From 9816f9e29fd858602c2bd6d64deb8e157a9c3be2 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 20 Oct 2016 16:42:14 +0200 Subject: Initial commit --- .gitignore | 89 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ LICENSE | 21 +++++++++++++++ README.md | 2 ++ 3 files changed, 112 insertions(+) create mode 100644 .gitignore create mode 100644 LICENSE create mode 100644 README.md diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..72364f9 --- /dev/null +++ b/.gitignore @@ -0,0 +1,89 @@ +# Byte-compiled / optimized / DLL files +__pycache__/ +*.py[cod] +*$py.class + +# C extensions +*.so + +# Distribution / packaging +.Python +env/ +build/ +develop-eggs/ +dist/ +downloads/ +eggs/ +.eggs/ +lib/ +lib64/ +parts/ +sdist/ +var/ +*.egg-info/ +.installed.cfg +*.egg + +# PyInstaller +# Usually these files are written by a python script from a template +# before PyInstaller builds the exe, so as to inject date/other infos into it. +*.manifest +*.spec + +# Installer logs +pip-log.txt +pip-delete-this-directory.txt + +# Unit test / coverage reports +htmlcov/ +.tox/ +.coverage +.coverage.* +.cache +nosetests.xml +coverage.xml +*,cover +.hypothesis/ + +# Translations +*.mo +*.pot + +# Django stuff: +*.log +local_settings.py + +# Flask stuff: +instance/ +.webassets-cache + +# Scrapy stuff: +.scrapy + +# Sphinx documentation +docs/_build/ + +# PyBuilder +target/ + +# IPython Notebook +.ipynb_checkpoints + +# pyenv +.python-version + +# celery beat schedule file +celerybeat-schedule + +# dotenv +.env + +# virtualenv +venv/ +ENV/ + +# Spyder project settings +.spyderproject + +# Rope project settings +.ropeproject diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..822bdb6 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2016 Rémi Flamary + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md new file mode 100644 index 0000000..d7749db --- /dev/null +++ b/README.md @@ -0,0 +1,2 @@ +# POT +Python Optimal Transport library -- cgit v1.2.3 From 2109443f5bea396114d1f9e0563ba5c396378c57 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 21 Oct 2016 09:26:17 +0200 Subject: add emd --- ot/__init__.py | 6 + ot/emd/EMD.h | 29 + ot/emd/EMD_wrap.cpp | 120 + ot/emd/__init__.py | 3 + ot/emd/core.h | 103 + ot/emd/emd.cpp | 6298 +++++++++++++++++++++++++++++++++++++++ ot/emd/emd.pyx | 31 + ot/emd/full_bipartitegraph.h | 215 ++ ot/emd/network_simplex_simple.h | 1543 ++++++++++ setup.py | 47 + 10 files changed, 8395 insertions(+) create mode 100644 ot/__init__.py create mode 100644 ot/emd/EMD.h create mode 100644 ot/emd/EMD_wrap.cpp create mode 100644 ot/emd/__init__.py create mode 100644 ot/emd/core.h create mode 100644 ot/emd/emd.cpp create mode 100644 ot/emd/emd.pyx create mode 100644 ot/emd/full_bipartitegraph.h create mode 100644 ot/emd/network_simplex_simple.h create mode 100755 setup.py diff --git a/ot/__init__.py b/ot/__init__.py new file mode 100644 index 0000000..d0ab3f7 --- /dev/null +++ b/ot/__init__.py @@ -0,0 +1,6 @@ + + +from emd import emd + + +__all__ = ["emd"] diff --git a/ot/emd/EMD.h b/ot/emd/EMD.h new file mode 100644 index 0000000..40d7192 --- /dev/null +++ b/ot/emd/EMD.h @@ -0,0 +1,29 @@ +/* This file is a c++ wrapper function for computing the transportation cost + * between two vectors given a cost matrix. + * + * It was written by Antoine Rolet (2014) and mainly consists of a wrapper + * of the code written by Nicolas Bonneel available on this page + * http://people.seas.harvard.edu/~nbonneel/FastTransport/ + * + * It was then modified to make it more amenable to python inline calling + * + * Please give relevant credit to the original author (Nicolas Bonneel) if + * you use this code for a publication. + * + */ + + +#ifndef EMD_H +#define EMD_H + +#include +#include +#include "network_simplex_simple.h" + +using namespace lemon; +typedef unsigned int node_id_type; + + +void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost); + +#endif diff --git a/ot/emd/EMD_wrap.cpp b/ot/emd/EMD_wrap.cpp new file mode 100644 index 0000000..52cd262 --- /dev/null +++ b/ot/emd/EMD_wrap.cpp @@ -0,0 +1,120 @@ +/* This file is a c++ wrapper function for computing the transportation cost + * between two vectors given a cost matrix. + * + * It was written by Antoine Rolet (2014) and mainly consists of a wrapper + * of the code written by Nicolas Bonneel available on this page + * http://people.seas.harvard.edu/~nbonneel/FastTransport/ + * + * It was then modified to make it more amenable to python inline calling + * + * Please give relevant credit to the original author (Nicolas Bonneel) if + * you use this code for a publication. + * + */ + +#include "EMD.h" + + +void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost) { +// beware M and C anre strored in row major C style!!! + int n, m, i,cur; + double max,max_iter; + + + typedef FullBipartiteDigraph Digraph; + DIGRAPH_TYPEDEFS(FullBipartiteDigraph); + + // Get the number of non zero coordinates for r and c + n=0; + for (node_id_type i=0; i0) { + n++; + } + } + m=0; + for (node_id_type i=0; i0) { + m++; + } + } + + + // Define the graph + + std::vector indI(n), indJ(m); + std::vector weights1(n), weights2(m); + Digraph di(n, m); + NetworkSimplexSimple net(di, true, n+m, n*m,max_iter); + + // Set supply and demand, don't account for 0 values (faster) + + max=0; + cur=0; + for (node_id_type i=0; i0) { + weights1[ di.nodeFromId(cur) ] = val; + max+=val; + indI[cur++]=i; + } + } + + // Demand is actually negative supply... + + max=0; + cur=0; + for (node_id_type i=0; i0) { + weights2[ di.nodeFromId(cur) ] = -val; + indJ[cur++]=i; + + max-=val; + } + } + + + net.supplyMap(&weights1[0], n, &weights2[0], m); + + // Set the cost of each edge + max=0; + for (node_id_type i=0; imax) { + max=val; + } + } + } + + + // Solve the problem with the network simplex algorithm + + int ret=net.run(); + if (ret!=(int)net.OPTIMAL) { + if (ret==(int)net.INFEASIBLE) { + std::cout << "Infeasible problem"; + } + if (ret==(int)net.UNBOUNDED) + { + std::cout << "Unbounded problem"; + } + } else + { + for (node_id_type i=0; i +#include + + +// Disable the following warnings when compiling with MSVC: +// C4250: 'class1' : inherits 'class2::member' via dominance +// C4355: 'this' : used in base member initializer list +// C4503: 'function' : decorated name length exceeded, name was truncated +// C4800: 'type' : forcing value to bool 'true' or 'false' (performance warning) +// C4996: 'function': was declared deprecated +#ifdef _MSC_VER +#pragma warning( disable : 4250 4355 4503 4800 4996 ) +#endif + +///\file +///\brief LEMON core utilities. +/// +///This header file contains core utilities for LEMON. +///It is automatically included by all graph types, therefore it usually +///do not have to be included directly. + +namespace lemon { + + /// \brief Dummy type to make it easier to create invalid iterators. + /// + /// Dummy type to make it easier to create invalid iterators. + /// See \ref INVALID for the usage. + struct Invalid { + public: + bool operator==(Invalid) { return true; } + bool operator!=(Invalid) { return false; } + bool operator< (Invalid) { return false; } + }; + + /// \brief Invalid iterators. + /// + /// \ref Invalid is a global type that converts to each iterator + /// in such a way that the value of the target iterator will be invalid. +#ifdef LEMON_ONLY_TEMPLATES + const Invalid INVALID = Invalid(); +#else + extern const Invalid INVALID; +#endif + + /// \addtogroup gutils + /// @{ + + ///Create convenience typedefs for the digraph types and iterators + + ///This \c \#define creates convenient type definitions for the following + ///types of \c Digraph: \c Node, \c NodeIt, \c Arc, \c ArcIt, \c InArcIt, + ///\c OutArcIt, \c BoolNodeMap, \c IntNodeMap, \c DoubleNodeMap, + ///\c BoolArcMap, \c IntArcMap, \c DoubleArcMap. + /// + ///\note If the graph type is a dependent type, ie. the graph type depend + ///on a template parameter, then use \c TEMPLATE_DIGRAPH_TYPEDEFS() + ///macro. +#define DIGRAPH_TYPEDEFS(Digraph) \ + typedef Digraph::Node Node; \ + typedef Digraph::Arc Arc; \ + + + ///Create convenience typedefs for the digraph types and iterators + + ///\see DIGRAPH_TYPEDEFS + /// + ///\note Use this macro, if the graph type is a dependent type, + ///ie. the graph type depend on a template parameter. +#define TEMPLATE_DIGRAPH_TYPEDEFS(Digraph) \ + typedef typename Digraph::Node Node; \ + typedef typename Digraph::Arc Arc; \ + + + +} //namespace lemon + +#endif diff --git a/ot/emd/emd.cpp b/ot/emd/emd.cpp new file mode 100644 index 0000000..27dc2de --- /dev/null +++ b/ot/emd/emd.cpp @@ -0,0 +1,6298 @@ +/* Generated by Cython 0.23.4 */ + +/* BEGIN: Cython Metadata +{ + "distutils": { + "depends": [ + "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/arrayobject.h", + "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/ufuncobject.h", + "ot/emd/EMD.h" + ], + "include_dirs": [ + "/usr/lib/python2.7/dist-packages/numpy/core/include", + "/home/rflamary/PYTHON/POT/ot/emd" + ], + "language": "c++" + } +} +END: Cython Metadata */ + +#define PY_SSIZE_T_CLEAN +#include "Python.h" +#ifndef Py_PYTHON_H + #error Python headers needed to compile C extensions, please install development version of Python. +#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03020000) + #error Cython requires Python 2.6+ or Python 3.2+. +#else +#define CYTHON_ABI "0_23_4" +#include +#ifndef offsetof +#define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) +#endif +#if !defined(WIN32) && !defined(MS_WINDOWS) + #ifndef __stdcall + #define __stdcall + #endif + #ifndef __cdecl + #define __cdecl + #endif + #ifndef __fastcall + #define __fastcall + #endif +#endif +#ifndef DL_IMPORT + #define DL_IMPORT(t) t +#endif +#ifndef DL_EXPORT + #define DL_EXPORT(t) t +#endif +#ifndef PY_LONG_LONG + #define PY_LONG_LONG LONG_LONG +#endif +#ifndef Py_HUGE_VAL + #define Py_HUGE_VAL HUGE_VAL +#endif +#ifdef PYPY_VERSION +#define CYTHON_COMPILING_IN_PYPY 1 +#define CYTHON_COMPILING_IN_CPYTHON 0 +#else +#define CYTHON_COMPILING_IN_PYPY 0 +#define CYTHON_COMPILING_IN_CPYTHON 1 +#endif +#if !defined(CYTHON_USE_PYLONG_INTERNALS) && CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x02070000 +#define CYTHON_USE_PYLONG_INTERNALS 1 +#endif +#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) +#define Py_OptimizeFlag 0 +#endif +#define __PYX_BUILD_PY_SSIZE_T "n" +#define CYTHON_FORMAT_SSIZE_T "z" +#if PY_MAJOR_VERSION < 3 + #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyClass_Type +#else + #define __Pyx_BUILTIN_MODULE_NAME "builtins" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyType_Type +#endif +#ifndef Py_TPFLAGS_CHECKTYPES + #define Py_TPFLAGS_CHECKTYPES 0 +#endif +#ifndef Py_TPFLAGS_HAVE_INDEX + #define Py_TPFLAGS_HAVE_INDEX 0 +#endif +#ifndef Py_TPFLAGS_HAVE_NEWBUFFER + #define Py_TPFLAGS_HAVE_NEWBUFFER 0 +#endif +#ifndef Py_TPFLAGS_HAVE_FINALIZE + #define Py_TPFLAGS_HAVE_FINALIZE 0 +#endif +#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) + #define CYTHON_PEP393_ENABLED 1 + #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ + 0 : _PyUnicode_Ready((PyObject *)(op))) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) + #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u) + #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) + #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) +#else + #define CYTHON_PEP393_ENABLED 0 + #define __Pyx_PyUnicode_READY(op) (0) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) + #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) + #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) + #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) +#endif +#if CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) +#else + #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ + PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains) + #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) +#endif +#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? 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PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) +#else + #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBaseString_Type PyUnicode_Type + #define PyStringObject PyUnicodeObject + #define PyString_Type PyUnicode_Type + #define PyString_Check PyUnicode_Check + #define PyString_CheckExact PyUnicode_CheckExact +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) + #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) +#else + #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) + #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) +#endif +#ifndef PySet_CheckExact + #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) +#endif +#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) +#if PY_MAJOR_VERSION >= 3 + #define PyIntObject PyLongObject + #define PyInt_Type PyLong_Type + #define PyInt_Check(op) PyLong_Check(op) + #define PyInt_CheckExact(op) PyLong_CheckExact(op) + #define PyInt_FromString PyLong_FromString + #define PyInt_FromUnicode PyLong_FromUnicode + #define PyInt_FromLong PyLong_FromLong + #define PyInt_FromSize_t PyLong_FromSize_t + #define PyInt_FromSsize_t PyLong_FromSsize_t + #define PyInt_AsLong PyLong_AsLong + #define PyInt_AS_LONG PyLong_AS_LONG + #define PyInt_AsSsize_t PyLong_AsSsize_t + #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask + #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask + #define PyNumber_Int PyNumber_Long +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBoolObject PyLongObject +#endif +#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY + #ifndef PyUnicode_InternFromString + #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) + #endif +#endif +#if PY_VERSION_HEX < 0x030200A4 + typedef long Py_hash_t; + #define __Pyx_PyInt_FromHash_t PyInt_FromLong + #define __Pyx_PyInt_AsHash_t PyInt_AsLong +#else + #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t + #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func)) +#else + #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) +#endif +#if PY_VERSION_HEX >= 0x030500B1 +#define __Pyx_PyAsyncMethodsStruct PyAsyncMethods +#define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) +#elif CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 +typedef struct { + unaryfunc am_await; + unaryfunc am_aiter; + unaryfunc am_anext; +} __Pyx_PyAsyncMethodsStruct; +#define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) +#else +#define __Pyx_PyType_AsAsync(obj) NULL +#endif +#ifndef CYTHON_RESTRICT + #if defined(__GNUC__) + #define CYTHON_RESTRICT __restrict__ + #elif defined(_MSC_VER) && _MSC_VER >= 1400 + #define CYTHON_RESTRICT __restrict + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_RESTRICT restrict + #else + #define CYTHON_RESTRICT + #endif +#endif +#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) + +#ifndef __cplusplus + #error "Cython files generated with the C++ option must be compiled with a C++ compiler." +#endif +#ifndef CYTHON_INLINE + #define CYTHON_INLINE inline +#endif +template +void __Pyx_call_destructor(T& x) { + x.~T(); +} +template +class __Pyx_FakeReference { + public: + __Pyx_FakeReference() : ptr(NULL) { } + __Pyx_FakeReference(const T& ref) : ptr(const_cast(&ref)) { } + T *operator->() { return ptr; } + operator T&() { return *ptr; } + private: + T *ptr; +}; + +#if defined(WIN32) || defined(MS_WINDOWS) + #define _USE_MATH_DEFINES +#endif +#include +#ifdef NAN +#define __PYX_NAN() ((float) NAN) +#else +static CYTHON_INLINE float __PYX_NAN() { + float value; + memset(&value, 0xFF, sizeof(value)); + return value; +} +#endif + + +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) +#else + #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) +#endif + +#ifndef __PYX_EXTERN_C + #ifdef __cplusplus + #define __PYX_EXTERN_C extern "C" + #else + #define __PYX_EXTERN_C extern + #endif +#endif + +#define __PYX_HAVE__ot__emd__emd +#define __PYX_HAVE_API__ot__emd__emd +#include "string.h" +#include "stdio.h" +#include "stdlib.h" +#include "numpy/arrayobject.h" +#include "numpy/ufuncobject.h" +#include "EMD.h" +#ifdef _OPENMP +#include +#endif /* _OPENMP */ + +#ifdef PYREX_WITHOUT_ASSERTIONS +#define CYTHON_WITHOUT_ASSERTIONS +#endif + +#ifndef CYTHON_UNUSED +# if defined(__GNUC__) +# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +#endif +#ifndef CYTHON_NCP_UNUSED +# if CYTHON_COMPILING_IN_CPYTHON +# define CYTHON_NCP_UNUSED +# else +# define CYTHON_NCP_UNUSED CYTHON_UNUSED +# endif +#endif +typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding; + const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; + +#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0 +#define __PYX_DEFAULT_STRING_ENCODING "" +#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString +#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#define __Pyx_uchar_cast(c) ((unsigned char)c) +#define __Pyx_long_cast(x) ((long)x) +#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ + (sizeof(type) < sizeof(Py_ssize_t)) ||\ + (sizeof(type) > sizeof(Py_ssize_t) &&\ + likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX) &&\ + (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ + v == (type)PY_SSIZE_T_MIN))) ||\ + (sizeof(type) == sizeof(Py_ssize_t) &&\ + (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX))) ) +#if defined (__cplusplus) && __cplusplus >= 201103L + #include + #define __Pyx_sst_abs(value) std::abs(value) +#elif SIZEOF_INT >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) abs(value) +#elif SIZEOF_LONG >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) labs(value) +#elif defined (_MSC_VER) && defined (_M_X64) + #define __Pyx_sst_abs(value) _abs64(value) +#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define __Pyx_sst_abs(value) llabs(value) +#elif defined (__GNUC__) + #define __Pyx_sst_abs(value) __builtin_llabs(value) +#else + #define __Pyx_sst_abs(value) ((value<0) ? -value : value) +#endif +static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*); +static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); +#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) +#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) +#define __Pyx_PyBytes_FromString PyBytes_FromString +#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#else + #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize +#endif +#define __Pyx_PyObject_AsSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) +#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) +#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) +#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) +#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) +#if PY_MAJOR_VERSION < 3 +static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) +{ + const Py_UNICODE *u_end = u; + while (*u_end++) ; + return (size_t)(u_end - u - 1); +} +#else +#define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen +#endif +#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) +#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode +#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode +#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) +#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) +#define __Pyx_PyBool_FromLong(b) ((b) ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False)) +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); +static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x); +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); +#if CYTHON_COMPILING_IN_CPYTHON +#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) +#else +#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) +#endif +#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII +static int __Pyx_sys_getdefaultencoding_not_ascii; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + PyObject* ascii_chars_u = NULL; + PyObject* ascii_chars_b = NULL; + const char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + if (strcmp(default_encoding_c, "ascii") == 0) { + __Pyx_sys_getdefaultencoding_not_ascii = 0; + } else { + char ascii_chars[128]; + int c; + for (c = 0; c < 128; c++) { + ascii_chars[c] = c; + } + __Pyx_sys_getdefaultencoding_not_ascii = 1; + ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); + if (!ascii_chars_u) goto bad; + ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); + if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { + PyErr_Format( + PyExc_ValueError, + "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", + default_encoding_c); + goto bad; + } + Py_DECREF(ascii_chars_u); + Py_DECREF(ascii_chars_b); + } + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + Py_XDECREF(ascii_chars_u); + Py_XDECREF(ascii_chars_b); + return -1; +} +#endif +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) +#else +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +static char* __PYX_DEFAULT_STRING_ENCODING; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c)); + if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; + strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + return -1; +} +#endif +#endif + + +/* Test for GCC > 2.95 */ +#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) + #define likely(x) __builtin_expect(!!(x), 1) + #define unlikely(x) __builtin_expect(!!(x), 0) +#else /* !__GNUC__ or GCC < 2.95 */ + #define likely(x) (x) + #define unlikely(x) (x) +#endif /* __GNUC__ */ + +static PyObject *__pyx_m; +static PyObject *__pyx_d; +static PyObject *__pyx_b; +static PyObject *__pyx_empty_tuple; +static PyObject *__pyx_empty_bytes; +static int __pyx_lineno; +static int __pyx_clineno = 0; +static const char * __pyx_cfilenm= __FILE__; +static const char *__pyx_filename; + +#if !defined(CYTHON_CCOMPLEX) + #if defined(__cplusplus) + #define CYTHON_CCOMPLEX 1 + #elif defined(_Complex_I) + #define CYTHON_CCOMPLEX 1 + #else + #define CYTHON_CCOMPLEX 0 + #endif +#endif +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #include + #else + #include + #endif +#endif +#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__) + #undef _Complex_I + #define _Complex_I 1.0fj +#endif + + +static const char *__pyx_f[] = { + "ot/emd/emd.pyx", + "__init__.pxd", + "type.pxd", +}; +#define IS_UNSIGNED(type) (((type) -1) > 0) +struct __Pyx_StructField_; +#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) +typedef struct { + const char* name; + struct __Pyx_StructField_* fields; + size_t size; + size_t arraysize[8]; + int ndim; + char typegroup; + char is_unsigned; + int flags; +} __Pyx_TypeInfo; +typedef struct __Pyx_StructField_ { + __Pyx_TypeInfo* type; + const char* name; + size_t offset; +} __Pyx_StructField; +typedef struct { + __Pyx_StructField* field; + size_t parent_offset; +} __Pyx_BufFmt_StackElem; +typedef struct { + __Pyx_StructField root; + __Pyx_BufFmt_StackElem* head; + size_t fmt_offset; + size_t new_count, enc_count; + size_t struct_alignment; + int is_complex; + char enc_type; + char new_packmode; + char enc_packmode; + char is_valid_array; +} __Pyx_BufFmt_Context; + + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":725 + * # in Cython to enable them only on the right systems. + * + * ctypedef npy_int8 int8_t # <<<<<<<<<<<<<< + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t + */ +typedef npy_int8 __pyx_t_5numpy_int8_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":726 + * + * ctypedef npy_int8 int8_t + * ctypedef npy_int16 int16_t # <<<<<<<<<<<<<< + * ctypedef npy_int32 int32_t + * ctypedef npy_int64 int64_t + */ +typedef npy_int16 __pyx_t_5numpy_int16_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":727 + * ctypedef npy_int8 int8_t + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< + * ctypedef npy_int64 int64_t + * #ctypedef npy_int96 int96_t + */ +typedef npy_int32 __pyx_t_5numpy_int32_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":728 + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t + * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< + * #ctypedef npy_int96 int96_t + * #ctypedef npy_int128 int128_t + */ +typedef npy_int64 __pyx_t_5numpy_int64_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":732 + * #ctypedef npy_int128 int128_t + * + * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t + */ +typedef npy_uint8 __pyx_t_5numpy_uint8_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":733 + * + * ctypedef npy_uint8 uint8_t + * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< + * ctypedef npy_uint32 uint32_t + * ctypedef npy_uint64 uint64_t + */ +typedef npy_uint16 __pyx_t_5numpy_uint16_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":734 + * ctypedef npy_uint8 uint8_t + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<< + * ctypedef npy_uint64 uint64_t + * #ctypedef npy_uint96 uint96_t + */ +typedef npy_uint32 __pyx_t_5numpy_uint32_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":735 + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t + * ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<< + * #ctypedef npy_uint96 uint96_t + * #ctypedef npy_uint128 uint128_t + */ +typedef npy_uint64 __pyx_t_5numpy_uint64_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":739 + * #ctypedef npy_uint128 uint128_t + * + * ctypedef npy_float32 float32_t # <<<<<<<<<<<<<< + * ctypedef npy_float64 float64_t + * #ctypedef npy_float80 float80_t + */ +typedef npy_float32 __pyx_t_5numpy_float32_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":740 + * + * ctypedef npy_float32 float32_t + * ctypedef npy_float64 float64_t # <<<<<<<<<<<<<< + * #ctypedef npy_float80 float80_t + * #ctypedef npy_float128 float128_t + */ +typedef npy_float64 __pyx_t_5numpy_float64_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":749 + * # The int types are mapped a bit surprising -- + * # numpy.int corresponds to 'l' and numpy.long to 'q' + * ctypedef npy_long int_t # <<<<<<<<<<<<<< + * ctypedef npy_longlong long_t + * ctypedef npy_longlong longlong_t + */ +typedef npy_long __pyx_t_5numpy_int_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":750 + * # numpy.int corresponds to 'l' and numpy.long to 'q' + * ctypedef npy_long int_t + * ctypedef npy_longlong long_t # <<<<<<<<<<<<<< + * ctypedef npy_longlong longlong_t + * + */ +typedef npy_longlong __pyx_t_5numpy_long_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":751 + * ctypedef npy_long int_t + * ctypedef npy_longlong long_t + * ctypedef npy_longlong longlong_t # <<<<<<<<<<<<<< + * + * ctypedef npy_ulong uint_t + */ +typedef npy_longlong __pyx_t_5numpy_longlong_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":753 + * ctypedef npy_longlong longlong_t + * + * ctypedef npy_ulong uint_t # <<<<<<<<<<<<<< + * ctypedef npy_ulonglong ulong_t + * ctypedef npy_ulonglong ulonglong_t + */ +typedef npy_ulong __pyx_t_5numpy_uint_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":754 + * + * ctypedef npy_ulong uint_t + * ctypedef npy_ulonglong ulong_t # <<<<<<<<<<<<<< + * ctypedef npy_ulonglong ulonglong_t + * + */ +typedef npy_ulonglong __pyx_t_5numpy_ulong_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":755 + * ctypedef npy_ulong uint_t + * ctypedef npy_ulonglong ulong_t + * ctypedef npy_ulonglong ulonglong_t # <<<<<<<<<<<<<< + * + * ctypedef npy_intp intp_t + */ +typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":757 + * ctypedef npy_ulonglong ulonglong_t + * + * ctypedef npy_intp intp_t # <<<<<<<<<<<<<< + * ctypedef npy_uintp uintp_t + * + */ +typedef npy_intp __pyx_t_5numpy_intp_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":758 + * + * ctypedef npy_intp intp_t + * ctypedef npy_uintp uintp_t # <<<<<<<<<<<<<< + * + * ctypedef npy_double float_t + */ +typedef npy_uintp __pyx_t_5numpy_uintp_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":760 + * ctypedef npy_uintp uintp_t + * + * ctypedef npy_double float_t # <<<<<<<<<<<<<< + * ctypedef npy_double double_t + * ctypedef npy_longdouble longdouble_t + */ +typedef npy_double __pyx_t_5numpy_float_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":761 + * + * ctypedef npy_double float_t + * ctypedef npy_double double_t # <<<<<<<<<<<<<< + * ctypedef npy_longdouble longdouble_t + * + */ +typedef npy_double __pyx_t_5numpy_double_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":762 + * ctypedef npy_double float_t + * ctypedef npy_double double_t + * ctypedef npy_longdouble longdouble_t # <<<<<<<<<<<<<< + * + * ctypedef npy_cfloat cfloat_t + */ +typedef npy_longdouble __pyx_t_5numpy_longdouble_t; +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + typedef ::std::complex< float > __pyx_t_float_complex; + #else + typedef float _Complex __pyx_t_float_complex; + #endif +#else + typedef struct { float real, imag; } __pyx_t_float_complex; +#endif + +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + typedef ::std::complex< double > __pyx_t_double_complex; + #else + typedef double _Complex __pyx_t_double_complex; + #endif +#else + typedef struct { double real, imag; } __pyx_t_double_complex; +#endif + + +/*--- Type declarations ---*/ + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":764 + * ctypedef npy_longdouble longdouble_t + * + * ctypedef npy_cfloat cfloat_t # <<<<<<<<<<<<<< + * ctypedef npy_cdouble cdouble_t + * ctypedef npy_clongdouble clongdouble_t + */ +typedef npy_cfloat __pyx_t_5numpy_cfloat_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":765 + * + * ctypedef npy_cfloat cfloat_t + * ctypedef npy_cdouble cdouble_t # <<<<<<<<<<<<<< + * ctypedef npy_clongdouble clongdouble_t + * + */ +typedef npy_cdouble __pyx_t_5numpy_cdouble_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":766 + * ctypedef npy_cfloat cfloat_t + * ctypedef npy_cdouble cdouble_t + * ctypedef npy_clongdouble clongdouble_t # <<<<<<<<<<<<<< + * + * ctypedef npy_cdouble complex_t + */ +typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":768 + * ctypedef npy_clongdouble clongdouble_t + * + * ctypedef npy_cdouble complex_t # <<<<<<<<<<<<<< + * + * cdef inline object PyArray_MultiIterNew1(a): + */ +typedef npy_cdouble __pyx_t_5numpy_complex_t; + +/* --- Runtime support code (head) --- */ +#ifndef CYTHON_REFNANNY + #define CYTHON_REFNANNY 0 +#endif +#if CYTHON_REFNANNY + typedef struct { + void (*INCREF)(void*, PyObject*, int); + void (*DECREF)(void*, PyObject*, int); + void (*GOTREF)(void*, PyObject*, int); + void (*GIVEREF)(void*, PyObject*, int); + void* (*SetupContext)(const char*, int, const char*); + void (*FinishContext)(void**); + } __Pyx_RefNannyAPIStruct; + static __Pyx_RefNannyAPIStruct *__Pyx_RefNanny = NULL; + static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname); + #define __Pyx_RefNannyDeclarations void *__pyx_refnanny = NULL; +#ifdef WITH_THREAD + #define __Pyx_RefNannySetupContext(name, acquire_gil)\ + if (acquire_gil) {\ + PyGILState_STATE __pyx_gilstate_save = PyGILState_Ensure();\ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__);\ + PyGILState_Release(__pyx_gilstate_save);\ + } else {\ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__);\ + } +#else + #define __Pyx_RefNannySetupContext(name, acquire_gil)\ + __pyx_refnanny = __Pyx_RefNanny->SetupContext((name), __LINE__, __FILE__) +#endif + #define __Pyx_RefNannyFinishContext()\ + __Pyx_RefNanny->FinishContext(&__pyx_refnanny) + #define __Pyx_INCREF(r) __Pyx_RefNanny->INCREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_DECREF(r) __Pyx_RefNanny->DECREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_GOTREF(r) __Pyx_RefNanny->GOTREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_GIVEREF(r) __Pyx_RefNanny->GIVEREF(__pyx_refnanny, (PyObject *)(r), __LINE__) + #define __Pyx_XINCREF(r) do { if((r) != NULL) {__Pyx_INCREF(r); }} while(0) + #define __Pyx_XDECREF(r) do { if((r) != NULL) {__Pyx_DECREF(r); }} while(0) + #define __Pyx_XGOTREF(r) do { if((r) != NULL) {__Pyx_GOTREF(r); }} while(0) + #define __Pyx_XGIVEREF(r) do { if((r) != NULL) {__Pyx_GIVEREF(r);}} while(0) +#else + #define __Pyx_RefNannyDeclarations + #define __Pyx_RefNannySetupContext(name, acquire_gil) + #define __Pyx_RefNannyFinishContext() + #define __Pyx_INCREF(r) Py_INCREF(r) + #define __Pyx_DECREF(r) Py_DECREF(r) + #define __Pyx_GOTREF(r) + #define __Pyx_GIVEREF(r) + #define __Pyx_XINCREF(r) Py_XINCREF(r) + #define __Pyx_XDECREF(r) Py_XDECREF(r) + #define __Pyx_XGOTREF(r) + #define __Pyx_XGIVEREF(r) +#endif +#define __Pyx_XDECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; __Pyx_XDECREF(tmp);\ + } while (0) +#define __Pyx_DECREF_SET(r, v) do {\ + PyObject *tmp = (PyObject *) r;\ + r = v; __Pyx_DECREF(tmp);\ + } while (0) +#define __Pyx_CLEAR(r) do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0) +#define __Pyx_XCLEAR(r) do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0) + +static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, + Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); + +static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); + +static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\ + PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,\ + const char* function_name); + +static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, + const char *name, int exact); + +static CYTHON_INLINE int __Pyx_GetBufferAndValidate(Py_buffer* buf, PyObject* obj, + __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack); +static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info); + +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) { + PyTypeObject* tp = Py_TYPE(obj); + if (likely(tp->tp_getattro)) + return tp->tp_getattro(obj, attr_name); +#if PY_MAJOR_VERSION < 3 + if (likely(tp->tp_getattr)) + return tp->tp_getattr(obj, PyString_AS_STRING(attr_name)); +#endif + return PyObject_GetAttr(obj, attr_name); +} +#else +#define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) +#endif + +static PyObject *__Pyx_GetBuiltinName(PyObject *name); + +static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name); + +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); +#else +#define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) +#endif + +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg); +#endif + +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg); + +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); + +static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb); +static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb); + +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); + +#if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY +static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) { + PyObject *value; + value = PyDict_GetItemWithError(d, key); + if (unlikely(!value)) { + if (!PyErr_Occurred()) { + PyObject* args = PyTuple_Pack(1, key); + if (likely(args)) + PyErr_SetObject(PyExc_KeyError, args); + Py_XDECREF(args); + } + return NULL; + } + Py_INCREF(value); + return value; +} +#else + #define __Pyx_PyDict_GetItem(d, key) PyObject_GetItem(d, key) +#endif + +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); + +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); + +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); + +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); + +typedef struct { + int code_line; + PyCodeObject* code_object; +} __Pyx_CodeObjectCacheEntry; +struct __Pyx_CodeObjectCache { + int count; + int max_count; + __Pyx_CodeObjectCacheEntry* entries; +}; +static struct __Pyx_CodeObjectCache __pyx_code_cache = {0,0,NULL}; +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line); +static PyCodeObject *__pyx_find_code_object(int code_line); +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); + +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename); + +typedef struct { + Py_ssize_t shape, strides, suboffsets; +} __Pyx_Buf_DimInfo; +typedef struct { + size_t refcount; + Py_buffer pybuffer; +} __Pyx_Buffer; +typedef struct { + __Pyx_Buffer *rcbuffer; + char *data; + __Pyx_Buf_DimInfo diminfo[8]; +} __Pyx_LocalBuf_ND; + +#if PY_MAJOR_VERSION < 3 + static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags); + static void __Pyx_ReleaseBuffer(Py_buffer *view); +#else + #define __Pyx_GetBuffer PyObject_GetBuffer + #define __Pyx_ReleaseBuffer PyBuffer_Release +#endif + + +static Py_ssize_t __Pyx_zeros[] = {0, 0, 0, 0, 0, 0, 0, 0}; +static Py_ssize_t __Pyx_minusones[] = {-1, -1, -1, -1, -1, -1, -1, -1}; + +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); + +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #define __Pyx_CREAL(z) ((z).real()) + #define __Pyx_CIMAG(z) ((z).imag()) + #else + #define __Pyx_CREAL(z) (__real__(z)) + #define __Pyx_CIMAG(z) (__imag__(z)) + #endif +#else + #define __Pyx_CREAL(z) ((z).real) + #define __Pyx_CIMAG(z) ((z).imag) +#endif +#if (defined(_WIN32) || defined(__clang__)) && defined(__cplusplus) && CYTHON_CCOMPLEX + #define __Pyx_SET_CREAL(z,x) ((z).real(x)) + #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) +#else + #define __Pyx_SET_CREAL(z,x) __Pyx_CREAL(z) = (x) + #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) +#endif + +static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float); + +#if CYTHON_CCOMPLEX + #define __Pyx_c_eqf(a, b) ((a)==(b)) + #define __Pyx_c_sumf(a, b) ((a)+(b)) + #define __Pyx_c_difff(a, b) ((a)-(b)) + #define __Pyx_c_prodf(a, b) ((a)*(b)) + #define __Pyx_c_quotf(a, b) ((a)/(b)) + #define __Pyx_c_negf(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zerof(z) ((z)==(float)0) + #define __Pyx_c_conjf(z) (::std::conj(z)) + #if 1 + #define __Pyx_c_absf(z) (::std::abs(z)) + #define __Pyx_c_powf(a, b) (::std::pow(a, b)) + #endif + #else + #define __Pyx_c_is_zerof(z) ((z)==0) + #define __Pyx_c_conjf(z) (conjf(z)) + #if 1 + #define __Pyx_c_absf(z) (cabsf(z)) + #define __Pyx_c_powf(a, b) (cpowf(a, b)) + #endif + #endif +#else + static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex); + static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex); + #if 1 + static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex, __pyx_t_float_complex); + #endif +#endif + +static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double); + +#if CYTHON_CCOMPLEX + #define __Pyx_c_eq(a, b) ((a)==(b)) + #define __Pyx_c_sum(a, b) ((a)+(b)) + #define __Pyx_c_diff(a, b) ((a)-(b)) + #define __Pyx_c_prod(a, b) ((a)*(b)) + #define __Pyx_c_quot(a, b) ((a)/(b)) + #define __Pyx_c_neg(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zero(z) ((z)==(double)0) + #define __Pyx_c_conj(z) (::std::conj(z)) + #if 1 + #define __Pyx_c_abs(z) (::std::abs(z)) + #define __Pyx_c_pow(a, b) (::std::pow(a, b)) + #endif + #else + #define __Pyx_c_is_zero(z) ((z)==0) + #define __Pyx_c_conj(z) (conj(z)) + #if 1 + #define __Pyx_c_abs(z) (cabs(z)) + #define __Pyx_c_pow(a, b) (cpow(a, b)) + #endif + #endif +#else + static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex); + static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex); + #if 1 + static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex, __pyx_t_double_complex); + #endif +#endif + +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); + +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value); + +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); + +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); + +static int __Pyx_check_binary_version(void); + +#if !defined(__Pyx_PyIdentifier_FromString) +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s) +#else + #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s) +#endif +#endif + +static PyObject *__Pyx_ImportModule(const char *name); + +static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict); + +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); + + +/* Module declarations from 'cpython.buffer' */ + +/* Module declarations from 'libc.string' */ + +/* Module declarations from 'libc.stdio' */ + +/* Module declarations from '__builtin__' */ + +/* Module declarations from 'cpython.type' */ +static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0; + +/* Module declarations from 'cpython' */ + +/* Module declarations from 'cpython.object' */ + +/* Module declarations from 'cpython.ref' */ + +/* Module declarations from 'libc.stdlib' */ + +/* Module declarations from 'numpy' */ + +/* Module declarations from 'numpy' */ +static PyTypeObject *__pyx_ptype_5numpy_dtype = 0; +static PyTypeObject *__pyx_ptype_5numpy_flatiter = 0; +static PyTypeObject *__pyx_ptype_5numpy_broadcast = 0; +static PyTypeObject *__pyx_ptype_5numpy_ndarray = 0; +static PyTypeObject *__pyx_ptype_5numpy_ufunc = 0; +static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, char *, char *, int *); /*proto*/ + +/* Module declarations from 'cython' */ + +/* Module declarations from 'ot.emd.emd' */ +static __Pyx_TypeInfo __Pyx_TypeInfo_double = { "double", NULL, sizeof(double), { 0 }, 0, 'R', 0, 0 }; +#define __Pyx_MODULE_NAME "ot.emd.emd" +int __pyx_module_is_main_ot__emd__emd = 0; + +/* Implementation of 'ot.emd.emd' */ +static PyObject *__pyx_builtin_ValueError; +static PyObject *__pyx_builtin_range; +static PyObject *__pyx_builtin_RuntimeError; +static char __pyx_k_B[] = "B"; +static char __pyx_k_G[] = "G"; +static char __pyx_k_H[] = "H"; +static char __pyx_k_I[] = "I"; +static char __pyx_k_L[] = "L"; +static char __pyx_k_M[] = "M"; +static char __pyx_k_O[] = "O"; 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__pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + if (PyDict_SetItem(__pyx_d, __pyx_n_s_emd, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + + /* "ot/emd/emd.pyx":1 + * # -*- coding: utf-8 -*- # <<<<<<<<<<<<<< + * """ + * Created on Thu Sep 11 08:42:08 2014 + */ + __pyx_t_1 = PyDict_New(); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + if (PyDict_SetItem(__pyx_d, __pyx_n_s_test, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":976 + * arr.base = baseptr + * + * cdef inline object get_array_base(ndarray arr): # <<<<<<<<<<<<<< + * if arr.base is NULL: + * return None + */ + + /*--- Wrapped vars code ---*/ + + goto __pyx_L0; + __pyx_L1_error:; + __Pyx_XDECREF(__pyx_t_1); + if (__pyx_m) { + if (__pyx_d) { + __Pyx_AddTraceback("init ot.emd.emd", __pyx_clineno, __pyx_lineno, __pyx_filename); + } + Py_DECREF(__pyx_m); __pyx_m = 0; + } else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_ImportError, "init ot.emd.emd"); + } + __pyx_L0:; + __Pyx_RefNannyFinishContext(); + #if PY_MAJOR_VERSION < 3 + return; + #else + return __pyx_m; + #endif +} + +/* --- Runtime support code --- */ +#if CYTHON_REFNANNY +static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) { + PyObject *m = NULL, *p = NULL; + void *r = NULL; + m = PyImport_ImportModule((char *)modname); + if (!m) goto end; + p = PyObject_GetAttrString(m, (char *)"RefNannyAPI"); + if (!p) goto end; + r = PyLong_AsVoidPtr(p); +end: + Py_XDECREF(p); + Py_XDECREF(m); + return (__Pyx_RefNannyAPIStruct *)r; +} +#endif + +static void __Pyx_RaiseArgtupleInvalid( + const char* func_name, + int exact, + Py_ssize_t num_min, + Py_ssize_t num_max, + Py_ssize_t num_found) +{ + Py_ssize_t num_expected; 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"" : "s", num_found); +} + +static void __Pyx_RaiseDoubleKeywordsError( + const char* func_name, + PyObject* kw_name) +{ + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION >= 3 + "%s() got multiple values for keyword argument '%U'", func_name, kw_name); + #else + "%s() got multiple values for keyword argument '%s'", func_name, + PyString_AsString(kw_name)); + #endif +} + +static int __Pyx_ParseOptionalKeywords( + PyObject *kwds, + PyObject **argnames[], + PyObject *kwds2, + PyObject *values[], + Py_ssize_t num_pos_args, + const char* function_name) +{ + PyObject *key = 0, *value = 0; + Py_ssize_t pos = 0; + PyObject*** name; + PyObject*** first_kw_arg = argnames + num_pos_args; + while (PyDict_Next(kwds, &pos, &key, &value)) { + name = first_kw_arg; + while (*name && (**name != key)) name++; + if (*name) { + values[name-argnames] = value; + continue; + } + name = first_kw_arg; + #if PY_MAJOR_VERSION < 3 + if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) { + while (*name) { + if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) + && _PyString_Eq(**name, key)) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + if ((**argname == key) || ( + (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) + && _PyString_Eq(**argname, key))) { + goto arg_passed_twice; + } + argname++; + } + } + } else + #endif + if (likely(PyUnicode_Check(key))) { + while (*name) { + int cmp = (**name == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : + #endif + PyUnicode_Compare(**name, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + int cmp = (**argname == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : + #endif + PyUnicode_Compare(**argname, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) goto arg_passed_twice; + argname++; + } + } + } else + goto invalid_keyword_type; + if (kwds2) { + if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; + } else { + goto invalid_keyword; + } + } + return 0; +arg_passed_twice: + __Pyx_RaiseDoubleKeywordsError(function_name, key); + goto bad; +invalid_keyword_type: + PyErr_Format(PyExc_TypeError, + "%.200s() keywords must be strings", function_name); + goto bad; +invalid_keyword: + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION < 3 + "%.200s() got an unexpected keyword argument '%.200s'", + function_name, PyString_AsString(key)); + #else + "%s() got an unexpected keyword argument '%U'", + function_name, key); + #endif +bad: + return -1; +} + +static void __Pyx_RaiseArgumentTypeInvalid(const char* name, PyObject *obj, PyTypeObject *type) { + PyErr_Format(PyExc_TypeError, + "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", + name, type->tp_name, Py_TYPE(obj)->tp_name); +} +static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, + const char *name, int exact) +{ + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (none_allowed && obj == Py_None) return 1; + else if (exact) { + if (likely(Py_TYPE(obj) == type)) return 1; + #if PY_MAJOR_VERSION == 2 + else if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; + #endif + } + else { + if (likely(PyObject_TypeCheck(obj, type))) return 1; + } + __Pyx_RaiseArgumentTypeInvalid(name, obj, type); + return 0; +} + +static CYTHON_INLINE int __Pyx_IsLittleEndian(void) { + unsigned int n = 1; + return *(unsigned char*)(&n) != 0; +} +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type) { + stack[0].field = &ctx->root; + stack[0].parent_offset = 0; + ctx->root.type = type; + ctx->root.name = "buffer dtype"; + ctx->root.offset = 0; + ctx->head = stack; + ctx->head->field = &ctx->root; + ctx->fmt_offset = 0; + ctx->head->parent_offset = 0; + ctx->new_packmode = '@'; + ctx->enc_packmode = '@'; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->is_complex = 0; + ctx->is_valid_array = 0; + ctx->struct_alignment = 0; + while (type->typegroup == 'S') { + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = 0; + type = type->fields->type; + } +} +static int __Pyx_BufFmt_ParseNumber(const char** ts) { + int count; + const char* t = *ts; + if (*t < '0' || *t > '9') { + return -1; + } else { + count = *t++ - '0'; + while (*t >= '0' && *t < '9') { + count *= 10; + count += *t++ - '0'; + } + } + *ts = t; + return count; +} +static int __Pyx_BufFmt_ExpectNumber(const char **ts) { + int number = __Pyx_BufFmt_ParseNumber(ts); + if (number == -1) + PyErr_Format(PyExc_ValueError,\ + "Does not understand character buffer dtype format string ('%c')", **ts); + return number; +} +static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { + PyErr_Format(PyExc_ValueError, + "Unexpected format string character: '%c'", ch); +} +static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { + switch (ch) { + case 'c': return "'char'"; + case 'b': return "'signed char'"; + case 'B': return "'unsigned char'"; + case 'h': return "'short'"; + case 'H': return "'unsigned short'"; + case 'i': return "'int'"; + case 'I': return "'unsigned int'"; + case 'l': return "'long'"; + case 'L': return "'unsigned long'"; + case 'q': return "'long long'"; + case 'Q': return "'unsigned long long'"; + case 'f': return (is_complex ? "'complex float'" : "'float'"); + case 'd': return (is_complex ? "'complex double'" : "'double'"); + case 'g': return (is_complex ? "'complex long double'" : "'long double'"); + case 'T': return "a struct"; + case 'O': return "Python object"; + case 'P': return "a pointer"; + case 's': case 'p': return "a string"; + case 0: return "end"; + default: return "unparseable format string"; + } +} +static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return 2; + case 'i': case 'I': case 'l': case 'L': return 4; + case 'q': case 'Q': return 8; + case 'f': return (is_complex ? 8 : 4); + case 'd': return (is_complex ? 16 : 8); + case 'g': { + PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); + return 0; + } + case 'O': case 'P': return sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { + switch (ch) { + case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(short); + case 'i': case 'I': return sizeof(int); + case 'l': case 'L': return sizeof(long); + #ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(PY_LONG_LONG); + #endif + case 'f': return sizeof(float) * (is_complex ? 2 : 1); + case 'd': return sizeof(double) * (is_complex ? 2 : 1); + case 'g': return sizeof(long double) * (is_complex ? 2 : 1); + case 'O': case 'P': return sizeof(void*); + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +typedef struct { char c; short x; } __Pyx_st_short; +typedef struct { char c; int x; } __Pyx_st_int; +typedef struct { char c; long x; } __Pyx_st_long; +typedef struct { char c; float x; } __Pyx_st_float; +typedef struct { char c; double x; } __Pyx_st_double; +typedef struct { char c; long double x; } __Pyx_st_longdouble; +typedef struct { char c; void *x; } __Pyx_st_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_st_float) - sizeof(float); + case 'd': return sizeof(__Pyx_st_double) - sizeof(double); + case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +/* These are for computing the padding at the end of the struct to align + on the first member of the struct. This will probably the same as above, + but we don't have any guarantees. + */ +typedef struct { short x; char c; } __Pyx_pad_short; +typedef struct { int x; char c; } __Pyx_pad_int; +typedef struct { long x; char c; } __Pyx_pad_long; +typedef struct { float x; char c; } __Pyx_pad_float; +typedef struct { double x; char c; } __Pyx_pad_double; +typedef struct { long double x; char c; } __Pyx_pad_longdouble; +typedef struct { void *x; char c; } __Pyx_pad_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); + case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); + case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { + switch (ch) { + case 'c': + return 'H'; + case 'b': case 'h': case 'i': + case 'l': case 'q': case 's': case 'p': + return 'I'; + case 'B': case 'H': case 'I': case 'L': case 'Q': + return 'U'; + case 'f': case 'd': case 'g': + return (is_complex ? 'C' : 'R'); + case 'O': + return 'O'; + case 'P': + return 'P'; + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { + if (ctx->head == NULL || ctx->head->field == &ctx->root) { + const char* expected; + const char* quote; + if (ctx->head == NULL) { + expected = "end"; + quote = ""; + } else { + expected = ctx->head->field->type->name; + quote = "'"; + } + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected %s%s%s but got %s", + quote, expected, quote, + __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); + } else { + __Pyx_StructField* field = ctx->head->field; + __Pyx_StructField* parent = (ctx->head - 1)->field; + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", + field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), + parent->type->name, field->name); + } +} +static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { + char group; + size_t size, offset, arraysize = 1; + if (ctx->enc_type == 0) return 0; + if (ctx->head->field->type->arraysize[0]) { + int i, ndim = 0; + if (ctx->enc_type == 's' || ctx->enc_type == 'p') { + ctx->is_valid_array = ctx->head->field->type->ndim == 1; + ndim = 1; + if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { + PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %zu", + ctx->head->field->type->arraysize[0], ctx->enc_count); + return -1; + } + } + if (!ctx->is_valid_array) { + PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", + ctx->head->field->type->ndim, ndim); + return -1; + } + for (i = 0; i < ctx->head->field->type->ndim; i++) { + arraysize *= ctx->head->field->type->arraysize[i]; + } + ctx->is_valid_array = 0; + ctx->enc_count = 1; + } + group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); + do { + __Pyx_StructField* field = ctx->head->field; + __Pyx_TypeInfo* type = field->type; + if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { + size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); + } else { + size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); + } + if (ctx->enc_packmode == '@') { + size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); + size_t align_mod_offset; + if (align_at == 0) return -1; + align_mod_offset = ctx->fmt_offset % align_at; + if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; + if (ctx->struct_alignment == 0) + ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, + ctx->is_complex); + } + if (type->size != size || type->typegroup != group) { + if (type->typegroup == 'C' && type->fields != NULL) { + size_t parent_offset = ctx->head->parent_offset + field->offset; + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = parent_offset; + continue; + } + if ((type->typegroup == 'H' || group == 'H') && type->size == size) { + } else { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + } + offset = ctx->head->parent_offset + field->offset; + if (ctx->fmt_offset != offset) { + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", + (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); + return -1; + } + ctx->fmt_offset += size; + if (arraysize) + ctx->fmt_offset += (arraysize - 1) * size; + --ctx->enc_count; + while (1) { + if (field == &ctx->root) { + ctx->head = NULL; + if (ctx->enc_count != 0) { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + break; + } + ctx->head->field = ++field; + if (field->type == NULL) { + --ctx->head; + field = ctx->head->field; + continue; + } else if (field->type->typegroup == 'S') { + size_t parent_offset = ctx->head->parent_offset + field->offset; + if (field->type->fields->type == NULL) continue; + field = field->type->fields; + ++ctx->head; + ctx->head->field = field; + ctx->head->parent_offset = parent_offset; + break; + } else { + break; + } + } + } while (ctx->enc_count); + ctx->enc_type = 0; + ctx->is_complex = 0; + return 0; +} +static CYTHON_INLINE PyObject * +__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) +{ + const char *ts = *tsp; + int i = 0, number; + int ndim = ctx->head->field->type->ndim; +; + ++ts; + if (ctx->new_count != 1) { + PyErr_SetString(PyExc_ValueError, + "Cannot handle repeated arrays in format string"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + while (*ts && *ts != ')') { + switch (*ts) { + case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; + default: break; + } + number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) + return PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %d", + ctx->head->field->type->arraysize[i], number); + if (*ts != ',' && *ts != ')') + return PyErr_Format(PyExc_ValueError, + "Expected a comma in format string, got '%c'", *ts); + if (*ts == ',') ts++; + i++; + } + if (i != ndim) + return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", + ctx->head->field->type->ndim, i); + if (!*ts) { + PyErr_SetString(PyExc_ValueError, + "Unexpected end of format string, expected ')'"); + return NULL; + } + ctx->is_valid_array = 1; + ctx->new_count = 1; + *tsp = ++ts; + return Py_None; +} +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { + int got_Z = 0; + while (1) { + switch(*ts) { + case 0: + if (ctx->enc_type != 0 && ctx->head == NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + if (ctx->head != NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + return ts; + case ' ': + case '\r': + case '\n': + ++ts; + break; + case '<': + if (!__Pyx_IsLittleEndian()) { + PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '>': + case '!': + if (__Pyx_IsLittleEndian()) { + PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '=': + case '@': + case '^': + ctx->new_packmode = *ts++; + break; + case 'T': + { + const char* ts_after_sub; + size_t i, struct_count = ctx->new_count; + size_t struct_alignment = ctx->struct_alignment; + ctx->new_count = 1; + ++ts; + if (*ts != '{') { + PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + ctx->enc_count = 0; + ctx->struct_alignment = 0; + ++ts; + ts_after_sub = ts; + for (i = 0; i != struct_count; ++i) { + ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); + if (!ts_after_sub) return NULL; + } + ts = ts_after_sub; + if (struct_alignment) ctx->struct_alignment = struct_alignment; + } + break; + case '}': + { + size_t alignment = ctx->struct_alignment; + ++ts; + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + if (alignment && ctx->fmt_offset % alignment) { + ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); + } + } + return ts; + case 'x': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->fmt_offset += ctx->new_count; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->enc_packmode = ctx->new_packmode; + ++ts; + break; + case 'Z': + got_Z = 1; + ++ts; + if (*ts != 'f' && *ts != 'd' && *ts != 'g') { + __Pyx_BufFmt_RaiseUnexpectedChar('Z'); + return NULL; + } + case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': + case 'l': case 'L': case 'q': case 'Q': + case 'f': case 'd': case 'g': + case 'O': case 'p': + if (ctx->enc_type == *ts && got_Z == ctx->is_complex && + ctx->enc_packmode == ctx->new_packmode) { + ctx->enc_count += ctx->new_count; + ctx->new_count = 1; + got_Z = 0; + ++ts; + break; + } + case 's': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_count = ctx->new_count; + ctx->enc_packmode = ctx->new_packmode; + ctx->enc_type = *ts; + ctx->is_complex = got_Z; + ++ts; + ctx->new_count = 1; + got_Z = 0; + break; + case ':': + ++ts; + while(*ts != ':') ++ts; + ++ts; + break; + case '(': + if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; + break; + default: + { + int number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + ctx->new_count = (size_t)number; + } + } + } +} +static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { + buf->buf = NULL; + buf->obj = NULL; + buf->strides = __Pyx_zeros; + buf->shape = __Pyx_zeros; + buf->suboffsets = __Pyx_minusones; +} +static CYTHON_INLINE int __Pyx_GetBufferAndValidate( + Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, + int nd, int cast, __Pyx_BufFmt_StackElem* stack) +{ + if (obj == Py_None || obj == NULL) { + __Pyx_ZeroBuffer(buf); + return 0; + } + buf->buf = NULL; + if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; + if (buf->ndim != nd) { + PyErr_Format(PyExc_ValueError, + "Buffer has wrong number of dimensions (expected %d, got %d)", + nd, buf->ndim); + goto fail; + } + if (!cast) { + __Pyx_BufFmt_Context ctx; + __Pyx_BufFmt_Init(&ctx, stack, dtype); + if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; + } + if ((unsigned)buf->itemsize != dtype->size) { + PyErr_Format(PyExc_ValueError, + "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)", + buf->itemsize, (buf->itemsize > 1) ? "s" : "", + dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); + goto fail; + } + if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; + return 0; +fail:; + __Pyx_ZeroBuffer(buf); + return -1; +} +static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { + if (info->buf == NULL) return; + if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; + __Pyx_ReleaseBuffer(info); +} + +static PyObject *__Pyx_GetBuiltinName(PyObject *name) { + PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name); + if (unlikely(!result)) { + PyErr_Format(PyExc_NameError, +#if PY_MAJOR_VERSION >= 3 + "name '%U' is not defined", name); +#else + "name '%.200s' is not defined", PyString_AS_STRING(name)); +#endif + } + return result; +} + +static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { + PyObject *result; +#if CYTHON_COMPILING_IN_CPYTHON + result = PyDict_GetItem(__pyx_d, name); + if (likely(result)) { + Py_INCREF(result); + } else { +#else + result = PyObject_GetItem(__pyx_d, name); + if (!result) { + PyErr_Clear(); +#endif + result = __Pyx_GetBuiltinName(name); + } + return result; +} + +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { + PyObject *result; + ternaryfunc call = func->ob_type->tp_call; + if (unlikely(!call)) + return PyObject_Call(func, arg, kw); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = (*call)(func, arg, kw); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { + PyObject *self, *result; + PyCFunction cfunc; + cfunc = PyCFunction_GET_FUNCTION(func); + self = PyCFunction_GET_SELF(func); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = cfunc(self, arg); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +#if CYTHON_COMPILING_IN_CPYTHON +static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *result; + PyObject *args = PyTuple_New(1); + if (unlikely(!args)) return NULL; + Py_INCREF(arg); + PyTuple_SET_ITEM(args, 0, arg); + result = __Pyx_PyObject_Call(func, args, NULL); + Py_DECREF(args); + return result; +} +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { +#ifdef __Pyx_CyFunction_USED + if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) { +#else + if (likely(PyCFunction_Check(func))) { +#endif + if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { + return __Pyx_PyObject_CallMethO(func, arg); + } + } + return __Pyx__PyObject_CallOneArg(func, arg); +} +#else +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *result; + PyObject *args = PyTuple_Pack(1, arg); + if (unlikely(!args)) return NULL; + result = __Pyx_PyObject_Call(func, args, NULL); + Py_DECREF(args); + return result; +} +#endif + +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (likely(PyObject_TypeCheck(obj, type))) + return 1; + PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", + Py_TYPE(obj)->tp_name, type->tp_name); + return 0; +} + +static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) { +#if CYTHON_COMPILING_IN_CPYTHON + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyThreadState *tstate = PyThreadState_GET(); + tmp_type = tstate->curexc_type; + tmp_value = tstate->curexc_value; + tmp_tb = tstate->curexc_traceback; + tstate->curexc_type = type; + tstate->curexc_value = value; + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +#else + PyErr_Restore(type, value, tb); +#endif +} +static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) { +#if CYTHON_COMPILING_IN_CPYTHON + PyThreadState *tstate = PyThreadState_GET(); + *type = tstate->curexc_type; + *value = tstate->curexc_value; + *tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +#else + PyErr_Fetch(type, value, tb); +#endif +} + +#if PY_MAJOR_VERSION < 3 +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, + CYTHON_UNUSED PyObject *cause) { + Py_XINCREF(type); + if (!value || value == Py_None) + value = NULL; + else + Py_INCREF(value); + if (!tb || tb == Py_None) + tb = NULL; + else { + Py_INCREF(tb); + if (!PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto raise_error; + } + } + if (PyType_Check(type)) { +#if CYTHON_COMPILING_IN_PYPY + if (!value) { + Py_INCREF(Py_None); + value = Py_None; + } +#endif + PyErr_NormalizeException(&type, &value, &tb); + } else { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto raise_error; + } + value = type; + type = (PyObject*) Py_TYPE(type); + Py_INCREF(type); + if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto raise_error; + } + } + __Pyx_ErrRestore(type, value, tb); + return; +raise_error: + Py_XDECREF(value); + Py_XDECREF(type); + Py_XDECREF(tb); + return; +} +#else +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { + PyObject* owned_instance = NULL; + if (tb == Py_None) { + tb = 0; + } else if (tb && !PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto bad; + } + if (value == Py_None) + value = 0; + if (PyExceptionInstance_Check(type)) { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto bad; + } + value = type; + type = (PyObject*) Py_TYPE(value); + } else if (PyExceptionClass_Check(type)) { + PyObject *instance_class = NULL; + if (value && PyExceptionInstance_Check(value)) { + instance_class = (PyObject*) Py_TYPE(value); + if (instance_class != type) { + int is_subclass = PyObject_IsSubclass(instance_class, type); + if (!is_subclass) { + instance_class = NULL; + } else if (unlikely(is_subclass == -1)) { + goto bad; + } else { + type = instance_class; + } + } + } + if (!instance_class) { + PyObject *args; + if (!value) + args = PyTuple_New(0); + else if (PyTuple_Check(value)) { + Py_INCREF(value); + args = value; + } else + args = PyTuple_Pack(1, value); + if (!args) + goto bad; + owned_instance = PyObject_Call(type, args, NULL); + Py_DECREF(args); + if (!owned_instance) + goto bad; + value = owned_instance; + if (!PyExceptionInstance_Check(value)) { + PyErr_Format(PyExc_TypeError, + "calling %R should have returned an instance of " + "BaseException, not %R", + type, Py_TYPE(value)); + goto bad; + } + } + } else { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto bad; + } +#if PY_VERSION_HEX >= 0x03030000 + if (cause) { +#else + if (cause && cause != Py_None) { +#endif + PyObject *fixed_cause; + if (cause == Py_None) { + fixed_cause = NULL; + } else if (PyExceptionClass_Check(cause)) { + fixed_cause = PyObject_CallObject(cause, NULL); + if (fixed_cause == NULL) + goto bad; + } else if (PyExceptionInstance_Check(cause)) { + fixed_cause = cause; + Py_INCREF(fixed_cause); + } else { + PyErr_SetString(PyExc_TypeError, + "exception causes must derive from " + "BaseException"); + goto bad; + } + PyException_SetCause(value, fixed_cause); + } + PyErr_SetObject(type, value); + if (tb) { +#if CYTHON_COMPILING_IN_PYPY + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); + Py_INCREF(tb); + PyErr_Restore(tmp_type, tmp_value, tb); + Py_XDECREF(tmp_tb); +#else + PyThreadState *tstate = PyThreadState_GET(); + PyObject* tmp_tb = tstate->curexc_traceback; + if (tb != tmp_tb) { + Py_INCREF(tb); + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_tb); + } +#endif + } +bad: + Py_XDECREF(owned_instance); + return; +} +#endif + +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { + PyErr_Format(PyExc_ValueError, + "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); +} + +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { + PyErr_Format(PyExc_ValueError, + "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", + index, (index == 1) ? "" : "s"); +} + +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { + PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); +} + +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { + PyObject *empty_list = 0; + PyObject *module = 0; + PyObject *global_dict = 0; + PyObject *empty_dict = 0; + PyObject *list; + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_import; + py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); + if (!py_import) + goto bad; + #endif + if (from_list) + list = from_list; + else { + empty_list = PyList_New(0); + if (!empty_list) + goto bad; + list = empty_list; + } + global_dict = PyModule_GetDict(__pyx_m); + if (!global_dict) + goto bad; + empty_dict = PyDict_New(); + if (!empty_dict) + goto bad; + { + #if PY_MAJOR_VERSION >= 3 + if (level == -1) { + if (strchr(__Pyx_MODULE_NAME, '.')) { + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_level = PyInt_FromLong(1); + if (!py_level) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, py_level, NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, 1); + #endif + if (!module) { + if (!PyErr_ExceptionMatches(PyExc_ImportError)) + goto bad; + PyErr_Clear(); + } + } + level = 0; + } + #endif + if (!module) { + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_level = PyInt_FromLong(level); + if (!py_level) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, py_level, NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, level); + #endif + } + } +bad: + #if PY_VERSION_HEX < 0x03030000 + Py_XDECREF(py_import); + #endif + Py_XDECREF(empty_list); + Py_XDECREF(empty_dict); + return module; +} + +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { + int start = 0, mid = 0, end = count - 1; + if (end >= 0 && code_line > entries[end].code_line) { + return count; + } + while (start < end) { + mid = start + (end - start) / 2; + if (code_line < entries[mid].code_line) { + end = mid; + } else if (code_line > entries[mid].code_line) { + start = mid + 1; + } else { + return mid; + } + } + if (code_line <= entries[mid].code_line) { + return mid; + } else { + return mid + 1; + } +} +static PyCodeObject *__pyx_find_code_object(int code_line) { + PyCodeObject* code_object; + int pos; + if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { + return NULL; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { + return NULL; + } + code_object = __pyx_code_cache.entries[pos].code_object; + Py_INCREF(code_object); + return code_object; +} +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { + int pos, i; + __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; + if (unlikely(!code_line)) { + return; + } + if (unlikely(!entries)) { + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); + if (likely(entries)) { + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = 64; + __pyx_code_cache.count = 1; + entries[0].code_line = code_line; + entries[0].code_object = code_object; + Py_INCREF(code_object); + } + return; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { + PyCodeObject* tmp = entries[pos].code_object; + entries[pos].code_object = code_object; + Py_DECREF(tmp); + return; + } + if (__pyx_code_cache.count == __pyx_code_cache.max_count) { + int new_max = __pyx_code_cache.max_count + 64; + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( + __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry)); + if (unlikely(!entries)) { + return; + } + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = new_max; + } + for (i=__pyx_code_cache.count; i>pos; i--) { + entries[i] = entries[i-1]; + } + entries[pos].code_line = code_line; + entries[pos].code_object = code_object; + __pyx_code_cache.count++; + Py_INCREF(code_object); +} + +#include "compile.h" +#include "frameobject.h" +#include "traceback.h" +static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( + const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyObject *py_srcfile = 0; + PyObject *py_funcname = 0; + #if PY_MAJOR_VERSION < 3 + py_srcfile = PyString_FromString(filename); + #else + py_srcfile = PyUnicode_FromString(filename); + #endif + if (!py_srcfile) goto bad; + if (c_line) { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #else + py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #endif + } + else { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromString(funcname); + #else + py_funcname = PyUnicode_FromString(funcname); + #endif + } + if (!py_funcname) goto bad; + py_code = __Pyx_PyCode_New( + 0, + 0, + 0, + 0, + 0, + __pyx_empty_bytes, /*PyObject *code,*/ + __pyx_empty_tuple, /*PyObject *consts,*/ + __pyx_empty_tuple, /*PyObject *names,*/ + __pyx_empty_tuple, /*PyObject *varnames,*/ + __pyx_empty_tuple, /*PyObject *freevars,*/ + __pyx_empty_tuple, /*PyObject *cellvars,*/ + py_srcfile, /*PyObject *filename,*/ + py_funcname, /*PyObject *name,*/ + py_line, + __pyx_empty_bytes /*PyObject *lnotab*/ + ); + Py_DECREF(py_srcfile); + Py_DECREF(py_funcname); + return py_code; +bad: + Py_XDECREF(py_srcfile); + Py_XDECREF(py_funcname); + return NULL; +} +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyFrameObject *py_frame = 0; + py_code = __pyx_find_code_object(c_line ? c_line : py_line); + if (!py_code) { + py_code = __Pyx_CreateCodeObjectForTraceback( + funcname, c_line, py_line, filename); + if (!py_code) goto bad; + __pyx_insert_code_object(c_line ? c_line : py_line, py_code); + } + py_frame = PyFrame_New( + PyThreadState_GET(), /*PyThreadState *tstate,*/ + py_code, /*PyCodeObject *code,*/ + __pyx_d, /*PyObject *globals,*/ + 0 /*PyObject *locals*/ + ); + if (!py_frame) goto bad; + py_frame->f_lineno = py_line; + PyTraceBack_Here(py_frame); +bad: + Py_XDECREF(py_code); + Py_XDECREF(py_frame); +} + +#if PY_MAJOR_VERSION < 3 +static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { + if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); + if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); + PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); + return -1; +} +static void __Pyx_ReleaseBuffer(Py_buffer *view) { + PyObject *obj = view->obj; + if (!obj) return; + if (PyObject_CheckBuffer(obj)) { + PyBuffer_Release(view); + return; + } + if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; } + Py_DECREF(obj); + view->obj = NULL; +} +#endif + + + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { + const int neg_one = (int) -1, const_zero = (int) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(int) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(int) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); + } + } else { + if (sizeof(int) <= sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(int), + little, !is_unsigned); + } +} + +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return ::std::complex< float >(x, y); + } + #else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return x + y*(__pyx_t_float_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + __pyx_t_float_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +#if CYTHON_CCOMPLEX +#else + static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + float denom = b.real * b.real + b.imag * b.imag; + z.real = (a.real * b.real + a.imag * b.imag) / denom; + z.imag = (a.imag * b.real - a.real * b.imag) / denom; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } + #if 1 + static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex z) { + #if !defined(HAVE_HYPOT) || defined(_MSC_VER) + return sqrtf(z.real*z.real + z.imag*z.imag); + #else + return hypotf(z.real, z.imag); + #endif + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + float r, lnr, theta, z_r, z_theta; + if (b.imag == 0 && b.real == (int)b.real) { + if (b.real < 0) { + float denom = a.real * a.real + a.imag * a.imag; + a.real = a.real / denom; + a.imag = -a.imag / denom; + b.real = -b.real; + } + switch ((int)b.real) { + case 0: + z.real = 1; + z.imag = 0; + return z; + case 1: + return a; + case 2: + z = __Pyx_c_prodf(a, a); + return __Pyx_c_prodf(a, a); + case 3: + z = __Pyx_c_prodf(a, a); + return __Pyx_c_prodf(z, a); + case 4: + z = __Pyx_c_prodf(a, a); + return __Pyx_c_prodf(z, z); + } + } + if (a.imag == 0) { + if (a.real == 0) { + return a; + } + r = a.real; + theta = 0; + } else { + r = __Pyx_c_absf(a); + theta = atan2f(a.imag, a.real); + } + lnr = logf(r); + z_r = expf(lnr * b.real - theta * b.imag); + z_theta = theta * b.real + lnr * b.imag; + z.real = z_r * cosf(z_theta); + z.imag = z_r * sinf(z_theta); + return z; + } + #endif +#endif + +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return ::std::complex< double >(x, y); + } + #else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return x + y*(__pyx_t_double_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + __pyx_t_double_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +#if CYTHON_CCOMPLEX +#else + static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + double denom = b.real * b.real + b.imag * b.imag; + z.real = (a.real * b.real + a.imag * b.imag) / denom; + z.imag = (a.imag * b.real - a.real * b.imag) / denom; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } + #if 1 + static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex z) { + #if !defined(HAVE_HYPOT) || defined(_MSC_VER) + return sqrt(z.real*z.real + z.imag*z.imag); + #else + return hypot(z.real, z.imag); + #endif + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + double r, lnr, theta, z_r, z_theta; + if (b.imag == 0 && b.real == (int)b.real) { + if (b.real < 0) { + double denom = a.real * a.real + a.imag * a.imag; + a.real = a.real / denom; + a.imag = -a.imag / denom; + b.real = -b.real; + } + switch ((int)b.real) { + case 0: + z.real = 1; + z.imag = 0; + return z; + case 1: + return a; + case 2: + z = __Pyx_c_prod(a, a); + return __Pyx_c_prod(a, a); + case 3: + z = __Pyx_c_prod(a, a); + return __Pyx_c_prod(z, a); + case 4: + z = __Pyx_c_prod(a, a); + return __Pyx_c_prod(z, z); + } + } + if (a.imag == 0) { + if (a.real == 0) { + return a; + } + r = a.real; + theta = 0; + } else { + r = __Pyx_c_abs(a); + theta = atan2(a.imag, a.real); + } + lnr = log(r); + z_r = exp(lnr * b.real - theta * b.imag); + z_theta = theta * b.real + lnr * b.imag; + z.real = z_r * cos(z_theta); + z.imag = z_r * sin(z_theta); + return z; + } + #endif +#endif + +#define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) +#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) +#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ + {\ + func_type value = func_value;\ + if (sizeof(target_type) < sizeof(func_type)) {\ + if (unlikely(value != (func_type) (target_type) value)) {\ + func_type zero = 0;\ + if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ + return (target_type) -1;\ + if (is_unsigned && unlikely(value < zero))\ + goto raise_neg_overflow;\ + else\ + goto raise_overflow;\ + }\ + }\ + return (target_type) value;\ + } + +#if CYTHON_USE_PYLONG_INTERNALS + #include "longintrepr.h" +#endif + +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { + const int neg_one = (int) -1, const_zero = (int) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(int) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (int) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { + return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { + return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { + return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (int) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(int) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, -(sdigit) digits[0]) + case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) + case -2: + if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + } +#endif + if (sizeof(int) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + int val; + PyObject *v = __Pyx_PyNumber_Int(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (int) -1; + } + } else { + int val; + PyObject *tmp = __Pyx_PyNumber_Int(x); + if (!tmp) return (int) -1; + val = __Pyx_PyInt_As_int(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to int"); + return (int) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to int"); + return (int) -1; +} + +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value) { + const enum NPY_TYPES neg_one = (enum NPY_TYPES) -1, const_zero = (enum NPY_TYPES) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(enum NPY_TYPES) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); + } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); + } + } else { + if (sizeof(enum NPY_TYPES) <= sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(enum NPY_TYPES) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(enum NPY_TYPES), + little, !is_unsigned); + } +} + +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { + const long neg_one = (long) -1, const_zero = (long) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(long) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(long) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); + } + } else { + if (sizeof(long) <= sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(long), + little, !is_unsigned); + } +} + +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { + const long neg_one = (long) -1, const_zero = (long) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(long) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (long) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { + return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { + return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { + return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (long) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(long) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, -(sdigit) digits[0]) + case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) + case -2: + if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + } +#endif + if (sizeof(long) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + long val; + PyObject *v = __Pyx_PyNumber_Int(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (long) -1; + } + } else { + long val; + PyObject *tmp = __Pyx_PyNumber_Int(x); + if (!tmp) return (long) -1; + val = __Pyx_PyInt_As_long(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to long"); + return (long) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to long"); + return (long) -1; +} + +static int __Pyx_check_binary_version(void) { + char ctversion[4], rtversion[4]; + PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); + PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); + if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { + char message[200]; + PyOS_snprintf(message, sizeof(message), + "compiletime version %s of module '%.100s' " + "does not match runtime version %s", + ctversion, __Pyx_MODULE_NAME, rtversion); + return PyErr_WarnEx(NULL, message, 1); + } + return 0; +} + +#ifndef __PYX_HAVE_RT_ImportModule +#define __PYX_HAVE_RT_ImportModule +static PyObject *__Pyx_ImportModule(const char *name) { + PyObject *py_name = 0; + PyObject *py_module = 0; + py_name = __Pyx_PyIdentifier_FromString(name); + if (!py_name) + goto bad; + py_module = PyImport_Import(py_name); + Py_DECREF(py_name); + return py_module; +bad: + Py_XDECREF(py_name); + return 0; +} +#endif + +#ifndef __PYX_HAVE_RT_ImportType +#define __PYX_HAVE_RT_ImportType +static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, + size_t size, int strict) +{ + PyObject *py_module = 0; + PyObject *result = 0; + PyObject *py_name = 0; + char warning[200]; + Py_ssize_t basicsize; +#ifdef Py_LIMITED_API + PyObject *py_basicsize; +#endif + py_module = __Pyx_ImportModule(module_name); + if (!py_module) + goto bad; + py_name = __Pyx_PyIdentifier_FromString(class_name); + if (!py_name) + goto bad; + result = PyObject_GetAttr(py_module, py_name); + Py_DECREF(py_name); + py_name = 0; + Py_DECREF(py_module); + py_module = 0; + if (!result) + goto bad; + if (!PyType_Check(result)) { + PyErr_Format(PyExc_TypeError, + "%.200s.%.200s is not a type object", + module_name, class_name); + goto bad; + } +#ifndef Py_LIMITED_API + basicsize = ((PyTypeObject *)result)->tp_basicsize; +#else + py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); + if (!py_basicsize) + goto bad; + basicsize = PyLong_AsSsize_t(py_basicsize); + Py_DECREF(py_basicsize); + py_basicsize = 0; + if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) + goto bad; +#endif + if (!strict && (size_t)basicsize > size) { + PyOS_snprintf(warning, sizeof(warning), + "%s.%s size changed, may indicate binary incompatibility", + module_name, class_name); + if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; + } + else if ((size_t)basicsize != size) { + PyErr_Format(PyExc_ValueError, + "%.200s.%.200s has the wrong size, try recompiling", + module_name, class_name); + goto bad; + } + return (PyTypeObject *)result; +bad: + Py_XDECREF(py_module); + Py_XDECREF(result); + return NULL; +} +#endif + +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { + while (t->p) { + #if PY_MAJOR_VERSION < 3 + if (t->is_unicode) { + *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); + } else if (t->intern) { + *t->p = PyString_InternFromString(t->s); + } else { + *t->p = PyString_FromStringAndSize(t->s, t->n - 1); + } + #else + if (t->is_unicode | t->is_str) { + if (t->intern) { + *t->p = PyUnicode_InternFromString(t->s); + } else if (t->encoding) { + *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); + } else { + *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); + } + } else { + *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); + } + #endif + if (!*t->p) + return -1; + ++t; + } + return 0; +} + +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { + return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); +} +static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) { + Py_ssize_t ignore; + return __Pyx_PyObject_AsStringAndSize(o, &ignore); +} +static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { +#if CYTHON_COMPILING_IN_CPYTHON && (__PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT) + if ( +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + __Pyx_sys_getdefaultencoding_not_ascii && +#endif + PyUnicode_Check(o)) { +#if PY_VERSION_HEX < 0x03030000 + char* defenc_c; + PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); + if (!defenc) return NULL; + defenc_c = PyBytes_AS_STRING(defenc); +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + { + char* end = defenc_c + PyBytes_GET_SIZE(defenc); + char* c; + for (c = defenc_c; c < end; c++) { + if ((unsigned char) (*c) >= 128) { + PyUnicode_AsASCIIString(o); + return NULL; + } + } + } +#endif + *length = PyBytes_GET_SIZE(defenc); + return defenc_c; +#else + if (__Pyx_PyUnicode_READY(o) == -1) return NULL; +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + if (PyUnicode_IS_ASCII(o)) { + *length = PyUnicode_GET_LENGTH(o); + return PyUnicode_AsUTF8(o); + } else { + PyUnicode_AsASCIIString(o); + return NULL; + } +#else + return PyUnicode_AsUTF8AndSize(o, length); +#endif +#endif + } else +#endif +#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) + if (PyByteArray_Check(o)) { + *length = PyByteArray_GET_SIZE(o); + return PyByteArray_AS_STRING(o); + } else +#endif + { + char* result; + int r = PyBytes_AsStringAndSize(o, &result, length); + if (unlikely(r < 0)) { + return NULL; + } else { + return result; + } + } +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { + int is_true = x == Py_True; + if (is_true | (x == Py_False) | (x == Py_None)) return is_true; + else return PyObject_IsTrue(x); +} +static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) { + PyNumberMethods *m; + const char *name = NULL; + PyObject *res = NULL; +#if PY_MAJOR_VERSION < 3 + if (PyInt_Check(x) || PyLong_Check(x)) +#else + if (PyLong_Check(x)) +#endif + return __Pyx_NewRef(x); + m = Py_TYPE(x)->tp_as_number; +#if PY_MAJOR_VERSION < 3 + if (m && m->nb_int) { + name = "int"; + res = PyNumber_Int(x); + } + else if (m && m->nb_long) { + name = "long"; + res = PyNumber_Long(x); + } +#else + if (m && m->nb_int) { + name = "int"; + res = PyNumber_Long(x); + } +#endif + if (res) { +#if PY_MAJOR_VERSION < 3 + if (!PyInt_Check(res) && !PyLong_Check(res)) { +#else + if (!PyLong_Check(res)) { +#endif + PyErr_Format(PyExc_TypeError, + "__%.4s__ returned non-%.4s (type %.200s)", + name, name, Py_TYPE(res)->tp_name); + Py_DECREF(res); + return NULL; + } + } + else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, + "an integer is required"); + } + return res; +} +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { + Py_ssize_t ival; + PyObject *x; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(b))) { + if (sizeof(Py_ssize_t) >= sizeof(long)) + return PyInt_AS_LONG(b); + else + return PyInt_AsSsize_t(x); + } +#endif + if (likely(PyLong_CheckExact(b))) { + #if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)b)->ob_digit; + const Py_ssize_t size = Py_SIZE(b); + if (likely(__Pyx_sst_abs(size) <= 1)) { + ival = likely(size) ? digits[0] : 0; + if (size == -1) ival = -ival; + return ival; + } else { + switch (size) { + case 2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + } + } + #endif + return PyLong_AsSsize_t(b); + } + x = PyNumber_Index(b); + if (!x) return -1; + ival = PyInt_AsSsize_t(x); + Py_DECREF(x); + return ival; +} +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { + return PyInt_FromSize_t(ival); +} + + +#endif /* Py_PYTHON_H */ diff --git a/ot/emd/emd.pyx b/ot/emd/emd.pyx new file mode 100644 index 0000000..d090cea --- /dev/null +++ b/ot/emd/emd.pyx @@ -0,0 +1,31 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Sep 11 08:42:08 2014 + +@author: rflamary +""" +import numpy as np +cimport numpy as np + +cimport cython + + + +cdef extern from "EMD.h": + void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost) + + + +@cython.boundscheck(False) +@cython.wraparound(False) +def emd( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M): + cdef int n1= M.shape[0] + cdef int n2= M.shape[1] + + cdef float cost=0 + cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([n1, n2]) + + # calling the function + EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost) + + return G diff --git a/ot/emd/full_bipartitegraph.h b/ot/emd/full_bipartitegraph.h new file mode 100644 index 0000000..87a1bec --- /dev/null +++ b/ot/emd/full_bipartitegraph.h @@ -0,0 +1,215 @@ +/* -*- mode: C++; indent-tabs-mode: nil; -*- + * + * This file has been adapted by Nicolas Bonneel (2013), + * from full_graph.h from LEMON, a generic C++ optimization library, + * to implement a lightweight fully connected bipartite graph. A previous + * version of this file is used as part of the Displacement Interpolation + * project, + * Web: http://www.cs.ubc.ca/labs/imager/tr/2011/DisplacementInterpolation/ + * + * + **** Original file Copyright Notice : + * Copyright (C) 2003-2010 + * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport + * (Egervary Research Group on Combinatorial Optimization, EGRES). + * + * Permission to use, modify and distribute this software is granted + * provided that this copyright notice appears in all copies. For + * precise terms see the accompanying LICENSE file. + * + * This software is provided "AS IS" with no warranty of any kind, + * express or implied, and with no claim as to its suitability for any + * purpose. + * + */ + +#ifndef LEMON_FULL_BIPARTITE_GRAPH_H +#define LEMON_FULL_BIPARTITE_GRAPH_H + +#include "core.h" + +///\ingroup graphs +///\file +///\brief FullBipartiteDigraph and FullBipartiteGraph classes. + + +namespace lemon { + + + class FullBipartiteDigraphBase { + public: + + typedef FullBipartiteDigraphBase Digraph; + + //class Node; + typedef int Node; + //class Arc; + typedef long long Arc; + + protected: + + int _node_num; + long long _arc_num; + + FullBipartiteDigraphBase() {} + + void construct(int n1, int n2) { _node_num = n1+n2; _arc_num = n1 * n2; _n1=n1; _n2=n2;} + + public: + + int _n1, _n2; + + + Node operator()(int ix) const { return Node(ix); } + static int index(const Node& node) { return node; } + + Arc arc(const Node& s, const Node& t) const { + if (s<_n1 && t>=_n1) + return Arc(s * _n2 + (t-_n1) ); + else + return Arc(-1); + } + + int nodeNum() const { return _node_num; } + long long arcNum() const { return _arc_num; } + + int maxNodeId() const { return _node_num - 1; } + long long maxArcId() const { return _arc_num - 1; } + + Node source(Arc arc) const { return arc / _n2; } + Node target(Arc arc) const { return (arc % _n2) + _n1; } + + static int id(Node node) { return node; } + static long long id(Arc arc) { return arc; } + + static Node nodeFromId(int id) { return Node(id);} + static Arc arcFromId(int id) { return Arc(id);} + + + Arc findArc(Node s, Node t, Arc prev = -1) const { + return prev == -1 ? arc(s, t) : -1; + } + + void first(Node& node) const { + node = _node_num - 1; + } + + static void next(Node& node) { + --node; + } + + void first(Arc& arc) const { + arc = _arc_num - 1; + } + + static void next(Arc& arc) { + --arc; + } + + void firstOut(Arc& arc, const Node& node) const { + if (node>=_n1) + arc = -1; + else + arc = (node + 1) * _n2 - 1; + } + + void nextOut(Arc& arc) const { + if (arc % _n2 == 0) arc = 0; + --arc; + } + + void firstIn(Arc& arc, const Node& node) const { + if (node<_n1) + arc = -1; + else + arc = _arc_num + node - _node_num; + } + + void nextIn(Arc& arc) const { + arc -= _n2; + if (arc < 0) arc = -1; + } + + }; + + /// \ingroup graphs + /// + /// \brief A directed full graph class. + /// + /// FullBipartiteDigraph is a simple and fast implmenetation of directed full + /// (complete) graphs. It contains an arc from each node to each node + /// (including a loop for each node), therefore the number of arcs + /// is the square of the number of nodes. + /// This class is completely static and it needs constant memory space. + /// Thus you can neither add nor delete nodes or arcs, however + /// the structure can be resized using resize(). + /// + /// This type fully conforms to the \ref concepts::Digraph "Digraph concept". + /// Most of its member functions and nested classes are documented + /// only in the concept class. + /// + /// This class provides constant time counting for nodes and arcs. + /// + /// \note FullBipartiteDigraph and FullBipartiteGraph classes are very similar, + /// but there are two differences. While this class conforms only + /// to the \ref concepts::Digraph "Digraph" concept, FullBipartiteGraph + /// conforms to the \ref concepts::Graph "Graph" concept, + /// moreover FullBipartiteGraph does not contain a loop for each + /// node as this class does. + /// + /// \sa FullBipartiteGraph + class FullBipartiteDigraph : public FullBipartiteDigraphBase { + typedef FullBipartiteDigraphBase Parent; + + public: + + /// \brief Default constructor. + /// + /// Default constructor. The number of nodes and arcs will be zero. + FullBipartiteDigraph() { construct(0,0); } + + /// \brief Constructor + /// + /// Constructor. + /// \param n The number of the nodes. + FullBipartiteDigraph(int n1, int n2) { construct(n1, n2); } + + + /// \brief Returns the node with the given index. + /// + /// Returns the node with the given index. Since this structure is + /// completely static, the nodes can be indexed with integers from + /// the range [0..nodeNum()-1]. + /// The index of a node is the same as its ID. + /// \sa index() + Node operator()(int ix) const { return Parent::operator()(ix); } + + /// \brief Returns the index of the given node. + /// + /// Returns the index of the given node. Since this structure is + /// completely static, the nodes can be indexed with integers from + /// the range [0..nodeNum()-1]. + /// The index of a node is the same as its ID. + /// \sa operator()() + static int index(const Node& node) { return Parent::index(node); } + + /// \brief Returns the arc connecting the given nodes. + /// + /// Returns the arc connecting the given nodes. + /*Arc arc(Node u, Node v) const { + return Parent::arc(u, v); + }*/ + + /// \brief Number of nodes. + int nodeNum() const { return Parent::nodeNum(); } + /// \brief Number of arcs. + long long arcNum() const { return Parent::arcNum(); } + }; + + + + +} //namespace lemon + + +#endif //LEMON_FULL_GRAPH_H diff --git a/ot/emd/network_simplex_simple.h b/ot/emd/network_simplex_simple.h new file mode 100644 index 0000000..64856a0 --- /dev/null +++ b/ot/emd/network_simplex_simple.h @@ -0,0 +1,1543 @@ +/* -*- mode: C++; indent-tabs-mode: nil; -*- + * + * + * This file has been adapted by Nicolas Bonneel (2013), + * from network_simplex.h from LEMON, a generic C++ optimization library, + * to implement a lightweight network simplex for mass transport, more + * memory efficient that the original file. A previous version of this file + * is used as part of the Displacement Interpolation project, + * Web: http://www.cs.ubc.ca/labs/imager/tr/2011/DisplacementInterpolation/ + * + * + **** Original file Copyright Notice : + * + * Copyright (C) 2003-2010 + * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport + * (Egervary Research Group on Combinatorial Optimization, EGRES). + * + * Permission to use, modify and distribute this software is granted + * provided that this copyright notice appears in all copies. For + * precise terms see the accompanying LICENSE file. + * + * This software is provided "AS IS" with no warranty of any kind, + * express or implied, and with no claim as to its suitability for any + * purpose. + * + */ + +#ifndef LEMON_NETWORK_SIMPLEX_SIMPLE_H +#define LEMON_NETWORK_SIMPLEX_SIMPLE_H +#define DEBUG_LVL 0 +#define EPSILON 10*2.2204460492503131e-016 +#define MAX_DEBUG_ITER 100000 + + +/// \ingroup min_cost_flow_algs +/// +/// \file +/// \brief Network Simplex algorithm for finding a minimum cost flow. + +// if your compiler has troubles with stdext or hashmaps, just comment the following line to use a slower std::map instead +//#define HASHMAP + +#include +#include +#include +#ifdef HASHMAP +#include +#else +#include +#endif +#include +//#include "core.h" +//#include "lmath.h" + +//#include "sparse_array_n.h" +#include "full_bipartitegraph.h" + +#define INVALIDNODE -1 +#define INVALID (-1) + +namespace lemon { + + + template + class ProxyObject; + + template + class SparseValueVector + { + public: + SparseValueVector(int n=0) + { + } + void resize(int n=0){}; + T operator[](const int id) const + { +#ifdef HASHMAP + typename stdext::hash_map::const_iterator it = data.find(id); +#else + typename std::map::const_iterator it = data.find(id); +#endif + if (it==data.end()) + return 0; + else + return it->second; + } + + ProxyObject operator[](const int id) + { + return ProxyObject( this, id ); + } + + //private: +#ifdef HASHMAP + stdext::hash_map data; +#else + std::map data; +#endif + + }; + + template + class ProxyObject { + public: + ProxyObject( SparseValueVector *v, int idx ){_v=v; _idx=idx;}; + ProxyObject & operator=( const T &v ) { + // If we get here, we know that operator[] was called to perform a write access, + // so we can insert an item in the vector if needed + if (v!=0) + _v->data[_idx]=v; + return *this; + } + + operator T() { + // If we get here, we know that operator[] was called to perform a read access, + // so we can simply return the existing object +#ifdef HASHMAP + typename stdext::hash_map::iterator it = _v->data.find(_idx); +#else + typename std::map::iterator it = _v->data.find(_idx); +#endif + if (it==_v->data.end()) + return 0; + else + return it->second; + } + + void operator+=(T val) + { + if (val==0) return; +#ifdef HASHMAP + typename stdext::hash_map::iterator it = _v->data.find(_idx); +#else + typename std::map::iterator it = _v->data.find(_idx); +#endif + if (it==_v->data.end()) + _v->data[_idx] = val; + else + { + T sum = it->second + val; + if (sum==0) + _v->data.erase(it); + else + it->second = sum; + } + } + void operator-=(T val) + { + if (val==0) return; +#ifdef HASHMAP + typename stdext::hash_map::iterator it = _v->data.find(_idx); +#else + typename std::map::iterator it = _v->data.find(_idx); +#endif + if (it==_v->data.end()) + _v->data[_idx] = -val; + else + { + T sum = it->second - val; + if (sum==0) + _v->data.erase(it); + else + it->second = sum; + } + } + + SparseValueVector *_v; + int _idx; + }; + + + + /// \addtogroup min_cost_flow_algs + /// @{ + + /// \brief Implementation of the primal Network Simplex algorithm + /// for finding a \ref min_cost_flow "minimum cost flow". + /// + /// \ref NetworkSimplexSimple implements the primal Network Simplex algorithm + /// for finding a \ref min_cost_flow "minimum cost flow" + /// \ref amo93networkflows, \ref dantzig63linearprog, + /// \ref kellyoneill91netsimplex. + /// This algorithm is a highly efficient specialized version of the + /// linear programming simplex method directly for the minimum cost + /// flow problem. + /// + /// In general, %NetworkSimplexSimple is the fastest implementation available + /// in LEMON for this problem. + /// Moreover, it supports both directions of the supply/demand inequality + /// constraints. For more information, see \ref SupplyType. + /// + /// Most of the parameters of the problem (except for the digraph) + /// can be given using separate functions, and the algorithm can be + /// executed using the \ref run() function. If some parameters are not + /// specified, then default values will be used. + /// + /// \tparam GR The digraph type the algorithm runs on. + /// \tparam V The number type used for flow amounts, capacity bounds + /// and supply values in the algorithm. By default, it is \c int. + /// \tparam C The number type used for costs and potentials in the + /// algorithm. By default, it is the same as \c V. + /// + /// \warning Both number types must be signed and all input data must + /// be integer. + /// + /// \note %NetworkSimplexSimple provides five different pivot rule + /// implementations, from which the most efficient one is used + /// by default. For more information, see \ref PivotRule. + template + class NetworkSimplexSimple + { + public: + + /// \brief Constructor. + /// + /// The constructor of the class. + /// + /// \param graph The digraph the algorithm runs on. + /// \param arc_mixing Indicate if the arcs have to be stored in a + /// mixed order in the internal data structure. + /// In special cases, it could lead to better overall performance, + /// but it is usually slower. Therefore it is disabled by default. + NetworkSimplexSimple(const GR& graph, bool arc_mixing, int nbnodes, long long nb_arcs,double maxiters) : + _graph(graph), //_arc_id(graph), + _arc_mixing(arc_mixing), _init_nb_nodes(nbnodes), _init_nb_arcs(nb_arcs), + MAX(std::numeric_limits::max()), + INF(std::numeric_limits::has_infinity ? + std::numeric_limits::infinity() : MAX) + { + // Reset data structures + reset(); + max_iter=maxiters; + } + + /// The type of the flow amounts, capacity bounds and supply values + typedef V Value; + /// The type of the arc costs + typedef C Cost; + + public: + + /// \brief Problem type constants for the \c run() function. + /// + /// Enum type containing the problem type constants that can be + /// returned by the \ref run() function of the algorithm. + enum ProblemType { + /// The problem has no feasible solution (flow). + INFEASIBLE, + /// The problem has optimal solution (i.e. it is feasible and + /// bounded), and the algorithm has found optimal flow and node + /// potentials (primal and dual solutions). + OPTIMAL, + /// The objective function of the problem is unbounded, i.e. + /// there is a directed cycle having negative total cost and + /// infinite upper bound. + UNBOUNDED + }; + + /// \brief Constants for selecting the type of the supply constraints. + /// + /// Enum type containing constants for selecting the supply type, + /// i.e. the direction of the inequalities in the supply/demand + /// constraints of the \ref min_cost_flow "minimum cost flow problem". + /// + /// The default supply type is \c GEQ, the \c LEQ type can be + /// selected using \ref supplyType(). + /// The equality form is a special case of both supply types. + enum SupplyType { + /// This option means that there are "greater or equal" + /// supply/demand constraints in the definition of the problem. + GEQ, + /// This option means that there are "less or equal" + /// supply/demand constraints in the definition of the problem. + LEQ + }; + + + + private: + + double max_iter; + TEMPLATE_DIGRAPH_TYPEDEFS(GR); + + typedef std::vector IntVector; + typedef std::vector UHalfIntVector; + typedef std::vector ValueVector; + typedef std::vector CostVector; + // typedef SparseValueVector CostVector; + typedef std::vector BoolVector; + // Note: vector is used instead of vector for efficiency reasons + + // State constants for arcs + enum ArcState { + STATE_UPPER = -1, + STATE_TREE = 0, + STATE_LOWER = 1 + }; + + typedef std::vector StateVector; + // Note: vector is used instead of vector for + // efficiency reasons + + private: + + // Data related to the underlying digraph + const GR &_graph; + int _node_num; + int _arc_num; + int _all_arc_num; + int _search_arc_num; + + // Parameters of the problem + SupplyType _stype; + Value _sum_supply; + + inline int _node_id(int n) const {return _node_num-n-1;} ; + + //IntArcMap _arc_id; + UHalfIntVector _source; + UHalfIntVector _target; + bool _arc_mixing; + public: + // Node and arc data + CostVector _cost; + ValueVector _supply; + ValueVector _flow; + //SparseValueVector _flow; + CostVector _pi; + + + private: + // Data for storing the spanning tree structure + IntVector _parent; + IntVector _pred; + IntVector _thread; + IntVector _rev_thread; + IntVector _succ_num; + IntVector _last_succ; + IntVector _dirty_revs; + BoolVector _forward; + StateVector _state; + int _root; + + // Temporary data used in the current pivot iteration + int in_arc, join, u_in, v_in, u_out, v_out; + int first, second, right, last; + int stem, par_stem, new_stem; + Value delta; + + const Value MAX; + + int mixingCoeff; + + public: + + /// \brief Constant for infinite upper bounds (capacities). + /// + /// Constant for infinite upper bounds (capacities). + /// It is \c std::numeric_limits::infinity() if available, + /// \c std::numeric_limits::max() otherwise. + const Value INF; + + private: + + // thank you to DVK and MizardX from StackOverflow for this function! + inline int sequence(int k) const { + int smallv = (k > num_total_big_subsequence_numbers) & 1; + + k -= num_total_big_subsequence_numbers * smallv; + int subsequence_length2 = subsequence_length- smallv; + int subsequence_num = (k / subsequence_length2) + num_big_subseqiences * smallv; + int subsequence_offset = (k % subsequence_length2) * mixingCoeff; + + return subsequence_offset + subsequence_num; + } + int subsequence_length; + int num_big_subseqiences; + int num_total_big_subsequence_numbers; + + inline int getArcID(const Arc &arc) const + { + //int n = _arc_num-arc._id-1; + int n = _arc_num-GR::id(arc)-1; + + //int a = mixingCoeff*(n%mixingCoeff) + n/mixingCoeff; + //int b = _arc_id[arc]; + if (_arc_mixing) + return sequence(n); + else + return n; + } + + // finally unused because too slow + inline int getSource(const int arc) const + { + //int a = _source[arc]; + //return a; + + int n = _arc_num-arc-1; + if (_arc_mixing) + n = mixingCoeff*(n%mixingCoeff) + n/mixingCoeff; + + int b; + if (n>=0) + b = _node_id(_graph.source(GR::arcFromId( n ) )); + else + { + n = arc+1-_arc_num; + if ( n<=_node_num) + b = _node_num; + else + if ( n>=_graph._n1) + b = _graph._n1; + else + b = _graph._n1-n; + } + + return b; + } + + + + // Implementation of the Block Search pivot rule + class BlockSearchPivotRule + { + private: + + // References to the NetworkSimplexSimple class + const UHalfIntVector &_source; + const UHalfIntVector &_target; + const CostVector &_cost; + const StateVector &_state; + const CostVector &_pi; + int &_in_arc; + int _search_arc_num; + + // Pivot rule data + int _block_size; + int _next_arc; + NetworkSimplexSimple &_ns; + + public: + + // Constructor + BlockSearchPivotRule(NetworkSimplexSimple &ns) : + _source(ns._source), _target(ns._target), + _cost(ns._cost), _state(ns._state), _pi(ns._pi), + _in_arc(ns.in_arc), _search_arc_num(ns._search_arc_num), + _next_arc(0),_ns(ns) + { + // The main parameters of the pivot rule + const double BLOCK_SIZE_FACTOR = 1.0; + const int MIN_BLOCK_SIZE = 10; + + _block_size = std::max( int(BLOCK_SIZE_FACTOR * + std::sqrt(double(_search_arc_num))), + MIN_BLOCK_SIZE ); + } + // Find next entering arc + bool findEnteringArc() { + Cost c, min = 0; + int e; + int cnt = _block_size; + double a; + for (e = _next_arc; e != _search_arc_num; ++e) { + c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); + if (c < min) { + min = c; + _in_arc = e; + } + if (--cnt == 0) { + a=fabs(_pi[_source[_in_arc]])>fabs(_pi[_target[_in_arc]]) ? fabs(_pi[_source[_in_arc]]):fabs(_pi[_target[_in_arc]]); + a=a>fabs(_cost[_in_arc])?a:fabs(_cost[_in_arc]); + if (min < -EPSILON*a) goto search_end; + cnt = _block_size; + } + } + for (e = 0; e != _next_arc; ++e) { + c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); + if (c < min) { + min = c; + _in_arc = e; + } + if (--cnt == 0) { + a=fabs(_pi[_source[_in_arc]])>fabs(_pi[_target[_in_arc]]) ? fabs(_pi[_source[_in_arc]]):fabs(_pi[_target[_in_arc]]); + a=a>fabs(_cost[_in_arc])?a:fabs(_cost[_in_arc]); + if (min < -EPSILON*a) goto search_end; + cnt = _block_size; + } + } + a=fabs(_pi[_source[_in_arc]])>fabs(_pi[_target[_in_arc]]) ? fabs(_pi[_source[_in_arc]]):fabs(_pi[_target[_in_arc]]); + a=a>fabs(_cost[_in_arc])?a:fabs(_cost[_in_arc]); + if (min >= -EPSILON*a) return false; + + search_end: + _next_arc = e; + return true; + } + + }; //class BlockSearchPivotRule + + + + public: + + + + int _init_nb_nodes; + long long _init_nb_arcs; + + /// \name Parameters + /// The parameters of the algorithm can be specified using these + /// functions. + + /// @{ + + + /// \brief Set the costs of the arcs. + /// + /// This function sets the costs of the arcs. + /// If it is not used before calling \ref run(), the costs + /// will be set to \c 1 on all arcs. + /// + /// \param map An arc map storing the costs. + /// Its \c Value type must be convertible to the \c Cost type + /// of the algorithm. + /// + /// \return (*this) + template + NetworkSimplexSimple& costMap(const CostMap& map) { + Arc a; _graph.first(a); + for (; a != INVALID; _graph.next(a)) { + _cost[getArcID(a)] = map[a]; + } + return *this; + } + + + /// \brief Set the costs of one arc. + /// + /// This function sets the costs of one arcs. + /// Done for memory reasons + /// + /// \param arc An arc. + /// \param arc A cost + /// + /// \return (*this) + template + NetworkSimplexSimple& setCost(const Arc& arc, const Value cost) { + _cost[getArcID(arc)] = cost; + return *this; + } + + + /// \brief Set the supply values of the nodes. + /// + /// This function sets the supply values of the nodes. + /// If neither this function nor \ref stSupply() is used before + /// calling \ref run(), the supply of each node will be set to zero. + /// + /// \param map A node map storing the supply values. + /// Its \c Value type must be convertible to the \c Value type + /// of the algorithm. + /// + /// \return (*this) + template + NetworkSimplexSimple& supplyMap(const SupplyMap& map) { + Node n; _graph.first(n); + for (; n != INVALIDNODE; _graph.next(n)) { + _supply[_node_id(n)] = map[n]; + } + return *this; + } + template + NetworkSimplexSimple& supplyMap(const SupplyMap* map1, int n1, const SupplyMap* map2, int n2) { + Node n; _graph.first(n); + for (; n != INVALIDNODE; _graph.next(n)) { + if (n + NetworkSimplexSimple& supplyMapAll(SupplyMap val1, int n1, SupplyMap val2, int n2) { + Node n; _graph.first(n); + for (; n != INVALIDNODE; _graph.next(n)) { + if (n(*this) + NetworkSimplexSimple& stSupply(const Node& s, const Node& t, Value k) { + for (int i = 0; i != _node_num; ++i) { + _supply[i] = 0; + } + _supply[_node_id(s)] = k; + _supply[_node_id(t)] = -k; + return *this; + } + + /// \brief Set the type of the supply constraints. + /// + /// This function sets the type of the supply/demand constraints. + /// If it is not used before calling \ref run(), the \ref GEQ supply + /// type will be used. + /// + /// For more information, see \ref SupplyType. + /// + /// \return (*this) + NetworkSimplexSimple& supplyType(SupplyType supply_type) { + _stype = supply_type; + return *this; + } + + /// @} + + /// \name Execution Control + /// The algorithm can be executed using \ref run(). + + /// @{ + + /// \brief Run the algorithm. + /// + /// This function runs the algorithm. + /// The paramters can be specified using functions \ref lowerMap(), + /// \ref upperMap(), \ref costMap(), \ref supplyMap(), \ref stSupply(), + /// \ref supplyType(). + /// For example, + /// \code + /// NetworkSimplexSimple ns(graph); + /// ns.lowerMap(lower).upperMap(upper).costMap(cost) + /// .supplyMap(sup).run(); + /// \endcode + /// + /// This function can be called more than once. All the given parameters + /// are kept for the next call, unless \ref resetParams() or \ref reset() + /// is used, thus only the modified parameters have to be set again. + /// If the underlying digraph was also modified after the construction + /// of the class (or the last \ref reset() call), then the \ref reset() + /// function must be called. + /// + /// \param pivot_rule The pivot rule that will be used during the + /// algorithm. For more information, see \ref PivotRule. + /// + /// \return \c INFEASIBLE if no feasible flow exists, + /// \n \c OPTIMAL if the problem has optimal solution + /// (i.e. it is feasible and bounded), and the algorithm has found + /// optimal flow and node potentials (primal and dual solutions), + /// \n \c UNBOUNDED if the objective function of the problem is + /// unbounded, i.e. there is a directed cycle having negative total + /// cost and infinite upper bound. + /// + /// \see ProblemType, PivotRule + /// \see resetParams(), reset() + ProblemType run() { +#if DEBUG_LVL>0 + mexPrintf("OPTIMAL = %d\nINFEASIBLE = %d\nUNBOUNDED = %d\n",OPTIMAL,INFEASIBLE,UNBOUNDED); + mexEvalString("drawnow;"); +#endif + + if (!init()) return INFEASIBLE; +#if DEBUG_LVL>0 + mexPrintf("Init done, starting iterations\n"); + mexEvalString("drawnow;"); +#endif + return start(); + } + + /// \brief Reset all the parameters that have been given before. + /// + /// This function resets all the paramaters that have been given + /// before using functions \ref lowerMap(), \ref upperMap(), + /// \ref costMap(), \ref supplyMap(), \ref stSupply(), \ref supplyType(). + /// + /// It is useful for multiple \ref run() calls. Basically, all the given + /// parameters are kept for the next \ref run() call, unless + /// \ref resetParams() or \ref reset() is used. + /// If the underlying digraph was also modified after the construction + /// of the class or the last \ref reset() call, then the \ref reset() + /// function must be used, otherwise \ref resetParams() is sufficient. + /// + /// For example, + /// \code + /// NetworkSimplexSimple ns(graph); + /// + /// // First run + /// ns.lowerMap(lower).upperMap(upper).costMap(cost) + /// .supplyMap(sup).run(); + /// + /// // Run again with modified cost map (resetParams() is not called, + /// // so only the cost map have to be set again) + /// cost[e] += 100; + /// ns.costMap(cost).run(); + /// + /// // Run again from scratch using resetParams() + /// // (the lower bounds will be set to zero on all arcs) + /// ns.resetParams(); + /// ns.upperMap(capacity).costMap(cost) + /// .supplyMap(sup).run(); + /// \endcode + /// + /// \return (*this) + /// + /// \see reset(), run() + NetworkSimplexSimple& resetParams() { + for (int i = 0; i != _node_num; ++i) { + _supply[i] = 0; + } + for (int i = 0; i != _arc_num; ++i) { + _cost[i] = 1; + } + _stype = GEQ; + return *this; + } + + + + int divid (int x, int y) + { + return (x-x%y)/y; + } + + /// \brief Reset the internal data structures and all the parameters + /// that have been given before. + /// + /// This function resets the internal data structures and all the + /// paramaters that have been given before using functions \ref lowerMap(), + /// \ref upperMap(), \ref costMap(), \ref supplyMap(), \ref stSupply(), + /// \ref supplyType(). + /// + /// It is useful for multiple \ref run() calls. Basically, all the given + /// parameters are kept for the next \ref run() call, unless + /// \ref resetParams() or \ref reset() is used. + /// If the underlying digraph was also modified after the construction + /// of the class or the last \ref reset() call, then the \ref reset() + /// function must be used, otherwise \ref resetParams() is sufficient. + /// + /// See \ref resetParams() for examples. + /// + /// \return (*this) + /// + /// \see resetParams(), run() + NetworkSimplexSimple& reset() { + // Resize vectors + _node_num = _init_nb_nodes; + _arc_num = _init_nb_arcs; + int all_node_num = _node_num + 1; + int max_arc_num = _arc_num + 2 * _node_num; + + _source.resize(max_arc_num); + _target.resize(max_arc_num); + + _cost.resize(max_arc_num); + _supply.resize(all_node_num); + _flow.resize(max_arc_num); + _pi.resize(all_node_num); + + _parent.resize(all_node_num); + _pred.resize(all_node_num); + _forward.resize(all_node_num); + _thread.resize(all_node_num); + _rev_thread.resize(all_node_num); + _succ_num.resize(all_node_num); + _last_succ.resize(all_node_num); + _state.resize(max_arc_num); + + + //_arc_mixing=false; + if (_arc_mixing) { + // Store the arcs in a mixed order + int k = std::max(int(std::sqrt(double(_arc_num))), 10); + mixingCoeff = k; + subsequence_length = _arc_num / mixingCoeff + 1; + num_big_subseqiences = _arc_num % mixingCoeff; + num_total_big_subsequence_numbers = subsequence_length * num_big_subseqiences; + + int i = 0, j = 0; + Arc a; _graph.first(a); + for (; a != INVALID; _graph.next(a)) { + _source[i] = _node_id(_graph.source(a)); + _target[i] = _node_id(_graph.target(a)); + //_arc_id[a] = i; + if ((i += k) >= _arc_num) i = ++j; + } + } else { + // Store the arcs in the original order + int i = 0; + Arc a; _graph.first(a); + for (; a != INVALID; _graph.next(a), ++i) { + _source[i] = _node_id(_graph.source(a)); + _target[i] = _node_id(_graph.target(a)); + //_arc_id[a] = i; + } + } + + // Reset parameters + resetParams(); + return *this; + } + + /// @} + + /// \name Query Functions + /// The results of the algorithm can be obtained using these + /// functions.\n + /// The \ref run() function must be called before using them. + + /// @{ + + /// \brief Return the total cost of the found flow. + /// + /// This function returns the total cost of the found flow. + /// Its complexity is O(e). + /// + /// \note The return type of the function can be specified as a + /// template parameter. For example, + /// \code + /// ns.totalCost(); + /// \endcode + /// It is useful if the total cost cannot be stored in the \c Cost + /// type of the algorithm, which is the default return type of the + /// function. + /// + /// \pre \ref run() must be called before using this function. + /*template + Number totalCost() const { + Number c = 0; + for (ArcIt a(_graph); a != INVALID; ++a) { + int i = getArcID(a); + c += Number(_flow[i]) * Number(_cost[i]); + } + return c; + }*/ + + template + Number totalCost() const { + Number c = 0; + + /*#ifdef HASHMAP + typename stdext::hash_map::const_iterator it; + #else + typename std::map::const_iterator it; + #endif + for (it = _flow.data.begin(); it!=_flow.data.end(); ++it) + c += Number(it->second) * Number(_cost[it->first]); + return c;*/ + + for (int i=0; i<_flow.size(); i++) + c += _flow[i] * Number(_cost[i]); + return c; + + } + +#ifndef DOXYGEN + Cost totalCost() const { + return totalCost(); + } +#endif + + /// \brief Return the flow on the given arc. + /// + /// This function returns the flow on the given arc. + /// + /// \pre \ref run() must be called before using this function. + Value flow(const Arc& a) const { + return _flow[getArcID(a)]; + } + + /// \brief Return the flow map (the primal solution). + /// + /// This function copies the flow value on each arc into the given + /// map. The \c Value type of the algorithm must be convertible to + /// the \c Value type of the map. + /// + /// \pre \ref run() must be called before using this function. + template + void flowMap(FlowMap &map) const { + Arc a; _graph.first(a); + for (; a != INVALID; _graph.next(a)) { + map.set(a, _flow[getArcID(a)]); + } + } + + /// \brief Return the potential (dual value) of the given node. + /// + /// This function returns the potential (dual value) of the + /// given node. + /// + /// \pre \ref run() must be called before using this function. + Cost potential(const Node& n) const { + return _pi[_node_id(n)]; + } + + /// \brief Return the potential map (the dual solution). + /// + /// This function copies the potential (dual value) of each node + /// into the given map. + /// The \c Cost type of the algorithm must be convertible to the + /// \c Value type of the map. + /// + /// \pre \ref run() must be called before using this function. + template + void potentialMap(PotentialMap &map) const { + Node n; _graph.first(n); + for (; n != INVALID; _graph.next(n)) { + map.set(n, _pi[_node_id(n)]); + } + } + + /// @} + + private: + + // Initialize internal data structures + bool init() { + if (_node_num == 0) return false; + /* + // Check the sum of supply values + _sum_supply = 0; + for (int i = 0; i != _node_num; ++i) { + _sum_supply += _supply[i]; + } + if ( !((_stype == GEQ && _sum_supply <= _epsilon ) || + (_stype == LEQ && _sum_supply >= -_epsilon )) ) return false; + */ + + // Initialize artifical cost + Cost ART_COST; + if (std::numeric_limits::is_exact) { + ART_COST = std::numeric_limits::max() / 2 + 1; + } else { + ART_COST = 0; + for (int i = 0; i != _arc_num; ++i) { + if (_cost[i] > ART_COST) ART_COST = _cost[i]; + } + ART_COST = (ART_COST + 1) * _node_num; + } + + // Initialize arc maps + for (int i = 0; i != _arc_num; ++i) { + //_flow[i] = 0; //by default, the sparse matrix is empty + _state[i] = STATE_LOWER; + } + + // Set data for the artificial root node + _root = _node_num; + _parent[_root] = -1; + _pred[_root] = -1; + _thread[_root] = 0; + _rev_thread[0] = _root; + _succ_num[_root] = _node_num + 1; + _last_succ[_root] = _root - 1; + _supply[_root] = -_sum_supply; + _pi[_root] = 0; + + // Add artificial arcs and initialize the spanning tree data structure + if (_sum_supply == 0) { + // EQ supply constraints + _search_arc_num = _arc_num; + _all_arc_num = _arc_num + _node_num; + for (int u = 0, e = _arc_num; u != _node_num; ++u, ++e) { + _parent[u] = _root; + _pred[u] = e; + _thread[u] = u + 1; + _rev_thread[u + 1] = u; + _succ_num[u] = 1; + _last_succ[u] = u; + _state[e] = STATE_TREE; + if (_supply[u] >= 0) { + _forward[u] = true; + _pi[u] = 0; + _source[e] = u; + _target[e] = _root; + _flow[e] = _supply[u]; + _cost[e] = 0; + } else { + _forward[u] = false; + _pi[u] = ART_COST; + _source[e] = _root; + _target[e] = u; + _flow[e] = -_supply[u]; + _cost[e] = ART_COST; + } + } + } + else if (_sum_supply > 0) { + // LEQ supply constraints + _search_arc_num = _arc_num + _node_num; + int f = _arc_num + _node_num; + for (int u = 0, e = _arc_num; u != _node_num; ++u, ++e) { + _parent[u] = _root; + _thread[u] = u + 1; + _rev_thread[u + 1] = u; + _succ_num[u] = 1; + _last_succ[u] = u; + if (_supply[u] >= 0) { + _forward[u] = true; + _pi[u] = 0; + _pred[u] = e; + _source[e] = u; + _target[e] = _root; + _flow[e] = _supply[u]; + _cost[e] = 0; + _state[e] = STATE_TREE; + } else { + _forward[u] = false; + _pi[u] = ART_COST; + _pred[u] = f; + _source[f] = _root; + _target[f] = u; + _flow[f] = -_supply[u]; + _cost[f] = ART_COST; + _state[f] = STATE_TREE; + _source[e] = u; + _target[e] = _root; + //_flow[e] = 0; //by default, the sparse matrix is empty + _cost[e] = 0; + _state[e] = STATE_LOWER; + ++f; + } + } + _all_arc_num = f; + } + else { + // GEQ supply constraints + _search_arc_num = _arc_num + _node_num; + int f = _arc_num + _node_num; + for (int u = 0, e = _arc_num; u != _node_num; ++u, ++e) { + _parent[u] = _root; + _thread[u] = u + 1; + _rev_thread[u + 1] = u; + _succ_num[u] = 1; + _last_succ[u] = u; + if (_supply[u] <= 0) { + _forward[u] = false; + _pi[u] = 0; + _pred[u] = e; + _source[e] = _root; + _target[e] = u; + _flow[e] = -_supply[u]; + _cost[e] = 0; + _state[e] = STATE_TREE; + } else { + _forward[u] = true; + _pi[u] = -ART_COST; + _pred[u] = f; + _source[f] = u; + _target[f] = _root; + _flow[f] = _supply[u]; + _state[f] = STATE_TREE; + _cost[f] = ART_COST; + _source[e] = _root; + _target[e] = u; + //_flow[e] = 0; //by default, the sparse matrix is empty + _cost[e] = 0; + _state[e] = STATE_LOWER; + ++f; + } + } + _all_arc_num = f; + } + + return true; + } + + // Find the join node + void findJoinNode() { + int u = _source[in_arc]; + int v = _target[in_arc]; + while (u != v) { + if (_succ_num[u] < _succ_num[v]) { + u = _parent[u]; + } else { + v = _parent[v]; + } + } + join = u; + } + + // Find the leaving arc of the cycle and returns true if the + // leaving arc is not the same as the entering arc + bool findLeavingArc() { + // Initialize first and second nodes according to the direction + // of the cycle + if (_state[in_arc] == STATE_LOWER) { + first = _source[in_arc]; + second = _target[in_arc]; + } else { + first = _target[in_arc]; + second = _source[in_arc]; + } + delta = INF; + int result = 0; + Value d; + int e; + + // Search the cycle along the path form the first node to the root + for (int u = first; u != join; u = _parent[u]) { + e = _pred[u]; + d = _forward[u] ? _flow[e] : INF ; + if (d < delta) { + delta = d; + u_out = u; + result = 1; + } + } + // Search the cycle along the path form the second node to the root + for (int u = second; u != join; u = _parent[u]) { + e = _pred[u]; + d = _forward[u] ? INF : _flow[e]; + if (d <= delta) { + delta = d; + u_out = u; + result = 2; + } + } + + if (result == 1) { + u_in = first; + v_in = second; + } else { + u_in = second; + v_in = first; + } + return result != 0; + } + + // Change _flow and _state vectors + void changeFlow(bool change) { + // Augment along the cycle + if (delta > 0) { + Value val = _state[in_arc] * delta; + _flow[in_arc] += val; + for (int u = _source[in_arc]; u != join; u = _parent[u]) { + _flow[_pred[u]] += _forward[u] ? -val : val; + } + for (int u = _target[in_arc]; u != join; u = _parent[u]) { + _flow[_pred[u]] += _forward[u] ? val : -val; + } + } + // Update the state of the entering and leaving arcs + if (change) { + _state[in_arc] = STATE_TREE; + _state[_pred[u_out]] = + (_flow[_pred[u_out]] == 0) ? STATE_LOWER : STATE_UPPER; + } else { + _state[in_arc] = -_state[in_arc]; + } + } + + // Update the tree structure + void updateTreeStructure() { + int u, w; + int old_rev_thread = _rev_thread[u_out]; + int old_succ_num = _succ_num[u_out]; + int old_last_succ = _last_succ[u_out]; + v_out = _parent[u_out]; + + u = _last_succ[u_in]; // the last successor of u_in + right = _thread[u]; // the node after it + + // Handle the case when old_rev_thread equals to v_in + // (it also means that join and v_out coincide) + if (old_rev_thread == v_in) { + last = _thread[_last_succ[u_out]]; + } else { + last = _thread[v_in]; + } + + // Update _thread and _parent along the stem nodes (i.e. the nodes + // between u_in and u_out, whose parent have to be changed) + _thread[v_in] = stem = u_in; + _dirty_revs.clear(); + _dirty_revs.push_back(v_in); + par_stem = v_in; + while (stem != u_out) { + // Insert the next stem node into the thread list + new_stem = _parent[stem]; + _thread[u] = new_stem; + _dirty_revs.push_back(u); + + // Remove the subtree of stem from the thread list + w = _rev_thread[stem]; + _thread[w] = right; + _rev_thread[right] = w; + + // Change the parent node and shift stem nodes + _parent[stem] = par_stem; + par_stem = stem; + stem = new_stem; + + // Update u and right + u = _last_succ[stem] == _last_succ[par_stem] ? + _rev_thread[par_stem] : _last_succ[stem]; + right = _thread[u]; + } + _parent[u_out] = par_stem; + _thread[u] = last; + _rev_thread[last] = u; + _last_succ[u_out] = u; + + // Remove the subtree of u_out from the thread list except for + // the case when old_rev_thread equals to v_in + // (it also means that join and v_out coincide) + if (old_rev_thread != v_in) { + _thread[old_rev_thread] = right; + _rev_thread[right] = old_rev_thread; + } + + // Update _rev_thread using the new _thread values + for (int i = 0; i != int(_dirty_revs.size()); ++i) { + u = _dirty_revs[i]; + _rev_thread[_thread[u]] = u; + } + + // Update _pred, _forward, _last_succ and _succ_num for the + // stem nodes from u_out to u_in + int tmp_sc = 0, tmp_ls = _last_succ[u_out]; + u = u_out; + while (u != u_in) { + w = _parent[u]; + _pred[u] = _pred[w]; + _forward[u] = !_forward[w]; + tmp_sc += _succ_num[u] - _succ_num[w]; + _succ_num[u] = tmp_sc; + _last_succ[w] = tmp_ls; + u = w; + } + _pred[u_in] = in_arc; + _forward[u_in] = (u_in == _source[in_arc]); + _succ_num[u_in] = old_succ_num; + + // Set limits for updating _last_succ form v_in and v_out + // towards the root + int up_limit_in = -1; + int up_limit_out = -1; + if (_last_succ[join] == v_in) { + up_limit_out = join; + } else { + up_limit_in = join; + } + + // Update _last_succ from v_in towards the root + for (u = v_in; u != up_limit_in && _last_succ[u] == v_in; + u = _parent[u]) { + _last_succ[u] = _last_succ[u_out]; + } + // Update _last_succ from v_out towards the root + if (join != old_rev_thread && v_in != old_rev_thread) { + for (u = v_out; u != up_limit_out && _last_succ[u] == old_last_succ; + u = _parent[u]) { + _last_succ[u] = old_rev_thread; + } + } else { + for (u = v_out; u != up_limit_out && _last_succ[u] == old_last_succ; + u = _parent[u]) { + _last_succ[u] = _last_succ[u_out]; + } + } + + // Update _succ_num from v_in to join + for (u = v_in; u != join; u = _parent[u]) { + _succ_num[u] += old_succ_num; + } + // Update _succ_num from v_out to join + for (u = v_out; u != join; u = _parent[u]) { + _succ_num[u] -= old_succ_num; + } + } + + // Update potentials + void updatePotential() { + Cost sigma = _forward[u_in] ? + _pi[v_in] - _pi[u_in] - _cost[_pred[u_in]] : + _pi[v_in] - _pi[u_in] + _cost[_pred[u_in]]; + // Update potentials in the subtree, which has been moved + int end = _thread[_last_succ[u_in]]; + for (int u = u_in; u != end; u = _thread[u]) { + _pi[u] += sigma; + } + } + + // Heuristic initial pivots + bool initialPivots() { + Value curr, total = 0; + std::vector supply_nodes, demand_nodes; + Node u; _graph.first(u); + for (; u != INVALIDNODE; _graph.next(u)) { + curr = _supply[_node_id(u)]; + if (curr > 0) { + total += curr; + supply_nodes.push_back(u); + } + else if (curr < 0) { + demand_nodes.push_back(u); + } + } + if (_sum_supply > 0) total -= _sum_supply; + if (total <= 0) return true; + + IntVector arc_vector; + if (_sum_supply >= 0) { + if (supply_nodes.size() == 1 && demand_nodes.size() == 1) { + // Perform a reverse graph search from the sink to the source + //typename GR::template NodeMap reached(_graph, false); + BoolVector reached(_node_num, false); + Node s = supply_nodes[0], t = demand_nodes[0]; + std::vector stack; + reached[t] = true; + stack.push_back(t); + while (!stack.empty()) { + Node u, v = stack.back(); + stack.pop_back(); + if (v == s) break; + Arc a; _graph.firstIn(a, v); + for (; a != INVALID; _graph.nextIn(a)) { + if (reached[u = _graph.source(a)]) continue; + int j = getArcID(a); + if (INF >= total) { + arc_vector.push_back(j); + reached[u] = true; + stack.push_back(u); + } + } + } + } else { + // Find the min. cost incomming arc for each demand node + for (int i = 0; i != int(demand_nodes.size()); ++i) { + Node v = demand_nodes[i]; + Cost c, min_cost = std::numeric_limits::max(); + Arc min_arc = INVALID; + Arc a; _graph.firstIn(a, v); + for (; a != INVALID; _graph.nextIn(a)) { + c = _cost[getArcID(a)]; + if (c < min_cost) { + min_cost = c; + min_arc = a; + } + } + if (min_arc != INVALID) { + arc_vector.push_back(getArcID(min_arc)); + } + } + } + } else { + // Find the min. cost outgoing arc for each supply node + for (int i = 0; i != int(supply_nodes.size()); ++i) { + Node u = supply_nodes[i]; + Cost c, min_cost = std::numeric_limits::max(); + Arc min_arc = INVALID; + Arc a; _graph.firstOut(a, u); + for (; a != INVALID; _graph.nextOut(a)) { + c = _cost[getArcID(a)]; + if (c < min_cost) { + min_cost = c; + min_arc = a; + } + } + if (min_arc != INVALID) { + arc_vector.push_back(getArcID(min_arc)); + } + } + } + + // Perform heuristic initial pivots + for (int i = 0; i != int(arc_vector.size()); ++i) { + in_arc = arc_vector[i]; + // l'erreur est probablement ici... + if (_state[in_arc] * (_cost[in_arc] + _pi[_source[in_arc]] - + _pi[_target[in_arc]]) >= 0) continue; + findJoinNode(); + bool change = findLeavingArc(); + if (delta >= MAX) return false; + changeFlow(change); + if (change) { + updateTreeStructure(); + updatePotential(); + } + } + return true; + } + + // Execute the algorithm + ProblemType start() { + return start(); + } + + template + ProblemType start() { + PivotRuleImpl pivot(*this); + double prevCost=-1; + + // Perform heuristic initial pivots + if (!initialPivots()) return UNBOUNDED; + +#if DEBUG_LVL>0 + int niter=0; +#endif + int iter_number=0; + //pivot.setDantzig(true); + // Execute the Network Simplex algorithm + while (pivot.findEnteringArc()) { + if(++iter_number>=max_iter&&max_iter>0){ + char errMess[1000]; + // sprintf( errMess, "RESULT MIGHT BE INACURATE\nMax number of iteration reached, currently \%d. Sometimes iterations go on in cycle even though the solution has been reached, to check if it's the case here have a look at the minimal reduced cost. If it is very close to machine precision, you might actually have the correct solution, if not try setting the maximum number of iterations a bit higher",iter_number ); + // mexWarnMsgTxt(errMess); + break; + } +#if DEBUG_LVL>0 + if(niter>MAX_DEBUG_ITER) + break; + if(++niter%1000==0||niter%1000==1){ + double curCost=totalCost(); + double sumFlow=0; + double a; + a= (fabs(_pi[_source[in_arc]])>=fabs(_pi[_target[in_arc]])) ? fabs(_pi[_source[in_arc]]) : fabs(_pi[_target[in_arc]]); + a=a>=fabs(_cost[in_arc])?a:fabs(_cost[in_arc]); + for (int i=0; i<_flow.size(); i++) { + sumFlow+=_state[i]*_flow[i]; + } + mexPrintf("Sum of the flow %.100f\n%d iterations, current cost=%.20f\nReduced cost=%.30f\nPrecision =%.30f\n",sumFlow,niter, curCost,_state[in_arc] * (_cost[in_arc] + _pi[_source[in_arc]] -_pi[_target[in_arc]]), -EPSILON*(a)); + mexPrintf("Arc in = (%d,%d)\n",_node_id(_source[in_arc]),_node_id(_target[in_arc])); + mexPrintf("Supplies = (%f,%f)\n",_supply[_source[in_arc]],_supply[_target[in_arc]]); + + mexPrintf("%.30f\n%.30f\n%.30f\n%.30f\n%",_cost[in_arc],_pi[_source[in_arc]],_pi[_target[in_arc]],a); + mexEvalString("drawnow;"); + } +#endif + + findJoinNode(); + bool change = findLeavingArc(); + if (delta >= MAX) return UNBOUNDED; + changeFlow(change); + if (change) { + updateTreeStructure(); + updatePotential(); + } +#if DEBUG_LVL>0 + else{ + mexPrintf("No change\n"); + } +#endif +#if DEBUG_LVL>1 + mexPrintf("Arc in = (%d,%d)\n",_source[in_arc],_target[in_arc]); +#endif + + } + + +#if DEBUG_LVL>0 + double curCost=totalCost(); + double sumFlow=0; + double a; + a= (fabs(_pi[_source[in_arc]])>=fabs(_pi[_target[in_arc]])) ? fabs(_pi[_source[in_arc]]) : fabs(_pi[_target[in_arc]]); + a=a>=fabs(_cost[in_arc])?a:fabs(_cost[in_arc]); + for (int i=0; i<_flow.size(); i++) { + sumFlow+=_state[i]*_flow[i]; + } + mexPrintf("Sum of the flow %.100f\n%d iterations, current cost=%.20f\nReduced cost=%.30f\nPrecision =%.30f",sumFlow,niter, curCost,_state[in_arc] * (_cost[in_arc] + _pi[_source[in_arc]] -_pi[_target[in_arc]]), -EPSILON*(a)); + mexPrintf("Arc in = (%d,%d)\n",_node_id(_source[in_arc]),_node_id(_target[in_arc])); + mexPrintf("Supplies = (%f,%f)\n",_supply[_source[in_arc]],_supply[_target[in_arc]]); + + mexEvalString("drawnow;"); +#endif + +#if DEBUG_LVL>1 + double sumFlow=0; + for (int i=0; i<_flow.size(); i++) { + sumFlow+=_state[i]*_flow[i]; + if (_state[i]==STATE_TREE) { + mexPrintf("Non zero value at (%d,%d)\n",_node_num+1-_source[i],_node_num+1-_target[i]); + } + } + mexPrintf("Sum of the flow %.100f\n%d iterations, current cost=%.20f\n",sumFlow,niter, totalCost()); + mexEvalString("drawnow;"); +#endif + // Check feasibility + for (int e = _search_arc_num; e != _all_arc_num; ++e) { + if (_flow[e] != 0){ + if (abs(_flow[e]) > EPSILON) + return INFEASIBLE; + else + _flow[e]=0; + + } + } + + // Shift potentials to meet the requirements of the GEQ/LEQ type + // optimality conditions + if (_sum_supply == 0) { + if (_stype == GEQ) { + Cost max_pot = -std::numeric_limits::max(); + for (int i = 0; i != _node_num; ++i) { + if (_pi[i] > max_pot) max_pot = _pi[i]; + } + if (max_pot > 0) { + for (int i = 0; i != _node_num; ++i) + _pi[i] -= max_pot; + } + } else { + Cost min_pot = std::numeric_limits::max(); + for (int i = 0; i != _node_num; ++i) { + if (_pi[i] < min_pot) min_pot = _pi[i]; + } + if (min_pot < 0) { + for (int i = 0; i != _node_num; ++i) + _pi[i] -= min_pot; + } + } + } + + return OPTIMAL; + } + + }; //class NetworkSimplexSimple + + ///@} + +} //namespace lemon + +#endif //LEMON_NETWORK_SIMPLEX_H diff --git a/setup.py b/setup.py new file mode 100755 index 0000000..3e31742 --- /dev/null +++ b/setup.py @@ -0,0 +1,47 @@ +#!/usr/bin/env python + +from distutils.core import setup, Extension +import numpy +from Cython.Distutils import build_ext +from Cython.Build import cythonize + +import os +import glob + +version=0.1 + +ROOT = os.path.abspath(os.path.dirname(__file__)) +README = open(os.path.join(ROOT, 'README.md')).read() + +setup(name='python-webgen', + version=version, + description='Python Optimal Transport Library', + long_description=README, + author=u'Remi Flamary', + author_email='remi.flamary@gmail.com', + url='https://github.com/rflamary/POT', + packages=['ot','ot.emd'], + ext_modules = cythonize(Extension( + "ot.emd.emd", # the extesion name + sources=["ot/emd/emd.pyx", "ot/emd/EMD_wrap.cpp"], # the Cython source and + # additional C++ source files + #extra_compile_args=['-fopenmp'], + #extra_link_args=['-fopenmp'], + language="c++", # generate and compile C++ code, + include_dirs=[numpy.get_include(),os.path.join(ROOT,'ot/emd')])), + platforms=['linux','macosx','windows'], + license = 'MIT', + scripts=[], + data_files=[], + requires=["numpy (>=1.11)"], + classifiers=[ + 'Development Status :: 4 - Beta', + 'Intended Audience :: Developers', + 'Environment :: Console', + 'Operating System :: OS Independent', + 'Operating System :: MacOS', + 'Operating System :: POSIX', + 'Programming Language :: Python', + 'Topic :: Utilities' + ] + ) -- cgit v1.2.3 From 581c6de782dca279edd97778cc474e7597788c0f Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 21 Oct 2016 10:51:27 +0200 Subject: demo+sinkhorn --- examples/demo_OT_1D.py | 102 +++++++++++++++++++++++++++++++++++++++++++++++++ ot/__init__.py | 6 +++ ot/bregman/__init__.py | 4 ++ ot/bregman/sinkhorn.py | 91 +++++++++++++++++++++++++++++++++++++++++++ ot/datasets.py | 10 +++++ ot/emd/emd.pyx | 16 ++++++++ ot/utils.py | 15 ++++++++ setup.py | 4 +- 8 files changed, 246 insertions(+), 2 deletions(-) create mode 100644 examples/demo_OT_1D.py create mode 100644 ot/bregman/__init__.py create mode 100644 ot/bregman/sinkhorn.py create mode 100644 ot/datasets.py create mode 100644 ot/utils.py diff --git a/examples/demo_OT_1D.py b/examples/demo_OT_1D.py new file mode 100644 index 0000000..29f2074 --- /dev/null +++ b/examples/demo_OT_1D.py @@ -0,0 +1,102 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Oct 21 09:51:45 2016 + +@author: rflamary +""" + +import numpy as np +import matplotlib.pylab as pl +from matplotlib import gridspec +import ot + + + +#%% parameters + +n=100 # nb bins + +ma=20 # mean of a +mb=60 # mean of b + +sa=20 # std of a +sb=60 # std of b + +# bin positions +x=np.arange(n,dtype=np.float64) + +# Gaussian distributions +a=np.exp(-(x-ma)**2/(2*sa^2)) +b=np.exp(-(x-mb)**2/(2*sb^2)) + +# normalization +a/=a.sum() +b/=b.sum() + +# loss matrix +M=ot.dist(x.reshape((n,1)),x.reshape((n,1))) +M/=M.max() + +#%% plot the distributions + +pl.figure(1) + +pl.plot(x,a,'b',label='Source distribution') +pl.plot(x,b,'r',label='Target distribution') + +pl.legend() + +#%% plot distributions and loss matrix + +pl.figure(2) +gs = gridspec.GridSpec(3, 3) + +ax1=pl.subplot(gs[0,1:]) +pl.plot(x,b,'r',label='Target distribution') +pl.yticks(()) + +#pl.axis('off') + +ax2=pl.subplot(gs[1:,0]) +pl.plot(a,x,'b',label='Source distribution') +pl.gca().invert_xaxis() +pl.gca().invert_yaxis() +pl.xticks(()) +#pl.ylim((0,n)) +#pl.axis('off') + +pl.subplot(gs[1:,1:],sharex=ax1,sharey=ax2) +pl.imshow(M,interpolation='nearest') + +pl.xlim((0,n)) +#pl.ylim((0,n)) +#pl.axis('off') + +#%% EMD + +G0=ot.emd(a,b,M) + +#%% plot EMD optimal tranport matrix +pl.figure(3) +gs = gridspec.GridSpec(3, 3) + +ax1=pl.subplot(gs[0,1:]) +pl.plot(x,b,'r',label='Target distribution') +pl.yticks(()) + +#pl.axis('off') + +ax2=pl.subplot(gs[1:,0]) +pl.plot(a,x,'b',label='Source distribution') +pl.gca().invert_xaxis() +pl.gca().invert_yaxis() +pl.xticks(()) +#pl.ylim((0,n)) +#pl.axis('off') + +pl.subplot(gs[1:,1:],sharex=ax1,sharey=ax2) +pl.imshow(G0,interpolation='nearest') + +pl.xlim((0,n)) +#pl.ylim((0,n)) +#pl.axis('off') \ No newline at end of file diff --git a/ot/__init__.py b/ot/__init__.py index d0ab3f7..beeae7f 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -1,6 +1,12 @@ + from emd import emd +from bregman import sinkhorn + +import utils +import datasets +from utils import dist,dots __all__ = ["emd"] diff --git a/ot/bregman/__init__.py b/ot/bregman/__init__.py new file mode 100644 index 0000000..e4016ea --- /dev/null +++ b/ot/bregman/__init__.py @@ -0,0 +1,4 @@ + + +from .sink import sinkhorn + diff --git a/ot/bregman/sinkhorn.py b/ot/bregman/sinkhorn.py new file mode 100644 index 0000000..798ac97 --- /dev/null +++ b/ot/bregman/sinkhorn.py @@ -0,0 +1,91 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Oct 21 09:40:21 2016 + +@author: rflamary +""" + +import numpy as np + + +def sinkhorn(a,b, M, reg,numItermax = 1000,stopThr=1e-9): + """ + Solve the optimal transport problem (OT) + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + where : + + - M is the metric cost matrix + - Omega is the entropic regularization term + - a and b are the sample weights + + Parameters + ---------- + a : (ns,) ndarray + samples in the source domain + b : (nt,) ndarray + samples in the target domain + M : (ns,nt) ndarray + loss matrix + reg: float() + Regularization term >0 + + + Returns + ------- + gamma: (ns x nt) ndarray + Optimal transportation matrix for the given parameters + + """ + # init data + Nini = len(a) + Nfin = len(b) + + + cpt = 0 + + # we assume that no distances are null except those of the diagonal of distances + u = np.ones(Nini)/Nini + v = np.ones(Nfin)/Nfin + uprev=np.zeros(Nini) + vprev=np.zeros(Nini) + + #print reg + + K = np.exp(-reg*M) + #print np.min(K) + + Kp = np.dot(np.diag(1/a),K) + transp = K + cpt = 0 + err=1 + while (err>stopThr and cpt Date: Fri, 21 Oct 2016 11:19:46 +0200 Subject: demo with sinkhorn --- examples/demo_OT_1D.py | 39 ++++++-- ot/__init__.py | 8 +- ot/bregman/sink.py | 91 +++++++++++++++++ ot/bregman/sinkhorn.py | 91 ----------------- ot/emd/emd.cpp | 267 +++++++++++++++++++++++++++++++++++++++++++------ ot/emd/emd.pyx | 44 ++++++-- ot/utils.py | 13 ++- 7 files changed, 411 insertions(+), 142 deletions(-) create mode 100644 ot/bregman/sink.py delete mode 100644 ot/bregman/sinkhorn.py diff --git a/examples/demo_OT_1D.py b/examples/demo_OT_1D.py index 29f2074..865b178 100644 --- a/examples/demo_OT_1D.py +++ b/examples/demo_OT_1D.py @@ -26,12 +26,8 @@ sb=60 # std of b x=np.arange(n,dtype=np.float64) # Gaussian distributions -a=np.exp(-(x-ma)**2/(2*sa^2)) -b=np.exp(-(x-mb)**2/(2*sb^2)) - -# normalization -a/=a.sum() -b/=b.sum() +a=ot.datasets.get_1D_gauss(n,ma,sa) +b=ot.datasets.get_1D_gauss(n,mb,sb) # loss matrix M=ot.dist(x.reshape((n,1)),x.reshape((n,1))) @@ -99,4 +95,33 @@ pl.imshow(G0,interpolation='nearest') pl.xlim((0,n)) #pl.ylim((0,n)) -#pl.axis('off') \ No newline at end of file +#pl.axis('off') + +#%% Sinkhorn +lambd=1e3 + +Gs=ot.sinkhorn(a,b,M,lambd) + + +#%% plot Sikhorn optimal tranport matrix +pl.figure(3) +gs = gridspec.GridSpec(3, 3) + +ax1=pl.subplot(gs[0,1:]) +pl.plot(x,b,'r',label='Target distribution') +pl.yticks(()) + +#pl.axis('off') + +ax2=pl.subplot(gs[1:,0]) +pl.plot(a,x,'b',label='Source distribution') +pl.gca().invert_xaxis() +pl.gca().invert_yaxis() +pl.xticks(()) +#pl.ylim((0,n)) +#pl.axis('off') + +pl.subplot(gs[1:,1:],sharex=ax1,sharey=ax2) +pl.imshow(Gs,interpolation='nearest') + +pl.xlim((0,n)) \ No newline at end of file diff --git a/ot/__init__.py b/ot/__init__.py index beeae7f..14c6181 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -1,11 +1,13 @@ +# utils submodules +import utils +import datasets - +# Ot functions from emd import emd from bregman import sinkhorn -import utils -import datasets + from utils import dist,dots diff --git a/ot/bregman/sink.py b/ot/bregman/sink.py new file mode 100644 index 0000000..8b97e1e --- /dev/null +++ b/ot/bregman/sink.py @@ -0,0 +1,91 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Oct 21 09:40:21 2016 + +@author: rflamary +""" + +import numpy as np + + +def sinkhorn(a,b, M, reg,numItermax = 1000,stopThr=1e-9): + """ + Solve the optimal transport problem (OT) + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + where : + + - M is the metric cost matrix + - Omega is the entropic regularization term + - a and b are the sample weights + + Parameters + ---------- + a : (ns,) ndarray + samples in the source domain + b : (nt,) ndarray + samples in the target domain + M : (ns,nt) ndarray + loss matrix + reg: float() + Regularization term >0 + + + Returns + ------- + gamma: (ns x nt) ndarray + Optimal transportation matrix for the given parameters + + """ + # init data + Nini = len(a) + Nfin = len(b) + + + cpt = 0 + + # we assume that no distances are null except those of the diagonal of distances + u = np.ones(Nini)/Nini + v = np.ones(Nfin)/Nfin + uprev=np.zeros(Nini) + vprev=np.zeros(Nini) + + #print reg + + K = np.exp(-M/reg) + #print np.min(K) + + Kp = np.dot(np.diag(1/a),K) + transp = K + cpt = 0 + err=1 + while (err>stopThr and cpt Date: Fri, 21 Oct 2016 11:26:51 +0200 Subject: demo with sinkhorn --- examples/demo_OT_1D.py | 96 +++++++++++++++++--------------------------------- 1 file changed, 32 insertions(+), 64 deletions(-) diff --git a/examples/demo_OT_1D.py b/examples/demo_OT_1D.py index 865b178..8ba40ba 100644 --- a/examples/demo_OT_1D.py +++ b/examples/demo_OT_1D.py @@ -44,27 +44,38 @@ pl.legend() #%% plot distributions and loss matrix -pl.figure(2) -gs = gridspec.GridSpec(3, 3) - -ax1=pl.subplot(gs[0,1:]) -pl.plot(x,b,'r',label='Target distribution') -pl.yticks(()) +def plotmat(M,title=''): + """ Plot a matrix woth the 1D distribution """ + gs = gridspec.GridSpec(3, 3) + + ax1=pl.subplot(gs[0,1:]) + pl.plot(x,b,'r',label='Target distribution') + pl.yticks(()) + pl.title(title) + + #pl.axis('off') + + ax2=pl.subplot(gs[1:,0]) + pl.plot(a,x,'b',label='Source distribution') + pl.gca().invert_xaxis() + pl.gca().invert_yaxis() + pl.xticks(()) + #pl.ylim((0,n)) + #pl.axis('off') + + pl.subplot(gs[1:,1:],sharex=ax1,sharey=ax2) + pl.imshow(M,interpolation='nearest') + + pl.xlim((0,n)) -#pl.axis('off') +pl.figure(2) -ax2=pl.subplot(gs[1:,0]) -pl.plot(a,x,'b',label='Source distribution') -pl.gca().invert_xaxis() -pl.gca().invert_yaxis() -pl.xticks(()) -#pl.ylim((0,n)) -#pl.axis('off') +plotmat(M,'Cost matrix M') -pl.subplot(gs[1:,1:],sharex=ax1,sharey=ax2) -pl.imshow(M,interpolation='nearest') -pl.xlim((0,n)) + + + #pl.ylim((0,n)) #pl.axis('off') @@ -72,56 +83,13 @@ pl.xlim((0,n)) G0=ot.emd(a,b,M) -#%% plot EMD optimal tranport matrix pl.figure(3) -gs = gridspec.GridSpec(3, 3) - -ax1=pl.subplot(gs[0,1:]) -pl.plot(x,b,'r',label='Target distribution') -pl.yticks(()) - -#pl.axis('off') - -ax2=pl.subplot(gs[1:,0]) -pl.plot(a,x,'b',label='Source distribution') -pl.gca().invert_xaxis() -pl.gca().invert_yaxis() -pl.xticks(()) -#pl.ylim((0,n)) -#pl.axis('off') - -pl.subplot(gs[1:,1:],sharex=ax1,sharey=ax2) -pl.imshow(G0,interpolation='nearest') - -pl.xlim((0,n)) -#pl.ylim((0,n)) -#pl.axis('off') +plotmat(G0,'OT matrix G0') #%% Sinkhorn -lambd=1e3 +lambd=1e-3 Gs=ot.sinkhorn(a,b,M,lambd) - -#%% plot Sikhorn optimal tranport matrix -pl.figure(3) -gs = gridspec.GridSpec(3, 3) - -ax1=pl.subplot(gs[0,1:]) -pl.plot(x,b,'r',label='Target distribution') -pl.yticks(()) - -#pl.axis('off') - -ax2=pl.subplot(gs[1:,0]) -pl.plot(a,x,'b',label='Source distribution') -pl.gca().invert_xaxis() -pl.gca().invert_yaxis() -pl.xticks(()) -#pl.ylim((0,n)) -#pl.axis('off') - -pl.subplot(gs[1:,1:],sharex=ax1,sharey=ax2) -pl.imshow(Gs,interpolation='nearest') - -pl.xlim((0,n)) \ No newline at end of file +pl.figure(4) +plotmat(Gs,'OT matrix Sinkhorn') -- cgit v1.2.3 From d29b40f4d875a58896dfb1635cbd007b8f6acfca Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 21 Oct 2016 14:49:10 +0200 Subject: correct version number --- ot/emd/emd.cpp | 94 +++++++++++++++++++++++++++++----------------------------- setup.py | 2 +- 2 files changed, 48 insertions(+), 48 deletions(-) diff --git a/ot/emd/emd.cpp b/ot/emd/emd.cpp index bd40d9c..2343af6 100644 --- a/ot/emd/emd.cpp +++ b/ot/emd/emd.cpp @@ -1229,7 +1229,7 @@ static PyObject *__pyx_codeobj__8; /* Python wrapper */ static PyObject *__pyx_pw_2ot_3emd_3emd_1emd(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ -static char __pyx_doc_2ot_3emd_3emd_emd[] = "\n Solves the Earth Movers distance problem and returns the optimal transport matrix\n \n .. math::\n \\gamma = arg\\min_\\gamma <\\gamma,M>_F \n \n s.t. \\gamma 1 = a\n \n \\gamma^T 1= b \n \n \\gamma\\geq 0\n where :\n \n - M is the metric cost matrix\n - a and b are the sample weights\n \n Parameters\n ----------\n a : (ns,) ndarray\n samples in the source domain\n b : (nt,) ndarray\n samples in the target domain\n M : (ns,nt) ndarray\n loss matrix \n \n \n Returns\n -------\n gamma: (ns x nt) ndarray\n Optimal transportation matrix for the given parameters\n \n "; +static char __pyx_doc_2ot_3emd_3emd_emd[] = "\n Solves the Earth Movers distance problem and returns the optimal transport matrix\n \n gamm=emd(a,b,M)\n \n .. math::\n \\gamma = arg\\min_\\gamma <\\gamma,M>_F \n \n s.t. \\gamma 1 = a\n \n \\gamma^T 1= b \n \n \\gamma\\geq 0\n where :\n \n - M is the metric cost matrix\n - a and b are the sample weights\n \n Parameters\n ----------\n a : (ns,) ndarray\n samples in the source domain (uniform waigth if empty)\n b : (nt,) ndarray\n samples in the target domain (uniform waigth if empty)\n M : (ns,nt) ndarray\n loss matrix \n \n \n Returns\n -------\n gamma: (ns x nt) ndarray\n Optimal transportation matrix for the given parameters\n \n "; static PyMethodDef __pyx_mdef_2ot_3emd_3emd_1emd = {"emd", (PyCFunction)__pyx_pw_2ot_3emd_3emd_1emd, METH_VARARGS|METH_KEYWORDS, __pyx_doc_2ot_3emd_3emd_emd}; static PyObject *__pyx_pw_2ot_3emd_3emd_1emd(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyArrayObject *__pyx_v_a = 0; @@ -1373,7 +1373,7 @@ static PyObject *__pyx_pf_2ot_3emd_3emd_emd(CYTHON_UNUSED PyObject *__pyx_self, } __pyx_pybuffernd_M.diminfo[0].strides = __pyx_pybuffernd_M.rcbuffer->pybuffer.strides[0]; __pyx_pybuffernd_M.diminfo[0].shape = __pyx_pybuffernd_M.rcbuffer->pybuffer.shape[0]; __pyx_pybuffernd_M.diminfo[1].strides = __pyx_pybuffernd_M.rcbuffer->pybuffer.strides[1]; __pyx_pybuffernd_M.diminfo[1].shape = __pyx_pybuffernd_M.rcbuffer->pybuffer.shape[1]; - /* "ot/emd/emd.pyx":54 + /* "ot/emd/emd.pyx":56 * * """ * cdef int n1= M.shape[0] # <<<<<<<<<<<<<< @@ -1382,7 +1382,7 @@ static PyObject *__pyx_pf_2ot_3emd_3emd_emd(CYTHON_UNUSED PyObject *__pyx_self, */ __pyx_v_n1 = (__pyx_v_M->dimensions[0]); - /* "ot/emd/emd.pyx":55 + /* "ot/emd/emd.pyx":57 * """ * cdef int n1= M.shape[0] * cdef int n2= M.shape[1] # <<<<<<<<<<<<<< @@ -1391,7 +1391,7 @@ static PyObject *__pyx_pf_2ot_3emd_3emd_emd(CYTHON_UNUSED PyObject *__pyx_self, */ __pyx_v_n2 = (__pyx_v_M->dimensions[1]); - /* "ot/emd/emd.pyx":57 + /* "ot/emd/emd.pyx":59 * cdef int n2= M.shape[1] * * cdef float cost=0 # <<<<<<<<<<<<<< @@ -1400,23 +1400,23 @@ static PyObject *__pyx_pf_2ot_3emd_3emd_emd(CYTHON_UNUSED PyObject *__pyx_self, */ __pyx_v_cost = 0.0; - /* "ot/emd/emd.pyx":58 + /* "ot/emd/emd.pyx":60 * * cdef float cost=0 * cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([n1, n2]) # <<<<<<<<<<<<<< * * if not len(a): */ - __pyx_t_2 = __Pyx_GetModuleGlobalName(__pyx_n_s_np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 58; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_2 = __Pyx_GetModuleGlobalName(__pyx_n_s_np); if (unlikely(!__pyx_t_2)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 60; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_2); - 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if (unlikely(__pyx_t_10 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 64; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (unlikely(__pyx_t_10 < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 66; __pyx_clineno = __LINE__; goto __pyx_L1_error;} } __pyx_t_14 = 0; __Pyx_DECREF_SET(__pyx_v_b, ((PyArrayObject *)__pyx_t_3)); __pyx_t_3 = 0; - /* "ot/emd/emd.pyx":63 + /* "ot/emd/emd.pyx":65 * a=np.ones((n1,))/n1 * * if not len(b): # <<<<<<<<<<<<<< @@ -1650,7 +1650,7 @@ static PyObject *__pyx_pf_2ot_3emd_3emd_emd(CYTHON_UNUSED PyObject *__pyx_self, */ } - /* "ot/emd/emd.pyx":67 + /* "ot/emd/emd.pyx":69 * * # calling the function * EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost) # <<<<<<<<<<<<<< @@ -1659,7 +1659,7 @@ static PyObject *__pyx_pf_2ot_3emd_3emd_emd(CYTHON_UNUSED PyObject *__pyx_self, */ EMD_wrap(__pyx_v_n1, __pyx_v_n2, ((double *)__pyx_v_a->data), ((double *)__pyx_v_b->data), ((double *)__pyx_v_M->data), ((double *)__pyx_v_G->data), ((double *)(&__pyx_v_cost))); - /* "ot/emd/emd.pyx":69 + /* "ot/emd/emd.pyx":71 * EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost) * * return G # <<<<<<<<<<<<<< diff --git a/setup.py b/setup.py index ddedb36..c20c3f5 100755 --- a/setup.py +++ b/setup.py @@ -8,7 +8,7 @@ from Cython.Build import cythonize import os #import glob -version=0.1 +version='0.1' ROOT = os.path.abspath(os.path.dirname(__file__)) README = open(os.path.join(ROOT, 'README.md')).read() -- cgit v1.2.3 From 89999e07871f82bd1738750b5bbcd97fae9ea4b2 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 21 Oct 2016 15:00:43 +0200 Subject: correct setup.py --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index c20c3f5..c7b0da0 100755 --- a/setup.py +++ b/setup.py @@ -20,7 +20,7 @@ setup(name='python-webgen', author=u'Remi Flamary', author_email='remi.flamary@gmail.com', url='https://github.com/rflamary/POT', - packages=['ot','ot.emd'], + packages=['ot','ot.emd','ot.bregman'], ext_modules = cythonize(Extension( "ot.emd.emd", # the extesion name sources=["ot/emd/emd.pyx", "ot/emd/EMD_wrap.cpp"], # the Cython source and -- cgit v1.2.3 From b9ae39adae1d1a16f6bfb79e73c0d67d3157e1de Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Oct 2016 10:28:21 +0200 Subject: update demo and add plot module --- examples/demo_OT_1D.py | 49 ++++++------------------------------------------- ot/__init__.py | 2 ++ ot/plot.py | 40 ++++++++++++++++++++++++++++++++++++++++ 3 files changed, 48 insertions(+), 43 deletions(-) create mode 100644 ot/plot.py diff --git a/examples/demo_OT_1D.py b/examples/demo_OT_1D.py index 8ba40ba..b17f902 100644 --- a/examples/demo_OT_1D.py +++ b/examples/demo_OT_1D.py @@ -7,7 +7,7 @@ Created on Fri Oct 21 09:51:45 2016 import numpy as np import matplotlib.pylab as pl -from matplotlib import gridspec + import ot @@ -16,18 +16,12 @@ import ot n=100 # nb bins -ma=20 # mean of a -mb=60 # mean of b - -sa=20 # std of a -sb=60 # std of b - # bin positions x=np.arange(n,dtype=np.float64) # Gaussian distributions -a=ot.datasets.get_1D_gauss(n,ma,sa) -b=ot.datasets.get_1D_gauss(n,mb,sb) +a=ot.datasets.get_1D_gauss(n,m=20,s=20) # m= mean, s= std +b=ot.datasets.get_1D_gauss(n,m=60,s=60) # loss matrix M=ot.dist(x.reshape((n,1)),x.reshape((n,1))) @@ -44,47 +38,16 @@ pl.legend() #%% plot distributions and loss matrix -def plotmat(M,title=''): - """ Plot a matrix woth the 1D distribution """ - gs = gridspec.GridSpec(3, 3) - - ax1=pl.subplot(gs[0,1:]) - pl.plot(x,b,'r',label='Target distribution') - pl.yticks(()) - pl.title(title) - - #pl.axis('off') - - ax2=pl.subplot(gs[1:,0]) - pl.plot(a,x,'b',label='Source distribution') - pl.gca().invert_xaxis() - pl.gca().invert_yaxis() - pl.xticks(()) - #pl.ylim((0,n)) - #pl.axis('off') - - pl.subplot(gs[1:,1:],sharex=ax1,sharey=ax2) - pl.imshow(M,interpolation='nearest') - - pl.xlim((0,n)) - pl.figure(2) +ot.plot.otplot1D(a,b,M,'Cost matrix M') -plotmat(M,'Cost matrix M') - - - - - -#pl.ylim((0,n)) -#pl.axis('off') #%% EMD G0=ot.emd(a,b,M) pl.figure(3) -plotmat(G0,'OT matrix G0') +ot.plot.otplot1D(a,b,G0,'OT matrix G0') #%% Sinkhorn lambd=1e-3 @@ -92,4 +55,4 @@ lambd=1e-3 Gs=ot.sinkhorn(a,b,M,lambd) pl.figure(4) -plotmat(Gs,'OT matrix Sinkhorn') +ot.plot.otplot1D(a,b,Gs,'OT matrix Sinkhorn') diff --git a/ot/__init__.py b/ot/__init__.py index 14c6181..fe55771 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -2,6 +2,8 @@ # utils submodules import utils import datasets +import plot + # Ot functions from emd import emd diff --git a/ot/plot.py b/ot/plot.py new file mode 100644 index 0000000..b8357b1 --- /dev/null +++ b/ot/plot.py @@ -0,0 +1,40 @@ + +import numpy as np +import matplotlib.pylab as pl +from matplotlib import gridspec + + +def otplot1D(a,b,M,title=''): + """ Plot a with the source and target 1D distribution """ + + na=M.shape[0] + nb=M.shape[1] + + gs = gridspec.GridSpec(3, 3) + + + xa=np.arange(na) + xb=np.arange(nb) + + + ax1=pl.subplot(gs[0,1:]) + pl.plot(xb,b,'r',label='Target distribution') + pl.yticks(()) + pl.title(title) + + #pl.axis('off') + + ax2=pl.subplot(gs[1:,0]) + pl.plot(a,xa,'b',label='Source distribution') + pl.gca().invert_xaxis() + pl.gca().invert_yaxis() + pl.xticks(()) + #pl.ylim((0,n)) + #pl.axis('off') + + pl.subplot(gs[1:,1:],sharex=ax1,sharey=ax2) + pl.imshow(M,interpolation='nearest') + + pl.xlim((0,nb)) + + -- cgit v1.2.3 From 8e1c66e7eadb35daa3f7c482cc820944d90fef33 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Oct 2016 11:04:07 +0200 Subject: update doc --- ot/plot.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/plot.py b/ot/plot.py index b8357b1..743ab4a 100644 --- a/ot/plot.py +++ b/ot/plot.py @@ -5,7 +5,7 @@ from matplotlib import gridspec def otplot1D(a,b,M,title=''): - """ Plot a with the source and target 1D distribution """ + """ Plot matrix M with the source and target 1D distribution """ na=M.shape[0] nb=M.shape[1] -- cgit v1.2.3 From 28681131adf681a713565768a6b94527600fb81f Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Oct 2016 11:32:37 +0200 Subject: add sphinx documentation --- doc/Makefile | 216 +++++++++++++++++++++++++++++++++++++ doc/make.bat | 263 +++++++++++++++++++++++++++++++++++++++++++++ doc/source/conf.py | 298 +++++++++++++++++++++++++++++++++++++++++++++++++++ doc/source/index.rst | 32 ++++++ ot/__init__.py | 6 +- 5 files changed, 813 insertions(+), 2 deletions(-) create mode 100644 doc/Makefile create mode 100644 doc/make.bat create mode 100644 doc/source/conf.py create mode 100644 doc/source/index.rst diff --git a/doc/Makefile b/doc/Makefile new file mode 100644 index 0000000..3511a59 --- /dev/null +++ b/doc/Makefile @@ -0,0 +1,216 @@ +# Makefile for Sphinx documentation +# + +# You can set these variables from the command line. +SPHINXOPTS = +SPHINXBUILD = sphinx-build +PAPER = +BUILDDIR = build + +# User-friendly check for sphinx-build +ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1) +$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/) +endif + +# Internal variables. +PAPEROPT_a4 = -D latex_paper_size=a4 +PAPEROPT_letter = -D latex_paper_size=letter +ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source +# the i18n builder cannot share the environment and doctrees with the others +I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source + +.PHONY: help +help: + @echo "Please use \`make ' where is one of" + @echo " html to make standalone HTML files" + @echo " dirhtml to make HTML files named index.html in directories" + @echo " singlehtml to make a single large HTML file" + @echo " pickle to make pickle files" + @echo " json to make JSON files" + @echo " htmlhelp to make HTML files and a HTML help project" + @echo " qthelp to make HTML files and a qthelp project" + @echo " applehelp to make an Apple Help Book" + @echo " devhelp to make HTML files and a Devhelp project" + @echo " epub to make an epub" + @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" + @echo " latexpdf to make LaTeX files and run them through pdflatex" + @echo " latexpdfja to make LaTeX files and run them through platex/dvipdfmx" + @echo " text to make text files" + @echo " man to make manual pages" + @echo " texinfo to make Texinfo files" + @echo " info to make Texinfo files and run them through makeinfo" + @echo " gettext to make PO message catalogs" + @echo " changes to make an overview of all changed/added/deprecated items" + @echo " xml to make Docutils-native XML files" + @echo " pseudoxml to make pseudoxml-XML files for display purposes" + @echo " linkcheck to check all external links for integrity" + @echo " doctest to run all doctests embedded in the documentation (if enabled)" + @echo " coverage to run coverage check of the documentation (if enabled)" + +.PHONY: clean +clean: + rm -rf $(BUILDDIR)/* + +.PHONY: html +html: + $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html + @echo + @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." + +.PHONY: dirhtml +dirhtml: + $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml + @echo + @echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml." + +.PHONY: singlehtml +singlehtml: + $(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml + @echo + @echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml." + +.PHONY: pickle +pickle: + $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle + @echo + @echo "Build finished; now you can process the pickle files." + +.PHONY: json +json: + $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json + @echo + @echo "Build finished; now you can process the JSON files." + +.PHONY: htmlhelp +htmlhelp: + $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp + @echo + @echo "Build finished; now you can run HTML Help Workshop with the" \ + ".hhp project file in $(BUILDDIR)/htmlhelp." + +.PHONY: qthelp +qthelp: + $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp + @echo + @echo "Build finished; now you can run "qcollectiongenerator" with the" \ + ".qhcp project file in $(BUILDDIR)/qthelp, like this:" + @echo "# qcollectiongenerator $(BUILDDIR)/qthelp/POT.qhcp" + @echo "To view the help file:" + @echo "# assistant -collectionFile $(BUILDDIR)/qthelp/POT.qhc" + +.PHONY: applehelp +applehelp: + $(SPHINXBUILD) -b applehelp $(ALLSPHINXOPTS) $(BUILDDIR)/applehelp + @echo + @echo "Build finished. The help book is in $(BUILDDIR)/applehelp." + @echo "N.B. You won't be able to view it unless you put it in" \ + "~/Library/Documentation/Help or install it in your application" \ + "bundle." + +.PHONY: devhelp +devhelp: + $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp + @echo + @echo "Build finished." + @echo "To view the help file:" + @echo "# mkdir -p $$HOME/.local/share/devhelp/POT" + @echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/POT" + @echo "# devhelp" + +.PHONY: epub +epub: + $(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub + @echo + @echo "Build finished. The epub file is in $(BUILDDIR)/epub." + +.PHONY: latex +latex: + $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex + @echo + @echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex." + @echo "Run \`make' in that directory to run these through (pdf)latex" \ + "(use \`make latexpdf' here to do that automatically)." + +.PHONY: latexpdf +latexpdf: + $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex + @echo "Running LaTeX files through pdflatex..." + $(MAKE) -C $(BUILDDIR)/latex all-pdf + @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." + +.PHONY: latexpdfja +latexpdfja: + $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex + @echo "Running LaTeX files through platex and dvipdfmx..." + $(MAKE) -C $(BUILDDIR)/latex all-pdf-ja + @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." + +.PHONY: text +text: + $(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text + @echo + @echo "Build finished. The text files are in $(BUILDDIR)/text." + +.PHONY: man +man: + $(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man + @echo + @echo "Build finished. The manual pages are in $(BUILDDIR)/man." + +.PHONY: texinfo +texinfo: + $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo + @echo + @echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo." + @echo "Run \`make' in that directory to run these through makeinfo" \ + "(use \`make info' here to do that automatically)." + +.PHONY: info +info: + $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo + @echo "Running Texinfo files through makeinfo..." + make -C $(BUILDDIR)/texinfo info + @echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo." + +.PHONY: gettext +gettext: + $(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale + @echo + @echo "Build finished. The message catalogs are in $(BUILDDIR)/locale." + +.PHONY: changes +changes: + $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes + @echo + @echo "The overview file is in $(BUILDDIR)/changes." + +.PHONY: linkcheck +linkcheck: + $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck + @echo + @echo "Link check complete; look for any errors in the above output " \ + "or in $(BUILDDIR)/linkcheck/output.txt." + +.PHONY: doctest +doctest: + $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest + @echo "Testing of doctests in the sources finished, look at the " \ + "results in $(BUILDDIR)/doctest/output.txt." + +.PHONY: coverage +coverage: + $(SPHINXBUILD) -b coverage $(ALLSPHINXOPTS) $(BUILDDIR)/coverage + @echo "Testing of coverage in the sources finished, look at the " \ + "results in $(BUILDDIR)/coverage/python.txt." + +.PHONY: xml +xml: + $(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml + @echo + @echo "Build finished. The XML files are in $(BUILDDIR)/xml." + +.PHONY: pseudoxml +pseudoxml: + $(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml + @echo + @echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml." diff --git a/doc/make.bat b/doc/make.bat new file mode 100644 index 0000000..f2313df --- /dev/null +++ b/doc/make.bat @@ -0,0 +1,263 @@ +@ECHO OFF + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set BUILDDIR=build +set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% source +set I18NSPHINXOPTS=%SPHINXOPTS% source +if NOT "%PAPER%" == "" ( + set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS% + set I18NSPHINXOPTS=-D latex_paper_size=%PAPER% %I18NSPHINXOPTS% +) + +if "%1" == "" goto help + +if "%1" == "help" ( + :help + echo.Please use `make ^` where ^ is one of + echo. html to make standalone HTML files + echo. dirhtml to make HTML files named index.html in directories + echo. singlehtml to make a single large HTML file + echo. pickle to make pickle files + echo. json to make JSON files + echo. htmlhelp to make HTML files and a HTML help project + echo. qthelp to make HTML files and a qthelp project + echo. devhelp to make HTML files and a Devhelp project + echo. epub to make an epub + echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter + echo. text to make text files + echo. man to make manual pages + echo. texinfo to make Texinfo files + echo. gettext to make PO message catalogs + echo. changes to make an overview over all changed/added/deprecated items + echo. xml to make Docutils-native XML files + echo. pseudoxml to make pseudoxml-XML files for display purposes + echo. linkcheck to check all external links for integrity + echo. doctest to run all doctests embedded in the documentation if enabled + echo. coverage to run coverage check of the documentation if enabled + goto end +) + +if "%1" == "clean" ( + for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i + del /q /s %BUILDDIR%\* + goto end +) + + +REM Check if sphinx-build is available and fallback to Python version if any +%SPHINXBUILD% 1>NUL 2>NUL +if errorlevel 9009 goto sphinx_python +goto sphinx_ok + +:sphinx_python + +set SPHINXBUILD=python -m sphinx.__init__ +%SPHINXBUILD% 2> nul +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.http://sphinx-doc.org/ + exit /b 1 +) + +:sphinx_ok + + +if "%1" == "html" ( + %SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The HTML pages are in %BUILDDIR%/html. + goto end +) + +if "%1" == "dirhtml" ( + %SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml. + goto end +) + +if "%1" == "singlehtml" ( + %SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml. + goto end +) + +if "%1" == "pickle" ( + %SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle + if errorlevel 1 exit /b 1 + echo. + echo.Build finished; now you can process the pickle files. + goto end +) + +if "%1" == "json" ( + %SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json + if errorlevel 1 exit /b 1 + echo. + echo.Build finished; now you can process the JSON files. + goto end +) + +if "%1" == "htmlhelp" ( + %SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp + if errorlevel 1 exit /b 1 + echo. + echo.Build finished; now you can run HTML Help Workshop with the ^ +.hhp project file in %BUILDDIR%/htmlhelp. + goto end +) + +if "%1" == "qthelp" ( + %SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp + if errorlevel 1 exit /b 1 + echo. + echo.Build finished; now you can run "qcollectiongenerator" with the ^ +.qhcp project file in %BUILDDIR%/qthelp, like this: + echo.^> qcollectiongenerator %BUILDDIR%\qthelp\POT.qhcp + echo.To view the help file: + echo.^> assistant -collectionFile %BUILDDIR%\qthelp\POT.ghc + goto end +) + +if "%1" == "devhelp" ( + %SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. + goto end +) + +if "%1" == "epub" ( + %SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The epub file is in %BUILDDIR%/epub. + goto end +) + +if "%1" == "latex" ( + %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex + if errorlevel 1 exit /b 1 + echo. + echo.Build finished; the LaTeX files are in %BUILDDIR%/latex. + goto end +) + +if "%1" == "latexpdf" ( + %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex + cd %BUILDDIR%/latex + make all-pdf + cd %~dp0 + echo. + echo.Build finished; the PDF files are in %BUILDDIR%/latex. + goto end +) + +if "%1" == "latexpdfja" ( + %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex + cd %BUILDDIR%/latex + make all-pdf-ja + cd %~dp0 + echo. + echo.Build finished; the PDF files are in %BUILDDIR%/latex. + goto end +) + +if "%1" == "text" ( + %SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The text files are in %BUILDDIR%/text. + goto end +) + +if "%1" == "man" ( + %SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The manual pages are in %BUILDDIR%/man. + goto end +) + +if "%1" == "texinfo" ( + %SPHINXBUILD% -b texinfo %ALLSPHINXOPTS% %BUILDDIR%/texinfo + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The Texinfo files are in %BUILDDIR%/texinfo. + goto end +) + +if "%1" == "gettext" ( + %SPHINXBUILD% -b gettext %I18NSPHINXOPTS% %BUILDDIR%/locale + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The message catalogs are in %BUILDDIR%/locale. + goto end +) + +if "%1" == "changes" ( + %SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes + if errorlevel 1 exit /b 1 + echo. + echo.The overview file is in %BUILDDIR%/changes. + goto end +) + +if "%1" == "linkcheck" ( + %SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck + if errorlevel 1 exit /b 1 + echo. + echo.Link check complete; look for any errors in the above output ^ +or in %BUILDDIR%/linkcheck/output.txt. + goto end +) + +if "%1" == "doctest" ( + %SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest + if errorlevel 1 exit /b 1 + echo. + echo.Testing of doctests in the sources finished, look at the ^ +results in %BUILDDIR%/doctest/output.txt. + goto end +) + +if "%1" == "coverage" ( + %SPHINXBUILD% -b coverage %ALLSPHINXOPTS% %BUILDDIR%/coverage + if errorlevel 1 exit /b 1 + echo. + echo.Testing of coverage in the sources finished, look at the ^ +results in %BUILDDIR%/coverage/python.txt. + goto end +) + +if "%1" == "xml" ( + %SPHINXBUILD% -b xml %ALLSPHINXOPTS% %BUILDDIR%/xml + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The XML files are in %BUILDDIR%/xml. + goto end +) + +if "%1" == "pseudoxml" ( + %SPHINXBUILD% -b pseudoxml %ALLSPHINXOPTS% %BUILDDIR%/pseudoxml + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The pseudo-XML files are in %BUILDDIR%/pseudoxml. + goto end +) + +:end diff --git a/doc/source/conf.py b/doc/source/conf.py new file mode 100644 index 0000000..0caefae --- /dev/null +++ b/doc/source/conf.py @@ -0,0 +1,298 @@ +# -*- coding: utf-8 -*- +# +# POT documentation build configuration file, created by +# sphinx-quickstart on Mon Oct 24 11:10:10 2016. +# +# This file is execfile()d with the current directory set to its +# containing dir. +# +# Note that not all possible configuration values are present in this +# autogenerated file. +# +# All configuration values have a default; values that are commented out +# serve to show the default. + +import sys +import os + +sys.path.insert(0, os.path.abspath("../..")) + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +#sys.path.insert(0, os.path.abspath('.')) + +# -- General configuration ------------------------------------------------ + +# If your documentation needs a minimal Sphinx version, state it here. +#needs_sphinx = '1.0' + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + 'sphinx.ext.autodoc', + 'sphinx.ext.doctest', + 'sphinx.ext.intersphinx', + 'sphinx.ext.todo', + 'sphinx.ext.coverage', + 'sphinx.ext.mathjax', + 'sphinx.ext.ifconfig', + 'sphinx.ext.viewcode', +] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# The suffix(es) of source filenames. +# You can specify multiple suffix as a list of string: +# source_suffix = ['.rst', '.md'] +source_suffix = '.rst' + +# The encoding of source files. +#source_encoding = 'utf-8-sig' + +# The master toctree document. +master_doc = 'index' + +# General information about the project. +project = u'POT' +copyright = u'2016, Rémi Flamary, Nicolas Courty' +author = u'Rémi Flamary, Nicolas Courty' + +# The version info for the project you're documenting, acts as replacement for +# |version| and |release|, also used in various other places throughout the +# built documents. +# +# The short X.Y version. +version = u'0.1' +# The full version, including alpha/beta/rc tags. +release = u'0.1' + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +# +# This is also used if you do content translation via gettext catalogs. +# Usually you set "language" from the command line for these cases. +language = None + +# There are two options for replacing |today|: either, you set today to some +# non-false value, then it is used: +#today = '' +# Else, today_fmt is used as the format for a strftime call. +#today_fmt = '%B %d, %Y' + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +exclude_patterns = [] + +# The reST default role (used for this markup: `text`) to use for all +# documents. +#default_role = None + +# If true, '()' will be appended to :func: etc. cross-reference text. +#add_function_parentheses = True + +# If true, the current module name will be prepended to all description +# unit titles (such as .. function::). +#add_module_names = True + +# If true, sectionauthor and moduleauthor directives will be shown in the +# output. They are ignored by default. +#show_authors = False + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = 'sphinx' + +# A list of ignored prefixes for module index sorting. +#modindex_common_prefix = [] + +# If true, keep warnings as "system message" paragraphs in the built documents. +#keep_warnings = False + +# If true, `todo` and `todoList` produce output, else they produce nothing. +todo_include_todos = True + + +# -- Options for HTML output ---------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +html_theme = 'alabaster' + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. +#html_theme_options = {} + +# Add any paths that contain custom themes here, relative to this directory. +#html_theme_path = [] + +# The name for this set of Sphinx documents. If None, it defaults to +# " v documentation". +#html_title = None + +# A shorter title for the navigation bar. Default is the same as html_title. +#html_short_title = None + +# The name of an image file (relative to this directory) to place at the top +# of the sidebar. +#html_logo = None + +# The name of an image file (relative to this directory) to use as a favicon of +# the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 +# pixels large. +#html_favicon = None + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] + +# Add any extra paths that contain custom files (such as robots.txt or +# .htaccess) here, relative to this directory. These files are copied +# directly to the root of the documentation. +#html_extra_path = [] + +# If not '', a 'Last updated on:' timestamp is inserted at every page bottom, +# using the given strftime format. +#html_last_updated_fmt = '%b %d, %Y' + +# If true, SmartyPants will be used to convert quotes and dashes to +# typographically correct entities. +#html_use_smartypants = True + +# Custom sidebar templates, maps document names to template names. +#html_sidebars = {} + +# Additional templates that should be rendered to pages, maps page names to +# template names. +#html_additional_pages = {} + +# If false, no module index is generated. +#html_domain_indices = True + +# If false, no index is generated. +#html_use_index = True + +# If true, the index is split into individual pages for each letter. +#html_split_index = False + +# If true, links to the reST sources are added to the pages. +#html_show_sourcelink = True + +# If true, "Created using Sphinx" is shown in the HTML footer. Default is True. +#html_show_sphinx = True + +# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. +#html_show_copyright = True + +# If true, an OpenSearch description file will be output, and all pages will +# contain a tag referring to it. The value of this option must be the +# base URL from which the finished HTML is served. +#html_use_opensearch = '' + +# This is the file name suffix for HTML files (e.g. ".xhtml"). +#html_file_suffix = None + +# Language to be used for generating the HTML full-text search index. +# Sphinx supports the following languages: +# 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja' +# 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr' +#html_search_language = 'en' + +# A dictionary with options for the search language support, empty by default. +# Now only 'ja' uses this config value +#html_search_options = {'type': 'default'} + +# The name of a javascript file (relative to the configuration directory) that +# implements a search results scorer. If empty, the default will be used. +#html_search_scorer = 'scorer.js' + +# Output file base name for HTML help builder. +htmlhelp_basename = 'POTdoc' + +# -- Options for LaTeX output --------------------------------------------- + +latex_elements = { +# The paper size ('letterpaper' or 'a4paper'). +#'papersize': 'letterpaper', + +# The font size ('10pt', '11pt' or '12pt'). +#'pointsize': '10pt', + +# Additional stuff for the LaTeX preamble. +#'preamble': '', + +# Latex figure (float) alignment +#'figure_align': 'htbp', +} + +# Grouping the document tree into LaTeX files. List of tuples +# (source start file, target name, title, +# author, documentclass [howto, manual, or own class]). +latex_documents = [ + (master_doc, 'POT.tex', u'POT Documentation', + u'Rémi Flamary, Nicolas Courty', 'manual'), +] + +# The name of an image file (relative to this directory) to place at the top of +# the title page. +#latex_logo = None + +# For "manual" documents, if this is true, then toplevel headings are parts, +# not chapters. +#latex_use_parts = False + +# If true, show page references after internal links. +#latex_show_pagerefs = False + +# If true, show URL addresses after external links. +#latex_show_urls = False + +# Documents to append as an appendix to all manuals. +#latex_appendices = [] + +# If false, no module index is generated. +#latex_domain_indices = True + + +# -- Options for manual page output --------------------------------------- + +# One entry per manual page. List of tuples +# (source start file, name, description, authors, manual section). +man_pages = [ + (master_doc, 'pot', u'POT Documentation', + [author], 1) +] + +# If true, show URL addresses after external links. +#man_show_urls = False + + +# -- Options for Texinfo output ------------------------------------------- + +# Grouping the document tree into Texinfo files. List of tuples +# (source start file, target name, title, author, +# dir menu entry, description, category) +texinfo_documents = [ + (master_doc, 'POT', u'POT Documentation', + author, 'POT', 'One line description of project.', + 'Miscellaneous'), +] + +# Documents to append as an appendix to all manuals. +#texinfo_appendices = [] + +# If false, no module index is generated. +#texinfo_domain_indices = True + +# How to display URL addresses: 'footnote', 'no', or 'inline'. +#texinfo_show_urls = 'footnote' + +# If true, do not generate a @detailmenu in the "Top" node's menu. +#texinfo_no_detailmenu = False + + +# Example configuration for intersphinx: refer to the Python standard library. +intersphinx_mapping = {'https://docs.python.org/': None} diff --git a/doc/source/index.rst b/doc/source/index.rst new file mode 100644 index 0000000..e96f544 --- /dev/null +++ b/doc/source/index.rst @@ -0,0 +1,32 @@ +.. POT documentation master file, created by + sphinx-quickstart on Mon Oct 24 11:10:10 2016. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to POT's documentation! +=============================== + +Contents: + +.. toctree:: + :maxdepth: 2 + +.. automodule:: ot + :members: +.. automodule:: ot.emd + :members: +.. automodule:: ot.bregman + :members: +.. automodule:: ot.utils + :members: +.. automodule:: ot.datasets + :members: +.. automodule:: ot.plot + :members: + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` diff --git a/ot/__init__.py b/ot/__init__.py index fe55771..ebe27ea 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -1,5 +1,7 @@ -# utils submodules +# Python Optimal Transport toolbox + + import utils import datasets import plot @@ -13,4 +15,4 @@ from bregman import sinkhorn from utils import dist,dots -__all__ = ["emd"] +__all__ = ["emd","sinkhorn","utils",'datasets','plot','dist','dot'] -- cgit v1.2.3 From def8eb0844e95d82b25c0a79c3be42bfaacb100c Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Oct 2016 11:36:28 +0200 Subject: add sphinx documentation --- ot/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/__init__.py b/ot/__init__.py index ebe27ea..3cf8e0b 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -15,4 +15,4 @@ from bregman import sinkhorn from utils import dist,dots -__all__ = ["emd","sinkhorn","utils",'datasets','plot','dist','dot'] +__all__ = ["emd","sinkhorn","utils",'datasets','plot','dist','dots'] -- cgit v1.2.3 From cfdfd3d4d2791d45cde0bf4512c15039c725bd3e Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Oct 2016 13:57:31 +0200 Subject: move doc --- doc/Makefile | 216 ------------------------------------ doc/make.bat | 263 -------------------------------------------- doc/source/conf.py | 298 -------------------------------------------------- doc/source/index.rst | 32 ------ docs/Makefile | 216 ++++++++++++++++++++++++++++++++++++ docs/make.bat | 263 ++++++++++++++++++++++++++++++++++++++++++++ docs/source/conf.py | 298 ++++++++++++++++++++++++++++++++++++++++++++++++++ docs/source/index.rst | 32 ++++++ 8 files changed, 809 insertions(+), 809 deletions(-) delete mode 100644 doc/Makefile delete mode 100644 doc/make.bat delete mode 100644 doc/source/conf.py delete mode 100644 doc/source/index.rst create mode 100644 docs/Makefile create mode 100644 docs/make.bat create mode 100644 docs/source/conf.py create mode 100644 docs/source/index.rst diff --git a/doc/Makefile b/doc/Makefile deleted file mode 100644 index 3511a59..0000000 --- a/doc/Makefile +++ /dev/null @@ -1,216 +0,0 @@ -# Makefile for Sphinx documentation -# - -# You can set these variables from the command line. -SPHINXOPTS = -SPHINXBUILD = sphinx-build -PAPER = -BUILDDIR = build - -# User-friendly check for sphinx-build -ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1) -$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/) -endif - -# Internal variables. -PAPEROPT_a4 = -D latex_paper_size=a4 -PAPEROPT_letter = -D latex_paper_size=letter -ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source -# the i18n builder cannot share the environment and doctrees with the others -I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source - -.PHONY: help -help: - @echo "Please use \`make ' where is one of" - @echo " html to make standalone HTML files" - @echo " dirhtml to make HTML files named index.html in directories" - @echo " singlehtml to make a single large HTML file" - @echo " pickle to make pickle files" - @echo " json to make JSON files" - @echo " htmlhelp to make HTML files and a HTML help project" - @echo " qthelp to make HTML files and a qthelp project" - @echo " applehelp to make an Apple Help Book" - @echo " devhelp to make HTML files and a Devhelp project" - @echo " epub to make an epub" - @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" - @echo " latexpdf to make LaTeX files and run them through pdflatex" - @echo " latexpdfja to make LaTeX files and run them through platex/dvipdfmx" - @echo " text to make text files" - @echo " man to make manual pages" - @echo " texinfo to make Texinfo files" - @echo " info to make Texinfo files and run them through makeinfo" - @echo " gettext to make PO message catalogs" - @echo " changes to make an overview of all changed/added/deprecated items" - @echo " xml to make Docutils-native XML files" - @echo " pseudoxml to make pseudoxml-XML files for display purposes" - @echo " linkcheck to check all external links for integrity" - @echo " doctest to run all doctests embedded in the documentation (if enabled)" - @echo " coverage to run coverage check of the documentation (if enabled)" - -.PHONY: clean -clean: - rm -rf $(BUILDDIR)/* - -.PHONY: html -html: - $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html - @echo - @echo "Build finished. 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If the directory is relative to the -# documentation root, use os.path.abspath to make it absolute, like shown here. -#sys.path.insert(0, os.path.abspath('.')) - -# -- General configuration ------------------------------------------------ - -# If your documentation needs a minimal Sphinx version, state it here. -#needs_sphinx = '1.0' - -# Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom -# ones. -extensions = [ - 'sphinx.ext.autodoc', - 'sphinx.ext.doctest', - 'sphinx.ext.intersphinx', - 'sphinx.ext.todo', - 'sphinx.ext.coverage', - 'sphinx.ext.mathjax', - 'sphinx.ext.ifconfig', - 'sphinx.ext.viewcode', -] - -# Add any paths that contain templates here, relative to this directory. -templates_path = ['_templates'] - -# The suffix(es) of source filenames. -# You can specify multiple suffix as a list of string: -# source_suffix = ['.rst', '.md'] -source_suffix = '.rst' - -# The encoding of source files. -#source_encoding = 'utf-8-sig' - -# The master toctree document. -master_doc = 'index' - -# General information about the project. -project = u'POT' -copyright = u'2016, Rémi Flamary, Nicolas Courty' -author = u'Rémi Flamary, Nicolas Courty' - -# The version info for the project you're documenting, acts as replacement for -# |version| and |release|, also used in various other places throughout the -# built documents. -# -# The short X.Y version. -version = u'0.1' -# The full version, including alpha/beta/rc tags. -release = u'0.1' - -# The language for content autogenerated by Sphinx. Refer to documentation -# for a list of supported languages. -# -# This is also used if you do content translation via gettext catalogs. -# Usually you set "language" from the command line for these cases. -language = None - -# There are two options for replacing |today|: either, you set today to some -# non-false value, then it is used: -#today = '' -# Else, today_fmt is used as the format for a strftime call. -#today_fmt = '%B %d, %Y' - -# List of patterns, relative to source directory, that match files and -# directories to ignore when looking for source files. -exclude_patterns = [] - -# The reST default role (used for this markup: `text`) to use for all -# documents. -#default_role = None - -# If true, '()' will be appended to :func: etc. cross-reference text. -#add_function_parentheses = True - -# If true, the current module name will be prepended to all description -# unit titles (such as .. function::). -#add_module_names = True - -# If true, sectionauthor and moduleauthor directives will be shown in the -# output. They are ignored by default. -#show_authors = False - -# The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' - -# A list of ignored prefixes for module index sorting. -#modindex_common_prefix = [] - -# If true, keep warnings as "system message" paragraphs in the built documents. -#keep_warnings = False - -# If true, `todo` and `todoList` produce output, else they produce nothing. -todo_include_todos = True - - -# -- Options for HTML output ---------------------------------------------- - -# The theme to use for HTML and HTML Help pages. See the documentation for -# a list of builtin themes. -html_theme = 'alabaster' - -# Theme options are theme-specific and customize the look and feel of a theme -# further. For a list of options available for each theme, see the -# documentation. -#html_theme_options = {} - -# Add any paths that contain custom themes here, relative to this directory. -#html_theme_path = [] - -# The name for this set of Sphinx documents. If None, it defaults to -# " v documentation". -#html_title = None - -# A shorter title for the navigation bar. Default is the same as html_title. -#html_short_title = None - -# The name of an image file (relative to this directory) to place at the top -# of the sidebar. -#html_logo = None - -# The name of an image file (relative to this directory) to use as a favicon of -# the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 -# pixels large. -#html_favicon = None - -# Add any paths that contain custom static files (such as style sheets) here, -# relative to this directory. They are copied after the builtin static files, -# so a file named "default.css" will overwrite the builtin "default.css". -html_static_path = ['_static'] - -# Add any extra paths that contain custom files (such as robots.txt or -# .htaccess) here, relative to this directory. These files are copied -# directly to the root of the documentation. -#html_extra_path = [] - -# If not '', a 'Last updated on:' timestamp is inserted at every page bottom, -# using the given strftime format. -#html_last_updated_fmt = '%b %d, %Y' - -# If true, SmartyPants will be used to convert quotes and dashes to -# typographically correct entities. -#html_use_smartypants = True - -# Custom sidebar templates, maps document names to template names. -#html_sidebars = {} - -# Additional templates that should be rendered to pages, maps page names to -# template names. -#html_additional_pages = {} - -# If false, no module index is generated. -#html_domain_indices = True - -# If false, no index is generated. -#html_use_index = True - -# If true, the index is split into individual pages for each letter. -#html_split_index = False - -# If true, links to the reST sources are added to the pages. -#html_show_sourcelink = True - -# If true, "Created using Sphinx" is shown in the HTML footer. Default is True. -#html_show_sphinx = True - -# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. -#html_show_copyright = True - -# If true, an OpenSearch description file will be output, and all pages will -# contain a tag referring to it. The value of this option must be the -# base URL from which the finished HTML is served. -#html_use_opensearch = '' - -# This is the file name suffix for HTML files (e.g. ".xhtml"). -#html_file_suffix = None - -# Language to be used for generating the HTML full-text search index. -# Sphinx supports the following languages: -# 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja' -# 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr' -#html_search_language = 'en' - -# A dictionary with options for the search language support, empty by default. -# Now only 'ja' uses this config value -#html_search_options = {'type': 'default'} - -# The name of a javascript file (relative to the configuration directory) that -# implements a search results scorer. If empty, the default will be used. -#html_search_scorer = 'scorer.js' - -# Output file base name for HTML help builder. -htmlhelp_basename = 'POTdoc' - -# -- Options for LaTeX output --------------------------------------------- - -latex_elements = { -# The paper size ('letterpaper' or 'a4paper'). -#'papersize': 'letterpaper', - -# The font size ('10pt', '11pt' or '12pt'). -#'pointsize': '10pt', - -# Additional stuff for the LaTeX preamble. -#'preamble': '', - -# Latex figure (float) alignment -#'figure_align': 'htbp', -} - -# Grouping the document tree into LaTeX files. List of tuples -# (source start file, target name, title, -# author, documentclass [howto, manual, or own class]). -latex_documents = [ - (master_doc, 'POT.tex', u'POT Documentation', - u'Rémi Flamary, Nicolas Courty', 'manual'), -] - -# The name of an image file (relative to this directory) to place at the top of -# the title page. -#latex_logo = None - -# For "manual" documents, if this is true, then toplevel headings are parts, -# not chapters. -#latex_use_parts = False - -# If true, show page references after internal links. -#latex_show_pagerefs = False - -# If true, show URL addresses after external links. -#latex_show_urls = False - -# Documents to append as an appendix to all manuals. -#latex_appendices = [] - -# If false, no module index is generated. -#latex_domain_indices = True - - -# -- Options for manual page output --------------------------------------- - -# One entry per manual page. List of tuples -# (source start file, name, description, authors, manual section). -man_pages = [ - (master_doc, 'pot', u'POT Documentation', - [author], 1) -] - -# If true, show URL addresses after external links. -#man_show_urls = False - - -# -- Options for Texinfo output ------------------------------------------- - -# Grouping the document tree into Texinfo files. List of tuples -# (source start file, target name, title, author, -# dir menu entry, description, category) -texinfo_documents = [ - (master_doc, 'POT', u'POT Documentation', - author, 'POT', 'One line description of project.', - 'Miscellaneous'), -] - -# Documents to append as an appendix to all manuals. -#texinfo_appendices = [] - -# If false, no module index is generated. -#texinfo_domain_indices = True - -# How to display URL addresses: 'footnote', 'no', or 'inline'. -#texinfo_show_urls = 'footnote' - -# If true, do not generate a @detailmenu in the "Top" node's menu. -#texinfo_no_detailmenu = False - - -# Example configuration for intersphinx: refer to the Python standard library. -intersphinx_mapping = {'https://docs.python.org/': None} diff --git a/doc/source/index.rst b/doc/source/index.rst deleted file mode 100644 index e96f544..0000000 --- a/doc/source/index.rst +++ /dev/null @@ -1,32 +0,0 @@ -.. POT documentation master file, created by - sphinx-quickstart on Mon Oct 24 11:10:10 2016. - You can adapt this file completely to your liking, but it should at least - contain the root `toctree` directive. - -Welcome to POT's documentation! -=============================== - -Contents: - -.. toctree:: - :maxdepth: 2 - -.. automodule:: ot - :members: -.. automodule:: ot.emd - :members: -.. automodule:: ot.bregman - :members: -.. automodule:: ot.utils - :members: -.. automodule:: ot.datasets - :members: -.. automodule:: ot.plot - :members: - -Indices and tables -================== - -* :ref:`genindex` -* :ref:`modindex` -* :ref:`search` diff --git a/docs/Makefile b/docs/Makefile new file mode 100644 index 0000000..3511a59 --- /dev/null +++ b/docs/Makefile @@ -0,0 +1,216 @@ +# Makefile for Sphinx documentation +# + +# You can set these variables from the command line. +SPHINXOPTS = +SPHINXBUILD = sphinx-build +PAPER = +BUILDDIR = build + +# User-friendly check for sphinx-build +ifeq ($(shell which $(SPHINXBUILD) >/dev/null 2>&1; echo $$?), 1) +$(error The '$(SPHINXBUILD)' command was not found. Make sure you have Sphinx installed, then set the SPHINXBUILD environment variable to point to the full path of the '$(SPHINXBUILD)' executable. Alternatively you can add the directory with the executable to your PATH. If you don't have Sphinx installed, grab it from http://sphinx-doc.org/) +endif + +# Internal variables. +PAPEROPT_a4 = -D latex_paper_size=a4 +PAPEROPT_letter = -D latex_paper_size=letter +ALLSPHINXOPTS = -d $(BUILDDIR)/doctrees $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source +# the i18n builder cannot share the environment and doctrees with the others +I18NSPHINXOPTS = $(PAPEROPT_$(PAPER)) $(SPHINXOPTS) source + +.PHONY: help +help: + @echo "Please use \`make ' where is one of" + @echo " html to make standalone HTML files" + @echo " dirhtml to make HTML files named index.html in directories" + @echo " singlehtml to make a single large HTML file" + @echo " pickle to make pickle files" + @echo " json to make JSON files" + @echo " htmlhelp to make HTML files and a HTML help project" + @echo " qthelp to make HTML files and a qthelp project" + @echo " applehelp to make an Apple Help Book" + @echo " devhelp to make HTML files and a Devhelp project" + @echo " epub to make an epub" + @echo " latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter" + @echo " latexpdf to make LaTeX files and run them through pdflatex" + @echo " latexpdfja to make LaTeX files and run them through platex/dvipdfmx" + @echo " text to make text files" + @echo " man to make manual pages" + @echo " texinfo to make Texinfo files" + @echo " info to make Texinfo files and run them through makeinfo" + @echo " gettext to make PO message catalogs" + @echo " changes to make an overview of all changed/added/deprecated items" + @echo " xml to make Docutils-native XML files" + @echo " pseudoxml to make pseudoxml-XML files for display purposes" + @echo " linkcheck to check all external links for integrity" + @echo " doctest to run all doctests embedded in the documentation (if enabled)" + @echo " coverage to run coverage check of the documentation (if enabled)" + +.PHONY: clean +clean: + rm -rf $(BUILDDIR)/* + +.PHONY: html +html: + $(SPHINXBUILD) -b html $(ALLSPHINXOPTS) $(BUILDDIR)/html + @echo + @echo "Build finished. The HTML pages are in $(BUILDDIR)/html." + +.PHONY: dirhtml +dirhtml: + $(SPHINXBUILD) -b dirhtml $(ALLSPHINXOPTS) $(BUILDDIR)/dirhtml + @echo + @echo "Build finished. The HTML pages are in $(BUILDDIR)/dirhtml." + +.PHONY: singlehtml +singlehtml: + $(SPHINXBUILD) -b singlehtml $(ALLSPHINXOPTS) $(BUILDDIR)/singlehtml + @echo + @echo "Build finished. The HTML page is in $(BUILDDIR)/singlehtml." + +.PHONY: pickle +pickle: + $(SPHINXBUILD) -b pickle $(ALLSPHINXOPTS) $(BUILDDIR)/pickle + @echo + @echo "Build finished; now you can process the pickle files." + +.PHONY: json +json: + $(SPHINXBUILD) -b json $(ALLSPHINXOPTS) $(BUILDDIR)/json + @echo + @echo "Build finished; now you can process the JSON files." + +.PHONY: htmlhelp +htmlhelp: + $(SPHINXBUILD) -b htmlhelp $(ALLSPHINXOPTS) $(BUILDDIR)/htmlhelp + @echo + @echo "Build finished; now you can run HTML Help Workshop with the" \ + ".hhp project file in $(BUILDDIR)/htmlhelp." + +.PHONY: qthelp +qthelp: + $(SPHINXBUILD) -b qthelp $(ALLSPHINXOPTS) $(BUILDDIR)/qthelp + @echo + @echo "Build finished; now you can run "qcollectiongenerator" with the" \ + ".qhcp project file in $(BUILDDIR)/qthelp, like this:" + @echo "# qcollectiongenerator $(BUILDDIR)/qthelp/POT.qhcp" + @echo "To view the help file:" + @echo "# assistant -collectionFile $(BUILDDIR)/qthelp/POT.qhc" + +.PHONY: applehelp +applehelp: + $(SPHINXBUILD) -b applehelp $(ALLSPHINXOPTS) $(BUILDDIR)/applehelp + @echo + @echo "Build finished. The help book is in $(BUILDDIR)/applehelp." + @echo "N.B. You won't be able to view it unless you put it in" \ + "~/Library/Documentation/Help or install it in your application" \ + "bundle." + +.PHONY: devhelp +devhelp: + $(SPHINXBUILD) -b devhelp $(ALLSPHINXOPTS) $(BUILDDIR)/devhelp + @echo + @echo "Build finished." + @echo "To view the help file:" + @echo "# mkdir -p $$HOME/.local/share/devhelp/POT" + @echo "# ln -s $(BUILDDIR)/devhelp $$HOME/.local/share/devhelp/POT" + @echo "# devhelp" + +.PHONY: epub +epub: + $(SPHINXBUILD) -b epub $(ALLSPHINXOPTS) $(BUILDDIR)/epub + @echo + @echo "Build finished. The epub file is in $(BUILDDIR)/epub." + +.PHONY: latex +latex: + $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex + @echo + @echo "Build finished; the LaTeX files are in $(BUILDDIR)/latex." + @echo "Run \`make' in that directory to run these through (pdf)latex" \ + "(use \`make latexpdf' here to do that automatically)." + +.PHONY: latexpdf +latexpdf: + $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex + @echo "Running LaTeX files through pdflatex..." + $(MAKE) -C $(BUILDDIR)/latex all-pdf + @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." + +.PHONY: latexpdfja +latexpdfja: + $(SPHINXBUILD) -b latex $(ALLSPHINXOPTS) $(BUILDDIR)/latex + @echo "Running LaTeX files through platex and dvipdfmx..." + $(MAKE) -C $(BUILDDIR)/latex all-pdf-ja + @echo "pdflatex finished; the PDF files are in $(BUILDDIR)/latex." + +.PHONY: text +text: + $(SPHINXBUILD) -b text $(ALLSPHINXOPTS) $(BUILDDIR)/text + @echo + @echo "Build finished. The text files are in $(BUILDDIR)/text." + +.PHONY: man +man: + $(SPHINXBUILD) -b man $(ALLSPHINXOPTS) $(BUILDDIR)/man + @echo + @echo "Build finished. The manual pages are in $(BUILDDIR)/man." + +.PHONY: texinfo +texinfo: + $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo + @echo + @echo "Build finished. The Texinfo files are in $(BUILDDIR)/texinfo." + @echo "Run \`make' in that directory to run these through makeinfo" \ + "(use \`make info' here to do that automatically)." + +.PHONY: info +info: + $(SPHINXBUILD) -b texinfo $(ALLSPHINXOPTS) $(BUILDDIR)/texinfo + @echo "Running Texinfo files through makeinfo..." + make -C $(BUILDDIR)/texinfo info + @echo "makeinfo finished; the Info files are in $(BUILDDIR)/texinfo." + +.PHONY: gettext +gettext: + $(SPHINXBUILD) -b gettext $(I18NSPHINXOPTS) $(BUILDDIR)/locale + @echo + @echo "Build finished. The message catalogs are in $(BUILDDIR)/locale." + +.PHONY: changes +changes: + $(SPHINXBUILD) -b changes $(ALLSPHINXOPTS) $(BUILDDIR)/changes + @echo + @echo "The overview file is in $(BUILDDIR)/changes." + +.PHONY: linkcheck +linkcheck: + $(SPHINXBUILD) -b linkcheck $(ALLSPHINXOPTS) $(BUILDDIR)/linkcheck + @echo + @echo "Link check complete; look for any errors in the above output " \ + "or in $(BUILDDIR)/linkcheck/output.txt." + +.PHONY: doctest +doctest: + $(SPHINXBUILD) -b doctest $(ALLSPHINXOPTS) $(BUILDDIR)/doctest + @echo "Testing of doctests in the sources finished, look at the " \ + "results in $(BUILDDIR)/doctest/output.txt." + +.PHONY: coverage +coverage: + $(SPHINXBUILD) -b coverage $(ALLSPHINXOPTS) $(BUILDDIR)/coverage + @echo "Testing of coverage in the sources finished, look at the " \ + "results in $(BUILDDIR)/coverage/python.txt." + +.PHONY: xml +xml: + $(SPHINXBUILD) -b xml $(ALLSPHINXOPTS) $(BUILDDIR)/xml + @echo + @echo "Build finished. The XML files are in $(BUILDDIR)/xml." + +.PHONY: pseudoxml +pseudoxml: + $(SPHINXBUILD) -b pseudoxml $(ALLSPHINXOPTS) $(BUILDDIR)/pseudoxml + @echo + @echo "Build finished. The pseudo-XML files are in $(BUILDDIR)/pseudoxml." diff --git a/docs/make.bat b/docs/make.bat new file mode 100644 index 0000000..f2313df --- /dev/null +++ b/docs/make.bat @@ -0,0 +1,263 @@ +@ECHO OFF + +REM Command file for Sphinx documentation + +if "%SPHINXBUILD%" == "" ( + set SPHINXBUILD=sphinx-build +) +set BUILDDIR=build +set ALLSPHINXOPTS=-d %BUILDDIR%/doctrees %SPHINXOPTS% source +set I18NSPHINXOPTS=%SPHINXOPTS% source +if NOT "%PAPER%" == "" ( + set ALLSPHINXOPTS=-D latex_paper_size=%PAPER% %ALLSPHINXOPTS% + set I18NSPHINXOPTS=-D latex_paper_size=%PAPER% %I18NSPHINXOPTS% +) + +if "%1" == "" goto help + +if "%1" == "help" ( + :help + echo.Please use `make ^` where ^ is one of + echo. html to make standalone HTML files + echo. dirhtml to make HTML files named index.html in directories + echo. singlehtml to make a single large HTML file + echo. pickle to make pickle files + echo. json to make JSON files + echo. htmlhelp to make HTML files and a HTML help project + echo. qthelp to make HTML files and a qthelp project + echo. devhelp to make HTML files and a Devhelp project + echo. epub to make an epub + echo. latex to make LaTeX files, you can set PAPER=a4 or PAPER=letter + echo. text to make text files + echo. man to make manual pages + echo. texinfo to make Texinfo files + echo. gettext to make PO message catalogs + echo. changes to make an overview over all changed/added/deprecated items + echo. xml to make Docutils-native XML files + echo. pseudoxml to make pseudoxml-XML files for display purposes + echo. linkcheck to check all external links for integrity + echo. doctest to run all doctests embedded in the documentation if enabled + echo. coverage to run coverage check of the documentation if enabled + goto end +) + +if "%1" == "clean" ( + for /d %%i in (%BUILDDIR%\*) do rmdir /q /s %%i + del /q /s %BUILDDIR%\* + goto end +) + + +REM Check if sphinx-build is available and fallback to Python version if any +%SPHINXBUILD% 1>NUL 2>NUL +if errorlevel 9009 goto sphinx_python +goto sphinx_ok + +:sphinx_python + +set SPHINXBUILD=python -m sphinx.__init__ +%SPHINXBUILD% 2> nul +if errorlevel 9009 ( + echo. + echo.The 'sphinx-build' command was not found. Make sure you have Sphinx + echo.installed, then set the SPHINXBUILD environment variable to point + echo.to the full path of the 'sphinx-build' executable. Alternatively you + echo.may add the Sphinx directory to PATH. + echo. + echo.If you don't have Sphinx installed, grab it from + echo.http://sphinx-doc.org/ + exit /b 1 +) + +:sphinx_ok + + +if "%1" == "html" ( + %SPHINXBUILD% -b html %ALLSPHINXOPTS% %BUILDDIR%/html + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The HTML pages are in %BUILDDIR%/html. + goto end +) + +if "%1" == "dirhtml" ( + %SPHINXBUILD% -b dirhtml %ALLSPHINXOPTS% %BUILDDIR%/dirhtml + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The HTML pages are in %BUILDDIR%/dirhtml. + goto end +) + +if "%1" == "singlehtml" ( + %SPHINXBUILD% -b singlehtml %ALLSPHINXOPTS% %BUILDDIR%/singlehtml + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The HTML pages are in %BUILDDIR%/singlehtml. + goto end +) + +if "%1" == "pickle" ( + %SPHINXBUILD% -b pickle %ALLSPHINXOPTS% %BUILDDIR%/pickle + if errorlevel 1 exit /b 1 + echo. + echo.Build finished; now you can process the pickle files. + goto end +) + +if "%1" == "json" ( + %SPHINXBUILD% -b json %ALLSPHINXOPTS% %BUILDDIR%/json + if errorlevel 1 exit /b 1 + echo. + echo.Build finished; now you can process the JSON files. + goto end +) + +if "%1" == "htmlhelp" ( + %SPHINXBUILD% -b htmlhelp %ALLSPHINXOPTS% %BUILDDIR%/htmlhelp + if errorlevel 1 exit /b 1 + echo. + echo.Build finished; now you can run HTML Help Workshop with the ^ +.hhp project file in %BUILDDIR%/htmlhelp. + goto end +) + +if "%1" == "qthelp" ( + %SPHINXBUILD% -b qthelp %ALLSPHINXOPTS% %BUILDDIR%/qthelp + if errorlevel 1 exit /b 1 + echo. + echo.Build finished; now you can run "qcollectiongenerator" with the ^ +.qhcp project file in %BUILDDIR%/qthelp, like this: + echo.^> qcollectiongenerator %BUILDDIR%\qthelp\POT.qhcp + echo.To view the help file: + echo.^> assistant -collectionFile %BUILDDIR%\qthelp\POT.ghc + goto end +) + +if "%1" == "devhelp" ( + %SPHINXBUILD% -b devhelp %ALLSPHINXOPTS% %BUILDDIR%/devhelp + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. + goto end +) + +if "%1" == "epub" ( + %SPHINXBUILD% -b epub %ALLSPHINXOPTS% %BUILDDIR%/epub + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The epub file is in %BUILDDIR%/epub. + goto end +) + +if "%1" == "latex" ( + %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex + if errorlevel 1 exit /b 1 + echo. + echo.Build finished; the LaTeX files are in %BUILDDIR%/latex. + goto end +) + +if "%1" == "latexpdf" ( + %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex + cd %BUILDDIR%/latex + make all-pdf + cd %~dp0 + echo. + echo.Build finished; the PDF files are in %BUILDDIR%/latex. + goto end +) + +if "%1" == "latexpdfja" ( + %SPHINXBUILD% -b latex %ALLSPHINXOPTS% %BUILDDIR%/latex + cd %BUILDDIR%/latex + make all-pdf-ja + cd %~dp0 + echo. + echo.Build finished; the PDF files are in %BUILDDIR%/latex. + goto end +) + +if "%1" == "text" ( + %SPHINXBUILD% -b text %ALLSPHINXOPTS% %BUILDDIR%/text + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The text files are in %BUILDDIR%/text. + goto end +) + +if "%1" == "man" ( + %SPHINXBUILD% -b man %ALLSPHINXOPTS% %BUILDDIR%/man + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The manual pages are in %BUILDDIR%/man. + goto end +) + +if "%1" == "texinfo" ( + %SPHINXBUILD% -b texinfo %ALLSPHINXOPTS% %BUILDDIR%/texinfo + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The Texinfo files are in %BUILDDIR%/texinfo. + goto end +) + +if "%1" == "gettext" ( + %SPHINXBUILD% -b gettext %I18NSPHINXOPTS% %BUILDDIR%/locale + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The message catalogs are in %BUILDDIR%/locale. + goto end +) + +if "%1" == "changes" ( + %SPHINXBUILD% -b changes %ALLSPHINXOPTS% %BUILDDIR%/changes + if errorlevel 1 exit /b 1 + echo. + echo.The overview file is in %BUILDDIR%/changes. + goto end +) + +if "%1" == "linkcheck" ( + %SPHINXBUILD% -b linkcheck %ALLSPHINXOPTS% %BUILDDIR%/linkcheck + if errorlevel 1 exit /b 1 + echo. + echo.Link check complete; look for any errors in the above output ^ +or in %BUILDDIR%/linkcheck/output.txt. + goto end +) + +if "%1" == "doctest" ( + %SPHINXBUILD% -b doctest %ALLSPHINXOPTS% %BUILDDIR%/doctest + if errorlevel 1 exit /b 1 + echo. + echo.Testing of doctests in the sources finished, look at the ^ +results in %BUILDDIR%/doctest/output.txt. + goto end +) + +if "%1" == "coverage" ( + %SPHINXBUILD% -b coverage %ALLSPHINXOPTS% %BUILDDIR%/coverage + if errorlevel 1 exit /b 1 + echo. + echo.Testing of coverage in the sources finished, look at the ^ +results in %BUILDDIR%/coverage/python.txt. + goto end +) + +if "%1" == "xml" ( + %SPHINXBUILD% -b xml %ALLSPHINXOPTS% %BUILDDIR%/xml + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The XML files are in %BUILDDIR%/xml. + goto end +) + +if "%1" == "pseudoxml" ( + %SPHINXBUILD% -b pseudoxml %ALLSPHINXOPTS% %BUILDDIR%/pseudoxml + if errorlevel 1 exit /b 1 + echo. + echo.Build finished. The pseudo-XML files are in %BUILDDIR%/pseudoxml. + goto end +) + +:end diff --git a/docs/source/conf.py b/docs/source/conf.py new file mode 100644 index 0000000..0caefae --- /dev/null +++ b/docs/source/conf.py @@ -0,0 +1,298 @@ +# -*- coding: utf-8 -*- +# +# POT documentation build configuration file, created by +# sphinx-quickstart on Mon Oct 24 11:10:10 2016. +# +# This file is execfile()d with the current directory set to its +# containing dir. +# +# Note that not all possible configuration values are present in this +# autogenerated file. +# +# All configuration values have a default; values that are commented out +# serve to show the default. + +import sys +import os + +sys.path.insert(0, os.path.abspath("../..")) + +# If extensions (or modules to document with autodoc) are in another directory, +# add these directories to sys.path here. If the directory is relative to the +# documentation root, use os.path.abspath to make it absolute, like shown here. +#sys.path.insert(0, os.path.abspath('.')) + +# -- General configuration ------------------------------------------------ + +# If your documentation needs a minimal Sphinx version, state it here. +#needs_sphinx = '1.0' + +# Add any Sphinx extension module names here, as strings. They can be +# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# ones. +extensions = [ + 'sphinx.ext.autodoc', + 'sphinx.ext.doctest', + 'sphinx.ext.intersphinx', + 'sphinx.ext.todo', + 'sphinx.ext.coverage', + 'sphinx.ext.mathjax', + 'sphinx.ext.ifconfig', + 'sphinx.ext.viewcode', +] + +# Add any paths that contain templates here, relative to this directory. +templates_path = ['_templates'] + +# The suffix(es) of source filenames. +# You can specify multiple suffix as a list of string: +# source_suffix = ['.rst', '.md'] +source_suffix = '.rst' + +# The encoding of source files. +#source_encoding = 'utf-8-sig' + +# The master toctree document. +master_doc = 'index' + +# General information about the project. +project = u'POT' +copyright = u'2016, Rémi Flamary, Nicolas Courty' +author = u'Rémi Flamary, Nicolas Courty' + +# The version info for the project you're documenting, acts as replacement for +# |version| and |release|, also used in various other places throughout the +# built documents. +# +# The short X.Y version. +version = u'0.1' +# The full version, including alpha/beta/rc tags. +release = u'0.1' + +# The language for content autogenerated by Sphinx. Refer to documentation +# for a list of supported languages. +# +# This is also used if you do content translation via gettext catalogs. +# Usually you set "language" from the command line for these cases. +language = None + +# There are two options for replacing |today|: either, you set today to some +# non-false value, then it is used: +#today = '' +# Else, today_fmt is used as the format for a strftime call. +#today_fmt = '%B %d, %Y' + +# List of patterns, relative to source directory, that match files and +# directories to ignore when looking for source files. +exclude_patterns = [] + +# The reST default role (used for this markup: `text`) to use for all +# documents. +#default_role = None + +# If true, '()' will be appended to :func: etc. cross-reference text. +#add_function_parentheses = True + +# If true, the current module name will be prepended to all description +# unit titles (such as .. function::). +#add_module_names = True + +# If true, sectionauthor and moduleauthor directives will be shown in the +# output. They are ignored by default. +#show_authors = False + +# The name of the Pygments (syntax highlighting) style to use. +pygments_style = 'sphinx' + +# A list of ignored prefixes for module index sorting. +#modindex_common_prefix = [] + +# If true, keep warnings as "system message" paragraphs in the built documents. +#keep_warnings = False + +# If true, `todo` and `todoList` produce output, else they produce nothing. +todo_include_todos = True + + +# -- Options for HTML output ---------------------------------------------- + +# The theme to use for HTML and HTML Help pages. See the documentation for +# a list of builtin themes. +html_theme = 'alabaster' + +# Theme options are theme-specific and customize the look and feel of a theme +# further. For a list of options available for each theme, see the +# documentation. +#html_theme_options = {} + +# Add any paths that contain custom themes here, relative to this directory. +#html_theme_path = [] + +# The name for this set of Sphinx documents. If None, it defaults to +# " v documentation". +#html_title = None + +# A shorter title for the navigation bar. Default is the same as html_title. +#html_short_title = None + +# The name of an image file (relative to this directory) to place at the top +# of the sidebar. +#html_logo = None + +# The name of an image file (relative to this directory) to use as a favicon of +# the docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 +# pixels large. +#html_favicon = None + +# Add any paths that contain custom static files (such as style sheets) here, +# relative to this directory. They are copied after the builtin static files, +# so a file named "default.css" will overwrite the builtin "default.css". +html_static_path = ['_static'] + +# Add any extra paths that contain custom files (such as robots.txt or +# .htaccess) here, relative to this directory. These files are copied +# directly to the root of the documentation. +#html_extra_path = [] + +# If not '', a 'Last updated on:' timestamp is inserted at every page bottom, +# using the given strftime format. +#html_last_updated_fmt = '%b %d, %Y' + +# If true, SmartyPants will be used to convert quotes and dashes to +# typographically correct entities. +#html_use_smartypants = True + +# Custom sidebar templates, maps document names to template names. +#html_sidebars = {} + +# Additional templates that should be rendered to pages, maps page names to +# template names. +#html_additional_pages = {} + +# If false, no module index is generated. +#html_domain_indices = True + +# If false, no index is generated. +#html_use_index = True + +# If true, the index is split into individual pages for each letter. +#html_split_index = False + +# If true, links to the reST sources are added to the pages. +#html_show_sourcelink = True + +# If true, "Created using Sphinx" is shown in the HTML footer. Default is True. +#html_show_sphinx = True + +# If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. +#html_show_copyright = True + +# If true, an OpenSearch description file will be output, and all pages will +# contain a tag referring to it. The value of this option must be the +# base URL from which the finished HTML is served. +#html_use_opensearch = '' + +# This is the file name suffix for HTML files (e.g. ".xhtml"). +#html_file_suffix = None + +# Language to be used for generating the HTML full-text search index. +# Sphinx supports the following languages: +# 'da', 'de', 'en', 'es', 'fi', 'fr', 'hu', 'it', 'ja' +# 'nl', 'no', 'pt', 'ro', 'ru', 'sv', 'tr' +#html_search_language = 'en' + +# A dictionary with options for the search language support, empty by default. +# Now only 'ja' uses this config value +#html_search_options = {'type': 'default'} + +# The name of a javascript file (relative to the configuration directory) that +# implements a search results scorer. If empty, the default will be used. +#html_search_scorer = 'scorer.js' + +# Output file base name for HTML help builder. +htmlhelp_basename = 'POTdoc' + +# -- Options for LaTeX output --------------------------------------------- + +latex_elements = { +# The paper size ('letterpaper' or 'a4paper'). +#'papersize': 'letterpaper', + +# The font size ('10pt', '11pt' or '12pt'). +#'pointsize': '10pt', + +# Additional stuff for the LaTeX preamble. +#'preamble': '', + +# Latex figure (float) alignment +#'figure_align': 'htbp', +} + +# Grouping the document tree into LaTeX files. List of tuples +# (source start file, target name, title, +# author, documentclass [howto, manual, or own class]). +latex_documents = [ + (master_doc, 'POT.tex', u'POT Documentation', + u'Rémi Flamary, Nicolas Courty', 'manual'), +] + +# The name of an image file (relative to this directory) to place at the top of +# the title page. +#latex_logo = None + +# For "manual" documents, if this is true, then toplevel headings are parts, +# not chapters. +#latex_use_parts = False + +# If true, show page references after internal links. +#latex_show_pagerefs = False + +# If true, show URL addresses after external links. +#latex_show_urls = False + +# Documents to append as an appendix to all manuals. +#latex_appendices = [] + +# If false, no module index is generated. +#latex_domain_indices = True + + +# -- Options for manual page output --------------------------------------- + +# One entry per manual page. List of tuples +# (source start file, name, description, authors, manual section). +man_pages = [ + (master_doc, 'pot', u'POT Documentation', + [author], 1) +] + +# If true, show URL addresses after external links. +#man_show_urls = False + + +# -- Options for Texinfo output ------------------------------------------- + +# Grouping the document tree into Texinfo files. List of tuples +# (source start file, target name, title, author, +# dir menu entry, description, category) +texinfo_documents = [ + (master_doc, 'POT', u'POT Documentation', + author, 'POT', 'One line description of project.', + 'Miscellaneous'), +] + +# Documents to append as an appendix to all manuals. +#texinfo_appendices = [] + +# If false, no module index is generated. +#texinfo_domain_indices = True + +# How to display URL addresses: 'footnote', 'no', or 'inline'. +#texinfo_show_urls = 'footnote' + +# If true, do not generate a @detailmenu in the "Top" node's menu. +#texinfo_no_detailmenu = False + + +# Example configuration for intersphinx: refer to the Python standard library. +intersphinx_mapping = {'https://docs.python.org/': None} diff --git a/docs/source/index.rst b/docs/source/index.rst new file mode 100644 index 0000000..e96f544 --- /dev/null +++ b/docs/source/index.rst @@ -0,0 +1,32 @@ +.. POT documentation master file, created by + sphinx-quickstart on Mon Oct 24 11:10:10 2016. + You can adapt this file completely to your liking, but it should at least + contain the root `toctree` directive. + +Welcome to POT's documentation! +=============================== + +Contents: + +.. toctree:: + :maxdepth: 2 + +.. automodule:: ot + :members: +.. automodule:: ot.emd + :members: +.. automodule:: ot.bregman + :members: +.. automodule:: ot.utils + :members: +.. automodule:: ot.datasets + :members: +.. automodule:: ot.plot + :members: + +Indices and tables +================== + +* :ref:`genindex` +* :ref:`modindex` +* :ref:`search` -- cgit v1.2.3 From 9c1d808ef2a45393dd1b8d8950411707b04617e7 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Oct 2016 14:18:27 +0200 Subject: add makefile --- Makefile | 32 ++++++++++++++++++++++++++++++++ 1 file changed, 32 insertions(+) create mode 100644 Makefile diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..4345854 --- /dev/null +++ b/Makefile @@ -0,0 +1,32 @@ + + +PYTHON=python + +help : + @echo "The following make targets are available:" + @echo " help - print this message" + @echo " build - build python package" + @echo " install - install python package (local user)" + @echo " sinstall - install python package (system with sudo)" + @echo " remove - remove the package (local user)" + @echo " sremove - remove the package (system with sudo)" + @echo " clean - remove any temporary files" + +build : + $(PYTHON) setup.py build + +install : + $(PYTHON) setup.py install --user + +sinstall : + sudo $(PYTHON) setup.py install + +remove : + $(PYTHON) setup.py install --user --record files.txt + tr '\n' '\0' < files.txt | xargs -0 rm -f -- + rm files.txt + +sremove : + $(PYTHON) setup.py install --record files.txt + tr '\n' '\0' < files.txt | sudo xargs -0 rm -f -- + rm files.txt -- cgit v1.2.3 From 04262dd46420304dd8d9bb01d37e4ceb3e44c11d Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Oct 2016 14:40:58 +0200 Subject: add notebook demo --- Makefile | 7 ++ examples/demo_1D_optimal_transport.ipynb | 165 +++++++++++++++++++++++++++++++ ot/__init__.py | 9 +- 3 files changed, 176 insertions(+), 5 deletions(-) create mode 100644 examples/demo_1D_optimal_transport.ipynb diff --git a/Makefile b/Makefile index 4345854..030f422 100644 --- a/Makefile +++ b/Makefile @@ -30,3 +30,10 @@ sremove : $(PYTHON) setup.py install --record files.txt tr '\n' '\0' < files.txt | sudo xargs -0 rm -f -- rm files.txt + +clean : + $(PYTHON) setup.py clean + +notebook : + ipython notebook --matplotlib=inline --notebook-dir=examples/ + diff --git a/examples/demo_1D_optimal_transport.ipynb b/examples/demo_1D_optimal_transport.ipynb new file mode 100644 index 0000000..67e16d9 --- /dev/null +++ b/examples/demo_1D_optimal_transport.ipynb @@ -0,0 +1,165 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:57e08b66b7b54b40dc86f8945521210bdf3f738b7f742ff83cc41f0cbec99f46" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "1D optimal transport demo" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# load all relevant modules\n", + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Dataset generation\n", + "n=100 # nb bins\n", + "a=ot.datasets.get_1D_gauss(n,m=20,s=20) # m= mean, s= std\n", + "b=ot.datasets.get_1D_gauss(n,m=60,s=60)\n", + "\n", + "# bin positions\n", + "x=np.arange(n,dtype=np.float64)\n", + "# loss matrix\n", + "M=ot.dist(x.reshape((n,1)),x.reshape((n,1)))\n", + "M/=M.max()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# plot the distributions\n", + "\n", + "pl.figure(1)\n", + "pl.plot(x,a,'b',label='Source distribution')\n", + "pl.plot(x,b,'r',label='Target distribution')\n", + "pl.legend()\n", + "pl.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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mjEkVkQRs5fAxETkRWAJ0Msbs8diHKSuOUDBmDLRpA3//u3MxtGwJixZB69bO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\nNSLyd9fyOBFZLCIbRORDEanrdKyBICJ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+ "text": [ + "" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# plot distributions and loss matrix\n", + "\n", + "pl.figure(2)\n", + "ot.plot.otplot1D(a,b,M,'Cost matrix M')\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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J3C0yDCMKTMDLmIsvdkMRA28+K1P86ayZ/tpDcd0pFk+8s8oEuVMkoVKYzFYb\nBfPtb1eKHxy6Cnk+EYdo3CkWT9yRLHeKZg2NX9rYKJQy54ADsgfqMgwj2ZgFXgGMGeMCbJUt/kz6\njmBGpoeaEM0YcbB44lCaY8ShEHdKn4Ra4CbgFUDZu1CqcG8090WiA1IiDpXlTrF44pnJ7U7pk1Bn\nRMECLiJ9gFeBtao6UURGArOB/YH/Bb6oqt39ZRhF4IADsm8ri/NaDV2Cye0kIOJQXGvc4ok7EmSN\n94MqkhnXuTu3nWuAZYH1HwK3qeqRwBbgijAbZoRHfX3OzXZeDSOhFCTgIjIMOBd4IJB9GvCUl54F\nnB9u04ywyPbKt7I5r74LJbh0+W/pu1NqSc3YrMUFwKr2lprA9uB6bWDdX4Jl0tM+wW0E9iVHfm8J\nWvMdOfLbA3kdgbzgtmC6PVCuLS2vLZDXgbPCd3h5wX3UWwLZO3Ms2wvI3x74TE+nL8F9tgtsF/Zu\nr6N1SwNtu+rYuasu77dbihTqQrkduB5oBBCRwcBmVd3rbV8LDA2/eUYY5Hg5RHmcV1/A09nHJw7l\n706xeOKFk4onLgIcFGdbekZeC1xEzgNaVHUpdHEUJdNpVIFkEnA7r4aRfAqxwMcDE0XkXNztswG4\nE2gUkT6etTYMWJftANMDUwGbmppoamrqRZONQmhubqa5uRlwL17OQPmc12wWuE/nQ03o3ugUiyee\nm/KJJ763BAahBK/ZQhHtRtxRETkVuM4brfA48EtVfVxE7gVeU9WfZthHu1OHET4vvwwnnyyoakbr\nOunn9f6n4cp/xvk3oavPlLR0F41SUqLj+2p3BNZ9MW0L7Nietk97ID+4np4mrVxwnSzlwiJ4A0n3\n0wfzs61nSwf9/rVZ1v196gL5wTKBn2RwNFH/LOn6DPn1gfV86fq0tLdtYB/YmuG1h3Eikv2a9enN\nfedG4FoRWYEbcvZgL45lRIh0zyli59UwEkK3JvKo6u+A33npdwGbpF0GJP685nOhBCl5d4rFE4+F\nZE7EtJmYRhnQHQGHPKNT4pixCRZPPOZ44gkV8BJw3RtR000XimEYCcEscCP5dNcC98noTunJGPFs\nedmweOIpSiSeeEKVMKHNNowANaRGFnQXC4DlpSs8nngyX4lpLhTDMIykYha4kXx66kIJEmsEQ7B4\n4hDrlPu+ER8/IkzAjeTTGxdKkB4PMfTL5FrPR6m4Uyo0nnhCBdxcKIZhGAnFLHAj+YThQvGpyAiG\nUPEvhChEibnDAAANr0lEQVSNqBDdxgTcSD5huVCClPyMzUz5vaWCA2CFGXqmiJiAG8knTAs8SEVa\n4xZPPEmYD9wwDCOhmAVuJJ8oXChBEuVOqcB44mFY4XtCOEYMmIAbySdqAYduuFPiCIAFKXeKBcDq\nEQmNF2QuFMMwjIRiFriRfKqJ3gL3KXl3SiXGEw/hoebe/EVKERNwI/kUw4USxOKJe+lScaeEMMQw\nob6IhDbbMAzDMAvcSD5RjQPPh8UTpzTiiYcwRjyhb+QxATeST7FdKEEsnriXjjOeeAgzNhMq4AW5\nUESkUUTmiMhyEXlDRE4Wkf1EZJ6IvCUiz4lIY9SNNcLFzqthJJtCLfA7gWdVdbKIVAMDgG8B81X1\nRyJyA3ATcGNE7TSioTzOa416FniMg3ktnjg25b745BVwERkInKKqlwKoagewVUQmAad6xWYBzZT6\nhW50UlbntbqDPvW72UudlxGTkFs8cUp6xiZkF/KEOpMLcaGMAj4QkYdEZLGIzBSROmCIqrYAqOqf\ngYOibKgROnZeDSPhFHLfqQZOAr6iqq+KyO04iyw9gm5CI+pWLGVzXvv2a6ehqpVWb91Z4jFa4VBh\nEQwh8fHEE/pS40IEfC2wRlVf9dafwl3oLSIyRFVbRORgYEO2A0yfPr0z3dTURFNTU48bbBRGc3Mz\nzc3NAKxfn7FI2ZzXqqoOaqt2wCC33krMIg4JmLGZKb+3JCgAFnQV8hIQ8OA1Wyiimt/AEpHfAX+v\nqitEZBp0Ohs3qeoPvYdd+6nqPr5SEdFC6jCi49VXYcwYQVW7KFq5nNdH2Mb1fEDbLtf81i0N7N1e\nB9u97m4ndbFu95b09M4M5TKld+bJD6530Sb/u2oLbGgjJTgdOKFrS1tP36c9kN+RIR2sNLgtPS9X\nfm8IKmF1nvz0vJq0bcF0TaBcbVqZ9HVwP+XaDPsELoFq3PyB/jCwAbauzNmxoiOy7zWbTqGu+68C\nj4pIDbASuAw3cvIJEbkcWAVM6U1jjVgoi/NaTQd1tEE/L2NQ0AoHc6d0NoLyfCFEDyIY+lX65yih\nLzUuSMBV9TVgTIZNZ4TbHKOYlMt5rWYPdcELth+dIg7mTrF44pA3nng5C7hhlDLOAk8TP0/EIWnW\nuMUT7x4hxRPvbrUlggWzMgzDSChmgRuJp4bd1Hc6TAKk+cTB3CnlGU+8J0MM/TIBn3gCMQE3Eo/z\ngecQs0S5UyyeeOb1QgghnnjCMBeKYRhGQjEL3Eg8NbTTkMmFEiQx7hSLJ56i2DM260gaJuBG4sk4\nCiUbJe9OqcQAWFAa8cSTh7lQDMMwEopZ4EbiKciFEqSk3SkWTzxVTzHdKQIM7MaxSwMTcCPxVNNB\nbbeFiyzulFIQcahcd0pcMzaTNwIFzIViGIaRWMwCNxJPt10oQSwAVoBKjieeTFvWBNxIPL0ScB8L\ngOVRqfHEkyngyWy1YRiGYRa4kXxCscAhAWPEofzdKXFNuU+mLWsCbiSe0AQcSnyIIZS+OyUpAbCg\n63dR1d0GlgTJvO0YhmEYZoEbyadbU+kLJbHuFHshRPfwv4u9PWlg7JiAG4knazzw3mLulAClHE88\njCGGyZTCZLbaMAKE6gPPRKKs8UqMJx7WEMPkUZAPXES+LiJ/FJHXReRREekrIiNFZJGIrBCRx0TE\nbgYJw86rYSSbvBeniAwFrgaOVtXdIvI4MBU4F7hNVeeIyL3AFcB9kbbWCI1yOq+RW+CQIHdKJcYT\nD2PGpobSumJTqHVVBQwQkb24X8Z64DO4Cx5gFjCdEr/QjX0oi/Pal90MYktxKit5d4oFwErVSVq5\nXCRTwPO6UFR1PXAbsBpYB2wFFgNbVNV/dLsWGBpVI43wsfNqGMmnEBfKIGASMAJ3kc8BJkTcLiNi\nyum89t29h6q2ndBYRCscStSdUonxxMMIgJXMcLKFuFDOAFaq6iYAEfkVMB4YJCJ9PGttGM6Ky8j0\n6dM7001NTTQ1NfWiyUYhNDc309zcDMD69RmLlM157bMb+myDBt+fUEwht3jiHnG6U6IOgFUcgtds\noYhqbt+PiIwFHgTGALuAh4BXgE8Dv1TVx72HXa+p6k8z7K/56jCi5dVXYcwYQVU71aWszuv2+2Hz\nlbR7L1RpbezPFgbRSoNbp4HtXnoHdV3yg+k2ajvXg+V2UNtl/x2e33sHtbTtcunWLQ3s3V4H272v\neDsp//R2b0lP78yQDu6zM7AtX76f7qJN/vlpC2xoIyXgHaREtC3LOl5ee4Z90tNkWA/m5crvDcEb\nR3We/PS8GqCagQNr2Lr1CyG1JxxEul6zmchrgavqyyLyJLAE940vAWYCzwKzReSfvLwHe99ko1iU\n1XndDWxNXZoNxOdOKb2HmmABsApxp5SvCwVVnQHMSMt+Fzg59BYZRaNszqsn4D41FFnEweKJd1Lu\n8cRLCwtmZRiGkVBslp2RfHYBG7tmdVrhEPNDTTB3SmcjKE13SgdJtWVNwI3ksxvYtm92KfjEwdwp\nyYknnjySedsxDMMwzAI3yoAMLpQg5k7B4onndaeU8SgUwyhp0kahZMLcKR4l706JK554Mp0RJuBG\n8tkFbCqsqFnjWDzxrPHEk0cybzuGYRiGWeBGGVCACyWIuVM8LJ44KSvcfOCGEQ87yfkQMxvmTsHi\niXfxiScPc6EYhmEklOTeegzDpxsPMdMxd4pHxccTrwrpmMXFBNxIPj10oQSJMwAWWDzx0ognnjzM\nhWIYhpFQzAI3kk87GWOhdJfYHmqCxRPvrDSuAFjJtGVNwI3Es3sn7NwIA0M4Viw+cbB44p3EGQAr\neSTztmMYhmGYBW4kn7a9sCEQ0CosS9zGiHufJe9OCfOhZrIwATcSTxvQAm44IUCS3SklPcQQStOd\nEsUQw2RgLhTDMIyEYha4ETrNzc00NTUVrb424L+A0/0Mz50ShhUO2d0przTv4OimkCpJJ5s75ZXf\nwTFRVZqFjmbAq7No8cTfBEb7DaAYEQyL/bsNoz4TcCN04hDwl4GPBjMj8IlDV3fKH5qrGNNUl32n\n3pLJnfL6CzCmKbo6M/FhM/T36iyaO2UFcHRgPfoAWEkUcHOhGIZhJBSzwCuAAw+MuwXR0gZ8CGxI\n3xDyQ03o6k7pTxUNnbZxhATcKVv7tkO9ehuK9GCzmtS/ASjSCyH6kFme4ppyX5qIquYv1ZsKRKKt\nwCgYVQ3tirfzahjRk++ajVzADcMwjGgwH7hhGEZCMQE3DMNIKCbgRmiIyI9EZLmILBWRp0RkYGDb\nTSLytrf9rJDrnSAib4rIChG5Icxje8cfJiLPi8gbIvIHEfmql7+fiMwTkbdE5DkRaYyg7j4islhE\n5nrrI0VkkdfXx0Qk1IEIItIoInO88/SGiJwcdT9F5Osi8kcReV1EHhWRvmH3U0QeFJEWEXk9kJe1\nXyJyl/d7XSoiJ4ZYZ6jXiAm4ESbzgONU9UTgbeAmABE5FpgCHAOcA9wjIqE8UBWRPsC/AmcDxwFT\nReTo3Ht1mw7gWlU9DhgHfMWr40ZgvqoeBTyP19+QuQZYFlj/IXCbqh4JbAGuCLm+O4FnVfUY4ATc\njJrI+ikiQ4GrgZNU9WO4YSR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+ "text": [ + "" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# EMD\n", + "\n", + "G0=ot.emd(a,b,M)\n", + "\n", + "pl.figure(3)\n", + "ot.plot.otplot1D(a,b,G0,'OT matrix G0')\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Sinkhorn optimal transport\n", + "lambd=1e-3\n", + "\n", + "Gs=ot.sinkhorn(a,b,M,lambd)\n", + "\n", + "pl.figure(4)\n", + "ot.plot.otplot1D(a,b,Gs,'OT matrix Sinkhorn')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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v9svXr4fZs2HWLHj4Ybj88nAx9QtfCG31p5wSPlOmoydS0hQCmqE99gjDC/Tv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\nL+KNAAAAAElFTkSuQmCC\n", + "text": [ + "" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/ot/__init__.py b/ot/__init__.py index 3cf8e0b..24350a5 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -1,18 +1,17 @@ - # Python Optimal Transport toolbox - +# All submodules and packages import utils import datasets import plot +import bregman -# Ot functions +# OT functions from emd import emd from bregman import sinkhorn - - +# utils functions from utils import dist,dots __all__ = ["emd","sinkhorn","utils",'datasets','plot','dist','dots'] -- cgit v1.2.3 From 838708a0134ab8e0e0ba7ee01bd97e7aa39b23bb Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Oct 2016 15:36:16 +0200 Subject: add plot and utils functions --- examples/demo_OT_1D.py | 6 +----- ot/__init__.py | 2 +- ot/datasets.py | 17 +++++++++++++++-- ot/plot.py | 10 ++++++++++ ot/utils.py | 5 +++++ 5 files changed, 32 insertions(+), 8 deletions(-) diff --git a/examples/demo_OT_1D.py b/examples/demo_OT_1D.py index b17f902..accf722 100644 --- a/examples/demo_OT_1D.py +++ b/examples/demo_OT_1D.py @@ -7,7 +7,6 @@ Created on Fri Oct 21 09:51:45 2016 import numpy as np import matplotlib.pylab as pl - import ot @@ -30,10 +29,8 @@ M/=M.max() #%% plot the distributions pl.figure(1) - pl.plot(x,a,'b',label='Source distribution') pl.plot(x,b,'r',label='Target distribution') - pl.legend() #%% plot distributions and loss matrix @@ -41,7 +38,6 @@ pl.legend() pl.figure(2) ot.plot.otplot1D(a,b,M,'Cost matrix M') - #%% EMD G0=ot.emd(a,b,M) @@ -50,8 +46,8 @@ pl.figure(3) ot.plot.otplot1D(a,b,G0,'OT matrix G0') #%% Sinkhorn -lambd=1e-3 +lambd=1e-3 Gs=ot.sinkhorn(a,b,M,lambd) pl.figure(4) diff --git a/ot/__init__.py b/ot/__init__.py index 24350a5..0a9e89b 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -12,6 +12,6 @@ from emd import emd from bregman import sinkhorn # utils functions -from utils import dist,dots +from utils import dist,dots,unif __all__ = ["emd","sinkhorn","utils",'datasets','plot','dist','dots'] diff --git a/ot/datasets.py b/ot/datasets.py index bb10ba4..3ebc2a1 100644 --- a/ot/datasets.py +++ b/ot/datasets.py @@ -1,10 +1,23 @@ import numpy as np - +import scipy as sp def get_1D_gauss(n,m,s): "return a 1D histogram for a gaussian distribution (n bins, mean m and std s) " x=np.arange(n,dtype=np.float64) h=np.exp(-(x-m)**2/(2*s^2)) - return h/h.sum() \ No newline at end of file + return h/h.sum() + + +def get_2D_samples_gauss(n,m,sigma): + "return samples from 2D gaussian (n samples, mean m and cov sigma) " + if np.isscalar(sigma): + sigma=np.array([sigma,]) + if len(sigma)>1: + P=sp.linalg.sqrtm(sigma) + res= np.random.randn(n,2).dot(P)+m + else: + res= np.random.randn(n,2)*np.sqrt(sigma)+m + return res + \ No newline at end of file diff --git a/ot/plot.py b/ot/plot.py index 743ab4a..f78daf6 100644 --- a/ot/plot.py +++ b/ot/plot.py @@ -38,3 +38,13 @@ def otplot1D(a,b,M,title=''): pl.xlim((0,nb)) +def otplot2D_samples(xs,xt,G,thr=1e-8,**kwargs): + """ Plot matrix M in 2D with lines using alpha values""" + if ('color' not in kwargs) and ('c' not in kwargs): + kwargs['color']='k' + mx=G.max() + for i in range(xs.shape[0]): + for j in range(xt.shape[0]): + if G[i,j]/mx>thr: + pl.plot([xs[i,0],xt[j,0]],[xs[i,1],xt[j,1]],alpha=G[i,j]/mx,**kwargs) + \ No newline at end of file diff --git a/ot/utils.py b/ot/utils.py index 582c3ff..5feb4c6 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -3,6 +3,11 @@ import numpy as np from scipy.spatial.distance import cdist +def unif(n): + """ return a uniform histogram (simplex) """ + return np.ones((n,))/n + + def dist(x1,x2=None,metric='sqeuclidean'): """Compute distance between samples in x1 and x2""" if x2 is None: -- cgit v1.2.3 From 4cdefdf572e41bc578625da53e82a4c2e455a62e Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Oct 2016 15:36:36 +0200 Subject: add 2d exmaple with empirical measure --- examples/demo_OT_2D_samples.py | 90 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 90 insertions(+) create mode 100644 examples/demo_OT_2D_samples.py diff --git a/examples/demo_OT_2D_samples.py b/examples/demo_OT_2D_samples.py new file mode 100644 index 0000000..2cda92e --- /dev/null +++ b/examples/demo_OT_2D_samples.py @@ -0,0 +1,90 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Oct 21 09:51:45 2016 + +@author: rflamary +""" + +import numpy as np +import matplotlib.pylab as pl +import ot + + + +#%% parameters + +n=20 # nb bins + +mu_s=np.array([0,0]) +cov_s=np.array([[1,0],[0,1]]) + +mu_t=np.array([4,4]) +cov_t=np.array([[1,-.8],[-.8,1]]) + +xs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s) +xt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t) + +a,b = ot.unif(n),ot.unif(n) + +# loss matrix +M=ot.dist(xs,xt) +#M/=M.max() + +#%% plot samples + +pl.figure(1) +pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') +pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') +pl.legend(loc=0) +pl.title('Source and traget distributions') + +pl.figure(2) +pl.imshow(M,interpolation='nearest') +pl.title('Cost matrix M') + + +#%% EMD + +G0=ot.emd(a,b,M) + + +pl.figure(3) +pl.imshow(G0,interpolation='nearest') +pl.title('Cost matrix M') + +pl.figure(4) +ot.plot.otplot2D_samples(xs,xt,G0,c=[.5,.5,1]) +pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') +pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') +pl.legend(loc=0) +pl.title('OT matrix') + + +#%% sinkhorn + +lambd=.8e-1 +Gs=ot.sinkhorn(a,b,M,lambd) + + +pl.figure(5) +pl.imshow(Gs,interpolation='nearest') +pl.title('Cost matrix M') + +pl.figure(6) +ot.plot.otplot2D_samples(xs,xt,Gs,color=[.5,.5,1]) +pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') +pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') +pl.legend(loc=0) +pl.title('OT matrix Sinkhorn') + +# +#pl.figure(3) +#ot.plot.otplot1D(a,b,G0,'OT matrix G0') +# +##%% Sinkhorn +# +#lambd=1e-3 +#Gs=ot.sinkhorn(a,b,M,lambd) +# +#pl.figure(4) +#ot.plot.otplot1D(a,b,Gs,'OT matrix Sinkhorn') -- cgit v1.2.3 From 6ee839d64d8b0f5f73fd5899032f2ae4bd8a7a51 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Oct 2016 15:43:37 +0200 Subject: change function name on plot --- examples/demo_1D_optimal_transport.ipynb | 19 ++++++++++--------- examples/demo_OT_1D.py | 6 +++--- examples/demo_OT_2D_samples.py | 4 ++-- ot/plot.py | 4 ++-- 4 files changed, 17 insertions(+), 16 deletions(-) diff --git a/examples/demo_1D_optimal_transport.ipynb b/examples/demo_1D_optimal_transport.ipynb index 67e16d9..354972e 100644 --- a/examples/demo_1D_optimal_transport.ipynb +++ b/examples/demo_1D_optimal_transport.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:57e08b66b7b54b40dc86f8945521210bdf3f738b7f742ff83cc41f0cbec99f46" + "signature": "sha256:70469d78395244f4978f6cd50bff1940285ade6e3a32e21040ca4851a189f8cc" }, "nbformat": 3, "nbformat_minor": 0, @@ -70,7 +70,7 @@ 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mjEkVkQRs5fAxETkRWAJ0Msbs8diHKSuOUDBmDLRpA3//u3MxtGwJixZB69bO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\nNSLyd9fyOBFZLCIbRORDEanrdKyBICJ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"text": [ - "" + "" ] } ], @@ -83,7 +83,7 @@ "# plot distributions and loss matrix\n", "\n", "pl.figure(2)\n", - "ot.plot.otplot1D(a,b,M,'Cost matrix M')\n" + "ot.plot.plot1D_mat(a,b,M,'Cost matrix M')\n" ], "language": "python", "metadata": {}, @@ -93,7 +93,7 @@ "output_type": "display_data", "png": 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J3C0yDCMKTMDLmIsvdkMRA28+K1P86ayZ/tpDcd0pFk+8s8oEuVMkoVKYzFYb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\njJrI+ikiQ4GrgZNU9WO4YSR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"text": [ - "" + "" ] } ], @@ -108,7 +108,7 @@ "G0=ot.emd(a,b,M)\n", "\n", "pl.figure(3)\n", - "ot.plot.otplot1D(a,b,G0,'OT matrix G0')\n" + "ot.plot.plot1D_mat(a,b,G0,'OT matrix G0')\n" ], "language": "python", "metadata": {}, @@ -118,7 +118,7 @@ "output_type": "display_data", "png": 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xUPb6ZtKGvkzSoIhYJml74JX1LaCxsfGD6YaGBhoaGtpdYatMU1MTTU1NACxd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\n/w/xqdjTkgKZjAAAAABJRU5ErkJggg==\n", "text": [ - "" + "" ] } ], @@ -134,7 +134,7 @@ "Gs=ot.sinkhorn(a,b,M,lambd)\n", "\n", "pl.figure(4)\n", - "ot.plot.otplot1D(a,b,Gs,'OT matrix Sinkhorn')" + "ot.plot.plot1D_mat(a,b,Gs,'OT matrix Sinkhorn')" ], "language": "python", "metadata": {}, @@ -144,7 +144,7 @@ "output_type": "display_data", "png": 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v9svXr4fZs2HWLHj4Ybj88nAx9QtfCG31p5wSPlOmoydS0hQCmqE99gjDC/Tv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\nL+KNAAAAAElFTkSuQmCC\n", "text": [ - "" + "" ] } ], @@ -156,7 +156,8 @@ "input": [], "language": "python", "metadata": {}, - "outputs": [] + "outputs": [], + "prompt_number": 6 } ], "metadata": {} diff --git a/examples/demo_OT_1D.py b/examples/demo_OT_1D.py index accf722..abc851d 100644 --- a/examples/demo_OT_1D.py +++ b/examples/demo_OT_1D.py @@ -36,14 +36,14 @@ pl.legend() #%% plot distributions and loss matrix pl.figure(2) -ot.plot.otplot1D(a,b,M,'Cost matrix M') +ot.plot.plot1D_mat(a,b,M,'Cost matrix M') #%% EMD G0=ot.emd(a,b,M) pl.figure(3) -ot.plot.otplot1D(a,b,G0,'OT matrix G0') +ot.plot.plot1D_mat(a,b,G0,'OT matrix G0') #%% Sinkhorn @@ -51,4 +51,4 @@ lambd=1e-3 Gs=ot.sinkhorn(a,b,M,lambd) pl.figure(4) -ot.plot.otplot1D(a,b,Gs,'OT matrix Sinkhorn') +ot.plot.plot1D_mat(a,b,Gs,'OT matrix Sinkhorn') diff --git a/examples/demo_OT_2D_samples.py b/examples/demo_OT_2D_samples.py index 2cda92e..f91bbb2 100644 --- a/examples/demo_OT_2D_samples.py +++ b/examples/demo_OT_2D_samples.py @@ -53,7 +53,7 @@ pl.imshow(G0,interpolation='nearest') pl.title('Cost matrix M') pl.figure(4) -ot.plot.otplot2D_samples(xs,xt,G0,c=[.5,.5,1]) +ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1]) pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') pl.legend(loc=0) @@ -71,7 +71,7 @@ pl.imshow(Gs,interpolation='nearest') pl.title('Cost matrix M') pl.figure(6) -ot.plot.otplot2D_samples(xs,xt,Gs,color=[.5,.5,1]) +ot.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1]) pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') pl.legend(loc=0) diff --git a/ot/plot.py b/ot/plot.py index f78daf6..ce5444e 100644 --- a/ot/plot.py +++ b/ot/plot.py @@ -4,7 +4,7 @@ import matplotlib.pylab as pl from matplotlib import gridspec -def otplot1D(a,b,M,title=''): +def plot1D_mat(a,b,M,title=''): """ Plot matrix M with the source and target 1D distribution """ na=M.shape[0] @@ -38,7 +38,7 @@ def otplot1D(a,b,M,title=''): pl.xlim((0,nb)) -def otplot2D_samples(xs,xt,G,thr=1e-8,**kwargs): +def plot2D_samples_mat(xs,xt,G,thr=1e-8,**kwargs): """ Plot matrix M in 2D with lines using alpha values""" if ('color' not in kwargs) and ('c' not in kwargs): kwargs['color']='k' -- cgit v1.2.3 From d130b55fd5845bf0848bb02cebc58ce1ae89f8a3 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Oct 2016 15:51:31 +0200 Subject: bregman as module --- ot/__init__.py | 2 +- ot/bregman/__init__.py | 4 --- ot/bregman/sink.py | 91 -------------------------------------------------- setup.py | 2 +- 4 files changed, 2 insertions(+), 97 deletions(-) delete mode 100644 ot/bregman/__init__.py delete mode 100644 ot/bregman/sink.py diff --git a/ot/__init__.py b/ot/__init__.py index 0a9e89b..f5490f7 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -14,4 +14,4 @@ from bregman import sinkhorn # utils functions from utils import dist,dots,unif -__all__ = ["emd","sinkhorn","utils",'datasets','plot','dist','dots'] +__all__ = ["emd","sinkhorn","utils",'datasets','bregman','plot','dist','dots'] diff --git a/ot/bregman/__init__.py b/ot/bregman/__init__.py deleted file mode 100644 index e4016ea..0000000 --- a/ot/bregman/__init__.py +++ /dev/null @@ -1,4 +0,0 @@ - - -from .sink import sinkhorn - diff --git a/ot/bregman/sink.py b/ot/bregman/sink.py deleted file mode 100644 index 8b97e1e..0000000 --- a/ot/bregman/sink.py +++ /dev/null @@ -1,91 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Fri Oct 21 09:40:21 2016 - -@author: rflamary -""" - -import numpy as np - - -def sinkhorn(a,b, M, reg,numItermax = 1000,stopThr=1e-9): - """ - Solve the optimal transport problem (OT) - - .. math:: - \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) - - s.t. \gamma 1 = a - - \gamma^T 1= b - - \gamma\geq 0 - where : - - - M is the metric cost matrix - - Omega is the entropic regularization term - - a and b are the sample weights - - Parameters - ---------- - a : (ns,) ndarray - samples in the source domain - b : (nt,) ndarray - samples in the target domain - M : (ns,nt) ndarray - loss matrix - reg: float() - Regularization term >0 - - - Returns - ------- - gamma: (ns x nt) ndarray - Optimal transportation matrix for the given parameters - - """ - # init data - Nini = len(a) - Nfin = len(b) - - - cpt = 0 - - # we assume that no distances are null except those of the diagonal of distances - u = np.ones(Nini)/Nini - v = np.ones(Nfin)/Nfin - uprev=np.zeros(Nini) - vprev=np.zeros(Nini) - - #print reg - - K = np.exp(-M/reg) - #print np.min(K) - - Kp = np.dot(np.diag(1/a),K) - transp = K - cpt = 0 - err=1 - while (err>stopThr and cpt Date: Mon, 24 Oct 2016 15:51:48 +0200 Subject: bregman as module --- ot/bregman.py | 91 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 91 insertions(+) create mode 100644 ot/bregman.py diff --git a/ot/bregman.py b/ot/bregman.py new file mode 100644 index 0000000..8b97e1e --- /dev/null +++ b/ot/bregman.py @@ -0,0 +1,91 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Oct 21 09:40:21 2016 + +@author: rflamary +""" + +import numpy as np + + +def sinkhorn(a,b, M, reg,numItermax = 1000,stopThr=1e-9): + """ + Solve the optimal transport problem (OT) + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + where : + + - M is the metric cost matrix + - Omega is the entropic regularization term + - a and b are the sample weights + + Parameters + ---------- + a : (ns,) ndarray + samples in the source domain + b : (nt,) ndarray + samples in the target domain + M : (ns,nt) ndarray + loss matrix + reg: float() + Regularization term >0 + + + Returns + ------- + gamma: (ns x nt) ndarray + Optimal transportation matrix for the given parameters + + """ + # init data + Nini = len(a) + Nfin = len(b) + + + cpt = 0 + + # we assume that no distances are null except those of the diagonal of distances + u = np.ones(Nini)/Nini + v = np.ones(Nfin)/Nfin + uprev=np.zeros(Nini) + vprev=np.zeros(Nini) + + #print reg + + K = np.exp(-M/reg) + #print np.min(K) + + Kp = np.dot(np.diag(1/a),K) + transp = K + cpt = 0 + err=1 + while (err>stopThr and cpt Date: Mon, 24 Oct 2016 16:08:55 +0200 Subject: add barycenter and unmixing --- ot/bregman.py | 89 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 89 insertions(+) diff --git a/ot/bregman.py b/ot/bregman.py index 8b97e1e..1761029 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -89,3 +89,92 @@ def sinkhorn(a,b, M, reg,numItermax = 1000,stopThr=1e-9): return np.dot(np.diag(u),np.dot(K,np.diag(v))) +def geometricBar(weights,alldistribT): + assert(len(weights)==alldistribT.shape[1]) + return np.exp(np.dot(np.log(alldistribT),weights.T)) + +def geometricMean(alldistribT): + return np.exp(np.mean(np.log(alldistribT),axis=1)) + +def projR(gamma,p): + #return np.dot(np.diag(p/np.maximum(np.sum(gamma,axis=1),1e-10)),gamma) + return np.multiply(gamma.T,p/np.maximum(np.sum(gamma,axis=1),1e-10)).T + +def projC(gamma,q): + #return (np.dot(np.diag(q/np.maximum(np.sum(gamma,axis=0),1e-10)),gamma.T)).T + return np.multiply(gamma,q/np.maximum(np.sum(gamma,axis=0),1e-10)) + + +def barycenter(A,M,reg, weights=None, numItermax = 1000, tol_error=1e-4,log=dict()): + """Compute the Regularizzed wassersteien barycenter of distributions A""" + + + if weights is None: + weights=np.ones(A.shape[1])/A.shape[1] + else: + assert(len(weights)==A.shape[1]) + + #compute Mmax once for all + #M = M/np.median(M) # suggested by G. Peyre + K = np.exp(-reg*M) + + cpt = 0 + err=1 + + UKv=np.dot(K,np.divide(A.T,np.sum(K,axis=0)).T) + u = (geometricMean(UKv)/UKv.T).T + + log = {'niter':0, 'all_err':[]} + + while (err>tol_error and cpttol_error and cpt Date: Mon, 24 Oct 2016 16:32:01 +0200 Subject: correction barcenter --- examples/demo_OT_2D_samples.py | 2 +- ot/__init__.py | 4 ++-- ot/bregman.py | 18 +++++++++++------- 3 files changed, 14 insertions(+), 10 deletions(-) diff --git a/examples/demo_OT_2D_samples.py b/examples/demo_OT_2D_samples.py index f91bbb2..992352c 100644 --- a/examples/demo_OT_2D_samples.py +++ b/examples/demo_OT_2D_samples.py @@ -62,7 +62,7 @@ pl.title('OT matrix') #%% sinkhorn -lambd=.8e-1 +lambd=1e-1 Gs=ot.sinkhorn(a,b,M,lambd) diff --git a/ot/__init__.py b/ot/__init__.py index ce9d157..dd74590 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -8,9 +8,9 @@ import bregman # OT functions from emd import emd -from bregman import sinkhorn +from bregman import sinkhorn,barycenter # utils functions from utils import dist,unif -__all__ = ["emd","sinkhorn","utils",'datasets','bregman','plot','dist','unif'] +__all__ = ["emd","sinkhorn","utils",'datasets','bregman','plot','dist','unif','barycenter'] diff --git a/ot/bregman.py b/ot/bregman.py index 1761029..e46506b 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -116,7 +116,7 @@ def barycenter(A,M,reg, weights=None, numItermax = 1000, tol_error=1e-4,log=dict #compute Mmax once for all #M = M/np.median(M) # suggested by G. Peyre - K = np.exp(-reg*M) + K = np.exp(-M/reg) cpt = 0 err=1 @@ -124,7 +124,8 @@ def barycenter(A,M,reg, weights=None, numItermax = 1000, tol_error=1e-4,log=dict UKv=np.dot(K,np.divide(A.T,np.sum(K,axis=0)).T) u = (geometricMean(UKv)/UKv.T).T - log = {'niter':0, 'all_err':[]} + log['niter']=0 + log['all_err']=[] while (err>tol_error and cpttol_error and cpt Date: Mon, 24 Oct 2016 16:32:22 +0200 Subject: add demo barycenter --- examples/demo_barycenter_1D.py | 60 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 60 insertions(+) create mode 100644 examples/demo_barycenter_1D.py diff --git a/examples/demo_barycenter_1D.py b/examples/demo_barycenter_1D.py new file mode 100644 index 0000000..200444b --- /dev/null +++ b/examples/demo_barycenter_1D.py @@ -0,0 +1,60 @@ +# -*- coding: utf-8 -*- +""" +Created on Fri Oct 21 09:51:45 2016 + +@author: rflamary +""" + +import numpy as np +import matplotlib.pylab as pl +import ot + + + +#%% parameters + +n=100 # nb bins + +# bin positions +x=np.arange(n,dtype=np.float64) + +# Gaussian distributions +a1=ot.datasets.get_1D_gauss(n,m=20,s=20) # m= mean, s= std +a2=ot.datasets.get_1D_gauss(n,m=60,s=60) + +A=np.vstack((a1,a2)).T +nbd=A.shape[1] + +# loss matrix +M=ot.utils.dist0(n) +M/=M.max() + +#%% plot the distributions + +pl.figure(1) +for i in range(nbd): + pl.plot(x,A[:,i]) +pl.title('Distributions') + +#%% barucenter computation + +# l2bary +bary_l2=A.mean(1) + +# wasserstein +reg=1e-2 +log=dict() +bary_wass=ot.bregman.barycenter(A,M,reg,log=log) + +pl.figure(2) +pl.clf() +pl.subplot(2,1,1) +for i in range(nbd): + pl.plot(x,A[:,i]) +pl.title('Distributions') + +pl.subplot(2,1,2) +pl.plot(x,bary_l2,'r',label='l2') +pl.plot(x,bary_wass,'g',label='Wasserstein') +pl.legend() +pl.title('Barycenters') -- cgit v1.2.3 From 2f2bee41dcc2685a2ee9adfd60253ebb3131e5e8 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Oct 2016 16:34:26 +0200 Subject: update demo barycenter --- examples/demo_barycenter_1D.py | 5 ++--- ot/bregman.py | 4 ++-- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/examples/demo_barycenter_1D.py b/examples/demo_barycenter_1D.py index 200444b..c9f63a2 100644 --- a/examples/demo_barycenter_1D.py +++ b/examples/demo_barycenter_1D.py @@ -42,9 +42,8 @@ pl.title('Distributions') bary_l2=A.mean(1) # wasserstein -reg=1e-2 -log=dict() -bary_wass=ot.bregman.barycenter(A,M,reg,log=log) +reg=1e-3 +bary_wass,log=ot.bregman.barycenter(A,M,reg) pl.figure(2) pl.clf() diff --git a/ot/bregman.py b/ot/bregman.py index e46506b..8362dc4 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -136,7 +136,7 @@ def barycenter(A,M,reg, weights=None, numItermax = 1000, tol_error=1e-4,log=dict log['all_err'].append(err) log['niter']=cpt - return geometricBar(weights,UKv) + return geometricBar(weights,UKv),log def unmixBregman(distrib,D,M,M0,h0,reg,reg0,alpha,numItermax = 1000, tol_error=1e-3,log=dict()): @@ -181,4 +181,4 @@ def unmixBregman(distrib,D,M,M0,h0,reg,reg0,alpha,numItermax = 1000, tol_error=1 log['niter']=cpt - return np.sum(K0,axis=1) + return np.sum(K0,axis=1),log -- cgit v1.2.3 From eff2e5304269482e1ce2e7d6e1271e068134bfa4 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Oct 2016 16:45:03 +0200 Subject: cleanup --- Makefile | 5 +++++ ot/bregman.py | 2 +- 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/Makefile b/Makefile index 030f422..ead5e3a 100644 --- a/Makefile +++ b/Makefile @@ -31,6 +31,11 @@ sremove : tr '\n' '\0' < files.txt | sudo xargs -0 rm -f -- rm files.txt +doc : + cd docs + make html + cd .. + clean : $(PYTHON) setup.py clean diff --git a/ot/bregman.py b/ot/bregman.py index 8362dc4..a74cd19 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -139,7 +139,7 @@ def barycenter(A,M,reg, weights=None, numItermax = 1000, tol_error=1e-4,log=dict return geometricBar(weights,UKv),log -def unmixBregman(distrib,D,M,M0,h0,reg,reg0,alpha,numItermax = 1000, tol_error=1e-3,log=dict()): +def unmix(distrib,D,M,M0,h0,reg,reg0,alpha,numItermax = 1000, tol_error=1e-3,log=dict()): """ distrib : distribution to unmix D : Dictionnary -- cgit v1.2.3 From 48f0963dd3af8c569ea52467ab52c6480f16be8f Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Oct 2016 16:49:11 +0200 Subject: readme --- Makefile | 5 ----- README.md | 8 ++++++++ 2 files changed, 8 insertions(+), 5 deletions(-) diff --git a/Makefile b/Makefile index ead5e3a..9a54e42 100644 --- a/Makefile +++ b/Makefile @@ -30,11 +30,6 @@ sremove : $(PYTHON) setup.py install --record files.txt tr '\n' '\0' < files.txt | sudo xargs -0 rm -f -- rm files.txt - -doc : - cd docs - make html - cd .. clean : $(PYTHON) setup.py clean diff --git a/README.md b/README.md index d7749db..d2d5049 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,10 @@ # POT Python Optimal Transport library + +This Python librarie is an open source implementation with several functions that allow to solve optimal transport problems in Python. + +It provides the following solvers: +* Linear program (LP) OT solver/ Earth Movers Distance (using code from Antoine Rolet and Nicolas Bonnel) +* Entropic regularization OT solver +* Bregman projection for Wasserstein barycenter and unmixing +* Optimal transport for domain adaptation -- cgit v1.2.3 From af99bcb8b307e6460e061bff205b1d1b5a575e46 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Oct 2016 16:51:01 +0200 Subject: readme 2 --- README.md | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index d2d5049..a4439d9 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,15 @@ Python Optimal Transport library This Python librarie is an open source implementation with several functions that allow to solve optimal transport problems in Python. It provides the following solvers: -* Linear program (LP) OT solver/ Earth Movers Distance (using code from Antoine Rolet and Nicolas Bonnel) +* Linear program (LP) OT solver/ Earth Movers Distance (using code from Antoine Rolet and Nicolas Bonneel) * Entropic regularization OT solver * Bregman projection for Wasserstein barycenter and unmixing * Optimal transport for domain adaptation + +Some demonstrations of what can be done are available in the examples folder. + + +## Installation + + +## References -- cgit v1.2.3 From 176ff069483b9ba630af8a00ce5edc104168c0a2 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Oct 2016 16:53:34 +0200 Subject: comments in modules --- ot/bregman.py | 4 +--- ot/datasets.py | 4 ++++ ot/plot.py | 4 ++++ ot/utils.py | 4 +++- 4 files changed, 12 insertions(+), 4 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index a74cd19..81804a7 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1,8 +1,6 @@ # -*- coding: utf-8 -*- """ -Created on Fri Oct 21 09:40:21 2016 - -@author: rflamary +Bregman projection for regularized Otimal transport """ import numpy as np diff --git a/ot/datasets.py b/ot/datasets.py index 3ebc2a1..cebfdac 100644 --- a/ot/datasets.py +++ b/ot/datasets.py @@ -1,3 +1,7 @@ +""" +Simple example datasets for OT +""" + import numpy as np import scipy as sp diff --git a/ot/plot.py b/ot/plot.py index ce5444e..d172c90 100644 --- a/ot/plot.py +++ b/ot/plot.py @@ -1,3 +1,7 @@ +""" +Functions for plotting OT matrices +""" + import numpy as np import matplotlib.pylab as pl diff --git a/ot/utils.py b/ot/utils.py index 5feb4c6..46c3775 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -1,4 +1,6 @@ - +""" +Various function that can be usefull +""" import numpy as np from scipy.spatial.distance import cdist -- cgit v1.2.3 From 041f9bca2e89d1d111b553159aed862291630b00 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Oct 2016 17:12:01 +0200 Subject: add domain adaptation --- ot/__init__.py | 4 +++- ot/da.py | 50 ++++++++++++++++++++++++++++++++++++++++++++++++++ ot/datasets.py | 39 ++++++++++++++++++++++++++++++++++++++- 3 files changed, 91 insertions(+), 2 deletions(-) create mode 100644 ot/da.py diff --git a/ot/__init__.py b/ot/__init__.py index dd74590..f63d1c2 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -5,12 +5,14 @@ import utils import datasets import plot import bregman +import da # OT functions from emd import emd from bregman import sinkhorn,barycenter +from da import sinkhorn_lpl1_mm # utils functions from utils import dist,unif -__all__ = ["emd","sinkhorn","utils",'datasets','bregman','plot','dist','unif','barycenter'] +__all__ = ["emd","sinkhorn","utils",'datasets','bregman','plot','dist','unif','barycenter','sinkhorn_lpl1_mm'] diff --git a/ot/da.py b/ot/da.py new file mode 100644 index 0000000..8ecd952 --- /dev/null +++ b/ot/da.py @@ -0,0 +1,50 @@ +""" +domain adaptation with optimal transport +""" +import numpy as np +from scipy.spatial.distance import cdist + +from bregman import sinkhorn + + + +def indices(a, func): + return [i for (i, val) in enumerate(a) if func(val)] + +def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1): + p=0.5 + epsilon = 1e-3 + + # init data + Nini = len(a) + Nfin = len(b) + + indices_labels = [] + idx_begin = np.min(labels_a) + for c in range(idx_begin,np.max(labels_a)+1): + idxc = indices(labels_a, lambda x: x==c) + indices_labels.append(idxc) + + W=np.zeros(M.shape) + + for cpt in range(10): + Mreg = M + eta*W + transp=sinkhorn(a,b,Mreg,reg,numItermax = 200) + # the transport has been computed. Check if classes are really separated + W = np.ones((Nini,Nfin)) + for t in range(Nfin): + column = transp[:,t] + all_maj = [] + for c in range(idx_begin,np.max(labels_a)+1): + col_c = column[indices_labels[c-idx_begin]] + if c!=-1: + maj = p*((sum(col_c)+epsilon)**(p-1)) + W[indices_labels[c-idx_begin],t]=maj + all_maj.append(maj) + + # now we majorize the unlabelled by the min of the majorizations + # do it only for unlabbled data + if idx_begin==-1: + W[indices_labels[0],t]=np.min(all_maj) + + return transp \ No newline at end of file diff --git a/ot/datasets.py b/ot/datasets.py index cebfdac..edc29a9 100644 --- a/ot/datasets.py +++ b/ot/datasets.py @@ -24,4 +24,41 @@ def get_2D_samples_gauss(n,m,sigma): else: res= np.random.randn(n,2)*np.sqrt(sigma)+m return res - \ No newline at end of file + +def get_data_classif(dataset,n,nz=.5,**kwargs): + """ + dataset generation + """ + if dataset.lower()=='3gauss': + y=np.floor((np.arange(n)*1.0/n*3))+1 + x=np.zeros((n,2)) + # class 1 + x[y==1,0]=-1.; x[y==1,1]=-1. + x[y==2,0]=-1.; x[y==2,1]=1. + x[y==3,0]=1. ; x[y==3,1]=0 + + x[y!=3,:]+=nz*np.random.randn(sum(y!=3),2) + x[y==3,:]+=2*nz*np.random.randn(sum(y==3),2) + + elif dataset.lower()=='3gauss2': + y=np.floor((np.arange(n)*1.0/n*4))+1 + x=np.zeros((n,2)) + y[y==4]=3 + # class 1 + x[y==1,0]=-1.; x[y==1,1]=-1. + x[y==2,0]=-1.; x[y==2,1]=1. + x[y==3,0]=1. ; x[y==3,1]=0 + + x[y!=3,:]+=nz*np.random.randn(sum(y!=3),2) + x[y==3,:]+=2*nz*np.random.randn(sum(y==3),2) +# elif dataset.lower()=='sinreg': +# +# x=np.random.rand(n,1) +# y=4*x+np.sin(2*np.pi*x)+nz*np.random.randn(n,1) + + else: + x=0 + y=0 + print("unknown dataset") + + return x,y \ No newline at end of file -- cgit v1.2.3 From 7b9f4e9d4b198183929c60eceec1a419b5212e2d Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 25 Oct 2016 10:24:55 +0200 Subject: set numpy doc format --- docs/source/conf.py | 2 +- docs/source/index.rst | 25 +++++++++++++++++++++++++ ot/bregman.py | 31 +++++++++++++++++++++++-------- 3 files changed, 49 insertions(+), 9 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 0caefae..e114e2c 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -38,7 +38,7 @@ extensions = [ 'sphinx.ext.coverage', 'sphinx.ext.mathjax', 'sphinx.ext.ifconfig', - 'sphinx.ext.viewcode', + 'sphinx.ext.viewcode','sphinx.ext.autodoc', 'sphinxcontrib.napoleon' ] # Add any paths that contain templates here, relative to this directory. diff --git a/docs/source/index.rst b/docs/source/index.rst index e96f544..8613bfd 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -11,16 +11,41 @@ Contents: .. toctree:: :maxdepth: 2 + +Module ot +========= + +This module provide easy access to solvers for the most common OT problems + .. automodule:: ot :members: + +Module ot.emd +========= .. automodule:: ot.emd :members: + +Module ot.bregman +========= + .. automodule:: ot.bregman :members: + +Module ot.utils +========= + .. automodule:: ot.utils :members: + +Module ot.datasets +========= + .. automodule:: ot.datasets :members: + +Module ot.plot +========= + .. automodule:: ot.plot :members: diff --git a/ot/bregman.py b/ot/bregman.py index 81804a7..17ec06f 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -8,7 +8,9 @@ import numpy as np def sinkhorn(a,b, M, reg,numItermax = 1000,stopThr=1e-9): """ - Solve the optimal transport problem (OT) + Solve the entropic regularization optimal transport problem and return the OT matrix + + The function solves the following optimization problem: .. math:: \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) @@ -20,17 +22,20 @@ def sinkhorn(a,b, M, reg,numItermax = 1000,stopThr=1e-9): \gamma\geq 0 where : - - M is the metric cost matrix - - Omega is the entropic regularization term - - a and b are the sample weights + - M is the (ns,nt) metric cost matrix + - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target weights (sum to 1) + + The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [1]_ + Parameters ---------- - a : (ns,) ndarray - samples in the source domain - b : (nt,) ndarray + a : np.ndarray (ns,) + samples weights in the source domain + b : np.ndarray (nt,) samples in the target domain - M : (ns,nt) ndarray + M : np.ndarray (ns,nt) loss matrix reg: float() Regularization term >0 @@ -41,6 +46,16 @@ def sinkhorn(a,b, M, reg,numItermax = 1000,stopThr=1e-9): gamma: (ns x nt) ndarray Optimal transportation matrix for the given parameters + References + ---------- + + .. [1] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013 + + + See Also + -------- + ot.emd.emd : Unregularized optimal ransport + """ # init data Nini = len(a) -- cgit v1.2.3 From ab27b8387201e57173255b2d22ad3c8d5057ad8b Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 25 Oct 2016 10:44:55 +0200 Subject: complet doc --- docs/source/conf.py | 2 +- docs/source/index.rst | 21 +++++++++++++++------ 2 files changed, 16 insertions(+), 7 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index e114e2c..51b71c9 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -38,7 +38,7 @@ extensions = [ 'sphinx.ext.coverage', 'sphinx.ext.mathjax', 'sphinx.ext.ifconfig', - 'sphinx.ext.viewcode','sphinx.ext.autodoc', 'sphinxcontrib.napoleon' + 'sphinx.ext.viewcode', 'sphinx.ext.napoleon' ] # Add any paths that contain templates here, relative to this directory. diff --git a/docs/source/index.rst b/docs/source/index.rst index 8613bfd..c95e847 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -11,9 +11,12 @@ Contents: .. toctree:: :maxdepth: 2 +Module list +=========== + Module ot -========= +--------- This module provide easy access to solvers for the most common OT problems @@ -21,34 +24,40 @@ This module provide easy access to solvers for the most common OT problems :members: Module ot.emd -========= +------------- .. automodule:: ot.emd :members: Module ot.bregman -========= +----------------- .. automodule:: ot.bregman :members: Module ot.utils -========= +--------------- .. automodule:: ot.utils :members: Module ot.datasets -========= +------------------ .. automodule:: ot.datasets :members: Module ot.plot -========= +-------------- .. automodule:: ot.plot :members: + +Examples +======== + +.. literalinclude:: ../../examples/demo_OT_1D.py + Indices and tables ================== -- cgit v1.2.3 From 8d45086670f2666a316a94a33179658d20ac5ec2 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 26 Oct 2016 15:06:25 +0200 Subject: add domain adaptation demo --- examples/demo_OTDA_2D.py | 117 +++++++++++++++++++++++++++++++++++++++++++++++ ot/da.py | 3 +- ot/datasets.py | 10 ++-- 3 files changed, 124 insertions(+), 6 deletions(-) create mode 100644 examples/demo_OTDA_2D.py diff --git a/examples/demo_OTDA_2D.py b/examples/demo_OTDA_2D.py new file mode 100644 index 0000000..fbaf56d --- /dev/null +++ b/examples/demo_OTDA_2D.py @@ -0,0 +1,117 @@ +# -*- coding: utf-8 -*- +""" +demo of Optimal transport for domain adaptation +""" + +import numpy as np +import matplotlib.pylab as pl +import ot + + + +#%% parameters + +n=150 # nb bins + +xs,ys=ot.datasets.get_data_classif('3gauss',n) +xt,yt=ot.datasets.get_data_classif('3gauss2',n) + +a,b = ot.unif(n),ot.unif(n) +# loss matrix +M=ot.dist(xs,xt) +#M/=M.max() + +#%% plot samples + +pl.figure(1) + +pl.subplot(2,2,1) +pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') +pl.legend(loc=0) +pl.title('Source distributions') + +pl.subplot(2,2,2) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') +pl.legend(loc=0) +pl.title('target distributions') + +pl.figure(2) +pl.imshow(M,interpolation='nearest') +pl.title('Cost matrix M') + + +#%% OT estimation + +# EMD +G0=ot.emd(a,b,M) + +# sinkhorn +lambd=1e-1 +Gs=ot.sinkhorn(a,b,M,lambd) + + +# Group lasso regularization +reg=1e-1 +eta=1e0 +Gg=ot.da.sinkhorn_lpl1_mm(a,ys.astype(np.int),b,M,reg,eta) + + +#%% visu matrices + +pl.figure(3) + +pl.subplot(2,3,1) +pl.imshow(G0,interpolation='nearest') +pl.title('OT matrix ') + +pl.subplot(2,3,2) +pl.imshow(Gs,interpolation='nearest') +pl.title('OT matrix Sinkhorn') + +pl.subplot(2,3,3) +pl.imshow(Gg,interpolation='nearest') +pl.title('OT matrix Group lasso') + +pl.subplot(2,3,4) +ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1]) +pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') + + +pl.subplot(2,3,5) +ot.plot.plot2D_samples_mat(xs,xt,Gs,c=[.5,.5,1]) +pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') + +pl.subplot(2,3,6) +ot.plot.plot2D_samples_mat(xs,xt,Gg,c=[.5,.5,1]) +pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') + +#%% sample interpolation + +xst0=n*G0.dot(xt) +xsts=n*Gs.dot(xt) +xstg=n*Gg.dot(xt) + +pl.figure(4) +pl.subplot(2,3,1) + + +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) +pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30) +pl.title('Interp samples') +pl.legend(loc=0) + +pl.subplot(2,3,2) + + +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) +pl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30) +pl.title('Interp samples Sinkhorn') + +pl.subplot(2,3,3) + +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) +pl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30) +pl.title('Interp samples Grouplasso') \ No newline at end of file diff --git a/ot/da.py b/ot/da.py index 8ecd952..3ef9fc7 100644 --- a/ot/da.py +++ b/ot/da.py @@ -47,4 +47,5 @@ def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1): if idx_begin==-1: W[indices_labels[0],t]=np.min(all_maj) - return transp \ No newline at end of file + return transp + \ No newline at end of file diff --git a/ot/datasets.py b/ot/datasets.py index edc29a9..f22e345 100644 --- a/ot/datasets.py +++ b/ot/datasets.py @@ -37,17 +37,17 @@ def get_data_classif(dataset,n,nz=.5,**kwargs): x[y==2,0]=-1.; x[y==2,1]=1. x[y==3,0]=1. ; x[y==3,1]=0 - x[y!=3,:]+=nz*np.random.randn(sum(y!=3),2) + x[y!=3,:]+=1.5*nz*np.random.randn(sum(y!=3),2) x[y==3,:]+=2*nz*np.random.randn(sum(y==3),2) elif dataset.lower()=='3gauss2': - y=np.floor((np.arange(n)*1.0/n*4))+1 + y=np.floor((np.arange(n)*1.0/n*3))+1 x=np.zeros((n,2)) y[y==4]=3 # class 1 - x[y==1,0]=-1.; x[y==1,1]=-1. - x[y==2,0]=-1.; x[y==2,1]=1. - x[y==3,0]=1. ; x[y==3,1]=0 + x[y==1,0]=-2.; x[y==1,1]=-2. + x[y==2,0]=-2.; x[y==2,1]=2. + x[y==3,0]=2. ; x[y==3,1]=0 x[y!=3,:]+=nz*np.random.randn(sum(y!=3),2) x[y==3,:]+=2*nz*np.random.randn(sum(y==3),2) -- cgit v1.2.3 From 22dcef6c1cf6bd7ae626ef768359c7495298ba29 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 26 Oct 2016 16:43:20 +0200 Subject: add conditionnal gradient and demo --- examples/demo_optim_OTreg.py | 47 ++++++++++++++ ot/__init__.py | 1 + ot/da.py | 2 - ot/optim.py | 150 +++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 198 insertions(+), 2 deletions(-) create mode 100644 examples/demo_optim_OTreg.py create mode 100644 ot/optim.py diff --git a/examples/demo_optim_OTreg.py b/examples/demo_optim_OTreg.py new file mode 100644 index 0000000..f12fdc1 --- /dev/null +++ b/examples/demo_optim_OTreg.py @@ -0,0 +1,47 @@ +# -*- coding: utf-8 -*- +""" +demo of Optimal transport for domain adaptation +""" + +import numpy as np +import matplotlib.pylab as pl +import ot + + + +#%% parameters + +n=100 # nb bins + +# bin positions +x=np.arange(n,dtype=np.float64) + +# Gaussian distributions +a=ot.datasets.get_1D_gauss(n,m=20,s=20) # m= mean, s= std +b=ot.datasets.get_1D_gauss(n,m=60,s=60) + +# loss matrix +M=ot.dist(x.reshape((n,1)),x.reshape((n,1))) +M/=M.max() + +#%% EMD + +G0=ot.emd(a,b,M) + +pl.figure(3) +ot.plot.plot1D_mat(a,b,G0,'OT matrix G0') + +#%% exampel of regularization with Frobnisu norm + +def f(G): + return 0.5*np.sum(G**2) + +def df(G): + return G + +reg=1e1 + +Greg=ot.optim.cg(a,b,M,reg,f,df,verbose=True) + +pl.figure(4) +ot.plot.plot1D_mat(a,b,Greg,'OT matrix G0') \ No newline at end of file diff --git a/ot/__init__.py b/ot/__init__.py index f63d1c2..35e42b7 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -6,6 +6,7 @@ import datasets import plot import bregman import da +import optim # OT functions from emd import emd diff --git a/ot/da.py b/ot/da.py index 3ef9fc7..ffaa97d 100644 --- a/ot/da.py +++ b/ot/da.py @@ -2,8 +2,6 @@ domain adaptation with optimal transport """ import numpy as np -from scipy.spatial.distance import cdist - from bregman import sinkhorn diff --git a/ot/optim.py b/ot/optim.py new file mode 100644 index 0000000..86371b9 --- /dev/null +++ b/ot/optim.py @@ -0,0 +1,150 @@ +# -*- coding: utf-8 -*- +""" +Created on Wed Oct 26 15:08:19 2016 + +@author: rflamary +""" + +import numpy as np +import scipy as sp +from scipy.optimize.linesearch import scalar_search_armijo +from emd import emd + +# The corresponding scipy function does not work for matrices +def line_search_armijo(f,xk,pk,gfk,old_fval,args=(),c1=1e-4,alpha0=0.99): + xk = np.atleast_1d(xk) + fc = [0] + + def phi(alpha1): + fc[0] += 1 + return f(xk + alpha1*pk, *args) + + if old_fval is None: + phi0 = phi(0.) + else: + phi0 = old_fval + + derphi0 = np.sum(pk*gfk) # Quickfix for matrices + alpha,phi1 = scalar_search_armijo(phi,phi0,derphi0,c1=c1,alpha0=alpha0) + + return alpha,fc[0],phi1 + + +def cg(a,b,M,reg,f,df,G0=None,numItermax = 200,stopThr=1e-9,verbose=False,log=False): + """ + Solve the general regularized OT problem with conditional gradient + + The function solves the following optimization problem: + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg*f(\gamma) + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + where : + + - M is the (ns,nt) metric cost matrix + - :math:`f` is the regularization term ( and df is its gradient) + - a and b are source and target weights (sum to 1) + + The algorithm used for solving the problem is conditional gradient as discussed in [1]_ + + + Parameters + ---------- + a : np.ndarray (ns,) + samples weights in the source domain + b : np.ndarray (nt,) + samples in the target domain + M : np.ndarray (ns,nt) + loss matrix + reg: float() + Regularization term >0 + + + Returns + ------- + gamma: (ns x nt) ndarray + Optimal transportation matrix for the given parameters + + References + ---------- + + .. [1] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. + + See Also + -------- + ot.emd.emd : Unregularized optimal ransport + ot.bregman.sinkhorn : Entropic regularized optimal transport + + """ + + loop=1 + + if log: + log={'loss':[]} + + if G0 is None: + G=np.outer(a,b) + else: + G=G0 + + def cost(G): + return np.sum(M*G)+reg*f(G) + + f_val=cost(G) + if log: + log['loss'].append(f_val) + + it=0 + + if verbose: + print('{:5s}|{:12s}|{:8s}'.format('It.','Loss','Delta loss')+'\n'+'-'*32) + print('{:5d}|{:8e}|{:8e}'.format(it,f_val,0)) + + while loop: + + it+=1 + old_fval=f_val + + + # problem linearization + Mi=M+reg*df(G) + + # solve linear program + Gc=emd(a,b,Mi) + + deltaG=Gc-G + + # line search + alpha,fc,f_val = line_search_armijo(f,G,deltaG,Mi,f_val) + + + G=G+alpha*deltaG + + # test convergence + if it>=numItermax: + loop=0 + + delta_fval=(f_val-old_fval)/abs(f_val) + if abs(delta_fval) Date: Wed, 26 Oct 2016 16:49:52 +0200 Subject: add conditionnal gradient and demo --- ot/optim.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/optim.py b/ot/optim.py index 86371b9..03ed83d 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -120,7 +120,7 @@ def cg(a,b,M,reg,f,df,G0=None,numItermax = 200,stopThr=1e-9,verbose=False,log=Fa deltaG=Gc-G # line search - alpha,fc,f_val = line_search_armijo(f,G,deltaG,Mi,f_val) + alpha,fc,f_val = line_search_armijo(cost,G,deltaG,Mi,f_val) G=G+alpha*deltaG -- cgit v1.2.3 From 03cca954887bf07debe5494716542d5fef8c0830 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 26 Oct 2016 17:03:04 +0200 Subject: redo init --- ot/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/__init__.py b/ot/__init__.py index 35e42b7..3cf3518 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -16,4 +16,4 @@ from da import sinkhorn_lpl1_mm # utils functions from utils import dist,unif -__all__ = ["emd","sinkhorn","utils",'datasets','bregman','plot','dist','unif','barycenter','sinkhorn_lpl1_mm'] +__all__ = ["emd","sinkhorn","utils",'datasets','bregman','plot','dist','unif','barycenter','sinkhorn_lpl1_mm','da','optim'] -- cgit v1.2.3 From afe642c252c0a838dbc35b3d624f6410e45d4c75 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 26 Oct 2016 17:09:26 +0200 Subject: redo --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index c20c3f5..f74cac3 100755 --- a/setup.py +++ b/setup.py @@ -13,7 +13,7 @@ version='0.1' ROOT = os.path.abspath(os.path.dirname(__file__)) README = open(os.path.join(ROOT, 'README.md')).read() -setup(name='python-webgen', +setup(name='POT', version=version, description='Python Optimal Transport Library', long_description=README, -- cgit v1.2.3 From 4563808ee8c867f18fa4c2ecb5c3f0bffa53825d Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 27 Oct 2016 09:13:10 +0200 Subject: demo optim --- examples/demo_optim_OTreg.py | 9 +++++---- ot/optim.py | 1 - 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/examples/demo_optim_OTreg.py b/examples/demo_optim_OTreg.py index f12fdc1..9ffb02d 100644 --- a/examples/demo_optim_OTreg.py +++ b/examples/demo_optim_OTreg.py @@ -34,12 +34,13 @@ ot.plot.plot1D_mat(a,b,G0,'OT matrix G0') #%% exampel of regularization with Frobnisu norm def f(G): - return 0.5*np.sum(G**2) + #return 0.5*np.sum(G**2) + return np.sum(G*np.log(G)) def df(G): - return G - -reg=1e1 +# return G + return np.log(G)+1 +reg=1e-3 Greg=ot.optim.cg(a,b,M,reg,f,df,verbose=True) diff --git a/ot/optim.py b/ot/optim.py index 03ed83d..66bbfb5 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -122,7 +122,6 @@ def cg(a,b,M,reg,f,df,G0=None,numItermax = 200,stopThr=1e-9,verbose=False,log=Fa # line search alpha,fc,f_val = line_search_armijo(cost,G,deltaG,Mi,f_val) - G=G+alpha*deltaG # test convergence -- cgit v1.2.3 From 708aadb3396129c56cf128be04b7e87304b95070 Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Thu, 27 Oct 2016 12:17:39 +0200 Subject: setuptools --- examples/demo_optim_OTreg.py | 2 +- ot/__init__.py | 18 +++++++++--------- setup.py | 23 ++++++++++++++--------- 3 files changed, 24 insertions(+), 19 deletions(-) diff --git a/examples/demo_optim_OTreg.py b/examples/demo_optim_OTreg.py index 9ffb02d..5e88bdb 100644 --- a/examples/demo_optim_OTreg.py +++ b/examples/demo_optim_OTreg.py @@ -40,7 +40,7 @@ def f(G): def df(G): # return G return np.log(G)+1 -reg=1e-3 +reg=1e-1 Greg=ot.optim.cg(a,b,M,reg,f,df,verbose=True) diff --git a/ot/__init__.py b/ot/__init__.py index 3cf3518..4c3c40e 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -1,17 +1,17 @@ # Python Optimal Transport toolbox # All submodules and packages -import utils -import datasets -import plot -import bregman -import da -import optim +from . import utils +from . import datasets +from . import plot +from . import bregman +from . import da +from . import optim # OT functions -from emd import emd -from bregman import sinkhorn,barycenter -from da import sinkhorn_lpl1_mm +from ot.emd import emd +from ot.bregman import sinkhorn,barycenter +from ot.da import sinkhorn_lpl1_mm # utils functions from utils import dist,unif diff --git a/setup.py b/setup.py index f74cac3..969ec99 100755 --- a/setup.py +++ b/setup.py @@ -1,10 +1,17 @@ #!/usr/bin/env python -from distutils.core import setup, Extension +from setuptools import setup, find_packages +from codecs import open +from os import path import numpy -#from Cython.Distutils import build_ext +from setuptools.extension import Extension from Cython.Build import cythonize + +here = path.abspath(path.dirname(__file__)) + + + import os #import glob @@ -17,16 +24,14 @@ setup(name='POT', version=version, description='Python Optimal Transport Library', long_description=README, - author=u'Remi Flamary', - author_email='remi.flamary@gmail.com', + author=u'Remi Flamary, Nicolas Courty', + author_email='remi.flamary@gmail.com, ncourty@gmail.com', url='https://github.com/rflamary/POT', - packages=['ot','ot.emd'], - ext_modules = cythonize(Extension( - "ot.emd.emd", # the extesion name + packages=find_packages(), + ext_modules = cythonize(Extension( + "ot.emd.emd", # the extension name sources=["ot/emd/emd.pyx", "ot/emd/EMD_wrap.cpp"], # the Cython source and # additional C++ source files - #extra_compile_args=['-fopenmp'], - #extra_link_args=['-fopenmp'], language="c++", # generate and compile C++ code, include_dirs=[numpy.get_include(),os.path.join(ROOT,'ot/emd')])), platforms=['linux','macosx','windows'], -- cgit v1.2.3 From e083f90ad09a3bd42beffea1e996f3b4a9b3ff76 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 27 Oct 2016 12:34:42 +0200 Subject: rename emd module to lp --- ot/__init__.py | 6 +- ot/emd/EMD.h | 29 - ot/emd/EMD_wrap.cpp | 120 - ot/emd/__init__.py | 3 - ot/emd/core.h | 103 - ot/emd/emd.cpp | 6507 --------------------------------------- ot/emd/emd.pyx | 71 - ot/emd/full_bipartitegraph.h | 215 -- ot/emd/network_simplex_simple.h | 1543 ---------- ot/lp/EMD.h | 29 + ot/lp/EMD_wrap.cpp | 120 + ot/lp/__init__.py | 3 + ot/lp/core.h | 103 + ot/lp/emd.cpp | 6507 +++++++++++++++++++++++++++++++++++++++ ot/lp/emd.pyx | 71 + ot/lp/full_bipartitegraph.h | 215 ++ ot/lp/network_simplex_simple.h | 1543 ++++++++++ ot/optim.py | 2 +- setup.py | 6 +- 19 files changed, 8599 insertions(+), 8597 deletions(-) delete mode 100644 ot/emd/EMD.h delete mode 100644 ot/emd/EMD_wrap.cpp delete mode 100644 ot/emd/__init__.py delete mode 100644 ot/emd/core.h delete mode 100644 ot/emd/emd.cpp delete mode 100644 ot/emd/emd.pyx delete mode 100644 ot/emd/full_bipartitegraph.h delete mode 100644 ot/emd/network_simplex_simple.h create mode 100644 ot/lp/EMD.h create mode 100644 ot/lp/EMD_wrap.cpp create mode 100644 ot/lp/__init__.py create mode 100644 ot/lp/core.h create mode 100644 ot/lp/emd.cpp create mode 100644 ot/lp/emd.pyx create mode 100644 ot/lp/full_bipartitegraph.h create mode 100644 ot/lp/network_simplex_simple.h diff --git a/ot/__init__.py b/ot/__init__.py index 4c3c40e..87119e5 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -5,15 +5,17 @@ from . import utils from . import datasets from . import plot from . import bregman +from . import lp from . import da from . import optim + # OT functions -from ot.emd import emd +from ot.lp import emd from ot.bregman import sinkhorn,barycenter from ot.da import sinkhorn_lpl1_mm # utils functions from utils import dist,unif -__all__ = ["emd","sinkhorn","utils",'datasets','bregman','plot','dist','unif','barycenter','sinkhorn_lpl1_mm','da','optim'] +__all__ = ["emd","sinkhorn","utils",'datasets','bregman','lp','plot','dist','unif','barycenter','sinkhorn_lpl1_mm','da','optim'] diff --git a/ot/emd/EMD.h b/ot/emd/EMD.h deleted file mode 100644 index 40d7192..0000000 --- a/ot/emd/EMD.h +++ /dev/null @@ -1,29 +0,0 @@ -/* This file is a c++ wrapper function for computing the transportation cost - * between two vectors given a cost matrix. - * - * It was written by Antoine Rolet (2014) and mainly consists of a wrapper - * of the code written by Nicolas Bonneel available on this page - * http://people.seas.harvard.edu/~nbonneel/FastTransport/ - * - * It was then modified to make it more amenable to python inline calling - * - * Please give relevant credit to the original author (Nicolas Bonneel) if - * you use this code for a publication. - * - */ - - -#ifndef EMD_H -#define EMD_H - -#include -#include -#include "network_simplex_simple.h" - -using namespace lemon; -typedef unsigned int node_id_type; - - -void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost); - -#endif diff --git a/ot/emd/EMD_wrap.cpp b/ot/emd/EMD_wrap.cpp deleted file mode 100644 index 52cd262..0000000 --- a/ot/emd/EMD_wrap.cpp +++ /dev/null @@ -1,120 +0,0 @@ -/* This file is a c++ wrapper function for computing the transportation cost - * between two vectors given a cost matrix. - * - * It was written by Antoine Rolet (2014) and mainly consists of a wrapper - * of the code written by Nicolas Bonneel available on this page - * http://people.seas.harvard.edu/~nbonneel/FastTransport/ - * - * It was then modified to make it more amenable to python inline calling - * - * Please give relevant credit to the original author (Nicolas Bonneel) if - * you use this code for a publication. - * - */ - -#include "EMD.h" - - -void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost) { -// beware M and C anre strored in row major C style!!! - int n, m, i,cur; - double max,max_iter; - - - typedef FullBipartiteDigraph Digraph; - DIGRAPH_TYPEDEFS(FullBipartiteDigraph); - - // Get the number of non zero coordinates for r and c - n=0; - for (node_id_type i=0; i0) { - n++; - } - } - m=0; - for (node_id_type i=0; i0) { - m++; - } - } - - - // Define the graph - - std::vector indI(n), indJ(m); - std::vector weights1(n), weights2(m); - Digraph di(n, m); - NetworkSimplexSimple net(di, true, n+m, n*m,max_iter); - - // Set supply and demand, don't account for 0 values (faster) - - max=0; - cur=0; - for (node_id_type i=0; i0) { - weights1[ di.nodeFromId(cur) ] = val; - max+=val; - indI[cur++]=i; - } - } - - // Demand is actually negative supply... - - max=0; - cur=0; - for (node_id_type i=0; i0) { - weights2[ di.nodeFromId(cur) ] = -val; - indJ[cur++]=i; - - max-=val; - } - } - - - net.supplyMap(&weights1[0], n, &weights2[0], m); - - // Set the cost of each edge - max=0; - for (node_id_type i=0; imax) { - max=val; - } - } - } - - - // Solve the problem with the network simplex algorithm - - int ret=net.run(); - if (ret!=(int)net.OPTIMAL) { - if (ret==(int)net.INFEASIBLE) { - std::cout << "Infeasible problem"; - } - if (ret==(int)net.UNBOUNDED) - { - std::cout << "Unbounded problem"; - } - } else - { - for (node_id_type i=0; i -#include - - -// Disable the following warnings when compiling with MSVC: -// C4250: 'class1' : inherits 'class2::member' via dominance -// C4355: 'this' : used in base member initializer list -// C4503: 'function' : decorated name length exceeded, name was truncated -// C4800: 'type' : forcing value to bool 'true' or 'false' (performance warning) -// C4996: 'function': was declared deprecated -#ifdef _MSC_VER -#pragma warning( disable : 4250 4355 4503 4800 4996 ) -#endif - -///\file -///\brief LEMON core utilities. -/// -///This header file contains core utilities for LEMON. -///It is automatically included by all graph types, therefore it usually -///do not have to be included directly. - -namespace lemon { - - /// \brief Dummy type to make it easier to create invalid iterators. - /// - /// Dummy type to make it easier to create invalid iterators. - /// See \ref INVALID for the usage. - struct Invalid { - public: - bool operator==(Invalid) { return true; } - bool operator!=(Invalid) { return false; } - bool operator< (Invalid) { return false; } - }; - - /// \brief Invalid iterators. - /// - /// \ref Invalid is a global type that converts to each iterator - /// in such a way that the value of the target iterator will be invalid. -#ifdef LEMON_ONLY_TEMPLATES - const Invalid INVALID = Invalid(); -#else - extern const Invalid INVALID; -#endif - - /// \addtogroup gutils - /// @{ - - ///Create convenience typedefs for the digraph types and iterators - - ///This \c \#define creates convenient type definitions for the following - 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PyMethod_New(func, self) : PyInstanceMethod_New(func)) -#else - #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) -#endif -#if PY_VERSION_HEX >= 0x030500B1 -#define __Pyx_PyAsyncMethodsStruct PyAsyncMethods -#define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) -#elif CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -typedef struct { - unaryfunc am_await; - unaryfunc am_aiter; - unaryfunc am_anext; -} __Pyx_PyAsyncMethodsStruct; -#define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) -#else -#define __Pyx_PyType_AsAsync(obj) NULL -#endif -#ifndef CYTHON_RESTRICT - #if defined(__GNUC__) - #define CYTHON_RESTRICT __restrict__ - #elif defined(_MSC_VER) && _MSC_VER >= 1400 - #define CYTHON_RESTRICT __restrict - #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L - #define CYTHON_RESTRICT restrict - #else - #define CYTHON_RESTRICT - #endif -#endif -#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) - -#ifndef __cplusplus - #error "Cython files generated with the C++ option must be compiled with a C++ compiler." -#endif -#ifndef CYTHON_INLINE - #define CYTHON_INLINE inline -#endif -template -void __Pyx_call_destructor(T& x) { - x.~T(); -} -template -class __Pyx_FakeReference { - public: - __Pyx_FakeReference() : ptr(NULL) { } - __Pyx_FakeReference(const T& ref) : ptr(const_cast(&ref)) { } - T *operator->() { return ptr; } - operator T&() { return *ptr; } - private: - T *ptr; -}; - -#if defined(WIN32) || defined(MS_WINDOWS) - #define _USE_MATH_DEFINES -#endif -#include -#ifdef NAN -#define __PYX_NAN() ((float) NAN) -#else -static CYTHON_INLINE float __PYX_NAN() { - float value; - memset(&value, 0xFF, sizeof(value)); - return value; -} -#endif - - -#if PY_MAJOR_VERSION >= 3 - #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) - #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) -#else - #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) - #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) -#endif - -#ifndef __PYX_EXTERN_C - #ifdef __cplusplus - #define __PYX_EXTERN_C extern "C" - #else - #define __PYX_EXTERN_C extern - #endif -#endif - -#define __PYX_HAVE__ot__emd__emd -#define __PYX_HAVE_API__ot__emd__emd -#include "string.h" -#include "stdio.h" -#include "stdlib.h" -#include "numpy/arrayobject.h" -#include "numpy/ufuncobject.h" -#include "EMD.h" -#ifdef _OPENMP -#include -#endif /* _OPENMP */ - -#ifdef PYREX_WITHOUT_ASSERTIONS -#define CYTHON_WITHOUT_ASSERTIONS -#endif - -#ifndef CYTHON_UNUSED -# if defined(__GNUC__) -# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) -# define CYTHON_UNUSED __attribute__ ((__unused__)) -# else -# define CYTHON_UNUSED -# endif -# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) -# define CYTHON_UNUSED __attribute__ ((__unused__)) -# else -# define CYTHON_UNUSED -# endif -#endif -#ifndef CYTHON_NCP_UNUSED -# if CYTHON_COMPILING_IN_CPYTHON -# define CYTHON_NCP_UNUSED -# else -# define CYTHON_NCP_UNUSED CYTHON_UNUSED -# endif -#endif -typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding; - const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; - -#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 -#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0 -#define __PYX_DEFAULT_STRING_ENCODING "" -#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString -#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize -#define __Pyx_uchar_cast(c) ((unsigned char)c) -#define __Pyx_long_cast(x) ((long)x) -#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ - (sizeof(type) < sizeof(Py_ssize_t)) ||\ - (sizeof(type) > sizeof(Py_ssize_t) &&\ - likely(v < (type)PY_SSIZE_T_MAX ||\ - v == (type)PY_SSIZE_T_MAX) &&\ - (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ - v == (type)PY_SSIZE_T_MIN))) ||\ - (sizeof(type) == sizeof(Py_ssize_t) &&\ - (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ - v == (type)PY_SSIZE_T_MAX))) ) -#if defined (__cplusplus) && __cplusplus >= 201103L - #include - #define __Pyx_sst_abs(value) std::abs(value) -#elif SIZEOF_INT >= SIZEOF_SIZE_T - #define __Pyx_sst_abs(value) abs(value) -#elif SIZEOF_LONG >= SIZEOF_SIZE_T - #define __Pyx_sst_abs(value) labs(value) -#elif defined (_MSC_VER) && defined (_M_X64) - #define __Pyx_sst_abs(value) _abs64(value) -#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L - #define __Pyx_sst_abs(value) llabs(value) -#elif defined (__GNUC__) - #define __Pyx_sst_abs(value) __builtin_llabs(value) -#else - #define __Pyx_sst_abs(value) ((value<0) ? -value : value) -#endif -static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*); -static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); -#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) -#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) -#define __Pyx_PyBytes_FromString PyBytes_FromString -#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); -#if PY_MAJOR_VERSION < 3 - #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString - #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize -#else - #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString - #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize -#endif -#define __Pyx_PyObject_AsSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) -#define __Pyx_PyObject_AsUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) -#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) -#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) -#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) -#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) -#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) -#if PY_MAJOR_VERSION < 3 -static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) -{ - const Py_UNICODE *u_end = u; - while (*u_end++) ; - return (size_t)(u_end - u - 1); -} -#else -#define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen -#endif -#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) -#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode -#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode -#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) -#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) -#define __Pyx_PyBool_FromLong(b) ((b) ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False)) -static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); -static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x); -static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); -static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); -#if CYTHON_COMPILING_IN_CPYTHON -#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) -#else -#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) -#endif -#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) -#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII -static int __Pyx_sys_getdefaultencoding_not_ascii; -static int __Pyx_init_sys_getdefaultencoding_params(void) { - PyObject* sys; - PyObject* default_encoding = NULL; - PyObject* ascii_chars_u = NULL; - PyObject* ascii_chars_b = NULL; - const char* default_encoding_c; - sys = PyImport_ImportModule("sys"); - if (!sys) goto bad; - default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); - Py_DECREF(sys); - if (!default_encoding) goto bad; - default_encoding_c = PyBytes_AsString(default_encoding); - if (!default_encoding_c) goto bad; - if (strcmp(default_encoding_c, "ascii") == 0) { - __Pyx_sys_getdefaultencoding_not_ascii = 0; - } else { - char ascii_chars[128]; - int c; - for (c = 0; c < 128; c++) { - ascii_chars[c] = c; - } - __Pyx_sys_getdefaultencoding_not_ascii = 1; - ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); - if (!ascii_chars_u) goto bad; - ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); - if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { - PyErr_Format( - PyExc_ValueError, - "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", - default_encoding_c); - goto bad; - } - Py_DECREF(ascii_chars_u); - Py_DECREF(ascii_chars_b); - } - Py_DECREF(default_encoding); - return 0; -bad: - Py_XDECREF(default_encoding); - Py_XDECREF(ascii_chars_u); - Py_XDECREF(ascii_chars_b); - return -1; -} -#endif -#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 -#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) -#else -#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) -#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT -static char* __PYX_DEFAULT_STRING_ENCODING; -static int __Pyx_init_sys_getdefaultencoding_params(void) { - PyObject* sys; - PyObject* default_encoding = NULL; - char* default_encoding_c; - sys = PyImport_ImportModule("sys"); - if (!sys) goto bad; - default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); - Py_DECREF(sys); - if (!default_encoding) goto bad; - default_encoding_c = PyBytes_AsString(default_encoding); - if (!default_encoding_c) goto bad; - __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c)); - if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; - strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); - Py_DECREF(default_encoding); - return 0; -bad: - Py_XDECREF(default_encoding); - return -1; -} -#endif -#endif - - -/* Test for GCC > 2.95 */ -#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) - #define likely(x) __builtin_expect(!!(x), 1) - #define unlikely(x) __builtin_expect(!!(x), 0) -#else /* !__GNUC__ or GCC < 2.95 */ - #define likely(x) (x) - #define unlikely(x) (x) -#endif /* __GNUC__ */ - -static PyObject *__pyx_m; 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- -/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":727 - * ctypedef npy_int8 int8_t - * ctypedef npy_int16 int16_t - * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< - * ctypedef npy_int64 int64_t - * #ctypedef npy_int96 int96_t - */ -typedef npy_int32 __pyx_t_5numpy_int32_t; - -/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":728 - * ctypedef npy_int16 int16_t - * ctypedef npy_int32 int32_t - * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< - * #ctypedef npy_int96 int96_t - * #ctypedef npy_int128 int128_t - */ -typedef npy_int64 __pyx_t_5numpy_int64_t; - -/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":732 - * #ctypedef npy_int128 int128_t - * - * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< - * ctypedef npy_uint16 uint16_t - * ctypedef npy_uint32 uint32_t - */ -typedef npy_uint8 __pyx_t_5numpy_uint8_t; - -/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":733 - * - * ctypedef npy_uint8 uint8_t - * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< - * ctypedef npy_uint32 uint32_t - * ctypedef npy_uint64 uint64_t - */ -typedef npy_uint16 __pyx_t_5numpy_uint16_t; 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"'complex long double'" : "'long double'"); - case 'T': return "a struct"; - case 'O': return "Python object"; - case 'P': return "a pointer"; - case 's': case 'p': return "a string"; - case 0: return "end"; - default: return "unparseable format string"; - } -} -static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return 2; - case 'i': case 'I': case 'l': case 'L': return 4; - case 'q': case 'Q': return 8; - case 'f': return (is_complex ? 8 : 4); - case 'd': return (is_complex ? 16 : 8); - case 'g': { - PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); - return 0; - } - case 'O': case 'P': return sizeof(void*); - default: - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } -} -static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { - switch (ch) { - case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return sizeof(short); - case 'i': case 'I': return sizeof(int); - case 'l': case 'L': return sizeof(long); - #ifdef HAVE_LONG_LONG - case 'q': case 'Q': return sizeof(PY_LONG_LONG); - #endif - case 'f': return sizeof(float) * (is_complex ? 2 : 1); - case 'd': return sizeof(double) * (is_complex ? 2 : 1); - case 'g': return sizeof(long double) * (is_complex ? 2 : 1); - case 'O': case 'P': return sizeof(void*); - default: { - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } - } -} -typedef struct { char c; short x; } __Pyx_st_short; -typedef struct { char c; int x; } __Pyx_st_int; -typedef struct { char c; long x; } __Pyx_st_long; -typedef struct { char c; float x; } __Pyx_st_float; -typedef struct { char c; double x; } __Pyx_st_double; -typedef struct { char c; long double x; } __Pyx_st_longdouble; -typedef struct { char c; void *x; } __Pyx_st_void_p; -#ifdef HAVE_LONG_LONG -typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; -#endif -static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); - case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); - case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); -#ifdef HAVE_LONG_LONG - case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); -#endif - case 'f': return sizeof(__Pyx_st_float) - sizeof(float); - case 'd': return sizeof(__Pyx_st_double) - sizeof(double); - case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); - case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); - default: - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } -} -/* These are for computing the padding at the end of the struct to align - on the first member of the struct. This will probably the same as above, - but we don't have any guarantees. - */ -typedef struct { short x; char c; } __Pyx_pad_short; -typedef struct { int x; char c; } __Pyx_pad_int; -typedef struct { long x; char c; } __Pyx_pad_long; -typedef struct { float x; char c; } __Pyx_pad_float; -typedef struct { double x; char c; } __Pyx_pad_double; -typedef struct { long double x; char c; } __Pyx_pad_longdouble; -typedef struct { void *x; char c; } __Pyx_pad_void_p; -#ifdef HAVE_LONG_LONG -typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; -#endif -static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); - case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); - case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); -#ifdef HAVE_LONG_LONG - case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); -#endif - case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); - case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); - case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); - case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); - default: - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } -} -static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { - switch (ch) { - case 'c': - return 'H'; - case 'b': case 'h': case 'i': - case 'l': case 'q': case 's': case 'p': - return 'I'; - case 'B': case 'H': case 'I': case 'L': case 'Q': - return 'U'; - case 'f': case 'd': case 'g': - return (is_complex ? 'C' : 'R'); - case 'O': - return 'O'; - case 'P': - return 'P'; - default: { - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } - } -} -static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { - if (ctx->head == NULL || ctx->head->field == &ctx->root) { - const char* expected; - const char* quote; - if (ctx->head == NULL) { - expected = "end"; - quote = ""; - } else { - expected = ctx->head->field->type->name; - quote = "'"; - } - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch, expected %s%s%s but got %s", - quote, expected, quote, - __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); - } else { - __Pyx_StructField* field = ctx->head->field; - __Pyx_StructField* parent = (ctx->head - 1)->field; - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", - field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), - parent->type->name, field->name); - } -} -static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { - char group; - size_t size, offset, arraysize = 1; - if (ctx->enc_type == 0) return 0; - if (ctx->head->field->type->arraysize[0]) { - int i, ndim = 0; - if (ctx->enc_type == 's' || ctx->enc_type == 'p') { - ctx->is_valid_array = ctx->head->field->type->ndim == 1; - ndim = 1; - if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { - PyErr_Format(PyExc_ValueError, - "Expected a dimension of size %zu, got %zu", - ctx->head->field->type->arraysize[0], ctx->enc_count); - return -1; - } - } - if (!ctx->is_valid_array) { - PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", - ctx->head->field->type->ndim, ndim); - return -1; - } - for (i = 0; i < ctx->head->field->type->ndim; i++) { - arraysize *= ctx->head->field->type->arraysize[i]; - } - ctx->is_valid_array = 0; - ctx->enc_count = 1; - } - group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); - do { - __Pyx_StructField* field = ctx->head->field; - __Pyx_TypeInfo* type = field->type; - if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { - size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); - } else { - size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); - } - if (ctx->enc_packmode == '@') { - size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); - size_t align_mod_offset; - if (align_at == 0) return -1; - align_mod_offset = ctx->fmt_offset % align_at; - if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; - if (ctx->struct_alignment == 0) - ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, - ctx->is_complex); - } - if (type->size != size || type->typegroup != group) { - if (type->typegroup == 'C' && type->fields != NULL) { - size_t parent_offset = ctx->head->parent_offset + field->offset; - ++ctx->head; - ctx->head->field = type->fields; - ctx->head->parent_offset = parent_offset; - continue; - } - if ((type->typegroup == 'H' || group == 'H') && type->size == size) { - } else { - __Pyx_BufFmt_RaiseExpected(ctx); - return -1; - } - } - offset = ctx->head->parent_offset + field->offset; - if (ctx->fmt_offset != offset) { - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", - (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); - return -1; - } - ctx->fmt_offset += size; - if (arraysize) - ctx->fmt_offset += (arraysize - 1) * size; - --ctx->enc_count; - while (1) { - if (field == &ctx->root) { - ctx->head = NULL; - if (ctx->enc_count != 0) { - __Pyx_BufFmt_RaiseExpected(ctx); - return -1; - } - break; - } - ctx->head->field = ++field; - if (field->type == NULL) { - --ctx->head; - field = ctx->head->field; - continue; - } else if (field->type->typegroup == 'S') { - size_t parent_offset = ctx->head->parent_offset + field->offset; - if (field->type->fields->type == NULL) continue; - field = field->type->fields; - ++ctx->head; - ctx->head->field = field; - ctx->head->parent_offset = parent_offset; - break; - } else { - break; - } - } - } while (ctx->enc_count); - ctx->enc_type = 0; - ctx->is_complex = 0; - return 0; -} -static CYTHON_INLINE PyObject * -__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) -{ - const char *ts = *tsp; - int i = 0, number; - int ndim = ctx->head->field->type->ndim; -; - ++ts; - if (ctx->new_count != 1) { - PyErr_SetString(PyExc_ValueError, - "Cannot handle repeated arrays in format string"); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - while (*ts && *ts != ')') { - switch (*ts) { - case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; - default: break; - } - number = __Pyx_BufFmt_ExpectNumber(&ts); - if (number == -1) return NULL; - if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) - return PyErr_Format(PyExc_ValueError, - "Expected a dimension of size %zu, got %d", - ctx->head->field->type->arraysize[i], number); - if (*ts != ',' && *ts != ')') - return PyErr_Format(PyExc_ValueError, - "Expected a comma in format string, got '%c'", *ts); - if (*ts == ',') ts++; - i++; - } - if (i != ndim) - return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", - ctx->head->field->type->ndim, i); - if (!*ts) { - PyErr_SetString(PyExc_ValueError, - "Unexpected end of format string, expected ')'"); - return NULL; - } - ctx->is_valid_array = 1; - ctx->new_count = 1; - *tsp = ++ts; - return Py_None; -} -static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { - int got_Z = 0; - while (1) { - switch(*ts) { - case 0: - if (ctx->enc_type != 0 && ctx->head == NULL) { - __Pyx_BufFmt_RaiseExpected(ctx); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - if (ctx->head != NULL) { - __Pyx_BufFmt_RaiseExpected(ctx); - return NULL; - } - return ts; - case ' ': - case '\r': - case '\n': - ++ts; - break; - case '<': - if (!__Pyx_IsLittleEndian()) { - PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); - return NULL; - } - ctx->new_packmode = '='; - ++ts; - break; - case '>': - case '!': - if (__Pyx_IsLittleEndian()) { - PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); - return NULL; - } - ctx->new_packmode = '='; - ++ts; - break; - case '=': - case '@': - case '^': - ctx->new_packmode = *ts++; - break; - case 'T': - { - const char* ts_after_sub; - size_t i, struct_count = ctx->new_count; - size_t struct_alignment = ctx->struct_alignment; - ctx->new_count = 1; - ++ts; - if (*ts != '{') { - PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_type = 0; - ctx->enc_count = 0; - ctx->struct_alignment = 0; - ++ts; - ts_after_sub = ts; - for (i = 0; i != struct_count; ++i) { - ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); - if (!ts_after_sub) return NULL; - } - ts = ts_after_sub; - if (struct_alignment) ctx->struct_alignment = struct_alignment; - } - break; - case '}': - { - size_t alignment = ctx->struct_alignment; - ++ts; - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_type = 0; - if (alignment && ctx->fmt_offset % alignment) { - ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); - } - } - return ts; - case 'x': - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->fmt_offset += ctx->new_count; - ctx->new_count = 1; - ctx->enc_count = 0; - ctx->enc_type = 0; - ctx->enc_packmode = ctx->new_packmode; - ++ts; - break; - case 'Z': - got_Z = 1; - ++ts; - if (*ts != 'f' && *ts != 'd' && *ts != 'g') { - __Pyx_BufFmt_RaiseUnexpectedChar('Z'); - return NULL; - } - case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': - case 'l': case 'L': case 'q': case 'Q': - case 'f': case 'd': case 'g': - case 'O': case 'p': - if (ctx->enc_type == *ts && got_Z == ctx->is_complex && - ctx->enc_packmode == ctx->new_packmode) { - ctx->enc_count += ctx->new_count; - ctx->new_count = 1; - got_Z = 0; - ++ts; - break; - } - case 's': - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_count = ctx->new_count; - ctx->enc_packmode = ctx->new_packmode; - ctx->enc_type = *ts; - ctx->is_complex = got_Z; - ++ts; - ctx->new_count = 1; - got_Z = 0; - break; - case ':': - ++ts; - while(*ts != ':') ++ts; - ++ts; - break; - case '(': - if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; - break; - default: - { - int number = __Pyx_BufFmt_ExpectNumber(&ts); - if (number == -1) return NULL; - ctx->new_count = (size_t)number; - } - } - } -} -static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { - buf->buf = NULL; - buf->obj = NULL; - buf->strides = __Pyx_zeros; - buf->shape = __Pyx_zeros; - buf->suboffsets = __Pyx_minusones; -} -static CYTHON_INLINE int __Pyx_GetBufferAndValidate( - Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, - int nd, int cast, __Pyx_BufFmt_StackElem* stack) -{ - if (obj == Py_None || obj == NULL) { - __Pyx_ZeroBuffer(buf); - return 0; - } - buf->buf = NULL; - if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; - if (buf->ndim != nd) { - PyErr_Format(PyExc_ValueError, - "Buffer has wrong number of dimensions (expected %d, got %d)", - nd, buf->ndim); - goto fail; - } - if (!cast) { - __Pyx_BufFmt_Context ctx; - __Pyx_BufFmt_Init(&ctx, stack, dtype); - if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; - } - if ((unsigned)buf->itemsize != dtype->size) { - PyErr_Format(PyExc_ValueError, - "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)", - buf->itemsize, (buf->itemsize > 1) ? "s" : "", - dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); - goto fail; - } - if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; - return 0; -fail:; - __Pyx_ZeroBuffer(buf); - return -1; -} -static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { - if (info->buf == NULL) return; - if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; - __Pyx_ReleaseBuffer(info); -} - -static PyObject *__Pyx_GetBuiltinName(PyObject *name) { - PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name); - if (unlikely(!result)) { - PyErr_Format(PyExc_NameError, -#if PY_MAJOR_VERSION >= 3 - "name '%U' is not defined", name); -#else - "name '%.200s' is not defined", PyString_AS_STRING(name)); -#endif - } - return result; -} - -static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { - PyObject *result; -#if CYTHON_COMPILING_IN_CPYTHON - result = PyDict_GetItem(__pyx_d, name); - if (likely(result)) { - Py_INCREF(result); - } else { -#else - result = PyObject_GetItem(__pyx_d, name); - if (!result) { - PyErr_Clear(); -#endif - result = __Pyx_GetBuiltinName(name); - } - return result; -} - -#if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { - PyObject *result; - ternaryfunc call = func->ob_type->tp_call; - if (unlikely(!call)) - return PyObject_Call(func, arg, kw); - if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) - return NULL; - result = (*call)(func, arg, kw); - Py_LeaveRecursiveCall(); - if (unlikely(!result) && unlikely(!PyErr_Occurred())) { - PyErr_SetString( - PyExc_SystemError, - "NULL result without error in PyObject_Call"); - } - return result; -} -#endif - -#if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { - PyObject *self, *result; - PyCFunction cfunc; - cfunc = PyCFunction_GET_FUNCTION(func); - self = PyCFunction_GET_SELF(func); - if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) - return NULL; - result = cfunc(self, arg); - Py_LeaveRecursiveCall(); - if (unlikely(!result) && unlikely(!PyErr_Occurred())) { - PyErr_SetString( - PyExc_SystemError, - "NULL result without error in PyObject_Call"); - } - return result; -} -#endif - -#if CYTHON_COMPILING_IN_CPYTHON -static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { - PyObject *result; - PyObject *args = PyTuple_New(1); - if (unlikely(!args)) return NULL; - Py_INCREF(arg); - PyTuple_SET_ITEM(args, 0, arg); - result = __Pyx_PyObject_Call(func, args, NULL); - Py_DECREF(args); - return result; -} -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { -#ifdef __Pyx_CyFunction_USED - if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) { -#else - if (likely(PyCFunction_Check(func))) { -#endif - if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { - return __Pyx_PyObject_CallMethO(func, arg); - } - } - return __Pyx__PyObject_CallOneArg(func, arg); -} -#else -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { - PyObject *result; - PyObject *args = PyTuple_Pack(1, arg); - if (unlikely(!args)) return NULL; - result = __Pyx_PyObject_Call(func, args, NULL); - Py_DECREF(args); - return result; -} -#endif - -static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { - if (unlikely(!type)) { - PyErr_SetString(PyExc_SystemError, "Missing type object"); - return 0; - } - if (likely(PyObject_TypeCheck(obj, type))) - return 1; - PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", - Py_TYPE(obj)->tp_name, type->tp_name); - return 0; -} - -static void __Pyx_RaiseBufferFallbackError(void) { - PyErr_SetString(PyExc_ValueError, - "Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!"); -} - -static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) { -#if CYTHON_COMPILING_IN_CPYTHON - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyThreadState *tstate = PyThreadState_GET(); - tmp_type = tstate->curexc_type; - tmp_value = tstate->curexc_value; - tmp_tb = tstate->curexc_traceback; - tstate->curexc_type = type; - tstate->curexc_value = value; - tstate->curexc_traceback = tb; - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -#else - PyErr_Restore(type, value, tb); -#endif -} -static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) { -#if CYTHON_COMPILING_IN_CPYTHON - PyThreadState *tstate = PyThreadState_GET(); - *type = tstate->curexc_type; - *value = tstate->curexc_value; - *tb = tstate->curexc_traceback; - tstate->curexc_type = 0; - tstate->curexc_value = 0; - tstate->curexc_traceback = 0; -#else - PyErr_Fetch(type, value, tb); -#endif -} - -#if PY_MAJOR_VERSION < 3 -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, - CYTHON_UNUSED PyObject *cause) { - Py_XINCREF(type); - if (!value || value == Py_None) - value = NULL; - else - Py_INCREF(value); - if (!tb || tb == Py_None) - tb = NULL; - else { - Py_INCREF(tb); - if (!PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto raise_error; - } - } - if (PyType_Check(type)) { -#if CYTHON_COMPILING_IN_PYPY - if (!value) { - Py_INCREF(Py_None); - value = Py_None; - } -#endif - PyErr_NormalizeException(&type, &value, &tb); - } else { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto raise_error; - } - value = type; - type = (PyObject*) Py_TYPE(type); - Py_INCREF(type); - if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto raise_error; - } - } - __Pyx_ErrRestore(type, value, tb); - return; -raise_error: - Py_XDECREF(value); - Py_XDECREF(type); - Py_XDECREF(tb); - return; -} -#else -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { - PyObject* owned_instance = NULL; - if (tb == Py_None) { - tb = 0; - } else if (tb && !PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto bad; - } - if (value == Py_None) - value = 0; - if (PyExceptionInstance_Check(type)) { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto bad; - } - value = type; - type = (PyObject*) Py_TYPE(value); - } else if (PyExceptionClass_Check(type)) { - PyObject *instance_class = NULL; - if (value && PyExceptionInstance_Check(value)) { - instance_class = (PyObject*) Py_TYPE(value); - if (instance_class != type) { - int is_subclass = PyObject_IsSubclass(instance_class, type); - if (!is_subclass) { - instance_class = NULL; - } else if (unlikely(is_subclass == -1)) { - goto bad; - } else { - type = instance_class; - } - } - } - if (!instance_class) { - PyObject *args; - if (!value) - args = PyTuple_New(0); - else if (PyTuple_Check(value)) { - Py_INCREF(value); - args = value; - } else - args = PyTuple_Pack(1, value); - if (!args) - goto bad; - owned_instance = PyObject_Call(type, args, NULL); - Py_DECREF(args); - if (!owned_instance) - goto bad; - value = owned_instance; - if (!PyExceptionInstance_Check(value)) { - PyErr_Format(PyExc_TypeError, - "calling %R should have returned an instance of " - "BaseException, not %R", - type, Py_TYPE(value)); - goto bad; - } - } - } else { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto bad; - } -#if PY_VERSION_HEX >= 0x03030000 - if (cause) { -#else - if (cause && cause != Py_None) { -#endif - PyObject *fixed_cause; - if (cause == Py_None) { - fixed_cause = NULL; - } else if (PyExceptionClass_Check(cause)) { - fixed_cause = PyObject_CallObject(cause, NULL); - if (fixed_cause == NULL) - goto bad; - } else if (PyExceptionInstance_Check(cause)) { - fixed_cause = cause; - Py_INCREF(fixed_cause); - } else { - PyErr_SetString(PyExc_TypeError, - "exception causes must derive from " - "BaseException"); - goto bad; - } - PyException_SetCause(value, fixed_cause); - } - PyErr_SetObject(type, value); - if (tb) { -#if CYTHON_COMPILING_IN_PYPY - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); - Py_INCREF(tb); - PyErr_Restore(tmp_type, tmp_value, tb); - Py_XDECREF(tmp_tb); -#else - PyThreadState *tstate = PyThreadState_GET(); - PyObject* tmp_tb = tstate->curexc_traceback; - if (tb != tmp_tb) { - Py_INCREF(tb); - tstate->curexc_traceback = tb; - Py_XDECREF(tmp_tb); - } -#endif - } -bad: - Py_XDECREF(owned_instance); - return; -} -#endif - -static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { - PyErr_Format(PyExc_ValueError, - "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); -} - -static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { - PyErr_Format(PyExc_ValueError, - "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", - index, (index == 1) ? "" : "s"); -} - -static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { - PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); -} - -static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { - PyObject *empty_list = 0; - PyObject *module = 0; - PyObject *global_dict = 0; - PyObject *empty_dict = 0; - PyObject *list; - #if PY_VERSION_HEX < 0x03030000 - PyObject *py_import; - py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); - if (!py_import) - goto bad; - #endif - if (from_list) - list = from_list; - else { - empty_list = PyList_New(0); - if (!empty_list) - goto bad; - list = empty_list; - } - global_dict = PyModule_GetDict(__pyx_m); - if (!global_dict) - goto bad; - empty_dict = PyDict_New(); - if (!empty_dict) - goto bad; - { - #if PY_MAJOR_VERSION >= 3 - if (level == -1) { - if (strchr(__Pyx_MODULE_NAME, '.')) { - #if PY_VERSION_HEX < 0x03030000 - PyObject *py_level = PyInt_FromLong(1); - if (!py_level) - goto bad; - module = PyObject_CallFunctionObjArgs(py_import, - name, global_dict, empty_dict, list, py_level, NULL); - Py_DECREF(py_level); - #else - module = PyImport_ImportModuleLevelObject( - name, global_dict, empty_dict, list, 1); - #endif - if (!module) { - if (!PyErr_ExceptionMatches(PyExc_ImportError)) - goto bad; - PyErr_Clear(); - } - } - level = 0; - } - #endif - if (!module) { - #if PY_VERSION_HEX < 0x03030000 - PyObject *py_level = PyInt_FromLong(level); - if (!py_level) - goto bad; - module = PyObject_CallFunctionObjArgs(py_import, - name, global_dict, empty_dict, list, py_level, NULL); - Py_DECREF(py_level); - #else - module = PyImport_ImportModuleLevelObject( - name, global_dict, empty_dict, list, level); - #endif - } - } -bad: - #if PY_VERSION_HEX < 0x03030000 - Py_XDECREF(py_import); - #endif - Py_XDECREF(empty_list); - Py_XDECREF(empty_dict); - return module; -} - -static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { - int start = 0, mid = 0, end = count - 1; - if (end >= 0 && code_line > entries[end].code_line) { - return count; - } - while (start < end) { - mid = start + (end - start) / 2; - if (code_line < entries[mid].code_line) { - end = mid; - } else if (code_line > entries[mid].code_line) { - start = mid + 1; - } else { - return mid; - } - } - if (code_line <= entries[mid].code_line) { - return mid; - } else { - return mid + 1; - } -} -static PyCodeObject *__pyx_find_code_object(int code_line) { - PyCodeObject* code_object; - int pos; - if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { - return NULL; - } - pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); - if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { - return NULL; - } - code_object = __pyx_code_cache.entries[pos].code_object; - Py_INCREF(code_object); - return code_object; -} -static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { - int pos, i; - __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; - if (unlikely(!code_line)) { - return; - } - if (unlikely(!entries)) { - entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); - if (likely(entries)) { - __pyx_code_cache.entries = entries; - __pyx_code_cache.max_count = 64; - __pyx_code_cache.count = 1; - entries[0].code_line = code_line; - entries[0].code_object = code_object; - Py_INCREF(code_object); - } - return; - } - pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); - if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { - PyCodeObject* tmp = entries[pos].code_object; - entries[pos].code_object = code_object; - Py_DECREF(tmp); - return; - } - if (__pyx_code_cache.count == __pyx_code_cache.max_count) { - int new_max = __pyx_code_cache.max_count + 64; - entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( - __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry)); - if (unlikely(!entries)) { - return; - } - __pyx_code_cache.entries = entries; - __pyx_code_cache.max_count = new_max; - } - for (i=__pyx_code_cache.count; i>pos; i--) { - entries[i] = entries[i-1]; - } - entries[pos].code_line = code_line; - entries[pos].code_object = code_object; - __pyx_code_cache.count++; - Py_INCREF(code_object); -} - -#include "compile.h" -#include "frameobject.h" -#include "traceback.h" -static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( - const char *funcname, int c_line, - int py_line, const char *filename) { - PyCodeObject *py_code = 0; - PyObject *py_srcfile = 0; - PyObject *py_funcname = 0; - #if PY_MAJOR_VERSION < 3 - py_srcfile = PyString_FromString(filename); - #else - py_srcfile = PyUnicode_FromString(filename); - #endif - if (!py_srcfile) goto bad; - if (c_line) { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - #else - py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - #endif - } - else { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromString(funcname); - #else - py_funcname = PyUnicode_FromString(funcname); - #endif - } - if (!py_funcname) goto bad; - py_code = __Pyx_PyCode_New( - 0, - 0, - 0, - 0, - 0, - __pyx_empty_bytes, /*PyObject *code,*/ - __pyx_empty_tuple, /*PyObject *consts,*/ - __pyx_empty_tuple, /*PyObject *names,*/ - __pyx_empty_tuple, /*PyObject *varnames,*/ - __pyx_empty_tuple, /*PyObject *freevars,*/ - __pyx_empty_tuple, /*PyObject *cellvars,*/ - py_srcfile, /*PyObject *filename,*/ - py_funcname, /*PyObject *name,*/ - py_line, - __pyx_empty_bytes /*PyObject *lnotab*/ - ); - Py_DECREF(py_srcfile); - Py_DECREF(py_funcname); - return py_code; -bad: - Py_XDECREF(py_srcfile); - Py_XDECREF(py_funcname); - return NULL; -} -static void __Pyx_AddTraceback(const char *funcname, int c_line, - int py_line, const char *filename) { - PyCodeObject *py_code = 0; - PyFrameObject *py_frame = 0; - py_code = __pyx_find_code_object(c_line ? c_line : py_line); - if (!py_code) { - py_code = __Pyx_CreateCodeObjectForTraceback( - funcname, c_line, py_line, filename); - if (!py_code) goto bad; - __pyx_insert_code_object(c_line ? c_line : py_line, py_code); - } - py_frame = PyFrame_New( - PyThreadState_GET(), /*PyThreadState *tstate,*/ - py_code, /*PyCodeObject *code,*/ - __pyx_d, /*PyObject *globals,*/ - 0 /*PyObject *locals*/ - ); - if (!py_frame) goto bad; - py_frame->f_lineno = py_line; - PyTraceBack_Here(py_frame); -bad: - Py_XDECREF(py_code); - Py_XDECREF(py_frame); -} - -#if PY_MAJOR_VERSION < 3 -static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { - if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); - if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); - PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); - return -1; -} -static void __Pyx_ReleaseBuffer(Py_buffer *view) { - PyObject *obj = view->obj; - if (!obj) return; - if (PyObject_CheckBuffer(obj)) { - PyBuffer_Release(view); - return; - } - if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; } - Py_DECREF(obj); - view->obj = NULL; -} -#endif - - - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { - const int neg_one = (int) -1, const_zero = (int) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(int) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(int) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); - } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); - } - } else { - if (sizeof(int) <= sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(int), - little, !is_unsigned); - } -} - -#if CYTHON_CCOMPLEX - #ifdef __cplusplus - static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { - return ::std::complex< float >(x, y); - } - #else - static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { - return x + y*(__pyx_t_float_complex)_Complex_I; - } - #endif -#else - static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { - __pyx_t_float_complex z; - z.real = x; - z.imag = y; - return z; - } -#endif - -#if CYTHON_CCOMPLEX -#else - static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex a, __pyx_t_float_complex b) { - return (a.real == b.real) && (a.imag == b.imag); - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - z.real = a.real + b.real; - z.imag = a.imag + b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - z.real = a.real - b.real; - z.imag = a.imag - b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - z.real = a.real * b.real - a.imag * b.imag; - z.imag = a.real * b.imag + a.imag * b.real; - return z; - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - float denom = b.real * b.real + b.imag * b.imag; - z.real = (a.real * b.real + a.imag * b.imag) / denom; - z.imag = (a.imag * b.real - a.real * b.imag) / denom; - return z; - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex a) { - __pyx_t_float_complex z; - z.real = -a.real; - z.imag = -a.imag; - return z; - } - static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex a) { - return (a.real == 0) && (a.imag == 0); - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex a) { - __pyx_t_float_complex z; - z.real = a.real; - z.imag = -a.imag; - return z; - } - #if 1 - static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex z) { - #if !defined(HAVE_HYPOT) || defined(_MSC_VER) - return sqrtf(z.real*z.real + z.imag*z.imag); - #else - return hypotf(z.real, z.imag); - #endif - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - float r, lnr, theta, z_r, z_theta; - if (b.imag == 0 && b.real == (int)b.real) { - if (b.real < 0) { - float denom = a.real * a.real + a.imag * a.imag; - a.real = a.real / denom; - a.imag = -a.imag / denom; - b.real = -b.real; - } - switch ((int)b.real) { - case 0: - z.real = 1; - z.imag = 0; - return z; - case 1: - return a; - case 2: - z = __Pyx_c_prodf(a, a); - return __Pyx_c_prodf(a, a); - case 3: - z = __Pyx_c_prodf(a, a); - return __Pyx_c_prodf(z, a); - case 4: - z = __Pyx_c_prodf(a, a); - return __Pyx_c_prodf(z, z); - } - } - if (a.imag == 0) { - if (a.real == 0) { - return a; - } - r = a.real; - theta = 0; - } else { - r = __Pyx_c_absf(a); - theta = atan2f(a.imag, a.real); - } - lnr = logf(r); - z_r = expf(lnr * b.real - theta * b.imag); - z_theta = theta * b.real + lnr * b.imag; - z.real = z_r * cosf(z_theta); - z.imag = z_r * sinf(z_theta); - return z; - } - #endif -#endif - -#if CYTHON_CCOMPLEX - #ifdef __cplusplus - static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { - return ::std::complex< double >(x, y); - } - #else - static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { - return x + y*(__pyx_t_double_complex)_Complex_I; - } - #endif -#else - static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { - __pyx_t_double_complex z; - z.real = x; - z.imag = y; - return z; - } -#endif - -#if CYTHON_CCOMPLEX -#else - static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) { - return (a.real == b.real) && (a.imag == b.imag); - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - z.real = a.real + b.real; - z.imag = a.imag + b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - z.real = a.real - b.real; - z.imag = a.imag - b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - z.real = a.real * b.real - a.imag * b.imag; - z.imag = a.real * b.imag + a.imag * b.real; - return z; - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - double denom = b.real * b.real + b.imag * b.imag; - z.real = (a.real * b.real + a.imag * b.imag) / denom; - z.imag = (a.imag * b.real - a.real * b.imag) / denom; - return z; - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex a) { - __pyx_t_double_complex z; - z.real = -a.real; - z.imag = -a.imag; - return z; - } - static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex a) { - return (a.real == 0) && (a.imag == 0); - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex a) { - __pyx_t_double_complex z; - z.real = a.real; - z.imag = -a.imag; - return z; - } - #if 1 - static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex z) { - #if !defined(HAVE_HYPOT) || defined(_MSC_VER) - return sqrt(z.real*z.real + z.imag*z.imag); - #else - return hypot(z.real, z.imag); - #endif - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - double r, lnr, theta, z_r, z_theta; - if (b.imag == 0 && b.real == (int)b.real) { - if (b.real < 0) { - double denom = a.real * a.real + a.imag * a.imag; - a.real = a.real / denom; - a.imag = -a.imag / denom; - b.real = -b.real; - } - switch ((int)b.real) { - case 0: - z.real = 1; - z.imag = 0; - return z; - case 1: - return a; - case 2: - z = __Pyx_c_prod(a, a); - return __Pyx_c_prod(a, a); - case 3: - z = __Pyx_c_prod(a, a); - return __Pyx_c_prod(z, a); - case 4: - z = __Pyx_c_prod(a, a); - return __Pyx_c_prod(z, z); - } - } - if (a.imag == 0) { - if (a.real == 0) { - return a; - } - r = a.real; - theta = 0; - } else { - r = __Pyx_c_abs(a); - theta = atan2(a.imag, a.real); - } - lnr = log(r); - z_r = exp(lnr * b.real - theta * b.imag); - z_theta = theta * b.real + lnr * b.imag; - z.real = z_r * cos(z_theta); - z.imag = z_r * sin(z_theta); - return z; - } - #endif -#endif - -#define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ - __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) -#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ - __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) -#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ - {\ - func_type value = func_value;\ - if (sizeof(target_type) < sizeof(func_type)) {\ - if (unlikely(value != (func_type) (target_type) value)) {\ - func_type zero = 0;\ - if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ - return (target_type) -1;\ - if (is_unsigned && unlikely(value < zero))\ - goto raise_neg_overflow;\ - else\ - goto raise_overflow;\ - }\ - }\ - return (target_type) value;\ - } - -#if CYTHON_USE_PYLONG_INTERNALS - #include "longintrepr.h" -#endif - -static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { - const int neg_one = (int) -1, const_zero = (int) 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(int) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (int) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (int) 0; - case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) - case 2: - if (8 * sizeof(int) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { - return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(int) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { - return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(int) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { - return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (int) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(int) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) - } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (int) 0; - case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, -(sdigit) digits[0]) - case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) - case -2: - if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(int) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(int) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(int) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { - return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - } -#endif - if (sizeof(int) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) - } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - int val; - PyObject *v = __Pyx_PyNumber_Int(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (int) -1; - } - } else { - int val; - PyObject *tmp = __Pyx_PyNumber_Int(x); - if (!tmp) return (int) -1; - val = __Pyx_PyInt_As_int(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to int"); - return (int) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to int"); - return (int) -1; -} - -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value) { - const enum NPY_TYPES neg_one = (enum NPY_TYPES) -1, const_zero = (enum NPY_TYPES) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(enum NPY_TYPES) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); - } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); - } - } else { - if (sizeof(enum NPY_TYPES) <= sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(enum NPY_TYPES) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(enum NPY_TYPES), - little, !is_unsigned); - } -} - -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { - const long neg_one = (long) -1, const_zero = (long) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(long) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(long) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); - } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); - } - } else { - if (sizeof(long) <= sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(long), - little, !is_unsigned); - } -} - -static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { - const long neg_one = (long) -1, const_zero = (long) 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(long) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (long) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (long) 0; - case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) - case 2: - if (8 * sizeof(long) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { - return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(long) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { - return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(long) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { - return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (long) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(long) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) - } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (long) 0; - case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, -(sdigit) digits[0]) - case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) - case -2: - if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(long) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(long) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(long) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - } -#endif - if (sizeof(long) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) - } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - long val; - PyObject *v = __Pyx_PyNumber_Int(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (long) -1; - } - } else { - long val; - PyObject *tmp = __Pyx_PyNumber_Int(x); - if (!tmp) return (long) -1; - val = __Pyx_PyInt_As_long(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to long"); - return (long) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to long"); - return (long) -1; -} - -static int __Pyx_check_binary_version(void) { - char ctversion[4], rtversion[4]; - PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); - PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); - if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { - char message[200]; - PyOS_snprintf(message, sizeof(message), - "compiletime version %s of module '%.100s' " - "does not match runtime version %s", - ctversion, __Pyx_MODULE_NAME, rtversion); - return PyErr_WarnEx(NULL, message, 1); - } - return 0; -} - -#ifndef __PYX_HAVE_RT_ImportModule -#define __PYX_HAVE_RT_ImportModule -static PyObject *__Pyx_ImportModule(const char *name) { - PyObject *py_name = 0; - PyObject *py_module = 0; - py_name = __Pyx_PyIdentifier_FromString(name); - if (!py_name) - goto bad; - py_module = PyImport_Import(py_name); - Py_DECREF(py_name); - return py_module; -bad: - Py_XDECREF(py_name); - return 0; -} -#endif - -#ifndef __PYX_HAVE_RT_ImportType -#define __PYX_HAVE_RT_ImportType -static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, - size_t size, int strict) -{ - PyObject *py_module = 0; - PyObject *result = 0; - PyObject *py_name = 0; - char warning[200]; - Py_ssize_t basicsize; -#ifdef Py_LIMITED_API - PyObject *py_basicsize; -#endif - py_module = __Pyx_ImportModule(module_name); - if (!py_module) - goto bad; - py_name = __Pyx_PyIdentifier_FromString(class_name); - if (!py_name) - goto bad; - result = PyObject_GetAttr(py_module, py_name); - Py_DECREF(py_name); - py_name = 0; - Py_DECREF(py_module); - py_module = 0; - if (!result) - goto bad; - if (!PyType_Check(result)) { - PyErr_Format(PyExc_TypeError, - "%.200s.%.200s is not a type object", - module_name, class_name); - goto bad; - } -#ifndef Py_LIMITED_API - basicsize = ((PyTypeObject *)result)->tp_basicsize; -#else - py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); - if (!py_basicsize) - goto bad; - basicsize = PyLong_AsSsize_t(py_basicsize); - Py_DECREF(py_basicsize); - py_basicsize = 0; - if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) - goto bad; -#endif - if (!strict && (size_t)basicsize > size) { - PyOS_snprintf(warning, sizeof(warning), - "%s.%s size changed, may indicate binary incompatibility", - module_name, class_name); 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indent-tabs-mode: nil; -*- - * - * This file has been adapted by Nicolas Bonneel (2013), - * from full_graph.h from LEMON, a generic C++ optimization library, - * to implement a lightweight fully connected bipartite graph. A previous - * version of this file is used as part of the Displacement Interpolation - * project, - * Web: http://www.cs.ubc.ca/labs/imager/tr/2011/DisplacementInterpolation/ - * - * - **** Original file Copyright Notice : - * Copyright (C) 2003-2010 - * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport - * (Egervary Research Group on Combinatorial Optimization, EGRES). - * - * Permission to use, modify and distribute this software is granted - * provided that this copyright notice appears in all copies. For - * precise terms see the accompanying LICENSE file. - * - * This software is provided "AS IS" with no warranty of any kind, - * express or implied, and with no claim as to its suitability for any - * purpose. - * - */ - -#ifndef LEMON_FULL_BIPARTITE_GRAPH_H -#define LEMON_FULL_BIPARTITE_GRAPH_H - -#include "core.h" - -///\ingroup graphs -///\file -///\brief FullBipartiteDigraph and FullBipartiteGraph classes. - - -namespace lemon { - - - class FullBipartiteDigraphBase { - public: - - typedef FullBipartiteDigraphBase Digraph; - - //class Node; - typedef int Node; - //class Arc; - typedef long long Arc; - - protected: - - int _node_num; - long long _arc_num; - - FullBipartiteDigraphBase() {} - - void construct(int n1, int n2) { _node_num = n1+n2; _arc_num = n1 * n2; _n1=n1; _n2=n2;} - - public: - - int _n1, _n2; - - - Node operator()(int ix) const { return Node(ix); } - static int index(const Node& node) { return node; } - - Arc arc(const Node& s, const Node& t) const { - if (s<_n1 && t>=_n1) - return Arc(s * _n2 + (t-_n1) ); - else - return Arc(-1); - } - - int nodeNum() const { return _node_num; } - long long arcNum() const { return _arc_num; } - - int maxNodeId() const { return _node_num - 1; } - long long maxArcId() const { return _arc_num - 1; } - - Node source(Arc arc) const { return arc / _n2; } - Node target(Arc arc) const { return (arc % _n2) + _n1; } - - static int id(Node node) { return node; } - static long long id(Arc arc) { return arc; } - - static Node nodeFromId(int id) { return Node(id);} - static Arc arcFromId(int id) { return Arc(id);} - - - Arc findArc(Node s, Node t, Arc prev = -1) const { - return prev == -1 ? arc(s, t) : -1; - } - - void first(Node& node) const { - node = _node_num - 1; - } - - static void next(Node& node) { - --node; - } - - void first(Arc& arc) const { - arc = _arc_num - 1; - } - - static void next(Arc& arc) { - --arc; - } - - void firstOut(Arc& arc, const Node& node) const { - if (node>=_n1) - arc = -1; - else - arc = (node + 1) * _n2 - 1; - } - - void nextOut(Arc& arc) const { - if (arc % _n2 == 0) arc = 0; - --arc; - } - - void firstIn(Arc& arc, const Node& node) const { - if (node<_n1) - arc = -1; - else - arc = _arc_num + node - _node_num; - } - - void nextIn(Arc& arc) const { - arc -= _n2; - if (arc < 0) arc = -1; - } - - }; - - /// \ingroup graphs - /// - /// \brief A directed full graph class. - /// - /// FullBipartiteDigraph is a simple and fast implmenetation of directed full - /// (complete) graphs. It contains an arc from each node to each node - /// (including a loop for each node), therefore the number of arcs - /// is the square of the number of nodes. - /// This class is completely static and it needs constant memory space. - /// Thus you can neither add nor delete nodes or arcs, however - /// the structure can be resized using resize(). - /// - /// This type fully conforms to the \ref concepts::Digraph "Digraph concept". - /// Most of its member functions and nested classes are documented - /// only in the concept class. - /// - /// This class provides constant time counting for nodes and arcs. - /// - /// \note FullBipartiteDigraph and FullBipartiteGraph classes are very similar, - /// but there are two differences. While this class conforms only - /// to the \ref concepts::Digraph "Digraph" concept, FullBipartiteGraph - /// conforms to the \ref concepts::Graph "Graph" concept, - /// moreover FullBipartiteGraph does not contain a loop for each - /// node as this class does. - /// - /// \sa FullBipartiteGraph - class FullBipartiteDigraph : public FullBipartiteDigraphBase { - typedef FullBipartiteDigraphBase Parent; - - public: - - /// \brief Default constructor. - /// - /// Default constructor. The number of nodes and arcs will be zero. - FullBipartiteDigraph() { construct(0,0); } - - /// \brief Constructor - /// - /// Constructor. - /// \param n The number of the nodes. - FullBipartiteDigraph(int n1, int n2) { construct(n1, n2); } - - - /// \brief Returns the node with the given index. - /// - /// Returns the node with the given index. Since this structure is - /// completely static, the nodes can be indexed with integers from - /// the range [0..nodeNum()-1]. - /// The index of a node is the same as its ID. - /// \sa index() - Node operator()(int ix) const { return Parent::operator()(ix); } - - /// \brief Returns the index of the given node. - /// - /// Returns the index of the given node. Since this structure is - /// completely static, the nodes can be indexed with integers from - /// the range [0..nodeNum()-1]. - /// The index of a node is the same as its ID. - /// \sa operator()() - static int index(const Node& node) { return Parent::index(node); } - - /// \brief Returns the arc connecting the given nodes. - /// - /// Returns the arc connecting the given nodes. - /*Arc arc(Node u, Node v) const { - return Parent::arc(u, v); - }*/ - - /// \brief Number of nodes. - int nodeNum() const { return Parent::nodeNum(); } - /// \brief Number of arcs. - long long arcNum() const { return Parent::arcNum(); } - }; - - - - -} //namespace lemon - - -#endif //LEMON_FULL_GRAPH_H diff --git a/ot/emd/network_simplex_simple.h b/ot/emd/network_simplex_simple.h deleted file mode 100644 index 64856a0..0000000 --- a/ot/emd/network_simplex_simple.h +++ /dev/null @@ -1,1543 +0,0 @@ -/* -*- mode: C++; indent-tabs-mode: nil; -*- - * - * - * This file has been adapted by Nicolas Bonneel (2013), - * from network_simplex.h from LEMON, a generic C++ optimization library, - * to implement a lightweight network simplex for mass transport, more - * memory efficient that the original file. A previous version of this file - * is used as part of the Displacement Interpolation project, - * Web: http://www.cs.ubc.ca/labs/imager/tr/2011/DisplacementInterpolation/ - * - * - **** Original file Copyright Notice : - * - * Copyright (C) 2003-2010 - * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport - * (Egervary Research Group on Combinatorial Optimization, EGRES). - * - * Permission to use, modify and distribute this software is granted - * provided that this copyright notice appears in all copies. For - * precise terms see the accompanying LICENSE file. - * - * This software is provided "AS IS" with no warranty of any kind, - * express or implied, and with no claim as to its suitability for any - * purpose. - * - */ - -#ifndef LEMON_NETWORK_SIMPLEX_SIMPLE_H -#define LEMON_NETWORK_SIMPLEX_SIMPLE_H -#define DEBUG_LVL 0 -#define EPSILON 10*2.2204460492503131e-016 -#define MAX_DEBUG_ITER 100000 - - -/// \ingroup min_cost_flow_algs -/// -/// \file -/// \brief Network Simplex algorithm for finding a minimum cost flow. - -// if your compiler has troubles with stdext or hashmaps, just comment the following line to use a slower std::map instead -//#define HASHMAP - -#include -#include -#include -#ifdef HASHMAP -#include -#else -#include -#endif -#include -//#include "core.h" -//#include "lmath.h" - -//#include "sparse_array_n.h" -#include "full_bipartitegraph.h" - -#define INVALIDNODE -1 -#define INVALID (-1) - -namespace lemon { - - - template - class ProxyObject; - - template - class SparseValueVector - { - public: - SparseValueVector(int n=0) - { - } - void resize(int n=0){}; - T operator[](const int id) const - { -#ifdef HASHMAP - typename stdext::hash_map::const_iterator it = data.find(id); -#else - typename std::map::const_iterator it = data.find(id); -#endif - if (it==data.end()) - return 0; - else - return it->second; - } - - ProxyObject operator[](const int id) - { - return ProxyObject( this, id ); - } - - //private: -#ifdef HASHMAP - stdext::hash_map data; -#else - std::map data; -#endif - - }; - - template - class ProxyObject { - public: - ProxyObject( SparseValueVector *v, int idx ){_v=v; _idx=idx;}; - ProxyObject & operator=( const T &v ) { - // If we get here, we know that operator[] was called to perform a write access, - // so we can insert an item in the vector if needed - if (v!=0) - _v->data[_idx]=v; - return *this; - } - - operator T() { - // If we get here, we know that operator[] was called to perform a read access, - // so we can simply return the existing object -#ifdef HASHMAP - typename stdext::hash_map::iterator it = _v->data.find(_idx); -#else - typename std::map::iterator it = _v->data.find(_idx); -#endif - if (it==_v->data.end()) - return 0; - else - return it->second; - } - - void operator+=(T val) - { - if (val==0) return; -#ifdef HASHMAP - typename stdext::hash_map::iterator it = _v->data.find(_idx); -#else - typename std::map::iterator it = _v->data.find(_idx); -#endif - if (it==_v->data.end()) - _v->data[_idx] = val; - else - { - T sum = it->second + val; - if (sum==0) - _v->data.erase(it); - else - it->second = sum; - } - } - void operator-=(T val) - { - if (val==0) return; -#ifdef HASHMAP - typename stdext::hash_map::iterator it = _v->data.find(_idx); -#else - typename std::map::iterator it = _v->data.find(_idx); -#endif - if (it==_v->data.end()) - _v->data[_idx] = -val; - else - { - T sum = it->second - val; - if (sum==0) - _v->data.erase(it); - else - it->second = sum; - } - } - - SparseValueVector *_v; - int _idx; - }; - - - - /// \addtogroup min_cost_flow_algs - /// @{ - - /// \brief Implementation of the primal Network Simplex algorithm - /// for finding a \ref min_cost_flow "minimum cost flow". - /// - /// \ref NetworkSimplexSimple implements the primal Network Simplex algorithm - /// for finding a \ref min_cost_flow "minimum cost flow" - /// \ref amo93networkflows, \ref dantzig63linearprog, - /// \ref kellyoneill91netsimplex. - /// This algorithm is a highly efficient specialized version of the - /// linear programming simplex method directly for the minimum cost - /// flow problem. - /// - /// In general, %NetworkSimplexSimple is the fastest implementation available - /// in LEMON for this problem. - /// Moreover, it supports both directions of the supply/demand inequality - /// constraints. For more information, see \ref SupplyType. - /// - /// Most of the parameters of the problem (except for the digraph) - /// can be given using separate functions, and the algorithm can be - /// executed using the \ref run() function. If some parameters are not - /// specified, then default values will be used. - /// - /// \tparam GR The digraph type the algorithm runs on. - /// \tparam V The number type used for flow amounts, capacity bounds - /// and supply values in the algorithm. By default, it is \c int. - /// \tparam C The number type used for costs and potentials in the - /// algorithm. By default, it is the same as \c V. - /// - /// \warning Both number types must be signed and all input data must - /// be integer. - /// - /// \note %NetworkSimplexSimple provides five different pivot rule - /// implementations, from which the most efficient one is used - /// by default. For more information, see \ref PivotRule. - template - class NetworkSimplexSimple - { - public: - - /// \brief Constructor. - /// - /// The constructor of the class. - /// - /// \param graph The digraph the algorithm runs on. - /// \param arc_mixing Indicate if the arcs have to be stored in a - /// mixed order in the internal data structure. - /// In special cases, it could lead to better overall performance, - /// but it is usually slower. Therefore it is disabled by default. - NetworkSimplexSimple(const GR& graph, bool arc_mixing, int nbnodes, long long nb_arcs,double maxiters) : - _graph(graph), //_arc_id(graph), - _arc_mixing(arc_mixing), _init_nb_nodes(nbnodes), _init_nb_arcs(nb_arcs), - MAX(std::numeric_limits::max()), - INF(std::numeric_limits::has_infinity ? - std::numeric_limits::infinity() : MAX) - { - // Reset data structures - reset(); - max_iter=maxiters; - } - - /// The type of the flow amounts, capacity bounds and supply values - typedef V Value; - /// The type of the arc costs - typedef C Cost; - - public: - - /// \brief Problem type constants for the \c run() function. - /// - /// Enum type containing the problem type constants that can be - /// returned by the \ref run() function of the algorithm. - enum ProblemType { - /// The problem has no feasible solution (flow). - INFEASIBLE, - /// The problem has optimal solution (i.e. it is feasible and - /// bounded), and the algorithm has found optimal flow and node - /// potentials (primal and dual solutions). - OPTIMAL, - /// The objective function of the problem is unbounded, i.e. - /// there is a directed cycle having negative total cost and - /// infinite upper bound. - UNBOUNDED - }; - - /// \brief Constants for selecting the type of the supply constraints. - /// - /// Enum type containing constants for selecting the supply type, - /// i.e. the direction of the inequalities in the supply/demand - /// constraints of the \ref min_cost_flow "minimum cost flow problem". - /// - /// The default supply type is \c GEQ, the \c LEQ type can be - /// selected using \ref supplyType(). - /// The equality form is a special case of both supply types. - enum SupplyType { - /// This option means that there are "greater or equal" - /// supply/demand constraints in the definition of the problem. - GEQ, - /// This option means that there are "less or equal" - /// supply/demand constraints in the definition of the problem. - LEQ - }; - - - - private: - - double max_iter; - TEMPLATE_DIGRAPH_TYPEDEFS(GR); - - typedef std::vector IntVector; - typedef std::vector UHalfIntVector; - typedef std::vector ValueVector; - typedef std::vector CostVector; - // typedef SparseValueVector CostVector; - typedef std::vector BoolVector; - // Note: vector is used instead of vector for efficiency reasons - - // State constants for arcs - enum ArcState { - STATE_UPPER = -1, - STATE_TREE = 0, - STATE_LOWER = 1 - }; - - typedef std::vector StateVector; - // Note: vector is used instead of vector for - // efficiency reasons - - private: - - // Data related to the underlying digraph - const GR &_graph; - int _node_num; - int _arc_num; - int _all_arc_num; - int _search_arc_num; - - // Parameters of the problem - SupplyType _stype; - Value _sum_supply; - - inline int _node_id(int n) const {return _node_num-n-1;} ; - - //IntArcMap _arc_id; - UHalfIntVector _source; - UHalfIntVector _target; - bool _arc_mixing; - public: - // Node and arc data - CostVector _cost; - ValueVector _supply; - ValueVector _flow; - //SparseValueVector _flow; - CostVector _pi; - - - private: - // Data for storing the spanning tree structure - IntVector _parent; - IntVector _pred; - IntVector _thread; - IntVector _rev_thread; - IntVector _succ_num; - IntVector _last_succ; - IntVector _dirty_revs; - BoolVector _forward; - StateVector _state; - int _root; - - // Temporary data used in the current pivot iteration - int in_arc, join, u_in, v_in, u_out, v_out; - int first, second, right, last; - int stem, par_stem, new_stem; - Value delta; - - const Value MAX; - - int mixingCoeff; - - public: - - /// \brief Constant for infinite upper bounds (capacities). - /// - /// Constant for infinite upper bounds (capacities). - /// It is \c std::numeric_limits::infinity() if available, - /// \c std::numeric_limits::max() otherwise. - const Value INF; - - private: - - // thank you to DVK and MizardX from StackOverflow for this function! - inline int sequence(int k) const { - int smallv = (k > num_total_big_subsequence_numbers) & 1; - - k -= num_total_big_subsequence_numbers * smallv; - int subsequence_length2 = subsequence_length- smallv; - int subsequence_num = (k / subsequence_length2) + num_big_subseqiences * smallv; - int subsequence_offset = (k % subsequence_length2) * mixingCoeff; - - return subsequence_offset + subsequence_num; - } - int subsequence_length; - int num_big_subseqiences; - int num_total_big_subsequence_numbers; - - inline int getArcID(const Arc &arc) const - { - //int n = _arc_num-arc._id-1; - int n = _arc_num-GR::id(arc)-1; - - //int a = mixingCoeff*(n%mixingCoeff) + n/mixingCoeff; - //int b = _arc_id[arc]; - if (_arc_mixing) - return sequence(n); - else - return n; - } - - // finally unused because too slow - inline int getSource(const int arc) const - { - //int a = _source[arc]; - //return a; - - int n = _arc_num-arc-1; - if (_arc_mixing) - n = mixingCoeff*(n%mixingCoeff) + n/mixingCoeff; - - int b; - if (n>=0) - b = _node_id(_graph.source(GR::arcFromId( n ) )); - else - { - n = arc+1-_arc_num; - if ( n<=_node_num) - b = _node_num; - else - if ( n>=_graph._n1) - b = _graph._n1; - else - b = _graph._n1-n; - } - - return b; - } - - - - // Implementation of the Block Search pivot rule - class BlockSearchPivotRule - { - private: - - // References to the NetworkSimplexSimple class - const UHalfIntVector &_source; - const UHalfIntVector &_target; - const CostVector &_cost; - const StateVector &_state; - const CostVector &_pi; - int &_in_arc; - int _search_arc_num; - - // Pivot rule data - int _block_size; - int _next_arc; - NetworkSimplexSimple &_ns; - - public: - - // Constructor - BlockSearchPivotRule(NetworkSimplexSimple &ns) : - _source(ns._source), _target(ns._target), - _cost(ns._cost), _state(ns._state), _pi(ns._pi), - _in_arc(ns.in_arc), _search_arc_num(ns._search_arc_num), - _next_arc(0),_ns(ns) - { - // The main parameters of the pivot rule - const double BLOCK_SIZE_FACTOR = 1.0; - const int MIN_BLOCK_SIZE = 10; - - _block_size = std::max( int(BLOCK_SIZE_FACTOR * - std::sqrt(double(_search_arc_num))), - MIN_BLOCK_SIZE ); - } - // Find next entering arc - bool findEnteringArc() { - Cost c, min = 0; - int e; - int cnt = _block_size; - double a; - for (e = _next_arc; e != _search_arc_num; ++e) { - c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); - if (c < min) { - min = c; - _in_arc = e; - } - if (--cnt == 0) { - a=fabs(_pi[_source[_in_arc]])>fabs(_pi[_target[_in_arc]]) ? fabs(_pi[_source[_in_arc]]):fabs(_pi[_target[_in_arc]]); - a=a>fabs(_cost[_in_arc])?a:fabs(_cost[_in_arc]); - if (min < -EPSILON*a) goto search_end; - cnt = _block_size; - } - } - for (e = 0; e != _next_arc; ++e) { - c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); - if (c < min) { - min = c; - _in_arc = e; - } - if (--cnt == 0) { - a=fabs(_pi[_source[_in_arc]])>fabs(_pi[_target[_in_arc]]) ? fabs(_pi[_source[_in_arc]]):fabs(_pi[_target[_in_arc]]); - a=a>fabs(_cost[_in_arc])?a:fabs(_cost[_in_arc]); - if (min < -EPSILON*a) goto search_end; - cnt = _block_size; - } - } - a=fabs(_pi[_source[_in_arc]])>fabs(_pi[_target[_in_arc]]) ? fabs(_pi[_source[_in_arc]]):fabs(_pi[_target[_in_arc]]); - a=a>fabs(_cost[_in_arc])?a:fabs(_cost[_in_arc]); - if (min >= -EPSILON*a) return false; - - search_end: - _next_arc = e; - return true; - } - - }; //class BlockSearchPivotRule - - - - public: - - - - int _init_nb_nodes; - long long _init_nb_arcs; - - /// \name Parameters - /// The parameters of the algorithm can be specified using these - /// functions. - - /// @{ - - - /// \brief Set the costs of the arcs. - /// - /// This function sets the costs of the arcs. - /// If it is not used before calling \ref run(), the costs - /// will be set to \c 1 on all arcs. - /// - /// \param map An arc map storing the costs. - /// Its \c Value type must be convertible to the \c Cost type - /// of the algorithm. - /// - /// \return (*this) - template - NetworkSimplexSimple& costMap(const CostMap& map) { - Arc a; _graph.first(a); - for (; a != INVALID; _graph.next(a)) { - _cost[getArcID(a)] = map[a]; - } - return *this; - } - - - /// \brief Set the costs of one arc. - /// - /// This function sets the costs of one arcs. - /// Done for memory reasons - /// - /// \param arc An arc. - /// \param arc A cost - /// - /// \return (*this) - template - NetworkSimplexSimple& setCost(const Arc& arc, const Value cost) { - _cost[getArcID(arc)] = cost; - return *this; - } - - - /// \brief Set the supply values of the nodes. - /// - /// This function sets the supply values of the nodes. - /// If neither this function nor \ref stSupply() is used before - /// calling \ref run(), the supply of each node will be set to zero. - /// - /// \param map A node map storing the supply values. - /// Its \c Value type must be convertible to the \c Value type - /// of the algorithm. - /// - /// \return (*this) - template - NetworkSimplexSimple& supplyMap(const SupplyMap& map) { - Node n; _graph.first(n); - for (; n != INVALIDNODE; _graph.next(n)) { - _supply[_node_id(n)] = map[n]; - } - return *this; - } - template - NetworkSimplexSimple& supplyMap(const SupplyMap* map1, int n1, const SupplyMap* map2, int n2) { - Node n; _graph.first(n); - for (; n != INVALIDNODE; _graph.next(n)) { - if (n - NetworkSimplexSimple& supplyMapAll(SupplyMap val1, int n1, SupplyMap val2, int n2) { - Node n; _graph.first(n); - for (; n != INVALIDNODE; _graph.next(n)) { - if (n(*this) - NetworkSimplexSimple& stSupply(const Node& s, const Node& t, Value k) { - for (int i = 0; i != _node_num; ++i) { - _supply[i] = 0; - } - _supply[_node_id(s)] = k; - _supply[_node_id(t)] = -k; - return *this; - } - - /// \brief Set the type of the supply constraints. - /// - /// This function sets the type of the supply/demand constraints. - /// If it is not used before calling \ref run(), the \ref GEQ supply - /// type will be used. - /// - /// For more information, see \ref SupplyType. - /// - /// \return (*this) - NetworkSimplexSimple& supplyType(SupplyType supply_type) { - _stype = supply_type; - return *this; - } - - /// @} - - /// \name Execution Control - /// The algorithm can be executed using \ref run(). - - /// @{ - - /// \brief Run the algorithm. - /// - /// This function runs the algorithm. - /// The paramters can be specified using functions \ref lowerMap(), - /// \ref upperMap(), \ref costMap(), \ref supplyMap(), \ref stSupply(), - /// \ref supplyType(). - /// For example, - /// \code - /// NetworkSimplexSimple ns(graph); - /// ns.lowerMap(lower).upperMap(upper).costMap(cost) - /// .supplyMap(sup).run(); - /// \endcode - /// - /// This function can be called more than once. All the given parameters - /// are kept for the next call, unless \ref resetParams() or \ref reset() - /// is used, thus only the modified parameters have to be set again. - /// If the underlying digraph was also modified after the construction - /// of the class (or the last \ref reset() call), then the \ref reset() - /// function must be called. - /// - /// \param pivot_rule The pivot rule that will be used during the - /// algorithm. For more information, see \ref PivotRule. - /// - /// \return \c INFEASIBLE if no feasible flow exists, - /// \n \c OPTIMAL if the problem has optimal solution - /// (i.e. it is feasible and bounded), and the algorithm has found - /// optimal flow and node potentials (primal and dual solutions), - /// \n \c UNBOUNDED if the objective function of the problem is - /// unbounded, i.e. there is a directed cycle having negative total - /// cost and infinite upper bound. - /// - /// \see ProblemType, PivotRule - /// \see resetParams(), reset() - ProblemType run() { -#if DEBUG_LVL>0 - mexPrintf("OPTIMAL = %d\nINFEASIBLE = %d\nUNBOUNDED = %d\n",OPTIMAL,INFEASIBLE,UNBOUNDED); - mexEvalString("drawnow;"); -#endif - - if (!init()) return INFEASIBLE; -#if DEBUG_LVL>0 - mexPrintf("Init done, starting iterations\n"); - mexEvalString("drawnow;"); -#endif - return start(); - } - - /// \brief Reset all the parameters that have been given before. - /// - /// This function resets all the paramaters that have been given - /// before using functions \ref lowerMap(), \ref upperMap(), - /// \ref costMap(), \ref supplyMap(), \ref stSupply(), \ref supplyType(). - /// - /// It is useful for multiple \ref run() calls. Basically, all the given - /// parameters are kept for the next \ref run() call, unless - /// \ref resetParams() or \ref reset() is used. - /// If the underlying digraph was also modified after the construction - /// of the class or the last \ref reset() call, then the \ref reset() - /// function must be used, otherwise \ref resetParams() is sufficient. - /// - /// For example, - /// \code - /// NetworkSimplexSimple ns(graph); - /// - /// // First run - /// ns.lowerMap(lower).upperMap(upper).costMap(cost) - /// .supplyMap(sup).run(); - /// - /// // Run again with modified cost map (resetParams() is not called, - /// // so only the cost map have to be set again) - /// cost[e] += 100; - /// ns.costMap(cost).run(); - /// - /// // Run again from scratch using resetParams() - /// // (the lower bounds will be set to zero on all arcs) - /// ns.resetParams(); - /// ns.upperMap(capacity).costMap(cost) - /// .supplyMap(sup).run(); - /// \endcode - /// - /// \return (*this) - /// - /// \see reset(), run() - NetworkSimplexSimple& resetParams() { - for (int i = 0; i != _node_num; ++i) { - _supply[i] = 0; - } - for (int i = 0; i != _arc_num; ++i) { - _cost[i] = 1; - } - _stype = GEQ; - return *this; - } - - - - int divid (int x, int y) - { - return (x-x%y)/y; - } - - /// \brief Reset the internal data structures and all the parameters - /// that have been given before. - /// - /// This function resets the internal data structures and all the - /// paramaters that have been given before using functions \ref lowerMap(), - /// \ref upperMap(), \ref costMap(), \ref supplyMap(), \ref stSupply(), - /// \ref supplyType(). - /// - /// It is useful for multiple \ref run() calls. Basically, all the given - /// parameters are kept for the next \ref run() call, unless - /// \ref resetParams() or \ref reset() is used. - /// If the underlying digraph was also modified after the construction - /// of the class or the last \ref reset() call, then the \ref reset() - /// function must be used, otherwise \ref resetParams() is sufficient. - /// - /// See \ref resetParams() for examples. - /// - /// \return (*this) - /// - /// \see resetParams(), run() - NetworkSimplexSimple& reset() { - // Resize vectors - _node_num = _init_nb_nodes; - _arc_num = _init_nb_arcs; - int all_node_num = _node_num + 1; - int max_arc_num = _arc_num + 2 * _node_num; - - _source.resize(max_arc_num); - _target.resize(max_arc_num); - - _cost.resize(max_arc_num); - _supply.resize(all_node_num); - _flow.resize(max_arc_num); - _pi.resize(all_node_num); - - _parent.resize(all_node_num); - _pred.resize(all_node_num); - _forward.resize(all_node_num); - _thread.resize(all_node_num); - _rev_thread.resize(all_node_num); - _succ_num.resize(all_node_num); - _last_succ.resize(all_node_num); - _state.resize(max_arc_num); - - - //_arc_mixing=false; - if (_arc_mixing) { - // Store the arcs in a mixed order - int k = std::max(int(std::sqrt(double(_arc_num))), 10); - mixingCoeff = k; - subsequence_length = _arc_num / mixingCoeff + 1; - num_big_subseqiences = _arc_num % mixingCoeff; - num_total_big_subsequence_numbers = subsequence_length * num_big_subseqiences; - - int i = 0, j = 0; - Arc a; _graph.first(a); - for (; a != INVALID; _graph.next(a)) { - _source[i] = _node_id(_graph.source(a)); - _target[i] = _node_id(_graph.target(a)); - //_arc_id[a] = i; - if ((i += k) >= _arc_num) i = ++j; - } - } else { - // Store the arcs in the original order - int i = 0; - Arc a; _graph.first(a); - for (; a != INVALID; _graph.next(a), ++i) { - _source[i] = _node_id(_graph.source(a)); - _target[i] = _node_id(_graph.target(a)); - //_arc_id[a] = i; - } - } - - // Reset parameters - resetParams(); - return *this; - } - - /// @} - - /// \name Query Functions - /// The results of the algorithm can be obtained using these - /// functions.\n - /// The \ref run() function must be called before using them. - - /// @{ - - /// \brief Return the total cost of the found flow. - /// - /// This function returns the total cost of the found flow. - /// Its complexity is O(e). - /// - /// \note The return type of the function can be specified as a - /// template parameter. For example, - /// \code - /// ns.totalCost(); - /// \endcode - /// It is useful if the total cost cannot be stored in the \c Cost - /// type of the algorithm, which is the default return type of the - /// function. - /// - /// \pre \ref run() must be called before using this function. - /*template - Number totalCost() const { - Number c = 0; - for (ArcIt a(_graph); a != INVALID; ++a) { - int i = getArcID(a); - c += Number(_flow[i]) * Number(_cost[i]); - } - return c; - }*/ - - template - Number totalCost() const { - Number c = 0; - - /*#ifdef HASHMAP - typename stdext::hash_map::const_iterator it; - #else - typename std::map::const_iterator it; - #endif - for (it = _flow.data.begin(); it!=_flow.data.end(); ++it) - c += Number(it->second) * Number(_cost[it->first]); - return c;*/ - - for (int i=0; i<_flow.size(); i++) - c += _flow[i] * Number(_cost[i]); - return c; - - } - -#ifndef DOXYGEN - Cost totalCost() const { - return totalCost(); - } -#endif - - /// \brief Return the flow on the given arc. - /// - /// This function returns the flow on the given arc. - /// - /// \pre \ref run() must be called before using this function. - Value flow(const Arc& a) const { - return _flow[getArcID(a)]; - } - - /// \brief Return the flow map (the primal solution). - /// - /// This function copies the flow value on each arc into the given - /// map. The \c Value type of the algorithm must be convertible to - /// the \c Value type of the map. - /// - /// \pre \ref run() must be called before using this function. - template - void flowMap(FlowMap &map) const { - Arc a; _graph.first(a); - for (; a != INVALID; _graph.next(a)) { - map.set(a, _flow[getArcID(a)]); - } - } - - /// \brief Return the potential (dual value) of the given node. - /// - /// This function returns the potential (dual value) of the - /// given node. - /// - /// \pre \ref run() must be called before using this function. - Cost potential(const Node& n) const { - return _pi[_node_id(n)]; - } - - /// \brief Return the potential map (the dual solution). - /// - /// This function copies the potential (dual value) of each node - /// into the given map. - /// The \c Cost type of the algorithm must be convertible to the - /// \c Value type of the map. - /// - /// \pre \ref run() must be called before using this function. - template - void potentialMap(PotentialMap &map) const { - Node n; _graph.first(n); - for (; n != INVALID; _graph.next(n)) { - map.set(n, _pi[_node_id(n)]); - } - } - - /// @} - - private: - - // Initialize internal data structures - bool init() { - if (_node_num == 0) return false; - /* - // Check the sum of supply values - _sum_supply = 0; - for (int i = 0; i != _node_num; ++i) { - _sum_supply += _supply[i]; - } - if ( !((_stype == GEQ && _sum_supply <= _epsilon ) || - (_stype == LEQ && _sum_supply >= -_epsilon )) ) return false; - */ - - // Initialize artifical cost - Cost ART_COST; - if (std::numeric_limits::is_exact) { - ART_COST = std::numeric_limits::max() / 2 + 1; - } else { - ART_COST = 0; - for (int i = 0; i != _arc_num; ++i) { - if (_cost[i] > ART_COST) ART_COST = _cost[i]; - } - ART_COST = (ART_COST + 1) * _node_num; - } - - // Initialize arc maps - for (int i = 0; i != _arc_num; ++i) { - //_flow[i] = 0; //by default, the sparse matrix is empty - _state[i] = STATE_LOWER; - } - - // Set data for the artificial root node - _root = _node_num; - _parent[_root] = -1; - _pred[_root] = -1; - _thread[_root] = 0; - _rev_thread[0] = _root; - _succ_num[_root] = _node_num + 1; - _last_succ[_root] = _root - 1; - _supply[_root] = -_sum_supply; - _pi[_root] = 0; - - // Add artificial arcs and initialize the spanning tree data structure - if (_sum_supply == 0) { - // EQ supply constraints - _search_arc_num = _arc_num; - _all_arc_num = _arc_num + _node_num; - for (int u = 0, e = _arc_num; u != _node_num; ++u, ++e) { - _parent[u] = _root; - _pred[u] = e; - _thread[u] = u + 1; - _rev_thread[u + 1] = u; - _succ_num[u] = 1; - _last_succ[u] = u; - _state[e] = STATE_TREE; - if (_supply[u] >= 0) { - _forward[u] = true; - _pi[u] = 0; - _source[e] = u; - _target[e] = _root; - _flow[e] = _supply[u]; - _cost[e] = 0; - } else { - _forward[u] = false; - _pi[u] = ART_COST; - _source[e] = _root; - _target[e] = u; - _flow[e] = -_supply[u]; - _cost[e] = ART_COST; - } - } - } - else if (_sum_supply > 0) { - // LEQ supply constraints - _search_arc_num = _arc_num + _node_num; - int f = _arc_num + _node_num; - for (int u = 0, e = _arc_num; u != _node_num; ++u, ++e) { - _parent[u] = _root; - _thread[u] = u + 1; - _rev_thread[u + 1] = u; - _succ_num[u] = 1; - _last_succ[u] = u; - if (_supply[u] >= 0) { - _forward[u] = true; - _pi[u] = 0; - _pred[u] = e; - _source[e] = u; - _target[e] = _root; - _flow[e] = _supply[u]; - _cost[e] = 0; - _state[e] = STATE_TREE; - } else { - _forward[u] = false; - _pi[u] = ART_COST; - _pred[u] = f; - _source[f] = _root; - _target[f] = u; - _flow[f] = -_supply[u]; - _cost[f] = ART_COST; - _state[f] = STATE_TREE; - _source[e] = u; - _target[e] = _root; - //_flow[e] = 0; //by default, the sparse matrix is empty - _cost[e] = 0; - _state[e] = STATE_LOWER; - ++f; - } - } - _all_arc_num = f; - } - else { - // GEQ supply constraints - _search_arc_num = _arc_num + _node_num; - int f = _arc_num + _node_num; - for (int u = 0, e = _arc_num; u != _node_num; ++u, ++e) { - _parent[u] = _root; - _thread[u] = u + 1; - _rev_thread[u + 1] = u; - _succ_num[u] = 1; - _last_succ[u] = u; - if (_supply[u] <= 0) { - _forward[u] = false; - _pi[u] = 0; - _pred[u] = e; - _source[e] = _root; - _target[e] = u; - _flow[e] = -_supply[u]; - _cost[e] = 0; - _state[e] = STATE_TREE; - } else { - _forward[u] = true; - _pi[u] = -ART_COST; - _pred[u] = f; - _source[f] = u; - _target[f] = _root; - _flow[f] = _supply[u]; - _state[f] = STATE_TREE; - _cost[f] = ART_COST; - _source[e] = _root; - _target[e] = u; - //_flow[e] = 0; //by default, the sparse matrix is empty - _cost[e] = 0; - _state[e] = STATE_LOWER; - ++f; - } - } - _all_arc_num = f; - } - - return true; - } - - // Find the join node - void findJoinNode() { - int u = _source[in_arc]; - int v = _target[in_arc]; - while (u != v) { - if (_succ_num[u] < _succ_num[v]) { - u = _parent[u]; - } else { - v = _parent[v]; - } - } - join = u; - } - - // Find the leaving arc of the cycle and returns true if the - // leaving arc is not the same as the entering arc - bool findLeavingArc() { - // Initialize first and second nodes according to the direction - // of the cycle - if (_state[in_arc] == STATE_LOWER) { - first = _source[in_arc]; - second = _target[in_arc]; - } else { - first = _target[in_arc]; - second = _source[in_arc]; - } - delta = INF; - int result = 0; - Value d; - int e; - - // Search the cycle along the path form the first node to the root - for (int u = first; u != join; u = _parent[u]) { - e = _pred[u]; - d = _forward[u] ? _flow[e] : INF ; - if (d < delta) { - delta = d; - u_out = u; - result = 1; - } - } - // Search the cycle along the path form the second node to the root - for (int u = second; u != join; u = _parent[u]) { - e = _pred[u]; - d = _forward[u] ? INF : _flow[e]; - if (d <= delta) { - delta = d; - u_out = u; - result = 2; - } - } - - if (result == 1) { - u_in = first; - v_in = second; - } else { - u_in = second; - v_in = first; - } - return result != 0; - } - - // Change _flow and _state vectors - void changeFlow(bool change) { - // Augment along the cycle - if (delta > 0) { - Value val = _state[in_arc] * delta; - _flow[in_arc] += val; - for (int u = _source[in_arc]; u != join; u = _parent[u]) { - _flow[_pred[u]] += _forward[u] ? -val : val; - } - for (int u = _target[in_arc]; u != join; u = _parent[u]) { - _flow[_pred[u]] += _forward[u] ? val : -val; - } - } - // Update the state of the entering and leaving arcs - if (change) { - _state[in_arc] = STATE_TREE; - _state[_pred[u_out]] = - (_flow[_pred[u_out]] == 0) ? STATE_LOWER : STATE_UPPER; - } else { - _state[in_arc] = -_state[in_arc]; - } - } - - // Update the tree structure - void updateTreeStructure() { - int u, w; - int old_rev_thread = _rev_thread[u_out]; - int old_succ_num = _succ_num[u_out]; - int old_last_succ = _last_succ[u_out]; - v_out = _parent[u_out]; - - u = _last_succ[u_in]; // the last successor of u_in - right = _thread[u]; // the node after it - - // Handle the case when old_rev_thread equals to v_in - // (it also means that join and v_out coincide) - if (old_rev_thread == v_in) { - last = _thread[_last_succ[u_out]]; - } else { - last = _thread[v_in]; - } - - // Update _thread and _parent along the stem nodes (i.e. the nodes - // between u_in and u_out, whose parent have to be changed) - _thread[v_in] = stem = u_in; - _dirty_revs.clear(); - _dirty_revs.push_back(v_in); - par_stem = v_in; - while (stem != u_out) { - // Insert the next stem node into the thread list - new_stem = _parent[stem]; - _thread[u] = new_stem; - _dirty_revs.push_back(u); - - // Remove the subtree of stem from the thread list - w = _rev_thread[stem]; - _thread[w] = right; - _rev_thread[right] = w; - - // Change the parent node and shift stem nodes - _parent[stem] = par_stem; - par_stem = stem; - stem = new_stem; - - // Update u and right - u = _last_succ[stem] == _last_succ[par_stem] ? - _rev_thread[par_stem] : _last_succ[stem]; - right = _thread[u]; - } - _parent[u_out] = par_stem; - _thread[u] = last; - _rev_thread[last] = u; - _last_succ[u_out] = u; - - // Remove the subtree of u_out from the thread list except for - // the case when old_rev_thread equals to v_in - // (it also means that join and v_out coincide) - if (old_rev_thread != v_in) { - _thread[old_rev_thread] = right; - _rev_thread[right] = old_rev_thread; - } - - // Update _rev_thread using the new _thread values - for (int i = 0; i != int(_dirty_revs.size()); ++i) { - u = _dirty_revs[i]; - _rev_thread[_thread[u]] = u; - } - - // Update _pred, _forward, _last_succ and _succ_num for the - // stem nodes from u_out to u_in - int tmp_sc = 0, tmp_ls = _last_succ[u_out]; - u = u_out; - while (u != u_in) { - w = _parent[u]; - _pred[u] = _pred[w]; - _forward[u] = !_forward[w]; - tmp_sc += _succ_num[u] - _succ_num[w]; - _succ_num[u] = tmp_sc; - _last_succ[w] = tmp_ls; - u = w; - } - _pred[u_in] = in_arc; - _forward[u_in] = (u_in == _source[in_arc]); - _succ_num[u_in] = old_succ_num; - - // Set limits for updating _last_succ form v_in and v_out - // towards the root - int up_limit_in = -1; - int up_limit_out = -1; - if (_last_succ[join] == v_in) { - up_limit_out = join; - } else { - up_limit_in = join; - } - - // Update _last_succ from v_in towards the root - for (u = v_in; u != up_limit_in && _last_succ[u] == v_in; - u = _parent[u]) { - _last_succ[u] = _last_succ[u_out]; - } - // Update _last_succ from v_out towards the root - if (join != old_rev_thread && v_in != old_rev_thread) { - for (u = v_out; u != up_limit_out && _last_succ[u] == old_last_succ; - u = _parent[u]) { - _last_succ[u] = old_rev_thread; - } - } else { - for (u = v_out; u != up_limit_out && _last_succ[u] == old_last_succ; - u = _parent[u]) { - _last_succ[u] = _last_succ[u_out]; - } - } - - // Update _succ_num from v_in to join - for (u = v_in; u != join; u = _parent[u]) { - _succ_num[u] += old_succ_num; - } - // Update _succ_num from v_out to join - for (u = v_out; u != join; u = _parent[u]) { - _succ_num[u] -= old_succ_num; - } - } - - // Update potentials - void updatePotential() { - Cost sigma = _forward[u_in] ? - _pi[v_in] - _pi[u_in] - _cost[_pred[u_in]] : - _pi[v_in] - _pi[u_in] + _cost[_pred[u_in]]; - // Update potentials in the subtree, which has been moved - int end = _thread[_last_succ[u_in]]; - for (int u = u_in; u != end; u = _thread[u]) { - _pi[u] += sigma; - } - } - - // Heuristic initial pivots - bool initialPivots() { - Value curr, total = 0; - std::vector supply_nodes, demand_nodes; - Node u; _graph.first(u); - for (; u != INVALIDNODE; _graph.next(u)) { - curr = _supply[_node_id(u)]; - if (curr > 0) { - total += curr; - supply_nodes.push_back(u); - } - else if (curr < 0) { - demand_nodes.push_back(u); - } - } - if (_sum_supply > 0) total -= _sum_supply; - if (total <= 0) return true; - - IntVector arc_vector; - if (_sum_supply >= 0) { - if (supply_nodes.size() == 1 && demand_nodes.size() == 1) { - // Perform a reverse graph search from the sink to the source - //typename GR::template NodeMap reached(_graph, false); - BoolVector reached(_node_num, false); - Node s = supply_nodes[0], t = demand_nodes[0]; - std::vector stack; - reached[t] = true; - stack.push_back(t); - while (!stack.empty()) { - Node u, v = stack.back(); - stack.pop_back(); - if (v == s) break; - Arc a; _graph.firstIn(a, v); - for (; a != INVALID; _graph.nextIn(a)) { - if (reached[u = _graph.source(a)]) continue; - int j = getArcID(a); - if (INF >= total) { - arc_vector.push_back(j); - reached[u] = true; - stack.push_back(u); - } - } - } - } else { - // Find the min. cost incomming arc for each demand node - for (int i = 0; i != int(demand_nodes.size()); ++i) { - Node v = demand_nodes[i]; - Cost c, min_cost = std::numeric_limits::max(); - Arc min_arc = INVALID; - Arc a; _graph.firstIn(a, v); - for (; a != INVALID; _graph.nextIn(a)) { - c = _cost[getArcID(a)]; - if (c < min_cost) { - min_cost = c; - min_arc = a; - } - } - if (min_arc != INVALID) { - arc_vector.push_back(getArcID(min_arc)); - } - } - } - } else { - // Find the min. cost outgoing arc for each supply node - for (int i = 0; i != int(supply_nodes.size()); ++i) { - Node u = supply_nodes[i]; - Cost c, min_cost = std::numeric_limits::max(); - Arc min_arc = INVALID; - Arc a; _graph.firstOut(a, u); - for (; a != INVALID; _graph.nextOut(a)) { - c = _cost[getArcID(a)]; - if (c < min_cost) { - min_cost = c; - min_arc = a; - } - } - if (min_arc != INVALID) { - arc_vector.push_back(getArcID(min_arc)); - } - } - } - - // Perform heuristic initial pivots - for (int i = 0; i != int(arc_vector.size()); ++i) { - in_arc = arc_vector[i]; - // l'erreur est probablement ici... - if (_state[in_arc] * (_cost[in_arc] + _pi[_source[in_arc]] - - _pi[_target[in_arc]]) >= 0) continue; - findJoinNode(); - bool change = findLeavingArc(); - if (delta >= MAX) return false; - changeFlow(change); - if (change) { - updateTreeStructure(); - updatePotential(); - } - } - return true; - } - - // Execute the algorithm - ProblemType start() { - return start(); - } - - template - ProblemType start() { - PivotRuleImpl pivot(*this); - double prevCost=-1; - - // Perform heuristic initial pivots - if (!initialPivots()) return UNBOUNDED; - -#if DEBUG_LVL>0 - int niter=0; -#endif - int iter_number=0; - //pivot.setDantzig(true); - // Execute the Network Simplex algorithm - while (pivot.findEnteringArc()) { - if(++iter_number>=max_iter&&max_iter>0){ - char errMess[1000]; - // sprintf( errMess, "RESULT MIGHT BE INACURATE\nMax number of iteration reached, currently \%d. Sometimes iterations go on in cycle even though the solution has been reached, to check if it's the case here have a look at the minimal reduced cost. If it is very close to machine precision, you might actually have the correct solution, if not try setting the maximum number of iterations a bit higher",iter_number ); - // mexWarnMsgTxt(errMess); - break; - } -#if DEBUG_LVL>0 - if(niter>MAX_DEBUG_ITER) - break; - if(++niter%1000==0||niter%1000==1){ - double curCost=totalCost(); - double sumFlow=0; - double a; - a= (fabs(_pi[_source[in_arc]])>=fabs(_pi[_target[in_arc]])) ? fabs(_pi[_source[in_arc]]) : fabs(_pi[_target[in_arc]]); - a=a>=fabs(_cost[in_arc])?a:fabs(_cost[in_arc]); - for (int i=0; i<_flow.size(); i++) { - sumFlow+=_state[i]*_flow[i]; - } - mexPrintf("Sum of the flow %.100f\n%d iterations, current cost=%.20f\nReduced cost=%.30f\nPrecision =%.30f\n",sumFlow,niter, curCost,_state[in_arc] * (_cost[in_arc] + _pi[_source[in_arc]] -_pi[_target[in_arc]]), -EPSILON*(a)); - mexPrintf("Arc in = (%d,%d)\n",_node_id(_source[in_arc]),_node_id(_target[in_arc])); - mexPrintf("Supplies = (%f,%f)\n",_supply[_source[in_arc]],_supply[_target[in_arc]]); - - mexPrintf("%.30f\n%.30f\n%.30f\n%.30f\n%",_cost[in_arc],_pi[_source[in_arc]],_pi[_target[in_arc]],a); - mexEvalString("drawnow;"); - } -#endif - - findJoinNode(); - bool change = findLeavingArc(); - if (delta >= MAX) return UNBOUNDED; - changeFlow(change); - if (change) { - updateTreeStructure(); - updatePotential(); - } -#if DEBUG_LVL>0 - else{ - mexPrintf("No change\n"); - } -#endif -#if DEBUG_LVL>1 - mexPrintf("Arc in = (%d,%d)\n",_source[in_arc],_target[in_arc]); -#endif - - } - - -#if DEBUG_LVL>0 - double curCost=totalCost(); - double sumFlow=0; - double a; - a= (fabs(_pi[_source[in_arc]])>=fabs(_pi[_target[in_arc]])) ? fabs(_pi[_source[in_arc]]) : fabs(_pi[_target[in_arc]]); - a=a>=fabs(_cost[in_arc])?a:fabs(_cost[in_arc]); - for (int i=0; i<_flow.size(); i++) { - sumFlow+=_state[i]*_flow[i]; - } - mexPrintf("Sum of the flow %.100f\n%d iterations, current cost=%.20f\nReduced cost=%.30f\nPrecision =%.30f",sumFlow,niter, curCost,_state[in_arc] * (_cost[in_arc] + _pi[_source[in_arc]] -_pi[_target[in_arc]]), -EPSILON*(a)); - mexPrintf("Arc in = (%d,%d)\n",_node_id(_source[in_arc]),_node_id(_target[in_arc])); - mexPrintf("Supplies = (%f,%f)\n",_supply[_source[in_arc]],_supply[_target[in_arc]]); - - mexEvalString("drawnow;"); -#endif - -#if DEBUG_LVL>1 - double sumFlow=0; - for (int i=0; i<_flow.size(); i++) { - sumFlow+=_state[i]*_flow[i]; - if (_state[i]==STATE_TREE) { - mexPrintf("Non zero value at (%d,%d)\n",_node_num+1-_source[i],_node_num+1-_target[i]); - } - } - mexPrintf("Sum of the flow %.100f\n%d iterations, current cost=%.20f\n",sumFlow,niter, totalCost()); - mexEvalString("drawnow;"); -#endif - // Check feasibility - for (int e = _search_arc_num; e != _all_arc_num; ++e) { - if (_flow[e] != 0){ - if (abs(_flow[e]) > EPSILON) - return INFEASIBLE; - else - _flow[e]=0; - - } - } - - // Shift potentials to meet the requirements of the GEQ/LEQ type - // optimality conditions - if (_sum_supply == 0) { - if (_stype == GEQ) { - Cost max_pot = -std::numeric_limits::max(); - for (int i = 0; i != _node_num; ++i) { - if (_pi[i] > max_pot) max_pot = _pi[i]; - } - if (max_pot > 0) { - for (int i = 0; i != _node_num; ++i) - _pi[i] -= max_pot; - } - } else { - Cost min_pot = std::numeric_limits::max(); - for (int i = 0; i != _node_num; ++i) { - if (_pi[i] < min_pot) min_pot = _pi[i]; - } - if (min_pot < 0) { - for (int i = 0; i != _node_num; ++i) - _pi[i] -= min_pot; - } - } - } - - return OPTIMAL; - } - - }; //class NetworkSimplexSimple - - ///@} - -} //namespace lemon - -#endif //LEMON_NETWORK_SIMPLEX_H diff --git a/ot/lp/EMD.h b/ot/lp/EMD.h new file mode 100644 index 0000000..40d7192 --- /dev/null +++ b/ot/lp/EMD.h @@ -0,0 +1,29 @@ +/* This file is a c++ wrapper function for computing the transportation cost + * between two vectors given a cost matrix. + * + * It was written by Antoine Rolet (2014) and mainly consists of a wrapper + * of the code written by Nicolas Bonneel available on this page + * http://people.seas.harvard.edu/~nbonneel/FastTransport/ + * + * It was then modified to make it more amenable to python inline calling + * + * Please give relevant credit to the original author (Nicolas Bonneel) if + * you use this code for a publication. + * + */ + + +#ifndef EMD_H +#define EMD_H + +#include +#include +#include "network_simplex_simple.h" + +using namespace lemon; +typedef unsigned int node_id_type; + + +void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost); + +#endif diff --git a/ot/lp/EMD_wrap.cpp b/ot/lp/EMD_wrap.cpp new file mode 100644 index 0000000..52cd262 --- /dev/null +++ b/ot/lp/EMD_wrap.cpp @@ -0,0 +1,120 @@ +/* This file is a c++ wrapper function for computing the transportation cost + * between two vectors given a cost matrix. + * + * It was written by Antoine Rolet (2014) and mainly consists of a wrapper + * of the code written by Nicolas Bonneel available on this page + * http://people.seas.harvard.edu/~nbonneel/FastTransport/ + * + * It was then modified to make it more amenable to python inline calling + * + * Please give relevant credit to the original author (Nicolas Bonneel) if + * you use this code for a publication. + * + */ + +#include "EMD.h" + + +void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost) { +// beware M and C anre strored in row major C style!!! + int n, m, i,cur; + double max,max_iter; + + + typedef FullBipartiteDigraph Digraph; + DIGRAPH_TYPEDEFS(FullBipartiteDigraph); + + // Get the number of non zero coordinates for r and c + n=0; + for (node_id_type i=0; i0) { + n++; + } + } + m=0; + for (node_id_type i=0; i0) { + m++; + } + } + + + // Define the graph + + std::vector indI(n), indJ(m); + std::vector weights1(n), weights2(m); + Digraph di(n, m); + NetworkSimplexSimple net(di, true, n+m, n*m,max_iter); + + // Set supply and demand, don't account for 0 values (faster) + + max=0; + cur=0; + for (node_id_type i=0; i0) { + weights1[ di.nodeFromId(cur) ] = val; + max+=val; + indI[cur++]=i; + } + } + + // Demand is actually negative supply... + + max=0; + cur=0; + for (node_id_type i=0; i0) { + weights2[ di.nodeFromId(cur) ] = -val; + indJ[cur++]=i; + + max-=val; + } + } + + + net.supplyMap(&weights1[0], n, &weights2[0], m); + + // Set the cost of each edge + max=0; + for (node_id_type i=0; imax) { + max=val; + } + } + } + + + // Solve the problem with the network simplex algorithm + + int ret=net.run(); + if (ret!=(int)net.OPTIMAL) { + if (ret==(int)net.INFEASIBLE) { + std::cout << "Infeasible problem"; + } + if (ret==(int)net.UNBOUNDED) + { + std::cout << "Unbounded problem"; + } + } else + { + for (node_id_type i=0; i +#include + + +// Disable the following warnings when compiling with MSVC: +// C4250: 'class1' : inherits 'class2::member' via dominance +// C4355: 'this' : used in base member initializer list +// C4503: 'function' : decorated name length exceeded, name was truncated +// C4800: 'type' : forcing value to bool 'true' or 'false' (performance warning) +// C4996: 'function': was declared deprecated +#ifdef _MSC_VER +#pragma warning( disable : 4250 4355 4503 4800 4996 ) +#endif + +///\file +///\brief LEMON core utilities. +/// +///This header file contains core utilities for LEMON. +///It is automatically included by all graph types, therefore it usually +///do not have to be included directly. + +namespace lemon { + + /// \brief Dummy type to make it easier to create invalid iterators. + /// + /// Dummy type to make it easier to create invalid iterators. + /// See \ref INVALID for the usage. + struct Invalid { + public: + bool operator==(Invalid) { return true; } + bool operator!=(Invalid) { return false; } + bool operator< (Invalid) { return false; } + }; + + /// \brief Invalid iterators. + /// + /// \ref Invalid is a global type that converts to each iterator + /// in such a way that the value of the target iterator will be invalid. +#ifdef LEMON_ONLY_TEMPLATES + const Invalid INVALID = Invalid(); +#else + extern const Invalid INVALID; +#endif + + /// \addtogroup gutils + /// @{ + + ///Create convenience typedefs for the digraph types and iterators + + ///This \c \#define creates convenient type definitions for the following + ///types of \c Digraph: \c Node, \c NodeIt, \c Arc, \c ArcIt, \c InArcIt, + ///\c OutArcIt, \c BoolNodeMap, \c IntNodeMap, \c DoubleNodeMap, + ///\c BoolArcMap, \c IntArcMap, \c DoubleArcMap. + /// + ///\note If the graph type is a dependent type, ie. the graph type depend + ///on a template parameter, then use \c TEMPLATE_DIGRAPH_TYPEDEFS() + ///macro. +#define DIGRAPH_TYPEDEFS(Digraph) \ + typedef Digraph::Node Node; \ + typedef Digraph::Arc Arc; \ + + + ///Create convenience typedefs for the digraph types and iterators + + ///\see DIGRAPH_TYPEDEFS + /// + ///\note Use this macro, if the graph type is a dependent type, + ///ie. the graph type depend on a template parameter. +#define TEMPLATE_DIGRAPH_TYPEDEFS(Digraph) \ + typedef typename Digraph::Node Node; \ + typedef typename Digraph::Arc Arc; \ + + + +} //namespace lemon + +#endif diff --git a/ot/lp/emd.cpp b/ot/lp/emd.cpp new file mode 100644 index 0000000..2343af6 --- /dev/null +++ b/ot/lp/emd.cpp @@ -0,0 +1,6507 @@ +/* Generated by Cython 0.23.4 */ + +/* BEGIN: Cython Metadata +{ + "distutils": { + "depends": [ + "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/arrayobject.h", + "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/ufuncobject.h", + "ot/emd/EMD.h" + ], + "include_dirs": [ + "/usr/lib/python2.7/dist-packages/numpy/core/include", + "/home/rflamary/PYTHON/POT/ot/emd" + ], + "language": "c++" + } +} +END: Cython Metadata */ + +#define PY_SSIZE_T_CLEAN +#include "Python.h" +#ifndef Py_PYTHON_H + #error Python headers needed to compile C extensions, please install development version of Python. +#elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03020000) + #error Cython requires Python 2.6+ or Python 3.2+. +#else +#define CYTHON_ABI "0_23_4" +#include +#ifndef offsetof +#define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) +#endif +#if !defined(WIN32) && !defined(MS_WINDOWS) + #ifndef __stdcall + #define __stdcall + #endif + #ifndef __cdecl + #define __cdecl + #endif + #ifndef __fastcall + #define __fastcall + #endif +#endif +#ifndef DL_IMPORT + #define DL_IMPORT(t) t +#endif +#ifndef DL_EXPORT + #define DL_EXPORT(t) t +#endif +#ifndef PY_LONG_LONG + #define PY_LONG_LONG LONG_LONG +#endif +#ifndef Py_HUGE_VAL + #define Py_HUGE_VAL HUGE_VAL +#endif +#ifdef PYPY_VERSION +#define CYTHON_COMPILING_IN_PYPY 1 +#define CYTHON_COMPILING_IN_CPYTHON 0 +#else +#define CYTHON_COMPILING_IN_PYPY 0 +#define CYTHON_COMPILING_IN_CPYTHON 1 +#endif +#if !defined(CYTHON_USE_PYLONG_INTERNALS) && CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x02070000 +#define CYTHON_USE_PYLONG_INTERNALS 1 +#endif +#if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) +#define Py_OptimizeFlag 0 +#endif +#define __PYX_BUILD_PY_SSIZE_T "n" +#define CYTHON_FORMAT_SSIZE_T "z" +#if PY_MAJOR_VERSION < 3 + #define __Pyx_BUILTIN_MODULE_NAME "__builtin__" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a+k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyClass_Type +#else + #define __Pyx_BUILTIN_MODULE_NAME "builtins" + #define __Pyx_PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos)\ + PyCode_New(a, k, l, s, f, code, c, n, v, fv, cell, fn, name, fline, lnos) + #define __Pyx_DefaultClassType PyType_Type +#endif +#ifndef Py_TPFLAGS_CHECKTYPES + #define Py_TPFLAGS_CHECKTYPES 0 +#endif +#ifndef Py_TPFLAGS_HAVE_INDEX + #define Py_TPFLAGS_HAVE_INDEX 0 +#endif +#ifndef Py_TPFLAGS_HAVE_NEWBUFFER + #define Py_TPFLAGS_HAVE_NEWBUFFER 0 +#endif +#ifndef Py_TPFLAGS_HAVE_FINALIZE + #define Py_TPFLAGS_HAVE_FINALIZE 0 +#endif +#if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) + #define CYTHON_PEP393_ENABLED 1 + #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ + 0 : _PyUnicode_Ready((PyObject *)(op))) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) + #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u) + #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) + #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) +#else + #define CYTHON_PEP393_ENABLED 0 + #define __Pyx_PyUnicode_READY(op) (0) + #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) + #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) + #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) + #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) + #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) +#endif +#if CYTHON_COMPILING_IN_PYPY + #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) PyNumber_Add(a, b) +#else + #define __Pyx_PyUnicode_Concat(a, b) PyUnicode_Concat(a, b) + #define __Pyx_PyUnicode_ConcatSafe(a, b) ((unlikely((a) == Py_None) || unlikely((b) == Py_None)) ?\ + PyNumber_Add(a, b) : __Pyx_PyUnicode_Concat(a, b)) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains) + #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) +#endif +#define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) +#define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyString_Format(a, b) PyUnicode_Format(a, b) +#else + #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBaseString_Type PyUnicode_Type + #define PyStringObject PyUnicodeObject + #define PyString_Type PyUnicode_Type + #define PyString_Check PyUnicode_Check + #define PyString_CheckExact PyUnicode_CheckExact +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyBaseString_Check(obj) PyUnicode_Check(obj) + #define __Pyx_PyBaseString_CheckExact(obj) PyUnicode_CheckExact(obj) +#else + #define __Pyx_PyBaseString_Check(obj) (PyString_Check(obj) || PyUnicode_Check(obj)) + #define __Pyx_PyBaseString_CheckExact(obj) (PyString_CheckExact(obj) || PyUnicode_CheckExact(obj)) +#endif +#ifndef PySet_CheckExact + #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) +#endif +#define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) +#if PY_MAJOR_VERSION >= 3 + #define PyIntObject PyLongObject + #define PyInt_Type PyLong_Type + #define PyInt_Check(op) PyLong_Check(op) + #define PyInt_CheckExact(op) PyLong_CheckExact(op) + #define PyInt_FromString PyLong_FromString + #define PyInt_FromUnicode PyLong_FromUnicode + #define PyInt_FromLong PyLong_FromLong + #define PyInt_FromSize_t PyLong_FromSize_t + #define PyInt_FromSsize_t PyLong_FromSsize_t + #define PyInt_AsLong PyLong_AsLong + #define PyInt_AS_LONG PyLong_AS_LONG + #define PyInt_AsSsize_t PyLong_AsSsize_t + #define PyInt_AsUnsignedLongMask PyLong_AsUnsignedLongMask + #define PyInt_AsUnsignedLongLongMask PyLong_AsUnsignedLongLongMask + #define PyNumber_Int PyNumber_Long +#endif +#if PY_MAJOR_VERSION >= 3 + #define PyBoolObject PyLongObject +#endif +#if PY_MAJOR_VERSION >= 3 && CYTHON_COMPILING_IN_PYPY + #ifndef PyUnicode_InternFromString + #define PyUnicode_InternFromString(s) PyUnicode_FromString(s) + #endif +#endif +#if PY_VERSION_HEX < 0x030200A4 + typedef long Py_hash_t; + #define __Pyx_PyInt_FromHash_t PyInt_FromLong + #define __Pyx_PyInt_AsHash_t PyInt_AsLong +#else + #define __Pyx_PyInt_FromHash_t PyInt_FromSsize_t + #define __Pyx_PyInt_AsHash_t PyInt_AsSsize_t +#endif +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyMethod_New(func, self, klass) ((self) ? PyMethod_New(func, self) : PyInstanceMethod_New(func)) +#else + #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) +#endif +#if PY_VERSION_HEX >= 0x030500B1 +#define __Pyx_PyAsyncMethodsStruct PyAsyncMethods +#define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) +#elif CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 +typedef struct { + unaryfunc am_await; + unaryfunc am_aiter; + unaryfunc am_anext; +} __Pyx_PyAsyncMethodsStruct; +#define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) +#else +#define __Pyx_PyType_AsAsync(obj) NULL +#endif +#ifndef CYTHON_RESTRICT + #if defined(__GNUC__) + #define CYTHON_RESTRICT __restrict__ + #elif defined(_MSC_VER) && _MSC_VER >= 1400 + #define CYTHON_RESTRICT __restrict + #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define CYTHON_RESTRICT restrict + #else + #define CYTHON_RESTRICT + #endif +#endif +#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) + +#ifndef __cplusplus + #error "Cython files generated with the C++ option must be compiled with a C++ compiler." +#endif +#ifndef CYTHON_INLINE + #define CYTHON_INLINE inline +#endif +template +void __Pyx_call_destructor(T& x) { + x.~T(); +} +template +class __Pyx_FakeReference { + public: + __Pyx_FakeReference() : ptr(NULL) { } + __Pyx_FakeReference(const T& ref) : ptr(const_cast(&ref)) { } + T *operator->() { return ptr; } + operator T&() { return *ptr; } + private: + T *ptr; +}; + +#if defined(WIN32) || defined(MS_WINDOWS) + #define _USE_MATH_DEFINES +#endif +#include +#ifdef NAN +#define __PYX_NAN() ((float) NAN) +#else +static CYTHON_INLINE float __PYX_NAN() { + float value; + memset(&value, 0xFF, sizeof(value)); + return value; +} +#endif + + +#if PY_MAJOR_VERSION >= 3 + #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) +#else + #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) + #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) +#endif + +#ifndef __PYX_EXTERN_C + #ifdef __cplusplus + #define __PYX_EXTERN_C extern "C" + #else + #define __PYX_EXTERN_C extern + #endif +#endif + +#define __PYX_HAVE__ot__emd__emd +#define __PYX_HAVE_API__ot__emd__emd +#include "string.h" +#include "stdio.h" +#include "stdlib.h" +#include "numpy/arrayobject.h" +#include "numpy/ufuncobject.h" +#include "EMD.h" +#ifdef _OPENMP +#include +#endif /* _OPENMP */ + +#ifdef PYREX_WITHOUT_ASSERTIONS +#define CYTHON_WITHOUT_ASSERTIONS +#endif + +#ifndef CYTHON_UNUSED +# if defined(__GNUC__) +# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) +# define CYTHON_UNUSED __attribute__ ((__unused__)) +# else +# define CYTHON_UNUSED +# endif +#endif +#ifndef CYTHON_NCP_UNUSED +# if CYTHON_COMPILING_IN_CPYTHON +# define CYTHON_NCP_UNUSED +# else +# define CYTHON_NCP_UNUSED CYTHON_UNUSED +# endif +#endif +typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding; + const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; + +#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 +#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0 +#define __PYX_DEFAULT_STRING_ENCODING "" +#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString +#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#define __Pyx_uchar_cast(c) ((unsigned char)c) +#define __Pyx_long_cast(x) ((long)x) +#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ + (sizeof(type) < sizeof(Py_ssize_t)) ||\ + (sizeof(type) > sizeof(Py_ssize_t) &&\ + likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX) &&\ + (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ + v == (type)PY_SSIZE_T_MIN))) ||\ + (sizeof(type) == sizeof(Py_ssize_t) &&\ + (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ + v == (type)PY_SSIZE_T_MAX))) ) +#if defined (__cplusplus) && __cplusplus >= 201103L + #include + #define __Pyx_sst_abs(value) std::abs(value) +#elif SIZEOF_INT >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) abs(value) +#elif SIZEOF_LONG >= SIZEOF_SIZE_T + #define __Pyx_sst_abs(value) labs(value) +#elif defined (_MSC_VER) && defined (_M_X64) + #define __Pyx_sst_abs(value) _abs64(value) +#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L + #define __Pyx_sst_abs(value) llabs(value) +#elif defined (__GNUC__) + #define __Pyx_sst_abs(value) __builtin_llabs(value) +#else + #define __Pyx_sst_abs(value) ((value<0) ? -value : value) +#endif +static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*); +static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); +#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) +#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) +#define __Pyx_PyBytes_FromString PyBytes_FromString +#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize +#else + #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString + #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize +#endif +#define __Pyx_PyObject_AsSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_AsUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) +#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) +#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) +#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) +#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) +#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) +#if PY_MAJOR_VERSION < 3 +static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) +{ + const Py_UNICODE *u_end = u; + while (*u_end++) ; + return (size_t)(u_end - u - 1); +} +#else +#define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen +#endif +#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) +#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode +#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode +#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) +#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) +#define __Pyx_PyBool_FromLong(b) ((b) ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False)) +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); +static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x); +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); +#if CYTHON_COMPILING_IN_CPYTHON +#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) +#else +#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) +#endif +#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII +static int __Pyx_sys_getdefaultencoding_not_ascii; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + PyObject* ascii_chars_u = NULL; + PyObject* ascii_chars_b = NULL; + const char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + if (strcmp(default_encoding_c, "ascii") == 0) { + __Pyx_sys_getdefaultencoding_not_ascii = 0; + } else { + char ascii_chars[128]; + int c; + for (c = 0; c < 128; c++) { + ascii_chars[c] = c; + } + __Pyx_sys_getdefaultencoding_not_ascii = 1; + ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); + if (!ascii_chars_u) goto bad; + ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); + if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { + PyErr_Format( + PyExc_ValueError, + "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", + default_encoding_c); + goto bad; + } + Py_DECREF(ascii_chars_u); + Py_DECREF(ascii_chars_b); + } + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + Py_XDECREF(ascii_chars_u); + Py_XDECREF(ascii_chars_b); + return -1; +} +#endif +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) +#else +#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) +#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT +static char* __PYX_DEFAULT_STRING_ENCODING; +static int __Pyx_init_sys_getdefaultencoding_params(void) { + PyObject* sys; + PyObject* default_encoding = NULL; + char* default_encoding_c; + sys = PyImport_ImportModule("sys"); + if (!sys) goto bad; + default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); + Py_DECREF(sys); + if (!default_encoding) goto bad; + default_encoding_c = PyBytes_AsString(default_encoding); + if (!default_encoding_c) goto bad; + __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c)); + if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; + strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); + Py_DECREF(default_encoding); + return 0; +bad: + Py_XDECREF(default_encoding); + return -1; +} +#endif +#endif + + +/* Test for GCC > 2.95 */ +#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) + #define likely(x) __builtin_expect(!!(x), 1) + #define unlikely(x) __builtin_expect(!!(x), 0) +#else /* !__GNUC__ or GCC < 2.95 */ + #define likely(x) (x) + #define unlikely(x) (x) +#endif /* __GNUC__ */ + +static PyObject *__pyx_m; +static PyObject *__pyx_d; +static PyObject *__pyx_b; +static PyObject *__pyx_empty_tuple; +static PyObject *__pyx_empty_bytes; +static int __pyx_lineno; +static int __pyx_clineno = 0; +static const char * __pyx_cfilenm= __FILE__; +static const char *__pyx_filename; + +#if !defined(CYTHON_CCOMPLEX) + #if defined(__cplusplus) + #define CYTHON_CCOMPLEX 1 + #elif defined(_Complex_I) + #define CYTHON_CCOMPLEX 1 + #else + #define CYTHON_CCOMPLEX 0 + #endif +#endif +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + #include + #else + #include + #endif +#endif +#if CYTHON_CCOMPLEX && !defined(__cplusplus) && defined(__sun__) && defined(__GNUC__) + #undef _Complex_I + #define _Complex_I 1.0fj +#endif + + +static const char *__pyx_f[] = { + "ot/emd/emd.pyx", + "__init__.pxd", + "type.pxd", +}; +#define IS_UNSIGNED(type) (((type) -1) > 0) +struct __Pyx_StructField_; +#define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) +typedef struct { + const char* name; + struct __Pyx_StructField_* fields; + size_t size; + size_t arraysize[8]; + int ndim; + char typegroup; + char is_unsigned; + int flags; +} __Pyx_TypeInfo; +typedef struct __Pyx_StructField_ { + __Pyx_TypeInfo* type; + const char* name; + size_t offset; +} __Pyx_StructField; +typedef struct { + __Pyx_StructField* field; + size_t parent_offset; +} __Pyx_BufFmt_StackElem; +typedef struct { + __Pyx_StructField root; + __Pyx_BufFmt_StackElem* head; + size_t fmt_offset; + size_t new_count, enc_count; + size_t struct_alignment; + int is_complex; + char enc_type; + char new_packmode; + char enc_packmode; + char is_valid_array; +} __Pyx_BufFmt_Context; + + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":725 + * # in Cython to enable them only on the right systems. + * + * ctypedef npy_int8 int8_t # <<<<<<<<<<<<<< + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t + */ +typedef npy_int8 __pyx_t_5numpy_int8_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":726 + * + * ctypedef npy_int8 int8_t + * ctypedef npy_int16 int16_t # <<<<<<<<<<<<<< + * ctypedef npy_int32 int32_t + * ctypedef npy_int64 int64_t + */ +typedef npy_int16 __pyx_t_5numpy_int16_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":727 + * ctypedef npy_int8 int8_t + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< + * ctypedef npy_int64 int64_t + * #ctypedef npy_int96 int96_t + */ +typedef npy_int32 __pyx_t_5numpy_int32_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":728 + * ctypedef npy_int16 int16_t + * ctypedef npy_int32 int32_t + * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< + * #ctypedef npy_int96 int96_t + * #ctypedef npy_int128 int128_t + */ +typedef npy_int64 __pyx_t_5numpy_int64_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":732 + * #ctypedef npy_int128 int128_t + * + * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t + */ +typedef npy_uint8 __pyx_t_5numpy_uint8_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":733 + * + * ctypedef npy_uint8 uint8_t + * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< + * ctypedef npy_uint32 uint32_t + * ctypedef npy_uint64 uint64_t + */ +typedef npy_uint16 __pyx_t_5numpy_uint16_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":734 + * ctypedef npy_uint8 uint8_t + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<< + * ctypedef npy_uint64 uint64_t + * #ctypedef npy_uint96 uint96_t + */ +typedef npy_uint32 __pyx_t_5numpy_uint32_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":735 + * ctypedef npy_uint16 uint16_t + * ctypedef npy_uint32 uint32_t + * ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<< + * #ctypedef npy_uint96 uint96_t + * #ctypedef npy_uint128 uint128_t + */ +typedef npy_uint64 __pyx_t_5numpy_uint64_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":739 + * #ctypedef npy_uint128 uint128_t + * + * ctypedef npy_float32 float32_t # <<<<<<<<<<<<<< + * ctypedef npy_float64 float64_t + * #ctypedef npy_float80 float80_t + */ +typedef npy_float32 __pyx_t_5numpy_float32_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":740 + * + * ctypedef npy_float32 float32_t + * ctypedef npy_float64 float64_t # <<<<<<<<<<<<<< + * #ctypedef npy_float80 float80_t + * #ctypedef npy_float128 float128_t + */ +typedef npy_float64 __pyx_t_5numpy_float64_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":749 + * # The int types are mapped a bit surprising -- + * # numpy.int corresponds to 'l' and numpy.long to 'q' + * ctypedef npy_long int_t # <<<<<<<<<<<<<< + * ctypedef npy_longlong long_t + * ctypedef npy_longlong longlong_t + */ +typedef npy_long __pyx_t_5numpy_int_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":750 + * # numpy.int corresponds to 'l' and numpy.long to 'q' + * ctypedef npy_long int_t + * ctypedef npy_longlong long_t # <<<<<<<<<<<<<< + * ctypedef npy_longlong longlong_t + * + */ +typedef npy_longlong __pyx_t_5numpy_long_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":751 + * ctypedef npy_long int_t + * ctypedef npy_longlong long_t + * ctypedef npy_longlong longlong_t # <<<<<<<<<<<<<< + * + * ctypedef npy_ulong uint_t + */ +typedef npy_longlong __pyx_t_5numpy_longlong_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":753 + * ctypedef npy_longlong longlong_t + * + * ctypedef npy_ulong uint_t # <<<<<<<<<<<<<< + * ctypedef npy_ulonglong ulong_t + * ctypedef npy_ulonglong ulonglong_t + */ +typedef npy_ulong __pyx_t_5numpy_uint_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":754 + * + * ctypedef npy_ulong uint_t + * ctypedef npy_ulonglong ulong_t # <<<<<<<<<<<<<< + * ctypedef npy_ulonglong ulonglong_t + * + */ +typedef npy_ulonglong __pyx_t_5numpy_ulong_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":755 + * ctypedef npy_ulong uint_t + * ctypedef npy_ulonglong ulong_t + * ctypedef npy_ulonglong ulonglong_t # <<<<<<<<<<<<<< + * + * ctypedef npy_intp intp_t + */ +typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":757 + * ctypedef npy_ulonglong ulonglong_t + * + * ctypedef npy_intp intp_t # <<<<<<<<<<<<<< + * ctypedef npy_uintp uintp_t + * + */ +typedef npy_intp __pyx_t_5numpy_intp_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":758 + * + * ctypedef npy_intp intp_t + * ctypedef npy_uintp uintp_t # <<<<<<<<<<<<<< + * + * ctypedef npy_double float_t + */ +typedef npy_uintp __pyx_t_5numpy_uintp_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":760 + * ctypedef npy_uintp uintp_t + * + * ctypedef npy_double float_t # <<<<<<<<<<<<<< + * ctypedef npy_double double_t + * ctypedef npy_longdouble longdouble_t + */ +typedef npy_double __pyx_t_5numpy_float_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":761 + * + * ctypedef npy_double float_t + * ctypedef npy_double double_t # <<<<<<<<<<<<<< + * ctypedef npy_longdouble longdouble_t + * + */ +typedef npy_double __pyx_t_5numpy_double_t; + +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":762 + * ctypedef npy_double float_t + * ctypedef npy_double double_t + * ctypedef npy_longdouble longdouble_t # <<<<<<<<<<<<<< + * + * ctypedef npy_cfloat cfloat_t + */ +typedef npy_longdouble __pyx_t_5numpy_longdouble_t; +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + typedef ::std::complex< float > __pyx_t_float_complex; + #else + typedef float _Complex __pyx_t_float_complex; 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r = NULL; __Pyx_DECREF(tmp);}} while(0) + +static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, + Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); + +static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); + +static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\ + PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,\ + const char* function_name); + +static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, + const char *name, int exact); + +static CYTHON_INLINE int __Pyx_GetBufferAndValidate(Py_buffer* buf, PyObject* obj, + __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack); +static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info); + +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) { + PyTypeObject* tp = Py_TYPE(obj); + if (likely(tp->tp_getattro)) + return tp->tp_getattro(obj, attr_name); 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+ static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex); + static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex); + #if 1 + static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex, __pyx_t_float_complex); + #endif +#endif + +static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double); + +#if CYTHON_CCOMPLEX + #define __Pyx_c_eq(a, b) ((a)==(b)) + #define __Pyx_c_sum(a, b) ((a)+(b)) + #define __Pyx_c_diff(a, b) ((a)-(b)) + #define __Pyx_c_prod(a, b) ((a)*(b)) + #define __Pyx_c_quot(a, b) ((a)/(b)) + #define __Pyx_c_neg(a) (-(a)) + #ifdef __cplusplus + #define __Pyx_c_is_zero(z) ((z)==(double)0) + #define __Pyx_c_conj(z) (::std::conj(z)) + #if 1 + #define __Pyx_c_abs(z) (::std::abs(z)) + #define __Pyx_c_pow(a, b) (::std::pow(a, b)) + #endif + #else + #define __Pyx_c_is_zero(z) ((z)==0) + #define __Pyx_c_conj(z) (conj(z)) + #if 1 + #define __Pyx_c_abs(z) (cabs(z)) + #define __Pyx_c_pow(a, b) (cpow(a, b)) + #endif + #endif +#else + static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex, __pyx_t_double_complex); 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+ +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); + +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); + +static int __Pyx_check_binary_version(void); + +#if !defined(__Pyx_PyIdentifier_FromString) +#if PY_MAJOR_VERSION < 3 + #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s) +#else + #define __Pyx_PyIdentifier_FromString(s) PyUnicode_FromString(s) +#endif +#endif + +static PyObject *__Pyx_ImportModule(const char *name); + +static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict); + +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); + + +/* Module declarations from 'cpython.buffer' */ + +/* Module declarations from 'libc.string' */ + +/* Module declarations from 'libc.stdio' */ + +/* Module declarations from '__builtin__' */ + +/* Module declarations from 'cpython.type' */ +static PyTypeObject *__pyx_ptype_7cpython_4type_type = 0; + +/* Module declarations from 'cpython' */ + +/* Module declarations from 'cpython.object' */ + +/* Module declarations from 'cpython.ref' */ + +/* Module declarations from 'libc.stdlib' */ + +/* Module declarations from 'numpy' */ + +/* Module declarations from 'numpy' */ +static PyTypeObject *__pyx_ptype_5numpy_dtype = 0; 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/* proto */ +static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ +static void __pyx_pf_5numpy_7ndarray_2__releasebuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info); /* proto */ +static PyObject *__pyx_tuple_; +static PyObject *__pyx_tuple__2; +static PyObject *__pyx_tuple__3; +static PyObject *__pyx_tuple__4; +static PyObject *__pyx_tuple__5; +static PyObject *__pyx_tuple__6; +static PyObject *__pyx_tuple__7; +static PyObject *__pyx_codeobj__8; + +/* "ot/emd/emd.pyx":21 + * @cython.boundscheck(False) + * @cython.wraparound(False) + * def emd( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M): # <<<<<<<<<<<<<< + * """ + * Solves the Earth Movers distance problem and returns the optimal transport matrix + */ + +/* Python wrapper */ +static PyObject *__pyx_pw_2ot_3emd_3emd_1emd(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); 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+ const char *more_or_less; + if (num_found < num_min) { + num_expected = num_min; + more_or_less = "at least"; + } else { + num_expected = num_max; + more_or_less = "at most"; + } + if (exact) { + more_or_less = "exactly"; + } + PyErr_Format(PyExc_TypeError, + "%.200s() takes %.8s %" CYTHON_FORMAT_SSIZE_T "d positional argument%.1s (%" CYTHON_FORMAT_SSIZE_T "d given)", + func_name, more_or_less, num_expected, + (num_expected == 1) ? "" : "s", num_found); +} + +static void __Pyx_RaiseDoubleKeywordsError( + const char* func_name, + PyObject* kw_name) +{ + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION >= 3 + "%s() got multiple values for keyword argument '%U'", func_name, kw_name); + #else + "%s() got multiple values for keyword argument '%s'", func_name, + PyString_AsString(kw_name)); + #endif +} + +static int __Pyx_ParseOptionalKeywords( + PyObject *kwds, + PyObject **argnames[], + PyObject *kwds2, + PyObject *values[], + Py_ssize_t num_pos_args, + const char* function_name) +{ + PyObject *key = 0, *value = 0; + Py_ssize_t pos = 0; + PyObject*** name; + PyObject*** first_kw_arg = argnames + num_pos_args; + while (PyDict_Next(kwds, &pos, &key, &value)) { + name = first_kw_arg; + while (*name && (**name != key)) name++; + if (*name) { + values[name-argnames] = value; + continue; + } + name = first_kw_arg; + #if PY_MAJOR_VERSION < 3 + if (likely(PyString_CheckExact(key)) || likely(PyString_Check(key))) { + while (*name) { + if ((CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**name) == PyString_GET_SIZE(key)) + && _PyString_Eq(**name, key)) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + if ((**argname == key) || ( + (CYTHON_COMPILING_IN_PYPY || PyString_GET_SIZE(**argname) == PyString_GET_SIZE(key)) + && _PyString_Eq(**argname, key))) { + goto arg_passed_twice; + } + argname++; + } + } + } else + #endif + if (likely(PyUnicode_Check(key))) { + while (*name) { + int cmp = (**name == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (PyUnicode_GET_SIZE(**name) != PyUnicode_GET_SIZE(key)) ? 1 : + #endif + PyUnicode_Compare(**name, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) { + values[name-argnames] = value; + break; + } + name++; + } + if (*name) continue; + else { + PyObject*** argname = argnames; + while (argname != first_kw_arg) { + int cmp = (**argname == key) ? 0 : + #if !CYTHON_COMPILING_IN_PYPY && PY_MAJOR_VERSION >= 3 + (PyUnicode_GET_SIZE(**argname) != PyUnicode_GET_SIZE(key)) ? 1 : + #endif + PyUnicode_Compare(**argname, key); + if (cmp < 0 && unlikely(PyErr_Occurred())) goto bad; + if (cmp == 0) goto arg_passed_twice; + argname++; + } + } + } else + goto invalid_keyword_type; + if (kwds2) { + if (unlikely(PyDict_SetItem(kwds2, key, value))) goto bad; + } else { + goto invalid_keyword; + } + } + return 0; +arg_passed_twice: + __Pyx_RaiseDoubleKeywordsError(function_name, key); + goto bad; +invalid_keyword_type: + PyErr_Format(PyExc_TypeError, + "%.200s() keywords must be strings", function_name); + goto bad; +invalid_keyword: + PyErr_Format(PyExc_TypeError, + #if PY_MAJOR_VERSION < 3 + "%.200s() got an unexpected keyword argument '%.200s'", + function_name, PyString_AsString(key)); + #else + "%s() got an unexpected keyword argument '%U'", + function_name, key); + #endif +bad: + return -1; +} + +static void __Pyx_RaiseArgumentTypeInvalid(const char* name, PyObject *obj, PyTypeObject *type) { + PyErr_Format(PyExc_TypeError, + "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", + name, type->tp_name, Py_TYPE(obj)->tp_name); +} +static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, + const char *name, int exact) +{ + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (none_allowed && obj == Py_None) return 1; + else if (exact) { + if (likely(Py_TYPE(obj) == type)) return 1; + #if PY_MAJOR_VERSION == 2 + else if ((type == &PyBaseString_Type) && likely(__Pyx_PyBaseString_CheckExact(obj))) return 1; + #endif + } + else { + if (likely(PyObject_TypeCheck(obj, type))) return 1; + } + __Pyx_RaiseArgumentTypeInvalid(name, obj, type); + return 0; +} + +static CYTHON_INLINE int __Pyx_IsLittleEndian(void) { + unsigned int n = 1; + return *(unsigned char*)(&n) != 0; +} +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type) { + stack[0].field = &ctx->root; + stack[0].parent_offset = 0; + ctx->root.type = type; + ctx->root.name = "buffer dtype"; + ctx->root.offset = 0; + ctx->head = stack; + ctx->head->field = &ctx->root; + ctx->fmt_offset = 0; + ctx->head->parent_offset = 0; + ctx->new_packmode = '@'; + ctx->enc_packmode = '@'; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->is_complex = 0; + ctx->is_valid_array = 0; + ctx->struct_alignment = 0; + while (type->typegroup == 'S') { + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = 0; + type = type->fields->type; + } +} +static int __Pyx_BufFmt_ParseNumber(const char** ts) { + int count; + const char* t = *ts; + if (*t < '0' || *t > '9') { + return -1; + } else { + count = *t++ - '0'; + while (*t >= '0' && *t < '9') { + count *= 10; + count += *t++ - '0'; + } + } + *ts = t; + return count; +} +static int __Pyx_BufFmt_ExpectNumber(const char **ts) { + int number = __Pyx_BufFmt_ParseNumber(ts); + if (number == -1) + PyErr_Format(PyExc_ValueError,\ + "Does not understand character buffer dtype format string ('%c')", **ts); + return number; +} +static void __Pyx_BufFmt_RaiseUnexpectedChar(char ch) { + PyErr_Format(PyExc_ValueError, + "Unexpected format string character: '%c'", ch); +} +static const char* __Pyx_BufFmt_DescribeTypeChar(char ch, int is_complex) { + switch (ch) { + case 'c': return "'char'"; + case 'b': return "'signed char'"; + case 'B': return "'unsigned char'"; + case 'h': return "'short'"; + case 'H': return "'unsigned short'"; + case 'i': return "'int'"; + case 'I': return "'unsigned int'"; + case 'l': return "'long'"; + case 'L': return "'unsigned long'"; + case 'q': return "'long long'"; + case 'Q': return "'unsigned long long'"; + case 'f': return (is_complex ? "'complex float'" : "'float'"); + case 'd': return (is_complex ? "'complex double'" : "'double'"); + case 'g': return (is_complex ? "'complex long double'" : "'long double'"); + case 'T': return "a struct"; + case 'O': return "Python object"; + case 'P': return "a pointer"; + case 's': case 'p': return "a string"; + case 0: return "end"; + default: return "unparseable format string"; + } +} +static size_t __Pyx_BufFmt_TypeCharToStandardSize(char ch, int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return 2; + case 'i': case 'I': case 'l': case 'L': return 4; + case 'q': case 'Q': return 8; + case 'f': return (is_complex ? 8 : 4); + case 'd': return (is_complex ? 16 : 8); + case 'g': { + PyErr_SetString(PyExc_ValueError, "Python does not define a standard format string size for long double ('g').."); + return 0; + } + case 'O': case 'P': return sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static size_t __Pyx_BufFmt_TypeCharToNativeSize(char ch, int is_complex) { + switch (ch) { + case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(short); + case 'i': case 'I': return sizeof(int); + case 'l': case 'L': return sizeof(long); + #ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(PY_LONG_LONG); + #endif + case 'f': return sizeof(float) * (is_complex ? 2 : 1); + case 'd': return sizeof(double) * (is_complex ? 2 : 1); + case 'g': return sizeof(long double) * (is_complex ? 2 : 1); + case 'O': case 'P': return sizeof(void*); + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +typedef struct { char c; short x; } __Pyx_st_short; +typedef struct { char c; int x; } __Pyx_st_int; +typedef struct { char c; long x; } __Pyx_st_long; +typedef struct { char c; float x; } __Pyx_st_float; +typedef struct { char c; double x; } __Pyx_st_double; +typedef struct { char c; long double x; } __Pyx_st_longdouble; +typedef struct { char c; void *x; } __Pyx_st_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { char c; PY_LONG_LONG x; } __Pyx_st_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToAlignment(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_st_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_st_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_st_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_st_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_st_float) - sizeof(float); + case 'd': return sizeof(__Pyx_st_double) - sizeof(double); + case 'g': return sizeof(__Pyx_st_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_st_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +/* These are for computing the padding at the end of the struct to align + on the first member of the struct. This will probably the same as above, + but we don't have any guarantees. + */ +typedef struct { short x; char c; } __Pyx_pad_short; +typedef struct { int x; char c; } __Pyx_pad_int; +typedef struct { long x; char c; } __Pyx_pad_long; +typedef struct { float x; char c; } __Pyx_pad_float; +typedef struct { double x; char c; } __Pyx_pad_double; +typedef struct { long double x; char c; } __Pyx_pad_longdouble; +typedef struct { void *x; char c; } __Pyx_pad_void_p; +#ifdef HAVE_LONG_LONG +typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; +#endif +static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { + switch (ch) { + case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; + case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); + case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); + case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); +#ifdef HAVE_LONG_LONG + case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); +#endif + case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); + case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); + case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); + case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); + default: + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } +} +static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { + switch (ch) { + case 'c': + return 'H'; + case 'b': case 'h': case 'i': + case 'l': case 'q': case 's': case 'p': + return 'I'; + case 'B': case 'H': case 'I': case 'L': case 'Q': + return 'U'; + case 'f': case 'd': case 'g': + return (is_complex ? 'C' : 'R'); + case 'O': + return 'O'; + case 'P': + return 'P'; + default: { + __Pyx_BufFmt_RaiseUnexpectedChar(ch); + return 0; + } + } +} +static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { + if (ctx->head == NULL || ctx->head->field == &ctx->root) { + const char* expected; + const char* quote; + if (ctx->head == NULL) { + expected = "end"; + quote = ""; + } else { + expected = ctx->head->field->type->name; + quote = "'"; + } + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected %s%s%s but got %s", + quote, expected, quote, + __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); + } else { + __Pyx_StructField* field = ctx->head->field; + __Pyx_StructField* parent = (ctx->head - 1)->field; + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", + field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), + parent->type->name, field->name); + } +} +static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { + char group; + size_t size, offset, arraysize = 1; + if (ctx->enc_type == 0) return 0; + if (ctx->head->field->type->arraysize[0]) { + int i, ndim = 0; + if (ctx->enc_type == 's' || ctx->enc_type == 'p') { + ctx->is_valid_array = ctx->head->field->type->ndim == 1; + ndim = 1; + if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { + PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %zu", + ctx->head->field->type->arraysize[0], ctx->enc_count); + return -1; + } + } + if (!ctx->is_valid_array) { + PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", + ctx->head->field->type->ndim, ndim); + return -1; + } + for (i = 0; i < ctx->head->field->type->ndim; i++) { + arraysize *= ctx->head->field->type->arraysize[i]; + } + ctx->is_valid_array = 0; + ctx->enc_count = 1; + } + group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); + do { + __Pyx_StructField* field = ctx->head->field; + __Pyx_TypeInfo* type = field->type; + if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { + size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); + } else { + size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); + } + if (ctx->enc_packmode == '@') { + size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); + size_t align_mod_offset; + if (align_at == 0) return -1; + align_mod_offset = ctx->fmt_offset % align_at; + if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; + if (ctx->struct_alignment == 0) + ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, + ctx->is_complex); + } + if (type->size != size || type->typegroup != group) { + if (type->typegroup == 'C' && type->fields != NULL) { + size_t parent_offset = ctx->head->parent_offset + field->offset; + ++ctx->head; + ctx->head->field = type->fields; + ctx->head->parent_offset = parent_offset; + continue; + } + if ((type->typegroup == 'H' || group == 'H') && type->size == size) { + } else { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + } + offset = ctx->head->parent_offset + field->offset; + if (ctx->fmt_offset != offset) { + PyErr_Format(PyExc_ValueError, + "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", + (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); + return -1; + } + ctx->fmt_offset += size; + if (arraysize) + ctx->fmt_offset += (arraysize - 1) * size; + --ctx->enc_count; + while (1) { + if (field == &ctx->root) { + ctx->head = NULL; + if (ctx->enc_count != 0) { + __Pyx_BufFmt_RaiseExpected(ctx); + return -1; + } + break; + } + ctx->head->field = ++field; + if (field->type == NULL) { + --ctx->head; + field = ctx->head->field; + continue; + } else if (field->type->typegroup == 'S') { + size_t parent_offset = ctx->head->parent_offset + field->offset; + if (field->type->fields->type == NULL) continue; + field = field->type->fields; + ++ctx->head; + ctx->head->field = field; + ctx->head->parent_offset = parent_offset; + break; + } else { + break; + } + } + } while (ctx->enc_count); + ctx->enc_type = 0; + ctx->is_complex = 0; + return 0; +} +static CYTHON_INLINE PyObject * +__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) +{ + const char *ts = *tsp; + int i = 0, number; + int ndim = ctx->head->field->type->ndim; +; + ++ts; + if (ctx->new_count != 1) { + PyErr_SetString(PyExc_ValueError, + "Cannot handle repeated arrays in format string"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + while (*ts && *ts != ')') { + switch (*ts) { + case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; + default: break; + } + number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) + return PyErr_Format(PyExc_ValueError, + "Expected a dimension of size %zu, got %d", + ctx->head->field->type->arraysize[i], number); + if (*ts != ',' && *ts != ')') + return PyErr_Format(PyExc_ValueError, + "Expected a comma in format string, got '%c'", *ts); + if (*ts == ',') ts++; + i++; + } + if (i != ndim) + return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", + ctx->head->field->type->ndim, i); + if (!*ts) { + PyErr_SetString(PyExc_ValueError, + "Unexpected end of format string, expected ')'"); + return NULL; + } + ctx->is_valid_array = 1; + ctx->new_count = 1; + *tsp = ++ts; + return Py_None; +} +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { + int got_Z = 0; + while (1) { + switch(*ts) { + case 0: + if (ctx->enc_type != 0 && ctx->head == NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + if (ctx->head != NULL) { + __Pyx_BufFmt_RaiseExpected(ctx); + return NULL; + } + return ts; + case ' ': + case '\r': + case '\n': + ++ts; + break; + case '<': + if (!__Pyx_IsLittleEndian()) { + PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '>': + case '!': + if (__Pyx_IsLittleEndian()) { + PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); + return NULL; + } + ctx->new_packmode = '='; + ++ts; + break; + case '=': + case '@': + case '^': + ctx->new_packmode = *ts++; + break; + case 'T': + { + const char* ts_after_sub; + size_t i, struct_count = ctx->new_count; + size_t struct_alignment = ctx->struct_alignment; + ctx->new_count = 1; + ++ts; + if (*ts != '{') { + PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); + return NULL; + } + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + ctx->enc_count = 0; + ctx->struct_alignment = 0; + ++ts; + ts_after_sub = ts; + for (i = 0; i != struct_count; ++i) { + ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); + if (!ts_after_sub) return NULL; + } + ts = ts_after_sub; + if (struct_alignment) ctx->struct_alignment = struct_alignment; + } + break; + case '}': + { + size_t alignment = ctx->struct_alignment; + ++ts; + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_type = 0; + if (alignment && ctx->fmt_offset % alignment) { + ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); + } + } + return ts; + case 'x': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->fmt_offset += ctx->new_count; + ctx->new_count = 1; + ctx->enc_count = 0; + ctx->enc_type = 0; + ctx->enc_packmode = ctx->new_packmode; + ++ts; + break; + case 'Z': + got_Z = 1; + ++ts; + if (*ts != 'f' && *ts != 'd' && *ts != 'g') { + __Pyx_BufFmt_RaiseUnexpectedChar('Z'); + return NULL; + } + case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': + case 'l': case 'L': case 'q': case 'Q': + case 'f': case 'd': case 'g': + case 'O': case 'p': + if (ctx->enc_type == *ts && got_Z == ctx->is_complex && + ctx->enc_packmode == ctx->new_packmode) { + ctx->enc_count += ctx->new_count; + ctx->new_count = 1; + got_Z = 0; + ++ts; + break; + } + case 's': + if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; + ctx->enc_count = ctx->new_count; + ctx->enc_packmode = ctx->new_packmode; + ctx->enc_type = *ts; + ctx->is_complex = got_Z; + ++ts; + ctx->new_count = 1; + got_Z = 0; + break; + case ':': + ++ts; + while(*ts != ':') ++ts; + ++ts; + break; + case '(': + if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; + break; + default: + { + int number = __Pyx_BufFmt_ExpectNumber(&ts); + if (number == -1) return NULL; + ctx->new_count = (size_t)number; + } + } + } +} +static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { + buf->buf = NULL; + buf->obj = NULL; + buf->strides = __Pyx_zeros; + buf->shape = __Pyx_zeros; + buf->suboffsets = __Pyx_minusones; +} +static CYTHON_INLINE int __Pyx_GetBufferAndValidate( + Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, + int nd, int cast, __Pyx_BufFmt_StackElem* stack) +{ + if (obj == Py_None || obj == NULL) { + __Pyx_ZeroBuffer(buf); + return 0; + } + buf->buf = NULL; + if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; + if (buf->ndim != nd) { + PyErr_Format(PyExc_ValueError, + "Buffer has wrong number of dimensions (expected %d, got %d)", + nd, buf->ndim); + goto fail; + } + if (!cast) { + __Pyx_BufFmt_Context ctx; + __Pyx_BufFmt_Init(&ctx, stack, dtype); + if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; + } + if ((unsigned)buf->itemsize != dtype->size) { + PyErr_Format(PyExc_ValueError, + "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)", + buf->itemsize, (buf->itemsize > 1) ? "s" : "", + dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); + goto fail; + } + if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; + return 0; +fail:; + __Pyx_ZeroBuffer(buf); + return -1; +} +static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { + if (info->buf == NULL) return; + if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; + __Pyx_ReleaseBuffer(info); +} + +static PyObject *__Pyx_GetBuiltinName(PyObject *name) { + PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name); + if (unlikely(!result)) { + PyErr_Format(PyExc_NameError, +#if PY_MAJOR_VERSION >= 3 + "name '%U' is not defined", name); +#else + "name '%.200s' is not defined", PyString_AS_STRING(name)); +#endif + } + return result; +} + +static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { + PyObject *result; +#if CYTHON_COMPILING_IN_CPYTHON + result = PyDict_GetItem(__pyx_d, name); + if (likely(result)) { + Py_INCREF(result); + } else { +#else + result = PyObject_GetItem(__pyx_d, name); + if (!result) { + PyErr_Clear(); +#endif + result = __Pyx_GetBuiltinName(name); + } + return result; +} + +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { + PyObject *result; + ternaryfunc call = func->ob_type->tp_call; + if (unlikely(!call)) + return PyObject_Call(func, arg, kw); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = (*call)(func, arg, kw); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +#if CYTHON_COMPILING_IN_CPYTHON +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { + PyObject *self, *result; + PyCFunction cfunc; + cfunc = PyCFunction_GET_FUNCTION(func); + self = PyCFunction_GET_SELF(func); + if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) + return NULL; + result = cfunc(self, arg); + Py_LeaveRecursiveCall(); + if (unlikely(!result) && unlikely(!PyErr_Occurred())) { + PyErr_SetString( + PyExc_SystemError, + "NULL result without error in PyObject_Call"); + } + return result; +} +#endif + +#if CYTHON_COMPILING_IN_CPYTHON +static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *result; + PyObject *args = PyTuple_New(1); + if (unlikely(!args)) return NULL; + Py_INCREF(arg); + PyTuple_SET_ITEM(args, 0, arg); + result = __Pyx_PyObject_Call(func, args, NULL); + Py_DECREF(args); + return result; +} +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { +#ifdef __Pyx_CyFunction_USED + if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) { +#else + if (likely(PyCFunction_Check(func))) { +#endif + if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { + return __Pyx_PyObject_CallMethO(func, arg); + } + } + return __Pyx__PyObject_CallOneArg(func, arg); +} +#else +static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { + PyObject *result; + PyObject *args = PyTuple_Pack(1, arg); + if (unlikely(!args)) return NULL; + result = __Pyx_PyObject_Call(func, args, NULL); + Py_DECREF(args); + return result; +} +#endif + +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { + if (unlikely(!type)) { + PyErr_SetString(PyExc_SystemError, "Missing type object"); + return 0; + } + if (likely(PyObject_TypeCheck(obj, type))) + return 1; + PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", + Py_TYPE(obj)->tp_name, type->tp_name); + return 0; +} + +static void __Pyx_RaiseBufferFallbackError(void) { + PyErr_SetString(PyExc_ValueError, + "Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!"); +} + +static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) { +#if CYTHON_COMPILING_IN_CPYTHON + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyThreadState *tstate = PyThreadState_GET(); + tmp_type = tstate->curexc_type; + tmp_value = tstate->curexc_value; + tmp_tb = tstate->curexc_traceback; + tstate->curexc_type = type; + tstate->curexc_value = value; + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +#else + PyErr_Restore(type, value, tb); +#endif +} +static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) { +#if CYTHON_COMPILING_IN_CPYTHON + PyThreadState *tstate = PyThreadState_GET(); + *type = tstate->curexc_type; + *value = tstate->curexc_value; + *tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +#else + PyErr_Fetch(type, value, tb); +#endif +} + +#if PY_MAJOR_VERSION < 3 +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, + CYTHON_UNUSED PyObject *cause) { + Py_XINCREF(type); + if (!value || value == Py_None) + value = NULL; + else + Py_INCREF(value); + if (!tb || tb == Py_None) + tb = NULL; + else { + Py_INCREF(tb); + if (!PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto raise_error; + } + } + if (PyType_Check(type)) { +#if CYTHON_COMPILING_IN_PYPY + if (!value) { + Py_INCREF(Py_None); + value = Py_None; + } +#endif + PyErr_NormalizeException(&type, &value, &tb); + } else { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto raise_error; + } + value = type; + type = (PyObject*) Py_TYPE(type); + Py_INCREF(type); + if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto raise_error; + } + } + __Pyx_ErrRestore(type, value, tb); + return; +raise_error: + Py_XDECREF(value); + Py_XDECREF(type); + Py_XDECREF(tb); + return; +} +#else +static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { + PyObject* owned_instance = NULL; + if (tb == Py_None) { + tb = 0; + } else if (tb && !PyTraceBack_Check(tb)) { + PyErr_SetString(PyExc_TypeError, + "raise: arg 3 must be a traceback or None"); + goto bad; + } + if (value == Py_None) + value = 0; + if (PyExceptionInstance_Check(type)) { + if (value) { + PyErr_SetString(PyExc_TypeError, + "instance exception may not have a separate value"); + goto bad; + } + value = type; + type = (PyObject*) Py_TYPE(value); + } else if (PyExceptionClass_Check(type)) { + PyObject *instance_class = NULL; + if (value && PyExceptionInstance_Check(value)) { + instance_class = (PyObject*) Py_TYPE(value); + if (instance_class != type) { + int is_subclass = PyObject_IsSubclass(instance_class, type); + if (!is_subclass) { + instance_class = NULL; + } else if (unlikely(is_subclass == -1)) { + goto bad; + } else { + type = instance_class; + } + } + } + if (!instance_class) { + PyObject *args; + if (!value) + args = PyTuple_New(0); + else if (PyTuple_Check(value)) { + Py_INCREF(value); + args = value; + } else + args = PyTuple_Pack(1, value); + if (!args) + goto bad; + owned_instance = PyObject_Call(type, args, NULL); + Py_DECREF(args); + if (!owned_instance) + goto bad; + value = owned_instance; + if (!PyExceptionInstance_Check(value)) { + PyErr_Format(PyExc_TypeError, + "calling %R should have returned an instance of " + "BaseException, not %R", + type, Py_TYPE(value)); + goto bad; + } + } + } else { + PyErr_SetString(PyExc_TypeError, + "raise: exception class must be a subclass of BaseException"); + goto bad; + } +#if PY_VERSION_HEX >= 0x03030000 + if (cause) { +#else + if (cause && cause != Py_None) { +#endif + PyObject *fixed_cause; + if (cause == Py_None) { + fixed_cause = NULL; + } else if (PyExceptionClass_Check(cause)) { + fixed_cause = PyObject_CallObject(cause, NULL); + if (fixed_cause == NULL) + goto bad; + } else if (PyExceptionInstance_Check(cause)) { + fixed_cause = cause; + Py_INCREF(fixed_cause); + } else { + PyErr_SetString(PyExc_TypeError, + "exception causes must derive from " + "BaseException"); + goto bad; + } + PyException_SetCause(value, fixed_cause); + } + PyErr_SetObject(type, value); + if (tb) { +#if CYTHON_COMPILING_IN_PYPY + PyObject *tmp_type, *tmp_value, *tmp_tb; + PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); + Py_INCREF(tb); + PyErr_Restore(tmp_type, tmp_value, tb); + Py_XDECREF(tmp_tb); +#else + PyThreadState *tstate = PyThreadState_GET(); + PyObject* tmp_tb = tstate->curexc_traceback; + if (tb != tmp_tb) { + Py_INCREF(tb); + tstate->curexc_traceback = tb; + Py_XDECREF(tmp_tb); + } +#endif + } +bad: + Py_XDECREF(owned_instance); + return; +} +#endif + +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { + PyErr_Format(PyExc_ValueError, + "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); +} + +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { + PyErr_Format(PyExc_ValueError, + "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", + index, (index == 1) ? "" : "s"); +} + +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { + PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); +} + +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { + PyObject *empty_list = 0; + PyObject *module = 0; + PyObject *global_dict = 0; + PyObject *empty_dict = 0; + PyObject *list; + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_import; + py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); + if (!py_import) + goto bad; + #endif + if (from_list) + list = from_list; + else { + empty_list = PyList_New(0); + if (!empty_list) + goto bad; + list = empty_list; + } + global_dict = PyModule_GetDict(__pyx_m); + if (!global_dict) + goto bad; + empty_dict = PyDict_New(); + if (!empty_dict) + goto bad; + { + #if PY_MAJOR_VERSION >= 3 + if (level == -1) { + if (strchr(__Pyx_MODULE_NAME, '.')) { + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_level = PyInt_FromLong(1); + if (!py_level) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, py_level, NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, 1); + #endif + if (!module) { + if (!PyErr_ExceptionMatches(PyExc_ImportError)) + goto bad; + PyErr_Clear(); + } + } + level = 0; + } + #endif + if (!module) { + #if PY_VERSION_HEX < 0x03030000 + PyObject *py_level = PyInt_FromLong(level); + if (!py_level) + goto bad; + module = PyObject_CallFunctionObjArgs(py_import, + name, global_dict, empty_dict, list, py_level, NULL); + Py_DECREF(py_level); + #else + module = PyImport_ImportModuleLevelObject( + name, global_dict, empty_dict, list, level); + #endif + } + } +bad: + #if PY_VERSION_HEX < 0x03030000 + Py_XDECREF(py_import); + #endif + Py_XDECREF(empty_list); + Py_XDECREF(empty_dict); + return module; +} + +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { + int start = 0, mid = 0, end = count - 1; + if (end >= 0 && code_line > entries[end].code_line) { + return count; + } + while (start < end) { + mid = start + (end - start) / 2; + if (code_line < entries[mid].code_line) { + end = mid; + } else if (code_line > entries[mid].code_line) { + start = mid + 1; + } else { + return mid; + } + } + if (code_line <= entries[mid].code_line) { + return mid; + } else { + return mid + 1; + } +} +static PyCodeObject *__pyx_find_code_object(int code_line) { + PyCodeObject* code_object; + int pos; + if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { + return NULL; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { + return NULL; + } + code_object = __pyx_code_cache.entries[pos].code_object; + Py_INCREF(code_object); + return code_object; +} +static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { + int pos, i; + __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; + if (unlikely(!code_line)) { + return; + } + if (unlikely(!entries)) { + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); + if (likely(entries)) { + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = 64; + __pyx_code_cache.count = 1; + entries[0].code_line = code_line; + entries[0].code_object = code_object; + Py_INCREF(code_object); + } + return; + } + pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); + if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { + PyCodeObject* tmp = entries[pos].code_object; + entries[pos].code_object = code_object; + Py_DECREF(tmp); + return; + } + if (__pyx_code_cache.count == __pyx_code_cache.max_count) { + int new_max = __pyx_code_cache.max_count + 64; + entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( + __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry)); + if (unlikely(!entries)) { + return; + } + __pyx_code_cache.entries = entries; + __pyx_code_cache.max_count = new_max; + } + for (i=__pyx_code_cache.count; i>pos; i--) { + entries[i] = entries[i-1]; + } + entries[pos].code_line = code_line; + entries[pos].code_object = code_object; + __pyx_code_cache.count++; + Py_INCREF(code_object); +} + +#include "compile.h" +#include "frameobject.h" +#include "traceback.h" +static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( + const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyObject *py_srcfile = 0; + PyObject *py_funcname = 0; + #if PY_MAJOR_VERSION < 3 + py_srcfile = PyString_FromString(filename); + #else + py_srcfile = PyUnicode_FromString(filename); + #endif + if (!py_srcfile) goto bad; + if (c_line) { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #else + py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); + #endif + } + else { + #if PY_MAJOR_VERSION < 3 + py_funcname = PyString_FromString(funcname); + #else + py_funcname = PyUnicode_FromString(funcname); + #endif + } + if (!py_funcname) goto bad; + py_code = __Pyx_PyCode_New( + 0, + 0, + 0, + 0, + 0, + __pyx_empty_bytes, /*PyObject *code,*/ + __pyx_empty_tuple, /*PyObject *consts,*/ + __pyx_empty_tuple, /*PyObject *names,*/ + __pyx_empty_tuple, /*PyObject *varnames,*/ + __pyx_empty_tuple, /*PyObject *freevars,*/ + __pyx_empty_tuple, /*PyObject *cellvars,*/ + py_srcfile, /*PyObject *filename,*/ + py_funcname, /*PyObject *name,*/ + py_line, + __pyx_empty_bytes /*PyObject *lnotab*/ + ); + Py_DECREF(py_srcfile); + Py_DECREF(py_funcname); + return py_code; +bad: + Py_XDECREF(py_srcfile); + Py_XDECREF(py_funcname); + return NULL; +} +static void __Pyx_AddTraceback(const char *funcname, int c_line, + int py_line, const char *filename) { + PyCodeObject *py_code = 0; + PyFrameObject *py_frame = 0; + py_code = __pyx_find_code_object(c_line ? c_line : py_line); + if (!py_code) { + py_code = __Pyx_CreateCodeObjectForTraceback( + funcname, c_line, py_line, filename); + if (!py_code) goto bad; + __pyx_insert_code_object(c_line ? c_line : py_line, py_code); + } + py_frame = PyFrame_New( + PyThreadState_GET(), /*PyThreadState *tstate,*/ + py_code, /*PyCodeObject *code,*/ + __pyx_d, /*PyObject *globals,*/ + 0 /*PyObject *locals*/ + ); + if (!py_frame) goto bad; + py_frame->f_lineno = py_line; + PyTraceBack_Here(py_frame); +bad: + Py_XDECREF(py_code); + Py_XDECREF(py_frame); +} + +#if PY_MAJOR_VERSION < 3 +static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { + if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); + if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); + PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); + return -1; +} +static void __Pyx_ReleaseBuffer(Py_buffer *view) { + PyObject *obj = view->obj; + if (!obj) return; + if (PyObject_CheckBuffer(obj)) { + PyBuffer_Release(view); + return; + } + if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; } + Py_DECREF(obj); + view->obj = NULL; +} +#endif + + + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { + const int neg_one = (int) -1, const_zero = (int) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(int) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(int) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); + } + } else { + if (sizeof(int) <= sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(int), + little, !is_unsigned); + } +} + +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return ::std::complex< float >(x, y); + } + #else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + return x + y*(__pyx_t_float_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { + __pyx_t_float_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +#if CYTHON_CCOMPLEX +#else + static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + float denom = b.real * b.real + b.imag * b.imag; + z.real = (a.real * b.real + a.imag * b.imag) / denom; + z.imag = (a.imag * b.real - a.real * b.imag) / denom; + return z; + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex a) { + __pyx_t_float_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } + #if 1 + static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex z) { + #if !defined(HAVE_HYPOT) || defined(_MSC_VER) + return sqrtf(z.real*z.real + z.imag*z.imag); + #else + return hypotf(z.real, z.imag); + #endif + } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + float r, lnr, theta, z_r, z_theta; + if (b.imag == 0 && b.real == (int)b.real) { + if (b.real < 0) { + float denom = a.real * a.real + a.imag * a.imag; + a.real = a.real / denom; + a.imag = -a.imag / denom; + b.real = -b.real; + } + switch ((int)b.real) { + case 0: + z.real = 1; + z.imag = 0; + return z; + case 1: + return a; + case 2: + z = __Pyx_c_prodf(a, a); + return __Pyx_c_prodf(a, a); + case 3: + z = __Pyx_c_prodf(a, a); + return __Pyx_c_prodf(z, a); + case 4: + z = __Pyx_c_prodf(a, a); + return __Pyx_c_prodf(z, z); + } + } + if (a.imag == 0) { + if (a.real == 0) { + return a; + } + r = a.real; + theta = 0; + } else { + r = __Pyx_c_absf(a); + theta = atan2f(a.imag, a.real); + } + lnr = logf(r); + z_r = expf(lnr * b.real - theta * b.imag); + z_theta = theta * b.real + lnr * b.imag; + z.real = z_r * cosf(z_theta); + z.imag = z_r * sinf(z_theta); + return z; + } + #endif +#endif + +#if CYTHON_CCOMPLEX + #ifdef __cplusplus + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return ::std::complex< double >(x, y); + } + #else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + return x + y*(__pyx_t_double_complex)_Complex_I; + } + #endif +#else + static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { + __pyx_t_double_complex z; + z.real = x; + z.imag = y; + return z; + } +#endif + +#if CYTHON_CCOMPLEX +#else + static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) { + return (a.real == b.real) && (a.imag == b.imag); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real + b.real; + z.imag = a.imag + b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real - b.real; + z.imag = a.imag - b.imag; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + z.real = a.real * b.real - a.imag * b.imag; + z.imag = a.real * b.imag + a.imag * b.real; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + double denom = b.real * b.real + b.imag * b.imag; + z.real = (a.real * b.real + a.imag * b.imag) / denom; + z.imag = (a.imag * b.real - a.real * b.imag) / denom; + return z; + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = -a.real; + z.imag = -a.imag; + return z; + } + static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex a) { + return (a.real == 0) && (a.imag == 0); + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex a) { + __pyx_t_double_complex z; + z.real = a.real; + z.imag = -a.imag; + return z; + } + #if 1 + static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex z) { + #if !defined(HAVE_HYPOT) || defined(_MSC_VER) + return sqrt(z.real*z.real + z.imag*z.imag); + #else + return hypot(z.real, z.imag); + #endif + } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + double r, lnr, theta, z_r, z_theta; + if (b.imag == 0 && b.real == (int)b.real) { + if (b.real < 0) { + double denom = a.real * a.real + a.imag * a.imag; + a.real = a.real / denom; + a.imag = -a.imag / denom; + b.real = -b.real; + } + switch ((int)b.real) { + case 0: + z.real = 1; + z.imag = 0; + return z; + case 1: + return a; + case 2: + z = __Pyx_c_prod(a, a); + return __Pyx_c_prod(a, a); + case 3: + z = __Pyx_c_prod(a, a); + return __Pyx_c_prod(z, a); + case 4: + z = __Pyx_c_prod(a, a); + return __Pyx_c_prod(z, z); + } + } + if (a.imag == 0) { + if (a.real == 0) { + return a; + } + r = a.real; + theta = 0; + } else { + r = __Pyx_c_abs(a); + theta = atan2(a.imag, a.real); + } + lnr = log(r); + z_r = exp(lnr * b.real - theta * b.imag); + z_theta = theta * b.real + lnr * b.imag; + z.real = z_r * cos(z_theta); + z.imag = z_r * sin(z_theta); + return z; + } + #endif +#endif + +#define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) +#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) +#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ + {\ + func_type value = func_value;\ + if (sizeof(target_type) < sizeof(func_type)) {\ + if (unlikely(value != (func_type) (target_type) value)) {\ + func_type zero = 0;\ + if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ + return (target_type) -1;\ + if (is_unsigned && unlikely(value < zero))\ + goto raise_neg_overflow;\ + else\ + goto raise_overflow;\ + }\ + }\ + return (target_type) value;\ + } + +#if CYTHON_USE_PYLONG_INTERNALS + #include "longintrepr.h" +#endif + +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { + const int neg_one = (int) -1, const_zero = (int) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(int) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (int) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { + return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { + return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { + return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (int) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(int) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, -(sdigit) digits[0]) + case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) + case -2: + if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + } +#endif + if (sizeof(int) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + int val; + PyObject *v = __Pyx_PyNumber_Int(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (int) -1; + } + } else { + int val; + PyObject *tmp = __Pyx_PyNumber_Int(x); + if (!tmp) return (int) -1; + val = __Pyx_PyInt_As_int(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to int"); + return (int) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to int"); + return (int) -1; +} + +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value) { + const enum NPY_TYPES neg_one = (enum NPY_TYPES) -1, const_zero = (enum NPY_TYPES) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(enum NPY_TYPES) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); + } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); + } + } else { + if (sizeof(enum NPY_TYPES) <= sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(enum NPY_TYPES) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(enum NPY_TYPES), + little, !is_unsigned); + } +} + +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { + const long neg_one = (long) -1, const_zero = (long) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(long) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(long) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); + } + } else { + if (sizeof(long) <= sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(long), + little, !is_unsigned); + } +} + +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { + const long neg_one = (long) -1, const_zero = (long) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(long) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (long) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { + return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { + return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { + return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (long) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(long) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) + } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (long) 0; + case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, -(sdigit) digits[0]) + case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) + case -2: + if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(long) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(long) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(long) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { + return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); + } + } + break; + } +#endif + if (sizeof(long) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) + } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + long val; + PyObject *v = __Pyx_PyNumber_Int(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (long) -1; + } + } else { + long val; + PyObject *tmp = __Pyx_PyNumber_Int(x); + if (!tmp) return (long) -1; + val = __Pyx_PyInt_As_long(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to long"); + return (long) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to long"); + return (long) -1; +} + +static int __Pyx_check_binary_version(void) { + char ctversion[4], rtversion[4]; + PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); + PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); + if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { + char message[200]; + PyOS_snprintf(message, sizeof(message), + "compiletime version %s of module '%.100s' " + "does not match runtime version %s", + ctversion, __Pyx_MODULE_NAME, rtversion); + return PyErr_WarnEx(NULL, message, 1); + } + return 0; +} + +#ifndef __PYX_HAVE_RT_ImportModule +#define __PYX_HAVE_RT_ImportModule +static PyObject *__Pyx_ImportModule(const char *name) { + PyObject *py_name = 0; + PyObject *py_module = 0; + py_name = __Pyx_PyIdentifier_FromString(name); + if (!py_name) + goto bad; + py_module = PyImport_Import(py_name); + Py_DECREF(py_name); + return py_module; +bad: + Py_XDECREF(py_name); + return 0; +} +#endif + +#ifndef __PYX_HAVE_RT_ImportType +#define __PYX_HAVE_RT_ImportType +static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, + size_t size, int strict) +{ + PyObject *py_module = 0; + PyObject *result = 0; + PyObject *py_name = 0; + char warning[200]; + Py_ssize_t basicsize; +#ifdef Py_LIMITED_API + PyObject *py_basicsize; +#endif + py_module = __Pyx_ImportModule(module_name); + if (!py_module) + goto bad; + py_name = __Pyx_PyIdentifier_FromString(class_name); + if (!py_name) + goto bad; + result = PyObject_GetAttr(py_module, py_name); + Py_DECREF(py_name); + py_name = 0; + Py_DECREF(py_module); + py_module = 0; + if (!result) + goto bad; + if (!PyType_Check(result)) { + PyErr_Format(PyExc_TypeError, + "%.200s.%.200s is not a type object", + module_name, class_name); + goto bad; + } +#ifndef Py_LIMITED_API + basicsize = ((PyTypeObject *)result)->tp_basicsize; +#else + py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); + if (!py_basicsize) + goto bad; + basicsize = PyLong_AsSsize_t(py_basicsize); + Py_DECREF(py_basicsize); + py_basicsize = 0; + if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) + goto bad; +#endif + if (!strict && (size_t)basicsize > size) { + PyOS_snprintf(warning, sizeof(warning), + "%s.%s size changed, may indicate binary incompatibility", + module_name, class_name); + if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; + } + else if ((size_t)basicsize != size) { + PyErr_Format(PyExc_ValueError, + "%.200s.%.200s has the wrong size, try recompiling", + module_name, class_name); + goto bad; + } + return (PyTypeObject *)result; +bad: + Py_XDECREF(py_module); + Py_XDECREF(result); + return NULL; +} +#endif + +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { + while (t->p) { + #if PY_MAJOR_VERSION < 3 + if (t->is_unicode) { + *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); + } else if (t->intern) { + *t->p = PyString_InternFromString(t->s); + } else { + *t->p = PyString_FromStringAndSize(t->s, t->n - 1); + } + #else + if (t->is_unicode | t->is_str) { + if (t->intern) { + *t->p = PyUnicode_InternFromString(t->s); + } else if (t->encoding) { + *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); + } else { + *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); + } + } else { + *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); + } + #endif + if (!*t->p) + return -1; + ++t; + } + return 0; +} + +static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { + return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); +} +static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) { + Py_ssize_t ignore; + return __Pyx_PyObject_AsStringAndSize(o, &ignore); +} +static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { +#if CYTHON_COMPILING_IN_CPYTHON && (__PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT) + if ( +#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + __Pyx_sys_getdefaultencoding_not_ascii && +#endif + PyUnicode_Check(o)) { +#if PY_VERSION_HEX < 0x03030000 + char* defenc_c; + PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); + if (!defenc) return NULL; + defenc_c = PyBytes_AS_STRING(defenc); +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + { + char* end = defenc_c + PyBytes_GET_SIZE(defenc); + char* c; + for (c = defenc_c; c < end; c++) { + if ((unsigned char) (*c) >= 128) { + PyUnicode_AsASCIIString(o); + return NULL; + } + } + } +#endif + *length = PyBytes_GET_SIZE(defenc); + return defenc_c; +#else + if (__Pyx_PyUnicode_READY(o) == -1) return NULL; +#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII + if (PyUnicode_IS_ASCII(o)) { + *length = PyUnicode_GET_LENGTH(o); + return PyUnicode_AsUTF8(o); + } else { + PyUnicode_AsASCIIString(o); + return NULL; + } +#else + return PyUnicode_AsUTF8AndSize(o, length); +#endif +#endif + } else +#endif +#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) + if (PyByteArray_Check(o)) { + *length = PyByteArray_GET_SIZE(o); + return PyByteArray_AS_STRING(o); + } else +#endif + { + char* result; + int r = PyBytes_AsStringAndSize(o, &result, length); + if (unlikely(r < 0)) { + return NULL; + } else { + return result; + } + } +} +static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { + int is_true = x == Py_True; + if (is_true | (x == Py_False) | (x == Py_None)) return is_true; + else return PyObject_IsTrue(x); +} +static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) { + PyNumberMethods *m; + const char *name = NULL; + PyObject *res = NULL; +#if PY_MAJOR_VERSION < 3 + if (PyInt_Check(x) || PyLong_Check(x)) +#else + if (PyLong_Check(x)) +#endif + return __Pyx_NewRef(x); + m = Py_TYPE(x)->tp_as_number; +#if PY_MAJOR_VERSION < 3 + if (m && m->nb_int) { + name = "int"; + res = PyNumber_Int(x); + } + else if (m && m->nb_long) { + name = "long"; + res = PyNumber_Long(x); + } +#else + if (m && m->nb_int) { + name = "int"; + res = PyNumber_Long(x); + } +#endif + if (res) { +#if PY_MAJOR_VERSION < 3 + if (!PyInt_Check(res) && !PyLong_Check(res)) { +#else + if (!PyLong_Check(res)) { +#endif + PyErr_Format(PyExc_TypeError, + "__%.4s__ returned non-%.4s (type %.200s)", + name, name, Py_TYPE(res)->tp_name); + Py_DECREF(res); + return NULL; + } + } + else if (!PyErr_Occurred()) { + PyErr_SetString(PyExc_TypeError, + "an integer is required"); + } + return res; +} +static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { + Py_ssize_t ival; + PyObject *x; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_CheckExact(b))) { + if (sizeof(Py_ssize_t) >= sizeof(long)) + return PyInt_AS_LONG(b); + else + return PyInt_AsSsize_t(x); + } +#endif + if (likely(PyLong_CheckExact(b))) { + #if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)b)->ob_digit; + const Py_ssize_t size = Py_SIZE(b); + if (likely(__Pyx_sst_abs(size) <= 1)) { + ival = likely(size) ? digits[0] : 0; + if (size == -1) ival = -ival; + return ival; + } else { + switch (size) { + case 2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -2: + if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -3: + if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case 4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + case -4: + if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { + return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); + } + break; + } + } + #endif + return PyLong_AsSsize_t(b); + } + x = PyNumber_Index(b); + if (!x) return -1; + ival = PyInt_AsSsize_t(x); + Py_DECREF(x); + return ival; +} +static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { + return PyInt_FromSize_t(ival); +} + + +#endif /* Py_PYTHON_H */ diff --git a/ot/lp/emd.pyx b/ot/lp/emd.pyx new file mode 100644 index 0000000..753b195 --- /dev/null +++ b/ot/lp/emd.pyx @@ -0,0 +1,71 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Sep 11 08:42:08 2014 + +@author: rflamary +""" +import numpy as np +cimport numpy as np + +cimport cython + + + +cdef extern from "EMD.h": + void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost) + + + +@cython.boundscheck(False) +@cython.wraparound(False) +def emd( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M): + """ + Solves the Earth Movers distance problem and returns the optimal transport matrix + + gamm=emd(a,b,M) + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + where : + + - M is the metric cost matrix + - a and b are the sample weights + + Parameters + ---------- + a : (ns,) ndarray + samples in the source domain (uniform waigth if empty) + b : (nt,) ndarray + samples in the target domain (uniform waigth if empty) + M : (ns,nt) ndarray + loss matrix + + + Returns + ------- + gamma: (ns x nt) ndarray + Optimal transportation matrix for the given parameters + + """ + cdef int n1= M.shape[0] + cdef int n2= M.shape[1] + + cdef float cost=0 + cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([n1, n2]) + + if not len(a): + a=np.ones((n1,))/n1 + + if not len(b): + b=np.ones((n2,))/n2 + + # calling the function + EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost) + + return G diff --git a/ot/lp/full_bipartitegraph.h b/ot/lp/full_bipartitegraph.h new file mode 100644 index 0000000..87a1bec --- /dev/null +++ b/ot/lp/full_bipartitegraph.h @@ -0,0 +1,215 @@ +/* -*- mode: C++; indent-tabs-mode: nil; -*- + * + * This file has been adapted by Nicolas Bonneel (2013), + * from full_graph.h from LEMON, a generic C++ optimization library, + * to implement a lightweight fully connected bipartite graph. A previous + * version of this file is used as part of the Displacement Interpolation + * project, + * Web: http://www.cs.ubc.ca/labs/imager/tr/2011/DisplacementInterpolation/ + * + * + **** Original file Copyright Notice : + * Copyright (C) 2003-2010 + * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport + * (Egervary Research Group on Combinatorial Optimization, EGRES). + * + * Permission to use, modify and distribute this software is granted + * provided that this copyright notice appears in all copies. For + * precise terms see the accompanying LICENSE file. + * + * This software is provided "AS IS" with no warranty of any kind, + * express or implied, and with no claim as to its suitability for any + * purpose. + * + */ + +#ifndef LEMON_FULL_BIPARTITE_GRAPH_H +#define LEMON_FULL_BIPARTITE_GRAPH_H + +#include "core.h" + +///\ingroup graphs +///\file +///\brief FullBipartiteDigraph and FullBipartiteGraph classes. + + +namespace lemon { + + + class FullBipartiteDigraphBase { + public: + + typedef FullBipartiteDigraphBase Digraph; + + //class Node; + typedef int Node; + //class Arc; + typedef long long Arc; + + protected: + + int _node_num; + long long _arc_num; + + FullBipartiteDigraphBase() {} + + void construct(int n1, int n2) { _node_num = n1+n2; _arc_num = n1 * n2; _n1=n1; _n2=n2;} + + public: + + int _n1, _n2; + + + Node operator()(int ix) const { return Node(ix); } + static int index(const Node& node) { return node; } + + Arc arc(const Node& s, const Node& t) const { + if (s<_n1 && t>=_n1) + return Arc(s * _n2 + (t-_n1) ); + else + return Arc(-1); + } + + int nodeNum() const { return _node_num; } + long long arcNum() const { return _arc_num; } + + int maxNodeId() const { return _node_num - 1; } + long long maxArcId() const { return _arc_num - 1; } + + Node source(Arc arc) const { return arc / _n2; } + Node target(Arc arc) const { return (arc % _n2) + _n1; } + + static int id(Node node) { return node; } + static long long id(Arc arc) { return arc; } + + static Node nodeFromId(int id) { return Node(id);} + static Arc arcFromId(int id) { return Arc(id);} + + + Arc findArc(Node s, Node t, Arc prev = -1) const { + return prev == -1 ? arc(s, t) : -1; + } + + void first(Node& node) const { + node = _node_num - 1; + } + + static void next(Node& node) { + --node; + } + + void first(Arc& arc) const { + arc = _arc_num - 1; + } + + static void next(Arc& arc) { + --arc; + } + + void firstOut(Arc& arc, const Node& node) const { + if (node>=_n1) + arc = -1; + else + arc = (node + 1) * _n2 - 1; + } + + void nextOut(Arc& arc) const { + if (arc % _n2 == 0) arc = 0; + --arc; + } + + void firstIn(Arc& arc, const Node& node) const { + if (node<_n1) + arc = -1; + else + arc = _arc_num + node - _node_num; + } + + void nextIn(Arc& arc) const { + arc -= _n2; + if (arc < 0) arc = -1; + } + + }; + + /// \ingroup graphs + /// + /// \brief A directed full graph class. + /// + /// FullBipartiteDigraph is a simple and fast implmenetation of directed full + /// (complete) graphs. It contains an arc from each node to each node + /// (including a loop for each node), therefore the number of arcs + /// is the square of the number of nodes. + /// This class is completely static and it needs constant memory space. + /// Thus you can neither add nor delete nodes or arcs, however + /// the structure can be resized using resize(). + /// + /// This type fully conforms to the \ref concepts::Digraph "Digraph concept". + /// Most of its member functions and nested classes are documented + /// only in the concept class. + /// + /// This class provides constant time counting for nodes and arcs. + /// + /// \note FullBipartiteDigraph and FullBipartiteGraph classes are very similar, + /// but there are two differences. While this class conforms only + /// to the \ref concepts::Digraph "Digraph" concept, FullBipartiteGraph + /// conforms to the \ref concepts::Graph "Graph" concept, + /// moreover FullBipartiteGraph does not contain a loop for each + /// node as this class does. + /// + /// \sa FullBipartiteGraph + class FullBipartiteDigraph : public FullBipartiteDigraphBase { + typedef FullBipartiteDigraphBase Parent; + + public: + + /// \brief Default constructor. + /// + /// Default constructor. The number of nodes and arcs will be zero. + FullBipartiteDigraph() { construct(0,0); } + + /// \brief Constructor + /// + /// Constructor. + /// \param n The number of the nodes. + FullBipartiteDigraph(int n1, int n2) { construct(n1, n2); } + + + /// \brief Returns the node with the given index. + /// + /// Returns the node with the given index. Since this structure is + /// completely static, the nodes can be indexed with integers from + /// the range [0..nodeNum()-1]. + /// The index of a node is the same as its ID. + /// \sa index() + Node operator()(int ix) const { return Parent::operator()(ix); } + + /// \brief Returns the index of the given node. + /// + /// Returns the index of the given node. Since this structure is + /// completely static, the nodes can be indexed with integers from + /// the range [0..nodeNum()-1]. + /// The index of a node is the same as its ID. + /// \sa operator()() + static int index(const Node& node) { return Parent::index(node); } + + /// \brief Returns the arc connecting the given nodes. + /// + /// Returns the arc connecting the given nodes. + /*Arc arc(Node u, Node v) const { + return Parent::arc(u, v); + }*/ + + /// \brief Number of nodes. + int nodeNum() const { return Parent::nodeNum(); } + /// \brief Number of arcs. + long long arcNum() const { return Parent::arcNum(); } + }; + + + + +} //namespace lemon + + +#endif //LEMON_FULL_GRAPH_H diff --git a/ot/lp/network_simplex_simple.h b/ot/lp/network_simplex_simple.h new file mode 100644 index 0000000..64856a0 --- /dev/null +++ b/ot/lp/network_simplex_simple.h @@ -0,0 +1,1543 @@ +/* -*- mode: C++; indent-tabs-mode: nil; -*- + * + * + * This file has been adapted by Nicolas Bonneel (2013), + * from network_simplex.h from LEMON, a generic C++ optimization library, + * to implement a lightweight network simplex for mass transport, more + * memory efficient that the original file. A previous version of this file + * is used as part of the Displacement Interpolation project, + * Web: http://www.cs.ubc.ca/labs/imager/tr/2011/DisplacementInterpolation/ + * + * + **** Original file Copyright Notice : + * + * Copyright (C) 2003-2010 + * Egervary Jeno Kombinatorikus Optimalizalasi Kutatocsoport + * (Egervary Research Group on Combinatorial Optimization, EGRES). + * + * Permission to use, modify and distribute this software is granted + * provided that this copyright notice appears in all copies. For + * precise terms see the accompanying LICENSE file. + * + * This software is provided "AS IS" with no warranty of any kind, + * express or implied, and with no claim as to its suitability for any + * purpose. + * + */ + +#ifndef LEMON_NETWORK_SIMPLEX_SIMPLE_H +#define LEMON_NETWORK_SIMPLEX_SIMPLE_H +#define DEBUG_LVL 0 +#define EPSILON 10*2.2204460492503131e-016 +#define MAX_DEBUG_ITER 100000 + + +/// \ingroup min_cost_flow_algs +/// +/// \file +/// \brief Network Simplex algorithm for finding a minimum cost flow. + +// if your compiler has troubles with stdext or hashmaps, just comment the following line to use a slower std::map instead +//#define HASHMAP + +#include +#include +#include +#ifdef HASHMAP +#include +#else +#include +#endif +#include +//#include "core.h" +//#include "lmath.h" + +//#include "sparse_array_n.h" +#include "full_bipartitegraph.h" + +#define INVALIDNODE -1 +#define INVALID (-1) + +namespace lemon { + + + template + class ProxyObject; + + template + class SparseValueVector + { + public: + SparseValueVector(int n=0) + { + } + void resize(int n=0){}; + T operator[](const int id) const + { +#ifdef HASHMAP + typename stdext::hash_map::const_iterator it = data.find(id); +#else + typename std::map::const_iterator it = data.find(id); +#endif + if (it==data.end()) + return 0; + else + return it->second; + } + + ProxyObject operator[](const int id) + { + return ProxyObject( this, id ); + } + + //private: +#ifdef HASHMAP + stdext::hash_map data; +#else + std::map data; +#endif + + }; + + template + class ProxyObject { + public: + ProxyObject( SparseValueVector *v, int idx ){_v=v; _idx=idx;}; + ProxyObject & operator=( const T &v ) { + // If we get here, we know that operator[] was called to perform a write access, + // so we can insert an item in the vector if needed + if (v!=0) + _v->data[_idx]=v; + return *this; + } + + operator T() { + // If we get here, we know that operator[] was called to perform a read access, + // so we can simply return the existing object +#ifdef HASHMAP + typename stdext::hash_map::iterator it = _v->data.find(_idx); +#else + typename std::map::iterator it = _v->data.find(_idx); +#endif + if (it==_v->data.end()) + return 0; + else + return it->second; + } + + void operator+=(T val) + { + if (val==0) return; +#ifdef HASHMAP + typename stdext::hash_map::iterator it = _v->data.find(_idx); +#else + typename std::map::iterator it = _v->data.find(_idx); +#endif + if (it==_v->data.end()) + _v->data[_idx] = val; + else + { + T sum = it->second + val; + if (sum==0) + _v->data.erase(it); + else + it->second = sum; + } + } + void operator-=(T val) + { + if (val==0) return; +#ifdef HASHMAP + typename stdext::hash_map::iterator it = _v->data.find(_idx); +#else + typename std::map::iterator it = _v->data.find(_idx); +#endif + if (it==_v->data.end()) + _v->data[_idx] = -val; + else + { + T sum = it->second - val; + if (sum==0) + _v->data.erase(it); + else + it->second = sum; + } + } + + SparseValueVector *_v; + int _idx; + }; + + + + /// \addtogroup min_cost_flow_algs + /// @{ + + /// \brief Implementation of the primal Network Simplex algorithm + /// for finding a \ref min_cost_flow "minimum cost flow". + /// + /// \ref NetworkSimplexSimple implements the primal Network Simplex algorithm + /// for finding a \ref min_cost_flow "minimum cost flow" + /// \ref amo93networkflows, \ref dantzig63linearprog, + /// \ref kellyoneill91netsimplex. + /// This algorithm is a highly efficient specialized version of the + /// linear programming simplex method directly for the minimum cost + /// flow problem. + /// + /// In general, %NetworkSimplexSimple is the fastest implementation available + /// in LEMON for this problem. + /// Moreover, it supports both directions of the supply/demand inequality + /// constraints. For more information, see \ref SupplyType. + /// + /// Most of the parameters of the problem (except for the digraph) + /// can be given using separate functions, and the algorithm can be + /// executed using the \ref run() function. If some parameters are not + /// specified, then default values will be used. + /// + /// \tparam GR The digraph type the algorithm runs on. + /// \tparam V The number type used for flow amounts, capacity bounds + /// and supply values in the algorithm. By default, it is \c int. + /// \tparam C The number type used for costs and potentials in the + /// algorithm. By default, it is the same as \c V. + /// + /// \warning Both number types must be signed and all input data must + /// be integer. + /// + /// \note %NetworkSimplexSimple provides five different pivot rule + /// implementations, from which the most efficient one is used + /// by default. For more information, see \ref PivotRule. + template + class NetworkSimplexSimple + { + public: + + /// \brief Constructor. + /// + /// The constructor of the class. + /// + /// \param graph The digraph the algorithm runs on. + /// \param arc_mixing Indicate if the arcs have to be stored in a + /// mixed order in the internal data structure. + /// In special cases, it could lead to better overall performance, + /// but it is usually slower. Therefore it is disabled by default. + NetworkSimplexSimple(const GR& graph, bool arc_mixing, int nbnodes, long long nb_arcs,double maxiters) : + _graph(graph), //_arc_id(graph), + _arc_mixing(arc_mixing), _init_nb_nodes(nbnodes), _init_nb_arcs(nb_arcs), + MAX(std::numeric_limits::max()), + INF(std::numeric_limits::has_infinity ? + std::numeric_limits::infinity() : MAX) + { + // Reset data structures + reset(); + max_iter=maxiters; + } + + /// The type of the flow amounts, capacity bounds and supply values + typedef V Value; + /// The type of the arc costs + typedef C Cost; + + public: + + /// \brief Problem type constants for the \c run() function. + /// + /// Enum type containing the problem type constants that can be + /// returned by the \ref run() function of the algorithm. + enum ProblemType { + /// The problem has no feasible solution (flow). + INFEASIBLE, + /// The problem has optimal solution (i.e. it is feasible and + /// bounded), and the algorithm has found optimal flow and node + /// potentials (primal and dual solutions). + OPTIMAL, + /// The objective function of the problem is unbounded, i.e. + /// there is a directed cycle having negative total cost and + /// infinite upper bound. + UNBOUNDED + }; + + /// \brief Constants for selecting the type of the supply constraints. + /// + /// Enum type containing constants for selecting the supply type, + /// i.e. the direction of the inequalities in the supply/demand + /// constraints of the \ref min_cost_flow "minimum cost flow problem". + /// + /// The default supply type is \c GEQ, the \c LEQ type can be + /// selected using \ref supplyType(). + /// The equality form is a special case of both supply types. + enum SupplyType { + /// This option means that there are "greater or equal" + /// supply/demand constraints in the definition of the problem. + GEQ, + /// This option means that there are "less or equal" + /// supply/demand constraints in the definition of the problem. + LEQ + }; + + + + private: + + double max_iter; + TEMPLATE_DIGRAPH_TYPEDEFS(GR); + + typedef std::vector IntVector; + typedef std::vector UHalfIntVector; + typedef std::vector ValueVector; + typedef std::vector CostVector; + // typedef SparseValueVector CostVector; + typedef std::vector BoolVector; + // Note: vector is used instead of vector for efficiency reasons + + // State constants for arcs + enum ArcState { + STATE_UPPER = -1, + STATE_TREE = 0, + STATE_LOWER = 1 + }; + + typedef std::vector StateVector; + // Note: vector is used instead of vector for + // efficiency reasons + + private: + + // Data related to the underlying digraph + const GR &_graph; + int _node_num; + int _arc_num; + int _all_arc_num; + int _search_arc_num; + + // Parameters of the problem + SupplyType _stype; + Value _sum_supply; + + inline int _node_id(int n) const {return _node_num-n-1;} ; + + //IntArcMap _arc_id; + UHalfIntVector _source; + UHalfIntVector _target; + bool _arc_mixing; + public: + // Node and arc data + CostVector _cost; + ValueVector _supply; + ValueVector _flow; + //SparseValueVector _flow; + CostVector _pi; + + + private: + // Data for storing the spanning tree structure + IntVector _parent; + IntVector _pred; + IntVector _thread; + IntVector _rev_thread; + IntVector _succ_num; + IntVector _last_succ; + IntVector _dirty_revs; + BoolVector _forward; + StateVector _state; + int _root; + + // Temporary data used in the current pivot iteration + int in_arc, join, u_in, v_in, u_out, v_out; + int first, second, right, last; + int stem, par_stem, new_stem; + Value delta; + + const Value MAX; + + int mixingCoeff; + + public: + + /// \brief Constant for infinite upper bounds (capacities). + /// + /// Constant for infinite upper bounds (capacities). + /// It is \c std::numeric_limits::infinity() if available, + /// \c std::numeric_limits::max() otherwise. + const Value INF; + + private: + + // thank you to DVK and MizardX from StackOverflow for this function! + inline int sequence(int k) const { + int smallv = (k > num_total_big_subsequence_numbers) & 1; + + k -= num_total_big_subsequence_numbers * smallv; + int subsequence_length2 = subsequence_length- smallv; + int subsequence_num = (k / subsequence_length2) + num_big_subseqiences * smallv; + int subsequence_offset = (k % subsequence_length2) * mixingCoeff; + + return subsequence_offset + subsequence_num; + } + int subsequence_length; + int num_big_subseqiences; + int num_total_big_subsequence_numbers; + + inline int getArcID(const Arc &arc) const + { + //int n = _arc_num-arc._id-1; + int n = _arc_num-GR::id(arc)-1; + + //int a = mixingCoeff*(n%mixingCoeff) + n/mixingCoeff; + //int b = _arc_id[arc]; + if (_arc_mixing) + return sequence(n); + else + return n; + } + + // finally unused because too slow + inline int getSource(const int arc) const + { + //int a = _source[arc]; + //return a; + + int n = _arc_num-arc-1; + if (_arc_mixing) + n = mixingCoeff*(n%mixingCoeff) + n/mixingCoeff; + + int b; + if (n>=0) + b = _node_id(_graph.source(GR::arcFromId( n ) )); + else + { + n = arc+1-_arc_num; + if ( n<=_node_num) + b = _node_num; + else + if ( n>=_graph._n1) + b = _graph._n1; + else + b = _graph._n1-n; + } + + return b; + } + + + + // Implementation of the Block Search pivot rule + class BlockSearchPivotRule + { + private: + + // References to the NetworkSimplexSimple class + const UHalfIntVector &_source; + const UHalfIntVector &_target; + const CostVector &_cost; + const StateVector &_state; + const CostVector &_pi; + int &_in_arc; + int _search_arc_num; + + // Pivot rule data + int _block_size; + int _next_arc; + NetworkSimplexSimple &_ns; + + public: + + // Constructor + BlockSearchPivotRule(NetworkSimplexSimple &ns) : + _source(ns._source), _target(ns._target), + _cost(ns._cost), _state(ns._state), _pi(ns._pi), + _in_arc(ns.in_arc), _search_arc_num(ns._search_arc_num), + _next_arc(0),_ns(ns) + { + // The main parameters of the pivot rule + const double BLOCK_SIZE_FACTOR = 1.0; + const int MIN_BLOCK_SIZE = 10; + + _block_size = std::max( int(BLOCK_SIZE_FACTOR * + std::sqrt(double(_search_arc_num))), + MIN_BLOCK_SIZE ); + } + // Find next entering arc + bool findEnteringArc() { + Cost c, min = 0; + int e; + int cnt = _block_size; + double a; + for (e = _next_arc; e != _search_arc_num; ++e) { + c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); + if (c < min) { + min = c; + _in_arc = e; + } + if (--cnt == 0) { + a=fabs(_pi[_source[_in_arc]])>fabs(_pi[_target[_in_arc]]) ? fabs(_pi[_source[_in_arc]]):fabs(_pi[_target[_in_arc]]); + a=a>fabs(_cost[_in_arc])?a:fabs(_cost[_in_arc]); + if (min < -EPSILON*a) goto search_end; + cnt = _block_size; + } + } + for (e = 0; e != _next_arc; ++e) { + c = _state[e] * (_cost[e] + _pi[_source[e]] - _pi[_target[e]]); + if (c < min) { + min = c; + _in_arc = e; + } + if (--cnt == 0) { + a=fabs(_pi[_source[_in_arc]])>fabs(_pi[_target[_in_arc]]) ? fabs(_pi[_source[_in_arc]]):fabs(_pi[_target[_in_arc]]); + a=a>fabs(_cost[_in_arc])?a:fabs(_cost[_in_arc]); + if (min < -EPSILON*a) goto search_end; + cnt = _block_size; + } + } + a=fabs(_pi[_source[_in_arc]])>fabs(_pi[_target[_in_arc]]) ? fabs(_pi[_source[_in_arc]]):fabs(_pi[_target[_in_arc]]); + a=a>fabs(_cost[_in_arc])?a:fabs(_cost[_in_arc]); + if (min >= -EPSILON*a) return false; + + search_end: + _next_arc = e; + return true; + } + + }; //class BlockSearchPivotRule + + + + public: + + + + int _init_nb_nodes; + long long _init_nb_arcs; + + /// \name Parameters + /// The parameters of the algorithm can be specified using these + /// functions. + + /// @{ + + + /// \brief Set the costs of the arcs. + /// + /// This function sets the costs of the arcs. + /// If it is not used before calling \ref run(), the costs + /// will be set to \c 1 on all arcs. + /// + /// \param map An arc map storing the costs. + /// Its \c Value type must be convertible to the \c Cost type + /// of the algorithm. + /// + /// \return (*this) + template + NetworkSimplexSimple& costMap(const CostMap& map) { + Arc a; _graph.first(a); + for (; a != INVALID; _graph.next(a)) { + _cost[getArcID(a)] = map[a]; + } + return *this; + } + + + /// \brief Set the costs of one arc. + /// + /// This function sets the costs of one arcs. + /// Done for memory reasons + /// + /// \param arc An arc. + /// \param arc A cost + /// + /// \return (*this) + template + NetworkSimplexSimple& setCost(const Arc& arc, const Value cost) { + _cost[getArcID(arc)] = cost; + return *this; + } + + + /// \brief Set the supply values of the nodes. + /// + /// This function sets the supply values of the nodes. + /// If neither this function nor \ref stSupply() is used before + /// calling \ref run(), the supply of each node will be set to zero. + /// + /// \param map A node map storing the supply values. + /// Its \c Value type must be convertible to the \c Value type + /// of the algorithm. + /// + /// \return (*this) + template + NetworkSimplexSimple& supplyMap(const SupplyMap& map) { + Node n; _graph.first(n); + for (; n != INVALIDNODE; _graph.next(n)) { + _supply[_node_id(n)] = map[n]; + } + return *this; + } + template + NetworkSimplexSimple& supplyMap(const SupplyMap* map1, int n1, const SupplyMap* map2, int n2) { + Node n; _graph.first(n); + for (; n != INVALIDNODE; _graph.next(n)) { + if (n + NetworkSimplexSimple& supplyMapAll(SupplyMap val1, int n1, SupplyMap val2, int n2) { + Node n; _graph.first(n); + for (; n != INVALIDNODE; _graph.next(n)) { + if (n(*this) + NetworkSimplexSimple& stSupply(const Node& s, const Node& t, Value k) { + for (int i = 0; i != _node_num; ++i) { + _supply[i] = 0; + } + _supply[_node_id(s)] = k; + _supply[_node_id(t)] = -k; + return *this; + } + + /// \brief Set the type of the supply constraints. + /// + /// This function sets the type of the supply/demand constraints. + /// If it is not used before calling \ref run(), the \ref GEQ supply + /// type will be used. + /// + /// For more information, see \ref SupplyType. + /// + /// \return (*this) + NetworkSimplexSimple& supplyType(SupplyType supply_type) { + _stype = supply_type; + return *this; + } + + /// @} + + /// \name Execution Control + /// The algorithm can be executed using \ref run(). + + /// @{ + + /// \brief Run the algorithm. + /// + /// This function runs the algorithm. + /// The paramters can be specified using functions \ref lowerMap(), + /// \ref upperMap(), \ref costMap(), \ref supplyMap(), \ref stSupply(), + /// \ref supplyType(). + /// For example, + /// \code + /// NetworkSimplexSimple ns(graph); + /// ns.lowerMap(lower).upperMap(upper).costMap(cost) + /// .supplyMap(sup).run(); + /// \endcode + /// + /// This function can be called more than once. All the given parameters + /// are kept for the next call, unless \ref resetParams() or \ref reset() + /// is used, thus only the modified parameters have to be set again. + /// If the underlying digraph was also modified after the construction + /// of the class (or the last \ref reset() call), then the \ref reset() + /// function must be called. + /// + /// \param pivot_rule The pivot rule that will be used during the + /// algorithm. For more information, see \ref PivotRule. + /// + /// \return \c INFEASIBLE if no feasible flow exists, + /// \n \c OPTIMAL if the problem has optimal solution + /// (i.e. it is feasible and bounded), and the algorithm has found + /// optimal flow and node potentials (primal and dual solutions), + /// \n \c UNBOUNDED if the objective function of the problem is + /// unbounded, i.e. there is a directed cycle having negative total + /// cost and infinite upper bound. + /// + /// \see ProblemType, PivotRule + /// \see resetParams(), reset() + ProblemType run() { +#if DEBUG_LVL>0 + mexPrintf("OPTIMAL = %d\nINFEASIBLE = %d\nUNBOUNDED = %d\n",OPTIMAL,INFEASIBLE,UNBOUNDED); + mexEvalString("drawnow;"); +#endif + + if (!init()) return INFEASIBLE; +#if DEBUG_LVL>0 + mexPrintf("Init done, starting iterations\n"); + mexEvalString("drawnow;"); +#endif + return start(); + } + + /// \brief Reset all the parameters that have been given before. + /// + /// This function resets all the paramaters that have been given + /// before using functions \ref lowerMap(), \ref upperMap(), + /// \ref costMap(), \ref supplyMap(), \ref stSupply(), \ref supplyType(). + /// + /// It is useful for multiple \ref run() calls. Basically, all the given + /// parameters are kept for the next \ref run() call, unless + /// \ref resetParams() or \ref reset() is used. + /// If the underlying digraph was also modified after the construction + /// of the class or the last \ref reset() call, then the \ref reset() + /// function must be used, otherwise \ref resetParams() is sufficient. + /// + /// For example, + /// \code + /// NetworkSimplexSimple ns(graph); + /// + /// // First run + /// ns.lowerMap(lower).upperMap(upper).costMap(cost) + /// .supplyMap(sup).run(); + /// + /// // Run again with modified cost map (resetParams() is not called, + /// // so only the cost map have to be set again) + /// cost[e] += 100; + /// ns.costMap(cost).run(); + /// + /// // Run again from scratch using resetParams() + /// // (the lower bounds will be set to zero on all arcs) + /// ns.resetParams(); + /// ns.upperMap(capacity).costMap(cost) + /// .supplyMap(sup).run(); + /// \endcode + /// + /// \return (*this) + /// + /// \see reset(), run() + NetworkSimplexSimple& resetParams() { + for (int i = 0; i != _node_num; ++i) { + _supply[i] = 0; + } + for (int i = 0; i != _arc_num; ++i) { + _cost[i] = 1; + } + _stype = GEQ; + return *this; + } + + + + int divid (int x, int y) + { + return (x-x%y)/y; + } + + /// \brief Reset the internal data structures and all the parameters + /// that have been given before. + /// + /// This function resets the internal data structures and all the + /// paramaters that have been given before using functions \ref lowerMap(), + /// \ref upperMap(), \ref costMap(), \ref supplyMap(), \ref stSupply(), + /// \ref supplyType(). + /// + /// It is useful for multiple \ref run() calls. Basically, all the given + /// parameters are kept for the next \ref run() call, unless + /// \ref resetParams() or \ref reset() is used. + /// If the underlying digraph was also modified after the construction + /// of the class or the last \ref reset() call, then the \ref reset() + /// function must be used, otherwise \ref resetParams() is sufficient. + /// + /// See \ref resetParams() for examples. + /// + /// \return (*this) + /// + /// \see resetParams(), run() + NetworkSimplexSimple& reset() { + // Resize vectors + _node_num = _init_nb_nodes; + _arc_num = _init_nb_arcs; + int all_node_num = _node_num + 1; + int max_arc_num = _arc_num + 2 * _node_num; + + _source.resize(max_arc_num); + _target.resize(max_arc_num); + + _cost.resize(max_arc_num); + _supply.resize(all_node_num); + _flow.resize(max_arc_num); + _pi.resize(all_node_num); + + _parent.resize(all_node_num); + _pred.resize(all_node_num); + _forward.resize(all_node_num); + _thread.resize(all_node_num); + _rev_thread.resize(all_node_num); + _succ_num.resize(all_node_num); + _last_succ.resize(all_node_num); + _state.resize(max_arc_num); + + + //_arc_mixing=false; + if (_arc_mixing) { + // Store the arcs in a mixed order + int k = std::max(int(std::sqrt(double(_arc_num))), 10); + mixingCoeff = k; + subsequence_length = _arc_num / mixingCoeff + 1; + num_big_subseqiences = _arc_num % mixingCoeff; + num_total_big_subsequence_numbers = subsequence_length * num_big_subseqiences; + + int i = 0, j = 0; + Arc a; _graph.first(a); + for (; a != INVALID; _graph.next(a)) { + _source[i] = _node_id(_graph.source(a)); + _target[i] = _node_id(_graph.target(a)); + //_arc_id[a] = i; + if ((i += k) >= _arc_num) i = ++j; + } + } else { + // Store the arcs in the original order + int i = 0; + Arc a; _graph.first(a); + for (; a != INVALID; _graph.next(a), ++i) { + _source[i] = _node_id(_graph.source(a)); + _target[i] = _node_id(_graph.target(a)); + //_arc_id[a] = i; + } + } + + // Reset parameters + resetParams(); + return *this; + } + + /// @} + + /// \name Query Functions + /// The results of the algorithm can be obtained using these + /// functions.\n + /// The \ref run() function must be called before using them. + + /// @{ + + /// \brief Return the total cost of the found flow. + /// + /// This function returns the total cost of the found flow. + /// Its complexity is O(e). + /// + /// \note The return type of the function can be specified as a + /// template parameter. For example, + /// \code + /// ns.totalCost(); + /// \endcode + /// It is useful if the total cost cannot be stored in the \c Cost + /// type of the algorithm, which is the default return type of the + /// function. + /// + /// \pre \ref run() must be called before using this function. + /*template + Number totalCost() const { + Number c = 0; + for (ArcIt a(_graph); a != INVALID; ++a) { + int i = getArcID(a); + c += Number(_flow[i]) * Number(_cost[i]); + } + return c; + }*/ + + template + Number totalCost() const { + Number c = 0; + + /*#ifdef HASHMAP + typename stdext::hash_map::const_iterator it; + #else + typename std::map::const_iterator it; + #endif + for (it = _flow.data.begin(); it!=_flow.data.end(); ++it) + c += Number(it->second) * Number(_cost[it->first]); + return c;*/ + + for (int i=0; i<_flow.size(); i++) + c += _flow[i] * Number(_cost[i]); + return c; + + } + +#ifndef DOXYGEN + Cost totalCost() const { + return totalCost(); + } +#endif + + /// \brief Return the flow on the given arc. + /// + /// This function returns the flow on the given arc. + /// + /// \pre \ref run() must be called before using this function. + Value flow(const Arc& a) const { + return _flow[getArcID(a)]; + } + + /// \brief Return the flow map (the primal solution). + /// + /// This function copies the flow value on each arc into the given + /// map. The \c Value type of the algorithm must be convertible to + /// the \c Value type of the map. + /// + /// \pre \ref run() must be called before using this function. + template + void flowMap(FlowMap &map) const { + Arc a; _graph.first(a); + for (; a != INVALID; _graph.next(a)) { + map.set(a, _flow[getArcID(a)]); + } + } + + /// \brief Return the potential (dual value) of the given node. + /// + /// This function returns the potential (dual value) of the + /// given node. + /// + /// \pre \ref run() must be called before using this function. + Cost potential(const Node& n) const { + return _pi[_node_id(n)]; + } + + /// \brief Return the potential map (the dual solution). + /// + /// This function copies the potential (dual value) of each node + /// into the given map. + /// The \c Cost type of the algorithm must be convertible to the + /// \c Value type of the map. + /// + /// \pre \ref run() must be called before using this function. + template + void potentialMap(PotentialMap &map) const { + Node n; _graph.first(n); + for (; n != INVALID; _graph.next(n)) { + map.set(n, _pi[_node_id(n)]); + } + } + + /// @} + + private: + + // Initialize internal data structures + bool init() { + if (_node_num == 0) return false; + /* + // Check the sum of supply values + _sum_supply = 0; + for (int i = 0; i != _node_num; ++i) { + _sum_supply += _supply[i]; + } + if ( !((_stype == GEQ && _sum_supply <= _epsilon ) || + (_stype == LEQ && _sum_supply >= -_epsilon )) ) return false; + */ + + // Initialize artifical cost + Cost ART_COST; + if (std::numeric_limits::is_exact) { + ART_COST = std::numeric_limits::max() / 2 + 1; + } else { + ART_COST = 0; + for (int i = 0; i != _arc_num; ++i) { + if (_cost[i] > ART_COST) ART_COST = _cost[i]; + } + ART_COST = (ART_COST + 1) * _node_num; + } + + // Initialize arc maps + for (int i = 0; i != _arc_num; ++i) { + //_flow[i] = 0; //by default, the sparse matrix is empty + _state[i] = STATE_LOWER; + } + + // Set data for the artificial root node + _root = _node_num; + _parent[_root] = -1; + _pred[_root] = -1; + _thread[_root] = 0; + _rev_thread[0] = _root; + _succ_num[_root] = _node_num + 1; + _last_succ[_root] = _root - 1; + _supply[_root] = -_sum_supply; + _pi[_root] = 0; + + // Add artificial arcs and initialize the spanning tree data structure + if (_sum_supply == 0) { + // EQ supply constraints + _search_arc_num = _arc_num; + _all_arc_num = _arc_num + _node_num; + for (int u = 0, e = _arc_num; u != _node_num; ++u, ++e) { + _parent[u] = _root; + _pred[u] = e; + _thread[u] = u + 1; + _rev_thread[u + 1] = u; + _succ_num[u] = 1; + _last_succ[u] = u; + _state[e] = STATE_TREE; + if (_supply[u] >= 0) { + _forward[u] = true; + _pi[u] = 0; + _source[e] = u; + _target[e] = _root; + _flow[e] = _supply[u]; + _cost[e] = 0; + } else { + _forward[u] = false; + _pi[u] = ART_COST; + _source[e] = _root; + _target[e] = u; + _flow[e] = -_supply[u]; + _cost[e] = ART_COST; + } + } + } + else if (_sum_supply > 0) { + // LEQ supply constraints + _search_arc_num = _arc_num + _node_num; + int f = _arc_num + _node_num; + for (int u = 0, e = _arc_num; u != _node_num; ++u, ++e) { + _parent[u] = _root; + _thread[u] = u + 1; + _rev_thread[u + 1] = u; + _succ_num[u] = 1; + _last_succ[u] = u; + if (_supply[u] >= 0) { + _forward[u] = true; + _pi[u] = 0; + _pred[u] = e; + _source[e] = u; + _target[e] = _root; + _flow[e] = _supply[u]; + _cost[e] = 0; + _state[e] = STATE_TREE; + } else { + _forward[u] = false; + _pi[u] = ART_COST; + _pred[u] = f; + _source[f] = _root; + _target[f] = u; + _flow[f] = -_supply[u]; + _cost[f] = ART_COST; + _state[f] = STATE_TREE; + _source[e] = u; + _target[e] = _root; + //_flow[e] = 0; //by default, the sparse matrix is empty + _cost[e] = 0; + _state[e] = STATE_LOWER; + ++f; + } + } + _all_arc_num = f; + } + else { + // GEQ supply constraints + _search_arc_num = _arc_num + _node_num; + int f = _arc_num + _node_num; + for (int u = 0, e = _arc_num; u != _node_num; ++u, ++e) { + _parent[u] = _root; + _thread[u] = u + 1; + _rev_thread[u + 1] = u; + _succ_num[u] = 1; + _last_succ[u] = u; + if (_supply[u] <= 0) { + _forward[u] = false; + _pi[u] = 0; + _pred[u] = e; + _source[e] = _root; + _target[e] = u; + _flow[e] = -_supply[u]; + _cost[e] = 0; + _state[e] = STATE_TREE; + } else { + _forward[u] = true; + _pi[u] = -ART_COST; + _pred[u] = f; + _source[f] = u; + _target[f] = _root; + _flow[f] = _supply[u]; + _state[f] = STATE_TREE; + _cost[f] = ART_COST; + _source[e] = _root; + _target[e] = u; + //_flow[e] = 0; //by default, the sparse matrix is empty + _cost[e] = 0; + _state[e] = STATE_LOWER; + ++f; + } + } + _all_arc_num = f; + } + + return true; + } + + // Find the join node + void findJoinNode() { + int u = _source[in_arc]; + int v = _target[in_arc]; + while (u != v) { + if (_succ_num[u] < _succ_num[v]) { + u = _parent[u]; + } else { + v = _parent[v]; + } + } + join = u; + } + + // Find the leaving arc of the cycle and returns true if the + // leaving arc is not the same as the entering arc + bool findLeavingArc() { + // Initialize first and second nodes according to the direction + // of the cycle + if (_state[in_arc] == STATE_LOWER) { + first = _source[in_arc]; + second = _target[in_arc]; + } else { + first = _target[in_arc]; + second = _source[in_arc]; + } + delta = INF; + int result = 0; + Value d; + int e; + + // Search the cycle along the path form the first node to the root + for (int u = first; u != join; u = _parent[u]) { + e = _pred[u]; + d = _forward[u] ? _flow[e] : INF ; + if (d < delta) { + delta = d; + u_out = u; + result = 1; + } + } + // Search the cycle along the path form the second node to the root + for (int u = second; u != join; u = _parent[u]) { + e = _pred[u]; + d = _forward[u] ? INF : _flow[e]; + if (d <= delta) { + delta = d; + u_out = u; + result = 2; + } + } + + if (result == 1) { + u_in = first; + v_in = second; + } else { + u_in = second; + v_in = first; + } + return result != 0; + } + + // Change _flow and _state vectors + void changeFlow(bool change) { + // Augment along the cycle + if (delta > 0) { + Value val = _state[in_arc] * delta; + _flow[in_arc] += val; + for (int u = _source[in_arc]; u != join; u = _parent[u]) { + _flow[_pred[u]] += _forward[u] ? -val : val; + } + for (int u = _target[in_arc]; u != join; u = _parent[u]) { + _flow[_pred[u]] += _forward[u] ? val : -val; + } + } + // Update the state of the entering and leaving arcs + if (change) { + _state[in_arc] = STATE_TREE; + _state[_pred[u_out]] = + (_flow[_pred[u_out]] == 0) ? STATE_LOWER : STATE_UPPER; + } else { + _state[in_arc] = -_state[in_arc]; + } + } + + // Update the tree structure + void updateTreeStructure() { + int u, w; + int old_rev_thread = _rev_thread[u_out]; + int old_succ_num = _succ_num[u_out]; + int old_last_succ = _last_succ[u_out]; + v_out = _parent[u_out]; + + u = _last_succ[u_in]; // the last successor of u_in + right = _thread[u]; // the node after it + + // Handle the case when old_rev_thread equals to v_in + // (it also means that join and v_out coincide) + if (old_rev_thread == v_in) { + last = _thread[_last_succ[u_out]]; + } else { + last = _thread[v_in]; + } + + // Update _thread and _parent along the stem nodes (i.e. the nodes + // between u_in and u_out, whose parent have to be changed) + _thread[v_in] = stem = u_in; + _dirty_revs.clear(); + _dirty_revs.push_back(v_in); + par_stem = v_in; + while (stem != u_out) { + // Insert the next stem node into the thread list + new_stem = _parent[stem]; + _thread[u] = new_stem; + _dirty_revs.push_back(u); + + // Remove the subtree of stem from the thread list + w = _rev_thread[stem]; + _thread[w] = right; + _rev_thread[right] = w; + + // Change the parent node and shift stem nodes + _parent[stem] = par_stem; + par_stem = stem; + stem = new_stem; + + // Update u and right + u = _last_succ[stem] == _last_succ[par_stem] ? + _rev_thread[par_stem] : _last_succ[stem]; + right = _thread[u]; + } + _parent[u_out] = par_stem; + _thread[u] = last; + _rev_thread[last] = u; + _last_succ[u_out] = u; + + // Remove the subtree of u_out from the thread list except for + // the case when old_rev_thread equals to v_in + // (it also means that join and v_out coincide) + if (old_rev_thread != v_in) { + _thread[old_rev_thread] = right; + _rev_thread[right] = old_rev_thread; + } + + // Update _rev_thread using the new _thread values + for (int i = 0; i != int(_dirty_revs.size()); ++i) { + u = _dirty_revs[i]; + _rev_thread[_thread[u]] = u; + } + + // Update _pred, _forward, _last_succ and _succ_num for the + // stem nodes from u_out to u_in + int tmp_sc = 0, tmp_ls = _last_succ[u_out]; + u = u_out; + while (u != u_in) { + w = _parent[u]; + _pred[u] = _pred[w]; + _forward[u] = !_forward[w]; + tmp_sc += _succ_num[u] - _succ_num[w]; + _succ_num[u] = tmp_sc; + _last_succ[w] = tmp_ls; + u = w; + } + _pred[u_in] = in_arc; + _forward[u_in] = (u_in == _source[in_arc]); + _succ_num[u_in] = old_succ_num; + + // Set limits for updating _last_succ form v_in and v_out + // towards the root + int up_limit_in = -1; + int up_limit_out = -1; + if (_last_succ[join] == v_in) { + up_limit_out = join; + } else { + up_limit_in = join; + } + + // Update _last_succ from v_in towards the root + for (u = v_in; u != up_limit_in && _last_succ[u] == v_in; + u = _parent[u]) { + _last_succ[u] = _last_succ[u_out]; + } + // Update _last_succ from v_out towards the root + if (join != old_rev_thread && v_in != old_rev_thread) { + for (u = v_out; u != up_limit_out && _last_succ[u] == old_last_succ; + u = _parent[u]) { + _last_succ[u] = old_rev_thread; + } + } else { + for (u = v_out; u != up_limit_out && _last_succ[u] == old_last_succ; + u = _parent[u]) { + _last_succ[u] = _last_succ[u_out]; + } + } + + // Update _succ_num from v_in to join + for (u = v_in; u != join; u = _parent[u]) { + _succ_num[u] += old_succ_num; + } + // Update _succ_num from v_out to join + for (u = v_out; u != join; u = _parent[u]) { + _succ_num[u] -= old_succ_num; + } + } + + // Update potentials + void updatePotential() { + Cost sigma = _forward[u_in] ? + _pi[v_in] - _pi[u_in] - _cost[_pred[u_in]] : + _pi[v_in] - _pi[u_in] + _cost[_pred[u_in]]; + // Update potentials in the subtree, which has been moved + int end = _thread[_last_succ[u_in]]; + for (int u = u_in; u != end; u = _thread[u]) { + _pi[u] += sigma; + } + } + + // Heuristic initial pivots + bool initialPivots() { + Value curr, total = 0; + std::vector supply_nodes, demand_nodes; + Node u; _graph.first(u); + for (; u != INVALIDNODE; _graph.next(u)) { + curr = _supply[_node_id(u)]; + if (curr > 0) { + total += curr; + supply_nodes.push_back(u); + } + else if (curr < 0) { + demand_nodes.push_back(u); + } + } + if (_sum_supply > 0) total -= _sum_supply; + if (total <= 0) return true; + + IntVector arc_vector; + if (_sum_supply >= 0) { + if (supply_nodes.size() == 1 && demand_nodes.size() == 1) { + // Perform a reverse graph search from the sink to the source + //typename GR::template NodeMap reached(_graph, false); + BoolVector reached(_node_num, false); + Node s = supply_nodes[0], t = demand_nodes[0]; + std::vector stack; + reached[t] = true; + stack.push_back(t); + while (!stack.empty()) { + Node u, v = stack.back(); + stack.pop_back(); + if (v == s) break; + Arc a; _graph.firstIn(a, v); + for (; a != INVALID; _graph.nextIn(a)) { + if (reached[u = _graph.source(a)]) continue; + int j = getArcID(a); + if (INF >= total) { + arc_vector.push_back(j); + reached[u] = true; + stack.push_back(u); + } + } + } + } else { + // Find the min. cost incomming arc for each demand node + for (int i = 0; i != int(demand_nodes.size()); ++i) { + Node v = demand_nodes[i]; + Cost c, min_cost = std::numeric_limits::max(); + Arc min_arc = INVALID; + Arc a; _graph.firstIn(a, v); + for (; a != INVALID; _graph.nextIn(a)) { + c = _cost[getArcID(a)]; + if (c < min_cost) { + min_cost = c; + min_arc = a; + } + } + if (min_arc != INVALID) { + arc_vector.push_back(getArcID(min_arc)); + } + } + } + } else { + // Find the min. cost outgoing arc for each supply node + for (int i = 0; i != int(supply_nodes.size()); ++i) { + Node u = supply_nodes[i]; + Cost c, min_cost = std::numeric_limits::max(); + Arc min_arc = INVALID; + Arc a; _graph.firstOut(a, u); + for (; a != INVALID; _graph.nextOut(a)) { + c = _cost[getArcID(a)]; + if (c < min_cost) { + min_cost = c; + min_arc = a; + } + } + if (min_arc != INVALID) { + arc_vector.push_back(getArcID(min_arc)); + } + } + } + + // Perform heuristic initial pivots + for (int i = 0; i != int(arc_vector.size()); ++i) { + in_arc = arc_vector[i]; + // l'erreur est probablement ici... + if (_state[in_arc] * (_cost[in_arc] + _pi[_source[in_arc]] - + _pi[_target[in_arc]]) >= 0) continue; + findJoinNode(); + bool change = findLeavingArc(); + if (delta >= MAX) return false; + changeFlow(change); + if (change) { + updateTreeStructure(); + updatePotential(); + } + } + return true; + } + + // Execute the algorithm + ProblemType start() { + return start(); + } + + template + ProblemType start() { + PivotRuleImpl pivot(*this); + double prevCost=-1; + + // Perform heuristic initial pivots + if (!initialPivots()) return UNBOUNDED; + +#if DEBUG_LVL>0 + int niter=0; +#endif + int iter_number=0; + //pivot.setDantzig(true); + // Execute the Network Simplex algorithm + while (pivot.findEnteringArc()) { + if(++iter_number>=max_iter&&max_iter>0){ + char errMess[1000]; + // sprintf( errMess, "RESULT MIGHT BE INACURATE\nMax number of iteration reached, currently \%d. Sometimes iterations go on in cycle even though the solution has been reached, to check if it's the case here have a look at the minimal reduced cost. If it is very close to machine precision, you might actually have the correct solution, if not try setting the maximum number of iterations a bit higher",iter_number ); + // mexWarnMsgTxt(errMess); + break; + } +#if DEBUG_LVL>0 + if(niter>MAX_DEBUG_ITER) + break; + if(++niter%1000==0||niter%1000==1){ + double curCost=totalCost(); + double sumFlow=0; + double a; + a= (fabs(_pi[_source[in_arc]])>=fabs(_pi[_target[in_arc]])) ? fabs(_pi[_source[in_arc]]) : fabs(_pi[_target[in_arc]]); + a=a>=fabs(_cost[in_arc])?a:fabs(_cost[in_arc]); + for (int i=0; i<_flow.size(); i++) { + sumFlow+=_state[i]*_flow[i]; + } + mexPrintf("Sum of the flow %.100f\n%d iterations, current cost=%.20f\nReduced cost=%.30f\nPrecision =%.30f\n",sumFlow,niter, curCost,_state[in_arc] * (_cost[in_arc] + _pi[_source[in_arc]] -_pi[_target[in_arc]]), -EPSILON*(a)); + mexPrintf("Arc in = (%d,%d)\n",_node_id(_source[in_arc]),_node_id(_target[in_arc])); + mexPrintf("Supplies = (%f,%f)\n",_supply[_source[in_arc]],_supply[_target[in_arc]]); + + mexPrintf("%.30f\n%.30f\n%.30f\n%.30f\n%",_cost[in_arc],_pi[_source[in_arc]],_pi[_target[in_arc]],a); + mexEvalString("drawnow;"); + } +#endif + + findJoinNode(); + bool change = findLeavingArc(); + if (delta >= MAX) return UNBOUNDED; + changeFlow(change); + if (change) { + updateTreeStructure(); + updatePotential(); + } +#if DEBUG_LVL>0 + else{ + mexPrintf("No change\n"); + } +#endif +#if DEBUG_LVL>1 + mexPrintf("Arc in = (%d,%d)\n",_source[in_arc],_target[in_arc]); +#endif + + } + + +#if DEBUG_LVL>0 + double curCost=totalCost(); + double sumFlow=0; + double a; + a= (fabs(_pi[_source[in_arc]])>=fabs(_pi[_target[in_arc]])) ? fabs(_pi[_source[in_arc]]) : fabs(_pi[_target[in_arc]]); + a=a>=fabs(_cost[in_arc])?a:fabs(_cost[in_arc]); + for (int i=0; i<_flow.size(); i++) { + sumFlow+=_state[i]*_flow[i]; + } + mexPrintf("Sum of the flow %.100f\n%d iterations, current cost=%.20f\nReduced cost=%.30f\nPrecision =%.30f",sumFlow,niter, curCost,_state[in_arc] * (_cost[in_arc] + _pi[_source[in_arc]] -_pi[_target[in_arc]]), -EPSILON*(a)); + mexPrintf("Arc in = (%d,%d)\n",_node_id(_source[in_arc]),_node_id(_target[in_arc])); + mexPrintf("Supplies = (%f,%f)\n",_supply[_source[in_arc]],_supply[_target[in_arc]]); + + mexEvalString("drawnow;"); +#endif + +#if DEBUG_LVL>1 + double sumFlow=0; + for (int i=0; i<_flow.size(); i++) { + sumFlow+=_state[i]*_flow[i]; + if (_state[i]==STATE_TREE) { + mexPrintf("Non zero value at (%d,%d)\n",_node_num+1-_source[i],_node_num+1-_target[i]); + } + } + mexPrintf("Sum of the flow %.100f\n%d iterations, current cost=%.20f\n",sumFlow,niter, totalCost()); + mexEvalString("drawnow;"); +#endif + // Check feasibility + for (int e = _search_arc_num; e != _all_arc_num; ++e) { + if (_flow[e] != 0){ + if (abs(_flow[e]) > EPSILON) + return INFEASIBLE; + else + _flow[e]=0; + + } + } + + // Shift potentials to meet the requirements of the GEQ/LEQ type + // optimality conditions + if (_sum_supply == 0) { + if (_stype == GEQ) { + Cost max_pot = -std::numeric_limits::max(); + for (int i = 0; i != _node_num; ++i) { + if (_pi[i] > max_pot) max_pot = _pi[i]; + } + if (max_pot > 0) { + for (int i = 0; i != _node_num; ++i) + _pi[i] -= max_pot; + } + } else { + Cost min_pot = std::numeric_limits::max(); + for (int i = 0; i != _node_num; ++i) { + if (_pi[i] < min_pot) min_pot = _pi[i]; + } + if (min_pot < 0) { + for (int i = 0; i != _node_num; ++i) + _pi[i] -= min_pot; + } + } + } + + return OPTIMAL; + } + + }; //class NetworkSimplexSimple + + ///@} + +} //namespace lemon + +#endif //LEMON_NETWORK_SIMPLEX_H diff --git a/ot/optim.py b/ot/optim.py index 66bbfb5..e6373ce 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -8,7 +8,7 @@ Created on Wed Oct 26 15:08:19 2016 import numpy as np import scipy as sp from scipy.optimize.linesearch import scalar_search_armijo -from emd import emd +from lp import emd # The corresponding scipy function does not work for matrices def line_search_armijo(f,xk,pk,gfk,old_fval,args=(),c1=1e-4,alpha0=0.99): diff --git a/setup.py b/setup.py index 969ec99..60b9a9f 100755 --- a/setup.py +++ b/setup.py @@ -30,15 +30,15 @@ setup(name='POT', packages=find_packages(), ext_modules = cythonize(Extension( "ot.emd.emd", # the extension name - sources=["ot/emd/emd.pyx", "ot/emd/EMD_wrap.cpp"], # the Cython source and + sources=["ot/lp/emd.pyx", "ot/lp/EMD_wrap.cpp"], # the Cython source and # additional C++ source files language="c++", # generate and compile C++ code, - include_dirs=[numpy.get_include(),os.path.join(ROOT,'ot/emd')])), + include_dirs=[numpy.get_include(),os.path.join(ROOT,'ot/lp')])), platforms=['linux','macosx','windows'], license = 'MIT', scripts=[], data_files=[], - requires=["numpy (>=1.11)"], + requires=["numpy (>=1.11)","scipy (>=0.17)"], classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', -- cgit v1.2.3 From f4e76b739242d73b7656f85dbbff1fd2be52c3a5 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 27 Oct 2016 12:38:30 +0200 Subject: bug emd --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 60b9a9f..c7870d3 100755 --- a/setup.py +++ b/setup.py @@ -29,7 +29,7 @@ setup(name='POT', url='https://github.com/rflamary/POT', packages=find_packages(), ext_modules = cythonize(Extension( - "ot.emd.emd", # the extension name + "ot.lp.emd", # the extension name sources=["ot/lp/emd.pyx", "ot/lp/EMD_wrap.cpp"], # the Cython source and # additional C++ source files language="c++", # generate and compile C++ code, -- cgit v1.2.3 From 537e9cfbf06341cb3bcdaecf1f99b2a0d4e9dfa8 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 27 Oct 2016 12:44:00 +0200 Subject: bug emd v2 --- ot/lp/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 65cee83..46662b7 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -1,3 +1,3 @@ -from . import emd +from .emd import emd -- cgit v1.2.3 From 6a02cfaf6c3a8ff30fc8970b18758ac64d5dd6e2 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 27 Oct 2016 15:47:42 +0200 Subject: update readme + demo notebook --- README.md | 16 +-- examples/Demo_1D_OT.ipynb | 166 +++++++++++++++++++++++++++++++ examples/demo_1D_optimal_transport.ipynb | 166 ------------------------------- 3 files changed, 176 insertions(+), 172 deletions(-) create mode 100644 examples/Demo_1D_OT.ipynb delete mode 100644 examples/demo_1D_optimal_transport.ipynb diff --git a/README.md b/README.md index a4439d9..f41d5c4 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,14 @@ -# POT +# POT: Python Optimal Transport library Python Optimal Transport library -This Python librarie is an open source implementation with several functions that allow to solve optimal transport problems in Python. +This Python librarie is an open source implementation of several functions that allow to solve optimal transport problems in Python. It provides the following solvers: -* Linear program (LP) OT solver/ Earth Movers Distance (using code from Antoine Rolet and Nicolas Bonneel) -* Entropic regularization OT solver -* Bregman projection for Wasserstein barycenter and unmixing -* Optimal transport for domain adaptation +* Linear program (LP) OT solver/ Earth Movers Distance (using code from Antoine Rolet and Nicolas Bonneel). +* Entropic regularization OT solver (Sinkhorn Knopp ALgorithm) +* Bregman projection for Wasserstein barycenter and unmixing. +* Optimal transport for domain adaptation (with group lasso regularization) +* Conditional gradient and Generalized conditional gradient for regularized OT. Some demonstrations of what can be done are available in the examples folder. @@ -15,4 +16,7 @@ Some demonstrations of what can be done are available in the examples folder. ## Installation +## Examples + + ## References diff --git a/examples/Demo_1D_OT.ipynb b/examples/Demo_1D_OT.ipynb new file mode 100644 index 0000000..354972e --- /dev/null +++ b/examples/Demo_1D_OT.ipynb @@ -0,0 +1,166 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:70469d78395244f4978f6cd50bff1940285ade6e3a32e21040ca4851a189f8cc" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "1D optimal transport demo" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# load all relevant modules\n", + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Dataset generation\n", + "n=100 # nb bins\n", + "a=ot.datasets.get_1D_gauss(n,m=20,s=20) # m= mean, s= std\n", + "b=ot.datasets.get_1D_gauss(n,m=60,s=60)\n", + "\n", + "# bin positions\n", + "x=np.arange(n,dtype=np.float64)\n", + "# loss matrix\n", + "M=ot.dist(x.reshape((n,1)),x.reshape((n,1)))\n", + "M/=M.max()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# plot the distributions\n", + "\n", + "pl.figure(1)\n", + "pl.plot(x,a,'b',label='Source distribution')\n", + "pl.plot(x,b,'r',label='Target distribution')\n", + "pl.legend()\n", + "pl.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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mjEkVkQRs5fAxETkRWAJ0Msbs8diHKSuOUDBmDLRpA3//u3MxtGwJixZB69bO\nxaAqYP58e+AsXWpn9/GGMXDbbbB5sy02CvQAVMpxIoIxpsL/8WUWAbnK9McAi4G1QLoxZp2ITBKR\nvq7VpgEJIpIJ3AaMdy0/B/heRFYBc4DRnif/cOJkE9ACWgwUgn7/3V7NT5/u/ckf7An/3/+GnBz7\nWaXKqcw7gIAEESZ3ACefbItwT3Ww0evw4ZCSAiNHOheDKqfUVGjSBJ54omKfX7PGFgWtXOlsKwQV\ncH6/A1Dec7oOAHQ4iJAzezb873/w0EMV30anTnDHHXDttdo8VJWLJgAf2bvXtsoL9EQwnrQpaAjZ\nscOW+7/2mq3YrYw774SDB+H5530Tm4oImgB8JDvbnnydrodLToasLGdjUF6aNAmuvhq6dav8tqpU\nseWPkybZpmBKeUETgI8UJACnJSdrEVBIyMqCOXPgnnt8t82TTrI9hStal6AijiYAH8nOhubNnY7C\n1gFs26ZFwUFv8mS46Sbft9+/7z5bDPT7777drgpLmgB8JFjuAKpXtx1Gc3OdjkSVaNMmeO89W3Hr\nay1awFVXwWOP+X7bKuxoAvCRrKzgSACg9QBB74EHbOVvXJx/tn/vvfCf/8Cvv/pn+ypsaALwkWAp\nAgIbh9YDBKn16+GDD+xIn/6SlASDB8Ojj/pvHyosaALwkWApAgKtCA5qDz4I48ZB3br+3c8//2lb\nBeldgCqFJgAfOHTI9gNITHQ6EksTQJDKzbWjfN50k//31aSJnUfg5Zf9vy8VsjQB+MCWLXYWsKgg\n+TU1AQSpqVPtsA/16gVmf2PGwIsvQl5eYPanQk6QnLJCW1ZW8JT/g41FK4GDzJEjNgGMGRO4fZ58\nMpx4om1xpFQxNAH4QDCV/8Nfw0GEwfh64ePtt6FdO+jQIbD7HTMGnn02sPtUIUMTgA8EWwKoXdv2\nB9i50+lIVKHnngvs1X+Byy+3/Q6+/z7w+1ZBTxOADwRbAgCtBwgqq1bB1q12mIZAi4mBG2+0CUgp\nD5oAfCDY6gBA6wGCynPP2ZY/VbyZgdUPrr8e3noLdu92Zv8qaHmVAESkj4isF5GNInJ3Me9XFZF0\nEckUkWUikuTxfpKI7BeRcb4KPJjoHYAq0e7d8M47cN11zsWQmAiXXKKzhqnjlJkARCQKeA64EOgA\nDBaRkzxWG4Wd+7c18BQwxeP9x4H3Kx9u8MnLg19+Kd9MfoGgCSBIzJoFffpAgwbOxnHddbZjmLYM\nUG68uQNlgSh8AAAcMklEQVToBmQaY7KNMXlAOtDfY53+wAzX87nYCeQBEJH+wGbsfMJhJyfHXmDF\nxDgdSVGaAILE9OkwYoTTUcA558D+/bB6tdORqCDiTQI4Adjq9jrHtazYdVyTyO8RkXgRqQXcBUwC\nHJ4qxT+CsfwftA4gKKxda8fmPv98pyOxvRSHD9diIFWEvyqBC072acCTxpiDHsvDRjCW/4PeAQSF\nGTPgmmsgOtrpSKzhw22R1J9/Oh2JChLeNEvYBrhX6jZ1LXOXAzQDtotINBBrjNklImcAV4rIFCAO\nyBeRQ8aYFzx3kpaWVvg8JSWFlJSU8nwPxwRrAoiLg/x8O0aRv8cdU8U4ehRefx0++8zpSP7SogV0\n7AgLFthxglTIycjIICMjw2fbE1NGpZDrhL4BW66fC3wDDDbGrHNb52agozHmZhFJBS4zxqR6bGci\nsN8Yc9x8dSJiyoojWF17LZx5prONPErSqRO8+SZ07ux0JBFo4UI78ueyZU5HUtSMGTB3Lsyf73Qk\nygdEBGNMhUtWyiwCcpXpjwEWYyty040x60Rkkoj0da02DUgQkUzgNmB8RQMKNcE0EYwnnRjGQdOn\nw8iRTkdxvKuugqVLYccOpyNRQaDMO4CABBHCdwAtW9r5Pdq0cTqS491yi50nfOxYpyOJML//bg+M\n7OzgLH+79lpo3x7uvNPpSFQl+f0OQJXs2DHbDDQpqex1naAVwQ5JT4eLLw7Okz/YZqkzZpS5mgp/\nmgAqITcX4uPtwGvBSKeGdMjMmXD11U5HUbKzz4Z9+2DNGqcjUQ7TBFAJwVz+D1oH4IisLNi4MTja\n/pckKspOTDNzptORKIdpAqiEn38Ozk5gBZo3tzGqAEpPtxWtwdY13NOQIbZPQIjWvSnf0ARQCT/9\nZOv6glXDhrbPz969TkcSQWbOtCfXYNe5M9SqFXzNVFVAaQKohGBPACJ2RsCffnI6kgixZg3s2QNn\nneV0JGUTsYlKi4EimiaASgj2BAA2Pk0AATJrFgwebMvYQ8HgwTBnjk4aH8FC5EgNTj/9BK1aOR1F\n6Vq10gQQEMaETvFPgRNPtFcIn3zidCTKIZoAKujAAduSrnFjpyMpnd4BBMiyZbZMPdTG3dBioIim\nCaCCNm+2Y2sF+92+JoAAKbj6lxAb8HbgQJg3Dw4eLHtdFXaC/PQVvEKh/B9sjJs2OR1FmDt61M65\nO2iQ05GUX6NGcPrp8H5YTtinyqAJoIJCJQE0awa//qpDwPtVRoYdDyTYK4RKkppq+y+oiKMJoII2\nbQqNBFClik0C2iHMj2bPDs2r/wKXXw4ffWSnjFQRRRNABYXKHQBoPYBfHTkC775ry9JDVXw89Oxp\n6wJURNEEUEGaABQAH38MbdsG75Cw3ho0yN7JqIiiCaAC8vLsXN/BPA6QO+0L4EehXvxToH9/WLIE\ndu92OhIVQF4lABHpIyLrRWSjiNxdzPtVRSRdRDJFZJmIJLmWny4iq90el/n6CzghOxuaNIGqVZ2O\nxDvaEshPDh+2xSYDBjgdSeXFxkLv3vB//+d0JCqAykwAIhIFPAdcCHQABovISR6rjQJ2GWNaA08B\nU1zL1wBdjTGnABcBU13bC2mhVPwDWgTkN4sWQZcuwd8b0FtaDBRxvDkZdwMyjTHZxpg8IB3o77FO\nf6BgiqG52AnkMcYcNsYccy2vARwjDIRaAjjxRDtMfX6+05GEmXAp/inQt6/t0bxzp9ORqADxJgGc\nAGx1e53jWlbsOq5J5PeISDyAiHQTkR+A74Ab3RJCyAq1BFCjBtSvb+stlI/88YedDPrKK52OxHdq\n1YKLLoK333Y6EhUgVfy03cL+8MaYb4COItIWeE1EPjDGHPH8QFpaWuHzlJQUUlJS/BRa5f30E5x5\nptNRlE9BMVCoN1YJGgsXQvfu0KCB05H4VmoqPPMMjB7tdCSqGBkZGWRkZPhse2LKmBFIRLoDacaY\nPq7X4wFjjHnUbZ0PXOssF5FoINcY07CYbX0C/MMYs8pjuSkrjmDSsSO88YYt/g0VI0faYeqvu87p\nSMLEFVfApZfaCdbDyeHDtoXD2rXhU7cRxkQEY0yFB6DypghoBdBKRJJFpCqQCnj2GJkPDHc9HwB8\n6gquuSshICLJQFsgq6LBBgNj7EBwoVQEBFoR7FN799ohlC8Li0ZtRVWvDv362bGNVNgrMwG4yvTH\nAIuBtUC6MWadiEwSkb6u1aYBCSKSCdwGjHctPxv4TkRWAW8DNxljdvn6SwRSbi7UqWMfoaRVK20K\n6jPvvQcpKVCvntOR+IeODRQxvKoDMMYswl69uy+b6Pb8T+C4vvDGmDeANyoZY1AJtQrgAnoH4EPp\n6TBsmNNR+M/f/ma/X1ZW6PR2VBUS8m3yAy3UE0AIVbUEp5074csvbTFJuIqJsa2b5sxxOhLlZ5oA\nyilUE0B8vJ285vffnY4kxL3zDvTpA7VrOx2Jf2kxUETQBFBOoTIMdHG0HsAH0tPtyTHcnXOOrfDa\nsMHpSJQfaQIopx9/hPbtnY6iYtq1g3XrnI4ihOXmwurVtrNUuIuOtkNc69AQYU0TQDkcPQqZmXCS\n50hIIaJ9e9u8W1XQnDm27L96dacjCYzUVJg1SyuOwpgmgHLYvBkSE22P+VDUoYO9g1EV9OabduL3\nSNG9u+0Ytnq105EoP9EEUA6hXPwDegdQKZmZsGWLbSIZKURg6FCb+FRY0gRQDqGeAJo3h99+06lf\nK+TNN+3In1X8NXxWkBo61BYD6VCyYUkTQDmsXWuLUUJVdLStv9CK4HIyxiaAoUOdjiTw2rWz5Z4+\nHIBMBQ9NAOUQ6ncAYOPXeoByWrHC/nv66c7G4RQtBgpbmgC8lJ9vm0S3a+d0JJWjFcEVUHD1LxUe\ndDG0pabCu+/CoUNOR6J8TBOAl37+GRo2DP0OoFoRXE5Hj9rOX5FY/FPghBOga1dYsMDpSJSPaQLw\nUqiX/xfQO4By+vhjW3veurXTkThLi4HCkiYAL4VD+T9Aixbwyy9w4IDTkYSISK389XTFFfDZZ7Ar\npEdzVx40AXgpXO4AoqOhbVtYv97pSELA/v0wf35kjP1Tlrp17SB4OkBcWNEE4KVwuQMArQfw2pw5\nduKXhsfNbhqZRo6EV191OgrlQ14lABHpIyLrRWSjiNxdzPtVRSRdRDJFZJmIJLmW/01EVorIdyKy\nQkR6+foLBEJ+vr1iDvUWQAW0HsBLr75qT3rKOv98OyDeDz84HYnykTITgIhEAc8BFwIdgMEi4jkc\n2ihglzGmNfAUMMW1/DegrzHmZGAE8LqP4g6orCxo0CD0poEsifYF8MLGjXbs7IsvdjqS4BEdDddc\no3cBYcSbO4BuQKYxJtsYkwekA/091ukPzHA9nwv0BjDGfGeM2eF6vhaoLiIxPok8gMKl/L9Ahw5a\nBFSm6dPh6qvt7FjqLyNGwBtvQF6e05EoH/AmAZwAbHV7neNaVuw6rknk94hIvPsKInIVsMqVREJK\nOJX/g20JtGMH/PGH05EEqfx8eO01Lf4pTps2tkns++87HYnyAX+NbFWky6SIdAAeAc4v6QNpaWmF\nz1NSUkhJSfFTaOW3di30Csn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+ "text": [ + "" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# plot distributions and loss matrix\n", + "\n", + "pl.figure(2)\n", + "ot.plot.plot1D_mat(a,b,M,'Cost matrix M')\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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J3C0yDCMKTMDLmIsvdkMRA28+K1P86ayZ/tpDcd0pFk+8s8oEuVMkoVKYzFYb\nBfPtb1eKHxy6Cnk+EYdo3CkWT9yRLHeKZg2NX9rYKJQy54ADsgfqMgwj2ZgFXgGMGeMCbJUt/kz6\njmBGpoeaEM0YcbB44lCaY8ShEHdKn4Ra4CbgFUDZu1CqcG8090WiA1IiDpXlTrF44pnJ7U7pk1Bn\nRMECLiJ9gFeBtao6UURGArOB/YH/Bb6oqt39ZRhF4IADsm8ri/NaDV2Cye0kIOJQXGvc4ok7EmSN\n94MqkhnXuTu3nWuAZYH1HwK3qeqRwBbgijAbZoRHfX3OzXZeDSOhFCTgIjIMOBd4IJB9GvCUl54F\nnB9u04ywyPbKt7I5r74LJbh0+W/pu1NqSc3YrMUFwKr2lprA9uB6bWDdX4Jl0tM+wW0E9iVHfm8J\nWvMdOfLbA3kdgbzgtmC6PVCuLS2vLZDXgbPCd3h5wX3UWwLZO3Ms2wvI3x74TE+nL8F9tgtsF/Zu\nr6N1SwNtu+rYuasu77dbihTqQrkduB5oBBCRwcBmVd3rbV8LDA2/eUYY5Hg5RHmcV1/A09nHJw7l\n706xeOKFk4onLgIcFGdbekZeC1xEzgNaVHUpdHEUJdNpVIFkEnA7r4aRfAqxwMcDE0XkXNztswG4\nE2gUkT6etTYMWJftANMDUwGbmppoamrqRZONQmhubqa5uRlwL17OQPmc12wWuE/nQ03o3ugUiyee\nm/KJJ763BAahBK/ZQhHtRtxRETkVuM4brfA48EtVfVxE7gVeU9WfZthHu1OHET4vvwwnnyyoakbr\nOunn9f6n4cp/xvk3oavPlLR0F41SUqLj+2p3BNZ9MW0L7Nietk97ID+4np4mrVxwnSzlwiJ4A0n3\n0wfzs61nSwf9/rVZ1v196gL5wTKBn2RwNFH/LOn6DPn1gfV86fq0tLdtYB/YmuG1h3Eikv2a9enN\nfedG4FoRWYEbcvZgL45lRIh0zyli59UwEkK3JvKo6u+A33npdwGbpF0GJP685nOhBCl5d4rFE4+F\nZE7EtJmYRhnQHQGHPKNT4pixCRZPPOZ44gkV8BJw3RtR000XimEYCcEscCP5dNcC98noTunJGPFs\nedmweOIpSiSeeEKVMKHNNowANaRGFnQXC4DlpSs8nngyX4lpLhTDMIykYha4kXx66kIJEmsEQ7B4\n4hDrlPu+ER8/IkzAjeTTGxdKkB4PMfTL5FrPR6m4Uyo0nnhCBdxcKIZhGAnFLHAj+YThQvGpyAiG\nUPEvhChEibnDAAANr0lEQVSNqBDdxgTcSD5huVCClPyMzUz5vaWCA2CFGXqmiJiAG8knTAs8SEVa\n4xZPPEmYD9wwDCOhmAVuJJ8oXChBEuVOqcB44mFY4XtCOEYMmIAbySdqAYduuFPiCIAFKXeKBcDq\nEQmNF2QuFMMwjIRiFriRfKqJ3gL3KXl3SiXGEw/hoebe/EVKERNwI/kUw4USxOKJe+lScaeEMMQw\nob6IhDbbMAzDMAvcSD5RjQPPh8UTpzTiiYcwRjyhb+QxATeST7FdKEEsnriXjjOeeAgzNhMq4AW5\nUESkUUTmiMhyEXlDRE4Wkf1EZJ6IvCUiz4lIY9SNNcLFzqthJJtCLfA7gWdVdbKIVAMDgG8B81X1\nRyJyA3ATcGNE7TSioTzOa416FniMg3ktnjg25b745BVwERkInKKqlwKoagewVUQmAad6xWYBzZT6\nhW50UlbntbqDPvW72UudlxGTkFs8cUp6xiZkF/KEOpMLcaGMAj4QkYdEZLGIzBSROmCIqrYAqOqf\ngYOibKgROnZeDSPhFHLfqQZOAr6iqq+KyO04iyw9gm5CI+pWLGVzXvv2a6ehqpVWb91Z4jFa4VBh\nEQwh8fHEE/pS40IEfC2wRlVf9dafwl3oLSIyRFVbRORgYEO2A0yfPr0z3dTURFNTU48bbBRGc3Mz\nzc3NAKxfn7FI2ZzXqqoOaqt2wCC33krMIg4JmLGZKb+3JCgAFnQV8hIQ8OA1Wyiimt/AEpHfAX+v\nqitEZBp0Ohs3qeoPvYdd+6nqPr5SEdFC6jCi49VXYcwYQVW7KFq5nNdH2Mb1fEDbLtf81i0N7N1e\nB9u97m4ndbFu95b09M4M5TKld+bJD6530Sb/u2oLbGgjJTgdOKFrS1tP36c9kN+RIR2sNLgtPS9X\nfm8IKmF1nvz0vJq0bcF0TaBcbVqZ9HVwP+XaDPsELoFq3PyB/jCwAbauzNmxoiOy7zWbTqGu+68C\nj4pIDbASuAw3cvIJEbkcWAVM6U1jjVgoi/NaTQd1tEE/L2NQ0AoHc6d0NoLyfCFEDyIY+lX65yih\nLzUuSMBV9TVgTIZNZ4TbHKOYlMt5rWYPdcELth+dIg7mTrF44pA3nng5C7hhlDLOAk8TP0/EIWnW\nuMUT7x4hxRPvbrUlggWzMgzDSChmgRuJp4bd1Hc6TAKk+cTB3CnlGU+8J0MM/TIBn3gCMQE3Eo/z\ngecQs0S5UyyeeOb1QgghnnjCMBeKYRhGQjEL3Eg8NbTTkMmFEiQx7hSLJ56i2DM260gaJuBG4sk4\nCiUbJe9OqcQAWFAa8cSTh7lQDMMwEopZ4EbiKciFEqSk3SkWTzxVTzHdKQIM7MaxSwMTcCPxVNNB\nbbeFiyzulFIQcahcd0pcMzaTNwIFzIViGIaRWMwCNxJPt10oQSwAVoBKjieeTFvWBNxIPL0ScB8L\ngOVRqfHEkyngyWy1YRiGYRa4kXxCscAhAWPEofzdKXFNuU+mLWsCbiSe0AQcSnyIIZS+OyUpAbCg\n63dR1d0GlgTJvO0YhmEYZoEbyadbU+kLJbHuFHshRPfwv4u9PWlg7JiAG4knazzw3mLulAClHE88\njCGGyZTCZLbaMAKE6gPPRKKs8UqMJx7WEMPkUZAPXES+LiJ/FJHXReRREekrIiNFZJGIrBCRx0TE\nbgYJw86rYSSbvBeniAwFrgaOVtXdIvI4MBU4F7hNVeeIyL3AFcB9kbbWCI1yOq+RW+CQIHdKJcYT\nD2PGpobSumJTqHVVBQwQkb24X8Z64DO4Cx5gFjCdEr/QjX0oi/Pal90MYktxKit5d4oFwErVSVq5\nXCRTwPO6UFR1PXAbsBpYB2wFFgNbVNV/dLsWGBpVI43wsfNqGMmnEBfKIGASMAJ3kc8BJkTcLiNi\nyum89t29h6q2ndBYRCscStSdUonxxMMIgJXMcLKFuFDOAFaq6iYAEfkVMB4YJCJ9PGttGM6Ky8j0\n6dM7001NTTQ1NfWiyUYhNDc309zcDMD69RmLlM157bMb+myDBt+fUEwht3jiHnG6U6IOgFUcgtds\noYhqbt+PiIwFHgTGALuAh4BXgE8Dv1TVx72HXa+p6k8z7K/56jCi5dVXYcwYQVU71aWszuv2+2Hz\nlbR7L1RpbezPFgbRSoNbp4HtXnoHdV3yg+k2ajvXg+V2UNtl/x2e33sHtbTtcunWLQ3s3V4H272v\neDsp//R2b0lP78yQDu6zM7AtX76f7qJN/vlpC2xoIyXgHaREtC3LOl5ee4Z90tNkWA/m5crvDcEb\nR3We/PS8GqCagQNr2Lr1CyG1JxxEul6zmchrgavqyyLyJLAE940vAWYCzwKzReSfvLwHe99ko1iU\n1XndDWxNXZoNxOdOKb2HmmABsApxp5SvCwVVnQHMSMt+Fzg59BYZRaNszqsn4D41FFnEweKJd1Lu\n8cRLCwtmZRiGkVBslp2RfHYBG7tmdVrhEPNDTTB3SmcjKE13SgdJtWVNwI3ksxvYtm92KfjEwdwp\nyYknnjySedsxDMMwzAI3yoAMLpQg5k7B4onndaeU8SgUwyhp0kahZMLcKR4l706JK554Mp0RJuBG\n8tkFbCqsqFnjWDzxrPHEk0cybzuGYRiGWeBGGVCACyWIuVM8LJ44KSvcfOCGEQ87yfkQMxvmTsHi\niXfxiScPc6EYhmEklOTeegzDpxsPMdMxd4pHxccTrwrpmMXFBNxIPj10oQSJMwAWWDzx0ognnjzM\nhWIYhpFQzAI3kk87GWOhdJfYHmqCxRPvrDSuAFjJtGVNwI3Es3sn7NwIA0M4Viw+cbB44p3EGQAr\neSTztmMYhmGYBW4kn7a9sCEQ0CosS9zGiHufJe9OCfOhZrIwATcSTxvQAm44IUCS3SklPcQQStOd\nEsUQw2RgLhTDMIyEYha4ETrNzc00NTUVrb424L+A0/0Mz50ShhUO2d0przTv4OimkCpJJ5s75ZXf\nwTFRVZqFjmbAq7No8cTfBEb7DaAYEQyL/bsNoz4TcCN04hDwl4GPBjMj8IlDV3fKH5qrGNNUl32n\n3pLJnfL6CzCmKbo6M/FhM/T36iyaO2UFcHRgPfoAWEkUcHOhGIZhJBSzwCuAAw+MuwXR0gZ8CGxI\n3xDyQ03o6k7pTxUNnbZxhATcKVv7tkO9ehuK9GCzmtS/ASjSCyH6kFme4ppyX5qIquYv1ZsKRKKt\nwCgYVQ3tirfzahjRk++ajVzADcMwjGgwH7hhGEZCMQE3DMNIKCbgRmiIyI9EZLmILBWRp0RkYGDb\nTSLytrf9rJDrnSAib4rIChG5Icxje8cfJiLPi8gbIvIHEfmql7+fiMwTkbdE5DkRaYyg7j4islhE\n5nrrI0VkkdfXx0Qk1IEIItIoInO88/SGiJwcdT9F5Osi8kcReV1EHhWRvmH3U0QeFJEWEXk9kJe1\nXyJyl/d7XSoiJ4ZYZ6jXiAm4ESbzgONU9UTgbeAmABE5FpgCHAOcA9wjIqE8UBWRPsC/AmcDxwFT\nReTo3Ht1mw7gWlU9DhgHfMWr40ZgvqoeBTyP19+QuQZYFlj/IXCbqh4JbAGuCLm+O4FnVfUY4ATc\njJrI+ikiQ4GrgZNU9WO4YSR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+ "text": [ + "" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# EMD\n", + "\n", + "G0=ot.emd(a,b,M)\n", + "\n", + "pl.figure(3)\n", + "ot.plot.plot1D_mat(a,b,G0,'OT matrix G0')\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# Sinkhorn optimal transport\n", + "lambd=1e-3\n", + "\n", + "Gs=ot.sinkhorn(a,b,M,lambd)\n", + "\n", + "pl.figure(4)\n", + "ot.plot.plot1D_mat(a,b,Gs,'OT matrix Sinkhorn')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 6 + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/examples/demo_1D_optimal_transport.ipynb b/examples/demo_1D_optimal_transport.ipynb deleted file mode 100644 index 354972e..0000000 --- a/examples/demo_1D_optimal_transport.ipynb +++ /dev/null @@ -1,166 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:70469d78395244f4978f6cd50bff1940285ade6e3a32e21040ca4851a189f8cc" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "1D optimal transport demo" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# load all relevant modules\n", - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Dataset generation\n", - "n=100 # nb bins\n", - "a=ot.datasets.get_1D_gauss(n,m=20,s=20) # m= mean, s= std\n", - "b=ot.datasets.get_1D_gauss(n,m=60,s=60)\n", - "\n", - "# bin positions\n", - "x=np.arange(n,dtype=np.float64)\n", - "# loss matrix\n", - "M=ot.dist(x.reshape((n,1)),x.reshape((n,1)))\n", - "M/=M.max()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# plot the distributions\n", - "\n", - "pl.figure(1)\n", - "pl.plot(x,a,'b',label='Source distribution')\n", - "pl.plot(x,b,'r',label='Target distribution')\n", - "pl.legend()\n", - "pl.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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mjEkVkQRs5fAxETkRWAJ0Msbs8diHKSuOUDBmDLRpA3//u3MxtGwJixZB69bO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\nz1NSUkhJSfFTaOW3di30Csn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J3C0yDCMKTMDLmIsvdkMRA28+K1P86ayZ/tpDcd0pFk+8s8oEuVMkoVKYzFYb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\njJrI+ikiQ4GrgZNU9WO4YSR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- "text": [ - "" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# EMD\n", - "\n", - "G0=ot.emd(a,b,M)\n", - "\n", - "pl.figure(3)\n", - "ot.plot.plot1D_mat(a,b,G0,'OT matrix G0')\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# Sinkhorn optimal transport\n", - "lambd=1e-3\n", - "\n", - "Gs=ot.sinkhorn(a,b,M,lambd)\n", - "\n", - "pl.figure(4)\n", - "ot.plot.plot1D_mat(a,b,Gs,'OT matrix Sinkhorn')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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v9svXr4fZs2HWLHj4Ybj88nAx9QtfCG31p5wSPlOmoydS0hQCmqE99gjDC/Tv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\nL+KNAAAAAElFTkSuQmCC\n", - "text": [ - "" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 6 - } - ], - "metadata": {} - } - ] -} \ No newline at end of file -- cgit v1.2.3 From 6bc9f4db4dda573c94506d632c48f0cd0f78c66d Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 27 Oct 2016 15:53:59 +0200 Subject: new ,notebook --- examples/Demo_1D_OT.ipynb | 52 ++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 42 insertions(+), 10 deletions(-) diff --git a/examples/Demo_1D_OT.ipynb b/examples/Demo_1D_OT.ipynb index 354972e..087b3bc 100644 --- a/examples/Demo_1D_OT.ipynb +++ b/examples/Demo_1D_OT.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:70469d78395244f4978f6cd50bff1940285ade6e3a32e21040ca4851a189f8cc" + "signature": "sha256:b65d32b626c6b5575ab808a448461a190dd0152a68ce83ddf7bbaf1aefb35c43" }, "nbformat": 3, "nbformat_minor": 0, @@ -30,11 +30,18 @@ "outputs": [], "prompt_number": 1 }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dataset generation" + ] + }, { "cell_type": "code", "collapsed": false, "input": [ - "# Dataset generation\n", + "\n", "n=100 # nb bins\n", "a=ot.datasets.get_1D_gauss(n,m=20,s=20) # m= mean, s= std\n", "b=ot.datasets.get_1D_gauss(n,m=60,s=60)\n", @@ -50,11 +57,17 @@ "outputs": [], "prompt_number": 2 }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting the distributions" + ] + }, { "cell_type": "code", "collapsed": false, "input": [ - "# plot the distributions\n", "\n", "pl.figure(1)\n", "pl.plot(x,a,'b',label='Source distribution')\n", @@ -70,12 +83,19 @@ "output_type": "display_data", "png": 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mjEkVkQRs5fAxETkRWAJ0Msbs8diHKSuOUDBmDLRpA3//u3MxtGwJixZB69bO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\nz1NSUkhJSfFTaOW3di30Csn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"text": [ - "" + "" ] } ], "prompt_number": 3 }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Distributions and loss matrix" + ] + }, { "cell_type": "code", "collapsed": false, @@ -93,18 +113,23 @@ "output_type": "display_data", "png": 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J3C0yDCMKTMDLmIsvdkMRA28+K1P86ayZ/tpDcd0pFk+8s8oEuVMkoVKYzFYb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\njJrI+ikiQ4GrgZNU9WO4YSR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"text": [ - "" + "" ] } ], "prompt_number": 4 }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## OT solution (Earth Mover's Distance)" + ] + }, { "cell_type": "code", "collapsed": false, "input": [ - "# EMD\n", - "\n", "G0=ot.emd(a,b,M)\n", "\n", "pl.figure(3)\n", @@ -118,17 +143,24 @@ "output_type": "display_data", "png": 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"text": [ - "" + "" ] } ], "prompt_number": 5 }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Regularized Optimal transport (Entropic regularization)" + ] + }, { "cell_type": "code", "collapsed": false, "input": [ - "# Sinkhorn optimal transport\n", + "# reg parameter\n", "lambd=1e-3\n", "\n", "Gs=ot.sinkhorn(a,b,M,lambd)\n", @@ -144,7 +176,7 @@ "output_type": "display_data", "png": 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"text": [ - "" + "" ] } ], -- cgit v1.2.3 From 0b1667deeef0640e0fcb7f125d0fb1658306b475 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 27 Oct 2016 16:24:58 +0200 Subject: better readme --- README.md | 38 +++++++++++++++++++++++++++++++------- 1 file changed, 31 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index f41d5c4..4f3d68f 100644 --- a/README.md +++ b/README.md @@ -1,16 +1,16 @@ # POT: Python Optimal Transport library Python Optimal Transport library -This Python librarie is an open source implementation of several functions that allow to solve optimal transport problems in Python. +This Python library is an open source implementation of several functions that allow to solve optimal transport problems in Python. It provides the following solvers: -* Linear program (LP) OT solver/ Earth Movers Distance (using code from Antoine Rolet and Nicolas Bonneel). -* Entropic regularization OT solver (Sinkhorn Knopp ALgorithm) -* Bregman projection for Wasserstein barycenter and unmixing. -* Optimal transport for domain adaptation (with group lasso regularization) -* Conditional gradient and Generalized conditional gradient for regularized OT. +* Linear program (LP) OT solver/ Earth Movers Distance (using code from Antoine Rolet and Nicolas Bonneel [1]). +* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2]. +* Bregman projections for Wasserstein barycenter [3] and unmixing [4]. +* Optimal transport for domain adaptation with group lasso regularization [5] +* Conditional gradient and Generalized conditional gradient for regularized OT [5]. -Some demonstrations of what can be done are available in the examples folder. +Some demonstrations (both in Python and Jupyter Notebook Format) are available in the examples folder. ## Installation @@ -18,5 +18,29 @@ Some demonstrations of what can be done are available in the examples folder. ## Examples +## Acknowledgements + +The main developers of this library are: +* Rémi Flamary +* Nicolas Courty + +This toolbox benefit a lot from Open Source research and we would like to thank the Following persons for providing some code (in various languages): + +* Gabriel Peyré (Wasserstein Barycenters in Matlab) +* Nicolas Bonneel ( C++ code for EMD) +* Antoine Rolet ( Mex file fro EMD ) +* Marco Cuturi (Sinkhorn Knopp in Matlab/Cuda) ## References + +[1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). Displacement interpolation using Lagrangian mass transport. In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM. + +[2] Cuturi, M. (2013). Sinkhorn distances: Lightspeed computation of optimal transport. In Advances in Neural Information Processing Systems (pp. 2292-2300). + +[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). Iterative Bregman projections for regularized transportation problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138. + +[4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, Supervised planetary unmixing with optimal transport, Whorkshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), 2016. + +[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 + +[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. -- cgit v1.2.3 From cb187e2cf8316ee7fe9a65f1d4381fb8baf8050f Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 27 Oct 2016 16:31:56 +0200 Subject: better readme --- README.md | 12 +++++++++--- 1 file changed, 9 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 4f3d68f..110cca0 100644 --- a/README.md +++ b/README.md @@ -1,14 +1,13 @@ # POT: Python Optimal Transport library -Python Optimal Transport library -This Python library is an open source implementation of several functions that allow to solve optimal transport problems in Python. +This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning. It provides the following solvers: * Linear program (LP) OT solver/ Earth Movers Distance (using code from Antoine Rolet and Nicolas Bonneel [1]). * Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2]. * Bregman projections for Wasserstein barycenter [3] and unmixing [4]. * Optimal transport for domain adaptation with group lasso regularization [5] -* Conditional gradient and Generalized conditional gradient for regularized OT [5]. +* Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. Some demonstrations (both in Python and Jupyter Notebook Format) are available in the examples folder. @@ -18,6 +17,11 @@ Some demonstrations (both in Python and Jupyter Notebook Format) are available i ## Examples +The examples folder contain several examples abnd use case for the library. Here is a list of the Ypython notebook if you want a quick look. + +* [1D Optimal transport](examples/Demo_1D_OT.ipynb) + + ## Acknowledgements The main developers of this library are: @@ -44,3 +48,5 @@ This toolbox benefit a lot from Open Source research and we would like to thank [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. + +[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567. -- cgit v1.2.3 From 8c88f7065c240c424d5bab80a813aa6e7ff879d1 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 27 Oct 2016 16:53:29 +0200 Subject: readme with references --- README.md | 42 ++++++++++++++++++++++++++++++------------ 1 file changed, 30 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index 110cca0..39ed68d 100644 --- a/README.md +++ b/README.md @@ -1,39 +1,57 @@ -# POT: Python Optimal Transport library +# POT: Python Optimal Transport This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning. It provides the following solvers: -* Linear program (LP) OT solver/ Earth Movers Distance (using code from Antoine Rolet and Nicolas Bonneel [1]). +* OT solver for the linear program/ Earth Movers Distance [1]. * Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2]. * Bregman projections for Wasserstein barycenter [3] and unmixing [4]. * Optimal transport for domain adaptation with group lasso regularization [5] * Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. -Some demonstrations (both in Python and Jupyter Notebook Format) are available in the examples folder. - +Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. ## Installation +The Library has been tested on Linux and MacOSX. It requires a C++ compiler for using the EMD solver and rely on the following Python modules: + +- Numpy (>=1.11) +- Scipy (>=0.17) + +To install the library, you can install it locally (after downloading it) on you machine using +``` +python setup.py install --user +``` + + + +After a correct installation, you should be able to import the module without errors: +```python +import ot +``` + +Note that for easier acesss the module is name ot instead of pot. ## Examples The examples folder contain several examples abnd use case for the library. Here is a list of the Ypython notebook if you want a quick look. -* [1D Optimal transport](examples/Demo_1D_OT.ipynb) +* [1D optimal transport](examples/Demo_1D_OT.ipynb) ## Acknowledgements -The main developers of this library are: -* Rémi Flamary -* Nicolas Courty +The contributors to this library are: +* [Rémi Flamary](http://remi.flamary.com/) +* [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/) +* [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/) This toolbox benefit a lot from Open Source research and we would like to thank the Following persons for providing some code (in various languages): -* Gabriel Peyré (Wasserstein Barycenters in Matlab) -* Nicolas Bonneel ( C++ code for EMD) -* Antoine Rolet ( Mex file fro EMD ) -* Marco Cuturi (Sinkhorn Knopp in Matlab/Cuda) +* [Gabriel Peyré](http://gpeyre.github.io/) (Wasserstein Barycenters in Matlab) +* [Nicolas Bonneel](http://liris.cnrs.fr/~nbonneel/) ( C++ code for EMD) +* [Antoine Rolet](https://arolet.github.io/) ( Mex file for EMD ) +* [Marco Cuturi](http://marcocuturi.net/) (Sinkhorn Knopp in Matlab/Cuda) ## References -- cgit v1.2.3 From 9ab569de69f391daad337e2d8fa17fb57a18f81c Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 27 Oct 2016 17:13:06 +0200 Subject: readme again --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 39ed68d..9793512 100644 --- a/README.md +++ b/README.md @@ -30,11 +30,11 @@ After a correct installation, you should be able to import the module without er import ot ``` -Note that for easier acesss the module is name ot instead of pot. +Note that for easier access the module is name ot instead of pot. ## Examples -The examples folder contain several examples abnd use case for the library. Here is a list of the Ypython notebook if you want a quick look. +The examples folder contain several examples and use case for the library. Here is a list of the Python notebook if you want a quick look. * [1D optimal transport](examples/Demo_1D_OT.ipynb) -- cgit v1.2.3 From 1c554920fbcbf15338a1cc3e50c7f27e0cb89597 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 27 Oct 2016 17:17:07 +0200 Subject: update demo 1D --- examples/demo_OT_1D.py | 2 +- examples/demo_OT_2D_samples.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/demo_OT_1D.py b/examples/demo_OT_1D.py index abc851d..fc2cbcf 100644 --- a/examples/demo_OT_1D.py +++ b/examples/demo_OT_1D.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- """ -Created on Fri Oct 21 09:51:45 2016 +Demo for 1D optimal transport @author: rflamary """ diff --git a/examples/demo_OT_2D_samples.py b/examples/demo_OT_2D_samples.py index 992352c..d7de2c7 100644 --- a/examples/demo_OT_2D_samples.py +++ b/examples/demo_OT_2D_samples.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- """ -Created on Fri Oct 21 09:51:45 2016 +Demo for 2D Optimal transport between empirical distributions @author: rflamary """ -- cgit v1.2.3 From d79fee64d4d8d318375f0ccba25bf71ae37ed1ea Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 27 Oct 2016 17:29:36 +0200 Subject: new notebook --- examples/Demo_2D_Ot_samples.ipynb | 233 ++++++++++++++++++++++++++++++++++++++ examples/demo_OT_2D_samples.py | 17 +-- 2 files changed, 242 insertions(+), 8 deletions(-) create mode 100644 examples/Demo_2D_Ot_samples.ipynb diff --git a/examples/Demo_2D_Ot_samples.ipynb b/examples/Demo_2D_Ot_samples.ipynb new file mode 100644 index 0000000..7e500f0 --- /dev/null +++ b/examples/Demo_2D_Ot_samples.ipynb @@ -0,0 +1,233 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:c74366ad23ce9af1038df1dfcbb934ba1d17d5cc990fc236b364db121e4eed9d" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data generation" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "n=20 # nb samples\n", + "\n", + "mu_s=np.array([0,0])\n", + "cov_s=np.array([[1,0],[0,1]])\n", + "\n", + "mu_t=np.array([4,4])\n", + "cov_t=np.array([[1,-.8],[-.8,1]])\n", + "\n", + "xs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s)\n", + "xt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t)\n", + "\n", + "a,b = ot.unif(n),ot.unif(n)\n", + "\n", + "# loss matrix\n", + "M=ot.dist(xs,xt)\n", + "M/=M.max()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot dataset" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "pl.figure(1)\n", + "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", + "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", + "pl.legend(loc=0)\n", + "pl.title('Source and traget distributions')\n", + "\n", + "pl.figure(2)\n", + "pl.imshow(M,interpolation='nearest')\n", + "pl.title('Cost matrix M')\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 3, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solve OT" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "G0=ot.emd(a,b,M)\n", + "\n", + "\n", + "pl.figure(3)\n", + "pl.imshow(G0,interpolation='nearest')\n", + "pl.title('Cost matrix M')\n", + "\n", + "pl.figure(4)\n", + "ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1])\n", + "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", + "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", + "pl.legend(loc=0)\n", + "pl.title('OT matrix')\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 4, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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DOum5JjinTGE0vm8ftebNm1uv/RIqOBwk6KtXPU90gF7LxmrlCmj48OCP06gI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\nXKmnTp1Sl8ulc+fO1YSEBHW5XKqqOm3aNG3btq1u27ZN3W63jh49Wrt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+ "text": [ + "" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solve OT with entropic regularization" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# reg term\n", + "lambd=5e-3\n", + "\n", + "Gs=ot.sinkhorn(a,b,M,lambd)\n", + "\n", + "pl.figure(5)\n", + "pl.imshow(Gs,interpolation='nearest')\n", + "pl.title('OT matrix sinkhorn')\n", + "\n", + "pl.figure(6)\n", + "ot.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1])\n", + "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", + "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", + "pl.legend(loc=0)\n", + "pl.title('OT matrix Sinkhorn with samples')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 6, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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NbQk0/9EOnPilnXjpVPwVDXltLYObgwcSuOO/duDkT27HDV/ZhZo/ZhldXc/T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jKQkZGBnJwcxMbGoqamJiB7fvnLX6KjowMzZszAueeeixdffLH3tf/6r//ClClTkJ6ejoyMDNjt\ndg9bvcfDZWdnezwnInR0dPT+zXoexcXF6OzsPElqOXz4MLq6upCdnY2MjAykp6fjBz/4Aerr6/u1\nd7hgyNzAINQQffs//xMoKeGfVv07VPvoA7fffjtmzZqFQ4cOoaWlBQ888MBJuro1aDlmzBiPMWtE\n1DvFCGCCfO655zzGq7W3t2PGjBkBBT/HjBmDZ555BkePHsWvfvUr3HrrraipqcGmTZvw61//Gm+8\n8QaamprQ2NiI+Pj4AaWDeh/feh5VVVW9mT5WFBUVITk52eN8mpubsXPnzj7tHU4YMjcwCDXee4/J\nNy2Nn6el8fP33gvtPvpAe3s7UlNTMXr0aOzevRtPP/10n9svXrwYH374ITZs2ACXy4UnnngCzZaF\n5c4778RDDz2E8vJyABw8/Mtf/gIAiI2NRVpaGioqKvzu/9VXX+0NmKampkIphVGjRqGtrQ2xsbHI\nzMyE3W7HAw88ALvdb+2iT3gT/3PPPYf9+/ejvb0dq1at8hgXJ9uWlJRg9uzZuOeee9De3g4iwoED\nB/Bez/X3Z+9wwpC5gUGo8fWvaxIWpKXx30O5jx748ox/8Ytf4Omnn0ZKSgpWrFhx0vxL7/fk5eVh\nzZo1WLFiBbKzs1FbW4uzzz4bcXFxAIBly5ZhxYoVuPbaa5GWloaZM2di06ZNve9fvXo1rrvuOmRk\nZOCtt946yZ5//vOfmDVrFlJSUrB06VI8/fTTGDNmDBYtWoR58+ZhwoQJmDhxInJycjxklMGc//Ll\ny3HDDTegqKgI0dHRvaP0vLdds2YNmpubMWXKFGRmZmLZsmW9Mos/e4cTZmycgcEw4nStAHW5XMjL\ny8Nbb72FCy64INzmBIw5c+ZgxYoVuPHGG0N+7HBXgBoYGBgAADZs2IDW1lbYbDasXLkSiYmJmDVr\nVrjNOm1gyNzAwCAo2Lp1K8aNG4e8vDxs3rwZf/3rXxE9wkZsjeRKVCOzGBgMI05XmcVg4DAyi4GB\ngYGBIXMDAwODUwGGzA0MDAxOAYys6ISBwQhDcXHxiA6qGYQOxcXFQ3r/UCcNrQbwDQBuAHUAbiGi\nY362NQFQAwMDgwEiVAHQR4noHCKaAWAdgAeHuL+QYjBDU4cbkWgTEJl2GZsCg7EpcESqXYFgqJOG\n2i1PE8Ee+ohBJP7jItEmIDLtMjYFBmNT4IhUuwLBkDVzpdTDAG4G0AzgkiFbZGBgYGAwYPTrmSul\n3lFKfW55fNHzcxEAENGPiWgsgBcBrBhugw0MDAwMTkbQKkCVUkUA3iais/28bqKfBgYGBoNAIAHQ\nIcksSqmJRHSg5+k3AewdijEGBgYGBoPDUFMTXwcwCRz4rAJwJxEdDZJtBgYGBgYBImSNtgwMDAwM\nhg8hLedXSj2qlNqrlNqllPofpVRKKI/vx6brlFJfKqVcSqmZYbbla0qpfUqpcqXUj8JpS489zyil\n6pRSn4fbFoFSqlAp9a5SandPMP7fw20TACil4pRSHyilPu2xKyJqLpRSUUqpT5RSa8Nti0ApVamU\n+qznWn0YbnsAQCmVqpR6rYefdiulwjpRQyk1qef6fNLzs6W/z3pIPXOl1OUA3iUit1LqpwCIiO4L\nmQG+bZoMlomeAvB/iOiTMNkRBaAcwGUAagHsBLCMiPaFw54emy4C0A7gBSKaHi47rFBK5QHII6Jd\nSqkkAB8D+EY4r5NAKZVARJ1KqVEA3gPw70QUVrJSSv0QwCwAKUS0OJy2CJRSFQBmEVFTuG0RKKWe\nA7CFiJ5VSkUDSCCi1jCbBaCXG2oAXEBE1f62C6lnTkSbiEgKi3YAKAzl8X2BiMqIaD+AcAdovwJg\nPxFVEZETwMvgVglhAxFtBxAxXzgAIKJjRLSr5/d2cNC9ILxWMYios+fXOHByQVg1TKVUIYCrAPy/\ncNrhAwoR1OSvRyGYR0TPAgARdUcKkffgcgAH+yJyILwX9FYA68N4/EhDAQDrP6sGEUJSkQqlVAmA\ncwF8EF5LGD2SxqcAjgF4h4h2htmkXwD4vwjzouIDBODvSqmdSqnbw20MgHEAjiulnu2RNf6glBod\nbqMsWApgTX8bBZ3M+ysy6tnmfgBOInop2McfrE0GIws9EsvrAL7v1VYibCAid0+fokIAFyilpobL\nFqXU1wHU9dzFKIT/ztOKuUR0Hviu4V975LxwIhrATAC/IaKZADoB3BtekxhKqRgAiwG81t+2QW+B\nS0RX9PW6UuoW8D/x0mAf2x/6sylCcATAWMvzwp6/GXihR9N8HcCfiOiNcNvjDSJqVUptBvA1AHvC\nZMZcAIuVUlcBGA0gWSn1AhHdHCZ7eiHpy0TUoJT6K1hi3B5Gk2oAVBPRRz3PXwcQ9gSEHlwJ4GMi\nauhvw1Bns3wNfNu3mIjsoTx2gAin97ITwESlVLFSKhbAMgCRkIEQaV4dAPwRwB4i+lW4DREopbKU\nUqk9v48GcAWAsAVlieg/iGgsEY0Hf5bejQQiV0ol9NxVQSmVCGAhgC/DaRMR1QGoVkpN6vnTZQjf\nIuyNGxCAxAKEXjN/EkASgHd6tKnfhvj4J0Ep9U2lVDWA2QDeUkqFRccnIheAfwOwEcBuAC8Tkd+K\n2lBAKfUSgPcBTFJKHVZKfTec9vTYNBfAtwFcaknd+lq47QIwBsBmpdQusIb/dyJ6O8w2RSJyAWzv\niS3sAPAmEW0Ms00A8O8AXuz5/50D4JEw2wOlVAI4+PmXgLY3RUMGBgYGIx8Rkx5kYGBgYDB4GDI3\nMDAwOAVgyNzAwMDgFIAhcwMDA4NTAIbMDQwMDE4BGDI3MDAwOAVgyNzAwMDgFIAhcwMDA4NTAP8f\nfBHRv735d5YAAAAASUVORK5CYII=\n", + "text": [ + "" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [] + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/examples/demo_OT_2D_samples.py b/examples/demo_OT_2D_samples.py index d7de2c7..5fc214f 100644 --- a/examples/demo_OT_2D_samples.py +++ b/examples/demo_OT_2D_samples.py @@ -13,7 +13,7 @@ import ot #%% parameters -n=20 # nb bins +n=20 # nb samples mu_s=np.array([0,0]) cov_s=np.array([[1,0],[0,1]]) @@ -28,7 +28,7 @@ a,b = ot.unif(n),ot.unif(n) # loss matrix M=ot.dist(xs,xt) -#M/=M.max() +M/=M.max() #%% plot samples @@ -50,32 +50,33 @@ G0=ot.emd(a,b,M) pl.figure(3) pl.imshow(G0,interpolation='nearest') -pl.title('Cost matrix M') +pl.title('OT matrix G0') pl.figure(4) ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1]) pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') pl.legend(loc=0) -pl.title('OT matrix') +pl.title('OT matrix with samples') #%% sinkhorn -lambd=1e-1 -Gs=ot.sinkhorn(a,b,M,lambd) +# reg term +lambd=5e-3 +Gs=ot.sinkhorn(a,b,M,lambd) pl.figure(5) pl.imshow(Gs,interpolation='nearest') -pl.title('Cost matrix M') +pl.title('OT matrix sinkhorn') pl.figure(6) ot.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1]) pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') pl.legend(loc=0) -pl.title('OT matrix Sinkhorn') +pl.title('OT matrix Sinkhorn with samples') # #pl.figure(3) -- cgit v1.2.3 From 0a4030fd809f677a4723b1cf38d6409b44160533 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 27 Oct 2016 17:39:39 +0200 Subject: Demo 2D samples --- examples/Demo_2D_Ot_samples.ipynb | 41 ++++++++++++++++++++------------------- examples/demo_OT_2D_samples.py | 19 +++--------------- 2 files changed, 24 insertions(+), 36 deletions(-) diff --git a/examples/Demo_2D_Ot_samples.ipynb b/examples/Demo_2D_Ot_samples.ipynb index 7e500f0..e20b09b 100644 --- a/examples/Demo_2D_Ot_samples.ipynb +++ b/examples/Demo_2D_Ot_samples.ipynb @@ -1,7 +1,7 @@ { "metadata": { "name": "", - "signature": "sha256:c74366ad23ce9af1038df1dfcbb934ba1d17d5cc990fc236b364db121e4eed9d" + "signature": "sha256:e01831efa84095a87a681a09f08c8991bbe439e010c2bc4cd3559dc4d3bf0bde" }, "nbformat": 3, "nbformat_minor": 0, @@ -43,7 +43,7 @@ "xs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s)\n", "xt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t)\n", "\n", - "a,b = ot.unif(n),ot.unif(n)\n", + "a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples\n", "\n", "# loss matrix\n", "M=ot.dist(xs,xt)\n", @@ -83,23 +83,23 @@ "output_type": "pyout", "prompt_number": 3, "text": [ - "" + "" ] }, { "metadata": {}, "output_type": "display_data", - "png": 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Cd5pZEcGPw4Pu/mQE7YqISIJ0BqiISBbTGaAiIgVEyVxEJA8omYuI5AElcxGR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\nXEQkDyiZi4jkASVzEZE88P8B8sPqteLhUE0AAAAASUVORK5CYII=\n", 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"text": [ - "" + "" ] } ], @@ -139,23 +139,23 @@ "output_type": "pyout", "prompt_number": 4, "text": [ - "" + "" ] }, { "metadata": {}, "output_type": "display_data", - "png": 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DOum5JjinTGE0vm8ftebNm1uv/RIqOBwk6KtXPU90gF7LxmrlCmj48OCP06gI\npptlOACbEKJS07R+AA4A+JkQYp/bcYrMQ4RAWRA7OtEpCfrQIb1utyfLX1YWI2ibjRFcdjaj95Ur\nSeq+Jvzk5x08yOX+6tVcvXz8MXXrtWs95xTkiuHqVd5TTSNJucpWdjvllDNn6CzJzeU5o6MZdael\ncVXjKjPU13PCLS3l6mnhQv01s5nEbTZzsiou5mpEVlQ0quWwrIxJzgEDODF6mpSvXOHfc8kSBhpe\n/37dtG5LMMl8JoA3AfRo/O9dIcRuD8cpMg8RAmFB7OhEZ0EBSdViIYlL3dsVRUWMxEtKSIoASWDK\nFMog/sg9xcW6NLJuHbXwo0eZjNy0yfM1yhVDQgInmZISnYBc72tKCs89aBAJulcvTgxDhrBV3IAB\njD5dJ4qiIhK5ptGSKJ0yDgcnhJMnOYHOmkXpp7aWUbtRI1gh6AA6fJh/q4ULm0841dWsTlldzfvR\nasemblq3RdVmUQDQfgtiRyc6q6t5/tRUPvSeImuzmVHo7du07U2YwInFaqVv21tS0hPq6rjkv3lT\nr3FSUEBpY9Qonk9ul5eQBaoOHSJBW610jcTFNSXTigqSeHExtXGzmZPMzJnUiz/4gLp/ZGRTYrt6\nlauBO+/krlkp0+Tm4vMEp4zGP/1U37Uaqu5MraG2luMsL2cNePfvjCy7cOAAVxXLl/txLY2e+7Kn\nu0/dFkXmCu2ugtiRiU6bjTLE2bPUfqOimk82tbWMgq9eJenOn88o9epVktmCBb5LKk6nXlRLNqXo\n00ffsLJuHf3Z7sjPJ+nU1dHamJbWXNqQZW/PneOEUFTEiH3ZMiZsZUXGbdvoNnHFJ58wgl28mNG7\npjGaP3KEE4jsLnTgACeEBx7wb/IKNtLTOTHedx8nMveEtazHXlzMaNzfio1VVcCRNzLxwLcnQaRn\nQJs0MUAjNy4UmXdztNeC2FGJTlnk6sgRjis2tnldkYYGkvaZMyRYqYsfPMhkZGysf8k+6Tbp35+k\nLQn3o48YacfF8f+uqKzkiiE9nYnN5GRu+tmwQbcPSs39wAG6ZiorKfnIjUxWKyPuykpOhq5laa1W\n4I9/pD6+fTsdHrKM7oEDPE9sLF//8EOuRtat86+GTDBht3PFc/06pThPLeqSkih7y6YZ3rz6niCt\nn/GfmPHQ5Z0Y9eoO9PylisybHKfIvOuhqgp44w1Gem2xIHZUojM7m0QFcGzudbsdDkbJ8fFMFq5c\nyYj6s88iQi6cAAAgAElEQVSobW/Y4LmAljeYzZwACgqY3Jwxo2kDh9jY5h50q5X69IULfM1qpQa+\nfj3fL48tK+MEUVLC38kmEdJFU1ICvPsur2PduqbEVVAA/PnP/N2zz5KLKioYsVZWcvIcO5b34eJF\nSiwzZvh5s4OI0lKWLBgypLm3HuCqZv9+ykZbtjT/u7eG6mquMOsKzHjk+k4M+C+lmXs8TpF510J7\nLYgdkeisqGAiLDdX15BdCVR2hT92jFF6TAyj4IQEkllUFLVmX1cHDQ2UPS5coHyxbBmTkGVljMbD\nwnh9rs+/00lXxbFjlEImTtRLBqxerfvV5Q7PCxcYzTc0UApxJfqbN0nMq1dzQnDFuXMktnHjmOjU\ntKYJzmXLyFEffMDP3LKl+arBKJAFxI4eZaTtyVVz+zaTnNOmcfL0t+FHE229ei96LlduFq/HKTLv\nOpAWxIEDGSH5E1F3RKLTaiXxXbrUlFRdkZ5Ootc0kvikSZQu9u+nNrxmje9kJh/+w4cZwcfEMFoU\ngiR6/Di1dvft4+npjOD79GFy8upVRs+bNukec+nxPnCA12CxcLJcskSPuh0OfnZSEmUV19otdjsJ\nOimJ71m9Wi9RO3AgVx1Dh/JeHT3KcXpygBgFFgtJurKSOzndXTVWK+9Vejptm+65Al/O/+mnnIC3\nbvVv30BXgyLzboi2WhADneh0OklKx4/TYrhqVXNCzs+nbm428/V772UE/9ln/N2GDd4363hCfr7e\nsm3dOn0pbzYzyWizkRRci12VlNChUlpK4m9o4JjmzCGZyomntJTjKi7m+e+9V2+eLFFdzW4/ffow\nSem687SiAnjrLR4jk6Aywbl2LZOFtbUcZ1UVj/G321EwkZbGXMDMmbwP7tp3RgavZdIkXp+/Ov/N\nm7zfc+bQ4eSPtt4Voci8m6GtFkTXROfGje1/cNLSGJH1788H2b2yYFkZpYysLBLm3Lkk/xMnWOlP\nRru+Tkayz2dqKollzhw6XFw3o0j5QjpfLBZKKImJjMTvuYfkUV/PKFIW5Gpo4IR08SLvy4gRegLV\nFdnZJHJps3ONphMTOVH26kXbYWkp78/UqXq5gZQURrmSvIxqObTbeT9v3aL84x5t22z663Fx3htn\ne0NtLQOSwkJOvEYtFBZsKDLvRmirBTGQic6SEkoV5eWUEKZNa97P8vhxRl1Ll1J26d2bRPbZZ1xG\nr1njuRGxJ8g64CdP0m3i2udTJsyqqppuRrHb9ffMnEkt/upV/hwZyUlETgQ3bzLS79GD5Lpund48\nWUK2bTtxguTmSl4OB0n78mVKEHFxnMSqqijfjB/PyeLgQU6AW7f6l9wNNoqLKRMNHcprcffi5+Qw\nHzF2LJPF/tTEAXSny/33c6XmLsd1Zygy7yZoqwUxUInO2lpGuTdvkhDdE5Wu7pDZs0mg/ftT/pBu\nkPXrPe/49IaUFI592DBOAK567Y0bPK/rZhRp+Tt8mMQeG8socs8eks6mTbo9sriYpFJSwhWD3L3o\nHi03NFBKKCujNOVqr6ysZO6ivJz3duRIumeWLeNE1rMn9fIPPiCpr1vX/lLEHQUhuGIymXjf5s5t\nOqFJS+K1a5TG/HXdtNfp0h2gyLwboC0WxEAlOu12JhVPnmQ0tWJF02jNbicJnDzJiDU6mlG3a80S\n1401vkBKFBUVvGbXSFgu0YuKmm5Gyc3le+x2Ev+4cSSmq1dJTrNnk5ysVpLSpUt83+zZTZsnu4/j\n73/nZ2zY0DSKTEmhrOJ0UjbJzGS+YONGEr7TyaTw+fOcxNy7BRkJNTWcsCwW6vjuzTXy8xmNDx/O\n6/M315KayglVVlE0atneUEOReRdHWyyIgUh0um5tHz68eWTsdHLjyLFjjIJjYvQJIy2NhCu1Z18l\nofp6vbBVZCQlGtdIOTmZ0fR99+lLdLOZScasLP5u1iz+e88ekvDatUxgCqGvUhwOXSbwloC8dYsS\nzqpVTXtqOhycJC9f5r/vuovar0xwahoj9Q8/JGlt2eK5Q5JRkJpKIvek4zscugd+7VpO5v5IdPX1\n+o7WzZv9S3R3Rygy78JoiwUxEInO/HxqvHV1JHHXXX5CkACOHKF7ITZWXzJXVvLhLSggUfoq67h6\nv+XOSlcHidXKJXpmJslx4sSmdkjZG9Th4LjT03nt8vMLC+nKKC9nBL5xo3e5x+nUVzTbtzfdhl5V\nxQRoVRUnTE2j3BAbS/lEFp06coQy0+LFxrUcyiRmUhJdOe4d2lrbOdsa0tI4od5zD3MrRt3RaiQo\nMu/C8NeC2N5EZ1UViSwtjVHa3LlNa6Lk5JAA6uoYictEocNBrfjUKRJrRITvia3sbBJ1WBijeHef\ncUYGiViSQq9eTe2QK1eSaBITeZ4ZMzi2Pn0YGR48yBVEjx78fUt1XiwWknWPHvRUu0ovt2+T3Hr3\n5vXL2uKyfor0S1dUeC46ZSQUFXEn54gRzCO4JjGdTn3nbExMc+28NVitXM2lpvL+eNru323RSmlf\nReZdFP5aENuT6Gxo4AN87hwTipGRTSOpkhJGm4WFJPlZs3RClM2Kw8MZjbs3IPaGykpODNnZjGzd\nl/CukWNcHIn79m2S84ABXDHceScnoH37GHXHxZFchSDhHzzIiWbuXBJTS/cxN5dla2fP5jXK63M6\nqb1fvMjzNjQwb7BsmT7BSk3Ymx/bKJCunIQETowyjyBRWqpPWJs3+79zXvrOW6oR363RSmlfReZd\nEP5YEF0TnY8+6kOtaLf3XrvG948fT1J1/bzKShJZSkrzdm7V1STLnBxG1O4WRW9wraK4cCHP654Q\nk/a3ceN4bvlZFRW6HRKgc8Zk0lcDYWGUiN5/n2MfN46k1NIEI1uYmUw81rUdW3U1nSg1NVwh3XEH\nV0nyfDYbx5WaymSskZvJV1dzhVNfz5WD6z1xJfm27EiVm7Bu3fJeI16BsOSZUfHCTiRu3IE1V5sW\nEAtmc4pxAN4CMAqAE8DrQoj/9nCcIvN2wB8LYnsSnVlZjOR79GAU5Vputa6OD/aVK4zUIyL0KMu1\nccP8+dSGfXEnyG3yBw9Sh169uvlEZbfrDhRZv9xk4vuWL6dE0rMnVwp79vA9cXGUC+rq+LvkZEog\nnja7uMNm46RZWAg88khTgsvI4KQgk6wREYzuJcnl55Pox4zhWI0chcrgYN685jXFKypI8k4nJyRf\nV1YS2dl8v5x4/fWddxdUVzOIuXIFWDA8EzHPTOKXzCUCCCaZjwYwWghxRdO0gQAuAtgihEhyO06R\neRvhjwVRepxHj/Yv0VlRQU0zL6+5vGGzMUI7fZra84oVTRNfWVl6L8r1633vflNURD27tpYPvCdX\ng2wccccdvP7r1zkOuVGoXz9OJLKeeHQ0yR3gQ2Iy8TpWrWKk3lr98/Jy5hdGjWI0KTV+p5MOjjNn\n+Hk9egBf+IKe5HU6KXGeOcNrmTnTt3sQCsiVw+3bTHK6ertdi2e5bqTy59yyFO7GjbQdKjSHlMRv\n3OB3edm9Zgz+z53Ajh3AKyGKzJudUNM+AvBrIcQRt98rMm8D/LEg5uaSiJYs8T3RWV9PkrpyRX+f\nK4Fdvsyk4vjxJERXr3FNjd7Nfs0aTjS+fGZtLckiKYkTw/z5zQlD+rHPneO5NY3vGTOGk42MFHNy\nGHkPHcpIeMgQvauPxUId39c64MnJ1HbdJQWLheVs5Uai8HBWPJQrnooKroR69mQU6+su1lCgsJAr\nCznZu64cKit5L+vqeB3+1ofJzWU07q1jkwKDhYQEftfmzeMzN9BuQM1c07SJAEwA7hdC1Li9psjc\nT/hjQfQ30enaeWfq1Ka2Pyl9HD3KCDw2tqkVz+mknnz8OKOKFSt8I0uHg++Lj2fkHx3tefldUsJo\nvF8/Ev3Jk/zMtWv1Le9WK8eXmEiyvvdeThLvvceVwpgxdJ64N77wdi+kjPPQQ02lpYwM/g2cThLf\n3XfzbxEWpjdMOHSIE21HNLkOFITgiubkSd7HWbOaviavY/FiRuT+RONSBrtyxfgboUKFkhKSeFoa\nA4XFi12++0ZzszRKLCYAPxFCfOzhdUXmfsIXC2JbEp2pqVxmDxrEqFcWlgIY1R4+TOKNiaGFzJWg\ncnI4rj59GH35arVLT9ebHK9d6/l9QlCmSEhgxFJYSNnHvQZ6SgrHMGkSx9+nD8d89qxeA9zXIk+1\ntYziHQ4SuYy2heAzdukSVyN1dXrBLk3T+1yWljJx6HoPjYbqak6ONhtlFdcJrqaG12E2Mxr39zrk\nLtBhwxjp+9MBqjugoIDf5+xsfqcXLvTfW+8rmQfELKVpWhiA9wD8nycil9i1a9fn/46Ojka0bLGu\n0Axnz5JYn37aO5G7Jjqfeab1RGdxse7+kL0lJUEWFtJ5UFZGG527JdBiIWGmpTFJ6euuv/JyfmZx\nMT/Tm7tFJtwcDuqscrv/Aw/oso/FwgkhL0+vkX3rll7iNjq6ebPklpCfT1lK9quU0Wh5OfDmm/w8\n2TJu82Zd/5UlYO+7j0RuVMshwPuzdy9JJCqqacQtS83OncuJzJ/rkLtAL1zg5OzecKS7IyeHJF5Y\nyABg61bfyxWYTCaYTCa/PzMgkbmmaW8BKBVCfLuFY1Rk7iN8sSD6k+i0WJiUunWLD7Rr4aiKCr6W\nkcHX5s9vOnnI2uTHjvGBjY72zaHhuhNTblbyNEbXhNuECYxgpk3TN/3IY65dowwg/d7V1dSxi4tJ\nslu3+hfxXLrEyWvjRj2pLASlo/h4rhwmTeI9e/RR3mdXj7svrphQoqFBbw6xbVtT6chbHRtfIXfO\ntnUXaFeFEJT44uMZEERGshxCeyf7YLpZIgDEA7gOQDT+930hxH634xSZtwKTiUTWmgXR10SnLPl6\n6hSJeMUKXaezWPilu36d+t2SJc3JMD+fUV3PnpRUfFmCS+I9coRkFxPj/WGvqmJUXVHBSWPo0Oay\nT0UFJ7baWkbHI0aQSG7coGvm4Yf9S9TZbCSyvDy+VzpvyspY7qCigpNFYSHH98gjlA4KCrgKGjmS\nE4CRrXbSHjluHDVs17+rDBTaUmpW1p0/e9bz5qLuCiG4WouP53MVFcXnLVB16dWmoU6I732PZNGS\nBfH6dUoNLSU6ZQLz0CG95KskLauVibBz5/iFW77ccwPeI0f44MfE+P7Q5uZybACTkt4mIyF4HZ99\nxii/Z0+S+JQp+uc4nfqGFZlcPHeO4+rZk7bB++9vfUyuMJs5Cd5xByeG3r0pFxw/zglvwABGsQcO\ncIKIi6MsIbexG11OcN1yv3590/tTX8+/TXY2vzv+1k53TUrHxRnbsRMsCMFnJD6e36OoKD63/iSP\nfYEi804Gq5Xui+99z7MF0ddEZ14eNWqrlQQppQDpJElIYFIzOrq500N25zlyRG+N5ksEWl3N96Sl\ntU7+FgtJITeXP8vqg65RTGEhI/a+fUnasjZKdTW94mvW+P/AyBoqsuqippHYPvyQScD77+c43nuP\nPvXISEpZH33E92/dauwG8HKsTifzDK5jTUvj/Zw6lRG1P6VmnU69vo57pcjuCqeTLqqEBH5vly/3\nfadzW6DIvJPAZOJ/Dgfw8svAD3/IL0V0NP8D9ESnxcJlv6dEZ1UVCTU9vXn7tBs3qHkPG0ay9SSX\nFBZSUhGCMoJ7uzdPsNuZqDx1ig95VFTLuvWNG/QxO50kzBUrmurvNhuj5MuXuZqYMkW3Gk6YQFnE\nX++yEHq51gcf5Hnq6qh937zJ1zdv5rH79nHymD6dK4cDB5q3nDMiEhP5t1uyhIGAHKssbnX7dtua\nKpeWUtIKC2M0b+TJLBhwOPi9SEjgM7h8eXO3V0dAkXknxK5d/M8VrSU6GxpoRz1/ngQZEUFClTqe\nlCViYz3XCKmvJ9HfvMlJwJfISwi928/IkYyUW9ruXVdHPTovj2QaF9d8VZCZSaK/806eTxb4GjiQ\nTou2dKCpq+MkaLXyHAMHckI5cIDRaa9enCBu3GBC9NFHSVh79zI5uG2bb5NaqCBLAGdnc6yuiczM\nTBJxW4pbuVpEZael7hyN2+1csZ44we/58uX8Hgfrnigy74RwJ/OWEp1yo8fRo3xgY2J0HTMvj5Fn\ndTV/P3168y+eTFQePqw3F/Yl6i0pIRlWVlIXb62U6cmTHGO/fnpnelfU1TF6TEtjktVqZYRst3NZ\n39aNOIWFvHfTpnEiq6wkSZvNXAFMm8bz79vHpOcjj+jVAWXnGyP3oZRt5+66i/q4lE5sNk7giYlt\nK25VXs5JAGA07m9Nlq6EhgZO8qdOMZiKimrqCgoWFJl3QphMurTSUqIzM5OEGhbGqEsmGsvKSJw5\nOZQw3OuOSxQVkcRsNhKoL71D6+o4vhs3mtsbPaGwkNbBykquFlataj4Z3brFa5w2jYnFTz4hmcyY\nwetuaxuxK1c4Qaxfz3PJmikTJlCy2bCB//7b30hWGzZQirl5k3KEP/1Igw3pKDl3juN2TZTn5nIy\nuvNOXrs/kpRrr0/ZQMPI0lJHwmrlvThzhpNlVFRoV2iKzDspWkp0lpeTpAoKGDnKdmTV1XwIk5IY\nyS5e7DmqtFp53LVrnDQ81URxh/SZm0yMWFeubHlzUl0drW+3bjGaeeyx5tbEqio6WUpLmZC7eJG7\nUocN899q6Aq7Xe889PDD+liGDOHEYzazS5DNRiKfP58T5Ycf8rM3bTJ2PRGzmWPt0aNpDRjX7fTu\nBO/reeXGqy1bfC+U1tVQV0cH1fnzXHFGRhqjmYgi804Ib4lO1x6Yy5bpZF1fz6jz4kVG4ZGRnt0n\nQjDqPHiQX9LYWN/K4mZmkhz79qWk0pLP3OFgJCOrFG7YwCSs+zguXqRGP3++Xi8kLIzHt8f2V1nJ\nJhKyXEB8PBN/S5fSxTNhAqPV27dJ8Bs2cFI5caJzeKZv3OAEuGwZr0lOwq5VJf3dTi+bdRw92jkS\nvR0Fi4Xfw0uXGLBERhpLXlJk3sngmugcOJAatsNB8ouP13dFDhzISOzcORL5tGmMsr01By4pIQnU\n1pLAfEkkms16OdzVq1uuhii9tp99xsjmrruojbtHuKWlTHA6HFxRJCRwpTB/vt72ra1IT+ckuHgx\n78OhQ5RXhg/nJLhmDQtLJSTwfm7cyInHbm9eq8RokDmEvDzeV9k+z7Xsr7w+fyYjuWGrtpZRvhEi\n0GCjqop6+NWrDCSWLTOmY0eReSeCTHQuXswv1K5drJV96BDJac0ayi1OJ794JhMf6lWrvEsSDQ2c\nBC5dYvbdl1reNhsJ4vx5fSwtkWxBASP30lKObePG5ht5HA5OOmfP0imTksLjx4whObWHSIXQzx0b\nS/nIYuEq4soV7oTcvp2f8cknzCnMmcP7527jMyJycpjkvPturjZkDqG4mNH4gAF0BnmbyD3BvUJi\nRETgdip2FlRU8Htz8yZXtEuXGrskgSLzTgKZ6JStyYqKgK99jZG53BUJMPo9epQRb0yM96y6TCwe\nOEBpYfXq1r+oUoY5dIiRdWxsyzv8ZIPnlBSS/ciRHL/75+TmMhofMIDHpaXRNunehq0tqK+n66K6\nmtd5+TITVXffzXrdstNPQwNXPIMGkbQKC5tGuEaEbIRx4QInyBkz9N+3ZwNPdTUlpsrKtlVI7Owo\nLWWwkpJCG++SJcbOkUgoMjc43BOdt25RS750iQ/cD37AqHHGDEabVitJ9p57vD/AZWWUO6qqSGS+\n9J6U0XVDAyPalrZ5ywbPZ8+SCIqKOCb3Tu0NDfq13XUXk5sAI6Dly9tfeKi4mE6ZkSP5gN5xB/Xw\nrCzmBWJjGYEXFZHIJ05kIbGpUzlBGtlyKBtdhIVRApITZFkZo/G2bOCRG8cOHKCs5d4irqujqIgS\nW0YGVyOLFhm7nZ87FJkbGC3t6BQC+NGPgBdeoF+4qIha+cyZ3iUBm41f1gsX9O3qrT2sFgsJNzmZ\n5/dmYwR0eefYMVq0qqsZYXsildRU+rnDw0m6djsJfdOmwOiRsqbLyJF096xfz6Su3DyzfTslqaQk\nrgrGjaPcsnmz7zXOQwXZYMS10YUQ1MWPH6fddNEi/6Jxi4XBQVkZo3Ejr0gCjbw8Phd5ebyfCxa0\n3e4aSigyNyha29FpNlNmkfVBFixoOZJNTiaRjR3LqLM1/VQ2Xj5xgkkz9y317sjIYLQbFkZp58oV\nz6RisZCIMjMZ+dbU8LybN7e+scgXOBw8v9yCP3MmpYaaGrpYRozghNG7t+4r79NHL5jlT1PrYKO+\nnknOggKWHJDyh9lMKcluJxG7tuzzBbJe+Zw5TJIbue56IJGdTZmqpIR5n3nzjL0aaw2KzA0I90Tn\n8eP6JqHaWkYRV6/yi/fCCy3XOamoIImXlVFS8aXuxu3bfM/QoUyoteQnLi3l7tCiIo711i2uKLZu\nbfo+WQHxwAHqj5WV/H1UFN8XiOV8dTXw9tuUj2QN7bFjdbveypWUDxwORuOZmRzr6tXNJSCjITub\nSc4pU3QJyNUy6G5F9AWyXnlhIf9evmwK6+wQgoFHfDy/g5GRtJt2hQlMkbnB4J7oBOha+f73GUWe\nOUPL3ooVLXuF7XbdwdFS0wdXlJWRbMvKSOKupWbdUVvLSebGDRJJ374kFU/uD7OZ5FlYSPLs2ZOT\nyrp1/jksWkJaGidAITjxLVlC2efAAb62fTuln5oanfCHDGGEaySvsDtk6d1Llzg5ye9EVRXvqcXS\nNstgUhJlrrbUK++MEILSXnw880qRkS1Lkp0RiswNAm87Oh0O4Pnn+RBPmMAHrzXySU1lJDpqFAmz\ntZrSViu/5Jcvt66lu3rX77uP8s7RoyTsBx5ouhNV1ho3mXh9vXtzFbFpE7vzBAJCMLq8eJE670MP\nUXMvL6esMnQoJ8a+fbl6eOst5g6WLWOCz8gPc3k5o/G+fUnYAwc2XeEsWsS/lz+rmro6Bgs5OW2r\nV97ZIF1b8fH8OSqKZgEj/93biqCSuaZpbwDYBKBICDHLyzHdjsw9JTplydvKSuC114Bvf5vSgWvJ\nW3dUVvJBLSpiwq+1RJ6sS370KN0vMTHeo335UBw+TH05NpaJy88+o9a4YkVTUikqYlnaykpGfQ6H\nrqEHyiFhNrMHZ1UVr3f+fK4kZKlX6ZvXNE6SH31EYnz0UWNLCq4eb1n/RNO4qti7lyS/dav/dUBS\nUxnNywJhnTHJ5yucTv7NExIYQERFNe1l2xURbDKPBFAD4C1F5oRMdI4axYjVkxTiqeStKxwOvXOM\n3ODRmqSSk0Mi7tmTRNiSe8G9kcWdd+qJOHet1W7nsRcv8txhYZwofPGx+wohmJg9doy6/FNPcQJ0\nOPjZKSmM0MeO5bF791KmmDqV3nEjk1hdHcdbXEwJSK50EhN5z9uSpKyv15POmzcHblVkRDgcnAhP\nnKCEJ/cUdGUSl/CVzAOSHhBCnNA0rYsv7HyHe6KzLV+49HQ+5HfcATz7bOs7JauqGF1nZjI6a6nO\nSWUlo/aMDCYPZ89mcvR3v+PW/eeea6q1pqZyhVFfT+Lu04dOnEAu5cvKeM9KSigxrFrF35vNes2V\nr36VtWesVuCPf2SSdtMmriCMjMxM3r/p0/m37NWL5C4nzrasKNLSGI3fcw/dT/40s+5MsNkoE548\nyZVjd5CQ2oqAaeaNZL6nu0fmnhKd3uBa8laiqopRaF4edfHWzmG3M3o/c0a3M3qLUK1WPhQXLrCE\nbUQEI9wDB0jsW7Y03WhUW0uCzcpiNGSzUVJZuDBw2qTdzmjr5ElGpU88oTdZSE4mYUlHh2z19pe/\n8Bq/8hVj11VxOPRqhq4+95QUer/vvZcSmD9JStk9KDU1cLZPI6Khgd/T06f5fYiKatp8ozsh6AnQ\n7k7mQlAeuH695R6d3uBwMKl44gRJOSqq5YdcCDoXDh6kPLJ6tXdiczoZ3ZhMXJquWsXkaUYGfcyT\nJ1NmcY3ujh/nf717U1aZMoURvz9V+VpDZiZrptTX07Uh28I5HHqDhQcfpL9dJpJPnuR4H3vM2Mmu\nsjImOQcM4CQ5YEBTWcR94vQFGRm8X23pHtRZUF/PRPzZs5SNIiO7X9kBdwRVZvEVu1wE4ujoaER7\ny/h1MjQ0MAlXUwM884z/G1QyM7nkHjyY0WZrm0OKikgKFkvrWmlaGgm/Xz8S4JgxjLA/+4yJz7i4\npgnV7GzKGhYLx9OvH33sgeywUlvL6DIlhRPN4sW6A6WykrVV+vShrNK/P33m77zD646J8dzw2igQ\nghPnkSP6KkbTKJt98knbZJGGBp7v1q22dQ/qDKit5erywgVe35e/3H3rqptMJphMJr/fF8jIfCIY\nmc/08nqXjMx9SXR6Q00NiTYri5HWjBkt6+u1tYyub94kUSxY4D06LSkhYcoGELJ1nOxGM2YME6Sy\n/nldHV0q6enUpx0O3xtY+ArZqu7QIT6oJSVMtMrJJDWVKwXpaZcOFtnG7PHHja2X1tZSPikvZ0J2\n5EgSsZy44uL872KUnc3rHzeOspunevWdGdXVlFIuX6bsFBlpbOksFAi2m+VtANEAhgEoAvCSEOJP\nbsd0OTJva6LT6eRSMiGBOxSXL2/ZieF0MmI5fpwe8Oho79XeLBYSfmJi0/Zudjvff/ly0240Tifw\nq1/xoerVi8dOn84IOJBb4MvKSHT19TyvxUJZZehQjuHYMboVHnyQhC3reCclUdr54hdb99WHEunp\nJF2pg4eFcZL++GPWplm3zj9ZxGbTZbsNG/TKiV0FlZWUzK5fZ1mJiIjAbTTralCbhjoY/iQ6XZGd\nTZKS8kVrLdIyMvg5AwYwevemxdvt1BlPnuTDsXy5TviFhYzGw8O5epC6d2oqf79vH6PGgQPpUglk\nokkmOM+d40oiKYmrgo0bOXlUV1NW6dmT0eyAAbRXvv8+3ys3DBnVduhwUMu/fp06+OTJTYl40yb/\ny/3m5nISGDWK35HOUKbVV5SX8/tw6xZdSEuXBjYP0xWhyLyD0NZEp8VC62BaGpONsn+nN1RUUIIp\nLDhi2RUAACAASURBVOTxUibxNJ6bN6mpjh7NJKXU3GXz37Nnm3ajKS2lfpufz2hozx7gZz9ruXJi\nW5CZyWh8xAhKKUeO6HVUpI784Yd6wlcI7ug7f57jmDOHUa5RvcSlpZx0hgzRi3nl5XGCbAsRu/by\nXL+e35GugpISrkRv3+ZqcfHirjVJdSQUmXcAXBOd7qVrvcHp5EYbk4lkGh3dcvKroUFvb9Za7ZXc\nXCZC7XZG7a7uiNJSjlU2gxgyhJru0aOUMzIzGQ2PGAG8/DLw0kt8X0s7UX2FTHCmp3NcBQWc/LZv\nZ9Qvmy9cvMhSAXffrbs/hKC3fO1a+t+NCNnL9OhROoPmz+c1yVor69Y177jUGvLz+fcaNsz/Xp5G\nRkEBv8/Z2STwhQu7pgunI6HIPMDwNdHp6h3Py+Ouv169GKW1FMXL2hyHD9OdEhPjXUM0mxnlZmWR\nTFybEQvBSDwhgeNYsIBEc/68XkvlnntIGDIyam0nqq+Q29UPHyaZLV7MyFwIauEDBnAilKS9bRtJ\n6+JFXs/EiZygtm/3rVdpKGCxcCVTWclrGj7cu4zlCxwOvavQ2rXta2ptJOTm8roKCxmQzJ9vXKnM\n6DCkNbGzwp9Ep8nEuiFHjtDBEBvberPdvDzq4k4nicybDbC+nrLJpUv8jLi4pg9IRQW1VqdT31CT\nksLo3WbjsZs3+++o8AWlpZy4rFa6ToRgfZWZMymt9OjB1cAHH+hb12traTmsrmZ0XlrKcRuxqS5A\niezjj3lNDz3Ea4qP5+S5enXTSdUXFBbyfIMG0a5o5D6UvkAIBhjx8dTGIyKY5O4KZWg7A1Rk3gr8\nSXQKwYdyyhTqnStXtrykrKkh6d++zQh7zhzPZOB0ksBNJp571aqmD75r/euICFr7Skr0Zst2O8+9\ncqXn6MjTTlRf4ZrgXL6cy+jLl5lXiIuj1i8EVwrnztGKeM893N356ae8T4WFlIO2bTPmtnS7Xd/E\ntGULJ56SEkbj/frxu+GPE8M1lyFb3HXmaFwITnQJCfxOR0YygOlOrek6EkpmaSf8SXTKSojl5cCv\nf916JURX54m0Jnojsdu3mQiVbhb33XDV1UxmyvrXAwaQ1G/d4nJf00g2HbEVOiOD0fiIEbpnfe9e\n6qQPP0z912JhktNmoyzRty9XCunpvDfHj9N2FxNjzB2dxcVcTQwdysmpb19ubjl5smky11e4TgJx\ncca2W7YGITgpJyTw7xsVxcnZiH/HzgxF5u1AWxKdAL/cu3axh6e311NTSWbDh9Nh4m23Z1ERk4hm\nM5fw7mU+XZv0LlhAXfLCBUZ8o0aRhJYuDVy3H1fU1nKCycggiU+fTonn739v2r4tO5tuD9koobCQ\nxDh+PN+zZ4/eENpoEIJ5huPHOdHMnctr/OgjktWWLf5tbnE6uTnm1Cnei3nzOm807nRylZKQwO9W\nVJR3t5VC+6E08zbCNdH5xS/6p/dpmvcvdGkpZY/KShKgN926poYrguRkPiQLFjQnY4uFEXBpKbfo\nV1YC//u/1JplpPf004HfDi3rpB85Qt1YtraTOzelzAIwcj19WtfoExJIjuvX6/W7H37YmDs6LRZe\nj8XC+3jHHTqxu9Yh9xWlpTxfWBirJho1J9AaHA6uVE+c4MoiNpZ/W0XixoCKzF0QiNK17vpzfT1J\n4Nq1pjsy3Y+32bh8P32aGmpUlOet27It2KxZ1PCPHOFnjBpF3bKjor7SUmrcDQ2UB+68U7fjXb6s\nJ25raxm91tYySeh0Umbp3ZsR+8mTjNgfe8yY27ZTUylbySRtTQ2J2GZjNO7PBCkE/6bSWSTrtHQ2\n2O2cxE+e5N8sKorOo854LZ0RSmbxE23d0ekNslLhsWM836pVnuWal14i6R05Ql07JsZz+7j6eo4v\nO5vyTFISyXvmTDpWRoyg/THQjgi7XY+qXcvf1tVRMrHZOP6BAzkZvvee3vHm2jVeV1QUJ5/33mN0\n+uCDxkt02my0VCYl0fs+YQIJ7PBhXa7yRwsuL9drymzZYux+pN5gs9E2euoUczVRUYEtuKbgGxSZ\n+wiZ6Lx2jdGiv6VrPSEri8Tbuzc3kHhrA5aXxz6gcXFMbnrzVqel6RX3BgygNj5rFqPk27cpXXRE\n7Y6MDEbjsueodGwUFHAFM2MGSVvTdG/7pk28jj17qPdv20YSfOcd6v6rVxsvQVZUxIlp+HCO327n\n+KurmVT25zshtXaTSZdkjHa9rcFq5TWcOUPyjopquWOVQsdCkbkPaGui0xsqK5m0zMkhaXnbsi/d\nL2VlwP/8D/DDH/I4d/eLa8W9mTO5ehg3jpuKEhL0tm2B3lFnsfBzMzM5UbiuVC5fZrS6cSOLStXX\nMwKtrKTUIksFzJpFt0d2NolSyj9GgusGq9WrOeabN/WkclSUf8ljs5nX3tDASaCzlXCtq+P9OH+e\n9suoKFZ+VAgtFJm3gvaUrnWHzUY98dw5buaJiPC9e4y33ZfZ2Zxohg/nZAPw4bpxg66QuDj/mxu0\nBvcEp6sv3W5nDfTsbE58w4dzC/p773FSWbmSlsjUVBLZxIl60vChhwI/1vaipob3t75e97fLpPLW\nrf5Foq4+/7ZIMqGGxcJczaVLnLgjI1uvqa8QPCg3SwsIRKIT4EOcmMgodtw4NlNor1PBbicpXLvG\nB6qwkFGt00nJY948ko0/rcZ8QUkJycxmA77whabSkNnM+zV0KJtv9O7Niev4cer0Q4cCb7xBAvza\n1/j6vn2UaaQbxEiQG5bmzaMDJyWF4501i8Tuz8ReVcVovLaWDag7UyRbVUU9/OpV2kcD8f1VCB26\nVWRuMpEgA5HoLCzkeerrKUW01WLn6n7Jz6fzA2DkuGgRdekDB3QXSaBbaMkE54ULTRteyHGlpXFM\ncmdpQwP15NJSJjJv3eLSfP16EoJsctGjh75JyCiw2eiPv32bSc4RI7jayMvjBOlPck/WoTl0iEFB\nRETn2fFoNtNeePMmXTvLlnX+UgJdGUpmcYMQwJe+xM0fbUl0SnKzWJgwTUriz/PmtX9J7XCQUE+f\n5rmmTOG5ExMZOXVUIi09ndG4e4IToMsmJoYkLxtGFBaypdzEiST2PXsYxW7ZQn97WRkTnffcQ8eN\nkaSGwkJuYBo9mnp/Tg7HL5O4/qx0qqsZ2VdWchLoLD0qy8pI4snJ3Lm6ZElgG5AodAyCKrNomrYO\nwGsAegB4Qwjxn4E4b6DgdDJaNJu5aaMtX+CjRxllJiRQT/7619vfwstkYhLx739nJC7Ln2oaSXPA\nAI430H5si0VvV+ee4AS42rhxgwT+7LMk9NJS3oO1a3k///xnaqtLlnC8GRkkS7nF3SgQgpPkyZMc\n+7RpXOlkZDA6b6l/qqdzyV238+dz01NniMaLikji6elc7f3zP3e99nMKAYjMNU3rASAFQAyAfADn\nATwqhEhyOy4kkbl0jhQVcZdkW+p2p6XxAXj8cRJCa92BfIHTyZXC1KnUmCWpmkzUy6W7IpAbM2Sz\n4aNH9drqroW35L26eZOT3w9+wPecOEGnSlwcibGsjNqyXN1cuMD3Pfigf+TY0aiuZpLTZiNxm810\n3kyezJWDP153i4XReFmZ/wnSUCE/n8FHTg4TswsWGM/fr9A6ghmZLwKQKoTIavzgvwHYAiCpxXcF\nCa6kPWqUf3W7JbmlpFBbXbiQemt7GzjU1zNhmJMDPPGEbuH73e+o2z7/fOCXvyUlJCO7vXmCU0Je\nl81GW+UPfwi8/jrv26pVXC3cd5+eJHQ6GaWmpbGbupEcEElJvF7Z1eboUcoLmzbpDaR9xc2b/PvP\nmcMJy+glXbOzSeJFRdTyt20LfMJcwXgIxNdyLIAcl59zQYLv9HAl7alT297AQertTifJUbYaM5mY\nfPrjH2n1+5d/8Z9oWoPNpncuck1wtoRevbhh5Mc/pvzz7rssMjV9OnMEYWGckN57j8c/84xxEp0N\nDXpVxkce4e9ef51uo699zT95obaWLpfCQlbOHDeuY8YcCAhB6SghgSuQyEhev9EnHoXAIah/6l0u\nbBgdHY3o9vYn8xNB/rjPYTIx4j54kN193nyTSbOvf522vchIRr6BXgLLBOfo0cBzz/lXc1u+54MP\nKLG8+aZOhOXlTHROmsTEqVESnfn5HO+4cZxgTp2iZLVxIycifyBr4Nx/PxO8Ro1sZSXOhAQ6iaKi\nOObOoOUreIbJZILJZPL7fYHQzJcA2CWEWNf4878BEO5J0FC7WdqLtjZwqKujlLJiBXXaadPo7927\nF/jrX4Hf/z7wEZ/Fwug0O5s+8KlT/Xu/00kynDGDOYL339fL+mZmMiKXdVqMAKeTxH36NHMPd9yh\nb7jauNE/yaqujpbTnBySuBGrOgIk8Vu3SOJOJ/3yM2YYZ2JVCByCZk3UNK0ngGQwAVoA4ByAx4QQ\nt9yO69Rk7i+k3i4E5Yof/IDJzNGjmZhbsoQSSExM4D5TJjiPHGELM/cEp69jzsmh9PPd71I+CQ8H\nvvlNfZfjtm3c7m0EVFXRB+90cu/AtWuUlNauZYTqTwI5NZV2RVkozIg9K51OOmpOnOBqYfny5rXu\nFboWguozb7Qm/gq6NfFnHo7pVmTuil27WPt7zx5GTnFxga/bIROcDgeTfO3xPtvtwE9+okfjTqde\nI+bxx42T6ExMpKa9aBFzDZ98Qilp0yb/NsHU13Mlk5nJCcFIjhwJh4OblE6c4LUtX84JVZF410dQ\nfeZCiP0AAlA4tuvBbidJvPlm29qMtQbXBGd0NM/f3qV2WJg+RquVMovdTunFCP7khga6S7KymOTL\nzAT+8pe29dNMS+MkO3kyE6RGs+7ZbFxtnTrFSdTI0o9CaNFtdoCGAjk5jBaLioAXX/QvAekL0tKo\nvd95JxORgdySbTJRqnnnHZLHunXGSKrl5THJeddd1Oz37SMBb97sXz9Nq5WrjdRUvnfy5I4bc1vQ\n0ED//unT9LRHRRnbTaPQcVDb+UMIq5W69a1beq3xQEbj7U1w+oKsLCY6o6IoY4Qarh3tN2ygVn7i\nBFcjCxb4d38zMjjJTpxIbd0otkqAks+5c7zOiRN5/ztLuQCFjoEi8xAhOZnR4uTJ3MUZSFnCNcE5\nZw4dJR2RpJM1y7dtM0bEajYzydmjh15qVwj/O/g0NOiT7KZNHTMJthW1tfT2X7hA/T8yMjA7jRU6\nPxSZBxk1NbS05eczwRnoJFpxMROcTmf7E5ze4HSSxJOTWYzMCM0VbtygPr50KeUUk4lE52/hsexs\nbuUfN46SkRG0f4Dfm1OnOIHeey93bBqtZLBCaKHIPEiQDR0OH2ZFxhUrArvBxGYD4uNpCwxUgtMT\nrFZq0Q0NLCAVarKzWkniubn05589Swli61b/IlabjVUur1+nPNMR7fXagspKFv+6fp11cpYt80/z\nV+g+UGQeBJSXM1qur9c71gcSMsE5Zgy13Y6qOW02M9E5bhwJL9SJzpwcyiqTJvGeHjtGX35EhH8T\nWW4uo/GRI7l5qH//jhuzrygvp9Z/6xZLIyxdymbYCgreoMi8A+F06mVVZRnYQEbLNTXc+p+TQ3IN\ndL0WV2Rns4BWZCQTnaH0LTudtFmeP8/NVElJbasZbrdTjrlyhQno++7rsCH7jJISknhqql78ywiT\ni4Lxoci8g1BQQCdE//7UrgNZa9y1l2RHJjglrlyhPe+BB9hQIpSoqGA0HhZGKeT4cUauK1b4t1LI\nz+dWflkbPtRRb2EhJ6jMTE76Cxcayz2jYHwoMg8wbDZGe1evcnPK7NmBjWJdE5xxcf53QvIHQtDV\nkZjIRGeoXRPXrtFquWgR70NxMaPxsWN9P4fDwdzChQuUpGbODO0qIzeXJJ6fTz18/nxjlgdQMD4U\nmQcQGRncJTh2LIkiENGeLNzlmuDsiB2i7mhoYKKzvp6JzlAu9evraeMsKGAUfuoUSXjVKv9KtxYW\nUhsfNIgTYSj7WWZmksTLykjic+cat+KiQudAULfzd1XU1VG7Tk/nkj2QvmSTiQnHffv0rvYdTUKV\nlUx0jhnDsrahTHRmZzdNcp4/zzHddZfv53DdSNSWrfyBghBMVicksIhaVBQdKqFOJCt0L6jI3AOE\noASxfz/125iYwNbsqKkBnn6a7oyOTnBK5OSw1+iyZXrfzlBAyiEXL3Ln5uXLLAvsb5XCkhJq4/36\nMRoPha1PCHryExK4woqMZKVGVYZWIZBQMksbIWuNl5ezZsf48YE7t8lEm93Fi/yMf/93Rm/tbUPX\nGqQmvXVrcCYObygvp8TTuzelquxs3mN/yum6OolWrep4WcrbGBITSeI9erCC4fTpqoKhQsdAkbkf\nMJnomrhwgWS7aBGjrI5quWWzAbt3t70Nna8Qgs6YGzeY6Bw5smM/r6VxXL1K58x997GU7t13M//g\nz4qntJTaeFgYJ4FAOol8gcPBTT4nTnBFEBXFyVGRuEJHQmnmfmDvXuriQrAxcUe7O4KREGtooCZd\nW8vStYFuEO0LTCb6qffuZeXIyZPpHfe3LooQrFuSkMAVzMKFwSVQu12vJR4ezvzJxImKxBWMhW5P\n5omJ9Fs//LD/1ffag46UVSorgb/9jRttQtlN/qOPeG/HjqU0AQDPP+9fqYDyckbjQnBSCmbdEpuN\nLqNTp7iqeeAB/xK0CgrBRLsec03THgKwC8AMAAuFEJcCMahgQLZIa2hgXZWICEaQHa1fS3TUZ+Tm\nMtG5ZAm3iofK3XH0KCfKZcuoja9fz0JS/pzj/Hn+jaKi/C+s1R5YrXot8fHj2QBjzJjgfLaCQlvR\n3pjtOoAHAPw+AGMJKlxJu3fvjtevg4Hr1+nA2bIldOVd5SSZlkaNfMgQ6srFxb6TudnMXbYNDXT9\nBKt6Y10da4mfO0dN/8knO3bzloJCINEuMhdCJAOApin1MJQQQq8M+MUvhpaAoqL4/4ED+d9vf+v7\n6sC1nMHSpYzqgxGNWyzU5C9epE3yy182RvlfBQV/0O01cyA4skpHoaGB2nRNTegSnRLFxRzLwIHA\nc88Bv/iF70ReVcVovLYWeOqp4Dhvqquph1+5QpfNV7/KBKeCQmdEq2SuadohAK6xngZAANgphNjj\nz4ftctEyoqOjEW0QFjXIMPxGVRUTnSNHMiIPVaLT6SQpnj7NDVZz55LEfbmvrrZFaQnt6J2TZjN9\n6jdusMbO888Hvj+rgkJbYTKZYDKZ/H5fQHzmmqYdA/CvLSVAjewz74zIywPefZeJwWXLQmeTKytj\nNN6rF73f/kS21dUsLtaWMrdtQVkZ7YXJyXot8VCuZBQUfEEofOZKNw8SZCu1uDjuPAwFhGCiMD6e\nG6788X4LwWs4cIA7OB9+uGOj8eJietTT0znOf/7n4HVSmjhxIrKysoLzYQqdGhMmTEBmZmab39+u\nyFzTtK0Afg1gOAAzgCtCiPVejlWReTshBOt8X7kCPPpo6Lq2m830fjscjKj98X5bLIzGy8r43o60\n/OXnk8RzcvRa4oGsseMLGqOq4H6oQqeEt++K2s7fxWCzkUArK+l7DkXTBVe3SUSE/x2Wbt7kimLO\nHOrpHaXx5+RwxVBUpNcSD1UZWkXmCr5CkXk3QHU1E53DhlGXDkWiU7pN6ur8b6pcW8tSv4WFfO+4\ncYEfnxCsJR4fz5VDRAQnjVAlhSUUmSv4ivaSubImGhz5+Ux0LlhAp0ewE51CsOriwYNMtkZE+Kdv\nJyVxZ+3993MzU6AjZCGA27dJ4nV1vEczZ6pa4grdDyoyNzASE0mEmzaxrnqwUVNDfdts9t9tUlfH\n3ag5OSTxCRMCOzYhOFHEx9MaGRXFHaZGqyWuInMFX6Fkli4IIfRWco8+yk48wYJsZyf17blz6Vbx\nR65ITWWbvenT/W860RqcTo4tIYFRflQUd20adQ+yIvOuhx/96Ee4ffs2/u///i+g51UySxeDzUZt\nuqKCOzqD3c/y0CE6TQoKOJH4o2/X19NumJnJCoOTJrVvLHJiAeicuXaNPvEBA4A1a1hS16gk3hlw\n4sQJfPe738XNmzcRFhaGGTNm4LXXXsP8+fNDPTTDw4gVTBSZGwjV1dTHhw7llvZgOzBSUlgtcPly\nbsf35/PT0hiNT57MfqaBsACaTNTAL1/mjs1hw+itnzCh65C464QVzHNUV1cjLi4Ov//977F9+3Y0\nNDQgISEBfTrAu+lwONBTJTE6HAZTGLsvCgqAP/yB1Q63bQsukZtMwA9+AOzYwUTn6dPshOTLjmKr\nlbr6J5+QaOPiAkPkDQ3U23/1KyY4H3qIVQy7WlOINuzaDsg5UlJSoGkaHn74YWiahj59+iA2Nhb3\n338/AEAIgZdffhkTJ07E6NGj8aUvfQnV1dUAgOPHj2O8Wz/FSZMm4ejRowAoQ2zfvh1PPvkkwsPD\n8eabb8LpdOKnP/0p7rnnHgwZMgQLFy5EXl4eACApKQlr1qzBsGHDMGPGDPzjH//wOu4///nPmDx5\nMgYPHozJkyfjnXfeAQCkp6cjJiYGw4cPx8iRI/HEE0+gqqqqyfheffVVzJ49G4MGDcKzzz6L4uJi\nbNiwAYMHD8aaNWtQWVkJAMjKykKPHj3w+uuvY+zYsRg7dix+8YtfeB3TmTNnEBERgaFDh2Lu3Lk4\nfvx4q+PtEAghgvIfP0rBExIThfj5z4W4eTPUIxHipZd8PzY9XYjXXhPio4+EqKsLzOcfO8YxPPWU\nEIAQ3/42fz52LDDnDzZa+977c78DeY6qqioxfPhw8dRTT4nPPvtMVFRUNHn9jTfeEFOmTBGZmZnC\nYrGIbdu2iSeffFIIIYTJZBLjx49vcvzEiRPFkSNHhBBC7Nq1S/Tu3Vt88sknQggh6uvrxc9//nMx\na9YskZqaKoQQ4tq1a6K8vFxYLBYxfvx48eabbwqn0ymuXLkiRowYIW7dutVszBaLRQwePPjzcxQW\nForExEQhhBC3b98Whw8fFjabTZSWlooVK1aIb33rW03Gt3TpUlFSUiLy8/PFyJEjxfz588XVq1eF\n1WoVq1atEj/+8Y+FEEJkZmYKTdPE448/Lurq6sT169fFiBEjmlyfvBe5ubli2LBhYv/+/UIIIQ4f\nPiyGDRsmSktLWxyvJ3j7rjT+vnWO9eWgQPynyLw5nE4h4uOF+OUvhcjLC/VoCF+IwWoVYt8+IX7x\nCyGSkztmHE5nYIgu1PD0vZcT1ksv8QmU//ZnwgrEOZKSksSXv/xlMX78eNGrVy+xefNmUVxcLIQQ\nIiYmRvzud7/7/Njk5GTRu3dv4XA4fCLzFStWNHl92rRpYs+ePc3G8O6774rly5c3+d1zzz33ObG6\nwmKxiKFDh4oPPvhA1LUSPXz00Udi3rx5Tcb39ttvf/7zgw8+KF544YXPf/71r38tHnjgASGETuYp\nKSmfv/6d73zn/7d35tFRV1ke/9xAooRsRAIJJBSbAW21JXiGIGIDiiISut0gqCAN4uE4E1HPDAPD\noAI2LmzuCrRCgxEERw+b2shRcEEQpLWVFhCBEExCUBJCBRJSlTt//Cpl9hRZ6lcJ73NOTtX7/V69\n960lt351733v6v333+99fmXG/JlnntFx48ZVmPvmm2/WFStWnJde1YYbc+MztwmXy3JN/PqrPYHO\nmqjL93r0qLUStXPn8y8Bdz60JFdKZSpXs6pPYZTGGKNXr1688cYbgOV2ueeee3j44YdJT08nKysL\nR7l8UofDQUlJCcePH/dp7MpumMzMTLp3716lX0ZGBjt27CDasyeEquJ2uxk7dmyVvqGhobz99tvM\nmzePCRMmcN111zF//nx69epFbm4uU6ZM4bPPPsPpdOJ2u71jltGx3Eb/bdq0qdJ2Op3etogQXy76\n73A4+P7776vVv2bNGjZs2ODV73K5GDJkSK16mwLjM7cBpxP+9jcrzW78+MAx5FDVmJf5Y0tKLH/6\n2rVWuuHttzf9ZlXNdWvi5khiYiLjx4/3GqxOnTpV2CAsIyOD4OBgOnbsSNu2bTlz5oz3nNvt5sSJ\nExXGq5zt0aVLF3766acq8yYkJDBo0CBOnjzJyZMnycvLo6CggJdffrlanUOHDmXz5s3k5OTQq1cv\nHnjgAQCmT59OUFAQe/fuJT8/nzfffLNBKaGqSmZmprd99OhROlWzkVBCQgLjxo2roP/06dNMnTq1\nWr2TJk2qt6a6MMbcz+TkWIHOHj2sYst27RniK1u3WnVFlyyx9oWZPNl/C5guBGPeGM+xPmPs37+f\nhQsXeoOQmZmZrFq1iv79+wMwZswYFi1axJEjR3A6ncyYMYPU1FSCgoJITEykqKiIDz74AJfLxZNP\nPsm5c+dqnW/ixInMnDmTgwcPAvDdd9+Rl5fHiBEjOHDgAG+++SYul4uSkhJ2797Nvn37qoyRm5vL\n+vXrOXPmDMHBwYSFhRHkWSXmdDoJCwsjPDycn3/+mXnz5p3/i1KJOXPmcPbsWfbu3cuyZctITU2t\n0ufee+9lw4YNbN68mdLSUoqKiti2bRtZWVnV6m3KrB5jzP3Ivn2wcqV1ZTtoUOC7Elwua9vY1ast\nvXfdZfb/bmzsMubh4eHs3LmTfv36ER4ezrXXXstVV13F/PnzAZgwYQJjx47l+uuvp0ePHoSGhvLC\nCy8AEBERwSuvvMLEiROJj48nPDy8gkuiOh599FFGjRrFTTfdRGRkJPfffz9nz54lLCyMzZs3s3r1\najp16kSnTp2YNm1atV8OpaWlLFy4kM6dO9O+fXs+/fRTXn31VQAef/xxvv76a6KiokhJSeGOO+6o\n8NjKvxR8yRP/wx/+QM+ePRk6dChTp07lhhtuqNInPj6edevWMXfuXGJiYnA4HMyfP5/S0tJa9TYF\nZgWoH1C18qS/+sra8bBzZ7sV1U5ZUWa3G558EqZPt1ZxVvbTGurGrABtfmRkZNC9e3dKSkq8V/7+\nwKwADXBcLisPOzfXCnQ2h/Jk5Y12q1b1C64ZDM2Z5vgFbIx5E1JYaK3oDA+3Kr4Hun/cYDBYFp9E\nuAAADMVJREFUBOJy/bpokDEXkWeBFKAY+An4s6oW1P6oC4Pjxy1f81VXNQ//eE0Yt4rhQsPhcOB2\nu+2Wcd40tGzcjcDHqloqIk9jJbdPr6HvBeMz37/fyiEfNszaW9tw4WJ85gZfaajPvEHefVXdoqql\nnuYOoAlqyDQPtm61Ap3bt1t7kI8ZYwy5wWDwH43pM58ArG7E8ZoVH39s5WHn5MDEiRAZabcig8Fw\nIVGnMReRj4CO5Q8BCsxQ1Q2ePjOAElV9q7axniiXFjFo0CAGtRCHbGEhfPst/O53VqCzMYsxGAyG\nC4utW7eytR5bYTY4z1xExgOTgCGqWlxLvxbnMy/Lxz54ENLT4bHHrECnycc2lGF85gZfsbVsnIgM\nAxYA16vqr3X0bXHGvAxVmDXL5GMbqmKMecth//79XHHFFZSUlDTJ+LYGQIEXgTDgIxHZIyKvNHC8\nZklzTTs0XLiEh4cTERFBREQErVq1IjQ01HusSQsoVENxcTFBQUFkZWX5dd76EMj55w3NZrlUVR2q\nmuT5e7CxhDU3jFvF4DObNkF+fsVj+fnWcT+Ncfr0aQoKCigoKMDhcLBp0ybvsTFjxviuAxqck62q\nAW0kmwtmo61Gwhhzg88MGAAzZvxmjPPzrfaAAf4dw0NZcYPybN++neTkZNq1a0d8fDyPPvoopaVW\nFnLZlfRrr71Gz549udKTg7tp0yYSExOJjo7mkUceoX///rz11m85EYsXL6Z37960b9+elJQUsrOz\nAWtDK7C24Y2IiGD9+vVVNO7fv5+BAwcSFRVFx44dGT9+vPfcgw8+SEJCApGRkSQnJ7Nz507vuenT\np3PvvfeSmppKeHg4SUlJHDlyhNmzZxMTE0P37t0rlHnr378/jz32GNdccw3t2rXjrrvu8pbLq0xe\nXh733XcfcXFxOBwOZs+e7ZPeJsOXChaN8YepNGS4AKnxc5+Xp/rgg6qHD1u3lcq2+URjjKEVqwSV\nsWvXLt29e7eqqh46dEgvvfRSXbx4sapaZeBEREeMGKGnTp3SoqIizc7O1rCwMH3//ffV5XLps88+\nqyEhIZqenq6qqqtXr9bLL79cDx48qC6XS2fOnKmDBw+uMF5WVlaNGm+77TZdsGCBt//27du951au\nXKmnTp1Sl8ulc+fO1YSEBHW5XKqqOm3aNG3btq1u27ZN3W63jh49Wrt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wd24wcMtRdH//MKrvPYS17ziXQuT1z8hgFmpYGLs32TXWjz461DN/7jmvxu2n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\n/XWSd0EBCSs62rHnqe6VuXkzf2evOqIabW3Ad75DmWbbNv7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"text": [ - "" + "" ] } ], @@ -194,29 +194,29 @@ { "metadata": {}, "output_type": "pyout", - "prompt_number": 6, + "prompt_number": 5, "text": [ - "" + "" ] }, { "metadata": {}, "output_type": "display_data", - "png": 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NbQk0/9EOnPilnXjpVPwVDXltLYObgwcSuOO/duDkT27HDV/ZhZo/ZhldXc/T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5eXmorKxEJBLBddddh0gkMq5rFVcNA/CLX/wCmzZtQn9/P2644YaUcnHct7a2\nFieccAKuueYa9Pf3Q0SwefNmrEuO/0j9PRAkHtdsgQyn9/u1gk57O/DMM6plx2KaGuWjHwWOOkq1\nc4qI5lThoqymRiM6jdFCDtnZVjt2tXBGXHI7JZHQ3Cz33Qc8+aQWgbj8cuD447Vd3l62GY+n+oyn\na+Pu/ntD2tuBJ1/246mjV+DIf5q40nHp2ncopN+zszVvTWGhauGJhN67J54ANm8GjjwSOO00YM6c\nfTv4ad/s2plnDr95fv/4nPAnoo2kZPKd/sEPfoA77rgDpaWluPLKK4fVv0w/prq6Gvfffz+uvPJK\nVFVVobm5GUcccQTy8/MBAOeddx6uvPJKfOITn4Df78eSJUvw1FNPDR3/rW99C5/61KdQUVGBRx55\nZFh/XnjhBSxduhSlpaU499xzcccdd2DmzJk4++yz8YEPfAALFizAwoULMX369BQaZXeu/6KLLsJn\nP/tZzJkzBzk5OUOl9NL3vf/++xEIBHDwwQejsrIS55133hDNMlJ/92cZHNRl+ObNChSzZimI9/YC\nv/0t8PzzSmMsXw6cfrpuy0nLjhSJaMbBnh6lVMrLFYRycqzf91hoFEAnjNdfB+68U4N9jjsOuPRS\nBafs7OEg7vqYp6endefzveE/nkhoBOvTTwN/+QtQEg/go2/vOUWaSGjGyFAICAZ1fI3RSay4WHPJ\nEMCjUaVTnnpKJ7+DD1YQr60dfp/2RfEiQKdI4vE4qqur8cgjj+D444+f6u6MWU488URceeWVOP/8\n8yf93PvqsxIMqiYeCqm+UF6uYLltm6aB7ewEFi/WiMni4pF57q4uDQry+1VTj8ettk3AFRmegTBd\nwmGbM+Wo7Y9i3vnLUHOo32rXyQhFc9aZQ9o923U/QCqIAxML4jx3ezuwYYOuYqqrgcNmBeBfs3t5\nZKh9x+M6OQE6fgxocvvP8ycSas/YsEEBfeFCnWhzc/eNoKfJLk7hyRjk8ccfx/vf/37k5eVh9erV\nKC4uxtKlS6e6W57shiQSqnF3den38nLVxAcHVTN/5x0F9+pqNZb5fBaEXRHRyaCzU7VF2sxjsVQQ\nB1ITV2WS3l6dPJgz5dOfBqblqDFRvr0akgRG8x8KjOl0igvo7BvPy217KmwzGlXw3rxZsXrGDODk\nk3Ucc/40CkWaYWXNyFWCOKmmgoLh48UJkZ+dOxXEIxEF8AULlMraF0B8vOKB+STKs88+iwsuuADx\neByHH36qudNdAAAgAElEQVQ4fvvb3yJnf1i/OfJeD9ePxYDubv0UFioIFRUpGL/yimp4ubnqcXLc\nccDMmSNr0oODCmjRKFBVpe0NDirIEMjHUo0nPWfKJZfo+QFAxI+eFavR/7mV6L5sBY54fA0SN65G\nwueHQerk4v7vauR7CuQu1x6NAm1tmt2xv19pp6VLNZdMYWHygExUqEORZtK+6d1TUJC5r+kg3tam\nIN7fr3lt6ur02F1y4vtwjVKPZvFkv5GpfFZCIUuDlJVZKoXJrmIxBaaBAQXSgw6y4JQOxomEcuI9\nPbpvebn+FolYVzhy4COJiBpIX3pJgWnJEjWkFhTYcwSDypnv3AksyG7Akk/WIbapHqitHZZj3G03\nXXYHyNMng0hE+7l9u47htGk60ZWUaJ9zc0dvbyTtOydn5HFiH9xsjh0dCuK9vcqF19bahGRjuk6H\n8omX+JHdN75UwrsjXtZETw44mexnRUQLEXd1KZCUl+v7GgopgG/bpgA+Z47u09OjWl51deaKOiJ6\nbEeHglBlpf6NRhVwcnOtRj4SsCQSSk289JJq8cccAxx6qLZDrXNgQGmebdt05XDk3ABKvrcSia+v\nQPb3tSiFlPmHnWdPaZV0I2kspiDe3q4rlkhEx7C6WgGcn5HaouadnsgrnfvOdCw//N7VpSAeCADz\n5umnuHjXbWWS1g0BRK9ZiS2fWIFTXprY8nuZZNLB3BiTBeAVANtF5JwM2z0w92SPZLKelXhcX/qu\nLuVPKyr0xW9tVRAnINTWKthv2WK18aRz0jCgjEa1vXBYAa2oyHqQUMMcDcRjMeXCX3lFNf5jj1VD\nHYXeGO++qxRGWZlSLpXZypHLt7WQMzlzc1MqAO0ukKcDOKkUUkjNzfrd71cQz8uzn3RtnFq3q32P\nZZXi9sUFcUDpsA0bdOxnz1Y6xeezXPpYrzOR0FUFtfqFOQ046v+bmPJ7u5KpAPOvAVgKoNQDc0/2\nhuztZyUS0Ze+t1fBuaJCQcSt3rNggTV0btqkYD5/fjIaEMOpCxHV2Lu7FcD9fgUxAhZpgpGA3PVM\nqa5WEK+pSQXdaFT7snmzAtXBBzv9eexRZH1AOd4hA2eP5Xh3h1ZJB3BAwW5wUPvb06PUTjyu/Zk5\n02rg+fk2cjVd+3b95MeqMbvat9uvQEDdCzs7dbxcEB+rayUnpqYmHdtwWFdhh8wMoPDbK9X/fQ/K\n741VJhXMjTGzAdwJYDWAr48HzGtra9HY2LjHffDkwJd58+ahoaFhQtukN4mrNZeXK0hv3aqa5cyZ\nCuLktnfuVIXM71ftOD3whkBLSgVQjpjGOdbuoO07E7ike6awmo+rNcfjuirYuFH7sGiR9pXtusE+\nmc6Rro3z/5HGieLuE49bEO/tVT/7REKBk3SKiAK46ycfi+nf8Wrfbn9cV01KIAC0/+JR1NcsQ9Ui\n/xAnnh8KwDyv7pi7kkRC7922bUBjo17jnDk6vgVh6w20O+X3dkcmG8x/DQXyMgDfGA+Ye+LJVAiN\nkF1dCgYVFQpAzJ9CV7XaWkudBIOqAQ8MpGrj6UAYi6lGODCgE0BpqYJVPK5ATvDKBLKZqvnQrZHn\niMd1tbBhg7azcKGlMIDUaj+UXQH5SB4gIx0/OKjXEo3qOLa26v7FxRrsVFxs26X7I7+7hsvd4eVd\nLZzH9/XppNbWBswqDmDx3QqwedP9yOpVwE2nltLbjcf1Hm/bpoFbWVmWmhnykrn+euBzn1OejdLY\nqJFZ118/vosZo0yan7kx5kwArSLymjFmOYART3q9c7HLly/H8uXL9/T0nngyLiF33dOjGhuDTrdu\n1XeyrExpCteIGY/ry93UpFiwZIm+3IzAJGDF46qdsu25c61hkkZO14fZLa+W7pmyfHmqNwz7sX07\nsH69fl+wIDXU3/VD35U27vZ7JG+WTMcSxGMxvc72dt1WVKTulSUlNuKSk0p+/u5p3+55R3KV7OtT\nCqSlRSeRY48FfD4/8o5Yjci/rsTW81dg0e/WACMAOemu3l69x62tuoqYP1/ptMLCtHG8+urhhbFv\nvlm/T5CsXbsWa9euHfdxe6yZG2NuAnAhgBiAQgAlAB4SkYvT9vM0c0+mTAYGFMQHBhSw/X4Foy1b\nVIueM0fB0c2RAihYbNqkIFZXp4ABpHK15It7evT/ykqrydEwyZB7wIIDPVP+9rfhninprozNzQri\n0agqhbNmpeZpSeeYR9O002mV0QAcsFQK6aHubqWPsrJSDcTkvvk7Q+V310c9HcRdCQZ17Hbu1Elk\nzhxdAeXm6oS4fj0wuLEBH71iuJGSE280qteyc6feu9xcndyrq3Vy4ngM63+SWol8dQXyf3SAcebO\nSU+BR7N4so+IiI3STCSU8igutr7hWVkK4HPmDM+9EYupFtzcrMdxqc3wb/ccPT0KLqWlOlFQ+4zF\n9OMa/EiTvPmmeqYUFVnPlEzRoS0tCkzhsGr6s2dbEAesX7rLH48FyF3tdiSwpVcKk3h1dqomzjwx\nJSW2oDOg18m+7S6Iu5NkJgkGrS2jokInNvahs1Mn595eYLYvgCMfXIm8f1cjpXxb3TEJ4p2dCuLh\nsPZ5+nSdFNzJfKSJrbERaHquAadcum95s+xf4YeeeDIGYZRmIKBL/KoqfQm3blWAnjFDqYzKyswv\nbCCgoBCPK8hWVVk3QhrtABtIlJendAdd7UirAJZvB5SH/8c/1Duluhr4yEcUnDPRGR0d6mYYDKoW\nPnu2DU83JlXTJ9hmojDSQXG0fbm/q4UDFsTz8vR6Cgp07PLyrH8829yVi+VIMpIWzslnYMCCeFkZ\n8L736cQcieg9bWnRsZo2DVi6IICym1cCN69GotSPxA2rYb65EgMrV6Mj5kdbm15fQYFO5JWVNmfO\nSBNiJKLn37JFvYGOe2wNBjfUI28SvFnGKlMeNOSJJxMl4bCCa1+fasl+vwLR1q0adUiD5lDYeJrQ\nDa21VbW+uXMVvBhBSACNRHSyiEattu/y2q6RM5HQ/vz979Yz5fjjhxeBAPQc3d2qiQcCuuSvrU2l\nU1z3vbFo4+mc+EggywhU9p1ZDNvbLeddWKgabGGhDaNP79No58gko4E4oADd0KAg7vPZ8YhEdFwD\nAd2npES19BmvPArp74ecdgZiPr9eV2sAA7/7E3piPvScdCby8/W+lZVpm1zZpPc9kdDnZutWYPC3\nj6L70GWorQUW3bUSWTclOfI//Ql49tkDx5tlLOKBuSd7Q0T0hevqUo2yvFzBZts2XQH7fGrMqqkZ\nXRvt7tb9EwkF8XStPSvLesDQD93vTzU4UnOndtraqu6F9fWadnbJEpszJf38vb0K4l1duhKorU3l\nnNO18bGAOPs9mtthLGYrH2VnK5i3tGg/SkpscM/06UoJDQ5a/3hOcOPVxkejUlwQb2pSrbugQMcj\nO1sn7FBIwTwc1vPPmaOrrawsINGlroPR61cjmOtH5xbV0nf+y2pkV/pRWqoTPUE8fSzpNtndrc9Q\nR4de/8JpAcz575XI+uDJMGecoQfSEArYOgl7IW+LB+aeHNASj1vXwuxsBfFoVIGztVVpifnzVfsa\nTSIR+9JWVOhx9GAALOCQUsnOthRDesAMvTe2b1f3ws5OBfAjj8wcts6JaONG7fO0aaq58/x03XOL\nRIxkrHQB0uXCMwE5aaBIxIL44KD2oa9Px5JujixrR/7fdSfk5DJWIM/kUpj+fWBAx6+pSfswd66e\nJxTS8xujIB6L6aRHl0zaMkSAto0BxK9dicDlKzDnV2vQ+fXVKJypQF5YmGrD4PnpWx4I6Cqgp8dO\nItXVOl4bX9LUvDU/GMHwme5zPkE+6B6Ye3JAyuCgdS30+VTL6ujQpXA8rgA+b96uEzeJKIWwbZt+\nnz1btU9q7zRyxmI2oKiiQrVVF4gIctnZOpEwZ8pxx6lnykgpa+mzTkPe/PnWkEcKxS06wfMRONNf\npUzAnU6xJBJ6HeGwbT+R0PHr7dXJhLliKip0IuREBVjayNXG3fONNta7AnHWOd2+XX+bPVvPw0ky\nN1f3CYX0ns+YYT2G6J2yfTvw1ls6ic4INeCcq+qw46/1KD6sFvn5qYZjgj99ywMB9YIZGNB2587V\n8ejvV6+ZHTv0vIf7GjDz/SMbPiOtAQSvXomK70xcdKgH5p4cUJJeACI3V7U3JrtasEA1tbEAy8CA\nHtfTo8dSG+d2fsjJFhcn82znpGpy0aiCwaZNqokXFSmIL1w4cj9CIQWH7dsVLOvq9HoIrkCqxguM\n3FY6MPK3dM+VwUE9bzhs08TGYgpefX0WxEMhm0wMsNfnGnZdN8jRxjodwEdaUYRCQPc9j2LrzGWI\nFPoxa1ZSa+4OoOytdYiefuYQiNOYzQlVxE4C69db+mtBZQCL7tLEYkU/WYOsm5RioRsp7RrBoNIp\nvb36TBQWqrG5pESfjfp6nRjKy4FDDgFm5Cc17Qxh/IGAGqybm4GqYANOvmTiPF08MPdkv5f0AhDM\nWNjQYJNdzZ9vfYJ31VY8bjP45eQoiDP/CmB9xiMRbZ8+4wzKcV0L+/vVoPnGG6olHnOMcrcjARy9\nIbZts4a8igq7ncv+9MhIep8YA+DRR2FOSuVkpdtysq6WGo9bLTwry2YojET0+vv7dSWSk2OrG5WX\n674MCuKqwK1Sv6sJZldauOud0tKi45HoCuCoB1ei6xurEfP5URIPoOLWlWi7ajX6sv3IyrJpFjhO\nvb06mTc2alsM0qopCmDaD1Yi/i2N/mSK2vi3NGXtwIDu39dny8gVFOgkUVxsufK+Pv1t0aKk/aRn\nOIUSv1a5+M0dfgQC2r9DawKo+uFu5G0ZJU+6OessD8w92T8lvQCEz6cgnJ7salf1nwnOBI8dOxS4\nKir0eJfHpgbb06MTRkmJas7pGmhXl3qmrF+vL/oxx4zs4ghom/X1OgEVFyvgk86hm6NLXbAvgD33\nUNtpHKx0p+bWJghHo3ZiKChQeqG/X0F8YEAnn5wcBcTSUh2PnBwbHATYvqT3jf1KH+f0fme6DvLS\nLS0KxAMDNkCnfVMA0/9zJXq+uAKHPLIG27+yGtFiP/x+vQ9MTtbdrcd2dFgf8Zkz9VNaChT+5VFk\nnbQM2ZUKivE4EOsIIPbMOnS//0yEw3reSETPW1amY0SuPBzWlQrtLUPXnQTbmM+PcFhXAzvfDcD3\n+jrEP3ImDjoIKDd7kLdlFL7dlJd7YO7J/iXpBSCyslRL2rkzNdnVaOLSJIACSFubGveys1Ubp4ZH\nicVUS+vpUXDw+62Bk8DU2qp8eEMDcMQRqTlTRvISaWjQT36+Th7V1QoOriFRxPppu6/HiHlLAgH0\nXaVVg2bdtwaR/6e+1LzmRMJmJszOVsBmLvHqav2d1FFlpX53KSP2hdr4aHRPJi2cv7u/cTJta7Na\nLzMpBgJJ7TwBHFzQgGM+XYcNj9cj76BalJTotTDXzc6dep+YKmDaNL2fZWWWD3ddNgcH9bzBoE0x\nEI3aYs65ufqstbToOaZPV+2+pCT1ut0I35YWvQ5APaRqamzunT2uQpTU9ru/sALT7rRavUezeLJf\nCLlpFoAoLbW+xZmSXY0kbjUZAmx/v2px4bC+XzU1qdo4NcWeHgUvv1+1Ndf9r6lJw+3b2xXAjz5a\ngWMk18BYTI+pr1ewqK7WT2GhAgm9UzJp48M0cUficQW9118HElsb8Ilv1CGyvh4yr3YowCcvz9I0\nzDUSjSpw5uXpGDPgh5keqcm7QLgrbXwIxNNoHxGkFItO18RJXfh8em927tR7PHMmMLNQ6ZGeL67A\njLt1kor5/Ojq0rGnB0sioc/IzJkK5px03ckxElEQ5zF0ZczLs94stMHQK2bOHBsv4KbnZUrflhY7\n2dfU6BgWFU1c0Wem7G17qQFnXZnKt3tg7sk+LekFIAoLVeNhsqsFC1KTXWUSl0ZxX+hYzGpQzLeR\nzo0zLwdztXAlQDpg0ybVxCMRrVF5xBEjB5cAegzTBGRnK5VRXW3d+uibzf7m5WX2AXfbJe1RX68v\n+uAgML9C+eXEN1bA3KKgV1DtHwLenh4F8Xhcrzs/X7XanBwFPxp62bYxOkYEr3RtnH3KSKUkqQC3\n8AUuvACJ236C/sp5Q2XiBpoDqN2xDrEzzsTgoPaHPH1NjfLR1f+1EsFvqqHS9ASQe/1KbLhYqRYa\nLHNzVROvqkqNOuXkGArp/eRKo7/frja4WhkY0G6K6Aqtpma4hxINxj09SucEg3ofqYXn5Gh7u5M0\nzH1eOBZcffoRwPEPr0TxqlS+3QNzT/ZJcQtA+Hy2gktnpy5x588fnuzKlXQahS80t1EjjUQULLic\n5/7xuH2hWbrNpRbeestW8zn6aOXF6c2RYoyE/Y1pcwFdqldXWzdDJtniJMG83pyIgFStksAfDuvE\n1tio22pqgFp/AKU3r0T4P1Yjq8KPvIEAclYpmAbgR3OztsEkXJ2d2v60aap1sr+kVNwo1ZG08fSx\nTv9toDmA/qtWIva1Fai5bw16v3QNcPPNeOuzq9Ee9aMyO4BD71uJnf+sofS9vQqEpHwAoOLFRyEn\nLkO8xD/0bOSHAih5Yx22H3UmcnJ0cuRkxPHKybFUCg294bCCbyxmATwnR38LBvU4v1/vU2mpvQ6u\nUOhrHgjo99JSOylzEt6V2+tIQiN8X5/em7Y2nTAKC4HDZycNpx5n7sm+LCKpBSB8Pn2gGxsVTObP\nz5zsyj0+E6i4oDo4aCMXc3P15Xe9H7hPIGAjRTlppOdMOfponQTSg0vcczLcfcsWy7dWVel7mJWl\nbdKNj+cnJeCmznULFRP8CeKFhRhK8FVQABT95VFkn6zGPbYbaAig57F1GPjgmUM0UkeHtldZmRpx\nyvzj2dlWGyd/nykH+kggTuDbvFlXDJV9DTj1sjpsfrIe23Nq0bklgMPuW4mOz63AoY+uwfqLdLLJ\nydEVEL2PCgsVcAnAwaClf/r6dJ9p02x+dN4LBjkFg6lBXeTGuS89dSIRmxiMeesZ7cproQdTf7+2\nWVKiz1BRkTVUu4FiYxUCONvv7saQEVZESw3OnAmYxzxvFk/2YUkvAJGbq0DDZFfz54/uCTISjZK+\nD9OYRqMKFtXV1p3Q7Udfn77oDMNnNZ933lHf8KVLVRNjkArbd4FcRDnczZv1BZ0+XQGHRjiGu7vH\nu8UoXOB2Xf4GBpROaWrStg46yEY+lpVZgCLgdnbqiiA313rmdHRonyor9TrSDXhAar/SXQ7dsXZp\nH94HVhTq7NTCGJGI+nTP/elKvHPmCky/aw3+8anV6M3yY0aoAWd/tQ7P3V2PcHUtfD5rlKXxMRbT\nexCNKrDzPgHaf2rPLo9Nd0uubuhmyFUPM1uSKsnPV/BmHhZj7KQWi9kKSQMD2rfSUjsJkqPPz9+1\n51T6c+9SPb292hcAyH/qUbQftAyzDvNj9uxku7swkno0iydTJm4BiIICfbHo21xXZ7XNTDIajZIu\n9Jnu7dWXe/p0y40TnMJhBXsR3VZYqGCc7plCEKA2zr6wD4CC2ObNqgHOmGFfeiaecn2zSckQdJiJ\nkJMaDY49Pba4QmWlpXUCAZsTxaVlurr0mukhU1BgqxpVVOhE5fK/BC3SDdQUCdjpmRczaeGRiNWE\nGxqUHqisBKpyNe/Ji2erR43p0bSzLRddg0W/uxlN52o4/Y4r1OebxsfBQb1ngGq+TM2QSFjtmYFh\nBN9QyE6SjNoMhew9o1cOk4Xl52tbPp9Nj8BrAXS8+vv1b36+zXFP6m9w0LY7Fm2cAM5zBIM6ycTj\nev5IRO/TzMIA5v1sJbK/O3b3RQ/MPZl0cQtAFBToC9rUtOtkV2OhUdL37+jAUCpTv1/B1Y3ijEb1\nPRkYsC9pc7OCeHu7auFHHaUvqwuyLpCxD4GALd5cVWUz7pGHTqdUCIwETsBSG/Tn7u7WNjs6tO8L\nF1p/cF4TaSC6V7a0KPjV1Oi1dnbaXCrp7pYEca4yXNdFavgu5eN+Jy3AXOYMld++XfebPj1ZzOGJ\nR9F/1DJEizUYx+cDZoQbsfhH/4x3/v0eJEr9KBNNUNV3rSa+6umxlAcnp3jcrpjKy/XZod2AFAnT\nDPT22vF2aSsmCysq0o/PZ72O4nF7HQRwcuqMJygq0rYI9mMxcLopAaJRSxexfyUl+n9Li/5fV6f3\nracxgNDXVyJ85QrU/nrXgUUemHsyKUKjI19KYyzQ7irZlUujjFah3t0/FFIDJ5fa06enAhlf2N5e\nfZlLS5V/fuUVfbGOPRY47DAFV7d4hOuOSCDv7VWtORBQECffyqW/GymZqUgxr4naejRqg58CgaRR\ns9Zqm6RCqJWSl29tVbAjiHPVU1amfXIpAAIb/dddNzuObTpdlZWVyh1zX/Z3xw6dNPx+W/OzsND2\nBdD7kJ8PlP31UQQOW4bcKj+Ki3Wf0M4Asl9ch+DyM1Faqn3s7NTz+HyWBiku1vYJtjR2Mt0tvW9o\n0GZ/jcHQuWj05DNFbZ2aPCcBFtbgOTgZ7MrA6QJ4PA4kHtYJLVzgRyym973cBJB4bh22HHwmcnPt\nSrSlRW0s7e3AnHgDTjy/bkwh/x6Ye7JXJRazBh3m9Whu1gd9tGRX46FRuD+gL05Hh4KAiAJqVZXV\nxt0w/FhMX9T6euXEi4s1UnPBAgtujJJ0jVo8F0uSdXQMB3FOAkxWRSoiJ8cCAcGdk0I0qpNbfb1O\nNLNmqeeOm+MkHNbvdJNradFPaamlU1hww+dTmsM1GLuUCjVZVxvneVwgd2mUWMwCIMeR7p1sLxTS\nsSwrU0Dq71d7gd+fjLSMWa27sNAG7BQW6j4M/qEGXVxs09Gmpx7IytJjuVLJzrZUGIN/cnKsBs5V\nj5tdkkZVlzLh5MFCH0wBzFqlmZQJArhLpYTDetxgWwDlt6qHUfEsP+KdAUSvWYmmL6/G7MPVUL1z\np7ofRqNqzzlkZgAl3x17yP+kgbkxJh/AswDyoJWLfiMiN2TYzwPzA0DcAhA5OfrS79w5erKr3aFR\n+JfBP62tCjK5uRZAXGCmkSknR7WfN95QTfbYY5O+zMlzEXSoPbv9YxKs9nY9BwND6FvsRhES4Eif\nEJhd8AyFtN8MXZ87V/vCc+fm2gCX4mIFJWriZWUWxHt6FASLimz6XVeoVadTRaR4gFQu3+Xw3X1o\nDOzrs6sfAmhhoU5qzC5YUmLjADgxEiSZGKuoSI+JxXRiHBy0GnRJibWn8DyFhdpOT08SKAdT+XDS\nLnl51iOFIE4vGPLp/f16TF6e3b+w0IK4iLaVycDpToIcLxo0eb84poWFQHCHhvH3fHEFKu9cg8H/\np+DMQKOsLKXSFiwAigbHnyZ3UjVzY0yRiAwYY7IBrAPwVRF5KW0fD8z3UxGxBSC4DGfa1NGSXY2H\nRkkHcMByxQzy8Pl0OU/jKV/cvj7t16ZN6iq3aJGCeGWlbY9aaCYjJz1JaISsqrJeENTe3Bff1eYJ\nkgQA+rG3tuokFwrpGFVXW/c2glN/v9VSWZrNjVSlP3Jeni0S7QoBhqBFsCYNkJ4HncZBbuP9oatc\nb6/e1/Z2G2hTUKBj3t+v2mVOjq3ARA+NggKbojYet0ZM0jTRqF5jQYEtCsFVRHGxfhhpSYqIGjr9\nyMlxu26H7B/vIbV7N6UBJw+3WhNrm7oGznTw5vhyogyHbSk91jptblbKrLUVqOhtwKdWaCqCrtLa\noSCnykqdlGlf2Z2Q/6kq6FwE1dK/IiIvp23zwHw/E7cABAG9tXX0ZFcjLe0zSSYA59/eXj0XtejK\nSsuNu7ky2tpUE9++XT1Tli61hkO2S9BL11yZyZA5xauqtH26zrnjQA2QfXQpC2q6fX3aZ2qhc+dq\nm9QOSX3QZc2dJCsqbIBTf7+2kZ2dGrXpjls6pQKkGlzZP167C5KuZh6J6Pna27XvLkjOnGk9WETU\nBlJSYl37WFJvcBBDPuR+vwIfx4AaMVcvBDnSI5xEWIbOXbG43ioMtSe1QtdGJs0Khy2fnptrOX13\n1UQqDrCUSjqAc0LmNUYidlt+vi1msn279i87W6NyF965Es0XrEDNvWvQ+lWNymXw2J7KZGvmWQBe\nBbAAwI9F5JsZ9vHAfD8RtwBEIqGKQ0fHyMmuxkOjjAbggOWXGTRSVGS1cb5ozDW9caP+v2SJeqZQ\nc3X74Ro5OalEowpQ9OmmrzuX/wR8twycOyG52i4DkdrbdYxisVQQ5xLe9dXu7lYApXcMq+WEQtqG\nSGrUpisupUK6xeVzXX9xgpJ73TyW/SBtQtAsKlJKIBZT0KIvfUWFnRg4mWRl6ZixFFs4bDVx0ijs\nHykJFrzo79eJgsKJli6Q/M6JjDQMJwTSHaS8uJJwjaAu9cVrz2TTcGkUjm96CcBAQJ+X7m5rIC4v\nByqyAph3x0p0fX01pEyjcmfdvhJ5NydTHEyATJVmXgrgdwD+RUTeSdsmq1atGvq+fPlyLF++fMLO\n7cmeC6M06V7V0aEPNg2a6cmuxkqj7ArAuY2aNl9++htnZ1tK5e239QNoIYhDDknNs+0+zi4FYYy2\n0dCgEwFBKzvbelNQG3e9VNyVh6uNMyVAW5u+4IDNG0Lt0K1aJKL77dihgDVjhtIpOTk2ECcatb7r\n6ePjUips2wVtBiJxDPidQUrxuC1GTfdR/qXxlj7tjAnw+WwkLINwQiH97vcrgJMi4bPi8+nYhsPa\nF1IrPp/1hKFXCoVh+dTEOX68N66/ubsfV0s5OXpO1wDt2kjYF9fLhWNKGwrvOd0Yc3LsWLW0WH/0\nSCS1SEbli48itETdM2nLMT17Vvdz7dq1WLt27dD3G264YWq8WYwx1wEIisj30373NPN9UBIJ61oY\nDutL3NamWsf8+cOTXaXTKCOlah0LgFMYih8K6f7FxRYUyYe++irw5psKIMcfr31LD0F3PV8IxgSi\nbdtsPvRZsyz/WlZmAdv1bHA9TVwAYImxzk5bE5QgTo3QnVjoVbF1q2ric+faLIY0JrMkHfOnp48j\nwb1T0UcAACAASURBVMb1UiHosL+sj8ltBCa6RoZCNrEY0/0ClsopLlaturfXZntkMBS16Px8nWyY\nA3xgQNskiOfmWrqD2jqpFK60XK6ehS/6+ux9J7fNjIS8TlIqItYHPCfHauI0Rrv2FhqryXFzPDm5\npWvhtCVwhUF6MT9fz22MTsJVVdo/cukVFcP9/CdSJtObZRqAqIj0GGMKAfwJwHdF5LG0/Tww34eE\nBSBo1OzutmCTnuxqrDTKeACc+3V12YRQxliwyMpSEHn5ZfVMmTkTOOEEzeHi9oHiaqmcaBjs4hYH\nJuCWlqYm0GKATHqlH/azv18BsKdHueXcXF2tuCDu5lwhiLNY9OzZen7mM+/oUIBgmbZMQECwAaxW\nSdDh9dKl0L0OwIJdKKTjyzwmfX16LYBNQ0BvGWZ79PlslCY18RkzbLEGUjTxuPUOoebq91ueOBjU\n39Npq6G8MgHdz+ezhk2u/hjwRK8WEUvbsOhGOoi7Bl1q0ARxN3CIKRcI6NT6BwZsIBYppIEB3b+6\nWp9BBgIx62O6n//ekMkE8yMA3AUgK/l5QERWZ9jPA/N9QFgAgtGRHR36wGdKduUahiYKwCnhsHU3\nFFFNh8DY3a2RmuvXKwieeKJSEul9SacVqNlnZelL2dio/8+da32XS0stYLiGRFIqbJscdF+fglpv\nr65Y3GK/BBMXxAE9prlZx9nvx1DQCItD9/baqM1MQEBKxfVISefBeU5XE6eBMCvLAlMkouegUZUJ\nuGioZNAP6R2uzqiJV1VZn/ZIRIE/GrXeM0ycVl6u+7vum9zHpU/CYauJl5frfU/3fXdBnB4nIlbD\ndukUwAK1a+imcZaGVZdGGQr4Se7f16fX1d9v3Ri5mqiu1vtdWmrT4jJYa6TEcBMtXtCQJ0NCjYxG\nzd5eBczq6uHJrsZCo+wugAM2+q+727bP8PiODuDFF5XXrqvTQJ8ZMzLnSnEDOKhV5+Xp9TU06Ms6\nd65N4FRWluoVkl4iLb24QX+/jSRtb1fQYck314gG2P719ekqIBi0L3xFheXLu7ttIqdMQMDJhZxy\nOufu+oW7AEabAbMEMuEWa342NWmbtEGQWotGbfZIAjUBtLJSJyy24XLdvNe0NXAFwFVCXp6lOWjI\nZBtZWdpuUVEqLcbrpesrJ8p4PNWQ7Ea1upMoPVVcLd11x+Q4UstnlsVAwE5Ifr8t4u2WjqN9oaTE\npkyeTPHA3JOhAhAdHfqQkt9MT3Y1FhplTwCcQv9rghCr3rS1qSbe1gYsXgwcemhqAQLXuOca+/jS\n5uQoUGzdqi9vba2lCkpLbd4NIDOlQtAkgJN2am/X42trLQBnSolLEA+H7eqC/s30GCkqUoAYCQjc\nUHrXwMk8Ka5Rk1QFqRRSOuTfmbaV3hfl5TYbJHleBkO5ZdVYwKK8XIGLub3pu02vEdoaSGmQ/6Zm\nzqCa/HwbwEMDa2GhjZ51nzFSMPRYGRxM9SNPN0bTruFy6ZzU6HbJ55Wh+vzdvc8VFba6VVubAvqi\nRToGwaAN1mJVo6kQD8zfw0IrPAN7urr05UtPdrUrGmUiABxIDTKiBlxWpi/Pyy9rfw89VDVpNziG\nmjfBn+DF/sdiCqT19TbCsqLCRh+WlKRq9eSg3Xa4aunttZNfS4u2U1enL/dIIM6qPqyvSQ2XQNjZ\naSMnM2WJdOkh0hJutCPBnFo5tVcaQqlh9/VpG/SrbmrSfrGCEvs/OGhTBtC/m1pwRYVNnUtNlEFA\n+fk2gVVhYeoEQ08Y8tSkQxjEU1BgJzjy9q5fPOkh2mhosGREpxsbQADnvWclIe7rrlwSCQvy1M4Z\nYCZi4xZYvq64WBWJykrdr6ND2+XqZCrFA/P3mFA7YyQhX3IaNJnsynUnzESjTBSAUxhoxKVwTo6C\n+Guv6Qt02GH6AtEIBqQa+agNuoEfzAvT2Givcdo0m7+a4feUTJSKiI5RT4+229Wl/aqq0vGie6A7\nGRDEA4HU+poVFTr2pKa6u23agYKC4eNHjpuaeFaWAiLBiJMGbQCkUJjK1c3BzeCdggJb8Yg5T+jp\nwUnM5awB/e7mDSdFEw7bTIY0FDMDId383CRY1MRdF8bCQm03K0vP5/rFu9G0jBxm4BH5cfLe7uTN\ncWP/eD840QGpUaBuOtpgMDUxG1MW5OSoeyujXDs67AolU1TzVIgH5u8RSSQUkNrb9cM82G6yq13R\nKBMN4IC+SG1t+hJxeb9jhxo1Z84EDj/cFgsgcLr8L/lRV5g6tqFBr3nuXH0JmUHRDfjh9dBzgct0\nV5sF7OQ3c+ZwL570SY6aeCKhmnhFhc3SyDB+Y1KBwKV3CDCcXAhSdNEDbAInIDUYhi6aTDxFbruo\nSCeit9+29gf6enP1UFhotXf6bjNMn8WVSdGIpHLh7D+pqeJi669OkCaIs8gEc8qTI2fOctfYTVsG\nQZx0UXZ2qosnx4srNE4EFLd0nOsnzgkvEtHrIX1ESiyRUE181ix9Rtvb9XxVVZmDtaZSPDA/wCUa\ntRV2aNhkMh8muxqNRtkbAE7h5JKToy9Ufb1q0QsXAkceqS8seduiIgtaBLdMXjOBgE1HO3u2voTU\nEunP7B7DiD8uzem1QM+K9nYdv1mzdMxcaifdu6K7W7VeYxTE6UpI320CM1cYvAaCjuvHzIhUtk8w\nct0Q6RZHbb2nxwIVMwgWFurYvvWWHk+KhP7ePT2WwqF7IaMyKyr0fxrFmUa2rMxy6zQ8cvVQVGS9\nYqjlupRLXp6NEmX64cJCawsA7CTCycjNl0K6xi2gwXFzXQep4XOVwBURYK+3r0+PZa3P4mLdb/t2\nPe+CBWrMpj95PK7vDJWLfU08MD9AhS5nO3bYepYLFii/Sw+BkWiUvQnggL5wdDfs6wPefVe18yOO\nUE6c/DQLEdBHmX1KN4rxejds0JeupkZXG9S2CQ7uMdR6qeXRM4Ug2tKiYDN3bqoRON0zhT7wzc3a\nDiuzu9u6uuzS3eezIOhqlLwmgh4NnAQoauFu5COpFGq+3Jf7BIM6tixMwZVAUZE+E8x1Ql9rukD6\nfAq49CsfHLRh8sz7xJze7D+pHdddkQbWUEj3ZQrc/n5L+bC4hZtLJRzWdnmdQGpumfQ4ARozmceG\n7obBoE2Xy+fOLdhcXq6KDVMktLRoG/Pm6X2PxfR5ikQUxN0Se/uieGB+AAn5XWbio4vbwoWqWbrG\nOWA418u/ewPA2fYjjwAHH6x9e/ttBZojj7QgTu21osImOUp3h3R/i0RUE3frhRIQmS+b10KvFoI8\nYLU2AsKOHfpi19XpC+1GBLpjkkhYEM/LUxAvKbH9GxjQZXo8rtvKy23/Cd4un0uOlyDJ31w3P/pO\n8z4z0IbFjmkLYGbInh5bcYg2gkBAnw/AThjTpun3vDwFLbpZki7Jy7MaN+kSjiMBll4frN5D0Gbg\nFemfggILnoODen+o6XI1RFDn6sd9bt2Se67WTVdHNzkZn2164nClU1WlHz4/nZ06LjNm6H03Rq9/\nYMCmUd6XQZzigfkBIDT0NTXZYgBz56om7vePzINPBoBTIhGdYK6/XiM043E1ah5xhPUvHhxUQGSU\nn9uXdI14cFBpmW3bFCwWL7aAUFycmdN2swIODOiHwEJvk4ULdWmdKdKSlEh7u2pxBQUWxN0lPAOB\nZs5UbZwaNwNT2BbBiRw0r5mh7sxnzujFgYHUGpgFBQq8/f02L019vU6Q2dl6fhqMg0F9Puglwpw2\nvM6qKptX3RibEdJdBRDEaYDlpEXQZ+j6wIC2ywhP5pdnSluCfnGx5a25P0Pz3fvupijgpMt7SYrF\ndTNkib9QyHLkeXk6HmVlqZNAW5s+PwsW6D6dnTqm9P3fW6H3e0M8MN+PhYmLGhpU083KUt/XefNs\nQEY6jTKZAM7zdHXZaM377gOuvVZBnF4TXJa73iUjaeOxmPLqjY0KCOSxg0H9W1JiDZg8hpQKPSpI\nK4RCOhmI2NULxyh9ReCCeFGR7suivtzOZEs+n25n+lX3PnBCYKQhj3UBjMDF4+nvnJ1taadAwOZ8\nEbEl20RsDhBy0Y2NNs83U/ey78XFNm0A+W2en1q56/pIAKV3TUGBrcXJPtL4SVsEDds0fJaWWkNw\nXp6dONyIS94/dxJwAZtumuTZuWqgeyFBnEnAiopsv1lvMydHnx+fz0Y7+/1qV9jbofd7Qzww389E\nRF+a5mbVtHp6VPtbuFCXidwHsCA+2QBOoab3t78BL7ygL9J//ZcWTBHRyM0TTrAJmdJ5cBfIRRR4\nGxoUlObN05eQRjtOBO4xgHU5oydJUZGCHosOL1hgKwy5EYau5tzWptdRUqL7FhdbEKaXEMvU0UAG\npBqWXYqF3ipME8vzuSlZmW2RWmxFhQIfvZHoR97aqucnzUA/dlJGfX02SRjpK3eCDwRswioaGEtK\ntC/0TCHvzQkBsBp7LGbpER5HgzPHlJx6SYk1PrpVgDgWLoC7MQKuaya9T+Jx645ImwddMMlxT59u\njbO8n8xGyYhmgvhURW1OpHhgvp8IA1W2blVwISVQV2fBBcgMiJMJ4IC+lAz+KSy0UXqlpcB3vgP8\n67/aEmgM2Envm+smuWOH0gd5eRplWVRkS7/RQ8U9hkvxQMDmwqbRb+dOm2OG/s2ZQJxG2rY2BcOa\nGutGSFBmQY6sLAUB5uFIB3GXIgqFUlOtuulZjdExY2IpBvOQw2Wdzfx81Sz7+mzb9Ium1tnba6NL\n6YWRk2PHg5o2Iyfz81OzHNJ2wYAa9pO+5DQmAtY24YbEu0E+JSU2AtPl3+nnzYmIE44bicnngvlO\n6D2Tl2epK4J4IqEKTWWlHsPnzhidvGnQrq7Wtrq7dYwyldjbH2WsYD5JqWI8SZfBQQWVLVsUOIqL\n1Vg4e3aq1p2e4pUvwWRzfsGgzRbIl575TlwjH8Elk3shX/CWFp28srOBgw6yIB6JWLqBwnGIx22S\nKhb3pdtjSYn6rTMNKYHWDRyKRnXV09am4HzoocNztbgFqglM1DJdjZ2eMqQn3Cx7DKahZhoIKMDQ\nCMntLS0K5NnZ2u/mZqVNaCDkRDMwYLl6Vv9hmoLCQuu2SF90aqCuF0t/v3XrIw1UUJBaw5STpIhN\nmsVVBMeU7ovMTd7XZ8/j+oATlEmfkTpJf57Yd9bmFLGaOI2ws2frOanlMxBqxw69l7NmqV2lv19X\nd4WFesxIxZkPZPE080kWpkVtaNCXYc4c1cQrKuw+6bzuZGvgrsTj1gOALmcsOJCdbaufv/EGcMYZ\nmUHc5aa3bNHv8+crcHNpTS44nVOnP30gYCvJdHWpJs6MhPSN5jFuXmsGK7FIM0uzUUinkFsmlUHO\nl4DkFoGOxex1A9boR+2fGfciEVtdhzx3W5ueKzfXgnh7u613yfzoItZoZ4wtjJyba9PW0mvEtUcU\nFGi7xmgfmCaAPup0B+Wqg5V9WJqP4E/jJemR0lLrI046JT8/1ZWQEZuc3N3MjxxHpsVl+ly2wTJx\nLPpM/3B3jHNz9V7u2KETY22ttS9xsjwQQdyjWfYhSSQUjDZtUhAC1KBZV2e5ShewpxrAKX19Nu0r\nl+9u4EcwaBM2uQE/rrggHotZGoS8MYsYpFNI4bCCeG+vjlF+voJ4a6uCMv3qXeOvC+KDgxYoWVTX\nTX1LN8C2Nj2GWn0waOtTMqc2KRt65vC6iopsNj8a5+gOSdc8po5l4i5qsjt3qnbOivSRiGriubm6\nL4tHMKNlLGZT1LrcNIUTLG0v1L4Zcu8COLnoSET7x2pOvb32+sjz07vGdZHk5MDVDydH1uIklUMQ\np6bNLIqkdejxwvB8v98mI6OnDEG8pUVXLiUlag9hvp+sLLviyfT8HQjigfk+ILGYvrQbN6qWVVmp\ntAKr97gAuK8AOKD9bm21mh9fYvoNE7D8fuvelqnPBPFwWMF39mybG5wpR13vAhEbVRkMWkqHIff0\nFyZf7J6XwBGJKIh3dCgw0BcbsBQJCxgPDlogoKbNFQeNroODqYY/gpQL4v39FmDpKcJxpK8z08q2\ntCjPS6Dv6bEFHYJBHRvX6MjoSq6C6OpHKosTLGDB0i2Tx1QG5MbdSvcMgmL/aTAlXcQKRfQSYk52\nGmlJNVGj5uqFwnZ5n9hnGl45cdBdELC5ZoqLdf/2dl3F0kOFvwF6fzmBTfU7M0wefRRYtsxGYwH6\nIOxGObnJLE4xG8DdAGYASAC4Q0R+lGG/9wyYDwwol7t1q/4/f75q4m64sMtDAvvGwyhiPTjo8kUN\nlIawcNimlc1k4AQUjDdtUvBh1B017bw81UzdHCqJhL741Nazs3W/jg79zJqlS2qCiTtmrg83ueWq\nKluazXUxZPh2OGwr/JDXZjZB15eZmqs7GVCzJVfuuuUBVmOlwZP5u3fuVC+l4mK9DvqMMwTepUSK\niizHTXpmcFDpFb/fFm6gNspMg+TLOQmR+gD0eoBUfpoBN6SX6MnClQKBlddMDx1OYrSTuM8xXRnJ\nebsZDcl7cyJmpSPXM4bPFVMaswatz6f3LhbT49yq9/vCuzNMAgF171q9Wm9a+vdxyGSCeTWAahF5\nzRjjA/AqgH8SkfVp+x3QYC6iYLVhg2peeXlqmKmttS9ZOnDvSw8hDbIMGWcZMCYdos84S7pl0sZ7\ne23+lDlz9NqZHCuRsLw4YKkDlmMjt5qdrdQH22AhaZebdVc0BPHubqVvqqutdkggHxxUIGDRiMpK\nq1Gy/Bdd3aiF08jqerDQU4ORiW5Cq0jEcsX0WqmosJolc4AzBYPfr7+xSALTE5Cf5zgxUVRlpe7T\n12dd91wAN8a6PnICJjXEZFfclxMVVxLUwgF7PCkOgjjH3wVmdzIOBrVdZoCkYZwTBt1BGZlKP3jA\nKgtc9W3dqs9SXZ2OIYttsKSgS63tS+9Qukh3AN3/vBLZ/7YCZT9bs1tADkwhzWKM+R2A20Tk6bTf\nD0gwj8dtNsDOTgWTxYtVO3Q1130RwAE7CXV3W+OfiE30RA+FsrLhYfiU/n6lUzo7bfZBY6yRjm5s\nPDYatXSLq+nR7bG2VrV58qoupcIPozv7+pR+mTYt1cOHBj/W7SwpsQmyqCHSIEgAZbIn1xeaBlVq\n4tRiuXKhGx6Di2jMZdAX854wf3hJiY1ApE2AeV0IoqzcU1io10abC3lwUjTkpnNzbYEJ1yuEeVfc\nHCakg2i0dQs8AHoMtWM3r7rrikmbA3l0rgyYNI33lwbdWEzPxVQObsZHrhzCYeXE29uVjpsxwxag\nZpm7/QXEAV2ZP/kkUNTWgAuvq9Mfamt3q60pAXNjTC2AtQAOF5H+tG0HFJiHw0olbNlifcMXLbIv\nArDvAjiFdTjJAQeDVqsC9EUtKUmlh9xrGRiw/vHTpyunmZtrU48WFNgoQGrIvb02615eng29DoUs\nJUO+2o0YdDXHHTt0Apk+3YbVE2gIPDQi0puEASvGKOgSvKndkg5ww/Opvbu5SQiiBNJEQs8VDivg\ndHfre1tYaP3De3qsRlpaqpNeW5uldjiuvI6cHBtMxLaZnIxATx68r88WgeBqh5ouXzeuOOhBQy2Y\nBSSysuwERR905rlxJ1CODTMxuhQcVy30TqLR2J280vlwQH9ratLV1fTp1q7S1ze86v2+DuSJhL5P\nTz+t9/jDxwRw6L0rYa5ZAazZjzTzJMWyFsCNIvL7DNv3ezAnSLzzjs0dcvDBqYWQR/Lq2JeEWmRP\nj14DtS9WqmF+cBoo07XxSEQnsZYWWyuxoMC6tDEUnABKw567rbtbj2fU3uzZdklPUHUpFVaECQat\nJs5rASx4sLISIxNd7wt3G+kFV2sFUjVOYyzIkdelQdEYW46vtFT/MoqVJdXoVVJVpf0NBm0IPqsP\nsQAFx4qeNO5YsoQeefbcXGtcdEvf+XyWY3evgznGuZogiNOoTW8lVgviODBQivQHjyO3zSyGBHE3\noIp1XUmvMKsjxzuRUADftk33mzfP5qhJr3q/r4I4n1VA+/3MM0ozfuADwNIFAeSs2s848+TJcgA8\nAuCPIvKfI+wjq1atGvq+fPlyLF++fI/PPRmSSOhLuH69gsHs2VqdhBFp+wOAU5gSlIEdrM9ID5FY\nzBo4gdRrYhKsHTv0ZV24UPfji06/YRaCcPlwAkDX/9/et8fGfZ1XnktRokSJlCiRIiXRsmTJsiy/\n7drxKzb92iZpHjUaNE63myZpUhRdZwt0k27bFKmT7h/bAMXuIsECW2y3aBZN0iDFbpukTmpLpuw8\n7DixHSuWZTm2JFIixcfwOZwZDsm5+8fR6XdnNHyTMxzyHmDAx7x+85uZc797vvN93wC/wJOTvH9r\na/5zKBpXFK1J9+k0JZxwux0WVomotQgpcaeEnErjRWqKgvU4GkEnAi0sfVcyMZezKk2Nh5PbQlKS\nbHgq/Jmc5Jc8mbS+42EnRe1SwvFrVVVmVRSJy/2SSuVXbTY02PsjEpfuHkobYesDRcdKZGpaT/he\nhP3Qw+IeSVPqqKg2Bs5Z2wP9XVubP23Je0opqvzdv5+3nW7Y9Uoj8vBzJ7nv+9/n5KzbbwfuvvuS\ndLQIN0t7ezva29v/9e/Pf/7zJSXzrwDo997/wQy3qbjIPJMhgb/5Jv8+eJB6uBobASvnQzYb5Pce\nG+OXRvMdtUUeH7cy/HBrDfDLdvYst8P19ebMUW8U6ax6e8PKQXXdSyRIaM7RnrlrV/65UzQuEh8e\nZiJ5cpLb78ZGI/nQAieb4fr1/N5IQlAxjLTzcCaoHkeOjqEhswPKlaLodv16s2cqMasKyLNn7TXq\neBQtq7nX2bOM4Hfs4OtQhaP82c6ZT1ukreIXOVq0WMmuKflKhUrqZii/dm1tflJXCVu9F1q8wvFr\n0raV09D5kwSlRVr/l2yixLiGNavgqrY2v5Tee2tbMTVF+bi62qqfC0vvVxKJh5ONwryPehMdOQLc\nd1++w2YpUUo3yz0AngVwAoC/dPkT7/13C25XMWTe38+e3J2djGwkpRSTHCoBySSJSNWCKsNWxFZd\nbbpy+PqmphhFnzvHL9zBgza1fHTUikqkh6toZt06Kzfv7SWJr1vH+4ckrgg77D6o6s7JSUam4exS\nEbBeQ38//25szO8hIi+0SF3RIWCkq0ZM6lgYOkkAi1wzGR6PkqgicblZlG9QcrG52eZxaihwSwuJ\namiIt1MeQbmJZJLHunUrr5N9T2QpaUeFSJs3m3sGyB/3Jg+6dgf6v5wshQ2utEMJOw+qT4oif7Wi\nzWSslcPQkFVqys6pRaawM2EySRJPJknimzbZsOvGRkuCCiuByIsRuP7/yitAezs54cEHbYe+XIhF\nQ/NELset3+uv84O6bx9XXG3rK43AAZuoouScGjgpIpyaulxS0Ze7s5MkvmEDI/Ht2/mFVmWiZkSO\njlrpuvTiDRuYCFL/lauuyrcMhjKJZJXBQco33ueTuPRgwPTZ3l7+LhIfGzNPuEhI0oF83Wr0pMSo\nxpxJMpCTRCXp6TRJXE3FpqZM7xZhqWBFVaNKImtIRHMzyXV42Fwrilh1Pz1OaHEUcUqG0XSfcG6m\n2u0ClpxVdK0ciAZG6L3W/QqHQqgjYTh/Uw4jNd1Sa2Etao2NPE/aCYT5ByGTsZ2Jeqxo2HVTU/Gp\n9+UqAArlE+Dy77z3tB0fPcpz/fDD3H2VApHMZ0F7O9DWxg/oyZOUUqqrKaMcOnT5TMlKg6bKKHpU\nWbyiqLAMPyTxri4uaoqkm5qsk6AaPKlT4eAgr5OHurqa9w/b2e7cmd87RB8BEWgiQdJ0zoYM6JhC\nEk+nSQqTk7YlVzWjolyRi/pv6/WFbWc15kxDH8JqThF+Tw+PS3JaR4cNx1BHx5oaWxyVWBweNl18\nxw5ePzho/VKUUJQsojyCjkWLkqyhhQlYdRBU5K7rwp4pem+1y9Ltw/miSuQqyatFTAloldhrYdNO\nrL7e9Hk1IyvWC2Viguesu9uS1ZLdmpqKT70vRzReSODTNa/r6KDNcGKCJH7gQGmPM5L5LPj0p4F3\nv5vRYHMzp+Ps2lWZzetDqMXr1JQVqXhv1jvJCmFPGO+tk6H3/LC2tFhlYiplTglp1N7zcUS+HR28\nbNnC7ad836HXXlG4POUazRYmNgG7jwYdDA5aHxdNEJLWLBICjMjlkQ/7nascXbZHRbyKUnVM0t/X\nreNnQ5H5xo18rXV1tjiEU+X7+21YsqpKNR90/XorDlq/3hZY6deyH27YYLsIOU0kYckqmMvx/3Kh\nyP+ey9lrBIyUpYnr/EuGAkxqkywjSUfHpVmiksycswWoWH/wXI7nrKOD7+euXXZsYT/4QpQyGi9M\nYM70vL29jMR7eoAHHuAYxHIEeJHMZ8Dp05yK85nPsHXqciUuSomwFF+WsOFhI4dczqa3A/ah7O0l\niU9O0lmwezf/rwG+ci+MjVFnliNEzyE5Zts2bjsVoSvSBYyEVInZ3U1S2LXLZmgqCRcOTVDfcvm7\ngXwpRYuF9+aNlhwQeuYlLYgsRUSKdoeG8gdDXLzI16oKye3bee4mJnidonE1xZqYsMVIWvaOHbb4\nKeEp26EWkb4+HqsibCVcw77g0r2dsx2QGlGJbMPiG8kpaoymxVo7IcAWO9lGtThqPJx88WFP+ro6\nO65in73eXu7oNm7k50Ayl0rvi5FgqaLx+RA4wNff3k6euPdeulRCh02pEcm8CNrbecnlgD//c0BO\nybY2XioV2SxJxjmSzsAA/y9/dW2tTaXRB3lggInJTIb5AfVRV/tWJb802kwNnhoaTD8+f96aWSmp\nFxKsqjenpkjgPT02mk1l2YUFKeorPjSUP/BAEaRztlgAVh0pi1wuZ8cS9pZRNBxOBFLrW5Goeoyr\n5asWLee4ixgZ4d/19Twn8qyH/UfkMpFdU69BEpf86dppqLhHY9nWr+f9QhLXMAvZPTVGThKLzrMS\noXq8cLRdqIdrsVQhkSyRw8OWhFUSeetW0/MLoV2FdnStrfbY6rsyHXEudzQ+XwIHzGb48svAFQgf\n4wAAIABJREFUbbfRWVhM1y81IpnPgiee4KWS4b25MhoarIxaJd1q5KS/neOX7623SDZ791rFZTpt\ngx+qq63IRwU2mzcbiV+8SPmguTl/qx7q4fp58SKJvL7eNHEtKqGLQklQ9fpWD/BwgdCx6bFlF1RS\nUpWq8kwrKRm6ZkTig4PWPVDtaGVv3L7dSv9F8HV1fL2plMlMoY0z7IeiCldVn+p4NYxBjhTtGBTl\nazekXURdnRUgSTICbHEIdfSw7ayiciB/FyNPuXqJe2/e+oYGG9cnKa6Yti2MjpLEx8bM6ZVK5Z+7\n6T6zwNITeViwpMef63NMTHCO7Q9/SOdaW9vK2q3HSUOrHJmMEVBjI0k9LGyRvqsP9OgoSXx4mBHU\nzTfbdl2JTLlT1AhK3Q0nJpgg7utjVH3rrXxcFbwoKgVsS9/bayR+5Ah/KtEnAhfC8nZpq2EDKVU5\nKgJVMjSZ5OtsauJtC22WOh5pxckkX6tup+Trhg1cnLZts+To4CCj8dpaJoKzWe5E5MfXIqGuidKY\nq6qsiZZzfJ+SSfN5K5IGLKGaTPJYFJ1L0hDZyrooeUQLmchcC528+son6L1R6b0Sq+Pj+bbO5mYr\nDJI8NB0RZjKUUxIJ7sh27uTxNTQwzzIdieu90Hu6FChG4DM9fyFyOeBnP+Nufc8e4GMfs8riSsSa\njczlZqk0yAEyMsIv3vg4v/CSVFT1JzIbG6OcMjjIL9++feaLVvStSrZcjvdVp8RUismsoSGbsSjy\nCNvChg2YurtJzPX1vE84QCIc7qvXIRJvbrYhFZJT9PgiciUeVagUTmdSu1U5RqT1isQTCWvw1NPD\nRJ16jNfX5/c9kS7e3MxjSySsz4uSjGF/FBX31Nfz3EmLVmm8GlABVvTjnC2c0tGVZFbiV1JL2PgK\nyG8jG5K4qkhVWKX3VP8fHTX5Sr1fQg97MXuhMDFhu7KmJuu/ri6UMxkHlpLEQwIP6xXm+xinTzO5\nuWkT8MgjDHBWKqLMsgqRSpGIVHGZSORXojY2WkSXTnMb3NdHElb/FPUoSSbN2SC7Xl2d9W3p7OSX\ndd8+kpo8yiILaZKy8/X389gaGiinFI5c01s/NcXjTiRIcNJW5SCRfq4va9jrPJQzZEXUfcJhDdoB\njIzkR+I9PXxdIvEtW3i8inY1SEJJu9FRS4zKH67XovFoSn6qklZ2yXDBU1StSFmuEfWNCYualJCU\n4ybsT6IdjRK12nWE+QQlYJUYra62HIQW+jCZrEVwuun1uRx3JB0d1gM+lcr3ms+EpSDyQv17MY/X\n2Qk8/TSDmIceYg3FSrcgRzJfRQjncKphk2xusiDKTaFCDUVQBw+ao0ORmZKFmvYjIlHb1myWzpZd\nu2w2o6bQhFG4+pQMDJgVTbcJpRQdl4YbF8opIRGrpFxuDfmd9Zh1dbZQiNjUlVFuGFWHKhLv7TXv\nu+yUGocGmN1y0yaSfGjvlLtEC4y87OPj1lFSerR2LUoyahEIOy0q2SoS1/mSJh7mBsKEbS5nDiFJ\nYloAczl7TwGTSRIJ6yGjXY8WEMAWnOmSmz091gFSu8DaWn4GZ5t6vxgSLyafLPSxhL4+4Ngx7hzb\n2mgznI8kU05EMl8lUCm+knmJhEWoNTUkRbkRzp3jh1VNsMKZkD091sBJSSpFVRrvNjVlhUJhbw5d\ntLVXJD4wYJpruN0HjHzVG2R0lPfduZPEEnb7CyUYuUDU2EmjzjZsMNujxo1pIdDzjY6anKIhF+fO\n2Yg0LXpK/mmXkM2aVtzRYYM5RPaShiSpyB2kBUjHE/Z9UVm9Eo5hBK0mXuGAY90/7Bmvc6LFTtF8\n2D42JHo5eAYHuQCHVlR1iZTDJfTnFyKR4K4OsKIxfdYKS++LYSFEvhwEDjAgaG9n9eY99wB33FFe\nm+FCEMm8wqEkoohGxTuKxCQFSMvs6uKX98ABc1CMjpLcRU5NTeY0UOQlW9lVV/F6NVnSF1jRoPck\n5r4+Rtc7d/L2itBFbPI5Z7OmVyvRpuRi2J9axS1ycsgFIu1bCcZwqLQSr6EmPjRkU4J6eykNrF9v\nfcDDHiDO8TVoMaqvp4Yub7kiW0XiImXnuBAq0abj0zlQf3J53bX4aTGULj0xYS4SSVfhTE09Xhg5\n6r2Q1VO30YKmNsOTkzabVYuKdHjtVMKRfSFGRhiJq80wYKX36iMzE+ZL4kuhf0+HTIY2w5deYsL+\n3ntXhs1wIYhkXsGQ/7m+nl8mTSFXCbdItKODpFVbS+1v2zbTZM+fJ2Fs3Wo9QhTRq+TeOSYpVa0p\nApIMoAhxfJwEOTzMx9qxwyJvEVBoewO4gExM8Fg1YECkryhTBJTNWjWnyFPks3GjLWCFNju5U5To\nSyT42qRR79hh/Vskb2SzvM3GjTZMoqfHCoqUEFRhj+QTRfayPer1ivDDaFt9YkS0mvij3jbhlHq9\nJiBfwgLyi6/CHY8i66oqm6QkK6MWDyVTtQgD+d0+Qyi/otF7el6Nd5sL5krky0ngAM//j3/MTrPX\nXENJRZ0wKxWRzCsQoVa7fbt98eXMUPe9zk4S+caNlEXk6hgdZYQ5PMwvojRskeeFC5RTampoxRKJ\nq+BEcy8lf6jZVDJJEtfwYUWc8jdreLBcMePjJPBwwIBurzmW6i8uu52Ibd06Xq+eL3JsKPGqxKbO\njayVXV3WXKqlhUQuZ4l06IsXuYDs2GHdEKVrSyrRzkX9STZtMuujFpixMUvE1tXx2MK+53od8uBr\n2tD4uOncInElkZVYDROf4a5H51y7i4EBvi8qiNL9JMfp86Qh0cUSnNksd3U9PVaP4NzMpfeFmAuJ\nLzeBAzxPr75KSWXXLnYz1PtW6YhkXkHwnjKBEolVVYwyASP2hgaS8blz/GIeOGAf1pERRuIjIyTd\nXbvyx4h1dDASV/Wl+no4R2ISScgDLaIbHbUpOYrEAavyC+dmplK8yKoWatnSlTUOTASqZJx2AZIf\nqqtJxIqmpQWrcZVsf8PDNq1o40a+bjlpJBPV1FAaunDBenx3dlrRj7oCNjWZzz6d5uM1N+cPcJAL\nRV7wMDHrvend4Ri1wUGL3rUg6afyDyJ07TpCO6aSnWHrWbU4EImrq6LurwTtdAnOqSl+XtSfXo8j\nyWm+Mknh7cOv+XISuB7/zTdpM6ypoc3wiiuW/nnKiUjmFYLxcWuZ2tBgEae+pE1NVjJdVUVtu7nZ\nFoCuLpJoc7NNphExnj3Ly9atJPGw5F5ygHqvVFXZaLZkMl8ekdVNOnjoigjbooYDBkRy6bTp/fJY\nh5KKoPJyuT8kUSixOThoo9JGRij7pFJ8TXv28CIZQbr3wAA1YJFdb69Nu9draWzk3wMDNi1J/0un\nrb0BYD7yMNkoIpY8pOHKSvjqdSgaD3MBkrRkrwwdObKMqmGYXEhqAaBdlKJ47YDkaimW4MzlzKGi\ndsVA/sDkuSIkaf1d7LrltP2dP0+bYSpFm+GhQyvfZrgQRDJf4fDeilHUVnRgwHTTxkYSzttv83/7\n9jHq1P16enh9UxNJXAnBdJpf1vPn+SVtbc2PVEXKSshJ1jh/nl8Kkbh81JIylGxT4YxGsRWzqqlZ\n08iI3U+RvzRwfRTUnlcRr6JQVVSqSEgXJXTr6thit6XFys61SGjQdDpt4xfHxixhrDa1jY08l5qW\no7mVmmmp0nqROGDPpcg39HxLmhFxa0EOdxeSY0TiitqB/Ahf74vkKLljpL/rPtLUlbieLsGZSFBi\nA6ylwmyl99N9bgFbfIphuQm1v582wwsXqInfdFPl2AwXglLPAP1rAO8F0OO9v3Ga20Qyv4R0mmSs\nhk4iG8B8y+fO8Qt65ZWMOicnSfbqC7JjB4lMnuaxMZJ4dzf/v3evabJKtsl5oeZVGs2WyeS7TcLe\n2iIQlYdnMjyOmhqz8wFWoTk8bFKJyDVsMiV5QTrw8LD1xtZtlNQbGTGveVcXSViOnT17bJcht0sm\nQ0mpr88WsJERu10yaZG8PO+aWF9XZ9W06bQtXIrERaDaNYmEJSGpMEmvIaxIVU4CyO8vo8VV/nTJ\nTXr9SmTK4RPOLg1L+GdKcA4P28KmRbqhgZf5tnue6etbioh4dJSa+KlTnLV5xx3TFzutJpSazO8F\nkATwlUjm0yOXs4nuSiYODNiAhQ0bqAGn04yo9+4lSSiCB3i/xkbzfWsqfH8/SeqKK2wrr4ZK2taL\nVIeGrM9IczO/2LpNOm0RnnRhkZj6h8jloCKfsTFzlYQRuMhNEWxINLIjqppVEfqTT7KXy9gYb9PZ\nacORr76auxDtAsIugCrRl7QhfRsw3bm5ma9RFkQ1vNJr0PGrwZduoxmpcq+ElaqF0XBI/DqfIl7p\n4upoqGEZapo1OMhzEA5cLiTx8PlnSnDKoZJI8P1VwVRYATqfz60QknapJI1Mhu6Un/4UuOUW2gzn\nYpVcLSi5zOKcuxLAtyKZF4fGiW3cSIJQBKttujRbEbKSZ6q+VAc9RXOjo4xCEwmL3kOfsshDcsCG\nDTaabWqKxKae5IrC5cSoqzPSUSQeDhiQphtOG9LrkoYbShCKxgHTwFVBuX49/5Y740tfAh57jK9N\n4+6uv54krqSgCFcE2NlpY9k0MFlRpxpvVVfztkB+AY3K32VNBMwRomg/mTQbpiJtSSWSU/S7RtaF\n1Z5ytgD8v7o1yio5OGj6v2QstQ8QiYfRuCo+iyU4s1mrAK6vNwvmjh1zj2LDr2mh372UmvTkJPDi\niyTyq6+mpCKdfy0hdk1cIQhL8bdvt97eKlsfHuZ1LS2MSNNpkhNg/mVN2HGOpHfuHMmrtZVeWiXY\nFI2r0+HEBK8bHuZjVlXxeerqrPOgIlkVl+ixNEhCnfQ0uFlDKtQhcNMms+fpGEK5QRdF/uEM0VTK\n/PCplI26O3mSfud77iGJa5SaZBv1a7lwgRcV4wwPG/koQbtly+WDlAFz8Sj6raqygcSyGqpoKyRj\nNbTSYhGOXFPCFLAEpRZfySzSvFMpPv7EBM+fCnO0KxBphm0E5PkPC43Cz5ksq5s38/G2bp1b6T1w\nuYQS5jRKnVTM5YATJ4BnnmHQ8ZGP8PMQMTMimS8jRkf5ha2tNVeKxmiNjPCLuXMns/CZDIlJTgyR\nl7zSIyPcNqdSjNyPHMkn0LCHtYpShoYYoVVXM3KvrSWJ9fbmyyhK2Ek66O3lY6ijoHqNDw3ll9Yr\nehSxiUQLI3Fp8Brp5j31fblTXn2VrUj7+oBvf5vJ3u5uvnY1D1PULO97Rwf/3rLFJJmaGiPjzZvt\n/NfU2GKkLoWyB4akq1maPT32eNrOy8cd9k7RUIpMJn8HJEJWfxnp7yryGRriY+r8q0eLKm3Dalot\nOEqeavEJz69G/qmBmEh8torH2TTwUpO490zSPv00X8ujj3LXGTE3lJTMn3jiiX/9va2tDW1tbaV8\n+pJhctJ6oezYYb7edJoEJhI/eNBK5NXvI5PhF1b9SxIJbpszGZJ4a6vp0ID1B5c/3HsSRl8fiaW1\nleSjHiAqghF5SwpR18CxMWqs0pd7e80rvWED/6+oUBKKZJ0woSYykqwi7fj8eevYKL/5rbcymdXc\nDFx7LfCHf8jHCKPUdJo7grNnbVGYnOTrlB4swlSCWXM7lWTU46kQRyPk9FgXL5pdUxKUdi56fXrd\nk5Mmv1RV2eg3vQ8auafqQw0RUdJbWnjYOgCwBVXnFLCovrBFbdhTR9OIZiu9n8mBspCeKkuFCxdI\n4skkbYbXXLM6bYZzQXt7O9rb2+d9v6XUzPeBmvkN01y/6jVzEWl/vxV19Pdb9zqN09q927RTJboU\n8aqvhmZzhrbEcPutEvrxcfNMj4yQNDQNXok7RXPqUiiSEhEPDvK41VFQOrmcJiqS0SIiQtNxFLoi\npNtns3wcle2rclLRaybDx9MYuakp4Itf5Di/UFaQ1t/dbdbGkRHbISjaHxvLbw2g5KSiXiVCRcoq\n31d1pjoZhouTdi5yroS9a6qrrWw+mzWnS/j8kqRE3kpuajHVuSqMxuXvL5bgHBriZ0PtGrZtI4kX\nK72fqwOlXESeSNBm2NlJTfzmm1e3zXAhKLWb5asA2gDsANAD4M+8939TcJtVTebZLKPByUlq4xMT\n/IAmEiSt7dupV+sLK99wOs2fqk68cIHRpwqEWlqKF3+o4lLT2YeGrLxcrWE1Si2sOFRUClifa/Ud\nSaVsgo+iU1VqhmXxQL6mWnhskpG6uowoFYVLL66q4qK2bZv1Q1m/HnjuOX6ppa/39Jikovt6b2PN\nJOHkchZ5S47QQik3RljE4xzP28SETfgRJLlo4ZPEoYhZgyRU0KNZmrpPKmWLmMh78+bLp9qLyMPz\nFhb/hNWigPnn+/r4njU02KCIEPOxEJaLxJNJ4Phx5kfuugt4xzvWhs1wIYhFQyWCIttEwkqjRcjp\nNL9oLS2mfW7dauXvgBVtXLhA0tq0iSSuvikhtIVXcyp5saWtq5RcQ3mle4o0lBzU0AbdNhxsLLlH\nlZiSUoB8e1yh1VBkpH7qnZ28vfTu2lrT8jULNGxqpXMZ9iPv6LD+I3LQ6Ng0jcd7I2JF4vJm67hE\nVHotmhwfNhRTAjTsPx42DtNrkSVTFathIdTIiLXU1eg3yS+F72XYr0TPsW6dVYaGCU45VDTGrqHB\nWgmHOzWhsKBnOqIuB5GPj3PW5osvMgq/996ZZ41GRDIvCTSHE7A2tSdPMtrdsoX/k3uhoYG/y/8s\n0pAjY8sWFsM0NFy+/VWvDQ2lCMlYl23bTGcPLYoiSMkQg4Nmw5MlcP16mzovf3gYgYfJtrDwB6Dj\n4P77SW5vvWV9sNXnQ7sP56x7o9wZYem/IuVEggtBf7/Z9LQohpq4iF3TkpwziUo9aQRF5IqwJRUB\npmGLUNJpnicdY9i6VrZH7+3YtJhrBqt2Q2H/mxDTReMq/tF5CfvqnDtn0llzs5XeL4TAdQyz3Wap\nMTlJn/hzzzFX1NbG1xExOyKZLyPUbnVw0AoyXn2VyT2N5tI0m+3bTR4YHbUugd3dJPHt24H9+y9v\ncJTLMSF0993WgKq3l8+riFpVm4WJMZG3yELJQ5GY9OvaWtPJw0gwjOalq4c6O2D/+9M/petAUsju\n3abXq6eJbHJKokrmEOHKJtnVxcVx3To+hsrsVRUrT7h0bZG4XCFVVebvVgGPfOHSonXsW7bwmDZu\nNN++krSSWXSsmYzJYdLDw/F3ctFoCtJ0VsDCBRbIP2bp82pTrH48muJUuNDPh8DDY5jrbZcC3gM/\n/zl18aYmJjfVKz1ibog+82VCKkUifvFF4D3v4fb3tdd43c6dJIiWFn7xNm1ilK7E3bZt/JJeuMAP\n9u235+udYRSey7F0+ZZbWL48OEiiuPJKe55i5dgi8JDERZpDQ9aFsaUl33dd+OUO/eHA5XbD8XHg\nJz9hx7q+Plarbt9uPVUAvkYtUrIDilgB87JfvMhzpF4zsvupZ43cLEqcqnxdux49rgg3LHXX8yn6\nD6sg5fVWa9rQa6/3QTKWFrx0mu+fkprSrMOhzcUQkriORS2EwwRnXx/PaTbLY929+/JWwqFEMx9S\nnu/tFwPvuRg9/TSP/QMfYCI/YvkQyXyOyOX4xR8Z4Zfrhz80kmluJsHu3s1oGbBhwJJAurutGOYd\n78hPuE1NWZEPwC92Ok3ib2+nXHPzzdbPfDqItNRlT9Y5eaYbG20qu6JjID9iDZ0V00Xjx4/zcv48\n8M1vWmL3xhuB224zEpcOrSSk5oJOTvJ89PRYolD9QuRGkewhn7rK7tV9UVq0XCTyi+v1iMTlOtm5\n01oojI7yokrM5maTZ0IpZeNGmzyvZmTpNJ9XxVczTbQPz5neH8CicRVdVVVxsf7FL/j5qq9n3iRs\nJRw+3nwJudTReFcXSXxkhH3Fr7127doMS4lI5nOAuvWp+KSrizrmww/TD6se4VVV1v5UDZwkHeza\nBdx5Z34RighcicmNGznq6vhxRvx/+7dmOauqYvRbDCLKTIYLiEaoqeNfayt/hiPbZtNyAYvGw+uc\nAx54gKTd1cXX+Vu/xf8rSg0n6IiMddHOZGTEfO8aHO2cDUXQ80qXVsXm5s3mHZcrJoxsNYrNe/6v\npYW3V48bST+qrJVnPJWyCF02Qu95Lnt7bZh0a6uV+89GUMWicZ0H9bwZG2OuQeX3hw/btJ8QCyXD\nUkbjAwOUUzo6mEe5+eb5N/OKWDgimc8AFf/IqnfsGEdSVVcD//RP7BexYQOTOXfeaYRTW0vy7+sj\n0d99d345uLRfNV4KE44PPMALwG1pUGdVFJIXOjttStGWLSTI5mbrVT7XhFiY5ATybX2KXLu7qXEr\n0lYkHnYKFIkDJFKVmmcy9tqvuIKPp94ugD2vujcmk3wskXh19eXl/bJpqseKRsZp+ER3d/4wa41X\nU7Izm7Uktcrve3ttXN+WLbwulKVmg4hc50DnQR0is1nKKZ2d/Pvaa0ni2nktloBLGY0nk8Czz1Ib\nv+su4P3vn1sLgYilRSTzaTA8TBJQom5oiDaqd72L12/aBHzuc7xeHffWreN9BgYYRd9zDz/U0pjD\n5ktqQ7uQL1t7OxeQdBo4fdrmXu7cycVD0sBcI0f9DlwejYvEJyasqZUWt127gPe+l88Xtl/N5bi7\nuPNO7mDOnjUbIkDyd46PExbxhF54eeg3bbIukSqOCtvR9vZaYlLNyDZu5GN3dNj5bm42i6OqcGXF\nVOvfbJa7hkSC75v6feu9mgsKo3HAchbqYCnXz8aNDAhaWoo7XxaKUkXj4+PAj37EAOfGG4HHH482\nw3IiknkBJiZIjkNDtl1XbxVVESpC0zgx9ccYHSWJHzmSv31XJCo/8lwr3KbrdvD00zymCxf4mPv2\n8XnnOnwXuJzIw4rQMBpXhWRXl7UI2LvXOji+5z32mJKLslngG9+glLJuHW+rBGZNjfVuD6srde5l\nNdywwQZP6DhFxqmUyUjbtpnGLm17cNBcLps3W8+VwUGTUkL/dzJpA6s3bzYffNgDZa7nNOyTEzY+\nkw31zTf5mPv22fSnpUKpovGpKdoMn32WdtpPftKS1RHlQyTzS5BfuKPDWqiqm53mL6rxUipFl8nw\nsDWl2rePGiFAwpC3ev16G3AwXxQj86Eh4I03eKy33kqpYr7lz6GdTfbD0Lmi31WB2dfH1797N6N/\ndSQsTKAqon/mGZLqlVfyXCWTNoZNZfhhq9xk0qyD6nQokhYpTk5ac7L163m9zqsagWnhlR1UxT99\nffxZW2u7CMBeXzrN59y3z2yO84UklTBHsG4dn7O/n8nvbJbncN++uQ9Mns/zA8tL5N7TuXXsGM/j\nb/4mF9yIlYE1S+aSKgBGeW+9xS+2hi/s3GljxnI5+72vj5Hlnj2UHPbv5wdaFYmKxBYjo0x3vOq9\n881vAtddxy1uW9v0EXwhikXjhVPgZS3s7rZIdutWLhoqlAnvD9j9jh8nkY+NAV/7mpHzDTcwUayS\nd0kqKsPXcAdNwZHFUXKItHlF+bI7aqSdks3S61VYlEjw8bdsMRePBoT09PA56upIsGHV6HwQLjah\nRq6irJdf5k81VtuyZWkJt1TRuGyGzgHvex8/9xErC2uazO+/n5ruyZP8kLa2mltBXmdFj2pG1dfH\n/+3fz+hE3fNUbBKWvy8lCkl7tsRoIcKCFeDyBCdgrp2BAdOmr7jCfNlhh0Qg33cO0OHS0kILX18f\n8PGPk8jCSF6LHmALw7p1PJc7d5qsIxlFUpeKchSFA/lDpHUM2gloZ6X+LNkspSKN69PAhmKl9nOF\nSFzHKBtkNgu88grPQWMjrahbty494ZaCyLu7Ofl+cJA2wyNHos1wpWLNknkyCXzve4zg9u+n9qcv\ntnp3VFVZn5BEwhJW9fU27Dhs6LRSUYzIQ6lFnvb+ftuFKJqVpqtEaCgXhf1Y+vq4MKpfiab4aBan\nrIdKVupx1GdEEbMGashrr06SatGrnZPIUTq7HC0bN/J66d1y+uj9U3OvhSQcw11NKKuoEtU5c6hs\n3cq2vtu3VyaJDw5yl3XmDHDffZT0os1wZWMFU9DSQ1LFxATwl39JOWHnThvoq7JuEVRPDwlG/l8V\nruRyJheUI0qZr6wSfvkliYjourqo++dyFqmqP3iYrA1dLtKFNWRaZKlIO5NhlD45yYsIXGSuPMKO\nHXwvEgneL+xR4pwtmokEb793LxcZFVmpwGdigset+aDOMbq/eJHvcV2dtUyYr2WuWAcKEbiklaoq\nnoO33+Zx3norP1fLsUNbbiIfG2Ni88QJ7ije+95oM6wUrNneLJ/7HPCFL/B3SSq6dHeTQLZto+yi\ngbqa21gJ/ZZFuqG9UJBXvLubxKgFTYuUEouFr1MuDUkhnZ0kTPV8UVVjNsvHUpdBJYKVlJSNUUQv\n//rYWH4pvvck4J07zZut5LIGNmzezNuoXW0iYa0BdN/56uHF+p3oeETiStj29tIeun49J0bt3r08\nEexyk3g2yxzMCy8wx3HffUvrtIlYOGJvllkgopLmPTpqenFjI4cIq9+GtPBKQWEjJyGbJfmeP28k\nrtmKExNmNyxs2iVo56LHGB83kg2HGKsRWE2N6eXZrJF6OLlHHn09j/cmrTQ0WBXp8LD1DtdQCpG0\n7KRaWLZt4+uardS+8JwJhQQe9ngJ+9y88Qb/d+gQdw3L9RlZTiKfmgJeeonR+P790WZYyVizkfkz\nz3Ab2dtLIhgYYGFJa6sNKA4n+1QCpovGNRKto4PEt3MnE5WbNhmRbtuW3wMcyCe1kRGb9jM8bEli\nee0nJmzIhdr9AtYfZts2nldF7ponqmOWI0W92EXi6qOiXUPYplctiOV/b2qiRj2XUnu9LiF8rYUI\nh1Mkk4zEUyn2TzlwYPmGKiwniXvPxP+xYzzvDz1Ef33EykOpJw29C8B/A1AF4K+9939R5DZlJ3PZ\nEaemSExvvUWSamkxTVVRY6WhGJGHJJ7NWjOwcMxa2NRKCPXxkRHKKX19XPBOn6YmLFlYRuX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"text": [ - "" + "" ] } ], - "prompt_number": 6 + "prompt_number": 5 }, { "cell_type": "code", @@ -224,7 +224,8 @@ "input": [], "language": "python", "metadata": {}, - "outputs": [] + "outputs": [], + "prompt_number": 5 } ], "metadata": {} diff --git a/examples/demo_OT_2D_samples.py b/examples/demo_OT_2D_samples.py index 5fc214f..e9ec78a 100644 --- a/examples/demo_OT_2D_samples.py +++ b/examples/demo_OT_2D_samples.py @@ -9,9 +9,7 @@ import numpy as np import matplotlib.pylab as pl import ot - - -#%% parameters +#%% parameters and data generation n=20 # nb samples @@ -24,7 +22,7 @@ cov_t=np.array([[1,-.8],[-.8,1]]) xs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s) xt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t) -a,b = ot.unif(n),ot.unif(n) +a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples # loss matrix M=ot.dist(xs,xt) @@ -47,7 +45,6 @@ pl.title('Cost matrix M') G0=ot.emd(a,b,M) - pl.figure(3) pl.imshow(G0,interpolation='nearest') pl.title('OT matrix G0') @@ -78,14 +75,4 @@ pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') pl.legend(loc=0) pl.title('OT matrix Sinkhorn with samples') -# -#pl.figure(3) -#ot.plot.otplot1D(a,b,G0,'OT matrix G0') -# -##%% Sinkhorn -# -#lambd=1e-3 -#Gs=ot.sinkhorn(a,b,M,lambd) -# -#pl.figure(4) -#ot.plot.otplot1D(a,b,Gs,'OT matrix Sinkhorn') + -- cgit v1.2.3 From f338718068422570d43f5dcccde6c1af22499d99 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 27 Oct 2016 17:45:29 +0200 Subject: final demo 2D --- Makefile | 7 +++---- README.md | 2 +- 2 files changed, 4 insertions(+), 5 deletions(-) diff --git a/Makefile b/Makefile index 9a54e42..c817639 100644 --- a/Makefile +++ b/Makefile @@ -11,7 +11,7 @@ help : @echo " remove - remove the package (local user)" @echo " sremove - remove the package (system with sudo)" @echo " clean - remove any temporary files" - + @echo " notebook - launch ipython notebook" build : $(PYTHON) setup.py build @@ -30,10 +30,9 @@ sremove : $(PYTHON) setup.py install --record files.txt tr '\n' '\0' < files.txt | sudo xargs -0 rm -f -- rm files.txt - + clean : $(PYTHON) setup.py clean - + notebook : ipython notebook --matplotlib=inline --notebook-dir=examples/ - diff --git a/README.md b/README.md index 9793512..d8afbfe 100644 --- a/README.md +++ b/README.md @@ -37,7 +37,7 @@ Note that for easier access the module is name ot instead of pot. The examples folder contain several examples and use case for the library. Here is a list of the Python notebook if you want a quick look. * [1D optimal transport](examples/Demo_1D_OT.ipynb) - +* [2D optimal transport on empirical distributions](examples/demo_OTDA_2D.py) ## Acknowledgements -- cgit v1.2.3 From 123a7c70488d68bff85bbad50c6dd8d390f1e728 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 27 Oct 2016 17:46:31 +0200 Subject: final demo 2D (bis) --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index d8afbfe..a1651e4 100644 --- a/README.md +++ b/README.md @@ -37,7 +37,7 @@ Note that for easier access the module is name ot instead of pot. The examples folder contain several examples and use case for the library. Here is a list of the Python notebook if you want a quick look. * [1D optimal transport](examples/Demo_1D_OT.ipynb) -* [2D optimal transport on empirical distributions](examples/demo_OTDA_2D.py) +* [2D optimal transport on empirical distributions](examples/Demo_2D_Ot_samples.ipynb) ## Acknowledgements -- cgit v1.2.3 From cf2d92e151f816e6ddcfc4b64cbda1f8f7bde9df Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 08:53:16 +0200 Subject: complete doc emd --- ot/bregman.py | 5 ++- ot/lp/__init__.py | 61 +++++++++++++++++++++++++- ot/lp/emd.cpp | 126 +++++++++++++++++++++++++++--------------------------- ot/lp/emd.pyx | 12 +++--- 4 files changed, 132 insertions(+), 72 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 17ec06f..0d2c099 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -37,7 +37,7 @@ def sinkhorn(a,b, M, reg,numItermax = 1000,stopThr=1e-9): samples in the target domain M : np.ndarray (ns,nt) loss matrix - reg: float() + reg: float Regularization term >0 @@ -54,7 +54,8 @@ def sinkhorn(a,b, M, reg,numItermax = 1000,stopThr=1e-9): See Also -------- - ot.emd.emd : Unregularized optimal ransport + ot.lp.emd : Unregularized OT + ot.optim.cg : General regularized OT """ # init data diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 46662b7..568e370 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -1,3 +1,62 @@ -from .emd import emd +from .emd import emd_c +import numpy as np + +def emd(a,b,M): + """ + Solves the Earth Movers distance problem and returns the optimal transport matrix + + gamm=emd(a,b,M) + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + where : + + - M is the metric cost matrix + - a and b are the sample weights + + Parameters + ---------- + a : (ns,) ndarray, float64 + Source histogram (uniform weigth if empty list) + b : (nt,) ndarray, float64 + Target histogram (uniform weigth if empty list) + M : (ns,nt) ndarray, float64 + loss matrix + + Examples + -------- + + Simple example with obvious solution. The function :func:emd accepts lists and + perform automatic conversion tu numpy arrays + + >>> a=[.5,.5] + >>> b=[.5,.5] + >>> M=[[0.,1.],[1.,0.]] + >>> ot.emd(a,b,M) + array([[ 0.5, 0. ], + [ 0. , 0.5]]) + + Returns + ------- + gamma: (ns x nt) ndarray + Optimal transportation matrix for the given parameters + + """ + a=np.asarray(a,dtype=np.float64) + b=np.asarray(b,dtype=np.float64) + + if len(a)==0: + a=np.ones((M.shape[0],),dtype=np.float64)/M.shape[0] + if len(b)==0: + b=np.ones((M.shape[1],),dtype=np.float64)/M.shape[1] + + return emd_c(a,b,M) + diff --git a/ot/lp/emd.cpp b/ot/lp/emd.cpp index 2343af6..26d243f 100644 --- a/ot/lp/emd.cpp +++ b/ot/lp/emd.cpp @@ -6,11 +6,11 @@ "depends": [ "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/arrayobject.h", "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/ufuncobject.h", - "ot/emd/EMD.h" + "ot/lp/EMD.h" ], "include_dirs": [ "/usr/lib/python2.7/dist-packages/numpy/core/include", - "/home/rflamary/PYTHON/POT/ot/emd" + "/home/rflamary/PYTHON/POT/ot/lp" ], "language": "c++" } @@ -260,8 +260,8 @@ static CYTHON_INLINE float __PYX_NAN() { #endif #endif -#define __PYX_HAVE__ot__emd__emd -#define __PYX_HAVE_API__ot__emd__emd +#define __PYX_HAVE__ot__lp__emd +#define __PYX_HAVE_API__ot__lp__emd #include "string.h" #include "stdio.h" #include "stdlib.h" @@ -497,7 +497,7 @@ static const char *__pyx_filename; static const char *__pyx_f[] = { - "ot/emd/emd.pyx", + "ot/lp/emd.pyx", "__init__.pxd", "type.pxd", }; @@ -1129,12 +1129,12 @@ static CYTHON_INLINE char *__pyx_f_5numpy__util_dtypestring(PyArray_Descr *, cha /* Module declarations from 'cython' */ -/* Module declarations from 'ot.emd.emd' */ +/* Module declarations from 'ot.lp.emd' */ static __Pyx_TypeInfo __Pyx_TypeInfo_double = { "double", NULL, sizeof(double), { 0 }, 0, 'R', 0, 0 }; -#define __Pyx_MODULE_NAME "ot.emd.emd" -int __pyx_module_is_main_ot__emd__emd = 0; +#define __Pyx_MODULE_NAME "ot.lp.emd" +int __pyx_module_is_main_ot__lp__emd = 0; -/* Implementation of 'ot.emd.emd' */ +/* Implementation of 'ot.lp.emd' */ static PyObject *__pyx_builtin_ValueError; static PyObject *__pyx_builtin_range; static PyObject *__pyx_builtin_RuntimeError; @@ -1161,21 +1161,21 @@ static char __pyx_k_Zg[] = "Zg"; static char __pyx_k_n1[] = "n1"; static char __pyx_k_n2[] = "n2"; static char __pyx_k_np[] = "np"; -static char __pyx_k_emd[] = "emd"; static char __pyx_k_cost[] = "cost"; static char __pyx_k_main[] = "__main__"; static char __pyx_k_ones[] = "ones"; static char __pyx_k_test[] = "__test__"; +static char __pyx_k_emd_c[] = "emd_c"; static char __pyx_k_numpy[] = "numpy"; static char __pyx_k_range[] = "range"; static char __pyx_k_zeros[] = "zeros"; static char __pyx_k_import[] = "__import__"; +static char __pyx_k_ot_lp_emd[] = "ot.lp.emd"; static char __pyx_k_ValueError[] = "ValueError"; -static char __pyx_k_ot_emd_emd[] = "ot.emd.emd"; static char __pyx_k_RuntimeError[] = "RuntimeError"; static char __pyx_k_ndarray_is_not_C_contiguous[] = "ndarray is not C contiguous"; static char __pyx_k_Created_on_Thu_Sep_11_08_42_08[] = "\nCreated on Thu Sep 11 08:42:08 2014\n\n@author: rflamary\n"; -static char __pyx_k_home_rflamary_PYTHON_POT_ot_emd[] = "/home/rflamary/PYTHON/POT/ot/emd/emd.pyx"; +static char __pyx_k_home_rflamary_PYTHON_POT_ot_lp[] = "/home/rflamary/PYTHON/POT/ot/lp/emd.pyx"; static char __pyx_k_unknown_dtype_code_in_numpy_pxd[] = "unknown dtype code in numpy.pxd (%d)"; static char __pyx_k_Format_string_allocated_too_shor[] = "Format string allocated too short, see comment in numpy.pxd"; static char __pyx_k_Non_native_byte_order_not_suppor[] = "Non-native byte order not supported"; @@ -1191,8 +1191,8 @@ static PyObject *__pyx_n_s_ValueError; static PyObject *__pyx_n_s_a; static PyObject *__pyx_n_s_b; static PyObject *__pyx_n_s_cost; -static PyObject *__pyx_n_s_emd; -static PyObject *__pyx_kp_s_home_rflamary_PYTHON_POT_ot_emd; +static PyObject *__pyx_n_s_emd_c; +static PyObject *__pyx_kp_s_home_rflamary_PYTHON_POT_ot_lp; static PyObject *__pyx_n_s_import; static PyObject *__pyx_n_s_main; static PyObject *__pyx_n_s_n1; @@ -1202,12 +1202,12 @@ static PyObject *__pyx_kp_u_ndarray_is_not_Fortran_contiguou; static PyObject *__pyx_n_s_np; static PyObject *__pyx_n_s_numpy; static PyObject *__pyx_n_s_ones; -static PyObject *__pyx_n_s_ot_emd_emd; +static PyObject *__pyx_n_s_ot_lp_emd; static PyObject *__pyx_n_s_range; static PyObject *__pyx_n_s_test; static PyObject *__pyx_kp_u_unknown_dtype_code_in_numpy_pxd; static PyObject *__pyx_n_s_zeros; -static PyObject *__pyx_pf_2ot_3emd_3emd_emd(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_a, PyArrayObject *__pyx_v_b, PyArrayObject *__pyx_v_M); /* proto */ +static PyObject *__pyx_pf_2ot_2lp_3emd_emd_c(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_a, PyArrayObject *__pyx_v_b, PyArrayObject *__pyx_v_M); /* proto */ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ static void __pyx_pf_5numpy_7ndarray_2__releasebuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info); /* proto */ static PyObject *__pyx_tuple_; @@ -1219,19 +1219,19 @@ static PyObject *__pyx_tuple__6; static PyObject *__pyx_tuple__7; static PyObject *__pyx_codeobj__8; -/* "ot/emd/emd.pyx":21 +/* "ot/lp/emd.pyx":21 * @cython.boundscheck(False) * @cython.wraparound(False) - * def emd( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M): # <<<<<<<<<<<<<< + * def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M): # <<<<<<<<<<<<<< * """ * Solves the Earth Movers distance problem and returns the optimal transport matrix */ /* Python wrapper */ -static PyObject *__pyx_pw_2ot_3emd_3emd_1emd(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ -static char __pyx_doc_2ot_3emd_3emd_emd[] = "\n Solves the Earth Movers distance problem and returns the optimal transport matrix\n \n gamm=emd(a,b,M)\n \n .. math::\n \\gamma = arg\\min_\\gamma <\\gamma,M>_F \n \n s.t. \\gamma 1 = a\n \n \\gamma^T 1= b \n \n \\gamma\\geq 0\n where :\n \n - M is the metric cost matrix\n - a and b are the sample weights\n \n Parameters\n ----------\n a : (ns,) ndarray\n samples in the source domain (uniform waigth if empty)\n b : (nt,) ndarray\n samples in the target domain (uniform waigth if empty)\n M : (ns,nt) ndarray\n loss matrix \n \n \n Returns\n -------\n gamma: (ns x nt) ndarray\n Optimal transportation matrix for the given parameters\n \n "; -static PyMethodDef __pyx_mdef_2ot_3emd_3emd_1emd = {"emd", (PyCFunction)__pyx_pw_2ot_3emd_3emd_1emd, METH_VARARGS|METH_KEYWORDS, __pyx_doc_2ot_3emd_3emd_emd}; -static PyObject *__pyx_pw_2ot_3emd_3emd_1emd(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { +static PyObject *__pyx_pw_2ot_2lp_3emd_1emd_c(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ +static char __pyx_doc_2ot_2lp_3emd_emd_c[] = "\n Solves the Earth Movers distance problem and returns the optimal transport matrix\n \n gamm=emd(a,b,M)\n \n .. math::\n \\gamma = arg\\min_\\gamma <\\gamma,M>_F \n \n s.t. \\gamma 1 = a\n \n \\gamma^T 1= b \n \n \\gamma\\geq 0\n where :\n \n - M is the metric cost matrix\n - a and b are the sample weights\n \n Parameters\n ----------\n a : (ns,) ndarray\n source histogram \n b : (nt,) ndarray\n target histogram\n M : (ns,nt) ndarray\n loss matrix \n \n \n Returns\n -------\n gamma: (ns x nt) ndarray\n Optimal transportation matrix for the given parameters\n \n "; +static PyMethodDef __pyx_mdef_2ot_2lp_3emd_1emd_c = {"emd_c", (PyCFunction)__pyx_pw_2ot_2lp_3emd_1emd_c, METH_VARARGS|METH_KEYWORDS, __pyx_doc_2ot_2lp_3emd_emd_c}; +static PyObject *__pyx_pw_2ot_2lp_3emd_1emd_c(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyArrayObject *__pyx_v_a = 0; PyArrayObject *__pyx_v_b = 0; PyArrayObject *__pyx_v_M = 0; @@ -1240,7 +1240,7 @@ static PyObject *__pyx_pw_2ot_3emd_3emd_1emd(PyObject *__pyx_self, PyObject *__p int __pyx_clineno = 0; PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations - __Pyx_RefNannySetupContext("emd (wrapper)", 0); + __Pyx_RefNannySetupContext("emd_c (wrapper)", 0); { static PyObject **__pyx_pyargnames[] = {&__pyx_n_s_a,&__pyx_n_s_b,&__pyx_n_s_M,0}; PyObject* values[3] = {0,0,0}; @@ -1262,16 +1262,16 @@ static PyObject *__pyx_pw_2ot_3emd_3emd_1emd(PyObject *__pyx_self, PyObject *__p case 1: if (likely((values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_b)) != 0)) kw_args--; else { - __Pyx_RaiseArgtupleInvalid("emd", 1, 3, 3, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + __Pyx_RaiseArgtupleInvalid("emd_c", 1, 3, 3, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L3_error;} } case 2: if (likely((values[2] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_M)) != 0)) kw_args--; else { - __Pyx_RaiseArgtupleInvalid("emd", 1, 3, 3, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + __Pyx_RaiseArgtupleInvalid("emd_c", 1, 3, 3, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L3_error;} } } if (unlikely(kw_args > 0)) { - if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "emd") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "emd_c") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L3_error;} } } else if (PyTuple_GET_SIZE(__pyx_args) != 3) { goto __pyx_L5_argtuple_error; @@ -1286,16 +1286,16 @@ static PyObject *__pyx_pw_2ot_3emd_3emd_1emd(PyObject *__pyx_self, PyObject *__p } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; - __Pyx_RaiseArgtupleInvalid("emd", 1, 3, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + __Pyx_RaiseArgtupleInvalid("emd_c", 1, 3, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L3_error;} __pyx_L3_error:; - __Pyx_AddTraceback("ot.emd.emd.emd", __pyx_clineno, __pyx_lineno, __pyx_filename); + __Pyx_AddTraceback("ot.lp.emd.emd_c", __pyx_clineno, __pyx_lineno, __pyx_filename); __Pyx_RefNannyFinishContext(); return NULL; __pyx_L4_argument_unpacking_done:; if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_a), __pyx_ptype_5numpy_ndarray, 1, "a", 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_b), __pyx_ptype_5numpy_ndarray, 1, "b", 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L1_error;} if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_M), __pyx_ptype_5numpy_ndarray, 1, "M", 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - __pyx_r = __pyx_pf_2ot_3emd_3emd_emd(__pyx_self, __pyx_v_a, __pyx_v_b, __pyx_v_M); + __pyx_r = __pyx_pf_2ot_2lp_3emd_emd_c(__pyx_self, __pyx_v_a, __pyx_v_b, __pyx_v_M); /* function exit code */ goto __pyx_L0; @@ -1306,7 +1306,7 @@ static PyObject *__pyx_pw_2ot_3emd_3emd_1emd(PyObject *__pyx_self, PyObject *__p return __pyx_r; } -static PyObject *__pyx_pf_2ot_3emd_3emd_emd(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_a, PyArrayObject *__pyx_v_b, PyArrayObject *__pyx_v_M) { +static PyObject *__pyx_pf_2ot_2lp_3emd_emd_c(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_a, PyArrayObject *__pyx_v_b, PyArrayObject *__pyx_v_M) { int __pyx_v_n1; int __pyx_v_n2; float __pyx_v_cost; @@ -1338,7 +1338,7 @@ static PyObject *__pyx_pf_2ot_3emd_3emd_emd(CYTHON_UNUSED PyObject *__pyx_self, int __pyx_lineno = 0; const char *__pyx_filename = NULL; int __pyx_clineno = 0; - __Pyx_RefNannySetupContext("emd", 0); + __Pyx_RefNannySetupContext("emd_c", 0); __Pyx_INCREF((PyObject *)__pyx_v_a); __Pyx_INCREF((PyObject *)__pyx_v_b); __pyx_pybuffer_G.pybuffer.buf = NULL; @@ -1373,7 +1373,7 @@ static PyObject *__pyx_pf_2ot_3emd_3emd_emd(CYTHON_UNUSED PyObject *__pyx_self, } __pyx_pybuffernd_M.diminfo[0].strides = __pyx_pybuffernd_M.rcbuffer->pybuffer.strides[0]; __pyx_pybuffernd_M.diminfo[0].shape = __pyx_pybuffernd_M.rcbuffer->pybuffer.shape[0]; __pyx_pybuffernd_M.diminfo[1].strides = __pyx_pybuffernd_M.rcbuffer->pybuffer.strides[1]; __pyx_pybuffernd_M.diminfo[1].shape = __pyx_pybuffernd_M.rcbuffer->pybuffer.shape[1]; - /* "ot/emd/emd.pyx":56 + /* "ot/lp/emd.pyx":56 * * """ * cdef int n1= M.shape[0] # <<<<<<<<<<<<<< @@ -1382,7 +1382,7 @@ static PyObject *__pyx_pf_2ot_3emd_3emd_emd(CYTHON_UNUSED PyObject *__pyx_self, */ __pyx_v_n1 = (__pyx_v_M->dimensions[0]); - /* "ot/emd/emd.pyx":57 + /* "ot/lp/emd.pyx":57 * """ * cdef int n1= M.shape[0] * cdef int n2= M.shape[1] # <<<<<<<<<<<<<< @@ -1391,7 +1391,7 @@ static PyObject *__pyx_pf_2ot_3emd_3emd_emd(CYTHON_UNUSED PyObject *__pyx_self, */ __pyx_v_n2 = (__pyx_v_M->dimensions[1]); - /* "ot/emd/emd.pyx":59 + /* "ot/lp/emd.pyx":59 * cdef int n2= M.shape[1] * * cdef float cost=0 # <<<<<<<<<<<<<< @@ -1400,7 +1400,7 @@ static PyObject *__pyx_pf_2ot_3emd_3emd_emd(CYTHON_UNUSED PyObject *__pyx_self, */ __pyx_v_cost = 0.0; - /* "ot/emd/emd.pyx":60 + /* "ot/lp/emd.pyx":60 * * cdef float cost=0 * cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([n1, n2]) # <<<<<<<<<<<<<< @@ -1464,7 +1464,7 @@ static PyObject *__pyx_pf_2ot_3emd_3emd_emd(CYTHON_UNUSED PyObject *__pyx_self, __pyx_v_G = ((PyArrayObject *)__pyx_t_1); __pyx_t_1 = 0; - /* "ot/emd/emd.pyx":62 + /* "ot/lp/emd.pyx":62 * cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([n1, n2]) * * if not len(a): # <<<<<<<<<<<<<< @@ -1475,7 +1475,7 @@ static PyObject *__pyx_pf_2ot_3emd_3emd_emd(CYTHON_UNUSED PyObject *__pyx_self, __pyx_t_8 = ((!(__pyx_t_7 != 0)) != 0); if (__pyx_t_8) { - /* "ot/emd/emd.pyx":63 + /* "ot/lp/emd.pyx":63 * * if not len(a): * a=np.ones((n1,))/n1 # <<<<<<<<<<<<<< @@ -1548,7 +1548,7 @@ static PyObject *__pyx_pf_2ot_3emd_3emd_emd(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_DECREF_SET(__pyx_v_a, ((PyArrayObject *)__pyx_t_4)); __pyx_t_4 = 0; - /* "ot/emd/emd.pyx":62 + /* "ot/lp/emd.pyx":62 * cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([n1, n2]) * * if not len(a): # <<<<<<<<<<<<<< @@ -1557,7 +1557,7 @@ static PyObject *__pyx_pf_2ot_3emd_3emd_emd(CYTHON_UNUSED PyObject *__pyx_self, */ } - /* "ot/emd/emd.pyx":65 + /* "ot/lp/emd.pyx":65 * a=np.ones((n1,))/n1 * * if not len(b): # <<<<<<<<<<<<<< @@ -1568,7 +1568,7 @@ static PyObject *__pyx_pf_2ot_3emd_3emd_emd(CYTHON_UNUSED PyObject *__pyx_self, __pyx_t_8 = ((!(__pyx_t_7 != 0)) != 0); if (__pyx_t_8) { - /* "ot/emd/emd.pyx":66 + /* "ot/lp/emd.pyx":66 * * if not len(b): * b=np.ones((n2,))/n2 # <<<<<<<<<<<<<< @@ -1641,7 +1641,7 @@ static PyObject *__pyx_pf_2ot_3emd_3emd_emd(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_DECREF_SET(__pyx_v_b, ((PyArrayObject *)__pyx_t_3)); __pyx_t_3 = 0; - /* "ot/emd/emd.pyx":65 + /* "ot/lp/emd.pyx":65 * a=np.ones((n1,))/n1 * * if not len(b): # <<<<<<<<<<<<<< @@ -1650,7 +1650,7 @@ static PyObject *__pyx_pf_2ot_3emd_3emd_emd(CYTHON_UNUSED PyObject *__pyx_self, */ } - /* "ot/emd/emd.pyx":69 + /* "ot/lp/emd.pyx":69 * * # calling the function * EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost) # <<<<<<<<<<<<<< @@ -1659,7 +1659,7 @@ static PyObject *__pyx_pf_2ot_3emd_3emd_emd(CYTHON_UNUSED PyObject *__pyx_self, */ EMD_wrap(__pyx_v_n1, __pyx_v_n2, ((double *)__pyx_v_a->data), ((double *)__pyx_v_b->data), ((double *)__pyx_v_M->data), ((double *)__pyx_v_G->data), ((double *)(&__pyx_v_cost))); - /* "ot/emd/emd.pyx":71 + /* "ot/lp/emd.pyx":71 * EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost) * * return G # <<<<<<<<<<<<<< @@ -1669,10 +1669,10 @@ static PyObject *__pyx_pf_2ot_3emd_3emd_emd(CYTHON_UNUSED PyObject *__pyx_self, __pyx_r = ((PyObject *)__pyx_v_G); goto __pyx_L0; - /* "ot/emd/emd.pyx":21 + /* "ot/lp/emd.pyx":21 * @cython.boundscheck(False) * @cython.wraparound(False) - * def emd( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M): # <<<<<<<<<<<<<< + * def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M): # <<<<<<<<<<<<<< * """ * Solves the Earth Movers distance problem and returns the optimal transport matrix */ @@ -1691,7 +1691,7 @@ static PyObject *__pyx_pf_2ot_3emd_3emd_emd(CYTHON_UNUSED PyObject *__pyx_self, __Pyx_SafeReleaseBuffer(&__pyx_pybuffernd_a.rcbuffer->pybuffer); __Pyx_SafeReleaseBuffer(&__pyx_pybuffernd_b.rcbuffer->pybuffer); __Pyx_ErrRestore(__pyx_type, __pyx_value, __pyx_tb);} - __Pyx_AddTraceback("ot.emd.emd.emd", __pyx_clineno, __pyx_lineno, __pyx_filename); + __Pyx_AddTraceback("ot.lp.emd.emd_c", __pyx_clineno, __pyx_lineno, __pyx_filename); __pyx_r = NULL; goto __pyx_L2; __pyx_L0:; @@ -3884,8 +3884,8 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = { {&__pyx_n_s_a, __pyx_k_a, sizeof(__pyx_k_a), 0, 0, 1, 1}, {&__pyx_n_s_b, __pyx_k_b, sizeof(__pyx_k_b), 0, 0, 1, 1}, {&__pyx_n_s_cost, __pyx_k_cost, sizeof(__pyx_k_cost), 0, 0, 1, 1}, - {&__pyx_n_s_emd, __pyx_k_emd, sizeof(__pyx_k_emd), 0, 0, 1, 1}, - {&__pyx_kp_s_home_rflamary_PYTHON_POT_ot_emd, __pyx_k_home_rflamary_PYTHON_POT_ot_emd, sizeof(__pyx_k_home_rflamary_PYTHON_POT_ot_emd), 0, 0, 1, 0}, + {&__pyx_n_s_emd_c, __pyx_k_emd_c, sizeof(__pyx_k_emd_c), 0, 0, 1, 1}, + {&__pyx_kp_s_home_rflamary_PYTHON_POT_ot_lp, __pyx_k_home_rflamary_PYTHON_POT_ot_lp, sizeof(__pyx_k_home_rflamary_PYTHON_POT_ot_lp), 0, 0, 1, 0}, {&__pyx_n_s_import, __pyx_k_import, sizeof(__pyx_k_import), 0, 0, 1, 1}, {&__pyx_n_s_main, __pyx_k_main, sizeof(__pyx_k_main), 0, 0, 1, 1}, {&__pyx_n_s_n1, __pyx_k_n1, sizeof(__pyx_k_n1), 0, 0, 1, 1}, @@ -3895,7 +3895,7 @@ static __Pyx_StringTabEntry __pyx_string_tab[] = { {&__pyx_n_s_np, __pyx_k_np, sizeof(__pyx_k_np), 0, 0, 1, 1}, {&__pyx_n_s_numpy, __pyx_k_numpy, sizeof(__pyx_k_numpy), 0, 0, 1, 1}, {&__pyx_n_s_ones, __pyx_k_ones, sizeof(__pyx_k_ones), 0, 0, 1, 1}, - {&__pyx_n_s_ot_emd_emd, __pyx_k_ot_emd_emd, sizeof(__pyx_k_ot_emd_emd), 0, 0, 1, 1}, + {&__pyx_n_s_ot_lp_emd, __pyx_k_ot_lp_emd, sizeof(__pyx_k_ot_lp_emd), 0, 0, 1, 1}, {&__pyx_n_s_range, __pyx_k_range, sizeof(__pyx_k_range), 0, 0, 1, 1}, {&__pyx_n_s_test, __pyx_k_test, sizeof(__pyx_k_test), 0, 0, 1, 1}, {&__pyx_kp_u_unknown_dtype_code_in_numpy_pxd, __pyx_k_unknown_dtype_code_in_numpy_pxd, sizeof(__pyx_k_unknown_dtype_code_in_numpy_pxd), 0, 1, 0, 0}, @@ -3981,17 +3981,17 @@ static int __Pyx_InitCachedConstants(void) { __Pyx_GOTREF(__pyx_tuple__6); __Pyx_GIVEREF(__pyx_tuple__6); - /* "ot/emd/emd.pyx":21 + /* "ot/lp/emd.pyx":21 * @cython.boundscheck(False) * @cython.wraparound(False) - * def emd( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M): # <<<<<<<<<<<<<< + * def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M): # <<<<<<<<<<<<<< * """ * Solves the Earth Movers distance problem and returns the optimal transport matrix */ __pyx_tuple__7 = PyTuple_Pack(7, __pyx_n_s_a, __pyx_n_s_b, __pyx_n_s_M, __pyx_n_s_n1, __pyx_n_s_n2, __pyx_n_s_cost, __pyx_n_s_G); if (unlikely(!__pyx_tuple__7)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_tuple__7); __Pyx_GIVEREF(__pyx_tuple__7); - __pyx_codeobj__8 = (PyObject*)__Pyx_PyCode_New(3, 0, 7, 0, 0, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__7, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_home_rflamary_PYTHON_POT_ot_emd, __pyx_n_s_emd, 21, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__8)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_codeobj__8 = (PyObject*)__Pyx_PyCode_New(3, 0, 7, 0, 0, __pyx_empty_bytes, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_tuple__7, __pyx_empty_tuple, __pyx_empty_tuple, __pyx_kp_s_home_rflamary_PYTHON_POT_ot_lp, __pyx_n_s_emd_c, 21, __pyx_empty_bytes); if (unlikely(!__pyx_codeobj__8)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_RefNannyFinishContext(); return 0; __pyx_L1_error:; @@ -4073,14 +4073,14 @@ PyMODINIT_FUNC PyInit_emd(void) #if PY_MAJOR_VERSION < 3 && (__PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT) if (__Pyx_init_sys_getdefaultencoding_params() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} #endif - if (__pyx_module_is_main_ot__emd__emd) { + if (__pyx_module_is_main_ot__lp__emd) { if (PyObject_SetAttrString(__pyx_m, "__name__", __pyx_n_s_main) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} } #if PY_MAJOR_VERSION >= 3 { PyObject *modules = PyImport_GetModuleDict(); if (unlikely(!modules)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - if (!PyDict_GetItemString(modules, "ot.emd.emd")) { - if (unlikely(PyDict_SetItemString(modules, "ot.emd.emd", __pyx_m) < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (!PyDict_GetItemString(modules, "ot.lp.emd")) { + if (unlikely(PyDict_SetItemString(modules, "ot.lp.emd", __pyx_m) < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} } } #endif @@ -4112,7 +4112,7 @@ PyMODINIT_FUNC PyInit_emd(void) if (__Pyx_patch_abc() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} #endif - /* "ot/emd/emd.pyx":7 + /* "ot/lp/emd.pyx":7 * @author: rflamary * """ * import numpy as np # <<<<<<<<<<<<<< @@ -4124,19 +4124,19 @@ PyMODINIT_FUNC PyInit_emd(void) if (PyDict_SetItem(__pyx_d, __pyx_n_s_np, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - /* "ot/emd/emd.pyx":21 + /* "ot/lp/emd.pyx":21 * @cython.boundscheck(False) * @cython.wraparound(False) - * def emd( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M): # <<<<<<<<<<<<<< + * def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M): # <<<<<<<<<<<<<< * """ * Solves the Earth Movers distance problem and returns the optimal transport matrix */ - __pyx_t_1 = PyCFunction_NewEx(&__pyx_mdef_2ot_3emd_3emd_1emd, NULL, __pyx_n_s_ot_emd_emd); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = PyCFunction_NewEx(&__pyx_mdef_2ot_2lp_3emd_1emd_c, NULL, __pyx_n_s_ot_lp_emd); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_1); - if (PyDict_SetItem(__pyx_d, __pyx_n_s_emd, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (PyDict_SetItem(__pyx_d, __pyx_n_s_emd_c, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - /* "ot/emd/emd.pyx":1 + /* "ot/lp/emd.pyx":1 * # -*- coding: utf-8 -*- # <<<<<<<<<<<<<< * """ * Created on Thu Sep 11 08:42:08 2014 @@ -4161,11 +4161,11 @@ PyMODINIT_FUNC PyInit_emd(void) __Pyx_XDECREF(__pyx_t_1); if (__pyx_m) { if (__pyx_d) { - __Pyx_AddTraceback("init ot.emd.emd", __pyx_clineno, __pyx_lineno, __pyx_filename); + __Pyx_AddTraceback("init ot.lp.emd", __pyx_clineno, __pyx_lineno, __pyx_filename); } Py_DECREF(__pyx_m); __pyx_m = 0; } else if (!PyErr_Occurred()) { - PyErr_SetString(PyExc_ImportError, "init ot.emd.emd"); + PyErr_SetString(PyExc_ImportError, "init ot.lp.emd"); } __pyx_L0:; __Pyx_RefNannyFinishContext(); diff --git a/ot/lp/emd.pyx b/ot/lp/emd.pyx index 753b195..de2d4a9 100644 --- a/ot/lp/emd.pyx +++ b/ot/lp/emd.pyx @@ -18,7 +18,7 @@ cdef extern from "EMD.h": @cython.boundscheck(False) @cython.wraparound(False) -def emd( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M): +def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M): """ Solves the Earth Movers distance problem and returns the optimal transport matrix @@ -39,11 +39,11 @@ def emd( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode= Parameters ---------- - a : (ns,) ndarray - samples in the source domain (uniform waigth if empty) - b : (nt,) ndarray - samples in the target domain (uniform waigth if empty) - M : (ns,nt) ndarray + a : (ns,) ndarray, float64 + source histogram + b : (nt,) ndarray, float64 + target histogram + M : (ns,nt) ndarray, float64 loss matrix -- cgit v1.2.3 From 0e8363ca13034537ca7bc64acd982f39b7a42123 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 09:14:19 +0200 Subject: doc sinkhorn --- docs/source/conf.py | 2 +- docs/source/index.rst | 2 +- examples/demo_OT_1D.py | 6 +++--- ot/bregman.py | 26 ++++++++++++++++++++++++-- ot/lp/__init__.py | 30 ++++++++++++++++++++++-------- 5 files changed, 51 insertions(+), 15 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 51b71c9..1c1bb40 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -118,7 +118,7 @@ todo_include_todos = True # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. -html_theme = 'alabaster' +html_theme = 'sphinx_rtd_theme' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the diff --git a/docs/source/index.rst b/docs/source/index.rst index c95e847..cd1029e 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -3,7 +3,7 @@ You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. -Welcome to POT's documentation! +POT's documentation! =============================== Contents: diff --git a/examples/demo_OT_1D.py b/examples/demo_OT_1D.py index fc2cbcf..6eaa2ff 100644 --- a/examples/demo_OT_1D.py +++ b/examples/demo_OT_1D.py @@ -8,7 +8,7 @@ Demo for 1D optimal transport import numpy as np import matplotlib.pylab as pl import ot - +from ot.datasets import get_1D_gauss as gauss #%% parameters @@ -19,8 +19,8 @@ n=100 # nb bins x=np.arange(n,dtype=np.float64) # Gaussian distributions -a=ot.datasets.get_1D_gauss(n,m=20,s=20) # m= mean, s= std -b=ot.datasets.get_1D_gauss(n,m=60,s=60) +a=gauss(n,m=20,s=20) # m= mean, s= std +b=gauss(n,m=60,s=60) # loss matrix M=ot.dist(x.reshape((n,1)),x.reshape((n,1))) diff --git a/ot/bregman.py b/ot/bregman.py index 0d2c099..5d93fd6 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -26,7 +26,7 @@ def sinkhorn(a,b, M, reg,numItermax = 1000,stopThr=1e-9): - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - a and b are source and target weights (sum to 1) - The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [1]_ + The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [2]_ Parameters @@ -46,10 +46,22 @@ def sinkhorn(a,b, M, reg,numItermax = 1000,stopThr=1e-9): gamma: (ns x nt) ndarray Optimal transportation matrix for the given parameters + + Examples + -------- + + >>> a=[.5,.5] + >>> b=[.5,.5] + >>> M=[[0.,1.],[1.,0.]] + >>> ot.sinkhorn(a,b,M,1) + array([[ 0.36552929, 0.13447071], + [ 0.13447071, 0.36552929]]) + + References ---------- - .. [1] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013 + .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013 See Also @@ -58,6 +70,16 @@ def sinkhorn(a,b, M, reg,numItermax = 1000,stopThr=1e-9): ot.optim.cg : General regularized OT """ + + a=np.asarray(a,dtype=np.float64) + b=np.asarray(b,dtype=np.float64) + M=np.asarray(M,dtype=np.float64) + + if len(a)==0: + a=np.ones((M.shape[0],),dtype=np.float64)/M.shape[0] + if len(b)==0: + b=np.ones((M.shape[1],),dtype=np.float64)/M.shape[1] + # init data Nini = len(a) Nfin = len(b) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 568e370..72b4cb8 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -7,7 +7,6 @@ def emd(a,b,M): """ Solves the Earth Movers distance problem and returns the optimal transport matrix - gamm=emd(a,b,M) .. math:: \gamma = arg\min_\gamma <\gamma,M>_F @@ -21,6 +20,8 @@ def emd(a,b,M): - M is the metric cost matrix - a and b are the sample weights + + Uses the algorithm proposed in [1]_ Parameters ---------- @@ -31,11 +32,17 @@ def emd(a,b,M): M : (ns,nt) ndarray, float64 loss matrix + Returns + ------- + gamma: (ns x nt) ndarray + Optimal transportation matrix for the given parameters + + Examples -------- - Simple example with obvious solution. The function :func:emd accepts lists and - perform automatic conversion tu numpy arrays + Simple example with obvious solution. The function emd accepts lists and + perform automatic conversion to numpy arrays >>> a=[.5,.5] >>> b=[.5,.5] @@ -43,15 +50,22 @@ def emd(a,b,M): >>> ot.emd(a,b,M) array([[ 0.5, 0. ], [ 0. , 0.5]]) + + References + ---------- - Returns - ------- - gamma: (ns x nt) ndarray - Optimal transportation matrix for the given parameters - + .. [1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). Displacement interpolation using Lagrangian mass transport. In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM. + + See Also + -------- + ot.bregman.sinkhorn : Entropic regularized OT + ot.optim.cg : General regularized OT + + """ a=np.asarray(a,dtype=np.float64) b=np.asarray(b,dtype=np.float64) + M=np.asarray(M,dtype=np.float64) if len(a)==0: a=np.ones((M.shape[0],),dtype=np.float64)/M.shape[0] -- cgit v1.2.3 From a0d8139af3407e567e1dc9a5e8c10d9218ddd185 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 09:54:11 +0200 Subject: doc index updated --- docs/source/all.rst | 47 ++++++++++++++++++++++++++++++++++ docs/source/examples.rst | 14 ++++++++++ docs/source/index.rst | 66 +++++++++++++++++++++++------------------------- ot/bregman.py | 5 ++++ 4 files changed, 97 insertions(+), 35 deletions(-) create mode 100644 docs/source/all.rst create mode 100644 docs/source/examples.rst diff --git a/docs/source/all.rst b/docs/source/all.rst new file mode 100644 index 0000000..30f5add --- /dev/null +++ b/docs/source/all.rst @@ -0,0 +1,47 @@ + + +Python modules +============== + +ot +-- + +This module provide easy access to solvers for the most common OT problems + +.. automodule:: ot + :members: + +ot.lp +----- +.. automodule:: ot.lp + :members: + +ot.bregman +---------- + +.. automodule:: ot.bregman + :members: + +ot.optim +-------- + +.. automodule:: ot.optim + :members: + +ot.utils +-------- + +.. automodule:: ot.utils + :members: + +ot.datasets +----------- + +.. automodule:: ot.datasets + :members: + +ot.plot +------- + +.. automodule:: ot.plot + :members: diff --git a/docs/source/examples.rst b/docs/source/examples.rst new file mode 100644 index 0000000..606b2f5 --- /dev/null +++ b/docs/source/examples.rst @@ -0,0 +1,14 @@ + + +Examples +============ + +1D Optimal transport +--------------------- + +.. literalinclude:: ../../examples/demo_OT_1D.py + +2D Optimal transport on empirical distributions +----------------------------------------------- + +.. literalinclude:: ../../examples/demo_OT_2D_samples.py diff --git a/docs/source/index.rst b/docs/source/index.rst index cd1029e..8452f00 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -3,60 +3,56 @@ You can adapt this file completely to your liking, but it should at least contain the root `toctree` directive. -POT's documentation! -=============================== +POT: Python Optimal Transport +============================= -Contents: -.. toctree:: - :maxdepth: 2 +This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning. -Module list -=========== +It provides the following solvers: +* OT solver for the linear program/ Earth Movers Distance [1]. +* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2]. +* Bregman projections for Wasserstein barycenter [3] and unmixing [4]. +* Optimal transport for domain adaptation with group lasso regularization [5] +* Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. -Module ot ---------- +Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. -This module provide easy access to solvers for the most common OT problems -.. automodule:: ot - :members: +Contents +-------- -Module ot.emd -------------- -.. automodule:: ot.emd - :members: +.. toctree:: + :maxdepth: 2 -Module ot.bregman ------------------ + self + all + examples -.. automodule:: ot.bregman - :members: +Examples +-------- -Module ot.utils ---------------- -.. automodule:: ot.utils - :members: -Module ot.datasets ------------------- -.. automodule:: ot.datasets - :members: +References +---------- -Module ot.plot --------------- +[1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). Displacement interpolation using Lagrangian mass transport. In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM. -.. automodule:: ot.plot - :members: +[2] Cuturi, M. (2013). Sinkhorn distances: Lightspeed computation of optimal transport. In Advances in Neural Information Processing Systems (pp. 2292-2300). +[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). Iterative Bregman projections for regularized transportation problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138. -Examples -======== +[4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, Supervised planetary unmixing with optimal transport, Whorkshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), 2016. + +[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 + +[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. + +[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567. -.. literalinclude:: ../../examples/demo_OT_1D.py Indices and tables ================== diff --git a/ot/bregman.py b/ot/bregman.py index 5d93fd6..b749b13 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -39,6 +39,11 @@ def sinkhorn(a,b, M, reg,numItermax = 1000,stopThr=1e-9): loss matrix reg: float Regularization term >0 + numItermax: int, optional + Max number of iterations + stopThr: float, optional + Stop threshol on error (>0) + Returns -- cgit v1.2.3 From 8cd50c55f398cc371db2ef334c803dec99cc209a Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 10:58:04 +0200 Subject: update doc optim+bregman; add log to sinkhorn --- ot/__init__.py | 7 ++++--- ot/bregman.py | 27 +++++++++++++++++++++------ ot/lp/emd.cpp | 2 +- ot/optim.py | 58 ++++++++++++++++++++++++++++++++++++++++++++++++++++------ 4 files changed, 78 insertions(+), 16 deletions(-) diff --git a/ot/__init__.py b/ot/__init__.py index 87119e5..863f408 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -1,13 +1,14 @@ # Python Optimal Transport toolbox # All submodules and packages +from . import lp +from . import bregman +from . import optim from . import utils from . import datasets from . import plot -from . import bregman -from . import lp from . import da -from . import optim + # OT functions diff --git a/ot/bregman.py b/ot/bregman.py index b749b13..08f965b 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1,12 +1,12 @@ # -*- coding: utf-8 -*- """ -Bregman projection for regularized Otimal transport +Bregman projections for regularized OT """ import numpy as np -def sinkhorn(a,b, M, reg,numItermax = 1000,stopThr=1e-9): +def sinkhorn(a,b, M, reg,numItermax = 1000,stopThr=1e-9,verbose=False,log=False): """ Solve the entropic regularization optimal transport problem and return the OT matrix @@ -43,14 +43,18 @@ def sinkhorn(a,b, M, reg,numItermax = 1000,stopThr=1e-9): Max number of iterations stopThr: float, optional Stop threshol on error (>0) - + verbose : int, optional + Print information along iterations + log : int, optional + record log if True Returns ------- gamma: (ns x nt) ndarray Optimal transportation matrix for the given parameters - + log: dict + log dictionary return only if log==True in parameters Examples -------- @@ -91,6 +95,8 @@ def sinkhorn(a,b, M, reg,numItermax = 1000,stopThr=1e-9): cpt = 0 + if log: + log={'loss':[]} # we assume that no distances are null except those of the diagonal of distances u = np.ones(Nini)/Nini @@ -124,10 +130,19 @@ def sinkhorn(a,b, M, reg,numItermax = 1000,stopThr=1e-9): # we can speed up the process by checking for the error only all the 10th iterations transp = np.dot(np.diag(u),np.dot(K,np.diag(v))) err = np.linalg.norm((np.sum(transp,axis=0)-b))**2 + if log: + log['loss'].append(err) + + if verbose: + if cpt%200 ==0: + print('{:5s}|{:12s}'.format('It.','Err')+'\n'+'-'*19) + print('{:5d}|{:8e}|'.format(cpt,err)) cpt = cpt +1 #print 'err=',err,' cpt=',cpt - - return np.dot(np.diag(u),np.dot(K,np.diag(v))) + if log: + return np.dot(np.diag(u),np.dot(K,np.diag(v))),log + else: + return np.dot(np.diag(u),np.dot(K,np.diag(v))) def geometricBar(weights,alldistribT): diff --git a/ot/lp/emd.cpp b/ot/lp/emd.cpp index 26d243f..6db54bb 100644 --- a/ot/lp/emd.cpp +++ b/ot/lp/emd.cpp @@ -1229,7 +1229,7 @@ static PyObject *__pyx_codeobj__8; /* Python wrapper */ static PyObject *__pyx_pw_2ot_2lp_3emd_1emd_c(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds); /*proto*/ -static char __pyx_doc_2ot_2lp_3emd_emd_c[] = "\n Solves the Earth Movers distance problem and returns the optimal transport matrix\n \n gamm=emd(a,b,M)\n \n .. math::\n \\gamma = arg\\min_\\gamma <\\gamma,M>_F \n \n s.t. \\gamma 1 = a\n \n \\gamma^T 1= b \n \n \\gamma\\geq 0\n where :\n \n - M is the metric cost matrix\n - a and b are the sample weights\n \n Parameters\n ----------\n a : (ns,) ndarray\n source histogram \n b : (nt,) ndarray\n target histogram\n M : (ns,nt) ndarray\n loss matrix \n \n \n Returns\n -------\n gamma: (ns x nt) ndarray\n Optimal transportation matrix for the given parameters\n \n "; +static char __pyx_doc_2ot_2lp_3emd_emd_c[] = "\n Solves the Earth Movers distance problem and returns the optimal transport matrix\n \n gamm=emd(a,b,M)\n \n .. math::\n \\gamma = arg\\min_\\gamma <\\gamma,M>_F \n \n s.t. \\gamma 1 = a\n \n \\gamma^T 1= b \n \n \\gamma\\geq 0\n where :\n \n - M is the metric cost matrix\n - a and b are the sample weights\n \n Parameters\n ----------\n a : (ns,) ndarray, float64\n source histogram \n b : (nt,) ndarray, float64\n target histogram\n M : (ns,nt) ndarray, float64\n loss matrix \n \n \n Returns\n -------\n gamma: (ns x nt) ndarray\n Optimal transportation matrix for the given parameters\n \n "; static PyMethodDef __pyx_mdef_2ot_2lp_3emd_1emd_c = {"emd_c", (PyCFunction)__pyx_pw_2ot_2lp_3emd_1emd_c, METH_VARARGS|METH_KEYWORDS, __pyx_doc_2ot_2lp_3emd_emd_c}; static PyObject *__pyx_pw_2ot_2lp_3emd_1emd_c(PyObject *__pyx_self, PyObject *__pyx_args, PyObject *__pyx_kwds) { PyArrayObject *__pyx_v_a = 0; diff --git a/ot/optim.py b/ot/optim.py index e6373ce..d1bf672 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -1,8 +1,6 @@ # -*- coding: utf-8 -*- """ -Created on Wed Oct 26 15:08:19 2016 - -@author: rflamary +Optimization algorithms for OT """ import numpy as np @@ -12,6 +10,42 @@ from lp import emd # The corresponding scipy function does not work for matrices def line_search_armijo(f,xk,pk,gfk,old_fval,args=(),c1=1e-4,alpha0=0.99): + """ + Armijo linesearch function that works with matrices + + find an approximate minimum of f(xk+alpha*pk) that satifies the + armijo conditions. + + Parameters + ---------- + + f : function + loss function + xk : np.ndarray + initial position + pk : np.ndarray + descent direction + gfk : np.ndarray + gradient of f at xk + old_fval: float + loss value at xk + args : tuple, optional + arguments given to f + c1 : float, optional + c1 const in armijo rule (>0) + alpha0 : float, optional + initial step (>0) + + Returns + ------- + alpha : float + step that satisfy armijo conditions + fc : int + nb of function call + fa : float + loss value at step alpha + + """ xk = np.atleast_1d(xk) fc = [0] @@ -61,14 +95,26 @@ def cg(a,b,M,reg,f,df,G0=None,numItermax = 200,stopThr=1e-9,verbose=False,log=Fa samples in the target domain M : np.ndarray (ns,nt) loss matrix - reg: float() + reg : float Regularization term >0 - + G0 : np.ndarray (ns,nt), optional + initial guess (default is indep joint density) + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : int, optional + Print information along iterations + log : int, optional + record log if True Returns ------- gamma: (ns x nt) ndarray Optimal transportation matrix for the given parameters + log: dict + log dictionary return only if log==True in parameters + References ---------- @@ -77,7 +123,7 @@ def cg(a,b,M,reg,f,df,G0=None,numItermax = 200,stopThr=1e-9,verbose=False,log=Fa See Also -------- - ot.emd.emd : Unregularized optimal ransport + ot.lp.emd : Unregularized optimal ransport ot.bregman.sinkhorn : Entropic regularized optimal transport """ -- cgit v1.2.3 From 3067c8873bf325808985453f0ac968d13435e032 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 11:22:23 +0200 Subject: update bregman with doc --- examples/demo_barycenter_1D.py | 2 +- ot/bregman.py | 91 +++++++++++++++++++++++++++++++++++------- ot/lp/__init__.py | 5 ++- ot/optim.py | 4 +- 4 files changed, 83 insertions(+), 19 deletions(-) diff --git a/examples/demo_barycenter_1D.py b/examples/demo_barycenter_1D.py index c9f63a2..2376f7b 100644 --- a/examples/demo_barycenter_1D.py +++ b/examples/demo_barycenter_1D.py @@ -43,7 +43,7 @@ bary_l2=A.mean(1) # wasserstein reg=1e-3 -bary_wass,log=ot.bregman.barycenter(A,M,reg) +bary_wass=ot.bregman.barycenter(A,M,reg) pl.figure(2) pl.clf() diff --git a/ot/bregman.py b/ot/bregman.py index 08f965b..b6cdf80 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -43,9 +43,9 @@ def sinkhorn(a,b, M, reg,numItermax = 1000,stopThr=1e-9,verbose=False,log=False) Max number of iterations stopThr: float, optional Stop threshol on error (>0) - verbose : int, optional + verbose : bool, optional Print information along iterations - log : int, optional + log : bool, optional record log if True @@ -96,7 +96,7 @@ def sinkhorn(a,b, M, reg,numItermax = 1000,stopThr=1e-9,verbose=False,log=False) cpt = 0 if log: - log={'loss':[]} + log={'err':[]} # we assume that no distances are null except those of the diagonal of distances u = np.ones(Nini)/Nini @@ -131,7 +131,7 @@ def sinkhorn(a,b, M, reg,numItermax = 1000,stopThr=1e-9,verbose=False,log=False) transp = np.dot(np.diag(u),np.dot(K,np.diag(v))) err = np.linalg.norm((np.sum(transp,axis=0)-b))**2 if log: - log['loss'].append(err) + log['err'].append(err) if verbose: if cpt%200 ==0: @@ -146,10 +146,12 @@ def sinkhorn(a,b, M, reg,numItermax = 1000,stopThr=1e-9,verbose=False,log=False) def geometricBar(weights,alldistribT): + """return the weighted geometric mean of distributions""" assert(len(weights)==alldistribT.shape[1]) return np.exp(np.dot(np.log(alldistribT),weights.T)) def geometricMean(alldistribT): + """return the geometric mean of distributions""" return np.exp(np.mean(np.log(alldistribT),axis=1)) def projR(gamma,p): @@ -161,16 +163,66 @@ def projC(gamma,q): return np.multiply(gamma,q/np.maximum(np.sum(gamma,axis=0),1e-10)) -def barycenter(A,M,reg, weights=None, numItermax = 1000, tol_error=1e-4,log=dict()): - """Compute the Regularizzed wassersteien barycenter of distributions A""" +def barycenter(A,M,reg, weights=None, numItermax = 1000, stopThr=1e-4,verbose=False,log=False): + """Compute the entropic regularized wasserstein barycenter of distributions A + + The function solves the following optimization problem: + + .. math:: + \mathbf{a} = arg\min_\mathbf{a} \sum_i W_{reg}(\mathbf{a},\mathbf{a}_i) + + where : + + - :math:`W_{reg}(\cdot,\cdot)` is the entropic regularized Wasserstein distance (see ot.bregman.sinkhorn) + - :math:`\mathbf{a}_i` are training distributions in the columns of matrix :math:`\mathbf{A}` + - reg and :math:`\mathbf{M}` are respectively the regularization term and the cost matrix for OT + + The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [3]_ + + Parameters + ---------- + A : np.ndarray (d,n) + n training distributions of size d + M : np.ndarray (ns,nt) + loss matrix for OT + reg: float + Regularization term >0 + numItermax: int, optional + Max number of iterations + stopThr: float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + a: (d,) ndarray + Wasserstein barycenter + log: dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). Iterative Bregman projections for regularized transportation problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138. + + + + """ if weights is None: weights=np.ones(A.shape[1])/A.shape[1] else: assert(len(weights)==A.shape[1]) + + if log: + log={'err':[]} - #compute Mmax once for all #M = M/np.median(M) # suggested by G. Peyre K = np.exp(-M/reg) @@ -180,19 +232,28 @@ def barycenter(A,M,reg, weights=None, numItermax = 1000, tol_error=1e-4,log=dict UKv=np.dot(K,np.divide(A.T,np.sum(K,axis=0)).T) u = (geometricMean(UKv)/UKv.T).T - log['niter']=0 - log['all_err']=[] - - while (err>tol_error and cptstopThr and cpt0) - verbose : int, optional + verbose : bool, optional Print information along iterations - log : int, optional + log : bool, optional record log if True Returns -- cgit v1.2.3 From 062d6fd23baa27e0334023af320230120b50c828 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 11:47:22 +0200 Subject: bregman doc finished --- ot/bregman.py | 89 +++++++++++++++++++++++++++++++++++++++++++++++++++-------- 1 file changed, 78 insertions(+), 11 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index b6cdf80..9183bea 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -183,7 +183,7 @@ def barycenter(A,M,reg, weights=None, numItermax = 1000, stopThr=1e-4,verbose=Fa ---------- A : np.ndarray (d,n) n training distributions of size d - M : np.ndarray (ns,nt) + M : np.ndarray (d,d) loss matrix for OT reg: float Regularization term >0 @@ -256,15 +256,73 @@ def barycenter(A,M,reg, weights=None, numItermax = 1000, stopThr=1e-4,verbose=Fa return geometricBar(weights,UKv) -def unmix(distrib,D,M,M0,h0,reg,reg0,alpha,numItermax = 1000, tol_error=1e-3,log=dict()): +def unmix(a,D,M,M0,h0,reg,reg0,alpha,numItermax = 1000, stopThr=1e-3,verbose=False,log=False): """ - distrib : distribution to unmix + Compute the unmixing of an observation with a given dictionary using Wasserstein distance + + The function solve the following optimization problem: + + .. math:: + \mathbf{h} = arg\min_\mathbf{h} (1- \\alpha) W_{M,reg}(\mathbf{a},\mathbf{Dh})+\\alpha W_{M0,reg0}(\mathbf{h}_0,\mathbf{h}) + + + where : + + - :math:`W_{M,reg}(\cdot,\cdot)` is the entropic regularized Wasserstein distance with M loss matrix (see ot.bregman.sinkhorn) + - :math:`\mathbf{a}` is an observed distribution, :math:`\mathbf{h}_0` is aprior on unmixing + - reg and :math:`\mathbf{M}` are respectively the regularization term and the cost matrix for OT data fitting + - reg0 and :math:`\mathbf{M0}` are respectively the regularization term and the cost matrix for regularization + - :math:`\\alpha`weight data fitting and regularization + + The optimization problem is solved suing the algorithm described in [4] + + + distrib : distribution to unmix D : Dictionnary M : Metric matrix in the space of the distributions to unmix M0 : Metric matrix in the space of the 'abundance values' to solve for h0 : prior on solution (generally uniform distribution) reg,reg0 : transport regularizations alpha : how much should we trust the prior ? ([0,1]) + + Parameters + ---------- + a : np.ndarray (d) + observed distribution + D : np.ndarray (d,n) + dictionary matrix + M : np.ndarray (d,d) + loss matrix + M0 : np.ndarray (n,n) + loss matrix + h0 : np.ndarray (n,) + prior on h + reg: float + Regularization term >0 (Wasserstein data fitting) + reg0: float + Regularization term >0 (Wasserstein reg with h0) + numItermax: int, optional + Max number of iterations + stopThr: float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + a: (d,) ndarray + Wasserstein barycenter + log: dict + log dictionary return only if log==True in parameters + + References + ---------- + + .. [4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, Supervised planetary unmixing with optimal transport, Whorkshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), 2016. + """ #M = M/np.median(M) @@ -277,12 +335,12 @@ def unmix(distrib,D,M,M0,h0,reg,reg0,alpha,numItermax = 1000, tol_error=1e-3,log err=1 cpt=0 #log = {'niter':0, 'all_err':[]} - log['niter']=0 - log['all_err']=[] + if log: + log={'err':[]} - while (err>tol_error and cptstopThr and cpt Date: Fri, 28 Oct 2016 11:53:13 +0200 Subject: change filename --- examples/Demo_2D_OT_samples.ipynb | 234 ++++++++++++++++++++++++++++++++++++++ examples/Demo_2D_Ot_samples.ipynb | 234 -------------------------------------- 2 files changed, 234 insertions(+), 234 deletions(-) create mode 100644 examples/Demo_2D_OT_samples.ipynb delete mode 100644 examples/Demo_2D_Ot_samples.ipynb diff --git a/examples/Demo_2D_OT_samples.ipynb b/examples/Demo_2D_OT_samples.ipynb new file mode 100644 index 0000000..e20b09b --- /dev/null +++ b/examples/Demo_2D_OT_samples.ipynb @@ -0,0 +1,234 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:e01831efa84095a87a681a09f08c8991bbe439e010c2bc4cd3559dc4d3bf0bde" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data generation" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "n=20 # nb samples\n", + "\n", + "mu_s=np.array([0,0])\n", + "cov_s=np.array([[1,0],[0,1]])\n", + "\n", + "mu_t=np.array([4,4])\n", + "cov_t=np.array([[1,-.8],[-.8,1]])\n", + "\n", + "xs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s)\n", + "xt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t)\n", + "\n", + "a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples\n", + "\n", + "# loss matrix\n", + "M=ot.dist(xs,xt)\n", + "M/=M.max()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot dataset" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "pl.figure(1)\n", + "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", + "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", + "pl.legend(loc=0)\n", + "pl.title('Source and traget distributions')\n", + "\n", + "pl.figure(2)\n", + "pl.imshow(M,interpolation='nearest')\n", + "pl.title('Cost matrix M')\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 3, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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Cd5pZEcGPw4Pu/mQE7YqISIJ0BqiISBbTGaAiIgVEyVxEJA8omYuI5AElcxGR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\nXEQkDyiZi4jkASVzEZE88P8B8sPqteLhUE0AAAAASUVORK5CYII=\n", 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+ "text": [ + "" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solve OT" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "G0=ot.emd(a,b,M)\n", + "\n", + "\n", + "pl.figure(3)\n", + "pl.imshow(G0,interpolation='nearest')\n", + "pl.title('Cost matrix M')\n", + "\n", + "pl.figure(4)\n", + "ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1])\n", + "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", + "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", + "pl.legend(loc=0)\n", + "pl.title('OT matrix')\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 4, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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wd24wcMtRdH//MKrvPYS17ziXQuT1z8hgFmpYGLs32TXWjz461DN/7jmvxu2n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\n/XWSd0EBCSs62rHnqe6VuXkzf2evOqIabW3Ad75DmWbbNv7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+ "text": [ + "" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solve OT with entropic regularization" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# reg term\n", + "lambd=5e-3\n", + "\n", + "Gs=ot.sinkhorn(a,b,M,lambd)\n", + "\n", + "pl.figure(5)\n", + "pl.imshow(Gs,interpolation='nearest')\n", + "pl.title('OT matrix sinkhorn')\n", + "\n", + "pl.figure(6)\n", + "ot.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1])\n", + "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", + "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", + "pl.legend(loc=0)\n", + "pl.title('OT matrix Sinkhorn with samples')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 5, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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5eXmorKxEJBLBddddh0gkMq5rFVcNA/CLX/wCmzZtQn9/P2644YaUcnHct7a2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+ "text": [ + "" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 5 + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/examples/Demo_2D_Ot_samples.ipynb b/examples/Demo_2D_Ot_samples.ipynb deleted file mode 100644 index e20b09b..0000000 --- a/examples/Demo_2D_Ot_samples.ipynb +++ /dev/null @@ -1,234 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:e01831efa84095a87a681a09f08c8991bbe439e010c2bc4cd3559dc4d3bf0bde" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data generation" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "n=20 # nb samples\n", - "\n", - "mu_s=np.array([0,0])\n", - "cov_s=np.array([[1,0],[0,1]])\n", - "\n", - "mu_t=np.array([4,4])\n", - "cov_t=np.array([[1,-.8],[-.8,1]])\n", - "\n", - "xs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s)\n", - "xt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t)\n", - "\n", - "a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples\n", - "\n", - "# loss matrix\n", - "M=ot.dist(xs,xt)\n", - "M/=M.max()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot dataset" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pl.figure(1)\n", - "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", - "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", - "pl.legend(loc=0)\n", - "pl.title('Source and traget distributions')\n", - "\n", - "pl.figure(2)\n", - "pl.imshow(M,interpolation='nearest')\n", - "pl.title('Cost matrix M')\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 3, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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Cd5pZEcGPw4Pu/mQE7YqISIJ0BqiISBbTGaAiIgVEyVxEJA8omYuI5AElcxGR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\nXEQkDyiZi4jkASVzEZE88P8B8sPqteLhUE0AAAAASUVORK5CYII=\n", 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- "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Solve OT" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "G0=ot.emd(a,b,M)\n", - "\n", - "\n", - "pl.figure(3)\n", - "pl.imshow(G0,interpolation='nearest')\n", - "pl.title('Cost matrix M')\n", - "\n", - "pl.figure(4)\n", - "ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1])\n", - "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", - "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", - "pl.legend(loc=0)\n", - "pl.title('OT matrix')\n", - "\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 4, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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wd24wcMtRdH//MKrvPYS17ziXQuT1z8hgFmpYGLs32TXWjz461DN/7jmvxu2n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\n/XWSd0EBCSs62rHnqe6VuXkzf2evOqIabW3Ad75DmWbbNv7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- "text": [ - "" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Solve OT with entropic regularization" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# reg term\n", - "lambd=5e-3\n", - "\n", - "Gs=ot.sinkhorn(a,b,M,lambd)\n", - "\n", - "pl.figure(5)\n", - "pl.imshow(Gs,interpolation='nearest')\n", - "pl.title('OT matrix sinkhorn')\n", - "\n", - "pl.figure(6)\n", - "ot.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1])\n", - "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", - "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", - "pl.legend(loc=0)\n", - "pl.title('OT matrix Sinkhorn with samples')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 5, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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5eXmorKxEJBLBddddh0gkMq5rFVcNA/CLX/wCmzZtQn9/P2644YaUcnHct7a2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- "text": [ - "" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 5 - } - ], - "metadata": {} - } - ] -} \ No newline at end of file -- cgit v1.2.3 From b1c74aea36bb428d4cb2d87ebec9f9f703cd29a5 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 11:53:53 +0200 Subject: update readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a1651e4..52cc230 100644 --- a/README.md +++ b/README.md @@ -37,7 +37,7 @@ Note that for easier access the module is name ot instead of pot. The examples folder contain several examples and use case for the library. Here is a list of the Python notebook if you want a quick look. * [1D optimal transport](examples/Demo_1D_OT.ipynb) -* [2D optimal transport on empirical distributions](examples/Demo_2D_Ot_samples.ipynb) +* [2D optimal transport on empirical distributions](examples/Demo_2D_OT_samples.ipynb) ## Acknowledgements -- cgit v1.2.3 From 5f02ebce58fb255d6e640e23d258bc4f2444b5e1 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 12:01:01 +0200 Subject: add notebook barycenter --- examples/Demo_1D_barycenter.ipynb | 168 ++++++++++++++++++++++++++++++++++++++ examples/demo_barycenter_1D.py | 5 +- 2 files changed, 171 insertions(+), 2 deletions(-) create mode 100644 examples/Demo_1D_barycenter.ipynb diff --git a/examples/Demo_1D_barycenter.ipynb b/examples/Demo_1D_barycenter.ipynb new file mode 100644 index 0000000..0d72471 --- /dev/null +++ b/examples/Demo_1D_barycenter.ipynb @@ -0,0 +1,168 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:52487801ef7f544098c7df1333a41148cb4d9e7393c84ea171d60b030b40c19f" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#1D Wasserstein barycenter demo" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dataset Generation" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "n=100 # nb bins\n", + "\n", + "# bin positions\n", + "x=np.arange(n,dtype=np.float64)\n", + "\n", + "# Gaussian distributions\n", + "a1=ot.datasets.get_1D_gauss(n,m=20,s=20) # m= mean, s= std\n", + "a2=ot.datasets.get_1D_gauss(n,m=60,s=60)\n", + "\n", + "# creating matrix A containing all distributions\n", + "A=np.vstack((a1,a2)).T\n", + "nbd=A.shape[1]\n", + "\n", + "# loss matrix + normalization\n", + "M=ot.utils.dist0(n)\n", + "M/=M.max()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot distributions" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "pl.figure(1)\n", + "for i in range(nbd):\n", + " pl.plot(x,A[:,i])\n", + "pl.title('Distributions')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 3, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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sf8EkgC1Amt/7Vu46f/lAa2CrW+9fX1V3i0g+8IWq7gEQkQ+AU4ETJoB4kJsLF1wQmWOV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\nLnj+eSdBxpLnlz5PbkEuT13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+ "text": [ + "" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Barycenter computation (for l2 and Wasserstein)" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# l2bary\n", + "bary_l2=A.mean(1)\n", + "\n", + "# wasserstein\n", + "reg=1e-3\n", + "bary_wass=ot.bregman.barycenter(A,M,reg)\n", + "\n", + "pl.figure(2)\n", + "pl.clf()\n", + "pl.subplot(2,1,1)\n", + "for i in range(nbd):\n", + " pl.plot(x,A[:,i])\n", + "pl.title('Distributions')\n", + "\n", + "pl.subplot(2,1,2)\n", + "pl.plot(x,bary_l2,'r',label='l2')\n", + "pl.plot(x,bary_wass,'g',label='Wasserstein')\n", + "pl.legend()\n", + "pl.title('Barycenters')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 4, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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VPvP60LVCVwbXHWzvBtIQr08ES0kqCKAWsEVENgBTgZ6xT/6pxf79cPGi+1fe9eubOxHL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\nNbfD8Yr9+/e7HYIVIGw2UF/YswdmzYK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+ "text": [ + "" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 4 + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/examples/demo_barycenter_1D.py b/examples/demo_barycenter_1D.py index 2376f7b..47b6be8 100644 --- a/examples/demo_barycenter_1D.py +++ b/examples/demo_barycenter_1D.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- """ -Created on Fri Oct 21 09:51:45 2016 +1D Wasserstein barycenter demo @author: rflamary """ @@ -22,10 +22,11 @@ x=np.arange(n,dtype=np.float64) a1=ot.datasets.get_1D_gauss(n,m=20,s=20) # m= mean, s= std a2=ot.datasets.get_1D_gauss(n,m=60,s=60) +# creating matrix A containing all distributions A=np.vstack((a1,a2)).T nbd=A.shape[1] -# loss matrix +# loss matrix + normalization M=ot.utils.dist0(n) M/=M.max() -- cgit v1.2.3 From 4f412fed1442a77be219abd2751ebacc4a854402 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 12:02:39 +0200 Subject: add notebook barycenter --- README.md | 1 + docs/source/examples.rst | 5 +++++ 2 files changed, 6 insertions(+) diff --git a/README.md b/README.md index 52cc230..743a8d9 100644 --- a/README.md +++ b/README.md @@ -38,6 +38,7 @@ The examples folder contain several examples and use case for the library. Here * [1D optimal transport](examples/Demo_1D_OT.ipynb) * [2D optimal transport on empirical distributions](examples/Demo_2D_OT_samples.ipynb) +* [1D Wasserstein barycenter](examples/Demo_1D_barycenter.ipynb) ## Acknowledgements diff --git a/docs/source/examples.rst b/docs/source/examples.rst index 606b2f5..9bd4e75 100644 --- a/docs/source/examples.rst +++ b/docs/source/examples.rst @@ -12,3 +12,8 @@ Examples ----------------------------------------------- .. literalinclude:: ../../examples/demo_OT_2D_samples.py + +1D Wasserstein barycenter +------------------------- + +.. literalinclude:: ../../examples/demo_barycenter_1D.py -- cgit v1.2.3 From c418ef460514a991d95bb2e2a1937b1aedd3e0c9 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 12:07:01 +0200 Subject: update readme --- README.md | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 743a8d9..e0f07b8 100644 --- a/README.md +++ b/README.md @@ -34,7 +34,9 @@ Note that for easier access the module is name ot instead of pot. ## Examples -The examples folder contain several examples and use case for the library. Here is a list of the Python notebook if you want a quick look. +The examples folder contain several examples and use case for the library. + + Here is a list of the Python notebook if you want a quick look: * [1D optimal transport](examples/Demo_1D_OT.ipynb) * [2D optimal transport on empirical distributions](examples/Demo_2D_OT_samples.ipynb) @@ -47,7 +49,7 @@ The contributors to this library are: * [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/) * [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/) -This toolbox benefit a lot from Open Source research and we would like to thank the Following persons for providing some code (in various languages): +This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): * [Gabriel Peyré](http://gpeyre.github.io/) (Wasserstein Barycenters in Matlab) * [Nicolas Bonneel](http://liris.cnrs.fr/~nbonneel/) ( C++ code for EMD) -- cgit v1.2.3 From f33087d2b1790dac773782bb0d91bcfe7ce6a079 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 12:31:07 +0200 Subject: doc utils.py --- ot/utils.py | 51 ++++++++++++++++++++++++++++++++++++++++++++++----- 1 file changed, 46 insertions(+), 5 deletions(-) diff --git a/ot/utils.py b/ot/utils.py index 46c3775..e5ec864 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -6,21 +6,62 @@ from scipy.spatial.distance import cdist def unif(n): - """ return a uniform histogram (simplex) """ + """ return a uniform histogram of length n (simplex) """ return np.ones((n,))/n def dist(x1,x2=None,metric='sqeuclidean'): - """Compute distance between samples in x1 and x2""" + """Compute distance between samples in x1 and x2 using function scipy.spatial.distance.cdist + + Parameters + ---------- + + x1 : np.array (n1,d) + matrix with n1 samples of size d + x2 : np.array (n2,d), optional + matrix with n2 samples of size d (if None then x2=x1) + metric : str, fun, optional + name of the metric to be computed (full list in the doc of scipy), If a string, + the distance function can be ‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘cityblock’, + ‘correlation’, ‘cosine’, ‘dice’, ‘euclidean’, ‘hamming’, ‘jaccard’, ‘kulsinski’, + ‘mahalanobis’, ‘matching’, ‘minkowski’, ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, + ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘wminkowski’, ‘yule’. + + + Returns + ------- + M : np.array (n1,n2) + distance matrix computed with given metric + + """ if x2 is None: return cdist(x1,x1,metric=metric) else: return cdist(x1,x2,metric=metric) -def dist0(n,method='linear'): - """Compute stardard cos matrices for OT problems""" +def dist0(n,method='lin_square'): + """Compute standard cost matrices of size (n,n) for OT problems + + Parameters + ---------- + + n : int + size of the cost matrix + method : str, optional + Type of loss matrix chosen from: + + * 'lin_square' : linear sampling between 0 and n-1, quadratic loss + + + Returns + ------- + M : np.array (n1,n2) + distance matrix computed with given metric + + + """ res=0 - if method=='linear': + if method=='lin_square': x=np.arange(n,dtype=np.float64).reshape((n,1)) res=dist(x,x) return res -- cgit v1.2.3 From 9523b1e95ed7c117554ff673532d150841092137 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 12:50:54 +0200 Subject: doc datasets.py --- ot/bregman.py | 14 ++++-------- ot/datasets.py | 71 +++++++++++++++++++++++++++++++++++++++++++++++++--------- ot/utils.py | 23 +++++++++++++++---- 3 files changed, 83 insertions(+), 25 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 9183bea..ad9a67a 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -276,14 +276,6 @@ def unmix(a,D,M,M0,h0,reg,reg0,alpha,numItermax = 1000, stopThr=1e-3,verbose=Fal The optimization problem is solved suing the algorithm described in [4] - - distrib : distribution to unmix - D : Dictionnary - M : Metric matrix in the space of the distributions to unmix - M0 : Metric matrix in the space of the 'abundance values' to solve for - h0 : prior on solution (generally uniform distribution) - reg,reg0 : transport regularizations - alpha : how much should we trust the prior ? ([0,1]) Parameters ---------- @@ -300,7 +292,9 @@ def unmix(a,D,M,M0,h0,reg,reg0,alpha,numItermax = 1000, stopThr=1e-3,verbose=Fal reg: float Regularization term >0 (Wasserstein data fitting) reg0: float - Regularization term >0 (Wasserstein reg with h0) + Regularization term >0 (Wasserstein reg with h0) + alpha: float + How much should we trust the prior ([0,1]) numItermax: int, optional Max number of iterations stopThr: float, optional @@ -318,7 +312,7 @@ def unmix(a,D,M,M0,h0,reg,reg0,alpha,numItermax = 1000, stopThr=1e-3,verbose=Fal log: dict log dictionary return only if log==True in parameters - References + References ---------- .. [4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, Supervised planetary unmixing with optimal transport, Whorkshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), 2016. diff --git a/ot/datasets.py b/ot/datasets.py index f22e345..6388d94 100644 --- a/ot/datasets.py +++ b/ot/datasets.py @@ -8,14 +8,50 @@ import scipy as sp def get_1D_gauss(n,m,s): - "return a 1D histogram for a gaussian distribution (n bins, mean m and std s) " + """return a 1D histogram for a gaussian distribution (n bins, mean m and std s) + + Parameters + ---------- + + n : int + number of bins in the histogram + m : float + mean value of the gaussian distribution + s : float + standard deviaton of the gaussian distribution + + + Returns + ------- + h : np.array (n,) + 1D histogram for a gaussian distribution + + """ x=np.arange(n,dtype=np.float64) h=np.exp(-(x-m)**2/(2*s^2)) return h/h.sum() def get_2D_samples_gauss(n,m,sigma): - "return samples from 2D gaussian (n samples, mean m and cov sigma) " + """return n samples drawn from 2D gaussian N(m,sigma) + + Parameters + ---------- + + n : int + number of bins in the histogram + m : np.array (2,) + mean value of the gaussian distribution + sigma : np.array (2,2) + covariance matrix of the gaussian distribution + + + Returns + ------- + X : np.array (n,2) + n samples drawn from N(m,sigma) + + """ if np.isscalar(sigma): sigma=np.array([sigma,]) if len(sigma)>1: @@ -26,8 +62,26 @@ def get_2D_samples_gauss(n,m,sigma): return res def get_data_classif(dataset,n,nz=.5,**kwargs): - """ - dataset generation + """ dataset generation for classification problems + + Parameters + ---------- + + dataset : str + type of classification problem (see code) + n : int + number of training samples + nz : float + noise level (>0) + + + Returns + ------- + X : np.array (n,d) + n observation of size d + y : np.array (n,) + labels of the samples + """ if dataset.lower()=='3gauss': y=np.floor((np.arange(n)*1.0/n*3))+1 @@ -50,15 +104,10 @@ def get_data_classif(dataset,n,nz=.5,**kwargs): x[y==3,0]=2. ; x[y==3,1]=0 x[y!=3,:]+=nz*np.random.randn(sum(y!=3),2) - x[y==3,:]+=2*nz*np.random.randn(sum(y==3),2) -# elif dataset.lower()=='sinreg': -# -# x=np.random.rand(n,1) -# y=4*x+np.sin(2*np.pi*x)+nz*np.random.randn(n,1) - + x[y==3,:]+=2*nz*np.random.randn(sum(y==3),2) else: x=0 y=0 print("unknown dataset") - return x,y \ No newline at end of file + return x,y.astype(int) \ No newline at end of file diff --git a/ot/utils.py b/ot/utils.py index e5ec864..2110c01 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -1,3 +1,4 @@ +# -*- coding: utf-8 -*- """ Various function that can be usefull """ @@ -6,7 +7,21 @@ from scipy.spatial.distance import cdist def unif(n): - """ return a uniform histogram of length n (simplex) """ + """ return a uniform histogram of length n (simplex) + + Parameters + ---------- + + n : int + number of bins in the histogram + + Returns + ------- + h : np.array (n,) + histogram of length n such that h_i=1/n for all i + + + """ return np.ones((n,))/n @@ -22,9 +37,9 @@ def dist(x1,x2=None,metric='sqeuclidean'): matrix with n2 samples of size d (if None then x2=x1) metric : str, fun, optional name of the metric to be computed (full list in the doc of scipy), If a string, - the distance function can be ‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘cityblock’, + the distance function can be ‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘cityblock’, ‘correlation’, ‘cosine’, ‘dice’, ‘euclidean’, ‘hamming’, ‘jaccard’, ‘kulsinski’, - ‘mahalanobis’, ‘matching’, ‘minkowski’, ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, + ‘mahalanobis’, ‘matching’, ‘minkowski’, ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘wminkowski’, ‘yule’. @@ -68,5 +83,5 @@ def dist0(n,method='lin_square'): def dots(*args): - """ Stupid but nice dots function for multiple matrix multiply """ + """ dots function for multiple matrix multiply """ return reduce(np.dot,args) \ No newline at end of file -- cgit v1.2.3 From 0d7df3b34b99a7578e7fbac68b40852dc6710524 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 13:35:53 +0200 Subject: doc plot.py --- examples/demo_barycenter_1D.py | 2 +- ot/plot.py | 42 ++++++++++++++++++++++++++++++++++++++++-- ot/utils.py | 2 ++ 3 files changed, 43 insertions(+), 3 deletions(-) diff --git a/examples/demo_barycenter_1D.py b/examples/demo_barycenter_1D.py index 47b6be8..db1ff4a 100644 --- a/examples/demo_barycenter_1D.py +++ b/examples/demo_barycenter_1D.py @@ -37,7 +37,7 @@ for i in range(nbd): pl.plot(x,A[:,i]) pl.title('Distributions') -#%% barucenter computation +#%% barycenter computation # l2bary bary_l2=A.mean(1) diff --git a/ot/plot.py b/ot/plot.py index d172c90..9737f8a 100644 --- a/ot/plot.py +++ b/ot/plot.py @@ -9,7 +9,25 @@ from matplotlib import gridspec def plot1D_mat(a,b,M,title=''): - """ Plot matrix M with the source and target 1D distribution """ + """ Plot matrix M with the source and target 1D distribution + + Creates a subplot with the source distribution a on the left and + target distribution b on the tot. The matrix M is shown in between. + + + Parameters + ---------- + + a : np.array (na,) + Source distribution + b : np.array (nb,) + Target distribution + M : np.array (na,nb) + Matrix to plot + + + + """ na=M.shape[0] nb=M.shape[1] @@ -43,7 +61,27 @@ def plot1D_mat(a,b,M,title=''): def plot2D_samples_mat(xs,xt,G,thr=1e-8,**kwargs): - """ Plot matrix M in 2D with lines using alpha values""" + """ Plot matrix M in 2D with lines using alpha values + + Plot lines between source and target 2D samples with a color + proportional to the value of the matrix G between samples. + + + Parameters + ---------- + + xs : np.array (ns,2) + Source samples positions + b : np.array (nt,2) + Target samples positions + G : np.array (na,nb) + OT matrix + thr : float, optional + threshold above which the line is drawn + **kwargs : dict + paameters given to the plot functions (default color is black if nothing given) + + """ if ('color' not in kwargs) and ('c' not in kwargs): kwargs['color']='k' mx=G.max() diff --git a/ot/utils.py b/ot/utils.py index 2110c01..24f65a8 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -45,6 +45,7 @@ def dist(x1,x2=None,metric='sqeuclidean'): Returns ------- + M : np.array (n1,n2) distance matrix computed with given metric @@ -70,6 +71,7 @@ def dist0(n,method='lin_square'): Returns ------- + M : np.array (n1,n2) distance matrix computed with given metric -- cgit v1.2.3 From 83ce401c1ed61c03501d6b6337f927d1c7d890ca Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 14:12:03 +0200 Subject: unknown module --- ot/__init__.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/ot/__init__.py b/ot/__init__.py index 863f408..4f4489e 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -12,9 +12,9 @@ from . import da # OT functions -from ot.lp import emd -from ot.bregman import sinkhorn,barycenter -from ot.da import sinkhorn_lpl1_mm +from .lp import emd +from .bregman import sinkhorn,barycenter +from .da import sinkhorn_lpl1_mm # utils functions from utils import dist,unif -- cgit v1.2.3 From e1fe245208e4f100dba42ab46db599d2f5794d68 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 14:15:54 +0200 Subject: relative path --- ot/da.py | 2 +- ot/optim.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/ot/da.py b/ot/da.py index ffaa97d..bd20014 100644 --- a/ot/da.py +++ b/ot/da.py @@ -2,7 +2,7 @@ domain adaptation with optimal transport """ import numpy as np -from bregman import sinkhorn +from .bregman import sinkhorn diff --git a/ot/optim.py b/ot/optim.py index 632eac1..1afbea3 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -6,7 +6,7 @@ Optimization algorithms for OT import numpy as np import scipy as sp from scipy.optimize.linesearch import scalar_search_armijo -from lp import emd +from .lp import emd # The corresponding scipy function does not work for matrices def line_search_armijo(f,xk,pk,gfk,old_fval,args=(),c1=1e-4,alpha0=0.99): -- cgit v1.2.3 From 4ced7428bd2be4ca008f12400afb445c5a6517c8 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 14:19:57 +0200 Subject: relative path --- ot/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/__init__.py b/ot/__init__.py index 4f4489e..0602eed 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -17,6 +17,6 @@ from .bregman import sinkhorn,barycenter from .da import sinkhorn_lpl1_mm # utils functions -from utils import dist,unif +from .utils import dist,unif __all__ = ["emd","sinkhorn","utils",'datasets','bregman','lp','plot','dist','unif','barycenter','sinkhorn_lpl1_mm','da','optim'] -- cgit v1.2.3 From 0d81de9909e8e9eb95858f0a043550b15898f172 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 15:13:48 +0200 Subject: doc da.py --- docs/source/all.rst | 8 +++-- ot/__init__.py | 15 +++++---- ot/bregman.py | 8 ++--- ot/da.py | 91 ++++++++++++++++++++++++++++++++++++++++++++++++++--- ot/lp/__init__.py | 77 ++++++++++++++++++++++----------------------- 5 files changed, 142 insertions(+), 57 deletions(-) diff --git a/docs/source/all.rst b/docs/source/all.rst index 30f5add..d5733b8 100644 --- a/docs/source/all.rst +++ b/docs/source/all.rst @@ -6,8 +6,6 @@ Python modules ot -- -This module provide easy access to solvers for the most common OT problems - .. automodule:: ot :members: @@ -28,6 +26,12 @@ ot.optim .. automodule:: ot.optim :members: +ot.da +-------- + +.. automodule:: ot.da + :members: + ot.utils -------- diff --git a/ot/__init__.py b/ot/__init__.py index 0602eed..72c820a 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -1,22 +1,21 @@ -# Python Optimal Transport toolbox +"""Python Optimal Transport toolbox""" # All submodules and packages -from . import lp +from . import lp from . import bregman -from . import optim +from . import optim from . import utils from . import datasets from . import plot from . import da - - # OT functions from .lp import emd -from .bregman import sinkhorn,barycenter +from .bregman import sinkhorn, barycenter from .da import sinkhorn_lpl1_mm # utils functions -from .utils import dist,unif +from .utils import dist, unif -__all__ = ["emd","sinkhorn","utils",'datasets','bregman','lp','plot','dist','unif','barycenter','sinkhorn_lpl1_mm','da','optim'] +__all__ = ["emd", "sinkhorn", "utils", 'datasets', 'bregman', 'lp', 'plot', + 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim'] diff --git a/ot/bregman.py b/ot/bregman.py index ad9a67a..2d82ae4 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -6,12 +6,12 @@ Bregman projections for regularized OT import numpy as np -def sinkhorn(a,b, M, reg,numItermax = 1000,stopThr=1e-9,verbose=False,log=False): +def sinkhorn(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, log=False): """ - Solve the entropic regularization optimal transport problem and return the OT matrix - + Solve the entropic regularization optimal transport problem + The function solves the following optimization problem: - + .. math:: \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) diff --git a/ot/da.py b/ot/da.py index bd20014..300d21a 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1,6 +1,8 @@ +# -*- coding: utf-8 -*- """ -domain adaptation with optimal transport +Domain adaptation with optimal transport """ + import numpy as np from .bregman import sinkhorn @@ -9,7 +11,88 @@ from .bregman import sinkhorn def indices(a, func): return [i for (i, val) in enumerate(a) if func(val)] -def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1): +def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerItermax = 200,stopInnerThr=1e-9,verbose=False,log=False): + """ + Solve the entropic regularization optimal transport problem with nonconvex group lasso regularization + + The function solves the following optimization problem: + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma)+ \eta \Omega_g(\gamma) + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + where : + + - M is the (ns,nt) metric cost matrix + - :math:`\Omega_e` is the entropic regularization term :math:`\Omega_e(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - :math:`\Omega_g` is the group lasso regulaization term :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^{1/2}_1` where :math:`\mathcal{I}_c` are the index of samples from class c in the source domain. + - a and b are source and target weights (sum to 1) + + The algorithm used for solving the problem is the generalised conditional gradient as proposed in [5]_ [7]_ + + + Parameters + ---------- + a : np.ndarray (ns,) + samples weights in the source domain + labels_a : np.ndarray (ns,) + labels of samples in the source domain + b : np.ndarray (nt,) + samples in the target domain + M : np.ndarray (ns,nt) + loss matrix + reg: float + Regularization term for entropic regularization >0 + eta: float, optional + Regularization term for group lasso regularization >0 + numItermax: int, optional + Max number of iterations + numInnerItermax: int, optional + Max number of iterations (inner sinkhorn solver) + stopInnerThr: float, optional + Stop threshold on error (inner sinkhorn solver) (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + gamma: (ns x nt) ndarray + Optimal transportation matrix for the given parameters + log: dict + log dictionary return only if log==True in parameters + + Examples + -------- + + >>> a=[.5,.5] + >>> b=[.5,.5] + >>> M=[[0.,1.],[1.,0.]] + >>> ot.sinkhorn(a,b,M,1) + array([[ 0.36552929, 0.13447071], + [ 0.13447071, 0.36552929]]) + + + References + ---------- + + .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 + + .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567. + + See Also + -------- + ot.lp.emd : Unregularized OT + ot.bregman.sinkhorn : Entropic regularized OT + ot.optim.cg : General regularized OT + + """ p=0.5 epsilon = 1e-3 @@ -25,9 +108,9 @@ def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1): W=np.zeros(M.shape) - for cpt in range(10): + for cpt in range(numItermax): Mreg = M + eta*W - transp=sinkhorn(a,b,Mreg,reg,numItermax = 200) + transp=sinkhorn(a,b,Mreg,reg,numItermax=numInnerItermax, stopThr=stopInnerThr) # the transport has been computed. Check if classes are really separated W = np.ones((Nini,Nfin)) for t in range(Nfin): diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 6c7822a..2adf937 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -1,31 +1,30 @@ +# -*- coding: utf-8 -*- """ Solvers for the original linear program OT problem """ +import numpy as np # import compiled emd from .emd import emd_c -import numpy as np -def emd(a,b,M): - """ - Solves the Earth Movers distance problem and returns the optimal transport matrix - - + +def emd(a, b, M): + """Solves the Earth Movers distance problem and returns the OT matrix + + .. math:: - \gamma = arg\min_\gamma <\gamma,M>_F - + \gamma = arg\min_\gamma <\gamma,M>_F + s.t. \gamma 1 = a - - \gamma^T 1= b - + \gamma^T 1= b \gamma\geq 0 where : - + - M is the metric cost matrix - a and b are the sample weights - + Uses the algorithm proposed in [1]_ - + Parameters ---------- a : (ns,) ndarray, float64 @@ -33,47 +32,47 @@ def emd(a,b,M): b : (nt,) ndarray, float64 Target histogram (uniform weigth if empty list) M : (ns,nt) ndarray, float64 - loss matrix - + loss matrix + Returns ------- gamma: (ns x nt) ndarray Optimal transportation matrix for the given parameters - - + + Examples -------- - + Simple example with obvious solution. The function emd accepts lists and - perform automatic conversion to numpy arrays - + perform automatic conversion to numpy arrays + >>> a=[.5,.5] >>> b=[.5,.5] >>> M=[[0.,1.],[1.,0.]] >>> ot.emd(a,b,M) array([[ 0.5, 0. ], [ 0. , 0.5]]) - + References ---------- - - .. [1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). Displacement interpolation using Lagrangian mass transport. In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM. - + + .. [1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. + (2011, December). Displacement interpolation using Lagrangian mass + transport. In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. + 158). ACM. + See Also -------- ot.bregman.sinkhorn : Entropic regularized OT - ot.optim.cg : General regularized OT - - - """ - a=np.asarray(a,dtype=np.float64) - b=np.asarray(b,dtype=np.float64) - M=np.asarray(M,dtype=np.float64) - - if len(a)==0: - a=np.ones((M.shape[0],),dtype=np.float64)/M.shape[0] - if len(b)==0: - b=np.ones((M.shape[1],),dtype=np.float64)/M.shape[1] - - return emd_c(a,b,M) + ot.optim.cg : General regularized OT""" + + a = np.asarray(a, dtype=np.float64) + b = np.asarray(b, dtype=np.float64) + M = np.asarray(M, dtype=np.float64) + + if len(a) == 0: + a = np.ones((M.shape[0], ), dtype=np.float64)/M.shape[0] + if len(b) == 0: + b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1] + return emd_c(a, b, M) -- cgit v1.2.3 From 1dd1ed6db5dda760839aac7bba291a00fcae2126 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 15:24:32 +0200 Subject: new notebook --- README.md | 2 + docs/source/examples.rst | 5 ++ examples/Demo_Optim_OTreg.ipynb | 173 ++++++++++++++++++++++++++++++++++++++++ examples/demo_optim_OTreg.py | 30 ++++--- 4 files changed, 199 insertions(+), 11 deletions(-) create mode 100644 examples/Demo_Optim_OTreg.ipynb diff --git a/README.md b/README.md index e0f07b8..4a6f7d1 100644 --- a/README.md +++ b/README.md @@ -41,6 +41,8 @@ The examples folder contain several examples and use case for the library. * [1D optimal transport](examples/Demo_1D_OT.ipynb) * [2D optimal transport on empirical distributions](examples/Demo_2D_OT_samples.ipynb) * [1D Wasserstein barycenter](examples/Demo_1D_barycenter.ipynb) +* [OT with user provided regularizat](examples/Demo_Optim_OTreg.ipynb) + ## Acknowledgements diff --git a/docs/source/examples.rst b/docs/source/examples.rst index 9bd4e75..5d26e0c 100644 --- a/docs/source/examples.rst +++ b/docs/source/examples.rst @@ -17,3 +17,8 @@ Examples ------------------------- .. literalinclude:: ../../examples/demo_barycenter_1D.py + +OT with user provided regularization +------------------------------------ + +.. literalinclude:: ../../examples/demo_optim_OTreg.py diff --git a/examples/Demo_Optim_OTreg.ipynb b/examples/Demo_Optim_OTreg.ipynb new file mode 100644 index 0000000..5731687 --- /dev/null +++ b/examples/Demo_Optim_OTreg.ipynb @@ -0,0 +1,173 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:4dc159882a3ef225eae256eb2ae57de153d04cc9de52ea36b1644e64a88de96a" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Regularized OT with generic solver" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dataset generation" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "n=100 # nb bins\n", + "\n", + "# bin positions\n", + "x=np.arange(n,dtype=np.float64)\n", + "\n", + "# Gaussian distributions\n", + "a=ot.datasets.get_1D_gauss(n,m=20,s=20) # m= mean, s= std\n", + "b=ot.datasets.get_1D_gauss(n,m=60,s=60)\n", + "\n", + "# loss matrix\n", + "M=ot.dist(x.reshape((n,1)),x.reshape((n,1)))\n", + "M/=M.max()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### EMD solution" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "G0=ot.emd(a,b,M)\n", + "\n", + "pl.figure(3)\n", + "ot.plot.plot1D_mat(a,b,G0,'OT matrix G0')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solution with Frobenius norm regularization" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def f(G): return 0.5*np.sum(G**2)\n", + "def df(G): return G\n", + "\n", + "reg=1e-1\n", + " \n", + "Gl2=ot.optim.cg(a,b,M,reg,f,df)\n", + "\n", + "pl.figure(3)\n", + "ot.plot.plot1D_mat(a,b,Gl2,'OT matrix Frob. reg')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solution with entropic regularization" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def f(G): return np.sum(G*np.log(G))\n", + "def df(G): return np.log(G)+1\n", + " \n", + "reg=1e-3\n", + " \n", + "Ge=ot.optim.cg(a,b,M,reg,f,df)\n", + "\n", + "pl.figure(4)\n", + "ot.plot.plot1D_mat(a,b,Ge,'OT matrix Entrop. reg')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 5 + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/examples/demo_optim_OTreg.py b/examples/demo_optim_OTreg.py index 5e88bdb..5e19be5 100644 --- a/examples/demo_optim_OTreg.py +++ b/examples/demo_optim_OTreg.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- """ -demo of Optimal transport for domain adaptation +Regularized OT with generic solver """ import numpy as np @@ -31,18 +31,26 @@ G0=ot.emd(a,b,M) pl.figure(3) ot.plot.plot1D_mat(a,b,G0,'OT matrix G0') -#%% exampel of regularization with Frobnisu norm +#%% Example with Frobenisu norm regularization + +def f(G): return 0.5*np.sum(G**2) +def df(G): return G -def f(G): - #return 0.5*np.sum(G**2) - return np.sum(G*np.log(G)) - -def df(G): -# return G - return np.log(G)+1 reg=1e-1 -Greg=ot.optim.cg(a,b,M,reg,f,df,verbose=True) +Gl2=ot.optim.cg(a,b,M,reg,f,df,verbose=True) + +pl.figure(3) +ot.plot.plot1D_mat(a,b,Gl2,'OT matrix Frob. reg') + +#%% Examlple with entropic regularization + +def f(G): return np.sum(G*np.log(G)) +def df(G): return np.log(G)+1 + +reg=1e-3 + +Ge=ot.optim.cg(a,b,M,reg,f,df,verbose=True) pl.figure(4) -ot.plot.plot1D_mat(a,b,Greg,'OT matrix G0') \ No newline at end of file +ot.plot.plot1D_mat(a,b,Ge,'OT matrix Entrop. reg') \ No newline at end of file -- cgit v1.2.3 From ca57a49f5f64a872debf03cb3db64b74e70318d4 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 15:25:56 +0200 Subject: new notebook --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 4a6f7d1..1be1208 100644 --- a/README.md +++ b/README.md @@ -41,7 +41,7 @@ The examples folder contain several examples and use case for the library. * [1D optimal transport](examples/Demo_1D_OT.ipynb) * [2D optimal transport on empirical distributions](examples/Demo_2D_OT_samples.ipynb) * [1D Wasserstein barycenter](examples/Demo_1D_barycenter.ipynb) -* [OT with user provided regularizat](examples/Demo_Optim_OTreg.ipynb) +* [OT with user provided regularization](examples/Demo_Optim_OTreg.ipynb) ## Acknowledgements -- cgit v1.2.3 From 996c6681513184c837c2c1f17af2cae9e5106676 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 15:41:21 +0200 Subject: update doc --- ot/da.py | 11 ----------- 1 file changed, 11 deletions(-) diff --git a/ot/da.py b/ot/da.py index 300d21a..083138f 100644 --- a/ot/da.py +++ b/ot/da.py @@ -67,23 +67,12 @@ def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerIter Optimal transportation matrix for the given parameters log: dict log dictionary return only if log==True in parameters - - Examples - -------- - - >>> a=[.5,.5] - >>> b=[.5,.5] - >>> M=[[0.,1.],[1.,0.]] - >>> ot.sinkhorn(a,b,M,1) - array([[ 0.36552929, 0.13447071], - [ 0.13447071, 0.36552929]]) References ---------- .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 - .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567. See Also -- cgit v1.2.3 From d3d8689b9230ea6066409ff44969817da6f5af50 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 16:23:57 +0200 Subject: first class for DA --- ot/da.py | 45 ++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 44 insertions(+), 1 deletion(-) diff --git a/ot/da.py b/ot/da.py index 083138f..19d41bc 100644 --- a/ot/da.py +++ b/ot/da.py @@ -5,6 +5,8 @@ Domain adaptation with optimal transport import numpy as np from .bregman import sinkhorn +from .lp import emd +from .utils import unif,dist @@ -118,4 +120,45 @@ def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerIter W[indices_labels[0],t]=np.min(all_maj) return transp - \ No newline at end of file + + + +class OTDA(): + """Class for optimal transport with domain adaptation""" + + def __init__(self): + self.xs=0 + self.xt=0 + self.G=0 + + + def fit(self,xs,xt,ws=None,wt=None): + self.xs=xs + self.xt=xt + + if wt is None: + wt=unif(xt.shape[0]) + if ws is None: + ws=unif(xs.shape[0]) + + self.ws=ws + self.wt=wt + + self.M=dist(xs,xt) + self.G=emd(ws,wt,self.M) + + def interp(self,direction=1): + """Barycentric interpolation for the source (1) or target (-1)""" + + if self.G and direction>0: + return (self.G/self.ws).dot(self.xt) + elif self.G and direction<0: + return (self.G.T/self.wt).dot(self.xs) + + + + + + + + \ No newline at end of file -- cgit v1.2.3 From a4e9e8a3a4724439939d04d95b0c29d0c5823f4f Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 16:42:55 +0200 Subject: RTD --- docs/source/conf.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/source/conf.py b/docs/source/conf.py index 1c1bb40..6ba42ab 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -76,6 +76,9 @@ release = u'0.1' # Usually you set "language" from the command line for these cases. language = None +MOCK_MODULES = ['numpy', 'scipy'] +sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) + # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' -- cgit v1.2.3 From 71c152ab92ddb9405a2d975e2f84cc3f155d6975 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 16:56:46 +0200 Subject: RTD2 --- docs/source/conf.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 6ba42ab..1c1bb40 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -76,9 +76,6 @@ release = u'0.1' # Usually you set "language" from the command line for these cases. language = None -MOCK_MODULES = ['numpy', 'scipy'] -sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) - # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' -- cgit v1.2.3 From 46e304dce30ab243c40174a4c6659cec358056d4 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 17:04:32 +0200 Subject: RTD3 --- docs/source/conf.py | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/docs/source/conf.py b/docs/source/conf.py index 1c1bb40..0031d77 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -14,9 +14,19 @@ import sys import os +from unittest.mock import MagicMock sys.path.insert(0, os.path.abspath("../..")) + +class Mock(MagicMock): + @classmethod + def __getattr__(cls, name): + return Mock() + +MOCK_MODULES = [ 'numpy', 'scipy'] +sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) + # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. -- cgit v1.2.3 From 8cd32fc567841b8f9557265bb1cf952310563bfd Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 17:07:42 +0200 Subject: RTD3 --- docs/source/conf.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 0031d77..bca78a2 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -14,18 +14,18 @@ import sys import os -from unittest.mock import MagicMock +#from unittest.mock import MagicMock sys.path.insert(0, os.path.abspath("../..")) -class Mock(MagicMock): - @classmethod - def __getattr__(cls, name): - return Mock() - -MOCK_MODULES = [ 'numpy', 'scipy'] -sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) +# class Mock(MagicMock): +# @classmethod +# def __getattr__(cls, name): +# return Mock() +# +# MOCK_MODULES = [ 'numpy', 'scipy'] +# sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the -- cgit v1.2.3 From 2438fa84d8095f21d3397ff70ddb32c51ed9855c Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 17:12:07 +0200 Subject: RTD3 --- docs/source/conf.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index bca78a2..cb8573f 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -14,18 +14,18 @@ import sys import os -#from unittest.mock import MagicMock +from unittest.mock import MagicMock sys.path.insert(0, os.path.abspath("../..")) -# class Mock(MagicMock): -# @classmethod -# def __getattr__(cls, name): -# return Mock() -# -# MOCK_MODULES = [ 'numpy', 'scipy'] -# sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) + class Mock(MagicMock): + @classmethod + def __getattr__(cls, name): + return Mock() + + MOCK_MODULES = [ 'emd'] + sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the -- cgit v1.2.3 From dabf0c22b7fd285301254539047291aa83a807a5 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 17:13:41 +0200 Subject: RTD3 --- docs/source/conf.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index cb8573f..aea2235 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -19,13 +19,13 @@ from unittest.mock import MagicMock sys.path.insert(0, os.path.abspath("../..")) - class Mock(MagicMock): - @classmethod - def __getattr__(cls, name): - return Mock() +class Mock(MagicMock): + @classmethod + def __getattr__(cls, name): + return Mock() - MOCK_MODULES = [ 'emd'] - sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) +MOCK_MODULES = [ 'emd'] +sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the -- cgit v1.2.3 From 88cf9b1aa33a809683d1205960988cb1d18318b5 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 17:14:50 +0200 Subject: RTD3 --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index aea2235..b56df38 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -24,7 +24,7 @@ class Mock(MagicMock): def __getattr__(cls, name): return Mock() -MOCK_MODULES = [ 'emd'] +MOCK_MODULES = [ 'emd','ot.lp.emd'] sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # If extensions (or modules to document with autodoc) are in another directory, -- cgit v1.2.3 From 29ddaf4dead412a65877559fe52d3255a0a55893 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 17:58:51 +0200 Subject: pip works! --- MANIFEST.in | 7 +++++++ setup.cfg | 2 ++ setup.py | 3 ++- 3 files changed, 11 insertions(+), 1 deletion(-) create mode 100644 MANIFEST.in create mode 100644 setup.cfg diff --git a/MANIFEST.in b/MANIFEST.in new file mode 100644 index 0000000..8c14549 --- /dev/null +++ b/MANIFEST.in @@ -0,0 +1,7 @@ +graft ot/lp/ +include README.md +include ot/lp/core.h +include ot/lp/EMD.h +include ot/lp/emd.pyx +include ot/lp/full_bipartitegraph.h +include ot/lp/network_simplex_simple.h diff --git a/setup.cfg b/setup.cfg new file mode 100644 index 0000000..b88034e --- /dev/null +++ b/setup.cfg @@ -0,0 +1,2 @@ +[metadata] +description-file = README.md diff --git a/setup.py b/setup.py index c7870d3..ade427f 100755 --- a/setup.py +++ b/setup.py @@ -15,7 +15,7 @@ here = path.abspath(path.dirname(__file__)) import os #import glob -version='0.1' +version='0.1.3' ROOT = os.path.abspath(os.path.dirname(__file__)) README = open(os.path.join(ROOT, 'README.md')).read() @@ -35,6 +35,7 @@ setup(name='POT', language="c++", # generate and compile C++ code, include_dirs=[numpy.get_include(),os.path.join(ROOT,'ot/lp')])), platforms=['linux','macosx','windows'], + download_url='https://github.com/rflamary/POT/archive/V0.1.tar.gz', license = 'MIT', scripts=[], data_files=[], -- cgit v1.2.3 From e5a1abce0411cf2529645b5c0a279c29086e9010 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 18:02:13 +0200 Subject: pip works! --- README.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 1be1208..55e9e8a 100644 --- a/README.md +++ b/README.md @@ -23,7 +23,10 @@ To install the library, you can install it locally (after downloading it) on you python setup.py install --user ``` - +The toolbox is also available on PyPI with a possibly slightly older version. You can install it with: +``` +pip install POT +``` After a correct installation, you should be able to import the module without errors: ```python -- cgit v1.2.3 From acfea8e233d683e7d505d64c0e5c4fb5d617918a Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Oct 2016 18:07:00 +0200 Subject: pipy v 0.1.4 --- setup.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/setup.py b/setup.py index ade427f..c352d03 100755 --- a/setup.py +++ b/setup.py @@ -15,7 +15,7 @@ here = path.abspath(path.dirname(__file__)) import os #import glob -version='0.1.3' +version='0.1.4' ROOT = os.path.abspath(os.path.dirname(__file__)) README = open(os.path.join(ROOT, 'README.md')).read() @@ -35,11 +35,11 @@ setup(name='POT', language="c++", # generate and compile C++ code, include_dirs=[numpy.get_include(),os.path.join(ROOT,'ot/lp')])), platforms=['linux','macosx','windows'], - download_url='https://github.com/rflamary/POT/archive/V0.1.tar.gz', + download_url='https://github.com/rflamary/POT/archive/V{}.tar.gz'.format(version), license = 'MIT', scripts=[], data_files=[], - requires=["numpy (>=1.11)","scipy (>=0.17)"], + requires=["numpy (>=1.11)","scipy (>=0.17)","cython (>=0.23)"], classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', -- cgit v1.2.3 From afc621c8571a4cdb734b94f7e384fe8dc0cd7823 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 31 Oct 2016 09:18:43 +0100 Subject: uodate readme +dependencies --- README.md | 8 +++++++- setup.py | 3 ++- 2 files changed, 9 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 55e9e8a..cd69e68 100644 --- a/README.md +++ b/README.md @@ -17,6 +17,12 @@ The Library has been tested on Linux and MacOSX. It requires a C++ compiler for - Numpy (>=1.11) - Scipy (>=0.17) +- Cython (>=0.23) +- Matplotlib (>=1.5) +Under debian based linux the dependencies can be installed with +``` +sudo apt-get install python-numpy python-scipy python-matplotlib cython +``` To install the library, you can install it locally (after downloading it) on you machine using ``` @@ -37,7 +43,7 @@ Note that for easier access the module is name ot instead of pot. ## Examples -The examples folder contain several examples and use case for the library. +The examples folder contain several examples and use case for the library. The full documentation is available on [Readthedoc](http://pot.readthedocs.io/) Here is a list of the Python notebook if you want a quick look: diff --git a/setup.py b/setup.py index c352d03..3397cda 100755 --- a/setup.py +++ b/setup.py @@ -39,7 +39,8 @@ setup(name='POT', license = 'MIT', scripts=[], data_files=[], - requires=["numpy (>=1.11)","scipy (>=0.17)","cython (>=0.23)"], + requires=["numpy (>=1.11)","scipy (>=0.17)","cython (>=0.23)","matplotlib (>=1.5)"], + install_requires=["numpy (>=1.11)","scipy (>=0.17)","cython (>=0.23)","matplotlib (>=1.5)"], classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', -- cgit v1.2.3 From d3122f035023d4619e19adccabd9cc342c415a3a Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 31 Oct 2016 09:24:56 +0100 Subject: rst description for pypi --- setup.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 3397cda..7d41a1b 100755 --- a/setup.py +++ b/setup.py @@ -18,7 +18,15 @@ import os version='0.1.4' ROOT = os.path.abspath(os.path.dirname(__file__)) -README = open(os.path.join(ROOT, 'README.md')).read() + + +# convert markdown readme to rst in pypandoc installed +try: + import pypandoc + README = pypandoc.convert('README.md', 'rst') +except (IOError, ImportError): + README = open(os.path.join(ROOT, 'README.md')).read() + setup(name='POT', version=version, -- cgit v1.2.3 From b2030a031b672a0618720d8622714a2e0d2db4f4 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 31 Oct 2016 09:25:48 +0100 Subject: redame update --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 7d41a1b..70a85c2 100755 --- a/setup.py +++ b/setup.py @@ -15,7 +15,7 @@ here = path.abspath(path.dirname(__file__)) import os #import glob -version='0.1.4' +version='0.1.5' ROOT = os.path.abspath(os.path.dirname(__file__)) -- cgit v1.2.3 From a5dd8c23736d2fd62c5627a20744a0662774a671 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 31 Oct 2016 09:27:35 +0100 Subject: redame update --- README.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/README.md b/README.md index cd69e68..1c3cb5d 100644 --- a/README.md +++ b/README.md @@ -3,6 +3,7 @@ This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning. It provides the following solvers: + * OT solver for the linear program/ Earth Movers Distance [1]. * Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2]. * Bregman projections for Wasserstein barycenter [3] and unmixing [4]. @@ -19,6 +20,8 @@ The Library has been tested on Linux and MacOSX. It requires a C++ compiler for - Scipy (>=0.17) - Cython (>=0.23) - Matplotlib (>=1.5) + + Under debian based linux the dependencies can be installed with ``` sudo apt-get install python-numpy python-scipy python-matplotlib cython @@ -56,6 +59,7 @@ The examples folder contain several examples and use case for the library. The f ## Acknowledgements The contributors to this library are: + * [Rémi Flamary](http://remi.flamary.com/) * [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/) * [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/) -- cgit v1.2.3 From f9dd6f89c79ad775bf63cb811034417afd37fc19 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 31 Oct 2016 09:30:00 +0100 Subject: readme update readthedoc --- README.md | 3 +++ 1 file changed, 3 insertions(+) diff --git a/README.md b/README.md index 1c3cb5d..2e81a55 100644 --- a/README.md +++ b/README.md @@ -1,5 +1,8 @@ # POT: Python Optimal Transport + +[![Documentation Status](https://readthedocs.org/projects/pot/badge/?version=latest)](http://pot.readthedocs.io/en/latest/?badge=latest) + This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning. It provides the following solvers: -- cgit v1.2.3 From b8d92d9c702dc66782b2216c9c63a97a91439ca4 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 31 Oct 2016 09:42:46 +0100 Subject: readme again --- README.md | 18 +++++++++++++----- docs/source/index.rst | 5 ----- 2 files changed, 13 insertions(+), 10 deletions(-) diff --git a/README.md b/README.md index 2e81a55..dd4c83e 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ [![Documentation Status](https://readthedocs.org/projects/pot/badge/?version=latest)](http://pot.readthedocs.io/en/latest/?badge=latest) - + This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning. It provides the following solvers: @@ -13,6 +13,12 @@ It provides the following solvers: * Optimal transport for domain adaptation with group lasso regularization [5] * Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. +We are also currently working on the following features: + +[] Image color adaptation demo +[] Scikit-learn inspired classes for domain adaptation +[] Mapping estimation as proposed in [8] + Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. ## Installation @@ -53,10 +59,10 @@ The examples folder contain several examples and use case for the library. The f Here is a list of the Python notebook if you want a quick look: -* [1D optimal transport](examples/Demo_1D_OT.ipynb) -* [2D optimal transport on empirical distributions](examples/Demo_2D_OT_samples.ipynb) -* [1D Wasserstein barycenter](examples/Demo_1D_barycenter.ipynb) -* [OT with user provided regularization](examples/Demo_Optim_OTreg.ipynb) +* [1D optimal transport](https://github.com/rflamary/POT/blob/master/examples/Demo_1D_OT.ipynb) +* [2D optimal transport on empirical distributions](https://github.com/rflamary/POT/blob/master/examples/Demo_2D_OT_samples.ipynb) +* [1D Wasserstein barycenter](https://github.com/rflamary/POT/blob/master/examples/Demo_1D_barycenter.ipynb) +* [OT with user provided regularization](https://github.com/rflamary/POT/blob/master/examples/Demo_Optim_OTreg.ipynb) ## Acknowledgements @@ -89,3 +95,5 @@ This toolbox benefit a lot from open source research and we would like to thank [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567. + +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. diff --git a/docs/source/index.rst b/docs/source/index.rst index 8452f00..adbabb6 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -30,11 +30,6 @@ Contents all examples -Examples --------- - - - References ---------- -- cgit v1.2.3 From 4fee02607357d2cc43f852c4d2497dc86d2686e1 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 31 Oct 2016 09:43:15 +0100 Subject: readme niaga --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index dd4c83e..5d95d0b 100644 --- a/README.md +++ b/README.md @@ -15,9 +15,9 @@ It provides the following solvers: We are also currently working on the following features: -[] Image color adaptation demo -[] Scikit-learn inspired classes for domain adaptation -[] Mapping estimation as proposed in [8] +[ ] Image color adaptation demo +[ ] Scikit-learn inspired classes for domain adaptation +[ ] Mapping estimation as proposed in [8] Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. -- cgit v1.2.3 From 03120093e2c3ce699b18812881a988335f8d8350 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 31 Oct 2016 09:43:47 +0100 Subject: readme niaga --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 5d95d0b..ab7be28 100644 --- a/README.md +++ b/README.md @@ -15,9 +15,9 @@ It provides the following solvers: We are also currently working on the following features: -[ ] Image color adaptation demo -[ ] Scikit-learn inspired classes for domain adaptation -[ ] Mapping estimation as proposed in [8] +- [ ] Image color adaptation demo +- [ ] Scikit-learn inspired classes for domain adaptation +- [ ] Mapping estimation as proposed in [8] Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. -- cgit v1.2.3 From 3f5189f8c3c0230bc10fc1c90349c5c340fe808a Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 31 Oct 2016 09:56:15 +0100 Subject: better version handling --- ot/__init__.py | 2 ++ setup.py | 18 ++++++++++++------ 2 files changed, 14 insertions(+), 6 deletions(-) diff --git a/ot/__init__.py b/ot/__init__.py index 72c820a..cf55711 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -17,5 +17,7 @@ from .da import sinkhorn_lpl1_mm # utils functions from .utils import dist, unif +__version__ = "0.1.6" + __all__ = ["emd", "sinkhorn", "utils", 'datasets', 'bregman', 'lp', 'plot', 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim'] diff --git a/setup.py b/setup.py index 70a85c2..9804422 100755 --- a/setup.py +++ b/setup.py @@ -3,19 +3,25 @@ from setuptools import setup, find_packages from codecs import open from os import path -import numpy from setuptools.extension import Extension from Cython.Build import cythonize - +import numpy +import re +import os here = path.abspath(path.dirname(__file__)) -import os + #import glob -version='0.1.5' +# dirty but working +__version__ = re.search( + r'__version__\s*=\s*[\'"]([^\'"]*)[\'"]', # It excludes inline comment too + open('ot/__init__.py').read()).group(1) +# The beautiful part is, I don't even need to check exceptions here. +# If something messes up, let the build process fail noisy, BEFORE my release! ROOT = os.path.abspath(os.path.dirname(__file__)) @@ -29,7 +35,7 @@ except (IOError, ImportError): setup(name='POT', - version=version, + version=__version__, description='Python Optimal Transport Library', long_description=README, author=u'Remi Flamary, Nicolas Courty', @@ -43,7 +49,7 @@ setup(name='POT', language="c++", # generate and compile C++ code, include_dirs=[numpy.get_include(),os.path.join(ROOT,'ot/lp')])), platforms=['linux','macosx','windows'], - download_url='https://github.com/rflamary/POT/archive/V{}.tar.gz'.format(version), + download_url='https://github.com/rflamary/POT/archive/V{}.tar.gz'.format(__version__), license = 'MIT', scripts=[], data_files=[], -- cgit v1.2.3 From 475810754e05bb64ce27264d0a7e533355e07dff Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 31 Oct 2016 09:57:32 +0100 Subject: update makefile --- Makefile | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/Makefile b/Makefile index c817639..0b17082 100644 --- a/Makefile +++ b/Makefile @@ -33,6 +33,11 @@ sremove : clean : $(PYTHON) setup.py clean + +uploadpypi: + python setup.py register + python setup.py sdist upload -r pypi + notebook : ipython notebook --matplotlib=inline --notebook-dir=examples/ -- cgit v1.2.3 From 9e40820bee3570354ffdd35a69e18bd16703719a Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 31 Oct 2016 13:43:48 +0100 Subject: firt DA class --- ot/da.py | 63 ++++++++++++++++++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 54 insertions(+), 9 deletions(-) diff --git a/ot/da.py b/ot/da.py index 19d41bc..11e420b 100644 --- a/ot/da.py +++ b/ot/da.py @@ -9,7 +9,6 @@ from .lp import emd from .utils import unif,dist - def indices(a, func): return [i for (i, val) in enumerate(a) if func(val)] @@ -124,15 +123,20 @@ def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerIter class OTDA(): - """Class for optimal transport with domain adaptation""" + """Class for domain adaptation with optimal transport""" - def __init__(self): + def __init__(self,metric='sqeuclidean'): + """ Class initialization""" self.xs=0 self.xt=0 self.G=0 + self.metric=metric + self.computed=False def fit(self,xs,xt,ws=None,wt=None): + """ Fit domain adaptation between samples is xs and xt (with optional + weights)""" self.xs=xs self.xt=xt @@ -144,17 +148,58 @@ class OTDA(): self.ws=ws self.wt=wt - self.M=dist(xs,xt) + self.M=dist(xs,xt,metric=self.metric) self.G=emd(ws,wt,self.M) + self.computed=True def interp(self,direction=1): - """Barycentric interpolation for the source (1) or target (-1)""" + """Barycentric interpolation for the source (1) or target (-1) + + This Barycentric interpolation solves for each source (resp target) + sample xs (resp xt) the following optimization problem: + + .. math:: + arg\min_x \sum_i \gamma_{k,i} c(x,x_i^t) + + where k is the index of the sample in xs + + For the moment only squared euclidean distance is provided but more + metric c can be used in teh future. + + """ + if direction>0: # >0 then source to target + G=self.G + w=self.ws.reshape((self.xs.shape[0],1)) + x=self.xt + else: + G=self.G.T + w=self.wt.reshape((self.xt.shape[0],1)) + x=self.xs + + if self.computed: + if self.metric=='sqeuclidean': + return np.dot(G/w,x) # weighted mean + else: + print("Warning, metric not handled yet, using weighted average") + return np.dot(G/w,x) # weighted mean + return None + else: + print("Warning, model not fitted yet, returning None") + return None + - if self.G and direction>0: - return (self.G/self.ws).dot(self.xt) - elif self.G and direction<0: - return (self.G.T/self.wt).dot(self.xs) + def predict(x,direction=1): + """ Out of sample mapping using the formulation from Ferradans + """ + if direction>0: # >0 then source to target + G=self.G + w=self.ws.reshape((self.xs.shape[0],1)) + x=self.xt + else: + G=self.G.T + w=self.wt.reshape((self.xt.shape[0],1)) + x=self.xs -- cgit v1.2.3 From 104627b2f69eb22d3f9010955e6765ac2b179faa Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 31 Oct 2016 14:23:40 +0100 Subject: commit doc --- docs/source/conf.py | 7 ++++++- ot/da.py | 39 ++++++++++++++++++++++++++++++++------- 2 files changed, 38 insertions(+), 8 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index b56df38..ffc0da8 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -14,9 +14,14 @@ import sys import os -from unittest.mock import MagicMock +try: + from unittest.mock import MagicMock +except ImportError: + from mock import MagicMock sys.path.insert(0, os.path.abspath("../..")) +sys.setrecursionlimit(1500) + class Mock(MagicMock): diff --git a/ot/da.py b/ot/da.py index 11e420b..87354b9 100644 --- a/ot/da.py +++ b/ot/da.py @@ -188,21 +188,46 @@ class OTDA(): return None - def predict(x,direction=1): + def predict(self,x,direction=1): """ Out of sample mapping using the formulation from Ferradans + It basically find the source sample the nearset to the nex sample and + apply the difference to the displaced source sample. + """ if direction>0: # >0 then source to target - G=self.G - w=self.ws.reshape((self.xs.shape[0],1)) - x=self.xt + xf=self.xt + x0=self.xs else: - G=self.G.T - w=self.wt.reshape((self.xt.shape[0],1)) - x=self.xs + xf=self.xs + x0=self.xt + + D0=dist(x,x0) # dist netween new samples an source + idx=np.argmin(D0,1) # closest one + xf=self.interp(direction)# interp the source samples + return xf[idx,:]+x-x0[idx,:] # aply the delta to the interpolation + +class OTDA_sinkhorn(OTDA): + + def fit(self,xs,xt,ws=None,wt=None,reg=1,**kwargs): + """ Fit domain adaptation between samples is xs and xt (with optional + weights)""" + self.xs=xs + self.xt=xt + if wt is None: + wt=unif(xt.shape[0]) + if ws is None: + ws=unif(xs.shape[0]) + + self.ws=ws + self.wt=wt + + self.M=dist(xs,xt,metric=self.metric) + self.G=sinkhorn(ws,wt,self.M,reg,**kwargs) + self.computed=True -- cgit v1.2.3 From fdceacbac0d4b0380c90ca2c942e5abd0f69df64 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 31 Oct 2016 14:36:34 +0100 Subject: add classes for entropic and group lasso regularization --- ot/da.py | 25 +++++++++++++++++++++++-- 1 file changed, 23 insertions(+), 2 deletions(-) diff --git a/ot/da.py b/ot/da.py index 87354b9..56963d8 100644 --- a/ot/da.py +++ b/ot/da.py @@ -210,8 +210,8 @@ class OTDA(): class OTDA_sinkhorn(OTDA): - - def fit(self,xs,xt,ws=None,wt=None,reg=1,**kwargs): + """Class for domain adaptation with optimal transport with entropic regularization""" + def fit(self,xs,xt,reg=1,ws=None,wt=None,**kwargs): """ Fit domain adaptation between samples is xs and xt (with optional weights)""" self.xs=xs @@ -230,5 +230,26 @@ class OTDA_sinkhorn(OTDA): self.computed=True +class OTDA_lpl1(OTDA): + """Class for domain adaptation with optimal transport with entropic an group regularization""" + + def fit(self,xs,ys,xt,reg=1,eta=1,ws=None,wt=None,**kwargs): + """ Fit domain adaptation between samples is xs and xt (with optional + weights)""" + self.xs=xs + self.xt=xt + + if wt is None: + wt=unif(xt.shape[0]) + if ws is None: + ws=unif(xs.shape[0]) + + self.ws=ws + self.wt=wt + + self.M=dist(xs,xt,metric=self.metric) + self.G=sinkhorn_lpl1_mm(ws,ys,wt,self.M,reg,eta,**kwargs) + self.computed=True + \ No newline at end of file -- cgit v1.2.3 From 543be68caf6e1804cbe1200f88efc32f3b38e91c Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 31 Oct 2016 14:38:27 +0100 Subject: origibnal doc --- docs/source/conf.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index ffc0da8..2bb0e81 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -14,13 +14,13 @@ import sys import os -try: - from unittest.mock import MagicMock -except ImportError: - from mock import MagicMock +#try: +from unittest.mock import MagicMock +#except ImportError: +# from mock import MagicMock sys.path.insert(0, os.path.abspath("../..")) -sys.setrecursionlimit(1500) +#sys.setrecursionlimit(1500) -- cgit v1.2.3 From c48829d2fcecb3204b18e15d6c51201728bd1770 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 31 Oct 2016 14:49:53 +0100 Subject: add domain adaptation demo --- examples/demo_OTDA_classes.py | 96 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 96 insertions(+) create mode 100644 examples/demo_OTDA_classes.py diff --git a/examples/demo_OTDA_classes.py b/examples/demo_OTDA_classes.py new file mode 100644 index 0000000..da25dcb --- /dev/null +++ b/examples/demo_OTDA_classes.py @@ -0,0 +1,96 @@ +# -*- coding: utf-8 -*- +""" +demo of Optimal transport for domain adaptation +""" + +import numpy as np +import matplotlib.pylab as pl +import ot + + + +#%% parameters + +n=150 # nb bins + +xs,ys=ot.datasets.get_data_classif('3gauss',n) +xt,yt=ot.datasets.get_data_classif('3gauss2',n) + +a,b = ot.unif(n),ot.unif(n) +# loss matrix +M=ot.dist(xs,xt) +#M/=M.max() + +#%% plot samples + +pl.figure(1) + +pl.subplot(2,2,1) +pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') +pl.legend(loc=0) +pl.title('Source distributions') + +pl.subplot(2,2,2) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') +pl.legend(loc=0) +pl.title('target distributions') + + +#%% OT estimation + +# EMD + + +da_emd=ot.da.OTDA() +da_emd.fit(xs,xt) + +# interpolate samples +xst0=da_emd.interp() + + +# sinkhorn +lambd=1e-1 +da_entrop=ot.da.OTDA_sinkhorn() +da_entrop.fit(xs,xt,reg=lambd) +xsts=da_entrop.interp() + +# Group lasso regularization +reg=1e-1 +eta=1e0 +da_lpl1=ot.da.OTDA_lpl1() +da_lpl1.fit(xs,ys,xt,reg=lambd,eta=eta) +xstg=da_lpl1.interp() + +#%% plot interpolated source samples +pl.figure(4) + +param_img={'interpolation':'nearest','cmap':'jet'} + +pl.subplot(2,3,1) +pl.imshow(da_emd.G,**param_img) +pl.title('OT matrix') + + +pl.subplot(2,3,2) +pl.imshow(da_entrop.G,**param_img) +pl.title('OT matrix sinkhorn') + +pl.subplot(2,3,3) +pl.imshow(da_lpl1.G,**param_img) +pl.title('OT matrix Group Lasso') + +pl.subplot(2,3,4) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) +pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30) +pl.title('Interp samples') +pl.legend(loc=0) + +pl.subplot(2,3,5) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) +pl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30) +pl.title('Interp samples Sinkhorn') + +pl.subplot(2,3,6) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) +pl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30) +pl.title('Interp samples Group Lasso') \ No newline at end of file -- cgit v1.2.3 From 2933531a140cb901befde30bbda96e1aecb9c7d8 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 31 Oct 2016 15:06:10 +0100 Subject: add notebook --- examples/Demo_2D_OT_DomainAdaptation.ipynb | 217 +++ examples/demo_OTDA_classes.py | 22 +- ot/lp/emd.cpp | 2517 ++++++++++++++++++++-------- 3 files changed, 2025 insertions(+), 731 deletions(-) create mode 100644 examples/Demo_2D_OT_DomainAdaptation.ipynb diff --git a/examples/Demo_2D_OT_DomainAdaptation.ipynb b/examples/Demo_2D_OT_DomainAdaptation.ipynb new file mode 100644 index 0000000..b713d5b --- /dev/null +++ b/examples/Demo_2D_OT_DomainAdaptation.ipynb @@ -0,0 +1,217 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Domain adaptation with optimal transport" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dataset generation (classification problem) " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n=150 # nb samples in source and target datasets\n", + "\n", + "xs,ys=ot.datasets.get_data_classif('3gauss',n)\n", + "xt,yt=ot.datasets.get_data_classif('3gauss2',n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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mudNPP/2EUoo333zTZvvhw4ejtebHH39MU/1KKfr3729zhUxrzdq1a3n++eep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\nKEXIAQAAShFyAACAUoQcAACglC+qn/WKtzKzIwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(1,(10,7))\n", + "\n", + "pl.subplot(2,2,1)\n", + "pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples')\n", + "pl.legend(loc=0)\n", + "pl.axis([-4,4,-4,4])\n", + "pl.title('Source distributions')\n", + "\n", + "pl.subplot(2,2,2)\n", + "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples')\n", + "pl.legend(loc=0)\n", + "pl.axis([-4,4,-4,4])\n", + "pl.title('Target distributions')\n", + "\n", + "pl.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "## Domain adaptation classes" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# LP problem\n", + "da_emd=ot.da.OTDA() # init class\n", + "da_emd.fit(xs,xt) # fit distributions\n", + "xst0=da_emd.interp() # interpolation of source samples\n", + "\n", + "\n", + "# sinkhorn regularization\n", + "lambd=1e-1\n", + "da_entrop=ot.da.OTDA_sinkhorn()\n", + "da_entrop.fit(xs,xt,reg=lambd)\n", + "xsts=da_entrop.interp()\n", + "\n", + "# Group lasso regularization\n", + "reg=1e-1\n", + "eta=1e0\n", + "da_lpl1=ot.da.OTDA_lpl1()\n", + "da_lpl1.fit(xs,ys,xt,reg=lambd,eta=eta)\n", + "xstg=da_lpl1.interp()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot OT matrices and interpolated samples " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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artIdDXkv+QcCIUR36Z+GQLd05DLgcx3uqRCims7oSFccGQSR+RPgFc65/y4c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O+Xw+n0Ag+DNQeq3nE2RtYTab6bJ00RbeypaQrcSp56ehqFQqdu9Qcd8OJV2mmbS1nL/aS47p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h5k795u/vjHT+jurmfRooWjvocQYvwppfDz8+Oee+LQ6Sppa1O0tup54YUCtm4NZP36eG6/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\nhBBCTGnS6RFCCCGEEEJMaf8PUJAvj+BeZCgAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(4,(10,7))\n", + "\n", + "param_img={'interpolation':'nearest','cmap':'jet'}\n", + "\n", + "pl.subplot(2,3,1)\n", + "pl.imshow(da_emd.G,**param_img)\n", + "pl.title('OT matrix')\n", + "\n", + "\n", + "pl.subplot(2,3,2)\n", + "pl.imshow(da_entrop.G,**param_img)\n", + "pl.title('OT matrix sinkhorn')\n", + "\n", + "pl.subplot(2,3,3)\n", + "pl.imshow(da_lpl1.G,**param_img)\n", + "pl.title('OT matrix Group Lasso')\n", + "\n", + "pl.subplot(2,3,4)\n", + "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)\n", + "pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30)\n", + "pl.title('Interp samples')\n", + "pl.legend(loc=0)\n", + "\n", + "pl.subplot(2,3,5)\n", + "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)\n", + "pl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30)\n", + "pl.title('Interp samples Sinkhorn')\n", + "\n", + "pl.subplot(2,3,6)\n", + "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)\n", + "pl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30)\n", + "pl.title('Interp samples Group Lasso')\n", + "\n", + "pl.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/demo_OTDA_classes.py b/examples/demo_OTDA_classes.py index da25dcb..f61625a 100644 --- a/examples/demo_OTDA_classes.py +++ b/examples/demo_OTDA_classes.py @@ -11,15 +11,13 @@ import ot #%% parameters -n=150 # nb bins +n=150 # nb samples in source and target datasets xs,ys=ot.datasets.get_data_classif('3gauss',n) xt,yt=ot.datasets.get_data_classif('3gauss2',n) -a,b = ot.unif(n),ot.unif(n) -# loss matrix -M=ot.dist(xs,xt) -#M/=M.max() + + #%% plot samples @@ -38,17 +36,13 @@ pl.title('target distributions') #%% OT estimation -# EMD - - -da_emd=ot.da.OTDA() -da_emd.fit(xs,xt) - -# interpolate samples -xst0=da_emd.interp() +# LP problem +da_emd=ot.da.OTDA() # init class +da_emd.fit(xs,xt) # fit distributions +xst0=da_emd.interp() # interpolation of source samples -# sinkhorn +# sinkhorn regularization lambd=1e-1 da_entrop=ot.da.OTDA_sinkhorn() da_entrop.fit(xs,xt,reg=lambd) diff --git a/ot/lp/emd.cpp b/ot/lp/emd.cpp index 6db54bb..81bb450 100644 --- a/ot/lp/emd.cpp +++ b/ot/lp/emd.cpp @@ -1,19 +1,20 @@ -/* Generated by Cython 0.23.4 */ +/* Generated by Cython 0.25.1 */ /* BEGIN: Cython Metadata { "distutils": { "depends": [ - "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/arrayobject.h", - "/usr/lib/python2.7/dist-packages/numpy/core/include/numpy/ufuncobject.h", + "/home/rflamary/.local/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h", + "/home/rflamary/.local/lib/python2.7/site-packages/numpy/core/include/numpy/ufuncobject.h", "ot/lp/EMD.h" ], "include_dirs": [ - "/usr/lib/python2.7/dist-packages/numpy/core/include", + "/home/rflamary/.local/lib/python2.7/site-packages/numpy/core/include", "/home/rflamary/PYTHON/POT/ot/lp" ], "language": "c++" - } + }, + "module_name": "ot.lp.emd" } END: Cython Metadata */ @@ -24,10 +25,10 @@ END: Cython Metadata */ #elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03020000) #error Cython requires Python 2.6+ or Python 3.2+. #else -#define CYTHON_ABI "0_23_4" +#define CYTHON_ABI "0_25_1" #include #ifndef offsetof -#define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) + #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) #endif #if !defined(WIN32) && !defined(MS_WINDOWS) #ifndef __stdcall @@ -46,6 +47,11 @@ END: Cython Metadata */ #ifndef DL_EXPORT #define DL_EXPORT(t) t #endif +#ifndef HAVE_LONG_LONG + #if PY_VERSION_HEX >= 0x03030000 || (PY_MAJOR_VERSION == 2 && PY_VERSION_HEX >= 0x02070000) + #define HAVE_LONG_LONG + #endif +#endif #ifndef PY_LONG_LONG #define PY_LONG_LONG LONG_LONG #endif @@ -53,17 +59,120 @@ END: Cython Metadata */ #define Py_HUGE_VAL HUGE_VAL #endif #ifdef PYPY_VERSION -#define CYTHON_COMPILING_IN_PYPY 1 -#define CYTHON_COMPILING_IN_CPYTHON 0 + #define CYTHON_COMPILING_IN_PYPY 1 + #define CYTHON_COMPILING_IN_PYSTON 0 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #undef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 0 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #undef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #undef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 1 + #undef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 0 + #undef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 0 + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 +#elif defined(PYSTON_VERSION) + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_PYSTON 1 + #define CYTHON_COMPILING_IN_CPYTHON 0 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #undef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 0 + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #undef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 0 + #undef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 0 #else -#define CYTHON_COMPILING_IN_PYPY 0 -#define CYTHON_COMPILING_IN_CPYTHON 1 + #define CYTHON_COMPILING_IN_PYPY 0 + #define CYTHON_COMPILING_IN_PYSTON 0 + #define CYTHON_COMPILING_IN_CPYTHON 1 + #ifndef CYTHON_USE_TYPE_SLOTS + #define CYTHON_USE_TYPE_SLOTS 1 + #endif + #if PY_MAJOR_VERSION < 3 + #undef CYTHON_USE_ASYNC_SLOTS + #define CYTHON_USE_ASYNC_SLOTS 0 + #elif !defined(CYTHON_USE_ASYNC_SLOTS) + #define CYTHON_USE_ASYNC_SLOTS 1 + #endif + #if PY_VERSION_HEX < 0x02070000 + #undef CYTHON_USE_PYLONG_INTERNALS + #define CYTHON_USE_PYLONG_INTERNALS 0 + #elif !defined(CYTHON_USE_PYLONG_INTERNALS) + #define CYTHON_USE_PYLONG_INTERNALS 1 + #endif + #ifndef CYTHON_USE_PYLIST_INTERNALS + #define CYTHON_USE_PYLIST_INTERNALS 1 + #endif + #ifndef CYTHON_USE_UNICODE_INTERNALS + #define CYTHON_USE_UNICODE_INTERNALS 1 + #endif + #if PY_VERSION_HEX < 0x030300F0 + #undef CYTHON_USE_UNICODE_WRITER + #define CYTHON_USE_UNICODE_WRITER 0 + #elif !defined(CYTHON_USE_UNICODE_WRITER) + #define CYTHON_USE_UNICODE_WRITER 1 + #endif + #ifndef CYTHON_AVOID_BORROWED_REFS + #define CYTHON_AVOID_BORROWED_REFS 0 + #endif + #ifndef CYTHON_ASSUME_SAFE_MACROS + #define CYTHON_ASSUME_SAFE_MACROS 1 + #endif + #ifndef CYTHON_UNPACK_METHODS + #define CYTHON_UNPACK_METHODS 1 + #endif + #ifndef CYTHON_FAST_THREAD_STATE + #define CYTHON_FAST_THREAD_STATE 1 + #endif + #ifndef CYTHON_FAST_PYCALL + #define CYTHON_FAST_PYCALL 1 + #endif +#endif +#if !defined(CYTHON_FAST_PYCCALL) +#define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) #endif -#if !defined(CYTHON_USE_PYLONG_INTERNALS) && CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x02070000 -#define CYTHON_USE_PYLONG_INTERNALS 1 +#if CYTHON_USE_PYLONG_INTERNALS + #include "longintrepr.h" + #undef SHIFT + #undef BASE + #undef MASK #endif #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) -#define Py_OptimizeFlag 0 + #define Py_OptimizeFlag 0 #endif #define __PYX_BUILD_PY_SSIZE_T "n" #define CYTHON_FORMAT_SSIZE_T "z" @@ -90,23 +199,45 @@ END: Cython Metadata */ #ifndef Py_TPFLAGS_HAVE_FINALIZE #define Py_TPFLAGS_HAVE_FINALIZE 0 #endif +#ifndef METH_FASTCALL + #define METH_FASTCALL 0x80 + typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject **args, + Py_ssize_t nargs, PyObject *kwnames); +#else + #define __Pyx_PyCFunctionFast _PyCFunctionFast +#endif +#if CYTHON_FAST_PYCCALL +#define __Pyx_PyFastCFunction_Check(func)\ + ((PyCFunction_Check(func) && METH_FASTCALL == PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST))) +#else +#define __Pyx_PyFastCFunction_Check(func) 0 +#endif #if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) #define CYTHON_PEP393_ENABLED 1 #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ 0 : _PyUnicode_Ready((PyObject *)(op))) #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u) #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) #else #define CYTHON_PEP393_ENABLED 0 + #define PyUnicode_1BYTE_KIND 1 + #define PyUnicode_2BYTE_KIND 2 + #define PyUnicode_4BYTE_KIND 4 #define __Pyx_PyUnicode_READY(op) (0) #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) + #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111) #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) + #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch) + #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u)) #endif #if CYTHON_COMPILING_IN_PYPY #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) @@ -119,6 +250,24 @@ END: Cython Metadata */ #if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains) #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) #endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check) + #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format) + #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) +#endif +#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc) + #define PyObject_Malloc(s) PyMem_Malloc(s) + #define PyObject_Free(p) PyMem_Free(p) + #define PyObject_Realloc(p) PyMem_Realloc(p) +#endif +#if CYTHON_COMPILING_IN_PYSTON + #define __Pyx_PyCode_HasFreeVars(co) PyCode_HasFreeVars(co) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno) +#else + #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) + #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno) +#endif #define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) #define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) #if PY_MAJOR_VERSION >= 3 @@ -126,6 +275,9 @@ END: Cython Metadata */ #else #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) #endif +#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII) + #define PyObject_ASCII(o) PyObject_Repr(o) +#endif #if PY_MAJOR_VERSION >= 3 #define PyBaseString_Type PyUnicode_Type #define PyStringObject PyUnicodeObject @@ -144,6 +296,7 @@ END: Cython Metadata */ #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) #endif #define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) +#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) #if PY_MAJOR_VERSION >= 3 #define PyIntObject PyLongObject #define PyInt_Type PyLong_Type @@ -182,18 +335,20 @@ END: Cython Metadata */ #else #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) #endif -#if PY_VERSION_HEX >= 0x030500B1 -#define __Pyx_PyAsyncMethodsStruct PyAsyncMethods -#define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) -#elif CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -typedef struct { - unaryfunc am_await; - unaryfunc am_aiter; - unaryfunc am_anext; -} __Pyx_PyAsyncMethodsStruct; -#define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) +#if CYTHON_USE_ASYNC_SLOTS + #if PY_VERSION_HEX >= 0x030500B1 + #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods + #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) + #else + typedef struct { + unaryfunc am_await; + unaryfunc am_aiter; + unaryfunc am_anext; + } __Pyx_PyAsyncMethodsStruct; + #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) + #endif #else -#define __Pyx_PyType_AsAsync(obj) NULL + #define __Pyx_PyType_AsAsync(obj) NULL #endif #ifndef CYTHON_RESTRICT #if defined(__GNUC__) @@ -225,6 +380,8 @@ class __Pyx_FakeReference { __Pyx_FakeReference(const T& ref) : ptr(const_cast(&ref)) { } T *operator->() { return ptr; } operator T&() { return *ptr; } + template bool operator ==(U other) { return *ptr == other; }; + template bool operator !=(U other) { return *ptr != other; }; private: T *ptr; }; @@ -242,8 +399,18 @@ static CYTHON_INLINE float __PYX_NAN() { return value; } #endif +#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL) +#define __Pyx_truncl trunc +#else +#define __Pyx_truncl truncl +#endif +#define __PYX_ERR(f_index, lineno, Ln_error) \ +{ \ + __pyx_filename = __pyx_f[f_index]; __pyx_lineno = lineno; __pyx_clineno = __LINE__; goto Ln_error; \ +} + #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) @@ -262,9 +429,9 @@ static CYTHON_INLINE float __PYX_NAN() { #define __PYX_HAVE__ot__lp__emd #define __PYX_HAVE_API__ot__lp__emd -#include "string.h" -#include "stdio.h" -#include "stdlib.h" +#include +#include +#include #include "numpy/arrayobject.h" #include "numpy/ufuncobject.h" #include "EMD.h" @@ -296,7 +463,7 @@ static CYTHON_INLINE float __PYX_NAN() { # define CYTHON_NCP_UNUSED CYTHON_UNUSED # endif #endif -typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding; +typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding; const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; #define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 @@ -370,15 +537,21 @@ static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) #define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) #define __Pyx_PyBool_FromLong(b) ((b) ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False)) static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); -static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x); +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); -#if CYTHON_COMPILING_IN_CPYTHON +#if CYTHON_ASSUME_SAFE_MACROS #define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) #else #define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) #endif #define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) +#if PY_MAJOR_VERSION >= 3 +#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) +#else +#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) +#endif +#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x)) #if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII static int __Pyx_sys_getdefaultencoding_not_ascii; static int __Pyx_init_sys_getdefaultencoding_params(void) { @@ -469,11 +642,13 @@ static PyObject *__pyx_d; static PyObject *__pyx_b; static PyObject *__pyx_empty_tuple; static PyObject *__pyx_empty_bytes; +static PyObject *__pyx_empty_unicode; static int __pyx_lineno; static int __pyx_clineno = 0; static const char * __pyx_cfilenm= __FILE__; static const char *__pyx_filename; +/* Header.proto */ #if !defined(CYTHON_CCOMPLEX) #if defined(__cplusplus) #define CYTHON_CCOMPLEX 1 @@ -501,6 +676,7 @@ static const char *__pyx_f[] = { "__init__.pxd", "type.pxd", }; +/* BufferFormatStructs.proto */ #define IS_UNSIGNED(type) (((type) -1) > 0) struct __Pyx_StructField_; #define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) @@ -537,7 +713,7 @@ typedef struct { } __Pyx_BufFmt_Context; -/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":725 +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":725 * # in Cython to enable them only on the right systems. * * ctypedef npy_int8 int8_t # <<<<<<<<<<<<<< @@ -546,7 +722,7 @@ typedef struct { */ typedef npy_int8 __pyx_t_5numpy_int8_t; -/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":726 +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":726 * * ctypedef npy_int8 int8_t * ctypedef npy_int16 int16_t # <<<<<<<<<<<<<< @@ -555,7 +731,7 @@ typedef npy_int8 __pyx_t_5numpy_int8_t; */ typedef npy_int16 __pyx_t_5numpy_int16_t; -/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":727 +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":727 * ctypedef npy_int8 int8_t * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< @@ -564,7 +740,7 @@ typedef npy_int16 __pyx_t_5numpy_int16_t; */ typedef npy_int32 __pyx_t_5numpy_int32_t; -/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":728 +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":728 * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< @@ -573,7 +749,7 @@ typedef npy_int32 __pyx_t_5numpy_int32_t; */ typedef npy_int64 __pyx_t_5numpy_int64_t; -/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":732 +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":732 * #ctypedef npy_int128 int128_t * * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< @@ -582,7 +758,7 @@ typedef npy_int64 __pyx_t_5numpy_int64_t; */ typedef npy_uint8 __pyx_t_5numpy_uint8_t; -/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":733 +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":733 * * ctypedef npy_uint8 uint8_t * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< @@ -591,7 +767,7 @@ typedef npy_uint8 __pyx_t_5numpy_uint8_t; */ typedef npy_uint16 __pyx_t_5numpy_uint16_t; -/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":734 +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":734 * ctypedef npy_uint8 uint8_t * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<< @@ -600,7 +776,7 @@ typedef npy_uint16 __pyx_t_5numpy_uint16_t; */ typedef npy_uint32 __pyx_t_5numpy_uint32_t; -/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":735 +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":735 * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t * ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<< @@ -609,7 +785,7 @@ typedef npy_uint32 __pyx_t_5numpy_uint32_t; */ typedef npy_uint64 __pyx_t_5numpy_uint64_t; -/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":739 +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":739 * #ctypedef npy_uint128 uint128_t * * ctypedef npy_float32 float32_t # <<<<<<<<<<<<<< @@ -618,7 +794,7 @@ typedef npy_uint64 __pyx_t_5numpy_uint64_t; */ typedef npy_float32 __pyx_t_5numpy_float32_t; -/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":740 +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":740 * * ctypedef npy_float32 float32_t * ctypedef npy_float64 float64_t # <<<<<<<<<<<<<< @@ -627,7 +803,7 @@ typedef npy_float32 __pyx_t_5numpy_float32_t; */ typedef npy_float64 __pyx_t_5numpy_float64_t; -/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":749 +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":749 * # The int types are mapped a bit surprising -- * # numpy.int corresponds to 'l' and numpy.long to 'q' * ctypedef npy_long int_t # <<<<<<<<<<<<<< @@ -636,7 +812,7 @@ typedef npy_float64 __pyx_t_5numpy_float64_t; */ typedef npy_long __pyx_t_5numpy_int_t; -/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":750 +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":750 * # numpy.int corresponds to 'l' and numpy.long to 'q' * ctypedef npy_long int_t * ctypedef npy_longlong long_t # <<<<<<<<<<<<<< @@ -645,7 +821,7 @@ typedef npy_long __pyx_t_5numpy_int_t; */ typedef npy_longlong __pyx_t_5numpy_long_t; -/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":751 +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":751 * ctypedef npy_long int_t * ctypedef npy_longlong long_t * ctypedef npy_longlong longlong_t # <<<<<<<<<<<<<< @@ -654,7 +830,7 @@ typedef npy_longlong __pyx_t_5numpy_long_t; */ typedef npy_longlong __pyx_t_5numpy_longlong_t; -/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":753 +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":753 * ctypedef npy_longlong longlong_t * * ctypedef npy_ulong uint_t # <<<<<<<<<<<<<< @@ -663,7 +839,7 @@ typedef npy_longlong __pyx_t_5numpy_longlong_t; */ typedef npy_ulong __pyx_t_5numpy_uint_t; -/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":754 +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":754 * * ctypedef npy_ulong uint_t * ctypedef npy_ulonglong ulong_t # <<<<<<<<<<<<<< @@ -672,7 +848,7 @@ typedef npy_ulong __pyx_t_5numpy_uint_t; */ typedef npy_ulonglong __pyx_t_5numpy_ulong_t; -/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":755 +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":755 * ctypedef npy_ulong uint_t * ctypedef npy_ulonglong ulong_t * ctypedef npy_ulonglong ulonglong_t # <<<<<<<<<<<<<< @@ -681,7 +857,7 @@ typedef npy_ulonglong __pyx_t_5numpy_ulong_t; */ typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t; -/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":757 +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":757 * ctypedef npy_ulonglong ulonglong_t * * ctypedef npy_intp intp_t # <<<<<<<<<<<<<< @@ -690,7 +866,7 @@ typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t; */ typedef npy_intp __pyx_t_5numpy_intp_t; -/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":758 +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":758 * * ctypedef npy_intp intp_t * ctypedef npy_uintp uintp_t # <<<<<<<<<<<<<< @@ -699,7 +875,7 @@ typedef npy_intp __pyx_t_5numpy_intp_t; */ typedef npy_uintp __pyx_t_5numpy_uintp_t; -/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":760 +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":760 * ctypedef npy_uintp uintp_t * * ctypedef npy_double float_t # <<<<<<<<<<<<<< @@ -708,7 +884,7 @@ typedef npy_uintp __pyx_t_5numpy_uintp_t; */ typedef npy_double __pyx_t_5numpy_float_t; -/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":761 +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":761 * * ctypedef npy_double float_t * ctypedef npy_double double_t # <<<<<<<<<<<<<< @@ -717,7 +893,7 @@ typedef npy_double __pyx_t_5numpy_float_t; */ typedef npy_double __pyx_t_5numpy_double_t; -/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":762 +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":762 * ctypedef npy_double float_t * ctypedef npy_double double_t * ctypedef npy_longdouble longdouble_t # <<<<<<<<<<<<<< @@ -725,6 +901,7 @@ typedef npy_double __pyx_t_5numpy_double_t; * ctypedef npy_cfloat cfloat_t */ typedef npy_longdouble __pyx_t_5numpy_longdouble_t; +/* Declarations.proto */ #if CYTHON_CCOMPLEX #ifdef __cplusplus typedef ::std::complex< float > __pyx_t_float_complex; @@ -734,7 +911,9 @@ typedef npy_longdouble __pyx_t_5numpy_longdouble_t; #else typedef struct { float real, imag; } __pyx_t_float_complex; #endif +static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float); +/* Declarations.proto */ #if CYTHON_CCOMPLEX #ifdef __cplusplus typedef ::std::complex< double > __pyx_t_double_complex; @@ -744,11 +923,12 @@ typedef npy_longdouble __pyx_t_5numpy_longdouble_t; #else typedef struct { double real, imag; } __pyx_t_double_complex; #endif +static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double); /*--- Type declarations ---*/ -/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":764 +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":764 * ctypedef npy_longdouble longdouble_t * * ctypedef npy_cfloat cfloat_t # <<<<<<<<<<<<<< @@ -757,7 +937,7 @@ typedef npy_longdouble __pyx_t_5numpy_longdouble_t; */ typedef npy_cfloat __pyx_t_5numpy_cfloat_t; -/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":765 +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":765 * * ctypedef npy_cfloat cfloat_t * ctypedef npy_cdouble cdouble_t # <<<<<<<<<<<<<< @@ -766,7 +946,7 @@ typedef npy_cfloat __pyx_t_5numpy_cfloat_t; */ typedef npy_cdouble __pyx_t_5numpy_cdouble_t; -/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":766 +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":766 * ctypedef npy_cfloat cfloat_t * ctypedef npy_cdouble cdouble_t * ctypedef npy_clongdouble clongdouble_t # <<<<<<<<<<<<<< @@ -775,7 +955,7 @@ typedef npy_cdouble __pyx_t_5numpy_cdouble_t; */ typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t; -/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":768 +/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":768 * ctypedef npy_clongdouble clongdouble_t * * ctypedef npy_cdouble complex_t # <<<<<<<<<<<<<< @@ -785,6 +965,7 @@ typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t; typedef npy_cdouble __pyx_t_5numpy_complex_t; /* --- Runtime support code (head) --- */ +/* Refnanny.proto */ #ifndef CYTHON_REFNANNY #define CYTHON_REFNANNY 0 #endif @@ -847,23 +1028,33 @@ typedef npy_cdouble __pyx_t_5numpy_complex_t; #define __Pyx_CLEAR(r) do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0) #define __Pyx_XCLEAR(r) do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0) +/* RaiseArgTupleInvalid.proto */ static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); +/* RaiseDoubleKeywords.proto */ static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); +/* ParseKeywords.proto */ static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\ PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,\ const char* function_name); +/* ArgTypeTest.proto */ static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, const char *name, int exact); +/* BufferFormatCheck.proto */ static CYTHON_INLINE int __Pyx_GetBufferAndValidate(Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack); static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info); +static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts); +static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, + __Pyx_BufFmt_StackElem* stack, + __Pyx_TypeInfo* type); // PROTO -#if CYTHON_COMPILING_IN_CPYTHON +/* PyObjectGetAttrStr.proto */ +#if CYTHON_USE_TYPE_SLOTS static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) { PyTypeObject* tp = Py_TYPE(obj); if (likely(tp->tp_getattro)) @@ -878,31 +1069,79 @@ static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject #define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) #endif +/* GetBuiltinName.proto */ static PyObject *__Pyx_GetBuiltinName(PyObject *name); +/* GetModuleGlobalName.proto */ static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name); +/* PyCFunctionFastCall.proto */ +#if CYTHON_FAST_PYCCALL +static CYTHON_INLINE PyObject *__Pyx_PyCFunction_FastCall(PyObject *func, PyObject **args, Py_ssize_t nargs); +#else +#define __Pyx_PyCFunction_FastCall(func, args, nargs) (assert(0), NULL) +#endif + +/* PyFunctionFastCall.proto */ +#if CYTHON_FAST_PYCALL +#define __Pyx_PyFunction_FastCall(func, args, nargs)\ + __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL) +#if 1 || PY_VERSION_HEX < 0x030600B1 +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs); +#else +#define __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs) _PyFunction_FastCallDict(func, args, nargs, kwargs) +#endif +#endif + +/* PyObjectCall.proto */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); #else #define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) #endif +/* PyObjectCallMethO.proto */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg); #endif +/* PyObjectCallOneArg.proto */ static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg); +/* ExtTypeTest.proto */ static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); +/* BufferFallbackError.proto */ static void __Pyx_RaiseBufferFallbackError(void); -static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb); -static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb); +/* PyThreadStateGet.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyThreadState_declare PyThreadState *__pyx_tstate; +#define __Pyx_PyThreadState_assign __pyx_tstate = PyThreadState_GET(); +#else +#define __Pyx_PyThreadState_declare +#define __Pyx_PyThreadState_assign +#endif + +/* PyErrFetchRestore.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ErrRestoreWithState(type, value, tb) __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +#define __Pyx_ErrRestoreWithState(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetchWithState(type, value, tb) PyErr_Fetch(type, value, tb) +#define __Pyx_ErrRestore(type, value, tb) PyErr_Restore(type, value, tb) +#define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb) +#endif +/* RaiseException.proto */ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); +/* DictGetItem.proto */ #if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) { PyObject *value; @@ -923,17 +1162,49 @@ static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) { #define __Pyx_PyDict_GetItem(d, key) PyObject_GetItem(d, key) #endif +/* RaiseTooManyValuesToUnpack.proto */ static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); +/* RaiseNeedMoreValuesToUnpack.proto */ static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); +/* RaiseNoneIterError.proto */ static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); +/* SaveResetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb) +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); +#else +#define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb) +#define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb) +#endif + +/* PyErrExceptionMatches.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err) +static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err); +#else +#define __Pyx_PyErr_ExceptionMatches(err) PyErr_ExceptionMatches(err) +#endif + +/* GetException.proto */ +#if CYTHON_FAST_THREAD_STATE +#define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb) +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); +#endif + +/* Import.proto */ static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); +/* CodeObjectCache.proto */ typedef struct { - int code_line; PyCodeObject* code_object; + int code_line; } __Pyx_CodeObjectCacheEntry; struct __Pyx_CodeObjectCache { int count; @@ -945,9 +1216,11 @@ static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int co static PyCodeObject *__pyx_find_code_object(int code_line); static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); +/* AddTraceback.proto */ static void __Pyx_AddTraceback(const char *funcname, int c_line, int py_line, const char *filename); +/* BufferStructDeclare.proto */ typedef struct { Py_ssize_t shape, strides, suboffsets; } __Pyx_Buf_DimInfo; @@ -970,11 +1243,14 @@ typedef struct { #endif +/* None.proto */ static Py_ssize_t __Pyx_zeros[] = {0, 0, 0, 0, 0, 0, 0, 0}; static Py_ssize_t __Pyx_minusones[] = {-1, -1, -1, -1, -1, -1, -1, -1}; +/* CIntToPy.proto */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); +/* RealImag.proto */ #if CYTHON_CCOMPLEX #ifdef __cplusplus #define __Pyx_CREAL(z) ((z).real()) @@ -987,7 +1263,8 @@ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); #define __Pyx_CREAL(z) ((z).real) #define __Pyx_CIMAG(z) ((z).imag) #endif -#if (defined(_WIN32) || defined(__clang__)) && defined(__cplusplus) && CYTHON_CCOMPLEX +#if defined(__cplusplus) && CYTHON_CCOMPLEX\ + && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103) #define __Pyx_SET_CREAL(z,x) ((z).real(x)) #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) #else @@ -995,94 +1272,98 @@ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) #endif -static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float); - +/* Arithmetic.proto */ #if CYTHON_CCOMPLEX - #define __Pyx_c_eqf(a, b) ((a)==(b)) - #define __Pyx_c_sumf(a, b) ((a)+(b)) - #define __Pyx_c_difff(a, b) ((a)-(b)) - #define __Pyx_c_prodf(a, b) ((a)*(b)) - #define __Pyx_c_quotf(a, b) ((a)/(b)) - #define __Pyx_c_negf(a) (-(a)) + #define __Pyx_c_eq_float(a, b) ((a)==(b)) + #define __Pyx_c_sum_float(a, b) ((a)+(b)) + #define __Pyx_c_diff_float(a, b) ((a)-(b)) + #define __Pyx_c_prod_float(a, b) ((a)*(b)) + #define __Pyx_c_quot_float(a, b) ((a)/(b)) + #define __Pyx_c_neg_float(a) (-(a)) #ifdef __cplusplus - #define __Pyx_c_is_zerof(z) ((z)==(float)0) - #define __Pyx_c_conjf(z) (::std::conj(z)) + #define __Pyx_c_is_zero_float(z) ((z)==(float)0) + #define __Pyx_c_conj_float(z) (::std::conj(z)) #if 1 - #define __Pyx_c_absf(z) (::std::abs(z)) - #define __Pyx_c_powf(a, b) (::std::pow(a, b)) + #define __Pyx_c_abs_float(z) (::std::abs(z)) + #define __Pyx_c_pow_float(a, b) (::std::pow(a, b)) #endif #else - #define __Pyx_c_is_zerof(z) ((z)==0) - #define __Pyx_c_conjf(z) (conjf(z)) + #define __Pyx_c_is_zero_float(z) ((z)==0) + #define __Pyx_c_conj_float(z) (conjf(z)) #if 1 - #define __Pyx_c_absf(z) (cabsf(z)) - #define __Pyx_c_powf(a, b) (cpowf(a, b)) + #define __Pyx_c_abs_float(z) (cabsf(z)) + #define __Pyx_c_pow_float(a, b) (cpowf(a, b)) #endif #endif #else - static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex); - static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex); + static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex); + static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex); #if 1 - static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex); #endif #endif -static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double); - +/* Arithmetic.proto */ #if CYTHON_CCOMPLEX - #define __Pyx_c_eq(a, b) ((a)==(b)) - #define __Pyx_c_sum(a, b) ((a)+(b)) - #define __Pyx_c_diff(a, b) ((a)-(b)) - #define __Pyx_c_prod(a, b) ((a)*(b)) - #define __Pyx_c_quot(a, b) ((a)/(b)) - #define __Pyx_c_neg(a) (-(a)) + #define __Pyx_c_eq_double(a, b) ((a)==(b)) + #define __Pyx_c_sum_double(a, b) ((a)+(b)) + #define __Pyx_c_diff_double(a, b) ((a)-(b)) + #define __Pyx_c_prod_double(a, b) ((a)*(b)) + #define __Pyx_c_quot_double(a, b) ((a)/(b)) + #define __Pyx_c_neg_double(a) (-(a)) #ifdef __cplusplus - #define __Pyx_c_is_zero(z) ((z)==(double)0) - #define __Pyx_c_conj(z) (::std::conj(z)) + #define __Pyx_c_is_zero_double(z) ((z)==(double)0) + #define __Pyx_c_conj_double(z) (::std::conj(z)) #if 1 - #define __Pyx_c_abs(z) (::std::abs(z)) - #define __Pyx_c_pow(a, b) (::std::pow(a, b)) + #define __Pyx_c_abs_double(z) (::std::abs(z)) + #define __Pyx_c_pow_double(a, b) (::std::pow(a, b)) #endif #else - #define __Pyx_c_is_zero(z) ((z)==0) - #define __Pyx_c_conj(z) (conj(z)) + #define __Pyx_c_is_zero_double(z) ((z)==0) + #define __Pyx_c_conj_double(z) (conj(z)) #if 1 - #define __Pyx_c_abs(z) (cabs(z)) - #define __Pyx_c_pow(a, b) (cpow(a, b)) + #define __Pyx_c_abs_double(z) (cabs(z)) + #define __Pyx_c_pow_double(a, b) (cpow(a, b)) #endif #endif #else - static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex); - static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex); + static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex); + static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex); #if 1 - static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex); #endif #endif -static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); - +/* CIntToPy.proto */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value); +/* CIntFromPy.proto */ +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); + +/* CIntToPy.proto */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); +/* CIntFromPy.proto */ static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); +/* CheckBinaryVersion.proto */ static int __Pyx_check_binary_version(void); +/* PyIdentifierFromString.proto */ #if !defined(__Pyx_PyIdentifier_FromString) #if PY_MAJOR_VERSION < 3 #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s) @@ -1091,10 +1372,13 @@ static int __Pyx_check_binary_version(void); #endif #endif +/* ModuleImport.proto */ static PyObject *__Pyx_ImportModule(const char *name); +/* TypeImport.proto */ static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict); +/* InitStrings.proto */ static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); @@ -1138,52 +1422,41 @@ int __pyx_module_is_main_ot__lp__emd = 0; static PyObject *__pyx_builtin_ValueError; static PyObject *__pyx_builtin_range; static PyObject *__pyx_builtin_RuntimeError; -static char __pyx_k_B[] = "B"; -static char __pyx_k_G[] = "G"; -static char __pyx_k_H[] = "H"; -static char __pyx_k_I[] = "I"; -static char __pyx_k_L[] = "L"; -static char __pyx_k_M[] = "M"; -static char __pyx_k_O[] = "O"; -static char __pyx_k_Q[] = "Q"; -static char __pyx_k_a[] = "a"; -static char __pyx_k_b[] = "b"; -static char __pyx_k_d[] = "d"; -static char __pyx_k_f[] = "f"; -static char __pyx_k_g[] = "g"; -static char __pyx_k_h[] = "h"; -static char __pyx_k_i[] = "i"; -static char __pyx_k_l[] = "l"; -static char __pyx_k_q[] = "q"; -static char __pyx_k_Zd[] = "Zd"; -static char __pyx_k_Zf[] = "Zf"; -static char __pyx_k_Zg[] = "Zg"; -static char __pyx_k_n1[] = "n1"; -static char __pyx_k_n2[] = "n2"; -static char __pyx_k_np[] = "np"; -static char __pyx_k_cost[] = "cost"; -static char __pyx_k_main[] = "__main__"; -static char __pyx_k_ones[] = "ones"; -static char __pyx_k_test[] = "__test__"; -static char __pyx_k_emd_c[] = "emd_c"; -static char __pyx_k_numpy[] = "numpy"; -static char __pyx_k_range[] = "range"; -static char __pyx_k_zeros[] = "zeros"; -static char __pyx_k_import[] = "__import__"; -static char __pyx_k_ot_lp_emd[] = "ot.lp.emd"; -static char __pyx_k_ValueError[] = "ValueError"; -static char __pyx_k_RuntimeError[] = "RuntimeError"; -static char __pyx_k_ndarray_is_not_C_contiguous[] = "ndarray is not C contiguous"; -static char __pyx_k_Created_on_Thu_Sep_11_08_42_08[] = "\nCreated on Thu Sep 11 08:42:08 2014\n\n@author: rflamary\n"; -static char __pyx_k_home_rflamary_PYTHON_POT_ot_lp[] = "/home/rflamary/PYTHON/POT/ot/lp/emd.pyx"; -static char __pyx_k_unknown_dtype_code_in_numpy_pxd[] = "unknown dtype code in numpy.pxd (%d)"; -static char __pyx_k_Format_string_allocated_too_shor[] = "Format string allocated too short, see comment in numpy.pxd"; -static char __pyx_k_Non_native_byte_order_not_suppor[] = "Non-native byte order not supported"; -static char __pyx_k_ndarray_is_not_Fortran_contiguou[] = "ndarray is not Fortran contiguous"; -static char __pyx_k_Format_string_allocated_too_shor_2[] = "Format string allocated too short."; +static PyObject *__pyx_builtin_ImportError; +static const char __pyx_k_G[] = "G"; +static const char __pyx_k_M[] = "M"; +static const char __pyx_k_a[] = "a"; +static const char __pyx_k_b[] = "b"; +static const char __pyx_k_n1[] = "n1"; +static const char __pyx_k_n2[] = "n2"; +static const char __pyx_k_np[] = "np"; +static const char __pyx_k_cost[] = "cost"; +static const char __pyx_k_main[] = "__main__"; +static const char __pyx_k_ones[] = "ones"; +static const char __pyx_k_test[] = "__test__"; +static const char __pyx_k_emd_c[] = "emd_c"; +static const char __pyx_k_numpy[] = "numpy"; +static const char __pyx_k_range[] = "range"; +static const char __pyx_k_zeros[] = "zeros"; +static const char __pyx_k_import[] = "__import__"; +static const char __pyx_k_ot_lp_emd[] = "ot.lp.emd"; +static const char __pyx_k_ValueError[] = "ValueError"; +static const char __pyx_k_ImportError[] = "ImportError"; +static const char __pyx_k_RuntimeError[] = "RuntimeError"; +static const char __pyx_k_ndarray_is_not_C_contiguous[] = "ndarray is not C contiguous"; +static const char __pyx_k_Created_on_Thu_Sep_11_08_42_08[] = "\nCreated on Thu Sep 11 08:42:08 2014\n\n@author: rflamary\n"; +static const char __pyx_k_home_rflamary_PYTHON_POT_ot_lp[] = "/home/rflamary/PYTHON/POT/ot/lp/emd.pyx"; +static const char __pyx_k_numpy_core_multiarray_failed_to[] = "numpy.core.multiarray failed to import"; +static const char __pyx_k_unknown_dtype_code_in_numpy_pxd[] = "unknown dtype code in numpy.pxd (%d)"; +static const char __pyx_k_Format_string_allocated_too_shor[] = "Format string allocated too short, see comment in numpy.pxd"; +static const char __pyx_k_Non_native_byte_order_not_suppor[] = "Non-native byte order not supported"; +static const char __pyx_k_ndarray_is_not_Fortran_contiguou[] = "ndarray is not Fortran contiguous"; +static const char __pyx_k_numpy_core_umath_failed_to_impor[] = "numpy.core.umath failed to import"; +static const char __pyx_k_Format_string_allocated_too_shor_2[] = "Format string allocated too short."; static PyObject *__pyx_kp_u_Format_string_allocated_too_shor; static PyObject *__pyx_kp_u_Format_string_allocated_too_shor_2; static PyObject *__pyx_n_s_G; +static PyObject *__pyx_n_s_ImportError; static PyObject *__pyx_n_s_M; static PyObject *__pyx_kp_u_Non_native_byte_order_not_suppor; static PyObject *__pyx_n_s_RuntimeError; @@ -1201,6 +1474,8 @@ static PyObject *__pyx_kp_u_ndarray_is_not_C_contiguous; static PyObject *__pyx_kp_u_ndarray_is_not_Fortran_contiguou; static PyObject *__pyx_n_s_np; static PyObject *__pyx_n_s_numpy; +static PyObject *__pyx_kp_s_numpy_core_multiarray_failed_to; +static PyObject *__pyx_kp_s_numpy_core_umath_failed_to_impor; static PyObject *__pyx_n_s_ones; static PyObject *__pyx_n_s_ot_lp_emd; static PyObject *__pyx_n_s_range; @@ -1217,7 +1492,10 @@ static PyObject *__pyx_tuple__4; static PyObject *__pyx_tuple__5; static PyObject *__pyx_tuple__6; static PyObject *__pyx_tuple__7; -static PyObject *__pyx_codeobj__8; +static PyObject *__pyx_tuple__8; +static PyObject *__pyx_tuple__9; +static PyObject *__pyx_tuple__10; +static PyObject *__pyx_codeobj__11; /* "ot/lp/emd.pyx":21 * @cython.boundscheck(False) @@ -1235,9 +1513,6 @@ static PyObject *__pyx_pw_2ot_2lp_3emd_1emd_c(PyObject *__pyx_self, PyObject *__ PyArrayObject *__pyx_v_a = 0; PyArrayObject *__pyx_v_b = 0; PyArrayObject *__pyx_v_M = 0; - int __pyx_lineno = 0; - const char *__pyx_filename = NULL; - int __pyx_clineno = 0; PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("emd_c (wrapper)", 0); @@ -1262,16 +1537,16 @@ static PyObject *__pyx_pw_2ot_2lp_3emd_1emd_c(PyObject *__pyx_self, PyObject *__ case 1: if (likely((values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_b)) != 0)) kw_args--; else { - __Pyx_RaiseArgtupleInvalid("emd_c", 1, 3, 3, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + __Pyx_RaiseArgtupleInvalid("emd_c", 1, 3, 3, 1); __PYX_ERR(0, 21, __pyx_L3_error) } case 2: if (likely((values[2] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_M)) != 0)) kw_args--; else { - __Pyx_RaiseArgtupleInvalid("emd_c", 1, 3, 3, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + __Pyx_RaiseArgtupleInvalid("emd_c", 1, 3, 3, 2); __PYX_ERR(0, 21, __pyx_L3_error) } } if (unlikely(kw_args > 0)) { - if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "emd_c") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "emd_c") < 0)) __PYX_ERR(0, 21, __pyx_L3_error) } } else if (PyTuple_GET_SIZE(__pyx_args) != 3) { goto __pyx_L5_argtuple_error; @@ -1286,15 +1561,15 @@ static PyObject *__pyx_pw_2ot_2lp_3emd_1emd_c(PyObject *__pyx_self, PyObject *__ } goto __pyx_L4_argument_unpacking_done; __pyx_L5_argtuple_error:; - __Pyx_RaiseArgtupleInvalid("emd_c", 1, 3, 3, PyTuple_GET_SIZE(__pyx_args)); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L3_error;} + __Pyx_RaiseArgtupleInvalid("emd_c", 1, 3, 3, PyTuple_GET_SIZE(__pyx_args)); 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__pyx_L6_bool_binop_done:; - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":216 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":216 * copy_shape = 0 * * if ((flags & pybuf.PyBUF_C_CONTIGUOUS == pybuf.PyBUF_C_CONTIGUOUS) # <<<<<<<<<<<<<< @@ -1873,20 +2204,20 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ if (__pyx_t_1) { - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":218 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":218 * if ((flags & pybuf.PyBUF_C_CONTIGUOUS == pybuf.PyBUF_C_CONTIGUOUS) * and not PyArray_CHKFLAGS(self, NPY_C_CONTIGUOUS)): * raise ValueError(u"ndarray is not C contiguous") # <<<<<<<<<<<<<< * * if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS) */ - __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple_, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 218; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple_, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 218, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - {__pyx_filename = __pyx_f[1]; __pyx_lineno = 218; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __PYX_ERR(1, 218, __pyx_L1_error) - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":216 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":216 * copy_shape = 0 * * if ((flags & pybuf.PyBUF_C_CONTIGUOUS == pybuf.PyBUF_C_CONTIGUOUS) # <<<<<<<<<<<<<< @@ -1895,7 +2226,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ } - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":220 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":220 * raise ValueError(u"ndarray is not C contiguous") * * if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS) # <<<<<<<<<<<<<< @@ -1909,7 +2240,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P goto __pyx_L9_bool_binop_done; } - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":221 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":221 * * if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS) * and not PyArray_CHKFLAGS(self, NPY_F_CONTIGUOUS)): # <<<<<<<<<<<<<< @@ -1920,7 +2251,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P __pyx_t_1 = __pyx_t_2; __pyx_L9_bool_binop_done:; - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":220 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":220 * raise ValueError(u"ndarray is not C contiguous") * * if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS) # <<<<<<<<<<<<<< @@ -1929,20 +2260,20 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ if (__pyx_t_1) { - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":222 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":222 * if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS) * and not PyArray_CHKFLAGS(self, NPY_F_CONTIGUOUS)): * raise ValueError(u"ndarray is not Fortran contiguous") # <<<<<<<<<<<<<< * * info.buf = PyArray_DATA(self) */ - __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__2, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 222; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__2, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 222, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - {__pyx_filename = __pyx_f[1]; __pyx_lineno = 222; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __PYX_ERR(1, 222, __pyx_L1_error) - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":220 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":220 * raise ValueError(u"ndarray is not C contiguous") * * if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS) # <<<<<<<<<<<<<< @@ -1951,7 +2282,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ } - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":224 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":224 * raise ValueError(u"ndarray is not Fortran contiguous") * * info.buf = PyArray_DATA(self) # <<<<<<<<<<<<<< @@ -1960,7 +2291,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ __pyx_v_info->buf = PyArray_DATA(__pyx_v_self); - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":225 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":225 * * info.buf = PyArray_DATA(self) * info.ndim = ndim # <<<<<<<<<<<<<< @@ -1969,7 +2300,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ __pyx_v_info->ndim = __pyx_v_ndim; - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":226 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":226 * info.buf = PyArray_DATA(self) * info.ndim = ndim * if copy_shape: # <<<<<<<<<<<<<< @@ -1979,7 +2310,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P __pyx_t_1 = (__pyx_v_copy_shape != 0); if (__pyx_t_1) { - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":229 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":229 * # Allocate new buffer for strides and shape info. * # This is allocated as one block, strides first. * info.strides = stdlib.malloc(sizeof(Py_ssize_t) * ndim * 2) # <<<<<<<<<<<<<< @@ -1988,7 +2319,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ __pyx_v_info->strides = ((Py_ssize_t *)malloc((((sizeof(Py_ssize_t)) * ((size_t)__pyx_v_ndim)) * 2))); - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":230 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":230 * # This is allocated as one block, strides first. * info.strides = stdlib.malloc(sizeof(Py_ssize_t) * ndim * 2) * info.shape = info.strides + ndim # <<<<<<<<<<<<<< @@ -1997,7 +2328,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ __pyx_v_info->shape = (__pyx_v_info->strides + __pyx_v_ndim); - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":231 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":231 * info.strides = stdlib.malloc(sizeof(Py_ssize_t) * ndim * 2) * info.shape = info.strides + ndim * for i in range(ndim): # <<<<<<<<<<<<<< @@ -2008,7 +2339,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":232 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":232 * info.shape = info.strides + ndim * for i in range(ndim): * info.strides[i] = PyArray_STRIDES(self)[i] # <<<<<<<<<<<<<< @@ -2017,7 +2348,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ (__pyx_v_info->strides[__pyx_v_i]) = (PyArray_STRIDES(__pyx_v_self)[__pyx_v_i]); - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":233 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":233 * for i in range(ndim): * info.strides[i] = PyArray_STRIDES(self)[i] * info.shape[i] = PyArray_DIMS(self)[i] # <<<<<<<<<<<<<< @@ -2027,7 +2358,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P (__pyx_v_info->shape[__pyx_v_i]) = (PyArray_DIMS(__pyx_v_self)[__pyx_v_i]); } - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":226 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":226 * info.buf = PyArray_DATA(self) * info.ndim = ndim * if copy_shape: # <<<<<<<<<<<<<< @@ -2037,7 +2368,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P goto __pyx_L11; } - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":235 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":235 * info.shape[i] = PyArray_DIMS(self)[i] * else: * info.strides = PyArray_STRIDES(self) # <<<<<<<<<<<<<< @@ -2047,7 +2378,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P /*else*/ { __pyx_v_info->strides = ((Py_ssize_t *)PyArray_STRIDES(__pyx_v_self)); - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":236 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":236 * else: * info.strides = PyArray_STRIDES(self) * info.shape = PyArray_DIMS(self) # <<<<<<<<<<<<<< @@ -2058,7 +2389,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P } __pyx_L11:; - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":237 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":237 * info.strides = PyArray_STRIDES(self) * info.shape = PyArray_DIMS(self) * info.suboffsets = NULL # <<<<<<<<<<<<<< @@ -2067,7 +2398,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ __pyx_v_info->suboffsets = NULL; - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":238 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":238 * info.shape = PyArray_DIMS(self) * info.suboffsets = NULL * info.itemsize = PyArray_ITEMSIZE(self) # <<<<<<<<<<<<<< @@ -2076,7 +2407,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ __pyx_v_info->itemsize = PyArray_ITEMSIZE(__pyx_v_self); - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":239 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":239 * info.suboffsets = NULL * info.itemsize = PyArray_ITEMSIZE(self) * info.readonly = not PyArray_ISWRITEABLE(self) # <<<<<<<<<<<<<< @@ -2085,7 +2416,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ __pyx_v_info->readonly = (!(PyArray_ISWRITEABLE(__pyx_v_self) != 0)); - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":242 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":242 * * cdef int t * cdef char* f = NULL # <<<<<<<<<<<<<< @@ -2094,7 +2425,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ __pyx_v_f = NULL; - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":243 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":243 * cdef int t * cdef char* f = NULL * cdef dtype descr = self.descr # <<<<<<<<<<<<<< @@ -2106,7 +2437,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P __pyx_v_descr = ((PyArray_Descr *)__pyx_t_3); __pyx_t_3 = 0; - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":246 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":246 * cdef int offset * * cdef bint hasfields = PyDataType_HASFIELDS(descr) # <<<<<<<<<<<<<< @@ -2115,7 +2446,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ __pyx_v_hasfields = PyDataType_HASFIELDS(__pyx_v_descr); - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":248 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":248 * cdef bint hasfields = PyDataType_HASFIELDS(descr) * * if not hasfields and not copy_shape: # <<<<<<<<<<<<<< @@ -2133,7 +2464,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P __pyx_L15_bool_binop_done:; if (__pyx_t_1) { - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":250 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":250 * if not hasfields and not copy_shape: * # do not call releasebuffer * info.obj = None # <<<<<<<<<<<<<< @@ -2146,7 +2477,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = Py_None; - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":248 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":248 * cdef bint hasfields = PyDataType_HASFIELDS(descr) * * if not hasfields and not copy_shape: # <<<<<<<<<<<<<< @@ -2156,7 +2487,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P goto __pyx_L14; } - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":253 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":253 * else: * # need to call releasebuffer * info.obj = self # <<<<<<<<<<<<<< @@ -2172,7 +2503,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P } __pyx_L14:; - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":255 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":255 * info.obj = self * * if not hasfields: # <<<<<<<<<<<<<< @@ -2182,7 +2513,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P __pyx_t_1 = ((!(__pyx_v_hasfields != 0)) != 0); if (__pyx_t_1) { - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":256 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":256 * * if not hasfields: * t = descr.type_num # <<<<<<<<<<<<<< @@ -2192,7 +2523,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P __pyx_t_4 = __pyx_v_descr->type_num; __pyx_v_t = __pyx_t_4; - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":257 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":257 * if not hasfields: * t = descr.type_num * if ((descr.byteorder == c'>' and little_endian) or # <<<<<<<<<<<<<< @@ -2212,7 +2543,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P } __pyx_L20_next_or:; - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":258 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":258 * t = descr.type_num * if ((descr.byteorder == c'>' and little_endian) or * (descr.byteorder == c'<' and not little_endian)): # <<<<<<<<<<<<<< @@ -2229,7 +2560,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P __pyx_t_1 = __pyx_t_2; __pyx_L19_bool_binop_done:; - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":257 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":257 * if not hasfields: * t = descr.type_num * if ((descr.byteorder == c'>' and little_endian) or # <<<<<<<<<<<<<< @@ -2238,20 +2569,20 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ if (__pyx_t_1) { - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":259 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":259 * if ((descr.byteorder == c'>' and little_endian) or * (descr.byteorder == c'<' and not little_endian)): * raise ValueError(u"Non-native byte order not supported") # <<<<<<<<<<<<<< * if t == NPY_BYTE: f = "b" * elif t == NPY_UBYTE: f = "B" */ - __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__3, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 259; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__3, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 259, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - {__pyx_filename = __pyx_f[1]; __pyx_lineno = 259; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __PYX_ERR(1, 259, __pyx_L1_error) - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":257 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":257 * if not hasfields: * t = descr.type_num * if ((descr.byteorder == c'>' and little_endian) or # <<<<<<<<<<<<<< @@ -2260,7 +2591,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ } - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":260 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":260 * (descr.byteorder == c'<' and not little_endian)): * raise ValueError(u"Non-native byte order not supported") * if t == NPY_BYTE: f = "b" # <<<<<<<<<<<<<< @@ -2269,10 +2600,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ switch (__pyx_v_t) { case NPY_BYTE: - __pyx_v_f = __pyx_k_b; + __pyx_v_f = ((char *)"b"); break; - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":261 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":261 * raise ValueError(u"Non-native byte order not supported") * if t == NPY_BYTE: f = "b" * elif t == NPY_UBYTE: f = "B" # <<<<<<<<<<<<<< @@ -2280,10 +2611,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * elif t == NPY_USHORT: f = "H" */ case NPY_UBYTE: - __pyx_v_f = __pyx_k_B; + __pyx_v_f = ((char *)"B"); break; - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":262 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":262 * if t == NPY_BYTE: f = "b" * elif t == NPY_UBYTE: f = "B" * elif t == NPY_SHORT: f = "h" # <<<<<<<<<<<<<< @@ -2291,10 +2622,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * elif t == NPY_INT: f = "i" */ case NPY_SHORT: - __pyx_v_f = __pyx_k_h; + __pyx_v_f = ((char *)"h"); break; - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":263 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":263 * elif t == NPY_UBYTE: f = "B" * elif t == NPY_SHORT: f = "h" * elif t == NPY_USHORT: f = "H" # <<<<<<<<<<<<<< @@ -2302,10 +2633,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * elif t == NPY_UINT: f = "I" */ case NPY_USHORT: - __pyx_v_f = __pyx_k_H; + __pyx_v_f = ((char *)"H"); break; - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":264 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":264 * elif t == NPY_SHORT: f = "h" * elif t == NPY_USHORT: f = "H" * elif t == NPY_INT: f = "i" # <<<<<<<<<<<<<< @@ -2313,10 +2644,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * elif t == NPY_LONG: f = "l" */ case NPY_INT: - __pyx_v_f = __pyx_k_i; + __pyx_v_f = ((char *)"i"); break; - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":265 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":265 * elif t == NPY_USHORT: f = "H" * elif t == NPY_INT: f = "i" * elif t == NPY_UINT: f = "I" # <<<<<<<<<<<<<< @@ -2324,10 +2655,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * elif t == NPY_ULONG: f = "L" */ case NPY_UINT: - __pyx_v_f = __pyx_k_I; + __pyx_v_f = ((char *)"I"); break; - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":266 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":266 * elif t == NPY_INT: f = "i" * elif t == NPY_UINT: f = "I" * elif t == NPY_LONG: f = "l" # <<<<<<<<<<<<<< @@ -2335,10 +2666,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * elif t == NPY_LONGLONG: f = "q" */ case NPY_LONG: - __pyx_v_f = __pyx_k_l; + __pyx_v_f = ((char *)"l"); break; - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":267 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":267 * elif t == NPY_UINT: f = "I" * elif t == NPY_LONG: f = "l" * elif t == NPY_ULONG: f = "L" # <<<<<<<<<<<<<< @@ -2346,10 +2677,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * elif t == NPY_ULONGLONG: f = "Q" */ case NPY_ULONG: - __pyx_v_f = __pyx_k_L; + __pyx_v_f = ((char *)"L"); break; - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":268 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":268 * elif t == NPY_LONG: f = "l" * elif t == NPY_ULONG: f = "L" * elif t == NPY_LONGLONG: f = "q" # <<<<<<<<<<<<<< @@ -2357,10 +2688,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * elif t == NPY_FLOAT: f = "f" */ case NPY_LONGLONG: - __pyx_v_f = __pyx_k_q; + __pyx_v_f = ((char *)"q"); break; - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":269 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":269 * elif t == NPY_ULONG: f = "L" * elif t == NPY_LONGLONG: f = "q" * elif t == NPY_ULONGLONG: f = "Q" # <<<<<<<<<<<<<< @@ -2368,10 +2699,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * elif t == NPY_DOUBLE: f = "d" */ case NPY_ULONGLONG: - __pyx_v_f = __pyx_k_Q; + __pyx_v_f = ((char *)"Q"); break; - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":270 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":270 * elif t == NPY_LONGLONG: f = "q" * elif t == NPY_ULONGLONG: f = "Q" * elif t == NPY_FLOAT: f = "f" # <<<<<<<<<<<<<< @@ -2379,10 +2710,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * elif t == NPY_LONGDOUBLE: f = "g" */ case NPY_FLOAT: - __pyx_v_f = __pyx_k_f; + __pyx_v_f = ((char *)"f"); break; - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":271 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":271 * elif t == NPY_ULONGLONG: f = "Q" * elif t == NPY_FLOAT: f = "f" * elif t == NPY_DOUBLE: f = "d" # <<<<<<<<<<<<<< @@ -2390,10 +2721,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * elif t == NPY_CFLOAT: f = "Zf" */ case NPY_DOUBLE: - __pyx_v_f = __pyx_k_d; + __pyx_v_f = ((char *)"d"); break; - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":272 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":272 * elif t == NPY_FLOAT: f = "f" * elif t == NPY_DOUBLE: f = "d" * elif t == NPY_LONGDOUBLE: f = "g" # <<<<<<<<<<<<<< @@ -2401,10 +2732,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * elif t == NPY_CDOUBLE: f = "Zd" */ case NPY_LONGDOUBLE: - __pyx_v_f = __pyx_k_g; + __pyx_v_f = ((char *)"g"); break; - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":273 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":273 * elif t == NPY_DOUBLE: f = "d" * elif t == NPY_LONGDOUBLE: f = "g" * elif t == NPY_CFLOAT: f = "Zf" # <<<<<<<<<<<<<< @@ -2412,10 +2743,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * elif t == NPY_CLONGDOUBLE: f = "Zg" */ case NPY_CFLOAT: - __pyx_v_f = __pyx_k_Zf; + __pyx_v_f = ((char *)"Zf"); break; - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":274 + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":274 * elif t == NPY_LONGDOUBLE: f = "g" * elif t == NPY_CFLOAT: f = "Zf" * elif t == NPY_CDOUBLE: f = "Zd" # <<<<<<<<<<<<<< @@ -2423,10 +2754,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * elif t == NPY_OBJECT: f = "O" */ case NPY_CDOUBLE: - __pyx_v_f = __pyx_k_Zd; 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if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = PyCFunction_NewEx(&__pyx_mdef_2ot_2lp_3emd_1emd_c, NULL, __pyx_n_s_ot_lp_emd); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 21, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); - if (PyDict_SetItem(__pyx_d, __pyx_n_s_emd_c, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (PyDict_SetItem(__pyx_d, __pyx_n_s_emd_c, __pyx_t_1) < 0) __PYX_ERR(0, 21, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "ot/lp/emd.pyx":1 @@ -4141,17 +4879,17 @@ PyMODINIT_FUNC PyInit_emd(void) * """ * Created on Thu Sep 11 08:42:08 2014 */ - __pyx_t_1 = PyDict_New(); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_t_1 = PyDict_New(); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 1, __pyx_L1_error) __Pyx_GOTREF(__pyx_t_1); - if (PyDict_SetItem(__pyx_d, __pyx_n_s_test, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (PyDict_SetItem(__pyx_d, __pyx_n_s_test, __pyx_t_1) < 0) __PYX_ERR(0, 1, __pyx_L1_error) __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":976 - * arr.base = baseptr + /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":997 + * raise ImportError("numpy.core.umath failed to import") * - * cdef inline object get_array_base(ndarray arr): # <<<<<<<<<<<<<< - * if arr.base is NULL: - * return None + * cdef inline int import_ufunc() except -1: # <<<<<<<<<<<<<< + * try: + * _import_umath() */ /*--- Wrapped vars code ---*/ @@ -4177,6 +4915,7 @@ PyMODINIT_FUNC PyInit_emd(void) } /* --- Runtime support code --- */ +/* Refnanny */ #if CYTHON_REFNANNY static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) { PyObject *m = NULL, *p = NULL; @@ -4193,6 +4932,7 @@ end: } #endif +/* RaiseArgTupleInvalid */ static void __Pyx_RaiseArgtupleInvalid( const char* func_name, int exact, @@ -4218,6 +4958,7 @@ static void __Pyx_RaiseArgtupleInvalid( (num_expected == 1) ? "" : "s", num_found); } +/* RaiseDoubleKeywords */ static void __Pyx_RaiseDoubleKeywordsError( const char* func_name, PyObject* kw_name) @@ -4231,6 +4972,7 @@ static void __Pyx_RaiseDoubleKeywordsError( #endif } +/* ParseKeywords */ static int __Pyx_ParseOptionalKeywords( PyObject *kwds, PyObject **argnames[], @@ -4332,6 +5074,7 @@ bad: return -1; } +/* ArgTypeTest */ static void __Pyx_RaiseArgumentTypeInvalid(const char* name, PyObject *obj, PyTypeObject *type) { PyErr_Format(PyExc_TypeError, "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", @@ -4358,6 +5101,7 @@ static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, in return 0; } +/* BufferFormatCheck */ static CYTHON_INLINE int __Pyx_IsLittleEndian(void) { unsigned int n = 1; return *(unsigned char*)(&n) != 0; @@ -4907,7 +5651,8 @@ static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { __Pyx_ReleaseBuffer(info); } -static PyObject *__Pyx_GetBuiltinName(PyObject *name) { +/* GetBuiltinName */ + static PyObject *__Pyx_GetBuiltinName(PyObject *name) { PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name); if (unlikely(!result)) { PyErr_Format(PyExc_NameError, @@ -4920,9 +5665,10 @@ static PyObject *__Pyx_GetBuiltinName(PyObject *name) { return result; } -static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { +/* GetModuleGlobalName */ + static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { PyObject *result; -#if CYTHON_COMPILING_IN_CPYTHON +#if !CYTHON_AVOID_BORROWED_REFS result = PyDict_GetItem(__pyx_d, name); if (likely(result)) { Py_INCREF(result); @@ -4937,7 +5683,148 @@ static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { return result; } -#if CYTHON_COMPILING_IN_CPYTHON +/* PyCFunctionFastCall */ + #if CYTHON_FAST_PYCCALL +static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) { + PyCFunctionObject *func = (PyCFunctionObject*)func_obj; + PyCFunction meth = PyCFunction_GET_FUNCTION(func); + PyObject *self = PyCFunction_GET_SELF(func); + PyObject *result; + int flags; + assert(PyCFunction_Check(func)); + assert(METH_FASTCALL == PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST)); + assert(nargs >= 0); + assert(nargs == 0 || args != NULL); + /* _PyCFunction_FastCallDict() must not be called with an exception set, + because it may clear it (directly or indirectly) and so the + caller loses its exception */ + assert(!PyErr_Occurred()); + return (*((__Pyx_PyCFunctionFast)meth)) (self, args, nargs, NULL); +} +#endif // CYTHON_FAST_PYCCALL + +/* PyFunctionFastCall */ + #if CYTHON_FAST_PYCALL +#include "frameobject.h" +static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, + PyObject *globals) { + PyFrameObject *f; + PyThreadState *tstate = PyThreadState_GET(); + PyObject **fastlocals; + Py_ssize_t i; + PyObject *result; + assert(globals != NULL); + /* XXX Perhaps we should create a specialized + PyFrame_New() that doesn't take locals, but does + take builtins without sanity checking them. + */ + assert(tstate != NULL); + f = PyFrame_New(tstate, co, globals, NULL); + if (f == NULL) { + return NULL; + } + fastlocals = f->f_localsplus; + for (i = 0; i < na; i++) { + Py_INCREF(*args); + fastlocals[i] = *args++; + } + result = PyEval_EvalFrameEx(f,0); + ++tstate->recursion_depth; + Py_DECREF(f); + --tstate->recursion_depth; + return result; +} +#if 1 || PY_VERSION_HEX < 0x030600B1 +static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs) { + PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); + PyObject *globals = PyFunction_GET_GLOBALS(func); + PyObject *argdefs = PyFunction_GET_DEFAULTS(func); + PyObject *closure; +#if PY_MAJOR_VERSION >= 3 + PyObject *kwdefs; +#endif + PyObject *kwtuple, **k; + PyObject **d; + Py_ssize_t nd; + Py_ssize_t nk; + PyObject *result; + assert(kwargs == NULL || PyDict_Check(kwargs)); + nk = kwargs ? PyDict_Size(kwargs) : 0; + if (Py_EnterRecursiveCall((char*)" while calling a Python object")) { + return NULL; + } + if ( +#if PY_MAJOR_VERSION >= 3 + co->co_kwonlyargcount == 0 && +#endif + likely(kwargs == NULL || nk == 0) && + co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { + if (argdefs == NULL && co->co_argcount == nargs) { + result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); + goto done; + } + else if (nargs == 0 && argdefs != NULL + && co->co_argcount == Py_SIZE(argdefs)) { + /* function called with no arguments, but all parameters have + a default value: use default values as arguments .*/ + args = &PyTuple_GET_ITEM(argdefs, 0); + result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); + goto done; + } + } + if (kwargs != NULL) { + Py_ssize_t pos, i; + kwtuple = PyTuple_New(2 * nk); + if (kwtuple == NULL) { + result = NULL; + goto done; + } + k = &PyTuple_GET_ITEM(kwtuple, 0); + pos = i = 0; + while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { + Py_INCREF(k[i]); + Py_INCREF(k[i+1]); + i += 2; + } + nk = i / 2; + } + else { + kwtuple = NULL; + k = NULL; + } + closure = PyFunction_GET_CLOSURE(func); +#if PY_MAJOR_VERSION >= 3 + kwdefs = PyFunction_GET_KW_DEFAULTS(func); +#endif + if (argdefs != NULL) { + d = &PyTuple_GET_ITEM(argdefs, 0); + nd = Py_SIZE(argdefs); + } + else { + d = NULL; + nd = 0; + } +#if PY_MAJOR_VERSION >= 3 + result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, + args, nargs, + k, (int)nk, + d, (int)nd, kwdefs, closure); +#else + result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, + args, nargs, + k, (int)nk, + d, (int)nd, closure); +#endif + Py_XDECREF(kwtuple); +done: + Py_LeaveRecursiveCall(); + return result; +} +#endif // CPython < 3.6 +#endif // CYTHON_FAST_PYCALL + +/* PyObjectCall */ + #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { PyObject *result; ternaryfunc call = func->ob_type->tp_call; @@ -4956,7 +5843,8 @@ static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg } #endif -#if CYTHON_COMPILING_IN_CPYTHON +/* PyObjectCallMethO */ + #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { PyObject *self, *result; PyCFunction cfunc; @@ -4975,7 +5863,8 @@ static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject } #endif -#if CYTHON_COMPILING_IN_CPYTHON +/* PyObjectCallOneArg */ + #if CYTHON_COMPILING_IN_CPYTHON static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { PyObject *result; PyObject *args = PyTuple_New(1); @@ -4987,6 +5876,11 @@ static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { return result; } static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { +#if CYTHON_FAST_PYCALL + if (PyFunction_Check(func)) { + return __Pyx_PyFunction_FastCall(func, &arg, 1); + } +#endif #ifdef __Pyx_CyFunction_USED if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) { #else @@ -4994,6 +5888,10 @@ static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObjec #endif if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { return __Pyx_PyObject_CallMethO(func, arg); +#if CYTHON_FAST_PYCCALL + } else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) { + return __Pyx_PyCFunction_FastCall(func, &arg, 1); +#endif } } return __Pyx__PyObject_CallOneArg(func, arg); @@ -5009,7 +5907,8 @@ static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObjec } #endif -static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { +/* ExtTypeTest */ + static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { if (unlikely(!type)) { PyErr_SetString(PyExc_SystemError, "Missing type object"); return 0; @@ -5021,15 +5920,16 @@ static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { return 0; } -static void __Pyx_RaiseBufferFallbackError(void) { +/* BufferFallbackError */ + static void __Pyx_RaiseBufferFallbackError(void) { PyErr_SetString(PyExc_ValueError, "Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!"); } -static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) { -#if CYTHON_COMPILING_IN_CPYTHON +/* PyErrFetchRestore */ + #if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { PyObject *tmp_type, *tmp_value, *tmp_tb; - PyThreadState *tstate = PyThreadState_GET(); tmp_type = tstate->curexc_type; tmp_value = tstate->curexc_value; tmp_tb = tstate->curexc_traceback; @@ -5039,27 +5939,22 @@ static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyOb Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); -#else - PyErr_Restore(type, value, tb); -#endif } -static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) { -#if CYTHON_COMPILING_IN_CPYTHON - PyThreadState *tstate = PyThreadState_GET(); +static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { *type = tstate->curexc_type; *value = tstate->curexc_value; *tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; -#else - PyErr_Fetch(type, value, tb); -#endif } +#endif -#if PY_MAJOR_VERSION < 3 +/* RaiseException */ + #if PY_MAJOR_VERSION < 3 static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, CYTHON_UNUSED PyObject *cause) { + __Pyx_PyThreadState_declare Py_XINCREF(type); if (!value || value == Py_None) value = NULL; @@ -5098,6 +5993,7 @@ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, goto raise_error; } } + __Pyx_PyThreadState_assign __Pyx_ErrRestore(type, value, tb); return; raise_error: @@ -5217,22 +6113,121 @@ bad: } #endif -static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { +/* RaiseTooManyValuesToUnpack */ + static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { PyErr_Format(PyExc_ValueError, "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); } -static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { +/* RaiseNeedMoreValuesToUnpack */ + static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { PyErr_Format(PyExc_ValueError, "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", index, (index == 1) ? "" : "s"); } -static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { +/* RaiseNoneIterError */ + static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); } -static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { +/* SaveResetException */ + #if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { + *type = tstate->exc_type; + *value = tstate->exc_value; + *tb = tstate->exc_traceback; + Py_XINCREF(*type); + Py_XINCREF(*value); + Py_XINCREF(*tb); +} +static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { + PyObject *tmp_type, *tmp_value, *tmp_tb; + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = type; + tstate->exc_value = value; + tstate->exc_traceback = tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +} +#endif + +/* PyErrExceptionMatches */ + #if CYTHON_FAST_THREAD_STATE +static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err) { + PyObject *exc_type = tstate->curexc_type; + if (exc_type == err) return 1; + if (unlikely(!exc_type)) return 0; + return PyErr_GivenExceptionMatches(exc_type, err); +} +#endif + +/* GetException */ + #if CYTHON_FAST_THREAD_STATE +static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { +#else +static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) { +#endif + PyObject *local_type, *local_value, *local_tb; +#if CYTHON_FAST_THREAD_STATE + PyObject *tmp_type, *tmp_value, *tmp_tb; + local_type = tstate->curexc_type; + local_value = tstate->curexc_value; + local_tb = tstate->curexc_traceback; + tstate->curexc_type = 0; + tstate->curexc_value = 0; + tstate->curexc_traceback = 0; +#else + PyErr_Fetch(&local_type, &local_value, &local_tb); +#endif + PyErr_NormalizeException(&local_type, &local_value, &local_tb); +#if CYTHON_FAST_THREAD_STATE + if (unlikely(tstate->curexc_type)) +#else + if (unlikely(PyErr_Occurred())) +#endif + goto bad; + #if PY_MAJOR_VERSION >= 3 + if (local_tb) { + if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) + goto bad; + } + #endif + Py_XINCREF(local_tb); + Py_XINCREF(local_type); + Py_XINCREF(local_value); + *type = local_type; + *value = local_value; + *tb = local_tb; +#if CYTHON_FAST_THREAD_STATE + tmp_type = tstate->exc_type; + tmp_value = tstate->exc_value; + tmp_tb = tstate->exc_traceback; + tstate->exc_type = local_type; + tstate->exc_value = local_value; + tstate->exc_traceback = local_tb; + Py_XDECREF(tmp_type); + Py_XDECREF(tmp_value); + Py_XDECREF(tmp_tb); +#else + PyErr_SetExcInfo(local_type, local_value, local_tb); +#endif + return 0; +bad: + *type = 0; + *value = 0; + *tb = 0; + Py_XDECREF(local_type); + Py_XDECREF(local_value); + Py_XDECREF(local_tb); + return -1; +} + +/* Import */ + static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { PyObject *empty_list = 0; PyObject *module = 0; PyObject *global_dict = 0; @@ -5305,7 +6300,8 @@ bad: return module; } -static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { +/* CodeObjectCache */ + static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { int start = 0, mid = 0, end = count - 1; if (end >= 0 && code_line > entries[end].code_line) { return count; @@ -5384,7 +6380,8 @@ static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { Py_INCREF(code_object); } -#include "compile.h" +/* AddTraceback */ + #include "compile.h" #include "frameobject.h" #include "traceback.h" static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( @@ -5457,7 +6454,7 @@ static void __Pyx_AddTraceback(const char *funcname, int c_line, 0 /*PyObject *locals*/ ); if (!py_frame) goto bad; - py_frame->f_lineno = py_line; + __Pyx_PyFrame_SetLineNumber(py_frame, py_line); PyTraceBack_Here(py_frame); bad: Py_XDECREF(py_code); @@ -5485,7 +6482,8 @@ static void __Pyx_ReleaseBuffer(Py_buffer *view) { #endif - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { + /* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { const int neg_one = (int) -1, const_zero = (int) 0; const int is_unsigned = neg_one > const_zero; if (is_unsigned) { @@ -5493,14 +6491,18 @@ static void __Pyx_ReleaseBuffer(Py_buffer *view) { return PyInt_FromLong((long) value); } else if (sizeof(int) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif } } else { if (sizeof(int) <= sizeof(long)) { return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif } } { @@ -5511,7 +6513,8 @@ static void __Pyx_ReleaseBuffer(Py_buffer *view) { } } -#if CYTHON_CCOMPLEX +/* Declarations */ + #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return ::std::complex< float >(x, y); @@ -5530,60 +6533,86 @@ static void __Pyx_ReleaseBuffer(Py_buffer *view) { } #endif -#if CYTHON_CCOMPLEX +/* Arithmetic */ + #if CYTHON_CCOMPLEX #else - static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { return (a.real == b.real) && (a.imag == b.imag); } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex a, __pyx_t_float_complex b) { + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - float denom = b.real * b.real + b.imag * b.imag; - z.real = (a.real * b.real + a.imag * b.imag) / denom; - z.imag = (a.imag * b.real - a.real * b.imag) / denom; - return z; + #if 1 + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + if (b.imag == 0) { + return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); + } else if (fabsf(b.real) >= fabsf(b.imag)) { + if (b.real == 0 && b.imag == 0) { + return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag); + } else { + float r = b.imag / b.real; + float s = 1.0 / (b.real + b.imag * r); + return __pyx_t_float_complex_from_parts( + (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); + } + } else { + float r = b.real / b.imag; + float s = 1.0 / (b.imag + b.real * r); + return __pyx_t_float_complex_from_parts( + (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); + } + } + #else + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + if (b.imag == 0) { + return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); + } else { + float denom = b.real * b.real + b.imag * b.imag; + return __pyx_t_float_complex_from_parts( + (a.real * b.real + a.imag * b.imag) / denom, + (a.imag * b.real - a.real * b.imag) / denom); + } } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex a) { + #endif + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = -a.real; z.imag = -a.imag; return z; } - static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex a) { + static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) { return (a.real == 0) && (a.imag == 0); } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex a) { + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 - static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex z) { + static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrtf(z.real*z.real + z.imag*z.imag); #else return hypotf(z.real, z.imag); #endif } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; float r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { @@ -5601,14 +6630,14 @@ static void __Pyx_ReleaseBuffer(Py_buffer *view) { case 1: return a; case 2: - z = __Pyx_c_prodf(a, a); - return __Pyx_c_prodf(a, a); + z = __Pyx_c_prod_float(a, a); + return __Pyx_c_prod_float(a, a); case 3: - z = __Pyx_c_prodf(a, a); - return __Pyx_c_prodf(z, a); + z = __Pyx_c_prod_float(a, a); + return __Pyx_c_prod_float(z, a); case 4: - z = __Pyx_c_prodf(a, a); - return __Pyx_c_prodf(z, z); + z = __Pyx_c_prod_float(a, a); + return __Pyx_c_prod_float(z, z); } } if (a.imag == 0) { @@ -5618,7 +6647,7 @@ static void __Pyx_ReleaseBuffer(Py_buffer *view) { r = a.real; theta = 0; } else { - r = __Pyx_c_absf(a); + r = __Pyx_c_abs_float(a); theta = atan2f(a.imag, a.real); } lnr = logf(r); @@ -5631,7 +6660,8 @@ static void __Pyx_ReleaseBuffer(Py_buffer *view) { #endif #endif -#if CYTHON_CCOMPLEX +/* Declarations */ + #if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return ::std::complex< double >(x, y); @@ -5650,60 +6680,86 @@ static void __Pyx_ReleaseBuffer(Py_buffer *view) { } #endif -#if CYTHON_CCOMPLEX +/* Arithmetic */ + #if CYTHON_CCOMPLEX #else - static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) { + static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { return (a.real == b.real) && (a.imag == b.imag); } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex a, __pyx_t_double_complex b) { + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex a, __pyx_t_double_complex b) { + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex a, __pyx_t_double_complex b) { + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - double denom = b.real * b.real + b.imag * b.imag; - z.real = (a.real * b.real + a.imag * b.imag) / denom; - z.imag = (a.imag * b.real - a.real * b.imag) / denom; - return z; + #if 1 + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + if (b.imag == 0) { + return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); + } else if (fabs(b.real) >= fabs(b.imag)) { + if (b.real == 0 && b.imag == 0) { + return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag); + } else { + double r = b.imag / b.real; + double s = 1.0 / (b.real + b.imag * r); + return __pyx_t_double_complex_from_parts( + (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); + } + } else { + double r = b.real / b.imag; + double s = 1.0 / (b.imag + b.real * r); + return __pyx_t_double_complex_from_parts( + (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); + } + } + #else + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + if (b.imag == 0) { + return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); + } else { + double denom = b.real * b.real + b.imag * b.imag; + return __pyx_t_double_complex_from_parts( + (a.real * b.real + a.imag * b.imag) / denom, + (a.imag * b.real - a.real * b.imag) / denom); + } } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex a) { + #endif + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = -a.real; z.imag = -a.imag; return z; } - static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex a) { + static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) { return (a.real == 0) && (a.imag == 0); } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex a) { + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 - static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex z) { + static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrt(z.real*z.real + z.imag*z.imag); #else return hypot(z.real, z.imag); #endif } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex a, __pyx_t_double_complex b) { + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; double r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { @@ -5721,14 +6777,14 @@ static void __Pyx_ReleaseBuffer(Py_buffer *view) { case 1: return a; case 2: - z = __Pyx_c_prod(a, a); - return __Pyx_c_prod(a, a); + z = __Pyx_c_prod_double(a, a); + return __Pyx_c_prod_double(a, a); case 3: - z = __Pyx_c_prod(a, a); - return __Pyx_c_prod(z, a); + z = __Pyx_c_prod_double(a, a); + return __Pyx_c_prod_double(z, a); case 4: - z = __Pyx_c_prod(a, a); - return __Pyx_c_prod(z, z); + z = __Pyx_c_prod_double(a, a); + return __Pyx_c_prod_double(z, z); } } if (a.imag == 0) { @@ -5738,7 +6794,7 @@ static void __Pyx_ReleaseBuffer(Py_buffer *view) { r = a.real; theta = 0; } else { - r = __Pyx_c_abs(a); + r = __Pyx_c_abs_double(a); theta = atan2(a.imag, a.real); } lnr = log(r); @@ -5751,7 +6807,8 @@ static void __Pyx_ReleaseBuffer(Py_buffer *view) { #endif #endif -#define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ +/* CIntFromPyVerify */ + #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) #define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) @@ -5772,11 +6829,39 @@ static void __Pyx_ReleaseBuffer(Py_buffer *view) { return (target_type) value;\ } -#if CYTHON_USE_PYLONG_INTERNALS - #include "longintrepr.h" +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value) { + const enum NPY_TYPES neg_one = (enum NPY_TYPES) -1, const_zero = (enum NPY_TYPES) 0; + const int is_unsigned = neg_one > const_zero; + if (is_unsigned) { + if (sizeof(enum NPY_TYPES) < sizeof(long)) { + return PyInt_FromLong((long) value); + } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned long)) { + return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned PY_LONG_LONG)) { + return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif + } + } else { + if (sizeof(enum NPY_TYPES) <= sizeof(long)) { + return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG + } else if (sizeof(enum NPY_TYPES) <= sizeof(PY_LONG_LONG)) { + return PyLong_FromLongLong((PY_LONG_LONG) value); #endif + } + } + { + int one = 1; int little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&value; + return _PyLong_FromByteArray(bytes, sizeof(enum NPY_TYPES), + little, !is_unsigned); + } +} -static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { +/* CIntFromPy */ + static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { const int neg_one = (int) -1, const_zero = (int) 0; const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 @@ -5843,15 +6928,17 @@ static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { #endif if (sizeof(int) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif } } else { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (int) 0; - case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, -(sdigit) digits[0]) + case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0])) case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) case -2: if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { @@ -5911,8 +6998,10 @@ static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { #endif if (sizeof(int) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif } } { @@ -5921,7 +7010,7 @@ static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else int val; - PyObject *v = __Pyx_PyNumber_Int(x); + PyObject *v = __Pyx_PyNumber_IntOrLong(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; @@ -5944,7 +7033,7 @@ static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { } } else { int val; - PyObject *tmp = __Pyx_PyNumber_Int(x); + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); if (!tmp) return (int) -1; val = __Pyx_PyInt_As_int(tmp); Py_DECREF(tmp); @@ -5960,33 +7049,8 @@ raise_neg_overflow: return (int) -1; } -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value) { - const enum NPY_TYPES neg_one = (enum NPY_TYPES) -1, const_zero = (enum NPY_TYPES) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(enum NPY_TYPES) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); - } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); - } - } else { - if (sizeof(enum NPY_TYPES) <= sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(enum NPY_TYPES) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(enum NPY_TYPES), - little, !is_unsigned); - } -} - -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { +/* CIntToPy */ + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { const long neg_one = (long) -1, const_zero = (long) 0; const int is_unsigned = neg_one > const_zero; if (is_unsigned) { @@ -5994,14 +7058,18 @@ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { return PyInt_FromLong((long) value); } else if (sizeof(long) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); +#ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); +#endif } } else { if (sizeof(long) <= sizeof(long)) { return PyInt_FromLong((long) value); +#ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { return PyLong_FromLongLong((PY_LONG_LONG) value); +#endif } } { @@ -6012,7 +7080,8 @@ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { } } -static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { +/* CIntFromPy */ + static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { const long neg_one = (long) -1, const_zero = (long) 0; const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 @@ -6079,15 +7148,17 @@ static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { #endif if (sizeof(long) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) +#ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) +#endif } } else { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (long) 0; - case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, -(sdigit) digits[0]) + case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0])) case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) case -2: if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { @@ -6147,8 +7218,10 @@ static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { #endif if (sizeof(long) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) +#ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) +#endif } } { @@ -6157,7 +7230,7 @@ static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else long val; - PyObject *v = __Pyx_PyNumber_Int(x); + PyObject *v = __Pyx_PyNumber_IntOrLong(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; @@ -6180,7 +7253,7 @@ static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { } } else { long val; - PyObject *tmp = __Pyx_PyNumber_Int(x); + PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); if (!tmp) return (long) -1; val = __Pyx_PyInt_As_long(tmp); Py_DECREF(tmp); @@ -6196,7 +7269,8 @@ raise_neg_overflow: return (long) -1; } -static int __Pyx_check_binary_version(void) { +/* CheckBinaryVersion */ + static int __Pyx_check_binary_version(void) { char ctversion[4], rtversion[4]; PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); @@ -6211,7 +7285,8 @@ static int __Pyx_check_binary_version(void) { return 0; } -#ifndef __PYX_HAVE_RT_ImportModule +/* ModuleImport */ + #ifndef __PYX_HAVE_RT_ImportModule #define __PYX_HAVE_RT_ImportModule static PyObject *__Pyx_ImportModule(const char *name) { PyObject *py_name = 0; @@ -6228,7 +7303,8 @@ bad: } #endif -#ifndef __PYX_HAVE_RT_ImportType +/* TypeImport */ + #ifndef __PYX_HAVE_RT_ImportType #define __PYX_HAVE_RT_ImportType static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict) @@ -6274,14 +7350,14 @@ static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class #endif if (!strict && (size_t)basicsize > size) { PyOS_snprintf(warning, sizeof(warning), - "%s.%s size changed, may indicate binary incompatibility", - module_name, class_name); + "%s.%s size changed, may indicate binary incompatibility. Expected %zd, got %zd", + module_name, class_name, basicsize, size); if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; } else if ((size_t)basicsize != size) { PyErr_Format(PyExc_ValueError, - "%.200s.%.200s has the wrong size, try recompiling", - module_name, class_name); + "%.200s.%.200s has the wrong size, try recompiling. Expected %zd, got %zd", + module_name, class_name, basicsize, size); goto bad; } return (PyTypeObject *)result; @@ -6292,7 +7368,8 @@ bad: } #endif -static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { +/* InitStrings */ + static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { while (t->p) { #if PY_MAJOR_VERSION < 3 if (t->is_unicode) { @@ -6392,8 +7469,10 @@ static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { if (is_true | (x == Py_False) | (x == Py_None)) return is_true; else return PyObject_IsTrue(x); } -static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) { +static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { +#if CYTHON_USE_TYPE_SLOTS PyNumberMethods *m; +#endif const char *name = NULL; PyObject *res = NULL; #if PY_MAJOR_VERSION < 3 @@ -6402,8 +7481,9 @@ static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) { if (PyLong_Check(x)) #endif return __Pyx_NewRef(x); +#if CYTHON_USE_TYPE_SLOTS m = Py_TYPE(x)->tp_as_number; -#if PY_MAJOR_VERSION < 3 + #if PY_MAJOR_VERSION < 3 if (m && m->nb_int) { name = "int"; res = PyNumber_Int(x); @@ -6412,11 +7492,14 @@ static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) { name = "long"; res = PyNumber_Long(x); } -#else + #else if (m && m->nb_int) { name = "int"; res = PyNumber_Long(x); } + #endif +#else + res = PyNumber_Int(x); #endif if (res) { #if PY_MAJOR_VERSION < 3 -- cgit v1.2.3 From dfc82797ae594b4ab2233a22beb41b62dea5fc4e Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 31 Oct 2016 15:07:43 +0100 Subject: update readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index ab7be28..f8b6e85 100644 --- a/README.md +++ b/README.md @@ -63,7 +63,7 @@ The examples folder contain several examples and use case for the library. The f * [2D optimal transport on empirical distributions](https://github.com/rflamary/POT/blob/master/examples/Demo_2D_OT_samples.ipynb) * [1D Wasserstein barycenter](https://github.com/rflamary/POT/blob/master/examples/Demo_1D_barycenter.ipynb) * [OT with user provided regularization](https://github.com/rflamary/POT/blob/master/examples/Demo_Optim_OTreg.ipynb) - +* [Domain adaptation with optimal transport](https://github.com/rflamary/POT/blob/master/examples/Demo_2D_OT_DomainAdaptation.ipynb) ## Acknowledgements -- cgit v1.2.3 From 21952bf0f19cc7e0c9f013cbc8c1342ed1122b8b Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 31 Oct 2016 15:10:38 +0100 Subject: final commit DA classes --- README.md | 2 +- docs/source/examples.rst | 5 +++++ 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index f8b6e85..6ca7e13 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,7 @@ It provides the following solvers: We are also currently working on the following features: - [ ] Image color adaptation demo -- [ ] Scikit-learn inspired classes for domain adaptation +- [x] Scikit-learn inspired classes for domain adaptation - [ ] Mapping estimation as proposed in [8] Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. diff --git a/docs/source/examples.rst b/docs/source/examples.rst index 5d26e0c..ee95f40 100644 --- a/docs/source/examples.rst +++ b/docs/source/examples.rst @@ -22,3 +22,8 @@ OT with user provided regularization ------------------------------------ .. literalinclude:: ../../examples/demo_optim_OTreg.py + +Domain adaptation with optimal transport +---------------------------------------- + +.. literalinclude:: ../../demo_OTDA_classes.py -- cgit v1.2.3 From 66fac72c909a4de2a427309593e30ddc3be26827 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 31 Oct 2016 15:11:43 +0100 Subject: final commit DA classes (2) --- docs/source/examples.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/examples.rst b/docs/source/examples.rst index ee95f40..f5c8dab 100644 --- a/docs/source/examples.rst +++ b/docs/source/examples.rst @@ -26,4 +26,4 @@ OT with user provided regularization Domain adaptation with optimal transport ---------------------------------------- -.. literalinclude:: ../../demo_OTDA_classes.py +.. literalinclude:: ../../examples/demo_OTDA_classes.py -- cgit v1.2.3 From b4af2d3b97294ceaabfadc6209d300e20a196b88 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 31 Oct 2016 15:28:26 +0100 Subject: v0.1.7 --- README.md | 2 +- ot/__init__.py | 2 +- ot/da.py | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 6ca7e13..e5365cb 100644 --- a/README.md +++ b/README.md @@ -55,7 +55,7 @@ Note that for easier access the module is name ot instead of pot. ## Examples -The examples folder contain several examples and use case for the library. The full documentation is available on [Readthedoc](http://pot.readthedocs.io/) +The examples folder contain several examples and use case for the library. The full documentation is available on [Readthedocs](http://pot.readthedocs.io/) Here is a list of the Python notebook if you want a quick look: diff --git a/ot/__init__.py b/ot/__init__.py index cf55711..5cf878c 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -17,7 +17,7 @@ from .da import sinkhorn_lpl1_mm # utils functions from .utils import dist, unif -__version__ = "0.1.6" +__version__ = "0.1.7" __all__ = ["emd", "sinkhorn", "utils", 'datasets', 'bregman', 'lp', 'plot', 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim'] diff --git a/ot/da.py b/ot/da.py index 56963d8..c9b8c7c 100644 --- a/ot/da.py +++ b/ot/da.py @@ -164,7 +164,7 @@ class OTDA(): where k is the index of the sample in xs For the moment only squared euclidean distance is provided but more - metric c can be used in teh future. + metric could be used in the future. """ if direction>0: # >0 then source to target -- cgit v1.2.3 From cc3da98e58d53e57b0f6c0240b8912970fe365c9 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 31 Oct 2016 15:37:28 +0100 Subject: v0.1.7 --- ot/da.py | 2 +- setup.py | 5 ----- 2 files changed, 1 insertion(+), 6 deletions(-) diff --git a/ot/da.py b/ot/da.py index c9b8c7c..3447437 100644 --- a/ot/da.py +++ b/ot/da.py @@ -122,7 +122,7 @@ def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerIter -class OTDA(): +class OTDA(object): """Class for domain adaptation with optimal transport""" def __init__(self,metric='sqeuclidean'): diff --git a/setup.py b/setup.py index 9804422..d9111e0 100755 --- a/setup.py +++ b/setup.py @@ -11,11 +11,6 @@ import os here = path.abspath(path.dirname(__file__)) - - - -#import glob - # dirty but working __version__ = re.search( r'__version__\s*=\s*[\'"]([^\'"]*)[\'"]', # It excludes inline comment too -- cgit v1.2.3 From 3f46a247b174918e24e2e448b21de21bb0037b2c Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 2 Nov 2016 12:04:15 +0100 Subject: add image color adaptation demo --- data/autumn.jpg | Bin 0 -> 621005 bytes data/fallingwater.jpg | Bin 0 -> 382775 bytes data/ocean_day.jpg | Bin 0 -> 76640 bytes data/ocean_sunset.jpg | Bin 0 -> 131739 bytes data/woods.jpg | Bin 0 -> 387260 bytes examples/demo_OTDA_color_images.py | 119 +++++++++++++++++++++++++++++++++++++ 6 files changed, 119 insertions(+) create mode 100644 data/autumn.jpg create mode 100644 data/fallingwater.jpg create mode 100644 data/ocean_day.jpg create mode 100644 data/ocean_sunset.jpg create mode 100644 data/woods.jpg create mode 100644 examples/demo_OTDA_color_images.py diff --git a/data/autumn.jpg b/data/autumn.jpg new file mode 100644 index 0000000..fddce1e Binary files /dev/null and b/data/autumn.jpg differ diff --git a/data/fallingwater.jpg b/data/fallingwater.jpg new file mode 100644 index 0000000..c5a533a Binary files /dev/null and b/data/fallingwater.jpg differ diff --git a/data/ocean_day.jpg b/data/ocean_day.jpg new file mode 100644 index 0000000..bbe5c62 Binary files /dev/null and b/data/ocean_day.jpg differ diff --git a/data/ocean_sunset.jpg b/data/ocean_sunset.jpg new file mode 100644 index 0000000..01c9e5d Binary files /dev/null and b/data/ocean_sunset.jpg differ diff --git a/data/woods.jpg b/data/woods.jpg new file mode 100644 index 0000000..a9a9532 Binary files /dev/null and b/data/woods.jpg differ diff --git a/examples/demo_OTDA_color_images.py b/examples/demo_OTDA_color_images.py new file mode 100644 index 0000000..7a08ea0 --- /dev/null +++ b/examples/demo_OTDA_color_images.py @@ -0,0 +1,119 @@ +# -*- coding: utf-8 -*- +""" +demo of Optimal transport for domain adaptation with image color adaptation as in [6] + +[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. +""" + +import numpy as np +import scipy.ndimage as spi +import matplotlib.pylab as pl +import ot + + +#%% Loading images + +I1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256 +I2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256 + +#%% Plot images + +pl.figure(1) + +pl.subplot(1,2,1) +pl.imshow(I1) +pl.title('Image 1') + +pl.subplot(1,2,2) +pl.imshow(I2) +pl.title('Image 2') + +pl.show() + +#%% Image conversion and dataset generation + +def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0]*I.shape[1],I.shape[2])) + +def mat2im(X,shape): + """Converts back a matrix to an image""" + return X.reshape(shape) + +X1=im2mat(I1) +X2=im2mat(I2) + +# training samples +nb=1000 +idx1=np.random.randint(X1.shape[0],size=(nb,)) +idx2=np.random.randint(X2.shape[0],size=(nb,)) + +xs=X1[idx1,:] +xt=X2[idx2,:] + +#%% domain adaptation between images + +# LP problem +da_emd=ot.da.OTDA() # init class +da_emd.fit(xs,xt) # fit distributions + + +# sinkhorn regularization +lambd=1e-1 +da_entrop=ot.da.OTDA_sinkhorn() +da_entrop.fit(xs,xt,reg=lambd) + + + +#%% prediction between images (using out of sample prediction as in [6]) + +X1t=da_emd.predict(X1) +X2t=da_emd.predict(X2,-1) + + +X1te=da_entrop.predict(X1) +X2te=da_entrop.predict(X2,-1) + + +def minmax(I): + return np.minimum(np.maximum(I,0),1) + +I1t=minmax(mat2im(X1t,I1.shape)) +I2t=minmax(mat2im(X2t,I2.shape)) + +I1te=minmax(mat2im(X1te,I1.shape)) +I2te=minmax(mat2im(X2te,I2.shape)) + +#%% plot all images + +pl.figure(2,(10,8)) + +pl.subplot(2,3,1) + +pl.imshow(I1) +pl.title('Image 1') + +pl.subplot(2,3,2) +pl.imshow(I1t) +pl.title('Image 1 Adapt') + + +pl.subplot(2,3,3) +pl.imshow(I1te) +pl.title('Image 1 Adapt (reg)') + +pl.subplot(2,3,4) + +pl.imshow(I2) +pl.title('Image 2') + +pl.subplot(2,3,5) +pl.imshow(I2t) +pl.title('Image 2 Adapt') + + +pl.subplot(2,3,6) +pl.imshow(I2te) +pl.title('Image 2 Adapt (reg)') + +pl.show() \ No newline at end of file -- cgit v1.2.3 From 3c4944cf705477c913e5feb8f2483d0fa40ed5e1 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 2 Nov 2016 12:25:05 +0100 Subject: add notebook for color transfer in images --- README.md | 2 + docs/source/examples.rst | 5 + examples/Demo_Image_ColorAdaptation.ipynb | 316 ++++++++++++++++++++++++++++++ examples/demo_OTDA_color_images.py | 26 ++- 4 files changed, 348 insertions(+), 1 deletion(-) create mode 100644 examples/Demo_Image_ColorAdaptation.ipynb diff --git a/README.md b/README.md index e5365cb..621da14 100644 --- a/README.md +++ b/README.md @@ -64,6 +64,8 @@ The examples folder contain several examples and use case for the library. The f * [1D Wasserstein barycenter](https://github.com/rflamary/POT/blob/master/examples/Demo_1D_barycenter.ipynb) * [OT with user provided regularization](https://github.com/rflamary/POT/blob/master/examples/Demo_Optim_OTreg.ipynb) * [Domain adaptation with optimal transport](https://github.com/rflamary/POT/blob/master/examples/Demo_2D_OT_DomainAdaptation.ipynb) +* [Color transfer in images](https://github.com/rflamary/POT/blob/master/examples/Demo_Image_ColorAdaptation.ipynb) + ## Acknowledgements diff --git a/docs/source/examples.rst b/docs/source/examples.rst index f5c8dab..6093299 100644 --- a/docs/source/examples.rst +++ b/docs/source/examples.rst @@ -27,3 +27,8 @@ Domain adaptation with optimal transport ---------------------------------------- .. literalinclude:: ../../examples/demo_OTDA_classes.py + +Color transfer in images +------------------------ + +.. literalinclude:: ../../examples/demo_OTDA_color_images.py diff --git a/examples/Demo_Image_ColorAdaptation.ipynb b/examples/Demo_Image_ColorAdaptation.ipynb new file mode 100644 index 0000000..16e5208 --- /dev/null +++ b/examples/Demo_Image_ColorAdaptation.ipynb @@ -0,0 +1,316 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Demo of OT Domain Adaptation for color transfer in images\n", + "\n", + "The color adaptation problem and the out of sample interpolation has been proposed in [6]:\n", + "\n", + "[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import scipy.ndimage as spi\n", + "import matplotlib.pylab as pl\n", + "import ot\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading images" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "I1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256\n", + "I2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting images" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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mTia0hLGlGUdRPJdlnZfEqBoxOLLbusg0fQycdE60mvwID4Ylse5O2ZcHU+LH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\n41yBpIqq38d4HxfdExL3lJffqz5zh0o6f6MfJwg6URVT45HwJ54UukTc9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QTmuMY+j45F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\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J3EjkYbtwM3Qdo3jh1nsXvllEaJzbyucijKlPqTbJCIUDgUMUGoEURz6MC2t0gm58nCIJ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fhX6o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vmv/VN/ir//kyMtKVkrJRlZY5/KUyHl6EbT0oi8iOlizmAe72NtM+QrmZgkaUrsm5A1ADwx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\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4xz+7HdCiqVAp2wpEYK5NyVtsMyPOf2nBvYT53DtihAdiVAQl50iEL3TOr28hR8uTGCQz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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(1,(10,5))\n", + "\n", + "pl.subplot(1,2,1)\n", + "pl.imshow(I1)\n", + "pl.title('Image 1')\n", + "\n", + "pl.subplot(1,2,2)\n", + "pl.imshow(I2)\n", + "pl.title('Image 2')\n", + "\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Convert image to matrix and dataset generation" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def im2mat(I):\n", + " \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n", + " return I.reshape((I.shape[0]*I.shape[1],I.shape[2]))\n", + "\n", + "def mat2im(X,shape):\n", + " \"\"\"Converts back a matrix to an image\"\"\"\n", + " return X.reshape(shape)\n", + "\n", + "X1=im2mat(I1)\n", + "X2=im2mat(I2)\n", + "\n", + "# training samples\n", + "nb=1000\n", + "idx1=np.random.randint(X1.shape[0],size=(nb,))\n", + "idx2=np.random.randint(X2.shape[0],size=(nb,))\n", + "\n", + "xs=X1[idx1,:]\n", + "xt=X2[idx2,:]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot the images distributions" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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aA6kPJza2iRMnMnz4cJyccpDdwZFFixZhYWFBzRq1GTn8OwAqUYWiRYrRs3dX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\nWxMkGvgdePr8LjOBSCFEmxou5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kkSP//8MwMHDmThggWU6/WMnnTl57Ir5SHgsh+jLefz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m1m\nyXkzSAwJYW1hIcvycokMDydYglvGjibSoKespZVHNmwhWKvl+ekzUSsUALTYbNyyZiUKWSZIo6Gt\ns5OFV17J2++8w5YtW5gyZQqPTDqbod0CZR6vl9vWraPe1olXCPr368ef7ruPyy677EffWw0NDYwe\nNZKKyioiDUG0d9kRSLz51lvk5uby4QcfYLFYmDBhAo88+iijR4/+0XOc4rbbbmPN3z/ky7nTe5z/\noOA4j+89jNvt/tX7lxs2bGDOnAtxu5zER+gpq+2gd3oqz7/wIrNmzYKf+Hvpt7fHqu0knpJdeFuq\nfLWqvq4S6PWA24ms1aNMG4KwdSDsFoTTzrPPPousUNJ5aCOOqkKcdaV0Ht6I19ZBenoaM2fO5OWX\nX8br9RIcHIzXaUcI3wePJElIsgLhtCMr1V9RCdRhyhyBuaODL774wm9GR0cHy1eswJjcF11oJJIk\nodTqiMgcgdPpZPLkyQi3G4VSTVhaf9R6Iy3H87j22mtJSkryz6nRaH71N32AAAECBAjwv4Qsyzzy\nyCPYnXY67Z2n5PXxer1syd2KpctCRkw68cExuN1uxmaMJEijQ5IkokOiyErqT31bA73TfVkNm72L\nquYajlQe40R9BeYuX9Hcr24jqK6uZtOmTYxKzCTKGAZAq62DDruVMJ2R1PA4ogyh5JQe4x97NvDx\nno24vG4SIyI5O3MwCeERbC86whvbVuL2erjppptYdM89KGSZmYNGoFP7/Ik+MfGMTO2DSlagV2to\n6bTy+eF9JIdHgRAoZQWjeqUzvd9gYowhNFrN/gLASWERnNt3AIUNNVgdvsK85u6fEydOxGzv4nhT\nPa/t3kpOVTkbigp4M3sHMdExxCTE0ys1lXvuuQeL1cpZKSm8nZ2N3e3iwkFZTOzdG5VC5t133sHj\n8SDLMgMGDACg0Wolu6qCtUXHyKurpbl7v1ZRQz3vHzjAJ0cOk5KSQkNTE9eOHEakwfeQOzU8jPMz\n+9JktbJ46yY2l55gTXERD23bQmhYGLfcfjs33X47e/bs4Z1330WWZSZPnsy4sWN5KnsPf8/PY2tF\nOdevXkVlRzvjkuK5dthANO0tzJ8/n1deeeVH31t79uyhorKK5BATMUE6InRaPF4Pd9x2G2+9+irT\nYiP4/aB+VB45xKSJE/0KkT8Wh8NBTU0NzTYbzq/t56vvtBFsNH6rf9nS0sKbb77JM888w969e/ml\nEzhTp06lvLyCJ596hosuv4EPP/yQvPyjZ6TK/a/wm1sKiEIFLdXQ4tvQ6TlZhBQah2wKR3jceMoO\ng8eDHNnr9A3jtPPggw/yz88+w+txg8eNvfRw94C+NtnZ2SArWLt2LQ8uXsxHf/87L7/8Mp3lBeiT\nB4Ak4Wyrp6uuDF1MYk+TtEEolEoaGxv959rb2/F6PKj0hh5tldogFCoVM2bMYMaMGTzzzDM0lB4j\nNCyMBx98kAceeODnuW4BAgQIECBAgDPmiiuuID8/n2eeeYaik8VkJPShuvkkLZYWpmVNJsoUydGT\nRZxsqyNI01M5zaTzffff86d7WPrKUrbn78Pt8aBWqHB6XChkBcHBwUydOtXfp6XFt8om5NSKGiHY\nXZaLVqmmtcuC1dmFy+NBIcs0WdqRJYnMhGRmDBnp93eig0PZXODzb3L25zBy1EhMuqAee6YAwvVG\nXF4PN485j1d3rSPCYGJbcQFeIbhy2FjSI317pEYn9+Zve7ewsTDffy5cb0QANpdvi8WqwkMkJSax\ndu1aHnroIV55+WVKmhoobqxHIcvoDQbqG+pRNsl4heDYsWMA7CotpVdYGHefO9mXWQOy4uJ4Yes2\n1q1bx8yZM5kxYwZGg5FHNm/A5fGgU6rocrtQKRQoJYkPDh3CqNWQERtJSblP9CvG1FPJeWBsDJ/m\nFhCclMRrB/YjyzKzZ8/mueee67EM8xSyLLNm7Vruvfde3n/vPTptNiTguhGDuGhAXwAu6N+Hv+7O\nYfGDD3Lttdei1Wq/Mc63IYTgvj/9iTGJsQyOieT1A/koJAmlJNNhsTA5OYFbRwxCkiTm9e/NtWu2\n8NDixazfsOGMxj/F0aNHOW/aVKpragF4Zt8R7h45GI1SwcH6Jj4uKuOa3//+G/2WL1/OlVdcgdPp\nRKNSYHO4mD1rJss+XX7Gr/HnIDIykjvuuOM/MtdvL7BCIGVMRGhNULoHOltwH9kIsgwI8AqUvYcj\n6wy4q4v8vSorK/03pio2FW2vTJAVuJtP0nX8ANrk/mjiUuksyqG9rZHZs89nyJAhHD58GGdDObJS\nhcPagUql/kae0N5Yg8ft7qESGB8fT3R0NJ311ei71zwD2Jpq8bhcjBo1inHjxnHnnXditVrR6/Uo\nulPUAQIECBAgQID/PMuXL+epp57i2LFjJCUlcdttt3HHHXfwwgsvcLz2OE6XE4PWQJTJt2olwhiG\n2+OmprWOhPDT3/XlTVVIksSNv/89AwYMQKVQMXPgRKJNEXR0Wdh0LBtZrUL5Fcn03r17YzAYKGmq\nJtYUgdPjpsXWgUqh5IKscaSEx9LptLOp6ADlLXV4hSCrV0qPrENWUiqbCw4zOD6NwqNHcbqcNJs7\nqGtvJTbElwUTQnCspooYUwghQXr6RMXRYG3H5nRg0GhJizitGqiQZYYkpPBF/gHM9i62lxRyoLIM\nWZJ4add6XB43kiSTGhmOTqfjmWee4cknn8RqtSJJEtdccw0rVqzgkhHDGJOWgtPtZlVuPjtLSilr\nbmb+8GH+oAogIzqKYJ2O1atXM3PmTJRKJRqNmjClkptGjyZKb+BESwsvZe9Go1Sw+PxzMWk1tNns\n3PnJKgD2VlQxIS3FP+Y/846iUigoLi4ms18//nDrrdx4443fuxrIZDKxdOlSXnzxRV544QUWLVrE\nsrxCPskrZERCLAuGDGBan1TWHd/E/v37mTBhwhndX42NjRSXlHDt0Exezcnj8swMrh7UH6Us81lx\nKX/NOcKXJyqY0TsFtULB9JREXvsWBb/vw+v1MufC3xFk72L1nOnsrW1gyd7D/LO4nGCthhqLlVEj\nR/Loo4/26FddXc38+ZcxrX8sj1w4hNAgDV/mn+TOj9fz6KOPsmTJkh9lx38rv72lgJHpyKYoKN4K\n1mak8ETkXgNBHwpeL2h0SCoNrtLDuMtzfcsFJZkPP/wQvF4klQZd+lBkjQ5ZpUYdm4oqKglnQxWW\nQ1vwWNvRJWWgTuxNQdFxwsLCuemG67jpuqtZvXo1zz77DF01lbTlZtNVX425pABzwX4mn3NOj3Ww\nSqWSxYsXY6kppyE3G2t9Na0nCmgu2MfESZMYO3Ys4HsyYjKZAkFVgAABAgQI8Avy2muvMW/ePE6W\nnyQ9Jp3O1k5uvvlmlEolOTk5zLtkHm6PG6fb6Zf1jjJFEh0cxbZje8itKKCiqZrtx/ZQ1lBJXEgU\nKoWK3Lw8RqVkEW3yiQcE64xM7DOCpqYmNm/e7J9fr9dz7733cqSmmM3H93O0rhQJGJbYh9SIOCRJ\nwqDRMbXfCH+fTru9x2vo7F6WV9xYTd/IePLy8lApFHy0dzvZJ4o4WlPFJ/t3UtbcwMT0TAAsji7k\n7tU7Trcbt7fnsjGLw46MxGs7NnKwsoxRCamck9qfILUGhaxgRuZASk6c4PBhX6ZMofBl40wmE1+u\nXcvgxATG9U5DIcvo1GrmDhuCsTv7kVdTS35NLVuKj7O3vAKz3Y7d5aK0tBSA9evX09zSwrUjRhBt\n8C1d6x0RwdwBA2nutPHSZp88eVt3sd60tDTe2neATw7nsb+qmge/3MCR2joyoyKZ0zsdg9XCzTff\nzEMPPXRG94TZbOb/nnkGg1rFtLRUZvdOJ6+ukbtWb6K8W+77zjvuwP01AbTvIigoCFmW2VNdR69g\nIzcPyyJIpfIpFfbvw6i4aL44XuZv39Jlx2g0fs+I32T37t0cLznBg6MGkxZi4vL+vVlz0XTGxkdT\nY7Hy4osvsmv3bkwmU49+H3zwAUoJshJD2XSsFrPdycxBicwflcJbb7zxo2z4b+Y3l7GSNHq8HfVg\ntyCnDEYR51u/LMf3wVO8F9FSg+vYLlCqUcb1RQ6Nx3l0E14BKNXIOmMPmXMAOciEq7kGJAgdPR2F\n1idp7k1Ix5yzEY1Gw9NPPw34nvSo1Woefewxag/vRqvVcd011/DMM8984+nHTTfdhFKp5C+PPELt\nkd1otFquueoqnn322cC+qQABAgQIEOBXgt1u58/3/ZmUmGSGZwz3f0cH64N5/rnnueOOO2huasao\nM2C2WThSlc/gpIHIskxWYn82FWznSEUBgtNy1c2WNrKS+pJTlkdIUE8nNiTI5yzX19f3OH/dddex\ndOlSjtaedq7D9D37Bqm16FRqPBLsLMonLjQcU5Aeh8vFpoJDqBVKXG43JU21BOuCWDByEttKCthS\nmItXCIwaHXMHjyEjKp7cmgoqW5vQqdQAOD1u1hflMb3fIJSygrqONrLLixEI2rps3DFmCrFGXx3O\n8b1683z2BkqbffW0Ghoa+DpOp5OY4J72y7JMjMmIxW7naF0dBXV1KCQJjxAoZRm310tFRQUej8c/\nZqyx5xhx3UFBSWMLO0rK+WR/LgCV5eW4vV5WHivE6xXIksS09DSuHXZaXObj/AKeevJJbr31ViIj\nI795M3yF119/nbbWVl6dMZWY7n1bs/uk8/s163jnQB6JJiOHDh9m1apVXHjhhd87FoDRaGT27Nms\nW72akXHR3/AFk4NN7KmpAyC/sZkvSiq47qabfnDcr3LqmqWGnL5mKcEmbhmSyfqKkwwaNKhHphR8\npYE+/PBDupxunlmbj9srWPzZYZ6aN5ze0SYad5WcUXHr/wV+c4GVMDf4slCAHHN6bawkScgxqXha\nfAXx1H0moDB01wPQh0FnK8rQSNzNtXjtNuTu4EkIL67mk0iyAlVYlD+oApA1WhRh0Sz79FN27NhJ\nUXERaWlp3HXnnVRVVtLa2orJZEKj0WC1Wrn//vv54MMP6ezsZMq557J48WJuuOEGrrvuOlpaWvxt\nAwQIECBAgAC/HvLz82lrb2Po0KE9nN20uFQKygvYuXMnm7dsJiM+HUmSOFySR2lDORqVhg6bGQkJ\nSZIZ23soaVFJdDq72H38IEcqjyEhUd580i9IAVDeXAP46kjdf//9OB0OZsycSVVVFa1NzQCMSutH\nYW0VJY0nyYhO8vet7Wimy+VEkiQ6XC7e2LyGCGMwbZ1W3F4PWqWa5PBoTjTVMjatH6F6PRcOHsWM\nAcP4Incfxxtq2VCUy4bCI5gdXQAoJZk/jJnC1tJj7K0o4UhNBUaNliarBaUso5BlYo0h/qAKuhUK\nY5LIri5FoVAwaNCgb1zXsPBwcqtPMn1Af/+Sv46uLsqbW0gLDaeyo42Fg4YzNDYBs8PBR/mHONpU\nT0lJCa+88oq/ztLBmpOMSjx9DQ6cPIlWqcTj8fLu7oNIwPD4OK4fNgyr08lrOQcoaWnBKwTnpvXc\nRzUlLZV/Hitk9+7d/O53v/ve+2Lzpk0MiYnyB1UAwVoNYxMT2FxWwf0TJvH47v1s2bLljAIrgJdf\nfpmB27eRU9eA2eHA1O0XOj0etlfV0GZ3cNkXGyhraWP4sGE8/PDDZzTuKU4plG6oOMklfU+rZm+o\nPIlGrfYLgnyVF154geNFRTw+fihz+yRjdjpZkp3HXf/Yz+Be4QwdPOg3EVTBbzCwou0kaLujcJcT\nvrph1OXw/+quPYaiz1ifmkn3eTnIiKTWYs3dijapL5JSg6P2BF5rO6h1eJ0Ovo5wOamsq6PeYkMV\nEU9hVS0LFiygoqKC+++/H/A9kTnnnHM5ePgQuqgEFMZIPl/zJavXrGFvdjYDBgz4waciAQIECBAg\nQIBfhlOlUhyunn6Ao9sv0Ov16IOCcDgdDMsYRFx4NBX1VThcLrpcdjweDwMTM+gdkwyAUatnQsYI\nlu1bg1atIbe6CLfHTWJoDE3WNvJqjmMyGln28Sf0lL0siwAAIABJREFUjkxApTOw4pNPsdpthAeZ\nCDMYGd0nE1OQno35B1h7NJuMqCQ6uqzsqyhEQkIIwaT0LJ+YRaeZPhHxxJhCWX5kF602C14hqO9o\n42BlKWF6I8nhkcwdMoaXtn9JbFIiVVVVvoxO7yyGxCejUaq4ZNAY3j2wnZMdLahkBVqVCrvLhUap\npNPp8EmxfyXw7HQ6cHrcXHf99cTFxfF1Hn74YW6++WZe2bKd8X3SsbtcbDhaSJBKRZ3FzMReaYyI\n9wVMoTodVw0ewb2bVhNnMvHq0qWMGjWKgQMG8Lec/TRYLPQKDSWvvo4tpaWoFQr0ajUTEnvR5XGz\nq7qSJdt38NDks7lx5Aj++OU6ACwOZw+bzA7fe2ow9BQX+zYMBgN1Ttc3zrfb7SSHBpNgMmJxOnuU\n2vkhEhIS2L8/h6GDB3Pjuq1cnpmBRqFgeXEprU4Xly1YgE6nY9KkSVx44YWo1eozHhsgNTWVBfPn\n88SyZdR32hgcFcHe2gY+KDzBbbffTlhY2Df6vPnaa1yQnsT8fr4gNEKn5YkJw9hcXcfBimb++c/A\nUsD/XaLToakSAE/5YRS9RyEpFAhHF56qo/5mXmsLXq8Xb2MpODtBocRZU4oucxTOymK6jh/0NfyK\ncqDb2YWjvgp1tE/1z9lci7O5DlVIBKHDxvvHlo/n88ijj3LTTTcRFhbG8uXL2b9/HzHDJ6EJDvfN\n36sPjQe38Ze//IVPP/30P3BhAgQIECBAgAD/Cv369WPAgAEcqzxGmDEUrVqLy+0mrzyfsLAwpkyZ\nwoIrruDVpa+SHJNIeHAYwYZgCsoLcbp8jnvo15b7BWl0qJVqurqDs2O1pRytPYFSoWDMWWexa+cu\nLh5+DuF6n+R6YmgUX+TtxCO8hBt85/onJOMVgv0nCiluqP6G3Udqy7h0yESGJvoK4J6qTdVsNaNW\nqShpqqOkybe0LMoYTO+oWCxdNt544w3mz5+P2uZkdK+ehW6TQiNoslm4eew5uD0e3ti7Da/wUm8x\ns6uqhLFJvZElifK2Zg7WVqLVarnnnnu+9bredNNNtLS0sOTRR3l7VzYAIVodt46YwJJdG4n72hI/\nvVpNiC4IlSxTVlrK6NGjfTW1gM+PHUUARrWG5OAQWrtsPDFpCobuwOPspBTu376JXZWVnJOaioxv\nNdOHuXncP3E8Ro0Gm8vFB0fyiImO9mfDvo/5l1/OJStXsrmsgskpPrXpnJo69tXUcf3QLD7KP0Zb\np+1H17Pq3bs3u7Ozue3WW1myYwcAQwYPYv3Hy5g0adKPGuvbeOvtt4mMjubN11/ntdxCQoODuf+B\nB1i8ePG3tq+tq+PCzJ6ZPa1SQbLJQHDfAWecjftf4DcXWEnhvSAhC1FxENFSibt9JWiNYGsHpRrV\nwLPxmpvxVObjOLwSPC6koGCEsws8Lrpyd4GsBJWGoPTBKMNi8VrbsBXnoFcrsRzdh6aqCIGE0+Lb\nmGjMyOphgy4xlc6K4+zevZvZs2ezadMmdMFh/qAKQFaq0EYmsGHDxu99PVVVVTz++OOsXr0GtVrN\npZdewj333ENISMj39gsQIECAAAEC/OsIIfj444/561//SkV5BUm9knB6nazd9yWhplDMnWaQ4LPP\nPkOr1fLQQw+xfdt2vty/mYiQcBwuJ5ZOCw888ADvvPMOVa11JEcm+Mdv6GjG4XYSaQzhnMzRaJQq\nCmvKyKk4RnNzM/Ghkf6gqqq1nu3HDyNLEha7jfKmOtweD0qFggGJKaTHJPDOtjUIr2DuqHGEG0ws\n37+LRnM7r+9ZixCCYJ2elDCfop8sSegUKi4bfBbxwWFUt7fwWf5+skuLycjIIDg4mP79+7Nl02as\nTjsGtU9MwisEx5vqiDP5fBClQsHwxBRWHj3MmF5prC7OZWfFcXQqNfXWDmIMJpxeL5fPn8/effu+\n9To/8MAD3HXXXURHRRGEhN3tJiJIT6zBxJH6Ws5KTPZnwWrMHTR1WjHbFciSxC0jRzMoJpZai5k3\nD+ZQYzYTqtFy0mJmSnKaP6gCSDQF0zc8koKGRlSyjBfIjIqgvLWNm1atISk4mKqODtxeLxs2bkSl\nUn2rveDbc7R06VLeeuMNdFot/7c3h38UFiMJQY3Zgl6tZkXRCZo7O3nssce+dRnkD5GVlcW27dtp\naWnB7XYTFRX1k+2/12g0PPfccyxZsoSWlhaioqK+N/M1dOhQNp0o4oZBfZC7bai12ihsNfPMRRf9\nJDb9t/CbC6xE60kkkwPf8wvA4wZbB4rETBTRyUhKNbIhFK+lBWFuRlLrEA4bqkhfFsrVWAVeN0EZ\nw1BHxAMgm8LRpQ3GcnQPL730EgUFBezdu5f8o1a8bl/dqx42uHxp4aCgIP9Pr9v1jRS51+1Ep/tu\n3f/q6mqGDx+B2WpFHxlHp9PNM8/+H6tXryY7O/tHpZYDBAgQIECAAGfOY489xuLFi4kJiybUEEL5\n8TKsVisXX3wxOp2O5ORkrrnmGpKSfEvVQkJCyN6bzaeffsrWrVsxGAwMGjQIs9nMgAEDWL9+PTIS\nKZEJmO1WjlQWIksS07PGoVGqaLK0oVVrSAiNoqy0lGCtbylaZUsda49mo1NrGNQrnfZOC+VN9fxz\n/w6GpvTBK7wcKCvG5fYwZcAQXG43b279EqfHQ7/YeIzaIArrTtLR1cnhmjIkfAHS9H6DSQjxPfBN\nCo1gWsYgVuTto76qmtGjRnPHnXewadMm3ti3hXPSMtGqVOyrLqXe0s6M/qflw7tcTpSyzNCEXmRX\nlhJrCMao0TA5OYOBUXEUtdTz3v695OXlkZWV9Y3rDD4/6b4//5n7778fhSTzwr7t9A2PYmvlCf52\neD/DYhM41lTPwbqTqGUZh8fDxZkDGRLrW16YYArm98NH8uCWTcgKCZ1SSafL+Y15rE4Hdq+Ltw8d\nRqdScu/ZZ2FzudhWWkmt2YLD46amw8yBAwc455xzvvPeWLhwIf/46CPOSopjdloSWytPUm+xMm3a\nNOb26UN7ezuhoaFcfvnlDB8+/Efddy6Xiw0bNtDY2MiIESO+dc/TT4VOpyMhIeEH29375z8zY8YM\nbtiYzfy+KbR0OViad5zIyEgWLlz4s9n3a+Q3F1hRX4yoL+ZUYV8AyRCKMr5Pj2ayPgRPeyPC6yFo\n8GTk7g8w2RCK48RBFP/P3nsHRlWt6/+fPX0mM5kkk94LCSmEhCIgRUDUIwp2RRERBctR9FhR9ByP\n5ahguSL23sCugB5RlN4JhATSe+/JZGaSmcnU/ftjQjAotnu8935/zOcvZs/ea69dwqxnve963oDh\nESHp4KxRQkICS5cu5ZprrqG8oQXXgI2+6hKCx0xBIpfjdbvpqyzCEBrKGWecgdvtpra2Foe1j76m\nKnRxqQiCL9pl72jihqW3nvRSVq5cibmvj4QJM5ApfQJsIC6Z4rztvP/++/z1dzrB+PHjx48fP35+\nnc7OTh599FFGxqUxKslnOy6KIgfLD7F582ZaW1t/1mxKqVSyYMECZs+ezUUXXsTq1auHfV/T2UBV\nRz1SqZTwsHD6TWYAvincRZu5e2g/AYFOp5Gy9noO1pcSoFRx1emzUA5GUQrrq9lTVcw3Bb70OY1c\niYhIfn0VPf19AFw0biKpET7hcXrqSD7Ysx2L3YbL47NLjxhMJzxGxKDxxKToEWypK2bFihUAmAds\nrCs5iIgv0pUZEUNCsM8avtvax76GGkZFxlLW6Ss2e3FGLsE/MvqKHmz3o48+OqmwAli+fDnbtm1j\ny+bNNFlMNJh7AchvbSK/tenYdPkQsSfYgUdpdUgFgXqTCblEwp7mJqbHJ5IWEoooiuxoqqepz4IA\nhKo16DVKFDIpCpmUi0aNBGBDSSWfHSnl4X/+kxtvvJHg4OCf9PPQoUOsXbuWOyaP5ayUBACuyslg\n+Q+76GhvZ+PGjSe9xl/j0KFDXHThBbS0tg1tu+jCC/nwo49Qq9W/cOSfy7nnnssnn3zC/cvu5frv\nfBb2s2bO5OVXXz3lMqhOPWEVmQrWXrBZwONCJpPhsZoQXQMIcp84EUURr7EVvB6khqghUQUgDfKZ\nSLiNbUhjRgxtdxnbEASB7OxswBei/fCjjxBUAbjMRrp2foMsMBh3nwnR7SE4dQQymYwnnniC7zZt\nQhkcSm9VEX3NtUhkCpx9vWRmZvKPf/zjpJfyzcaNBIRFDYkqAJVOjyY4lO+++84vrPz48ePHj58/\nge3bt+N2uxkRc9w1TRAERsSksK1wB0eOHGHChAknPf76668nLy8PgFGxqWTFpOIVvRTUl1Ld2cjG\njRt56qmn2LJlC9tK8zBaLcxIHUuP1UxdTysDLicer4dtFfkIgsBpySOHRBVAbuIISprr6B8YQKNQ\nYh6wIiDgcrkJVgfg9HoYER41tL9cKiMnPomtpUeHtlV1t5NsCGdffSV1PZ1D9an2NlUSotFy0aix\nhOsCqehsY33xYbRyJeNiEthcU8bKrf/GK4o43W5EoLa3m8JWX62obXUVGO1Wuu39hKq1hKh92TUr\nV67k448+QiFXcNElF3PvvfcSGho67P7W1dYiFSSIohelREJqaBhlXZ2MDo/ksvRRBMgV7GisY11l\nKRurKsgKP16suKSrE48ocklmOhsrqpBJBB7bs4OEQD0DbjcdNisAz888h7yOVj4uL8VosxOi8QkW\nURQ53NxGQpCeamMvu3bt4oILLqCiooIVK1awY9s2goKCCIuIQCmVMjPpuAuhXCrh/LQknt2TT29v\n788Ksl/DZrNx3uzZhEtFHr90JvFBWrbVtvL0xm9YtmwZzz77LC+88AJr3n8fi8XM9Jlnsnz5clJT\nU3+98f8Al19+OZdeeimNjY1oNBrCw8P/R877f41TT1i1V4FUDvowcDlx93UDAq6SXUhjMxBkclyt\nVWD1rY8STwgVSxRqBIUae10RoseNTB+K29KDq7mSefPmkZiYCPj+03ziiScxmXrRpY3G63TgsfWj\niElGotZQXV5IQUEBL7z4IgHR8QRn5ODo7cHW3ozH5USwmlmwYMEvKn2VSkWvyfrTLzweVKqTpxD6\n8ePHjx8/fv44dXV1ADhdTvrtVmwOG4EaHa7B1H+j0XjSY1taWvj666/Rq7Xo1VrGJWYNfTc5dQxd\n/b18+OGHZGdns3XLFpp7O5mYNIr8pnIcLhcjwmLwil6qunzlYTxeL84TCsyKoojL68GLF5vLV/RX\nRCQjIo7KrlbcHg9eUUT6o+UHTrdryOhBJkjYVF6ITCJFLpWSGRGD3eWkpL2ZPucAC8afToTOF9HK\niozBZLexubKUhJBQ4ntCaDQZCVUHMD4qgdY+MzW9XQQGBmKxWNjfUkd8YDCjI6KpNfawv8V3LxUS\nKY6eXiKDgnlh1fOs+/JLDuTlERwcTEdHB7feeis1tbWEBwQwOjKKFouZ4s4O1DI51+eMRyGVAnBu\nShp15l6OdrbzSfFRciOjabGY2VBRRkpICHNGjkQtV7D2yFGUUilOjxsEkAxeu0f0Mj02ga9rqvjX\n5t1cmp2OTqVkc1Ut5V09XD9mNNXGXlQqFUePHmXqlCmoEDk9KpKenm42H/WJU4fbjUZxXOzaXO6h\nZ/NH+PLLL+nq7ubFK88mJtAnRv+SGkezuZ933n6LyooKtm7ZwsyEaNLVCr757FPWffEFu/fuJSsr\n61da/88gkUiIjo5my5YtmEwmJk+eTEJCwh9qy2az8cMPP+BwOJg+fToRERG/ftBvbHfz5s2Ulpb+\nR9o7kVNPWAF4XGBshSFPfRHR3o+7Ku8nu3otPTgaSlAm+F5Kt6kT0WknODgYa1sN/Q2lyBUKFl9/\nHatWrRo6zmAw8MwzT7NkyRKUIeHINMejXh7nAH1AfX09He3thGTmIggCqpBQVCG+2ZmuA9tpa2vj\nl5h/1VU88uij2M1JqPU++0tzezNWUw/z5s37b9wgP378+PHjx8/JODbI235kJy73cTttucw3kNbp\ndCc9tqWlBVEUcXu9RJ2wrEAQBII0Ourr62lqOp7e1mu1MOBycvmYmegG0+iyo1P4onA7CqmMkuZ6\nMmISMGgDEUWRoqY6+gd8NaY8gE6hpt9p50Bj5dC58mormZQyEkEQMNms5NfXIJNKAYGkkDBqejqQ\nS6XcNOlMNIPGBWq5gvzmOsK1JxbcDUJE5K2Du4a2WV1OEvQhnDsii211FWyuK0cATotJ4NKMHATB\nZ/n+RdkRDrY0EKRSEx6g5YrMXGYm9vPs/h28+OKLuN1unnziCVyD4rHf6SQpOJiLs7J4cvs2FBLZ\nkKg6RoI+iNLuTnY11PN9TTUA46OjuXaMb7yVFOy773eMm8CoMF9kpcbUy8N7dvJiYT4PTz6Dv0+a\nymtHDrN6z0EADBo1fz1tLHmtbYQaDEyfPp3LLr0UvVTC0zOmoh6MGO5vaWPFvjxW7TvM/WdMQCII\ndNvsfFlShYDP2OKP0NjYiF6tGhJVxxgZGoTVVsH3P/zAs7MmMjU2EoAbctNZ9O1uHnroIb744os/\ndM7fy44dO7jyiito7+wEfELrxhtv5MUXX0R6wjP6Jb744guWLL4ek9kCgFwu48EHfa6E/x2DjvXr\n13P99Yvo7TX/4TZ+jVNPWCWPBX04FG0FmRxJwkjE3i5EUxc4faYW0qhU5FHJiB4XrsZSXC2VuI2t\nCBIZ3sFI1gMPPMCNN95Ic3MzMTEx6PX6n5xq9uzZSCRSHD0dw4SVo9tX1Xr06NGkjRxJc08n2tjE\noe/dNisDFvNQWuHJuOuuu/j3N9+Qd2AHAcGhiF4PNnMvV1511a8WrfPjx48fP378/DHGjRuHgIBM\nImNS+nhCtMF0mLvIry1AEARGjhz5k2M2btzIypUrOVJ4xLdB9NLc24G0voQWYweCIBAbEkmnxUig\n0UhRUREyiRS310OzqZMkQ9SQqAIICQgkNiicPocNk72fj/ZuISrIgN3pwGTrJ0St4/zU8RxoqaCy\np5VUQxRTEzJQSGWsPbKT3ZVlFDU1EKjW0GzsQRAGTSvSc9lZW4YoiuRGxw+JKoCRYVEcbKqlzthN\nsuF4fc2q7g4EBKbFpzA5PgW728Wm6lI+LDrIealZ1PR2IRUEPKLI4bYmCtuaCQ0I4NKMXKbFp5DX\n0kCXtZ8xkT5TsDCNlszQCN5/7z2qa2r4S0oa0xOSsLtdrCsr4c1DB4nQarE6XdhcffQ5HOgG17SJ\nokhJVycJ+iBuGDueB7b8QHZEOLdOmoDL42FTVTWbqqqRCgIH29uI0uowqNWkBAUzMsRAhbGH27f/\nQFyAjmZrPwIgl0kRRHgzvxARmL9gAW63m02bNnF15sghUQUwMToSg1rF3sZWblj3PZG6AEo7e5BJ\nBEINhp+tA/VbyM7OxmwfoKTTSFb48Tb2N3WgUasxKOVMiTke1dEq5MxNjuGd/8aart9DV1cXc+ec\nT3awhveunEGUVsVn5U08/tprJCUlndRS/0TKysq48sp5nJcTwUOXjEerkvHGljoefvhhUlNTmT9/\n/h/qX2VlJVdccTlzpoXx5N/G0dhm55yb9/2htn6JU6MM8o8Q1FrobQO3EyEsBm/VEcTeLiRBkQh6\n338SotMGMgUSlRbFiHEglSM6BxDdjsG6VQL33nsvu3btIjMzc5io6u/v5+uvv+aZZ55hy5YtzJk7\nB1tdGf0NVbgsvVibqrHVlHDxxRczYsQIHnzgAWwdrfSUFODo7cHa3kzPkQNERkb+al2DgIAAdu7Y\nwTvvvMP5Z5/JJXPPZ/369axds+aUqXDtx48fP378/E/T39+PiMi45FzC9WHIpDJiQqLIjs9CFEUa\nGhqG7b9q1SrOP/98Sg4fJSogBL1aS7/DTt+AlfLWWgwaPUFKLcXNVThcToqKiggN0DMyIh6lTI7d\n6cD9M5EOt9eDZDDyc/HFFyMJUGF1OQhR67g6ewYhmkBkEhlqmYLz0sYRrNYSoFBx42nnoFcFYLbb\naDb2oFEoCNUGIgDFbY0MDDr5uU44Z3xQCHKJlM+PHCS/uZ5mUy9bqkrZW19NsFrD2SMyCVAoCdVo\nuSJrHBJB4N9VxSCFiUmJhGg0eLxeEoNCMNpsvHxwF42mHgBkUgmTYo+njbm8Hjo7O8kKj2BOWjo6\npRKdQkmHtd+3PwI6uRxRhOfy9lDY0UqlsZs3Cw9R3dtDmsHAC3n78SJS1t3NlppaVuzczafFJaTo\ngzgjNp6Dba38c/cOOgfXVzlFkfPOO4+FN9xAytTJLFu+nK++/hq1WoPV7eaMmFgmRkXx0dq1nH3W\nWchkMhzu4ffIC7g8XhQSCeEaDV63SGpwEHa3h+UPPkhXVxfr1q1jy5YtuE9I4QRfqtrGjRv56quv\nMJuPR1Zmz55NVmYG/9iSz7eVjRR3GHlxfzHry+oZO24cTo/3JwYeA24PEomAzWYbtr2jo4N169bx\nww8/4HL9tIDx70UURZ588knsNjt3jU8jPTQQvUrBktwULsuI48XVz//mtl5//XVCtEpeWzyG5HAt\n4YEqHrw4g5lZEbz0wupfb+AkvPnmm+i1ctauGEtaopaQoJPb5f93OOUiVmJTKVi6B/9dCRIpsrQJ\nSAZd/TzdTXhqC/FGJCHVhSBIpEgC9AhyOer08Xht/diO7ECQq1l6222DUSmfiHn//fe55ZZbsFqH\nr3uKT0igtaGC/poSpFIp86++mpdfegmAhQsXYjab+ec//0nHYAj99NMn89577/6mqt5KpZJFixax\naNGi/9Qt8uPHjx8/fvz8AvX19QCEnJDKF6L1mRJMmDCBu+++m5UrV2IymVh+/3JGRiYxLilrKAVu\nV0U+zb3tzMmchl7tSx3Mspn5umQnEboQzs+a7DOmiMvgo8M/UNfTRrvFSGSgL1rR2NtBq7kbrUrN\n1KlTueeee7ji8stxuVz0uty0W3uJ1hkYcDuJ0AYhPWHCNT00moK2Om6ddi42l4MP8nYiAq2WXsZF\nJ1HW1UphayNjYxMJDfD1r6i9GZfXQ4Q2kK9LCoHjHsu5kcNtua0uB27Ry6yRaZw1GME7LzOTd/Yf\noNdm5+6JM3ju4E42VBQjEQTmpGYSOGjGVW3spqy7E41GQ3ygb3zm9Lh5/sBuugZFUGt/P8ekRJu1\nj1cO+5ZzyAZTxb6pqkQuk/HRxx9z1513suaIb+3TPeMnkj2Y/ndJ6kge2rOT9VUV5IRHUNdr5Nkl\nS4YVtL3jjjvwulw8d8Z0DIPOexW9Rv6xdy+TTz+djYfzmZEQR0SABlEU+XdVDRankzCNiqIu33gz\nUKfj0UcfpaWlhbjY2CGRHBMVxUeffMK0adMA+Pjjj/nrzTdjGhRUGrWKJ1es5Pbbb0cmk/H9D5u5\n/rrrePz7733tarU88sgjzJo1i6lTp/J5eR2XpychCAItfVY+L6/D6nQRGx3NK6+9xhVXXMEDDzzA\ns888M5RaGRUZwZq1H3LmmWf+5D3/LbS1tXHZpZewd99+AC5bt4dpcaG8+JfxBKkUjI0I5tPSI3i9\n3t806d/Q0MCoGC1K+fDUwXFJej48VP+H+gi+v9nRqVpUyt+ekvhHOOWEFX1GhBE5CCERYLXgrTmK\nuyYfefZMBEFAYojF01SG19SBVBeC6Hbh7Tchj/FVlJZotMhCIvFYLdTX1VFZWUl6ejr79+9n0aJF\nKMKiCc48DRCwNVXjaG+iubmFyy+7lPvuu4+4uLhhLjcAt912GzfccAMVFRXo9fohAww/fvz48ePH\nz59DT08Pzz//PP/++t/IFXLmzZvHX//6199kW52ZmQlAh7mLWEP00PYOcycCAqnhSTz99NPEx8cT\nExPDgGOAjJjkofUhgiAQExxOk7GNrdUHCVRpSQ9PJEYfTnRgGF7RM7SvVCpl1sjT+LZkLxuO7iIy\nMASvKNLZ14tEkCBTKrnzzjuZOWMGeEVCNYE4PC4+K9lNtC4E84ANp8eFw+1CObgGTBRF6no7ERFZ\ndzSPxt4upFIZaampKFUqDhUVo5LJUMnkvL5/G/FBBuwuFx39ZnKi4rggPZd9TTVsri4lJSQUm8tF\njbGLGYlpQ/0u7fStE5+Wctw5USqRMDUlmfcO5OHyehkfFcfuplqyRo3iy6IiCjvbEIAaYzczZ8zw\nFRwuLmG2KPJlWQnt/b5olUYmJ0Au56rMbOJ0eo52tvNRWREikG4Io6HPgt3t4rPPP+fCCy8kICCA\nxYsXI7PahkQVQKBSydTYOL6tq2FXcxOXXXopF1544bBn/fWGDUyOiBwSVQAjg0PICDEw4HAw4PVy\ny3ebyQ4PxWgfoNHSxwUjk2nqsxKVPIL3PviAtLQ03nzzTf75z3+ycFQa5yTFYbQ7eK2whDNnzOCs\ns8/mwosuYunSpZwRHcG1k8agkEj4pLKGv/3tb3i9XvIOHKCkqIjE5GTWrl1LZmYmqampQzVLly5d\nyrMvvsiG6kYMKgX57d2EqVQ8M3k8n9Y0cPXVV3P06FFWrFjBzTlpXJIaj9Hu4LmCci6YO4fKqmqi\no6P5rVRWVvLss8/y0do1KEUPb/zlNMZFBrO3pZt/7C7i7s0FvDVnIjsau0hPS/3NmVSZmZm8sOkb\nLHYXgWrf++rxePn8QAsOt4KxOaOZcsZ07rrrLpKSkn5zf7Oysnhm4wZ6LU6CA09e7Pi/y6mXLxYR\njyQiDkGuQAgKRZI2BgasiObOwR1E8HoRnQN4TJ04yveBAPKI47aZotc7NEMjk/m06UsvvYRMo0U7\nMhepWotUHYA2dTTSAB2CSs3nn3/+s6LqGCqVipycHL+o8uPHjx8/fv5kurq6mDBhAiueXEF7cycN\nNU0su3cZs2adhcPh+NXjc3JymHXmLI40FlHbWY/ZZqayrZqSpnISQ2MZHZdBvCGGVatWDY0TvN7j\niVrNxnYO1B5Fq1YTGW7A7h1gc+UBSttr8YjewWUHxxly65PJiBuZgsagJycnhwcefIBN329i0aJF\niF4vyeGRaNUq+h12QjWBmAdsuCUiXgG+KNnvncomAAAgAElEQVRPfW8nZZ3NfFq8h06rGZ1STb2x\nE1EUSdIbkJj6KSkuQURkfHQSZydlMj1hJCa7jY5+M6MiYpgQk8SR9ia215ajksm5cvRpnBabQIPZ\nyBdlBTSZe6ns6WBvcy3gcy38MW6P77NEEHAPRjH27dvHe++9R+6MMxg9fRrvvvsu323axH333UdN\nTzerDuxmf3MjkVodQUoVNreLa7NzyTCEoVUomBwbzwWp6XhFkdLuLrxSCZu+/54LL7yQp556ijlz\n5mAzmXB7vT9x5XN7vSD6+tPV1fWT9DyZXI5bHH4NAJ02K/n5+cRoAkjR6ynt6qHR0sdZyfGYHQ4K\nWju46JJLyMzMRKPRsHrVKmbGx3BVZioGtYrUED3/mDIeEDm0cyd//etfMaiUPHBaLvE6LZEBGm7P\nHUW6IZi777qLPd9uJNnWR8We3Vx99dW8+uqrmEymof6sXr2ajRs3YlVqKO0yceuodNaecwZjwg08\nNjGXMLWKV19+mbMTo7k5J41wjYp0g56nzxiL6Hbzzjvv/Op7f4z8/HzGjR3D52vep89q44lp2ZyV\nGEGwSsH5KdE8MCmTzfUd3PlDPt/WtHLvfff/5rZvuukmPEg5+8ndvPJDDfuquhn/963Ud1nJCZCR\n7TTx8TtvMW7MGEpKSgBf0eRt27bx7bffDkuf/DFLlixBIlVw3q15fLenk6Iqy2/u0+/hlItYCQEn\nOPVogwAB0WH31a9qrwOPy5cS2N0ECMgi45EofTMVHosRT28HgkJFekYmKYMzMVXV1UgCAoe5lQiC\ngDwwGJepB4/HQ0tLy0mFlR8/fvz48ePnf4ZnnnmG5qZmJo2aimbQEKK3z8j+/ftYs2YNixcv/tU2\nPvv8MxYvXsy6desA329+oiGWMQmjADAEBFFUX86ZZ56JTqfjaFMFp6fmAgIH64qJCg3lzHHjkUgk\nvuLCZaXkN5ThFb2EaoPwil4kggS310NBcyUKuZw9e/cyfvz4Yf247rrr8DpdXHP6TDSDBg51XR18\nc/QQ2REJFHU08Pjjj/Pggw+yrnT/sHU4AXIlRvo5NzWb0ZG+CeTvqo5ytKOZXYMOgnKJlGnxaXTZ\n+ijuaKa4owXwOb7F6IOQS6U0m00opFKqejo40u6zgQ9UqhCAH8ormJs9Cokg4HC72VFdTYxOj8vj\n4VBbI26PhxEpKbz/wQfD3OuMRiPPD7ot1/b6igF7RS/BKhUmxwBJ+uG1oFKCQhCBO8ZM4O3yo3z8\n8cdkZWXx9wcfZPaIZDJCDfzX/oPsbW1hSowvbbHDamVXcyMzkuKYEBPFEzt38sknn3DNNdcMtXvZ\n5Zfz3DPPcF5iEvGDRYd3NjfRY7czNzmJ67IyEAZF4uMHDrKtrgnPoHh79NFHefWVl3nzrbepa2hg\n9pjMYX3WKxXEB+rIDAniQFsn6UH6YSmbgiCQFRxEs6WP92ZMRTqYRvrc0RLeeO013njjDRYvXsxL\nL72EXC5n9uzZ6DQaZiTGsiD9eKRQJpGQFRTIjtYOckYMj0oFKuQkBwVSW1vLb+XuO+8kTq3ghuxE\n7tpWyLiI4YYcYyN8z2ZTo5Gnn36a66677je16/F4ePrpp3E4nFS12Xnw0xIkAnhFeGn6aBaMjAPg\nMYeLWV/tZ/n993Hr0tu4/rpraW3zGcMFaNQ88uhj3H333cPajomJ4fvvN7P4+kWcd8v+33ytv5dT\nTliJ/SbgR576lh5AxNPTjKezHux9gIAQEIg0OByPpQd3ewPWfjOCVIbX3A2CT4iljkgZElJZmZkc\nPlLki2YN/lGIoojL1AMSCQql8qRe/s3NzaxevZpt27cTEhzMwoULueqqq/wGFH78+PHjx8+fwLp1\n6wkPjhgSVQDBuhBCAg1s2LDhNwmr4OBgvvzySx5++GEee+wxzsqcil593Ia8s89IYlISy5cvJyw0\njLq6WlpNnQRrArE57ExNzhn6nRcEgVHJKZTX1xMREUFnZydfHN1BiEpHl9WE0+vmq6++GiaqHA4H\nb731FmvXrCUnNmFIVAEkhUUQotHSYOpiREoKl1xyCX9/8EFUcgWzUrKICQymydzDlmrfjH9isM+8\nq9dupbijhcQgAzNTRqKQyjjYVMfW+jK0cqUvkjYoGmbOnMneXbtxuN1U9nQwIS6BWSPSaO/rQy6V\nEh6g5ZmdW9lfX09lZyfRej1VXV24PB5idUE8d3AHEkHgmuwx5LU2M3fuXMrKyobSuxZcfTV7du7k\n2jG5xAfpWbFjFxIk9Nh9RgyVxh7SDccnq8t7upBLJCQE6pkUHs36deuYOnUqLreb81JTCJDLOT02\nhtePFrCloQ6tQkFJdxcGjYaL0lPRK5Wkhobw9ddfDxNW9957L+u//JJlu3aSHRrKgMdD+WCdsktS\nj48DZRIJF49IobCrG5kgsGLqJOQSKWsqqrj0kktISkigsLOHOSMSh9ruGUwd/EtCLANuD3ltnXxW\nWcPe1g48osiEyHAOdnSSEqgbqjkmCAJXp6awvq6B2fFRvPv22xgMBp588kkA0rMyyd+9G68oIhk8\nxuHxcKTXTEhICHkdPSzITB7qg9HuoNJo4tqMjF995wH6+vrYsWsXT56RTYbB977vbelmzo8E296W\nbiSCQMGRI7+rQPGTTz7JSy+9yN8vSOPS8dHUd9tY/FYhoktkftrxNXxBSjmL02N54JuN/PDDZqaO\nCOTT66agV8t4ZVsD99xzDwkJCVx22WXD2p84cSJFxaWUlJRQUFDAwoULf3Pffiun3si9owlvczWi\nrR9vVyveigJAgP5en6iSKZGERoPbhbu5ClloDEhliE47or0PQRUAEimCMoBvv/uO3sFZlNtuuw2v\ncwBL6SFcZiMuSy99pfl47Fa8dhtLFi/+2WK/VVVV5Obmsmr1akobW9h16DALFixg8eLFf7iInB8/\nfvz48ePn5EgHo0QnIiL+rno7ALfffjv6QD35DUW0mzsx2SwUNBTT0ttGc1Mzb73+JqLZQaguBKfb\nhU10+s51wvmPfX7uuec4dOgQ866+iqScdK67YTFHjhzh3HPPHdrX5XJx/vnns3TpUkSvr9jviW15\nRC8Wh4177r0Xt9uNCJw1IouRYVFolSoywmOYkeKLoBw7vrC9EYVUymXZ44jU6QlWa4aiL8EaDaPC\no1BKZUgFic/Vzuvhw6JDeL0iXlFEJpESE6jH7fVSa+xBKZMRqg6g124ndORIMrOzkUiltPSZGBkS\nyrLTz2B0eBQLs8cgA9544w3ANzb69rvvuDh9JONiogkLCGBaYgJt/RYsTicamZx3iws42NZCnamX\ntSVH+aamkilRcb66UXbbMBdFrygiCAJLxuZwy/ixIIGjXZ3MSk5gfnY6Hf1WPF4vXpGfTGq73W4c\nDgciUGUy0dRnGdbujzn22SuKHGjrJFkfyPJxuegUChKTk9nd1MZrBSXUmSwcauvkoZ15BMjknJUQ\ny/TYKPpdLl47WoZGJiVEqeCDsiqa+21MiAj/2fNMjYng8tQEXnnpJbq6utiyZQuzZ59HpbGXv+8v\noMRoorDLyD178+lzubl+yRJ2NnXw5IEiKowW9rV2ccuWAwgSCddee+0vvOXHOSYkPSKkheiYFhvG\nP3YX8Vl5EzWmfj4oqWfFwUrmzZv3u0SV1+vlhdWruG5aPH87J4XYEDVT0wxcMCYCryhy4p/rsfdS\nIxf4+OaxjE3QkxIewDPzMpmZEcbzq/7rpP0fNWrUn1Y0+ZSLWBEUidhYgdhQPny7IEESHI58RO6Q\nY4+r+giuxnKEgEAkMgXq9LEADNQU4TEbcbtcNDc3ExwcTG5uLuvWrWPxkiV0Hdl7rFEkEgnXX38d\n//VfP/+A77//fvoHHISPm4p0sFZEf1sT7777LkuWLGHKlCl/1p3w48ePHz9+TkkuvexSnlr5FP32\nPrSDjnw95m6M5p5hjnC/hZCQELZs3cL8+fPZVe5zpgsICCAxMRFLVy9npkxALvUNt0rbazjSVklM\nTAxFNdWEBwcjlUoRRZEj1VVD44+xY8fy+uuvn/ScH3/8MVu2bOGCUROo7m6jrLWJ7NgEdCrfsoWq\njjbMdhsLFy7kxhtvZMOGDQDEBg5P2YoJ9KVs1fV2MSYqAZPdSqROj3xQXLZaTBS2NXFeWhbjYnyp\ngjaXk7cP+er/WFwOvFoNVmM3+S1NpISEsqmqjM5BkwkBkEulzJ07d6gPYaGhjNWHcHbS8UG3Qioj\nWhtIdbWvmO+xtLSUH9V8mps+kn6Hk7yWFuxuFzY3vF1UMLT+DOBwVzt72ppwDa7rWn7ffchlMjZU\nVLEgOwuJIJATGc7munokgsD2+kY21dQDEKhQYHE6+dcJdUBXrlxJU1MTSqkE66A1eaBCTp/TxScV\nVdw02pfmaHY4WFXgc0r0Ap9V11BtNnP32FxG6LQo5HJWrFjBvx57jHWVdQAkBGpZccYEAuQy1pZX\n4wVWT5vEKIPvuTT09XPTtj38u76RK0ckI5dI8Ioi71VUoZZJOS0iFAnwYXktCfHx2AcGAIgMD+eg\npZ8fNu8GIC4mhvUbNvDRhx+iVcr5d20zn1T4SgJEBKhwOF0UFBRwzjnnnPSdO4ZWq+WsWbN4t+Ag\n5yVH8fysMSzbfoRlO3z12SQSgSvnzeP1N9781bZ+TH9/P51dPUxKiRu2/apJsby9s5G3yxq4ISsR\ngC67gzfLmoiKiiIjyIVGMXwy5PQUPW/nVf+u8/+nOPWElXsAYkaCw+arZ+Vx+8LaohdZdMowxx5Z\nTApOYxtivwlJlC80LYoiHosRQZAgUyiIi/O9ANXV1WzevJkRKSmMzs5m8uTJTJ8+nVGjRg1VaD8R\nURT56quv0ManDIkqgIDIWKxNtaxfv94vrPz48ePHj5//MPfccw9ffvkl+4v3YNCH4vV66TF3k5GR\nwXvvvcc777zD3LlzufHGG39T6ZMxY8ZQWlrK0aNHsVgsJCYmEh8fz8T47CFRBTAyPJHSrjpmz57N\ne+++yxfbthIVGkqP2YzFaiVYo+Paa68lOzub7Ozsk57vq6++IkofQmyQgWB1AE293azdt4MEQxg2\nl4M2Uy+XXHIJ7777LoIgDKXXNZmNjAyLGmqn2exLadtcU0KNsZMuqwWr08H7+fvQKBQIgkCAXMGY\n6OODXY1cwfiYeDbXlCMCDz/8MDExMVy7cCFrCw8Rpglg0ZjxBKnUFLa3sr2uZlh0IC0tjbrK4YPe\nAbeblj4L8wZt2UeMGAFAVU8PEzW+FDCpREJ6eCh5LS0EKpVYBqNIs1NSmBgTjdFu57OycgY8bu6f\nOBGADTU1dAJb6xqoMJqI0wVQYTRhdbnwiCLTomI4JzEBh9vDJxUV9LlcvPjCC7zx+utccOGFLFmy\nhLVr1uB0uzkzPpbzUgb3La+iqKuH7xsaKeruITVIz4H2dgCWjs5idJiBil4TbxSXcefO3ZgdThJq\narjjzjtpbWvjlVde8dUx9Xj5rLKWSnM/zWYLY8MMQ6IKIEGnZWZMFFuaW7li01bGhhko6zXRYrVx\n/2nZaOQyvqxuBOCi+AjCNCq2NXdQ12vEJUhYs2YNqampjBs3DqlUylXz5jE/K4FFo5Mo77agUchI\nC9Zy0Zd7Wb9+/U+EVXd3Ny+//DLbtmxBq9Ny1fyrufLKK3lu1SrOmDqVmZ/uYFq0gc4BXxR2/vz5\nPPXUU8TExJz03T0ZWq2WyIgw9lQZuWT88bTCII3PGfCePSV8UtNGrEbJlpYe1IF6Lpo7l0/WvEv/\ngButyvd3Jooiu6tMpI1M/919+E9w6qUCejzQXA5djaAOBJ1h6CvR+9PiewB4vUh0QXisFgYqCxDt\nVkSHjeuvu46goCD2799PTk4Or7z+OvlVdezOO8Rjjz3G4cOHTyqq/Pjx48ePHz//Oxz77V6xYgUZ\n2enkjsshJSWFsrIyig4dpayglGX3LmPy5MkndRk7EUEQyMnJYdq0ab8qxtLS0pgydSqIYLXYMKgC\nmZ0xkfMzT0clV/Dyyy//5Jj29nZ27tw5FM3xil5azUYcHjeX5U5mbGwy7eZezA4Ha9asYc2aNRw4\ncIC9e/dy//33IyCwubqYss4WzAM2itub2NlQwdw5c/mv557DMCIRu8eNVCJBp1DSN2CnoqudH5ed\ntTodNJiM2FzOoa3l5eWEhoZyxbx5iKLIgtxxjDCEEhoQwFkpqYyJiuadt98eSnW86+67qerp4svy\nYjr6+6g39fJe8WFEiYQbbrgBgJSUFObOmcPnxSUcaGqmx2Yjr7mZz4tLyQwPI0AhRy6VMikmmgvS\nUokICCAjNJSl48fh9npp6utDLZfzl4QElBIJ8+fPZ8zUqfTrg7nwiivIzc1lpMHA9aOyiNPpiA/U\n+a5TFOmrrKS3uJi777qL6dOmYTaZSA3Wc3NuFgmBOtJCglg2cSwauQyNTEqb1UpRTw8Oj5fFmemc\nkxBHpEbD9JhobhmdRZd9gFGGYDxdnfzlL39h9erVLFu2jMMFBZx1wYV06YI5beaZTJ48+SdukHa3\nm16HA1EUiddp2NrSRqd9gFty0skND2FNWQ2HO3uYHhNOr8PJ6sIKJMCESAN43Cy95RYiIyORSqV4\nPB7cHg/t/XZkEoGxUSGkG4abrv2YlpYWxo8dw4rH/4WirYL2Iwe4+uqrWXjNNWRlZVF49Cg3Lr0d\nc2QiIybP4Ouvv2bNmjXDRJXT6WT//v3k5eX9bDHkHyORSPjbHXfx3u4mnt5YRW2nlW2lXSx8o5Do\nqAjWrl1L9ISp9ESnsPTuezhcWMh9992HwyNwxSuH2V/TS1lrH3/7sISdFd3cceddv3i+PwvhVFnH\nIwjCWCCfkaeD0w51hUhScpEERSBazXjKDyBotChGTRmWCij2tqPWaLANFf0VQIBrFlzD66+/hlKp\nZMzYsZTVNqDNHIsglSGKIrb6SpwdTTQ1Nv5iXYBLL72UbzZ9T2jOxGGpgMaKYnbv3u2PWPnx4+f/\neQ4fPsy4ceMAxomiePh/uz//Vzj2u5Sfn8/YsWP/t7tzyuLxeJg9ezY//PADE0eMxzA44Wqx97G/\nOo9//OMfPPTQQ7+73WlTp1FaWDQ8FbCjhiOtlZSXl3PhBRcgMTuYkDDcNGBHdSEJ2SPZvn07AAMD\nA9xyyy28/977eAYngMPCwujq6ho6JlyrZ1JCGt9XHeW2v93O+PHjuW3pUjoH9xEQGBuXQI+1n3pj\n99BxqampHDp0iMDAQGafey6Hdu/l6uzxaOS+8cim6lIOtzUxOy2Djr5+Ctubh9b3yCUSXF4vwUFB\n9A7afutVKu6dOmPY9RxubeHL0iLsdjsqla8A8PPPP8/fH3yQ/sGxVVxsLO+9/z4zZ84cOs5kMpGY\nkIDZcnxNU1Z4GBPjYng735dytzB7FJNjhxcmfmjHTgbcbixO5+C1+1Izj51LJpWiUCiYGRXJlYMR\nss0NjbxfWso/Jk4gPcQXMao3W/jngTwkUinnJcQyPzNt2Hme3J+P0T6ARiaj1Ohbb//yjKnE6o6L\n6j6nk6s3bWX5uBymREXwfnk1n9fWU1xczBOPP86HH32EdzBtMTs7m+LiYp6fOpGskCA+qa7jg/Jq\n7INrxZQSCeEBGuwKJd09Pb5nIPONOTODtRztNvP3iaOYm+wTNp22ARZ9v5/zL7uCaxYuZMn119PQ\n1AT4zB/unpTB3LQYNtW0cd/WQjZt2jQsYrVkyRI2fPIhn155GtGBvhTTr8tauffbIr7//nvOPvts\nfolPP/2Uv922lPZO3zsYFxPNK6+9zvnnn3/SY7xeL8uWLeOFF1bjdPrSLkePyuKjTz4dqh13Ilu3\nbuW6RQtpbPK5VQbqtPzr8Se47bbbfrF/f9bv0ikprARNIGL5XgS1DmmSL9Turj4M5i4EpQZJYAhe\nixHRYSM3N5cDBw6wb98+6urqCA0NJTc3l9jBP+Tm5mbi4uLQjsxBGRo5dD633Yr58O4h5R4eHs6V\nV17JjTfeOMzEoqqqikmnn05ffz/yIAO4nNiM3SxatIi33377pDMJfvz48fP/Cn5h9fP4hdX/DR5+\n+GEeeeQRQrTBTEqdMOy7I/VFhMQYOFp09He3m5+fzxlnnIHX5SFKF0qfw0q31cSyZctYuXIlc+bM\nIW/nXmanTxj6rfd6vWwo2cO8BfOH1ljddNNNvPP224yLSyE+OJSufjN7ayuQS6WclzEGy4CdvXUV\n9DsHCI+IYMSIEezevZuUkHDGRyfiFUXymmtpshi5evzpyCQSzAN2SttacQaoqKmtxW63ExAQwNnJ\n6YyLPl630+pwsDpvO+Cr83RmQiqpIaF0WPvYVFuBzeVEIZUxJyWDvLYmmvtM3DN1BvpBAQWwrrSY\ngrYWBhwO5HL50Pa+vj7y8vJQq9VMnDjxZ01D1q1bx2WXXoooigSpVQSpVNT3mhABnVJBdlgYC3+U\nMtk7MMCD27ajlctZmJ5FqFLF80UFuLxe5qenk6DTcbS7my+qq9Ep5Dw/YwaCILAy7yASAe4/bbiV\n/eqCIxRZzEQrFUyJiaKwsxu5RML4yHDeLy5nwOPBI4ro9XrMZjNLR2dxTsLxtMkD7Z08fvAwz02d\nSFpwEHa3m6u+307u2LEUFRayeFQKY8INlBtNvF5UjVcqw2q1kqzXUWPu49KUeGYnxmAccPBacSX1\nFit/f+ghLr74Yjo7Oxk9ejRjc3NpbW8nXK3kqwunDxs3vllUzYe1bXg8HkYH67ghIxGVVMqaygY2\nNbaTEaqnrNvMFZdfzseffDLs2NCQYC5LDeHOqcfXwomiyOz395Ez9UwCAwNpa2tl3Ljx3HLLLcTH\nH39v9u7dy7Rp0zg3O4ybz0zA7RV54Yd6dlf1kp9/+BfTXMGXglhQUIDBYGDMmDG/Ohb2eDwcOHAA\nu93OxIkTf1P67p/1u3TqrbEa4sfLHQFBAoIE0e3CY+pCotUjMURSWFhIY2Mj06dPJzk5mdbWVjSa\n4/asx2Yafhy+9QzYsRTlAQItLS3ItIF02Rzcv3w5r73+Ovv27iU83OfwkpqaypHCQp5//vlhduvz\n58/3iyo/fvz48ePnT8TpdLLquVWoFWoE4aerIwRBwOM5yTKBX0GlUiGTyugfGKDV0jkU6dHpfGYZ\nd9xxB2d/8w376kvIivQJoIKWKvoH7FxwwQUA9PT08M477zAmNonRMb6SLUGaAJQyOd+VFuD2ekgy\nhKNXa/ikYC/t7e30dvcQpNJwXlrOkN323PQxvHN4FwVNDZyTMYpgTQC13V30ORzk5eWRnJyM+CN7\n7mNUGH21gaSCwLS4FCbHJgIQptGikStYU5yPKIr8u6aMtOBQWvoFPjxymNlp6QSp1RS2tZLf2vyz\n4xmdTsesWbN+9t5ZrVZeffVV7r//fgJVKhK1OmrMJup7TUyZOpWe7m4a6mrZ19xCqFrDpJhojPYB\nPikrAwFuHz2WxEA9VaZeeh0O7hk3jiyDLxIZrdXi8npZV13N2yUlzElKwuZ2E6iQ/6QfEkFAIkio\nMZmpMVkYHRpCn8PJK4XFCECSXotCKqPc6IvYvVFShlwqJSc0hPJeE68cLUUCfFxVy4Pjc5EIAgJw\n6NAh/pozkgtH+MRIfGAAapmMx/b5DCDarDamRoVxW+6xdUI6UvQ6rvh2J1KplJycnKE+SqVSojSq\nnzw7AKlEwOl0olPIWDUlB5XMJ16fmJRNncVKN3Leeecdrrnmmp88I6/Xi1Ty0zb77E42bNhASkgg\nI3QqXt29m9deeYWt27czZswYAJ5ftYqUCC0vLxo91Mabi/VMe3wfL7zwwi8aswCEhob+akRs2HVK\npb5Uyv8DnJLCSrR0g92CEDloSGHvA3Mn0sgE5HHHQ72icwBPay0HDhzg9r/9jW83bgRArlBw8003\n8eyzzxIXF0dGZiY1LY0ogsMQJBJsDZWDdR5EAtOyUIf7UgHddhuNRb71Vy+88MLQeWJjY3n66af/\n526AHz9+/Pjx44euri7MFjPxhjgae5owWU0EBfiySqwOK+3mDq7966I/1Pbtt9+ORBS5aPw0lHIF\noihS1FjDQw89xPz58znrrLN47bXXuPvuu6kq8rm3HVuKMHfuXKZNncay+5bhcrmICTIMa/vY5167\nlQhdECEaLSqZnPCAQHrs/cTrDcMG2lKJhFh9MM1mX8qaZcBOWUcrDrebiRMnotPpSElJIb+9icyw\nKJQy3/CwvKsDnVxBn8tJUtBwR8Ekve/zmfEjONLVSru1D68oYnYM8Ga+zx1RIgio5XJmzJo1LFp1\nMkRRZOXKlTz+r39hs9pIDzGwJDsHmUSC2+vlneKjHNi3nzUfruWplSvJP3yYr6uq+KqqCgCNWo1a\nKiMxUA9Am82X/pcRMrzvmSEhfAnsaGpme5OvoLEEX/pfot5Xm6m138rBjg7kKhUKqYzHJ59G/GCa\n38GOTp46dAQBgXKjaaiIrcPj5fnCo3gH5+2zDcGclZDKc4dL2N3WQYfNjnNQqI85oajumHDfZ4Vc\nxoDLzdjw4c/coFaSHBxIS0vLsO2dXV2cHxvOl7XNbG3qYFa8L3vK5HCyrqaF0NBQ0qXeIVEFvvds\nUqSB3XaRRYsW/eyzmHvBhXy54Quuzo3DoPHVSPuiuAWjzcHirATuPy0VQRCwOFzM31TAbbfeyu69\nPlfssrISTk/RDxNmCpmECUk6yktLfvZ8/3/h1BNWzWVg9c0seHvbEU0diKYOEEWEHxUKBPD2+/Zb\nuXIlZVVVqEZmItHqcBu7eekl38LS9PR0QoKDKS8vx5K/EyEoFFd3OzJtIKJHjupH7jsytQZFWCSf\nfPrpMGHlx48fP378+PHR29vLm2++ya5duwgKCmLBggWcffbZf0oWh8FgQKPRIJVKCdLo2VeVR0Rg\nOBKJhHZTB+ER4dx5552/u12z2czWrVuZkJKBcnC9kiAIZMYmUdnexPr167nrrru48cYbmT9/Pnfc\ncQdvvfUWoyLiSQyJwDJgo/BwAbfddl02P6cAACAASURBVBsSQUKnxUSY9njx4Q6Lb3wSqPStfbEM\n2Bhwu0gNjaCjwUxrXy/iYO0m8NU9auszYXU6+a6siKrODkRE/pKWQXiAjuKOVgpqalAqlbxRuJcU\nvQGzc4AGsxEBn0Bq7jMRF3h8KUNzn8/UI0wTwNSYJD6rPEqmIYLSng5itDrUcgWt1n7kKiUrn3rq\nZ+/T1q1bee+99zD29DB5yhTkcjnLly9ndGQYR61WzktKRjZYV0omkTA7KYWiQ/tZdO21VFRW0t3d\nTVFREU6nk4yMDEpLS7n5ppvoGbBjUKkJU/vuT43JRGrwcbe9arMZiSAwJjSU/K4uMjIyqKmo4J/7\nDzAuPAyJIOFQR4cvwiSKTImKGBJVAOPDw9DIpDT3+YTbXxJiOTM+CuOAg/dLq7E4XDw0KZf0EN/9\n+qa2iVeLy7E4ndx888289tprlPaYSAg83mZpj++ZLr3tdp5/7jlKjSYu4Xh6ncXpoqnPOuTweIzo\nqEgsTgdnxkbw4J4j/Lu2BYNaybamDpAruHDWLLZsWI/L60U+eC9FUeSo0ULSqNyTvcI8/MgjfL/p\nO857fx9nJYXSbXOyo64LqSCwNDd56N0KVMpZkhXHPTv30dnZSXh4OMnJKRwu2DXsHfR4RQqb+pk+\n+7fXtvp/kVNPWA2KKuRqMHcNutSIqNRqBhorEaRy3xqrvl7EZl/x3sLCQtSjxyAL8c0eSHU6RLeb\nF196CUQRuT4YWYAOV5+ZwIE+XIOnEgTJT34IBIkEl8uFHz9+/Pjx42c4zc3NTJ48mdbWVrTqINxe\nFx988AH33nsvT51kcP7fQaVScdNNN7F69WrSIkYQqjPQburA7rSjVCnZtm0bBoPh1xs6gWPpgycW\nm3W4XYiA5UeGDGq1mq+/+oqRYTGMj/MNOkMDAtGrA/iq5ABTpkzh0MGDKGRy4oINdPVb2FlVSoBC\nSZA6gHaLiV115UgEgREhEeS31NFt62drbRnjY46vsbI4BgjQaOiTSXB5PSzIPY2owchOpC4Qm8vF\ngFrJ7PPOY/euXSSGp3LfvHncf999uGx2djTUoJHJSQ0Jo93ax8bqUkLVASTpQ6js9RkUqAbXSdnd\nbtqs/cyZO5dbb70VmUyG1+sddj8eeughHnvsMSIDdQQpFXy/aRMiMCo8lKnxcRxt7xoSVR6vlw6b\nlT6nw/fZ7ebNN9/kkUceISMjg4qKCoKCgoiNjSUwMJCXigq5Oi2DKE0AeoWS14qKuC4ra2iN1frq\nak6PjOT69HQeP3yYiooKvF4vUQEamqz9SBCI0WmpM1vwOBxDguQYNWYLNreHMJWK5CAdN+Uct/ZO\n1gdy0+Y91Jr7hoSVQipFqdfz6Usvcdlll9He1sZbm75DQCBKq8ZoH+CtklomjB/P00//f+ydd3hU\nVfrHP3f6TCZlZtJ7SEIKvYbQO0jTFayoCHZ0Lax1FRVF/Omqa1tdK4sFXMBGU1wsNOmdkJCEkgBJ\nSM+kTSYz8/7+GBiMFMWy7q7zeZ7zwNw559xzzr0397xz3vN9/0JJSQkffPAB8YFmxibGUN3Swit7\nCtBodW0C+S5cuJDDxUc4JML1me3I7NSefx0pZXdlLQ6P8OSsWZhMJj744AMe3LiHWzqmoFereH9/\nETvLq/n0jjvOeg8nJSWxdfsOLrzwQj7dsR29Wo3NqKPO0Yruey6CerV3fE7Ob2/74+2MGLGUBxbm\nMn14Im638PzKgxRVNDJ9+vSznvPXpKysjLKyMpKTk33uuL8Gvz/DSmdCldITRa1FRJBjeUhtKY7m\nZgBaC3f5svbr14+LLrqInTt3ora0XbIVpzd+QmCnXmgCvBfIWVmGvSCH7t27s3vfPlwOBy01Vegt\n3j/KnlYnzooyLrn8sn9PX/348ePHj5//Ih544AEqyivJTMpCrzMiIhyvKuYvf/kLl19++a8i8vHk\nk09y/PhxFixYwHcFvZqbm+napSs33nQjzzzzDLrvxJv8IaxWK7179aJwfwHxoRF4PMKWA7kUVXpj\nHc2ZM4eKigqee+457HY75RUVdEju3KYOmykQs8HIoEGDCAkJYfny5ae+s9moqqpi3pbVAERFRuJp\nsFNUW0lqaBTbjh0ir7KEveVeNzetSo2iKDw+ezbFxcW8//Zcn1F1ksQQKysLcnnppZfQaDRtznXV\n5Mk4PW4+LTjlxhUZEMilaZ0RETaWFKNCYVd5KUMTkqlqbqKloY69u/cwatQoAFKSk3nl1VcZMWIE\nOTk5PP7444xKacfw5EQURaHW4eDFDVuoa2kh0RKMSavhy+IiMqw2Pj1YQF2L16hSKwqRJpNXQe+F\nF3js0UepPqFKqFIUPCLYUXhy+2ZfWwO1Wp7Zts33uVtYGJkWC/ds+JZqR4uvbGljE3BqF74lJASV\nw8H6kjIuSk7EZvSKcmw97jUkKxwOJoYnthnHMJOBGLOJ4hOrWfur68ipruX111/nkksuAeDVv/+d\n3r178detOb7d/qFWK3fcdRedOnRgX14eAHP3FfL2vsIT1ziCZStWEBnpdfVrbGzkxuuvZ0hMGJEm\nA3NzD+E+cf+eNEjvueceAHQaDRsq7az6bD0ARoOBZ5991reX72yUlJSwbds27u+dyrUd4yiyNzNq\n8QbeyT3CDZ28/Xa6PbyTe5SunTr5VLCHDx/OK6+8wj13/4n3v/Xeg8FBgcybN4/evXuf7XS/CpWV\nldxw/XV8umQpIkKAycgfb7+Diy+++Fc53+/OsFKsUShqr5+voigQmYzUlIAhAAQ0tljctaVIk50H\nH3zQ9+uKp96O+jt/hNzV1ejConxGFYAuNJLWsqNER0ezPz8ft0pNbc4O9LYwVFodLVXHsQYH/yTZ\nVj9+/Pjx4+d/GRFh0aLF2IKi0eu8LlyKohBhi6Oy7hiLFi2ie/fuuN1uPvnkEz7++GPcbjdjx47l\nsssu+1F7eM6EXq/n/fff5/HHH+fiP1xM7r5cUm2JhBiDqGis4pW/vYLb7eZvf/vbedX73F//yvBh\nw/l81ybcbjctra30jE7FZgqmrKGa1/7+Gi6Xi5dffhmz2Uxlo51Ea7ivfH1LM40OB+3bt+eJJ54g\nNzeXnJwc4uPj6dWrF8XFxWzdupXQ0FD69+/PZZddxkcffUR8iA2tSoPL4yIu0IoglDXZ6dShIzfc\ncANvvvkmNY0NNLU6fbLqAKX1dqIiI9sYVU1NTcy46y7MGi29wqNwelzkVlZQ73RiUGvYVHqEwroq\nqpoaCdTrSbOEsr+2imN1tWjUalS1dVyb4RVaWFt6hHHjxrFt2zY++ugjTHo9Q9ol+Dx7QgwGBibG\ns3x/IWpFYUJ6Kh/syWXL8VK6hIcxKD6WZpeLzw4c4mh9PYmVldx5550MiImmSqWioLaW8e2TSAwO\n4vlNOxgYHU0Hm42k4GCsej0bSkt5a98+rkhNJSEwkKe2b6d7TBgdtGrWHS5jbHIcPSLDOFbfyMK8\ng9Q7ndTU1nJPt87Mzc3nT2s20CcqAofLxYZSr6iHRa+joNbO6O9c93pnK6UNTRjVap7ZtocNZRVk\n9e7NVVdd5cvzyCOPcLy0lGs7tqNbhJW86jrm5RxmyjXXkB4SxJzendGpVLxfWMyu6hr++tfnueWW\nW9rc419++SV19fXc3L8jcWYTV6bFs6eyjiMNTbyy5wBdLEHcmN6OVo+HN/MPk1NjZ86cOWRkZDB4\n8OA2CtVnIj8/n1tvvRWdWkW1w0lJg4PEYBNTO8bz1NYCVh+tJM1i5qtjVZQ7Wvls/gttvLRuueUW\nJk+ezDfffINKpWLIkCEEBAScxxP08xERJowbS+G+3bw6qT2dos0sy6nkqaefory8/Fc55+8vQPD3\nVX9Uat9xRaVGbYtB264HGrOFWbNmMXz4cMLCw2nO3YurtgZxuWg9Xoa4WgEFd1Mj8t2gZyoVRqOR\nHdu3M23qtYSG2lA7GgmUVqbfdBPbt28/zT/Wjx8/fvz4+T1RX1/Pvn37Tgu+63K1olJ9X3ZbQaVS\n43Q6cblcTJw4kUmTJrH0k2V8tuxzrr76aoYPH4HD4fhZbWpoaGDX7l10iEgl0RpLiDGI1NAkUmwJ\nvPHGG1SdiB10LkpKSti/fz8ul4t+/fqxectmBg0bSmOLg94xaaSFxhFqCqJjeCKdwxN5++23qamp\n4eabbya34gj7K47hdLuobLSz5lAONpuNSZMmAZCRkcGkSZPo3dsrz56QkMDEiRMZNGgQarWaDz74\ngJdffpnI1HbEJsTRuWtXDOFWLAmxPDRzJmvWrsVsNnPVVVdhMBhYmreXisYGHK5Wth0rZm95KX+8\n/fY2/Vm4cCFHjh5lVFIK3aNiGNUujTt69SMmMIjyVgfVATqGjx/HSy+9xMBhw6gNMNCtfz9Gjx5N\nkN7ANemdaW+x0d5iY0p6ZwI0Gp5//nmcTidaleo0JTudWo0Aaw4X0zkynECdjrigQKZ16Uiq1ULn\n8DBu69kNtaKwe9cuuoeHMyYxkdzqaiZmpDA6OYH0UAv94qLZWFZGq8eDSaPhQF0dK4qKUAHby8tZ\nVFhIfIiZW7Iy2X6sklHt4rg8M4VUazCDE6K5rUcHnwBFqNHAnOyeDI2NJr+mluL6Bjx4V7hcHg9f\nFZfwaWERDc5WiuwNPLVlN4pajRIWjt0WwaOPPc6qL7/0xfAqLy9n7ttvc01mIldkJpJuC+Ki1Dj+\n2C0Vl9vNrRnJ9IkIpXuYlaezOhMfaGbt2rWn/XDgPBGny6hWU+9spd7poneklW5h3r1kf8xMoXeY\nlX4RobyS3Q2zVsPixYu56KKLMJvN7N+//zQhjJMsWbKETh07kr97Fz3DglmQe4wxH25kSWEpM3q2\nY3y7CLYcr+H9/Uc5Wt+E0Wj0raR9l6CgICZMmMC4ceP+7UYVwLp169iwaTP/uCKN67NjyEoI5vEx\nyfxpcBzz57//q5zzd7diJbXliC3WJ6sqld5gaTTXo5wICqgoChIQwqZNm+nVqxe1NTWIR2jeua1N\nXc7jR3EePwqKCl1YFLrQCJx1NWi1WlJSUnjzzTd58803/6398+PHjx8/fv5TaWlp4Z577uH111+n\npaUFnVbHlGun8Pzzz2MymRg5ciRrvllHmCUatco7Ramtr6CpuYELLriA+fPn8+mnn5Ke0BFrUBgA\ndQ01rFu3lldfffUnCU2cZM+ePQCEm9vuqQo329hfcZCCgoKz7rcqKCjghutvYPUar2teZGQkc+bM\nYerUqVx11VUsXbqU6MC2ZaODbOwoO0B+fj6zZ8/m6NGjfPDBB3x7OBfwBs39+JNPfvSEVKPRMH36\n9B/cwxIaGsryFSu4ZNIk/rFtI+Cd99xwww3ce++9bfIuXrwYjUrF/BzvNonYwGDGpqTRPSKapYV5\n7M3J8blI3nbbbb5yPbt3J8kc5HNJA697WlJAELt37uLa56/liSeeYGfpcbpHeyfkTrebTcdKiY6K\n4tO8QpbkFaIoCv1jo9sYYAFaLe0sIeyvqqZDRjqljU0I0Ok7KnqXZqZS7XDwZs4p10W1oqDTqNl/\nwpgPDzBwqMZOY6uLrt9T4MuwhaBRKWi0OpYfLubWTplcnZ7KVZLCP/LyOdLQ6JWnR0GAuTkFzM0p\n8PXzs5UrGT58+BnHf9WqVbS6XPSOanvOXic+lzQ1k2bxCpWoVSq6W4PZvWPHafUMHjwYnVbLnWt2\nUFTfhEsEo1pNmFGHQaUi03JK7MSoUdMr1MKuQ4d49913eeC++zhWWgrAwAEDeP2NN0g7ESzZ4XBw\n3dRr6R8RzHP9Mjne5GTmpjw2l9dyz+p9PLXZ65o4ODaUSSmR3PL1XnRuJ7fecgtfnQhq/Z/Cnj17\nUKsURqa13c4zOt3GX74q/lXO+bszrGiuw1OwCSUwDHHUQ0M1oIBKhcd9SlTC01QPOj279uXhaW3F\n1C0baWlGnC20VlXgrq3CEJOAJigEV30djqNFOMtLULRa5s+fz6BBg7jxxht/u3768ePHjx8//2Hc\ndtttzH17LqEhcZjDQmhsrmPu3LnU1dXxz3/+kyeffJJ+/fqRd3grQSYbre4WauwVjB8/nmHDhjFu\n3DiCzRZAofBoHiKCJchGiNnKggULfpZhFRfnDexa56jHZjqlIFfnqEdRFGJiYs5Yrr6+nsGDBtFY\nW0/vuHQMGh2Ha8qYNm0awcHBxMbGAlDTXE9k4KkJXnVzPeANuaLX61mwYAGPPfYYW7ZsISwsjCFD\nhrRxy/slGThwIEeOHmXVqlXU1tbSt29fEhMT2+RZuXIly5cvp31IKD3ConG4W1lXUsR7e3eQYrUR\nFhp6VvfLuIQEthw42EYVTkQodTTRNzGBfv36cemll/LBokXsLa/AYjCQU1lNo8vFJwsXcfmll2Lw\nuGl1uTlir29Tt8vjoaypGaPBQHF9PcnB3m0axXX1WAx6tpaUs6+ympPb5R5//HEefeQR3B4PfWyh\n9IuIpLqlhY8PH+LvG/ehUSkcrqunU/ipa1PS0ITLI9x43XW88sorlDQ108ESQm5NLfm1dUQYDQyO\nieSfhYdpF2QmQKvhSH0jtc5W5r377lmNqrq6Ou48IRhRWFNPYvApVcDCGm8/w4z6NmUK65uI75J2\nWl2hoaEY9HqONjZxc6ckOliD2Hy8hnl5RahR+Ka0gvXHq1CrFAZFhpJTY0dnsXLNNdcwMj6cmUM6\nU+Vo5c09Oxg6eBD78vYTHBzM119/TWV1DXdl98YtMPWrHWgUhaey0okw6vnwUClLi8qpa2llxeEK\n4swGDHoVX69eTUVFBWFhYWfs+9koLy/n7bffZvfu3cTFxXHdddfRvn37Hy74I4iNjcXtEfaUNtI5\n+tRY7zhWj1oBt5yj8E/k9+cKqNZASxNSWQwNNYACYTEQaEHxeBC3C1f5YaS+Em1YPCqrdyOeSqNB\nE2RBHWTBXVOJMT4ZQ2wimqAQDDEJGBNTACEwvQs6Wzizn3iizSZYP378+PHj5/dMWVkZc+fOJdya\nSIQtkQBjCOHWBCKs7Vi4cCEHDx4kLCyMZcuWcellk9AHQGxCJM8++wwffvghiqLQ1NREs6ORvKI9\n1DfV0eioJ784h4bmehoaGn5W+/r37++V7C4voLqpFo94OF5fSUHVYcaOHeszvL7P+++/T1lZGf0S\nOhAZaMWsN9IrNp3IIBuzZ88mOzubjh07srWskPJGb73H7FXsLj/MqJGj2hg0qampXHnllYwYMeJX\nM6pOotPpGDNmDFdeeeVpRhXAk3OeJC4whEuSO5AcbKWDNYLJaV1wuFzsLj/OrbfddlYJ/OnTp3PM\nXsfywwU0tDppcDpZdqiA0no7t9xyC4qi8P777/PCiy+iiYqhyAOjL7yQzVu2sH37dhobG7m1W2dG\nJMWzt7KKlQcP0+xyUeNw8F5OLg1OJ1OnTWNdSSl51TUkBAYyf08es9du4e1d+yitb6CiyStEUXT4\nMB6Ph642GzekZ5BpsdA/MpK7O3ehqrmFJEsgnxQcZuOx47g8Hg7V1vPythyiIiJ47rnnWLlyJQk9\nerCi+AhF9Q3oVCpiAkwMjIrg1o5pGNUajjc5vCtyiYlceeWVZx3zd955h+qqKtJDAnltZyGbS6tw\nezzsrajlua1eZcfPikupcrRgd7byZu4BdldWM/3WW2lububgwYO++3zJkiXYGxq4v0d7JqfF0zUs\nhBs7JnFDhyRcIvx5Ww6F9Q3sqanjT5v3UO5owajX0yvSylN9M+gTaWVsYgSvDOxIeXkF77zzDoDP\npdasVbP88HHKmlp4a1AXLkqKJDvSwl/6ZNA/0sLuSjuljQ5KG1soqvUKwLWcEBj5sezatYvM9HQe\ne3gmh1d/zpsvv0CHDpksWrTovOo5GxdccAEJcbFMWZDH1iN2Wt0ePtpVzuNfHGZkR+sPV/AT+P2t\nWLlP7IeKboei0SLFeahCo/DkbUPEg3PfGgA0obGorVFIawuu0gM4y46hj0nAcyLYnMbSdglXa7HR\nfAg8LQ60ITaOHMilvr6eoKAg/Pjx48ePn987OTk5uN1uggLavj+DAkI5Rj4jR47kwIEDAHTs2JF5\n78xj8ODBbfJGRkbidDlpH5dBaIhX6KG2vpp9h/f4FMl+KiqViiVLljB27Fg25G/3Hc/Ozmbu3Lln\nLbdz506CjGa2H8unrN4bgNekNRAWEMyePXtQFIUlS5YwZswY/pV3qt6s3r155913flabf0127dpJ\n1yBrG+PJrNUTZQpEE2blz3/+81nLjhgxgueee4777ruPb0u9qnA6nY7nn3+eYcOGAV7Xxdtuu62N\nCyHAE088QUJQIIE6HVlRkZQ3NrG88CDLCg8CEGAy8d577zFx4kR27NjBwo0b8Zz4IbvZ7ea+7K6k\nWIIREdYeKeXNt94CoNv33DijTCbCDAYKquwowEvbTrkNGvQ6Nqxei16vZ+TIkYwcOZINGzbwhwsv\n5HhFBTurarh17SbUikLfyDBuyEzlwa27mHbppecc0w8//BCNSiGv1rs6NXPtLp8qoEalYvYTT/DY\nrFl8dsTrpqcAOq2WZ599linXXEN9QwMGvZ4p3wnq2z86tM05+kfbeG3vIf7UKZnLU2IREVYeLWfm\n1jyOHDvKHzomtLmmUQEG0mxB7NrldfccMGAAep2O9/Yfo9nlJjkogPhAoy+/oigMjw1lfVkNC0Z0\n4Xizkyu+2EmNqM66qns2brhuGpFqN4su60WoUYfD5eG21XlcN20qo0eP/tmy6Js3b8ZkMpFbcIw+\nf93qOz6ig4X7xsTz2Z7qn1X/mfj9GVahCWA/DqWHEJ13I6EndysoCqBCMRjRJXZCdeI7tDpUag3O\nIweRRjtovMve7qYG1IZTN5q70fsLgkqnx1lVjjkw8DfZqOfHjx8/fvz8J3Jy0uVoaUSvM/mOO1q8\n78+So2UkRqWjoFB08CijRo1m69YtdOrUyZf32LFjBJqCfEYVQEigFWuQ7awb8c+HlJQUcnNz+frr\nrykqKiIzM5OsrKxzBicODQ3F3txAq1ZPz9hUDFodh6qOU1R7HKvVyt///nc2b97M+PHjuffeexER\nMjIy6NOnz68S9PiXIiY6hvLjlW2OuTwealpbmH7JJT+ownjXXXdx1VVX8cUXXwAwatQoQkNDz1kG\nvPfJiuZmXB4PGpWKCanJDIiL4e3dOUhwCDk5OQQGBrJgwQI2bNhAr7BwssLDebcgn07hVlIsXtdA\nRVEYEBfF10fKKG9yUNzQ2OY8ja2t1JxYYUkJCSbCaGBvTR0Nra0sX/EZXbu2DZ6bnZ3NjLvv5r77\n7mN4bCRZ4TaONjSx8EAxG49XEh4RcU5X1E8++YTVq1fTLyqUUfGRVDtamJ9fjNPtISEwgPrAEO6/\n/34+/eQTNm/ZQoRRx8DYMHZW1PLt+vVcnhZLr4gk9lXXM+/tt0jLyASgsLaBrmGnFP4Kar3P08Co\nUN84jI6LYOGhMg42O8mvbTsOzS43xfYmJp54PkNDQ3n4kUd48MEHiQ80Ud7koN7pIlB3ymTIrWkg\nwqRHURQiTXqmd4zngY35VFVVeeX833+f2tpaBgwYwGWXXYbRaOT7HDhwgC3btvP28ExCjd59egaN\nikez2rFkwSY+++wzLv0BQ/Vc5OXlMXLEcDpZ9fxjTBp7Kxv5YF85ZU1OXrwylcYW90+u+1z8/lwB\nA62Q2BUQcHqXLtHqQDyAB3E04q4rRzxupNVJ69F8xOPmscceo1NSAiG4CQ4JwVl8EJe9FhHBVV9H\n8+EC1AFmXE2NtJaXctONN6JWf1/ZyI8fP378+Pl9kp6eTv/+/Tlec4iGZu/7s7G5jpLKAhRFRWps\nZ6yB4VgCw0iO7ohapeG5555rU8fhw4fRqE+f0GvUWsrKyn6RdqpUKoYNG8a0adN+lPFjNpvxiDCw\nXSeSbFFEBdnITszAagzEXmfn1unTWbb4I15+8SWuv/56RITs7Ow29ZaXl/8o1cF/J7fcOp3cmgo2\nHz9Kq8dNvbOFZUX7cbhcTJs27YxlRISysjJqarwrd2FhYUyePJnJkyf/KKMK4LrrrqPR2cqC3P3Y\nW1pwut3sPF5Bsb2eBx54wLeKMfvxx+lsszEtLY2OVisqRcGkbbteoCgKJo2G5JQUVpeVsrasFJfH\nQ4WjmdfyctHodMyaNQtDTCyFHmHIBRewYeNGhg4delq7WltbeebppxkZG8VNmal0DbUyLjGWGV3S\nafV4+OsLL5xRGe8kTzz+ON3CrTzUM4PeEVZGJ0TxZHZn6pyt7K6q5ebp05l48cVs2ryZSJMevUbN\nwvyjHKhtZGqHBG7p3I6eERauyYjnrq7t2LV7Nwadjjlb95NbbUdE2HK8hpd2HSDSqCc6wNDm/EEa\n74rS8sPHWVhwDKfbw/EmBzM35uFwe7j22mtpbGzk2LFj3HvvvSxYsICw1HScHuGejbmUNDpwuj0s\nOlDChwfLuCI1yld3iN77TM6aNYsePXow//VX2bLkQ6ZOnUqPbt3Iz88/bTyaT8SPtejbPs8n62ps\nbDytzPnw/PPPE6JV+NclHbksI5zHBySxa1pPAnUa/m95EQ6n52fVfzZ+f4YVoGh0YAyCQCtKUCi4\nW1GndAedAVBwlR7EsXctjtxv0TTV8o9//IOZM2eybetWyo8fZ++ePbRPbkfDvp3UbV5DQ84OPC0t\nuBsbaDqYx7hxY5k9e/Zv3U0/fvz48ePnP4oPPviA1PbJHDy6k70H1nDg6A5UKgg0hbQxmFQqFQH6\nIDZv3tymvFqtprahGkdLs++Ys9VJVV0Fqu+HU/k3cfjwYSwBgZj1bd2l3OJBo6gYk9aD4e06M759\nD5JCwrn5ppsoPaHItnbtWnp0705ERAShoaEMHDDQ55L1W3PLLbdw880388WRQp7evpYXdm/gQGMd\n7773Lunp6aflX7FiBR0yM4mKisJqtTJ61CgKCwvP+7wZGRnMmzePnJo6Zq7dwL1fr+WTggNMnz6d\nm266CfBKje/LzaWL9ZSrYkZIIF2T2AAAIABJREFUCJtLymlsPSVEdtTeQGF1LbfeeiuTLr2Ut/bv\n58Z1a7ln0yaKXC4+/uQTHn74YXbv3Utp2XE++ugjevbsecZ2HTt2jIqqKnp/T0Gwi82CUauluPjc\nKnPbd+4kO6Kta2WM2Uis2UhaejoJCQl8/Mkn3NMzlfcu6MncUT24qXMSbhEGRLc954AYr5F654wZ\nlLe4mPbldvotXs3ta3bR4HJjb22lusXpy1/c0MTG8hp6Z2Vx7dSpzNlaQJ9Faxn16UY2Vjfy1ttv\n89isWVgtFmJjY0mMi6OqqootW7fy7HPPsbq0iiFLN9Jl8Roe2pJPR6uZGzt49xy6PcL8/BI0isLL\nL7/M9MxY1o/vwbJRnflsTDeOHiwkLS2Ngf37sX37KVfYoKAgAgwG5u4raaNJMHffMVQq1RmN2/Nh\n2+ZNjIgPwqg9tchh0qoZnWTl/Q3H6f9/pyst/hL8/lwBAREPOJtRjKEoYbFIXiU4GlGHJ+A+uh+A\nDh06cN999zFu3DgsFkub8rGxsezZvZuvv/6a/Px8YmNjaWpqoqamhj59+py2fOzHjx8/fvz48bp5\n7dy5k2+++Yb9+/fTrl073nzzTT5f8UUbBTkAp8tx2p6N7t27U1xUzO4D2wmzRKJSFMprjiMiZGZm\n/ru7A0BUVBRNzhZcbjeaE54qrW4XdY5GesQkE3jC4FKrVHSNTuJwbQWLFy9m8ODBjBgxgmCtgQEJ\naXjEw74dOxk4cCB79uwhPj7+394Xh8PBwoUL2bBhAzabjTvuuIMZM2bw1VdfYTKZGDdu3BkDy65Z\ns4YJ48eTGBTMZanpOFwu1q9bz4D+/dmXm3vaPOqHmDx5MmPHjmXZsmU0NTUxdOhQUlJSfN9rtVqs\nISGUnBCoABgdF8/OqioeWbOVfrGROFxuNpZW0KFDB6ZOncqtt97Kgw8+yLp16wgJCWH8+PG+LRsN\nDQ3Mnz+f7du3ExUVxZQpU04T9LDZbGg1GoobGukWdkr44Hizg+bW1h/c4xcVEUGR/XQ3vCqni4mj\nR/PII4/4Vmta3B4MGjUDYmy8tvsQB+1NJIecUrU7VOetZ/To0cyaNYs5c+bwr3/9C0VR2L8/D3t1\nNZO/2sbY+Aicbg/Li4+jUyvU1dayZOlS7r33Xr755hsCAwMZO3YsY8dcwJ7t27gtM4Z2QUZWHa3i\ntttuQ0SYNGkSd911F1d0jiQj3MzaQzV8ebCKe9bnkRoSwMriSvbVNJAcbKSquZW7OiegUXmf40yL\nmalp0byee4zK/N0MHTKYnbt2ExUVxagRw1GLmyWHKihd2sLwOCu7KutZcbiKO+64g4SEhB97u5yR\n6NhY9m07dNrxvVXNBAQGERwS8oPG8E9CRH4XCegOCPGdhJAIAUSV2l1UHfp5/x+dIurEjgIIIElJ\nSdLY2Ch+/Pjx4+fnsW3btpN/W7vLf8D74D8lnXwvbdu27WeO8H83q1atEkAiLLHSJbmvdEnpJ1G2\nBAHkww8/bJP3q6++EkACDAGi1ehEq9FKgNEsgCxcuPAXaY/dbpfa2tofnf/QoUOi0WgkLiRcxmf2\nkYmd+0vX6HYCSN/4dLmiywBfuqxzf9FrdTJnzhy5+uqrJchokqu69JUp3frLlG795YpOfcSg08m9\n9977i/TlfCgrK5P0tDQBJDIwSAL0elGpVPL666//YNlRI0dKTGCQzMrqJ4/36S+P9+kvd3frJRq1\nWp555plfpb3333+/aNVquS4tXV7pP0Ce7J0l6RaLqFUqsVksEhsTI3fffbdUV1efs56DBw9KQlyc\nqBRFEkOCxKTTiVajkUWLFp2W95qrrxazXid/7t5RFo8cIH8b0EsyrCFis1p/cM44a9Ys0ahVcne3\nNFk+boDMH9lHBkSHiUatFpVKJSatRhICjaKAxJgN8s9xveWrSwZImFEnVoNW/jaki6yeNEDmjugu\n7SyB0j4lRdxut9TV1UnvXj0FkHYhgRKs0wggGTaz2IxaCTfp5LKMaMmKDpERI0ac1q6Tz9RrgzrI\n3sv7+9JFSeESGR4ura2tMnBAf7EatfLepZ1l35395cZesWLUqESjKKICGRVnk2kZ0ZJgNkjR5AFt\n0qM924lKQfZMzxJrgF5mzJgh7733ngDy9UXdZP6ITOkbGSxWvUaCdBqJjooSj8dzfjfDGVi6dKkA\n8mB2vNTc0U+q7+grD/SJE0CsZp0khJp+lffSb/5i+Xcln2EFgqKIEpMq6k4DRIlOFkDUKd1FCQoV\nNHpBoxMURR555JGfci39+PHjx8938BtW534v/d4NKxGRp556SlQqtSiKIipFJYqiyIMPPnjGCdbT\nTz8t6pN5Vd68DzzwwM+ejOXk5Mjw4cN9P7Bm9+kj33777Y8qu2jRIjEaDKKAqFVqASQ4KEgiAy1y\nWef+PsMqO95ruGzcuFHS2reX9NAon1F1MiWE2GTw4ME/qy8/hcmTJ4vZYJDrOnWVB7L6yT29sqVb\neISo1WopLi4+Z1mrxSJDY+N9RtXJlBQcIpdffvmv0t7m5ma5cMIEAUSr9o65OSBAli5del71jBo5\nUiICTPJs3+7y/vB+8taQPtInMkxMRsNpRllNTY0M6Of9QV6n8Z4z1GaT9evX/+B5Wlpa5NJLL/WW\nVXvvX5PRKBq1WkbGh8vy8X1k1UX95K1h3cRm0MmAGJt8PKGPBGjUotN4jSWD1vtvfFys5OTkiIjI\njBkzJECnldcHdJKNF/WTdROyZUr7WAHkvQndZMvUAfLxpJ6i06jl//7v/05r19NPPy0BOq3suaxf\nG8PqbwMzBZCOHTLFbDaL2hsLWXRqRQAxalSiOvGsbLmkt7wxJEMAeX9YR59RVXB5P+loDZC+ccFy\nZEZ/mZAWKgP795M777xTkq2Bcnxa/zbpmX4pAsiQQYNk165d53Udz8SsWbNEpVKJRq0Stcrb7uEZ\nwdL6al/Z+mCXX+W99Lt0BcQQAG4X7uI8qKsAoxlPSSHSZEcdmYz7+CEUYwD/mDePRx999LdurR8/\nfvz48fM/zb333svkyZNZunQpHo+HMWPGnDG2EsA999zDlVdeydKlS3G73YwZM4akpKTT8jkcDhYt\nWsS2bduIiIjg6quv9gXr/T4lJSUM6N8fl8NJx8h2qBQVebv3MXTIELZs3UrHjh3P2f5JkyYxbNgw\nPv30U+x2O4MGDeLIkSNceOGFfHVwDzGBVupbmjlcW84fLrqI3r17ExkZSeH3VPdEhPpW5zlFEH4N\nnE4nCxcupF9kNOEmr3tcq8dNiN6IeDzcfvvtzJ0794xugAAR4RFUfk98w+3xUO1sISIi4ldps8Fg\n4JNPP2Xbtm2sW7cOi8XCRRdddF5hbiorK1n5xRfckJFCpMnrsmlQq7kmNZFb125hxowZvPTSS5jN\nXje8kJAQVq9dy7p169i2bRuRkZGMHTuWVatWcddddxEcHMyVV155xgC3tbW1ZGVloVar8Xg8DB48\nGLvdzkN//jPTOyahP+FGmhBo4rLUGF7dc4jc6gY0JhO7tu/gwIED7Nu3j8TERMaOHetTZXx33jwm\nxIXR2ebtt0al4ob0eD45XMbTGwvpGhHM0gMVxMbFceONN57WroiICJpaXZQ1tRD1HcGLg/YmbxDd\n0iKaGhq5JjkKg1rFypIqDjU4uKdPAo+t87raFdY1MyTGSlZEENd/s49LkiOIMun59HA5h+qb+WBk\nJ0SEPRXN6Klk69atHLU3YXe2EqQ7tbeyoLYJs05Nyb6tDBo4gJ27dv8sl8CHH36Ya6+9luXLl/P+\n++9zdP92Vt7R4ddV4/wlrbT/5MTJFStbjPfXDa1WNFrtCWtVEcUYKOqoVFECbYKiiCosSqxW20+w\nj/348ePHz3fxr1id+73kX7H65XC73WK326W4uFiSk70eKUEms2g1GtFqtbJ48eIzlnvooYdEp9HK\n8NReMiqtj4xKy5JRaVliNpjkmmuuOa82eDwesdvt4nK55Msvv5TBgweLyWSShPgEmT17trS0tIiI\n+NyhekYnylVd+sqVnbOlU4R3pWHVqlW++hoaGnxlfi3q6+sFkHHtUuWBrH4ypUNnMag1olIUsRoM\n3n+tFtm+ffsZyz/11FOigFzULkUezeonf+7ZR3qFR4oCv8jKw49pv9PpPO9yhw4dEkDu7poh7w/v\n50vvDO0rakURBSQ2Olry8/PPWL62tlayevUSQKIDzWLW60RRFHnppZfa5Pvqq6/EbDKJTqOWhJBA\nUSmKREdGyp133ilmvU6+uLCvrLqony891Mu7snnhhAlnPfdJjAa9/LFDomy8qF+blBhkEp1OK6E2\nq9x0001SWloqDodDmpqa2pS32+1iCQ6WrEiLfD62p2y8OEteH9RBgnUamZAcJjuv7SNxgXoZHWOT\nHROyJPcP2dI7NEg6hXldcKMjIyXNGihfTOguOVdkyyXJ4aJTKaJSkBSLURZd2kn2/zFbekYHCiA2\no15ig7xueJEmney4tKeUTu0nbw1NF4NaJdP7REvh3b3FEqCXP/3pT+d9Tc/GZZddJv1TQ8TzWj9x\n/C1b1t3bye8K+LM6+l1XQJCY2FhZsGCBZGR6lzo5sXSPSiWapPaiMQXIpEmTftLF8+PHjx8/p/Ab\nVud+L/kNq5+P2+2WZ555RiIjI73uSjqdaNUa6ZXUWYZkZMuA9r0lPChUDAaDVFVVnVZ++LBhYgsI\nlnCzxTdPsJmCJDLQJinJKT+qDR6PR1544QWJjo4WQCwhIfLQQw+ddcLv8Xjkjjvu8P7Yq9H49trM\nmTNHRET+9a9/Sc8ePQQQjUYjV1xxhZSUlPz0QfoBunbpIgnBIXJPr2wJ0eslxmyWO3r1kAf7Zcsf\ne3aX6KBASWvf/owul48++qioFK+rlU6l8hklwI92p/wpfPrpp9Kpo3d/vF6vl2uvvVYqKyt/dHm3\n2y0JcXHSK9wm7w3r6zOsbspMFUDu6ZUm0UEB0r9f3zOWv/3228Wk08qcnh3kkxHZsnBoloyLixRF\nUSQ3N1dERBwOh4SHhUrX8BD55wU95bMLs2Xu8G4SH2yWDhle97mHe6X5jKovLuwrPSMsPvfG2Oho\neeGFF8447k6nU1LatZN2gSZZMz7bZ1S9NaizADJ//nwRESkoKJAJE8aLSqUSQAYOGCCbNm3y1bNi\nxQrR6XSiOuHup1aQ+ECDbLiqt6y7spekhBh9bn+ZwQFyRVKE7/p+8cUXEhfjveeDDToBJLVdO7nh\nhhtOuA6qfG6ED3SLl+LJfeTYVX3k7cFpolIQ5YRbIeA7x7DkEBmeHCL9+2afz+1wTl599VVRFGRU\nZohoTrgF/hrvJUXklMTh/zKKonQHthEUjmIMhIYqpLGWBQsWcO9993Hs2DHEFAgBASi1VahcLm64\n4Qb++Mc/kpGR8Vs3348fP37+a9m+fTs9evQA6CEi238o/++Fk++lbdu20b1799+6Ob8ZLS0tfPzx\nxz5FtsmTJxMeHv7DBb/DzJkzmT17NpGWMELMwdibGiipKiMqJJz0qGQAnC4n3xZu5/XXX+eKK67g\nn//8J6tWraK6upqDBw9yoLAQg1pHYkgUKkWhqO44Dc5munTtwo4dPyzNPHv2bGbOnEmiJYzIgGCq\nmhs4WFPOVVddxT/mzTtruby8PFasWIFGo+HCCy8kISGBNWvWMHToUMJNAaRaQml2ucitKicyNoad\nu3b51Ox+ST7//HPGjh2LzWCkoqmRKZ06EhsU6Pv+UG0d83P2sWXLltMkyVPatSOksZGsqEgO1Nah\nValIt1qYm5vHpVOm8Morr/jy1tfX88EHH1BQUEBqaiqXX365LzbV+bBs2TImTJhAujWYrHAb1Y4W\nviqpIDU9nU1btvxgAOOTzJ8/n8mTJ9PBGkKPUAtHG5tYXVJO70grt3dvz4aSSl7cUcChQ4dOc0+1\nhAQzxGJmSqrXXc3hdrO6tIK38osZNnIk8+fPZ926dYwfP56/D+lCQtCpwNjrS6qYvSWfoUOGsG7t\nGkbHhRFnNvJNSRU5VXayw0IYFhXKlqo6Vh6r4PHHH+ehhx5qc/5p06Yxb+5cUCAp0MSYuHCqW1r5\n+HAZwbZQ7v/znxk9ejSDBg5A3dzAFe3D0KtVLC6s5EhTK5s2b6FDhw6MGjmS9Wu+4aruEbSzGflX\nfhVfFtQwpl0oG47V4nC5ub59NDEmAx8XV7C5wo4C3Hjzzbz66qu0tLSwZMkSDh48SFpaGuPGjUOt\nVjN40CA2fLseq15NgEbN2gld27jh3bqugJUldhxOJ3EBOqZnRiEovJFXxpHGFoYMG8HnK1ee971x\nJoqLi2mf0g6LUcWfhkRR1+xm9hfH4Bd+L/3uDCslrhOKIcBrVZbuJ7NdHF9/9RUPP/ww7733Hg2N\njYjHg0ZvAI8HV6uT2bNn8+CDD/7WXfDjx4+f/0r8htWZ8RtW3r1NQ4YMIT8/H5MxgBanA41Gy0cf\nfciIESNQq9WoVN74VC6XC0VRUJ/Yi+J2uxERGhoaiIyMJDzIRlLUKYnyoxWlHCwtIjulOwatHhFh\nfeFW7poxg3feeccXUFir1tDqdqFSVAyJ74Ze452QuzxuVhfvpFd2FmvXrj1nP062IcYQRNeoU3tC\nCqvK2FFWRGFhIe3atfvR4zJ8+HB2b9rM+JRMVCcmorWOZj7M3cXrb7zB9ddf/6PrOh++/PJLbv/j\n7ezL3cdtPbsTrNf7vqtqaubvO3ayatUqhg0b1qZcqM1GZ5OJofFxbY6/lbOP7DFjmD9/PgC7du1i\nxLBhVFVXYzMFUNXUiM1q5V9ffkmXLl3Oq609unWjqfgwt3dJ843Robp6ntqWw+LFi5k4ceKPrmvJ\nkiXcdeedHDp0CKtBx9D4CCYkR6NRqdhfbefRDTns3LmTLl26ICK0trai1WrRaDRMS41nXHwUh+2N\nPLYjlxpnK6F6HdXOVoKCg7njzjt59NFHWTymFwHfCWC8v6aeO9fsZf369Xz++ee89cYblFdW4nG5\nmBAfzh2Zp+6XV/KK+LzCTklZGWazGREhPz+f9PR0jGoVLW4PerUKh9uDSgG3QLTZRHmTA61Wi9vl\nYtkfumDRaxGg1ePh0uU5DLtwIjfceCMDBgzgxYvaMzTVKyO/u6SeqR/so9UjRBh1lDU5CTdomds/\nk6RAA1eszqHIraayutr3fH6fTZs20adPH14bnspHBZU0tXj454i2IRFmby9i3oEq9OJm6x+6Eqjz\njk9Ni4vuH25n1IV/YPHixT/6Op6N2tpaunTpTMnRI+x/qCtJNgPbjzTS85k98Au/l36XAYLBG7xP\nAqzs3bOHgIAAXnnlFWbOnAmALi4VTXJnNCld0IRG89BDD/Htt9/+xi3248ePHz9+/re4+eabKS46\nQlpMR9KiOpIZ2xUNGsaPH49Op8NsNjNx4kQGDRqETqfDYDAwduxYLrjgAvR6PXq9nlGjRtHS0kK4\nJbRN3Sc/25sbAKior6LV5eLTTz+ltqoagE4xyQxJ6441IIgwU7DPqALQqNREBlgpOxHM91zk5OTQ\n2NhIQkjbNsSHhCIibNy48bzGZcOGDSQGWXwGA0CIwUhYYNCvOh8ZNmwYX3/zNRqNht3lFW2+211R\ngUGvP+OPAAMHDSKnpoZWj8d3rKKpmWK7nQEDBgDerSeXXnIJ+hYn92V04Z7UDtyX0QV9i5PLLr2U\n8/mhv7W1le07d9IjrO0YJQUHEm4OYMOGDefV7wkTJvD5ypUIMCE5hotTY9GcMBjWHK3AZrEQExPD\nnXfeSUhwMHq9nm5dupCW1p7PjpZz76bd3LlpN7XOVvqHW3m+VyfezO5KBB7eeO01AFYVtx3PVcUV\nhAQH0a1bNx577DGOlZYyb948PMB1qW1jmA2LslHf2Mi2bdu4++67sYaEkJ6ejlqBMIOOT0d246ux\nvUgNMhFl1LN4UBc+GdiJJUO60N6kQ4Xw1OZDZC/YTNb7m/jTN/l0thlZv24tGzduJECvZXCKN9aY\n2yPcvbSAtBATX4/vxlfju7FybBfMOg33bC1EASbEhVJrt59TBGLDhg0YNGpGJ1jJigxiY7mdonqH\n7/umVjfLjtSiMxgZE2f1GVUAFr2GEbEWykpKzus6no3777+fkqNHyUoIJMlm+OECP4PfpyrgCcTV\ngk6vR6fTAfDmW2+hCrKiDjoR+E1R0ITHojTWMW/ePPr27fsbttaPHz9+/Pj536G6upply5YRa0vA\npPe6tjW1NNLY0kCA0UxocCjOVicff/wJIATpgvCIhxUrVqDT6Ii1RKNSFPbs3AOAw9lCgOGUq5Wj\nxTuJq2uqp67JTqm9gsGDB/PNN99g0hoID7QQfcIQ0qm1NDqavd4s35ksOjytRP+AW6KIUFBQAECD\n04HFeMpNr9HZAniDy54PVouVeoejzTGPeGh0Os+7rvMlPDycGTNm8Je//IUah4O4wECK7HZyKip5\n+OGHzxjsd+bMmfRdsYI39ubQLczruri1ooLk5GSuvvpqADZv3kx+QQE3Jqdj0XlXwiw6PWMiYng9\nP48tW7bQu3fvH9VGjUZDoNlMlaOlzXGHy019y48bIxFh06ZNrFy5Er1ez6RJk5g6dSrvzJtHcX0j\nScFmdlXUsqWsmueff55LJk1k07ffMjoxjMjkUL4tOUJueS0AaUFmbklPpLrFyfIjx5m5M5e/9OjA\n9clx3L0th7Fjx/LGZ59xuL6J9iFmtlXUsb6kiueeew6j0ehr08l2lza3kPqd1a2yZm8/Zz74IFs2\nbeLiuFDi4218VVbF5ko72yvtZISYybc38VT3VOJPqPuF6nXc0yGBq9btZVt5PdOzYjGoFT7MqWDr\ncTsZGZFYrVaanS4qG1sJN+vYesROid3JX4enEm70zo/jzAb+1CWO6WvzKaxv5khjCzZLyDkNK5vN\nhsPl5niTk8vTw5i3r4wLV+5lSvtIzFo18w9UUu2Cjh3bU3Q0/7TyRU2tOOpqmTVrFqNGjSIrK+sn\nqfm53W7efWcewUY1h6sdFJQ38/GeGg5WOH648E/gd7diJR6Xd4NZUx3UlBJoNvvcCqqrq1E0ujb5\nFUVB1Bpqamp+i+b68ePHjx8//1Wc3MT9Q9TV1SEiaDV6X/7S6iOYDAGkx2cQFhJOdGgMgaZAr8tf\nawONrY0AhAXaiLFEERUSSceYDFSKikNlxTS1NANeI+tgWTE6nY6jNaU0q1q59957efjhhwGvkWLU\nnnJzi7GEUu9s4kBtCR7xICIcsZdzvKGa66677qx9aG5uZvTo0Vx99dWoVSp2lxVjd3jb0NTaws7j\nxURHRZ3mOvdDXHf9dRTWVlFUW+11PXO72XismMYWB9dcc8151fVj+e51mzNnDs899xx2g5EVBw7S\nbA7kb3/721lD0HTr1o3Va9bQISuLz4uK2VhRyaQrrmTtunU+qfKT8yirTt+m7Ekjq7q6+ke3VVEU\npk6bxurSCnKrvfdRs8vFBwWHafV4mDx58jnLu1wurrzySrKzs3n2ySd57OGHSU1NJTk5mVmPPUau\nU8Wbew7SEBzGvHnz6NixI9+sXsOMHu2YnBnHsIQwHuqTSrfwYAxqFU/0SGdUTDiXJ8XwcNf2HGxo\nYmNFDWEGb9+mTJnC7CeeYI9TzYu7DlJltvH2229z1113tWnX0KFDiYmK4sW8YipOGI1FDc28eaCE\njh0yWbt+PY90SmJ6WjzjYsN4tkcaA8JDeC3vKLXOVgAijW3HN+rE55t6xnBttygu7xzJO5MysRg0\nmAMDmThxIgEBJmZ9cYiaplbsDhcAMQFt64kxeT+vLqvlg6IKrp12+nPx3ef+oosuIjgwkPvWH6bV\nLSwe34FOoQE8u/sIj24rol3vfqxZt47bbr+dtSW1vJ1XhssjtHo8vJJTwvZyOwfz83nxqSfJzs7m\nissvx+VynfvGOANOp5OmZgdqFRyrayX9iV08/vlR/rmj6ocL/xR+SSWMn5OAW4FDQDOwEeh1jrz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0NVNQyNDmZy+wRmdU7m2W5p9I/0zJ3r4sJ5rUtbXuycSo8QP948eJL1+cVoFHh4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nAw88wMI8N+1Skz8i6EIwcEkF4ezmrKqchywWi3HJxRfx37nzqJViJhjRCIQ0FEWhTnZt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\nZNM0TKy6CdDLl3T12n4226q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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(2,(10,5))\n", + "\n", + "pl.subplot(1,2,1)\n", + "pl.scatter(xs[:,0],xs[:,2],c=xs)\n", + "pl.axis([0,1,0,1])\n", + "pl.xlabel('Red')\n", + "pl.ylabel('Blue')\n", + "pl.title('Image 1')\n", + "\n", + "pl.subplot(1,2,2)\n", + "#pl.imshow(I2)\n", + "pl.scatter(xt[:,0],xt[:,2],c=xt)\n", + "pl.axis([0,1,0,1])\n", + "pl.xlabel('Red')\n", + "pl.ylabel('Blue')\n", + "pl.title('Image 2')\n", + "\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Domain adaptation between images color spaces" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# LP problem\n", + "da_emd=ot.da.OTDA() # init class\n", + "da_emd.fit(xs,xt) # fit distributions\n", + "\n", + "\n", + "# sinkhorn regularization\n", + "lambd=1e-1\n", + "da_entrop=ot.da.OTDA_sinkhorn()\n", + "da_entrop.fit(xs,xt,reg=lambd)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Image adaptation with out of sample extension as in [6]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "X1t=da_emd.predict(X1)\n", + "X2t=da_emd.predict(X2,-1)\n", + "\n", + "\n", + "X1te=da_entrop.predict(X1)\n", + "X2te=da_entrop.predict(X2,-1)\n", + "\n", + "\n", + "def minmax(I):\n", + " return np.minimum(np.maximum(I,0),1)\n", + "\n", + "I1t=minmax(mat2im(X1t,I1.shape))\n", + "I2t=minmax(mat2im(X2t,I2.shape))\n", + "\n", + "I1te=minmax(mat2im(X1te,I1.shape))\n", + "I2te=minmax(mat2im(X2te,I2.shape))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot all adapted images" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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3pmwM7MbRccnWdI+bXMVW7IMP7nkAbnSep0LNvb4EYHnkmwI+tT2zcsX8T1QVN7iSDz/z\nCO84fiefqmdNHid2LdF9MVbSgzv2uTGw8p4KdTMcvnDNEn3ygqUp/QSAvRA4WBbUAc5UjmkNN43S\nHI9zJ07n83XFZuE5W9WUIvh9Y8vedWpSMSyEzaLkj55+hPuP3saNRcFndqesDBzbtTVaet6LcK4O\nrDrhxqKkFOHx2RSA7Vk7f+4cOx7arVFg4OCmgeOJPWVzUHB6WjW+5YdK4fxuoCpsgNcV+BLCFKpI\nIBfREAF8EJ44/wS3rN/SpL1rZG3+qYlvaMiqd+xqTag9lYR9NGQcM15zFROFcxQEasBzWCoUGMe0\nx0p4emokZOxgN0AINnfPOGFVlTRCPC25uYiwinIgruB/+NzTfOHhGzlSwCNTWPZwV+nYqwMPV8qd\nA8fTWXve5DwPzuIAVuXIQDgoHu8CpVOGBTyzB5tzHMKJyvK4f6ScroVnZ3BIHKdCwKktA/cv1VRB\n+OSetctdQxhKIKiAE87NhPN7E37uxAm4ijQEPns68g133cHhsfViouf2KfFaGmuJH7CJ5JreXLx3\nPBDXClKmee4ZXSauvc3CmK1BOv9ue+p9jz3On73t1iwPbfiKNH8aPiP7nb6ncd4ua/ka367Z+bvb\nufbS9sqn1L/x2OO887Zbm7ZVQvzsljM9M//WeSQeLvEZ0qzJYuu9aJbfZ8+L/MZjT/BVt92S8SKG\nOq3L8TPlvIgXIecP4/0Q648KIm07W/Lumh8SLyLtaATw8X0itp47ERDlfY89wZ+99Za29tKOk6Y1\n9PKt3rxfdGHbz7XSgmuKBsFLHIepL7T7VPoZFM7u7PGuRx+FK0RHrpZL3g8DPxsJVQrjuQT8zGXS\n7wHI8hH86s2AMcENsxgTNaJEFFp809Au9mto0jqEuq6pxcVBRXwmTstQ4wpvgy4+5YnSQRQG1AkS\n7FKd2M9sJvsAVTGCtePNZHCBjLglImr5J4HOBBAbHIl4JiKXhCowIaBOEx+x8gGqthAm5tstGLup\nvOmZGjvJ2CFQjJADx+OgdYjEew1tju8JCs7eIzgEacqXBMjgjJl3mcCkQa29nTdCkAiDSW0tsU2z\nQ7WZXvo8dZJihKwcb9JKTOQiX+Diu8EhLp7cPJdHytaLohqZFxfLHYlrM49juQsx0cyE5Jivd/nK\ngktE0TlCFEKUAH4Eq8ctjd1ARPDBhpk4GkYEoBTHjtaUaTGMRNEnUiKd07hjH6fxZZ+zUOOc1TEE\nKKIQiSpWbDXmJrWj9QwSBA8EacjG1XZJeSl0ZA9g4D3HDqwA8L4zFeqU5ZFwdMXqtDw1geOh2FZf\nf9wYnGcmS5TjwLDY47baqn0HwjNV4Fd3Pf7AEhdik71pZYmDdcms2uVB2eTZvUusjC2/N60ucWjq\neGRvy/IYrLDpA8sePh1M6rndW1lWXckdonzwWcdTgwFvHG4CcHwQ0LP2rqdKe+YmMaHo4sB+b0BD\nQ46tLMU62DOHRu1RKrcsD/idiztsVEMK9bx13d792J7l82ht7XGPLzi+VHJiZ9Y8+/FdE2xePx40\nvwdeOeCFjWJA4Tx7xZhPhwE3bCxxajLlruWCoDCOUuHpWcVuFZgKbLFEgeOeNcv3oS37nHoYDYfc\nVE6pVTlSljy6M2U0gouMGC5NuWdkZXhkZ4+iFEZD0gwzkp0zFokfXUBDvCtYHiw3v88MjHhs1I5p\nETgkwrlaWZkVSKHslfVlaUjlAyiMKiLB8mwn1i8SpadVGY+Uz18STtfwsalwQwioKhPvWdPQLNKJ\n0blvWfj9HWG9qSHUzrM7XOLYAM6JcvcS3DHynNwS1nzFsYFjY2ppHxgV/PTZirdtWL4PbitHhsID\nBzyJhlTB2mkv0pH7l62W98V58d5TMw544YYBDJ3wtoHwyMzKffuK0Z8zMe0FbMyemMLxwnOsgKpq\ntk9fbRoCnwUdOTQec2OkIyHSWxHZJzAlXiQNumbtyIQdh1ChURjOeJH4QmNqkwLYNdkGe5FBbK4L\n+/WkST05LDxHV1ba9ZGMZ5pDrqBreBFJ5enmD43utoGba4dFAk5CUkwnHilvz1HhObayTAhEAaHR\nlcf82vcEzduhFSXSWpg/lxQPmvGRqoKLPIOqKSydtsJZ0xhzbbSIjgwLz7Hllo40wmsSZpvGNwEj\nF8jm35T4oBACOEWiorkjLMXPIt4LKKKRZ3HpfldgMgEsCYWBoffcENcK4zmkGVPzQiKYYm8SNJoj\nrG4iEts48iJE5iU2VlJiJ16krk3h5CXypE4Rjcpsb32jIXurhKjc74rGXCE6clUEJlX9ZRE5BPxD\nzBT+p8A7VfXU8z1XNcQnMu1xBqWJaoKGNur3uhnJijibEGY1cEw1gPcmwCiZZSiOY+9xKoi0GuBa\nNapc2nKAdXRiiINrZjK1s09tRp1QO6GIVqJKHJXYAPYKiqeWNFhDHPjJlGALrIo3IUms4EJWvvQl\nljkRijTecgubUKCqzTMi2kwORFDnm8EoUYgJLhFrq6v6lkiLhkyTQaMml0TYQ2wjMGtbpBgiOTGV\nOOGMkpmAkrU32QTpEPQ06a2tm0UpWDu4XLpK0iiZ4JdpZtpFxsaW8zRpkla8qWVMnNowEVmrlJAX\nsiYgTqjUenQqSoGLApi9v/JixCcowbXtorZaIiLMRCnFNX3rXGuBDAgqQtksBiagpV4OQF3XJuCF\nYPnGt4RY3mRRUK3xSLQMurhQdQX9q43Pho6crZULFfzBxYqvPFjy2CRwYqZsxSp913rgKfVMnS12\nf7BldHiqW9w5XOFPLgU+JGNuXV7m4W0Teu4sbZBIaYLIiQl8pC7YCmNuKYUbhquNv8/DuzVPzba4\nY7gSKwGXauHDO7t88coSj+9MkGIEwEDgBOCdUDqHClyqHH+843nrpgkuf3RuiaerwGhc8OYRPDHb\n4NHpjI3SM9MdAjUPb1u59gZmYqpZ5RM7u2yMZ5zY2uOoX2fX2yh5MFoFtrD6702s/md9wdmJsjMI\nzNQE6DvGm5zfnfDgTmBtNOD+A1ZuY9qMNm3IEuphXaasj0rO7Jlg48cjLu5NeKY2ddENvmB9MOVJ\nrVhVa89bl80K91SoeUpnFKrcNBhysa6YRRriMCnwbGXC1frAwQCODkomdeDR3ZmN1lFLAxYxbQRr\nZ0RRp0gQ3BDKYGWQYaDAswMMMEWKAsPKg8KkrBnOfCfLKv4eYXRkNlPWSyvHmUibdrBYvs9sQwlM\nVQkusC2Ondqxk8VlusFVXHKOescG60MKByKB8gpvGSm/sl3wnavCmWrGhYmwV1QsiyMEDwRu8CXP\nVvDVqyUno8XyvigMPbpb81QFD1WO79wQ7lw2C2PhpaEhT+zUfHQr8AVLA1QD4uDpENitBIJQivCR\nS7AsgmrFW5aED+3Cxcpxc+kaRscVL6SBvnL4bOhI7Yzum4XClIpIxlhq4nIjX5HJTYnqJlXszBbd\nfRG4EruBGN+SM8hhvvk0XU+Wi4y1l0S7M08FtftpxNbZs45oiaEtq2Z167SDauNpk8+rcFkxo22H\ntngS2yP+jjxAm6uQH6farlpZ+UgWlwXvnF/bVTPBKVv7M8knWW8aXqTz9vht4RBuBZlcCGrGfVZA\ndW0vtelyYTCDgIs8lVnCujxoQtPuaehFfkiyxLVY/StMCT6TCh9HQhI0g2jDKmkqjHb5q0rNQyAV\nobGMxrFjK0BS1ipOWl5EgwmAzguqwXjl4IEaDSashToq1qNwp9ZobYPsb6VXHFct6IOq/hjwYy/l\nGUFMY9gQgNiZLgkH0tUiYNaN3JVMgFoTs9hO3KSBdKKR2LVjsTWVSjRjRtemeC+5lmlSWTR306Rz\nSeAmCMx8vKeRSGZaJYcNVhAKbaWJ0D2AuSWvGfWoRKOFLJr2pTW9KkqQ1m0vSKuJUdW2PdU0XQ6b\nWCoQQrKuJOYakklZ0iSKfVNHi0h7PWvDrgzRIlmlmr5I6Vo3wDTx6kYr1wqDhQq1c7EdJZtKrWAT\nGkpkLZd6KUQBNWnROguKZuMmIwwdNVWWF9IK2K3mKmmPXFx4TCfspXVEVEk9kaxKZir3sf/qEOy7\nZItKW8wGDlPAiIhZnpxSUzcEPGCWL20mScgWCxvXrnEB7XaUiETXgI78etXxUunIQBwf3K64dVRw\noSrxKBoqnqiG7MwqHvcldVCOjDx1rQwZcKQsOF9PEYHbhyt4gUe29pAwBBQZGxl9ZmK9cXwsbMqA\nDR00/TOM7nrVnufN44JHoyXrhqFnU6Eer/H0LOB9QVATAraDCReFeG4arHKprlkS2AnKvzNZjQPD\nklVX4xCepmSzrNFyxHM7E6DkDcsj0ig5uWMCzVmUpaJg3Q/ie5RCoRTh09t7rA9L1gvH6WnFoaUR\nFydTVoYDVJXdShl4myyPTGYcLAtuHhScnkx4IlqwyhBwAsulMB4Je7VyonJM6prDI2uHEzs7VKoc\nHgw46APng2OtHPDwtvKIcxwADg0dT+5VbJRjJlXFvcsFKyKcp+RcmDLJuMal6Ce9E+fkR7drHDAq\nhL1oHb91aPV/dM8KuhTpWeXgiHM8V3nUQRhCEZmkcWGDfaqwMwEtFXHgh9IwJ5XAaObRQjk6cDwb\nLTlURr9qZ8yHV8eedTvLcX7tJibGJw25cLIuGUQlWuVCM1fP4EGFx1XYcMpIhO3gWCbgHDxTee4d\nwIN14BiOP9yZ8cUrA87WysN7NTcNHINCeXQnukUWsCKOrcxr4fYBfN5IKRG2KhgUgRN7cLiwfjs5\nqzkydHgHewROz5QbC8+4UKSyNePYUBgJnNwTnq2M+g0E1gvl0V3A0SgorhW8VDoiKrSrJnG9yFas\n6ObV3I8Kr8YqEf/qpBQkrQNdwURS3vH3PqEqSydRaND5u5k2PuWXmN8qfz4pK7N3hTgPfFaXxP+Q\nLBALBIfG2kArOLS8CGhoBRWNlW7uZeuqYta1urkmHYEnRCm1y4tEhn1BGSBzLYTOtoq8zVIf5O54\nGjuitYQ1zRDLYvxXaKW4TPppGynEfFKbN3xAVq955BaeTpo5RiDnRVzDQ7V5t5YpayUV8OI7z1t9\npRGUbNtEck1MPGt37HXeHZ93kVcIGB8UNHR4a3HmheMQCIImVyAf0OBINpGQmxZjmxHb+UrzItdS\nlLwXhMPc51yIlhxNWhKJ2h1tJn3urqdZq9oYEoax4ypJEn60iBBaV7b4TGNBQExKbp4Bc8jINP6N\nFaclbcZEp+HWFXY8dC0dRHdBaT8hk95p3eiIdWv8WjMCrmJEyTkXiU2U9iWVu7X45ERSnCOIEDKL\nk5IsPxkxyYWT5l/bLuYGQ9MXzWIg0FkRAE2bNrTtC42TDCIhTP3TlMHa2ZgKmpkjTvYR8EZLN09V\nYgVFhdpFF860eJF8jds6zGN+j1AS2uPrrM2i61/SjOT0s9WySUOkggbKKPx11U37KUO+r6wpA+YG\naSZvpUIpG5fS1B4atU6+JeSq+Oieamkl+nO7xu2viJaten9RrhsMxHH/6ipvChf4YL1sbaLK4WEJ\nw5LpNIA35u6is/0wNy87jsioyUMUtgO8c7Vg1U94MFq0lzFLji8mnNqdcW5Wc9+qPfeJi2apCWHI\nXjHibG3WnhuKkiergql6s4h7OFTGF1XRGiOOdRdAhmxVFYUEDg/bg97XBiUbbgpMORcGlMCN4yGf\n2d5lRYUnIpk/Pm7J/aFB2Xx3CDtVjfeOOig3jwdMZjVbVWCpCNxzYMxHL01wAvdENxMROD/dZbMo\nuBACy2Wb39GxZ0kqPhZgvTSLzyPbwuZwzOFBdCGLbVUp4Apwyop3zOoKoeDgcMDdg5rtMGRFPBom\nnKsGnAYuoiyVReeo+7XS8j0zMUnymB9TS2Cn2GNFYKjCXmVlTIzOrjPB6Zai5IlpRRU8IOCENd+w\nhwBs1cAwKjQgegQYVKwKE4FTQZtVVb3R0AG2+NfALC4uLnpFFhP7XSQZq6zR2jPBsimCwy3V7FYe\nP7VxVoxrJigDVWYi3FwqnwzCp2fwpSPlAzvKFx12rA6JpoPAhyq4a2Tr0OkqxLEnLDvPeF4ZF4xc\nVUGYTYT3bQf+8oqleX0UeJ+tZkxVeONqu+H2WGXKw81BVHLtwK0jODMVBk4pPdx9QAgBHr3WJKaX\nCBdX8Xa+XEABAAAgAElEQVQ/TWTcnWtWwnb9pOVFsjwSw5zW83mrTK5/bZjybAlznQRJMdyuVY2I\n0xGCTGhreJE53mie+U3ld5q5+EvO23QXg6AtD5LfUYju6IlPkub5tI+mWT2ze7Ymxro0S6I0VWp4\nkfkXLqhTxnPPtfT8E/uX23i1c33eWc8hTRsh3XI27aBpzCx2w7P6ZhYVEi/S3l+0H2zRe/J7qS27\ntrI5s90cgiqFi4rdRvjT/S9blEczTqWRwoK0vGs2gpqsc/dK5xJPKxSAONA68ZQB8dEz53l68pXA\ndSUwAeAdtTNtnBcxP8zovqUSrQ3RFUmIFogkZWPm4wKYZf7DgGmao5DVCDHBGE7EGzPtAqo1iG8F\ngUwrYAxs1y3D3fhWcK1J3WFuVQCD6GMaxMyj+UZqp+aCuGivDoA6syKQhDjavUxgHSsSLXLeLFye\nzF84Eq/c4gQg6nBH34KIj4x+xrMnoSWO9kpiP2iyWEUHQWk1PUbIulqU5OqValup7aNJAoQmq4hG\nVwfX7kNKboGaNhkXReMLrje8GcURnGln62T+bepmLw2AxJUn7Q9yMZ02Gierv2iwfVjBiE2+cCQk\nmSMXrNp9XtYgrVAchWsXhajDnw9RERCowQl1Tmyafuzm29DlpO0ibmB1UCULqNh4q1Stj2IedSLI\nIohaW3nnmGlAUmAIMW228zYAJDEEQusmeh3ipvVNxDt+/tKI3cmUW1Y8962PuTQLPLY1YXsifNGR\nEb93bpf7N5Z4/fqYAByrAzNngQNEhC/dHHIiONAx63Fwro12uRCENaesLXtECspZ4GOzwOHxOne4\nPaQMXAp7BNZZd8qFGcy07cc1B2eqUafMdx26iW0Zsq3CjUPhgBc+ObM+uK+c4CvlKQb80Zlt7t6w\nZ5edcnR5iQcRjrvF7t3ndMhegIkXlr2nwspRiiKDggcGBR5lqsItB5YRlNvchGdDwQTH61aX2A1w\nKE6ArTjRVmRKUOG2Q8c5UxfUqqwMjC6GKHgeiQLDn57d4rTAjctDPjYVji7bRvpLKA9PClYELhBg\nVHI2jt9nt3f5qo0RH5kKa3EsfuTilHuXx6z4NQAuygVuLZY5t7PEwWXBOWEtzpp6ZoLT6b0JN3jH\nbrHCYReQac25lRuo6yGngvBl60P+v4sm2LaWAaMhU2A1MlBblVJjVroapY7CVqiHCDUFM2ovHFvz\nPDepeMOK56NbkaDFrk6MxBKe6CmHj/fqHc8w/71rwSaqEsbi+NTU88Chw9y1MuQ3t2ZUCr9xxnIF\nOFQ6bhXhI9vmtnj/anRVruNnHHt/sA1fuAQ4+BdnrHz3Ss19Dn5pG94+Cjy4G8ugyo4IAx8YqHD7\nyHHHWPjEbuSOgDeueSYqvH5deWZPwRmN8g5et3z90pAGLu7HjWu7uOhpEqVqF5lf8a5Zd3wj1Jhy\nLzohWXbJEtQINNIwu6iFDUFaNrdR6qXfc7zIPO4/dKhZh11k5pOFqcCYbIVm319TTeis1/PQ+O5m\nnaBVXEMrtDQBBugKL7knTKqHfRHuP3SoXVc1F366gk2yhHWE1TlhcF5Y0li3XHCpk2dHSiPZ+h7/\nNQrZXNEteang9QcPNoJ0stSl/PKUHQE6CY3SFQPm9ypZe3TrlNczFW0+8Mii37lO9vUHDzb309hK\n5c7LMlf9fYVo+kGwYCeRNxTMAt1Y4Ui8CI2VyYkpbqsAKbqaOpAgSGGB1NqgGVmFrxCuK4HJ2sk6\nslATFCrN3Iqc4DRzmYvXa2K7dqTuLuOZ9nt035csKO1nbmnI3ZnmGeUGR9+SEQB7YRNoQTKi4dw+\ny0jrx9siWcRSSZ1zaKiz9GYtgRhpLSkAJbVDcv3STv6tUFTjj72JFzMSy2gJWpR0XxCMvE7xWt28\nc1EbmguYRLFB50znzjmc0iwiArijb8naISM03Y+IRVHkFhAgyQnEIjK3P/2L+x3reuiNrYn+eZo8\njc3URu3es0bK3PeO+T1sSTmQLyYarQoEM893F5nLabOuMJV6GXHPoYOcm1S8fn3MkirLKjw6C6gM\nuWdVeGh7yqZTbl4esFUVzKJl8BxwSGYcaFgMODczC9DG0AQSVWXDt4IuGIFdKkZMC0eJCalL2URf\ncebuNRDlXG0MzPr8pNk8zvkAq9lzm/H7yWrA+SAckMBdR9dZn7XlKzWw4h1LRbe/PjUzaWVDlC3g\nBidUWnMhOJYHJZtFxSO1cec3yx5P6ihaeYVPhBGHZcKGVDxZDxERNlxyIbRnisJG55sPH+ZicNTP\nI2C/49CAoHBBy333Tk4tn3XfKqFEgOUx3sEB73lox949KDxLpVBHIeBIuULthJMSWMfo5XOztNzZ\n+vD6lVWODwN/vG0Kg5VRwaHVIwTg6PKYda8sDZIPfnx/1jXTSc2Ng5LC1eQ4U7eJChHWS9dcWx84\ndkLJko8uhPX++eVHL/xbakfh6xirRdkOG3zkdGgEsBynbZMMX7Jp4/VCZeV973Zs34HdL7J+ulfs\n3ifjfrKt4PnYVHn9kpX393ccry8Dh13JyhDOT4QjRWAmNRI1ak5hKcB2V4cIXN80BBI/YUKgC9K4\njYlgewaiYJPc9LpUP2fRW/fy1G5txLSsjZLAktYJUXTOSPdCbfqGw4eyX1aGRmE6X7csz3Zd7ubX\nrEGZwjDt90lrZcjnjWsZnmZNZT8v0liYUB5IZX6BuuVCzjyaQAtz1xOP0IZOoRH0dO6ZvLzzSMJn\nHgzjDYcPd97RFH8BL3I5V7x5p5bYvM13WZCmWz954d8ZQXv9wc3WsHD5bDOXxnY0pzJ1LmTvzNPW\noi0vIlm5nG3/SGMz55VEF8dZ3M8hv7K4rgQmnMOlCF0u7dNJrk7WcEGyaxD357g0cmldu6LQEt2N\nkKRxiAKXCEhoOsQ2vwv4ognwYEWKbhy+jYRmeaf3hGgJcuY+J5CizrWTMwkR3Qh45vMc4rssSEMR\nGbggmZCVBWjASRN6vEZtQSVWRxJxcji1uoXkXhj33oToXlhIRuyagZsWwhi0QlsByCZzaBn7JDBF\nd6XgLApbstAI0aoUVRyt5sS2CirmNmZ7qWj282gsf+g4KaY6mvDWGnO7wS8SxVFMUJCsxZNl0cUr\nIbZhiG1lL3MEMUuN1dP6xmtrYidoRxsXxATXppxCHIhmVfK+LaPQWi6tULngnyLeRBc5hOAg1KEZ\nK0kxoAKSIhESv0MTvr5IDI0ITmsTpp2LbW0R+kTExpIIosFcNUMVy/M8VPoax3PViI3hCuemJgSt\niTKLk7/SETeORzw8w8aRwm2DPVSVx6ZLXKpLTtcFa7Ed16MQ//jeiJVMJjgerTAVnguxL9ermucY\nsKfKxrBmw2uz2I19XBxKx7CqW0t3zH9DlPO1ctE5AgMmAVZczVaw5za8zd+DdegwPiuFjbFTdcn5\nIGzE/Wv3ebM0PMeAgw4OM+WiLyi84+D6EpfELF0V8MlQMhKoCIzEs07g8VCw5EqOux2WCuGhmXHp\nm1EwezyssBumvLGsOTVzTNQ1QmCiD4ei1evDswEr4jnKHuMCnpkV3BRDo29Ht73DcR48o+ZEfHjg\neDLA0MOXrHnOxb1eU+za6emMoR8ymQVWiooS5VM7E+5eHnF6OmO3qjg+HnFyBsejO3CNWbI+b22A\nE/jYVsWnGXCTt1Byn9reBkyQGotycnvCjioP79H41y2XnuNFyY2FmYg+eukiIyecmg44PJgy1cAN\nZRRagje3wfEOs1r5ivGYc3XNw9MpW1Nl2qxU5nI8zMzaNYp40F2PG8FyUXDLgQGPnpsywHP3ypDH\noyD59Wsz/nBPeNtYKYLnTKjZdo7dmXIIxyMOTu7AuDDyeH6n5hg1H6fkTl+xPTX6dLvMoIZndq0f\nv27FBNz1kTIMNQcG8NzUc9QXbE1gpYzh1Z2wHAI3D5ShCltRELvSrjQvO8S2CCTZJ8kDkBhH7Vyz\nRIkVTXs6Uvq4RmbcrzZraiusJFbS9hBHVryTJjGn2Vo6x4m3+4Ncwxd0rDtkwmAqeeJFInVR2zTe\neMQYTxNtSw5yVy9JSruYeS4qzisimzdmCj8FvHSK00nTHB3SHPlhWwf2tf0CZKx5x12yy4u0SkMH\nMWqua8rWWKi0rUFTR0lWu0xgnqtH+86uwjjbZdBap3KlqkZrpirOte0lkrWjtsJoup+HLm9FQqtn\nWm+afU55p6TvscLp2VRjFWlc9wVptq10a9k+k7xdfBMMQxCpwEOojZ8NUREhRF6EpNRtn7/SvMj1\nJTBBp/Pnr80jRHWMw85aSoznfH4ds7YTCLkunu4z0rq9WTmSMNK9nsN7b8JWmknq4oOhqYtzLtMe\n5Jasgtwass8UTbaxcY5wLjLXp7JqDCzgFrQHpH0qzy+/S5wkxoCHfRY6oLF2pdJrJogVl8m9E2wh\nLgqNpkJNBxJc03xNWtUitnFX9bZofBSxdo1peL4McS9UniZNbh+FlkSo6khMJAmQmcCU2rwtzP5y\n5dbNTlmz73VdL2xf51xnLiys91z1a9IeJqgkueW05cuLsKhs+yI0XWdIzPv5II29aJ9VJ+KSeGoR\njhaBmSprLiBR6z5JLmgONnxgN/5+dK1k6aIwUmWYGMT4zCAo58ee46GmUtu0X2F9sSw1B33NThAg\nMM3I892DirM6wEdmqw6lhYUnKS+UgZOOAiCNC+88GwIDF5gFx7hhNgzP6SC6RbR9W4pFbbvVF2lp\nYyfOhwOuYE2UpwbL6LRmIz9LAbN+nXUlT4aCoSjj51nU1p3nfD0juJLdespN5Yz5Afu0s3aQOkRF\nS80FLQgINxUCMUJeg8GAraqmUuX4eMBKMWVTC9vnNRjwbDD3pqe3pwzdiJGz8b/kPWXsp5UinsUX\nGyS5CqZ2Wx6UvGPZXMI/Hc/YOlHNOsUoCmHkC1a05CxWxnsHI0qB10nByZ1d7hyucKGe8rvTPZwq\nQ+D+lRGPzqYsxX46Mau5dakNBX+2mjIQxxna64/vTLhrc0jAojaurpgQG2TGkTiM/vW5wDvW9vfB\n/UvKJ3aF0QJuYBTP5jgVFXUx5iC/vF1zs1e+mIJhjBK5MYzsk8Ag2+ioqgxjXcZzAZVeK+iszcm9\nbMG6NO9GRsO4zglFaT23i9m1zDE/vSvLs402lgoGZOJCYqpzYaZdX1zLwkjLTTdK5fh9kfDTqWli\n4lup0ISuBUKRqL6o9Sa5j8U4Arj9TbvPSqTZNdcpdPv+3NIktMJdupfyyPefJZc5E2rneJEFZXck\nIcN+z+/u02iZs16KwltsLC/tniekFWDSyzrWrTSW5suSR+fKxqWkKswhufTvq0fcGrII0mn1FkGM\nF1GFEIpO3eaK2xH40uufz8L2SuC6Epi8hX0AEpPaJQbzMWTMJG77liQLtd0hSvMdr60gIHObYdsQ\nzsZtOqKwpYKoi9HP5phK8a2ZMwZSSFJy0iG2fHD6Ip1qSVQb5Mx7q2GhqZflk+tw4sFlUfuRrDP2\nnI8akP2Cp6rEc5P2C1zQRuwTojAIJO/s7IEmTVM3AUQbrZOqUjsoNO1vMJLrNd4z/ZJZoqgJLmr1\nwCxtYhqGuk59Zcyp0+60bOlFu7FUs7/UjkZ4XPO7jZhn5Zs6268lWsf+i3HVgzGuztGcXyTRCmd7\n0aBsGAXNCFm0ODZBh9o0dn6VkUhBQbxtfq1t0UuEJn8uESWHBQLRRPxcO1ZyjWUeNbEZrhqtqWJX\nXd1KUC7OoXpuXlxPeGC4zfGhlf+h6RJBlDVn83K7KpinIcOqYDsIK2lXPo6LQVj31b5N2iuxQ1Yu\nQelqnpkViJgmfjW6bZUFrG4rEx84M3Yc2wpUKninhKrgLCU3erO+PBsXEBHP6eARCYyoGaLseWUW\nfCTg7eECpRfqOH9HLrAXkjXRxuJQAqecYy/47HBs+wwKm85EyPMx5xACmz5wXj3jOC/XnL3zkB3i\n1TmzywmMXU1V2b5PUyrYvDun8Xwo4HR031sWWC7KGBx8TNoCI8BarNSFlLlP6s0CUXNt3KmUJ3XE\nfX7WtFnp4HVLgUkQHps5jonjgaUBldQ8fOESd40OAPDlmwOMRiqP7DluLGaciuG+jw29MS5x7mzG\n9kgHmm965bEoFFyIYveBwvOp7W3uXbHAGJvlOpMQWBl5JtuW5nfOzygILI0uwRBOVmvAmNVZwckw\nIQxrPrilQMm9S4FzVcVBL3x6MuGrV1KYC/NqWFkquLP0PDibcttSKywBPDurOKU1j00H3ORroKYY\njvnAHmyWNX9mMAMCt8ccbyuV39pxPDASntgb8NfWlfeeK9gLjnGhhDj3/6MD8LGLNUepuXnkqYua\nYWOlaxmtiU/stTILjqU4fZJ1e7SIE7uO4GhdnM2lOmMJJaniWzSWHCBtAs5XakuzSIBqvUI678/W\nalOoZuuHRGNA8rho+c/2vUkOE5udqSwNI3wZHimtUNL8b/Nt91bN1759tll3M96tUfguYOY13l/E\nWwBtNFfJyz4vdMzzIgveo92gBMmq4+MB9OmMJx+FIZXMxb3Ze6aZpSsF1n4eXqQZD7bm5wJdvo+s\ny89E/jdzY5xvn6DtvrBOXeNYSPS+FWpp+1O6bZU757RNLZ3refAv2+ucv1Oy8dcaFczLqn1/5ziY\npl3jWIkSlSSLLq0X2cJOfQVxXQlMtaMJw56iFXU3DC4iOF0L0uVM1fMwq9BlpGVJrkqAmGXIYUJW\nSP0YEmlpn0kDw8WN/20o8UT8tDFrzmM+ct/8vhQ6b+uiFZRMsLicda61TOX5ZZs3M03W/LP5u2D/\nnpu8fKqK9x4JUQhbVOw0+ZpHE/PYDawtQmPpmbfANHt4IoVwdesGmUKXy5z6Kp2jZYKEdjQuBTHA\nhdvft/lmV4jalkw4aaIdZiboSkyzVoTntxRZ9WN71nb2iZtbkpILIcTzx7w0Fq+ElghaOesQGmld\nxA5yLiUJpO3i2+Z72eJdN3iOARJdvUZlxcVQMpAZFcIxP2HKoJO+kMDYOYbZPpUbXcWeOkqZt0t2\ncXwwZS8sJrFnljyrExi6mgEFF3XIkhd26prTkeldiXtNLlZpLjl2S8fEB9an4HzNhdoEshvEBIYA\nnFLPiq8pA7bPJeJCjBJ3RGcU2bNrUdjYri8vCKsKq0XNrIYDTjmj7caUlfiOneCb776Z60AU3jaC\n23f45SJNcGPlisXZ7LgqacNwjbyw5KMyrJ7tz4ju+N+djTi6vMKG2qG8dezrA8WMGwYeqDhSVJ3n\nk7vfVpwXKxoYiHLAV5wIQ0A4EoeMlXLMXmF9fkiUraqGoI2FSoC3DpUnanvoZHzd2mjAyXgQ8LHC\n+uXOA/D0jlAp7NQVvzs1Qfp2X7DkHYeHBb99aYeAcrN47l4uCdN5/XRLp07HMfzUVs1os+BoubjN\nAH55S9jxMHbKN622hwysec/9q1FoGgj/+znlm9bsnWuF5wNnprz9oJV/EBfDIivFzF1OB319wdRY\naT0k/jINu8MtXtJkThGann0BupqY9EUQyfe/SCOsqLaOYPPZuzZ5u71gES9yGT5pPt9F+/suhyQA\npecXChOdvLvvFzIGPXt+URN2eC8WsBmaXNrM1Tft9d6Pbr8l77umDK4VMlyyBs2VW/NnofVGuVwd\nVLvln1NAi9h+/eT6lucxz6+59vGo6O2+L6ma0xErHtmnDOw2xxwfOHe7K7zZAG/atqm/SUhp/KfD\nmS0/odbQ7Kn0ef8lnu4qEZHrSmASpcMEJjNkc7roZbCvbdNz5J3uqTXtjLFDRpuzAhquMQ3EzJc3\nna9AZNhziTw95ZRQW1978dHKIA1hqzOpXpzrEBWg9Q3NNEjGE8cNi41foOsIXBL3ezmf8vCdsJ7J\nkgZJuxLbS5zd14AmRj5ZZ7JypXZ1cTZo1jedfUN2Fw0ORJt9O7V0BZKWkMZ6uDZKi7gUxjU+m7RG\n4ppDcjXu56klBbyIVp46E1gV4u7CaOHRJjR5EYTUCkrdsRimNlIBH5nFWXTzDMQTwZ0jerBQO9tv\nVtP2s+VhY6xpOwWcNFZLsH1xkhaxqLl0jYGj9XdPBxzbGHU47whadxbE1K62gGqiqGa7m1MK5L8r\nFB+y/WOQRXG6fi1MTpUDVaAAznnH62SXafCcHJScW/wEANtZ9Ms1V3EhODa9cbtna5tnq3XBydqz\nIkJwMzYGE04JrFTKKHh21eGiALUaD4S9oMs4ZqzolC0ZsuQ9zIzlGqkxtOqFldE2p3aWqGYFhwls\nibmfrvuandpxMgZNSK6W25Vj5JN1GXZq0/+vFcpOGHAJx9ApG66CGs6op1ZH7R0XqsR2wVJRsAcs\neeVSbSveXlytVouKUYCL6YiG3CItwnpRs1wFnmHIXqNAUlRbF6CgZolYdxWVwFYomoVzjoQ00pdz\nws1lzU5wnAojNgiNm+FyZF6W1VsfFcIzYYQCSw4OoqxEa8knoxJmEjxSwC4Fw3rK0AmPMuDGULEd\n2+GQzhg6cFJTqwlbm1ozdPB08Bx3Mx6aOu4atkzMxQDPTSvuWBrGY4AtkuBjqtwehZUnJ4GVwvPE\nZMpbDox5onK8ddmef3wauG005qFJye2lcOaShah7lB0eGC0373FY2O7Hd1vXxAfWRzwRo+KNZcBZ\n9TwQg3FcWim4NIWHpnCwGPDWwRY7Imz4AaNlx5uGOx0X5RlQiVCqcqq2PY9hXHCiFv7GQcdeXEM8\nytsPlg112/UwqszaNIgKqCIdJn4d0xBo5xlCPOCejpLvxeqWJGUC5KxsIFkqsk/JbRbtukpMlVzd\n5tfp9DU9ldaCRrBLAohqu6eZxdam1iqS5U/rkZHfN5f/biFclsc8k9/s1WnqnGrWtkNTBy5vxcrr\nmrdqnsZIiTR1rK3zmtRt+yXFdtpDHdtLsvybtV0aoaAVBOYEwXgacCqDOkhb5pPLWdpmkVfA2IRu\nP5ApTisNkXNtHSxzC6iXRGsz4S21RWxQpzZ+21NGjbVqWQRNUkunjZqdedIKzU5cHMPdjmmsRk0e\ndt133Pm04W0B85hACWoh+C3atWvecyVxfQlMIo0mPvclTa2emP991o1MozFvYWoGewgWfjsTXRtr\nRJCY5/wK3mX0wQ7FtU17kYkINYU4ixsPFnI0yz/Lpv0M2u5N0rYcSRsStA1G0bRDxMJ9RBrwhV8o\nludWJUGadkjWs3liOV/fBeLoXN263I/3vumneVeyJgKcRAEuExITmv1PcUXPrR4pfHoaEm3dbPN7\nJZURhqCNcNCYtYG8IS24g+Xhc4JBa2GSuOnetD0xpHCeRwiNMN+Y+tP4I7PczbWd7Y/qWpyS5dI1\nQ7Ad/7nmcpFG0MU9Vx1NoHbTz1s7JQr0OGMIAq3/cFhkFrhOsBKUA2Vgd+ZYrwOjYWAkyiwS3sei\ndeC2qisMbUThSDDmeSw0VuSEUzPHcqntIAXWqsCqCzwxW+YAyrIzRrmI+VV1Ff0kPMvRteu5ylE6\n21MDcL6CW3TGziBQq7Bdt8LbGEWdsiddUj4INZew87iOMCU4jxPhQhU45isuamZJ856l2lE7x0xh\ns6jZ0y4duVQHNobKrPLszQWXGMRBuavCKMA5dSwVnlGoKZ2wJI6dOj2Rs0ndK/Mo4uo703QKvU36\nw65mJ6Zxc+N+O07AJ3zJOYFNgUPehMVJMAXGqWhZui8eEHwysyoe8cIEZRNlzdXNvrRDkZ48WI8Y\nE9jUGbeUjpPBArWsSWBcDjgge1yMeW2pMK0DWzhujkEutmqzbp6Lc/vwsOSAD9w8KEgGyw/u2Ttv\nWhKe2N2F6Nb51nXr4yeqFZ6b7rExHnDncAWA48OuZemhS3vcszzm8d1Jc+0jcY/V7d7yOToouV+n\n7EQaslkWuKBMFtCQm5zj6RDYKa38d8qElZhsFNecJyNdvz0OnQKjPUMJiLqo3In9dX3LS8knL+Pc\n46ITB/P+tTITbGj0Vh0Lk2bP5vuIIBOsdJ7GZ2vIHC+QGNZ0tdbQnLWXhI88l9xyk0kDHX6p4bni\ntVawa/NommjBOAqxDAqNa/78K4nlS3usW0uMtO788888D+bZq/Rs2v/TSdPwkvE9ie9KHZbXJeWT\n2jd/Z+SnTKDOgxRYfeoUbjuYUJL2f6WSSlbOfK9U7vZon235G14mps+tRDnr11gRaV0x2/p32645\nEyp7ft7C2IymODZTaRf1jePy1ivJykVWVxETjES1cX/0c59XCteVwIRzdiCntFqGXGBoI7RFZJYL\n0djpkuLLR2oXzxuS5NjpBPA2aRNj621o1dghoBqZCduL1PWl9MFZoIN4GGqRNiynQvp28HWikcQM\ng4Dz2ZAUS5NrC2zwREk9NkRHAHTJpzzgaotrb2dDSEvkkrIgtVF0EZRoJdOmLea7ILoTajvVGs2A\nzJUjgziPSja9JXMvbLqraJ5NVigJ1u6S+jcJVT4j0omgxyL5xsUg9pMjatTiniNPc5itVx+Lo9HK\nplSieI26G01hUmnSphLnwmnu0wymNVLfXZTMmujsPCygPYQ31gkbMyVJYM2i7zVtlIhzciGIVkMX\nDyPO+qw9kzZprPIIfKmPo2ZpThcQaAVPJ+Dx1C7EM7euX4GpGjouFkVD+c6HIYUK61iwgduii1Rq\n8yQoDYNQh4Jp7blUGs05F4YElXj2m3BgYEuBLyqCOnbrEeMaqNo9TA8z4D6dEiYjBNh1wnI8N2wn\nlukIgc/IkCNhwlYJB33NU+EAIlGIcDUXYmhwRFmWmgNxuZ4Gzwyh8FAG21+06zzOw7pWrBdwXgoO\nEFiOdtrtOC/WpEKBLfXsRovaeaccqWtWvGdawZZ4Noua05RU6tjKVnbRwJYbUsZrWzJkK7ZjvtAf\nLgPboWCnDqg3S9n5TMhcT/uo0ibgeH3kS7brwBTYq4WAsBoj0p2Nc8+5REMCm2LL80YIPKklAqwW\nNVtR4HzOmaC04uyaE9iLtPYGnRqNivQ9CVkHPYBnD88TsXD3uikBTynCZxhzh9/jU7Mht5dTbtgY\nUr4dfjYAACAASURBVNVTKiwK1Y1zzELuAugLeGOhjTB4ZjrAlUNULWLhRO2Mto9uK3cPD/DwNqwu\nWb8nK88Hzga2K+VLNscgyi3LA3arwLYq42hZu2XZ6vKHlybcPBgjg2We2J5ydMnzzM62ncUUMY0M\nydORs787TDktnj2ELYUtVfYGQ26cTbkp0exIH6cIIqb5tk31RUtD9HqXmKTRMs0z5Ln2TelcNJqq\n7fUkNKU0aa3Jnohpum5hQee1//PPmaIr7UsJQhNhNy9R5/m5GloU4rnAT0k5F5lot6+eXcEkfTdL\ni8bAADGVtALjQl5E8rz3rznN3h3N35SlnpdEs+cSW75PmIpJG+UguZdJc0Jkx7qVH4/SLYmdOTk/\nHjoWNNfyP43yR9sIfDXdPmi4LhfHylx5YwU75Ws8hOaEw8YapfvrH+JzXlp3Op0TThpeIVO+Wl4p\nEEWWNtVf4nftjpk07o2jjNsEGiUD0dsr7d50DR250riuBKb8wDAHzWFl+9LNqa9CiJ3n5g/dipKx\nCMQ9J9n6b5HSMrnfibScZPuRvdekAJfy1iy+f6ZK6ggVcQCFyJwafRPSEGu1DG0ZgtCcwSDNatQK\nS7Y/Kp4gntW50QxlZWilfTsjJinNJLvXTm6XneSdCAFpFmTWu+yZvH3S1UjoVBfv12pcLmMfuNQW\niwhfDIFtbW1t3BCPLH2y1iULYNoKNb8XzNrAFhhUmo2zjasBuaDXJdIIjVZM5wTpWgPqY5CF2HRN\nAJMYd7wI0joESiSsiYik/srqZOMz7wKJkb1C55rlmMzmFh7WZeM9b/PkqOHNe5KAhX8XFTy2Qs/7\nlV9PGMzinjGEZSr2EGo8UzVjf+rbaeieC3Q2OAailD6wHV25RhKoRFiRKUOpuagjVOFAmHFJCiZ4\nagcHdZdZDD29GWCFCeelxEtgLI5KjbEfaLLqKXe4XRw2XrbjfpehVPhozTkQLVXiArXCcgicl4Ll\nYsrJasiaBDbdhJLAOW/PT+KYP8oOp2XEbjRbDnyNcyaE7KqPQf1hzykHo8txYusHVNQqFKLNPqVo\nEKEsLOLfgRCYOlgmcB5PDYyi2+wMx+kgrBAYOcckjnGz+AKCuQtKaCwYVbb8D52jlIoVhC1Xsls7\nBpniopZkffOUohxiygUtGIpnyU2ZBGEYJ38ABgiTumSZmjE1p9XjBdabvaj2qRiZ3aTihBaMgFW1\nVrkQ33lc7PeOFhzwwimGOOCQn+ElsF07xjHDQZzHybI5UQUnXFTHhVo47ipKqfFIY8E7EQqe2dnj\n7pUxKxI4IIFx5Fa2CseeCl+0WRgjFCpOV8Jjkwk3FCWrhXDjwPZRPbizy2YxYL0ouTQoWFPl4NDz\n+KTinvEI53YbGjKICrvj0tKQgcBFX3CMitMUHKsrRkAZ2+pZd4Cjuk0Z6Xyt5tLpNcTAELpIF3dd\nQbIvTtMW//xGvD33Oyka5/jbuXzbdbR5LtLypGRrhKM5oSN/naWJ6TVTTHYkhe5a2bA4YPt7Y9CW\nWKp9dcsDwUnOsNAKEIo0bvspvHiSIdrcFyi6aes0fy8x+p2MUkMlnm7umU7ZG/6quz7OI7dyqLbB\ndboCYnQXbOqV7x9r+Ss63yyQRLMXnpYX6bi6ad6G9mKVbN3WrNpzaKxDUUDp8BCNIJSsUikf++Ij\nT6ck4YlYipbnyN0N5/k9kdazpakLEuvT8iIs4AEbQSmuh9FGEoVXe7FTiZ5YVxbXlcCUtA6NZgE6\njZ0zfTmcyzcvzo8sbRjHfPASiYFonn8r1CQk4pKbrhsLvULyPRDCvjIkwU40hZRUGxwhChT5q9Jk\nihdbS0Ji6Nv6p308kg16yDd7SlMvJ90JbRNEbY9PapO5BmjOXUrnSREbJhHoXCOiWf6irVZDZJ9g\nm0+65jC53G1ubmK1e6t0n3BrdYsEIrlqSibI+E6ztgf4pYUpTWulCS+eErfEMPZfJJAWSaeOglBD\nVuILzPLo4rlYzflsWb3SPnpRQV27Z+hydbG0sUzxlfPufE2bpTqgFL5AQ4iikY2fpEk3QTvqtwRK\nEQh2GKUg1nnXscCk3qqw6qYIUATPcvwOsBwtNSelG/zBO6FEGShMszYtMf/wkrphBEbUVBid2taC\nmZTNQjtGGUiN8yWK7Xmr4P+n7l1iNUmyPK/fOWbu3+M+4p3Pqq6uruqeafVoHkCrZxDDphcIFogt\nCIHEDgnEAiQ2LEYMG5AQSLBBIAQLHhrBAhBiRoAQDBo0oJlpTY80Lbqruqq6MysyIzIibsSN+z3c\nzQ6Lc8zdvxuRWf2ozKQ85Rn3+z5/mJuZHzv/8/if6R0+CvRANs+D64ESSnZSYx2hHOTJlALAIw7s\nOUeBdSdspTJYYiCxblp/NGJbCtWE8xbXGgBpJ4k+jr1kwOjZqufBeAifcSkDGyopFzaDX3AbXpJd\naWySwkvpeG7GGYVO4CzAxNMgURhRRGCj1RdeUwYRCkKSQhXYxrtzPTFM+px8xooHskdFWEsKQNWA\n5GKsgRes2dXKNgm9QX9L6dur0VmmWOapJS7SgIh7R/x6kfczet+c4wxvKuZ5ZIvtPSIvCa9d1ZRj\nw72QdxbU9TUAZBeg9UwLO5RX1rOjomnkLJbnFJTkZ6lyc37GO7LnxpQfWs83xEP9ejF6sUmQpTFx\no8I385aNVHamPGqIZr/BknC3c4/aPTPOUuI8dWQ9UmvluOpYHcew/M7WbDHlN4ZLfu38mjwoPz/E\nSrWQ5+frA3qYQzWTSOSMBqujCG+JHP+Z2ho4csXPv1tKxVlEvKlIvqmDtItaKMNvhqK7XrBYe8Sm\nG89qRftuXnNnjcNOdJGlqnva+AbmfC7J5xBYLBq9WMdbntWi3bQmyfR5iW+Wf88azayLGBNieHPZ\nuXViA1CyOH95ir3l79a0Nw28p2daO6vpIreOXdZAOtVDQs+KT7M3Sbh1yOlYEfBkoea1/j2596ld\ndjq7gehJZ2XWFSSM7i2Xegn05aQxMnn7dHFQO6YZ1CdQGf+bvExResUW9w3EN7V0/l2mBi49TEls\n8mz1ky6SFhE4X60u8ocWWyLyF0XkvxeRj0Skisg/+ZZj/k0R+VhEbkTkfxaR7976/Z6I/BciciUi\nz0XkPxGRs9vXeaOxQUYAHjfaXJUiCSc0EJD05nlvk84i4b1IJMm0/KiahCoe7kGE8VVpDCISMa3u\ntRLRiAlWz1XRRBInP1f8+hPAE6UiC68EEK/emDwPQXF2MrM6TeR2jpj/W1O73lxzhUjOH3AXsHtJ\n5nuf7k3x9b1dvxU9FXPrQqfZyQfi+criGu1cEfWCpl5dDBEhSTptp3oi+XRvVYq4wFBNcY8ZBE8y\nXyTo4Jvwk9hni0sVmcIIRRRE/QVqUkJD6Gvy42OpSCnhPdQAj0zzpM0DCbOGqWLqY1ljbwHsc25Z\nUI1iJBJIogaAG0XIUZA2m8+35jmYCvhaQkh47KBi6p9FEiZKEUXNd1RYlCn2Gg1JSSk5w4120zO1\n52l/JxV69bmpmkg5kTXRpTyNqQYFfwu7UxNaQd22/3Hjhr9OGXKhI3d18Lk+KhucJONqPOdqPOf3\nyzkvxvM3zrtjRsKp5dvWm3DNilyVoa5ZmbAy+F69z5N6xuvSgQnJCq9Kz6vSc54KT9OGUoXnQ0+1\nTIdSLXM9btkNW9bFQ3svKLyuHWPpGUvPcey5Lr1fd+4JQPhNvccr7Vhp5Vu2YzSnc76pmdel43Xp\neFQO7Evie/U+pfZcHbe8Lplc4bIOvKbjR9LzUAYqyrkVttn3Cx240IFtLtzTvSfV9QX6wifjlk/G\nLVe24jINrAzeqUf+nF5xISMZ49xGntSeCx250JFOC50WMoaKcYOykpELHfgmB25qx75zi94NmR+n\nbnraivKxbjizyha4pHKmhTP1vK3zeuCVKDeiiBrbCN+9lnXsq9jXPLMNrzUhSdmmxAs2PLMtZh09\niUM2DtkY0opdyTy1FQnjYYYNiU2AmsYOuE6V81R4qJWLXElaubKep+L7k9i1Kho1sTYiHC0xmvAM\n4U/mkS2ZvSbuSOH7rHkoyjYLf6I7cCGJ+2J8Q/f8fl1zCVyOQrbEcVCOg7IzYVPgnQr7Pfzg5chm\nUDaDcr4SPlwo7jfJOKjyrR4eMrJPW9ZDQUQ4rDt2q8x+ldmvOw6bxF84f+0yJFdkfeSwTuhq4Gaj\njKuBre1YJeEm2BffLkN+0pv6k7evVxeZlaf2t7+JU0YHbwNGb4u88JXDnNhI3NymwXBl0izxsf5J\ngCrzO7py7Iq0Tgq2t8QLsRPGTSIHxOZ19ASB+DmVgllxPcDjeJiMsqF3NCMAMkObaUkwP6M0bzlM\nZAINCM7/tV7z9bTpIpOHK0zfKoqoBCifw+nftk/TStwjU/FldQawrX+bLGH63L6bgMtijkq7J2/X\nRaZsMREHphLPJnO72xhhDipauN/JPVufTfNg8XubA8hCl3wTvk1Pumivmut4KQxVPjfmEWiPmm4D\n0xgvms65bI/cytow11M1+ltl7pMJhEc3JvHIFcXHKYmRze2A0xMGPf+U0/QWOZJ/CnLkD7P9UTxM\nZ8BvAP8p8N/e/lFE/nXgXwL+eeB3gX8L+Gsi8stm1mh8/kvgXeDXcYPqfwb8R8A/+4V3lsihoYXj\nzTSaMAOjzwvzaiFjME+QMs/QKdmyzb8aFJFdHD2lLauCefFDf6FnMVji2rkJL5vb9nk0jG3xEFl6\nZMJWEc/SGNredIsLY3bPRRaJWkFNOrw5m5pAnVIG4pCi7cUJRbmGcF8Ikbf1a3ue9vv8N5M1ZGnl\nQICUTlypDQAsz124Xxa/nt61CZSZRbAd19wut+aDzXNkGYonIlgSxhh/J0iIRtt8fhunyaJ4qz/a\ngtl+E4Eeneo3zf08e+kEnzNFOCF0mLoqwgBa+HlLfmwexml+1ErOCaufYyB4Sw+aGWlBonJCpmLV\n88jKm9SyX8A+/QfdvjYZ4gAHPrMzLFf2JuxNuBu/n4eZclVv9xaTQG892d6ux6MnfdQk7ETZJKOW\nxFgzL7Jg1vFIPTPlJvJy9pYZNfHU4D4HzqRwE5PkihXFhJVVzGYR7SEOilWZ2Kza9n4QGPQSeY7M\nSchjhPR9n7tcpcTFlINnjCWT8sD3uGBj8B058vc5526EetXwgIzFy0xXMZ6xoRd4Uj3EaxPeo09q\n5lPbsEuZROU9blinShbIpXIWAOz2VgxurOcsF1Qgl8LWRqwqD/SaV1Ux67kTzJV7Lbyk47UpL2Ms\n3o1p4ZW0Uiy8PlQbdVB8HWGW55EjdV07RISN1cnznYHNQjqlwetFXVJ5mTs2dccu9cDooXgC96Ww\nrvBEN9yMlXd04EaMV2MXifQ2Aar7LZmc0/d3gysx97G5sLDAGvhVGfghKx7h3qRPpafXwoUM3B2N\nkoUbPH/q55PPs5Z3UYCHSXl/zUTmsBHjYdpRBx+/pnQcDVYZLFX2OgNUAAtq+420rDTPZSwoWw4Y\nsKk7N+4tDGBfogyBr1MXIQx6ZmEwm5VB//X039Mzwxthp3JkWvt1DqdiMV9EbPJUT++/OZCZy6zM\nN22hVGlxD+IYCzfE7SV9YkLlzSC1JUnDbe+Nh/rb5LkQNYotLfJ28s+yb24nVjhD7tw/SU6Bptz6\nvGjgdNV5HWbWw8SPmXPIfAGevCwsIm7e0jfzx9t6VfM8NRa9hUK38Ba179p15npIc5/7eyMTpfsU\ngh+XWqir0cw272T6LDQwvNQ3jYwwLOZQWeh2zSNV5Vao3aIPpq6ZvFqhH0206ta6DlXDakKmdapd\naBHjzOKn0LP8eeZnavf4kuXIH3j7QwMmM/urwF8FkLdr0P8K8JfN7H+IY/454BPgnwL+ioj8MvCP\nAf+gmf2dOOZfBv5HEfnXzOzx5999dg+bNBuFsHB0MhdDXSS4A6hQzCAJ2ggQmAd/8XxI8qvmeMlG\n8eupCGPYKCw8DB5qEO+jzCxzB6uRZGnkoFis4opOjbybpni3Qq0NIFWdle+maI/YRF8tcR23EkUN\ngRQvWQOENhMlDGGLyChjhHQV8fs2Np4u7jP559SV+pHqXg2bAafhfdI4+yWUNCJJMMUkdyKEEJoR\ndDxZj2RJqy5kwvk/gat55TmpG3USIhleqUYr3nLCluauxbENDNQFUIgfaaF/hiCa2oqyRHAzC04b\nt3YX8XES5jHVOlsH/fkjvKa2grIJtUaO7qFZTfCYOfFELzOjIAmkVE/kV79PZp7jXYrnUWixfWKF\noxh5UTfKEzkje2oBCJt4tVKx5J5FIDxl7bEdmK6SMKtNf/jt65QhVpTPzA3IL01ZVbhrMBNw+HEf\nyYYP7ebk3Nd03JSM5sq5jRxTjHNeWjbBKqz7IyrGu8VD6F7Q89COXNrIJ7JmlUcYhVUeSANhlDQ6\njOfWcZeBv2vnfKAjKiOP7MBRhOuc2FvHq3HFuhsYQoA9kD0g7HxW8IQNK5w+/d1Qop/Yhn01Lgxy\nMvYiDKp8Zls3+qTCK/qoUuWbSmVdje+Lk6C8b4WnZI5jz5UIDww2nYcgfigDGNzTGwzhEzuD6iF1\nj3XLI9mTKwwx4t8ftzzUgUsdOKNwXTI3lqGDy8iaesKGs1Q4wym1qwkvxzVnMqAKWx3ZF+UT6/mG\n7ni3jKAy5XgBjDgpx76pb4v8tGzM3+Phka8QzoHREkrLdxIuqAiJB7Xwio5LcY6wjkTjENom5RoH\nWSUpYpVOC6N5r/6uOFNfrt6+n4t2vlDhfjW+KSPPw+DxQRkoIrxUJVU4Boj50Hbsq3KmiW+kAxgU\nS/wSByfcwGXcb8qWP8uOHe6VOyS4GArf4sBOEiTjQ46TDLlYXfvfBjeHSwC2+pKnacsD9TfegKN0\nrG10+bcoDeH6qCuNuzT62lzfLkP6L64E8gfavl5dxE4MkrNRcAEqzIFN4pbtUgMHqfdXM5zdBihm\nuGeFua5ZwQ1oHnHSwEMoKq3MRegXDXQNsS4jESUT61ptS5wsDKUtBCraVK0ZQedEh8JSX5kJAtB5\nPfF55BdvWEUFhgCAnQbnpbW83xnk5CAPmeghYo6V6BQvidIKn8qCmTj6LXSGKYakRcVPw+O9PWEo\nkQWznMzzmnbsrUF5yzb5FCcQNP//1gWmvm1Ni247vVfTRWRCtye6yOc0A5CFNFuA36aXLL6fA5lP\nPUUtB7+1txhkVVrGkKeQVLKFIZ9WK8yvliLMTqTOulitDCozSJzaE6O+AE0O4GL+qE3vwFt1kf+/\nA6Yv2kTk28B7wP/avjOzlyLyN4G/APwV4M8Dz5uAiu1/wfv914D/7ifcA5gHXhBKdGi2VocIrNqJ\npb0SlNx4KJ0Fmk8xSGOraSEzup9l8BLxMg0stDCuBfJfeLkEBz+l2iI3hojbPJ3x7oFqzwhUd9PX\nuF4S9YS88RYld1uoFutFm1amLdTOP49mJ88kIm/Sly4tFgKtVoPgoK1N3r7OFgqmPKbZYzd7zU4F\nzvLT0pNyYjFa/Nmo1OdaB4vrv22JfMvXZTlnAtggi3466ZfPueiivTOrXbQxTjMWuUaL2OITwLcY\nt/lep/PMC+lVouwNijgdq3rIoec3RQxyA8jJZbzSwKFbjDemWC1Tm1rO2dso4pcMiI2RsIpfN8e7\n84bZ7ae8fdkyxIAH4e1BekcqJJ5GldT3j4UjxoZWgNW3KkKisl0deV07Oip7y3RauVNceXyqPQno\nuoFjI4lYzFsDOipdGERWwfDW5UpXjRxR4fdkxAxWIqx05KZmXtaOA4pKRc3Yrg4M9bR8ccLY2ojg\nuVilOrC+jkK4ZwysWQf7g+dDCco6H0nVw4u1VgYS2/7Ibui5trUz4QV5zPf1nM1YaVUWcjLW5uxR\nr3GFPlnBTDxXC+O1bOnxeme/L1vWZhxQvq17XtQVFnlTpXac68Dj6h6792Kcal1o18YJGMKUdTKG\nEQYSj9JrAJ6k9XTIUIW1GBrFbRul+nt2pNNTj9fBKTVZxbu4Cq7vx+Hp22tPZ5U1lQ0eSpiBA8LZ\nwnMF7pVCPMet3WXrb/DEhPfjyOk6miEycmWZdyIX6qMAWfdtmHLLALIkeg0lMURLd8uok0TYdIXH\nkrE9vCcDV6PyYX+kmEISHjKyOaZp7RvrHVRGUq7cyzeA8Lzc4X09TJ7GmnpW9bCwBi9kiKzDiLjD\nrCJpC7Vwk450VUlqJMuhDH6uxvdT2b4KXWQq+r0gPWgh99Fb3k/1lL206ReTZz/6YuYYdDnb1o7T\ntWJWzGcnc9NFYiVtyui0vsp0ZpWZo23Wv2/rIix0EVfYHfw1UEVDJfMx7U5NJ5jWOZo1ecmXRbE3\ndYF5aWk3P/lnzn3GdSqJi+fFtc3mfqnW8n3nXOPTbaEztXbSdHe9dQSTzmjT59auL/bGLbeJeONz\n/j1Va2Jtf9ua21DGZCCfz5hZh2X+kjf+RMVrG8l8GW7PBRE3+pg5SYXXiarB/hc6oi2AeDRrBrBu\nEBhVWS/0jp+oi8gcbjqHFvr6mM08B/RL1kXetv20SR/ew3v8k1vffxK/tWM+Xf5oZkVEni2OeetW\ndRFKBky4XhJdhVIrlj1kiU59gKt7nAyPr2zhBxrWe0tCqZXOEoeMWzBiECe/VVVMZFEsdRZAQ7NM\nC6yKcJSKiLKieWNcwU0IRWyq3VOEyUtQIqRMxanARxH68GBpNUfWZnQGdVmHKjwE/pK4QmKqU+2m\nhuKbJ6K9Y2N44Lzv/diKTSFa1dx64FEYMlFjaq1xP2NIrfbQDEDam6ftxRIozSIUbU7M7IaNWcg9\nMEIJ61MDQy1eFvw+XZAhxNAjcQ1sFlciMoG6yeJhLQfN224yyXDE5ORFnbZmLbM20vP7aUFFP5N8\nnFKUsvh7WS29sRd6LDokyT7PpC2MzTNU6VCKuXeyRHs0rH1esNYmD56ZIQuv5VA9/8B9DUZjTpxj\nvwO8GyfCytpYAtrabb7YVzMvKWWGvSVP8Ke4faky5KX0/L96BgirMAAYcCMbvnO45ghc5zW5Vq7T\nGqFydzzyQnuuLXFWCh+ah0Yd1Bf+65wpZnxzuOFvn93n3QE6hWrKMUKxeiqv6BinmkLh7UT4Qd7C\naJjCnxpe8vfTOdopv7p/xivrGCVxVKXXymfWcykDgnBFx1GV92TkWVmTxes8vbCOjyXzC8mZ9u4M\nI1c5c1kGLhm4WmWKCWZKF7TPV5a4JvMt3VHNKbbP+iNjgJVvSmUwYSUj1sEPbcM3rRFCrFnnI5TK\ngHJMyuuh50O74bGuEYzLNDAinA3uYV9h/K6ueZTcW4MpfS4Ynk/6sO5ZFfiMDaVASrCVIypOnPF6\n7KgILxFyOnKeBjqM3xnvAfBdnvOYM26sZ6sHzODKev60POcHTXlNhS3wumQwY5MKr4bMhY6sI3Tw\nZvTnf9cGPpWed+3IKPAZHS8JQ1LI0sMtHWmHomasYKq/Na8dTnM+AB1wz/bsJbFFJ51lokwfM2tV\nxI7sTNih7FW5FuGB9ZgZz1S4Lok76vLijg38ieHI75eeX1m/5vGwZp1GzsxYFWFbAPE5t+6PjEMi\n6w2ow+jnxw09ynk/UkiQtmCV4dgDa7pVPI9BrXtEVrOSrBvEKtuxhCFJyKJgCVUnR6l6q7N++tuX\nKkdMJFw0MiEmr47oqvbERAYzBTRtzXNdJE0LRmO2daUwmTKKazdNLs/h/ItEfRY6o0GJpFjDFbsS\n9q0sSzU4cnviN3+MCJ/EPZFOcmSxfgdLKj7WzZOqNLbUmTDL+68ZDmfj2nJ5TdM6EyAMp9ufHoJT\nRdlsEV4si7yoebw8v5pFKNn8P18v25q/sPfJog2tPy30FOS0vxsUmNZ0Iu1h4dJqGPLE9SNNyjOB\niQaupvC96anndt3epCl5iwOmq4Y+0R5OsVij37zS8j7+zPOYNSO+mZwcB4ROOEdKicy0VmmhizRD\nf9MijCh/UmUa9+ZxtPbuaOhsTReR+dyJxS/u57qiUNUNh1/H9lWx5C3w+x/9mPE3/2vGvDlRQPXn\nfo38zT+PiKKxWGRzq06Lb/Rir4FaJ2uaoV3yULkQYIKRLWi7IyQNoKRAzI1Zrr2MZhM4SQijGqvq\nTsZB8fC/aHtpVqM4vk22LMLYLFXYlOzXtkY00CaUNStSgIsSoEaAlBzstOc+IQeYlHvmlxscxMkc\nu9y8CxL8nw3cQSRIipBMqQvWv0k+idcRovpLWHX2+k0FZxeCrJ2fgn5yWUer1jqBLhFhVWCXKuu4\n0AkzUQi6JY0li+vHIXNvnAjNz9kaAJxaxBRPfnrdU0vg7es1od/OqXFtlXkVO/U6zaAlRjoWKw+L\nHHMDNJVa61SvylSwALStKO/Sdl6bF7C1VYXBvJZBI4eIH50Uo1mCfvy32X38/0wA1cyw4cDXsP1U\nZMj/8du/QZ8zfSwXe5QP3vs5/uT73+LMRjLKjVXu1yNW4XmEQTUikFThhXjux2PpONMjBxJjVX7x\n6H6Dnx9e8jyteZlWaKNa7lxuvTu4F+I8wMajYYfiXohvDa/5Xr7kT42vuBwGfmt1D6WSUBKVj9MW\nlYIOikhFOuW9MvDd4Yrf6SILy2CVhLXNc/uq63iS1xQZMTOe5B4EHh4OmBk/lg29VtZ4fgsGPzZ/\nxnd1rhOUi3vvL0rhF3Q/0X0/lsTaNjySI8ncs3XWDciAkzooPI7r7dXosvABOz4b17OHLBk9lSrC\nS8lspGNnHY818fN5x82x50lek63yAbvwZgkpGWPpuUyvuZZEvA78LveoxUH+jfWIwLf1hr9R7/NL\nwSxXAgyOpSdR0Vx4lAdksRi3nKt75cAO4RGvOZpyZFbAtp+zdi+9V0Nox318dYfCFc4aJxhdgrEY\nax1pxa7XxQ+WJFwV41Ldi/iCFdTKHRmnQV5XIBW66gAsh1HjnTygwEoqZ6VyppWnXRfPVChW6YDU\nVcbhEuoApqzVZcgwzOGLxeo8XmaYZfYjrBOTotOMZr0MlAgz+FufPOf//OR5aEAGlnk9LryEUYMg\nqgAAIABJREFUX+32U5Ej/9P3vs865abVYQZ/+p1H/Ol3Hk0F1z2X0C81kwssDFNtXa3NC+JKtLxx\n7NyapnROdsNpIfTQ35Z0X6vXbBNuRVjQjHhM7K8zucLiqQMPTGyu0tZc/2Jhopz6pBkh/V5KmWDC\nMpdpVoxVlmczeTumeRQGrdZ5y+bV6EONVIt57ZyHrcYaWyPMUAMItSOSzCBCAux4ny9p75tnpYFM\noTfjgJeBgFkXmW8tiE7qQvtm7q9QwERu1Vj8nClni7kiJxNhbnfLbxdmQ/Os7yx6eWEIbQDztj7D\nsj+lheHFXAlWLgcudXq2atXD9kI/rcGynNSLVCxFZLW6AJLehtHqTELR5pn5OIi5HPx7Tz7jN548\nbT2CkdiPR77K7acNmB7jz/8up5add4C/szjmneVJ4qb1e7xpDTrZ+j/zT9Pf/dbMV294Dk8tDOKK\ntwqMycioFzCd6KM9xC2hjFbpcyiaoWAWc4t8TYbVKPYaszKZh5q1QS+loFGZvVglB9ucVKOk5s2R\nAHDhuQnkXtWT75LDZQphsahtojuts+kMzFxYuVBOePigpEXdqOqC5SBGZzrRPZ6s48m9ZIOFJyqE\nh78E8+uswUZTxNtcJCwLtU4hCCpCZwmrdfJ+SXgxUgtPUEHNi87WLHTVX4YSx4J7MUYJ65EqFp42\nRJAUVg1cqg1UOpPwei36pvWBzILztthJGp4tCWsK0zo3SRWNl3yRTTmdX5nrJ/lPt4CTNILOGCsz\nJKyGEhYUm1aTORbcItbXrJDSnK9UJ/rX8NBhFFW3ZEeYgZlQNTHSctcifFKF29TihMdpCgERD/9M\nmoEaScM+73udswJFhPU3fpXNN3/V2y0GpXJ4+RHP/vq/zZe0faky5Nf/xJ/l186EK1m5UmKVQRN5\nfM3jvOG+7Tln5Lc2F/zicM2lDZQEKxvBPGflXj3wg7zll8s1VKaQpk9WG35xuOZZv6KYcb8ephCd\nb417Pu0ST3JmzcjL45pVFvaivJKOD2zH43zGhoGXXc8ndsY5R8QSV93Ac9nw7jAw6sin3Zpf3T+j\n5ASi/Ljf8mnueH/YsZMVG0ZS1/Oy9Hw47thpIlG5zsaN9Lw7jjzOHU/XK6i+CNajIL3w/XrGvTTw\nMhTuTyUUZhNP+FF4XOHdceBp12PVWBPv++je/U6EDQPPug0dhWfS8aEdOVZ4EsViVTLf6gasVl7i\ndZCaJfMb9UhFyGq8bwO70vFJl/i27Lk7DjyVFX1XEEa2NvI8r3kW7Syje6N6HSgq7Czzge1RMx7X\nNe9iPJMVigO0Yp6YrGpcHbekdMRKRvUUBVVJbDB+UO5QgPM0oFJOlBEribN65CZCIBlnT+yLcc09\njuSQynvkhFyi1I6DZ7qzJtYzyxjGRiub7J48cCv1BSOoskOoVjApPMyVwOO8nvK0lKdDYiXGx/2K\nO9VIMsuQ511mVTNVKpJhM3Tss1CloqZYeILWh5EO5UBlhTIOK0QLnWTKeE5aFayOWBlZR65BEQ/o\n/ovvP+Af/eABSxny29cH/tX/+7e+6FX9425fqhz5J77zC3zz8nwygEnF3xGpzhAnvlY7mYh7GufN\ndRENw2OecnDdEl+t1UMM3UHmaJHbzOGFGqRTESqli5CxkOsNEDQFfgp3IggAJq9nQKFJoRfXoWig\nTSYG2wYuCnNETujuiHjkTQTsTNde9DFTjtV039moOUMLiXuG144IQ2cO0xLc6GwLENe8cNrGJgBH\nNaJsxgL2LYyirR2055H5ag00CcYY92zmkDfTaGwKObxNKKuTst9g5+yFak/u2ULTE5z0XvNiLuyb\nizPjeRegtwHwybvWPFIiE8CabyHTfGs6pE3EWYR3KQzaizaZuU5Rzai1gTXB1LwGqtp0nVb615ke\nY3wJXSTItjz8z8IkxuQt+3PvPuIfeO8RSznyo9c7/r2//ZtvjMCXtf1UU6bM7HdxIfTr7TsRucTj\ngf9GfPV/AXdF5M8tTv11vG/+5hdfP0BRcmrBokFLqIpqMICUOefFv9dJSW8eiJQ8hXIZ2rbcc06n\nngpzwgQIS3Ncy3BLnogwWg3K6YXF/jZyj9+quifGstfmWd67tWmFsld74/u37kG/naoX5fy846Ih\nWJqfZalYa0pU9UKTIkEuoG/eu4h7j2oS0Pl5RYSUZ4r25fey+Luqn18EyDp9TjGuVYk6TTL1XUrp\n9HrRv20cVJWU9OTet9sw06G/uRerE3lEu+eXsac0t2emafWtE6UTvXXOF7dnyjdabNP8rzjboSyZ\nBOfFrb0LtVZKeC9Hq4y3ismVahRTdscaNAJf3vZVyJA1kZOl8Ljf8HA88MAObCn83f4O5+PAA9th\nZnQoHcqP0pqsA/c48BLlYXHLluFe6WzCaB2jdWRzD8+As69VceKBB0f3ZK/HhGQl5cpe4YO654qe\nzzp4kd2bdC4D+dY4XLDnIIl7tuPTdMbjLjOI8P1u6wulCBdyYGcZBL5drvn7+YKtVS7syGs6Vgxc\nsAc17tmO4PwlZeEOOy6s8qd2L0hSWTUPhiPLabUwhWPnmnlKlZxKhFh4fauXuuJ7+QIR4Wm3Yt8p\nI/4uuvsDHnc9j/OK59Kz7zNdqiTxuXwvD3QhD/okdFJZiyt6d+1AlwqfSc+nsuYVPVerjk9lzaey\n5mI1sMlHPpU1L6RnHWqNAtt+ZNMd6alkjF4rq3Rk2x+xAmerA6tkVEs4/ca8b6KwrSW34HcYtWZq\nTdM+ULlOc/2uRsneaNkv04EHesMDvaHHTvZH6TUXVHqMB3pDN0UiuPK8VCy/k15CSmSEfYLr1FEi\nQuJBKjxIEcPY9pAP99M4XVMQjkk4lOQK6S32IzU3GF7u4XIPHcpeCqsIFfdQqUKtlc3qBhtveDX2\njOWcI8o+QGkTYE2GXA/2pcsQ+PLlCHh4WRaZauu1JUZkUaaDJqNlCm0GQcSjQRq5TlNofbfY3QtS\nF64KkTmvUvB3opEGeEGTBgza8XNO9bzN66qFIdiEKZqjGVHbup8Rjg14MOtNcLpGL9erZCFafpIu\n0vQiOc2nVtEFkGodA8270Hab9hks+TMwkRupzJ4LCTkiAYaW54vMn1s+WPPoicgEKvRtz3KrLxpz\nsmIn+wQIRdzQGiBsHnOb9K95tBb/SZMIrf852UEXf8vUbSfH4XmUJzTqt4Bdjrm97OuTFoks+tOf\nzUxQnWEnEGDJDQpScbKSAEttjoG5fhLEZS31pciCaItTObIf5SuRI7e3P/QdxWsUfJe5/35BRP4M\n8MzMfg/494F/Q0R+B/gB8JeB3ycSKM3st0TkrwH/sYj8iziV538A/FdfzEoDSZ0atR+h5DTFChdR\nehOSKKNaeG/USQqaUt0mUNc5cUK8EC3crOX5mHkujaY8DWhRzymxJlByAK+maKOgHtdqWd0DpTOx\nhIgsKD4jl0nEax2IYgqjVGfiC6/UmIS1eV0oATpNjHhsqtRmoQovRbzLouLpwioTS2ALzzN8sjbf\nRcLdpInktYY8iwnC6qS0eghKCbeRLELS2nNgQXBQKtI59aMlRU04Ut2bVqGmcI6IIFa9flOeWQA9\nFtn7x2OpKySNelczvWZlzr8SwtIxeYXmRSATrIjMlN1NCJUa7uOF67mFsaVYYMbFS+9FROewxSbL\nKpWWlzSFaQoTiBmpU2ibqswV4WV+eUQEkhPXj+IgJidlHEeP+xefD9QazIaxwIqHx1gAxiZkzWZg\nVFLEGKNoUicuMJAkUyjOBEYn4GWTcQDmWHwLmvERz0H542xfpwy5kAN/b/WQX7l5ze/0l3RmPO3W\nPM2JD4bKr4w3/N56xTuD2zM/6RQtiXuy40aUvUK2nrNxzytcOX4dNbAe2oEjiVfW0zGSEDoKe3p2\norxve25qRpOyrhUblLOa+DitWFWQWjivwnXqeYFwZgmpIzomHlB4xobOCquSuepH7rFHrOfhUDGB\nj/o13z1cs6Vyz3ZcpQ2/VF/zmW6gwrftyFWG52y4U/e8thUfDEd2XWGXMjd0SKr8xtkdLjhydxCE\nI887hWClstqRTBhkw4d1z5OceP9Y+ahLvEjCC9ZA4cNh5NxGntsZIsqzPt6PiQLU+MXxmo/6FQXl\neV5xNh4puaKl59NVzy8dr/m+bXlHRz5kz4+7DSVywLZUUhI+61e8O47RPuNx2vBe2fGujByq8Xy1\nZn2sFM1OtGDwRDI/rzua03hfMiloQsea6HLBEN61PZ+KEy88Zs0lA514ntazccW7aceO5MWE8XC5\na4R1rPKflO007y7TgefljH0LcQytN+vIEfiUNZnChpGDdlzaHusyLyTRj37B76abSYb0VumoULIX\n+NWO3dixA15Y5rvpJR+PKz5Izoh3VTPdwcMA78S9j1q5xJkREdgNwqM8kDzgGkH4KGU+tJF9UjDl\nYzo+rCOvOzha5t3V3mVIXnNBoWOPBCBthVKXMqSPdXkJAv6o29cpRxQYqitQIhp5pVBM6cTBVKkR\n2iUR/bBQqrEwbNU5TaCldTQacKuuyE/2MBG32DMrwW7tn0PGGlhKcY9KM47N3gSLlX7ODSJCrpgA\nlBoB8NxA0oenRyDC4ELxXng4Grho12s5yklc96nzo0/3g9Mwxaa8L70/MyOgzmFgC13ETQXzfGp5\n06EQINY8Yf5V89C1NrfQw5ZW58b0eBSbIgLnhX9xzJJRzhbHSNzAmL2FPsbiwCiuU+00NBDmzzMz\n4rwla562tvlZdRrZ5oVrukj0W4Ck1mdNjrRaWu34BhrL1DbPsU/Tu+xHuxd01kVk6pSodYnEvcMr\npa0nQlcKcKfqRdRTOA6SusdKYrA+TxdRcR2tfMWpTH8UiPYPAf8bM7j/d+P7/xz4F8zs3xGRLV7L\n4C7w14F/3Oa6BwD/DPAf4ow0FfhvcArQL97Cxa99nuix3ZoeFnJA08z0JWENS4uaBdVNDtPsVBGk\nMrHZufXAaaWPtYQVyaduqp5Tk+aTMXFwIlMss9OKGy5kRnMiBQc7Pl3SAp7kEIpJ0xQWJpIoVPcc\nBYCgenhaVYL2PCxXtYVZxcst/l1jsJvCAoIuWmt1avMI9vc4Vo9i9mP996NCCjSp1rwe7QV2x2rF\nC+56EWEHHDUodgZ1EoMwIHg8t78N3qoGYuIlcKucQYrFp3q7kNliY3gfSDCy1HiRWyidmYeiifnL\npVFwokiQhcQcmTyMbfxFOBYPCfLYfCZp2jxBLKz9szt6LjQoIRzNChrgq9V48n62mbAknqWFMkyL\ng7V8ruKLEg4sBSF7gAOlVjS8gN5/DtBSSiQzD9GLkDwlmAwj1MONWtHGZc0KAdUgQVEXyiMtZCBA\nnTDPucXC8UfcvjYZkky4KJVXfU8L5NilnntlYCfCTqEX5XlKXn6gdGQt9JbozHNEPuthLDNDXdbK\n5Xjk09WKMiodnsN4fzjyo27Ntibu1x2DCO/YjidsuRN1k0oWhtTz3vGaJ6ln0IG9ZNYGRQZ+rux5\nQc9GRp6njh0952lPGhI7SRyz8MGwY8C4c1R6YAe8sgsG3UPqSQgPxh3ZjPuD8KQXkMwGYxDhYoQN\nhb0kVoy8SsK2GK9zJZXM2ehEAx0jT/OGD4drXgk8TWtGhJdpZO3aAmZKp3sO2fheesC9smdTKh1C\nb7Cunj/0WVrxqWx5LcqRnoe8YiUgtXKgo6/wNGfO9cBQXAGpwKDCoIXX2vOg7rmgESsUVD3kz4qD\nGFPlXh0pmhHBDRAYH0gFMmbCWCqbVKEWjMQB5VIrhcorS9yzI2bwma6mgLpXknhntefanDzjbh1Y\nW+G37JLzPEQosmC5IgZdWI6PptwP9WcfCsiudqytsE4FERgsI15MjV4qWyt0wVTkzIsufTopHJOw\nqSOdjTy3NXd1DwYPpPIK5U4aeJ59/XiY96yqoVSu6Di3Qg1mzW2wYG0SbGXgM1uxZkBRzlLhI02s\nx8p5Mi7LkaEztsVZHLUOXHcrzsoOEaUYVDIr6k+QIT+V4JavURcJz5/CaOCGRn/GEorGzO4VYCB0\nh6Y8+yszh1e1+kDGgpQhPhcg2ZxbM4Wm2dQcECYwNd/XtxTXaMDKw5oWij7MYW82gzGBCRw1RVcs\n0gaW91qCrgXwaOdPXtIFUGrP1nTeie68HRvfD1rDWDmTG7WLzGQZhCKtE+BoYW2jLB6IFm5Ow1M0\nEobWKu+jCBkTJsKqZW4PAcCaUb21uZE8FVvWWZpoHqaHnkBr6AK2GO/R5u9Pxqc9wqIPZxI0m8ig\nhNAFrZ1zyvl3Cl5l8f/FoLVzT0IvvY2tzmebo9Vkys1v80qj/1smh9PAh2EpQE8bIYk2ptBbm07f\nyrV8kRxJ03h8NdsfpQ7T/85PCOUzs78E/KUv+P0FP7Ew3JubiFvKj3VENGONKKCC5Nmj0CatyqIo\nJwTiD6V0ER6q4oplzhmqg5kioDn550XezEnkQoy2IhNtcHOB5+pkETAXRFVcIZdKgAcoy0BfgOSp\nb11M6hIvW4kCGy3+tt3fpE1WXPiqhwgUcctQMzNolwnHaXjDwmLA0pkdE9IkLGdyalWZLD7e3oyG\ne5WZhSZAh1DnNz2UlRYWVq26ZS1Fbk8LZWxiK0BmDPrUzhPrhDnw8c+e16WtunaApxI5Qlqap08n\nmuxlmGEpBVGh1OJ5ZKp0tVlabH5Bp+cn2uSLoi5B1qJ9U3+yyLkihOXkedLFv82WlCfQT1wjiVLG\nkZSzA5vomxRzvJhF/S4lRRhixtzLSniWop9L5KdM7ImSUfMq3oQA7OKBRmY2QFu8V3+c7euUIa+0\n4wOBaxFEhXUpnFvhGT0bLRRLdBhHqfRJWDOyZ8UZe4ooYxJGlAQ8jyIQ61JZmWFirLpCX4xXsuLj\nlaKSURt5seqowDv7I2MeeZb76dyjJHo8fnLnvlsAPtjv+axfcaPCla3ZWuFOPVJU3evZV86K8fF6\nTRNMl3XAgAur3C0jz3vjSOY6J36cvLbO/amKVmWvmaE4VbZJgtHoSfR19OK6AmMHWmAtyqUVrvKa\nkiqHoOce1WXIC9lwxsiqOHPb+/UGEWEQpZI5ChyjHlk2o4ry6DhSdQSt7BOMImAdK3NrQJNlIDwa\nCldZuDAHItesWMuBQToOASrujyOHrmBjxx32J1rjJ/kcgO8O/vyvZE2XExd1z2vNfJQ3POJIwb1g\nB0t81HVYhXtHr6uVVcgJpLjFPYlxVkY+lg01GS+HFfvUsU4D3xwcJF9px6hCri6BAVahJl7Tc08G\nqml40U8pijuZlbQXzB4rz/0s3GfHMXndLisdd7ITj6wpmMFQlcToOTW9MRyEuzoyKOwssaWwtYKZ\nM58OJpzrQC6G5JG74tTvrIz79cjLzj1upffn2LPCzHjdbUkG5/XAIEI1/Qky5I+v6HydckQBSV5X\nyNd/l52TMhmPNxeUnRXgObStKbQNMdFW6FC25xyk1DR8mp4RE3tSfP2CsihZMnloJpV9VoDbiZP3\nQ+Zc2NaYtpylAB7VKo3MgXimdvdl3swEmqSBiDBOxr+NMc3ivKkUR7u1ufJfAzW5yTAuPE2b0/nj\nOcqNYiOeO9bG5llaHtuIFFqR7yUjr0XfNdExrXfL52UZgsdUG8tD6hY5ViITQDFrfdq8Tk09m8Pb\n2ngvQ+hzdEyNMRexKTdWpjkhkZ8VkQAy62lTj93S3076PIAiEp62NH/vfTjP3YRHqKgKVm2iem9e\nJY+icR+1E4/4Wmu1gLl+lWrTRbz1TRdJuD5b1Imnqn6xHJGfjuHlD7x99UGAf4zNQ6uELE6YXIIh\nJUfeR3Ntdyn5QNRwByYnFFCDlbmXxpNq8ZcmPBEa4WQdi/crOYMH4PHr8bWITxYRdw1m1EPUYhvU\n6cBzqxcQx5u2l0Mm4YKAVUXVKaFVLQCNTch9wncmHMXoAqFUNbrqL0j1wvPUxOzViocYjaAlb+jf\nNxXPp8o1PDziZkfF2a0EBwbOWNpYUVzwTmDR5pepAZgWQz1Y5ErVMoUbduQQ0AVJyqCwHltSoVtE\nj3hf1eapKXOuzshc18LMwwmT+jHmEo5UPEyzShtoJ+iQWkmaPCQwcnckebygqM4heVpd8EW8w9IG\nNQlOM7BG7WmRR+cesiwLT6cIkgSKswVqNVKa88dSAy6x2JUY8xwOuSJBPZ/TFO7X5kOxEp7IVjHL\npvDCigvHhHv3ChErXPGwvKWHrhiWXGLWIIKwMjMXinhMM3huzs/qVgWeW8fDemRdD+w0caUdD8cj\nmzoy9FtAeFQKj1drtmNhzYF9TuRS2ZbCw+OBQZSncsk79RWHJDxJKxR4cDjyou/5xvEVB80QBVif\nRo2iz9Y9fVt8QnFUMX686nnvOPBZP0ufH6wveJG2vFdeoFIo4oBiUEWSh1QeNZHNEB2xYcXKDnya\nVyQd2NSRoyW2NiAifGd4QRVhR8dv9/f49vEVL7RDs/Ht8Tl76Xi18nY+I9NL5cdyxztOlWdmfOf4\ngldJOKuV0YPCIELq3hl2PE09N6lDMC7ZcXc8kmvld/M9quAgBl/opcIx+8IopaeidLVOYPIi5Onv\nySWiyrf3z3gwFvZJeThWBkloyVRJ/F6+yz/8+mNeqsCgfGN8wd9bXUIqvKbjzAbeOQ5ccOBI5of9\nmvvmxXCPx55rSdyXGzqBG+lRHdiMxj+yu+Jx3qIRlnzFirvHI9+u1w4GDX7UbRGETQXpCiv1DJYn\nacVTS9yvBUhc6oGXARgndW8M0h/zZPJe4AdpxX0GVmMLmXIN6FJGlMoeZS0jl3IgqbBe7ej2Ss0G\nXcXEqKPHQazzAIOyI9MPhZTBqq8FdwKYj+prYzIhZUjV0I0rP1dsQD1M+5WtPd/0LTKkLwObMnJI\niZ1kXmOcSSGP41tlyIkG+zO4FYAaBALiC3BjYmuqLECSAMChCCMzhs8S+Y3FFhTj8ybCLQu6LJjX\nTkOsLNaMVqy25YaAr98OeQK90YLvfVWTxQ0lGOVEotaRapBExeouMkfAV5dujZSikVu1ezR1KAnh\neQRQikfbT8846SI4uMoiUw1CiBSLhS+kraFzTxAK+2xylABrzWAMC89NSyWQYFTGjZ6C65idzV4o\nxetXTmkPxuxxYmY/bEN+u+YWLPPMZOHlar3hhsxqNrHwNcDWLlM1frOmeszXXwZauhdeIIwsjZAk\nhXduIuYIfbcB1qSTv5JkdaIIt6kfIsrJLPKKbCL5EnXjuhtaa3S6ek6kGKpu/PXnTiQxxEZqRLTc\nliNe7cAJqLyYcGVUPamreipHvlpd5GcKMPUE4QJCkRpW/EiwlNlyI+3zwnNRRSdhVYEOnwRlenHD\nFStMrti2TYXP4vPSgiPYFAa23LrIPVkm5E+CJSwHM5tZdZBhlZx9gjX2PE2nBBQVWGueLBBF3Suy\nDHNo156MBO0lDcuSCVCYipNKgAxr1oV41iYk2pxcJiJOnw061ana9qm/yseMELgIrC1F7apQ8c3I\n1ajZWfcQ4ahuQe1QKJVelDG7UEnTlePRVGhByQ2E1gAVY61YWrQfQVIKoeLPrDZbK5bPqJKivoBN\nCZxvs9y1+adBJtGpU08TC8jJOaokqQ7QmIkYxraQWRBwBEAfIi/Pajm5/3KMmxXKhfnpvCXOKWKT\nV6vNk7EYSTOjjf6eiHsL2/iPYhEW6POjAWvM5+TP6vbABjZqKMpOjFGUVa28SpmdJVZB5TyKe5rW\n8e5tB4uwX+G1dlx1iQ/tineORz7p16BGLsrGCs+rUiUxyCxeu5JBy2mNsKJ0dUASpJro7JSu/cF4\npJdCboVbU+XOMLIX4dgJlITKSKEjlTKFJffV69280o5rFVJZQTqyTx5SVsbMLx5eUTAe2MhHacMz\nVlSEVQA8V77gPbsCoI5eZ+dlylStvNYEI5xjqA6s6ECVkjoejTeAIanyOidEEnfGwRWdNJd7tqin\noRjnUnkZhq67UWDWgpb726MXox06ZUD5peNL/lb3CEN4VCvFlJ/bv+BHmw0y9iQp/N3uAZ+mLY/s\nhrOy54Nx5BPpeZYTdwbhXt0jxcHhD/otd8oBGTu2tqeTHU+k5z0b+J3uDoMIoh7FlYsTt3yaNoxk\nVEY6I5izmPoOICdFcubFUXmkxWvwxdO3vzotpAqfac9KjN4Gvs2RB4MDy2e6cvkJ3LEDR/F8Or+H\nMvaFdFCe0XNuA9f7DQdRHoj32VXdkjFWEYVmC/3CZUiE2mTDGlNBbipobBWKGtaHIj4I0sFxVLqk\njDZSUqZo4ihzMd2XkrinxcmYcBnyLLk3UxcEOz+LWw7Dp4pSQ033VWjpB2phbjZ1p8psYGyHdcEq\nuHDSTJ9v6cdvzJ95fYEWMra8FgRxxMKTc3pJCWNunX+Pf1LkYbc6gV60dAFUxAsmN//VGHmOU3tY\ngrjFA8+4IcLdwyNT5zWcClOh2XigExVr6e1prprwVNQGBafHifdxPiU+nzLdmYUXbAGyinnplc78\n2CwLAHq7o7EpWmaOuvG86BoAhUWbPNKleR7nXKfbYywBgpbz4XY/WwNF7driEUA69ffc6ZM2GxFJ\nEGF11NnoX+frgetjTV97qy5idXIEOL69pYhE/7j3LU2AXkQ9bUVCF5n0mfn6tRbQSFMJI6PV4h61\nr1gX+ZmSWkPUe3Ayh4RqCkt8hDUxx+F6st48gZ0hTSkinmMygSNHsiqeFG8QLG1xU5GJzUzC8rEM\no8rhZbFbDG1VxSesCWNcqyQXTi30rF0nacIyztJmzrplypTP0ybRlBQXlCO5U1aSwkLgbRWEISwA\nKhKsglC1ojorv6j4/ZJ7VUy8MKyIW7ual0KCFTBF/9Rof3K+7EnIpmkMZMrd8fN9VxK56lzTSj22\n2J/JraGqToudROnx9quqU4knZ5Az9WdK8R/MwFA1YdoYFGMcq/l54iFAFnTxo87gcMmoV4Jkw8Lk\n10XYn3uJlNwYEON+Gv1MswA1K1EsXkUdlFcLFjrUBa6Aag52K6e6cBkkIP5sQwAUkYSB+jKIAAAg\nAElEQVShkTDaSEQsxjCDOjVy1ZkZUmRmkASlVgeXWRNFFNE0WY/AAVKLwe8lgQgpJ5Imcsp0KiTJ\nrJKib5OFPyPbx/2WZMaVJqp0aARLruL3pQy5OI5cdR2vkv/6Kq25Shuu0ooDGwrKS+24GI9cHAc6\ngyd5i2XjWd5wo5lRhCI+5rVmrCSec061HrQySse7w47OjB+t7yI1MbDCasdrXXE5VO6Pex5351hR\nPuov2GtmPUC1hFgmm5EtU7JwnVakdOT9/Z5neQvWc2bHicJVDfp85NAJ2h1ZUfjGcE2qORL1hYuh\n8LHcQUQ4GwvrUcj5yJO8IuVCEmdz28gAGEfrWdcCOvBBvcLywL26I9fMEIVokwgdwpWccSVnXBTj\nIgxFW4OqSi9uJW0ypHNLCykfSPlAV5RvHV/zw9WWu3XgQR14lnpUKutc2VZjrQcOGc4Z+Fa5YmsD\nVVf83uqc40r4uWHgKm8QtlyacWnGw3HHHTuCGGszfqj3WFP4VNdc58qYRt4bjBvJvJbEQROvZM3z\nznjvuGNF5dwKWx250IGbbLwz7jh2I70Z30k3XHKgU8Ok5xeG1x6CicvZB+a/iVUuy5GLcnDvFcL9\neuBFzrzMiReywoCndsaxZJfZ+47PbIuReWUbRtx497yeIUn4bbskW+WZnXPFhpesXYZ0QV2s8NLO\nuBrOEa1UNVJJpIOQBkVreMcXMiT1PqdT4kSGHGQucv7QRhDhuttwoxtudEW5LmwPB9bXR4bXP7te\nauA0/J80GSubR2gpRyB01RlFuIEKOWGDa3BLZVb2p8KkuCJssSNRyqStu/GONZa+tsZKkAKliBYp\n7S6u3Tac4deRuT3thzQd6wCmgbmwN0YjXWfoRMO7YnFtYWz6C0tPS2Nom0O92gUbVsnxvJ4nppPX\nRWKOtT5rOkJre5M1LOSIYOHha7uHcjXlf8pZEh+/1lZw/aZrFOv+qHEcMd4Omtv4L3PMiDa3fHWI\nMPiYN07RHqx4DWBb6D0ikx5r070iONEkxpupDleSxt7n88hxz5yO4hjUYgdToni5Xz+1+9GqIcWM\nM29TsUglYXY6nOgisgxRDF1EQDybM/TYU10kSeginOoink3qcy4nj0bqgK4KXQUdR2+LGTbOdQK/\niu1nysPUhMAyjrEprB6CFC/ANFkdCVcN4WCcUItrTCrwUBezOZQKXGmu1RnywCO7JqsHEX/ZhJPN\nIAPC1Tq1O9rePjNbhNpz+TxvFqK5IBvMwKptLcm/FK+vUzDIKUI73KWcUK+tMU3ieNkawEkth6m1\nRybQ0mJUc9dRSiFFIcUpLHBp7QD3TrV8spnPdPKWgYcCOuichUdSjdyZuEwIo7Y8TJ4fgVxgzIux\nWSwi3rfNHY1bchfWh7GGNTssvEkTo5UTSvXZezZ7DItFuOK00DGN03LcGlit6kLJLGira50sOO0c\nFWdPcmE4QlaknLZhoGIGZ5Y5RLjMsn3LYycWmdav4jlZfq+gVQ1kWEOg9tN0EiDC/Myv5TknlZVl\nh3G5pc764papc/7fz+CmCHtZ0zEwkElWOEjPN+wlP9Yzjg5dJxCeqqc3v0griiSn7gcOAJYY1cNd\nRQuvckdBuFuODD4zue4TVuDCM8P5cLzmhyRGUawmXnQdhs/fZJAtYRWqJCdnqZmihfvjnkPqOS+F\nKgmRwjHrVPes04FjWkHxAsci0KnB6GN3lDWpemFYrUZXjWPeIHqg0DGgJFO0wkvteK+84v5YeKXZ\nVY6qXMgANpfD1mCsO+aoMzPG+56VoyRII7BmoEQcOrw/eI7NTjskFSftLoVXukZs9Dpy4dWTLPx/\n3L1bzGVZkt/1i1hr733O+S75VWZduy5dXX2ZnvaMMXhsY2ssX4S4WAgJgQQSAgGvvMETEki8gXiy\nhGwk4M2PXF9AGGGQ5cFC1njs8ZixxzPt7qmprmtWVn75Xc45e++1goeItffJ7PHY45npVnlLWVn5\nfefsy1prx4p/xD/+kWtClshG5WPZIVKZRBnM6LRyXkau411/aZrZ57rY2wkjiWfhv3G44/ubHXNk\n0b6nXtP0sE5s5wNj6vlILl3lc+ypqWNjXsHxqcwc2bCVmXsyb9U7PpItJe0X5Sox9QweYKq8PlY+\nSROPc+K10d/JCzmEk+GjeGHHNSCWhKc68FQ3vDbf8qAW7qPOqB0V4dwmPmPDnSXOZWJOibN5lU9X\n4HHKMG/5I/aE7+mODUap6vtXTOB9HSgIL0tkNqtQg3JROt8Z8qiecer8/DYLkqCvoBSEctIeIXG2\ndwrmzWCcz0c2Y1DXFUjmzYylkpo86pf0EHFb0pL6i8hPU0O150FFYyU1hbZGF1uc/pPhKPH5Vuvj\nX4nyA1ZAttRJSfMV2p64ZpPcUeY59oHCSR/C56Nfp4BokfymsVN+WPDHjKV/moiLHSCyKLImsdUZ\nX56nAbA4f+wnNaTxCSChASirGTlpqN/5ZxcKoz2flWl+w3M1Y+Lz0vbgikTvSDnJ1OiJIAaLwEK7\nV7N1rJrCcBvQ9TrrnFsLaEbZRru/YhZ+hCyfbaIYlefHtt2HEK1cGpAg7m19JAhluZZt87XmrIgk\nEiqJ8sK5fb6sRM35AtvWZytSUaozg7Qu9uX07W3fSouHWBdV6hr+oIa4yTLXkV3qginGyfi2EgwR\nYS6ewVdpL42DXkPJFH7UieovFWDyzMZJrUfMgJWQWJaoqWlofX0TfdF5njf6Fp2al0jXyuL+Lour\nCUeoaiihyMn9eKRgKN6jcBaPsszmdSNV3VD1bfHhqLgsAMrBjnNN16jSnBzcZZGlcWCVJhEdhkY8\nW2AS96GCzcUzQrZylP1ViMhDXV/s9qKdcqStvZztGiaoZCS5VPrKs/bRqeZGYVboxKmDniSzZfG3\nF8sXvEdHgpGGhlua6goyS2SFkkHRFYxaBql1sTwiThVI1UGNmCyKfySN9H64vVqcOqAucV5qZdCO\nYwKtZXl2wAEcwdM9IZarKqVWRvW6Jqf22WIAm9piH0ajrc/WsLAkIUXzXtGYs5RDPjwt9+Bj6HM4\nS2yQEj3jRVbn0Rqwc9Pd5lgxb3qsydX2xHtjlVLo4sKtwFJVlzFtwQUTY1MFNLKpJFx9qNH+0klO\n78t3dMV4275Y1IR+PT1EU8GOxuvllvfTS2ztyK14HU2X3Lm2KTPIgTvtUCnsCkydoVKWl+myUerU\no2EilaE6XeI2d1zKkY+6HT0TS7eeWqgG35qe8H5/ydNhw1t3d3yy2XJmI/dDpo5bdhVEjtymnt1s\nPMkDfS1sClx3yrFkyD5nRTK/tnuJfi4kMZ7kDV0t7HNiN3mNiyl0tdCVjiywS0euuy0yzi7LXzNP\nUuao7vyUANuHkt22iZLnoCu2KJ+4Y5Wqca9KsYGhzJj1kIRajLu89inCMp2N3MuGZ3lgQ6afZ6ZO\nGcrklMNwR6wkkhasJPIsXDJynzsuS+Feey6iY+vDcuADe42stzyaJj7vOlJNPJxGPk8bhmJY0ALf\ntBtuU+KNcc9Hw8C2Gp9Kx8MyMQ5Cnj1ODZCy8Tr33MmGr01P+YINb88T/9fuHd45Om1xDGGebjKu\nRXgmiU5GjnhQqEPROvHL2x395L2dPs+JX+tfBuD1caRS+db4zIfH4KN+x4PZ19V1Hnh3unEJezmy\nsY6alS9K5lxWO1YNLphREx7LhpdtZCdHvrAzd6wAmRMXMjqV+MSGaHXwXhSGsmfSLakKvR0Zpy1d\nuocK21EoamTzxvEAIhOGYQm2ewmQdGJD9J8MGwLh8KZFSmHZQFrWYgEdJw44sNBfagT5GiI6dWXT\nCahd8IOsPoksznx8nshE2arCV1hrh5Kw9L5p7YxjR3F5d2ky0xbiFavjXE+u0a7Y6qRO76VlfbLg\nLI5GF2/udAtqN4EDXwjub8RFlmwT0Ea21RJZBA19n7fnpNYXX4TwRUJAZW3A+pv4InETSpxXnr+H\nmFVam46WNVNO6W3Nb3ke2AqNvcTy3QWsxJ6+qBqbB7jnCCafHg0UNyGI5f7VA9pek8VClSy2XAKI\nOp/Y1+vJ+a2tqTa2Igsgb29loxM26qADp7jpRbzCluvJc75ICyjURZSsNR92qqRFoK8GADRXfm6B\nMjEseQYuWwh5SKPyVTSUQn8cduRLBZi8AWS8rBI1MNUWOtxQYK+VTp4fxAawWmNUszVD0qIca2dj\nvP8Pi1bAc5mj584bIg4FqMmvb9oWoy/Q9qIsBXgoqfUWwov/mzhFH1ZgCmeOuGc/or6kARFbi0hV\nXcHmNKMDa1q5VgvaW6E35SjlxPiu5zMzaor4gUvsLcIBqup1Y7BEuVrN0a4K91KXXj+eUj/5zAtj\n/WIg5bTWDIrPUzVqGBfBm50huvY1CmNQqfTmDYATnnWr0sByu1BEcCtMWVBLjAKbEqpcJ/fkwMeW\n1Llhzt9XB3C5tKJRp/qJhspe7HprhcaagYRmW1yoBHPDmyqopKUXmNTIHJqtDW4BEV2k8WsJamhi\nqYFb18OJ4ENkUf25K52mJWJYzNeDp/7bHcca15bqbwBZQRJWPLL8wtR96Y59TvyGXoAkHtUveHm+\n5VA2fDScoVS+fXjMX9++wksv1BOdlz2zKps8s2HPIW+hDJyPozeCBT7bOBiY6BETJga2HKhqvFKO\nTinmeTuyKxNTUj7oL5lt4Ku3d2zlwBPbcDZWpj6h2etP9vSUrPTseanC+Vh4NmQuSs+c3Ka8e3Bn\n+3vnD0I6Cq7mwiSZUcQBYIn7zAWVCRPjlXHPdbdlZ8KYjWTFv65OHSok7qXnNbvlK4c9v745a+HP\ndVFU/7MfXLgil8Kh65hFOB9nSDBFEOJqdLtwkI4pC+8dbvhBv+Wuz6i41Hr7zDinUJkTqibUChIK\ndcfkLuA2NmuxxKbOjGnLph644YouVa7KxDPdomlcnKJNrYxZ+aIfeK9e80U956t2z70KexLn7Dkk\n3yI1an0e1D1/r3/IYJXv9xf8ibsP+PUuhDHiXdrkmW4qvGoTNzYwzcrjbgQ7w+rEq+ORSVy/8o25\n8nWe8swyPVAQHqcN4DTZD7sNO/Garp1N3EjHIScGKzwsR75gw9frLU+zj0Mu3g/sMT0v2+gOirrt\nfS17XZNFfUJZIv4tkOauX9/tsWpMZUef750mNBs73VPC3hwGYzN1qNqSEXRKlNuQYi4TbNWL1odk\njMf0T4QNgaAltT5LLT64+OZCNt/HX5Q9FnVRBd+dWkqA5l2vJ2+HRlsQc0eyCTy8eIjZ0jqjqtBV\niyBaAwArKHBfxHfbVrBvELQy91ly3ERZAOGatXKQsvo2S4ZH1oBrq39anqXthWUZIroQsFq6Q7bH\nNz+nyUkuI567uUAtE9RimhLO/KbCQVZhK7P1M0sGZzFbK1JdZumkBskFEfzfM848WMZfVlGLZYqj\nfUdrf1KrO/1en3VybQEpHnAQQqHZwsfh9JF97F0mXWKO3SupnPTrssaCMWo9meclttoE0da16D5w\nA3Orf3baY1Hj343m6Y9opPADLZ7JtAlMtLWmsT400JnRGtWKOfCypXYqfBFdkwNi/qyEryQhMQ4E\nyP/x2ZEvFWDqhAVAaOsEENkYq0bJoXBirpzXVNfaiwysNK+YEH8BG4pe09XtBQR3yiUmr7AqnyV1\nFF8SSK0ONqyumZhqSw4yheNqApJ0acKltAzJ2qenCVAIq4Sn0xq8cWg2QtkvHPoANNQQGUgr1axD\nEa2YVa+hMQce3izsNDroG+ZKeQvwV1da31LnMxfvAi3BtVWhEwcr3tB2Tf7mmK/lvCcGShFmWxPR\nk0JvPq+mXvPQvpMsAMxJAU0K41XVjVS1GXJwfJfYNFgIo1qKTKIanfl6yVVDkvwEOKbIRInzdhfQ\nU30zaqnnSdwpaIIZlYgI1cpGPDq+OCMRWWnUhdmamo5TpJImSMWzqNbqwOoy7tM0+Rznill2AKVe\nfDzSVK5ifkqAzriXhZoY/Va831as46oBUP1506KcJWH0nfapOSKEApJ/kx37S3K8Md7w2raAQS6Z\nvSjS73ntYDxJWz4dEt8YP+Oj7op3Drd8vNmxYc+8gWH2yNid+s9QkP7AtQqXR6Nn4pP0EmeTccEd\nw1zJNnMzCLdyxdl8YFNnPhguyEWYMjxIBy7niY+HHV2p3HUdcz5wwS1lqB4tLV5DdWkTd5aY6sBr\n81M+7q4gMgJSM6kaj7sLALZTZVMq2SpPh54u1Ngu54mP+46LqXIg0dWJOxn4uE+MjWxiMEnHmD0z\n8ubxlsfdhl31pqqPN5VE4q5L3EvHy8UdcRL0pqTJCYlFK6NEzj/ZQokuCDedcVVHzzKJcb9Rhupg\n/sE0c913C1DcLMEXAakcox5yqPCw3PNJvza0/bubKy5G417hB90DLuvEqMqn/RkX04zOcAiluidd\nzzAbuU58Jg/Y94KWSqGnY2Kohpi/D7e69WfMhY0VVAtfr4/5YLvhjeMNHw/9Ugd3l5S71PPm4cBN\n9owYdUsnE7MZn+Udj8oeofJh2nBVMirGXhK32oEV9rnwjePIq+O0BJ/EhENyJc4jPU+6LZc2casD\nV+XovfW6mVlHtqWjDjPZwNSoCRjdkcnDLdN0TqcTkyXOZeQuGx2FZEI/9xzTkb7bhxNV6KuLT4xp\nXGzIsS9oFboS9OdmM62QMUpyBUiZ/R52u5nSNrkvsQ0B8CYd6mCp7cUS4NN8/0qxP2vsU43AveKi\nECdwuLICLyKrEHhqofXFdyLkvvgQDczkABISGYU1Pxr+TquzaedBFoXXFpyrtJzZus82sNUa6y69\neSKL4w55gBxWR/aUxgYOXCTZAnzmAFGNkryAu/DXGvGtsQ3FXigxsEZTO/mOulNbbaUStvOmZY9f\nxzNGx9ktQGy5DmKW57CobWpOe2RgmsiWtVqhQFlGKMb5uPt9rVdrvtVCL7RVXKrVUq33J8uaOgUI\nDZ+3c8yNrqi2gKjW6qZHnpsXQRYQ1cR9VizcAJpfrwFGtJ74bKyIEo3EQCWTGKkeYDcvnylWHEBp\nY8+EL8ILvkhoz6eUwIi+T/5dD4Z7eQwCKcvyfD9qX+RLBZgauAE3EHNpjT0jyxLOXkqJltVeM0gs\naVogdOFtrYnSxikNzvUSemiSkyt4aXw+X3h+oRy0p1OOrzdIXf/dMkFuGGXNBkUBnJxkY5pCXZ0r\nXfZpMnN5aH+29a3PmihlRlT9PpaIYTxj8ka8hRA7mOsiq92yWhbnF1VqKcv3c85OiYtNoJQSySeN\ndDYRkfICPYkHaJmg0jZ7VsPUqHklGjQuBZPVqZVz3JCDK5+Lit9bC4EswCD40y4fLqTiWTIPbKwc\ncQhbViqSgKDYNerjIkca91s8nbdkvyTmU1Wx4g53Mi8ErUkoxV/mXJwO19LxTRFJ8bmrUXcmqvTN\nYGaiBk+j/0RdGvS1OrWUPHktmjBzJ1RSWuimjetczeg1mtyGQWzvwDaAY1OGVBEswVRKjIEhlpZC\n0NmcAtu6prel3b8QNf0yHUnmoO460L7XTGdO57qyA8yea37reAsCW7vnItpcPum2Lp9drjkfjR17\nroeOy3mmCJxN0BXltfoErc4Jn1W5PFYuucYEHus5QzEOUWfz0njgJrur/ZLtuWXLrhrbOqMVhnzH\njXVUUbZ1JNFxIz0f5yuy1ajbE/Y9nB3h9fkLAH7p/HUymUkS2zLSmbCdjXsZuE89Xzt8xm13Rdt6\nr8pMtht2tbKpE39n+zIX88iswkjizeMNYsb3d1dcp55+mpmk46rueefglDRD+CQ9ICGMwU2/nCb2\nnXJZZu615146zssRE3iqOwYb6YqSgQuOPCjVgUFNpAim7M3BShbPDj2oBUTIVvl86MOW+6J+43Bw\ne6U9qRqjCpugbD/LiT4ilmrGhsIhCb0pMzCXjn0nPBhHnumGp3nmrEzMoqh5haxW4WVuybXyG8MD\nsoxsKLyySMLDJrJdHw4DIjCkKVafkLLR1WmhwVzVwkPb81m/5TBDLjMP7ci2zh4USWc8lgnFuJrg\nnJmP7Iyt+DM9iOxTl2ZKf2QqHUmFQW+x6oJDaVIX/RFjzoVBE2MK2gsVRkVl8pqRmjh2E2ej25BD\n/7wNuZycxTCn7MAaxTrjXo9sJ+HQG9sxeyQ8PEuROZrv2kI3u+jWuqwv43FapO7NNSOiLq0exY9W\nRwqrU2r2Aq2NAFbIIirQnNPnhE8bCArFtgZeGqXMt5qgotlpzYrXi5y6ls13abS50+NU1KclJpp/\nkPGAKFjQOde93cxFqVo9VatdahSw54CZObhudeRNeKCNT7vHU0XBJBr4sAWy7bkArP84alBDKrv9\nzD/fwNaaVXFfZaXutTlKATabcGQDq0KAsFNAKasSYLWY+6hlQ6J2+wUwqWJYDTAnrYKIpR7fb60F\nfAUa6yiuHPphS/mD94GqmOgyZrnx/uK7bX2Ax/HL4iOfKHxqm2uJea9e+22tJ6h/Xl21alnoi/DV\niS9iGF1yO9KSF+2d2ZCWIDOsIHSudV2/lmjcUpe6L7+JL8KP9PhSAaam/tVqLVKIACxlYLoCiyW7\nhC9oPTVQLaoSqnSqunA21xooY5JYdMmXrjvIq0iBLHxc1x5J2euXMicGtUQ/JvEsERLqIGH4IJRW\nWvYpsl2B3bHkf6sm71gdAgZWKrMZQ+4CectCz/K6F6d0VBXq7BmHQkx4Tgsn+Rgv0CYyRIKDv9nq\ncwIYNbRJe3UFNW+mkByssBYjIkIyd9YbnQ5YAFqTqu5pzv9qEL1WIl66iCAQUSWv/xKq6vJ8i5Lf\nYjBwLmwAYC3OxV82hgq1ixfbQFNipjzX12iO8WgNCAHS7N8fpToNMyW0erbFU+PSgnfUzBJ5yyKU\n4tS9VjDZ5eS1RIarF9ZKMwDR59jv07wvl3YhOx/PrBgSPaIEqAqDpRVQYlFoWZf3xWvv/NlS9L0h\nKJiq6qAVj/K54bJQTAs5YG2gb52nL+0RSj1HNc7qxAOByYx9p/TFaHlNo7JPLGCpqvLStOelae/0\nAPWGeldTQc0do6Mo02AMe2EUpxX88u41furuU8aUSGa8ZrdQ4GMucKcDBql8/fAEE2EnE+/rI94p\nT3mWe0C5qnf87fNXee3+Kbty5PHuIe8drvmbZ6/zz9x8iAFj9xKv8ARTYUqZrx2ekqyytYlf3b7E\nI64pNfG9zRWdFT5MjzAzPus3PKoHdCzMnHGGg5+f2n8GwN84f51n3YYH44GiGZOZvQxId+TMJkbN\n/L2zhwC8PB+wopzVA69y5P3+Aa9OX/C5XdDXmR9sz3gwH3lzvGaIJoamcK1bbnXg1fmGKsK2Tgw2\n81k+453xC/YhLz7YzON0xkvlht8Yrvj2+JTdOJKs8jR7U9dDblUaictyx3UayOmAYlznS3QyDilz\nWUeSuTrqQTMmXqt1Nnkm/7xUOq28NE98lM6X/ixndeRjPSenysV05CYPvD8Y1OwOEMYX6pS6B3aP\nWWVOyrsH79v0q8OOl+aRp7nnah657hKfcMbVNLKVRLKZ677nqQ08nA+8XO+5t8yr7Hk/bxlK4SEH\nXrIDpq6oOmuhZncH+zyRZrcLtfRonimdOyNjgPR+zCQpFJ3pgEOnbKcNUJlRNmYcRcnM5Nnp6aNW\n+ipMKLNmKkZHpadSTenLgOpMPydU56UeswIXm3mpOVlKT77MUpucOO8RXGphR4saWA3PskX7lwh+\n/GcJ3BK1Y3HeJXvRvhPb40zsM0IwLqJuKChaLdJvugYJSz0RXIgIZLgzxBaw1L+0fTrhAYtWWnCa\nhbEAZhnvD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P5nEfnWC58ZROTPichjEbkRkf9BRF594TNvi8j/KiJ3IvKxiPyXIvIPvRdR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xbI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\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iUSy42NoPp6YmUnEEVVywLUtWXcLmrS6UBdIRRQzeAAsExH5GUTxFLe8LVTIsxN1qRDwv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UnuQ5yHJuWzLTNSTNiYeRnsQWZF/YFQp2MGcvpc5+zSceO+MnXkMAKlqM6giiVprkADAifSVs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\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XAww7x11u/L/zS85pITxlBZx51mJEhF+zgMlK8oEPfGPIhvOF1Sp/ywv63HNeEw/J8Ydn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c2hPQBgXI4w2WCDguaQSqBNDksZ/28MP0GnEdIOUUXqWf6TJupakRgghEGuWgvEiNsR4iVm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\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s75C4kvoOy0n2JPKCDSceqphRbeSim8k4QR9ri20D3KqYZyu/SCZ5Lixyq8FfDbreInIn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\nSI0wElzl1mIxnivSqSimz7m2xNGsdUGqb3SNLnR8dFnypui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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(2,(10,8))\n", + "\n", + "pl.subplot(2,3,1)\n", + "\n", + "pl.imshow(I1)\n", + "pl.title('Image 1')\n", + "\n", + "pl.subplot(2,3,2)\n", + "pl.imshow(I1t)\n", + "pl.title('Image 1 Adapt')\n", + "\n", + "\n", + "pl.subplot(2,3,3)\n", + "pl.imshow(I1te)\n", + "pl.title('Image 1 Adapt (reg)')\n", + "\n", + "pl.subplot(2,3,4)\n", + "\n", + "pl.imshow(I2)\n", + "pl.title('Image 2')\n", + "\n", + "pl.subplot(2,3,5)\n", + "pl.imshow(I2t)\n", + "pl.title('Image 2 Adapt')\n", + "\n", + "\n", + "pl.subplot(2,3,6)\n", + "pl.imshow(I2te)\n", + "pl.title('Image 2 Adapt (reg)')\n", + "\n", + "pl.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/examples/demo_OTDA_color_images.py b/examples/demo_OTDA_color_images.py index 7a08ea0..715707d 100644 --- a/examples/demo_OTDA_color_images.py +++ b/examples/demo_OTDA_color_images.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- """ -demo of Optimal transport for domain adaptation with image color adaptation as in [6] +Demo of Optimal transport for domain adaptation with image color adaptation as in [6] [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. """ @@ -51,6 +51,30 @@ idx2=np.random.randint(X2.shape[0],size=(nb,)) xs=X1[idx1,:] xt=X2[idx2,:] +#%% Plot image distributions + + +pl.figure(2,(10,5)) + +pl.subplot(1,2,1) +pl.scatter(xs[:,0],xs[:,2],c=xs) +pl.axis([0,1,0,1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 1') + +pl.subplot(1,2,2) +#pl.imshow(I2) +pl.scatter(xt[:,0],xt[:,2],c=xt) +pl.axis([0,1,0,1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 2') + +pl.show() + + + #%% domain adaptation between images # LP problem -- cgit v1.2.3 From 981351165dbab740145d109b00782f0c41f2244b Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 2 Nov 2016 17:13:43 +0100 Subject: add mapping estimation (still debugging) --- ot/da.py | 228 +++++++++++++++++++++++++++++++++++++++++------------------- ot/optim.py | 106 ++++++++++++++-------------- 2 files changed, 208 insertions(+), 126 deletions(-) diff --git a/ot/da.py b/ot/da.py index 3447437..7cfbca1 100644 --- a/ot/da.py +++ b/ot/da.py @@ -7,6 +7,7 @@ import numpy as np from .bregman import sinkhorn from .lp import emd from .utils import unif,dist +from .optim import cg def indices(a, func): @@ -15,81 +16,81 @@ def indices(a, func): def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerItermax = 200,stopInnerThr=1e-9,verbose=False,log=False): """ Solve the entropic regularization optimal transport problem with nonconvex group lasso regularization - + The function solves the following optimization problem: - + .. math:: \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma)+ \eta \Omega_g(\gamma) - + s.t. \gamma 1 = a - - \gamma^T 1= b - + + \gamma^T 1= b + \gamma\geq 0 where : - + - M is the (ns,nt) metric cost matrix - :math:`\Omega_e` is the entropic regularization term :math:`\Omega_e(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - :math:`\Omega_g` is the group lasso regulaization term :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^{1/2}_1` where :math:`\mathcal{I}_c` are the index of samples from class c in the source domain. - a and b are source and target weights (sum to 1) - + The algorithm used for solving the problem is the generalised conditional gradient as proposed in [5]_ [7]_ - - + + Parameters ---------- a : np.ndarray (ns,) samples weights in the source domain labels_a : np.ndarray (ns,) - labels of samples in the source domain + labels of samples in the source domain b : np.ndarray (nt,) samples in the target domain M : np.ndarray (ns,nt) - loss matrix + loss matrix reg: float Regularization term for entropic regularization >0 eta: float, optional - Regularization term for group lasso regularization >0 + Regularization term for group lasso regularization >0 numItermax: int, optional Max number of iterations numInnerItermax: int, optional Max number of iterations (inner sinkhorn solver) stopInnerThr: float, optional - Stop threshold on error (inner sinkhorn solver) (>0) + Stop threshold on error (inner sinkhorn solver) (>0) verbose : bool, optional Print information along iterations log : bool, optional - record log if True - - + record log if True + + Returns ------- gamma: (ns x nt) ndarray Optimal transportation matrix for the given parameters log: dict - log dictionary return only if log==True in parameters - - + log dictionary return only if log==True in parameters + + References ---------- - + .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567. - + See Also -------- ot.lp.emd : Unregularized OT ot.bregman.sinkhorn : Entropic regularized OT ot.optim.cg : General regularized OT - - """ + + """ p=0.5 epsilon = 1e-3 # init data Nini = len(a) Nfin = len(b) - + indices_labels = [] idx_begin = np.min(labels_a) for c in range(idx_begin,np.max(labels_a)+1): @@ -117,14 +118,96 @@ def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerIter # do it only for unlabbled data if idx_begin==-1: W[indices_labels[0],t]=np.min(all_maj) - + return transp +def joint_OT_mapping_linear(xs,xt,mu=1,eta=0.001,bias=False,verbose=False,verbose2=False,numItermax = 100,numInnerItermax = 20,stopInnerThr=1e-9,stopThr=1e-6,log=False,**kwargs): + """Joint Ot and mapping estimation (uniform weights and ) + """ + + ns,nt,d=xs.shape[0],xt.shape[0],xt.shape[1] + + if bias: + xs1=np.hstack((xs,np.ones((ns,1)))) + I=eta*np.eye(d+1) + I[-1]=0 + I0=I[:,:-1] + sel=lambda x : x[:-1,:] + else: + xs1=xs + I=eta*np.eye(d) + I0=I + sel=lambda x : x + + if log: + log={'err':[]} + + a,b=unif(ns),unif(nt) + M=dist(xs,xt) + G=emd(a,b,M) + + vloss=[] + + def loss(L,G): + return np.sum((xs1.dot(L)-ns*G.dot(xt))**2)+mu*np.sum(G*M)+eta*np.sum(sel(L-I0)**2) + + def solve_L(G): + """ solve problem with fixed G""" + xst=ns*G.dot(xt) + return np.linalg.solve(xs1.T.dot(xs1)+I,xs1.T.dot(xst)+I0) + + def solve_G(L,G0): + xsi=xs1.dot(L) + def f(G): + return np.sum((xsi-ns*G.dot(xt))**2) + def df(G): + return -2*ns*(xsi-ns*G.dot(xt)).dot(xt.T) + G=cg(a,b,M,1.0/mu,f,df,G0=G0,numItermax=numInnerItermax,stopThr=stopInnerThr) + return G + + + L=solve_L(G) + + vloss.append(loss(L,G)) + + if verbose: + print('{:5s}|{:12s}|{:8s}'.format('It.','Loss','Delta loss')+'\n'+'-'*32) + print('{:5d}|{:8e}|{:8e}'.format(0,vloss[-1],0)) + + + # regul matrix + loop=1 + it=0 + + while loop: + + it+=1 + + # update G + G=solve_G(L,G) + + #update L + L=solve_L(G) + + vloss.append(loss(L,G)) + + if abs(vloss[-1]-vloss[-2])0: # >0 then source to target + if direction>0: # >0 then source to target G=self.G w=self.ws.reshape((self.xs.shape[0],1)) x=self.xt @@ -175,81 +258,80 @@ class OTDA(object): G=self.G.T w=self.wt.reshape((self.xt.shape[0],1)) x=self.xs - + if self.computed: if self.metric=='sqeuclidean': return np.dot(G/w,x) # weighted mean else: print("Warning, metric not handled yet, using weighted average") - return np.dot(G/w,x) # weighted mean - return None + return np.dot(G/w,x) # weighted mean + return None else: print("Warning, model not fitted yet, returning None") return None - - + + def predict(self,x,direction=1): - """ Out of sample mapping using the formulation from Ferradans - - It basically find the source sample the nearset to the nex sample and + """ Out of sample mapping using the formulation from Ferradans + + It basically find the source sample the nearset to the nex sample and apply the difference to the displaced source sample. - + """ - if direction>0: # >0 then source to target + if direction>0: # >0 then source to target xf=self.xt x0=self.xs else: - xf=self.xs + xf=self.xs x0=self.xt - + D0=dist(x,x0) # dist netween new samples an source idx=np.argmin(D0,1) # closest one xf=self.interp(direction)# interp the source samples return xf[idx,:]+x-x0[idx,:] # aply the delta to the interpolation - - + + class OTDA_sinkhorn(OTDA): """Class for domain adaptation with optimal transport with entropic regularization""" def fit(self,xs,xt,reg=1,ws=None,wt=None,**kwargs): - """ Fit domain adaptation between samples is xs and xt (with optional + """ Fit domain adaptation between samples is xs and xt (with optional weights)""" self.xs=xs self.xt=xt - + if wt is None: wt=unif(xt.shape[0]) if ws is None: ws=unif(xs.shape[0]) - + self.ws=ws self.wt=wt - + self.M=dist(xs,xt,metric=self.metric) self.G=sinkhorn(ws,wt,self.M,reg,**kwargs) - self.computed=True - - + self.computed=True + + class OTDA_lpl1(OTDA): """Class for domain adaptation with optimal transport with entropic an group regularization""" - - + + def fit(self,xs,ys,xt,reg=1,eta=1,ws=None,wt=None,**kwargs): - """ Fit domain adaptation between samples is xs and xt (with optional + """ Fit domain adaptation between samples is xs and xt (with optional weights)""" self.xs=xs self.xt=xt - + if wt is None: wt=unif(xt.shape[0]) if ws is None: ws=unif(xs.shape[0]) - + self.ws=ws self.wt=wt - + self.M=dist(xs,xt,metric=self.metric) self.G=sinkhorn_lpl1_mm(ws,ys,wt,self.M,reg,eta,**kwargs) - self.computed=True - - \ No newline at end of file + self.computed=True + diff --git a/ot/optim.py b/ot/optim.py index 1afbea3..dcefd24 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -12,21 +12,21 @@ from .lp import emd def line_search_armijo(f,xk,pk,gfk,old_fval,args=(),c1=1e-4,alpha0=0.99): """ Armijo linesearch function that works with matrices - - find an approximate minimum of f(xk+alpha*pk) that satifies the - armijo conditions. - + + find an approximate minimum of f(xk+alpha*pk) that satifies the + armijo conditions. + Parameters ---------- f : function loss function - xk : np.ndarray + xk : np.ndarray initial position - pk : np.ndarray + pk : np.ndarray descent direction gfk : np.ndarray - gradient of f at xk + gradient of f at xk old_fval: float loss value at xk args : tuple, optional @@ -35,7 +35,7 @@ def line_search_armijo(f,xk,pk,gfk,old_fval,args=(),c1=1e-4,alpha0=0.99): c1 const in armijo rule (>0) alpha0 : float, optional initial step (>0) - + Returns ------- alpha : float @@ -44,49 +44,49 @@ def line_search_armijo(f,xk,pk,gfk,old_fval,args=(),c1=1e-4,alpha0=0.99): nb of function call fa : float loss value at step alpha - + """ xk = np.atleast_1d(xk) fc = [0] - + def phi(alpha1): fc[0] += 1 return f(xk + alpha1*pk, *args) - + if old_fval is None: phi0 = phi(0.) else: phi0 = old_fval - + derphi0 = np.sum(pk*gfk) # Quickfix for matrices alpha,phi1 = scalar_search_armijo(phi,phi0,derphi0,c1=c1,alpha0=alpha0) - + return alpha,fc[0],phi1 def cg(a,b,M,reg,f,df,G0=None,numItermax = 200,stopThr=1e-9,verbose=False,log=False): """ Solve the general regularized OT problem with conditional gradient - + The function solves the following optimization problem: - + .. math:: \gamma = arg\min_\gamma <\gamma,M>_F + reg*f(\gamma) - + s.t. \gamma 1 = a - - \gamma^T 1= b - + + \gamma^T 1= b + \gamma\geq 0 where : - + - M is the (ns,nt) metric cost matrix - :math:`f` is the regularization term ( and df is its gradient) - a and b are source and target weights (sum to 1) - + The algorithm used for solving the problem is conditional gradient as discussed in [1]_ - - + + Parameters ---------- a : np.ndarray (ns,) @@ -94,7 +94,7 @@ def cg(a,b,M,reg,f,df,G0=None,numItermax = 200,stopThr=1e-9,verbose=False,log=Fa b : np.ndarray (nt,) samples in the target domain M : np.ndarray (ns,nt) - loss matrix + loss matrix reg : float Regularization term >0 G0 : np.ndarray (ns,nt), optional @@ -107,87 +107,87 @@ def cg(a,b,M,reg,f,df,G0=None,numItermax = 200,stopThr=1e-9,verbose=False,log=Fa Print information along iterations log : bool, optional record log if True - + Returns ------- gamma: (ns x nt) ndarray Optimal transportation matrix for the given parameters log: dict - log dictionary return only if log==True in parameters - - + log dictionary return only if log==True in parameters + + References ---------- - + .. [1] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. - + See Also -------- ot.lp.emd : Unregularized optimal ransport ot.bregman.sinkhorn : Entropic regularized optimal transport - + """ - + loop=1 - + if log: log={'loss':[]} - + if G0 is None: G=np.outer(a,b) else: G=G0 - + def cost(G): return np.sum(M*G)+reg*f(G) - + f_val=cost(G) if log: log['loss'].append(f_val) - + it=0 - + if verbose: print('{:5s}|{:12s}|{:8s}'.format('It.','Loss','Delta loss')+'\n'+'-'*32) print('{:5d}|{:8e}|{:8e}'.format(it,f_val,0)) - + while loop: - + it+=1 old_fval=f_val - - + + # problem linearization Mi=M+reg*df(G) - + # solve linear program Gc=emd(a,b,Mi) - + deltaG=Gc-G - + # line search alpha,fc,f_val = line_search_armijo(cost,G,deltaG,Mi,f_val) - + G=G+alpha*deltaG - + # test convergence if it>=numItermax: loop=0 - + delta_fval=(f_val-old_fval)/abs(f_val) if abs(delta_fval) Date: Thu, 3 Nov 2016 14:53:52 +0100 Subject: add mapping estimation (still debugging) --- ot/da.py | 174 ++++++++++++++++++++++++++++++++++++++++++++++++++++++--- ot/datasets.py | 62 ++++++++++++-------- ot/utils.py | 51 +++++++++-------- 3 files changed, 234 insertions(+), 53 deletions(-) diff --git a/ot/da.py b/ot/da.py index 7cfbca1..66680cd 100644 --- a/ot/da.py +++ b/ot/da.py @@ -6,13 +6,15 @@ Domain adaptation with optimal transport import numpy as np from .bregman import sinkhorn from .lp import emd -from .utils import unif,dist +from .utils import unif,dist,kernel from .optim import cg def indices(a, func): return [i for (i, val) in enumerate(a) if func(val)] + + def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerItermax = 200,stopInnerThr=1e-9,verbose=False,log=False): """ Solve the entropic regularization optimal transport problem with nonconvex group lasso regularization @@ -129,13 +131,15 @@ def joint_OT_mapping_linear(xs,xt,mu=1,eta=0.001,bias=False,verbose=False,verbos if bias: xs1=np.hstack((xs,np.ones((ns,1)))) - I=eta*np.eye(d+1) + xstxs=xs1.T.dot(xs1) + I=np.eye(d+1) I[-1]=0 I0=I[:,:-1] sel=lambda x : x[:-1,:] else: xs1=xs - I=eta*np.eye(d) + xstxs=xs1.T.dot(xs1) + I=np.eye(d) I0=I sel=lambda x : x @@ -143,20 +147,22 @@ def joint_OT_mapping_linear(xs,xt,mu=1,eta=0.001,bias=False,verbose=False,verbos log={'err':[]} a,b=unif(ns),unif(nt) - M=dist(xs,xt) + M=dist(xs,xt)*ns G=emd(a,b,M) vloss=[] def loss(L,G): + """Compute full loss""" return np.sum((xs1.dot(L)-ns*G.dot(xt))**2)+mu*np.sum(G*M)+eta*np.sum(sel(L-I0)**2) def solve_L(G): - """ solve problem with fixed G""" + """ solve L problem with fixed G (least square)""" xst=ns*G.dot(xt) - return np.linalg.solve(xs1.T.dot(xs1)+I,xs1.T.dot(xst)+I0) + return np.linalg.solve(xstxs+eta*I,xs1.T.dot(xst)+eta*I0) def solve_G(L,G0): + """Update G with CG algorithm""" xsi=xs1.dot(L) def f(G): return np.sum((xsi-ns*G.dot(xt))**2) @@ -175,8 +181,11 @@ def joint_OT_mapping_linear(xs,xt,mu=1,eta=0.001,bias=False,verbose=False,verbos print('{:5d}|{:8e}|{:8e}'.format(0,vloss[-1],0)) - # regul matrix - loop=1 + # init loop + if numItermax>0: + loop=1 + else: + loop=0 it=0 while loop: @@ -191,6 +200,9 @@ def joint_OT_mapping_linear(xs,xt,mu=1,eta=0.001,bias=False,verbose=False,verbos vloss.append(loss(L,G)) + if it>=numItermax: + loop=0 + if abs(vloss[-1]-vloss[-2])0: + loop=1 + else: + loop=0 + it=0 + + while loop: + + it+=1 + + # update G + G=solve_G(L,G) + + #update L + L=solve_L(G) + + vloss.append(loss(L,G)) + + if it>=numItermax: + loop=0 + + if abs(vloss[-1]-vloss[-2])0) - + Returns ------- X : np.array (n,d) - n observation of size d + n observation of size d y : np.array (n,) - labels of the samples + labels of the samples """ if dataset.lower()=='3gauss': @@ -90,10 +91,10 @@ def get_data_classif(dataset,n,nz=.5,**kwargs): x[y==1,0]=-1.; x[y==1,1]=-1. x[y==2,0]=-1.; x[y==2,1]=1. x[y==3,0]=1. ; x[y==3,1]=0 - + x[y!=3,:]+=1.5*nz*np.random.randn(sum(y!=3),2) x[y==3,:]+=2*nz*np.random.randn(sum(y==3),2) - + elif dataset.lower()=='3gauss2': y=np.floor((np.arange(n)*1.0/n*3))+1 x=np.zeros((n,2)) @@ -102,12 +103,29 @@ def get_data_classif(dataset,n,nz=.5,**kwargs): x[y==1,0]=-2.; x[y==1,1]=-2. x[y==2,0]=-2.; x[y==2,1]=2. x[y==3,0]=2. ; x[y==3,1]=0 - + x[y!=3,:]+=nz*np.random.randn(sum(y!=3),2) - x[y==3,:]+=2*nz*np.random.randn(sum(y==3),2) + x[y==3,:]+=2*nz*np.random.randn(sum(y==3),2) + + elif dataset.lower()=='gaussrot' : + rot=np.array([[np.cos(theta),-np.sin(theta)],[np.sin(theta),np.cos(theta)]]) + m1=np.array([-1,-1]) + m2=np.array([1,1]) + y=np.floor((np.arange(n)*1.0/n*2))+1 + n1=np.sum(y==1) + n2=np.sum(y==2) + x=np.zeros((n,2)) + + x[y==1,:]=get_2D_samples_gauss(n1,m1,nz) + x[y==2,:]=get_2D_samples_gauss(n2,m2,nz) + + x=x.dot(rot) + + + else: x=0 y=0 print("unknown dataset") - + return x,y.astype(int) \ No newline at end of file diff --git a/ot/utils.py b/ot/utils.py index 24f65a8..47fe77f 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -6,28 +6,34 @@ import numpy as np from scipy.spatial.distance import cdist +def kernel(x1,x2,method='gaussian',sigma=1,**kwargs): + """Compute kernel matrix""" + if method.lower() in ['gaussian','gauss','rbf']: + K=np.exp(dist(x1,x2)/(2*sigma**2)) + return K + def unif(n): - """ return a uniform histogram of length n (simplex) - + """ return a uniform histogram of length n (simplex) + Parameters ---------- n : int number of bins in the histogram - + Returns ------- h : np.array (n,) - histogram of length n such that h_i=1/n for all i - - + histogram of length n such that h_i=1/n for all i + + """ return np.ones((n,))/n def dist(x1,x2=None,metric='sqeuclidean'): """Compute distance between samples in x1 and x2 using function scipy.spatial.distance.cdist - + Parameters ---------- @@ -36,28 +42,29 @@ def dist(x1,x2=None,metric='sqeuclidean'): x2 : np.array (n2,d), optional matrix with n2 samples of size d (if None then x2=x1) metric : str, fun, optional - name of the metric to be computed (full list in the doc of scipy), If a string, + name of the metric to be computed (full list in the doc of scipy), If a string, the distance function can be ‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘cityblock’, ‘correlation’, ‘cosine’, ‘dice’, ‘euclidean’, ‘hamming’, ‘jaccard’, ‘kulsinski’, ‘mahalanobis’, ‘matching’, ‘minkowski’, ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘wminkowski’, ‘yule’. - + Returns ------- - + M : np.array (n1,n2) distance matrix computed with given metric - + """ if x2 is None: - return cdist(x1,x1,metric=metric) - else: - return cdist(x1,x2,metric=metric) - + x2=x1 + + return cdist(x1,x2,metric=metric) + + def dist0(n,method='lin_square'): """Compute standard cost matrices of size (n,n) for OT problems - + Parameters ---------- @@ -68,21 +75,21 @@ def dist0(n,method='lin_square'): * 'lin_square' : linear sampling between 0 and n-1, quadratic loss - + Returns ------- - + M : np.array (n1,n2) - distance matrix computed with given metric - - + distance matrix computed with given metric + + """ res=0 if method=='lin_square': x=np.arange(n,dtype=np.float64).reshape((n,1)) res=dist(x,x) return res - + def dots(*args): """ dots function for multiple matrix multiply """ -- cgit v1.2.3 From 86b1c88eb0c2c43853bca38de96d2278cc90ceba Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 3 Nov 2016 16:28:46 +0100 Subject: add mapping estimation with kernels (still debugging) --- ot/da.py | 35 +++++++++++++++++++++-------------- 1 file changed, 21 insertions(+), 14 deletions(-) diff --git a/ot/da.py b/ot/da.py index 66680cd..e4aa0be 100644 --- a/ot/da.py +++ b/ot/da.py @@ -217,25 +217,23 @@ def joint_OT_mapping_linear(xs,xt,mu=1,eta=0.001,bias=False,verbose=False,verbos return G,L -def joint_OT_mapping_kernel(xs,xt,mu=1,eta=0.001,kernel='gaussian',sigma=1,bias=False,verbose=False,verbose2=False,numItermax = 100,numInnerItermax = 20,stopInnerThr=1e-9,stopThr=1e-6,log=False,**kwargs): +def joint_OT_mapping_kernel(xs,xt,mu=1,eta=0.001,kerneltype='gaussian',sigma=1,bias=False,verbose=False,verbose2=False,numItermax = 100,numInnerItermax = 20,stopInnerThr=1e-9,stopThr=1e-6,log=False,**kwargs): """Joint Ot and mapping estimation (uniform weights and ) """ ns,nt,d=xs.shape[0],xt.shape[0],xt.shape[1] + K=kernel(xs,xs,method=kerneltype,sigma=sigma) if bias: - K= - xs1=np.hstack((xs,np.ones((ns,1)))) - xstxs=xs1.T.dot(xs1) - I=np.eye(d+1) + K1=np.hstack((K,np.ones((ns,1)))) + I=np.eye(ns+1) I[-1]=0 - I0=I[:,:-1] + K0 = K1.T.dot(K1)+eta*I sel=lambda x : x[:-1,:] else: - xs1=xs - xstxs=xs1.T.dot(xs1) - I=np.eye(d) - I0=I + K1=K + I=np.eye(ns) + K0=K+eta*I sel=lambda x : x if log: @@ -249,16 +247,21 @@ def joint_OT_mapping_kernel(xs,xt,mu=1,eta=0.001,kernel='gaussian',sigma=1,bias= def loss(L,G): """Compute full loss""" - return np.sum((xs1.dot(L)-ns*G.dot(xt))**2)+mu*np.sum(G*M)+eta*np.sum(sel(L-I0)**2) + return np.sum((K1.dot(L)-ns*G.dot(xt))**2)+mu*np.sum(G*M)+eta*np.sum(sel(L)**2) - def solve_L(G): + def solve_L_nobias(G): """ solve L problem with fixed G (least square)""" xst=ns*G.dot(xt) - return np.linalg.solve(xstxs+eta*I,xs1.T.dot(xst)+eta*I0) + return np.linalg.solve(K0,xst) + + def solve_L_bias(G): + """ solve L problem with fixed G (least square)""" + xst=ns*G.dot(xt) + return np.linalg.solve(K0,K1.T.dot(xst)) def solve_G(L,G0): """Update G with CG algorithm""" - xsi=xs1.dot(L) + xsi=K1.dot(L) def f(G): return np.sum((xsi-ns*G.dot(xt))**2) def df(G): @@ -266,6 +269,10 @@ def joint_OT_mapping_kernel(xs,xt,mu=1,eta=0.001,kernel='gaussian',sigma=1,bias= G=cg(a,b,M,1.0/mu,f,df,G0=G0,numItermax=numInnerItermax,stopThr=stopInnerThr) return G + if bias: + solve_L=solve_L_bias + else: + solve_L=solve_L_nobias L=solve_L(G) -- cgit v1.2.3 From 7e16b7a80f3a2896351262a02af27a60401b6a5e Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 3 Nov 2016 17:07:22 +0100 Subject: add mapping estimation with kernels (still debugging) --- ot/da.py | 48 ++++++++++++++++++++++++++++++++++++++++++++---- ot/datasets.py | 6 +++--- 2 files changed, 47 insertions(+), 7 deletions(-) diff --git a/ot/da.py b/ot/da.py index e4aa0be..49fa79e 100644 --- a/ot/da.py +++ b/ot/da.py @@ -247,7 +247,7 @@ def joint_OT_mapping_kernel(xs,xt,mu=1,eta=0.001,kerneltype='gaussian',sigma=1,b def loss(L,G): """Compute full loss""" - return np.sum((K1.dot(L)-ns*G.dot(xt))**2)+mu*np.sum(G*M)+eta*np.sum(sel(L)**2) + return np.sum((K1.dot(L)-ns*G.dot(xt))**2)+mu*np.sum(G*M)+eta*np.trace(L.T.dot(K0).dot(L)) def solve_L_nobias(G): """ solve L problem with fixed G (least square)""" @@ -450,11 +450,11 @@ class OTDA_lpl1(OTDA): self.G=sinkhorn_lpl1_mm(ws,ys,wt,self.M,reg,eta,**kwargs) self.computed=True -class OTDA_mapping(OTDA): +class OTDA_mapping_linear(OTDA): """Class for optimal transport with joint linear mapping estimation""" - def __init__(self,metric='sqeuclidean'): + def __init__(self): """ Class initialization""" @@ -463,8 +463,8 @@ class OTDA_mapping(OTDA): self.G=0 self.L=0 self.bias=False - self.metric=metric self.computed=False + self.metric='sqeuclidean' def fit(self,xs,xt,mu=1,eta=1,bias=False,**kwargs): """ Fit domain adaptation between samples is xs and xt (with optional @@ -473,6 +473,7 @@ class OTDA_mapping(OTDA): self.xt=xt self.bias=bias + self.ws=unif(xs.shape[0]) self.wt=unif(xt.shape[0]) @@ -498,3 +499,42 @@ class OTDA_mapping(OTDA): print("Warning, model not fitted yet, returning None") return None +class OTDA_mapping_kernel(OTDA_mapping_linear): + """Class for optimal transport with joint linear mapping estimation""" + + + + def fit(self,xs,xt,mu=1,eta=1,bias=False,kerneltype='gaussian',sigma=1,**kwargs): + """ Fit domain adaptation between samples is xs and xt (with optional + weights)""" + self.xs=xs + self.xt=xt + self.bias=bias + + self.ws=unif(xs.shape[0]) + self.wt=unif(xt.shape[0]) + self.kernel=kerneltype + self.sigma=sigma + self.kwargs=kwargs + + + self.G,self.L=joint_OT_mapping_kernel(xs,xt,mu=mu,eta=eta,bias=bias,**kwargs) + self.computed=True + + + def predict(self,x): + """ Out of sample mapping using the formulation from Ferradans + + It basically find the source sample the nearset to the nex sample and + apply the difference to the displaced source sample. + + """ + + if self.computed: + K=kernel(x,self.xs,method=self.kernel,sigma=self.sigma,**self.kwargs) + if self.bias: + K=np.hstack((K,np.ones((x.shape[0],1)))) + return K.dot(self.L) + else: + print("Warning, model not fitted yet, returning None") + return None \ No newline at end of file diff --git a/ot/datasets.py b/ot/datasets.py index 588f501..c750812 100644 --- a/ot/datasets.py +++ b/ot/datasets.py @@ -108,9 +108,9 @@ def get_data_classif(dataset,n,nz=.5,theta=0,**kwargs): x[y==3,:]+=2*nz*np.random.randn(sum(y==3),2) elif dataset.lower()=='gaussrot' : - rot=np.array([[np.cos(theta),-np.sin(theta)],[np.sin(theta),np.cos(theta)]]) - m1=np.array([-1,-1]) - m2=np.array([1,1]) + rot=np.array([[np.cos(theta),np.sin(theta)],[-np.sin(theta),np.cos(theta)]]) + m1=np.array([-1,1]) + m2=np.array([1,-1]) y=np.floor((np.arange(n)*1.0/n*2))+1 n1=np.sum(y==1) n2=np.sum(y==2) -- cgit v1.2.3 From 0ea30e9ca2398caeb853662012b09148a25cffff Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 3 Nov 2016 17:12:26 +0100 Subject: add mapping estimation with kernels (smaller bugs) --- ot/da.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/ot/da.py b/ot/da.py index 49fa79e..8bb3b97 100644 --- a/ot/da.py +++ b/ot/da.py @@ -229,11 +229,13 @@ def joint_OT_mapping_kernel(xs,xt,mu=1,eta=0.001,kerneltype='gaussian',sigma=1,b I=np.eye(ns+1) I[-1]=0 K0 = K1.T.dot(K1)+eta*I + Kreg=I sel=lambda x : x[:-1,:] else: K1=K I=np.eye(ns) K0=K+eta*I + Kreg=K sel=lambda x : x if log: @@ -247,7 +249,7 @@ def joint_OT_mapping_kernel(xs,xt,mu=1,eta=0.001,kerneltype='gaussian',sigma=1,b def loss(L,G): """Compute full loss""" - return np.sum((K1.dot(L)-ns*G.dot(xt))**2)+mu*np.sum(G*M)+eta*np.trace(L.T.dot(K0).dot(L)) + return np.sum((K1.dot(L)-ns*G.dot(xt))**2)+mu*np.sum(G*M)+eta*np.trace(L.T.dot(Kreg).dot(L)) def solve_L_nobias(G): """ solve L problem with fixed G (least square)""" -- cgit v1.2.3 From 405f352dc562eb17a2d6d7ca17c2ce14b19f2668 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 4 Nov 2016 10:10:44 +0100 Subject: add mapping estimation with kernels works! --- ot/da.py | 32 ++++++++++++++++++++++++-------- ot/optim.py | 2 ++ ot/utils.py | 2 +- 3 files changed, 27 insertions(+), 9 deletions(-) diff --git a/ot/da.py b/ot/da.py index 8bb3b97..ad2f8b5 100644 --- a/ot/da.py +++ b/ot/da.py @@ -123,7 +123,7 @@ def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerIter return transp -def joint_OT_mapping_linear(xs,xt,mu=1,eta=0.001,bias=False,verbose=False,verbose2=False,numItermax = 100,numInnerItermax = 20,stopInnerThr=1e-9,stopThr=1e-6,log=False,**kwargs): +def joint_OT_mapping_linear(xs,xt,mu=1,eta=0.001,bias=False,verbose=False,verbose2=False,numItermax = 100,numInnerItermax = 10,stopInnerThr=1e-6,stopThr=1e-5,log=False,**kwargs): """Joint Ot and mapping estimation (uniform weights and ) """ @@ -209,7 +209,7 @@ def joint_OT_mapping_linear(xs,xt,mu=1,eta=0.001,bias=False,verbose=False,verbos if verbose: if it%20==0: print('{:5s}|{:12s}|{:8s}'.format('It.','Loss','Delta loss')+'\n'+'-'*32) - print('{:5d}|{:8e}|{:8e}'.format(it,vloss[-1],abs(vloss[-1]-vloss[-2])/abs(vloss[-2]))) + print('{:5d}|{:8e}|{:8e}'.format(it,vloss[-1],(vloss[-1]-vloss[-2])/abs(vloss[-2]))) if log: log['loss']=vloss return G,L,log @@ -217,7 +217,7 @@ def joint_OT_mapping_linear(xs,xt,mu=1,eta=0.001,bias=False,verbose=False,verbos return G,L -def joint_OT_mapping_kernel(xs,xt,mu=1,eta=0.001,kerneltype='gaussian',sigma=1,bias=False,verbose=False,verbose2=False,numItermax = 100,numInnerItermax = 20,stopInnerThr=1e-9,stopThr=1e-6,log=False,**kwargs): +def joint_OT_mapping_kernel(xs,xt,mu=1,eta=0.001,kerneltype='gaussian',sigma=1,bias=False,verbose=False,verbose2=False,numItermax = 100,numInnerItermax = 10,stopInnerThr=1e-6,stopThr=1e-5,log=False,**kwargs): """Joint Ot and mapping estimation (uniform weights and ) """ @@ -228,15 +228,31 @@ def joint_OT_mapping_kernel(xs,xt,mu=1,eta=0.001,kerneltype='gaussian',sigma=1,b K1=np.hstack((K,np.ones((ns,1)))) I=np.eye(ns+1) I[-1]=0 - K0 = K1.T.dot(K1)+eta*I - Kreg=I - sel=lambda x : x[:-1,:] + Kp=np.eye(ns+1) + Kp[:ns,:ns]=K + + # ls regu + #K0 = K1.T.dot(K1)+eta*I + #Kreg=I + + # RKHS regul + K0 = K1.T.dot(K1)+eta*Kp + Kreg=Kp + else: K1=K I=np.eye(ns) + + # ls regul + #K0 = K1.T.dot(K1)+eta*I + #Kreg=I + + # proper kernel ridge K0=K+eta*I Kreg=K - sel=lambda x : x + + + if log: log={'err':[]} @@ -313,7 +329,7 @@ def joint_OT_mapping_kernel(xs,xt,mu=1,eta=0.001,kerneltype='gaussian',sigma=1,b if verbose: if it%20==0: print('{:5s}|{:12s}|{:8s}'.format('It.','Loss','Delta loss')+'\n'+'-'*32) - print('{:5d}|{:8e}|{:8e}'.format(it,vloss[-1],abs(vloss[-1]-vloss[-2])/abs(vloss[-2]))) + print('{:5d}|{:8e}|{:8e}'.format(it,vloss[-1],(vloss[-1]-vloss[-2])/abs(vloss[-2]))) if log: log['loss']=vloss return G,L,log diff --git a/ot/optim.py b/ot/optim.py index dcefd24..2b8f565 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -159,6 +159,8 @@ def cg(a,b,M,reg,f,df,G0=None,numItermax = 200,stopThr=1e-9,verbose=False,log=Fa # problem linearization Mi=M+reg*df(G) + # set M positive + Mi+=Mi.min() # solve linear program Gc=emd(a,b,Mi) diff --git a/ot/utils.py b/ot/utils.py index 47fe77f..d3df8fa 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -9,7 +9,7 @@ from scipy.spatial.distance import cdist def kernel(x1,x2,method='gaussian',sigma=1,**kwargs): """Compute kernel matrix""" if method.lower() in ['gaussian','gauss','rbf']: - K=np.exp(dist(x1,x2)/(2*sigma**2)) + K=np.exp(-dist(x1,x2)/(2*sigma**2)) return K def unif(n): -- cgit v1.2.3 From 5992b14bedc1d51b10278474aa2f41f4ce822c38 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 4 Nov 2016 10:18:20 +0100 Subject: add demo mapping --- docs/source/examples.rst | 5 ++ examples/demo_OTDA_mapping.py | 110 ++++++++++++++++++++++++++++++++++++++++++ ot/da.py | 4 +- 3 files changed, 117 insertions(+), 2 deletions(-) create mode 100644 examples/demo_OTDA_mapping.py diff --git a/docs/source/examples.rst b/docs/source/examples.rst index 6093299..f209543 100644 --- a/docs/source/examples.rst +++ b/docs/source/examples.rst @@ -32,3 +32,8 @@ Color transfer in images ------------------------ .. literalinclude:: ../../examples/demo_OTDA_color_images.py + +OT mapping estimation for domain adaptation +------------------------------------------- + +.. literalinclude:: ../../examples/demo_OTDA_mapping.py diff --git a/examples/demo_OTDA_mapping.py b/examples/demo_OTDA_mapping.py new file mode 100644 index 0000000..f5da2ff --- /dev/null +++ b/examples/demo_OTDA_mapping.py @@ -0,0 +1,110 @@ +# -*- coding: utf-8 -*- +""" +Demo of OT mapping estimation for somain adaptation +""" + +import numpy as np +import matplotlib.pylab as pl +import ot + + + +#%% dataset generation + +np.random.seed(0) + +n=100 # nb samples in source and target datasets +theta=2*np.pi/20 +nz=0.1 +xs,ys=ot.datasets.get_data_classif('gaussrot',n,nz=nz) +xt,yt=ot.datasets.get_data_classif('gaussrot',n,theta=theta,nz=nz) + +# one of the target mode changes its variance (no linear mapping) +xt[yt==2]*=3 +xt=xt+4 + + +#%% plot samples + +pl.figure(1,(8,5)) +pl.clf() + +pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') + +pl.legend(loc=0) +pl.title('Source and target distributions') + + + +#%% OT linear mapping estimation + +eta=1e-8 # quadratic regularization for regression +mu=1e0 # weight of the OT linear term +bias=True # estimate a bias + +ot_mapping=ot.da.OTDA_mapping_linear() +ot_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True) + +xst=ot_mapping.predict(xs) # use the estimated mapping +xst0=ot_mapping.interp() # use barycentric mapping + + +pl.figure(2,(10,7)) +pl.clf() +pl.subplot(2,2,1) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.3) +pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='barycentric mapping') +pl.title("barycentric mapping") + +pl.subplot(2,2,2) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.3) +pl.scatter(xst[:,0],xst[:,1],c=ys,marker='+',label='Learned mapping') +pl.title("Learned mapping") + + + +#%% Kernel mapping estimation + +eta=1e-5 # quadratic regularization for regression +mu=1e-1 # weight of the OT linear term +bias=True # estimate a bias +sigma=1 # sigma bandwidth fot gaussian kernel + + +ot_mapping_kernel=ot.da.OTDA_mapping_kernel() +ot_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 10,verbose=True) + +xst_kernel=ot_mapping_kernel.predict(xs) # use the estimated mapping +xst0_kernel=ot_mapping_kernel.interp() # use barycentric mapping + + +#%% Plotting the mapped samples + +pl.figure(2,(10,7)) +pl.clf() +pl.subplot(2,2,1) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) +pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Mapped source samples') +pl.title("Bary. mapping (linear)") +pl.legend(loc=0) + +pl.subplot(2,2,2) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) +pl.scatter(xst[:,0],xst[:,1],c=ys,marker='+',label='Learned mapping') +pl.title("Estim. mapping (linear)") + +pl.subplot(2,2,3) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) +pl.scatter(xst0_kernel[:,0],xst0_kernel[:,1],c=ys,marker='+',label='barycentric mapping') +pl.title("Bary. mapping (kernel)") + +pl.subplot(2,2,4) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) +pl.scatter(xst_kernel[:,0],xst_kernel[:,1],c=ys,marker='+',label='Learned mapping') +pl.title("Estim. mapping (kernel)") + + + + + diff --git a/ot/da.py b/ot/da.py index ad2f8b5..4e5fda2 100644 --- a/ot/da.py +++ b/ot/da.py @@ -203,7 +203,7 @@ def joint_OT_mapping_linear(xs,xt,mu=1,eta=0.001,bias=False,verbose=False,verbos if it>=numItermax: loop=0 - if abs(vloss[-1]-vloss[-2])=numItermax: loop=0 - if abs(vloss[-1]-vloss[-2]) Date: Fri, 4 Nov 2016 10:40:27 +0100 Subject: add notebook + better dependencies in setup.py --- examples/Demo_2D_OTmapping_DomainAdaptation.ipynb | 283 ++++++++++++++++++++++ examples/demo_OTDA_mapping.py | 2 +- setup.py | 6 +- 3 files changed, 287 insertions(+), 4 deletions(-) create mode 100644 examples/Demo_2D_OTmapping_DomainAdaptation.ipynb diff --git a/examples/Demo_2D_OTmapping_DomainAdaptation.ipynb b/examples/Demo_2D_OTmapping_DomainAdaptation.ipynb new file mode 100644 index 0000000..4b75f58 --- /dev/null +++ b/examples/Demo_2D_OTmapping_DomainAdaptation.ipynb @@ -0,0 +1,283 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## OT mapping estimation for domain adaptation" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dataset generation" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "np.random.seed(0) # makes example reproducible\n", + "\n", + "n=100 # nb samples in source and target datasets\n", + "theta=2*np.pi/20\n", + "nz=0.1\n", + "xs,ys=ot.datasets.get_data_classif('gaussrot',n,nz=nz)\n", + "xt,yt=ot.datasets.get_data_classif('gaussrot',n,theta=theta,nz=nz)\n", + "\n", + "# one of the target mode changes its variance (no linear mapping)\n", + "xt[yt==2]*=3\n", + "xt=xt+4\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Plot source and target datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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iSpWq5M+fn0KFClGiRClWrVrllOfw4cOEhYVx8+bNDKqlEEI82KRHVAjxwLp2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fEQ8K3M/EDtcVBs4PCJzLy+1OIqphbufwTMzcwHyq6nSiJiH6APeD/wUeCRzLys3Ko6X22769mZ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\nRK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(1,(8,5))\n", + "pl.clf()\n", + "\n", + "pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples')\n", + "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples')\n", + "\n", + "pl.legend(loc=0)\n", + "pl.title('Source and target distributions')\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### OT linear mapping estimation" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 0|4.009366e+03|0.000000e+00\n", + " 1|3.999933e+03|-2.352753e-03\n", + " 2|3.999520e+03|-1.031984e-04\n", + " 3|3.999362e+03|-3.936391e-05\n", + " 4|3.999281e+03|-2.032868e-05\n", + " 5|3.999238e+03|-1.083083e-05\n", + " 6|3.999229e+03|-2.125291e-06\n" + ] + } + ], + "source": [ + "\n", + "eta=1e-8 # quadratic regularization for regression\n", + "mu=1e0 # weight of the OT linear term\n", + "bias=True # estimate a bias\n", + "\n", + "ot_mapping=ot.da.OTDA_mapping_linear()\n", + "ot_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True)\n", + "\n", + "xst=ot_mapping.predict(xs) # use the estimated mapping\n", + "xst0=ot_mapping.interp() # use barycentric mapping\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### OT kernel mapping estimation" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 0|4.026411e+02|0.000000e+00\n", + " 1|3.991051e+02|-8.782091e-03\n", + " 2|3.987950e+02|-7.769290e-04\n", + " 3|3.986577e+02|-3.442631e-04\n", + " 4|3.985741e+02|-2.096899e-04\n", + " 5|3.985159e+02|-1.460105e-04\n", + " 6|3.984729e+02|-1.078536e-04\n", + " 7|3.984436e+02|-7.368218e-05\n", + " 8|3.984214e+02|-5.574123e-05\n", + " 9|3.984025e+02|-4.740267e-05\n", + " 10|3.983871e+02|-3.865975e-05\n", + " 11|3.983744e+02|-3.175451e-05\n", + " 12|3.983642e+02|-2.561334e-05\n", + " 13|3.983558e+02|-2.116042e-05\n", + " 14|3.983479e+02|-1.982104e-05\n", + " 15|3.983413e+02|-1.643931e-05\n", + " 16|3.983351e+02|-1.567880e-05\n", + " 17|3.983296e+02|-1.366612e-05\n", + " 18|3.983270e+02|-6.571092e-06\n" + ] + } + ], + "source": [ + "\n", + "eta=1e-5 # quadratic regularization for regression\n", + "mu=1e-1 # weight of the OT linear term\n", + "bias=True # estimate a bias\n", + "sigma=1 # sigma bandwidth fot gaussian kernel\n", + "\n", + "\n", + "ot_mapping_kernel=ot.da.OTDA_mapping_kernel()\n", + "ot_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 20,verbose=True)\n", + "\n", + "xst_kernel=ot_mapping_kernel.predict(xs) # use the estimated mapping\n", + "xst0_kernel=ot_mapping_kernel.interp() # use barycentric mapping" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting the mapped samples" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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hYQF5eWn4+3uZyUopOXs2FWfnQNzdte/s7e0JCmrIpUuCS5cuXadalWTJkiW0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\nCgpqdHC4a2LZsmXodDp0Oh1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cuHAGDWpOVFQiY8euY/HigXTqFGx1g2uFQnHzoQwZrm6+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dPaytjMDNVaDe3S57+Qy6HodOtKUHeLWq1uRMmWqdfrjN64Qeittwh985sE3/tePAMDDAwNrZOC\nXklbRwfpWIzxmRkcViuVapVMqURrX9+W6w2Hw0yOjlLOZkGSMLvd9Bw+jGtpEJpAIBAImmdtxuNO\nZh1isRilRILepbIvgPZAgFtTU0xevIjXZFpnn1GpuPGXf9lUhsZisaC3WkmkUnjdbqrVKvl8ntDc\nHM6+vobKWbPv/CApmu30W9wnWFQpe3nN8f8L+MP9WJBAsN9UKhUuv/02mZkZbEYjiqJwa3aWVHc3\nQ8eO7UlCcSd0dXdTLBQIj40RWVigXCziDQTo7O5GUqnI5nLozeYN08d3csPeTbP9TvD7/SQ6Oxmf\nmcGkVlOXZfL1Oo62ttvqWE5NTRG5cQNLPs/017/O0Xe/m3woxHXgodOnN80I2e12jg4PMzszQzoe\nR2M0NmYIbHZNKpXi5sWLmGWZYEsLsqIQiUa5Xi7z0DvesSPHTyAQCATrKxL2K+sQGx/nu1/+Mkc+\n+lH8/f0bBraKxSI6lWpdVt1kMHD1D/+QGy+80Di2bJ9PPfMMI+fONZWh0ev1dPT1MX7xIrOXLpGO\nRsmkUsgmE31uN4lEAo/Hw6lnnqHnPe8h+vbb/NUzz2z7zrsJTN5v7HSOjKiLENx3zM/PkwmF6AkG\nG1+Uy5UKk1NTxHy+O9asrdVqOXbiBO2dnRjcbtKTk3h9PlCpiMRiZKtVeru7N3SsDlKaWKvVcvT4\ncaI+H4l4nLm5Oar5PAvhMBdSKZw+H/0DAzuqB96Oer3O9FtvoY5EKIYX2/oWxsexHjpEPJcj09e3\nZW+O3W7HbrejKEpTJXDzkQiqYhF/e3vjWHsgwOj0NLFYjI6Ojr2/lEAgEDwArK1IePm553jk53+e\nluPHt5x11gyhUIiLf/u3jDz/PAQChGMxOg4fXrdHGwwGyvX6OhtQLJcZ/OhHeeoTn2jY5x/4nd/B\n6vezsGRrmq2gaG9vp1Qq8drUFFqtloGTJ/F6PBSKRW68/TbZ7m7SiQTFhQWKS1Ubvoce2vKdb3dg\n8l5AjK4WHHjSySRmrXZVtF+v06FTFLLZ7B1VnVqWgvyeJ55gsr2dyPQ02XwevcVCb1fXppOCm00T\n3y+a8VqtlmAwSKVSQT8xQZvTid1qpVgqER4b44aicOzEiX0bGFqtVpn6i79g8n//78axb3/mMwB0\nfPjDVN797qbu0+x6ivk8xjWOmCRJ6FQqKpVKk6sWCAQCwdqKhNGXXmL0pZcaFQm7zTqk02nGL1/G\ntCTa4rZYMMsyE5cvY7FYVpUBe71eZt1upmdnafF4UKlUxJNJJIuFruPHV5UZJ27e5O8//enG781W\nUCiKQqlYpMfvp2eFI+W02/nnt95ibnqaTq8Xp9mMotHQ8eEPk6rV2Fwj9MFAODKCA49arW40yq1E\ngbvWfK1Wq+np6aGzs3Pxy7xe31SJ23Yb9v2kGV+v14lMT+MymRqN9BazmaBKxezcHAuHDmGz2fbl\nWTqdjkM/9mMET59GFY/z7c98hieefRZDRwd5s3nLHpndYLHZCM/MrDomyzIVRRFlZbcBSZJ+Dfgg\nMAAUge8Av6IoyuhdXZhAINgzw2fP0v7YY0y9+mojAPXks8/S9thjLITDTWUdNgryzVy9yvy//Avl\niQkAbvzTP+Ho70dyu4kdOrTKkTEajXS0tvLKH/0RC08/jd7pxOhw0L+iV3LZPh/+wAc4ffbsjioo\nIpEIoakproyMIC0FwvwtLUiShCzLpGMxWjwe2pbu4bDZsPt8pHM5yuXyvlYw3G8IR0Zw4HF7vUQn\nJljI5bBaLACkMhnqOt1dn8yu0Wh21Bey2YZ9P2rGVyoVaqXSOifCZDRSj8cpl8v79iyVSkXv6dOM\nqlRUl46pfT5Kbjftg4NYlv5f7Bc+v5/ozAxToRAelwtZlokmk5hbWtaJEAj2hSeALwJvsmjX/jvw\nd5IkDSqKUryrKxMIBHtiubF/2YkB+NbSz0/9xm8wfPbstpUIGwX5Lv/BHzC6ordl+utfZxqwP/00\nvqXByStRFQrc/NrXePinfoqWEyewWCyrApCb2eftSt7m5ua4eeECJqDNZiM0O8vk1atUq1U629oo\nFIsUCwW8a76vOGw24pEI+XxeODICwUGmpaWFTH8/4Vu3mE8mF2tcTSbaBwZ2Pe33XuNe0IzP5XJE\no1FKhQImi4WWlpYtMx06nQ6d2cxCLtcYNgmQy+fRGAwNlZb9wu/3Iw0Pc0tR6PrIR1D8frpOnqSz\ns3NfnwNgtVoZfOghJsfGCCeTSJKEvbubQzucBSBoDkVR3rPyd0mS/j0QBYaBV+/GmgSCB4lsNksu\nl0Oj0eB0OjcdIbBbhs+epe2xx3jjy19m9KWXVmU5tqpE2CrIF3j/+4mUy3Sp1Vz8X/+Lkz/zM1g7\nOrgyP0+lWt30Hgujo2i1WgoeD56urk3ftZmSt3q9zsz4OBaVCn9LCy67nWouR3p+nqlbtzAZjcTT\naUweD+Y1AbdypYJqaQ7aMqVSibm5OZLRKGqNBq/Ph9/vv2OiRncD4cgIDjySJNHX34+3pYVMJrM4\nG8Th2Dep33uBuy0GkEgkuP7WW9QyGQxaLbFajbDLxeCJE+ua6GVZJpVKUSwW0VssJGIxVPE4VouF\nUrlMNJ2mpa8Pq9W67+v0+Xy0vve9PPwDP4BGo7mtm7vL5cLpdFIsFpEkSZSU3VkcLFaPJu/2QgSC\ng4wsy9wcHWV+cpJ6sQgqFXq7nd4jR2hpadm35yz3iZq9XkZfemmVE7NO9ph/rUb4zuc+x3eff75x\nfGWQz/mBD+A8epRCKASA5PGQMRqxdXdjXrFfbxYo7Dxzhv5nniHQ1UVnZ+euejpLpRKlhQXalsqo\njUYjhwYGCJlMXBsfZzaXo3NwEGdPD8mpKcxmM0aDgUq1SjgWw97Z2agoKJVKXLpwgfzcHDazmVq9\nzs1QiExPD4NDQwd2jplwZAQPBMtN9gclA7OWu6kZL8sy46OjaAoFupeyG4qiMDU7y/jNm6ukjSuV\nCteuXCEVCiHV69SBvKKg1Otkslk0Oh3BoSG6Dx26beuVJIlKMsk/3wFRBEmS9r3/RrA10uJ/tv8J\nvKooytW7vR6B4CAzOzvL3PXrBJxOjE4noXCY69/9LpdHRhh+4gm6e3r2dXbWyizHZg4G/Gs1Qu8P\n/iDfff55nnz2Wb71mc+sCvIlKxUC7e2oXC5yP/zDVF0uWrq7MVerOFaseW2gsP9nf5aOEydwBALU\nq1Um3n4btVpN+wqVSti8Z3Vlv47O5UKt1VKuVFByOa594xsMfuhDdHR3I9tsnHz8cTweD+Vymesq\nFaG5OZRqFUWlwhYM0j8w0LCvkUiEfDhMT3t7I0hXLJWYmZyk1e8/sGXNwpERCA4Qd0MzfmFhgUIy\nSceKTVKSJFrcbuYSCQqFAmazGYDJiQlSExN0+HwY9HpqtRoz4TAah4PBo0fR6/U7Kr3arUrb/SSK\nINgxvwccAR7f7sRPfvKT6zKzZ86c4cyZM7dpaQLBwSISCmHT67GYzYzeukUyFKLdaCSWzTJ/5QrF\ndJqh06f3LYi4sg9ls0oEAFQqwiMjZJZEV5Sl623t7Y1zNMUic34/SjrN07/0S0iSRCKVwux2r1Iz\nXQ4U5vN5ALpPnaLjoYcaf6/LMnOTkwQCAdRq9bY9qyvtj9/vxxMMErl2DVM6zcgLL+B/5zsp2Gx4\nu7sbfbx6vZ7jJ0+S7uparGbQ63E6nauyLIn5eaxG46pKA6PBgKZeZ2Fh4a44Mi+++CIvvvjiqmOZ\nTGZfnyEcGYHgALGdeste5Jm3u7YQj3P5pZcY/NCHMK3YMBVl0YRUq1VioRAeux3DkrOi0WgItLYy\nlUhQr9d33D+ybBDaHnusqfe6H0URBM0jSdKXgPcATyiKEt7u/C984QucOqDTrgUPJrIsU6vV0Gq1\n+yZfvxmKolAtl7HpdGRzOVLz8wQdDowGA+VqlUBLC4VCgZmpqT05MpvZnq0qEV5+7rlV2ZploYCx\nv/s7epfk9o1GI0dOnmT81i3m43FQFCyBAN29vY3g20o0TicdH/4wnjWZF4vJRKRQoFKpYDQaN80U\nPfqpT3H8J39ynf0x1+uoymXG3noLgJvnz9P6yCP4bTbK5TKpVIp6vY7Vat2yskSt1VJZkpJeSV1R\ntiwry2az5PP5Rn/Tfg6m3igwNDIywvDw8L49QzgyAsEDxF4yEZtda7FYMDmdzP3LvzDywgt0PvUU\nRrebWCKBJRBoGIR6vY5cq6Fb0yui02qRazXqG2zAm7HWIZl59VW+9ZnP0P7YY1u+170giiC4PSw5\nMT8CPKUoyvTdXo9AcCeRZXmxzGtqimqphN5sJtjZuShycpscGkmSsLvdpMbGMOr1SLUaRoOBUrkM\nWi1GoxGNXk8mmUSW5V33aGxntzaqRGi2b9Rut3Py1CkKhQIAJpNp08/L1dHBoY99DGVNuXC+UEBj\nMKDT6bZ89sU//mPOrfgC3+i1eeoppl55pXF89EtfYhQo/tIv4XjPe6hkMkiKgmQw4Ovupq+/f8PP\nssXn48bMDIViEdOSnY0nk6hMpg2dn3q9zuiNG8Smp5FLJRRJwuzxcPjo0fuqh1g4MgLBA8DtzEQU\nolEsxSITSyn8m9/9LlPhMJbubgZ6extGQa/XY3Q4SMdiWFZEu1KZDDqLZcMI2GasdUiWpTj/5ctf\nxuT1bvped1sUQXB7kCTp94AzwPuBvCRJrUt/yiiKUrp7KxMI7gyTk5NMX7qETa/HYTSykEpxM5FA\nPnmStra22/bcto4O0tEoczMzZAsF4skk+XIZZ3s7FquV+XgcrdW6K2dqO7u1MlOzNhC1k75RSZKa\nsj82mw2n38/s+Dh+txutRsPM7Cwz0ShtR4+SyWRwOp2bPtvi9zcyMi99/OP8wO/8DombNznyb/8t\n737++VV2yXnkCBOhEPpikc5gEJVKxUIuR/jGDaw2G4HA+jGYPp+PdG8vs5OTSLEYsqKgNpvpOnJk\nw5lss7OzREZHCTidWL1earUas5EINy5f5tQ73rGvmZnbycGUMBAIBKs4/9Wvcm54uBEBeunjH+fc\n8DDnv/rVba9dWDIkK41JeGSEhXC4ce8/+/7v59qSMszlL32JkU9/mur586uiQJIk0XnoECWtlqlQ\niGQ6zWwkQjyfJ3Do0I5UvYbPnuWZ8+fpf9/7Vh0ffemlLd/L6vevMmjLP4uysvueTwA24GVgbsW/\nH7uLaxII7gjlcpnwxAQeiwWf14vVYiHQ2opNq2VmbIxarXbbnm232xkaHiZ49CgVq5VQPk9rXx9d\n3d3k8nkypRK+9vZdOTLb2a3lTE0uvHkV6X73jR4eHMTb18dcLscrb77J5evX0Wk0qJJJLv3zPzM+\nPr7ps9faH6vfz8i5c5jc7nV2SdvRgUqrxd/S0si+WC0WzBoN83NzG65NpVIxeOQIxx57jI6HHuLQ\n6dOcfOc714kQwGJZYHh6GofR2Jivp9FoCPp85ONxksn7R/Dx/nC3BALBnthLJmK7cqyd3Nvr9aI6\nfZrZmRkyqRR6j4f+9nb8OzQyyxGvh3/+5xl96SWeePZZvr1GkWYr7oYoguD2oSiKCMoJHlgKhQLV\nQgHbGrljm8VCNpOhVCrt+9DflTgcDk6dPk1ndze3rl2jkEgwFg6jMhjw9fcTDAZ3dd+NbIu9vR0F\n1gXXYOMKg+36RneKXq9n6NgxNDoduVSKvpMnsS19tulsltDoKB6PB7vdvvmzVSpOPfNMIxjYkI1W\nqRp2KVmpoJakdQ6gVqOhXNo8ySxJEi6Xa1ulOFmWqVUqWNbMwNFoNEiKclud3/1GODICwQPAXuSZ\nt3NUdnpvt9uN2+1eHEy6x9rt1uPHeeo3foP2xx7j2zt4r/02bgKBQHC30Gq1qJYkfFeWA5UrFdQ6\n3b4Pp9wMt9uN/dFHSaVS1Go1LBbLnuaBbWRbbnzzm6sCa7DzXseNxAN2KoSTy2Twu1wNJwbAYbMR\nS6VIp9Nb9pjc+Mu/ZOTcuS3XX0kkqKvVFEsljEvDoRVFIZvP4+vq2nZ926FWq8j+TvIAACAASURB\nVLG6XGSnpnCuWGu+UEDS6XZU6n23EY6MQPAAkEgkiMzNEZ+eZuDsWSpLTYnNsNaYzL7xBn3vfe+6\nzX6nWY79aEBddkhWDkITCASCBwmLxYLD52NufJy21laMBgOFYpFoKkXrwMCO1SD3gkajwev17us9\nV9qW5cAasGFwrRmHZCPxgOzsLK/85m+iGRzEdvgwziUJ5k2dwE0CcZIkNZQ6N2NtcHC5V+bwBz7Q\nOMfpdOJqb2d6fByH2YxGrSaVzaJ1uwnsMsO1lraODq7GYkyGQjhsNiqVCqlCgda+vg17au5VhCMj\nEBxw5ufnufHWW2hKJZxmM/r3vIfJ6Wm0LteGDYObYfH7OfXMM4ycO8fps2cxer3k83nUavVi5G2P\nWY5MJkN0fp5CPo/ZasXn8zVdDiFKxQQCwYNM/8AA1+p1QpEIcrWKSqfDdegQPb29d3tpe2atbVnr\noKzMxIdHRjZVONtIPKAQiyEDo6+/DkDk29+mFIkwZ7EQO36coydONNTIAJLJJPFYjFQmQ2JmBpvV\ninmpvzOby6Ho9TgcjlXPXOtYrQ0OWv1+/v7Tn+b02bONYyqViiNHjzLrdDIfClGsVvEODNDW3r5v\nZYIul4sjw8PMTE2RSiZRG410Hz5MW1vbbZfu3k+EIyMQHGDq9TpTt25hrNUIrlCuicRiTN28SUtL\nS1PKJMsGIPjww4ycO8fVf/gHaufPIxkMGLxe7K2t9B0+vOsp9tFolNG330bJ5TDodKTLZeanpxk8\nebKpqdCiVEwgEDzIGAwGTp46RTqdplKpYDAYsNls99UX0p2yMoDVjDLnZv2eK7ny5S8DcOJnfoaM\n00mktZWOjg4ApqenmbhyBVWphAHIpdP842uvMdDTg06rpaJWE+jvX1VWtpz9WQiHefq551Y7V5v0\nyiyvWaPR0NnZSWdn576UYm/Ecj9NrVZDpVLtWiL7biIcGYHgAFMoFChmMrStiBABuBwOJuNxcrnc\nqujRZqw1AK/+8i8Di5t998c+RnhigquVCg+dPr1qqnAz1Ot1JkZH0VcqBJcMBsBUKMTErVs4H374\nQBtjgUAg2A8kSdrT4Mn7jZUBrLUDMDfqO1lb0tX/vvdx9Md/nEy1yuzrr3P993+fJ559Fs/AACaP\nh3S9Tnx+no6ODgqFAtM3buDSanEviSoEW1u5cOUKOb2eQz09eLxePB4PkiStc6xGzp2j68kn6fq+\n72s4M830yixzu23g/SK1vBH378oFAsG2qNVqVGr1umGT9XodSa1u2ulYawCO/of/QN+jj2LyeDCZ\nTHQGg0zMz5NMJndcH72wsEAxnabL41l13Ot2M5dMUigUmmo8rFarRKNRspkMWp0Or9d7Xw31EggE\nAsHuaEY9c21J1+hLL/H0c89hcTrJxGIAeAYG8AwMAJCKRFAt2ch0Ok01l8O1QspYr9fT29VFTqdj\nYHBwlbOxNvgH8Ocf+QinnnmmkZkRc832B+HICAQHGJPJhL21lfmJCQx6PRqNhnq9TjgWw7aDWttl\nA7DcxNgyNNTY7GFRElKSZcrl8o7XKC1JTK5tkJRlGTaQn9yIUqnE5bffZmFuDoNKRU2WmTOZODQ0\ndFuHwQkEAoHg7tOseuZCOEz82rXG7+GRESx9feskjUvlMrlqlb41ktbNMnz2LAvh8KqMCyxmZpYz\nSXtRExX8K8KREQgOOD19fVwtFhmLRNAoCjXA7PXS29+/43S1NRCg/+MfR14zvLJSraKoVBiWZCK3\nI5PJkEgkqNVqmEwmdDYb87EY7YEAkiQhyzKxZBJbR0dTgzJnZmbIhUL0tLU1UuSxRIKpGzdwu907\nGrYpEAgEgvuT7YRfNuuTGfjpn6bzJ36CaKlELhSiDHi6u/H5fMDirBytxUIynca9VL5Xq9VILSwQ\nOHZsnS1ddla6nnySP//IRwA2zbgIsZq9IRwZgeCAYzabeejhh4nH45TLZfR6PW63e1ezBax+P+/6\nzGe49sYbRGIxnHY7lUqF+WQSazDYVH12KBRi4soVlEIBjUpFWVHAYgG9npvT0+jVasqyjMHt5lBv\n77bOlizLxGZncdlsDSdGURTcTifJUIh0Oi0cGYFAIHgA2E74ZbNyLrPPR81gIJlMIssyDocDt9vd\nKL82mUx0HD7MxJUrpKen0Wk0FGo1rIEA7SvKzdaupev7vq+h9rlZxkWI1ewN4cgIBA8AGo2mEVna\nKy0tLVRPniQ0Ps50MolKo8HR3U3f4cPb9twUi0Umr1/HCrQsNfZXazUmQiHc/f3YbDZKxSImsxmv\n19t0hgcWS9Qq1Srh+XkS0Sh1WSZTqdCWy607d6fDzwQCgUBwb5DL5SgUCmi1Wux2+4ZKW5vt8duV\nc22lktnR0YHVaiUej1Mtlwk6HLS0tKySZ17LyjIykXG5PQhHRiAQ7JhgMEhrayuFQgGNRtO07HIq\nlaKWy+Fd0bei1WhwWq3kUymOHj2643I3lUqFx+8ndPEic0tKMQ6TiXw+T7FQYG5ykmAwuGqNGw1E\n24hkMsmtW7fIZzK4PB78wSBer1eoqAkEAsEdpl6vc+vmTaJTU1SLRVQaDRavl4GhISwWyyrnZbs9\nfrflXE6nc13lgaIopNPpRsWDw+FYZSOaybiI4NruEY6MQPAAsh+bpkaj2dX034308FUq1WJz/y5p\na29n4tYtbpw/T4/HQ6VcRmUwcGpwkMLCAuFwmJ6enqZmDSxz9epVXvubv6ESi2HW65kxmQh1dDD4\nyCMcOnRo12sVCAQCwc4JhULMXbuG3+XC5vFQrlQIzc1xHTj18MMN58Xd10e1WAQ23+PVdjuBM2e4\nNDqKZnyclmCQYDC445LrcrnM9atXSc/NLQ4i1WpxBAIcHhzcUUVBs8E1wXqEIyMQHGA2c1ju1qZp\nt9vRmEwk02kMtRrXvvENDn/wgySLRfxDQ7vOdJhMJjoOHaI4N4fVYkGr0+FwOLA7HERiMdLxOPT0\nbNrouVa3PxqN8sbLL2PMZjk9MICsKMSTSbJzc0xeu0Zra2tTktACgUAg2DuyLBOemsJpMmFbUtvU\n63S4NBrG//mfGS0UKIyNATSa62HjPb5YLHJpZIRSNIrTaqVeKjF54QKZVIqjx483NZZg2bZannyS\nfDpNW2srRoOBYqlEaGKCMY2GoWPHmrpPs8E1wcYIR0YgOMCsdVju9qZpNpsJ9vUxc+0axRs3GHnh\nBZT+fvyPP07bJg2TO7m30+Ohd8VQTYBKpYJ5KTLWrG7/7MwM1VSKrtZWVGo1KqDV46E0P08qHCab\nzQpHRiAQCO4QtVqNWqWyLssx/tJLjLzwAiObXLfRHj83N0dxfp6AycSN//N/GPzQh3D4fEzNzJAI\nBmlZklzeqnJh2bY+/MUv0nPsGMaldRkNBnxuN9G5OQo9PVuWXSuKwreef543P/e5xrGthmIKNkY4\nMgLBA0SzGYlmqNfrxONx8vk8Go0Gt9u97Zf7hXAYQypFi8HA+FITvrFexwvUMxlostdmI1wuF9NW\nK5FYjNal6crpbJaSJNG9JHTQrG5/pVhEp9WuKneTVCrUQEmWN2wuFQgEAsHtQavVYrTZyEajjYwM\nQMcP/RCq48fpP36chdHRRoBKazTy5x/5yIZ7fDoex2oyUUomGXnhBTqfegqPx4O6XieXyzUcmY0q\nF5aDgaE33gAgMzpKzmpF7fNhWhrqrNfrkXM5qtXqpu+jKAq3bt5Ee+wYj37+8+QmJrj8pS9x/Nd/\nnVPvex+uzs59/fwOMsKREQjuIcrlMslkknq9jsViwW6376rcarPMy+EPfGBfJglXKhWuXLpEOhRC\nqyjUZJkZu52eoaEt1dE2mnb8nV/9Vb7D3iNQFouF3qNHGbtyhdFQCElRUJlMtA0ONgxT49xtGj1t\nLhdai4VUPo/VbEar0VCt1Yhms7QeOtSUzLRAIBAI9gdJkmjr7OR6IsFsJILNYqFcqZBUFLq/93vp\nGxoivOTgLNu0zfb4ejZL+sYNNIkEAHNvvMG1b3wD0+OP07HNQMrXfvd3ef23f7vx++gXv8gocOrj\nH2f47FkAMtksWrN5y2xMJpNh7tYtOjo7sQ0NEW9t5fKXvoTa4UDx+URZ2Q4QjoxAcI8Qi8UYvXyZ\nSiaDCkCnw9vZyeGBgaZqdlfSbObF2tdHzmIhHo1iLhbx+Xwbbr5rU+wzMzNkpqboDgTQLTVHhqNR\nxq5exel0otfrN1zXcmkXsGdnaiN8Ph8Oh4NUKoUsy9hsNqxW67rztlORsavVlF57jWJvL8VKBVW9\nTjKXw9jRwclHH91SblMgEAgE+09rayucOsXMxATRbBaNXk/HiRN0LmUvVjovW+3x0b/9W85//vON\n31//3d8FIFAu871nz24aCMzlcnDoEI987nPUw2HOf/7zBD76UWpeL/YTJ8jmcuQLBRaqVQ4NDGwp\nHJDJZJDKZWytrZQrFRaAnh//cVQmE5FQiK6urr1/YA8IwpERCO4ByuUyo5cvoysU6AwGUalU5PJ5\nZm/exGqzbTpwazO26wWx+P0M/6f/xNTcHPpkEoNOR6JcZt7t5sjJk9jt9lX3W5lit/h8REMhnBZL\nw4mBxR6Sm7OzpFKpRlZmrQO0trQLNi/v2i0GgwH/Hp0iKZ9n+k/+hMf/4A/IGwyUKxUG/H5OnDyJ\nZ6l8QCAQCAR3ltbWVlpaWqhUKmg0mlVBvmYHSz75qU/hffxxRv/6rxn/2tdoeeopoq+8QvvwMAuj\no1z84z/mu88/3zh/ORAI0PVjP8YP/PIvE79+nfPAiaefZkaSKHu9xKtV9A4HfR0dBAKBLdcgSRIK\nkFlYYOzmTSrZLLbhYSLz86SvX+fE8PCqQc7NKI1GIhESiQR6vR6Xy4XT6XwgRgXs2JGRJOkJ4NPA\nMOAHPqAoyjf3e2ECwYNEIpGgkk7T2dbW6L+wmM1YczkiodCOHZntekGMXi/u970PbT5PoLUVWKzZ\nnQyFmBwf58RDDzWiUsCqyJQsyxRjMaxryqtUKhUoyqq+kq3U0Xar4387WRuJc1Yq9Pb3YwsERM2y\nQCAQ3ANIkrRp1r8ZbIEA7/jgB5n95uJX1+grrwDw+m/9Fq//1m/x6Kc+xTPnz68LBF66cAH7UsWC\nyePh1Mc/jisYpFws0j44SDAYRKPRbOg8LITDfGepqf+dv/RLOBwOFIOBi5cuYa3V6PZ6kWWZeq2G\nXK8zMTbGkaNHG9dvZUuLxSIv/9M/MXHhAqpSCa3BgLO9naFHHqGvv//A93TuJiNjBt4C/m/gG/u7\nHIHgwaRer6OWpHUbjl6nY6FS2fV9N3MWstkspUyG4JITA4vGweN0Eo3FKBaLG/azLEemjvzcz6F5\n17twrujhSWUyaEwmbDZbU+pozUbP7iRr3/mvnnkGEAoyAoFAcNB47Bd/Ea1Oh7uvj7//9KdXVS5s\nFAiMyDK1+Xlg0ZEZPnt20fmYnUWr1W5ZSpYLhxtZnuM/+ZP4T53C7vNx+fXX0RsMzMdiVABXezuu\n1laSkQjlvj4qyeSWtlSWZb77ne8w9tpr9Ho8uHw+FvJ5EuEw1994A6fLta5H9KCxY0dGUZS/Af4G\nQHoQclYCwR3AYrGgaLUUikVMS+lkRVFIZbN4BwZ2fd+tnIXl1PZmf9usn8XW3s71v/5rFEni1vQ0\nVpOJSqVCSZJoP3IEi8XCy5/73I7V0WRZJp1OU6vVMJvNd0XeeLuSPDF9WSAQCA4G/pMned9Xv8rs\nm28u/r6mcmFtINAXDHJjbo50NovDZqNerxOORtE5HOgrFV5+7rl1tmEhHCZ68SJTr77aOHb9L/6C\n+LVrGINBOvr68BgMSIDZYsHpcFCqVEgXCsiyvG2/ayqVYvbWLfxWK36vF1hUTavHYkRjMeKx2CpH\n5iDaMNEjIxDcAzgcDjwdHczcuoXdaESr0ZBeWEDjcu15vspG2Gw2jE4n0Xic4FI/iyzLxFIp7F1d\nGAwGDJv0swCc/+IX+akPf5h6ayuZRAKzwUC3z9fYMJud17JMLpfj+pUr5GIx5FoNrdFIa1cXvX19\ndzQtvl1Jnpi+LBAIBDtDUZTFqgO1+p7q2chkMoSmp5k+fx6AcDhMy9I6YX0g0OfzkT9yhPDYGPMz\nMyBJGJ1ODg8NUZma2tA2bFTZ8K3PfAaA9iee4PBzz2Gu1fC63Y2/J+bnMfv9GAyGbW1pqVRCqVbR\nr8kGmYxGaokEtVpt1fGDaMOEIyMQ3ANIksTAkSNY7XYioRClSgXP4cO0tbdvqLq1VzQaDYcOH2b0\n4kVuTk2h12go1mqYvF66e3rWX6BSceqZZyjEYmRmZgBIXbuG32iktaNj3YbY7LwWWCyru3bpEuX5\neTpbW9HrdGQWFpi9dg2D0UjHmgGXa7kdEaa1kbi7PUhUIBAI7kfm5+eZnZ6mmM2iMxrxLzXC73eA\nqlwuLyqBSRJ2u31bZclMJsPlN99EyWbxOJ30/+RPEo1EuHH9OoNHjmzocEmSRG9vL36/n4WFBVQq\nFdpSidLU1Ka2YfjsWdofe4ypV1/l20sOzJPPPotnYADP0BA1t5vxixcpzs5i0OtZKBSQrFb6Dh1C\nkqRtbaler8dstZIvFKhUqw0BnlyhQE2jwb0kTnOQbZikKJsVlzRxsSTJbNHsL0nSKeD8k08+uU4F\n6cyZM5w5c2bXzxYIDjKKotyRyFUulyMWi1EqFjFbLLS0tKybnAzw8nPPrYsqLbNVuVgzTkYikeDi\na6/R3dKySgVtPh6nYjbz8DvfuanRWwiHefm55xg5d45nzp/fV/WzlWz2/vdS78yLL77Iiy++uOpY\nJpPhW9/6FsCwoiibDb9+4Fi2TefPn+fUbfo/IxA86ITDYUZHRjAqClaLhUKxyEKlQvvRo/RsFDDb\nJbOzs0zeuEFlYQEAnc1Gz+DgljPNrly6RGpsjEMrKh7yhQJz2SwnHn8ch8PR1LObtQ3hkRHODQ8D\nrLNVsViMyNwcpXwei8NBIBhc9515M1tar9d58/XXufbd76IrlXAYDOQLBSL5PANPPcXT73oXWq32\nnrJhIyMjDC9+Fvtil+5IRuYLX/iCMBYCwQ64U+l3i8WCZcWU5M3Y7fyXZhr6K5UKkiyvcmIADHo9\nhXKZer2+qSOTC4cZOXdu2/XvlZ2Wyt0NNgoOrTAYBxqhpikQ3FvIskxoYgKzJBFYcijsViv6dJrw\nxATBYHDDoNlOSaVSjF26hE2S6AwEUBSFaCLBrUuXMJvNG1Y0KIpCOh7HseZvZpMJJR4nl8tt6cis\ndCp2ahtOPfPMur95vV68S/0tm7GZLVWr1Rw9cQKVWs3k6ChzsRhql4tT3/u9nD59uiFAcD/YsN0i\nSssEAsG23M75LyaTCUmnI18oYF4xjDO7sIDR50OjWb9NbdZAmY/FaD1+fN9T5TsplRPcFYSapkBw\nD1EqlSguLBCw2VYdd9hsxObmyOfz++LIxKJRVKUSLSsyK/6WFm5OTRGNRjd0ZCRJQqvTNTI4y8iy\njCJJG9qclazsM/GfOtWUbVguV74dTfZms5nTjzzCwJEjDbGctaV1B9mG7WaOjBnoBZZDxockSToB\nJBVFmdnPxQkEgnuP/Z7/YrPZMBsMvPqlLzH0wQ9i9/vJLCxQ1mrp7uzcMDu1VQPlfqbKE4kEszMz\n5DIZjBYLdq32npt9IxBqmgLBvYZGo0Gt0VCuVBpKnADlSgWVRrOts9As5VJpXaM7gE6tprrF6AJf\neztjIyNYlwJo9Xqdufl5DE4nLpdrw2s26zNR2+1k6nWO/cf/SF6SqFar66SYb/e4AUmSbks/7f3A\nbv4nnQb+CVCW/n1+6fj/A/z0Pq1LIBDco+z3hixJEq0WC3/9Z3+G/13vouxyYfR6Geju3lT/frMG\nyvbv+R5ajx/fl3XNz89z48IFtOUyVrOZwtwc05JEz8/8zH3fHCkQCAS3E51Oh7etjbkrVzDo9RgN\nBirVKnPRKNb2dmxrMjW7xWq3kxgbQ5blRglyvV6nJMtYtvhiHwwGyS0sMDc1BYkEMmBwOuk/enRT\noYDNpJC7f+qn6PiRH6H1+76P6clJUqUSQydOYFpRYdAMd0Ia+V4cRL1XdjNH5hXgYI8JFQgEe6JW\nq1GtVtHpdA0py41YjnDFLl4EwK0otNhsONvbsW0xxGs5TW7yehuOzMAHP7hvqXJZlpkeH8dYrxNs\na1tcm9NJLJFgZmyM1tbWbVVxBAKB4EGmq7ubUrFIaHYWpVpFUamwBgIcHhzctz7Q1tZW5r1eJkIh\n3A4HiqKQSKex+P1b9p2o1WoGjxzBodHw5le/ytDHPkb74OCW+/raPpP3fOUrpDQaDFotPR0dSJJE\nrVZjcnaWSZuNI0NDO3qXOyGNfC8Oot4rokdGIBDsG7IsMzU1RWR6mmqphN5kItDVRVtbW1MlYv/v\nJz4BNF8eZvH7efRTn2r8vF8Ui0WKmQzBNQ2fTrudVDRKPp8XjswB4JOf/KRQ1BQIbhM6nY5jJ06Q\n6eqiWCyi0+lwOp37Kr1sNBoZeughpiYmSEWjAHgOH6aruxu9Xr/ltZIkIeXzXPjCF3j4Ix/Zdk9f\n22di6esjl8vR1drasG8ajQaP00kqEqHS19eUnTjI0sibqWnuJ8KREQgE+8b42BjTly/jNpsxm80s\n5PPcGhlBlmU6OzvXnb9XJRWr38+7P//57U/cIWq1GpVaTbVWw7jieLVWQ1Krt8wyCe4fhKKmQHB7\nkSQJh8PRtJzxbrBYLAwdO0a1WgVY15+yEXtxHpbLs0wtLShL82RWolKpUGo1mh1vslnJ2m76PZef\nea+0Ct4JNU3hyAgEgn2hVCoRmZqixWbDtWS0TEYjqkSCuclJgsHgugbPe1VJxWAw4PT5mL95E4Ne\nj06rpVarEY7FsAaDD2xTpUAgENyrNOPALLMX52G5PKtcLhOKRoknk7QsDZ5UFIVkOo21o2PbjNAy\n+yGNnM/nCc3MkIhEUKlUeINB2tvbH4jKAeHICASCfaFYLFIrFtf1tlgtFtLpNKVSadOZNfdiA+Kh\n3l7KpRKT4TAqWUaWJMytrfQNDNwz0S7BIkJNUyAQ7IT9cB70ej2d/f2MXbxIIRRCr9ORKxbROJ10\ndnc3fZ+9BvSKxSKXL1ygHI3itNmo1+tMv/UW2XSa4ydPHvgKAuHICASCfUGn06HS6SiWSliXHJZC\nPM7In/wJnve+d8to2b3YgGg0Gjlx6hTJZJJisYher8ftdu+bbKhgXxFqmgKBoGk2cx4URSGdTpPJ\nZJAkCafTuWUGvq2tDaPRyHw4TKlYJOBw4Pf7mxo0vZbdBvTC4TDFaJTe9vZGmZvDbmciFCLe1kZr\na+uO13I/ISyyQCDYF8xmM+5AgPDNm6hUKswmE7Hpaa794R/y7h/5kabT7PvFfkhZqtXqbScuC+4+\nQk1TIBDshpXOgyzL3BwdJTI+jlQuIysKarOZzsFBOjo6Nr2H2+3G7XbveS27DeilEwmsRuOqXh2d\nVotOUcjlcsKREQgEgmbp7e+nXq8zceUKpUiEwsxiVY86FiM8MnJHVVjuhJSlQCAQCO5fVjoPkUiE\n8Ogofrsd69KX/0QqxeS1a9jt9nUKh/cKWp2O/JLQwUqqsvxAVBAc/DcUCAR3DL1ez/GTJwl//etc\n+B//o3F8p7LKu6VSqRC6fp1cOEzu5k3gYElZCgQCgeD2EJufxwCN0mhYnB+WnJoimUzes45Mi8/H\n9ZkZ0tksDpsNRVGIJhJorNZ9yRTd6whHRiAQ7Dvv/IVf4Pi/+3d7aqTcKfF4nJtXrnD993+f6a9/\nvXF8L1KWAoFAIHgwqNdqG2Yw1JKELMt3YUXN0dLSwsLAAHNjY0Snp1EAnc3GocHBXfXq3G8IR0Yg\nEOwbiqIwPz9PeG6OSrFIfUmG+XbLKpdKJW5cuoSuUODxj32M4fe9j5m33uLNz32O7/nsZznyrnfd\nU4poAoFAILi3cHo8jE9NUa/XG0pfpXKZmlp9T0vuS5JEb28vPp+PTCaDSqXC6XRiMBju9tLuCMKR\nEQgE+8b4+DgzV69ikiQMOh2zk5MAZLNZduJG1Ot18vn8omiA2byt3HEymaSaydDV1oYkSViWJKDf\nBKTW1ntiNo1AIBAI7l18Ph9Rv5+xUAi72Ywsy2RLJdzd3Y0Srf0QkdmIer2OSqXak7S/xWJ5IDIw\naxGOjEBwB6lUKiwsLCBJEna7/Z7Vd9/NZl0oFAiPj+MxmRoDMc3HjpH48IfJbNCIuBnRaJTJmzcp\nptOgUmFraaGnrw+bzbbpNbVaDZWirDICJo+HgY98BPU9HEkTCAQCwb2BXq/n2EMPMdvSQnJ+Hkml\noicQIBAINGz1fovIRKNRZmdmKGQy6E0mAh0d+P1+MatsBwhHRiC4Q8zOzjI5Okolm0VSqTC5XPQO\nDuJyue7amqrVKul0mnq9js1mw2QyAc1t1oqiADQ23IWFBar5PM62tsY5Jo+H05/4BIlajWq1uu3k\n5VQqxY233sJQrdLudCLLMvMzM1wrFjn58MObSjibzWZkjYZiqYRxKZ1udLsJ/OiP4h0Y2NmHIhAI\nBIIHEo1Gs2hPvF7UajV2ux2NRsNCOEwuHG6Ix+yHiEw4HGb0wgUM9ToOs5lCIsFoNEr52DG6dzBQ\n80FHODICwR0gkUhw6+JF7CoVHX4/sqIQnp/n2ttvc+rRRzEajXd8TfF4nFtXr1JMpVBkGa3ZjNNm\nw6XVErlwAfjXzRr+dcPO5/OEZmZIRCKoVCq8wSDt7e2UYjEm//RP8X/0o9h8vsZ19XodSa1epXG/\nGZFwGKlQINje3jjWGQxyMxQiHo8TDAY3vM7pdOJqb2d6fByn2YxGoyG9sIDK4SC4wrESCAQCgWAj\nKpUKVy5dIhMKoVUU6opCyGSi+8gRxr72NV75zd9snLuZiEy1WqVYLKLRaBqBwY2QZZnQxARmIBAI\nAOC020mkUsyNjxMIBO747LX7FeHICAR3gPlwGG2lQsvSl2o10B4IMDo9WJiEQwAAIABJREFUTTwe\np33FF/c7QbFY5MalS2jzeXr9flQqFalMhjd/93eZ+tM/bZy3vFnD4ob9jl/5FS5fuEA5GsVpsyHL\nMjNvv002ncZVrTL99a8TfPRRjrS0oFKpKFcqxNNpgsePN1VGV1hYwLzGqVOpVOgkiXK5vOl1KpWK\nwaEhQnY78zMz1Gs1nD09tHd23tNNmgKBQHC/Uy6XSaVSyLKM3W7HbDbf7SXtirm5OdJTU3QHAuiW\nqgfiySRT168z+NGPcvj9799UiVNRFGZmZpidmKCSy6HSaHAFAvT09W3YdF8sFilmswTXSDo7bDbi\nkQj5fF44Mk0iHBmB4A5QzOcxrNmUJElCp1JRqVTu+HoSiQTVdJqu9vZGaZjL4aD73/wbDr3//Vjy\n+VWbNSxmZMLhMMVolN729kaGRVupcOuVV8gs3TszO8vIK69gcrlQu904urro7Oxsal0mq5VEOLzq\nmCzLVBRl201dq9XS3d1NV1cXiqI0lQESCAQCwe6JRCKMXb1KJZsFRUFjMhHo7eXQoUN3rM+jWq0S\ni8XI5XJotVrcbveWPZWwaFey2SyKomC1WtFoNERnZ3GYzQ0nBpbmyMzMUNXraVshGrNWiTMcDjP+\n9ts4dDp8LheVSoXIzZtUq1VOPPTQus9Co9Gg0mgoVyqNcmiASrWKSqN5IAZZ7hfikxII7gBWh4PI\n7OyqY7VajbKi3JWyslqthhrWba52n4+q1Yp/aWNdu1nffOMNrEbjKidh7Jvf5MILLzR+v/q5zwFw\n4hd/kcf/83/G5XI1LWrg8/uJh0LMRiK4l3tkEglMXi8ej6epe0iSJBolBQKB4DaTy+W4dfkyxmqV\nzmCwkdmfuXoVq9VKy5J65O2kXC5z5eJFMrOz6CWJar1OyGym59ixRsnWWtLpNDevXaOQTKLIMnqb\nja7+fmRZRrXGdkiS9P+zd+fhcZVl48e/92SZ7M2+NmnSvbSlNGVHdlkFXFAqguiLsrgvrxuv+oKK\nvio/wRVFFgXZREChiEChArJDS+mWtmmzp5N93zOZ5/fHc5JOppM2SdPMpL0/1zVXmzNnuc/J5Nzz\nrAdh75jQhJwcTr/xxlHT+RtjqK2sJCEigkwnT7mjo4mMjKTG46GtqIiUlJRR+3W73aTl5lJfUkKM\n202M282g18uehgaSCgq0J8EEaEFGqWmQk5tLY00NlTU1pKWkMOTz0djSQmJODhkZGdMeT1xcHF6X\ni4HBwZHaJ2MMHd3dZBcWkpCUtM/NGiAqOprugBnIllx6KbJwIdFeL6/fcMOoJvfECZ5bSkoKi445\nhorSUqpbW+2sZbNnM2/BAm1mV0qpMNLc3Iy3o4Mcvxb3lFmz6OzqoqGubloKMtXV1XRUVzM3L48o\npxWjvqmJ8pISUlNT9+nW1dfXR8l770FbGwVOF+imlhZK33uPuPR0WhsaSE1OHql8a+/sRGJjmeV0\nAUvMydnnwcper5eBnh5SA8bExMbEYAYH6evrCxr73Hnz6O/ro2rPHsTrxbhcJOTmsnDxYq2MmwAt\nyCg1DRITEzmquJiKsjLqm5sRl4u0BQsomjfvgDN5HQppaWkkz55NRUUFac400K3t7UQkJ5OTm0ti\nYuI+N2uAzOxstldX09bRQXJSEsYYOoHkFSvIjonhdQ7+4ZeZmZmkpaVN6DkySimlppfX6yUqSBfe\nqKgoBvYzpnGq+Hw+GmprSU1MHCnEAGSmpbGzpobW1lZyAirjmpqa6GtuZmFBwUheycnMpLymhgiX\ni/jcXHbV1JDgdjM0NES/y0Xe4sX77aoWGRlJdFwc3a2tzPJrSenr74fIyDEfTOl2uzn6mGNoKyyk\nt7cXt9tNSkpK2D6WIVxpQUapaZKSkkJycTH9/f2ISEhbGCIiIjhq2TKqkpJoqKkBn4+koiIKCgv3\n26SdmZlJ15Il1O7aRUNVFQaITkpi7pIlJIoEbcWZbHwH6uOslFIqdBISEhgQ2adlv6u3l9nT9FgB\n4/MFHQ/p3x3MX39/P9FBHjwZ63bjGxzk6OJi6urqaGtpISoqivTMzAP2mhAR8ubMYWdTE43NzcxK\nSqK/v5+65mZmzZlDsvNctWBcLldIH8FwONCCjFLTSETGrJ2Zbm63mwULFjB37lx8Pt+4WoZEhHnz\n5pGVlUV7ezsul4uUlJSRcwrWiqOUUurw49+yn5qUZMfIdHTgTk/fpyXkUHC5XKTn5FC3dSvJzvEB\n27sgLm6kO5i/2NhYBrCPBfBv+eju7SWrqAi3282cOXPGPUHNsJycHLwrVlBTVkZbczOuyEjS5s9n\n/sKF2qPgENOCjFJHuIiIiAk3ZSckJJCQkHCIIlJKKRXuIiMjOWrZMqpnzaKxthafz0f6okXkFxTs\n9xkqU2l2fj7tzc3sqq4m3hkwPxgZScFRRwWdBjo9PZ3arCzKa2rISksjIiKCppYWXElJZB9E4UtE\nKCgoICcnh56eHqKioqbtGhzptCCjlFJKKaUmzO12M3/+fObNmxeSae/j4+M5etUq6urqaG9tJcHt\nJiMzc8xZLqOjo1myfDllsbE0NDRgfD7isrJYMH/+lHRnjoqKCtoSpA4dLcgopZRSSqlJC+W09zEx\nMRQWFkJh4bjWT0hI4OhjjqG3txefz0dcXJx2/5rBtCCjlFJKKaWOKKF4hpuaevroa6WUUkoppdSM\nowUZpZRSSiml1IyjBRmllFJKKaXUjKMFGaWUUkoppdSMowUZpZRSSiml1IxzxBdkHnrooVCHMCaN\nbXI0tsnR2CYv3ONTaiL08xycXpex6bUJTq/LoTepgoyIfEFEykWkV0TeEJHjpjqw6RLOHzKNbXI0\ntsnR2CYv3OM7UhxOuSmU9PMcnF6Xsem1CU6vy6E34YKMiKwGfgHcCKwE3gOeFZHgj1FVSimlDjHN\nTUopdeSZTIvM14A7jDH3GWO2A9cDPcDVUxqZUkopNX6am5RS6ggzoYKMiEQBq4AXhpcZYwzwPHDS\n1IamlFJKHZjmJqWUOjJFTnD9dCACqA9YXg8sCrJ+DEBJScnEI5sm7e3tbNiwIdRhBKWxTY7GNjka\n2+SFa3x+996YUMYxDQ673BRK4fp5DjW9LmPTaxOcXpd9TXVeEltpNc6VRXKAWuAkY8ybfst/DrzP\nGHNywPqfAB6YikCVUkpN2hXGmAdDHcShorlJKaVmnCnJSxNtkWkChoCsgOWZ7FsTBvAscAVQAfRN\nNDillFIHJQYoxN6LD2eam5RSamaY0rw0oRYZABF5A3jTGPMV52cBqoBfG2NumYqglFJKqYnQ3KSU\nUkeeibbIANwK3Csi64G3sDPFxAF/nsK4lFJKqYnQ3KSUUkeYCRdkjDGPOPPy/xDbjL8ROM8Y0zjV\nwSmllFLjoblJKaWOPBPuWqaUUkoppZRSoTaZB2IqpZRSSimlVEgdsoKMiJwqIk+KSK2I+ETkkkN1\nrIkQkRtE5C0R6RCRehH5u4gsDHVcACJyvYi8JyLtzus1ETk/1HEF41xHn4jcGupYAETkRice/9e2\nUMc1TERyReQvItIkIj3O77k4DOIqD3LdfCLymzCIzSUiPxKRMuea7RKR74U6rmEikiAivxSRCie+\nV0Tk2BDEccB7rYj8UET2OHGuFZH50x1nOBGRLzif/V4ReUNEjgt1TKEUznkxnIRb3gu1cM1roRbu\nuWu6TFduOpQtMvHYPspfAMKp/9qpwG+AE4D3A1HAcyISG9KorGrg29gnVK8C1gFPiMiSkEYVwEn6\n1wDvhTqWAFuwfeOzndf7QhuOJSLJwKtAP3AesAT4b6A1lHE5jmXv9coGzsH+vT4SyqAc3wGuAz4P\nLAa+BXxLRL4Y0qj2uhs4GzuN7zJgLfC880yT6bTfe62IfBv4IvZaHg90A8+KSPR0BhkuRGQ18Avg\nRmAl9j72rDO+5kgVznkxLIRx3guJMM9roRbuuWu6TEtumpYxMiLiAz5kjHnykB9sgpzk1QCcZox5\nJdTxBBKRZuAbxpg/hToWsLXQwHrgc8D3gXeNMV8PbVS2RQb4oDEm7GqDROSn2Af1nR7qWA5ERH4J\nXGiMCXltrIisAeqMMdf4LXsU6DHGXBW6yEBEYoBO4GJjzDN+y98BnjbG/G+I4trnXisie4BbjDG3\nOT8nYZ+t8iljTDgUWKeVBJ+muRo7TfPPQxpcmAj3vDjdwjXvhdJMymvTLZxzV6gcytykY2QgGVtS\nbAl1IP6cpsmPY6cPfT3U8fj5HbDGGLMu1IEEscBpwtwtIveLSH6oA3JcDLwjIo843TY2iMhnQx1U\nIBGJwrYu3B3qWByvAWeLyAIAEVkBnAI8HdKorEggAlsb6a+XMGkJBBCRImxL2wvDy4wxHcCbwEmh\niitUnM/4KkZfDwM8zxF4PfYjLPNiCIVz3guVGZHXQiScc1dYmMrcNJnnyBw2nJq4XwKvGGPCYjyF\niCzDFlyGa3w/bIzZHtqoLKdgdQy2O1K4eQP4NLADyAFuAl4WkWXGmO4QxgUwF1uT9wvgx9juG78W\nkT5jzP0hjWy0DwOzgHtDHYjjp0ASsF1EhrAVL981xjwc2rDAGNMlIq8D3xeR7dhapE9gb8ClIQ1u\ntGzsF9LAp9vXO+8dadKxBdBg12PR9IcTfsIxL4ZSmOe9UJopeS0UwjZ3hZEpy01HdEEGuB04CltS\nDhfbgRXYGrFLgftE5LRQF2ZEZDY2uZ1jjBkMZSzBGGOe9ftxi4i8BVQClwGh7pbnAt4yxnzf+fk9\nEVmKTQLhdMO/GviXMaYu1IE4VmMLBx8HtmG/TPxKRPYYY/4S0sisK4F7gFrAC2wAHgTCrntjEEJ4\njV0MNb0ee4VjXgyJcM97ITZT8loohHvuCmcTvhcfsV3LROS3wIXAGcYYT6jjGWaM8RpjyowxG4wx\n38UOLPxKqOPCdsfIANaLyKCIDAKnA18RkQGnFi9sGGPagZ1AOMzO5AFKApaVAAUhiCUoESnADvK9\nM9Sx+Pk58H/GmL8ZY7YaYx4AbgNuCHFcABhjyo0xZ2IHNOYbY04EooHy0EY2Sh02MWQFLM9k35qw\nI0ETMIRej6DCNS+G0IzKe9Ms7PNaCIV17goTU5abjsiCjHOz/iBwpjGmKtTxHIALcIc6CGwf8uXY\nmoUVzusdbM3LChNmT1Z1BmfOw95sQ+1V9u22sgjbYhQursbePMKpD28c+9bM+Aiz+5YxptcYUy8i\nKdjZe/4R6piGGWPKsQnj7OFlzoDKE7D9uI8oTq36ekZfD3F+PuKuh78Zlheny4zKe9NsJuS1UJkR\nuSuUpjI3HbKuZSISj60NH66xmOsMeGoxxlQfquOOI67bgcuBS4BuERkuDbYbY/pCFReAiPwY+Bd2\nBp1E7MDr04FzQxkXgDPOZFR/aRHpBpqNMYG1MtNORG4B1mBvonnAD7DdfR4KZVyO24BXReQG7LTG\nJwCfxU7lGXLOF7lPA382xvhCHI6/NcB3RaQa2IrtsvU14K6QRuUQkXOx97cdwAJsLVwJ8OdpjuNA\n99pfAt8TkV1ABfAjoAZ4YjrjDCO3AveKyHrgLexnKo5p/r2Fk3DOi6EU7nkvxMI6r4VYWOeu6TJt\nuckYc0he2C/gPmwzvv/rnkN1zHHGFSymIeCqUMblxHYXUIad+agOeA44K9Rx7SfedcCtoY7DieUh\n5w+gF6jCjlUoCnVcfvFdCGwCerA3tqtDHZNfbOc4fwPzQx1LQFzx2C+d5dj55UuxBdTIUMfmxPcx\nYJfzmasFfgUkhiCOA95rsZNf7HE+f8+G2+86BNfs807i7MVOrnJsqGMK8fUI27wYbq9wynuhfoVz\nXgvxdQnr3DWN12FactO0PEdGKaWUUkoppaaS9tdTSimllFJKzThakFFKKaWUUkrNOFqQUUoppZRS\nSs04WpBRSimllFJKzThakFFKKaWUUkrNOFqQUUoppZRSSs04WpBRSimllFJKzThakFFKKaWUUkrN\nOFqQUUoppZRSSs04WpBRSimllFJKzThakFFKKaWUUkrNOFqQUWocROQ8EfGJyPEhjOFGEdno9/Mi\nJ6bPhyqmgyEi1zvxZ/ot2yAiN4YyLqVUaInIiyLy71DHMRM599T/DeHxjxeRfhHJ91tWISJPhiqm\ngyEic5xrepXfsp+KyOuhjEvtpQWZGUJEPuX8Mfm/6kVknYicH+r4jhAmVAcWkRTgq8BPQhXDIWDY\n95r+HPi6c75KqTAyRh4afg1NpKJHRJY4lTMFQd42gG/qIj+iBLuvTqebgQeMMdV+y0IZz6FwG3CM\niFwU6kAURIY6ADUhBvg+UAEIkAV8GnhaRC4yxjwdutAOb8aYZ0Uk1hgzEKIQrgMGgUdDdPzp8jfg\nN9jz/WmIY1FK7cs/DwXaNYH9HAXcCPwbqAp475xJRaYAYgFvKA4sIscA7wdODMXxp4sxpl5EngC+\nATwV6niOdFqQmXmeMcZsGP5BRO4B6oHLgYMuyIiIANHGmP6D3dfhJoSFGIBPAX83xkxrLaWIRAIY\nY6YlMRpjhkTk79jz1YKMUuFpVB6aJGGMmvrput8cjkKcp/4LqDLGvDXdBxaRGGNM3zQe8hHgEREp\nMsaUT+NxVQDtWjbDGWPagF4CamBE5Bsi8qqINIlIj4i8IyKXBm7vdAn4tYh8QkS2AH3ABSJS7nyh\nDFzfLSLtIvL7icYqIm+IyFsislJE/iMi3SKyQ0Qucd4/W0TeduLdKiKnBWw/V0TuEJGdzjqNIvKQ\niMwOWG947MUJInK3iLSISJvz/8SAdetE5BERuVBE3hORXhHZHNhkHGyMjN/5LBeRl5yYqkXkK0HO\nfa6IPO2cc52I/FxELhrPuBsRWQwsAtaO4xq7ROReJ5YL/Janishvnfj6nWv49YBth8fcfMH5/JRh\nP1tz/c7/EhG5SURqnWM8KyJzgsRxioisdT4rXSLywgS6nTwPLBSRReNcXykVZkTk407e6XDuA5tE\n5EvOe5/CfhEEeNGva9ppzvsvisg6v32d7qzzMbHd0Wqc/f5NRBJFJFpEfim2u3WniNwjIlGTjPsm\n51gLROR+J3c0iMgPnffzReQfzjl5gtxHo0Tkh865tzn3v5dF5IyA9YbHXnxdRL4qdhxJj3PuSwPW\n/bNzXkXOPbfLuQd/P0j8o8bI+J3PPGc/rU5c94hITMC2MWK/DzQ61/cfIpIbuM/9+CD2/n1AYrsp\nekXkZ37LxLkWW8Tm4joR+YOIJAdsWyEiT4rIuWK/M/QB1/qd/69F5INic3mfs7/zgsSQ61yHOr/1\nrh5P/M55CnDJONdXh4i2yMw8s0QkDfsHlAl8GYgH/hKw3peBJ4D7gWjg49jag4uMMf8KWPds4GPA\n74AmoMzZ7psikuwUloZdAiQEOd54GCfmJ5ztHwa+6MR1FfBL4LfOsb8NPCoiBX61LCcBK533a4F5\nwOeBYhFZZowZ9DsOwB+BRuB7wFLgeiAP8B9TZIBlTjy/A1qAzwJ/F5EzjTGvBKwb7Hyeds7lQex1\nvlVENhpjXgIQkSTgRSAZ+AX2Gn8S231iPH2HT3bWe3d/K4lIBPAAcBFwsTHmBWd5AvAKkAr8AXvt\nTgP+n4ikG2P+J2BXnwMigNuxBeR2v/duBPqxrSVpwLeAPwNn+sVxPvZ3/DownPw+i/3CcqIxZtMB\nzvcd7Of7FGDHAdZVSk2/4TzkzxhjWgBE5Bzs/XAt9h4BsAR7L/sN8DLwa+BL2DEV2511Sob3NcZx\nbwB6gP8D5jvbD2LH0yRj708nYlt0y5x9T9Twsf8KbMPmog8A3xWRFmy31xec5Z8AbhGRt/xyRRJw\nNfAQNgclAp8BnhGR44Pc/z6Fzam/BWKArwAviMhyY0yjX0wu4BnsffWb2Dz2AxGJMMbcNI7zeQR7\nTb4DFGPvyfXYazrsXuCjwH3Am8DpwD8ZR54SkVyggAPkKWfda4HfAzcbY/wnd/kjcBVwD/AroAj7\nOz5GRE4xxgz5ndNi7GfsDmc7/1xxKvARbA7rxH4felRE5vh9RjOdcxzCfhabgAuAu0QkwRjz6/2d\ngzGmQ0R2Y/PUrw50zuoQMsboawa8sDc7X5BXD/DJIOu7A36OADYBawOW+7CJYFHA8gXOe9cGLH8C\n2D3Jc3gde9O4xG/Zcuc4A8DRfssvdta9bKxzcpad5mx/qd+y65xl/wFcfsu/5+zz/X7LPM6y8/yW\nJQMNwCt+y85z1js+yPl8xG9ZDLbwdJ/fsv8JctwYbH/yUfsc47r93FnPFbB8kXOenweigMeBDuDU\ngPVuBlqB/IDlt2Jb4DID9tcIJAWse57z3gYgwm/5N53Y5jo/u4By4PGA7eOw/eD/EfB7Gho+vt9y\ncZb/v1D/3elLX/ra+2LsPOQDevzWuw1oOcC+LnX+zk8L8t6/gXV+P5/uHOO9gPvPA84+ngrY/lWg\nbJLneKNzrNv9lrmc+5cX+G+/5bOAbuAev2UCRAbsM8nJNXf6LZvjHKcLyPZbfpyz/P/5LfuTc563\nBex3DbbVPNVvmQ/43yDn88eAbR8DGvx+Xhl4XGf5Pc6x/9d/eZDrdpaz/YVB3isHnnT+/2Vnf/8T\nsM77nO1XByw/x1n+8YD9jcqpAeffCxT6LRv+nvF5v2V3ATVAcsD2D2IrNN0Bv6erghzrGWBLKP4W\n9bX3pV3LZhaDrS1/v/O6AnvDv1tEPjRqRb8xLk6zbAr2i31xkP2+aIwZVfNtjCnF1lZc4befFOwX\n2vsP4hyajTEj0zAaYzZjv0xvNKNrqt7EJoS5Y5xTlIikYmvMeoKclwH+YEaPKfmts88LA9YtN8Y8\n63ecNmyCPElEZh3gfFqMMY/7bdsHrPePG3vNdhtjng9Y7+4D7HtYGtBlxh4fEwv8A9sqcq4x5j8B\n738UWAf0iEja8AvbNB6NrVHy97AxpmOMY91l9taKgf1M+f+ejsfe+B8KOFYc9rN6JgdgbIboANIP\ntK5SatoF5qHh1wV+67QBCcG68xykewPuP286/94TsN6bQL6ITPY7jsHv/uzce4dbiv/kt7wd2xLg\nn6eMccb4OF2lUrD32XcInn//boyp89v+bSf+wDwFtteAv986+37/OM7njoBl/wHSnBZ7sC08BttS\n4u832PM+kDRn+9axVhCRb2B7XnzTGBM4A+dHsZ+bFwJyx7vYwl5g7ij3z6kB1hpjKoZ/cL5ndDA6\nL38EWxCMCDjec9gCarDfVaBWNE+FnHYtm3neNqMH+z+MrSX/rYg85XcDvQj4LnAM4PbbPtiX4Yox\njnUf8BsRyTd2KsXLsDX/DxxE/NVBlrUHWT7cnWlkGl4RicOe06eAHPbeXA32xhNo1Aw6xpg2EWnE\nftH2Vxpk253OvwXA5iDvDwucbQec1g+/n+ewt8vEmPEdwP4SyY3Y7oVnGmPeCPL+PGwL24eDvDfc\nPc5fxX6OFfh7Gk5aw7+nBc6/fx3jWEZE3ObAk0mMORBYKRVyo/JQELdjuys/LSJ7sF8OH/GvMJqk\nsfJEsOUubF4Y84v1AQTe29uBPuN0TQpYnuq/QOwYoK9juz/5j9UpC3KcYHlgJ/aLvT9fkO13Yu+V\n+4xTDCLwfPzv3V3sbXkIHLg+kTwFY+eqM7Ddnn9qjLk1yPsL2NsbIlCwPLW/AfbBvme04uQpEclw\njnUttmfAeI4XjOapMKAFmRnOGGNE5EVsc+0CoERETsV2AXsRW3PmwXYfuxo7u1mg3jF2/zC2i8AV\n2DERVwDvGGN2jrH+eAxNcLn/TfGP2OR4K/AWtobFYLtUjbfmbTw1SxNZbzxxH6xmIN7pCx3seP/E\ndsW7QUReM34z/oiIOLH8E1sTFsz2gJ/H+jzAgc/Xhf2dfJnghTew3QjH5MSciO2zrJSaYYwxjWKn\n4j0P21JzAfBfInKvMea/DmLXB5M/puJYBzyOiFyJbbV5HNstuMHZ7n8Y3SKwP1Odp2Dy12i8X9Sb\nnX2N9QywLdjCwydF5E6z70xfLuyYnU+MEVNjwM8Hm6fA9i65d4x1DzSWE+y5ap4KMS3IHB6Gf4/D\nTcQfwf6RnxfwpfYzE9mpMaZVRP4JXCEiD2K7IH15CuKdrI9g+/mODE50msWTxlh/AXu7Hgx3sUsH\nKoOsF2ih82+wFpeJqsQOTA0W33gMFzSKCF479h/szfgJ4EERWe10zxou6FYAccaYdUG2nWq7scmi\n/SCOV+TsY6yCkFIqzDm555/OC7EzXV4rIj8yxpRx+NZkX4rtSjyqRUWcWc+CCJYHFrBvnnJhC0L+\nOWA4TwWuOxmVzjGKsPfxwGMciH+eCqYJ28r0KvC8M3i/zu/93diJh14bR4v9wWrETgIQcZB5sQjY\nODUhqcnSMTIznNjnfJyHreUe/uI3hE0SkX7rFWKnRpyov2Bn/LoFO9Bxny5DYqftzZvEvidqiH0/\ns18bY10Brg/oI/0l7HUJnLWtSEZPVZyCrRV63ekDfbCexU5hPPKQN6eb3HineXwdez7HjrWCMeYZ\n4EpsYe+ugLcfAc4QkdMDtxORFKcFZDzG88XjDWyz/rdEJDbI8cbTn3iVc6zXxhmXUiqMOOMXAw13\n0R3u6tyNva8lB1n3UMSUL9Mzpftw/vU/9gnYWTeD+ZAz49fwuscDJxD8uXBfDPLzAHYWtYP1LPb3\n8fmA5cN5c7+MMXuw9/795ak92PE8scBaJ9cOewT7nWWfaZ5FJGIc41XHzRnz9BhwqQRMde0c74B5\nypmNdB62YKZCSFtkZhYBLhSRJc7PmdjuXvOA/zPGdDnLn8L2z33WaUnJwt6cSoGjJ3jMf2KbjD8G\nPG2MGdWMKiJubAHqGYIPTpxK/wQ+KyK92L7B78O2ErWNsX4C9mb5OHaK5WuB540xgc9j2Q7cLyK3\nY8/1WmxyvSFgvcl2U/gdtovf4yLyS2xt0FXs7d+93yRhjCkRkVJsAnh4P+v9Texzcu4UkU5jzFed\nt36CnT70ObEPUN2IvTYrsAWfTOyECQdywPM3xnhF5Bps69BmEbneObbQAAAgAElEQVQP2APMduKv\nBVYfYDfnAKXGmMAub0qp0AvMQ/5edQZZ3+UUZtZhZ4YqxH7p3miMGa5w24j90v9tp7W8H3ghMMeM\nM57x+At2lstDXYH7FPAREfkHNmfNxY7D2MreXhP+dgGvOC1Ww9MvN2IrD/31A+eLyL3YCqMLsV32\nfmyMaT7YoI0xG0TkMeCrzhf5N7CzxQ23GI2nIusJ4EP7W8EYs9up1HsJm5POMsZ0GmNeFpE7gO84\n3RKfw3aJX4htyfkytrveVPkOdtzOmyJyJ3bioFRsRdpZHHgQ/3DF5JopjElNghZkZhYD/MDv5z7s\nl/DrjTF3jqxkzItiH+r0HewYl3LsXP5F7FuQMeznBmWMGRSRv2K/iN+3n7jG200g2HpjbR+4/Hrs\nOV+FnanlZeyX41eDbG+wyeMa4IfY6af/DHyVfW0FvgH8DHvTLsVOqRw4+9dYMQYzstwY0+60hvwW\n24LUiZ0RZwt24oTxPI34T8A3ROS6gHEyo45vjLnHqSn6hYi0G2NuNMZ0icgp2OmnL8U+fbkNO9vO\nDYzua7y/3+MBz9WJ4TkRORn4PrY2Lx47Tut17HNsxiT2WTgfxj5vRykVfgLzkL//wk4W8hdshdDn\nsJVCddjnqoxsZ4ypF5HrsPegu7D36DOx9/Xh4wQed6x4xhv3WDM/jtd47vd/FpEsbP45F/sF+Qrs\nZDmnBdn2Pieur2Irld4EvmSMqQ9Yz4udWewP2LE3ncBNxpgfBYllst32Pom9V1+OLZCsxVY87WR8\neeoe4AsicrIxxr9FfVRMxpitTi+ItcCTInK+MabfGPM5EXkHe+1+jD3nCuw1enWs/QUY1/cJY0yD\n0/r1v9ic8zlsReZW9j77yH/bQB/FPqIh2AQOahqJ05VeqTGJyK3YB3plmb0PpwxbTnK8HVhujNl2\ngHU9wH+MMZdNS3Cjj/0d7M063Riz35l1nNrN3dh58B+ajvhCQUQ+jk3Uc4PMDqSUUocFEZmDrWT8\nxhizePmv+yfss9LGGg96yDitIxuAK8aTe0TkeWCPMeaqQx5ciIhINnYGucuMMU+FOp4j3YSbWEUk\nV0T+IiJNItIjIu+JyHjm21YzkNN17ErgbzOhEBOunOvo/3MctrVo84EKMQDOl/rbsE+TPpx9C/vQ\nNy3EqAnR3KTUwQnMU46vYrsAvhzkvWD+B1gtIgVTFlj4+QrwnhZiwsOEupY5/VhfxQ4sOw87C8UC\nJj9PuwpTzjzr52CbT1OBX4c2ohnvnyKyE/tk6jRsE34htqvXuBhjfojtJnfYMsboF081YZqblJoS\n3xKRVdhHN3ix43DOA+4wxtSOZwfGmLcY/ey6w47/zKkq9CY6RuY7QJUx5rN+y6Zi2j8Vfo7CzrFe\nj+2vO5451Weig+lPPBH/wvYfvxLbEroFOw7niWk4tlKHO81NaiY62PGlU+11bAXm97ATE1RhH7j8\nk2k4tlKTMqExMiKyFTs7VT52Nota4HZjTOB0r0oppdS00NyklFJHpokWZHqxtQK/AB7FznX+S+Ba\nY8z9QdZPwzZLVjC+GS+UUkpNnRhsF8Znp2KK1nCluUkppWaMKc1LEy3I9ANvGWNO9Vv2K+BYY8wp\nQdb/BHZ6WaWUUqFzhTHmwVAHcahoblJKqRlnSvLSRMfIeNj79PhhJdiH6gVTAXD//fezZEmwZ2eF\n3te+9jVuu+22UIcRlMY2ORrb5Ghskxeu8ZWUlHDllVeCcy8+jB1WuSlcP08Q3rFBeMensU2OxjY5\n4RrbVOeliRZkXgUWBSxbxNiDKvsAlixZQnFxeE5GNGvWLI1tEjS2ydHYJiecY4Pwj4/Dv/vUYZWb\nwvnzFM6xQXjHp7FNjsY2OeEcm2NK8tJEnyNzG3CiiNwgIvOc5vnPYp9YrpRSSoWC5iallDoCTagg\nY4x5B/gwcDmwGfgu8BVjzMOHIDallFLqgDQ3KaXUkWmiXcswxjwNPH0IYlFKKaUmRXOTUkodeSba\nteywc/nll4c6hDFpbJOjsU2OxjZ54R6fmlnC+fMUzrFBeMensU2OxjY54RzbVJrQ9MsT3rlIMbB+\n/fr14T7gSCmlDjsbNmxg1apVAKuMMRtCHU+40NyklFKhMdV56YhvkVFKKaWUUkrNPFqQUUoppZRS\nSs04WpBRSimllFJKzThakFFKKaWUUkrNOFqQUUoppZRSSs04WpBRSimllFJKzThakFFKKaWUUkrN\nOFqQUUoppZRSSs04WpBRSimllFJKzThakFFKKaWUUkrNOFqQUUoppZRSSs04WpBRSimllFJKzTha\nkFFKKaWUUkrNOFqQUUoppZRSSs04WpBRSimllFJKzThakFFKKaWUUkrNOFqQUUoppZRSSs04WpBR\nSimllFJKzThakFFKKaWUUkrNOFqQUUoppZRSSs04WpBRSimllFJKzThakFFKKaWUUkrNOFqQUUop\npZRSSs04WpBRSimllFJKzThakFFKKaWUUkrNOJGhDuBI1N7eTk1NLc3NHcTGRpOXl01OTg4iAoDX\n60VEiIiICHGkSimljgTGGOrr66mt9dDV1UdqahJ5eTmkpqaOrDM4OEhERAQul9aBKqXCwxFVkOns\n7KSrq4vIyEhSUlKIjJz+029tbWXDhm10dhri45Po7Oxnz54dLF3aQ05ODhUVldTXtwKQm5tOQUE+\ncXFx0x6nUkqpQ8/n89HW1kZ/fz8xMTEkJyePVGpNp6qqKjZvrgBiiYlJoKysHY+nhZUrFyMiVFZW\n09bWQ1RUBAUF2eTn52tlm1Iq5I6IgszQ0BA7d5ZSWdlAX58PEUN6ejxLly4kOTl5wvvr6emhv78f\nt9s94UJGRUUVXV1Cfn7RyLL29la2b6+kvLyGzk4Xs2bZGrBt2+pobe1g1aoVREdHTzhOpZRS4au3\nt5ctW0qor+/A64WoKCE3N5mjjlqM2+2e0L6MMXR1deH1eomPj59Qzujv72fXrhrc7hRSU9MBSElJ\nY8+eajZs2IQxUQwMRJOUlEpvbz8bNpTR3d3D0qVHTShGpZSaahNqHxaRG0XEF/DadqiCmyq1tbXs\n2OEhNjaL/PyFZGfPo6lpiC1bduD1ese9H6/Xy8svv8t11/2NJ554k1deWc+2bSUMDg6Oa/vBwUFK\nS5v5+99raWrqGVmelJTMnj1NVFe3EBubzV//Wo7XG8Ps2XPxeDppaGiY8DkrpdSRYqbmpu3bd1JT\n00Va2hzy8xeSnDybsrJWdu3aPaH9dHd389xzb3D99Y/x5JNv8corb1NVVYUxZlzbd3V1UVXVySOP\nVIzKTcnJqZSWVtHZ6cPtTufBB3cBCaSnz6aqqpGOjo4JxamUUlNtMh1dtwBZQLbzet+URjTFjDFU\nV9cREzOLhIREANraBlizpoHS0lZaWlrGva/du8t4661q7r+/ApcrC7c7g+3b68addFwuF62t/dx7\n77ZRyWJoaIje3j5iYhJpaenjzjs30NTUQ0REBBERsXR0dE7spJVS6sgzo3JTV1cX9fXtpKfnEB3t\npqmph3vv3QYksWdPC319fePaz9DQEJs2bWPz5lYefLASyMDrTeC998qor68f1z5cLhft7YPcdde7\no3JTX18fvb0DJCen0NTUM5Kb4uMT6O83dHd3T+LMlVJq6kyma5nXGNM45ZEcIj6fj/7+QaKj40eW\nNTX1cPfdG1m8uHjcLTLl5U2sW7cLj8eW/UpLW4mIiMDtTqKmponCwl5iY2Pxer00NzfT29tLdHQ0\naWlpuN1uPJ5OPJ4umptt3+fNmz0j8Q0NtRMREU1tbR8dHU0AbN9u/+3r62LZsswpux5KKXWYmlG5\naXBwkMFBH9HRtgvZcEHhpJOyiY0dGldu8ng62bq1ik2bPLS0xAKwa1cbkZHpDA25qK7eQ3Z2NmAL\nJU1NTXi9XuLi4khLSyMiIgKPp5Pa2h4aGmzPgj/84R1Wr15GYWECdXV76Olxs2NHK/X1PsDmJq/X\nizH9REVFHYpLo5RS4zaZgswCEakF+oDXgRuMMdVTG9bUiYiIIDU1kcrKDgYHba3XcCGhurqL0tIu\nRDrJyUnc737++Mf1/PSnb4/8fPPN/wHgM59ZwYc+lMzAwAAAmzZtpa6uk95eH0NDfeTkzKK4eBl3\n3LGRH/zgpZHtf/azN0b+/6lPzSUpKZ7f/GbDPvu/7LI8PvaxE/B4OrnjjvVcd92qkVh9Ph9NTU00\nNTXj8xnS0lLIzMzUAZhKqSPRjMpN8fHxxMVFUVFRh9cbM5KX3nuvhmXL4mltHSQhYf/7uOOO9aPy\nCuzNHZ/85BLy8/MwxtDc3MzmzTtobR2gv9+LyADz5uWwYsWyffbxyivVvPJKNR//+BwSEtzcdVcl\nULnP/q+6qpBPfjIlaG4aGBigoaGBlpY2oqOjyMhIJzU1NSSTGCilDm8TLci8AXwa2AHkADcBL4vI\nMmNM2LYxFxTMprFxG3ff/Tp//WvZyPJf/3onv/71Tm688XRuuumM/e7j+uuPY84cH6WlXm69dQPf\n+96pLF6cTlRUP7GxA8TExLBz5y4qKtrweiNpbe2jubmPu+/eyRVX1PPZz57NJZcsYsMGD9dcswaA\nX/7yVObOTSInJ5GoqEjOOiuPJ57YyJ//XMf558dTUBDF8uUJNDY20tYWyw9+8BKXXLKInJxEjDFs\n376DXbvqMcaNiLB7dz1z5jSybNlRIZmRTSmlQmTG5abo6Gjmzs3lrrte4uGH9xYUfvWrTQDceGP0\nAfPSddet4tRTs9i0qZSGhhh++tPXR3LT4GAryckJeL1etm3bRX19P319Pjo6+mlu7uHOO//N17/e\nyXXXreKkk2bzyivV3HzzywB86UsrWLUqnezsRD72sWU0Njby1FNbefjhFi64IIG5c90cd1waLS0t\neDzeUbmpv7+fjRs3s2dPJ5GRcfh8XnbvrmPJknzmzp17yK6nUurINKFvu8aYZ/1+3CIib2Grai4D\n/jTWdl/72teYNWvWqGWXX345l19++UQOP2mpqamsWnUUxkRxxhnplJV1c8stW/nDHy7kuONmk5Oz\n/2qvzs5O+vqayMgYZPdu2yVs7txEsrNddHR0UVCQjzEGj6eF9vY+2tth1qw0jOnl3//ewty5VZx0\n0h5ycvKJjd3bFN/dPUBtbSONjU2kproZGuohL8/+Si666DiKiwuorW3hqadKAHv91q0r54473uHj\nH59PV1c9KSl5xMXZbnMDA/1UVFSQkVFPXl7eIbiSSqlw9dBDD/HQQw+NWtbe3h6iaKbXTM1NhYWF\nfPObXs47bzabN7dw660l3HLLaZx55iJyc/ffSwAgMrKP9HQvKSmd1NfbXnVFRQmkpNgeArNn59La\n2kpdXTttbUN0d0eQnJxJc3MH69aVcPTRmzn55KN5/fWakUIMwG9+8x4Aq1fP4fLL5xAZ2UpRURLQ\nwiWXHM/KlXPYubOSf/xjAz6fnWVzODdddFEWAwM9zJ49b6RCrb29ldLSGjIyMkhMPPB5KaUOD9OR\nlw6q2t4Y0y4iO4H5+1vvtttuo7i4+GAOddBSU1M599wT8Hq9bNxYzy23bOW442ZTXJyz3+1aWlp4\n990SOjp8xMTkkpHRy1lnddDZuQuRAo4+uoDCwkL6+/vp7Oyira2XiIgsGhoGqKmxg/Tr612sW7eT\nsrIqbrvtzZF9f/e79v+f+tQyvvjF5bz77gZ6evq56qqjmD8/j+hoN//4RyUPPLB5ZJtvfnMtYD8I\nq1cXjhRiAKKj3URGxtPU1KIFGaWOMMG+gG/YsIFVq1aFKKLQmSm5SUQoLl7AypXzefvtGm69tYSz\nzlp8wLwEUFlZyZYtFQwNuUlJmU9sbAnnnBOPz7eHpKQc5s1bSEZGBnV1dbS3d9DZ6SYmJg2Pp5fa\nWttItXv3AC+8UMKHPrSIhQvjuP329bz6agOXXJLAscfOZ+XKhaSlxbFuXQXR0S4+85kVFBXlEBkZ\nyTPP1PPgg1tH4hnOTR7PbL7whVWjegXMmpVCVVUj7e3tWpBR6ggyHXnpoB7PKyIJwDzAMzXhHHqR\nkZHk5SVx442nH7AlxhhDWVklXV0uCgrmkZ2dy9lnn8rXv34G8+dncvzxK4iLy+RHP/oPbW1eYmMj\naW9v49VX9/D977/I3XdvBOCpp9r40pfeo7u7m5/8ZD7nnGOfXXPKKYl89atHsWyZm4GBQRITk4mJ\nieGSS7JJT7fPpznxxNkAXHXVQuffowEoLEyirKyD7dubRs0yY4yhp6eXqqoqysrsrDUTmWJaKaVm\nupmWm0RkQt2Be3t7KS2txu1OJS+vgNmz8/nAB87h6qtXsnRpDieddCyQwE03vUh3twuXy0tvbz//\n/nf5qNy0Zk0nH/vYWu6661Vqakowph+AvLwMYmJcdHQ04HIJyckZREQIl11WNJKbTjopH4BrrlkG\n7M1N+flxlJa2jcpLAMZAW1sb5eXllJeX09LSMu7poZVSaiwTapERkVuANdgm+zzgB4AXeGh/24Wb\nnJzEA/Y9BjvLS3NzFykpWaOWZ2Rk4/F00d/fj8czMNI/eNGiuWzYsJNFiwzf+tYxVFS08cgjFVx1\nVR4LFkTS3FzJO+90MzSUAUBPTx/R0W46OgZobW0lMTGB3t4uKivL6ejw0t8fSW1tLwA7dtj5+u+7\nz/af/tnP3nWieZtrrinmuutWUVvbwj33bOD88zPweHoQicTlGiQvbxbLly+d8APWlFJqJjgcclNO\nTsK4KtgAOjo66OoaZPbs1JFlIkJWVg49Pa34fD48nq6R3LRw4Wx27XqXo48uYMGCYygra+bRR6u5\n8spslixxU1e3nY0bY3nttS6WLIlgaKiT+PgCGhs7yc3tJTk5gZKSEqqqyvB42hgcdFNebicn2Lq1\nFdibm26/fSewk898ZgWf+9zxALS2NtPWVsf27b24XPGICNHRVcyfn8PChQt0EgCl1KRNtGvZbOBB\nIA1oBF4BTjTGNE91YOHA5XLhcgk+39Co5T7fEBUVXTz++L9ZvjwXgA0bPKxcmc3SpYuorKwlMjKS\nhIREHnkE4uO76euDF1/sYeNGH2Dn9n/33UHeffddzjknl6OPzmZoaIi2tgZ6ezvZtq2F//xnbz/C\nN9+sA+Ccc+aydm0Zf/zjRaSmDlBd3cSsWQnU1FSwY0cdTzzRwBlnzGPOHNuCMzg4QFVVBbNmVbFg\nwYJpuGpKKTXtZnxuGm8FG9jcJGJnrvSfpbKxsZvy8mYefvhfLFqUDtjctGTJbObPb6K9vYPs7ESS\nklJ49NFq4uO7qKnppqJC6HEaUIaGYtm9u5v09EZSU2Po7u6mpaWF9vZGNm2KYuPGylG56bXXaoG9\nuen22y8gIaGLwcEBamurGBoapK6ukX/9q5FPf3oRc+bYisGurk5KSz2kpCSTmamPGFBKTc5EB/tP\nzwjIMOF2u8nOTqG0tJG4uAR8PjuPfkNDHevWNfDkkzWA7SM8PBPZd797CqtXH4vH04LH080FFySR\nn59JQkIi557bx+LFXeze3c7bb7dxyikppKZ2kZ/vpampgaoqDytWLGXOnEIWL27j1FM7qalp4777\nPNx558UUF+fQ2NjD2rVlrFqVy8qV2TQ1NdHc3ILP56Ovz3YhS0vbmxSioqJJTEyltraRefPm4XId\nVG9CpZQKO0dabkpOTiY5OYbGxjoyM3Pweocwxsdjj23lkUcqR607nJu+/e0TuPTSOTQ1dRIZ2cZ5\n5yWyYEERzzxTz/PPdwJdAOzc2cXOndDauoMPfaiIysp+qqo8nHHG+5g1K4PCwlZOPrmV1tZ+/vjH\n6n1y0wkn5LN8eToNDQ20t3cQFRVJR0cfa9a0sHr13kJXQkIibW3NNDU1a0FGKTVpOkfvAcybV0Rd\nXSOvvvoCHR2DdHb2Y0wMra22b+9VVx3Nffdt4pZbzuGss4pITY0mIyOGoqI5TnexCIyJZ+PGHfT3\nR2PMIMbYwkRcXBzR0e34fF10draQnZ1EcfFxxMbGUVAwB4CXXtoMeCguzqG4OAePp3Ok+4GIkJGR\ngdcbg8fTRXW1bbXZsaMZl8tFenoc6elxuFwufD6j/ZGVUuowEBUVxVFHzeell97khRc20dfno69v\ngLy8BE4+OY/XXqsdlZvOPLOQ5ORIcnISGBoaorq6msTEOLzeCBYsaCQjI5+dOztZv74NgDPOyCA2\n1oNIF4ODvSxbNpfFi5cTGRlJUdEcjDG89NIGoDpoboqKiiIvLw+XKwmPp4vKSjuL2vCzcoZzk4iL\noSFfiK6iUupwoAWZcXC5IkhKSiYzM5a//72Wxx7b+4y14X7BJSUNnH/+LHbsaGTbNkhMdJOamkR1\ndS19fYl0dQ2ybl0Dmzf3jWy7dm2t878err46h/PPzyU2Ni7g2KP7DgfrfhD4QLMf//gVAK65pphr\nry2mo6OVhQvT9EGZSil1mPD5fLhcUWRlZeJ2x/D44zU89ljFyPvDuWnLFg+nn+5mx452duxwkZGR\nCPjYvbsKSMbnG2Tbtjbee693ZNsXX2wEIlm0KJsVK6KJisoeNRmBiJCcHM3XvrZyZEzPeHLT8MM0\nr7mmmKuvXs7QUA9paQVTel2UUkcWLcgcgMfjob3dx4oVx9Hc3MsZZ6Qwa1Yq99xj59n/yldOoKmp\nm+Jiw5NPvkJvbz+9vf24XC7c7kE8nh5KS3Pwel10dg6SnQ1udxeVlQksXBjBlVfOJSVlFj5fNyUl\n2zAGiooW4HK5GBwcICnJNypZBHPddatGPWzzi19cyNy5aWRlJVBVtYu0tGgKCvKn65IppZQ6hIwx\nVFTYWctyc1NoaurhjDNmEReXwF/+sg2wucnjaWfx4l7+9a+36OvrZ3BwkIgIFy5XF1VVUbz1VifH\nHJNIdXUT8+cP0tjoo73dzcUXx3HhhYuJj3ezZ08jTU2VrFp1AhkZdnxLR0cbs2fHctllJ+93OuXh\n3PTOO7Vcd90/+dznFrBoUSapqW48nnLmzEnVbmVKqYOiBZkx9PX10dfXR11dAzExdpaVxx4r4c47\nN4xa71e/epPrr19MXV09g4OxtLdHACnAEOXlVXR3D/Hyyw2jtjnuuEgqK2HnziGam7s4/fRjiYyM\nZvPmTWzcuIWurk6ysnIYGOhi5cocrrpq6X6n5szJSSQnZ28y+eAHjyYjw3Y1SEvLJCcnh/j4+DG3\nV0opNTN0dXXR09NDc3MnCQm5PPDAvnkJbG76xCfyaG7uAWLo6IgGEoiMHKS21kN3dwKlpYO0tnbS\n0gKLF8eSkdHD669DVJQhMtLH3LnzGBwsoKNjPa+++hrLlx9FTEwsLlc/S5bMPuAzYQJz08UXH0VG\nhu3inJWVTnZ29oSmnVZKqUB6BwkwNDREWVk5VVX11NZ28eijOzn++Fmcc04Gl166hNNPn8P27U3c\nfPN/OPvs2fz3f59Kbe0WysrigCiSk7OIi5tFWZmH6upYGhuH9jlGZ2c68+fDrl1d1NQYnnxyK2vW\nVHD99UuZN68Q6CAzM4+cnAVkZWWN+0Y/PH3n0qUFo5KHUkqpma23t5cdO0qpr29jz55uHn54Gx/4\nQDsf+chSTj/djqncsqWOn/70dc47r5Brr13J9u1v09eXQmenl+zsufT2+ti+vYo9e+JobIwAfDQ1\n2TEqHo+bxMQ4oIP29mjKy/upry/loYd2cPXVi8nOjsTlaqeoKJOsrLlkZGSMO/bh3HTMMfM0Nyml\nppQWZAJUVFSwZUs1SUkZGBPLmjVvkJHhY+PGdyguPoGUFDdNTbaF5aabziA7O5677mogPz8CYwZJ\nTU3EGMPzz1dTU7NvIQZg+/aukf8/9lgdYAfpv/12NatWxZGTE8vy5UuJiYmZUOwTmb5TKaXUzODz\n+di6tYSqqk7S03Nwubp47rm3yMsrIy8vlcWL5zMw0I/HUwPAzTe/n4iIXu6+u4d584ZwuWKJjIzi\nrbd289JLtYAtxPgrL+8e+f8LL7TzwgvtLFjgprS0n3feqWHlykjS0nJYvnzphOPX3KSUOlR0Ll4/\ng4ODVFbWMTAQT12dj1277Awu0dHZbN26h/Xr38HjKSU/P5JvfONY5s3LpLGxj8cfr8cYF93d7fT2\ndlFT48Hl6ic1dezZWPLy7MMpZ88epKAgFoDych9vvtnNhg11eL3eQ3/CSimlwl5bWxseTwdRUelU\nV/eN5KaBgSRee20LGzduorm5kiVLkrjhhpPIy0uiubmfp59uYWDAR2dnK17vIOnpXrKyYKzGlNzc\n4bzUxVFHufD5ogCorDS89FIb77xTo7NfKqXCirbI+BkYGKC/38uzz9bzpz9tHln+hz9sB+CLX8zl\n0kuXk5yczEkn9eHxdLF1awsA3d2RGBNJeXkJlZW9VFXBueemkpwcxyOP7Bl1nI98JJ2kpHj+/OdK\namqiADtbzBtv7OGNNyA1NZIPfKCE5OQUHnpoJ1/+8snMnp08sr3H08kdd6znuutWaTO9Ukod5vr7\n+xkagqefLhs1HuaBB+zMl1//ej4f/vDx9PZGkJPTg8fTxe7d9gmXPT1RDA11smXLBioru6mvd/HB\nD2bS0zPE2rV7nxf6gQ+kM2tWBA8+WE9NTQK2xcb2Hhh+6OWbb3Zz6qlbcbtj+Otfd/HlL59Mbm7S\nyD40NymlppsWZPxER0cTGxvFuefO5uyz54+MhfnGN46lsFA4//xjR/oF33HHq6Omlbz77goAFi7s\np6enA8igq2uAY47J5YwzInjxxWqOO24WWVlRFBYOkJKSxNFHu5yHmrkoKeliyRI3xxyTz0MP7eKl\nl7aRmJjGLbesZ+lSNx/96IkjA/Y9ni5+8IOXuOSSReNOFl6vl56eHiIjI4mLizvwBkoppcKC2+0m\nMhIuuqho1DjNL31pOatWzeKcc44jNTWJm256cVReAvjLX2x3s8LCbrzefiCV9vZ+iosLKC8fYteu\nNoqKYjjhhHja2zs57bQEYmL6EEmgqspLSUkXK1cmsGhRBsR7CfAAACAASURBVA8/XM4LL2zF7Y7j\nZz/bwMqVCVx66Ykj4zgnk5v6+voYGBggJiaG6OjoKb1uSqnDnxZk/ERFRTFnTg5tbRUkJMQyOGhr\nmrKyfJx55nzmzds7TeR1163ipJPy+d3v3mLNmp3cdtuZPP30ZtaubQJsYee113p47bVtXHzxHC65\nJJfjjhPy8txs2lSBy5XDBRdk4fG0kZQUS0lJF6eckk9urr2RZ2QUkpycAqynsbGf7dt3kpMzj7q6\nbjZs8ACM/At2MGWwxGGMoaamhrKyGrq6BoiKcpGTk8qCBfMmPAZHKaXU9EtJSSE3N5mKimZmz85i\nYMDe6+fOjeLcc48iJ8fmquHpjhsbe0Zy0803H88//7mT118HsJVhL77Yzosvbua007KBWC6/PJXk\n5B5aWnq48MIFVFdX0tPjIyYmkZKSLhYtymJgoB2A0tIoIiNt97KXXqoiPj6W2bNz8fnYJzeNlZfA\nVq7t3l1GdXUD/f1DxMREUliYTVFRES6X9npXSo2PFmQcxhja2trw+XwkJwttbTW4XC5Wr84jPz8S\nEaivryc9PZ2IiAhychLxeLpYs2YnAJmZhgsumM8JJyzkmWc28847nRQVRZKT48Lrrae8vJP8/Fm4\nXCnU13uBLhYsWExkZBVVVeWkphqam+ucrmrRvPhiBQkJttm/qcnFq6/WUlpaw29/u7dbwTXXrBn5\n/403nh50MGVdXR0bN+4mOjqZ1NRsBgYGKC2tY2BggJUrV2jCUEqpMDY0NERTUxMxMdHExw/Q1lZJ\nRIRh9epc5syJpaenh9bWVlJSUkamO96wwTOSm9LSIDc3HWjbZ981NQ2Ulfloaemnr89LY6OXxMRB\nliw5xilk1JCQAA8/vHtkm3vv3dvt+ve/L+X3vy/ltNMKePnlqpHlw7lprLwEsHNnKdu315GSkkVa\nWhzd3V1s3lyFy+WiqKhoCq6cUupIoAUZbCGmtHQXpaV7GBhw0dLiZe1aD+efn8zllxcwNBTDjh0t\niNQxZ04aaWn5NDb2jdQ6XXTRAqqq2nnttWbWrNl7My8v91JeDoWFA1RURFNcHM+WLZ2ceeYJeDy7\nqa3dTWJiEr29EbS0+Pj737sA2yLz6KMVI/v52c/eBODznz+G9euvHXnw5Z13XkxxcQ5A0AdmGmOo\nrKzF5YonPd22JkVHu4mKisLjqaKoqI3U1NRDcUmVUkodpIGBATZv3kpNTRtNTT6ee66W889PJy0N\nVq+ei0gMmzbtwe2uYfHifGJjM/B4ukZy03nnFVJX18OJJ6bx/9m70+jIzvLQ9/9d8zxIVSWVZrXU\nknoe1G2bMDQQGxOIHQhZgAk5SdaK3ZAVsgKZ1jkZbN+YrISTQ0hYJHG8AifhEsecc3LhMgRjQuxr\nhthGsnseJLVmlaSqkmqeq/b9oFZZUqvdGkrz8/tia6vq3W8JvJ963uF5m5t1/PCHY7z0UoKmpjx2\nexyDwQVoGBkp0tDgpKGhienpEfR6qKqy0Nycx+tNU13dxo0bOb797Sj33uvB5bLxv//3EL/92920\nt2s5deoIGo32lth0u4OcU6kUo6Mhqqv92Gxzs0kuVxWlUonh4QANDQ3o9frN+jMLIXYwSWSAcDjM\n9evj2O212GwOUqkQzzzzQ5zOGPfdd4LW1nYAstkMg4PD/OM/DvK5z71Wfv83v9nHN78Jb32ri9/4\njU5eeWWUl15KceCAHp1uGr3eydAQTEzACy/E6O6G/fs70OtT7N+/D61Ww+HDI9xzzz384Ad9/OM/\nDvDAA+2k01G+970gn/zkCTo69Lz73XfR0lJdvu/Jk/5yIrOcYrFIMpnFal2crBiNJgqFuQ2kQggh\ntqfR0VGGh6PU1bWSSMT46ld/wOHDDkZGArztbe/E4XACEI3Ocu3aKC+8cL088AXw7LNDPPss3Huv\nl3vvbcBozAFQKum5dOn1uPCNb8wCs/zMz7Tx5jc30tBgw+v1odPlMRgUTp16E9/8Zi/f/naU06db\nKJViANTV6bj7bj8nTjQs6vedYlMmkyGbLeB2Lz6o2WKxEovNksvlJJERQqyIrCsCwuEZpqdLjI3l\nuHo1xNWrIQBGRmBgIE4oNFf9xWg0YTTaue8+Dz09j/DUUw8A8Md/fIJf/3UXIyPjfP3r57l2bRaA\n0dEZLlxw09s792d+/vkgAM8+28fERAqt1orX60FV4xw7VsPx4w2cOtUEwP79Zvbtm9vD0tiocP/9\nXeUkZv5wsduNds3TarVYrUaSycSi69lsBp1ubgOpEEKI7UdVVcbHg2SzZgYGYuW4NDSUYWQEAoHX\nl4o5nW7Safj5n2+ip+cRnnzyZwH4/d8/xPvfr+WVV8b413/9IRcvzr9njKNH+3jnO+cSiTNn5mLL\n/v12DAY9Ho8Xq9VMMpmgsbERq9WCxzM3c5LPF4hG4wCYTCWamxvL/VhpbDIajRiNOtLp5KLr6XQK\nk0knm/6FECsmMzLMzVx897vjPPPMi4uuf+c7Ub7znR4efljl7NluADQaDS6XkZMn/czOziUskcg0\nyaTK8PDi0SWrVYfLFSAatROPv/5gv3QpwqVLEc6csaLTxXA4VByOahRFYf/+Oj760QM0N1sYGZng\nQx/y8/a3H6ClpaX8/pUeLqYoCk1NdQSD1wmHgzgcTnK5HKFQgKYmBy6X645tCCGE2HyqqlIqlfi3\nfxvly1++Wr7+xS/2AZDNDtHZ2Vy+rigavF4zra1+Rkbmljgnk1HC4SzRqJHLl4MkEnOFaLRaN7FY\nEq12FKjCYJib/bh06QrDwzmi0SBNTXZqa43Y7XOzPocP7+Ohh2apri6h1eb4lV9p5e1vP7JoefJK\nY5PVaqWhoZpr1wKoqorZbCGRiJNIhDh6tFlmY4QQKyaJDFBV5eb++728+90H0OsN5dKW73mPlbe/\n/QBHj3YC8yWMY3R1taCqKrOz43R3W/j+91OYzbdObk1NmQFz+ed3vMPGf/xHgvvu0+HzGRgf1zMz\nk+TgwSqKRYhEZqiudvOJT7yJYHCSgwetvOlNJ8tll9fC7/dz7FiBGzfGCIcj6HQKbW1VdHS0y0Z/\nIYTYpjQaDTU1VZw5k+S++36O69dneOKJF3n44f2UStO8+90d5demUkkMhhJOp5NkMsn4+DBHj1r5\n939PksmYgXw5iQFuDrp1EgrNzdY/99wkAP/+73PLjS9eDPLxjxvp6KglmYxgs9mpqbHxiU/cQyAw\nSkNDE6dPn0RRlDV/vo6O/SiKwvh4mERiCrNZz6FDjTQ3N9/5zUIIcZMkMoDX6+XEiToGB8MYDDY8\nnhIA73hHA21tJjKZIJOTOrLZBD6fCZPJxMWLFzl3rodYLEtfX+oN2/f5knR31/Gud7XxH//Ry8/+\n7D1UVXn4pV/6Gr/yK2+lWIzg8xlIpyOcOzfGc88F+OAHW7nrrkPrSmJgflamCb/fXz5HZr1tCiGE\n2HhNTY2Ew1GmpiL4blb/7+iw0NbWCsSYnCxRLBZR1TQNDU4KhQIXL17k/PlzTEyYCIWSt23b7y/R\n2Znmwx9+K9euxfjLvxzgd37nFE6nnT/6o//A4dgHpKitNRKNjhMMltDpFBoa7Bw61LWuJAbmjjs4\nePAAra3p8jkystxZCLFaksgwt5fk8OGDeDyTTE+H0Ols/NZvHecXfuEtmM0FpqaCN2dj9CSTWb71\nrf/g4sVJrlyJ4HBUAynq6/WMj+cXtfv2t9dht2eprtbzqU/9NLGYyoMPBtFo7Fy7Nldaub8/gsdT\nwmbT85a3HEFVB3jmmZf41KfuLR++WQl6vR6n01mx9oQQQmwsq9XKyZNHCAQCmEyTnD3bxbvedZTO\nznqmp6cJhWYolUrEYlmmpqL88IevcflyiOvX87S21hAKzeLxKIRCKhYLpG6Oub373U1YLDMcOVLF\ngw+e5lvfugAMcPz46/tdLBYrhUKOxsYGDh92MjQU4p//+Tq/+ZsnKjoYZjabMZvNd36hEEIsQxKZ\nm3Q6HQ0NDTQ0zFVfuf/+139XU1PD6Ogovb2zGAxOMpkZLl2y8txzZmAuMixNYtrajPh8Gaqr8xw8\n2ITH48FqTWMyafnEJ/6t/Lonnpjbl/Oxjx3gXe+6G4/HU+7PagUCcZ58soezZ7tXfKqyEEKI7cti\nsdDW1kZbWxvvfe/r1xsbG2lsbOTcuQvMzqrodCZyOSuDgyrPP58G5vZwhkJzh1eqahKwsn+/Gas1\nQkODwuHD7TgcDmpqzPz8z+9jdjZDf/8MAK+8Mk5DQ4Hm5ixNTU6KxRSf+czLfOhDx6mvX/mgmMQl\nIcRGkkRmBeZq209gNDoxmczk8yrvec9B9u2r5utfP8/kpIZ77rEzORkjn08zPm7hxIkiXV166utr\nOH26E6/XSzwe54EH/LzjHW385Ccz/MM/vMp/+S+HOHbMwNGjrfT2BlZ1MvJSgUCCxx9/gQcf7JSA\nIYQQu1wsFmNiYgavt55YLIKimHjggRaqquD554eZnNRy/LiZ4eEETU1Zzp2zcuoUHDpko62tjhMn\nurBarRw92oRO179okO2v/uplAH71Vwv8xm/Y1hybJC4JITaSJDIrkM/nSafzmM0uNBoNOp0Wo1FD\nQ4OHycm5DfOdnXre9jYTx47dxYsvxnnggbmZneeeC/Hudzeh0+lwu920t9fxyitDRKNzS8symThN\nTZ18+9vj/OVf/mv5nis5GVkIIcTelc1myWaL+HxWUqkEqlrA4TDQ1tbIv/zLGAAHDih88IPVHDjw\nDp59dpr3vrceo9HAs89O87a3zS1frqur4/3vb6OhwUJPT4QXXpjk3ntr0euNfOlL5/nSl86X77nS\n2BQIxBcdzjn/T1jdAJ0QQrwRSWRWQK/XY7UaiUYT+Hx+NBojFy8OEQ7PJTF3312Dz+fkwIFOzpy5\nm498ZG7avbc3wOc+9z1+6ZfuomnueBi+850Qjz/eU277q18d4atfHeFTn7qHnp5HVnwy8kK3C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mOfNDCCHElqirq0NRFIaHx0kmg9hseg4dapd9m0KITSWJzA7gcrk4ftxFLpdDo9HIGTJCCCG2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\nAOjOJ9ftJm14VELcAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "pl.figure(2,(10,7))\n", + "pl.clf()\n", + "pl.subplot(2,2,1)\n", + "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2)\n", + "pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Mapped source samples')\n", + "pl.title(\"Bary. mapping (linear)\")\n", + "pl.legend(loc=0)\n", + "\n", + "pl.subplot(2,2,2)\n", + "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2)\n", + "pl.scatter(xst[:,0],xst[:,1],c=ys,marker='+',label='Learned mapping')\n", + "pl.title(\"Estim. mapping (linear)\")\n", + "\n", + "pl.subplot(2,2,3)\n", + "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2)\n", + "pl.scatter(xst0_kernel[:,0],xst0_kernel[:,1],c=ys,marker='+',label='barycentric mapping')\n", + "pl.title(\"Bary. mapping (kernel)\")\n", + "\n", + "pl.subplot(2,2,4)\n", + "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2)\n", + "pl.scatter(xst_kernel[:,0],xst_kernel[:,1],c=ys,marker='+',label='Learned mapping')\n", + "pl.title(\"Estim. mapping (kernel)\")\n", + "\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Barycentric mapping on the left, estimated mapping on the right. We can see that the change \n", + "in variance of the mode do not allow for a good linear mapping. In this case the kernel \n", + "mapping (lower right) allows for a far better estimation\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/examples/demo_OTDA_mapping.py b/examples/demo_OTDA_mapping.py index f5da2ff..7a9af61 100644 --- a/examples/demo_OTDA_mapping.py +++ b/examples/demo_OTDA_mapping.py @@ -11,7 +11,7 @@ import ot #%% dataset generation -np.random.seed(0) +np.random.seed(0) # makes example reproducible n=100 # nb samples in source and target datasets theta=2*np.pi/20 diff --git a/setup.py b/setup.py index d9111e0..144c325 100755 --- a/setup.py +++ b/setup.py @@ -11,7 +11,7 @@ import os here = path.abspath(path.dirname(__file__)) -# dirty but working +# dirty but working __version__ = re.search( r'__version__\s*=\s*[\'"]([^\'"]*)[\'"]', # It excludes inline comment too open('ot/__init__.py').read()).group(1) @@ -48,8 +48,8 @@ setup(name='POT', license = 'MIT', scripts=[], data_files=[], - requires=["numpy (>=1.11)","scipy (>=0.17)","cython (>=0.23)","matplotlib (>=1.5)"], - install_requires=["numpy (>=1.11)","scipy (>=0.17)","cython (>=0.23)","matplotlib (>=1.5)"], + requires=["numpy","scipy","cython","matplotlib"], + install_requires=["numpy","scipy","cython","matplotlib"], classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', -- cgit v1.2.3 From 5cb4db95450bb754b7e167173c31c9505e473f2b Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 4 Nov 2016 10:41:46 +0100 Subject: update readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 621da14..ec679e8 100644 --- a/README.md +++ b/README.md @@ -65,7 +65,7 @@ The examples folder contain several examples and use case for the library. The f * [OT with user provided regularization](https://github.com/rflamary/POT/blob/master/examples/Demo_Optim_OTreg.ipynb) * [Domain adaptation with optimal transport](https://github.com/rflamary/POT/blob/master/examples/Demo_2D_OT_DomainAdaptation.ipynb) * [Color transfer in images](https://github.com/rflamary/POT/blob/master/examples/Demo_Image_ColorAdaptation.ipynb) - +* [OT mapping estimation for domain adaptation](https://github.com/rflamary/POT/blob/master/examples/Demo_2D_OTmapping_DomainAdaptation.ipynb) ## Acknowledgements -- cgit v1.2.3 From 67b4b7eb128f659c8a693617ca107418aabc4a88 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 4 Nov 2016 10:57:19 +0100 Subject: V0.1.8 --- ot/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/__init__.py b/ot/__init__.py index 5cf878c..c359881 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -17,7 +17,7 @@ from .da import sinkhorn_lpl1_mm # utils functions from .utils import dist, unif -__version__ = "0.1.7" +__version__ = "0.1.8" __all__ = ["emd", "sinkhorn", "utils", 'datasets', 'bregman', 'lp', 'plot', 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim'] -- cgit v1.2.3 From 23ea435b1fd89707d7acc5e173d22fb017cb9abc Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 4 Nov 2016 10:59:34 +0100 Subject: update readme --- README.md | 9 ++------- 1 file changed, 2 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index ec679e8..97eed9a 100644 --- a/README.md +++ b/README.md @@ -12,12 +12,7 @@ It provides the following solvers: * Bregman projections for Wasserstein barycenter [3] and unmixing [4]. * Optimal transport for domain adaptation with group lasso regularization [5] * Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. - -We are also currently working on the following features: - -- [ ] Image color adaptation demo -- [x] Scikit-learn inspired classes for domain adaptation -- [ ] Mapping estimation as proposed in [8] +* Joint OT matix and mapping etsimation [8]. Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -57,7 +52,7 @@ Note that for easier access the module is name ot instead of pot. The examples folder contain several examples and use case for the library. The full documentation is available on [Readthedocs](http://pot.readthedocs.io/) - Here is a list of the Python notebook if you want a quick look: + Here is a list of the Python notebooks if you want a quick look: * [1D optimal transport](https://github.com/rflamary/POT/blob/master/examples/Demo_1D_OT.ipynb) * [2D optimal transport on empirical distributions](https://github.com/rflamary/POT/blob/master/examples/Demo_2D_OT_samples.ipynb) -- cgit v1.2.3 From 29f1f15617aac3d3b76528f9ed89264efaaf66d0 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 4 Nov 2016 11:01:25 +0100 Subject: V0.1.9 --- ot/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/__init__.py b/ot/__init__.py index c359881..7d79058 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -17,7 +17,7 @@ from .da import sinkhorn_lpl1_mm # utils functions from .utils import dist, unif -__version__ = "0.1.8" +__version__ = "0.1.9" __all__ = ["emd", "sinkhorn", "utils", 'datasets', 'bregman', 'lp', 'plot', 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim'] -- cgit v1.2.3 From 3c256469c435dadbcc93bc0d2a08046cbee79a57 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 4 Nov 2016 11:20:39 +0100 Subject: update doc --- Makefile | 7 ++- README.md | 2 +- docs/source/index.rst | 33 +---------- docs/source/readme.rst | 147 +++++++++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 155 insertions(+), 34 deletions(-) create mode 100644 docs/source/readme.rst diff --git a/Makefile b/Makefile index 0b17082..a4fae8e 100644 --- a/Makefile +++ b/Makefile @@ -11,7 +11,7 @@ help : @echo " remove - remove the package (local user)" @echo " sremove - remove the package (system with sudo)" @echo " clean - remove any temporary files" - @echo " notebook - launch ipython notebook" + @echo " notebook - launch ipython notebook" build : $(PYTHON) setup.py build @@ -33,11 +33,14 @@ sremove : clean : $(PYTHON) setup.py clean - + uploadpypi: python setup.py register python setup.py sdist upload -r pypi +rdoc: + pandoc pandoc --from=markdown --to=rst --output=docs/source/readme.rst README.md + notebook : ipython notebook --matplotlib=inline --notebook-dir=examples/ diff --git a/README.md b/README.md index 97eed9a..2a0ce90 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,7 @@ It provides the following solvers: * Bregman projections for Wasserstein barycenter [3] and unmixing [4]. * Optimal transport for domain adaptation with group lasso regularization [5] * Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. -* Joint OT matix and mapping etsimation [8]. +* Joint OT matrix and mapping etsimation [8]. Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. diff --git a/docs/source/index.rst b/docs/source/index.rst index adbabb6..acfe766 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -6,20 +6,6 @@ POT: Python Optimal Transport ============================= - -This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning. - -It provides the following solvers: - -* OT solver for the linear program/ Earth Movers Distance [1]. -* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2]. -* Bregman projections for Wasserstein barycenter [3] and unmixing [4]. -* Optimal transport for domain adaptation with group lasso regularization [5] -* Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. - -Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. - - Contents -------- @@ -30,23 +16,8 @@ Contents all examples - -References ----------- - -[1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). Displacement interpolation using Lagrangian mass transport. In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM. - -[2] Cuturi, M. (2013). Sinkhorn distances: Lightspeed computation of optimal transport. In Advances in Neural Information Processing Systems (pp. 2292-2300). - -[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). Iterative Bregman projections for regularized transportation problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138. - -[4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, Supervised planetary unmixing with optimal transport, Whorkshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), 2016. - -[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 - -[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. - -[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567. +.. include:: ../readme.rst + :start-line: 5 Indices and tables diff --git a/docs/source/readme.rst b/docs/source/readme.rst new file mode 100644 index 0000000..5fa1dfd --- /dev/null +++ b/docs/source/readme.rst @@ -0,0 +1,147 @@ +POT: Python Optimal Transport +============================= + +|Documentation Status| + +This open source Python library provide several solvers for optimization +problems related to Optimal Transport for signal, image processing and +machine learning. + +It provides the following solvers: + +- OT solver for the linear program/ Earth Movers Distance [1]. +- Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2]. +- Bregman projections for Wasserstein barycenter [3] and unmixing [4]. +- Optimal transport for domain adaptation with group lasso + regularization [5] +- Conditional gradient [6] and Generalized conditional gradient for + regularized OT [7]. +- Joint OT matrix and mapping etsimation [8]. + +Some demonstrations (both in Python and Jupyter Notebook format) are +available in the examples folder. + +Installation +------------ + +The Library has been tested on Linux and MacOSX. It requires a C++ +compiler for using the EMD solver and rely on the following Python +modules: + +- Numpy (>=1.11) +- Scipy (>=0.17) +- Cython (>=0.23) +- Matplotlib (>=1.5) + +Under debian based linux the dependencies can be installed with + +:: + + sudo apt-get install python-numpy python-scipy python-matplotlib cython + +To install the library, you can install it locally (after downloading +it) on you machine using + +:: + + python setup.py install --user + +The toolbox is also available on PyPI with a possibly slightly older +version. You can install it with: + +:: + + pip install POT + +After a correct installation, you should be able to import the module +without errors: + +.. code:: python + + import ot + +Note that for easier access the module is name ot instead of pot. + +Examples +-------- + +The examples folder contain several examples and use case for the +library. The full documentation is available on +`Readthedocs `__ + +Here is a list of the Python notebooks if you want a quick look: + +- `1D optimal + transport `__ +- `2D optimal transport on empirical + distributions `__ +- `1D Wasserstein + barycenter `__ +- `OT with user provided + regularization `__ +- `Domain adaptation with optimal + transport `__ +- `Color transfer in + images `__ +- `OT mapping estimation for domain + adaptation `__ + +Acknowledgements +---------------- + +The contributors to this library are: + +- `Rémi Flamary `__ +- `Nicolas Courty `__ +- `Laetitia Chapel `__ + +This toolbox benefit a lot from open source research and we would like +to thank the following persons for providing some code (in various +languages): + +- `Gabriel Peyré `__ (Wasserstein Barycenters + in Matlab) +- `Nicolas Bonneel `__ ( C++ code for + EMD) +- `Antoine Rolet `__ ( Mex file for EMD ) +- `Marco Cuturi `__ (Sinkhorn Knopp in + Matlab/Cuda) + +References +---------- + +[1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, +December). Displacement interpolation using Lagrangian mass transport. +In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM. + +[2] Cuturi, M. (2013). Sinkhorn distances: Lightspeed computation of +optimal transport. In Advances in Neural Information Processing Systems +(pp. 2292-2300). + +[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. +(2015). Iterative Bregman projections for regularized transportation +problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138. + +[4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, +Supervised planetary unmixing with optimal transport, Whorkshop on +Hyperspectral Image and Signal Processing : Evolution in Remote Sensing +(WHISPERS), 2016. + +[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport +for Domain Adaptation," in IEEE Transactions on Pattern Analysis and +Machine Intelligence , vol.PP, no.99, pp.1-1 + +[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). +Regularized discrete optimal transport. SIAM Journal on Imaging +Sciences, 7(3), 1853-1882. + +[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized +conditional gradient: analysis of convergence and applications. arXiv +preprint arXiv:1510.06567. + +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation +for discrete optimal transport", Neural Information Processing Systems +(NIPS), 2016. + +.. |Documentation Status| image:: https://readthedocs.org/projects/pot/badge/?version=latest + :target: http://pot.readthedocs.io/en/latest/?badge=latest -- cgit v1.2.3 From 0cd4ee7ac02f78016bd8e7229b608b63801ab907 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 4 Nov 2016 11:22:18 +0100 Subject: update doc 2 --- docs/source/index.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/index.rst b/docs/source/index.rst index acfe766..41483b8 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -16,7 +16,7 @@ Contents all examples -.. include:: ../readme.rst +.. include:: readme.rst :start-line: 5 -- cgit v1.2.3 From 5fe917cd92541c1d869e342a841756cd53927a8a Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 4 Nov 2016 11:24:45 +0100 Subject: update doc 3 --- Makefile | 2 +- README.md | 2 +- docs/source/readme.rst | 2 +- 3 files changed, 3 insertions(+), 3 deletions(-) diff --git a/Makefile b/Makefile index a4fae8e..b1ecdcb 100644 --- a/Makefile +++ b/Makefile @@ -39,7 +39,7 @@ uploadpypi: python setup.py sdist upload -r pypi rdoc: - pandoc pandoc --from=markdown --to=rst --output=docs/source/readme.rst README.md + pandoc --from=markdown --to=rst --output=docs/source/readme.rst README.md notebook : diff --git a/README.md b/README.md index 2a0ce90..cd9e281 100644 --- a/README.md +++ b/README.md @@ -12,7 +12,7 @@ It provides the following solvers: * Bregman projections for Wasserstein barycenter [3] and unmixing [4]. * Optimal transport for domain adaptation with group lasso regularization [5] * Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. -* Joint OT matrix and mapping etsimation [8]. +* Joint OT matrix and mapping estimation [8]. Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 5fa1dfd..7b88cad 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -16,7 +16,7 @@ It provides the following solvers: regularization [5] - Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. -- Joint OT matrix and mapping etsimation [8]. +- Joint OT matrix and mapping estimation [8]. Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. -- cgit v1.2.3 From 151aa9b0c92d151bc9ed0edbe2217506652f19ec Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 4 Nov 2016 11:45:14 +0100 Subject: doc linear mapping estimation --- ot/da.py | 75 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++- 1 file changed, 74 insertions(+), 1 deletion(-) diff --git a/ot/da.py b/ot/da.py index 4e5fda2..76bc6a3 100644 --- a/ot/da.py +++ b/ot/da.py @@ -124,7 +124,80 @@ def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerIter return transp def joint_OT_mapping_linear(xs,xt,mu=1,eta=0.001,bias=False,verbose=False,verbose2=False,numItermax = 100,numInnerItermax = 10,stopInnerThr=1e-6,stopThr=1e-5,log=False,**kwargs): - """Joint Ot and mapping estimation (uniform weights and ) + """Joint OT and linear mapping estimation as proposed in [8] + + The function solves the following optimization problem: + + .. math:: + \min_{\gamma,L}\quad \|L(X_s) -n_s\gamma X_t\|^2_F + \mu<\gamma,M>_F + \eta \|L -I\|^2_F + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + where : + + - M is the (ns,nt) squared euclidean cost matrix between samples in Xs and Xt (scaled by ns) + - :math:`L` is a dxd linear operator that approximates the barycentric mapping + - :math:`I` is the identity matrix (neutral linear mapping) + - a and b are uniform source and target weights + + The problem consist in solving jointly an optimal transport matrix + :math:`\gamma` and a linear mapping that fits the barycentric mapping + :math:`n_s\gamma X_t` + + The algorithm used for solving the problem is the block coordinate + descent that alternates between updates of G (using conditionnal gradient) + abd the update of L using a classical least square solver. + + + Parameters + ---------- + xs : np.ndarray (ns,d) + samples in the source domain + xt : np.ndarray (nt,d) + samples in the target domain + mu: float,optional + Weight for the linear OT loss (>0) + eta: float, optional + Regularization term for the linear mapping L (>0) + bias: bool,optional + Estimate linear mapping with constant bias + numItermax: int, optional + Max number of BCD iterations + stopThr: float, optional + Stop threshold on relative loss decrease (>0) + numInnerItermax: int, optional + Max number of iterations (inner CG solver) + stopInnerThr: float, optional + Stop threshold on error (inner CG solver) (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + gamma: (ns x nt) ndarray + Optimal transportation matrix for the given parameters + L: (d x d) ndarray + Linear mapping matrix (d+1 x d if bias) + log: dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. + + See Also + -------- + ot.lp.emd : Unregularized OT + ot.optim.cg : General regularized OT + """ ns,nt,d=xs.shape[0],xt.shape[0],xt.shape[1] -- cgit v1.2.3 From a5f2569859424a00100f7ad29ae4d715ee90c29f Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 4 Nov 2016 12:09:36 +0100 Subject: doc nonlinear mapping estimation --- README.md | 2 + docs/source/readme.rst | 3 + ot/bregman.py | 232 ++++++++++++++++++++++++------------------------- ot/da.py | 123 +++++++++++++++++++++----- ot/optim.py | 6 +- 5 files changed, 226 insertions(+), 140 deletions(-) diff --git a/README.md b/README.md index cd9e281..f73cbe5 100644 --- a/README.md +++ b/README.md @@ -62,6 +62,8 @@ The examples folder contain several examples and use case for the library. The f * [Color transfer in images](https://github.com/rflamary/POT/blob/master/examples/Demo_Image_ColorAdaptation.ipynb) * [OT mapping estimation for domain adaptation](https://github.com/rflamary/POT/blob/master/examples/Demo_2D_OTmapping_DomainAdaptation.ipynb) +You can also see the notebooks with [Jupyter nbviewer](https://nbviewer.jupyter.org/github/rflamary/POT/tree/master/examples/). + ## Acknowledgements The contributors to this library are: diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 7b88cad..653adaf 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -86,6 +86,9 @@ Here is a list of the Python notebooks if you want a quick look: - `OT mapping estimation for domain adaptation `__ +You can also see the notebooks with `Jupyter +nbviewer `__. + Acknowledgements ---------------- diff --git a/ot/bregman.py b/ot/bregman.py index 2d82ae4..a770c5a 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -14,21 +14,21 @@ def sinkhorn(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, log=Fa .. math:: \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) - + s.t. \gamma 1 = a - - \gamma^T 1= b - + + \gamma^T 1= b + \gamma\geq 0 where : - + - M is the (ns,nt) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - a and b are source and target weights (sum to 1) - + The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [2]_ - - + + Parameters ---------- a : np.ndarray (ns,) @@ -36,79 +36,79 @@ def sinkhorn(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, log=Fa b : np.ndarray (nt,) samples in the target domain M : np.ndarray (ns,nt) - loss matrix - reg: float + loss matrix + reg : float Regularization term >0 - numItermax: int, optional + numItermax : int, optional Max number of iterations - stopThr: float, optional + stopThr : float, optional Stop threshol on error (>0) verbose : bool, optional Print information along iterations log : bool, optional - record log if True - - + record log if True + + Returns ------- - gamma: (ns x nt) ndarray + gamma : (ns x nt) ndarray Optimal transportation matrix for the given parameters - log: dict - log dictionary return only if log==True in parameters + log : dict + log dictionary return only if log==True in parameters Examples -------- - + >>> a=[.5,.5] >>> b=[.5,.5] >>> M=[[0.,1.],[1.,0.]] >>> ot.sinkhorn(a,b,M,1) array([[ 0.36552929, 0.13447071], [ 0.13447071, 0.36552929]]) - - + + References ---------- - + .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013 - - + + See Also -------- ot.lp.emd : Unregularized OT ot.optim.cg : General regularized OT - - """ - + + """ + a=np.asarray(a,dtype=np.float64) b=np.asarray(b,dtype=np.float64) M=np.asarray(M,dtype=np.float64) - + if len(a)==0: a=np.ones((M.shape[0],),dtype=np.float64)/M.shape[0] if len(b)==0: - b=np.ones((M.shape[1],),dtype=np.float64)/M.shape[1] - + b=np.ones((M.shape[1],),dtype=np.float64)/M.shape[1] + # init data Nini = len(a) Nfin = len(b) - - + + cpt = 0 if log: log={'err':[]} - + # we assume that no distances are null except those of the diagonal of distances u = np.ones(Nini)/Nini - v = np.ones(Nfin)/Nfin + v = np.ones(Nfin)/Nfin uprev=np.zeros(Nini) vprev=np.zeros(Nini) #print reg - + K = np.exp(-M/reg) #print np.min(K) - + Kp = np.dot(np.diag(1/a),K) transp = K cpt = 0 @@ -120,10 +120,10 @@ def sinkhorn(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, log=Fa print('Warning: numerical errrors') if cpt!=0: u = uprev - v = vprev + v = vprev break uprev = u - vprev = v + vprev = v v = np.divide(b,np.dot(K.T,u)) u = 1./np.dot(Kp,v) if cpt%10==0: @@ -131,14 +131,14 @@ def sinkhorn(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, log=Fa transp = np.dot(np.diag(u),np.dot(K,np.diag(v))) err = np.linalg.norm((np.sum(transp,axis=0)-b))**2 if log: - log['err'].append(err) - + log['err'].append(err) + if verbose: if cpt%200 ==0: print('{:5s}|{:12s}'.format('It.','Err')+'\n'+'-'*19) print('{:5d}|{:8e}|'.format(cpt,err)) cpt = cpt +1 - #print 'err=',err,' cpt=',cpt + #print 'err=',err,' cpt=',cpt if log: return np.dot(np.diag(u),np.dot(K,np.diag(v))),log else: @@ -147,12 +147,12 @@ def sinkhorn(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, log=Fa def geometricBar(weights,alldistribT): """return the weighted geometric mean of distributions""" - assert(len(weights)==alldistribT.shape[1]) - return np.exp(np.dot(np.log(alldistribT),weights.T)) + assert(len(weights)==alldistribT.shape[1]) + return np.exp(np.dot(np.log(alldistribT),weights.T)) -def geometricMean(alldistribT): +def geometricMean(alldistribT): """return the geometric mean of distributions""" - return np.exp(np.mean(np.log(alldistribT),axis=1)) + return np.exp(np.mean(np.log(alldistribT),axis=1)) def projR(gamma,p): #return np.dot(np.diag(p/np.maximum(np.sum(gamma,axis=1),1e-10)),gamma) @@ -161,65 +161,65 @@ def projR(gamma,p): def projC(gamma,q): #return (np.dot(np.diag(q/np.maximum(np.sum(gamma,axis=0),1e-10)),gamma.T)).T return np.multiply(gamma,q/np.maximum(np.sum(gamma,axis=0),1e-10)) - + def barycenter(A,M,reg, weights=None, numItermax = 1000, stopThr=1e-4,verbose=False,log=False): """Compute the entropic regularized wasserstein barycenter of distributions A The function solves the following optimization problem: - + .. math:: \mathbf{a} = arg\min_\mathbf{a} \sum_i W_{reg}(\mathbf{a},\mathbf{a}_i) - + where : - + - :math:`W_{reg}(\cdot,\cdot)` is the entropic regularized Wasserstein distance (see ot.bregman.sinkhorn) - :math:`\mathbf{a}_i` are training distributions in the columns of matrix :math:`\mathbf{A}` - reg and :math:`\mathbf{M}` are respectively the regularization term and the cost matrix for OT - + The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [3]_ - + Parameters ---------- A : np.ndarray (d,n) - n training distributions of size d + n training distributions of size d M : np.ndarray (d,d) - loss matrix for OT - reg: float + loss matrix for OT + reg : float Regularization term >0 - numItermax: int, optional + numItermax : int, optional Max number of iterations - stopThr: float, optional + stopThr : float, optional Stop threshol on error (>0) verbose : bool, optional Print information along iterations log : bool, optional - record log if True - - + record log if True + + Returns ------- - a: (d,) ndarray + a : (d,) ndarray Wasserstein barycenter - log: dict - log dictionary return only if log==True in parameters + log : dict + log dictionary return only if log==True in parameters + - References ---------- - + .. [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). Iterative Bregman projections for regularized transportation problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138. - - - + + + """ - - + + if weights is None: weights=np.ones(A.shape[1])/A.shape[1] else: assert(len(weights)==A.shape[1]) - + if log: log={'err':[]} @@ -231,130 +231,130 @@ def barycenter(A,M,reg, weights=None, numItermax = 1000, stopThr=1e-4,verbose=Fa UKv=np.dot(K,np.divide(A.T,np.sum(K,axis=0)).T) u = (geometricMean(UKv)/UKv.T).T - + while (err>stopThr and cpt0 (Wasserstein data fitting) - reg0: float - Regularization term >0 (Wasserstein reg with h0) - alpha: float + reg0 : float + Regularization term >0 (Wasserstein reg with h0) + alpha : float How much should we trust the prior ([0,1]) - numItermax: int, optional + numItermax : int, optional Max number of iterations - stopThr: float, optional + stopThr : float, optional Stop threshol on error (>0) verbose : bool, optional Print information along iterations log : bool, optional - record log if True - - + record log if True + + Returns ------- - a: (d,) ndarray + a : (d,) ndarray Wasserstein barycenter - log: dict - log dictionary return only if log==True in parameters - + log : dict + log dictionary return only if log==True in parameters + References ---------- - + .. [4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, Supervised planetary unmixing with optimal transport, Whorkshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), 2016. """ - - #M = M/np.median(M) + + #M = M/np.median(M) K = np.exp(-M/reg) - - #M0 = M0/np.median(M0) + + #M0 = M0/np.median(M0) K0 = np.exp(-M0/reg0) old = h0 err=1 - cpt=0 + cpt=0 #log = {'niter':0, 'all_err':[]} if log: log={'err':[]} - - + + while (err>stopThr and cpt0 - eta: float, optional + eta : float, optional Regularization term for group lasso regularization >0 - numItermax: int, optional + numItermax : int, optional Max number of iterations - numInnerItermax: int, optional + numInnerItermax : int, optional Max number of iterations (inner sinkhorn solver) - stopInnerThr: float, optional + stopInnerThr : float, optional Stop threshold on error (inner sinkhorn solver) (>0) verbose : bool, optional Print information along iterations @@ -67,9 +66,9 @@ def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerIter Returns ------- - gamma: (ns x nt) ndarray + gamma : (ns x nt) ndarray Optimal transportation matrix for the given parameters - log: dict + log : dict log dictionary return only if log==True in parameters @@ -145,7 +144,10 @@ def joint_OT_mapping_linear(xs,xt,mu=1,eta=0.001,bias=False,verbose=False,verbos The problem consist in solving jointly an optimal transport matrix :math:`\gamma` and a linear mapping that fits the barycentric mapping - :math:`n_s\gamma X_t` + :math:`n_s\gamma X_t`. + + One can also estimate a mapping with constant bias (see supplementary + material of [8]) using the bias optional argument. The algorithm used for solving the problem is the block coordinate descent that alternates between updates of G (using conditionnal gradient) @@ -158,19 +160,19 @@ def joint_OT_mapping_linear(xs,xt,mu=1,eta=0.001,bias=False,verbose=False,verbos samples in the source domain xt : np.ndarray (nt,d) samples in the target domain - mu: float,optional + mu : float,optional Weight for the linear OT loss (>0) - eta: float, optional + eta : float, optional Regularization term for the linear mapping L (>0) - bias: bool,optional + bias : bool,optional Estimate linear mapping with constant bias - numItermax: int, optional + numItermax : int, optional Max number of BCD iterations - stopThr: float, optional + stopThr : float, optional Stop threshold on relative loss decrease (>0) - numInnerItermax: int, optional + numInnerItermax : int, optional Max number of iterations (inner CG solver) - stopInnerThr: float, optional + stopInnerThr : float, optional Stop threshold on error (inner CG solver) (>0) verbose : bool, optional Print information along iterations @@ -180,11 +182,11 @@ def joint_OT_mapping_linear(xs,xt,mu=1,eta=0.001,bias=False,verbose=False,verbos Returns ------- - gamma: (ns x nt) ndarray + gamma : (ns x nt) ndarray Optimal transportation matrix for the given parameters - L: (d x d) ndarray + L : (d x d) ndarray Linear mapping matrix (d+1 x d if bias) - log: dict + log : dict log dictionary return only if log==True in parameters @@ -291,10 +293,89 @@ def joint_OT_mapping_linear(xs,xt,mu=1,eta=0.001,bias=False,verbose=False,verbos def joint_OT_mapping_kernel(xs,xt,mu=1,eta=0.001,kerneltype='gaussian',sigma=1,bias=False,verbose=False,verbose2=False,numItermax = 100,numInnerItermax = 10,stopInnerThr=1e-6,stopThr=1e-5,log=False,**kwargs): - """Joint Ot and mapping estimation (uniform weights and ) + """Joint OT and nonlinear mapping estimation with kernels as proposed in [8] + + The function solves the following optimization problem: + + .. math:: + \min_{\gamma,L\in\mathcal{H}}\quad \|L(X_s) -n_s\gamma X_t\|^2_F + \mu<\gamma,M>_F + \eta \|L\|^2_\mathcal{H} + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + where : + + - M is the (ns,nt) squared euclidean cost matrix between samples in Xs and Xt (scaled by ns) + - :math:`L` is a ns x d linear operator on a kernel matrix that approximates the barycentric mapping + - a and b are uniform source and target weights + + The problem consist in solving jointly an optimal transport matrix + :math:`\gamma` and the nonlinear mapping that fits the barycentric mapping + :math:`n_s\gamma X_t`. + + One can also estimate a mapping with constant bias (see supplementary + material of [8]) using the bias optional argument. + + The algorithm used for solving the problem is the block coordinate + descent that alternates between updates of G (using conditionnal gradient) + abd the update of L using a classical kernel least square solver. + + + Parameters + ---------- + xs : np.ndarray (ns,d) + samples in the source domain + xt : np.ndarray (nt,d) + samples in the target domain + mu : float,optional + Weight for the linear OT loss (>0) + eta : float, optional + Regularization term for the linear mapping L (>0) + bias : bool,optional + Estimate linear mapping with constant bias + kerneltype : str,optional + kernel used by calling function ot.utils.kernel (gaussian by default) + sigma : float, optional + Gaussian kernel bandwidth. + numItermax : int, optional + Max number of BCD iterations + stopThr : float, optional + Stop threshold on relative loss decrease (>0) + numInnerItermax : int, optional + Max number of iterations (inner CG solver) + stopInnerThr : float, optional + Stop threshold on error (inner CG solver) (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + gamma : (ns x nt) ndarray + Optimal transportation matrix for the given parameters + L : (ns x d) ndarray + Nonlinear mapping matrix (ns+1 x d if bias) + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. + + See Also + -------- + ot.lp.emd : Unregularized OT + ot.optim.cg : General regularized OT + """ - ns,nt,d=xs.shape[0],xt.shape[0],xt.shape[1] + ns,nt=xs.shape[0],xt.shape[0] K=kernel(xs,xs,method=kerneltype,sigma=sigma) if bias: diff --git a/ot/optim.py b/ot/optim.py index 2b8f565..7ed658c 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -27,7 +27,7 @@ def line_search_armijo(f,xk,pk,gfk,old_fval,args=(),c1=1e-4,alpha0=0.99): descent direction gfk : np.ndarray gradient of f at xk - old_fval: float + old_fval : float loss value at xk args : tuple, optional arguments given to f @@ -110,9 +110,9 @@ def cg(a,b,M,reg,f,df,G0=None,numItermax = 200,stopThr=1e-9,verbose=False,log=Fa Returns ------- - gamma: (ns x nt) ndarray + gamma : (ns x nt) ndarray Optimal transportation matrix for the given parameters - log: dict + log : dict log dictionary return only if log==True in parameters -- cgit v1.2.3 From f5e9a1336af4959fcf4fee0dddd6257cc5cfa064 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 4 Nov 2016 12:21:53 +0100 Subject: etter doc for classes --- ot/da.py | 61 +++++++++++++++++++++++++++++++------------------------------ 1 file changed, 31 insertions(+), 30 deletions(-) diff --git a/ot/da.py b/ot/da.py index 72ca3ac..90b96ba 100644 --- a/ot/da.py +++ b/ot/da.py @@ -151,7 +151,7 @@ def joint_OT_mapping_linear(xs,xt,mu=1,eta=0.001,bias=False,verbose=False,verbos The algorithm used for solving the problem is the block coordinate descent that alternates between updates of G (using conditionnal gradient) - abd the update of L using a classical least square solver. + and the update of L using a classical least square solver. Parameters @@ -320,7 +320,7 @@ def joint_OT_mapping_kernel(xs,xt,mu=1,eta=0.001,kerneltype='gaussian',sigma=1,b The algorithm used for solving the problem is the block coordinate descent that alternates between updates of G (using conditionnal gradient) - abd the update of L using a classical kernel least square solver. + and the update of L using a classical kernel least square solver. Parameters @@ -492,7 +492,15 @@ def joint_OT_mapping_kernel(xs,xt,mu=1,eta=0.001,kerneltype='gaussian',sigma=1,b class OTDA(object): - """Class for domain adaptation with optimal transport""" + """Class for domain adaptation with optimal transport as proposed in [5] + + + References + ---------- + + .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 + + """ def __init__(self,metric='sqeuclidean'): """ Class initialization""" @@ -504,8 +512,7 @@ class OTDA(object): def fit(self,xs,xt,ws=None,wt=None): - """ Fit domain adaptation between samples is xs and xt (with optional - weights)""" + """ Fit domain adaptation between samples is xs and xt (with optional weights)""" self.xs=xs self.xt=xt @@ -522,7 +529,7 @@ class OTDA(object): self.computed=True def interp(self,direction=1): - """Barycentric interpolation for the source (1) or target (-1) + """Barycentric interpolation for the source (1) or target (-1) samples This Barycentric interpolation solves for each source (resp target) sample xs (resp xt) the following optimization problem: @@ -558,10 +565,16 @@ class OTDA(object): def predict(self,x,direction=1): - """ Out of sample mapping using the formulation from Ferradans + """ Out of sample mapping using the formulation from [6] + + For each sample x to map, it finds the nearest source sample xs and + map the samle x to the position xst+(x-xs) wher xst is the barycentric + interpolation of source sample xs. + + References + ---------- - It basically find the source sample the nearset to the nex sample and - apply the difference to the displaced source sample. + .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. """ if direction>0: # >0 then source to target @@ -582,8 +595,7 @@ class OTDA_sinkhorn(OTDA): """Class for domain adaptation with optimal transport with entropic regularization""" def fit(self,xs,xt,reg=1,ws=None,wt=None,**kwargs): - """ Fit domain adaptation between samples is xs and xt (with optional - weights)""" + """ Fit regularized domain adaptation between samples is xs and xt (with optional weights)""" self.xs=xs self.xt=xt @@ -601,12 +613,12 @@ class OTDA_sinkhorn(OTDA): class OTDA_lpl1(OTDA): - """Class for domain adaptation with optimal transport with entropic an group regularization""" + """Class for domain adaptation with optimal transport with entropic and group regularization""" def fit(self,xs,ys,xt,reg=1,eta=1,ws=None,wt=None,**kwargs): - """ Fit domain adaptation between samples is xs and xt (with optional - weights)""" + """ Fit regularized domain adaptation between samples is xs and xt (with optional weights), + See ot.da.sinkhorn_lpl1_mm for fit parameters"""" self.xs=xs self.xt=xt @@ -623,7 +635,7 @@ class OTDA_lpl1(OTDA): self.computed=True class OTDA_mapping_linear(OTDA): - """Class for optimal transport with joint linear mapping estimation""" + """Class for optimal transport with joint linear mapping estimation as in [8]""" def __init__(self): @@ -657,12 +669,7 @@ class OTDA_mapping_linear(OTDA): def predict(self,x): - """ Out of sample mapping using the formulation from Ferradans - - It basically find the source sample the nearset to the nex sample and - apply the difference to the displaced source sample. - - """ + """ Out of sample mapping estimated during the call to fit""" if self.computed: if self.bias: x=np.hstack((x,np.ones((x.shape[0],1)))) @@ -672,13 +679,12 @@ class OTDA_mapping_linear(OTDA): return None class OTDA_mapping_kernel(OTDA_mapping_linear): - """Class for optimal transport with joint linear mapping estimation""" + """Class for optimal transport with joint nonlinear mapping estimation as in [8]""" def fit(self,xs,xt,mu=1,eta=1,bias=False,kerneltype='gaussian',sigma=1,**kwargs): - """ Fit domain adaptation between samples is xs and xt (with optional - weights)""" + """ Fit domain adaptation between samples is xs and xt """ self.xs=xs self.xt=xt self.bias=bias @@ -695,12 +701,7 @@ class OTDA_mapping_kernel(OTDA_mapping_linear): def predict(self,x): - """ Out of sample mapping using the formulation from Ferradans - - It basically find the source sample the nearset to the nex sample and - apply the difference to the displaced source sample. - - """ + """ Out of sample mapping estimated during the call to fit""" if self.computed: K=kernel(x,self.xs,method=self.kernel,sigma=self.sigma,**self.kwargs) -- cgit v1.2.3 From a65ff1bfe33fa8a9200d2899c74b7ce582bf8761 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 4 Nov 2016 13:32:41 +0100 Subject: working --- ot/da.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/ot/da.py b/ot/da.py index 90b96ba..fb40782 100644 --- a/ot/da.py +++ b/ot/da.py @@ -617,8 +617,7 @@ class OTDA_lpl1(OTDA): def fit(self,xs,ys,xt,reg=1,eta=1,ws=None,wt=None,**kwargs): - """ Fit regularized domain adaptation between samples is xs and xt (with optional weights), - See ot.da.sinkhorn_lpl1_mm for fit parameters"""" + """ Fit regularized domain adaptation between samples is xs and xt (with optional weights), See ot.da.sinkhorn_lpl1_mm for fit parameters""" self.xs=xs self.xt=xt -- cgit v1.2.3 From 94bc743954bbcefda22ee6a28623164641debb79 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 4 Nov 2016 16:09:27 +0100 Subject: new example for mapping --- examples/demo_OTDA_mapping_color_images.py | 157 +++++++++++++++++++++++++++++ 1 file changed, 157 insertions(+) create mode 100644 examples/demo_OTDA_mapping_color_images.py diff --git a/examples/demo_OTDA_mapping_color_images.py b/examples/demo_OTDA_mapping_color_images.py new file mode 100644 index 0000000..2744f6c --- /dev/null +++ b/examples/demo_OTDA_mapping_color_images.py @@ -0,0 +1,157 @@ +# -*- coding: utf-8 -*- +""" +Demo of Optimal transport for domain adaptation with image color adaptation as in [6] with mapping estimation from [8] + +[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized + discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for + discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. + + +""" + +import numpy as np +import scipy.ndimage as spi +import matplotlib.pylab as pl +import ot + + +#%% Loading images + +I1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256 +I2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256 + +#%% Plot images + +pl.figure(1) + +pl.subplot(1,2,1) +pl.imshow(I1) +pl.title('Image 1') + +pl.subplot(1,2,2) +pl.imshow(I2) +pl.title('Image 2') + +pl.show() + +#%% Image conversion and dataset generation + +def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0]*I.shape[1],I.shape[2])) + +def mat2im(X,shape): + """Converts back a matrix to an image""" + return X.reshape(shape) + +X1=im2mat(I1) +X2=im2mat(I2) + +# training samples +nb=1000 +idx1=np.random.randint(X1.shape[0],size=(nb,)) +idx2=np.random.randint(X2.shape[0],size=(nb,)) + +xs=X1[idx1,:] +xt=X2[idx2,:] + +#%% Plot image distributions + + +pl.figure(2,(10,5)) + +pl.subplot(1,2,1) +pl.scatter(xs[:,0],xs[:,2],c=xs) +pl.axis([0,1,0,1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 1') + +pl.subplot(1,2,2) +#pl.imshow(I2) +pl.scatter(xt[:,0],xt[:,2],c=xt) +pl.axis([0,1,0,1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 2') + +pl.show() + + + +#%% domain adaptation between images +def minmax(I): + return np.minimum(np.maximum(I,0),1) +# LP problem +da_emd=ot.da.OTDA() # init class +da_emd.fit(xs,xt) # fit distributions + +X1t=da_emd.predict(X1) # out of sample +I1t=minmax(mat2im(X1t,I1.shape)) + +# sinkhorn regularization +lambd=1e-1 +da_entrop=ot.da.OTDA_sinkhorn() +da_entrop.fit(xs,xt,reg=lambd) + +X1te=da_entrop.predict(X1) +I1te=minmax(mat2im(X1te,I1.shape)) + +# linear mapping estimation +eta=1e-8 # quadratic regularization for regression +mu=1e0 # weight of the OT linear term +bias=True # estimate a bias + +ot_mapping=ot.da.OTDA_mapping_linear() +ot_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True) + +X1tl=ot_mapping.predict(X1) # use the estimated mapping +I1tl=minmax(mat2im(X1tl,I1.shape)) + +# nonlinear mapping estimation +eta=1e-2 # quadratic regularization for regression +mu=1e0 # weight of the OT linear term +bias=False # estimate a bias +sigma=1 # sigma bandwidth fot gaussian kernel + + +ot_mapping_kernel=ot.da.OTDA_mapping_kernel() +ot_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 10,verbose=True) + +X1tn=ot_mapping_kernel.predict(X1) # use the estimated mapping +I1tn=minmax(mat2im(X1tn,I1.shape)) +#%% plot images + + +pl.figure(2,(10,8)) + +pl.subplot(2,3,1) + +pl.imshow(I1) +pl.title('Im. 1') + +pl.subplot(2,3,2) + +pl.imshow(I2) +pl.title('Im. 2') + + +pl.subplot(2,3,3) +pl.imshow(I1t) +pl.title('Im. 1 Interp LP') + +pl.subplot(2,3,4) +pl.imshow(I1te) +pl.title('Im. 1 Interp Entrop') + + +pl.subplot(2,3,5) +pl.imshow(I1tl) +pl.title('Im. 1 Linear mapping') + +pl.subplot(2,3,6) +pl.imshow(I1tn) +pl.title('Im. 1 nonlinear mapping') + +pl.show() \ No newline at end of file -- cgit v1.2.3 From a99f096797480c2b7a4c69bfffa211efc8c11d73 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 7 Nov 2016 09:35:18 +0100 Subject: add demo notebook for mapping in color adaptation --- README.md | 2 + examples/Demo_Image_ColorAdaptation_mapping.ipynb | 349 ++++++++++++++++++++++ examples/demo_OTDA_mapping.py | 5 +- 3 files changed, 355 insertions(+), 1 deletion(-) create mode 100644 examples/Demo_Image_ColorAdaptation_mapping.ipynb diff --git a/README.md b/README.md index f73cbe5..4088bc2 100644 --- a/README.md +++ b/README.md @@ -61,6 +61,8 @@ The examples folder contain several examples and use case for the library. The f * [Domain adaptation with optimal transport](https://github.com/rflamary/POT/blob/master/examples/Demo_2D_OT_DomainAdaptation.ipynb) * [Color transfer in images](https://github.com/rflamary/POT/blob/master/examples/Demo_Image_ColorAdaptation.ipynb) * [OT mapping estimation for domain adaptation](https://github.com/rflamary/POT/blob/master/examples/Demo_2D_OTmapping_DomainAdaptation.ipynb) +* [OT mapping estimation for color transfer in images](https://github.com/rflamary/POT/blob/master/examples/Demo_Image_ColorAdaptation_mapping.ipynb) + You can also see the notebooks with [Jupyter nbviewer](https://nbviewer.jupyter.org/github/rflamary/POT/tree/master/examples/). diff --git a/examples/Demo_Image_ColorAdaptation_mapping.ipynb b/examples/Demo_Image_ColorAdaptation_mapping.ipynb new file mode 100644 index 0000000..51b91ed --- /dev/null +++ b/examples/Demo_Image_ColorAdaptation_mapping.ipynb @@ -0,0 +1,349 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Color adaptation with OT mapping estimation\n", + "\n", + "Demo of Optimal transport for domain adaptation with image color adaptation as in [6] with mapping estimation from [8]\n", + "\n", + "[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized\n", + " discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.\n", + " \n", + "[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, \"Mapping estimation for\n", + " discrete optimal transport\", Neural Information Processing Systems (NIPS), 2016.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import scipy.ndimage as spi\n", + "import matplotlib.pylab as pl\n", + "import ot\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading and plotting images" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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mTia0hLGlGUdRPJdlnZfEqBoxOLLbusg0fQycdE60mvwID4Ylse5O2ZcHU+LH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\n41yBpIqq38d4HxfdExL3lJffqz5zh0o6f6MfJwg6URVT45HwJ54UukTc9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QTmuMY+j45F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\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J3EjkYbtwM3Qdo3jh1nsXvllEaJzbyucijKlPqTbJCIUDgUMUGoEURz6MC2t0gm58nCIJ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fhX6o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vmv/VN/ir//kyMtKVkrJRlZY5/KUyHl6EbT0oi8iOlizmAe72NtM+QrmZgkaUrsm5A1ADwx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\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4xz+7HdCiqVAp2wpEYK5NyVtsMyPOf2nBvYT53DtihAdiVAQl50iEL3TOr28hR8uTGCQz\nxLYkrOtsJkChM3rjJpSK7I3JN2XMQ9rMDCGhOTweNmV9kjQ2it7CuJr1Lp/L2wPtaDAONE3dOsTE\nwB08AB+0HmPde6CfzdZheDk0FdAyn7mQfdymB0FM8+k3bFBHeNbmdD1Yu/E5vcW9RSLXSY4r429+\nkeT9qeu2N/0n//iv+N3jxi7OPUfHIGlhRsGQxNF52L6h8buN6JyWGWBpgfaWeUAwt/CJTorVjVZ5\nu5RAAEev2RgTiewTge8IKopidIusnxvpUS0KKJmCTCOmAlhnSYk+KVrCrGiIKVN8foKyNot2tYFm\nZbvL9OK4OPF4IHYHMakXnIEnB9f5+eMzL+Jkd7LegkQjkN3MSMWxEdSxrzIgYvpy8+fNz9nHmOtC\nFK1ZQ0rdcIoOHpZoTlUfPBC+4JQDhz5UcVNSaqy58HY9cC2kAbV3hkd3XP4vf3f4lITWW3Tw3chJ\no1EkGSR8vM5glcKwzrIEdEkF0pQ9qkQh2i3omEw5Jkwa4wIPqyBFp+SQ8LdYRJCoON0GjlAsGh85\ngc3QYy8ZGY1FBo8LbItzWgyRHjQ3c479kWuFcxdem9ObkHtISNtQ8joYPTxBdSxfKWfEfa61BlBL\nBJJiEhTDYzCDkGX6qsv8OWHgNwJGEPc2zgrZhZQ6q0fb+NIS7mdEMqncHjAYQzkMLAVco1s0Blqf\nPxdiEosyGDfN2Sx0iTV9TmHT9OBEUR5THhsCGtOOgcYUU0NaikAxoaqTsenhAzyF30YDk+3uNIfW\nB0rBdX6PU1ZrElvKIoXDOvuwGfjrs+EYobRx9kpch9M94j8QDakt0SV4KTEJzK5RwGMUj98viwSw\nSXKoPvxr4T+UKFrmZPUm0+MWjzG+rj/YrakTv9OtntjvAbihx8so6UatdeE1f53V5FklqwouIZFX\nBHXFNSbP4VMRnMhHjbVB+bEe/Le/SPL+9KVz01in10FnQaOl0PYahUrKpC1PQh50ekhNcmb1FVTo\nHqZYNzg9vQ8jbFlgVHKaHqHeuVwulBITonVbuV6v9P4WI9aUuJ47SEYSdGnAwI+K3zrfLpADTHAc\nV3R5wFIhLbHIrsuC98HeG1oK/ThIqqyAYBzna3h3rLO/XOgpsSwLohnxxL7v09y68vDuKar41nhY\nF9BAbtoCxortr0g7ZljfrY1zUFKOCJd2MK6di4QEycYAb/Q9sgyGlqCeNIPmqC5UXTiV+RYvaxyS\nj529H3EIGIbIEj6frAFQiLqWtL676+lLiWmbzY4aSRj7me6dUgqtRYe71hrj8qOSxqDPrmIQVUIK\nts2MjFIKXaJFs64nSjtIOXOtB7Vs5CJRkAm0rPQR0InjstP7wfP7b5Fk1BaL6L7vuB9hPs2JvARk\n4vPLF1DlenlFpFPyQtqeWIvyzfN72nXHZqjj21LYNCM5UWvl08srSYN0+PLpheux87y8UZ5WXr98\nol+u5HWheuPp/Ue+fPkyuzzCr54/4LVyeb3Q2iAvmWVZApTw/IHzl1f8aLTxiWVZuF6vbCkoaml7\ngn2nfNj46fWFj4+/ISFUq2iOjSw/vyPZCG/XlH2llEhrYrz8SE7C0Q0thawZFedyqVjdGW3iv9IK\nNibmv9wXX88FmV1BkxH0RQ8iVZIU+nCJgkuvn2lXxfMGKNQr9fqFe0JEzhhKXhaKrtS9MfwNLQv0\njvUdpudRs4Ju9H2nAdepwRFJ4AnrHdErbo1+9Djk5RVlZbd2W4CiETJxtszDpBDvjKZE8o504uBY\nj1kwK2N3sCtmX6dA63aifjmHnyELQxJ4CTnNNChr+RCbV/ZJmw3YSPY2v6cGS8ivcEeXWB9lfWD0\nPUJp8yQp9Q5S4HRiCCwTw9tnM8mt0PMVz2l2RNv91/7l+vuvgbIPWFNh4DQxjhL+jwigjulHkXE3\nmzfv9AlmyCPfz/+kkO6Yg4ngMtARiOwIsfB/7e+NQ0r4am6ThT5sdmpjKtXNSZ6iMeGTMAbhwVe9\nT5RUIalwsWnWt+j0F5l94lGnnCjw47ROTpklCV2ENxt4ktj7yCS3mIamTBqhTriZ/BJCsyB7pe6w\nCIVEUdiI7n0UfyHhGT0IsCI+PR4zIF79vpfcjO4+5VJBlJXwUooRLVSlGKTckTHIKQhrQbKNRuzw\ngZI46sFSopm2LMq26Lw3kTE3dKEehusS8kBz8By+4um1COlwI3ssOFkTjWhe7L2RPEJ1xxHT55sP\nMgrjGSUggTzfj0xqwk9vjUUaJc9DZNJ7I3QRJfr8Tp8T/EEcSJMC185aEpaUNBSvhqfAi4sqrXea\nNLoIbRiLbCQVrmWlGXRz7FinDNTZrYeq10dMQ7qhusIIH9U4RhTAJmFZwMM/TebYG7OipI2bFCxk\nhG6OqFLESGZBx9OVd0ui20PQ2ophFrLu6PFEsZjyEn6oFlO0oUI1ofn0iXs0wLNMifLtfVInORQJ\nHPtdgqpR/JsnLBTMjDUazmMEMht1FqLYUPcIPTbh0Chq+gRaGGvQILWjzeKdS4lsMxtK4BghL1RN\nQb0bnWExYV1TkCEPCbDVyUuct1xIrliCron3HrJelZhaCtBKRBLsBq2Er956wzzTfGDiNELWmiUa\nIAseJF8hzlpi3DohG3HOvQ0lJAkq8I6Y9Il7gLAcxpQqA4RoP2QnlqCJMxSKL1NtEsWTyiQ0+7gr\noZRoJGUX9Ge125/j+osqmMYM9uz7KzOkIDpUeSGvC+UU046jGqdThGKeSnTF+xjYuHC8vMLlAssa\nB+j9AlGHozmxLylkfVuJULRhrBMxfsoLozwH0KAUXGILU01If4AC5SHQpKpK7YN2VJ62E6JCa456\np+9nsBZGbVWKbuw426SPdVG8X1nLoPfPuDt5S2RX6uUlZAb2Nn9/o7WdXQqpbDFK1gLtgLwERVB0\n+iSULW+RNaHCtZ3prdEJuUes0ookmYfMqTt2m4fHTNkW9ssVGwf4zrWXOATPIqysK10WWNdp/gsI\nQutzdG4GNhjHAVlJJbNfw/Buw0I+OaLzqTlAG4+PTyHPksy17qynR2rdY7KBk9B7Noal6JxeX8/s\nk+pUVHm1QVlhLc+8U2U5bbTe2HIUWNaunF8/8+X1cxQYP/0rlmVlv1ZOz88IA8mJy37gIpx//78F\nWghIkjCH5emZ09MDCef1cuV/f/s93/76V+xvX5A22N49shCTy3ZUfv3rb/nh7YWeJLKvxPjxy2fe\n5w88f/iG5deFzz/9xH49+PLjv0DWkG+qvNI/fjcXV8NkcDo9kZaFow/q25lvv/nA6fGRty+f8evB\nN7/+FcsSYIk//OEPbNvC+fzGQoQgjv0FlzD9igh+fAmggipj29hOW0xLxgXNC7qs5KNh/ULdX0KC\nNhHUKZ/Iyxqo7mns7WOf2J3w12gp5JxJqvRxkHIcQtq+I3cdPNNsDX68IKkEzXDEIVLSipui64ID\n13FFiqMEsEVLRstTdN77daKTF1xzJIX7wC1M4liPn+0NUHLeYArR3EdAZVp4nSBBmwTN8jDpgAek\nPqlABc0R1lzKiebzMFrA/QSy4cSfPwgcss1gXmwgqdMvEplWOd4L8UZiICOTXKF33BsmCe+G9HRr\nCcahTRO+XyBl8uPHMPe7RBf3OMADXLE9PSGi7HWnT+9G0UJrMQHOyxoy4V+uf+slc/O/7p3hocdX\nacg8YM1+OVJiapJQii3habI4aN4IWngLs7wRRT6hEPDZcb95mmCCfuZPNw9f0RCJXEFuMKBxn0rG\nfxkyohSagNmVjk5Wco2jtjm7RHfbs87JjqATO4/HIVHMaA6jO6iTl5U29pgAGZNoF3u3ALhH7oo5\nWQIwIeZkF6Q6XZwiN5xxj6w8hCgBZqTECPmqzwnssDlh9qBk3q4oOmK/WXK6G9/DTWZ4DwldH8Km\nYTYTmXJWUVp3UirEkFgZEh6tMQIkUZbEahY2YgbdWhjccxQSsazEtFbUaGNmKokQcXExv/AS8JWc\nw5dV68xi8zjAt73fv/uUrwAzIDekh0njz9ncA6vpHTDQRFAtcwJPNKaGUd04fHBNsCpseeKiUwyy\n9+rUScQz6fO4FURaAEktJo/unFQimNeDMueuWAMv9nVqMcIXFMCHgEDYpPClFJPM4TZbAXZ/F9xj\nqW2aOAR6bxw0QrkD7eyYxQTC8lfCmtgRPlLJiKaY6FgoIWyCe1oPRUy8K3GN23PjxmDMCd+UtcZT\nMLex+dxqxKaMOUGNXLVoMsv8Xbo7SkI9gys9E7ExZjgJfCL4mZmXEq6zKWeIHLK8ohLT2IJBCmJx\nH4Out7Obc81QbXBYxyWHKkpC7RCk1SAKaso8z3dBp0cuJLmDTcucVAp10icnEijgHBJnXDPnfG9W\nzPVtTu0GwQkoKRoadThJ830ChWuAaCyK3KEhv8MM9yj8TQKa4+YhsxydRolpF0H0/OnPzBX/iyqY\ntqy0tuPrGuPQ3sF2hgqjjjhwTdTw29uneIhKiQUwJTSfWB7f09ZHfFwxVUpaI219f0MorOWR07qy\nWgJ9xezM29sFIZFSIeeVRTPq4aUa7aBZaFVTStiSMY/At17fUOCnH34/wyRLAAVSZl2XQLWWQnND\njk5rEV/Y6wU8uPWSMhA0McZLfBH9AC1hhDcH3dCyQp4ocUmM4zK7fAlyLMgdx+wSZKwWcIhFQ9a3\nt8jwuAEPhsptiI21io49NKU9UebiJiLTBjyNeSjtOMCP+c+MkVPcpzECwSQS42l9jI3FjW0r7JdG\nWjbabWy7PSBLjJTH/gopwuZkNFyURZW9Ot46+XFDSpALk8UkiqyUXKZsafB+feB8/cT5pTNyRf84\n6WpLFGXDOuW0oqclFv2kEYpWlOv5DWmD5eN7toenmOK8e8ZUgiRG4qcvLzw/fJgFeuV5gTY69Xwh\nq3IcV/afGt9++EgphXfv3rFp5rvTE9u28fbpCw/vPvD4699yfjsDcLTOb3/7D/nDl0+Mx53nx0cu\nlwv97cr5yyd+87vf0Wyhv154+/yZ5XSKBWc00rEzzle0JP7qr37H3/7x91wuO5fzmW+/+44//vSJ\n0VpMzPWRdPqGpSg+OketZImJbWuNd1vi9eWnOESLotsDbWLqhwvp8ZtoHqQSuUitM4Yg86CtGlQt\nUUhpYE1wa7Ta8KJoXql1zAlJmN6jFQrJO2NOB0ev9E4U3VnBrmH6vZxDfjmpk22/QBnT+K3RD0kW\n0gq/4JKRFJsCIxZmUoapo5ay0ctDhMASXdcVgxz0x7SsM+9D6f3A6uzXaxRXLge1xsTZxsT5umHy\nyDyXgkUoMO60JCAl3l2LnCtfpk58EIWRZjRvQdczJ+UVk1N4L6WhozGU8N1dL/FnFbxXsMGRQg4Y\nPXsNCXHOvPz0gi4LOSVSC9njUEMlNu6+8zOK6C/X33eZT6hPoMiiUEHDUyRfgx7rYciU+jjQvYHI\nHVUtc4KSUjQT4r+KqdGYh/Cfy+hFQ/wW4otJw1K5U+H+tc84lQPG178/4XPtDsN6nbQvccdHJy+R\n2JrngcznRMsmzvnmqQi5IJyvV7IYMQGOY7qIomjsk+rht9VEm3/WxTmEkL2bsSSllETusLqQk1BS\nSHNuFNibNBCio3/7TmyG72pS1AKv755DLm9On7KofURhJBoTqEtr00/iEVgrHgXZPLxmUQ7rpEnM\n9dHxaiysIakXm0HwUdrlPBtD8annZ4r9AmBJcpctq0cBRxvoojwsgTlwnEUnwWxGkYhsEyoR1MGI\nV2nUFn4Xwe/kzqB7Bqo+qYRUT5zkM3soJbrFZ7MbftuUNkJm1ynh5xnhgBwW0rQbmCbIjYF5lgk0\naJNi6gK9EZK1eTD2myTYpzdFYrrZzChJSSlgCrfHW1ME3ev0ww2cXY3m6StpDjDTUM5Mvw3EbA0N\nqEO8aLBEaF48B7PIKilRp/zV3VFXqjjHxMKLh+rlJqv129RMYhIbTYBZ6N2kagoMD7/hpFI09Whk\nAKVXVsmUOUVWidbF1v1e2F4nZQ5C1eNu7DrQJOHpkpDkpXXhwUcE1qpSfBa+7jQJJYuNyDYUETpt\nZg7GNHaosntHCd9UMmcvIW9HlNN8iyG+mz6nTKSAR6RJq7uDPsLwiEpGJCZs7h4SxwG3G3TNdkeT\nD/UpE1T223rncPQ4h04rZEzCnWgGzvXo+Jk8+c9x/UV5mPgn/5T0/ltUN3oLmsayFGqdeQm9TcnJ\n9A1Y3CUXmfhhwbLAUu43UjVh9YrLTnZjaPzxJNBSAtb5ISYQwWtU+ilR8xbRByKRzO5hwhMPMpzn\noOjslys5Fx5Oj/RuHPsZbxVaRdZHLBkPZcEktL9pXCEXLBW8hTxwWVd6XrB9J2Wlt+imCIbvX6I7\npjO4UhK2PLAuJ1Iq1LqDxGFwdJ3yghXsirceAXdy20DWSJhXv0+YwoAXXhyVCaiAOMSa3dRF6NzM\nqEEzEg2yDKqx4FskO7uDuZCXAgqrRufCb52mYTyUkEOklBh1p3sgrbNEUJyNRvfKaI2+H/dFVnSD\nkkmndQYOBkRjkNDUKemJvh80cUiC1ZBrLmUJyYkKbjvrDINLR48JRlHsWjmOg4eHBy6fv6BLYV0X\n9rcrp6dnhlVsVErSCBjEWR8fYjrXjCbOly9fADj2ne3DM+No2BiUSUGqU4aScyYvJzbN/PU/+C1f\nPv9IcaGer/C0sB+VbnGg+KGeyUumvl1i7H4+M1Q5vX/i2iKUsYmj3Xh8eooudo+Mjm1bOV/PdEnk\nNTKznJmt5D6JRrODfnufZgje84ePXJoFiGROM7CYAqe8URZhv14nRjvDOINdwX922FkEaxlZYlID\nGbH4TkCwdeEW3ux9FtwiMDqSehQEuk6ZT0jdNC343kLWZk5PSxg9rKL3Q6zC8QYkUl4Z8xmNU2WH\ndsUl37vpQDzTZvgaJEQzi8860bgyMarjUkMqkBXNESApOZNYGTbTLb3FuzIMxiQh5UA/xxeu83sz\n9NiR5RTeCa9335K0K5KWyErRE3Jacb+GmdydPEKKJZpRPWH7C2lUejoFZWsG9uZ1o7cAeGSJ8G0b\nIeVSHKs7/P5/hV88TP/G9dXD9A/59cNKl/KzgmIgfpOsxAQ1pzK7/M6Qfj/oq8jXDjUDiMIr6cTo\nEzJMU7n7DdwdUonSwcMzZRKZeFUdtdgDk0aOzNBEGsLhgB+45ng3ep2HfgJG4AVIqByk1Ehaohsv\nxjLS3EumVGlKdxy7Sw2PEodFQWKfhfD6EPtw0YRN74fS4t0BCg51JanTUwWURZRiI+T2vZGTINaR\nlAIJ7SFJ8xl8fSI8V2O0aPqJBOZbp4R4ZLIYhYFNz5GZUbKB91mGTRrgPCyrZdxaSOPGCOkaQdzr\nY7kXuoNx968lnV4dSay5oHgEBAPIQGXMzJzAeLvFoTVvlVs0wypOyikCquekiJlP18fA9TYBd9LI\n9xBeazfa7IjogOn3EGKJxnv4q1QgJRaxkHmlM+YbtUHFqS2em97HnSZnQ8ATQzLmPQ7FssyCKHys\nokEs7i0Os35bswEL8HQ0rEafa9dc1/wmQ+73qQUef+dtOuUkdsb9vqFx/hvDIQuDKELcieLIBZu5\nEml6+kw0ppIegc3d030SlqtRE7Tp4xmEHCws81H8mntIDyVNT5EyvH5dFG51m4S5p5mFtBZCbSFK\nssFT0nsBLSI80ufUycnrIwOP++yT8nebvNAZmiPPcRjHbGIMMwYZSQcpG+/6yoMnauK+XqgGQCEA\nGfPMLIMhG2kYOhxJheYjit8lw5RahhxTkdRo7nEmnlNkFcGS3b+CdZ6vB87uFkVOz3eZapYj4NIz\n8kVGoPhdlSudKo7cCNgTN76HGXn6oMLn+KU1/ofPX+CXHKav1x368I//GTw8TU0jc8OX8OtIGLA1\nZzC5d6LcQhKGOzJlNuKGpzU8NsCa83xw2twIpiHQFdFE3iKUdgynjz1wlikWwTGO+8gwMTCTmOgM\nC/24xGSJVpFUIK+M9hYLbK3klJBloeSFPsYcy8P1euW0bfR+cNSO5EIi3X8vSVCPC6LCFNfGw1Vy\n+KokPERFU+AZ7yz/kBJczm+kcYROfprFFQfrmKZYnG9j6HkQdOm0eqAssWHlmN4NCyrO6CM2Czni\nwXaHadr3ucgMs8hkmkRAO3bcByOVgAq0oKSoTpmSBUnHZmtevGOTLmgopWRyKqxLSA2tB+Zy38+M\nFllQWgqSMuNojNZIdvDw7gOD0O4f5zPl4cSY0IkhDb8ecWCRKF56a4ze0HJi256Q0vFu9FYDHU+M\npOlhtpVZSGpKDIS0ZtZt5WHZ7t/nkjOXvfLu3QcWG5QSm96aF4794PP17T7JGMBoAQvZWyNr4re/\n+Q1tv9L74NPreWZgGO8fHnh+euLtfGavnToaroKMipBxh/38hbrviCjr0/MEY/RY2POClg0fhh3T\nN5aElGKTsd5JfsPs+yzSZ0ewjblZeeD4p4k2fEnhgzIpEZw35TVFHes1cPw+SOspFlhNWLOQr2Jk\nq9jcEPN6imdvDNAcB4RbcTMa0sCiPUvSr4HAJsbUhzCxdtArqR/RSc23EOZpcpWM6hKHJwvJQJiR\nw++BSciVdOBkchJoQWwiF7AgIY2+45S7eTsKRw0amMSzYmPiglNG1xKaf5e7nCI2j1jLkCCRqYZl\n31UxXWMCO3H9SAr/mCimEz+tiWR7eFBSCoM6OqfRfm96yDzAo4KfXxn/4n+EXwqmf+O67U3/8b/3\nO361LVSTeXskMlOMe5BkTINibp9FgxwmN4/LDLL0G7I6RSzDbP6ZTcnLnArcrsSgCPN+K21EgXww\nkJvUjshH6QK5CwfQ0DvOO018rwkUHVPyJyiJhFGIgFPEWVwBCwmWTb+GBODhZhTfbqh7QjYjIqwy\nD+jI9A0KmKPyNa9l6XBtwkg1/Ho2oxb9q4ohacxeAojgrDoP7pppw9iSziYmVLsVbjdnjGK5oG4U\nJunSbK7R0ewpOTIUVW7oZCW5IqNTcmTiJMILlOSr0gK4m+IjmoO6SAAAIABJREFUWLvOfx4hr0ki\nc2pYJyUhqzFGNFi7hC8ja9yLeH4SpfQ5CZIoCDzWrLsHmZ/JwGZWkd/ydH72HsfzFWtjmnEgSWMt\nyzmxyC3EttJdcVd2c9qIOANF6QOqDJBEb3Bt0+OpoLJEZt2UpjtRSLlNuJTIfYIj6UZyzKFGsElR\nk45beLx8TrVySriNicmXOWVS2g0q4kYnVAUiieFBezQCLR72uQkfYfbUmD6/ua6a9wDlWOz9de7d\n6sT+InN6afE0i84JrcfPTiLI8FBFzGtY4N2Z8BDD6R7vt6jGuuwjGgS3KZL7/TmPZ527h/HWnGM2\nAjQ52dP9vXmYf8YBbwEXUfW4XzaDaeca1EnRPDHDKfNdhubhrVTm7yxxXxLCooLOqaXNoO1hxMQ4\nB31SEbJ/LZhsvgtxj4LAXEf83OEWU1eZk0a1SUcMMIQnDc7AbWI+57SdGXw7z7oizkvvf9aC6S9K\nkhfSusyQM+yBwFQC7yvcCCcaAarzYbJtUs7M8ONH8Hgxkm5APNy7OEINTwQ6s1fs/iJyXIESh42t\nBF7cnUxDfNCOnfXpHeaF07LRaiVeqwPNJQx3y2MQvUZjKXG4hxQP577T7Bpnt5wn3bzwVndyC1Nk\nSYlWJ1UrZ7IM8hY0MljxHkGYg/BQ2LiAGTY6VspE0vpM7lbWLdPlASchPsjeSSyUdWWvLYIX+8G2\nRTe9j8FRd1SjszdGp9ZjkojkTtTLGvYp7xXtFdmUdo3OCXlBU+boVwTQsiLbCRs9ioS3V8g59N+9\nk9Y8/SGDh20l58xhG4hQjwMdNQJhRdn9DeuNbXuinFZkKYgWuoMcBrmG/MNj8Xr98Q/hcXn4yLtv\nvqH1hoiFXE4faOtAl0zK4ed4+fLC9m7j9LBQ64HmNTS6tqI5wdFIKjy8f+LlfMYtk3NimLFWSDly\nOaoPzl9e6Hvl+eGR9d07/u6Pf6DVK2ad7Mo3v/oOckLMQv5HmFrTWvj0+oXszt4u/M//0/dYv5If\nH9me3pM1cRyVS9v54ff/B8ce98tVWU8njus5xuIT2Z1P78LkOwy3io8AhWjfsese20zWmNrhjN6n\naTM6xOSvh3dIjNqRvEZhPqEFKSm9Vm42ijE6QgROZ3E8b7Tq6HIil0d8NAotcKcWAZxSIsCY7WMU\noZqx4/V+2Ll1LxWJxsBwfH1At4XUJsRkymKKRf6Fmd1DcEUzvm1QNrRXIIq+WAvCl8SoM0A0JlXi\nU/JHTHS97yAL3VdS2SL6oF1QXRmHRRND7T6pcw/Z0LDBTSEfE87YQMfliuQlpnEpDt2YzYDT6U3p\nXwJFjyAGOicMkqZkBYc2ka02u+cCPUUwbmtRSIOE9NciHFhu0I02p4V/ZnTrX+LVzNl7B1/m4caD\nDULIz+6+DA1SqkypWu/RBGFSF7MIZuHRwO9iu5CueMjK0rB7QYLeSKxG90Amj7kXFJ1Ieo0DIhIH\nGffQTSQdKAYesjIDsml8Ro0pRLLwHaTZ8Q+RdMi8JOe7wR35Gnx589J1M/pM5L36PLq6U9UpLuTu\n5KQxITLHls6JxEPqnMgh35oF/+gDS0sUR9axXNhS5ESZZZo5aUSTzcYIf4/kmNzi8zue+UwYpWS6\nCDqbfrk7qeTwSt2Iu/OAr+4MNboYJgHP8Jv0jWgYqirWbz6RRNZlNvw81o3pSRJJdw+Gk0EkAGAq\ndLvlCMUzs3eDbiSNhqwNQ7N8lay53L+fpYRkbrjTfNwnIWIyf/d5cxRaj6DhMtca80Blq5cohOnI\n9IF2M2pzqgeUBgy3OSmYz/6YDaj4//B4itykXNz3jdshN+CpHnJq1wniis/ns4hHEp2o4m8NuGAM\nMj1l08NER0ixr4jfC6Z55JvvXtzLNj07BoGul5BJ9rajGuqZRwup6JDIJYucO4ARAd+mXydMOgsM\nu4U2zMunHNVn8TZlskI0n5NFA3HIwCy8TOYef89U9IwR5QFIWEZsTKVFnhCPr368yyyMEeFIO+5R\nYLSW4l2zKE7cPPD7CqaJbU7WJAllBFikzclO0DsDaW4easPqHrlUHtLc4YKMAWZxDv+ZPE5nGLZI\nIO0jLsDvjR2bkljDkR4z9a4yn/U4V/j890anIJE96jal71F4z1Xwz3b9RRVMfpwZGGmEUdKS4OtC\noEQFa4O8nUiyzcXK0EmSKiVz9GfcAg1sSbApHcME709xeJhYX3qNLrcopg2fJmmfG8SWCrQLo1eW\nJTOOnSGZ+vojlAyTSlQtJkuW2gyWrVQ94ZrQvCDaSWXB0xKFjg2sdaxDKQ8kzogLvcWDuW6zYCAz\n+hFBq+NMq4H8JGVySTw8faDVincjSSGvmaPW2dVxjutOlujMaV6pptB/4nrNLOvGqZyw5RR4ztYw\nVZbtRG8NGc62FNwLVQKXPo4aE76UkLTHRv70LZiQViFpYpkY7WPf6Q7j8hnrB/npiXoMSHmG2TmI\n0Y59HuLgfFwiXJSQKq2lUE6nAG4kRWdm03k/yKmQX65sKL5kKIk1L2QJKaOcHskM1CqXGpKVfT94\nXE98eHrmp3Hm/PlCuYDnzLauPL9/z/F6JTXjIQnntyAUbmXh3fbM27hSZXB5eeO75YFKRRWO3ti3\nLTaiMdi/vPDbf/ev+bsff8BT5ugHv/rwEewdH7/5wPf/8vd8+eETv/7rv+K8v1FKodbK+XVHt8I3\n33ykLIWXL58p337Ec2bxTLtcGL2BOTUZlkFPC0tZWbaVozXKw0bKMZFTPXE5X6KDlBcuLKHLbzsm\nQV7EDcrCDbBSyhqLpA963WEEdUt0IW/Pc0JTSKXQ+8Cvn2ndyMsWQIgxUyZGbJh9VHR/DVjJ/jaD\n/JxdZnWlgUqOCbEx7AJaSAV8ex9gEAmJTu818P54PCN0xt7QOujakbKgKTZ5cQLjvy3cw2/7Aa9/\nZEiBWyM8n0KC6BVcMY1g5pFvXiSDFlIjE4tCAw08+vAAM+Q+p1kJ93QnP7kv8XflEp7CqclGNZ7n\n4xIBjDljxIFMNAq4WSKh+WOsi4AtRKE0J2mYY/1yzyhhff7qC2tnRg8Po+/hS6NsUST2Bt6nPFfw\nXBjt8udf7P8fXiLyN8A/+hP/6r9y9/9MRFbgvwT+I6Je+O+B/9Td//izn/HXwH8N/IfAK/DfAP+F\n+89ap3//34/mgvYWJn5CzqMisYYmIQ1gxCTWR1C0gkRnZEmMfiuPUoAX3DjmwSuOnhF0GROaODTo\nlPsIykFIYAZGyvMg7xKTJgHtMCS6xgmf74+ivd8LpiQRgkkPL2EnKnH3wHvrTPRKvuDq08cRjYf0\nM6/BGIM1F5IrzRvJc3ij1CNXygKvXVNIU12d0VcuGNfrEjhhAVUHwguZOuSUad3YmnPxmOK4tXsX\nPumUb4lPqWscJNtENh95UFzYBwHDwEI6N6V0WUN2BYCHL9YIT5+IsTIxyiPUC0vOWI/4goNo9pl1\njjoo6zIn3sKOwaSAlpRYtM1gzxLyzBGFWptU1tr28It4RyQhRGe+9SWmkYQn84ZgfpuFd60VkUxO\neSo8xvQ8KSrzIKuFMSKkOnr2IYmvU1qlaQsRm8Sz1FJ44noNz8qSDCmGsyG5s5IiQqIk6viaEWZT\nCpYmcGZ4NHUiAUgRMToDxGauXchYi9xyrgJo5Kq4J9TnPbOQiZvEBBOiMKrJWN1ZBbqG7D8JkSU0\njEWMJkqZB/OSUjj2mvM6OjuQ3VlJqCf6Wm+pEROFH9O3czcOLzS5Yt2QdU5oJ7zhmPdyzGdFmVO+\nFJ7t4YrfviY9opkxhKHGSZTizjHzmQzQZGTXAJfN99SmDC/Q5zZtJApEg3SYYCoUEUiOeweJAii5\nsBoMbRP1D4cf7L7y1hMmg+xK0YRIp8wm65BbpEA0WhYPeAeiMen8GYwmttBoXqhm3MN353NSqCmk\nrMUFS5nFjMWdHDiWUNR4SP9dgnCZ+sBTSJXdb7TQ/9ul+f/V6y+qYNLTI5yeGNNfgSRoPrsXO4yD\n43yAL4HzXlfy8hHHGOMg9WOOB0E5IUVYloJ1w0xnenkLaUxtICPkPHmjLCdyLrTrT/RWqfUa5j5J\noJCWBNZIj99A39kWqNawbpPgtuKp4GXBxdB1RVOG6069nJF8xi02hpwSvXfqxaBNnLEIpPfst87i\n5QdsHNHpX5ZA2ywfwq/Tdur1jKpyenigjkq9XkkpQu/GGKTtiZQ77lAvV1YfjAG2KbVVWh/khwfE\nO/16Dew6s+umsYgvy4J2wXXAUqj7lWED6wXJGRlBmRENGMRx/hFUWR8eKGVjef8upoHA08fn6NBJ\nFBnH+UJZt5DUSYyEkzsvXqeEcmDrEi/75Ur79EPIw1TZ3SjrShNlIbFYpn5+4e3YGcNITxceHx+n\npGywlQeen59Yl8wPP/4dbvCYMkONvTYuPcIa++PCm1vQE8vG8pi4HDv7D3/k+fmZp/XE+e2NA6iv\nF4Y727ry23VDxHh6//H/ZO/dQm3ttvSsp7Xe+/eNeVj/Ydfe2bsq5CCEKIqgIUK8EASDEMmFQcRD\nrhIE0SCiCPGIKAoKIkE84Y1RiRdiELyIliBeGSESQwgoaiASUu6qffjXvw5zjjG+3ntrXrx9zLVq\n51CBquzKtlaHtfe/5pxrzjG/8R16a+19n5evfCffnPmbv/nraWm01vjh66/owNvv/YByt2PXM19/\n9Zreu+SMwOV4Ii/BuDwv6tDk9NlnHM9XnuZkzMHlfMF843E/ETkotREUjiO4v3vk9devJUcrCe/f\n8fDll1wvZy5nNQdKuePhs29KTeedY0yen8/Kxeqd46oJVGtNW66iu37MM8dV5nXOV8ZM8EK9f3zR\noo/+TFyU72X+IK9c2YhNSNWypH65QvXGGCqWr6upUR2/uxfxMoLChT6Hirp4ovjGdvdIWpNkYKyA\n0H2DY8IhX1EvmzaKWaXIGyHfI2D759CWJ+GGaQ5hvjHlpk3QRkx9a9IHZtDslbrNBljipxO576z4\nDH3PKY+DnjGDnBe4PmFZV/J5IU0p8RNNevr1qvvMzZ9YbnQlYF50bMaUDJhCVnV/bQbW7tnr6khq\nvEccg1nuKZszo0O7V4FWCrMflL1SlwJlTJnmOd39GO7uv+z127mx5rX+VuB/AP6r9fc/BPwu4B8A\n3gL/AfBHgb8LwMwc+GPA/wv8DuBngP8CZWX+y7/UD7dUWOPYm2xpc1JDneyCYUNQBawtOlTQzYh0\nPKCXfMF1F18SvKkcGfMixcLax/el7RdMoWPLa+qra2+uwqqZ6GlzFePuUhdEQieYltpw+8bItQHP\nQsbN69GXbLWoeE8nqZKfh4iLxRb4wfNlwpRLTmQ2KXboGKQk3pmTU5VSwoE2fE0a4N6MViutGNf9\nntevX/PZ/U5d+ULGUIC55cqdWXloH8nPcHX3b5Io4MXTohxAFY3X1fkvxelAWMMJ5dPY9WXzp985\nySEwwViTjeqN7ImPNYkzX82+Rb49FS79wDD2WthQZqG68QqhLu4cQxtqX3JnphpE7k4rIhYWjQdo\nWyMQvbK40ecC3ehg4JFspRCWWPQ1uVjQHTTFCNPz+PaxnB9ka7MsyE0axJ3SQork4Ke2MevBVlQ0\n1SmoEQx6HuCyLFjZdY9LSQgzJK3KXHK0EsjTd5urr9e4ZHRGeXkv5QPUREzY8lx+qDXNMZi2LSKg\nU9d98XkeqwCTpylTE/o35jwneA4uVjjZM6/KwZ0XohasbdQY5C0sfIjUG570NBUbOMXgzpLHqJSt\nKmh4ee5iQkXZayMFetB0BRXgc2hynPI5vpmToxSe6MQoXF1TntVbgTTauHXwbr6upK3JryZEB1UH\nCEdRN7fAV5Z0MNbNIwwsk87EplpvYwwOT8yTUw2GCQIycjJD4eyVm83kBRuELWofrBebH6Zsinxh\nNQv1cypGsao4hfhQ6Nwkpu7OWD7Nsc6OXNfbRIXVNCRlBTpwfAqu/YvXC/Tht/ztbJ9/Qw+PzJUV\nhAzV3hAqtzLjmdoq/ejyQLCCbc/vAYO9gd/r3moL33k8Szh6y4moRTeiSJivoex42yn+CitQW8HL\nxoglPbhclzTIgAHjgpXK1nZIp552rodG3OUYIgllkg+Pa1oz2douWk3KCHo5Ohe/pYgX2uwi6Zlx\nwQVXIPGvvod9+W1tEj2FBA/dZK0Jxw3AcUA9gykYMWPDrUE1whPyQzBvzGRvjetQsK/P5RcBIi+Y\nFXKkfCpVYIzWGv35GWqltqYMq6VHBhRgF0HvxwoT1ZN137UhMwyrCsa9Pl+VGF6r0Mrjyv2+8/T6\naz2cgNPDA+fzmf1uh805n8+UtnMcMsYe3/s58vFL7vY78rFyf3/P5XLh8tXrl5sNoe//+OoVvV+p\nrgISdAG3psJwP514/sFbrtdnvvOtb9G2O3747jWv332twndJMXpM5UB844G4HNyXjbo3Mo33754x\nBtumTbuliH7FnXkcktCVynLW4C6pgRcZh2UATeXuDBWpXA9s23R+R1DaCXeTP8mdvBwrlDQodsfd\n3R3x7gfwU9/iuF4XtCOw7QRxxiyYlytwgm2n7ieIYNwKF0PT09NpJZAvI+k41ub9WSOPWiHrwnAj\niev1oi3tlBSl1sYwEyBlypuWE1x6Gn3vh3uSZB5XbV1vK/rt5gDbCd/u4LhgMahlE5Y4guwLYDIn\n5IRahVMeQ4XguHkQFArK0xv44tuUdkfGrQMnL8joC9RwPGHbJiBJD3LoeignFR+GSe4WoV/f7GXj\n4C5gzBjjQ+D2VpfX6iZlGZQuz9FYhKOXf2OmSZI783K8vCdWnRJKqWcJuYxQwRVT12kfuqftmzbC\nFvp4roDbTLxt1FSWCW5UL8TTW8ZPWHCtmf0h4O/LzN9qZp8B3wf+4cz8b9bn/0bg/wB+R2b+CTP7\nXcB/C/x0Zv5gfc0/DvxbwLcyc/xlfs5vA/7kP/Kbf5pv3p80CU3ksalCvieJz+RSoR9QXTOMczF8\nBGV1UVlegFP7QLqCilvDcnILj60ejBUau3GTyi1pdC7Pgol6VfUiJa8CpjkjDaWXafJUFiEvDQF1\nFgf9ADILVMicFJB/JMDDiZuc++blSW2k0tVJzyWZ2RdNT5ufqaxEDCLZbtAHMw5TfIdZKiepFIjJ\n3dYYc1BCsiUViscH8MragJZSSCtkDhUHubrwLLBAcXJ2dejXdOUm4/c+KDap7lTspaiITNH7pN+g\n2G3WFyLmpa2CCWU4TjXXmgUjFTy+r4aQ1V0kVzPcOkllhmPKOMBMDalbAXiMTitGWQG4MxWLQE62\n6lziwEMN1rmGoCq65bPSe37LXVRmTTVWNo/Oo5O7PGrA/cl4WCTW19creKXPZKRLRqezkm35vPoo\nWJ0cQwqczCWZWstp63kl6WK6gA5prvgKr4yYjAgd85vfah1jTWmLvKlAHwNck5iUWo9T2MKTI7mc\nTaiwYfKZZ3KqCk6tJJdYYb8haIKZcQbmCGUjmQBJPSZRbHl7DNLl/ynADJonOeQJjQl13fcjgx7G\nXPueIwVOOjJXMQ8f77iTiWXB0tf1GcxUsHSs5kZZE2ePeUuoInRR6lG7nl/VnN0Wkr5WXYtLBjuX\nbLaG1FWDpCxppc5nXgrVnkH32xZyXQPwEh0jSaP+3EJ7FeJbftFvJoLjpJTl3V2vYUTgN1mx2aI3\n6++dm7cpcLsBS/R7yE3tawuigvBNH/yZd1/DJw/TX2L1J473B9RvUE+7PDMYIwbkoM9DaEnUPTZz\n6p3GqLMf2Ol+oVK12ctlKrRWqHdf4gPdzJbO9To61jZK/4a039dBrzJoH5cJvMdCnWi3Qts2Zg9K\n2bH9c8bxpIcPcDmgne7JmOQWjHGhOcT1zNP5PfN4D2W+mL4Tg9rwY5LesP2BXhWWWmvhgc5hK9/o\n9c/BZ6+wtiRq9cScl3UxGFzPwg/XCuVLddISwQpywrTVmFMK+FiF4vlygVZg34nng7KCUWfsbFV5\nTDknRFdYJuCt0Tg4nt/r+9RNDaGYYA2rRRTB7BzXK7U14nJV4XJXYUouGHaHW5LjynEkJa6MI/Dt\nkW3fhMe1K3evKu/ffQ1nGMfB9lPf4fPPP2fOSb79is9/5rfw/vmZ/OFr3v3C97Q5XA+Rz169Ynv1\nCnNhytupMsbg3KR9Bzjmhbfnd8ynN4zjSt0bf+7NzxPHYLvb2R52Pj89YqXy7u1bPvv81Ys0wU6N\nV9uJ93Ew+mRvldbBrsr2srbxzS8+Z4zOfIS3b95y5GRm8OUXX3A5rgsLGlwvF3KZRm3bqfebcqKu\nZ0qrjOdn0ouQ3mbgjdPDI0d8jWcSr+6V9fAA17/wXV791De5ziCPK7Zv3J92YlbO54P68AVzvGd/\nFMLWwlUUAg93O30ezOhYbtztGxjqUFHoWYTlBY5xwBi4T7I+SqrqRpqKrxH9RZpj5jKfexImEEK1\nSbw/09omXXXpFBoxHd/qyh3Rpo333yOm+lI9P3S3zAwrJ9KVC2X9Sd34FYRpXsg+8LbT9srxC38W\n/+xz5vs3wv5iKEvK8RIUn1BP8okck9IgTo/Y7MzLG8wg5nKdmxNZuVHFSpEUJmOAFUqtkixcu8Ky\nU1Nt6fU3vDb2uslzsLpq8mIehGlz7cuD1S/XhVy9/VFOh5Uq+MQcsBcsC16WV2HqwUwkeT5jcSVL\nY1S9f7UU+kzy/O7HcHP/lVumwJjfC/w760O/HT3r/sfb12Tm/2lmfx74O4E/gaZKf+ZWLK31s8B/\nBPwtwJ/+K/3MRmGfMAq0tqurnZ26F+F6N6dWo9exJKHBcGdumnyU5T3aS9WmL9Wxx5OwQ2Z+c8FL\npy0CKfjK+OoZmN2xWr0wVTTfwlsVTyRogGWyE4IPGMTKSlNRL/etl8JdavJkU5u+cGjreZam1zan\nNm3NtZH31Hnlrk1jeOUckvEWW56sCHQXhvfVPwL83Oh7k81UxhmwHWqMln2N3rLzTSo90Ia+Od6T\nrQfHpilDbYXWBT2JWP36MSmphoxZcK5Jncme0E63gm9ALDlVGhaVZgOnUtxoC9wRy6Qe87IaVRuX\nDPrsWAzu8zYBMK5lxZrE88vko8yktLOiAj6vWEgOPWjMKMxwzmXnksEJKD1Jc4bt1EyOEQwGzYPI\nviY2B2BEymdt1tiXryiBYw6eVnD1VsBaoftglEEpk6fTiTfta+ajvCNzwDVXJlaBnJ0+CjEPtlwy\nwbOQ351JJ8lFL9S0c+JZsFIZc4WQLnpsmuSO7sbjqamIobCVxttDYbsRS6oYkpi2Ag1jUpbXSceh\nmEtokIlZIa7B1Quzy+eSmYQr30dep+DZBOIoNjG2lZ2WCNEgL2m8yKjlZ3+ZmKEQ3HLDmLsaCWYF\nt6bii0lW/ZvLFNjAlp622Jq6hQF1ARgmQhppGpSmhmoh1QR01AxbHvywKeksTp9Dk8+SEJLU2hD0\nJYua4w7Y1LES8jw56Oue4OywPEMaMIyQHNBMRVpG0kzF5owKBFZumP4gC9zgGgCXNEqCUbgONRjC\nJnN5n5pJbm+RCyuzJlE2YDVeZq7cMtZ9MFW8gYos0fP+Kh4Iv4LrJ6pgsu0e6i7JyujKj3AXEz6D\n5qKZRQZHP9R9vtwq2UJ6iq6FsZX6ou2NYzKeD71LJBxX2t1J6O2EMWUtpF/wcOrqfEUWfHWE+tEZ\nY2B3lR5XtrpDiq6VY7BdX3M89UXkKQoFy0lpd3pQtAdJoU6VdhIcNa0wTuflMdjxy1nqvAwuM5m2\nqbhKo9Z7mQJjkP0KoZtO2zfK3T3HiJcgUiKky368E7DgRpmJCVeFiro78xjLN1Tg8RHDuJ6fMSbX\no2vTmXr4zGcFndZW6e2B8nCnsMIMYty05iLLARze8Ps75uzYPCg+mXHSA7U08vJGUsZSONWdWe65\nlo3qg+d+xt259klc5R3JaWyvHrm8/i7PU7hNjvd89f4vCMnaHvG7B0kt/KCY83Z0ylc/VPHVr3iT\nfra4QlVba8ToNJfG3WsTHv7VoyR6l4N3b9/x/etryraztcLlcqGOyl4qbpV3b95yOQSnKKVw3iWz\nOtmJasHb1+8kZczg7u6B+7ZzPp95+/2v+OYXX3KJ4BgH+fBIKc62yYuVqc7aq1efM2LyNoz7x8+5\n30/k8YyXxpunJx5+5tuUp4PL8zNzJOP8Dvrk7fM7qBvl4XPuto33r99gceDtxHh+hxNcvv9aF15p\nmhTNybvrQWlthdJOzucur8TdA7Vs1M8FGDGMOh8X+lRZQPitgNmotckM7M7sXV66UvDmtIJkcqsj\ndT3OMozWIkrQkge4GTG6CpCPyFEaz8nMrUiBN3KttkraLqM0RilNReju8hZeRXdMmv55WRklRwJP\nROzEqLC9133CkjEadqyJa901tapGcRXlCjWe2ojGIvyYqYBZnqWZMtBjTVPWmWTTRueYkxznNb3W\n1LGUxC3pkZIuHgHzGS9gfAZ1XXujk/PA6ppW2MrQuZwhh3BZVvSg3zYVqgnElcykz/VA9Y8MzT8Z\n6/cAnwP/2fr7t4EjM9/+yNf9AvCd9d/fWX//0c/fPvdXLJhiOL6dmFaUYzOmpoEpKbFFUEoKQhOS\nPfVQWr1lQjFOsfEe2FJSrkDe1TCwur1QrCw/mLGHKSMmSUYevDgcyr08vVXS4hmTOUMhlSZPBmgy\ndI0brKBRM9iMD9PQJeczIGdw2JpGRqqTvYAC3dTsUq9kW40RKbAweEhlxJAQSEJkGId1DEnpAuPe\njBKFixktXfLhojDmba7sm4Rrqyq50hkhOmxiPLgId2NMsqgbX2t5kURFzhVPmDyOk/KRAM7OUWDW\noMVkINR/myCwy5BUvxWucwERxoA66CU5z3c8xCuwnVYqr+2yCsGkVk0Bn6KpuZrwPiY2nDkGl6Nw\ndKj1kZ865DVJn1yGKIg7SV3etxLfV14bhVkuPKbxYMmprkmkVbZ8D1PPm+3VkghObUqzTiwq1648\nnle28e1v7Hzj/mDbC+wVzsb7U/LVGc5HkEen1Ipn5Xq+pojDAAAgAElEQVRVYfF93kLsFL8nzpWn\nvvHUncyrvJGmbMpLLBhJaJp/nYW+vDhHa8zjoHQn3JldMQ3FFLY6M7nGEJBh6H1IeIFOiOR50NeE\nyeqH7Wwxybhu9M+Za4rqhVIqEQcZ8uAobwvSjdOcxA1hbirCBAFZk3xzYk3R7Bdt1p1+DCIG0DQt\nIkTPWxAdQw2MziJATk19gY+mxVJXvHwcZ1uNDfOxvDsKQo4pOeNhlTDJbDe29dgULGiSS30iNPr1\nFh6MscUCUwQ8uxoNI4ORdb1+qCh4N0jcg5qBhwiDRCx7s62954cDYhnreCW45mKWjbo6elnGkuoF\n7eOqZ01XE8hUU0eFtrzJNvU+blboIQLmj3P9RBVMRLJtDzqhFl7TcNyV3TKOC8WdUmQaHH1okzQn\nFBOmMlPZPYwXM3uplW1faPEchA3681v624TTHXU7aTO310UICdGp6lleJgrmuyQ27yTNuzyd8TZf\ndNZXdmi6kJhBtBNWHzXhKgleKXXTDeT9V4Qt8zgblYPx5g2+L+nbtlFME4sZhV4K23aH18pxvdC2\njYxJaxtzDuYM6maMSPZSmN44rmfq8+CYV0ozwofQ4OnMy5W2b8zxxHy6UtpObvcf5D+lsbed6ANK\n0Joz/QtGFsnFjmfJH4SRWQSuiTcnhgzlxZt+Hr42GRf8LIphzk7WR8rdZypmMol5MM5PjIXZLacT\nMbSBfHh4INM4X87447eEWp+BffVDvHzOtskoexyHirz3iker7oyQ16e1u0VYGkR0Rh/067N6nGvj\nfrftxJj84Lu/IH/Y6Y6tVubKvbjOwbxesZXtFMXoc7Dd3/Hlt39aRL/rlW988SXn90+cz0/kmHz+\n6hVtV4H+/rjyxeePPD8/8y4O7r/xivvaGOcLz8/PPL9/WjdlV/5YBtTCdneCy1uen99wvpx5ePwM\ni6D/8MKld/jsnlYr/XpQvr7nm9/5Nm+fzrx//8TToZDayJV/VCvewEuhbY2SQoIfx4GNQZ993dAK\nxZZu+vyWaxh1xovWefgzMhtv2N3n6mKPoWyQPgSBsEXdygFjSHZGV2PEG60YbduWXMPBFBbNEKXO\nWyEz8P3uZToMMFaWiSY+mgDRO9MOIpTJgbkKr1JxbytIsYCvc1sjU6y5plCAeZD5hbp2tWB2Zo4k\n+0Edz8wjsP0LwEU4DG3wMtftxpeUwmKFOCZW5A9kTQuKr4lAulLo7z5/QVNnBKNf5d04PeCbQxqn\nvWKePH/9BiKo205pGxE7cx6Lhif/go1U/IElDMkM59BG21Ym2o2C5RYLNPETtX4/8N9l5s//El+n\n3dQvvX7Jr/nZH36P9loTSV0byU8/PPIbXz1KMjMn9uIlC3mLLGhh3Hvl3A++ZOOrHPJABKJamTyM\nc3ZWggbuQ8Q8g5pNeXIJTIVJYk72zmauc6UlmxtWlAWFG5cOhlDWwpkb1+NQdIBLGhWzq0O9lLjD\n5CfIY12zrkm/mbH5lf2+EeOgtjN13zAK/rSQyH7V5Jvkbd3U/Z6DsH1JgcApfDU72Rp3ceaOwing\nzuR3UTEWeDHuNhn8naT4mbo3cMfjFXMOOl2yd1MwKkUeRDs0rYDE7yWnD4Pqk3ugWeJDskUVfWWh\nzCs5G1sxtraCXGfBs9JCUidr11V4DmoIVX2TyLkHlo4vg37fktNdxdjY8pmy3zO9YSOwOqEE49lE\niy0blwOejzP39YE+TdCBfqf7kQ+u14CszOm8K99Y8Ak43smjErXRKfR5h4+DWR64hBP5zPjzG2lf\ncPhXy7x/z11eaEUSxTH25XkaRDasGvfHbwC/4uXgKIVtDrZ5ZVilIxqs4RymWJM5g1ILxYKohUmy\njSpCaaJQ7ZDHJYuaQe7O5goYLwHmxpWglQ9B2mUGrUg6SM4XqM4WmmYYmsJmsSXX1KRnuGFW8Vmx\n7dB+MpNamuR0EQq9XTLQSbzIbVnX900GCTef2JJx2kEpS+7a2wuSPQzmTLq0ckJs24ohwLg3FQmx\nIDAgkMRhEEWQGEEzwErg6TiFxhTFLibTXcWZKcJlkEu6p8kyU77GiOBc0H6LQotVJEYSroYNZgKZ\nWKrJM1VUXk33A0NADSHf9b1e3hdzyoKS7GuqW1lhviTHmlB6GuPjfpzZy/QQk+drrsbidy8XfnBc\nX27IAb9IAvrjWD9RBVPOZ47DIdYG3JbWXvq7hft2rL/BTnew3ZNXTXW2WiE71+uFYhoX1nbH5bgw\n53uinkg7ARu2tw+hkZnk5Sy+/b5DKcQxaNvOiMrps1+nydJ6prpXrFZdgOdnSpXMq9RKMedUG7md\nOPqZ3g98r2yl4aVpw10S457mysm42mQWJ30Trao2+jHI64VZNnXN+pXnN98VaIH1dA+lIkcGc7+j\neWEzeaOqb2x3J47aJfGISS3CmnJ/j40L8/J2uXIbUQo+nlY4XCP6G56edSGWcuJ6mdAHs0+2dmJu\nDbcpok49cR7B6e6eUY3xnOR4Yh5XSaKKSwvcXpH3TWShGLwqJ3pMLr1TqnpLNdShrX1ifcIJ0grX\ngH55VkbTZVDv7pYkxChlcnn+LnnVhCLNaI9f0Frj+elpUf2M0RMrJ7wkXna2reE5uSvJ6e6Bax+8\nP7+nXy7qeHjhenRiJlFgG4Pr01vuHh758js/zVdvX/NTn33O+6/fcj4ufP3d7/Hl/QPfqo+cz4Nv\nf+PX0TB6TL73wx9wPD3z7oevOabCLIUETsr54Eh4vjwxayNbe/ELJcmVIpjA5cLzVCdw2+55/ear\nFYZYlUN1PbheD+JyJZ6f+bm/8HPs96+4O91zjaDtjcKJVhvv3j4x37/DLTinOuNzSO6QlrT7e+a1\nq3hKdQCTZXpud+ourk6btyYZQ78uPH2nX99w2/hk+ML55yLNSWN/vxX69Yn+9DWk07YTyYaVE629\n4uCKjUnepGpjMF068JwTQ/lexATu1AlHpmRf3ocsHR/OPHfKtoGLgglDk9twoFG2nbx7RcQQvO8Y\nRFzAr/Ro1Lryb+pGDsN6J/oTQSdLxesjyYbHGU6vsHGVLn8q28nnoBQHV6cQIMsuoqMLuHHzaJSy\nMWdSMsjjIshETJ6PRAhyGb5598QVbRTxqinhVMFq7Q5fGVnF9V7FvMo/NQObNwS2E3WHsv/Y7vG/\n3GVmvxH4ncDf/9GHfx7YzOyzH5ky/To+TJF+Hvg7fuTbfXv9/49Onv6i9Q/+pu/wmz7/jDIulGKM\nceXmLFIresE3XIChiBB6Ogdmg2GB58HfEODxQN4aRawpIBsjQBkzcw0nTVjlWMCDJp+OJHhJ9MkI\nIwr041jnNCuKQvKbJNizMdwXEVMTF7eF8Y0lnlvSu2LaNEbAcwSjGlsml955e5Vcx7wQz/KB+PJW\nmEv+vtVG9MsKe5/kODR1cecuks2MQmX4zvsI3gfKnAlR3WQeL9TLxKqeb7UY1rWJvSGiY03ImrEk\n2GuTXZPaDUvDL5Myk6Paaso5I5yzS1abJhmwGuBqrIbBNip7QCe5pq1JW+MaTRtLUjJLdDFPV2Bt\nmUk1SdKtTPy1Nr+v4h4vxuTCFvXlXuozsQUQwA3PJg+VCVD6OEVLC9uEgU5BtQ+ghGNpXFPglmd3\nLmZcHchdUAIPLu1B0q+8MvPElk5hMhL2obyg6k61Qkl5kt2Mt/uF6Ac14LGLYDtKobmkUhsNZwpx\n7r6mhFdigbKmshqIWJj9kgrVjWP9XURQ4esdM8EktiKgzV6LlC25trDVpKBAXj5BBQJcvs6Yg+u4\nqjHYA/eqEPFyG/YvFLl9yHIqy5/j3LaYTvV8objZghzcIBciOk5KLMGDOSMHx5pMVZMrYkvJK48p\nX6KyNCVoc1NuVzhcUKHo7jyjpluSfE2So3HLZgovzLFIlTOJ6JQy8bk8raarxiMZ5qQuZ/qQ2mFk\np4obzKCKjLkmOmVJ3+q69gtJ9ZAPM9d5Cngap5rLfwfGRH2JNY2bzjRN7UYoAWoiwMNGvHiYIlOE\nP4yeSXrjElJnfPO08WWT9WOgRsfzGLx996PCgb926yelYFqhSSYcY1xgGeDMNqxuqzMi81nbXuEx\nsPNrRm54Osf5PRGCAZTiHL1zHBfMC4yUl2M+kQxyDqabRoZt43R6pM+D69tn4YfH1Lj3vpLjSQjK\nNWVwlvQog1Z25vVJHd99h0zenS+Qg+gHuLE/fE40Zx5G80KJwcxJTnWG7vEXCaKNswAXdePA2Yvw\nzeFOTqM0X/IJW0hIwSI6MuzGODjmleerJFK+b0QfH0zwTEpqIkXZwYSJTuu6wZUqco4/ypdkxsyF\nOd4b6ZMsjfuSlJI8PT/zdH0jWci48up0x1EaTwRbbFhVATcJePoKf0rm0E3kh0UPdZpTUQZTzIkf\nF3yvXI6DfH8wIxmlgVdyXDX+nioool+Ip7dsthHNqLXibozn91wzmf2g0ej9YN8q9/eShfXxnuP9\nWSHId3d89fo11z7IdXNT8T3Z2yaTJsH17dcwO5c++Pl3b/GZnJ8unEphT+PqyfvLO746vsd4eoY/\ne13ghFSrpDTqq3v2e4X3jqumYM/nzlYbD5/dYSFazdu+00dX6LGLqnZ9814hf2ZMe01m1zEIPVSt\nNRW4p21hQyf93ddcZ2A+mbWSvknqOdaDptTVGZWp2bwye6e/vUi6ppELwt5qCjX6WYAFT7gO4riA\nO7VBCYUyZ9lekL4xDvJYhL2UTMS9cIyr/H6hQqKPwOINM99KbkdZUtQKtYrodlH2mPDZRef8TDKf\n1EQwh+nr/HZsQNSKNcfozEgVWJcncg68SMYWs2riNAeTgbWKs4Pv5PGGPiZUZ3aoZZMxOBvYIyeb\njOsZeEfB6U8TiYdkaiV1HMdQh9SB0bWJZIFJlMcl6MPMAXYL5JS8SrK5Cl6VBl+cUav8JKtLqmOj\nPJ28vJc0eEwl2q9vcys+5blOaTIu70Qq+Pg+/Nf3+v2owPljH33sTwID+HuAG/ThtwK/Efjj62v+\nF+BfNLNvfuRj+nuBN8D//kv90P/p68Hd0xNenRJ94fobpSsPSRe5CHPuiZfJGGX56ZK7bFgY1Rst\nDrqJaHbN5JKTa0r7H5iABWuTNpvoWMo4CdJCXgTf9LOmLxLeSUTTVVwJQqCO9izyVHk3Se7k8F4x\nGy4VRLImQYUXiEJoEtBi4nYi3Zg+2VOvPdb0Sqq6SfFGCaPZK3XYSUq9ncOGtU5N1Ny7dvCG7zBS\nEuqSSfGijMAVHF8TOg+AMUaSe6wutfGA88zkks4o8hRdcy5QQ3KYca3ydZQe7G64x5LIagoYQ8VF\nY90Xi/MeGOlM18QuiiA2JzNKVcymzOqr+JwKr25F2XrFFZiutBx5iWxO7rxyqWvzbU5bEto0YcTN\nnem6Tn0a7x2sSJKY2V823sURZcw3clZmwqvrlS8AamMUybT61MToFjhsxbE52YrTi+Er1yg9iThw\nS5pBieBbBe7uK9tMqCJ3uifHZIGw4N5jTXkGmVMSZ68cY6jgaGoyZ6oxXVa790A+8bJCxCMms58Z\n5pxHsKHpSeZg2IJ9pPxKPdckpqqYcVvBqgZ1r+omFxffiyRyCm6yiqNYhctkPZJC3iQBWCRlUwEC\nZh+HCLN+l2SaL0qjsrNSidXchGexlE0KZZV3jjX1cS/UWul5mwyxJk6SkcciW4aDNTXIW2+Kv7IA\nOhT9m7ELQhULWlGqK9x3TXSrFcaysvhU/lGmqj030RAJPUOnB8O1N6tD12Fd3ji31RyIsQ6aCgtf\nE6ZcQIsMuRLdqwrK9ZxZ4geMFWS7/uYu2EhdUK2GcVRFJ5R0jtTQ5Me5flIoef8o8Ed+tV/Hp/Vp\nfVqf1q/h9Xsz87/81X4Rf7llotz8OeCPZOa/9COf+w8RVvz3oYylfw+IzPwYK/6nEFb8DwI/jXKY\n/pPM/Ff+Cj/ztwF/8m/7zs/waj9h80QW0a52P2hdJDJ1bGELp9RJLYM2d0kzTd6aiKSUykMfjIKa\nH2Gc1wasz1AI5q2L646JVfchNBbtQy6+NigZazNllCGgAm6EGXdZeLRCzysPFHZ3DrMFE3GszyXB\nkXcpMC6xutOoAu0ENgfd7ug55N9LTcclmxEF0NKX4TxofiyDtyacoNdeU7ksuxWeW7DNZDNNxUSk\nWx3tTN55UENABCuraWeOT3XkaynUHmRJNgb7Or4UkR+LO3c9uTajzmT45K5AzcF57CLDujLjRirP\nqtUmP0camznXHPQIIaDNGFOQCsdefFMlRfzDhgLpV0aa8qyWJGkv7Al3y+d7I/SNQ++mF9E2q4mC\n2LxgfbKbCKojg4a8NplJicIw+XOsIcIgmp7nCvK9SdAOl5+aTHzAYQoQvQ8Ydktpuo29k82dPeIF\nQjBKUoaw827G06z0CMZMahXcw1ITPRUgAy9tBdV+wId3VDBZ6hjFen26PlZ4qu+8m0kenbZAJrZC\nxmsp9JQvKCKVHbs28r7kgHPKl5YZDHP6mKQ5Yg2rQKkr2SlxjpAM+3ZcM6WkkF8wqRYvagWLW9Cq\njvtcxyxRweT2UcRqJFhh5IJBrA/7hyMt0qbdmlewm0h5A51v9RZEHcGRyhJLAlJBxTFjwUtu8bf6\nE9RFWzQqCmXPVN6WIEX2QnY1WOhxx1MFVMbgacl0Y0Ul2PIgddOkbmayW7K7CwhWjb6ufcWjCVR0\nI/5tqGgGOEyeqshkmHGeSS/GmJONJTdGRZMnvBuDP/X2DXyi5P2i9bOIevT/AJdf3ZfyaX1an9an\n9WtqnYDfjO7Dfz2v3wn8BuA//Ut87p9BjeP/GgXX/vfAH7h9MjPDzH43ouL9ceAJ+MPAv/pX84Mt\njZyJ+ZWcwVYKZQTm6pgXMwqTqEVckgBqBy9cjkFthRITnwfn1Xl30wzijoCc1CJJV9n1s+Yc9JXz\npE6tuulzDHora9MjTyHu7KUsemOsTY46zUdNtjwYNTgdhZGixE2/tcVVrERW6pzagLkkayU1NdhR\n6KuaxuPFHZYEzYxiQkeXLVceiyYxzVZmj36M/k0OXqX+TbNOqaLsbQuvbExeFadEiFo3AQK3yamq\nYJpxoZ2K6GwuSZ7XRonQ5GRKtvSQRtryyKDN+V27rtcOFFH0tgLYwZhJmzouu0+2WulL3tZSm+cZ\nU1Ky2dm3EzNySQcLW8rPsvmgeBWJM5JG0NyYzEWsDdiSVpxck2ciGUURG606ZolZyMc6BcBwM3o9\nyCiSTc2DO3P5tjJhDoUar7gCs4k4Awrfrmha35csappzDAWO92nURZfDUz67jjbpJoWBlyubG3ut\nC2mfjD6IlSXZp0A1PUyB4qZjWe1D4VYyX15foKDSGYOja9JpazPvi1ioCVnQzNmrZLCekk9KJKei\nxVasSWJslrQqf/lpeeQkDa0v+Oxu7SWfUwHqamCkfQilTUsVNsU4EsHB1iR0zlyTqPwIZJCwcrXK\nkrLz8nEVScLdK+PoRkK9SfYGySVXaOuqhIr5UiSsgit0XLoZc8kGXaWl/FCrMCtZXtQOC3OnAnGB\nuUgWTn/SXCVXOLyKzsSYnot6F4xcxiwrxDTeBGTRsfbrAtGYfIoZUBhq/KA3UnTAwZjLuxVqDgmc\nc4timPS4lX/QM2Qj+TGun4gJ06f1aX1an9an9Wn99bY+TJh+PQ/b/pJ/U0uhxaC9eAiSzZLMQiXY\nPSmk4D44g4nPSf3IxGwrr+nOjNMykVskF9MGJwKmy1NiUzK4wpoQ3SYOiManiYLRzDm5E0XYaDPn\n/phs1XjPFfNKpK3u9ypPUnk+1+n0DI6EqxnHh565NmBrOhUrTNYk1NXrTuUWWaamTu7UNP2+azPp\nK7IjMnFrlJyr2EqqJfvt90Lyoc01wYG2JGmI4rWCvd2EgbYqCRzr9/dFsGweCzsu0Ioj6fHMfAmH\nTl9S+ZRnIlMTokrSPoqdMUSLjSWl2rZGIelj0Ae3GCgey8a5H2wVCGeG6IR9dsGXpmwFYw5qq5hN\n/GVKk3R3YkwF7BZthucMkrqyr1LgjjWpcZac0ZJWCzYVUFqrJIobc9HejEbnkql4ilEZU8Grk1x5\nfRVzIxmSaaUxQsf0Rq3r1snbGTg/vBdT2i36MMVbutE/wlBbStrFlD9mklAcT19kueAyGz2hmQAo\nwoKLSMoQUMUdLAYzyyLjJWGFoQRfQMVLS9Z7Hy/xfreA2VySOUrRNIRkEQ8UGu2a3IxUsRAm323H\nOEJF0i2aeYMV7SlcZGauTT+Y28vEIm9F1pq6sKZtdpsGub9ISq+oQL2tunxit8yilxyvEGjCrZAT\nhZH7DWPOKkQMj7Eg/1puvxjyU8h1XPXOYmqajPDlgVK830EnbWNMHYthiz64ft9KoUfSzUXW9Vsx\nl+wG915W/pK8TFeQR9BuMfEq5G8FkwHve+d/+zRh+rQ+rU/r0/q0Pq2fjNVn0Kf8Q9UQ9dFidXK1\nCdgMdhvyI2SwoY37nJPZBla0yW2BEL3mhMn/1Ie6w7Oqk10jOVZY7JYmylWZ1JQvZjhs0yCSWQtX\nK5xpzMvBZgYuv8K9C1R0ZpCt0ab8Eclqz8MLnTFWjozBkgBqg1aWTNBXh1tSvEkAR/qLl9Ju/gbL\nFVBrchqujd7eK15hWlJXfo6bpkOCOkoWJOrZRsnJzgCbyBEpGY8bWJHkSaTKSbHKZULxAw9tD/Mm\nU0v5QkAQiZtfqxYZ8G0OIisz5JNyD4W9D5n/qzutVIJjYcpNflKgeOWwSeLMkTxlMqaKyGRtYjmU\nE0TBS2LV8CwqaLNg7qsImlQqUddM8Rbwakk1eaeTCiu3pzoUnWXahGeSrjc4I9dETYU4MZgmKNKM\npDNESkwFZdcVnF1Ko4fjJSVtnJOZVdCMFZRrGHMqa25jaHqUIVS4JyMH4UabhRFTjQHXJlq+5lSV\nMVmaMINcQa4xVbAJv4tH0tO4TjhVXW8C+pQ1rWRRGyCi09rGOOT1lXxsfV9Tob5bUE/OHE6Erywj\nvU9hsb7X2rSnroWyJrXNnc0mdU1zxsoFDFRsmWvqGwuoU9yZqSyqOdYVY0KP34J8zYTSj5zcBJ8e\nxrDgxJL1maY2zLloqCIMEsubtgoqgbO68p4iIXTt4QqUjjB5iFNofmKqwWG3CaK8sz2EPY9MgR1W\n8b6ngo2tqiD2dXzmKrAS6MU4SK5rGovJ8xYBl5wCkFXRqA9uE6y83XV0P+KDVPEjoeOPZX0qmD6t\nT+vT+rQ+rU/rl7FuSo2bXuPWA/1gFRdwYcrhjafTlz/EirEvuVlJhTebOWOGAECZPKcyW0ZqKlSH\n8lzMnJ7y9eQ0Do0LiMM43DhwjgOeI3lqB1aSzeAh/CVPKX3wmRt3PZlWqCssctjKDSNfaFjdl3Iu\nFbYrKIFRFoo+lpcjcdIUqCui34L4LP+Vr4IsMzQxAmKqOEyXl6muaYCbNnvXHAI2ELSpzvNhghrc\n8JKNpExNY2plRT4YiTJdIqp8YGEkyZlgFIFgZFTnhTQYa6PmCebSUbZSpJvKFMktgz4HvXdGreSQ\n6vGaSUFBn7WIkGiu76kplOSRZmBjUr2RfXlvIjXFcsEkYgbHkvmxpkiisulcc5c8bwbMDMn4CHpO\nybcM2vLD3CZ//RDoKF3vk2SM69w0W+jqFU67zu85pyBLaTCa0NXTGH55oXhKYqeJXdsm1UU9Ixsz\nnZYrON4WFKS4cijJhd2eLxviSONiTg8IBk9xFUAqlDWU3Zh+EG4cxRjzlhtWaBELr60yo5oR7iKP\nWlEtBlAK20L0T4KaFZ9BzsKxUPwqKF2Et4w1WZOPRhMrFcTny5laC5cF3gmMOn0Frup3FOhBE0MS\n9uUPagaXtfcvRcVOLDJCIsndzRcIvITYYgoWLquLsS1pX8RNqnvzZ61pXgS4JMJWb3JPXeNSEwab\nCZhki4iYmfhU8Q+qZUvcpo+3ydkvnvzsK/Ld0l7kekNXNyXhlReWWpDwdT1l0qu8TSPnuius/7VY\nUkZe/FWxptE/zvWpYPq0Pq1P69P6tD6tX8ZKZ0nibh9AcqKiokZUrtAUxnxJm+aLryFCkpdiQZgk\nM/FSYMG0ykjp9mOFwA6HPrWliAy2KERRRuG0xnNqCoUZVirWp7xTGG+aMuguaXx/wuMMHgy+8MHm\nhYLTFqnPDI7QRmfLuNmauGbhmsEocElNFnJJim7/a5SVHfphY1PwVSwl4TomxYS8jrXhO2HUXLK/\nVVTtXjW9Q3Kgt5kc6O9b+jLCa1rkGC2CWhUWGzHYLPGSy/MBZgPPQk3jyZIHF5Z5rNemLaDM5ZvZ\nAgYUyGP5ShDu26vkbyFaZvWibW6uAxWhQiwlbRPVTfI73BQZktqwWsJuxm5wzUnO1Neg4tbWZA5Y\nVLVF0vNCLOhARyhoUHGdJH1tSM2FtS4LVnDz35hLTkkoJLha4WJBT70/CdTaYBFRvfiClOjf1qqi\nkFjHzo0Sg+swznNyzAvFN3ouTHWCW2piWCSdLKUwa1KtEjMF0bBYPhrYEpid3QJYHrpSOULfM1aY\nqhvIhqOCxm2u7/BhHTfpHatIXtKyq0GOICM5D5FmbQXFT0B4hA+ZThDrPE6ySPoXUzmJmUuOJpLF\nkk4GHmvkBUwl8q739JYiqGLg5nuaOV/e83yRP4rQlwZlLmkd+SKti+XvAw3qbk6f5s5c18fITjM4\nmcibM9WkiVshn7xMvW5iX72mdY0s+W+mpL9hhZIsOMw6/xP6aroUixef4uB4abBE3Boy8mYeloxi\n+NS9Sm66hJy6/tbt5DZV/nGuTwXTp/VpfVqf1qf1af1yVsjbgkNaoWdQXRvIi4tiVbs2ttpnTbJq\n2sKU5KhmcCnBsEIJUcNudcaIyWHOFaNbpaVIXdfb1MCcd3brLkO3XLEbC2Ueg758LWFOzCvgPHml\n2cFz+so+CWokxVPQihtpzMEzaaOQ1bnGYA4j3bUhMvlI3NTtz9Q0pYS2WrlIY54QJbjlPUW0ZWaH\nwiRtdbuXhM9nUtMpGM9F8iXPRk5tLgfarDqCOZ9OM1YAAB70SURBVDzYBxN9iUpJ47y61Q+oqCpF\nBvONQndBHE7pvGeyGWSvVG7SRm1ot7htHIP0ig0VXjUPwQbM6KVSSI7eMd8ohDDiCJFcvXBdQIWG\nshpHBM/Ip1TMSO/sODURpCOCHgOvmkY0Vd7y34wT+70z86yYBwsGkzoKRw62vbGZwzgUhFoqIye9\naurXbueCT6wM6lBsRQkVwqyMv0HQZ9JaZUZX9z/8Zap6jIOZ+YFCF66A+NDEaSPZS4N0LkzM13tr\n7cXHcuSg4pQR9AgOFLo6ZvLoCiVuud6LJTvLHBjOqWjy0w1GFqGrbdJjSMYZdZXvk2IGDNw35pzL\nw2S6bl1SxUyH9TpFyzMGgjzYIryt5A3AwRz9BFu5joNg+aAmjCLfWTPnmsEMZaJF8lKEGAJQaEIW\nhG9CcafuIe5GriIprMDhCt/NoBZJZAsFTJ5Ad006Zypgd+JU5BtzM3pOainkUI7TMdX4OIqKgpEg\nPrk8jNNyNXKcDHt5781XdIeFSIak4lesiBYJmKk4mllezvkXRoSrIxMjqG7srraLAqX1PqjydCZV\njalUO2ZxLn6s6yeiYDKzPwD8c8B3gD8N/FOZ+b/+6r6qvzbLzP4F4PcAfxNwRsSmP5iZ/9dHX7MD\n/y7wDyHi088C/2Rmfu+jr/kNwH8M/N0Io/ufA/983viN/z9Y61j9m8Afysx/dn3s1+SxMbOfAf5t\nhE6+B/5v4Pd9bIQ0s38d+MeAL4D/GfgnMvPPfvT5L4F/H/jdKDjmjwL/dGY+/bh+j1/ptXDR/xqi\nbH4HYaP/cGb+Gz/ydb/mjs2n9Su4jBdEb0yZnZ9iUly0qzLUlZV3XJud2uFc9Ce7CqaeQR2Thkhy\nuYzxY3VyJ6KZBcnMNdlKdcSraSpSaqFPdWCPcWW6M93xRYJzJJu7eQIiCwfyKjxH0xSD5Or7h0nQ\nMorXHETCQZHcynNhmAPfPqCDk3U8XEjom9keTLkw2EsezG1l+iKbBWdB1dThR8WjR4UApc+MF1JZ\ne/ExJP3lv1dm1Zr0WWpSc7gxx6C4cdjGJSc1QAZ9YydoDfk3QhtwgLssL9/5cJljSgK2USaUhDYK\npUCh0vpBdcCMI5McA7cly5tJxkGabiSzKNPKM/G+8dqSbskJp1nD2OA6lGX1kWvjMZPXbw/a1qht\nsk/j0Xa66XjtWZnXC3txwgbEwCPwgGJ6zUKxB9En57kmFx7kmoTWqgycUit99tXrt5eJh5nRvDG6\nIBuanMprZDdQAcplwiSvBCNncM6LviY0eahr+lC8LfWm8OxzeZcodUEkbsHtuibMjTSdm26TMMdn\nw8KX1eiGzy7rnNM5GGmUunG3ioKYuRDMH07KRaIHY5Ejg8hOmNPSXgALFwvMjTkPZlTSDXejjLHO\nd12rlktet0iUdtty5MKqG4I/aPS8/FsfUfZeZKo6nrYEvx9e75K68v+1d/6xtpbVnf98n+fdex+Q\nMJgBQWMb21EpNcYf6IBphU4YdWpjm45/SJtGp4lo/DEx4yRtGTXTlElLaPlRirSmtrHFtpYyaSqx\nIw7yT6UVBgasUbBjBn/19l5EEfDee/Z+3+dZ88d63n32PXDpZeCce84962MMd+/9nnPed+137zzr\nWWt9v54sDe17QjmzmPekDNYEOuY2g2TMJVJa+PfJyqzQuFljuVuep5l5NbQxG8TBZDymgtWp13uS\nvAprXtebaOqJaFn43F1O5JqXv6+OCbTBmhkpd3S10tV+ef3IRTbWWhui4TOdc22vSt6OT5gkvQW4\nEngHcCcuD3uLpBevGAyeSLwW+B3gLvz9+Q3gM5LOMbPD7Zhr8IXxm4FHgQ/jC7hVT5G/xheH5wPP\nA24AFsAHt+1KthBJrwYuwRPoVfZcbCSNi/zPAm8AHgJeBDy8cswvA+8F3oZ71fw3/HN0jpmNQkF/\nCpyJG3xOcVnljwC/sC0XsjX8CvBO4K24AemrgI9J+p6ZXQd7OjbBM0SljdG0xUqnREfHOCeuJIqM\nwbJ796hw2BJlaLYsyeioLFTpmLTKzEbLiczbVrqaGVRIZhTBtLakQzBp1SYbChNz+WOyGzzOraNm\nr+qoenJU68Y8zaIpWk2qG59mianNfUaK6qajZsxbe5KZmGOk2pTxvIlwOfAO8N2DBzn9lFOWbXae\n/7SZDF92b2qp8Wcxka1r090+v2B4lSy3GZxxlqFWo3YsV3fDuHhsrXvVvOJXKD7rQUdtvUJrtWDJ\nF5yZUdsNVIxkacODh9Fzpr0XrXWoAmXiLYfVjIUZGlxGXfIqEdWWC+ZBRhoSuYivrB/iR05Z84V4\n7ya2VVA6T/AmBguMoQ5k85gzSl63Be+3zcUE7LCYHMQlrq3n1Kkbyk5zYZI7cjEmSazlxJpNmoFp\nMxcuA7YQnWYkG0iTylAWJK15S2gpDIbP04nmNSSy2bLKMP63mrnwQEtAXV5d1Cogg2V669v9Kjr5\nHFRVpXSuzHjvYz2vPskX0SbRkVgfFqTcMZ97peIkdcs5LhfLUIt/9g0Fk3v7KGPVmDKlyuhrXQqP\nDHh7W62uuljkn4UxHTVrJt+tZNtlT2ZmxdtbB5OLIzTFRZKrFHbK9BNXpjR5gp6V6a0wAKl6Nch9\nqLwiNCZdVr2VUaJVulwcYkweH1if84KTZv5ZkH/e/HQ3EqZiG4nO0DZGioQKLqufbCnFraGnyI9L\n2VUN83Ki0H9PxVuDN+anvO1zbAV9NNfW/phJyf29SjUmJJJSa8vzv+dGxK3ipdFs2JjX2ua6jEOp\n0pVxMyJvJIz4ZpGrbbo6oYoYyva25O14WXFJnwfuMLP3tccCvglca2ZXHNeT2wYknQ48CFxgZp+T\ndCrwbeBiMxtd688G7gPON7M7Jf0k8EnguWNSKemdwOXAGWY2PNHf2i1IOgW4G3gX8CHgHjN7/16N\njaTLgdeY2YVPcsw+4DfN7Or2+FTgAPA2M7tR0jnAl3B5znvaMW8APgU838z2b/V1bAWSbgb2m9kl\nK8/dBBwys7e2x3syNsHTZ5QVf+EZZ/Gs2YRJZTk0PajSGc2Asy0a8STIJIoNbT4ika0pXEksTMvF\nO8mYVF+cj7uwEytMmyRxHiWKJUpXSMVQNXplFpZYtCTMmMBKclKzoeID3p6I0drVKielxIRKGTwp\nszYv0qvSl0Ivcdistcw4o3FuXlm83f/QAf7VvzyjtV+5Wl1qO/zQhCC0UdTPSkt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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "I1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256\n", + "I2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256\n", + "\n", + "#%% Plot images\n", + "\n", + "pl.figure(1,(10,5))\n", + "\n", + "pl.subplot(1,2,1)\n", + "pl.imshow(I1)\n", + "pl.title('Image 1')\n", + "\n", + "pl.subplot(1,2,2)\n", + "pl.imshow(I2)\n", + "pl.title('Image 2')\n", + "\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Image conversion (toi matrices) and subsampling" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def im2mat(I):\n", + " \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n", + " return I.reshape((I.shape[0]*I.shape[1],I.shape[2]))\n", + "\n", + "def mat2im(X,shape):\n", + " \"\"\"Converts back a matrix to an image\"\"\"\n", + " return X.reshape(shape)\n", + "\n", + "X1=im2mat(I1)\n", + "X2=im2mat(I2)\n", + "\n", + "# training samples\n", + "nb=1000\n", + "idx1=np.random.randint(X1.shape[0],size=(nb,))\n", + "idx2=np.random.randint(X2.shape[0],size=(nb,))\n", + "\n", + "xs=X1[idx1,:]\n", + "xt=X2[idx2,:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot image distributions" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Tvw3wA9AJ2AfkAWYDFqDfKwo7Xfryyy/5ceRIMpZvQd48ZQi/eooJEydx82Yg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sQKPRMGPGDLvgh9VqZceOHezfvx8PDw9mz55NV1cXO3fuRKVScf311xMUFHRJW3d3d+bMmUN4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4fdbqdm927KbC7a/eRZQ6nVfwyXy8X06dNxuVwMHTqU/gMG8u233zIoMYTbW0VzqMbK4txq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EBrp41+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\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YMIGuXbsSjUaZPXs2GzdupGXLlowYMaIhxdPv9zN9+nRmz5qFTZLw1K/NZAioMUw+qKzD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\nePzxx5EkiUAgQH5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(2,(10,5))\n", + "\n", + "pl.subplot(1,2,1)\n", + "pl.scatter(xs[:,0],xs[:,2],c=xs)\n", + "pl.axis([0,1,0,1])\n", + "pl.xlabel('Red')\n", + "pl.ylabel('Blue')\n", + "pl.title('Image 1')\n", + "\n", + "pl.subplot(1,2,2)\n", + "#pl.imshow(I2)\n", + "pl.scatter(xt[:,0],xt[:,2],c=xt)\n", + "pl.axis([0,1,0,1])\n", + "pl.xlabel('Red')\n", + "pl.ylabel('Blue')\n", + "pl.title('Image 2')\n", + "\n", + "pl.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Domain adaptation and mapping between images" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 0|3.699980e+02|0.000000e+00\n", + " 1|3.608346e+02|-2.476614e-02\n", + " 2|3.606710e+02|-4.534048e-04\n", + " 3|3.605854e+02|-2.373172e-04\n", + " 4|3.605308e+02|-1.515104e-04\n", + " 5|3.604930e+02|-1.048652e-04\n", + " 6|3.604655e+02|-7.607409e-05\n", + " 7|3.604444e+02|-5.868306e-05\n", + " 8|3.604277e+02|-4.642246e-05\n", + " 9|3.604141e+02|-3.764735e-05\n", + " 10|3.604028e+02|-3.124268e-05\n", + " 11|3.603933e+02|-2.632307e-05\n", + " 12|3.603852e+02|-2.260049e-05\n", + " 13|3.603782e+02|-1.938188e-05\n", + " 14|3.603721e+02|-1.706719e-05\n", + " 15|3.603667e+02|-1.489910e-05\n", + " 16|3.603619e+02|-1.336306e-05\n", + " 17|3.603576e+02|-1.189587e-05\n", + " 18|3.603537e+02|-1.066658e-05\n", + " 19|3.603534e+02|-9.986781e-07\n", + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 0|3.619308e+02|0.000000e+00\n", + " 1|3.568950e+02|-1.391388e-02\n", + " 2|3.567799e+02|-3.225305e-04\n", + " 3|3.567404e+02|-1.105949e-04\n", + " 4|3.567137e+02|-7.490749e-05\n", + " 5|3.566940e+02|-5.504299e-05\n", + " 6|3.566790e+02|-4.230200e-05\n", + " 7|3.566671e+02|-3.324452e-05\n", + " 8|3.566575e+02|-2.697764e-05\n", + " 9|3.566496e+02|-2.210773e-05\n", + " 10|3.566429e+02|-1.873910e-05\n" + ] + } + ], + "source": [ + "def minmax(I):\n", + " return np.minimum(np.maximum(I,0),1)\n", + "# LP problem\n", + "da_emd=ot.da.OTDA() # init class\n", + "da_emd.fit(xs,xt) # fit distributions\n", + "\n", + "X1t=da_emd.predict(X1) # out of sample\n", + "I1t=minmax(mat2im(X1t,I1.shape))\n", + "\n", + "# sinkhorn regularization\n", + "lambd=1e-1\n", + "da_entrop=ot.da.OTDA_sinkhorn()\n", + "da_entrop.fit(xs,xt,reg=lambd)\n", + "\n", + "X1te=da_entrop.predict(X1)\n", + "I1te=minmax(mat2im(X1te,I1.shape))\n", + "\n", + "# linear mapping estimation\n", + "eta=1e-8 # quadratic regularization for regression\n", + "mu=1e0 # weight of the OT linear term\n", + "bias=True # estimate a bias\n", + "\n", + "ot_mapping=ot.da.OTDA_mapping_linear()\n", + "ot_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True)\n", + "\n", + "X1tl=ot_mapping.predict(X1) # use the estimated mapping\n", + "I1tl=minmax(mat2im(X1tl,I1.shape))\n", + "\n", + "# nonlinear mapping estimation\n", + "eta=1e-2 # quadratic regularization for regression\n", + "mu=1e0 # weight of the OT linear term\n", + "bias=False # estimate a bias\n", + "sigma=1 # sigma bandwidth fot gaussian kernel\n", + "\n", + "\n", + "ot_mapping_kernel=ot.da.OTDA_mapping_kernel()\n", + "ot_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 10,verbose=True)\n", + "\n", + "X1tn=ot_mapping_kernel.predict(X1) # use the estimated mapping\n", + "I1tn=minmax(mat2im(X1tn,I1.shape))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting adapted images" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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PZXomc7e3igQf31bumyYO+zpPd/t54T18Vzjv2TYCuGTKm1V4RhVVeGLr3FJy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OnCylcGG+lkYdVBlZ55jjzaDZgK1lgkWfpLxzFmDnXF6ywcx475TiDdszKRD58NXFihGsV4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\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d+Eg2vtKUG4GbmNfyYAppwu8syr7BI48ony91Yf8DpvZ+qBomsAtyM/LYBw1EA6I9U63O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vPgNp6FJACj/xU9c8edb40vfu0Urh03dWPv/WEz7zofI771/wpHtqvLbOSWEVjyxr8a7N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\nqTI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7h8nEpxWi2jHxhphrkl1uo6QgnS7okzSXNnqz2syYJHb/GhD9YbDWS0ZWl7o1dVof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B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\n4pQQKi60maUKV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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% plot images\n", + "\n", + "\n", + "pl.figure(2,(10,8))\n", + "\n", + "pl.subplot(2,3,1)\n", + "\n", + "pl.imshow(I1)\n", + "pl.title('Im. 1')\n", + "\n", + "pl.subplot(2,3,2)\n", + "\n", + "pl.imshow(I2)\n", + "pl.title('Im. 2')\n", + "\n", + "\n", + "pl.subplot(2,3,3)\n", + "pl.imshow(I1t)\n", + "pl.title('Im. 1 Interp LP')\n", + "\n", + "pl.subplot(2,3,4)\n", + "pl.imshow(I1te)\n", + "pl.title('Im. 1 Interp Entrop')\n", + "\n", + "\n", + "pl.subplot(2,3,5)\n", + "pl.imshow(I1tl)\n", + "pl.title('Im. 1 Linear mapping')\n", + "\n", + "pl.subplot(2,3,6)\n", + "pl.imshow(I1tn)\n", + "pl.title('Im. 1 nonlinear mapping')\n", + "\n", + "pl.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/examples/demo_OTDA_mapping.py b/examples/demo_OTDA_mapping.py index 7a9af61..50c1df2 100644 --- a/examples/demo_OTDA_mapping.py +++ b/examples/demo_OTDA_mapping.py @@ -1,6 +1,9 @@ # -*- coding: utf-8 -*- """ -Demo of OT mapping estimation for somain adaptation +Demo of OT mapping estimation for domain adaptation [8] + +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for + discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. """ import numpy as np -- cgit v1.2.3 From e485078116660e53b47aa1f7b96288b9b413c3eb Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 7 Nov 2016 09:35:34 +0100 Subject: update doc --- docs/source/readme.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 653adaf..e219eb5 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -85,6 +85,8 @@ Here is a list of the Python notebooks if you want a quick look: images `__ - `OT mapping estimation for domain adaptation `__ +- `OT mapping estimation for color transfer in + images `__ You can also see the notebooks with `Jupyter nbviewer `__. -- cgit v1.2.3 From 8b41e141e8ce4ad14a458cb363f46d3176644116 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 7 Nov 2016 16:51:42 +0100 Subject: add log and epsilon scaling stabilizations --- README.md | 6 +- examples/demo_OT_1D.py | 4 +- examples/demo_optim_OTreg.py | 10 +- ot/bregman.py | 349 +++++++++++++++++++++++++++++++++++++++++++ ot/datasets.py | 2 +- 5 files changed, 362 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 4088bc2..c6d480b 100644 --- a/README.md +++ b/README.md @@ -8,7 +8,7 @@ This open source Python library provide several solvers for optimization problem It provides the following solvers: * OT solver for the linear program/ Earth Movers Distance [1]. -* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2]. +* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10]. * Bregman projections for Wasserstein barycenter [3] and unmixing [4]. * Optimal transport for domain adaptation with group lasso regularization [5] * Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. @@ -98,3 +98,7 @@ This toolbox benefit a lot from open source research and we would like to thank [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. + +[9] Schmitzer, B. (2016). Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems. arXiv preprint arXiv:1610.06519. + +[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. diff --git a/examples/demo_OT_1D.py b/examples/demo_OT_1D.py index 6eaa2ff..df65a60 100644 --- a/examples/demo_OT_1D.py +++ b/examples/demo_OT_1D.py @@ -19,8 +19,8 @@ n=100 # nb bins x=np.arange(n,dtype=np.float64) # Gaussian distributions -a=gauss(n,m=20,s=20) # m= mean, s= std -b=gauss(n,m=60,s=60) +a=gauss(n,m=20,s=5) # m= mean, s= std +b=gauss(n,m=60,s=10) # loss matrix M=ot.dist(x.reshape((n,1)),x.reshape((n,1))) diff --git a/examples/demo_optim_OTreg.py b/examples/demo_optim_OTreg.py index 5e19be5..0a8c583 100644 --- a/examples/demo_optim_OTreg.py +++ b/examples/demo_optim_OTreg.py @@ -17,8 +17,8 @@ n=100 # nb bins x=np.arange(n,dtype=np.float64) # Gaussian distributions -a=ot.datasets.get_1D_gauss(n,m=20,s=20) # m= mean, s= std -b=ot.datasets.get_1D_gauss(n,m=60,s=60) +a=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std +b=ot.datasets.get_1D_gauss(n,m=60,s=10) # loss matrix M=ot.dist(x.reshape((n,1)),x.reshape((n,1))) @@ -37,7 +37,7 @@ def f(G): return 0.5*np.sum(G**2) def df(G): return G reg=1e-1 - + Gl2=ot.optim.cg(a,b,M,reg,f,df,verbose=True) pl.figure(3) @@ -47,9 +47,9 @@ ot.plot.plot1D_mat(a,b,Gl2,'OT matrix Frob. reg') def f(G): return np.sum(G*np.log(G)) def df(G): return np.log(G)+1 - + reg=1e-3 - + Ge=ot.optim.cg(a,b,M,reg,f,df,verbose=True) pl.figure(4) diff --git a/ot/bregman.py b/ot/bregman.py index a770c5a..b132225 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -144,6 +144,355 @@ def sinkhorn(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, log=Fa else: return np.dot(np.diag(u),np.dot(K,np.diag(v))) +def sinkhorn_stabilized(a,b, M, reg, numItermax = 1000,tau=1e3, stopThr=1e-9,warmstart=None, verbose=False,print_period=20, log=False): + """ + Solve the entropic regularization OT problem with log stabilization + + The function solves the following optimization problem: + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + where : + + - M is the (ns,nt) metric cost matrix + - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target weights (sum to 1) + + The algorithm used for solving the problem is the Sinkhorn-Knopp matrix + scaling algorithm as proposed in [2]_ but with the log stabilization + proposed in [10]_ an defined in [9]_ (Algo 3.1) . + + + Parameters + ---------- + a : np.ndarray (ns,) + samples weights in the source domain + b : np.ndarray (nt,) + samples in the target domain + M : np.ndarray (ns,nt) + loss matrix + reg : float + Regularization term >0 + tau : float + thershold for max value in u or v for log scaling + warmstart : tible of vectors + if given then sarting values for alpha an beta log scalings + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + gamma : (ns x nt) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + Examples + -------- + + >>> a=[.5,.5] + >>> b=[.5,.5] + >>> M=[[0.,1.],[1.,0.]] + >>> ot.sinkhorn(a,b,M,1) + array([[ 0.36552929, 0.13447071], + [ 0.13447071, 0.36552929]]) + + + References + ---------- + + .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013 + + .. [9] Schmitzer, B. (2016). Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems. arXiv preprint arXiv:1610.06519. + + .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + + + See Also + -------- + ot.lp.emd : Unregularized OT + ot.optim.cg : General regularized OT + + """ + + a=np.asarray(a,dtype=np.float64) + b=np.asarray(b,dtype=np.float64) + M=np.asarray(M,dtype=np.float64) + + if len(a)==0: + a=np.ones((M.shape[0],),dtype=np.float64)/M.shape[0] + if len(b)==0: + b=np.ones((M.shape[1],),dtype=np.float64)/M.shape[1] + + # init data + na = len(a) + nb = len(b) + + + cpt = 0 + if log: + log={'err':[]} + + # we assume that no distances are null except those of the diagonal of distances + if warmstart is None: + alpha,beta=np.zeros(na),np.zeros(nb) + else: + alpha,beta=warmstart + u,v = np.ones(na)/na,np.ones(nb)/nb + uprev,vprev=np.zeros(na),np.zeros(nb) + + + #print reg + + + def get_K(alpha,beta): + """log space computation""" + return np.exp(-(M-alpha.reshape((na,1))-beta.reshape((1,nb)))/reg) + + def get_Gamma(alpha,beta,u,v): + """log space gamma computation""" + return np.exp(-(M-alpha.reshape((na,1))-beta.reshape((1,nb)))/reg+np.log(u.reshape((na,1)))+np.log(v.reshape((1,nb)))) + + #print np.min(K) + + K=get_K(alpha,beta) + transp = K + loop=1 + cpt = 0 + err=1 + while loop: + + if np.abs(u).max()>tau or np.abs(v).max()>tau: + alpha,beta=alpha+reg*np.log(u),beta+reg*np.log(v) + u,v = np.ones(na)/na,np.ones(nb)/nb + K=get_K(alpha,beta) + + uprev = u + vprev = v + v = b/np.dot(K.T,u) + u = a/np.dot(K,v) + + + + if cpt%print_period==0: + # we can speed up the process by checking for the error only all the 10th iterations + transp = get_Gamma(alpha,beta,u,v) + err = np.linalg.norm((np.sum(transp,axis=0)-b))**2 + if log: + log['err'].append(err) + + if verbose: + if cpt%(print_period*20) ==0: + print('{:5s}|{:12s}'.format('It.','Err')+'\n'+'-'*19) + print('{:5d}|{:8e}|'.format(cpt,err)) + + + if err<=stopThr: + loop=False + + if cpt>=numItermax: + loop=False + + + if np.any(np.dot(K.T,u)==0) or np.any(np.isnan(u)) or np.any(np.isnan(v)): + # we have reached the machine precision + # come back to previous solution and quit loop + print('Warning: numerical errrors') + if cpt!=0: + u = uprev + v = vprev + break + + cpt = cpt +1 + #print 'err=',err,' cpt=',cpt + if log: + log['logu']=alpha/reg+np.log(u) + log['logv']=beta/reg+np.log(v) + log['alpha']=alpha+reg*np.log(u) + log['beta']=beta+reg*np.log(v) + log['warmstart']=(log['alpha'],log['beta']) + return get_Gamma(alpha,beta,u,v),log + else: + return get_Gamma(alpha,beta,u,v) + +def sinkhorn_epsilon_scaling(a,b, M, reg, numItermax = 100, epsilon0=1e4, numInnerItermax = 100,tau=1e3, stopThr=1e-9,warmstart=None, verbose=False,print_period=10, log=False): + """ + Solve the entropic regularization optimal transport problem with log + stabilization and epsilon scaling. + + The function solves the following optimization problem: + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + where : + + - M is the (ns,nt) metric cost matrix + - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target weights (sum to 1) + + The algorithm used for solving the problem is the Sinkhorn-Knopp matrix + scaling algorithm as proposed in [2]_ but with the log stabilization + proposed in [10]_ and the log scaling proposed in [9]_ algorithm 3.2 + + + Parameters + ---------- + a : np.ndarray (ns,) + samples weights in the source domain + b : np.ndarray (nt,) + samples in the target domain + M : np.ndarray (ns,nt) + loss matrix + reg : float + Regularization term >0 + tau : float + thershold for max value in u or v for log scaling + tau : float + thershold for max value in u or v for log scaling + warmstart : tible of vectors + if given then sarting values for alpha an beta log scalings + numItermax : int, optional + Max number of iterations + numInnerItermax : int, optional + Max number of iterationsin the inner slog stabilized sinkhorn + epsilon0 : int, optional + first epsilon regularization value (then exponential decrease to reg) + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + gamma : (ns x nt) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + Examples + -------- + + >>> a=[.5,.5] + >>> b=[.5,.5] + >>> M=[[0.,1.],[1.,0.]] + >>> ot.sinkhorn(a,b,M,1) + array([[ 0.36552929, 0.13447071], + [ 0.13447071, 0.36552929]]) + + + References + ---------- + + .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013 + + .. [9] Schmitzer, B. (2016). Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems. arXiv preprint arXiv:1610.06519. + + See Also + -------- + ot.lp.emd : Unregularized OT + ot.optim.cg : General regularized OT + + """ + + a=np.asarray(a,dtype=np.float64) + b=np.asarray(b,dtype=np.float64) + M=np.asarray(M,dtype=np.float64) + + if len(a)==0: + a=np.ones((M.shape[0],),dtype=np.float64)/M.shape[0] + if len(b)==0: + b=np.ones((M.shape[1],),dtype=np.float64)/M.shape[1] + + # init data + na = len(a) + nb = len(b) + + # nrelative umerical precision with 64 bits + numItermin = 35 + numItermax=max(numItermin,numItermax) # ensure that last velue is exact + + + cpt = 0 + if log: + log={'err':[]} + + # we assume that no distances are null except those of the diagonal of distances + if warmstart is None: + alpha,beta=np.zeros(na),np.zeros(nb) + else: + alpha,beta=warmstart + + + def get_K(alpha,beta): + """log space computation""" + return np.exp(-(M-alpha.reshape((na,1))-beta.reshape((1,nb)))/reg) + + #print np.min(K) + def get_reg(n): # exponential decreasing + return (epsilon0-reg)*np.exp(-n)+reg + + loop=1 + cpt = 0 + err=1 + while loop: + + regi=get_reg(cpt) + + G,logi=sinkhorn_stabilized(a,b, M, regi, numItermax = numInnerItermax,tau=1e3, stopThr=1e-9,warmstart=(alpha,beta), verbose=False,print_period=20,tau=tau, log=True) + + alpha=logi['alpha'] + beta=logi['beta'] + + if cpt>=numItermax: + loop=False + + if cpt%(print_period)==0: # spsion nearly converged + # we can speed up the process by checking for the error only all the 10th iterations + transp = G + err = np.linalg.norm((np.sum(transp,axis=0)-b))**2+np.linalg.norm((np.sum(transp,axis=1)-a))**2 + if log: + log['err'].append(err) + + if verbose: + if cpt%(print_period*10) ==0: + print('{:5s}|{:12s}'.format('It.','Err')+'\n'+'-'*19) + print('{:5d}|{:8e}|'.format(cpt,err)) + + if err<=stopThr and cpt>numItermin: + loop=False + + cpt = cpt +1 + #print 'err=',err,' cpt=',cpt + if log: + log['alpha']=alpha + log['beta']=beta + log['warmstart']=(log['alpha'],log['beta']) + return G,log + else: + return G + def geometricBar(weights,alldistribT): """return the weighted geometric mean of distributions""" diff --git a/ot/datasets.py b/ot/datasets.py index c750812..8605691 100644 --- a/ot/datasets.py +++ b/ot/datasets.py @@ -28,7 +28,7 @@ def get_1D_gauss(n,m,s): """ x=np.arange(n,dtype=np.float64) - h=np.exp(-(x-m)**2/(2*s^2)) + h=np.exp(-(x-m)**2/(2*s**2)) return h/h.sum() -- cgit v1.2.3 From 4c49303fa4e1321a559ecb4066ae47a6a3f4b9d5 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 7 Nov 2016 16:53:14 +0100 Subject: v0.1.10 --- ot/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/__init__.py b/ot/__init__.py index 7d79058..c887cad 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -17,7 +17,7 @@ from .da import sinkhorn_lpl1_mm # utils functions from .utils import dist, unif -__version__ = "0.1.9" +__version__ = "0.1.10" __all__ = ["emd", "sinkhorn", "utils", 'datasets', 'bregman', 'lp', 'plot', 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim'] -- cgit v1.2.3 From 7337331111d02692e32a13dd02cb9e54b84f058b Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 7 Nov 2016 16:53:55 +0100 Subject: v0.1.10 --- Makefile | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/Makefile b/Makefile index b1ecdcb..e3808df 100644 --- a/Makefile +++ b/Makefile @@ -35,7 +35,7 @@ clean : $(PYTHON) setup.py clean uploadpypi: - python setup.py register + #python setup.py register python setup.py sdist upload -r pypi rdoc: -- cgit v1.2.3 From 611d22fd135f77bba9ba4d4651b689a859c36a82 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 7 Nov 2016 16:55:41 +0100 Subject: doc update --- docs/source/readme.rst | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/docs/source/readme.rst b/docs/source/readme.rst index e219eb5..92fc097 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -10,7 +10,8 @@ machine learning. It provides the following solvers: - OT solver for the linear program/ Earth Movers Distance [1]. -- Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2]. +- Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] + and stabilized version [9][10]. - Bregman projections for Wasserstein barycenter [3] and unmixing [4]. - Optimal transport for domain adaptation with group lasso regularization [5] @@ -148,5 +149,12 @@ preprint arXiv:1510.06567. for discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. +[9] Schmitzer, B. (2016). Stabilized Sparse Scaling Algorithms for +Entropy Regularized Transport Problems. arXiv preprint arXiv:1610.06519. + +[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). +Scaling algorithms for unbalanced transport problems. arXiv preprint +arXiv:1607.05816. + .. |Documentation Status| image:: https://readthedocs.org/projects/pot/badge/?version=latest :target: http://pot.readthedocs.io/en/latest/?badge=latest -- cgit v1.2.3 From 8b079cb1735e063709d4421241d23d11e32b55cb Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 7 Nov 2016 17:01:20 +0100 Subject: v0.1.11 --- ot/bregman.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/bregman.py b/ot/bregman.py index b132225..91c526a 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -460,7 +460,7 @@ def sinkhorn_epsilon_scaling(a,b, M, reg, numItermax = 100, epsilon0=1e4, numInn regi=get_reg(cpt) - G,logi=sinkhorn_stabilized(a,b, M, regi, numItermax = numInnerItermax,tau=1e3, stopThr=1e-9,warmstart=(alpha,beta), verbose=False,print_period=20,tau=tau, log=True) + G,logi=sinkhorn_stabilized(a,b, M, regi, numItermax = numInnerItermax, stopThr=1e-9,warmstart=(alpha,beta), verbose=False,print_period=20,tau=tau, log=True) alpha=logi['alpha'] beta=logi['beta'] -- cgit v1.2.3 From 194d3668f963a0e0deccca8e2bc5bc638af2193d Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 7 Nov 2016 17:02:04 +0100 Subject: v0.1.11 --- ot/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/__init__.py b/ot/__init__.py index c887cad..08b57eb 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -17,7 +17,7 @@ from .da import sinkhorn_lpl1_mm # utils functions from .utils import dist, unif -__version__ = "0.1.10" +__version__ = "0.1.11" __all__ = ["emd", "sinkhorn", "utils", 'datasets', 'bregman', 'lp', 'plot', 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim'] -- cgit v1.2.3 From 8097bdda7c0fbd469eecb55fc0a1e93dd53b7fb8 Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Mon, 7 Nov 2016 23:45:38 +0100 Subject: gcg --- examples/demo_OTDA_classes.py | 2 +- examples/demo_optim_OTreg.py | 19 +++++- ot/optim.py | 136 +++++++++++++++++++++++++++++++++++++++++- 3 files changed, 152 insertions(+), 5 deletions(-) diff --git a/examples/demo_OTDA_classes.py b/examples/demo_OTDA_classes.py index f61625a..8a97c80 100644 --- a/examples/demo_OTDA_classes.py +++ b/examples/demo_OTDA_classes.py @@ -56,7 +56,7 @@ da_lpl1.fit(xs,ys,xt,reg=lambd,eta=eta) xstg=da_lpl1.interp() #%% plot interpolated source samples -pl.figure(4) +pl.figure(4,(15,10)) param_img={'interpolation':'nearest','cmap':'jet'} diff --git a/examples/demo_optim_OTreg.py b/examples/demo_optim_OTreg.py index 0a8c583..a49b00c 100644 --- a/examples/demo_optim_OTreg.py +++ b/examples/demo_optim_OTreg.py @@ -31,7 +31,7 @@ G0=ot.emd(a,b,M) pl.figure(3) ot.plot.plot1D_mat(a,b,G0,'OT matrix G0') -#%% Example with Frobenisu norm regularization +#%% Example with Frobenius norm regularization def f(G): return 0.5*np.sum(G**2) def df(G): return G @@ -43,7 +43,7 @@ Gl2=ot.optim.cg(a,b,M,reg,f,df,verbose=True) pl.figure(3) ot.plot.plot1D_mat(a,b,Gl2,'OT matrix Frob. reg') -#%% Examlple with entropic regularization +#%% Example with entropic regularization def f(G): return np.sum(G*np.log(G)) def df(G): return np.log(G)+1 @@ -53,4 +53,17 @@ reg=1e-3 Ge=ot.optim.cg(a,b,M,reg,f,df,verbose=True) pl.figure(4) -ot.plot.plot1D_mat(a,b,Ge,'OT matrix Entrop. reg') \ No newline at end of file +ot.plot.plot1D_mat(a,b,Ge,'OT matrix Entrop. reg') + +#%% Example with Frobenius norm + entropic regularization with gcg + +def f(G): return 0.5*np.sum(G**2) +def df(G): return G + +reg1=1e-3 +reg2=1e-3 + +Gel2=ot.optim.gcg(a,b,M,reg1,reg2,f,df,verbose=True) + +pl.figure(5) +ot.plot.plot1D_mat(a,b,Gel2,'OT entropic + matrix Frob. reg') \ No newline at end of file diff --git a/ot/optim.py b/ot/optim.py index 7ed658c..598e23f 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -4,9 +4,9 @@ Optimization algorithms for OT """ import numpy as np -import scipy as sp from scipy.optimize.linesearch import scalar_search_armijo from .lp import emd +from .bregman import sinkhorn_stabilized # The corresponding scipy function does not work for matrices def line_search_armijo(f,xk,pk,gfk,old_fval,args=(),c1=1e-4,alpha0=0.99): @@ -195,3 +195,137 @@ def cg(a,b,M,reg,f,df,G0=None,numItermax = 200,stopThr=1e-9,verbose=False,log=Fa else: return G +def gcg(a,b,M,reg1,reg2,f,df,G0=None,numItermax = 200,stopThr=1e-9,verbose=False,log=False): + """ + Solve the general regularized OT problem with the generalized conditional gradient + + The function solves the following optimization problem: + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg1\cdot\Omega(\gamma) + reg2\cdot f(\gamma) + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + where : + + - M is the (ns,nt) metric cost matrix + - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - :math:`f` is the regularization term ( and df is its gradient) + - a and b are source and target weights (sum to 1) + + The algorithm used for solving the problem is the generalized conditional gradient as discussed in [5,7]_ + + + Parameters + ---------- + a : np.ndarray (ns,) + samples weights in the source domain + b : np.ndarray (nt,) + samples in the target domain + M : np.ndarray (ns,nt) + loss matrix + reg1 : float + Entropic Regularization term >0 + reg2 : float + Second Regularization term >0 + G0 : np.ndarray (ns,nt), optional + initial guess (default is indep joint density) + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + Returns + ------- + gamma : (ns x nt) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 + .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567. + + See Also + -------- + ot.optim.cg : conditional gradient + + """ + + loop=1 + + if log: + log={'loss':[]} + + if G0 is None: + G=np.outer(a,b) + else: + G=G0 + + def cost(G): + return np.sum(M*G)+ reg1*np.sum(G*np.log(G)) + reg2*f(G) + + f_val=cost(G) + if log: + log['loss'].append(f_val) + + it=0 + + if verbose: + print('{:5s}|{:12s}|{:8s}'.format('It.','Loss','Delta loss')+'\n'+'-'*32) + print('{:5d}|{:8e}|{:8e}'.format(it,f_val,0)) + + while loop: + + it+=1 + old_fval=f_val + + + # problem linearization + Mi=M+reg2*df(G) + # set M positive + Mi+=Mi.min() + + # solve linear program with Sinkhorn + Gc = sinkhorn_stabilized(a,b, Mi, reg1) + + deltaG=Gc-G + + # line search + alpha,fc,f_val = line_search_armijo(cost,G,deltaG,Mi,f_val) + + G=G+alpha*deltaG + + # test convergence + if it>=numItermax: + loop=0 + + delta_fval=(f_val-old_fval)/abs(f_val) + if abs(delta_fval) Date: Tue, 8 Nov 2016 08:14:28 +0100 Subject: update doc --- docs/source/conf.py | 16 ++++++++++------ 1 file changed, 10 insertions(+), 6 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 2bb0e81..eadb396 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -71,7 +71,7 @@ source_suffix = '.rst' master_doc = 'index' # General information about the project. -project = u'POT' +project = u'POT Python Optimal Transport library' copyright = u'2016, Rémi Flamary, Nicolas Courty' author = u'Rémi Flamary, Nicolas Courty' @@ -79,10 +79,14 @@ author = u'Rémi Flamary, Nicolas Courty' # |version| and |release|, also used in various other places throughout the # built documents. # + +__version__ = re.search( + r'__version__\s*=\s*[\'"]([^\'"]*)[\'"]', # It excludes inline comment too + open('ot/__init__.py').read()).group(1) # The short X.Y version. -version = u'0.1' +version = __version__ # The full version, including alpha/beta/rc tags. -release = u'0.1' +release = __version__ # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. @@ -247,7 +251,7 @@ latex_elements = { # (source start file, target name, title, # author, documentclass [howto, manual, or own class]). latex_documents = [ - (master_doc, 'POT.tex', u'POT Documentation', + (master_doc, 'POT.tex', u'POT Python Optimal Transport library', u'Rémi Flamary, Nicolas Courty', 'manual'), ] @@ -277,7 +281,7 @@ latex_documents = [ # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ - (master_doc, 'pot', u'POT Documentation', + (master_doc, 'pot', u'POT Python Optimal Transport library Documentation', [author], 1) ] @@ -291,7 +295,7 @@ man_pages = [ # (source start file, target name, title, author, # dir menu entry, description, category) texinfo_documents = [ - (master_doc, 'POT', u'POT Documentation', + (master_doc, 'POT', u'POT Python Optimal Transport library Documentation', author, 'POT', 'One line description of project.', 'Miscellaneous'), ] -- cgit v1.2.3 From 46eb2aba620283e3bfb2ab6a0fe7bdb212f29c46 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 8 Nov 2016 08:18:17 +0100 Subject: update doc 2 --- docs/source/conf.py | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/source/conf.py b/docs/source/conf.py index eadb396..8a85e06 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -14,6 +14,7 @@ import sys import os +import re #try: from unittest.mock import MagicMock #except ImportError: -- cgit v1.2.3 From db70a830756eb5d6a9c3d76fefc479b5a3daade6 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 8 Nov 2016 08:19:55 +0100 Subject: update doc 3 --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 8a85e06..f48a868 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -83,7 +83,7 @@ author = u'Rémi Flamary, Nicolas Courty' __version__ = re.search( r'__version__\s*=\s*[\'"]([^\'"]*)[\'"]', # It excludes inline comment too - open('ot/__init__.py').read()).group(1) + open('../../ot/__init__.py').read()).group(1) # The short X.Y version. version = __version__ # The full version, including alpha/beta/rc tags. -- cgit v1.2.3 From 2ccba4bf4bd0a13f82ffeb34c05e97c80466a030 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 8 Nov 2016 09:19:30 +0100 Subject: travis CI --- Makefile | 3 +++ ot/__init__.py | 1 + ot/bregman.py | 7 +++++-- ot/lp/__init__.py | 2 +- requirements.txt | 5 +++++ test/test_load_module.py | 10 ++++++++++ travis.yml | 16 ++++++++++++++++ 7 files changed, 41 insertions(+), 3 deletions(-) create mode 100644 requirements.txt create mode 100644 test/test_load_module.py create mode 100644 travis.yml diff --git a/Makefile b/Makefile index e3808df..34e9f20 100644 --- a/Makefile +++ b/Makefile @@ -33,6 +33,9 @@ sremove : clean : $(PYTHON) setup.py clean + +test: + pytest uploadpypi: #python setup.py register diff --git a/ot/__init__.py b/ot/__init__.py index 08b57eb..a925a6f 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -1,5 +1,6 @@ """Python Optimal Transport toolbox""" + # All submodules and packages from . import lp from . import bregman diff --git a/ot/bregman.py b/ot/bregman.py index 91c526a..568ead2 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -59,6 +59,7 @@ def sinkhorn(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, log=Fa Examples -------- + >>> import ot >>> a=[.5,.5] >>> b=[.5,.5] >>> M=[[0.,1.],[1.,0.]] @@ -203,10 +204,11 @@ def sinkhorn_stabilized(a,b, M, reg, numItermax = 1000,tau=1e3, stopThr=1e-9,war Examples -------- + >>> import ot >>> a=[.5,.5] >>> b=[.5,.5] >>> M=[[0.,1.],[1.,0.]] - >>> ot.sinkhorn(a,b,M,1) + >>> ot.bregman.sinkhorn_stabilized(a,b,M,1) array([[ 0.36552929, 0.13447071], [ 0.13447071, 0.36552929]]) @@ -394,10 +396,11 @@ def sinkhorn_epsilon_scaling(a,b, M, reg, numItermax = 100, epsilon0=1e4, numInn Examples -------- + >>> import ot >>> a=[.5,.5] >>> b=[.5,.5] >>> M=[[0.,1.],[1.,0.]] - >>> ot.sinkhorn(a,b,M,1) + >>> ot.bregman.sinkhorn_epsilon_scaling(a,b,M,1) array([[ 0.36552929, 0.13447071], [ 0.13447071, 0.36552929]]) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 2adf937..1e55f5a 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -45,7 +45,7 @@ def emd(a, b, M): Simple example with obvious solution. The function emd accepts lists and perform automatic conversion to numpy arrays - + >>> import ot >>> a=[.5,.5] >>> b=[.5,.5] >>> M=[[0.,1.],[1.,0.]] diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..cd73c15 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,5 @@ +POT +numpy +scipy +cython +matplotlib diff --git a/test/test_load_module.py b/test/test_load_module.py new file mode 100644 index 0000000..a04c5df --- /dev/null +++ b/test/test_load_module.py @@ -0,0 +1,10 @@ + + +import ot +import doctest + +# test lp solver +doctest.testmod(ot.lp,verbose=True) + +# test bregman solver +doctest.testmod(ot.bregman,verbose=True) diff --git a/travis.yml b/travis.yml new file mode 100644 index 0000000..c73bd8f --- /dev/null +++ b/travis.yml @@ -0,0 +1,16 @@ +language: python +python: + - "2.6" + - "2.7" + - "3.2" + - "3.3" + - "3.4" + # does not have headers provided, please ask https://launchpad.net/~pypy/+archive/ppa + # maintainers to fix their pypy-dev package. + - "pypy" +# command to install dependencies +install: + - pip install . + - pip install -r requirements.txt +# command to run tests +script: pytest -- cgit v1.2.3 From e610d685405f570f6269e1867cf965630bd6bfe7 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 8 Nov 2016 09:20:24 +0100 Subject: travis 2 --- .travis.yml | 16 ++++++++++++++++ travis.yml | 16 ---------------- 2 files changed, 16 insertions(+), 16 deletions(-) create mode 100644 .travis.yml delete mode 100644 travis.yml diff --git a/.travis.yml b/.travis.yml new file mode 100644 index 0000000..c73bd8f --- /dev/null +++ b/.travis.yml @@ -0,0 +1,16 @@ +language: python +python: + - "2.6" + - "2.7" + - "3.2" + - "3.3" + - "3.4" + # does not have headers provided, please ask https://launchpad.net/~pypy/+archive/ppa + # maintainers to fix their pypy-dev package. + - "pypy" +# command to install dependencies +install: + - pip install . + - pip install -r requirements.txt +# command to run tests +script: pytest diff --git a/travis.yml b/travis.yml deleted file mode 100644 index c73bd8f..0000000 --- a/travis.yml +++ /dev/null @@ -1,16 +0,0 @@ -language: python -python: - - "2.6" - - "2.7" - - "3.2" - - "3.3" - - "3.4" - # does not have headers provided, please ask https://launchpad.net/~pypy/+archive/ppa - # maintainers to fix their pypy-dev package. - - "pypy" -# command to install dependencies -install: - - pip install . - - pip install -r requirements.txt -# command to run tests -script: pytest -- cgit v1.2.3 From 9cc998f506a2530b7b13a8ecd932854cf09912a1 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 8 Nov 2016 09:21:56 +0100 Subject: travis 3 --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index c73bd8f..2e9fd85 100644 --- a/.travis.yml +++ b/.travis.yml @@ -10,7 +10,7 @@ python: - "pypy" # command to install dependencies install: - - pip install . - pip install -r requirements.txt + - pip install . # command to run tests script: pytest -- cgit v1.2.3 From 6f328e065f6e8c877931609686bdfa10e7d29853 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 8 Nov 2016 09:25:35 +0100 Subject: travis 4 --- .travis.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.travis.yml b/.travis.yml index 2e9fd85..cf92ee8 100644 --- a/.travis.yml +++ b/.travis.yml @@ -10,7 +10,7 @@ python: - "pypy" # command to install dependencies install: - - pip install -r requirements.txt - - pip install . + - pip install --user -r requirements.txt + - python setup.py install --user # command to run tests script: pytest -- cgit v1.2.3 From bb610ef0e23889efcc25cb25cc1852cc1de44ee4 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 8 Nov 2016 09:27:05 +0100 Subject: travis 5 --- .travis.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.travis.yml b/.travis.yml index cf92ee8..0e0ab41 100644 --- a/.travis.yml +++ b/.travis.yml @@ -10,7 +10,7 @@ python: - "pypy" # command to install dependencies install: - - pip install --user -r requirements.txt - - python setup.py install --user + - pip install -r requirements.txt + - python setup.py install # command to run tests script: pytest -- cgit v1.2.3 From da9a3d4fa4fc4b86b05538b1db5910ee7233e0ec Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 8 Nov 2016 09:28:08 +0100 Subject: travis 6 --- requirements.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index cd73c15..d7b38b0 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,4 +1,3 @@ -POT numpy scipy cython -- cgit v1.2.3 From 05959d1c570060fdbe44b37049077e28bdba40d8 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 8 Nov 2016 09:34:29 +0100 Subject: travis 7 --- .travis.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.travis.yml b/.travis.yml index 0e0ab41..26fb52a 100644 --- a/.travis.yml +++ b/.travis.yml @@ -10,6 +10,7 @@ python: - "pypy" # command to install dependencies install: + - pip install --only-binary=numpy,scipy numpy scipy - pip install -r requirements.txt - python setup.py install # command to run tests -- cgit v1.2.3 From 3a65d6b3693c59d8273a6b8b96a2faafed6dd860 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 8 Nov 2016 09:41:47 +0100 Subject: travis 8 --- .travis.yml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.travis.yml b/.travis.yml index 26fb52a..9ab8797 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,16 +1,16 @@ language: python python: - - "2.6" + - "2.7" - - "3.2" - "3.3" - "3.4" # does not have headers provided, please ask https://launchpad.net/~pypy/+archive/ppa # maintainers to fix their pypy-dev package. - - "pypy" + # - "pypy" +before_install: + - sudo apt-get install libblas-dev liblapack-dev libatlas-base-dev gfortran # command to install dependencies install: - - pip install --only-binary=numpy,scipy numpy scipy - pip install -r requirements.txt - python setup.py install # command to run tests -- cgit v1.2.3 From daffc937892da56aafbcd3607ed47e82b08b40e5 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 8 Nov 2016 09:47:42 +0100 Subject: travis pytest --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 9ab8797..8acffbb 100644 --- a/.travis.yml +++ b/.travis.yml @@ -14,4 +14,4 @@ install: - pip install -r requirements.txt - python setup.py install # command to run tests -script: pytest +script: pytest -t test -- cgit v1.2.3 From 2e20a42897c59e2b0ab957982bacd2a5a1a1445a Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 8 Nov 2016 10:07:49 +0100 Subject: travis pytest2 --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 8acffbb..88f6517 100644 --- a/.travis.yml +++ b/.travis.yml @@ -14,4 +14,4 @@ install: - pip install -r requirements.txt - python setup.py install # command to run tests -script: pytest -t test +script: python test/test_load_module.py -v -- cgit v1.2.3 From 391a2d35bf3430bc02058f57bdf2dd765f2d81bf Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 8 Nov 2016 10:20:12 +0100 Subject: travis should work --- .travis.yml | 2 -- 1 file changed, 2 deletions(-) diff --git a/.travis.yml b/.travis.yml index 88f6517..8547582 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,8 +1,6 @@ language: python python: - - "2.7" - - "3.3" - "3.4" # does not have headers provided, please ask https://launchpad.net/~pypy/+archive/ppa # maintainers to fix their pypy-dev package. -- cgit v1.2.3 From 76aaeef0636b8c6dab3fa6158a92607faed78c57 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 8 Nov 2016 10:23:56 +0100 Subject: add travis badge to readme --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index c6d480b..8994832 100644 --- a/README.md +++ b/README.md @@ -2,6 +2,8 @@ [![Documentation Status](https://readthedocs.org/projects/pot/badge/?version=latest)](http://pot.readthedocs.io/en/latest/?badge=latest) +[![Build Status](https://travis-ci.org/rflamary/POT.svg?branch=masterhttps://travis-ci.org/rflamary/POT.svg?branch=master)](https://travis-ci.org/rflamary/POT) + This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning. -- cgit v1.2.3 From 916d0332be51f705e8d6a575e82906ba0e3e61ba Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 8 Nov 2016 10:29:17 +0100 Subject: new badge --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 8994832..dedde6b 100644 --- a/README.md +++ b/README.md @@ -2,7 +2,7 @@ [![Documentation Status](https://readthedocs.org/projects/pot/badge/?version=latest)](http://pot.readthedocs.io/en/latest/?badge=latest) -[![Build Status](https://travis-ci.org/rflamary/POT.svg?branch=masterhttps://travis-ci.org/rflamary/POT.svg?branch=master)](https://travis-ci.org/rflamary/POT) +[![Build Status](https://travis-ci.org/rflamary/POT.svg?branch=master)](https://travis-ci.org/rflamary/POT) This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning. -- cgit v1.2.3 From 23887a73d03714cd427c13f9a61774f22be24de4 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 8 Nov 2016 10:38:16 +0100 Subject: add pypi badge --- README.md | 4 ++-- docs/source/index.rst | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index dedde6b..0e550a2 100644 --- a/README.md +++ b/README.md @@ -1,8 +1,8 @@ # POT: Python Optimal Transport - -[![Documentation Status](https://readthedocs.org/projects/pot/badge/?version=latest)](http://pot.readthedocs.io/en/latest/?badge=latest) +[![PyPI version](https://badge.fury.io/py/POT.svg)](https://badge.fury.io/py/POT) [![Build Status](https://travis-ci.org/rflamary/POT.svg?branch=master)](https://travis-ci.org/rflamary/POT) +[![Documentation Status](https://readthedocs.org/projects/pot/badge/?version=latest)](http://pot.readthedocs.io/en/latest/?badge=latest) This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning. diff --git a/docs/source/index.rst b/docs/source/index.rst index 41483b8..024c4e9 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -17,7 +17,7 @@ Contents examples .. include:: readme.rst - :start-line: 5 + :start-line: 6 Indices and tables -- cgit v1.2.3 From 0d1f3eb3c41c0b06edc70647037b6cda581e8e2d Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 8 Nov 2016 13:09:34 +0100 Subject: update doc --- docs/source/readme.rst | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 92fc097..cc37bdf 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -1,7 +1,7 @@ POT: Python Optimal Transport ============================= -|Documentation Status| +|PyPI version| |Build Status| |Documentation Status| This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and @@ -156,5 +156,9 @@ Entropy Regularized Transport Problems. arXiv preprint arXiv:1610.06519. Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. +.. |PyPI version| image:: https://badge.fury.io/py/POT.svg + :target: https://badge.fury.io/py/POT +.. |Build Status| image:: https://travis-ci.org/rflamary/POT.svg?branch=master + :target: https://travis-ci.org/rflamary/POT .. |Documentation Status| image:: https://readthedocs.org/projects/pot/badge/?version=latest :target: http://pot.readthedocs.io/en/latest/?badge=latest -- cgit v1.2.3 From 22036bd9db2cd5cc8329b27ca740ff4d9c114fb7 Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Tue, 8 Nov 2016 23:15:49 +0100 Subject: da with GL --- examples/demo_OTDA_classes.py | 37 ++++++++++---- examples/demo_optim_OTreg.py | 4 +- ot/da.py | 116 ++++++++++++++++++++++++++++++++++++++++++ ot/optim.py | 13 +++-- 4 files changed, 154 insertions(+), 16 deletions(-) diff --git a/examples/demo_OTDA_classes.py b/examples/demo_OTDA_classes.py index 8a97c80..43fd37e 100644 --- a/examples/demo_OTDA_classes.py +++ b/examples/demo_OTDA_classes.py @@ -48,43 +48,62 @@ da_entrop=ot.da.OTDA_sinkhorn() da_entrop.fit(xs,xt,reg=lambd) xsts=da_entrop.interp() -# Group lasso regularization +# non-convex Group lasso regularization reg=1e-1 eta=1e0 da_lpl1=ot.da.OTDA_lpl1() -da_lpl1.fit(xs,ys,xt,reg=lambd,eta=eta) +da_lpl1.fit(xs,ys,xt,reg=reg,eta=eta) xstg=da_lpl1.interp() + +# True Group lasso regularization +reg=1e-1 +eta=1e1 +da_l1l2=ot.da.OTDA_l1l2() +da_l1l2.fit(xs,ys,xt,reg=reg,eta=eta,numItermax=20,verbose=True) +xstgl=da_l1l2.interp() + + #%% plot interpolated source samples -pl.figure(4,(15,10)) +pl.figure(4,(15,8)) param_img={'interpolation':'nearest','cmap':'jet'} -pl.subplot(2,3,1) +pl.subplot(2,4,1) pl.imshow(da_emd.G,**param_img) pl.title('OT matrix') -pl.subplot(2,3,2) +pl.subplot(2,4,2) pl.imshow(da_entrop.G,**param_img) pl.title('OT matrix sinkhorn') -pl.subplot(2,3,3) +pl.subplot(2,4,3) pl.imshow(da_lpl1.G,**param_img) +pl.title('OT matrix non-convex Group Lasso') + +pl.subplot(2,4,4) +pl.imshow(da_l1l2.G,**param_img) pl.title('OT matrix Group Lasso') -pl.subplot(2,3,4) + +pl.subplot(2,4,5) pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30) pl.title('Interp samples') pl.legend(loc=0) -pl.subplot(2,3,5) +pl.subplot(2,4,6) pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) pl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30) pl.title('Interp samples Sinkhorn') -pl.subplot(2,3,6) +pl.subplot(2,4,7) pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) pl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30) +pl.title('Interp samples non-convex Group Lasso') + +pl.subplot(2,4,8) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) +pl.scatter(xstgl[:,0],xstgl[:,1],c=ys,marker='+',label='Transp samples',s=30) pl.title('Interp samples Group Lasso') \ No newline at end of file diff --git a/examples/demo_optim_OTreg.py b/examples/demo_optim_OTreg.py index a49b00c..0de6b08 100644 --- a/examples/demo_optim_OTreg.py +++ b/examples/demo_optim_OTreg.py @@ -60,8 +60,8 @@ ot.plot.plot1D_mat(a,b,Ge,'OT matrix Entrop. reg') def f(G): return 0.5*np.sum(G**2) def df(G): return G -reg1=1e-3 -reg2=1e-3 +reg1=1e-1 +reg2=1e-1 Gel2=ot.optim.gcg(a,b,M,reg1,reg2,f,df,verbose=True) diff --git a/ot/da.py b/ot/da.py index fb40782..81b6a35 100644 --- a/ot/da.py +++ b/ot/da.py @@ -8,6 +8,7 @@ from .bregman import sinkhorn from .lp import emd from .utils import unif,dist,kernel from .optim import cg +from .optim import gcg def indices(a, func): @@ -122,6 +123,100 @@ def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerIter return transp +def sinkhorn_l1l2_gl(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerItermax = 200,stopInnerThr=1e-9,verbose=False,log=False): + """ + Solve the entropic regularization optimal transport problem with group lasso regularization + + The function solves the following optimization problem: + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma)+ \eta \Omega_g(\gamma) + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + where : + + - M is the (ns,nt) metric cost matrix + - :math:`\Omega_e` is the entropic regularization term :math:`\Omega_e(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - :math:`\Omega_g` is the group lasso regulaization term :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^2` where :math:`\mathcal{I}_c` are the index of samples from class c in the source domain. + - a and b are source and target weights (sum to 1) + + The algorithm used for solving the problem is the generalised conditional gradient as proposed in [5]_ [7]_ + + + Parameters + ---------- + a : np.ndarray (ns,) + samples weights in the source domain + labels_a : np.ndarray (ns,) + labels of samples in the source domain + b : np.ndarray (nt,) + samples in the target domain + M : np.ndarray (ns,nt) + loss matrix + reg : float + Regularization term for entropic regularization >0 + eta : float, optional + Regularization term for group lasso regularization >0 + numItermax : int, optional + Max number of iterations + numInnerItermax : int, optional + Max number of iterations (inner sinkhorn solver) + stopInnerThr : float, optional + Stop threshold on error (inner sinkhorn solver) (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + gamma : (ns x nt) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 + .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567. + + See Also + -------- + ot.optim.gcg : Generalized conditional gradient for OT problems + + """ + lstlab=np.unique(labels_a) + + def f(G): + res=0 + for i in range(G.shape[1]): + for lab in lstlab: + temp=G[labels_a==lab,i] + res+=np.linalg.norm(temp) + return res + + def df(G): + W=np.zeros(G.shape) + for i in range(G.shape[1]): + for lab in lstlab: + temp=G[labels_a==lab,i] + n=np.linalg.norm(temp) + if n: + W[labels_a==lab,i]=temp/n + return W + + + return gcg(a,b,M,reg,eta,f,df,G0=None,numItermax = numItermax,numInnerItermax=numInnerItermax, stopThr=stopInnerThr,verbose=verbose,log=log) + + + def joint_OT_mapping_linear(xs,xt,mu=1,eta=0.001,bias=False,verbose=False,verbose2=False,numItermax = 100,numInnerItermax = 10,stopInnerThr=1e-6,stopThr=1e-5,log=False,**kwargs): """Joint OT and linear mapping estimation as proposed in [8] @@ -632,6 +727,27 @@ class OTDA_lpl1(OTDA): self.M=dist(xs,xt,metric=self.metric) self.G=sinkhorn_lpl1_mm(ws,ys,wt,self.M,reg,eta,**kwargs) self.computed=True + +class OTDA_l1l2(OTDA): + """Class for domain adaptation with optimal transport with entropic and group lasso regularization""" + + + def fit(self,xs,ys,xt,reg=1,eta=1,ws=None,wt=None,**kwargs): + """ Fit regularized domain adaptation between samples is xs and xt (with optional weights), See ot.da.sinkhorn_lpl1_gl for fit parameters""" + self.xs=xs + self.xt=xt + + if wt is None: + wt=unif(xt.shape[0]) + if ws is None: + ws=unif(xs.shape[0]) + + self.ws=ws + self.wt=wt + + self.M=dist(xs,xt,metric=self.metric) + self.G=sinkhorn_l1l2_gl(ws,ys,wt,self.M,reg,eta,**kwargs) + self.computed=True class OTDA_mapping_linear(OTDA): """Class for optimal transport with joint linear mapping estimation as in [8]""" diff --git a/ot/optim.py b/ot/optim.py index 598e23f..d807824 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -7,6 +7,7 @@ import numpy as np from scipy.optimize.linesearch import scalar_search_armijo from .lp import emd from .bregman import sinkhorn_stabilized +from .bregman import sinkhorn # The corresponding scipy function does not work for matrices def line_search_armijo(f,xk,pk,gfk,old_fval,args=(),c1=1e-4,alpha0=0.99): @@ -195,7 +196,7 @@ def cg(a,b,M,reg,f,df,G0=None,numItermax = 200,stopThr=1e-9,verbose=False,log=Fa else: return G -def gcg(a,b,M,reg1,reg2,f,df,G0=None,numItermax = 200,stopThr=1e-9,verbose=False,log=False): +def gcg(a,b,M,reg1,reg2,f,df,G0=None,numItermax = 10,numInnerItermax = 200,stopThr=1e-9,verbose=False,log=False): """ Solve the general regularized OT problem with the generalized conditional gradient @@ -235,6 +236,8 @@ def gcg(a,b,M,reg1,reg2,f,df,G0=None,numItermax = 200,stopThr=1e-9,verbose=False initial guess (default is indep joint density) numItermax : int, optional Max number of iterations + numInnerItermax : int, optional + Max number of iterations of Sinkhorn stopThr : float, optional Stop threshol on error (>0) verbose : bool, optional @@ -293,16 +296,16 @@ def gcg(a,b,M,reg1,reg2,f,df,G0=None,numItermax = 200,stopThr=1e-9,verbose=False # problem linearization Mi=M+reg2*df(G) - # set M positive - Mi+=Mi.min() # solve linear program with Sinkhorn - Gc = sinkhorn_stabilized(a,b, Mi, reg1) + #Gc = sinkhorn_stabilized(a,b, Mi, reg1, numItermax = numInnerItermax) + Gc = sinkhorn(a,b, Mi, reg1, numItermax = numInnerItermax) deltaG=Gc-G # line search - alpha,fc,f_val = line_search_armijo(cost,G,deltaG,Mi,f_val) + dcost=Mi+reg1*np.sum(deltaG*(1+np.log(G))) #?? + alpha,fc,f_val = line_search_armijo(cost,G,deltaG,dcost,f_val) G=G+alpha*deltaG -- cgit v1.2.3 From 530eb0413f9c3cf3305ea5154ac4286c91665302 Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Wed, 9 Nov 2016 00:15:10 +0100 Subject: da with GL --- examples/demo_OTDA_classes.py | 2 +- ot/optim.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/demo_OTDA_classes.py b/examples/demo_OTDA_classes.py index 43fd37e..bd52fdf 100644 --- a/examples/demo_OTDA_classes.py +++ b/examples/demo_OTDA_classes.py @@ -58,7 +58,7 @@ xstg=da_lpl1.interp() # True Group lasso regularization reg=1e-1 -eta=1e1 +eta=1e0 da_l1l2=ot.da.OTDA_l1l2() da_l1l2.fit(xs,ys,xt,reg=reg,eta=eta,numItermax=20,verbose=True) xstgl=da_l1l2.interp() diff --git a/ot/optim.py b/ot/optim.py index d807824..760b3c6 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -304,7 +304,7 @@ def gcg(a,b,M,reg1,reg2,f,df,G0=None,numItermax = 10,numInnerItermax = 200,stopT deltaG=Gc-G # line search - dcost=Mi+reg1*np.sum(deltaG*(1+np.log(G))) #?? + dcost=Mi+reg1*(1+np.log(G)) #?? alpha,fc,f_val = line_search_armijo(cost,G,deltaG,dcost,f_val) G=G+alpha*deltaG -- cgit v1.2.3 From e3c0d3e1251e70df25614f8dc458e46897d2efa7 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 24 Nov 2016 10:42:07 +0100 Subject: add michael to contributors --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index 0e550a2..4bd03f9 100644 --- a/README.md +++ b/README.md @@ -75,6 +75,7 @@ The contributors to this library are: * [Rémi Flamary](http://remi.flamary.com/) * [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/) * [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/) +* [Michael Perrot](http://perso.univ-st-etienne.fr/pem82055/) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): -- cgit v1.2.3 From 0a7ce083728568d86554c7b3541ea72ff1d758e1 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 1 Dec 2016 16:21:27 +0100 Subject: update barycenter demo --- examples/Demo_1D_barycenter.ipynb | 437 ++++++++++++++++++++++++-------------- examples/demo_OTDA_classes.py | 2 +- examples/demo_barycenter_1D.py | 86 +++++++- ot/datasets.py | 2 +- 4 files changed, 366 insertions(+), 161 deletions(-) diff --git a/examples/Demo_1D_barycenter.ipynb b/examples/Demo_1D_barycenter.ipynb index 0d72471..c93d00b 100644 --- a/examples/Demo_1D_barycenter.ipynb +++ b/examples/Demo_1D_barycenter.ipynb @@ -1,168 +1,297 @@ { - "metadata": { - "name": "", - "signature": "sha256:52487801ef7f544098c7df1333a41148cb4d9e7393c84ea171d60b030b40c19f" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ + "cells": [ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "#1D Wasserstein barycenter demo" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Dataset Generation" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "n=100 # nb bins\n", - "\n", - "# bin positions\n", - "x=np.arange(n,dtype=np.float64)\n", - "\n", - "# Gaussian distributions\n", - "a1=ot.datasets.get_1D_gauss(n,m=20,s=20) # m= mean, s= std\n", - "a2=ot.datasets.get_1D_gauss(n,m=60,s=60)\n", - "\n", - "# creating matrix A containing all distributions\n", - "A=np.vstack((a1,a2)).T\n", - "nbd=A.shape[1]\n", - "\n", - "# loss matrix + normalization\n", - "M=ot.utils.dist0(n)\n", - "M/=M.max()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot distributions" - ] - }, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1D Wasserstein barycenter demo" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot\n", + "\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "from matplotlib.collections import PolyCollection\n", + "from matplotlib.colors import colorConverter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dataset Generation" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n=100 # nb bins\n", + "\n", + "# bin positions\n", + "x=np.arange(n,dtype=np.float64)\n", + "\n", + "# Gaussian distributions\n", + "a1=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std\n", + "a2=ot.datasets.get_1D_gauss(n,m=60,s=8)\n", + "\n", + "# creating matrix A containing all distributions\n", + "A=np.vstack((a1,a2)).T\n", + "nbd=A.shape[1]\n", + "\n", + "# loss matrix + normalization\n", + "M=ot.utils.dist0(n)\n", + "M/=M.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot distributions" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "pl.figure(1)\n", - "for i in range(nbd):\n", - " pl.plot(x,A[:,i])\n", - "pl.title('Distributions')" - ], - "language": "python", + "data": { + "image/png": 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tH/b/IY3rNuZ/5v1PrV5XJNGosAiBzZvh4MHkLixAS07l+D228DF27t/Jz86t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\nZbkOA17PaGHhh6wsGD7cTomekABDh7odkaopIhA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pk1KHSzteytvfve11KCZCqoVpv8f6w6vqEqCniHQBXhORD1X1aOAH0tPTjy37\nfD58Pl+YQqu6DRvgrLO8jqJiSquBonX+4lgzN2cuZ7Q6g6Z1mnodSkgN7TmUfyz+B7eddpvXoZgy\nZGZmkpmZGbL9BZMAtgD+l41W7jp/+UBrYKuIJAP1VXW3fwFVzRaRA0BPYHngQfwTQLSLlU5g/koT\nwHnneR1JfMhYlcHQnkO9DiPkBnQcwE3v3cS2/dvipm0jngT+OB4/fnyV9hdMFdBSoKOItBGRFGAo\nENhjZA4w2l2+FvgUQETaugkBEWkDdAFyqxSxx1SdgeBiNQGYqtt7eC+ffP8JV3aNi4fafqZmtZoM\n6jKIt757y+tQTASUmwDcOv2xwHxgNZChqmtEZLyIDHSLTQZSRSQHuBd4yF3/a2CliCwH3gZuD7wz\niDXbtkG9es4rlnTsaI+Chsp72e/ha+ujYc2GXocSFjY2UOIIqg1AVefh/Hr3XzfOb/kI8Iu+8Kr6\nBvBGFWOMKrHWAFzK7gBCJ2NVBqN6j/I6jLC5sP2FjHp3FLkFubRt2NbrcEwYxfwz+ZEW6wlA1etI\nYtvOgztZuHkhg7oM8jqUsKmeXJ2ru13NzNUzvQ7FhJklgAqK1QTQuLEzec2uXV5HEtveWfMOAzoO\noG5KXa9DCauhPa0aKBFYAqigWHwCqJS1A1RdxqoMhvaIv6d/Ap3b5ly2HdhG9s5sr0MxYWQJoIK+\n+w66d/c6isrp1g3WrPE6iti1bf82VmxfwaWdLvU6lLBLTkrmuu7X2dAQcc4SQAUUFUFODnQNHAkp\nRnTvDqtXex1F7Jq5eiaDOg+iZrWaXocSEUN7DmX6qumoNRzFLUsAFbBxIzRvDnXqeB1J5fTo4dzB\nmMqZljWN4b2Gex1GxJzR6gwOFx1mxfYVXodiwsQSQAXEcvUP2B1AVeTsymHT3k1c2P5Cr0OJGBFh\nRK8RTPt2mtehmDCxBFABsZ4A2raF//wH9tvUrxU2LWsaQ3oMoVpSuIbPik4jeo1g+qrpFJfYULLx\nyBJABaxe7VSjxKrkZKf9whqCK0ZVmZY1jRG9R3gdSsR1a9qN5nWbk5mb6XUoJgwsAVRArN8BgBO/\ntQNUzNKtSwHo16Kfx5F4Y0SvEUzLsmqgeGQJIEjFxZCd7TxKGcusIbjipn07jRG9RiAi5ReOQ0N7\nDuXdte9yqPCQ16GYELMEEKTvv4dmzaBujHcAtYbgiikqKSJjdQYjeiVe9U+plvVb0vfkvry/7n2v\nQzEhZgmiKV9RAAAVxklEQVQgSLFe/1/K7gAq5uONH9O2YVs6NenkdSiesmqg+GQJIEjxUP8P0K4d\n7NgBBw54HUlsmJY1LaF//Ze6qttVfJb7GbsPxfRo7iaAJYAgxcsdQHIydOkCa9d6HUn0239kP3Oy\n58TlzF8V1aBmAwZ0HGADxMUZSwBBipc7ALB2gGDNXD0TX1sfzeo08zqUqHBjnxuZ8s0Ur8MwIRRU\nAhCRASKyVkTWiciDZWxPEZEMEckRkUUikuauv1BElonIShFZKiIxOSNtcbHziznWnwAqZe0AwZny\nzRRu7HOj12FEjYvaX8S2/dtY9cMqr0MxIVJuAhCRJGAicAnQAxgmIoHDoY0BdqtqJ+BZYIK7/j/A\nQFU9BbgBeD1EcUdUbi40bRp700Aej/UFKN+6XetYv3s9l3W6zOtQokZyUjLXn3I9U1bYXUC8COYO\noD+Qo6p5qloIZACDA8oMBqa6y7OACwBUdaWqbneXVwM1RaR6SCKPoHip/y/Vo4dVAZXn1W9eZWTv\nkVRPjrnTNaxu6HMDb2S9QWFxodehmBAIJgG0BDb7vc9315VZxp1EvkBEGvsXEJFrgOVuEokp8VT/\nD86TQNu3w48/eh1JdCouKea1la9Z9U8ZOjfpTKfGnfgg5wOvQzEhEK6RrX7WZVJEegD/C1x0vA+k\np6cfW/b5fPh8vjCFVnGrV8N5Mdl6UbZq1aBTJ6ddo29fr6OJPh9t/IgW9VrQo1kc3faFUGlj8OCu\ngRUBJtwyMzPJzMwM2f6kvMkeROQMIF1VB7jvHwJUVZ/wK/OhW2axiCQD21S1mbutFfAJMFpVvzrO\nMTSaJ53o2xdeeAFOP93rSEJn+HC49FIYNcrrSKLPkFlD8LXxcXu/270OJSrtP7Kf1n9rzbq71tkT\nUh4TEVS10mOUBFMFtBToKCJtRCQFGArMDigzBxjtLl8LfOoG1xB4H3jweBf/aBdvTwCV6t4dVtnD\nHL+w6+Au/rX+X/bs/wnUq1GPK7teyesrY/KZDuOn3ATg1umPBeYDq4EMVV0jIuNFZKBbbDKQKiI5\nwL3AQ+76O4EOwJ9FZIWILBeR1JD/FWG0Zg20bAn163sdSWj17Qtff+11FNHn1W9e5YouV9CoViOv\nQ4lqt/a9lUlfT6JES7wOxVRBuVVAEQkiiquAJk+Gzz6DN97wOpLQ2rkTOnSAPXsgyboDAlCiJXR+\nrjPTrprG6a3iqL4vDFSVX036FU9e9CQXdThu054Js0hUASW0xYvjq+6/VGqq07fBhoT4yfwN82lQ\nswH9W/b3OpSoJyLc0e8Onl/6vNehmCqwBFCOeE0A4Pxdixd7HUX0eGHpC9xx2h0JO+5/RQ3vNZwv\n8r5g095NXodiKskSwAn8+CPk5MApp3gdSXhYAvhJbkEuCzcvZFivYV6HEjPqptRlZO+RvPz1y16H\nYirJEsAJfP019OoFNWp4HUl49O8PS5Z4HUV0ePnrl7m+9/XUrl7b61Biyu2n3c4/l/+To8VHvQ7F\nVIIlgBOI5+ofgD59nGkuDx70OhJvHSk6wuQVk/ndab/zOpSY061pN3o068E7a97xOhRTCZYATmDJ\nkvhOADVrOuMCLV/udSTemvXdLHqf1JsuqV28DiUm3XHaHUxcMtHrMEwlWAI4gXi/AwBrB1BVnlr0\nFPecfo/XocSswV0Hk78vn6/yY7KvZ0KzBHAc27Y5jcAdOngdSXj175/YCeCjjR9RWFxowz5XQbWk\natx/5v1MWDih/MImqlgCOI7Fi52LY7w/EXj66YndEPzEwif477P/mySxfwpVcdOvbmLBpgVk78z2\nOhRTAXbWH0e81/+X6tQJ9u1zJopPNMu2LiNnVw7Detqjn1VVJ6UOd/a7kye/fNLrUEwFWAI4jkSo\n/wfnDidRq4GeWPgE9515n036EiJj+4/lnTXvsHX/Vq9DMUGyBFCG4mJYtgz69fM6kshIxASQsyuH\nzNxMbj71Zq9DiRtNajdhVO9RPPvVs16HYoJkCaAM2dnOODmpMTVuaeUlYjvAU18+xe/6/o66KXW9\nDiWu/P7M3zN5xWQKDhd4HYoJgiWAMixYAGee6XUUkVOaAI4mSGfO7/d8z6w1s7j79Lu9DiXutG3Y\nliu6XMEzi57xOhQTBEsAZZg7FwYM8DqKyElNhS5dnMSXCMZljuOu/nfRtE5Tr0OJS+N+M47nlz7P\njgMJ+GRBjLEEEODwYWf8/0RKAACXX+4kvniXtSOL+Rvmc9+Z93kdStxq27Ato3qP4q///qvXoZhy\nBJUARGSAiKwVkXUi8mAZ21NEJENEckRkkYikuesbi8inIrJfRP4R6uDD4fPPnQHgmjTxOpLISpQE\n8PCnD/OHX/+B+jXibIq3KPPHc/7Im1lvsnHPRq9DMSdQbgIQkSRgInAJ0AMYJiJdA4qNAXaraifg\nWaC0S+Bh4BHg/pBFHGZz5zoXw0Rz6qlQUAAbNngdSfgs2LSArB1ZNuhbBDSt05S7+t/FuMxxXodi\nTiCYO4D+QI6q5qlqIZABDA4oMxiY6i7PAi4AUNWDqvolcCRE8YaVauImgKQkuOyy+L0LUFUe+vgh\nxvvGU6NanI7vHWXuO/M+PtrwEd/u+NbrUMxxBJMAWgKb/d7nu+vKLONOIl8gIo1DEmEEZWc7T8L0\n7u11JN6I52qgt9e8zd4jexnZe6TXoSSMejXq8cdz/sjv//V7onXO70RXLUz7rfAIOunp6ceWfT4f\nPp8vhOEEZ+5c51dwvI//czwXXQQ33AAHDkDdOHo8fu/hvdwz7x5mXDOD5KRkr8NJKLf3u51XvnmF\naVnTLPmGQGZmJpmZmSHbn5SXmUXkDCBdVQe47x8CVFWf8CvzoVtmsYgkA9tUtZnf9tFAX1Ut88Fr\nEdFo+IVw/vlw771wxRVeR+KdCy6Au++GwYGVfDHsrg/u4lDRIf55xT+9DiUhLdmyhMEZg1l9x2oa\n14q5ioGoJiKoaqV/sgZTBbQU6CgibUQkBRgKzA4oMwcY7S5fC3xaVqyVDTIS9u6FpUudC2Aii7dq\noKVblvLWd28x4SIbqtgr/Vv25+puV/PQxw95HYoJUG4CcOv0xwLzgdVAhqquEZHxIjLQLTYZSBWR\nHOBe4Nj/aRH5HngaGC0im8p4gigqfPQRnH021KnjdSTeuvxy+OADp0E81hWVFHHr+7fy5EVP2i9P\nj/31/L8yN2cuCzYlSG/DGFFuFVBEgoiCKqDRo+G00+CuuzwNw3OqzhDRM2c6j4bGsicXPsm8DfP4\neNTHSKI27ESRmatnMv7z8Sy7ZRm1qtfyOpy4UNUqIEsAwO7dzsxfa9ZA8+aehRE10tNh+3Z46SWv\nI6m8JVuWMPDNgSy+eTHtGrXzOhyD8yjusLeH0bBmQ14aGMMnVxSJRBtA3HvlFafqwy7+jttugxkz\nYM8eryOpnILDBQyZNYSXBr5kF/8oIiK8POhlPt74MTNWzfA6HIPdAVBcDB07QkZGYkwAE6zhw50q\nsftibMgcVeWat66hRd0WPHfZc16HY8qwfNtyLnnjEhaNWUTHxh29Diem2R1AFc2d64z9bxf/n7vr\nLnj+eSdBxpLnlz5PbkEuT13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- "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(1)\n", + "for i in range(nbd):\n", + " pl.plot(x,A[:,i])\n", + "pl.title('Distributions')\n", + "pl.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Barycenter computation (for l2 and Wasserstein)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "markdown", + "data": { + "image/png": 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eoap2PGAmtm+Hb76Btvl3+f4Jq1DB9cZMm+Z3JsYvKWkpdJ3YldJFS/N+x/eJ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+ "text/plain": [ + "" + ] + }, "metadata": {}, - "source": [ - "### Barycenter computation (for l2 and Wasserstein)" - ] - }, + "output_type": "display_data" + } + ], + "source": [ + "# l2bary\n", + "bary_l2=A.mean(1)\n", + "\n", + "# wasserstein\n", + "reg=1e-3\n", + "bary_wass=ot.bregman.barycenter(A,M,reg)\n", + "\n", + "pl.figure(2)\n", + "pl.clf()\n", + "pl.subplot(2,1,1)\n", + "for i in range(nbd):\n", + " pl.plot(x,A[:,i])\n", + "pl.title('Distributions')\n", + "pl.xticks([])\n", + "\n", + "pl.subplot(2,1,2)\n", + "pl.plot(x,bary_l2,'r',label='l2')\n", + "pl.plot(x,bary_wass,'g',label='Wasserstein')\n", + "pl.legend()\n", + "pl.title('Barycenters')\n", + "\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "## Barycenter interpolation" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "\n", + "nbalpha=11\n", + "alphalist=np.linspace(0,1,nbalpha)\n", + "\n", + "\n", + "B_l2=np.zeros((n,nbalpha))\n", + "\n", + "B_wass=np.copy(B_l2)\n", + "\n", + "for i in range(0,nbalpha):\n", + " alpha=alphalist[i]\n", + " weights=np.array([1-alpha,alpha])\n", + " B_l2[:,i]=A.dot(weights)\n", + " B_wass[:,i]=ot.bregman.barycenter(A,M,reg,weights)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot interpolation" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "cell_type": "code", - "collapsed": false, - "input": [ - "# l2bary\n", - "bary_l2=A.mean(1)\n", - "\n", - "# wasserstein\n", - "reg=1e-3\n", - "bary_wass=ot.bregman.barycenter(A,M,reg)\n", - "\n", - "pl.figure(2)\n", - "pl.clf()\n", - "pl.subplot(2,1,1)\n", - "for i in range(nbd):\n", - " pl.plot(x,A[:,i])\n", - "pl.title('Distributions')\n", - "\n", - "pl.subplot(2,1,2)\n", - "pl.plot(x,bary_l2,'r',label='l2')\n", - "pl.plot(x,bary_wass,'g',label='Wasserstein')\n", - "pl.legend()\n", - "pl.title('Barycenters')" - ], - "language": "python", + "data": { + "image/png": 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MnDkTy5cvxw033ICXXnoJJSUlqKysRE5Ojt/+FRUVmDhxIhYsWIBRo0Zh3bp1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Vq1eTmZmpX7P6+vpIL7qEgoKiCKs7pJa6Y5KId6brrO74+KFbIUqSSGHBbs7UFbJgwSzuu+/r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\n48aNo3PnznW8S6tDGmR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weyALvx7OFxu/4L1f36NU7lI8XelpWpRoQbrggFs51hW2AkiiY8fMUMcjR8xK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\nNbfD8Yr9+/e7HYIVIGw2UF/YswdmzYK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4//KPT/+VhFqiNiEJskSNwC9vSYVYuOCD1Wp1EOLo6GgoFAoAgF6vr8nmS512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Uk0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\nAlhjjJkMNANWAFvucIW1Uk3S/wOA7iEME+ag6QAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, "metadata": {}, - "outputs": [], - "prompt_number": 4 + "output_type": "display_data" } ], - "metadata": {} + "source": [ + "\n", + "pl.figure(3)\n", + "\n", + "#pl.subplot(1,2,1)\n", + "cmap=pl.cm.get_cmap('viridis')\n", + "verts = []\n", + "zs = alphalist\n", + "for i,z in enumerate(zs):\n", + " ys = B_l2[:,i]\n", + " verts.append(list(zip(x, ys)))\n", + "\n", + "ax = pl.gcf().gca(projection='3d')\n", + "\n", + "poly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist])\n", + "poly.set_alpha(0.7)\n", + "ax.add_collection3d(poly, zs=zs, zdir='y')\n", + "\n", + "ax.set_xlabel('x')\n", + "ax.set_xlim3d(0, n)\n", + "ax.set_ylabel('$\\\\alpha$')\n", + "ax.set_ylim3d(0,1)\n", + "ax.set_zlabel('')\n", + "ax.set_zlim3d(0, B_l2.max()*1.01)\n", + "pl.title('Barycenter interpolation with l2')\n", + "\n", + "pl.show()\n", + "\n", + "pl.figure(4)\n", + "\n", + "#pl.subplot(1,2,1)\n", + "cmap=pl.cm.get_cmap('viridis')\n", + "verts = []\n", + "zs = alphalist\n", + "for i,z in enumerate(zs):\n", + " ys = B_wass[:,i]\n", + " verts.append(list(zip(x, ys)))\n", + "\n", + "ax = pl.gcf().gca(projection='3d')\n", + "\n", + "poly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist])\n", + "poly.set_alpha(0.7)\n", + "ax.add_collection3d(poly, zs=zs, zdir='y')\n", + "\n", + "ax.set_xlabel('x')\n", + "ax.set_xlim3d(0, n)\n", + "ax.set_ylabel('$\\\\alpha$')\n", + "ax.set_ylim3d(0,1)\n", + "ax.set_zlabel('')\n", + "ax.set_zlim3d(0, B_l2.max()*1.01)\n", + "pl.title('Barycenter interpolation with Wasserstein')\n", + "\n", + "pl.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" } - ] -} \ No newline at end of file + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/examples/demo_OTDA_classes.py b/examples/demo_OTDA_classes.py index bd52fdf..4111ace 100644 --- a/examples/demo_OTDA_classes.py +++ b/examples/demo_OTDA_classes.py @@ -58,7 +58,7 @@ xstg=da_lpl1.interp() # True Group lasso regularization reg=1e-1 -eta=1e0 +eta=2e0 da_l1l2=ot.da.OTDA_l1l2() da_l1l2.fit(xs,ys,xt,reg=reg,eta=eta,numItermax=20,verbose=True) xstgl=da_l1l2.interp() diff --git a/examples/demo_barycenter_1D.py b/examples/demo_barycenter_1D.py index db1ff4a..7e478c2 100644 --- a/examples/demo_barycenter_1D.py +++ b/examples/demo_barycenter_1D.py @@ -9,7 +9,9 @@ import numpy as np import matplotlib.pylab as pl import ot - +from mpl_toolkits.mplot3d import Axes3D +from matplotlib.collections import PolyCollection +from matplotlib.colors import colorConverter #%% parameters @@ -19,8 +21,8 @@ n=100 # nb bins x=np.arange(n,dtype=np.float64) # Gaussian distributions -a1=ot.datasets.get_1D_gauss(n,m=20,s=20) # m= mean, s= std -a2=ot.datasets.get_1D_gauss(n,m=60,s=60) +a1=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std +a2=ot.datasets.get_1D_gauss(n,m=60,s=8) # creating matrix A containing all distributions A=np.vstack((a1,a2)).T @@ -39,12 +41,15 @@ pl.title('Distributions') #%% barycenter computation +alpha=0.2 # 0<=alpha<=1 +weights=np.array([1-alpha,alpha]) + # l2bary -bary_l2=A.mean(1) +bary_l2=A.dot(weights) # wasserstein reg=1e-3 -bary_wass=ot.bregman.barycenter(A,M,reg) +bary_wass=ot.bregman.barycenter(A,M,reg,weights) pl.figure(2) pl.clf() @@ -58,3 +63,74 @@ pl.plot(x,bary_l2,'r',label='l2') pl.plot(x,bary_wass,'g',label='Wasserstein') pl.legend() pl.title('Barycenters') + + +#%% barycenter interpolation + +nbalpha=11 +alphalist=np.linspace(0,1,nbalpha) + + +B_l2=np.zeros((n,nbalpha)) + +B_wass=np.copy(B_l2) + +for i in range(0,nbalpha): + alpha=alphalist[i] + weights=np.array([1-alpha,alpha]) + B_l2[:,i]=A.dot(weights) + B_wass[:,i]=ot.bregman.barycenter(A,M,reg,weights) + +#%% plot interpolation + +pl.figure(3,(10,5)) + +#pl.subplot(1,2,1) +cmap=pl.cm.get_cmap('viridis') +verts = [] +zs = alphalist +for i,z in enumerate(zs): + ys = B_l2[:,i] + verts.append(list(zip(x, ys))) + +ax = pl.gcf().gca(projection='3d') + +poly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist]) +poly.set_alpha(0.7) +ax.add_collection3d(poly, zs=zs, zdir='y') + +ax.set_xlabel('x') +ax.set_xlim3d(0, n) +ax.set_ylabel('$\\alpha$') +ax.set_ylim3d(0,1) +ax.set_zlabel('') +ax.set_zlim3d(0, B_l2.max()*1.01) +pl.title('Barycenter interpolation with l2') + +pl.show() + +pl.figure(4,(10,5)) + +#pl.subplot(1,2,1) +cmap=pl.cm.get_cmap('viridis') +verts = [] +zs = alphalist +for i,z in enumerate(zs): + ys = B_wass[:,i] + verts.append(list(zip(x, ys))) + +ax = pl.gcf().gca(projection='3d') + +poly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist]) +poly.set_alpha(0.7) +ax.add_collection3d(poly, zs=zs, zdir='y') + +ax.set_xlabel('x') +ax.set_xlim3d(0, n) +ax.set_ylabel('$\\alpha$') +ax.set_ylim3d(0,1) +ax.set_zlabel('') +ax.set_zlim3d(0, B_l2.max()*1.01) +pl.title('Barycenter interpolation with Wasserstein') + +pl.show() \ No newline at end of file diff --git a/ot/datasets.py b/ot/datasets.py index 8605691..5c1ef78 100644 --- a/ot/datasets.py +++ b/ot/datasets.py @@ -128,4 +128,4 @@ def get_data_classif(dataset,n,nz=.5,theta=0,**kwargs): y=0 print("unknown dataset") - return x,y.astype(int) \ No newline at end of file + return x,y \ No newline at end of file -- cgit v1.2.3 From e2733d354c87bdc3cbc58a5e3e838e688fafe627 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 2 Dec 2016 10:20:12 +0100 Subject: cleanup pyflakes issue #23 --- examples/demo_OTDA_classes.py | 1 - examples/demo_barycenter_1D.py | 3 +-- ot/optim.py | 3 +-- 3 files changed, 2 insertions(+), 5 deletions(-) diff --git a/examples/demo_OTDA_classes.py b/examples/demo_OTDA_classes.py index 4111ace..b2da7de 100644 --- a/examples/demo_OTDA_classes.py +++ b/examples/demo_OTDA_classes.py @@ -3,7 +3,6 @@ demo of Optimal transport for domain adaptation """ -import numpy as np import matplotlib.pylab as pl import ot diff --git a/examples/demo_barycenter_1D.py b/examples/demo_barycenter_1D.py index 7e478c2..297b872 100644 --- a/examples/demo_barycenter_1D.py +++ b/examples/demo_barycenter_1D.py @@ -9,9 +9,8 @@ import numpy as np import matplotlib.pylab as pl import ot -from mpl_toolkits.mplot3d import Axes3D from matplotlib.collections import PolyCollection -from matplotlib.colors import colorConverter + #%% parameters diff --git a/ot/optim.py b/ot/optim.py index 760b3c6..79f4f66 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -6,7 +6,6 @@ Optimization algorithms for OT import numpy as np from scipy.optimize.linesearch import scalar_search_armijo from .lp import emd -from .bregman import sinkhorn_stabilized from .bregman import sinkhorn # The corresponding scipy function does not work for matrices @@ -261,7 +260,7 @@ def gcg(a,b,M,reg1,reg2,f,df,G0=None,numItermax = 10,numInnerItermax = 200,stopT See Also -------- - ot.optim.cg : conditional gradient + ot.optim.cg : conditional gradient """ -- cgit v1.2.3 From 8dbfd3edae649f5f3e87be4a3ce446c59729b2f7 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 2 Dec 2016 10:40:09 +0100 Subject: restore 3d axes in exmaple --- examples/demo_barycenter_1D.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/demo_barycenter_1D.py b/examples/demo_barycenter_1D.py index 297b872..5466332 100644 --- a/examples/demo_barycenter_1D.py +++ b/examples/demo_barycenter_1D.py @@ -8,7 +8,7 @@ import numpy as np import matplotlib.pylab as pl import ot - +from mpl_toolkits.mplot3d import Axes3D #necessary for 3d plot even if not used from matplotlib.collections import PolyCollection -- cgit v1.2.3 From f439f777084690ecbf54bcd8d67dadc883fffa31 Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Fri, 2 Dec 2016 12:49:38 +0100 Subject: first attempt to support sphinx-gallery --- docs/source/conf.py | 30 ++++-- docs/source/index.rst | 2 +- examples/demo_OTDA_2D.py | 117 --------------------- examples/demo_OTDA_classes.py | 108 -------------------- examples/demo_OTDA_color_images.py | 143 -------------------------- examples/demo_OTDA_mapping.py | 113 --------------------- examples/demo_OTDA_mapping_color_images.py | 157 ---------------------------- examples/demo_OT_1D.py | 54 ---------- examples/demo_OT_2D_samples.py | 78 -------------- examples/demo_barycenter_1D.py | 135 ------------------------ examples/demo_optim_OTreg.py | 69 ------------- examples/plot_OTDA_2D.py | 120 ++++++++++++++++++++++ examples/plot_OTDA_classes.py | 111 ++++++++++++++++++++ examples/plot_OTDA_color_images.py | 145 ++++++++++++++++++++++++++ examples/plot_OTDA_mapping.py | 110 ++++++++++++++++++++ examples/plot_OTDA_mapping_color_images.py | 158 +++++++++++++++++++++++++++++ examples/plot_OT_1D.py | 56 ++++++++++ examples/plot_OT_2D_samples.py | 76 ++++++++++++++ examples/plot_barycenter_1D.py | 135 ++++++++++++++++++++++++ examples/plot_optim_OTreg.py | 69 +++++++++++++ requirements.txt | 1 + 21 files changed, 1003 insertions(+), 984 deletions(-) delete mode 100644 examples/demo_OTDA_2D.py delete mode 100644 examples/demo_OTDA_classes.py delete mode 100644 examples/demo_OTDA_color_images.py delete mode 100644 examples/demo_OTDA_mapping.py delete mode 100644 examples/demo_OTDA_mapping_color_images.py delete mode 100644 examples/demo_OT_1D.py delete mode 100644 examples/demo_OT_2D_samples.py delete mode 100644 examples/demo_barycenter_1D.py delete mode 100644 examples/demo_optim_OTreg.py create mode 100644 examples/plot_OTDA_2D.py create mode 100644 examples/plot_OTDA_classes.py create mode 100644 examples/plot_OTDA_color_images.py create mode 100644 examples/plot_OTDA_mapping.py create mode 100644 examples/plot_OTDA_mapping_color_images.py create mode 100644 examples/plot_OT_1D.py create mode 100644 examples/plot_OT_2D_samples.py create mode 100644 examples/plot_barycenter_1D.py create mode 100644 examples/plot_optim_OTreg.py diff --git a/docs/source/conf.py b/docs/source/conf.py index f48a868..61505cc 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -15,23 +15,25 @@ import sys import os import re +import sphinx_gallery + #try: -from unittest.mock import MagicMock +# from unittest.mock import MagicMock #except ImportError: # from mock import MagicMock -sys.path.insert(0, os.path.abspath("../..")) +# sys.path.insert(0, os.path.abspath("../..")) #sys.setrecursionlimit(1500) -class Mock(MagicMock): - @classmethod - def __getattr__(cls, name): - return Mock() +# class Mock(MagicMock): +# @classmethod +# def __getattr__(cls, name): +# return Mock() -MOCK_MODULES = [ 'emd','ot.lp.emd'] -sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) +# MOCK_MODULES = [ 'emd','ot.lp.emd'] +# sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the @@ -54,7 +56,9 @@ extensions = [ 'sphinx.ext.coverage', 'sphinx.ext.mathjax', 'sphinx.ext.ifconfig', - 'sphinx.ext.viewcode', 'sphinx.ext.napoleon' + 'sphinx.ext.viewcode', + 'sphinx.ext.napoleon', + 'sphinx_gallery.gen_gallery', ] # Add any paths that contain templates here, relative to this directory. @@ -316,3 +320,11 @@ texinfo_documents = [ # Example configuration for intersphinx: refer to the Python standard library. intersphinx_mapping = {'https://docs.python.org/': None} + +sphinx_gallery_conf = { + 'examples_dirs': '../../examples', + 'gallery_dirs': 'auto_examples', + 'reference_url': { + 'numpy': 'http://docs.scipy.org/doc/numpy-1.9.1', + 'scipy': 'http://docs.scipy.org/doc/scipy-0.17.0/reference'} +} diff --git a/docs/source/index.rst b/docs/source/index.rst index 024c4e9..e3c4d36 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -14,7 +14,7 @@ Contents self all - examples + auto_examples .. include:: readme.rst :start-line: 6 diff --git a/examples/demo_OTDA_2D.py b/examples/demo_OTDA_2D.py deleted file mode 100644 index fbaf56d..0000000 --- a/examples/demo_OTDA_2D.py +++ /dev/null @@ -1,117 +0,0 @@ -# -*- coding: utf-8 -*- -""" -demo of Optimal transport for domain adaptation -""" - -import numpy as np -import matplotlib.pylab as pl -import ot - - - -#%% parameters - -n=150 # nb bins - -xs,ys=ot.datasets.get_data_classif('3gauss',n) -xt,yt=ot.datasets.get_data_classif('3gauss2',n) - -a,b = ot.unif(n),ot.unif(n) -# loss matrix -M=ot.dist(xs,xt) -#M/=M.max() - -#%% plot samples - -pl.figure(1) - -pl.subplot(2,2,1) -pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') -pl.legend(loc=0) -pl.title('Source distributions') - -pl.subplot(2,2,2) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') -pl.legend(loc=0) -pl.title('target distributions') - -pl.figure(2) -pl.imshow(M,interpolation='nearest') -pl.title('Cost matrix M') - - -#%% OT estimation - -# EMD -G0=ot.emd(a,b,M) - -# sinkhorn -lambd=1e-1 -Gs=ot.sinkhorn(a,b,M,lambd) - - -# Group lasso regularization -reg=1e-1 -eta=1e0 -Gg=ot.da.sinkhorn_lpl1_mm(a,ys.astype(np.int),b,M,reg,eta) - - -#%% visu matrices - -pl.figure(3) - -pl.subplot(2,3,1) -pl.imshow(G0,interpolation='nearest') -pl.title('OT matrix ') - -pl.subplot(2,3,2) -pl.imshow(Gs,interpolation='nearest') -pl.title('OT matrix Sinkhorn') - -pl.subplot(2,3,3) -pl.imshow(Gg,interpolation='nearest') -pl.title('OT matrix Group lasso') - -pl.subplot(2,3,4) -ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1]) -pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') - - -pl.subplot(2,3,5) -ot.plot.plot2D_samples_mat(xs,xt,Gs,c=[.5,.5,1]) -pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') - -pl.subplot(2,3,6) -ot.plot.plot2D_samples_mat(xs,xt,Gg,c=[.5,.5,1]) -pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') - -#%% sample interpolation - -xst0=n*G0.dot(xt) -xsts=n*Gs.dot(xt) -xstg=n*Gg.dot(xt) - -pl.figure(4) -pl.subplot(2,3,1) - - -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) -pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30) -pl.title('Interp samples') -pl.legend(loc=0) - -pl.subplot(2,3,2) - - -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) -pl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30) -pl.title('Interp samples Sinkhorn') - -pl.subplot(2,3,3) - -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) -pl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30) -pl.title('Interp samples Grouplasso') \ No newline at end of file diff --git a/examples/demo_OTDA_classes.py b/examples/demo_OTDA_classes.py deleted file mode 100644 index b2da7de..0000000 --- a/examples/demo_OTDA_classes.py +++ /dev/null @@ -1,108 +0,0 @@ -# -*- coding: utf-8 -*- -""" -demo of Optimal transport for domain adaptation -""" - -import matplotlib.pylab as pl -import ot - - - -#%% parameters - -n=150 # nb samples in source and target datasets - -xs,ys=ot.datasets.get_data_classif('3gauss',n) -xt,yt=ot.datasets.get_data_classif('3gauss2',n) - - - - -#%% plot samples - -pl.figure(1) - -pl.subplot(2,2,1) -pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') -pl.legend(loc=0) -pl.title('Source distributions') - -pl.subplot(2,2,2) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') -pl.legend(loc=0) -pl.title('target distributions') - - -#%% OT estimation - -# LP problem -da_emd=ot.da.OTDA() # init class -da_emd.fit(xs,xt) # fit distributions -xst0=da_emd.interp() # interpolation of source samples - - -# sinkhorn regularization -lambd=1e-1 -da_entrop=ot.da.OTDA_sinkhorn() -da_entrop.fit(xs,xt,reg=lambd) -xsts=da_entrop.interp() - -# non-convex Group lasso regularization -reg=1e-1 -eta=1e0 -da_lpl1=ot.da.OTDA_lpl1() -da_lpl1.fit(xs,ys,xt,reg=reg,eta=eta) -xstg=da_lpl1.interp() - - -# True Group lasso regularization -reg=1e-1 -eta=2e0 -da_l1l2=ot.da.OTDA_l1l2() -da_l1l2.fit(xs,ys,xt,reg=reg,eta=eta,numItermax=20,verbose=True) -xstgl=da_l1l2.interp() - - -#%% plot interpolated source samples -pl.figure(4,(15,8)) - -param_img={'interpolation':'nearest','cmap':'jet'} - -pl.subplot(2,4,1) -pl.imshow(da_emd.G,**param_img) -pl.title('OT matrix') - - -pl.subplot(2,4,2) -pl.imshow(da_entrop.G,**param_img) -pl.title('OT matrix sinkhorn') - -pl.subplot(2,4,3) -pl.imshow(da_lpl1.G,**param_img) -pl.title('OT matrix non-convex Group Lasso') - -pl.subplot(2,4,4) -pl.imshow(da_l1l2.G,**param_img) -pl.title('OT matrix Group Lasso') - - -pl.subplot(2,4,5) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) -pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30) -pl.title('Interp samples') -pl.legend(loc=0) - -pl.subplot(2,4,6) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) -pl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30) -pl.title('Interp samples Sinkhorn') - -pl.subplot(2,4,7) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) -pl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30) -pl.title('Interp samples non-convex Group Lasso') - -pl.subplot(2,4,8) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) -pl.scatter(xstgl[:,0],xstgl[:,1],c=ys,marker='+',label='Transp samples',s=30) -pl.title('Interp samples Group Lasso') \ No newline at end of file diff --git a/examples/demo_OTDA_color_images.py b/examples/demo_OTDA_color_images.py deleted file mode 100644 index 715707d..0000000 --- a/examples/demo_OTDA_color_images.py +++ /dev/null @@ -1,143 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Demo of Optimal transport for domain adaptation with image color adaptation as in [6] - -[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. -""" - -import numpy as np -import scipy.ndimage as spi -import matplotlib.pylab as pl -import ot - - -#%% Loading images - -I1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256 -I2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256 - -#%% Plot images - -pl.figure(1) - -pl.subplot(1,2,1) -pl.imshow(I1) -pl.title('Image 1') - -pl.subplot(1,2,2) -pl.imshow(I2) -pl.title('Image 2') - -pl.show() - -#%% Image conversion and dataset generation - -def im2mat(I): - """Converts and image to matrix (one pixel per line)""" - return I.reshape((I.shape[0]*I.shape[1],I.shape[2])) - -def mat2im(X,shape): - """Converts back a matrix to an image""" - return X.reshape(shape) - -X1=im2mat(I1) -X2=im2mat(I2) - -# training samples -nb=1000 -idx1=np.random.randint(X1.shape[0],size=(nb,)) -idx2=np.random.randint(X2.shape[0],size=(nb,)) - -xs=X1[idx1,:] -xt=X2[idx2,:] - -#%% Plot image distributions - - -pl.figure(2,(10,5)) - -pl.subplot(1,2,1) -pl.scatter(xs[:,0],xs[:,2],c=xs) -pl.axis([0,1,0,1]) -pl.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 1') - -pl.subplot(1,2,2) -#pl.imshow(I2) -pl.scatter(xt[:,0],xt[:,2],c=xt) -pl.axis([0,1,0,1]) -pl.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 2') - -pl.show() - - - -#%% domain adaptation between images - -# LP problem -da_emd=ot.da.OTDA() # init class -da_emd.fit(xs,xt) # fit distributions - - -# sinkhorn regularization -lambd=1e-1 -da_entrop=ot.da.OTDA_sinkhorn() -da_entrop.fit(xs,xt,reg=lambd) - - - -#%% prediction between images (using out of sample prediction as in [6]) - -X1t=da_emd.predict(X1) -X2t=da_emd.predict(X2,-1) - - -X1te=da_entrop.predict(X1) -X2te=da_entrop.predict(X2,-1) - - -def minmax(I): - return np.minimum(np.maximum(I,0),1) - -I1t=minmax(mat2im(X1t,I1.shape)) -I2t=minmax(mat2im(X2t,I2.shape)) - -I1te=minmax(mat2im(X1te,I1.shape)) -I2te=minmax(mat2im(X2te,I2.shape)) - -#%% plot all images - -pl.figure(2,(10,8)) - -pl.subplot(2,3,1) - -pl.imshow(I1) -pl.title('Image 1') - -pl.subplot(2,3,2) -pl.imshow(I1t) -pl.title('Image 1 Adapt') - - -pl.subplot(2,3,3) -pl.imshow(I1te) -pl.title('Image 1 Adapt (reg)') - -pl.subplot(2,3,4) - -pl.imshow(I2) -pl.title('Image 2') - -pl.subplot(2,3,5) -pl.imshow(I2t) -pl.title('Image 2 Adapt') - - -pl.subplot(2,3,6) -pl.imshow(I2te) -pl.title('Image 2 Adapt (reg)') - -pl.show() \ No newline at end of file diff --git a/examples/demo_OTDA_mapping.py b/examples/demo_OTDA_mapping.py deleted file mode 100644 index 50c1df2..0000000 --- a/examples/demo_OTDA_mapping.py +++ /dev/null @@ -1,113 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Demo of OT mapping estimation for domain adaptation [8] - -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for - discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. -""" - -import numpy as np -import matplotlib.pylab as pl -import ot - - - -#%% dataset generation - -np.random.seed(0) # makes example reproducible - -n=100 # nb samples in source and target datasets -theta=2*np.pi/20 -nz=0.1 -xs,ys=ot.datasets.get_data_classif('gaussrot',n,nz=nz) -xt,yt=ot.datasets.get_data_classif('gaussrot',n,theta=theta,nz=nz) - -# one of the target mode changes its variance (no linear mapping) -xt[yt==2]*=3 -xt=xt+4 - - -#%% plot samples - -pl.figure(1,(8,5)) -pl.clf() - -pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') - -pl.legend(loc=0) -pl.title('Source and target distributions') - - - -#%% OT linear mapping estimation - -eta=1e-8 # quadratic regularization for regression -mu=1e0 # weight of the OT linear term -bias=True # estimate a bias - -ot_mapping=ot.da.OTDA_mapping_linear() -ot_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True) - -xst=ot_mapping.predict(xs) # use the estimated mapping -xst0=ot_mapping.interp() # use barycentric mapping - - -pl.figure(2,(10,7)) -pl.clf() -pl.subplot(2,2,1) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.3) -pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='barycentric mapping') -pl.title("barycentric mapping") - -pl.subplot(2,2,2) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.3) -pl.scatter(xst[:,0],xst[:,1],c=ys,marker='+',label='Learned mapping') -pl.title("Learned mapping") - - - -#%% Kernel mapping estimation - -eta=1e-5 # quadratic regularization for regression -mu=1e-1 # weight of the OT linear term -bias=True # estimate a bias -sigma=1 # sigma bandwidth fot gaussian kernel - - -ot_mapping_kernel=ot.da.OTDA_mapping_kernel() -ot_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 10,verbose=True) - -xst_kernel=ot_mapping_kernel.predict(xs) # use the estimated mapping -xst0_kernel=ot_mapping_kernel.interp() # use barycentric mapping - - -#%% Plotting the mapped samples - -pl.figure(2,(10,7)) -pl.clf() -pl.subplot(2,2,1) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) -pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Mapped source samples') -pl.title("Bary. mapping (linear)") -pl.legend(loc=0) - -pl.subplot(2,2,2) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) -pl.scatter(xst[:,0],xst[:,1],c=ys,marker='+',label='Learned mapping') -pl.title("Estim. mapping (linear)") - -pl.subplot(2,2,3) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) -pl.scatter(xst0_kernel[:,0],xst0_kernel[:,1],c=ys,marker='+',label='barycentric mapping') -pl.title("Bary. mapping (kernel)") - -pl.subplot(2,2,4) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) -pl.scatter(xst_kernel[:,0],xst_kernel[:,1],c=ys,marker='+',label='Learned mapping') -pl.title("Estim. mapping (kernel)") - - - - - diff --git a/examples/demo_OTDA_mapping_color_images.py b/examples/demo_OTDA_mapping_color_images.py deleted file mode 100644 index 2744f6c..0000000 --- a/examples/demo_OTDA_mapping_color_images.py +++ /dev/null @@ -1,157 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Demo of Optimal transport for domain adaptation with image color adaptation as in [6] with mapping estimation from [8] - -[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized - discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for - discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. - - -""" - -import numpy as np -import scipy.ndimage as spi -import matplotlib.pylab as pl -import ot - - -#%% Loading images - -I1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256 -I2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256 - -#%% Plot images - -pl.figure(1) - -pl.subplot(1,2,1) -pl.imshow(I1) -pl.title('Image 1') - -pl.subplot(1,2,2) -pl.imshow(I2) -pl.title('Image 2') - -pl.show() - -#%% Image conversion and dataset generation - -def im2mat(I): - """Converts and image to matrix (one pixel per line)""" - return I.reshape((I.shape[0]*I.shape[1],I.shape[2])) - -def mat2im(X,shape): - """Converts back a matrix to an image""" - return X.reshape(shape) - -X1=im2mat(I1) -X2=im2mat(I2) - -# training samples -nb=1000 -idx1=np.random.randint(X1.shape[0],size=(nb,)) -idx2=np.random.randint(X2.shape[0],size=(nb,)) - -xs=X1[idx1,:] -xt=X2[idx2,:] - -#%% Plot image distributions - - -pl.figure(2,(10,5)) - -pl.subplot(1,2,1) -pl.scatter(xs[:,0],xs[:,2],c=xs) -pl.axis([0,1,0,1]) -pl.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 1') - -pl.subplot(1,2,2) -#pl.imshow(I2) -pl.scatter(xt[:,0],xt[:,2],c=xt) -pl.axis([0,1,0,1]) -pl.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 2') - -pl.show() - - - -#%% domain adaptation between images -def minmax(I): - return np.minimum(np.maximum(I,0),1) -# LP problem -da_emd=ot.da.OTDA() # init class -da_emd.fit(xs,xt) # fit distributions - -X1t=da_emd.predict(X1) # out of sample -I1t=minmax(mat2im(X1t,I1.shape)) - -# sinkhorn regularization -lambd=1e-1 -da_entrop=ot.da.OTDA_sinkhorn() -da_entrop.fit(xs,xt,reg=lambd) - -X1te=da_entrop.predict(X1) -I1te=minmax(mat2im(X1te,I1.shape)) - -# linear mapping estimation -eta=1e-8 # quadratic regularization for regression -mu=1e0 # weight of the OT linear term -bias=True # estimate a bias - -ot_mapping=ot.da.OTDA_mapping_linear() -ot_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True) - -X1tl=ot_mapping.predict(X1) # use the estimated mapping -I1tl=minmax(mat2im(X1tl,I1.shape)) - -# nonlinear mapping estimation -eta=1e-2 # quadratic regularization for regression -mu=1e0 # weight of the OT linear term -bias=False # estimate a bias -sigma=1 # sigma bandwidth fot gaussian kernel - - -ot_mapping_kernel=ot.da.OTDA_mapping_kernel() -ot_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 10,verbose=True) - -X1tn=ot_mapping_kernel.predict(X1) # use the estimated mapping -I1tn=minmax(mat2im(X1tn,I1.shape)) -#%% plot images - - -pl.figure(2,(10,8)) - -pl.subplot(2,3,1) - -pl.imshow(I1) -pl.title('Im. 1') - -pl.subplot(2,3,2) - -pl.imshow(I2) -pl.title('Im. 2') - - -pl.subplot(2,3,3) -pl.imshow(I1t) -pl.title('Im. 1 Interp LP') - -pl.subplot(2,3,4) -pl.imshow(I1te) -pl.title('Im. 1 Interp Entrop') - - -pl.subplot(2,3,5) -pl.imshow(I1tl) -pl.title('Im. 1 Linear mapping') - -pl.subplot(2,3,6) -pl.imshow(I1tn) -pl.title('Im. 1 nonlinear mapping') - -pl.show() \ No newline at end of file diff --git a/examples/demo_OT_1D.py b/examples/demo_OT_1D.py deleted file mode 100644 index df65a60..0000000 --- a/examples/demo_OT_1D.py +++ /dev/null @@ -1,54 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Demo for 1D optimal transport - -@author: rflamary -""" - -import numpy as np -import matplotlib.pylab as pl -import ot -from ot.datasets import get_1D_gauss as gauss - - -#%% parameters - -n=100 # nb bins - -# bin positions -x=np.arange(n,dtype=np.float64) - -# Gaussian distributions -a=gauss(n,m=20,s=5) # m= mean, s= std -b=gauss(n,m=60,s=10) - -# loss matrix -M=ot.dist(x.reshape((n,1)),x.reshape((n,1))) -M/=M.max() - -#%% plot the distributions - -pl.figure(1) -pl.plot(x,a,'b',label='Source distribution') -pl.plot(x,b,'r',label='Target distribution') -pl.legend() - -#%% plot distributions and loss matrix - -pl.figure(2) -ot.plot.plot1D_mat(a,b,M,'Cost matrix M') - -#%% EMD - -G0=ot.emd(a,b,M) - -pl.figure(3) -ot.plot.plot1D_mat(a,b,G0,'OT matrix G0') - -#%% Sinkhorn - -lambd=1e-3 -Gs=ot.sinkhorn(a,b,M,lambd) - -pl.figure(4) -ot.plot.plot1D_mat(a,b,Gs,'OT matrix Sinkhorn') diff --git a/examples/demo_OT_2D_samples.py b/examples/demo_OT_2D_samples.py deleted file mode 100644 index e9ec78a..0000000 --- a/examples/demo_OT_2D_samples.py +++ /dev/null @@ -1,78 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Demo for 2D Optimal transport between empirical distributions - -@author: rflamary -""" - -import numpy as np -import matplotlib.pylab as pl -import ot - -#%% parameters and data generation - -n=20 # nb samples - -mu_s=np.array([0,0]) -cov_s=np.array([[1,0],[0,1]]) - -mu_t=np.array([4,4]) -cov_t=np.array([[1,-.8],[-.8,1]]) - -xs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s) -xt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t) - -a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples - -# loss matrix -M=ot.dist(xs,xt) -M/=M.max() - -#%% plot samples - -pl.figure(1) -pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') -pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') -pl.legend(loc=0) -pl.title('Source and traget distributions') - -pl.figure(2) -pl.imshow(M,interpolation='nearest') -pl.title('Cost matrix M') - - -#%% EMD - -G0=ot.emd(a,b,M) - -pl.figure(3) -pl.imshow(G0,interpolation='nearest') -pl.title('OT matrix G0') - -pl.figure(4) -ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1]) -pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') -pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') -pl.legend(loc=0) -pl.title('OT matrix with samples') - - -#%% sinkhorn - -# reg term -lambd=5e-3 - -Gs=ot.sinkhorn(a,b,M,lambd) - -pl.figure(5) -pl.imshow(Gs,interpolation='nearest') -pl.title('OT matrix sinkhorn') - -pl.figure(6) -ot.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1]) -pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') -pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') -pl.legend(loc=0) -pl.title('OT matrix Sinkhorn with samples') - - diff --git a/examples/demo_barycenter_1D.py b/examples/demo_barycenter_1D.py deleted file mode 100644 index 5466332..0000000 --- a/examples/demo_barycenter_1D.py +++ /dev/null @@ -1,135 +0,0 @@ -# -*- coding: utf-8 -*- -""" -1D Wasserstein barycenter demo - -@author: rflamary -""" - -import numpy as np -import matplotlib.pylab as pl -import ot -from mpl_toolkits.mplot3d import Axes3D #necessary for 3d plot even if not used -from matplotlib.collections import PolyCollection - - -#%% parameters - -n=100 # nb bins - -# bin positions -x=np.arange(n,dtype=np.float64) - -# Gaussian distributions -a1=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std -a2=ot.datasets.get_1D_gauss(n,m=60,s=8) - -# creating matrix A containing all distributions -A=np.vstack((a1,a2)).T -nbd=A.shape[1] - -# loss matrix + normalization -M=ot.utils.dist0(n) -M/=M.max() - -#%% plot the distributions - -pl.figure(1) -for i in range(nbd): - pl.plot(x,A[:,i]) -pl.title('Distributions') - -#%% barycenter computation - -alpha=0.2 # 0<=alpha<=1 -weights=np.array([1-alpha,alpha]) - -# l2bary -bary_l2=A.dot(weights) - -# wasserstein -reg=1e-3 -bary_wass=ot.bregman.barycenter(A,M,reg,weights) - -pl.figure(2) -pl.clf() -pl.subplot(2,1,1) -for i in range(nbd): - pl.plot(x,A[:,i]) -pl.title('Distributions') - -pl.subplot(2,1,2) -pl.plot(x,bary_l2,'r',label='l2') -pl.plot(x,bary_wass,'g',label='Wasserstein') -pl.legend() -pl.title('Barycenters') - - -#%% barycenter interpolation - -nbalpha=11 -alphalist=np.linspace(0,1,nbalpha) - - -B_l2=np.zeros((n,nbalpha)) - -B_wass=np.copy(B_l2) - -for i in range(0,nbalpha): - alpha=alphalist[i] - weights=np.array([1-alpha,alpha]) - B_l2[:,i]=A.dot(weights) - B_wass[:,i]=ot.bregman.barycenter(A,M,reg,weights) - -#%% plot interpolation - -pl.figure(3,(10,5)) - -#pl.subplot(1,2,1) -cmap=pl.cm.get_cmap('viridis') -verts = [] -zs = alphalist -for i,z in enumerate(zs): - ys = B_l2[:,i] - verts.append(list(zip(x, ys))) - -ax = pl.gcf().gca(projection='3d') - -poly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist]) -poly.set_alpha(0.7) -ax.add_collection3d(poly, zs=zs, zdir='y') - -ax.set_xlabel('x') -ax.set_xlim3d(0, n) -ax.set_ylabel('$\\alpha$') -ax.set_ylim3d(0,1) -ax.set_zlabel('') -ax.set_zlim3d(0, B_l2.max()*1.01) -pl.title('Barycenter interpolation with l2') - -pl.show() - -pl.figure(4,(10,5)) - -#pl.subplot(1,2,1) -cmap=pl.cm.get_cmap('viridis') -verts = [] -zs = alphalist -for i,z in enumerate(zs): - ys = B_wass[:,i] - verts.append(list(zip(x, ys))) - -ax = pl.gcf().gca(projection='3d') - -poly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist]) -poly.set_alpha(0.7) -ax.add_collection3d(poly, zs=zs, zdir='y') - -ax.set_xlabel('x') -ax.set_xlim3d(0, n) -ax.set_ylabel('$\\alpha$') -ax.set_ylim3d(0,1) -ax.set_zlabel('') -ax.set_zlim3d(0, B_l2.max()*1.01) -pl.title('Barycenter interpolation with Wasserstein') - -pl.show() \ No newline at end of file diff --git a/examples/demo_optim_OTreg.py b/examples/demo_optim_OTreg.py deleted file mode 100644 index 0de6b08..0000000 --- a/examples/demo_optim_OTreg.py +++ /dev/null @@ -1,69 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Regularized OT with generic solver -""" - -import numpy as np -import matplotlib.pylab as pl -import ot - - - -#%% parameters - -n=100 # nb bins - -# bin positions -x=np.arange(n,dtype=np.float64) - -# Gaussian distributions -a=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std -b=ot.datasets.get_1D_gauss(n,m=60,s=10) - -# loss matrix -M=ot.dist(x.reshape((n,1)),x.reshape((n,1))) -M/=M.max() - -#%% EMD - -G0=ot.emd(a,b,M) - -pl.figure(3) -ot.plot.plot1D_mat(a,b,G0,'OT matrix G0') - -#%% Example with Frobenius norm regularization - -def f(G): return 0.5*np.sum(G**2) -def df(G): return G - -reg=1e-1 - -Gl2=ot.optim.cg(a,b,M,reg,f,df,verbose=True) - -pl.figure(3) -ot.plot.plot1D_mat(a,b,Gl2,'OT matrix Frob. reg') - -#%% Example with entropic regularization - -def f(G): return np.sum(G*np.log(G)) -def df(G): return np.log(G)+1 - -reg=1e-3 - -Ge=ot.optim.cg(a,b,M,reg,f,df,verbose=True) - -pl.figure(4) -ot.plot.plot1D_mat(a,b,Ge,'OT matrix Entrop. reg') - -#%% Example with Frobenius norm + entropic regularization with gcg - -def f(G): return 0.5*np.sum(G**2) -def df(G): return G - -reg1=1e-1 -reg2=1e-1 - -Gel2=ot.optim.gcg(a,b,M,reg1,reg2,f,df,verbose=True) - -pl.figure(5) -ot.plot.plot1D_mat(a,b,Gel2,'OT entropic + matrix Frob. reg') \ No newline at end of file diff --git a/examples/plot_OTDA_2D.py b/examples/plot_OTDA_2D.py new file mode 100644 index 0000000..db04c5e --- /dev/null +++ b/examples/plot_OTDA_2D.py @@ -0,0 +1,120 @@ +# -*- coding: utf-8 -*- +""" +=============================================== +demo of Optimal transport for domain adaptation +=============================================== + +""" + +import numpy as np +import matplotlib.pylab as pl +import ot + + + +#%% parameters + +n=150 # nb bins + +xs,ys=ot.datasets.get_data_classif('3gauss',n) +xt,yt=ot.datasets.get_data_classif('3gauss2',n) + +a,b = ot.unif(n),ot.unif(n) +# loss matrix +M=ot.dist(xs,xt) +#M/=M.max() + +#%% plot samples + +pl.figure(1) + +pl.subplot(2,2,1) +pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') +pl.legend(loc=0) +pl.title('Source distributions') + +pl.subplot(2,2,2) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') +pl.legend(loc=0) +pl.title('target distributions') + +pl.figure(2) +pl.imshow(M,interpolation='nearest') +pl.title('Cost matrix M') + + +#%% OT estimation + +# EMD +G0=ot.emd(a,b,M) + +# sinkhorn +lambd=1e-1 +Gs=ot.sinkhorn(a,b,M,lambd) + + +# Group lasso regularization +reg=1e-1 +eta=1e0 +Gg=ot.da.sinkhorn_lpl1_mm(a,ys.astype(np.int),b,M,reg,eta) + + +#%% visu matrices + +pl.figure(3) + +pl.subplot(2,3,1) +pl.imshow(G0,interpolation='nearest') +pl.title('OT matrix ') + +pl.subplot(2,3,2) +pl.imshow(Gs,interpolation='nearest') +pl.title('OT matrix Sinkhorn') + +pl.subplot(2,3,3) +pl.imshow(Gg,interpolation='nearest') +pl.title('OT matrix Group lasso') + +pl.subplot(2,3,4) +ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1]) +pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') + + +pl.subplot(2,3,5) +ot.plot.plot2D_samples_mat(xs,xt,Gs,c=[.5,.5,1]) +pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') + +pl.subplot(2,3,6) +ot.plot.plot2D_samples_mat(xs,xt,Gg,c=[.5,.5,1]) +pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') + +#%% sample interpolation + +xst0=n*G0.dot(xt) +xsts=n*Gs.dot(xt) +xstg=n*Gg.dot(xt) + +pl.figure(4) +pl.subplot(2,3,1) + + +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) +pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30) +pl.title('Interp samples') +pl.legend(loc=0) + +pl.subplot(2,3,2) + + +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) +pl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30) +pl.title('Interp samples Sinkhorn') + +pl.subplot(2,3,3) + +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) +pl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30) +pl.title('Interp samples Grouplasso') \ No newline at end of file diff --git a/examples/plot_OTDA_classes.py b/examples/plot_OTDA_classes.py new file mode 100644 index 0000000..999be53 --- /dev/null +++ b/examples/plot_OTDA_classes.py @@ -0,0 +1,111 @@ +# -*- coding: utf-8 -*- +""" +=============================================== +demo of Optimal transport for domain adaptation +=============================================== + +""" + +import matplotlib.pylab as pl +import ot + + + +#%% parameters + +n=150 # nb samples in source and target datasets + +xs,ys=ot.datasets.get_data_classif('3gauss',n) +xt,yt=ot.datasets.get_data_classif('3gauss2',n) + + + + +#%% plot samples + +pl.figure(1) + +pl.subplot(2,2,1) +pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') +pl.legend(loc=0) +pl.title('Source distributions') + +pl.subplot(2,2,2) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') +pl.legend(loc=0) +pl.title('target distributions') + + +#%% OT estimation + +# LP problem +da_emd=ot.da.OTDA() # init class +da_emd.fit(xs,xt) # fit distributions +xst0=da_emd.interp() # interpolation of source samples + + +# sinkhorn regularization +lambd=1e-1 +da_entrop=ot.da.OTDA_sinkhorn() +da_entrop.fit(xs,xt,reg=lambd) +xsts=da_entrop.interp() + +# non-convex Group lasso regularization +reg=1e-1 +eta=1e0 +da_lpl1=ot.da.OTDA_lpl1() +da_lpl1.fit(xs,ys,xt,reg=reg,eta=eta) +xstg=da_lpl1.interp() + + +# True Group lasso regularization +reg=1e-1 +eta=2e0 +da_l1l2=ot.da.OTDA_l1l2() +da_l1l2.fit(xs,ys,xt,reg=reg,eta=eta,numItermax=20,verbose=True) +xstgl=da_l1l2.interp() + + +#%% plot interpolated source samples +pl.figure(4,(15,8)) + +param_img={'interpolation':'nearest','cmap':'jet'} + +pl.subplot(2,4,1) +pl.imshow(da_emd.G,**param_img) +pl.title('OT matrix') + + +pl.subplot(2,4,2) +pl.imshow(da_entrop.G,**param_img) +pl.title('OT matrix sinkhorn') + +pl.subplot(2,4,3) +pl.imshow(da_lpl1.G,**param_img) +pl.title('OT matrix non-convex Group Lasso') + +pl.subplot(2,4,4) +pl.imshow(da_l1l2.G,**param_img) +pl.title('OT matrix Group Lasso') + + +pl.subplot(2,4,5) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) +pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30) +pl.title('Interp samples') +pl.legend(loc=0) + +pl.subplot(2,4,6) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) +pl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30) +pl.title('Interp samples Sinkhorn') + +pl.subplot(2,4,7) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) +pl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30) +pl.title('Interp samples non-convex Group Lasso') + +pl.subplot(2,4,8) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) +pl.scatter(xstgl[:,0],xstgl[:,1],c=ys,marker='+',label='Transp samples',s=30) +pl.title('Interp samples Group Lasso') \ No newline at end of file diff --git a/examples/plot_OTDA_color_images.py b/examples/plot_OTDA_color_images.py new file mode 100644 index 0000000..7ddaa2b --- /dev/null +++ b/examples/plot_OTDA_color_images.py @@ -0,0 +1,145 @@ +# -*- coding: utf-8 -*- +""" +===================================================================================== +Demo of Optimal transport for domain adaptation with image color adaptation as in [6] +===================================================================================== + +[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. +""" + +import numpy as np +import scipy.ndimage as spi +import matplotlib.pylab as pl +import ot + + +#%% Loading images + +I1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256 +I2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256 + +#%% Plot images + +pl.figure(1) + +pl.subplot(1,2,1) +pl.imshow(I1) +pl.title('Image 1') + +pl.subplot(1,2,2) +pl.imshow(I2) +pl.title('Image 2') + +pl.show() + +#%% Image conversion and dataset generation + +def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0]*I.shape[1],I.shape[2])) + +def mat2im(X,shape): + """Converts back a matrix to an image""" + return X.reshape(shape) + +X1=im2mat(I1) +X2=im2mat(I2) + +# training samples +nb=1000 +idx1=np.random.randint(X1.shape[0],size=(nb,)) +idx2=np.random.randint(X2.shape[0],size=(nb,)) + +xs=X1[idx1,:] +xt=X2[idx2,:] + +#%% Plot image distributions + + +pl.figure(2,(10,5)) + +pl.subplot(1,2,1) +pl.scatter(xs[:,0],xs[:,2],c=xs) +pl.axis([0,1,0,1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 1') + +pl.subplot(1,2,2) +#pl.imshow(I2) +pl.scatter(xt[:,0],xt[:,2],c=xt) +pl.axis([0,1,0,1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 2') + +pl.show() + + + +#%% domain adaptation between images + +# LP problem +da_emd=ot.da.OTDA() # init class +da_emd.fit(xs,xt) # fit distributions + + +# sinkhorn regularization +lambd=1e-1 +da_entrop=ot.da.OTDA_sinkhorn() +da_entrop.fit(xs,xt,reg=lambd) + + + +#%% prediction between images (using out of sample prediction as in [6]) + +X1t=da_emd.predict(X1) +X2t=da_emd.predict(X2,-1) + + +X1te=da_entrop.predict(X1) +X2te=da_entrop.predict(X2,-1) + + +def minmax(I): + return np.minimum(np.maximum(I,0),1) + +I1t=minmax(mat2im(X1t,I1.shape)) +I2t=minmax(mat2im(X2t,I2.shape)) + +I1te=minmax(mat2im(X1te,I1.shape)) +I2te=minmax(mat2im(X2te,I2.shape)) + +#%% plot all images + +pl.figure(2,(10,8)) + +pl.subplot(2,3,1) + +pl.imshow(I1) +pl.title('Image 1') + +pl.subplot(2,3,2) +pl.imshow(I1t) +pl.title('Image 1 Adapt') + + +pl.subplot(2,3,3) +pl.imshow(I1te) +pl.title('Image 1 Adapt (reg)') + +pl.subplot(2,3,4) + +pl.imshow(I2) +pl.title('Image 2') + +pl.subplot(2,3,5) +pl.imshow(I2t) +pl.title('Image 2 Adapt') + + +pl.subplot(2,3,6) +pl.imshow(I2te) +pl.title('Image 2 Adapt (reg)') + +pl.show() \ No newline at end of file diff --git a/examples/plot_OTDA_mapping.py b/examples/plot_OTDA_mapping.py new file mode 100644 index 0000000..779fa76 --- /dev/null +++ b/examples/plot_OTDA_mapping.py @@ -0,0 +1,110 @@ +# -*- coding: utf-8 -*- +""" +======================================================= +Demo of OT mapping estimation for domain adaptation [8] +======================================================= + +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for + discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. +""" + +import numpy as np +import matplotlib.pylab as pl +import ot + + + +#%% dataset generation + +np.random.seed(0) # makes example reproducible + +n=100 # nb samples in source and target datasets +theta=2*np.pi/20 +nz=0.1 +xs,ys=ot.datasets.get_data_classif('gaussrot',n,nz=nz) +xt,yt=ot.datasets.get_data_classif('gaussrot',n,theta=theta,nz=nz) + +# one of the target mode changes its variance (no linear mapping) +xt[yt==2]*=3 +xt=xt+4 + + +#%% plot samples + +pl.figure(1,(8,5)) +pl.clf() + +pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') + +pl.legend(loc=0) +pl.title('Source and target distributions') + + + +#%% OT linear mapping estimation + +eta=1e-8 # quadratic regularization for regression +mu=1e0 # weight of the OT linear term +bias=True # estimate a bias + +ot_mapping=ot.da.OTDA_mapping_linear() +ot_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True) + +xst=ot_mapping.predict(xs) # use the estimated mapping +xst0=ot_mapping.interp() # use barycentric mapping + + +pl.figure(2,(10,7)) +pl.clf() +pl.subplot(2,2,1) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.3) +pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='barycentric mapping') +pl.title("barycentric mapping") + +pl.subplot(2,2,2) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.3) +pl.scatter(xst[:,0],xst[:,1],c=ys,marker='+',label='Learned mapping') +pl.title("Learned mapping") + + + +#%% Kernel mapping estimation + +eta=1e-5 # quadratic regularization for regression +mu=1e-1 # weight of the OT linear term +bias=True # estimate a bias +sigma=1 # sigma bandwidth fot gaussian kernel + + +ot_mapping_kernel=ot.da.OTDA_mapping_kernel() +ot_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 10,verbose=True) + +xst_kernel=ot_mapping_kernel.predict(xs) # use the estimated mapping +xst0_kernel=ot_mapping_kernel.interp() # use barycentric mapping + + +#%% Plotting the mapped samples + +pl.figure(2,(10,7)) +pl.clf() +pl.subplot(2,2,1) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) +pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Mapped source samples') +pl.title("Bary. mapping (linear)") +pl.legend(loc=0) + +pl.subplot(2,2,2) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) +pl.scatter(xst[:,0],xst[:,1],c=ys,marker='+',label='Learned mapping') +pl.title("Estim. mapping (linear)") + +pl.subplot(2,2,3) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) +pl.scatter(xst0_kernel[:,0],xst0_kernel[:,1],c=ys,marker='+',label='barycentric mapping') +pl.title("Bary. mapping (kernel)") + +pl.subplot(2,2,4) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) +pl.scatter(xst_kernel[:,0],xst_kernel[:,1],c=ys,marker='+',label='Learned mapping') +pl.title("Estim. mapping (kernel)") diff --git a/examples/plot_OTDA_mapping_color_images.py b/examples/plot_OTDA_mapping_color_images.py new file mode 100644 index 0000000..0cd6c9c --- /dev/null +++ b/examples/plot_OTDA_mapping_color_images.py @@ -0,0 +1,158 @@ +# -*- coding: utf-8 -*- +""" +====================================================================================================================== +Demo of Optimal transport for domain adaptation with image color adaptation as in [6] with mapping estimation from [8] +====================================================================================================================== + +[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized + discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for + discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. + +""" + +import numpy as np +import scipy.ndimage as spi +import matplotlib.pylab as pl +import ot + + +#%% Loading images + +I1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256 +I2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256 + +#%% Plot images + +pl.figure(1) + +pl.subplot(1,2,1) +pl.imshow(I1) +pl.title('Image 1') + +pl.subplot(1,2,2) +pl.imshow(I2) +pl.title('Image 2') + +pl.show() + +#%% Image conversion and dataset generation + +def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0]*I.shape[1],I.shape[2])) + +def mat2im(X,shape): + """Converts back a matrix to an image""" + return X.reshape(shape) + +X1=im2mat(I1) +X2=im2mat(I2) + +# training samples +nb=1000 +idx1=np.random.randint(X1.shape[0],size=(nb,)) +idx2=np.random.randint(X2.shape[0],size=(nb,)) + +xs=X1[idx1,:] +xt=X2[idx2,:] + +#%% Plot image distributions + + +pl.figure(2,(10,5)) + +pl.subplot(1,2,1) +pl.scatter(xs[:,0],xs[:,2],c=xs) +pl.axis([0,1,0,1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 1') + +pl.subplot(1,2,2) +#pl.imshow(I2) +pl.scatter(xt[:,0],xt[:,2],c=xt) +pl.axis([0,1,0,1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 2') + +pl.show() + + + +#%% domain adaptation between images +def minmax(I): + return np.minimum(np.maximum(I,0),1) +# LP problem +da_emd=ot.da.OTDA() # init class +da_emd.fit(xs,xt) # fit distributions + +X1t=da_emd.predict(X1) # out of sample +I1t=minmax(mat2im(X1t,I1.shape)) + +# sinkhorn regularization +lambd=1e-1 +da_entrop=ot.da.OTDA_sinkhorn() +da_entrop.fit(xs,xt,reg=lambd) + +X1te=da_entrop.predict(X1) +I1te=minmax(mat2im(X1te,I1.shape)) + +# linear mapping estimation +eta=1e-8 # quadratic regularization for regression +mu=1e0 # weight of the OT linear term +bias=True # estimate a bias + +ot_mapping=ot.da.OTDA_mapping_linear() +ot_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True) + +X1tl=ot_mapping.predict(X1) # use the estimated mapping +I1tl=minmax(mat2im(X1tl,I1.shape)) + +# nonlinear mapping estimation +eta=1e-2 # quadratic regularization for regression +mu=1e0 # weight of the OT linear term +bias=False # estimate a bias +sigma=1 # sigma bandwidth fot gaussian kernel + + +ot_mapping_kernel=ot.da.OTDA_mapping_kernel() +ot_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 10,verbose=True) + +X1tn=ot_mapping_kernel.predict(X1) # use the estimated mapping +I1tn=minmax(mat2im(X1tn,I1.shape)) +#%% plot images + + +pl.figure(2,(10,8)) + +pl.subplot(2,3,1) + +pl.imshow(I1) +pl.title('Im. 1') + +pl.subplot(2,3,2) + +pl.imshow(I2) +pl.title('Im. 2') + + +pl.subplot(2,3,3) +pl.imshow(I1t) +pl.title('Im. 1 Interp LP') + +pl.subplot(2,3,4) +pl.imshow(I1te) +pl.title('Im. 1 Interp Entrop') + + +pl.subplot(2,3,5) +pl.imshow(I1tl) +pl.title('Im. 1 Linear mapping') + +pl.subplot(2,3,6) +pl.imshow(I1tn) +pl.title('Im. 1 nonlinear mapping') + +pl.show() \ No newline at end of file diff --git a/examples/plot_OT_1D.py b/examples/plot_OT_1D.py new file mode 100644 index 0000000..a8bbbd6 --- /dev/null +++ b/examples/plot_OT_1D.py @@ -0,0 +1,56 @@ +# -*- coding: utf-8 -*- +""" +============================= +Demo for 1D optimal transport +============================= + +@author: rflamary +""" + +import numpy as np +import matplotlib.pylab as pl +import ot +from ot.datasets import get_1D_gauss as gauss + + +#%% parameters + +n=100 # nb bins + +# bin positions +x=np.arange(n,dtype=np.float64) + +# Gaussian distributions +a=gauss(n,m=20,s=5) # m= mean, s= std +b=gauss(n,m=60,s=10) + +# loss matrix +M=ot.dist(x.reshape((n,1)),x.reshape((n,1))) +M/=M.max() + +#%% plot the distributions + +pl.figure(1) +pl.plot(x,a,'b',label='Source distribution') +pl.plot(x,b,'r',label='Target distribution') +pl.legend() + +#%% plot distributions and loss matrix + +pl.figure(2) +ot.plot.plot1D_mat(a,b,M,'Cost matrix M') + +#%% EMD + +G0=ot.emd(a,b,M) + +pl.figure(3) +ot.plot.plot1D_mat(a,b,G0,'OT matrix G0') + +#%% Sinkhorn + +lambd=1e-3 +Gs=ot.sinkhorn(a,b,M,lambd) + +pl.figure(4) +ot.plot.plot1D_mat(a,b,Gs,'OT matrix Sinkhorn') diff --git a/examples/plot_OT_2D_samples.py b/examples/plot_OT_2D_samples.py new file mode 100644 index 0000000..e55eff8 --- /dev/null +++ b/examples/plot_OT_2D_samples.py @@ -0,0 +1,76 @@ +# -*- coding: utf-8 -*- +""" +Demo for 2D Optimal transport between empirical distributions + +@author: rflamary +""" + +import numpy as np +import matplotlib.pylab as pl +import ot + +#%% parameters and data generation + +n=20 # nb samples + +mu_s=np.array([0,0]) +cov_s=np.array([[1,0],[0,1]]) + +mu_t=np.array([4,4]) +cov_t=np.array([[1,-.8],[-.8,1]]) + +xs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s) +xt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t) + +a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples + +# loss matrix +M=ot.dist(xs,xt) +M/=M.max() + +#%% plot samples + +pl.figure(1) +pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') +pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') +pl.legend(loc=0) +pl.title('Source and traget distributions') + +pl.figure(2) +pl.imshow(M,interpolation='nearest') +pl.title('Cost matrix M') + + +#%% EMD + +G0=ot.emd(a,b,M) + +pl.figure(3) +pl.imshow(G0,interpolation='nearest') +pl.title('OT matrix G0') + +pl.figure(4) +ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1]) +pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') +pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') +pl.legend(loc=0) +pl.title('OT matrix with samples') + + +#%% sinkhorn + +# reg term +lambd=5e-3 + +Gs=ot.sinkhorn(a,b,M,lambd) + +pl.figure(5) +pl.imshow(Gs,interpolation='nearest') +pl.title('OT matrix sinkhorn') + +pl.figure(6) +ot.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1]) +pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') +pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') +pl.legend(loc=0) +pl.title('OT matrix Sinkhorn with samples') diff --git a/examples/plot_barycenter_1D.py b/examples/plot_barycenter_1D.py new file mode 100644 index 0000000..5466332 --- /dev/null +++ b/examples/plot_barycenter_1D.py @@ -0,0 +1,135 @@ +# -*- coding: utf-8 -*- +""" +1D Wasserstein barycenter demo + +@author: rflamary +""" + +import numpy as np +import matplotlib.pylab as pl +import ot +from mpl_toolkits.mplot3d import Axes3D #necessary for 3d plot even if not used +from matplotlib.collections import PolyCollection + + +#%% parameters + +n=100 # nb bins + +# bin positions +x=np.arange(n,dtype=np.float64) + +# Gaussian distributions +a1=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std +a2=ot.datasets.get_1D_gauss(n,m=60,s=8) + +# creating matrix A containing all distributions +A=np.vstack((a1,a2)).T +nbd=A.shape[1] + +# loss matrix + normalization +M=ot.utils.dist0(n) +M/=M.max() + +#%% plot the distributions + +pl.figure(1) +for i in range(nbd): + pl.plot(x,A[:,i]) +pl.title('Distributions') + +#%% barycenter computation + +alpha=0.2 # 0<=alpha<=1 +weights=np.array([1-alpha,alpha]) + +# l2bary +bary_l2=A.dot(weights) + +# wasserstein +reg=1e-3 +bary_wass=ot.bregman.barycenter(A,M,reg,weights) + +pl.figure(2) +pl.clf() +pl.subplot(2,1,1) +for i in range(nbd): + pl.plot(x,A[:,i]) +pl.title('Distributions') + +pl.subplot(2,1,2) +pl.plot(x,bary_l2,'r',label='l2') +pl.plot(x,bary_wass,'g',label='Wasserstein') +pl.legend() +pl.title('Barycenters') + + +#%% barycenter interpolation + +nbalpha=11 +alphalist=np.linspace(0,1,nbalpha) + + +B_l2=np.zeros((n,nbalpha)) + +B_wass=np.copy(B_l2) + +for i in range(0,nbalpha): + alpha=alphalist[i] + weights=np.array([1-alpha,alpha]) + B_l2[:,i]=A.dot(weights) + B_wass[:,i]=ot.bregman.barycenter(A,M,reg,weights) + +#%% plot interpolation + +pl.figure(3,(10,5)) + +#pl.subplot(1,2,1) +cmap=pl.cm.get_cmap('viridis') +verts = [] +zs = alphalist +for i,z in enumerate(zs): + ys = B_l2[:,i] + verts.append(list(zip(x, ys))) + +ax = pl.gcf().gca(projection='3d') + +poly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist]) +poly.set_alpha(0.7) +ax.add_collection3d(poly, zs=zs, zdir='y') + +ax.set_xlabel('x') +ax.set_xlim3d(0, n) +ax.set_ylabel('$\\alpha$') +ax.set_ylim3d(0,1) +ax.set_zlabel('') +ax.set_zlim3d(0, B_l2.max()*1.01) +pl.title('Barycenter interpolation with l2') + +pl.show() + +pl.figure(4,(10,5)) + +#pl.subplot(1,2,1) +cmap=pl.cm.get_cmap('viridis') +verts = [] +zs = alphalist +for i,z in enumerate(zs): + ys = B_wass[:,i] + verts.append(list(zip(x, ys))) + +ax = pl.gcf().gca(projection='3d') + +poly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist]) +poly.set_alpha(0.7) +ax.add_collection3d(poly, zs=zs, zdir='y') + +ax.set_xlabel('x') +ax.set_xlim3d(0, n) +ax.set_ylabel('$\\alpha$') +ax.set_ylim3d(0,1) +ax.set_zlabel('') +ax.set_zlim3d(0, B_l2.max()*1.01) +pl.title('Barycenter interpolation with Wasserstein') + +pl.show() \ No newline at end of file diff --git a/examples/plot_optim_OTreg.py b/examples/plot_optim_OTreg.py new file mode 100644 index 0000000..0de6b08 --- /dev/null +++ b/examples/plot_optim_OTreg.py @@ -0,0 +1,69 @@ +# -*- coding: utf-8 -*- +""" +Regularized OT with generic solver +""" + +import numpy as np +import matplotlib.pylab as pl +import ot + + + +#%% parameters + +n=100 # nb bins + +# bin positions +x=np.arange(n,dtype=np.float64) + +# Gaussian distributions +a=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std +b=ot.datasets.get_1D_gauss(n,m=60,s=10) + +# loss matrix +M=ot.dist(x.reshape((n,1)),x.reshape((n,1))) +M/=M.max() + +#%% EMD + +G0=ot.emd(a,b,M) + +pl.figure(3) +ot.plot.plot1D_mat(a,b,G0,'OT matrix G0') + +#%% Example with Frobenius norm regularization + +def f(G): return 0.5*np.sum(G**2) +def df(G): return G + +reg=1e-1 + +Gl2=ot.optim.cg(a,b,M,reg,f,df,verbose=True) + +pl.figure(3) +ot.plot.plot1D_mat(a,b,Gl2,'OT matrix Frob. reg') + +#%% Example with entropic regularization + +def f(G): return np.sum(G*np.log(G)) +def df(G): return np.log(G)+1 + +reg=1e-3 + +Ge=ot.optim.cg(a,b,M,reg,f,df,verbose=True) + +pl.figure(4) +ot.plot.plot1D_mat(a,b,Ge,'OT matrix Entrop. reg') + +#%% Example with Frobenius norm + entropic regularization with gcg + +def f(G): return 0.5*np.sum(G**2) +def df(G): return G + +reg1=1e-1 +reg2=1e-1 + +Gel2=ot.optim.gcg(a,b,M,reg1,reg2,f,df,verbose=True) + +pl.figure(5) +ot.plot.plot1D_mat(a,b,Gel2,'OT entropic + matrix Frob. reg') \ No newline at end of file diff --git a/requirements.txt b/requirements.txt index d7b38b0..319f5e1 100644 --- a/requirements.txt +++ b/requirements.txt @@ -2,3 +2,4 @@ numpy scipy cython matplotlib +sphinx-gallery -- cgit v1.2.3 From eb3ebcbcac97d814d5a8d249c18273a46052116c Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 2 Dec 2016 13:46:29 +0100 Subject: try sphinx gallery --- examples/plot_OTDA_color_images.py | 2 +- examples/plot_OTDA_mapping_color_images.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/plot_OTDA_color_images.py b/examples/plot_OTDA_color_images.py index 7ddaa2b..b19cfe3 100644 --- a/examples/plot_OTDA_color_images.py +++ b/examples/plot_OTDA_color_images.py @@ -142,4 +142,4 @@ pl.subplot(2,3,6) pl.imshow(I2te) pl.title('Image 2 Adapt (reg)') -pl.show() \ No newline at end of file +pl.show() diff --git a/examples/plot_OTDA_mapping_color_images.py b/examples/plot_OTDA_mapping_color_images.py index 0cd6c9c..fbb9ee4 100644 --- a/examples/plot_OTDA_mapping_color_images.py +++ b/examples/plot_OTDA_mapping_color_images.py @@ -155,4 +155,4 @@ pl.subplot(2,3,6) pl.imshow(I1tn) pl.title('Im. 1 nonlinear mapping') -pl.show() \ No newline at end of file +pl.show() -- cgit v1.2.3 From 449045fda6070c3b9b5a599305357713a164e7ee Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 2 Dec 2016 14:07:17 +0100 Subject: nearly there gallery --- examples/README.txt | 3 +++ examples/plot_OTDA_color_images.py | 2 +- examples/plot_OTDA_mapping_color_images.py | 2 +- examples/plot_OT_2D_samples.py | 2 ++ examples/plot_barycenter_1D.py | 3 +++ examples/plot_optim_OTreg.py | 4 ++++ 6 files changed, 14 insertions(+), 2 deletions(-) create mode 100644 examples/README.txt diff --git a/examples/README.txt b/examples/README.txt new file mode 100644 index 0000000..83d114a --- /dev/null +++ b/examples/README.txt @@ -0,0 +1,3 @@ +============ +POT Examples +============ diff --git a/examples/plot_OTDA_color_images.py b/examples/plot_OTDA_color_images.py index b19cfe3..15fbab0 100644 --- a/examples/plot_OTDA_color_images.py +++ b/examples/plot_OTDA_color_images.py @@ -4,7 +4,7 @@ Demo of Optimal transport for domain adaptation with image color adaptation as in [6] ===================================================================================== -[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. +[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. """ import numpy as np diff --git a/examples/plot_OTDA_mapping_color_images.py b/examples/plot_OTDA_mapping_color_images.py index fbb9ee4..0349bf6 100644 --- a/examples/plot_OTDA_mapping_color_images.py +++ b/examples/plot_OTDA_mapping_color_images.py @@ -4,7 +4,7 @@ Demo of Optimal transport for domain adaptation with image color adaptation as in [6] with mapping estimation from [8] ====================================================================================================================== -[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized +[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. diff --git a/examples/plot_OT_2D_samples.py b/examples/plot_OT_2D_samples.py index e55eff8..138690f 100644 --- a/examples/plot_OT_2D_samples.py +++ b/examples/plot_OT_2D_samples.py @@ -1,6 +1,8 @@ # -*- coding: utf-8 -*- """ +============================================================= Demo for 2D Optimal transport between empirical distributions +============================================================= @author: rflamary """ diff --git a/examples/plot_barycenter_1D.py b/examples/plot_barycenter_1D.py index 5466332..30eecbf 100644 --- a/examples/plot_barycenter_1D.py +++ b/examples/plot_barycenter_1D.py @@ -1,6 +1,9 @@ # -*- coding: utf-8 -*- """ +============================== 1D Wasserstein barycenter demo +============================== + @author: rflamary """ diff --git a/examples/plot_optim_OTreg.py b/examples/plot_optim_OTreg.py index 0de6b08..3c4d3f4 100644 --- a/examples/plot_optim_OTreg.py +++ b/examples/plot_optim_OTreg.py @@ -1,6 +1,10 @@ # -*- coding: utf-8 -*- """ +================================== Regularized OT with generic solver +================================== + + """ import numpy as np -- cgit v1.2.3 From 01f15d4ac4a909ca7e71f6b8f6bca3edf88d7e47 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 2 Dec 2016 14:29:22 +0100 Subject: small update --- examples/plot_OTDA_classes.py | 1 + ot/datasets.py | 6 +++--- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/examples/plot_OTDA_classes.py b/examples/plot_OTDA_classes.py index 999be53..c00cef6 100644 --- a/examples/plot_OTDA_classes.py +++ b/examples/plot_OTDA_classes.py @@ -11,6 +11,7 @@ import ot + #%% parameters n=150 # nb samples in source and target datasets diff --git a/ot/datasets.py b/ot/datasets.py index 5c1ef78..7816833 100644 --- a/ot/datasets.py +++ b/ot/datasets.py @@ -124,8 +124,8 @@ def get_data_classif(dataset,n,nz=.5,theta=0,**kwargs): else: - x=0 - y=0 + x=np.array(0) + y=np.array(0) print("unknown dataset") - return x,y \ No newline at end of file + return x,y.astype(int) \ No newline at end of file -- cgit v1.2.3 From 4e723cbd3ac52750f28719cbd9a4119b113b28a9 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 2 Dec 2016 14:54:04 +0100 Subject: temporary disbly shinx-gallery --- docs/source/conf.py | 8 ++++---- docs/source/index.rst | 2 +- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 61505cc..51164b5 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -22,7 +22,7 @@ import sphinx_gallery #except ImportError: # from mock import MagicMock -# sys.path.insert(0, os.path.abspath("../..")) +sys.path.insert(0, os.path.abspath("../..")) #sys.setrecursionlimit(1500) @@ -58,7 +58,7 @@ extensions = [ 'sphinx.ext.ifconfig', 'sphinx.ext.viewcode', 'sphinx.ext.napoleon', - 'sphinx_gallery.gen_gallery', +# 'sphinx_gallery.gen_gallery', ] # Add any paths that contain templates here, relative to this directory. @@ -70,13 +70,13 @@ templates_path = ['_templates'] source_suffix = '.rst' # The encoding of source files. -#source_encoding = 'utf-8-sig' +source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. -project = u'POT Python Optimal Transport library' +project = u'POT Python Optimal Transport' copyright = u'2016, Rémi Flamary, Nicolas Courty' author = u'Rémi Flamary, Nicolas Courty' diff --git a/docs/source/index.rst b/docs/source/index.rst index e3c4d36..07c4f9a 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -17,7 +17,7 @@ Contents auto_examples .. include:: readme.rst - :start-line: 6 + :start-line: 5 Indices and tables -- cgit v1.2.3 From fd6df63f0775ba869f0dff839d5ff0713cdb89dc Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 2 Dec 2016 15:01:38 +0100 Subject: temporary readdthedoc correction --- docs/source/conf.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 51164b5..dddc4fe 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -18,7 +18,7 @@ import re import sphinx_gallery #try: -# from unittest.mock import MagicMock +from unittest.mock import MagicMock #except ImportError: # from mock import MagicMock @@ -27,13 +27,13 @@ sys.path.insert(0, os.path.abspath("../..")) -# class Mock(MagicMock): -# @classmethod -# def __getattr__(cls, name): -# return Mock() +class Mock(MagicMock): + @classmethod + def __getattr__(cls, name): + return Mock() -# MOCK_MODULES = [ 'emd','ot.lp.emd'] -# sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) +MOCK_MODULES = [ 'emd','ot.lp.emd'] +sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the -- cgit v1.2.3 From 7609f9e6a4103e13beb294873f4dac562b1d45e1 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 2 Dec 2016 15:33:30 +0100 Subject: working doc with gallery --- docs/source/conf.py | 37 +++++++++++++++--------------- docs/source/index.rst | 4 ++-- examples/README.txt | 1 - examples/plot_OTDA_2D.py | 6 ++--- examples/plot_OTDA_classes.py | 6 ++--- examples/plot_OTDA_color_images.py | 6 ++--- examples/plot_OTDA_mapping.py | 6 ++--- examples/plot_OTDA_mapping_color_images.py | 6 ++--- examples/plot_OT_1D.py | 6 ++--- examples/plot_OT_2D_samples.py | 6 ++--- 10 files changed, 42 insertions(+), 42 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index dddc4fe..f76c184 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -17,23 +17,23 @@ import os import re import sphinx_gallery -#try: -from unittest.mock import MagicMock -#except ImportError: -# from mock import MagicMock - -sys.path.insert(0, os.path.abspath("../..")) -#sys.setrecursionlimit(1500) - - - -class Mock(MagicMock): - @classmethod - def __getattr__(cls, name): - return Mock() - -MOCK_MODULES = [ 'emd','ot.lp.emd'] -sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) +##try: +#from unittest.mock import MagicMock +##except ImportError: +## from mock import MagicMock +# +#sys.path.insert(0, os.path.abspath("../..")) +##sys.setrecursionlimit(1500) +# +# +# +#class Mock(MagicMock): +# @classmethod +# def __getattr__(cls, name): +# return Mock() +# +#MOCK_MODULES = [ 'emd','ot.lp.emd'] +#sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the @@ -58,7 +58,7 @@ extensions = [ 'sphinx.ext.ifconfig', 'sphinx.ext.viewcode', 'sphinx.ext.napoleon', -# 'sphinx_gallery.gen_gallery', + 'sphinx_gallery.gen_gallery', ] # Add any paths that contain templates here, relative to this directory. @@ -324,6 +324,7 @@ intersphinx_mapping = {'https://docs.python.org/': None} sphinx_gallery_conf = { 'examples_dirs': '../../examples', 'gallery_dirs': 'auto_examples', + 'mod_example_dir': '../modules/generated/', 'reference_url': { 'numpy': 'http://docs.scipy.org/doc/numpy-1.9.1', 'scipy': 'http://docs.scipy.org/doc/scipy-0.17.0/reference'} diff --git a/docs/source/index.rst b/docs/source/index.rst index 07c4f9a..b8eabcb 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -10,11 +10,11 @@ Contents -------- .. toctree:: - :maxdepth: 2 + :maxdepth: 3 self all - auto_examples + auto_examples/index .. include:: readme.rst :start-line: 5 diff --git a/examples/README.txt b/examples/README.txt index 83d114a..f8643b8 100644 --- a/examples/README.txt +++ b/examples/README.txt @@ -1,3 +1,2 @@ -============ POT Examples ============ diff --git a/examples/plot_OTDA_2D.py b/examples/plot_OTDA_2D.py index db04c5e..a1fb804 100644 --- a/examples/plot_OTDA_2D.py +++ b/examples/plot_OTDA_2D.py @@ -1,8 +1,8 @@ # -*- coding: utf-8 -*- """ -=============================================== -demo of Optimal transport for domain adaptation -=============================================== +============================== +OT for empirical distributions +============================== """ diff --git a/examples/plot_OTDA_classes.py b/examples/plot_OTDA_classes.py index c00cef6..089b45b 100644 --- a/examples/plot_OTDA_classes.py +++ b/examples/plot_OTDA_classes.py @@ -1,8 +1,8 @@ # -*- coding: utf-8 -*- """ -=============================================== -demo of Optimal transport for domain adaptation -=============================================== +======================== +OT for domain adaptation +======================== """ diff --git a/examples/plot_OTDA_color_images.py b/examples/plot_OTDA_color_images.py index 15fbab0..68eee44 100644 --- a/examples/plot_OTDA_color_images.py +++ b/examples/plot_OTDA_color_images.py @@ -1,8 +1,8 @@ # -*- coding: utf-8 -*- """ -===================================================================================== -Demo of Optimal transport for domain adaptation with image color adaptation as in [6] -===================================================================================== +======================================================== +OT for domain adaptation with image color adaptation [6] +======================================================== [6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. """ diff --git a/examples/plot_OTDA_mapping.py b/examples/plot_OTDA_mapping.py index 779fa76..78b57e7 100644 --- a/examples/plot_OTDA_mapping.py +++ b/examples/plot_OTDA_mapping.py @@ -1,8 +1,8 @@ # -*- coding: utf-8 -*- """ -======================================================= -Demo of OT mapping estimation for domain adaptation [8] -======================================================= +=============================================== +OT mapping estimation for domain adaptation [8] +=============================================== [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. diff --git a/examples/plot_OTDA_mapping_color_images.py b/examples/plot_OTDA_mapping_color_images.py index 0349bf6..f07dc6c 100644 --- a/examples/plot_OTDA_mapping_color_images.py +++ b/examples/plot_OTDA_mapping_color_images.py @@ -1,8 +1,8 @@ # -*- coding: utf-8 -*- """ -====================================================================================================================== -Demo of Optimal transport for domain adaptation with image color adaptation as in [6] with mapping estimation from [8] -====================================================================================================================== +==================================================================================== +OT for domain adaptation with image color adaptation [6] with mapping estimation [8] +==================================================================================== [6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. diff --git a/examples/plot_OT_1D.py b/examples/plot_OT_1D.py index a8bbbd6..e5719eb 100644 --- a/examples/plot_OT_1D.py +++ b/examples/plot_OT_1D.py @@ -1,8 +1,8 @@ # -*- coding: utf-8 -*- """ -============================= -Demo for 1D optimal transport -============================= +==================== +1D optimal transport +==================== @author: rflamary """ diff --git a/examples/plot_OT_2D_samples.py b/examples/plot_OT_2D_samples.py index 138690f..6c39ad4 100644 --- a/examples/plot_OT_2D_samples.py +++ b/examples/plot_OT_2D_samples.py @@ -1,8 +1,8 @@ # -*- coding: utf-8 -*- """ -============================================================= -Demo for 2D Optimal transport between empirical distributions -============================================================= +==================================================== +2D Optimal transport between empirical distributions +==================================================== 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b/docs/source/auto_examples/auto_examples_python.zip differ diff --git a/docs/source/auto_examples/demo_OT_1D_test.ipynb b/docs/source/auto_examples/demo_OT_1D_test.ipynb new file mode 100644 index 0000000..87317ea --- /dev/null +++ b/docs/source/auto_examples/demo_OT_1D_test.ipynb @@ -0,0 +1,54 @@ +{ + "nbformat_minor": 0, + "nbformat": 4, + "cells": [ + { + "execution_count": null, + "cell_type": "code", + "source": [ + "%matplotlib inline" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "\nDemo for 1D optimal transport\n\n@author: rflamary\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\nfrom ot.datasets import get_1D_gauss as gauss\n\n\n#%% parameters\n\nn=100 # nb bins\n\n# bin positions\nx=np.arange(n,dtype=np.float64)\n\n# Gaussian distributions\na=gauss(n,m=n*.2,s=5) # m= mean, s= std\nb=gauss(n,m=n*.6,s=10)\n\n# loss matrix\nM=ot.dist(x.reshape((n,1)),x.reshape((n,1)))\nM/=M.max()\n\n#%% plot the distributions\n\npl.figure(1)\npl.plot(x,a,'b',label='Source distribution')\npl.plot(x,b,'r',label='Target distribution')\npl.legend()\n\n#%% plot distributions and loss matrix\n\npl.figure(2)\not.plot.plot1D_mat(a,b,M,'Cost matrix M')\n\n#%% EMD\n\nG0=ot.emd(a,b,M)\n\npl.figure(3)\not.plot.plot1D_mat(a,b,G0,'OT matrix G0')\n\n#%% Sinkhorn\n\nlambd=1e-3\nGs=ot.sinkhorn(a,b,M,lambd,verbose=True)\n\npl.figure(4)\not.plot.plot1D_mat(a,b,Gs,'OT matrix Sinkhorn')\n\n#%% Sinkhorn\n\nlambd=1e-4\nGss,log=ot.bregman.sinkhorn_stabilized(a,b,M,lambd,verbose=True,log=True)\nGss2,log2=ot.bregman.sinkhorn_stabilized(a,b,M,lambd,verbose=True,log=True,warmstart=log['warmstart'])\n\npl.figure(5)\not.plot.plot1D_mat(a,b,Gss,'OT matrix Sinkhorn stabilized')\n\n#%% Sinkhorn\n\nlambd=1e-11\nGss=ot.bregman.sinkhorn_epsilon_scaling(a,b,M,lambd,verbose=True)\n\npl.figure(5)\not.plot.plot1D_mat(a,b,Gss,'OT matrix Sinkhorn stabilized')" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "name": "python2", + "language": "python" + }, + "language_info": { + "mimetype": "text/x-python", + "nbconvert_exporter": "python", + "name": "python", + "file_extension": ".py", + "version": "2.7.12", + "pygments_lexer": "ipython2", + "codemirror_mode": { + "version": 2, + "name": "ipython" + } + } + } +} \ No newline at end of file diff --git a/docs/source/auto_examples/demo_OT_1D_test.py b/docs/source/auto_examples/demo_OT_1D_test.py new file mode 100644 index 0000000..9edc377 --- /dev/null +++ b/docs/source/auto_examples/demo_OT_1D_test.py @@ -0,0 +1,71 @@ +# -*- coding: utf-8 -*- +""" +Demo for 1D optimal transport + +@author: rflamary +""" + +import numpy as np +import matplotlib.pylab as pl +import ot +from ot.datasets import get_1D_gauss as gauss + + +#%% parameters + +n=100 # nb bins + +# bin positions +x=np.arange(n,dtype=np.float64) + +# Gaussian distributions +a=gauss(n,m=n*.2,s=5) # m= mean, s= std +b=gauss(n,m=n*.6,s=10) + +# loss matrix +M=ot.dist(x.reshape((n,1)),x.reshape((n,1))) +M/=M.max() + +#%% plot the distributions + +pl.figure(1) +pl.plot(x,a,'b',label='Source distribution') +pl.plot(x,b,'r',label='Target distribution') +pl.legend() + +#%% plot distributions and loss matrix + +pl.figure(2) +ot.plot.plot1D_mat(a,b,M,'Cost matrix M') + +#%% EMD + +G0=ot.emd(a,b,M) + +pl.figure(3) +ot.plot.plot1D_mat(a,b,G0,'OT matrix G0') + +#%% Sinkhorn + +lambd=1e-3 +Gs=ot.sinkhorn(a,b,M,lambd,verbose=True) + +pl.figure(4) +ot.plot.plot1D_mat(a,b,Gs,'OT matrix Sinkhorn') + +#%% Sinkhorn + +lambd=1e-4 +Gss,log=ot.bregman.sinkhorn_stabilized(a,b,M,lambd,verbose=True,log=True) +Gss2,log2=ot.bregman.sinkhorn_stabilized(a,b,M,lambd,verbose=True,log=True,warmstart=log['warmstart']) + +pl.figure(5) +ot.plot.plot1D_mat(a,b,Gss,'OT matrix Sinkhorn stabilized') + +#%% Sinkhorn + +lambd=1e-11 +Gss=ot.bregman.sinkhorn_epsilon_scaling(a,b,M,lambd,verbose=True) + +pl.figure(5) +ot.plot.plot1D_mat(a,b,Gss,'OT matrix Sinkhorn stabilized') diff --git a/docs/source/auto_examples/demo_OT_1D_test.rst b/docs/source/auto_examples/demo_OT_1D_test.rst new file mode 100644 index 0000000..aebeb1d --- /dev/null +++ b/docs/source/auto_examples/demo_OT_1D_test.rst @@ -0,0 +1,99 @@ + + +.. _sphx_glr_auto_examples_demo_OT_1D_test.py: + + +Demo for 1D optimal transport + +@author: rflamary + + + +.. code-block:: python + + + import numpy as np + import matplotlib.pylab as pl + import ot + from ot.datasets import get_1D_gauss as gauss + + + #%% parameters + + n=100 # nb bins + + # bin positions + x=np.arange(n,dtype=np.float64) + + # Gaussian distributions + a=gauss(n,m=n*.2,s=5) # m= mean, s= std + b=gauss(n,m=n*.6,s=10) + + # loss matrix + M=ot.dist(x.reshape((n,1)),x.reshape((n,1))) + M/=M.max() + + #%% plot the distributions + + pl.figure(1) + pl.plot(x,a,'b',label='Source distribution') + pl.plot(x,b,'r',label='Target distribution') + pl.legend() + + #%% plot distributions and loss matrix + + pl.figure(2) + ot.plot.plot1D_mat(a,b,M,'Cost matrix M') + + #%% EMD + + G0=ot.emd(a,b,M) + + pl.figure(3) + ot.plot.plot1D_mat(a,b,G0,'OT matrix G0') + + #%% Sinkhorn + + lambd=1e-3 + Gs=ot.sinkhorn(a,b,M,lambd,verbose=True) + + pl.figure(4) + ot.plot.plot1D_mat(a,b,Gs,'OT matrix Sinkhorn') + + #%% Sinkhorn + + lambd=1e-4 + Gss,log=ot.bregman.sinkhorn_stabilized(a,b,M,lambd,verbose=True,log=True) + Gss2,log2=ot.bregman.sinkhorn_stabilized(a,b,M,lambd,verbose=True,log=True,warmstart=log['warmstart']) + + pl.figure(5) + ot.plot.plot1D_mat(a,b,Gss,'OT matrix Sinkhorn stabilized') + + #%% Sinkhorn + + lambd=1e-11 + Gss=ot.bregman.sinkhorn_epsilon_scaling(a,b,M,lambd,verbose=True) + + pl.figure(5) + ot.plot.plot1D_mat(a,b,Gss,'OT matrix Sinkhorn stabilized') + +**Total running time of the script:** ( 0 minutes 0.000 seconds) + + + +.. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: demo_OT_1D_test.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: demo_OT_1D_test.ipynb ` + +.. rst-class:: sphx-glr-signature + + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/demo_OT_2D_sampleslarge.ipynb b/docs/source/auto_examples/demo_OT_2D_sampleslarge.ipynb new file mode 100644 index 0000000..584a936 --- /dev/null +++ b/docs/source/auto_examples/demo_OT_2D_sampleslarge.ipynb @@ -0,0 +1,54 @@ +{ + "nbformat_minor": 0, + "nbformat": 4, + "cells": [ + { + "execution_count": null, + "cell_type": "code", + "source": [ + "%matplotlib inline" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "\nDemo for 2D Optimal transport between empirical distributions\n\n@author: rflamary\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n#%% parameters and data generation\n\nn=5000 # nb samples\n\nmu_s=np.array([0,0])\ncov_s=np.array([[1,0],[0,1]])\n\nmu_t=np.array([4,4])\ncov_t=np.array([[1,-.8],[-.8,1]])\n\nxs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s)\nxt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t)\n\na,b = ot.unif(n),ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM=ot.dist(xs,xt)\nM/=M.max()\n\n#%% plot samples\n\n#pl.figure(1)\n#pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n#pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n#pl.legend(loc=0)\n#pl.title('Source and traget distributions')\n#\n#pl.figure(2)\n#pl.imshow(M,interpolation='nearest')\n#pl.title('Cost matrix M')\n#\n\n#%% EMD\n\nG0=ot.emd(a,b,M)\n\n#pl.figure(3)\n#pl.imshow(G0,interpolation='nearest')\n#pl.title('OT matrix G0')\n#\n#pl.figure(4)\n#ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1])\n#pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n#pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n#pl.legend(loc=0)\n#pl.title('OT matrix with samples')\n\n\n#%% sinkhorn\n\n# reg term\nlambd=5e-3\n\nGs=ot.sinkhorn(a,b,M,lambd)\n\n#pl.figure(5)\n#pl.imshow(Gs,interpolation='nearest')\n#pl.title('OT matrix sinkhorn')\n#\n#pl.figure(6)\n#ot.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1])\n#pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n#pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n#pl.legend(loc=0)\n#pl.title('OT matrix Sinkhorn with samples')\n#" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "name": "python2", + "language": "python" + }, + "language_info": { + "mimetype": "text/x-python", + "nbconvert_exporter": "python", + "name": "python", + "file_extension": ".py", + "version": "2.7.12", + "pygments_lexer": "ipython2", + "codemirror_mode": { + "version": 2, + "name": "ipython" + } + } + } +} \ No newline at end of file diff --git a/docs/source/auto_examples/demo_OT_2D_sampleslarge.py b/docs/source/auto_examples/demo_OT_2D_sampleslarge.py new file mode 100644 index 0000000..ee3e8f7 --- /dev/null +++ b/docs/source/auto_examples/demo_OT_2D_sampleslarge.py @@ -0,0 +1,78 @@ +# -*- coding: utf-8 -*- +""" +Demo for 2D Optimal transport between empirical distributions + +@author: rflamary +""" + +import numpy as np +import matplotlib.pylab as pl +import ot + +#%% parameters and data generation + +n=5000 # nb samples + +mu_s=np.array([0,0]) +cov_s=np.array([[1,0],[0,1]]) + +mu_t=np.array([4,4]) +cov_t=np.array([[1,-.8],[-.8,1]]) + +xs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s) +xt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t) + +a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples + +# loss matrix +M=ot.dist(xs,xt) +M/=M.max() + +#%% plot samples + +#pl.figure(1) +#pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') +#pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') +#pl.legend(loc=0) +#pl.title('Source and traget distributions') +# +#pl.figure(2) +#pl.imshow(M,interpolation='nearest') +#pl.title('Cost matrix M') +# + +#%% EMD + +G0=ot.emd(a,b,M) + +#pl.figure(3) +#pl.imshow(G0,interpolation='nearest') +#pl.title('OT matrix G0') +# +#pl.figure(4) +#ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1]) +#pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') +#pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') +#pl.legend(loc=0) +#pl.title('OT matrix with samples') + + +#%% sinkhorn + +# reg term +lambd=5e-3 + +Gs=ot.sinkhorn(a,b,M,lambd) + +#pl.figure(5) +#pl.imshow(Gs,interpolation='nearest') +#pl.title('OT matrix sinkhorn') +# +#pl.figure(6) +#ot.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1]) +#pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') +#pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') +#pl.legend(loc=0) +#pl.title('OT matrix Sinkhorn with samples') +# + diff --git a/docs/source/auto_examples/demo_OT_2D_sampleslarge.rst b/docs/source/auto_examples/demo_OT_2D_sampleslarge.rst new file mode 100644 index 0000000..f5dbb0d --- /dev/null +++ b/docs/source/auto_examples/demo_OT_2D_sampleslarge.rst @@ -0,0 +1,106 @@ + + +.. _sphx_glr_auto_examples_demo_OT_2D_sampleslarge.py: + + +Demo for 2D Optimal transport between empirical distributions + +@author: rflamary + + + +.. code-block:: python + + + import numpy as np + import matplotlib.pylab as pl + import ot + + #%% parameters and data generation + + n=5000 # nb samples + + mu_s=np.array([0,0]) + cov_s=np.array([[1,0],[0,1]]) + + mu_t=np.array([4,4]) + cov_t=np.array([[1,-.8],[-.8,1]]) + + xs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s) + xt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t) + + a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples + + # loss matrix + M=ot.dist(xs,xt) + M/=M.max() + + #%% plot samples + + #pl.figure(1) + #pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') + #pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') + #pl.legend(loc=0) + #pl.title('Source and traget distributions') + # + #pl.figure(2) + #pl.imshow(M,interpolation='nearest') + #pl.title('Cost matrix M') + # + + #%% EMD + + G0=ot.emd(a,b,M) + + #pl.figure(3) + #pl.imshow(G0,interpolation='nearest') + #pl.title('OT matrix G0') + # + #pl.figure(4) + #ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1]) + #pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') + #pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') + #pl.legend(loc=0) + #pl.title('OT matrix with samples') + + + #%% sinkhorn + + # reg term + lambd=5e-3 + + Gs=ot.sinkhorn(a,b,M,lambd) + + #pl.figure(5) + #pl.imshow(Gs,interpolation='nearest') + #pl.title('OT matrix sinkhorn') + # + #pl.figure(6) + 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+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_1D_thumb.png + + :ref:`sphx_glr_auto_examples_plot_OT_1D.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_OT_1D + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_optim_OTreg_thumb.png + + :ref:`sphx_glr_auto_examples_plot_optim_OTreg.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_optim_OTreg + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_2D_samples_thumb.png + + :ref:`sphx_glr_auto_examples_plot_OT_2D_samples.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_OT_2D_samples + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OTDA_color_images_thumb.png + + :ref:`sphx_glr_auto_examples_plot_OTDA_color_images.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_OTDA_color_images + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OTDA_classes_thumb.png + + :ref:`sphx_glr_auto_examples_plot_OTDA_classes.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_OTDA_classes + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OTDA_2D_thumb.png + + :ref:`sphx_glr_auto_examples_plot_OTDA_2D.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_OTDA_2D + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_barycenter_1D_thumb.png + + :ref:`sphx_glr_auto_examples_plot_barycenter_1D.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_barycenter_1D + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OTDA_mapping_color_images_thumb.png + + :ref:`sphx_glr_auto_examples_plot_OTDA_mapping_color_images.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_OTDA_mapping_color_images + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OTDA_mapping_thumb.png + + :ref:`sphx_glr_auto_examples_plot_OTDA_mapping.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_OTDA_mapping +.. raw:: html + +
+ + + +.. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download all examples in Python source code: auto_examples_python.zip ` + + + + .. container:: sphx-glr-download + + :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip ` + +.. rst-class:: sphx-glr-signature + + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OTDA_2D.ipynb b/docs/source/auto_examples/plot_OTDA_2D.ipynb new file mode 100644 index 0000000..2ffb256 --- /dev/null +++ b/docs/source/auto_examples/plot_OTDA_2D.ipynb @@ -0,0 +1,54 @@ +{ + "nbformat_minor": 0, + "nbformat": 4, + "cells": [ + { + "execution_count": null, + "cell_type": "code", + "source": [ + "%matplotlib inline" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "\n# OT for empirical distributions\n\n\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n\n\n#%% parameters\n\nn=150 # nb bins\n\nxs,ys=ot.datasets.get_data_classif('3gauss',n)\nxt,yt=ot.datasets.get_data_classif('3gauss2',n)\n\na,b = ot.unif(n),ot.unif(n)\n# loss matrix\nM=ot.dist(xs,xt)\n#M/=M.max()\n\n#%% plot samples\n\npl.figure(1)\n\npl.subplot(2,2,1)\npl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples')\npl.legend(loc=0)\npl.title('Source distributions')\n\npl.subplot(2,2,2)\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples')\npl.legend(loc=0)\npl.title('target distributions')\n\npl.figure(2)\npl.imshow(M,interpolation='nearest')\npl.title('Cost matrix M')\n\n\n#%% OT estimation\n\n# EMD\nG0=ot.emd(a,b,M)\n\n# sinkhorn\nlambd=1e-1\nGs=ot.sinkhorn(a,b,M,lambd)\n\n\n# Group lasso regularization\nreg=1e-1\neta=1e0\nGg=ot.da.sinkhorn_lpl1_mm(a,ys.astype(np.int),b,M,reg,eta)\n\n\n#%% visu matrices\n\npl.figure(3)\n\npl.subplot(2,3,1)\npl.imshow(G0,interpolation='nearest')\npl.title('OT matrix ')\n\npl.subplot(2,3,2)\npl.imshow(Gs,interpolation='nearest')\npl.title('OT matrix Sinkhorn')\n\npl.subplot(2,3,3)\npl.imshow(Gg,interpolation='nearest')\npl.title('OT matrix Group lasso')\n\npl.subplot(2,3,4)\not.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1])\npl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples')\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples')\n\n\npl.subplot(2,3,5)\not.plot.plot2D_samples_mat(xs,xt,Gs,c=[.5,.5,1])\npl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples')\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples')\n\npl.subplot(2,3,6)\not.plot.plot2D_samples_mat(xs,xt,Gg,c=[.5,.5,1])\npl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples')\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples')\n\n#%% sample interpolation\n\nxst0=n*G0.dot(xt)\nxsts=n*Gs.dot(xt)\nxstg=n*Gg.dot(xt)\n\npl.figure(4)\npl.subplot(2,3,1)\n\n\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5)\npl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30)\npl.title('Interp samples')\npl.legend(loc=0)\n\npl.subplot(2,3,2)\n\n\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5)\npl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30)\npl.title('Interp samples Sinkhorn')\n\npl.subplot(2,3,3)\n\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5)\npl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30)\npl.title('Interp samples Grouplasso')" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "name": "python2", + "language": "python" + }, + "language_info": { + "mimetype": "text/x-python", + "nbconvert_exporter": "python", + "name": "python", + "file_extension": ".py", + "version": "2.7.12", + "pygments_lexer": "ipython2", + "codemirror_mode": { + "version": 2, + "name": "ipython" + } + } + } +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OTDA_2D.py b/docs/source/auto_examples/plot_OTDA_2D.py new file mode 100644 index 0000000..a1fb804 --- /dev/null +++ b/docs/source/auto_examples/plot_OTDA_2D.py @@ -0,0 +1,120 @@ +# -*- coding: utf-8 -*- +""" +============================== +OT for empirical distributions +============================== + +""" + +import numpy as np +import matplotlib.pylab as pl +import ot + + + +#%% parameters + +n=150 # nb bins + +xs,ys=ot.datasets.get_data_classif('3gauss',n) +xt,yt=ot.datasets.get_data_classif('3gauss2',n) + +a,b = ot.unif(n),ot.unif(n) +# loss matrix +M=ot.dist(xs,xt) +#M/=M.max() + +#%% plot samples + +pl.figure(1) + +pl.subplot(2,2,1) +pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') +pl.legend(loc=0) +pl.title('Source distributions') + +pl.subplot(2,2,2) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') +pl.legend(loc=0) +pl.title('target distributions') + +pl.figure(2) +pl.imshow(M,interpolation='nearest') +pl.title('Cost matrix M') + + +#%% OT estimation + +# EMD +G0=ot.emd(a,b,M) + +# sinkhorn +lambd=1e-1 +Gs=ot.sinkhorn(a,b,M,lambd) + + +# Group lasso regularization +reg=1e-1 +eta=1e0 +Gg=ot.da.sinkhorn_lpl1_mm(a,ys.astype(np.int),b,M,reg,eta) + + +#%% visu matrices + +pl.figure(3) + +pl.subplot(2,3,1) +pl.imshow(G0,interpolation='nearest') +pl.title('OT matrix ') + +pl.subplot(2,3,2) +pl.imshow(Gs,interpolation='nearest') +pl.title('OT matrix Sinkhorn') + +pl.subplot(2,3,3) +pl.imshow(Gg,interpolation='nearest') +pl.title('OT matrix Group lasso') + +pl.subplot(2,3,4) +ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1]) +pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') + + +pl.subplot(2,3,5) +ot.plot.plot2D_samples_mat(xs,xt,Gs,c=[.5,.5,1]) +pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') + +pl.subplot(2,3,6) +ot.plot.plot2D_samples_mat(xs,xt,Gg,c=[.5,.5,1]) +pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') + +#%% sample interpolation + +xst0=n*G0.dot(xt) +xsts=n*Gs.dot(xt) +xstg=n*Gg.dot(xt) + +pl.figure(4) +pl.subplot(2,3,1) + + +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) +pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30) +pl.title('Interp samples') +pl.legend(loc=0) + +pl.subplot(2,3,2) + + +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) +pl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30) +pl.title('Interp samples Sinkhorn') + +pl.subplot(2,3,3) + +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) +pl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30) +pl.title('Interp samples Grouplasso') \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OTDA_2D.rst b/docs/source/auto_examples/plot_OTDA_2D.rst new file mode 100644 index 0000000..b535bb0 --- /dev/null +++ b/docs/source/auto_examples/plot_OTDA_2D.rst @@ -0,0 +1,175 @@ + + +.. _sphx_glr_auto_examples_plot_OTDA_2D.py: + + +============================== +OT for empirical distributions +============================== + + + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OTDA_2D_001.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OTDA_2D_002.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OTDA_2D_003.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OTDA_2D_004.png + :scale: 47 + + + + + +.. code-block:: python + + + import numpy as np + import matplotlib.pylab as pl + import ot + + + + #%% parameters + + n=150 # nb bins + + xs,ys=ot.datasets.get_data_classif('3gauss',n) + xt,yt=ot.datasets.get_data_classif('3gauss2',n) + + a,b = ot.unif(n),ot.unif(n) + # loss matrix + M=ot.dist(xs,xt) + #M/=M.max() + + #%% plot samples + + pl.figure(1) + + pl.subplot(2,2,1) + pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') + pl.legend(loc=0) + pl.title('Source distributions') + + pl.subplot(2,2,2) + pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') + pl.legend(loc=0) + pl.title('target distributions') + + pl.figure(2) + pl.imshow(M,interpolation='nearest') + pl.title('Cost matrix M') + + + #%% OT estimation + + # EMD + G0=ot.emd(a,b,M) + + # sinkhorn + lambd=1e-1 + Gs=ot.sinkhorn(a,b,M,lambd) + + + # Group lasso regularization + reg=1e-1 + eta=1e0 + Gg=ot.da.sinkhorn_lpl1_mm(a,ys.astype(np.int),b,M,reg,eta) + + + #%% visu matrices + + pl.figure(3) + + pl.subplot(2,3,1) + pl.imshow(G0,interpolation='nearest') + pl.title('OT matrix ') + + pl.subplot(2,3,2) + pl.imshow(Gs,interpolation='nearest') + pl.title('OT matrix Sinkhorn') + + pl.subplot(2,3,3) + pl.imshow(Gg,interpolation='nearest') + pl.title('OT matrix Group lasso') + + pl.subplot(2,3,4) + ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1]) + pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') + pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') + + + pl.subplot(2,3,5) + ot.plot.plot2D_samples_mat(xs,xt,Gs,c=[.5,.5,1]) + pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') + pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') + + pl.subplot(2,3,6) + ot.plot.plot2D_samples_mat(xs,xt,Gg,c=[.5,.5,1]) + pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') + pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') + + #%% sample interpolation + + xst0=n*G0.dot(xt) + xsts=n*Gs.dot(xt) + xstg=n*Gg.dot(xt) + + pl.figure(4) + pl.subplot(2,3,1) + + + pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) + pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30) + pl.title('Interp samples') + pl.legend(loc=0) + + pl.subplot(2,3,2) + + + pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) + pl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30) + pl.title('Interp samples Sinkhorn') + + pl.subplot(2,3,3) + + pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) + pl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30) + pl.title('Interp samples Grouplasso') +**Total running time of the script:** ( 0 minutes 17.372 seconds) + + + +.. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_OTDA_2D.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_OTDA_2D.ipynb ` + +.. rst-class:: sphx-glr-signature + + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OTDA_classes.ipynb b/docs/source/auto_examples/plot_OTDA_classes.ipynb new file mode 100644 index 0000000..d9fcb87 --- /dev/null +++ b/docs/source/auto_examples/plot_OTDA_classes.ipynb @@ -0,0 +1,54 @@ +{ + "nbformat_minor": 0, + "nbformat": 4, + "cells": [ + { + "execution_count": null, + "cell_type": "code", + "source": [ + "%matplotlib inline" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "\n# OT for domain adaptation\n\n\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "import matplotlib.pylab as pl\nimport ot\n\n\n\n\n#%% parameters\n\nn=150 # nb samples in source and target datasets\n\nxs,ys=ot.datasets.get_data_classif('3gauss',n)\nxt,yt=ot.datasets.get_data_classif('3gauss2',n)\n\n\n\n\n#%% plot samples\n\npl.figure(1)\n\npl.subplot(2,2,1)\npl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples')\npl.legend(loc=0)\npl.title('Source distributions')\n\npl.subplot(2,2,2)\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples')\npl.legend(loc=0)\npl.title('target distributions')\n\n\n#%% OT estimation\n\n# LP problem\nda_emd=ot.da.OTDA() # init class\nda_emd.fit(xs,xt) # fit distributions\nxst0=da_emd.interp() # interpolation of source samples\n\n\n# sinkhorn regularization\nlambd=1e-1\nda_entrop=ot.da.OTDA_sinkhorn()\nda_entrop.fit(xs,xt,reg=lambd)\nxsts=da_entrop.interp()\n\n# non-convex Group lasso regularization\nreg=1e-1\neta=1e0\nda_lpl1=ot.da.OTDA_lpl1()\nda_lpl1.fit(xs,ys,xt,reg=reg,eta=eta)\nxstg=da_lpl1.interp()\n\n\n# True Group lasso regularization\nreg=1e-1\neta=2e0\nda_l1l2=ot.da.OTDA_l1l2()\nda_l1l2.fit(xs,ys,xt,reg=reg,eta=eta,numItermax=20,verbose=True)\nxstgl=da_l1l2.interp()\n\n\n#%% plot interpolated source samples\npl.figure(4,(15,8))\n\nparam_img={'interpolation':'nearest','cmap':'jet'}\n\npl.subplot(2,4,1)\npl.imshow(da_emd.G,**param_img)\npl.title('OT matrix')\n\n\npl.subplot(2,4,2)\npl.imshow(da_entrop.G,**param_img)\npl.title('OT matrix sinkhorn')\n\npl.subplot(2,4,3)\npl.imshow(da_lpl1.G,**param_img)\npl.title('OT matrix non-convex Group Lasso')\n\npl.subplot(2,4,4)\npl.imshow(da_l1l2.G,**param_img)\npl.title('OT matrix Group Lasso')\n\n\npl.subplot(2,4,5)\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)\npl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30)\npl.title('Interp samples')\npl.legend(loc=0)\n\npl.subplot(2,4,6)\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)\npl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30)\npl.title('Interp samples Sinkhorn')\n\npl.subplot(2,4,7)\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)\npl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30)\npl.title('Interp samples non-convex Group Lasso')\n\npl.subplot(2,4,8)\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)\npl.scatter(xstgl[:,0],xstgl[:,1],c=ys,marker='+',label='Transp samples',s=30)\npl.title('Interp samples Group Lasso')" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "name": "python2", + "language": "python" + }, + "language_info": { + "mimetype": "text/x-python", + "nbconvert_exporter": "python", + "name": "python", + "file_extension": ".py", + "version": "2.7.12", + "pygments_lexer": "ipython2", + "codemirror_mode": { + "version": 2, + "name": "ipython" + } + } + } +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OTDA_classes.py b/docs/source/auto_examples/plot_OTDA_classes.py new file mode 100644 index 0000000..089b45b --- /dev/null +++ b/docs/source/auto_examples/plot_OTDA_classes.py @@ -0,0 +1,112 @@ +# -*- coding: utf-8 -*- +""" +======================== +OT for domain adaptation +======================== + +""" + +import matplotlib.pylab as pl +import ot + + + + +#%% parameters + +n=150 # nb samples in source and target datasets + +xs,ys=ot.datasets.get_data_classif('3gauss',n) +xt,yt=ot.datasets.get_data_classif('3gauss2',n) + + + + +#%% plot samples + +pl.figure(1) + +pl.subplot(2,2,1) +pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') +pl.legend(loc=0) +pl.title('Source distributions') + +pl.subplot(2,2,2) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') +pl.legend(loc=0) +pl.title('target distributions') + + +#%% OT estimation + +# LP problem +da_emd=ot.da.OTDA() # init class +da_emd.fit(xs,xt) # fit distributions +xst0=da_emd.interp() # interpolation of source samples + + +# sinkhorn regularization +lambd=1e-1 +da_entrop=ot.da.OTDA_sinkhorn() +da_entrop.fit(xs,xt,reg=lambd) +xsts=da_entrop.interp() + +# non-convex Group lasso regularization +reg=1e-1 +eta=1e0 +da_lpl1=ot.da.OTDA_lpl1() +da_lpl1.fit(xs,ys,xt,reg=reg,eta=eta) +xstg=da_lpl1.interp() + + +# True Group lasso regularization +reg=1e-1 +eta=2e0 +da_l1l2=ot.da.OTDA_l1l2() +da_l1l2.fit(xs,ys,xt,reg=reg,eta=eta,numItermax=20,verbose=True) +xstgl=da_l1l2.interp() + + +#%% plot interpolated source samples +pl.figure(4,(15,8)) + +param_img={'interpolation':'nearest','cmap':'jet'} + +pl.subplot(2,4,1) +pl.imshow(da_emd.G,**param_img) +pl.title('OT matrix') + + +pl.subplot(2,4,2) +pl.imshow(da_entrop.G,**param_img) +pl.title('OT matrix sinkhorn') + +pl.subplot(2,4,3) +pl.imshow(da_lpl1.G,**param_img) +pl.title('OT matrix non-convex Group Lasso') + +pl.subplot(2,4,4) +pl.imshow(da_l1l2.G,**param_img) +pl.title('OT matrix Group Lasso') + + +pl.subplot(2,4,5) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) +pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30) +pl.title('Interp samples') +pl.legend(loc=0) + +pl.subplot(2,4,6) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) +pl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30) +pl.title('Interp samples Sinkhorn') + +pl.subplot(2,4,7) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) +pl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30) +pl.title('Interp samples non-convex Group Lasso') + +pl.subplot(2,4,8) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) +pl.scatter(xstgl[:,0],xstgl[:,1],c=ys,marker='+',label='Transp samples',s=30) +pl.title('Interp samples Group Lasso') \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OTDA_classes.rst b/docs/source/auto_examples/plot_OTDA_classes.rst new file mode 100644 index 0000000..097e9fc --- /dev/null +++ b/docs/source/auto_examples/plot_OTDA_classes.rst @@ -0,0 +1,190 @@ + + +.. _sphx_glr_auto_examples_plot_OTDA_classes.py: + + +======================== +OT for domain adaptation +======================== + + + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OTDA_classes_001.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OTDA_classes_004.png + :scale: 47 + + +.. rst-class:: sphx-glr-script-out + + Out:: + + It. |Loss |Delta loss + -------------------------------- + 0|9.171271e+00|0.000000e+00 + 1|2.133783e+00|-3.298127e+00 + 2|1.895941e+00|-1.254484e-01 + 3|1.844628e+00|-2.781709e-02 + 4|1.824983e+00|-1.076467e-02 + 5|1.815453e+00|-5.249337e-03 + 6|1.808104e+00|-4.064733e-03 + 7|1.803558e+00|-2.520475e-03 + 8|1.801061e+00|-1.386155e-03 + 9|1.799391e+00|-9.279565e-04 + 10|1.797176e+00|-1.232778e-03 + 11|1.795465e+00|-9.529479e-04 + 12|1.795316e+00|-8.322362e-05 + 13|1.794523e+00|-4.418932e-04 + 14|1.794444e+00|-4.390599e-05 + 15|1.794395e+00|-2.710318e-05 + 16|1.793713e+00|-3.804028e-04 + 17|1.793110e+00|-3.359479e-04 + 18|1.792829e+00|-1.569563e-04 + 19|1.792621e+00|-1.159469e-04 + It. |Loss |Delta loss + -------------------------------- + 20|1.791334e+00|-7.187689e-04 + + + + +| + + +.. code-block:: python + + + import matplotlib.pylab as pl + import ot + + + + + #%% parameters + + n=150 # nb samples in source and target datasets + + xs,ys=ot.datasets.get_data_classif('3gauss',n) + xt,yt=ot.datasets.get_data_classif('3gauss2',n) + + + + + #%% plot samples + + pl.figure(1) + + pl.subplot(2,2,1) + pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') + pl.legend(loc=0) + pl.title('Source distributions') + + pl.subplot(2,2,2) + pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') + pl.legend(loc=0) + pl.title('target distributions') + + + #%% OT estimation + + # LP problem + da_emd=ot.da.OTDA() # init class + da_emd.fit(xs,xt) # fit distributions + xst0=da_emd.interp() # interpolation of source samples + + + # sinkhorn regularization + lambd=1e-1 + da_entrop=ot.da.OTDA_sinkhorn() + da_entrop.fit(xs,xt,reg=lambd) + xsts=da_entrop.interp() + + # non-convex Group lasso regularization + reg=1e-1 + eta=1e0 + da_lpl1=ot.da.OTDA_lpl1() + da_lpl1.fit(xs,ys,xt,reg=reg,eta=eta) + xstg=da_lpl1.interp() + + + # True Group lasso regularization + reg=1e-1 + eta=2e0 + da_l1l2=ot.da.OTDA_l1l2() + da_l1l2.fit(xs,ys,xt,reg=reg,eta=eta,numItermax=20,verbose=True) + xstgl=da_l1l2.interp() + + + #%% plot interpolated source samples + pl.figure(4,(15,8)) + + param_img={'interpolation':'nearest','cmap':'jet'} + + pl.subplot(2,4,1) + pl.imshow(da_emd.G,**param_img) + pl.title('OT matrix') + + + pl.subplot(2,4,2) + pl.imshow(da_entrop.G,**param_img) + pl.title('OT matrix sinkhorn') + + pl.subplot(2,4,3) + pl.imshow(da_lpl1.G,**param_img) + pl.title('OT matrix non-convex Group Lasso') + + pl.subplot(2,4,4) + pl.imshow(da_l1l2.G,**param_img) + pl.title('OT matrix Group Lasso') + + + pl.subplot(2,4,5) + pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) + pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30) + pl.title('Interp samples') + pl.legend(loc=0) + + pl.subplot(2,4,6) + pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) + pl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30) + pl.title('Interp samples Sinkhorn') + + pl.subplot(2,4,7) + pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) + pl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30) + pl.title('Interp samples non-convex Group Lasso') + + pl.subplot(2,4,8) + pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) + pl.scatter(xstgl[:,0],xstgl[:,1],c=ys,marker='+',label='Transp samples',s=30) + pl.title('Interp samples Group Lasso') +**Total running time of the script:** ( 0 minutes 2.225 seconds) + + + +.. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_OTDA_classes.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_OTDA_classes.ipynb ` + +.. rst-class:: sphx-glr-signature + + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OTDA_color_images.ipynb b/docs/source/auto_examples/plot_OTDA_color_images.ipynb new file mode 100644 index 0000000..d174828 --- /dev/null +++ b/docs/source/auto_examples/plot_OTDA_color_images.ipynb @@ -0,0 +1,54 @@ +{ + "nbformat_minor": 0, + "nbformat": 4, + "cells": [ + { + "execution_count": null, + "cell_type": "code", + "source": [ + "%matplotlib inline" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "\n========================================================\nOT for domain adaptation with image color adaptation [6]\n========================================================\n\n[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "import numpy as np\nimport scipy.ndimage as spi\nimport matplotlib.pylab as pl\nimport ot\n\n\n#%% Loading images\n\nI1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256\nI2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256\n\n#%% Plot images\n\npl.figure(1)\n\npl.subplot(1,2,1)\npl.imshow(I1)\npl.title('Image 1')\n\npl.subplot(1,2,2)\npl.imshow(I2)\npl.title('Image 2')\n\npl.show()\n\n#%% Image conversion and dataset generation\n\ndef im2mat(I):\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0]*I.shape[1],I.shape[2]))\n\ndef mat2im(X,shape):\n \"\"\"Converts back a matrix to an image\"\"\"\n return X.reshape(shape)\n\nX1=im2mat(I1)\nX2=im2mat(I2)\n\n# training samples\nnb=1000\nidx1=np.random.randint(X1.shape[0],size=(nb,))\nidx2=np.random.randint(X2.shape[0],size=(nb,))\n\nxs=X1[idx1,:]\nxt=X2[idx2,:]\n\n#%% Plot image distributions\n\n\npl.figure(2,(10,5))\n\npl.subplot(1,2,1)\npl.scatter(xs[:,0],xs[:,2],c=xs)\npl.axis([0,1,0,1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 1')\n\npl.subplot(1,2,2)\n#pl.imshow(I2)\npl.scatter(xt[:,0],xt[:,2],c=xt)\npl.axis([0,1,0,1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 2')\n\npl.show()\n\n\n\n#%% domain adaptation between images\n\n# LP problem\nda_emd=ot.da.OTDA() # init class\nda_emd.fit(xs,xt) # fit distributions\n\n\n# sinkhorn regularization\nlambd=1e-1\nda_entrop=ot.da.OTDA_sinkhorn()\nda_entrop.fit(xs,xt,reg=lambd)\n\n\n\n#%% prediction between images (using out of sample prediction as in [6])\n\nX1t=da_emd.predict(X1)\nX2t=da_emd.predict(X2,-1)\n\n\nX1te=da_entrop.predict(X1)\nX2te=da_entrop.predict(X2,-1)\n\n\ndef minmax(I):\n return np.minimum(np.maximum(I,0),1)\n\nI1t=minmax(mat2im(X1t,I1.shape))\nI2t=minmax(mat2im(X2t,I2.shape))\n\nI1te=minmax(mat2im(X1te,I1.shape))\nI2te=minmax(mat2im(X2te,I2.shape))\n\n#%% plot all images\n\npl.figure(2,(10,8))\n\npl.subplot(2,3,1)\n\npl.imshow(I1)\npl.title('Image 1')\n\npl.subplot(2,3,2)\npl.imshow(I1t)\npl.title('Image 1 Adapt')\n\n\npl.subplot(2,3,3)\npl.imshow(I1te)\npl.title('Image 1 Adapt (reg)')\n\npl.subplot(2,3,4)\n\npl.imshow(I2)\npl.title('Image 2')\n\npl.subplot(2,3,5)\npl.imshow(I2t)\npl.title('Image 2 Adapt')\n\n\npl.subplot(2,3,6)\npl.imshow(I2te)\npl.title('Image 2 Adapt (reg)')\n\npl.show()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "name": "python2", + "language": "python" + }, + "language_info": { + "mimetype": "text/x-python", + "nbconvert_exporter": "python", + "name": "python", + "file_extension": ".py", + "version": "2.7.12", + "pygments_lexer": "ipython2", + "codemirror_mode": { + "version": 2, + "name": "ipython" + } + } + } +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OTDA_color_images.py b/docs/source/auto_examples/plot_OTDA_color_images.py new file mode 100644 index 0000000..68eee44 --- /dev/null +++ b/docs/source/auto_examples/plot_OTDA_color_images.py @@ -0,0 +1,145 @@ +# -*- coding: utf-8 -*- +""" +======================================================== +OT for domain adaptation with image color adaptation [6] +======================================================== + +[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. +""" + +import numpy as np +import scipy.ndimage as spi +import matplotlib.pylab as pl +import ot + + +#%% Loading images + +I1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256 +I2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256 + +#%% Plot images + +pl.figure(1) + +pl.subplot(1,2,1) +pl.imshow(I1) +pl.title('Image 1') + +pl.subplot(1,2,2) +pl.imshow(I2) +pl.title('Image 2') + +pl.show() + +#%% Image conversion and dataset generation + +def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0]*I.shape[1],I.shape[2])) + +def mat2im(X,shape): + """Converts back a matrix to an image""" + return X.reshape(shape) + +X1=im2mat(I1) +X2=im2mat(I2) + +# training samples +nb=1000 +idx1=np.random.randint(X1.shape[0],size=(nb,)) +idx2=np.random.randint(X2.shape[0],size=(nb,)) + +xs=X1[idx1,:] +xt=X2[idx2,:] + +#%% Plot image distributions + + +pl.figure(2,(10,5)) + +pl.subplot(1,2,1) +pl.scatter(xs[:,0],xs[:,2],c=xs) +pl.axis([0,1,0,1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 1') + +pl.subplot(1,2,2) +#pl.imshow(I2) +pl.scatter(xt[:,0],xt[:,2],c=xt) +pl.axis([0,1,0,1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 2') + +pl.show() + + + +#%% domain adaptation between images + +# LP problem +da_emd=ot.da.OTDA() # init class +da_emd.fit(xs,xt) # fit distributions + + +# sinkhorn regularization +lambd=1e-1 +da_entrop=ot.da.OTDA_sinkhorn() +da_entrop.fit(xs,xt,reg=lambd) + + + +#%% prediction between images (using out of sample prediction as in [6]) + +X1t=da_emd.predict(X1) +X2t=da_emd.predict(X2,-1) + + +X1te=da_entrop.predict(X1) +X2te=da_entrop.predict(X2,-1) + + +def minmax(I): + return np.minimum(np.maximum(I,0),1) + +I1t=minmax(mat2im(X1t,I1.shape)) +I2t=minmax(mat2im(X2t,I2.shape)) + +I1te=minmax(mat2im(X1te,I1.shape)) +I2te=minmax(mat2im(X2te,I2.shape)) + +#%% plot all images + +pl.figure(2,(10,8)) + +pl.subplot(2,3,1) + +pl.imshow(I1) +pl.title('Image 1') + +pl.subplot(2,3,2) +pl.imshow(I1t) +pl.title('Image 1 Adapt') + + +pl.subplot(2,3,3) +pl.imshow(I1te) +pl.title('Image 1 Adapt (reg)') + +pl.subplot(2,3,4) + +pl.imshow(I2) +pl.title('Image 2') + +pl.subplot(2,3,5) +pl.imshow(I2t) +pl.title('Image 2 Adapt') + + +pl.subplot(2,3,6) +pl.imshow(I2te) +pl.title('Image 2 Adapt (reg)') + +pl.show() diff --git a/docs/source/auto_examples/plot_OTDA_color_images.rst b/docs/source/auto_examples/plot_OTDA_color_images.rst new file mode 100644 index 0000000..a982a90 --- /dev/null +++ b/docs/source/auto_examples/plot_OTDA_color_images.rst @@ -0,0 +1,191 @@ + + +.. _sphx_glr_auto_examples_plot_OTDA_color_images.py: + + +======================================================== +OT for domain adaptation with image color adaptation [6] +======================================================== + +[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. + + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OTDA_color_images_001.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OTDA_color_images_002.png + :scale: 47 + + + + + +.. code-block:: python + + + import numpy as np + import scipy.ndimage as spi + import matplotlib.pylab as pl + import ot + + + #%% Loading images + + I1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256 + I2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256 + + #%% Plot images + + pl.figure(1) + + pl.subplot(1,2,1) + pl.imshow(I1) + pl.title('Image 1') + + pl.subplot(1,2,2) + pl.imshow(I2) + pl.title('Image 2') + + pl.show() + + #%% Image conversion and dataset generation + + def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0]*I.shape[1],I.shape[2])) + + def mat2im(X,shape): + """Converts back a matrix to an image""" + return X.reshape(shape) + + X1=im2mat(I1) + X2=im2mat(I2) + + # training samples + nb=1000 + idx1=np.random.randint(X1.shape[0],size=(nb,)) + idx2=np.random.randint(X2.shape[0],size=(nb,)) + + xs=X1[idx1,:] + xt=X2[idx2,:] + + #%% Plot image distributions + + + pl.figure(2,(10,5)) + + pl.subplot(1,2,1) + pl.scatter(xs[:,0],xs[:,2],c=xs) + pl.axis([0,1,0,1]) + pl.xlabel('Red') + pl.ylabel('Blue') + pl.title('Image 1') + + pl.subplot(1,2,2) + #pl.imshow(I2) + pl.scatter(xt[:,0],xt[:,2],c=xt) + pl.axis([0,1,0,1]) + pl.xlabel('Red') + pl.ylabel('Blue') + pl.title('Image 2') + + pl.show() + + + + #%% domain adaptation between images + + # LP problem + da_emd=ot.da.OTDA() # init class + da_emd.fit(xs,xt) # fit distributions + + + # sinkhorn regularization + lambd=1e-1 + da_entrop=ot.da.OTDA_sinkhorn() + da_entrop.fit(xs,xt,reg=lambd) + + + + #%% prediction between images (using out of sample prediction as in [6]) + + X1t=da_emd.predict(X1) + X2t=da_emd.predict(X2,-1) + + + X1te=da_entrop.predict(X1) + X2te=da_entrop.predict(X2,-1) + + + def minmax(I): + return np.minimum(np.maximum(I,0),1) + + I1t=minmax(mat2im(X1t,I1.shape)) + I2t=minmax(mat2im(X2t,I2.shape)) + + I1te=minmax(mat2im(X1te,I1.shape)) + I2te=minmax(mat2im(X2te,I2.shape)) + + #%% plot all images + + pl.figure(2,(10,8)) + + pl.subplot(2,3,1) + + pl.imshow(I1) + pl.title('Image 1') + + pl.subplot(2,3,2) + pl.imshow(I1t) + pl.title('Image 1 Adapt') + + + pl.subplot(2,3,3) + pl.imshow(I1te) + pl.title('Image 1 Adapt (reg)') + + pl.subplot(2,3,4) + + pl.imshow(I2) + pl.title('Image 2') + + pl.subplot(2,3,5) + pl.imshow(I2t) + pl.title('Image 2 Adapt') + + + pl.subplot(2,3,6) + pl.imshow(I2te) + pl.title('Image 2 Adapt (reg)') + + pl.show() + +**Total running time of the script:** ( 0 minutes 24.815 seconds) + + + +.. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_OTDA_color_images.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_OTDA_color_images.ipynb ` + +.. rst-class:: sphx-glr-signature + + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OTDA_mapping.ipynb b/docs/source/auto_examples/plot_OTDA_mapping.ipynb new file mode 100644 index 0000000..ec405af --- /dev/null +++ b/docs/source/auto_examples/plot_OTDA_mapping.ipynb @@ -0,0 +1,54 @@ +{ + "nbformat_minor": 0, + "nbformat": 4, + "cells": [ + { + "execution_count": null, + "cell_type": "code", + "source": [ + "%matplotlib inline" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "\n===============================================\nOT mapping estimation for domain adaptation [8]\n===============================================\n\n[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, \"Mapping estimation for\n discrete optimal transport\", Neural Information Processing Systems (NIPS), 2016.\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n\n\n#%% dataset generation\n\nnp.random.seed(0) # makes example reproducible\n\nn=100 # nb samples in source and target datasets\ntheta=2*np.pi/20\nnz=0.1\nxs,ys=ot.datasets.get_data_classif('gaussrot',n,nz=nz)\nxt,yt=ot.datasets.get_data_classif('gaussrot',n,theta=theta,nz=nz)\n\n# one of the target mode changes its variance (no linear mapping)\nxt[yt==2]*=3\nxt=xt+4\n\n\n#%% plot samples\n\npl.figure(1,(8,5))\npl.clf()\n\npl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples')\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples')\n\npl.legend(loc=0)\npl.title('Source and target distributions')\n\n\n\n#%% OT linear mapping estimation\n\neta=1e-8 # quadratic regularization for regression\nmu=1e0 # weight of the OT linear term\nbias=True # estimate a bias\n\not_mapping=ot.da.OTDA_mapping_linear()\not_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True)\n\nxst=ot_mapping.predict(xs) # use the estimated mapping\nxst0=ot_mapping.interp() # use barycentric mapping\n\n\npl.figure(2,(10,7))\npl.clf()\npl.subplot(2,2,1)\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.3)\npl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='barycentric mapping')\npl.title(\"barycentric mapping\")\n\npl.subplot(2,2,2)\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.3)\npl.scatter(xst[:,0],xst[:,1],c=ys,marker='+',label='Learned mapping')\npl.title(\"Learned mapping\")\n\n\n\n#%% Kernel mapping estimation\n\neta=1e-5 # quadratic regularization for regression\nmu=1e-1 # weight of the OT linear term\nbias=True # estimate a bias\nsigma=1 # sigma bandwidth fot gaussian kernel\n\n\not_mapping_kernel=ot.da.OTDA_mapping_kernel()\not_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 10,verbose=True)\n\nxst_kernel=ot_mapping_kernel.predict(xs) # use the estimated mapping\nxst0_kernel=ot_mapping_kernel.interp() # use barycentric mapping\n\n\n#%% Plotting the mapped samples\n\npl.figure(2,(10,7))\npl.clf()\npl.subplot(2,2,1)\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2)\npl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Mapped source samples')\npl.title(\"Bary. mapping (linear)\")\npl.legend(loc=0)\n\npl.subplot(2,2,2)\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2)\npl.scatter(xst[:,0],xst[:,1],c=ys,marker='+',label='Learned mapping')\npl.title(\"Estim. mapping (linear)\")\n\npl.subplot(2,2,3)\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2)\npl.scatter(xst0_kernel[:,0],xst0_kernel[:,1],c=ys,marker='+',label='barycentric mapping')\npl.title(\"Bary. mapping (kernel)\")\n\npl.subplot(2,2,4)\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2)\npl.scatter(xst_kernel[:,0],xst_kernel[:,1],c=ys,marker='+',label='Learned mapping')\npl.title(\"Estim. mapping (kernel)\")" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "name": "python2", + "language": "python" + }, + "language_info": { + "mimetype": "text/x-python", + "nbconvert_exporter": "python", + "name": "python", + "file_extension": ".py", + "version": "2.7.12", + "pygments_lexer": "ipython2", + "codemirror_mode": { + "version": 2, + "name": "ipython" + } + } + } +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OTDA_mapping.py b/docs/source/auto_examples/plot_OTDA_mapping.py new file mode 100644 index 0000000..78b57e7 --- /dev/null +++ b/docs/source/auto_examples/plot_OTDA_mapping.py @@ -0,0 +1,110 @@ +# -*- coding: utf-8 -*- +""" +=============================================== +OT mapping estimation for domain adaptation [8] +=============================================== + +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for + discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. +""" + +import numpy as np +import matplotlib.pylab as pl +import ot + + + +#%% dataset generation + +np.random.seed(0) # makes example reproducible + +n=100 # nb samples in source and target datasets +theta=2*np.pi/20 +nz=0.1 +xs,ys=ot.datasets.get_data_classif('gaussrot',n,nz=nz) +xt,yt=ot.datasets.get_data_classif('gaussrot',n,theta=theta,nz=nz) + +# one of the target mode changes its variance (no linear mapping) +xt[yt==2]*=3 +xt=xt+4 + + +#%% plot samples + +pl.figure(1,(8,5)) +pl.clf() + +pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') + +pl.legend(loc=0) +pl.title('Source and target distributions') + + + +#%% OT linear mapping estimation + +eta=1e-8 # quadratic regularization for regression +mu=1e0 # weight of the OT linear term +bias=True # estimate a bias + +ot_mapping=ot.da.OTDA_mapping_linear() +ot_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True) + +xst=ot_mapping.predict(xs) # use the estimated mapping +xst0=ot_mapping.interp() # use barycentric mapping + + +pl.figure(2,(10,7)) +pl.clf() +pl.subplot(2,2,1) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.3) +pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='barycentric mapping') +pl.title("barycentric mapping") + +pl.subplot(2,2,2) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.3) +pl.scatter(xst[:,0],xst[:,1],c=ys,marker='+',label='Learned mapping') +pl.title("Learned mapping") + + + +#%% Kernel mapping estimation + +eta=1e-5 # quadratic regularization for regression +mu=1e-1 # weight of the OT linear term +bias=True # estimate a bias +sigma=1 # sigma bandwidth fot gaussian kernel + + +ot_mapping_kernel=ot.da.OTDA_mapping_kernel() +ot_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 10,verbose=True) + +xst_kernel=ot_mapping_kernel.predict(xs) # use the estimated mapping +xst0_kernel=ot_mapping_kernel.interp() # use barycentric mapping + + +#%% Plotting the mapped samples + +pl.figure(2,(10,7)) +pl.clf() +pl.subplot(2,2,1) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) +pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Mapped source samples') +pl.title("Bary. mapping (linear)") +pl.legend(loc=0) + +pl.subplot(2,2,2) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) +pl.scatter(xst[:,0],xst[:,1],c=ys,marker='+',label='Learned mapping') +pl.title("Estim. mapping (linear)") + +pl.subplot(2,2,3) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) +pl.scatter(xst0_kernel[:,0],xst0_kernel[:,1],c=ys,marker='+',label='barycentric mapping') +pl.title("Bary. mapping (kernel)") + +pl.subplot(2,2,4) +pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) +pl.scatter(xst_kernel[:,0],xst_kernel[:,1],c=ys,marker='+',label='Learned mapping') +pl.title("Estim. mapping (kernel)") diff --git a/docs/source/auto_examples/plot_OTDA_mapping.rst b/docs/source/auto_examples/plot_OTDA_mapping.rst new file mode 100644 index 0000000..18da90d --- /dev/null +++ b/docs/source/auto_examples/plot_OTDA_mapping.rst @@ -0,0 +1,186 @@ + + +.. _sphx_glr_auto_examples_plot_OTDA_mapping.py: + + +=============================================== +OT mapping estimation for domain adaptation [8] +=============================================== + +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for + discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. + + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OTDA_mapping_001.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OTDA_mapping_002.png + :scale: 47 + + +.. rst-class:: sphx-glr-script-out + + Out:: + + It. |Loss |Delta loss + -------------------------------- + 0|4.009366e+03|0.000000e+00 + 1|3.999933e+03|-2.352753e-03 + 2|3.999520e+03|-1.031984e-04 + 3|3.999362e+03|-3.936391e-05 + 4|3.999281e+03|-2.032868e-05 + 5|3.999238e+03|-1.083083e-05 + 6|3.999229e+03|-2.125291e-06 + It. |Loss |Delta loss + -------------------------------- + 0|4.026841e+02|0.000000e+00 + 1|3.990791e+02|-8.952439e-03 + 2|3.987954e+02|-7.107124e-04 + 3|3.986554e+02|-3.512453e-04 + 4|3.985721e+02|-2.087997e-04 + 5|3.985141e+02|-1.456184e-04 + 6|3.984729e+02|-1.034624e-04 + 7|3.984435e+02|-7.366943e-05 + 8|3.984199e+02|-5.922497e-05 + 9|3.984016e+02|-4.593063e-05 + 10|3.983867e+02|-3.733061e-05 + + + + +| + + +.. code-block:: python + + + import numpy as np + import matplotlib.pylab as pl + import ot + + + + #%% dataset generation + + np.random.seed(0) # makes example reproducible + + n=100 # nb samples in source and target datasets + theta=2*np.pi/20 + nz=0.1 + xs,ys=ot.datasets.get_data_classif('gaussrot',n,nz=nz) + xt,yt=ot.datasets.get_data_classif('gaussrot',n,theta=theta,nz=nz) + + # one of the target mode changes its variance (no linear mapping) + xt[yt==2]*=3 + xt=xt+4 + + + #%% plot samples + + pl.figure(1,(8,5)) + pl.clf() + + pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') + pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') + + pl.legend(loc=0) + pl.title('Source and target distributions') + + + + #%% OT linear mapping estimation + + eta=1e-8 # quadratic regularization for regression + mu=1e0 # weight of the OT linear term + bias=True # estimate a bias + + ot_mapping=ot.da.OTDA_mapping_linear() + ot_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True) + + xst=ot_mapping.predict(xs) # use the estimated mapping + xst0=ot_mapping.interp() # use barycentric mapping + + + pl.figure(2,(10,7)) + pl.clf() + pl.subplot(2,2,1) + pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.3) + pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='barycentric mapping') + pl.title("barycentric mapping") + + pl.subplot(2,2,2) + pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.3) + pl.scatter(xst[:,0],xst[:,1],c=ys,marker='+',label='Learned mapping') + pl.title("Learned mapping") + + + + #%% Kernel mapping estimation + + eta=1e-5 # quadratic regularization for regression + mu=1e-1 # weight of the OT linear term + bias=True # estimate a bias + sigma=1 # sigma bandwidth fot gaussian kernel + + + ot_mapping_kernel=ot.da.OTDA_mapping_kernel() + ot_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 10,verbose=True) + + xst_kernel=ot_mapping_kernel.predict(xs) # use the estimated mapping + xst0_kernel=ot_mapping_kernel.interp() # use barycentric mapping + + + #%% Plotting the mapped samples + + pl.figure(2,(10,7)) + pl.clf() + pl.subplot(2,2,1) + pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) + pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Mapped source samples') + pl.title("Bary. mapping (linear)") + pl.legend(loc=0) + + pl.subplot(2,2,2) + pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) + pl.scatter(xst[:,0],xst[:,1],c=ys,marker='+',label='Learned mapping') + pl.title("Estim. mapping (linear)") + + pl.subplot(2,2,3) + pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) + pl.scatter(xst0_kernel[:,0],xst0_kernel[:,1],c=ys,marker='+',label='barycentric mapping') + pl.title("Bary. mapping (kernel)") + + pl.subplot(2,2,4) + pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) + pl.scatter(xst_kernel[:,0],xst_kernel[:,1],c=ys,marker='+',label='Learned mapping') + pl.title("Estim. mapping (kernel)") + +**Total running time of the script:** ( 0 minutes 0.882 seconds) + + + +.. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_OTDA_mapping.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_OTDA_mapping.ipynb ` + +.. rst-class:: sphx-glr-signature + + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OTDA_mapping_color_images.ipynb b/docs/source/auto_examples/plot_OTDA_mapping_color_images.ipynb new file mode 100644 index 0000000..1136cc3 --- /dev/null +++ b/docs/source/auto_examples/plot_OTDA_mapping_color_images.ipynb @@ -0,0 +1,54 @@ +{ + "nbformat_minor": 0, + "nbformat": 4, + "cells": [ + { + "execution_count": null, + "cell_type": "code", + "source": [ + "%matplotlib inline" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "\n====================================================================================\nOT for domain adaptation with image color adaptation [6] with mapping estimation [8]\n====================================================================================\n\n[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized\n discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.\n[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, \"Mapping estimation for\n discrete optimal transport\", Neural Information Processing Systems (NIPS), 2016.\n\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "import numpy as np\nimport scipy.ndimage as spi\nimport matplotlib.pylab as pl\nimport ot\n\n\n#%% Loading images\n\nI1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256\nI2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256\n\n#%% Plot images\n\npl.figure(1)\n\npl.subplot(1,2,1)\npl.imshow(I1)\npl.title('Image 1')\n\npl.subplot(1,2,2)\npl.imshow(I2)\npl.title('Image 2')\n\npl.show()\n\n#%% Image conversion and dataset generation\n\ndef im2mat(I):\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0]*I.shape[1],I.shape[2]))\n\ndef mat2im(X,shape):\n \"\"\"Converts back a matrix to an image\"\"\"\n return X.reshape(shape)\n\nX1=im2mat(I1)\nX2=im2mat(I2)\n\n# training samples\nnb=1000\nidx1=np.random.randint(X1.shape[0],size=(nb,))\nidx2=np.random.randint(X2.shape[0],size=(nb,))\n\nxs=X1[idx1,:]\nxt=X2[idx2,:]\n\n#%% Plot image distributions\n\n\npl.figure(2,(10,5))\n\npl.subplot(1,2,1)\npl.scatter(xs[:,0],xs[:,2],c=xs)\npl.axis([0,1,0,1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 1')\n\npl.subplot(1,2,2)\n#pl.imshow(I2)\npl.scatter(xt[:,0],xt[:,2],c=xt)\npl.axis([0,1,0,1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 2')\n\npl.show()\n\n\n\n#%% domain adaptation between images\ndef minmax(I):\n return np.minimum(np.maximum(I,0),1)\n# LP problem\nda_emd=ot.da.OTDA() # init class\nda_emd.fit(xs,xt) # fit distributions\n\nX1t=da_emd.predict(X1) # out of sample\nI1t=minmax(mat2im(X1t,I1.shape))\n\n# sinkhorn regularization\nlambd=1e-1\nda_entrop=ot.da.OTDA_sinkhorn()\nda_entrop.fit(xs,xt,reg=lambd)\n\nX1te=da_entrop.predict(X1)\nI1te=minmax(mat2im(X1te,I1.shape))\n\n# linear mapping estimation\neta=1e-8 # quadratic regularization for regression\nmu=1e0 # weight of the OT linear term\nbias=True # estimate a bias\n\not_mapping=ot.da.OTDA_mapping_linear()\not_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True)\n\nX1tl=ot_mapping.predict(X1) # use the estimated mapping\nI1tl=minmax(mat2im(X1tl,I1.shape))\n\n# nonlinear mapping estimation\neta=1e-2 # quadratic regularization for regression\nmu=1e0 # weight of the OT linear term\nbias=False # estimate a bias\nsigma=1 # sigma bandwidth fot gaussian kernel\n\n\not_mapping_kernel=ot.da.OTDA_mapping_kernel()\not_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 10,verbose=True)\n\nX1tn=ot_mapping_kernel.predict(X1) # use the estimated mapping\nI1tn=minmax(mat2im(X1tn,I1.shape))\n#%% plot images\n\n\npl.figure(2,(10,8))\n\npl.subplot(2,3,1)\n\npl.imshow(I1)\npl.title('Im. 1')\n\npl.subplot(2,3,2)\n\npl.imshow(I2)\npl.title('Im. 2')\n\n\npl.subplot(2,3,3)\npl.imshow(I1t)\npl.title('Im. 1 Interp LP')\n\npl.subplot(2,3,4)\npl.imshow(I1te)\npl.title('Im. 1 Interp Entrop')\n\n\npl.subplot(2,3,5)\npl.imshow(I1tl)\npl.title('Im. 1 Linear mapping')\n\npl.subplot(2,3,6)\npl.imshow(I1tn)\npl.title('Im. 1 nonlinear mapping')\n\npl.show()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "name": "python2", + "language": "python" + }, + "language_info": { + "mimetype": "text/x-python", + "nbconvert_exporter": "python", + "name": "python", + "file_extension": ".py", + "version": "2.7.12", + "pygments_lexer": "ipython2", + "codemirror_mode": { + "version": 2, + "name": "ipython" + } + } + } +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OTDA_mapping_color_images.py b/docs/source/auto_examples/plot_OTDA_mapping_color_images.py new file mode 100644 index 0000000..f07dc6c --- /dev/null +++ b/docs/source/auto_examples/plot_OTDA_mapping_color_images.py @@ -0,0 +1,158 @@ +# -*- coding: utf-8 -*- +""" +==================================================================================== +OT for domain adaptation with image color adaptation [6] with mapping estimation [8] +==================================================================================== + +[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized + discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for + discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. + +""" + +import numpy as np +import scipy.ndimage as spi +import matplotlib.pylab as pl +import ot + + +#%% Loading images + +I1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256 +I2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256 + +#%% Plot images + +pl.figure(1) + +pl.subplot(1,2,1) +pl.imshow(I1) +pl.title('Image 1') + +pl.subplot(1,2,2) +pl.imshow(I2) +pl.title('Image 2') + +pl.show() + +#%% Image conversion and dataset generation + +def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0]*I.shape[1],I.shape[2])) + +def mat2im(X,shape): + """Converts back a matrix to an image""" + return X.reshape(shape) + +X1=im2mat(I1) +X2=im2mat(I2) + +# training samples +nb=1000 +idx1=np.random.randint(X1.shape[0],size=(nb,)) +idx2=np.random.randint(X2.shape[0],size=(nb,)) + +xs=X1[idx1,:] +xt=X2[idx2,:] + +#%% Plot image distributions + + +pl.figure(2,(10,5)) + +pl.subplot(1,2,1) +pl.scatter(xs[:,0],xs[:,2],c=xs) +pl.axis([0,1,0,1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 1') + +pl.subplot(1,2,2) +#pl.imshow(I2) +pl.scatter(xt[:,0],xt[:,2],c=xt) +pl.axis([0,1,0,1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 2') + +pl.show() + + + +#%% domain adaptation between images +def minmax(I): + return np.minimum(np.maximum(I,0),1) +# LP problem +da_emd=ot.da.OTDA() # init class +da_emd.fit(xs,xt) # fit distributions + +X1t=da_emd.predict(X1) # out of sample +I1t=minmax(mat2im(X1t,I1.shape)) + +# sinkhorn regularization +lambd=1e-1 +da_entrop=ot.da.OTDA_sinkhorn() +da_entrop.fit(xs,xt,reg=lambd) + +X1te=da_entrop.predict(X1) +I1te=minmax(mat2im(X1te,I1.shape)) + +# linear mapping estimation +eta=1e-8 # quadratic regularization for regression +mu=1e0 # weight of the OT linear term +bias=True # estimate a bias + +ot_mapping=ot.da.OTDA_mapping_linear() +ot_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True) + +X1tl=ot_mapping.predict(X1) # use the estimated mapping +I1tl=minmax(mat2im(X1tl,I1.shape)) + +# nonlinear mapping estimation +eta=1e-2 # quadratic regularization for regression +mu=1e0 # weight of the OT linear term +bias=False # estimate a bias +sigma=1 # sigma bandwidth fot gaussian kernel + + +ot_mapping_kernel=ot.da.OTDA_mapping_kernel() +ot_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 10,verbose=True) + +X1tn=ot_mapping_kernel.predict(X1) # use the estimated mapping +I1tn=minmax(mat2im(X1tn,I1.shape)) +#%% plot images + + +pl.figure(2,(10,8)) + +pl.subplot(2,3,1) + +pl.imshow(I1) +pl.title('Im. 1') + +pl.subplot(2,3,2) + +pl.imshow(I2) +pl.title('Im. 2') + + +pl.subplot(2,3,3) +pl.imshow(I1t) +pl.title('Im. 1 Interp LP') + +pl.subplot(2,3,4) +pl.imshow(I1te) +pl.title('Im. 1 Interp Entrop') + + +pl.subplot(2,3,5) +pl.imshow(I1tl) +pl.title('Im. 1 Linear mapping') + +pl.subplot(2,3,6) +pl.imshow(I1tn) +pl.title('Im. 1 nonlinear mapping') + +pl.show() diff --git a/docs/source/auto_examples/plot_OTDA_mapping_color_images.rst b/docs/source/auto_examples/plot_OTDA_mapping_color_images.rst new file mode 100644 index 0000000..60be3a4 --- /dev/null +++ b/docs/source/auto_examples/plot_OTDA_mapping_color_images.rst @@ -0,0 +1,246 @@ + + +.. _sphx_glr_auto_examples_plot_OTDA_mapping_color_images.py: + + +==================================================================================== +OT for domain adaptation with image color adaptation [6] with mapping estimation [8] +==================================================================================== + +[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized + discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for + discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. + + + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OTDA_mapping_color_images_001.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OTDA_mapping_color_images_002.png + :scale: 47 + + +.. rst-class:: sphx-glr-script-out + + Out:: + + It. |Loss |Delta loss + -------------------------------- + 0|3.624802e+02|0.000000e+00 + 1|3.547180e+02|-2.141395e-02 + 2|3.545494e+02|-4.753955e-04 + 3|3.544646e+02|-2.391784e-04 + 4|3.544126e+02|-1.466280e-04 + 5|3.543775e+02|-9.921805e-05 + 6|3.543518e+02|-7.245828e-05 + 7|3.543323e+02|-5.491924e-05 + 8|3.543170e+02|-4.342401e-05 + 9|3.543046e+02|-3.472174e-05 + 10|3.542945e+02|-2.878681e-05 + 11|3.542859e+02|-2.417065e-05 + 12|3.542786e+02|-2.058131e-05 + 13|3.542723e+02|-1.768262e-05 + 14|3.542668e+02|-1.551616e-05 + 15|3.542620e+02|-1.371909e-05 + 16|3.542577e+02|-1.213326e-05 + 17|3.542538e+02|-1.085481e-05 + 18|3.542531e+02|-1.996006e-06 + It. |Loss |Delta loss + -------------------------------- + 0|3.555768e+02|0.000000e+00 + 1|3.510071e+02|-1.285164e-02 + 2|3.509110e+02|-2.736701e-04 + 3|3.508748e+02|-1.031476e-04 + 4|3.508506e+02|-6.910585e-05 + 5|3.508330e+02|-5.014608e-05 + 6|3.508195e+02|-3.839166e-05 + 7|3.508090e+02|-3.004218e-05 + 8|3.508005e+02|-2.417627e-05 + 9|3.507935e+02|-2.004621e-05 + 10|3.507876e+02|-1.681731e-05 + + + + +| + + +.. code-block:: python + + + import numpy as np + import scipy.ndimage as spi + import matplotlib.pylab as pl + import ot + + + #%% Loading images + + I1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256 + I2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256 + + #%% Plot images + + pl.figure(1) + + pl.subplot(1,2,1) + pl.imshow(I1) + pl.title('Image 1') + + pl.subplot(1,2,2) + pl.imshow(I2) + pl.title('Image 2') + + pl.show() + + #%% Image conversion and dataset generation + + def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0]*I.shape[1],I.shape[2])) + + def mat2im(X,shape): + """Converts back a matrix to an image""" + return X.reshape(shape) + + X1=im2mat(I1) + X2=im2mat(I2) + + # training samples + nb=1000 + idx1=np.random.randint(X1.shape[0],size=(nb,)) + idx2=np.random.randint(X2.shape[0],size=(nb,)) + + xs=X1[idx1,:] + xt=X2[idx2,:] + + #%% Plot image distributions + + + pl.figure(2,(10,5)) + + pl.subplot(1,2,1) + pl.scatter(xs[:,0],xs[:,2],c=xs) + pl.axis([0,1,0,1]) + pl.xlabel('Red') + pl.ylabel('Blue') + pl.title('Image 1') + + pl.subplot(1,2,2) + #pl.imshow(I2) + pl.scatter(xt[:,0],xt[:,2],c=xt) + pl.axis([0,1,0,1]) + pl.xlabel('Red') + pl.ylabel('Blue') + pl.title('Image 2') + + pl.show() + + + + #%% domain adaptation between images + def minmax(I): + return np.minimum(np.maximum(I,0),1) + # LP problem + da_emd=ot.da.OTDA() # init class + da_emd.fit(xs,xt) # fit distributions + + X1t=da_emd.predict(X1) # out of sample + I1t=minmax(mat2im(X1t,I1.shape)) + + # sinkhorn regularization + lambd=1e-1 + da_entrop=ot.da.OTDA_sinkhorn() + da_entrop.fit(xs,xt,reg=lambd) + + X1te=da_entrop.predict(X1) + I1te=minmax(mat2im(X1te,I1.shape)) + + # linear mapping estimation + eta=1e-8 # quadratic regularization for regression + mu=1e0 # weight of the OT linear term + bias=True # estimate a bias + + ot_mapping=ot.da.OTDA_mapping_linear() + ot_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True) + + X1tl=ot_mapping.predict(X1) # use the estimated mapping + I1tl=minmax(mat2im(X1tl,I1.shape)) + + # nonlinear mapping estimation + eta=1e-2 # quadratic regularization for regression + mu=1e0 # weight of the OT linear term + bias=False # estimate a bias + sigma=1 # sigma bandwidth fot gaussian kernel + + + ot_mapping_kernel=ot.da.OTDA_mapping_kernel() + ot_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 10,verbose=True) + + X1tn=ot_mapping_kernel.predict(X1) # use the estimated mapping + I1tn=minmax(mat2im(X1tn,I1.shape)) + #%% plot images + + + pl.figure(2,(10,8)) + + pl.subplot(2,3,1) + + pl.imshow(I1) + pl.title('Im. 1') + + pl.subplot(2,3,2) + + pl.imshow(I2) + pl.title('Im. 2') + + + pl.subplot(2,3,3) + pl.imshow(I1t) + pl.title('Im. 1 Interp LP') + + pl.subplot(2,3,4) + pl.imshow(I1te) + pl.title('Im. 1 Interp Entrop') + + + pl.subplot(2,3,5) + pl.imshow(I1tl) + pl.title('Im. 1 Linear mapping') + + pl.subplot(2,3,6) + pl.imshow(I1tn) + pl.title('Im. 1 nonlinear mapping') + + pl.show() + +**Total running time of the script:** ( 1 minutes 59.537 seconds) + + + +.. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_OTDA_mapping_color_images.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_OTDA_mapping_color_images.ipynb ` + +.. rst-class:: sphx-glr-signature + + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OT_1D.ipynb b/docs/source/auto_examples/plot_OT_1D.ipynb new file mode 100644 index 0000000..17d0b21 --- /dev/null +++ b/docs/source/auto_examples/plot_OT_1D.ipynb @@ -0,0 +1,54 @@ +{ + "nbformat_minor": 0, + "nbformat": 4, + "cells": [ + { + "execution_count": null, + "cell_type": "code", + "source": [ + "%matplotlib inline" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "\n# 1D optimal transport\n\n\n@author: rflamary\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\nfrom ot.datasets import get_1D_gauss as gauss\n\n\n#%% parameters\n\nn=100 # nb bins\n\n# bin positions\nx=np.arange(n,dtype=np.float64)\n\n# Gaussian distributions\na=gauss(n,m=20,s=5) # m= mean, s= std\nb=gauss(n,m=60,s=10)\n\n# loss matrix\nM=ot.dist(x.reshape((n,1)),x.reshape((n,1)))\nM/=M.max()\n\n#%% plot the distributions\n\npl.figure(1)\npl.plot(x,a,'b',label='Source distribution')\npl.plot(x,b,'r',label='Target distribution')\npl.legend()\n\n#%% plot distributions and loss matrix\n\npl.figure(2)\not.plot.plot1D_mat(a,b,M,'Cost matrix M')\n\n#%% EMD\n\nG0=ot.emd(a,b,M)\n\npl.figure(3)\not.plot.plot1D_mat(a,b,G0,'OT matrix G0')\n\n#%% Sinkhorn\n\nlambd=1e-3\nGs=ot.sinkhorn(a,b,M,lambd)\n\npl.figure(4)\not.plot.plot1D_mat(a,b,Gs,'OT matrix Sinkhorn')" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "name": "python2", + "language": "python" + }, + "language_info": { + "mimetype": "text/x-python", + "nbconvert_exporter": "python", + "name": "python", + "file_extension": ".py", + "version": "2.7.12", + "pygments_lexer": "ipython2", + "codemirror_mode": { + "version": 2, + "name": "ipython" + } + } + } +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OT_1D.py b/docs/source/auto_examples/plot_OT_1D.py new file mode 100644 index 0000000..e5719eb --- /dev/null +++ b/docs/source/auto_examples/plot_OT_1D.py @@ -0,0 +1,56 @@ +# -*- coding: utf-8 -*- +""" +==================== +1D optimal transport +==================== + +@author: rflamary +""" + +import numpy as np +import matplotlib.pylab as pl +import ot +from ot.datasets import get_1D_gauss as gauss + + +#%% parameters + +n=100 # nb bins + +# bin positions +x=np.arange(n,dtype=np.float64) + +# Gaussian distributions +a=gauss(n,m=20,s=5) # m= mean, s= std +b=gauss(n,m=60,s=10) + +# loss matrix +M=ot.dist(x.reshape((n,1)),x.reshape((n,1))) +M/=M.max() + +#%% plot the distributions + +pl.figure(1) +pl.plot(x,a,'b',label='Source distribution') +pl.plot(x,b,'r',label='Target distribution') +pl.legend() + +#%% plot distributions and loss matrix + +pl.figure(2) +ot.plot.plot1D_mat(a,b,M,'Cost matrix M') + +#%% EMD + +G0=ot.emd(a,b,M) + +pl.figure(3) +ot.plot.plot1D_mat(a,b,G0,'OT matrix G0') + +#%% Sinkhorn + +lambd=1e-3 +Gs=ot.sinkhorn(a,b,M,lambd) + +pl.figure(4) +ot.plot.plot1D_mat(a,b,Gs,'OT matrix Sinkhorn') diff --git a/docs/source/auto_examples/plot_OT_1D.rst b/docs/source/auto_examples/plot_OT_1D.rst new file mode 100644 index 0000000..941fd54 --- /dev/null +++ b/docs/source/auto_examples/plot_OT_1D.rst @@ -0,0 +1,112 @@ + + +.. _sphx_glr_auto_examples_plot_OT_1D.py: + + +==================== +1D optimal transport +==================== + +@author: rflamary + + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_001.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_002.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_003.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_004.png + :scale: 47 + + + + + +.. code-block:: python + + + import numpy as np + import matplotlib.pylab as pl + import ot + from ot.datasets import get_1D_gauss as gauss + + + #%% parameters + + n=100 # nb bins + + # bin positions + x=np.arange(n,dtype=np.float64) + + # Gaussian distributions + a=gauss(n,m=20,s=5) # m= mean, s= std + b=gauss(n,m=60,s=10) + + # loss matrix + M=ot.dist(x.reshape((n,1)),x.reshape((n,1))) + M/=M.max() + + #%% plot the distributions + + pl.figure(1) + pl.plot(x,a,'b',label='Source distribution') + pl.plot(x,b,'r',label='Target distribution') + pl.legend() + + #%% plot distributions and loss matrix + + pl.figure(2) + ot.plot.plot1D_mat(a,b,M,'Cost matrix M') + + #%% EMD + + G0=ot.emd(a,b,M) + + pl.figure(3) + ot.plot.plot1D_mat(a,b,G0,'OT matrix G0') + + #%% Sinkhorn + + lambd=1e-3 + Gs=ot.sinkhorn(a,b,M,lambd) + + pl.figure(4) + ot.plot.plot1D_mat(a,b,Gs,'OT matrix Sinkhorn') + +**Total running time of the script:** ( 0 minutes 0.597 seconds) + + + +.. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_OT_1D.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_OT_1D.ipynb ` + +.. rst-class:: sphx-glr-signature + + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OT_2D_samples.ipynb b/docs/source/auto_examples/plot_OT_2D_samples.ipynb new file mode 100644 index 0000000..e8ec1d1 --- /dev/null +++ b/docs/source/auto_examples/plot_OT_2D_samples.ipynb @@ -0,0 +1,54 @@ +{ + "nbformat_minor": 0, + "nbformat": 4, + "cells": [ + { + "execution_count": null, + "cell_type": "code", + "source": [ + "%matplotlib inline" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "\n# 2D Optimal transport between empirical distributions\n\n\n@author: rflamary\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n#%% parameters and data generation\n\nn=20 # nb samples\n\nmu_s=np.array([0,0])\ncov_s=np.array([[1,0],[0,1]])\n\nmu_t=np.array([4,4])\ncov_t=np.array([[1,-.8],[-.8,1]])\n\nxs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s)\nxt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t)\n\na,b = ot.unif(n),ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM=ot.dist(xs,xt)\nM/=M.max()\n\n#%% plot samples\n\npl.figure(1)\npl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\npl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\npl.legend(loc=0)\npl.title('Source and traget distributions')\n\npl.figure(2)\npl.imshow(M,interpolation='nearest')\npl.title('Cost matrix M')\n\n\n#%% EMD\n\nG0=ot.emd(a,b,M)\n\npl.figure(3)\npl.imshow(G0,interpolation='nearest')\npl.title('OT matrix G0')\n\npl.figure(4)\not.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1])\npl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\npl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix with samples')\n\n\n#%% sinkhorn\n\n# reg term\nlambd=5e-3\n\nGs=ot.sinkhorn(a,b,M,lambd)\n\npl.figure(5)\npl.imshow(Gs,interpolation='nearest')\npl.title('OT matrix sinkhorn')\n\npl.figure(6)\not.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1])\npl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\npl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix Sinkhorn with samples')" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "name": "python2", + "language": "python" + }, + "language_info": { + "mimetype": "text/x-python", + "nbconvert_exporter": "python", + "name": "python", + "file_extension": ".py", + "version": "2.7.12", + "pygments_lexer": "ipython2", + "codemirror_mode": { + "version": 2, + "name": "ipython" + } + } + } +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OT_2D_samples.py b/docs/source/auto_examples/plot_OT_2D_samples.py new file mode 100644 index 0000000..6c39ad4 --- /dev/null +++ b/docs/source/auto_examples/plot_OT_2D_samples.py @@ -0,0 +1,78 @@ +# -*- coding: utf-8 -*- +""" +==================================================== +2D Optimal transport between empirical distributions +==================================================== + +@author: rflamary +""" + +import numpy as np +import matplotlib.pylab as pl +import ot + +#%% parameters and data generation + +n=20 # nb samples + +mu_s=np.array([0,0]) +cov_s=np.array([[1,0],[0,1]]) + +mu_t=np.array([4,4]) +cov_t=np.array([[1,-.8],[-.8,1]]) + +xs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s) +xt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t) + +a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples + +# loss matrix +M=ot.dist(xs,xt) +M/=M.max() + +#%% plot samples + +pl.figure(1) +pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') +pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') +pl.legend(loc=0) +pl.title('Source and traget distributions') + +pl.figure(2) +pl.imshow(M,interpolation='nearest') +pl.title('Cost matrix M') + + +#%% EMD + +G0=ot.emd(a,b,M) + +pl.figure(3) +pl.imshow(G0,interpolation='nearest') +pl.title('OT matrix G0') + +pl.figure(4) +ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1]) +pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') +pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') +pl.legend(loc=0) +pl.title('OT matrix with samples') + + +#%% sinkhorn + +# reg term +lambd=5e-3 + +Gs=ot.sinkhorn(a,b,M,lambd) + +pl.figure(5) +pl.imshow(Gs,interpolation='nearest') +pl.title('OT matrix sinkhorn') + +pl.figure(6) +ot.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1]) +pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') +pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') +pl.legend(loc=0) +pl.title('OT matrix Sinkhorn with samples') diff --git a/docs/source/auto_examples/plot_OT_2D_samples.rst b/docs/source/auto_examples/plot_OT_2D_samples.rst new file mode 100644 index 0000000..bc86cb8 --- /dev/null +++ b/docs/source/auto_examples/plot_OT_2D_samples.rst @@ -0,0 +1,144 @@ + + +.. _sphx_glr_auto_examples_plot_OT_2D_samples.py: + + +==================================================== +2D Optimal transport between empirical distributions +==================================================== + +@author: rflamary + + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_001.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_002.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_003.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_004.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_005.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_006.png + :scale: 47 + + + + + +.. code-block:: python + + + import numpy as np + import matplotlib.pylab as pl + import ot + + #%% parameters and data generation + + n=20 # nb samples + + mu_s=np.array([0,0]) + cov_s=np.array([[1,0],[0,1]]) + + mu_t=np.array([4,4]) + cov_t=np.array([[1,-.8],[-.8,1]]) + + xs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s) + xt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t) + + a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples + + # loss matrix + M=ot.dist(xs,xt) + M/=M.max() + + #%% plot samples + + pl.figure(1) + pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') + pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') + pl.legend(loc=0) + pl.title('Source and traget distributions') + + pl.figure(2) + pl.imshow(M,interpolation='nearest') + pl.title('Cost matrix M') + + + #%% EMD + + G0=ot.emd(a,b,M) + + pl.figure(3) + pl.imshow(G0,interpolation='nearest') + pl.title('OT matrix G0') + + pl.figure(4) + ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1]) + pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') + pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') + pl.legend(loc=0) + pl.title('OT matrix with samples') + + + #%% sinkhorn + + # reg term + lambd=5e-3 + + Gs=ot.sinkhorn(a,b,M,lambd) + + pl.figure(5) + pl.imshow(Gs,interpolation='nearest') + pl.title('OT matrix sinkhorn') + + pl.figure(6) + ot.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1]) + pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') + pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') + pl.legend(loc=0) + pl.title('OT matrix Sinkhorn with samples') + +**Total running time of the script:** ( 0 minutes 1.051 seconds) + + + +.. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_OT_2D_samples.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_OT_2D_samples.ipynb ` + +.. rst-class:: sphx-glr-signature + + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_barycenter_1D.ipynb b/docs/source/auto_examples/plot_barycenter_1D.ipynb new file mode 100644 index 0000000..36f3975 --- /dev/null +++ b/docs/source/auto_examples/plot_barycenter_1D.ipynb @@ -0,0 +1,54 @@ +{ + "nbformat_minor": 0, + "nbformat": 4, + "cells": [ + { + "execution_count": null, + "cell_type": "code", + "source": [ + "%matplotlib inline" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "\n# 1D Wasserstein barycenter demo\n\n\n\n@author: rflamary\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\nfrom mpl_toolkits.mplot3d import Axes3D #necessary for 3d plot even if not used\nfrom matplotlib.collections import PolyCollection\n\n\n#%% parameters\n\nn=100 # nb bins\n\n# bin positions\nx=np.arange(n,dtype=np.float64)\n\n# Gaussian distributions\na1=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std\na2=ot.datasets.get_1D_gauss(n,m=60,s=8)\n\n# creating matrix A containing all distributions\nA=np.vstack((a1,a2)).T\nnbd=A.shape[1]\n\n# loss matrix + normalization\nM=ot.utils.dist0(n)\nM/=M.max()\n\n#%% plot the distributions\n\npl.figure(1)\nfor i in range(nbd):\n pl.plot(x,A[:,i])\npl.title('Distributions')\n\n#%% barycenter computation\n\nalpha=0.2 # 0<=alpha<=1\nweights=np.array([1-alpha,alpha])\n\n# l2bary\nbary_l2=A.dot(weights)\n\n# wasserstein\nreg=1e-3\nbary_wass=ot.bregman.barycenter(A,M,reg,weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2,1,1)\nfor i in range(nbd):\n pl.plot(x,A[:,i])\npl.title('Distributions')\n\npl.subplot(2,1,2)\npl.plot(x,bary_l2,'r',label='l2')\npl.plot(x,bary_wass,'g',label='Wasserstein')\npl.legend()\npl.title('Barycenters')\n\n\n#%% barycenter interpolation\n\nnbalpha=11\nalphalist=np.linspace(0,1,nbalpha)\n\n\nB_l2=np.zeros((n,nbalpha))\n\nB_wass=np.copy(B_l2)\n\nfor i in range(0,nbalpha):\n alpha=alphalist[i]\n weights=np.array([1-alpha,alpha])\n B_l2[:,i]=A.dot(weights)\n B_wass[:,i]=ot.bregman.barycenter(A,M,reg,weights)\n\n#%% plot interpolation\n\npl.figure(3,(10,5))\n\n#pl.subplot(1,2,1)\ncmap=pl.cm.get_cmap('viridis')\nverts = []\nzs = alphalist\nfor i,z in enumerate(zs):\n ys = B_l2[:,i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\n\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0,1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max()*1.01)\npl.title('Barycenter interpolation with l2')\n\npl.show()\n\npl.figure(4,(10,5))\n\n#pl.subplot(1,2,1)\ncmap=pl.cm.get_cmap('viridis')\nverts = []\nzs = alphalist\nfor i,z in enumerate(zs):\n ys = B_wass[:,i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\n\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0,1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max()*1.01)\npl.title('Barycenter interpolation with Wasserstein')\n\npl.show()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "name": "python2", + "language": "python" + }, + "language_info": { + "mimetype": "text/x-python", + "nbconvert_exporter": "python", + "name": "python", + "file_extension": ".py", + "version": "2.7.12", + "pygments_lexer": "ipython2", + "codemirror_mode": { + "version": 2, + "name": "ipython" + } + } + } +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_barycenter_1D.py b/docs/source/auto_examples/plot_barycenter_1D.py new file mode 100644 index 0000000..30eecbf --- /dev/null +++ b/docs/source/auto_examples/plot_barycenter_1D.py @@ -0,0 +1,138 @@ +# -*- coding: utf-8 -*- +""" +============================== +1D Wasserstein barycenter demo +============================== + + +@author: rflamary +""" + +import numpy as np +import matplotlib.pylab as pl +import ot +from mpl_toolkits.mplot3d import Axes3D #necessary for 3d plot even if not used +from matplotlib.collections import PolyCollection + + +#%% parameters + +n=100 # nb bins + +# bin positions +x=np.arange(n,dtype=np.float64) + +# Gaussian distributions +a1=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std +a2=ot.datasets.get_1D_gauss(n,m=60,s=8) + +# creating matrix A containing all distributions +A=np.vstack((a1,a2)).T +nbd=A.shape[1] + +# loss matrix + normalization +M=ot.utils.dist0(n) +M/=M.max() + +#%% plot the distributions + +pl.figure(1) +for i in range(nbd): + pl.plot(x,A[:,i]) +pl.title('Distributions') + +#%% barycenter computation + +alpha=0.2 # 0<=alpha<=1 +weights=np.array([1-alpha,alpha]) + +# l2bary +bary_l2=A.dot(weights) + +# wasserstein +reg=1e-3 +bary_wass=ot.bregman.barycenter(A,M,reg,weights) + +pl.figure(2) +pl.clf() +pl.subplot(2,1,1) +for i in range(nbd): + pl.plot(x,A[:,i]) +pl.title('Distributions') + +pl.subplot(2,1,2) +pl.plot(x,bary_l2,'r',label='l2') +pl.plot(x,bary_wass,'g',label='Wasserstein') +pl.legend() +pl.title('Barycenters') + + +#%% barycenter interpolation + +nbalpha=11 +alphalist=np.linspace(0,1,nbalpha) + + +B_l2=np.zeros((n,nbalpha)) + +B_wass=np.copy(B_l2) + +for i in range(0,nbalpha): + alpha=alphalist[i] + weights=np.array([1-alpha,alpha]) + B_l2[:,i]=A.dot(weights) + B_wass[:,i]=ot.bregman.barycenter(A,M,reg,weights) + +#%% plot interpolation + +pl.figure(3,(10,5)) + +#pl.subplot(1,2,1) +cmap=pl.cm.get_cmap('viridis') +verts = [] +zs = alphalist +for i,z in enumerate(zs): + ys = B_l2[:,i] + verts.append(list(zip(x, ys))) + +ax = pl.gcf().gca(projection='3d') + +poly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist]) +poly.set_alpha(0.7) +ax.add_collection3d(poly, zs=zs, zdir='y') + +ax.set_xlabel('x') +ax.set_xlim3d(0, n) +ax.set_ylabel('$\\alpha$') +ax.set_ylim3d(0,1) +ax.set_zlabel('') +ax.set_zlim3d(0, B_l2.max()*1.01) +pl.title('Barycenter interpolation with l2') + +pl.show() + +pl.figure(4,(10,5)) + +#pl.subplot(1,2,1) +cmap=pl.cm.get_cmap('viridis') +verts = [] +zs = alphalist +for i,z in enumerate(zs): + ys = B_wass[:,i] + verts.append(list(zip(x, ys))) + +ax = pl.gcf().gca(projection='3d') + +poly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist]) +poly.set_alpha(0.7) +ax.add_collection3d(poly, zs=zs, zdir='y') + +ax.set_xlabel('x') +ax.set_xlim3d(0, n) +ax.set_ylabel('$\\alpha$') +ax.set_ylim3d(0,1) +ax.set_zlabel('') +ax.set_zlim3d(0, B_l2.max()*1.01) +pl.title('Barycenter interpolation with Wasserstein') + +pl.show() \ No newline at end of file diff --git a/docs/source/auto_examples/plot_barycenter_1D.rst b/docs/source/auto_examples/plot_barycenter_1D.rst new file mode 100644 index 0000000..1b15c77 --- /dev/null +++ b/docs/source/auto_examples/plot_barycenter_1D.rst @@ -0,0 +1,193 @@ + + +.. _sphx_glr_auto_examples_plot_barycenter_1D.py: + + +============================== +1D Wasserstein barycenter demo +============================== + + +@author: rflamary + + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_001.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_002.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_003.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_004.png + :scale: 47 + + + + + +.. code-block:: python + + + import numpy as np + import matplotlib.pylab as pl + import ot + from mpl_toolkits.mplot3d import Axes3D #necessary for 3d plot even if not used + from matplotlib.collections import PolyCollection + + + #%% parameters + + n=100 # nb bins + + # bin positions + x=np.arange(n,dtype=np.float64) + + # Gaussian distributions + a1=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std + a2=ot.datasets.get_1D_gauss(n,m=60,s=8) + + # creating matrix A containing all distributions + A=np.vstack((a1,a2)).T + nbd=A.shape[1] + + # loss matrix + normalization + M=ot.utils.dist0(n) + M/=M.max() + + #%% plot the distributions + + pl.figure(1) + for i in range(nbd): + pl.plot(x,A[:,i]) + pl.title('Distributions') + + #%% barycenter computation + + alpha=0.2 # 0<=alpha<=1 + weights=np.array([1-alpha,alpha]) + + # l2bary + bary_l2=A.dot(weights) + + # wasserstein + reg=1e-3 + bary_wass=ot.bregman.barycenter(A,M,reg,weights) + + pl.figure(2) + pl.clf() + pl.subplot(2,1,1) + for i in range(nbd): + pl.plot(x,A[:,i]) + pl.title('Distributions') + + pl.subplot(2,1,2) + pl.plot(x,bary_l2,'r',label='l2') + pl.plot(x,bary_wass,'g',label='Wasserstein') + pl.legend() + pl.title('Barycenters') + + + #%% barycenter interpolation + + nbalpha=11 + alphalist=np.linspace(0,1,nbalpha) + + + B_l2=np.zeros((n,nbalpha)) + + B_wass=np.copy(B_l2) + + for i in range(0,nbalpha): + alpha=alphalist[i] + weights=np.array([1-alpha,alpha]) + B_l2[:,i]=A.dot(weights) + B_wass[:,i]=ot.bregman.barycenter(A,M,reg,weights) + + #%% plot interpolation + + pl.figure(3,(10,5)) + + #pl.subplot(1,2,1) + cmap=pl.cm.get_cmap('viridis') + verts = [] + zs = alphalist + for i,z in enumerate(zs): + ys = B_l2[:,i] + verts.append(list(zip(x, ys))) + + ax = pl.gcf().gca(projection='3d') + + poly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist]) + poly.set_alpha(0.7) + ax.add_collection3d(poly, zs=zs, zdir='y') + + ax.set_xlabel('x') + ax.set_xlim3d(0, n) + ax.set_ylabel('$\\alpha$') + ax.set_ylim3d(0,1) + ax.set_zlabel('') + ax.set_zlim3d(0, B_l2.max()*1.01) + pl.title('Barycenter interpolation with l2') + + pl.show() + + pl.figure(4,(10,5)) + + #pl.subplot(1,2,1) + cmap=pl.cm.get_cmap('viridis') + verts = [] + zs = alphalist + for i,z in enumerate(zs): + ys = B_wass[:,i] + verts.append(list(zip(x, ys))) + + ax = pl.gcf().gca(projection='3d') + + poly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist]) + poly.set_alpha(0.7) + ax.add_collection3d(poly, zs=zs, zdir='y') + + ax.set_xlabel('x') + ax.set_xlim3d(0, n) + ax.set_ylabel('$\\alpha$') + ax.set_ylim3d(0,1) + ax.set_zlabel('') + ax.set_zlim3d(0, B_l2.max()*1.01) + pl.title('Barycenter interpolation with Wasserstein') + + pl.show() +**Total running time of the script:** ( 0 minutes 2.274 seconds) + + + +.. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_barycenter_1D.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_barycenter_1D.ipynb ` + +.. rst-class:: sphx-glr-signature + + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_optim_OTreg.ipynb b/docs/source/auto_examples/plot_optim_OTreg.ipynb new file mode 100644 index 0000000..250ea72 --- /dev/null +++ b/docs/source/auto_examples/plot_optim_OTreg.ipynb @@ -0,0 +1,54 @@ +{ + "nbformat_minor": 0, + "nbformat": 4, + "cells": [ + { + "execution_count": null, + "cell_type": "code", + "source": [ + "%matplotlib inline" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "\n# Regularized OT with generic solver\n\n\n\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n\n\n#%% parameters\n\nn=100 # nb bins\n\n# bin positions\nx=np.arange(n,dtype=np.float64)\n\n# Gaussian distributions\na=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std\nb=ot.datasets.get_1D_gauss(n,m=60,s=10)\n\n# loss matrix\nM=ot.dist(x.reshape((n,1)),x.reshape((n,1)))\nM/=M.max()\n\n#%% EMD\n\nG0=ot.emd(a,b,M)\n\npl.figure(3)\not.plot.plot1D_mat(a,b,G0,'OT matrix G0')\n\n#%% Example with Frobenius norm regularization\n\ndef f(G): return 0.5*np.sum(G**2)\ndef df(G): return G\n\nreg=1e-1\n\nGl2=ot.optim.cg(a,b,M,reg,f,df,verbose=True)\n\npl.figure(3)\not.plot.plot1D_mat(a,b,Gl2,'OT matrix Frob. reg')\n\n#%% Example with entropic regularization\n\ndef f(G): return np.sum(G*np.log(G))\ndef df(G): return np.log(G)+1\n\nreg=1e-3\n\nGe=ot.optim.cg(a,b,M,reg,f,df,verbose=True)\n\npl.figure(4)\not.plot.plot1D_mat(a,b,Ge,'OT matrix Entrop. reg')\n\n#%% Example with Frobenius norm + entropic regularization with gcg\n\ndef f(G): return 0.5*np.sum(G**2)\ndef df(G): return G\n\nreg1=1e-1\nreg2=1e-1\n\nGel2=ot.optim.gcg(a,b,M,reg1,reg2,f,df,verbose=True)\n\npl.figure(5)\not.plot.plot1D_mat(a,b,Gel2,'OT entropic + matrix Frob. reg')" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "name": "python2", + "language": "python" + }, + "language_info": { + "mimetype": "text/x-python", + "nbconvert_exporter": "python", + "name": "python", + "file_extension": ".py", + "version": "2.7.12", + "pygments_lexer": "ipython2", + "codemirror_mode": { + "version": 2, + "name": "ipython" + } + } + } +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_optim_OTreg.py b/docs/source/auto_examples/plot_optim_OTreg.py new file mode 100644 index 0000000..3c4d3f4 --- /dev/null +++ b/docs/source/auto_examples/plot_optim_OTreg.py @@ -0,0 +1,73 @@ +# -*- coding: utf-8 -*- +""" +================================== +Regularized OT with generic solver +================================== + + +""" + +import numpy as np +import matplotlib.pylab as pl +import ot + + + +#%% parameters + +n=100 # nb bins + +# bin positions +x=np.arange(n,dtype=np.float64) + +# Gaussian distributions +a=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std +b=ot.datasets.get_1D_gauss(n,m=60,s=10) + +# loss matrix +M=ot.dist(x.reshape((n,1)),x.reshape((n,1))) +M/=M.max() + +#%% EMD + +G0=ot.emd(a,b,M) + +pl.figure(3) +ot.plot.plot1D_mat(a,b,G0,'OT matrix G0') + +#%% Example with Frobenius norm regularization + +def f(G): return 0.5*np.sum(G**2) +def df(G): return G + +reg=1e-1 + +Gl2=ot.optim.cg(a,b,M,reg,f,df,verbose=True) + +pl.figure(3) +ot.plot.plot1D_mat(a,b,Gl2,'OT matrix Frob. reg') + +#%% Example with entropic regularization + +def f(G): return np.sum(G*np.log(G)) +def df(G): return np.log(G)+1 + +reg=1e-3 + +Ge=ot.optim.cg(a,b,M,reg,f,df,verbose=True) + +pl.figure(4) +ot.plot.plot1D_mat(a,b,Ge,'OT matrix Entrop. reg') + +#%% Example with Frobenius norm + entropic regularization with gcg + +def f(G): return 0.5*np.sum(G**2) +def df(G): return G + +reg1=1e-1 +reg2=1e-1 + +Gel2=ot.optim.gcg(a,b,M,reg1,reg2,f,df,verbose=True) + +pl.figure(5) +ot.plot.plot1D_mat(a,b,Gel2,'OT entropic + matrix Frob. reg') \ No newline at end of file diff --git a/docs/source/auto_examples/plot_optim_OTreg.rst b/docs/source/auto_examples/plot_optim_OTreg.rst new file mode 100644 index 0000000..d6397ba --- /dev/null +++ b/docs/source/auto_examples/plot_optim_OTreg.rst @@ -0,0 +1,583 @@ + + +.. _sphx_glr_auto_examples_plot_optim_OTreg.py: + + +================================== +Regularized OT with generic solver +================================== + + + + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_optim_OTreg_003.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_optim_OTreg_004.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_optim_OTreg_005.png + :scale: 47 + + +.. rst-class:: sphx-glr-script-out + + Out:: + + It. |Loss |Delta loss + -------------------------------- + 0|1.760578e-01|0.000000e+00 + 1|1.669467e-01|-5.457501e-02 + 2|1.665639e-01|-2.298130e-03 + 3|1.664378e-01|-7.572776e-04 + 4|1.664077e-01|-1.811855e-04 + 5|1.663912e-01|-9.936787e-05 + 6|1.663852e-01|-3.555826e-05 + 7|1.663814e-01|-2.305693e-05 + 8|1.663785e-01|-1.760450e-05 + 9|1.663767e-01|-1.078011e-05 + 10|1.663751e-01|-9.525192e-06 + 11|1.663737e-01|-8.396466e-06 + 12|1.663727e-01|-6.086938e-06 + 13|1.663720e-01|-4.042609e-06 + 14|1.663713e-01|-4.160914e-06 + 15|1.663707e-01|-3.823502e-06 + 16|1.663702e-01|-3.022440e-06 + 17|1.663697e-01|-3.181249e-06 + 18|1.663692e-01|-2.698532e-06 + 19|1.663687e-01|-3.258253e-06 + It. |Loss |Delta loss + -------------------------------- + 20|1.663682e-01|-2.741118e-06 + 21|1.663678e-01|-2.624135e-06 + 22|1.663673e-01|-2.645179e-06 + 23|1.663670e-01|-1.957237e-06 + 24|1.663666e-01|-2.261541e-06 + 25|1.663663e-01|-1.851305e-06 + 26|1.663660e-01|-1.942296e-06 + 27|1.663657e-01|-2.092896e-06 + 28|1.663653e-01|-1.924361e-06 + 29|1.663651e-01|-1.625455e-06 + 30|1.663648e-01|-1.641123e-06 + 31|1.663645e-01|-1.566666e-06 + 32|1.663643e-01|-1.338514e-06 + 33|1.663641e-01|-1.222711e-06 + 34|1.663639e-01|-1.221805e-06 + 35|1.663637e-01|-1.440781e-06 + 36|1.663634e-01|-1.520091e-06 + 37|1.663632e-01|-1.288193e-06 + 38|1.663630e-01|-1.123055e-06 + 39|1.663628e-01|-1.024487e-06 + It. |Loss |Delta loss + -------------------------------- + 40|1.663627e-01|-1.079606e-06 + 41|1.663625e-01|-1.172093e-06 + 42|1.663623e-01|-1.047880e-06 + 43|1.663621e-01|-1.010577e-06 + 44|1.663619e-01|-1.064438e-06 + 45|1.663618e-01|-9.882375e-07 + 46|1.663616e-01|-8.532647e-07 + 47|1.663615e-01|-9.930189e-07 + 48|1.663613e-01|-8.728955e-07 + 49|1.663612e-01|-9.524214e-07 + 50|1.663610e-01|-9.088418e-07 + 51|1.663609e-01|-7.639430e-07 + 52|1.663608e-01|-6.662611e-07 + 53|1.663607e-01|-7.133700e-07 + 54|1.663605e-01|-7.648141e-07 + 55|1.663604e-01|-6.557516e-07 + 56|1.663603e-01|-7.304213e-07 + 57|1.663602e-01|-6.353809e-07 + 58|1.663601e-01|-7.968279e-07 + 59|1.663600e-01|-6.367159e-07 + It. |Loss |Delta loss + -------------------------------- + 60|1.663599e-01|-5.610790e-07 + 61|1.663598e-01|-5.787466e-07 + 62|1.663596e-01|-6.937777e-07 + 63|1.663596e-01|-5.599432e-07 + 64|1.663595e-01|-5.813048e-07 + 65|1.663594e-01|-5.724600e-07 + 66|1.663593e-01|-6.081892e-07 + 67|1.663592e-01|-5.948732e-07 + 68|1.663591e-01|-4.941833e-07 + 69|1.663590e-01|-5.213739e-07 + 70|1.663589e-01|-5.127355e-07 + 71|1.663588e-01|-4.349251e-07 + 72|1.663588e-01|-5.007084e-07 + 73|1.663587e-01|-4.880265e-07 + 74|1.663586e-01|-4.931950e-07 + 75|1.663585e-01|-4.981309e-07 + 76|1.663584e-01|-3.952959e-07 + 77|1.663584e-01|-4.544857e-07 + 78|1.663583e-01|-4.237579e-07 + 79|1.663582e-01|-4.382386e-07 + It. |Loss |Delta loss + -------------------------------- + 80|1.663582e-01|-3.646051e-07 + 81|1.663581e-01|-4.197994e-07 + 82|1.663580e-01|-4.072764e-07 + 83|1.663580e-01|-3.994645e-07 + 84|1.663579e-01|-4.842721e-07 + 85|1.663578e-01|-3.276486e-07 + 86|1.663578e-01|-3.737346e-07 + 87|1.663577e-01|-4.282043e-07 + 88|1.663576e-01|-4.020937e-07 + 89|1.663576e-01|-3.431951e-07 + 90|1.663575e-01|-3.052335e-07 + 91|1.663575e-01|-3.500538e-07 + 92|1.663574e-01|-3.063176e-07 + 93|1.663573e-01|-3.576367e-07 + 94|1.663573e-01|-3.224681e-07 + 95|1.663572e-01|-3.673221e-07 + 96|1.663572e-01|-3.635561e-07 + 97|1.663571e-01|-3.527236e-07 + 98|1.663571e-01|-2.788548e-07 + 99|1.663570e-01|-2.727141e-07 + It. |Loss |Delta loss + -------------------------------- + 100|1.663570e-01|-3.127278e-07 + 101|1.663569e-01|-2.637504e-07 + 102|1.663569e-01|-2.922750e-07 + 103|1.663568e-01|-3.076454e-07 + 104|1.663568e-01|-2.911509e-07 + 105|1.663567e-01|-2.403398e-07 + 106|1.663567e-01|-2.439790e-07 + 107|1.663567e-01|-2.634542e-07 + 108|1.663566e-01|-2.452203e-07 + 109|1.663566e-01|-2.852991e-07 + 110|1.663565e-01|-2.165490e-07 + 111|1.663565e-01|-2.450250e-07 + 112|1.663564e-01|-2.685294e-07 + 113|1.663564e-01|-2.821800e-07 + 114|1.663564e-01|-2.237390e-07 + 115|1.663563e-01|-1.992842e-07 + 116|1.663563e-01|-2.166739e-07 + 117|1.663563e-01|-2.086064e-07 + 118|1.663562e-01|-2.435945e-07 + 119|1.663562e-01|-2.292497e-07 + It. |Loss |Delta loss + -------------------------------- + 120|1.663561e-01|-2.366209e-07 + 121|1.663561e-01|-2.138746e-07 + 122|1.663561e-01|-2.009637e-07 + 123|1.663560e-01|-2.386258e-07 + 124|1.663560e-01|-1.927442e-07 + 125|1.663560e-01|-2.081681e-07 + 126|1.663559e-01|-1.759123e-07 + 127|1.663559e-01|-1.890771e-07 + 128|1.663559e-01|-1.971315e-07 + 129|1.663558e-01|-2.101983e-07 + 130|1.663558e-01|-2.035645e-07 + 131|1.663558e-01|-1.984492e-07 + 132|1.663557e-01|-1.849064e-07 + 133|1.663557e-01|-1.795703e-07 + 134|1.663557e-01|-1.624087e-07 + 135|1.663557e-01|-1.689557e-07 + 136|1.663556e-01|-1.644308e-07 + 137|1.663556e-01|-1.618007e-07 + 138|1.663556e-01|-1.483013e-07 + 139|1.663555e-01|-1.708771e-07 + It. |Loss |Delta loss + -------------------------------- + 140|1.663555e-01|-2.013847e-07 + 141|1.663555e-01|-1.721217e-07 + 142|1.663554e-01|-2.027911e-07 + 143|1.663554e-01|-1.764565e-07 + 144|1.663554e-01|-1.677151e-07 + 145|1.663554e-01|-1.351982e-07 + 146|1.663553e-01|-1.423360e-07 + 147|1.663553e-01|-1.541112e-07 + 148|1.663553e-01|-1.491601e-07 + 149|1.663553e-01|-1.466407e-07 + 150|1.663552e-01|-1.801524e-07 + 151|1.663552e-01|-1.714107e-07 + 152|1.663552e-01|-1.491257e-07 + 153|1.663552e-01|-1.513799e-07 + 154|1.663551e-01|-1.354539e-07 + 155|1.663551e-01|-1.233818e-07 + 156|1.663551e-01|-1.576219e-07 + 157|1.663551e-01|-1.452791e-07 + 158|1.663550e-01|-1.262867e-07 + 159|1.663550e-01|-1.316379e-07 + It. |Loss |Delta loss + -------------------------------- + 160|1.663550e-01|-1.295447e-07 + 161|1.663550e-01|-1.283286e-07 + 162|1.663550e-01|-1.569222e-07 + 163|1.663549e-01|-1.172942e-07 + 164|1.663549e-01|-1.399809e-07 + 165|1.663549e-01|-1.229432e-07 + 166|1.663549e-01|-1.326191e-07 + 167|1.663548e-01|-1.209694e-07 + 168|1.663548e-01|-1.372136e-07 + 169|1.663548e-01|-1.338395e-07 + 170|1.663548e-01|-1.416497e-07 + 171|1.663548e-01|-1.298576e-07 + 172|1.663547e-01|-1.190590e-07 + 173|1.663547e-01|-1.167083e-07 + 174|1.663547e-01|-1.069425e-07 + 175|1.663547e-01|-1.217780e-07 + 176|1.663547e-01|-1.140754e-07 + 177|1.663546e-01|-1.160707e-07 + 178|1.663546e-01|-1.101798e-07 + 179|1.663546e-01|-1.114904e-07 + It. |Loss |Delta loss + -------------------------------- + 180|1.663546e-01|-1.064022e-07 + 181|1.663546e-01|-9.258231e-08 + 182|1.663546e-01|-1.213120e-07 + 183|1.663545e-01|-1.164296e-07 + 184|1.663545e-01|-1.188762e-07 + 185|1.663545e-01|-9.394153e-08 + 186|1.663545e-01|-1.028656e-07 + 187|1.663545e-01|-1.115348e-07 + 188|1.663544e-01|-9.768310e-08 + 189|1.663544e-01|-1.021806e-07 + 190|1.663544e-01|-1.086303e-07 + 191|1.663544e-01|-9.879008e-08 + 192|1.663544e-01|-1.050210e-07 + 193|1.663544e-01|-1.002463e-07 + 194|1.663543e-01|-1.062747e-07 + 195|1.663543e-01|-9.348538e-08 + 196|1.663543e-01|-7.992512e-08 + 197|1.663543e-01|-9.558020e-08 + 198|1.663543e-01|-9.993772e-08 + 199|1.663543e-01|-8.588499e-08 + It. |Loss |Delta loss + -------------------------------- + 200|1.663543e-01|-8.737134e-08 + It. |Loss |Delta loss + -------------------------------- + 0|1.692289e-01|0.000000e+00 + 1|1.617643e-01|-4.614437e-02 + 2|1.612546e-01|-3.161037e-03 + 3|1.611040e-01|-9.349544e-04 + 4|1.610346e-01|-4.310179e-04 + 5|1.610072e-01|-1.701719e-04 + 6|1.609947e-01|-7.759814e-05 + 7|1.609934e-01|-7.941439e-06 + 8|1.609841e-01|-5.797180e-05 + 9|1.609838e-01|-1.559407e-06 + 10|1.609685e-01|-9.530282e-05 + 11|1.609666e-01|-1.142129e-05 + 12|1.609541e-01|-7.799970e-05 + 13|1.609496e-01|-2.780416e-05 + 14|1.609385e-01|-6.887105e-05 + 15|1.609334e-01|-3.174241e-05 + 16|1.609231e-01|-6.420777e-05 + 17|1.609115e-01|-7.189949e-05 + 18|1.608815e-01|-1.865331e-04 + 19|1.608799e-01|-1.013039e-05 + It. |Loss |Delta loss + -------------------------------- + 20|1.608695e-01|-6.468606e-05 + 21|1.608686e-01|-5.738419e-06 + 22|1.608661e-01|-1.495923e-05 + 23|1.608657e-01|-2.784611e-06 + 24|1.608633e-01|-1.512408e-05 + 25|1.608624e-01|-5.397916e-06 + 26|1.608617e-01|-4.115218e-06 + 27|1.608561e-01|-3.503396e-05 + 28|1.608479e-01|-5.098773e-05 + 29|1.608452e-01|-1.659203e-05 + 30|1.608399e-01|-3.298319e-05 + 31|1.608330e-01|-4.302183e-05 + 32|1.608310e-01|-1.273465e-05 + 33|1.608280e-01|-1.827713e-05 + 34|1.608231e-01|-3.039842e-05 + 35|1.608212e-01|-1.229256e-05 + 36|1.608200e-01|-6.900556e-06 + 37|1.608159e-01|-2.554039e-05 + 38|1.608103e-01|-3.521137e-05 + 39|1.608058e-01|-2.795180e-05 + It. |Loss |Delta loss + -------------------------------- + 40|1.608040e-01|-1.119118e-05 + 41|1.608027e-01|-8.193369e-06 + 42|1.607994e-01|-2.026719e-05 + 43|1.607985e-01|-5.819902e-06 + 44|1.607978e-01|-4.048170e-06 + 45|1.607978e-01|-3.007470e-07 + 46|1.607950e-01|-1.705375e-05 + 47|1.607927e-01|-1.430186e-05 + 48|1.607925e-01|-1.166526e-06 + 49|1.607911e-01|-9.069406e-06 + 50|1.607910e-01|-3.804209e-07 + 51|1.607910e-01|-5.942399e-08 + 52|1.607910e-01|-2.321380e-07 + 53|1.607907e-01|-1.877655e-06 + 54|1.607906e-01|-2.940224e-07 + 55|1.607877e-01|-1.814208e-05 + 56|1.607841e-01|-2.236496e-05 + 57|1.607810e-01|-1.951355e-05 + 58|1.607804e-01|-3.578228e-06 + 59|1.607789e-01|-9.442277e-06 + It. |Loss |Delta loss + -------------------------------- + 60|1.607779e-01|-5.997371e-06 + 61|1.607754e-01|-1.564408e-05 + 62|1.607742e-01|-7.693285e-06 + 63|1.607727e-01|-9.030547e-06 + 64|1.607719e-01|-5.103894e-06 + 65|1.607693e-01|-1.605420e-05 + 66|1.607676e-01|-1.047837e-05 + 67|1.607675e-01|-6.026848e-07 + 68|1.607655e-01|-1.240216e-05 + 69|1.607632e-01|-1.434674e-05 + 70|1.607618e-01|-8.829808e-06 + 71|1.607606e-01|-7.581824e-06 + 72|1.607590e-01|-1.009457e-05 + 73|1.607586e-01|-2.222963e-06 + 74|1.607577e-01|-5.564775e-06 + 75|1.607574e-01|-1.932763e-06 + 76|1.607573e-01|-8.148685e-07 + 77|1.607554e-01|-1.187660e-05 + 78|1.607546e-01|-4.557651e-06 + 79|1.607537e-01|-5.911902e-06 + It. |Loss |Delta loss + -------------------------------- + 80|1.607529e-01|-4.710187e-06 + 81|1.607528e-01|-8.866080e-07 + 82|1.607522e-01|-3.620627e-06 + 83|1.607514e-01|-5.091281e-06 + 84|1.607498e-01|-9.932095e-06 + 85|1.607487e-01|-6.852804e-06 + 86|1.607478e-01|-5.373596e-06 + 87|1.607473e-01|-3.287295e-06 + 88|1.607470e-01|-1.666655e-06 + 89|1.607469e-01|-5.293790e-07 + 90|1.607466e-01|-2.051914e-06 + 91|1.607456e-01|-6.422797e-06 + 92|1.607456e-01|-1.110433e-07 + 93|1.607451e-01|-2.803849e-06 + 94|1.607451e-01|-2.608066e-07 + 95|1.607441e-01|-6.290352e-06 + 96|1.607429e-01|-7.298455e-06 + 97|1.607429e-01|-8.969905e-09 + 98|1.607427e-01|-7.923968e-07 + 99|1.607427e-01|-3.519286e-07 + It. |Loss |Delta loss + -------------------------------- + 100|1.607426e-01|-3.563804e-07 + 101|1.607410e-01|-1.004042e-05 + 102|1.607410e-01|-2.124801e-07 + 103|1.607398e-01|-7.556935e-06 + 104|1.607398e-01|-7.606853e-08 + 105|1.607385e-01|-8.058684e-06 + 106|1.607383e-01|-7.393061e-07 + 107|1.607381e-01|-1.504958e-06 + 108|1.607377e-01|-2.508807e-06 + 109|1.607371e-01|-4.004631e-06 + 110|1.607365e-01|-3.580156e-06 + 111|1.607364e-01|-2.563573e-07 + 112|1.607354e-01|-6.390137e-06 + 113|1.607348e-01|-4.119553e-06 + 114|1.607339e-01|-5.299475e-06 + 115|1.607335e-01|-2.316767e-06 + 116|1.607330e-01|-3.444737e-06 + 117|1.607324e-01|-3.467980e-06 + 118|1.607320e-01|-2.374632e-06 + 119|1.607319e-01|-7.978255e-07 + It. |Loss |Delta loss + -------------------------------- + 120|1.607312e-01|-4.221434e-06 + 121|1.607310e-01|-1.324597e-06 + 122|1.607304e-01|-3.650359e-06 + 123|1.607298e-01|-3.732712e-06 + 124|1.607295e-01|-1.994082e-06 + 125|1.607289e-01|-3.954139e-06 + 126|1.607286e-01|-1.532372e-06 + 127|1.607286e-01|-1.167223e-07 + 128|1.607283e-01|-2.157376e-06 + 129|1.607279e-01|-2.253077e-06 + 130|1.607274e-01|-3.301532e-06 + 131|1.607269e-01|-2.650754e-06 + 132|1.607264e-01|-3.595551e-06 + 133|1.607262e-01|-1.159425e-06 + 134|1.607258e-01|-2.512411e-06 + 135|1.607255e-01|-1.998792e-06 + 136|1.607251e-01|-2.486536e-06 + 137|1.607246e-01|-2.782996e-06 + 138|1.607246e-01|-2.922470e-07 + 139|1.607242e-01|-2.071131e-06 + It. |Loss |Delta loss + -------------------------------- + 140|1.607237e-01|-3.154193e-06 + 141|1.607235e-01|-1.194962e-06 + 142|1.607232e-01|-2.035251e-06 + 143|1.607232e-01|-6.027855e-08 + 144|1.607229e-01|-1.555696e-06 + 145|1.607228e-01|-1.081740e-06 + 146|1.607225e-01|-1.881070e-06 + 147|1.607224e-01|-4.100096e-07 + 148|1.607223e-01|-7.785200e-07 + 149|1.607222e-01|-2.094072e-07 + 150|1.607220e-01|-1.440814e-06 + 151|1.607217e-01|-1.997794e-06 + 152|1.607214e-01|-2.011022e-06 + 153|1.607212e-01|-8.808854e-07 + 154|1.607211e-01|-7.245877e-07 + 155|1.607207e-01|-2.217159e-06 + 156|1.607201e-01|-3.817891e-06 + 157|1.607200e-01|-7.409600e-07 + 158|1.607198e-01|-1.497698e-06 + 159|1.607195e-01|-1.729666e-06 + It. |Loss |Delta loss + -------------------------------- + 160|1.607195e-01|-2.115187e-07 + 161|1.607192e-01|-1.643727e-06 + 162|1.607192e-01|-1.712969e-07 + 163|1.607189e-01|-1.805877e-06 + 164|1.607189e-01|-1.209827e-07 + 165|1.607185e-01|-2.060002e-06 + 166|1.607182e-01|-1.961341e-06 + 167|1.607181e-01|-1.020366e-06 + 168|1.607179e-01|-9.760982e-07 + 169|1.607178e-01|-7.219236e-07 + 170|1.607175e-01|-1.837718e-06 + 171|1.607174e-01|-3.337578e-07 + 172|1.607173e-01|-5.298564e-07 + 173|1.607173e-01|-6.864278e-08 + 174|1.607173e-01|-2.008419e-07 + 175|1.607171e-01|-1.375630e-06 + 176|1.607168e-01|-1.911257e-06 + 177|1.607167e-01|-2.709815e-07 + 178|1.607167e-01|-1.390953e-07 + 179|1.607165e-01|-1.199675e-06 + It. |Loss |Delta loss + -------------------------------- + 180|1.607165e-01|-1.457259e-07 + 181|1.607163e-01|-1.049154e-06 + 182|1.607163e-01|-2.753577e-09 + 183|1.607163e-01|-6.972814e-09 + 184|1.607161e-01|-1.552100e-06 + 185|1.607159e-01|-1.068596e-06 + 186|1.607157e-01|-1.247724e-06 + 187|1.607155e-01|-1.158164e-06 + 188|1.607155e-01|-2.616199e-07 + 189|1.607154e-01|-3.595874e-07 + 190|1.607154e-01|-5.334527e-08 + 191|1.607153e-01|-3.452744e-07 + 192|1.607153e-01|-1.239593e-07 + 193|1.607152e-01|-8.184984e-07 + 194|1.607150e-01|-1.316308e-06 + 195|1.607150e-01|-7.100882e-09 + 196|1.607148e-01|-1.393958e-06 + 197|1.607146e-01|-1.242735e-06 + 198|1.607144e-01|-1.123993e-06 + 199|1.607143e-01|-3.512071e-07 + It. |Loss |Delta loss + -------------------------------- + 200|1.607143e-01|-2.151971e-10 + It. |Loss |Delta loss + -------------------------------- + 0|-4.988764e-01|0.000000e+00 + 1|-4.993932e-01|-1.034993e-03 + 2|-4.993933e-01|-9.845917e-08 + 3|-4.993933e-01|-9.206594e-12 + + + + +| + + +.. code-block:: python + + + import numpy as np + import matplotlib.pylab as pl + import ot + + + + #%% parameters + + n=100 # nb bins + + # bin positions + x=np.arange(n,dtype=np.float64) + + # Gaussian distributions + a=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std + b=ot.datasets.get_1D_gauss(n,m=60,s=10) + + # loss matrix + M=ot.dist(x.reshape((n,1)),x.reshape((n,1))) + M/=M.max() + + #%% EMD + + G0=ot.emd(a,b,M) + + pl.figure(3) + ot.plot.plot1D_mat(a,b,G0,'OT matrix G0') + + #%% Example with Frobenius norm regularization + + def f(G): return 0.5*np.sum(G**2) + def df(G): return G + + reg=1e-1 + + Gl2=ot.optim.cg(a,b,M,reg,f,df,verbose=True) + + pl.figure(3) + ot.plot.plot1D_mat(a,b,Gl2,'OT matrix Frob. reg') + + #%% Example with entropic regularization + + def f(G): return np.sum(G*np.log(G)) + def df(G): return np.log(G)+1 + + reg=1e-3 + + Ge=ot.optim.cg(a,b,M,reg,f,df,verbose=True) + + pl.figure(4) + ot.plot.plot1D_mat(a,b,Ge,'OT matrix Entrop. reg') + + #%% Example with Frobenius norm + entropic regularization with gcg + + def f(G): return 0.5*np.sum(G**2) + def df(G): return G + + reg1=1e-1 + reg2=1e-1 + + Gel2=ot.optim.gcg(a,b,M,reg1,reg2,f,df,verbose=True) + + pl.figure(5) + ot.plot.plot1D_mat(a,b,Gel2,'OT entropic + matrix Frob. reg') +**Total running time of the script:** ( 0 minutes 2.358 seconds) + + + +.. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_optim_OTreg.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_optim_OTreg.ipynb ` + +.. rst-class:: sphx-glr-signature + + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/conf.py b/docs/source/conf.py index f76c184..1a12639 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -58,7 +58,7 @@ extensions = [ 'sphinx.ext.ifconfig', 'sphinx.ext.viewcode', 'sphinx.ext.napoleon', - 'sphinx_gallery.gen_gallery', +# 'sphinx_gallery.gen_gallery', ] # Add any paths that contain templates here, relative to this directory. -- cgit v1.2.3 From ecac4ba865460cbb677520a24fc4d2c34442d45a Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 2 Dec 2016 15:39:33 +0100 Subject: readthedoc --- docs/source/conf.py | 34 +++++++++++++++++----------------- 1 file changed, 17 insertions(+), 17 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 1a12639..d93dff5 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -17,23 +17,23 @@ import os import re import sphinx_gallery -##try: -#from unittest.mock import MagicMock -##except ImportError: -## from mock import MagicMock -# -#sys.path.insert(0, os.path.abspath("../..")) -##sys.setrecursionlimit(1500) -# -# -# -#class Mock(MagicMock): -# @classmethod -# def __getattr__(cls, name): -# return Mock() -# -#MOCK_MODULES = [ 'emd','ot.lp.emd'] -#sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) +#try: +from unittest.mock import MagicMock +#except ImportError: +# from mock import MagicMock + +sys.path.insert(0, os.path.abspath("../..")) +#sys.setrecursionlimit(1500) + + + +class Mock(MagicMock): + @classmethod + def __getattr__(cls, name): + return Mock() + +MOCK_MODULES = [ 'emd','ot.lp.emd'] +sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the -- cgit v1.2.3 From 17315cb08ebf777626dc9aec4bf140cfb5c42503 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 2 Dec 2016 15:49:09 +0100 Subject: readthedoc 2 --- docs/source/conf.py | 11 ++--------- 1 file changed, 2 insertions(+), 9 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index d93dff5..e0d4e0b 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -17,23 +17,16 @@ import os import re import sphinx_gallery -#try: +# !!!! allow readthedoc compilation from unittest.mock import MagicMock -#except ImportError: -# from mock import MagicMock - sys.path.insert(0, os.path.abspath("../..")) -#sys.setrecursionlimit(1500) - - - class Mock(MagicMock): @classmethod def __getattr__(cls, name): return Mock() - MOCK_MODULES = [ 'emd','ot.lp.emd'] sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) +# !!!! # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the -- cgit v1.2.3 From bb6020dd69933735a6cf821eabcbedca92f2ecb4 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 5 Jan 2017 13:40:38 +0100 Subject: more efficient sinkhorn --- ot/bregman.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 568ead2..fd22259 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -141,9 +141,9 @@ def sinkhorn(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, log=Fa cpt = cpt +1 #print 'err=',err,' cpt=',cpt if log: - return np.dot(np.diag(u),np.dot(K,np.diag(v))),log + return u.reshape((-1,1))*K*v.reshape((1,-1)),log else: - return np.dot(np.diag(u),np.dot(K,np.diag(v))) + return u.reshape((-1,1))*K*v.reshape((1,-1)) def sinkhorn_stabilized(a,b, M, reg, numItermax = 1000,tau=1e3, stopThr=1e-9,warmstart=None, verbose=False,print_period=20, log=False): """ -- cgit v1.2.3 From b90ea88f8f0cf32aef102277588f80042c6cfcaa Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 5 Jan 2017 13:43:44 +0100 Subject: update demo --- examples/plot_OT_2D_samples.py | 2 +- examples/plot_optim_OTreg.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/plot_OT_2D_samples.py b/examples/plot_OT_2D_samples.py index 6c39ad4..3b95083 100644 --- a/examples/plot_OT_2D_samples.py +++ b/examples/plot_OT_2D_samples.py @@ -13,7 +13,7 @@ import ot #%% parameters and data generation -n=20 # nb samples +n=2 # nb samples mu_s=np.array([0,0]) cov_s=np.array([[1,0],[0,1]]) diff --git a/examples/plot_optim_OTreg.py b/examples/plot_optim_OTreg.py index 3c4d3f4..c585445 100644 --- a/examples/plot_optim_OTreg.py +++ b/examples/plot_optim_OTreg.py @@ -64,7 +64,7 @@ ot.plot.plot1D_mat(a,b,Ge,'OT matrix Entrop. reg') def f(G): return 0.5*np.sum(G**2) def df(G): return G -reg1=1e-1 +reg1=1e-3 reg2=1e-1 Gel2=ot.optim.gcg(a,b,M,reg1,reg2,f,df,verbose=True) -- cgit v1.2.3 From e1cd68bd8e8a2c7334595d1b4ea0bc9dccfd3013 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 5 Jan 2017 13:48:09 +0100 Subject: add tic() and toc() functions for easy coarse timing --- ot/__init__.py | 2 +- ot/utils.py | 20 ++++++++++++++++++++ 2 files changed, 21 insertions(+), 1 deletion(-) diff --git a/ot/__init__.py b/ot/__init__.py index a925a6f..5c67dc5 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -16,7 +16,7 @@ from .bregman import sinkhorn, barycenter from .da import sinkhorn_lpl1_mm # utils functions -from .utils import dist, unif +from .utils import dist, unif, tic, toc, toq __version__ = "0.1.11" diff --git a/ot/utils.py b/ot/utils.py index d3df8fa..2f68775 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -6,6 +6,26 @@ import numpy as np from scipy.spatial.distance import cdist +import time +__time_tic_toc=time.time() + +def tic(): + """ Python implementation of Matlab tic() function """ + global __time_tic_toc + __time_tic_toc=time.time() + +def toc(message='Elapsed time : {} s'): + """ Python implementation of Matlab toc() function """ + t=time.time() + print(message.format(t-__time_tic_toc)) + return t-__time_tic_toc + +def toq(): + """ Python implementation of Julia toc() function """ + t=time.time() + return t-__time_tic_toc + + def kernel(x1,x2,method='gaussian',sigma=1,**kwargs): """Compute kernel matrix""" if method.lower() in ['gaussian','gauss','rbf']: -- cgit v1.2.3 From 7102b90af73db265bfa0d61bf39b9cd7a0b2d525 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 5 Jan 2017 13:48:44 +0100 Subject: version 0.1.12 --- ot/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/__init__.py b/ot/__init__.py index 5c67dc5..55016a4 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -18,7 +18,7 @@ from .da import sinkhorn_lpl1_mm # utils functions from .utils import dist, unif, tic, toc, toq -__version__ = "0.1.11" +__version__ = "0.1.12" __all__ = ["emd", "sinkhorn", "utils", 'datasets', 'bregman', 'lp', 'plot', 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim'] -- cgit v1.2.3 From 4c344cfb57d6b32ae433777ae4d8f85c69d498e8 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 5 Jan 2017 13:49:47 +0100 Subject: update doc --- docs/source/readme.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/source/readme.rst b/docs/source/readme.rst index cc37bdf..a76de51 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -100,6 +100,7 @@ The contributors to this library are: - `Rémi Flamary `__ - `Nicolas Courty `__ - `Laetitia Chapel `__ +- `Michael Perrot `__ This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various -- cgit v1.2.3 From 0b806374d33ae83d39846096a1838b096c0c0b8e Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 7 Feb 2017 17:17:34 +0100 Subject: log return for sinkhorn --- ot/bregman.py | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/ot/bregman.py b/ot/bregman.py index fd22259..27f8ff5 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -139,6 +139,10 @@ def sinkhorn(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, log=Fa print('{:5s}|{:12s}'.format('It.','Err')+'\n'+'-'*19) print('{:5d}|{:8e}|'.format(cpt,err)) cpt = cpt +1 + if log: + log['u']=u + log['v']=v + #print 'err=',err,' cpt=',cpt if log: return u.reshape((-1,1))*K*v.reshape((1,-1)),log -- cgit v1.2.3 From df32d77316e79a663312544129048f8fee949817 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 10 Mar 2017 10:18:12 +0100 Subject: first try --- ot/__init__.py | 4 +- ot/lp/__init__.py | 107 +++++++++++++++++++++++++++++++++++++++++++++++++++++- 2 files changed, 108 insertions(+), 3 deletions(-) diff --git a/ot/__init__.py b/ot/__init__.py index 55016a4..ee294d8 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -11,7 +11,7 @@ from . import plot from . import da # OT functions -from .lp import emd +from .lp import emd, emd2 from .bregman import sinkhorn, barycenter from .da import sinkhorn_lpl1_mm @@ -20,5 +20,5 @@ from .utils import dist, unif, tic, toc, toq __version__ = "0.1.12" -__all__ = ["emd", "sinkhorn", "utils", 'datasets', 'bregman', 'lp', 'plot', +__all__ = ["emd", "emd2", "sinkhorn", "utils", 'datasets', 'bregman', 'lp', 'plot', 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim'] diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 1e55f5a..5358083 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -6,7 +6,7 @@ Solvers for the original linear program OT problem import numpy as np # import compiled emd from .emd import emd_c - +import multiprocessing def emd(a, b, M): """Solves the Earth Movers distance problem and returns the OT matrix @@ -70,9 +70,114 @@ def emd(a, b, M): b = np.asarray(b, dtype=np.float64) M = np.asarray(M, dtype=np.float64) + # if empty array given then use unifor distributions if len(a) == 0: a = np.ones((M.shape[0], ), dtype=np.float64)/M.shape[0] if len(b) == 0: b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1] return emd_c(a, b, M) + +def emd2(a, b, M,processes=None): + """Solves the Earth Movers distance problem and returns the loss + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + + s.t. \gamma 1 = a + \gamma^T 1= b + \gamma\geq 0 + where : + + - M is the metric cost matrix + - a and b are the sample weights + + Uses the algorithm proposed in [1]_ + + Parameters + ---------- + a : (ns,) ndarray, float64 + Source histogram (uniform weigth if empty list) + b : (nt,) ndarray, float64 + Target histogram (uniform weigth if empty list) + M : (ns,nt) ndarray, float64 + loss matrix + + Returns + ------- + gamma: (ns x nt) ndarray + Optimal transportation matrix for the given parameters + + + Examples + -------- + + Simple example with obvious solution. The function emd accepts lists and + perform automatic conversion to numpy arrays + >>> import ot + >>> a=[.5,.5] + >>> b=[.5,.5] + >>> M=[[0.,1.],[1.,0.]] + >>> ot.emd2(a,b,M) + 0.0 + + References + ---------- + + .. [1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. + (2011, December). Displacement interpolation using Lagrangian mass + transport. In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. + 158). ACM. + + See Also + -------- + ot.bregman.sinkhorn : Entropic regularized OT + ot.optim.cg : General regularized OT""" + + a = np.asarray(a, dtype=np.float64) + b = np.asarray(b, dtype=np.float64) + M = np.asarray(M, dtype=np.float64) + + # if empty array given then use unifor distributions + if len(a) == 0: + a = np.ones((M.shape[0], ), dtype=np.float64)/M.shape[0] + if len(b) == 0: + b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1] + + if len(b.shape)==1: + return np.sum(emd_c(a, b, M)*M) + else: + nb=b.shape[1] + ls=[(a,b[:,k],M) for k in range(nb)] + # run emd in multiprocessing + res=parmap(emd2, ls,processes) + np.array(res) +# with Pool(processes) as p: +# res=p.map(f, ls) +# return np.array(res) + + +def fun(f, q_in, q_out): + while True: + i, x = q_in.get() + if i is None: + break + q_out.put((i, f(x))) + +def parmap(f, X, nprocs): + q_in = multiprocessing.Queue(1) + q_out = multiprocessing.Queue() + + proc = [multiprocessing.Process(target=fun, args=(f, q_in, q_out)) + for _ in range(nprocs)] + for p in proc: + p.daemon = True + p.start() + + sent = [q_in.put((i, x)) for i, x in enumerate(X)] + [q_in.put((None, None)) for _ in range(nprocs)] + res = [q_out.get() for _ in range(len(sent))] + + [p.join() for p in proc] + + return [x for i, x in sorted(res)] \ No newline at end of file -- cgit v1.2.3 From 84219d9bd87acd9bbb6d1a832cf4ccaee53fed0b Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 10 Mar 2017 10:26:18 +0100 Subject: runs but not quicker --- ot/lp/__init__.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 5358083..57c5b7b 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -78,7 +78,7 @@ def emd(a, b, M): return emd_c(a, b, M) -def emd2(a, b, M,processes=None): +def emd2(a, b, M,processes=multiprocessing.cpu_count()): """Solves the Earth Movers distance problem and returns the loss .. math:: @@ -149,9 +149,11 @@ def emd2(a, b, M,processes=None): else: nb=b.shape[1] ls=[(a,b[:,k],M) for k in range(nb)] + def f(l): + return emd2(l[0],l[1],l[2]) # run emd in multiprocessing - res=parmap(emd2, ls,processes) - np.array(res) + res=parmap(f, ls,processes) + return np.array(res) # with Pool(processes) as p: # res=p.map(f, ls) # return np.array(res) @@ -164,7 +166,7 @@ def fun(f, q_in, q_out): break q_out.put((i, f(x))) -def parmap(f, X, nprocs): +def parmap(f, X, nprocs=multiprocessing.cpu_count()): q_in = multiprocessing.Queue(1) q_out = multiprocessing.Queue() -- cgit v1.2.3 From a84f2c3e23edd1fa89975bd77b08672f518d5ca4 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 14 Mar 2017 10:30:45 +0100 Subject: add emd2+ multiproc --- ot/__init__.py | 3 +- ot/lp/__init__.py | 44 +- ot/lp/emd.cpp | 3566 +++++++++++++++++++++--------------------------- ot/lp/emd.pyx | 60 + ot/utils.py | 31 +- test/test_emd_multi.py | 48 + 6 files changed, 1683 insertions(+), 2069 deletions(-) create mode 100644 test/test_emd_multi.py diff --git a/ot/__init__.py b/ot/__init__.py index ee294d8..9a253d2 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -20,5 +20,6 @@ from .utils import dist, unif, tic, toc, toq __version__ = "0.1.12" -__all__ = ["emd", "emd2", "sinkhorn", "utils", 'datasets', 'bregman', 'lp', 'plot', +__all__ = ["emd", "emd2", "sinkhorn", "utils", 'datasets', 'bregman', 'lp', + 'plot', 'tic', 'toc', 'toq', 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim'] diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 57c5b7b..5674cf6 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -5,9 +5,12 @@ Solvers for the original linear program OT problem import numpy as np # import compiled emd -from .emd import emd_c +from .emd import emd_c, emd2_c +from ..utils import parmap import multiprocessing + + def emd(a, b, M): """Solves the Earth Movers distance problem and returns the OT matrix @@ -145,41 +148,14 @@ def emd2(a, b, M,processes=multiprocessing.cpu_count()): b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1] if len(b.shape)==1: - return np.sum(emd_c(a, b, M)*M) + return emd2_c(a, b, M) else: nb=b.shape[1] - ls=[(a,b[:,k],M) for k in range(nb)] - def f(l): - return emd2(l[0],l[1],l[2]) - # run emd in multiprocessing - res=parmap(f, ls,processes) + #res=[emd2_c(a,b[:,i].copy(),M) for i in range(nb)] + def f(b): + return emd2_c(a,b,M) + res= parmap(f, [b[:,i] for i in range(nb)],processes) return np.array(res) -# with Pool(processes) as p: -# res=p.map(f, ls) -# return np.array(res) -def fun(f, q_in, q_out): - while True: - i, x = q_in.get() - if i is None: - break - q_out.put((i, f(x))) - -def parmap(f, X, nprocs=multiprocessing.cpu_count()): - q_in = multiprocessing.Queue(1) - q_out = multiprocessing.Queue() - - proc = [multiprocessing.Process(target=fun, args=(f, q_in, q_out)) - for _ in range(nprocs)] - for p in proc: - p.daemon = True - p.start() - - sent = [q_in.put((i, x)) for i, x in enumerate(X)] - [q_in.put((None, None)) for _ in range(nprocs)] - res = [q_out.get() for _ in range(len(sent))] - - [p.join() for p in proc] - - return [x for i, x in sorted(res)] \ No newline at end of file + \ No newline at end of file diff --git a/ot/lp/emd.cpp b/ot/lp/emd.cpp index 81bb450..03f5b88 100644 --- a/ot/lp/emd.cpp +++ b/ot/lp/emd.cpp @@ -1,20 +1,19 @@ -/* Generated by Cython 0.25.1 */ +/* Generated by Cython 0.23.4 */ /* BEGIN: Cython Metadata { "distutils": { "depends": [ - "/home/rflamary/.local/lib/python2.7/site-packages/numpy/core/include/numpy/arrayobject.h", - "/home/rflamary/.local/lib/python2.7/site-packages/numpy/core/include/numpy/ufuncobject.h", + "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/arrayobject.h", + "/usr/local/lib/python2.7/dist-packages/numpy/core/include/numpy/ufuncobject.h", "ot/lp/EMD.h" ], "include_dirs": [ - "/home/rflamary/.local/lib/python2.7/site-packages/numpy/core/include", + "/usr/local/lib/python2.7/dist-packages/numpy/core/include", "/home/rflamary/PYTHON/POT/ot/lp" ], "language": "c++" - }, - "module_name": "ot.lp.emd" + } } END: Cython Metadata */ @@ -25,10 +24,10 @@ END: Cython Metadata */ #elif PY_VERSION_HEX < 0x02060000 || (0x03000000 <= PY_VERSION_HEX && PY_VERSION_HEX < 0x03020000) #error Cython requires Python 2.6+ or Python 3.2+. #else -#define CYTHON_ABI "0_25_1" +#define CYTHON_ABI "0_23_4" #include #ifndef offsetof - #define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) +#define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) #endif #if !defined(WIN32) && !defined(MS_WINDOWS) #ifndef __stdcall @@ -47,11 +46,6 @@ END: Cython Metadata */ #ifndef DL_EXPORT #define DL_EXPORT(t) t #endif -#ifndef HAVE_LONG_LONG - #if PY_VERSION_HEX >= 0x03030000 || (PY_MAJOR_VERSION == 2 && PY_VERSION_HEX >= 0x02070000) - #define HAVE_LONG_LONG - #endif -#endif #ifndef PY_LONG_LONG #define PY_LONG_LONG LONG_LONG #endif @@ -59,120 +53,17 @@ END: Cython Metadata */ #define Py_HUGE_VAL HUGE_VAL #endif #ifdef PYPY_VERSION - #define CYTHON_COMPILING_IN_PYPY 1 - #define CYTHON_COMPILING_IN_PYSTON 0 - #define CYTHON_COMPILING_IN_CPYTHON 0 - #undef CYTHON_USE_TYPE_SLOTS - #define CYTHON_USE_TYPE_SLOTS 0 - #undef CYTHON_USE_ASYNC_SLOTS - #define CYTHON_USE_ASYNC_SLOTS 0 - #undef CYTHON_USE_PYLIST_INTERNALS - #define CYTHON_USE_PYLIST_INTERNALS 0 - #undef CYTHON_USE_UNICODE_INTERNALS - #define CYTHON_USE_UNICODE_INTERNALS 0 - #undef CYTHON_USE_UNICODE_WRITER - #define CYTHON_USE_UNICODE_WRITER 0 - #undef CYTHON_USE_PYLONG_INTERNALS - #define CYTHON_USE_PYLONG_INTERNALS 0 - #undef CYTHON_AVOID_BORROWED_REFS - #define CYTHON_AVOID_BORROWED_REFS 1 - #undef CYTHON_ASSUME_SAFE_MACROS - #define CYTHON_ASSUME_SAFE_MACROS 0 - #undef CYTHON_UNPACK_METHODS - #define CYTHON_UNPACK_METHODS 0 - #undef CYTHON_FAST_THREAD_STATE - #define CYTHON_FAST_THREAD_STATE 0 - #undef CYTHON_FAST_PYCALL - #define CYTHON_FAST_PYCALL 0 -#elif defined(PYSTON_VERSION) - #define CYTHON_COMPILING_IN_PYPY 0 - #define CYTHON_COMPILING_IN_PYSTON 1 - #define CYTHON_COMPILING_IN_CPYTHON 0 - #ifndef CYTHON_USE_TYPE_SLOTS - #define CYTHON_USE_TYPE_SLOTS 1 - #endif - #undef CYTHON_USE_ASYNC_SLOTS - #define CYTHON_USE_ASYNC_SLOTS 0 - #undef CYTHON_USE_PYLIST_INTERNALS - #define CYTHON_USE_PYLIST_INTERNALS 0 - #ifndef CYTHON_USE_UNICODE_INTERNALS - #define CYTHON_USE_UNICODE_INTERNALS 1 - #endif - #undef CYTHON_USE_UNICODE_WRITER - #define CYTHON_USE_UNICODE_WRITER 0 - #undef CYTHON_USE_PYLONG_INTERNALS - #define CYTHON_USE_PYLONG_INTERNALS 0 - #ifndef CYTHON_AVOID_BORROWED_REFS - #define CYTHON_AVOID_BORROWED_REFS 0 - #endif - #ifndef CYTHON_ASSUME_SAFE_MACROS - #define CYTHON_ASSUME_SAFE_MACROS 1 - #endif - #ifndef CYTHON_UNPACK_METHODS - #define CYTHON_UNPACK_METHODS 1 - #endif - #undef CYTHON_FAST_THREAD_STATE - #define CYTHON_FAST_THREAD_STATE 0 - #undef CYTHON_FAST_PYCALL - #define CYTHON_FAST_PYCALL 0 +#define CYTHON_COMPILING_IN_PYPY 1 +#define CYTHON_COMPILING_IN_CPYTHON 0 #else - #define CYTHON_COMPILING_IN_PYPY 0 - #define CYTHON_COMPILING_IN_PYSTON 0 - #define CYTHON_COMPILING_IN_CPYTHON 1 - #ifndef CYTHON_USE_TYPE_SLOTS - #define CYTHON_USE_TYPE_SLOTS 1 - #endif - #if PY_MAJOR_VERSION < 3 - #undef CYTHON_USE_ASYNC_SLOTS - #define CYTHON_USE_ASYNC_SLOTS 0 - #elif !defined(CYTHON_USE_ASYNC_SLOTS) - #define CYTHON_USE_ASYNC_SLOTS 1 - #endif - #if PY_VERSION_HEX < 0x02070000 - #undef CYTHON_USE_PYLONG_INTERNALS - #define CYTHON_USE_PYLONG_INTERNALS 0 - #elif !defined(CYTHON_USE_PYLONG_INTERNALS) - #define CYTHON_USE_PYLONG_INTERNALS 1 - #endif - #ifndef CYTHON_USE_PYLIST_INTERNALS - #define CYTHON_USE_PYLIST_INTERNALS 1 - #endif - #ifndef CYTHON_USE_UNICODE_INTERNALS - #define CYTHON_USE_UNICODE_INTERNALS 1 - #endif - #if PY_VERSION_HEX < 0x030300F0 - #undef CYTHON_USE_UNICODE_WRITER - #define CYTHON_USE_UNICODE_WRITER 0 - #elif !defined(CYTHON_USE_UNICODE_WRITER) - #define CYTHON_USE_UNICODE_WRITER 1 - #endif - #ifndef CYTHON_AVOID_BORROWED_REFS - #define CYTHON_AVOID_BORROWED_REFS 0 - #endif - #ifndef CYTHON_ASSUME_SAFE_MACROS - #define CYTHON_ASSUME_SAFE_MACROS 1 - #endif - #ifndef CYTHON_UNPACK_METHODS - #define CYTHON_UNPACK_METHODS 1 - #endif - #ifndef CYTHON_FAST_THREAD_STATE - #define CYTHON_FAST_THREAD_STATE 1 - #endif - #ifndef CYTHON_FAST_PYCALL - #define CYTHON_FAST_PYCALL 1 - #endif +#define CYTHON_COMPILING_IN_PYPY 0 +#define CYTHON_COMPILING_IN_CPYTHON 1 #endif -#if !defined(CYTHON_FAST_PYCCALL) -#define CYTHON_FAST_PYCCALL (CYTHON_FAST_PYCALL && PY_VERSION_HEX >= 0x030600B1) -#endif -#if CYTHON_USE_PYLONG_INTERNALS - #include "longintrepr.h" - #undef SHIFT - #undef BASE - #undef MASK +#if !defined(CYTHON_USE_PYLONG_INTERNALS) && CYTHON_COMPILING_IN_CPYTHON && PY_VERSION_HEX >= 0x02070000 +#define CYTHON_USE_PYLONG_INTERNALS 1 #endif #if CYTHON_COMPILING_IN_PYPY && PY_VERSION_HEX < 0x02070600 && !defined(Py_OptimizeFlag) - #define Py_OptimizeFlag 0 +#define Py_OptimizeFlag 0 #endif #define __PYX_BUILD_PY_SSIZE_T "n" #define CYTHON_FORMAT_SSIZE_T "z" @@ -199,45 +90,23 @@ END: Cython Metadata */ #ifndef Py_TPFLAGS_HAVE_FINALIZE #define Py_TPFLAGS_HAVE_FINALIZE 0 #endif -#ifndef METH_FASTCALL - #define METH_FASTCALL 0x80 - typedef PyObject *(*__Pyx_PyCFunctionFast) (PyObject *self, PyObject **args, - Py_ssize_t nargs, PyObject *kwnames); -#else - #define __Pyx_PyCFunctionFast _PyCFunctionFast -#endif -#if CYTHON_FAST_PYCCALL -#define __Pyx_PyFastCFunction_Check(func)\ - ((PyCFunction_Check(func) && METH_FASTCALL == PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST))) -#else -#define __Pyx_PyFastCFunction_Check(func) 0 -#endif #if PY_VERSION_HEX > 0x03030000 && defined(PyUnicode_KIND) #define CYTHON_PEP393_ENABLED 1 #define __Pyx_PyUnicode_READY(op) (likely(PyUnicode_IS_READY(op)) ?\ 0 : _PyUnicode_Ready((PyObject *)(op))) #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_LENGTH(u) #define __Pyx_PyUnicode_READ_CHAR(u, i) PyUnicode_READ_CHAR(u, i) - #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) PyUnicode_MAX_CHAR_VALUE(u) #define __Pyx_PyUnicode_KIND(u) PyUnicode_KIND(u) #define __Pyx_PyUnicode_DATA(u) PyUnicode_DATA(u) #define __Pyx_PyUnicode_READ(k, d, i) PyUnicode_READ(k, d, i) - #define __Pyx_PyUnicode_WRITE(k, d, i, ch) PyUnicode_WRITE(k, d, i, ch) - #define __Pyx_PyUnicode_IS_TRUE(u) (0 != (likely(PyUnicode_IS_READY(u)) ? PyUnicode_GET_LENGTH(u) : PyUnicode_GET_SIZE(u))) #else #define CYTHON_PEP393_ENABLED 0 - #define PyUnicode_1BYTE_KIND 1 - #define PyUnicode_2BYTE_KIND 2 - #define PyUnicode_4BYTE_KIND 4 #define __Pyx_PyUnicode_READY(op) (0) #define __Pyx_PyUnicode_GET_LENGTH(u) PyUnicode_GET_SIZE(u) #define __Pyx_PyUnicode_READ_CHAR(u, i) ((Py_UCS4)(PyUnicode_AS_UNICODE(u)[i])) - #define __Pyx_PyUnicode_MAX_CHAR_VALUE(u) ((sizeof(Py_UNICODE) == 2) ? 65535 : 1114111) #define __Pyx_PyUnicode_KIND(u) (sizeof(Py_UNICODE)) #define __Pyx_PyUnicode_DATA(u) ((void*)PyUnicode_AS_UNICODE(u)) #define __Pyx_PyUnicode_READ(k, d, i) ((void)(k), (Py_UCS4)(((Py_UNICODE*)d)[i])) - #define __Pyx_PyUnicode_WRITE(k, d, i, ch) (((void)(k)), ((Py_UNICODE*)d)[i] = ch) - #define __Pyx_PyUnicode_IS_TRUE(u) (0 != PyUnicode_GET_SIZE(u)) #endif #if CYTHON_COMPILING_IN_PYPY #define __Pyx_PyUnicode_Concat(a, b) PyNumber_Add(a, b) @@ -250,24 +119,6 @@ END: Cython Metadata */ #if CYTHON_COMPILING_IN_PYPY && !defined(PyUnicode_Contains) #define PyUnicode_Contains(u, s) PySequence_Contains(u, s) #endif -#if CYTHON_COMPILING_IN_PYPY && !defined(PyByteArray_Check) - #define PyByteArray_Check(obj) PyObject_TypeCheck(obj, &PyByteArray_Type) -#endif -#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Format) - #define PyObject_Format(obj, fmt) PyObject_CallMethod(obj, "__format__", "O", fmt) -#endif -#if CYTHON_COMPILING_IN_PYPY && !defined(PyObject_Malloc) - #define PyObject_Malloc(s) PyMem_Malloc(s) - #define PyObject_Free(p) PyMem_Free(p) - #define PyObject_Realloc(p) PyMem_Realloc(p) -#endif -#if CYTHON_COMPILING_IN_PYSTON - #define __Pyx_PyCode_HasFreeVars(co) PyCode_HasFreeVars(co) - #define __Pyx_PyFrame_SetLineNumber(frame, lineno) PyFrame_SetLineNumber(frame, lineno) -#else - #define __Pyx_PyCode_HasFreeVars(co) (PyCode_GetNumFree(co) > 0) - #define __Pyx_PyFrame_SetLineNumber(frame, lineno) (frame)->f_lineno = (lineno) -#endif #define __Pyx_PyString_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : __Pyx_PyString_Format(a, b)) #define __Pyx_PyUnicode_FormatSafe(a, b) ((unlikely((a) == Py_None)) ? PyNumber_Remainder(a, b) : PyUnicode_Format(a, b)) #if PY_MAJOR_VERSION >= 3 @@ -275,9 +126,6 @@ END: Cython Metadata */ #else #define __Pyx_PyString_Format(a, b) PyString_Format(a, b) #endif -#if PY_MAJOR_VERSION < 3 && !defined(PyObject_ASCII) - #define PyObject_ASCII(o) PyObject_Repr(o) -#endif #if PY_MAJOR_VERSION >= 3 #define PyBaseString_Type PyUnicode_Type #define PyStringObject PyUnicodeObject @@ -296,7 +144,6 @@ END: Cython Metadata */ #define PySet_CheckExact(obj) (Py_TYPE(obj) == &PySet_Type) #endif #define __Pyx_TypeCheck(obj, type) PyObject_TypeCheck(obj, (PyTypeObject *)type) -#define __Pyx_PyException_Check(obj) __Pyx_TypeCheck(obj, PyExc_Exception) #if PY_MAJOR_VERSION >= 3 #define PyIntObject PyLongObject #define PyInt_Type PyLong_Type @@ -335,20 +182,18 @@ END: Cython Metadata */ #else #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) #endif -#if CYTHON_USE_ASYNC_SLOTS - #if PY_VERSION_HEX >= 0x030500B1 - #define __Pyx_PyAsyncMethodsStruct PyAsyncMethods - #define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) - #else - typedef struct { - unaryfunc am_await; - unaryfunc am_aiter; - unaryfunc am_anext; - } __Pyx_PyAsyncMethodsStruct; - #define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) - #endif +#if PY_VERSION_HEX >= 0x030500B1 +#define __Pyx_PyAsyncMethodsStruct PyAsyncMethods +#define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) +#elif CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 +typedef struct { + unaryfunc am_await; + unaryfunc am_aiter; + unaryfunc am_anext; +} __Pyx_PyAsyncMethodsStruct; +#define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) #else - #define __Pyx_PyType_AsAsync(obj) NULL +#define __Pyx_PyType_AsAsync(obj) NULL #endif #ifndef CYTHON_RESTRICT #if defined(__GNUC__) @@ -380,8 +225,6 @@ class __Pyx_FakeReference { __Pyx_FakeReference(const T& ref) : ptr(const_cast(&ref)) { } T *operator->() { return ptr; } operator T&() { return *ptr; } - template bool operator ==(U other) { return *ptr == other; }; - template bool operator !=(U other) { return *ptr != other; }; private: T *ptr; }; @@ -399,18 +242,8 @@ static CYTHON_INLINE float __PYX_NAN() { return value; } #endif -#if defined(__CYGWIN__) && defined(_LDBL_EQ_DBL) -#define __Pyx_truncl trunc -#else -#define __Pyx_truncl truncl -#endif -#define __PYX_ERR(f_index, lineno, Ln_error) \ -{ \ - __pyx_filename = __pyx_f[f_index]; __pyx_lineno = lineno; __pyx_clineno = __LINE__; goto Ln_error; \ -} - #if PY_MAJOR_VERSION >= 3 #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) @@ -429,9 +262,9 @@ static CYTHON_INLINE float __PYX_NAN() { #define __PYX_HAVE__ot__lp__emd #define __PYX_HAVE_API__ot__lp__emd -#include -#include -#include +#include "string.h" +#include "stdio.h" +#include "stdlib.h" #include "numpy/arrayobject.h" #include "numpy/ufuncobject.h" #include "EMD.h" @@ -463,7 +296,7 @@ static CYTHON_INLINE float __PYX_NAN() { # define CYTHON_NCP_UNUSED CYTHON_UNUSED # endif #endif -typedef struct {PyObject **p; const char *s; const Py_ssize_t n; const char* encoding; +typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding; const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; #define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 @@ -537,21 +370,15 @@ static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) #define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) #define __Pyx_PyBool_FromLong(b) ((b) ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False)) static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); -static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x); +static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x); static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); -#if CYTHON_ASSUME_SAFE_MACROS +#if CYTHON_COMPILING_IN_CPYTHON #define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) #else #define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) #endif #define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) -#if PY_MAJOR_VERSION >= 3 -#define __Pyx_PyNumber_Int(x) (PyLong_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Long(x)) -#else -#define __Pyx_PyNumber_Int(x) (PyInt_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Int(x)) -#endif -#define __Pyx_PyNumber_Float(x) (PyFloat_CheckExact(x) ? __Pyx_NewRef(x) : PyNumber_Float(x)) #if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII static int __Pyx_sys_getdefaultencoding_not_ascii; static int __Pyx_init_sys_getdefaultencoding_params(void) { @@ -642,13 +469,11 @@ static PyObject *__pyx_d; static PyObject *__pyx_b; static PyObject *__pyx_empty_tuple; static PyObject *__pyx_empty_bytes; -static PyObject *__pyx_empty_unicode; static int __pyx_lineno; static int __pyx_clineno = 0; static const char * __pyx_cfilenm= __FILE__; static const char *__pyx_filename; -/* Header.proto */ #if !defined(CYTHON_CCOMPLEX) #if defined(__cplusplus) #define CYTHON_CCOMPLEX 1 @@ -676,7 +501,6 @@ static const char *__pyx_f[] = { "__init__.pxd", "type.pxd", }; -/* BufferFormatStructs.proto */ #define IS_UNSIGNED(type) (((type) -1) > 0) struct __Pyx_StructField_; #define __PYX_BUF_FLAGS_PACKED_STRUCT (1 << 0) @@ -713,7 +537,7 @@ typedef struct { } __Pyx_BufFmt_Context; -/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":725 +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":725 * # in Cython to enable them only on the right systems. * * ctypedef npy_int8 int8_t # <<<<<<<<<<<<<< @@ -722,7 +546,7 @@ typedef struct { */ typedef npy_int8 __pyx_t_5numpy_int8_t; -/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":726 +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":726 * * ctypedef npy_int8 int8_t * ctypedef npy_int16 int16_t # <<<<<<<<<<<<<< @@ -731,7 +555,7 @@ typedef npy_int8 __pyx_t_5numpy_int8_t; */ typedef npy_int16 __pyx_t_5numpy_int16_t; -/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":727 +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":727 * ctypedef npy_int8 int8_t * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t # <<<<<<<<<<<<<< @@ -740,7 +564,7 @@ typedef npy_int16 __pyx_t_5numpy_int16_t; */ typedef npy_int32 __pyx_t_5numpy_int32_t; -/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":728 +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":728 * ctypedef npy_int16 int16_t * ctypedef npy_int32 int32_t * ctypedef npy_int64 int64_t # <<<<<<<<<<<<<< @@ -749,7 +573,7 @@ typedef npy_int32 __pyx_t_5numpy_int32_t; */ typedef npy_int64 __pyx_t_5numpy_int64_t; -/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":732 +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":732 * #ctypedef npy_int128 int128_t * * ctypedef npy_uint8 uint8_t # <<<<<<<<<<<<<< @@ -758,7 +582,7 @@ typedef npy_int64 __pyx_t_5numpy_int64_t; */ typedef npy_uint8 __pyx_t_5numpy_uint8_t; -/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":733 +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":733 * * ctypedef npy_uint8 uint8_t * ctypedef npy_uint16 uint16_t # <<<<<<<<<<<<<< @@ -767,7 +591,7 @@ typedef npy_uint8 __pyx_t_5numpy_uint8_t; */ typedef npy_uint16 __pyx_t_5numpy_uint16_t; -/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":734 +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":734 * ctypedef npy_uint8 uint8_t * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t # <<<<<<<<<<<<<< @@ -776,7 +600,7 @@ typedef npy_uint16 __pyx_t_5numpy_uint16_t; */ typedef npy_uint32 __pyx_t_5numpy_uint32_t; -/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":735 +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":735 * ctypedef npy_uint16 uint16_t * ctypedef npy_uint32 uint32_t * ctypedef npy_uint64 uint64_t # <<<<<<<<<<<<<< @@ -785,7 +609,7 @@ typedef npy_uint32 __pyx_t_5numpy_uint32_t; */ typedef npy_uint64 __pyx_t_5numpy_uint64_t; -/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":739 +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":739 * #ctypedef npy_uint128 uint128_t * * ctypedef npy_float32 float32_t # <<<<<<<<<<<<<< @@ -794,7 +618,7 @@ typedef npy_uint64 __pyx_t_5numpy_uint64_t; */ typedef npy_float32 __pyx_t_5numpy_float32_t; -/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":740 +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":740 * * ctypedef npy_float32 float32_t * ctypedef npy_float64 float64_t # <<<<<<<<<<<<<< @@ -803,7 +627,7 @@ typedef npy_float32 __pyx_t_5numpy_float32_t; */ typedef npy_float64 __pyx_t_5numpy_float64_t; -/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":749 +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":749 * # The int types are mapped a bit surprising -- * # numpy.int corresponds to 'l' and numpy.long to 'q' * ctypedef npy_long int_t # <<<<<<<<<<<<<< @@ -812,7 +636,7 @@ typedef npy_float64 __pyx_t_5numpy_float64_t; */ typedef npy_long __pyx_t_5numpy_int_t; -/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":750 +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":750 * # numpy.int corresponds to 'l' and numpy.long to 'q' * ctypedef npy_long int_t * ctypedef npy_longlong long_t # <<<<<<<<<<<<<< @@ -821,7 +645,7 @@ typedef npy_long __pyx_t_5numpy_int_t; */ typedef npy_longlong __pyx_t_5numpy_long_t; -/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":751 +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":751 * ctypedef npy_long int_t * ctypedef npy_longlong long_t * ctypedef npy_longlong longlong_t # <<<<<<<<<<<<<< @@ -830,7 +654,7 @@ typedef npy_longlong __pyx_t_5numpy_long_t; */ typedef npy_longlong __pyx_t_5numpy_longlong_t; -/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":753 +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":753 * ctypedef npy_longlong longlong_t * * ctypedef npy_ulong uint_t # <<<<<<<<<<<<<< @@ -839,7 +663,7 @@ typedef npy_longlong __pyx_t_5numpy_longlong_t; */ typedef npy_ulong __pyx_t_5numpy_uint_t; -/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":754 +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":754 * * ctypedef npy_ulong uint_t * ctypedef npy_ulonglong ulong_t # <<<<<<<<<<<<<< @@ -848,7 +672,7 @@ typedef npy_ulong __pyx_t_5numpy_uint_t; */ typedef npy_ulonglong __pyx_t_5numpy_ulong_t; -/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":755 +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":755 * ctypedef npy_ulong uint_t * ctypedef npy_ulonglong ulong_t * ctypedef npy_ulonglong ulonglong_t # <<<<<<<<<<<<<< @@ -857,7 +681,7 @@ typedef npy_ulonglong __pyx_t_5numpy_ulong_t; */ typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t; -/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":757 +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":757 * ctypedef npy_ulonglong ulonglong_t * * ctypedef npy_intp intp_t # <<<<<<<<<<<<<< @@ -866,7 +690,7 @@ typedef npy_ulonglong __pyx_t_5numpy_ulonglong_t; */ typedef npy_intp __pyx_t_5numpy_intp_t; -/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":758 +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":758 * * ctypedef npy_intp intp_t * ctypedef npy_uintp uintp_t # <<<<<<<<<<<<<< @@ -875,7 +699,7 @@ typedef npy_intp __pyx_t_5numpy_intp_t; */ typedef npy_uintp __pyx_t_5numpy_uintp_t; -/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":760 +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":760 * ctypedef npy_uintp uintp_t * * ctypedef npy_double float_t # <<<<<<<<<<<<<< @@ -884,7 +708,7 @@ typedef npy_uintp __pyx_t_5numpy_uintp_t; */ typedef npy_double __pyx_t_5numpy_float_t; -/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":761 +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":761 * * ctypedef npy_double float_t * ctypedef npy_double double_t # <<<<<<<<<<<<<< @@ -893,7 +717,7 @@ typedef npy_double __pyx_t_5numpy_float_t; */ typedef npy_double __pyx_t_5numpy_double_t; -/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":762 +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":762 * ctypedef npy_double float_t * ctypedef npy_double double_t * ctypedef npy_longdouble longdouble_t # <<<<<<<<<<<<<< @@ -901,7 +725,6 @@ typedef npy_double __pyx_t_5numpy_double_t; * ctypedef npy_cfloat cfloat_t */ typedef npy_longdouble __pyx_t_5numpy_longdouble_t; -/* Declarations.proto */ #if CYTHON_CCOMPLEX #ifdef __cplusplus typedef ::std::complex< float > __pyx_t_float_complex; @@ -911,9 +734,7 @@ typedef npy_longdouble __pyx_t_5numpy_longdouble_t; #else typedef struct { float real, imag; } __pyx_t_float_complex; #endif -static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float); -/* Declarations.proto */ #if CYTHON_CCOMPLEX #ifdef __cplusplus typedef ::std::complex< double > __pyx_t_double_complex; @@ -923,12 +744,11 @@ static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(floa #else typedef struct { double real, imag; } __pyx_t_double_complex; #endif -static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double); /*--- Type declarations ---*/ -/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":764 +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":764 * ctypedef npy_longdouble longdouble_t * * ctypedef npy_cfloat cfloat_t # <<<<<<<<<<<<<< @@ -937,7 +757,7 @@ static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(do */ typedef npy_cfloat __pyx_t_5numpy_cfloat_t; -/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":765 +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":765 * * ctypedef npy_cfloat cfloat_t * ctypedef npy_cdouble cdouble_t # <<<<<<<<<<<<<< @@ -946,7 +766,7 @@ typedef npy_cfloat __pyx_t_5numpy_cfloat_t; */ typedef npy_cdouble __pyx_t_5numpy_cdouble_t; -/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":766 +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":766 * ctypedef npy_cfloat cfloat_t * ctypedef npy_cdouble cdouble_t * ctypedef npy_clongdouble clongdouble_t # <<<<<<<<<<<<<< @@ -955,7 +775,7 @@ typedef npy_cdouble __pyx_t_5numpy_cdouble_t; */ typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t; -/* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":768 +/* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":768 * ctypedef npy_clongdouble clongdouble_t * * ctypedef npy_cdouble complex_t # <<<<<<<<<<<<<< @@ -965,7 +785,6 @@ typedef npy_clongdouble __pyx_t_5numpy_clongdouble_t; typedef npy_cdouble __pyx_t_5numpy_complex_t; /* --- Runtime support code (head) --- */ -/* Refnanny.proto */ #ifndef CYTHON_REFNANNY #define CYTHON_REFNANNY 0 #endif @@ -1028,33 +847,7 @@ typedef npy_cdouble __pyx_t_5numpy_complex_t; #define __Pyx_CLEAR(r) do { PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);} while(0) #define __Pyx_XCLEAR(r) do { if((r) != NULL) {PyObject* tmp = ((PyObject*)(r)); r = NULL; __Pyx_DECREF(tmp);}} while(0) -/* RaiseArgTupleInvalid.proto */ -static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, - Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); - -/* RaiseDoubleKeywords.proto */ -static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); - -/* ParseKeywords.proto */ -static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\ - PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,\ - const char* function_name); - -/* ArgTypeTest.proto */ -static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, - const char *name, int exact); - -/* BufferFormatCheck.proto */ -static CYTHON_INLINE int __Pyx_GetBufferAndValidate(Py_buffer* buf, PyObject* obj, - __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack); -static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info); -static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts); -static void __Pyx_BufFmt_Init(__Pyx_BufFmt_Context* ctx, - __Pyx_BufFmt_StackElem* stack, - __Pyx_TypeInfo* type); // PROTO - -/* PyObjectGetAttrStr.proto */ -#if CYTHON_USE_TYPE_SLOTS +#if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject* attr_name) { PyTypeObject* tp = Py_TYPE(obj); if (likely(tp->tp_getattro)) @@ -1069,79 +862,48 @@ static CYTHON_INLINE PyObject* __Pyx_PyObject_GetAttrStr(PyObject* obj, PyObject #define __Pyx_PyObject_GetAttrStr(o,n) PyObject_GetAttr(o,n) #endif -/* GetBuiltinName.proto */ static PyObject *__Pyx_GetBuiltinName(PyObject *name); -/* GetModuleGlobalName.proto */ -static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name); +static void __Pyx_RaiseArgtupleInvalid(const char* func_name, int exact, + Py_ssize_t num_min, Py_ssize_t num_max, Py_ssize_t num_found); -/* PyCFunctionFastCall.proto */ -#if CYTHON_FAST_PYCCALL -static CYTHON_INLINE PyObject *__Pyx_PyCFunction_FastCall(PyObject *func, PyObject **args, Py_ssize_t nargs); -#else -#define __Pyx_PyCFunction_FastCall(func, args, nargs) (assert(0), NULL) -#endif +static void __Pyx_RaiseDoubleKeywordsError(const char* func_name, PyObject* kw_name); -/* PyFunctionFastCall.proto */ -#if CYTHON_FAST_PYCALL -#define __Pyx_PyFunction_FastCall(func, args, nargs)\ - __Pyx_PyFunction_FastCallDict((func), (args), (nargs), NULL) -#if 1 || PY_VERSION_HEX < 0x030600B1 -static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs); -#else -#define __Pyx_PyFunction_FastCallDict(func, args, nargs, kwargs) _PyFunction_FastCallDict(func, args, nargs, kwargs) -#endif -#endif +static int __Pyx_ParseOptionalKeywords(PyObject *kwds, PyObject **argnames[],\ + PyObject *kwds2, PyObject *values[], Py_ssize_t num_pos_args,\ + const char* function_name); + +static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, int none_allowed, + const char *name, int exact); + +static CYTHON_INLINE int __Pyx_GetBufferAndValidate(Py_buffer* buf, PyObject* obj, + __Pyx_TypeInfo* dtype, int flags, int nd, int cast, __Pyx_BufFmt_StackElem* stack); +static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info); + +static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name); -/* PyObjectCall.proto */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw); #else #define __Pyx_PyObject_Call(func, arg, kw) PyObject_Call(func, arg, kw) #endif -/* PyObjectCallMethO.proto */ #if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg); #endif -/* PyObjectCallOneArg.proto */ static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg); -/* ExtTypeTest.proto */ static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type); -/* BufferFallbackError.proto */ static void __Pyx_RaiseBufferFallbackError(void); -/* PyThreadStateGet.proto */ -#if CYTHON_FAST_THREAD_STATE -#define __Pyx_PyThreadState_declare PyThreadState *__pyx_tstate; -#define __Pyx_PyThreadState_assign __pyx_tstate = PyThreadState_GET(); -#else -#define __Pyx_PyThreadState_declare -#define __Pyx_PyThreadState_assign -#endif - -/* PyErrFetchRestore.proto */ -#if CYTHON_FAST_THREAD_STATE -#define __Pyx_ErrRestoreWithState(type, value, tb) __Pyx_ErrRestoreInState(PyThreadState_GET(), type, value, tb) -#define __Pyx_ErrFetchWithState(type, value, tb) __Pyx_ErrFetchInState(PyThreadState_GET(), type, value, tb) -#define __Pyx_ErrRestore(type, value, tb) __Pyx_ErrRestoreInState(__pyx_tstate, type, value, tb) -#define __Pyx_ErrFetch(type, value, tb) __Pyx_ErrFetchInState(__pyx_tstate, type, value, tb) -static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); -static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); -#else -#define __Pyx_ErrRestoreWithState(type, value, tb) PyErr_Restore(type, value, tb) -#define __Pyx_ErrFetchWithState(type, value, tb) PyErr_Fetch(type, value, tb) -#define __Pyx_ErrRestore(type, value, tb) PyErr_Restore(type, value, tb) -#define __Pyx_ErrFetch(type, value, tb) PyErr_Fetch(type, value, tb) -#endif +static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb); +static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb); -/* RaiseException.proto */ +#define __Pyx_BufPtrCContig2d(type, buf, i0, s0, i1, s1) ((type)((char*)buf + i0 * s0) + i1) static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause); -/* DictGetItem.proto */ #if PY_MAJOR_VERSION >= 3 && !CYTHON_COMPILING_IN_PYPY static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) { PyObject *value; @@ -1162,49 +924,17 @@ static PyObject *__Pyx_PyDict_GetItem(PyObject *d, PyObject* key) { #define __Pyx_PyDict_GetItem(d, key) PyObject_GetItem(d, key) #endif -/* RaiseTooManyValuesToUnpack.proto */ static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected); -/* RaiseNeedMoreValuesToUnpack.proto */ static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index); -/* RaiseNoneIterError.proto */ static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void); -/* SaveResetException.proto */ -#if CYTHON_FAST_THREAD_STATE -#define __Pyx_ExceptionSave(type, value, tb) __Pyx__ExceptionSave(__pyx_tstate, type, value, tb) -static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); -#define __Pyx_ExceptionReset(type, value, tb) __Pyx__ExceptionReset(__pyx_tstate, type, value, tb) -static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb); -#else -#define __Pyx_ExceptionSave(type, value, tb) PyErr_GetExcInfo(type, value, tb) -#define __Pyx_ExceptionReset(type, value, tb) PyErr_SetExcInfo(type, value, tb) -#endif - -/* PyErrExceptionMatches.proto */ -#if CYTHON_FAST_THREAD_STATE -#define __Pyx_PyErr_ExceptionMatches(err) __Pyx_PyErr_ExceptionMatchesInState(__pyx_tstate, err) -static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err); -#else -#define __Pyx_PyErr_ExceptionMatches(err) PyErr_ExceptionMatches(err) -#endif - -/* GetException.proto */ -#if CYTHON_FAST_THREAD_STATE -#define __Pyx_GetException(type, value, tb) __Pyx__GetException(__pyx_tstate, type, value, tb) -static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb); -#else -static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb); -#endif - -/* Import.proto */ static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level); -/* CodeObjectCache.proto */ typedef struct { - PyCodeObject* code_object; int code_line; + PyCodeObject* code_object; } __Pyx_CodeObjectCacheEntry; struct __Pyx_CodeObjectCache { int count; @@ -1216,11 +946,9 @@ static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int co static PyCodeObject *__pyx_find_code_object(int code_line); static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object); -/* AddTraceback.proto */ static void __Pyx_AddTraceback(const char *funcname, int c_line, int py_line, const char *filename); -/* BufferStructDeclare.proto */ typedef struct { Py_ssize_t shape, strides, suboffsets; } __Pyx_Buf_DimInfo; @@ -1243,14 +971,13 @@ typedef struct { #endif -/* None.proto */ static Py_ssize_t __Pyx_zeros[] = {0, 0, 0, 0, 0, 0, 0, 0}; static Py_ssize_t __Pyx_minusones[] = {-1, -1, -1, -1, -1, -1, -1, -1}; -/* CIntToPy.proto */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); -/* RealImag.proto */ +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); + #if CYTHON_CCOMPLEX #ifdef __cplusplus #define __Pyx_CREAL(z) ((z).real()) @@ -1263,8 +990,7 @@ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); #define __Pyx_CREAL(z) ((z).real) #define __Pyx_CIMAG(z) ((z).imag) #endif -#if defined(__cplusplus) && CYTHON_CCOMPLEX\ - && (defined(_WIN32) || defined(__clang__) || (defined(__GNUC__) && (__GNUC__ >= 5 || __GNUC__ == 4 && __GNUC_MINOR__ >= 4 )) || __cplusplus >= 201103) +#if (defined(_WIN32) || defined(__clang__)) && defined(__cplusplus) && CYTHON_CCOMPLEX #define __Pyx_SET_CREAL(z,x) ((z).real(x)) #define __Pyx_SET_CIMAG(z,y) ((z).imag(y)) #else @@ -1272,98 +998,92 @@ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value); #define __Pyx_SET_CIMAG(z,y) __Pyx_CIMAG(z) = (y) #endif -/* Arithmetic.proto */ +static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float, float); + #if CYTHON_CCOMPLEX - #define __Pyx_c_eq_float(a, b) ((a)==(b)) - #define __Pyx_c_sum_float(a, b) ((a)+(b)) - #define __Pyx_c_diff_float(a, b) ((a)-(b)) - #define __Pyx_c_prod_float(a, b) ((a)*(b)) - #define __Pyx_c_quot_float(a, b) ((a)/(b)) - #define __Pyx_c_neg_float(a) (-(a)) + #define __Pyx_c_eqf(a, b) ((a)==(b)) + #define __Pyx_c_sumf(a, b) ((a)+(b)) + #define __Pyx_c_difff(a, b) ((a)-(b)) + #define __Pyx_c_prodf(a, b) ((a)*(b)) + #define __Pyx_c_quotf(a, b) ((a)/(b)) + #define __Pyx_c_negf(a) (-(a)) #ifdef __cplusplus - #define __Pyx_c_is_zero_float(z) ((z)==(float)0) - #define __Pyx_c_conj_float(z) (::std::conj(z)) + #define __Pyx_c_is_zerof(z) ((z)==(float)0) + #define __Pyx_c_conjf(z) (::std::conj(z)) #if 1 - #define __Pyx_c_abs_float(z) (::std::abs(z)) - #define __Pyx_c_pow_float(a, b) (::std::pow(a, b)) + #define __Pyx_c_absf(z) (::std::abs(z)) + #define __Pyx_c_powf(a, b) (::std::pow(a, b)) #endif #else - #define __Pyx_c_is_zero_float(z) ((z)==0) - #define __Pyx_c_conj_float(z) (conjf(z)) + #define __Pyx_c_is_zerof(z) ((z)==0) + #define __Pyx_c_conjf(z) (conjf(z)) #if 1 - #define __Pyx_c_abs_float(z) (cabsf(z)) - #define __Pyx_c_pow_float(a, b) (cpowf(a, b)) + #define __Pyx_c_absf(z) (cabsf(z)) + #define __Pyx_c_powf(a, b) (cpowf(a, b)) #endif #endif #else - static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex, __pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex); - static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex); + static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex); + static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex); #if 1 - static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex); - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex, __pyx_t_float_complex); + static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex); + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex, __pyx_t_float_complex); #endif #endif -/* Arithmetic.proto */ +static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double, double); + #if CYTHON_CCOMPLEX - #define __Pyx_c_eq_double(a, b) ((a)==(b)) - #define __Pyx_c_sum_double(a, b) ((a)+(b)) - #define __Pyx_c_diff_double(a, b) ((a)-(b)) - #define __Pyx_c_prod_double(a, b) ((a)*(b)) - #define __Pyx_c_quot_double(a, b) ((a)/(b)) - #define __Pyx_c_neg_double(a) (-(a)) + #define __Pyx_c_eq(a, b) ((a)==(b)) + #define __Pyx_c_sum(a, b) ((a)+(b)) + #define __Pyx_c_diff(a, b) ((a)-(b)) + #define __Pyx_c_prod(a, b) ((a)*(b)) + #define __Pyx_c_quot(a, b) ((a)/(b)) + #define __Pyx_c_neg(a) (-(a)) #ifdef __cplusplus - #define __Pyx_c_is_zero_double(z) ((z)==(double)0) - #define __Pyx_c_conj_double(z) (::std::conj(z)) + #define __Pyx_c_is_zero(z) ((z)==(double)0) + #define __Pyx_c_conj(z) (::std::conj(z)) #if 1 - #define __Pyx_c_abs_double(z) (::std::abs(z)) - #define __Pyx_c_pow_double(a, b) (::std::pow(a, b)) + #define __Pyx_c_abs(z) (::std::abs(z)) + #define __Pyx_c_pow(a, b) (::std::pow(a, b)) #endif #else - #define __Pyx_c_is_zero_double(z) ((z)==0) - #define __Pyx_c_conj_double(z) (conj(z)) + #define __Pyx_c_is_zero(z) ((z)==0) + #define __Pyx_c_conj(z) (conj(z)) #if 1 - #define __Pyx_c_abs_double(z) (cabs(z)) - #define __Pyx_c_pow_double(a, b) (cpow(a, b)) + #define __Pyx_c_abs(z) (cabs(z)) + #define __Pyx_c_pow(a, b) (cpow(a, b)) #endif #endif #else - static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex, __pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex); - static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex); + static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex); + static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex); #if 1 - static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex); - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex, __pyx_t_double_complex); + static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex); + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex, __pyx_t_double_complex); #endif #endif -/* CIntToPy.proto */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value); -/* CIntFromPy.proto */ -static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *); - -/* CIntToPy.proto */ static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value); -/* CIntFromPy.proto */ static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *); -/* CheckBinaryVersion.proto */ static int __Pyx_check_binary_version(void); -/* PyIdentifierFromString.proto */ #if !defined(__Pyx_PyIdentifier_FromString) #if PY_MAJOR_VERSION < 3 #define __Pyx_PyIdentifier_FromString(s) PyString_FromString(s) @@ -1372,13 +1092,10 @@ static int __Pyx_check_binary_version(void); #endif #endif -/* ModuleImport.proto */ static PyObject *__Pyx_ImportModule(const char *name); -/* TypeImport.proto */ static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict); -/* InitStrings.proto */ static int __Pyx_InitStrings(__Pyx_StringTabEntry *t); @@ -1419,44 +1136,57 @@ static __Pyx_TypeInfo __Pyx_TypeInfo_double = { "double", NULL, sizeof(double), int __pyx_module_is_main_ot__lp__emd = 0; /* Implementation of 'ot.lp.emd' */ -static PyObject *__pyx_builtin_ValueError; static PyObject *__pyx_builtin_range; +static PyObject *__pyx_builtin_ValueError; static PyObject *__pyx_builtin_RuntimeError; -static PyObject *__pyx_builtin_ImportError; -static const char __pyx_k_G[] = "G"; -static const char __pyx_k_M[] = "M"; -static const char __pyx_k_a[] = "a"; -static const char __pyx_k_b[] = "b"; -static const char __pyx_k_n1[] = "n1"; -static const char __pyx_k_n2[] = "n2"; -static const char __pyx_k_np[] = "np"; -static const char __pyx_k_cost[] = "cost"; -static const char __pyx_k_main[] = "__main__"; -static const char __pyx_k_ones[] = "ones"; -static const char __pyx_k_test[] = "__test__"; -static const char __pyx_k_emd_c[] = "emd_c"; -static const char __pyx_k_numpy[] = "numpy"; -static const char __pyx_k_range[] = "range"; -static const char __pyx_k_zeros[] = "zeros"; -static const char __pyx_k_import[] = "__import__"; -static const char __pyx_k_ot_lp_emd[] = "ot.lp.emd"; -static const char __pyx_k_ValueError[] = "ValueError"; -static const char __pyx_k_ImportError[] = "ImportError"; -static const char __pyx_k_RuntimeError[] = "RuntimeError"; -static const char __pyx_k_ndarray_is_not_C_contiguous[] = "ndarray is not C contiguous"; -static const char __pyx_k_Created_on_Thu_Sep_11_08_42_08[] = "\nCreated on Thu Sep 11 08:42:08 2014\n\n@author: rflamary\n"; -static const char __pyx_k_home_rflamary_PYTHON_POT_ot_lp[] = "/home/rflamary/PYTHON/POT/ot/lp/emd.pyx"; -static const char __pyx_k_numpy_core_multiarray_failed_to[] = "numpy.core.multiarray failed to import"; -static const char __pyx_k_unknown_dtype_code_in_numpy_pxd[] = "unknown dtype code in numpy.pxd (%d)"; -static const char __pyx_k_Format_string_allocated_too_shor[] = "Format string allocated too short, see comment in numpy.pxd"; -static const char __pyx_k_Non_native_byte_order_not_suppor[] = "Non-native byte order not supported"; -static const char __pyx_k_ndarray_is_not_Fortran_contiguou[] = "ndarray is not Fortran contiguous"; -static const char __pyx_k_numpy_core_umath_failed_to_impor[] = "numpy.core.umath failed to import"; -static const char __pyx_k_Format_string_allocated_too_shor_2[] = "Format string allocated too short."; +static char __pyx_k_B[] = "B"; +static char __pyx_k_G[] = "G"; +static char __pyx_k_H[] = "H"; +static char __pyx_k_I[] = "I"; +static char __pyx_k_L[] = "L"; +static char __pyx_k_M[] = "M"; +static char __pyx_k_O[] = "O"; +static char __pyx_k_Q[] = "Q"; +static char __pyx_k_a[] = "a"; +static char __pyx_k_b[] = "b"; +static char __pyx_k_d[] = "d"; +static char __pyx_k_f[] = "f"; +static char __pyx_k_g[] = "g"; +static char __pyx_k_h[] = "h"; +static char __pyx_k_i[] = "i"; +static char __pyx_k_j[] = "j"; +static char __pyx_k_l[] = "l"; +static char __pyx_k_q[] = "q"; +static char __pyx_k_Zd[] = "Zd"; +static char __pyx_k_Zf[] = "Zf"; +static char __pyx_k_Zg[] = "Zg"; +static char __pyx_k_n1[] = "n1"; +static char __pyx_k_n2[] = "n2"; +static char __pyx_k_np[] = "np"; +static char __pyx_k_cost[] = "cost"; +static char __pyx_k_main[] = "__main__"; +static char __pyx_k_ones[] = "ones"; +static char __pyx_k_test[] = "__test__"; +static char __pyx_k_emd_c[] = "emd_c"; +static char __pyx_k_numpy[] = "numpy"; +static char __pyx_k_range[] = "range"; +static char __pyx_k_zeros[] = "zeros"; +static char __pyx_k_emd2_c[] = "emd2_c"; +static char __pyx_k_import[] = "__import__"; +static char __pyx_k_ot_lp_emd[] = "ot.lp.emd"; +static char __pyx_k_ValueError[] = "ValueError"; +static char __pyx_k_RuntimeError[] = "RuntimeError"; +static char __pyx_k_ndarray_is_not_C_contiguous[] = "ndarray is not C contiguous"; +static char __pyx_k_Created_on_Thu_Sep_11_08_42_08[] = "\nCreated on Thu Sep 11 08:42:08 2014\n\n@author: rflamary\n"; +static char __pyx_k_home_rflamary_PYTHON_POT_ot_lp[] = "/home/rflamary/PYTHON/POT/ot/lp/emd.pyx"; +static char __pyx_k_unknown_dtype_code_in_numpy_pxd[] = "unknown dtype code in numpy.pxd (%d)"; +static char __pyx_k_Format_string_allocated_too_shor[] = "Format string allocated too short, see comment in numpy.pxd"; +static char __pyx_k_Non_native_byte_order_not_suppor[] = "Non-native byte order not supported"; +static char __pyx_k_ndarray_is_not_Fortran_contiguou[] = "ndarray is not Fortran contiguous"; +static char __pyx_k_Format_string_allocated_too_shor_2[] = "Format string allocated too short."; static PyObject *__pyx_kp_u_Format_string_allocated_too_shor; static PyObject *__pyx_kp_u_Format_string_allocated_too_shor_2; static PyObject *__pyx_n_s_G; -static PyObject *__pyx_n_s_ImportError; static PyObject *__pyx_n_s_M; static PyObject *__pyx_kp_u_Non_native_byte_order_not_suppor; static PyObject *__pyx_n_s_RuntimeError; @@ -1464,9 +1194,12 @@ static PyObject *__pyx_n_s_ValueError; static PyObject *__pyx_n_s_a; static PyObject *__pyx_n_s_b; static PyObject *__pyx_n_s_cost; +static PyObject *__pyx_n_s_emd2_c; static PyObject *__pyx_n_s_emd_c; static PyObject *__pyx_kp_s_home_rflamary_PYTHON_POT_ot_lp; +static PyObject *__pyx_n_s_i; static PyObject *__pyx_n_s_import; +static PyObject *__pyx_n_s_j; static PyObject *__pyx_n_s_main; static PyObject *__pyx_n_s_n1; static PyObject *__pyx_n_s_n2; @@ -1474,8 +1207,6 @@ static PyObject *__pyx_kp_u_ndarray_is_not_C_contiguous; static PyObject *__pyx_kp_u_ndarray_is_not_Fortran_contiguou; static PyObject *__pyx_n_s_np; static PyObject *__pyx_n_s_numpy; -static PyObject *__pyx_kp_s_numpy_core_multiarray_failed_to; -static PyObject *__pyx_kp_s_numpy_core_umath_failed_to_impor; static PyObject *__pyx_n_s_ones; static PyObject *__pyx_n_s_ot_lp_emd; static PyObject *__pyx_n_s_range; @@ -1483,6 +1214,7 @@ static PyObject *__pyx_n_s_test; static PyObject *__pyx_kp_u_unknown_dtype_code_in_numpy_pxd; static PyObject *__pyx_n_s_zeros; static PyObject *__pyx_pf_2ot_2lp_3emd_emd_c(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_a, PyArrayObject *__pyx_v_b, PyArrayObject *__pyx_v_M); /* proto */ +static PyObject *__pyx_pf_2ot_2lp_3emd_2emd2_c(CYTHON_UNUSED PyObject *__pyx_self, PyArrayObject *__pyx_v_a, PyArrayObject *__pyx_v_b, PyArrayObject *__pyx_v_M); /* proto */ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info, int __pyx_v_flags); /* proto */ static void __pyx_pf_5numpy_7ndarray_2__releasebuffer__(PyArrayObject *__pyx_v_self, Py_buffer *__pyx_v_info); /* proto */ static PyObject *__pyx_tuple_; @@ -1492,10 +1224,9 @@ static PyObject *__pyx_tuple__4; static PyObject *__pyx_tuple__5; static PyObject *__pyx_tuple__6; static PyObject *__pyx_tuple__7; -static PyObject *__pyx_tuple__8; static PyObject *__pyx_tuple__9; -static PyObject *__pyx_tuple__10; -static PyObject *__pyx_codeobj__11; +static PyObject *__pyx_codeobj__8; +static PyObject *__pyx_codeobj__10; /* "ot/lp/emd.pyx":21 * @cython.boundscheck(False) @@ -1513,6 +1244,9 @@ static PyObject *__pyx_pw_2ot_2lp_3emd_1emd_c(PyObject *__pyx_self, PyObject *__ PyArrayObject *__pyx_v_a = 0; PyArrayObject *__pyx_v_b = 0; PyArrayObject *__pyx_v_M = 0; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; PyObject *__pyx_r = 0; __Pyx_RefNannyDeclarations __Pyx_RefNannySetupContext("emd_c (wrapper)", 0); @@ -1537,16 +1271,16 @@ static PyObject *__pyx_pw_2ot_2lp_3emd_1emd_c(PyObject *__pyx_self, PyObject *__ case 1: if (likely((values[1] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_b)) != 0)) kw_args--; else { - __Pyx_RaiseArgtupleInvalid("emd_c", 1, 3, 3, 1); __PYX_ERR(0, 21, __pyx_L3_error) + __Pyx_RaiseArgtupleInvalid("emd_c", 1, 3, 3, 1); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L3_error;} } case 2: if (likely((values[2] = PyDict_GetItem(__pyx_kwds, __pyx_n_s_M)) != 0)) kw_args--; else { - __Pyx_RaiseArgtupleInvalid("emd_c", 1, 3, 3, 2); __PYX_ERR(0, 21, __pyx_L3_error) + __Pyx_RaiseArgtupleInvalid("emd_c", 1, 3, 3, 2); {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L3_error;} } } if (unlikely(kw_args > 0)) { - if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "emd_c") < 0)) __PYX_ERR(0, 21, __pyx_L3_error) + if (unlikely(__Pyx_ParseOptionalKeywords(__pyx_kwds, __pyx_pyargnames, 0, values, pos_args, "emd_c") < 0)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L3_error;} } } else if (PyTuple_GET_SIZE(__pyx_args) != 3) { goto __pyx_L5_argtuple_error; 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__pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_b), __pyx_ptype_5numpy_ndarray, 1, "b", 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + if (unlikely(!__Pyx_ArgTypeTest(((PyObject *)__pyx_v_M), __pyx_ptype_5numpy_ndarray, 1, "M", 0))) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __pyx_r = __pyx_pf_2ot_2lp_3emd_emd_c(__pyx_self, __pyx_v_a, __pyx_v_b, __pyx_v_M); /* function exit code */ @@ -1610,6 +1344,9 @@ static PyObject *__pyx_pf_2ot_2lp_3emd_emd_c(CYTHON_UNUSED PyObject *__pyx_self, PyObject *__pyx_t_12 = NULL; PyObject *__pyx_t_13 = NULL; PyArrayObject *__pyx_t_14 = NULL; + int __pyx_lineno = 0; + const char *__pyx_filename = NULL; + int __pyx_clineno = 0; __Pyx_RefNannySetupContext("emd_c", 0); __Pyx_INCREF((PyObject *)__pyx_v_a); __Pyx_INCREF((PyObject *)__pyx_v_b); @@ -1631,17 +1368,17 @@ static PyObject *__pyx_pf_2ot_2lp_3emd_emd_c(CYTHON_UNUSED PyObject *__pyx_self, __pyx_pybuffernd_M.rcbuffer = &__pyx_pybuffer_M; 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- /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":216 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":216 * copy_shape = 0 * * if ((flags & pybuf.PyBUF_C_CONTIGUOUS == pybuf.PyBUF_C_CONTIGUOUS) # <<<<<<<<<<<<<< @@ -2184,7 +2411,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P goto __pyx_L6_bool_binop_done; } - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":217 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":217 * * if ((flags & pybuf.PyBUF_C_CONTIGUOUS == pybuf.PyBUF_C_CONTIGUOUS) * and not PyArray_CHKFLAGS(self, NPY_C_CONTIGUOUS)): # <<<<<<<<<<<<<< @@ -2195,7 +2422,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P __pyx_t_1 = __pyx_t_2; __pyx_L6_bool_binop_done:; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":216 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":216 * copy_shape = 0 * * if ((flags & pybuf.PyBUF_C_CONTIGUOUS == pybuf.PyBUF_C_CONTIGUOUS) # <<<<<<<<<<<<<< @@ -2204,20 +2431,20 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ if (__pyx_t_1) { - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":218 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":218 * if ((flags & pybuf.PyBUF_C_CONTIGUOUS == pybuf.PyBUF_C_CONTIGUOUS) * and not PyArray_CHKFLAGS(self, NPY_C_CONTIGUOUS)): * raise ValueError(u"ndarray is not C contiguous") # <<<<<<<<<<<<<< * * if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS) */ - __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple_, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 218, __pyx_L1_error) + __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple_, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 218; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - __PYX_ERR(1, 218, __pyx_L1_error) + {__pyx_filename = __pyx_f[1]; __pyx_lineno = 218; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":216 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":216 * copy_shape = 0 * * if ((flags & pybuf.PyBUF_C_CONTIGUOUS == pybuf.PyBUF_C_CONTIGUOUS) # <<<<<<<<<<<<<< @@ -2226,7 +2453,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ } - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":220 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":220 * raise ValueError(u"ndarray is not C contiguous") * * if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS) # <<<<<<<<<<<<<< @@ -2240,7 +2467,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P goto __pyx_L9_bool_binop_done; } - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":221 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":221 * * if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS) * and not PyArray_CHKFLAGS(self, NPY_F_CONTIGUOUS)): # <<<<<<<<<<<<<< @@ -2251,7 +2478,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P __pyx_t_1 = __pyx_t_2; __pyx_L9_bool_binop_done:; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":220 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":220 * raise ValueError(u"ndarray is not C contiguous") * * if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS) # <<<<<<<<<<<<<< @@ -2260,20 +2487,20 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ if (__pyx_t_1) { - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":222 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":222 * if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS) * and not PyArray_CHKFLAGS(self, NPY_F_CONTIGUOUS)): * raise ValueError(u"ndarray is not Fortran contiguous") # <<<<<<<<<<<<<< * * info.buf = PyArray_DATA(self) */ - __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__2, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 222, __pyx_L1_error) + __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__2, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 222; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - __PYX_ERR(1, 222, __pyx_L1_error) + {__pyx_filename = __pyx_f[1]; __pyx_lineno = 222; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":220 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":220 * raise ValueError(u"ndarray is not C contiguous") * * if ((flags & pybuf.PyBUF_F_CONTIGUOUS == pybuf.PyBUF_F_CONTIGUOUS) # <<<<<<<<<<<<<< @@ -2282,7 +2509,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ } - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":224 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":224 * raise ValueError(u"ndarray is not Fortran contiguous") * * info.buf = PyArray_DATA(self) # <<<<<<<<<<<<<< @@ -2291,7 +2518,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ __pyx_v_info->buf = PyArray_DATA(__pyx_v_self); - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":225 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":225 * * info.buf = PyArray_DATA(self) * info.ndim = ndim # <<<<<<<<<<<<<< @@ -2300,7 +2527,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ __pyx_v_info->ndim = __pyx_v_ndim; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":226 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":226 * info.buf = PyArray_DATA(self) * info.ndim = ndim * if copy_shape: # <<<<<<<<<<<<<< @@ -2310,7 +2537,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P __pyx_t_1 = (__pyx_v_copy_shape != 0); if (__pyx_t_1) { - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":229 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":229 * # Allocate new buffer for strides and shape info. * # This is allocated as one block, strides first. * info.strides = stdlib.malloc(sizeof(Py_ssize_t) * ndim * 2) # <<<<<<<<<<<<<< @@ -2319,7 +2546,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ __pyx_v_info->strides = ((Py_ssize_t *)malloc((((sizeof(Py_ssize_t)) * ((size_t)__pyx_v_ndim)) * 2))); - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":230 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":230 * # This is allocated as one block, strides first. * info.strides = stdlib.malloc(sizeof(Py_ssize_t) * ndim * 2) * info.shape = info.strides + ndim # <<<<<<<<<<<<<< @@ -2328,7 +2555,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ __pyx_v_info->shape = (__pyx_v_info->strides + __pyx_v_ndim); - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":231 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":231 * info.strides = stdlib.malloc(sizeof(Py_ssize_t) * ndim * 2) * info.shape = info.strides + ndim * for i in range(ndim): # <<<<<<<<<<<<<< @@ -2339,7 +2566,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P for (__pyx_t_5 = 0; __pyx_t_5 < __pyx_t_4; __pyx_t_5+=1) { __pyx_v_i = __pyx_t_5; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":232 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":232 * info.shape = info.strides + ndim * for i in range(ndim): * info.strides[i] = PyArray_STRIDES(self)[i] # <<<<<<<<<<<<<< @@ -2348,7 +2575,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ (__pyx_v_info->strides[__pyx_v_i]) = (PyArray_STRIDES(__pyx_v_self)[__pyx_v_i]); - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":233 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":233 * for i in range(ndim): * info.strides[i] = PyArray_STRIDES(self)[i] * info.shape[i] = PyArray_DIMS(self)[i] # <<<<<<<<<<<<<< @@ -2358,7 +2585,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P (__pyx_v_info->shape[__pyx_v_i]) = (PyArray_DIMS(__pyx_v_self)[__pyx_v_i]); } - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":226 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":226 * info.buf = PyArray_DATA(self) * info.ndim = ndim * if copy_shape: # <<<<<<<<<<<<<< @@ -2368,7 +2595,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P goto __pyx_L11; } - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":235 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":235 * info.shape[i] = PyArray_DIMS(self)[i] * else: * info.strides = PyArray_STRIDES(self) # <<<<<<<<<<<<<< @@ -2378,7 +2605,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P /*else*/ { __pyx_v_info->strides = ((Py_ssize_t *)PyArray_STRIDES(__pyx_v_self)); - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":236 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":236 * else: * info.strides = PyArray_STRIDES(self) * info.shape = PyArray_DIMS(self) # <<<<<<<<<<<<<< @@ -2389,7 +2616,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P } __pyx_L11:; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":237 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":237 * info.strides = PyArray_STRIDES(self) * info.shape = PyArray_DIMS(self) * info.suboffsets = NULL # <<<<<<<<<<<<<< @@ -2398,7 +2625,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ __pyx_v_info->suboffsets = NULL; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":238 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":238 * info.shape = PyArray_DIMS(self) * info.suboffsets = NULL * info.itemsize = PyArray_ITEMSIZE(self) # <<<<<<<<<<<<<< @@ -2407,7 +2634,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ __pyx_v_info->itemsize = PyArray_ITEMSIZE(__pyx_v_self); - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":239 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":239 * info.suboffsets = NULL * info.itemsize = PyArray_ITEMSIZE(self) * info.readonly = not PyArray_ISWRITEABLE(self) # <<<<<<<<<<<<<< @@ -2416,7 +2643,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ __pyx_v_info->readonly = (!(PyArray_ISWRITEABLE(__pyx_v_self) != 0)); - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":242 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":242 * * cdef int t * cdef char* f = NULL # <<<<<<<<<<<<<< @@ -2425,7 +2652,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ __pyx_v_f = NULL; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":243 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":243 * cdef int t * cdef char* f = NULL * cdef dtype descr = self.descr # <<<<<<<<<<<<<< @@ -2437,7 +2664,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P __pyx_v_descr = ((PyArray_Descr *)__pyx_t_3); __pyx_t_3 = 0; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":246 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":246 * cdef int offset * * cdef bint hasfields = PyDataType_HASFIELDS(descr) # <<<<<<<<<<<<<< @@ -2446,7 +2673,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ __pyx_v_hasfields = PyDataType_HASFIELDS(__pyx_v_descr); - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":248 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":248 * cdef bint hasfields = PyDataType_HASFIELDS(descr) * * if not hasfields and not copy_shape: # <<<<<<<<<<<<<< @@ -2464,7 +2691,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P __pyx_L15_bool_binop_done:; if (__pyx_t_1) { - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":250 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":250 * if not hasfields and not copy_shape: * # do not call releasebuffer * info.obj = None # <<<<<<<<<<<<<< @@ -2477,7 +2704,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P __Pyx_DECREF(__pyx_v_info->obj); __pyx_v_info->obj = Py_None; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":248 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":248 * cdef bint hasfields = PyDataType_HASFIELDS(descr) * * if not hasfields and not copy_shape: # <<<<<<<<<<<<<< @@ -2487,7 +2714,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P goto __pyx_L14; } - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":253 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":253 * else: * # need to call releasebuffer * info.obj = self # <<<<<<<<<<<<<< @@ -2503,7 +2730,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P } __pyx_L14:; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":255 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":255 * info.obj = self * * if not hasfields: # <<<<<<<<<<<<<< @@ -2513,7 +2740,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P __pyx_t_1 = ((!(__pyx_v_hasfields != 0)) != 0); if (__pyx_t_1) { - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":256 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":256 * * if not hasfields: * t = descr.type_num # <<<<<<<<<<<<<< @@ -2523,7 +2750,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P __pyx_t_4 = __pyx_v_descr->type_num; __pyx_v_t = __pyx_t_4; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":257 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":257 * if not hasfields: * t = descr.type_num * if ((descr.byteorder == c'>' and little_endian) or # <<<<<<<<<<<<<< @@ -2543,7 +2770,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P } __pyx_L20_next_or:; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":258 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":258 * t = descr.type_num * if ((descr.byteorder == c'>' and little_endian) or * (descr.byteorder == c'<' and not little_endian)): # <<<<<<<<<<<<<< @@ -2560,7 +2787,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P __pyx_t_1 = __pyx_t_2; __pyx_L19_bool_binop_done:; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":257 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":257 * if not hasfields: * t = descr.type_num * if ((descr.byteorder == c'>' and little_endian) or # <<<<<<<<<<<<<< @@ -2569,20 +2796,20 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ if (__pyx_t_1) { - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":259 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":259 * if ((descr.byteorder == c'>' and little_endian) or * (descr.byteorder == c'<' and not little_endian)): * raise ValueError(u"Non-native byte order not supported") # <<<<<<<<<<<<<< * if t == NPY_BYTE: f = "b" * elif t == NPY_UBYTE: f = "B" */ - __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__3, NULL); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 259, __pyx_L1_error) + __pyx_t_3 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_tuple__3, NULL); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 259; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); __Pyx_Raise(__pyx_t_3, 0, 0, 0); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - __PYX_ERR(1, 259, __pyx_L1_error) + {__pyx_filename = __pyx_f[1]; __pyx_lineno = 259; __pyx_clineno = __LINE__; goto __pyx_L1_error;} - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":257 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":257 * if not hasfields: * t = descr.type_num * if ((descr.byteorder == c'>' and little_endian) or # <<<<<<<<<<<<<< @@ -2591,7 +2818,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ } - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":260 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":260 * (descr.byteorder == c'<' and not little_endian)): * raise ValueError(u"Non-native byte order not supported") * if t == NPY_BYTE: f = "b" # <<<<<<<<<<<<<< @@ -2600,10 +2827,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ switch (__pyx_v_t) { case NPY_BYTE: - __pyx_v_f = ((char *)"b"); + __pyx_v_f = __pyx_k_b; break; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":261 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":261 * raise ValueError(u"Non-native byte order not supported") * if t == NPY_BYTE: f = "b" * elif t == NPY_UBYTE: f = "B" # <<<<<<<<<<<<<< @@ -2611,10 +2838,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * elif t == NPY_USHORT: f = "H" */ case NPY_UBYTE: - __pyx_v_f = ((char *)"B"); + __pyx_v_f = __pyx_k_B; break; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":262 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":262 * if t == NPY_BYTE: f = "b" * elif t == NPY_UBYTE: f = "B" * elif t == NPY_SHORT: f = "h" # <<<<<<<<<<<<<< @@ -2622,10 +2849,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * elif t == NPY_INT: f = "i" */ case NPY_SHORT: - __pyx_v_f = ((char *)"h"); + __pyx_v_f = __pyx_k_h; break; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":263 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":263 * elif t == NPY_UBYTE: f = "B" * elif t == NPY_SHORT: f = "h" * elif t == NPY_USHORT: f = "H" # <<<<<<<<<<<<<< @@ -2633,10 +2860,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * elif t == NPY_UINT: f = "I" */ case NPY_USHORT: - __pyx_v_f = ((char *)"H"); + __pyx_v_f = __pyx_k_H; break; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":264 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":264 * elif t == NPY_SHORT: f = "h" * elif t == NPY_USHORT: f = "H" * elif t == NPY_INT: f = "i" # <<<<<<<<<<<<<< @@ -2644,10 +2871,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * elif t == NPY_LONG: f = "l" */ case NPY_INT: - __pyx_v_f = ((char *)"i"); + __pyx_v_f = __pyx_k_i; break; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":265 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":265 * elif t == NPY_USHORT: f = "H" * elif t == NPY_INT: f = "i" * elif t == NPY_UINT: f = "I" # <<<<<<<<<<<<<< @@ -2655,10 +2882,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * elif t == NPY_ULONG: f = "L" */ case NPY_UINT: - __pyx_v_f = ((char *)"I"); + __pyx_v_f = __pyx_k_I; break; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":266 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":266 * elif t == NPY_INT: f = "i" * elif t == NPY_UINT: f = "I" * elif t == NPY_LONG: f = "l" # <<<<<<<<<<<<<< @@ -2666,10 +2893,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * elif t == NPY_LONGLONG: f = "q" */ case NPY_LONG: - __pyx_v_f = ((char *)"l"); + __pyx_v_f = __pyx_k_l; break; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":267 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":267 * elif t == NPY_UINT: f = "I" * elif t == NPY_LONG: f = "l" * elif t == NPY_ULONG: f = "L" # <<<<<<<<<<<<<< @@ -2677,10 +2904,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * elif t == NPY_ULONGLONG: f = "Q" */ case NPY_ULONG: - __pyx_v_f = ((char *)"L"); + __pyx_v_f = __pyx_k_L; break; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":268 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":268 * elif t == NPY_LONG: f = "l" * elif t == NPY_ULONG: f = "L" * elif t == NPY_LONGLONG: f = "q" # <<<<<<<<<<<<<< @@ -2688,10 +2915,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * elif t == NPY_FLOAT: f = "f" */ case NPY_LONGLONG: - __pyx_v_f = ((char *)"q"); + __pyx_v_f = __pyx_k_q; break; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":269 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":269 * elif t == NPY_ULONG: f = "L" * elif t == NPY_LONGLONG: f = "q" * elif t == NPY_ULONGLONG: f = "Q" # <<<<<<<<<<<<<< @@ -2699,10 +2926,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * elif t == NPY_DOUBLE: f = "d" */ case NPY_ULONGLONG: - __pyx_v_f = ((char *)"Q"); + __pyx_v_f = __pyx_k_Q; break; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":270 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":270 * elif t == NPY_LONGLONG: f = "q" * elif t == NPY_ULONGLONG: f = "Q" * elif t == NPY_FLOAT: f = "f" # <<<<<<<<<<<<<< @@ -2710,10 +2937,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * elif t == NPY_LONGDOUBLE: f = "g" */ case NPY_FLOAT: - __pyx_v_f = ((char *)"f"); + __pyx_v_f = __pyx_k_f; break; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":271 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":271 * elif t == NPY_ULONGLONG: f = "Q" * elif t == NPY_FLOAT: f = "f" * elif t == NPY_DOUBLE: f = "d" # <<<<<<<<<<<<<< @@ -2721,10 +2948,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * elif t == NPY_CFLOAT: f = "Zf" */ case NPY_DOUBLE: - __pyx_v_f = ((char *)"d"); + __pyx_v_f = __pyx_k_d; break; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":272 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":272 * elif t == NPY_FLOAT: f = "f" * elif t == NPY_DOUBLE: f = "d" * elif t == NPY_LONGDOUBLE: f = "g" # <<<<<<<<<<<<<< @@ -2732,10 +2959,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * elif t == NPY_CDOUBLE: f = "Zd" */ case NPY_LONGDOUBLE: - __pyx_v_f = ((char *)"g"); + __pyx_v_f = __pyx_k_g; break; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":273 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":273 * elif t == NPY_DOUBLE: f = "d" * elif t == NPY_LONGDOUBLE: f = "g" * elif t == NPY_CFLOAT: f = "Zf" # <<<<<<<<<<<<<< @@ -2743,10 +2970,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * elif t == NPY_CLONGDOUBLE: f = "Zg" */ case NPY_CFLOAT: - __pyx_v_f = ((char *)"Zf"); + __pyx_v_f = __pyx_k_Zf; break; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":274 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":274 * elif t == NPY_LONGDOUBLE: f = "g" * elif t == NPY_CFLOAT: f = "Zf" * elif t == NPY_CDOUBLE: f = "Zd" # <<<<<<<<<<<<<< @@ -2754,10 +2981,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * elif t == NPY_OBJECT: f = "O" */ case NPY_CDOUBLE: - __pyx_v_f = ((char *)"Zd"); + __pyx_v_f = __pyx_k_Zd; break; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":275 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":275 * elif t == NPY_CFLOAT: f = "Zf" * elif t == NPY_CDOUBLE: f = "Zd" * elif t == NPY_CLONGDOUBLE: f = "Zg" # <<<<<<<<<<<<<< @@ -2765,10 +2992,10 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * else: */ case NPY_CLONGDOUBLE: - __pyx_v_f = ((char *)"Zg"); + __pyx_v_f = __pyx_k_Zg; break; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":276 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":276 * elif t == NPY_CDOUBLE: f = "Zd" * elif t == NPY_CLONGDOUBLE: f = "Zg" * elif t == NPY_OBJECT: f = "O" # <<<<<<<<<<<<<< @@ -2776,37 +3003,37 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P * raise ValueError(u"unknown dtype code in numpy.pxd (%d)" % t) */ case NPY_OBJECT: - __pyx_v_f = ((char *)"O"); + __pyx_v_f = __pyx_k_O; break; default: - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":278 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":278 * elif t == NPY_OBJECT: f = "O" * else: * raise ValueError(u"unknown dtype code in numpy.pxd (%d)" % t) # <<<<<<<<<<<<<< * info.format = f * return */ - __pyx_t_3 = __Pyx_PyInt_From_int(__pyx_v_t); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 278, __pyx_L1_error) + __pyx_t_3 = __Pyx_PyInt_From_int(__pyx_v_t); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 278; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); - __pyx_t_6 = PyUnicode_Format(__pyx_kp_u_unknown_dtype_code_in_numpy_pxd, __pyx_t_3); if (unlikely(!__pyx_t_6)) __PYX_ERR(1, 278, __pyx_L1_error) + __pyx_t_6 = PyUnicode_Format(__pyx_kp_u_unknown_dtype_code_in_numpy_pxd, __pyx_t_3); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 278; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; - __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) __PYX_ERR(1, 278, __pyx_L1_error) + __pyx_t_3 = PyTuple_New(1); if (unlikely(!__pyx_t_3)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 278; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_3); __Pyx_GIVEREF(__pyx_t_6); PyTuple_SET_ITEM(__pyx_t_3, 0, __pyx_t_6); __pyx_t_6 = 0; - __pyx_t_6 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_t_3, NULL); if (unlikely(!__pyx_t_6)) __PYX_ERR(1, 278, __pyx_L1_error) + __pyx_t_6 = __Pyx_PyObject_Call(__pyx_builtin_ValueError, __pyx_t_3, NULL); if (unlikely(!__pyx_t_6)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 278; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_6); __Pyx_DECREF(__pyx_t_3); __pyx_t_3 = 0; __Pyx_Raise(__pyx_t_6, 0, 0, 0); __Pyx_DECREF(__pyx_t_6); __pyx_t_6 = 0; - __PYX_ERR(1, 278, __pyx_L1_error) + {__pyx_filename = __pyx_f[1]; __pyx_lineno = 278; 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- /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":283 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":283 * else: * info.format = stdlib.malloc(_buffer_format_string_len) * info.format[0] = c'^' # Native data types, manual alignment # <<<<<<<<<<<<<< @@ -2853,7 +3080,7 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ (__pyx_v_info->format[0]) = '^'; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":284 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":284 * info.format = stdlib.malloc(_buffer_format_string_len) * info.format[0] = c'^' # Native data types, manual alignment * offset = 0 # <<<<<<<<<<<<<< @@ -2862,17 +3089,17 @@ static int __pyx_pf_5numpy_7ndarray___getbuffer__(PyArrayObject *__pyx_v_self, P */ __pyx_v_offset = 0; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":285 + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":285 * info.format[0] = c'^' # Native data types, manual alignment * offset = 0 * f = _util_dtypestring(descr, info.format + 1, # <<<<<<<<<<<<<< * info.format + _buffer_format_string_len, * &offset) */ - __pyx_t_7 = __pyx_f_5numpy__util_dtypestring(__pyx_v_descr, (__pyx_v_info->format + 1), (__pyx_v_info->format + 0xFF), (&__pyx_v_offset)); 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if (unlikely(!__pyx_ptype_5numpy_ndarray)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 181; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __pyx_ptype_5numpy_ufunc = __Pyx_ImportType("numpy", "ufunc", sizeof(PyUFuncObject), 0); if (unlikely(!__pyx_ptype_5numpy_ufunc)) {__pyx_filename = __pyx_f[1]; __pyx_lineno = 861; __pyx_clineno = __LINE__; goto __pyx_L1_error;} /*--- Variable import code ---*/ /*--- Function import code ---*/ /*--- Execution code ---*/ #if defined(__Pyx_Generator_USED) || defined(__Pyx_Coroutine_USED) - if (__Pyx_patch_abc() < 0) __PYX_ERR(0, 1, __pyx_L1_error) + if (__Pyx_patch_abc() < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} #endif /* "ot/lp/emd.pyx":7 @@ -4857,9 +4692,9 @@ PyMODINIT_FUNC PyInit_emd(void) * cimport numpy as np * */ - __pyx_t_1 = __Pyx_Import(__pyx_n_s_numpy, 0, -1); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 7, __pyx_L1_error) + __pyx_t_1 = __Pyx_Import(__pyx_n_s_numpy, 0, -1); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_1); - if (PyDict_SetItem(__pyx_d, __pyx_n_s_np, __pyx_t_1) < 0) __PYX_ERR(0, 7, __pyx_L1_error) + if (PyDict_SetItem(__pyx_d, __pyx_n_s_np, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 7; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "ot/lp/emd.pyx":21 @@ -4869,9 +4704,21 @@ PyMODINIT_FUNC PyInit_emd(void) * """ * Solves the Earth Movers distance problem and returns the optimal transport matrix */ - __pyx_t_1 = PyCFunction_NewEx(&__pyx_mdef_2ot_2lp_3emd_1emd_c, NULL, __pyx_n_s_ot_lp_emd); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 21, __pyx_L1_error) + __pyx_t_1 = PyCFunction_NewEx(&__pyx_mdef_2ot_2lp_3emd_1emd_c, NULL, __pyx_n_s_ot_lp_emd); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_GOTREF(__pyx_t_1); + if (PyDict_SetItem(__pyx_d, __pyx_n_s_emd_c, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 21; __pyx_clineno = __LINE__; goto __pyx_L1_error;} + __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; + + /* "ot/lp/emd.pyx":75 + * @cython.boundscheck(False) + * @cython.wraparound(False) + * def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M): # <<<<<<<<<<<<<< + * """ + * Solves the Earth Movers distance problem and returns the optimal transport matrix + */ + __pyx_t_1 = PyCFunction_NewEx(&__pyx_mdef_2ot_2lp_3emd_3emd2_c, NULL, __pyx_n_s_ot_lp_emd); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 75; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_1); - if (PyDict_SetItem(__pyx_d, __pyx_n_s_emd_c, __pyx_t_1) < 0) __PYX_ERR(0, 21, __pyx_L1_error) + if (PyDict_SetItem(__pyx_d, __pyx_n_s_emd2_c, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 75; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; /* "ot/lp/emd.pyx":1 @@ -4879,17 +4726,17 @@ PyMODINIT_FUNC PyInit_emd(void) * """ * Created on Thu Sep 11 08:42:08 2014 */ - __pyx_t_1 = PyDict_New(); if (unlikely(!__pyx_t_1)) __PYX_ERR(0, 1, __pyx_L1_error) + __pyx_t_1 = PyDict_New(); if (unlikely(!__pyx_t_1)) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_GOTREF(__pyx_t_1); - if (PyDict_SetItem(__pyx_d, __pyx_n_s_test, __pyx_t_1) < 0) __PYX_ERR(0, 1, __pyx_L1_error) + if (PyDict_SetItem(__pyx_d, __pyx_n_s_test, __pyx_t_1) < 0) {__pyx_filename = __pyx_f[0]; __pyx_lineno = 1; __pyx_clineno = __LINE__; goto __pyx_L1_error;} __Pyx_DECREF(__pyx_t_1); __pyx_t_1 = 0; - /* "../../.local/lib/python2.7/site-packages/Cython/Includes/numpy/__init__.pxd":997 - * raise ImportError("numpy.core.umath failed to import") + /* "../../../../usr/lib/python2.7/dist-packages/Cython/Includes/numpy/__init__.pxd":976 + * arr.base = baseptr * - * cdef inline int import_ufunc() except -1: # <<<<<<<<<<<<<< - * try: - * _import_umath() + * cdef inline object get_array_base(ndarray arr): # <<<<<<<<<<<<<< + * if arr.base is NULL: + * return None */ /*--- Wrapped vars code ---*/ @@ -4915,7 +4762,6 @@ PyMODINIT_FUNC PyInit_emd(void) } /* --- Runtime support code --- */ -/* Refnanny */ #if CYTHON_REFNANNY static __Pyx_RefNannyAPIStruct *__Pyx_RefNannyImportAPI(const char *modname) { PyObject *m = NULL, *p = NULL; @@ -4932,7 +4778,19 @@ end: } #endif -/* RaiseArgTupleInvalid */ +static PyObject *__Pyx_GetBuiltinName(PyObject *name) { + PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name); + if (unlikely(!result)) { + PyErr_Format(PyExc_NameError, +#if PY_MAJOR_VERSION >= 3 + "name '%U' is not defined", name); +#else + "name '%.200s' is not defined", PyString_AS_STRING(name)); +#endif + } + return result; +} + static void __Pyx_RaiseArgtupleInvalid( const char* func_name, int exact, @@ -4958,7 +4816,6 @@ static void __Pyx_RaiseArgtupleInvalid( (num_expected == 1) ? "" : "s", num_found); } -/* RaiseDoubleKeywords */ static void __Pyx_RaiseDoubleKeywordsError( const char* func_name, PyObject* kw_name) @@ -4972,7 +4829,6 @@ static void __Pyx_RaiseDoubleKeywordsError( #endif } -/* ParseKeywords */ static int __Pyx_ParseOptionalKeywords( PyObject *kwds, PyObject **argnames[], @@ -5074,7 +4930,6 @@ bad: return -1; } -/* ArgTypeTest */ static void __Pyx_RaiseArgumentTypeInvalid(const char* name, PyObject *obj, PyTypeObject *type) { PyErr_Format(PyExc_TypeError, "Argument '%.200s' has incorrect type (expected %.200s, got %.200s)", @@ -5101,7 +4956,6 @@ static CYTHON_INLINE int __Pyx_ArgTypeTest(PyObject *obj, PyTypeObject *type, in return 0; } -/* BufferFormatCheck */ static CYTHON_INLINE int __Pyx_IsLittleEndian(void) { unsigned int n = 1; return *(unsigned char*)(&n) != 0; @@ -5651,24 +5505,9 @@ static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { __Pyx_ReleaseBuffer(info); } -/* GetBuiltinName */ - static PyObject *__Pyx_GetBuiltinName(PyObject *name) { - PyObject* result = __Pyx_PyObject_GetAttrStr(__pyx_b, name); - if (unlikely(!result)) { - PyErr_Format(PyExc_NameError, -#if PY_MAJOR_VERSION >= 3 - "name '%U' is not defined", name); -#else - "name '%.200s' is not defined", PyString_AS_STRING(name)); -#endif - } - return result; -} - -/* GetModuleGlobalName */ - static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { +static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { PyObject *result; -#if !CYTHON_AVOID_BORROWED_REFS +#if CYTHON_COMPILING_IN_CPYTHON result = PyDict_GetItem(__pyx_d, name); if (likely(result)) { Py_INCREF(result); @@ -5683,148 +5522,7 @@ static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { return result; } -/* PyCFunctionFastCall */ - #if CYTHON_FAST_PYCCALL -static CYTHON_INLINE PyObject * __Pyx_PyCFunction_FastCall(PyObject *func_obj, PyObject **args, Py_ssize_t nargs) { - PyCFunctionObject *func = (PyCFunctionObject*)func_obj; - PyCFunction meth = PyCFunction_GET_FUNCTION(func); - PyObject *self = PyCFunction_GET_SELF(func); - PyObject *result; - int flags; - assert(PyCFunction_Check(func)); - assert(METH_FASTCALL == PyCFunction_GET_FLAGS(func) & ~(METH_CLASS | METH_STATIC | METH_COEXIST)); - assert(nargs >= 0); - assert(nargs == 0 || args != NULL); - /* _PyCFunction_FastCallDict() must not be called with an exception set, - because it may clear it (directly or indirectly) and so the - caller loses its exception */ - assert(!PyErr_Occurred()); - return (*((__Pyx_PyCFunctionFast)meth)) (self, args, nargs, NULL); -} -#endif // CYTHON_FAST_PYCCALL - -/* PyFunctionFastCall */ - #if CYTHON_FAST_PYCALL -#include "frameobject.h" -static PyObject* __Pyx_PyFunction_FastCallNoKw(PyCodeObject *co, PyObject **args, Py_ssize_t na, - PyObject *globals) { - PyFrameObject *f; - PyThreadState *tstate = PyThreadState_GET(); - PyObject **fastlocals; - Py_ssize_t i; - PyObject *result; - assert(globals != NULL); - /* XXX Perhaps we should create a specialized - PyFrame_New() that doesn't take locals, but does - take builtins without sanity checking them. - */ - assert(tstate != NULL); - f = PyFrame_New(tstate, co, globals, NULL); - if (f == NULL) { - return NULL; - } - fastlocals = f->f_localsplus; - for (i = 0; i < na; i++) { - Py_INCREF(*args); - fastlocals[i] = *args++; - } - result = PyEval_EvalFrameEx(f,0); - ++tstate->recursion_depth; - Py_DECREF(f); - --tstate->recursion_depth; - return result; -} -#if 1 || PY_VERSION_HEX < 0x030600B1 -static PyObject *__Pyx_PyFunction_FastCallDict(PyObject *func, PyObject **args, int nargs, PyObject *kwargs) { - PyCodeObject *co = (PyCodeObject *)PyFunction_GET_CODE(func); - PyObject *globals = PyFunction_GET_GLOBALS(func); - PyObject *argdefs = PyFunction_GET_DEFAULTS(func); - PyObject *closure; -#if PY_MAJOR_VERSION >= 3 - PyObject *kwdefs; -#endif - PyObject *kwtuple, **k; - PyObject **d; - Py_ssize_t nd; - Py_ssize_t nk; - PyObject *result; - assert(kwargs == NULL || PyDict_Check(kwargs)); - nk = kwargs ? PyDict_Size(kwargs) : 0; - if (Py_EnterRecursiveCall((char*)" while calling a Python object")) { - return NULL; - } - if ( -#if PY_MAJOR_VERSION >= 3 - co->co_kwonlyargcount == 0 && -#endif - likely(kwargs == NULL || nk == 0) && - co->co_flags == (CO_OPTIMIZED | CO_NEWLOCALS | CO_NOFREE)) { - if (argdefs == NULL && co->co_argcount == nargs) { - result = __Pyx_PyFunction_FastCallNoKw(co, args, nargs, globals); - goto done; - } - else if (nargs == 0 && argdefs != NULL - && co->co_argcount == Py_SIZE(argdefs)) { - /* function called with no arguments, but all parameters have - a default value: use default values as arguments .*/ - args = &PyTuple_GET_ITEM(argdefs, 0); - result =__Pyx_PyFunction_FastCallNoKw(co, args, Py_SIZE(argdefs), globals); - goto done; - } - } - if (kwargs != NULL) { - Py_ssize_t pos, i; - kwtuple = PyTuple_New(2 * nk); - if (kwtuple == NULL) { - result = NULL; - goto done; - } - k = &PyTuple_GET_ITEM(kwtuple, 0); - pos = i = 0; - while (PyDict_Next(kwargs, &pos, &k[i], &k[i+1])) { - Py_INCREF(k[i]); - Py_INCREF(k[i+1]); - i += 2; - } - nk = i / 2; - } - else { - kwtuple = NULL; - k = NULL; - } - closure = PyFunction_GET_CLOSURE(func); -#if PY_MAJOR_VERSION >= 3 - kwdefs = PyFunction_GET_KW_DEFAULTS(func); -#endif - if (argdefs != NULL) { - d = &PyTuple_GET_ITEM(argdefs, 0); - nd = Py_SIZE(argdefs); - } - else { - d = NULL; - nd = 0; - } -#if PY_MAJOR_VERSION >= 3 - result = PyEval_EvalCodeEx((PyObject*)co, globals, (PyObject *)NULL, - args, nargs, - k, (int)nk, - d, (int)nd, kwdefs, closure); -#else - result = PyEval_EvalCodeEx(co, globals, (PyObject *)NULL, - args, nargs, - k, (int)nk, - d, (int)nd, closure); -#endif - Py_XDECREF(kwtuple); -done: - Py_LeaveRecursiveCall(); - return result; -} -#endif // CPython < 3.6 -#endif // CYTHON_FAST_PYCALL - -/* PyObjectCall */ - #if CYTHON_COMPILING_IN_CPYTHON +#if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { PyObject *result; ternaryfunc call = func->ob_type->tp_call; @@ -5843,8 +5541,7 @@ static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg } #endif -/* PyObjectCallMethO */ - #if CYTHON_COMPILING_IN_CPYTHON +#if CYTHON_COMPILING_IN_CPYTHON static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { PyObject *self, *result; PyCFunction cfunc; @@ -5863,8 +5560,7 @@ static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject } #endif -/* PyObjectCallOneArg */ - #if CYTHON_COMPILING_IN_CPYTHON +#if CYTHON_COMPILING_IN_CPYTHON static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { PyObject *result; PyObject *args = PyTuple_New(1); @@ -5876,11 +5572,6 @@ static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { return result; } static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { -#if CYTHON_FAST_PYCALL - if (PyFunction_Check(func)) { - return __Pyx_PyFunction_FastCall(func, &arg, 1); - } -#endif #ifdef __Pyx_CyFunction_USED if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) { #else @@ -5888,10 +5579,6 @@ static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObjec #endif if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { return __Pyx_PyObject_CallMethO(func, arg); -#if CYTHON_FAST_PYCCALL - } else if (PyCFunction_GET_FLAGS(func) & METH_FASTCALL) { - return __Pyx_PyCFunction_FastCall(func, &arg, 1); -#endif } } return __Pyx__PyObject_CallOneArg(func, arg); @@ -5907,8 +5594,7 @@ static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObjec } #endif -/* ExtTypeTest */ - static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { +static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { if (unlikely(!type)) { PyErr_SetString(PyExc_SystemError, "Missing type object"); return 0; @@ -5920,16 +5606,15 @@ static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObjec return 0; } -/* BufferFallbackError */ - static void __Pyx_RaiseBufferFallbackError(void) { +static void __Pyx_RaiseBufferFallbackError(void) { PyErr_SetString(PyExc_ValueError, "Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!"); } -/* PyErrFetchRestore */ - #if CYTHON_FAST_THREAD_STATE -static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { +static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) { +#if CYTHON_COMPILING_IN_CPYTHON PyObject *tmp_type, *tmp_value, *tmp_tb; + PyThreadState *tstate = PyThreadState_GET(); tmp_type = tstate->curexc_type; tmp_value = tstate->curexc_value; tmp_tb = tstate->curexc_traceback; @@ -5939,22 +5624,27 @@ static CYTHON_INLINE void __Pyx_ErrRestoreInState(PyThreadState *tstate, PyObjec Py_XDECREF(tmp_type); Py_XDECREF(tmp_value); Py_XDECREF(tmp_tb); +#else + PyErr_Restore(type, value, tb); +#endif } -static CYTHON_INLINE void __Pyx_ErrFetchInState(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { +static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) { +#if CYTHON_COMPILING_IN_CPYTHON + PyThreadState *tstate = PyThreadState_GET(); *type = tstate->curexc_type; *value = tstate->curexc_value; *tb = tstate->curexc_traceback; tstate->curexc_type = 0; tstate->curexc_value = 0; tstate->curexc_traceback = 0; -} +#else + PyErr_Fetch(type, value, tb); #endif +} -/* RaiseException */ - #if PY_MAJOR_VERSION < 3 +#if PY_MAJOR_VERSION < 3 static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, CYTHON_UNUSED PyObject *cause) { - __Pyx_PyThreadState_declare Py_XINCREF(type); if (!value || value == Py_None) value = NULL; @@ -5993,7 +5683,6 @@ static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, goto raise_error; } } - __Pyx_PyThreadState_assign __Pyx_ErrRestore(type, value, tb); return; raise_error: @@ -6113,121 +5802,22 @@ bad: } #endif -/* RaiseTooManyValuesToUnpack */ - static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { +static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { PyErr_Format(PyExc_ValueError, "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); } -/* RaiseNeedMoreValuesToUnpack */ - static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { +static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { PyErr_Format(PyExc_ValueError, "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", index, (index == 1) ? "" : "s"); } -/* RaiseNoneIterError */ - static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { +static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); } -/* SaveResetException */ - #if CYTHON_FAST_THREAD_STATE -static CYTHON_INLINE void __Pyx__ExceptionSave(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { - *type = tstate->exc_type; - *value = tstate->exc_value; - *tb = tstate->exc_traceback; - Py_XINCREF(*type); - Py_XINCREF(*value); - Py_XINCREF(*tb); -} -static CYTHON_INLINE void __Pyx__ExceptionReset(PyThreadState *tstate, PyObject *type, PyObject *value, PyObject *tb) { - PyObject *tmp_type, *tmp_value, *tmp_tb; - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = type; - tstate->exc_value = value; - tstate->exc_traceback = tb; - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -} -#endif - -/* PyErrExceptionMatches */ - #if CYTHON_FAST_THREAD_STATE -static CYTHON_INLINE int __Pyx_PyErr_ExceptionMatchesInState(PyThreadState* tstate, PyObject* err) { - PyObject *exc_type = tstate->curexc_type; - if (exc_type == err) return 1; - if (unlikely(!exc_type)) return 0; - return PyErr_GivenExceptionMatches(exc_type, err); -} -#endif - -/* GetException */ - #if CYTHON_FAST_THREAD_STATE -static int __Pyx__GetException(PyThreadState *tstate, PyObject **type, PyObject **value, PyObject **tb) { -#else -static int __Pyx_GetException(PyObject **type, PyObject **value, PyObject **tb) { -#endif - PyObject *local_type, *local_value, *local_tb; -#if CYTHON_FAST_THREAD_STATE - PyObject *tmp_type, *tmp_value, *tmp_tb; - local_type = tstate->curexc_type; - local_value = tstate->curexc_value; - local_tb = tstate->curexc_traceback; - tstate->curexc_type = 0; - tstate->curexc_value = 0; - tstate->curexc_traceback = 0; -#else - PyErr_Fetch(&local_type, &local_value, &local_tb); -#endif - PyErr_NormalizeException(&local_type, &local_value, &local_tb); -#if CYTHON_FAST_THREAD_STATE - if (unlikely(tstate->curexc_type)) -#else - if (unlikely(PyErr_Occurred())) -#endif - goto bad; - #if PY_MAJOR_VERSION >= 3 - if (local_tb) { - if (unlikely(PyException_SetTraceback(local_value, local_tb) < 0)) - goto bad; - } - #endif - Py_XINCREF(local_tb); - Py_XINCREF(local_type); - Py_XINCREF(local_value); - *type = local_type; - *value = local_value; - *tb = local_tb; -#if CYTHON_FAST_THREAD_STATE - tmp_type = tstate->exc_type; - tmp_value = tstate->exc_value; - tmp_tb = tstate->exc_traceback; - tstate->exc_type = local_type; - tstate->exc_value = local_value; - tstate->exc_traceback = local_tb; - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -#else - PyErr_SetExcInfo(local_type, local_value, local_tb); -#endif - return 0; -bad: - *type = 0; - *value = 0; - *tb = 0; - Py_XDECREF(local_type); - Py_XDECREF(local_value); - Py_XDECREF(local_tb); - return -1; -} - -/* Import */ - static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { +static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { PyObject *empty_list = 0; PyObject *module = 0; PyObject *global_dict = 0; @@ -6300,8 +5890,7 @@ bad: return module; } -/* CodeObjectCache */ - static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { +static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { int start = 0, mid = 0, end = count - 1; if (end >= 0 && code_line > entries[end].code_line) { return count; @@ -6380,8 +5969,7 @@ static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { Py_INCREF(code_object); } -/* AddTraceback */ - #include "compile.h" +#include "compile.h" #include "frameobject.h" #include "traceback.h" static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( @@ -6454,7 +6042,7 @@ static void __Pyx_AddTraceback(const char *funcname, int c_line, 0 /*PyObject *locals*/ ); if (!py_frame) goto bad; - __Pyx_PyFrame_SetLineNumber(py_frame, py_line); + py_frame->f_lineno = py_line; PyTraceBack_Here(py_frame); bad: Py_XDECREF(py_code); @@ -6482,8 +6070,7 @@ static void __Pyx_ReleaseBuffer(Py_buffer *view) { #endif - /* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { + static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { const int neg_one = (int) -1, const_zero = (int) 0; const int is_unsigned = neg_one > const_zero; if (is_unsigned) { @@ -6491,18 +6078,14 @@ static void __Pyx_ReleaseBuffer(Py_buffer *view) { return PyInt_FromLong((long) value); } else if (sizeof(int) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif } } else { if (sizeof(int) <= sizeof(long)) { return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif } } { @@ -6513,8 +6096,216 @@ static void __Pyx_ReleaseBuffer(Py_buffer *view) { } } -/* Declarations */ - #if CYTHON_CCOMPLEX +#define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) +#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ + __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) +#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ + {\ + func_type value = func_value;\ + if (sizeof(target_type) < sizeof(func_type)) {\ + if (unlikely(value != (func_type) (target_type) value)) {\ + func_type zero = 0;\ + if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ + return (target_type) -1;\ + if (is_unsigned && unlikely(value < zero))\ + goto raise_neg_overflow;\ + else\ + goto raise_overflow;\ + }\ + }\ + return (target_type) value;\ + } + +#if CYTHON_USE_PYLONG_INTERNALS + #include "longintrepr.h" +#endif + +static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { + const int neg_one = (int) -1, const_zero = (int) 0; + const int is_unsigned = neg_one > const_zero; +#if PY_MAJOR_VERSION < 3 + if (likely(PyInt_Check(x))) { + if (sizeof(int) < sizeof(long)) { + __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) + } else { + long val = PyInt_AS_LONG(x); + if (is_unsigned && unlikely(val < 0)) { + goto raise_neg_overflow; + } + return (int) val; + } + } else +#endif + if (likely(PyLong_Check(x))) { + if (is_unsigned) { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { + return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { + return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { + return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); + } + } + break; + } +#endif +#if CYTHON_COMPILING_IN_CPYTHON + if (unlikely(Py_SIZE(x) < 0)) { + goto raise_neg_overflow; + } +#else + { + int result = PyObject_RichCompareBool(x, Py_False, Py_LT); + if (unlikely(result < 0)) + return (int) -1; + if (unlikely(result == 1)) + goto raise_neg_overflow; + } +#endif + if (sizeof(int) <= sizeof(unsigned long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) + } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) + } + } else { +#if CYTHON_USE_PYLONG_INTERNALS + const digit* digits = ((PyLongObject*)x)->ob_digit; + switch (Py_SIZE(x)) { + case 0: return (int) 0; + case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, -(sdigit) digits[0]) + case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) + case -2: + if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 2: + if (8 * sizeof(int) > 1 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -3: + if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 3: + if (8 * sizeof(int) > 2 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case -4: + if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + case 4: + if (8 * sizeof(int) > 3 * PyLong_SHIFT) { + if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { + __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) + } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { + return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); + } + } + break; + } +#endif + if (sizeof(int) <= sizeof(long)) { + __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) + } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { + __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) + } + } + { +#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) + PyErr_SetString(PyExc_RuntimeError, + "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); +#else + int val; + PyObject *v = __Pyx_PyNumber_Int(x); + #if PY_MAJOR_VERSION < 3 + if (likely(v) && !PyLong_Check(v)) { + PyObject *tmp = v; + v = PyNumber_Long(tmp); + Py_DECREF(tmp); + } + #endif + if (likely(v)) { + int one = 1; int is_little = (int)*(unsigned char *)&one; + unsigned char *bytes = (unsigned char *)&val; + int ret = _PyLong_AsByteArray((PyLongObject *)v, + bytes, sizeof(val), + is_little, !is_unsigned); + Py_DECREF(v); + if (likely(!ret)) + return val; + } +#endif + return (int) -1; + } + } else { + int val; + PyObject *tmp = __Pyx_PyNumber_Int(x); + if (!tmp) return (int) -1; + val = __Pyx_PyInt_As_int(tmp); + Py_DECREF(tmp); + return val; + } +raise_overflow: + PyErr_SetString(PyExc_OverflowError, + "value too large to convert to int"); + return (int) -1; +raise_neg_overflow: + PyErr_SetString(PyExc_OverflowError, + "can't convert negative value to int"); + return (int) -1; +} + +#if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { return ::std::complex< float >(x, y); @@ -6533,86 +6324,60 @@ static void __Pyx_ReleaseBuffer(Py_buffer *view) { } #endif -/* Arithmetic */ - #if CYTHON_CCOMPLEX +#if CYTHON_CCOMPLEX #else - static CYTHON_INLINE int __Pyx_c_eq_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex a, __pyx_t_float_complex b) { return (a.real == b.real) && (a.imag == b.imag); } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sum_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_diff_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prod_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } - #if 1 - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - if (b.imag == 0) { - return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); - } else if (fabsf(b.real) >= fabsf(b.imag)) { - if (b.real == 0 && b.imag == 0) { - return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.imag); - } else { - float r = b.imag / b.real; - float s = 1.0 / (b.real + b.imag * r); - return __pyx_t_float_complex_from_parts( - (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); - } - } else { - float r = b.real / b.imag; - float s = 1.0 / (b.imag + b.real * r); - return __pyx_t_float_complex_from_parts( - (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); - } - } - #else - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quot_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { - if (b.imag == 0) { - return __pyx_t_float_complex_from_parts(a.real / b.real, a.imag / b.real); - } else { - float denom = b.real * b.real + b.imag * b.imag; - return __pyx_t_float_complex_from_parts( - (a.real * b.real + a.imag * b.imag) / denom, - (a.imag * b.real - a.real * b.imag) / denom); - } + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex a, __pyx_t_float_complex b) { + __pyx_t_float_complex z; + float denom = b.real * b.real + b.imag * b.imag; + z.real = (a.real * b.real + a.imag * b.imag) / denom; + z.imag = (a.imag * b.real - a.real * b.imag) / denom; + return z; } - #endif - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_neg_float(__pyx_t_float_complex a) { + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = -a.real; z.imag = -a.imag; return z; } - static CYTHON_INLINE int __Pyx_c_is_zero_float(__pyx_t_float_complex a) { + static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex a) { return (a.real == 0) && (a.imag == 0); } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conj_float(__pyx_t_float_complex a) { + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex a) { __pyx_t_float_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 - static CYTHON_INLINE float __Pyx_c_abs_float(__pyx_t_float_complex z) { + static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrtf(z.real*z.real + z.imag*z.imag); #else return hypotf(z.real, z.imag); #endif } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_pow_float(__pyx_t_float_complex a, __pyx_t_float_complex b) { + static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex a, __pyx_t_float_complex b) { __pyx_t_float_complex z; float r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { @@ -6630,14 +6395,14 @@ static void __Pyx_ReleaseBuffer(Py_buffer *view) { case 1: return a; case 2: - z = __Pyx_c_prod_float(a, a); - return __Pyx_c_prod_float(a, a); + z = __Pyx_c_prodf(a, a); + return __Pyx_c_prodf(a, a); case 3: - z = __Pyx_c_prod_float(a, a); - return __Pyx_c_prod_float(z, a); + z = __Pyx_c_prodf(a, a); + return __Pyx_c_prodf(z, a); case 4: - z = __Pyx_c_prod_float(a, a); - return __Pyx_c_prod_float(z, z); + z = __Pyx_c_prodf(a, a); + return __Pyx_c_prodf(z, z); } } if (a.imag == 0) { @@ -6647,7 +6412,7 @@ static void __Pyx_ReleaseBuffer(Py_buffer *view) { r = a.real; theta = 0; } else { - r = __Pyx_c_abs_float(a); + r = __Pyx_c_absf(a); theta = atan2f(a.imag, a.real); } lnr = logf(r); @@ -6660,8 +6425,7 @@ static void __Pyx_ReleaseBuffer(Py_buffer *view) { #endif #endif -/* Declarations */ - #if CYTHON_CCOMPLEX +#if CYTHON_CCOMPLEX #ifdef __cplusplus static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { return ::std::complex< double >(x, y); @@ -6680,86 +6444,60 @@ static void __Pyx_ReleaseBuffer(Py_buffer *view) { } #endif -/* Arithmetic */ - #if CYTHON_CCOMPLEX +#if CYTHON_CCOMPLEX #else - static CYTHON_INLINE int __Pyx_c_eq_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) { return (a.real == b.real) && (a.imag == b.imag); } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real + b.real; z.imag = a.imag + b.imag; return z; } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real - b.real; z.imag = a.imag - b.imag; return z; } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; z.real = a.real * b.real - a.imag * b.imag; z.imag = a.real * b.imag + a.imag * b.real; return z; } - #if 1 - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - if (b.imag == 0) { - return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); - } else if (fabs(b.real) >= fabs(b.imag)) { - if (b.real == 0 && b.imag == 0) { - return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.imag); - } else { - double r = b.imag / b.real; - double s = 1.0 / (b.real + b.imag * r); - return __pyx_t_double_complex_from_parts( - (a.real + a.imag * r) * s, (a.imag - a.real * r) * s); - } - } else { - double r = b.real / b.imag; - double s = 1.0 / (b.imag + b.real * r); - return __pyx_t_double_complex_from_parts( - (a.real * r + a.imag) * s, (a.imag * r - a.real) * s); - } - } - #else - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { - if (b.imag == 0) { - return __pyx_t_double_complex_from_parts(a.real / b.real, a.imag / b.real); - } else { - double denom = b.real * b.real + b.imag * b.imag; - return __pyx_t_double_complex_from_parts( - (a.real * b.real + a.imag * b.imag) / denom, - (a.imag * b.real - a.real * b.imag) / denom); - } + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex a, __pyx_t_double_complex b) { + __pyx_t_double_complex z; + double denom = b.real * b.real + b.imag * b.imag; + z.real = (a.real * b.real + a.imag * b.imag) / denom; + z.imag = (a.imag * b.real - a.real * b.imag) / denom; + return z; } - #endif - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg_double(__pyx_t_double_complex a) { + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = -a.real; z.imag = -a.imag; return z; } - static CYTHON_INLINE int __Pyx_c_is_zero_double(__pyx_t_double_complex a) { + static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex a) { return (a.real == 0) && (a.imag == 0); } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj_double(__pyx_t_double_complex a) { + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex a) { __pyx_t_double_complex z; z.real = a.real; z.imag = -a.imag; return z; } #if 1 - static CYTHON_INLINE double __Pyx_c_abs_double(__pyx_t_double_complex z) { + static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex z) { #if !defined(HAVE_HYPOT) || defined(_MSC_VER) return sqrt(z.real*z.real + z.imag*z.imag); #else return hypot(z.real, z.imag); #endif } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow_double(__pyx_t_double_complex a, __pyx_t_double_complex b) { + static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex a, __pyx_t_double_complex b) { __pyx_t_double_complex z; double r, lnr, theta, z_r, z_theta; if (b.imag == 0 && b.real == (int)b.real) { @@ -6777,14 +6515,14 @@ static void __Pyx_ReleaseBuffer(Py_buffer *view) { case 1: return a; case 2: - z = __Pyx_c_prod_double(a, a); - return __Pyx_c_prod_double(a, a); + z = __Pyx_c_prod(a, a); + return __Pyx_c_prod(a, a); case 3: - z = __Pyx_c_prod_double(a, a); - return __Pyx_c_prod_double(z, a); + z = __Pyx_c_prod(a, a); + return __Pyx_c_prod(z, a); case 4: - z = __Pyx_c_prod_double(a, a); - return __Pyx_c_prod_double(z, z); + z = __Pyx_c_prod(a, a); + return __Pyx_c_prod(z, z); } } if (a.imag == 0) { @@ -6794,7 +6532,7 @@ static void __Pyx_ReleaseBuffer(Py_buffer *view) { r = a.real; theta = 0; } else { - r = __Pyx_c_abs_double(a); + r = __Pyx_c_abs(a); theta = atan2(a.imag, a.real); } lnr = log(r); @@ -6807,30 +6545,7 @@ static void __Pyx_ReleaseBuffer(Py_buffer *view) { #endif #endif -/* CIntFromPyVerify */ - #define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ - __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) -#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ - __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) -#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ - {\ - func_type value = func_value;\ - if (sizeof(target_type) < sizeof(func_type)) {\ - if (unlikely(value != (func_type) (target_type) value)) {\ - func_type zero = 0;\ - if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ - return (target_type) -1;\ - if (is_unsigned && unlikely(value < zero))\ - goto raise_neg_overflow;\ - else\ - goto raise_overflow;\ - }\ - }\ - return (target_type) value;\ - } - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value) { +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value) { const enum NPY_TYPES neg_one = (enum NPY_TYPES) -1, const_zero = (enum NPY_TYPES) 0; const int is_unsigned = neg_one > const_zero; if (is_unsigned) { @@ -6838,18 +6553,14 @@ static void __Pyx_ReleaseBuffer(Py_buffer *view) { return PyInt_FromLong((long) value); } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned PY_LONG_LONG)) { return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif } } else { if (sizeof(enum NPY_TYPES) <= sizeof(long)) { return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG } else if (sizeof(enum NPY_TYPES) <= sizeof(PY_LONG_LONG)) { return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif } } { @@ -6860,197 +6571,7 @@ static void __Pyx_ReleaseBuffer(Py_buffer *view) { } } -/* CIntFromPy */ - static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { - const int neg_one = (int) -1, const_zero = (int) 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(int) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (int) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (int) 0; - case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) - case 2: - if (8 * sizeof(int) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { - return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(int) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { - return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(int) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { - return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (int) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(int) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (int) 0; - case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, (sdigit) (-(sdigit)digits[0])) - case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) - case -2: - if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(int) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(int) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(int) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { - return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - } -#endif - if (sizeof(int) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG - } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - int val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (int) -1; - } - } else { - int val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); - if (!tmp) return (int) -1; - val = __Pyx_PyInt_As_int(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to int"); - return (int) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to int"); - return (int) -1; -} - -/* CIntToPy */ - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { +static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { const long neg_one = (long) -1, const_zero = (long) 0; const int is_unsigned = neg_one > const_zero; if (is_unsigned) { @@ -7058,18 +6579,14 @@ raise_neg_overflow: return PyInt_FromLong((long) value); } else if (sizeof(long) <= sizeof(unsigned long)) { return PyLong_FromUnsignedLong((unsigned long) value); -#ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); -#endif } } else { if (sizeof(long) <= sizeof(long)) { return PyInt_FromLong((long) value); -#ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { return PyLong_FromLongLong((PY_LONG_LONG) value); -#endif } } { @@ -7080,8 +6597,7 @@ raise_neg_overflow: } } -/* CIntFromPy */ - static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { +static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { const long neg_one = (long) -1, const_zero = (long) 0; const int is_unsigned = neg_one > const_zero; #if PY_MAJOR_VERSION < 3 @@ -7148,17 +6664,15 @@ raise_neg_overflow: #endif if (sizeof(long) <= sizeof(unsigned long)) { __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) -#ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) -#endif } } else { #if CYTHON_USE_PYLONG_INTERNALS const digit* digits = ((PyLongObject*)x)->ob_digit; switch (Py_SIZE(x)) { case 0: return (long) 0; - case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, (sdigit) (-(sdigit)digits[0])) + case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, -(sdigit) digits[0]) case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) case -2: if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { @@ -7218,10 +6732,8 @@ raise_neg_overflow: #endif if (sizeof(long) <= sizeof(long)) { __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) -#ifdef HAVE_LONG_LONG } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) -#endif } } { @@ -7230,7 +6742,7 @@ raise_neg_overflow: "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); #else long val; - PyObject *v = __Pyx_PyNumber_IntOrLong(x); + PyObject *v = __Pyx_PyNumber_Int(x); #if PY_MAJOR_VERSION < 3 if (likely(v) && !PyLong_Check(v)) { PyObject *tmp = v; @@ -7253,7 +6765,7 @@ raise_neg_overflow: } } else { long val; - PyObject *tmp = __Pyx_PyNumber_IntOrLong(x); + PyObject *tmp = __Pyx_PyNumber_Int(x); if (!tmp) return (long) -1; val = __Pyx_PyInt_As_long(tmp); Py_DECREF(tmp); @@ -7269,8 +6781,7 @@ raise_neg_overflow: return (long) -1; } -/* CheckBinaryVersion */ - static int __Pyx_check_binary_version(void) { +static int __Pyx_check_binary_version(void) { char ctversion[4], rtversion[4]; PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); @@ -7285,8 +6796,7 @@ raise_neg_overflow: return 0; } -/* ModuleImport */ - #ifndef __PYX_HAVE_RT_ImportModule +#ifndef __PYX_HAVE_RT_ImportModule #define __PYX_HAVE_RT_ImportModule static PyObject *__Pyx_ImportModule(const char *name) { PyObject *py_name = 0; @@ -7303,8 +6813,7 @@ bad: } #endif -/* TypeImport */ - #ifndef __PYX_HAVE_RT_ImportType +#ifndef __PYX_HAVE_RT_ImportType #define __PYX_HAVE_RT_ImportType static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, size_t size, int strict) @@ -7350,14 +6859,14 @@ static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class #endif if (!strict && (size_t)basicsize > size) { PyOS_snprintf(warning, sizeof(warning), - "%s.%s size changed, may indicate binary incompatibility. Expected %zd, got %zd", - module_name, class_name, basicsize, size); + "%s.%s size changed, may indicate binary incompatibility", + module_name, class_name); if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; } else if ((size_t)basicsize != size) { PyErr_Format(PyExc_ValueError, - "%.200s.%.200s has the wrong size, try recompiling. Expected %zd, got %zd", - module_name, class_name, basicsize, size); + "%.200s.%.200s has the wrong size, try recompiling", + module_name, class_name); goto bad; } return (PyTypeObject *)result; @@ -7368,8 +6877,7 @@ bad: } #endif -/* InitStrings */ - static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { +static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { while (t->p) { #if PY_MAJOR_VERSION < 3 if (t->is_unicode) { @@ -7469,10 +6977,8 @@ static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { if (is_true | (x == Py_False) | (x == Py_None)) return is_true; else return PyObject_IsTrue(x); } -static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { -#if CYTHON_USE_TYPE_SLOTS +static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) { PyNumberMethods *m; -#endif const char *name = NULL; PyObject *res = NULL; #if PY_MAJOR_VERSION < 3 @@ -7481,9 +6987,8 @@ static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { if (PyLong_Check(x)) #endif return __Pyx_NewRef(x); -#if CYTHON_USE_TYPE_SLOTS m = Py_TYPE(x)->tp_as_number; - #if PY_MAJOR_VERSION < 3 +#if PY_MAJOR_VERSION < 3 if (m && m->nb_int) { name = "int"; res = PyNumber_Int(x); @@ -7492,14 +6997,11 @@ static CYTHON_INLINE PyObject* __Pyx_PyNumber_IntOrLong(PyObject* x) { name = "long"; res = PyNumber_Long(x); } - #else +#else if (m && m->nb_int) { name = "int"; res = PyNumber_Long(x); } - #endif -#else - res = PyNumber_Int(x); #endif if (res) { #if PY_MAJOR_VERSION < 3 diff --git a/ot/lp/emd.pyx b/ot/lp/emd.pyx index de2d4a9..46794ab 100644 --- a/ot/lp/emd.pyx +++ b/ot/lp/emd.pyx @@ -69,3 +69,63 @@ def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mod EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost) return G + +@cython.boundscheck(False) +@cython.wraparound(False) +def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M): + """ + Solves the Earth Movers distance problem and returns the optimal transport loss + + gamm=emd(a,b,M) + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + where : + + - M is the metric cost matrix + - a and b are the sample weights + + Parameters + ---------- + a : (ns,) ndarray, float64 + source histogram + b : (nt,) ndarray, float64 + target histogram + M : (ns,nt) ndarray, float64 + loss matrix + + + Returns + ------- + gamma: (ns x nt) ndarray + Optimal transportation matrix for the given parameters + + """ + cdef int n1= M.shape[0] + cdef int n2= M.shape[1] + + cdef float cost=0 + cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([n1, n2]) + + if not len(a): + a=np.ones((n1,))/n1 + + if not len(b): + b=np.ones((n2,))/n2 + + # calling the function + EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost) + + cost=0 + for i in range(n1): + for j in range(n2): + cost+=G[i,j]*M[i,j] + + return cost + diff --git a/ot/utils.py b/ot/utils.py index 2f68775..e5cd6c0 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -4,7 +4,7 @@ Various function that can be usefull """ import numpy as np from scipy.spatial.distance import cdist - +import multiprocessing import time __time_tic_toc=time.time() @@ -113,4 +113,31 @@ def dist0(n,method='lin_square'): def dots(*args): """ dots function for multiple matrix multiply """ - return reduce(np.dot,args) \ No newline at end of file + return reduce(np.dot,args) + +def fun(f, q_in, q_out): + """ Utility function for parmap with no serializing problems """ + while True: + i, x = q_in.get() + if i is None: + break + q_out.put((i, f(x))) + +def parmap(f, X, nprocs=multiprocessing.cpu_count()): + """ paralell map for multiprocessing """ + q_in = multiprocessing.Queue(1) + q_out = multiprocessing.Queue() + + proc = [multiprocessing.Process(target=fun, args=(f, q_in, q_out)) + for _ in range(nprocs)] + for p in proc: + p.daemon = True + p.start() + + sent = [q_in.put((i, x)) for i, x in enumerate(X)] + [q_in.put((None, None)) for _ in range(nprocs)] + res = [q_out.get() for _ in range(len(sent))] + + [p.join() for p in proc] + + return [x for i, x in sorted(res)] \ No newline at end of file diff --git a/test/test_emd_multi.py b/test/test_emd_multi.py new file mode 100644 index 0000000..ee0a20e --- /dev/null +++ b/test/test_emd_multi.py @@ -0,0 +1,48 @@ +#!/usr/bin/env python2 +# -*- coding: utf-8 -*- +""" +Created on Fri Mar 10 09:56:06 2017 + +@author: rflamary +""" + +import numpy as np +import pylab as pl +import ot + +from ot.datasets import get_1D_gauss as gauss +reload(ot.lp) + +#%% parameters + +n=5000 # nb bins + +# bin positions +x=np.arange(n,dtype=np.float64) + +# Gaussian distributions +a=gauss(n,m=20,s=5) # m= mean, s= std + +ls= range(20,1000,10) +nb=len(ls) +b=np.zeros((n,nb)) +for i in range(nb): + b[:,i]=gauss(n,m=ls[i],s=10) + +# loss matrix +M=ot.dist(x.reshape((n,1)),x.reshape((n,1))) +#M/=M.max() + +#%% + +print('Computing {} EMD '.format(nb)) + +# emd loss 1 proc +ot.tic() +emd_loss4=ot.emd2(a,b,M,1) +ot.toc('1 proc : {} s') + +# emd loss multipro proc +ot.tic() +emd_loss4=ot.emd2(a,b,M) +ot.toc('multi proc : {} s') -- cgit v1.2.3 From 0bbd36c0b569e58c3d47177e816b3541fac4b916 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 21 Mar 2017 09:32:06 +0100 Subject: cleanupt cpp wrapper name --- ot/lp/__init__.py | 2 +- ot/lp/emd.pyx | 131 ----------------------------------------------------- ot/lp/emd_wrap.pyx | 131 +++++++++++++++++++++++++++++++++++++++++++++++++++++ setup.py | 4 +- 4 files changed, 134 insertions(+), 134 deletions(-) delete mode 100644 ot/lp/emd.pyx create mode 100644 ot/lp/emd_wrap.pyx diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 5674cf6..2e7c52e 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -5,7 +5,7 @@ Solvers for the original linear program OT problem import numpy as np # import compiled emd -from .emd import emd_c, emd2_c +from .emd_wrap import emd_c, emd2_c from ..utils import parmap import multiprocessing diff --git a/ot/lp/emd.pyx b/ot/lp/emd.pyx deleted file mode 100644 index 46794ab..0000000 --- a/ot/lp/emd.pyx +++ /dev/null @@ -1,131 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Created on Thu Sep 11 08:42:08 2014 - -@author: rflamary -""" -import numpy as np -cimport numpy as np - -cimport cython - - - -cdef extern from "EMD.h": - void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost) - - - -@cython.boundscheck(False) -@cython.wraparound(False) -def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M): - """ - Solves the Earth Movers distance problem and returns the optimal transport matrix - - gamm=emd(a,b,M) - - .. math:: - \gamma = arg\min_\gamma <\gamma,M>_F - - s.t. \gamma 1 = a - - \gamma^T 1= b - - \gamma\geq 0 - where : - - - M is the metric cost matrix - - a and b are the sample weights - - Parameters - ---------- - a : (ns,) ndarray, float64 - source histogram - b : (nt,) ndarray, float64 - target histogram - M : (ns,nt) ndarray, float64 - loss matrix - - - Returns - ------- - gamma: (ns x nt) ndarray - Optimal transportation matrix for the given parameters - - """ - cdef int n1= M.shape[0] - cdef int n2= M.shape[1] - - cdef float cost=0 - cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([n1, n2]) - - if not len(a): - a=np.ones((n1,))/n1 - - if not len(b): - b=np.ones((n2,))/n2 - - # calling the function - EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost) - - return G - -@cython.boundscheck(False) -@cython.wraparound(False) -def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M): - """ - Solves the Earth Movers distance problem and returns the optimal transport loss - - gamm=emd(a,b,M) - - .. math:: - \gamma = arg\min_\gamma <\gamma,M>_F - - s.t. \gamma 1 = a - - \gamma^T 1= b - - \gamma\geq 0 - where : - - - M is the metric cost matrix - - a and b are the sample weights - - Parameters - ---------- - a : (ns,) ndarray, float64 - source histogram - b : (nt,) ndarray, float64 - target histogram - M : (ns,nt) ndarray, float64 - loss matrix - - - Returns - ------- - gamma: (ns x nt) ndarray - Optimal transportation matrix for the given parameters - - """ - cdef int n1= M.shape[0] - cdef int n2= M.shape[1] - - cdef float cost=0 - cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([n1, n2]) - - if not len(a): - a=np.ones((n1,))/n1 - - if not len(b): - b=np.ones((n2,))/n2 - - # calling the function - EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost) - - cost=0 - for i in range(n1): - for j in range(n2): - cost+=G[i,j]*M[i,j] - - return cost - diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx new file mode 100644 index 0000000..46794ab --- /dev/null +++ b/ot/lp/emd_wrap.pyx @@ -0,0 +1,131 @@ +# -*- coding: utf-8 -*- +""" +Created on Thu Sep 11 08:42:08 2014 + +@author: rflamary +""" +import numpy as np +cimport numpy as np + +cimport cython + + + +cdef extern from "EMD.h": + void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost) + + + +@cython.boundscheck(False) +@cython.wraparound(False) +def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M): + """ + Solves the Earth Movers distance problem and returns the optimal transport matrix + + gamm=emd(a,b,M) + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + where : + + - M is the metric cost matrix + - a and b are the sample weights + + Parameters + ---------- + a : (ns,) ndarray, float64 + source histogram + b : (nt,) ndarray, float64 + target histogram + M : (ns,nt) ndarray, float64 + loss matrix + + + Returns + ------- + gamma: (ns x nt) ndarray + Optimal transportation matrix for the given parameters + + """ + cdef int n1= M.shape[0] + cdef int n2= M.shape[1] + + cdef float cost=0 + cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([n1, n2]) + + if not len(a): + a=np.ones((n1,))/n1 + + if not len(b): + b=np.ones((n2,))/n2 + + # calling the function + EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost) + + return G + +@cython.boundscheck(False) +@cython.wraparound(False) +def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M): + """ + Solves the Earth Movers distance problem and returns the optimal transport loss + + gamm=emd(a,b,M) + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + where : + + - M is the metric cost matrix + - a and b are the sample weights + + Parameters + ---------- + a : (ns,) ndarray, float64 + source histogram + b : (nt,) ndarray, float64 + target histogram + M : (ns,nt) ndarray, float64 + loss matrix + + + Returns + ------- + gamma: (ns x nt) ndarray + Optimal transportation matrix for the given parameters + + """ + cdef int n1= M.shape[0] + cdef int n2= M.shape[1] + + cdef float cost=0 + cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([n1, n2]) + + if not len(a): + a=np.ones((n1,))/n1 + + if not len(b): + b=np.ones((n2,))/n2 + + # calling the function + EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost) + + cost=0 + for i in range(n1): + for j in range(n2): + cost+=G[i,j]*M[i,j] + + return cost + diff --git a/setup.py b/setup.py index 144c325..51c6861 100755 --- a/setup.py +++ b/setup.py @@ -38,8 +38,8 @@ setup(name='POT', url='https://github.com/rflamary/POT', packages=find_packages(), ext_modules = cythonize(Extension( - "ot.lp.emd", # the extension name - sources=["ot/lp/emd.pyx", "ot/lp/EMD_wrap.cpp"], # the Cython source and + "ot.lp.emd_wrap", # the extension name + sources=["ot/lp/emd_wrap.pyx", "ot/lp/EMD_wrap.cpp"], # the Cython source and # additional C++ source files language="c++", # generate and compile C++ code, include_dirs=[numpy.get_include(),os.path.join(ROOT,'ot/lp')])), -- cgit v1.2.3 From b30a380fe5a5115cd0a5596f08903259c077f12c Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 7 Apr 2017 10:58:15 +0200 Subject: avoid filename conclict in windows --- ot/lp/EMD_wrap.cpp | 120 - ot/lp/EMD_wrapper.cpp | 120 + ot/lp/emd.cpp | 7092 ------------------------------------------------- setup.py | 2 +- 4 files changed, 121 insertions(+), 7213 deletions(-) delete mode 100644 ot/lp/EMD_wrap.cpp create mode 100644 ot/lp/EMD_wrapper.cpp delete mode 100644 ot/lp/emd.cpp diff --git a/ot/lp/EMD_wrap.cpp b/ot/lp/EMD_wrap.cpp deleted file mode 100644 index 52cd262..0000000 --- a/ot/lp/EMD_wrap.cpp +++ /dev/null @@ -1,120 +0,0 @@ -/* This file is a c++ wrapper function for computing the transportation cost - * between two vectors given a cost matrix. - * - * It was written by Antoine Rolet (2014) and mainly consists of a wrapper - * of the code written by Nicolas Bonneel available on this page - * http://people.seas.harvard.edu/~nbonneel/FastTransport/ - * - * It was then modified to make it more amenable to python inline calling - * - * Please give relevant credit to the original author (Nicolas Bonneel) if - * you use this code for a publication. - * - */ - -#include "EMD.h" - - -void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost) { -// beware M and C anre strored in row major C style!!! - int n, m, i,cur; - double max,max_iter; - - - typedef FullBipartiteDigraph Digraph; - DIGRAPH_TYPEDEFS(FullBipartiteDigraph); - - // Get the number of non zero coordinates for r and c - n=0; - for (node_id_type i=0; i0) { - n++; - } - } - m=0; - for (node_id_type i=0; i0) { - m++; - } - } - - - // Define the graph - - std::vector indI(n), indJ(m); - std::vector weights1(n), weights2(m); - Digraph di(n, m); - NetworkSimplexSimple net(di, true, n+m, n*m,max_iter); - - // Set supply and demand, don't account for 0 values (faster) - - max=0; - cur=0; - for (node_id_type i=0; i0) { - weights1[ di.nodeFromId(cur) ] = val; - max+=val; - indI[cur++]=i; - } - } - - // Demand is actually negative supply... - - max=0; - cur=0; - for (node_id_type i=0; i0) { - weights2[ di.nodeFromId(cur) ] = -val; - indJ[cur++]=i; - - max-=val; - } - } - - - net.supplyMap(&weights1[0], n, &weights2[0], m); - - // Set the cost of each edge - max=0; - for (node_id_type i=0; imax) { - max=val; - } - } - } - - - // Solve the problem with the network simplex algorithm - - int ret=net.run(); - if (ret!=(int)net.OPTIMAL) { - if (ret==(int)net.INFEASIBLE) { - std::cout << "Infeasible problem"; - } - if (ret==(int)net.UNBOUNDED) - { - std::cout << "Unbounded problem"; - } - } else - { - for (node_id_type i=0; i0) { + n++; + } + } + m=0; + for (node_id_type i=0; i0) { + m++; + } + } + + + // Define the graph + + std::vector indI(n), indJ(m); + std::vector weights1(n), weights2(m); + Digraph di(n, m); + NetworkSimplexSimple net(di, true, n+m, n*m,max_iter); + + // Set supply and demand, don't account for 0 values (faster) + + max=0; + cur=0; + for (node_id_type i=0; i0) { + weights1[ di.nodeFromId(cur) ] = val; + max+=val; + indI[cur++]=i; + } + } + + // Demand is actually negative supply... + + max=0; + cur=0; + for (node_id_type i=0; i0) { + weights2[ di.nodeFromId(cur) ] = -val; + indJ[cur++]=i; + + max-=val; + } + } + + + net.supplyMap(&weights1[0], n, &weights2[0], m); + + // Set the cost of each edge + max=0; + for (node_id_type i=0; imax) { + max=val; + } + } + } + + + // Solve the problem with the network simplex algorithm + + int ret=net.run(); + if (ret!=(int)net.OPTIMAL) { + if (ret==(int)net.INFEASIBLE) { + std::cout << "Infeasible problem"; + } + if (ret==(int)net.UNBOUNDED) + { + std::cout << "Unbounded problem"; + } + } else + { + for (node_id_type i=0; i -#ifndef offsetof -#define offsetof(type, member) ( (size_t) & ((type*)0) -> member ) -#endif -#if !defined(WIN32) && !defined(MS_WINDOWS) - 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PyMethod_New(func, self) : PyInstanceMethod_New(func)) -#else - #define __Pyx_PyMethod_New(func, self, klass) PyMethod_New(func, self, klass) -#endif -#if PY_VERSION_HEX >= 0x030500B1 -#define __Pyx_PyAsyncMethodsStruct PyAsyncMethods -#define __Pyx_PyType_AsAsync(obj) (Py_TYPE(obj)->tp_as_async) -#elif CYTHON_COMPILING_IN_CPYTHON && PY_MAJOR_VERSION >= 3 -typedef struct { - unaryfunc am_await; - unaryfunc am_aiter; - unaryfunc am_anext; -} __Pyx_PyAsyncMethodsStruct; -#define __Pyx_PyType_AsAsync(obj) ((__Pyx_PyAsyncMethodsStruct*) (Py_TYPE(obj)->tp_reserved)) -#else -#define __Pyx_PyType_AsAsync(obj) NULL -#endif -#ifndef CYTHON_RESTRICT - #if defined(__GNUC__) - #define CYTHON_RESTRICT __restrict__ - #elif defined(_MSC_VER) && _MSC_VER >= 1400 - #define CYTHON_RESTRICT __restrict - #elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L - #define CYTHON_RESTRICT restrict - #else - #define CYTHON_RESTRICT - #endif -#endif -#define __Pyx_void_to_None(void_result) ((void)(void_result), Py_INCREF(Py_None), Py_None) - -#ifndef __cplusplus - #error "Cython files generated with the C++ option must be compiled with a C++ compiler." -#endif -#ifndef CYTHON_INLINE - #define CYTHON_INLINE inline -#endif -template -void __Pyx_call_destructor(T& x) { - x.~T(); -} -template -class __Pyx_FakeReference { - public: - __Pyx_FakeReference() : ptr(NULL) { } - __Pyx_FakeReference(const T& ref) : ptr(const_cast(&ref)) { } - T *operator->() { return ptr; } - operator T&() { return *ptr; } - private: - T *ptr; -}; - -#if defined(WIN32) || defined(MS_WINDOWS) - #define _USE_MATH_DEFINES -#endif -#include -#ifdef NAN -#define __PYX_NAN() ((float) NAN) -#else -static CYTHON_INLINE float __PYX_NAN() { - float value; - memset(&value, 0xFF, sizeof(value)); - return value; -} -#endif - - -#if PY_MAJOR_VERSION >= 3 - #define __Pyx_PyNumber_Divide(x,y) PyNumber_TrueDivide(x,y) - #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceTrueDivide(x,y) -#else - #define __Pyx_PyNumber_Divide(x,y) PyNumber_Divide(x,y) - #define __Pyx_PyNumber_InPlaceDivide(x,y) PyNumber_InPlaceDivide(x,y) -#endif - -#ifndef __PYX_EXTERN_C - #ifdef __cplusplus - #define __PYX_EXTERN_C extern "C" - #else - #define __PYX_EXTERN_C extern - #endif -#endif - -#define __PYX_HAVE__ot__lp__emd -#define __PYX_HAVE_API__ot__lp__emd -#include "string.h" -#include "stdio.h" -#include "stdlib.h" -#include "numpy/arrayobject.h" -#include "numpy/ufuncobject.h" -#include "EMD.h" -#ifdef _OPENMP -#include -#endif /* _OPENMP */ - -#ifdef PYREX_WITHOUT_ASSERTIONS -#define CYTHON_WITHOUT_ASSERTIONS -#endif - -#ifndef CYTHON_UNUSED -# if defined(__GNUC__) -# if !(defined(__cplusplus)) || (__GNUC__ > 3 || (__GNUC__ == 3 && __GNUC_MINOR__ >= 4)) -# define CYTHON_UNUSED __attribute__ ((__unused__)) -# else -# define CYTHON_UNUSED -# endif -# elif defined(__ICC) || (defined(__INTEL_COMPILER) && !defined(_MSC_VER)) -# define CYTHON_UNUSED __attribute__ ((__unused__)) -# else -# define CYTHON_UNUSED -# endif -#endif -#ifndef CYTHON_NCP_UNUSED -# if CYTHON_COMPILING_IN_CPYTHON -# define CYTHON_NCP_UNUSED -# else -# define CYTHON_NCP_UNUSED CYTHON_UNUSED -# endif -#endif -typedef struct {PyObject **p; char *s; const Py_ssize_t n; const char* encoding; - const char is_unicode; const char is_str; const char intern; } __Pyx_StringTabEntry; - -#define __PYX_DEFAULT_STRING_ENCODING_IS_ASCII 0 -#define __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT 0 -#define __PYX_DEFAULT_STRING_ENCODING "" -#define __Pyx_PyObject_FromString __Pyx_PyBytes_FromString -#define __Pyx_PyObject_FromStringAndSize __Pyx_PyBytes_FromStringAndSize -#define __Pyx_uchar_cast(c) ((unsigned char)c) -#define __Pyx_long_cast(x) ((long)x) -#define __Pyx_fits_Py_ssize_t(v, type, is_signed) (\ - (sizeof(type) < sizeof(Py_ssize_t)) ||\ - (sizeof(type) > sizeof(Py_ssize_t) &&\ - likely(v < (type)PY_SSIZE_T_MAX ||\ - v == (type)PY_SSIZE_T_MAX) &&\ - (!is_signed || likely(v > (type)PY_SSIZE_T_MIN ||\ - v == (type)PY_SSIZE_T_MIN))) ||\ - (sizeof(type) == sizeof(Py_ssize_t) &&\ - (is_signed || likely(v < (type)PY_SSIZE_T_MAX ||\ - v == (type)PY_SSIZE_T_MAX))) ) -#if defined (__cplusplus) && __cplusplus >= 201103L - #include - #define __Pyx_sst_abs(value) std::abs(value) -#elif SIZEOF_INT >= SIZEOF_SIZE_T - #define __Pyx_sst_abs(value) abs(value) -#elif SIZEOF_LONG >= SIZEOF_SIZE_T - #define __Pyx_sst_abs(value) labs(value) -#elif defined (_MSC_VER) && defined (_M_X64) - #define __Pyx_sst_abs(value) _abs64(value) -#elif defined (__STDC_VERSION__) && __STDC_VERSION__ >= 199901L - #define __Pyx_sst_abs(value) llabs(value) -#elif defined (__GNUC__) - #define __Pyx_sst_abs(value) __builtin_llabs(value) -#else - #define __Pyx_sst_abs(value) ((value<0) ? -value : value) -#endif -static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject*); -static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject*, Py_ssize_t* length); -#define __Pyx_PyByteArray_FromString(s) PyByteArray_FromStringAndSize((const char*)s, strlen((const char*)s)) -#define __Pyx_PyByteArray_FromStringAndSize(s, l) PyByteArray_FromStringAndSize((const char*)s, l) -#define __Pyx_PyBytes_FromString PyBytes_FromString -#define __Pyx_PyBytes_FromStringAndSize PyBytes_FromStringAndSize -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char*); -#if PY_MAJOR_VERSION < 3 - #define __Pyx_PyStr_FromString __Pyx_PyBytes_FromString - #define __Pyx_PyStr_FromStringAndSize __Pyx_PyBytes_FromStringAndSize -#else - #define __Pyx_PyStr_FromString __Pyx_PyUnicode_FromString - #define __Pyx_PyStr_FromStringAndSize __Pyx_PyUnicode_FromStringAndSize -#endif -#define __Pyx_PyObject_AsSString(s) ((signed char*) __Pyx_PyObject_AsString(s)) -#define __Pyx_PyObject_AsUString(s) ((unsigned char*) __Pyx_PyObject_AsString(s)) -#define __Pyx_PyObject_FromCString(s) __Pyx_PyObject_FromString((const char*)s) -#define __Pyx_PyBytes_FromCString(s) __Pyx_PyBytes_FromString((const char*)s) -#define __Pyx_PyByteArray_FromCString(s) __Pyx_PyByteArray_FromString((const char*)s) -#define __Pyx_PyStr_FromCString(s) __Pyx_PyStr_FromString((const char*)s) -#define __Pyx_PyUnicode_FromCString(s) __Pyx_PyUnicode_FromString((const char*)s) -#if PY_MAJOR_VERSION < 3 -static CYTHON_INLINE size_t __Pyx_Py_UNICODE_strlen(const Py_UNICODE *u) -{ - const Py_UNICODE *u_end = u; - while (*u_end++) ; - return (size_t)(u_end - u - 1); -} -#else -#define __Pyx_Py_UNICODE_strlen Py_UNICODE_strlen -#endif -#define __Pyx_PyUnicode_FromUnicode(u) PyUnicode_FromUnicode(u, __Pyx_Py_UNICODE_strlen(u)) -#define __Pyx_PyUnicode_FromUnicodeAndLength PyUnicode_FromUnicode -#define __Pyx_PyUnicode_AsUnicode PyUnicode_AsUnicode -#define __Pyx_NewRef(obj) (Py_INCREF(obj), obj) -#define __Pyx_Owned_Py_None(b) __Pyx_NewRef(Py_None) -#define __Pyx_PyBool_FromLong(b) ((b) ? __Pyx_NewRef(Py_True) : __Pyx_NewRef(Py_False)) -static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject*); -static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x); -static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject*); -static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t); -#if CYTHON_COMPILING_IN_CPYTHON -#define __pyx_PyFloat_AsDouble(x) (PyFloat_CheckExact(x) ? PyFloat_AS_DOUBLE(x) : PyFloat_AsDouble(x)) -#else -#define __pyx_PyFloat_AsDouble(x) PyFloat_AsDouble(x) -#endif -#define __pyx_PyFloat_AsFloat(x) ((float) __pyx_PyFloat_AsDouble(x)) -#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII -static int __Pyx_sys_getdefaultencoding_not_ascii; -static int __Pyx_init_sys_getdefaultencoding_params(void) { - PyObject* sys; - PyObject* default_encoding = NULL; - PyObject* ascii_chars_u = NULL; - PyObject* ascii_chars_b = NULL; - const char* default_encoding_c; - sys = PyImport_ImportModule("sys"); - if (!sys) goto bad; - default_encoding = PyObject_CallMethod(sys, (char*) "getdefaultencoding", NULL); - Py_DECREF(sys); - if (!default_encoding) goto bad; - default_encoding_c = PyBytes_AsString(default_encoding); - if (!default_encoding_c) goto bad; - if (strcmp(default_encoding_c, "ascii") == 0) { - __Pyx_sys_getdefaultencoding_not_ascii = 0; - } else { - char ascii_chars[128]; - int c; - for (c = 0; c < 128; c++) { - ascii_chars[c] = c; - } - __Pyx_sys_getdefaultencoding_not_ascii = 1; - ascii_chars_u = PyUnicode_DecodeASCII(ascii_chars, 128, NULL); - if (!ascii_chars_u) goto bad; - ascii_chars_b = PyUnicode_AsEncodedString(ascii_chars_u, default_encoding_c, NULL); - if (!ascii_chars_b || !PyBytes_Check(ascii_chars_b) || memcmp(ascii_chars, PyBytes_AS_STRING(ascii_chars_b), 128) != 0) { - PyErr_Format( - PyExc_ValueError, - "This module compiled with c_string_encoding=ascii, but default encoding '%.200s' is not a superset of ascii.", - default_encoding_c); - goto bad; - } - Py_DECREF(ascii_chars_u); - Py_DECREF(ascii_chars_b); - } - Py_DECREF(default_encoding); - return 0; -bad: - Py_XDECREF(default_encoding); - Py_XDECREF(ascii_chars_u); - Py_XDECREF(ascii_chars_b); - return -1; -} -#endif -#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT && PY_MAJOR_VERSION >= 3 -#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_DecodeUTF8(c_str, size, NULL) -#else -#define __Pyx_PyUnicode_FromStringAndSize(c_str, size) PyUnicode_Decode(c_str, size, __PYX_DEFAULT_STRING_ENCODING, NULL) -#if __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT -static char* __PYX_DEFAULT_STRING_ENCODING; -static int __Pyx_init_sys_getdefaultencoding_params(void) { - PyObject* sys; - PyObject* default_encoding = NULL; - char* default_encoding_c; - sys = PyImport_ImportModule("sys"); - if (!sys) goto bad; - default_encoding = PyObject_CallMethod(sys, (char*) (const char*) "getdefaultencoding", NULL); - Py_DECREF(sys); - if (!default_encoding) goto bad; - default_encoding_c = PyBytes_AsString(default_encoding); - if (!default_encoding_c) goto bad; - __PYX_DEFAULT_STRING_ENCODING = (char*) malloc(strlen(default_encoding_c)); - if (!__PYX_DEFAULT_STRING_ENCODING) goto bad; - strcpy(__PYX_DEFAULT_STRING_ENCODING, default_encoding_c); - Py_DECREF(default_encoding); - return 0; -bad: - Py_XDECREF(default_encoding); - return -1; -} -#endif -#endif - - -/* Test for GCC > 2.95 */ -#if defined(__GNUC__) && (__GNUC__ > 2 || (__GNUC__ == 2 && (__GNUC_MINOR__ > 95))) - #define likely(x) __builtin_expect(!!(x), 1) - #define unlikely(x) __builtin_expect(!!(x), 0) -#else /* !__GNUC__ or GCC < 2.95 */ - #define likely(x) (x) - #define unlikely(x) (x) -#endif /* __GNUC__ */ - -static PyObject *__pyx_m; 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This will probably the same as above, - but we don't have any guarantees. - */ -typedef struct { short x; char c; } __Pyx_pad_short; -typedef struct { int x; char c; } __Pyx_pad_int; -typedef struct { long x; char c; } __Pyx_pad_long; -typedef struct { float x; char c; } __Pyx_pad_float; -typedef struct { double x; char c; } __Pyx_pad_double; -typedef struct { long double x; char c; } __Pyx_pad_longdouble; -typedef struct { void *x; char c; } __Pyx_pad_void_p; -#ifdef HAVE_LONG_LONG -typedef struct { PY_LONG_LONG x; char c; } __Pyx_pad_longlong; -#endif -static size_t __Pyx_BufFmt_TypeCharToPadding(char ch, CYTHON_UNUSED int is_complex) { - switch (ch) { - case '?': case 'c': case 'b': case 'B': case 's': case 'p': return 1; - case 'h': case 'H': return sizeof(__Pyx_pad_short) - sizeof(short); - case 'i': case 'I': return sizeof(__Pyx_pad_int) - sizeof(int); - case 'l': case 'L': return sizeof(__Pyx_pad_long) - sizeof(long); -#ifdef HAVE_LONG_LONG - case 'q': case 'Q': return sizeof(__Pyx_pad_longlong) - sizeof(PY_LONG_LONG); -#endif - case 'f': return sizeof(__Pyx_pad_float) - sizeof(float); - case 'd': return sizeof(__Pyx_pad_double) - sizeof(double); - case 'g': return sizeof(__Pyx_pad_longdouble) - sizeof(long double); - case 'P': case 'O': return sizeof(__Pyx_pad_void_p) - sizeof(void*); - default: - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } -} -static char __Pyx_BufFmt_TypeCharToGroup(char ch, int is_complex) { - switch (ch) { - case 'c': - return 'H'; - case 'b': case 'h': case 'i': - case 'l': case 'q': case 's': case 'p': - return 'I'; - case 'B': case 'H': case 'I': case 'L': case 'Q': - return 'U'; - case 'f': case 'd': case 'g': - return (is_complex ? 'C' : 'R'); - case 'O': - return 'O'; - case 'P': - return 'P'; - default: { - __Pyx_BufFmt_RaiseUnexpectedChar(ch); - return 0; - } - } -} -static void __Pyx_BufFmt_RaiseExpected(__Pyx_BufFmt_Context* ctx) { - if (ctx->head == NULL || ctx->head->field == &ctx->root) { - const char* expected; - const char* quote; - if (ctx->head == NULL) { - expected = "end"; - quote = ""; - } else { - expected = ctx->head->field->type->name; - quote = "'"; - } - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch, expected %s%s%s but got %s", - quote, expected, quote, - __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex)); - } else { - __Pyx_StructField* field = ctx->head->field; - __Pyx_StructField* parent = (ctx->head - 1)->field; - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch, expected '%s' but got %s in '%s.%s'", - field->type->name, __Pyx_BufFmt_DescribeTypeChar(ctx->enc_type, ctx->is_complex), - parent->type->name, field->name); - } -} -static int __Pyx_BufFmt_ProcessTypeChunk(__Pyx_BufFmt_Context* ctx) { - char group; - size_t size, offset, arraysize = 1; - if (ctx->enc_type == 0) return 0; - if (ctx->head->field->type->arraysize[0]) { - int i, ndim = 0; - if (ctx->enc_type == 's' || ctx->enc_type == 'p') { - ctx->is_valid_array = ctx->head->field->type->ndim == 1; - ndim = 1; - if (ctx->enc_count != ctx->head->field->type->arraysize[0]) { - PyErr_Format(PyExc_ValueError, - "Expected a dimension of size %zu, got %zu", - ctx->head->field->type->arraysize[0], ctx->enc_count); - return -1; - } - } - if (!ctx->is_valid_array) { - PyErr_Format(PyExc_ValueError, "Expected %d dimensions, got %d", - ctx->head->field->type->ndim, ndim); - return -1; - } - for (i = 0; i < ctx->head->field->type->ndim; i++) { - arraysize *= ctx->head->field->type->arraysize[i]; - } - ctx->is_valid_array = 0; - ctx->enc_count = 1; - } - group = __Pyx_BufFmt_TypeCharToGroup(ctx->enc_type, ctx->is_complex); - do { - __Pyx_StructField* field = ctx->head->field; - __Pyx_TypeInfo* type = field->type; - if (ctx->enc_packmode == '@' || ctx->enc_packmode == '^') { - size = __Pyx_BufFmt_TypeCharToNativeSize(ctx->enc_type, ctx->is_complex); - } else { - size = __Pyx_BufFmt_TypeCharToStandardSize(ctx->enc_type, ctx->is_complex); - } - if (ctx->enc_packmode == '@') { - size_t align_at = __Pyx_BufFmt_TypeCharToAlignment(ctx->enc_type, ctx->is_complex); - size_t align_mod_offset; - if (align_at == 0) return -1; - align_mod_offset = ctx->fmt_offset % align_at; - if (align_mod_offset > 0) ctx->fmt_offset += align_at - align_mod_offset; - if (ctx->struct_alignment == 0) - ctx->struct_alignment = __Pyx_BufFmt_TypeCharToPadding(ctx->enc_type, - ctx->is_complex); - } - if (type->size != size || type->typegroup != group) { - if (type->typegroup == 'C' && type->fields != NULL) { - size_t parent_offset = ctx->head->parent_offset + field->offset; - ++ctx->head; - ctx->head->field = type->fields; - ctx->head->parent_offset = parent_offset; - continue; - } - if ((type->typegroup == 'H' || group == 'H') && type->size == size) { - } else { - __Pyx_BufFmt_RaiseExpected(ctx); - return -1; - } - } - offset = ctx->head->parent_offset + field->offset; - if (ctx->fmt_offset != offset) { - PyErr_Format(PyExc_ValueError, - "Buffer dtype mismatch; next field is at offset %" CYTHON_FORMAT_SSIZE_T "d but %" CYTHON_FORMAT_SSIZE_T "d expected", - (Py_ssize_t)ctx->fmt_offset, (Py_ssize_t)offset); - return -1; - } - ctx->fmt_offset += size; - if (arraysize) - ctx->fmt_offset += (arraysize - 1) * size; - --ctx->enc_count; - while (1) { - if (field == &ctx->root) { - ctx->head = NULL; - if (ctx->enc_count != 0) { - __Pyx_BufFmt_RaiseExpected(ctx); - return -1; - } - break; - } - ctx->head->field = ++field; - if (field->type == NULL) { - --ctx->head; - field = ctx->head->field; - continue; - } else if (field->type->typegroup == 'S') { - size_t parent_offset = ctx->head->parent_offset + field->offset; - if (field->type->fields->type == NULL) continue; - field = field->type->fields; - ++ctx->head; - ctx->head->field = field; - ctx->head->parent_offset = parent_offset; - break; - } else { - break; - } - } - } while (ctx->enc_count); - ctx->enc_type = 0; - ctx->is_complex = 0; - return 0; -} -static CYTHON_INLINE PyObject * -__pyx_buffmt_parse_array(__Pyx_BufFmt_Context* ctx, const char** tsp) -{ - const char *ts = *tsp; - int i = 0, number; - int ndim = ctx->head->field->type->ndim; -; - ++ts; - if (ctx->new_count != 1) { - PyErr_SetString(PyExc_ValueError, - "Cannot handle repeated arrays in format string"); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - while (*ts && *ts != ')') { - switch (*ts) { - case ' ': case '\f': case '\r': case '\n': case '\t': case '\v': continue; - default: break; - } - number = __Pyx_BufFmt_ExpectNumber(&ts); - if (number == -1) return NULL; - if (i < ndim && (size_t) number != ctx->head->field->type->arraysize[i]) - return PyErr_Format(PyExc_ValueError, - "Expected a dimension of size %zu, got %d", - ctx->head->field->type->arraysize[i], number); - if (*ts != ',' && *ts != ')') - return PyErr_Format(PyExc_ValueError, - "Expected a comma in format string, got '%c'", *ts); - if (*ts == ',') ts++; - i++; - } - if (i != ndim) - return PyErr_Format(PyExc_ValueError, "Expected %d dimension(s), got %d", - ctx->head->field->type->ndim, i); - if (!*ts) { - PyErr_SetString(PyExc_ValueError, - "Unexpected end of format string, expected ')'"); - return NULL; - } - ctx->is_valid_array = 1; - ctx->new_count = 1; - *tsp = ++ts; - return Py_None; -} -static const char* __Pyx_BufFmt_CheckString(__Pyx_BufFmt_Context* ctx, const char* ts) { - int got_Z = 0; - while (1) { - switch(*ts) { - case 0: - if (ctx->enc_type != 0 && ctx->head == NULL) { - __Pyx_BufFmt_RaiseExpected(ctx); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - if (ctx->head != NULL) { - __Pyx_BufFmt_RaiseExpected(ctx); - return NULL; - } - return ts; - case ' ': - case '\r': - case '\n': - ++ts; - break; - case '<': - if (!__Pyx_IsLittleEndian()) { - PyErr_SetString(PyExc_ValueError, "Little-endian buffer not supported on big-endian compiler"); - return NULL; - } - ctx->new_packmode = '='; - ++ts; - break; - case '>': - case '!': - if (__Pyx_IsLittleEndian()) { - PyErr_SetString(PyExc_ValueError, "Big-endian buffer not supported on little-endian compiler"); - return NULL; - } - ctx->new_packmode = '='; - ++ts; - break; - case '=': - case '@': - case '^': - ctx->new_packmode = *ts++; - break; - case 'T': - { - const char* ts_after_sub; - size_t i, struct_count = ctx->new_count; - size_t struct_alignment = ctx->struct_alignment; - ctx->new_count = 1; - ++ts; - if (*ts != '{') { - PyErr_SetString(PyExc_ValueError, "Buffer acquisition: Expected '{' after 'T'"); - return NULL; - } - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_type = 0; - ctx->enc_count = 0; - ctx->struct_alignment = 0; - ++ts; - ts_after_sub = ts; - for (i = 0; i != struct_count; ++i) { - ts_after_sub = __Pyx_BufFmt_CheckString(ctx, ts); - if (!ts_after_sub) return NULL; - } - ts = ts_after_sub; - if (struct_alignment) ctx->struct_alignment = struct_alignment; - } - break; - case '}': - { - size_t alignment = ctx->struct_alignment; - ++ts; - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_type = 0; - if (alignment && ctx->fmt_offset % alignment) { - ctx->fmt_offset += alignment - (ctx->fmt_offset % alignment); - } - } - return ts; - case 'x': - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->fmt_offset += ctx->new_count; - ctx->new_count = 1; - ctx->enc_count = 0; - ctx->enc_type = 0; - ctx->enc_packmode = ctx->new_packmode; - ++ts; - break; - case 'Z': - got_Z = 1; - ++ts; - if (*ts != 'f' && *ts != 'd' && *ts != 'g') { - __Pyx_BufFmt_RaiseUnexpectedChar('Z'); - return NULL; - } - case 'c': case 'b': case 'B': case 'h': case 'H': case 'i': case 'I': - case 'l': case 'L': case 'q': case 'Q': - case 'f': case 'd': case 'g': - case 'O': case 'p': - if (ctx->enc_type == *ts && got_Z == ctx->is_complex && - ctx->enc_packmode == ctx->new_packmode) { - ctx->enc_count += ctx->new_count; - ctx->new_count = 1; - got_Z = 0; - ++ts; - break; - } - case 's': - if (__Pyx_BufFmt_ProcessTypeChunk(ctx) == -1) return NULL; - ctx->enc_count = ctx->new_count; - ctx->enc_packmode = ctx->new_packmode; - ctx->enc_type = *ts; - ctx->is_complex = got_Z; - ++ts; - ctx->new_count = 1; - got_Z = 0; - break; - case ':': - ++ts; - while(*ts != ':') ++ts; - ++ts; - break; - case '(': - if (!__pyx_buffmt_parse_array(ctx, &ts)) return NULL; - break; - default: - { - int number = __Pyx_BufFmt_ExpectNumber(&ts); - if (number == -1) return NULL; - ctx->new_count = (size_t)number; - } - } - } -} -static CYTHON_INLINE void __Pyx_ZeroBuffer(Py_buffer* buf) { - buf->buf = NULL; - buf->obj = NULL; - buf->strides = __Pyx_zeros; - buf->shape = __Pyx_zeros; - buf->suboffsets = __Pyx_minusones; -} -static CYTHON_INLINE int __Pyx_GetBufferAndValidate( - Py_buffer* buf, PyObject* obj, __Pyx_TypeInfo* dtype, int flags, - int nd, int cast, __Pyx_BufFmt_StackElem* stack) -{ - if (obj == Py_None || obj == NULL) { - __Pyx_ZeroBuffer(buf); - return 0; - } - buf->buf = NULL; - if (__Pyx_GetBuffer(obj, buf, flags) == -1) goto fail; - if (buf->ndim != nd) { - PyErr_Format(PyExc_ValueError, - "Buffer has wrong number of dimensions (expected %d, got %d)", - nd, buf->ndim); - goto fail; - } - if (!cast) { - __Pyx_BufFmt_Context ctx; - __Pyx_BufFmt_Init(&ctx, stack, dtype); - if (!__Pyx_BufFmt_CheckString(&ctx, buf->format)) goto fail; - } - if ((unsigned)buf->itemsize != dtype->size) { - PyErr_Format(PyExc_ValueError, - "Item size of buffer (%" CYTHON_FORMAT_SSIZE_T "d byte%s) does not match size of '%s' (%" CYTHON_FORMAT_SSIZE_T "d byte%s)", - buf->itemsize, (buf->itemsize > 1) ? "s" : "", - dtype->name, (Py_ssize_t)dtype->size, (dtype->size > 1) ? "s" : ""); - goto fail; - } - if (buf->suboffsets == NULL) buf->suboffsets = __Pyx_minusones; - return 0; -fail:; - __Pyx_ZeroBuffer(buf); - return -1; -} -static CYTHON_INLINE void __Pyx_SafeReleaseBuffer(Py_buffer* info) { - if (info->buf == NULL) return; - if (info->suboffsets == __Pyx_minusones) info->suboffsets = NULL; - __Pyx_ReleaseBuffer(info); -} - -static CYTHON_INLINE PyObject *__Pyx_GetModuleGlobalName(PyObject *name) { - PyObject *result; -#if CYTHON_COMPILING_IN_CPYTHON - result = PyDict_GetItem(__pyx_d, name); - if (likely(result)) { - Py_INCREF(result); - } else { -#else - result = PyObject_GetItem(__pyx_d, name); - if (!result) { - PyErr_Clear(); -#endif - result = __Pyx_GetBuiltinName(name); - } - return result; -} - -#if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_Call(PyObject *func, PyObject *arg, PyObject *kw) { - PyObject *result; - ternaryfunc call = func->ob_type->tp_call; - if (unlikely(!call)) - return PyObject_Call(func, arg, kw); - if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) - return NULL; - result = (*call)(func, arg, kw); - Py_LeaveRecursiveCall(); - if (unlikely(!result) && unlikely(!PyErr_Occurred())) { - PyErr_SetString( - PyExc_SystemError, - "NULL result without error in PyObject_Call"); - } - return result; -} -#endif - -#if CYTHON_COMPILING_IN_CPYTHON -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallMethO(PyObject *func, PyObject *arg) { - PyObject *self, *result; - PyCFunction cfunc; - cfunc = PyCFunction_GET_FUNCTION(func); - self = PyCFunction_GET_SELF(func); - if (unlikely(Py_EnterRecursiveCall((char*)" while calling a Python object"))) - return NULL; - result = cfunc(self, arg); - Py_LeaveRecursiveCall(); - if (unlikely(!result) && unlikely(!PyErr_Occurred())) { - PyErr_SetString( - PyExc_SystemError, - "NULL result without error in PyObject_Call"); - } - return result; -} -#endif - -#if CYTHON_COMPILING_IN_CPYTHON -static PyObject* __Pyx__PyObject_CallOneArg(PyObject *func, PyObject *arg) { - PyObject *result; - PyObject *args = PyTuple_New(1); - if (unlikely(!args)) return NULL; - Py_INCREF(arg); - PyTuple_SET_ITEM(args, 0, arg); - result = __Pyx_PyObject_Call(func, args, NULL); - Py_DECREF(args); - return result; -} -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { -#ifdef __Pyx_CyFunction_USED - if (likely(PyCFunction_Check(func) || PyObject_TypeCheck(func, __pyx_CyFunctionType))) { -#else - if (likely(PyCFunction_Check(func))) { -#endif - if (likely(PyCFunction_GET_FLAGS(func) & METH_O)) { - return __Pyx_PyObject_CallMethO(func, arg); - } - } - return __Pyx__PyObject_CallOneArg(func, arg); -} -#else -static CYTHON_INLINE PyObject* __Pyx_PyObject_CallOneArg(PyObject *func, PyObject *arg) { - PyObject *result; - PyObject *args = PyTuple_Pack(1, arg); - if (unlikely(!args)) return NULL; - result = __Pyx_PyObject_Call(func, args, NULL); - Py_DECREF(args); - return result; -} -#endif - -static CYTHON_INLINE int __Pyx_TypeTest(PyObject *obj, PyTypeObject *type) { - if (unlikely(!type)) { - PyErr_SetString(PyExc_SystemError, "Missing type object"); - return 0; - } - if (likely(PyObject_TypeCheck(obj, type))) - return 1; - PyErr_Format(PyExc_TypeError, "Cannot convert %.200s to %.200s", - Py_TYPE(obj)->tp_name, type->tp_name); - return 0; -} - -static void __Pyx_RaiseBufferFallbackError(void) { - PyErr_SetString(PyExc_ValueError, - "Buffer acquisition failed on assignment; and then reacquiring the old buffer failed too!"); -} - -static CYTHON_INLINE void __Pyx_ErrRestore(PyObject *type, PyObject *value, PyObject *tb) { -#if CYTHON_COMPILING_IN_CPYTHON - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyThreadState *tstate = PyThreadState_GET(); - tmp_type = tstate->curexc_type; - tmp_value = tstate->curexc_value; - tmp_tb = tstate->curexc_traceback; - tstate->curexc_type = type; - tstate->curexc_value = value; - tstate->curexc_traceback = tb; - Py_XDECREF(tmp_type); - Py_XDECREF(tmp_value); - Py_XDECREF(tmp_tb); -#else - PyErr_Restore(type, value, tb); -#endif -} -static CYTHON_INLINE void __Pyx_ErrFetch(PyObject **type, PyObject **value, PyObject **tb) { -#if CYTHON_COMPILING_IN_CPYTHON - PyThreadState *tstate = PyThreadState_GET(); - *type = tstate->curexc_type; - *value = tstate->curexc_value; - *tb = tstate->curexc_traceback; - tstate->curexc_type = 0; - tstate->curexc_value = 0; - tstate->curexc_traceback = 0; -#else - PyErr_Fetch(type, value, tb); -#endif -} - -#if PY_MAJOR_VERSION < 3 -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, - CYTHON_UNUSED PyObject *cause) { - Py_XINCREF(type); - if (!value || value == Py_None) - value = NULL; - else - Py_INCREF(value); - if (!tb || tb == Py_None) - tb = NULL; - else { - Py_INCREF(tb); - if (!PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto raise_error; - } - } - if (PyType_Check(type)) { -#if CYTHON_COMPILING_IN_PYPY - if (!value) { - Py_INCREF(Py_None); - value = Py_None; - } -#endif - PyErr_NormalizeException(&type, &value, &tb); - } else { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto raise_error; - } - value = type; - type = (PyObject*) Py_TYPE(type); - Py_INCREF(type); - if (!PyType_IsSubtype((PyTypeObject *)type, (PyTypeObject *)PyExc_BaseException)) { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto raise_error; - } - } - __Pyx_ErrRestore(type, value, tb); - return; -raise_error: - Py_XDECREF(value); - Py_XDECREF(type); - Py_XDECREF(tb); - return; -} -#else -static void __Pyx_Raise(PyObject *type, PyObject *value, PyObject *tb, PyObject *cause) { - PyObject* owned_instance = NULL; - if (tb == Py_None) { - tb = 0; - } else if (tb && !PyTraceBack_Check(tb)) { - PyErr_SetString(PyExc_TypeError, - "raise: arg 3 must be a traceback or None"); - goto bad; - } - if (value == Py_None) - value = 0; - if (PyExceptionInstance_Check(type)) { - if (value) { - PyErr_SetString(PyExc_TypeError, - "instance exception may not have a separate value"); - goto bad; - } - value = type; - type = (PyObject*) Py_TYPE(value); - } else if (PyExceptionClass_Check(type)) { - PyObject *instance_class = NULL; - if (value && PyExceptionInstance_Check(value)) { - instance_class = (PyObject*) Py_TYPE(value); - if (instance_class != type) { - int is_subclass = PyObject_IsSubclass(instance_class, type); - if (!is_subclass) { - instance_class = NULL; - } else if (unlikely(is_subclass == -1)) { - goto bad; - } else { - type = instance_class; - } - } - } - if (!instance_class) { - PyObject *args; - if (!value) - args = PyTuple_New(0); - else if (PyTuple_Check(value)) { - Py_INCREF(value); - args = value; - } else - args = PyTuple_Pack(1, value); - if (!args) - goto bad; - owned_instance = PyObject_Call(type, args, NULL); - Py_DECREF(args); - if (!owned_instance) - goto bad; - value = owned_instance; - if (!PyExceptionInstance_Check(value)) { - PyErr_Format(PyExc_TypeError, - "calling %R should have returned an instance of " - "BaseException, not %R", - type, Py_TYPE(value)); - goto bad; - } - } - } else { - PyErr_SetString(PyExc_TypeError, - "raise: exception class must be a subclass of BaseException"); - goto bad; - } -#if PY_VERSION_HEX >= 0x03030000 - if (cause) { -#else - if (cause && cause != Py_None) { -#endif - PyObject *fixed_cause; - if (cause == Py_None) { - fixed_cause = NULL; - } else if (PyExceptionClass_Check(cause)) { - fixed_cause = PyObject_CallObject(cause, NULL); - if (fixed_cause == NULL) - goto bad; - } else if (PyExceptionInstance_Check(cause)) { - fixed_cause = cause; - Py_INCREF(fixed_cause); - } else { - PyErr_SetString(PyExc_TypeError, - "exception causes must derive from " - "BaseException"); - goto bad; - } - PyException_SetCause(value, fixed_cause); - } - PyErr_SetObject(type, value); - if (tb) { -#if CYTHON_COMPILING_IN_PYPY - PyObject *tmp_type, *tmp_value, *tmp_tb; - PyErr_Fetch(&tmp_type, &tmp_value, &tmp_tb); - Py_INCREF(tb); - PyErr_Restore(tmp_type, tmp_value, tb); - Py_XDECREF(tmp_tb); -#else - PyThreadState *tstate = PyThreadState_GET(); - PyObject* tmp_tb = tstate->curexc_traceback; - if (tb != tmp_tb) { - Py_INCREF(tb); - tstate->curexc_traceback = tb; - Py_XDECREF(tmp_tb); - } -#endif - } -bad: - Py_XDECREF(owned_instance); - return; -} -#endif - -static CYTHON_INLINE void __Pyx_RaiseTooManyValuesError(Py_ssize_t expected) { - PyErr_Format(PyExc_ValueError, - "too many values to unpack (expected %" CYTHON_FORMAT_SSIZE_T "d)", expected); -} - -static CYTHON_INLINE void __Pyx_RaiseNeedMoreValuesError(Py_ssize_t index) { - PyErr_Format(PyExc_ValueError, - "need more than %" CYTHON_FORMAT_SSIZE_T "d value%.1s to unpack", - index, (index == 1) ? "" : "s"); -} - -static CYTHON_INLINE void __Pyx_RaiseNoneNotIterableError(void) { - PyErr_SetString(PyExc_TypeError, "'NoneType' object is not iterable"); -} - -static PyObject *__Pyx_Import(PyObject *name, PyObject *from_list, int level) { - PyObject *empty_list = 0; - PyObject *module = 0; - PyObject *global_dict = 0; - PyObject *empty_dict = 0; - PyObject *list; - #if PY_VERSION_HEX < 0x03030000 - PyObject *py_import; - py_import = __Pyx_PyObject_GetAttrStr(__pyx_b, __pyx_n_s_import); - if (!py_import) - goto bad; - #endif - if (from_list) - list = from_list; - else { - empty_list = PyList_New(0); - if (!empty_list) - goto bad; - list = empty_list; - } - global_dict = PyModule_GetDict(__pyx_m); - if (!global_dict) - goto bad; - empty_dict = PyDict_New(); - if (!empty_dict) - goto bad; - { - #if PY_MAJOR_VERSION >= 3 - if (level == -1) { - if (strchr(__Pyx_MODULE_NAME, '.')) { - #if PY_VERSION_HEX < 0x03030000 - PyObject *py_level = PyInt_FromLong(1); - if (!py_level) - goto bad; - module = PyObject_CallFunctionObjArgs(py_import, - name, global_dict, empty_dict, list, py_level, NULL); - Py_DECREF(py_level); - #else - module = PyImport_ImportModuleLevelObject( - name, global_dict, empty_dict, list, 1); - #endif - if (!module) { - if (!PyErr_ExceptionMatches(PyExc_ImportError)) - goto bad; - PyErr_Clear(); - } - } - level = 0; - } - #endif - if (!module) { - #if PY_VERSION_HEX < 0x03030000 - PyObject *py_level = PyInt_FromLong(level); - if (!py_level) - goto bad; - module = PyObject_CallFunctionObjArgs(py_import, - name, global_dict, empty_dict, list, py_level, NULL); - Py_DECREF(py_level); - #else - module = PyImport_ImportModuleLevelObject( - name, global_dict, empty_dict, list, level); - #endif - } - } -bad: - #if PY_VERSION_HEX < 0x03030000 - Py_XDECREF(py_import); - #endif - Py_XDECREF(empty_list); - Py_XDECREF(empty_dict); - return module; -} - -static int __pyx_bisect_code_objects(__Pyx_CodeObjectCacheEntry* entries, int count, int code_line) { - int start = 0, mid = 0, end = count - 1; - if (end >= 0 && code_line > entries[end].code_line) { - return count; - } - while (start < end) { - mid = start + (end - start) / 2; - if (code_line < entries[mid].code_line) { - end = mid; - } else if (code_line > entries[mid].code_line) { - start = mid + 1; - } else { - return mid; - } - } - if (code_line <= entries[mid].code_line) { - return mid; - } else { - return mid + 1; - } -} -static PyCodeObject *__pyx_find_code_object(int code_line) { - PyCodeObject* code_object; - int pos; - if (unlikely(!code_line) || unlikely(!__pyx_code_cache.entries)) { - return NULL; - } - pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); - if (unlikely(pos >= __pyx_code_cache.count) || unlikely(__pyx_code_cache.entries[pos].code_line != code_line)) { - return NULL; - } - code_object = __pyx_code_cache.entries[pos].code_object; - Py_INCREF(code_object); - return code_object; -} -static void __pyx_insert_code_object(int code_line, PyCodeObject* code_object) { - int pos, i; - __Pyx_CodeObjectCacheEntry* entries = __pyx_code_cache.entries; - if (unlikely(!code_line)) { - return; - } - if (unlikely(!entries)) { - entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Malloc(64*sizeof(__Pyx_CodeObjectCacheEntry)); - if (likely(entries)) { - __pyx_code_cache.entries = entries; - __pyx_code_cache.max_count = 64; - __pyx_code_cache.count = 1; - entries[0].code_line = code_line; - entries[0].code_object = code_object; - Py_INCREF(code_object); - } - return; - } - pos = __pyx_bisect_code_objects(__pyx_code_cache.entries, __pyx_code_cache.count, code_line); - if ((pos < __pyx_code_cache.count) && unlikely(__pyx_code_cache.entries[pos].code_line == code_line)) { - PyCodeObject* tmp = entries[pos].code_object; - entries[pos].code_object = code_object; - Py_DECREF(tmp); - return; - } - if (__pyx_code_cache.count == __pyx_code_cache.max_count) { - int new_max = __pyx_code_cache.max_count + 64; - entries = (__Pyx_CodeObjectCacheEntry*)PyMem_Realloc( - __pyx_code_cache.entries, (size_t)new_max*sizeof(__Pyx_CodeObjectCacheEntry)); - if (unlikely(!entries)) { - return; - } - __pyx_code_cache.entries = entries; - __pyx_code_cache.max_count = new_max; - } - for (i=__pyx_code_cache.count; i>pos; i--) { - entries[i] = entries[i-1]; - } - entries[pos].code_line = code_line; - entries[pos].code_object = code_object; - __pyx_code_cache.count++; - Py_INCREF(code_object); -} - -#include "compile.h" -#include "frameobject.h" -#include "traceback.h" -static PyCodeObject* __Pyx_CreateCodeObjectForTraceback( - const char *funcname, int c_line, - int py_line, const char *filename) { - PyCodeObject *py_code = 0; - PyObject *py_srcfile = 0; - PyObject *py_funcname = 0; - #if PY_MAJOR_VERSION < 3 - py_srcfile = PyString_FromString(filename); - #else - py_srcfile = PyUnicode_FromString(filename); - #endif - if (!py_srcfile) goto bad; - if (c_line) { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - #else - py_funcname = PyUnicode_FromFormat( "%s (%s:%d)", funcname, __pyx_cfilenm, c_line); - #endif - } - else { - #if PY_MAJOR_VERSION < 3 - py_funcname = PyString_FromString(funcname); - #else - py_funcname = PyUnicode_FromString(funcname); - #endif - } - if (!py_funcname) goto bad; - py_code = __Pyx_PyCode_New( - 0, - 0, - 0, - 0, - 0, - __pyx_empty_bytes, /*PyObject *code,*/ - __pyx_empty_tuple, /*PyObject *consts,*/ - __pyx_empty_tuple, /*PyObject *names,*/ - __pyx_empty_tuple, /*PyObject *varnames,*/ - __pyx_empty_tuple, /*PyObject *freevars,*/ - __pyx_empty_tuple, /*PyObject *cellvars,*/ - py_srcfile, /*PyObject *filename,*/ - py_funcname, /*PyObject *name,*/ - py_line, - __pyx_empty_bytes /*PyObject *lnotab*/ - ); - Py_DECREF(py_srcfile); - Py_DECREF(py_funcname); - return py_code; -bad: - Py_XDECREF(py_srcfile); - Py_XDECREF(py_funcname); - return NULL; -} -static void __Pyx_AddTraceback(const char *funcname, int c_line, - int py_line, const char *filename) { - PyCodeObject *py_code = 0; - PyFrameObject *py_frame = 0; - py_code = __pyx_find_code_object(c_line ? c_line : py_line); - if (!py_code) { - py_code = __Pyx_CreateCodeObjectForTraceback( - funcname, c_line, py_line, filename); - if (!py_code) goto bad; - __pyx_insert_code_object(c_line ? c_line : py_line, py_code); - } - py_frame = PyFrame_New( - PyThreadState_GET(), /*PyThreadState *tstate,*/ - py_code, /*PyCodeObject *code,*/ - __pyx_d, /*PyObject *globals,*/ - 0 /*PyObject *locals*/ - ); - if (!py_frame) goto bad; - py_frame->f_lineno = py_line; - PyTraceBack_Here(py_frame); -bad: - Py_XDECREF(py_code); - Py_XDECREF(py_frame); -} - -#if PY_MAJOR_VERSION < 3 -static int __Pyx_GetBuffer(PyObject *obj, Py_buffer *view, int flags) { - if (PyObject_CheckBuffer(obj)) return PyObject_GetBuffer(obj, view, flags); - if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) return __pyx_pw_5numpy_7ndarray_1__getbuffer__(obj, view, flags); - PyErr_Format(PyExc_TypeError, "'%.200s' does not have the buffer interface", Py_TYPE(obj)->tp_name); - return -1; -} -static void __Pyx_ReleaseBuffer(Py_buffer *view) { - PyObject *obj = view->obj; - if (!obj) return; - if (PyObject_CheckBuffer(obj)) { - PyBuffer_Release(view); - return; - } - if (PyObject_TypeCheck(obj, __pyx_ptype_5numpy_ndarray)) { __pyx_pw_5numpy_7ndarray_3__releasebuffer__(obj, view); return; } - Py_DECREF(obj); - view->obj = NULL; -} -#endif - - - static CYTHON_INLINE PyObject* __Pyx_PyInt_From_int(int value) { - const int neg_one = (int) -1, const_zero = (int) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(int) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(int) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); - } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); - } - } else { - if (sizeof(int) <= sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(int), - little, !is_unsigned); - } -} - -#define __PYX_VERIFY_RETURN_INT(target_type, func_type, func_value)\ - __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 0) -#define __PYX_VERIFY_RETURN_INT_EXC(target_type, func_type, func_value)\ - __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, 1) -#define __PYX__VERIFY_RETURN_INT(target_type, func_type, func_value, exc)\ - {\ - func_type value = func_value;\ - if (sizeof(target_type) < sizeof(func_type)) {\ - if (unlikely(value != (func_type) (target_type) value)) {\ - func_type zero = 0;\ - if (exc && unlikely(value == (func_type)-1 && PyErr_Occurred()))\ - return (target_type) -1;\ - if (is_unsigned && unlikely(value < zero))\ - goto raise_neg_overflow;\ - else\ - goto raise_overflow;\ - }\ - }\ - return (target_type) value;\ - } - -#if CYTHON_USE_PYLONG_INTERNALS - #include "longintrepr.h" -#endif - -static CYTHON_INLINE int __Pyx_PyInt_As_int(PyObject *x) { - const int neg_one = (int) -1, const_zero = (int) 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(int) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(int, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (int) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (int) 0; - case 1: __PYX_VERIFY_RETURN_INT(int, digit, digits[0]) - case 2: - if (8 * sizeof(int) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 2 * PyLong_SHIFT) { - return (int) (((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(int) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 3 * PyLong_SHIFT) { - return (int) (((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(int) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) >= 4 * PyLong_SHIFT) { - return (int) (((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (int) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(int) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(int, unsigned long, PyLong_AsUnsignedLong(x)) - } else if (sizeof(int) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(int, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (int) 0; - case -1: __PYX_VERIFY_RETURN_INT(int, sdigit, -(sdigit) digits[0]) - case 1: __PYX_VERIFY_RETURN_INT(int, digit, +digits[0]) - case -2: - if (8 * sizeof(int) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(int) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - return (int) ((((((int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(int) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(int) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - return (int) ((((((((int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(int) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { - return (int) (((int)-1)*(((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(int) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(int, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(int) - 1 > 4 * PyLong_SHIFT) { - return (int) ((((((((((int)digits[3]) << PyLong_SHIFT) | (int)digits[2]) << PyLong_SHIFT) | (int)digits[1]) << PyLong_SHIFT) | (int)digits[0]))); - } - } - break; - } -#endif - if (sizeof(int) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(int, long, PyLong_AsLong(x)) - } else if (sizeof(int) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(int, PY_LONG_LONG, PyLong_AsLongLong(x)) - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - int val; - PyObject *v = __Pyx_PyNumber_Int(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (int) -1; - } - } else { - int val; - PyObject *tmp = __Pyx_PyNumber_Int(x); - if (!tmp) return (int) -1; - val = __Pyx_PyInt_As_int(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to int"); - return (int) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to int"); - return (int) -1; -} - -#if CYTHON_CCOMPLEX - #ifdef __cplusplus - static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { - return ::std::complex< float >(x, y); - } - #else - static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { - return x + y*(__pyx_t_float_complex)_Complex_I; - } - #endif -#else - static CYTHON_INLINE __pyx_t_float_complex __pyx_t_float_complex_from_parts(float x, float y) { - __pyx_t_float_complex z; - z.real = x; - z.imag = y; - return z; - } -#endif - -#if CYTHON_CCOMPLEX -#else - static CYTHON_INLINE int __Pyx_c_eqf(__pyx_t_float_complex a, __pyx_t_float_complex b) { - return (a.real == b.real) && (a.imag == b.imag); - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_sumf(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - z.real = a.real + b.real; - z.imag = a.imag + b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_difff(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - z.real = a.real - b.real; - z.imag = a.imag - b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_prodf(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - z.real = a.real * b.real - a.imag * b.imag; - z.imag = a.real * b.imag + a.imag * b.real; - return z; - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_quotf(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - float denom = b.real * b.real + b.imag * b.imag; - z.real = (a.real * b.real + a.imag * b.imag) / denom; - z.imag = (a.imag * b.real - a.real * b.imag) / denom; - return z; - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_negf(__pyx_t_float_complex a) { - __pyx_t_float_complex z; - z.real = -a.real; - z.imag = -a.imag; - return z; - } - static CYTHON_INLINE int __Pyx_c_is_zerof(__pyx_t_float_complex a) { - return (a.real == 0) && (a.imag == 0); - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_conjf(__pyx_t_float_complex a) { - __pyx_t_float_complex z; - z.real = a.real; - z.imag = -a.imag; - return z; - } - #if 1 - static CYTHON_INLINE float __Pyx_c_absf(__pyx_t_float_complex z) { - #if !defined(HAVE_HYPOT) || defined(_MSC_VER) - return sqrtf(z.real*z.real + z.imag*z.imag); - #else - return hypotf(z.real, z.imag); - #endif - } - static CYTHON_INLINE __pyx_t_float_complex __Pyx_c_powf(__pyx_t_float_complex a, __pyx_t_float_complex b) { - __pyx_t_float_complex z; - float r, lnr, theta, z_r, z_theta; - if (b.imag == 0 && b.real == (int)b.real) { - if (b.real < 0) { - float denom = a.real * a.real + a.imag * a.imag; - a.real = a.real / denom; - a.imag = -a.imag / denom; - b.real = -b.real; - } - switch ((int)b.real) { - case 0: - z.real = 1; - z.imag = 0; - return z; - case 1: - return a; - case 2: - z = __Pyx_c_prodf(a, a); - return __Pyx_c_prodf(a, a); - case 3: - z = __Pyx_c_prodf(a, a); - return __Pyx_c_prodf(z, a); - case 4: - z = __Pyx_c_prodf(a, a); - return __Pyx_c_prodf(z, z); - } - } - if (a.imag == 0) { - if (a.real == 0) { - return a; - } - r = a.real; - theta = 0; - } else { - r = __Pyx_c_absf(a); - theta = atan2f(a.imag, a.real); - } - lnr = logf(r); - z_r = expf(lnr * b.real - theta * b.imag); - z_theta = theta * b.real + lnr * b.imag; - z.real = z_r * cosf(z_theta); - z.imag = z_r * sinf(z_theta); - return z; - } - #endif -#endif - -#if CYTHON_CCOMPLEX - #ifdef __cplusplus - static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { - return ::std::complex< double >(x, y); - } - #else - static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { - return x + y*(__pyx_t_double_complex)_Complex_I; - } - #endif -#else - static CYTHON_INLINE __pyx_t_double_complex __pyx_t_double_complex_from_parts(double x, double y) { - __pyx_t_double_complex z; - z.real = x; - z.imag = y; - return z; - } -#endif - -#if CYTHON_CCOMPLEX -#else - static CYTHON_INLINE int __Pyx_c_eq(__pyx_t_double_complex a, __pyx_t_double_complex b) { - return (a.real == b.real) && (a.imag == b.imag); - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_sum(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - z.real = a.real + b.real; - z.imag = a.imag + b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_diff(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - z.real = a.real - b.real; - z.imag = a.imag - b.imag; - return z; - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_prod(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - z.real = a.real * b.real - a.imag * b.imag; - z.imag = a.real * b.imag + a.imag * b.real; - return z; - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_quot(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - double denom = b.real * b.real + b.imag * b.imag; - z.real = (a.real * b.real + a.imag * b.imag) / denom; - z.imag = (a.imag * b.real - a.real * b.imag) / denom; - return z; - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_neg(__pyx_t_double_complex a) { - __pyx_t_double_complex z; - z.real = -a.real; - z.imag = -a.imag; - return z; - } - static CYTHON_INLINE int __Pyx_c_is_zero(__pyx_t_double_complex a) { - return (a.real == 0) && (a.imag == 0); - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_conj(__pyx_t_double_complex a) { - __pyx_t_double_complex z; - z.real = a.real; - z.imag = -a.imag; - return z; - } - #if 1 - static CYTHON_INLINE double __Pyx_c_abs(__pyx_t_double_complex z) { - #if !defined(HAVE_HYPOT) || defined(_MSC_VER) - return sqrt(z.real*z.real + z.imag*z.imag); - #else - return hypot(z.real, z.imag); - #endif - } - static CYTHON_INLINE __pyx_t_double_complex __Pyx_c_pow(__pyx_t_double_complex a, __pyx_t_double_complex b) { - __pyx_t_double_complex z; - double r, lnr, theta, z_r, z_theta; - if (b.imag == 0 && b.real == (int)b.real) { - if (b.real < 0) { - double denom = a.real * a.real + a.imag * a.imag; - a.real = a.real / denom; - a.imag = -a.imag / denom; - b.real = -b.real; - } - switch ((int)b.real) { - case 0: - z.real = 1; - z.imag = 0; - return z; - case 1: - return a; - case 2: - z = __Pyx_c_prod(a, a); - return __Pyx_c_prod(a, a); - case 3: - z = __Pyx_c_prod(a, a); - return __Pyx_c_prod(z, a); - case 4: - z = __Pyx_c_prod(a, a); - return __Pyx_c_prod(z, z); - } - } - if (a.imag == 0) { - if (a.real == 0) { - return a; - } - r = a.real; - theta = 0; - } else { - r = __Pyx_c_abs(a); - theta = atan2(a.imag, a.real); - } - lnr = log(r); - z_r = exp(lnr * b.real - theta * b.imag); - z_theta = theta * b.real + lnr * b.imag; - z.real = z_r * cos(z_theta); - z.imag = z_r * sin(z_theta); - return z; - } - #endif -#endif - -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_enum__NPY_TYPES(enum NPY_TYPES value) { - const enum NPY_TYPES neg_one = (enum NPY_TYPES) -1, const_zero = (enum NPY_TYPES) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(enum NPY_TYPES) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); - } else if (sizeof(enum NPY_TYPES) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); - } - } else { - if (sizeof(enum NPY_TYPES) <= sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(enum NPY_TYPES) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(enum NPY_TYPES), - little, !is_unsigned); - } -} - -static CYTHON_INLINE PyObject* __Pyx_PyInt_From_long(long value) { - const long neg_one = (long) -1, const_zero = (long) 0; - const int is_unsigned = neg_one > const_zero; - if (is_unsigned) { - if (sizeof(long) < sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(long) <= sizeof(unsigned long)) { - return PyLong_FromUnsignedLong((unsigned long) value); - } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { - return PyLong_FromUnsignedLongLong((unsigned PY_LONG_LONG) value); - } - } else { - if (sizeof(long) <= sizeof(long)) { - return PyInt_FromLong((long) value); - } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { - return PyLong_FromLongLong((PY_LONG_LONG) value); - } - } - { - int one = 1; int little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&value; - return _PyLong_FromByteArray(bytes, sizeof(long), - little, !is_unsigned); - } -} - -static CYTHON_INLINE long __Pyx_PyInt_As_long(PyObject *x) { - const long neg_one = (long) -1, const_zero = (long) 0; - const int is_unsigned = neg_one > const_zero; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_Check(x))) { - if (sizeof(long) < sizeof(long)) { - __PYX_VERIFY_RETURN_INT(long, long, PyInt_AS_LONG(x)) - } else { - long val = PyInt_AS_LONG(x); - if (is_unsigned && unlikely(val < 0)) { - goto raise_neg_overflow; - } - return (long) val; - } - } else -#endif - if (likely(PyLong_Check(x))) { - if (is_unsigned) { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (long) 0; - case 1: __PYX_VERIFY_RETURN_INT(long, digit, digits[0]) - case 2: - if (8 * sizeof(long) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 2 * PyLong_SHIFT) { - return (long) (((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - case 3: - if (8 * sizeof(long) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 3 * PyLong_SHIFT) { - return (long) (((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - case 4: - if (8 * sizeof(long) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) >= 4 * PyLong_SHIFT) { - return (long) (((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0])); - } - } - break; - } -#endif -#if CYTHON_COMPILING_IN_CPYTHON - if (unlikely(Py_SIZE(x) < 0)) { - goto raise_neg_overflow; - } -#else - { - int result = PyObject_RichCompareBool(x, Py_False, Py_LT); - if (unlikely(result < 0)) - return (long) -1; - if (unlikely(result == 1)) - goto raise_neg_overflow; - } -#endif - if (sizeof(long) <= sizeof(unsigned long)) { - __PYX_VERIFY_RETURN_INT_EXC(long, unsigned long, PyLong_AsUnsignedLong(x)) - } else if (sizeof(long) <= sizeof(unsigned PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(long, unsigned PY_LONG_LONG, PyLong_AsUnsignedLongLong(x)) - } - } else { -#if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)x)->ob_digit; - switch (Py_SIZE(x)) { - case 0: return (long) 0; - case -1: __PYX_VERIFY_RETURN_INT(long, sdigit, -(sdigit) digits[0]) - case 1: __PYX_VERIFY_RETURN_INT(long, digit, +digits[0]) - case -2: - if (8 * sizeof(long) - 1 > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 2: - if (8 * sizeof(long) > 1 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 2 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - return (long) ((((((long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case -3: - if (8 * sizeof(long) - 1 > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 3: - if (8 * sizeof(long) > 2 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 3 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - return (long) ((((((((long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case -4: - if (8 * sizeof(long) - 1 > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, long, -(long) (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - return (long) (((long)-1)*(((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - case 4: - if (8 * sizeof(long) > 3 * PyLong_SHIFT) { - if (8 * sizeof(unsigned long) > 4 * PyLong_SHIFT) { - __PYX_VERIFY_RETURN_INT(long, unsigned long, (((((((((unsigned long)digits[3]) << PyLong_SHIFT) | (unsigned long)digits[2]) << PyLong_SHIFT) | (unsigned long)digits[1]) << PyLong_SHIFT) | (unsigned long)digits[0]))) - } else if (8 * sizeof(long) - 1 > 4 * PyLong_SHIFT) { - return (long) ((((((((((long)digits[3]) << PyLong_SHIFT) | (long)digits[2]) << PyLong_SHIFT) | (long)digits[1]) << PyLong_SHIFT) | (long)digits[0]))); - } - } - break; - } -#endif - if (sizeof(long) <= sizeof(long)) { - __PYX_VERIFY_RETURN_INT_EXC(long, long, PyLong_AsLong(x)) - } else if (sizeof(long) <= sizeof(PY_LONG_LONG)) { - __PYX_VERIFY_RETURN_INT_EXC(long, PY_LONG_LONG, PyLong_AsLongLong(x)) - } - } - { -#if CYTHON_COMPILING_IN_PYPY && !defined(_PyLong_AsByteArray) - PyErr_SetString(PyExc_RuntimeError, - "_PyLong_AsByteArray() not available in PyPy, cannot convert large numbers"); -#else - long val; - PyObject *v = __Pyx_PyNumber_Int(x); - #if PY_MAJOR_VERSION < 3 - if (likely(v) && !PyLong_Check(v)) { - PyObject *tmp = v; - v = PyNumber_Long(tmp); - Py_DECREF(tmp); - } - #endif - if (likely(v)) { - int one = 1; int is_little = (int)*(unsigned char *)&one; - unsigned char *bytes = (unsigned char *)&val; - int ret = _PyLong_AsByteArray((PyLongObject *)v, - bytes, sizeof(val), - is_little, !is_unsigned); - Py_DECREF(v); - if (likely(!ret)) - return val; - } -#endif - return (long) -1; - } - } else { - long val; - PyObject *tmp = __Pyx_PyNumber_Int(x); - if (!tmp) return (long) -1; - val = __Pyx_PyInt_As_long(tmp); - Py_DECREF(tmp); - return val; - } -raise_overflow: - PyErr_SetString(PyExc_OverflowError, - "value too large to convert to long"); - return (long) -1; -raise_neg_overflow: - PyErr_SetString(PyExc_OverflowError, - "can't convert negative value to long"); - return (long) -1; -} - -static int __Pyx_check_binary_version(void) { - char ctversion[4], rtversion[4]; - PyOS_snprintf(ctversion, 4, "%d.%d", PY_MAJOR_VERSION, PY_MINOR_VERSION); - PyOS_snprintf(rtversion, 4, "%s", Py_GetVersion()); - if (ctversion[0] != rtversion[0] || ctversion[2] != rtversion[2]) { - char message[200]; - PyOS_snprintf(message, sizeof(message), - "compiletime version %s of module '%.100s' " - "does not match runtime version %s", - ctversion, __Pyx_MODULE_NAME, rtversion); - return PyErr_WarnEx(NULL, message, 1); - } - return 0; -} - -#ifndef __PYX_HAVE_RT_ImportModule -#define __PYX_HAVE_RT_ImportModule -static PyObject *__Pyx_ImportModule(const char *name) { - PyObject *py_name = 0; - PyObject *py_module = 0; - py_name = __Pyx_PyIdentifier_FromString(name); - if (!py_name) - goto bad; - py_module = PyImport_Import(py_name); - Py_DECREF(py_name); - return py_module; -bad: - Py_XDECREF(py_name); - return 0; -} -#endif - -#ifndef __PYX_HAVE_RT_ImportType -#define __PYX_HAVE_RT_ImportType -static PyTypeObject *__Pyx_ImportType(const char *module_name, const char *class_name, - size_t size, int strict) -{ - PyObject *py_module = 0; - PyObject *result = 0; - PyObject *py_name = 0; - char warning[200]; - Py_ssize_t basicsize; -#ifdef Py_LIMITED_API - PyObject *py_basicsize; -#endif - py_module = __Pyx_ImportModule(module_name); - if (!py_module) - goto bad; - py_name = __Pyx_PyIdentifier_FromString(class_name); - if (!py_name) - goto bad; - result = PyObject_GetAttr(py_module, py_name); - Py_DECREF(py_name); - py_name = 0; - Py_DECREF(py_module); - py_module = 0; - if (!result) - goto bad; - if (!PyType_Check(result)) { - PyErr_Format(PyExc_TypeError, - "%.200s.%.200s is not a type object", - module_name, class_name); - goto bad; - } -#ifndef Py_LIMITED_API - basicsize = ((PyTypeObject *)result)->tp_basicsize; -#else - py_basicsize = PyObject_GetAttrString(result, "__basicsize__"); - if (!py_basicsize) - goto bad; - basicsize = PyLong_AsSsize_t(py_basicsize); - Py_DECREF(py_basicsize); - py_basicsize = 0; - if (basicsize == (Py_ssize_t)-1 && PyErr_Occurred()) - goto bad; -#endif - if (!strict && (size_t)basicsize > size) { - PyOS_snprintf(warning, sizeof(warning), - "%s.%s size changed, may indicate binary incompatibility", - module_name, class_name); - if (PyErr_WarnEx(NULL, warning, 0) < 0) goto bad; - } - else if ((size_t)basicsize != size) { - PyErr_Format(PyExc_ValueError, - "%.200s.%.200s has the wrong size, try recompiling", - module_name, class_name); - goto bad; - } - return (PyTypeObject *)result; -bad: - Py_XDECREF(py_module); - Py_XDECREF(result); - return NULL; -} -#endif - -static int __Pyx_InitStrings(__Pyx_StringTabEntry *t) { - while (t->p) { - #if PY_MAJOR_VERSION < 3 - if (t->is_unicode) { - *t->p = PyUnicode_DecodeUTF8(t->s, t->n - 1, NULL); - } else if (t->intern) { - *t->p = PyString_InternFromString(t->s); - } else { - *t->p = PyString_FromStringAndSize(t->s, t->n - 1); - } - #else - if (t->is_unicode | t->is_str) { - if (t->intern) { - *t->p = PyUnicode_InternFromString(t->s); - } else if (t->encoding) { - *t->p = PyUnicode_Decode(t->s, t->n - 1, t->encoding, NULL); - } else { - *t->p = PyUnicode_FromStringAndSize(t->s, t->n - 1); - } - } else { - *t->p = PyBytes_FromStringAndSize(t->s, t->n - 1); - } - #endif - if (!*t->p) - return -1; - ++t; - } - return 0; -} - -static CYTHON_INLINE PyObject* __Pyx_PyUnicode_FromString(const char* c_str) { - return __Pyx_PyUnicode_FromStringAndSize(c_str, (Py_ssize_t)strlen(c_str)); -} -static CYTHON_INLINE char* __Pyx_PyObject_AsString(PyObject* o) { - Py_ssize_t ignore; - return __Pyx_PyObject_AsStringAndSize(o, &ignore); -} -static CYTHON_INLINE char* __Pyx_PyObject_AsStringAndSize(PyObject* o, Py_ssize_t *length) { -#if CYTHON_COMPILING_IN_CPYTHON && (__PYX_DEFAULT_STRING_ENCODING_IS_ASCII || __PYX_DEFAULT_STRING_ENCODING_IS_DEFAULT) - if ( -#if PY_MAJOR_VERSION < 3 && __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - __Pyx_sys_getdefaultencoding_not_ascii && -#endif - PyUnicode_Check(o)) { -#if PY_VERSION_HEX < 0x03030000 - char* defenc_c; - PyObject* defenc = _PyUnicode_AsDefaultEncodedString(o, NULL); - if (!defenc) return NULL; - defenc_c = PyBytes_AS_STRING(defenc); -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - { - char* end = defenc_c + PyBytes_GET_SIZE(defenc); - char* c; - for (c = defenc_c; c < end; c++) { - if ((unsigned char) (*c) >= 128) { - PyUnicode_AsASCIIString(o); - return NULL; - } - } - } -#endif - *length = PyBytes_GET_SIZE(defenc); - return defenc_c; -#else - if (__Pyx_PyUnicode_READY(o) == -1) return NULL; -#if __PYX_DEFAULT_STRING_ENCODING_IS_ASCII - if (PyUnicode_IS_ASCII(o)) { - *length = PyUnicode_GET_LENGTH(o); - return PyUnicode_AsUTF8(o); - } else { - PyUnicode_AsASCIIString(o); - return NULL; - } -#else - return PyUnicode_AsUTF8AndSize(o, length); -#endif -#endif - } else -#endif -#if (!CYTHON_COMPILING_IN_PYPY) || (defined(PyByteArray_AS_STRING) && defined(PyByteArray_GET_SIZE)) - if (PyByteArray_Check(o)) { - *length = PyByteArray_GET_SIZE(o); - return PyByteArray_AS_STRING(o); - } else -#endif - { - char* result; - int r = PyBytes_AsStringAndSize(o, &result, length); - if (unlikely(r < 0)) { - return NULL; - } else { - return result; - } - } -} -static CYTHON_INLINE int __Pyx_PyObject_IsTrue(PyObject* x) { - int is_true = x == Py_True; - if (is_true | (x == Py_False) | (x == Py_None)) return is_true; - else return PyObject_IsTrue(x); -} -static CYTHON_INLINE PyObject* __Pyx_PyNumber_Int(PyObject* x) { - PyNumberMethods *m; - const char *name = NULL; - PyObject *res = NULL; -#if PY_MAJOR_VERSION < 3 - if (PyInt_Check(x) || PyLong_Check(x)) -#else - if (PyLong_Check(x)) -#endif - return __Pyx_NewRef(x); - m = Py_TYPE(x)->tp_as_number; -#if PY_MAJOR_VERSION < 3 - if (m && m->nb_int) { - name = "int"; - res = PyNumber_Int(x); - } - else if (m && m->nb_long) { - name = "long"; - res = PyNumber_Long(x); - } -#else - if (m && m->nb_int) { - name = "int"; - res = PyNumber_Long(x); - } -#endif - if (res) { -#if PY_MAJOR_VERSION < 3 - if (!PyInt_Check(res) && !PyLong_Check(res)) { -#else - if (!PyLong_Check(res)) { -#endif - PyErr_Format(PyExc_TypeError, - "__%.4s__ returned non-%.4s (type %.200s)", - name, name, Py_TYPE(res)->tp_name); - Py_DECREF(res); - return NULL; - } - } - else if (!PyErr_Occurred()) { - PyErr_SetString(PyExc_TypeError, - "an integer is required"); - } - return res; -} -static CYTHON_INLINE Py_ssize_t __Pyx_PyIndex_AsSsize_t(PyObject* b) { - Py_ssize_t ival; - PyObject *x; -#if PY_MAJOR_VERSION < 3 - if (likely(PyInt_CheckExact(b))) { - if (sizeof(Py_ssize_t) >= sizeof(long)) - return PyInt_AS_LONG(b); - else - return PyInt_AsSsize_t(x); - } -#endif - if (likely(PyLong_CheckExact(b))) { - #if CYTHON_USE_PYLONG_INTERNALS - const digit* digits = ((PyLongObject*)b)->ob_digit; - const Py_ssize_t size = Py_SIZE(b); - if (likely(__Pyx_sst_abs(size) <= 1)) { - ival = likely(size) ? digits[0] : 0; - if (size == -1) ival = -ival; - return ival; - } else { - switch (size) { - case 2: - if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { - return (Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case -2: - if (8 * sizeof(Py_ssize_t) > 2 * PyLong_SHIFT) { - return -(Py_ssize_t) (((((size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case 3: - if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { - return (Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case -3: - if (8 * sizeof(Py_ssize_t) > 3 * PyLong_SHIFT) { - return -(Py_ssize_t) (((((((size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case 4: - if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { - return (Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - case -4: - if (8 * sizeof(Py_ssize_t) > 4 * PyLong_SHIFT) { - return -(Py_ssize_t) (((((((((size_t)digits[3]) << PyLong_SHIFT) | (size_t)digits[2]) << PyLong_SHIFT) | (size_t)digits[1]) << PyLong_SHIFT) | (size_t)digits[0])); - } - break; - } - } - #endif - return PyLong_AsSsize_t(b); - } - x = PyNumber_Index(b); - if (!x) return -1; - ival = PyInt_AsSsize_t(x); - Py_DECREF(x); - return ival; -} -static CYTHON_INLINE PyObject * __Pyx_PyInt_FromSize_t(size_t ival) { - return PyInt_FromSize_t(ival); -} - - -#endif /* Py_PYTHON_H */ diff --git a/setup.py b/setup.py index 51c6861..63d071c 100755 --- a/setup.py +++ b/setup.py @@ -39,7 +39,7 @@ setup(name='POT', packages=find_packages(), ext_modules = cythonize(Extension( "ot.lp.emd_wrap", # the extension name - sources=["ot/lp/emd_wrap.pyx", "ot/lp/EMD_wrap.cpp"], # the Cython source and + sources=["ot/lp/emd_wrap.pyx", "ot/lp/EMD_wrapper.cpp"], # the Cython source and # additional C++ source files language="c++", # generate and compile C++ code, include_dirs=[numpy.get_include(),os.path.join(ROOT,'ot/lp')])), -- cgit v1.2.3 From 140baad30ab822deeccd6f1cb4fedc3136370ab4 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 7 Apr 2017 14:00:10 +0200 Subject: add WDA --- examples/plot_WDA.py | 63 ++++++++++++++++++++++++++++++++ ot/dr.py | 101 +++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 164 insertions(+) create mode 100644 examples/plot_WDA.py create mode 100644 ot/dr.py diff --git a/examples/plot_WDA.py b/examples/plot_WDA.py new file mode 100644 index 0000000..8d24bdc --- /dev/null +++ b/examples/plot_WDA.py @@ -0,0 +1,63 @@ +# -*- coding: utf-8 -*- +""" +==================== +1D optimal transport +==================== + +@author: rflamary +""" + +import numpy as np +import matplotlib.pylab as pl +import ot +from ot.datasets import get_1D_gauss as gauss +from ot.dr import wda + + +#%% parameters + +n=1000 # nb samples in source and target datasets +nz=0.2 +xs,ys=ot.datasets.get_data_classif('3gauss',n,nz) +xt,yt=ot.datasets.get_data_classif('3gauss',n,nz) + +nbnoise=8 + +xs=np.hstack((xs,np.random.randn(n,nbnoise))) +xt=np.hstack((xt,np.random.randn(n,nbnoise))) + +#%% plot samples + +pl.figure(1) + + +pl.scatter(xt[:,0],xt[:,1],c=ys,marker='+',label='Source samples') +pl.legend(loc=0) +pl.title('Discriminant dimensions') + + +#%% plot distributions and loss matrix +p=2 +reg=1 +k=10 +maxiter=100 + +P,proj = wda(xs,ys,p,reg,k,maxiter=maxiter) + +#%% plot samples + +xsp=proj(xs) +xtp=proj(xt) + +pl.figure(1,(10,5)) + +pl.subplot(1,2,1) +pl.scatter(xsp[:,0],xsp[:,1],c=ys,marker='+',label='Projected samples') +pl.legend(loc=0) +pl.title('Projected training samples') + + +pl.subplot(1,2,2) +pl.scatter(xtp[:,0],xtp[:,1],c=ys,marker='+',label='Projected samples') +pl.legend(loc=0) +pl.title('Projected test samples') diff --git a/ot/dr.py b/ot/dr.py new file mode 100644 index 0000000..3732d81 --- /dev/null +++ b/ot/dr.py @@ -0,0 +1,101 @@ +# -*- coding: utf-8 -*- +""" +Domain adaptation with optimal transport +""" + + +import autograd.numpy as np +from pymanopt.manifolds import Stiefel +from pymanopt import Problem +from pymanopt.solvers import SteepestDescent, TrustRegions + +def dist(x1,x2): + """ Compute squared euclidena distance between samples + """ + x1p2=np.sum(np.square(x1),1) + x2p2=np.sum(np.square(x2),1) + return x1p2.reshape((-1,1))+x2p2.reshape((1,-1))-2*np.dot(x1,x2.T) + +def sinkhorn(w1,w2,M,reg,k): + """ + Simple solver for Sinkhorn algorithm with fixed number of iteration + """ + K=np.exp(-M/reg) + ui=np.ones((M.shape[0],)) + vi=np.ones((M.shape[1],)) + for i in range(k): + vi=w2/(np.dot(K.T,ui)) + ui=w1/(np.dot(K,vi)) + G=ui.reshape((M.shape[0],1))*K*vi.reshape((1,M.shape[1])) + return G + +def split_classes(X,y): + """ + split samples in X by classes in y + """ + lstsclass=np.unique(y) + return [X[y==i,:].astype(np.float32) for i in lstsclass] + + + +def wda(X,y,p=2,reg=1,k=10,solver = None,maxiter=100,verbose=0): + """ + Wasserstein Discriminant Analysis + + The function solves the following optimization problem: + + .. math:: + P = arg\min_P \frac{\sum_i W(PX^i,PX^i)}{\sum_{i,j\neq i} W(PX^i,PX^j)} + + where : + + - :math:`W` is entropic regularized Wasserstein distances + - :math:`X^i` are samples in the dataset corresponding to class i + + """ + + mx=np.mean(X) + X-=mx.reshape((1,-1)) + + # data split between classes + d=X.shape[1] + xc=split_classes(X,y) + # compute uniform weighs + wc=[np.ones((x.shape[0]),dtype=np.float32)/x.shape[0] for x in xc] + + def cost(P): + # wda loss + loss_b=0 + loss_w=0 + + for i,xi in enumerate(xc): + xi=np.dot(xi,P) + for j,xj in enumerate(xc[i:]): + xj=np.dot(xj,P) + M=dist(xi,xj) + G=sinkhorn(wc[i],wc[j+i],M,reg,k) + if j==0: + loss_w+=np.sum(G*M) + else: + loss_b+=np.sum(G*M) + + # loss inversed because minimization + return loss_w/loss_b + + + # declare manifold and problem + manifold = Stiefel(d, p) + problem = Problem(manifold=manifold, cost=cost) + + # declare solver and solve + if solver is None: + solver= SteepestDescent(maxiter=maxiter,logverbosity=verbose) + elif solver in ['tr','TrustRegions']: + solver= TrustRegions(maxiter=maxiter,logverbosity=verbose) + + Popt = solver.solve(problem) + + def proj(X): + return (X-mx.reshape((1,-1))).dot(Popt) + + return Popt, proj -- cgit v1.2.3 From 3cc99e6590fa87ae8705fc93315590b27bf84efc Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 7 Apr 2017 14:12:37 +0200 Subject: better dicumentation --- README.md | 2 ++ docs/source/conf.py | 2 +- ot/dr.py | 59 +++++++++++++++++++++++++++++++++++++++++++++++------ 3 files changed, 56 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index 4bd03f9..15cd599 100644 --- a/README.md +++ b/README.md @@ -105,3 +105,5 @@ This toolbox benefit a lot from open source research and we would like to thank [9] Schmitzer, B. (2016). Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems. arXiv preprint arXiv:1610.06519. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + +[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). Wasserstein Discriminant Analysis. arXiv preprint arXiv:1608.08063. \ No newline at end of file diff --git a/docs/source/conf.py b/docs/source/conf.py index e0d4e0b..529d410 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -24,7 +24,7 @@ class Mock(MagicMock): @classmethod def __getattr__(cls, name): return Mock() -MOCK_MODULES = [ 'emd','ot.lp.emd'] +MOCK_MODULES = [ 'emd','ot.lp.emd_wrap'] sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # !!!! diff --git a/ot/dr.py b/ot/dr.py index 3732d81..3965149 100644 --- a/ot/dr.py +++ b/ot/dr.py @@ -1,16 +1,15 @@ # -*- coding: utf-8 -*- """ -Domain adaptation with optimal transport +Dimension reduction with optimal transport """ - import autograd.numpy as np from pymanopt.manifolds import Stiefel from pymanopt import Problem from pymanopt.solvers import SteepestDescent, TrustRegions def dist(x1,x2): - """ Compute squared euclidena distance between samples + """ Compute squared euclidean distance between samples """ x1p2=np.sum(np.square(x1),1) x2p2=np.sum(np.square(x2),1) @@ -40,18 +39,66 @@ def split_classes(X,y): def wda(X,y,p=2,reg=1,k=10,solver = None,maxiter=100,verbose=0): """ - Wasserstein Discriminant Analysis + Wasserstein Discriminant Analysis [11]_ The function solves the following optimization problem: .. math:: - P = arg\min_P \frac{\sum_i W(PX^i,PX^i)}{\sum_{i,j\neq i} W(PX^i,PX^j)} + P = \\text{arg}\min_P \\frac{\\sum_i W(PX^i,PX^i)}{\\sum_{i,j\\neq i} W(PX^i,PX^j)} where : - + + - :math:`P` is a linear projection operator in the Stiefel(p,d) manifold - :math:`W` is entropic regularized Wasserstein distances - :math:`X^i` are samples in the dataset corresponding to class i + Parameters + ---------- + a : np.ndarray (ns,) + samples weights in the source domain + b : np.ndarray (nt,) + samples in the target domain + M : np.ndarray (ns,nt) + loss matrix + reg : float + Regularization term >0 + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + gamma : (ns x nt) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + Examples + -------- + + >>> import ot + >>> a=[.5,.5] + >>> b=[.5,.5] + >>> M=[[0.,1.],[1.,0.]] + >>> ot.sinkhorn(a,b,M,1) + array([[ 0.36552929, 0.13447071], + [ 0.13447071, 0.36552929]]) + + + References + ---------- + + .. [11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). Wasserstein Discriminant Analysis. arXiv preprint arXiv:1608.08063. + + + + """ mx=np.mean(X) -- cgit v1.2.3 From 461d269538196a679e65aa3804c7ab88dce6dfaa Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 7 Apr 2017 14:24:08 +0200 Subject: doc wda --- ot/dr.py | 45 ++++++++++++++++----------------------------- 1 file changed, 16 insertions(+), 29 deletions(-) diff --git a/ot/dr.py b/ot/dr.py index 3965149..14a92c1 100644 --- a/ot/dr.py +++ b/ot/dr.py @@ -54,41 +54,28 @@ def wda(X,y,p=2,reg=1,k=10,solver = None,maxiter=100,verbose=0): Parameters ---------- - a : np.ndarray (ns,) - samples weights in the source domain - b : np.ndarray (nt,) - samples in the target domain - M : np.ndarray (ns,nt) - loss matrix - reg : float - Regularization term >0 - numItermax : int, optional - Max number of iterations - stopThr : float, optional - Stop threshol on error (>0) - verbose : bool, optional + X : numpy.ndarray (n,d) + Training samples + y : np.ndarray (n,) + labels for training samples + p : int, optional + size of dimensionnality reduction + reg : float, optional + Regularization term >0 (entropic regularization) + solver : str, optional + None for steepest decsent or 'TrustRegions' for trust regions algorithm + else shoudl be a pymanopt.sovers + verbose : int, optional Print information along iterations - log : bool, optional - record log if True + Returns ------- - gamma : (ns x nt) ndarray + P : (d x p) ndarray Optimal transportation matrix for the given parameters - log : dict - log dictionary return only if log==True in parameters - - Examples - -------- - - >>> import ot - >>> a=[.5,.5] - >>> b=[.5,.5] - >>> M=[[0.,1.],[1.,0.]] - >>> ot.sinkhorn(a,b,M,1) - array([[ 0.36552929, 0.13447071], - [ 0.13447071, 0.36552929]]) + proj : fun + projectiuon function including mean centering References -- cgit v1.2.3 From c1a9cb2f24676f81251155b46c6af857182f19be Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 7 Apr 2017 14:26:34 +0200 Subject: readthedoc error --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 529d410..bad72d1 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -24,7 +24,7 @@ class Mock(MagicMock): @classmethod def __getattr__(cls, name): return Mock() -MOCK_MODULES = [ 'emd','ot.lp.emd_wrap'] +MOCK_MODULES = [ 'emd_wrap','ot.lp.emd_wrap'] sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # !!!! -- cgit v1.2.3 From a4c899066735957e91c6a97cbe04e669c80ccc6d Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 7 Apr 2017 14:33:55 +0200 Subject: readthedoc mock trick --- docs/source/conf.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index bad72d1..7123877 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -18,7 +18,13 @@ import re import sphinx_gallery # !!!! allow readthedoc compilation -from unittest.mock import MagicMock +try: + from unittest.mock import MagicMock +except ImportError: + from mock import Mock as MagicMock + ## check whether in the source directory... +# + sys.path.insert(0, os.path.abspath("../..")) class Mock(MagicMock): @classmethod -- cgit v1.2.3 From e6a3fdc5b2cee6bf9913a1cd2c7e017d266628a4 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 7 Apr 2017 14:34:07 +0200 Subject: readthedoc test --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 7123877..6ea99c2 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -30,7 +30,7 @@ class Mock(MagicMock): @classmethod def __getattr__(cls, name): return Mock() -MOCK_MODULES = [ 'emd_wrap','ot.lp.emd_wrap'] +MOCK_MODULES = ['ot.lp.emd_wrap'] sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # !!!! -- cgit v1.2.3 From 19f29bb235becd47bf85f3bad81bc316d5410fdd Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 7 Apr 2017 14:53:48 +0200 Subject: smal changes --- MANIFEST.in | 3 ++- docs/source/readme.rst | 3 +++ 2 files changed, 5 insertions(+), 1 deletion(-) diff --git a/MANIFEST.in b/MANIFEST.in index 8c14549..2fd6a10 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -2,6 +2,7 @@ graft ot/lp/ include README.md include ot/lp/core.h include ot/lp/EMD.h -include ot/lp/emd.pyx +include ot/lp/EMD_wrapper.cpp +include ot/lp/emd_wrap.pyx include ot/lp/full_bipartitegraph.h include ot/lp/network_simplex_simple.h diff --git a/docs/source/readme.rst b/docs/source/readme.rst index a76de51..1d68303 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -157,6 +157,9 @@ Entropy Regularized Transport Problems. arXiv preprint arXiv:1610.06519. Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. +[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). +Wasserstein Discriminant Analysis. arXiv preprint arXiv:1608.08063. + .. |PyPI version| image:: https://badge.fury.io/py/POT.svg :target: https://badge.fury.io/py/POT .. |Build Status| image:: https://travis-ci.org/rflamary/POT.svg?branch=master -- cgit v1.2.3 From 2d08696603e331be7a0adfb402ee8ace5d455465 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 7 Apr 2017 15:20:39 +0200 Subject: correct doc --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 6ea99c2..d58f160 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -29,7 +29,7 @@ sys.path.insert(0, os.path.abspath("../..")) class Mock(MagicMock): @classmethod def __getattr__(cls, name): - return Mock() + return MagicMock() MOCK_MODULES = ['ot.lp.emd_wrap'] sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # !!!! -- cgit v1.2.3 From ce0348d47393fbcd4748ca2dc9bc315b6871e4bf Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 7 Apr 2017 15:32:27 +0200 Subject: V0.2 --- README.md | 2 ++ docs/source/readme.rst | 2 ++ ot/__init__.py | 2 +- 3 files changed, 5 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 15cd599..f2d6144 100644 --- a/README.md +++ b/README.md @@ -15,6 +15,8 @@ It provides the following solvers: * Optimal transport for domain adaptation with group lasso regularization [5] * Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. * Joint OT matrix and mapping estimation [8]. +* Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt). + Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 1d68303..e1d4459 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -18,6 +18,8 @@ It provides the following solvers: - Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. - Joint OT matrix and mapping estimation [8]. +- Wasserstein Discriminant Analysis [11] (requires autograd + + pymanopt). Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. diff --git a/ot/__init__.py b/ot/__init__.py index 9a253d2..d89b3a3 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -18,7 +18,7 @@ from .da import sinkhorn_lpl1_mm # utils functions from .utils import dist, unif, tic, toc, toq -__version__ = "0.1.12" +__version__ = "0.2" __all__ = ["emd", "emd2", "sinkhorn", "utils", 'datasets', 'bregman', 'lp', 'plot', 'tic', 'toc', 'toq', -- cgit v1.2.3 From d48874e26025f68a267160db03fe8968b4b6224e Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 7 Apr 2017 15:41:13 +0200 Subject: doc with dr --- docs/source/all.rst | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/docs/source/all.rst b/docs/source/all.rst index d5733b8..2348785 100644 --- a/docs/source/all.rst +++ b/docs/source/all.rst @@ -32,6 +32,13 @@ ot.da .. automodule:: ot.da :members: +ot.dr +-------- + +.. automodule:: ot.dr + :members: + + ot.utils -------- -- cgit v1.2.3 From 92538abf67e5118431396c92caf82071866dcbe5 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 7 Apr 2017 15:42:13 +0200 Subject: doc update --- ot/lp/__init__.py | 3 +++ 1 file changed, 3 insertions(+) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 2e7c52e..db3da78 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -48,6 +48,7 @@ def emd(a, b, M): Simple example with obvious solution. The function emd accepts lists and perform automatic conversion to numpy arrays + >>> import ot >>> a=[.5,.5] >>> b=[.5,.5] @@ -117,6 +118,8 @@ def emd2(a, b, M,processes=multiprocessing.cpu_count()): Simple example with obvious solution. The function emd accepts lists and perform automatic conversion to numpy arrays + + >>> import ot >>> a=[.5,.5] >>> b=[.5,.5] -- cgit v1.2.3 From 7bcae0fa807d67b36c44d5e0abeb45df8c65c3c6 Mon Sep 17 00:00:00 2001 From: Leo gautheron Date: Thu, 13 Apr 2017 10:53:29 +0200 Subject: update bregman file - change commented prints to python3 compatible syntax - Correct issue that could cause the sinkhorn algo to stop with u and v containing nan/infinite numbers: - Assign uprev and vprev before changing u and v. - Then update u and v. - Then check if u and v contain nan, but ALSO infinite values. - if there are issues, then display error (with 2 r, not 3 :p) along with the iteration number (there may have errors at iteration 0) --- ot/bregman.py | 50 ++++++++++++++++++++++++-------------------------- 1 file changed, 24 insertions(+), 26 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 27f8ff5..b06eaeb 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -102,31 +102,30 @@ def sinkhorn(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, log=Fa # we assume that no distances are null except those of the diagonal of distances u = np.ones(Nini)/Nini v = np.ones(Nfin)/Nfin - uprev=np.zeros(Nini) - vprev=np.zeros(Nini) - #print reg + #print(reg) K = np.exp(-M/reg) - #print np.min(K) + #print(np.min(K)) Kp = np.dot(np.diag(1/a),K) transp = K cpt = 0 err=1 while (err>stopThr and cpt Date: Tue, 18 Apr 2017 15:05:39 +0200 Subject: Performance improvement sinkhorn Doing the computation this way is equivalent and allows to reduce the space complexity required from O(max(a, b)^2) to O(a*b) (especially usefull to transport a small number of sources example to a lot of target) This also allows to decrease the computation time. --- ot/bregman.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index b06eaeb..1deccce 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -108,7 +108,8 @@ def sinkhorn(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, log=Fa K = np.exp(-M/reg) #print(np.min(K)) - Kp = np.dot(np.diag(1/a),K) + Kp = (1/a).reshape(-1, 1) * K + transp = K cpt = 0 err=1 @@ -128,7 +129,7 @@ def sinkhorn(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, log=Fa break if cpt%10==0: # we can speed up the process by checking for the error only all the 10th iterations - transp = np.dot(np.diag(u),np.dot(K,np.diag(v))) + transp = u.reshape(-1, 1) * (K * v) err = np.linalg.norm((np.sum(transp,axis=0)-b))**2 if log: log['err'].append(err) -- cgit v1.2.3 From 691c97a033e54359e8c933e3bdd34bf5cf40151d Mon Sep 17 00:00:00 2001 From: Leo gautheron Date: Tue, 18 Apr 2017 16:04:03 +0200 Subject: little cleanup sinkhorn --- ot/bregman.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 1deccce..0453f14 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -94,8 +94,6 @@ def sinkhorn(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, log=Fa Nini = len(a) Nfin = len(b) - - cpt = 0 if log: log={'err':[]} @@ -109,8 +107,6 @@ def sinkhorn(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, log=Fa #print(np.min(K)) Kp = (1/a).reshape(-1, 1) * K - - transp = K cpt = 0 err=1 while (err>stopThr and cpt Date: Tue, 18 Apr 2017 16:25:38 +0200 Subject: Update README.md Add Leo as contributor --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index f2d6144..3d944db 100644 --- a/README.md +++ b/README.md @@ -78,6 +78,7 @@ The contributors to this library are: * [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/) * [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/) * [Michael Perrot](http://perso.univ-st-etienne.fr/pem82055/) +* [Léo Gautheron](https://github.com/aje) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): @@ -108,4 +109,4 @@ This toolbox benefit a lot from open source research and we would like to thank [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. -[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). Wasserstein Discriminant Analysis. arXiv preprint arXiv:1608.08063. \ No newline at end of file +[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). Wasserstein Discriminant Analysis. arXiv preprint arXiv:1608.08063. -- cgit v1.2.3 From 16f51f971607efab2c73958d207c582b389406c8 Mon Sep 17 00:00:00 2001 From: Leo gautheron Date: Thu, 20 Apr 2017 12:12:15 +0200 Subject: sinkhorn GPU implementation --- ot/__init__.py | 6 +- ot/bregman.py | 6 +- ot/da.py | 23 +- ot/gpu/__init__.py | 6 + ot/gpu/bregman.py | 83 + ot/gpu/cudamat/.gitignore | 38 + ot/gpu/cudamat/CHANGELOG | 41 + ot/gpu/cudamat/CONTRIBUTE.md | 74 + ot/gpu/cudamat/INSTALL.md | 53 + ot/gpu/cudamat/LICENSE | 21 + ot/gpu/cudamat/README.md | 65 + ot/gpu/cudamat/cudamat/__init__.py | 2 + ot/gpu/cudamat/cudamat/cudamat.cu | 1633 +++ ot/gpu/cudamat/cudamat/cudamat.cuh | 35 + ot/gpu/cudamat/cudamat/cudamat.py | 1575 ++ ot/gpu/cudamat/cudamat/cudamat_kernels.cu | 804 ++ ot/gpu/cudamat/cudamat/cudamat_kernels.cuh | 92 + ot/gpu/cudamat/cudamat/learn.cu | 34 + ot/gpu/cudamat/cudamat/learn.py | 21 + ot/gpu/cudamat/cudamat/learn_kernels.cu | 10 + ot/gpu/cudamat/cudamat/learn_kernels.cuh | 12 + ot/gpu/cudamat/cudamat/rnd_multipliers_32bit.txt | 16028 +++++++++++++++++++++ ot/gpu/cudamat/examples/bench_cudamat.py | 97 + ot/gpu/cudamat/examples/nn_cudamat.py | 133 + ot/gpu/cudamat/examples/rbm_cudamat.py | 98 + ot/gpu/cudamat/examples/rbm_numpy.py | 72 + ot/gpu/cudamat/examples/util.py | 22 + ot/gpu/cudamat/setup.py | 121 + ot/gpu/cudamat/test/test_cudamat.py | 1217 ++ ot/gpu/cudamat/test/test_learn.py | 27 + ot/gpu/da.py | 81 + 31 files changed, 22514 insertions(+), 16 deletions(-) create mode 100644 ot/gpu/__init__.py create mode 100644 ot/gpu/bregman.py create mode 100644 ot/gpu/cudamat/.gitignore create mode 100644 ot/gpu/cudamat/CHANGELOG create mode 100644 ot/gpu/cudamat/CONTRIBUTE.md create mode 100644 ot/gpu/cudamat/INSTALL.md create mode 100644 ot/gpu/cudamat/LICENSE create mode 100644 ot/gpu/cudamat/README.md create mode 100644 ot/gpu/cudamat/cudamat/__init__.py create mode 100644 ot/gpu/cudamat/cudamat/cudamat.cu create mode 100644 ot/gpu/cudamat/cudamat/cudamat.cuh create mode 100644 ot/gpu/cudamat/cudamat/cudamat.py create mode 100644 ot/gpu/cudamat/cudamat/cudamat_kernels.cu create mode 100644 ot/gpu/cudamat/cudamat/cudamat_kernels.cuh create mode 100644 ot/gpu/cudamat/cudamat/learn.cu create mode 100644 ot/gpu/cudamat/cudamat/learn.py create mode 100644 ot/gpu/cudamat/cudamat/learn_kernels.cu create mode 100644 ot/gpu/cudamat/cudamat/learn_kernels.cuh create mode 100644 ot/gpu/cudamat/cudamat/rnd_multipliers_32bit.txt create mode 100644 ot/gpu/cudamat/examples/bench_cudamat.py create mode 100644 ot/gpu/cudamat/examples/nn_cudamat.py create mode 100644 ot/gpu/cudamat/examples/rbm_cudamat.py create mode 100644 ot/gpu/cudamat/examples/rbm_numpy.py create mode 100644 ot/gpu/cudamat/examples/util.py create mode 100755 ot/gpu/cudamat/setup.py create mode 100644 ot/gpu/cudamat/test/test_cudamat.py create mode 100644 ot/gpu/cudamat/test/test_learn.py create mode 100644 ot/gpu/da.py diff --git a/ot/__init__.py b/ot/__init__.py index d89b3a3..702269c 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -9,6 +9,7 @@ from . import utils from . import datasets from . import plot from . import da +from . import gpu # OT functions from .lp import emd, emd2 @@ -20,6 +21,7 @@ from .utils import dist, unif, tic, toc, toq __version__ = "0.2" -__all__ = ["emd", "emd2", "sinkhorn", "utils", 'datasets', 'bregman', 'lp', +__all__ = ["emd", "emd2", "sinkhorn", "utils", 'datasets', 'bregman', 'lp', 'plot', 'tic', 'toc', 'toq', - 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim'] + 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim', + 'gpu'] diff --git a/ot/bregman.py b/ot/bregman.py index 0453f14..c46e5dc 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -112,9 +112,11 @@ def sinkhorn(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, log=Fa while (err>stopThr and cpt stopThr and cpt < numItermax): + uprev_GPU = u_GPU.copy() + vprev_GPU = v_GPU.copy() + + KtransposeU_GPU = K_GPU.transpose().dot(u_GPU) + b_GPU.divide(KtransposeU_GPU, target=v_GPU) + ones_GPU.divide(Kp_GPU.dot(v_GPU), target=u_GPU) + + if (np.any(KtransposeU_GPU.asarray() == 0) or + not u_GPU.allfinite() or not v_GPU.allfinite()): + # we have reached the machine precision + # come back to previous solution and quit loop + print('Warning: numerical errors at iteration', cpt) + u_GPU = uprev_GPU.copy() + v_GPU = vprev_GPU.copy() + break + if cpt % 10 == 0: + # we can speed up the process by checking for the error only all + # the 10th iterations + K_GPU.mult_by_col(u_GPU, target=tmp_GPU) + tmp_GPU.mult_by_row(v_GPU.transpose(), target=tmp_GPU) + + bcopy_GPU = b_GPU.copy().transpose() + bcopy_GPU.add_sums(tmp_GPU, axis=0, beta=-1) + err = bcopy_GPU.euclid_norm()**2 + if log: + log['err'].append(err) + + if verbose: + if cpt % 200 == 0: + print('{:5s}|{:12s}'.format('It.', 'Err')+'\n'+'-'*19) + print('{:5d}|{:8e}|'.format(cpt, err)) + cpt += 1 + if log: + log['u'] = u_GPU.asarray() + log['v'] = v_GPU.asarray() + + # print('err=',err,' cpt=',cpt) + K_GPU.mult_by_col(u_GPU, target=K_GPU) + K_GPU.mult_by_row(v_GPU.transpose(), target=K_GPU) + if log: + return K_GPU.asarray(), log + else: + return K_GPU.asarray() diff --git a/ot/gpu/cudamat/.gitignore b/ot/gpu/cudamat/.gitignore new file mode 100644 index 0000000..7a10978 --- /dev/null +++ b/ot/gpu/cudamat/.gitignore @@ -0,0 +1,38 @@ +*.py[cod] + +# C extensions +*.so + +# Packages +*.egg +*.egg-info +dist +build +eggs +parts +bin +var +sdist +develop-eggs +.installed.cfg +lib +lib64 + +# Installer logs +pip-log.txt + +# Unit test / coverage reports +.coverage +.tox +nosetests.xml + +# Translations +*.mo + +# Mr Developer +.mr.developer.cfg +.project +.pydevproject + +# Vim +*.swp diff --git a/ot/gpu/cudamat/CHANGELOG b/ot/gpu/cudamat/CHANGELOG new file mode 100644 index 0000000..446df0f --- /dev/null +++ b/ot/gpu/cudamat/CHANGELOG @@ -0,0 +1,41 @@ +Version 0.3 +- Added equality testing. Contributed by Ryan P. Adams. +- Added flag that determines whether threads are synced after each call. +- Added direct blas calls for two elementwise operations. Contributed by Vincent Vanhoucke. +- Added the set_selected_columns. Contributed by Tijmen Tieleman. +- Added tanh, abs, and log_1_plus_exp. Contributed by Ilya Sutskever. +- Some bug fixes. Contributed by Jonathan Taylor. +- Added the select_columns method. Code contributed by Tijmen Tieleman. +- on_device should now work as intended. +- allocate_device_memory now returns an error when cublasAlloc fails. +- Fixed bug in max that showed up when an entire column was negative. +- Fixed bug in activation computations in examples/rbm_numpy.py. +- Added get_col_slice and set_col_slice methods. +- Added init and shutdown methods to shorten cublas_init and cublas_shutdown. +- Added bound checking to the various slicing methods. +- Fixed problem with pow and negative numbers. +- Added support for matrix powers in pow. + +Version 0.2 +- Methods add, subtract, mult, divide can now take scalars as well as instances of CUDAMatrix. +- Deprecated add_scalar, mult_by_scalar, div_by_scalar. +- Methods now return target or self to make chaining operations easier. +- Added asarray method. +- Added transpose method. +- Added sqrt and pow functions. +- Added the sigmoid method to the module level. +- Added add_row_vec. +- Added empty. Now when you don't provide a target or pre-allocated temporary storage cudamat methods will not take up CPU RAM or transfer anything between the CPU and GPU. +- Added get_row_slice and set_row_slice. +- Added less_than_scalar, greater_than, greater_than_scalar. +- Added max (axis=1 is currently not supported.) + +Version 0.1.5 +- Added shape attribute and reshape method. + +Version 0.1 +- Most methods now throw python exceptions instead of exiting after encountering an error. +- The CUDAMatrix constructor now automatically converts ndarray objects to float32 in FORTRAN order. +- Renamed scalar_mult to mult_by_scalar and scalar_div to div_by_scalar. +- Added log and exp functions. +- Removed add_row_sums and sum_rows. diff --git a/ot/gpu/cudamat/CONTRIBUTE.md b/ot/gpu/cudamat/CONTRIBUTE.md new file mode 100644 index 0000000..a98a39f --- /dev/null +++ b/ot/gpu/cudamat/CONTRIBUTE.md @@ -0,0 +1,74 @@ +Development installation +------------------------ + +If you want to develop cudamat, you should clone the github repository instead +of downloading a release. Furthermore, it is useful to install it in editable +mode. Instead of copying the files somewhere Python can find them, this will +point Python directly to the directory you install it from. Either of the +following commands will do: + +```bash +# a) Install for your user in editable mode: +python setup.py develop --prefix=~/.local +# b) Install for your user in editable mode, but with pip: +pip install --user --editable . +``` + +As for the [standard installation](INSTALL.md), you can set the `NVCC_FLAGS` +environment variable to compile for a specific architecture. + +Update after local changes +-------------------------- + +Your changes to `.py` files will show up immediately the next time you import +cudamat. Changes to `.cu` and `.cuh` files require a recompilation triggered +by just running the above installation command again. + +Update after remote changes +--------------------------- + +To obtain the latest version, just pull in the remote changes: + +```bash +git checkout master +git fetch origin +git merge origin/master +``` + +Then recompile as per the instructions in the previous section. + +Contribute back +--------------- + +If you created a great new feature that is useful to the rest of the world, +and it even comes with docstrings and updated tests, we will gladly incorporate +it into cudamat. To do that, you will need to send us a pull request from your +fork. + +If you haven't forked cudamat yet, log in to your github account, go to +https://github.com/cudamat/cudamat and hit the "Fork" button. +Now instead of having `origin` point to `cudamat/cudamat`, you will want to have +it point to your fork, and have `upstream` point to the official project: + +```bash +git remote rename origin upstream +git remote add origin git@github.com:yourusername/cudamat +git fetch origin +``` + +Create a branch to house your changes: + +```bash +git checkout -b my-new-feature +``` + +Hack away, then add your changes, commit and push: + +```bash +git add all.py my.py changes.py +git commit -m 'Added a new feature that does this and that' +git push origin my-new-feature +``` + +Now send us a pull request asking to merge `yourusername:my-new-feature` into +`cudamat:master` and we will come back to you! diff --git a/ot/gpu/cudamat/INSTALL.md b/ot/gpu/cudamat/INSTALL.md new file mode 100644 index 0000000..95cfef3 --- /dev/null +++ b/ot/gpu/cudamat/INSTALL.md @@ -0,0 +1,53 @@ +Prerequisites +------------- + +cudamat needs the following to be installed first: + +* Python 2.x or 3.x and numpy +* The CUDA SDK +* nose for running the tests (optional) + +Installation +------------ + +Once you have installed the prerequisites and downloaded cudamat, switch to the +cudamat directory and run either of the following commands to install it: + +```bash +# a) Install for your user: +python setup.py install --user +# b) Install for your user, but with pip: +pip install --user . +# c) Install system-wide: +sudo python setup.py install +# d) Install system-wide, but with pip: +sudo pip install . +``` + +If your Nvidia GPU supports a higher Compute Capability than the default one of +your CUDA toolkit, you can set the `NVCCFLAGS` environment variable when +installing cudamat to compile it for your architecture. For example, to install +for your user for a GTX 780 Ti (Compute Capability 3.5), you would run: + +```bash +NVCCFLAGS=-arch=sm_35 python setup.py install --user +``` + +To compile for both Compute Capability 2.0 and 3.5, you would run: + +```bash +NVCCFLAGS="-gencode arch=compute_20,code=sm_20 -gencode arch=compute_35,code=sm_35" ... +``` + +Testing +------- + +To test your setup, run the included unit tests and optionally the benchmark: + +```bash +cd test # so it doesn't try importing cudamat from the source directory +# Run tests +nosetests +# Run benchmark +python ../examples/bench_cudamat.py +``` diff --git a/ot/gpu/cudamat/LICENSE b/ot/gpu/cudamat/LICENSE new file mode 100644 index 0000000..43b4816 --- /dev/null +++ b/ot/gpu/cudamat/LICENSE @@ -0,0 +1,21 @@ +Copyright (c) 2009-2013, Volodymyr Mnih +All rights reserved. + +Redistribution and use in source and binary forms, with or without modification, are permitted provided that +the following conditions are met: + + * Redistributions of source code must retain the above copyright notice, this list of conditions and the +following disclaimer. + * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and +the following disclaimer in the documentation and/or other materials provided with the distribution. + * Neither the name of the nor the names of its contributors may be used to endorse or +promote products derived from this software without specific prior written permission. + +THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED +WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A +PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY +DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, +PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR +OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH +DAMAGE. diff --git a/ot/gpu/cudamat/README.md b/ot/gpu/cudamat/README.md new file mode 100644 index 0000000..9d2dd6f --- /dev/null +++ b/ot/gpu/cudamat/README.md @@ -0,0 +1,65 @@ +CUDAMat +======= + +The aim of the cudamat project is to make it easy to perform basic matrix calculations on CUDA-enabled GPUs from Python. cudamat provides a Python matrix class that performs calculations on a GPU. At present, some of the operations our GPU matrix class supports include: + +* Easy conversion to and from instances of `numpy.ndarray`. +* Limited slicing support. +* Matrix multiplication and transpose. +* Elementwise addition, subtraction, multiplication, and division. +* Elementwise application of exp, log, pow, sqrt. +* Summation, maximum and minimum along rows or columns. +* Conversion of CUDA errors into Python exceptions. + +The current feature set of cudamat is biased towards features needed for implementing some common machine learning algorithms. We have included implementations of feedforward neural networks and restricted Boltzmann machines in the examples that come with cudamat. + +Example: + +```python +import numpy as np +import cudamat as cm + +cm.cublas_init() + +# create two random matrices and copy them to the GPU +a = cm.CUDAMatrix(np.random.rand(32, 256)) +b = cm.CUDAMatrix(np.random.rand(256, 32)) + +# perform calculations on the GPU +c = cm.dot(a, b) +d = c.sum(axis = 0) + +# copy d back to the host (CPU) and print +print(d.asarray()) +``` + +Documentation +------------- + +An overview of the main features of cudamat can be found in the technical report: + +[CUDAMat: A CUDA-based matrix class for Python](http://www.cs.toronto.edu/~vmnih/docs/cudamat_tr.pdf), Volodymyr Mnih, UTML TR 2009-004. + +Download +-------- + +You can obtain the latest release from the repository by typing: + +```bash +git clone https://github.com/cudamat/cudamat.git +``` + +You can also download one of the releases from the [releases](https://github.com/cudamat/cudamat/releases) section. + +Installation +------------ + +cudamat uses setuptools and can be installed via pip. +For details, please see [INSTALL.md](INSTALL.md). + +Development +----------- + +If you want to contribute new features or improvements, you're welcome to fork +cudamat on github and send us your pull requests! +Please see [CONTRIBUTE.md](CONTRIBUTE.md) if you need any help with that. diff --git a/ot/gpu/cudamat/cudamat/__init__.py b/ot/gpu/cudamat/cudamat/__init__.py new file mode 100644 index 0000000..13b072d --- /dev/null +++ b/ot/gpu/cudamat/cudamat/__init__.py @@ -0,0 +1,2 @@ +from .cudamat import * +from . import learn diff --git a/ot/gpu/cudamat/cudamat/cudamat.cu b/ot/gpu/cudamat/cudamat/cudamat.cu new file mode 100644 index 0000000..522f9cc --- /dev/null +++ b/ot/gpu/cudamat/cudamat/cudamat.cu @@ -0,0 +1,1633 @@ +#include +#include +#include +#include +#include +#include "cudamat_kernels.cuh" +#include "cudamat.cuh" + +extern "C" { + +/* ------------------------------ CUBLAS init/shutdown ------------------------------ */ + +inline bool check_cublas_error() { + cublasStatus status = cublasGetError(); + + return status != CUBLAS_STATUS_SUCCESS; +} + +inline bool checkCUDAError() { + cudaError_t err = cudaGetLastError(); + + if (cudaSuccess != err) + printf("%s\n", cudaGetErrorString( err)); + return cudaSuccess != err; +} + +EXPORT const char* get_last_cuda_error() { + cudaError_t err = cudaGetLastError(); + + return cudaGetErrorString( err); +} + +EXPORT const char* get_last_clib_error() { + return strerror(errno); +} + +EXPORT int cublas_init() { + cublasInit(); + if (check_cublas_error()) + return CUBLAS_ERROR; + else + return 0; +} + +EXPORT int cublas_shutdown() { + cublasShutdown(); + cudaThreadExit(); + + return 0; +} + + +EXPORT int cuda_set_device(int deviceId) { + cudaSetDevice(deviceId); + + if (checkCUDAError()) + return CUDA_ERROR; + else + return 0; +} + +EXPORT int init_random(rnd_struct* rnd_state, int seed, char* cudamatpath) { + unsigned int * host_mults; + host_mults = (unsigned int*)malloc(NUM_RND_STREAMS * sizeof(unsigned int)); + FILE * pFile; + + pFile = fopen (cudamatpath,"r"); + if (pFile == NULL) { + return ERROR_FILE_OPEN; + } + + for (int i = 0; i < NUM_RND_STREAMS; i++) { + if (fscanf (pFile, "%u", &host_mults[i]) != 1) { + return ERROR_FILE_SCAN; + } + } + fclose (pFile); + + cublasAlloc(NUM_RND_STREAMS, sizeof(unsigned int), (void**)&rnd_state->dev_mults); + cublasAlloc(NUM_RND_STREAMS, sizeof(unsigned long long), (void**)&rnd_state->dev_words); + cublasSetVector(NUM_RND_STREAMS, sizeof(unsigned int), host_mults, 1, rnd_state->dev_mults, 1); + //cudaMalloc((void **)&rnd_state->dev_mults, NUM_RND_STREAMS * sizeof(unsigned int)); + //cudaMalloc((void **)&rnd_state->dev_words, NUM_RND_STREAMS * sizeof(unsigned long long)); + //cudaMemcpy(rnd_state->dev_mults, host_mults, NUM_RND_STREAMS * sizeof(unsigned int), cudaMemcpyHostToDevice); + cudaThreadSynchronize(); + + kSeedRandom<<>>(rnd_state->dev_mults, rnd_state->dev_words, seed); + + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + else + return 0; +} + +/* ------------------------------ Utility routines ------------------------------ */ + +EXPORT int get_leading_dimension(cudamat* mat) { + return mat->is_trans ? mat->size[1] : mat->size[0]; +} + +EXPORT int get_nonleading_dimension(cudamat* mat) { + return mat->is_trans ? mat->size[0] : mat->size[1]; +} + +EXPORT void set_transpose(cudamat* mat, int is_trans) { + mat->is_trans = is_trans; +} + +inline char get_transpose_char(cudamat* mat) { + return mat->is_trans ? 't' : 'n'; +} + +EXPORT void cuda_sync_threads() { + cudaThreadSynchronize(); +} + +/* ------------------------------ Allocating/moving data ------------------------------ */ + +EXPORT int allocate_device_memory(cudamat* mat) { + int len = mat->size[0]*mat->size[1]; + + cublasStatus stat; + + stat = cublasAlloc(len, sizeof(mat->data_device[0]), (void**)&mat->data_device); + + if (stat != CUBLAS_STATUS_SUCCESS || check_cublas_error()) { + checkCUDAError(); + return CUBLAS_ERROR; + } + + mat->on_device = 1; + return 0; +} + +EXPORT int copy_to_host(cudamat* mat) { + int len = mat->size[0]*mat->size[1]; + + if (mat->on_device) { + cublasGetVector(len, sizeof(mat->data_host[0]), mat->data_device, 1, mat->data_host, 1); + + if (check_cublas_error()) + return CUBLAS_ERROR; + } else + return ERROR_NOT_ON_DEVICE; + + return 0; +} + +EXPORT int copy_to_device(cudamat* mat) { + int len = mat->size[0]*mat->size[1]; + int err_code = 0; + + //if (!mat->owns_data) + // return VIEW_ERROR; + + if (!mat->on_device) { + err_code = allocate_device_memory(mat); + if (err_code) + return err_code; + } + + cublasSetVector(len, sizeof(mat->data_host[0]), mat->data_host, 1, mat->data_device, 1); + + if (check_cublas_error()) + return CUBLAS_ERROR; + + return 0; +} + +EXPORT int copy_on_device(cudamat* mat1, cudamat* mat2) { + int len = mat1->size[0]*mat1->size[1]; + + if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + cublasDcopy(len, mat1->data_device, 1, mat2->data_device, 1); + + if (check_cublas_error()) + return CUBLAS_ERROR; + else + return 0; +} + +EXPORT int get_row_slice(cudamat* source, cudamat* target, unsigned int start, unsigned int end) { + int height = source->size[0]; + int width = source->size[1]; + + if ((end - start) != target->size[0] || source->size[1] != target->size[1] || start >= end || end > height) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + dim3 kernelBlockGrid((int)ceil((end - start)/32.), (int)ceil(width/32.), 1); + dim3 kernelBlockDim(32, 1, 1); + + kGetRowSlice<<>>(source->data_device, target->data_device, start, end, width, height); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + else + return 0; +} + +EXPORT int set_row_slice(cudamat* source, cudamat* target, unsigned int start, unsigned int end) { + int height = target->size[0]; + int width = target->size[1]; + + if ((end - start) != source->size[0] || source->size[1] != target->size[1] || start >= end || end > height) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + dim3 kernelBlockGrid((int)ceil((end - start)/32.), (int)ceil(width/32.), 1); + dim3 kernelBlockDim(32, 1, 1); + + kSetRowSlice<<>>(source->data_device, target->data_device, start, end, width, height); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + else + return 0; +} + +EXPORT int copy_transpose(cudamat* source, cudamat* target) { + unsigned int height = source->size[0]; + unsigned int width = source->size[1]; + + if (source->size[0] != target->size[1] || source->size[1] != target->size[0]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + // setup execution parameters + unsigned int grid_x = height / COPY_BLOCK_SIZE; + if (height % COPY_BLOCK_SIZE) + grid_x++; + + unsigned int grid_y = width / COPY_BLOCK_SIZE; + if (width % COPY_BLOCK_SIZE) + grid_y++; + + dim3 grid(grid_x, grid_y, 1); + dim3 threads(COPY_BLOCK_SIZE, COPY_BLOCK_SIZE, 1); + + kTranspose<<< grid, threads >>>(target->data_device, source->data_device, height, width); + + if (checkCUDAError()) + return CUDA_ERROR; + else + return 0; +} + +EXPORT int free_device_memory(cudamat* mat) { + if (mat->owns_data && mat->on_device) { + cublasStatus stat; + + stat = cublasFree(mat->data_device); + mat->on_device = 0; + + if (stat != CUBLAS_STATUS_SUCCESS || check_cublas_error()) + return CUBLAS_ERROR; + } + + return 0; +} + +EXPORT int reshape(cudamat* mat, unsigned int m, unsigned int n) { + if (mat->size[0] * mat->size[1] != m * n) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + mat->size[0] = m; + mat->size[1] = n; + + return 0; +} + +EXPORT int get_slice(cudamat* source, cudamat* target, unsigned int first_col, unsigned int last_col) { + if (source->is_trans) + return ERROR_TRANSPOSED; + + if (!source->on_device) + return ERROR_NOT_ON_DEVICE; + + if (last_col > source->size[1] || (first_col >= last_col)) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + int num_rows = source->size[0]; + + target->data_host = 0; + target->data_device = source->data_device + first_col * num_rows; + target->on_device = 1; + target->on_host = 0; + target->size[0] = source->size[0]; + target->size[1] = last_col - first_col; + target->is_trans = 0; + target->owns_data = 0; + + return 0; +} + +EXPORT int get_vector_slice(cudamat* source, cudamat* target, unsigned int first_ind, unsigned int last_ind) { + // source must be a vector + if (source->size[0] > 1 && source->size[1] > 1) + return ERROR_GENERIC; + + if (source->is_trans) + return ERROR_TRANSPOSED; + + if (!source->on_device) + return ERROR_NOT_ON_DEVICE; + + if (first_ind >= last_ind) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + int num_rows = source->size[0]; + + target->data_host = 0; + target->data_device = source->data_device + first_ind * num_rows; + target->on_device = 1; + target->on_host = 0; + target->is_trans = 0; + target->owns_data = 0; + + if (source->size[0] > 1) { + if (last_ind > source->size[0]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + target->size[0] = last_ind - first_ind; + target->size[1] = 1; + } else { + if (last_ind > source->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + target->size[0] = 1; + target->size[1] = last_ind - first_ind; + } + + return 0; +} + +/* ------------------------------ Initialization routines ------------------------------ */ + +EXPORT void init_from_array(cudamat* mat, double* data, int m, int n) { + mat->data_host = data; + mat->size[0] = m; + mat->size[1] = n; + mat->on_device = 0; + mat->on_host = 1; + mat->is_trans = 0; + mat->owns_data = 1; +} + +EXPORT int init_empty(cudamat* mat, int m, int n) { + mat->size[0] = m; + mat->size[1] = n; + mat->on_device = 0; + mat->on_host = 0; + mat->is_trans = 0; + mat->owns_data = 1; + + return allocate_device_memory(mat); +} + +/* ------------------------------ Random number generation ------------------------------ */ +EXPORT int fill_with_rand(rnd_struct* rnd_state, cudamat* mat) { + int len = mat->size[0] * mat->size[1]; + + if (!mat->on_device) + return ERROR_NOT_ON_DEVICE; + + kRandomUniform<<>>(rnd_state->dev_mults, rnd_state->dev_words, mat->data_device, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + else + return 0; +} + +EXPORT int fill_with_randn(rnd_struct* rnd_state, cudamat* mat) { + int len = mat->size[0] * mat->size[1]; + + if (!mat->on_device) + return ERROR_NOT_ON_DEVICE; + + kRandomGaussian<<>>(rnd_state->dev_mults, rnd_state->dev_words, mat->data_device, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + else + return 0; +} +/* ------------------------------ Algebraic operations ------------------------------ */ + +EXPORT int add_col_vec(cudamat* mat, cudamat* vec, cudamat* target) { + unsigned int h = mat->size[0], + w = mat->size[1]; + + if (!mat->on_device || !vec->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->is_trans) + return ERROR_TRANSPOSED; + + if (mat->size[0] != vec->size[0] || vec->size[1] != 1 || + mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kAddColVector<<>>(mat->data_device, vec->data_device, target->data_device, w, h); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) { + return CUDA_ERROR; + } + + return 0; +} + +EXPORT int add_col_mult(cudamat* mat, cudamat* vec, cudamat* target, double mult) { + unsigned int h = mat->size[0], + w = mat->size[1]; + + if (!mat->on_device || !vec->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->is_trans) + return ERROR_TRANSPOSED; + + if (mat->size[0] != vec->size[0] || vec->size[1] != 1 || + mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kAddColMult<<>>(mat->data_device, vec->data_device, target->data_device, mult, w, h); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int add_row_vec(cudamat* mat, cudamat* vec, cudamat* target) { + unsigned int h = mat->size[0], + w = mat->size[1]; + + if (!mat->on_device || !vec->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->is_trans) + return ERROR_TRANSPOSED; + + if (mat->size[1] != vec->size[1] || vec->size[0] != 1 || + mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kAddRowVector<<>>(mat->data_device, vec->data_device, target->data_device, w, h); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int mult_by_col_vec(cudamat* mat, cudamat* vec, cudamat* target) { + unsigned int h = mat->size[0], + w = mat->size[1]; + + if (!mat->on_device || !vec->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->is_trans) + return ERROR_TRANSPOSED; + + if (mat->size[0] != vec->size[0] || vec->size[1] != 1 || + mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kMultByColVector<<>>(mat->data_device, vec->data_device, target->data_device, w, h); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int mult_by_row_vec(cudamat* mat, cudamat* vec, cudamat* target) { + unsigned int h = mat->size[0], + w = mat->size[1]; + + if (!mat->on_device || !vec->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->is_trans) + return ERROR_TRANSPOSED; + + if (mat->size[1] != vec->size[1] || vec->size[0] != 1 || + mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kMultByRowVector<<>>(mat->data_device, vec->data_device, target->data_device, w, h); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int divide_by_col_vec(cudamat* mat, cudamat* vec, cudamat* target) { + unsigned int h = mat->size[0], + w = mat->size[1]; + + if (!mat->on_device || !vec->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->is_trans) + return ERROR_TRANSPOSED; + + if (mat->size[0] != vec->size[0] || vec->size[1] != 1 || + mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kDivByColVector<<>>(mat->data_device, vec->data_device, target->data_device, w, h); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int divide_by_row_vec(cudamat* mat, cudamat* vec, cudamat* target) { + unsigned int h = mat->size[0], + w = mat->size[1]; + + if (!mat->on_device || !vec->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->is_trans) + return ERROR_TRANSPOSED; + + if (mat->size[1] != vec->size[1] || vec->size[0] != 1 || + mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kDivByRowVector<<>>(mat->data_device, vec->data_device, target->data_device, w, h); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int less_than(cudamat* mat1, cudamat* mat2, cudamat* target) { + int len = mat1->size[0]*mat1->size[1]; + + if (!mat1->on_device || !mat2->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat1->is_trans != mat2->is_trans) + return ERROR_TRANSPOSEDNESS; + + if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1] || + mat1->size[0] != target->size[0] || mat1->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kLessThan<<>>(mat1->data_device, mat2->data_device, target->data_device, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int less_than_scalar(cudamat* mat, double val, cudamat* target) { + int len = mat->size[0]*mat->size[1]; + + if (!mat->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->is_trans != target->is_trans) + return ERROR_TRANSPOSEDNESS; + + if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kLessThanScalar<<>>(mat->data_device, val, target->data_device, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int greater_than(cudamat* mat1, cudamat* mat2, cudamat* target) { + int len = mat1->size[0]*mat1->size[1]; + + if (!mat1->on_device || !mat2->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat1->is_trans != mat2->is_trans) + return ERROR_TRANSPOSEDNESS; + + if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1] || + mat1->size[0] != target->size[0] || mat1->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kGreaterThan<<>>(mat1->data_device, mat2->data_device, target->data_device, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int greater_than_scalar(cudamat* mat, double val, cudamat* target) { + int len = mat->size[0]*mat->size[1]; + + if (!mat->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->is_trans != target->is_trans) + return ERROR_TRANSPOSEDNESS; + + if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kGreaterThanScalar<<>>(mat->data_device, val, target->data_device, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int equals(cudamat* mat1, cudamat* mat2, cudamat* target) { + int len = mat1->size[0]*mat1->size[1]; + + if (!mat1->on_device || !mat2->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat1->is_trans != mat2->is_trans) + return ERROR_TRANSPOSEDNESS; + + if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1] || + mat1->size[0] != target->size[0] || mat1->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kEquals<<>>(mat1->data_device, mat2->data_device, target->data_device, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int equals_scalar(cudamat* mat, double val, cudamat* target) { + int len = mat->size[0]*mat->size[1]; + + if (!mat->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->is_trans != target->is_trans) + return ERROR_TRANSPOSEDNESS; + + if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kEqualsScalar<<>>(mat->data_device, val, target->data_device, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int minimum(cudamat* mat1, cudamat* mat2, cudamat* target) { + int len = mat1->size[0]*mat1->size[1]; + + if (!mat1->on_device || !mat2->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat1->is_trans != mat2->is_trans) + return ERROR_TRANSPOSEDNESS; + + if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1] || + mat1->size[0] != target->size[0] || mat1->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kMinimum<<>>(mat1->data_device, mat2->data_device, target->data_device, len); + + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int minimum_scalar(cudamat* mat, double val, cudamat* target) { + int len = mat->size[0]*mat->size[1]; + + if (!mat->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->is_trans != target->is_trans) + return ERROR_TRANSPOSEDNESS; + + if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kMinimumScalar<<>>(mat->data_device, val, target->data_device, len); + + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int maximum(cudamat* mat1, cudamat* mat2, cudamat* target) { + int len = mat1->size[0]*mat1->size[1]; + + if (!mat1->on_device || !mat2->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat1->is_trans != mat2->is_trans) + return ERROR_TRANSPOSEDNESS; + + if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1] || + mat1->size[0] != target->size[0] || mat1->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kMaximum<<>>(mat1->data_device, mat2->data_device, target->data_device, len); + + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int maximum_scalar(cudamat* mat, double val, cudamat* target) { + int len = mat->size[0]*mat->size[1]; + + if (!mat->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->is_trans != target->is_trans) + return ERROR_TRANSPOSEDNESS; + + if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kMaximumScalar<<>>(mat->data_device, val, target->data_device, len); + + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int min_by_axis(cudamat* mat, cudamat* target, int axis) { + unsigned int h = mat->size[0], + w = mat->size[1]; + + if (!mat->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->is_trans) + return ERROR_TRANSPOSED; + + if (axis == 0) { + if (target->size[0] != 1 || target->size[1] != mat->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kMinColumnwise<<>>(mat->data_device, target->data_device, w, h); + } else { + if (target->size[1] != 1 || target->size[0] != mat->size[0]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kMinRowwise<<>>(mat->data_device, target->data_device, w, h); + } + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int max_by_axis(cudamat* mat, cudamat* target, int axis) { + unsigned int h = mat->size[0], + w = mat->size[1]; + + if (!mat->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->is_trans) + return ERROR_TRANSPOSED; + + if (axis == 0) { + if (target->size[0] != 1 || target->size[1] != mat->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kMaxColumnwise<<>>(mat->data_device, target->data_device, w, h); + } else { + if (target->size[1] != 1 || target->size[0] != mat->size[0]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kMaxRowwise<<>>(mat->data_device, target->data_device, w, h); + } + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int argmin_by_axis(cudamat* mat, cudamat* target, int axis) { + unsigned int h = mat->size[0], + w = mat->size[1]; + + if (!mat->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->is_trans) + return ERROR_TRANSPOSED; + + if (axis == 0) { + if (target->size[0] != 1 || target->size[1] != mat->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kArgMinColumnwise<<>>(mat->data_device, target->data_device, w, h); + } else { + if (target->size[1] != 1 || target->size[0] != mat->size[0]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kArgMinRowwise<<>>(mat->data_device, target->data_device, w, h); + } + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int argmax_by_axis(cudamat* mat, cudamat* target, int axis) { + unsigned int h = mat->size[0], + w = mat->size[1]; + + if (!mat->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->is_trans) + return ERROR_TRANSPOSED; + + if (axis == 0) { + if (target->size[0] != 1 || target->size[1] != mat->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kArgMaxColumnwise<<>>(mat->data_device, target->data_device, w, h); + } else { + if (target->size[1] != 1 || target->size[0] != mat->size[0]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kArgMaxRowwise<<>>(mat->data_device, target->data_device, w, h); + } + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int sign(cudamat* mat, cudamat* target) { + int len = mat->size[0]*mat->size[1]; + + if (!mat->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->is_trans != target->is_trans) + return ERROR_TRANSPOSEDNESS; + + if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kSign<<>>(mat->data_device, target->data_device, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int apply_sigmoid(cudamat* mat, cudamat* target) { + unsigned int len = mat->size[0] * mat->size[1]; + + if (!mat->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kApplySigmoid<<>>(mat->data_device, target->data_device, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int apply_tanh(cudamat* mat, cudamat* target) { + unsigned int len = mat->size[0] * mat->size[1]; + + if (!mat->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kApplyTanh<<>>(mat->data_device, target->data_device, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int apply_soft_threshold(cudamat* mat, double alpha, cudamat* target) { + unsigned int len = mat->size[0] * mat->size[1]; + + if (!mat->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kApplySoftThreshold<<>>(mat->data_device, alpha, target->data_device, len); + + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int apply_abs(cudamat* mat, cudamat* target) { + unsigned int len = mat->size[0] * mat->size[1]; + + if (!mat->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kApplyAbs<<>>(mat->data_device, target->data_device, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int apply_log_1_plus_exp(cudamat* mat, cudamat* target) { + unsigned int len = mat->size[0] * mat->size[1]; + + if (!mat->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kApplyLog1PlusExp<<>>(mat->data_device, target->data_device, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int apply_log(cudamat* mat, cudamat* target) { + unsigned int len = mat->size[0] * mat->size[1]; + + if (!mat->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kLog<<>>(mat->data_device, target->data_device, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int apply_exp(cudamat* mat, cudamat* target) { + unsigned int len = mat->size[0] * mat->size[1]; + + if (!mat->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kExp<<>>(mat->data_device, target->data_device, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int apply_gamma(cudamat* mat, cudamat* target) { + unsigned int len = mat->size[0] * mat->size[1]; + + if (!mat->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kGamma<<>>(mat->data_device, target->data_device, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int apply_lgamma(cudamat* mat, cudamat* target) { + unsigned int len = mat->size[0] * mat->size[1]; + + if (!mat->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kLogGamma<<>>(mat->data_device, target->data_device, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int apply_sqrt(cudamat* mat, cudamat* target) { + unsigned int len = mat->size[0] * mat->size[1]; + + if (!mat->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kSqrt<<>>(mat->data_device, target->data_device, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int apply_pow(cudamat* mat, double pow, cudamat* target) { + unsigned int len = mat->size[0] * mat->size[1]; + + if (!mat->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kPow<<>>(mat->data_device, pow, target->data_device, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int apply_pow_matrix(cudamat* mat, cudamat* pow, cudamat* target) { + unsigned int len = mat->size[0] * mat->size[1]; + + if (!mat->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + if (mat->size[0] != pow->size[0] || mat->size[1] != pow->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kPowMatrix<<>>(mat->data_device, pow->data_device, target->data_device, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int reciprocal(cudamat* mat, cudamat* target) { + unsigned int len = mat->size[0] * mat->size[1]; + + if (!mat->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kReciprocal<<>>(mat->data_device, target->data_device, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int dot(cudamat* mat1, cudamat* mat2, cudamat* target, double beta, double alpha) { + if (!mat1->on_device || !mat2->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (get_leading_dimension(mat1) != get_leading_dimension(target) || + get_nonleading_dimension(mat2) != get_nonleading_dimension(target) || + get_nonleading_dimension(mat1) != get_leading_dimension(mat2)) { + return ERROR_INCOMPATIBLE_DIMENSIONS; + } + int m = get_leading_dimension(mat1), + k = get_leading_dimension(mat2), + n = get_nonleading_dimension(mat2); + + // gemv if second matrix is a (column) vector + if (n == 1) { + cublasDgemv(get_transpose_char(mat1), mat1->size[0], mat1->size[1], + alpha, mat1->data_device, mat1->size[0], + mat2->data_device, 1, + beta, target->data_device, 1); + } + // gemv if first matrix is a (row) vector + else if (m == 1) { + cublasDgemv(mat2->is_trans ? 'n' : 't', mat2->size[0], mat2->size[1], + alpha, mat2->data_device, mat2->size[0], + mat1->data_device, 1, + beta, target->data_device, 1); + } + // gemm otherwise + else { + cublasDgemm(get_transpose_char(mat1), get_transpose_char(mat2), + m, n, k, + alpha, mat1->data_device, mat1->size[0], + mat2->data_device, mat2->size[0], + beta, target->data_device, target->size[0]); + } + + if (check_cublas_error()) + return CUBLAS_ERROR; + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + return 0; +} + +EXPORT double vdot(cudamat* mat1, cudamat* mat2, int* err_code) { + int len = mat1->size[0]*mat1->size[1]; + double res; + + if (!mat1->on_device || !mat2->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat1->is_trans != mat2->is_trans) { + *err_code = ERROR_TRANSPOSEDNESS; + return 0; + } + + if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1]) { + *err_code = ERROR_INCOMPATIBLE_DIMENSIONS; + return 0; + } + + res = cublasDdot(len, mat1->data_device, 1, mat2->data_device, 1); + + if (check_cublas_error()) { + *err_code = CUBLAS_ERROR; + return -1.; + } else { + *err_code = 0; + return res; + } +} + +/* Perform the operation mat1 = mat1 + alpha * mat2. mat1 and mat2 must + have the same transposedness. */ +EXPORT int add_mult(cudamat* mat1, cudamat* mat2, double alpha) { + int len = mat1->size[0]*mat1->size[1]; + + if (!mat1->on_device || !mat2->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat1->is_trans != mat2->is_trans) + return ERROR_TRANSPOSEDNESS; + + if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + cublasDaxpy(len, alpha, mat2->data_device, 1, mat1->data_device, 1); + + if (check_cublas_error()) + return CUBLAS_ERROR; + + return 0; +} + +EXPORT int add_elementwise(cudamat* mat1, cudamat* mat2, cudamat* target) { + int len = mat1->size[0]*mat1->size[1]; + + if (!mat1->on_device || !mat2->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat1->is_trans != mat2->is_trans) + return ERROR_TRANSPOSEDNESS; + + if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1] || + mat1->size[0] != target->size[0] || mat1->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + if (mat1 == target) { + cublasDaxpy(len, 1, mat2->data_device, 1, mat1->data_device, 1); + + if (check_cublas_error()) + return CUBLAS_ERROR; + + } else { + kAdd<<>>(mat1->data_device, mat2->data_device, target->data_device, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + } + + return 0; +} + +EXPORT int subtract_elementwise(cudamat* mat1, cudamat* mat2, cudamat* target) { + int len = mat1->size[0]*mat1->size[1]; + + if (!mat1->on_device || !mat2->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat1->is_trans != mat2->is_trans) + return ERROR_TRANSPOSEDNESS; + + if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1] || + mat1->size[0] != target->size[0] || mat1->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kSubtract<<>>(mat1->data_device, mat2->data_device, target->data_device, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int divide_elementwise(cudamat* mat1, cudamat* mat2, cudamat* target) { + int len = mat1->size[0]*mat1->size[1]; + + if (!mat1->on_device || !mat2->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat1->is_trans != mat2->is_trans) + return ERROR_TRANSPOSEDNESS; + + if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1] || + mat1->size[0] != target->size[0] || mat1->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kDivide<<>>(mat1->data_device, mat2->data_device, target->data_device, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +/* Elementwise multiplication of 2 matrices */ +EXPORT int mult_elementwise(cudamat* mat1, cudamat* mat2, cudamat* target) { + int len = mat1->size[0]*mat1->size[1]; + + if (!mat1->on_device || !mat2->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat1->is_trans != mat2->is_trans) + return ERROR_TRANSPOSEDNESS; + + if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1] || + mat1->size[0] != target->size[0] || mat1->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kMult<<>>(mat1->data_device, mat2->data_device, target->data_device, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int assign_scalar(cudamat* mat, double alpha) { + int len = mat->size[0]*mat->size[1]; + + if (!mat->on_device) + return ERROR_NOT_ON_DEVICE; + + kAssignScalar<<>>(mat->data_device, alpha, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int mult_by_scalar(cudamat* mat, double alpha, cudamat* target) { + int len = mat->size[0]*mat->size[1]; + + if (!mat->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + if (mat == target) { + cublasDscal(len, alpha, mat->data_device, 1); + + if (check_cublas_error()) + return CUBLAS_ERROR; + + } else { + kMultScalar<<>>(mat->data_device, alpha, target->data_device, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + } + + return 0; +} + +EXPORT int divide_by_scalar(cudamat* mat, double alpha, cudamat* target) { + int len = mat->size[0]*mat->size[1]; + + if (!mat->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kDivideScalar<<>>(mat->data_device, alpha, target->data_device, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int add_scalar(cudamat* mat, double alpha, cudamat* target) { + int len = mat->size[0]*mat->size[1]; + + if (!mat->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kAddScalar<<>>(mat->data_device, alpha, target->data_device, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT double euclid_norm(cudamat* mat, int* err_code) { + int len = mat->size[0]*mat->size[1]; + + if (!mat->on_device) { + *err_code = ERROR_NOT_ON_DEVICE; + return -1.; + } + + double res = cublasDnrm2(len, mat->data_device, 1); + + if (check_cublas_error()) { + *err_code = CUBLAS_ERROR; + return -1.; + } else { + *err_code = 0; + return res; + } +} + +EXPORT double manhattan_norm(cudamat* mat, int* err_code) { + int len = mat->size[0]*mat->size[1]; + + if (!mat->on_device) { + *err_code = ERROR_NOT_ON_DEVICE; + return -1.; + } + + double res = cublasDasum(len, mat->data_device, 1); + + if (check_cublas_error()) { + *err_code = CUBLAS_ERROR; + return -1.; + } else { + *err_code = 0; + return res; + } +} + +EXPORT int selectRows(cudamat* source, cudamat* target, cudamat* indices){ + const int nRetRows = indices->size[1]; + + if (nRetRows==0) return 0; + + dim3 gridDim((nRetRows+31)/32); + dim3 blockDim(32); + + kSelectRows<<>>(source->data_device, target->data_device, indices->data_device, nRetRows, source->size[0], source->size[1]); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + else + return 0; +} + +EXPORT int setSelectedRows(cudamat* target, cudamat* source, cudamat* indices){ + const int nSetRows = indices->size[1]; + + if (nSetRows==0) + return 0; + + dim3 gridDim((nSetRows+31)/32); + dim3 blockDim(32); + + kSetSelectedRows<<>>(target->data_device, source->data_device, indices->data_device, nSetRows, target->size[0], target->size[1]); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + else + return 0; +} + +EXPORT int where(cudamat* condition_mat, cudamat* if_mat, cudamat* else_mat, cudamat* target) { + unsigned int len = condition_mat->size[0] * condition_mat->size[1]; + + if (!condition_mat->on_device || !target->on_device) + return ERROR_NOT_ON_DEVICE; + + if (condition_mat->size[0] != target->size[0] || condition_mat->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + if (condition_mat->size[0] != if_mat->size[0] || condition_mat->size[1] != if_mat->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + if (condition_mat->size[0] != else_mat->size[0] || condition_mat->size[1] != else_mat->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kWhere<<>>(condition_mat->data_device, + if_mat->data_device, else_mat->data_device, target->data_device, len); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +EXPORT int correlate(cudamat* source, cudamat* kernel, cudamat* dest) { + int len = source->size[0] * source->size[1]; + + if (!source->on_device || !kernel->on_device || !dest->on_device) + return ERROR_NOT_ON_DEVICE; + + if (source->size[0] != dest->size[0] || source->size[1] != dest->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + if (kernel->size[0] % 2 == 0 || kernel->size[1] % 2 == 0 || + kernel->size[0] > source->size[0] || kernel->size[1] > source->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kCorrelate<<>>(source->data_device, + kernel->data_device, dest->data_device, source->size[1], source->size[0], + kernel->size[1], kernel->size[0]); + + if (SYNC_THREADS) + cudaThreadSynchronize(); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +} diff --git a/ot/gpu/cudamat/cudamat/cudamat.cuh b/ot/gpu/cudamat/cudamat/cudamat.cuh new file mode 100644 index 0000000..e4e2e34 --- /dev/null +++ b/ot/gpu/cudamat/cudamat/cudamat.cuh @@ -0,0 +1,35 @@ +#if defined(_WIN32) || defined(__CYGWIN__) + #define EXPORT __declspec(dllexport) +#else + #define EXPORT __attribute__ ((visibility("default"))) +#endif + +#define SYNC_THREADS 1 + +#define ERROR_INCOMPATIBLE_DIMENSIONS -1 +#define CUBLAS_ERROR -2 +#define CUDA_ERROR -3 +#define VIEW_ERROR -4 +#define ERROR_TRANSPOSED -5 +#define ERROR_GENERIC -6 +#define ERROR_TRANSPOSEDNESS -7 +#define ERROR_NOT_ON_DEVICE -8 +#define ERROR_UNSUPPORTED -9 +#define ERROR_FILE_OPEN -10 +#define ERROR_FILE_SCAN -11 + +struct cudamat { + double* data_host; + double* data_device; + int on_device; + int on_host; + int size[2]; + int is_trans; // 0 or 1 + int owns_data; +}; + +struct rnd_struct { + unsigned int* dev_mults; + unsigned long long* dev_words; +}; + diff --git a/ot/gpu/cudamat/cudamat/cudamat.py b/ot/gpu/cudamat/cudamat/cudamat.py new file mode 100644 index 0000000..f855ed2 --- /dev/null +++ b/ot/gpu/cudamat/cudamat/cudamat.py @@ -0,0 +1,1575 @@ +import os +import platform +import warnings +import sysconfig +import sys + +import ctypes as ct +import numpy as np + +def load_library(basename): + if platform.system() == 'Windows': + ext = '.dll' + else: + ext = sysconfig.get_config_var('SO') + return ct.cdll.LoadLibrary(os.path.join( + os.path.dirname(__file__) or os.path.curdir, + basename + ext)) + +_cudamat = load_library('libcudamat') + +_cudamat.get_last_cuda_error.restype = ct.c_char_p +_cudamat.get_last_clib_error.restype = ct.c_char_p +_cudamat.cublas_init.restype = ct.c_int +_cudamat.cublas_shutdown.restype = ct.c_int +_cudamat.cuda_set_device.restype = ct.c_int +_cudamat.init_random.restype = ct.c_int + +_cudamat.init_empty.restype = ct.c_int +_cudamat.reshape.restype = ct.c_int +_cudamat.copy_to_host.restype = ct.c_int +_cudamat.allocate_device_memory = ct.c_int +_cudamat.copy_to_device.restype = ct.c_int +_cudamat.copy_on_device.restype = ct.c_int +_cudamat.free_device_memory.restype = ct.c_int + +_cudamat.get_slice.restype = ct.c_int +_cudamat.get_row_slice.restype = ct.c_int +_cudamat.set_row_slice.restype = ct.c_int +_cudamat.copy_transpose.restype = ct.c_int +_cudamat.get_vector_slice.restype = ct.c_int +_cudamat.fill_with_rand.restype = ct.c_int +_cudamat.fill_with_randn.restype = ct.c_int + +_cudamat.add_col_vec.restype = ct.c_int +_cudamat.add_col_mult.restype = ct.c_int +_cudamat.add_row_vec.restype = ct.c_int +_cudamat.mult_by_col_vec.restype = ct.c_int +_cudamat.mult_by_row_vec.restype = ct.c_int +_cudamat.divide_by_col_vec.restype = ct.c_int +_cudamat.divide_by_row_vec.restype = ct.c_int + +_cudamat.less_than.restype = ct.c_int +_cudamat.less_than_scalar.restype = ct.c_int +_cudamat.greater_than.restype = ct.c_int +_cudamat.greater_than_scalar.restype = ct.c_int +_cudamat.equals.restype = ct.c_int +_cudamat.equals_scalar.restype = ct.c_int +_cudamat.minimum.restype = ct.c_int +_cudamat.minimum_scalar.restype = ct.c_int +_cudamat.maximum.restype = ct.c_int +_cudamat.maximum_scalar.restype = ct.c_int +_cudamat.min_by_axis.restype = ct.c_int +_cudamat.max_by_axis.restype = ct.c_int +_cudamat.argmin_by_axis.restype = ct.c_int +_cudamat.argmax_by_axis.restype = ct.c_int +_cudamat.sign.restype = ct.c_int +_cudamat.apply_sigmoid.restype = ct.c_int +_cudamat.apply_tanh.restype = ct.c_int +_cudamat.apply_soft_threshold.restype = ct.c_int +_cudamat.apply_abs.restype = ct.c_int +_cudamat.apply_log_1_plus_exp.restype = ct.c_int +_cudamat.apply_log.restype = ct.c_int +_cudamat.apply_exp.restype = ct.c_int +_cudamat.apply_gamma.restype = ct.c_int +_cudamat.apply_lgamma.restype = ct.c_int +_cudamat.apply_sqrt.restype = ct.c_int +_cudamat.apply_pow.restype = ct.c_int +_cudamat.apply_pow_matrix.restype = ct.c_int +_cudamat.reciprocal.restype = ct.c_int + +_cudamat.add_elementwise.restype = ct.c_int +_cudamat.subtract_elementwise.restype = ct.c_int +_cudamat.divide_elementwise.restype = ct.c_int +_cudamat.mult_elementwise.restype = ct.c_int +_cudamat.assign_scalar.restype = ct.c_int +_cudamat.mult_by_scalar.restype = ct.c_int +_cudamat.divide_by_scalar.restype = ct.c_int +_cudamat.add_scalar.restype = ct.c_int + +_cudamat.euclid_norm.restype = ct.c_double +_cudamat.manhattan_norm.restype = ct.c_double +_cudamat.selectRows.restype = ct.c_int +_cudamat.setSelectedRows.restype = ct.c_int +_cudamat.vdot.restype = ct.c_double +_cudamat.dot.restype = ct.c_int + +_cudamat.where.restype = ct.c_int + +_cudamat.correlate.restype = ct.c_int + + +def deprecated(func): + """This is a decorator which can be used to mark functions + as deprecated. It will result in a warning being emmitted + when the function is used.""" + + def newFunc(*args, **kwargs): + warnings.warn("Call to deprecated function %s." % func.__name__, + category=DeprecationWarning) + return func(*args, **kwargs) + newFunc.__name__ = func.__name__ + newFunc.__doc__ = func.__doc__ + newFunc.__dict__.update(func.__dict__) + return newFunc + + +class CUDAMatException(Exception): + pass + + +def get_last_cuda_error(): + errmsg = _cudamat.get_last_cuda_error() + if sys.version_info >= (3,): + return bytes(errmsg).decode() + else: + return str(errmsg) + + +def get_last_clib_error(): + errmsg = _cudamat.get_last_clib_error() + if sys.version_info >= (3,): + return bytes(errmsg).decode() + else: + return str(errmsg) + + +def generate_exception(err_code, **kwargs): + """ + Return a CUDAMatException object based on the error code err_code. + Additional arguments are error-specific and optional. + """ + + if err_code == -1: + return CUDAMatException("Incompatible matrix dimensions.") + elif err_code == -2: + return CUDAMatException("CUBLAS error.") + elif err_code == -3: + return CUDAMatException("CUDA error: " + get_last_cuda_error()) + elif err_code == -4: + return CUDAMatException("Operation not supported on views.") + elif err_code == -5: + return CUDAMatException("Operation not supported on " + "transposed matrices.") + elif err_code == -6: + return CUDAMatException("") + elif err_code == -7: + return CUDAMatException("Incompatible transposedness.") + elif err_code == -8: + return CUDAMatException("Matrix is not in device memory.") + elif err_code == -9: + return CUDAMatException("Operation not supported.") + elif err_code == -10: + filepath = kwargs.get("filepath",""); + if filepath: + filepath = ": '%s'" % filepath + return CUDAMatException("Cannot open file%s: %s" % (filepath,get_last_clib_error())) + elif err_code == -11: + filepath = kwargs.get("filepath",""); + if filepath: + filepath = ": '%s'" % filepath + return CUDAMatException("Cannot parse file%s." % filepath) + else: + return CUDAMatException("") + + +class cudamat(ct.Structure): + _fields_ = [('data_host', ct.POINTER(ct.c_double)), + ('data_device', ct.POINTER(ct.c_double)), + ('on_device', ct.c_int), + ('on_host', ct.c_int), + ('size', ct.c_int * 2), + ('is_trans', ct.c_int), + ('owns_data', ct.c_int)] + + +class rnd_struct(ct.Structure): + _fields_ = [('dev_rnd_mults', ct.POINTER(ct.c_uint)), + ('dev_rnd_words', ct.POINTER(ct.c_longlong))] + + +class TransposedCUDAMatrix(object): + def __init__(self, mat): + self.mat = cudamat() + ct.memmove(ct.pointer(self.mat), ct.pointer(mat), ct.sizeof(self.mat)) + self.mat.is_trans = 1 + self.p_mat = ct.pointer(self.mat) + + +class CUDAMatrix(object): + """ + A CUDAMatrix object represents a matrix of single precision floating point + numbers on a GPU. + """ + + def __init__(self, array, copy_to_device=True, copy_on_host=True): + """ + Initializes a new matrix object in one of two ways. If array is a numpy + ndarray, memory for a matrix with the same dimensions is allocated on + the GPU. If the copy_to_device flag is set to True, the GPU matrix is + initialized with the given ndarray. If the copy_on_host flag is set to + True, a copy of the matrix will be created in host memory even if the + matrix is of the correct type (float64, Fortran-contiguous order). + If array is not an ndarray, it must be a cudamat structure (typically + the user will never use this way of calling __init__). + """ + + if type(array) in [np.ndarray, np.memmap]: + # Convert array to float64 in FORTRAN order + array = reformat(array, copy=copy_on_host) + + # Initialize as a ndarray-tied matrix. + self.mat = cudamat() + self.size = self.mat.size + self.p_mat = ct.pointer(self.mat) + self.numpy_array = array + + _cudamat.init_from_array( + self.p_mat, + array.ctypes.data_as(ct.POINTER(ct.c_double)), + ct.c_int(array.shape[0]), + ct.c_int(array.shape[1])) + + if copy_to_device: + err_code = _cudamat.copy_to_device(self.p_mat) + if err_code: + raise generate_exception(err_code) + + else: + # Initialize based on existing cudamat structure. + mat = array + self.mat = mat + self.p_mat = ct.pointer(self.mat) + + self.T = TransposedCUDAMatrix(self.mat) + + # Keep a reference to free device memory in case of a crash. + self.__free_device_memory = _cudamat.free_device_memory + + def __del__(self): + try: + if 'p_mat' in self.__dict__: + err_code = self.__free_device_memory(self.p_mat) + if err_code: + raise generate_exception(err_code) + except AttributeError: + pass + + @staticmethod + def init_random(seed=0): + """ + Initialize and seed the random number generator. + """ + + CUDAMatrix.rndInitialized = 1 + CUDAMatrix.rnd_state = rnd_struct() + CUDAMatrix.rnd_state_p = ct.pointer(CUDAMatrix.rnd_state) + + cudamat_path = os.path.join(os.path.abspath(os.path.dirname(__file__)), + 'rnd_multipliers_32bit.txt') + + if sys.version_info >= (3,): + cudamat_path = cudamat_path.encode(sys.getfilesystemencoding()) + + err_code = _cudamat.init_random(CUDAMatrix.rnd_state_p, + ct.c_int(seed), + cudamat_path) + if err_code: + if sys.version_info >= (3,): + cudamat_path = cudamat_path.decode(sys.getfilesystemencoding()) + raise generate_exception(err_code, filepath=cudamat_path) + + @property + def shape(self): + return (self.mat.size[0], self.mat.size[1]) + + def reshape(self, shape): + """ + Reshapes self to have the given shape. The number of elements cannot + change as this only changes how the contents are interpreted. + """ + + m = ct.c_uint(shape[0]) + n = ct.c_uint(shape[1]) + + # Reshape the default matrix + err_code = _cudamat.reshape(self.p_mat, m, n) + if err_code: + raise generate_exception(err_code) + # Reshape the transposed matrix + err_code = _cudamat.reshape(self.T.p_mat, m, n) + if err_code: + raise generate_exception(err_code) + # Reshape the CPU matrix + if self.mat.on_host: + self.numpy_array = np.reshape(self.numpy_array, shape, order='F') + + return self + + def asarray(self): + """ + Copies the matrix to an ndarray on the CPU and returns it. + """ + + self.copy_to_host() + + return self.numpy_array + + def copy_to_device(self): + """ + Copy the matrix to the GPU. + """ + + err_code = _cudamat.copy_to_device(self.p_mat) + if err_code: + raise generate_exception(err_code) + + def copy_to_host(self): + """ + Copy the matrix to the CPU. + """ + + if not self.mat.on_host: + # allocate host storage if necessary + m = self.mat.size[0] + n = self.mat.size[1] + + self.numpy_array = np.empty((m, n), dtype=np.float64, order='F') + self.mat.data_host = \ + self.numpy_array.ctypes.data_as(ct.POINTER(ct.c_double)) + + self.mat.on_host = 1 + + err_code = _cudamat.copy_to_host(self.p_mat) + if err_code: + raise generate_exception(err_code) + + def copy(self, include_host=False): + """ + Create a copy of the matrix on GPU. If include_host is True, also + creates a copy of the matrix on CPU if there was any. + """ + + new_mat = empty(self.shape).assign(self) + + if include_host and self.mat.on_host: + new_mat.numpy_array = self.numpy_array.copy() + new_mat.mat.data_host = \ + new_mat.numpy_array.ctypes.data_as(ct.POINTER(ct.c_double)) + new_mat.mat.on_host = 1 + + return new_mat + + def assign(self, val): + """Assign val to self, where val can be a scalar or a CUDAMatrix + with the same dimensions as self. """ + + if isinstance(val, CUDAMatrix): + err_code = _cudamat.copy_on_device(val.p_mat, self.p_mat) + elif isinstance(val, (int, float)): + err_code = _cudamat.assign_scalar(self.p_mat, ct.c_double(val)) + else: + raise ValueError("Assigned value must be of type" + "CUDAMatrix, int, or float.") + + if err_code: + raise generate_exception(err_code) + + return self + + def free_device_memory(self): + """ + Free memory used up by the matrix on the GPU. + """ + + err_code = _cudamat.free_device_memory(self.p_mat) + if err_code: + raise generate_exception(err_code) + + def set_trans(self, is_trans): + """ + Set the transposedness flag to is_trans. + """ + + _cudamat.set_transpose(self.p_mat, ct.c_int(1 * is_trans)) + + def slice(self, first_col, last_col, include_host=False): + """ + Creates a view into a consecutive range of columns of an existing + matrix on GPU. If include_host is set to True, also creates a view + into the CPU copy of the matrix (i.e., the numpy_array). + """ + mat = cudamat() + + if self.mat.size[0] == 1 or self.mat.size[1] == 1: + err_code = _cudamat.get_vector_slice(self.p_mat, + ct.pointer(mat), + ct.c_int(first_col), + ct.c_int(last_col)) + else: + err_code = _cudamat.get_slice(self.p_mat, + ct.pointer(mat), + ct.c_int(first_col), + ct.c_int(last_col)) + + if err_code: + raise generate_exception(err_code) + + new_mat = CUDAMatrix(mat) + + try: + new_mat.sliceof = self.sliceof + except: + new_mat.sliceof = self + + # reproduce the slice on the host as well (if requested) + if include_host and self.mat.on_host: + new_mat.numpy_array = self.numpy_array[:, first_col:last_col] + new_mat.mat.data_host = \ + new_mat.numpy_array.ctypes.data_as(ct.POINTER(ct.c_double)) + new_mat.mat.on_host = 1 + + return new_mat + + def get_col_slice(self, first_col, last_col, target=None): + """ + Get the columns with indices first_col through last_col. If a target + is provided, columns are copied into the target. Otherwise, returns a + view into the existing memory on GPU. + """ + col_slice = self.slice(first_col, last_col) + + if target: + target.assign(col_slice) + return target + else: + return col_slice + + def set_col_slice(self, first_col, last_col, mat): + """ + Assign the contents of mat to the columns with indices first_col + through last_col. + """ + self.slice(first_col, last_col).assign(mat) + + return self + + def get_row_slice(self, start, end, target=None): + """ + Get the rows with indices start through end. If target is not provided + memory for a new matrix will be allocated. + """ + + width = self.shape[1] + + if not target: + target = empty((end-start, width)) + + err_code = _cudamat.get_row_slice(self.p_mat, + target.p_mat, + ct.c_int(start), + ct.c_int(end)) + if err_code: + raise generate_exception(err_code) + + return target + + def set_row_slice(self, start, end, mat): + """ + Assign the contents of mat to the rows with indices start through end. + """ + + err_code = _cudamat.set_row_slice(mat.p_mat, self.p_mat, + ct.c_int(start), ct.c_int(end)) + if err_code: + raise generate_exception(err_code) + + return self + + def transpose(self, target=None): + """ + Return a transposed copy of the matrix. + """ + if not target: + target = empty((self.shape[1], self.shape[0])) + + err_code = _cudamat.copy_transpose(self.p_mat, target.p_mat) + if err_code: + raise generate_exception(err_code) + + return target + + def fill_with_rand(self): + """ + Fill matrix on the GPU with random numbers drawn from the uniform + distribution over the (0,1) interval. + """ + + err_code = _cudamat.fill_with_rand(CUDAMatrix.rnd_state_p, self.p_mat) + if err_code: + raise generate_exception(err_code) + + return self + + def fill_with_randn(self): + """ + Fill matrix on the GPU with random numbers drawn from + the standard normal distribution. + """ + + err_code = _cudamat.fill_with_randn(CUDAMatrix.rnd_state_p, self.p_mat) + if err_code: + raise generate_exception(err_code) + + return self + + def add_col_vec(self, vec, target=None): + """ + Add vector vec to every column of the matrix. If a target is provided, + it is used to store the result instead of self. + """ + + if not target: + target = self + + err_code = _cudamat.add_col_vec(self.p_mat, vec.p_mat, target.p_mat) + if err_code: + raise generate_exception(err_code) + + return target + + def add_col_mult(self, vec, mult, target=None): + """ + Add a multiple of vector vec to every column of the matrix. If a target + is provided, it is used to store the result instead of self. + """ + + if not target: + target = self + + err_code = _cudamat.add_col_mult(self.p_mat, vec.p_mat, + target.p_mat, ct.c_double(mult)) + if err_code: + raise generate_exception(err_code) + + return target + + def add_row_vec(self, vec, target=None): + """ + Add vector vec to every row of the matrix. If a target is provided, + it is used to store the result instead of self. + """ + + if not target: + target = self + + err_code = _cudamat.add_row_vec(self.p_mat, vec.p_mat, target.p_mat) + if err_code: + raise generate_exception(err_code) + + return target + + def mult_by_col(self, vec, target=None): + """ + Multiply vector vec into every column of the matrix. If a target is + provided, it is used to store the result instead of self. + """ + + if not target: + target = self + + err_code = _cudamat.mult_by_col_vec(self.p_mat, vec.p_mat, target.p_mat) + if err_code: + raise generate_exception(err_code) + + return target + + def mult_by_row(self, vec, target=None): + """ + Multiply vector vec into every row of the matrix. If a target is + provided, it is used to store the result instead of self. + """ + + if not target: + target = self + + err_code = _cudamat.mult_by_row_vec(self.p_mat, vec.p_mat, target.p_mat) + if err_code: + raise generate_exception(err_code) + + return target + + def div_by_col(self, vec, target=None): + """ + Divide every column of the matrix by vector vec. If a target is + provided, it is used to store the result instead of self. + """ + + if not target: + target = self + + err_code = _cudamat.divide_by_col_vec(self.p_mat, vec.p_mat, + target.p_mat) + if err_code: + raise generate_exception(err_code) + + return target + + def div_by_row(self, vec, target=None): + """ + Divide every row of the matrix by vector vec. If a target is + provided, it is used to store the result instead of self. + """ + + if not target: + target = self + + err_code = _cudamat.divide_by_row_vec(self.p_mat, vec.p_mat, + target.p_mat) + if err_code: + raise generate_exception(err_code) + + return target + + def sum(self, axis, target=None, mult=1.): + """ + Sum the matrix along the given dimension, where 0 represents the leading + dimension and 1 represents the non-leading dimension. If a target is + not provided, a new vector is created for storing the result. The result + is multiplied by the given factor mult (defaults to 1). + """ + + return sum(self, axis, target, mult) + + def mean(self, axis, target=None): + """ + Compute the mean of the matrix along the given dimension, where 0 + represents the leading dimension and 1 represents the non-leading + dimension. If a target is not provided, a new vector is created for + storing the result. + """ + + return mean(self, axis, target) + + def add_sums(self, mat, axis, mult=1., beta=1.): + """ + Add a multiple of the sums of the matrix mat along the given dimension + to self. Self is scaled by beta before adding anything. + """ + + m = _cudamat.get_leading_dimension(mat.p_mat) + n = _cudamat.get_nonleading_dimension(mat.p_mat) + + if axis == 0: + # sum along leading dimension + check_ones_matrix(m) + left = CUDAMatrix.ones.slice(0, m) + left.set_trans(True) + right = mat + + elif axis == 1: + # sum along non-leading dimension + left = mat + check_ones_matrix(n) + right = CUDAMatrix.ones.slice(0, n) + + err_code = _cudamat.dot(left.p_mat, right.p_mat, self.p_mat, + ct.c_double(beta), ct.c_double(mult)) + if err_code: + raise generate_exception(err_code) + + return self + + def less_than(self, val, target=None): + """ + Perform the operation target = 1. * (self < val), + where val can be a matrix or a scalar. + """ + + if not target: + target = self + + if isinstance(val, (int, float)): + err_code = _cudamat.less_than_scalar(self.p_mat, ct.c_double(val), + target.p_mat) + else: + err_code = _cudamat.less_than(self.p_mat, val.p_mat, target.p_mat) + + if err_code: + raise generate_exception(err_code) + + return target + + def greater_than(self, val, target=None): + """ + Perform the operation target = 1. * (self > val), + where val can be a matrix or a scalar. + """ + + if not target: + target = self + + if isinstance(val, (int, float)): + err_code = _cudamat.greater_than_scalar(self.p_mat, + ct.c_double(val), + target.p_mat) + else: + err_code = _cudamat.greater_than(self.p_mat, val.p_mat, + target.p_mat) + + if err_code: + raise generate_exception(err_code) + + return target + + def equals(self, val, target=None): + """ + Perform the operation target = 1. * (self == val), + where val can be a matrix or a scalar. + """ + + if not target: + target = self + + if isinstance(val, (int, float)): + err_code = _cudamat.equals_scalar(self.p_mat, ct.c_double(val), + target.p_mat) + else: + err_code = _cudamat.equals(self.p_mat, val.p_mat, target.p_mat) + + if err_code: + raise generate_exception(err_code) + + return target + + def minimum(self, val, target=None): + """ + Perform the element-wise operation target = min(self, val), where + val can be a matrix or a scalar. + """ + + if not target: + target = self + + if isinstance(val, (int, float)): + err_code = _cudamat.minimum_scalar(self.p_mat, ct.c_double(val), + target.p_mat) + else: + err_code = _cudamat.minimum(self.p_mat, val.p_mat, target.p_mat) + + if err_code: + raise generate_exception(err_code) + + return target + + def maximum(self, val, target=None): + """ + Perform the element-wise operation target = max(self, val), where + val can be a matrix or a scalar. + """ + + if not target: + target = self + + if isinstance(val, (int, float)): + err_code = _cudamat.maximum_scalar(self.p_mat, ct.c_double(val), + target.p_mat) + else: + err_code = _cudamat.maximum(self.p_mat, val.p_mat, target.p_mat) + + if err_code: + raise generate_exception(err_code) + + return target + + def min(self, axis, target=None): + """ + Find the minimum value along the given dimension, where 0 represents the + leading dimension and 1 represents the non-leading dimension. If a + target is not prvided, a new vector is created for storing the result. + """ + + m, n = self.shape + + if axis == 0: + if not target: + target = empty((1, n)) + + elif axis == 1: + if not target: + target = empty((m, 1)) + + err_code = _cudamat.min_by_axis(self.p_mat, target.p_mat, + ct.c_int(axis)) + if err_code: + raise generate_exception(err_code) + + return target + + def max(self, axis, target=None): + """ + Find the maximum value along the given dimension, where 0 represents the + leading dimension and 1 represents the non-leading dimension. If a + target is not prvided, a new vector is created for storing the result. + """ + + m, n = self.shape + + if axis == 0: + if not target: + target = empty((1, n)) + + elif axis == 1: + if not target: + target = empty((m, 1)) + + err_code = _cudamat.max_by_axis(self.p_mat, target.p_mat, + ct.c_int(axis)) + if err_code: + raise generate_exception(err_code) + + return target + + def argmin(self, axis, target=None): + """ + Find the index of the minimum value along the given dimension, where 0 + represents the leading dimension and 1 represents the non-leading + dimension. If a target is not provided, a new vector is created for + storing the result. + """ + + m, n = self.shape + + if axis == 0: + if not target: + target = empty((1, n)) + + elif axis == 1: + if not target: + target = empty((m, 1)) + + err_code = _cudamat.argmin_by_axis(self.p_mat, target.p_mat, + ct.c_int(axis)) + if err_code: + raise generate_exception(err_code) + + return target + + def argmax(self, axis, target=None): + """ + Find the index of the maximum value along the given dimension, where 0 + represents the leading dimension and 1 represents the non-leading + dimension. If a target is not provided, a new vector is created for + storing the result. + """ + + m, n = self.shape + + if axis == 0: + if not target: + target = empty((1, n)) + + elif axis == 1: + if not target: + target = empty((m, 1)) + + err_code = _cudamat.argmax_by_axis(self.p_mat, target.p_mat, + ct.c_int(axis)) + if err_code: + raise generate_exception(err_code) + + return target + + def sign(self, target=None): + """ + Find the sign of each element of the matrix. + """ + + if not target: + target = empty((self.mat.size[0], self.mat.size[1])) + + err_code = _cudamat.sign(self.p_mat, target.p_mat) + if err_code: + raise generate_exception(err_code) + + return target + + def apply_sigmoid(self, target=None): + """ + Apply the logistic sigmoid to each element of the matrix. + """ + + return sigmoid(self, target) + + def apply_tanh(self, target=None): + """ + Apply the tanh to each element of the matrix. + """ + + return tanh(self, target) + + def apply_soft_threshold(self, alpha, target=None): + """ + Apply the soft threshold function to each element of the matrix: + + x = sign(x) * max(0, abs(x) - alpha) + """ + + return soft_threshold(self, alpha, target) + + def reciprocal(self, target=None): + """ + Find the reciprocal of each element of the matrix. + """ + + if not target: + target = self + + err_code = _cudamat.reciprocal(self.p_mat, target.p_mat) + if err_code: + raise generate_exception(err_code) + + return target + + def dot(self, mat2, target=None): + """ + Multiply the matrix by mat2 from the right. + """ + + return dot(self, mat2, target) + + def add_dot(self, m1, m2, mult=1., beta=1.): + """ + Add the dot product of m1 and m2 to the matrix, scaled by mult. + Self is scaled by beta before adding anything. + """ + + err_code = _cudamat.dot(m1.p_mat, m2.p_mat, self.p_mat, + ct.c_double(beta), ct.c_double(mult)) + if err_code: + raise generate_exception(err_code) + + return self + + def subtract_dot(self, m1, m2, mult=1., beta=1.): + """ + Subtract the dot product of m1 and m2 from the matrix, scaled by mult. + Self is scaled by beta before subtracting anything. + """ + + return self.add_dot(m1, m2, mult=-1. * mult, beta=beta) + + def add_mult(self, mat2, alpha=1.): + """ + Add multiple of mat2 to the matrix. + """ + + err_code = _cudamat.add_mult(self.p_mat, mat2.p_mat, ct.c_double(alpha)) + if err_code: + raise generate_exception(err_code) + + return self + + def subtract_mult(self, mat2, alpha=1.): + """ + Subtract a multiple of mat2 from the matrix. + """ + + err_code = _cudamat.add_mult(self.p_mat, mat2.p_mat, + ct.c_double(-1. * alpha)) + if err_code: + raise generate_exception(err_code) + + return self + + def add(self, val, target=None): + """Add val to self, where val can be a scalar or a CUDAMatrix with the + same dimensions as self. """ + + if not target: + target = self + + if isinstance(val, CUDAMatrix): + err_code = _cudamat.add_elementwise(self.p_mat, val.p_mat, + target.p_mat) + elif isinstance(val, (int, float)): + err_code = _cudamat.add_scalar(self.p_mat, ct.c_double(val), + target.p_mat) + else: + raise ValueError("Value must be of type CUDAMatrix, int, or float.") + + if err_code: + raise generate_exception(err_code) + + return target + + def subtract(self, val, target=None): + """Subtract val from self, where val can be a scalar or a CUDAMatrix with + the same dimensions as self. """ + + if not target: + target = self + + if isinstance(val, CUDAMatrix): + err_code = _cudamat.subtract_elementwise(self.p_mat, val.p_mat, + target.p_mat) + elif isinstance(val, (int, float)): + err_code = _cudamat.add_scalar(self.p_mat, ct.c_double(-1*val), + target.p_mat) + else: + raise ValueError("Value must be of type CUDAMatrix, int, or float.") + + if err_code: + raise generate_exception(err_code) + + return target + + def divide(self, val, target=None): + """Divide self by val, where val can be a scalar or + a CUDAMatrix with the same dimensions as self. """ + + if not target: + target = self + + if isinstance(val, CUDAMatrix): + err_code = _cudamat.divide_elementwise(self.p_mat, val.p_mat, + target.p_mat) + elif isinstance(val, (int, float)): + err_code = _cudamat.divide_by_scalar(self.p_mat, ct.c_double(val), + target.p_mat) + else: + raise ValueError("Value must be of type CUDAMatrix, int, or float.") + + if err_code: + raise generate_exception(err_code) + + return target + + def mult(self, val, target=None): + """Multiply self by val, where val can be a scalar or a CUDAMatrix with + the same dimensions as self. """ + + if not target: + target = self + + if isinstance(val, CUDAMatrix): + err_code = _cudamat.mult_elementwise(self.p_mat, val.p_mat, + target.p_mat) + elif isinstance(val, (int, float)): + err_code = _cudamat.mult_by_scalar(self.p_mat, ct.c_double(val), + target.p_mat) + else: + raise ValueError("Value must be of type CUDAMatrix, int, or float.") + + if err_code: + raise generate_exception(err_code) + + return target + + @deprecated + def assign_scalar(self, alpha): + """ + Assign scalar alpha to every element of the matrix. + """ + + err_code = _cudamat.assign_scalar(self.p_mat, ct.c_double(alpha)) + if err_code: + raise generate_exception(err_code) + + return self + + @deprecated + def mult_by_scalar(self, alpha, target=None): + """ + Multiply the matrix by a scalar. + """ + + if not target: + target = self + + err_code = _cudamat.mult_by_scalar(self.p_mat, ct.c_double(alpha), + target.p_mat) + if err_code: + raise generate_exception(err_code) + + return target + + @deprecated + def div_by_scalar(self, alpha, target=None): + """ + Divide the matrix by a scalar. + """ + + if not target: + target = self + + err_code = _cudamat.divide_by_scalar(self.p_mat, ct.c_double(alpha), + target.p_mat) + if err_code: + raise generate_exception(err_code) + + return target + + @deprecated + def add_scalar(self, alpha, target=None): + """ + Increment the matrix by a scalar. + """ + + if not target: + target = self + + err_code = _cudamat.add_scalar(self.p_mat, ct.c_double(alpha), + target.p_mat) + if err_code: + raise generate_exception(err_code) + + return target + + def euclid_norm(self): + """ + Returns the L2 norm of the matrix flattened to a vector. + """ + err_code = ct.c_int(0) + res = _cudamat.euclid_norm(self.p_mat, ct.byref(err_code)) + + if err_code: + raise generate_exception(err_code.value) + + return res + + def manhattan_norm(self): + """ + Returns the L1 norm of the matrix flattened to a vector. + """ + err_code = ct.c_int(0) + res = _cudamat.manhattan_norm(self.p_mat, ct.byref(err_code)) + + if err_code: + raise generate_exception(err_code.value) + + return res + + def allfinite(self): + """ + Checks if all entries in this matrix are finite, i.e., there is no + NaN and no positive or negative infinity. + """ + # Caveat: For a very large matrix of very large finite numbers, the + # manhattan norm may overflow and allfinite() may return False. + return np.isfinite(self.manhattan_norm()) + + def select_columns(self, indices, target): + """ + Copies some columns of self into target. + must be a row vector. Its elements are float64's representing + integers, e.g. "34.0" means the integer "34". + After this call, for all r,c, target[r,c]=self[r,indices[c]]. + This returns target. + Negative indices are interpreted in the usual Python way: all + elements of had better be in the range + [-self.shape[1], self.shape[1]-1]. + This does bounds checking, but out of bounds indices do not raise an + exception (because the programmer was lazy). Instead, they result + in NaN values in . + """ + + err_code = _cudamat.selectRows(self.p_mat, target.p_mat, indices.p_mat) + + if err_code: + raise generate_exception(err_code) + + return target + + def set_selected_columns(self, indices, source): + """ + copies all columns of source into some columns of self. + must be a row vector. Its elements are float64's representing + integers, e.g. "34.0" means the integer "34". after this call, for all + r,c, self[r,indices[c]]=source[r,c]. This returns self. + Negative indices are interpreted in the usual Python way: all elements + of had better be in the range + [-self.shape[1], self.shape[1]-1]. + This does bounds checking, but out of bounds indices do not raise an + exception (because the programmer was lazy). Instead, they result in NaN + values in . + """ + + err_code = _cudamat.setSelectedRows(self.p_mat, source.p_mat, + indices.p_mat) + if err_code: + raise generate_exception(err_code) + + return self + + +def empty(shape): + """ + Creates and returns a new CUDAMatrix with the given shape. + """ + + mat = cudamat() + err_code = _cudamat.init_empty(ct.pointer(mat), ct.c_int(shape[0]), + ct.c_int(shape[1])) + + if err_code: + raise generate_exception(err_code) + + return CUDAMatrix(mat) + + +def check_ones_matrix(min_size): + if min_size > CUDAMatrix.ones.shape[0]: + raise CUDAMatException( + 'Not enough memory allocated for reduction. ' + '({} needed, {} actual), use cudamat.init() ' + 'to allocate more'.format(min_size, CUDAMatrix.ones.shape[0])) + + +def sum(mat, axis, target=None, mult=1.): + """ + Sum the matrix along the given dimension, where 0 represents the leading + dimension and 1 represents the non-leading dimension. If a target is + not provided, a new vector is created for storing the result. The result + is multiplied by the given factor mult (defaults to 1). + """ + + m = _cudamat.get_leading_dimension(mat.p_mat) + n = _cudamat.get_nonleading_dimension(mat.p_mat) + + if axis == 0: + # sum along leading dimension + check_ones_matrix(m) + left = CUDAMatrix.ones.slice(0, m) + left.set_trans(True) + right = mat + + if not target: + target = empty((1, n)) + + elif axis == 1: + # sum along non-leading dimension + left = mat + check_ones_matrix(n) + right = CUDAMatrix.ones.slice(0, n) + + if not target: + target = empty((m, 1)) + + err_code = _cudamat.dot(left.p_mat, right.p_mat, target.p_mat, + ct.c_double(0.), ct.c_double(mult)) + if err_code: + raise generate_exception(err_code) + + return target + + +def mean(mat, axis, target=None): + """ + Compute the mean of the matrix along the given dimension, where 0 represents + the leading dimension and 1 represents the non-leading dimension. If a + target is not provided, a new vector is created for storing the result. + """ + + return sum(mat, axis, target=target, mult=1. / mat.shape[axis]) + + +def dot(m1, m2, target=None, beta=0., alpha=1.): + """ + Find the dot product between m1 and m2 and store in target: + target = beta*target + alpha*(m1 m2) + If no target is given, it will be created automatically, but not + initialized -- so beta should be left at its default value zero. + """ + + if not target: + m = _cudamat.get_leading_dimension(m1.p_mat) + n = _cudamat.get_nonleading_dimension(m2.p_mat) + + target = empty((m, n)) + + err_code = _cudamat.dot(m1.p_mat, m2.p_mat, + target.p_mat, ct.c_double(beta), + ct.c_double(alpha)) + if err_code: + raise generate_exception(err_code) + + return target + + +def vdot(m1, m2): + """ + Compute the vector dot product of matrices m1 and m2. + """ + + err_code = ct.c_int(0) + res = _cudamat.vdot(m1.p_mat, m2.p_mat, ct.byref(err_code)) + + if err_code: + raise generate_exception(err_code.value) + + return res + + +def sigmoid(mat, target=None): + """ + Apply the logistic sigmoid to each element of the matrix mat. + """ + + if not target: + target = mat + + err_code = _cudamat.apply_sigmoid(mat.p_mat, target.p_mat) + if err_code: + raise generate_exception(err_code) + + return target + + +def tanh(mat, target=None): + """ + Apply the tanh to each element of the matrix mat. + """ + + if not target: + target = mat + + err_code = _cudamat.apply_tanh(mat.p_mat, target.p_mat) + if err_code: + raise generate_exception(err_code) + + return target + + +def soft_threshold(mat, alpha, target=None): + """ + Apply the soft threshold function to each element of the matrix: + + mat = sign(mat) * max(0, abs(mat) - alpha) + """ + + if not target: + target = mat + + err_code = _cudamat.apply_soft_threshold(mat.p_mat, ct.c_double(alpha), + target.p_mat) + if err_code: + raise generate_exception(err_code) + + return target + + +def abs(mat, target=None): + """ + Apply abs to each element of the matrix mat. + """ + + if not target: + target = mat + + err_code = _cudamat.apply_abs(mat.p_mat, target.p_mat) + if err_code: + raise generate_exception(err_code) + + return target + + +def log_1_plus_exp(mat, target=None): + """ + Apply log(1+exp(x)) to each element of the matrix mat. + """ + + if not target: + target = mat + + err_code = _cudamat.apply_log_1_plus_exp(mat.p_mat, target.p_mat) + if err_code: + raise generate_exception(err_code) + + return target + + +def log(mat, target=None): + """ + Find the natural logarithm of each element of the matrix mat. + """ + + if not target: + target = mat + + err_code = _cudamat.apply_log(mat.p_mat, target.p_mat) + if err_code: + raise generate_exception(err_code) + + return target + + +def exp(mat, target=None): + """ + Apply the exponential function to each element of the matrix mat. + """ + + if not target: + target = mat + + err_code = _cudamat.apply_exp(mat.p_mat, target.p_mat) + if err_code: + raise generate_exception(err_code) + + return target + + +def gamma(mat, target=None): + """ + Apply the gamma function to each element of the matrix mat. + """ + + if not target: + target = mat + + err_code = _cudamat.apply_gamma(mat.p_mat, target.p_mat) + if err_code: + raise generate_exception(err_code) + + return target + + +def lgamma(mat, target=None): + """ + Apply the log gamma function to each element of the matrix mat. + """ + + if not target: + target = mat + + err_code = _cudamat.apply_lgamma(mat.p_mat, target.p_mat) + if err_code: + raise generate_exception(err_code) + + return target + + +def sqrt(mat, target=None): + """ + Compute the square root of each element of the matrix mat. + """ + + if not target: + target = mat + + err_code = _cudamat.apply_sqrt(mat.p_mat, target.p_mat) + if err_code: + raise generate_exception(err_code) + + return target + + +def pow(mat, p, target=None): + """ + If p is a scalar, compute the 'p'th power of each element of the matrix mat, + otherwise raise each element of the matrix mat to the power given by the + corresponding element of the matrix p. + """ + + if not target: + target = mat + + if isinstance(p, CUDAMatrix): + err_code = _cudamat.apply_pow_matrix(mat.p_mat, p.p_mat, target.p_mat) + elif isinstance(p, (int, float)): + err_code = _cudamat.apply_pow(mat.p_mat, ct.c_double(p), target.p_mat) + else: + raise ValueError("Value must be of type CUDAMatrix, int, or float.") + + if err_code: + raise generate_exception(err_code) + + return target + + +def where(condition_mat, if_mat, else_mat, target=None): + """ + For each element i, j, store if_math[i, j] in target[i,j] if + condition_mat[i, j] is True, and else_mat[i, j] otherwise. + """ + if not target: + target = condition_mat + + err_code = _cudamat.where(condition_mat.p_mat, if_mat.p_mat, + else_mat.p_mat, target.p_mat) + if err_code: + raise generate_exception(err_code) + + return target + + +def correlate(mat, kernel, target=None): + """ + Cross-correlate a matrix with a kernel matrix. + The kernel matrix is centered over each element of the matrix mat. + Width and height of the kernel matrix must be an odd integer. + If a target is not provided, a new matrix is created for storing the result. + Note that this function cannot operate in-place. + """ + if not target: + m = _cudamat.get_leading_dimension(mat.p_mat) + n = _cudamat.get_nonleading_dimension(mat.p_mat) + + target = empty((m, n)) + + err_code = _cudamat.correlate(mat.p_mat, kernel.p_mat, target.p_mat) + if err_code: + raise generate_exception(err_code) + + return target + + +def cuda_sync_threads(): + _cudamat.cuda_sync_threads() + + +def reformat(array, copy=True): + """ + Returns array as a float64 array in FORTRAN order. + If copy is set to False, the array will only be copied if it is not already + in the correct format. + """ + return np.array(array, dtype=np.float64, order='F', copy=copy) + + +def cuda_set_device(dev_id): + """ + Selects the CUDA device with the given ID. + """ + + err_code = _cudamat.cuda_set_device(ct.c_int(dev_id)) + if err_code: + raise generate_exception(err_code) + + +def cublas_init(max_ones=(1024*256)): + """ + Initialize Cublas. + + 'max_ones' is an optional argument that determines the length of + the largest sum that can be computed using Cublas matrix multiply. + A larger value causes more memory to be allocated for this purpose. + """ + + err = _cudamat.cublas_init() + if err: + raise CUDAMatException('error initializing CUBLAS: (err=%u)' % err) + CUDAMatrix.ones = empty((max_ones, 1)).assign(1.0) + +init = cublas_init + + +def cublas_shutdown(): + """ + Shut down Cublas. + """ + + CUDAMatrix.ones = 0 + _cudamat.cublas_shutdown() + +shutdown = cublas_shutdown diff --git a/ot/gpu/cudamat/cudamat/cudamat_kernels.cu b/ot/gpu/cudamat/cudamat/cudamat_kernels.cu new file mode 100644 index 0000000..707b387 --- /dev/null +++ b/ot/gpu/cudamat/cudamat/cudamat_kernels.cu @@ -0,0 +1,804 @@ +#include "cudamat_kernels.cuh" +#include "float.h" + +/* ------------------------- Random number generation ------------------------- */ + +__global__ void kSeedRandom(unsigned int* rndMults, unsigned long long* rndWords, unsigned int seed) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + + // The initial x is the seed and the initial carry is 1 + unsigned long long rndWord = ((unsigned long long)seed << 32) + 1; + const unsigned int rndMult = rndMults[idx]; + /* + * Run the chain for a few steps so that all the streams have a chance + * to differentiate. They start out generating similar random numbers + * because all the multipliers are similar. + */ + for(unsigned int i = 0; i < NUM_RND_BURNIN; i++) { + rndWord = rndMult * LOW_BITS(rndWord) + HIGH_BITS(rndWord); + } + rndWords[idx] = rndWord; +} + +__global__ void kRandomUniform(unsigned int* rndMults, unsigned long long* rndWords, double* gData, unsigned int numElements) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + unsigned long long rndWord = rndWords[idx]; + const unsigned int rndMult = rndMults[idx]; + + for(unsigned int i = idx; i < numElements; i += NUM_RND_STREAMS) { + rndWord = rndMult * LOW_BITS(rndWord) + HIGH_BITS(rndWord); + gData[i] = (__uint2double_rn(LOW_BITS(rndWord)) + 1.0f) / 4294967296.0f; + } + rndWords[idx] = rndWord; +} + +__global__ void kRandomGaussian(unsigned int* rndMults, unsigned long long* rndWords, double* gData, unsigned int numElements) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + unsigned long long rndWord = rndWords[idx]; + const unsigned int rndMult = rndMults[idx]; + + double rnd1, rnd2, R, T; + for(unsigned int i = idx; i < numElements; i += 2*NUM_RND_STREAMS) { + rndWord = rndMult * LOW_BITS(rndWord) + HIGH_BITS(rndWord); + rnd1 = (__uint2double_rn(LOW_BITS(rndWord)) + 1.0f) / 4294967296.0f; + rndWord = rndMult * LOW_BITS(rndWord) + HIGH_BITS(rndWord); + rnd2 = (__uint2double_rn(LOW_BITS(rndWord)) + 1.0f) / 4294967296.0f; + T = 2 * PI * rnd2; + R = sqrtf(-2 * __logf(rnd1)); + gData[i] = R * __cosf(T); + if (i + NUM_RND_STREAMS < numElements) + gData[i + NUM_RND_STREAMS] = R * __sinf(T); + } + rndWords[idx] = rndWord; +} + +/* ------------------------- Data copying ------------------------- */ + +/* +Copy row slice from source to target. There is a block for every 32x32 chunk being copied. +*/ +__global__ void kGetRowSlice(double* source, double* target, int start, int end, int width, int height) { + const int row = start + blockIdx.x * 32 + threadIdx.x; + const int start_col = blockIdx.y * 32; + + const int end_col = (start_col + 32 < width) ? start_col + 32: width; + + const int target_height = end - start; + + if (row < end) { + for (int cur_col = start_col; cur_col < end_col; cur_col++) + target[cur_col * target_height + row - start] = source[cur_col * height + row]; + } +} + +__global__ void kSetRowSlice(double* source, double* target, int start, int end, int width, int height) { + const int row = start + blockIdx.x * 32 + threadIdx.x; + const int start_col = blockIdx.y * 32; + + const int end_col = (start_col + 32 < width) ? start_col + 32: width; + + const int source_height = end - start; + + if (row < end) { + for (int cur_col = start_col; cur_col < end_col; cur_col++) + target[cur_col * height + row] = source[cur_col * source_height + row - start]; + //source[cur_col * height + row - start] = target[cur_col * target_height + row]; + } +} + +__global__ void kTranspose(double *odata, double *idata, int width, int height) { + __shared__ double block[COPY_BLOCK_SIZE][COPY_BLOCK_SIZE+1]; + + // read the matrix tile into shared memory + unsigned int xIndex = blockIdx.x * COPY_BLOCK_SIZE + threadIdx.x; + unsigned int yIndex = blockIdx.y * COPY_BLOCK_SIZE + threadIdx.y; + + if((xIndex < width) && (yIndex < height)) { + unsigned int index_in = yIndex * width + xIndex; + + block[threadIdx.y][threadIdx.x] = idata[index_in]; + } + + __syncthreads(); + + // write the transposed matrix tile to global memory + xIndex = blockIdx.y * COPY_BLOCK_SIZE + threadIdx.x; + yIndex = blockIdx.x * COPY_BLOCK_SIZE + threadIdx.y; + + if((xIndex < height) && (yIndex < width)) { + unsigned int index_out = yIndex * height + xIndex; + + odata[index_out] = block[threadIdx.x][threadIdx.y]; + } +} + +/* ------------------------- Mathematical operations ------------------------- */ + +__global__ void kLessThan(double* mat1, double* mat2, double* target, unsigned int len) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < len; i += numThreads) { + target[i] = mat1[i] < mat2[i]; + } +} + +__global__ void kLessThanScalar(double* mat, double val, double* target, unsigned int len) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < len; i += numThreads) { + target[i] = mat[i] < val; + } +} + +__global__ void kGreaterThan(double* mat1, double* mat2, double* target, unsigned int len) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < len; i += numThreads) { + target[i] = mat1[i] > mat2[i]; + } +} + +__global__ void kGreaterThanScalar(double* mat, double val, double* target, unsigned int len) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < len; i += numThreads) { + target[i] = mat[i] > val; + } +} + +__global__ void kEquals(double* mat1, double* mat2, double* target, unsigned int len) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < len; i += numThreads) { + target[i] = mat1[i] == mat2[i]; + } +} + +__global__ void kEqualsScalar(double* mat, double val, double* target, unsigned int len) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < len; i += numThreads) { + target[i] = mat[i] == val; + } +} + +__global__ void kMinimum(double* mat1, double* mat2, double* target, unsigned int len) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < len; i += numThreads) { + target[i] = fminf(mat1[i], mat2[i]); + } +} + +__global__ void kMinimumScalar(double* mat, double val, double* target, unsigned int len) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < len; i += numThreads) { + target[i] = fminf(mat[i], val); + } +} + +__global__ void kMaximum(double* mat1, double* mat2, double* target, unsigned int len) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < len; i += numThreads) { + target[i] = fmaxf(mat1[i], mat2[i]); + } +} + +__global__ void kMaximumScalar(double* mat, double val, double* target, unsigned int len) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < len; i += numThreads) { + target[i] = fmaxf(mat[i], val); + } +} + +__global__ void kMinColumnwise(double* mat, double* target, unsigned int width, unsigned int height) { + __shared__ double min_vals[32]; + double cur_min = FLT_MAX; + double val = 0; + + for (unsigned int i = threadIdx.x; i < height; i += 32) { + val = mat[blockIdx.x * height + i]; + + if (val < cur_min) + cur_min = val; + } + + min_vals[threadIdx.x] = cur_min; + + __syncthreads(); + + if (threadIdx.x == 0) { + cur_min = FLT_MAX; + + for (unsigned int i = 0; i < 32; i++) + if (min_vals[i] < cur_min) + cur_min = min_vals[i]; + + target[blockIdx.x] = cur_min; + } +} + +__global__ void kMinRowwise(double* mat, double* target, unsigned int width, unsigned int height) { + __shared__ double min_vals[32]; + double cur_min = FLT_MAX; + double val = 0; + + for (unsigned int i = threadIdx.x; i < width; i += 32) { + val = mat[i * height + blockIdx.x]; + + if (val < cur_min) + cur_min = val; + } + + min_vals[threadIdx.x] = cur_min; + + __syncthreads(); + + if (threadIdx.x == 0) { + cur_min = FLT_MAX; + + for (unsigned int i = 0; i < 32; i++) + if (min_vals[i] < cur_min) + cur_min = min_vals[i]; + + target[blockIdx.x] = cur_min; + } +} + +__global__ void kMaxColumnwise(double* mat, double* target, unsigned int width, unsigned int height) { + __shared__ double max_vals[32]; + double cur_max = -FLT_MAX; + double val = 0; + + for (unsigned int i = threadIdx.x; i < height; i += 32) { + val = mat[blockIdx.x * height + i]; + + if (val > cur_max) + cur_max = val; + } + + max_vals[threadIdx.x] = cur_max; + + __syncthreads(); + + if (threadIdx.x == 0) { + cur_max = -FLT_MAX; + + for (unsigned int i = 0; i < 32; i++) + if (max_vals[i] > cur_max) + cur_max = max_vals[i]; + + target[blockIdx.x] = cur_max; + } +} + +__global__ void kMaxRowwise(double* mat, double* target, unsigned int width, unsigned int height) { + __shared__ double max_vals[32]; + double cur_max = -FLT_MAX; + double val = 0; + + for (unsigned int i = threadIdx.x; i < width; i += 32) { + val = mat[i * height + blockIdx.x]; + + if (val > cur_max) + cur_max = val; + } + + max_vals[threadIdx.x] = cur_max; + + __syncthreads(); + + if (threadIdx.x == 0) { + cur_max = -FLT_MAX; + + for (unsigned int i = 0; i < 32; i++) + if (max_vals[i] > cur_max) + cur_max = max_vals[i]; + + target[blockIdx.x] = cur_max; + } +} + +__global__ void kArgMinColumnwise(double* mat, double* target, unsigned int width, unsigned int height) { + __shared__ double min_vals[32]; + __shared__ unsigned int min_args[32]; + double cur_min = FLT_MAX; + unsigned int cur_arg = 0; + double val = 0; + + for (unsigned int i = threadIdx.x; i < height; i += 32) { + val = mat[blockIdx.x * height + i]; + + if (val < cur_min) { + cur_min = val; + cur_arg = i; + } + } + + min_vals[threadIdx.x] = cur_min; + min_args[threadIdx.x] = cur_arg; + + __syncthreads(); + + if (threadIdx.x == 0) { + cur_min = FLT_MAX; + cur_arg = 0; + + for (unsigned int i = 0; i < 32; i++) + if (min_vals[i] < cur_min) { + cur_min = min_vals[i]; + cur_arg = min_args[i]; + } + + target[blockIdx.x] = cur_arg; + } +} + +__global__ void kArgMinRowwise(double* mat, double* target, unsigned int width, unsigned int height) { + __shared__ double min_vals[32]; + __shared__ unsigned int min_args[32]; + double cur_min = FLT_MAX; + unsigned int cur_arg = 0; + double val = 0; + + for (unsigned int i = threadIdx.x; i < width; i += 32) { + val = mat[i * height + blockIdx.x]; + + if (val < cur_min) { + cur_min = val; + cur_arg = i; + } + } + + min_vals[threadIdx.x] = cur_min; + min_args[threadIdx.x] = cur_arg; + + __syncthreads(); + + if (threadIdx.x == 0) { + cur_min = FLT_MAX; + cur_arg = 0; + + for (unsigned int i = 0; i < 32; i++) + if (min_vals[i] < cur_min) { + cur_min = min_vals[i]; + cur_arg = min_args[i]; + } + + target[blockIdx.x] = cur_arg; + } +} + +__global__ void kArgMaxColumnwise(double* mat, double* target, unsigned int width, unsigned int height) { + __shared__ double max_vals[32]; + __shared__ unsigned int max_args[32]; + double cur_max = -FLT_MAX; + unsigned int cur_arg = 0; + double val = 0; + + for (unsigned int i = threadIdx.x; i < height; i += 32) { + val = mat[blockIdx.x * height + i]; + + if (val > cur_max) { + cur_max = val; + cur_arg = i; + } + } + + max_vals[threadIdx.x] = cur_max; + max_args[threadIdx.x] = cur_arg; + + __syncthreads(); + + if (threadIdx.x == 0) { + cur_max = -FLT_MAX; + cur_arg = 0; + + for (unsigned int i = 0; i < 32; i++) + if (max_vals[i] > cur_max) { + cur_max = max_vals[i]; + cur_arg = max_args[i]; + } + + target[blockIdx.x] = cur_arg; + } +} + +__global__ void kArgMaxRowwise(double* mat, double* target, unsigned int width, unsigned int height) { + __shared__ double max_vals[32]; + __shared__ unsigned int max_args[32]; + double cur_max = -FLT_MAX; + unsigned int cur_arg = 0; + double val = 0; + + for (unsigned int i = threadIdx.x; i < width; i += 32) { + val = mat[i * height + blockIdx.x]; + + if (val > cur_max) { + cur_max = val; + cur_arg = i; + } + } + + max_vals[threadIdx.x] = cur_max; + max_args[threadIdx.x] = cur_arg; + + __syncthreads(); + + if (threadIdx.x == 0) { + cur_max = -FLT_MAX; + cur_arg = 0; + + for (unsigned int i = 0; i < 32; i++) + if (max_vals[i] > cur_max) { + cur_max = max_vals[i]; + cur_arg = max_args[i]; + } + + target[blockIdx.x] = cur_arg; + } +} + +__global__ void kSign(double* mat, double* target, unsigned int len) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < len; i += numThreads) { + target[i] = mat[i] ? copysignf(1., mat[i]) : 0.; + } +} + +__global__ void kApplySigmoid(double* mat, double* target, unsigned int len) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < len; i += numThreads) { + target[i] = 1 / (1 + __expf(-mat[i])); + } +} + + +__global__ void kApplyTanh(double* mat, double* target, unsigned int len) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + double mat_i, exp2x; + + for (unsigned int i = idx; i < len; i += numThreads) { + mat_i = mat[i]; + exp2x = __expf(2 * mat_i); + target[i] = 1 - 2 / (exp2x + 1); + } +} + +__global__ void kApplySoftThreshold(double* mat, double alpha, double* target, unsigned int len) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < len; i += numThreads) { + double f = mat[i]; + target[i] = f > 0 ? max(0., f - alpha) : min(0., f + alpha); + } +} + +__global__ void kApplyAbs(double* mat, double* target, unsigned int len) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < len; i += numThreads) { + target[i] = mat[i] * ((mat[i] > 0) - (mat[i] < 0)); + } +} + +__global__ void kApplyLog1PlusExp(double* mat, double* target, unsigned int len) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + double mat_i; + + for (unsigned int i = idx; i < len; i += numThreads) { + mat_i = mat[i]; + if (mat_i > 0) + target[i] = (__logf(1 + __expf(-mat_i)) + mat_i); + else + target[i] = __logf(1 + __expf(mat_i)); + } +} + +__global__ void kLog(double* mat, double* target, unsigned int len) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < len; i += numThreads) { + target[i] = __logf(mat[i]); + } +} + +__global__ void kExp(double* mat, double* target, unsigned int len) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < len; i += numThreads) { + target[i] = __expf(mat[i]); + } +} + +__global__ void kGamma(double* mat, double* target, unsigned int len) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < len; i += numThreads) { + target[i] = tgammaf(mat[i]); + } +} + +__global__ void kLogGamma(double* mat, double* target, unsigned int len) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < len; i += numThreads) { + target[i] = lgammaf(mat[i]); + } +} + +__global__ void kSqrt(double* mat, double* target, unsigned int len) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < len; i += numThreads) { + target[i] = sqrt(mat[i]); + } +} + +__global__ void kPow(double* mat, double pow, double* target, unsigned int len) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < len; i += numThreads) { + target[i] = powf(mat[i], pow); + } +} + +__global__ void kPowMatrix(double* mat, double* pow, double* target, unsigned int len) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < len; i += numThreads) { + target[i] = powf(mat[i], pow[i]); + } +} + +__global__ void kReciprocal(double* mat, double* target, unsigned int len) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < len; i += numThreads) + target[i] = 1.f / mat[i]; +} + +__global__ void kAddColVector(double* mat, double* vec, double* tgtMat, unsigned int width, + unsigned int height) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < width * height; i += numThreads) { + tgtMat[i] = mat[i] + vec[i % height]; + } +} + +__global__ void kAddRowVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < width * height; i += numThreads) { + tgtMat[i] = mat[i] + vec[i / height]; + } +} + +__global__ void kAddColMult(double* mat, double* vec, double* tgtMat, double mult, + unsigned int width, unsigned int height) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < width * height; i += numThreads) { + tgtMat[i] = mat[i] + mult * vec[i % height]; + } +} + +__global__ void kMultByColVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < width * height; i += numThreads) { + tgtMat[i] = mat[i] * vec[i % height]; + } +} + +__global__ void kMultByRowVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < width * height; i += numThreads) { + tgtMat[i] = mat[i] * vec[i / height]; + } +} + +__global__ void kDivByColVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < width * height; i += numThreads) { + tgtMat[i] = mat[i] / vec[i % height]; + } +} + +__global__ void kDivByRowVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < width * height; i += numThreads) { + tgtMat[i] = mat[i] / vec[i / height]; + } +} + +__global__ void kAdd(double* a, double* b, double* dest, unsigned int numEls) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < numEls; i += numThreads) { + dest[i] = a[i] + b[i]; + } +} + +__global__ void kSubtract(double* a, double* b, double* dest, unsigned int numEls) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < numEls; i += numThreads) { + dest[i] = a[i] - b[i]; + } +} + +__global__ void kDivide(double* a, double* b, double* dest, unsigned int numEls) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < numEls; i += numThreads) { + dest[i] = a[i] / b[i]; + } +} + +__global__ void kMult(double* a, double* b, double* dest, unsigned int numEls) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < numEls; i += numThreads) { + dest[i] = a[i] * b[i]; + } +} + +__global__ void kMultScalar(double* mat, double alpha, double* dest, unsigned int len) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < len; i += numThreads) { + dest[i] = alpha * mat[i]; + } +} + +__global__ void kAssignScalar(double* dest, double alpha, unsigned int len) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < len; i += numThreads) { + dest[i] = alpha; + } +} + +__global__ void kDivideScalar(double* mat, double alpha, double* dest, unsigned int len) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < len; i += numThreads) { + dest[i] = mat[i] / alpha; + } +} + +__global__ void kAddScalar(double* a, double alpha, double* dest, unsigned int numEls) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for (unsigned int i = idx; i < numEls; i += numThreads) { + dest[i] = a[i] + alpha; + } +} + +__global__ void kSelectRows(double* source, double* target, double* indices, int nRowIs, int nCols, int nSourceRows){ + __shared__ int sourceRowIndices[32]; + const int startTargetRowI = blockIdx.x * 32; + const int tid = threadIdx.x; + const int localNRowIs = min(32, nRowIs-startTargetRowI); + + // cooperatively load 32 row indices + if (tid < localNRowIs){ + sourceRowIndices[tid] = int(indices[startTargetRowI + tid]); + if (sourceRowIndices[tid]<0) + sourceRowIndices[tid] += nSourceRows; + if (sourceRowIndices[tid]<0 || sourceRowIndices[tid]>=nSourceRows) + sourceRowIndices[tid] = -1; + } + __syncthreads(); + + // copy 32 rows + for (int i=0; i=nTargetRows) + targetRowIndices[tid] = -1; + } + __syncthreads(); + + // copy 32 rows + for (int i=0; i= 0 && x < width && y >= 0 && y < height) + sum += source[j] * kernel[(kwidth * kheight / 2) + w * kheight + h]; + } + } + dest[i] = sum; + } +} diff --git a/ot/gpu/cudamat/cudamat/cudamat_kernels.cuh b/ot/gpu/cudamat/cudamat/cudamat_kernels.cuh new file mode 100644 index 0000000..25e2858 --- /dev/null +++ b/ot/gpu/cudamat/cudamat/cudamat_kernels.cuh @@ -0,0 +1,92 @@ +#ifndef NVMATRIX_KERNEL_H_ +#define NVMATRIX_KERNEL_H_ + +#define NUM_RND_BLOCKS 96 +#define NUM_RND_THREADS_PER_BLOCK 128 +#define NUM_RND_STREAMS (NUM_RND_BLOCKS * NUM_RND_THREADS_PER_BLOCK) + +/* + * Defines for getting the values at the lower and upper 32 bits + * of a 64-bit number. + */ +#define LOW_BITS(x) ((x) & 0xffffffff) +#define HIGH_BITS(x) ((x) >> 32) + +/* + * Number of iterations to run random number generator upon initialization. + */ +#define NUM_RND_BURNIN 100 + +/* + * CUDA grid dimensions for different types of kernels + */ +#define COPY_BLOCK_SIZE 16 +# +// element-wise kernels use min(ceil(N / 512), 4096) blocks of 512 threads +#define MAX_VECTOR_OP_BLOCKS 4096 +#define MAX_VECTOR_OP_THREADS_PER_BLOCK 512 +#define NUM_VECTOR_OP_BLOCKS(N) (min(((N) + MAX_VECTOR_OP_THREADS_PER_BLOCK - 1)/MAX_VECTOR_OP_THREADS_PER_BLOCK, MAX_VECTOR_OP_BLOCKS)) +#define NUM_VECTOR_OP_THREADS_PER_BLOCK(N) (min((N), MAX_VECTOR_OP_THREADS_PER_BLOCK)) + +#define PI 3.1415926535897932f + +__global__ void kSeedRandom(unsigned int* randMults, unsigned long long* randWords, unsigned int seed); +__global__ void kRandomUniform(unsigned int* randMults, unsigned long long* randWords, double* gData, unsigned int numElements); +__global__ void kRandomGaussian(unsigned int* rndMults, unsigned long long* rndWords, double* gData, unsigned int numElements); + +__global__ void kGetRowSlice(double* source, double* target, int start, int end, int width, int height); +__global__ void kTranspose(double *odata, double *idata, int width, int height); +__global__ void kSetRowSlice(double* source, double* target, int start, int end, int width, int height); + +__global__ void kLessThan(double* mat1, double* mat2, double* target, unsigned int len); +__global__ void kLessThanScalar(double* mat, double val, double* target, unsigned int len); +__global__ void kGreaterThan(double* mat1, double* mat2, double* target, unsigned int len); +__global__ void kGreaterThanScalar(double* mat, double val, double* target, unsigned int len); +__global__ void kEquals(double* mat1, double* mat2, double* target, unsigned int len); +__global__ void kEqualsScalar(double* mat, double val, double* target, unsigned int len); +__global__ void kMinimum(double* mat1, double* mat2, double* target, unsigned int len); +__global__ void kMinimumScalar(double* mat, double val, double* target, unsigned int len); +__global__ void kMaximum(double* mat1, double* mat2, double* target, unsigned int len); +__global__ void kMaximumScalar(double* mat, double val, double* target, unsigned int len); +__global__ void kMinColumnwise(double* mat, double* target, unsigned int width, unsigned int height); +__global__ void kMinRowwise(double* mat, double* target, unsigned int width, unsigned int height); +__global__ void kMaxColumnwise(double* mat, double* target, unsigned int width, unsigned int height); +__global__ void kMaxRowwise(double* mat, double* target, unsigned int width, unsigned int height); +__global__ void kArgMinColumnwise(double* mat, double* target, unsigned int width, unsigned int height); +__global__ void kArgMinRowwise(double* mat, double* target, unsigned int width, unsigned int height); +__global__ void kArgMaxColumnwise(double* mat, double* target, unsigned int width, unsigned int height); +__global__ void kArgMaxRowwise(double* mat, double* target, unsigned int width, unsigned int height); +__global__ void kSign(double* mat, double* target, unsigned int len); +__global__ void kApplySigmoid(double* mat, double* target, unsigned int len); +__global__ void kApplyTanh(double* mat, double* target, unsigned int len); +__global__ void kApplySoftThreshold(double* mat, double alpha, double* target, unsigned int len); +__global__ void kApplyAbs(double* mat, double* target, unsigned int len); +__global__ void kApplyLog1PlusExp(double* mat, double* target, unsigned int len); +__global__ void kLog(double* mat, double* target, unsigned int len); +__global__ void kExp(double* mat, double* target, unsigned int len); +__global__ void kGamma(double* mat, double* target, unsigned int len); +__global__ void kLogGamma(double* mat, double* target, unsigned int len); +__global__ void kSqrt(double* mat, double* target, unsigned int len); +__global__ void kPow(double* mat, double pow, double* target, unsigned int len); +__global__ void kPowMatrix(double* mat, double* pow, double* target, unsigned int len); +__global__ void kReciprocal(double* mat, double* target, unsigned int len); +__global__ void kAddColVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height); +__global__ void kAddRowVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height); +__global__ void kAddColMult(double* mat, double* vec, double* tgtMat, double mult, unsigned int width, unsigned int height); +__global__ void kMultByColVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height); +__global__ void kMultByRowVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height); +__global__ void kDivByColVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height); +__global__ void kDivByRowVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height); +__global__ void kAdd(double* a, double* b, double* dest, unsigned int numEls); +__global__ void kSubtract(double* a, double* b, double* dest, unsigned int numEls); +__global__ void kMult(double* a, double* b, double* dest, unsigned int numEls); +__global__ void kDivide(double* a, double* b, double* dest, unsigned int numEls); +__global__ void kMultScalar(double* mat, double alpha, double* dest, unsigned int len); +__global__ void kAssignScalar(double* dest, double alpha, unsigned int len); +__global__ void kDivideScalar(double* mat, double alpha, double* dest, unsigned int len); +__global__ void kAddScalar(double* a, double alpha, double* dest, unsigned int numEls); +__global__ void kSelectRows(double* source, double* target, double* indices, int nRowIs, int nCols, int nSourceRows); +__global__ void kSetSelectedRows(double* target, double* source, double* indices, int nRowIs, int nCols, int nTargetRows); +__global__ void kWhere(double* condition_mat, double* if_mat, double* else_mat, double* target, unsigned int len); +__global__ void kCorrelate(double* source, double* kernel, double* dest, int width, int height, int fwidth, int fheight); +#endif diff --git a/ot/gpu/cudamat/cudamat/learn.cu b/ot/gpu/cudamat/cudamat/learn.cu new file mode 100644 index 0000000..3d9260c --- /dev/null +++ b/ot/gpu/cudamat/cudamat/learn.cu @@ -0,0 +1,34 @@ +#include +#include +#include +#include "learn_kernels.cuh" +#include "cudamat.cuh" + +extern "C" { + +inline bool checkCUDAError() { + cudaError_t err = cudaGetLastError(); + + if (cudaSuccess != err) + printf("%s\n", cudaGetErrorString( err)); + return cudaSuccess != err; +} + +EXPORT int mult_by_sigmoid_deriv(cudamat* target, cudamat* acts) { + int len = acts->size[0]*acts->size[1]; + + if (acts->is_trans != target->is_trans) + return ERROR_TRANSPOSED; + + if (acts->size[0] != target->size[0] || acts->size[1] != target->size[1]) + return ERROR_INCOMPATIBLE_DIMENSIONS; + + kMultiplyBySigmoidGrad<<>>(acts->data_device, target->data_device, len); + + if (checkCUDAError()) + return CUDA_ERROR; + + return 0; +} + +} diff --git a/ot/gpu/cudamat/cudamat/learn.py b/ot/gpu/cudamat/cudamat/learn.py new file mode 100644 index 0000000..741ca13 --- /dev/null +++ b/ot/gpu/cudamat/cudamat/learn.py @@ -0,0 +1,21 @@ +import os + +import ctypes as ct +import numpy as np + +from cudamat import load_library, generate_exception + +_cudalearn = load_library('libcudalearn') + +_cudalearn.mult_by_sigmoid_deriv.restype = ct.c_int + +def mult_by_sigmoid_deriv(target, acts): + """ + target = target * acts * (1 - acts) + + Useful for doing backprop in neural networks with logistic units. + """ + + err_code = _cudalearn.mult_by_sigmoid_deriv(target.p_mat, acts.p_mat) + if err_code: + raise generate_exception(err_code) diff --git a/ot/gpu/cudamat/cudamat/learn_kernels.cu b/ot/gpu/cudamat/cudamat/learn_kernels.cu new file mode 100644 index 0000000..8e897ba --- /dev/null +++ b/ot/gpu/cudamat/cudamat/learn_kernels.cu @@ -0,0 +1,10 @@ +#include "learn_kernels.cuh" + +__global__ void kMultiplyBySigmoidGrad(double* act, double* target, const unsigned int len) { + const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; + const unsigned int numThreads = blockDim.x * gridDim.x; + + for(unsigned int i = idx; i < len; i+= numThreads) { + target[i] = target[i] * act[i] * (1.0f - act[i]); + } +} diff --git a/ot/gpu/cudamat/cudamat/learn_kernels.cuh b/ot/gpu/cudamat/cudamat/learn_kernels.cuh new file mode 100644 index 0000000..26c8abd --- /dev/null +++ b/ot/gpu/cudamat/cudamat/learn_kernels.cuh @@ -0,0 +1,12 @@ +#ifndef EBM_KERNELS_H_ +#define EBM_KERNELS_H_ + +#define NUM_VECTOR_OP_BLOCKS 4096 +#define NUM_VECTOR_OP_THREADS_PER_BLOCK 512 + +#define NUM_SPARSE_GRAD_BLOCKS 4096 +#define NUM_SPARSE_GRAD_THREADS_PER_BLOCK 512 + +__global__ void kMultiplyBySigmoidGrad(double* act, double* target, const unsigned int len); + +#endif diff --git a/ot/gpu/cudamat/cudamat/rnd_multipliers_32bit.txt b/ot/gpu/cudamat/cudamat/rnd_multipliers_32bit.txt new file mode 100644 index 0000000..098d31d --- /dev/null +++ b/ot/gpu/cudamat/cudamat/rnd_multipliers_32bit.txt @@ -0,0 +1,16028 @@ +4294967118 +4294966893 +4294966830 +4294966284 +4294966164 +4294965708 +4294965675 +4294964880 +4294964568 +4294963860 +4294963023 +4294962654 +4294962540 +4294962330 +4294962063 +4294959894 +4294959600 +4294959030 +4294958094 +4294957665 +4294956564 +4294956270 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b/ot/gpu/cudamat/examples/bench_cudamat.py @@ -0,0 +1,97 @@ +from __future__ import print_function, division +import sys +import numpy as np +import cudamat as cmt +import time +import timeit +from inspect import getmodule, getmembers, isfunction +try: from itertools import ifilter as filter +except: pass + +# heat-up time in seconds before starting the benchmark +HEATUP = 2 + +# shapes used for the small and large test matrix +XS_SHAPE = (400, 256) +XL_SHAPE = (4096, 4096) + +# timeit number and repeat parameter +NUM_ITER = 100 +NUM_REPEATS = 5 + +def setup(shape): + """Creates two matrices and corresponding row/column vectors""" + mat = cmt.empty(shape).fill_with_randn() + mat2 = cmt.empty(shape).fill_with_randn() + col = cmt.empty((shape[0], 1)).assign(0) + row = cmt.empty((1, shape[1])).assign(0) + return mat, mat2, col, row + +def bench_dot(X, Y, col, row): + cmt.dot(X.T, Y) + +def bench_add(X, Y, col, row): + X.add(Y) +bench_add.repeats = 5 # 5 times more repetitions than usual + +def bench_mult(X, Y, col, row): + X.mult(Y) + +def bench_sigm(X, Y, col, row): + X.apply_sigmoid() + +def bench_colsum(X, Y, col, row): + X.sum(axis=0, target=row) + +def bench_rowsum(X, Y, col, row): + X.sum(axis=1, target=col) + +def bench_addcolsum(X, Y, col, row): + row.add_sums(X, axis=0, mult=3.2, beta=0.2) + +def bench_addrowsum(X, Y, col, row): + col.add_sums(X, axis=1, mult=3.2, beta=0.2) + +def bench_colmax(X, Y, col, row): + X.max(axis=0, target=row) + +def bench_rowmax(X, Y, col, row): + X.max(axis=1, target=col) + +def bench_addcolmult(X, Y, col, row): + X.add_col_mult(col, mult=3.2) + +def heatup(duration): + """Heat-up the GPU for a while so it enters full-performance mode""" + t1 = time.time() + while time.time() - t1 < duration: + cmt.dot(cmt.empty((200, 200)), cmt.empty((200, 200))) + +def main(): + cmt.init() + cmt.CUDAMatrix.init_random() + if HEATUP: + print("heating up for %g seconds..." % HEATUP, end=' ') + sys.stdout.flush() + heatup(HEATUP) + print("done.") + print("small matrix shape:", XS_SHAPE) + print("large matrix shape:", XL_SHAPE) + for funcname, func in filter(lambda f: f[0].startswith('bench_'), + getmembers(getmodule(main), isfunction)): + print("%-15s" % funcname[len('bench_'):], end=' ') + sys.stdout.flush() + for size, shape, factor in ('small', XS_SHAPE, 10), ('large', XL_SHAPE, 1): + repeat = NUM_REPEATS * getattr(func, 'repeats', 1) + time = min(timeit.repeat(\ + setup="from __main__ import setup, %s\nmats = setup(%s)" % (funcname, shape), + stmt="%s(*mats)" % funcname, repeat=repeat, + number=NUM_ITER * factor)) / (NUM_ITER * factor) + print("%.3es (%s) " % (time, size), end=' ') + sys.stdout.flush() + print() + cmt.shutdown() + +if __name__=="__main__": + main() + diff --git a/ot/gpu/cudamat/examples/nn_cudamat.py b/ot/gpu/cudamat/examples/nn_cudamat.py new file mode 100644 index 0000000..7c56c7d --- /dev/null +++ b/ot/gpu/cudamat/examples/nn_cudamat.py @@ -0,0 +1,133 @@ +# This file shows how to implement a single hidden layer neural network for +# performing binary classification on the GPU using cudamat. + +from __future__ import division +import pdb +import time +import numpy as np +import cudamat as cm +from cudamat import learn as cl +import util + +# initialize CUDA +cm.cublas_init() + +# load data +util.load('mnist49.dat', globals()) + +# Put training data onto the GPU. +dat_train = dat_train/255. +dat_train = dat_train - (np.mean(dat_train, 1)+10**-8)[:, np.newaxis] +dev_train = cm.CUDAMatrix(dat_train) +dev_lbl = cm.CUDAMatrix(lbl_train) + +# training parameters +epsilon = 0.01 +momentum = 0.9 + +num_epochs = 30 +batch_size = 128 +num_batches = dat_train.shape[1]//batch_size + +# model parameters +dim_in = dat_train.shape[0] +dim_out = 1 +num_hid = 1024 + +# initialize weights +w_w1 = cm.CUDAMatrix(dim_in ** -0.5 * np.random.randn(dim_in, num_hid)) +w_b1 = cm.CUDAMatrix(np.zeros((num_hid, 1))) +w_w2 = cm.CUDAMatrix(num_hid ** -0.5 * np.random.randn(num_hid, dim_out)) +w_b2 = cm.CUDAMatrix(np.zeros((dim_out, 1))) + +# initialize weight update matrices +wu_w1 = cm.empty(w_w1.shape).assign(0) +wu_b1 = cm.empty(w_b1.shape).assign(0) +wu_w2 = cm.empty(w_w2.shape).assign(0) +wu_b2 = cm.empty(w_b2.shape).assign(0) + +# initialize temporary storage +h = cm.empty((num_hid, batch_size)) +out = cm.empty((dim_out, batch_size)) +delta = cm.empty((num_hid, batch_size)) + +# Train neural network. +start_time = time.time() +for epoch in range(num_epochs): + print("Epoch %i" % (epoch + 1)) + err = [] + + for batch in range(num_batches): + # get current minibatch + inp = dev_train.slice(batch*batch_size,(batch + 1)*batch_size) + target = dev_lbl.slice(batch*batch_size,(batch + 1)*batch_size) + + # forward pass + cm.dot(w_w1.T, inp, target = h) + + h.add_col_vec(w_b1) + h.apply_sigmoid() + + cm.dot(w_w2.T, h, target = out) + + out.add_col_vec(w_b2) + out.apply_sigmoid() + + # back prop errors + out.subtract(target) # compute error + + # gradients for w_w2 and w_b2 + wu_w2.add_dot(h, out.T, beta = momentum) + wu_b2.add_sums(out, axis = 1, beta = momentum) + + # compute delta + cm.dot(w_w2, out, target = delta) + + # delta = delta * h * (1 - h) + cl.mult_by_sigmoid_deriv(delta, h) + + # gradients for w_w1 and w_b1 + wu_w1.add_dot(inp, delta.T, beta = momentum) + wu_b1.add_sums(delta, axis = 1, beta = momentum) + + # update weights + w_w1.subtract_mult(wu_w1, epsilon/batch_size) + w_b1.subtract_mult(wu_b1, epsilon/batch_size) + w_w2.subtract_mult(wu_w2, epsilon/batch_size) + w_b2.subtract_mult(wu_b2, epsilon/batch_size) + + # calculate error on current minibatch + err.append(np.abs(out.asarray())>0.5) + + print("Training misclassification rate: %f" % np.mean(err)) + print("Time: %f" % (time.time() - start_time)) + +# Evaluate neural network on test data. + +# Load test data onto the GPU. +dat_test = dat_test/255. +dat_test = dat_test - np.mean(dat_test, 1)[:, np.newaxis] +dev_test = cm.CUDAMatrix(dat_test) +dev_lbl = cm.CUDAMatrix(lbl_test) + +# Initalize temporary storage. +h = cm.empty((num_hid, dat_test.shape[1])) +out = cm.empty((dim_out, dat_test.shape[1])) + +# forward pass +cm.dot(w_w1.T, dev_test, target = h) + +h.add_col_vec(w_b1) +h.apply_sigmoid() + +cm.dot(w_w2.T, h, target = out) + +out.add_col_vec(w_b2) +out.apply_sigmoid() + +# compute error +out.subtract(dev_lbl) + +print("Testing misclassification rate: %f" % np.mean(np.abs(out.asarray())>0.5)) + +cm.cublas_shutdown() diff --git a/ot/gpu/cudamat/examples/rbm_cudamat.py b/ot/gpu/cudamat/examples/rbm_cudamat.py new file mode 100644 index 0000000..3f6a900 --- /dev/null +++ b/ot/gpu/cudamat/examples/rbm_cudamat.py @@ -0,0 +1,98 @@ +from __future__ import division +import time +import numpy as np +import cudamat as cm +import util + +# initialize CUDA +cm.cublas_init() +cm.CUDAMatrix.init_random(1) + +# load data +util.load('mnist.dat', globals()) +dev_dat = cm.CUDAMatrix(cm.reformat(dat/255.)) + +# training parameters +epsilon = 0.1 +momentum = 0.9 + +num_epochs = 30 +batch_size = 128 +num_batches = dat.shape[1]//batch_size + +# model parameters +num_vis = dat.shape[0] +num_hid = 4096 + +# initialize weights +w_vh = cm.CUDAMatrix(0.1 * np.random.randn(num_vis, num_hid)) +w_v = cm.CUDAMatrix(np.zeros((num_vis, 1))) +w_h = cm.CUDAMatrix(-4.*np.ones((num_hid, 1))) + +# initialize weight updates +wu_vh = cm.CUDAMatrix(np.zeros((num_vis, num_hid))) +wu_v = cm.CUDAMatrix(np.zeros((num_vis, 1))) +wu_h = cm.CUDAMatrix(np.zeros((num_hid, 1))) + +# initialize temporary storage +v = cm.empty((num_vis, batch_size)) +h = cm.empty((num_hid, batch_size)) +r = cm.empty((num_hid, batch_size)) + +start_time = time.time() +for epoch in range(num_epochs): + print("Epoch %i" % (epoch + 1)) + err = [] + + for batch in range(num_batches): + # get current minibatch + v_true = dev_dat.slice(batch*batch_size,(batch + 1)*batch_size) + v.assign(v_true) + + # apply momentum + wu_vh.mult(momentum) + wu_v.mult(momentum) + wu_h.mult(momentum) + + # positive phase + cm.dot(w_vh.T, v, target = h) + h.add_col_vec(w_h) + h.apply_sigmoid() + + wu_vh.add_dot(v, h.T) + wu_v.add_sums(v, axis = 1) + wu_h.add_sums(h, axis = 1) + + # sample hiddens + r.fill_with_rand() + r.less_than(h, target = h) + + # negative phase + cm.dot(w_vh, h, target = v) + v.add_col_vec(w_v) + v.apply_sigmoid() + + cm.dot(w_vh.T, v, target = h) + h.add_col_vec(w_h) + h.apply_sigmoid() + + wu_vh.subtract_dot(v, h.T) + wu_v.add_sums(v, axis = 1, mult = -1.) + wu_h.add_sums(h, axis = 1, mult = -1.) + + # update weights + w_vh.add_mult(wu_vh, epsilon/batch_size) + w_v.add_mult(wu_v, epsilon/batch_size) + w_h.add_mult(wu_h, epsilon/batch_size) + + # calculate reconstruction error + v.subtract(v_true) + err.append(v.euclid_norm()**2/(num_vis*batch_size)) + + print("Mean squared error: %f" % np.mean(err)) + print("Time: %f" % (time.time() - start_time)) + +w_vh.copy_to_host() +util.save('weights.dat', 'w_vh', {'w_vh': w_vh.numpy_array}) + +cm.cublas_shutdown() diff --git a/ot/gpu/cudamat/examples/rbm_numpy.py b/ot/gpu/cudamat/examples/rbm_numpy.py new file mode 100644 index 0000000..1331566 --- /dev/null +++ b/ot/gpu/cudamat/examples/rbm_numpy.py @@ -0,0 +1,72 @@ +from __future__ import division +import time +import numpy as np +import util + +# load data +util.load('mnist.dat', globals()) +dat = dat/255. + +# training parameters +epsilon = 0.01 +momentum = 0.9 + +num_epochs = 10 +batch_size = 64 +num_batches = dat.shape[1]//batch_size + +# model parameters +num_vis = dat.shape[0] +num_hid = 1024 + +# initialize weights +w_vh = 0.1 * np.random.randn(num_vis, num_hid) +w_v = np.zeros((num_vis, 1)) +w_h = np.zeros((num_hid, 1)) + +# initialize weight updates +wu_vh = np.zeros((num_vis, num_hid)) +wu_v = np.zeros((num_vis, 1)) +wu_h = np.zeros((num_hid, 1)) + +start_time = time.time() +for epoch in range(num_epochs): + print("Epoch %i" % (epoch + 1)) + err = [] + + for batch in range(num_batches): + v_true = dat[:, batch*batch_size:(batch + 1)*batch_size] + v = v_true + + # apply momentum + wu_vh *= momentum + wu_v *= momentum + wu_h *= momentum + + # positive phase + h = 1. / (1 + np.exp(-(np.dot(w_vh.T, v) + w_h))) + + wu_vh += np.dot(v, h.T) + wu_v += v.sum(1)[:, np.newaxis] + wu_h += h.sum(1)[:, np.newaxis] + + # sample hiddens + h = 1. * (h > np.random.rand(num_hid, batch_size)) + + # negative phase + v = 1. / (1 + np.exp(-(np.dot(w_vh, h) + w_v))) + h = 1. / (1 + np.exp(-(np.dot(w_vh.T, v) + w_h))) + + wu_vh -= np.dot(v, h.T) + wu_v -= v.sum(1)[:, np.newaxis] + wu_h -= h.sum(1)[:, np.newaxis] + + # update weights + w_vh += epsilon/batch_size * wu_vh + w_v += epsilon/batch_size * wu_v + w_h += epsilon/batch_size * wu_h + + err.append(np.mean((v - v_true)**2)) + + print("Mean squared error: %f" % np.mean(err)) + print("Time: %f" % (time.time() - start_time)) diff --git a/ot/gpu/cudamat/examples/util.py b/ot/gpu/cudamat/examples/util.py new file mode 100644 index 0000000..79ceead --- /dev/null +++ b/ot/gpu/cudamat/examples/util.py @@ -0,0 +1,22 @@ +from __future__ import division +import gzip +try: import cPickle as pickle +except: import pickle + +def save(fname, var_list, source_dict): + var_list = [var.strip() for var in var_list.split() if len(var.strip())>0] + fo = gzip.GzipFile(fname, 'wb') + pickle.dump(var_list, fo) + for var in var_list: + pickle.dump(source_dict[var], fo, protocol=2) + fo.close() + +def load(fname, target_dict, verbose = True): + fo = gzip.GzipFile(fname, 'rb') + var_list = pickle.load(fo) + if verbose: + print(var_list) + for var in var_list: + target_dict[var] = pickle.load(fo) + fo.close() + diff --git a/ot/gpu/cudamat/setup.py b/ot/gpu/cudamat/setup.py new file mode 100755 index 0000000..ad386d1 --- /dev/null +++ b/ot/gpu/cudamat/setup.py @@ -0,0 +1,121 @@ +#!/usr/bin/env python + +import os +# on Windows, we need the original PATH without Anaconda's compiler in it: +PATH = os.environ.get('PATH') +from distutils.spawn import spawn, find_executable +from setuptools import setup, find_packages, Extension +from setuptools.command.build_ext import build_ext +import sys + +# CUDA specific config +# nvcc is assumed to be in user's PATH +nvcc_compile_args = ['-O', '--ptxas-options=-v', '--compiler-options=-fPIC'] +nvcc_compile_args = os.environ.get('NVCCFLAGS', '').split() + nvcc_compile_args +cuda_libs = ['cublas'] + +cudamat_ext = Extension('cudamat.libcudamat', + sources=['cudamat/cudamat.cu', + 'cudamat/cudamat_kernels.cu'], + libraries=cuda_libs, + extra_compile_args=nvcc_compile_args) +cudalearn_ext = Extension('cudamat.libcudalearn', + sources=['cudamat/learn.cu', + 'cudamat/learn_kernels.cu'], + libraries=cuda_libs, + extra_compile_args=nvcc_compile_args) + + +class CUDA_build_ext(build_ext): + """ + Custom build_ext command that compiles CUDA files. + Note that all extension source files will be processed with this compiler. + """ + def build_extensions(self): + self.compiler.src_extensions.append('.cu') + self.compiler.set_executable('compiler_so', 'nvcc') + self.compiler.set_executable('linker_so', 'nvcc --shared') + if hasattr(self.compiler, '_c_extensions'): + self.compiler._c_extensions.append('.cu') # needed for Windows + self.compiler.spawn = self.spawn + build_ext.build_extensions(self) + + def spawn(self, cmd, search_path=1, verbose=0, dry_run=0): + """ + Perform any CUDA specific customizations before actually launching + compile/link etc. commands. + """ + if (sys.platform == 'darwin' and len(cmd) >= 2 and cmd[0] == 'nvcc' and + cmd[1] == '--shared' and cmd.count('-arch') > 0): + # Versions of distutils on OSX earlier than 2.7.9 inject + # '-arch x86_64' which we need to strip while using nvcc for + # linking + while True: + try: + index = cmd.index('-arch') + del cmd[index:index+2] + except ValueError: + break + elif self.compiler.compiler_type == 'msvc': + # There are several things we need to do to change the commands + # issued by MSVCCompiler into one that works with nvcc. In the end, + # it might have been easier to write our own CCompiler class for + # nvcc, as we're only interested in creating a shared library to + # load with ctypes, not in creating an importable Python extension. + # - First, we replace the cl.exe or link.exe call with an nvcc + # call. In case we're running Anaconda, we search cl.exe in the + # original search path we captured further above -- Anaconda + # inserts a MSVC version into PATH that is too old for nvcc. + cmd[:1] = ['nvcc', '--compiler-bindir', + os.path.dirname(find_executable("cl.exe", PATH)) + or cmd[0]] + # - Secondly, we fix a bunch of command line arguments. + for idx, c in enumerate(cmd): + # create .dll instead of .pyd files + if '.pyd' in c: cmd[idx] = c = c.replace('.pyd', '.dll') + # replace /c by -c + if c == '/c': cmd[idx] = '-c' + # replace /DLL by --shared + elif c == '/DLL': cmd[idx] = '--shared' + # remove --compiler-options=-fPIC + elif '-fPIC' in c: del cmd[idx] + # replace /Tc... by ... + elif c.startswith('/Tc'): cmd[idx] = c[3:] + # replace /Fo... by -o ... + elif c.startswith('/Fo'): cmd[idx:idx+1] = ['-o', c[3:]] + # replace /LIBPATH:... by -L... + elif c.startswith('/LIBPATH:'): cmd[idx] = '-L' + c[9:] + # replace /OUT:... by -o ... + elif c.startswith('/OUT:'): cmd[idx:idx+1] = ['-o', c[5:]] + # remove /EXPORT:initlibcudamat or /EXPORT:initlibcudalearn + elif c.startswith('/EXPORT:'): del cmd[idx] + # replace cublas.lib by -lcublas + elif c == 'cublas.lib': cmd[idx] = '-lcublas' + # - Finally, we pass on all arguments starting with a '/' to the + # compiler or linker, and have nvcc handle all other arguments + if '--shared' in cmd: + pass_on = '--linker-options=' + # we only need MSVCRT for a .dll, remove CMT if it sneaks in: + cmd.append('/NODEFAULTLIB:libcmt.lib') + else: + pass_on = '--compiler-options=' + cmd = ([c for c in cmd if c[0] != '/'] + + [pass_on + ','.join(c for c in cmd if c[0] == '/')]) + # For the future: Apart from the wrongly set PATH by Anaconda, it + # would suffice to run the following for compilation on Windows: + # nvcc -c -O -o .obj .cu + # And the following for linking: + # nvcc --shared -o .dll .obj .obj -lcublas + # This could be done by a NVCCCompiler class for all platforms. + spawn(cmd, search_path, verbose, dry_run) + +setup(name="cudamat", + version="0.3", + description="Performs linear algebra computation on the GPU via CUDA", + ext_modules=[cudamat_ext, cudalearn_ext], + packages=find_packages(exclude=['examples', 'test']), + include_package_data=True, + package_data={'cudamat': ['rnd_multipliers_32bit.txt']}, + author="Volodymyr Mnih", + url="https://github.com/cudamat/cudamat", + cmdclass={'build_ext': CUDA_build_ext}) diff --git a/ot/gpu/cudamat/test/test_cudamat.py b/ot/gpu/cudamat/test/test_cudamat.py new file mode 100644 index 0000000..800243c --- /dev/null +++ b/ot/gpu/cudamat/test/test_cudamat.py @@ -0,0 +1,1217 @@ +import numpy as np +import nose +import cudamat as cm + +def setup(): + cm.cublas_init() + +def teardown(): + cm.cublas_shutdown() + +def test_reshape(): + m = 256 + n = 1 + cm1 = np.array(np.random.rand(n, m)*10, dtype=np.float64, order='F') + cm2 = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + + gm1 = cm.CUDAMatrix(cm1) + gm2 = cm.CUDAMatrix(cm2) + + gm1.reshape((m, n)) + gm2.assign(gm1) + gm1.reshape((n, m)) + + gm1.copy_to_host() + gm2.copy_to_host() + + assert np.max(np.abs(gm1.numpy_array - gm2.numpy_array.T)) < 10**-2, "Error in CUDAMatrix.reshape exceeded threshold" + +def test_T_field(): + m = 256 + n = 128 + cm1 = np.array(np.random.rand(n, m)*10, dtype=np.float64, order='F') + cm2 = np.array(np.random.rand(m, 1)*10, dtype=np.float64, order='F') + gm1 = cm.CUDAMatrix(cm1) + gm2 = cm.CUDAMatrix(cm2) + + # test dot + gm = cm.dot(gm2.T, gm1.T) + c = np.dot(cm2.T, cm1.T) + gm.copy_to_host() + + assert np.max(np.abs(gm.numpy_array - c)) < 10**-2, "Error in CUDAMatrix.dot with TransposedCUDAMatrix exceeded threshold" + + # test add_dot + cm3 = np.array(np.random.rand(1, n)*10, dtype=np.float64, order='F') + gm3 = cm.CUDAMatrix(cm3) + gm3.add_dot(gm2.T, gm1.T) + c = cm3 + np.dot(cm2.T, cm1.T) + gm3.copy_to_host() + + assert np.max(np.abs(gm3.numpy_array - c)) < 10**-2, "Error in CUDAMatrix.add_dot TransposedCUDAMatrix exceeded threshold" + + # test add_sums + gm2.add_sums(gm1.T, axis = 1) + c = cm2 + np.atleast_2d(cm1.sum(0)).T + gm2.copy_to_host() + + assert np.max(np.abs(gm2.numpy_array - c)) < 10**-2, "Error in CUDAMatrix.add_sums TransposedCUDAMatrix exceeded threshold" + +def test_assign(): + m = 256 + n = 128 + a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + b = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + + m1.assign(m2) + m1.copy_to_host() + + assert np.max(np.abs(m1.numpy_array - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.assign exceeded threshold" + +def test_assign_scalar(): + m = 256 + n = 128 + a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + + m1 = cm.CUDAMatrix(a) + + m1.assign(np.pi) + m1.copy_to_host() + + assert np.max(np.abs(m1.numpy_array - np.pi)) < 10**-4, "Error in CUDAMatrix.assign_scalar exceeded threshold" + +def test_get_row_slice(): + m = 256 + n = 128 + start = 11 + end = 54 + + a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + b = np.array(np.random.rand(end-start, n)*10, dtype=np.float64, order='F') + + c = np.array(a[start:end,:], order='F') + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + m1.get_row_slice(start, end, target = m2) + m3 = m1.get_row_slice(start, end) + m2.copy_to_host() + m3.copy_to_host() + + assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.get_row_slice exceeded threshold" + assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.get_row_slice exceeded threshold" + +def test_set_row_slice(): + m = 256 + n = 128 + start = 11 + end = 54 + + a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + b = np.array(np.random.rand(end-start, n)*10, dtype=np.float64, order='F') + + c = a.copy() + c[start:end,:] = b + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + m1.set_row_slice(start, end, m2) + m1.copy_to_host() + + assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.set_row_slice exceeded threshold" + +def test_transpose(): + m = 6 + n = 128 + + a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + b = np.array(np.random.rand(n, m), dtype=np.float64, order='F') + + c = a.copy().T + + m = cm.CUDAMatrix(a) + mt1 = cm.CUDAMatrix(b) + m.transpose(target = mt1) + mt2 = m.transpose() + + mt1.copy_to_host() + mt2.copy_to_host() + + assert np.max(np.abs(c - mt1.numpy_array)) < 10**-4, "Error in CUDAMatrix.transpose exceeded threshold" + assert np.max(np.abs(c - mt2.numpy_array)) < 10**-4, "Error in CUDAMatrix.transpose exceeded threshold" + +def test_slice(): + m = 256 + n = 128 + a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + + c = np.array(a[:,32:64], order='F') + + m1 = cm.CUDAMatrix(a) + m2 = m1.slice(32, 64) + m2.copy_to_host() + + assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.slice exceeded threshold" + + +def test_add_col_vec(): + m = 250 + n = 120 + a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + b = np.array(np.random.rand(m, 1)*10, dtype=np.float64, order='F') + t = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + + c = a + b + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + m3 = cm.CUDAMatrix(t) + + m1.add_col_vec(m2, target = m3) + m1.add_col_vec(m2) + m1.copy_to_host() + m3.copy_to_host() + + assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.add_col_vec exceeded threshold" + assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.add_col_vec exceeded threshold" + +def test_add_col_mult(): + m = 256 + n = 128 + mult = np.pi + a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + b = np.array(np.random.rand(m, 1)*10, dtype=np.float64, order='F') + t = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + + c = a + mult * b + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + m3 = cm.CUDAMatrix(t) + + m1.add_col_mult(m2, mult, target = m3) + m1.add_col_mult(m2, mult) + m1.copy_to_host() + m3.copy_to_host() + + assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.add_col_mult exceeded threshold" + assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.add_col_mult exceeded threshold" + +def test_add_row_vec(): + m = 256 + n = 128 + a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + b = np.array(np.random.rand(1, n)*10, dtype=np.float64, order='F') + t = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + + c = a + b + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + m3 = cm.CUDAMatrix(t) + + m1.add_row_vec(m2, target = m3) + m1.add_row_vec(m2) + m1.copy_to_host() + m3.copy_to_host() + + assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.add_row_vec exceeded threshold" + assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.add_row_vec exceeded threshold" + +def test_mult_by_col(): + m = 256 + n = 128 + a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + b = np.array(np.random.rand(m, 1)*10, dtype=np.float64, order='F') + t = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + + c = a * b + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + m3 = cm.CUDAMatrix(t) + + m1.mult_by_col(m2, target = m3) + m1.mult_by_col(m2) + m1.copy_to_host() + m3.copy_to_host() + + assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.mult_by_col exceeded threshold" + assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.mult_by_col exceeded threshold" + +def test_mult_by_row(): + m = 256 + n = 128 + a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + b = np.array(np.random.rand(1, n)*10, dtype=np.float64, order='F') + t = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + + c = a * b + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + m3 = cm.CUDAMatrix(t) + + m1.mult_by_row(m2, target = m3) + m1.mult_by_row(m2) + m1.copy_to_host() + m3.copy_to_host() + + assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.mult_by_row exceeded threshold" + assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.mult_by_row exceeded threshold" + +def test_div_by_col(): + m = 256 + n = 128 + a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + b = np.array(np.random.rand(m, 1)*10, dtype=np.float64, order='F') + 0.1 + t = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + + c = a / b + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + m3 = cm.CUDAMatrix(t) + + m1.div_by_col(m2, target = m3) + m1.div_by_col(m2) + m1.copy_to_host() + m3.copy_to_host() + + assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.div_by_col exceeded threshold" + assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.div_by_col exceeded threshold" + +def test_div_by_row(): + m = 256 + n = 128 + a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + b = np.array(np.random.rand(1, n)*10, dtype=np.float64, order='F') + 0.1 + t = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + + c = a / b + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + m3 = cm.CUDAMatrix(t) + + m1.div_by_row(m2, target = m3) + m1.div_by_row(m2) + m1.copy_to_host() + m3.copy_to_host() + + assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.div_by_row exceeded threshold" + assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.div_by_row exceeded threshold" + +def test_sum(): + m = 256 + n = 128 + a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + t1 = np.array(np.random.rand(1, n)*10, dtype=np.float64, order='F') + t2 = np.array(np.random.rand(m, 1)*10, dtype=np.float64, order='F') + + mult = 0.8 + c1 = np.atleast_2d(a.sum(0)) * mult + c2 = np.atleast_2d(a.sum(1)).T + + m = cm.CUDAMatrix(a) + mt1 = cm.CUDAMatrix(t1) + mt2 = cm.CUDAMatrix(t2) + + m.sum(axis = 0, target = mt1, mult = mult) + mt1r = m.sum(axis = 0, mult = mult) + + m.sum(axis = 1, target = mt2) + mt2r = m.sum(axis = 1) + + mt1.copy_to_host() + mt1r.copy_to_host() + mt2.copy_to_host() + mt2r.copy_to_host() + + assert np.max(np.abs(c1 - mt1.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold" + assert np.max(np.abs(c1 - mt1r.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold" + assert np.max(np.abs(c2 - mt2.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold" + assert np.max(np.abs(c2 - mt2r.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold" + +def test_sum_trans(): + m = 256 + n = 128 + a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + t1 = np.array(np.random.rand(1, m)*10, dtype=np.float64, order='F') + t2 = np.array(np.random.rand(n, 1)*10, dtype=np.float64, order='F') + + c1 = np.atleast_2d(a.T.sum(0)) + c2 = np.atleast_2d(a.T.sum(1)).T + + m = cm.CUDAMatrix(a) + m.set_trans(True) + mt1 = cm.CUDAMatrix(t1) + mt2 = cm.CUDAMatrix(t2) + + m.sum(axis = 0, target = mt1) + mt1r = m.sum(axis = 0) + + m.sum(axis = 1, target = mt2) + mt2r = m.sum(axis = 1) + + mt1.copy_to_host() + mt1r.copy_to_host() + mt2.copy_to_host() + mt2r.copy_to_host() + + assert np.max(np.abs(c1 - mt1.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold" + assert np.max(np.abs(c1 - mt1r.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold" + assert np.max(np.abs(c2 - mt2.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold" + assert np.max(np.abs(c2 - mt2r.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold" + +def test_mean(): + m = 256 + n = 128 + a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + t1 = np.array(np.random.rand(1, n)*10, dtype=np.float64, order='F') + t2 = np.array(np.random.rand(m, 1)*10, dtype=np.float64, order='F') + + c1 = np.atleast_2d(a.mean(0)) + c2 = np.atleast_2d(a.mean(1)).T + + m = cm.CUDAMatrix(a) + mt1 = cm.CUDAMatrix(t1) + mt2 = cm.CUDAMatrix(t2) + + m.mean(axis = 0, target = mt1) + mt1r = m.mean(axis = 0) + + m.mean(axis = 1, target = mt2) + mt2r = m.mean(axis = 1) + + mt1.copy_to_host() + mt1r.copy_to_host() + mt2.copy_to_host() + mt2r.copy_to_host() + + assert np.max(np.abs(c1 - mt1.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold" + assert np.max(np.abs(c1 - mt1r.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold" + assert np.max(np.abs(c2 - mt2.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold" + assert np.max(np.abs(c2 - mt2r.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold" + +def test_add_sums(): + m = 256 + n = 128 + + a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + t1 = np.array(np.random.rand(m, 1)*10, dtype=np.float64, order='F') + t2 = np.array(np.random.rand(1, n)*10, dtype=np.float64, order='F') + + mult = np.pi + beta = 0.7 + + c1 = beta * t1 + mult * np.atleast_2d(a.sum(1)).T + c2 = t2 + np.atleast_2d(a.sum(0)) + + m = cm.CUDAMatrix(a) + mt1 = cm.CUDAMatrix(t1) + mt2 = cm.CUDAMatrix(t2) + + mt1.add_sums(m, axis = 1, mult = np.pi, beta = beta) + mt2.add_sums(m, axis = 0) + + mt1.copy_to_host() + mt2.copy_to_host() + + assert np.max(np.abs(c1 - mt1.numpy_array)) < 10**-3, "Error in CUDAMatrix.add_sums exceeded threshold" + assert np.max(np.abs(c2 - mt2.numpy_array)) < 10**-3, "Error in CUDAMatrix.add_sums exceeded threshold" + + +def test_less_than(): + m = 256 + n = 128 + a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + t1 = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + t2 = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + v = 0.1 + + r1 = 1 * (a < b) + r2 = 1 * (a < v) + + da = cm.CUDAMatrix(a) + db = cm.CUDAMatrix(b) + dt1 = cm.CUDAMatrix(t1) + dt2 = cm.CUDAMatrix(t2) + + da.less_than(db, target = dt1) + da.less_than(v, target = dt2) + da.less_than(db) + + da.copy_to_host() + dt1.copy_to_host() + dt2.copy_to_host() + + assert np.max(np.abs(r1 - da.numpy_array)) < 10**-4, "Error in CUDAMatrix.less_than exceeded threshold" + assert np.max(np.abs(r1 - dt1.numpy_array)) < 10**-4, "Error in CUDAMatrix.less_than exceeded threshold" + assert np.max(np.abs(r2 - dt2.numpy_array)) < 10**-4, "Error in CUDAMatrix.less_than exceeded threshold" + +def test_greater_than(): + m = 256 + n = 128 + a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + t1 = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + t2 = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + v = 0.1 + + r1 = 1 * (a > b) + r2 = 1 * (a > v) + + da = cm.CUDAMatrix(a) + db = cm.CUDAMatrix(b) + dt1 = cm.CUDAMatrix(t1) + dt2 = cm.CUDAMatrix(t2) + + da.greater_than(db, target = dt1) + da.greater_than(v, target = dt2) + da.greater_than(db) + + da.copy_to_host() + dt1.copy_to_host() + dt2.copy_to_host() + + assert np.max(np.abs(r1 - da.numpy_array)) < 10**-4, "Error in CUDAMatrix.greater_than exceeded threshold" + assert np.max(np.abs(r1 - dt1.numpy_array)) < 10**-4, "Error in CUDAMatrix.greater_than exceeded threshold" + assert np.max(np.abs(r2 - dt2.numpy_array)) < 10**-4, "Error in CUDAMatrix.greater_than exceeded threshold" + +def test_minimum(): + m = 256 + n = 128 + a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + t1 = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + t2 = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + v = 0.1 + + r1 = np.minimum(a, b) + r2 = np.minimum(a, v) + + da = cm.CUDAMatrix(a) + db = cm.CUDAMatrix(b) + dt1 = cm.CUDAMatrix(t1) + dt2 = cm.CUDAMatrix(t2) + + da.minimum(db, target = dt1) + da.minimum(v, target = dt2) + da.minimum(db) + + da.copy_to_host() + dt1.copy_to_host() + dt2.copy_to_host() + + assert np.max(np.abs(r1 - da.numpy_array)) < 10**-4, "Error in CUDAMatrix.minimum exceeded threshold" + assert np.max(np.abs(r1 - dt1.numpy_array)) < 10**-4, "Error in CUDAMatrix.minimum exceeded threshold" + assert np.max(np.abs(r2 - dt2.numpy_array)) < 10**-4, "Error in CUDAMatrix.minimum exceeded threshold" + +def test_maximum(): + m = 256 + n = 128 + a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + t1 = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + t2 = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + v = 0.1 + + r1 = np.maximum(a, b) + r2 = np.maximum(a, v) + + da = cm.CUDAMatrix(a) + db = cm.CUDAMatrix(b) + dt1 = cm.CUDAMatrix(t1) + dt2 = cm.CUDAMatrix(t2) + + da.maximum(db, target = dt1) + da.maximum(v, target = dt2) + da.maximum(db) + + da.copy_to_host() + dt1.copy_to_host() + dt2.copy_to_host() + + assert np.max(np.abs(r1 - da.numpy_array)) < 10**-4, "Error in CUDAMatrix.maximum exceeded threshold" + assert np.max(np.abs(r1 - dt1.numpy_array)) < 10**-4, "Error in CUDAMatrix.maximum exceeded threshold" + assert np.max(np.abs(r2 - dt2.numpy_array)) < 10**-4, "Error in CUDAMatrix.maximum exceeded threshold" + +def test_minmax(): + m = 256 + n = 128 + for op in 'min', 'max', 'argmin', 'argmax': + for sign in (1, -1): + a = np.array(np.random.randn(m, n)*10*sign, dtype=np.float64, order='F') + t0 = np.array(np.random.rand(1, n)*10, dtype=np.float64, order='F') + t1 = np.array(np.random.rand(m, 1)*10, dtype=np.float64, order='F') + + r0 = np.atleast_2d(getattr(a, op)(0)) + r1 = np.atleast_2d(getattr(a, op)(1)) + + da = cm.CUDAMatrix(a) + dr10 = cm.CUDAMatrix(t0) + dr11 = cm.CUDAMatrix(t1) + + getattr(da, op)(axis = 0, target = dr10) + getattr(da, op)(axis = 1, target = dr11) + dr20 = getattr(da, op)(axis = 0) + dr21 = getattr(da, op)(axis = 1) + + dr10.copy_to_host() + dr11.copy_to_host() + dr20.copy_to_host() + dr21.copy_to_host() + + assert np.max(np.abs(r0 - dr10.numpy_array)) < 10**-4, "Error in CUDAMatrix.%s exceeded threshold" % op + assert np.max(np.abs(r1 - dr11.numpy_array.T)) < 10**-4, "Error in CUDAMatrix.%s exceeded threshold" % op + assert np.max(np.abs(r0 - dr20.numpy_array)) < 10**-4, "Error in CUDAMatrix.%s exceeded threshold" % op + assert np.max(np.abs(r1 - dr21.numpy_array.T)) < 10**-4, "Error in CUDAMatrix.%s exceeded threshold" % op + +def test_sign(): + m = 256 + n = 128 + a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + a[0,0] = 0. + a[0,1] = -0. + t = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + + c = np.sign(a) + + m1 = cm.CUDAMatrix(a) + m3 = cm.CUDAMatrix(t) + + m2 = m1.sign() + m1.sign(target = m3) + + m2.copy_to_host() + m3.copy_to_host() + + assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.sign exceeded threshold" + assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.sign exceeded threshold" + +def test_sigmoid(): + m = 256 + n = 128 + a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + + c = 1. / (1. + np.exp(-a)) + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + m1.apply_sigmoid(target = m2) + m1.apply_sigmoid() + + m1.copy_to_host() + m2.copy_to_host() + + assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.apply_sigmoid exceeded threshold" + assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.apply_sigmoid exceeded threshold" + +def test_tanh(): + m = 256 + n = 128 + a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + + c = np.tanh(a) + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + m1.apply_tanh(target = m2) + m1.apply_tanh() + + m1.copy_to_host() + m2.copy_to_host() + + assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.apply_tanh exceeded threshold" + assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.apply_tanh exceeded threshold" + +def test_soft_threshold(): + m = 256 + n = 128 + a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + + alpha = 0.5 + c = np.sign(a) * np.maximum(0, np.abs(a) - alpha) + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + m1.apply_soft_threshold(alpha, target = m2) + m1.apply_soft_threshold(alpha) + + m1.copy_to_host() + m2.copy_to_host() + + assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.apply_soft_threshold exceeded threshold" + assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.apply_soft_threshold exceeded threshold" + +def test_log(): + m = 256 + n = 128 + a = np.array(np.random.rand(m, n)*10+0.1, dtype=np.float64, order='F') + b = np.array(np.random.rand(m, n)*10+0.1, dtype=np.float64, order='F') + + c = np.log(a) + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + cm.log(m1, target = m2) + cm.log(m1) + + m1.copy_to_host() + m2.copy_to_host() + + assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in cudamat.log exceeded threshold" + assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in cudamat.log exceeded threshold" + +def test_exp(): + m = 256 + n = 128 + a = np.array(np.random.randn(m, n), dtype=np.float64, order='F') + b = np.array(np.random.randn(m, n), dtype=np.float64, order='F') + + c = np.exp(a) + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + cm.exp(m1, target = m2) + cm.exp(m1) + + m1.copy_to_host() + m2.copy_to_host() + + assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in cudamat.exp exceeded threshold" + assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in cudamat.exp exceeded threshold" + +def test_gamma(): + m = 256 + n = 128 + a = np.array(np.random.rand(m, n)*5, dtype=np.float64, order='F') + b = np.array(np.random.rand(m, n)*5, dtype=np.float64, order='F') + + from scipy.special import gamma + c = gamma(a) + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + cm.gamma(m1, target = m2) + cm.gamma(m1) + + m1.copy_to_host() + m2.copy_to_host() + + assert np.max(np.abs(c - m1.numpy_array)) < 10**-2, "Error in cudamat.gamma exceeded threshold" + assert np.max(np.abs(c - m2.numpy_array)) < 10**-2, "Error in cudamat.gamma exceeded threshold" + +def test_lgamma(): + m = 256 + n = 128 + a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + b = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + + from scipy.special import gammaln + c = gammaln(a) + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + cm.lgamma(m1, target = m2) + cm.lgamma(m1) + + m1.copy_to_host() + m2.copy_to_host() + + assert np.max(np.abs(c - m1.numpy_array)) < 10**-2, "Error in cudamat.lgamma exceeded threshold " + str(np.max(np.abs(c - m1.numpy_array))) + assert np.max(np.abs(c - m2.numpy_array)) < 10**-2, "Error in cudamat.lgamma exceeded threshold" + +def test_sqrt(): + m = 256 + n = 128 + a = np.array(np.random.rand(m, n)*20, dtype=np.float64, order='F') + b = np.array(np.random.rand(m, n), dtype=np.float64, order='F') + + c = np.sqrt(a) + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + cm.sqrt(m1, target = m2) + cm.sqrt(m1) + + m1.copy_to_host() + m2.copy_to_host() + + assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in cudamat.sqrt exceeded threshold" + assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in cudamat.sqrt exceeded threshold" + +def test_pow(): + m = 256 + n = 128 + a = np.array(np.random.randn(m, n)*20, dtype=np.float64, order='F') + b = np.array(np.random.rand(m, n), dtype=np.float64, order='F') + p = 2 + + c = a**p + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + cm.pow(m1, p, target = m2) + cm.pow(m1, p) + + m1.copy_to_host() + m2.copy_to_host() + + assert np.max(np.abs(c - m1.numpy_array)) < 10**-3, "Error in cudamat.pow exceeded threshold" + assert np.max(np.abs(c - m2.numpy_array)) < 10**-3, "Error in cudamat.pow exceeded threshold" + +def test_pow_matrix(): + m = 256 + n = 128 + a = np.array(np.random.rand(m, n)*20, dtype=np.float64, order='F') + b = np.array(np.random.rand(m, n), dtype=np.float64, order='F') + p = np.array(np.random.randn(m, n), dtype=np.float64, order='F') + + + c = a**p + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + mp = cm.CUDAMatrix(p) + cm.pow(m1, mp, target = m2) + cm.pow(m1, mp) + + m1.copy_to_host() + m2.copy_to_host() + + assert np.max(np.abs(c - m1.numpy_array)) < 10**-2, "Error in cudamat.pow exceeded threshold" + assert np.max(np.abs(c - m2.numpy_array)) < 10**-2, "Error in cudamat.pow exceeded threshold" + +def test_reciprocal(): + m = 256 + n = 128 + a = np.array(np.random.rand(m, n)*10+10**-3, dtype=np.float64, order='F') + b = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + + c = 1. / a + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + m1.reciprocal(target = m2) + m1.reciprocal() + + m1.copy_to_host() + m2.copy_to_host() + + assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.reciprocal exceeded threshold" + assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.reciprocal exceeded threshold" + +def test_add_mult(): + m = 256 + n = 128 + alpha = np.pi + a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + + c = a + np.pi * b + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + m1.add_mult(m2, np.pi) + m1.copy_to_host() + + assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.add_mult exceeded threshold" + +def test_subtract_mult(): + m = 256 + n = 128 + alpha = np.pi + a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + + c = a - np.pi * b + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + m1.subtract_mult(m2, np.pi) + m1.copy_to_host() + + assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.subtract_mult exceeded threshold" + +def test_add(): + m = 256 + n = 128 + a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + b = np.array(1.+np.random.rand(m, n)*10, dtype=np.float64, order='F') + t = np.array(np.empty((m, n)), dtype=np.float64, order='F') + + c = a + b + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + m3 = cm.CUDAMatrix(t) + + m1.add(m2, target = m3) + m1.add(m2) + + m3.copy_to_host() + m1.copy_to_host() + + assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.add exceeded threshold" + assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.add exceeded threshold" + +def test_subtract(): + m = 256 + n = 128 + a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + b = np.array(1.+np.random.rand(m, n)*10, dtype=np.float64, order='F') + t = np.array(np.empty((m, n)), dtype=np.float64, order='F') + + c = a - b + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + m3 = cm.CUDAMatrix(t) + + m1.subtract(m2, target = m3) + m1.subtract(m2) + + m3.copy_to_host() + m1.copy_to_host() + + assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.subtract exceeded threshold" + assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.subtract exceeded threshold" + +def test_divide(): + m = 256 + n = 128 + a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + b = np.array(1.+np.random.rand(m, n)*10, dtype=np.float64, order='F') + t = np.array(np.empty((m, n)), dtype=np.float64, order='F') + + c = a / b + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + m3 = cm.CUDAMatrix(t) + + m1.divide(m2, target = m3) + m1.divide(m2) + + m3.copy_to_host() + m1.copy_to_host() + + assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.div exceeded threshold" + assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.div exceeded threshold" + +def test_mult(): + m = 256 + n = 128 + a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + t = np.array(np.empty((m, n)), dtype=np.float64, order='F') + + c = a * b + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + m3 = cm.CUDAMatrix(t) + + m1.mult(m2, target = m3) + m1.mult(m2) + + m3.copy_to_host() + m1.copy_to_host() + + assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.multiply exceeded threshold" + assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.multiply exceeded threshold" + +def test_scalar_mult(): + m = 256 + n = 128 + alpha = np.pi + a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + t = np.array(np.empty((m, n)), dtype=np.float64, order='F') + + c = a * alpha + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(t) + + m1.mult(alpha, target = m2) + m1.mult(alpha) + + m1.copy_to_host() + m2.copy_to_host() + + assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.mult exceeded threshold" + assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.mult exceeded threshold" + +def test_scalar_div(): + m = 256 + n = 128 + alpha = np.pi + a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + t = np.array(np.empty((m, n)), dtype=np.float64, order='F') + + c = a / alpha + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(t) + + m1.divide(alpha, target = m2) + m1.divide(alpha) + + m1.copy_to_host() + m2.copy_to_host() + + assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.divide exceeded threshold" + assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.divide exceeded threshold" + +def test_add_scalar(): + m = 256 + n = 128 + alpha = np.pi + a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + t = np.array(np.empty((m, n)), dtype=np.float64, order='F') + + c = a + alpha + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(t) + + m1.add(alpha, target = m2) + m1.add(alpha) + + m1.copy_to_host() + m2.copy_to_host() + + assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.add_scalar exceeded threshold" + assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.add_scalar exceeded threshold" + +def test_dot(): + m = 128 + k = 256 + n = 64 + a = np.array(np.random.randn(m, k)*10, dtype=np.float64, order='F') + b = np.array(np.random.randn(k, n)*10, dtype=np.float64, order='F') + c = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + + alpha = 2. + beta = 0.3 + r = beta * c + alpha * np.dot(a, b) + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + m3 = cm.CUDAMatrix(c) + m3 = cm.dot(m1, m2, target = m3, alpha = alpha, beta = beta) + m3.copy_to_host() + + assert np.max(np.abs(r - m3.numpy_array)) < 10**-2, "Error in CUDAMatrix.dot exceeded threshold" + +def test_dot_trans(): + m = 128 + k = 256 + n = 64 + a = np.array(np.random.randn(k, m)*10, dtype=np.float64, order='F') + b = np.array(np.random.randn(k, n)*10, dtype=np.float64, order='F') + + c = np.dot(a.T, b) + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + m1.set_trans(True); + m3 = cm.dot(m1, m2) + m3.copy_to_host() + + assert np.max(np.abs(c - m3.numpy_array)) < 10**-2, "Error in CUDAMatrix.dot exceeded threshold" + +def test_dot_vect(): + m = 128 + k = 256 + n = 1 + a = np.array(np.random.randn(m, k)*10, dtype=np.float64, order='F') + b = np.array(np.random.randn(k, n)*10, dtype=np.float64, order='F') + A = cm.CUDAMatrix(a) + B = cm.CUDAMatrix(b) + + c = np.dot(a, b) + C = cm.dot(A, B) + assert np.max(np.abs(c - C.asarray())) < 10**-2, "Error in CUDAMatrix.dot exceeded threshold" + + c = np.dot(a.T, b[:m]) + C = cm.dot(A.T, B.slice(0, m)) + assert np.max(np.abs(c - C.asarray())) < 10**-2, "Error in CUDAMatrix.dot exceeded threshold" + + c = np.dot(b.T, a.T) + C = cm.dot(B.T, A.T) + assert np.max(np.abs(c - C.asarray())) < 10**-2, "Error in CUDAMatrix.dot exceeded threshold" + + c = np.dot(b[:m].T, a) + C = cm.dot(B.slice(0, m).reshape((1, m)), A) + assert np.max(np.abs(c - C.asarray())) < 10**-2, "Error in CUDAMatrix.dot exceeded threshold" + +def test_add_dot(): + m = 128 + k = 256 + n = 64 + a = np.array(np.random.randn(m, k)*10, dtype=np.float64, order='F') + b = np.array(np.random.randn(k, n)*10, dtype=np.float64, order='F') + c = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + + mult = 2.1 + beta = 0.8 + res = beta * c + mult * np.dot(a, b) + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + m3 = cm.CUDAMatrix(c) + m3.add_dot(m1, m2, mult = mult, beta = beta) + + m3.copy_to_host() + + assert np.max(np.abs(res - m3.numpy_array)) < 10**-2, "Error in CUDAMatrix.add_dot exceeded threshold" + +def test_vdot(): + m = 64 + n = 64 + a = np.array(np.random.randn(m, n), dtype=np.float64, order='F') + b = np.array(np.random.randn(m, n), dtype=np.float64, order='F') + + true_res = np.vdot(a, b) + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + + res = cm.vdot(m1, m2) + + assert np.abs(res - true_res) < 10**-2, "Error in CUDAMatrix.vdot exceeded threshold" + +def test_subtract_dot(): + m = 128 + k = 256 + n = 64 + a = np.array(np.random.randn(m, k)*10, dtype=np.float64, order='F') + b = np.array(np.random.randn(k, n)*10, dtype=np.float64, order='F') + c = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + + res = c - np.dot(a, b) + + m1 = cm.CUDAMatrix(a) + m2 = cm.CUDAMatrix(b) + m3 = cm.CUDAMatrix(c) + m3.subtract_dot(m1, m2) + + m3.copy_to_host() + + assert np.max(np.abs(res - m3.numpy_array)) < 10**-2, "Error in CUDAMatrix.subtract_dot exceeded threshold" + +def test_random(): + cm.CUDAMatrix.init_random(1) + m1 = cm.CUDAMatrix(np.array(np.empty((128,256)), dtype=np.float64, order='F')) + m2 = cm.CUDAMatrix(np.array(np.empty((128,256)), dtype=np.float64, order='F')) + + m1.fill_with_rand() + m1.copy_to_host() + m2.fill_with_randn() + m2.copy_to_host() + + assert np.abs(np.mean(m1.numpy_array) - 0.5) < 10**-2, "Error in CUDAMatrix.fill_with_rand threshold" + assert np.abs(np.mean(m2.numpy_array)) < 10**-2, "Error in CUDAMatrix.fill_with_randn threshold" + +def test_euclid_norm(): + m = 256 + n = 128 + a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + + m = cm.CUDAMatrix(a) + + n1 = np.sqrt(np.sum(a**2)) + n2 = m.euclid_norm() + + assert np.abs(n1-n2) < 10**-2, "Error in CUDAMatrix.euclid_norm exceeded threshold" + +def test_manhattan_norm(): + m = 256 + n = 128 + a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') + + m = cm.CUDAMatrix(a) + + n1 = np.sum(np.abs(a), dtype=np.double) + n2 = m.manhattan_norm() + + assert np.abs(n1-n2) < 2e-2, "Error in CUDAMatrix.manhattan_norm exceeded threshold (%f != %f)" % (n1, n2) + +def test_allfinite(): + a = cm.empty((10, 20)).assign(1).divide(0) # NaN + b = cm.empty((10, 20)).assign(1e20).mult(1e20) # Inf + c = cm.empty((10, 20)).assign(1) # 1.0 + + assert (not a.allfinite()) and (not b.allfinite()) and c.allfinite(), "CUDAMatrix.allfinite does not work" + +def test_select_columns(): + m = 256 + n = 128 + k = 8 + + s = np.array(np.random.randn(m, n), dtype=np.float64, order='F') + i_l = [0, 1, 2, 3, 5, 10, 12, 20] + i = np.array(i_l).T[np.newaxis, :] + t = np.empty((m, k)) + + s_d = cm.CUDAMatrix(s) + i_d = cm.CUDAMatrix(i) + t_d = cm.CUDAMatrix(t) + + s_d.select_columns(i_d, t_d) + res = s[:,i_l] + + assert np.max(np.abs(res - t_d.asarray())) < 10**-4, "Error in CUDAMatrix.select_columns exceeded threshold" + + +def test_where(): + m = 256 + n = 128 + a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + z = np.zeros_like(a) + res = np.where(a > 0, a, z); + + a_d = cm.CUDAMatrix(a) + z_d = cm.CUDAMatrix(z) + res_d = cm.empty(a_d.shape) + a_d.greater_than(0, res_d) + cm.where(res_d, a_d, z_d) + assert np.abs(res-res_d.asarray()).max() < 1e-2, "Error in cudamat.where" + + +def test_correlate(): + m = 64 + n = 32 + km = 17 + kn = 11 + + a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') + k = np.array(np.random.randn(km, kn)*10, dtype=np.float64, order='F') + + res = np.zeros_like(a) + for i in range(len(a)): + for j in range(len(a[0])): + for h in range(-(km/2), km/2 + 1): + for w in range(-(kn/2), kn/2 + 1): + if i+h >= 0 and i+h < m and j+w >= 0 and j+w < n: + res[i][j] += a[i + h][j + w] * k[km/2 + h][kn/2 + w] + + a_d = cm.CUDAMatrix(a) + k_d = cm.CUDAMatrix(k) + + res_d = cm.correlate(a_d, k_d) + assert np.abs(res-res_d.asarray()).max() < 1e-2, "Error in cudamat.correlate" + + +if __name__ == '__main__': + nose.runmodule() diff --git a/ot/gpu/cudamat/test/test_learn.py b/ot/gpu/cudamat/test/test_learn.py new file mode 100644 index 0000000..954757a --- /dev/null +++ b/ot/gpu/cudamat/test/test_learn.py @@ -0,0 +1,27 @@ +import pdb +import numpy as np +import nose +import cudamat as cm + +def setup(): + cm.cublas_init() + +def teardown(): + cm.cublas_shutdown() + +def test_mult_by_sigmoid_deriv(): + m = 256 + n = 128 + c_targets = np.array(np.random.randn(m, n)*10, dtype=np.float32, order='F') + c_acts = np.array(np.random.rand(m, n), dtype=np.float32, order='F') + + g_targets = cm.CUDAMatrix(c_targets) + g_acts = cm.CUDAMatrix(c_acts) + + c_targets = c_targets * c_acts * (1. - c_acts) + cm.learn.mult_by_sigmoid_deriv(g_targets, g_acts) + + assert np.max(np.abs(c_acts - g_acts.asarray())) < 10**-2, "Error in cudamat.learn.mult_by_sigmoid_deriv exceeded threshold" + +if __name__ == '__main__': + nose.runmodule() diff --git a/ot/gpu/da.py b/ot/gpu/da.py new file mode 100644 index 0000000..7d04b5b --- /dev/null +++ b/ot/gpu/da.py @@ -0,0 +1,81 @@ +# -*- coding: utf-8 -*- +""" +Domain adaptation with optimal transport and GPU +""" + +import numpy as np +from ..utils import unif +from ..da import OTDA +from .bregman import sinkhornGPU + + +def pairwiseEuclideanGPU(a, b, returnAsGPU=False, squared=False, cudamat=None): + # a is shape (n, f) and b shape (m, f). Return matrix c of shape (n, m). + # First compute in c_GPU the squared euclidean distance. And return its + # square root. At each cell [i,j] of c, we want to have + # sum{k in range(f)} ( (a[i,k] - b[j,k])^2 ). We know that + # (a-b)^2 = a^2 -2ab +b^2. Thus we want to have in each cell of c: + # sum{k in range(f)} ( a[i,k]^2 -2a[i,k]b[j,k] +b[j,k]^2). + + a_GPU = cudamat.CUDAMatrix(a) + b_GPU = cudamat.CUDAMatrix(b) + + # Multiply a by b transpose to obtain in each cell [i,j] of c the + # value sum{k in range(f)} ( a[i,k]b[j,k] ) + c_GPU = cudamat.dot(a_GPU, b_GPU.transpose()) + # multiply by -2 to have sum{k in range(f)} ( -2a[i,k]b[j,k] ) + c_GPU.mult(-2) + + # Compute the vectors of the sum of squared elements. + a_GPU = cudamat.pow(a_GPU, 2).sum(axis=1) + b_GPU = cudamat.pow(b_GPU, 2).sum(axis=1) + + # Add the vectors in each columns (respectivly rows) of c. + # sum{k in range(f)} ( a[i,k]^2 -2a[i,k]b[j,k] ) + c_GPU.add_col_vec(a_GPU) + # sum{k in range(f)} ( a[i,k]^2 -2a[i,k]b[j,k] +b[j,k]^2) + c_GPU.add_row_vec(b_GPU.transpose()) + + if not squared: + c_GPU = cudamat.sqrt(c_GPU) + + if returnAsGPU: + return c_GPU + else: + return c_GPU.asarray() + + +class OTDA_sinkhorn_GPU(OTDA): + def fit(self, xs, xt, reg=1, ws=None, wt=None, norm=None): + import cudamat + cudamat.init() + xs = np.asarray(xs, dtype=np.float64) + xt = np.asarray(xt, dtype=np.float64) + + self.xs = xs + self.xt = xt + + if wt is None: + wt = unif(xt.shape[0]) + if ws is None: + ws = unif(xs.shape[0]) + + self.ws = ws + self.wt = wt + + self.M_GPU = pairwiseEuclideanGPU(xs, xt, returnAsGPU=True, + squared=True, cudamat=cudamat) + + if norm == "median": + self.M_GPU.divide(float(np.median(self.M_GPU.asarray()))) + elif norm == "max": + self.M_GPU.divide(float(np.max(self.M_GPU.asarray()))) + elif norm == "log": + M = np.log(1 + self.M_GPU.asarray()) + self.M_GPU = cudamat.CUDAMatrix(M) + elif norm == "loglog": + M = np.log(1 + np.log(1 + self.M_GPU.asarray())) + self.M_GPU = cudamat.CUDAMatrix(M) + + self.G = sinkhornGPU(ws, wt, self.M_GPU, reg, cudamat=cudamat) + self.computed = True -- cgit v1.2.3 From 67f09ad4089594457cb00702dd3aa8a6a94ec2eb Mon Sep 17 00:00:00 2001 From: Leo gautheron Date: Thu, 20 Apr 2017 13:51:30 +0200 Subject: changes from feedback --- ot/__init__.py | 4 +- ot/gpu/bregman.py | 2 +- ot/gpu/cudamat/.gitignore | 38 - ot/gpu/cudamat/CHANGELOG | 41 - ot/gpu/cudamat/CONTRIBUTE.md | 74 - ot/gpu/cudamat/INSTALL.md | 53 - ot/gpu/cudamat/LICENSE | 21 - ot/gpu/cudamat/README.md | 65 - ot/gpu/cudamat/cudamat/__init__.py | 2 - ot/gpu/cudamat/cudamat/cudamat.cu | 1633 --- ot/gpu/cudamat/cudamat/cudamat.cuh | 35 - ot/gpu/cudamat/cudamat/cudamat.py | 1575 -- ot/gpu/cudamat/cudamat/cudamat_kernels.cu | 804 -- ot/gpu/cudamat/cudamat/cudamat_kernels.cuh | 92 - ot/gpu/cudamat/cudamat/learn.cu | 34 - ot/gpu/cudamat/cudamat/learn.py | 21 - ot/gpu/cudamat/cudamat/learn_kernels.cu | 10 - ot/gpu/cudamat/cudamat/learn_kernels.cuh | 12 - ot/gpu/cudamat/cudamat/rnd_multipliers_32bit.txt | 16028 --------------------- ot/gpu/cudamat/examples/bench_cudamat.py | 97 - ot/gpu/cudamat/examples/nn_cudamat.py | 133 - ot/gpu/cudamat/examples/rbm_cudamat.py | 98 - ot/gpu/cudamat/examples/rbm_numpy.py | 72 - ot/gpu/cudamat/examples/util.py | 22 - ot/gpu/cudamat/setup.py | 121 - ot/gpu/cudamat/test/test_cudamat.py | 1217 -- ot/gpu/cudamat/test/test_learn.py | 27 - ot/gpu/da.py | 6 +- 28 files changed, 5 insertions(+), 22332 deletions(-) delete mode 100644 ot/gpu/cudamat/.gitignore delete mode 100644 ot/gpu/cudamat/CHANGELOG delete mode 100644 ot/gpu/cudamat/CONTRIBUTE.md delete mode 100644 ot/gpu/cudamat/INSTALL.md delete mode 100644 ot/gpu/cudamat/LICENSE delete mode 100644 ot/gpu/cudamat/README.md delete mode 100644 ot/gpu/cudamat/cudamat/__init__.py delete mode 100644 ot/gpu/cudamat/cudamat/cudamat.cu delete mode 100644 ot/gpu/cudamat/cudamat/cudamat.cuh delete mode 100644 ot/gpu/cudamat/cudamat/cudamat.py delete mode 100644 ot/gpu/cudamat/cudamat/cudamat_kernels.cu delete mode 100644 ot/gpu/cudamat/cudamat/cudamat_kernels.cuh delete mode 100644 ot/gpu/cudamat/cudamat/learn.cu delete mode 100644 ot/gpu/cudamat/cudamat/learn.py delete mode 100644 ot/gpu/cudamat/cudamat/learn_kernels.cu delete mode 100644 ot/gpu/cudamat/cudamat/learn_kernels.cuh delete mode 100644 ot/gpu/cudamat/cudamat/rnd_multipliers_32bit.txt delete mode 100644 ot/gpu/cudamat/examples/bench_cudamat.py delete mode 100644 ot/gpu/cudamat/examples/nn_cudamat.py delete mode 100644 ot/gpu/cudamat/examples/rbm_cudamat.py delete mode 100644 ot/gpu/cudamat/examples/rbm_numpy.py delete mode 100644 ot/gpu/cudamat/examples/util.py delete mode 100755 ot/gpu/cudamat/setup.py delete mode 100644 ot/gpu/cudamat/test/test_cudamat.py delete mode 100644 ot/gpu/cudamat/test/test_learn.py diff --git a/ot/__init__.py b/ot/__init__.py index 702269c..13abcda 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -9,7 +9,6 @@ from . import utils from . import datasets from . import plot from . import da -from . import gpu # OT functions from .lp import emd, emd2 @@ -23,5 +22,4 @@ __version__ = "0.2" __all__ = ["emd", "emd2", "sinkhorn", "utils", 'datasets', 'bregman', 'lp', 'plot', 'tic', 'toc', 'toq', - 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim', - 'gpu'] + 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim'] diff --git a/ot/gpu/bregman.py b/ot/gpu/bregman.py index 3fdf11b..4d4a8b7 100644 --- a/ot/gpu/bregman.py +++ b/ot/gpu/bregman.py @@ -6,7 +6,7 @@ Bregman projections for regularized OT with GPU import numpy as np -def sinkhornGPU(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False, +def sinkhorn(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False, log=False, cudamat=None): # init data Nini = len(a) diff --git a/ot/gpu/cudamat/.gitignore b/ot/gpu/cudamat/.gitignore deleted file mode 100644 index 7a10978..0000000 --- a/ot/gpu/cudamat/.gitignore +++ /dev/null @@ -1,38 +0,0 @@ -*.py[cod] - -# C extensions -*.so - -# Packages -*.egg -*.egg-info -dist -build -eggs -parts -bin -var -sdist -develop-eggs -.installed.cfg -lib -lib64 - -# Installer logs -pip-log.txt - -# Unit test / coverage reports -.coverage -.tox -nosetests.xml - -# Translations -*.mo - -# Mr Developer -.mr.developer.cfg -.project -.pydevproject - -# Vim -*.swp diff --git a/ot/gpu/cudamat/CHANGELOG b/ot/gpu/cudamat/CHANGELOG deleted file mode 100644 index 446df0f..0000000 --- a/ot/gpu/cudamat/CHANGELOG +++ /dev/null @@ -1,41 +0,0 @@ -Version 0.3 -- Added equality testing. Contributed by Ryan P. Adams. -- Added flag that determines whether threads are synced after each call. -- Added direct blas calls for two elementwise operations. Contributed by Vincent Vanhoucke. -- Added the set_selected_columns. Contributed by Tijmen Tieleman. -- Added tanh, abs, and log_1_plus_exp. Contributed by Ilya Sutskever. -- Some bug fixes. Contributed by Jonathan Taylor. -- Added the select_columns method. Code contributed by Tijmen Tieleman. -- on_device should now work as intended. -- allocate_device_memory now returns an error when cublasAlloc fails. -- Fixed bug in max that showed up when an entire column was negative. -- Fixed bug in activation computations in examples/rbm_numpy.py. -- Added get_col_slice and set_col_slice methods. -- Added init and shutdown methods to shorten cublas_init and cublas_shutdown. -- Added bound checking to the various slicing methods. -- Fixed problem with pow and negative numbers. -- Added support for matrix powers in pow. - -Version 0.2 -- Methods add, subtract, mult, divide can now take scalars as well as instances of CUDAMatrix. -- Deprecated add_scalar, mult_by_scalar, div_by_scalar. -- Methods now return target or self to make chaining operations easier. -- Added asarray method. -- Added transpose method. -- Added sqrt and pow functions. -- Added the sigmoid method to the module level. -- Added add_row_vec. -- Added empty. Now when you don't provide a target or pre-allocated temporary storage cudamat methods will not take up CPU RAM or transfer anything between the CPU and GPU. -- Added get_row_slice and set_row_slice. -- Added less_than_scalar, greater_than, greater_than_scalar. -- Added max (axis=1 is currently not supported.) - -Version 0.1.5 -- Added shape attribute and reshape method. - -Version 0.1 -- Most methods now throw python exceptions instead of exiting after encountering an error. -- The CUDAMatrix constructor now automatically converts ndarray objects to float32 in FORTRAN order. -- Renamed scalar_mult to mult_by_scalar and scalar_div to div_by_scalar. -- Added log and exp functions. -- Removed add_row_sums and sum_rows. diff --git a/ot/gpu/cudamat/CONTRIBUTE.md b/ot/gpu/cudamat/CONTRIBUTE.md deleted file mode 100644 index a98a39f..0000000 --- a/ot/gpu/cudamat/CONTRIBUTE.md +++ /dev/null @@ -1,74 +0,0 @@ -Development installation ------------------------- - -If you want to develop cudamat, you should clone the github repository instead -of downloading a release. Furthermore, it is useful to install it in editable -mode. Instead of copying the files somewhere Python can find them, this will -point Python directly to the directory you install it from. Either of the -following commands will do: - -```bash -# a) Install for your user in editable mode: -python setup.py develop --prefix=~/.local -# b) Install for your user in editable mode, but with pip: -pip install --user --editable . -``` - -As for the [standard installation](INSTALL.md), you can set the `NVCC_FLAGS` -environment variable to compile for a specific architecture. - -Update after local changes --------------------------- - -Your changes to `.py` files will show up immediately the next time you import -cudamat. Changes to `.cu` and `.cuh` files require a recompilation triggered -by just running the above installation command again. - -Update after remote changes ---------------------------- - -To obtain the latest version, just pull in the remote changes: - -```bash -git checkout master -git fetch origin -git merge origin/master -``` - -Then recompile as per the instructions in the previous section. - -Contribute back ---------------- - -If you created a great new feature that is useful to the rest of the world, -and it even comes with docstrings and updated tests, we will gladly incorporate -it into cudamat. To do that, you will need to send us a pull request from your -fork. - -If you haven't forked cudamat yet, log in to your github account, go to -https://github.com/cudamat/cudamat and hit the "Fork" button. -Now instead of having `origin` point to `cudamat/cudamat`, you will want to have -it point to your fork, and have `upstream` point to the official project: - -```bash -git remote rename origin upstream -git remote add origin git@github.com:yourusername/cudamat -git fetch origin -``` - -Create a branch to house your changes: - -```bash -git checkout -b my-new-feature -``` - -Hack away, then add your changes, commit and push: - -```bash -git add all.py my.py changes.py -git commit -m 'Added a new feature that does this and that' -git push origin my-new-feature -``` - -Now send us a pull request asking to merge `yourusername:my-new-feature` into -`cudamat:master` and we will come back to you! diff --git a/ot/gpu/cudamat/INSTALL.md b/ot/gpu/cudamat/INSTALL.md deleted file mode 100644 index 95cfef3..0000000 --- a/ot/gpu/cudamat/INSTALL.md +++ /dev/null @@ -1,53 +0,0 @@ -Prerequisites -------------- - -cudamat needs the following to be installed first: - -* Python 2.x or 3.x and numpy -* The CUDA SDK -* nose for running the tests (optional) - -Installation ------------- - -Once you have installed the prerequisites and downloaded cudamat, switch to the -cudamat directory and run either of the following commands to install it: - -```bash -# a) Install for your user: -python setup.py install --user -# b) Install for your user, but with pip: -pip install --user . -# c) Install system-wide: -sudo python setup.py install -# d) Install system-wide, but with pip: -sudo pip install . -``` - -If your Nvidia GPU supports a higher Compute Capability than the default one of -your CUDA toolkit, you can set the `NVCCFLAGS` environment variable when -installing cudamat to compile it for your architecture. For example, to install -for your user for a GTX 780 Ti (Compute Capability 3.5), you would run: - -```bash -NVCCFLAGS=-arch=sm_35 python setup.py install --user -``` - -To compile for both Compute Capability 2.0 and 3.5, you would run: - -```bash -NVCCFLAGS="-gencode arch=compute_20,code=sm_20 -gencode arch=compute_35,code=sm_35" ... -``` - -Testing -------- - -To test your setup, run the included unit tests and optionally the benchmark: - -```bash -cd test # so it doesn't try importing cudamat from the source directory -# Run tests -nosetests -# Run benchmark -python ../examples/bench_cudamat.py -``` diff --git a/ot/gpu/cudamat/LICENSE b/ot/gpu/cudamat/LICENSE deleted file mode 100644 index 43b4816..0000000 --- a/ot/gpu/cudamat/LICENSE +++ /dev/null @@ -1,21 +0,0 @@ -Copyright (c) 2009-2013, Volodymyr Mnih -All rights reserved. - -Redistribution and use in source and binary forms, with or without modification, are permitted provided that -the following conditions are met: - - * Redistributions of source code must retain the above copyright notice, this list of conditions and the -following disclaimer. - * Redistributions in binary form must reproduce the above copyright notice, this list of conditions and -the following disclaimer in the documentation and/or other materials provided with the distribution. - * Neither the name of the nor the names of its contributors may be used to endorse or -promote products derived from this software without specific prior written permission. - -THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED -WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A -PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY -DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, -PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER -CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR -OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH -DAMAGE. diff --git a/ot/gpu/cudamat/README.md b/ot/gpu/cudamat/README.md deleted file mode 100644 index 9d2dd6f..0000000 --- a/ot/gpu/cudamat/README.md +++ /dev/null @@ -1,65 +0,0 @@ -CUDAMat -======= - -The aim of the cudamat project is to make it easy to perform basic matrix calculations on CUDA-enabled GPUs from Python. cudamat provides a Python matrix class that performs calculations on a GPU. At present, some of the operations our GPU matrix class supports include: - -* Easy conversion to and from instances of `numpy.ndarray`. -* Limited slicing support. -* Matrix multiplication and transpose. -* Elementwise addition, subtraction, multiplication, and division. -* Elementwise application of exp, log, pow, sqrt. -* Summation, maximum and minimum along rows or columns. -* Conversion of CUDA errors into Python exceptions. - -The current feature set of cudamat is biased towards features needed for implementing some common machine learning algorithms. We have included implementations of feedforward neural networks and restricted Boltzmann machines in the examples that come with cudamat. - -Example: - -```python -import numpy as np -import cudamat as cm - -cm.cublas_init() - -# create two random matrices and copy them to the GPU -a = cm.CUDAMatrix(np.random.rand(32, 256)) -b = cm.CUDAMatrix(np.random.rand(256, 32)) - -# perform calculations on the GPU -c = cm.dot(a, b) -d = c.sum(axis = 0) - -# copy d back to the host (CPU) and print -print(d.asarray()) -``` - -Documentation -------------- - -An overview of the main features of cudamat can be found in the technical report: - -[CUDAMat: A CUDA-based matrix class for Python](http://www.cs.toronto.edu/~vmnih/docs/cudamat_tr.pdf), Volodymyr Mnih, UTML TR 2009-004. - -Download --------- - -You can obtain the latest release from the repository by typing: - -```bash -git clone https://github.com/cudamat/cudamat.git -``` - -You can also download one of the releases from the [releases](https://github.com/cudamat/cudamat/releases) section. - -Installation ------------- - -cudamat uses setuptools and can be installed via pip. -For details, please see [INSTALL.md](INSTALL.md). - -Development ------------ - -If you want to contribute new features or improvements, you're welcome to fork -cudamat on github and send us your pull requests! -Please see [CONTRIBUTE.md](CONTRIBUTE.md) if you need any help with that. diff --git a/ot/gpu/cudamat/cudamat/__init__.py b/ot/gpu/cudamat/cudamat/__init__.py deleted file mode 100644 index 13b072d..0000000 --- a/ot/gpu/cudamat/cudamat/__init__.py +++ /dev/null @@ -1,2 +0,0 @@ -from .cudamat import * -from . import learn diff --git a/ot/gpu/cudamat/cudamat/cudamat.cu b/ot/gpu/cudamat/cudamat/cudamat.cu deleted file mode 100644 index 522f9cc..0000000 --- a/ot/gpu/cudamat/cudamat/cudamat.cu +++ /dev/null @@ -1,1633 +0,0 @@ -#include -#include -#include -#include -#include -#include "cudamat_kernels.cuh" -#include "cudamat.cuh" - -extern "C" { - -/* ------------------------------ CUBLAS init/shutdown ------------------------------ */ - -inline bool check_cublas_error() { - cublasStatus status = cublasGetError(); - - return status != CUBLAS_STATUS_SUCCESS; -} - -inline bool checkCUDAError() { - cudaError_t err = cudaGetLastError(); - - if (cudaSuccess != err) - printf("%s\n", cudaGetErrorString( err)); - return cudaSuccess != err; -} - -EXPORT const char* get_last_cuda_error() { - cudaError_t err = cudaGetLastError(); - - return cudaGetErrorString( err); -} - -EXPORT const char* get_last_clib_error() { - return strerror(errno); -} - -EXPORT int cublas_init() { - cublasInit(); - if (check_cublas_error()) - return CUBLAS_ERROR; - else - return 0; -} - -EXPORT int cublas_shutdown() { - cublasShutdown(); - cudaThreadExit(); - - return 0; -} - - -EXPORT int cuda_set_device(int deviceId) { - cudaSetDevice(deviceId); - - if (checkCUDAError()) - return CUDA_ERROR; - else - return 0; -} - -EXPORT int init_random(rnd_struct* rnd_state, int seed, char* cudamatpath) { - unsigned int * host_mults; - host_mults = (unsigned int*)malloc(NUM_RND_STREAMS * sizeof(unsigned int)); - FILE * pFile; - - pFile = fopen (cudamatpath,"r"); - if (pFile == NULL) { - return ERROR_FILE_OPEN; - } - - for (int i = 0; i < NUM_RND_STREAMS; i++) { - if (fscanf (pFile, "%u", &host_mults[i]) != 1) { - return ERROR_FILE_SCAN; - } - } - fclose (pFile); - - cublasAlloc(NUM_RND_STREAMS, sizeof(unsigned int), (void**)&rnd_state->dev_mults); - cublasAlloc(NUM_RND_STREAMS, sizeof(unsigned long long), (void**)&rnd_state->dev_words); - cublasSetVector(NUM_RND_STREAMS, sizeof(unsigned int), host_mults, 1, rnd_state->dev_mults, 1); - //cudaMalloc((void **)&rnd_state->dev_mults, NUM_RND_STREAMS * sizeof(unsigned int)); - //cudaMalloc((void **)&rnd_state->dev_words, NUM_RND_STREAMS * sizeof(unsigned long long)); - //cudaMemcpy(rnd_state->dev_mults, host_mults, NUM_RND_STREAMS * sizeof(unsigned int), cudaMemcpyHostToDevice); - cudaThreadSynchronize(); - - kSeedRandom<<>>(rnd_state->dev_mults, rnd_state->dev_words, seed); - - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - else - return 0; -} - -/* ------------------------------ Utility routines ------------------------------ */ - -EXPORT int get_leading_dimension(cudamat* mat) { - return mat->is_trans ? mat->size[1] : mat->size[0]; -} - -EXPORT int get_nonleading_dimension(cudamat* mat) { - return mat->is_trans ? mat->size[0] : mat->size[1]; -} - -EXPORT void set_transpose(cudamat* mat, int is_trans) { - mat->is_trans = is_trans; -} - -inline char get_transpose_char(cudamat* mat) { - return mat->is_trans ? 't' : 'n'; -} - -EXPORT void cuda_sync_threads() { - cudaThreadSynchronize(); -} - -/* ------------------------------ Allocating/moving data ------------------------------ */ - -EXPORT int allocate_device_memory(cudamat* mat) { - int len = mat->size[0]*mat->size[1]; - - cublasStatus stat; - - stat = cublasAlloc(len, sizeof(mat->data_device[0]), (void**)&mat->data_device); - - if (stat != CUBLAS_STATUS_SUCCESS || check_cublas_error()) { - checkCUDAError(); - return CUBLAS_ERROR; - } - - mat->on_device = 1; - return 0; -} - -EXPORT int copy_to_host(cudamat* mat) { - int len = mat->size[0]*mat->size[1]; - - if (mat->on_device) { - cublasGetVector(len, sizeof(mat->data_host[0]), mat->data_device, 1, mat->data_host, 1); - - if (check_cublas_error()) - return CUBLAS_ERROR; - } else - return ERROR_NOT_ON_DEVICE; - - return 0; -} - -EXPORT int copy_to_device(cudamat* mat) { - int len = mat->size[0]*mat->size[1]; - int err_code = 0; - - //if (!mat->owns_data) - // return VIEW_ERROR; - - if (!mat->on_device) { - err_code = allocate_device_memory(mat); - if (err_code) - return err_code; - } - - cublasSetVector(len, sizeof(mat->data_host[0]), mat->data_host, 1, mat->data_device, 1); - - if (check_cublas_error()) - return CUBLAS_ERROR; - - return 0; -} - -EXPORT int copy_on_device(cudamat* mat1, cudamat* mat2) { - int len = mat1->size[0]*mat1->size[1]; - - if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - cublasDcopy(len, mat1->data_device, 1, mat2->data_device, 1); - - if (check_cublas_error()) - return CUBLAS_ERROR; - else - return 0; -} - -EXPORT int get_row_slice(cudamat* source, cudamat* target, unsigned int start, unsigned int end) { - int height = source->size[0]; - int width = source->size[1]; - - if ((end - start) != target->size[0] || source->size[1] != target->size[1] || start >= end || end > height) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - dim3 kernelBlockGrid((int)ceil((end - start)/32.), (int)ceil(width/32.), 1); - dim3 kernelBlockDim(32, 1, 1); - - kGetRowSlice<<>>(source->data_device, target->data_device, start, end, width, height); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - else - return 0; -} - -EXPORT int set_row_slice(cudamat* source, cudamat* target, unsigned int start, unsigned int end) { - int height = target->size[0]; - int width = target->size[1]; - - if ((end - start) != source->size[0] || source->size[1] != target->size[1] || start >= end || end > height) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - dim3 kernelBlockGrid((int)ceil((end - start)/32.), (int)ceil(width/32.), 1); - dim3 kernelBlockDim(32, 1, 1); - - kSetRowSlice<<>>(source->data_device, target->data_device, start, end, width, height); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - else - return 0; -} - -EXPORT int copy_transpose(cudamat* source, cudamat* target) { - unsigned int height = source->size[0]; - unsigned int width = source->size[1]; - - if (source->size[0] != target->size[1] || source->size[1] != target->size[0]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - // setup execution parameters - unsigned int grid_x = height / COPY_BLOCK_SIZE; - if (height % COPY_BLOCK_SIZE) - grid_x++; - - unsigned int grid_y = width / COPY_BLOCK_SIZE; - if (width % COPY_BLOCK_SIZE) - grid_y++; - - dim3 grid(grid_x, grid_y, 1); - dim3 threads(COPY_BLOCK_SIZE, COPY_BLOCK_SIZE, 1); - - kTranspose<<< grid, threads >>>(target->data_device, source->data_device, height, width); - - if (checkCUDAError()) - return CUDA_ERROR; - else - return 0; -} - -EXPORT int free_device_memory(cudamat* mat) { - if (mat->owns_data && mat->on_device) { - cublasStatus stat; - - stat = cublasFree(mat->data_device); - mat->on_device = 0; - - if (stat != CUBLAS_STATUS_SUCCESS || check_cublas_error()) - return CUBLAS_ERROR; - } - - return 0; -} - -EXPORT int reshape(cudamat* mat, unsigned int m, unsigned int n) { - if (mat->size[0] * mat->size[1] != m * n) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - mat->size[0] = m; - mat->size[1] = n; - - return 0; -} - -EXPORT int get_slice(cudamat* source, cudamat* target, unsigned int first_col, unsigned int last_col) { - if (source->is_trans) - return ERROR_TRANSPOSED; - - if (!source->on_device) - return ERROR_NOT_ON_DEVICE; - - if (last_col > source->size[1] || (first_col >= last_col)) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - int num_rows = source->size[0]; - - target->data_host = 0; - target->data_device = source->data_device + first_col * num_rows; - target->on_device = 1; - target->on_host = 0; - target->size[0] = source->size[0]; - target->size[1] = last_col - first_col; - target->is_trans = 0; - target->owns_data = 0; - - return 0; -} - -EXPORT int get_vector_slice(cudamat* source, cudamat* target, unsigned int first_ind, unsigned int last_ind) { - // source must be a vector - if (source->size[0] > 1 && source->size[1] > 1) - return ERROR_GENERIC; - - if (source->is_trans) - return ERROR_TRANSPOSED; - - if (!source->on_device) - return ERROR_NOT_ON_DEVICE; - - if (first_ind >= last_ind) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - int num_rows = source->size[0]; - - target->data_host = 0; - target->data_device = source->data_device + first_ind * num_rows; - target->on_device = 1; - target->on_host = 0; - target->is_trans = 0; - target->owns_data = 0; - - if (source->size[0] > 1) { - if (last_ind > source->size[0]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - target->size[0] = last_ind - first_ind; - target->size[1] = 1; - } else { - if (last_ind > source->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - target->size[0] = 1; - target->size[1] = last_ind - first_ind; - } - - return 0; -} - -/* ------------------------------ Initialization routines ------------------------------ */ - -EXPORT void init_from_array(cudamat* mat, double* data, int m, int n) { - mat->data_host = data; - mat->size[0] = m; - mat->size[1] = n; - mat->on_device = 0; - mat->on_host = 1; - mat->is_trans = 0; - mat->owns_data = 1; -} - -EXPORT int init_empty(cudamat* mat, int m, int n) { - mat->size[0] = m; - mat->size[1] = n; - mat->on_device = 0; - mat->on_host = 0; - mat->is_trans = 0; - mat->owns_data = 1; - - return allocate_device_memory(mat); -} - -/* ------------------------------ Random number generation ------------------------------ */ -EXPORT int fill_with_rand(rnd_struct* rnd_state, cudamat* mat) { - int len = mat->size[0] * mat->size[1]; - - if (!mat->on_device) - return ERROR_NOT_ON_DEVICE; - - kRandomUniform<<>>(rnd_state->dev_mults, rnd_state->dev_words, mat->data_device, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - else - return 0; -} - -EXPORT int fill_with_randn(rnd_struct* rnd_state, cudamat* mat) { - int len = mat->size[0] * mat->size[1]; - - if (!mat->on_device) - return ERROR_NOT_ON_DEVICE; - - kRandomGaussian<<>>(rnd_state->dev_mults, rnd_state->dev_words, mat->data_device, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - else - return 0; -} -/* ------------------------------ Algebraic operations ------------------------------ */ - -EXPORT int add_col_vec(cudamat* mat, cudamat* vec, cudamat* target) { - unsigned int h = mat->size[0], - w = mat->size[1]; - - if (!mat->on_device || !vec->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->is_trans) - return ERROR_TRANSPOSED; - - if (mat->size[0] != vec->size[0] || vec->size[1] != 1 || - mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kAddColVector<<>>(mat->data_device, vec->data_device, target->data_device, w, h); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) { - return CUDA_ERROR; - } - - return 0; -} - -EXPORT int add_col_mult(cudamat* mat, cudamat* vec, cudamat* target, double mult) { - unsigned int h = mat->size[0], - w = mat->size[1]; - - if (!mat->on_device || !vec->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->is_trans) - return ERROR_TRANSPOSED; - - if (mat->size[0] != vec->size[0] || vec->size[1] != 1 || - mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kAddColMult<<>>(mat->data_device, vec->data_device, target->data_device, mult, w, h); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int add_row_vec(cudamat* mat, cudamat* vec, cudamat* target) { - unsigned int h = mat->size[0], - w = mat->size[1]; - - if (!mat->on_device || !vec->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->is_trans) - return ERROR_TRANSPOSED; - - if (mat->size[1] != vec->size[1] || vec->size[0] != 1 || - mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kAddRowVector<<>>(mat->data_device, vec->data_device, target->data_device, w, h); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int mult_by_col_vec(cudamat* mat, cudamat* vec, cudamat* target) { - unsigned int h = mat->size[0], - w = mat->size[1]; - - if (!mat->on_device || !vec->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->is_trans) - return ERROR_TRANSPOSED; - - if (mat->size[0] != vec->size[0] || vec->size[1] != 1 || - mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kMultByColVector<<>>(mat->data_device, vec->data_device, target->data_device, w, h); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int mult_by_row_vec(cudamat* mat, cudamat* vec, cudamat* target) { - unsigned int h = mat->size[0], - w = mat->size[1]; - - if (!mat->on_device || !vec->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->is_trans) - return ERROR_TRANSPOSED; - - if (mat->size[1] != vec->size[1] || vec->size[0] != 1 || - mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kMultByRowVector<<>>(mat->data_device, vec->data_device, target->data_device, w, h); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int divide_by_col_vec(cudamat* mat, cudamat* vec, cudamat* target) { - unsigned int h = mat->size[0], - w = mat->size[1]; - - if (!mat->on_device || !vec->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->is_trans) - return ERROR_TRANSPOSED; - - if (mat->size[0] != vec->size[0] || vec->size[1] != 1 || - mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kDivByColVector<<>>(mat->data_device, vec->data_device, target->data_device, w, h); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int divide_by_row_vec(cudamat* mat, cudamat* vec, cudamat* target) { - unsigned int h = mat->size[0], - w = mat->size[1]; - - if (!mat->on_device || !vec->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->is_trans) - return ERROR_TRANSPOSED; - - if (mat->size[1] != vec->size[1] || vec->size[0] != 1 || - mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kDivByRowVector<<>>(mat->data_device, vec->data_device, target->data_device, w, h); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int less_than(cudamat* mat1, cudamat* mat2, cudamat* target) { - int len = mat1->size[0]*mat1->size[1]; - - if (!mat1->on_device || !mat2->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat1->is_trans != mat2->is_trans) - return ERROR_TRANSPOSEDNESS; - - if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1] || - mat1->size[0] != target->size[0] || mat1->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kLessThan<<>>(mat1->data_device, mat2->data_device, target->data_device, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int less_than_scalar(cudamat* mat, double val, cudamat* target) { - int len = mat->size[0]*mat->size[1]; - - if (!mat->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->is_trans != target->is_trans) - return ERROR_TRANSPOSEDNESS; - - if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kLessThanScalar<<>>(mat->data_device, val, target->data_device, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int greater_than(cudamat* mat1, cudamat* mat2, cudamat* target) { - int len = mat1->size[0]*mat1->size[1]; - - if (!mat1->on_device || !mat2->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat1->is_trans != mat2->is_trans) - return ERROR_TRANSPOSEDNESS; - - if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1] || - mat1->size[0] != target->size[0] || mat1->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kGreaterThan<<>>(mat1->data_device, mat2->data_device, target->data_device, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int greater_than_scalar(cudamat* mat, double val, cudamat* target) { - int len = mat->size[0]*mat->size[1]; - - if (!mat->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->is_trans != target->is_trans) - return ERROR_TRANSPOSEDNESS; - - if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kGreaterThanScalar<<>>(mat->data_device, val, target->data_device, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int equals(cudamat* mat1, cudamat* mat2, cudamat* target) { - int len = mat1->size[0]*mat1->size[1]; - - if (!mat1->on_device || !mat2->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat1->is_trans != mat2->is_trans) - return ERROR_TRANSPOSEDNESS; - - if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1] || - mat1->size[0] != target->size[0] || mat1->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kEquals<<>>(mat1->data_device, mat2->data_device, target->data_device, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int equals_scalar(cudamat* mat, double val, cudamat* target) { - int len = mat->size[0]*mat->size[1]; - - if (!mat->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->is_trans != target->is_trans) - return ERROR_TRANSPOSEDNESS; - - if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kEqualsScalar<<>>(mat->data_device, val, target->data_device, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int minimum(cudamat* mat1, cudamat* mat2, cudamat* target) { - int len = mat1->size[0]*mat1->size[1]; - - if (!mat1->on_device || !mat2->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat1->is_trans != mat2->is_trans) - return ERROR_TRANSPOSEDNESS; - - if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1] || - mat1->size[0] != target->size[0] || mat1->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kMinimum<<>>(mat1->data_device, mat2->data_device, target->data_device, len); - - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int minimum_scalar(cudamat* mat, double val, cudamat* target) { - int len = mat->size[0]*mat->size[1]; - - if (!mat->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->is_trans != target->is_trans) - return ERROR_TRANSPOSEDNESS; - - if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kMinimumScalar<<>>(mat->data_device, val, target->data_device, len); - - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int maximum(cudamat* mat1, cudamat* mat2, cudamat* target) { - int len = mat1->size[0]*mat1->size[1]; - - if (!mat1->on_device || !mat2->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat1->is_trans != mat2->is_trans) - return ERROR_TRANSPOSEDNESS; - - if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1] || - mat1->size[0] != target->size[0] || mat1->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kMaximum<<>>(mat1->data_device, mat2->data_device, target->data_device, len); - - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int maximum_scalar(cudamat* mat, double val, cudamat* target) { - int len = mat->size[0]*mat->size[1]; - - if (!mat->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->is_trans != target->is_trans) - return ERROR_TRANSPOSEDNESS; - - if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kMaximumScalar<<>>(mat->data_device, val, target->data_device, len); - - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int min_by_axis(cudamat* mat, cudamat* target, int axis) { - unsigned int h = mat->size[0], - w = mat->size[1]; - - if (!mat->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->is_trans) - return ERROR_TRANSPOSED; - - if (axis == 0) { - if (target->size[0] != 1 || target->size[1] != mat->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kMinColumnwise<<>>(mat->data_device, target->data_device, w, h); - } else { - if (target->size[1] != 1 || target->size[0] != mat->size[0]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kMinRowwise<<>>(mat->data_device, target->data_device, w, h); - } - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int max_by_axis(cudamat* mat, cudamat* target, int axis) { - unsigned int h = mat->size[0], - w = mat->size[1]; - - if (!mat->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->is_trans) - return ERROR_TRANSPOSED; - - if (axis == 0) { - if (target->size[0] != 1 || target->size[1] != mat->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kMaxColumnwise<<>>(mat->data_device, target->data_device, w, h); - } else { - if (target->size[1] != 1 || target->size[0] != mat->size[0]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kMaxRowwise<<>>(mat->data_device, target->data_device, w, h); - } - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int argmin_by_axis(cudamat* mat, cudamat* target, int axis) { - unsigned int h = mat->size[0], - w = mat->size[1]; - - if (!mat->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->is_trans) - return ERROR_TRANSPOSED; - - if (axis == 0) { - if (target->size[0] != 1 || target->size[1] != mat->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kArgMinColumnwise<<>>(mat->data_device, target->data_device, w, h); - } else { - if (target->size[1] != 1 || target->size[0] != mat->size[0]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kArgMinRowwise<<>>(mat->data_device, target->data_device, w, h); - } - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int argmax_by_axis(cudamat* mat, cudamat* target, int axis) { - unsigned int h = mat->size[0], - w = mat->size[1]; - - if (!mat->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->is_trans) - return ERROR_TRANSPOSED; - - if (axis == 0) { - if (target->size[0] != 1 || target->size[1] != mat->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kArgMaxColumnwise<<>>(mat->data_device, target->data_device, w, h); - } else { - if (target->size[1] != 1 || target->size[0] != mat->size[0]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kArgMaxRowwise<<>>(mat->data_device, target->data_device, w, h); - } - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int sign(cudamat* mat, cudamat* target) { - int len = mat->size[0]*mat->size[1]; - - if (!mat->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->is_trans != target->is_trans) - return ERROR_TRANSPOSEDNESS; - - if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kSign<<>>(mat->data_device, target->data_device, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int apply_sigmoid(cudamat* mat, cudamat* target) { - unsigned int len = mat->size[0] * mat->size[1]; - - if (!mat->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kApplySigmoid<<>>(mat->data_device, target->data_device, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int apply_tanh(cudamat* mat, cudamat* target) { - unsigned int len = mat->size[0] * mat->size[1]; - - if (!mat->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kApplyTanh<<>>(mat->data_device, target->data_device, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int apply_soft_threshold(cudamat* mat, double alpha, cudamat* target) { - unsigned int len = mat->size[0] * mat->size[1]; - - if (!mat->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kApplySoftThreshold<<>>(mat->data_device, alpha, target->data_device, len); - - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int apply_abs(cudamat* mat, cudamat* target) { - unsigned int len = mat->size[0] * mat->size[1]; - - if (!mat->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kApplyAbs<<>>(mat->data_device, target->data_device, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int apply_log_1_plus_exp(cudamat* mat, cudamat* target) { - unsigned int len = mat->size[0] * mat->size[1]; - - if (!mat->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kApplyLog1PlusExp<<>>(mat->data_device, target->data_device, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int apply_log(cudamat* mat, cudamat* target) { - unsigned int len = mat->size[0] * mat->size[1]; - - if (!mat->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kLog<<>>(mat->data_device, target->data_device, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int apply_exp(cudamat* mat, cudamat* target) { - unsigned int len = mat->size[0] * mat->size[1]; - - if (!mat->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kExp<<>>(mat->data_device, target->data_device, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int apply_gamma(cudamat* mat, cudamat* target) { - unsigned int len = mat->size[0] * mat->size[1]; - - if (!mat->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kGamma<<>>(mat->data_device, target->data_device, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int apply_lgamma(cudamat* mat, cudamat* target) { - unsigned int len = mat->size[0] * mat->size[1]; - - if (!mat->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kLogGamma<<>>(mat->data_device, target->data_device, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int apply_sqrt(cudamat* mat, cudamat* target) { - unsigned int len = mat->size[0] * mat->size[1]; - - if (!mat->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kSqrt<<>>(mat->data_device, target->data_device, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int apply_pow(cudamat* mat, double pow, cudamat* target) { - unsigned int len = mat->size[0] * mat->size[1]; - - if (!mat->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kPow<<>>(mat->data_device, pow, target->data_device, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int apply_pow_matrix(cudamat* mat, cudamat* pow, cudamat* target) { - unsigned int len = mat->size[0] * mat->size[1]; - - if (!mat->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - if (mat->size[0] != pow->size[0] || mat->size[1] != pow->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kPowMatrix<<>>(mat->data_device, pow->data_device, target->data_device, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int reciprocal(cudamat* mat, cudamat* target) { - unsigned int len = mat->size[0] * mat->size[1]; - - if (!mat->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kReciprocal<<>>(mat->data_device, target->data_device, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int dot(cudamat* mat1, cudamat* mat2, cudamat* target, double beta, double alpha) { - if (!mat1->on_device || !mat2->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (get_leading_dimension(mat1) != get_leading_dimension(target) || - get_nonleading_dimension(mat2) != get_nonleading_dimension(target) || - get_nonleading_dimension(mat1) != get_leading_dimension(mat2)) { - return ERROR_INCOMPATIBLE_DIMENSIONS; - } - int m = get_leading_dimension(mat1), - k = get_leading_dimension(mat2), - n = get_nonleading_dimension(mat2); - - // gemv if second matrix is a (column) vector - if (n == 1) { - cublasDgemv(get_transpose_char(mat1), mat1->size[0], mat1->size[1], - alpha, mat1->data_device, mat1->size[0], - mat2->data_device, 1, - beta, target->data_device, 1); - } - // gemv if first matrix is a (row) vector - else if (m == 1) { - cublasDgemv(mat2->is_trans ? 'n' : 't', mat2->size[0], mat2->size[1], - alpha, mat2->data_device, mat2->size[0], - mat1->data_device, 1, - beta, target->data_device, 1); - } - // gemm otherwise - else { - cublasDgemm(get_transpose_char(mat1), get_transpose_char(mat2), - m, n, k, - alpha, mat1->data_device, mat1->size[0], - mat2->data_device, mat2->size[0], - beta, target->data_device, target->size[0]); - } - - if (check_cublas_error()) - return CUBLAS_ERROR; - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - return 0; -} - -EXPORT double vdot(cudamat* mat1, cudamat* mat2, int* err_code) { - int len = mat1->size[0]*mat1->size[1]; - double res; - - if (!mat1->on_device || !mat2->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat1->is_trans != mat2->is_trans) { - *err_code = ERROR_TRANSPOSEDNESS; - return 0; - } - - if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1]) { - *err_code = ERROR_INCOMPATIBLE_DIMENSIONS; - return 0; - } - - res = cublasDdot(len, mat1->data_device, 1, mat2->data_device, 1); - - if (check_cublas_error()) { - *err_code = CUBLAS_ERROR; - return -1.; - } else { - *err_code = 0; - return res; - } -} - -/* Perform the operation mat1 = mat1 + alpha * mat2. mat1 and mat2 must - have the same transposedness. */ -EXPORT int add_mult(cudamat* mat1, cudamat* mat2, double alpha) { - int len = mat1->size[0]*mat1->size[1]; - - if (!mat1->on_device || !mat2->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat1->is_trans != mat2->is_trans) - return ERROR_TRANSPOSEDNESS; - - if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - cublasDaxpy(len, alpha, mat2->data_device, 1, mat1->data_device, 1); - - if (check_cublas_error()) - return CUBLAS_ERROR; - - return 0; -} - -EXPORT int add_elementwise(cudamat* mat1, cudamat* mat2, cudamat* target) { - int len = mat1->size[0]*mat1->size[1]; - - if (!mat1->on_device || !mat2->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat1->is_trans != mat2->is_trans) - return ERROR_TRANSPOSEDNESS; - - if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1] || - mat1->size[0] != target->size[0] || mat1->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - if (mat1 == target) { - cublasDaxpy(len, 1, mat2->data_device, 1, mat1->data_device, 1); - - if (check_cublas_error()) - return CUBLAS_ERROR; - - } else { - kAdd<<>>(mat1->data_device, mat2->data_device, target->data_device, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - } - - return 0; -} - -EXPORT int subtract_elementwise(cudamat* mat1, cudamat* mat2, cudamat* target) { - int len = mat1->size[0]*mat1->size[1]; - - if (!mat1->on_device || !mat2->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat1->is_trans != mat2->is_trans) - return ERROR_TRANSPOSEDNESS; - - if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1] || - mat1->size[0] != target->size[0] || mat1->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kSubtract<<>>(mat1->data_device, mat2->data_device, target->data_device, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int divide_elementwise(cudamat* mat1, cudamat* mat2, cudamat* target) { - int len = mat1->size[0]*mat1->size[1]; - - if (!mat1->on_device || !mat2->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat1->is_trans != mat2->is_trans) - return ERROR_TRANSPOSEDNESS; - - if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1] || - mat1->size[0] != target->size[0] || mat1->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kDivide<<>>(mat1->data_device, mat2->data_device, target->data_device, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -/* Elementwise multiplication of 2 matrices */ -EXPORT int mult_elementwise(cudamat* mat1, cudamat* mat2, cudamat* target) { - int len = mat1->size[0]*mat1->size[1]; - - if (!mat1->on_device || !mat2->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat1->is_trans != mat2->is_trans) - return ERROR_TRANSPOSEDNESS; - - if (mat1->size[0] != mat2->size[0] || mat1->size[1] != mat2->size[1] || - mat1->size[0] != target->size[0] || mat1->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kMult<<>>(mat1->data_device, mat2->data_device, target->data_device, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int assign_scalar(cudamat* mat, double alpha) { - int len = mat->size[0]*mat->size[1]; - - if (!mat->on_device) - return ERROR_NOT_ON_DEVICE; - - kAssignScalar<<>>(mat->data_device, alpha, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int mult_by_scalar(cudamat* mat, double alpha, cudamat* target) { - int len = mat->size[0]*mat->size[1]; - - if (!mat->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - if (mat == target) { - cublasDscal(len, alpha, mat->data_device, 1); - - if (check_cublas_error()) - return CUBLAS_ERROR; - - } else { - kMultScalar<<>>(mat->data_device, alpha, target->data_device, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - } - - return 0; -} - -EXPORT int divide_by_scalar(cudamat* mat, double alpha, cudamat* target) { - int len = mat->size[0]*mat->size[1]; - - if (!mat->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kDivideScalar<<>>(mat->data_device, alpha, target->data_device, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int add_scalar(cudamat* mat, double alpha, cudamat* target) { - int len = mat->size[0]*mat->size[1]; - - if (!mat->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (mat->size[0] != target->size[0] || mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kAddScalar<<>>(mat->data_device, alpha, target->data_device, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT double euclid_norm(cudamat* mat, int* err_code) { - int len = mat->size[0]*mat->size[1]; - - if (!mat->on_device) { - *err_code = ERROR_NOT_ON_DEVICE; - return -1.; - } - - double res = cublasDnrm2(len, mat->data_device, 1); - - if (check_cublas_error()) { - *err_code = CUBLAS_ERROR; - return -1.; - } else { - *err_code = 0; - return res; - } -} - -EXPORT double manhattan_norm(cudamat* mat, int* err_code) { - int len = mat->size[0]*mat->size[1]; - - if (!mat->on_device) { - *err_code = ERROR_NOT_ON_DEVICE; - return -1.; - } - - double res = cublasDasum(len, mat->data_device, 1); - - if (check_cublas_error()) { - *err_code = CUBLAS_ERROR; - return -1.; - } else { - *err_code = 0; - return res; - } -} - -EXPORT int selectRows(cudamat* source, cudamat* target, cudamat* indices){ - const int nRetRows = indices->size[1]; - - if (nRetRows==0) return 0; - - dim3 gridDim((nRetRows+31)/32); - dim3 blockDim(32); - - kSelectRows<<>>(source->data_device, target->data_device, indices->data_device, nRetRows, source->size[0], source->size[1]); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - else - return 0; -} - -EXPORT int setSelectedRows(cudamat* target, cudamat* source, cudamat* indices){ - const int nSetRows = indices->size[1]; - - if (nSetRows==0) - return 0; - - dim3 gridDim((nSetRows+31)/32); - dim3 blockDim(32); - - kSetSelectedRows<<>>(target->data_device, source->data_device, indices->data_device, nSetRows, target->size[0], target->size[1]); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - else - return 0; -} - -EXPORT int where(cudamat* condition_mat, cudamat* if_mat, cudamat* else_mat, cudamat* target) { - unsigned int len = condition_mat->size[0] * condition_mat->size[1]; - - if (!condition_mat->on_device || !target->on_device) - return ERROR_NOT_ON_DEVICE; - - if (condition_mat->size[0] != target->size[0] || condition_mat->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - if (condition_mat->size[0] != if_mat->size[0] || condition_mat->size[1] != if_mat->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - if (condition_mat->size[0] != else_mat->size[0] || condition_mat->size[1] != else_mat->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kWhere<<>>(condition_mat->data_device, - if_mat->data_device, else_mat->data_device, target->data_device, len); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -EXPORT int correlate(cudamat* source, cudamat* kernel, cudamat* dest) { - int len = source->size[0] * source->size[1]; - - if (!source->on_device || !kernel->on_device || !dest->on_device) - return ERROR_NOT_ON_DEVICE; - - if (source->size[0] != dest->size[0] || source->size[1] != dest->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - if (kernel->size[0] % 2 == 0 || kernel->size[1] % 2 == 0 || - kernel->size[0] > source->size[0] || kernel->size[1] > source->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kCorrelate<<>>(source->data_device, - kernel->data_device, dest->data_device, source->size[1], source->size[0], - kernel->size[1], kernel->size[0]); - - if (SYNC_THREADS) - cudaThreadSynchronize(); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -} diff --git a/ot/gpu/cudamat/cudamat/cudamat.cuh b/ot/gpu/cudamat/cudamat/cudamat.cuh deleted file mode 100644 index e4e2e34..0000000 --- a/ot/gpu/cudamat/cudamat/cudamat.cuh +++ /dev/null @@ -1,35 +0,0 @@ -#if defined(_WIN32) || defined(__CYGWIN__) - #define EXPORT __declspec(dllexport) -#else - #define EXPORT __attribute__ ((visibility("default"))) -#endif - -#define SYNC_THREADS 1 - -#define ERROR_INCOMPATIBLE_DIMENSIONS -1 -#define CUBLAS_ERROR -2 -#define CUDA_ERROR -3 -#define VIEW_ERROR -4 -#define ERROR_TRANSPOSED -5 -#define ERROR_GENERIC -6 -#define ERROR_TRANSPOSEDNESS -7 -#define ERROR_NOT_ON_DEVICE -8 -#define ERROR_UNSUPPORTED -9 -#define ERROR_FILE_OPEN -10 -#define ERROR_FILE_SCAN -11 - -struct cudamat { - double* data_host; - double* data_device; - int on_device; - int on_host; - int size[2]; - int is_trans; // 0 or 1 - int owns_data; -}; - -struct rnd_struct { - unsigned int* dev_mults; - unsigned long long* dev_words; -}; - diff --git a/ot/gpu/cudamat/cudamat/cudamat.py b/ot/gpu/cudamat/cudamat/cudamat.py deleted file mode 100644 index f855ed2..0000000 --- a/ot/gpu/cudamat/cudamat/cudamat.py +++ /dev/null @@ -1,1575 +0,0 @@ -import os -import platform -import warnings -import sysconfig -import sys - -import ctypes as ct -import numpy as np - -def load_library(basename): - if platform.system() == 'Windows': - ext = '.dll' - else: - ext = sysconfig.get_config_var('SO') - return ct.cdll.LoadLibrary(os.path.join( - os.path.dirname(__file__) or os.path.curdir, - basename + ext)) - -_cudamat = load_library('libcudamat') - -_cudamat.get_last_cuda_error.restype = ct.c_char_p -_cudamat.get_last_clib_error.restype = ct.c_char_p -_cudamat.cublas_init.restype = ct.c_int -_cudamat.cublas_shutdown.restype = ct.c_int -_cudamat.cuda_set_device.restype = ct.c_int -_cudamat.init_random.restype = ct.c_int - -_cudamat.init_empty.restype = ct.c_int -_cudamat.reshape.restype = ct.c_int -_cudamat.copy_to_host.restype = ct.c_int -_cudamat.allocate_device_memory = ct.c_int -_cudamat.copy_to_device.restype = ct.c_int -_cudamat.copy_on_device.restype = ct.c_int -_cudamat.free_device_memory.restype = ct.c_int - -_cudamat.get_slice.restype = ct.c_int -_cudamat.get_row_slice.restype = ct.c_int -_cudamat.set_row_slice.restype = ct.c_int -_cudamat.copy_transpose.restype = ct.c_int -_cudamat.get_vector_slice.restype = ct.c_int -_cudamat.fill_with_rand.restype = ct.c_int -_cudamat.fill_with_randn.restype = ct.c_int - -_cudamat.add_col_vec.restype = ct.c_int -_cudamat.add_col_mult.restype = ct.c_int -_cudamat.add_row_vec.restype = ct.c_int -_cudamat.mult_by_col_vec.restype = ct.c_int -_cudamat.mult_by_row_vec.restype = ct.c_int -_cudamat.divide_by_col_vec.restype = ct.c_int -_cudamat.divide_by_row_vec.restype = ct.c_int - -_cudamat.less_than.restype = ct.c_int -_cudamat.less_than_scalar.restype = ct.c_int -_cudamat.greater_than.restype = ct.c_int -_cudamat.greater_than_scalar.restype = ct.c_int -_cudamat.equals.restype = ct.c_int -_cudamat.equals_scalar.restype = ct.c_int -_cudamat.minimum.restype = ct.c_int -_cudamat.minimum_scalar.restype = ct.c_int -_cudamat.maximum.restype = ct.c_int -_cudamat.maximum_scalar.restype = ct.c_int -_cudamat.min_by_axis.restype = ct.c_int -_cudamat.max_by_axis.restype = ct.c_int -_cudamat.argmin_by_axis.restype = ct.c_int -_cudamat.argmax_by_axis.restype = ct.c_int -_cudamat.sign.restype = ct.c_int -_cudamat.apply_sigmoid.restype = ct.c_int -_cudamat.apply_tanh.restype = ct.c_int -_cudamat.apply_soft_threshold.restype = ct.c_int -_cudamat.apply_abs.restype = ct.c_int -_cudamat.apply_log_1_plus_exp.restype = ct.c_int -_cudamat.apply_log.restype = ct.c_int -_cudamat.apply_exp.restype = ct.c_int -_cudamat.apply_gamma.restype = ct.c_int -_cudamat.apply_lgamma.restype = ct.c_int -_cudamat.apply_sqrt.restype = ct.c_int -_cudamat.apply_pow.restype = ct.c_int -_cudamat.apply_pow_matrix.restype = ct.c_int -_cudamat.reciprocal.restype = ct.c_int - -_cudamat.add_elementwise.restype = ct.c_int -_cudamat.subtract_elementwise.restype = ct.c_int -_cudamat.divide_elementwise.restype = ct.c_int -_cudamat.mult_elementwise.restype = ct.c_int -_cudamat.assign_scalar.restype = ct.c_int -_cudamat.mult_by_scalar.restype = ct.c_int -_cudamat.divide_by_scalar.restype = ct.c_int -_cudamat.add_scalar.restype = ct.c_int - -_cudamat.euclid_norm.restype = ct.c_double -_cudamat.manhattan_norm.restype = ct.c_double -_cudamat.selectRows.restype = ct.c_int -_cudamat.setSelectedRows.restype = ct.c_int -_cudamat.vdot.restype = ct.c_double -_cudamat.dot.restype = ct.c_int - -_cudamat.where.restype = ct.c_int - -_cudamat.correlate.restype = ct.c_int - - -def deprecated(func): - """This is a decorator which can be used to mark functions - as deprecated. It will result in a warning being emmitted - when the function is used.""" - - def newFunc(*args, **kwargs): - warnings.warn("Call to deprecated function %s." % func.__name__, - category=DeprecationWarning) - return func(*args, **kwargs) - newFunc.__name__ = func.__name__ - newFunc.__doc__ = func.__doc__ - newFunc.__dict__.update(func.__dict__) - return newFunc - - -class CUDAMatException(Exception): - pass - - -def get_last_cuda_error(): - errmsg = _cudamat.get_last_cuda_error() - if sys.version_info >= (3,): - return bytes(errmsg).decode() - else: - return str(errmsg) - - -def get_last_clib_error(): - errmsg = _cudamat.get_last_clib_error() - if sys.version_info >= (3,): - return bytes(errmsg).decode() - else: - return str(errmsg) - - -def generate_exception(err_code, **kwargs): - """ - Return a CUDAMatException object based on the error code err_code. - Additional arguments are error-specific and optional. - """ - - if err_code == -1: - return CUDAMatException("Incompatible matrix dimensions.") - elif err_code == -2: - return CUDAMatException("CUBLAS error.") - elif err_code == -3: - return CUDAMatException("CUDA error: " + get_last_cuda_error()) - elif err_code == -4: - return CUDAMatException("Operation not supported on views.") - elif err_code == -5: - return CUDAMatException("Operation not supported on " - "transposed matrices.") - elif err_code == -6: - return CUDAMatException("") - elif err_code == -7: - return CUDAMatException("Incompatible transposedness.") - elif err_code == -8: - return CUDAMatException("Matrix is not in device memory.") - elif err_code == -9: - return CUDAMatException("Operation not supported.") - elif err_code == -10: - filepath = kwargs.get("filepath",""); - if filepath: - filepath = ": '%s'" % filepath - return CUDAMatException("Cannot open file%s: %s" % (filepath,get_last_clib_error())) - elif err_code == -11: - filepath = kwargs.get("filepath",""); - if filepath: - filepath = ": '%s'" % filepath - return CUDAMatException("Cannot parse file%s." % filepath) - else: - return CUDAMatException("") - - -class cudamat(ct.Structure): - _fields_ = [('data_host', ct.POINTER(ct.c_double)), - ('data_device', ct.POINTER(ct.c_double)), - ('on_device', ct.c_int), - ('on_host', ct.c_int), - ('size', ct.c_int * 2), - ('is_trans', ct.c_int), - ('owns_data', ct.c_int)] - - -class rnd_struct(ct.Structure): - _fields_ = [('dev_rnd_mults', ct.POINTER(ct.c_uint)), - ('dev_rnd_words', ct.POINTER(ct.c_longlong))] - - -class TransposedCUDAMatrix(object): - def __init__(self, mat): - self.mat = cudamat() - ct.memmove(ct.pointer(self.mat), ct.pointer(mat), ct.sizeof(self.mat)) - self.mat.is_trans = 1 - self.p_mat = ct.pointer(self.mat) - - -class CUDAMatrix(object): - """ - A CUDAMatrix object represents a matrix of single precision floating point - numbers on a GPU. - """ - - def __init__(self, array, copy_to_device=True, copy_on_host=True): - """ - Initializes a new matrix object in one of two ways. If array is a numpy - ndarray, memory for a matrix with the same dimensions is allocated on - the GPU. If the copy_to_device flag is set to True, the GPU matrix is - initialized with the given ndarray. If the copy_on_host flag is set to - True, a copy of the matrix will be created in host memory even if the - matrix is of the correct type (float64, Fortran-contiguous order). - If array is not an ndarray, it must be a cudamat structure (typically - the user will never use this way of calling __init__). - """ - - if type(array) in [np.ndarray, np.memmap]: - # Convert array to float64 in FORTRAN order - array = reformat(array, copy=copy_on_host) - - # Initialize as a ndarray-tied matrix. - self.mat = cudamat() - self.size = self.mat.size - self.p_mat = ct.pointer(self.mat) - self.numpy_array = array - - _cudamat.init_from_array( - self.p_mat, - array.ctypes.data_as(ct.POINTER(ct.c_double)), - ct.c_int(array.shape[0]), - ct.c_int(array.shape[1])) - - if copy_to_device: - err_code = _cudamat.copy_to_device(self.p_mat) - if err_code: - raise generate_exception(err_code) - - else: - # Initialize based on existing cudamat structure. - mat = array - self.mat = mat - self.p_mat = ct.pointer(self.mat) - - self.T = TransposedCUDAMatrix(self.mat) - - # Keep a reference to free device memory in case of a crash. - self.__free_device_memory = _cudamat.free_device_memory - - def __del__(self): - try: - if 'p_mat' in self.__dict__: - err_code = self.__free_device_memory(self.p_mat) - if err_code: - raise generate_exception(err_code) - except AttributeError: - pass - - @staticmethod - def init_random(seed=0): - """ - Initialize and seed the random number generator. - """ - - CUDAMatrix.rndInitialized = 1 - CUDAMatrix.rnd_state = rnd_struct() - CUDAMatrix.rnd_state_p = ct.pointer(CUDAMatrix.rnd_state) - - cudamat_path = os.path.join(os.path.abspath(os.path.dirname(__file__)), - 'rnd_multipliers_32bit.txt') - - if sys.version_info >= (3,): - cudamat_path = cudamat_path.encode(sys.getfilesystemencoding()) - - err_code = _cudamat.init_random(CUDAMatrix.rnd_state_p, - ct.c_int(seed), - cudamat_path) - if err_code: - if sys.version_info >= (3,): - cudamat_path = cudamat_path.decode(sys.getfilesystemencoding()) - raise generate_exception(err_code, filepath=cudamat_path) - - @property - def shape(self): - return (self.mat.size[0], self.mat.size[1]) - - def reshape(self, shape): - """ - Reshapes self to have the given shape. The number of elements cannot - change as this only changes how the contents are interpreted. - """ - - m = ct.c_uint(shape[0]) - n = ct.c_uint(shape[1]) - - # Reshape the default matrix - err_code = _cudamat.reshape(self.p_mat, m, n) - if err_code: - raise generate_exception(err_code) - # Reshape the transposed matrix - err_code = _cudamat.reshape(self.T.p_mat, m, n) - if err_code: - raise generate_exception(err_code) - # Reshape the CPU matrix - if self.mat.on_host: - self.numpy_array = np.reshape(self.numpy_array, shape, order='F') - - return self - - def asarray(self): - """ - Copies the matrix to an ndarray on the CPU and returns it. - """ - - self.copy_to_host() - - return self.numpy_array - - def copy_to_device(self): - """ - Copy the matrix to the GPU. - """ - - err_code = _cudamat.copy_to_device(self.p_mat) - if err_code: - raise generate_exception(err_code) - - def copy_to_host(self): - """ - Copy the matrix to the CPU. - """ - - if not self.mat.on_host: - # allocate host storage if necessary - m = self.mat.size[0] - n = self.mat.size[1] - - self.numpy_array = np.empty((m, n), dtype=np.float64, order='F') - self.mat.data_host = \ - self.numpy_array.ctypes.data_as(ct.POINTER(ct.c_double)) - - self.mat.on_host = 1 - - err_code = _cudamat.copy_to_host(self.p_mat) - if err_code: - raise generate_exception(err_code) - - def copy(self, include_host=False): - """ - Create a copy of the matrix on GPU. If include_host is True, also - creates a copy of the matrix on CPU if there was any. - """ - - new_mat = empty(self.shape).assign(self) - - if include_host and self.mat.on_host: - new_mat.numpy_array = self.numpy_array.copy() - new_mat.mat.data_host = \ - new_mat.numpy_array.ctypes.data_as(ct.POINTER(ct.c_double)) - new_mat.mat.on_host = 1 - - return new_mat - - def assign(self, val): - """Assign val to self, where val can be a scalar or a CUDAMatrix - with the same dimensions as self. """ - - if isinstance(val, CUDAMatrix): - err_code = _cudamat.copy_on_device(val.p_mat, self.p_mat) - elif isinstance(val, (int, float)): - err_code = _cudamat.assign_scalar(self.p_mat, ct.c_double(val)) - else: - raise ValueError("Assigned value must be of type" - "CUDAMatrix, int, or float.") - - if err_code: - raise generate_exception(err_code) - - return self - - def free_device_memory(self): - """ - Free memory used up by the matrix on the GPU. - """ - - err_code = _cudamat.free_device_memory(self.p_mat) - if err_code: - raise generate_exception(err_code) - - def set_trans(self, is_trans): - """ - Set the transposedness flag to is_trans. - """ - - _cudamat.set_transpose(self.p_mat, ct.c_int(1 * is_trans)) - - def slice(self, first_col, last_col, include_host=False): - """ - Creates a view into a consecutive range of columns of an existing - matrix on GPU. If include_host is set to True, also creates a view - into the CPU copy of the matrix (i.e., the numpy_array). - """ - mat = cudamat() - - if self.mat.size[0] == 1 or self.mat.size[1] == 1: - err_code = _cudamat.get_vector_slice(self.p_mat, - ct.pointer(mat), - ct.c_int(first_col), - ct.c_int(last_col)) - else: - err_code = _cudamat.get_slice(self.p_mat, - ct.pointer(mat), - ct.c_int(first_col), - ct.c_int(last_col)) - - if err_code: - raise generate_exception(err_code) - - new_mat = CUDAMatrix(mat) - - try: - new_mat.sliceof = self.sliceof - except: - new_mat.sliceof = self - - # reproduce the slice on the host as well (if requested) - if include_host and self.mat.on_host: - new_mat.numpy_array = self.numpy_array[:, first_col:last_col] - new_mat.mat.data_host = \ - new_mat.numpy_array.ctypes.data_as(ct.POINTER(ct.c_double)) - new_mat.mat.on_host = 1 - - return new_mat - - def get_col_slice(self, first_col, last_col, target=None): - """ - Get the columns with indices first_col through last_col. If a target - is provided, columns are copied into the target. Otherwise, returns a - view into the existing memory on GPU. - """ - col_slice = self.slice(first_col, last_col) - - if target: - target.assign(col_slice) - return target - else: - return col_slice - - def set_col_slice(self, first_col, last_col, mat): - """ - Assign the contents of mat to the columns with indices first_col - through last_col. - """ - self.slice(first_col, last_col).assign(mat) - - return self - - def get_row_slice(self, start, end, target=None): - """ - Get the rows with indices start through end. If target is not provided - memory for a new matrix will be allocated. - """ - - width = self.shape[1] - - if not target: - target = empty((end-start, width)) - - err_code = _cudamat.get_row_slice(self.p_mat, - target.p_mat, - ct.c_int(start), - ct.c_int(end)) - if err_code: - raise generate_exception(err_code) - - return target - - def set_row_slice(self, start, end, mat): - """ - Assign the contents of mat to the rows with indices start through end. - """ - - err_code = _cudamat.set_row_slice(mat.p_mat, self.p_mat, - ct.c_int(start), ct.c_int(end)) - if err_code: - raise generate_exception(err_code) - - return self - - def transpose(self, target=None): - """ - Return a transposed copy of the matrix. - """ - if not target: - target = empty((self.shape[1], self.shape[0])) - - err_code = _cudamat.copy_transpose(self.p_mat, target.p_mat) - if err_code: - raise generate_exception(err_code) - - return target - - def fill_with_rand(self): - """ - Fill matrix on the GPU with random numbers drawn from the uniform - distribution over the (0,1) interval. - """ - - err_code = _cudamat.fill_with_rand(CUDAMatrix.rnd_state_p, self.p_mat) - if err_code: - raise generate_exception(err_code) - - return self - - def fill_with_randn(self): - """ - Fill matrix on the GPU with random numbers drawn from - the standard normal distribution. - """ - - err_code = _cudamat.fill_with_randn(CUDAMatrix.rnd_state_p, self.p_mat) - if err_code: - raise generate_exception(err_code) - - return self - - def add_col_vec(self, vec, target=None): - """ - Add vector vec to every column of the matrix. If a target is provided, - it is used to store the result instead of self. - """ - - if not target: - target = self - - err_code = _cudamat.add_col_vec(self.p_mat, vec.p_mat, target.p_mat) - if err_code: - raise generate_exception(err_code) - - return target - - def add_col_mult(self, vec, mult, target=None): - """ - Add a multiple of vector vec to every column of the matrix. If a target - is provided, it is used to store the result instead of self. - """ - - if not target: - target = self - - err_code = _cudamat.add_col_mult(self.p_mat, vec.p_mat, - target.p_mat, ct.c_double(mult)) - if err_code: - raise generate_exception(err_code) - - return target - - def add_row_vec(self, vec, target=None): - """ - Add vector vec to every row of the matrix. If a target is provided, - it is used to store the result instead of self. - """ - - if not target: - target = self - - err_code = _cudamat.add_row_vec(self.p_mat, vec.p_mat, target.p_mat) - if err_code: - raise generate_exception(err_code) - - return target - - def mult_by_col(self, vec, target=None): - """ - Multiply vector vec into every column of the matrix. If a target is - provided, it is used to store the result instead of self. - """ - - if not target: - target = self - - err_code = _cudamat.mult_by_col_vec(self.p_mat, vec.p_mat, target.p_mat) - if err_code: - raise generate_exception(err_code) - - return target - - def mult_by_row(self, vec, target=None): - """ - Multiply vector vec into every row of the matrix. If a target is - provided, it is used to store the result instead of self. - """ - - if not target: - target = self - - err_code = _cudamat.mult_by_row_vec(self.p_mat, vec.p_mat, target.p_mat) - if err_code: - raise generate_exception(err_code) - - return target - - def div_by_col(self, vec, target=None): - """ - Divide every column of the matrix by vector vec. If a target is - provided, it is used to store the result instead of self. - """ - - if not target: - target = self - - err_code = _cudamat.divide_by_col_vec(self.p_mat, vec.p_mat, - target.p_mat) - if err_code: - raise generate_exception(err_code) - - return target - - def div_by_row(self, vec, target=None): - """ - Divide every row of the matrix by vector vec. If a target is - provided, it is used to store the result instead of self. - """ - - if not target: - target = self - - err_code = _cudamat.divide_by_row_vec(self.p_mat, vec.p_mat, - target.p_mat) - if err_code: - raise generate_exception(err_code) - - return target - - def sum(self, axis, target=None, mult=1.): - """ - Sum the matrix along the given dimension, where 0 represents the leading - dimension and 1 represents the non-leading dimension. If a target is - not provided, a new vector is created for storing the result. The result - is multiplied by the given factor mult (defaults to 1). - """ - - return sum(self, axis, target, mult) - - def mean(self, axis, target=None): - """ - Compute the mean of the matrix along the given dimension, where 0 - represents the leading dimension and 1 represents the non-leading - dimension. If a target is not provided, a new vector is created for - storing the result. - """ - - return mean(self, axis, target) - - def add_sums(self, mat, axis, mult=1., beta=1.): - """ - Add a multiple of the sums of the matrix mat along the given dimension - to self. Self is scaled by beta before adding anything. - """ - - m = _cudamat.get_leading_dimension(mat.p_mat) - n = _cudamat.get_nonleading_dimension(mat.p_mat) - - if axis == 0: - # sum along leading dimension - check_ones_matrix(m) - left = CUDAMatrix.ones.slice(0, m) - left.set_trans(True) - right = mat - - elif axis == 1: - # sum along non-leading dimension - left = mat - check_ones_matrix(n) - right = CUDAMatrix.ones.slice(0, n) - - err_code = _cudamat.dot(left.p_mat, right.p_mat, self.p_mat, - ct.c_double(beta), ct.c_double(mult)) - if err_code: - raise generate_exception(err_code) - - return self - - def less_than(self, val, target=None): - """ - Perform the operation target = 1. * (self < val), - where val can be a matrix or a scalar. - """ - - if not target: - target = self - - if isinstance(val, (int, float)): - err_code = _cudamat.less_than_scalar(self.p_mat, ct.c_double(val), - target.p_mat) - else: - err_code = _cudamat.less_than(self.p_mat, val.p_mat, target.p_mat) - - if err_code: - raise generate_exception(err_code) - - return target - - def greater_than(self, val, target=None): - """ - Perform the operation target = 1. * (self > val), - where val can be a matrix or a scalar. - """ - - if not target: - target = self - - if isinstance(val, (int, float)): - err_code = _cudamat.greater_than_scalar(self.p_mat, - ct.c_double(val), - target.p_mat) - else: - err_code = _cudamat.greater_than(self.p_mat, val.p_mat, - target.p_mat) - - if err_code: - raise generate_exception(err_code) - - return target - - def equals(self, val, target=None): - """ - Perform the operation target = 1. * (self == val), - where val can be a matrix or a scalar. - """ - - if not target: - target = self - - if isinstance(val, (int, float)): - err_code = _cudamat.equals_scalar(self.p_mat, ct.c_double(val), - target.p_mat) - else: - err_code = _cudamat.equals(self.p_mat, val.p_mat, target.p_mat) - - if err_code: - raise generate_exception(err_code) - - return target - - def minimum(self, val, target=None): - """ - Perform the element-wise operation target = min(self, val), where - val can be a matrix or a scalar. - """ - - if not target: - target = self - - if isinstance(val, (int, float)): - err_code = _cudamat.minimum_scalar(self.p_mat, ct.c_double(val), - target.p_mat) - else: - err_code = _cudamat.minimum(self.p_mat, val.p_mat, target.p_mat) - - if err_code: - raise generate_exception(err_code) - - return target - - def maximum(self, val, target=None): - """ - Perform the element-wise operation target = max(self, val), where - val can be a matrix or a scalar. - """ - - if not target: - target = self - - if isinstance(val, (int, float)): - err_code = _cudamat.maximum_scalar(self.p_mat, ct.c_double(val), - target.p_mat) - else: - err_code = _cudamat.maximum(self.p_mat, val.p_mat, target.p_mat) - - if err_code: - raise generate_exception(err_code) - - return target - - def min(self, axis, target=None): - """ - Find the minimum value along the given dimension, where 0 represents the - leading dimension and 1 represents the non-leading dimension. If a - target is not prvided, a new vector is created for storing the result. - """ - - m, n = self.shape - - if axis == 0: - if not target: - target = empty((1, n)) - - elif axis == 1: - if not target: - target = empty((m, 1)) - - err_code = _cudamat.min_by_axis(self.p_mat, target.p_mat, - ct.c_int(axis)) - if err_code: - raise generate_exception(err_code) - - return target - - def max(self, axis, target=None): - """ - Find the maximum value along the given dimension, where 0 represents the - leading dimension and 1 represents the non-leading dimension. If a - target is not prvided, a new vector is created for storing the result. - """ - - m, n = self.shape - - if axis == 0: - if not target: - target = empty((1, n)) - - elif axis == 1: - if not target: - target = empty((m, 1)) - - err_code = _cudamat.max_by_axis(self.p_mat, target.p_mat, - ct.c_int(axis)) - if err_code: - raise generate_exception(err_code) - - return target - - def argmin(self, axis, target=None): - """ - Find the index of the minimum value along the given dimension, where 0 - represents the leading dimension and 1 represents the non-leading - dimension. If a target is not provided, a new vector is created for - storing the result. - """ - - m, n = self.shape - - if axis == 0: - if not target: - target = empty((1, n)) - - elif axis == 1: - if not target: - target = empty((m, 1)) - - err_code = _cudamat.argmin_by_axis(self.p_mat, target.p_mat, - ct.c_int(axis)) - if err_code: - raise generate_exception(err_code) - - return target - - def argmax(self, axis, target=None): - """ - Find the index of the maximum value along the given dimension, where 0 - represents the leading dimension and 1 represents the non-leading - dimension. If a target is not provided, a new vector is created for - storing the result. - """ - - m, n = self.shape - - if axis == 0: - if not target: - target = empty((1, n)) - - elif axis == 1: - if not target: - target = empty((m, 1)) - - err_code = _cudamat.argmax_by_axis(self.p_mat, target.p_mat, - ct.c_int(axis)) - if err_code: - raise generate_exception(err_code) - - return target - - def sign(self, target=None): - """ - Find the sign of each element of the matrix. - """ - - if not target: - target = empty((self.mat.size[0], self.mat.size[1])) - - err_code = _cudamat.sign(self.p_mat, target.p_mat) - if err_code: - raise generate_exception(err_code) - - return target - - def apply_sigmoid(self, target=None): - """ - Apply the logistic sigmoid to each element of the matrix. - """ - - return sigmoid(self, target) - - def apply_tanh(self, target=None): - """ - Apply the tanh to each element of the matrix. - """ - - return tanh(self, target) - - def apply_soft_threshold(self, alpha, target=None): - """ - Apply the soft threshold function to each element of the matrix: - - x = sign(x) * max(0, abs(x) - alpha) - """ - - return soft_threshold(self, alpha, target) - - def reciprocal(self, target=None): - """ - Find the reciprocal of each element of the matrix. - """ - - if not target: - target = self - - err_code = _cudamat.reciprocal(self.p_mat, target.p_mat) - if err_code: - raise generate_exception(err_code) - - return target - - def dot(self, mat2, target=None): - """ - Multiply the matrix by mat2 from the right. - """ - - return dot(self, mat2, target) - - def add_dot(self, m1, m2, mult=1., beta=1.): - """ - Add the dot product of m1 and m2 to the matrix, scaled by mult. - Self is scaled by beta before adding anything. - """ - - err_code = _cudamat.dot(m1.p_mat, m2.p_mat, self.p_mat, - ct.c_double(beta), ct.c_double(mult)) - if err_code: - raise generate_exception(err_code) - - return self - - def subtract_dot(self, m1, m2, mult=1., beta=1.): - """ - Subtract the dot product of m1 and m2 from the matrix, scaled by mult. - Self is scaled by beta before subtracting anything. - """ - - return self.add_dot(m1, m2, mult=-1. * mult, beta=beta) - - def add_mult(self, mat2, alpha=1.): - """ - Add multiple of mat2 to the matrix. - """ - - err_code = _cudamat.add_mult(self.p_mat, mat2.p_mat, ct.c_double(alpha)) - if err_code: - raise generate_exception(err_code) - - return self - - def subtract_mult(self, mat2, alpha=1.): - """ - Subtract a multiple of mat2 from the matrix. - """ - - err_code = _cudamat.add_mult(self.p_mat, mat2.p_mat, - ct.c_double(-1. * alpha)) - if err_code: - raise generate_exception(err_code) - - return self - - def add(self, val, target=None): - """Add val to self, where val can be a scalar or a CUDAMatrix with the - same dimensions as self. """ - - if not target: - target = self - - if isinstance(val, CUDAMatrix): - err_code = _cudamat.add_elementwise(self.p_mat, val.p_mat, - target.p_mat) - elif isinstance(val, (int, float)): - err_code = _cudamat.add_scalar(self.p_mat, ct.c_double(val), - target.p_mat) - else: - raise ValueError("Value must be of type CUDAMatrix, int, or float.") - - if err_code: - raise generate_exception(err_code) - - return target - - def subtract(self, val, target=None): - """Subtract val from self, where val can be a scalar or a CUDAMatrix with - the same dimensions as self. """ - - if not target: - target = self - - if isinstance(val, CUDAMatrix): - err_code = _cudamat.subtract_elementwise(self.p_mat, val.p_mat, - target.p_mat) - elif isinstance(val, (int, float)): - err_code = _cudamat.add_scalar(self.p_mat, ct.c_double(-1*val), - target.p_mat) - else: - raise ValueError("Value must be of type CUDAMatrix, int, or float.") - - if err_code: - raise generate_exception(err_code) - - return target - - def divide(self, val, target=None): - """Divide self by val, where val can be a scalar or - a CUDAMatrix with the same dimensions as self. """ - - if not target: - target = self - - if isinstance(val, CUDAMatrix): - err_code = _cudamat.divide_elementwise(self.p_mat, val.p_mat, - target.p_mat) - elif isinstance(val, (int, float)): - err_code = _cudamat.divide_by_scalar(self.p_mat, ct.c_double(val), - target.p_mat) - else: - raise ValueError("Value must be of type CUDAMatrix, int, or float.") - - if err_code: - raise generate_exception(err_code) - - return target - - def mult(self, val, target=None): - """Multiply self by val, where val can be a scalar or a CUDAMatrix with - the same dimensions as self. """ - - if not target: - target = self - - if isinstance(val, CUDAMatrix): - err_code = _cudamat.mult_elementwise(self.p_mat, val.p_mat, - target.p_mat) - elif isinstance(val, (int, float)): - err_code = _cudamat.mult_by_scalar(self.p_mat, ct.c_double(val), - target.p_mat) - else: - raise ValueError("Value must be of type CUDAMatrix, int, or float.") - - if err_code: - raise generate_exception(err_code) - - return target - - @deprecated - def assign_scalar(self, alpha): - """ - Assign scalar alpha to every element of the matrix. - """ - - err_code = _cudamat.assign_scalar(self.p_mat, ct.c_double(alpha)) - if err_code: - raise generate_exception(err_code) - - return self - - @deprecated - def mult_by_scalar(self, alpha, target=None): - """ - Multiply the matrix by a scalar. - """ - - if not target: - target = self - - err_code = _cudamat.mult_by_scalar(self.p_mat, ct.c_double(alpha), - target.p_mat) - if err_code: - raise generate_exception(err_code) - - return target - - @deprecated - def div_by_scalar(self, alpha, target=None): - """ - Divide the matrix by a scalar. - """ - - if not target: - target = self - - err_code = _cudamat.divide_by_scalar(self.p_mat, ct.c_double(alpha), - target.p_mat) - if err_code: - raise generate_exception(err_code) - - return target - - @deprecated - def add_scalar(self, alpha, target=None): - """ - Increment the matrix by a scalar. - """ - - if not target: - target = self - - err_code = _cudamat.add_scalar(self.p_mat, ct.c_double(alpha), - target.p_mat) - if err_code: - raise generate_exception(err_code) - - return target - - def euclid_norm(self): - """ - Returns the L2 norm of the matrix flattened to a vector. - """ - err_code = ct.c_int(0) - res = _cudamat.euclid_norm(self.p_mat, ct.byref(err_code)) - - if err_code: - raise generate_exception(err_code.value) - - return res - - def manhattan_norm(self): - """ - Returns the L1 norm of the matrix flattened to a vector. - """ - err_code = ct.c_int(0) - res = _cudamat.manhattan_norm(self.p_mat, ct.byref(err_code)) - - if err_code: - raise generate_exception(err_code.value) - - return res - - def allfinite(self): - """ - Checks if all entries in this matrix are finite, i.e., there is no - NaN and no positive or negative infinity. - """ - # Caveat: For a very large matrix of very large finite numbers, the - # manhattan norm may overflow and allfinite() may return False. - return np.isfinite(self.manhattan_norm()) - - def select_columns(self, indices, target): - """ - Copies some columns of self into target. - must be a row vector. Its elements are float64's representing - integers, e.g. "34.0" means the integer "34". - After this call, for all r,c, target[r,c]=self[r,indices[c]]. - This returns target. - Negative indices are interpreted in the usual Python way: all - elements of had better be in the range - [-self.shape[1], self.shape[1]-1]. - This does bounds checking, but out of bounds indices do not raise an - exception (because the programmer was lazy). Instead, they result - in NaN values in . - """ - - err_code = _cudamat.selectRows(self.p_mat, target.p_mat, indices.p_mat) - - if err_code: - raise generate_exception(err_code) - - return target - - def set_selected_columns(self, indices, source): - """ - copies all columns of source into some columns of self. - must be a row vector. Its elements are float64's representing - integers, e.g. "34.0" means the integer "34". after this call, for all - r,c, self[r,indices[c]]=source[r,c]. This returns self. - Negative indices are interpreted in the usual Python way: all elements - of had better be in the range - [-self.shape[1], self.shape[1]-1]. - This does bounds checking, but out of bounds indices do not raise an - exception (because the programmer was lazy). Instead, they result in NaN - values in . - """ - - err_code = _cudamat.setSelectedRows(self.p_mat, source.p_mat, - indices.p_mat) - if err_code: - raise generate_exception(err_code) - - return self - - -def empty(shape): - """ - Creates and returns a new CUDAMatrix with the given shape. - """ - - mat = cudamat() - err_code = _cudamat.init_empty(ct.pointer(mat), ct.c_int(shape[0]), - ct.c_int(shape[1])) - - if err_code: - raise generate_exception(err_code) - - return CUDAMatrix(mat) - - -def check_ones_matrix(min_size): - if min_size > CUDAMatrix.ones.shape[0]: - raise CUDAMatException( - 'Not enough memory allocated for reduction. ' - '({} needed, {} actual), use cudamat.init() ' - 'to allocate more'.format(min_size, CUDAMatrix.ones.shape[0])) - - -def sum(mat, axis, target=None, mult=1.): - """ - Sum the matrix along the given dimension, where 0 represents the leading - dimension and 1 represents the non-leading dimension. If a target is - not provided, a new vector is created for storing the result. The result - is multiplied by the given factor mult (defaults to 1). - """ - - m = _cudamat.get_leading_dimension(mat.p_mat) - n = _cudamat.get_nonleading_dimension(mat.p_mat) - - if axis == 0: - # sum along leading dimension - check_ones_matrix(m) - left = CUDAMatrix.ones.slice(0, m) - left.set_trans(True) - right = mat - - if not target: - target = empty((1, n)) - - elif axis == 1: - # sum along non-leading dimension - left = mat - check_ones_matrix(n) - right = CUDAMatrix.ones.slice(0, n) - - if not target: - target = empty((m, 1)) - - err_code = _cudamat.dot(left.p_mat, right.p_mat, target.p_mat, - ct.c_double(0.), ct.c_double(mult)) - if err_code: - raise generate_exception(err_code) - - return target - - -def mean(mat, axis, target=None): - """ - Compute the mean of the matrix along the given dimension, where 0 represents - the leading dimension and 1 represents the non-leading dimension. If a - target is not provided, a new vector is created for storing the result. - """ - - return sum(mat, axis, target=target, mult=1. / mat.shape[axis]) - - -def dot(m1, m2, target=None, beta=0., alpha=1.): - """ - Find the dot product between m1 and m2 and store in target: - target = beta*target + alpha*(m1 m2) - If no target is given, it will be created automatically, but not - initialized -- so beta should be left at its default value zero. - """ - - if not target: - m = _cudamat.get_leading_dimension(m1.p_mat) - n = _cudamat.get_nonleading_dimension(m2.p_mat) - - target = empty((m, n)) - - err_code = _cudamat.dot(m1.p_mat, m2.p_mat, - target.p_mat, ct.c_double(beta), - ct.c_double(alpha)) - if err_code: - raise generate_exception(err_code) - - return target - - -def vdot(m1, m2): - """ - Compute the vector dot product of matrices m1 and m2. - """ - - err_code = ct.c_int(0) - res = _cudamat.vdot(m1.p_mat, m2.p_mat, ct.byref(err_code)) - - if err_code: - raise generate_exception(err_code.value) - - return res - - -def sigmoid(mat, target=None): - """ - Apply the logistic sigmoid to each element of the matrix mat. - """ - - if not target: - target = mat - - err_code = _cudamat.apply_sigmoid(mat.p_mat, target.p_mat) - if err_code: - raise generate_exception(err_code) - - return target - - -def tanh(mat, target=None): - """ - Apply the tanh to each element of the matrix mat. - """ - - if not target: - target = mat - - err_code = _cudamat.apply_tanh(mat.p_mat, target.p_mat) - if err_code: - raise generate_exception(err_code) - - return target - - -def soft_threshold(mat, alpha, target=None): - """ - Apply the soft threshold function to each element of the matrix: - - mat = sign(mat) * max(0, abs(mat) - alpha) - """ - - if not target: - target = mat - - err_code = _cudamat.apply_soft_threshold(mat.p_mat, ct.c_double(alpha), - target.p_mat) - if err_code: - raise generate_exception(err_code) - - return target - - -def abs(mat, target=None): - """ - Apply abs to each element of the matrix mat. - """ - - if not target: - target = mat - - err_code = _cudamat.apply_abs(mat.p_mat, target.p_mat) - if err_code: - raise generate_exception(err_code) - - return target - - -def log_1_plus_exp(mat, target=None): - """ - Apply log(1+exp(x)) to each element of the matrix mat. - """ - - if not target: - target = mat - - err_code = _cudamat.apply_log_1_plus_exp(mat.p_mat, target.p_mat) - if err_code: - raise generate_exception(err_code) - - return target - - -def log(mat, target=None): - """ - Find the natural logarithm of each element of the matrix mat. - """ - - if not target: - target = mat - - err_code = _cudamat.apply_log(mat.p_mat, target.p_mat) - if err_code: - raise generate_exception(err_code) - - return target - - -def exp(mat, target=None): - """ - Apply the exponential function to each element of the matrix mat. - """ - - if not target: - target = mat - - err_code = _cudamat.apply_exp(mat.p_mat, target.p_mat) - if err_code: - raise generate_exception(err_code) - - return target - - -def gamma(mat, target=None): - """ - Apply the gamma function to each element of the matrix mat. - """ - - if not target: - target = mat - - err_code = _cudamat.apply_gamma(mat.p_mat, target.p_mat) - if err_code: - raise generate_exception(err_code) - - return target - - -def lgamma(mat, target=None): - """ - Apply the log gamma function to each element of the matrix mat. - """ - - if not target: - target = mat - - err_code = _cudamat.apply_lgamma(mat.p_mat, target.p_mat) - if err_code: - raise generate_exception(err_code) - - return target - - -def sqrt(mat, target=None): - """ - Compute the square root of each element of the matrix mat. - """ - - if not target: - target = mat - - err_code = _cudamat.apply_sqrt(mat.p_mat, target.p_mat) - if err_code: - raise generate_exception(err_code) - - return target - - -def pow(mat, p, target=None): - """ - If p is a scalar, compute the 'p'th power of each element of the matrix mat, - otherwise raise each element of the matrix mat to the power given by the - corresponding element of the matrix p. - """ - - if not target: - target = mat - - if isinstance(p, CUDAMatrix): - err_code = _cudamat.apply_pow_matrix(mat.p_mat, p.p_mat, target.p_mat) - elif isinstance(p, (int, float)): - err_code = _cudamat.apply_pow(mat.p_mat, ct.c_double(p), target.p_mat) - else: - raise ValueError("Value must be of type CUDAMatrix, int, or float.") - - if err_code: - raise generate_exception(err_code) - - return target - - -def where(condition_mat, if_mat, else_mat, target=None): - """ - For each element i, j, store if_math[i, j] in target[i,j] if - condition_mat[i, j] is True, and else_mat[i, j] otherwise. - """ - if not target: - target = condition_mat - - err_code = _cudamat.where(condition_mat.p_mat, if_mat.p_mat, - else_mat.p_mat, target.p_mat) - if err_code: - raise generate_exception(err_code) - - return target - - -def correlate(mat, kernel, target=None): - """ - Cross-correlate a matrix with a kernel matrix. - The kernel matrix is centered over each element of the matrix mat. - Width and height of the kernel matrix must be an odd integer. - If a target is not provided, a new matrix is created for storing the result. - Note that this function cannot operate in-place. - """ - if not target: - m = _cudamat.get_leading_dimension(mat.p_mat) - n = _cudamat.get_nonleading_dimension(mat.p_mat) - - target = empty((m, n)) - - err_code = _cudamat.correlate(mat.p_mat, kernel.p_mat, target.p_mat) - if err_code: - raise generate_exception(err_code) - - return target - - -def cuda_sync_threads(): - _cudamat.cuda_sync_threads() - - -def reformat(array, copy=True): - """ - Returns array as a float64 array in FORTRAN order. - If copy is set to False, the array will only be copied if it is not already - in the correct format. - """ - return np.array(array, dtype=np.float64, order='F', copy=copy) - - -def cuda_set_device(dev_id): - """ - Selects the CUDA device with the given ID. - """ - - err_code = _cudamat.cuda_set_device(ct.c_int(dev_id)) - if err_code: - raise generate_exception(err_code) - - -def cublas_init(max_ones=(1024*256)): - """ - Initialize Cublas. - - 'max_ones' is an optional argument that determines the length of - the largest sum that can be computed using Cublas matrix multiply. - A larger value causes more memory to be allocated for this purpose. - """ - - err = _cudamat.cublas_init() - if err: - raise CUDAMatException('error initializing CUBLAS: (err=%u)' % err) - CUDAMatrix.ones = empty((max_ones, 1)).assign(1.0) - -init = cublas_init - - -def cublas_shutdown(): - """ - Shut down Cublas. - """ - - CUDAMatrix.ones = 0 - _cudamat.cublas_shutdown() - -shutdown = cublas_shutdown diff --git a/ot/gpu/cudamat/cudamat/cudamat_kernels.cu b/ot/gpu/cudamat/cudamat/cudamat_kernels.cu deleted file mode 100644 index 707b387..0000000 --- a/ot/gpu/cudamat/cudamat/cudamat_kernels.cu +++ /dev/null @@ -1,804 +0,0 @@ -#include "cudamat_kernels.cuh" -#include "float.h" - -/* ------------------------- Random number generation ------------------------- */ - -__global__ void kSeedRandom(unsigned int* rndMults, unsigned long long* rndWords, unsigned int seed) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - - // The initial x is the seed and the initial carry is 1 - unsigned long long rndWord = ((unsigned long long)seed << 32) + 1; - const unsigned int rndMult = rndMults[idx]; - /* - * Run the chain for a few steps so that all the streams have a chance - * to differentiate. They start out generating similar random numbers - * because all the multipliers are similar. - */ - for(unsigned int i = 0; i < NUM_RND_BURNIN; i++) { - rndWord = rndMult * LOW_BITS(rndWord) + HIGH_BITS(rndWord); - } - rndWords[idx] = rndWord; -} - -__global__ void kRandomUniform(unsigned int* rndMults, unsigned long long* rndWords, double* gData, unsigned int numElements) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - unsigned long long rndWord = rndWords[idx]; - const unsigned int rndMult = rndMults[idx]; - - for(unsigned int i = idx; i < numElements; i += NUM_RND_STREAMS) { - rndWord = rndMult * LOW_BITS(rndWord) + HIGH_BITS(rndWord); - gData[i] = (__uint2double_rn(LOW_BITS(rndWord)) + 1.0f) / 4294967296.0f; - } - rndWords[idx] = rndWord; -} - -__global__ void kRandomGaussian(unsigned int* rndMults, unsigned long long* rndWords, double* gData, unsigned int numElements) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - unsigned long long rndWord = rndWords[idx]; - const unsigned int rndMult = rndMults[idx]; - - double rnd1, rnd2, R, T; - for(unsigned int i = idx; i < numElements; i += 2*NUM_RND_STREAMS) { - rndWord = rndMult * LOW_BITS(rndWord) + HIGH_BITS(rndWord); - rnd1 = (__uint2double_rn(LOW_BITS(rndWord)) + 1.0f) / 4294967296.0f; - rndWord = rndMult * LOW_BITS(rndWord) + HIGH_BITS(rndWord); - rnd2 = (__uint2double_rn(LOW_BITS(rndWord)) + 1.0f) / 4294967296.0f; - T = 2 * PI * rnd2; - R = sqrtf(-2 * __logf(rnd1)); - gData[i] = R * __cosf(T); - if (i + NUM_RND_STREAMS < numElements) - gData[i + NUM_RND_STREAMS] = R * __sinf(T); - } - rndWords[idx] = rndWord; -} - -/* ------------------------- Data copying ------------------------- */ - -/* -Copy row slice from source to target. There is a block for every 32x32 chunk being copied. -*/ -__global__ void kGetRowSlice(double* source, double* target, int start, int end, int width, int height) { - const int row = start + blockIdx.x * 32 + threadIdx.x; - const int start_col = blockIdx.y * 32; - - const int end_col = (start_col + 32 < width) ? start_col + 32: width; - - const int target_height = end - start; - - if (row < end) { - for (int cur_col = start_col; cur_col < end_col; cur_col++) - target[cur_col * target_height + row - start] = source[cur_col * height + row]; - } -} - -__global__ void kSetRowSlice(double* source, double* target, int start, int end, int width, int height) { - const int row = start + blockIdx.x * 32 + threadIdx.x; - const int start_col = blockIdx.y * 32; - - const int end_col = (start_col + 32 < width) ? start_col + 32: width; - - const int source_height = end - start; - - if (row < end) { - for (int cur_col = start_col; cur_col < end_col; cur_col++) - target[cur_col * height + row] = source[cur_col * source_height + row - start]; - //source[cur_col * height + row - start] = target[cur_col * target_height + row]; - } -} - -__global__ void kTranspose(double *odata, double *idata, int width, int height) { - __shared__ double block[COPY_BLOCK_SIZE][COPY_BLOCK_SIZE+1]; - - // read the matrix tile into shared memory - unsigned int xIndex = blockIdx.x * COPY_BLOCK_SIZE + threadIdx.x; - unsigned int yIndex = blockIdx.y * COPY_BLOCK_SIZE + threadIdx.y; - - if((xIndex < width) && (yIndex < height)) { - unsigned int index_in = yIndex * width + xIndex; - - block[threadIdx.y][threadIdx.x] = idata[index_in]; - } - - __syncthreads(); - - // write the transposed matrix tile to global memory - xIndex = blockIdx.y * COPY_BLOCK_SIZE + threadIdx.x; - yIndex = blockIdx.x * COPY_BLOCK_SIZE + threadIdx.y; - - if((xIndex < height) && (yIndex < width)) { - unsigned int index_out = yIndex * height + xIndex; - - odata[index_out] = block[threadIdx.x][threadIdx.y]; - } -} - -/* ------------------------- Mathematical operations ------------------------- */ - -__global__ void kLessThan(double* mat1, double* mat2, double* target, unsigned int len) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < len; i += numThreads) { - target[i] = mat1[i] < mat2[i]; - } -} - -__global__ void kLessThanScalar(double* mat, double val, double* target, unsigned int len) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < len; i += numThreads) { - target[i] = mat[i] < val; - } -} - -__global__ void kGreaterThan(double* mat1, double* mat2, double* target, unsigned int len) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < len; i += numThreads) { - target[i] = mat1[i] > mat2[i]; - } -} - -__global__ void kGreaterThanScalar(double* mat, double val, double* target, unsigned int len) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < len; i += numThreads) { - target[i] = mat[i] > val; - } -} - -__global__ void kEquals(double* mat1, double* mat2, double* target, unsigned int len) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < len; i += numThreads) { - target[i] = mat1[i] == mat2[i]; - } -} - -__global__ void kEqualsScalar(double* mat, double val, double* target, unsigned int len) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < len; i += numThreads) { - target[i] = mat[i] == val; - } -} - -__global__ void kMinimum(double* mat1, double* mat2, double* target, unsigned int len) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < len; i += numThreads) { - target[i] = fminf(mat1[i], mat2[i]); - } -} - -__global__ void kMinimumScalar(double* mat, double val, double* target, unsigned int len) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < len; i += numThreads) { - target[i] = fminf(mat[i], val); - } -} - -__global__ void kMaximum(double* mat1, double* mat2, double* target, unsigned int len) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < len; i += numThreads) { - target[i] = fmaxf(mat1[i], mat2[i]); - } -} - -__global__ void kMaximumScalar(double* mat, double val, double* target, unsigned int len) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < len; i += numThreads) { - target[i] = fmaxf(mat[i], val); - } -} - -__global__ void kMinColumnwise(double* mat, double* target, unsigned int width, unsigned int height) { - __shared__ double min_vals[32]; - double cur_min = FLT_MAX; - double val = 0; - - for (unsigned int i = threadIdx.x; i < height; i += 32) { - val = mat[blockIdx.x * height + i]; - - if (val < cur_min) - cur_min = val; - } - - min_vals[threadIdx.x] = cur_min; - - __syncthreads(); - - if (threadIdx.x == 0) { - cur_min = FLT_MAX; - - for (unsigned int i = 0; i < 32; i++) - if (min_vals[i] < cur_min) - cur_min = min_vals[i]; - - target[blockIdx.x] = cur_min; - } -} - -__global__ void kMinRowwise(double* mat, double* target, unsigned int width, unsigned int height) { - __shared__ double min_vals[32]; - double cur_min = FLT_MAX; - double val = 0; - - for (unsigned int i = threadIdx.x; i < width; i += 32) { - val = mat[i * height + blockIdx.x]; - - if (val < cur_min) - cur_min = val; - } - - min_vals[threadIdx.x] = cur_min; - - __syncthreads(); - - if (threadIdx.x == 0) { - cur_min = FLT_MAX; - - for (unsigned int i = 0; i < 32; i++) - if (min_vals[i] < cur_min) - cur_min = min_vals[i]; - - target[blockIdx.x] = cur_min; - } -} - -__global__ void kMaxColumnwise(double* mat, double* target, unsigned int width, unsigned int height) { - __shared__ double max_vals[32]; - double cur_max = -FLT_MAX; - double val = 0; - - for (unsigned int i = threadIdx.x; i < height; i += 32) { - val = mat[blockIdx.x * height + i]; - - if (val > cur_max) - cur_max = val; - } - - max_vals[threadIdx.x] = cur_max; - - __syncthreads(); - - if (threadIdx.x == 0) { - cur_max = -FLT_MAX; - - for (unsigned int i = 0; i < 32; i++) - if (max_vals[i] > cur_max) - cur_max = max_vals[i]; - - target[blockIdx.x] = cur_max; - } -} - -__global__ void kMaxRowwise(double* mat, double* target, unsigned int width, unsigned int height) { - __shared__ double max_vals[32]; - double cur_max = -FLT_MAX; - double val = 0; - - for (unsigned int i = threadIdx.x; i < width; i += 32) { - val = mat[i * height + blockIdx.x]; - - if (val > cur_max) - cur_max = val; - } - - max_vals[threadIdx.x] = cur_max; - - __syncthreads(); - - if (threadIdx.x == 0) { - cur_max = -FLT_MAX; - - for (unsigned int i = 0; i < 32; i++) - if (max_vals[i] > cur_max) - cur_max = max_vals[i]; - - target[blockIdx.x] = cur_max; - } -} - -__global__ void kArgMinColumnwise(double* mat, double* target, unsigned int width, unsigned int height) { - __shared__ double min_vals[32]; - __shared__ unsigned int min_args[32]; - double cur_min = FLT_MAX; - unsigned int cur_arg = 0; - double val = 0; - - for (unsigned int i = threadIdx.x; i < height; i += 32) { - val = mat[blockIdx.x * height + i]; - - if (val < cur_min) { - cur_min = val; - cur_arg = i; - } - } - - min_vals[threadIdx.x] = cur_min; - min_args[threadIdx.x] = cur_arg; - - __syncthreads(); - - if (threadIdx.x == 0) { - cur_min = FLT_MAX; - cur_arg = 0; - - for (unsigned int i = 0; i < 32; i++) - if (min_vals[i] < cur_min) { - cur_min = min_vals[i]; - cur_arg = min_args[i]; - } - - target[blockIdx.x] = cur_arg; - } -} - -__global__ void kArgMinRowwise(double* mat, double* target, unsigned int width, unsigned int height) { - __shared__ double min_vals[32]; - __shared__ unsigned int min_args[32]; - double cur_min = FLT_MAX; - unsigned int cur_arg = 0; - double val = 0; - - for (unsigned int i = threadIdx.x; i < width; i += 32) { - val = mat[i * height + blockIdx.x]; - - if (val < cur_min) { - cur_min = val; - cur_arg = i; - } - } - - min_vals[threadIdx.x] = cur_min; - min_args[threadIdx.x] = cur_arg; - - __syncthreads(); - - if (threadIdx.x == 0) { - cur_min = FLT_MAX; - cur_arg = 0; - - for (unsigned int i = 0; i < 32; i++) - if (min_vals[i] < cur_min) { - cur_min = min_vals[i]; - cur_arg = min_args[i]; - } - - target[blockIdx.x] = cur_arg; - } -} - -__global__ void kArgMaxColumnwise(double* mat, double* target, unsigned int width, unsigned int height) { - __shared__ double max_vals[32]; - __shared__ unsigned int max_args[32]; - double cur_max = -FLT_MAX; - unsigned int cur_arg = 0; - double val = 0; - - for (unsigned int i = threadIdx.x; i < height; i += 32) { - val = mat[blockIdx.x * height + i]; - - if (val > cur_max) { - cur_max = val; - cur_arg = i; - } - } - - max_vals[threadIdx.x] = cur_max; - max_args[threadIdx.x] = cur_arg; - - __syncthreads(); - - if (threadIdx.x == 0) { - cur_max = -FLT_MAX; - cur_arg = 0; - - for (unsigned int i = 0; i < 32; i++) - if (max_vals[i] > cur_max) { - cur_max = max_vals[i]; - cur_arg = max_args[i]; - } - - target[blockIdx.x] = cur_arg; - } -} - -__global__ void kArgMaxRowwise(double* mat, double* target, unsigned int width, unsigned int height) { - __shared__ double max_vals[32]; - __shared__ unsigned int max_args[32]; - double cur_max = -FLT_MAX; - unsigned int cur_arg = 0; - double val = 0; - - for (unsigned int i = threadIdx.x; i < width; i += 32) { - val = mat[i * height + blockIdx.x]; - - if (val > cur_max) { - cur_max = val; - cur_arg = i; - } - } - - max_vals[threadIdx.x] = cur_max; - max_args[threadIdx.x] = cur_arg; - - __syncthreads(); - - if (threadIdx.x == 0) { - cur_max = -FLT_MAX; - cur_arg = 0; - - for (unsigned int i = 0; i < 32; i++) - if (max_vals[i] > cur_max) { - cur_max = max_vals[i]; - cur_arg = max_args[i]; - } - - target[blockIdx.x] = cur_arg; - } -} - -__global__ void kSign(double* mat, double* target, unsigned int len) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < len; i += numThreads) { - target[i] = mat[i] ? copysignf(1., mat[i]) : 0.; - } -} - -__global__ void kApplySigmoid(double* mat, double* target, unsigned int len) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < len; i += numThreads) { - target[i] = 1 / (1 + __expf(-mat[i])); - } -} - - -__global__ void kApplyTanh(double* mat, double* target, unsigned int len) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - double mat_i, exp2x; - - for (unsigned int i = idx; i < len; i += numThreads) { - mat_i = mat[i]; - exp2x = __expf(2 * mat_i); - target[i] = 1 - 2 / (exp2x + 1); - } -} - -__global__ void kApplySoftThreshold(double* mat, double alpha, double* target, unsigned int len) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < len; i += numThreads) { - double f = mat[i]; - target[i] = f > 0 ? max(0., f - alpha) : min(0., f + alpha); - } -} - -__global__ void kApplyAbs(double* mat, double* target, unsigned int len) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < len; i += numThreads) { - target[i] = mat[i] * ((mat[i] > 0) - (mat[i] < 0)); - } -} - -__global__ void kApplyLog1PlusExp(double* mat, double* target, unsigned int len) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - double mat_i; - - for (unsigned int i = idx; i < len; i += numThreads) { - mat_i = mat[i]; - if (mat_i > 0) - target[i] = (__logf(1 + __expf(-mat_i)) + mat_i); - else - target[i] = __logf(1 + __expf(mat_i)); - } -} - -__global__ void kLog(double* mat, double* target, unsigned int len) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < len; i += numThreads) { - target[i] = __logf(mat[i]); - } -} - -__global__ void kExp(double* mat, double* target, unsigned int len) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < len; i += numThreads) { - target[i] = __expf(mat[i]); - } -} - -__global__ void kGamma(double* mat, double* target, unsigned int len) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < len; i += numThreads) { - target[i] = tgammaf(mat[i]); - } -} - -__global__ void kLogGamma(double* mat, double* target, unsigned int len) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < len; i += numThreads) { - target[i] = lgammaf(mat[i]); - } -} - -__global__ void kSqrt(double* mat, double* target, unsigned int len) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < len; i += numThreads) { - target[i] = sqrt(mat[i]); - } -} - -__global__ void kPow(double* mat, double pow, double* target, unsigned int len) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < len; i += numThreads) { - target[i] = powf(mat[i], pow); - } -} - -__global__ void kPowMatrix(double* mat, double* pow, double* target, unsigned int len) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < len; i += numThreads) { - target[i] = powf(mat[i], pow[i]); - } -} - -__global__ void kReciprocal(double* mat, double* target, unsigned int len) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < len; i += numThreads) - target[i] = 1.f / mat[i]; -} - -__global__ void kAddColVector(double* mat, double* vec, double* tgtMat, unsigned int width, - unsigned int height) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < width * height; i += numThreads) { - tgtMat[i] = mat[i] + vec[i % height]; - } -} - -__global__ void kAddRowVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < width * height; i += numThreads) { - tgtMat[i] = mat[i] + vec[i / height]; - } -} - -__global__ void kAddColMult(double* mat, double* vec, double* tgtMat, double mult, - unsigned int width, unsigned int height) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < width * height; i += numThreads) { - tgtMat[i] = mat[i] + mult * vec[i % height]; - } -} - -__global__ void kMultByColVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < width * height; i += numThreads) { - tgtMat[i] = mat[i] * vec[i % height]; - } -} - -__global__ void kMultByRowVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < width * height; i += numThreads) { - tgtMat[i] = mat[i] * vec[i / height]; - } -} - -__global__ void kDivByColVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < width * height; i += numThreads) { - tgtMat[i] = mat[i] / vec[i % height]; - } -} - -__global__ void kDivByRowVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < width * height; i += numThreads) { - tgtMat[i] = mat[i] / vec[i / height]; - } -} - -__global__ void kAdd(double* a, double* b, double* dest, unsigned int numEls) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < numEls; i += numThreads) { - dest[i] = a[i] + b[i]; - } -} - -__global__ void kSubtract(double* a, double* b, double* dest, unsigned int numEls) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < numEls; i += numThreads) { - dest[i] = a[i] - b[i]; - } -} - -__global__ void kDivide(double* a, double* b, double* dest, unsigned int numEls) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < numEls; i += numThreads) { - dest[i] = a[i] / b[i]; - } -} - -__global__ void kMult(double* a, double* b, double* dest, unsigned int numEls) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < numEls; i += numThreads) { - dest[i] = a[i] * b[i]; - } -} - -__global__ void kMultScalar(double* mat, double alpha, double* dest, unsigned int len) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < len; i += numThreads) { - dest[i] = alpha * mat[i]; - } -} - -__global__ void kAssignScalar(double* dest, double alpha, unsigned int len) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < len; i += numThreads) { - dest[i] = alpha; - } -} - -__global__ void kDivideScalar(double* mat, double alpha, double* dest, unsigned int len) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < len; i += numThreads) { - dest[i] = mat[i] / alpha; - } -} - -__global__ void kAddScalar(double* a, double alpha, double* dest, unsigned int numEls) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for (unsigned int i = idx; i < numEls; i += numThreads) { - dest[i] = a[i] + alpha; - } -} - -__global__ void kSelectRows(double* source, double* target, double* indices, int nRowIs, int nCols, int nSourceRows){ - __shared__ int sourceRowIndices[32]; - const int startTargetRowI = blockIdx.x * 32; - const int tid = threadIdx.x; - const int localNRowIs = min(32, nRowIs-startTargetRowI); - - // cooperatively load 32 row indices - if (tid < localNRowIs){ - sourceRowIndices[tid] = int(indices[startTargetRowI + tid]); - if (sourceRowIndices[tid]<0) - sourceRowIndices[tid] += nSourceRows; - if (sourceRowIndices[tid]<0 || sourceRowIndices[tid]>=nSourceRows) - sourceRowIndices[tid] = -1; - } - __syncthreads(); - - // copy 32 rows - for (int i=0; i=nTargetRows) - targetRowIndices[tid] = -1; - } - __syncthreads(); - - // copy 32 rows - for (int i=0; i= 0 && x < width && y >= 0 && y < height) - sum += source[j] * kernel[(kwidth * kheight / 2) + w * kheight + h]; - } - } - dest[i] = sum; - } -} diff --git a/ot/gpu/cudamat/cudamat/cudamat_kernels.cuh b/ot/gpu/cudamat/cudamat/cudamat_kernels.cuh deleted file mode 100644 index 25e2858..0000000 --- a/ot/gpu/cudamat/cudamat/cudamat_kernels.cuh +++ /dev/null @@ -1,92 +0,0 @@ -#ifndef NVMATRIX_KERNEL_H_ -#define NVMATRIX_KERNEL_H_ - -#define NUM_RND_BLOCKS 96 -#define NUM_RND_THREADS_PER_BLOCK 128 -#define NUM_RND_STREAMS (NUM_RND_BLOCKS * NUM_RND_THREADS_PER_BLOCK) - -/* - * Defines for getting the values at the lower and upper 32 bits - * of a 64-bit number. - */ -#define LOW_BITS(x) ((x) & 0xffffffff) -#define HIGH_BITS(x) ((x) >> 32) - -/* - * Number of iterations to run random number generator upon initialization. - */ -#define NUM_RND_BURNIN 100 - -/* - * CUDA grid dimensions for different types of kernels - */ -#define COPY_BLOCK_SIZE 16 -# -// element-wise kernels use min(ceil(N / 512), 4096) blocks of 512 threads -#define MAX_VECTOR_OP_BLOCKS 4096 -#define MAX_VECTOR_OP_THREADS_PER_BLOCK 512 -#define NUM_VECTOR_OP_BLOCKS(N) (min(((N) + MAX_VECTOR_OP_THREADS_PER_BLOCK - 1)/MAX_VECTOR_OP_THREADS_PER_BLOCK, MAX_VECTOR_OP_BLOCKS)) -#define NUM_VECTOR_OP_THREADS_PER_BLOCK(N) (min((N), MAX_VECTOR_OP_THREADS_PER_BLOCK)) - -#define PI 3.1415926535897932f - -__global__ void kSeedRandom(unsigned int* randMults, unsigned long long* randWords, unsigned int seed); -__global__ void kRandomUniform(unsigned int* randMults, unsigned long long* randWords, double* gData, unsigned int numElements); -__global__ void kRandomGaussian(unsigned int* rndMults, unsigned long long* rndWords, double* gData, unsigned int numElements); - -__global__ void kGetRowSlice(double* source, double* target, int start, int end, int width, int height); -__global__ void kTranspose(double *odata, double *idata, int width, int height); -__global__ void kSetRowSlice(double* source, double* target, int start, int end, int width, int height); - -__global__ void kLessThan(double* mat1, double* mat2, double* target, unsigned int len); -__global__ void kLessThanScalar(double* mat, double val, double* target, unsigned int len); -__global__ void kGreaterThan(double* mat1, double* mat2, double* target, unsigned int len); -__global__ void kGreaterThanScalar(double* mat, double val, double* target, unsigned int len); -__global__ void kEquals(double* mat1, double* mat2, double* target, unsigned int len); -__global__ void kEqualsScalar(double* mat, double val, double* target, unsigned int len); -__global__ void kMinimum(double* mat1, double* mat2, double* target, unsigned int len); -__global__ void kMinimumScalar(double* mat, double val, double* target, unsigned int len); -__global__ void kMaximum(double* mat1, double* mat2, double* target, unsigned int len); -__global__ void kMaximumScalar(double* mat, double val, double* target, unsigned int len); -__global__ void kMinColumnwise(double* mat, double* target, unsigned int width, unsigned int height); -__global__ void kMinRowwise(double* mat, double* target, unsigned int width, unsigned int height); -__global__ void kMaxColumnwise(double* mat, double* target, unsigned int width, unsigned int height); -__global__ void kMaxRowwise(double* mat, double* target, unsigned int width, unsigned int height); -__global__ void kArgMinColumnwise(double* mat, double* target, unsigned int width, unsigned int height); -__global__ void kArgMinRowwise(double* mat, double* target, unsigned int width, unsigned int height); -__global__ void kArgMaxColumnwise(double* mat, double* target, unsigned int width, unsigned int height); -__global__ void kArgMaxRowwise(double* mat, double* target, unsigned int width, unsigned int height); -__global__ void kSign(double* mat, double* target, unsigned int len); -__global__ void kApplySigmoid(double* mat, double* target, unsigned int len); -__global__ void kApplyTanh(double* mat, double* target, unsigned int len); -__global__ void kApplySoftThreshold(double* mat, double alpha, double* target, unsigned int len); -__global__ void kApplyAbs(double* mat, double* target, unsigned int len); -__global__ void kApplyLog1PlusExp(double* mat, double* target, unsigned int len); -__global__ void kLog(double* mat, double* target, unsigned int len); -__global__ void kExp(double* mat, double* target, unsigned int len); -__global__ void kGamma(double* mat, double* target, unsigned int len); -__global__ void kLogGamma(double* mat, double* target, unsigned int len); -__global__ void kSqrt(double* mat, double* target, unsigned int len); -__global__ void kPow(double* mat, double pow, double* target, unsigned int len); -__global__ void kPowMatrix(double* mat, double* pow, double* target, unsigned int len); -__global__ void kReciprocal(double* mat, double* target, unsigned int len); -__global__ void kAddColVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height); -__global__ void kAddRowVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height); -__global__ void kAddColMult(double* mat, double* vec, double* tgtMat, double mult, unsigned int width, unsigned int height); -__global__ void kMultByColVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height); -__global__ void kMultByRowVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height); -__global__ void kDivByColVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height); -__global__ void kDivByRowVector(double* mat, double* vec, double* tgtMat, unsigned int width, unsigned int height); -__global__ void kAdd(double* a, double* b, double* dest, unsigned int numEls); -__global__ void kSubtract(double* a, double* b, double* dest, unsigned int numEls); -__global__ void kMult(double* a, double* b, double* dest, unsigned int numEls); -__global__ void kDivide(double* a, double* b, double* dest, unsigned int numEls); -__global__ void kMultScalar(double* mat, double alpha, double* dest, unsigned int len); -__global__ void kAssignScalar(double* dest, double alpha, unsigned int len); -__global__ void kDivideScalar(double* mat, double alpha, double* dest, unsigned int len); -__global__ void kAddScalar(double* a, double alpha, double* dest, unsigned int numEls); -__global__ void kSelectRows(double* source, double* target, double* indices, int nRowIs, int nCols, int nSourceRows); -__global__ void kSetSelectedRows(double* target, double* source, double* indices, int nRowIs, int nCols, int nTargetRows); -__global__ void kWhere(double* condition_mat, double* if_mat, double* else_mat, double* target, unsigned int len); -__global__ void kCorrelate(double* source, double* kernel, double* dest, int width, int height, int fwidth, int fheight); -#endif diff --git a/ot/gpu/cudamat/cudamat/learn.cu b/ot/gpu/cudamat/cudamat/learn.cu deleted file mode 100644 index 3d9260c..0000000 --- a/ot/gpu/cudamat/cudamat/learn.cu +++ /dev/null @@ -1,34 +0,0 @@ -#include -#include -#include -#include "learn_kernels.cuh" -#include "cudamat.cuh" - -extern "C" { - -inline bool checkCUDAError() { - cudaError_t err = cudaGetLastError(); - - if (cudaSuccess != err) - printf("%s\n", cudaGetErrorString( err)); - return cudaSuccess != err; -} - -EXPORT int mult_by_sigmoid_deriv(cudamat* target, cudamat* acts) { - int len = acts->size[0]*acts->size[1]; - - if (acts->is_trans != target->is_trans) - return ERROR_TRANSPOSED; - - if (acts->size[0] != target->size[0] || acts->size[1] != target->size[1]) - return ERROR_INCOMPATIBLE_DIMENSIONS; - - kMultiplyBySigmoidGrad<<>>(acts->data_device, target->data_device, len); - - if (checkCUDAError()) - return CUDA_ERROR; - - return 0; -} - -} diff --git a/ot/gpu/cudamat/cudamat/learn.py b/ot/gpu/cudamat/cudamat/learn.py deleted file mode 100644 index 741ca13..0000000 --- a/ot/gpu/cudamat/cudamat/learn.py +++ /dev/null @@ -1,21 +0,0 @@ -import os - -import ctypes as ct -import numpy as np - -from cudamat import load_library, generate_exception - -_cudalearn = load_library('libcudalearn') - -_cudalearn.mult_by_sigmoid_deriv.restype = ct.c_int - -def mult_by_sigmoid_deriv(target, acts): - """ - target = target * acts * (1 - acts) - - Useful for doing backprop in neural networks with logistic units. - """ - - err_code = _cudalearn.mult_by_sigmoid_deriv(target.p_mat, acts.p_mat) - if err_code: - raise generate_exception(err_code) diff --git a/ot/gpu/cudamat/cudamat/learn_kernels.cu b/ot/gpu/cudamat/cudamat/learn_kernels.cu deleted file mode 100644 index 8e897ba..0000000 --- a/ot/gpu/cudamat/cudamat/learn_kernels.cu +++ /dev/null @@ -1,10 +0,0 @@ -#include "learn_kernels.cuh" - -__global__ void kMultiplyBySigmoidGrad(double* act, double* target, const unsigned int len) { - const unsigned int idx = blockIdx.x * blockDim.x + threadIdx.x; - const unsigned int numThreads = blockDim.x * gridDim.x; - - for(unsigned int i = idx; i < len; i+= numThreads) { - target[i] = target[i] * act[i] * (1.0f - act[i]); - } -} diff --git a/ot/gpu/cudamat/cudamat/learn_kernels.cuh b/ot/gpu/cudamat/cudamat/learn_kernels.cuh deleted file mode 100644 index 26c8abd..0000000 --- a/ot/gpu/cudamat/cudamat/learn_kernels.cuh +++ /dev/null @@ -1,12 +0,0 @@ -#ifndef EBM_KERNELS_H_ -#define EBM_KERNELS_H_ - -#define NUM_VECTOR_OP_BLOCKS 4096 -#define NUM_VECTOR_OP_THREADS_PER_BLOCK 512 - -#define NUM_SPARSE_GRAD_BLOCKS 4096 -#define NUM_SPARSE_GRAD_THREADS_PER_BLOCK 512 - -__global__ void kMultiplyBySigmoidGrad(double* act, double* target, const unsigned int len); - -#endif diff --git a/ot/gpu/cudamat/cudamat/rnd_multipliers_32bit.txt b/ot/gpu/cudamat/cudamat/rnd_multipliers_32bit.txt deleted file mode 100644 index 098d31d..0000000 --- a/ot/gpu/cudamat/cudamat/rnd_multipliers_32bit.txt +++ /dev/null @@ -1,16028 +0,0 @@ -4294967118 -4294966893 -4294966830 -4294966284 -4294966164 -4294965708 -4294965675 -4294964880 -4294964568 -4294963860 -4294963023 -4294962654 -4294962540 -4294962330 -4294962063 -4294959894 -4294959600 -4294959030 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mode 100644 index b3a5c19..0000000 --- a/ot/gpu/cudamat/examples/bench_cudamat.py +++ /dev/null @@ -1,97 +0,0 @@ -from __future__ import print_function, division -import sys -import numpy as np -import cudamat as cmt -import time -import timeit -from inspect import getmodule, getmembers, isfunction -try: from itertools import ifilter as filter -except: pass - -# heat-up time in seconds before starting the benchmark -HEATUP = 2 - -# shapes used for the small and large test matrix -XS_SHAPE = (400, 256) -XL_SHAPE = (4096, 4096) - -# timeit number and repeat parameter -NUM_ITER = 100 -NUM_REPEATS = 5 - -def setup(shape): - """Creates two matrices and corresponding row/column vectors""" - mat = cmt.empty(shape).fill_with_randn() - mat2 = cmt.empty(shape).fill_with_randn() - col = cmt.empty((shape[0], 1)).assign(0) - row = cmt.empty((1, shape[1])).assign(0) - return mat, mat2, col, row - -def bench_dot(X, Y, col, row): - cmt.dot(X.T, Y) - -def bench_add(X, Y, col, row): - X.add(Y) -bench_add.repeats = 5 # 5 times more repetitions than usual - -def bench_mult(X, Y, col, row): - X.mult(Y) - -def bench_sigm(X, Y, col, row): - X.apply_sigmoid() - -def bench_colsum(X, Y, col, row): - X.sum(axis=0, target=row) - -def bench_rowsum(X, Y, col, row): - X.sum(axis=1, target=col) - -def bench_addcolsum(X, Y, col, row): - row.add_sums(X, axis=0, mult=3.2, beta=0.2) - -def bench_addrowsum(X, Y, col, row): - col.add_sums(X, axis=1, mult=3.2, beta=0.2) - -def bench_colmax(X, Y, col, row): - X.max(axis=0, target=row) - -def bench_rowmax(X, Y, col, row): - X.max(axis=1, target=col) - -def bench_addcolmult(X, Y, col, row): - X.add_col_mult(col, mult=3.2) - -def heatup(duration): - """Heat-up the GPU for a while so it enters full-performance mode""" - t1 = time.time() - while time.time() - t1 < duration: - cmt.dot(cmt.empty((200, 200)), cmt.empty((200, 200))) - -def main(): - cmt.init() - cmt.CUDAMatrix.init_random() - if HEATUP: - print("heating up for %g seconds..." % HEATUP, end=' ') - sys.stdout.flush() - heatup(HEATUP) - print("done.") - print("small matrix shape:", XS_SHAPE) - print("large matrix shape:", XL_SHAPE) - for funcname, func in filter(lambda f: f[0].startswith('bench_'), - getmembers(getmodule(main), isfunction)): - print("%-15s" % funcname[len('bench_'):], end=' ') - sys.stdout.flush() - for size, shape, factor in ('small', XS_SHAPE, 10), ('large', XL_SHAPE, 1): - repeat = NUM_REPEATS * getattr(func, 'repeats', 1) - time = min(timeit.repeat(\ - setup="from __main__ import setup, %s\nmats = setup(%s)" % (funcname, shape), - stmt="%s(*mats)" % funcname, repeat=repeat, - number=NUM_ITER * factor)) / (NUM_ITER * factor) - print("%.3es (%s) " % (time, size), end=' ') - sys.stdout.flush() - print() - cmt.shutdown() - -if __name__=="__main__": - main() - diff --git a/ot/gpu/cudamat/examples/nn_cudamat.py b/ot/gpu/cudamat/examples/nn_cudamat.py deleted file mode 100644 index 7c56c7d..0000000 --- a/ot/gpu/cudamat/examples/nn_cudamat.py +++ /dev/null @@ -1,133 +0,0 @@ -# This file shows how to implement a single hidden layer neural network for -# performing binary classification on the GPU using cudamat. - -from __future__ import division -import pdb -import time -import numpy as np -import cudamat as cm -from cudamat import learn as cl -import util - -# initialize CUDA -cm.cublas_init() - -# load data -util.load('mnist49.dat', globals()) - -# Put training data onto the GPU. -dat_train = dat_train/255. -dat_train = dat_train - (np.mean(dat_train, 1)+10**-8)[:, np.newaxis] -dev_train = cm.CUDAMatrix(dat_train) -dev_lbl = cm.CUDAMatrix(lbl_train) - -# training parameters -epsilon = 0.01 -momentum = 0.9 - -num_epochs = 30 -batch_size = 128 -num_batches = dat_train.shape[1]//batch_size - -# model parameters -dim_in = dat_train.shape[0] -dim_out = 1 -num_hid = 1024 - -# initialize weights -w_w1 = cm.CUDAMatrix(dim_in ** -0.5 * np.random.randn(dim_in, num_hid)) -w_b1 = cm.CUDAMatrix(np.zeros((num_hid, 1))) -w_w2 = cm.CUDAMatrix(num_hid ** -0.5 * np.random.randn(num_hid, dim_out)) -w_b2 = cm.CUDAMatrix(np.zeros((dim_out, 1))) - -# initialize weight update matrices -wu_w1 = cm.empty(w_w1.shape).assign(0) -wu_b1 = cm.empty(w_b1.shape).assign(0) -wu_w2 = cm.empty(w_w2.shape).assign(0) -wu_b2 = cm.empty(w_b2.shape).assign(0) - -# initialize temporary storage -h = cm.empty((num_hid, batch_size)) -out = cm.empty((dim_out, batch_size)) -delta = cm.empty((num_hid, batch_size)) - -# Train neural network. -start_time = time.time() -for epoch in range(num_epochs): - print("Epoch %i" % (epoch + 1)) - err = [] - - for batch in range(num_batches): - # get current minibatch - inp = dev_train.slice(batch*batch_size,(batch + 1)*batch_size) - target = dev_lbl.slice(batch*batch_size,(batch + 1)*batch_size) - - # forward pass - cm.dot(w_w1.T, inp, target = h) - - h.add_col_vec(w_b1) - h.apply_sigmoid() - - cm.dot(w_w2.T, h, target = out) - - out.add_col_vec(w_b2) - out.apply_sigmoid() - - # back prop errors - out.subtract(target) # compute error - - # gradients for w_w2 and w_b2 - wu_w2.add_dot(h, out.T, beta = momentum) - wu_b2.add_sums(out, axis = 1, beta = momentum) - - # compute delta - cm.dot(w_w2, out, target = delta) - - # delta = delta * h * (1 - h) - cl.mult_by_sigmoid_deriv(delta, h) - - # gradients for w_w1 and w_b1 - wu_w1.add_dot(inp, delta.T, beta = momentum) - wu_b1.add_sums(delta, axis = 1, beta = momentum) - - # update weights - w_w1.subtract_mult(wu_w1, epsilon/batch_size) - w_b1.subtract_mult(wu_b1, epsilon/batch_size) - w_w2.subtract_mult(wu_w2, epsilon/batch_size) - w_b2.subtract_mult(wu_b2, epsilon/batch_size) - - # calculate error on current minibatch - err.append(np.abs(out.asarray())>0.5) - - print("Training misclassification rate: %f" % np.mean(err)) - print("Time: %f" % (time.time() - start_time)) - -# Evaluate neural network on test data. - -# Load test data onto the GPU. -dat_test = dat_test/255. -dat_test = dat_test - np.mean(dat_test, 1)[:, np.newaxis] -dev_test = cm.CUDAMatrix(dat_test) -dev_lbl = cm.CUDAMatrix(lbl_test) - -# Initalize temporary storage. -h = cm.empty((num_hid, dat_test.shape[1])) -out = cm.empty((dim_out, dat_test.shape[1])) - -# forward pass -cm.dot(w_w1.T, dev_test, target = h) - -h.add_col_vec(w_b1) -h.apply_sigmoid() - -cm.dot(w_w2.T, h, target = out) - -out.add_col_vec(w_b2) -out.apply_sigmoid() - -# compute error -out.subtract(dev_lbl) - -print("Testing misclassification rate: %f" % np.mean(np.abs(out.asarray())>0.5)) - -cm.cublas_shutdown() diff --git a/ot/gpu/cudamat/examples/rbm_cudamat.py b/ot/gpu/cudamat/examples/rbm_cudamat.py deleted file mode 100644 index 3f6a900..0000000 --- a/ot/gpu/cudamat/examples/rbm_cudamat.py +++ /dev/null @@ -1,98 +0,0 @@ -from __future__ import division -import time -import numpy as np -import cudamat as cm -import util - -# initialize CUDA -cm.cublas_init() -cm.CUDAMatrix.init_random(1) - -# load data -util.load('mnist.dat', globals()) -dev_dat = cm.CUDAMatrix(cm.reformat(dat/255.)) - -# training parameters -epsilon = 0.1 -momentum = 0.9 - -num_epochs = 30 -batch_size = 128 -num_batches = dat.shape[1]//batch_size - -# model parameters -num_vis = dat.shape[0] -num_hid = 4096 - -# initialize weights -w_vh = cm.CUDAMatrix(0.1 * np.random.randn(num_vis, num_hid)) -w_v = cm.CUDAMatrix(np.zeros((num_vis, 1))) -w_h = cm.CUDAMatrix(-4.*np.ones((num_hid, 1))) - -# initialize weight updates -wu_vh = cm.CUDAMatrix(np.zeros((num_vis, num_hid))) -wu_v = cm.CUDAMatrix(np.zeros((num_vis, 1))) -wu_h = cm.CUDAMatrix(np.zeros((num_hid, 1))) - -# initialize temporary storage -v = cm.empty((num_vis, batch_size)) -h = cm.empty((num_hid, batch_size)) -r = cm.empty((num_hid, batch_size)) - -start_time = time.time() -for epoch in range(num_epochs): - print("Epoch %i" % (epoch + 1)) - err = [] - - for batch in range(num_batches): - # get current minibatch - v_true = dev_dat.slice(batch*batch_size,(batch + 1)*batch_size) - v.assign(v_true) - - # apply momentum - wu_vh.mult(momentum) - wu_v.mult(momentum) - wu_h.mult(momentum) - - # positive phase - cm.dot(w_vh.T, v, target = h) - h.add_col_vec(w_h) - h.apply_sigmoid() - - wu_vh.add_dot(v, h.T) - wu_v.add_sums(v, axis = 1) - wu_h.add_sums(h, axis = 1) - - # sample hiddens - r.fill_with_rand() - r.less_than(h, target = h) - - # negative phase - cm.dot(w_vh, h, target = v) - v.add_col_vec(w_v) - v.apply_sigmoid() - - cm.dot(w_vh.T, v, target = h) - h.add_col_vec(w_h) - h.apply_sigmoid() - - wu_vh.subtract_dot(v, h.T) - wu_v.add_sums(v, axis = 1, mult = -1.) - wu_h.add_sums(h, axis = 1, mult = -1.) - - # update weights - w_vh.add_mult(wu_vh, epsilon/batch_size) - w_v.add_mult(wu_v, epsilon/batch_size) - w_h.add_mult(wu_h, epsilon/batch_size) - - # calculate reconstruction error - v.subtract(v_true) - err.append(v.euclid_norm()**2/(num_vis*batch_size)) - - print("Mean squared error: %f" % np.mean(err)) - print("Time: %f" % (time.time() - start_time)) - -w_vh.copy_to_host() -util.save('weights.dat', 'w_vh', {'w_vh': w_vh.numpy_array}) - -cm.cublas_shutdown() diff --git a/ot/gpu/cudamat/examples/rbm_numpy.py b/ot/gpu/cudamat/examples/rbm_numpy.py deleted file mode 100644 index 1331566..0000000 --- a/ot/gpu/cudamat/examples/rbm_numpy.py +++ /dev/null @@ -1,72 +0,0 @@ -from __future__ import division -import time -import numpy as np -import util - -# load data -util.load('mnist.dat', globals()) -dat = dat/255. - -# training parameters -epsilon = 0.01 -momentum = 0.9 - -num_epochs = 10 -batch_size = 64 -num_batches = dat.shape[1]//batch_size - -# model parameters -num_vis = dat.shape[0] -num_hid = 1024 - -# initialize weights -w_vh = 0.1 * np.random.randn(num_vis, num_hid) -w_v = np.zeros((num_vis, 1)) -w_h = np.zeros((num_hid, 1)) - -# initialize weight updates -wu_vh = np.zeros((num_vis, num_hid)) -wu_v = np.zeros((num_vis, 1)) -wu_h = np.zeros((num_hid, 1)) - -start_time = time.time() -for epoch in range(num_epochs): - print("Epoch %i" % (epoch + 1)) - err = [] - - for batch in range(num_batches): - v_true = dat[:, batch*batch_size:(batch + 1)*batch_size] - v = v_true - - # apply momentum - wu_vh *= momentum - wu_v *= momentum - wu_h *= momentum - - # positive phase - h = 1. / (1 + np.exp(-(np.dot(w_vh.T, v) + w_h))) - - wu_vh += np.dot(v, h.T) - wu_v += v.sum(1)[:, np.newaxis] - wu_h += h.sum(1)[:, np.newaxis] - - # sample hiddens - h = 1. * (h > np.random.rand(num_hid, batch_size)) - - # negative phase - v = 1. / (1 + np.exp(-(np.dot(w_vh, h) + w_v))) - h = 1. / (1 + np.exp(-(np.dot(w_vh.T, v) + w_h))) - - wu_vh -= np.dot(v, h.T) - wu_v -= v.sum(1)[:, np.newaxis] - wu_h -= h.sum(1)[:, np.newaxis] - - # update weights - w_vh += epsilon/batch_size * wu_vh - w_v += epsilon/batch_size * wu_v - w_h += epsilon/batch_size * wu_h - - err.append(np.mean((v - v_true)**2)) - - print("Mean squared error: %f" % np.mean(err)) - print("Time: %f" % (time.time() - start_time)) diff --git a/ot/gpu/cudamat/examples/util.py b/ot/gpu/cudamat/examples/util.py deleted file mode 100644 index 79ceead..0000000 --- a/ot/gpu/cudamat/examples/util.py +++ /dev/null @@ -1,22 +0,0 @@ -from __future__ import division -import gzip -try: import cPickle as pickle -except: import pickle - -def save(fname, var_list, source_dict): - var_list = [var.strip() for var in var_list.split() if len(var.strip())>0] - fo = gzip.GzipFile(fname, 'wb') - pickle.dump(var_list, fo) - for var in var_list: - pickle.dump(source_dict[var], fo, protocol=2) - fo.close() - -def load(fname, target_dict, verbose = True): - fo = gzip.GzipFile(fname, 'rb') - var_list = pickle.load(fo) - if verbose: - print(var_list) - for var in var_list: - target_dict[var] = pickle.load(fo) - fo.close() - diff --git a/ot/gpu/cudamat/setup.py b/ot/gpu/cudamat/setup.py deleted file mode 100755 index ad386d1..0000000 --- a/ot/gpu/cudamat/setup.py +++ /dev/null @@ -1,121 +0,0 @@ -#!/usr/bin/env python - -import os -# on Windows, we need the original PATH without Anaconda's compiler in it: -PATH = os.environ.get('PATH') -from distutils.spawn import spawn, find_executable -from setuptools import setup, find_packages, Extension -from setuptools.command.build_ext import build_ext -import sys - -# CUDA specific config -# nvcc is assumed to be in user's PATH -nvcc_compile_args = ['-O', '--ptxas-options=-v', '--compiler-options=-fPIC'] -nvcc_compile_args = os.environ.get('NVCCFLAGS', '').split() + nvcc_compile_args -cuda_libs = ['cublas'] - -cudamat_ext = Extension('cudamat.libcudamat', - sources=['cudamat/cudamat.cu', - 'cudamat/cudamat_kernels.cu'], - libraries=cuda_libs, - extra_compile_args=nvcc_compile_args) -cudalearn_ext = Extension('cudamat.libcudalearn', - sources=['cudamat/learn.cu', - 'cudamat/learn_kernels.cu'], - libraries=cuda_libs, - extra_compile_args=nvcc_compile_args) - - -class CUDA_build_ext(build_ext): - """ - Custom build_ext command that compiles CUDA files. - Note that all extension source files will be processed with this compiler. - """ - def build_extensions(self): - self.compiler.src_extensions.append('.cu') - self.compiler.set_executable('compiler_so', 'nvcc') - self.compiler.set_executable('linker_so', 'nvcc --shared') - if hasattr(self.compiler, '_c_extensions'): - self.compiler._c_extensions.append('.cu') # needed for Windows - self.compiler.spawn = self.spawn - build_ext.build_extensions(self) - - def spawn(self, cmd, search_path=1, verbose=0, dry_run=0): - """ - Perform any CUDA specific customizations before actually launching - compile/link etc. commands. - """ - if (sys.platform == 'darwin' and len(cmd) >= 2 and cmd[0] == 'nvcc' and - cmd[1] == '--shared' and cmd.count('-arch') > 0): - # Versions of distutils on OSX earlier than 2.7.9 inject - # '-arch x86_64' which we need to strip while using nvcc for - # linking - while True: - try: - index = cmd.index('-arch') - del cmd[index:index+2] - except ValueError: - break - elif self.compiler.compiler_type == 'msvc': - # There are several things we need to do to change the commands - # issued by MSVCCompiler into one that works with nvcc. In the end, - # it might have been easier to write our own CCompiler class for - # nvcc, as we're only interested in creating a shared library to - # load with ctypes, not in creating an importable Python extension. - # - First, we replace the cl.exe or link.exe call with an nvcc - # call. In case we're running Anaconda, we search cl.exe in the - # original search path we captured further above -- Anaconda - # inserts a MSVC version into PATH that is too old for nvcc. - cmd[:1] = ['nvcc', '--compiler-bindir', - os.path.dirname(find_executable("cl.exe", PATH)) - or cmd[0]] - # - Secondly, we fix a bunch of command line arguments. - for idx, c in enumerate(cmd): - # create .dll instead of .pyd files - if '.pyd' in c: cmd[idx] = c = c.replace('.pyd', '.dll') - # replace /c by -c - if c == '/c': cmd[idx] = '-c' - # replace /DLL by --shared - elif c == '/DLL': cmd[idx] = '--shared' - # remove --compiler-options=-fPIC - elif '-fPIC' in c: del cmd[idx] - # replace /Tc... by ... - elif c.startswith('/Tc'): cmd[idx] = c[3:] - # replace /Fo... by -o ... - elif c.startswith('/Fo'): cmd[idx:idx+1] = ['-o', c[3:]] - # replace /LIBPATH:... by -L... - elif c.startswith('/LIBPATH:'): cmd[idx] = '-L' + c[9:] - # replace /OUT:... by -o ... - elif c.startswith('/OUT:'): cmd[idx:idx+1] = ['-o', c[5:]] - # remove /EXPORT:initlibcudamat or /EXPORT:initlibcudalearn - elif c.startswith('/EXPORT:'): del cmd[idx] - # replace cublas.lib by -lcublas - elif c == 'cublas.lib': cmd[idx] = '-lcublas' - # - Finally, we pass on all arguments starting with a '/' to the - # compiler or linker, and have nvcc handle all other arguments - if '--shared' in cmd: - pass_on = '--linker-options=' - # we only need MSVCRT for a .dll, remove CMT if it sneaks in: - cmd.append('/NODEFAULTLIB:libcmt.lib') - else: - pass_on = '--compiler-options=' - cmd = ([c for c in cmd if c[0] != '/'] + - [pass_on + ','.join(c for c in cmd if c[0] == '/')]) - # For the future: Apart from the wrongly set PATH by Anaconda, it - # would suffice to run the following for compilation on Windows: - # nvcc -c -O -o .obj .cu - # And the following for linking: - # nvcc --shared -o .dll .obj .obj -lcublas - # This could be done by a NVCCCompiler class for all platforms. - spawn(cmd, search_path, verbose, dry_run) - -setup(name="cudamat", - version="0.3", - description="Performs linear algebra computation on the GPU via CUDA", - ext_modules=[cudamat_ext, cudalearn_ext], - packages=find_packages(exclude=['examples', 'test']), - include_package_data=True, - package_data={'cudamat': ['rnd_multipliers_32bit.txt']}, - author="Volodymyr Mnih", - url="https://github.com/cudamat/cudamat", - cmdclass={'build_ext': CUDA_build_ext}) diff --git a/ot/gpu/cudamat/test/test_cudamat.py b/ot/gpu/cudamat/test/test_cudamat.py deleted file mode 100644 index 800243c..0000000 --- a/ot/gpu/cudamat/test/test_cudamat.py +++ /dev/null @@ -1,1217 +0,0 @@ -import numpy as np -import nose -import cudamat as cm - -def setup(): - cm.cublas_init() - -def teardown(): - cm.cublas_shutdown() - -def test_reshape(): - m = 256 - n = 1 - cm1 = np.array(np.random.rand(n, m)*10, dtype=np.float64, order='F') - cm2 = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - - gm1 = cm.CUDAMatrix(cm1) - gm2 = cm.CUDAMatrix(cm2) - - gm1.reshape((m, n)) - gm2.assign(gm1) - gm1.reshape((n, m)) - - gm1.copy_to_host() - gm2.copy_to_host() - - assert np.max(np.abs(gm1.numpy_array - gm2.numpy_array.T)) < 10**-2, "Error in CUDAMatrix.reshape exceeded threshold" - -def test_T_field(): - m = 256 - n = 128 - cm1 = np.array(np.random.rand(n, m)*10, dtype=np.float64, order='F') - cm2 = np.array(np.random.rand(m, 1)*10, dtype=np.float64, order='F') - gm1 = cm.CUDAMatrix(cm1) - gm2 = cm.CUDAMatrix(cm2) - - # test dot - gm = cm.dot(gm2.T, gm1.T) - c = np.dot(cm2.T, cm1.T) - gm.copy_to_host() - - assert np.max(np.abs(gm.numpy_array - c)) < 10**-2, "Error in CUDAMatrix.dot with TransposedCUDAMatrix exceeded threshold" - - # test add_dot - cm3 = np.array(np.random.rand(1, n)*10, dtype=np.float64, order='F') - gm3 = cm.CUDAMatrix(cm3) - gm3.add_dot(gm2.T, gm1.T) - c = cm3 + np.dot(cm2.T, cm1.T) - gm3.copy_to_host() - - assert np.max(np.abs(gm3.numpy_array - c)) < 10**-2, "Error in CUDAMatrix.add_dot TransposedCUDAMatrix exceeded threshold" - - # test add_sums - gm2.add_sums(gm1.T, axis = 1) - c = cm2 + np.atleast_2d(cm1.sum(0)).T - gm2.copy_to_host() - - assert np.max(np.abs(gm2.numpy_array - c)) < 10**-2, "Error in CUDAMatrix.add_sums TransposedCUDAMatrix exceeded threshold" - -def test_assign(): - m = 256 - n = 128 - a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - b = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - - m1.assign(m2) - m1.copy_to_host() - - assert np.max(np.abs(m1.numpy_array - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.assign exceeded threshold" - -def test_assign_scalar(): - m = 256 - n = 128 - a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - - m1 = cm.CUDAMatrix(a) - - m1.assign(np.pi) - m1.copy_to_host() - - assert np.max(np.abs(m1.numpy_array - np.pi)) < 10**-4, "Error in CUDAMatrix.assign_scalar exceeded threshold" - -def test_get_row_slice(): - m = 256 - n = 128 - start = 11 - end = 54 - - a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - b = np.array(np.random.rand(end-start, n)*10, dtype=np.float64, order='F') - - c = np.array(a[start:end,:], order='F') - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - m1.get_row_slice(start, end, target = m2) - m3 = m1.get_row_slice(start, end) - m2.copy_to_host() - m3.copy_to_host() - - assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.get_row_slice exceeded threshold" - assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.get_row_slice exceeded threshold" - -def test_set_row_slice(): - m = 256 - n = 128 - start = 11 - end = 54 - - a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - b = np.array(np.random.rand(end-start, n)*10, dtype=np.float64, order='F') - - c = a.copy() - c[start:end,:] = b - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - m1.set_row_slice(start, end, m2) - m1.copy_to_host() - - assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.set_row_slice exceeded threshold" - -def test_transpose(): - m = 6 - n = 128 - - a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - b = np.array(np.random.rand(n, m), dtype=np.float64, order='F') - - c = a.copy().T - - m = cm.CUDAMatrix(a) - mt1 = cm.CUDAMatrix(b) - m.transpose(target = mt1) - mt2 = m.transpose() - - mt1.copy_to_host() - mt2.copy_to_host() - - assert np.max(np.abs(c - mt1.numpy_array)) < 10**-4, "Error in CUDAMatrix.transpose exceeded threshold" - assert np.max(np.abs(c - mt2.numpy_array)) < 10**-4, "Error in CUDAMatrix.transpose exceeded threshold" - -def test_slice(): - m = 256 - n = 128 - a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - - c = np.array(a[:,32:64], order='F') - - m1 = cm.CUDAMatrix(a) - m2 = m1.slice(32, 64) - m2.copy_to_host() - - assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.slice exceeded threshold" - - -def test_add_col_vec(): - m = 250 - n = 120 - a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - b = np.array(np.random.rand(m, 1)*10, dtype=np.float64, order='F') - t = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - - c = a + b - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - m3 = cm.CUDAMatrix(t) - - m1.add_col_vec(m2, target = m3) - m1.add_col_vec(m2) - m1.copy_to_host() - m3.copy_to_host() - - assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.add_col_vec exceeded threshold" - assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.add_col_vec exceeded threshold" - -def test_add_col_mult(): - m = 256 - n = 128 - mult = np.pi - a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - b = np.array(np.random.rand(m, 1)*10, dtype=np.float64, order='F') - t = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - - c = a + mult * b - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - m3 = cm.CUDAMatrix(t) - - m1.add_col_mult(m2, mult, target = m3) - m1.add_col_mult(m2, mult) - m1.copy_to_host() - m3.copy_to_host() - - assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.add_col_mult exceeded threshold" - assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.add_col_mult exceeded threshold" - -def test_add_row_vec(): - m = 256 - n = 128 - a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - b = np.array(np.random.rand(1, n)*10, dtype=np.float64, order='F') - t = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - - c = a + b - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - m3 = cm.CUDAMatrix(t) - - m1.add_row_vec(m2, target = m3) - m1.add_row_vec(m2) - m1.copy_to_host() - m3.copy_to_host() - - assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.add_row_vec exceeded threshold" - assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.add_row_vec exceeded threshold" - -def test_mult_by_col(): - m = 256 - n = 128 - a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - b = np.array(np.random.rand(m, 1)*10, dtype=np.float64, order='F') - t = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - - c = a * b - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - m3 = cm.CUDAMatrix(t) - - m1.mult_by_col(m2, target = m3) - m1.mult_by_col(m2) - m1.copy_to_host() - m3.copy_to_host() - - assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.mult_by_col exceeded threshold" - assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.mult_by_col exceeded threshold" - -def test_mult_by_row(): - m = 256 - n = 128 - a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - b = np.array(np.random.rand(1, n)*10, dtype=np.float64, order='F') - t = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - - c = a * b - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - m3 = cm.CUDAMatrix(t) - - m1.mult_by_row(m2, target = m3) - m1.mult_by_row(m2) - m1.copy_to_host() - m3.copy_to_host() - - assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.mult_by_row exceeded threshold" - assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.mult_by_row exceeded threshold" - -def test_div_by_col(): - m = 256 - n = 128 - a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - b = np.array(np.random.rand(m, 1)*10, dtype=np.float64, order='F') + 0.1 - t = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - - c = a / b - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - m3 = cm.CUDAMatrix(t) - - m1.div_by_col(m2, target = m3) - m1.div_by_col(m2) - m1.copy_to_host() - m3.copy_to_host() - - assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.div_by_col exceeded threshold" - assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.div_by_col exceeded threshold" - -def test_div_by_row(): - m = 256 - n = 128 - a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - b = np.array(np.random.rand(1, n)*10, dtype=np.float64, order='F') + 0.1 - t = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - - c = a / b - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - m3 = cm.CUDAMatrix(t) - - m1.div_by_row(m2, target = m3) - m1.div_by_row(m2) - m1.copy_to_host() - m3.copy_to_host() - - assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.div_by_row exceeded threshold" - assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.div_by_row exceeded threshold" - -def test_sum(): - m = 256 - n = 128 - a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - t1 = np.array(np.random.rand(1, n)*10, dtype=np.float64, order='F') - t2 = np.array(np.random.rand(m, 1)*10, dtype=np.float64, order='F') - - mult = 0.8 - c1 = np.atleast_2d(a.sum(0)) * mult - c2 = np.atleast_2d(a.sum(1)).T - - m = cm.CUDAMatrix(a) - mt1 = cm.CUDAMatrix(t1) - mt2 = cm.CUDAMatrix(t2) - - m.sum(axis = 0, target = mt1, mult = mult) - mt1r = m.sum(axis = 0, mult = mult) - - m.sum(axis = 1, target = mt2) - mt2r = m.sum(axis = 1) - - mt1.copy_to_host() - mt1r.copy_to_host() - mt2.copy_to_host() - mt2r.copy_to_host() - - assert np.max(np.abs(c1 - mt1.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold" - assert np.max(np.abs(c1 - mt1r.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold" - assert np.max(np.abs(c2 - mt2.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold" - assert np.max(np.abs(c2 - mt2r.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold" - -def test_sum_trans(): - m = 256 - n = 128 - a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - t1 = np.array(np.random.rand(1, m)*10, dtype=np.float64, order='F') - t2 = np.array(np.random.rand(n, 1)*10, dtype=np.float64, order='F') - - c1 = np.atleast_2d(a.T.sum(0)) - c2 = np.atleast_2d(a.T.sum(1)).T - - m = cm.CUDAMatrix(a) - m.set_trans(True) - mt1 = cm.CUDAMatrix(t1) - mt2 = cm.CUDAMatrix(t2) - - m.sum(axis = 0, target = mt1) - mt1r = m.sum(axis = 0) - - m.sum(axis = 1, target = mt2) - mt2r = m.sum(axis = 1) - - mt1.copy_to_host() - mt1r.copy_to_host() - mt2.copy_to_host() - mt2r.copy_to_host() - - assert np.max(np.abs(c1 - mt1.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold" - assert np.max(np.abs(c1 - mt1r.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold" - assert np.max(np.abs(c2 - mt2.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold" - assert np.max(np.abs(c2 - mt2r.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold" - -def test_mean(): - m = 256 - n = 128 - a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - t1 = np.array(np.random.rand(1, n)*10, dtype=np.float64, order='F') - t2 = np.array(np.random.rand(m, 1)*10, dtype=np.float64, order='F') - - c1 = np.atleast_2d(a.mean(0)) - c2 = np.atleast_2d(a.mean(1)).T - - m = cm.CUDAMatrix(a) - mt1 = cm.CUDAMatrix(t1) - mt2 = cm.CUDAMatrix(t2) - - m.mean(axis = 0, target = mt1) - mt1r = m.mean(axis = 0) - - m.mean(axis = 1, target = mt2) - mt2r = m.mean(axis = 1) - - mt1.copy_to_host() - mt1r.copy_to_host() - mt2.copy_to_host() - mt2r.copy_to_host() - - assert np.max(np.abs(c1 - mt1.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold" - assert np.max(np.abs(c1 - mt1r.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold" - assert np.max(np.abs(c2 - mt2.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold" - assert np.max(np.abs(c2 - mt2r.numpy_array)) < 10**-3, "Error in CUDAMatrix.sum exceeded threshold" - -def test_add_sums(): - m = 256 - n = 128 - - a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - t1 = np.array(np.random.rand(m, 1)*10, dtype=np.float64, order='F') - t2 = np.array(np.random.rand(1, n)*10, dtype=np.float64, order='F') - - mult = np.pi - beta = 0.7 - - c1 = beta * t1 + mult * np.atleast_2d(a.sum(1)).T - c2 = t2 + np.atleast_2d(a.sum(0)) - - m = cm.CUDAMatrix(a) - mt1 = cm.CUDAMatrix(t1) - mt2 = cm.CUDAMatrix(t2) - - mt1.add_sums(m, axis = 1, mult = np.pi, beta = beta) - mt2.add_sums(m, axis = 0) - - mt1.copy_to_host() - mt2.copy_to_host() - - assert np.max(np.abs(c1 - mt1.numpy_array)) < 10**-3, "Error in CUDAMatrix.add_sums exceeded threshold" - assert np.max(np.abs(c2 - mt2.numpy_array)) < 10**-3, "Error in CUDAMatrix.add_sums exceeded threshold" - - -def test_less_than(): - m = 256 - n = 128 - a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - t1 = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - t2 = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - v = 0.1 - - r1 = 1 * (a < b) - r2 = 1 * (a < v) - - da = cm.CUDAMatrix(a) - db = cm.CUDAMatrix(b) - dt1 = cm.CUDAMatrix(t1) - dt2 = cm.CUDAMatrix(t2) - - da.less_than(db, target = dt1) - da.less_than(v, target = dt2) - da.less_than(db) - - da.copy_to_host() - dt1.copy_to_host() - dt2.copy_to_host() - - assert np.max(np.abs(r1 - da.numpy_array)) < 10**-4, "Error in CUDAMatrix.less_than exceeded threshold" - assert np.max(np.abs(r1 - dt1.numpy_array)) < 10**-4, "Error in CUDAMatrix.less_than exceeded threshold" - assert np.max(np.abs(r2 - dt2.numpy_array)) < 10**-4, "Error in CUDAMatrix.less_than exceeded threshold" - -def test_greater_than(): - m = 256 - n = 128 - a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - t1 = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - t2 = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - v = 0.1 - - r1 = 1 * (a > b) - r2 = 1 * (a > v) - - da = cm.CUDAMatrix(a) - db = cm.CUDAMatrix(b) - dt1 = cm.CUDAMatrix(t1) - dt2 = cm.CUDAMatrix(t2) - - da.greater_than(db, target = dt1) - da.greater_than(v, target = dt2) - da.greater_than(db) - - da.copy_to_host() - dt1.copy_to_host() - dt2.copy_to_host() - - assert np.max(np.abs(r1 - da.numpy_array)) < 10**-4, "Error in CUDAMatrix.greater_than exceeded threshold" - assert np.max(np.abs(r1 - dt1.numpy_array)) < 10**-4, "Error in CUDAMatrix.greater_than exceeded threshold" - assert np.max(np.abs(r2 - dt2.numpy_array)) < 10**-4, "Error in CUDAMatrix.greater_than exceeded threshold" - -def test_minimum(): - m = 256 - n = 128 - a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - t1 = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - t2 = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - v = 0.1 - - r1 = np.minimum(a, b) - r2 = np.minimum(a, v) - - da = cm.CUDAMatrix(a) - db = cm.CUDAMatrix(b) - dt1 = cm.CUDAMatrix(t1) - dt2 = cm.CUDAMatrix(t2) - - da.minimum(db, target = dt1) - da.minimum(v, target = dt2) - da.minimum(db) - - da.copy_to_host() - dt1.copy_to_host() - dt2.copy_to_host() - - assert np.max(np.abs(r1 - da.numpy_array)) < 10**-4, "Error in CUDAMatrix.minimum exceeded threshold" - assert np.max(np.abs(r1 - dt1.numpy_array)) < 10**-4, "Error in CUDAMatrix.minimum exceeded threshold" - assert np.max(np.abs(r2 - dt2.numpy_array)) < 10**-4, "Error in CUDAMatrix.minimum exceeded threshold" - -def test_maximum(): - m = 256 - n = 128 - a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - t1 = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - t2 = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - v = 0.1 - - r1 = np.maximum(a, b) - r2 = np.maximum(a, v) - - da = cm.CUDAMatrix(a) - db = cm.CUDAMatrix(b) - dt1 = cm.CUDAMatrix(t1) - dt2 = cm.CUDAMatrix(t2) - - da.maximum(db, target = dt1) - da.maximum(v, target = dt2) - da.maximum(db) - - da.copy_to_host() - dt1.copy_to_host() - dt2.copy_to_host() - - assert np.max(np.abs(r1 - da.numpy_array)) < 10**-4, "Error in CUDAMatrix.maximum exceeded threshold" - assert np.max(np.abs(r1 - dt1.numpy_array)) < 10**-4, "Error in CUDAMatrix.maximum exceeded threshold" - assert np.max(np.abs(r2 - dt2.numpy_array)) < 10**-4, "Error in CUDAMatrix.maximum exceeded threshold" - -def test_minmax(): - m = 256 - n = 128 - for op in 'min', 'max', 'argmin', 'argmax': - for sign in (1, -1): - a = np.array(np.random.randn(m, n)*10*sign, dtype=np.float64, order='F') - t0 = np.array(np.random.rand(1, n)*10, dtype=np.float64, order='F') - t1 = np.array(np.random.rand(m, 1)*10, dtype=np.float64, order='F') - - r0 = np.atleast_2d(getattr(a, op)(0)) - r1 = np.atleast_2d(getattr(a, op)(1)) - - da = cm.CUDAMatrix(a) - dr10 = cm.CUDAMatrix(t0) - dr11 = cm.CUDAMatrix(t1) - - getattr(da, op)(axis = 0, target = dr10) - getattr(da, op)(axis = 1, target = dr11) - dr20 = getattr(da, op)(axis = 0) - dr21 = getattr(da, op)(axis = 1) - - dr10.copy_to_host() - dr11.copy_to_host() - dr20.copy_to_host() - dr21.copy_to_host() - - assert np.max(np.abs(r0 - dr10.numpy_array)) < 10**-4, "Error in CUDAMatrix.%s exceeded threshold" % op - assert np.max(np.abs(r1 - dr11.numpy_array.T)) < 10**-4, "Error in CUDAMatrix.%s exceeded threshold" % op - assert np.max(np.abs(r0 - dr20.numpy_array)) < 10**-4, "Error in CUDAMatrix.%s exceeded threshold" % op - assert np.max(np.abs(r1 - dr21.numpy_array.T)) < 10**-4, "Error in CUDAMatrix.%s exceeded threshold" % op - -def test_sign(): - m = 256 - n = 128 - a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - a[0,0] = 0. - a[0,1] = -0. - t = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - - c = np.sign(a) - - m1 = cm.CUDAMatrix(a) - m3 = cm.CUDAMatrix(t) - - m2 = m1.sign() - m1.sign(target = m3) - - m2.copy_to_host() - m3.copy_to_host() - - assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.sign exceeded threshold" - assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.sign exceeded threshold" - -def test_sigmoid(): - m = 256 - n = 128 - a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - - c = 1. / (1. + np.exp(-a)) - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - m1.apply_sigmoid(target = m2) - m1.apply_sigmoid() - - m1.copy_to_host() - m2.copy_to_host() - - assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.apply_sigmoid exceeded threshold" - assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.apply_sigmoid exceeded threshold" - -def test_tanh(): - m = 256 - n = 128 - a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - - c = np.tanh(a) - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - m1.apply_tanh(target = m2) - m1.apply_tanh() - - m1.copy_to_host() - m2.copy_to_host() - - assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.apply_tanh exceeded threshold" - assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.apply_tanh exceeded threshold" - -def test_soft_threshold(): - m = 256 - n = 128 - a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - - alpha = 0.5 - c = np.sign(a) * np.maximum(0, np.abs(a) - alpha) - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - m1.apply_soft_threshold(alpha, target = m2) - m1.apply_soft_threshold(alpha) - - m1.copy_to_host() - m2.copy_to_host() - - assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.apply_soft_threshold exceeded threshold" - assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.apply_soft_threshold exceeded threshold" - -def test_log(): - m = 256 - n = 128 - a = np.array(np.random.rand(m, n)*10+0.1, dtype=np.float64, order='F') - b = np.array(np.random.rand(m, n)*10+0.1, dtype=np.float64, order='F') - - c = np.log(a) - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - cm.log(m1, target = m2) - cm.log(m1) - - m1.copy_to_host() - m2.copy_to_host() - - assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in cudamat.log exceeded threshold" - assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in cudamat.log exceeded threshold" - -def test_exp(): - m = 256 - n = 128 - a = np.array(np.random.randn(m, n), dtype=np.float64, order='F') - b = np.array(np.random.randn(m, n), dtype=np.float64, order='F') - - c = np.exp(a) - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - cm.exp(m1, target = m2) - cm.exp(m1) - - m1.copy_to_host() - m2.copy_to_host() - - assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in cudamat.exp exceeded threshold" - assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in cudamat.exp exceeded threshold" - -def test_gamma(): - m = 256 - n = 128 - a = np.array(np.random.rand(m, n)*5, dtype=np.float64, order='F') - b = np.array(np.random.rand(m, n)*5, dtype=np.float64, order='F') - - from scipy.special import gamma - c = gamma(a) - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - cm.gamma(m1, target = m2) - cm.gamma(m1) - - m1.copy_to_host() - m2.copy_to_host() - - assert np.max(np.abs(c - m1.numpy_array)) < 10**-2, "Error in cudamat.gamma exceeded threshold" - assert np.max(np.abs(c - m2.numpy_array)) < 10**-2, "Error in cudamat.gamma exceeded threshold" - -def test_lgamma(): - m = 256 - n = 128 - a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - b = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - - from scipy.special import gammaln - c = gammaln(a) - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - cm.lgamma(m1, target = m2) - cm.lgamma(m1) - - m1.copy_to_host() - m2.copy_to_host() - - assert np.max(np.abs(c - m1.numpy_array)) < 10**-2, "Error in cudamat.lgamma exceeded threshold " + str(np.max(np.abs(c - m1.numpy_array))) - assert np.max(np.abs(c - m2.numpy_array)) < 10**-2, "Error in cudamat.lgamma exceeded threshold" - -def test_sqrt(): - m = 256 - n = 128 - a = np.array(np.random.rand(m, n)*20, dtype=np.float64, order='F') - b = np.array(np.random.rand(m, n), dtype=np.float64, order='F') - - c = np.sqrt(a) - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - cm.sqrt(m1, target = m2) - cm.sqrt(m1) - - m1.copy_to_host() - m2.copy_to_host() - - assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in cudamat.sqrt exceeded threshold" - assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in cudamat.sqrt exceeded threshold" - -def test_pow(): - m = 256 - n = 128 - a = np.array(np.random.randn(m, n)*20, dtype=np.float64, order='F') - b = np.array(np.random.rand(m, n), dtype=np.float64, order='F') - p = 2 - - c = a**p - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - cm.pow(m1, p, target = m2) - cm.pow(m1, p) - - m1.copy_to_host() - m2.copy_to_host() - - assert np.max(np.abs(c - m1.numpy_array)) < 10**-3, "Error in cudamat.pow exceeded threshold" - assert np.max(np.abs(c - m2.numpy_array)) < 10**-3, "Error in cudamat.pow exceeded threshold" - -def test_pow_matrix(): - m = 256 - n = 128 - a = np.array(np.random.rand(m, n)*20, dtype=np.float64, order='F') - b = np.array(np.random.rand(m, n), dtype=np.float64, order='F') - p = np.array(np.random.randn(m, n), dtype=np.float64, order='F') - - - c = a**p - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - mp = cm.CUDAMatrix(p) - cm.pow(m1, mp, target = m2) - cm.pow(m1, mp) - - m1.copy_to_host() - m2.copy_to_host() - - assert np.max(np.abs(c - m1.numpy_array)) < 10**-2, "Error in cudamat.pow exceeded threshold" - assert np.max(np.abs(c - m2.numpy_array)) < 10**-2, "Error in cudamat.pow exceeded threshold" - -def test_reciprocal(): - m = 256 - n = 128 - a = np.array(np.random.rand(m, n)*10+10**-3, dtype=np.float64, order='F') - b = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - - c = 1. / a - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - m1.reciprocal(target = m2) - m1.reciprocal() - - m1.copy_to_host() - m2.copy_to_host() - - assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.reciprocal exceeded threshold" - assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.reciprocal exceeded threshold" - -def test_add_mult(): - m = 256 - n = 128 - alpha = np.pi - a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - - c = a + np.pi * b - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - m1.add_mult(m2, np.pi) - m1.copy_to_host() - - assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.add_mult exceeded threshold" - -def test_subtract_mult(): - m = 256 - n = 128 - alpha = np.pi - a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - - c = a - np.pi * b - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - m1.subtract_mult(m2, np.pi) - m1.copy_to_host() - - assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.subtract_mult exceeded threshold" - -def test_add(): - m = 256 - n = 128 - a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - b = np.array(1.+np.random.rand(m, n)*10, dtype=np.float64, order='F') - t = np.array(np.empty((m, n)), dtype=np.float64, order='F') - - c = a + b - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - m3 = cm.CUDAMatrix(t) - - m1.add(m2, target = m3) - m1.add(m2) - - m3.copy_to_host() - m1.copy_to_host() - - assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.add exceeded threshold" - assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.add exceeded threshold" - -def test_subtract(): - m = 256 - n = 128 - a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - b = np.array(1.+np.random.rand(m, n)*10, dtype=np.float64, order='F') - t = np.array(np.empty((m, n)), dtype=np.float64, order='F') - - c = a - b - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - m3 = cm.CUDAMatrix(t) - - m1.subtract(m2, target = m3) - m1.subtract(m2) - - m3.copy_to_host() - m1.copy_to_host() - - assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.subtract exceeded threshold" - assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.subtract exceeded threshold" - -def test_divide(): - m = 256 - n = 128 - a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - b = np.array(1.+np.random.rand(m, n)*10, dtype=np.float64, order='F') - t = np.array(np.empty((m, n)), dtype=np.float64, order='F') - - c = a / b - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - m3 = cm.CUDAMatrix(t) - - m1.divide(m2, target = m3) - m1.divide(m2) - - m3.copy_to_host() - m1.copy_to_host() - - assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.div exceeded threshold" - assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.div exceeded threshold" - -def test_mult(): - m = 256 - n = 128 - a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - b = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - t = np.array(np.empty((m, n)), dtype=np.float64, order='F') - - c = a * b - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - m3 = cm.CUDAMatrix(t) - - m1.mult(m2, target = m3) - m1.mult(m2) - - m3.copy_to_host() - m1.copy_to_host() - - assert np.max(np.abs(c - m3.numpy_array)) < 10**-4, "Error in CUDAMatrix.multiply exceeded threshold" - assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.multiply exceeded threshold" - -def test_scalar_mult(): - m = 256 - n = 128 - alpha = np.pi - a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - t = np.array(np.empty((m, n)), dtype=np.float64, order='F') - - c = a * alpha - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(t) - - m1.mult(alpha, target = m2) - m1.mult(alpha) - - m1.copy_to_host() - m2.copy_to_host() - - assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.mult exceeded threshold" - assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.mult exceeded threshold" - -def test_scalar_div(): - m = 256 - n = 128 - alpha = np.pi - a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - t = np.array(np.empty((m, n)), dtype=np.float64, order='F') - - c = a / alpha - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(t) - - m1.divide(alpha, target = m2) - m1.divide(alpha) - - m1.copy_to_host() - m2.copy_to_host() - - assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.divide exceeded threshold" - assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.divide exceeded threshold" - -def test_add_scalar(): - m = 256 - n = 128 - alpha = np.pi - a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - t = np.array(np.empty((m, n)), dtype=np.float64, order='F') - - c = a + alpha - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(t) - - m1.add(alpha, target = m2) - m1.add(alpha) - - m1.copy_to_host() - m2.copy_to_host() - - assert np.max(np.abs(c - m1.numpy_array)) < 10**-4, "Error in CUDAMatrix.add_scalar exceeded threshold" - assert np.max(np.abs(c - m2.numpy_array)) < 10**-4, "Error in CUDAMatrix.add_scalar exceeded threshold" - -def test_dot(): - m = 128 - k = 256 - n = 64 - a = np.array(np.random.randn(m, k)*10, dtype=np.float64, order='F') - b = np.array(np.random.randn(k, n)*10, dtype=np.float64, order='F') - c = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - - alpha = 2. - beta = 0.3 - r = beta * c + alpha * np.dot(a, b) - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - m3 = cm.CUDAMatrix(c) - m3 = cm.dot(m1, m2, target = m3, alpha = alpha, beta = beta) - m3.copy_to_host() - - assert np.max(np.abs(r - m3.numpy_array)) < 10**-2, "Error in CUDAMatrix.dot exceeded threshold" - -def test_dot_trans(): - m = 128 - k = 256 - n = 64 - a = np.array(np.random.randn(k, m)*10, dtype=np.float64, order='F') - b = np.array(np.random.randn(k, n)*10, dtype=np.float64, order='F') - - c = np.dot(a.T, b) - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - m1.set_trans(True); - m3 = cm.dot(m1, m2) - m3.copy_to_host() - - assert np.max(np.abs(c - m3.numpy_array)) < 10**-2, "Error in CUDAMatrix.dot exceeded threshold" - -def test_dot_vect(): - m = 128 - k = 256 - n = 1 - a = np.array(np.random.randn(m, k)*10, dtype=np.float64, order='F') - b = np.array(np.random.randn(k, n)*10, dtype=np.float64, order='F') - A = cm.CUDAMatrix(a) - B = cm.CUDAMatrix(b) - - c = np.dot(a, b) - C = cm.dot(A, B) - assert np.max(np.abs(c - C.asarray())) < 10**-2, "Error in CUDAMatrix.dot exceeded threshold" - - c = np.dot(a.T, b[:m]) - C = cm.dot(A.T, B.slice(0, m)) - assert np.max(np.abs(c - C.asarray())) < 10**-2, "Error in CUDAMatrix.dot exceeded threshold" - - c = np.dot(b.T, a.T) - C = cm.dot(B.T, A.T) - assert np.max(np.abs(c - C.asarray())) < 10**-2, "Error in CUDAMatrix.dot exceeded threshold" - - c = np.dot(b[:m].T, a) - C = cm.dot(B.slice(0, m).reshape((1, m)), A) - assert np.max(np.abs(c - C.asarray())) < 10**-2, "Error in CUDAMatrix.dot exceeded threshold" - -def test_add_dot(): - m = 128 - k = 256 - n = 64 - a = np.array(np.random.randn(m, k)*10, dtype=np.float64, order='F') - b = np.array(np.random.randn(k, n)*10, dtype=np.float64, order='F') - c = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - - mult = 2.1 - beta = 0.8 - res = beta * c + mult * np.dot(a, b) - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - m3 = cm.CUDAMatrix(c) - m3.add_dot(m1, m2, mult = mult, beta = beta) - - m3.copy_to_host() - - assert np.max(np.abs(res - m3.numpy_array)) < 10**-2, "Error in CUDAMatrix.add_dot exceeded threshold" - -def test_vdot(): - m = 64 - n = 64 - a = np.array(np.random.randn(m, n), dtype=np.float64, order='F') - b = np.array(np.random.randn(m, n), dtype=np.float64, order='F') - - true_res = np.vdot(a, b) - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - - res = cm.vdot(m1, m2) - - assert np.abs(res - true_res) < 10**-2, "Error in CUDAMatrix.vdot exceeded threshold" - -def test_subtract_dot(): - m = 128 - k = 256 - n = 64 - a = np.array(np.random.randn(m, k)*10, dtype=np.float64, order='F') - b = np.array(np.random.randn(k, n)*10, dtype=np.float64, order='F') - c = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - - res = c - np.dot(a, b) - - m1 = cm.CUDAMatrix(a) - m2 = cm.CUDAMatrix(b) - m3 = cm.CUDAMatrix(c) - m3.subtract_dot(m1, m2) - - m3.copy_to_host() - - assert np.max(np.abs(res - m3.numpy_array)) < 10**-2, "Error in CUDAMatrix.subtract_dot exceeded threshold" - -def test_random(): - cm.CUDAMatrix.init_random(1) - m1 = cm.CUDAMatrix(np.array(np.empty((128,256)), dtype=np.float64, order='F')) - m2 = cm.CUDAMatrix(np.array(np.empty((128,256)), dtype=np.float64, order='F')) - - m1.fill_with_rand() - m1.copy_to_host() - m2.fill_with_randn() - m2.copy_to_host() - - assert np.abs(np.mean(m1.numpy_array) - 0.5) < 10**-2, "Error in CUDAMatrix.fill_with_rand threshold" - assert np.abs(np.mean(m2.numpy_array)) < 10**-2, "Error in CUDAMatrix.fill_with_randn threshold" - -def test_euclid_norm(): - m = 256 - n = 128 - a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - - m = cm.CUDAMatrix(a) - - n1 = np.sqrt(np.sum(a**2)) - n2 = m.euclid_norm() - - assert np.abs(n1-n2) < 10**-2, "Error in CUDAMatrix.euclid_norm exceeded threshold" - -def test_manhattan_norm(): - m = 256 - n = 128 - a = np.array(np.random.rand(m, n)*10, dtype=np.float64, order='F') - - m = cm.CUDAMatrix(a) - - n1 = np.sum(np.abs(a), dtype=np.double) - n2 = m.manhattan_norm() - - assert np.abs(n1-n2) < 2e-2, "Error in CUDAMatrix.manhattan_norm exceeded threshold (%f != %f)" % (n1, n2) - -def test_allfinite(): - a = cm.empty((10, 20)).assign(1).divide(0) # NaN - b = cm.empty((10, 20)).assign(1e20).mult(1e20) # Inf - c = cm.empty((10, 20)).assign(1) # 1.0 - - assert (not a.allfinite()) and (not b.allfinite()) and c.allfinite(), "CUDAMatrix.allfinite does not work" - -def test_select_columns(): - m = 256 - n = 128 - k = 8 - - s = np.array(np.random.randn(m, n), dtype=np.float64, order='F') - i_l = [0, 1, 2, 3, 5, 10, 12, 20] - i = np.array(i_l).T[np.newaxis, :] - t = np.empty((m, k)) - - s_d = cm.CUDAMatrix(s) - i_d = cm.CUDAMatrix(i) - t_d = cm.CUDAMatrix(t) - - s_d.select_columns(i_d, t_d) - res = s[:,i_l] - - assert np.max(np.abs(res - t_d.asarray())) < 10**-4, "Error in CUDAMatrix.select_columns exceeded threshold" - - -def test_where(): - m = 256 - n = 128 - a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - z = np.zeros_like(a) - res = np.where(a > 0, a, z); - - a_d = cm.CUDAMatrix(a) - z_d = cm.CUDAMatrix(z) - res_d = cm.empty(a_d.shape) - a_d.greater_than(0, res_d) - cm.where(res_d, a_d, z_d) - assert np.abs(res-res_d.asarray()).max() < 1e-2, "Error in cudamat.where" - - -def test_correlate(): - m = 64 - n = 32 - km = 17 - kn = 11 - - a = np.array(np.random.randn(m, n)*10, dtype=np.float64, order='F') - k = np.array(np.random.randn(km, kn)*10, dtype=np.float64, order='F') - - res = np.zeros_like(a) - for i in range(len(a)): - for j in range(len(a[0])): - for h in range(-(km/2), km/2 + 1): - for w in range(-(kn/2), kn/2 + 1): - if i+h >= 0 and i+h < m and j+w >= 0 and j+w < n: - res[i][j] += a[i + h][j + w] * k[km/2 + h][kn/2 + w] - - a_d = cm.CUDAMatrix(a) - k_d = cm.CUDAMatrix(k) - - res_d = cm.correlate(a_d, k_d) - assert np.abs(res-res_d.asarray()).max() < 1e-2, "Error in cudamat.correlate" - - -if __name__ == '__main__': - nose.runmodule() diff --git a/ot/gpu/cudamat/test/test_learn.py b/ot/gpu/cudamat/test/test_learn.py deleted file mode 100644 index 954757a..0000000 --- a/ot/gpu/cudamat/test/test_learn.py +++ /dev/null @@ -1,27 +0,0 @@ -import pdb -import numpy as np -import nose -import cudamat as cm - -def setup(): - cm.cublas_init() - -def teardown(): - cm.cublas_shutdown() - -def test_mult_by_sigmoid_deriv(): - m = 256 - n = 128 - c_targets = np.array(np.random.randn(m, n)*10, dtype=np.float32, order='F') - c_acts = np.array(np.random.rand(m, n), dtype=np.float32, order='F') - - g_targets = cm.CUDAMatrix(c_targets) - g_acts = cm.CUDAMatrix(c_acts) - - c_targets = c_targets * c_acts * (1. - c_acts) - cm.learn.mult_by_sigmoid_deriv(g_targets, g_acts) - - assert np.max(np.abs(c_acts - g_acts.asarray())) < 10**-2, "Error in cudamat.learn.mult_by_sigmoid_deriv exceeded threshold" - -if __name__ == '__main__': - nose.runmodule() diff --git a/ot/gpu/da.py b/ot/gpu/da.py index 7d04b5b..8b45772 100644 --- a/ot/gpu/da.py +++ b/ot/gpu/da.py @@ -6,7 +6,7 @@ Domain adaptation with optimal transport and GPU import numpy as np from ..utils import unif from ..da import OTDA -from .bregman import sinkhornGPU +from .bregman import sinkhorn def pairwiseEuclideanGPU(a, b, returnAsGPU=False, squared=False, cudamat=None): @@ -46,7 +46,7 @@ def pairwiseEuclideanGPU(a, b, returnAsGPU=False, squared=False, cudamat=None): class OTDA_sinkhorn_GPU(OTDA): - def fit(self, xs, xt, reg=1, ws=None, wt=None, norm=None): + def fit(self, xs, xt, reg=1, ws=None, wt=None, norm=None, **kwargs): import cudamat cudamat.init() xs = np.asarray(xs, dtype=np.float64) @@ -77,5 +77,5 @@ class OTDA_sinkhorn_GPU(OTDA): M = np.log(1 + np.log(1 + self.M_GPU.asarray())) self.M_GPU = cudamat.CUDAMatrix(M) - self.G = sinkhornGPU(ws, wt, self.M_GPU, reg, cudamat=cudamat) + self.G = sinkhorn(ws, wt, self.M_GPU, reg, cudamat=cudamat) self.computed = True -- cgit v1.2.3 From 9c5cc82b2e6f6dcd94a24a47f414c278c717ee7a Mon Sep 17 00:00:00 2001 From: Leo gautheron Date: Thu, 20 Apr 2017 14:06:17 +0200 Subject: missing argument function sinkhorn --- ot/gpu/da.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/gpu/da.py b/ot/gpu/da.py index 8b45772..4c583a7 100644 --- a/ot/gpu/da.py +++ b/ot/gpu/da.py @@ -77,5 +77,5 @@ class OTDA_sinkhorn_GPU(OTDA): M = np.log(1 + np.log(1 + self.M_GPU.asarray())) self.M_GPU = cudamat.CUDAMatrix(M) - self.G = sinkhorn(ws, wt, self.M_GPU, reg, cudamat=cudamat) + self.G = sinkhorn(ws, wt, self.M_GPU, reg, cudamat=cudamat, **kwargs) self.computed = True -- cgit v1.2.3 From fb95157f867d749551fad9a4988eb3dd2813096e Mon Sep 17 00:00:00 2001 From: Leo gautheron Date: Thu, 20 Apr 2017 15:05:53 +0200 Subject: more changes from feeback in addition add the posibility to normalize the cost matrix through the function fit --- ot/da.py | 26 ++++++++++++++++++++++---- ot/gpu/__init__.py | 3 ++- ot/gpu/bregman.py | 4 ++-- ot/gpu/da.py | 39 ++++++++++++++++++++++----------------- 4 files changed, 48 insertions(+), 24 deletions(-) diff --git a/ot/da.py b/ot/da.py index d7b8492..d607e50 100644 --- a/ot/da.py +++ b/ot/da.py @@ -606,7 +606,7 @@ class OTDA(object): self.computed=False - def fit(self,xs,xt,ws=None,wt=None): + def fit(self,xs,xt,ws=None,wt=None,norm=None): """ Fit domain adaptation between samples is xs and xt (with optional weights)""" self.xs=xs self.xt=xt @@ -620,6 +620,7 @@ class OTDA(object): self.wt=wt self.M=dist(xs,xt,metric=self.metric) + self.normalize() self.G=emd(ws,wt,self.M) self.computed=True @@ -684,11 +685,25 @@ class OTDA(object): xf=self.interp(direction)# interp the source samples return xf[idx,:]+x-x0[idx,:] # aply the delta to the interpolation + def normalizeM(self, norm): + """ + It may help to normalize the cost matrix self.M if there are numerical + errors during the sinkhorn based algorithms. + """ + if norm == "median": + self.M /= float(np.median(self.M)) + elif norm == "max": + self.M /= float(np.max(self.M)) + elif norm == "log": + self.M = np.log(1 + self.M) + elif norm == "loglog": + self.M = np.log(1 + np.log(1 + self.M)) + class OTDA_sinkhorn(OTDA): """Class for domain adaptation with optimal transport with entropic regularization""" - def fit(self,xs,xt,reg=1,ws=None,wt=None,**kwargs): + def fit(self,xs,xt,reg=1,ws=None,wt=None,norm=None,**kwargs): """ Fit regularized domain adaptation between samples is xs and xt (with optional weights)""" self.xs=xs self.xt=xt @@ -702,6 +717,7 @@ class OTDA_sinkhorn(OTDA): self.wt=wt self.M=dist(xs,xt,metric=self.metric) + self.normalizeM(norm) self.G=sinkhorn(ws,wt,self.M,reg,**kwargs) self.computed=True @@ -710,7 +726,7 @@ class OTDA_lpl1(OTDA): """Class for domain adaptation with optimal transport with entropic and group regularization""" - def fit(self,xs,ys,xt,reg=1,eta=1,ws=None,wt=None,**kwargs): + def fit(self,xs,ys,xt,reg=1,eta=1,ws=None,wt=None,norm=None,**kwargs): """ Fit regularized domain adaptation between samples is xs and xt (with optional weights), See ot.da.sinkhorn_lpl1_mm for fit parameters""" self.xs=xs self.xt=xt @@ -724,6 +740,7 @@ class OTDA_lpl1(OTDA): self.wt=wt self.M=dist(xs,xt,metric=self.metric) + self.normalizeM(norm) self.G=sinkhorn_lpl1_mm(ws,ys,wt,self.M,reg,eta,**kwargs) self.computed=True @@ -731,7 +748,7 @@ class OTDA_l1l2(OTDA): """Class for domain adaptation with optimal transport with entropic and group lasso regularization""" - def fit(self,xs,ys,xt,reg=1,eta=1,ws=None,wt=None,**kwargs): + def fit(self,xs,ys,xt,reg=1,eta=1,ws=None,wt=None,norm=None,**kwargs): """ Fit regularized domain adaptation between samples is xs and xt (with optional weights), See ot.da.sinkhorn_lpl1_gl for fit parameters""" self.xs=xs self.xt=xt @@ -745,6 +762,7 @@ class OTDA_l1l2(OTDA): self.wt=wt self.M=dist(xs,xt,metric=self.metric) + self.normalizeM(norm) self.G=sinkhorn_l1l2_gl(ws,ys,wt,self.M,reg,eta,**kwargs) self.computed=True diff --git a/ot/gpu/__init__.py b/ot/gpu/__init__.py index 9319208..40b11c0 100644 --- a/ot/gpu/__init__.py +++ b/ot/gpu/__init__.py @@ -2,5 +2,6 @@ from . import bregman from . import da +from .bregman import sinkhorn -__all__ = ["bregman", "da"] +__all__ = ["bregman", "da", "sinkhorn"] diff --git a/ot/gpu/bregman.py b/ot/gpu/bregman.py index 4d4a8b7..f91f15f 100644 --- a/ot/gpu/bregman.py +++ b/ot/gpu/bregman.py @@ -4,10 +4,11 @@ Bregman projections for regularized OT with GPU """ import numpy as np +import cudamat def sinkhorn(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False, - log=False, cudamat=None): + log=False): # init data Nini = len(a) Nfin = len(b) @@ -74,7 +75,6 @@ def sinkhorn(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False, log['u'] = u_GPU.asarray() log['v'] = v_GPU.asarray() - # print('err=',err,' cpt=',cpt) K_GPU.mult_by_col(u_GPU, target=K_GPU) K_GPU.mult_by_row(v_GPU.transpose(), target=K_GPU) if log: diff --git a/ot/gpu/da.py b/ot/gpu/da.py index 4c583a7..0121e7b 100644 --- a/ot/gpu/da.py +++ b/ot/gpu/da.py @@ -7,9 +7,10 @@ import numpy as np from ..utils import unif from ..da import OTDA from .bregman import sinkhorn +import cudamat -def pairwiseEuclideanGPU(a, b, returnAsGPU=False, squared=False, cudamat=None): +def pairwiseEuclideanGPU(a, b, returnAsGPU=False, squared=False): # a is shape (n, f) and b shape (m, f). Return matrix c of shape (n, m). # First compute in c_GPU the squared euclidean distance. And return its # square root. At each cell [i,j] of c, we want to have @@ -45,9 +46,24 @@ def pairwiseEuclideanGPU(a, b, returnAsGPU=False, squared=False, cudamat=None): return c_GPU.asarray() -class OTDA_sinkhorn_GPU(OTDA): +class OTDA_GPU(OTDA): + def normalizeM(self, norm): + if norm == "median": + self.M_GPU.divide(float(np.median(self.M_GPU.asarray()))) + elif norm == "max": + self.M_GPU.divide(float(np.max(self.M_GPU.asarray()))) + elif norm == "log": + self.M_GPU.add(1) + cudamat.log(self.M_GPU) + elif norm == "loglog": + self.M_GPU.add(1) + cudamat.log(self.M_GPU) + self.M_GPU.add(1) + cudamat.log(self.M_GPU) + + +class OTDA_sinkhorn(OTDA_GPU): def fit(self, xs, xt, reg=1, ws=None, wt=None, norm=None, **kwargs): - import cudamat cudamat.init() xs = np.asarray(xs, dtype=np.float64) xt = np.asarray(xt, dtype=np.float64) @@ -64,18 +80,7 @@ class OTDA_sinkhorn_GPU(OTDA): self.wt = wt self.M_GPU = pairwiseEuclideanGPU(xs, xt, returnAsGPU=True, - squared=True, cudamat=cudamat) - - if norm == "median": - self.M_GPU.divide(float(np.median(self.M_GPU.asarray()))) - elif norm == "max": - self.M_GPU.divide(float(np.max(self.M_GPU.asarray()))) - elif norm == "log": - M = np.log(1 + self.M_GPU.asarray()) - self.M_GPU = cudamat.CUDAMatrix(M) - elif norm == "loglog": - M = np.log(1 + np.log(1 + self.M_GPU.asarray())) - self.M_GPU = cudamat.CUDAMatrix(M) - - self.G = sinkhorn(ws, wt, self.M_GPU, reg, cudamat=cudamat, **kwargs) + squared=True) + self.normalizeM(norm) + self.G = sinkhorn(ws, wt, self.M_GPU, reg, **kwargs) self.computed = True -- cgit v1.2.3 From dabb16cd6601b92c5d6b8854c361ccf7343d96af Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 20 Apr 2017 15:40:08 +0200 Subject: add dependencies discussion in readme --- README.md | 22 +++++++++++++++++++--- 1 file changed, 19 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 3d944db..33ff301 100644 --- a/README.md +++ b/README.md @@ -37,7 +37,7 @@ sudo apt-get install python-numpy python-scipy python-matplotlib cython To install the library, you can install it locally (after downloading it) on you machine using ``` -python setup.py install --user +python setup.py install --user # for user install (no root) ``` The toolbox is also available on PyPI with a possibly slightly older version. You can install it with: @@ -52,6 +52,22 @@ import ot Note that for easier access the module is name ot instead of pot. + +### Dependencies + +Some sub-modules require additional dependences which are discussed below + +* ot.dr (Wasserstein dimensionality rediuction) depends on autograd and pymanopt that can be installed with: +``` +pip install pymanopt autograd +``` +* ot.gpu (GPU accelerated OT) depends on cudamat that have to be installed with: +``` +git clone https://github.com/cudamat/cudamat.git +cd cudamat +python setup.py install --user # for user install (no root) +``` + ## Examples The examples folder contain several examples and use case for the library. The full documentation is available on [Readthedocs](http://pot.readthedocs.io/) @@ -77,8 +93,8 @@ The contributors to this library are: * [Rémi Flamary](http://remi.flamary.com/) * [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/) * [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/) -* [Michael Perrot](http://perso.univ-st-etienne.fr/pem82055/) -* [Léo Gautheron](https://github.com/aje) +* [Michael Perrot](http://perso.univ-st-etienne.fr/pem82055/) (Mapping estimation) +* [Léo Gautheron](https://github.com/aje) (GPU implementation) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): -- cgit v1.2.3 From 2cc2f069b9241efdf2d0e0be1f7fbb6e6ab9dc45 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 20 Apr 2017 15:40:25 +0200 Subject: update doc --- docs/source/readme.rst | 26 +++++++++++++++++++++++++- 1 file changed, 25 insertions(+), 1 deletion(-) diff --git a/docs/source/readme.rst b/docs/source/readme.rst index e1d4459..13cf572 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -47,7 +47,7 @@ it) on you machine using :: - python setup.py install --user + python setup.py install --user # for user install (no root) The toolbox is also available on PyPI with a possibly slightly older version. You can install it with: @@ -65,6 +65,28 @@ without errors: Note that for easier access the module is name ot instead of pot. +Dependencies +~~~~~~~~~~~~ + +Some sub-modules require additional dependences which are discussed +below + +- ot.dr (Wasserstein dimensionality rediuction) depends on autograd and + pymanopt that can be installed with: + + :: + + pip install pymanopt autograd + +- ot.gpu (GPU accelerated OT) depends on cudamat that have to be + installed with: + + :: + + git clone https://github.com/cudamat/cudamat.git + cd cudamat + python setup.py install --user # for user install (no root) + Examples -------- @@ -103,6 +125,8 @@ The contributors to this library are: - `Nicolas Courty `__ - `Laetitia Chapel `__ - `Michael Perrot `__ + (Mapping estimation) +- `Léo Gautheron `__ (GPU implementation) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various -- cgit v1.2.3 From 26c128963fefaf7610fa6a1d48a676efc71dd137 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 20 Apr 2017 15:47:51 +0200 Subject: add test gpu --- test/test_gpu_sinkhorn.py | 26 ++++++++++++++++++++++++++ 1 file changed, 26 insertions(+) create mode 100644 test/test_gpu_sinkhorn.py diff --git a/test/test_gpu_sinkhorn.py b/test/test_gpu_sinkhorn.py new file mode 100644 index 0000000..bfa2cd2 --- /dev/null +++ b/test/test_gpu_sinkhorn.py @@ -0,0 +1,26 @@ +import ot +import numpy as np +import time +import ot.gpu + +def describeRes(r): + print("min:{:.3E}, max::{:.3E}, mean::{:.3E}, std::{:.3E}".format(np.min(r),np.max(r),np.mean(r),np.std(r))) + + +for n in [5000, 10000, 15000, 20000]: + print(n) + a = np.random.rand(n // 4, 100) + b = np.random.rand(n, 100) + time1 = time.time() + transport = ot.da.OTDA_sinkhorn() + transport.fit(a, b) + G1 = transport.G + time2 = time.time() + transport = ot.gpu.da.OTDA_sinkhorn() + transport.fit(a, b) + G2 = transport.G + time3 = time.time() + print("Normal sinkhorn, time: {:6.2f} sec ".format(time2 - time1)) + describeRes(G1) + print(" GPU sinkhorn, time: {:6.2f} sec ".format(time3 - time2)) + describeRes(G2) \ No newline at end of file -- cgit v1.2.3 From ad693e32e78df399ccaea07402613813a6c4d8db Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 20 Apr 2017 15:49:22 +0200 Subject: update readme --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 33ff301..53faac7 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ This open source Python library provide several solvers for optimization problem It provides the following solvers: * OT solver for the linear program/ Earth Movers Distance [1]. -* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10]. +* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10] with optional GPU implementation. * Bregman projections for Wasserstein barycenter [3] and unmixing [4]. * Optimal transport for domain adaptation with group lasso regularization [5] * Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. @@ -57,11 +57,11 @@ Note that for easier access the module is name ot instead of pot. Some sub-modules require additional dependences which are discussed below -* ot.dr (Wasserstein dimensionality rediuction) depends on autograd and pymanopt that can be installed with: +* *ot.dr* (Wasserstein dimensionality rediuction) depends on autograd and pymanopt that can be installed with: ``` pip install pymanopt autograd ``` -* ot.gpu (GPU accelerated OT) depends on cudamat that have to be installed with: +* *ot.gpu* (GPU accelerated OT) depends on cudamat that have to be installed with: ``` git clone https://github.com/cudamat/cudamat.git cd cudamat -- cgit v1.2.3 From 4cd6973a821faeaed54f6ab3e8c4aa6d5cb3f9eb Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 20 Apr 2017 15:52:38 +0200 Subject: Update README.md --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 53faac7..04d339f 100644 --- a/README.md +++ b/README.md @@ -57,11 +57,11 @@ Note that for easier access the module is name ot instead of pot. Some sub-modules require additional dependences which are discussed below -* *ot.dr* (Wasserstein dimensionality rediuction) depends on autograd and pymanopt that can be installed with: +* **ot.dr** (Wasserstein dimensionality rediuction) depends on autograd and pymanopt that can be installed with: ``` pip install pymanopt autograd ``` -* *ot.gpu* (GPU accelerated OT) depends on cudamat that have to be installed with: +* **ot.gpu** (GPU accelerated OT) depends on cudamat that have to be installed with: ``` git clone https://github.com/cudamat/cudamat.git cd cudamat -- cgit v1.2.3 From e6604ea5ceb57ab1276454e509b22181c938d20b Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 20 Apr 2017 15:53:27 +0200 Subject: Update README.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 04d339f..052ffc2 100644 --- a/README.md +++ b/README.md @@ -10,7 +10,7 @@ This open source Python library provide several solvers for optimization problem It provides the following solvers: * OT solver for the linear program/ Earth Movers Distance [1]. -* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10] with optional GPU implementation. +* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10] with optional GPU implementation (required cudamat). * Bregman projections for Wasserstein barycenter [3] and unmixing [4]. * Optimal transport for domain adaptation with group lasso regularization [5] * Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. -- cgit v1.2.3 From b33146062f847edd7c535a78535eae47058ba8fe Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 21 Apr 2017 09:08:38 +0200 Subject: small normalization bug --- README.md | 2 ++ ot/da.py | 2 +- 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 53faac7..46c3649 100644 --- a/README.md +++ b/README.md @@ -68,6 +68,8 @@ cd cudamat python setup.py install --user # for user install (no root) ``` +obviously you need CUDA installed and a compatible GPU. + ## Examples The examples folder contain several examples and use case for the library. The full documentation is available on [Readthedocs](http://pot.readthedocs.io/) diff --git a/ot/da.py b/ot/da.py index d607e50..44ce829 100644 --- a/ot/da.py +++ b/ot/da.py @@ -620,7 +620,7 @@ class OTDA(object): self.wt=wt self.M=dist(xs,xt,metric=self.metric) - self.normalize() + self.normalizeM(norm) self.G=emd(ws,wt,self.M) self.computed=True -- cgit v1.2.3 From 8fc74ed792cda0fc0073dab218f7fdc08bf3c1ba Mon Sep 17 00:00:00 2001 From: Leo gautheron Date: Fri, 21 Apr 2017 12:38:39 +0200 Subject: performance improvement sinkhorn lpl1 - instead of updating individually for each target examples, update for all target examples at once using numpy functions. This allows for a faster computation (for me, divided by 4 on 3000*100 random matricies and random labels in [0,1]). - if I understoud correctly, a value of -1 in the array labels_a meant that we didn't have a label for this example. But in machine learning, we often encounter the binary case where we say we have the positive class (+1) and negative class (-1); thus with a dataset like this, the algorithm wouldn't work as expected. I replaced the default value for 'no label' to '-99' instead of '-1', and I added a parameter to modify it. --- ot/da.py | 45 ++++++++++++++++++++++----------------------- 1 file changed, 22 insertions(+), 23 deletions(-) diff --git a/ot/da.py b/ot/da.py index 44ce829..c944c0d 100644 --- a/ot/da.py +++ b/ot/da.py @@ -11,11 +11,7 @@ from .optim import cg from .optim import gcg -def indices(a, func): - return [i for (i, val) in enumerate(a) if func(val)] - - -def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerItermax = 200,stopInnerThr=1e-9,verbose=False,log=False): +def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerItermax = 200,stopInnerThr=1e-9,unlabelledValue=-99,verbose=False,log=False): """ Solve the entropic regularization optimal transport problem with nonconvex group lasso regularization @@ -46,7 +42,7 @@ def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerIter labels_a : np.ndarray (ns,) labels of samples in the source domain b : np.ndarray (nt,) - samples in the target domain + samples weights in the target domain M : np.ndarray (ns,nt) loss matrix reg : float @@ -59,6 +55,8 @@ def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerIter Max number of iterations (inner sinkhorn solver) stopInnerThr : float, optional Stop threshold on error (inner sinkhorn solver) (>0) + unlabelledValue : int, optional + this value in array labels_a means this is an unlabelled example verbose : bool, optional Print information along iterations log : bool, optional @@ -94,9 +92,9 @@ def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerIter Nfin = len(b) indices_labels = [] - idx_begin = np.min(labels_a) - for c in range(idx_begin,np.max(labels_a)+1): - idxc = indices(labels_a, lambda x: x==c) + classes = np.unique(labels_a) + for c in classes: + idxc, = np.where(labels_a == c) indices_labels.append(idxc) W=np.zeros(M.shape) @@ -106,20 +104,21 @@ def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerIter transp=sinkhorn(a,b,Mreg,reg,numItermax=numInnerItermax, stopThr=stopInnerThr) # the transport has been computed. Check if classes are really separated W = np.ones((Nini,Nfin)) - for t in range(Nfin): - column = transp[:,t] - all_maj = [] - for c in range(idx_begin,np.max(labels_a)+1): - col_c = column[indices_labels[c-idx_begin]] - if c!=-1: - maj = p*((sum(col_c)+epsilon)**(p-1)) - W[indices_labels[c-idx_begin],t]=maj - all_maj.append(maj) - - # now we majorize the unlabelled by the min of the majorizations - # do it only for unlabbled data - if idx_begin==-1: - W[indices_labels[0],t]=np.min(all_maj) + all_majs = [] + idx_unlabelled = -1 + for (i, c) in enumerate(classes): + if c != unlabelledValue: + majs = np.sum(transp[indices_labels[i]], axis=0) + majs = p*((majs+epsilon)**(p-1)) + W[indices_labels[i]] = majs + all_majs.append(majs) + else: + idx_unlabelled = i + + # now we majorize the unlabelled (if there are any) by the min of + # the majorizations. do it only for unlabbled data + if idx_unlabelled != -1: + W[indices_labels[idx_unlabelled]] = np.min(all_majs, axis=0) return transp -- cgit v1.2.3 From e2cf8538b05f026d73c6033777699af77e7508b5 Mon Sep 17 00:00:00 2001 From: Leo gautheron Date: Mon, 24 Apr 2017 10:43:44 +0200 Subject: add GPU implementation sinkhorn lpl1 --- ot/gpu/bregman.py | 12 +++-- ot/gpu/da.py | 109 +++++++++++++++++++++++++++++++++++++++++ test/test_gpu_sinkhorn_lpl1.py | 28 +++++++++++ 3 files changed, 146 insertions(+), 3 deletions(-) create mode 100644 test/test_gpu_sinkhorn_lpl1.py diff --git a/ot/gpu/bregman.py b/ot/gpu/bregman.py index f91f15f..cc610b7 100644 --- a/ot/gpu/bregman.py +++ b/ot/gpu/bregman.py @@ -8,7 +8,7 @@ import cudamat def sinkhorn(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False, - log=False): + log=False, returnAsGPU=False): # init data Nini = len(a) Nfin = len(b) @@ -77,7 +77,13 @@ def sinkhorn(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False, K_GPU.mult_by_col(u_GPU, target=K_GPU) K_GPU.mult_by_row(v_GPU.transpose(), target=K_GPU) + + if returnAsGPU: + res = K_GPU + else: + res = K_GPU.asarray() + if log: - return K_GPU.asarray(), log + return res, log else: - return K_GPU.asarray() + return res diff --git a/ot/gpu/da.py b/ot/gpu/da.py index 0121e7b..700a5e4 100644 --- a/ot/gpu/da.py +++ b/ot/gpu/da.py @@ -11,6 +11,27 @@ import cudamat def pairwiseEuclideanGPU(a, b, returnAsGPU=False, squared=False): + """ + Compute the pairwise euclidean distance between matrices a and b. + + + Parameters + ---------- + a : np.ndarray (n, f) + first matrice + b : np.ndarray (m, f) + second matrice + returnAsGPU : boolean, optional (default False) + if True, returns cudamat matrix still on GPU, else return np.ndarray + squared : boolean, optional (default False) + if True, return squared euclidean distance matrice + + + Returns + ------- + c : (n x m) np.ndarray or cudamat.CUDAMatrix + pairwise euclidean distance distance matrix + """ # a is shape (n, f) and b shape (m, f). Return matrix c of shape (n, m). # First compute in c_GPU the squared euclidean distance. And return its # square root. At each cell [i,j] of c, we want to have @@ -46,6 +67,69 @@ def pairwiseEuclideanGPU(a, b, returnAsGPU=False, squared=False): return c_GPU.asarray() +def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10, + numInnerItermax=200, stopInnerThr=1e-9, + unlabelledValue=-99, verbose=False, log=False): + p = 0.5 + epsilon = 1e-3 + + # init data + Nfin = len(b) + + indices_labels = [] + classes = np.unique(labels_a) + for c in classes: + idxc, = np.where(labels_a == c) + indices_labels.append(cudamat.CUDAMatrix(idxc.reshape(1, -1))) + + Mreg_GPU = cudamat.empty(M_GPU.shape) + W_GPU = cudamat.empty(M_GPU.shape).assign(0) + + for cpt in range(numItermax): + Mreg_GPU.assign(M_GPU) + Mreg_GPU.add_mult(W_GPU, eta) + transp_GPU = sinkhorn(a, b, Mreg_GPU, reg, numItermax=numInnerItermax, + stopThr=stopInnerThr, returnAsGPU=True) + # the transport has been computed. Check if classes are really + # separated + W_GPU.assign(1) + W_GPU = W_GPU.transpose() + all_majs_GPU = [] + idx_unlabelled = -1 + for (i, c) in enumerate(classes): + if c != unlabelledValue: + (_, nbRow) = indices_labels[i].shape + tmpC_GPU = cudamat.empty((Nfin, nbRow)).assign(0) + transp_GPU.transpose().select_columns(indices_labels[i], + tmpC_GPU) + majs_GPU = tmpC_GPU.sum(axis=1).add(epsilon) + cudamat.pow(majs_GPU, (p-1)) + majs_GPU.mult(p) + all_majs_GPU.append(majs_GPU) + + tmpC_GPU.assign(0) + tmpC_GPU.add_col_vec(majs_GPU) + W_GPU.set_selected_columns(indices_labels[i], tmpC_GPU) + else: + idx_unlabelled = i + + # now we majorize the unlabelled (if there are any) by the min of + # the majorizations. do it only for unlabbled data + if idx_unlabelled != -1: + all_majs = np.array([m_GPU.asarray() for m_GPU in all_majs_GPU]) + minMaj_GPU = (cudamat.CUDAMatrix(all_majs).min(axis=0) + .transpose()) + (_, nbRow) = indices_labels[idx_unlabelled].shape + tmpC_GPU = cudamat.empty((Nfin, nbRow)).assign(0) + + tmpC_GPU.add_col_vec(minMaj_GPU) + W_GPU.set_selected_columns(indices_labels[idx_unlabelled], + tmpC_GPU) + W_GPU = W_GPU.transpose() + + return transp_GPU.asarray() + + class OTDA_GPU(OTDA): def normalizeM(self, norm): if norm == "median": @@ -84,3 +168,28 @@ class OTDA_sinkhorn(OTDA_GPU): self.normalizeM(norm) self.G = sinkhorn(ws, wt, self.M_GPU, reg, **kwargs) self.computed = True + + +class OTDA_lpl1(OTDA_GPU): + def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None, + **kwargs): + cudamat.init() + xs = np.asarray(xs, dtype=np.float64) + xt = np.asarray(xt, dtype=np.float64) + + self.xs = xs + self.xt = xt + + if wt is None: + wt = unif(xt.shape[0]) + if ws is None: + ws = unif(xs.shape[0]) + + self.ws = ws + self.wt = wt + + self.M_GPU = pairwiseEuclideanGPU(xs, xt, returnAsGPU=True, + squared=True) + self.normalizeM(norm) + self.G = sinkhorn_lpl1_mm(ws, ys, wt, self.M_GPU, reg, eta, **kwargs) + self.computed = True diff --git a/test/test_gpu_sinkhorn_lpl1.py b/test/test_gpu_sinkhorn_lpl1.py new file mode 100644 index 0000000..e6cdd31 --- /dev/null +++ b/test/test_gpu_sinkhorn_lpl1.py @@ -0,0 +1,28 @@ +import ot +import numpy as np +import time +import ot.gpu + + +def describeRes(r): + print("min:{:.3E}, max:{:.3E}, mean:{:.3E}, std:{:.3E}" + .format(np.min(r), np.max(r), np.mean(r), np.std(r))) + +for n in [5000, 10000, 15000, 20000]: + print(n) + a = np.random.rand(n // 4, 100) + labels_a = np.random.randint(10, size=(n // 4)) + b = np.random.rand(n, 100) + time1 = time.time() + transport = ot.da.OTDA_lpl1() + transport.fit(a, labels_a, b) + G1 = transport.G + time2 = time.time() + transport = ot.gpu.da.OTDA_lpl1() + transport.fit(a, labels_a, b) + G2 = transport.G + time3 = time.time() + print("Normal sinkhorn lpl1, time: {:6.2f} sec ".format(time2 - time1)) + describeRes(G1) + print(" GPU sinkhorn lpl1, time: {:6.2f} sec ".format(time3 - time2)) + describeRes(G2) -- cgit v1.2.3 From 9df60d467ddd3316334578adac8a80667cfa8759 Mon Sep 17 00:00:00 2001 From: Leo gautheron Date: Mon, 24 Apr 2017 10:56:26 +0200 Subject: Remove unnecessary parameter --- ot/da.py | 37 +++++++++++-------------------------- ot/gpu/da.py | 41 ++++++++++------------------------------- 2 files changed, 21 insertions(+), 57 deletions(-) diff --git a/ot/da.py b/ot/da.py index c944c0d..557e2aa 100644 --- a/ot/da.py +++ b/ot/da.py @@ -11,7 +11,7 @@ from .optim import cg from .optim import gcg -def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerItermax = 200,stopInnerThr=1e-9,unlabelledValue=-99,verbose=False,log=False): +def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerItermax = 200,stopInnerThr=1e-9,verbose=False,log=False): """ Solve the entropic regularization optimal transport problem with nonconvex group lasso regularization @@ -55,8 +55,6 @@ def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerIter Max number of iterations (inner sinkhorn solver) stopInnerThr : float, optional Stop threshold on error (inner sinkhorn solver) (>0) - unlabelledValue : int, optional - this value in array labels_a means this is an unlabelled example verbose : bool, optional Print information along iterations log : bool, optional @@ -84,41 +82,28 @@ def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerIter ot.optim.cg : General regularized OT """ - p=0.5 + p = 0.5 epsilon = 1e-3 - # init data - Nini = len(a) - Nfin = len(b) - indices_labels = [] classes = np.unique(labels_a) for c in classes: idxc, = np.where(labels_a == c) indices_labels.append(idxc) - W=np.zeros(M.shape) + W = np.zeros(M.shape) for cpt in range(numItermax): Mreg = M + eta*W - transp=sinkhorn(a,b,Mreg,reg,numItermax=numInnerItermax, stopThr=stopInnerThr) - # the transport has been computed. Check if classes are really separated - W = np.ones((Nini,Nfin)) - all_majs = [] - idx_unlabelled = -1 + transp = sinkhorn(a, b, Mreg, reg, numItermax=numInnerItermax, + stopThr=stopInnerThr) + # the transport has been computed. Check if classes are really + # separated + W = np.ones(M.shape) for (i, c) in enumerate(classes): - if c != unlabelledValue: - majs = np.sum(transp[indices_labels[i]], axis=0) - majs = p*((majs+epsilon)**(p-1)) - W[indices_labels[i]] = majs - all_majs.append(majs) - else: - idx_unlabelled = i - - # now we majorize the unlabelled (if there are any) by the min of - # the majorizations. do it only for unlabbled data - if idx_unlabelled != -1: - W[indices_labels[idx_unlabelled]] = np.min(all_majs, axis=0) + majs = np.sum(transp[indices_labels[i]], axis=0) + majs = p*((majs+epsilon)**(p-1)) + W[indices_labels[i]] = majs return transp diff --git a/ot/gpu/da.py b/ot/gpu/da.py index 700a5e4..399e769 100644 --- a/ot/gpu/da.py +++ b/ot/gpu/da.py @@ -69,11 +69,9 @@ def pairwiseEuclideanGPU(a, b, returnAsGPU=False, squared=False): def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10, numInnerItermax=200, stopInnerThr=1e-9, - unlabelledValue=-99, verbose=False, log=False): + verbose=False, log=False): p = 0.5 epsilon = 1e-3 - - # init data Nfin = len(b) indices_labels = [] @@ -94,37 +92,18 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10, # separated W_GPU.assign(1) W_GPU = W_GPU.transpose() - all_majs_GPU = [] - idx_unlabelled = -1 for (i, c) in enumerate(classes): - if c != unlabelledValue: - (_, nbRow) = indices_labels[i].shape - tmpC_GPU = cudamat.empty((Nfin, nbRow)).assign(0) - transp_GPU.transpose().select_columns(indices_labels[i], - tmpC_GPU) - majs_GPU = tmpC_GPU.sum(axis=1).add(epsilon) - cudamat.pow(majs_GPU, (p-1)) - majs_GPU.mult(p) - all_majs_GPU.append(majs_GPU) - - tmpC_GPU.assign(0) - tmpC_GPU.add_col_vec(majs_GPU) - W_GPU.set_selected_columns(indices_labels[i], tmpC_GPU) - else: - idx_unlabelled = i - - # now we majorize the unlabelled (if there are any) by the min of - # the majorizations. do it only for unlabbled data - if idx_unlabelled != -1: - all_majs = np.array([m_GPU.asarray() for m_GPU in all_majs_GPU]) - minMaj_GPU = (cudamat.CUDAMatrix(all_majs).min(axis=0) - .transpose()) - (_, nbRow) = indices_labels[idx_unlabelled].shape + (_, nbRow) = indices_labels[i].shape tmpC_GPU = cudamat.empty((Nfin, nbRow)).assign(0) + transp_GPU.transpose().select_columns(indices_labels[i], tmpC_GPU) + majs_GPU = tmpC_GPU.sum(axis=1).add(epsilon) + cudamat.pow(majs_GPU, (p-1)) + majs_GPU.mult(p) + + tmpC_GPU.assign(0) + tmpC_GPU.add_col_vec(majs_GPU) + W_GPU.set_selected_columns(indices_labels[i], tmpC_GPU) - tmpC_GPU.add_col_vec(minMaj_GPU) - W_GPU.set_selected_columns(indices_labels[idx_unlabelled], - tmpC_GPU) W_GPU = W_GPU.transpose() return transp_GPU.asarray() -- cgit v1.2.3 From 37977810ab538fe461ef8f3ec16434c59f6f7c5a Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 9 Jun 2017 13:11:41 +0200 Subject: add doc ot.gpu.bregman --- ot/gpu/bregman.py | 74 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 74 insertions(+) diff --git a/ot/gpu/bregman.py b/ot/gpu/bregman.py index cc610b7..2c3e317 100644 --- a/ot/gpu/bregman.py +++ b/ot/gpu/bregman.py @@ -9,6 +9,80 @@ import cudamat def sinkhorn(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False, log=False, returnAsGPU=False): + """ + Solve the entropic regularization optimal transport problem on GPU + + The function solves the following optimization problem: + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + where : + + - M is the (ns,nt) metric cost matrix + - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target weights (sum to 1) + + The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [2]_ + + + Parameters + ---------- + a : np.ndarray (ns,) + samples weights in the source domain + b : np.ndarray (nt,) + samples in the target domain + M_GPU : cudamat.CUDAMatrix (ns,nt) + loss matrix + reg : float + Regularization term >0 + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + returnAsGPU : bool, optional + return the OT matrix as a cudamat.CUDAMatrix + + Returns + ------- + gamma : (ns x nt) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + Examples + -------- + + >>> import ot + >>> a=[.5,.5] + >>> b=[.5,.5] + >>> M=[[0.,1.],[1.,0.]] + >>> ot.sinkhorn(a,b,M,1) + array([[ 0.36552929, 0.13447071], + [ 0.13447071, 0.36552929]]) + + + References + ---------- + + .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013 + + + See Also + -------- + ot.lp.emd : Unregularized OT + ot.optim.cg : General regularized OT + + """ # init data Nini = len(a) Nfin = len(b) -- cgit v1.2.3 From 2eaee96a2a927476cbd8ca31754a89afd0825916 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 9 Jun 2017 13:18:04 +0200 Subject: add doc and correct encoding --- ot/__init__.py | 6 +++++- ot/bregman.py | 4 ++-- ot/utils.py | 8 ++++---- 3 files changed, 11 insertions(+), 7 deletions(-) diff --git a/ot/__init__.py b/ot/__init__.py index 13abcda..b2af88b 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -1,4 +1,8 @@ -"""Python Optimal Transport toolbox""" +"""Python Optimal Transport toolbox + + + +""" # All submodules and packages diff --git a/ot/bregman.py b/ot/bregman.py index c46e5dc..00dca88 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -508,11 +508,11 @@ def geometricMean(alldistribT): return np.exp(np.mean(np.log(alldistribT),axis=1)) def projR(gamma,p): - #return np.dot(np.diag(p/np.maximum(np.sum(gamma,axis=1),1e-10)),gamma) + """return the KL projection on the row constrints """ return np.multiply(gamma.T,p/np.maximum(np.sum(gamma,axis=1),1e-10)).T def projC(gamma,q): - #return (np.dot(np.diag(q/np.maximum(np.sum(gamma,axis=0),1e-10)),gamma.T)).T + """return the KL projection on the column constrints """ return np.multiply(gamma,q/np.maximum(np.sum(gamma,axis=0),1e-10)) diff --git a/ot/utils.py b/ot/utils.py index e5cd6c0..fc6b0d2 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -63,10 +63,10 @@ def dist(x1,x2=None,metric='sqeuclidean'): matrix with n2 samples of size d (if None then x2=x1) metric : str, fun, optional name of the metric to be computed (full list in the doc of scipy), If a string, - the distance function can be ‘braycurtis’, ‘canberra’, ‘chebyshev’, ‘cityblock’, - ‘correlation’, ‘cosine’, ‘dice’, ‘euclidean’, ‘hamming’, ‘jaccard’, ‘kulsinski’, - ‘mahalanobis’, ‘matching’, ‘minkowski’, ‘rogerstanimoto’, ‘russellrao’, ‘seuclidean’, - ‘sokalmichener’, ‘sokalsneath’, ‘sqeuclidean’, ‘wminkowski’, ‘yule’. + the distance function can be 'braycurtis', 'canberra', 'chebyshev', 'cityblock', + 'correlation', 'cosine', 'dice', 'euclidean', 'hamming', 'jaccard', 'kulsinski', + 'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', + 'sokalmichener', 'sokalsneath', 'sqeuclidean', 'wminkowski', 'yule'. Returns -- cgit v1.2.3 From c7a5e3290527c372aa203c18df5f054409e8a60c Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 9 Jun 2017 13:28:40 +0200 Subject: update .gitignore with sphinwgallery temop files --- .gitignore | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/.gitignore b/.gitignore index 72364f9..21be4c9 100644 --- a/.gitignore +++ b/.gitignore @@ -2,10 +2,20 @@ __pycache__/ *.py[cod] *$py.class +.spyproject + +# sphinx-gallery temp files +docs/source/auto_examples/*.pickle +docs/source/auto_examples/*.md5 +docs/auto_examples/ +docs/modules/ # C extensions *.so +# Cython output +ot/lp/emd_wrap.cpp + # Distribution / packaging .Python env/ -- cgit v1.2.3 From 05da582675c89ab20998e1a9505bf3c220e296b8 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 9 Jun 2017 13:57:07 +0200 Subject: update doc --- .../source/auto_examples/auto_examples_jupyter.zip | Bin 36979 -> 39268 bytes docs/source/auto_examples/auto_examples_python.zip | Bin 25361 -> 26670 bytes 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+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_WDA_thumb.png + + :ref:`sphx_glr_auto_examples_plot_WDA.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_WDA + .. raw:: html
diff --git a/docs/source/auto_examples/plot_OT_2D_samples.ipynb b/docs/source/auto_examples/plot_OT_2D_samples.ipynb index e8ec1d1..7d42ba7 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.ipynb +++ b/docs/source/auto_examples/plot_OT_2D_samples.ipynb @@ -24,7 +24,7 @@ "execution_count": null, "cell_type": "code", "source": [ - "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n#%% parameters and data generation\n\nn=20 # nb samples\n\nmu_s=np.array([0,0])\ncov_s=np.array([[1,0],[0,1]])\n\nmu_t=np.array([4,4])\ncov_t=np.array([[1,-.8],[-.8,1]])\n\nxs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s)\nxt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t)\n\na,b = ot.unif(n),ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM=ot.dist(xs,xt)\nM/=M.max()\n\n#%% plot samples\n\npl.figure(1)\npl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\npl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\npl.legend(loc=0)\npl.title('Source and traget distributions')\n\npl.figure(2)\npl.imshow(M,interpolation='nearest')\npl.title('Cost matrix M')\n\n\n#%% EMD\n\nG0=ot.emd(a,b,M)\n\npl.figure(3)\npl.imshow(G0,interpolation='nearest')\npl.title('OT matrix G0')\n\npl.figure(4)\not.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1])\npl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\npl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix with samples')\n\n\n#%% sinkhorn\n\n# reg term\nlambd=5e-3\n\nGs=ot.sinkhorn(a,b,M,lambd)\n\npl.figure(5)\npl.imshow(Gs,interpolation='nearest')\npl.title('OT matrix sinkhorn')\n\npl.figure(6)\not.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1])\npl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\npl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix Sinkhorn with samples')" + "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n#%% parameters and data generation\n\nn=2 # nb samples\n\nmu_s=np.array([0,0])\ncov_s=np.array([[1,0],[0,1]])\n\nmu_t=np.array([4,4])\ncov_t=np.array([[1,-.8],[-.8,1]])\n\nxs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s)\nxt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t)\n\na,b = ot.unif(n),ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM=ot.dist(xs,xt)\nM/=M.max()\n\n#%% plot samples\n\npl.figure(1)\npl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\npl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\npl.legend(loc=0)\npl.title('Source and traget distributions')\n\npl.figure(2)\npl.imshow(M,interpolation='nearest')\npl.title('Cost matrix M')\n\n\n#%% EMD\n\nG0=ot.emd(a,b,M)\n\npl.figure(3)\npl.imshow(G0,interpolation='nearest')\npl.title('OT matrix G0')\n\npl.figure(4)\not.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1])\npl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\npl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix with samples')\n\n\n#%% sinkhorn\n\n# reg term\nlambd=5e-3\n\nGs=ot.sinkhorn(a,b,M,lambd)\n\npl.figure(5)\npl.imshow(Gs,interpolation='nearest')\npl.title('OT matrix sinkhorn')\n\npl.figure(6)\not.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1])\npl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\npl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix Sinkhorn with samples')" ], "outputs": [], "metadata": { diff --git a/docs/source/auto_examples/plot_OT_2D_samples.py b/docs/source/auto_examples/plot_OT_2D_samples.py index 6c39ad4..3b95083 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.py +++ b/docs/source/auto_examples/plot_OT_2D_samples.py @@ -13,7 +13,7 @@ import ot #%% parameters and data generation -n=20 # nb samples +n=2 # nb samples mu_s=np.array([0,0]) cov_s=np.array([[1,0],[0,1]]) diff --git a/docs/source/auto_examples/plot_OT_2D_samples.rst b/docs/source/auto_examples/plot_OT_2D_samples.rst index bc86cb8..01e5f31 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.rst +++ b/docs/source/auto_examples/plot_OT_2D_samples.rst @@ -58,7 +58,7 @@ #%% parameters and data generation - n=20 # nb samples + n=2 # nb samples mu_s=np.array([0,0]) cov_s=np.array([[1,0],[0,1]]) @@ -122,7 +122,7 @@ pl.legend(loc=0) pl.title('OT matrix Sinkhorn with samples') -**Total running time of the script:** ( 0 minutes 1.051 seconds) +**Total running time of the script:** ( 0 minutes 0.406 seconds) diff --git a/docs/source/auto_examples/plot_WDA.ipynb b/docs/source/auto_examples/plot_WDA.ipynb new file mode 100644 index 0000000..6d641a7 --- /dev/null +++ b/docs/source/auto_examples/plot_WDA.ipynb @@ -0,0 +1,54 @@ +{ + "nbformat_minor": 0, + "nbformat": 4, + "cells": [ + { + "execution_count": null, + "cell_type": "code", + "source": [ + "%matplotlib inline" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "\n# WAsserstein Discriminant Analysis\n\n\n@author: rflamary\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\nfrom ot.datasets import get_1D_gauss as gauss\nfrom ot.dr import wda\n\n\n#%% parameters\n\nn=1000 # nb samples in source and target datasets\nnz=0.2\nxs,ys=ot.datasets.get_data_classif('3gauss',n,nz)\nxt,yt=ot.datasets.get_data_classif('3gauss',n,nz)\n\nnbnoise=8\n\nxs=np.hstack((xs,np.random.randn(n,nbnoise)))\nxt=np.hstack((xt,np.random.randn(n,nbnoise)))\n\n#%% plot samples\n\npl.figure(1)\n\n\npl.scatter(xt[:,0],xt[:,1],c=ys,marker='+',label='Source samples')\npl.legend(loc=0)\npl.title('Discriminant dimensions')\n\n\n#%% plot distributions and loss matrix\np=2\nreg=1\nk=10\nmaxiter=100\n\nP,proj = wda(xs,ys,p,reg,k,maxiter=maxiter)\n\n#%% plot samples\n\nxsp=proj(xs)\nxtp=proj(xt)\n\npl.figure(1,(10,5))\n\npl.subplot(1,2,1)\npl.scatter(xsp[:,0],xsp[:,1],c=ys,marker='+',label='Projected samples')\npl.legend(loc=0)\npl.title('Projected training samples')\n\n\npl.subplot(1,2,2)\npl.scatter(xtp[:,0],xtp[:,1],c=ys,marker='+',label='Projected samples')\npl.legend(loc=0)\npl.title('Projected test samples')" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "name": "python2", + "language": "python" + }, + "language_info": { + "mimetype": "text/x-python", + "nbconvert_exporter": "python", + "name": "python", + "file_extension": ".py", + "version": "2.7.12", + "pygments_lexer": "ipython2", + "codemirror_mode": { + "version": 2, + "name": "ipython" + } + } + } +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_WDA.py b/docs/source/auto_examples/plot_WDA.py new file mode 100644 index 0000000..94b7ef4 --- /dev/null +++ b/docs/source/auto_examples/plot_WDA.py @@ -0,0 +1,63 @@ +# -*- coding: utf-8 -*- +""" +================================= +WAsserstein Discriminant Analysis +================================= + +@author: rflamary +""" + +import numpy as np +import matplotlib.pylab as pl +import ot +from ot.datasets import get_1D_gauss as gauss +from ot.dr import wda + + +#%% parameters + +n=1000 # nb samples in source and target datasets +nz=0.2 +xs,ys=ot.datasets.get_data_classif('3gauss',n,nz) +xt,yt=ot.datasets.get_data_classif('3gauss',n,nz) + +nbnoise=8 + +xs=np.hstack((xs,np.random.randn(n,nbnoise))) +xt=np.hstack((xt,np.random.randn(n,nbnoise))) + +#%% plot samples + +pl.figure(1) + + +pl.scatter(xt[:,0],xt[:,1],c=ys,marker='+',label='Source samples') +pl.legend(loc=0) +pl.title('Discriminant dimensions') + + +#%% plot distributions and loss matrix +p=2 +reg=1 +k=10 +maxiter=100 + +P,proj = wda(xs,ys,p,reg,k,maxiter=maxiter) + +#%% plot samples + +xsp=proj(xs) +xtp=proj(xt) + +pl.figure(1,(10,5)) + +pl.subplot(1,2,1) +pl.scatter(xsp[:,0],xsp[:,1],c=ys,marker='+',label='Projected samples') +pl.legend(loc=0) +pl.title('Projected training samples') + + +pl.subplot(1,2,2) +pl.scatter(xtp[:,0],xtp[:,1],c=ys,marker='+',label='Projected samples') +pl.legend(loc=0) +pl.title('Projected test samples') diff --git a/docs/source/auto_examples/plot_WDA.rst b/docs/source/auto_examples/plot_WDA.rst new file mode 100644 index 0000000..379a133 --- /dev/null +++ b/docs/source/auto_examples/plot_WDA.rst @@ -0,0 +1,127 @@ + + +.. _sphx_glr_auto_examples_plot_WDA.py: + + +================================= +WAsserstein Discriminant Analysis +================================= + +@author: rflamary + + + + +.. image:: /auto_examples/images/sphx_glr_plot_WDA_001.png + :align: center + + +.. rst-class:: sphx-glr-script-out + + Out:: + + Compiling cost function... + Computing gradient of cost function... + iter cost val grad. norm + 1 +7.5272200933021116e-01 8.85804426e-01 + 2 +2.5764223980223788e-01 3.04501586e-01 + 3 +1.6018169776696620e-01 1.78298483e-01 + 4 +1.4560944642106255e-01 1.42133298e-01 + 5 +1.0243843483991794e-01 1.23342675e-01 + 6 +7.8856617504010643e-02 1.05379766e-01 + 7 +7.7620851864404483e-02 1.04044062e-01 + 8 +7.3160520861018416e-02 8.33770034e-02 + 9 +6.6999294576662857e-02 2.87368977e-02 + 10 +6.6250206928793964e-02 1.72155066e-03 + 11 +6.6247631521353170e-02 2.43806911e-04 + 12 +6.6247596955965438e-02 1.40066459e-04 + 13 +6.6247580176638649e-02 4.77471577e-06 + 14 +6.6247580163923028e-02 3.00484279e-06 + 15 +6.6247580159235792e-02 1.91039983e-06 + 16 +6.6247580156889613e-02 9.56038747e-07 + Terminated - min grad norm reached after 16 iterations, 7.78 seconds. + + + + +| + + +.. code-block:: python + + + import numpy as np + import matplotlib.pylab as pl + import ot + from ot.datasets import get_1D_gauss as gauss + from ot.dr import wda + + + #%% parameters + + n=1000 # nb samples in source and target datasets + nz=0.2 + xs,ys=ot.datasets.get_data_classif('3gauss',n,nz) + xt,yt=ot.datasets.get_data_classif('3gauss',n,nz) + + nbnoise=8 + + xs=np.hstack((xs,np.random.randn(n,nbnoise))) + xt=np.hstack((xt,np.random.randn(n,nbnoise))) + + #%% plot samples + + pl.figure(1) + + + pl.scatter(xt[:,0],xt[:,1],c=ys,marker='+',label='Source samples') + pl.legend(loc=0) + pl.title('Discriminant dimensions') + + + #%% plot distributions and loss matrix + p=2 + reg=1 + k=10 + maxiter=100 + + P,proj = wda(xs,ys,p,reg,k,maxiter=maxiter) + + #%% plot samples + + xsp=proj(xs) + xtp=proj(xt) + + pl.figure(1,(10,5)) + + pl.subplot(1,2,1) + pl.scatter(xsp[:,0],xsp[:,1],c=ys,marker='+',label='Projected samples') + pl.legend(loc=0) + pl.title('Projected training samples') + + + pl.subplot(1,2,2) + pl.scatter(xtp[:,0],xtp[:,1],c=ys,marker='+',label='Projected samples') + pl.legend(loc=0) + pl.title('Projected test samples') + +**Total running time of the script:** ( 0 minutes 14.134 seconds) + + + +.. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_WDA.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_WDA.ipynb ` + +.. rst-class:: sphx-glr-signature + + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_optim_OTreg.ipynb b/docs/source/auto_examples/plot_optim_OTreg.ipynb index 250ea72..00d4702 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.ipynb +++ b/docs/source/auto_examples/plot_optim_OTreg.ipynb @@ -24,7 +24,7 @@ "execution_count": null, "cell_type": "code", "source": [ - "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n\n\n#%% parameters\n\nn=100 # nb bins\n\n# bin positions\nx=np.arange(n,dtype=np.float64)\n\n# Gaussian distributions\na=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std\nb=ot.datasets.get_1D_gauss(n,m=60,s=10)\n\n# loss matrix\nM=ot.dist(x.reshape((n,1)),x.reshape((n,1)))\nM/=M.max()\n\n#%% EMD\n\nG0=ot.emd(a,b,M)\n\npl.figure(3)\not.plot.plot1D_mat(a,b,G0,'OT matrix G0')\n\n#%% Example with Frobenius norm regularization\n\ndef f(G): return 0.5*np.sum(G**2)\ndef df(G): return G\n\nreg=1e-1\n\nGl2=ot.optim.cg(a,b,M,reg,f,df,verbose=True)\n\npl.figure(3)\not.plot.plot1D_mat(a,b,Gl2,'OT matrix Frob. reg')\n\n#%% Example with entropic regularization\n\ndef f(G): return np.sum(G*np.log(G))\ndef df(G): return np.log(G)+1\n\nreg=1e-3\n\nGe=ot.optim.cg(a,b,M,reg,f,df,verbose=True)\n\npl.figure(4)\not.plot.plot1D_mat(a,b,Ge,'OT matrix Entrop. reg')\n\n#%% Example with Frobenius norm + entropic regularization with gcg\n\ndef f(G): return 0.5*np.sum(G**2)\ndef df(G): return G\n\nreg1=1e-1\nreg2=1e-1\n\nGel2=ot.optim.gcg(a,b,M,reg1,reg2,f,df,verbose=True)\n\npl.figure(5)\not.plot.plot1D_mat(a,b,Gel2,'OT entropic + matrix Frob. reg')" + "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n\n\n#%% parameters\n\nn=100 # nb bins\n\n# bin positions\nx=np.arange(n,dtype=np.float64)\n\n# Gaussian distributions\na=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std\nb=ot.datasets.get_1D_gauss(n,m=60,s=10)\n\n# loss matrix\nM=ot.dist(x.reshape((n,1)),x.reshape((n,1)))\nM/=M.max()\n\n#%% EMD\n\nG0=ot.emd(a,b,M)\n\npl.figure(3)\not.plot.plot1D_mat(a,b,G0,'OT matrix G0')\n\n#%% Example with Frobenius norm regularization\n\ndef f(G): return 0.5*np.sum(G**2)\ndef df(G): return G\n\nreg=1e-1\n\nGl2=ot.optim.cg(a,b,M,reg,f,df,verbose=True)\n\npl.figure(3)\not.plot.plot1D_mat(a,b,Gl2,'OT matrix Frob. reg')\n\n#%% Example with entropic regularization\n\ndef f(G): return np.sum(G*np.log(G))\ndef df(G): return np.log(G)+1\n\nreg=1e-3\n\nGe=ot.optim.cg(a,b,M,reg,f,df,verbose=True)\n\npl.figure(4)\not.plot.plot1D_mat(a,b,Ge,'OT matrix Entrop. reg')\n\n#%% Example with Frobenius norm + entropic regularization with gcg\n\ndef f(G): return 0.5*np.sum(G**2)\ndef df(G): return G\n\nreg1=1e-3\nreg2=1e-1\n\nGel2=ot.optim.gcg(a,b,M,reg1,reg2,f,df,verbose=True)\n\npl.figure(5)\not.plot.plot1D_mat(a,b,Gel2,'OT entropic + matrix Frob. reg')" ], "outputs": [], "metadata": { diff --git a/docs/source/auto_examples/plot_optim_OTreg.py b/docs/source/auto_examples/plot_optim_OTreg.py index 3c4d3f4..c585445 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.py +++ b/docs/source/auto_examples/plot_optim_OTreg.py @@ -64,7 +64,7 @@ ot.plot.plot1D_mat(a,b,Ge,'OT matrix Entrop. reg') def f(G): return 0.5*np.sum(G**2) def df(G): return G -reg1=1e-1 +reg1=1e-3 reg2=1e-1 Gel2=ot.optim.gcg(a,b,M,reg1,reg2,f,df,verbose=True) diff --git a/docs/source/auto_examples/plot_optim_OTreg.rst b/docs/source/auto_examples/plot_optim_OTreg.rst index d6397ba..0dff327 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.rst +++ b/docs/source/auto_examples/plot_optim_OTreg.rst @@ -483,10 +483,11 @@ Regularized OT with generic solver 200|1.607143e-01|-2.151971e-10 It. |Loss |Delta loss -------------------------------- - 0|-4.988764e-01|0.000000e+00 - 1|-4.993932e-01|-1.034993e-03 - 2|-4.993933e-01|-9.845917e-08 - 3|-4.993933e-01|-9.206594e-12 + 0|1.693084e-01|0.000000e+00 + 1|1.610121e-01|-5.152589e-02 + 2|1.609378e-01|-4.622297e-04 + 3|1.609284e-01|-5.830043e-05 + 4|1.609284e-01|-1.111580e-12 @@ -554,14 +555,14 @@ Regularized OT with generic solver def f(G): return 0.5*np.sum(G**2) def df(G): return G - reg1=1e-1 + reg1=1e-3 reg2=1e-1 Gel2=ot.optim.gcg(a,b,M,reg1,reg2,f,df,verbose=True) pl.figure(5) ot.plot.plot1D_mat(a,b,Gel2,'OT entropic + matrix Frob. reg') -**Total running time of the script:** ( 0 minutes 2.358 seconds) +**Total running time of the script:** ( 0 minutes 2.422 seconds) diff --git a/docs/source/conf.py b/docs/source/conf.py index d58f160..42e2b79 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -25,7 +25,8 @@ except ImportError: ## check whether in the source directory... # -sys.path.insert(0, os.path.abspath("../..")) + +#!!! This should be commented when executing sphinx-gallery class Mock(MagicMock): @classmethod def __getattr__(cls, name): @@ -37,7 +38,10 @@ sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. -#sys.path.insert(0, os.path.abspath('.')) +sys.path.insert(0, os.path.abspath('.')) +sys.path.insert(0, os.path.abspath('..')) +sys.path.insert(0, os.path.abspath("../..")) + # -- General configuration ------------------------------------------------ diff --git a/examples/plot_WDA.py b/examples/plot_WDA.py index 8d24bdc..bbe3888 100644 --- a/examples/plot_WDA.py +++ b/examples/plot_WDA.py @@ -1,8 +1,8 @@ # -*- coding: utf-8 -*- """ -==================== -1D optimal transport -==================== +================================= +Wasserstein Discriminant Analysis +================================= @author: rflamary """ -- cgit v1.2.3 From a632c40b97c51534bfe65fee8b493780df118039 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 13 Jun 2017 14:39:59 +0200 Subject: make sinkhorn more general with method selection --- ot/bregman.py | 110 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++-- 1 file changed, 107 insertions(+), 3 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 00dca88..6b3c68b 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -5,8 +5,112 @@ Bregman projections for regularized OT import numpy as np +def sinkhorn(a,b, M, reg,method='sinkhorn', numItermax = 1000, stopThr=1e-9, verbose=False, log=False,**kwargs): + u""" + Solve the entropic regularization optimal transport problem + + The function solves the following optimization problem: + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + where : + + - M is the (ns,nt) metric cost matrix + - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target weights (sum to 1) + + The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [2]_ + + + Parameters + ---------- + a : np.ndarray (ns,) + samples weights in the source domain + b : np.ndarray (nt,) + samples in the target domain + M : np.ndarray (ns,nt) + loss matrix + reg : float + Regularization term >0 + method : str + method used for the solver either 'sinkhorn', 'sinkhorn_stabilized' or + 'sinkhorn_epsilon_scaling', see those function for specific parameters + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + gamma : (ns x nt) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + Examples + -------- + + >>> import ot + >>> a=[.5,.5] + >>> b=[.5,.5] + >>> M=[[0.,1.],[1.,0.]] + >>> ot.sinkhorn(a,b,M,1) + array([[ 0.36552929, 0.13447071], + [ 0.13447071, 0.36552929]]) + + + References + ---------- + + .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013 + + .. [9] Schmitzer, B. (2016). Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems. arXiv preprint arXiv:1610.06519. + + .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + + + + See Also + -------- + ot.lp.emd : Unregularized OT + ot.optim.cg : General regularized OT + ot.bregman.sinkhorn_knopp : Classic Sinkhorn [2] + ot.bregman.sinkhorn_stabilized: Stabilized sinkhorn [9][10] + ot.bregman.sinkhorn_epsilon_scaling: Sinkhorn with epslilon scaling [9][10] + + """ + + if method.lower()=='sinkhorn': + sink= lambda: sinkhorn_knopp(a,b, M, reg,numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log,**kwargs) + elif method.lower()=='sinkhorn_stabilized': + sink= lambda: sinkhorn_stabilized(a,b, M, reg,numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log, **kwargs) + elif method.lower()=='sinkhorn_epsilon_scaling': + sink= lambda: sinkhorn_epsilon_scaling(a,b, M, reg,numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log, **kwargs) + else: + print('Warning : unknown method using classic Sinkhorn Knopp') + sink= lambda: sinkhorn_knopp(a,b, M, reg, **kwargs) + + return sink() + + + + -def sinkhorn(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, log=False): +def sinkhorn_knopp(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, log=False,**kwargs): """ Solve the entropic regularization optimal transport problem @@ -147,7 +251,7 @@ def sinkhorn(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, log=Fa else: return u.reshape((-1,1))*K*v.reshape((1,-1)) -def sinkhorn_stabilized(a,b, M, reg, numItermax = 1000,tau=1e3, stopThr=1e-9,warmstart=None, verbose=False,print_period=20, log=False): +def sinkhorn_stabilized(a,b, M, reg, numItermax = 1000,tau=1e3, stopThr=1e-9,warmstart=None, verbose=False,print_period=20, log=False,**kwargs): """ Solve the entropic regularization OT problem with log stabilization @@ -331,7 +435,7 @@ def sinkhorn_stabilized(a,b, M, reg, numItermax = 1000,tau=1e3, stopThr=1e-9,war else: return get_Gamma(alpha,beta,u,v) -def sinkhorn_epsilon_scaling(a,b, M, reg, numItermax = 100, epsilon0=1e4, numInnerItermax = 100,tau=1e3, stopThr=1e-9,warmstart=None, verbose=False,print_period=10, log=False): +def sinkhorn_epsilon_scaling(a,b, M, reg, numItermax = 100, epsilon0=1e4, numInnerItermax = 100,tau=1e3, stopThr=1e-9,warmstart=None, verbose=False,print_period=10, log=False,**kwargs): """ Solve the entropic regularization optimal transport problem with log stabilization and epsilon scaling. -- cgit v1.2.3 From 3af9b06b6b3c24eb02931cd5fbf798034dd6b8a1 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 13 Jun 2017 14:40:19 +0200 Subject: add em2 computation example --- examples/plot_compute_emd.py | 57 ++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 57 insertions(+) create mode 100644 examples/plot_compute_emd.py diff --git a/examples/plot_compute_emd.py b/examples/plot_compute_emd.py new file mode 100644 index 0000000..87b39a6 --- /dev/null +++ b/examples/plot_compute_emd.py @@ -0,0 +1,57 @@ +# -*- coding: utf-8 -*- +""" +==================== +1D optimal transport +==================== + +@author: rflamary +""" + +import numpy as np +import matplotlib.pylab as pl +import ot +from ot.datasets import get_1D_gauss as gauss + + +#%% parameters + +n=100 # nb bins +n_target=10 # nb target distributions + + +# bin positions +x=np.arange(n,dtype=np.float64) + +lst_m=np.linspace(20,90,n_target) + +# Gaussian distributions +a=gauss(n,m=20,s=5) # m= mean, s= std + +B=np.zeros((n,n_target)) + +for i,m in enumerate(lst_m): + B[:,i]=gauss(n,m=m,s=5) + +# loss matrix +M=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'euclidean') +M2=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'sqeuclidean') + +#%% plot the distributions + +pl.figure(1) +pl.subplot(2,1,1) +pl.plot(x,a,'b',label='Source distribution') +pl.title('Source distribution') +pl.subplot(2,1,2) +pl.plot(x,B,label='Target distributions') +pl.title('Target distributions') + +#%% plot distributions and loss matrix + +emd=ot.emd2(a,B,M) +emd2=ot.emd2(a,B,M2) +pl.figure(2) +pl.plot(emd,label='Euclidean loss') +pl.plot(emd,label='Squared Euclidean loss') +pl.legend() + -- cgit v1.2.3 From a7bed093f91922e18fa5902c4d1d63b9712d5794 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 13 Jun 2017 15:14:45 +0200 Subject: implement paralell sinkhorn --- examples/plot_OT_1D.py | 2 +- examples/plot_compute_emd.py | 31 +++++++++++++++++++------ ot/bregman.py | 54 ++++++++++++++++++++++++++++++++------------ 3 files changed, 64 insertions(+), 23 deletions(-) diff --git a/examples/plot_OT_1D.py b/examples/plot_OT_1D.py index e5719eb..6661aa3 100644 --- a/examples/plot_OT_1D.py +++ b/examples/plot_OT_1D.py @@ -50,7 +50,7 @@ ot.plot.plot1D_mat(a,b,G0,'OT matrix G0') #%% Sinkhorn lambd=1e-3 -Gs=ot.sinkhorn(a,b,M,lambd) +Gs=ot.sinkhorn(a,b,M,lambd,verbose=True) pl.figure(4) ot.plot.plot1D_mat(a,b,Gs,'OT matrix Sinkhorn') diff --git a/examples/plot_compute_emd.py b/examples/plot_compute_emd.py index 87b39a6..226bc97 100644 --- a/examples/plot_compute_emd.py +++ b/examples/plot_compute_emd.py @@ -32,10 +32,11 @@ B=np.zeros((n,n_target)) for i,m in enumerate(lst_m): B[:,i]=gauss(n,m=m,s=5) -# loss matrix +# loss matrix and normalization M=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'euclidean') +M/=M.max() M2=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'sqeuclidean') - +M2/=M2.max() #%% plot the distributions pl.figure(1) @@ -46,12 +47,28 @@ pl.subplot(2,1,2) pl.plot(x,B,label='Target distributions') pl.title('Target distributions') -#%% plot distributions and loss matrix +#%% Compute and plot distributions and loss matrix + +d_emd=ot.emd2(a,B,M) # direct computation of EMD +d_emd2=ot.emd2(a,B,M2) # direct computation of EMD with loss M3 + -emd=ot.emd2(a,B,M) -emd2=ot.emd2(a,B,M2) pl.figure(2) -pl.plot(emd,label='Euclidean loss') -pl.plot(emd,label='Squared Euclidean loss') +pl.plot(d_emd,label='Euclidean EMD') +pl.plot(d_emd2,label='Squared Euclidean EMD') +pl.title('EMD distances') pl.legend() +#%% +reg=1e-2 +d_sinkhorn=ot.sinkhorn(a,B,M,reg) +d_sinkhorn2=ot.sinkhorn(a,B,M2,reg) + +pl.figure(2) +pl.clf() +pl.plot(d_emd,label='Euclidean EMD') +pl.plot(d_emd2,label='Squared Euclidean EMD') +pl.plot(d_sinkhorn,label='Euclidean Sinkhorn') +pl.plot(d_emd2,label='Squared Euclidean Sinkhorn') +pl.title('EMD distances') +pl.legend() \ No newline at end of file diff --git a/ot/bregman.py b/ot/bregman.py index 6b3c68b..3a9b15f 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -7,7 +7,7 @@ import numpy as np def sinkhorn(a,b, M, reg,method='sinkhorn', numItermax = 1000, stopThr=1e-9, verbose=False, log=False,**kwargs): u""" - Solve the entropic regularization optimal transport problem + Solve the entropic regularization optimal transport problem and return the OT matrix The function solves the following optimization problem: @@ -107,12 +107,9 @@ def sinkhorn(a,b, M, reg,method='sinkhorn', numItermax = 1000, stopThr=1e-9, ver return sink() - - - def sinkhorn_knopp(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, log=False,**kwargs): """ - Solve the entropic regularization optimal transport problem + Solve the entropic regularization optimal transport problem and return the OT matrix The function solves the following optimization problem: @@ -188,22 +185,35 @@ def sinkhorn_knopp(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, a=np.asarray(a,dtype=np.float64) b=np.asarray(b,dtype=np.float64) M=np.asarray(M,dtype=np.float64) + if len(a)==0: a=np.ones((M.shape[0],),dtype=np.float64)/M.shape[0] if len(b)==0: b=np.ones((M.shape[1],),dtype=np.float64)/M.shape[1] + # init data Nini = len(a) Nfin = len(b) + + if len(b.shape)>1: + nbb=b.shape[1] + else: + nbb=0 + if log: log={'err':[]} # we assume that no distances are null except those of the diagonal of distances - u = np.ones(Nini)/Nini - v = np.ones(Nfin)/Nfin + if nbb: + u = np.ones((Nini,nbb))/Nini + v = np.ones((Nfin,nbb))/Nfin + else: + u = np.ones(Nini)/Nini + v = np.ones(Nfin)/Nfin + #print(reg) @@ -231,8 +241,11 @@ def sinkhorn_knopp(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, break if cpt%10==0: # we can speed up the process by checking for the error only all the 10th iterations - transp = u.reshape(-1, 1) * (K * v) - err = np.linalg.norm((np.sum(transp,axis=0)-b))**2 + if nbb: + err = np.sum((u-uprev)**2)/np.sum((u)**2)+np.sum((v-vprev)**2)/np.sum((v)**2) + else: + transp = u.reshape(-1, 1) * (K * v) + err = np.linalg.norm((np.sum(transp,axis=0)-b))**2 if log: log['err'].append(err) @@ -244,12 +257,23 @@ def sinkhorn_knopp(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, if log: log['u']=u log['v']=v - - #print('err=',err,' cpt=',cpt) - if log: - return u.reshape((-1,1))*K*v.reshape((1,-1)),log - else: - return u.reshape((-1,1))*K*v.reshape((1,-1)) + + if nbb: #return only loss + res=np.zeros((nbb)) + for i in range(nbb): + res[i]=np.sum(u[:,i].reshape((-1,1))*K*v[:,i].reshape((1,-1))*M) + if log: + return res,log + else: + return res + + else: # return OT matrix + + if log: + return u.reshape((-1,1))*K*v.reshape((1,-1)),log + else: + return u.reshape((-1,1))*K*v.reshape((1,-1)) + def sinkhorn_stabilized(a,b, M, reg, numItermax = 1000,tau=1e3, stopThr=1e-9,warmstart=None, verbose=False,print_period=20, log=False,**kwargs): """ -- cgit v1.2.3 From 38e96f88eb520b9fa8333686565b082d2921e131 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 13 Jun 2017 15:50:11 +0200 Subject: implement paralell sinkhorn stabilized --- examples/plot_OT_2D_samples.py | 4 +-- examples/plot_compute_emd.py | 9 +++--- ot/bregman.py | 68 +++++++++++++++++++++++++++++++----------- 3 files changed, 58 insertions(+), 23 deletions(-) diff --git a/examples/plot_OT_2D_samples.py b/examples/plot_OT_2D_samples.py index 3b95083..edfb781 100644 --- a/examples/plot_OT_2D_samples.py +++ b/examples/plot_OT_2D_samples.py @@ -13,7 +13,7 @@ import ot #%% parameters and data generation -n=2 # nb samples +n=50 # nb samples mu_s=np.array([0,0]) cov_s=np.array([[1,0],[0,1]]) @@ -62,7 +62,7 @@ pl.title('OT matrix with samples') #%% sinkhorn # reg term -lambd=5e-3 +lambd=5e-4 Gs=ot.sinkhorn(a,b,M,lambd) diff --git a/examples/plot_compute_emd.py b/examples/plot_compute_emd.py index 226bc97..08de6ee 100644 --- a/examples/plot_compute_emd.py +++ b/examples/plot_compute_emd.py @@ -16,7 +16,7 @@ from ot.datasets import get_1D_gauss as gauss #%% parameters n=100 # nb bins -n_target=10 # nb target distributions +n_target=50 # nb target distributions # bin positions @@ -61,14 +61,15 @@ pl.legend() #%% reg=1e-2 -d_sinkhorn=ot.sinkhorn(a,B,M,reg) +d_sinkhorn=ot.sinkhorn(a,B,M,reg,method='sinkhorn_stabilized') +d_sinkhorn0=ot.sinkhorn(a,B,M,reg) d_sinkhorn2=ot.sinkhorn(a,B,M2,reg) pl.figure(2) pl.clf() pl.plot(d_emd,label='Euclidean EMD') pl.plot(d_emd2,label='Squared Euclidean EMD') -pl.plot(d_sinkhorn,label='Euclidean Sinkhorn') -pl.plot(d_emd2,label='Squared Euclidean Sinkhorn') +pl.plot(d_sinkhorn,'+',label='Euclidean Sinkhorn') +pl.plot(d_sinkhorn2,'+',label='Squared Euclidean Sinkhorn') pl.title('EMD distances') pl.legend() \ No newline at end of file diff --git a/ot/bregman.py b/ot/bregman.py index 3a9b15f..e847f24 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -32,8 +32,9 @@ def sinkhorn(a,b, M, reg,method='sinkhorn', numItermax = 1000, stopThr=1e-9, ver ---------- a : np.ndarray (ns,) samples weights in the source domain - b : np.ndarray (nt,) - samples in the target domain + b : np.ndarray (nt,) or np.ndarray (nt,nbb) + samples in the target domain, compute sinkhorn with multiple targets + and fixed M if b is a matrix (return OT loss + dual variables in log) M : np.ndarray (ns,nt) loss matrix reg : float @@ -134,8 +135,9 @@ def sinkhorn_knopp(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, ---------- a : np.ndarray (ns,) samples weights in the source domain - b : np.ndarray (nt,) - samples in the target domain + b : np.ndarray (nt,) or np.ndarray (nt,nbb) + samples in the target domain, compute sinkhorn with multiple targets + and fixed M if b is a matrix (return OT loss + dual variables in log) M : np.ndarray (ns,nt) loss matrix reg : float @@ -369,11 +371,17 @@ def sinkhorn_stabilized(a,b, M, reg, numItermax = 1000,tau=1e3, stopThr=1e-9,war if len(b)==0: b=np.ones((M.shape[1],),dtype=np.float64)/M.shape[1] + # test if multiple target + if len(b.shape)>1: + nbb=b.shape[1] + a=a[:,np.newaxis] + else: + nbb=0 + # init data na = len(a) nb = len(b) - cpt = 0 if log: log={'err':[]} @@ -383,10 +391,11 @@ def sinkhorn_stabilized(a,b, M, reg, numItermax = 1000,tau=1e3, stopThr=1e-9,war alpha,beta=np.zeros(na),np.zeros(nb) else: alpha,beta=warmstart - u,v = np.ones(na)/na,np.ones(nb)/nb - - #print(reg) - + + if nbb: + u,v = np.ones((na,nbb))/na,np.ones((nb,nbb))/nb + else: + u,v = np.ones(na)/na,np.ones(nb)/nb def get_K(alpha,beta): """log space computation""" @@ -405,22 +414,32 @@ def sinkhorn_stabilized(a,b, M, reg, numItermax = 1000,tau=1e3, stopThr=1e-9,war err=1 while loop: + # remove numerical problems and store them in K if np.abs(u).max()>tau or np.abs(v).max()>tau: - alpha,beta=alpha+reg*np.log(u),beta+reg*np.log(v) - u,v = np.ones(na)/na,np.ones(nb)/nb + if nbb: + alpha,beta=alpha+reg*np.max(np.log(u),1),beta+reg*np.max(np.log(v)) + else: + alpha,beta=alpha+reg*np.log(u),beta+reg*np.log(v) + if nbb: + u,v = np.ones((na,nbb))/na,np.ones((nb,nbb))/nb + else: + u,v = np.ones(na)/na,np.ones(nb)/nb K=get_K(alpha,beta) uprev = u vprev = v + + # sinkhorn update v = b/np.dot(K.T,u) u = a/np.dot(K,v) - - if cpt%print_period==0: # we can speed up the process by checking for the error only all the 10th iterations - transp = get_Gamma(alpha,beta,u,v) - err = np.linalg.norm((np.sum(transp,axis=0)-b))**2 + if nbb: + err = np.sum((u-uprev)**2)/np.sum((u)**2)+np.sum((v-vprev)**2)/np.sum((v)**2) + else: + transp = get_Gamma(alpha,beta,u,v) + err = np.linalg.norm((np.sum(transp,axis=0)-b))**2 if log: log['err'].append(err) @@ -448,6 +467,8 @@ def sinkhorn_stabilized(a,b, M, reg, numItermax = 1000,tau=1e3, stopThr=1e-9,war break cpt = cpt +1 + + #print('err=',err,' cpt=',cpt) if log: log['logu']=alpha/reg+np.log(u) @@ -455,9 +476,22 @@ def sinkhorn_stabilized(a,b, M, reg, numItermax = 1000,tau=1e3, stopThr=1e-9,war log['alpha']=alpha+reg*np.log(u) log['beta']=beta+reg*np.log(v) log['warmstart']=(log['alpha'],log['beta']) - return get_Gamma(alpha,beta,u,v),log + if nbb: + res=np.zeros((nbb)) + for i in range(nbb): + res[i]=np.sum(get_Gamma(alpha,beta,u[:,i],v[:,i])*M) + return res,log + + else: + return get_Gamma(alpha,beta,u,v),log else: - return get_Gamma(alpha,beta,u,v) + if nbb: + res=np.zeros((nbb)) + for i in range(nbb): + res[i]=np.sum(get_Gamma(alpha,beta,u[:,i],v[:,i])*M) + return res + else: + return get_Gamma(alpha,beta,u,v) def sinkhorn_epsilon_scaling(a,b, M, reg, numItermax = 100, epsilon0=1e4, numInnerItermax = 100,tau=1e3, stopThr=1e-9,warmstart=None, verbose=False,print_period=10, log=False,**kwargs): """ -- cgit v1.2.3 From 2bcc24aa05078cfbf160be06fc9ad166bee52904 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 13 Jun 2017 15:51:01 +0200 Subject: example compute emd --- examples/plot_compute_emd.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/examples/plot_compute_emd.py b/examples/plot_compute_emd.py index 08de6ee..c7063e8 100644 --- a/examples/plot_compute_emd.py +++ b/examples/plot_compute_emd.py @@ -61,8 +61,7 @@ pl.legend() #%% reg=1e-2 -d_sinkhorn=ot.sinkhorn(a,B,M,reg,method='sinkhorn_stabilized') -d_sinkhorn0=ot.sinkhorn(a,B,M,reg) +d_sinkhorn=ot.sinkhorn(a,B,M,reg) d_sinkhorn2=ot.sinkhorn(a,B,M2,reg) pl.figure(2) -- cgit v1.2.3 From 77bcf836272faf1a40fdea97131b85390e935be3 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 20 Jun 2017 14:51:12 +0200 Subject: add clean zeros function for sparse distributions --- ot/bregman.py | 2 ++ ot/utils.py | 7 +++++++ 2 files changed, 9 insertions(+) diff --git a/ot/bregman.py b/ot/bregman.py index e847f24..a13345d 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -108,6 +108,8 @@ def sinkhorn(a,b, M, reg,method='sinkhorn', numItermax = 1000, stopThr=1e-9, ver return sink() + + def sinkhorn_knopp(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, log=False,**kwargs): """ Solve the entropic regularization optimal transport problem and return the OT matrix diff --git a/ot/utils.py b/ot/utils.py index fc6b0d2..7ad7637 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -50,6 +50,13 @@ def unif(n): """ return np.ones((n,))/n +def clean_zeros(a,b,M): + """ Remove all components with zeros weights in a and b + """ + M2=M[a>0,:][:,b>0].copy() # copy force c style matrix (froemd) + a2=a[a>0] + b2=b[b>0] + return a2,b2,M2 def dist(x1,x2=None,metric='sqeuclidean'): """Compute distance between samples in x1 and x2 using function scipy.spatial.distance.cdist -- cgit v1.2.3 From d983766316198ce7ba3eb242f672a826c4b3db97 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 23 Jun 2017 09:00:43 +0200 Subject: add links 1/2 --- README.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 7e90088..4a9ee18 100644 --- a/README.md +++ b/README.md @@ -107,13 +107,13 @@ This toolbox benefit a lot from open source research and we would like to thank ## References -[1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). Displacement interpolation using Lagrangian mass transport. In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM. +[1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). [Displacement interpolation using Lagrangian mass transport](https://people.csail.mit.edu/sparis/publi/2011/sigasia/Bonneel_11_Displacement_Interpolation.pdf). In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM. -[2] Cuturi, M. (2013). Sinkhorn distances: Lightspeed computation of optimal transport. In Advances in Neural Information Processing Systems (pp. 2292-2300). +[2] Cuturi, M. (2013). [Sinkhorn distances: Lightspeed computation of optimal transport](https://arxiv.org/pdf/1306.0895.pdf). In Advances in Neural Information Processing Systems (pp. 2292-2300). -[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). Iterative Bregman projections for regularized transportation problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138. +[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). [Iterative Bregman projections for regularized transportation problems](https://arxiv.org/pdf/1412.5154.pdf). SIAM Journal on Scientific Computing, 37(2), A1111-A1138. -[4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, Supervised planetary unmixing with optimal transport, Whorkshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), 2016. +[4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, [Supervised planetary unmixing with optimal transport](https://hal.archives-ouvertes.fr/hal-01377236/document), Whorkshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), 2016. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 @@ -123,7 +123,7 @@ This toolbox benefit a lot from open source research and we would like to thank [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. -[9] Schmitzer, B. (2016). Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems. arXiv preprint arXiv:1610.06519. +[9] Schmitzer, B. (2016). [Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems](https://arxiv.org/pdf/1610.06519.pdf). arXiv preprint arXiv:1610.06519. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. -- cgit v1.2.3 From d80b3e009141904ce9d280d65058d2c8a568b8b3 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 23 Jun 2017 09:06:34 +0200 Subject: links 2/2 --- README.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index 4a9ee18..7a06486 100644 --- a/README.md +++ b/README.md @@ -115,16 +115,16 @@ This toolbox benefit a lot from open source research and we would like to thank [4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, [Supervised planetary unmixing with optimal transport](https://hal.archives-ouvertes.fr/hal-01377236/document), Whorkshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), 2016. -[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 +[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, [Optimal Transport for Domain Adaptation](https://arxiv.org/pdf/1507.00504.pdf), in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 -[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. +[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). [Regularized discrete optimal transport](https://arxiv.org/pdf/1307.5551.pdf). SIAM Journal on Imaging Sciences, 7(3), 1853-1882. -[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567. +[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). [Generalized conditional gradient: analysis of convergence and applications](https://arxiv.org/pdf/1510.06567.pdf). arXiv preprint arXiv:1510.06567. -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, [Mapping estimation for discrete optimal transport](http://remi.flamary.com/biblio/perrot2016mapping.pdf), Neural Information Processing Systems (NIPS), 2016. [9] Schmitzer, B. (2016). [Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems](https://arxiv.org/pdf/1610.06519.pdf). arXiv preprint arXiv:1610.06519. -[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. +[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). [Scaling algorithms for unbalanced transport problems](https://arxiv.org/pdf/1607.05816.pdf). arXiv preprint arXiv:1607.05816. -[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). Wasserstein Discriminant Analysis. arXiv preprint arXiv:1608.08063. +[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). [Wasserstein Discriminant Analysis](https://arxiv.org/pdf/1608.08063.pdf). arXiv preprint arXiv:1608.08063. -- cgit v1.2.3 From bb0047b374634a410ae3ded063784638a401b3f6 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 23 Jun 2017 09:12:20 +0200 Subject: test travis --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 8547582..244dbbb 100644 --- a/.travis.yml +++ b/.travis.yml @@ -6,7 +6,7 @@ python: # maintainers to fix their pypy-dev package. # - "pypy" before_install: - - sudo apt-get install libblas-dev liblapack-dev libatlas-base-dev gfortran + - sudo apt-get install libblas-dev liblapack-dev libatlas-base-dev #gfortran # command to install dependencies install: - pip install -r requirements.txt -- cgit v1.2.3 From 11239a9848e97201b3d4aa04224f3421b2c3974a Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 23 Jun 2017 13:10:27 +0200 Subject: add apt update tarvis --- .travis.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.travis.yml b/.travis.yml index 244dbbb..ebdd225 100644 --- a/.travis.yml +++ b/.travis.yml @@ -6,6 +6,7 @@ python: # maintainers to fix their pypy-dev package. # - "pypy" before_install: + - sudo apt-get update -q - sudo apt-get install libblas-dev liblapack-dev libatlas-base-dev #gfortran # command to install dependencies install: -- cgit v1.2.3 From 4fba2c9c479e8f410a23ef24458effc29fc3f7f0 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 3 Jul 2017 14:51:47 +0200 Subject: debug bregman stabilized --- ot/bregman.py | 20 +++++++++++--------- 1 file changed, 11 insertions(+), 9 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index a13345d..68be01c 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -416,6 +416,16 @@ def sinkhorn_stabilized(a,b, M, reg, numItermax = 1000,tau=1e3, stopThr=1e-9,war err=1 while loop: + + + uprev = u + vprev = v + + # sinkhorn update + v = b/(np.dot(K.T,u)+1e-16) + u = a/(np.dot(K,v)+1e-16) + + # remove numerical problems and store them in K if np.abs(u).max()>tau or np.abs(v).max()>tau: if nbb: @@ -428,12 +438,6 @@ def sinkhorn_stabilized(a,b, M, reg, numItermax = 1000,tau=1e3, stopThr=1e-9,war u,v = np.ones(na)/na,np.ones(nb)/nb K=get_K(alpha,beta) - uprev = u - vprev = v - - # sinkhorn update - v = b/np.dot(K.T,u) - u = a/np.dot(K,v) if cpt%print_period==0: # we can speed up the process by checking for the error only all the 10th iterations @@ -458,9 +462,7 @@ def sinkhorn_stabilized(a,b, M, reg, numItermax = 1000,tau=1e3, stopThr=1e-9,war loop=False - if (np.any(np.dot(K.T,u)==0) or - np.any(np.isnan(u)) or np.any(np.isnan(v)) or - np.any(np.isinf(u)) or np.any(np.isinf(v))): + if np.any(np.isnan(u)) or np.any(np.isnan(v)): # we have reached the machine precision # come back to previous solution and quit loop print('Warning: numerical errors at iteration', cpt) -- cgit v1.2.3 From f639518e9b96c5904122e62e024ed4ae369ceb33 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 3 Jul 2017 16:03:13 +0200 Subject: add example norm --- examples/plot_OT_L1_vs_L2.py | 110 +++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 110 insertions(+) create mode 100644 examples/plot_OT_L1_vs_L2.py diff --git a/examples/plot_OT_L1_vs_L2.py b/examples/plot_OT_L1_vs_L2.py new file mode 100644 index 0000000..1ab44a0 --- /dev/null +++ b/examples/plot_OT_L1_vs_L2.py @@ -0,0 +1,110 @@ +# -*- coding: utf-8 -*- +""" +==================================================== +2D Optimal transport between empirical distributions +==================================================== + +Stoile the figure idea from: +https://arxiv.org/pdf/1706.07650.pdf + + +@author: rflamary +""" + +import numpy as np +import matplotlib.pylab as pl +import ot + +#%% parameters and data generation + +for data in range(2): + + if data: + n=20 # nb samples + xs=np.zeros((n,2)) + xs[:,0]=np.arange(n)+1 + xs[:,1]=(np.arange(n)+1)*-0.001 # to make it strictly convex... + + xt=np.zeros((n,2)) + xt[:,1]=np.arange(n)+1 + else: + + n=50 # nb samples + xtot=np.zeros((n+1,2)) + xtot[:,0]=np.cos((np.arange(n+1)+1.0)*0.9/(n+2)*2*np.pi) + xtot[:,1]=np.sin((np.arange(n+1)+1.0)*0.9/(n+2)*2*np.pi) + + xs=xtot[:n,:] + xt=xtot[1:,:] + + + + a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples + + # loss matrix + M1=ot.dist(xs,xt,metric='euclidean') + M1/=M1.max() + + # loss matrix + M2=ot.dist(xs,xt,metric='sqeuclidean') + M2/=M2.max() + + # loss matrix + Mp=np.sqrt(ot.dist(xs,xt,metric='euclidean')) + Mp/=Mp.max() + + #%% plot samples + + pl.figure(1+3*data) + pl.clf() + pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') + pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') + pl.axis('equal') + pl.title('Source and traget distributions') + + pl.figure(2+3*data,(15,5)) + pl.subplot(1,3,1) + pl.imshow(M1,interpolation='nearest') + pl.title('Eucidean cost') + pl.subplot(1,3,2) + pl.imshow(M2,interpolation='nearest') + pl.title('Squared Euclidean cost') + + pl.subplot(1,3,3) + pl.imshow(Mp,interpolation='nearest') + pl.title('Sqrt Euclidean cost') + #%% EMD + + G1=ot.emd(a,b,M1) + G2=ot.emd(a,b,M2) + Gp=ot.emd(a,b,Mp) + + pl.figure(3+3*data,(15,5)) + + pl.subplot(1,3,1) + ot.plot.plot2D_samples_mat(xs,xt,G1,c=[.5,.5,1]) + pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') + pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') + pl.axis('equal') + #pl.legend(loc=0) + pl.title('OT Euclidean') + + pl.subplot(1,3,2) + + ot.plot.plot2D_samples_mat(xs,xt,G2,c=[.5,.5,1]) + pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') + pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') + pl.axis('equal') + #pl.legend(loc=0) + pl.title('OT squared Euclidean') + + pl.subplot(1,3,3) + + ot.plot.plot2D_samples_mat(xs,xt,Gp,c=[.5,.5,1]) + pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') + pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') + pl.axis('equal') + #pl.legend(loc=0) + pl.title('OT sqrt Euclidean') + +#%% sinkhorn -- cgit v1.2.3 From 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b/docs/source/auto_examples/images/thumb/sphx_glr_test_OT_2D_samples_stabilized_thumb.png differ diff --git a/docs/source/auto_examples/index.rst b/docs/source/auto_examples/index.rst index 868ca76..1695300 100644 --- a/docs/source/auto_examples/index.rst +++ b/docs/source/auto_examples/index.rst @@ -81,6 +81,26 @@ POT Examples /auto_examples/plot_OT_2D_samples +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_compute_emd_thumb.png + + :ref:`sphx_glr_auto_examples_plot_compute_emd.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_compute_emd + .. raw:: html
@@ -141,6 +161,26 @@ POT Examples /auto_examples/plot_OTDA_2D +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_L1_vs_L2_thumb.png + + :ref:`sphx_glr_auto_examples_plot_OT_L1_vs_L2.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_OT_L1_vs_L2 + .. raw:: html
diff --git a/docs/source/auto_examples/plot_OT_1D.ipynb b/docs/source/auto_examples/plot_OT_1D.ipynb index 17d0b21..8715b97 100644 --- a/docs/source/auto_examples/plot_OT_1D.ipynb +++ b/docs/source/auto_examples/plot_OT_1D.ipynb @@ -24,7 +24,7 @@ "execution_count": null, "cell_type": "code", "source": [ - "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\nfrom ot.datasets import get_1D_gauss as gauss\n\n\n#%% parameters\n\nn=100 # nb bins\n\n# bin positions\nx=np.arange(n,dtype=np.float64)\n\n# Gaussian distributions\na=gauss(n,m=20,s=5) # m= mean, s= std\nb=gauss(n,m=60,s=10)\n\n# loss matrix\nM=ot.dist(x.reshape((n,1)),x.reshape((n,1)))\nM/=M.max()\n\n#%% plot the distributions\n\npl.figure(1)\npl.plot(x,a,'b',label='Source distribution')\npl.plot(x,b,'r',label='Target distribution')\npl.legend()\n\n#%% plot distributions and loss matrix\n\npl.figure(2)\not.plot.plot1D_mat(a,b,M,'Cost matrix M')\n\n#%% EMD\n\nG0=ot.emd(a,b,M)\n\npl.figure(3)\not.plot.plot1D_mat(a,b,G0,'OT matrix G0')\n\n#%% Sinkhorn\n\nlambd=1e-3\nGs=ot.sinkhorn(a,b,M,lambd)\n\npl.figure(4)\not.plot.plot1D_mat(a,b,Gs,'OT matrix Sinkhorn')" + "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\nfrom ot.datasets import get_1D_gauss as gauss\n\n\n#%% parameters\n\nn=100 # nb bins\n\n# bin positions\nx=np.arange(n,dtype=np.float64)\n\n# Gaussian distributions\na=gauss(n,m=20,s=5) # m= mean, s= std\nb=gauss(n,m=60,s=10)\n\n# loss matrix\nM=ot.dist(x.reshape((n,1)),x.reshape((n,1)))\nM/=M.max()\n\n#%% plot the distributions\n\npl.figure(1)\npl.plot(x,a,'b',label='Source distribution')\npl.plot(x,b,'r',label='Target distribution')\npl.legend()\n\n#%% plot distributions and loss matrix\n\npl.figure(2)\not.plot.plot1D_mat(a,b,M,'Cost matrix M')\n\n#%% EMD\n\nG0=ot.emd(a,b,M)\n\npl.figure(3)\not.plot.plot1D_mat(a,b,G0,'OT matrix G0')\n\n#%% Sinkhorn\n\nlambd=1e-3\nGs=ot.sinkhorn(a,b,M,lambd,verbose=True)\n\npl.figure(4)\not.plot.plot1D_mat(a,b,Gs,'OT matrix Sinkhorn')" ], "outputs": [], "metadata": { diff --git a/docs/source/auto_examples/plot_OT_1D.py b/docs/source/auto_examples/plot_OT_1D.py index e5719eb..6661aa3 100644 --- a/docs/source/auto_examples/plot_OT_1D.py +++ b/docs/source/auto_examples/plot_OT_1D.py @@ -50,7 +50,7 @@ ot.plot.plot1D_mat(a,b,G0,'OT matrix G0') #%% Sinkhorn lambd=1e-3 -Gs=ot.sinkhorn(a,b,M,lambd) +Gs=ot.sinkhorn(a,b,M,lambd,verbose=True) pl.figure(4) ot.plot.plot1D_mat(a,b,Gs,'OT matrix Sinkhorn') diff --git a/docs/source/auto_examples/plot_OT_1D.rst b/docs/source/auto_examples/plot_OT_1D.rst index 941fd54..44b715b 100644 --- a/docs/source/auto_examples/plot_OT_1D.rst +++ b/docs/source/auto_examples/plot_OT_1D.rst @@ -36,7 +36,29 @@ :scale: 47 +.. rst-class:: sphx-glr-script-out + Out:: + + It. |Err + ------------------- + 0|8.187970e-02| + 10|3.460174e-02| + 20|6.633335e-03| + 30|9.797798e-04| + 40|1.389606e-04| + 50|1.959016e-05| + 60|2.759079e-06| + 70|3.885166e-07| + 80|5.470605e-08| + 90|7.702918e-09| + 100|1.084609e-09| + 110|1.527180e-10| + + + + +| .. code-block:: python @@ -85,12 +107,12 @@ #%% Sinkhorn lambd=1e-3 - Gs=ot.sinkhorn(a,b,M,lambd) + Gs=ot.sinkhorn(a,b,M,lambd,verbose=True) pl.figure(4) ot.plot.plot1D_mat(a,b,Gs,'OT matrix Sinkhorn') -**Total running time of the script:** ( 0 minutes 0.597 seconds) +**Total running time of the script:** ( 0 minutes 0.674 seconds) diff --git a/docs/source/auto_examples/plot_OT_2D_samples.ipynb b/docs/source/auto_examples/plot_OT_2D_samples.ipynb index 7d42ba7..fad0467 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.ipynb +++ b/docs/source/auto_examples/plot_OT_2D_samples.ipynb @@ -24,7 +24,7 @@ "execution_count": null, "cell_type": "code", "source": [ - "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n#%% parameters and data generation\n\nn=2 # nb samples\n\nmu_s=np.array([0,0])\ncov_s=np.array([[1,0],[0,1]])\n\nmu_t=np.array([4,4])\ncov_t=np.array([[1,-.8],[-.8,1]])\n\nxs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s)\nxt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t)\n\na,b = ot.unif(n),ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM=ot.dist(xs,xt)\nM/=M.max()\n\n#%% plot samples\n\npl.figure(1)\npl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\npl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\npl.legend(loc=0)\npl.title('Source and traget distributions')\n\npl.figure(2)\npl.imshow(M,interpolation='nearest')\npl.title('Cost matrix M')\n\n\n#%% EMD\n\nG0=ot.emd(a,b,M)\n\npl.figure(3)\npl.imshow(G0,interpolation='nearest')\npl.title('OT matrix G0')\n\npl.figure(4)\not.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1])\npl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\npl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix with samples')\n\n\n#%% sinkhorn\n\n# reg term\nlambd=5e-3\n\nGs=ot.sinkhorn(a,b,M,lambd)\n\npl.figure(5)\npl.imshow(Gs,interpolation='nearest')\npl.title('OT matrix sinkhorn')\n\npl.figure(6)\not.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1])\npl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\npl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix Sinkhorn with samples')" + "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n#%% parameters and data generation\n\nn=50 # nb samples\n\nmu_s=np.array([0,0])\ncov_s=np.array([[1,0],[0,1]])\n\nmu_t=np.array([4,4])\ncov_t=np.array([[1,-.8],[-.8,1]])\n\nxs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s)\nxt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t)\n\na,b = ot.unif(n),ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM=ot.dist(xs,xt)\nM/=M.max()\n\n#%% plot samples\n\npl.figure(1)\npl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\npl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\npl.legend(loc=0)\npl.title('Source and traget distributions')\n\npl.figure(2)\npl.imshow(M,interpolation='nearest')\npl.title('Cost matrix M')\n\n\n#%% EMD\n\nG0=ot.emd(a,b,M)\n\npl.figure(3)\npl.imshow(G0,interpolation='nearest')\npl.title('OT matrix G0')\n\npl.figure(4)\not.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1])\npl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\npl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix with samples')\n\n\n#%% sinkhorn\n\n# reg term\nlambd=5e-4\n\nGs=ot.sinkhorn(a,b,M,lambd)\n\npl.figure(5)\npl.imshow(Gs,interpolation='nearest')\npl.title('OT matrix sinkhorn')\n\npl.figure(6)\not.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1])\npl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\npl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix Sinkhorn with samples')" ], "outputs": [], "metadata": { diff --git a/docs/source/auto_examples/plot_OT_2D_samples.py b/docs/source/auto_examples/plot_OT_2D_samples.py index 3b95083..edfb781 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.py +++ b/docs/source/auto_examples/plot_OT_2D_samples.py @@ -13,7 +13,7 @@ import ot #%% parameters and data generation -n=2 # nb samples +n=50 # nb samples mu_s=np.array([0,0]) cov_s=np.array([[1,0],[0,1]]) @@ -62,7 +62,7 @@ pl.title('OT matrix with samples') #%% sinkhorn # reg term -lambd=5e-3 +lambd=5e-4 Gs=ot.sinkhorn(a,b,M,lambd) diff --git a/docs/source/auto_examples/plot_OT_2D_samples.rst b/docs/source/auto_examples/plot_OT_2D_samples.rst index 01e5f31..e05e591 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.rst +++ b/docs/source/auto_examples/plot_OT_2D_samples.rst @@ -46,7 +46,16 @@ :scale: 47 +.. rst-class:: sphx-glr-script-out + Out:: + + ('Warning: numerical errors at iteration', 0) + + + + +| .. code-block:: python @@ -58,7 +67,7 @@ #%% parameters and data generation - n=2 # nb samples + n=50 # nb samples mu_s=np.array([0,0]) cov_s=np.array([[1,0],[0,1]]) @@ -107,7 +116,7 @@ #%% sinkhorn # reg term - lambd=5e-3 + lambd=5e-4 Gs=ot.sinkhorn(a,b,M,lambd) @@ -122,7 +131,7 @@ pl.legend(loc=0) pl.title('OT matrix Sinkhorn with samples') -**Total running time of the script:** ( 0 minutes 0.406 seconds) +**Total running time of the script:** ( 0 minutes 0.623 seconds) diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb b/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb new file mode 100644 index 0000000..46283ac --- /dev/null +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb @@ -0,0 +1,54 @@ +{ + "nbformat_minor": 0, + "nbformat": 4, + "cells": [ + { + "execution_count": null, + "cell_type": "code", + "source": [ + "%matplotlib inline" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "\n# 2D Optimal transport for different metrics\n\n\nStole the figure idea from Fig. 1 and 2 in \nhttps://arxiv.org/pdf/1706.07650.pdf\n\n\n@author: rflamary\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n#%% parameters and data generation\n\nfor data in range(2):\n\n if data:\n n=20 # nb samples\n xs=np.zeros((n,2))\n xs[:,0]=np.arange(n)+1\n xs[:,1]=(np.arange(n)+1)*-0.001 # to make it strictly convex...\n \n xt=np.zeros((n,2))\n xt[:,1]=np.arange(n)+1\n else:\n \n n=50 # nb samples\n xtot=np.zeros((n+1,2))\n xtot[:,0]=np.cos((np.arange(n+1)+1.0)*0.9/(n+2)*2*np.pi)\n xtot[:,1]=np.sin((np.arange(n+1)+1.0)*0.9/(n+2)*2*np.pi)\n \n xs=xtot[:n,:]\n xt=xtot[1:,:]\n \n \n \n a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples\n \n # loss matrix\n M1=ot.dist(xs,xt,metric='euclidean')\n M1/=M1.max()\n \n # loss matrix\n M2=ot.dist(xs,xt,metric='sqeuclidean')\n M2/=M2.max()\n \n # loss matrix\n Mp=np.sqrt(ot.dist(xs,xt,metric='euclidean'))\n Mp/=Mp.max()\n \n #%% plot samples\n \n pl.figure(1+3*data)\n pl.clf()\n pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n pl.axis('equal')\n pl.title('Source and traget distributions')\n \n pl.figure(2+3*data,(15,5))\n pl.subplot(1,3,1)\n pl.imshow(M1,interpolation='nearest')\n pl.title('Eucidean cost')\n pl.subplot(1,3,2)\n pl.imshow(M2,interpolation='nearest')\n pl.title('Squared Euclidean cost')\n \n pl.subplot(1,3,3)\n pl.imshow(Mp,interpolation='nearest')\n pl.title('Sqrt Euclidean cost')\n #%% EMD\n \n G1=ot.emd(a,b,M1)\n G2=ot.emd(a,b,M2)\n Gp=ot.emd(a,b,Mp)\n \n pl.figure(3+3*data,(15,5))\n \n pl.subplot(1,3,1)\n ot.plot.plot2D_samples_mat(xs,xt,G1,c=[.5,.5,1])\n pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n pl.axis('equal')\n #pl.legend(loc=0)\n pl.title('OT Euclidean')\n \n pl.subplot(1,3,2)\n \n ot.plot.plot2D_samples_mat(xs,xt,G2,c=[.5,.5,1])\n pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n pl.axis('equal')\n #pl.legend(loc=0)\n pl.title('OT squared Euclidean')\n \n pl.subplot(1,3,3)\n \n ot.plot.plot2D_samples_mat(xs,xt,Gp,c=[.5,.5,1])\n pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n pl.axis('equal')\n #pl.legend(loc=0)\n pl.title('OT sqrt Euclidean')" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "name": "python2", + "language": "python" + }, + "language_info": { + "mimetype": "text/x-python", + "nbconvert_exporter": "python", + "name": "python", + "file_extension": ".py", + "version": "2.7.12", + "pygments_lexer": "ipython2", + "codemirror_mode": { + "version": 2, + "name": "ipython" + } + } + } +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.py b/docs/source/auto_examples/plot_OT_L1_vs_L2.py new file mode 100644 index 0000000..9bb92fe --- /dev/null +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.py @@ -0,0 +1,108 @@ +# -*- coding: utf-8 -*- +""" +========================================== +2D Optimal transport for different metrics +========================================== + +Stole the figure idea from Fig. 1 and 2 in +https://arxiv.org/pdf/1706.07650.pdf + + +@author: rflamary +""" + +import numpy as np +import matplotlib.pylab as pl +import ot + +#%% parameters and data generation + +for data in range(2): + + if data: + n=20 # nb samples + xs=np.zeros((n,2)) + xs[:,0]=np.arange(n)+1 + xs[:,1]=(np.arange(n)+1)*-0.001 # to make it strictly convex... + + xt=np.zeros((n,2)) + xt[:,1]=np.arange(n)+1 + else: + + n=50 # nb samples + xtot=np.zeros((n+1,2)) + xtot[:,0]=np.cos((np.arange(n+1)+1.0)*0.9/(n+2)*2*np.pi) + xtot[:,1]=np.sin((np.arange(n+1)+1.0)*0.9/(n+2)*2*np.pi) + + xs=xtot[:n,:] + xt=xtot[1:,:] + + + + a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples + + # loss matrix + M1=ot.dist(xs,xt,metric='euclidean') + M1/=M1.max() + + # loss matrix + M2=ot.dist(xs,xt,metric='sqeuclidean') + M2/=M2.max() + + # loss matrix + Mp=np.sqrt(ot.dist(xs,xt,metric='euclidean')) + Mp/=Mp.max() + + #%% plot samples + + pl.figure(1+3*data) + pl.clf() + pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') + pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') + pl.axis('equal') + pl.title('Source and traget distributions') + + pl.figure(2+3*data,(15,5)) + pl.subplot(1,3,1) + pl.imshow(M1,interpolation='nearest') + pl.title('Eucidean cost') + pl.subplot(1,3,2) + pl.imshow(M2,interpolation='nearest') + pl.title('Squared Euclidean cost') + + pl.subplot(1,3,3) + pl.imshow(Mp,interpolation='nearest') + pl.title('Sqrt Euclidean cost') + #%% EMD + + G1=ot.emd(a,b,M1) + G2=ot.emd(a,b,M2) + Gp=ot.emd(a,b,Mp) + + pl.figure(3+3*data,(15,5)) + + pl.subplot(1,3,1) + ot.plot.plot2D_samples_mat(xs,xt,G1,c=[.5,.5,1]) + pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') + pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') + pl.axis('equal') + #pl.legend(loc=0) + pl.title('OT Euclidean') + + pl.subplot(1,3,2) + + ot.plot.plot2D_samples_mat(xs,xt,G2,c=[.5,.5,1]) + pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') + pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') + pl.axis('equal') + #pl.legend(loc=0) + pl.title('OT squared Euclidean') + + pl.subplot(1,3,3) + + ot.plot.plot2D_samples_mat(xs,xt,Gp,c=[.5,.5,1]) + pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') + pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') + pl.axis('equal') + #pl.legend(loc=0) + pl.title('OT sqrt Euclidean') diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst new file mode 100644 index 0000000..4e94bef --- /dev/null +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst @@ -0,0 +1,174 @@ + + +.. _sphx_glr_auto_examples_plot_OT_L1_vs_L2.py: + + +========================================== +2D Optimal transport for different metrics +========================================== + +Stole the figure idea from Fig. 1 and 2 in +https://arxiv.org/pdf/1706.07650.pdf + + +@author: rflamary + + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_001.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_002.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_003.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_004.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_005.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_006.png + :scale: 47 + + + + + +.. code-block:: python + + + import numpy as np + import matplotlib.pylab as pl + import ot + + #%% parameters and data generation + + for data in range(2): + + if data: + n=20 # nb samples + xs=np.zeros((n,2)) + xs[:,0]=np.arange(n)+1 + xs[:,1]=(np.arange(n)+1)*-0.001 # to make it strictly convex... + + xt=np.zeros((n,2)) + xt[:,1]=np.arange(n)+1 + else: + + n=50 # nb samples + xtot=np.zeros((n+1,2)) + xtot[:,0]=np.cos((np.arange(n+1)+1.0)*0.9/(n+2)*2*np.pi) + xtot[:,1]=np.sin((np.arange(n+1)+1.0)*0.9/(n+2)*2*np.pi) + + xs=xtot[:n,:] + xt=xtot[1:,:] + + + + a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples + + # loss matrix + M1=ot.dist(xs,xt,metric='euclidean') + M1/=M1.max() + + # loss matrix + M2=ot.dist(xs,xt,metric='sqeuclidean') + M2/=M2.max() + + # loss matrix + Mp=np.sqrt(ot.dist(xs,xt,metric='euclidean')) + Mp/=Mp.max() + + #%% plot samples + + pl.figure(1+3*data) + pl.clf() + pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') + pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') + pl.axis('equal') + pl.title('Source and traget distributions') + + pl.figure(2+3*data,(15,5)) + pl.subplot(1,3,1) + pl.imshow(M1,interpolation='nearest') + pl.title('Eucidean cost') + pl.subplot(1,3,2) + pl.imshow(M2,interpolation='nearest') + pl.title('Squared Euclidean cost') + + pl.subplot(1,3,3) + pl.imshow(Mp,interpolation='nearest') + pl.title('Sqrt Euclidean cost') + #%% EMD + + G1=ot.emd(a,b,M1) + G2=ot.emd(a,b,M2) + Gp=ot.emd(a,b,Mp) + + pl.figure(3+3*data,(15,5)) + + pl.subplot(1,3,1) + ot.plot.plot2D_samples_mat(xs,xt,G1,c=[.5,.5,1]) + pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') + pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') + pl.axis('equal') + #pl.legend(loc=0) + pl.title('OT Euclidean') + + pl.subplot(1,3,2) + + ot.plot.plot2D_samples_mat(xs,xt,G2,c=[.5,.5,1]) + pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') + pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') + pl.axis('equal') + #pl.legend(loc=0) + pl.title('OT squared Euclidean') + + pl.subplot(1,3,3) + + ot.plot.plot2D_samples_mat(xs,xt,Gp,c=[.5,.5,1]) + pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') + pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') + pl.axis('equal') + #pl.legend(loc=0) + pl.title('OT sqrt Euclidean') + +**Total running time of the script:** ( 0 minutes 1.417 seconds) + + + +.. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_OT_L1_vs_L2.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_OT_L1_vs_L2.ipynb ` + +.. rst-class:: sphx-glr-signature + + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OT_conv.ipynb b/docs/source/auto_examples/plot_OT_conv.ipynb new file mode 100644 index 0000000..7fc4af0 --- /dev/null +++ b/docs/source/auto_examples/plot_OT_conv.ipynb @@ -0,0 +1,54 @@ +{ + "nbformat_minor": 0, + "nbformat": 4, + "cells": [ + { + "execution_count": null, + "cell_type": "code", + "source": [ + "%matplotlib inline" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "\n# 1D Wasserstein barycenter demo\n\n\n\n@author: rflamary\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\nfrom mpl_toolkits.mplot3d import Axes3D #necessary for 3d plot even if not used\nimport scipy as sp\nimport scipy.signal as sps\n#%% parameters\n\nn=10 # nb bins\n\n# bin positions\nx=np.arange(n,dtype=np.float64)\n\nxx,yy=np.meshgrid(x,x)\n\n\nxpos=np.hstack((xx.reshape(-1,1),yy.reshape(-1,1)))\n\nM=ot.dist(xpos)\n\n\nI0=((xx-5)**2+(yy-5)**2<3**2)*1.0\nI1=((xx-7)**2+(yy-7)**2<3**2)*1.0\n\nI0/=I0.sum()\nI1/=I1.sum()\n\ni0=I0.ravel()\ni1=I1.ravel()\n\nM=M[i0>0,:][:,i1>0].copy()\ni0=i0[i0>0]\ni1=i1[i1>0]\nItot=np.concatenate((I0[:,:,np.newaxis],I1[:,:,np.newaxis]),2)\n\n\n#%% plot the distributions\n\npl.figure(1)\npl.subplot(2,2,1)\npl.imshow(I0)\npl.subplot(2,2,2)\npl.imshow(I1)\n\n\n#%% barycenter computation\n\nalpha=0.5 # 0<=alpha<=1\nweights=np.array([1-alpha,alpha])\n\n\ndef conv2(I,k):\n return sp.ndimage.convolve1d(sp.ndimage.convolve1d(I,k,axis=1),k,axis=0)\n\ndef conv2n(I,k):\n res=np.zeros_like(I)\n for i in range(I.shape[2]):\n res[:,:,i]=conv2(I[:,:,i],k)\n return res\n\n\ndef get_1Dkernel(reg,thr=1e-16,wmax=1024):\n w=max(min(wmax,2*int((-np.log(thr)*reg)**(.5))),3)\n x=np.arange(w,dtype=np.float64)\n return np.exp(-((x-w/2)**2)/reg)\n \nthr=1e-16\nreg=1e0\n\nk=get_1Dkernel(reg)\npl.figure(2)\npl.plot(k)\n\nI05=conv2(I0,k)\n\npl.figure(1)\npl.subplot(2,2,1)\npl.imshow(I0)\npl.subplot(2,2,2)\npl.imshow(I05)\n\n#%%\n\nG=ot.emd(i0,i1,M)\nr0=np.sum(M*G)\n\nreg=1e-1\nGs=ot.bregman.sinkhorn_knopp(i0,i1,M,reg=reg)\nrs=np.sum(M*Gs)\n\n#%%\n\ndef mylog(u):\n tmp=np.log(u)\n tmp[np.isnan(tmp)]=0\n return tmp\n\ndef sinkhorn_conv(a,b, reg, numItermax = 1000, stopThr=1e-9, verbose=False, log=False,**kwargs):\n\n\n a=np.asarray(a,dtype=np.float64)\n b=np.asarray(b,dtype=np.float64)\n \n \n if len(b.shape)>2:\n nbb=b.shape[2]\n a=a[:,:,np.newaxis]\n else:\n nbb=0\n \n\n if log:\n log={'err':[]}\n\n # we assume that no distances are null except those of the diagonal of distances\n if nbb:\n u = np.ones((a.shape[0],a.shape[1],nbb))/(np.prod(a.shape[:2]))\n v = np.ones((a.shape[0],a.shape[1],nbb))/(np.prod(b.shape[:2]))\n a0=1.0/(np.prod(b.shape[:2]))\n else:\n u = np.ones((a.shape[0],a.shape[1]))/(np.prod(a.shape[:2]))\n v = np.ones((a.shape[0],a.shape[1]))/(np.prod(b.shape[:2]))\n a0=1.0/(np.prod(b.shape[:2]))\n \n \n k=get_1Dkernel(reg)\n \n if nbb:\n K=lambda I: conv2n(I,k)\n else:\n K=lambda I: conv2(I,k)\n\n cpt = 0\n err=1\n while (err>stopThr and cpt0,:][:,i1>0].copy() +i0=i0[i0>0] +i1=i1[i1>0] +Itot=np.concatenate((I0[:,:,np.newaxis],I1[:,:,np.newaxis]),2) + + +#%% plot the distributions + +pl.figure(1) +pl.subplot(2,2,1) +pl.imshow(I0) +pl.subplot(2,2,2) +pl.imshow(I1) + + +#%% barycenter computation + +alpha=0.5 # 0<=alpha<=1 +weights=np.array([1-alpha,alpha]) + + +def conv2(I,k): + return sp.ndimage.convolve1d(sp.ndimage.convolve1d(I,k,axis=1),k,axis=0) + +def conv2n(I,k): + res=np.zeros_like(I) + for i in range(I.shape[2]): + res[:,:,i]=conv2(I[:,:,i],k) + return res + + +def get_1Dkernel(reg,thr=1e-16,wmax=1024): + w=max(min(wmax,2*int((-np.log(thr)*reg)**(.5))),3) + x=np.arange(w,dtype=np.float64) + return np.exp(-((x-w/2)**2)/reg) + +thr=1e-16 +reg=1e0 + +k=get_1Dkernel(reg) +pl.figure(2) +pl.plot(k) + +I05=conv2(I0,k) + +pl.figure(1) +pl.subplot(2,2,1) +pl.imshow(I0) +pl.subplot(2,2,2) +pl.imshow(I05) + +#%% + +G=ot.emd(i0,i1,M) +r0=np.sum(M*G) + +reg=1e-1 +Gs=ot.bregman.sinkhorn_knopp(i0,i1,M,reg=reg) +rs=np.sum(M*Gs) + +#%% + +def mylog(u): + tmp=np.log(u) + tmp[np.isnan(tmp)]=0 + return tmp + +def sinkhorn_conv(a,b, reg, numItermax = 1000, stopThr=1e-9, verbose=False, log=False,**kwargs): + + + a=np.asarray(a,dtype=np.float64) + b=np.asarray(b,dtype=np.float64) + + + if len(b.shape)>2: + nbb=b.shape[2] + a=a[:,:,np.newaxis] + else: + nbb=0 + + + if log: + log={'err':[]} + + # we assume that no distances are null except those of the diagonal of distances + if nbb: + u = np.ones((a.shape[0],a.shape[1],nbb))/(np.prod(a.shape[:2])) + v = np.ones((a.shape[0],a.shape[1],nbb))/(np.prod(b.shape[:2])) + a0=1.0/(np.prod(b.shape[:2])) + else: + u = np.ones((a.shape[0],a.shape[1]))/(np.prod(a.shape[:2])) + v = np.ones((a.shape[0],a.shape[1]))/(np.prod(b.shape[:2])) + a0=1.0/(np.prod(b.shape[:2])) + + + k=get_1Dkernel(reg) + + if nbb: + K=lambda I: conv2n(I,k) + else: + K=lambda I: conv2(I,k) + + cpt = 0 + err=1 + while (err>stopThr and cpt", line 86, in + TypeError: unsupported operand type(s) for *: 'float' and 'Mock' + + + + + +.. code-block:: python + + + import numpy as np + import matplotlib.pylab as pl + import ot + from mpl_toolkits.mplot3d import Axes3D #necessary for 3d plot even if not used + import scipy as sp + import scipy.signal as sps + #%% parameters + + n=10 # nb bins + + # bin positions + x=np.arange(n,dtype=np.float64) + + xx,yy=np.meshgrid(x,x) + + + xpos=np.hstack((xx.reshape(-1,1),yy.reshape(-1,1))) + + M=ot.dist(xpos) + + + I0=((xx-5)**2+(yy-5)**2<3**2)*1.0 + I1=((xx-7)**2+(yy-7)**2<3**2)*1.0 + + I0/=I0.sum() + I1/=I1.sum() + + i0=I0.ravel() + i1=I1.ravel() + + M=M[i0>0,:][:,i1>0].copy() + i0=i0[i0>0] + i1=i1[i1>0] + Itot=np.concatenate((I0[:,:,np.newaxis],I1[:,:,np.newaxis]),2) + + + #%% plot the distributions + + pl.figure(1) + pl.subplot(2,2,1) + pl.imshow(I0) + pl.subplot(2,2,2) + pl.imshow(I1) + + + #%% barycenter computation + + alpha=0.5 # 0<=alpha<=1 + weights=np.array([1-alpha,alpha]) + + + def conv2(I,k): + return sp.ndimage.convolve1d(sp.ndimage.convolve1d(I,k,axis=1),k,axis=0) + + def conv2n(I,k): + res=np.zeros_like(I) + for i in range(I.shape[2]): + res[:,:,i]=conv2(I[:,:,i],k) + return res + + + def get_1Dkernel(reg,thr=1e-16,wmax=1024): + w=max(min(wmax,2*int((-np.log(thr)*reg)**(.5))),3) + x=np.arange(w,dtype=np.float64) + return np.exp(-((x-w/2)**2)/reg) + + thr=1e-16 + reg=1e0 + + k=get_1Dkernel(reg) + pl.figure(2) + pl.plot(k) + + I05=conv2(I0,k) + + pl.figure(1) + pl.subplot(2,2,1) + pl.imshow(I0) + pl.subplot(2,2,2) + pl.imshow(I05) + + #%% + + G=ot.emd(i0,i1,M) + r0=np.sum(M*G) + + reg=1e-1 + Gs=ot.bregman.sinkhorn_knopp(i0,i1,M,reg=reg) + rs=np.sum(M*Gs) + + #%% + + def mylog(u): + tmp=np.log(u) + tmp[np.isnan(tmp)]=0 + return tmp + + def sinkhorn_conv(a,b, reg, numItermax = 1000, stopThr=1e-9, verbose=False, log=False,**kwargs): + + + a=np.asarray(a,dtype=np.float64) + b=np.asarray(b,dtype=np.float64) + + + if len(b.shape)>2: + nbb=b.shape[2] + a=a[:,:,np.newaxis] + else: + nbb=0 + + + if log: + log={'err':[]} + + # we assume that no distances are null except those of the diagonal of distances + if nbb: + u = np.ones((a.shape[0],a.shape[1],nbb))/(np.prod(a.shape[:2])) + v = np.ones((a.shape[0],a.shape[1],nbb))/(np.prod(b.shape[:2])) + a0=1.0/(np.prod(b.shape[:2])) + else: + u = np.ones((a.shape[0],a.shape[1]))/(np.prod(a.shape[:2])) + v = np.ones((a.shape[0],a.shape[1]))/(np.prod(b.shape[:2])) + a0=1.0/(np.prod(b.shape[:2])) + + + k=get_1Dkernel(reg) + + if nbb: + K=lambda I: conv2n(I,k) + else: + K=lambda I: conv2(I,k) + + cpt = 0 + err=1 + while (err>stopThr and cpt` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_OT_conv.ipynb ` + +.. rst-class:: sphx-glr-signature + + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_WDA.ipynb b/docs/source/auto_examples/plot_WDA.ipynb index 6d641a7..408a605 100644 --- a/docs/source/auto_examples/plot_WDA.ipynb +++ b/docs/source/auto_examples/plot_WDA.ipynb @@ -15,7 +15,7 @@ }, { "source": [ - "\n# WAsserstein Discriminant Analysis\n\n\n@author: rflamary\n\n" + "\n# Wasserstein Discriminant Analysis\n\n\n@author: rflamary\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_WDA.py b/docs/source/auto_examples/plot_WDA.py index 94b7ef4..bbe3888 100644 --- a/docs/source/auto_examples/plot_WDA.py +++ b/docs/source/auto_examples/plot_WDA.py @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- """ ================================= -WAsserstein Discriminant Analysis +Wasserstein Discriminant Analysis ================================= @author: rflamary diff --git a/docs/source/auto_examples/plot_WDA.rst b/docs/source/auto_examples/plot_WDA.rst index 379a133..540555d 100644 --- a/docs/source/auto_examples/plot_WDA.rst +++ b/docs/source/auto_examples/plot_WDA.rst @@ -4,7 +4,7 @@ ================================= -WAsserstein Discriminant Analysis +Wasserstein Discriminant Analysis ================================= @author: rflamary @@ -23,23 +23,26 @@ WAsserstein Discriminant Analysis Compiling cost function... Computing gradient of cost function... iter cost val grad. norm - 1 +7.5272200933021116e-01 8.85804426e-01 - 2 +2.5764223980223788e-01 3.04501586e-01 - 3 +1.6018169776696620e-01 1.78298483e-01 - 4 +1.4560944642106255e-01 1.42133298e-01 - 5 +1.0243843483991794e-01 1.23342675e-01 - 6 +7.8856617504010643e-02 1.05379766e-01 - 7 +7.7620851864404483e-02 1.04044062e-01 - 8 +7.3160520861018416e-02 8.33770034e-02 - 9 +6.6999294576662857e-02 2.87368977e-02 - 10 +6.6250206928793964e-02 1.72155066e-03 - 11 +6.6247631521353170e-02 2.43806911e-04 - 12 +6.6247596955965438e-02 1.40066459e-04 - 13 +6.6247580176638649e-02 4.77471577e-06 - 14 +6.6247580163923028e-02 3.00484279e-06 - 15 +6.6247580159235792e-02 1.91039983e-06 - 16 +6.6247580156889613e-02 9.56038747e-07 - Terminated - min grad norm reached after 16 iterations, 7.78 seconds. + 1 +5.2427396265941129e-01 8.16627951e-01 + 2 +1.7904850059627236e-01 1.91366819e-01 + 3 +1.6985797253002377e-01 1.70940682e-01 + 4 +1.3903474972292729e-01 1.28606342e-01 + 5 +7.4961734618782416e-02 6.41973980e-02 + 6 +7.1900245222486239e-02 4.25693592e-02 + 7 +7.0472023318269614e-02 2.34599232e-02 + 8 +6.9917568641317152e-02 5.66542766e-03 + 9 +6.9885086242452696e-02 4.05756115e-04 + 10 +6.9884967432653489e-02 2.16836017e-04 + 11 +6.9884923649884148e-02 5.74961622e-05 + 12 +6.9884921818258436e-02 3.83257203e-05 + 13 +6.9884920459612282e-02 9.97486224e-06 + 14 +6.9884920414414409e-02 7.33567875e-06 + 15 +6.9884920388431387e-02 5.23889187e-06 + 16 +6.9884920385183902e-02 4.91959084e-06 + 17 +6.9884920373983223e-02 3.56451669e-06 + 18 +6.9884920369701245e-02 2.88858709e-06 + 19 +6.9884920361621208e-02 1.82294279e-07 + Terminated - min grad norm reached after 19 iterations, 9.65 seconds. @@ -105,7 +108,7 @@ WAsserstein Discriminant Analysis pl.legend(loc=0) pl.title('Projected test samples') -**Total running time of the script:** ( 0 minutes 14.134 seconds) +**Total running time of the script:** ( 0 minutes 16.902 seconds) diff --git a/docs/source/auto_examples/plot_compute_emd.ipynb b/docs/source/auto_examples/plot_compute_emd.ipynb new file mode 100644 index 0000000..4162144 --- /dev/null +++ b/docs/source/auto_examples/plot_compute_emd.ipynb @@ -0,0 +1,54 @@ +{ + "nbformat_minor": 0, + "nbformat": 4, + "cells": [ + { + "execution_count": null, + "cell_type": "code", + "source": [ + "%matplotlib inline" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "\n# 1D optimal transport\n\n\n@author: rflamary\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\nfrom ot.datasets import get_1D_gauss as gauss\n\n\n#%% parameters\n\nn=100 # nb bins\nn_target=50 # nb target distributions\n\n\n# bin positions\nx=np.arange(n,dtype=np.float64)\n\nlst_m=np.linspace(20,90,n_target)\n\n# Gaussian distributions\na=gauss(n,m=20,s=5) # m= mean, s= std\n\nB=np.zeros((n,n_target))\n\nfor i,m in enumerate(lst_m):\n B[:,i]=gauss(n,m=m,s=5)\n\n# loss matrix and normalization\nM=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'euclidean')\nM/=M.max()\nM2=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'sqeuclidean')\nM2/=M2.max()\n#%% plot the distributions\n\npl.figure(1)\npl.subplot(2,1,1)\npl.plot(x,a,'b',label='Source distribution')\npl.title('Source distribution')\npl.subplot(2,1,2)\npl.plot(x,B,label='Target distributions')\npl.title('Target distributions')\n\n#%% Compute and plot distributions and loss matrix\n\nd_emd=ot.emd2(a,B,M) # direct computation of EMD\nd_emd2=ot.emd2(a,B,M2) # direct computation of EMD with loss M3\n\n\npl.figure(2)\npl.plot(d_emd,label='Euclidean EMD')\npl.plot(d_emd2,label='Squared Euclidean EMD')\npl.title('EMD distances')\npl.legend()\n\n#%%\nreg=1e-2\nd_sinkhorn=ot.sinkhorn(a,B,M,reg)\nd_sinkhorn2=ot.sinkhorn(a,B,M2,reg)\n\npl.figure(2)\npl.clf()\npl.plot(d_emd,label='Euclidean EMD')\npl.plot(d_emd2,label='Squared Euclidean EMD')\npl.plot(d_sinkhorn,'+',label='Euclidean Sinkhorn')\npl.plot(d_sinkhorn2,'+',label='Squared Euclidean Sinkhorn')\npl.title('EMD distances')\npl.legend()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "name": "python2", + "language": "python" + }, + "language_info": { + "mimetype": "text/x-python", + "nbconvert_exporter": "python", + "name": "python", + "file_extension": ".py", + "version": "2.7.12", + "pygments_lexer": "ipython2", + "codemirror_mode": { + "version": 2, + "name": "ipython" + } + } + } +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_compute_emd.py b/docs/source/auto_examples/plot_compute_emd.py new file mode 100644 index 0000000..c7063e8 --- /dev/null +++ b/docs/source/auto_examples/plot_compute_emd.py @@ -0,0 +1,74 @@ +# -*- coding: utf-8 -*- +""" +==================== +1D optimal transport +==================== + +@author: rflamary +""" + +import numpy as np +import matplotlib.pylab as pl +import ot +from ot.datasets import get_1D_gauss as gauss + + +#%% parameters + +n=100 # nb bins +n_target=50 # nb target distributions + + +# bin positions +x=np.arange(n,dtype=np.float64) + +lst_m=np.linspace(20,90,n_target) + +# Gaussian distributions +a=gauss(n,m=20,s=5) # m= mean, s= std + +B=np.zeros((n,n_target)) + +for i,m in enumerate(lst_m): + B[:,i]=gauss(n,m=m,s=5) + +# loss matrix and normalization +M=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'euclidean') +M/=M.max() +M2=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'sqeuclidean') +M2/=M2.max() +#%% plot the distributions + +pl.figure(1) +pl.subplot(2,1,1) +pl.plot(x,a,'b',label='Source distribution') +pl.title('Source distribution') +pl.subplot(2,1,2) +pl.plot(x,B,label='Target distributions') +pl.title('Target distributions') + +#%% Compute and plot distributions and loss matrix + +d_emd=ot.emd2(a,B,M) # direct computation of EMD +d_emd2=ot.emd2(a,B,M2) # direct computation of EMD with loss M3 + + +pl.figure(2) +pl.plot(d_emd,label='Euclidean EMD') +pl.plot(d_emd2,label='Squared Euclidean EMD') +pl.title('EMD distances') +pl.legend() + +#%% +reg=1e-2 +d_sinkhorn=ot.sinkhorn(a,B,M,reg) +d_sinkhorn2=ot.sinkhorn(a,B,M2,reg) + +pl.figure(2) +pl.clf() +pl.plot(d_emd,label='Euclidean EMD') +pl.plot(d_emd2,label='Squared Euclidean EMD') +pl.plot(d_sinkhorn,'+',label='Euclidean Sinkhorn') +pl.plot(d_sinkhorn2,'+',label='Squared Euclidean Sinkhorn') +pl.title('EMD distances') +pl.legend() \ No newline at end of file diff --git a/docs/source/auto_examples/plot_compute_emd.rst b/docs/source/auto_examples/plot_compute_emd.rst new file mode 100644 index 0000000..4c7445b --- /dev/null +++ b/docs/source/auto_examples/plot_compute_emd.rst @@ -0,0 +1,119 @@ + + +.. _sphx_glr_auto_examples_plot_compute_emd.py: + + +==================== +1D optimal transport +==================== + +@author: rflamary + + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_compute_emd_001.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_compute_emd_002.png + :scale: 47 + + + + + +.. code-block:: python + + + import numpy as np + import matplotlib.pylab as pl + import ot + from ot.datasets import get_1D_gauss as gauss + + + #%% parameters + + n=100 # nb bins + n_target=50 # nb target distributions + + + # bin positions + x=np.arange(n,dtype=np.float64) + + lst_m=np.linspace(20,90,n_target) + + # Gaussian distributions + a=gauss(n,m=20,s=5) # m= mean, s= std + + B=np.zeros((n,n_target)) + + for i,m in enumerate(lst_m): + B[:,i]=gauss(n,m=m,s=5) + + # loss matrix and normalization + M=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'euclidean') + M/=M.max() + M2=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'sqeuclidean') + M2/=M2.max() + #%% plot the distributions + + pl.figure(1) + pl.subplot(2,1,1) + pl.plot(x,a,'b',label='Source distribution') + pl.title('Source distribution') + pl.subplot(2,1,2) + pl.plot(x,B,label='Target distributions') + pl.title('Target distributions') + + #%% Compute and plot distributions and loss matrix + + d_emd=ot.emd2(a,B,M) # direct computation of EMD + d_emd2=ot.emd2(a,B,M2) # direct computation of EMD with loss M3 + + + pl.figure(2) + pl.plot(d_emd,label='Euclidean EMD') + pl.plot(d_emd2,label='Squared Euclidean EMD') + pl.title('EMD distances') + pl.legend() + + #%% + reg=1e-2 + d_sinkhorn=ot.sinkhorn(a,B,M,reg) + d_sinkhorn2=ot.sinkhorn(a,B,M2,reg) + + pl.figure(2) + pl.clf() + pl.plot(d_emd,label='Euclidean EMD') + pl.plot(d_emd2,label='Squared Euclidean EMD') + pl.plot(d_sinkhorn,'+',label='Euclidean Sinkhorn') + pl.plot(d_sinkhorn2,'+',label='Squared Euclidean Sinkhorn') + pl.title('EMD distances') + pl.legend() +**Total running time of the script:** ( 0 minutes 0.521 seconds) + + + +.. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_compute_emd.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_compute_emd.ipynb ` + +.. rst-class:: sphx-glr-signature + + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_optim_OTreg.ipynb b/docs/source/auto_examples/plot_optim_OTreg.ipynb index 00d4702..5ded922 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.ipynb +++ b/docs/source/auto_examples/plot_optim_OTreg.ipynb @@ -24,7 +24,7 @@ "execution_count": null, "cell_type": "code", "source": [ - "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n\n\n#%% parameters\n\nn=100 # nb bins\n\n# bin positions\nx=np.arange(n,dtype=np.float64)\n\n# Gaussian distributions\na=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std\nb=ot.datasets.get_1D_gauss(n,m=60,s=10)\n\n# loss matrix\nM=ot.dist(x.reshape((n,1)),x.reshape((n,1)))\nM/=M.max()\n\n#%% EMD\n\nG0=ot.emd(a,b,M)\n\npl.figure(3)\not.plot.plot1D_mat(a,b,G0,'OT matrix G0')\n\n#%% Example with Frobenius norm regularization\n\ndef f(G): return 0.5*np.sum(G**2)\ndef df(G): return G\n\nreg=1e-1\n\nGl2=ot.optim.cg(a,b,M,reg,f,df,verbose=True)\n\npl.figure(3)\not.plot.plot1D_mat(a,b,Gl2,'OT matrix Frob. reg')\n\n#%% Example with entropic regularization\n\ndef f(G): return np.sum(G*np.log(G))\ndef df(G): return np.log(G)+1\n\nreg=1e-3\n\nGe=ot.optim.cg(a,b,M,reg,f,df,verbose=True)\n\npl.figure(4)\not.plot.plot1D_mat(a,b,Ge,'OT matrix Entrop. reg')\n\n#%% Example with Frobenius norm + entropic regularization with gcg\n\ndef f(G): return 0.5*np.sum(G**2)\ndef df(G): return G\n\nreg1=1e-3\nreg2=1e-1\n\nGel2=ot.optim.gcg(a,b,M,reg1,reg2,f,df,verbose=True)\n\npl.figure(5)\not.plot.plot1D_mat(a,b,Gel2,'OT entropic + matrix Frob. reg')" + "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n\n\n#%% parameters\n\nn=100 # nb bins\n\n# bin positions\nx=np.arange(n,dtype=np.float64)\n\n# Gaussian distributions\na=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std\nb=ot.datasets.get_1D_gauss(n,m=60,s=10)\n\n# loss matrix\nM=ot.dist(x.reshape((n,1)),x.reshape((n,1)))\nM/=M.max()\n\n#%% EMD\n\nG0=ot.emd(a,b,M)\n\npl.figure(3)\not.plot.plot1D_mat(a,b,G0,'OT matrix G0')\n\n#%% Example with Frobenius norm regularization\n\ndef f(G): return 0.5*np.sum(G**2)\ndef df(G): return G\n\nreg=1e-1\n\nGl2=ot.optim.cg(a,b,M,reg,f,df,verbose=True)\n\npl.figure(3)\not.plot.plot1D_mat(a,b,Gl2,'OT matrix Frob. reg')\n\n#%% Example with entropic regularization\n\ndef f(G): return np.sum(G*np.log(G))\ndef df(G): return np.log(G)+1\n\nreg=1e-3\n\nGe=ot.optim.cg(a,b,M,reg,f,df,verbose=True)\n\npl.figure(4)\not.plot.plot1D_mat(a,b,Ge,'OT matrix Entrop. reg')\n\n#%% Example with Frobenius norm + entropic regularization with gcg\n\ndef f(G): return 0.5*np.sum(G**2)\ndef df(G): return G\n\nreg1=1e-3\nreg2=1e-1\n\nGel2=ot.optim.gcg(a,b,M,reg1,reg2,f,df,verbose=True)\n\npl.figure(5)\not.plot.plot1D_mat(a,b,Gel2,'OT entropic + matrix Frob. reg')\npl.show()" ], "outputs": [], "metadata": { diff --git a/docs/source/auto_examples/plot_optim_OTreg.py b/docs/source/auto_examples/plot_optim_OTreg.py index c585445..8abb426 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.py +++ b/docs/source/auto_examples/plot_optim_OTreg.py @@ -70,4 +70,5 @@ reg2=1e-1 Gel2=ot.optim.gcg(a,b,M,reg1,reg2,f,df,verbose=True) pl.figure(5) -ot.plot.plot1D_mat(a,b,Gel2,'OT entropic + matrix Frob. reg') \ No newline at end of file +ot.plot.plot1D_mat(a,b,Gel2,'OT entropic + matrix Frob. reg') +pl.show() \ No newline at end of file diff --git a/docs/source/auto_examples/plot_optim_OTreg.rst b/docs/source/auto_examples/plot_optim_OTreg.rst index 0dff327..70cd26c 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.rst +++ b/docs/source/auto_examples/plot_optim_OTreg.rst @@ -562,7 +562,8 @@ Regularized OT with generic solver pl.figure(5) ot.plot.plot1D_mat(a,b,Gel2,'OT entropic + matrix Frob. reg') -**Total running time of the script:** ( 0 minutes 2.422 seconds) + pl.show() +**Total running time of the script:** ( 0 minutes 2.319 seconds) diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 13cf572..625cebf 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -11,7 +11,8 @@ It provides the following solvers: - OT solver for the linear program/ Earth Movers Distance [1]. - Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] - and stabilized version [9][10]. + and stabilized version [9][10] with optional GPU implementation + (required cudamat). - Bregman projections for Wasserstein barycenter [3] and unmixing [4]. - Optimal transport for domain adaptation with group lasso regularization [5] @@ -71,14 +72,14 @@ Dependencies Some sub-modules require additional dependences which are discussed below -- ot.dr (Wasserstein dimensionality rediuction) depends on autograd and - pymanopt that can be installed with: +- **ot.dr** (Wasserstein dimensionality rediuction) depends on autograd + and pymanopt that can be installed with: :: pip install pymanopt autograd -- ot.gpu (GPU accelerated OT) depends on cudamat that have to be +- **ot.gpu** (GPU accelerated OT) depends on cudamat that have to be installed with: :: @@ -87,6 +88,8 @@ below cd cudamat python setup.py install --user # for user install (no root) +obviously you need CUDA installed and a compatible GPU. + Examples -------- @@ -144,47 +147,59 @@ References ---------- [1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, -December). Displacement interpolation using Lagrangian mass transport. +December). `Displacement interpolation using Lagrangian mass +transport `__. In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM. -[2] Cuturi, M. (2013). Sinkhorn distances: Lightspeed computation of -optimal transport. In Advances in Neural Information Processing Systems -(pp. 2292-2300). +[2] Cuturi, M. (2013). `Sinkhorn distances: Lightspeed computation of +optimal transport `__. In Advances +in Neural Information Processing Systems (pp. 2292-2300). [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. -(2015). Iterative Bregman projections for regularized transportation -problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138. +(2015). `Iterative Bregman projections for regularized transportation +problems `__. SIAM Journal on +Scientific Computing, 37(2), A1111-A1138. [4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, -Supervised planetary unmixing with optimal transport, Whorkshop on -Hyperspectral Image and Signal Processing : Evolution in Remote Sensing -(WHISPERS), 2016. +`Supervised planetary unmixing with optimal +transport `__, +Whorkshop on Hyperspectral Image and Signal Processing : Evolution in +Remote Sensing (WHISPERS), 2016. -[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport -for Domain Adaptation," in IEEE Transactions on Pattern Analysis and -Machine Intelligence , vol.PP, no.99, pp.1-1 +[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, `Optimal Transport +for Domain Adaptation `__, in IEEE +Transactions on Pattern Analysis and Machine Intelligence , vol.PP, +no.99, pp.1-1 [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). -Regularized discrete optimal transport. SIAM Journal on Imaging -Sciences, 7(3), 1853-1882. +`Regularized discrete optimal +transport `__. SIAM Journal on +Imaging Sciences, 7(3), 1853-1882. -[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized -conditional gradient: analysis of convergence and applications. arXiv -preprint arXiv:1510.06567. +[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). `Generalized +conditional gradient: analysis of convergence and +applications `__. arXiv preprint +arXiv:1510.06567. -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation -for discrete optimal transport", Neural Information Processing Systems -(NIPS), 2016. +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, `Mapping estimation +for discrete optimal +transport `__, +Neural Information Processing Systems (NIPS), 2016. -[9] Schmitzer, B. (2016). Stabilized Sparse Scaling Algorithms for -Entropy Regularized Transport Problems. arXiv preprint arXiv:1610.06519. +[9] Schmitzer, B. (2016). `Stabilized Sparse Scaling Algorithms for +Entropy Regularized Transport +Problems `__. arXiv preprint +arXiv:1610.06519. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). -Scaling algorithms for unbalanced transport problems. arXiv preprint +`Scaling algorithms for unbalanced transport +problems `__. arXiv preprint arXiv:1607.05816. [11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). -Wasserstein Discriminant Analysis. arXiv preprint arXiv:1608.08063. +`Wasserstein Discriminant +Analysis `__. arXiv preprint +arXiv:1608.08063. .. |PyPI version| image:: https://badge.fury.io/py/POT.svg :target: https://badge.fury.io/py/POT diff --git a/examples/plot_OT_L1_vs_L2.py b/examples/plot_OT_L1_vs_L2.py index 1ab44a0..9bb92fe 100644 --- a/examples/plot_OT_L1_vs_L2.py +++ b/examples/plot_OT_L1_vs_L2.py @@ -1,10 +1,10 @@ # -*- coding: utf-8 -*- """ -==================================================== -2D Optimal transport between empirical distributions -==================================================== +========================================== +2D Optimal transport for different metrics +========================================== -Stoile the figure idea from: +Stole the figure idea from Fig. 1 and 2 in https://arxiv.org/pdf/1706.07650.pdf @@ -106,5 +106,3 @@ for data in range(2): pl.axis('equal') #pl.legend(loc=0) pl.title('OT sqrt Euclidean') - -#%% sinkhorn diff --git a/examples/plot_optim_OTreg.py b/examples/plot_optim_OTreg.py index c585445..8abb426 100644 --- a/examples/plot_optim_OTreg.py +++ b/examples/plot_optim_OTreg.py @@ -70,4 +70,5 @@ reg2=1e-1 Gel2=ot.optim.gcg(a,b,M,reg1,reg2,f,df,verbose=True) pl.figure(5) -ot.plot.plot1D_mat(a,b,Gel2,'OT entropic + matrix Frob. reg') \ No newline at end of file +ot.plot.plot1D_mat(a,b,Gel2,'OT entropic + matrix Frob. reg') +pl.show() \ No newline at end of file -- cgit v1.2.3 From f3cca07ded6d01d4e1658fa6391263fe756a04f6 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 3 Jul 2017 16:39:43 +0200 Subject: doc dr --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 42e2b79..a099ce0 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -31,7 +31,7 @@ class Mock(MagicMock): @classmethod def __getattr__(cls, name): return MagicMock() -MOCK_MODULES = ['ot.lp.emd_wrap'] +MOCK_MODULES = ['ot.lp.emd_wrap','autograd','pymanopt'] sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # !!!! -- cgit v1.2.3 From 8ff51eb898c3cb112d16a4a629639eddcef62516 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 3 Jul 2017 16:43:23 +0200 Subject: add mock in doc conf file --- docs/source/conf.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index a099ce0..1304464 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -31,7 +31,8 @@ class Mock(MagicMock): @classmethod def __getattr__(cls, name): return MagicMock() -MOCK_MODULES = ['ot.lp.emd_wrap','autograd','pymanopt'] +MOCK_MODULES = ['ot.lp.emd_wrap','autograd.numpy','pymanopt.manifolds','pymanopt' + 'pymanopt.solvers','cudamat'] sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # !!!! -- cgit v1.2.3 From 3ba85d6c8a1dac2722c5f51d4b2c49e3ed2ebd0f Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 3 Jul 2017 16:45:59 +0200 Subject: doc update --- ot/dr.py | 8 +++----- 1 file changed, 3 insertions(+), 5 deletions(-) diff --git a/ot/dr.py b/ot/dr.py index 14a92c1..9187b57 100644 --- a/ot/dr.py +++ b/ot/dr.py @@ -9,15 +9,14 @@ from pymanopt import Problem from pymanopt.solvers import SteepestDescent, TrustRegions def dist(x1,x2): - """ Compute squared euclidean distance between samples + """ Compute squared euclidean distance between samples (autograd) """ x1p2=np.sum(np.square(x1),1) x2p2=np.sum(np.square(x2),1) return x1p2.reshape((-1,1))+x2p2.reshape((1,-1))-2*np.dot(x1,x2.T) def sinkhorn(w1,w2,M,reg,k): - """ - Simple solver for Sinkhorn algorithm with fixed number of iteration + """Sinkhorn algorithm with fixed number of iteration (autograd) """ K=np.exp(-M/reg) ui=np.ones((M.shape[0],)) @@ -29,8 +28,7 @@ def sinkhorn(w1,w2,M,reg,k): return G def split_classes(X,y): - """ - split samples in X by classes in y + """split samples in X by classes in y """ lstsclass=np.unique(y) return [X[y==i,:].astype(np.float32) for i in lstsclass] -- cgit v1.2.3 From 5345d1e51fa1c9320bbdf1218a29c81d4a8d411c Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 3 Jul 2017 17:08:55 +0200 Subject: add notebooks --- examples/Demo_Compute_EMD.ipynb | 167 ++++++++++ examples/Demo_Ground_Loss.ipynb | 345 +++++++++++++++++++++ .../Demo_Wasserstein_Discriminant_Analysis.ipynb | 210 +++++++++++++ 3 files changed, 722 insertions(+) create mode 100644 examples/Demo_Compute_EMD.ipynb create mode 100644 examples/Demo_Ground_Loss.ipynb create mode 100644 examples/Demo_Wasserstein_Discriminant_Analysis.ipynb diff --git a/examples/Demo_Compute_EMD.ipynb b/examples/Demo_Compute_EMD.ipynb new file mode 100644 index 0000000..cda9a59 --- /dev/null +++ b/examples/Demo_Compute_EMD.ipynb @@ -0,0 +1,167 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Compute and plot EMD" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot\n", + "from ot.datasets import get_1D_gauss as gauss" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generate data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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EwzCM70VVyU4twlHhbOiq1Lsm1WRkGIbRkDITC1j7wWHSj+YTGhnAiGld6dg/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YVw/XwGT5zA2ea94O/tMtPpdLHWaKLuYhfWfm/u/q6iFgbsLlalm6g6fQK7RYGXX/QxksuGO+0W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\n7yOw6hNkxFBWLpzE863vEdWjXDfmOm4cf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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "n=100 # nb bins\n", + "n_target=25 # nb target distributions\n", + "\n", + "\n", + "# bin positions\n", + "x=np.arange(n,dtype=np.float64)\n", + "\n", + "lst_m=np.linspace(20,90,n_target)\n", + "\n", + "# Gaussian distributions\n", + "a=gauss(n,m=20,s=5) # m= mean, s= std\n", + "\n", + "B=np.zeros((n,n_target))\n", + "\n", + "for i,m in enumerate(lst_m):\n", + " B[:,i]=gauss(n,m=m,s=5)\n", + " \n", + "\n", + "# loss matrix and normalization\n", + "M=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'euclidean')\n", + "M/=M.max()\n", + "\n", + "\n", + "M2=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'sqeuclidean')\n", + "M2/=M2.max()\n", + "\n", + "\n", + "# plot the distributions\n", + "\n", + "pl.figure(1)\n", + "pl.subplot(2,1,1)\n", + "pl.plot(x,a,'b',label='Source distribution')\n", + "pl.xticks([])\n", + "pl.title('Source distribution')\n", + "pl.subplot(2,1,2)\n", + "pl.plot(x,B,label='Target distributions')\n", + "pl.title('Target distributions')\n", + "pl.show() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute EMD and sinkhorn distances " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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8atatW9dYjC2l7HJYWBj29vaMHTsWR0dHunfvnuHO2aSkJEaNGsX06dONj2VW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\n7tz7ex5PNrEisNTLMmfdTGSY1jjjCFA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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "d_emd=ot.emd2(a,B,M) # direct computation of EMD\n", + "d_emd2=ot.emd2(a,B,M2) # direct computation of EMD with loss M2\n", + "\n", + "reg=1e-2\n", + "d_sinkhorn=ot.sinkhorn(a,B,M,reg) # sinkhorn returns a list of distance if B is a matrix\n", + "d_sinkhorn2=ot.sinkhorn(a,B,M2,reg)\n", + "\n", + "pl.figure(2)\n", + "pl.clf()\n", + "pl.plot(d_emd,label='Euclidean EMD')\n", + "pl.plot(d_emd2,label='Squared Euclidean EMD')\n", + "pl.plot(d_sinkhorn,'+',label='Euclidean Sinkhorn')\n", + "pl.plot(d_sinkhorn2,'+',label='Squared Euclidean Sinkhorn')\n", + "pl.title('EMD distances')\n", + "pl.legend()\n", + "pl.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/Demo_Ground_Loss.ipynb b/examples/Demo_Ground_Loss.ipynb new file mode 100644 index 0000000..a39a07f --- /dev/null +++ b/examples/Demo_Ground_Loss.ipynb @@ -0,0 +1,345 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Choice of the ground loss\n", + "\n", + "Data from [Fig. 1 and 2 in here](https://arxiv.org/pdf/1706.07650.pdf)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dataset 1" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n=20 # nb samples\n", + "xs=np.zeros((n,2))\n", + "xs[:,0]=np.arange(n)+1\n", + "xs[:,1]=(np.arange(n)+1)*-0.001 # to make it strictly convex...\n", + "\n", + "xt=np.zeros((n,2))\n", + "xt[:,1]=np.arange(n)+1\n", + "a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples\n", + "\n", + "pl.figure(1)\n", + "pl.clf()\n", + "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", + "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", + "pl.axis('equal')\n", + "pl.title('Source and traget distributions')\n", + "pl.legend()\n", + "pl.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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JmhXzSdIiMpskzcTU79Bt8XTgNTtc9+iIeA/wSeCFmfmB7Yoi4nzgfIDB2mkc\nXZ18rZlTLEuzE6X6UfGWyilu2c1+cUyD2var9QCb/WofWauf4naq9pHFegD6o1J5t1er7/c3S/UA\n/V6tzbC3Uapf6R4t1QOsFtp0Yor7YTZ2lU/j2bTSOwiDyQ+Kas4A0CkGWrdWn91ubfsAvVofo0Gt\nj+zXx7Q5rLXZHNb2oVoPMKpmeLEepsj9YoZXMx9gs5r71Uzu17OjlsnLn029w6exuTp5x9mp73MW\nD9Nq/ahXH1P18Z3F5/ZpHnsxqD1Xl+cPg9pzO8CgOB+o1gMMu/Odcww69TGtdu8q1Q+LfQw79XnT\nNG0mset36CJiAHw98PvbXP1u4L6Z+RDgvwBv3Gk7mXlBZp6Xmef1VtZ3OyxJmkk+jWfToLs2v8FK\n2jdmnU3ddedN0n42i1MunwS8OzOv3XpFZt6cmbe0P18M9CPijBn0KUmTMJ8kLSKzSdLMzGJB9wx2\nOGUgIu4VEdH+/Ii2v+tn0KckTcJ8krSIzCZJM7Orz9BFxDrwtcD3jl32PIDMfBnwzcD3RcQGcDvw\n9Mw8aSerS9o/zCdJi8hskjRru1rQZeatwD22XPaysZ9fCrx0N31I0jTMJ0mLyGySNGuz+rMFkiRJ\nkqQ95oJOkiRJkpaUCzpJkiRJWlIu6CRJkiRpSbmgkyRJkqQl5YJOkiRJkpbUrv5swbxkB46uxeQN\nCqXjfVSMerVOcopbdlRsM+rPtx5gNKj96ZvNQXH7w/qf1hkNRrUGvWI90OnX2vT6m6X6QW+jVA+w\nUmwz7Nbq13p3leoBVrtHJ67tcAr8GaUIcqXwII96OGW32KZTC7Ps1V/Hq7YZ9bvF+vqYNofFMQ1r\nt+vmoH7fVdtU83KaPkbVTJ5iTOXnomKGx6CWrwD9weT5F53lz6YM2FidfD+qcyAAirdT1mKA7NXv\nh3Kb4nwgqvMNoFvso/JYBRj06sfDsF/rozrfABgW26wU5yiV+cYxw05xTJ1aHysxxVwu6nOtSfgO\nnSRJkiQtKRd0kiRJkrSkXNBJkiRJ0pJyQSdJkiRJS8oFnSRJkiQtKRd0kiRJkrSkXNBJkiRJ0pJy\nQSdJkiRJS8oFnSRJkiQtKRd0kiRJkrSkXNBJkiRJ0pLqnewBbKsDm6sxcXlOsSyttsnufOsBRsU2\no36xfpC1BsCo+AgZDWt95BRjol9rE4NRuYtef7NU3+9vlOqHxXqA1d7RUv1a767a9ru17QOsdSbv\noxNT3NfvOHc5AAAZrUlEQVQLJjvBaLVw4MXkOTbeR6m+Wwuz7E4xpl6tj1Gv1sdoUA/xzWGtzeag\nNqZqPcDmsFpf72NU7WNQ3f4UzxPVHC9mcrdfz/CVweR5dipkEx3YXCvcTvWHHtkp3k7Vw7o7xf3Q\nqz02OsXHUrdbf+z1B7Xn90GvNt+YZv6w0qu1qc43AFaKc4jqnGOqOUq3Ng+qzGkAhp0pbqcp2kzC\nd+gkSZIkaUm5oJMkSZKkJTXRgi4iLoyI6yLi/WOXnR4Rl0TER9v/T9uh7bPamo9GxLNmNXBJMpsk\nLSKzSdJemvQduouAJ2657EXAX2bmA4C/bH+/m4g4HfhJ4JHAI4Cf3CnAJGkKF2E2SVo8F2E2Sdoj\nEy3oMvNtwA1bLn4a8Mr251cC37BN068DLsnMGzLzRuASPj/gJGkqZpOkRWQ2SdpLu/kM3ZmZeU37\n86eAM7epORu4cuz3q9rLJGlezCZJi8hskjQXM/lSlMxMYFff+xsR50fEpRFx6cbtt85iWJL2uVln\n09GN22Y0Mkn72ayzafOWW2Y0MknLaDcLumsj4t4A7f/XbVNzNXDu2O/ntJd9nsy8IDPPy8zzeqvr\nuxiWpH1ubtnU763NfLCS9o25ZVP3wIGZD1bS8tjNgu5NwLFvX3oW8Efb1Pw58ISIOK39UO8T2ssk\naV7MJkmLyGySNBeT/tmC1wB/BzwwIq6KiOcCvwB8bUR8FPia9nci4ryI+G2AzLwB+Bngne2/n24v\nk6RdM5skLSKzSdJe6k1SlJnP2OGqx29Teynw3WO/XwhcONXoJOk4zCZJi8hskrSXZvKlKJIkSZKk\nvTfRO3R7LTuwUfjugYzp+phrfbdWD5C92hdejYr3Xk5xb48GxTENRrUO+vUv+YrhZqm+N9go9zEo\ntlnp1+rX+kdL9QCrvVqb9d5dpfoD3TtL9dU2nd19odti6ASjlf7E5dNkE91ao+zU6kfF7QOM+rUA\nzF6tj83BNGOabx+bw1J522YP+hjU6kfD2nG3Wcx8gBzWcr9TzPDhSj0v1waTt+nEqZBNSa4U7odp\n9rk4D4pOrY/oFucPQKdb66PbK84fevUxDXq1+cCwOH9YKW4f6vOHteL8AepzjvVebc6x1qmPqdpm\n2CnOszr1edNK1PNsEr5DJ0mSJElLygWdJEmSJC0pF3SSJEmStKRc0EmSJEnSknJBJ0mSJElLygWd\nJEmSJC0pF3SSJEmStKRc0EmSJEnSknJBJ0mSJElLygWdJEmSJC0pF3SSJEmStKR6J3sA28mAjZVC\ng5iij+JSNrtZrK9tH+pjGvWLY+rV6gFyUGzTG5XKY1CrB+gNNkr1g8FmuY+Vfq2Ptf7RUv1qr1YP\ncKB/Z6n+YO+O2vZ7te0DHOxO3kc36vf1oskINlYLB3enHk5ZbFPPsvqYRv1am1GvVr/ZL5U3fRTH\ntDmobX9zWL+dNofzrQfYXKll8uawVj9aqR+nsVLL2P6wmK/Du0r1AIeGlWyqPzcunE7SWZ38do0p\n5k0Ub6dOtb5bf+x1i216xfpBrz5/GPRqj++VYv0084e1Xu0YWi/WA6x3a21Wu7X9WCtuH2CtU9zv\nTm0etBL1+6Lax6R8h06SJEmSlpQLOkmSJElaUi7oJEmSJGlJuaCTJEmSpCXlgk6SJEmSlpQLOkmS\nJElaUi7oJEmSJGlJuaCTJEmSpCV1wgVdRFwYEddFxPvHLvvliPhwRLw3It4QEUd2aHtFRLwvIi6L\niEtnOXBJMp8kLSKzSdJemuQduouAJ2657BLgSzPzy4F/AH70OO0fl5kPzczzphuiJO3oIswnSYvn\nIswmSXvkhAu6zHwbcMOWy96SmRvtr28HzpnD2CTpuMwnSYvIbJK0l2bxGbrvAt68w3UJvCUi3hUR\n58+gL0mqMJ8kLSKzSdLM9HbTOCJ+DNgAXr1DyWMy8+qI+ALgkoj4cPuq1XbbOh84H6B35DQ213Li\ncUxeOd5hrTy7tV6yW9v+VH30interQfoj0rlnWJ9r79ZqgcYDDZOXDRmpV+rB1gf3FWqPzC4s1R/\nqH9HqX6aNge6tTEd7t5eqm/a3DpxbZfaY2O3ZpVP49k0XD3Cxnrh4C7mDEB2ao2y+LLcqFsfVDXP\nRr1aH5v92vYBRoNa/eagNqbRsLb9po9i/Uo9k6ttRqvFPlbqx2lvWMvYtZVavh5eqefl6cPbJq7t\ndZY/m3pnHGZQuB8i6o+9aptOp1Y/zf3Q6xbnHN3anGNYrG/a1I6HYa84p+keLdUDrPdqx9x6t1bf\n9FGbcxzsVuc09RxY69TGVK1fL9YDrET9/pvE1O/QRcSzgacA35GZ2x61mXl1+/91wBuAR+y0vcy8\nIDPPy8zzuuvr0w5LkmaaT+PZ1BuaTZKmN69s6h4ym6T9bKoFXUQ8EfgR4Oszc9uXwSJiPSIOHvsZ\neALw/u1qJWlWzCdJi8hskjQvk/zZgtcAfwc8MCKuiojnAi8FDtKcCnBZRLysrT0rIi5um54J/E1E\nvAf4e+BPM/PP5rIXkvYl80nSIjKbJO2lE36GLjOfsc3Fr9ih9pPAk9ufPw48ZFejk6TjMJ8kLSKz\nSdJemsW3XEqSJEmSTgIXdJIkSZK0pFzQSZIkSdKSckEnSZIkSUvKBZ0kSZIkLSkXdJIkSZK0pFzQ\nSZIkSdKSOuHfoTsZsgObKzl5fUzRSWfy7Tf1tfLsFrcP9TH1avXRG9W2D3SLbXr9zVJ9v79RqgdY\nKbZZ6x8t93FgcGetvl+rP9i/o1QPcLBXa3O4d3utvntbqR7gUHfyMXWj/vhbOJ1gY3XyMJgmm7Ka\nNZ1aJ9mtbR9gVHymKNf36zfUqF+sH9TqN4v1AKNhLZM3i/UAo9Vam1ypZXJvpZ7Jq6t3leoPrdTy\n8rRhPZvOGN4ycW0varfRIup0RqwOJ78fIuqPvWLU0O3UMr9aD9Avtul3i3OUTv2xsdKtzTlWurVj\nbrW4fYD1Xu2Ym6aPg4X5AMBap5Yb1XqA9U5tv6v1a8V6gPWo37aT8B06SZIkSVpSLugkSZIkaUm5\noJMkSZKkJeWCTpIkSZKWlAs6SZIkSVpSLugkSZIkaUm5oJMkSZKkJeWCTpIkSZKWlAs6SZIkSVpS\nLugkSZIkaUm5oJMkSZKkJdU72QPYVicZrW5OXh9T9FFt08na5ru1+qbNaK59dIvbB+j3C/cDMOht\nlOqH/Vo9wFr/aKl+tVerBzjUv6NUf7BYf6R/e6ke4HC31uZw97ZS/ZFiPcCRzuRtutQff4smO3B0\nbfLwyGmyqfgyWxbrR936oLL4TDEq1le3D7DZr9WPBsX6YT3DNwe1NqOVKY6JYpveSi1jV9fuLNUD\nHF6t5d/pK7eW6r9geEupHuDeg5smru1H7XluEXUiOTC8q1RfFcU2vU7tsdqN+vFQ7WPQqR0Pg279\nsVHtY7VbnNMU6wHWOpM/NgDWurV6gAPdWg5Ux3SoU583rXVqebYetTFV6wHWio+PSfkOnSRJkiQt\nKRd0kiRJkrSkTrigi4gLI+K6iHj/2GUvjoirI+Ky9t+Td2j7xIj4SERcHhEvmuXAJcl8krSIzCZJ\ne2mSd+guAp64zeW/mpkPbf9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3AefN8DZfpwmZw2OXzWW/aYLvGuAozXnNz6U5j/8vgY8C\nfwGc3taeB/z2WNvvau/3y4HnzKjvy2nOtT52nx/7FrCzgIuPd//sst9Xtffje2mC5t5b+93peNht\n3+3lFx27f8dqZ7bPp+q/7e4PzCeYUz5hNs01m47T99zzabt+28svwmya5vFrNjl3mnk+7dDvKZ1N\nO/XdXn4RC5pP0Q5AkiRJkrRkFu2US0mSJEnShFzQSZIkSdKSckEnSZIkSUvKBZ0kSZIkLSkXdJIk\nSZK0pFzQSZIkSdKSckEnSZIkSUvKBZ0kSZIkLan/H/+IDxLdPXIjAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# loss matrix\n", + "M1=ot.dist(xs,xt,metric='euclidean')\n", + "M1/=M1.max()\n", + "\n", + "# loss matrix\n", + "M2=ot.dist(xs,xt,metric='sqeuclidean')\n", + "M2/=M2.max()\n", + "\n", + "# loss matrix\n", + "Mp=np.sqrt(ot.dist(xs,xt,metric='euclidean'))\n", + "Mp/=Mp.max()\n", + "\n", + "pl.figure(2,(15,5))\n", + "pl.subplot(1,3,1)\n", + "pl.imshow(M1,interpolation='nearest')\n", + "pl.title('Eucidean cost')\n", + "pl.subplot(1,3,2)\n", + "pl.imshow(M2,interpolation='nearest')\n", + "pl.title('Squared Euclidean cost')\n", + "\n", + "pl.subplot(1,3,3)\n", + "pl.imshow(Mp,interpolation='nearest')\n", + "pl.title('Sqrt Euclidean cost')\n", + "pl.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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SjykpKqxxNUgispTDgUOnCuGhxOdRsfHzwJqPPJbEJiIKCYcDGRJIf+1f+GdWKmqtm2lY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\n4att6nLyg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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + " \n", + "G1=ot.emd(a,b,M1)\n", + "G2=ot.emd(a,b,M2)\n", + "Gp=ot.emd(a,b,Mp)\n", + "\n", + "pl.figure(3,(15,5))\n", + "\n", + "pl.subplot(1,3,1)\n", + "ot.plot.plot2D_samples_mat(xs,xt,G1,c=[.5,.5,1])\n", + "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", + "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", + "pl.axis('equal')\n", + "#pl.legend(loc=0)\n", + "pl.title('OT Euclidean')\n", + "\n", + "pl.subplot(1,3,2)\n", + "\n", + "ot.plot.plot2D_samples_mat(xs,xt,G2,c=[.5,.5,1])\n", + "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", + "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", + "pl.axis('equal')\n", + "#pl.legend(loc=0)\n", + "pl.title('OT squared Euclidean')\n", + "\n", + "pl.subplot(1,3,3)\n", + "\n", + "ot.plot.plot2D_samples_mat(xs,xt,Gp,c=[.5,.5,1])\n", + "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", + "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", + "pl.axis('equal')\n", + "#pl.legend(loc=0)\n", + "pl.title('OT sqrt Euclidean')\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dataset 2" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n=50 # nb samples\n", + "xtot=np.zeros((n+1,2))\n", + "xtot[:,0]=np.cos((np.arange(n+1)+1.0)*0.9/(n+2)*2*np.pi+.06*np.pi)\n", + "xtot[:,1]=np.sin((np.arange(n+1)+1.0)*0.9/(n+2)*2*np.pi+.06*np.pi)\n", + "\n", + "xs=xtot[:n,:]\n", + "xt=xtot[1:,:]\n", + "\n", + "a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples\n", + "\n", + "pl.figure(1)\n", + "pl.clf()\n", + "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", + "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", + "pl.axis('equal')\n", + "pl.title('Source and traget distributions')\n", + "pl.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# loss matrix\n", + "M1=ot.dist(xs,xt,metric='euclidean')\n", + "M1/=M1.max()\n", + "\n", + "# loss matrix\n", + "M2=ot.dist(xs,xt,metric='sqeuclidean')\n", + "M2/=M2.max()\n", + "\n", + "# loss matrix\n", + "Mp=np.sqrt(ot.dist(xs,xt,metric='euclidean'))\n", + "Mp/=Mp.max()\n", + "\n", + "pl.figure(2,(15,5))\n", + "pl.subplot(1,3,1)\n", + "pl.imshow(M1,interpolation='nearest')\n", + "pl.title('Eucidean cost')\n", + "pl.subplot(1,3,2)\n", + "pl.imshow(M2,interpolation='nearest')\n", + "pl.title('Squared Euclidean cost')\n", + "\n", + "pl.subplot(1,3,3)\n", + "pl.imshow(Mp,interpolation='nearest')\n", + "pl.title('Sqrt Euclidean cost')\n", + "pl.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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woWJvBcTKmluyWv3ZtM5c3qjZdR0ysonaCYiCiNRN5Thi95Kk/L1IO+RuG+Du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\ntROiKGG1Ex27EsRt6lPUCPaG1ryWFuk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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + " \n", + "G1=ot.emd(a,b,M1)\n", + "G2=ot.emd(a,b,M2)\n", + "Gp=ot.emd(a,b,Mp)\n", + "\n", + "pl.figure(3,(15,5))\n", + "\n", + "pl.subplot(1,3,1)\n", + "ot.plot.plot2D_samples_mat(xs,xt,G1,c=[.5,.5,1])\n", + "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", + "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", + "pl.axis('equal')\n", + "#pl.legend(loc=0)\n", + "pl.title('OT Euclidean')\n", + "\n", + "pl.subplot(1,3,2)\n", + "\n", + "ot.plot.plot2D_samples_mat(xs,xt,G2,c=[.5,.5,1])\n", + "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", + "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", + "pl.axis('equal')\n", + "#pl.legend(loc=0)\n", + "pl.title('OT squared Euclidean')\n", + "\n", + "pl.subplot(1,3,3)\n", + "\n", + "ot.plot.plot2D_samples_mat(xs,xt,Gp,c=[.5,.5,1])\n", + "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", + "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", + "pl.axis('equal')\n", + "#pl.legend(loc=0)\n", + "pl.title('OT sqrt Euclidean')\n", + "pl.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/examples/Demo_Wasserstein_Discriminant_Analysis.ipynb b/examples/Demo_Wasserstein_Discriminant_Analysis.ipynb new file mode 100644 index 0000000..fb36289 --- /dev/null +++ b/examples/Demo_Wasserstein_Discriminant_Analysis.ipynb @@ -0,0 +1,210 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Demonstration of Wasserstein Discriminant Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot\n", + "from ot.datasets import get_1D_gauss as gauss\n", + "from ot.dr import wda" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generate dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "n=1000 # nb samples in source and target datasets\n", + "nz=0.2\n", + "xs,ys=ot.datasets.get_data_classif('3gauss',n,nz)\n", + "xt,yt=ot.datasets.get_data_classif('3gauss',n,nz)\n", + "\n", + "nbnoise=8\n", + "\n", + "xs=np.hstack((xs,np.random.randn(n,nbnoise)))\n", + "xt=np.hstack((xt,np.random.randn(n,nbnoise)))\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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VUrlxjS6s70fSQnzFo/S99rG/3psWPuUjR1mCKJzBqr3IXm0zbXRx3OcDPI1g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fAAAg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\nj0tKSujq6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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(1,(10,5))\n", + "\n", + "pl.subplot(1,2,1)\n", + "pl.scatter(xt[:,0],xt[:,1],c=ys,marker='+',label='Source samples')\n", + "pl.legend(loc=0)\n", + "pl.title('Discriminant dimensions')\n", + "\n", + "pl.subplot(1,2,2)\n", + "pl.scatter(xt[:,2],xt[:,3],c=ys,marker='+',label='Source samples')\n", + "pl.legend(loc=0)\n", + "pl.title('Other dimensions')\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute WDA" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Compiling cost function...\n", + "Computing gradient of cost function...\n", + " iter\t\t cost val\t grad. norm\n", + " 1\t+3.9157922235693188e-01\t5.71597367e-01\n", + " 2\t+1.3819067207672747e-01\t1.45380541e-01\n", + " 3\t+1.3514783259634658e-01\t1.56401980e-01\n", + " 4\t+1.2366637320576453e-01\t1.39774670e-01\n", + " 5\t+8.7917719796676133e-02\t1.02114467e-01\n", + " 6\t+8.4889100715160537e-02\t1.00158402e-01\n", + " 7\t+7.4573884583769998e-02\t6.88332229e-02\n", + " 8\t+7.1655335462508282e-02\t5.32376275e-02\n", + " 9\t+7.0479018645171101e-02\t4.37283221e-02\n", + " 10\t+6.8400612315063808e-02\t6.65409711e-03\n", + " 11\t+6.8364466366572729e-02\t2.96611209e-03\n", + " 12\t+6.8355577537242695e-02\t8.64992482e-05\n", + " 13\t+6.8355570205523991e-02\t1.40803543e-05\n", + " 14\t+6.8355570014368830e-02\t2.34884058e-06\n", + " 15\t+6.8355570008960018e-02\t2.23203146e-07\n", + "Terminated - min grad norm reached after 15 iterations, 7.87 seconds.\n", + "\n" + ] + } + ], + "source": [ + "p=2\n", + "reg=1\n", + "k=10\n", + "maxiter=100\n", + "\n", + "P,proj = wda(xs,ys,p,reg,k,maxiter=maxiter)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Project and plot samples" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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X5tTEGOkPWaGekSyMtFEVJfJiikPXwEmrVAm9tzfs3b22rIe9DnsZEkKGR7Am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\nlCmvv568KwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# data projection (also removes center)\n", + "xsp=proj(xs)\n", + "xtp=proj(xt)\n", + "\n", + "pl.figure(1,(10,5))\n", + "\n", + "pl.subplot(1,2,1)\n", + "pl.scatter(xsp[:,0],xsp[:,1],c=ys,marker='+',label='Projected samples')\n", + "pl.legend(loc=0)\n", + "pl.title('Projected training samples')\n", + "\n", + "\n", + "pl.subplot(1,2,2)\n", + "pl.scatter(xtp[:,0],xtp[:,1],c=ys,marker='+',label='Projected samples')\n", + "pl.legend(loc=0)\n", + "pl.title('Projected test samples')\n", + "pl.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} -- cgit v1.2.3 From fb6c12afa7864afd9c288e5e3dc0295488238679 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 3 Jul 2017 17:11:48 +0200 Subject: add notebooks to reamde --- README.md | 3 +++ docs/source/readme.rst | 6 ++++++ 2 files changed, 9 insertions(+) diff --git a/README.md b/README.md index 7a06486..9664f3d 100644 --- a/README.md +++ b/README.md @@ -77,6 +77,8 @@ The examples folder contain several examples and use case for the library. The f Here is a list of the Python notebooks if you want a quick look: * [1D optimal transport](https://github.com/rflamary/POT/blob/master/examples/Demo_1D_OT.ipynb) +* [OT Ground Loss](https://github.com/rflamary/POT/blob/master/examples/Demo_Ground_Loss.ipynb) +* [Multiple EMD computation](https://github.com/rflamary/POT/blob/master/examples/Demo_Compute_EMD.ipynb) * [2D optimal transport on empirical distributions](https://github.com/rflamary/POT/blob/master/examples/Demo_2D_OT_samples.ipynb) * [1D Wasserstein barycenter](https://github.com/rflamary/POT/blob/master/examples/Demo_1D_barycenter.ipynb) * [OT with user provided regularization](https://github.com/rflamary/POT/blob/master/examples/Demo_Optim_OTreg.ipynb) @@ -84,6 +86,7 @@ The examples folder contain several examples and use case for the library. The f * [Color transfer in images](https://github.com/rflamary/POT/blob/master/examples/Demo_Image_ColorAdaptation.ipynb) * [OT mapping estimation for domain adaptation](https://github.com/rflamary/POT/blob/master/examples/Demo_2D_OTmapping_DomainAdaptation.ipynb) * [OT mapping estimation for color transfer in images](https://github.com/rflamary/POT/blob/master/examples/Demo_Image_ColorAdaptation_mapping.ipynb) +* [Wasserstein Discriminant Analysis](https://github.com/rflamary/POT/blob/master/examples/Demo_Wasserstein_Discriminant_Analysis.ipynb) You can also see the notebooks with [Jupyter nbviewer](https://nbviewer.jupyter.org/github/rflamary/POT/tree/master/examples/). diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 625cebf..9d8c49f 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -101,6 +101,10 @@ Here is a list of the Python notebooks if you want a quick look: - `1D optimal transport `__ +- `OT Ground + Loss `__ +- `Multiple EMD + computation `__ - `2D optimal transport on empirical distributions `__ - `1D Wasserstein @@ -115,6 +119,8 @@ Here is a list of the Python notebooks if you want a quick look: adaptation `__ - `OT mapping estimation for color transfer in images `__ +- `Wasserstein Discriminant + Analysis `__ You can also see the notebooks with `Jupyter nbviewer `__. -- cgit v1.2.3 From 02ccd64f1826861ecbfaafa1f4629f97ff447d9b Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 4 Jul 2017 09:21:49 +0200 Subject: add autograd mock --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 1304464..8b9a255 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -31,7 +31,7 @@ class Mock(MagicMock): @classmethod def __getattr__(cls, name): return MagicMock() -MOCK_MODULES = ['ot.lp.emd_wrap','autograd.numpy','pymanopt.manifolds','pymanopt' +MOCK_MODULES = ['ot.lp.emd_wrap','autograd','autograd.numpy','pymanopt.manifolds','pymanopt' 'pymanopt.solvers','cudamat'] sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # !!!! -- cgit v1.2.3 From 12f8e54feba94226d996ff24f80247041cad3c94 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 4 Jul 2017 09:23:50 +0200 Subject: add autograd mock --- docs/source/conf.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 8b9a255..d69b756 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -31,8 +31,8 @@ class Mock(MagicMock): @classmethod def __getattr__(cls, name): return MagicMock() -MOCK_MODULES = ['ot.lp.emd_wrap','autograd','autograd.numpy','pymanopt.manifolds','pymanopt' - 'pymanopt.solvers','cudamat'] +MOCK_MODULES = ['ot.lp.emd_wrap','autograd','pymanopt','cudamat'] +# 'autograd.numpy','pymanopt.manifolds','pymanopt.solvers', sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # !!!! -- cgit v1.2.3 From 96c353f2c437393d205d0e6c55e33e0122bb2ec7 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 4 Jul 2017 09:26:10 +0200 Subject: add autograd mock again --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index d69b756..ff08899 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -31,7 +31,7 @@ class Mock(MagicMock): @classmethod def __getattr__(cls, name): return MagicMock() -MOCK_MODULES = ['ot.lp.emd_wrap','autograd','pymanopt','cudamat'] +MOCK_MODULES = ['ot.lp.emd_wrap','autograd','pymanopt','cudamat','autograd.numpy','pymanopt.manifolds','pymanopt.solvers'] # 'autograd.numpy','pymanopt.manifolds','pymanopt.solvers', sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # !!!! -- cgit v1.2.3 From 12986e122ee2a6e56890df6a81dc53568cf7835f Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 4 Jul 2017 09:30:29 +0200 Subject: update thumb image --- .../images/thumb/sphx_glr_plot_optim_OTreg_thumb.png | Bin 2894 -> 21750 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/docs/source/auto_examples/images/thumb/sphx_glr_plot_optim_OTreg_thumb.png b/docs/source/auto_examples/images/thumb/sphx_glr_plot_optim_OTreg_thumb.png index 3015582..2a72060 100644 Binary files a/docs/source/auto_examples/images/thumb/sphx_glr_plot_optim_OTreg_thumb.png and b/docs/source/auto_examples/images/thumb/sphx_glr_plot_optim_OTreg_thumb.png differ -- cgit v1.2.3 From 315d812b18fcb1170b4b84907b87aeaadaa9a196 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 4 Jul 2017 09:31:25 +0200 Subject: delete useless images --- .../images/thumb/sphx_glr_demo_OT_1D_test_thumb.png | Bin 3101 -> 0 bytes .../thumb/sphx_glr_demo_OT_2D_sampleslarge_thumb.png | Bin 3101 -> 0 bytes .../sphx_glr_plot_OT_2D_samples_sinkhornlp_thumb.png | Bin 2894 -> 0 bytes 3 files changed, 0 insertions(+), 0 deletions(-) delete mode 100644 docs/source/auto_examples/images/thumb/sphx_glr_demo_OT_1D_test_thumb.png delete mode 100644 docs/source/auto_examples/images/thumb/sphx_glr_demo_OT_2D_sampleslarge_thumb.png delete mode 100644 docs/source/auto_examples/images/thumb/sphx_glr_plot_OT_2D_samples_sinkhornlp_thumb.png diff --git a/docs/source/auto_examples/images/thumb/sphx_glr_demo_OT_1D_test_thumb.png b/docs/source/auto_examples/images/thumb/sphx_glr_demo_OT_1D_test_thumb.png deleted file mode 100644 index cbc8e0f..0000000 Binary files a/docs/source/auto_examples/images/thumb/sphx_glr_demo_OT_1D_test_thumb.png and /dev/null differ diff --git a/docs/source/auto_examples/images/thumb/sphx_glr_demo_OT_2D_sampleslarge_thumb.png b/docs/source/auto_examples/images/thumb/sphx_glr_demo_OT_2D_sampleslarge_thumb.png deleted file mode 100644 index cbc8e0f..0000000 Binary files a/docs/source/auto_examples/images/thumb/sphx_glr_demo_OT_2D_sampleslarge_thumb.png and /dev/null differ diff --git a/docs/source/auto_examples/images/thumb/sphx_glr_plot_OT_2D_samples_sinkhornlp_thumb.png b/docs/source/auto_examples/images/thumb/sphx_glr_plot_OT_2D_samples_sinkhornlp_thumb.png deleted file mode 100644 index 3015582..0000000 Binary files a/docs/source/auto_examples/images/thumb/sphx_glr_plot_OT_2D_samples_sinkhornlp_thumb.png and /dev/null differ -- cgit v1.2.3 From 05765e26dec18697d9eb60ac5f3a7610af6ae8d2 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 5 Jul 2017 17:11:17 +0200 Subject: add FDA for comparison --- examples/plot_WDA.py | 13 +++++++--- ot/dr.py | 73 +++++++++++++++++++++++++++++++++++++++++++++++++--- 2 files changed, 78 insertions(+), 8 deletions(-) diff --git a/examples/plot_WDA.py b/examples/plot_WDA.py index bbe3888..d2eaf6d 100644 --- a/examples/plot_WDA.py +++ b/examples/plot_WDA.py @@ -11,7 +11,7 @@ import numpy as np import matplotlib.pylab as pl import ot from ot.datasets import get_1D_gauss as gauss -from ot.dr import wda +from ot.dr import wda, fda #%% parameters @@ -36,7 +36,12 @@ pl.legend(loc=0) pl.title('Discriminant dimensions') -#%% plot distributions and loss matrix +#%% Comlpute FDA +p=2 + +Pfda,projfda = fda(xs,ys,p) + +#%% Compute WDA p=2 reg=1 k=10 @@ -46,8 +51,8 @@ P,proj = wda(xs,ys,p,reg,k,maxiter=maxiter) #%% plot samples -xsp=proj(xs) -xtp=proj(xt) +xsp=projfda(xs) +xtp=projfda(xt) pl.figure(1,(10,5)) diff --git a/ot/dr.py b/ot/dr.py index 9187b57..fdb4daa 100644 --- a/ot/dr.py +++ b/ot/dr.py @@ -7,6 +7,7 @@ import autograd.numpy as np from pymanopt.manifolds import Stiefel from pymanopt import Problem from pymanopt.solvers import SteepestDescent, TrustRegions +import scipy.linalg as la def dist(x1,x2): """ Compute squared euclidean distance between samples (autograd) @@ -32,9 +33,73 @@ def split_classes(X,y): """ lstsclass=np.unique(y) return [X[y==i,:].astype(np.float32) for i in lstsclass] + + +def fda(X,y,p=2,reg=1e-16): + """ + Fisher Discriminant Analysis + + Parameters + ---------- + X : numpy.ndarray (n,d) + Training samples + y : np.ndarray (n,) + labels for training samples + p : int, optional + size of dimensionnality reduction + reg : float, optional + Regularization term >0 (ridge regularization) + Returns + ------- + P : (d x p) ndarray + Optimal transportation matrix for the given parameters + proj : fun + projection function including mean centering + + + """ + + mx=np.mean(X) + X-=mx.reshape((1,-1)) + + # data split between classes + d=X.shape[1] + xc=split_classes(X,y) + nc=len(xc) + + p=min(nc-1,p) + + Cw=0 + for x in xc: + Cw+=np.cov(x,rowvar=False) + Cw/=nc + + mxc=np.zeros((d,nc)) + + for i in range(nc): + mxc[:,i]=np.mean(xc[i]) + + mx0=np.mean(mxc,1) + Cb=0 + for i in range(nc): + Cb+=(mxc[:,i]-mx0).reshape((-1,1))*(mxc[:,i]-mx0).reshape((1,-1)) + + w,V=la.eig(Cb,Cw+reg*np.eye(d)) + + idx=np.argsort(w.real) + + Popt=V[:,idx[-p:]] + + + + def proj(X): + return (X-mx.reshape((1,-1))).dot(Popt) + + return Popt, proj + def wda(X,y,p=2,reg=1,k=10,solver = None,maxiter=100,verbose=0): """ Wasserstein Discriminant Analysis [11]_ @@ -73,16 +138,13 @@ def wda(X,y,p=2,reg=1,k=10,solver = None,maxiter=100,verbose=0): P : (d x p) ndarray Optimal transportation matrix for the given parameters proj : fun - projectiuon function including mean centering + projection function including mean centering References ---------- .. [11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). Wasserstein Discriminant Analysis. arXiv preprint arXiv:1608.08063. - - - """ @@ -131,3 +193,6 @@ def wda(X,y,p=2,reg=1,k=10,solver = None,maxiter=100,verbose=0): return (X-mx.reshape((1,-1))).dot(Popt) return Popt, proj + + + -- cgit v1.2.3 From 20547ee48dbc5dd0f3127983e22c48e18260e35f Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 6 Jul 2017 09:17:52 +0200 Subject: update wda example --- examples/plot_WDA.py | 54 +++++++++++++++++++++++++++++++++++++++------------- 1 file changed, 41 insertions(+), 13 deletions(-) diff --git a/examples/plot_WDA.py b/examples/plot_WDA.py index d2eaf6d..d96b27f 100644 --- a/examples/plot_WDA.py +++ b/examples/plot_WDA.py @@ -18,8 +18,17 @@ from ot.dr import wda, fda n=1000 # nb samples in source and target datasets nz=0.2 -xs,ys=ot.datasets.get_data_classif('3gauss',n,nz) -xt,yt=ot.datasets.get_data_classif('3gauss',n,nz) + +# generate circle dataset +t=np.random.rand(n)*2*np.pi +ys=np.floor((np.arange(n)*1.0/n*3))+1 +xs=np.concatenate((np.cos(t).reshape((-1,1)),np.sin(t).reshape((-1,1))),1) +xs=xs*ys.reshape(-1,1)+nz*np.random.randn(n,2) + +t=np.random.rand(n)*2*np.pi +yt=np.floor((np.arange(n)*1.0/n*3))+1 +xt=np.concatenate((np.cos(t).reshape((-1,1)),np.sin(t).reshape((-1,1))),1) +xt=xt*yt.reshape(-1,1)+nz*np.random.randn(n,2) nbnoise=8 @@ -27,42 +36,61 @@ xs=np.hstack((xs,np.random.randn(n,nbnoise))) xt=np.hstack((xt,np.random.randn(n,nbnoise))) #%% plot samples +pl.figure(1,(10,5)) -pl.figure(1) - - +pl.subplot(1,2,1) pl.scatter(xt[:,0],xt[:,1],c=ys,marker='+',label='Source samples') pl.legend(loc=0) pl.title('Discriminant dimensions') +pl.subplot(1,2,2) +pl.scatter(xt[:,2],xt[:,3],c=ys,marker='+',label='Source samples') +pl.legend(loc=0) +pl.title('Other dimensions') +pl.show() -#%% Comlpute FDA +#%% Compute FDA p=2 Pfda,projfda = fda(xs,ys,p) #%% Compute WDA p=2 -reg=1 +reg=1e-1 k=10 maxiter=100 -P,proj = wda(xs,ys,p,reg,k,maxiter=maxiter) +Pwda,projwda = wda(xs,ys,p,reg,k,maxiter=maxiter) #%% plot samples xsp=projfda(xs) xtp=projfda(xt) -pl.figure(1,(10,5)) +xspw=projwda(xs) +xtpw=projwda(xt) -pl.subplot(1,2,1) +pl.figure(1,(10,10)) + +pl.subplot(2,2,1) pl.scatter(xsp[:,0],xsp[:,1],c=ys,marker='+',label='Projected samples') pl.legend(loc=0) -pl.title('Projected training samples') +pl.title('Projected training samples FDA') -pl.subplot(1,2,2) +pl.subplot(2,2,2) pl.scatter(xtp[:,0],xtp[:,1],c=ys,marker='+',label='Projected samples') pl.legend(loc=0) -pl.title('Projected test samples') +pl.title('Projected test samples FDA') + + +pl.subplot(2,2,3) +pl.scatter(xspw[:,0],xspw[:,1],c=ys,marker='+',label='Projected samples') +pl.legend(loc=0) +pl.title('Projected training samples WDA') + + +pl.subplot(2,2,4) +pl.scatter(xtpw[:,0],xtpw[:,1],c=ys,marker='+',label='Projected samples') +pl.legend(loc=0) +pl.title('Projected test samples WDA') -- cgit v1.2.3 From 382aa0ec377721eb7ce905fc77fde61494d459d2 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 6 Jul 2017 09:22:52 +0200 Subject: update notebook --- .../Demo_Wasserstein_Discriminant_Analysis.ipynb | 121 ++++++++++++++------- examples/plot_WDA.py | 2 +- 2 files changed, 85 insertions(+), 38 deletions(-) diff --git a/examples/Demo_Wasserstein_Discriminant_Analysis.ipynb b/examples/Demo_Wasserstein_Discriminant_Analysis.ipynb index fb36289..2d3424e 100644 --- a/examples/Demo_Wasserstein_Discriminant_Analysis.ipynb +++ b/examples/Demo_Wasserstein_Discriminant_Analysis.ipynb @@ -9,7 +9,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -19,7 +19,7 @@ "import matplotlib.pylab as pl\n", "import ot\n", "from ot.datasets import get_1D_gauss as gauss\n", - "from ot.dr import wda" + "from ot.dr import wda, fda" ] }, { @@ -31,7 +31,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": { "collapsed": true }, @@ -39,27 +39,36 @@ "source": [ "n=1000 # nb samples in source and target datasets\n", "nz=0.2\n", - "xs,ys=ot.datasets.get_data_classif('3gauss',n,nz)\n", - "xt,yt=ot.datasets.get_data_classif('3gauss',n,nz)\n", + "\n", + "# generate circle dataset\n", + "t=np.random.rand(n)*2*np.pi\n", + "ys=np.floor((np.arange(n)*1.0/n*3))+1\n", + "xs=np.concatenate((np.cos(t).reshape((-1,1)),np.sin(t).reshape((-1,1))),1)\n", + "xs=xs*ys.reshape(-1,1)+nz*np.random.randn(n,2)\n", + "\n", + "t=np.random.rand(n)*2*np.pi\n", + "yt=np.floor((np.arange(n)*1.0/n*3))+1\n", + "xt=np.concatenate((np.cos(t).reshape((-1,1)),np.sin(t).reshape((-1,1))),1)\n", + "xt=xt*yt.reshape(-1,1)+nz*np.random.randn(n,2)\n", "\n", "nbnoise=8\n", "\n", "xs=np.hstack((xs,np.random.randn(n,nbnoise)))\n", - "xt=np.hstack((xt,np.random.randn(n,nbnoise)))\n" + "xt=np.hstack((xt,np.random.randn(n,nbnoise)))" ] }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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VUrlxjS6s70fSQnzFo/S99rG/3psWPuUjR1mCKJzBqr3IXm0zbXRx3OcDPI1g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fAAAg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\nj0tKSujq6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5xeANtikAcsGvUwnD+38aP71OJsmF72awEYSl6uPA54FXhBD7zPf+RUr5qwCO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\nc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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -81,6 +90,26 @@ "pl.show()" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute Fisher discriminant" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "p=2\n", + "\n", + "Pfda,projfda = fda(xs,ys,p)" + ] + }, { "cell_type": "markdown", "metadata": {}, @@ -90,7 +119,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -102,22 +131,25 @@ "Compiling cost function...\n", "Computing gradient of cost function...\n", " iter\t\t cost val\t grad. norm\n", - " 1\t+3.9157922235693188e-01\t5.71597367e-01\n", - " 2\t+1.3819067207672747e-01\t1.45380541e-01\n", - " 3\t+1.3514783259634658e-01\t1.56401980e-01\n", - " 4\t+1.2366637320576453e-01\t1.39774670e-01\n", - " 5\t+8.7917719796676133e-02\t1.02114467e-01\n", - " 6\t+8.4889100715160537e-02\t1.00158402e-01\n", - " 7\t+7.4573884583769998e-02\t6.88332229e-02\n", - " 8\t+7.1655335462508282e-02\t5.32376275e-02\n", - " 9\t+7.0479018645171101e-02\t4.37283221e-02\n", - " 10\t+6.8400612315063808e-02\t6.65409711e-03\n", - " 11\t+6.8364466366572729e-02\t2.96611209e-03\n", - " 12\t+6.8355577537242695e-02\t8.64992482e-05\n", - " 13\t+6.8355570205523991e-02\t1.40803543e-05\n", - " 14\t+6.8355570014368830e-02\t2.34884058e-06\n", - " 15\t+6.8355570008960018e-02\t2.23203146e-07\n", - "Terminated - min grad norm reached after 15 iterations, 7.87 seconds.\n", + " 1\t+7.3324064745743989e-01\t5.95226695e-01\n", + " 2\t+4.4853951178403229e-01\t8.20231271e-02\n", + " 3\t+4.4576445851595303e-01\t1.93811424e-01\n", + " 4\t+4.3562246733680465e-01\t1.61725387e-01\n", + " 5\t+4.0969564472790077e-01\t1.32513662e-01\n", + " 6\t+2.9388094458668662e-01\t2.00393708e-01\n", + " 7\t+2.7344485803563923e-01\t1.99320846e-01\n", + " 8\t+2.2883182995370804e-01\t2.82035999e-02\n", + " 9\t+2.2828598049696755e-01\t7.08646563e-03\n", + " 10\t+2.2825222787200100e-01\t3.22559822e-04\n", + " 11\t+2.2825220998825529e-01\t2.82197677e-04\n", + " 12\t+2.2825216255973643e-01\t9.26049149e-05\n", + " 13\t+2.2825215676825239e-01\t6.60508771e-06\n", + " 14\t+2.2825215674473881e-01\t3.35243645e-06\n", + " 15\t+2.2825215673980734e-01\t1.97305673e-06\n", + " 16\t+2.2825215673953242e-01\t1.86785394e-06\n", + " 17\t+2.2825215673856641e-01\t1.41420230e-06\n", + " 18\t+2.2825215673757782e-01\t6.92577574e-07\n", + "Terminated - min grad norm reached after 18 iterations, 8.84 seconds.\n", "\n" ] } @@ -128,7 +160,7 @@ "k=10\n", "maxiter=100\n", "\n", - "P,proj = wda(xs,ys,p,reg,k,maxiter=maxiter)" + "Pwda,projwda = wda(xs,ys,p,reg,k,maxiter=maxiter)\n" ] }, { @@ -140,16 +172,16 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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X5tTEGOkPWaGekSyMtFEVJfJiikPXwEmrVAm9tzfs3b22rIe9DnsZEkKGR7Am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\nEBIBGlWEEEIIIRGgUUUIIYQQEgEaVYQQQgghEaBRRQghhBA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ITLP6Z/1SCFEhpax2bfMD4AcA06dPN56sNDKYJ/djtzxDXlEeBOoAk5FjMBgMidDG6fpd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\noqvBpVvJL4XokU6vU2wrT6A/XFuEw60L0VNuEJfc/W4/LH1hVNrIZM4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42RCsjRfduqrXD0dCe0pBXubRATPdWQ463F7iXLYLKQmV6sCpVEjENnox2DFRdgvOSH84GvQCf0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\nrGPd1vAqKeX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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -158,21 +190,36 @@ ], "source": [ "# data projection (also removes center)\n", - "xsp=proj(xs)\n", - "xtp=proj(xt)\n", + "xsp=projfda(xs)\n", + "xtp=projfda(xt)\n", "\n", - "pl.figure(1,(10,5))\n", + "xspw=projwda(xs)\n", + "xtpw=projwda(xt)\n", "\n", - "pl.subplot(1,2,1)\n", + "pl.figure(1,(10,10))\n", + "\n", + "pl.subplot(2,2,1)\n", "pl.scatter(xsp[:,0],xsp[:,1],c=ys,marker='+',label='Projected samples')\n", "pl.legend(loc=0)\n", - "pl.title('Projected training samples')\n", + "pl.title('Projected training samples FDA')\n", "\n", "\n", - "pl.subplot(1,2,2)\n", + "pl.subplot(2,2,2)\n", "pl.scatter(xtp[:,0],xtp[:,1],c=ys,marker='+',label='Projected samples')\n", "pl.legend(loc=0)\n", - "pl.title('Projected test samples')\n", + "pl.title('Projected test samples FDA')\n", + "\n", + "\n", + "pl.subplot(2,2,3)\n", + "pl.scatter(xspw[:,0],xspw[:,1],c=ys,marker='+',label='Projected samples')\n", + "pl.legend(loc=0)\n", + "pl.title('Projected training samples WDA')\n", + "\n", + "\n", + "pl.subplot(2,2,4)\n", + "pl.scatter(xtpw[:,0],xtpw[:,1],c=ys,marker='+',label='Projected samples')\n", + "pl.legend(loc=0)\n", + "pl.title('Projected test samples WDA')\n", "pl.show()" ] }, diff --git a/examples/plot_WDA.py b/examples/plot_WDA.py index d96b27f..5fa2ab1 100644 --- a/examples/plot_WDA.py +++ b/examples/plot_WDA.py @@ -56,7 +56,7 @@ Pfda,projfda = fda(xs,ys,p) #%% Compute WDA p=2 -reg=1e-1 +reg=1e0 k=10 maxiter=100 -- cgit v1.2.3 From 132d5471eca26923a9a6239a5ab51623f209bf39 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 6 Jul 2017 10:15:32 +0200 Subject: move notebooks --- Makefile | 2 +- README.md | 22 +- docs/source/readme.rst | 22 +- examples/Demo_1D_OT.ipynb | 198 ------------ examples/Demo_1D_barycenter.ipynb | 297 ------------------ examples/Demo_2D_OT_DomainAdaptation.ipynb | 217 ------------- examples/Demo_2D_OT_samples.ipynb | 234 -------------- examples/Demo_2D_OTmapping_DomainAdaptation.ipynb | 283 ----------------- examples/Demo_Compute_EMD.ipynb | 167 ---------- examples/Demo_Ground_Loss.ipynb | 345 -------------------- examples/Demo_Image_ColorAdaptation.ipynb | 316 ------------------- examples/Demo_Image_ColorAdaptation_mapping.ipynb | 349 --------------------- examples/Demo_Optim_OTreg.ipynb | 173 ---------- .../Demo_Wasserstein_Discriminant_Analysis.ipynb | 257 --------------- notebooks/Demo_1D_OT.ipynb | 198 ++++++++++++ notebooks/Demo_1D_barycenter.ipynb | 297 ++++++++++++++++++ notebooks/Demo_2D_OT_DomainAdaptation.ipynb | 217 +++++++++++++ notebooks/Demo_2D_OT_samples.ipynb | 234 ++++++++++++++ notebooks/Demo_2D_OTmapping_DomainAdaptation.ipynb | 283 +++++++++++++++++ notebooks/Demo_Compute_EMD.ipynb | 167 ++++++++++ notebooks/Demo_Ground_Loss.ipynb | 345 ++++++++++++++++++++ notebooks/Demo_Image_ColorAdaptation.ipynb | 316 +++++++++++++++++++ notebooks/Demo_Image_ColorAdaptation_mapping.ipynb | 349 +++++++++++++++++++++ notebooks/Demo_Optim_OTreg.ipynb | 173 ++++++++++ .../Demo_Wasserstein_Discriminant_Analysis.ipynb | 257 +++++++++++++++ setup.py | 2 +- 26 files changed, 2860 insertions(+), 2860 deletions(-) delete mode 100644 examples/Demo_1D_OT.ipynb delete mode 100644 examples/Demo_1D_barycenter.ipynb delete mode 100644 examples/Demo_2D_OT_DomainAdaptation.ipynb delete mode 100644 examples/Demo_2D_OT_samples.ipynb delete mode 100644 examples/Demo_2D_OTmapping_DomainAdaptation.ipynb delete mode 100644 examples/Demo_Compute_EMD.ipynb delete mode 100644 examples/Demo_Ground_Loss.ipynb delete mode 100644 examples/Demo_Image_ColorAdaptation.ipynb delete mode 100644 examples/Demo_Image_ColorAdaptation_mapping.ipynb delete mode 100644 examples/Demo_Optim_OTreg.ipynb delete mode 100644 examples/Demo_Wasserstein_Discriminant_Analysis.ipynb create mode 100644 notebooks/Demo_1D_OT.ipynb create mode 100644 notebooks/Demo_1D_barycenter.ipynb create mode 100644 notebooks/Demo_2D_OT_DomainAdaptation.ipynb create mode 100644 notebooks/Demo_2D_OT_samples.ipynb create mode 100644 notebooks/Demo_2D_OTmapping_DomainAdaptation.ipynb create mode 100644 notebooks/Demo_Compute_EMD.ipynb create mode 100644 notebooks/Demo_Ground_Loss.ipynb create mode 100644 notebooks/Demo_Image_ColorAdaptation.ipynb create mode 100644 notebooks/Demo_Image_ColorAdaptation_mapping.ipynb create mode 100644 notebooks/Demo_Optim_OTreg.ipynb create mode 100644 notebooks/Demo_Wasserstein_Discriminant_Analysis.ipynb diff --git a/Makefile b/Makefile index 34e9f20..22c1b50 100644 --- a/Makefile +++ b/Makefile @@ -46,4 +46,4 @@ rdoc: notebook : - ipython notebook --matplotlib=inline --notebook-dir=examples/ + ipython notebook --matplotlib=inline --notebook-dir=notebooks/ diff --git a/README.md b/README.md index 9664f3d..09da51b 100644 --- a/README.md +++ b/README.md @@ -76,17 +76,17 @@ The examples folder contain several examples and use case for the library. The f Here is a list of the Python notebooks if you want a quick look: -* [1D optimal transport](https://github.com/rflamary/POT/blob/master/examples/Demo_1D_OT.ipynb) -* [OT Ground Loss](https://github.com/rflamary/POT/blob/master/examples/Demo_Ground_Loss.ipynb) -* [Multiple EMD computation](https://github.com/rflamary/POT/blob/master/examples/Demo_Compute_EMD.ipynb) -* [2D optimal transport on empirical distributions](https://github.com/rflamary/POT/blob/master/examples/Demo_2D_OT_samples.ipynb) -* [1D Wasserstein barycenter](https://github.com/rflamary/POT/blob/master/examples/Demo_1D_barycenter.ipynb) -* [OT with user provided regularization](https://github.com/rflamary/POT/blob/master/examples/Demo_Optim_OTreg.ipynb) -* [Domain adaptation with optimal transport](https://github.com/rflamary/POT/blob/master/examples/Demo_2D_OT_DomainAdaptation.ipynb) -* [Color transfer in images](https://github.com/rflamary/POT/blob/master/examples/Demo_Image_ColorAdaptation.ipynb) -* [OT mapping estimation for domain adaptation](https://github.com/rflamary/POT/blob/master/examples/Demo_2D_OTmapping_DomainAdaptation.ipynb) -* [OT mapping estimation for color transfer in images](https://github.com/rflamary/POT/blob/master/examples/Demo_Image_ColorAdaptation_mapping.ipynb) -* [Wasserstein Discriminant Analysis](https://github.com/rflamary/POT/blob/master/examples/Demo_Wasserstein_Discriminant_Analysis.ipynb) +* [1D optimal transport](https://github.com/rflamary/POT/blob/master/notebooks/Demo_1D_OT.ipynb) +* [OT Ground Loss](https://github.com/rflamary/POT/blob/master/notebooks/Demo_Ground_Loss.ipynb) +* [Multiple EMD computation](https://github.com/rflamary/POT/blob/master/notebooks/Demo_Compute_EMD.ipynb) +* [2D optimal transport on empirical distributions](https://github.com/rflamary/POT/blob/master/notebooks/Demo_2D_OT_samples.ipynb) +* [1D Wasserstein barycenter](https://github.com/rflamary/POT/blob/master/notebooks/Demo_1D_barycenter.ipynb) +* [OT with user provided regularization](https://github.com/rflamary/POT/blob/master/notebooks/Demo_Optim_OTreg.ipynb) +* [Domain adaptation with optimal transport](https://github.com/rflamary/POT/blob/master/notebooks/Demo_2D_OT_DomainAdaptation.ipynb) +* [Color transfer in images](https://github.com/rflamary/POT/blob/master/notebooks/Demo_Image_ColorAdaptation.ipynb) +* [OT mapping estimation for domain adaptation](https://github.com/rflamary/POT/blob/master/notebooks/Demo_2D_OTmapping_DomainAdaptation.ipynb) +* [OT mapping estimation for color transfer in images](https://github.com/rflamary/POT/blob/master/notebooks/Demo_Image_ColorAdaptation_mapping.ipynb) +* [Wasserstein Discriminant Analysis](https://github.com/rflamary/POT/blob/master/notebooks/Demo_Wasserstein_Discriminant_Analysis.ipynb) You can also see the notebooks with [Jupyter nbviewer](https://nbviewer.jupyter.org/github/rflamary/POT/tree/master/examples/). diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 9d8c49f..6898296 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -100,27 +100,27 @@ library. The full documentation is available on Here is a list of the Python notebooks if you want a quick look: - `1D optimal - transport `__ + transport `__ - `OT Ground - Loss `__ + Loss `__ - `Multiple EMD - computation `__ + computation `__ - `2D optimal transport on empirical - distributions `__ + distributions `__ - `1D Wasserstein - barycenter `__ + barycenter `__ - `OT with user provided - regularization `__ + regularization `__ - `Domain adaptation with optimal - transport `__ + transport `__ - `Color transfer in - images `__ + images `__ - `OT mapping estimation for domain - adaptation `__ + adaptation `__ - `OT mapping estimation for color transfer in - images `__ + images `__ - `Wasserstein Discriminant - Analysis `__ + Analysis `__ You can also see the notebooks with `Jupyter nbviewer `__. diff --git a/examples/Demo_1D_OT.ipynb b/examples/Demo_1D_OT.ipynb deleted file mode 100644 index 087b3bc..0000000 --- a/examples/Demo_1D_OT.ipynb +++ /dev/null @@ -1,198 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:b65d32b626c6b5575ab808a448461a190dd0152a68ce83ddf7bbaf1aefb35c43" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "1D optimal transport demo" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# load all relevant modules\n", - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Dataset generation" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "\n", - "n=100 # nb bins\n", - "a=ot.datasets.get_1D_gauss(n,m=20,s=20) # m= mean, s= std\n", - "b=ot.datasets.get_1D_gauss(n,m=60,s=60)\n", - "\n", - "# bin positions\n", - "x=np.arange(n,dtype=np.float64)\n", - "# loss matrix\n", - "M=ot.dist(x.reshape((n,1)),x.reshape((n,1)))\n", - "M/=M.max()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plotting the distributions" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "\n", - "pl.figure(1)\n", - "pl.plot(x,a,'b',label='Source distribution')\n", - "pl.plot(x,b,'r',label='Target distribution')\n", - "pl.legend()\n", - "pl.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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mjEkVkQRs5fAxETkRWAJ0Msbs8diHKSuOUDBmDLRpA3//u3MxtGwJixZB69bO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\nz1NSUkhJSfFTaOW3di30Csn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- "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Distributions and loss matrix" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# plot distributions and loss matrix\n", - "\n", - "pl.figure(2)\n", - "ot.plot.plot1D_mat(a,b,M,'Cost matrix M')\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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J3C0yDCMKTMDLmIsvdkMRA28+K1P86ayZ/tpDcd0pFk+8s8oEuVMkoVKYzFYb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\njJrI+ikiQ4GrgZNU9WO4YSR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- "text": [ - "" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## OT solution (Earth Mover's Distance)" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "G0=ot.emd(a,b,M)\n", - "\n", - "pl.figure(3)\n", - "ot.plot.plot1D_mat(a,b,G0,'OT matrix G0')\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Regularized Optimal transport (Entropic regularization)" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# reg parameter\n", - "lambd=1e-3\n", - "\n", - "Gs=ot.sinkhorn(a,b,M,lambd)\n", - "\n", - "pl.figure(4)\n", - "ot.plot.plot1D_mat(a,b,Gs,'OT matrix Sinkhorn')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 6 - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/examples/Demo_1D_barycenter.ipynb b/examples/Demo_1D_barycenter.ipynb deleted file mode 100644 index c93d00b..0000000 --- a/examples/Demo_1D_barycenter.ipynb +++ /dev/null @@ -1,297 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1D Wasserstein barycenter demo" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot\n", - "\n", - "from mpl_toolkits.mplot3d import Axes3D\n", - "from matplotlib.collections import PolyCollection\n", - "from matplotlib.colors import colorConverter" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Dataset Generation" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n=100 # nb bins\n", - "\n", - "# bin positions\n", - "x=np.arange(n,dtype=np.float64)\n", - "\n", - "# Gaussian distributions\n", - "a1=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std\n", - "a2=ot.datasets.get_1D_gauss(n,m=60,s=8)\n", - "\n", - "# creating matrix A containing all distributions\n", - "A=np.vstack((a1,a2)).T\n", - "nbd=A.shape[1]\n", - "\n", - "# loss matrix + normalization\n", - "M=ot.utils.dist0(n)\n", - "M/=M.max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot distributions" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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tH/b/IY3rNuZ/5v1PrV5XJNGosAiBzZvh4MHkLixAS07l+D228DF27t/Jz86t\n/b0lmtVvxh1n38FjCx9jy54ttX59kUShwiIEkn0PiygVFnI89h/ez4PzHuQ7fb5DRosMLxl+fPaP\nSU9LZ8J7E7xcXyQRqLAIgbAVFtrLQqrjbx/9jS1fbuHn5/3cW4aWDVvyo2/9iD9/8Ge+2PuFtxwi\nPqmwCIGiImjVKriRVzLLyIB9+2Cr7kYtMTpYcpD7372f63tfT7dW3bxmGXvOWEpKS3jo/fjv9imS\nDFRYhECyT9yM0l4WUl1Pf/w064vX84vzfuE7Cm0at+HWs27loQUPUby/2HcckVqnwiIEwlJYaC8L\nqY5SV8of3vkD2ZnZ9Grby3ccAH7y7Z+w99Be/vLhX3xHEal1KixCICyFRYsWwZbkKiwkFv9c/U9W\nbV/FT7/9U99RvtKhaQduOv0m/vLhXygpLfEdR6RWqbBIcqWlsG5dOAoL0F4WErtHFz5Kn3Z9OLvj\n2b6jHGHMWWNYX7yematn+o4iUqtUWCS5zz4L9rBI5vuElKUlpxKLTbs38dKKlxiTNabG7wkSq/4d\n+tO3fV8eXfio7ygitUqFRZILy1LTKBUWEou/5v+V+un1ufH0G31H+QYzY0zWGF5e+bK2+ZaUosIi\nyUXfhDvXzr2Wapz2spCqKikt4YmPnuD6XtfTvEFz33EqdMPpN9AwvSF/zf+r7ygitUaFRZIrKoLW\nraFJE99J4iMjA/bvh88/951EEt3M1TNZX7yeMWeN8R2lUs3qN+OG02/giY+e4HDpYd9xRGqFCosk\nF5YVIVFacipV9ejCR+nbvi/9O/T3HeWoRmeNZsOuDbz2yWu+o4jUChUWSS5shUV0SEeFhRzNhl0b\neHnlywk5abO8szqcRb8T+/FY/mO+o4jUChUWSW7t2nAVFi1aBF9acipH89f8v9IwvSE3nH6D7yhV\nMiZrDK9+8iqfFn/qO4pIjVNhkcRKSmD9+vAsNY3SyhA5muikzZzeOTSr38x3nCrJ6Z1Do7qNeCL/\nCd9RRGpctQoLM7vdzNaa2T4ze8/MjjrIaWYjzawg0n6xmQ05SttHzazUzH5cnWyp5LPP4NChcPVY\ngAoLObrXVr3Ghl0bEnrSZnlN6zflxtNv1CROSQkxFxZmdh3wR2AccCawGJhpZq0raT8AeBZ4HOgL\nzABmmFnPCtpeA3wL2BhrrlQUtj0solRYyNH89aO/cmb7Mzmrw1m+o8RkdNZoNu3exOurXvcdRaRG\nVafHYiyHqLAnAAAgAElEQVTwqHPuKedcIXArsBf4XiXt7wBec86Nd86tcM6NA/KBH5VtZGYdgYeA\nGwCV9FUQtj0sojIygm3KtZeFlLd933ZeWfkKo84Y5TtKzM5sfya92vTimSXP+I4iUqNiKizMrC6Q\nBcyOHnPOOWAWMKCSpw2IPF7WzLLtLZjW/RTwgHOuIJZMqayoCNq0gcaNfSeJr+heFlu2+E4iiWbq\n8qmUuBKu73297ygxMzNuPP1GXih8gd0HdvuOI1JjYu2xaA3UAcr/yd8CtK/kOe2r0P4e4KBz7uEY\n86S0sC01jdJeFlKZZ5Y8w6BTBtG+SWV/bhLbDaffwL7D+5hROMN3FJEakx6n8xgQS8f1V+3NLAv4\nMcF8jZiMHTuW5s2P3Mo3JyeHnJycWE+VlIqKwrciBI7cy+Kcc7xGkQSyvng9b697m0nDJvmOUm2d\nW3TmvJPP45klz/CdM77jO46kgNzcXHJzc484VlxcXKPXjLWw2AaUAO3KHW/LN3slojYfo/15QBvg\n0zIb3dQBxpvZnc65LpWFmTBhAv369at6+pBZuxaysnyniL/mzaFlS+1lIUfKXZJLg/QGXJt5re8o\nx+XG02/k9ldvZ8ueLbRrUv5Po0h8VfRhOz8/n6wafPOIaSjEOXcIWAgMih6LzI8YBMyr5Gnzy7aP\nuDRyHIK5FX2AM8p8bQIeAAbHki+VRPewCONQCGhliHzTM0ue4eruVyfN3hWVGdlzJGmWxnPLnvMd\nRaRGVGdVyHhgtJmNMrMewCNAI2ASgJk9ZWa/L9P+T8AQM7vLzLqb2W8IJoA+DOCc2+GcW172CzgE\nbHbOfVLtVxZymzbB4cMqLCQ1LNmyhCWfL0nI26PHqlWjVgw5dYhWh0hoxVxYOOeeB+4G7gU+Iuht\nGOyc2xpp0okyEzOdc/OBHGA0sAjIBoZFCohKLxNrrlQT1j0sojIyNBQiX3t2ybOc0PAELj/1ct9R\n4uLG029kwcYFrNq+yncUkbir1uRN59xEYGIlj11cwbE8IC+G81c6r0ICa9YE38O2h0XUKacEe1mU\nlECdOr7TiE+lrpRnlz7LyJ4jqVennu84cTG0+1Ca1GvCs0ue5dcX/Np3HJG40r1CktTq1dChAzRq\n5DtJzejaFQ4eDIZ8JLW9u/5d1hevD8UwSFSjuo3IzszmmSXP4LQTnISMCosktWYNdAlxv070ta1e\n7TeH+PfMkmc4ufnJnHvyub6jxNUNvW9g5RcrWfjZQt9RROJKhUWSWr06+FQfVhkZYKbCItUdLDnI\nlOVTuKH3DaRZuP5cDeoyiLaN2/LMx5rEKeESrv9SU0jYeywaNICOHb+eSyKpaeaqmWzft50b+4Rn\nGCQqPS2d63tdz+RlkykpLfEdRyRuVFgkod274fPPw91jAcHrU49FapuyfAo92/Skd9vevqPUiOt6\nX8fmPZuZ92ll2wCJJB8VFkkougwzFQoL9VikrgOHD/DiihcZkTnCd5Qac06nc+jYtCNTlk/xHUUk\nblRYJKHop/gwD4VA8PrUY5G6Zq2ZRfGBYkb2Guk7So1JszSGZw4nryCPUlfqO45IXKiwSEKrV0OT\nJsEt08Osa1fYvh127vSdRHyYWjCV7q2606tNL99RatTIXiPZtHsT8z+df+zGIklAhUUSik7c/Pqe\nbeEU7ZHRcEjqOVhykBmFMxjZcyQW8n/o3z7p25zY5ESmLp/qO4pIXKiwSEJhX2oaFX2NKixSz5tr\n32Tn/p2M6Bne+RVR0eGQqQVTNRwioaDCIgmFfalp1AknQLNmmmeRiqYsm0K3E7rRp10f31FqxYie\nI9iwawMLNi7wHUXkuKmwSDKHDwc3IEuFHgszrQxJRYdKDjFjxQxG9BwR+mGQqPNOPo92jdsxZZlW\nh0jyU2GRZDZsCIqLVOixAK0MSUVvFb3F9n3bGdkzvKtByquTVofszGymFkzVvUMk6amwSDLRN9lU\n6LEAbZKViqYsm0KXll3o276v7yi1amTPkawvXs8Hmz7wHUXkuKiwSDKrV0NaWnhvl15e166wfj0c\nOuQ7idSGw6WHmV44PSVWg5R3fufzadOojVaHSNJTYZFk1qyBk0+GunV9J6kdXbpAaSmsW+c7idSG\nOUVz+GLfFymxGqS89LR0sjOzmbJ8ioZDJKmpsEgyqbLUNEpLTlPL1OVTyWiRQdaJWb6jeDGi5wiK\ndhaR/1m+7ygi1abCIsmkylLTqJNOgvR0zbNIBSWlJUwrmMbwzOEpNwwSdWHGhbRu1Fr3DpGkpsIi\niTiXej0W6enBfBL1WITfu5++y9a9W1NyGCQqPS2dYd2HMa1gmoZDJGmpsEgiO3ZAcXFq9ViAlpym\nimkF0+jQtAPf6vgt31G8urbHtXyy/ROWb13uO4pItaiwSCKpttQ0SptkhZ9zjumF07mm+zWkWWr/\nWRrUZRBN6zVlWsE031FEqiW1/wtOMqlyu/Tyoj0W6hkOr/zP8llfvJ7szGzfUbxrkN6AK0+7kumF\n031HEakWFRZJZM2a4P4ZLVr4TlK7unaFPXtg2zbfSaSmTCuYxgkNT2Bg54G+oySE7B7ZfLT5I9bu\nWOs7ikjMVFgkkVSbuBkVfc2aZxFe0wunc3X3q6lbJ0U2aDmGId2GUL9OffVaSFJSYZFEUm2paVT0\nNauwCKeCrQUUbCvg2h7X+o6SMJrUa8JlXS/TPAtJSioskkiq9lg0bQpt2mgCZ1hNL5xO47qNubTL\npb6jJJTszGzmfTqPzXs2+44iEhMVFkniwIHgzqap2GMBWnIaZtMLp3NFtytoWLeh7ygJZehpQ0mz\nNF4ofMF3FJGYqLBIEkVFwaqIVOyxAC05Dav1xev5cNOHWg1SgVaNWnFBxgVMK9RwiCSXahUWZna7\nma01s31m9p6Z9T9G+5FmVhBpv9jMhpR7fFzk8T1mtt3M3jCz1N4lp5xUXWoapR6LcJpeMJ16depx\nRbcrfEdJSNk9snlz7Zvs3L/TdxSRKou5sDCz64A/AuOAM4HFwEwza11J+wHAs8DjQF9gBjDDzHqW\nabYCuB3oDZwLFAH/NLNWseYLqzVroF496NjRdxI/unaFTZtg3z7fSSSephdO55Iul9CsfjPfURLS\nNT2u4XDpYV5e+bLvKCJVVp0ei7HAo865p5xzhcCtwF7ge5W0vwN4zTk33jm3wjk3DsgHfhRt4Jyb\n7Jx70zlX5JwrAO4CmgF9qpEvlFavhowMqFPHdxI/oj01a7WsPzQ+//Jz5q6fS3YPDYNUpmOzjpzd\n8WytDpGkElNhYWZ1gSxgdvSYC+6UMwsYUMnTBkQeL2tmZe0j1xgD7CToDRGCHotUnV8Bun16GL24\n4kUAru5+teckiS07M5vXV73O3kN7fUcRqZJYeyxaA3WALeWObwHaV/Kc9lVpb2ZXmtluYD9BL8el\nzrntMeYLrVRdahp14onQoIHmWYTJ9MLpnH/y+bRp3MZ3lIR2bY9r2Xd4H6+vet13FJEqSY/TeQyI\n5U4OFbV/EziDoHj5ATDFzL7lnKt0I+exY8fSvHnzI47l5OSQk5MTQ5TEV1oafFL//vd9J/EnLS0Y\nDlm1yncSiYfi/cXMWjOLBy990HeUhNetVTd6t+3N9MLpWj0jMcvNzSU3N/eIY8XFxTV6zVgLi21A\nCdCu3PG2fLNXImpzVdo75/YBayJfC8xsJfB94P7KwkyYMIF+/fpVOXyy+vTTYNJijx6+k/jVvTus\nWOE7hcTDq5+8ysGSg9pts4qye2Tzp/f/xMGSg9SrU893HEkiFX3Yzs/PJysrq8auGdNQiHPuELAQ\nGBQ9ZmYW+XleJU+bX7Z9xKWR48fKVj+WfGFVWBh8797dbw7funf/+nchyW164XTO6nAWJzU/yXeU\npJCdmU3xgWLmFM3xHUXkmKqzKmQ8MNrMRplZD+ARoBEwCcDMnjKz35dp/ydgiJndZWbdzew3BBNA\nH460b2RmvzOzs83sZDPrZ2Z/AzoAU6r9ykJkxQqoXx86d/adxK8ePYLemy+/9J1Ejse+Q/t49ZNX\ntRokBn3a9eGUFqdodYgkhZgLC+fc88DdwL3ARwRLQgc757ZGmnSizMRM59x8IAcYDSwCsoFhzrnl\nkSYlQA9gKsF+Fi8CLYHzIktPU15hIXTrlrpLTaOiPTYrV/rNIcfnjTVv8OWhL7k2U8MgVWVmZGdm\nM6NwBiWlJb7jiBxVtXbedM5NdM5lOOcaOucGOOc+LPPYxc6575Vrn+ec6xFp38c5N7PMYwecc8Od\ncydFHu/knLvWOZdf/ZcVLitWaH4FfF1YaJ5FcptWMI3M1pn0aK1/1LHIzsxmy5dbeG/De76jiByV\n7hWSBAoLVVgAtGwJ7dppnkUyO1RyiJdWvqTVDdVwTqdzaN+kvYZDJOGpsEhwu3cHW1mn+sTNKE3g\nTG5vr3ub7fu2azVINaRZGtd0v4bphdMJ9iUUSUwqLBJctNtfPRaBHj00FJLMphVM4+TmJ9PvxPAv\nE68J2ZnZrN25lsVbtCmxJC4VFgku+iZ62ml+cySK6F4WpaW+k0isSl0pM1bMILtHNsEqdYnVhRkX\n0qJBCw2HSEJTYZHgCguhQwdopps/AkGPxb59sGGD7yQSqwUbF7Bp9yatBjkOdevUZehpQ5leON13\nFJFKqbBIcIWFml9RVvR3oXkWyWdawTTaNGrDuSed6ztKUsvOzGbp50tZ+YXWXUtiUmGR4LTU9EgZ\nGVCvngqLZOOcY1rBNIZ1H0adtBTfkOU4Xdb1MhqmN2R6gXotJDGpsEhgJSXBZlAqLL5Wp04w30QT\nOJPLks+XsHrHai0zjYNGdRsxpNsQphVqnoUkJhUWCWz9ejhwQEMh5WnJafLJW55H8/rNufiUi31H\nCYXhmcNZsHEB64vX+44i8g0qLBJY9M1TPRZH0pLT5DO1YCpXd7+a+um6r2A8XHXaVdSrU0+rQyQh\nqbBIYCtWQMOGcJJuAHmE7t1h48Zg8zBJfMu3Lmf51uWM6DnCd5TQaFa/GYO7Dmbq8qm+o4h8gwqL\nBFZYGMwnSNP/S0eI9uDoZmTJIW95Hk3qNeGyrpf5jhIqI3qO4N1P32Xjro2+o4gcQW9ZCUwrQiqm\nJafJZWrBVIaeNpQG6Q18RwmVoacNpW5aXQ2HSMJRYZHAtIdFxZo1gxNPVGGRDFZ+sZKPt3ysYZAa\n0LJhSy7pcglTCzQcIolFhUWCKi6GzZvVY1EZTeBMDnnL82hUtxGXn3q57yihNKLnCOaum8vmPZt9\nRxH5igqLBBV901SPRcW05DQ5TC2YypXdrqRR3Ua+o4TSsO7DSLM0bZYlCUWFRYKKvmnq5mMV69ED\nPvkk2ERMEtOaHWvI/yyfkT1H+o4SWq0atWJQl0EaDpGEosIiQa1YAZ06QZMmvpMkpu7dYf/+YBMx\nSUx5y/NomN6QId2G+I4SaiMyRzCnaA5bv9zqO4oIoMIiYRUWan7F0UR/NxoOSVxTC6YypNsQmtRT\ndVyTrulxDQAzCmd4TiISUGGRoLTU9OhOPhkaNNAEzkS1buc6FmxcwIhMrQapaW0at+HCjAs1HCIJ\nQ4VFAiopCeYPaOJm5dLSgvkn6rFITNMKplG/Tn2uPO1K31FSwojMEcxeM5sv9n7hO4qICotEVFQE\nBw+qx+JYtOQ0cU1ZPoXBpw6mWf1mvqOkhGszr6XUlfLCihd8RxFRYZGIop/C1WNxdFpympjW7VzH\n/A3ztRqkFrVv0p6BnQcyeelk31FEVFgkosJCaNwYOnb0nSSx9egRbCJWXOw7iZQ1eelkGqQ3YFj3\nYb6jpJSc3jnMXjubLXu2+I4iKU6FRQIqKAg+jevmY0cXHSoqKPCbQ440edlkhp42lKb1m/qOklJG\n9BxBmqXpjqfind66EtDixXDGGb5TJL6ePaFOneD3JYmhcFshizYvIqd3ju8oKadVo1Zc1vUycpfm\n+o4iKU6FRYI5fBiWLIG+fX0nSXwNGkBmJixa5DuJROUuyaVZ/WbaFMuTnN45vPvpu6wv1s5x4k+1\nCgszu93M1prZPjN7z8z6H6P9SDMriLRfbGZDyjyWbmb3m9nHZrbHzDaa2ZNmdmJ1siW7FSvgwAEV\nFlXVt68Ki0ThnCN3aS7Zmdm6Rbonw7oPo0F6A03iFK9iLizM7Drgj8A44ExgMTDTzFpX0n4A8Czw\nONAXmAHMMLOekSaNIsf/K3K+a4HuQEqum4q+SWoopGr69oWPP9Y9QxJB/mf5fLL9Ew2DeNS0flOu\nOu0qDYeIV9XpsRgLPOqce8o5VwjcCuwFvldJ+zuA15xz451zK5xz44B84EcAzrldzrnBzrk859wn\nzrkFkceyzKxTNfIltUWL4JRToHlz30mSQ9++sHcvrFrlO4nkLs2lbeO2XHzKxb6jpLSc3jks2ryI\nwm1aiy1+xFRYmFldIAuYHT3mnHPALGBAJU8bEHm8rJlHaQ/QAnDAzljyhcGiRRoGiUW0Z0fDIX6V\nulKeW/YcI3uOJD0t3XeclHZFtytoVr8ZuUvUayF+xNpj0RqoA5RfKL0FaF/Jc9rH0t7M6gP3Ac86\n5/bEmC+pOafCIlatWwd3gVVh4dc7699hw64NGgZJAA3SG3Btj2vJXZpL8LlPpHbFa1WIEfQwHFd7\nM0sHpkQeuy0+0ZLHpk2wbZsKi1hpAqd/uUtyObn5yQw46WgdkVJbcnrn8Mn2T8j/LN93FElBsfZZ\nbgNKgHbljrflm70SUZur0r5MUXEScHFVeivGjh1L83KTEXJycsjJSc5PTdE3RxUWsenbF554wneK\n1HWo5BBTC6ZyS99bSDOtYE8Eg7oMok2jNkxeOpmsDlm+44hHubm55OYeOSxWXMPbFcdUWDjnDpnZ\nQmAQ8CKAmVnk54cqedr8Ch6/NHKcyDmiRUUX4CLn3I6q5JkwYQL9+vWL5SUktEWLoGVLOOkk30mS\nS9++wdbemzdD+8oG5KTGzF47m217t2kYJIGkp6UzsudIJi+bzP2X3q+CL4VV9GE7Pz+frKyaKzir\n869tPDDazEaZWQ/gEYIlo5MAzOwpM/t9mfZ/AoaY2V1m1t3MfkMwAfThSPs6QB7QD7gJqGtm7SJf\ndav5upLSRx8Fb5JmvpMkl2gPj4ZD/Hj646fp0boHfdurqy2R5Jyew4ZdG3h73du+o0iKibmwcM49\nD9wN3At8BPQBBjvntkaadKLMxEzn3HwgBxgNLAKygWHOueVl2l8V+b4I2AR8FvmeUgO2mrhZPaec\nAk2bqrDwoXh/MdMKpvHdM76LqSJOKOeedC5dW3Zl0qJJvqNIiqlW/5hzbqJzLsM519A5N8A592GZ\nxy52zn2vXPs851yPSPs+zrmZZR5b55yrU+4rLfI9ZUrtXbtg9WoVFtWRlqYJnL48v+x5DpQc4Dt9\nvuM7ipRjZtzc92amLJ/C7gO7fceRFKKBtwTx8cfBdxUW1aPCwo9JiydxWdfL6Niso+8oUoFRZ4xi\n36F9uuOp1CoVFgli0SKoV+/rW4FLbPr2hZUr4csvfSdJHSu2rWDep/O4+YybfUeRSpzc/GQGdRnE\npMWTfEeRFKLCIkEsWgS9egXFhcSub99gg7ElS3wnSR1PLn6SFg1aMKzHMN9R5ChuPuNm3l73Nqu3\nr/YdRVKECosEoYmbx6dnT0hP13BIbSkpLeGpxU+R0ztHdzJNcNdmXkuz+s14cvGTvqNIilBhkQAO\nHYKlS1VYHI8GDSAzU4VFbZm1ZhYbd2/klr63+I4ix9CobiOu63UdTy5+klJX6juOpAAVFglgxQo4\ncECFxfHSBM7aM2nxJHq26clZHc7yHUWq4Ja+t7C+eD1vrX3LdxRJASosEkD0zTB6p06pnr59g9U1\nJSW+k4Tbjn07mF4wnZvPuFl7VySJczqdw2mtTtMkTqkVKiwSwKJFwSZP5W57IjHq2xf27YNPPvGd\nJNyeW/Ych0sPc1Ofm3xHkSoyM24+42byluex68Au33Ek5FRYJABN3IyPaI+PhkNq1t8X/Z3LT72c\nE5ue6DuKxGDUGaM4UHKA55c97zuKhJwKC8+cU2ERL61aBTdwU2FRc5Z9vowFGxdwc9+bfUeRGHVs\n1pFLu1zK3z76m+8oEnIqLDzbuBG++EKFRbxoAmfNmvjBRNo1bsfV3a/2HUWqYXTWaOZvmM+izfqP\nRGqOCgvPFiwIvofo7u9e9esHH3wApVpVF3e7DuziqY+f4gf9fkC9OtrJLRld3f1qOjXrxJ8X/Nl3\nFAkxFRaezZ0LGRnQqZPvJOFw/vmwfTsUFPhOEj7/WPwP9h3ax5izxviOItWUnpbOmKwxPLPkGXbs\n2+E7joSUCgvP3n47eDOU+DjnnGAHzrdT5r64tcM5x8QPJzKsxzA6NVMVnMx+0O8HHC49rNupS41R\nYeHRrl3BfICBA30nCY/GjSErK+gJkvj517p/sXzrcm7vf7vvKHKc2jVpx4ieI5j44UTtxCk1QoWF\nR/PmBXMBVFjE1/nnBz0WzvlOEh5//uDPZLbO5KKMi3xHkTi4vf/trNq+ijdWv+E7ioSQCguP5s6F\ntm2hWzffScJl4MBgtU1Rke8k4bBx10amF0zntv63aafNkPj2Sd/mjHZn8OcPNIlT4k+FhUdvvx28\nCepvdXyde27wXfMs4uOxhY/RsG5DRp0xyncUiRMz4/b+t/Pyypcp2lnkO46EjAoLT/bvD5aaauJm\n/J1wApx+uuZZxMPBkoM8lv8Y3+nzHZrVb+Y7jsTRDaffQLP6zXjkw0d8R5GQUWHhyYIFcPCg5lfU\nlIED1WMRD9MLprN5z2ZN2gyhxvUac0vfW3gi/wn2H97vO46EiAoLT+bODW46dvrpvpOE0/nnBzcj\n27zZd5Lk9vAHD3NhxoX0atvLdxSpAbf1v40v9n3Bc0uf8x1FQkSFhSdvvx3MBahTx3eScIoOMWk4\npPrmfTqPd9a/w51n3+k7itSQbq26cWW3K3lg3gNaeipxo8LCg8OHg6WmGgapOR06QNeuKiyOxx/e\n+QM92/RkaPehvqNIDfr5eT9n+dblvLTiJd9RJCRUWHiwaBHs2aOJmzVN8yyq7+MtH/Pyypf5+Xk/\nJ830ZyLMzj35XAZ2Hsjv3/k9Tpu/SBzoL4YHb78NDRrAWWf5ThJu558PH38MO3f6TpJ87nvnPjJa\nZHB97+t9R5Fa8PPzfs6CjQt4q+gt31EkBFRYeDB3LgwYAPV0g8gaNXBgsPvmu+/6TpJcVm1fxXPL\nnuOn3/4p6WnpvuNILRjcdTBntj+TP7zzB99RJARUWNSy0tKgsNAwSM3r0gVOPFHzLGL14LsP0rpR\na27pe4vvKFJLzIx7zruHWWtm8cHGD3zHkSSnwqKWFRbCF19o4mZtMNM8i1ht2r2JSYsncdc5d9Gw\nbkPfcaQWDc8cTrcTuqnXQo5btQoLM7vdzNaa2T4ze8/M+h+j/UgzK4i0X2xmQ8o9fq2ZvW5mW82s\n1Mz6VCdXMnj77eC23uec4ztJajj/fPjwQ9i713eS5DB+/ngapjfkh/1/6DuK1LI6aXX4z3P/k+mF\n0ynYWuA7jiSxmAsLM7sO+CMwDjgTWAzMNLPWlbQfADwLPA70BWYAM8ysZ5lmjYF3gP8EQj0tee7c\n4LbejRv7TpIaBg6EQ4fg/fd9J0l82/dt55EPH+H2/rdr++4U9Z0zvkPHph25/937fUeRJFadHoux\nwKPOuaecc4XArcBe4HuVtL8DeM05N945t8I5Nw7IB34UbeCce9o591tgNhDaW3KVlsJbb2l+RW3q\n1Su4d8hbmux+TBPmT6DElXDHOXf4jiKe1KtTj7sH3M3THz/Nqu2rfMeRJBVTYWFmdYEsggIAABcs\nfJ4FDKjkaQMij5c18yjtQ2vBAvjsMxiq/YZqTVoaXHEFTJ/uO0li27R7E+PfG88dZ99B28ZtfccR\nj8acNYb2Tdrz/978f76jSJKKtceiNVAH2FLu+BagfSXPaR9j+9DKy4O2bb++rbfUjuHDYelSWLnS\nd5LENe6tcTRMb8g9593jO4p41qhuI/77ov/m+WXP8/4GjSFK7OK1KsSIbW5ErO2TnnNBYXHttbo/\nSG0bPDiY05KX5ztJYlr2+TL+tuhv/Grgr2jRoIXvOJIARp0xitPbns5P3/ipduOUmMW6+802oARo\nV+54W77ZKxG1Ocb2VTZ27FiaN29+xLGcnBxycnKO99Rxt2gRrF0bfHqW2tWwYTAckpcHP/+57zSJ\n557Z95DRIkMrQeQrddLq8OClD3L5M5fz4ooXGdZjmO9IUk25ubnk5uYecay4uLhGr2mxVqNm9h7w\nvnPujsjPBqwHHnLOPVhB+8lAQ+fcsDLH3gUWO+duK9e2M7AGONM59/FRMvQDFi5cuJB+/frFlN+X\nX/4SJk6ELVugbl3faVLPc8/B9dcHxV1Ghu80iWNO0RwuevIinhvxHP/W6998x5EE4pzjsqcv49Pi\nT1l621Ltwhoi+fn5ZGVlAWQ55/Ljff7qDIWMB0ab2Sgz6wE8AjQCJgGY2VNm9vsy7f8EDDGzu8ys\nu5n9hmAC6MPRBmbW0szOAHoRDJP0MLMzzKx8T0fSysuDYcNUVPhyxRVQvz5Mm+Y7SeIodaX89I2f\n8q2O32Jkz5G+40iCMTMeuOQBVn6xkifyn/AdR5JIzIWFc+554G7gXuAjoA8w2Dm3NdKkE2UmZjrn\n5gM5wGhgEZANDHPOLS9z2qsj53qJYO5FLsGS1DGx5ktEy5cHO25qGMSfpk2DuRaaZ/G155c9z4eb\nPuTBSx8k6HgUOdKZJ57JTX1uYtyccew+sNt3HEkS1Zq86Zyb6JzLcM41dM4NcM59WOaxi51z3yvX\nPs851yPSvo9zbma5x590zqU55+qU+7q3ei8rseTlQZMmcMklvpOktuHDYd482LTJdxL/9h/ezy9m\n/4Kru1/NwM7aX14q99uLf0vx/mIenPeNkW6RCuleIbUgLw+uuiq4Vbr4M3RosJ269rSA3779Wzbs\n2gwEmukAABOoSURBVMD9l2iHRTm6k5ufzN0D7ub+d++ncFuh7ziSBFRY1LDVq2HxYg2DJIKWLWHQ\nIA2HLNq8iPvfvZ9fDvwlPVr38B1HksAvB/6Szs078/0Xv0+pK/UdRxKcCosalpcXLHccMuTYbaXm\nDR8O//oXbN167LZhdLj0MN9/8fv0aN1Dm2FJlTWs25Anrn6CeZ/OY+IHE33HkQSnwqKG5eXB5Zfr\npmOJ4pprgu8vvOA3hy/j549n0eZF/O3qv1GvTj3fcSSJDOw8kB+e9UPumXUP63au8x1HEpgKixr0\n6afB/UE0DJI42rQJ7niaisMhK79Yybg547jrnLvo37G/7ziShO675D5OaHgCY14eox05pVIqLGpQ\nXl6wb8VVV/lOImUNHw6zZ8P27b6T1J5SV8q/v/jvdGzakf+66L98x5Ek1ax+Mx656hFmrp7JPz7+\nh+84kqBUWNSQkhL485+Drvdyu46LZyNHghk8+qjvJLXn0Q8fZe76uTw+9HEa1W3kO44ksSu6XcFN\nfW7iztfvZPOezb7jSAJSYVFDXngBVq2Cn/7UdxIpr107+O534aGH4MAB32lq3uLNi7n7n3czJmsM\nF51yke84EgITBk+gXp163DTtJg6XHvYdRxKMCosa4Bw88ABccAH011B2Qrr77uC+LU8/7TtJzdqx\nbwfZz2fTvXV3Jgye4DuOhETrRq2ZPGIyc4rm8Ks3f+U7jiQYFRY14J134P334Wc/851EKtO9e3Dv\nlgcfhNKQLssvdaWMmjGKHft2kPdveTSs29B3JAmRCzMu5P5L7ue+d+9jeoF2nZOvqbCoAQ88AL16\nae+KRPfTn8KKFfDyy76T1Izfvf07Xln5Cs9kP0OXll18x5EQumvAXYzoOYLvzvguK7at8B1HEoQK\nizhbvjx4o/rJT4IJgpK4vv3t4OuBB3wnib/XV73OuDnj+M2Fv2FIN1W4UjPMjL9d/Tc6NutI9vPZ\n7Dm4x3ckSQAqLOLsf/4HOnSAG27wnUSq4mc/g3ffhfnzfSeJn7U71nJD3g0M6TaEXw78pe84EnJN\n6zdl+nXTWV+8Xlt+C6DCIq42bQomA955J9TTpoZJYejQYL7FgyG5cePGXRu55B+XcELDE3j62qdJ\nM/0nLjWvR+sePHnNk0xZNoU7X79Tm2elOP3ViaOHHgruCzJ6tO8kUlVpacGw1YwZsHKl7zTHZ8ue\nLQx6ahCHSg4xa9QsWjZs6TuSpJDszGweueoR/m/B/3HPrHtUXKQwFRZxsnMn/OUvMGaMNsRKNjfd\nBG3bJnevxfZ927n0H5ey68AuZo+aTUaLDN+RJAWNzhrNhMETeGDeA9z7r3t9xxFP0n0HCIu77gr2\nr7jzTt9JJFYNGsA99wT/H373u3Deeb4TxaZ4fzGDnx7MZ3s+4183/4turbr5jiQp7M5z7mTfoX38\n4s1f0LBuQ352rtbdpxoVFnHw0kvw97/DX/8aTNyU5PMf/wFTpvz/9u49OqrqXuD49zdJSEwQQV7h\nKY/wEEoBBaKCWASBRgtyCyJ6kQK+Cl0i1y6E2weot2JdSgWEIsYuQJCH4lVpNcEgl6WApgRBK+Gl\nKQghQoBAVl7kse8f+yQMIYkzk0kOCb8Pa6/MnNlz5sdvkjO/OY+9bWGxdy80bOh2RL45m3eWe9be\nw+Ezh9k6aSs9mvdwOySlmHP7HPKK8ng66WlCPaHMvGUmopfJXTX0UEg1ZWbCI4/A3XfD5MluR6MC\nFRICK1dCRkbdGdjsQOYBYuNj2Z+5n8T/TKRPdB+3Q1KqzDM/e4bZA2fz1OanmPaPaRQWF7odkqol\nWlhU0/TpUFgIr7+u41bUdTEx9jyLv/4VNm92O5qqbf52M7HxsYR6Qkl+OJkBbQa4HZJSlxAR5g+b\nT/wv4nnjyzcYvno4p3NPux2WqgVaWFTD+vWwYQMsXQqtWrkdjQqGxx+HYcNgyhR7Qu6VxhjD4i8W\nE7cmjtva3cbOqTvpfH1nt8NSqlJTb5pK0kNJ/Ovkv4iNjyX1VKrbIakapoVFgE6cgGnT4L77YPx4\nt6NRweLx2HNlsrNhxgy3o7lUVn4WUz6YwhMJT/DkLU+yacImrovQS5DUlW/wDYNJfjiZiNAIbnnj\nFt76+i29HLUe08IiAPn59iS/sDBYssTtaFSwtW8PCxfCqlW2XQk+OPABPZf2ZOO+jawYvYKXhr9E\niCfE7bCU8lnHJh3ZMXUHcV3iePDdBxm1bhTHzh9zOyxVA7Sw8FNOjh2t8dNPYc0aaNbM7YhUTZg0\nyZ6M+6tf2fNn3HIq5xT3v3M/o9eNpm90X/ZN38ekPpPcC0ipamgU3oi1v1zLe+PfIyU9hZ5Le7I8\nZbkOA17PaGHhh6wsGD7cTomekABDh7odkaopIhA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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(1)\n", - "for i in range(nbd):\n", - " pl.plot(x,A[:,i])\n", - "pl.title('Distributions')\n", - "pl.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Barycenter computation (for l2 and Wasserstein)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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eoap2PGAmtm+Hb76Btvl3+f4Jq1DB9cZMm+Z3JsYvKWkpdJ3YldJFS/N+x/eJ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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# l2bary\n", - "bary_l2=A.mean(1)\n", - "\n", - "# wasserstein\n", - "reg=1e-3\n", - "bary_wass=ot.bregman.barycenter(A,M,reg)\n", - "\n", - "pl.figure(2)\n", - "pl.clf()\n", - "pl.subplot(2,1,1)\n", - "for i in range(nbd):\n", - " pl.plot(x,A[:,i])\n", - "pl.title('Distributions')\n", - "pl.xticks([])\n", - "\n", - "pl.subplot(2,1,2)\n", - "pl.plot(x,bary_l2,'r',label='l2')\n", - "pl.plot(x,bary_wass,'g',label='Wasserstein')\n", - "pl.legend()\n", - "pl.title('Barycenters')\n", - "\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "## Barycenter interpolation" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "\n", - "nbalpha=11\n", - "alphalist=np.linspace(0,1,nbalpha)\n", - "\n", - "\n", - "B_l2=np.zeros((n,nbalpha))\n", - "\n", - "B_wass=np.copy(B_l2)\n", - "\n", - "for i in range(0,nbalpha):\n", - " alpha=alphalist[i]\n", - " weights=np.array([1-alpha,alpha])\n", - " B_l2[:,i]=A.dot(weights)\n", - " B_wass[:,i]=ot.bregman.barycenter(A,M,reg,weights)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot interpolation" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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MnDkTy5cvxw033ICXXnoJJSUlqKysRE5Ojt/+FRUVmDhxIhYsWIBRo0Zh3bp1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Vq1eTmZmpX7P6+vpIL7qEgoKiCKs7pJa6Y5KId6brrO74+KFbIUqSSGHBbs7UFbJgwSzuu+/r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\n48aNo3PnznW8S6tDGmR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4//KPT/+VhFqiNiEJskSNwC9vSYVYuOCD1Wp1EOLo6GgoFAoAgF6vr8nmS512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Uk0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\nAlhjjJkMNANWAFvucIW1Uk3S/wOA7iEME+ag6QAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "pl.figure(3)\n", - "\n", - "#pl.subplot(1,2,1)\n", - "cmap=pl.cm.get_cmap('viridis')\n", - "verts = []\n", - "zs = alphalist\n", - "for i,z in enumerate(zs):\n", - " ys = B_l2[:,i]\n", - " verts.append(list(zip(x, ys)))\n", - "\n", - "ax = pl.gcf().gca(projection='3d')\n", - "\n", - "poly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist])\n", - "poly.set_alpha(0.7)\n", - "ax.add_collection3d(poly, zs=zs, zdir='y')\n", - "\n", - "ax.set_xlabel('x')\n", - "ax.set_xlim3d(0, n)\n", - "ax.set_ylabel('$\\\\alpha$')\n", - "ax.set_ylim3d(0,1)\n", - "ax.set_zlabel('')\n", - "ax.set_zlim3d(0, B_l2.max()*1.01)\n", - "pl.title('Barycenter interpolation with l2')\n", - "\n", - "pl.show()\n", - "\n", - "pl.figure(4)\n", - "\n", - "#pl.subplot(1,2,1)\n", - "cmap=pl.cm.get_cmap('viridis')\n", - "verts = []\n", - "zs = alphalist\n", - "for i,z in enumerate(zs):\n", - " ys = B_wass[:,i]\n", - " verts.append(list(zip(x, ys)))\n", - "\n", - "ax = pl.gcf().gca(projection='3d')\n", - "\n", - "poly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist])\n", - "poly.set_alpha(0.7)\n", - "ax.add_collection3d(poly, zs=zs, zdir='y')\n", - "\n", - "ax.set_xlabel('x')\n", - "ax.set_xlim3d(0, n)\n", - "ax.set_ylabel('$\\\\alpha$')\n", - "ax.set_ylim3d(0,1)\n", - "ax.set_zlabel('')\n", - "ax.set_zlim3d(0, B_l2.max()*1.01)\n", - "pl.title('Barycenter interpolation with Wasserstein')\n", - "\n", - "pl.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/examples/Demo_2D_OT_DomainAdaptation.ipynb b/examples/Demo_2D_OT_DomainAdaptation.ipynb deleted file mode 100644 index b713d5b..0000000 --- a/examples/Demo_2D_OT_DomainAdaptation.ipynb +++ /dev/null @@ -1,217 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Domain adaptation with optimal transport" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Dataset generation (classification problem) " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n=150 # nb samples in source and target datasets\n", - "\n", - "xs,ys=ot.datasets.get_data_classif('3gauss',n)\n", - "xt,yt=ot.datasets.get_data_classif('3gauss2',n)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot datasets" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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mudNPP/2EUoo333zTZvvhw4ejtebHH39MU/1KKfr3729zhUxrzdq1a3n++eep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\nuJRSCvgUWKe13ml2POlRSt2hlEoErgBfA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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(1,(10,7))\n", - "\n", - "pl.subplot(2,2,1)\n", - "pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples')\n", - "pl.legend(loc=0)\n", - "pl.axis([-4,4,-4,4])\n", - "pl.title('Source distributions')\n", - "\n", - "pl.subplot(2,2,2)\n", - "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples')\n", - "pl.legend(loc=0)\n", - "pl.axis([-4,4,-4,4])\n", - "pl.title('Target distributions')\n", - "\n", - "pl.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "## Domain adaptation classes" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# LP problem\n", - "da_emd=ot.da.OTDA() # init class\n", - "da_emd.fit(xs,xt) # fit distributions\n", - "xst0=da_emd.interp() # interpolation of source samples\n", - "\n", - "\n", - "# sinkhorn regularization\n", - "lambd=1e-1\n", - "da_entrop=ot.da.OTDA_sinkhorn()\n", - "da_entrop.fit(xs,xt,reg=lambd)\n", - "xsts=da_entrop.interp()\n", - "\n", - "# Group lasso regularization\n", - "reg=1e-1\n", - "eta=1e0\n", - "da_lpl1=ot.da.OTDA_lpl1()\n", - "da_lpl1.fit(xs,ys,xt,reg=lambd,eta=eta)\n", - "xstg=da_lpl1.interp()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot OT matrices and interpolated samples " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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artIdDXkv+QcCIUR36Z+GQLd05DLgcx3uqRCims7oSFccGQSR+RPgFc65/y4c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O+Xw+n0Ag+DNQeq3nE2RtYTab6bJ00RbeypaQrcSp56ehqFQqdu9Qcd8OJV2mmbS1nL/aS47p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h5k795u/vjHT+jurmfRooWjvocQYvwppfDz8+Oee+LQ6Sppa1O0tup54YUCtm4NZP36eG6/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\nhBBCTGnS6RFCCCGEEEJMaf8PUJAvj+BeZCgAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(4,(10,7))\n", - "\n", - "param_img={'interpolation':'nearest','cmap':'jet'}\n", - "\n", - "pl.subplot(2,3,1)\n", - "pl.imshow(da_emd.G,**param_img)\n", - "pl.title('OT matrix')\n", - "\n", - "\n", - "pl.subplot(2,3,2)\n", - "pl.imshow(da_entrop.G,**param_img)\n", - "pl.title('OT matrix sinkhorn')\n", - "\n", - "pl.subplot(2,3,3)\n", - "pl.imshow(da_lpl1.G,**param_img)\n", - "pl.title('OT matrix Group Lasso')\n", - "\n", - "pl.subplot(2,3,4)\n", - "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)\n", - "pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30)\n", - "pl.title('Interp samples')\n", - "pl.legend(loc=0)\n", - "\n", - "pl.subplot(2,3,5)\n", - "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)\n", - "pl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30)\n", - "pl.title('Interp samples Sinkhorn')\n", - "\n", - "pl.subplot(2,3,6)\n", - "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)\n", - "pl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30)\n", - "pl.title('Interp samples Group Lasso')\n", - "\n", - "pl.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/examples/Demo_2D_OT_samples.ipynb b/examples/Demo_2D_OT_samples.ipynb deleted file mode 100644 index e20b09b..0000000 --- a/examples/Demo_2D_OT_samples.ipynb +++ /dev/null @@ -1,234 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:e01831efa84095a87a681a09f08c8991bbe439e010c2bc4cd3559dc4d3bf0bde" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data generation" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "n=20 # nb samples\n", - "\n", - "mu_s=np.array([0,0])\n", - "cov_s=np.array([[1,0],[0,1]])\n", - "\n", - "mu_t=np.array([4,4])\n", - "cov_t=np.array([[1,-.8],[-.8,1]])\n", - "\n", - "xs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s)\n", - "xt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t)\n", - "\n", - "a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples\n", - "\n", - "# loss matrix\n", - "M=ot.dist(xs,xt)\n", - "M/=M.max()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot dataset" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pl.figure(1)\n", - "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", - "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", - "pl.legend(loc=0)\n", - "pl.title('Source and traget distributions')\n", - "\n", - "pl.figure(2)\n", - "pl.imshow(M,interpolation='nearest')\n", - "pl.title('Cost matrix M')\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 3, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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Cd5pZEcGPw4Pu/mQE7YqISIJ0BqiISBbTGaAiIgVEyVxEJA8omYuI5AElcxGR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\nXEQkDyiZi4jkASVzEZE88P8B8sPqteLhUE0AAAAASUVORK5CYII=\n", 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- "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Solve OT" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "G0=ot.emd(a,b,M)\n", - "\n", - "\n", - "pl.figure(3)\n", - "pl.imshow(G0,interpolation='nearest')\n", - "pl.title('Cost matrix M')\n", - "\n", - "pl.figure(4)\n", - "ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1])\n", - "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", - "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", - "pl.legend(loc=0)\n", - "pl.title('OT matrix')\n", - "\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 4, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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wd24wcMtRdH//MKrvPYS17ziXQuT1z8hgFmpYGLs32TXWjz461DN/7jmvxu2n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\n/XWSd0EBCSs62rHnqe6VuXkzf2evOqIabW3Ad75DmWbbNv7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- "text": [ - "" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Solve OT with entropic regularization" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# reg term\n", - "lambd=5e-3\n", - "\n", - "Gs=ot.sinkhorn(a,b,M,lambd)\n", - "\n", - "pl.figure(5)\n", - "pl.imshow(Gs,interpolation='nearest')\n", - "pl.title('OT matrix sinkhorn')\n", - "\n", - "pl.figure(6)\n", - "ot.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1])\n", - "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", - "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", - "pl.legend(loc=0)\n", - "pl.title('OT matrix Sinkhorn with samples')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 5, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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5eXmorKxEJBLBddddh0gkMq5rFVcNA/CLX/wCmzZtQn9/P2644YaUcnHct7a2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- "text": [ - "" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 5 - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/examples/Demo_2D_OTmapping_DomainAdaptation.ipynb b/examples/Demo_2D_OTmapping_DomainAdaptation.ipynb deleted file mode 100644 index 4b75f58..0000000 --- a/examples/Demo_2D_OTmapping_DomainAdaptation.ipynb +++ /dev/null @@ -1,283 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## OT mapping estimation for domain adaptation" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Dataset generation" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "np.random.seed(0) # makes example reproducible\n", - "\n", - "n=100 # nb samples in source and target datasets\n", - "theta=2*np.pi/20\n", - "nz=0.1\n", - "xs,ys=ot.datasets.get_data_classif('gaussrot',n,nz=nz)\n", - "xt,yt=ot.datasets.get_data_classif('gaussrot',n,theta=theta,nz=nz)\n", - "\n", - "# one of the target mode changes its variance (no linear mapping)\n", - "xt[yt==2]*=3\n", - "xt=xt+4\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "### Plot source and target datasets" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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iSpWq5M+fn0KFClGiRClWrVrllOfw4cOEhYVx8+bNDKqlEEI82KRHVAjxwLp2\n7RrffPMN+/bto1ChQrz66qv4+flldLU4c+YMNWvW4to1L6AFkIlDh7by3HPP8/vv68mZMyft23dk\n69bNAOTI4cuoUSMfrLX7hBDiASCBqBDigbR3715q1apDePgFPDzyYLNFMHz4O6xa9T3Vq1e/63L3\n79/P9u3byZs3L7Vr177jotFJmTJlCteu3cBmCwV8ANC6GDCV9957n23bthMebgNaAdm4cuUv3njj\nDXx9fWnfvv1d110IIR41aXppXin1jlLK7vLYm5bHFEI8/LTWvPJKOy5eVGj9OjExPbDb+3Ljhh8t\nW7YiJiYm1WXeuHGDF19sSalSpWjXrh3169enSJGid7WGX1hYGDZbAeKCUMOKzVaMP/7YyPnz57DZ\n2gClgSDgOZQqxXvvfZDqYwkhxKMsPcaI/g3kBfI5HtXS4ZhCiIfY/v372blzBzZbbcDXkZoFu70B\n58+fvatB/v3792fp0hXAC8AQoAunTkXToEGjVN93OSAgAA+Pi4DdKV2pC3h5ZcLDwx/I6bRN62D+\n+Wd/krc3FEKI/6L0CERjtdYXtNbnHY+L6XBMIcRD7PLly47fsrtsMc8vXbqUqvKioqL48suZ2O1V\ngCcAL6AANltzzp07w7Jly+Lz2u121qxZw7BhwxgzZgzHjh1LVF7nzp2Jjb0I/ADcAGKAP9H6ADVr\nVsdmiwCiXPY6Rf78gTKzXgghEkiPQLSYUuqUUuqwUmquUiooHY4phHiIlStXjixZsgF/uWzZiVIW\nqlSpkqryIiIiuHnzBhDosiUPVqt3fLAZFRVFvXr1adCgAWPGfM7Qoe9QpEgwU6dOddqrUqVKTJo0\nCQ+PncBYlPoQWE3v3r0ZP348Xl6eWCyLgQvATWATSu3k//6v1x3rGR0dzdy5c2nXrh2dO3dm9erV\n0oMqhHikpXUgugnoCDQEugOPAeuVUlnS+LhCiIdY1qxZGT78LWAzSi0EtgPLUGotPXp0Jygodd9n\n8+bNS44cvsAhly0nsNluULp0aQBGjx7Nb7/9AbQlNrYPdvub2O0V6NatG9u2bXPas0ePHpw8eYKp\nU6cwfvynbNq0iaZNm3Ly5EmWL19GlizhwETgQ+BHtLYzb963XLhwwW0dIyMjqVGjJq+++irz5//G\nnDnf06hRI7p06SLBqBD/Mbdu3cJisTB27NiMrkqaS9NZ81rr1Qme/q2U2gIcw0wlnZnUfn379iVH\njhxOaW3gk7sNAAAgAElEQVTatKFNmzZpUk8hxINn4MCB5MqVizFjPuLw4RXkzx9Inz4f0K9fv1SV\ns2fPHmbMmEGBAoFcubLFkfoEcAGr9WeKFStNo0aNAJg+/Uvs9gpAMUc+L8z36N20aNGCY8eOOV1a\nz5s3L507d2bQoEH07fsmsbFmElWxYiWw2+0olRetKwDFgWvs3buQHj16smjRwkT1/Pjjj9m6NQzo\njM0WBGhgB19++SUtWrTg2WefTdV5C+GOxZJ8/5NSil9++YUaNWqkQ41S7/fff+fnn39mwIAB+Pj4\nJL+DuCfz5s1j3rx5TmlXrly5b+Wn6/JNWusrSql/gKJ3yvfJJ59QsWLFdKqVEOJBpJQiNDSU0NBQ\nbDbbXS2zNHfuXDp06IDFkhW7PTdKWdF6K2DW96xRow5ffTUnvuzLly8Brv97PIEcnDhxgg0bNlCt\nWjWio6P53//+x9SpMzh9+hTR0TeB2kA54DKHD/+E3X4deBXwd5STC5utOt999x2XLl0iZ07nyUxz\n587Dbo+bZQ+ggApYrVuZP3++BKLivpg7d67T89mzZ7N27Vrmzp3r1PNeqlSp9K5aiq1fv55Ro0bR\no0cPCUTTgbuOwLCwMEJCQu5L+ekaiCqlsgLBwJz0PK4Q4uF2N0HopUuXCA3tit1eFrv9ecy/u+tY\nrXMpWdKPlStXULhwYad9nnjiCbZt2wU8xe2RS2eB8yhlZdu2bVSpUoW6deuxYcMGtA7AzJwPAWo6\n8ufCbn8Z+Aw4we1AFCA3drvNbSAaFRUF5HI5C4Xd7sWNGzc4c+YMGzduJFu2bNSuXRtPT89Ut4nI\neNHR0axbt45r165RrVo1AgIC0vX4r7zyitPzP//8k7Vr1973K46xsbEAeHjc/zBDhqo8WtJ6HdGP\nlFI1lFKFlFJVgO+AWGBeMrsKIcQ9WblypWOCUn1uf+fOis1WlT17/nYb3A4fPgw4DcwCdgC/Y743\n50ZrG2fPniU4uCh//PE7WnsApzAz5r1cSsoJZAN2A/uBq470Pfj5+bsd49q4cQM8PPbgPNv+LHCc\nK1euEBRUkJYtW9KwYUMCA4PkPtUPobVr1xIQUJAmTZrw8ssvU7BgIQYMGPDABlY3b95k2LBhhISE\nkCNHjvgvQRs2bHDKd+DAASwWCxMnTuTjjz+mSJEieHt7c+TIEQCOHDlCkyZNyJIlC/ny5WPgwIGs\nXLkSi8XCli1bnMrasGED9evXJ0eOHGTNmpW6des65RkyZAhvv/02APny5cNisWC1Wjl//nyS57F/\n/35eeOEF8uXLh7e3NwULFqRdu3bcuHEjPs+0adOoU6cOefPmxdvbm3LlyvHll18mKitfvny0atWK\ntWvXEhISgo+PDxUqVGDjxo0AfPvtt5QpUwZvb28qV67Mnj17nPZv3bo1efLk4eDBg9StW5esWbMS\nFBTEhx9+mJKXhBMnTtC+fXvy5s1L5syZKV++fKJeboBx48ZRunRpsmTJQq5cuahcuTJLlixJ0THS\nW1r3iBYAvgFyY6aP/gE8rbWOSOPjCiH+48yHjAIyu2zxTrDd2fPPP0/NmjUdE5aOA1bMeNFLWCwe\njBkzJkHukkAtzBJOmzFLJMddJrwEXAeuAUcd9cgJXOTtt8e77c0cOnQoS5Z8x7VrU4mNLQtEY7Xu\nwt8/P2vXrsVc+q8IXCciYg3PPtuUw4cPkT9//lS2jLifVq5cyccff8KBAwcpViyYN998gxdeeCFR\nvlOnTvHcc82Ijq4K/A/Ii802nY8/fovChQvTq5f7FRVsNhtHjhzBx8eHwEDXVR/SVkREBHPmzKF1\n69Z0796dy5cvM336dOrXr09YWBglS5Z0yj958mRsNhs9e/bEw8ODHDlycPXqVWrVqsXly5fp168f\nfn5+fPXVV6xZsybRUmY//vgjzZo145lnnmHUqFEATJ8+nVq1arFp0ybKly9PmzZtOHz4MIsXL2bS\npElkz26WdPP19cWdmzdvUr9+fSwWC3379sXf358TJ06wfPlyrl+/jre3+X8wadIkKlWqRPPmzbFY\nLCxdupQuXbqglOK1116LL08pxZ49e+jYsSM9evQga9asjBkzhueee45PP/2UkSNH0qNHD2JjY3nv\nvfdo3bo1u3fvdto/OjqaRo0aUbt2bVq2bMnKlSsZOnQoAIMHD07y9Th16hRPPfUUPj4+9OnTh1y5\ncrFy5Urat29PVFQUXbt2BWDChAn079+ftm3b8uabb3Ljxg3++usvNm/eTIsWLVL02qcrrfUD88D8\nl9Xbt2/XQghxLw4dOqQBDQ00jHA83tZQUhcoUFDHxsa63S8iIkJXqlRZA9pi8dKAVsqqLZZ8Gjpp\nGKihqQarhmc09NegNBTX8IaGDhryOra31fCmhoYalG7cuLG22+13rHPHjh117tz+OiAgSA8YMEAX\nKvSYhnIJzmGEhkHaYsmkP/jgg7Rqvv+87du36+Q+jyZNmqQBbbVW1fCWtlhqaEB/8sknifK+++67\n2mrNouGyBh3/UKq1Llq0pNvy582bpwMDCznex+iqVWvovXv33rdz1Frr3r17a4vF4nabzWZL9Hdy\n8eJFnTt3bt27d+/4tP3792ullPbz89NXrlxxyv/ee+9pi8Wi16xZE59248YNHRwcrC0Wi968eXP8\nsQoXLqybN2/utH9kZKQOCgrSzZo1i0979913tcVi0efOnUv2/DZt2qSVUvqHH364Y76bN28mSqtd\nu7YuW7asU1q+fPm01WrVO3bsiE9bvny5Vkrp7Nmz67Nnz8anjx8/3ukctda6devW2mKx6MGDBzuV\nW79+fZ0lSxZ99erV+PoopfSYMWPi87Rt21YXLlw4Pk+c5s2b6zx58uiYmBittdaNGjXSlSpVuuP5\nJie593/cdqCivsfYLz3WERVCiHQXHBxM7969gZ+Ab4FfUWoGSh1g3LiPkxx3mitXLjZv/pN169Yx\nZsy79OrVC61t2O0vAgUxvZ5PAlUwy0p5YXpZ/8GMC52NuQDUCtObmh14BqjMxo2bsNlsd6zzzJkz\nCQ8/x6lTxxk7diynT5/EXFxKyBuLJQ///vvvXbaOuFeRkZEMGDAYCMVm+x14F7v9V6A3Q4cO4+rV\nq075jx49ilKlAOcVYbR+muPHjyYqf9WqVbRp04ZTpyoCPwJfs2nTeWrUqMPFi+lzX5i4y96mnppL\nly5hs9moWLEiYWFhifK3bt06vocyzurVqwkODqZevXrxaZkzZ6Zz585O+bZs2cKxY8do06YNERER\n8Y+oqChq165910NR4npKV61adcc7qHl53R5ec+XKFcLDw6lRowb79u0jOjraKW+FChV44okn4p9X\nrlwZgEaNGpE3b16ndK11/BCFhFx7wHv16sWNGzeSPE+bzcayZcto1qwZ0dHRTm3UsGFDIiIi4nte\nfX19OXr0KDt37kzyfB8kEogKIR5ZQ4cOxd8/L3AA+AOtz6O15sSJE27zb9u2jd69e9OmTRt27dpF\naGgogYGBKJUZyOOSuwAQjVmbNIpcuXIzc+ZMOnfujIdHVqCES/6CXLlyKdXLnhQrVgKlXAPOa9jt\n5xJdGhXpZ/PmzURGXgXewAy9wPHzdW7ciEw0jrJUqVLY7bsw435vU2oNJUoknqH+/vtjsFiqAosx\nS4i9gs22loiICGbNmnW/TydJ06dPp2zZsnh5eZE7d278/f1Zu3at2/ex6+Q/gGPHjhEcHJwovWhR\n58VzDh48CMDLL79Mnjx54h/+/v7MnTuXyMjIVN+KF6BEiRL06tWLiRMnkjt3bpo0acIXX3zB9evX\nnfL99ttv1K5dmyxZspAzZ078/f0ZNWoUWutEXyoKFizo9DxuuckCBQq4TXe9E5yXl1eivMWLF0dr\n7fZObgCnT58mMjKSCRMmOLVPnjx56NGjB0D8ONmhQ4fi6elJhQoVKFmyJG+88UaisbgPknSdNS+E\nEOlp8OAhREREYu6n4Q/YgLX079+fpk2bUrx48fi8H330EQMHDkQpL7T24ttvFzBy5CiqVHkGrW9i\nJjElnOF8BLO00zICAgJ5993RLF++gsOHDxMbexUzkSnhmL5/yZkzd5Jj2ZIyaNAAOnTogBmLWgEz\n8/8XsmXL7kgXGeF2D9o1ly3XXLYbHTp0YPTo97l2rQk227tAXmA6Wn/P4MFfJyp/x44d2O1DuR3k\nAgRisVTir79c7ziWNqZPn07Xrl1p1aoVb731Fn5+flitVkaOHOn2xgxx4y3vhll3VzF+/Pgkl47K\nlCnTXZU9YcIEQkNDWb58OT/99BO9evVi7NixbNq0CX9/f/bv30+DBg14/PHH+eyzzyhQoACZMmVi\n6dKlTJw4Ebvd7lReUldTkkrXKZiMllyeuDp06tQpyRUO4nppy5Urxz///MPKlSv58ccfWbBgARMm\nTOCDDz5g0KBBydYlvUkgKoR4JNlsNubPn4/NVoXbSyhZgTpYLH/x7bffMnz4cADWrVvHwIFmkoDW\nscAtIDOXL1/ihx9WAZmABZgZ+H7AXuLWIjULgZgPCKs1CJstM+Zi0yzgOUzw+jdKbad//9GpXoqq\nffv2XLhwgREjRnH9uunVKF68DF9//RW5crku9yTSS+XKlcmfP4izZ99G66WYIRs3UGo4uXPno3r1\n6k75c+fOzc8/r6Fdu47s3WvWhM2WzZfRoz9NtKQSQP78gRw6tMsl9Sawn4CA6onyp4XFixdTpkwZ\n5s+f75Q+cODAFJdRqFAhDh1yvaPZ7R7QOMHBwWityZEjB3Xq1Lljma6TnFKifPnylC9fnmHDhvHr\nr79Sp04dpk+fztChQ1m6dCmxsbH88MMP+Pn5xe/z/fffp/o4KXHr1i1Onjzp1Cv6zz//AKa93AkI\nCMDb2xutdbLtA5AlSxZefvllXn75ZWJiYnj22WcZOXKk48t26tsvLcmleSHEI+PWrVvMnTuXLl26\n0LdvX6KjbwGudxT2QCmv+EtzWmvatWsP+AKFMAFFF2Aw0AfTq6mArMBCYDKwATNOdBBQmdOnTwEt\nsNk6A22BHph/r0uAz/H03Mibb/a9696Ifv36cfbsaf744w927tzJnj27qVChwl2VJe4PDw8PZs+e\ngafn71itBYEmWK0F8fBY60hPvDJChQoV+Pvvv9i9ezcbN27k7NlTvPHGG27L7927G0rNBz7HBKBn\ngE5ofYVOnTql4ZndZrVaE/XUrV+/3u340KQ0bNiQI0eOsGbNmvi0qKioREsjPf300wQFBTF27Fi3\nK1qEh4fH/54li/mbvnz5crLHv3r1aqIezXLlygHEj/2MW+s0Yb6IiAi3yyLdL59//nn871prJk6c\niLe3N7Vq1XKb39PTk2bNmjFv3rz4oDWhhO3jOobY09OTkiVLYrPZiImJuT8ncB9Jj6gQ4pFw+fJl\natasza5df+HhEUDcepxKrUfrJ7j97+4gsbGX43sV/vrrL86ePQ20wASOz3N7cpAv0Axzz/iSwEmg\nMVCeuGWg4AZmhTovYAZwDjNBKR+5c0exYMF8Hn/8cXLnzn1P55clSxaqVq16T2WI+6t+/frs3fs3\nU6ZM4cCBAxQt2p5u3bo5DflwpZSibNmyyZbdu3dv9uzZy7Rp/4dSfdDaRubMPsycOfeO5d9PTZs2\npWfPnvHr1x46dIipU6dSunTpRMFdUnr16sXkyZNp0aIFffr0IU+ePMyZMyd+/GRc75yHhwfTpk2j\nWbNmlCtXjvbt2xMQEMDJkydZu3YtgYGBfPvttwCEhISgtWbQoEG8+OKLeHp60rx5c7eX7letWsXA\ngQN56aWXKFasGLdu3WL27Nl4eXnFL7PVqFEjhg4dSuPGjenSpQuXL19m6tSpBAYGOgV490vWrFlZ\nuHAhFy5cICQkhBUrVvDzzz8zevToRJO9Evr444/5448/ePLJJwkNDaVUqVKEh4ezbds2/vzzT06d\nOgVAzZo1CQ4O5umnn8bf35/du3czZcoUWrRocdfDG9KSBKJCiEfCO++8w549B4BQYmMDMXc8+g2t\nf8Ni+QK7/XHgChbLTmrVqkf9+vUBEiyEHbcGqOvl7rjnPzt+ZuJ2EAqmtyoGc5+OQpg7LJ0FdnP9\nuneKLqOJh1dwcDBjx4697+VarVamTp1Cv35v8ssvv5AlSxaee+65VI8xTomkLtV269aN8PBwpk+f\nzqpVqyhTpgwLFy5kxowZ7Nq1K0Vl5MiRg99++43evXvzySefkC1bNjp37kzZsmVp27YtmTPfXue3\nQYMGbNy4kdGjRzNhwgQiIyPJnz8/zzzzDN27d4/PV61aNd5++22mT5/OihUr0Fpz5swZ/P39Ex0/\nJCSEevXqsXTpUs6cOUOWLFmoUKECa9asiR9TWbZsWRYuXMjw4cPp168fgYGB9O3bFy8vL3r27Jno\nPN2d653SXXl5ebF69Wq6d+/Ot99+i6+vL++9916iNURdywwICGDr1q2MGjWKRYsWce7cOfz8/Chb\ntqzTgvg9evRg/vz5jBs3juvXrxMUFMTAgQPj1yp90KiUDKJNL0qpisD27du3y73mhRCpkjNnbi5f\nLgk0SJBqw2r9lKCg3Fy4EIGvry+dO7/G4MGD4ydWnDt3jsDAAths1YFNmPvFN0lQxm7MzOViwEFM\nwNoBM9kkEpgJXARKAS25PblkE/AjBw8eTDRDWDz44u6lLZ9HaePDDz/krbfeIjw8PNHtbh9lbdq0\nYd26dXe8E9SDILn3f4J7zYdorVM+VsMN6REVQjwSoqIiSTwe1IpSPjRq1IjJkye73S9v3rx0796N\nSZMmo3UBYAumh7M4Zlzen47fW2OxfE6mTDe4eXMy5vL7dcxY0Lj7zSfs/QgBfuSzzz4jKioKu93O\nc889R7NmzVI9YUmIh9mtW7ecVhGIiopi2rRplCtX7j8VhAr3JBAVQjwS6tSpw5o1O7DZnsIsqwRw\nnNjYc8leHv/000/JmTMnH330MbduKWAn5l7znpglk+oBFuz2Ajz+eBbat2/nWJDaD7Mk1CWc7xFP\n/PPPP/8cD4+8gIVZs2bRoEEjVqxYdl/Gap0+bSYwZc+enTp16jyQ47+EePbZZylevDiPP/44ERER\nfPXVVxw9epTFixdndNXEA0ACUSHEI2H06FH8/HM1YDo2W1nMept/ERJS2e29vxPy8PDgiSee4Nat\nm5iJSFeAq5jZ73HBXSxKHSUs7Ca7du3E9IRGAaUxyzn9grnzUnbMQverMT2kzYiNjbsLy0HWrJnH\ntGnTkry3eErY7Xb69+/PZ5+Nx243d2ry8/Pn22/nyZhU8cBp3LgxM2fOZO7cudjtdsqWLcuSJUto\n1qxZRlctQzxoyydlNBkjKoR4qEVHRzN//nxWrVrFlStXOH/+Anv37iNLliyULFkcb29v8ubNS8eO\nHalbt26S5YSEVOKvvy5ht7fDzI7/EjNTvirm0vt64DCmh/QEJtjshhkzet6RPxozdvQiZi1SMMFp\nI+IWw1dqPpUr5+TPPzfe9TlPmDCB119/A6hD3CL3FssavLzOcPjwIfLnz3/XZQtDxoiK/7L0HCMq\n64gKIR5aUVFR1KpVmw4dOrBw4QZ++ukvtm/fRu3atVBKsXHjFtasOcr8+WuoV68eb7/9dpJl/f33\nbuz2ophezCDMxKMTwHRMkHkEaIpZ3ukyZgxo3Ex7f+B1TO/pGcxSTg2B5piAdDbm8j1onZnr110v\n46fOp5+Ox0yqqo5Z3zQfdntLbt2KZfbs2fdUthBCpCcJRIUQD63PPvuMzZu3Ap2w2Tpjs3UDWvLD\nD98TERGJ3d4baEtsbHegNqNHj2bv3r1uywoIKIAJIm9iJiwdB57i9r/JYEzwiSPN5lJCZpTKhFJe\nwP8BzwCPA6858m8BrmG1HqBx4wbci+PHj+F8+1AAbyyWPPz7r+t96YUQ4sElgagQ4qH1zTfzsdtL\nYi5/xykLBGC3Z8GM1wTTy1kVq9XbaYKE3W5nypQpVKxYiQsXzgG7gE8w93X/G7N2aNzC3ZEJjlEK\n2I7pGY0ThtZX0bowtydLAWTGBLF7sVqnkTt3Dvr27XtP512iREmUcg04r2GznaV06dL3VLYQQqQn\nCUSFEA8tcytALzdbMgOu498tKOURf1s/gNDQULp378GOHVeJjAzG/Ev0wtyT/ia3L70r4DQQ5ii3\njuPnBGAeSk0DVhIQEIhSt3CmgfNkynSTjh1fYuvWzfc8hnPQoAFovR9Y6ajXP1it8/D1zcGrr756\nT2ULIUR6klnzQoiH1rPPNmbixBnYbDWBbI7U88BRlMqK1tHcnvW+m9jYazRpYhar/+uvvxz3u26K\nuW/8GcyyTdcxM+GfxQS0R4D5QCywHKXWo5QHdnskJmA9gda3yJTJi65dQxkxYgTwB1AZE4T+Dpxn\nxYrVNGhwb5fk47z66quEh4fz9tsjuH59GwAlS5bj66+/Ilcu1ztDiXuxb9++jK6CEOkuPd/3Mmte\nCPHQOnnyJBUrPsmlS1HExpYFYrBa/yYoKIAzZ05js/kQG1scpa6g9X5at27N11/PJSIigpdffplf\nflmPmc1eBnO/+K8x38/7Y4LQOD8DfzBo0AAiIiKYOXMmNltBoK0j/y0slq8pWtSH559vyscff4zF\n4on5/2pj1KhRDBs27L6ff1RUFDt37iR79uyULl1aloW5j44fP06pUqWIirq3iWVCPKx8fHzYt28f\nBQsWTLTtfs6al0BUCPFQO378OB988AFLl67A09OTNm1aMXjwYE6fPs2YMWP57bf1+Pn58fTTT7Fl\ny1a2bduKUh5obcEszxQNHMCMMz2G6Vnt53KUv4ClREZGsmLFClq3bg28ye0xqAD/AN+wb98+rFYr\n33//PRaLheeff57ChQundTOINHD8+HHCw8MzuhpCZAg/Pz+3QSjILT6FECJewYIFmTx5cqJbeObM\nmZM5c8xSRkuXLqVFixYoVQgojNZnge6AryP3v5gllnJh1gA9gVnCCczl9d2ULFkaHx8fYmJiHOmu\n/z7NBKWYmBhKlixJnz597udpigxQsGDBJD+IhRD3h0xWEkI80rTWDB48FAjGbm+PuWtSeW4HoQCP\nYZZDuoiZmDQXM7ZzN/ANcJh33x0FQL169bBaPTD3oI9jBzYRGBgks9aFECIVJBAVQjzSLl68yIED\n+9D6cW7/y3M3JMks05QpUybq1KmKp+fvwGKKFbOwYMECXnzxRQDy5cvH228PB37HYpkNrMZqnYpS\nBxk//lOsVmvan5QQQjwi5NK8EOKR5u3tjdXqgc12zZEStwbo05gJSmDGd56hS5cujBkzhly5cnHr\n1i2ioqLw9fVNNAlo+PDhlC5dmgkTPufo0eM88URlBgzoT7Vq1dLtvIQQ4lEggagQ4pHm4+PDiy+2\nYPHiH7HZgjH3jt8PTAKKYbHEYLcfoUmTZ5k8eTIeHubfopeXF15e7tYoBaUULVu2pGXLlqmuz9mz\nZ4mNjSUwMFBmuQsh/vPk0rwQ4pH32WefUaRIfmAynp5zsFiiUEpTvDjUrRvMjBnTWbr0u/ggNC3s\n2LGDp59+hvz58xMUFESZMuVYt25dmh1PCCEeBtIjKoR45OXLl49du/5i4cKFbNmyhTx58vDqq6/y\n2GOPpcvxT5w4Qc2atYmK8gFaAB7s37+FRo0as3nzJlmuTgjxnyWBqBDiPyFz5sy8+uqrqb4F5uHD\nh1m3bh0+Pj40bdoUX1/f5HdyMWnSJKKiYrDZ2gPeAGhdHPiCsWPHMn/+fLf7aa1ZuHAhn38+kaNH\nj1Ox4hMMGNCfqlWrproOQgjxIJJL80II4YbdbqdXr14ULVqUbt268+qrrxIQEJhk0HgnW7duc9yJ\nyTtBqgexsUXZsmVbkvuNHDmSl19+mQ0bTnLiRAArV26ievUafPfdd6k/ISGEeABJICqEEG58+OGH\nTJo0CWgIDAX6ceNGMG3btuPAgQMA/P3338yePZsff/yR2NjYJMsKDAzAwyMC12WjLJYLBAYGuN3n\n9OnTvPvue0ANx/qnDbDZugJFef31PthstvtwlkIIkbEkEBVCCBcXL17knXdGAaWBZzB3TcoGNEMp\nb7744guaNWtOuXLl6NixI40bN6Zw4SLs2LHDbXmhoaHExl4AVgM3gRjgD+z2w3Tv3s3tPmvXrsVm\ni3UcP44FrZ/m5Mnj7N+//76drxBCZBQZIyqEEC6mTJlCbGwMkM9liwd2ey6+//57Dh8+BjTHBKsX\nOHv2exo2bMSxY0fx9vZ22qtatWqMGzeOAQMGYrdvARRa2+jXrx+vvPKK2zp4eno6fnPtaY1x2S5E\n2jh9+jSLFy8mKiqKOnXqUKlSpYyukngESSAqhBAu1q37GciMWei+GrcvHl1D61McOaKw26sBjzvS\nA7DZmnPhwucsW7aM1q1bJyqzb9++vPzyyyxfvpyYmBiaNGlCcHBwknVo3LgxmTN7c/PmOuB5Rx1u\nYbH8QYkSZShWrNj9O2EhXEybNo2ePXqA1ngoxWCbjZdatuTrb76RL0Hivkq3QFQpNQR4D/hUa/1m\neh1XCCFSK1u2rFgsPtjtJ4EFQCUgCvgNq9WCzRYDuI7tzI3V6s3x48eTLDcgIIDu3bunqA6+vr5M\nmjSRzp07Y7UeJzY2D1brCTJlUsyY8a0shi/SzN9//023bt2oqDX1gUzAbmDJ4sWMGzeOQYMGZXAN\nxaMkXcaIKqUqAaHAzvQ4nhBC3It27dpht4cDIcAZ4CtgMXCRN9/sg69vLuCQy14nsNluULZs2ftW\nj9dee42tW7fy2msv0rBhYd58sxd79/7NM888k/zOQtylWbNmkc1qpQnmuoAF0/dfVmumffFFxlZO\nPHLSvEdUKZUVmAt0AYan9fGEEOJeNW/enNdee42ZM2diteYCcmGzXaRJk8a8++67+Pr68tZbwzCT\nmMwYUQ+PXyhWrAwNGza8r3UJCQlh6tSp97VMIe7k/Pnz+GqN1SXdD/g3PDwjqiQeYenRIzoRWKG1\n/hB6NVYAACAASURBVDkdjiWEEPfMYrEwY8YMfv75Z3r0eIXQ0FasXLmSFSuWkylTJgYPHszw4cPw\n9v4LmAYspVatyqxd+xNWq+vHtxAPl0qVKnHKbudSgjQ7cMBq5amnnsqoaolHlNJaJ5/rbgtXqjUw\nBHhSax2jlPoF2JHUGFGlVEVg+/bt2+WWd0KIB961a9f4559/8Pf3JygoKKOrI8R9cfXqVcqUKkXk\nuXM8Y7PhA4QpxVFg3c8/U6tWrYytoMhwYWFhhISEAIRorcPupaw0uzSvlCoAfArU11rHpGbfvn37\nkiNHDqe0Nm3a0KZNm/tYQyGEuDfZsmWL+2csxCMje/bs/L5hA//Xuzff//ADWmvKlCzJ8o8+kiD0\nP2jevHnMm/f/7N15mM11/8fx5/ecM5hhLCHGLtkSMaOQNWVIkaUwRBsmP9yV0t1dCuXu7m67k7YZ\nJUSTSkRZi7FkbSZRiIQmTrYYhhnmnPP9/XFmppljxqxnziyvx3W5OJ/z/X4+7+m+LvfbZ3l/ojK0\nxcfHF1j/XpsRNQzjLuALwAmkHu+04r5axAmUNT0G14yoiIhI0XH27FmSkpKoXr26KjVImmIxIwp8\nA7T0aJsN7AFe8kxCRURKC6fTycGDBwkICKBWrcyv+BQpCipWrEjFihV9HYaUYF47rGSa5nnTNHen\n/wWcB06ZprnHW+OKiBRln376KQ0bNqJx48bUrl2bTp06s2eP/koUkdKpsO+a1yyoiJRaK1euZPDg\nwcTF+QPDgAFs2bKPLl268ddff/k6PBGRQleoiahpmt11q5KIlFb//veLWCz1gEFAY6AVTue9/PXX\nX3z44Yc+jk5EpPAV9oyoiEip9cMPO3C5GpPxr96KGEZtduzY4auwRER8RomoiEghcR9MOu7Rmgyc\n1KElESmVlIiKiBSSsWPHAD8BW3EnoOeAJZhmEg8++KBPYxMR8QWv3zUvIiJuY8eOZdeuXbz//vsY\nxkpM00XZsuX48MOPaNq0qa/DExEpdEpERUQKidVqZebMmTz++OOsXbuWgIAA+vbtS5UqVXwdmoiI\nTygRFREpZM2aNaNZs2a+DkNExOe0R1REREREfEKJqIiIiIj4hBJREREREfEJJaIiIiIi4hNKREVE\nRETEJ5SIioiIiIhPKBEVEREREZ9QIioiIiIiPqFEVERERER8QomoiIiIiPiEElERERER8QndNS8i\nIlKCXbp0ic8//5zVq1fj7+/P4MGD6dKlC4Zh+Do0Ec2IioiIlFQJCQl06dyZYcOGsXrePD6bOZNu\n3brxyCOPYJqmr8PzifPnz/PUU08RVKMG5f39ub1XL7Zs2eLrsEotzYiKiIiUUP/973/5ISaGh4C6\nDgcmsBWYMWMG/fr1o3v37j6OsHA5nU569ezJts2bae1yURH48Ztv6PLtt6yNjqZjx46+DrHU0Yyo\niIhICTV/7lxaOp3UTflsAO2A6jYbUVFRPozMN7766is2fvcdYS4XvYFOwENOJ9VdLiY984yvwyuV\nNCMqIiJSQp0/f546Hm0G4G+anD9/3hch+dTatWup5udHw+TktDYbcIPLxcoNG3C5XFgsBT9Hd/78\neb788ktOnTpFu3btuPHGG7VHN4USURERkRKqR8+eLFuwgI5OJ+VS2uxAnNPJrbfe6svQfCIwMJBE\n08RBxgToPFDe398ryeGaNWsY2L8/Z86exWYYOEyTXqGhLFy0iICAgAIfr7jR0ryIiEgJNenZZ3H6\n+xNptbIWWA7MtVppef31DB061NfhFbohQ4Zw3uFgLeBMabMDMVYrw4YPL/BE9PTp0/Tr25eqCQk8\nAjxtmgwC1n77LU899VSBjlVcKREVEREpoZo1a8aWbdvocffd/FixInE1a/J/jz7Kug0b8Pf393V4\nha5Fixa8/PLLfAdMt9mItNmIABo2bcq0adMKfLwFCxZw4cIF+rlcVMGddF0HtHM6mfX++ySn2yJQ\nWmlpXkRExEcuXbrEvHnzWLRoEaZp0rdvX0aMGEG5cuWyfzmHmjdvzieffFJg/RV3EydOpEePHsyb\nN48zZ87QpUsXBg0aVKD/zVPZ7XYqWK0EOhwZ2q8GzicmkpCQQJUqVQp83OJEiaiIiIgPXLx4kdt7\n9SI6OpoGFguGabLs66+ZM3s2q7/5RvsHvah169a0bt3a6+O0adOGeIeDPyDDobFfgHp16lC5cmWv\nx1DUaWleRETEBz788EPWrVvHCOA+l4sRpsmDwNYtW4iIiPB1eFIA7rzzTlo0b86nVivbgV+BxcAu\nYNJzz+nkPEpERUREfOLzzz7jGqBhura6QBPT5FMtpZcINpuNb9asofudd7LcMJgHHK1WjRkzZjBy\n5Ehfh1ckaGleRETEB5IvXcKWyTWbNsDhsadQiq+aNWuyaPFiTp06xZkzZ6hXrx5+fn6+DqvI0Iyo\niIiID9zZty+/WiwcT9d2CvjFYqHPXXf5KizxkqpVq9KoUSMloR6UiIqIiPhAeHg4TZs25QOrlcXA\nl8BMq5X6DRsybtw4X4cnUii8mogahvGwYRg/GoYRn/Jrk2EYvbw5poiISHFQsWJFNm7axD+feYbk\nZs242LQpE/75TzZv3cpVV13l6/BECoW3Z0TjgH8CISm/1gBfGobR3MvjioiIFHmVK1dm6tSp/LRn\nDz/v3cu///1vqlat6uuw8s3pdPLaa6/RsH59/Gw2bmjZkqioKF+HJUWQVw8rmab5tUfTJMMwxgDt\ngT3eHFtERER8Y/z48US89x4tTZPmwIGff2bo0KH89ddfjB071tfhSRFSaHtEDcOwGIYxBAgANhfW\nuCIiIlJ4Dh06xHvvvUcP06Q/0A4Yapq0AZ6bNImLFy/6OEIpSryeiBqGcb1hGOeAi8A7QH/TNPd6\ne1wREREpfBs2bMBMSTzTCwb+OnOGn3/+2RdhSRFVGHVE9wI3AJWBgcBcwzC6XCkZfeyxx6hUqVKG\ntrCwMMLCwrwaqIiIiORPxYoVAUgA0t/efs7jeykeoqKiLtvfGx8fX2D9G2YmxXS9yTCM1cCvpmmO\nyeS7YCAmJiaG4ODgQo1LRERE8i8pKYnatWpR9cwZBpom5YB44GOrlXqtW7Pt++99HaLkU2xsLCEh\nIQAhpmnG5qcvX9QRtQBlfTCuiIiIeFm5cuX4OCqKuDJleN1iIdLPjzcNA7NKFWbPnevr8KSI8erS\nvGEY/waW4y7jFAgMA7oCod4cV0RERHynZ8+eHPjtN+bMmcPhw4e5/vrrGT58+GXb7kS8vUe0BjAX\nCMI9M78TCDVNc42XxxUREREf8vf3p2HDhtSpU4cePXooCZVMebuO6Ehv9i8iIiJFzwcffMC4sWNJ\nSinV5GezMXnKFJ555hkfRyZFTWGcmhcREZFSYuvWrYwaNYrWpkl3wApscjiYNGkS1113Hf379/d1\niFKE+OKwkoiIiJRQkZGRVLVa6YP7cEgAcBtQ32rl7bfe8m1wUuRoRlRERKSU+/PPP5k/fz5Hjx4l\nJCSEgQMHUrZs3grcHD50iOoOx2UzXUFOJ78fOpTvWKVk0YyoiIhIKfbVV1/RoH59/vXkk8ydMYNh\nw4bRskULjhw5kqf+Wt1wA3E2G5fStTmB32w2WrVuXSAxlyamafLWW2/RuFEjbFYrzZs25YMPPqCw\n68B7ixJRERGRUurs2bOEDR5Mw+RkJrhcjEtOZgxw4vBhxjz8cJ76HDt2LC4/P+ZbLOwDfgMWGAan\nXC6emDixIMMvFZ5++mnGjx+P/2+/0dPlwrJ/PyNHjuQ///mPr0MrEEpERURESqnFixeTcOECvU0T\n/5S2GkAnh4Ovvv6aU6dO5brPRo0asWLlSvyvvZaPcddwvFinDgu/+IL27dsXYPSFa9euXYwaNYoO\n7doRFhbGhg0bvD7m8ePHee3VV+mG+470m4BBpkkH4MVp0zh79qzXY/A2JaIiIiKl1OnTp/GzWKjg\n0V4J95JwXhOdzp07s3vvXvbs2cOuXbs4cPAgd911V77j9ZWVK1cSEhzMwtmzubBtG2s//5wuXboQ\nGRnp1XG3bNlCssNBG4/2NsD5xERiYmK8On5hUCIqIiJSSnXs2JFkl4uf07WZwI9A7aAg6tWrl+e+\nDcOgWbNmXH/99Vit1vyG6jNOp5PwUaOo53Qy1uFgABDucBAMPPrII8THx3tt7MDAQADOebSnfi4J\nlwQoERURESml2rZtS98+fVhisbACiAWiDIOfgOenTSvWCWRB2bVrF4fj4uhkmmmlhiy47ytPTEpi\n9erVXhu7c+fO1KlVi28sFs6ntJ0D1litNG3cmDZtPOdKix8loiIiIqXYgk8/5fEnn+SXSpVYAlib\nNOHjjz/mwQcf9HVoXrF9+3Yefvhh+vfvz7Rp0zh+/PgVn089nW54tBse33uDzWYjasECTvn784bF\nQqSfH28YBhcCA5kfFYVheEZV/BhF6fi/YRjBQExMTAzBwcG+DkdERKTUME2T5ORkypQp4+tQ2Ldv\nH3PmzOHYsWPceOONDBs2jAoVPHey5t5bb73F+PHjucpmo4rDQZzFQsXKlVm/cSPNmzfP9B2n00mD\nevXwt9sZYppYcW9fWAbsKluWo3Y7VapUyXdsV3L8+HHmzp3LgQMHaNq0KSNGjOCqq67y6phXEhsb\nS0hICECIaZqx+elLiaiIiIgUGXPnzuXBBx6gnGFQxTA46nRSt04d1m3YQP369fPc79GjR6lfrx7B\nTie9cC8JJwBzrFau79yZNWvXZvnu0qVLGdC/P5UNg/oOB3arlaNOJ2+++Sbjx4/Pc0zFVUEmolqa\nFxERkVxbu3Ytffv04dqGDekVGsry5cvz3eexY8cYNXIk17tcPOp0MtLhYKxpcvboUcaPG5evvhct\nWoTpctGdv5OfCkAHp5O10dFXLFXVp08ftmzdSq/Bg3Fcfz033Xknq1evLpVJaEHTFZ8iIiKSK3Pn\nzuW+++6jltVKPaeT3XFx9F69mhkzZjAuHwnjwoULcToc9AT8Utqq4k4Wv162jPj4+DyfFL948SJW\nw8DmsRKcepHppUuXLn8pnZCQED6aNy9PY0vWNCMqIiIiOZaUlMRjjzxCS2BkyjL3g04nbYF/Pvlk\nvoqsJyQk4GexUM6jvQLgcrlITEzMc989e/bkkstF+nVkJ/C9YXD9dddRs2bNPPcteadEVERERHLs\n+++/568zZ+jA30mEAdwMXEhMZN26dXnu+5ZbbiHJ6eSndG0uINYwaNq4MTVq1Mhz3y1atGDUqFEs\nx33l6LdApNVKnMXCa//7X4k4gV4cKREVERGRHLPZ3Lv6HB7tqZ/9/PzIqxtvvJGBAwbwpcXCEmAT\nMNti4QDw0ssv5ztZfO+993j3vffwv+EGDtSoQfs77mDDxo2Ehobmq1/JO52aFxERkRxzOBzuckZ/\n/slg08QP9xL3QsBeqRJH//yTcuU8F9dz7tKlS7z88svMjIjg+IkTtG3blmefe07JYhFSkKfmdVhJ\nREREcsxms/H+rFn07dOHGUBthwO7zcY5l4uomTPzlYQClClThkmTJjFp0qSCCViKNCWiIiIikiu9\nevXix507eeedd9i7Zw9drr2WMWPGcMMNN/g6NClmlIiKiIhIrjVv3pwZM2b4Ogwp5nRYSURERCSd\nvXv3MnbsWG5u354hQ4YQHR3t65BKLCWiIiIiIinWrVtHm9atmRcZScLWrUR//jm33HILb775pq9D\nK5GUiIqIiIgApmny8OjR1EhOZrzDwUDgYaeTm4AnnniCEydO+DrEEkeJqIiIiBQ58fHxTJ48meub\nN6d5kyY89dRTXk8Ef/31V/bu20dHlyvtilED6AokJyezfPlyr45fGumwkoiIiBQp586do9PNN7P/\nl19o7nRSFnjz1Vf5bMECtm7fTrVq1bwyrsvlAtzJZ3qps3ZFqfZ6SaEZURERESlSZs6cyZ49e3jQ\n6aQf0BcY7XRyNC6O6dOne23cxo0bc+0117DZMDLcHLUR8LPZ6Nmzp9fGLq2UiIqIiEiRsuzrr2lk\nmqS/Wb4K0NTp5Ksvv/TauBaLhbfeeYc4q5V3bTaWAu9bLHwHPDRyZL7uupfMKREVERERr3I6nWzc\nuJFVq1YRHx+f7fM2mw1nJvfKOwC/MmW8EOHfevbsybbt27l1wAB+8ffnj5Tl+vfee4/g1q2Ji4vz\n6viljRJRERER8Zro6Gga1KtH586d6dmzJ7Vq1uTll1++4jt333MPv5kmB9K1/QH8YrFwz+DBXo0X\noHXr1iScPQuXLjEEeBoYARzevZv+d92FaZr89ttvPP7449zavTv3338/mzZt8npcJZFRlDbeGoYR\nDMTExMQQHBzs63BEREQkH/744w+aNmlCjYsX6e5y4Q98D2wBPv74Y8LCwjJ979KlS/S54w5WffMN\n9S0WrKbJQdOkXbt2fLtmDQEBAV6N++DBg1xzzTX0A1qna98PzAfef/99Hhk/Hi5dor7TyQmbjRMO\nB++88w5jxozxamxFQWxsLCEhIQAhpmnG5qcvzYiKiIiIV7z//vu4Ll1isMtFXaAa0AtobBi8/uqr\nWb5XpkwZvlq2jDlz5tD6zju57o47iIiMZG10tFeTUNM02bx5M1FRUQDU9vg+9fMLU6dy1cWL/MPp\nZBAwxuGgLfDoI49w8uRJr8VXEnm1fJNhGP8C+gPNgERgE/BP0zT3eXNcERER8b1ff/2Vq10uynm0\n1zNNvt+//4rv+vn5MWLECEaMGOG9ANPZvXs3A/v3Z+++v1OUL4EHAGvK59StAofj4hgMlE35bAFu\nAb5PTmbZsmWFFnNJ4O0Z0c7ADKAdcBvgB6wyDMPfy+OKiIiIj5UtWxa7aZLk0X4IvL68nhtJSUmE\n3nYbZw4c4D7gcdwzt0eBeYAd2A4st1rp0rkzcPlMXmqy6nA4kJzz6oyoaZq90382DON+4DgQgrss\nl4iISKl06dIlDh48SJUqVbj66qt9HY5XOBwOnMAnwK1AAO6E7gBw1aVLvgwtg8WLF3PEbmcsUD2l\nrT1wDvdSbgRgMQwG9u9PRGQkN4aEsPXQIa4xzbQEdDNgtViyrDUaGxvLBx98wLFjxwgJCWHkyJFU\nr14902dLk8LeI1oZMIG/CnlcERGRIsE0Td5++23q1KpFs2bNqFmzJnf07s2RI0d8HZpXXGWxcBr4\nAPcSaQxwDeByOnP0/g8//EBYWBiNGjSgY4cOzJ49u8BvOPrtt9+oYLPhmRbWxZ20fPXVV/xx5Aif\nfvYZVapU4c233uKQxcJ7NhvLgdkWC+uAZyZNonZtz52l7tJPISEhREVG8sMXXzB50iRaNG/O3r17\nC/TnKI4KLRE1DMMA3gA2mqa5u7DGFRERKUpmzZrFuHHjqHPqFCOAO02TDStXEtymDR9++CGnT5/2\ndYgFplu3bpxyuRiEe6/lvcB44KzVSvfbbsv2/XXr1tG+XTu++fxzrj58mBPbtvHAAw8wbty4Ao2z\nSZMmJDgc/OnRfgioXKkSoaGhBAUFpbX37t2bTZs3c8uAAfx1zTVc07Urn3/+OVOmTLmsb7vdzvhx\n42gLjHc4GGGa/MPlgjNn+L9ScMI+O4VWvskwjHeBnkBH0zTtWTwTDMR06dKFSpUqZfguLCwsyzIP\nIiIixYFpmjRq2JDyhw9zN+ACVgDb0j3jX64cM99/n2HDhvkmyAKUlJREuxtvZP+ePbRxOvEHdlmt\nJJQpw+YtW2jVqlWW75qmSXDr1vz100+McLnS9hJuwf3f7Oeff+a6664rkDgvXbpE08aNSThyhB5O\nJ1WB3UC0YfDsc89lmmDm1Lvvvsv4sWN5wjRJf0AmFlgCnDx5kqpVq+Yrfm+KiopKqyKQKj4+nvXr\n10MBlG/y6h7RVIZhvAX0BjpnlYSm97///U91REVEpMRJSEjg4OHDDEj5HIM7CQ0FbgSSgNVJSdw3\nYgRt2rQpsETLV8qVK0f0+vVMmTKFj+fN48KFC9zWowdTpk69YhIKcOLECXbs3MlAMiYrbYE1FgvL\nly8vsP8+ZcqU4Zs1axh8zz1E/fAD4L5bfvzYsTz77LP56jspKQmLYeDnMfFXLt33RVlmE4Hp6ojm\nm9eX5lOS0LuAW0zT/N3b44mIiBRVAQEBVAoM5FjK5xigOXAz7rIygUBfoLzFwgcffOCjKAtWlSpV\nmD59OidOneJ8YiJfLllCmzZtsn3PZnOnn55n0J2AyzTx8/Mr0DgbNWrE9pgYdu3axbfffsuRo0d5\n4403sFrdx5FM02Tr1q0sWrSIgwcP5rjf0NBQkl0ufvD4Gb43DJo3bUqtWrWy7cM0TX766Sc2btzI\nuXPncvmTFW1eTUQNw3gHGAYMBc4bhlEj5ZdnSTEREZEC9/PPPzNy5EjatmnDXX37smLFCp/GY7Va\nGRUezjaLhZ3AWaCmxzM2oKrLVWIPL+XUVVddRbcuXdhitXIhpc0E1gEuw6Bfv34FPqZhGFx//fV0\n7949w4n2X3/9lRtatqR9+/YMGDCARo0aMTQsLEezmS1atGDkyJEsAz41DNYA71utHDYMXvvf/3Af\nocnaTz/9RJsbbqBly5Z07tyZoBo1ePHFFwv8wJaveHtG9GGgIhCNuxxX6q9BXh5XRERKuejoaEKC\ng1k4Zw6uHTuIWbaM22+/nZdeesmncb3wwgv0vvNOvsB908te3HtFUyXgvlf9hhtu8EV4RcqMt9/m\nUmAgb1qtRAHv2Gzum3H++U8qVKhQKDE4HA56hYby5969DAeeAO4wTRZ++ikTJkzIUR8RERG8/c47\nlGnZkn3VqxPSqxfr1q/n9ttvv+J7Z8+e5dZbbuHY7t0MxZ1UtUpM5JlnniEyMjK/P1qRoLvmRUSk\nxDFNkxbNm3Nh/37udblIXcRdDWy1Wvk9Li7DKWhf2LFjB7NmzeKtGTNoinvvYyLwndXKpYoV2fvL\nL6ozCRw9epR3332X7du3c+HCBfb98gvHjh/HMAzu6N2bd959l7p163pt/K+//po777yT0UD6RfR1\nwOayZTl+4gSBgYFeGTsiIoL/GzOGf5gmldO1f24YJDZowK+//eaVcbOju+ZFRESu4ODBg+z55Rc6\npEtCwX3dn8PpZNmyZb4KLU3r1q158803WfDpp1yoU4d5wEKgQdu2RK9bpyQ0Ra1atXjhhRcIDw9n\nw4YNXHX8OGG4ZyW/W7mSrp07k5iY6LXxDxw4gM0w8PxnSz0g6eJF/vzTs+hTwdmzZw/VbbYMSShA\nQ9PkwMGDOHNYi7UoUyIqIiIlTlb77orOGuDf7rnnHn47dIhffvmF33//nc1bttCyZUtfh1XkvDB1\nKo0Mg0GQNoM81OHg0OHDLFiwwGvjNm3aFIdpEufRfhAIKFcuR4eN8qphw4accjpJ8Gj/A6hTq1ba\nQariTImoiIiUOA0aNOC6Zs3YbLGQnNJmAhsAm9VK7969r/B24bNarTRp0sSrS8zFmWma7Ni5k2am\nSfp/YlQHrvbzIyYmxmtj33bbbVzXrBmLrFZ+Bk4C3wHfGQYP/9//Ub58ea+Nfe+99xIQEMBnFgtH\ncO8f3gj8aBj849FHvTZuYVIiKiIiJY5hGLzz3nv8abPxts3GYmCm1com4N8vvujz/aGl2e+//86C\nBQtYuXIlycnJ2b+A+3/P6lWrctyj/SIQ73JRs6Zn7YGCY7VaWbFqFdfddBOfAW8Ba61WRo4ezX/+\n8x+vjQtQtWpVlq1YgaNGDWYCrwJrLRbGjhvH448/7tWxC0uhFLQXKWx2+zkiImIIDw8hKMg7m8hF\npGjr2rUrP+zYwRtvvMEPMTG0q1uXh8eMITQ01NehlUpOp5Px48cT8d57uFIOSte8+mo+W7iQTp06\nZflecnIySUlJjH74YV568UXqulxcD1wAlhsGTouFESNGeDX2unXrsnHTJvbu3cvRo0dp0aIFNWrU\n8OqYqTp27Mih339n3bp1nD59mg4dOmR6n31xpVPzUiLFxtoJCYkkJmY0wcGa+RAR8bWXXnqJZ55+\nmttMk9ZAPLDSYuGUvz8HDx++7JrL06dPM3HiRObPm0fSxYs0a9KEipUqsW37dspYLCSbJuXKluWj\nefMYOHCgT36m0qogT81rRlRKFLv9HHZ7ArGx7ptkU38PCqqgmVERER968403aG2a3JzyOQC42+Xi\njcRE5s2bxyOPPJL2rNPpJPS229j94490cDqpDPy0fz/bTJOXX34Zm81GpUqV6N+/P1WqVPHFjyMF\nRImolCgRETFMnbou7fOoUUsBmDy5K1OmdPNRVCIipZvD4cB+7Bg3erRXAKparRw6dChD+7Jly/g+\nNpYHgPopba1Mk48Ng7mzZ7Pr55+9H7QUCh1Wknyx288xZUo0dnvRuPs2PDyEmJjRzJzZB4CZM/sQ\nEzOa8PCQtGeKWswiIiWdzWajUcOGeJZfPw2ccDi47rrrMrRv3ryZyjZbWhIKYAAtTJOfdu/m/Pnz\nXo5YCosSUckXuz2BqVPXYbd7VjnzjaCgQIKDg9L2hab+Of2yfFGLWUSkNHjyqaf4CVgO2HFfbfqJ\n1Ur1atUICwvL8GzVqlU573LheZP7aSDA35+yZcsWSszifUpEJU/s9nPExtoz7MWMjbUX+CxjXmcv\ng4IqMHlyV4KC/r6LuLBiFhGRy40aNYqXXnqJ3eXLEwF8AtRu2ZI10dGX3RsfFhaGYbXyNe5rT03c\nBeS3Wa2MuO8+bDbtLCwpdGpe8mTKlOgMezFTFfRezII8/V5YMYuISNYSEhLYtWsXVapUoVmzZlk+\nt2DBAkYMH47pdBJgsRDvcNDupptYuWoVlSpVKsSIxZNOzYvPhYeH0LdvU2Jj7YwatZSZM/ukLIFX\nyP7lHPDG6XdvxywiItmrUKECHTp0yPa5wYMH07VrV6Kiojh16hQdO3akZ8+eWCxazC1JlIhKngQF\nBWZICNPvyywI3jj97u2YRUSKI5fLxdmzZwkMDCxyd5fXrFmTxx57zNdhiBfpnxWSL5ntxSwI1zML\n/wAAIABJREFUnqffX3mlB6NHB9OvX9N8933ixPkMv4uIlEamafL6669Tp1YtqlSpQrWqVZk0aVKO\nr90UKQhKRCVfgoICmTKlW4EXi/c8/R4UFEhkZCwuV0H0bnj8LiJS+kydOpXHH3+cmseOcTfQPD6e\nl158kVEjRxZI/6ZpEhsbyxdffMHu3bsLpM9UBw4c4KGHHqJe7do0a9yY559/XiWdiiklolKkWSww\nenRw2sn2/Jx0Tz01HxcXD0BcXLxOzYtIqXTu3Dle+e9/6QjcBVwP9AR6miZzP/qIgwcP5qv/o0eP\ncnP79oSEhDBw4EBatGhBr549OXPmTL5j379/Pze2bcvCuXOpc/Qo/r/+yrSpUwnt0YNLly7lu38p\nXEpEpUjIqkzT4sW/EBkZy8SJqwH3XtGQkEgiImJyPUZERAwhIZFp+03z05eISHH2008/cSEpies9\n2lvinsncunVrnvs2TZMB/fqxJzaWMGAiMBDY8O23PHD//XnuN9ULzz+Pee4cDzsc9AT6AcNdLjZt\n3szChQvz3b8ULh1WkiIhtch8375NMyzzF8RJd7v9HBERMfTr11Sn5kVEgGrVqgHuAvHpj2z+5fF9\nXsTExLB1+3aGAk1S2loCyU4nXy5Zwu+//069evXy3P/yZcto6XTin66tHlDbamX58uWXFcfPSbyz\nZ8/m5MmT3HTTTTzwwANUrlw5z/FJ7igRlSKtIE66p09y07+rU/MiUlo1btyYDu3bs2b7dqo5nVwN\nnAFWWK3UrVmTbt265bnv335zX+RZ16O9Lu7Z0kOHDuUrES1bpgyeC/AmcMkwKFeuXK76mj59Oo8+\n+iiVbTYqu1x8tmABr7/6Khu++44GDRrkOUbJOSWi4nWpM5Lh4SGXHWryRr3Q7Pq2WPDKSX8RkeLk\no3nzuPWWW3gnLo4qfn7EOxxUqViR5YsW5evmotQi9QeB9DfIHwQshsG1116b475M02T79u0sXboU\ni8VCv379GDx0KO9On06w00kN3EloLO476wcNGpTjvg8dOsSECRNoD4Q6HFhwJ+Nzjh3jsUcfZdHi\nxTnuS/JOiah4XVbL7pDzeqFBQRWYMKE98+fvzHGS6o1apCIiJUWjRo34Zf/+tFPtDRo0YNCgQQQG\n5m8SoFWrVnS/5Ra+Xr+eS04ndYDfgG+tVsIGD6ZWrVo56sflcjFq1ChmzZpFoM2GC3j++ecZPXo0\n1zRpQsTevdQDkiwW/nQ6GTlyJLfeemuO4/z888+xAd35+8BMZaCd08mSpUu5cOECAQEBufnRJQ+U\niIrX2O3n2LnzGG+/vR3IfLYzp3tAg4ICGTasFSEhkQwb1ipHiahuUhIRubKyZcvmek9lTnz2+ecM\nv/deFi9fDrhnQocMGkREZGSO+5g/fz6zZs2iD9DG4cAEtgORkZF8/PHHxMfHs3r1asqXL8+QIUO4\n/fbbMYycl+VLTEzEZhjYgAspff8GJOFOgpOSkpSIFgIlouI1OZmRzMke0Lwu3+smJRER37jqqqv4\netkyDh48yKFDh2jSpAm1a9fOVR8fzppFI4uFkHQFpNsDP1utfBIVxZdLlvDwww/nOcYePXrw3HPP\nsR3YDJwH0m8aeDg8nE8WLMjXlaKmaRIdHc2SJUuwWq306dOHLl265CphLumUiEqBS50JPXDgLx55\npB3Tp7vLgEya1IVOnerSqlWNy9650g1NWSW0Eya057XXemYbj7dufxIRkStr2LAhDRs2zNO7p06e\npFImt5hUdDo5depUfkOjXbt23D1wIJ8vXIg/MBb30jzAT7hnde9fsYLevXvnqf+9e/fSMzSU3+Pi\nKI97Vvi1115j2NChzJk7t8hdp+orqiMqGWRVzzM3IiJi6NVrPvPm7UpLQgGmTVvP5s1/ZDqLeaUb\nmjyv+5w0qTMAoaGNchSPt25/EhER7+nctSv7rVaS0rWdB36zWuncpUu++zcMg4+joqgQEEAwfyeh\nAC2Aq202Fi1alKe+7XY7N7Vty+9xcfQBHgcmmCb9gfkff8xHH32U7/hLCiWikkHqwSK7PSGP75+j\nQ4e6acniiBGtAOjR4xpWrLiX8PCQXPfped1ns2bVAahevXyeYhQRkaJvwoQJWAICmGW1shXYAsyy\nWgmoWJFx48YVyBh+fn6ULVv2sgufDdwJkiuP90q//fbbXDh/nnpASEpfBnADcA0wZ/bsPMdc0igR\nFeDv6y/T78PMy/WX7tnQeUybtgGAuXN3AtCwYWV69myUr1nJgrzuU0RECkdiYiIHDhwgISF3ExzX\nXHMNG777jpAePVhhGKyyWLj59tv5bvPmXO83vZK+/fqx02Yj/f+T7AP+dDjo27dvnvr8bsMGygIV\nM/muIhB/+nSe+i2JlIgKkL/rL9Mv53suo7/ySg9Gjw5mzJi2eYorfd8Fed2niIh4l8Ph4Omnn6ZG\n9epce+21VK9WjTFjxpCYmJjjPlq2bMmy5ctJSkoiKSmJJUuX0rRp0wKNc8qUKQRcdRXvWq0sBj42\nDD4xDO7o3Zs777wzT31WrV4di2GwH0iffl8A9gLdclFmqqRTIirA5fswZ87sQ0zM6Bwtpadfzvdc\nRu/evSEREX1o3TooV/tPU5/dufMYU6euY+3aQ6xc+Svz5w/IU4w5GUszqyJSVFy6dInnn3+eOrVq\nUbZMGTrefDMrV64s9DgOHjzIjh07uHjxYq7ffeKJJ3j5pZdodf48I4CbL15kVmQkI4YPz3VfZcqU\nwc/PL9fv5US9evWI3bGDcY8/Di1bUr19e956+20WLV6c5wNFDzzwAAmmCcBMYAOwEYgAbAEBPPro\nowUVfrGnRFSAy/dhpv75SkvpV1rOz+ykek73n9rt51iz5hBTp65j48Y4AF59dRNbthzhr78S02Lc\nvv1IAd3AlL99sSIiBck0TQYPGsQLU6dS027nluRkjmzdyu23387iQrrtZ9++fdzcoQPXXHMNbdq0\noVbNmrz11ls5fv+vv/7i3XfeoYtp0gP3vsguwO0uF58vXMi+ffu8FXqeBAUF8d///pcfdu5k46ZN\njBkzJl+Jb+/evZk4cSIXcc+IrgG+ASrXq8e277/P1xWnJY3KN0kGuSl1lF2d0NRaobmpA5o6OxkZ\nGQu4T9oD/PDDnwDMnOluHzCgGZGRsYSHt81zIurN60VFRPJq27ZtLP7ySwYCLVPa2rlcRBkGTz35\nJHfddZdX61CeO3eObl264Dh5krtx72ncceYM48ePp3Llytx7773Z9rF7924uJSfTzKO9GfAlsGPH\nDpo0aVLwwRcRhmHw8ssvM3z4cL744gscDgd9+vThpptu8nVoRY5XE1HDMDoDE3EfGgsC+pmmucSb\nY0r+pJY6yomc3lyU06s27fZzhIUtZN26w1mOuXPnMcaPX06TJlcBGe+P/+gj98GoJ564WVeAikix\nFR0dTTmrlRZOZ1qbBWhtmny2fz/Hjx+nRo3L6zEXlKioKI4dP85406RKSls94IJh8J9//ztHiWhQ\nkHvl6jiQPtLjHt+XdC1btqRly5bZP1iKeXtGtDywA5gFLPTyWFLIcnpzUWrCumbNQSZOXM0rr/Sg\ne/eGlyWsdnsC69Ydpn//Ztx8c920Q0mZ2bfvL+Dv5HH06OC0WVRdASoixVlgYCDJLhdJQPoLJs8D\nFosFf39/r46/a9currbZqJKcnKH9WtPkq717MU0z2xnZRo0a0a1rV9Z89x2VHA7qAseAZVYrTRo2\npGPHjt77AaRY8WoiaprmCmAFgKH7rEqs7JbzUxPWPXtOpn1On7B6LpEvWrQ37fsZM25n1aoDLF3q\n3k/05JM30737NcTFxTNq1FJeeaUHQUGBGQ4a7dlzMkfL67oCVESKooEDB/LYo4+yMjmZOwE/4CSw\nyWajT+/eVKyYWVGgglOvXj3+cjpJBNKnvEeB2kFBOd4WMG/+fHqFhjJr927KWCxccrmoX6sWi5cs\nyde1mQDnz58nKiqK7du3U716dUaMGFGil/pLMu0RlXzL6XJ+tWr+GX5P9eqrm3j99S0Z2p59di3B\nwTXp1KkuN99cl6VL9xEcXJOwsOtp3TooLWndsePPy2ZO7733C0aPDs7xbUq6AlREipIaNWrw/gcf\n8MD997PfMKhsGNgdDurXqsWMXBwYyqvhw4cz+bnnWHjxIr1Mk4rAD8AOw2Da+PE57qd27dr8uGsX\nq1evZs+ePTRs2JDevXvn+/T7kSNH6NKpEwcPHSLIZuOMafLSf/7DB7Nmcd999+Wrbyl8SkTF61Jn\nPOPizgIQF3eW2Fh7trOWsbF/snjxL4SHhzB5clfCw0PSnk9NHrMquRQZGUuFCmVyeBd9zvfFiogU\nhuHDh9OhQwfmzJnDsWPHuPHGGxk6dCjly3v/RrmaNWvy5ZIlDBk0iLfOnElrf+jBB5k4cWKu+rJY\nLPTs2ZOePbP/uzin/jF+PKfi4hgLVHM4SAa+BkaNHEnPnj2pWbNmgY0l3meYKXWuvD6QYbjI5rCS\nYRjBQEyXLl2oVKlShu/CwsIICwvzcpSSF3b7OSIiYjIkiulNmRKd4VBQqtRDQamJ6p49J7n33i8A\nLtuvmVX/mb07aVIXpk1bz4oV99Kq1dWXvZtdvCIi4r4RaeXKlcTHx9OxY0euvfZaX4dEQkIClStV\noofLRft07YnAaxYL/5s+vcCu/xS3qKgooqKiMrTFx8ezfv16gBDTNGPz03+RnBH93//+R3BwsK/D\nkBxKrcPZt2/TTBO77A4Fpb4zf/5Ohg1ryfz5uzLs14yNtWfZv+c+Tzf3P67i4uI5efLCZe9mF6+I\niIC/vz/9+vXzdRgZJCYm4nS58NxIVRbwMwzOnj3ri7BKtMwmAmNjYwkJyd9lMqmKZCIqxUNO63Bm\ndigoKKgCr766CXCXW7LbE3j99S3MmzeA+fN3ceLE+cv6vVKdz6CgCrRvX5stW46k3XOfeqI+9d0T\nJ85z8mRihrvqs+pPRESKnmrVqtG8aVN27NvHdaaZdivPHiDR6eSWW27xZXiSB15dmjcMozxwLWAA\nscAEYC3wl2macZk8HwzExMTEaEa0GMhuyd1T+iVxuz2BkJBIIOPJ+Fde6cH+/ae4cCGZefN2ZTru\nlfpPTYzTJ6HZUd1QEZHiY8mSJfTr14+6hkFzl4tTwA6Lhdt79+bLJUu8Wuw/t/bu3cu/p03jm1Wr\nCAgIYNiIETz55JNUqFC8D8emmxHN99K8txPRrrgTT89B5pim+WAmzysRLUY8E7/0S+5ZzTDa7efY\nufMYGzfGpd2alJXRo4MJD2+bIbGcN28A3bs3yOaQk52QkEjmzRtAYmJyWmx161ZMmxGdOHF1juIV\nEZGiZ9WqVbzw/PNs27aN6tWqMSo8nKeeeoqyZcv6OrQ0e/bsof1NN2FNTKSF08kF4CeLheAbb2Td\n+vWUKVPG1yHmWUEmot6uI7oO3WdfYuWlDmdmpZrSmzSpM5061ad69YC0PaT+/n+X+khMTMZuT8Bu\nT8gygUw9Ud+9e4O0++M995zmNF4RESl6QkNDCQ0N9XUYVzR16lRsiYmMdjopl9LW2uVi1tatLFy4\nUAewUyhJlHzLTR3O0NBGADz0UJtMv582bQObN8elzFQGEhERk3YaHtz7PkNCIgkJiSQsbGGm5ZtS\nyzG5E+XLY1PdUBER3zpy5AgPPfQQVSpVonLFiowYPpyDBw/6OqwCtXL5clqmS0LBfVVqbZuNVatW\n+SqsIkeJqORb+sQvK3b7OWJj7Wm1RNM/O2lSZwD69GnCihXDCA//+yReeHgIMTGjmTmzD+Au6xQT\nM5p58wawbt3htBnP9ONMmRKdlqBmFltO4hUREe84efIkHdq147O5c2l59iytz51j6Sef0P6mm/jj\njz98HV6BKVeuHBc92kwgCbx+TWtxokRUCkVERAwhIZFpez1T94e2b1+bTp3qAzBlSjd69rz2sqQx\n/RJ66p+bN6+W6TippZk8E1QRESka3nnnHY7Z7Yx0OLgVuAUY6XCQcPo0b7zxhq/DKzBhw4bxo9XK\nnymfTWArcMrhYPDgwT6MrGhR+SYpFNnVEs1uqTwoqAITJrTnl19OEhHxPY0bVwX+3u9psYDLlbNS\nTyIi4jtrvvmGRi4X6a+tqQA0dTr5ZuVKePVVX4VWoJ599lm+WbWKiJ9/pq7FQpLFwnGHg3HjxtGl\nSxdfh1dkKBGVQpHdwabsyicFBQUSGFiWoUO/yNCeOsPatWt91q07fFl7Tksz6bYlEZHCUSEwkMOp\nswfpXDAMqlWs6KOoCl6VKlXYsm0b8+fP59tvv6V8+fIMHTqU7t27F6kSU76mRFQKVV4PCtnt5+jQ\noS6TJnVm2rQNjBjRirlzd/LKKz3o3r1hhhnRzGZcs+9fty2JiBSGocOGMWzZMn4EWqW07QX2A48O\nH+67wLwgICCAUaNGMWrUKF+HUmQpEZVClXpQKLciImIyFM+fO3cnAPv3n+KJJ26+7HmVZhIRKZqG\nDBnCsq+/Zv7HH7PBZsMCHHc46HvnnTz00EO+Dk8KmRJR8bmcLIt77jFNvYFpzJi2GZ7L7YxrTq8p\nFRGRgmGxWPho3jzuu/9+Fi1ahMvlok+fPtx+++1YLDpDXdp49Wal3NLNSqVT6k1IMTGjs53FzM2z\nkH2Sm9trSkVEpGTZsWMHz0+dSvTatQQGBjLi/vv517/+RUBAgK9DK7KKzc1KIleSl9nI3M94Xnnv\nZ3an+UVExDvOnDnDpk2bKFeuHJ07d8bPzy/7lwpYTEwMnTp2JNDh4Aank3Px8bz84ousj47m27Vr\nsdmUJnmb5sDF6zyLzKfyrC2aemtSRERMln3ltBh9agH99ElubKz9shiyqlOqZXkREe959dVXqRUU\nxB133MGtt95K3dq1WbFiRaHH8eykSVRyOBjtdNIN6AMMcblYv3EjX331VaHHUxopERWvy6rIfFa3\nJqW/WSmvcpvkZjXTmlUSLSIiefPZZ58xceJEWiUl8Q9gNFDx5En63XUXBw4cyHV/SUlJfPTRRzzx\nxBO88cYbnDhxIsfvfvvtt9zgdJJ+LvYa4GqbjW+++SbXsUjuac5ZfCa72qJXkt3ez9wuuWd1ml9l\nnUSkoDkcDpYuXcrKlSspW7Ys99xzD506dfJ1WIXmjddfp5HFwu3p6ojeY5pMdzqZOXMmL730Uo77\nOnToELd07cqh33+nmp8fZ5xOnnn6ab5csoTbbrst2/f9/f25kJycoc2J+xpO7REtHEpExWtyugc0\nL7VFs0sQ85Pk5iZ2EZHcSExM5I7evVkbHU0Nm41LwJtvvsn48eOZPn16kSl0npyczMKFC1mxYgV+\nfn7cfffdhIaG5iq+U6dOERcXR/369alSpUpa+6/793OdRzH7MkBNl4tff/01V3E+9OCDnD1yhLFA\n9eRkzgOLkpIYdPfdHLHbs73TfeiwYcyNjKSl00kNwAVsAM46HAwZMiRXsUjeaGlevCany+M53fcJ\nWe/9zGz/p7vvvBXQz8v+VRGR7LzxxhtsWL+e4cAYh4PxDge9gBkzZrB69Wpfhwe4k+Uet95KWFgY\n38ybx5LZs+nVqxcPPPAALo8EMjPnz5/nwQcfJKhmTdq0aUONq68mPDycpKQkAJo0a8Zhi4X0NXsu\nAnaLhaZNm+Y4zqNHj7Jm7Vq6Op1UT2krD/Q2TU7Hx/P1119n28cLL7xA/caNeQ/4wGrlLZuNaOC5\n555T9Z5CohlR8ZqslsctFnfZpLxcp+lZ2D41UYTMSy7ltYC+TtOLiDd8NHs2LVwuGqV8tgDtgB9s\nNubPn09oaKgPo3ObPn063333HfcBDZ1OTGAHMGfOHPr160e/fv2u+P79993H0sWLucXppB5wyOHg\nw/ffJykxkTlz5/L4E0/Qv39/vsL9sycB0RYLpp9frm4gOn36NECGO+sBKnp8fyVVq1bl+9hYPv74\nY6Kjo6lUqRLDhg2jQ4cOOY5D8keJqHhNVsvjsbH2PO+7zCpBdI9XcElifpf2RUQyk5CQQF2PNgMI\ncDo5d65oHIr8+KOPaO5y0TDlswG0Ab63Wvnkk0+umIgeOHCAzxcupC+QOp9YB/BzuZg3bx4v/uc/\n9OvXjxkzZvD0U08Rc/48AHWDgvhq7lwaNGiQ4zgbN25M1SpV+PH0adK/tSvl95tvvvzWvcz4+/vz\n0EMP6VYnH9HSvHhdUFAFJkxoz4kT53NUUunKfWVebslbJZfyurQvIpKZW0ND2W2zkZSu7QRwGOje\nvbuPosro/IULZLazspzLxfmEhEy++dvPP/8MQGOP9saAyzTZs2cPAOPGjcN+7BjffvstmzZt4uDh\nw7n++cuUKcOU55/nB2AB8AOwHPjaYmHwoEG0aNEiV/2JbygRFa8LCgokMLAsvXrNL7B9l4WVIOZm\n/6qISHb+9a9/4SpXjplWK+uB1cBsq5VrGzXivvvu83V4AIT26sUem40L6dpOAIeAHtlsHahTpw4A\ndo92u8f3AOXLl6d79+506NABq9Wap1jHjRvHhx9+iKNRI74E9lepwlNPP83cjz7KU39S+HTFpxQK\nu/0ca9Yc4t57v2DSpC5Mm7Y+w75LJXoiUlrs3r2byZMns/zrrylbtixDhg5lypQpVK9ePfuXC8Gh\nQ4doGxyM89w5WjocJAM7rVbqNWrEtu+/JzAw67+vTdPkprZt+W3nTvo6HNTFncAutdlo1aED69av\n91rcFy9epEyZMkWm8kBJpis+pVhJLYWUmJhaq839jx9/fz8loSJS6lx33XV89tlnvg4jSw0aNGDr\n9u288MILfL10KWX8/Bg1ZAjPPvvsFZNQAMMwWLhoEXf27s3slGV6gODrryfqk0+8GnfZsmW92r94\nh2ZExeumTInOcNI9vcxOuouISPFmmibr1q3jwIEDNGnShE6dOmmmsgTRjKgUK4V10l1ERIoGwzDo\n1q0b3bp183UoUsQpERWvUykkERERyYxOzUuhUSkkERERSU8zolJo8nrLkYiIiJRMmhEVEREREZ9Q\nIioiIiIiPqFEVERERER8QomoiIiIiPiEElERERER8QkloiIiIiLiE4WSiBqGMdYwjIOGYSQahrHF\nMIwbC2NcERERESm6vJ6IGoYxGHgNmAy0AX4EVhqGUc3bY4uIiIhI0VUYM6KPARGmac41TXMv8DBw\nAXiwEMYWERERkSLKq4moYRh+QAjwbWqbaZom8A3QwZtji4iIiEjR5u0Z0WqAFTjm0X4MqOnlsUVE\nRESkCPPVXfMGYGb15WOPPUalSpUytIWFhREWFubtuEREREQkRVRUFFFRURna4uPjC6x/w71S7h0p\nS/MXgIGmaS5J1z4bqGSaZn+P54OBmJiYGIKDg70Wl4iIiIjkTWxsLCEhIQAhpmnG5qcvry7Nm6aZ\nDMQAt6a2GYZhpHze5M2xRURERKRoK4yl+deBOYZhxADbcJ+iDwBmF8LYIiIiIlJEeT0RNU3z05Sa\noc8DNYAdQE/TNE94e2wRERERKboK5bCSaZrvAO8UxlgiIiIiUjzornkRERER8QkloiIiIiLiE0pE\nRURERMQnlIiKiIiIiE8oERURERERn1AiKiIiIiI+oURURERERHxCiaiIiIiI+IQSUZFi7JzdTvSU\nKZyz2zP9nNVzIiIiRYESUZFiLMFuZ93UqSSkJJien1Md27mTdVOncmznTl+EKSIikqlCueJTRArW\nObudBLsde2wsAAfXrOHknj1pM5722FgunDjBhZMn8a9WjbiNGwGI27iR8tWrUyEoiMCgIJ/FLyIi\nAmCYpunrGNIYhhEMxMTExBAcHOzrcESKrOgpU1g3dWqe328/YQI9X3uNc3Y7MRERhISHKzEVEZEc\niY2NJSQkBCDENM3Y/PSlpXmRYigkPJzRMTH0mTkTgB6vvMKAefPo8corAPSZOZN7V6yg2YABV+wn\nq6V8ERGRwqCleZFiKNBjab1h9+4EBQenLdVXqluXuM2b6fLss3R55hn2LFrEhmnT6DxpEvU7dcLE\nvXyf+nzq71qyFxGRwqQZUZFirEJQEF0nT6ZCSvKY+tkE99K9y0VQcDD1O3UCoH6nTsRt3sz8Xr2I\nDAlh6ahRACwdNYrIkBBiIiK8FqtO7ouIiCfNiIoUY4FBQXSbMiVDW9O+fS+b6QyoUYOukydzdatW\nXN2qFU379k37fumoUfSZOZOg4OC0hNYbUrcBNO3bV7OuIiICKBEVKVFiIiIyHGJKnfHsOnlyhoTV\nMxEMCg4mKOWAYEEfYPI84Z/6+4UTJ/h11SpufuIJJaYiIqWUElGREiQkPDxtRjQnM52eS/tQ8DOX\nWSXHqVoNG6ZEVESklFIiKlKCeB5iSj/TmdXzqTOlWc1c5vcAk2dy3OOVVwgMCuLk3r2snzZNB6VE\nREoxJaIiJUjqsnrTfv0um+nMTk6X9XPLMzk+tX8/qydOLPBxRESk+FEiKlKCpF9Wz21Sl9tl/dxK\n3QbQtF8/2oaHF+pBKRERKZqUiIoUU+kPFQH5XlbP7bJ+bmV2wj91nApBQbrhSUSkFFIdUZFiKv2t\nSDEREQVWF9TzAFNB1f/07Cf9OLrhSUSkdFIiKlLMnEuZ+Ty4Zg0AB9esoW6HDgxbsSLtys8+M2cy\nOiYmbbbU8/0rJZapM5epM5OpSeLxnTvzlZB6JpuBQUGEhIdfNpNrj41V0XsRkVJCS/MixYznoaLU\ngz9dJ09OK1R/pWX1nJZn8jxFf3jjRjZMm0adDh1ytXx+pdP43jogJSIixYMSUZFipmm/flRt3Jhf\nV61i59y5tBoxgto33sixn34Ci+WyZfW87iP1TBI3TJsGwPa336Z89eo53n96pWTT2wekRESkaDNM\n0/R1DGkMwwgGYmJiYgguwEMSIiVJ9JQpGRK79EbHxGSYCbXHxhIZEsLomBh+WbIk0/dSZx89b1RK\nncmMnjKFfUuXZvledtLPiHomm6mJbPo4C/KAlIiIFLzY2FhCQkIAQkzTjM1PX5oRFSlrSdK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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(1,(8,5))\n", - "pl.clf()\n", - "\n", - "pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples')\n", - "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples')\n", - "\n", - "pl.legend(loc=0)\n", - "pl.title('Source and target distributions')\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### OT linear mapping estimation" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 0|4.009366e+03|0.000000e+00\n", - " 1|3.999933e+03|-2.352753e-03\n", - " 2|3.999520e+03|-1.031984e-04\n", - " 3|3.999362e+03|-3.936391e-05\n", - " 4|3.999281e+03|-2.032868e-05\n", - " 5|3.999238e+03|-1.083083e-05\n", - " 6|3.999229e+03|-2.125291e-06\n" - ] - } - ], - "source": [ - "\n", - "eta=1e-8 # quadratic regularization for regression\n", - "mu=1e0 # weight of the OT linear term\n", - "bias=True # estimate a bias\n", - "\n", - "ot_mapping=ot.da.OTDA_mapping_linear()\n", - "ot_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True)\n", - "\n", - "xst=ot_mapping.predict(xs) # use the estimated mapping\n", - "xst0=ot_mapping.interp() # use barycentric mapping\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### OT kernel mapping estimation" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 0|4.026411e+02|0.000000e+00\n", - " 1|3.991051e+02|-8.782091e-03\n", - " 2|3.987950e+02|-7.769290e-04\n", - " 3|3.986577e+02|-3.442631e-04\n", - " 4|3.985741e+02|-2.096899e-04\n", - " 5|3.985159e+02|-1.460105e-04\n", - " 6|3.984729e+02|-1.078536e-04\n", - " 7|3.984436e+02|-7.368218e-05\n", - " 8|3.984214e+02|-5.574123e-05\n", - " 9|3.984025e+02|-4.740267e-05\n", - " 10|3.983871e+02|-3.865975e-05\n", - " 11|3.983744e+02|-3.175451e-05\n", - " 12|3.983642e+02|-2.561334e-05\n", - " 13|3.983558e+02|-2.116042e-05\n", - " 14|3.983479e+02|-1.982104e-05\n", - " 15|3.983413e+02|-1.643931e-05\n", - " 16|3.983351e+02|-1.567880e-05\n", - " 17|3.983296e+02|-1.366612e-05\n", - " 18|3.983270e+02|-6.571092e-06\n" - ] - } - ], - "source": [ - "\n", - "eta=1e-5 # quadratic regularization for regression\n", - "mu=1e-1 # weight of the OT linear term\n", - "bias=True # estimate a bias\n", - "sigma=1 # sigma bandwidth fot gaussian kernel\n", - "\n", - "\n", - "ot_mapping_kernel=ot.da.OTDA_mapping_kernel()\n", - "ot_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 20,verbose=True)\n", - "\n", - "xst_kernel=ot_mapping_kernel.predict(xs) # use the estimated mapping\n", - "xst0_kernel=ot_mapping_kernel.interp() # use barycentric mapping" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plotting the mapped samples" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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hYQF5eWn4+3uZyUopOXs2FWfnQNzdte/s7e0JCmrIpUuCS5cuXadalWTJkiW0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\nCgpqdHC4a2LZsmXodDp0Oh1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cuHAGDWpOVFQiY8euY/HigXTqFGx1g2uFQnHzoQwZrm6+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dPaytjMDNVaDe3S57+Qy6HodOtKUHeLWq1uRMmWqdfrjN64Qeittwh985sE3/tePAMDDAwNrZOC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mOfNDCCHElqirq0NRFIaHx0kmg9hseg4dapd9m0KITSWJzA7gcrk4ftxFLpdDo9HIGTJCCCG2\nlKIo1NXV4ff7yefz6HQ6NBpZ5CGE2FzyjXgHkUMLhRBCbCeKokhsEkJsGRk+EUIIIYQQQuw4ksgI\nIYQQQgghdhxJZIQQQgghhBA7jiQyQgghhBBCiB1HEhkhhBBCCCHEjiOJjBBCCCGEEGLHkURGCCGE\nEEIIseNIIiOEEEIIIYTYcSSREUIIIYQQQuw4ksgIIYQQQgghdhxJZITYRRKJBP39/fS+8gpXLl9m\nZmam4veIBwI8/9hjxAOBire9FqqqkkqlSKVSqKq61d0RQgixRDgc5vLFi/S+8gr9/f0kk8mK32O7\nxaZSqUQymSSTyWx1V3Y13VZ3QAhRGZFIhEuvvkphdhab2czMxATTw8O0Hz1KfX19xe6TCAR44fHH\n6XzwQex+f8XaXYtoNMqN/n7ioRAA9upqWtvbcblcW9ovIYQQc0ZHR7lx4QL6fB6jwcD46CjB8XEO\nnTiBw+Go2H22U2wKBAKMDQ6SikbR6nRU1dWxr60Nk8m0pf3ajSSREWKXGB4cRInFaG9qQlEUAKZC\nIYauXcPr9WIwGNbVfjwQIBEIEOjtBSj/0+b3b0nQSKfTXH7tNUqzs9RWVaEoCsHxcS4nEhw7fRqr\n1brpfRJCCPG6TCbDyPXruHQ6vDU1wNws+uDoKEM3bnD0+PF132O7xaZgMMj1V1/FCjQ4neTyeaav\nXSObyXDsxAk0GlkMVUmSyAixC2QyGWLBID6Xq5zEAHirqrg+MUEsFsPj8azrHj1PPskLjz9e/vkb\nDz8MwD2f+hRGu53us2c3NWhMT0+TDYfZvyBxs5jN9I2MMDU1xb59+zatL0IIIW4Vi8XIxuM0L1gV\noCgK1W434VCIXC637kG27RabxkZGMBWL1NXVAWA2mTAZjQxPTDDb0kJ1dfWm9WUvkLRQiF1Ao9Gg\naDSUluwRKZVKKIqCoijrXj/cffYsj/T08MBTTwHwwFNP8UhPD+3vehcvPP44iU1el5xKJjHr9YsS\nN0VRsBqNpOLxit0nm80yPDzMa729XLpwgampKdmLmn/0MgAAIABJREFUI4QQK6DRaFAU5ZZn5m6N\nTaVSiVQshtViWXTdaDCgFIsV3S8Tj8fp6+vjtZ4erl29SiQSqVjbO4kkMkJsQ6t9sBsMBtw1NQRn\nZigWi8Dc9P1kMIilqgqn01leP7zWh7rd78d/8iT+kycByv9u8XrX1N56mcxmMvn8LdfTuRymCi0r\ny2QyXHj1VW709JAPBIjduMGVl1+m7/p1SWaEEHvOamOT0+nE5HIxGQyWrxUKBUKRCFV+P3q9flfF\nJo1Gg9FiIb0kYckXCqgazbpnn+bNzMxw/uWXCVy8SGFqiuDVq1x4+WWmpqYq0v5OIkvLhNiG1rJp\nsbWtjVQiQf/EBAYgXyqhd7locLkInj9fsfXDNr+fM48+ChoNgd7eW9pdT9ur4fV6mXA6GZ2YoOZm\nwAqGw2jsdnw+X0XuMT4+TmJ8nPbGRrRaLQDxRILAwAC+mhopKiCE2FNWG5v0ej3tBw9y/fx5+kZG\n0CkKWVXFUVeHx2RaNoZsVGzarD0z9U1NXJueJjQzg8vhIF8oMDE9jbW2lqqqqnW3r6oqg/39aBIJ\nWpuaytcnpqYYun6d6upqdLq98/V+73xSIbaZbDZLOp1Gr9eXN6avZ9OixWLhWHc3oVCIVCqFwWDA\n4/Hw8mc+s+z64TOPPsrbH3ts1f22+/28/bHHeP6xx5Ztd7VtxwMBep58ctXrmG02G53HjnHj2jWG\nblYtM7tcdHR0VKwSzszUFE6brZzEANhtNiZnZohGo5LICCF2nWQyST6fx2w2YzQagfXFJq/Xi+We\newiFQuTzeSwWC16vlx9++tObGptW2+5aY1NtbS3ZI0cYv3GD8NQUGp0OR1MTHV1di2LJWqVSKZIz\nM9Qt2WvjqapiKBQiHo/jdrvXfZ+dQhIZITZZqVRiaGiIwOAg+VQKjcGAu7aW/Z2dt920uNIHsMFg\nKG8wnNd99iydDz5IoLeXbzz8MA889RT+kyexrXNk6nbtAqtqez0lMz0eD263m3g8jqqqOByOigSK\neYpGQ6lUuuW6qqpSeUYIsatks1n6r19nZmKCYi6H3mKhtqWF1tbWdccmq9V6SyXJzY5Nq213rbFJ\nURRaWlqoq6sjkUig0+mw2+2L9nOux/zeoqWxSVVVFEXZc7FJEhkhNtn4+DjDFy7gtdlw+HxkslkC\nAwNcK5U4+cgjFX+w25eMmC1cS7we6223UiUztVrths2M+OrqGAgEqMrlMN5c2zwTiaC1WmU2Rgix\na6iqyrUrV5i5cQN/dTVml4tYIsHIhQvodLoNSTq2Y2yaj0vAumOTwWCoyFKypSwWCw6fj+nhYSxm\nMxqNBlVVmQoGsXi92O32it9zO5NERohNVCqVmBgexmUyUXXzi7BNp6NBq2VsYgJ1375FD9xKPdjh\n9fXD6x3tqlS76x3h2wx+v5/ZlhaGRkYwqCrFUgksFpoPHNhzwUIIsXvF43FmAwEafD4sZjMAVS4X\nhUKBiaEhGt7ylg1JOmB7xaalcQm2Z2za197O5WSSvrExjBoNuVIJg9tNZ1eXzMgIITZOPp8nn07j\nvhko5plNJkr5PNlsFtiYB/v8+uFKW2u7G7WsoJL0ej2Hjx4lVFdHLBZDq9VizOXo+9KXqNrkswmE\nEGKjZLNZitksliWFUqwWC/FUinw+j1ar3fWxaT4uAds6Ntntdo6fPk0wGCSdTlOMRBj92tfQdXZu\nddc2nSQyQmwivV6P0WolHo1iW7BeOJVOozEYMJlMwMY92LeTjVpWUGlarZaamhpqbp5KHejtXfOe\nHiGE2I5MJhM6k4lEMrkoNiVSKQwWC3q9Htj9sWlpXILtG5uMRiMNDQ3AXFx65k//lMMf+MCei0uS\nyAixiTQaDXXNzfT19KANhXDY7WSyWaZnZ6lua9uTy5U2allBpVVqT48QQmw3drud6vp6Jvr68BWL\nmIxG4okEkUyG9oMHK1pEZafYCbHpdnEJ9k5sWlUioyjKx4CPAy03L10C/i9VVb9T4X4JsWvV1dVR\nLBYZHxwkGomgNRio6eqirb29YlVNdpKdMsK3E/b07FUSm4RYv46uLrQ6HaHxcYqRCAarldaOjvKo\n/16zE2LT7eIS7J3YtNoZmVHg94H+mz//CvB1RVGOq6p6pZIdE2K3UhSFpqYm6urqyufIzC8p2ymS\nySQTExNEw2H0RiM1fj81NTW7OhHbCXt69jCJTUKsk16vp+vAATKtreTzeUwmU3lJ2U4RDoeZnJgg\nFY9jc7mo9ft39ZkqlToGYSdbVSKjquq3llz6Q0VRPg7cA0iwEGIV5mvL7zTxeJyLvb3kwmEcViuZ\nXI6ro6MkDh6kvb19q7u3YXbKnp69SGKTEJVjMpl23OAawMTEBH3nzmHI5TCbTMwEg4TGxug6cQKv\n17vV3dsQEpfWsUdGURQN8EHAAvy4Yj0SQmxroyMjFGZmaG9qKs/ARGIxJgYGqK2txWazbXEPN9ZO\nWDe9l0lsEmLvKRQKDPf1YVcUahcshRsLBBjq76e6unrPlSXeK1b9v6qiKIcVRYkDWeBvgPerqnq1\n4j0TQmw7pVKJ2elp3A7HomVkLoeDYipFLBbbwt5tjvl103thE+VOIrFJiL0rkUiQjcepXrKMrNrt\nJhWJkEqltqhnm2MvD7CtZUbmKnAMcAEfAP5JUZS3vVHA+OQnP4nT6Vx07aGHHuKhhx5aw+2FEJsl\nHgjQ8+STdN88M0VRFDQaDcV8ftHrSqUS3Pyd2BpPP/00Tz/99KJr0Wh0i3qzJSQ2CbEHLI1LMFcR\nVFEUCoUCet3rX22LxSIajWbXx6btWphgM+KSoqrq+hpQlOeAflVVP77M704CPT09PZzcY2v2hNgN\nAr29/H13N4/09JTX3Q4MDDDy2mu01NVhNBhQVZXA9DRZs5lTP/VTGI3GLe61mNfb20t3dzdAt6qq\nvXd6/W4isUmI3Wm5uKSqKj0vv0xucpKmurq5AbdikaHxcZytrRw9fnyLey3mVTouVeIcGQ0g31yE\n2EXe6MyUxsZGErEYw6Oj6Eol8qUSBqeT/YcOSRIjthOJTULsInc6y6vjwAGu5PP0jY2hVxTygK22\nlrb9+7ew12KjrfYcmU8D/8ZcqUs78IvAGeBdle+aEGKr3OnMlMNHjzLT0EAikUCn01FdXY3FYtmq\n7oo9TmKTELvfneKSw+HgxF13EQqFyGazmEwmPB7PjishLVZntTMyNcA/AX4gCpwH3qWq6vcr3TEh\nxNa505kp2WyWVCpFPp9Hr9fvyVOfxbYisUmIXW4lZ3klk0kymcyW7Y1Zbv+O2FirPUfm1zaqI0KI\n7eONatOHw2GunjtHPhLBoNWSLZWY8Pk4eOzYppVelmAhFpLYJMTud6czUwYHBxm5ehVNJoNGURhT\nFKqbmzl4+DA6XSV2UtxZIhDghccfp/PBByU2bZLdXcZBCLEuS0s6FotFBq5eRZdMsr+piZaGBvY3\nNJCdnuZGf/8dWquc+WCRCAQ27Z5CCCG23nKlhqPRKKPXruExGmlraqK1sZFmr5fw4CABiRO72uak\nqEKIbaNUKqGq6oqWgy0t6RiLxUjNzNDi9ZbPkdFoNHirqghOT5PJZDb0ROg7bfYUQgixMxWLxXKJ\n/zeyXKnhSCSCmkrh9nrL10xGIzaDgeDkJI2NjRvR5TKJTVtHEhkh9ohsNsvo6CjT4+OopRJun4+m\n5uZVLQdTVRVVVdEsOAwTmPv55u820p02e26l5Za7ZbNZZmdnKZVK2O127Hb7lvZRCCG2m1gsxujw\nMJFgEI1Wi6+hgaamplVt0ldVddEhzfM0Gg1qqVTJ7i5ru8am2y3DjsfjxONxNBoNbrd7R1cclURG\niD2gUChw+eJFokNDVDkcaDQaQteuEQuHOXrq1IorjtntdkxOJ8GZGfw+HzAXQEKzs9gaGjZ0NgZW\nttlzqyxdGz01NUX/5cvkolFQVbQWC3VtbbS1tS0bcIUQYq9JJBJc7O2lODuL2+GgmM0ycu4ciViM\nI8eOrXizvtPpRDUYSCST2KxWYC7uxdJp9tXWbuRHALZvbFoal1RVZWBggImBAYqpFCgKBqeT9oMH\nqamp2dK+rpUkMkLsAeFwmMjYGK319RhujnK5nU76R0YIBAK0tbWtqB29Xk9LRwd9588zMDKC2Wgk\nlc2idTpp3YQv6Hfa7LkSmUyGRCKBRqPB6XSuu+LacksKMuk0g+PjOGw2muvr0Wg0RGIxxi5fxmaz\nUbsJgVUIIba7ifFxCjMztDU1leOH3WZjeHSUmcZGPB7PitpxuVzUtrUxcf06xtlZdDodiWwWZ2Pj\npjxvKxGb4vE4mUwGo9GI3W5fVzy93VK3lFbL2PAwPrsdl8dDqVRiMhik/+JF7Hb7jjxGQRIZIfaA\nRCKBvlQqJzEAiqJgM5uJhEKwwkQGwO/3YzabmZqcJJ1MUud0Ultbu2kVy2D5zZ53oqoqw8PDjA8M\nkI3H0Wi1WD0e9h84gMvlWnNfbrekoPmDH+S+3/3dcjByORzEEwmmJyclkRFCCGA2GMRhtS760m4y\nGtEWiyQSiRUnMoqisL+jA5fbTWh6mkKhQLvHQ01NDQaDYaO6f4u1xKZ8Ps/1q1cJj49TyGTQGgy4\n6+roPHBgzUu+bheXus6epeW978XlcABzS+/8Ph99o6OEw2FJZIQQ25NOp6O4zPV8Po91DcvBXC7X\nur78r9dymz3vZHp6mqGLF6k2mahqaCBfKDA5Pc3VfJ6T99yz5mC33JKCUk0N0WDwlhE1g8FALpNZ\n032EEGK3MZjN5CKRRddUVaUIqy6ZrNFoqKmp2dIlUmuJTYM3bjDd10e9x4PN6yWVTjN+4wbXFYUj\nx46tqR+3W+o2MDGxaEAT5pJAnaJQKBTWdK+tJuWXhdgDPB4PWrudwPQ0pZsbH2ejUbIaDTXbYH/J\nZpicmMCsqlS73SiKgkGvp8HvJz0zQygUWnO7dr9/0TIC/8mTNN19N1qPh2wuV36dqqrEkkmc1dXr\n/ixCCLEb1NbVkSwWicbjwFxVzYmpKQxOJ9V74FmZzWYJjo3hc7nKe3ssZjN+j4fZyUkSicSa2l0u\nLvlPnqSms5NYMrmoME82l6Og0WC9ef+dRmZkhNgDrFYr7YcPM3D5Mn3j4yiA1mKh4cABvAvKVe40\n+XyemZkZisUiNpsNx83p8uVkkklMS6bpNRoNWlUln8+vuy8LlxRYqqtxNzYyNDhIlc2GVqtlJhrF\nUF1NXV3duu8lhBC7QW1tLYkDBwgMDjI5OwsaDUank45DhzCbzVvdvTVLpVJEo1EA3G73bQvh5PN5\nCrkcpiWxy2wyUYxE1h2bli51q6urIzQxwcDICFVOJ8VikZl4nKp9+3Zs4iiJjBB7RG1tLW63m9nZ\nWVRVxeFwbPoITCqVIhaLoSgKbrd7XWuXZ2ZmuH7xIpnZWVBVFKMRX3MznV1dy1a6sbvdzIRCeBc8\nrHP5PEWNpiIBc+mSggOHDjHqcBAcH6dYLOLt6qKhsXHHjnoJIUSlze9t8dfVEYvF0Gq1644Nq6Wq\n6twZaakUBoMBl8u1riIwIyMjjFy7Rj6RmKsKZrfT0tVFfX39La81mUwYLBbiiQTmBclONB5Hb7Gs\nOzYtjUtWq5XDJ08yNjrKzNQUWr2eprY2Ghsb1134ZqtIIiPEHmI0Gt9wo/ntas6vl6qqDA0NMd7f\nTz6ZBEXB6HTSduDAmtYz53I5rl24gCYWo83vR6vVEk8kmLh+HZvdvuzhZ3X19cwEAgyPjVHlclEo\nFglFIjgbGzdkJMpgMNDW1sa+ffvmzt5ZYRlRIYTYa2w2220LxmxUXIK5GZFrV64QHhtDzeVQNRrs\nNTV0HTq0pgI24XCYwYsXqTIYqLoZh4LhMDcuXsRms+F0Ohe9XqfTUd/ayo3XXqMUDGK3Wkml08yk\nUjQeOrQhRxrY7XYOHDxIqasLRVF2/HEAElmFEGXzNecTgQC5XI7R0VEunj/P9WvXmJmZWXO7oVCI\n4UuXcGk0dDQ0sL+uDkMqRf/FiySTyVW3NzMzQ2Z2lvra2vIokt1mw2E0Mjk2tux7XC4XB06cwFRf\nz3Q2S0RVqTlwgINHjmzoSNRKTqoWQgixvIVxCeaqcA4MDHDx/Hlu3Lix5n0kAMPDwwT7+6l3OOho\nbKTV5yM9McG1y5fL+0lXIzg9jS6fL+/FVBQFn8eDmkrddi9mY2Mj7SdPkrfZmEylSBmN7Dtxgn2r\nqCa6FhqNZscnMSAzMkLsefP15uH1WvMjL73E5QsXyBcKuH0+IsUigYEBWg4dorm5edX3mJ6cxFQq\nUXWz0pmiKNTV1NA3MkIoFFr1cqtCoYAGbkkQjAYDsWz2tqc8V1dXU1VVRTabRavVrurkaCGEEJtn\nubNQIpEIgVAInV6PSa8nnMsRcLk4cPw4VVVVq2q/UCgwNTqK1+nEcnMJl0Gvp6G2lpFgkGg0itvt\nXlWb+VwOwzLV1vQaDYXb7HdRFIWGhgb8fj/5fB69Xr9jl3ltBUlkhNjjltabB/jOr/86AMd/7ddo\n/NjHAJiJRBi5dg2Px7PqxCObySybNKy15KPVagW9nlQ6XQ5AAJF4HPcdDuZUFGVDpuuFEEJUzu3O\nQmn78Id55+/8DjC3bHlkfJwbfX2477prVTMMhUKBUj6PYUk8MOj1FPP5NcUmh8tFaGCAUqlUHmgr\nFApkSiVsdvsbvler1UoCswaSyAixx83XmwfKNecP/M7vUNvaSt2CqW2300lobIxIJLLqRMbhdjM+\nOrpopiRfKJBTlDVtfne5XHiamhjt78dtsaDX64nEYih2O/WNjRu6ploIIcTGW3oWyr1//dfMlkq0\ntLaWXzO/dGtsZoZEIoH9DsnCQkajEbPDQTQcLpc+htc32q8lNtXU1DBdW8vA6CjVTieqqjITi2Gv\nr8fn8626PXFnksgIscfZ/f5bvuy7OjtxNjVhWbIJ/nZLtu7E7/cTHB/nxugoVU4npVKJcDSKs6lp\nxSc3L6QoCp0HDmC125kcHSWZz+NobaWxuRmXy0Xgxg1eePxxOh98UBIZIYTYgZbGJt+xYxQSCcxL\nNsyvlaIoNLa2cnV2ltGJCRx2O+lMhmgmQ8PBg2s65d5kMnHo+HFGR0YIBQJzy6gPH6axqQm9Xi+D\nbBtAEhkhdojNeADO15yvPniQ8OQkbqezfLryTCSC3mbDdXOfy2rMl3wcGRoiMj0NWi31R47Q1Ny8\n6tOb5+l0OlpaWmhubqZUKs1VLgsECNy4sWhN9fznkqAhhBCVt9GxaT4u+draiI6OEpyeprGuDkVR\nUFWV6VAIa23tmqqM1dTUoHR3MzY8zEwshs5mo+3gQRoaGtbcX4vFQmdXF/s7OoDFeznnCxfIIFvl\nSCIjxA6xGQ/A+ZrzmUyG7LlzDExMYNZqKZRKFI1GWg8dWtMoFcyVfDx05AiFQgFFUSq2FnhhW7db\nU33m0UcX1dIXQghRGRsdmxaehbLPbOZKKkXfyAhmvZ50Po+hqoq2jo41V+Dy+Xx4vV6KxSJarbZi\nlbwWJjDLFS4AGWSrBElkhNjmtuIBaDKZOHriBMGGBmLRKDq9Ho/Hs+oKLstZ6wzMSixdU/3AU0/h\nbGyk/7vfJR4ISMAQQogK2YrYVFVVxfG77yYYDJJKJKix2fD5fGseYJunKMqGxqbbDbI1nznDB55+\nWmLTOkgiI8Q2t1WzDAaDgfr6+mVPI96ulq6p9p88SalU4j8/+1kOP/SQBAshhKiQrYpNVqt1TRvx\nt9Jyg2yKwcD/+8u/TEIG2dZFTmkTYpvrPnuWR3p6eOCppwB44KmneKSnh+6zZ4kHAjz/2GPEb54D\ns5NtxGe58uKL/Ph//S8AXvo//4fzzz5LbGKiYu0LIcReJbFp5ex+P/6TJ/GfPAlAKBKh79w5AF7+\n2te49vzzu+JvtRUkkRFim1v6AJz/d7vff8uJxztZJT+Lze/Hd9ddvPhbv8Wlz3wGgAt/9mf8P+9+\nN8//xV+su30hhNjrJDatntnno+rkSX78u7/Llc9+FoDX/uRP+Jd3vIMf/tVfrbv9vUiWlgmxQ8xX\nbrHtginoRCJBLpfDbDZTiEQqvs5a73bT8alPcXh2lvz4OC8+8QRv/cM/RKmpQVNfX97UKYQQYn0W\nxqadvKm9VCoRj8cpFovY7XYyodCynwXW/nlyBgMdn/gEHkUh2t9fjk0ZpxPf6dMV+yx7iSQyQuwQ\nCyu37NRgkc1muX71KrOBAMVsFr3FwtTXv87FL3yh/JpKrLNOpVJozGZaWluZvXlAmqerC2tLC2Ox\nGOl0ek2lOoUQQiy2MDY9/9hjO7JyZDQape/KFZLhMKViEZPTSfDrX+fVv/zL8mvmPwus/fOkUiks\nLhf+xkb0N4sLeLq6UL1eMjK4tiaSyAixA+3UMsPXr14lPDBAnceDpbqaWCJB4sQJfuZrX0MXDJY3\nQfpPnlzXzJNer0drMJDJZrF4PJx8+GEsHg+ZbBadwYBer6/gpxJCCAHLb2pf7/N8o2WzWa6cO0dp\ndpZGrxedVks4EsF08iQf/N73SA8OLvoswJo/j16vp8jc7M9CmWwW4xoOh15orx62KXtkhNiB3miT\n5Upt9GbMpe3H43FmAwHqvV5sVisajQaXw0F9czN5hwPv0aPA4nXWa+2/3W7HVVvLRDCIYrPRffYs\nqsVCcHYWT309RqOxYp9LCCHEnDfaN7NSG/mMXa7tUChEOhymqa4Ok9GITqejxuPBbrdT9Hpv+Sx3\n+jxv1H+Px4PR7WYsEMDgcnHi4YfJ6HRkFIXaurp1fbbdtC9pNSSREWIHqkSw2OiH3tL2c7kcxWwW\ns8m06HUWs5liNouxunpVe4Du1P+Ori4czc0MhUK8+JOf8PzLLzMajZLP50kkEhX7XEIIIRZbz57O\njXzGLtd2LpdDryiLDrAEMJtMpBOJVX+WN+q/0Wik88gRlKoqBhMJYkeP8urYGOFkknQ6TT6fX/Vn\nit9cZr5wqXmgt3fPDLbJ0jIhdrC1BItK7q8pFAqMj48zPTFBMZ/HrCjYNRrMZvMt7WudTnRmM/Fk\nEufNfSsA8UQCvdlMVXPzipbFrbT/JpOJo8eP8+NUCl04TFdrKy6Hg5m+PhKzsxzp7sZsNhOJRMqF\nBxwOx21Pdd6p+5KEEGKzLdw3s1KVfMamUinGx8YIT06i1eux6XTYVJXg+fO3tG02m8krCoVCYdGh\nmIl0murGxhV/lpX2v6qqigNHj/LjSAR7KkVHSwt6vZ6hV18lFolw+OhRSqUSkUiEUqmEw+HAbDbf\n9r47dal5pSiqqm5c44pyEujp6enh5M2RYyHE1lq6GXPeah96pVKJi+fPEx4cxGk2o9VoePUf/oHh\nr3512defefRRaj/0IQJXr+J1ODCbTMQTCWYzGdpOnKCpqani/Q+FQlz40Y9o9ngw3VxOpqoq/SMj\nuNvbKWSzxKamUAsFNAYD1Q0NdB44sOwemkr93TZTb28v3d3dAN2qqvbe6fV7hcQmIbafSj1j0+k0\n53t6yExP47LZKJZKnPvSlxhZJjadefRR3vwHf8BrP/kJ6UAAX3X13B6Z2VnyJhOHT5/G5XJVvP8D\nAwOMnTtHW2NjeSYom8sxHAzia28nGgySiURAVdFbrdS3t9PS0rLsQNvCBGrpvqTtOMhW6bgkMzJC\n7DGV2ow5MzNDeGSEhqoq8oUCaqnEqYcewnP6NL79+5fdvG/2etHpdEyNjBCOxTBYLLQdOEBjY2PF\n+18sFgmHw2jy+XISA6AoCnaLhfM/+QnNbjfNtbUYDQaSqRRj/f0YTSb2d3Rs2N9NCCHErSr1jJ2Y\nmCA9NUVTTQ3pdBq9Xk/3hz9M9T334ASe/9SnFrWt1+s5dOwYAxYLwakp1FIJi9dLW3v7ipOY1fQ/\nn88zPTGB3WJZtJzNaDBQSKW4/OqrNLtctPv9aDQaZqNRhi9dwmq14vP5brmvfUnCsnDZ+V6wqkRG\nUZT/Crwf6ALSwI+A31dV9foG9E0IsQEq9dCLx+NEh4YY+N73cJ84gc7hQG82o/f5UHw+/DeTk6Xt\nt+/fT1NzM/l8HuPNjZWV7H+xWGRkZITJkRECY2MEh4exWSzUer3l0axINEo+HqeusxOjwQCA1WLB\n63QyPTpKS2vrLbMyez1YbGcSm4TY+Sr1jJ0NBomNjfHvX/wi7lOnMDgcGJ1O9NXVWN3uZdu2WCwc\nOXaMdDpNqVTCYrHcdpnxWvufTqcZGhxkJhBgoK8PTSaDyWjE5XC83vd4HFSVus7O8v2rXC6SqRRT\ngcCyicxet9rN/m8FPg/cDdwL6IHvKopy+8V7QohdKZ/PM3r5MuPf+AZujYYWnw8bMNbfTyKZfMP9\nOwaDAavVuuokZqHbtT/Q38/QuXNYslnafT4sisKrL71EYHoagFgiQSyXw+FwLJqpATAZjRQLhTfc\ncLmbDibdRSQ2CSGAuX2XE5cuMfHtb+PV6WjweNAkEgwPDaF3u9/w+W02m7FaratOYhZaLkYUCgUu\nnT/P9JUruBSF/T4fqWCQ13p7iScSqKrKZDCIajLhtttvub/BYCCbTq/6vnvBqr5FqKr6noU/K4ry\nK8A00A38oHLdEmL3UlWVXC6HTqfb0tPl1/PQiwcCzFy6RHZ8fO7nkRG0Wi1Fo5FCsUhJVde02XM1\nlms/lUoxNTxMjdOJy+EgFQphuHSJYmMjr7z2Gh0dHeisVvYdPUpkcpJILIbb6Sy/PxKLYXa5MC2p\nrFYqlQiHwySTSbRaLXf93u9hsVg27LOJ1ZHYJMT6FYtFCoUCBoNhXV/k12u9sSne10fqZmyKDA+j\nAnlFoVgsYvR4NnxP43KxKRQKEZ+cZF9DA/lIhOB3vkPn3XdzZWiIly9coLGhAb3dTtexY4QHB8kX\nCuUDM1VVJZ5K4W9tveVeuVyOUChENpvFZDLx5j/4gz13Ttp698i4ABWYqUBfhNj1AoEAY0NDZOJx\ndEYjNY2NNDc3b0lCs55EY2mVlNe++EUAfPc7i2IBAAAgAElEQVTfj/8DH8D2BhVWYO7hOzU1xUwo\nhFarxePz4fP5bil/uVrpdJp8Oo2zqgqAVCjE1a98hXv/9m8J2u00HTtGTU0NDoeDfouF0UuXyOXz\n5cIDKaCjtXVRP/L5PFcuXSI8MoKuWKSgqow4nbQfPkxNTc26+is2jMQmIVaoWCwyPDzM1OgohWwW\ns8NBQ0sLtbW1W9Kf9camVxfGpn/4BwBq3vMe6j/wAQw3lxLfTiKRYHJykkQ0islioaa2FvfN5Wjr\nkU6n0ZVK6HU6oqEQrz71FO8/c4YjJ04Q1enoOHUKt9uNXq/nfDbL4MgIHpcLjUbDTCSCzu3Gv+Sc\nmXg8zpXz50kGg+iBPGCrqeHAkSPYbLZ193mnWHMio8yl658DfqCq6uXKdUmI3SkQCHC9txcLUGO3\nk0ylOP+DHzA+NsaRo0dxOp1bOgq2Gt1nz+J585s5//Wv0/eFL3D0N38T+759VDc2Egdcb3BCcS6X\n4+K5c0RHR7EZjRSLRYKDg0Q7O+lYsC54LfR6PYV4nPFz5zCZTISuXgVgpq8P4/79eIxGHDfXI+9r\na8NgNBIYGSGZyWDyeOhsbr4leI+PjzMzOEhzTQ0moxFVVQlMT9N/6RJOp/OW2RuxtSQ2CbE6169d\nI3D1KtU2G2aTifDUFP85MMC+I0dob2/fUbPP3WfPYjp6lMHnnuP63/0dRz7xCVz79///7N15fON3\nfeD/10f3LVmWZcv3NWPPmfE4Z0kyIYUkwDLQsls6JYWWJWQp7RbY/dHlQbpJ+KXdHtwtoQm//W1h\noWnor3SX7P7YAC0JWZpwzJEwk/H4GF+yJVmWbEmWLOv67h+WhcfHWJLla/x5Ph7zyPg7Ot7yTL5v\nvz/H+0NVQwOzavV1P8vs7CyXzp8nOzuLSa9nOpViamyMA8eP49nkci2tVks8HCYYjxO6cgWA6b4+\nMk7nYncxIQotlo8cP86ozUZwchJyOezt7TS3tl5TnCiKwsCVK6SCQTrr6xdXRGSzjExMMGQwcFNP\nz6bi3Us2MyPzJHAYeMNGD/zoRz+KfdnyDYAzZ85w5syZTby9JO0duVwO78gIJqChro7E/DyBQIC5\niQkmh4ZIBIPUd3bSffjwnpgWtno8dNfUMOX3M/ClL+E6dIiaQ4cIz86itliob2hY97k+n4/I+Dgd\nDQ2FPTLxRILJoSFq3G6c+dmUsuKyWgm/9BIvP/30NdfPffazAAROneJdzzyDNd8Nprm5mcbGRjKZ\nDFqtdlURpSgK/vFxHCZTYT+NEIK6mhoGJiaYmZnZdIKrlGeeeYZnnnnmmmuRSGSHotlRMjdJUpHm\n5uYIjo9TX12NzWIhNDNDaGqKqdFRpr1eIj09NHR20tbWticG2qweD0fe/GZm8nsi644dw9beTigS\nwV5fj2udQTZFURgeGkJEo3Q0NRU+qz8YZKS/H5fLtanc7HK5CL70Ej/+6lcL11564onC768uy016\nvZ6DXV10dHaSy+XWfN+5uTliwSCNNTWFFR1qtZo6lwvf1BTxeByz2Vx2vJWyHXmprEJGCPGXwFuB\nuxRF2fDo0M997nOyV7+0r4VHR3n9ySc58au/unjDHBkhFQzS5fEwGQ7j1OkIDQ0xbDBwsKtrp8Mt\nikaj4fiddxL+8IdJV1cznU5jbW6mpa0N67IDL1eaDgSwGY3XbPQ3m0yIYJBIJLKpQkYIwS//wR9Q\ne/fdxKenifb3M/Dkk/Q+9hgNbW18+33vY87nu6azjEqluu5yg1w2u2rpn0qlQigKuVzuuvHEfD7O\nPvUUvQ8/vOX9/Nf6AXxZv/59QeYmSSpNcHiYof/yX6j/7d9mXqPh6uAg+mSSI01NzKTTWHM5xl9/\nHbPZvGeW0lqtVo7fcw+RD36QeZsNBag7fJiW1tZ1G8wkk0nmQiFqq6quKdhqnE4GfT6i0SjV1dVl\nx2QwGLjn4x+n7u67CZ47x8CXvsShj32MznvvRTs7y7cefHBVblKr1esuO8/lcpDLrVqOrVarUbLZ\nXZObtiMvlVzI5BPFO4BTiqKMVSwSSdoGiqLsyKjSfDDIyNe/Ttsdd6B3OomFQjQ4naAoqDUabFYr\nxmyWoNdLW3v7npiVAXC1tfEv//IvyWQy5HK5Ddcfw2IRkFnrJitERf5uatrbuaetjUgkwsTPfsbA\nk0/S0tlJOt/xZfCll5iamkKr1WKz2xn8H/9j3Zu5EIIqt5vpK1dwOhyF+KJzc6iMxusWbAChkRFe\nfPxx3HfdxYFNjuhJ1ydzk7SX7VRuSoVCjD37LDP33w9uN5lYjJa6OiLRKFqdDpfTSdLvZ8rv3zOF\nDEBDdze//tRTpFIphBAb3ntFPv/kVhwSn1MUhEpVkb+bpsOHqe/qor+1lYEvfYljb3wjDo8Hn29x\nzKX/xReZmpqiuqUFm93OuaefXjc3mc1mDDYb4dlZPMtaModmZjBVVW24HDAwOMiLjz9O3alTHKyt\n3fT+1J1U6jkyTwJngNNAXAix9K86oihKstLBSVKlzM/P4/V6CU5OohKCmoYGGhsb0a9ov1tpSyfu\nBl97DQDvxYskFxZIzc4iLBamZmYwud1YLBbmk0nC8TjpdHrP/cBbShvlmro6BrxekgsLheVaM5EI\nGI0V2VQJi0nJ4XCgPnKEllOn+NaDDxb+7J8+8pHC7xtPn8b77W/Tdfr0uqNSTc3NzAaDDI6NYTOb\nSafTxLNZGrq7C/ttVor5fAyeP8/AP/4jABf+4R8Yv3qVg7ffTtuxYxX5jNIvyNwk7VUzMzNMjI8T\nDYfR6vV4mpqor6/f8h8sl3LT3MAAAFd/+lPMra0QizFvtxOZn8fT2IhKrUav12/Y+ne3KmZwDRZn\nTOxuN1NDQ5iNxsWZDUUhEAxicjpXLUEtl1qtpvHQIU49+ihjP/gB38wve4bFgzoBWs6cof3tb+fF\nxx9fNzdpNBqaOzsZePVVRrxeTAYD8WSSnMHAwY6OdWdyZsbHef2VVxj/0Y8AOP+tbzExNsbRO++k\ntqOjIp9xu5U6I/NvWOwE88KK678NfK0SAUlSpS0sLHDxwgUSk5NU2WwoisL4hQtEwmGO9/Rs6iyT\njazs7jXw5S8zABhOnUK57z5qGxtp7ehACLFu698bTV1dHeHWVkZHR9EpCjlFIZ7L4WxqQlGUio5M\nWj0e3vXMM8z5fFz4znf4ySOPcNsf/AHOxkbmQiF8w8MATP7sZ8Bi28+VScNisXD85puZnJxkNhhE\nZzDQ5PFcd3Typc99jp/++Z8Xvh740pcYAAK/+Zu4v/zlXbF2+QYjc5O054TDYV4/dw4xN4fdaiU5\nM8PA1BTziQQHDh7c0vdemZv6vvxlAPRveAPqf/kvqWlpwePxoCgK0Xic+vb2LY1nN2jr6GB+bo7B\niQn0KhULmQwpjYZmp5P5+fmKdQJb6soW8/k4euYMP/7Wt/j5f/pP3Px7v4fV7QaTCe8PfwiA79w5\nYO3c5PF40Ol0+CcnScRiVDU0UFdff93l2S98+tO89sUvFr7u/8u/pB8IfOAD/PpTT+3JmZlSz5HZ\ne59Q2vf8fj9zPh+dTU2FUQqH3c7ViQmCjY1bulm79+GH6Tp9Gt+5czz30EO8/Stfwd7djTcSITY7\ni93pZCGdJjQ7u2br3xuRRqPhyLFjhOrrCYVCjI2Oko3FmJuc5NVgEGttLd2HD1esU47V48FYUwM/\n/SkA9ceOMfrii5z7ylcKj/kfDz8MwKlHH12z7afZbObAgQNw4EBR71n7lrdwW10dupkZXnriCe56\n5BFc3d34kkmmp6dlIVNhMjdJe9H46CiqeJzWpqbCtdloFP/wMPUNDVt6n1iZm/7F009jam9nNBiE\nXA5bVRXReJzw7Cy66upd09RkK1ksFm66+WaCwSDT09PMjI2hTqXwXblCcGyMmqYmDnZ1Vey4BKvH\nQ1qvR5tfGjbn9fKzv/iLax7z3EMPAevnpurq6qL37mSzWapOneKNXV1kfL5CbrJ1dBBSqYhEIhVb\nFbGdtm4oWpJ2idlwGItef83NR6vRoBeCWDS6pTdo64pRFM/Jk3hOnqQtl2NiYoLJ0VHCCwsYamro\nbmnZcA3ydm4e30pqtRq3200sFkOXSNDqdmO1WEguLOAdG+OKEJw4ebJiMzPZbBatzcaR970Pk8vF\noXe9i5ZTpwhevsz//qM/4tRnPkPXPfdU7ERkldWKq7sb3cwMAK7ublzd3cTGx0mn0xV5D0mS9q50\nOs1cOIxzxZIlh82Gf2yMWCy2pYXMytxU39uL5+RJ2hcWGBsbY8rrhVwOR0fHqta/a7lRcpNer6e+\nvp4pnw9bNktDfT0GvZ7o3By+K1cwGI20rXEwZbmWclPPQw/Rds89HHrXuwDwXrjATz/9aR548kma\nb7utIrkpk8mgsVqpqa5mIf/vbik3RfdwbpKFjHTD0+n1xDOZVdczuRyabdqLsvKkYpVKRVNTEw0N\nDWSzWTQaTVE/tM/5fNddN7uXZDIZ/GNjuGw2rPkkadDraaitxev3E4lEcDgcFXkvnU5HVUcH2je+\nkct///ccete7Fm/esRgALbfdhqeC3atsVVV4x8awV1dz8qGHMLlcZDIZFhRlT53JIEnS1lCpVKg0\nGtKp1DXX05kMKrV6S5c8L7cyN+n1eg4cOEB7ezuKohQdx42UmyKRCNFAgJa6OvT5PTY2i4WFhQX8\nY2M0NzdXbFbGbDajMRqJLixgrK7GlG8P7Q8GAWi69daK5SadTofBaiUaClHlchVyU3RuDo3RuGdz\nk5yOl254NW43aY1mcUM5i91hgqEQmEybaqdYiqU1sStv8CqVas3zS1aK+Xz4zp0rrJdd+n3Mt2GH\n2V0rnU6TS6UKG/6XGPR6sul0RUeHhBA0t7UxF4tx7itfwT88jG9qijm9nmO/+7u4Krz+u66uDl11\nNYH5eTrOnCGp0TA8MYHN46Gmpqai7yVJ0t6jVqtxNzYSisWYTy72o8hms/gCAUzV1RUbxNnIerlJ\nXWQxdaPmJiWTKRQxSwx6PdlUiswaA6PlMplM2EwmLn/ta4z19zMTiTDq9SLcbm7+9/8ea319xd5L\nCEFTWxsJIZjJZDjwnvcwB/jCYdzNzRXbA7Td5IyMdMOrrq6m6dAhJgYGCI6NoQBaq5W2rq6KdSLZ\nais3Zm60bnYnzM7OEvD7icdiWO12auvq1u3qBYsjfwarlcjsLOZlI0HRuTm0FRwdWurOA2DNF0eT\nAwM4DQbajx+n833vq/i+JLPZzJGeHkaHh5mZngag9tAhWlpb91xHOkmStkZzczPxWAzvxASkUuRU\nKoxOJwePHNm2GZnN2u25SVEUpqamCAYCZNJpqlwu6urqrtux1Gg0ojYYiM3NFVYLwGJuMlRXF90J\nbSNLuUkdCAAwOznJglaLs72dnje8Addv/EZF3me52tpa6O1lfGSE6WgUtcFAW1cXTcv2ae01e+P/\nFEnaBCEE7e3tuN1uIpFIoTXvXppGXatpgOfkyYrt6disQCBA/6uvIhIJjHo9fq+XqfFxuk+cWDXr\nlclkCIVCJBIJ1AYDoVQKxe/HbrUWWlDXHzpUsfXhKxMtwOV8y0vNo49ycIsOjLTZbBy76SbS6TRC\niD3zg4kkSdtDq9Vy7KabmGluJpFIoNFoqK6u3lODHbs9Nw0ODDBx5QoGRUGtVnN1dJQpj4djPT2r\nOoQm881Y0uk0wmRifGoKd37VQCQWIwF0tbZWbO/mytz08z/5E2CxCHTdd19F3mMttbW1uN1u0un0\ndQ/d3CtkZpX2DYvFsmenTtdrGrAbZLNZRgYGMKTTNDQ1kZieZvI738H2S7/EsNlMVVVVYcZjfn6e\nS6+9xvTrrzP1/PO43/xmslVVRDUakskkGr2e1gMHaG5urlh8S4kW2JFku5d+KJEkaXsJIXA6nddt\nmbub7ebcFIlEmBwaos5mw5bP/dlslqHxcSbcbjqWnZsSCoV49Qc/YPTv/o7m+++HqirmNRpmAFUy\nibG6mq7WVurq6ioW304WgUKIis0s7TRZyEjSHrJyY+ZuEIvFSEYitORnXhLT05z7yld4y+23E5+Z\nIZFIFArIkeFh4hMTuFUqfvzssxx/29tIajRgNnP85En0K7rLVcLKRAu7K9lKkiRJlReJRCCZxOZ2\nk5ieLjR6cVgsTPt8hUImk8kw8PrrZLxexr75TXpPn8bm8TAyMYG7oYH2jg50Ol3FZmKW7OYicC+R\nm/0laQ9Zb2NmMWI+Hy/kD+GqpKXZllwud811JZdDqFSFm38qlcJ38SKaYJDZwUEAwv39aGdmiAwN\nkUwmt3SKO+bz8do3vsHtH/vYrioEJUmS9rrNDLJtZW5S8r9fGmBLTE+TUxRUy3LNRF8fUz/9Kaqp\nKQCm+/qIDg2hDgbxXbqEWq3eVBFzvc8n89LmyRkZSdontqo9psViwVxdzdjly1RptYSuXAFg5Px5\n3EYj2UgEzGZyuRzjzz3HyNe/XnjuS088AUDzu99N7m1v21QcG51jMOfz8cpnP8sHz57d8+1BJUmS\ndpOlQbZybFVucjgcZJJJBn/8YzL5IsJ/8SLJ6moOnDpVeNzFr36V85/+dOHrpbwE0Pabv0nuHe/Y\nVBxLny/m860aiJR5afNkISNJN7ilzijL22PC4ghaJW6cKpWKzu5unvurv+LFr361cL3vySfpe/JJ\nRL57jV6v5+CDD1LX04N22Yn36ro6sjU1WK3WTcVxvWQhSZIk7R7Lu0luVW6yWCzEf/ITfvT5zxeu\nvZzfUG9+5BEO3XILALd+6EOI9nayg4Oc++xnueuRR3B1dzMZCFB19GjZe0lW5t5zTz9N69134zp0\nCFQqyOVWfXao3OffL2QhI0k3uO1oj+lwOHjgP/5HvL/2awQuXODHn/wkb3nySZqWnUgshKDrllu4\nrNEQOX8egIzTibq1lc5jx67bDvN61ksWrffei9Xj2fJCTpIkSSrNWt0ktyI33fvxj3P4ne9k+OWX\nefkTn+DUZz5D5113YW9sLDympr2dQw88wMX//t8BUNXWEjGZsN50E12b6Gq51mf81oMPAtBw++1M\nvPJK4frSZ4fd07p6r5CFjCTd4LarM0pNezs17e346ur48Sc/SdNtt63auOh0Ojl6881c1WrpfP/7\ncRw7RttNN23qkMj1ksXJD36Qex57bNefcyBJkrTfbFc3yaUN9RarlZc/8Qm67rlnzQ31ra2tKPfe\nS+Lhh1E3N+M+eBBPff11z0LbSO/DDxPz+Tj39NOr/qz2+HHe9qUvrfrsgNwrUyJZyEjSDW67OqMs\n7VHpeuc7r7vp026303P33fTcfXdF3ne9ZHHu6aexejy7/pwDSZKk/WZ5XlKUxS35W5Gbis1LQgja\njx+n/a/+qmLvvbRvqPXuuwszMcvzj+xYVhmykJGkfSAQCDAcCND23vcy5POBz0ddXV1F20ku37C5\nnTMdxSQLmTAkSZJ2l2w2i9frZWR8nLb3vpfxcBjj7CwOh6Ni77FTeWmJ1eNZ3BOTtzL/7MYjFfYa\nWchI0g1uYmKCwQsXMCoKve97H4n5efrPniV9/PimDp5MJpPMzc2xMD0Nc3MELlwAyt+DEgqF8E9O\nkojFMNvt1Dc0FJ3QrB4Prffey8kPfpBzTz+9ZrEiE4YkSdLuoCgKV/r6CPT34zCZOPne9xKJxbh0\n9ixHens3VczEYjHCo6NkZmeJ9PUB5eelXC6Hz+djyucjm8lQVVNDfX09RqOx6NeweDyF3LTSZrq9\nSYtkISNJN7BsNov36lWsajV1+X0oVXY7wVAI79WreDyekk+eVxSF4eFhfMPDLMzNMfbss4w+80zh\nz8vZgzI5OcnAq6+iS6UwGgyEp6YITUzQ3dNT9P6Z5WfsrFWsyIQhSZK0O0SjUYKjozS6XJhNJgCc\nDgfD4+N4x8bKKmRSqRT9fX2EJycZ+uu/ZuzZZwt/Vk5eWiq2/AMDWDQaNBoN4xMThPx+jvb0YMrH\nvZGNcpO0ObKQkaQbWCKRIBmLUbMiKTjsdmaCQeLxeMkJw+fzMXbxIi6zGUd9PbVnzlB7880kpqa4\n+Kd/WvIelHQ6zejAAFYhqMt3knEDXp+P0aEhqqurC4dubkQWK5IkSbvf3NwcpFKFImaJ3WolEgqR\ny+WKvu8vGRwYYHpwkHqXC89v/RahN72J4XPn6P/yl8vaGzk7O8vU8DCNTmchzhqnk6HxcSYnJ+ns\n7Cz6tWRu2jqykJGkG5hGo0Gl0ZBKpzEsa2+cSqVQ5UeYSjU5Po5ZrcaZL4Cq6uuxut1c+NGPgNL3\noMzNzbEQjVLvdl9zvbqqCu/MDIlEAovFsuHrJJNJ/H4/kZkZdHo9NW431dXVFd0HJEmSJG2eRqMh\nJwTZbBa1Wl24nkqn0VqtJd+35+fnCU1MUFddjcVsBrMZc00NuVyOfsB55EjJeyOj0ShiRbGlUqmw\nm82E/P6iC5lIJELA72c+Hsdss1FbW7vpc9OkX5CFjCTdwIxGI06Ph0B/P3qdDr1ORyqdxhcMYm9r\nK6pAWE5RFBYSCewGwzXXNRoNBoeDno9+tOSpc5VKhVCpyGQyaJcVVplMBpVKdU2SW08ikeDi+fMk\nAgHMej3xTIap4WGaDx+mra2tpHgkSZKkreV0OjFWVTHh99NQV4darWYuHicyP0/H4cMlFzKpVIps\nKoXRbr/muqO+npYzZ9BVV5cco0qlIsdi3lseTyabRV3kIODU1BT9r76KMjeHUa9ncnSUwOgoh3p6\ncDqdJcckrSYLGUm6wXUcOEAqlWJ0chKRzZJTqbA1NnKgq6vk1xJCYK2qYm5srDAjA5BcWEBXU0Pv\nJz+JtYiEEQqFCE5NkU6lsDkcaG02AtPTNNfXo8oXNVPhMPa2tqI2VY6PjTHv99PZ3FxYjhCencU7\nMIDb7cZsNpf8WSVJkqStodVqOXj0KP0XLzLo8yFyOYTBQO3BgzQ0NJT8ekajEa3RSGxu7prclDUY\nOPCBD+Bqbd3wNbLZLFNTU4SCQYQQGM1mhNFIMBTC7XIBMJ9MEltYoLOIGLPZLMP9/ehTKRqWNdYZ\nm5jg6sAAVbfeKlcMVIAsZCTpBmcwGLipp4eZ1laSySR6vR6n01ny+uMlDU1NXAoE8Pp8VNntpNJp\ngrOzOFpbqaqq2vD5w8PDjL3+OppMBp1Gw/TQEFgsCLOZAa8XrRCkAUtdHR0HDmz4erlcjpDfj9Nu\nL3ymTCaDSCQY+sY38NTU0NnTU9ZnlSRJkraG0+mk9447CIfDZDIZLBYL9hUzKsXS6XR42toYfe01\nstksZpOJuXicmWSS1uPH0el0131+Npvl0s9/Tmh0FGO+uAgqCjmLhWgmQ2RsDBWQ1Wio6ejAU8TK\ng1gsRjISoXnZzEsqncaQyXDhi1+k8fHHqSthn420NlnISNI+oFKpqC5jan0t1dXVdPf0MHb1Kv5I\nBJVGg+fwYVrb2jYsjuLxON6BAaoNhsKoWTab5er4ODXd3TgOH2ZhYQGDwYDL5Sqqo5oQAoRAURSS\nCwtMTE4Snp4mPjLC6LPP0vVrvyYLGUmSpF1Iq9VSW1tbkddqbW1FrVYzOTJCJB5HazDQ3t1NU1PT\nhs+dmpoiNDpKS01NYT/pfDLJ2PQ0TcePo1aryeVyWK1WqqqqihoIXJptURSFSCzGxMQE8UiE2atX\nGf2bv2H2Ax+QhUwFyEJGkqSSud1uampqSCaTqNXqDUe7lkQiETLxOM5l0+xqtZoqm43Z6WkOlbE2\nWgiBu6GB0fPnGRsbIzEygimTYWFwEICRH/4Qt9uNu6OjpPMDIN8Wur+fudlZql0umtra8Hg8Zc9m\nSZIkSVtDpVLR0tJCQ0MD6XQanU5X1B5LgJlQCKMQ1zTFMRoM6IHUwgJd3d0lx2O1WjE5nQyPjjIX\nDpMLBNCnUiQHBgC4/P3vL85CNTaWlJsURaG/v5/xkREyCwt4mptpbGqq2GDlXiMLGUmSyiKEKOlQ\nsOXPW7l5UlEUNrNSuKmpiasDAwyfO4f+Zz/j8ve/X/iz/i98gf4vfKHk8wMuXLjAj7/7XZRwGLPJ\nxIxeT+DqVQ7ddhsHy9hfJEmSJG09TZkdORVFWfNauftYVCoVnd3d/K/+fmZHR9GeP8/E975X+PPX\n/viPee2P/7ik3JRKpXjhn/6J/pdfRp9OYzQamb5yhamDBzl+++0Vm93aS2QhI0nStnE4HGgsFqbD\nYWryo0eZTIaZuTka29vLThh6vZ6W9nYWJiexdXbS8da3kpqc5Cef/jTHf//3sff00HvffUW/XiAQ\n4PxLL2FfWODwsWPksllCs7PMTE8z1teHp75ets+UJEm6QThdLgJDQ8wnkxjzXTnjiQQplYqqTXQX\nq6qqWpwtUavRHzxIx9veRtrn4+U//VO6P/QhOt/6Vg729hb9en2XLzPwyit02O3Uu91kMhn809OE\nhocZramhpqZm360YkIWMJEnbxmQy0drdzdVLl4iMjqJVq0kqCtaGBqo0Gl547DF6H3645CVgS69t\nqaqis7kZIQTTfX0AmFtacPf0lPSaE+Pj5KJRGvJn26jUamqcTuYDASLBIHNzc7KQkSRJKlLM5+Ps\nU0+VfX/fam63m3BnJ+NDQ+gUBUVRSKvVeA4c2PSSLbvTiS4ep6m+HqCQm4zt7dSVkJsWFhaYGB7G\notHgzsek0WioqapiPhwm7PMxPz+/77p0ykJGkm4g25ksUqkUwWCQaCSCVqfD5XLhWNb2cj2NjY1Y\nLBamp6fJZjJYbTZqamqY/vnPefHxx+k6fbqs2KurqxlzOJjw+6mrqcFYXU33b/4muFzUlvh6mYUF\n9AYDmWz2FxeFQMnlyORyRa+7liRJkmDO59vU/b0U8XicYDDIfCKByWzG7XZvuAxapVLRfegQrpoa\nZmdmQAiqqqqorq4mHghsKq/Wejz0eb2EZ2epstvRORx0/PqvY2tvL6lIymQyKNkser2eTDaLJp+H\ntBoNqYUFcrAvc5MsZCRpF4nH44RCIa5Iw7kAACAASURBVLLZLFarteQ2yduVLJLJJBdffZXY5CQG\nlYpMLsekyUT7kSM0NjZu+HyHw4HD4SDm8zHn8zHt9+M7dw6g8F+Lx1PSZzCZTHQdP87g668z5Pej\nKAqN73sfjZ2d1NTUlPT5qmpq0JpMzMzNYTYaMeh0LCwsMBWN0tjdXVSbaUmSpBuBoijMzs4SiUQQ\nQuBwOMpuk7zVQqEQfa++Snp2FoNGQyCTYbK6msMnTmwYs0qlWmwMk5+JX7LZvOp2u0kcOcLE0BBT\nXi9CCDp/53c4cPgwhhWHS1+PwWDA6XYTHh8nEA7TUFODRq1mJhollsnQ3dJS0uvdKGQhI0m7hM/n\nY+jSJTLRKJmZGca//32OvP/9nHzjGzfcuLhUEJRaDIRCIQI+H/PxOFaHgzqPB5vNtmGs42NjxCcm\n6GhoKMQWDIUYvXKF6urqopsAnH3qKV58/PFrrj330EMAJW2AXOJyubDfcQeRSIRcLofNZivrxu6p\nr6f+4EGGX3uNPp8PkU4TmZ/H0dnJydtvL6ottCRJ0l6Xy+UY6O/HPzSESKVIhsP4fvADen/3dzl6\n++0bPr+c3JTNZgkEAkz5/WTTaapra/F4POiXdRRbL9ar/f2o5+ZozS8xVhSF0fwBlCd6e0vah1lu\nXl1JCEFbWxt1dXXEYjFUKtXiftESGxKo1WqaOzqY8fsJjIwQGR8ns7BALJej7ZZbOHTkSEmvd6OQ\nhYwk7QLz8/Ncff11TJkMdS0tTM/P86O//Vuqe3uZOHiQlpaW6z5/ZUFQTDEwMTHB4GuvoU2lMBoM\nBCYnCXq9HOrpwXmdzY25XI7g5CROm+2aG7HL6STs9TI7O1t0IdP78MN0nT4NLCaJ5x56iLd/5St4\nTp7EUuaMklarxZU/hblcVquVE7feisPlYmJ4mPlUis6mJo4dOyb3xkiStG8Eg0F8/f3UOxxYzGam\nEwle/sY3qL71Vhq6ujacnS41NymKwpW+PgKDg5jVatRqNSMTEwQ9Ho719Fx3YCoWi5EIh2lyuQoF\nixCCGqcT3/Q0iUSipP0j5eTV6zEajWV1+lyuvr4e9d13M9LQwNTkJKjVnGxv58iRI2V1arsRlPyp\nhRB3Af8X0At4gHcqivLtSgcmSftJOBwmFY3iNhqZ7usrbAbMer30/+M/4nzLW647ArRUEBRbDKRS\nKUb6+7GpVNTml4LVAqNeLyNDQ1RVVZXVQazUZ1jXGNnynDyJ5+TJkt+70mw2G8dPnODw0aOoVKp9\n1wlmL5F5SZK2xvTUFHpFQTU/z/T4eCE3xfv6GPjhDzl0660VzU3hcJip4WGaqqsx5X/od2ezDHm9\n+D0eWltbN4x5Ze4qtxtmqbFvl9raWtxuN9lsFrVaXfbnu1GUk5nNwAXgw8DqptuSJJUsl8uhAvq+\n9S3+4cEHeemJJwB47Qtf4J//9b/m7FNPXff5Vo/nmgJg6ffrJZhYLEYqGsW1YjStuqqKeDhMMpkk\n5vPxwmOPEfP5rnmMSqXC5fEQjkbJLtsMH56dRWM2r1qHvN7rrGTxeDj16KNbmiSKjWU5jUYji5jd\nT+YlSdoCmUwGjUbD5b//+2tyU9+Xv8x33vnOiuemaDSKOp0uFDGwuKTKajQy7fcD69/HLRYLRoeD\nYChUuKYoCtPhMGanE5PJVLheTC4oNfZylZOXhBBoNJp9X8RAGYWMoij/S1GU/6goyn+j9AFYSZLW\nYLPZwGCg6YEH+JWvf527HnkEgIMf+hD3//3f0/vww0W9TrHFgEqlQqjVZHO5a65nczlEfvZhaYPj\n3Bo318amJkweD4NeL16fj6vj44RTKZq7uq5JFsB1X2c5Y00Nh3/nd4jmcszOzq55ONlmFRuLtLfI\nvCRJW8PpcjGXSnHgHe+4Jjcd+PCHedd3v7sluSm3xvVcLoc6v3Rqvfu4Wq2m7eBBUgYDg2NjTPj9\nDI6NkbVYaD9w4Jof+kvJBRqHg5Mf+xhxIUgkEht/2BLJvLQ5+3NBnSTtMjabjbq2NnxXrmCy2xH5\n03mre3s5dv/9Ra/rtXo8Ra3dtdlsmKur8QUCNDc0IIQgk8kQDIepPnBgw02VJpOJ4ydPEggEiMzM\nYNfrqXG7r9lbU8pGydnZWa5cvEgiFEKlKKDV4mpupuvQoYqs+63Upk1JkqT9pLa2lmBjI77xcWxO\nJ0q+A2TjG97AoXvvLbrdb7G5qaqqijGTidDMDNX5FQPzySRz6TQHi7hX19TUYLjtNgKBAPPxOE6L\nhbq6ukIOLTUXeL1eRoeHsdx5J97RUfzT0zQVsW9V2j6ykJGkXUAIwYGDB7HZ7QR8PlLAsd/7PU7c\nc8+WHG6lVqvp7O6mL5Wif2wMrRCkhcDi8VBjMuE7d27DG71er6e5uRmam9d8j2I3SmYyGfovXSIX\nCtFRV4dGoyExP8/4wABGs5n29vZNf95Kb9qUJEnaD3Q6HUeOH8fvdhP0+TB0dnLiIx/h2J13bsmZ\nJTabjZZDhxi5fJnw6CgqIchqNLg7OjArSlG5yWq1rtuUpZRcEIlEuHrpEnYhcDU1AYtLqEcuXcJi\nsWz6oEw5wFYZYjPLN4QQOa6zqVIIcRI4e/fdd69aN3/mzBnOnDlT9ntLkrR5yWSS6elpUqkURqMR\nl8vFj/7oj1a1RAa4/WMf4/7PfKbo115+k165UXL5TToYDHLp5Zdpr629ZvYlGAqR0Ou57a67Nr1H\npdhY9rJnnnmGZ5555pprkUiEH/7whwC9iqKc25HAttlGeSn/GJmbJGkXi0ajhMPhQht9p9PJDz/1\nqTVzUykDUqXkgsHBQSZfe43OFbMvw14vrq4uurq7y/58AC889timP89utx15aVtmZD73uc9xchd0\nIZIk6VoGg2HVAZYrO7Xc/cgj/PCJJ+i8776SXntlR7L1upFls1mUbHbVEjKtVksukyGbzaJSqfBd\nuMDzH/kI93/+83hOnNiSWPaytX4AP3fuHL29vTsU0e4nc5Mk7U42m23VmWYrc9PBt7+dWz78YWqP\nHy/6dUvJBdlMBu0as05atZrUwkLh63Jz027tilZJ25GX5NIySZKusfJG78qPOpnya6NLtdEmT7PZ\njNpoJDo3h81iKVyfjUaxNDcXDp+cvnSJ0RdfZPrSpZILmc1QFEW2uZQkSdphK3NT/3PPcc9jj5U1\nq15M8wGL1cpkLlfo3AaLA2/xVIq6ZR0/y81Nmx1gy+Vy5HK5fXt+zJJyzpExA538ojNMuxDiJiCs\nKMp4JYOTJGkHqVSc/OAHCy0hy12/u9EmT6vVSm1rK5N9fSTm59HrdERjMXImE00tLfguXGD60iUG\nv/tdgMJ/XUeOlFzQlNri2e/3M3j2LFeffZa2f/WvaOvpoSHfHEHaPWRekqT9IebzkQgGOfj2t9P/\n3HOFvASl5aZimg+43W589fVc9Xpx2mwIIQhHo1g8Hsy5HEPPP09ienrTuanUvJTNZhkfH2f0/HlG\n/+Ef6HrwQTp7eze9Z2evKnmPjBDiFPADVvfq/6qiKO9f8diTwNmzZ8/K6XtJ2mO2c/1uNptlYmIC\n//g46YUFrE4njc3NOJ1O/vqeexh98cVVz2k5dYrfeuGFisax3OTkJAPnz5MeHORHH/kId33xi4jm\nZlqOHatIA4LtsGwK/4beI1NKXso/XuYmSdqD1stLsDW5KZlMMj4+TnByEhQFl8dDY1MTP/mzP1s3\njq3MTYqicPn11wn09yMmJnjx936P2z/zGUxHj3J4jxQzlc5LJc/IKIryIuUdpClJ0h6ymfW7CwsL\nLCwsYDAY0Ol0Gz5erVbT3NxMU1MTiqJcs7n//s9/vjAj89rXvsbx976Xzvvuw3XkyKY+3/Vks1kG\nfvITsuPjqKanFy9OTaHWaLji82F/4AGqizhhWtoeMi9J0v6wXl4CispN8XicXC6HyWQqquuawWDg\nwIEDdHR0ABRyU+/DD9N0xx2FGZntyk3RaBTvhQvY5udJBAIAaGdmSFy8yMXpaU6+8Y03TAObYu3v\nhXWSJK2rnPW7mUyGq0NDBL1eMskkGoOButZW2traVnUei/l8nH3qKXoffrjwPkKIVcu2PCdOFKbp\nX/va1+i87z6Ovec9lfiI60omkwz/3d8x+jd/U7i2dKI1AB//OG/50z/d0hgkSZKka5W7ryQejzM0\nMMBsIICSzWKw2Wjp7KSurm7VY9fKTSvz18o4tis3JRIJJr79bV7+5jcL15bnpuwf/iH3fupTWxrD\nbiMLGUmSrquU9buDAwP4Ll/G7XBgcjqJJxKM/fznCCFWLcdaOs246/TpokaQXEeO0HLq1JaOdi3R\naDQ0nT7NgbvvZsHr5aUnnuCuRx7B0trKVCLBiQce2PIYJEmSpLWVkpcymQyvv/YaSb+fuupqNBoN\n4dlZ+i9cQHvLLauWY+323OR54AFueutbmR0cLOQmUVtL2mjk5re8Zctj2G1kISNJ0nUVeyLz/Pw8\n014vtVVVOPJtM/U6HYqi4BsZoampCa1WW/YhYJ4TJ7Z0T8xyer2e+mPHCPT1Ycl3TbN1dJCw2Wg6\neZK6zs5tiUOSJElardi8BBAKhZibmqKjvr7Q4au+tpZRr5dJr7dQyKyXm+D6+Wk7c1NVVRXOgwdZ\nmJ7Gkc9DxuZmki4XXSdPYquv35Y4dhO5pliSpIpIJpNkkkksJtM1180mE5lkkmQyCSyerPx0b2/h\nROXnHnqIp3t7OfvUU9se8/V0dHZS1dbGrFZLy7vfTUgILE1NHOjqkl3LJEmS9ohkMolGUVa1KTab\nTMQjkcLX6+Wm3ZSfNBoNXUePona5CCoKLe9+N3NmM3VdXavOhNsv5IyMJEkVodfrUev1xOfnsVut\nheuJ+Xk0ej16vR7YO4eA6XQ6jp84QaStjfk3vQm9Xo/D4Vi1VlqSJEnavfR6PRkhrjkPBhZz0/Lz\n0TbbSGC72O12Tt52GzMHD5K5/37MZvOqw0P3E1nISJJUESaTCVdjI/6+vsWvjUbm4nGC0SjNx48X\nupdt9hCw7SSEwOFw4HA4djoUSZIkqQzV1dVY3G5GJyfx1NQU9sgsaDR0LpvF2Eu5SaPRUFPmIdU3\nGlnISJJUMZ0HDiCEYNrrJTA3h8ZgoPHIEVrXaFVc6iFgkiRJklQqrVbLoWPHGNDpmJiaglwOncXC\ngQMHcLlcqx4vc9PeIgsZSZIqRqvV0n3oEPOtrYVzZAwGw5qPLWWzZrESiQThcJhsNovVaqWqqkru\nZ5EkSdrnLBYLJ06eJB6Pk81mMZvNq/bMLKl0blIUhUgkQjQaRQhBVVUVFoulYq+/38lCRpKkilje\ne99SV4dOpyvqwLFK8fv9DF26RDoaRQXktFpqWlroPnx4W+OQJEmSdo/luclcWwusPhdmq97PUlfH\nQH8/vqEhWFhAURTUFgst3d00NzdvWQz7iSxkJEmqiKXe+/ZbbyXjcJBKJrE6nTQ0Na3q018Jy5OF\n2m5n6NIljOk0rU1NCCFIzM8zPjSEzeGgqamp4u8vSZIk7X5LucnU00PG4QBFobqujqbmZkwrumxW\n8v26Tp8moVIxeeUKHrsda76ICs3MMHL5Mna7HbvdXvH3329k+x1JkjYllu+7v9Rz//L//J/Ezp1D\nHwoRHx3l9bNnmZ6eLuk1U6kUoVCImZkZstnsmo9ZShZzPh8zMzOko1FqXa7CUjKT0YhVpyMwMbG5\nDyhJkiTtOUu5yfvTnwIw9PzzZC5dQjs1hf/SJS6eP184FqBYiUSCUChENBpFUZQ132/5OTT9L7yA\nCIexLltKVl1VhRKPEw6HN/kJJZAzMpK0rRKJBNFoFJVKhcPhKHTy2svOPvUULz7+eOHrgSefZAA4\n+dBD9D78MGMTE4yPjlJdXV3UfpWJiQlGBwZYiEQQajXm6mo6u7upqqoC1jm0zOMhFQ4jVkzVazUa\n5lOpyn1YSZKkG4yiKESjURKJBDqdDofDcUMsx12Zm/q//GX6WcxNPQ89xOD4OH6/f81mNCtls1mG\nBgcJjI6Snp9HrdPhqKuj69Chwj7Qle+3dB5N55kzHDh27JrX06hU6w7SSaWRhYwkbQNFURgZGWFi\ncJB0PA5CoLfb6Tx8GLfbXfbrLl9etd6pw9czPz/PzMwMuVwOq9WKzWYrenN8KpVCpVIVeu9feeEF\nXvx3/467HnkEV3c3pnw3GIfdzvTMDOl0esPCLRQKMfjaa9hUKpo8HrK5HL5AgL5Uip7bbsNgMKyb\nLFrOnKHt4EHM+aUCiqIwOzeHp6Wl5O+LJEnSfpBOp7ly+TIhrxc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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "pl.figure(2,(10,7))\n", - "pl.clf()\n", - "pl.subplot(2,2,1)\n", - "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2)\n", - "pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Mapped source samples')\n", - "pl.title(\"Bary. mapping (linear)\")\n", - "pl.legend(loc=0)\n", - "\n", - "pl.subplot(2,2,2)\n", - "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2)\n", - "pl.scatter(xst[:,0],xst[:,1],c=ys,marker='+',label='Learned mapping')\n", - "pl.title(\"Estim. mapping (linear)\")\n", - "\n", - "pl.subplot(2,2,3)\n", - "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2)\n", - "pl.scatter(xst0_kernel[:,0],xst0_kernel[:,1],c=ys,marker='+',label='barycentric mapping')\n", - "pl.title(\"Bary. mapping (kernel)\")\n", - "\n", - "pl.subplot(2,2,4)\n", - "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2)\n", - "pl.scatter(xst_kernel[:,0],xst_kernel[:,1],c=ys,marker='+',label='Learned mapping')\n", - "pl.title(\"Estim. mapping (kernel)\")\n", - "\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Barycentric mapping on the left, estimated mapping on the right. We can see that the change \n", - "in variance of the mode do not allow for a good linear mapping. In this case the kernel \n", - "mapping (lower right) allows for a far better estimation\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/examples/Demo_Compute_EMD.ipynb b/examples/Demo_Compute_EMD.ipynb deleted file mode 100644 index cda9a59..0000000 --- a/examples/Demo_Compute_EMD.ipynb +++ /dev/null @@ -1,167 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Compute and plot EMD" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot\n", - "from ot.datasets import get_1D_gauss as gauss" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generate data" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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EwzCM70VVyU4twlHhbOiq1Lsm1WRkGIbRkDITC1j7wWHSj+YTGhnAiGld6dg/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YVw/XwGT5zA2ea94O/tMtPpdLHWaKLuYhfWfm/u/q6iFgbsLlalm6g6fQK7RYGXX/QxksuGO+0W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\n7yOw6hNkxFBWLpzE863vEdWjXDfmOm4cf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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "n=100 # nb bins\n", - "n_target=25 # nb target distributions\n", - "\n", - "\n", - "# bin positions\n", - "x=np.arange(n,dtype=np.float64)\n", - "\n", - "lst_m=np.linspace(20,90,n_target)\n", - "\n", - "# Gaussian distributions\n", - "a=gauss(n,m=20,s=5) # m= mean, s= std\n", - "\n", - "B=np.zeros((n,n_target))\n", - "\n", - "for i,m in enumerate(lst_m):\n", - " B[:,i]=gauss(n,m=m,s=5)\n", - " \n", - "\n", - "# loss matrix and normalization\n", - "M=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'euclidean')\n", - "M/=M.max()\n", - "\n", - "\n", - "M2=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'sqeuclidean')\n", - "M2/=M2.max()\n", - "\n", - "\n", - "# plot the distributions\n", - "\n", - "pl.figure(1)\n", - "pl.subplot(2,1,1)\n", - "pl.plot(x,a,'b',label='Source distribution')\n", - "pl.xticks([])\n", - "pl.title('Source distribution')\n", - "pl.subplot(2,1,2)\n", - "pl.plot(x,B,label='Target distributions')\n", - "pl.title('Target distributions')\n", - "pl.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute EMD and sinkhorn distances " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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8atatW9dYjC2l7HJYWBj29vaMHTsWR0dHunfvnuHO2aSkJEaNGsX06dONj2VW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\n7tz7ex5PNrEisNTLMmfdTGSY1jjjCFA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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "d_emd=ot.emd2(a,B,M) # direct computation of EMD\n", - "d_emd2=ot.emd2(a,B,M2) # direct computation of EMD with loss M2\n", - "\n", - "reg=1e-2\n", - "d_sinkhorn=ot.sinkhorn(a,B,M,reg) # sinkhorn returns a list of distance if B is a matrix\n", - "d_sinkhorn2=ot.sinkhorn(a,B,M2,reg)\n", - "\n", - "pl.figure(2)\n", - "pl.clf()\n", - "pl.plot(d_emd,label='Euclidean EMD')\n", - "pl.plot(d_emd2,label='Squared Euclidean EMD')\n", - "pl.plot(d_sinkhorn,'+',label='Euclidean Sinkhorn')\n", - "pl.plot(d_sinkhorn2,'+',label='Squared Euclidean Sinkhorn')\n", - "pl.title('EMD distances')\n", - "pl.legend()\n", - "pl.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/Demo_Ground_Loss.ipynb b/examples/Demo_Ground_Loss.ipynb deleted file mode 100644 index a39a07f..0000000 --- a/examples/Demo_Ground_Loss.ipynb +++ /dev/null @@ -1,345 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Choice of the ground loss\n", - "\n", - "Data from [Fig. 1 and 2 in here](https://arxiv.org/pdf/1706.07650.pdf)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Dataset 1" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "n=20 # nb samples\n", - "xs=np.zeros((n,2))\n", - "xs[:,0]=np.arange(n)+1\n", - "xs[:,1]=(np.arange(n)+1)*-0.001 # to make it strictly convex...\n", - "\n", - "xt=np.zeros((n,2))\n", - "xt[:,1]=np.arange(n)+1\n", - "a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples\n", - "\n", - "pl.figure(1)\n", - "pl.clf()\n", - "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", - "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", - "pl.axis('equal')\n", - "pl.title('Source and traget distributions')\n", - "pl.legend()\n", - "pl.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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JmhXzSdIiMpskzcTU79Bt8XTgNTtc9+iIeA/wSeCFmfmB7Yoi4nzgfIDB2mkc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\ne2mSd+guAp64zeW/mpkPbf9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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# loss matrix\n", - "M1=ot.dist(xs,xt,metric='euclidean')\n", - "M1/=M1.max()\n", - "\n", - "# loss matrix\n", - "M2=ot.dist(xs,xt,metric='sqeuclidean')\n", - "M2/=M2.max()\n", - "\n", - "# loss matrix\n", - "Mp=np.sqrt(ot.dist(xs,xt,metric='euclidean'))\n", - "Mp/=Mp.max()\n", - "\n", - "pl.figure(2,(15,5))\n", - "pl.subplot(1,3,1)\n", - "pl.imshow(M1,interpolation='nearest')\n", - "pl.title('Eucidean cost')\n", - "pl.subplot(1,3,2)\n", - "pl.imshow(M2,interpolation='nearest')\n", - "pl.title('Squared Euclidean cost')\n", - "\n", - "pl.subplot(1,3,3)\n", - "pl.imshow(Mp,interpolation='nearest')\n", - "pl.title('Sqrt Euclidean cost')\n", - "pl.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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w5Uci8sGy3qnAIAf23RaPxsnjsaFZT1zpZ9w76cOamWvWGNaIgi9HQU1KeVhK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\nR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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - " \n", - "G1=ot.emd(a,b,M1)\n", - "G2=ot.emd(a,b,M2)\n", - "Gp=ot.emd(a,b,Mp)\n", - "\n", - "pl.figure(3,(15,5))\n", - "\n", - "pl.subplot(1,3,1)\n", - "ot.plot.plot2D_samples_mat(xs,xt,G1,c=[.5,.5,1])\n", - "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", - "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", - "pl.axis('equal')\n", - "#pl.legend(loc=0)\n", - "pl.title('OT Euclidean')\n", - "\n", - "pl.subplot(1,3,2)\n", - "\n", - "ot.plot.plot2D_samples_mat(xs,xt,G2,c=[.5,.5,1])\n", - "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", - "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", - "pl.axis('equal')\n", - "#pl.legend(loc=0)\n", - "pl.title('OT squared Euclidean')\n", - "\n", - "pl.subplot(1,3,3)\n", - "\n", - "ot.plot.plot2D_samples_mat(xs,xt,Gp,c=[.5,.5,1])\n", - "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", - "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", - "pl.axis('equal')\n", - "#pl.legend(loc=0)\n", - "pl.title('OT sqrt Euclidean')\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Dataset 2" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "n=50 # nb samples\n", - "xtot=np.zeros((n+1,2))\n", - "xtot[:,0]=np.cos((np.arange(n+1)+1.0)*0.9/(n+2)*2*np.pi+.06*np.pi)\n", - "xtot[:,1]=np.sin((np.arange(n+1)+1.0)*0.9/(n+2)*2*np.pi+.06*np.pi)\n", - "\n", - "xs=xtot[:n,:]\n", - "xt=xtot[1:,:]\n", - "\n", - "a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples\n", - "\n", - "pl.figure(1)\n", - "pl.clf()\n", - "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", - "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", - "pl.axis('equal')\n", - "pl.title('Source and traget distributions')\n", - "pl.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# loss matrix\n", - "M1=ot.dist(xs,xt,metric='euclidean')\n", - "M1/=M1.max()\n", - "\n", - "# loss matrix\n", - "M2=ot.dist(xs,xt,metric='sqeuclidean')\n", - "M2/=M2.max()\n", - "\n", - "# loss matrix\n", - "Mp=np.sqrt(ot.dist(xs,xt,metric='euclidean'))\n", - "Mp/=Mp.max()\n", - "\n", - "pl.figure(2,(15,5))\n", - "pl.subplot(1,3,1)\n", - "pl.imshow(M1,interpolation='nearest')\n", - "pl.title('Eucidean cost')\n", - "pl.subplot(1,3,2)\n", - "pl.imshow(M2,interpolation='nearest')\n", - "pl.title('Squared Euclidean cost')\n", - "\n", - "pl.subplot(1,3,3)\n", - "pl.imshow(Mp,interpolation='nearest')\n", - "pl.title('Sqrt Euclidean cost')\n", - "pl.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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woWJvBcTKmluyWv3ZtM5c3qjZdR0ysonaCYiCiNRN5Thi95Kk/L1IO+RuG+Du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\ntROiKGG1Ex27EsRt6lPUCPaG1ryWFuk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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - " \n", - "G1=ot.emd(a,b,M1)\n", - "G2=ot.emd(a,b,M2)\n", - "Gp=ot.emd(a,b,Mp)\n", - "\n", - "pl.figure(3,(15,5))\n", - "\n", - "pl.subplot(1,3,1)\n", - "ot.plot.plot2D_samples_mat(xs,xt,G1,c=[.5,.5,1])\n", - "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", - "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", - "pl.axis('equal')\n", - "#pl.legend(loc=0)\n", - "pl.title('OT Euclidean')\n", - "\n", - "pl.subplot(1,3,2)\n", - "\n", - "ot.plot.plot2D_samples_mat(xs,xt,G2,c=[.5,.5,1])\n", - "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", - "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", - "pl.axis('equal')\n", - "#pl.legend(loc=0)\n", - "pl.title('OT squared Euclidean')\n", - "\n", - "pl.subplot(1,3,3)\n", - "\n", - "ot.plot.plot2D_samples_mat(xs,xt,Gp,c=[.5,.5,1])\n", - "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", - "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", - "pl.axis('equal')\n", - "#pl.legend(loc=0)\n", - "pl.title('OT sqrt Euclidean')\n", - "pl.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/examples/Demo_Image_ColorAdaptation.ipynb b/examples/Demo_Image_ColorAdaptation.ipynb deleted file mode 100644 index 16e5208..0000000 --- a/examples/Demo_Image_ColorAdaptation.ipynb +++ /dev/null @@ -1,316 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Demo of OT Domain Adaptation for color transfer in images\n", - "\n", - "The color adaptation problem and the out of sample interpolation has been proposed in [6]:\n", - "\n", - "[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import scipy.ndimage as spi\n", - "import matplotlib.pylab as pl\n", - "import ot\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Loading images" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "I1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256\n", - "I2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plotting images" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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mTia0hLGlGUdRPJdlnZfEqBoxOLLbusg0fQycdE60mvwID4Ylse5O2ZcHU+LH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\n41yBpIqq38d4HxfdExL3lJffqz5zh0o6f6MfJwg6URVT45HwJ54UukTc9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QTmuMY+j45F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\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J3EjkYbtwM3Qdo3jh1nsXvllEaJzbyucijKlPqTbJCIUDgUMUGoEURz6MC2t0gm58nCIJ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fhX6o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vmv/VN/ir//kyMtKVkrJRlZY5/KUyHl6EbT0oi8iOlizmAe72NtM+QrmZgkaUrsm5A1ADwx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\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4xz+7HdCiqVAp2wpEYK5NyVtsMyPOf2nBvYT53DtihAdiVAQl50iEL3TOr28hR8uTGCQz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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(1,(10,5))\n", - "\n", - "pl.subplot(1,2,1)\n", - "pl.imshow(I1)\n", - "pl.title('Image 1')\n", - "\n", - "pl.subplot(1,2,2)\n", - "pl.imshow(I2)\n", - "pl.title('Image 2')\n", - "\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Convert image to matrix and dataset generation" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def im2mat(I):\n", - " \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n", - " return I.reshape((I.shape[0]*I.shape[1],I.shape[2]))\n", - "\n", - "def mat2im(X,shape):\n", - " \"\"\"Converts back a matrix to an image\"\"\"\n", - " return X.reshape(shape)\n", - "\n", - "X1=im2mat(I1)\n", - "X2=im2mat(I2)\n", - "\n", - "# training samples\n", - "nb=1000\n", - "idx1=np.random.randint(X1.shape[0],size=(nb,))\n", - "idx2=np.random.randint(X2.shape[0],size=(nb,))\n", - "\n", - "xs=X1[idx1,:]\n", - "xt=X2[idx2,:]\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot the images distributions" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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aA6kPJza2iRMnMnz4cJyccpDdwZFFixZhYWFBzRq1GTn8OwAqUYWiRYrRs3dX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\nWxMkGvgdePr8LjOBSCFEmxou5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kkSP//8MwMHDmThggWU6/WMnnTl57Ir5SHgsh+jLefz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m1m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0NVNQyNDmZy+wRmdU7m2W5p9I/0zJ3r4sJ5rUtbXuycSo8QP948eJL1+cVoFHh4\n/0Hu+eZbnBYrb779dnPbPl2zhnR/K8EmQ/M5X4MOvSK4qiuZldmKf/VK5ZlOiThKTvP6q68CsKW4\nvMWYbS0ux6iqBOi1BAQGkZSYSJq/R8xiU0E5l4X48lCq55n1uatbMWNQHEFWjxf2ypQAXAJ/2HAY\nB8L9/SOpd7iZs+UUdy04jFaB+btPc6SkDoNG4cvjVZTXOWkTYmZtfjluEf721QmKGhwMbe2LQatw\nRZp/c9tUReGadH8OHjpMfn6+p9+rV9HKX49eVRge3TLtmNhAXMDR6gYOVdax9XQVgyLtBJnOzsFI\ni57eoRbWnkdw5MdSW1vLF9u2c0OyHzqNglZV+PCqeDKCTKhA/0gbR8YkU35TO965PJovCmuY/rkn\nlODU9flEPr+Hq/919Ge343z8x5cCnkFEXgJeusBnfb73/qdbPtK05/iM5GqTgSVVZUhtNVNmzmwO\n0ObFixcvXn7f/Gb3pv8S6uvrufzyy/nmm28wGO2oqobFiz9g+fLlbNu27bzCAz+H7y5LuxgTJkzg\nsssu45133mH79u2sWbMGh9uJ9jvqvY6m+7nb7cZsNl+oKKqqqvj444+prq6mV69eJCYm/qg2V1ZW\n0r17d45nZxNp9kGjqCx6910+Wr6cHV99RVRU1HnzfT9I8sVwuVysXbuWY8eOkZycTI8ePS64fMnl\ncrFu3Tqys7Np06YNNpuNwu+oHJ+hXtxYrdaL1ltbW0vvXr04uH8/qT4+6FWVpe+/z/Jly9i+Y8cF\nlwWazWbWb9jA0qVL+fTTT9FqtRiNRioqKnA6nbw7fz4n9u6lvc1KWUkJf/nTn9i6ZQvLli/nj3/8\nI3PffpuXDmfTMygABdhcXIJD8UizL1y4ELfLzU3pMc3BeTPD/DhZUceSDz7AqtcxPi4SbdOSsstD\nAzlSVc2qU4WsLizCLYJNp8XpFrSqgo9eyzfFlaT72Rgc4dn2odeo3JQQye7SSvaVVxMVFUVsbCzd\nwsIYPHgwMTExzX21WC0cr3ewOrcYX72OjEAbRXUOShucPJQWQ4qvBYAEm5nbWoXx511H6dmjB89t\n3UJhfSNJNjNflVaxPL+Ia6KCWX26nJtvvJGjR45QgErPMDv//PYkBXWNeJx0UO9seT0rmoQXwsPD\nKSkqZOnuYjYfruBESQNDwv3wN2hZlV/ODf8+6Fn6ptFwwzuH6d3Kxrvbi7ht8xG2F1fz0GVhvPxl\nIQ6XUNPgwm46axaU17lQFKX5mdhitVJRLLhEqHa48DOcTVvW6FGW1KsK4zcfxahRKG04V22ytNFN\n4A/MwUtBp9Oh02kpqT9bh0WnYViCL98U1/NWr2j8jZ72jU7w46uiOp7fW4RG8bQxxKylwXnud+SX\n4L/GsPrNqCwCFERvBLcbd3EeAHaDjml/e5wHH3zwP9s+L168ePHi5T/EokWL2LlzJ8GhrTAYPMaJ\n2x1E0emjzJgxg/nz5//H2ta6dWtmzpxJbW0toaGhHC/OIykkBo2qodHp4GTpKfQaLY0uJw88cH4n\n4Ycffsi4ceOoqTm7H2jixIm88sorlxxiZc6cORw5fJg+ka2wNakbtnIGsT7/CE8++STPPvvsz+rn\n0aNHGTJ4MIcOH24+1yEjg49XrCA0tKXSf3Z2NkOHDOHAdzyKkZGR5FVXkF1dQbzVjohwqKqc3Ooq\nZl1/3UXrnj9/Pt/s2cMtrVoR0vRA3SUoiDnZ2Tz66KPMmTPngnm1Wi0jR45k5MiRLc5f1r07IQY9\nt8XFNRs/KeU+zP/4YzZu3Ejv3r1Zu24d99x9N+9u3w5AVseOvPf88yQlJfHWW2/hazI0G1VnCLeZ\naHQUEWO2NZd7hgiziZ0lFZx5dF54NJ+VuUXcmxaHAhQ3OOgc6Nsij6ooRJpNRCfH0zYtjfnvvgvA\n+++/zx/vvZf3Fi2ie/fu5Jw4wbHKWl7ZVwuAv0FH52CPEmaMteWeoDNxm267/XZKy8pYsHcvbsBH\nq+G6mFDc4qa8oZFx48Zx6NAhRn34Ife3i8TfoGXRsdPUu9yoCrzx5SlaB5nxNWmpdbh46bM8VAU6\nduzIxo0befuzQgR4ISuOrADPPrYb44IZ/9khcmsbEaeL3DIXC7bXI8BXxdUAfHvas89MoyjMXpvH\nXwdFodeq5Fc08mJ530cAACAASURBVPrnpxk4cAD+TWqR110/jj/eew8aReHRPXn8IyMKg0Ylv7aR\nVw4V0tpu5GBFPYX1DgQ4UdPIp3kV9IvwzMGlJ8r5srCKd3+GCucZdDod14y8hhc+WsKweDtJfkac\nbuHDI+WEmLTNRtUZ0vyNuAFFYNP1CWSGmtlZUEvHdy7uif0p/P4Mq8YGQCD/MIoIGreL5158kUmT\nJqHT6X4wuxcvXrx48fL/lTVr1mAyWZuNKgBV1WAw+LBy5cofXZ6IMG/ePJ544gmOHj2KuIWQ0BDu\nuusu7r333hYCE5eK2Wxm/vz5jBw5km3Hv8WsN1DTUA94NtD369fvnId7gOPHjzNq1Cj8DWY6xLRG\np9GSX1nGnDlzSE5O5v7777+k+tesWUOgydJsVAEYtVpCjTY+WbHiZxlWIsLwq6+mKDeXK6ISCDKa\nOFVXw+Z9+xl3/fV8unZti7Qjhg+nMCeHkTHxhJjM5NXWsDovB5PRyPLcYwSZLbiBktoaxowZ84Oy\n6WvWrCHKam02qgBMWi3JViurfsL1b2hoYOtnnzE8MqKF8ZNq98FuNLJ69Wp69+5N586d+XLbNgoL\nPWHjQkJCmtMajUaKq2v509pvMWg1ZIb70jcuiD2nK/H39+dYRTnljQ589Z5nOLcIX5dVoigQ62ui\nos6BWa+hzuHiHzsPISioCuwqrWRETCiaJk9glcPJ4Zo6uvj5sWDBAlpZTZQ1OjGoCjgaGH71VQwf\nMZIjBw4yuXU0nfx9yKtr4IXDuaw8WYyqKHxZVEGU5ey8+KLIE2qiU6dObNiwgV49evDt/v04RHjv\nZCEg+Pv5kZ+fz8iRI7lh/HienjePKB8zoVYjxys8xtuJ0nqueedbkgJNHCutp8EluAWWLVtGqNlA\n+yAbx2sam40qz3VTuSrKn9cOF7JpUiobjlXy8LqTZPpZmNk2ggEbDvFlbjUDInzJCDDz1525bDpS\nRaSfnm9P1aI3GHjxxbPO+sTERAIDAyktKWZZbhmr8stJtBn5tqKOAKOOOV2jGbH2CFUON9PahbKp\nsIqbNx0j0cdAo1s4Ud3I2DFjGD169I+eR9+lsLCQxx57jE9Xr6KitpGMeQdICzRxut7N6WpPUOGp\nX+bzaV4V5Y0uugabKaxzYtOptAk0kBl6YW/2L8F/xR6r35RAz5c1K709D01+gP3793PnnXd6jSov\nXrx48fK7x2QyIeI+I67RjNvt+knL5KdPn86NN97I8aMnsOptKKKQk5PDlClTGDFixDn1XCpXXnkl\nhw4d4r777yMkMhz/AH9SUlN5++23zxs0FuCtt95CAVKDIzHq9GhUlSjfAEKtdl584YVLrttkMp0T\nDBbAKS7MZjNz585l4sSJPP300zid5y6HuhhffPEFe/bupUtAKMEmM4qiEG620tEviLXr1nH06FGc\nTierVq3i4Ycf5utvvqFHYAihZguKohBpsdIjNJzaujpaJbTiijGjGTnuej755BMWLFjwg145k8lE\ng/vc61/vcmH+Cddfo9Gg1+mod7WMGeQUodHlOmfJZkhICCEhIRw+fJi5c+fy2GOPMeORR7BqNXTw\ntZNgMvNp9mke3rCfXQXlPPLIIwT4B/DPg8f5oqiUPWWVvHzoBMera3ELOBuFbkF+BGn1lNY5cQq4\nRLgyOpi82nqe+jabnSUVfHa6jH98cwSD0cTXu3ahApVOJ91D7MTbTBTWN+JodPD+okWMCAuga4Ad\njaIQbTbyx6Qoj0Hfvz/vHjvNvKOn+Lq0ivePFfLGkQKuveYaWrVqRUBAAJu3bsVqNtMobnrH+TA8\nJRDqq7hi6FCWL1/O23Pnsnr1aoaMHU/fa8ayZMkS7rr7HhpcQoqPGaNDpbXVhAboGeNDiEVHnElP\nhMlAjdN1zryscrgwalW0qkK/BDs3ZQTxVWkNep2WsbEBlNU5qWx0clWMP0v6JXFVlB+xWgNhFj29\ne19OfHw84IlPOGTIEELVOm5ND6JLhIVal1AvwrT24awamESoWY/DLcTZ9KzJr2Rez3jeuCwGg6qQ\nV+9ixYoVvLtgAaqqsnnzZt5++20+//zzH/UbUFxcTLcunXn7lRcYEapwY2s/rDqVAyV1nK5uYMaM\nGZiNBp7bW0RyqIHRbe1sK6llc0ENUTYd5ecJUvxL8/vzWFWVo6oaNm7ceFEZRy9evHjx4uX3xujR\no3njjTeori7Bag1AURQaG2ppqK9k3LhJP6qs3NxcnnjiCYJ9ggmyewKlBvsEc7Ikh9qGWj7++GPW\nrVtH3759f1Jb4+LiePLJJ3nyyScvKX1eXh4WvRHN95aN2Qwmcpo26F8KY8aM4YMPPuBkVRmRVl8U\nRaGorpr8mkpOHzjITTfd1Jx2+rRprFy1it69e19yGwH8DS2fTwKa3m/cuJFHHn6YnJMnmz/bXVpM\nqNmCrqlfgQaPAXTk6BEefexRrr322h/Vt3nz5vF1aSnt/f1RFIW8mhr2V1Ux9e67L7mcM2i1WkaM\nHMnKDz8kzW4nwGDALcLawkLqHA5GjRrVIr3D4WDihAm8M29e8zmbVsP01ESsWs8ja7cgP546kE23\nbt2466676NevH7fdeitvbd4MQGR4OGpZBcm+Fv6YFova5JFanVfMouwCALoE24m2GnnvaAHP7D8O\neCJ/P/nUo0x+4AHCzQZmZiRg1HjGtEtJJU9+mwMixFpaXpswox6TVkvfvn1p3749L734Iv86fhqj\nQc+Nt0zgmWeeaU47a9YsqmtrmTUgloxwz16jse2CuGPZEe666w9cddVV9O/fvznoMcCwYcN45525\n7KuopNHtCQA8MMmPe7qGMWP9SWoqXPQLsbMwp5h52UXcGB+EoihkV9Wz5GQpA5LOBh9vHWii3iVU\nOVzc3TqUnaU1rD1VyZenq+kcbOX+tDBW5ZazLKeMJ5qEZBwOB5Pvv4+BcT78s19k83g+t6OQl3cW\nMSTKjlmr8tjXp2h0C12DrWw8VYVGVYi3GTjVKIwffwODBw/m5MmTXHXFMHZ9/U1zm7p0yuLDZctb\neCkvxPPPP09hfh5fXp1AjM3j7f5DWgCd/n0ERHj4r3/BJbBgRBQjkz39/nOPYHq8nc2xskaqHW5e\n3V3Kben+F6vmZ/H7M6wa6nniySe9RpUXL168ePHyPfr27csdd9zByy+/TH1tOYqioa6uioyMDKZN\nm3bJ5Wzbto0777wTt9uNv+2sQIWiKPhbA6iqq8KgM/DJJ5/8ZMPqx9KuXTveeust6p0OjE2KeSJC\nSV01bdu1u+RyRo4cydgxY1j43nscrixFoyiU1Faj1+lxORq5LCyaELOF0oY6thXkMXjQIGpqay8a\nr+cMbdu2BeBkdRWJdr/m8zk1VWg0GqZNnQq1tYxMiMfPYOBYZSUb8vJ5+/B+2tj9yAgI4nh1JSrg\nbzazcuXKH2VYDR48mAkTJjBnzhx2lJejUxTyqqvplJX1k/agiwjdunVj6ZIlPHngINEWC5UuF2X1\n9cyaNYukpKQW6WfOnMmCd9/l2ugwOvr78o+9h+kU4NtsVAHEWszEmE2cbDIu27Rpw8ZNmygoKKC2\ntpaGhgZSUlLoEx7QbAQAXB7mz7+yC3ADu0qqGBQVSHqAjeJ6B9uLKngvu4Bhw4bxwAMP0DfUr9mo\nAsj0t+Gn11Lthq/KqsjwO7vkbm9lDXVOJx06dKBfv348/PDD5OXlERoaek7crmXLlhHho282qgAs\neg2DEv2YtzvvvGOobTLatq/7hEf7RRJk1mHWa6htdLEjr5p0u4UUu5nRUQG8fLiApbmlBBi07C2v\nJcym4/asswbLoj0lGFSFCV9kE2c1EGcx8G15HRO3ZJPqb8UhwqGyGkZdew2jRo0iOzubhx56iPyC\nQmZfEddiPG9IC+CFr4q4fn02tS43BXUO/pQeyqLsMsoaXYxcf4xdxdW0ik/gscceQ0S4dsQIio8d\nYvHgWLJCTGw5VcP9W7/h+uvG8unadT84nxa/v4hhUZZmowogwcfAkGgbJ+oaMWpVtp+qpU+spflz\nk05lYoYf9646RZBJ5c41eTz7VTG6X2nN3u/OsJozZw633HLLf7oZXrx48eLFy38diqLw0EMP4efn\nx86dO/H19WXAgAGMHTv2kv+QXLlyJcOGDUNVPcvO3G43GvXsEjS3eCQFBEGv1/Ppp59SXFxMp06d\nmpce/RrccMMNzJw5k90FOcTaA9BrtORVllJSU8Ub06dfcjmqqjL/3Xe5ftw4Fi9ejNPppHXr1vzl\nL3+hY3A44VbPw3SQyUJWSAQb8o7z0ksvcdddd/1g2a1bt+bqq6/m4+XLKa6vxaDRUudycKSqkqxO\nWXzxxRf0iYwg0GhEURQSfX2pbHTw1enTHKgo40BFGU63mza+fpxqqMdgMPxgnd9FURRef/11Ro0a\nxfvvv099fT0DBw5k+PDhbN26lfLycrp06dJCJe9iTJs2jccff5x4uxX0GvJr62lwuXjqqafO2dMm\nIrz4wgt0DfCle5DHo2DQqDS43Oekq3e70bhcuN3uZoP1jLDHGXnw+u/la3C5cQN6RWFRdgENLjfJ\nfhYOV9Sy5HghCh7FSFWBuu/ldQONbjeRUTGsPH4cjQKdA+zk1tazKL+EtmmpFBUVsXLlSvr06XNB\npUm9Xk+9041bBFVROFpSR05FA7kVDVwsZO2DDz5E9w8/5PkvCrg2NYAGl5s3dxbR4BJ2lNXw7vHT\nXB5i54uSKnJqGzEaFXyNnn1l645VEudr4MUvC9hdUEt6mIn2YWa+yKlmc55nT9KsWbNYsWIFWq2W\nGbfdxjXXXMORI0fo1qUz7jqP0Eu1o+WYnHlfUNdIVrCFO5ID2XCqmqOVDfj5+ZHYdxC39OjB+PHj\nsVqt7Ny5ky937GD+gGi6h1s4XeugutHNyDgrL61bz+HDhy+q0Ll06VIOHjxIdOS5QaarHC58jRpe\nvyKC1i8e4r1vy7mj49lwDJUNLhQ8XslJ6b7kVDoREfYWN1xk1H8iv2RQrP/mg99xIEYvXrx4+U/y\nvxog+Nc+/tvuSw6HQyZNmiRKU5BSQEJDQ2Xr1q2XXIbb7ZakpCSxmqySGJkkiqKI3WyXlMhUSY1K\nkzYRyWLUGUWr8QQaDQkOaRGsdfz48dLY2Pir9XH//v3SrVu3s/0LCZW33nrrZ5f7+uuvCyADohNk\nVGJq8zE8wROE9I477rjksg4ePCgB/v4txuVMANwzR4DRINclJcrtaakyNCZGABkZGysGVZUAg0F6\nhIQKIOvXr//ZfduyZYuEhYaeDWarqnLrrbeK0+m8aL5jx46JoigyIDxIHuuYIo91TJF/ZCZLot0q\nrRISxOVytUhfV1cngFwXGyH/zEyVf2amSr/QQNGrqjyUnCDPZ6bJ85lpMi42orktSYmJsn///nPq\n7tqli4SZDfLPLm1kTo80ee2yVOkZ6idaRZEufj5i0ajNQWQVzr5OnjxZALHpNPLPrER5r2eaLOyR\nKmNiPfN0zJgx8ve//118bNbmsYiPi2v5nQkJlg0bNpx3TF588UUB5OYOIZIeam5xTc0mo+Tk5Fxw\nPJcvXy4JcXEt2nzmOBNY2GQwSGZmpphNxhbnaQpCfH26v+y6O0V23Z0iX92VLAMTfUQB0et0zels\nFovMnTtXxo4ZIxFWo3wxIEkSbQZpG2SUr25KlkO3pcneiSkyJMFHtAqSbD87N6MtOhkYZpOYiIhz\n2r9kyRIB5OuxSXJXu0DRKi2DND/66KMX7LvL5ZK4mGhp42sQjYIsHxwrVRPSpGpCmnwwMEYUkBcG\nhUnN1FQJtWile5RZ6qenSsOf0mTl9bFi0CotxktVkECz+qvcl/7jN5bf6vhvu4F58eLFy+8Fr2H1\nv3FfmjFjhscQsoVIeHCSBAfEicloEZvNR0pLSy+pjOPHjwsgkUFRkhyTIuEB4Z4HJ1UjFoO16QHU\n85BjNBrFZrRIUkCspIUkSaRPqKiqKtOnT/+VeyqyePFi6du3ryS2aiWDBg2SFStW/KzyDh06JIC0\nDQhuYVh1DY0UQBYtWnRJ5bjdbumYmSk+RqMMjIyS8YlJ0jc8UvSqKoEGo9yUmCRDo6LFR6cTf4NB\nbktNkbYB/qJXVUnz8xOTRiNq08PjrbfeKm63+2f1q6SkRHxsNomxWWVSYpxMTkmSAWEhoiqKzJw5\n86J5X331VVFAHslo02xYPdYxRW5sFSWAHD58+Jy+x8fFSgd/e7Nh9Vh6Gwk1GkQBibeaJdToeYgP\n0Ovk3oQoCbeYJTYmRhwOR4uyvvnmGzEaDKJTFUnxtYhd7zHk2/pYxVerlWC9x5DoGWiXx9PiZXa7\nBOnoZxONRiPRkZFi0qiiURRJtVvEvymvRkHapqXJypUrpba2Vvbu3St/+9vfRFUUualjsLwzJlHu\n7h4mdqNGdKoq/fr2kZdeeknGjxsnya0TpXevnjJv3jxp166dKCBWjSp/bRMhH3ZJksdSoyTEZJCO\nHTpc9Jq5XC658cYbxaDVyJS2obJyQJK81i1WWvkYxGIyyb59+0REpLKyUvbs2SMlJSWSl5cnbVon\nCSArb05sNqx23Z0iD1wWLIBcH+8nnw5sJZ/0T5ArouyiKIpYLWa5KylI9g5NlokJ/qJREJNWkcsi\nrRJo0ooKYlCQP7YJkjY+Bom36mVSqwBp7WuWIYMHN1/T9957T/pe3lsS4mI8xmmirwDyYFqg7Lk6\nUTYNjpc+YRYx6HRy7Nix8/Z7//79Asj7faPk8jCLAJIZaJKMQI8BOSDeKuUPpsiOiQnNxlMrf730\nijGLVkUSQ/SyflqMlL6UJHMnhYvVoIiv6dcxrH5/qoBevHjx4sWLlxaICM899xxmky82iz+qqkGv\nM+JrC6O6upoFCxZcUjlnFHbPLPezW32JC4vHarJS01BNSEgIo0Zdy+TJk2loaCDKJwyz3oRW1RBo\n8SPA5MuLL76I63sqcr8kixYtYtSoUez47HMaTpfw5abNDBky5CfJpIsIO3fuZM+ePbRq1Yq9JafZ\nV1pEaX0dR8pL2X46nwB//3NEGs5Xzmeffcbs2bPZ8dVXdAkIItJiRa9qiLXZ6BIcQnGDZxldhMVC\nz7AwShsaWJubx56SUgQ4UllJvN1GrI8VVVE4cfw47vMECv4xzJ8/n5qaGkZEhhFqMmLSaugc5E+6\nn53nn3vuzB8E50Wn0yGA43ttcLg9eb4vta8oClOnTWdnaQWLc/I5Xl3L/opqXE1BarOra6l0OBgU\nEsDDyfEk+Vi5ITKE4ydOnBMKoG3btnywZAlOt7C/vAajotDebuFAdQ2VTidFjQ5SbWYmxYcTbjYQ\natTzh4QIbDotrZOTqXO5ibcYKW90UNroJNKsZ0CoH3UnjzFo0CDeeustUlNTefvNOVzeys7VaQHs\nzq/hha2n8FU0DAq0c/yLz7nzzjtZuvg9EjRFlB/dyfjx4+nYsSMC3BYfwmWBPpi1GjL9rNwbH8yO\nnTvZ3hTL63w0Njby73/9izGxvlwZ7YdNpyHVz8Q/OkRSU1dH7149OX36NDabjbS0NPz9/QkPD2fW\n408AUP+95XyrDleS4mtkStsQQkw6Ii167mwTiJ9BQ319A3VOJw/szOXN7FI6hZqJs+vZll9NZb2T\nW+L8cQo8f6CIBLuezCATC46XcaSillsmTAA8S0HHjBlD3bEd9PCrwKJX+ffRcoZEWnkgLYggo5Yk\nu4HXu0ViULlgnLQzvysugcX9o3mjZwQFtQ52FdfTL87CQ10DWXKwkuHv56BTYdn4aC6LNVPvEpxu\nmH97OD2SzPiYNFzfzc6frwqiuuHXCRDsNay8ePHixYuX3zkNDQ0UFRWh17XcR6XR6DAajBw/fvyS\nygkPD6dTp06UV5c1G0cGnQFVVdFqtezevZtFixZhMpkw6g3oNS1DnZh1JioqKqiqqvpF+vV9Ghsb\nufvuuwkyWegUEkWbgBA6BkcSZfNl6tSplJeXX3JZeXl5dOnShczMTEaOHMmRI0ew2mzsLTnNpyez\n2Vl0irDwcHbu2nXRcvbv309KcjLdu3dnypQpAAR/T9o8yOh5X90k3x7S9P5oRQX+BgMGVeX6pDgu\njwhlSEwkQ2MiWLV6NR999NEl9+d8nDhxAn+TEauu5Zb8CLOJgsJCDn8niPH3GTZsGHqdjtX5Rbib\nDLA6p4tNp0vJ7NCB6Ojoc/JMnDiRJ598kr0NLv558BjvHMuldYdMli5bBsCkuEiuCA9u3lcVaTKg\nVdXzzs99+/ahKgqPpMbyWLsE7k2K4pHUODSqik6nI8HWcoy1qkKMUYfZbObZZ5+lWKPnVF0jXQNt\nPJkRx80JIcxMi6RfqC8PPfggFRUVnDiZS1KgEYdLeHNbId38bDybHMekqBCeah3NkCBfGh0ubugY\nxMxBkdzSKYg333wTgDbfq//M+4t918rKyqiurSXFt2XeKIseH51KeVkpTz/99Dn5+vfvj93Hxotf\nFOFwNV0Lh5vjpY1k+JtQFAWHW3h4Vz5D1xyltN6J0+Vi7rEyVp2q4qle4bw9KJolV8bx7uAYVFXh\njWOluPDsP9OpCn/vEMKqQXFY9Vq2bNlCdnY2TzzxBFN7BvH+6Ej+0T+Uz2+LRwQyAlq236JTaWPX\nX7Dv8fHxpLdty5N7S6lzuhkVb+ebEQmEm7WsP15Dv3ePc/OyXCrFgMMNX5+q55Wrw7gu3Y5WhQ4x\nLX/XOsWbcP46dpXXsPLixYsXL15+7xgMBqKiomlorG1x3ulspK6+lpSUlEsu65VXXkGj05B96ihH\n845w6ORByqrKGDBgAAsWLOCyyy7jnXfeoa6hnqqGmhZ5qxpqCAkOwcfH5xfp1/fZtWsXRUVFxPh4\npMTB4ymJ9Q2gvr6e9evXX1I5IsJVV13Fvq+/oVtwBMOiEsgKDKO+pgYfm43BgwezZs0ack6ePK8B\ncYbGxkYGDhjA6ZwcBkZEMSgiCoDcmuoW6fJqa1AAe1MQ3Nwaz7gJUNHYSLK/HdN3lPNibFaCLGaW\nL18OQH19Pc899xzdunalY4cO/PnPf+b555/n8t69SW/fnnvvvZcTJ06c077k5GSKa+soa2hscT67\nqgatonDVlVde0CsWFBTEc88/z7aiMp7ef5x3juTw5L5sKhUNr7z66nnzKIrCAw88QH5BATt37uTY\nsWNs3rKFrl27YjGbOVDVcr4crq7F6Xafd34uePdd2totRJrPPlSHGvVk+FrQ63R8W1XfwuNW53Jx\ntNajKHjPPffw3vvv48YjzjD96+PMP3aacoeLqyL9qamtZdOmTSS3bs3uU7UcLamjvN7F8BD/ZuU8\nRVEYHuJPvUv45pTne3VFih86jYqqKHxV1nQNRdhYVMmDe06gUWHx4sUcPHjwvOMTEBCA1WLhmb0F\n3Lw5myf2nOJEdQMHKuqodLhpH2Zk+dIPm8tdvHgxgwYOpHuXLnTsmMXa7CqGzcvmvo9OMvSdbOpc\nwmdFtbhEePlAEUtzKpiSGszG/gm80y2KOKsenQKXR3pU9hpdwh835hNh0TGvbxRfjkzgkawQPsmt\n4vFvigg367gyysri9xcxdswYtIqwPbeW9dme+exv1hJg1rD+VE2LsS9pcLK7uJbw8PALzotXXn+d\nQzWQ9kE2Y9bm0GH5CU7VuXjy6WeYP38+W7ZsobyyimnTpvGXT0+T8eJx5u8qx+mG9ftb/q59+m01\nxl9Jvu93pwroxYsXL168eGmJoihMmzaVO++8E1XRYDbZcbkcVNeVEBYW9oNL2b5LRkYGO3bsoGfP\npmVJRgsCfLLiE1asWIGPyYqqqKiKSnbZSSJswZj1ZsrrKimtK2fyHybzySefkJqaSmxs7C/azzMB\nct3fW8J25iHvhwLonmHbtm189dVXdA+JJNTkeeiMtvr8H3vvHSZFlfdv31Wdc89MT84wJJUMkkRy\nUBEUAwqsoCKiIiq6BtQVQd014JpARQQDIJJEkiigApJzzgOTY0+H6Zzq90ePDaMYd/d9nufdvq+r\nL6juqlPnnDo9XZ/6JkJShP3WKr5dv4Hdu3axfccOCgoKfrGdVatWUVJayrCcPBIaMvhlanVsq6ok\nGImQotFQ7vawp7aaJJUaXyhMscvNrppq0jUaLjOb+b6y8meFYSVJIixJyOVygsEg1wwezObNm2mi\n0yOXJP5+4CUikkSeTodBJuPD995jzgcfcN/99/PMM89gNpuBaF2rZ55+moXnSuiXnoJJIeew3clR\nh5NuSQlsP3mSb7/9lv79+19yfPfeey/t2rXj5ZdfpqioiLFdu/Lkk0+SnZ39q/Or0Who3759bFun\n0zHxwQd57dVXCUkSSUoF3nCELXUO2rVte8k6YRUVFSRFfu6qGJYkRFHkbL2LdwvLGZiaiDcc5suK\nOiS5gnvvvZdIJMIL06cjAGaVDKNCzoZKO1tqnDzQLC02x0OGDuXVV18l0pBFMPST6xBq2JQ1iK1Q\nREICCgoK+PDMGSQkzrp8bKhxcnm6moG5Rr5bt5JOa9ew8dvvuPLKKxu19/TTT+Nyu8kzqsnXq/ih\nsp6vSh0YFCK5CUoSdTJ8MlksTfqyZcton6wjXytna+EpVEolPfpfi8/r5eo2KlatXMn5ej8P7yxh\nd62Xv+QnMDo/mubfopLzZqdMrv3uHF8X1XNDgZlNpS7KXEFWX5tLy4SoYB3Z3Ey1L8Tc43X8tXUy\nRa4A5VV2BGcdt+aaOGzz8ZclpTzbJ5l7r0wiHIFt1R4m76rgjoIE6vxhXjlcQ1iSfrWgdpcuXTh8\n9Cjvvvsuhw4eZGB2NuPHj6dTp06N9nvppZcwmUxMeeoplCIYlAKj3ivj1dtSaZOtYvUBF6+ssaJR\nCPhCv+zK+meJC6s4ceLEiRMnDhMmTMBms/Hiiy9RbT0HQMeOHVmwYAE6ne43jm7M2rVrqampIS8l\nG41SjdPjot7rIjcpA6MmWsMnGA5xprqIUmcVAGq1mszMzFjRX0EQuPnmm/noo4/QarX/ljG2b9+e\n7KxsztfVS7b+YwAAIABJREFUYVJFiwVLksRZey0GveF319QqLCwELhTu/ZGkhuK8nROTOeCw8uyz\nz/LZZ5/9Yjtnz55FpVDERBXA1WnpbCwvZWtVtJitABgVCqx+H0vPR69Ljk5H34wMBEAhk3GszsEV\niWaMDXFLJ+1O6jxehg8fzqJFi/h+0yZuycgiW6OlzOvlhNvFNampXGGMFlH1hcN8XFzEjBkz+Ofr\nrzPxwQd588030ev1zP7gA24YNowlRaUAqESRvikWuiclsN1qi83FpTh//jwTH3iAPXv3AlGLodPh\n4IM5c/5wPdFnnnmGlV9+ycYTJ2LvmYwG3nv//UvWCFOr1RytrORUvYfmhuj6KXR52W9zoVKrmT17\nNk8/9RTbj50HoHlBAevmzSM3N5eVK1ey5Ycf+OsVGXSyRNer3R/iyb1FzD5bhVar4c6xY6izRV1H\nd5e6EIBF5bU8W5CFQhQJSxILy2vQKUTaZGiRJIlFB6yEIxK2ujoSlDI+OFdNBBjfw8KtHaIp5r3B\nCJOXlzH5kYf5Yeu22HhOnDjBa6+9xn1NLYzKbdg3nMz9e0uoDAR56OoUpqytIL9phKZNmwIwsZWF\nsc2j+/pCEe7dXk55aSlbt2+nSV4e3VN1XJ+l59XD1bhDEdolNHbRy9UpMSlE1p6LCqtiZwCtXIiJ\nqh/pYNHwTkhiV42b7dVeeiTr+Kh7BgpRQJIkph+u4eVNtQxqpsfhDzM4Q8fXZS4+O+cAoKVRSUuz\nhurq6l9dA7m5ufzjH//41X0kSeKjDz/k6nQtqwdkUOcPc/eWSsZ+EE3DLwoQkcDl//eLKogLqzhx\n4sSJEycOUavVlClTmDRpEkeOHCEhIYEWLVr8qba+/PJLdCotGmX0Bszlc6GSK2OiCkAhk5OgNeEn\nSL/+/Vn31ToqKipIMyaQrDfh8Hn44osv0Ov1sbgUAK/Xy5w5c1i6ZAmhcJhhw4YxYcIEjEYjdrud\nWbNmsXr1alRKJbeOGMFdd90Vq+ckk8mY8+Ecrr/+erZXFmGQK3GHQ3gCfj7++OPfLSBbtmwJQI3P\nQ4b2Ql2dap8HAUhQqsnT6FmyeDE9evRg3LhxlxQSLVu2xB8MUuvzYWn4XCWTkazWUOPzcU1GFgkK\nFRq5HHvAz7KS80iAW5LYVFFBqcdDhGgR2QWnz5Oj1+KPSJS73IweNYoBAwYwYsQIMrVasjVRcXHW\n40Ivk3G54YK7pVomo4PZzKbaWgp0Wt566y06duzIHXfcQa9evVAqlbTTa7ncZCRFrUIpihS63I3m\n4qeEw2GuveYaaorOMyYvg3S1kmNON58vWoTRZGLmzJm/a65/5Mknn+Ts6dPcmptMmwQdZd4Ay0rq\nGHPHHRw9dixmbZQkieXLlxMMBhEF+MeJYgr0alSiyDGnB4Uo4Pf52LlzJ6Xl5Rw4cAC1Wk1BQQFz\n5szhmaencPLkKTL16pioAjCr5PROM7GiuA5BDNFGJ+OWTjnIBVhRYue7SieHXF5GHjqDWhDwSxK+\ncASVQsbrmyooc4Y5b/XwwAMPMHPmTP7ZNps9dW6WVdi4oa05dh6NQuTGNkZe2bAdm81GQkLUgrRm\nzRrUchm3ZF+0r0xkRHYCLxyv5K+ryjCbTBSdOc2AFD2bat3c3vTCvmq5yG15Rp7dvZvNmzdTXFrK\ncz2y6JyspXeangFfF7LT6qF/+oX1fNrpxxGMsKnUzU2rovW7PCGJQ1YvbS6Kk9pRFZ3XcT+UEZbg\n3mZmFOIFl8gHWiQy54ydAXPPgwTekMS+IU04ZvejkYukqmV0+qqI2/7k35sfqa2tZdq0aZw+cxpV\nopIFZ52MLjCyZlAWh6w+On1ZzKSHHqZv377YbDbGjBnzL53vUsRjrOLEiRMnTpw4MfR6PV27dv3T\nogqIxS/9lJ9mkZMkiXqXi7Wr16CRyVHJFFQ6bVjd9STrTaTpzXz66adYrVYgGivUr18/Hn74YY7s\n3c/JA4d46sknadu2LStXrqRz584897e/cebgYQ7v3sMDDzzAtddcQzAYjJ1z4MCBHDhwgDvHjaNV\n547ccvtt7Ny5k4EDB/LNN9/w6aefsmnTJnw+3y+OLysri6zMTPbUVlLkclAfDHDWaeOorYYcnRG1\nXI5EtDjyQ5MmMXDAAPx+P2fOnGHDhg2UlJQAcO2119KsoIAtNVUUueqxB/wcqrNyzG5DAs67XHjD\nYco8br6vrkQQBObOnUv/66/HrlAQikTI0mhIVyiQJImqYJi2PXvy+eef8/EnnyAIAoIQLRgUuzbA\npZ7VR6ToZ0Mz0zDK5Ux7/nkKCwvZvXs3t44YwT5HPSUeL85giCMOJ6uqaujQvj09e/a85Bxt3LiR\n4ydOcHOGhZZGHSalgm4WM30sZj6cMwen0/mL8/tT6uvr+XDOHAamm+iVZiZBpeAKs4478iycPHWK\n9evXx/adOHEiN998M+r6OjonGdDKRM67fTiCIbK1KgIRid7JJj795BNcLhdXXnklzZo1o3/fvkx+\n5BHqj+4j7LASCIY4XOem8KJYrAgSCoWCZI2KR1ulka9Xka1TMbFFCikaJRJgUsrolKIlsSGIp0+/\nAShzO9J94DA2btzITTfdBIAkgVbecBv+kwvSkF+i0ffox+v400SMkYaDH3r4EepsdoanG7jcoI6d\n41Lter3e6DZQ6Q1ysM7L0Bwji87bmXWqlrP1fr6tdPHgnjKyDHL+OTCNDKMcpRzkAjz4QyVfF9dz\n1uHn3SNWPjxeR1ZOLo89/sSlhhM7b3eLhqltk9lU7eG5gzWoZQJWf4g7tlciV6m5uyGb4J+hoqKC\nDu3a8uH7sxh+uR6LWcaEH6q47dtywhGJLH00PrF169akpKT86vf7XyFusYoTJ06cOHHi/FsZPnw4\n33//PR6/F61Kg0Gjx+524vS6MDVYeAKhIDaPE5koo0VqFrIGd65yu5Vyh5UknQG9SkPIXktxcTFJ\nSUnMmzePHTt2cEVqBnqlmlKHDZvXzfnz5xk2bBgCAq0sqSTropYGm9fDt999x2effcYdd9wR61+r\nVq145513AAiFQjzyyCO8++67sUyGAmAwGHjr7bcbPdUOhUI8+uijsZTwMkFgT21l7HOtTE6bBAve\nUIjCegc5BgNNTUa+++EHOrRvz7Hjx6PtCwK33Hwzc+fN45v167n9ttv4bufORnNoNBg45rRzzBl1\nN1MqFMz58EPuvPNOFAoFn3/+OddnZJDd4CZpDwRYXlHBFVe0jsXERSIR3G435R4P5zxu8rU6mur0\n7LbbOOhw0K4hlsodCnHQYaeZQY9MFMnWaThVVBRzKQPIy83l25IS1lfVANCnd28WLFz4iyL6zJkz\nyESBHG1jS12eTsM3VVbKy8t/d5KSiooKfH4/TQ2Wxm3p1chFkTNnzgDRGKRZs2YxItfCgPQfxxbm\nxcOllHkDGOQy7muaRpJSwbc1DkpKSkhMTGTu3Lns2r2Lv7XNpKleyVP7Sil1B3jhUBkAWVolo5ta\n2FTtJikpiaYRFzLxwrgjEtQHgnRK0jClbToyUSAiSbx9rJoftmyhorISnU7Hiy++yEsvvoAMWFhi\nZXx+Mh+cq2XxfhujOycB4PKHWbrPhtGgp66uLhbvNnToUB599FEWFtu4Mz8pNrbPSx3k5eby7qyZ\nSMD8EjsZajnBiMTHZ+q4t2V0zlzBMJ+dd5Cfm8MtN92ECEzZU0GdP8yP6Ue0MoFZp6zMPBV9kJGh\nlzNvaCa5JiXXFES/tzcuKcUmGHhgS9S1TqlQcN8DE/nnP/+JKIosW7KYd05W0sWiQS2Lutq+edyK\nShR4o3MaiSoZIUli+qFaPj4bXdvNmzZl3Zefkp6e/rvWw0+x2+1079aNemsl+x/IJtccFVGrT7i5\n+bMKVhW72FLpRSaT8fzUZykuKf9T5/k9xIVVnDhx4sSJE+ffyt13382CBQvYuXMnerUu9sS/uK4C\nmT0aRxGRIkiSRFaCJSaqAFKNCVQ5bdi9biKShEwmY+XKlTz66KMcOnQIjUKBTqmmyuWkxFFHptFM\nqt5AIBTinM3K6bpqzGoNCpmMBI0Ws0bLl19+yeDBg5k1axbff/cdJpOJv9xxB8OHD2fq1KnMmjmT\ngoQE0nQ6XIEgJ6y1eNxuxo4dS05ODn369AFg+vTpvPP222RodJR6XMgFkcv1ZlQyGZ5wkBP1djZW\nFBOIhFGIIq2TEtEpFCRrNJw8cYLuqSlYNGoqPV5WfPEFMrmchQsXcu1117Fj504KDAZaGA34IxH2\n2uwYDQYsFgsmk4nH/vpXbrvtNiDqapmu1cZEFYBZqaSJRsPyZct45ZVo3aKXX36ZNWvWYFbK+aKi\njByNFgVR4bi+ppoj9U5McgWFHjcKQaBXShIRSaLY7SUUDnN9WgrZajVFXi8by8oYOHAgjz/xBJmZ\nmTRr1uxn112SJFauXMlHH31E4dkzhCMSx5xuLjddcKkrdHtQq1RkZmb+7vWUkZGBWqXijNNLc+OF\nMZ9z+QhFIjRr1oxVq1bx0ksvoZYJ9Ek1xfbRyWUMSDez4HwNKSo5p+q9KAQfSoUilkRjxRdf0DpB\nSzOjmrePVVLiDjA0z0yvDAN1vhDzT1l55Ug5aWlp9Ordh41fRmtkyRvE1el6H96wxC35CTHBJQoC\nt+QnsHFrMevXr6e2tpZnn32Wm1uaSdMZeHdfLU8dKSVPq+SjHVa2nHGRm6hkT5GbQEjCqISB/ftx\n/OQpFAoFBQUFPPvss0yfPp2tdV6y1TJ2O/z4EfAWFXHb5UZS9Xq+O+em0BZABD44WcfmSjf5BiU7\nan34EfHaihnV3MQRKxy3+XmylYUeFi1HHX7+frwGk1LEGZKQqbW0tECu6UK9MbsvzHlHkNtHD6Gi\nvJzyslK69biKyZMnI2/IStmsRUu+/uocV607R48ULQdtPgpdQaa1tbC+ws3q0nocgQghCSZPnszI\nkSNp3779JePkLoXH42Hu3LmsXLECQRC4ftgw5sx+n7LSIh7oYo6JKoAhLXVclqxk/NZq7P4wKpWC\nvAQb8x/LoNwa4rZpvx7T9WeIC6s4ceLEiRMnzr9MeXk5Z8+eJT8/n6ysLL799ls+/PBDli9fTigU\not7p5MDBg6gVCmSCiMvvjboM/YLFw+Xz4vR7MRj0TJ82DaNKTViS8AQCnKiuwBcMYNHqyU+IPr3X\nKpRcrlCyu6yIanc9mcbok34pEqG+vp527dpRW1NDgkJFEImVq1YxduxYli1dSq7RSFNzNJZFp1Ci\nkcvZWlaKVqHk+eefp3fv3thsNl55+WUsKjWGhrpOHczJZKgvxGXJBRkHnbVk63V0SE5GfVEKdJNS\nSb4x+tTfYFIQkSQWLVrE8OHDmfHaa7QymeiafMEiY1Ao+KK4BMHnw1lRwahRo1i7Zg2fzp+PJElc\natYELrhbhsNh/vn667RJMNI73cJxez2nHC5c4ajz2D333MP2bds4evQoiUoF/VOTCUQirCitoD4U\noqPJQJuG/iY0pHlfs24dM2fNIj8//5LX7OGHH+att94iW6vFKAooRIHPiisZkm6hhVHHcaebTbUO\nxk+YgMFguGQbl0Kv13PP+PG8N2sWGrksGmPl8bO81EarFi3o378//fv2JUmlwBsK/9KSIkEtY5et\nHlcwwnVDhpCYGE3scLGL6h6rm+6pOkY2i66rTJ2SJ9ormbiliM5XduGJJ55gyeLFvHaskltyE5AL\nAkvO1110BS6+HtHtF6ZPx+2qp2eOnnHtou1elqxmyTE7m4pdiIKAsz5EdVBgQLKB4VkmXKEI9+45\nz+rVq7nxxhsBmDZtGl27duXDOXOoqqxkbJcufLVmNWmBCtwBiTd3WmmdoKJToprtNV7kSJyrD1AZ\nUTBm/H18ve4r0j0V3H1ZAgO/PM8jzZO4LScqQrO1CnRygQf2VSITRZ5/4kmeeeYZHvm6gtFtTIDA\n6zttRBCZO3cuLSwaCgwin39ykoXz57Pxu+9o0qQJGzdswKIV0YoCJZ4gSll0Dj4pdHCmPkiPFDWJ\nahG5AGtXr2LKlCmXFFWRSISDBw/i8/lo3749arUat9tN/7592L1nD/1SNYQliUkbNiABiRrx0t8J\nAUxpWVx31VWs/OIzVr2UhUErsu+U/7eW3Z8iLqzixIkTJ06cOH8al8vFuHHjWLJkCZFIJFq/58Yb\nmTt3LhMnTmTixImsXbuW6667juzEFMzaqPUiEApyprqMSoeNJK0hdnNV7bQBYPO6aNWqFWdOn+by\n1Aw0iuiTc7vXw8maqPtd5k8SQijlcjQKBZ5QNKbK7vPi8Ps4f+4cDmsdXZIzYmKnzOXko48+AqB5\nWlqjdoyqaOFZpSiwedMmkhITcTgcRCQJH1Dji8aoJCt/Usi3IUtghk4XO0+t10u110tHS1Jsv3Ak\nQpXHiyRJ3HLLLQDIVUp84TDqhiQMZqUSlSjiCAYhGEQnk7Fg4ULGjB3LsGHDWLZsGWUeD5kNVitH\nMMhZr5cHb74ZiMYl1dTW0jkrFZkgcEWCkSsSoq53cwvLSEtL4/CRI9x3333Mfv99Pm9wj/rx5rRH\nUmKjseVpo2M9ceLEJYXVvn37eOutt7g2OYnuCdGbdVcozHslZXxZXgPlNYiiyF9Gj2bGjBk/O/63\nePXVV3HV1/PxJ5+wtCjqjnhl584sXrIEmUzGiRMnuMyoZktNPd9XOeiXFhXWnlCYjZV2rjBruL9V\nGv5whNeOVHLq5ImoQBUEbrjxRh7+/jtO2j0EIxKXJzbOQpmolpOiVXDu3Dnatm3L4iVLuPeee/jr\n3misnMlgQC4KLD1Xx1MNroCSJLHkXB0qUWDf/v0IgsCADhfWQBOziie6p1LsjnDW6uHh5sl0tTRO\nnmJQKTh16lSj96699lquvfba2Pa7s2bRpkDNkmNOnr7CwtDs6DWu8oYYs60UVYKFAwcPkZqailbz\nPte30lPmDhKSoFNi4/XbuWF73D33sHtX1DX1q7Mu1p2N1qHKSE8jGKzi3jZmHuuUgCAI1AcijF5X\nxYMTH2DWu+8RCAYZ2MLAkuMu5vRIp8Cg5M4fyvmu0ssnPdPonxGd29POAEO+Pc8rr7zCyy+/3Kgf\nW7Zs4e6xYzhdGM2CaUlM4B+vvIrdbmfv3r1s7JtOxyQ1X5a42FgZ/S4OLtAy/4CT+7uayDZFHwR8\ndcrN0eoAy5f/k9dee40OzZQYtP/Z9BLx5BVx4sSJEydOnD/NnXfeybJly7AYEshNySTZlMiqVasY\nNXJUbJ8vvvgCnVqDSXPhxlEpV5CgNRAOhzlZXUaRtYozNeVUOm2MGjWKoqIiAoEACWpNTFQBGNUa\nlLKoaCmx26iodxBuKFIbCIfwBoM4fF6OVJdzsKoMuShytrCQNLW2kQUpQ2dAKUZFTKWrcUHe+kCA\nUCRCIBxGJYrY7HbSNBquzsigR1oaSQ2CrtRb3+g4ayAaEL+rqpptFRX8UF7Ot6XROB2t4sK599TU\nUuZx0ykxkaFZmfRItuAKhvi2ojJmPXEEAvgjETqazQxOTUUjkyECCxcuZMSIEfTp3ZtVFRWsrajg\nm8pKPi8uRqXRcMMNNwDRGLHEhATKPY2D9O2BIE6vLxY/NWPGDFJTU1CKAlcYdVyZaGgYW+PjShq2\nHQ7Hz9bAkSNHGD9+PBqZjC7mC3FTermMbmYjoiiyZs0aiouL+ejjj2NZGv8IKpWKufPmUVxczLp1\n6zh06BA7d+0iNzcXiNaGqg1G6JNq5LPztbxytJS5Z6t4cn8RjmCYW/OjQlElExmSbebU6TMx0XLX\nXXfRqWMnXjxciUyAE3Zv4znzh6j2BmM1yW688UZKy8v5/vvv2bhxIytXryYUkdhd62HCtiLeOVbN\npB0lbCiv555WSbRK0KDVajha29hKYveFKXH4UCmVHHE0nu/z7gD1/iCfffYZt956KytXrvxZ8heA\n/LxcdpR6SdPIuT7rghUwVSPn5hwTbpeb1NTUhn3zOFDrJ0MnRybAAVvjc+5v2D6wbx+b1q/jhV4W\nVt+aydM9klArZGRmZSETBe5vZ47F1hmUIndfbmDHzl2oVCrkchkZBjlNzAquW1/KAzsqOW4P0CZB\nGRNVAM2MSm7MUvPF0iWN+lBUVMQ1gwdhcVexon8aG6/NoL85yLhx45g75wOuTdfQMSn6/Vtd5qFl\ng+tf1ww1GoVIh3eKGbuskqGfljN8QQV9evdi6NChOJ1O9p8O4PZeuqD1v4u4sIoTJ06cOHHi/GHK\nyspYuXIly5YtI1Fvwqw3olIoMeuMJOlNrFm7JnbjGmkQPj9NdCAIAkaTkTvGjiGnWVOu7tuHefPm\nMWnSJAwGA4FAgIvdqyKSxKmaSgLhEAalCo1cwdm6Wg5VlmH3ejjekDlPECAkRSiwJKFvuIkXL+Ef\nJgjR4q1lLhcHq6vwBoMUOx3sraxABnhCIcJE3fI6paSQoFJh0WjompqKQhQ56LBS7nXjC4co8tRz\n1G3niiuuQBAEPMEQgXCEtpZEklRKdlfXUuJyY/f7Oeusp11CApeZTZiVSpoaDHRPtlDl81Hq8VDh\n8fJtZSVamYzWRhNZGi2DUqNWtWPHjqFUKvlq3TrGjB3Lebebc65oCvWQ10ufPr35+uuvAbjl1ls5\nWOdgd40NVzBEqdvL2vIakpOTueWWW/B4PHTq2JGKyipuyUnj6rRE9Irojeq66hqO17twhUIcddaz\nvroWAX6WYGDFihW0b9eOIwf2I/BzF0WRqPVmwIABfyiu6mICgQD79u3j2LFjZGRkMGjQIFq3bt1o\nn3Hjx3Pc7kYARuQkEZYk9lpdeMIR7muRTLbugphr8E7Dbo8mT9BqtWz87jtefvVVTOYEtlS4WFZY\nR60vxCm7j9cOVCIgxGLXAJRKJb169aJv374oG+qHmRUyZAIU1vvJ0il46cp0BmUbEQVo2rSA74rq\n+fRwHdXuIMdrfbywrRq1RsuwG25gaZmT5aV2av0h9tV5mHokKvJU5Sc5tGE1w4YN46abbqK8vHHi\nhYcnP0qJM3hJNziZQCwhy4/7bihx8dkpB1dnaHnztJUvy5zU+kN8X+1m6ok6LmvZkp27d/O3HmZu\nammgaYKSUVcYmXylKVqPTJJi8/cjPyY3NJvNjBo5ipn76rmxhY6hzXUcq/dT4w/H4tEa909o1D+A\n2bNnI4+EWNQnmavTNbRPUjGzu4XOqVoqyssbtROWJPQKkX5ZGqZvqeOJHgnc0dbAnlI/35/zcPll\nl/H1N+uRyWTk5+fj8ka48W+V7Dvlp6z2l4sR/yvEXQHjxIkTJ06cOL+b2tpaxo4dy5o1a2Lv6VSN\nXYq0qgtuY82bN2fIkCHMnTuXeq8HQ0M9pWA4hNPvYeSoUbz//vtYrVbuuusu7rrrLiRJQiGXE5Ek\niETIMJpQyRXUuutx+LxclpSKWR09R33Az5GaCo5UVyAArVJTSNZH3Q0dPh9na6107NiJ40cOk6kz\noGhwtav2uvGHw3RISqHM46LcFX39iABk6/WUu92kaDSNRKFMFLGo1VR4PGy3XcgKOOS6IUx9fiqd\nOnUiy6CjVULUHS1Lp+XrknI2V1zYN13TeM5+3N7QsI9GlHFNWhryBhdJjUxGolIZy6S3detWPv34\nYyCaNjsgSXQzmzjt8TJq5EjUajVlDTfh26rr+KE6GgfUrKApS5ctR6vV8vrrr3Pi5ElEYFuNjXNu\nX2zswYjE8oqqWP8Mchkmg4kuXbrE3vP7/dwzbhzNdGq6JhiYV1TJfqeLjqao5cQXjrDb5WbwoEEo\nFBeSCvwRPvnkE/762GNU10Td/y5r1ZK58z5q1A+A48ePIxdFttTUE4xIsXEAvH6sitYJGu5uloxO\nLmNdmQNRICaIAHQ6HZMnT2bSpEl0796dpbt3s+Rs1C1VKZfx/gcfNMqSeDEdOnQgNTkZmcuOIxBm\nWqd0UrXR8R63+Thm8zLn1Yc4ffo0r8+YwYKj0XaTkxJRKiQWL14MwMzTVmaejmbkEwGjQmRQhp4D\ndT7O2KOW3y9XrGD48OF8MGcOZrOZ8ePHs2nTJj777DO+rXTTL71h7QfCrKzwMHTYDbF+jhs3jpKS\nEl55+R/4A1Ex9uyRmtjnXa+8krF33cWECRPoltl4fXbP1BCJ1BEB5h11cF/baEyiPxTho2P1dGjX\nlvT0dP75xhv88MMP/GNbYSzboE6rZZ/Vw45qL11Tou2WukMsLaonu1kWXq8XTcP6P378OB0TFRgU\nF2w/giDQK0XJ6fMh1pR7OeEM0NKoZHCGjrt3VLNoYCqzjzp5YO2FseRmZ7Fpy5bYupPL5UQk2HvM\nT6cJpZe8jv8O4sIqTpw4ceLEifO7kCSJIUOGsH/ffpINSYiCSJWzBm/Aj0yU4fS68Pi8hCLRp9A/\numkNHTqUwYMH8/XXX2NUaxEFEXfQhzkhgeeeew5Jkhg2bBh7du0iR5eAVq7ifH0t3nAIuShyqKKU\nRK0Ou9eDUamOiSoAg1JFolqL3e8lLEmcrKnF6vEQkSSsHi9du3Xj/fffp9fVV7OjuhyLSo0vHMLq\n85Kq0ZKk1qCRK6jyetDJ5VyWkIhMECisd1LiciEXBOr8/lg8DkQtZ3V+P7m5uaxYsYLi4mJatmxJ\n8+bNCYfDJCYmcLC2jnK3B5NSQZnLQzAS4aabbqJ9+/Y888wz1Pr9JF3kElfjj7qJ3X777WzevBl5\nXR2JF934ByIR7KEQV111Ffv27WPQoIEkKeR0TjSjEAQOOerZWGulrdFIcV0d+XotN2WlEkFiT109\nZV4fV/XsybRp02jTpg0ASxYvJl2joMIToNof5LrMJJLVCk47vWyrcaAWBTK1auyhCHX+AJ+8806j\nQsdbt26l1mrllvx00lRK2hl1fFFVw+F6F2aFnJMeH4JazcsXWXr+COvWrWPMmDF0NOkYlZ+KNyLx\ndUkRA/r3Z8nSpXz55ZeUlpbSpk0bPluwgB4WHcNzzHxaaGVfnYfrMk20T9RQ5gmyuMjG3/aXoRTA\nGojLAFlYAAAgAElEQVSuz40bNzJ9+nSUSiW33norN9xwA3K5nF27dnH06FEWLVpEamoq48ePj4mw\nyspK3n//ffbu3UtaWhrjxo3jyiuv5M233+b2229HIcDEraV0T9XhC0fYWeOle9dujBo1CpVKxeTJ\nk9mxYwfnz59n0qRJXGXRMrx1Kv6IxNzzNs66g4zqYKR9hpplh+t54WAtChHuKTDTOUnDMYefD1av\n5LZbb2XdN98gCALz58+n3unk6TVrWFPuxqIU2VzrR6bVMW369Nh8CoLAtGnTmDRpEjt27MBgMJCS\nksKpU6fIzc2lXbt27NmzB4CD1X565Vxw3TtQFV2fEyZMYMZ777GlPECBUcamcj9Wv8TXn7+FIAg8\n99xzFBed57FWZnqnaDjs8POPE06UJiO3fl/JgAwNBoXI2jI3ClHg3OkT3Dv+Hj75dD4A+fn5fPpN\nCF84glp2QVztsQZp0fIy3G4XV284xdAMTSwz46j1VVyXp+P6PA0bywIYExLZun1HLDkJwMGDB2mV\nrODAhAy2FPk4XB3gkXW2P7Uufw3hUv6a/39EEIQOwN69e/fSoUOH/+nuxIkTJ85/Dfv27aNjx44A\nHSVJ2vc/3Z//Lfxf/F3atm0bPXr0IN2UErNKldur8AejwioYDqFRqIhIEfyhILfffjvz589HFEUC\ngQCzZ89m/vz5uF1uBg0exCOPPEJmZia7du2iS5cuNDUmY1ZpCYRDHK4rI8ecQJJGR6XLicPnwxcM\nYlSpucyS2qhfp201eJCYNn06tbW1rF69GpVSyW233859992HVqulqKiIxx9/nCWLF6NXKMnWGcjQ\n6REFAV8oxObKUtolJZHRUANLkiS2VFQQliJ4w2GaGI00NZmISBLH6+oo93hYtGgRI0aMaNSX1atX\nc/3119PabKLa58cXDpOoUhKOSLhUKr5at44uXbqgEEW6WpLI0Giw+v3sqLXiDoVYsHAhTqeTCRMm\n0MFspqXegDcSYZetDmskwjfr1zN48GCCXi9j8rJQNli0JEliaWklrlAIhUxkTF5GzP0xFJGYd66U\nMAL+cJgXX3yRKVOm0KljB2pOHqPY7Wdkfio5ugui6btKG3vq6mnWrBmXXXY5t48cSYsWLWjevHks\nRurrr79m8ODBPNgkg2SVkogkccDhYretngpfgCFDh/L666//oqXnt+jXpw9nd+/gtjQzCQo5OrkM\nTzjC306V4w+HSVKrSFeIFPqC+ENhOiRqGds0icl7SuiTauCW3IRYWyccPl49XoVOFFDJROqCUXHV\nKkGDPyJR6PBx2223sWDBgl9M/X3kyBF69eyJ1+WipUFORUCi0u3n7bffZuLEiWzbto1//OPvbN+2\nnVAwEBVe4++NrcGLGXr99RzbvJFZrZMRBYFaf4hSb5DnjlfTr7meR65OwuoOcev8MiY0M3N73oUU\n8t9Wupl6uJZDhw7FXCJDoRAffvgh8z/5BKfDTu9+/Xn00UfJycn5Q3MuSRLdunah6Pghpl5lpl2q\nih1lPp7fauOqvoP4cuVKli5dyuz336eyooxOV3Zl8uTJtG7dGrvdTnpaKhPzNUxqYY61ub7Sw907\no9akfKMcnUKkT46a8W2MrDzr5tltDoqLi8nMzOTkyZO0vuIKBmaoeKadGb1c5IOTTt466mDRokUM\nGjSIt99+my+XL0MQBK4bOgy1Ws3KFcvxerwMuvY6Hn74YdJ+kpAmJyeH5FAVu8enc84WYm+FnxFL\nauHf/LsUF1Zx4sSJE+c/SlxYXZr/i79Ls2fP5t5776VJck7MehOOhCm2liNJEjkJqagbEk04fW4q\nnFZWrFjBsGHDfrXdefPmcdddd9HBktOQaczHKUcVrVMz0FzkQlbmtFPmdNA2JQNdw3l8oSCHait5\n8qmnmH7R0/lLEQgEyMzMRO7x0jrBgiBE439OOuoocdXTPysLRUNCC4BjtjqK6utRKJUN8V5RBEFg\n4sSJvPXWWz87x8svv8zUZ5/lhsyMRu+Xezxsrq7h3LlzdOvaBUetFe9F8SUamUhQECkvL8disTBl\nyhRee+01QqFoLEiyxcKChQt54403+GbdOjLVSq5Lbywwd1htHLA7udykp29qUqPP1lXUUB8Ik6VR\nscvu5NSpUyxcuJDp06aBFOGxy3IauTued3lZdL6ajRs38vzUqWzesgWAxIQEnps6lUmTJuFyuchI\nT6O5XGBoWlJsPldUWDkniZRVVPxMUPxevF4viYmJBHw+IkTjhTqbddySnsj7RTXUBoJMa5mBTBDw\nhCO8UVhNhS/I5FYpvHKsisdapdDKdMGyKUkS9+4q5nKDmkeaJrOmysnScgcvdEwj16Bke5WbWcet\nLF++PJbe/Kf0uron5/fv4fnLkjAqZEQkibnnbKyv9lJcUvKHCtw2zculY8jO8AwDM07XssfuR4SY\n+1yXbDXXX6bnma9r+ahbOk30F6yX7lCEa74rYeHChdx+++1/YnZ/nbKyMobfeAO7du+Jvdevbx8W\nL1nayAr0U/bv30+HDh1YdXUabRMuWGODEYmmq4oBOHRHFsnaC9+xc44g3T8rZ8OGDfTr1w+Ixu2N\nu+tOrLZoDJxKqeBvz01lypQpf3gsVquV8feMY/kXKwBoYZFzsnF81b/1dynuChgnTpw4ceL8F7J9\n+3Y+/vhj6urq6N69O3feeScmk+lXj/nRtc8fCqBWRG+cZKIMATCodTFRBWBU63D43SxduvQ3hdWP\nT9XdIT96hRplQxyUO+hvJKw08uj/D1VXYNFoEQQBq89DTk4ODz300G+OWalU8uabbzJ69Gi8kWrM\ncgX14RB1Xg9KmQy50NhS4QyGaNe+PTt37qS0tJT33nsPlUrFpEmTsFgslzxHTk4OvmCQ+mAQw0V9\nt/oDaDQaUlNTefudmYwYMQKjWolBJsMbjlDn8/P3v79IcnIyAH//+9956KGH2Lp1K3q9nj59+hAM\nBhk4cCBamUiNP0BYkpBdJIYqfX4koMoXaOS6KEkSVb4AaSolXcxGDro8LF++nN69e/PSSy8SCESo\n8QdJUV+4fhXeAAqFnLF33IHHWssNaYmYFHIOOd089NBDGI1Gxo4dy6uvzWDChAlUB8NkqxQU+4OU\nur188MEHf1pUAYy/5x5Cfj/XpZpoplNx1uPnqyon/rBEqS9AR5M2NnatTOTaFCPvF9Xy5qkaBOCc\nK9BIWJV5g4QlOOT0sdPm4ZpUI19X17OrxkOuQUm3VB1ry6Pr9VLCqrq6ms1bfuDBZlFRBdGEKLfn\nmPmm0s2QIUPo0KEDo0ePplevXr85vty8fE4d3MVTR6up9kdv9Idn6rnaoqXYE2RukYMP6qMZGE84\nAo2E1QlH1C3vx+/jv5vMzEx27NzF3r17KSwspFWrVj9LFgLRpDRr165lyZIlBAIBunXrhiiKHLQH\nGgmrA7YL2RAP1vjpn3uRi2F1oNFY7HY7hYWF9OrTF5/PR8+ePbnnnntISmr8oODX2LZtGx9//DE2\nm43du3ZRX1vGtD4Gpm2qR6mAZXcmUe0Kc98S+x+em98iLqzixIkTJ06c/zJeeuklnn76aVQqFQIC\ny5Yt4/UZr7N121ays7N/8bj+/fvTJL8JFeXlJGhMqBRKPH4vYSly6ax7CPj9v12Is0+fPjRv1oyS\nohKyNMSEVbHdhkwQMarUuAJ+iuw25IKITqmk1usG4KGHH2bKlCm/KHR+ysiRI8nMzGTGjBkcPXKE\nDk2bcs011/DYY49xuM5KM5MZURAodDqp83mZN3UqCoWC/Pz8n9XbuRQ33HADKcnJbKu1crnRSIpa\nRanHyymXiwn3349Go+Hmm29m06ZNzHjtNQ4fOkSLvDwenDQpliodomnN7XY7gwYNQt+QjOPdd98F\nIBKR8EkSG6tq6ZpkjmYotDsp9/lJVymp8AfYXGOjU6KRiAQ7rXbswRCDkpMQBQGZILB7926mPPUU\ngiShEARWltRyzUUxVjusLrp268YPW7YwIS8NS0OR4ByNCl9E4qUXXmDs2LHce++95OXl8eYbb3Dy\nxAma5uTwyoQJv9uSUl1djc1mo0mTJigUCmw2GwcOHGDBwoXclGbi6qRoIow8rQq1KPJ5eTQupkdi\n45pPqoZscdcNvYHly5ezqsxBokpG+wQtpZ4gnxRaSWqwMi0rt9MlQYtCFAhd5LmlFqPr1WazUVVV\nRU5OTkwc/mixVP8kJZ5SFACJ8qOHqDxxlDlz5jBlyhRefPHFXx33Aw8+yM0NNccMcoGh6TruaxJ1\nXbzcqCJHq2DSwWoAZp62YVCIdE5Sc8wR4LVTDtq1aU23bt1+1xz/GQRBoFOnTnTq1OmSn0ciEcaO\nuYNP5y+gZYIajQwWLVqEJTGR107Wk6QS6Zui4ZA9wBOHHbRq3gyjycRT2w6jEAU6p6nYVu5j6k4n\nA/r3o6CggKKiIq6+qgcVFRV0SVNS7JJYu3Ytoijy+OOP/65+v/DCCzz77LPkZ6rIsgiUlPhI0Ys4\n/BJKmcC3D6SQpJOxryTw2439GSRJ+q94AR0Aae/evVKcOHHixPn/jr1790qABHSQ/hf8Hvxvef1P\n/S6dOHFCAiSdziClpmRKaalZksWSJimVSmnEiBG/efzJkyelZgXNfrymsZcoiFJTS6bUIiVHapGS\nI+UkpEqANHfu3N/Vr9OnT0stmrdo1KZWrvjZeX58yUVR6tSp0786HTE++ugjSaPRxNpXKBTSyy+/\n/IfbWbdunZSTkx1rR2j4d/jw4ZLH4/nN491utzRu3DhJqYiOXavRSJMnT5ZKSkokuVwuXabTSeMz\nM6X+iYmSQhB+Ni8tdZrYOWNzJQhSf0ui9EiTHGlgcqIESCqVUkpWK6QHW2VIdzVLlcxK2c/aysnJ\nkZK1aunZ5tmNXtenJkiAFAgEYv1evHixlJOdFTu2W9eu0pEjR35xnMXFxdLgQYNi+1uSEqVOHTtK\nctmFfkxtni69dUV27PV8i/TYZ9enmqR32+RI77bJkWa2zpZaG7VSbk62FAqFpKZN8iXxJ2NJUcql\n6S3SpN5JOkktCtID+RYJkJ5smyJ92jtHer5DdL1269ZNUsjlEiDpdVppypQpUigUkiKRiKSQidLl\nRpX0ebdsaVmPHGlZjxzpznyzBEhTmiRKX3TIlEZlGCVA2rdv329e6/79+0taWbR/L15ukTb2zI69\nNlyVJWlEQerXr5/U6+qejcbS5orLpXPnzv2u9fif4osvvpAA6fVOCVLh8CypcHiWtKx3sqSSiVKz\npk0a9feyFi2k06dPSyUlJVLH9u0afdajW1epqqpKkiRJumn4cCnTqJQO3J4mWcdnSTX3ZEoPttVL\ngiBIp0+f/s0+HT16NHot7jBI3m8zJf/3WdLxhWlSRpIoZZtkUp8ClRT5Z7YU+We2tGdy6n/kdylu\nsYoTJ06cOHH+i1i8eDFyuQK9zhhzFZPL5CiVGpYtW0YoFEIu/+Xbg+bNm3Pi5Ak2b95MaWkpzZo1\n49ChQzz26KOUOKrRylVEJAlXIBojc+jQIY4fP06rVq0AKC8vZ86cOZw8eZKmTZsybtw4cnJyKCgo\n4NjxY2zZsoVdu3bx+OOPk6Y1oJbLKa634woG0MkUyEUZfilMRBB4/fXX/23zMmbMGG688UbWr19P\nKBSib9++Mbe838uePXu47rrrSFYp6JmSSFiCE/VufILIjBkzYimlf42//OUvrF65kjY6LSlKExV+\nP2++8Qa7d+8mHArRJSUFURAo0GrJVavZ4XBw1O1GDoSAE24vmSolEuAKhXCGI2hEAVswyMrKGs56\nvPS6+mo2bd7MVTlJaOQiGrnIuOZpnHR4WFViI0upoDQQpKykBAkJTziMVnYhLqbSH8SSlBRbJxs2\nbGDEiBG00Ku5IysJbyTC9/v30rVLFw4fOUJeXl6jMfr9fvr26U1daSm3pJpJVMjY5/Sye+9eWuvV\nnHKH8UtQ4guQqLywFku9QSBalHrevHkUeoNkqeQc8wQo8wZYMu8NZDIZDz38CJMmTUIpCHQyq+ls\n1tLaqIm6CHoCRCSJWedqUYgCe2vcbKtys7PWh8lo4NCe3YywaMnXKDno8vHy3/9OOBxmzJgxBMMR\njjv9PHqggk6JWko8AfY2FNX9wealQB9d+0pRZOLEiSxYsOBnY7+Y0aNHs2HDBpSiwGlXgK6JF9ZH\nhS+MNyJx5513MmrUKPbu3cvx48fJy8ujR48eP6sJ90fZsWMHs2fPZv/+/ahUKlq1aoUoivj9fjp2\n7Midd96J2Wz+xeMXL17MFYlqbsi5YDlsn6hiSKaaEwoF+/fv5/Dhw+Tk5NCzZ89YQpDde/exfft2\nzp49S4sWLejcuTOCELUUrlixguevNJBtiF5zURB4oqOJeSd8LF26lCeffPJXx7RkyRISjAqe+osR\nscGC2SRDzv3D9Tz7gRNPUCIQklDK/7W5+zXiwipOnDhx4sT5L8Ln88VuOi5GFARCodBvCisAURTp\n3bt3bLtLly706tWL6dOns3HDRqprqpEkCX+9h1kzZ/LWW2/x0Ucf0aRJEwYNGoTf70cjV+ALBXnl\nlVdYtWoVAwYMQBRFevXqRa9evfjqq6/YvnUruVoTLc3JlLodVHvdyEUYduMNPPXUU7Rv3/4PjT0S\niVBSUoJer79kzIbRaOSmm276Q21ezKuvvopeIadncmLMNTJNo2JtRS0zZ87k1Vdfje1rtVpxuVxk\nZ2cjiiIOh4O9e/eyfPlyeiaYaa6P3rCmq1XIBIEfGpJHyC+6oVaIIllqNUfdbn4Mx5cLApWBAKlK\nJQEpWrC3PhzhaL2boCTRtWtXJj74IJs2b0Z5kVubKAg0MWgAG20MOlIDQfbVR90tl5VbuTY1AZM8\nGmO1z+nhmWcfjd3cv/Tii2RrVdyekRAbdxOtitfOVNKpY0cOHznSKLHD8uXLOXO2kEfzUkhXRV0M\nm+nU+CMRCr0BwhLkahQsq7CjEUUKdCrOuv0sqbDR5crOfPjhh/Tt25dZM2dyrKSYtle2Z8ETT9Cz\nZ0/eeOMNHnnkEUwKOQlyge02LyddASY1kbPN6qbIG8RisXD//fdTXV3N11+tRaVSM2Zob2bPns3D\nOYl0aYjNukyvQkTg7bfeYtCgQQCMzDRy2h1ka40bs0LG+Bwzc4rt1AbCTDxaSUSCJnoFB3btoFXL\nFnyx4ksGDx5MZWUl4XCYjIyM2Lxdd911JJhM4HWxqKSeTLWcqy1airxBXj9tJzkpiRtvvBGr1YrF\nYmHkyJG/mLHwjzB16lSef/555AKIAqSoZezcuROzQiDPoGTxos+Y8corfL9lCwUFBZdsw+v1YrjE\nnwmDQsTj8dCuXTvatm1LeXk51dXVsSx9NTU1ZGdn061bt0bi8NNPPyUciWBUNh6fSgYKIXq+38Lr\n9aJRiSh+0i+jXkQC6jwRxiy08mQ/A+es/5kCwf/61YkTJ06cOHHi/J9h4MCBBAIB/P4LNyqSJOEP\n+Ljqqqsa1Sn6PQSDQR5//HE6tG/P/PnzqaquAgma6JLJ0SaSr7FgkKkYd/fdjBw5EiEUpkWihXxz\nAs0TLagEgdGjRxMMBhu1u3DhQi6/ojUn7TXsqSmj0uOiRcuWnDx1isWLF/9hUbVw4ULycnPJy8sj\nOTmZa665hqKioj/Uxm+xd88eUhTyRvFmClHEopCxb1808VhRURHXXHMNycnJ5OXlkZudTffu3bEk\nJcWyouVoGl+DXI065jt13O2OvR+RJI64XIhAD70eATDJZdyZmcrNaRbuykyjiUaNAPgiEfoPGMA3\n33zDkCFDkMtE9lldP7qlArDf6kIA8tQqmjWc8yqznmp/kFnnK/n7mVLWVNvo268fTz/9dOy4gwcO\n0EyjaDRuvVxGtkaJw27jiZ/ExyxduhSzQhYTVT9yuV6DKxwhW6Pgrtwk9DKRd87X8PDRUt4+X4Mz\nGAYETpw4wejRo9m2fTvFpWWsWr2anj17UlhYyOTJkxlg0fFKy2SeaZbMCy1SCEoSz52sYn1ttAC0\n1Wpl3969PP300xSeL+L4yZN0/X/s3Xd8VFXawPHfvdP7TCokgQAiXYqI9BJAxKUIFhR1LVhRFLu+\nyiLiIq517bLgglgABYVdEFRUpAiCgEhV6S0kIWUymT73nvePiYFIQEEQXc/385k/Mtxy5tw73PvM\nee5zOnQAoK2ret+3dVsJhcO43W6sZhPflke554wUXmtZmyeaZiBIVvIriCWo4zDy786ZPHlOGlM6\nZ9DcZeCKoZfTvl07ateuTU5ODq1btmThwoU8/PDDnNGgPqV+P/6YRlQXjPuuhPOX7eWmNQX47V4m\nvv46Fw0eRFpaGvXq1aNhg/rMmDHjmOfgz1m7di2PPvoo6WaVM91GZnRNoyCicVGOjc971+Ktjql8\n2D0dQ7CUEbfdetTt9OnTh6+Komz2H3pWqTiqMS8/Rt9+/Vm8eDFtW7ciJyeH2rVr07JFC85u05rM\nzEzq1q1Li6ZNmD9/ftXxuH3EbaRbVN7YXEFUO3ROfrAtTGkkUWPxjJratL8oypwlkar3IlHBv/8b\nJMWpUC9F5f11Ydo8Xcilb5ScSPf9LDliJUmSJEl/It26dWPgwIHMnTuXaDSCajCQSCSryP2S4gw/\ndfvttzNx4kS8FhteVwrhRIzScAVF0QDZ9uQIRobVzQ+BAnbv3k2DlFQMlb+6G1SVDIeTrYWFLF68\nuCqwANi9ezedu3SmdlZtateuzcUXX1w1qnW85syZw5VXXkmG1Uorr4+YrrPks8/o1rUbmzZvwuFw\n/PxGjiIajTJz5kwWLVpENBYjflgJdUgGrQFdUKdOHYLBID26daPkwAHaO93YDCpbi0tYvn8/DR1W\nvGY7X5cGKIknyDos9a64Muhs4LCytKyMPZEwQkBBLEZUCGqbTKwLhRBAB6+rKm3PqCp0TXGzbV+E\nRo0a0ap1a/bs2UOzZs0YfuttvPjii0zdWsgZbisF4TjbAhHau524jUa2hMIoQHuPk05eF9vCEcKa\nzrLyUFWhiR9lZWVxYM+Oap87IQSFsQS1TUZmzJhBt+7dWbFiBQcPHmTOnDmoQDCh4TAe+pz7onFM\nikJhNIHToDK4tpuXdhSTaTbQ1mPDa1D5bP06unftysbNm49I1XzvvfewGFQG1XJVVQzMtBhp5rSw\noizMOR4rnXw2SuMacz/9hLzu3Vi/cRNWq5WsrGR5/F2ROGfYD1Xg2xWJoygKWVlZPPB/D/Hoo49y\n78YC2vts7A7H+aosggoUx3XubO7DaUqen1aDytUNnIxcWcT6NavpkGKhlcfM0j0/0Pf880EILs6x\n06ZeChvKo8zYE6ZRkyYMGTKEtm3b0qlTJ9q0akW8pJD7G3vwmVXm5Rdy+eWX43A46N+/f43noMvl\n4rLLLqOwsJB58+ZhNBq56KKLyMvLQ1EUpk+fjtdioigaZ3RrL6tL4ggB9zTzYKocya5tMzCsnpUx\nH39CSUlJjSXWr776av716qsMWbKZC7MtOIwKc/bHUOwuBg0axPl9zqOFS+HVc72UxnTGfLuROg4D\nL5zrwWFUmbxtNwMHDGDxkiVs2bKFaCzGpA4+hn1VSo9ZBQyob2NneYIPtoUxKrB169ajfgd/lJeX\nx8AB/bly7IdcmhcmN9PArEVh9hQmSHer7C3RaeA28GBbJ8VhnXu/DPzsNo+XHLGSJEmSpD8RRVF4\n7733eOKJJ6ibWwe73Uq/fv1Yvnw5nTp1qlrO7/ezZ88e9u3bV20Op8MdOHCASZMmkWJ1kGZ34zBb\nSLO7SHe4KY+HievJdBtVUVGofJ7rJyXNjZWBUigUqnpv1KhRtG/fnomvvcYXCxcyceJERo0aRUVF\nxQl95rGPPkqq1cpZHi/pVivZdjst3R727NnNtGnTfvF2ioqKKC0trfq7rKyMjh06cNVVV/H+W29R\nlJ9PQSjCxrJy4rpOVNNZU+LHH4ly0003MX36dHbt3k2ey8OZdjtZZgvtnC4yjSZK4xrNXQ58JiPL\nSkspiiWD3fxIlJVl5TgMKr0zfbT2ONgbibI7GsVpNGBWFPLjcSr05AxI1p8EnpbKv/O3b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nAw88wMI8N+1Skz8i6EIwcEkF4ezmrKqchywWi3HJxRfx37nzqJViJhjRCIQ0FEWhTnZt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\nr9esId1qpiKhE9V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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(2,(10,5))\n", - "\n", - "pl.subplot(1,2,1)\n", - "pl.scatter(xs[:,0],xs[:,2],c=xs)\n", - "pl.axis([0,1,0,1])\n", - "pl.xlabel('Red')\n", - "pl.ylabel('Blue')\n", - "pl.title('Image 1')\n", - "\n", - "pl.subplot(1,2,2)\n", - "#pl.imshow(I2)\n", - "pl.scatter(xt[:,0],xt[:,2],c=xt)\n", - "pl.axis([0,1,0,1])\n", - "pl.xlabel('Red')\n", - "pl.ylabel('Blue')\n", - "pl.title('Image 2')\n", - "\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Domain adaptation between images color spaces" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# LP problem\n", - "da_emd=ot.da.OTDA() # init class\n", - "da_emd.fit(xs,xt) # fit distributions\n", - "\n", - "\n", - "# sinkhorn regularization\n", - "lambd=1e-1\n", - "da_entrop=ot.da.OTDA_sinkhorn()\n", - "da_entrop.fit(xs,xt,reg=lambd)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Image adaptation with out of sample extension as in [6]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "X1t=da_emd.predict(X1)\n", - "X2t=da_emd.predict(X2,-1)\n", - "\n", - "\n", - "X1te=da_entrop.predict(X1)\n", - "X2te=da_entrop.predict(X2,-1)\n", - "\n", - "\n", - "def minmax(I):\n", - " return np.minimum(np.maximum(I,0),1)\n", - "\n", - "I1t=minmax(mat2im(X1t,I1.shape))\n", - "I2t=minmax(mat2im(X2t,I2.shape))\n", - "\n", - "I1te=minmax(mat2im(X1te,I1.shape))\n", - "I2te=minmax(mat2im(X2te,I2.shape))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot all adapted images" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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3pmwM7MbRccnWdI+bXMVW7IMP7nkAbnSep0LNvb4EYHnkmwI+tT2zcsX8T1QVN7iSDz/z\nCO84fiefqmdNHid2LdF9MVbSgzv2uTGw8p4KdTMcvnDNEn3ygqUp/QSAvRA4WBbUAc5UjmkNN43S\nHI9zJ07n83XFZuE5W9WUIvh9Y8vedWpSMSyEzaLkj55+hPuP3saNRcFndqesDBzbtTVaet6LcK4O\nrDrhxqKkFOHx2RSA7Vk7f+4cOx7arVFg4OCmgeOJPWVzUHB6WjW+5YdK4fxuoCpsgNcV+BLCFKpI\nIBfREAF8EJ44/wS3rN/SpL1rZG3+qYlvaMiqd+xqTag9lYR9NGQcM15zFROFcxQEasBzWCoUGMe0\nx0p4emokZOxgN0AINnfPOGFVlTRCPC25uYiwinIgruB/+NzTfOHhGzlSwCNTWPZwV+nYqwMPV8qd\nA8fTWXve5DwPzuIAVuXIQDgoHu8CpVOGBTyzB5tzHMKJyvK4f6ScroVnZ3BIHKdCwKktA/cv1VRB\n+OSetctdQxhKIKiAE87NhPN7E37uxAm4ijQEPns68g133cHhsfViouf2KfFaGmuJH7CJ5JreXLx3\nPBDXClKmee4ZXSauvc3CmK1BOv9ue+p9jz3On73t1iwPbfiKNH8aPiP7nb6ncd4ua/ka367Z+bvb\nufbS9sqn1L/x2OO887Zbm7ZVQvzsljM9M//WeSQeLvEZ0qzJYuu9aJbfZ8+L/MZjT/BVt92S8SKG\nOq3L8TPlvIgXIecP4/0Q648KIm07W/Lumh8SLyLtaATw8X0itp47ERDlfY89wZ+99Za29tKOk6Y1\n9PKt3rxfdGHbz7XSgmuKBsFLHIepL7T7VPoZFM7u7PGuRx+FK0RHrpZL3g8DPxsJVQrjuQT8zGXS\n7wHI8hH86s2AMcENsxgTNaJEFFp809Au9mto0jqEuq6pxcVBRXwmTstQ4wpvgy4+5YnSQRQG1AkS\n7FKd2M9sJvsAVTGCtePNZHCBjLglImr5J4HOBBAbHIl4JiKXhCowIaBOEx+x8gGqthAm5tstGLup\nvOmZGjvJ2CFQjJADx+OgdYjEew1tju8JCs7eIzgEacqXBMjgjJl3mcCkQa29nTdCkAiDSW0tsU2z\nQ7WZXvo8dZJihKwcb9JKTOQiX+Diu8EhLp7cPJdHytaLohqZFxfLHYlrM49juQsx0cyE5Jivd/nK\ngktE0TlCFEKUAH4Eq8ctjd1ARPDBhpk4GkYEoBTHjtaUaTGMRNEnUiKd07hjH6fxZZ+zUOOc1TEE\nKKIQiSpWbDXmJrWj9QwSBA8EacjG1XZJeSl0ZA9g4D3HDqwA8L4zFeqU5ZFwdMXqtDw1geOh2FZf\nf9wYnGcmS5TjwLDY47baqn0HwjNV4Fd3Pf7AEhdik71pZYmDdcms2uVB2eTZvUusjC2/N60ucWjq\neGRvy/IYrLDpA8sePh1M6rndW1lWXckdonzwWcdTgwFvHG4CcHwQ0LP2rqdKe+YmMaHo4sB+b0BD\nQ46tLMU62DOHRu1RKrcsD/idiztsVEMK9bx13d792J7l82ht7XGPLzi+VHJiZ9Y8+/FdE2xePx40\nvwdeOeCFjWJA4Tx7xZhPhwE3bCxxajLlruWCoDCOUuHpWcVuFZgKbLFEgeOeNcv3oS37nHoYDYfc\nVE6pVTlSljy6M2U0gouMGC5NuWdkZXhkZ4+iFEZD0gwzkp0zFokfXUBDvCtYHiw3v88MjHhs1I5p\nETgkwrlaWZkVSKHslfVlaUjlAyiMKiLB8mwn1i8SpadVGY+Uz18STtfwsalwQwioKhPvWdPQLNKJ\n0blvWfj9HWG9qSHUzrM7XOLYAM6JcvcS3DHynNwS1nzFsYFjY2ppHxgV/PTZirdtWL4PbitHhsID\nBzyJhlTB2mkv0pH7l62W98V58d5TMw544YYBDJ3wtoHwyMzKffuK0Z8zMe0FbMyemMLxwnOsgKpq\ntk9fbRoCnwUdOTQec2OkIyHSWxHZJzAlXiQNumbtyIQdh1ChURjOeJH4QmNqkwLYNdkGe5FBbK4L\n+/WkST05LDxHV1ba9ZGMZ5pDrqBreBFJ5enmD43utoGba4dFAk5CUkwnHilvz1HhObayTAhEAaHR\nlcf82vcEzduhFSXSWpg/lxQPmvGRqoKLPIOqKSydtsJZ0xhzbbSIjgwLz7Hllo40wmsSZpvGNwEj\nF8jm35T4oBACOEWiorkjLMXPIt4LKKKRZ3HpfldgMgEsCYWBoffcENcK4zmkGVPzQiKYYm8SNJoj\nrG4iEts48iJE5iU2VlJiJ16krk3h5CXypE4Rjcpsb32jIXurhKjc74rGXCE6clUEJlX9ZRE5BPxD\nzBT+p8A7VfXU8z1XNcQnMu1xBqWJaoKGNur3uhnJijibEGY1cEw1gPcmwCiZZSiOY+9xKoi0GuBa\nNapc2nKAdXRiiINrZjK1s09tRp1QO6GIVqJKHJXYAPYKiqeWNFhDHPjJlGALrIo3IUms4EJWvvQl\nljkRijTecgubUKCqzTMi2kwORFDnm8EoUYgJLhFrq6v6lkiLhkyTQaMml0TYQ2wjMGtbpBgiOTGV\nOOGMkpmAkrU32QTpEPQ06a2tm0UpWDu4XLpK0iiZ4JdpZtpFxsaW8zRpkla8qWVMnNowEVmrlJAX\nsiYgTqjUenQqSoGLApi9v/JixCcowbXtorZaIiLMRCnFNX3rXGuBDAgqQtksBiagpV4OQF3XJuCF\nYPnGt4RY3mRRUK3xSLQMurhQdQX9q43Pho6crZULFfzBxYqvPFjy2CRwYqZsxSp913rgKfVMnS12\nf7BldHiqW9w5XOFPLgU+JGNuXV7m4W0Teu4sbZBIaYLIiQl8pC7YCmNuKYUbhquNv8/DuzVPzba4\nY7gSKwGXauHDO7t88coSj+9MkGIEwEDgBOCdUDqHClyqHH+843nrpgkuf3RuiaerwGhc8OYRPDHb\n4NHpjI3SM9MdAjUPb1u59gZmYqpZ5RM7u2yMZ5zY2uOoX2fX2yh5MFoFtrD6702s/md9wdmJsjMI\nzNQE6DvGm5zfnfDgTmBtNOD+A1ZuY9qMNm3IEuphXaasj0rO7Jlg48cjLu5NeKY2ddENvmB9MOVJ\nrVhVa89bl80K91SoeUpnFKrcNBhysa6YRRriMCnwbGXC1frAwQCODkomdeDR3ZmN1lFLAxYxbQRr\nZ0RRp0gQ3BDKYGWQYaDAswMMMEWKAsPKg8KkrBnOfCfLKv4eYXRkNlPWSyvHmUibdrBYvs9sQwlM\nVQkusC2Ondqxk8VlusFVXHKOescG60MKByKB8gpvGSm/sl3wnavCmWrGhYmwV1QsiyMEDwRu8CXP\nVvDVqyUno8XyvigMPbpb81QFD1WO79wQ7lw2C2PhpaEhT+zUfHQr8AVLA1QD4uDpENitBIJQivCR\nS7AsgmrFW5aED+3Cxcpxc+kaRscVL6SBvnL4bOhI7Yzum4XClIpIxlhq4nIjX5HJTYnqJlXszBbd\nfRG4EruBGN+SM8hhvvk0XU+Wi4y1l0S7M08FtftpxNbZs45oiaEtq2Z167SDauNpk8+rcFkxo22H\ntngS2yP+jjxAm6uQH6farlpZ+UgWlwXvnF/bVTPBKVv7M8knWW8aXqTz9vht4RBuBZlcCGrGfVZA\ndW0vtelyYTCDgIs8lVnCujxoQtPuaehFfkiyxLVY/StMCT6TCh9HQhI0g2jDKmkqjHb5q0rNQyAV\nobGMxrFjK0BS1ipOWl5EgwmAzguqwXjl4IEaDSashToq1qNwp9ZobYPsb6VXHFct6IOq/hjwYy/l\nGUFMY9gQgNiZLgkH0tUiYNaN3JVMgFoTs9hO3KSBdKKR2LVjsTWVSjRjRtemeC+5lmlSWTR306Rz\nSeAmCMx8vKeRSGZaJYcNVhAKbaWJ0D2AuSWvGfWoRKOFLJr2pTW9KkqQ1m0vSKuJUdW2PdU0XQ6b\nWCoQQrKuJOYakklZ0iSKfVNHi0h7PWvDrgzRIlmlmr5I6Vo3wDTx6kYr1wqDhQq1c7EdJZtKrWAT\nGkpkLZd6KUQBNWnROguKZuMmIwwdNVWWF9IK2K3mKmmPXFx4TCfspXVEVEk9kaxKZir3sf/qEOy7\nZItKW8wGDlPAiIhZnpxSUzcEPGCWL20mScgWCxvXrnEB7XaUiETXgI78etXxUunIQBwf3K64dVRw\noSrxKBoqnqiG7MwqHvcldVCOjDx1rQwZcKQsOF9PEYHbhyt4gUe29pAwBBQZGxl9ZmK9cXwsbMqA\nDR00/TOM7nrVnufN44JHoyXrhqFnU6Eer/H0LOB9QVATAraDCReFeG4arHKprlkS2AnKvzNZjQPD\nklVX4xCepmSzrNFyxHM7E6DkDcsj0ig5uWMCzVmUpaJg3Q/ie5RCoRTh09t7rA9L1gvH6WnFoaUR\nFydTVoYDVJXdShl4myyPTGYcLAtuHhScnkx4IlqwyhBwAsulMB4Je7VyonJM6prDI2uHEzs7VKoc\nHgw46APng2OtHPDwtvKIcxwADg0dT+5VbJRjJlXFvcsFKyKcp+RcmDLJuMal6Ce9E+fkR7drHDAq\nhL1oHb91aPV/dM8KuhTpWeXgiHM8V3nUQRhCEZmkcWGDfaqwMwEtFXHgh9IwJ5XAaObRQjk6cDwb\nLTlURr9qZ8yHV8eedTvLcX7tJibGJw25cLIuGUQlWuVCM1fP4EGFx1XYcMpIhO3gWCbgHDxTee4d\nwIN14BiOP9yZ8cUrA87WysN7NTcNHINCeXQnukUWsCKOrcxr4fYBfN5IKRG2KhgUgRN7cLiwfjs5\nqzkydHgHewROz5QbC8+4UKSyNePYUBgJnNwTnq2M+g0E1gvl0V3A0SgorhW8VDoiKrSrJnG9yFas\n6ObV3I8Kr8YqEf/qpBQkrQNdwURS3vH3PqEqSydRaND5u5k2PuWXmN8qfz4pK7N3hTgPfFaXxP+Q\nLBALBIfG2kArOLS8CGhoBRWNlW7uZeuqYta1urkmHYEnRCm1y4tEhn1BGSBzLYTOtoq8zVIf5O54\nGjuitYQ1zRDLYvxXaKW4TPppGynEfFKbN3xAVq955BaeTpo5RiDnRVzDQ7V5t5YpayUV8OI7z1t9\npRGUbNtEck1MPGt37HXeHZ93kVcIGB8UNHR4a3HmheMQCIImVyAf0OBINpGQmxZjmxHb+UrzItdS\nlLwXhMPc51yIlhxNWhKJ2h1tJn3urqdZq9oYEoax4ypJEn60iBBaV7b4TGNBQExKbp4Bc8jINP6N\nFaclbcZEp+HWFXY8dC0dRHdBaT8hk95p3eiIdWv8WjMCrmJEyTkXiU2U9iWVu7X45ERSnCOIEDKL\nk5IsPxkxyYWT5l/bLuYGQ9MXzWIg0FkRAE2bNrTtC42TDCIhTP3TlMHa2ZgKmpkjTvYR8EZLN09V\nYgVFhdpFF860eJF8jds6zGN+j1AS2uPrrM2i61/SjOT0s9WySUOkggbKKPx11U37KUO+r6wpA+YG\naSZvpUIpG5fS1B4atU6+JeSq+Oieamkl+nO7xu2viJaten9RrhsMxHH/6ipvChf4YL1sbaLK4WEJ\nw5LpNIA35u6is/0wNy87jsioyUMUtgO8c7Vg1U94MFq0lzFLji8mnNqdcW5Wc9+qPfeJi2apCWHI\nXjHibG3WnhuKkiergql6s4h7OFTGF1XRGiOOdRdAhmxVFYUEDg/bg97XBiUbbgpMORcGlMCN4yGf\n2d5lRYUnIpk/Pm7J/aFB2Xx3CDtVjfeOOig3jwdMZjVbVWCpCNxzYMxHL01wAvdENxMROD/dZbMo\nuBACy2Wb39GxZ0kqPhZgvTSLzyPbwuZwzOFBdCGLbVUp4Apwyop3zOoKoeDgcMDdg5rtMGRFPBom\nnKsGnAYuoiyVReeo+7XS8j0zMUnymB9TS2Cn2GNFYKjCXmVlTIzOrjPB6Zai5IlpRRU8IOCENd+w\nhwBs1cAwKjQgegQYVKwKE4FTQZtVVb3R0AG2+NfALC4uLnpFFhP7XSQZq6zR2jPBsimCwy3V7FYe\nP7VxVoxrJigDVWYi3FwqnwzCp2fwpSPlAzvKFx12rA6JpoPAhyq4a2Tr0OkqxLEnLDvPeF4ZF4xc\nVUGYTYT3bQf+8oqleX0UeJ+tZkxVeONqu+H2WGXKw81BVHLtwK0jODMVBk4pPdx9QAgBHr3WJKaX\nCBdX8Xa+XEABAAAgAElEQVQ/TWTcnWtWwnb9pOVFsjwSw5zW83mrTK5/bZjybAlznQRJMdyuVY2I\n0xGCTGhreJE53mie+U3ld5q5+EvO23QXg6AtD5LfUYju6IlPkub5tI+mWT2ze7Ymxro0S6I0VWp4\nkfkXLqhTxnPPtfT8E/uX23i1c33eWc8hTRsh3XI27aBpzCx2w7P6ZhYVEi/S3l+0H2zRe/J7qS27\ntrI5s90cgiqFi4rdRvjT/S9blEczTqWRwoK0vGs2gpqsc/dK5xJPKxSAONA68ZQB8dEz53l68pXA\ndSUwAeAdtTNtnBcxP8zovqUSrQ3RFUmIFogkZWPm4wKYZf7DgGmao5DVCDHBGE7EGzPtAqo1iG8F\ngUwrYAxs1y3D3fhWcK1J3WFuVQCD6GMaxMyj+UZqp+aCuGivDoA6syKQhDjavUxgHSsSLXLeLFye\nzF84Eq/c4gQg6nBH34KIj4x+xrMnoSWO9kpiP2iyWEUHQWk1PUbIulqU5OqValup7aNJAoQmq4hG\nVwfX7kNKboGaNhkXReMLrje8GcURnGln62T+bepmLw2AxJUn7Q9yMZ02Gierv2iwfVjBiE2+cCQk\nmSMXrNp9XtYgrVAchWsXhajDnw9RERCowQl1Tmyafuzm29DlpO0ibmB1UCULqNh4q1Stj2IedSLI\nIohaW3nnmGlAUmAIMW228zYAJDEEQusmeh3ipvVNxDt+/tKI3cmUW1Y8962PuTQLPLY1YXsifNGR\nEb93bpf7N5Z4/fqYAByrAzNngQNEhC/dHHIiONAx63Fwro12uRCENaesLXtECspZ4GOzwOHxOne4\nPaQMXAp7BNZZd8qFGcy07cc1B2eqUafMdx26iW0Zsq3CjUPhgBc+ObM+uK+c4CvlKQb80Zlt7t6w\nZ5edcnR5iQcRjrvF7t3ndMhegIkXlr2nwspRiiKDggcGBR5lqsItB5YRlNvchGdDwQTH61aX2A1w\nKE6ArTjRVmRKUOG2Q8c5UxfUqqwMjC6GKHgeiQLDn57d4rTAjctDPjYVji7bRvpLKA9PClYELhBg\nVHI2jt9nt3f5qo0RH5kKa3EsfuTilHuXx6z4NQAuygVuLZY5t7PEwWXBOWEtzpp6ZoLT6b0JN3jH\nbrHCYReQac25lRuo6yGngvBl60P+v4sm2LaWAaMhU2A1MlBblVJjVroapY7CVqiHCDUFM2ovHFvz\nPDepeMOK56NbkaDFrk6MxBKe6CmHj/fqHc8w/71rwSaqEsbi+NTU88Chw9y1MuQ3t2ZUCr9xxnIF\nOFQ6bhXhI9vmtnj/anRVruNnHHt/sA1fuAQ4+BdnrHz3Ss19Dn5pG94+Cjy4G8ugyo4IAx8YqHD7\nyHHHWPjEbuSOgDeueSYqvH5deWZPwRmN8g5et3z90pAGLu7HjWu7uOhpEqVqF5lf8a5Zd3wj1Jhy\nLzohWXbJEtQINNIwu6iFDUFaNrdR6qXfc7zIPO4/dKhZh11k5pOFqcCYbIVm319TTeis1/PQ+O5m\nnaBVXEMrtDQBBugKL7knTKqHfRHuP3SoXVc1F366gk2yhHWE1TlhcF5Y0li3XHCpk2dHSiPZ+h7/\nNQrZXNEteang9QcPNoJ0stSl/PKUHQE6CY3SFQPm9ypZe3TrlNczFW0+8Mii37lO9vUHDzb309hK\n5c7LMlf9fYVo+kGwYCeRNxTMAt1Y4Ui8CI2VyYkpbqsAKbqaOpAgSGGB1NqgGVmFrxCuK4HJ2sk6\nslATFCrN3Iqc4DRzmYvXa2K7dqTuLuOZ9nt035csKO1nbmnI3ZnmGeUGR9+SEQB7YRNoQTKi4dw+\ny0jrx9siWcRSSZ1zaKiz9GYtgRhpLSkAJbVDcv3STv6tUFTjj72JFzMSy2gJWpR0XxCMvE7xWt28\nc1EbmguYRLFB50znzjmc0iwiArijb8naISM03Y+IRVHkFhAgyQnEIjK3P/2L+x3reuiNrYn+eZo8\njc3URu3es0bK3PeO+T1sSTmQLyYarQoEM893F5nLabOuMJV6GXHPoYOcm1S8fn3MkirLKjw6C6gM\nuWdVeGh7yqZTbl4esFUVzKJl8BxwSGYcaFgMODczC9DG0AQSVWXDt4IuGIFdKkZMC0eJCalL2URf\ncebuNRDlXG0MzPr8pNk8zvkAq9lzm/H7yWrA+SAckMBdR9dZn7XlKzWw4h1LRbe/PjUzaWVDlC3g\nBidUWnMhOJYHJZtFxSO1cec3yx5P6ihaeYVPhBGHZcKGVDxZDxERNlxyIbRnisJG55sPH+ZicNTP\nI2C/49CAoHBBy333Tk4tn3XfKqFEgOUx3sEB73lox949KDxLpVBHIeBIuULthJMSWMfo5XOztNzZ\n+vD6lVWODwN/vG0Kg5VRwaHVIwTg6PKYda8sDZIPfnx/1jXTSc2Ng5LC1eQ4U7eJChHWS9dcWx84\ndkLJko8uhPX++eVHL/xbakfh6xirRdkOG3zkdGgEsBynbZMMX7Jp4/VCZeV973Zs34HdL7J+ulfs\n3ifjfrKt4PnYVHn9kpX393ccry8Dh13JyhDOT4QjRWAmNRI1ak5hKcB2V4cIXN80BBI/YUKgC9K4\njYlgewaiYJPc9LpUP2fRW/fy1G5txLSsjZLAktYJUXTOSPdCbfqGw4eyX1aGRmE6X7csz3Zd7ubX\nrEGZwjDt90lrZcjnjWsZnmZNZT8v0liYUB5IZX6BuuVCzjyaQAtz1xOP0IZOoRH0dO6ZvLzzSMJn\nHgzjDYcPd97RFH8BL3I5V7x5p5bYvM13WZCmWz954d8ZQXv9wc3WsHD5bDOXxnY0pzJ1LmTvzNPW\noi0vIlm5nG3/SGMz55VEF8dZ3M8hv7K4rgQmnMOlCF0u7dNJrk7WcEGyaxD357g0cmldu6LQEt2N\nkKRxiAKXCEhoOsQ2vwv4ognwYEWKbhy+jYRmeaf3hGgJcuY+J5CizrWTMwkR3Qh45vMc4rssSEMR\nGbggmZCVBWjASRN6vEZtQSVWRxJxcji1uoXkXhj33oToXlhIRuyagZsWwhi0QlsByCZzaBn7JDBF\nd6XgLApbstAI0aoUVRyt5sS2CirmNmZ7qWj282gsf+g4KaY6mvDWGnO7wS8SxVFMUJCsxZNl0cUr\nIbZhiG1lL3MEMUuN1dP6xmtrYidoRxsXxATXppxCHIhmVfK+LaPQWi6tULngnyLeRBc5hOAg1KEZ\nK0kxoAKSIhESv0MTvr5IDI0ITmsTpp2LbW0R+kTExpIIosFcNUMVy/M8VPoax3PViI3hCuemJgSt\niTKLk7/SETeORzw8w8aRwm2DPVSVx6ZLXKpLTtcFa7Ed16MQ//jeiJVMJjgerTAVnguxL9ermucY\nsKfKxrBmw2uz2I19XBxKx7CqW0t3zH9DlPO1ctE5AgMmAVZczVaw5za8zd+DdegwPiuFjbFTdcn5\nIGzE/Wv3ebM0PMeAgw4OM+WiLyi84+D6EpfELF0V8MlQMhKoCIzEs07g8VCw5EqOux2WCuGhmXHp\nm1EwezyssBumvLGsOTVzTNQ1QmCiD4ei1evDswEr4jnKHuMCnpkV3BRDo29Ht73DcR48o+ZEfHjg\neDLA0MOXrHnOxb1eU+za6emMoR8ymQVWiooS5VM7E+5eHnF6OmO3qjg+HnFyBsejO3CNWbI+b22A\nE/jYVsWnGXCTt1Byn9reBkyQGotycnvCjioP79H41y2XnuNFyY2FmYg+eukiIyecmg44PJgy1cAN\nZRRagje3wfEOs1r5ivGYc3XNw9MpW1Nl2qxU5nI8zMzaNYp40F2PG8FyUXDLgQGPnpsywHP3ypDH\noyD59Wsz/nBPeNtYKYLnTKjZdo7dmXIIxyMOTu7AuDDyeH6n5hg1H6fkTl+xPTX6dLvMoIZndq0f\nv27FBNz1kTIMNQcG8NzUc9QXbE1gpYzh1Z2wHAI3D5ShCltRELvSrjQvO8S2CCTZJ8kDkBhH7Vyz\nRIkVTXs6Uvq4RmbcrzZraiusJFbS9hBHVryTJjGn2Vo6x4m3+4Ncwxd0rDtkwmAqeeJFInVR2zTe\neMQYTxNtSw5yVy9JSruYeS4qzisimzdmCj8FvHSK00nTHB3SHPlhWwf2tf0CZKx5x12yy4u0SkMH\nMWqua8rWWKi0rUFTR0lWu0xgnqtH+86uwjjbZdBap3KlqkZrpirOte0lkrWjtsJoup+HLm9FQqtn\nWm+afU55p6TvscLp2VRjFWlc9wVptq10a9k+k7xdfBMMQxCpwEOojZ8NUREhRF6EpNRtn7/SvMj1\nJTBBp/Pnr80jRHWMw85aSoznfH4ds7YTCLkunu4z0rq9WTmSMNK9nsN7b8JWmknq4oOhqYtzLtMe\n5Jasgtwass8UTbaxcY5wLjLXp7JqDCzgFrQHpH0qzy+/S5wkxoCHfRY6oLF2pdJrJogVl8m9E2wh\nLgqNpkJNBxJc03xNWtUitnFX9bZofBSxdo1peL4McS9UniZNbh+FlkSo6khMJAmQmcCU2rwtzP5y\n5dbNTlmz73VdL2xf51xnLiys91z1a9IeJqgkueW05cuLsKhs+yI0XWdIzPv5II29aJ9VJ+KSeGoR\njhaBmSprLiBR6z5JLmgONnxgN/5+dK1k6aIwUmWYGMT4zCAo58ee46GmUtu0X2F9sSw1B33NThAg\nMM3I892DirM6wEdmqw6lhYUnKS+UgZOOAiCNC+88GwIDF5gFx7hhNgzP6SC6RbR9W4pFbbvVF2lp\nYyfOhwOuYE2UpwbL6LRmIz9LAbN+nXUlT4aCoSjj51nU1p3nfD0juJLdespN5Yz5Afu0s3aQOkRF\nS80FLQgINxUCMUJeg8GAraqmUuX4eMBKMWVTC9vnNRjwbDD3pqe3pwzdiJGz8b/kPWXsp5UinsUX\nGyS5CqZ2Wx6UvGPZXMI/Hc/YOlHNOsUoCmHkC1a05CxWxnsHI0qB10nByZ1d7hyucKGe8rvTPZwq\nQ+D+lRGPzqYsxX46Mau5dakNBX+2mjIQxxna64/vTLhrc0jAojaurpgQG2TGkTiM/vW5wDvW9vfB\n/UvKJ3aF0QJuYBTP5jgVFXUx5iC/vF1zs1e+mIJhjBK5MYzsk8Ag2+ioqgxjXcZzAZVeK+iszcm9\nbMG6NO9GRsO4zglFaT23i9m1zDE/vSvLs402lgoGZOJCYqpzYaZdX1zLwkjLTTdK5fh9kfDTqWli\n4lup0ISuBUKRqL6o9Sa5j8U4Arj9TbvPSqTZNdcpdPv+3NIktMJdupfyyPefJZc5E2rneJEFZXck\nIcN+z+/u02iZs16KwltsLC/tniekFWDSyzrWrTSW5suSR+fKxqWkKswhufTvq0fcGrII0mn1FkGM\nF1GFEIpO3eaK2xH40uufz8L2SuC6Epi8hX0AEpPaJQbzMWTMJG77liQLtd0hSvMdr60gIHObYdsQ\nzsZtOqKwpYKoi9HP5phK8a2ZMwZSSFJy0iG2fHD6Ip1qSVQb5Mx7q2GhqZflk+tw4sFlUfuRrDP2\nnI8akP2Cp6rEc5P2C1zQRuwTojAIJO/s7IEmTVM3AUQbrZOqUjsoNO1vMJLrNd4z/ZJZoqgJLmr1\nwCxtYhqGuk59Zcyp0+60bOlFu7FUs7/UjkZ4XPO7jZhn5Zs6268lWsf+i3HVgzGuztGcXyTRCmd7\n0aBsGAXNCFm0ODZBh9o0dn6VkUhBQbxtfq1t0UuEJn8uESWHBQLRRPxcO1ZyjWUeNbEZrhqtqWJX\nXd1KUC7OoXpuXlxPeGC4zfGhlf+h6RJBlDVn83K7KpinIcOqYDsIK2lXPo6LQVj31b5N2iuxQ1Yu\nQelqnpkViJgmfjW6bZUFrG4rEx84M3Yc2wpUKninhKrgLCU3erO+PBsXEBHP6eARCYyoGaLseWUW\nfCTg7eECpRfqOH9HLrAXkjXRxuJQAqecYy/47HBs+wwKm85EyPMx5xACmz5wXj3jOC/XnL3zkB3i\n1TmzywmMXU1V2b5PUyrYvDun8Xwo4HR031sWWC7KGBx8TNoCI8BarNSFlLlP6s0CUXNt3KmUJ3XE\nfX7WtFnp4HVLgUkQHps5jonjgaUBldQ8fOESd40OAPDlmwOMRiqP7DluLGaciuG+jw29MS5x7mzG\n9kgHmm965bEoFFyIYveBwvOp7W3uXbHAGJvlOpMQWBl5JtuW5nfOzygILI0uwRBOVmvAmNVZwckw\nIQxrPrilQMm9S4FzVcVBL3x6MuGrV1KYC/NqWFkquLP0PDibcttSKywBPDurOKU1j00H3ORroKYY\njvnAHmyWNX9mMAMCt8ccbyuV39pxPDASntgb8NfWlfeeK9gLjnGhhDj3/6MD8LGLNUepuXnkqYua\nYWOlaxmtiU/stTILjqU4fZJ1e7SIE7uO4GhdnM2lOmMJJaniWzSWHCBtAs5XakuzSIBqvUI678/W\nalOoZuuHRGNA8rho+c/2vUkOE5udqSwNI3wZHimtUNL8b/Nt91bN1759tll3M96tUfguYOY13l/E\nWwBtNFfJyz4vdMzzIgveo92gBMmq4+MB9OmMJx+FIZXMxb3Ze6aZpSsF1n4eXqQZD7bm5wJdvo+s\ny89E/jdzY5xvn6DtvrBOXeNYSPS+FWpp+1O6bZU757RNLZ3refAv2+ucv1Oy8dcaFczLqn1/5ziY\npl3jWIkSlSSLLq0X2cJOfQVxXQlMtaMJw56iFXU3DC4iOF0L0uVM1fMwq9BlpGVJrkqAmGXIYUJW\nSP0YEmlpn0kDw8WN/20o8UT8tDFrzmM+ct/8vhQ6b+uiFZRMsLicda61TOX5ZZs3M03W/LP5u2D/\nnpu8fKqK9x4JUQhbVOw0+ZpHE/PYDawtQmPpmbfANHt4IoVwdesGmUKXy5z6Kp2jZYKEdjQuBTHA\nhdvft/lmV4jalkw4aaIdZiboSkyzVoTntxRZ9WN71nb2iZtbkpILIcTzx7w0Fq+ElghaOesQGmld\nxA5yLiUJpO3i2+Z72eJdN3iOARJdvUZlxcVQMpAZFcIxP2HKoJO+kMDYOYbZPpUbXcWeOkqZt0t2\ncXwwZS8sJrFnljyrExi6mgEFF3XIkhd26prTkeldiXtNLlZpLjl2S8fEB9an4HzNhdoEshvEBIYA\nnFLPiq8pA7bPJeJCjBJ3RGcU2bNrUdjYri8vCKsKq0XNrIYDTjmj7caUlfiOneCb776Z60AU3jaC\n23f45SJNcGPlisXZ7LgqacNwjbyw5KMyrJ7tz4ju+N+djTi6vMKG2qG8dezrA8WMGwYeqDhSVJ3n\nk7vfVpwXKxoYiHLAV5wIQ0A4EoeMlXLMXmF9fkiUraqGoI2FSoC3DpUnanvoZHzd2mjAyXgQ8LHC\n+uXOA/D0jlAp7NQVvzs1Qfp2X7DkHYeHBb99aYeAcrN47l4uCdN5/XRLp07HMfzUVs1os+BoubjN\nAH55S9jxMHbKN622hwysec/9q1FoGgj/+znlm9bsnWuF5wNnprz9oJV/EBfDIivFzF1OB319wdRY\naT0k/jINu8MtXtJkThGann0BupqY9EUQyfe/SCOsqLaOYPPZuzZ5u71gES9yGT5pPt9F+/suhyQA\npecXChOdvLvvFzIGPXt+URN2eC8WsBmaXNrM1Tft9d6Pbr8l77umDK4VMlyyBs2VW/NnofVGuVwd\nVLvln1NAi9h+/eT6lucxz6+59vGo6O2+L6ma0xErHtmnDOw2xxwfOHe7K7zZAG/atqm/SUhp/KfD\nmS0/odbQ7Kn0ef8lnu4qEZHrSmASpcMEJjNkc7roZbCvbdNz5J3uqTXtjLFDRpuzAhquMQ3EzJc3\nna9AZNhziTw95ZRQW1978dHKIA1hqzOpXpzrEBWg9Q3NNEjGE8cNi41foOsIXBL3ezmf8vCdsJ7J\nkgZJuxLbS5zd14AmRj5ZZ7JypXZ1cTZo1jedfUN2Fw0ORJt9O7V0BZKWkMZ6uDZKi7gUxjU+m7RG\n4ppDcjXu56klBbyIVp46E1gV4u7CaOHRJjR5EYTUCkrdsRimNlIBH5nFWXTzDMQTwZ0jerBQO9tv\nVtP2s+VhY6xpOwWcNFZLsH1xkhaxqLl0jYGj9XdPBxzbGHU47whadxbE1K62gGqiqGa7m1MK5L8r\nFB+y/WOQRXG6fi1MTpUDVaAAznnH62SXafCcHJScW/wEANtZ9Ms1V3EhODa9cbtna5tnq3XBydqz\nIkJwMzYGE04JrFTKKHh21eGiALUaD4S9oMs4ZqzolC0ZsuQ9zIzlGqkxtOqFldE2p3aWqGYFhwls\nibmfrvuandpxMgZNSK6W25Vj5JN1GXZq0/+vFcpOGHAJx9ApG66CGs6op1ZH7R0XqsR2wVJRsAcs\neeVSbSveXlytVouKUYCL6YiG3CItwnpRs1wFnmHIXqNAUlRbF6CgZolYdxWVwFYomoVzjoQ00pdz\nws1lzU5wnAojNgiNm+FyZF6W1VsfFcIzYYQCSw4OoqxEa8knoxJmEjxSwC4Fw3rK0AmPMuDGULEd\n2+GQzhg6cFJTqwlbm1ozdPB08Bx3Mx6aOu4atkzMxQDPTSvuWBrGY4AtkuBjqtwehZUnJ4GVwvPE\nZMpbDox5onK8ddmef3wauG005qFJye2lcOaShah7lB0eGC0373FY2O7Hd1vXxAfWRzwRo+KNZcBZ\n9TwQg3FcWim4NIWHpnCwGPDWwRY7Imz4AaNlx5uGOx0X5RlQiVCqcqq2PY9hXHCiFv7GQcdeXEM8\nytsPlg112/UwqszaNIgKqCIdJn4d0xBo5xlCPOCejpLvxeqWJGUC5KxsIFkqsk/JbRbtukpMlVzd\n5tfp9DU9ldaCRrBLAohqu6eZxdam1iqS5U/rkZHfN5f/biFclsc8k9/s1WnqnGrWtkNTBy5vxcrr\nmrdqnsZIiTR1rK3zmtRt+yXFdtpDHdtLsvybtV0aoaAVBOYEwXgacCqDOkhb5pPLWdpmkVfA2IRu\nP5ApTisNkXNtHSxzC6iXRGsz4S21RWxQpzZ+21NGjbVqWQRNUkunjZqdedIKzU5cHMPdjmmsRk0e\ndt133Pm04W0B85hACWoh+C3atWvecyVxfQlMIo0mPvclTa2emP991o1MozFvYWoGewgWfjsTXRtr\nRJCY5/wK3mX0wQ7FtU17kYkINYU4ixsPFnI0yz/Lpv0M2u5N0rYcSRsStA1G0bRDxMJ9RBrwhV8o\nludWJUGadkjWs3liOV/fBeLoXN263I/3vumneVeyJgKcRAEuExITmv1PcUXPrR4pfHoaEm3dbPN7\nJZURhqCNcNCYtYG8IS24g+Xhc4JBa2GSuOnetD0xpHCeRwiNMN+Y+tP4I7PczbWd7Y/qWpyS5dI1\nQ7Ad/7nmcpFG0MU9Vx1NoHbTz1s7JQr0OGMIAq3/cFhkFrhOsBKUA2Vgd+ZYrwOjYWAkyiwS3sei\ndeC2qisMbUThSDDmeSw0VuSEUzPHcqntIAXWqsCqCzwxW+YAyrIzRrmI+VV1Ff0kPMvRteu5ylE6\n21MDcL6CW3TGziBQq7Bdt8LbGEWdsiddUj4INZew87iOMCU4jxPhQhU45isuamZJ856l2lE7x0xh\ns6jZ0y4duVQHNobKrPLszQWXGMRBuavCKMA5dSwVnlGoKZ2wJI6dOj2Rs0ndK/Mo4uo703QKvU36\nw65mJ6Zxc+N+O07AJ3zJOYFNgUPehMVJMAXGqWhZui8eEHwysyoe8cIEZRNlzdXNvrRDkZ48WI8Y\nE9jUGbeUjpPBArWsSWBcDjgge1yMeW2pMK0DWzhujkEutmqzbp6Lc/vwsOSAD9w8KEgGyw/u2Ttv\nWhKe2N2F6Nb51nXr4yeqFZ6b7rExHnDncAWA48OuZemhS3vcszzm8d1Jc+0jcY/V7d7yOToouV+n\n7EQaslkWuKBMFtCQm5zj6RDYKa38d8qElZhsFNecJyNdvz0OnQKjPUMJiLqo3In9dX3LS8knL+Pc\n46ITB/P+tTITbGj0Vh0Lk2bP5vuIIBOsdJ7GZ2vIHC+QGNZ0tdbQnLWXhI88l9xyk0kDHX6p4bni\ntVawa/NommjBOAqxDAqNa/78K4nlS3usW0uMtO788888D+bZq/Rs2v/TSdPwkvE9ie9KHZbXJeWT\n2jd/Z+SnTKDOgxRYfeoUbjuYUJL2f6WSSlbOfK9U7vZon235G14mps+tRDnr11gRaV0x2/p32645\nEyp7ft7C2IymODZTaRf1jePy1ivJykVWVxETjES1cX/0c59XCteVwIRzdiCntFqGXGBoI7RFZJYL\n0djpkuLLR2oXzxuS5NjpBPA2aRNj621o1dghoBqZCduL1PWl9MFZoIN4GGqRNiynQvp28HWikcQM\ng4Dz2ZAUS5NrC2zwREk9NkRHAHTJpzzgaotrb2dDSEvkkrIgtVF0EZRoJdOmLea7ILoTajvVGs2A\nzJUjgziPSja9JXMvbLqraJ5NVigJ1u6S+jcJVT4j0omgxyL5xsUg9pMjatTiniNPc5itVx+Lo9HK\nplSieI26G01hUmnSphLnwmnu0wymNVLfXZTMmujsPCygPYQ31gkbMyVJYM2i7zVtlIhzciGIVkMX\nDyPO+qw9kzZprPIIfKmPo2ZpThcQaAVPJ+Dx1C7EM7euX4GpGjouFkVD+c6HIYUK61iwgduii1Rq\n8yQoDYNQh4Jp7blUGs05F4YElXj2m3BgYEuBLyqCOnbrEeMaqNo9TA8z4D6dEiYjBNh1wnI8N2wn\nlukIgc/IkCNhwlYJB33NU+EAIlGIcDUXYmhwRFmWmgNxuZ4Gzwyh8FAG21+06zzOw7pWrBdwXgoO\nEFiOdtrtOC/WpEKBLfXsRovaeaccqWtWvGdawZZ4Noua05RU6tjKVnbRwJYbUsZrWzJkK7ZjvtAf\nLgPboWCnDqg3S9n5TMhcT/uo0ibgeH3kS7brwBTYq4WAsBoj0p2Nc8+5REMCm2LL80YIPKklAqwW\nNVtR4HzOmaC04uyaE9iLtPYGnRqNivQ9CVkHPYBnD88TsXD3uikBTynCZxhzh9/jU7Mht5dTbtgY\nUr4dfjYAACAASURBVNVTKiwK1Y1zzELuAugLeGOhjTB4ZjrAlUNULWLhRO2Mto9uK3cPD/DwNqwu\nWb8nK88Hzga2K+VLNscgyi3LA3arwLYq42hZu2XZ6vKHlybcPBgjg2We2J5ydMnzzM62ncUUMY0M\nydORs787TDktnj2ELYUtVfYGQ26cTbkp0exIH6cIIqb5tk31RUtD9HqXmKTRMs0z5Ln2TelcNJqq\n7fUkNKU0aa3Jnohpum5hQee1//PPmaIr7UsJQhNhNy9R5/m5GloU4rnAT0k5F5lot6+eXcEkfTdL\ni8bAADGVtALjQl5E8rz3rznN3h3N35SlnpdEs+cSW75PmIpJG+UguZdJc0Jkx7qVH4/SLYmdOTk/\nHjoWNNfyP43yR9sIfDXdPmi4LhfHylx5YwU75Ws8hOaEw8YapfvrH+JzXlp3Op0TThpeIVO+Wl4p\nEEWWNtVf4nftjpk07o2jjNsEGiUD0dsr7d50DR250riuBKb8wDAHzWFl+9LNqa9CiJ3n5g/dipKx\nCMQ9J9n6b5HSMrnfibScZPuRvdekAJfy1iy+f6ZK6ggVcQCFyJwafRPSEGu1DG0ZgtCcwSDNatQK\nS7Y/Kp4gntW50QxlZWilfTsjJinNJLvXTm6XneSdCAFpFmTWu+yZvH3S1UjoVBfv12pcLmMfuNQW\niwhfDIFtbW1t3BCPLH2y1iULYNoKNb8XzNrAFhhUmo2zjasBuaDXJdIIjVZM5wTpWgPqY5CF2HRN\nAJMYd7wI0joESiSsiYik/srqZOMz7wKJkb1C55rlmMzmFh7WZeM9b/PkqOHNe5KAhX8XFTy2Qs/7\nlV9PGMzinjGEZSr2EGo8UzVjf+rbaeieC3Q2OAailD6wHV25RhKoRFiRKUOpuagjVOFAmHFJCiZ4\nagcHdZdZDD29GWCFCeelxEtgLI5KjbEfaLLqKXe4XRw2XrbjfpehVPhozTkQLVXiArXCcgicl4Ll\nYsrJasiaBDbdhJLAOW/PT+KYP8oOp2XEbjRbDnyNcyaE7KqPQf1hzykHo8txYusHVNQqFKLNPqVo\nEKEsLOLfgRCYOlgmcB5PDYyi2+wMx+kgrBAYOcckjnGz+AKCuQtKaCwYVbb8D52jlIoVhC1Xsls7\nBpniopZkffOUohxiygUtGIpnyU2ZBGEYJ38ABgiTumSZmjE1p9XjBdabvaj2qRiZ3aTihBaMgFW1\nVrkQ33lc7PeOFhzwwimGOOCQn+ElsF07xjHDQZzHybI5UQUnXFTHhVo47ipKqfFIY8E7EQqe2dnj\n7pUxKxI4IIFx5Fa2CseeCl+0WRgjFCpOV8Jjkwk3FCWrhXDjwPZRPbizy2YxYL0ouTQoWFPl4NDz\n+KTinvEI53YbGjKICrvj0tKQgcBFX3CMitMUHKsrRkAZ2+pZd4Cjuk0Z6Xyt5tLpNcTAELpIF3dd\nQbIvTtMW//xGvD33Oyka5/jbuXzbdbR5LtLypGRrhKM5oSN/naWJ6TVTTHYkhe5a2bA4YPt7Y9CW\nWKp9dcsDwUnOsNAKEIo0bvspvHiSIdrcFyi6aes0fy8x+p2MUkMlnm7umU7ZG/6quz7OI7dyqLbB\ndboCYnQXbOqV7x9r+Ss63yyQRLMXnpYX6bi6ad6G9mKVbN3WrNpzaKxDUUDp8BCNIJSsUikf++Ij\nT6ck4YlYipbnyN0N5/k9kdazpakLEuvT8iIs4AEbQSmuh9FGEoVXe7FTiZ5YVxbXlcCUtA6NZgE6\njZ0zfTmcyzcvzo8sbRjHfPASiYFonn8r1CQk4pKbrhsLvULyPRDCvjIkwU40hZRUGxwhChT5q9Jk\nihdbS0Ji6Nv6p308kg16yDd7SlMvJ90JbRNEbY9PapO5BmjOXUrnSREbJhHoXCOiWf6irVZDZJ9g\nm0+65jC53G1ubmK1e6t0n3BrdYsEIrlqSibI+E6ztgf4pYUpTWulCS+eErfEMPZfJJAWSaeOglBD\nVuILzPLo4rlYzflsWb3SPnpRQV27Z+hydbG0sUzxlfPufE2bpTqgFL5AQ4iikY2fpEk3QTvqtwRK\nEQh2GKUg1nnXscCk3qqw6qYIUATPcvwOsBwtNSelG/zBO6FEGShMszYtMf/wkrphBEbUVBid2taC\nmZTNQjtGGUiN8yWK7Xmr4P+n7l1iNUmyPK/fOWbu3+M+4p3Pqq6uruqeafVoHkCrZxDDphcIFogt\nCIHEDgnEAiQ2LEYMG5AQSLBBIAQLHhrBAhBiRoAQDBo0oJlpTY80Lbqruqq6MysyIzIibsSN+z3c\nzQ6Lc8zdvxuRWf2ozKQ85Rn3+z5/mJuZHzv/8/if6R0+CvRANs+D64ESSnZSYx2hHOTJlALAIw7s\nOUeBdSdspTJYYiCxblp/NGJbCtWE8xbXGgBpJ4k+jr1kwOjZqufBeAifcSkDGyopFzaDX3AbXpJd\naWySwkvpeG7GGYVO4CzAxNMgURhRRGCj1RdeUwYRCkKSQhXYxrtzPTFM+px8xooHskdFWEsKQNWA\n5GKsgRes2dXKNgm9QX9L6dur0VmmWOapJS7SgIh7R/x6kfczet+c4wxvKuZ5ZIvtPSIvCa9d1ZRj\nw72QdxbU9TUAZBeg9UwLO5RX1rOjomnkLJbnFJTkZ6lyc37GO7LnxpQfWs83xEP9ejF6sUmQpTFx\no8I385aNVHamPGqIZr/BknC3c4/aPTPOUuI8dWQ9UmvluOpYHcew/M7WbDHlN4ZLfu38mjwoPz/E\nSrWQ5+frA3qYQzWTSOSMBqujCG+JHP+Z2ho4csXPv1tKxVlEvKlIvqmDtItaKMNvhqK7XrBYe8Sm\nG89qRftuXnNnjcNOdJGlqnva+AbmfC7J5xBYLBq9WMdbntWi3bQmyfR5iW+Wf88azayLGBNieHPZ\nuXViA1CyOH95ir3l79a0Nw28p2daO6vpIreOXdZAOtVDQs+KT7M3Sbh1yOlYEfBkoea1/j2596ld\ndjq7gehJZ2XWFSSM7i2Xegn05aQxMnn7dHFQO6YZ1CdQGf+bvExResUW9w3EN7V0/l2mBi49TEls\n8mz1ky6SFhE4X60u8ocWWyLyF0XkvxeRj0Skisg/+ZZj/k0R+VhEbkTkfxaR7976/Z6I/BciciUi\nz0XkPxGRs9vXeaOxQUYAHjfaXJUiCSc0EJD05nlvk84i4b1IJMm0/KiahCoe7kGE8VVpDCISMa3u\ntRLRiAlWz1XRRBInP1f8+hPAE6UiC68EEK/emDwPQXF2MrM6TeR2jpj/W1O73lxzhUjOH3AXsHtJ\n5nuf7k3x9b1dvxU9FXPrQqfZyQfi+criGu1cEfWCpl5dDBEhSTptp3oi+XRvVYq4wFBNcY8ZBE8y\nXyTo4Jvwk9hni0sVmcIIRRRE/QVqUkJD6Gvy42OpSCnhPdQAj0zzpM0DCbOGqWLqY1ljbwHsc25Z\nUI1iJBJIogaAG0XIUZA2m8+35jmYCvhaQkh47KBi6p9FEiZKEUXNd1RYlCn2Gg1JSSk5w4120zO1\n52l/JxV69bmpmkg5kTXRpTyNqQYFfwu7UxNaQd22/3Hjhr9OGXKhI3d18Lk+KhucJONqPOdqPOf3\nyzkvxvM3zrtjRsKp5dvWm3DNilyVoa5ZmbAy+F69z5N6xuvSgQnJCq9Kz6vSc54KT9OGUoXnQ0+1\nTIdSLXM9btkNW9bFQ3svKLyuHWPpGUvPcey5Lr1fd+4JQPhNvccr7Vhp5Vu2YzSnc76pmdel43Xp\neFQO7Evie/U+pfZcHbe8Lplc4bIOvKbjR9LzUAYqyrkVttn3Cx240IFtLtzTvSfV9QX6wifjlk/G\nLVe24jINrAzeqUf+nF5xISMZ49xGntSeCx250JFOC50WMoaKcYOykpELHfgmB25qx75zi94NmR+n\nbnraivKxbjizyha4pHKmhTP1vK3zeuCVKDeiiBrbCN+9lnXsq9jXPLMNrzUhSdmmxAs2PLMtZh09\niUM2DtkY0opdyTy1FQnjYYYNiU2AmsYOuE6V81R4qJWLXElaubKep+L7k9i1Kho1sTYiHC0xmvAM\n4U/mkS2ZvSbuSOH7rHkoyjYLf6I7cCGJ+2J8Q/f8fl1zCVyOQrbEcVCOg7IzYVPgnQr7Pfzg5chm\nUDaDcr4SPlwo7jfJOKjyrR4eMrJPW9ZDQUQ4rDt2q8x+ldmvOw6bxF84f+0yJFdkfeSwTuhq4Gaj\njKuBre1YJeEm2BffLkN+0pv6k7evVxeZlaf2t7+JU0YHbwNGb4u88JXDnNhI3NymwXBl0izxsf5J\ngCrzO7py7Iq0Tgq2t8QLsRPGTSIHxOZ19ASB+DmVgllxPcDjeJiMsqF3NCMAMkObaUkwP6M0bzlM\nZAINCM7/tV7z9bTpIpOHK0zfKoqoBCifw+nftk/TStwjU/FldQawrX+bLGH63L6bgMtijkq7J2/X\nRaZsMREHphLPJnO72xhhDipauN/JPVufTfNg8XubA8hCl3wTvk1Pumivmut4KQxVPjfmEWiPmm4D\n0xgvms65bI/cytow11M1+ltl7pMJhEc3JvHIFcXHKYmRze2A0xMGPf+U0/QWOZJ/CnLkD7P9UTxM\nZ8BvAP8p8N/e/lFE/nXgXwL+eeB3gX8L+Gsi8stm1mh8/kvgXeDXcYPqfwb8R8A/+4V3lsihoYXj\nzTSaMAOjzwvzaiFjME+QMs/QKdmyzb8aFJFdHD2lLauCefFDf6FnMVji2rkJL5vb9nk0jG3xEFl6\nZMJWEc/SGNredIsLY3bPRRaJWkFNOrw5m5pAnVIG4pCi7cUJRbmGcF8Ikbf1a3ue9vv8N5M1ZGnl\nQICUTlypDQAsz124Xxa/nt61CZSZRbAd19wut+aDzXNkGYonIlgSxhh/J0iIRtt8fhunyaJ4qz/a\ngtl+E4Eeneo3zf08e+kEnzNFOCF0mLoqwgBa+HlLfmwexml+1ErOCaufYyB4Sw+aGWlBonJCpmLV\n88jKm9SyX8A+/QfdvjYZ4gAHPrMzLFf2JuxNuBu/n4eZclVv9xaTQG892d6ux6MnfdQk7ETZJKOW\nxFgzL7Jg1vFIPTPlJvJy9pYZNfHU4D4HzqRwE5PkihXFhJVVzGYR7SEOilWZ2Kza9n4QGPQSeY7M\nSchjhPR9n7tcpcTFlINnjCWT8sD3uGBj8B058vc5526EetXwgIzFy0xXMZ6xoRd4Uj3EaxPeo09q\n5lPbsEuZROU9blinShbIpXIWAOz2VgxurOcsF1Qgl8LWRqwqD/SaV1Ux67kTzJV7Lbyk47UpL2Ms\n3o1p4ZW0Uiy8PlQbdVB8HWGW55EjdV07RISN1cnznYHNQjqlwetFXVJ5mTs2dccu9cDooXgC96Ww\nrvBEN9yMlXd04EaMV2MXifQ2Aar7LZmc0/d3gysx97G5sLDAGvhVGfghKx7h3qRPpafXwoUM3B2N\nkoUbPH/q55PPs5Z3UYCHSXl/zUTmsBHjYdpRBx+/pnQcDVYZLFX2OgNUAAtq+420rDTPZSwoWw4Y\nsKk7N+4tDGBfogyBr1MXIQx6ZmEwm5VB//X039Mzwxthp3JkWvt1DqdiMV9EbPJUT++/OZCZy6zM\nN22hVGlxD+IYCzfE7SV9YkLlzSC1JUnDbe+Nh/rb5LkQNYotLfJ28s+yb24nVjhD7tw/SU6Bptz6\nvGjgdNV5HWbWw8SPmXPIfAGevCwsIm7e0jfzx9t6VfM8NRa9hUK38Ba179p15npIc5/7eyMTpfsU\ngh+XWqir0cw272T6LDQwvNQ3jYwwLOZQWeh2zSNV5Vao3aIPpq6ZvFqhH0206ta6DlXDakKmdapd\naBHjzOKn0LP8eeZnavf4kuXIH3j7QwMmM/urwF8FkLdr0P8K8JfN7H+IY/454BPgnwL+ioj8MvCP\nAf+gmf2dOOZfBv5HEfnXzOzx5999dg+bNBuFsHB0MhdDXSS4A6hQzCAJ2ggQmAd/8XxI8qvmeMlG\n8eupCGPYKCw8DB5qEO+jzCxzB6uRZGnkoFis4opOjbybpni3Qq0NIFWdle+maI/YRF8tcR23EkUN\ngRQvWQOENhMlDGGLyChjhHQV8fs2Np4u7jP559SV+pHqXg2bAafhfdI4+yWUNCJJMMUkdyKEEJoR\ndDxZj2RJqy5kwvk/gat55TmpG3USIhleqUYr3nLCluauxbENDNQFUIgfaaF/hiCa2oqyRHAzC04b\nt3YX8XES5jHVOlsH/fkjvKa2grIJtUaO7qFZTfCYOfFELzOjIAmkVE/kV79PZp7jXYrnUWixfWKF\noxh5UTfKEzkje2oBCJt4tVKx5J5FIDxl7bEdmK6SMKtNf/jt65QhVpTPzA3IL01ZVbhrMBNw+HEf\nyYYP7ebk3Nd03JSM5sq5jRxTjHNeWjbBKqz7IyrGu8VD6F7Q89COXNrIJ7JmlUcYhVUeSANhlDQ6\njOfWcZeBv2vnfKAjKiOP7MBRhOuc2FvHq3HFuhsYQoA9kD0g7HxW8IQNK5w+/d1Qop/Yhn01Lgxy\nMvYiDKp8Zls3+qTCK/qoUuWbSmVdje+Lk6C8b4WnZI5jz5UIDww2nYcgfigDGNzTGwzhEzuD6iF1\nj3XLI9mTKwwx4t8ftzzUgUsdOKNwXTI3lqGDy8iaesKGs1Q4wym1qwkvxzVnMqAKWx3ZF+UT6/mG\n7ni3jKAy5XgBjDgpx76pb4v8tGzM3+Phka8QzoHREkrLdxIuqAiJB7Xwio5LcY6wjkTjENom5RoH\nWSUpYpVOC6N5r/6uOFNfrt6+n4t2vlDhfjW+KSPPw+DxQRkoIrxUJVU4Boj50Hbsq3KmiW+kAxgU\nS/wSByfcwGXcb8qWP8uOHe6VOyS4GArf4sBOEiTjQ46TDLlYXfvfBjeHSwC2+pKnacsD9TfegKN0\nrG10+bcoDeH6qCuNuzT62lzfLkP6L64E8gfavl5dxE4MkrNRcAEqzIFN4pbtUgMHqfdXM5zdBihm\nuGeFua5ZwQ1oHnHSwEMoKq3MRegXDXQNsS4jESUT61ptS5wsDKUtBCraVK0ZQedEh8JSX5kJAtB5\nPfF55BdvWEUFhgCAnQbnpbW83xnk5CAPmeghYo6V6BQvidIKn8qCmTj6LXSGKYakRcVPw+O9PWEo\nkQWznMzzmnbsrUF5yzb5FCcQNP//1gWmvm1Ni247vVfTRWRCtye6yOc0A5CFNFuA36aXLL6fA5lP\nPUUtB7+1txhkVVrGkKeQVLKFIZ9WK8yvliLMTqTOulitDCozSJzaE6O+AE0O4GL+qE3vwFt1kf+/\nA6Yv2kTk28B7wP/avjOzlyLyN4G/APwV4M8Dz5uAiu1/wfv914D/7ifcA5gHXhBKdGi2VocIrNqJ\npb0SlNx4KJ0Fmk8xSGOraSEzup9l8BLxMg0stDCuBfJfeLkEBz+l2iI3hojbPJ3x7oFqzwhUd9PX\nuF4S9YS88RYld1uoFutFm1amLdTOP49mJ88kIm/Sly4tFgKtVoPgoK1N3r7OFgqmPKbZYzd7zU4F\nzvLT0pNyYjFa/Nmo1OdaB4vrv22JfMvXZTlnAtggi3466ZfPueiivTOrXbQxTjMWuUaL2OITwLcY\nt/lep/PMC+lVouwNijgdq3rIoec3RQxyA8jJZbzSwKFbjDemWC1Tm1rO2dso4pcMiI2RsIpfN8e7\n84bZ7ae8fdkyxIAH4e1BekcqJJ5GldT3j4UjxoZWgNW3KkKisl0deV07Oip7y3RauVNceXyqPQno\nuoFjI4lYzFsDOipdGERWwfDW5UpXjRxR4fdkxAxWIqx05KZmXtaOA4pKRc3Yrg4M9bR8ccLY2ojg\nuVilOrC+jkK4ZwysWQf7g+dDCco6H0nVw4u1VgYS2/7Ibui5trUz4QV5zPf1nM1YaVUWcjLW5uxR\nr3GFPlnBTDxXC+O1bOnxeme/L1vWZhxQvq17XtQVFnlTpXac68Dj6h6792Kcal1o18YJGMKUdTKG\nEQYSj9JrAJ6k9XTIUIW1GBrFbRul+nt2pNNTj9fBKTVZxbu4Cq7vx+Hp22tPZ5U1lQ0eSpiBA8LZ\nwnMF7pVCPMet3WXrb/DEhPfjyOk6miEycmWZdyIX6qMAWfdtmHLLALIkeg0lMURLd8uok0TYdIXH\nkrE9vCcDV6PyYX+kmEISHjKyOaZp7RvrHVRGUq7cyzeA8Lzc4X09TJ7GmnpW9bCwBi9kiKzDiLjD\nrCJpC7Vwk450VUlqJMuhDH6uxvdT2b4KXWQq+r0gPWgh99Fb3k/1lL206ReTZz/6YuYYdDnb1o7T\ntWJWzGcnc9NFYiVtyui0vsp0ZpWZo23Wv2/rIix0EVfYHfw1UEVDJfMx7U5NJ5jWOZo1ecmXRbE3\ndYF5aWk3P/lnzn3GdSqJi+fFtc3mfqnW8n3nXOPTbaEztXbSdHe9dQSTzmjT59auL/bGLbeJeONz\n/j1Va2Jtf9ua21DGZCCfz5hZh2X+kjf+RMVrG8l8GW7PBRE3+pg5SYXXiarB/hc6oi2AeDRrBrBu\nEBhVWS/0jp+oi8gcbjqHFvr6mM08B/RL1kXetv20SR/ew3v8k1vffxK/tWM+Xf5oZkVEni2OeetW\ndRFKBky4XhJdhVIrlj1kiU59gKt7nAyPr2zhBxrWe0tCqZXOEoeMWzBiECe/VVVMZFEsdRZAQ7NM\nC6yKcJSKiLKieWNcwU0IRWyq3VOEyUtQIqRMxanARxH68GBpNUfWZnQGdVmHKjwE/pK4QmKqU+2m\nhuKbJ6K9Y2N44Lzv/diKTSFa1dx64FEYMlFjaq1xP2NIrfbQDEDam6ftxRIozSIUbU7M7IaNWcg9\nMEIJ61MDQy1eFvw+XZAhxNAjcQ1sFlciMoG6yeJhLQfN224yyXDE5ORFnbZmLbM20vP7aUFFP5N8\nnFKUsvh7WS29sRd6LDokyT7PpC2MzTNU6VCKuXeyRHs0rH1esNYmD56ZIQuv5VA9/8B9DUZjTpxj\nvwO8GyfCytpYAtrabb7YVzMvKWWGvSVP8Ke4faky5KX0/L96BgirMAAYcCMbvnO45ghc5zW5Vq7T\nGqFydzzyQnuuLXFWCh+ah0Yd1Bf+65wpZnxzuOFvn93n3QE6hWrKMUKxeiqv6BinmkLh7UT4Qd7C\naJjCnxpe8vfTOdopv7p/xivrGCVxVKXXymfWcykDgnBFx1GV92TkWVmTxes8vbCOjyXzC8mZ9u4M\nI1c5c1kGLhm4WmWKCWZKF7TPV5a4JvMt3VHNKbbP+iNjgJVvSmUwYSUj1sEPbcM3rRFCrFnnI5TK\ngHJMyuuh50O74bGuEYzLNDAinA3uYV9h/K6ueZTcW4MpfS4Ynk/6sO5ZFfiMDaVASrCVIypOnPF6\n7KgILxFyOnKeBjqM3xnvAfBdnvOYM26sZ6sHzODKev60POcHTXlNhS3wumQwY5MKr4bMhY6sI3Tw\nZvTnf9cGPpWed+3IKPAZHS8JQ1LI0sMtHWmHomasYKq/Na8dTnM+AB1wz/bsJbFFJ51lokwfM2tV\nxI7sTNih7FW5FuGB9ZgZz1S4Lok76vLijg38ieHI75eeX1m/5vGwZp1GzsxYFWFbAPE5t+6PjEMi\n6w2ow+jnxw09ynk/UkiQtmCV4dgDa7pVPI9BrXtEVrOSrBvEKtuxhCFJyKJgCVUnR6l6q7N++tuX\nKkdMJFw0MiEmr47oqvbERAYzBTRtzXNdJE0LRmO2daUwmTKKazdNLs/h/ItEfRY6o0GJpFjDFbsS\n9q0sSzU4cnviN3+MCJ/EPZFOcmSxfgdLKj7WzZOqNLbUmTDL+68ZDmfj2nJ5TdM6EyAMp9ufHoJT\nRdlsEV4si7yoebw8v5pFKNn8P18v25q/sPfJog2tPy30FOS0vxsUmNZ0Iu1h4dJqGPLE9SNNyjOB\niQaupvC96anndt3epCl5iwOmq4Y+0R5OsVij37zS8j7+zPOYNSO+mZwcB4ROOEdKicy0VmmhizRD\nf9MijCh/UmUa9+ZxtPbuaOhsTReR+dyJxS/u57qiUNUNh1/H9lWx5C3w+x/9mPE3/2vGvDlRQPXn\nfo38zT+PiKKxWGRzq06Lb/Rir4FaJ2uaoV3yULkQYIKRLWi7IyQNoKRAzI1Zrr2MZhM4SQijGqvq\nTsZB8fC/aHtpVqM4vk22LMLYLFXYlOzXtkY00CaUNStSgIsSoEaAlBzstOc+IQeYlHvmlxscxMkc\nu9y8CxL8nw3cQSRIipBMqQvWv0k+idcRovpLWHX2+k0FZxeCrJ2fgn5yWUer1jqBLhFhVWCXKuu4\n0AkzUQi6JY0li+vHIXNvnAjNz9kaAJxaxBRPfnrdU0vg7es1od/OqXFtlXkVO/U6zaAlRjoWKw+L\nHHMDNJVa61SvylSwALStKO/Sdl6bF7C1VYXBvJZBI4eIH50Uo1mCfvy32X38/0wA1cyw4cDXsP1U\nZMj/8du/QZ8zfSwXe5QP3vs5/uT73+LMRjLKjVXu1yNW4XmEQTUikFThhXjux2PpONMjBxJjVX7x\n6H6Dnx9e8jyteZlWaKNa7lxuvTu4F+I8wMajYYfiXohvDa/5Xr7kT42vuBwGfmt1D6WSUBKVj9MW\nlYIOikhFOuW9MvDd4Yrf6SILy2CVhLXNc/uq63iS1xQZMTOe5B4EHh4OmBk/lg29VtZ4fgsGPzZ/\nxnd1rhOUi3vvL0rhF3Q/0X0/lsTaNjySI8ncs3XWDciAkzooPI7r7dXosvABOz4b17OHLBk9lSrC\nS8lspGNnHY818fN5x82x50lek63yAbvwZgkpGWPpuUyvuZZEvA78LveoxUH+jfWIwLf1hr9R7/NL\nwSxXAgyOpSdR0Vx4lAdksRi3nKt75cAO4RGvOZpyZFbAtp+zdi+9V0Nox318dYfCFc4aJxhdgrEY\nax1pxa7XxQ+WJFwV41Ldi/iCFdTKHRmnQV5XIBW66gAsh1HjnTygwEoqZ6VyppWnXRfPVChW6YDU\nVcbhEuoApqzVZcgwzOGLxeo8XmaYZfYjrBOTotOMZr0MlAgz+FufPOf//OR5aEAGlnk9LryEUYMg\nqgAAIABJREFUX+32U5Ej/9P3vs865abVYQZ/+p1H/Ol3Hk0F1z2X0C81kwssDFNtXa3NC+JKtLxx\n7NyapnROdsNpIfTQ35Z0X6vXbBNuRVjQjHhM7K8zucLiqQMPTGyu0tZc/2Jhopz6pBkh/V5KmWDC\nMpdpVoxVlmczeTumeRQGrdZ5y+bV6EONVIt57ZyHrcYaWyPMUAMItSOSzCBCAux4ny9p75tnpYFM\noTfjgJeBgFkXmW8tiE7qQvtm7q9QwERu1Vj8nClni7kiJxNhbnfLbxdmQ/Os7yx6eWEIbQDztj7D\nsj+lheHFXAlWLgcudXq2atXD9kI/rcGynNSLVCxFZLW6AJLehtHqTELR5pn5OIi5HPx7Tz7jN548\nbT2CkdiPR77K7acNmB7jz/8up5add4C/szjmneVJ4qb1e7xpDTrZ+j/zT9Pf/dbMV294Dk8tDOKK\ntwqMycioFzCd6KM9xC2hjFbpcyiaoWAWc4t8TYbVKPYaszKZh5q1QS+loFGZvVglB9ucVKOk5s2R\nAHDhuQnkXtWT75LDZQphsahtojuts+kMzFxYuVBOePigpEXdqOqC5SBGZzrRPZ6s48m9ZIOFJyqE\nh78E8+uswUZTxNtcJCwLtU4hCCpCZwmrdfJ+SXgxUgtPUEHNi87WLHTVX4YSx4J7MUYJ65EqFp42\nRJAUVg1cqg1UOpPwei36pvWBzILztthJGp4tCWsK0zo3SRWNl3yRTTmdX5nrJ/lPt4CTNILOGCsz\nJKyGEhYUm1aTORbcItbXrJDSnK9UJ/rX8NBhFFW3ZEeYgZlQNTHSctcifFKF29TihMdpCgERD/9M\nmoEaScM+73udswJFhPU3fpXNN3/V2y0GpXJ4+RHP/vq/zZe0faky5Nf/xJ/l186EK1m5UmKVQRN5\nfM3jvOG+7Tln5Lc2F/zicM2lDZQEKxvBPGflXj3wg7zll8s1VKaQpk9WG35xuOZZv6KYcb8ephCd\nb417Pu0ST3JmzcjL45pVFvaivJKOD2zH43zGhoGXXc8ndsY5R8QSV93Ac9nw7jAw6sin3Zpf3T+j\n5ASi/Ljf8mnueH/YsZMVG0ZS1/Oy9Hw47thpIlG5zsaN9Lw7jjzOHU/XK6i+CNajIL3w/XrGvTTw\nMhTuTyUUZhNP+FF4XOHdceBp12PVWBPv++je/U6EDQPPug0dhWfS8aEdOVZ4EsViVTLf6gasVl7i\ndZCaJfMb9UhFyGq8bwO70vFJl/i27Lk7DjyVFX1XEEa2NvI8r3kW7Syje6N6HSgq7Czzge1RMx7X\nNe9iPJMVigO0Yp6YrGpcHbekdMRKRvUUBVVJbDB+UO5QgPM0oFJOlBEribN65CZCIBlnT+yLcc09\njuSQynvkhFyi1I6DZ7qzJtYzyxjGRiub7J48cCv1BSOoskOoVjApPMyVwOO8nvK0lKdDYiXGx/2K\nO9VIMsuQ511mVTNVKpJhM3Tss1CloqZYeILWh5EO5UBlhTIOK0QLnWTKeE5aFayOWBlZR65BEQ/o\n/ovvP+Af/eABSxny29cH/tX/+7e+6FX9425fqhz5J77zC3zz8nwygEnF3xGpzhAnvlY7mYh7GufN\ndRENw2OecnDdEl+t1UMM3UHmaJHbzOGFGqRTESqli5CxkOsNEDQFfgp3IggAJq9nQKFJoRfXoWig\nTSYG2wYuCnNETujuiHjkTQTsTNde9DFTjtV039moOUMLiXuG144IQ2cO0xLc6GwLENe8cNrGJgBH\nNaJsxgL2LYyirR2055H5ag00CcYY92zmkDfTaGwKObxNKKuTst9g5+yFak/u2ULTE5z0XvNiLuyb\nizPjeRegtwHwybvWPFIiE8CabyHTfGs6pE3EWYR3KQzaizaZuU5Rzai1gTXB1LwGqtp0nVb615ke\nY3wJXSTItjz8z8IkxuQt+3PvPuIfeO8RSznyo9c7/r2//ZtvjMCXtf1UU6bM7HdxIfTr7TsRucTj\ngf9GfPV/AXdF5M8tTv11vG/+5hdfP0BRcmrBokFLqIpqMICUOefFv9dJSW8eiJQ8hXIZ2rbcc06n\nngpzwgQIS3Ncy3BLnogwWg3K6YXF/jZyj9+quifGstfmWd67tWmFsld74/u37kG/naoX5fy846Ih\nWJqfZalYa0pU9UKTIkEuoG/eu4h7j2oS0Pl5RYSUZ4r25fey+Luqn18EyDp9TjGuVYk6TTL1XUrp\n9HrRv20cVJWU9OTet9sw06G/uRerE3lEu+eXsac0t2emafWtE6UTvXXOF7dnyjdabNP8rzjboSyZ\nBOfFrb0LtVZKeC9Hq4y3ismVahRTdscaNAJf3vZVyJA1kZOl8Ljf8HA88MAObCn83f4O5+PAA9th\nZnQoHcqP0pqsA/c48BLlYXHLluFe6WzCaB2jdWRzD8+As69VceKBB0f3ZK/HhGQl5cpe4YO654qe\nzzp4kd2bdC4D+dY4XLDnIIl7tuPTdMbjLjOI8P1u6wulCBdyYGcZBL5drvn7+YKtVS7syGs6Vgxc\nsAc17tmO4PwlZeEOOy6s8qd2L0hSWTUPhiPLabUwhWPnmnlKlZxKhFh4fauXuuJ7+QIR4Wm3Yt8p\nI/4uuvsDHnc9j/OK59Kz7zNdqiTxuXwvD3QhD/okdFJZiyt6d+1AlwqfSc+nsuYVPVerjk9lzaey\n5mI1sMlHPpU1L6RnHWqNAtt+ZNMd6alkjF4rq3Rk2x+xAmerA6tkVEs4/ca8b6KwrSW34HcYtWZq\nTdM+ULlOc/2uRsneaNkv04EHesMDvaHHTvZH6TUXVHqMB3pDN0UiuPK8VCy/k15CSmSEfYLr1FEi\nQuJBKjxIEcPY9pAP99M4XVMQjkk4lOQK6S32IzU3GF7u4XIPHcpeCqsIFfdQqUKtlc3qBhtveDX2\njOWcI8o+QGkTYE2GXA/2pcsQ+PLlCHh4WRaZauu1JUZkUaaDJqNlCm0GQcSjQRq5TlNofbfY3QtS\nF64KkTmvUvB3opEGeEGTBgza8XNO9bzN66qFIdiEKZqjGVHbup8Rjg14MOtNcLpGL9erZCFafpIu\n0vQiOc2nVtEFkGodA8270Hab9hks+TMwkRupzJ4LCTkiAYaW54vMn1s+WPPoicgEKvRtz3KrLxpz\nsmIn+wQIRdzQGiBsHnOb9K95tBb/SZMIrf852UEXf8vUbSfH4XmUJzTqt4Bdjrm97OuTFoks+tOf\nzUxQnWEnEGDJDQpScbKSAEttjoG5fhLEZS31pciCaItTObIf5SuRI7e3P/QdxWsUfJe5/35BRP4M\n8MzMfg/494F/Q0R+B/gB8JeB3ycSKM3st0TkrwH/sYj8iziV538A/FdfzEoDSZ0atR+h5DTFChdR\nehOSKKNaeG/USQqaUt0mUNc5cUK8EC3crOX5mHkujaY8DWhRzymxJlByAK+maKOgHtdqWd0DpTOx\nhIgsKD4jl0nEax2IYgqjVGfiC6/UmIS1eV0oATpNjHhsqtRmoQovRbzLouLpwioTS2ALzzN8sjbf\nRcLdpInktYY8iwnC6qS0eghKCbeRLELS2nNgQXBQKtI59aMlRU04Ut2bVqGmcI6IIFa9flOeWQA9\nFtn7x2OpKySNelczvWZlzr8SwtIxeYXmRSATrIjMlN1NCJUa7uOF67mFsaVYYMbFS+9FROewxSbL\nKpWWlzSFaQoTiBmpU2ibqswV4WV+eUQEkhPXj+IgJidlHEeP+xefD9QazIaxwIqHx1gAxiZkzWZg\nVFLEGKNoUicuMJAkUyjOBEYn4GWTcQDmWHwLmvERz0H542xfpwy5kAN/b/WQX7l5ze/0l3RmPO3W\nPM2JD4bKr4w3/N56xTuD2zM/6RQtiXuy40aUvUK2nrNxzytcOX4dNbAe2oEjiVfW0zGSEDoKe3p2\norxve25qRpOyrhUblLOa+DitWFWQWjivwnXqeYFwZgmpIzomHlB4xobOCquSuepH7rFHrOfhUDGB\nj/o13z1cs6Vyz3ZcpQ2/VF/zmW6gwrftyFWG52y4U/e8thUfDEd2XWGXMjd0SKr8xtkdLjhydxCE\nI887hWClstqRTBhkw4d1z5OceP9Y+ahLvEjCC9ZA4cNh5NxGntsZIsqzPt6PiQLU+MXxmo/6FQXl\neV5xNh4puaKl59NVzy8dr/m+bXlHRz5kz4+7DSVywLZUUhI+61e8O47RPuNx2vBe2fGujByq8Xy1\nZn2sFM1OtGDwRDI/rzua03hfMiloQsea6HLBEN61PZ+KEy88Zs0lA514ntazccW7aceO5MWE8XC5\na4R1rPKflO007y7TgefljH0LcQytN+vIEfiUNZnChpGDdlzaHusyLyTRj37B76abSYb0VumoULIX\n+NWO3dixA15Y5rvpJR+PKz5Izoh3VTPdwcMA78S9j1q5xJkREdgNwqM8kDzgGkH4KGU+tJF9UjDl\nYzo+rCOvOzha5t3V3mVIXnNBoWOPBCBthVKXMqSPdXkJAv6o29cpRxQYqitQIhp5pVBM6cTBVKkR\n2iUR/bBQqrEwbNU5TaCldTQacKuuyE/2MBG32DMrwW7tn0PGGlhKcY9KM47N3gSLlX7ODSJCrpgA\nlBoB8NxA0oenRyDC4ELxXng4Grho12s5yklc96nzo0/3g9Mwxaa8L70/MyOgzmFgC13ETQXzfGp5\n06EQINY8Yf5V89C1NrfQw5ZW58b0eBSbIgLnhX9xzJJRzhbHSNzAmL2FPsbiwCiuU+00NBDmzzMz\n4rwla562tvlZdRrZ5oVrukj0W4Ck1mdNjrRaWu34BhrL1DbPsU/Tu+xHuxd01kVk6pSodYnEvcMr\npa0nQlcKcKfqRdRTOA6SusdKYrA+TxdRcR2tfMWpTH8UiPYPAf8bM7j/d+P7/xz4F8zs3xGRLV7L\n4C7w14F/3Oa6BwD/DPAf4ow0FfhvcArQL97Cxa99nuix3ZoeFnJA08z0JWENS4uaBdVNDtPsVBGk\nMrHZufXAaaWPtYQVyaduqp5Tk+aTMXFwIlMss9OKGy5kRnMiBQc7Pl3SAp7kEIpJ0xQWJpIoVPcc\nBYCgenhaVYL2PCxXtYVZxcst/l1jsJvCAoIuWmt1avMI9vc4Vo9i9mP996NCCjSp1rwe7QV2x2rF\nC+56EWEHHDUodgZ1EoMwIHg8t78N3qoGYuIlcKucQYrFp3q7kNliY3gfSDCy1HiRWyidmYeiifnL\npVFwokiQhcQcmTyMbfxFOBYPCfLYfCZp2jxBLKz9szt6LjQoIRzNChrgq9V48n62mbAknqWFMkyL\ng7V8ruKLEg4sBSF7gAOlVjS8gN5/DtBSSiQzD9GLkDwlmAwj1MONWtHGZc0KAdUgQVEXyiMtZCBA\nnTDPucXC8UfcvjYZkky4KJVXfU8L5NilnntlYCfCTqEX5XlKXn6gdGQt9JbozHNEPuthLDNDXdbK\n5Xjk09WKMiodnsN4fzjyo27Ntibu1x2DCO/YjidsuRN1k0oWhtTz3vGaJ6ln0IG9ZNYGRQZ+rux5\nQc9GRp6njh0952lPGhI7SRyz8MGwY8C4c1R6YAe8sgsG3UPqSQgPxh3ZjPuD8KQXkMwGYxDhYoQN\nhb0kVoy8SsK2GK9zJZXM2ehEAx0jT/OGD4drXgk8TWtGhJdpZO3aAmZKp3sO2fheesC9smdTKh1C\nb7Cunj/0WVrxqWx5LcqRnoe8YiUgtXKgo6/wNGfO9cBQXAGpwKDCoIXX2vOg7rmgESsUVD3kz4qD\nGFPlXh0pmhHBDRAYH0gFMmbCWCqbVKEWjMQB5VIrhcorS9yzI2bwma6mgLpXknhntefanDzjbh1Y\nW+G37JLzPEQosmC5IgZdWI6PptwP9WcfCsiudqytsE4FERgsI15MjV4qWyt0wVTkzIsufTopHJOw\nqSOdjTy3NXd1DwYPpPIK5U4aeJ59/XiY96yqoVSu6Di3Qg1mzW2wYG0SbGXgM1uxZkBRzlLhI02s\nx8p5Mi7LkaEztsVZHLUOXHcrzsoOEaUYVDIr6k+QIT+V4JavURcJz5/CaOCGRn/GEorGzO4VYCB0\nh6Y8+yszh1e1+kDGgpQhPhcg2ZxbM4Wm2dQcECYwNd/XtxTXaMDKw5oWij7MYW82gzGBCRw1RVcs\n0gaW91qCrgXwaOdPXtIFUGrP1nTeie68HRvfD1rDWDmTG7WLzGQZhCKtE+BoYW2jLB6IFm5Ow1M0\nEobWKu+jCBkTJsKqZW4PAcCaUb21uZE8FVvWWZpoHqaHnkBr6AK2GO/R5u9Pxqc9wqIPZxI0m8ig\nhNAFrZ1zyvl3Cl5l8f/FoLVzT0IvvY2tzmebo9Vkys1v80qj/1smh9PAh2EpQE8bIYk2ptBbm07f\nyrV8kRxJ03h8NdsfpQ7T/85PCOUzs78E/KUv+P0FP7Ew3JubiFvKj3VENGONKKCC5Nmj0CatyqIo\nJwTiD6V0ER6q4oplzhmqg5kioDn550XezEnkQoy2IhNtcHOB5+pkETAXRFVcIZdKgAcoy0BfgOSp\nb11M6hIvW4kCGy3+tt3fpE1WXPiqhwgUcctQMzNolwnHaXjDwmLA0pkdE9IkLGdyalWZLD7e3oyG\ne5WZhSZAh1DnNz2UlRYWVq26ZS1Fbk8LZWxiK0BmDPrUzhPrhDnw8c+e16WtunaApxI5Qlqap08n\nmuxlmGEpBVGh1OJ5ZKp0tVlabH5Bp+cn2uSLoi5B1qJ9U3+yyLkihOXkedLFv82WlCfQT1wjiVLG\nkZSzA5vomxRzvJhF/S4lRRhixtzLSniWop9L5KdM7ImSUfMq3oQA7OKBRmY2QFu8V3+c7euUIa+0\n4wOBaxFEhXUpnFvhGT0bLRRLdBhHqfRJWDOyZ8UZe4ooYxJGlAQ8jyIQ61JZmWFirLpCX4xXsuLj\nlaKSURt5seqowDv7I2MeeZb76dyjJHo8fnLnvlsAPtjv+axfcaPCla3ZWuFOPVJU3evZV86K8fF6\nTRNMl3XAgAur3C0jz3vjSOY6J36cvLbO/amKVmWvmaE4VbZJgtHoSfR19OK6AmMHWmAtyqUVrvKa\nkiqHoOce1WXIC9lwxsiqOHPb+/UGEWEQpZI5ChyjHlk2o4ry6DhSdQSt7BOMImAdK3NrQJNlIDwa\nCldZuDAHItesWMuBQToOASrujyOHrmBjxx32J1rjJ/kcgO8O/vyvZE2XExd1z2vNfJQ3POJIwb1g\nB0t81HVYhXtHr6uVVcgJpLjFPYlxVkY+lg01GS+HFfvUsU4D3xwcJF9px6hCri6BAVahJl7Tc08G\nqml40U8pijuZlbQXzB4rz/0s3GfHMXndLisdd7ITj6wpmMFQlcToOTW9MRyEuzoyKOwssaWwtYKZ\nM58OJpzrQC6G5JG74tTvrIz79cjLzj1upffn2LPCzHjdbUkG5/XAIEI1/Qky5I+v6HydckQBSV5X\nyNd/l52TMhmPNxeUnRXgObStKbQNMdFW6FC25xyk1DR8mp4RE3tSfP2CsihZMnloJpV9VoDbiZP3\nQ+Zc2NaYtpylAB7VKo3MgXimdvdl3swEmqSBiDBOxr+NMc3ivKkUR7u1ufJfAzW5yTAuPE2b0/nj\nOcqNYiOeO9bG5llaHtuIFFqR7yUjr0XfNdExrXfL52UZgsdUG8tD6hY5ViITQDFrfdq8Tk09m8Pb\n2ngvQ+hzdEyNMRexKTdWpjkhkZ8VkQAy62lTj93S3076PIAiEp62NH/vfTjP3YRHqKgKVm2iem9e\nJY+icR+1E4/4Wmu1gLl+lWrTRbz1TRdJuD5b1Imnqn6xHJGfjuHlD7x99UGAf4zNQ6uELE6YXIIh\nJUfeR3Ntdyn5QNRwByYnFFCDlbmXxpNq8ZcmPBEa4WQdi/crOYMH4PHr8bWITxYRdw1m1EPUYhvU\n6cBzqxcQx5u2l0Mm4YKAVUXVKaFVLQCNTch9wncmHMXoAqFUNbrqL0j1wvPUxOzViocYjaAlb+jf\nNxXPp8o1PDziZkfF2a0EBwbOWNpYUVzwTmDR5pepAZgWQz1Y5ErVMoUbduQQ0AVJyqCwHltSoVtE\nj3hf1eapKXOuzshc18LMwwmT+jHmEo5UPEyzShtoJ+iQWkmaPCQwcnckebygqM4heVpd8EW8w9IG\nNQlOM7BG7WmRR+cesiwLT6cIkgSKswVqNVKa88dSAy6x2JUY8xwOuSJBPZ/TFO7X5kOxEp7IVjHL\npvDCigvHhHv3ChErXPGwvKWHrhiWXGLWIIKwMjMXinhMM3huzs/qVgWeW8fDemRdD+w0caUdD8cj\nmzoy9FtAeFQKj1drtmNhzYF9TuRS2ZbCw+OBQZSncsk79RWHJDxJKxR4cDjyou/5xvEVB80QBVif\nRo2iz9Y9fVt8QnFUMX686nnvOPBZP0ufH6wveJG2vFdeoFIo4oBiUEWSh1QeNZHNEB2xYcXKDnya\nVyQd2NSRoyW2NiAifGd4QRVhR8dv9/f49vEVL7RDs/Ht8Tl76Xi18nY+I9NL5cdyxztOlWdmfOf4\ngldJOKuV0YPCIELq3hl2PE09N6lDMC7ZcXc8kmvld/M9quAgBl/opcIx+8IopaeidLVOYPIi5Onv\nySWiyrf3z3gwFvZJeThWBkloyVRJ/F6+yz/8+mNeqsCgfGN8wd9bXUIqvKbjzAbeOQ5ccOBI5of9\nmvvmxXCPx55rSdyXGzqBG+lRHdiMxj+yu+Jx3qIRlnzFirvHI9+u1w4GDX7UbRGETQXpCiv1DJYn\nacVTS9yvBUhc6oGXARgndW8M0h/zZPJe4AdpxX0GVmMLmXIN6FJGlMoeZS0jl3IgqbBe7ej2Ss0G\nXcXEqKPHQazzAIOyI9MPhZTBqq8FdwKYj+prYzIhZUjV0I0rP1dsQD1M+5WtPd/0LTKkLwObMnJI\niZ1kXmOcSSGP41tlyIkG+zO4FYAaBALiC3BjYmuqLECSAMChCCMzhs8S+Y3FFhTj8ybCLQu6LJjX\nTkOsLNaMVqy25YaAr98OeQK90YLvfVWTxQ0lGOVEotaRapBExeouMkfAV5dujZSikVu1ezR1KAnh\neQRQikfbT8846SI4uMoiUw1CiBSLhS+kraFzTxAK+2xylABrzWAMC89NSyWQYFTGjZ6C65idzV4o\nxetXTmkPxuxxYmY/bEN+u+YWLPPMZOHlar3hhsxqNrHwNcDWLlM1frOmeszXXwZauhdeIIwsjZAk\nhXduIuYIfbcB1qSTv5JkdaIIt6kfIsrJLPKKbCL5EnXjuhtaa3S6ek6kGKpu/PXnTiQxxEZqRLTc\nliNe7cAJqLyYcGVUPamreipHvlpd5GcKMPUE4QJCkRpW/EiwlNlyI+3zwnNRRSdhVYEOnwRlenHD\nFStMrti2TYXP4vPSgiPYFAa23LrIPVkm5E+CJSwHM5tZdZBhlZx9gjX2PE2nBBQVWGueLBBF3Suy\nDHNo156MBO0lDcuSCVCYipNKgAxr1oV41iYk2pxcJiJOnw061ana9qm/yseMELgIrC1F7apQ8c3I\n1ajZWfcQ4ahuQe1QKJVelDG7UEnTlePRVGhByQ2E1gAVY61YWrQfQVIKoeLPrDZbK5bPqJKivoBN\nCZxvs9y1+adBJtGpU08TC8jJOaokqQ7QmIkYxraQWRBwBEAfIi/Pajm5/3KMmxXKhfnpvCXOKWKT\nV6vNk7EYSTOjjf6eiHsL2/iPYhEW6POjAWvM5+TP6vbABjZqKMpOjFGUVa28SpmdJVZB5TyKe5rW\n8e5tB4uwX+G1dlx1iQ/tineORz7p16BGLsrGCs+rUiUxyCxeu5JBy2mNsKJ0dUASpJro7JSu/cF4\npJdCboVbU+XOMLIX4dgJlITKSKEjlTKFJffV69280o5rFVJZQTqyTx5SVsbMLx5eUTAe2MhHacMz\nVlSEVQA8V77gPbsCoI5eZ+dlylStvNYEI5xjqA6s6ECVkjoejTeAIanyOidEEnfGwRWdNJd7tqin\noRjnUnkZhq67UWDWgpb726MXox06ZUD5peNL/lb3CEN4VCvFlJ/bv+BHmw0y9iQp/N3uAZ+mLY/s\nhrOy54Nx5BPpeZYTdwbhXt0jxcHhD/otd8oBGTu2tqeTHU+k5z0b+J3uDoMIoh7FlYsTt3yaNoxk\nVEY6I5izmPoOICdFcubFUXmkxWvwxdO3vzotpAqfac9KjN4Gvs2RB4MDy2e6cvkJ3LEDR/F8Or+H\nMvaFdFCe0XNuA9f7DQdRHoj32VXdkjFWEYVmC/3CZUiE2mTDGlNBbipobBWKGtaHIj4I0sFxVLqk\njDZSUqZo4ihzMd2XkrinxcmYcBnyLLk3UxcEOz+LWw7Dp4pSQ033VWjpB2phbjZ1p8psYGyHdcEq\nuHDSTJ9v6cdvzJ95fYEWMra8FgRxxMKTc3pJCWNunX+Pf1LkYbc6gV60dAFUxAsmN//VGHmOU3tY\ngrjFA8+4IcLdwyNT5zWcClOh2XigExVr6e1prprwVNQGBafHifdxPiU+nzLdmYUXbAGyinnplc78\n2CwLAHq7o7EpWmaOuvG86BoAhUWbPNKleR7nXKfbYywBgpbz4XY/WwNF7driEUA69ffc6ZM2GxFJ\nEGF11NnoX+frgetjTV97qy5idXIEOL69pYhE/7j3LU2AXkQ9bUVCF5n0mfn6tRbQSFMJI6PV4h61\nr1gX+ZmSWkPUe3Ayh4RqCkt8hDUxx+F6st48gZ0hTSkinmMygSNHsiqeFG8QLG1xU5GJzUzC8rEM\no8rhZbFbDG1VxSesCWNcqyQXTi30rF0nacIyztJmzrplypTP0ybRlBQXlCO5U1aSwkLgbRWEISwA\nKhKsglC1ojorv6j4/ZJ7VUy8MKyIW7ual0KCFTBF/9Rof3K+7EnIpmkMZMrd8fN9VxK56lzTSj22\n2J/JraGqToudROnx9quqU4knZ5Az9WdK8R/MwFA1YdoYFGMcq/l54iFAFnTxo87gcMmoV4Jkw8Lk\n10XYn3uJlNwYEON+Gv1MswA1K1EsXkUdlFcLFjrUBa6Aag52K6e6cBkkIP5sQwAUkYSB+jKIAAAg\nAElEQVShkTDaSEQsxjCDOjVy1ZkZUmRmkASlVgeXWRNFFNE0WY/AAVKLwe8lgQgpJ5Imcsp0KiTJ\nrJKib5OFPyPbx/2WZMaVJqp0aARLruL3pQy5OI5cdR2vkv/6Kq25Shuu0ooDGwrKS+24GI9cHAc6\ngyd5i2XjWd5wo5lRhCI+5rVmrCSec061HrQySse7w47OjB+t7yI1MbDCasdrXXE5VO6Pex5351hR\nPuov2GtmPUC1hFgmm5EtU7JwnVakdOT9/Z5neQvWc2bHicJVDfp85NAJ2h1ZUfjGcE2qORL1hYuh\n8LHcQUQ4GwvrUcj5yJO8IuVCEmdz28gAGEfrWdcCOvBBvcLywL26I9fMEIVokwgdwpWccSVnXBTj\nIgxFW4OqSi9uJW0ypHNLCykfSPlAV5RvHV/zw9WWu3XgQR14lnpUKutc2VZjrQcOGc4Z+Fa5YmsD\nVVf83uqc40r4uWHgKm8QtlyacWnGw3HHHTuCGGszfqj3WFP4VNdc58qYRt4bjBvJvJbEQROvZM3z\nznjvuGNF5dwKWx250IGbbLwz7jh2I70Z30k3XHKgU8Ok5xeG1x6CicvZB+a/iVUuy5GLcnDvFcL9\neuBFzrzMiReywoCndsaxZJfZ+47PbIuReWUbRtx497yeIUn4bbskW+WZnXPFhpesXYZ0QV2s8NLO\nuBrOEa1UNVJJpIOQBkVreMcXMiT1PqdT4kSGHGQucv7QRhDhuttwoxtudEW5LmwPB9bXR4bXP7te\nauA0/J80GSubR2gpRyB01RlFuIEKOWGDa3BLZVb2p8KkuCJssSNRyqStu/GONZa+tsZKkAKliBYp\n7S6u3Tac4deRuT3thzQd6wCmgbmwN0YjXWfoRMO7YnFtYWz6C0tPS2Nom0O92gUbVsnxvJ4nppPX\nRWKOtT5rOkJre5M1LOSIYOHha7uHcjXlf8pZEh+/1lZw/aZrFOv+qHEcMd4Omtv4L3PMiDa3fHWI\nMPiYN07RHqx4DWBb6D0ikx5r070iONEkxpupDleSxt7n88hxz5yO4hjUYgdToni5Xz+1+9GqIcWM\nM29TsUglYXY6nOgisgxRDF1EQDybM/TYU10kSeginOoink3qcy4nj0bqgK4KXQUdR2+LGTbOdQK/\niu1nysPUhMAyjrEprB6CFC/ANFkdCVcN4WCcUItrTCrwUBezOZQKXGmu1RnywCO7JqsHEX/ZhJPN\nIAPC1Tq1O9rePjNbhNpz+TxvFqK5IBvMwKptLcm/FK+vUzDIKUI73KWcUK+tMU3ieNkawEkth6m1\nRybQ0mJUc9dRSiFFIcUpLHBp7QD3TrV8spnPdPKWgYcCOuichUdSjdyZuEwIo7Y8TJ4fgVxgzIux\nWSwi3rfNHY1bchfWh7GGNTssvEkTo5UTSvXZezZ7DItFuOK00DGN03LcGlit6kLJLGira50sOO0c\nFWdPcmE4QlaknLZhoGIGZ5Y5RLjMsn3LYycWmdav4jlZfq+gVQ1kWEOg9tN0EiDC/Myv5TknlZVl\nh3G5pc764papc/7fz+CmCHtZ0zEwkElWOEjPN+wlP9Yzjg5dJxCeqqc3v0griiSn7gcOAJYY1cNd\nRQuvckdBuFuODD4zue4TVuDCM8P5cLzmhyRGUawmXnQdhs/fZJAtYRWqJCdnqZmihfvjnkPqOS+F\nKgmRwjHrVPes04FjWkHxAsci0KnB6GN3lDWpemFYrUZXjWPeIHqg0DGgJFO0wkvteK+84v5YeKXZ\nVY6qXMgANpfD1mCsO+aoMzPG+56VoyRII7BmoEQcOrw/eI7NTjskFSftLoVXukZs9Dpy4dWTLPx/\n3L1bzGVZkt/1i1hr733O+S75VWZduy5dXX2ZnvaMMXhsY2ssX4S4WAgJgQQSAgGvvMETEki8gXiy\nhGwk4M2PXF9AGGGQ5cFC1njs8ZixxzPt7qmprmtWVn75Xc45e++1goeItffJ7PHY45npVnlLWVn5\nfefsy1prx4p/xD/+kWtClshG5WPZIVKZRBnM6LRyXkau411/aZrZ57rY2wkjiWfhv3G44/ubHXNk\n0b6nXtP0sE5s5wNj6vlILl3lc+ypqWNjXsHxqcwc2bCVmXsyb9U7PpItJe0X5Sox9QweYKq8PlY+\nSROPc+K10d/JCzmEk+GjeGHHNSCWhKc68FQ3vDbf8qAW7qPOqB0V4dwmPmPDnSXOZWJOibN5lU9X\n4HHKMG/5I/aE7+mODUap6vtXTOB9HSgIL0tkNqtQg3JROt8Z8qiecer8/DYLkqCvoBSEctIeIXG2\ndwrmzWCcz0c2Y1DXFUjmzYylkpo86pf0EHFb0pL6i8hPU0O150FFYyU1hbZGF1uc/pPhKPH5Vuvj\nX4nyA1ZAttRJSfMV2p64ZpPcUeY59oHCSR/C56Nfp4BokfymsVN+WPDHjKV/moiLHSCyKLImsdUZ\nX56nAbA4f+wnNaTxCSChASirGTlpqN/5ZxcKoz2flWl+w3M1Y+Lz0vbgikTvSDnJ1OiJIAaLwEK7\nV7N1rJrCcBvQ9TrrnFsLaEbZRru/YhZ+hCyfbaIYlefHtt2HEK1cGpAg7m19JAhluZZt87XmrIgk\nEiqJ8sK5fb6sRM35AtvWZytSUaozg7Qu9uX07W3fSouHWBdV6hr+oIa4yTLXkV3qginGyfi2EgwR\nYS6ewVdpL42DXkPJFH7UieovFWDyzMZJrUfMgJWQWJaoqWlofX0TfdF5njf6Fp2al0jXyuL+Lour\nCUeoaiihyMn9eKRgKN6jcBaPsszmdSNV3VD1bfHhqLgsAMrBjnNN16jSnBzcZZGlcWCVJhEdhkY8\nW2AS96GCzcUzQrZylP1ViMhDXV/s9qKdcqStvZztGiaoZCS5VPrKs/bRqeZGYVboxKmDniSzZfG3\nF8sXvEdHgpGGhlua6goyS2SFkkHRFYxaBql1sTwiThVI1UGNmCyKfySN9H64vVqcOqAucV5qZdCO\nYwKtZXl2wAEcwdM9IZarKqVWRvW6Jqf22WIAm9piH0ajrc/WsLAkIUXzXtGYs5RDPjwt9+Bj6HM4\nS2yQEj3jRVbn0Rqwc9Pd5lgxb3qsydX2xHtjlVLo4sKtwFJVlzFtwQUTY1MFNLKpJFx9qNH+0klO\n78t3dMV4275Y1IR+PT1EU8GOxuvllvfTS2ztyK14HU2X3Lm2KTPIgTvtUCnsCkydoVKWl+myUerU\no2EilaE6XeI2d1zKkY+6HT0TS7eeWqgG35qe8H5/ydNhw1t3d3yy2XJmI/dDpo5bdhVEjtymnt1s\nPMkDfS1sClx3yrFkyD5nRTK/tnuJfi4kMZ7kDV0t7HNiN3mNiyl0tdCVjiywS0euuy0yzi7LXzNP\nUuao7vyUANuHkt22iZLnoCu2KJ+4Y5Wqca9KsYGhzJj1kIRajLu89inCMp2N3MuGZ3lgQ6afZ6ZO\nGcrklMNwR6wkkhasJPIsXDJynzsuS+Feey6iY+vDcuADe42stzyaJj7vOlJNPJxGPk8bhmJY0ALf\ntBtuU+KNcc9Hw8C2Gp9Kx8MyMQ5Cnj1ODZCy8Tr33MmGr01P+YINb88T/9fuHd45Om1xDGGebjKu\nRXgmiU5GjnhQqEPROvHL2x395L2dPs+JX+tfBuD1caRS+db4zIfH4KN+x4PZ19V1Hnh3unEJezmy\nsY6alS9K5lxWO1YNLphREx7LhpdtZCdHvrAzd6wAmRMXMjqV+MSGaHXwXhSGsmfSLakKvR0Zpy1d\nuocK21EoamTzxvEAIhOGYQm2ewmQdGJD9J8MGwLh8KZFSmHZQFrWYgEdJw44sNBfagT5GiI6dWXT\nCahd8IOsPoksznx8nshE2arCV1hrh5Kw9L5p7YxjR3F5d2ky0xbiFavjXE+u0a7Y6qRO76VlfbLg\nLI5GF2/udAtqN4EDXwjub8RFlmwT0Ea21RJZBA19n7fnpNYXX4TwRUJAZW3A+pv4InETSpxXnr+H\nmFVam46WNVNO6W3Nb3ke2AqNvcTy3QWsxJ6+qBqbB7jnCCafHg0UNyGI5f7VA9pek8VClSy2XAKI\nOp/Y1+vJ+a2tqTa2Igsgb29loxM26qADp7jpRbzCluvJc75ICyjURZSsNR92qqRFoK8GADRXfm6B\nMjEseQYuWwh5SKPyVTSUQn8cduRLBZi8AWS8rBI1MNUWOtxQYK+VTp4fxAawWmNUszVD0qIca2dj\nvP8Pi1bAc5mj584bIg4FqMmvb9oWoy/Q9qIsBXgoqfUWwov/mzhFH1ZgCmeOuGc/or6kARFbi0hV\nXcHmNKMDa1q5VgvaW6E35SjlxPiu5zMzaor4gUvsLcIBqup1Y7BEuVrN0a4K91KXXj+eUj/5zAtj\n/WIg5bTWDIrPUzVqGBfBm50huvY1CmNQqfTmDYATnnWr0sByu1BEcCtMWVBLjAKbEqpcJ/fkwMeW\n1Llhzt9XB3C5tKJRp/qJhspe7HprhcaagYRmW1yoBHPDmyqopKUXmNTIHJqtDW4BEV2k8WsJamhi\nqYFb18OJ4ENkUf25K52mJWJYzNeDp/7bHcca15bqbwBZQRJWPLL8wtR96Y59TvyGXoAkHtUveHm+\n5VA2fDScoVS+fXjMX9++wksv1BOdlz2zKps8s2HPIW+hDJyPozeCBT7bOBiY6BETJga2HKhqvFKO\nTinmeTuyKxNTUj7oL5lt4Ku3d2zlwBPbcDZWpj6h2etP9vSUrPTseanC+Vh4NmQuSs+c3Ka8e3Bn\n+3vnD0I6Cq7mwiSZUcQBYIn7zAWVCRPjlXHPdbdlZ8KYjWTFv65OHSok7qXnNbvlK4c9v745a+HP\ndVFU/7MfXLgil8Kh65hFOB9nSDBFEOJqdLtwkI4pC+8dbvhBv+Wuz6i41Hr7zDinUJkTqibUChIK\ndcfkLuA2NmuxxKbOjGnLph644YouVa7KxDPdomlcnKJNrYxZ+aIfeK9e80U956t2z70KexLn7Dkk\n3yI1an0e1D1/r3/IYJXv9xf8ibsP+PUuhDHiXdrkmW4qvGoTNzYwzcrjbgQ7w+rEq+ORSVy/8o25\n8nWe8swyPVAQHqcN4DTZD7sNO/Garp1N3EjHIScGKzwsR75gw9frLU+zj0Mu3g/sMT0v2+gOirrt\nfS17XZNFfUJZIv4tkOauX9/tsWpMZUef750mNBs73VPC3hwGYzN1qNqSEXRKlNuQYi4TbNWL1odk\njMf0T4QNgaAltT5LLT64+OZCNt/HX5Q9FnVRBd+dWkqA5l2vJ2+HRlsQc0eyCTy8eIjZ0jqjqtBV\niyBaAwArKHBfxHfbVrBvELQy91ly3ERZAOGatXKQsvo2S4ZH1oBrq39anqXthWUZIroQsFq6Q7bH\nNz+nyUkuI567uUAtE9RimhLO/KbCQVZhK7P1M0sGZzFbK1JdZumkBskFEfzfM848WMZfVlGLZYqj\nfUdrf1KrO/1en3VybQEpHnAQQqHZwsfh9JF97F0mXWKO3SupnPTrssaCMWo9meclttoE0da16D5w\nA3Orf3baY1Hj343m6Y9opPADLZ7JtAlMtLWmsT400JnRGtWKOfCypXYqfBFdkwNi/qyEryQhMQ4E\nyP/x2ZEvFWDqhAVAaOsEENkYq0bJoXBirpzXVNfaiwysNK+YEH8BG4pe09XtBQR3yiUmr7AqnyV1\nFF8SSK0ONqyumZhqSw4yheNqApJ0acKltAzJ2qenCVAIq4Sn0xq8cWg2QtkvHPoANNQQGUgr1axD\nEa2YVa+hMQce3izsNDroG+ZKeQvwV1da31LnMxfvAi3BtVWhEwcr3tB2Tf7mmK/lvCcGShFmWxPR\nk0JvPq+mXvPQvpMsAMxJAU0K41XVjVS1GXJwfJfYNFgIo1qKTKIanfl6yVVDkvwEOKbIRInzdhfQ\nU30zaqnnSdwpaIIZlYgI1cpGPDq+OCMRWWnUhdmamo5TpJImSMWzqNbqwOoy7tM0+Rznill2AKVe\nfDzSVK5ifkqAzriXhZoY/Va831as46oBUP1506KcJWH0nfapOSKEApJ/kx37S3K8Md7w2raAQS6Z\nvSjS73ntYDxJWz4dEt8YP+Oj7op3Drd8vNmxYc+8gWH2yNid+s9QkP7AtQqXR6Nn4pP0EmeTccEd\nw1zJNnMzCLdyxdl8YFNnPhguyEWYMjxIBy7niY+HHV2p3HUdcz5wwS1lqB4tLV5DdWkTd5aY6sBr\n81M+7q4gMgJSM6kaj7sLALZTZVMq2SpPh54u1Ngu54mP+46LqXIg0dWJOxn4uE+MjWxiMEnHmD0z\n8ubxlsfdhl31pqqPN5VE4q5L3EvHy8UdcRL0pqTJCYlFK6NEzj/ZQokuCDedcVVHzzKJcb9Rhupg\n/sE0c913C1DcLMEXAakcox5yqPCw3PNJvza0/bubKy5G417hB90DLuvEqMqn/RkX04zOcAiluidd\nzzAbuU58Jg/Y94KWSqGnY2Kohpi/D7e69WfMhY0VVAtfr4/5YLvhjeMNHw/9Ugd3l5S71PPm4cBN\n9owYdUsnE7MZn+Udj8oeofJh2nBVMirGXhK32oEV9rnwjePIq+O0BJ/EhENyJc4jPU+6LZc2casD\nV+XovfW6mVlHtqWjDjPZwNSoCRjdkcnDLdN0TqcTkyXOZeQuGx2FZEI/9xzTkb7bhxNV6KuLT4xp\nXGzIsS9oFboS9OdmM62QMUpyBUiZ/R52u5nSNrkvsQ0B8CYd6mCp7cUS4NN8/0qxP2vsU43AveKi\nECdwuLICLyKrEHhqofXFdyLkvvgQDczkABISGYU1Pxr+TquzaedBFoXXFpyrtJzZus82sNUa6y69\neSKL4w55gBxWR/aUxgYOXCTZAnzmAFGNkryAu/DXGvGtsQ3FXigxsEZTO/mOulNbbaUStvOmZY9f\nxzNGx9ktQGy5DmKW57CobWpOe2RgmsiWtVqhQFlGKMb5uPt9rVdrvtVCL7RVXKrVUq33J8uaOgUI\nDZ+3c8yNrqi2gKjW6qZHnpsXQRYQ1cR9VizcAJpfrwFGtJ74bKyIEo3EQCWTGKkeYDcvnylWHEBp\nY8+EL8ILvkhoz6eUwIi+T/5dD4Z7eQwCKcvyfD9qX+RLBZgauAE3EHNpjT0jyxLOXkqJltVeM0gs\naVogdOFtrYnSxikNzvUSemiSkyt4aXw+X3h+oRy0p1OOrzdIXf/dMkFuGGXNBkUBnJxkY5pCXZ0r\nXfZpMnN5aH+29a3PmihlRlT9PpaIYTxj8ka8hRA7mOsiq92yWhbnF1VqKcv3c85OiYtNoJQSySeN\ndDYRkfICPYkHaJmg0jZ7VsPUqHklGjQuBZPVqZVz3JCDK5+Lit9bC4EswCD40y4fLqTiWTIPbKwc\ncQhbViqSgKDYNerjIkca91s8nbdkvyTmU1Wx4g53Mi8ErUkoxV/mXJwO19LxTRFJ8bmrUXcmqvTN\nYGaiBk+j/0RdGvS1OrWUPHktmjBzJ1RSWuimjetczeg1mtyGQWzvwDaAY1OGVBEswVRKjIEhlpZC\n0NmcAtu6prel3b8QNf0yHUnmoO460L7XTGdO57qyA8yea37reAsCW7vnItpcPum2Lp9drjkfjR17\nroeOy3mmCJxN0BXltfoErc4Jn1W5PFYuucYEHus5QzEOUWfz0njgJrur/ZLtuWXLrhrbOqMVhnzH\njXVUUbZ1JNFxIz0f5yuy1ajbE/Y9nB3h9fkLAH7p/HUymUkS2zLSmbCdjXsZuE89Xzt8xm13Rdt6\nr8pMtht2tbKpE39n+zIX88iswkjizeMNYsb3d1dcp55+mpmk46rueefglDRD+CQ9ICGMwU2/nCb2\nnXJZZu615146zssRE3iqOwYb6YqSgQuOPCjVgUFNpAim7M3BShbPDj2oBUTIVvl86MOW+6J+43Bw\ne6U9qRqjCpugbD/LiT4ilmrGhsIhCb0pMzCXjn0nPBhHnumGp3nmrEzMoqh5haxW4WVuybXyG8MD\nsoxsKLyySMLDJrJdHw4DIjCkKVafkLLR1WmhwVzVwkPb81m/5TBDLjMP7ci2zh4USWc8lgnFuJrg\nnJmP7Iyt+DM9iOxTl2ZKf2QqHUmFQW+x6oJDaVIX/RFjzoVBE2MK2gsVRkVl8pqRmjh2E2ej25BD\n/7wNuZycxTCn7MAaxTrjXo9sJ+HQG9sxeyQ8PEuROZrv2kI3u+jWuqwv43FapO7NNSOiLq0exY9W\nRwqrU2r2Aq2NAFbIIirQnNPnhE8bCArFtgZeGqXMt5qgotlpzYrXi5y6ls13abS50+NU1KclJpp/\nkPGAKFjQOde93cxFqVo9VatdahSw54CZObhudeRNeKCNT7vHU0XBJBr4sAWy7bkArP84alBDKrv9\nzD/fwNaaVXFfZaXutTlKATabcGQDq0KAsFNAKasSYLWY+6hlQ6J2+wUwqWJYDTAnrYKIpR7fb60F\nfAUa6yiuHPphS/mD94GqmOgyZrnx/uK7bX2Ax/HL4iOfKHxqm2uJea9e+22tJ6h/Xl21alnoi/DV\niS9iGF1yO9KSF+2d2ZCWIDOsIHSudV2/lmjcUpe6L7+JL8KP9PhSAaam/tVqLVKIACxlYLoCiyW7\nhC9oPTVQLaoSqnSqunA21xooY5JYdMmXrjvIq0iBLHxc1x5J2euXMicGtUQ/JvEsERLqIGH4IJRW\nWvYpsl2B3bHkf6sm71gdAgZWKrMZQ+4CectCz/K6F6d0VBXq7BmHQkx4Tgsn+Rgv0CYyRIKDv9nq\ncwIYNbRJe3UFNW+mkByssBYjIkIyd9YbnQ5YAFqTqu5pzv9qEL1WIl66iCAQUSWv/xKq6vJ8i5Lf\nYjBwLmwAYC3OxV82hgq1ixfbQFNipjzX12iO8WgNCAHS7N8fpToNMyW0erbFU+PSgnfUzBJ5yyKU\n4tS9VjDZ5eS1RIarF9ZKMwDR59jv07wvl3YhOx/PrBgSPaIEqAqDpRVQYlFoWZf3xWvv/NlS9L0h\nKJiq6qAVj/K54bJQTAs5YG2gb52nL+0RSj1HNc7qxAOByYx9p/TFaHlNo7JPLGCpqvLStOelae/0\nAPWGeldTQc0do6Mo02AMe2EUpxX88u41furuU8aUSGa8ZrdQ4GMucKcDBql8/fAEE2EnE+/rI94p\nT3mWe0C5qnf87fNXee3+Kbty5PHuIe8drvmbZ6/zz9x8iAFj9xKv8ARTYUqZrx2ekqyytYlf3b7E\nI64pNfG9zRWdFT5MjzAzPus3PKoHdCzMnHGGg5+f2n8GwN84f51n3YYH44GiGZOZvQxId+TMJkbN\n/L2zhwC8PB+wopzVA69y5P3+Aa9OX/C5XdDXmR9sz3gwH3lzvGaIJoamcK1bbnXg1fmGKsK2Tgw2\n81k+453xC/YhLz7YzON0xkvlht8Yrvj2+JTdOJKs8jR7U9dDblUaictyx3UayOmAYlznS3QyDilz\nWUeSuTrqQTMmXqt1Nnkm/7xUOq28NE98lM6X/ixndeRjPSenysV05CYPvD8Y1OwOEMYX6pS6B3aP\nWWVOyrsH79v0q8OOl+aRp7nnah657hKfcMbVNLKVRLKZ677nqQ08nA+8XO+5t8yr7Hk/bxlK4SEH\nXrIDpq6oOmuhZncH+zyRZrcLtfRonimdOyNjgPR+zCQpFJ3pgEOnbKcNUJlRNmYcRcnM5Nnp6aNW\n+ipMKLNmKkZHpadSTenLgOpMPydU56UeswIXm3mpOVlKT77MUpucOO8RXGphR4saWA3PskX7lwh+\n/GcJ3BK1Y3HeJXvRvhPb40zsM0IwLqJuKChaLdJvugYJSz0RXIgIZLgzxBaw1L+0fTrhAYtWWnCa\nhbEAZhnvD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P5nEfnWC58ZROTPichjEbkRkf9BRF594TNvi8j/KiJ3IvKxiPyXIvIPvRdR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xbI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\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iUSy42NoPp6YmUnEEVVywLUtWXcLmrS6UBdIRRQzeAAsExH5GUTxFLe8LVTIsxN1qRDwv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UnuQ5yHJuWzLTNSTNiYeRnsQWZF/YFQp2MGcvpc5+zSceO+MnXkMAKlqM6giiVprkADAifSVs\nOxs6d6QD9yWvXXWknMprMTZFlrENFYYwMX82p5QD50ONtgXlTlzNXOxALwldIWmgFwqLWXXE94Zy\nyXS7iVjyWJxXD12phMTqXD4219no48M3mytyXnVETFA2GHMFW0i6bS6/znAxHC9vlGWJJWOsIAdJ\n8yp8BRr0mpQ0EI2P97UCZbo12KVq1IxcATUDQlJ5yQb1Pq2ViNexvFy+3hcNueTRw6lrrnNAY00x\nlry5YRPLGovJUFZYEL6RnLQNdte9XmYfdUS5p/KzepvqCKXvblk6VttFp0ANKw0v+h6mJGptsAYf\nt8J4YDR7bPgrGXD1BINNPugWU/ZKTOiVmPyKJ5PNg+XZck2zHWyhochX2t5AbFtR0C5fb6/ezybW\nu+CpkFxvtV90vICuEBCwaaVDlymUxy48d6qNw5NZqiOvuPDy1OqEvrA65BFtfq0XAV/qrvM3DL38\npAammUGGc/rgKOfmxbCTq4vC2SN5tA7Gcun7lWI1GcsXkjTeSYWTqQSbWs3tt00abmzjn0/J956Z\nHP7gh3aK5HM/qan0q4cPMuHK4nTpziOTb+GMDn5vm5yJoh57mQPr+RHv6C7Ga6wo3WetdJgphIkQ\nenBPoVHLOimZCnrtjpqs9MCugy0urC6pXE37db4V1Ow1NOp1tjcX9cF2DZQks4UkBiISRNGflYQZ\n3xpiOlWnhlCkS+5shqkQWxzLwNfMboYHg2QPofYqggrksN7AYAu9v6KxWTgn92EyE86TL8eNR8LT\nJlFiFh88l6F0sg1jN73HFgSy2EcWFqQAi2euUBCMySRNqH45y7T448NnNqCccsW8hyladN82xmIV\nrJ32b9g8pIfOhxixX3uGX7HjXgsUcwPbGN6EfyX6wNoYvtD5VDKhztcphi6GxvIafKfBCk+ZMbOB\n4Wwr5c9IPt0Uf/wgtOuhJ5+zeRpcYzFdq2HbzTlD2vNqNe33/sqJq1nz4ALIxlZMrH9AVT+9K0tx\nuD2aPjciannC1rDvr6FlU+iJQQ81Cvgu/f56Pnw3yepo6lyekdFqbN3pa1JDRv9+PMBReMacMKGm\nYbuM3F35YWD2fjEbTqy0KfMmS0NbXs+PvRUR8m1GDMgmvz7wQL/fl8mo5NQKXrIOxJiuQ1o2yaV/\nn65wkPnktCGJCxr2t+viOUI1xHTwuy91/iua32Acu9LccsI4AGdeYvCu5/tKnHrqMx43xpDEZiB/\njt/v1FXsx851Ptg+vTFotttBHZskNRT2szv0Jw0J7mQtr9bVXN++sd/fOIbx7Vdf+fLLTzzfi3md\nWOjw/3Y9176bZvv8xjClb/YpX2jcbgyMeQmtjttnzq/fMAY2IK+Hwnn2TYNy9zJ9v76bjb7ks5gV\nhBffv35l//SGZTLuY/lQLkgF6lznO3G8fAILK+lWmI/Jjsk2MN8Q2z2h36RS2As2lwwUw007kQoB\nBOGTKvlM2XTfznPSs4ljZ+w7Pg4q4csvfi7pzBDINs+iU7LE2CTFbF7JYK4o5qlmuqs4r3fKHH98\npyKYJUCoQ74c+wnXEICaUpXgTV8bY8gb0uveZFNT3rGLYcIWC6Em1d3X4IHqTC3Efq5UytC+IjOn\n51xDxVRqa0v6Z1mKgK8WQFkC+WgWiJBLEhuMxYYIsPdl2RPQCHpp1bmiwtVMi4XoHxkdkwctPuRh\nYi2161G9iWR1CP1vMDvJloxb6p8m6lL/9WJTsvGQd2UFXmMB3hp/zIouqYY6p1ilRa9ZqheRSFJN\nd6BehDgUqY36KIuhuG3baB9YSL48PWD1IhrbVh2ZRdbFNjaGGc95ctx28iyKp/yfBo+afOysilCq\nXb5kzVoj8KqNsfqTyrWDL6Bez4bbukVe7sGlImBg7VinAmusOEvhL14lqR1B8oRUX5Z1fsyGkh+q\njsjvr99drlOBAOyiW2EMMYyOXvs2DWvZUKolgXNXL5LdaGHl8j/VWpFhECW2f9t3sAGW1JD3KCvw\nWXgsBY611nOYi9X8SFBukic1YYR63F4+f7zJFdH/m7x+UgNTknzNi9/Jjdvh2JzMOih/Mlq06i8j\nmQ1Wgww9oIVj50PIJ4rDvlrJM9Uy0k8zSC3Zwpwa0p5uDWcq2z9biMrvVvNA0rcfMgkLPJvb64Cr\n5pnND67G9+5N5VwD2lDhcIOUPENiwMFFM8cgrqfCvKrl6eHB4Y4lXIim9yoOW76XAWXNxcXwH1Na\nHou1uJkQmKvBUZa/lbixC9MNGkF3M0u3apgGm24YprhM7yXXch0G5s5wHcjeQpbDpCVmOKOKsEu7\npEzs2BjJnioYMdRgTAxz4+eebFpcwhYKP4g+OLO4mxM2GJtzx8i1jHGPnXk91TTtwWM2lzVXS4IZ\nNWhPMnV4VAQ/zGul0RWDonpgBbsbj4LdhFYplMGXnlkD7lkrWnM2RyRjyfIkWbQla4Beul/df4MI\neWZ2N3YTy7FxSNJD6OBYw1BlMzokLzJTBP5QxHR6E3PKa8XQYdHNVcURot9xXxHL0IS87H4xOqUr\n751ZLL1M015kOH7WkgSs/Q7rni1k8LRZUCaz7k/0imo6J14Dv2+Sm6VRMXVAmu5Fp+jceM2/Dcz3\nd5YIWAxUaZhy1XY1pg0fy02GYsu7jXQlRirQI7HLGDap9F9DZ2vlTK21CCXfgtk6Oqt00JrD8j92\n1schy5JG9L7Tj1NR4W1YOJ3nx+uydYhKQqMYcA+lJXpqoEobOkfrwqugNh4WHAh4cNfag64lZTyT\n3g7J/J5PEslFbZ60H2qQY18yGSGZ9ZjAO2PbxAJvxnj7LB/nZjCabbxhVYy7WKUtdsamVLz6iwex\nrUHFYNt3bneIDvAv7HvQ5ew/v3E+nhyHs22fGFtg3sxL7dntLXj/9p2miePg8RfBRFK4BsYuT8b8\n+k5HcNwG379+w2ZTa0+eUgAL37UbTiFqxthRMxWFlZKszvd3Inb6mmyHWLZ8llKu3OmnpERFrhhf\naNvEIloz/BMdq4YcN/Kc+o7NlepnTj1OYtsx6zVMalkurQXWj8dD9R1nOwZ0cj4vbscN25T8ec1m\nzNZi35XQag71TNw3MQsfNUThPs8yuPReulRb40wmkoO2Ei3kk/wJXw6KmbZgbCGPTDdlS17b4CPE\n4aydbq/7dM4VNW0pQHeF3Lk2eKqOoL/7WiJqZXQrEED+D0mu5LPRuopcUmx1E2Kn21nAmgYxLahd\nS7OJxRio3mh2khwKW/f22qFErdfSqXut5U9yU+rfR8Ldpk9HEmOja4Ur1IV142yc3ezZnKCB3pXO\nGYY8MR5YOh31Yx1ZvYjk0pK9+us1NoTlGgpXfDvaj9mGktBn4K0+BhO4GKHPoK/GVxqdPhfnvjW2\nmJEQdY7fgrwmMYLI0HNiaOecB2PbOc9TMsljMM+iLOmpz9jQe2JOgccOj/Na/jVjaeOAJRXPpVig\n1zoHBVC9xqiqhDYBHlHyaKXOAQ2+LbxMa5KlmkHeRwWM7UrwdcN9X+mrtiSBYlHNWqEeLjmpAk6C\n7ok38pCaYhfDF89ZCt3ADcKU4NxNp1IXzafOuIn6FRmVVqKgAnKukt3CTENkTic8OVvgX19iBL1+\ns8DLT2pgUtsw+KHhcU7FP8dgt407D46S5OuOIpxzamv9M5Qcl9fkE5OrjUcPyZMM9gjOnqtp3T6M\nnNPXgrNKzplrN8rkn+OcZvwyL34WzfdZSh4CDtcwdHQyc6eBy5IvVox2fGjXR1rr0GzpY6udIyfn\neWFDC8B8GMHFNncyDLfGC3Ia1xqWylpLVXsxR5nsaCjb/WS4KF28qHbpbRebpAfF0HaU+kiqMZoD\nX0huy0tRqRj0htjGks7o0BihQUmpcbaaqSTGEuz0xxeIu/M51nK4kCa2a3B1YQzO3dhKyWTRReRk\n3xWz/egiaxXwnsQKcdh2+J7Ge5f2plyTbShJSnwVEEE1nIV2DnjJa2WDwldMq3HvizTl/SvMPZA2\nWQzji7HxcLyE3odWuSPXgxil5MUSBlkKtsC+MzjYmFy98+7G3XYZqoGRMotbBE8PQCleXuDnkyhj\nv5p0cHRwVdXy0Th7w9OarzO5t7P3NzbfeXrjdsf9xM/kL7s5evCgSHf8WRwe2p3xQnnQQVupIIqR\nyfdNb8p/wuCwfHVDjcn3JxGNueOxr0QneK2R7Voxtk+hKmZQ17ViTxfLMjWB2JDZVbJLUQHlSlKk\njK1O6coToXfhZE5u1065om5l+lZc7BKA0LnMPa+YYHf1MyU5SseAvBhvkm/29yf2fslvNbSbRN6n\nO+ul0O8P/SyXvE6rRat+tT5N2gJ28iQ3Ib0+i9lO6MFhtAv5dnm7hi09bIOZ9qY8cOwytqFUtVpJ\nTuPLHWpi1Vzfnoy3HY4Ds8W02dqiAJJV680DsO0bb7c7PkJR3ovZeTye4IYv9nv4TuzFdQbH2xe6\njO/1nT7XMGQP9uOOe/H5y41v307mY7J9ucP7E7+pmc1ZnA32+RPHpWWsEUPNnEvR0ObM54ntO2Yn\nvu1Kv0Of16xkv+36rKugL8ah3Sfn11yz5NrtYmhgmhqEY5ec57htnO/f6C21IJJgXrDdPxG2Hsoy\nNkvG7aDNyPkgYtcajPdUDH3pu8GKOO4YT+Z7cdzFSM02rvMB7MzrxMdGVrPvB1NmFJ7XAyOY15Ld\nzQdfx53jk9O3jcPg8URJqnmJGcsm40naXcqrn/JV0Kkz5WIS3pg5zo6NFIAIYhColfr2GoQEoPqS\ntlVAnGpuLVyyN+AVApWor+xZjGV693LKc6WeFcfcGSEZk+TyMD0YJZlb1wuLl9Thw+PMYsZCQ/NH\n/HjWktDp7cpvmXiKoc9ujClfC5Lb2UpfK+sF+BblYpZ6XqTMzevnxY4bMNpJdzBnjF7SUlvGJ62P\nOZc/3V1LT7uWTHlbfUrrOY0w0k3g4IoFr5ayxF8yaPR7zYPbttPD2EqAccVi3pYHPjC22DBP5mXs\nm1irc7uoU8NJxWQMA0vuY/DMSV0T3zb6auLQgEtpwyJjJ67myiJc91C4+h/xjKzE1fp4Df1KBDT5\n09NKsPhKW/WCOQ1zec0c9baSPbhAihUIFCsYjbEGjtI6gRjqi3SbGAOdZzUMei4/HdQlRkkyfw3X\n5gNnUtPYhnxNs2HOE9jFXrqW7g7fPsJEZl1S+UydPHs+ePYN39SvHgbZrjrSc91jhfvF5cdvvBf5\nSQ1M+GSbpxDIdiyMw4rhRZgSSy6aE8PSOPtiPwJMOfB7N2Swe7JTnJaLG5i8tZE1uOwJbIBYqEYm\nNsmMpzxFJnj5B5ojB7/cikcv83c1Nx+EJ/R3Ko3NtPQwVhDB1sv82U3bUEx5a//TzZ7Mdip2cKcy\nmKGBpiOxhVaSRbp8KNWLSLYmcc6Xltekee5utsXi4M02m3MUUcZsI2yyRRCdnEtHHdharNlr/45J\nq4t+t7cWRRJC1jx8RXgXo8WSxIpMrTZ+5cabbTjv2DA+tRMVksUt3XKGMx0iYY/BjQsiaFfYhqVp\nhDDDXMsU37XXm0kzF5uSfjDWwZCxdmd5sKVxenLisEyUKydG6AzFtCHDMmuZsDVYEhS7y+TPQDIB\nfUCMVphGmX34D9zHQuQmNpqLZtib4mZtl4SK/thP4LaG/6uZo7V40hTUMEjGENo3bRHgtkMbl624\n827eK8kJ+zHIHtx6kFZsNvg+k8PhwVoA11NSGuDNjYzBO4otfVZxzskDoVTdsG/OYc10/xcSCH9q\nl9u1ljjqWVIAU1OVWK1hfyFthqKka4wlF5CWntX4DIzyXuhxoQjhAJ9o/wXk2lmS7Iy+mMsc26np\n5Qp5DvwVprACGVaahw6qiZL5XM2J148RsE5hvpFXYfNUg1DyI5gPxraRs6QnRyxHeSgyjxV8sYIZ\nvBWZ0C+jOq0wGgt5Ho6X6bzpC2rkWtfRagxNm9+jp3Z8xM7WGoCshLCOoXjkEZLY5PMb477Jb7Ai\n981KNZtm29VwcU3yfNLHG+dZfPlZ82XcGeVcS4J0HDeYUyi/DX72KajYeOuDNIfnhX37RO9qNsZx\n4/v3qYj/1sFcneT5ZA+TFDXQAllr9n1XM2joMwAh6RjnfDL2jTmT2LYVobzixV3eVXcYfuO6nvjY\ntRiyJmOPtbNGTAOXTP3b211Ld3PJ566L7fMnvDeI5tgHs5Jt12frpuXsXc77WeT7iW0bHRt7Qnwa\nYqnf1zrvcSxgd8f2k2443ydXXhw/u4sF8W355oL5PPFhPK+nzpvrqfPFgE937r7JDG7F85xc709O\n+zWJ4uaY3RUd/RuW0vzrvixS7IEvBgndtyXrEbHUK6/s9MoL9lVH8B/ZHFO3UUNMNMsvEma0y+w/\n+tfriM6D6epFKFt1ZBIVxBiMbPkks7HtJbFbw/h4Rd5Lst4O1Not2Eu2h+pHmwYRG65o9BIjox1F\nMvBLWqlexFoyde0rlPz8x/NQZ24hxrMXCFTl9JhY6jVoy8I6G6vkh/LBWIlr2kG0lC1dL/hXwHPY\nh9+oHbF7vtxEtgD3luyx9xs+E783b7ERqQAqvVp52suaxrmH1hn0vkCw51R64baGkG3jeuTy+0i6\n1q4ApcHazekwW5Hsw6Ue0Q6/pQdKX4qSkr/xJeVf98l666tGw8pBBBQ4UmuAUie4zqX1buy1lH7t\n85LPbFvJIiH5IpKGvlhIEeeDy5t8isE2BoNJ7y52S0pEzNQvZ2+yETRSKtna5dasNDv1OVraW0rk\nY+3+w+iA9A1x1w5WXFVknVzdnOaSdo4G27BqhXv9Bq+f1MD0B9fJz/ZLzSrOUXA32C1JXhuCm2nw\nvoP3wdfrHcvAYmPn0sNKEnXyC4+1tLWZ5qTBQXNN6ftFiTqnJbMn6UEUvHFByNtjdjHnYLqagmAu\nQ7YOQSw561xJakoze4am/61FY2dChIyQ7nCzwVcLLI3hFwM1DL4yRK2btyyeeTFNCyEVb6lx+8HG\naMNt4m2S22CsFkCRomgBr2PcI7j5BdeOb4rzdDf9vS42W8b4gloo0Ra+Nlgpba0dDZZAjMmdhfzW\ng23c+b+pe3te27YkTeuJiDHmXGvtfc69Nz+qEgqpBV47CKkNwMDCwMHoH4DAwUECtQPCB34CDg7C\nwmgJAxBSI4GJgQO4LSGV1JQ66yPzfp2991pzjhERGDH2yapSdauLrMzkTufce84+Z+291pxjjIh4\n3+fVrIfuFOMeyd+K5HE52VIWcQWO7J+nHg9xUi/sMsscaoXwPqlC88mVzTotBinG9+YVipZFwsv3\nA23WQmBZ5LKNkuw8zqC3dxTYJNTKdBpVJIgWejkyyqSe5fMpOWPlysy1EFkUWCIxPqNoyr3JFjU1\nCKkCNMwIK9/INWtBuufCy9OJbnTgYcpbCD2Tay+yTOFYa6I0tHS/M4yJVEEslXNzzzW0bxttJGHO\n93nhgyeerSRJWZTE0RrnPAmFI6KyZmbl8gTQelEpc1bA3nUmV364BZNNQaJoabE2h4jKItJWsI/3\noqgW/J0YRxVXbQNK5qIzyTjpfcNnrSG5GgykrGlBrnupvCYxqxuYs2StTa26xZIco7E3UAmaOBmG\n94ZiuDgaXqCKpKY6SJmAs5Ea5Diw3nFfwaXbTpigTKbUtMlfD1TLP5KATmH6JDAawTThPdQus/67\nOnula/dFpiJZa63CBmKNvu/4HLWRx5JsaHL58MT53aeaePWSrG4fnxBRWi9supMly6MaL7hhe+O2\nKxnB/eXBFz/+wP0x6V3xmDzenH/p9z7wKpMbcKx7Ph5w98BVeD2D2wU2IAlyB2+Nl+8HJ8HHU7j1\nnZO5cljgNQJZMtXMmpo1Lb+ERTDToQvdGufbUc9SV7hXYWG6Jozr8NP6VhMdq5GZe/0dzY1U6Jfl\nIVl2M0+rZtn6nm3rCAs0sabX5a1ThgSX67Wog15NlAxHptCl4C5+n6gdyNYRT3Blv10q10mEPIOQ\nQUzHto412K/XCsTOrJ8tl/0vGwAAIABJREFUym/CDI7ldeuXK7qte2eD821gW+CvD+aczDOwrlic\n0HZynvgoX6DNKup+uKvIYhMs/15ITZRSag+xFQReJLTKSjLt1W1XqwlQUlCkGaQUqXC+B8Ku8ZtQ\nUsjU8gaJKRq+fGMGXsoOM+NMR5mcs7NLoh6YVDRFUXPfw9UnujLmXLUIlJJIVqQGmZ/hDyALblCN\ntIpWeAdEUFlCSZF604mATWIx1lhz+lov8UKtm0lNPlbmhVmBaHSTKhQRMCoDzqqhoIu8Vg702k/J\n8i1CWQHKOVbNnxT5vI6AsvVqGrtP9svOGI62ICaMI/np8zPTJju6KJKQx+ARwRTh4cHFhY1S9WQX\nmjbOMXGSmwtsHZtRsn8fDAEZ1XAnc3mYlv98KYyklZVhTKeX6KH6WPou5ZRFQsySEibrsyqZnYog\nURK796SPWqpkNcjWz7KKWW25pv+1xusGSOVaNWlIzpoKRYdexesWNan0UWciWq/XcMXagp1RDcAU\nX5/7r+7Lui+kqLBehR0jGb0mnJU/WXE9zaiYAwvMg5GD6Yposi3/lsRgRiu4Vejne+23df2gCqZf\nZuNI53KWSf6U4K7CV6J0BusZR7VzWR6DbuUy2RhsVhke5VcBYdBNmaMq+O1dnKIDo3yxEYmtubQy\nmbbGkFkUmAgr4EQKc1GLppcGXsLZo6h9bz7ZWiuJGVq5RTkRi5JrRaDvwVwiXAlsa+gsI97Q0pKX\nZGrUZELamvS8m7vr0HBhVLL7mrwINfnZMJoETaBrY8ySA5o0DpGij1gjJOkeC+qw19lfnSnCRQKk\nighbSdkudfPGkiWIbowZdE32bccleH5PZo7GL60RbfIvqPBFP0kfGMan6NhsDBOmdc5QXsTYWmMb\nQDu5ROMM5zBlOkzd2Jh8sU0u0zhJvpmNA+ORwtW0fEq+UK5Z+u7RalrUELoVVruoR1JYblFubVbX\nLXdmOHct6o60ytYwqUyoKuATz2WyFWEuko9pYe1Ty4eQWR40VkiyZ/AmCQOmNHKRyCZai3gobydc\nMyHLCzZTaGMgUdP2sbCpR+5ML930lOR0ZbqzpyB555NuWCrSnBk7I5P7/eRhsB8nLkkrTmh1lVGO\ncdZ0IQJmBSbK/P9T5ORf7zqXSFO8Y5Wut+QvCmN81smXobYkURWwvGSnlw0/Jyn1PCeOmRFHHbrD\n63D9nuPUIsmYjNVV0KwGw7sN7GJChnGzIlQWnr82Ap2D9MSkAk5zOHR931VrgqqjpjdW0AOJQuWn\nJK3vaOtcPXAB7xP1ZOI1vDJFszw007PM2wsbX/r/ojxBHd6JkvKaKemC7sZ83LHWoF/olwvj2+9p\nz5cquMLQETz9+CvOlzv7tvF23mna0H2F8eaiRmWyS8ncZDP63jnuB/tV+cnPPvBw58NzgQ4iOvfp\n/OGfvfAHP9n52VZT6p7GH+uGPEo6+3xRXs7J/XWyfTB6CNLguhtvQzhViDHxBgRcPhgfPn7JOZxP\n332ie/nNnr54KtDPDK4tmCsaoTVwn7h2ttZwc8w7IvAYD3pvbNt6v+gc51nTh2Z1KM6aiBdWOJYc\nJ+ltxYNGrcfvWT5oNbkqaLLjm3BfqL2MB5z38hFkcmZNK73NwvS+nriVIe8+A3cn5lw+F8HHqO6z\nNnh5EEwco10b4/Vga8Y5JmpbNQY1mN5pDB5fPzht4m/VpW9RnfFY4KQ5gozGHuVlMpSMH+4aAiXt\nbnEi0Ys5kHVwbKvYyCVzfY9oIKWyz1ge4KaEK/SgezVxS/Zdh3u0nseUCVFQpczBmQqrMIulagC4\nSAWvXteQ6t0z41NRKUqrthW7MsuHw/TPmO5JNXiklQdKl5f13Ucl1tk8qEdi+bSWvyq1mpBpic/6\n/3eFaLHT6n7P1agh5LMcj6ipaKxQb7T8xO8RCtr08yG/a6docuDLf1SenooYeZ+Qa5aENVVpJrgn\nZnDdOy7Btq2J4N54RPCLtzd+/+OF3/944eG1x7+8HegxmVnP4kOd4zHpe6NVPgxdyst8CMjh9d6E\nsl+Vm+zMLTnHAzkpj9+28PNeDSdf3tWuVXiF9JLaahbi32BSII4mqyjKIuExKwiZ7X0PW1mYi6Yc\nkjRZkSGxmoHR63yipRJKAQ0jtsrYDO+QA+ZJWxj8M6oAdnO2ELjPaponC35U8LEajC4/rtSZWM9q\nBsRSMMQsy8wgkFFNZ9VgSMdi4NOZEtghn9eRgmJURlNEEmFcPq8j1Zj7bV4/qIJp5sTTGKn0mFxb\nPZBvDj/qWmnBKGNONlXaSlYPT0YMEmGbk705bSipxozBZhtf6IPu5T+aCSOUu01WXAhixn0dgkTL\nTO0kk1mkKqByATbI5OKxNMKFCE1d/abVxY18D4VLZlT3pq0RvZPMWeGVuWroMedaYIoiUsbNRHwZ\n89LXGLMoKuTEsPJGoIwoH85FatPtKjzsPX4v6OzLWFgHPpFlJs/KVDBR1IRNFRXlMSZijSaNqaOQ\n3qI8wjkyubadwnSUj+i6VYfkYsnbNL5T5TGfkHNy0eBLCz5IBdUeAm8kU4XNPjI9iT7ZU7iY8TZL\n/nNXZ09o2SHhZXOeh/Lj3fh2wkNhjApjpZocmNSB9E7h4nwGVo1XWlbwn+lGSHBGFTlDKtuiMlZY\nki6wldnQFchgRnCGLqBDLNlCoNbw6eXZksYQ4z6Sqb0+u3mQKVw5gJVyPQs9bmMiNmiZjFGempTg\n1ZcbzQfWWgFJfIAl12HcmzBkAs70CiA2v9dGfjTGeGM2w1zZPAkNduk0Ky3xI51zjKWJN85MvoxE\nLRj6g1o2/sJVKRNW3r61GZgC4WhvSycuxDnRbrRtL3LeMZij5J1yjvLxjUYqeJzodkO5l6+nKfgF\nm5OpJ6eDSaKW9TqivNMkIoIZtYZEtW+BelYYa+Kzpie0RbjD1rQGyNogCxyh9fVaXdb5+kZs+2c5\nTtzvq7G71o/w6gL7km+kL59jNX9UlqTlnaiXVPdSjP7FjbbvPBJ0u2ASaL+S11sd6NpOHA9SgvH2\nwC4duTRuzx+57IZujZev39iuDWsb8xioCftuHPfJ8enk+vEDwmDMoEXntm1EwNMXxp/+cvAQ54++\nnvw/Huw7fPHc2DfjtlHS7ExMoX/5oTw8cnIx4fKjje/eDjSFr1+DXbQmaCEcefKhG7effsGn15PH\nmBxvB227kGrszdikYU15sYK1tONAdiMey5cZsOu1VtZYd10eZeBu5f/EITQxCrXOipEQn/ioEPbU\nij0QKiB0jlkHCu0V53acq2gOdFaTI1pb5msjz0ELOO81URUXxv2OmuJzMrwmrJJJ23YyhXl/oJm0\n1sHheHlAeE0jJCEOkuR8CY77ybCCnuhUpjq79ILlaMfv9/IojJKKjqicuJSEH/AaAtVs9SW7TU9m\nU1omzPKuiNUh3+e7qf5XwZ+eCV57raFIlNIhYmDawEYdZBtkFJX1zMmYRVo0kQoHNVh0kYInyah1\nJAVi8nkdyXcvJcuI/76OlB8yMz9PyNNrSlO5aIuE57OmXxLlwfQ6pNa9XVgjyfhVQOsq1i0Fl+WT\nWdL9TK3pGotrtxkqhoeUPyejYhiiDv3FqfHydlZtQGuVW9lUUO0c56xGjrQivRqIKnMG7jXlRQpe\npKNx2yvse9uU7+6TYzp//P3g598+6F14vly47p0vn437dO7DaSn0/QMjnJDJpSlNOnedqAuf+klf\nIKpIY6ZzFWW7Xbmzzq3nxHrJEZu8+8jhlB0TwcYke0UyqNR71bLBu7c0FJdZIKFW60X1sWodwWqS\nmEmt5dMWNOK96HRUWwGwTCEqf20eo0BRzbGj7u+xPOmYkDGXLLTkl5KJj4FoNRRnrqw2d0R7FcW+\ngDArxHm6Q3p5Yw0kKx8wpxL+qKLbV7NQgn1BIzIqn8nTiSjIyiDpuSRPy+3727p+UKvWDbikV6qy\nJEfCs8LNSop1SulkZTfQJSsL40kO3hB6Bjft3KwynMyT6MFbPPiggeqdk50jgje/VKihJ+fyFnzQ\n8hfsdhAuhDQeUlrNGc6uHV+FWUr5mdKUWN6iWNODrPu3gBM0zIx3YQxSo/QmVtGGmRyzyGxEcs2g\nB8X8zyBWYrNGFoVfGkktmkqwI4wsY/uJo6brhqsH6chKgN+ZXJtUJhSlgf52QR6+WlLFpnDxZOrg\nqfcykGfyhSqhwkC4aLHxJeJXY1ltnFodiW/Z+XkTNoRnKb6UsvGND55N+VGrEayL0r3C2V4NhiiX\nLI9Dtuo2XLUxmZwiuBtzbrwKnD64dOWLTHKriVTb6tDvDkjneY3w7z2rADUteZSXlC4+T/Oo8b8V\n4aWtggZW9+w4alNgbQiVgku3zz1GHp6oNU4vGcZxJgeGnA+uIuh8LMPpRoZymJMkdhwlLzwHM6w2\nXA3MoGsZUS2LeDhiAGVwfVHnzMEtrNQdMZhz8sbKwbLqlhOBaSHlq+tbSe2a8DQLbBBZBfIZzi9k\nctWNMc7fxeP/N3K1RY6qnCwKcCGQKyjarBXEYC+Eqe6KlnKbLiXJa3rh8tRKUjCdtI3vPn3Ph+sz\nzU9eX06GDGbu0C5c4o2ZWsGKFEFKLAmX0reb4QpMYV947Mwq0sMTb0rLgKlkW9OIKE14mXkTsQqe\nVGuEVadvu1wIgjiC8XjUaeMdmrI6lBkD0dqUa6d9x7e+o4ejjOgL5RqZzHTyfBSiuynj7YXYGhqT\n/eON4b7qL+fl8UAQ2uh0GpenTjtrMv3Fjz+U9y+Cp9vHgiNMuF0X4l6y8kTkxqbBqQX8fXyC13Gg\nXp3Tt/vgmhufHm983JWvfnwt3LLBh77hY/LKZJpyod5TW53np60TPpkKpwc54RuqSLp+aHxonXHZ\niTgxFc5I8gAR4+mpvF0PKThHv1g1NRb8Y0ZUvszKnWIr0qlpku09XDw4Xt5q0tcFcfCjwpOttc+/\nnjGglQfJ2sCPhBPOMWl7YzzeyEia3ur+mcEM8PO19qEZK7cFZK/pZO8dWjXDxOF4vRcDUuDwkx4D\n7YXSH8fAxwFzQDOmDdwn6bX2hzSkdTwbpx/oADwwrbWvieLiPMbJdi2Z6w/5asXOLpobicwirdFq\nStpCCxNtax1RQelk80JBA1te0Z7EBs2d0M4jnYv0lUFjDJ2MaAuVvYrcZAnO61AbAaEN0ZoAcSb7\nmjjXpKUatIJiGVWg2SpcV/hs+UsWeTMTRT8XTYquNaeACO8NGrTkYcupjKgWRpgEq0Ztrn0xkvJQ\nR51FIgomJeGffYExCpuucmCtkR4r9wjuowJQ+6ZIblUwZZHiLvu2fpZkb3sVqg7bJcmZZMtSVuiF\ndqkIGDx4PJJjDMQSCeOMoB3K23jj9qa8PW+01RjvqlhOXJ1TlOc6JiwAQ7LrRsasSX4kOYVPBH6c\ntM24htLaBY9B6+XJZiREY1ufz9yivFHKWjtqWjOlTknIWkeaFkG51LKr0Z6kD3zKO8WbSCdWEaLv\nnu4cYL3sHzbwIagbIwbWlMyzgF6+laR8eTSdoxrFkdhcB6DVy6s12tcZKBheoegzY0XJDCRb7Sfu\nRM41gRVCreSFYYtNoaCN6YrLKMFdFmVPROvsT/AIZ7eCEv1Wn/vf6qv9mteN4CtpjGqt06RC4naZ\nqHWOdF4Svp0NwpltmQ3jAq1xEOw4jCeE4JnB69EqaykNk6/4qd25yGCPYN93LvFAe/KYNVo0Mzxn\n5RbIyaYbLZS71Pj02sqEHFq687dRFJAmJV9IFTSCaa0qdRV8Rn34Cq9UMbHFQlWHLESjc4pzP5Og\n6EMek64d9aA1rQJIvMy6JCINtTsmhfP9oq0QOhFObzzawRcebCZc40LTg02EXeDFDc8y1X1nxsbE\npmO2c+ugnJw0ZByEbtwwwgrwcCOgF8b24cmuYJ6w7fwiG3JssI36HHW9DhemT1x3iINLF7ZetJ0G\nRAqHXFG/E0NW3sFJp0JYSw4xuUeNrzUGM7VwtgSOcQnjtDoU3sVxrnyRiemDNoM3Nx4MTpItqot8\nyuTKCrY0rWnROKu4IFaoWnKKwKjA0JJUgeSA7J/DiT2M4QcqBSCR4t/TOKrra4Uv7nPSZvLwyYx1\noFFnqII74oLIWbpkjInRZeA58Qm7VdF5jsEtrTbonKswEh76AIwuSp/BQybdGyMO9lYeMzHh9Mor\nO2cFRF+bol649B/qFQGMvsy3kxNn8xKOhBlO+QXql2TO7wsPPkAvG+kDRvByUB24yhOFcO6P79Gs\nCa9mIPON2L9gzI1d7jxmoeyn9kL0SxGzzAwZgAUHYN2YWR1X6YqMA9/6yhsJCitcfrrKgqmsFuvt\nM5EKFeb5INJgONo2Yh54sWAroyyTyEqdf+9qQhUruXJStBnkgcgGCdttX4bw6v75vJevKoQWHaMI\nmH3vvF6uRDg+g9AkYvDp6ztf/ewnXJ8b8/WOPV2Zj/JW7vsFU6epc3qjb3WoG4/J9tTh1dmfd/70\n+weZVaC4CvvztqhdG5+OQXwbCJPtyRg9ka1M0twfpDeOrQIV0xq2DzYa4b8y6z9mIJeasMysIEhf\nh8qbNY5roDK5T+gIz89PhE8kJo9X5W067SLYYbTLhRknrdfpQjdl7zuP4yTCyWOZ7TPIMzlf75Wr\ntm2lX5gwEUhD2qwswm/vpHUYEzQYjxPOiW17xXel4UzsDeY5SLeCeIiQu5S/yIxURw9gq8O9qVTx\nOJy2dUYqvL7StUMk5iX3FNfyaroT24b5JOPEYjA96NsOfhDWSKp7/ThK5t72HRnvdIsf7lW01L1k\nYDoZGgV88YReDVAccsmuT52LbsnCLUweDBggBHhNCtOdVxqKYjoLF+2T2K61tm8Hj1learde2H8p\nT0ujIaFEnxxAN63YkLTlnTyJsCpsau6AZxnxEarRG8uHRxU3NZzw+nvhgJU8P+vne89jmg7tPafP\njEbWHrnotBWwMhCpCWvfSpKVIuBC5RoJtI6xoUx2rbyq04XIB+lF53OZjDHR7cK+FyAiXFcGVbK1\nC9kSiYG3isgITeLutL0hI2ht45usfVCz7A62Go7inVdO8l6Tun41mipzK5+2jomgJemdtY6oDIxG\nOhytipnTSw1AwsjEGQQ1KbugHFulJ52zlC8Xu5DqgNNm+TJTJ5trSa4XjRES2WrCNf2sn3sWSY8s\nWWSsDC6j8PUeQgXZG6KD6Y0Y53qeB2gFXdsq0haQlBCnuTErMG5Nr2uqlUmpGmQWMVhXg5UgZeJR\nhepESD/RNV2s6Iz6b2LyrgSvTNFJk8rBapWCV5aGqPf87k4DmhjMKJvCb/H6axdMIvJvAP8J8HeA\nfw74u5n5P/ylr/nPgH8f+BL434D/IDP/7z/3518B/yXwb1OD4v8O+HuZ+fpPe22TYOMsbW4mMo3R\ngqFldJ5xoWnlHn3NVhK5LKnbOINJ8FgjPEnhm6iRdeiF72NiCb+83xANdh989MEHbXxJshtIepnY\n1pInaTxIXsRp1Mh9UgAAqOnL89aXnvQsOZjJ8rJAiRtAtYyQSTJXAjgtyShT45yTKcIZrLq8KvnW\nGocroJxRssEjpQ5sUlKjZh/oa1Re0r264Z/bSQ5H+oVkcufEpLFZMDPYJfgyWpFfMvkkykbjJYXb\nUZlK3eBmFx55sonRPbm0CsgdCSOSo9XmMVrhu29MfnxZ2TLroZw0XglGdr7twt6eOKXkZhevzpVF\n5+tWn/utVY5FZmOT6lxJlDSQhN23KpKCMre64q6kzvLmqHHzkl6+yuCn1jh58HGHr1BeR5CXQeQg\neuOW9bk41bEalszpVBRArofboF0wLcQ3WYSxI7yoMWMsFLBjnmxRHR/LQL1IYJbHCuVdYahzVLdp\nje+7V0daBAbH2nCqKzP8PfOrDtO3gLeAKYXgFHEieumho9G0c4zJKclFa7qCGI8RHCm8SOA0xqgi\n+AxnR7GA+69JyftdriFlmp2MVDZJcq0h4hP6jr5PMZXKBIlEZ1Y78VGH3F+Zmuu9rzyJtiQHlCdS\n17T49TsQYVhns+qWap5UQj0l7c1JtKItCiwPQTVQMhPd9s/y3nc5n0hNuyunZG2GEYSV1DAeJ3Lb\nFjYc4jg+wypcqWnSXHKv1WIZlM9Bc4XZLiy69qfPgZi6SEWRTr9u3L97oz1/rInZ42CmYdeN4cH1\neaNvXzHnRLpxv9+xbefr71/ob7W5X0mulxtvb3euW0ne+nXnw251f746JsHxKKztOZ2O8ns/2TGr\nAxUC7sLbCNw6w+9s/YIfcH8b1XiRpOXGSwgvL5OnKxW06qC7YJrsqsxH7Rm7Xgp8QPk5xj2YA+y5\nzOK2K3sGQef1+zs//urK68vgw5dXPsqNl9cH/akTmdy2C8ZWtLtFBnu67by+HNCFERUe21qnfbWh\n8Jlu2lpNd9Od8RrYDm5K+igvihpCI5aHxR81EYxReh1/DFIHSEdM0DOWNGx5D1pRqypbrsz8W2sF\nFojyp068KFyyvDWlqcG2KgZzLoreLCLinAeWwoizPDmjtFTujrD8/stc/+tcv9uzSGJUE+uayem1\njhjVbNH1HoWUryVn1mRRBcYkZBXC9T2UjG4aIRU1oiRygFQiOuYviCoDY7NqoGmeKwoFKNwUYf55\nHRkC7azaNCWxvi1p1goJXaju8JIJWmlugRLaZRRJNK0CrgHwBWEa4C1q2pAFnWqVRMucBWgSpXzl\nS85peinqbPJ+iilpniXngK2XjD/yABS1Omz3lqjseCsQy0hHxXg7H5xexV5vxmYbxznZ7EBsY9s2\nWivliJ/B7M70sj+MdHoqt+dFP30HWoRwxIqEGQ9k34iZPA7HGqgIlhsvOOMe2LagT0Fl8mmyheFL\nvaGyf56iJeWJ5xB8o/aNVg3eiM7pd263jfNtctk7l114zIm1gmtt1mnaiUXNdEm27IwoMmha3XuG\nLtIvCyFeCPeRBfUYB6jNZXmvqZ6l1rRxqWLSk1WJ12cySxlVBNkVUk5tix5FDI61n9VVU7n3IO+Z\nyZTyNBVFUcgsUmoTY8ogQ2hWTao64w/UYeQgsi0aak3uu+WyxcQ/42rxN3P9f5kwPQH/F/BfU4vL\nX7hE5D8F/kPg3wP+EPgvgP9ZRP52Zr7Pz/5b4PeBf5OCGP03wH8F/Dv/tBcuSWXSYjCWgVU8iLzS\ntuSXvnOXBx9bskthujMDlw6rg2rrkItIYaEjIUr+ES54q4d+0HiZwaaLoJXOT+n8RJzn7nQLbgK3\nVN5WN6AtlO/Myu0ACrEtdbAdAuHKlMo7aKbVcVkeAbJC4NyUI0qWNnOSGSgTHZ2BkXaweVudmeCu\n1OifSqueVGilZONxwLZXAjup7E34bgS3Ds2Ux9LK3prw7RQe2pl+8GTGVSpMdV/c/qbKlsFdgiN2\nPsRkyKBLMvyNbjvqBeN4WoY+YfB9lO7JaHRLWszKP6A26O9icrPO9IZr45BW6N7WeAC712MRM4jY\neMmkRxLNmAm714G+sUMkb60WsSGNHgf7JuCTYQKh1efJBhpcUzgiCd05Q2jq7FvwmMKu0Ek2rYnL\nMQ5cG10a2azkldRBuWdyEmQvpLqI4rP8YA85eaLhnJzU57O3nY7zdh5oFAq+e3WsZFRBdlEvmcsM\nhmQxCIOSBvbqAnr26taUgJPq31hNuNRrI6SVjwUYnjyaIDFpCq6N6ckmySnJ4ZNPKNOVyQnaSQqL\nHnNRgkL+6gf0n/363a0hLD9RTM6s8EDxIPW64ANGyAHW6l1sBTjAyoQvVjkhuf61eHc3L0IQyUJS\nC50OCiOipjYKenaaQrdgtg0hadLgvNfnah3Rhutkvcjq2NVmVralRBfZrZkWvW+epBYpdCLQ+yKg\nsWQLgbivXBwFJtK0YAbECpFcCGixKiCbkSjzGEg3RIM5Ar0q4+VRDBKEiJOYgV123r6/LxnpG3Z9\nQlsFul62zpTE9kYTitcZynw4h1Qz4dPrK1989ZEd5/Vt8pMPDbsZP/9eeX09ESrAuncwN56+2BdF\nS/j0MnluypiK9WdmBJ71no0BasrpFbrbXPn0qDVkirBRBu7HWet9s2TOwalG8zJbX28bErPWURHO\nkdU91uBy65znIE0YZ8k6b09XHm8nfetczNivILLx+jIJpGS1zyX//qBXTp90bRznpF2k8pXMcA/k\nAWM+aFvh4y0LtnF5eiZTuL98X3tBBrrX8dnvJ/44UEna8zP+WpIalersRnoFc5arv47uaiW7U1ux\nBxM08emYdTLPCtn0s4hbw2uCsG1IFoF2iiDnwUkVseSs+zNBrRPnWJQ2+5ug5P3O1hGQz+vIMKWl\nVO6WXDCDOJWpB2qt6Lbr+S1/4QqtzSWLo3hy1RQpyWwEXKTC5lsq+CiJZQShgfmFTaH3CroVU7Yo\nv0lk0qUw0LH5ktouap9RMsolYZMFOKq8xtJyBSXHnVHAIvMq/DIcCGQGbiBTSfU1Ia3G4cya0gpA\nLOx4VoBx+Lt/M/ApaDfmOYhW/uDI+t6tNY6j1Bv4gVpHWvn6GkYsa0EpqxMVw2ZyysRa8BiDqxlI\n55yD29bRJ+H7U7gfEwW6FAVYQ7jcNnJhy99O56aNTMVtx7OAOWlR2VFhHO+AHE8eI+nhhEkVSaGc\nXt9neZgPxiLBCRWXgs6ypZoUv4d6T4yGnwX28QQNYdPOzJoe9l7rhmrjeC0lkTanR2ds1VCrSY1V\nlpRCeE2ZQhyZjZkHrVmppVKIPCsPFGHMOxIlkcum5bN0J96zJq2BL3DHoqemFPpdFoSomoil6al7\nThBW4HW+gxpG0WlzcZYjFjCizrM1VwKmlzIny56SUrOpTeFw4bLkor/N669dMGXmPwD+AYCI/FUn\np78H/OeZ+T+ur/l3gT8B/i7w90XkbwP/FvB3MvP/XF/zHwH/k4j8x5n5x/+k1/4jGp9Sadb4IiYf\nV0dnStAJvtzvpG88cD5IseDNjKGVmdRZXWXgiJWpnHN1aqsAS3IVVADGQNhnyeJ+uWB00zpPCsPh\nFzPo4Xy1t2W+r3wYRDJbAAAgAElEQVSEq1cS+4wiuLxX5Oivuod4MehdKtROdYWejsR14l7epIZg\naYiePLAKj1NHpW5AiQqEfJ/YiLx3bwaiyssIgp3D1uEB5zULRXqdg9Y634Wwa3KGY61xT+HMes9O\ngo9h7A2uptxdeFnF3+hJzsoE+snlwtdx8DiUlzQ+dviyGzcGZ5yEwDmTS79yjKyUaYQeBRZwDdxP\nqhYQDmu8ZHIV5WIn17SVCSpcXPjFFL4dgTQj5+RJi1YnbmjrXHyy98C8gh4PkeqqhUB7cMmSzjWp\nhV68kPQZFy7bYFNZ3jAtf4DtVAzTysNRZ4837tmY7qga95PqskkSDlOdLaUymgQ2h6ttnJ54K2rP\nqRObyds6IAdC6xvaqjODlT+tS0IqpivW1IOHvJYsL2tMrTEgvTo71okMBg55WRkp62uiFjNNcBNe\nfLBlJ6QxPHBWRlckZB0oPYK/+pH/612/yzXk0OS6CYeXF5BlvD8kSlrWJtDJ9JKxeE1dglxFaB04\nMnNJCyovJbNodjZLa95S6lCRtsyvQouCa4yR+N7YKUmMH68M4LLv6+BAfU5rDUkvktX7GqKiq6OX\ny4R9kFbTyvT1/I+JM3B/J1VB9R5HHWBUV7FWPkiWN+H9qvDYWkMQhTFwaWQfyMsgvEz/RHC+vGHb\njsd9yVoC2XZ8euHVtXH/9D379cblYuyXnW+/fSUjeLsn0QYxqiP75VfJz789ePnuE/8olKePV378\n8cKHS+MxHEy5H3f2D43HfaC6PFkutEtJXF/m4EkSSeXRjE8EVy9P5Zeb4XvnFiUB+uYhfPfNJ9re\nOI+TrV9rsiPC1pRocBMhLOliPKJxtZJKu0xum3E+hO2pM4/JxMjHidHRm3C5aUkU10Tw0htDKvTA\nenLx8lA4wfF4BTFev3vPYulUB7gkhWm69ofg8uEjfk62y1ZGc5+kC+P1WJ9f0PaNdt0ro6t3FMf2\nTnqw708AnI8H83zAytpSa+SYxKiDTN8VNyWWhyqnE90WUbKeodzKwzTPN7COLGokKCo1rRKEyImJ\n4emr9P/1rt/lOvIIx3IyVGguaCs896Cyi3KjkM9Z/qWM93UE3HIF0a51JEsiG1JnkZZW8QeS9IBp\nCxOdNa3qCB6DI5RpnV1qojj9wRDhZju21aQHtA7rUnmNEiX/qnWkpO2ZRQ5OKkfKMj9PDyQCx3Ff\nfiUpGbCGwwJKqRQwKdWQ9HUf1fv0F9YRrAJspaEd5KhMPHddUxgvcEMsBUkUWCqgyKxLotatYar0\n1pjHQUzn1CIU5lLf3C7Cp7cHxzz505nsfePDZecmyrEaUMc8ebpcOc9Zga9QTXirc8TI4EKF/g5p\nvGVyzUQ1SpXROlucWG98d07ifkdkmRV0X3tCgbJCKzP0PX7lpND/pfgJtrXOixkZzkCw6SQVFNy3\n8i01qbNI343hhetKTTZ30rwaRXOUquEsVRGzPGghRXhOoySYIhj7IsFSqgXmklovRHgGTVspoxxS\nitxYPm7BpNfym7EativLaw0ryIp26VY5xMGEFdYbxufma8tVzIcy8yxaoirudTZXgsw6sw+EjZW3\n+usPqv9a19+oh0lE/kXgZ8D/+v57mfm9iPzvwL8O/H3gXwO+eV+g1vW/UP3UfxX47/9J//6mxi21\nJEjZeAvjSYOuJyIbmic/1Tpo+3BChG9J7JyoL3nFkh9lFhVpSvk+lLWo+JIqWO3DZwpOZWw8dedP\nXXmdyT6CL7ZOl2S2G3/osBN0NzaT5b2ZuEnJreJ9nlkp0r46e0WmCk4abVYBddBw12XUBBNnz8QX\ntrjcKDWebJL8qNWUy3Og5ovNXwtXWCIuGAdOsmejtcGI5ElaHcC9YgSHJhHCjHooDeW0xE14pSYg\nH10+hw6eEuRDsBZ87Y0/+f7k9JMuRpfGLxLmW/LRgtul8bN0/pZVsOU3I5n9UtKEMD5Np4XyaUvG\nWZkTUkMgpAkP23iTyh4ao/EUZ2nsFT5GaZIzgpdNGWncGXxU5WoXvtDAKGnhlJKfaBYq+paTZoXg\nDk3uIWWqlo4ENAtUs7DPnoRHaaiXnnpXoQfc1XgRReYq6jwZVmF/QZJZ8pTXWUjrc9ThSkRQ2TgJ\nELBsNCsC0qaVObN10LMoakdOXnMjPcAHpsJF4KA6OCVRLIKTxOBtjdiNUdIrLXT1azM+LXLNfToX\naQglLf3xIhIFwpSSoDWpkFMA0d8cyvM3vYY01eqCijFDMFVCL1VEth0iaFaUr1gSo+GJek3ochZM\nJnNWVzgqA2tGPZFhUdkpS8K2fiaMbVGGTrg0mLOkWK2T1uhaZCVmSaEwqxBa97VKL/LZkj/k/BV8\nRKjJjyhLVrHCVT9TrKjMFKk1xEZtQa71b0iAbB31JHMSJsunRPkwpMAiyiSDhaYFPU7CKiFFxlGg\ngzaXRBHIIKXQv7Y17i+vHMdBaxsxRwUqxuARju6dT989eP105+3779mfntguO/fXN37+j4L90nn6\nsPPxqy/52Y+euPXOL745YKuDvqfzzXeJRWO0O392FrjDon1eQ9yUb8JxHxwjuTDLu9Abm3T2p/Lq\nnNbwOZhycHGjfbHxk0vjMeGjO6EwfaI0wLh1YBNCNyKDN+1Yhx69wpB7YKZsW+N6KWkaWd6FkZ2Y\nwtY7dz0ZHsx51rM3F/XscZT000uSdHz6jtGvjPsrr8vDplK5YdqquacrvHPbbwx70Lcr5/0smfB8\ncI6DOAZ+nqhZATcIxAscklJwgJiBE8uw76SWHEfajhOMOZYnyUlRLCrIotsihnmgGlSSjuPpS0I2\nfqM5TL/xs4gZLkUA89SCP6jVlFaN9MpWSrFfrSMB6hPCiZmVZRa+psDQJMr/nE6afPbJ/rmfCs3O\nGOs91SDPUi1I76DlSx0+GA9dEl77TOWt3Khqn1STGPAq6Itu5DXRBKA8QX95HSkiH7gK5vGrdYRS\nVVjvJXXVd7LmguyErPVQiv47vCS/msgZZFcsQfAKr20FLCgYTUm0RNYeOAbnDFqL9+8KcOYZSFOO\nMzh+MQpkIEZrxjkG3768Frjq0rlen/jR7cJmje+OA41SIQXOp8dAxZgMHmGkacGNA2hCmPLqgfvk\nmMkuj/W9KtaVJ7tVIynKS/+Iwa6CbjvPDcQ6OQaTXOHAZaswTezaGPeS+z6k7BrvFODWaqJy2Tvp\n4B6V+RQwmqHS8Civ+5xRGVdIFehZe0C8K6wA96POtnEuKV5J/3MR8rQQV2ClDoo26zPImszNnPhS\nvFSGaH3eSO1WpVaIz2Hw/q5gIOpeSUEo2MOIiXpNUtMaWtUZm1GFeSZNBzmVXdf/Ay4/bKz4z6jH\n4k/+0u//yfqz96/50z//h5npIvL1n/uav/K6xuCjGVcprbxSxJXJFbrz09joW3By50WuHEzu9168\ne0pO5HNRQ/KsSUNGVd0roXiI0Fjyv1w3g56ICo+ZbGp8PSaXrfPtNKx1bpl80MkjFDErOo0Hukza\niDAoKcW6D5hRGSrAIupVMGVKTa6gCG27lDmzZeEkaY1jZC2+rOwjda7WeZTVkr2VeT2tNkFbk6cz\ntlIOy0R5At5oomymK316ELYRs2AZmclYVBNZkoKvdeHLEbpdOaKyXQRZx4eGZ3UTHyN5ss7bVLbv\nJv9Qlgl1f8FdabzyJI23aOT1ysx7BXb6WQfGPNi6ImnMaYX0XK+kCQ+fiAofAqINNlOeI/hKB3c6\nZhsjH/xjyi/yJMqzdTImBwppnKp8EbC1ks9dUrhrQOw8enVq9xBSnKc+yFY0udmUEbAL+HTaLPpZ\nU2cKDDP6SI4IXluSR9EHLWHmoG/GJW114HwtZEazO0HJQOdIEuP1rDBEjyyPlBfQvrca/cuio5Ul\nt8JsIyiZ1QD2By2eaG0USjp37pEMtDZZl4JXUJOR0hXnGq0HLkGXOjBDwTV+g9dvdA2RSORdKmLV\nFUkTxK7Qkqvd0B9/ZHzzNbFf8eOgPWpjq81+kCOWpOBEMCIECeU95dyl0tuX5aMKh7WGaK7YAxGm\nBhJzHbTqUJkzic2KaOaLKHUuipSdFVSbVSRlVn4KwB7lNQtVZsDyX1enD8oEPYMWlBTQfVHTalqV\nUhksWZod0JpniVUuS7xjkZf/ICXJbSf8oAHaOrJv4F55ke4gvQhZPsnHXLr1ZD6OZTwX+tPO6+sd\neXsDFMFo+xWfwePlTsTg8vErHq9vzHvwy59/X4fR5yv+uKNNaNcrOYJ+q1/lCum+MlwU61fyozHd\nGK+1hnAxNJzHSxUD21UQnN6LGPflVzvHUcbjxzj5x29VXD0/GV9ujdeMoo+mMDS4ckH34oJ93Kmc\notx4Ow9aazy3hmfjYlX8eMC2GyOCq8IbjfbSuL/d0bwQOCHGeDnKE5vBPJM4B5mdeb9j+15ZVuuw\nW4btknyLl5z08fIJacb9+0/EZvg4kZbkIwrTq4JtnYxgv+2MqHtSDmofk0BHIF0w3YuIN09Ut4IA\nNCuPShh4TbaJahLmyv1RL+lZimFr7UiR33Rw7W90HcHXgVJryoK8S+3KU3e1Hd8VcuBakq/2iJVV\nWIHS6azg7AL4OPmrdYS/eBb5y+uIZHnhNCbRS0IbarTM8rO5FMxj1pRIVIlZsr8Up8T2dbANKfIl\nwM7yL0lNBCxYvqeacqOBjKQFuDVkVjQBqeszDqxpeZ/wKr7XAdk11sGcaiRIhbnSO0HJclWt1uWo\nGI6ic678tYwFSqog3V/RWuu9Pz2QcDJ1gWyqOJzDSXFUCyx1f3Ne3r7lz4Iq2pYM0nonPGltJ/1A\netY6YoJjqGykKjOtEP7UeqszGAsfb14TLm3KBjxdO3NSWZdx8vVbgkwuzfjQjUNgzHr/Zgtu7PRb\nvel7wDhPhnR8FSSbCiYXLpeaRoYrA2VENUvfZmDH5CEPdCiB1+cUzgGlaCorJREFH1FriKyzCIvO\nR0FILCA0mLMK9/SoM5DXhLEadOWbVMrm0tZJQXHEa+KUEmjUptSk13QpV4ZWlPe8Zghlx2BRnEWp\nXKrUAktYTTJt5S+l/fqKl7/O9dui5FX79df8mv/jj/4hWzNMaroiIvzLv/cH/Cv//B9gtjG1kqkf\nx+STBZG9RoEjka60qENnRABG1xpRnjhEybK2RQtiYQy1ZRn1Uis5m8CsrYIH+gjCWkmsgG8plPez\ndZ4l2C0ZI5g22VIhG7yjxV0Y2jhzFU5D6WrsBIfU6FIp3bnzLgMKmhWNZCxCTQ8ttj3OZMNzkgsV\nLAIzleEQMRki2Gnseq8upFR6/YwyHVvWRjcWblikJibiyp2sA9vyDbgH1ooyo0AzwVrRWuYMbpp8\nxcGPtuRbvZABP0F5aBVpv2zJeRw8+aSfB4cPfnQ+MzflT1BePXk7k8fDaduOn49KnCdXfoPg5+QX\nZCGZLWiSDOvsDTwnXTaCwTiUr+fJYx7c9o2w4GMmz+F8LYFtHQnntt9KLiJ1EHjNzi/2yVcu/DIH\nuwSaD0ZG0e8Srqo890ot71G/9xrCsWhC4p2pkxQraQ2OWWLiWLQ6GP+/1L3Ljy1Zlub1W2vvbWbn\nHPd740ZExiMrMzs7uxoBQkBTrQIxZYCYIMYMmfE/MGjErCUkJMSIEUyYMGDABIREIwatVomneFTT\nrcqMiszKjNd9uPs5ZrYfazFYdm9lqUSiqnx12Sgeft2v2zHbe6+1vu/3yaADtzZxFSV7Z9EIdAvn\nZRzCJ52YxZE8UUZM61yjX7s6PB35Fd0cIZPLwDXTbdCaso+QPijhTZtGTD2UzAQkic5X1sxA+b9+\n9mP+ny8/J7hK8feovx2s+K9kDen/6L9C8nJ8tYIK+dPfp/zgXyXNC2mZSSXT0oJ5Q0t04OyYepqf\nIvfCKxw69T6ioJRjDYl8kD9dQyyFX+HtGmIK21G4WQqkcEsL2UNaF91gB8kkEqkAtYUJ2zwmyBZy\nveJADioaFrlsEZAJ4p0qgSRWDh/WsYYccyiaaEyIcFwPepkq7oOkGbfj/xGeB3eOiZVguh/ynki3\nZ7O4h3h4W+oOkpApwSEvG+5xyN47Ugq9NWQuR+ikMC0TllMEym6D6TQh2fn44w/oIvStcXc5sY9G\nnl+wrhvb9SnW1FbpDw+8f/4UuUs8ro2x74ztSlvBTyd0vWJHILmcLojAvnVe3lYkGdOpkHPGbCbP\nyq025ik2/do6P/78yh9dV07vnxFxTvPMSYTNKvPz6Pw+v7vgtoefwIW2wc/YOE8LV4HJ4+DcreMW\nkp8ijReLcEkzr2ewtXHrjZqUXqPR4makOQrTUSFPx9/VE9IbnsNHwW1nc5DWWeaZWg+IgIAnmM53\nSO+k5R4ZkfFno5PKRL9d6bWGCsJayH9TSIU6I2TkdTDKHjJlJ1Do1tBDRgOhlFBPSFbqT/4B9pM/\niAOZxSTd6/pLLQa/xPUrWUdu/+d/jUzL2+M/qHD+7t/m9IPfQyRjquScaFvI/nE5uDBRMBlTrCPy\nljznDIPk4906MmkUFXggwT2HRP/dWUSMqul4F508BlUKWcMvGQUSoJlMCoLiiIO2HJONtz7iiVC8\n2IgJIR4kxbcIiCZCOgidHrbEo2Edk6OuglpMRGKiFEWScQQVH6VSFNSxiAyL5oul9k4m6D6ORlGO\nPeeY5gspMixHyE7smFYxQhnk3SC9zTCK3CvP8TF6izNUKsZ9XmgmeI93o3lkE7VeaX2P6alVfOyc\n0jNsyuzdsd4x3xnNsXlG64pbzEtamlGg9sGtR8xDyhpZWpbJU6Ye02LBqM243q582Rp5mVGMkguT\nOs0reYkGxrPlnqIHHAHHmvJGG4sNHutgxugyaD0kdR1YcuY0ZzYmHgtIHazeqZ2j4Xrcs6ThU/SG\nCMeZOh0o8yg0tRNe/BEAh5hPxpTbBVSnsJEc+41JTAPfZieZOTGzs4BKHEOALiO82G1gKSIossaW\n18WiIS5HcW8D9cirvP74f6Z+9j8db2CU+/YbXkd+1QXTz4hX9GP+bGfnI+B/+bmv+ejn/5CIJOAF\nf74b9Geuf/1v/E2+dXnOSU+glZGC1rZkpZSCUdgTtKTIU2LIjs6ODmWtPYoVjYLJPTroi7zVpOR3\n3VM5JlfqscBPB0V1P/ICUrytoXHNM711RDI6V5a+0KXxNJyXeebcNp5n4bvTxOyB4E0UXnmjuWPe\nma0AQtVGnd+uJOF7qF4O6o4i6QYW4bpGgCRUNDrTMRynpJmkgZr2PuJh1cTUoRY9dOSBvVSFpAV1\nYxz3w9pOyoEwHqaMUZFyfKpUVFMYfy00pWWayCJHngtIyuxbxSSTk/JTlK/lFJtyzrxug1pLIMx7\npqQ7bjnxlCrfyZk/6ZnVJopCHhtNE8Oc0ZWpZGRdw5fWosgsArcuMHaeWmZkQxYoEl3xJU+c1bjd\ntsB/t8a1Ki0t/LGG9OHSB+k6mBKU2xN3JRDrO4JPna/lni/NuHkmtYp7YnFFZVAwFh+cRHh/6sw9\nTLGmjVWX6F5ReDYltjbYRSlmTObk0Wl6mBpHp/mK6QQWi/AYThK4qDIdoXiTNW7eaXuiErhQUWW3\nCK89uR0yhEomRuttCE9u9O6YpsC8HxuSWkXT4KyVkyoyhC5gttMM/uVPP+b3P/0kzriE7ODHb17x\nn//B3/tl1olfdP1a15D0T/1bpOd/jVSWkBiKk1Imn2bmZxdKjpyp5f7M9XHF+orlHESy0VEPBPPP\nryGlZJonaDtTKu/WkOqGmVEcaopihnzIAkd0dIsmhkxo3+iSyZMxesGl4xKdT+8dcuby/EP6bUNL\nCh38HkHEZo4QL6mPihaLDrNHlzMmO4SZf7TQj2s0nCYX/MD3koKUpssSFLV0jLt7RVJG9xHBitZj\n8zwKTs0zwgg5sxm27egyofMUE7PWSCVFULd1VEqYfFt4rPL9hXma6fsN0chW2p8aSZRUJvZ140km\n1nWlLIXb1w943TCBeVnI5YSpsNeV8wff4s11RaqSUmFsUaDSjQWFcsa2FcmZ/baCKCkJe9to1xrd\ncnGm8wnTkKpNpzuWc+PVTx7wMajXle22oXPh1XgT8m0L+7HOiZSUu2d3pOlGq045TzxqZl432tgY\nm5JGjYaYdnIqLCm8iffPEs8mY9eM3ZzWhYvNNBdOZ2XfVtoQSllQEdraSCX8Kdt6w+qOTBmqwZRZ\n1z0+o9NMnqY4XO6V2lZ8K1i7Rkc+BUgicujSUexA0hT7Ta3hTxmErw5BpxKH2hEmfMQ5Pbtj7EEj\ntd6hdZbv/D58519B5hKhp55or35I/Xv//i+3Uvzi69e6jtz9i/8m5b3vkPNMfydhCnmnlhKqFSDn\nxKiCS4tn3hQZRwA5f/YskrNSx1uvSsibRJx2vL95OC1zTIbDHzOOs0h4oKaI06CQU8XGjOmAEWh8\ns4Fo5nw6vYslSCT2tuEcTYSco4gZLWR/LsgQyoGNjlz76PC7HOvI0bQNxk2UWM4xLYpjTEycjZhE\nmAfowYPUlji8xZKOyfnRoOk9PD160IzND+RDfL/4/kcmlEHKOc5JtMgrzIW6t0Brp0TrnVt3ukfD\nt657eKdYSamQdMFcaKMyz3cRCTOIKVn3WEfMKHvD04K1iiSlH0G+KkHw82ocycCkGsWzAklnSm6s\na41CokVUC5rBI7Q+hHGOZOFLHpnnBU3bQTEsXKUw2UYvO7Yp2RukCBeBRPFKKsr5VLg3Y5tgrs5w\nPQos5bQIfbTI8rNQS8QzGNO57g23jou++8yaexTuSQ9lhqBu9NGwmhBpobSQID6rx8hIyciR0WQa\n38P9LRgrJoPpuD8uR5CzOln1mDIdXigznn3/9/Dv/140OnEwZXv5Ga/+27/7F14c/rLXr7Rgcvcf\nisjPCOLM/w4gIs8IPfB/cnzZ3wfeE5G/9XPa4X+NWNz+wS/6/g96xymdMBL308KS9TBAC5RCWWYM\nKLcNdOe6Gx2h9Xp0VglZytHpkuOwsaQ5TP/i4c9xUA8pW+rKJJWcYR7Gbjk6wO5Mw6njRtLCTsN2\nwQnTbcohq9E0sbXBP7RELvfMLmRroDOnI9/j5hFK2nWCLqFp1fA5uYwjmMxwm0lqTBIBs0aiamGW\nEZSbsqAeJsnRGmsHTxNlGFoSSkW1MCWjFGWMkB2mImBgwynThA0YHuGpLuFlEldEj8OgRhdCNdOb\nI1oCfesShlEJJChzIg+ljRpBdH2waYzhh09sEj/X1VCZ+InOzJfCWQVrnbIsyIhOfhs9OhAKzRqa\nCrV2qnsYiTXhM7hlxl4xKaR55uXDxjfinEY6yG8ZUkzJmsGyNlouDGmYJCqZx5vj2lgEuDWaDdYl\nDIgzUCXzpQjTSKScyGXhvjs/6sacVpZSKF7pIpRdSCm6b5eUkD6obqhGwdPNabZieaLZGYAlbYCj\nYuBvpaIOo1EtDtqn4ZxKOiSAnVuK52E2Qu7hiY2BWaCxT+KMSSminETYx2AnYXqiy84zn1m8cc4D\n9VjDqyk3k2PTEyYTVpyUf31iml/3GqJlRuZMM2U5z+TTKSarCmVeuDw/06xy+/KK+QOjh7zJR8OO\nMOzk4eVQD/kj5hT1kFDZYM6xwbsbWgQbiYkI8XOc/eiw4k5pTpe3IZ6NWgGia5ZLClOrlghafP0U\nmVAV6thwkYPO5QdgIrLC3MPYbRp/Nz9AN+CMFAe1gH+ENmNM03HgU+b7E64JH53t1vBWQ5PeO77M\njLbFodChnBdGtTgiTTOpHbS80wkZnV5D/pMo4CEvGmmO3wELutWU8bf3d8C+DooNkjhlnkhLYSmZ\nfV2Zl4Vt20hJ4nu4UFuD1iArecmYV/RyokyOrcb5+Zl92zFdMGtY7WGA3ityOmNPb2LqpROpSNCv\nHLanG6kU5g8vvP7pTyOeIvmB2Ve6Gb5ttBGNj/l8ovbBrGdsCC9/+oBkRRX8a4IANs/0WpFpolrI\nbkZLpFPjeklom/jqsZG9cb67OyK2BslLUMGsM88nWK+RYZeBvVOr02tFc2K63MXnpRE7YEf33Q5U\n9X694Yehe8qOlhOi4bmTIBUcsQhBvhq9470HZlgyepIjZHfG9x3UkXRkdE0J8cTp+RzhqkUD57zW\nYy8RhmVohqZfLw74172OIBmyRjxBFiTHNFgO+Ms0zeF1tZDR9dYRF8xayMMkzhguQjKjEdTV6YAw\nDDzgRw7gSA75VeaYdHOAijymNsVg0y2aGF6pnTiLjMhjQg4iL862NiQFLdO9wSHQE4v9Vd6CKpxj\nohQHZ3/rsHePcOw45R7yZmdo4S2yPGcNGa8ZtQdpDZEI+s45GreaSKqR//WWupYS6iMO1Hl698+m\nR2BqErI7I+WQ5vlxFknhG5MEo4HnQKHnt83vLDAKY3SSZroH4l4FOJRHw6IIFI2MS5kWpjywCvlU\n4veg4HTsKCCxjqeCti2kjJTodR/RMr0eXs0psW3XmNjIwExxP2b9o9Nxkg1yyuG1bwVUuF4rEEHb\n4YM2mug7C0cVwXfHLKFTRyYo28TDfiXLoMgcAbTaSf1P1xHSBL7H553BasSmjNEjF1AnILxkIjFF\ntKMBmNwZDOq7Yn3gOlHSOIqbAJ0px/NB2FSEAGGJZFI5wnk0/LxBiCxRkKbIgUox32Ckgg//OUlp\n+Me9hk/rN3n9ZXKYLsDvcswcgB+IyL8AvHT3z4H/CPj3ROQfAz8C/gPgxxwGSnf/QxH5b4D/VET+\nXQLl+R8D/8UvotIAnBa4u8vMWihTYk4pgmSts3km8vAKIhvFNi40bltoXo3wiqhrVMDJObuwemCh\nO4ZJVLrdg3STh3OXnZKNZJ1LTqzu7BYp2u3wBqXRuEfIk/LQozvke6Nq5gHnLNBaxWvom4vm415O\nJOnosXkqIQfUFJ3OpAkfGt9Pj4Pr4QFXgTAn7WRNx0Fp0MzYtw0dxotjKmDp2HiP3IWiUTAkSeGh\nOMg5muVI4R7YGEdhRGDV04RKx0UoJHIpcQgwZ4wb0oXN4ZSWkG+4I81jjCXH5E6clEJOaR4gDDuM\nEWpGl3gJttEA72QAACAASURBVNaCLKewqFJzUNpycpY58rWu+6CkHOSuAS4xcdKUAoEtSttW9r0h\n80RKQafCEuyDYR3LhWsp1BTwi94bpR20sFyYrfIC46Ubl31gmrjkwSJOHxpSmiKkPvFSBnecoApP\nUhE3pkMCydsQUpGYMLhi3VBKJGVrIVdjkad4rVwxWdm7Qx/0pGyHpCYj5KTsIfSlkMkK9565jg5i\n7O5HV0vCizIy2ftRNEcHKalSTBiaGeqsPpCUyKLU4SEZldBMF+HorjlPQ3Hu/qLLxj8xa4jeLZS7\ne07nM6dzYTqfWU4RJDr2ytPjTk+B3ZV9Q1vD2ggQxJFergMkRedXjTDBYiGZcdhGY1Jl0UQlvXu2\nk3WwEpK9IhjOFrsKDMhdoYSnQFyDVuY9TLdDGdqQEdQh0Sim9hRtX6khdeoEcEZkEElaIbsVIk8F\nT4jVAwkrUCZSb0gpgbAfxqidcX1C+ts1Js4GbpU8xdflVLBeSSnAFDrifUrJ0ZIZ28D3CvlY2XIC\nnUnW47Ammel0wcZOH4PtuoasJivlPFEuJ+o+YI38pbfd5HzkUOmUY/N+B6cwvMf6p8PZXlbSgaGd\nTwswMFHK5CzPLpgPHl9V8nkBUkhu2pHHsmRsNSjK49dPcLthpzsGimlMU2pt0AeaE65h6DeE7fE1\n3gLso7mgo1HyzN5CvpgmQU93ZBus2x4Khm3C64W2v+bu/h4z5fV6PXwuMXELUZagKSHzROmD3gdp\nWrAxSNOE7ZEdJ4B0xazjRxHrqTI2OXwKTpomhoTvqJQ5oAEO7XYDnNEj1JOUyGmCyWA4+bzgclDz\nZMYwRNMR7itoOdbgNpAW6/50XphyoYvRemd/vJHn8y+1hvzW15EpU/KClMJUlJQL2QpNO2LG3jsj\nx4RFPaBTY4ygUroy1FELVH97m2vj0eCwNJDDg5pxJofdEskV947KIB1HN82GYezHHTAzUs/kmTj0\nEwTZwcATZEt0sYC5ICGjdGiHtE6OQ2k/CgexwRAnu+Ian3UUSgnxg2anChJTJ9GQskday8DaBgdp\nNJqAAIOSUvwZAi+dEIYH8MZE0eQHkTQkeRCN2DCHZvSIcRBJJAm5r3nAWJz42WUKtPow8Hb8PgdK\nOJkEWEMAPQpPObxieFABbeO2WSiNBFKKGAEnkbJFXpkY2z5ifSMkgW5RLFjWkLCq0PaO9EqXGU0p\niHUobRiMHn53UYZHMWy+4T1kZyKJ1AdZMtX6ITl0RGbSGLQezX/xjPfCziOTThjKjYoeRXEgVeMe\niIQ0Og8P+m3K8JaSOfygBkrICsUOKWconzoxVXQJqIgRz4mkHPc4Cdba0agLGb8f55GU3zb2pgCi\nWARbB6A1pKk9+mEoiep2pBsIKcd7MpSgNyZHmP/iC8cvcf1lJkx/G/jvOXqkwH94/Pf/DPh33P3v\nisiZyDJ4D/gfgX/j53IPAP5tIizuvyMMGv8lgQD9hddHdwt//cULugs6J4okZhF2r0gbYDvVKg/b\nHtMTE+6nzGPv7McI3PNBY5HMZs5FKskrky88hUGGZIGolAQmQqbg7GwWXYmZCZlnnsZAdKIINN04\nM/hrCidxzrnx0DZuVfimnyiZgxikYJ0h8ZaaRKdaFCYXZBCdKCEIMoeeM3Hc7aRYEs5SqOrvFoA+\njM2NuRnfx9E0eCPC6lB9DnOlO4WBe0KGH52SgTiUnNm7oSmQDuc5DvjDQlSW86CNTGkNUmHbGicx\niiqmhZw3DNj7zkmFUhWZClhsEoOgj0lJDIN60MisNoYMig/6UFZgTmFgB3gsnY+u8DqBuPHkEx8s\nwmQ7D3LhbFceKLgnmm1MWUiW6a0zLDJRkCD9tb3FgjElrHt0SvZB9Ra+tF5j/I9Qe+eK8bVnSoFX\nrtArD+MuAmalkBjcyMxjjfBQiVyq5RBIEQBYvHtkGhGTR3EnZZhb4+Ie3a186Mct0TU2wUkVcg5j\nqkThWwYUIGVhUjgPGAzWETr5loTZAzV6EadZo6ohJXEdnS0liqVAexKF4kyhpMytw1MavBlOpZAQ\nUmp8QOTTlJ55UOOR7S+4ZPy567e3hnzvI+6/+wP6AJ2UOSUuWXgzJ6ottNcP+ICHV2+ObpiiU5iq\nuypJgvzmbqhbfDZueN8pMtM10K9tyEEFisw1kZlOZdJ4v7AMJaHWGXJCk0OKQFtMQzabnV4dqmAk\nknuYklXBwgej/QiBxhgqYb4/plfJwSTyK1KKxgIAUpC3/pcEIgVxoe+DVjvSOjMnWnaQSh+BIXeX\nkFqYYXS8B8Vr9CB9ldOJsW74iJiB6W75OTlgeC9cE6wVyYnt8TWpzORS4FJo2xqeouvGdJqRvSNl\nAQ+Ag9PJp8x0KrQK3hqUifH0BB7YXHqEtcb+H1OsdVzJttDahmvi2o1nH9wj7rHJt05vIUGsa+Wk\nguRCX3dGG5BP8bUCbDeGK8wT4jtGIdWNXnfQCepGOuRpdKONHr6iBM2ddh3Ipu/WkBoaHOTxAUe5\nvXnAxVnKQkvCkgq4se7jgOs4010JTPGSqWtkwJkqA2PUAAjESd3wlEnJY0p5UMnCIwJaJsoykyXj\ndNr1hopiYpzeex4eDU2MumJdmd87sa1r7ImqyBwHUsmKdGWZM70a621jv94wKUwHhrHOM/l0ZvKZ\nq2zI/isBx/zW1pH705nLhx8wzClayEmZRdi8s9ZB8iu1N251Z7wFAsghSdSDkBdt0QiMdYjI6kYZ\nU2Q1Ev5jE4KgJoKnme6dYv0AQuQoqL0zfIqGZIp7m2QiJSEXoW0DN2d0SCmg7kkVhh3huvJuHTEB\n8WAZejqCWeWQaqJH9wyQyH3MmhgSa6U4WDfaIZ+bdWYkcI9p25CY4MqIM4rJIbcjppxjRMgq5u/C\nsks5pHd69B6RkDbaQFNi2I6gMRVLGdOOuDN6D6WPcRRskbfpEpN/LWA9H+9nRno9yIQef34M4jgg\nYEJLndQnhjdocPXO6RTqBPeIgRiHDXT0wYzQNUXjy0KaLxLNbu8VM4WSQs5sUXAObYhnsH40gxw8\ngN3dB5KjaUc3pGeS12MdOUAfh7SvpR3HKJIZKhQXcKOaHsRXR0soHtJRmBYxDPAp1EbqHJ97WBeS\npShaowsQ9FGNBomKhD9bO1YbnZBrJj1IoSnyl8wElUy3KKhUY415+wLjmSlBH1BtMEYPFoEPOH6W\npEzqExX/0z3tN3T9ZXKY/gc4hKr/31/zd4C/8wv+/2v+f4Ph/vx1vrxHubvgtTKy8mgTm++c1Tip\n8tRmri9fIaMh9sgqJ8Y2c8uJbjUQwt3RtqHFaT7xyuGehQmjeZiwkxSGDkpJeEo82YEBz04eoJZo\nI6p2s8HmkNPCT9kp4qSe6bYw9zBSuxYKjaKJi0D2xpoHzTI9Z5TAg08uWBrMrhSHfTnIMxp5CZOE\nNymlQRsNNae3kAmW8UTahCl3Pht3dK3YCKy1SjsmDo2eE703FnXOsyFDqJrYekxoGJE9pEwkGWhV\ndEnMwNIGn8ydXYVX6oye6B3u9AnziT0ZqQ1yiXDJ022H3NAivMJ5bzK2q1DLBUsJ9RvPCZP6xz6T\n5ZEfrYk3bTCfhbYVigovNTP3K9/LBcuDF1PibJWf7hPXMvPxuPJq79SUWF49YXPlk9OZ/2NLPMl7\neN3oHIjVrKTeoutke4A7utNb5ET5YYrthxRRk9H3kC7mrHxbn5hnZ1F4UwebFlQmntuNF0TIX0uD\n5I0yVkyUnUJP0Ym2VjlZDPYvGhjUbpGL5SVRZYosLE/kSWkWi53rYAZynhEZzN7D2HqM2KcpOtzF\nnGuOXIQ5G4sl9i5s1nkmJTZNUR6l8KBK6oMhmScGeRZGU64lyF8ZUJ9Y3HjqiQdNJB085F9uDP7b\nXEOeTxfO9wuPW8MFqjm+b2hKFIfNJ15+9scBEBhXss9oz/QiuG2R3SYSBZQERQ6H3OeQjqQpDqXW\nGclIUmhLbLhKoofZIEz8PSQkyXbcwdKMyxWhUB3EZ0T/1Cswm74LbJRhIUNtDnM6zMqCl+gaespx\ni4uiWtCipJxIecLFcPNAYw+n1chD8f2Gtij89wh4YnQOubAdcp+O5RS5LQqU/A5Zu99uAEitQWnT\ne0QHtjbSeSEvE+1x5fKt9wJB3iujOnWv5LGTyxJei62j80J5dibtMHJ0RHvdmU6F6+udeVqCzDdu\npFPhMs08u9zRfOfLz79hf7yyfPge/bqHD2cMxm3lg+9/SkL41ocXHOfVy8ZI0PadV198w/myUP/k\nNVYG3/7d7/OTH/0UzwVrG7cWpC1PivQd7WB2jUK1Ge63I+Az5EtiBpqCetbgbSbNNCnL3Xuc7k48\nvrqGfFkL6onzXBgW8QdlbzQMnQPBSxGmtLBdN7w31AaX04nywYX15UorC35ZQJS+1iM09QB4QGQN\nqjDNOaRMBy5cUqbVQZkumAlJovhqzZkvCfwFdd3wYZzPC8afAktuW2eslbM7T+YRpNwNyoTJoImi\nfki6bjtrG6QkPB0RBb/M9dtcR+6WC8tyYq+ReROqog2ZE3MWbvvEbX0Tcquxo55JNdGKYl4pFiGd\n3nsUG6pxwB0TTmTPiIY6ZqSgofYpihX1FO/EkIOyZ6HeOLD/JhmXDXqmu1C78JaK5yWR3UJRkmJS\nUAW8C0xvi6aEZyE3kJyDIqpHESVvD8hH7gqB9E/m9DEYCPQd6eDJWCXD6O8Q4aMEgCGPwciK08Kr\nlY+JmQRiGgcd8XvZmCIb6Z2XWzATlnPBRKGH16ibk1MEIzvht8sGFCXtzigRqttGJS+ZthtZJkwU\n8Q0viZNnTtOFwc7T00ZrFZ0KjiFNAmTgndPlDhV4dlkQGTw8RRSHe2WtK6kovm5INp6dn/P6esW9\nhDeYICx6VsT7Ad9oRJ64Y75zwAkxsXdqAFXF27H3JiEno+SJkhO6h+RRJPajJYWv3TQUTBGafNBT\ni6BDaX2g6sfZJqElMVqAqjyHN156gKlUAzOflEOO6OSDAq361kuvDFPKaQoYjAeAaoxAorvN7wjS\nc4kiW4cejfYIgT+5sVr4SoMNoQyJUHC1oMym5rQDVnHTf8Ileb/N6wtVsmWkFIpVuu9s28Z1u7Gu\nlaUe6EIdrOVM3Ro57ZwZFA1MKuq8tySKDE7WmAW+7E4uheejcpUSH0wuiKR4KA6Tdx8DtfDQDBlk\nT5EmIcIYnckHluVQsvbASia48yeeHDYykqLjexLljLN5pxRF3TFrFIuquyu8txo/SysiQh4RTlrF\n6TT2oTyzzDPb+XQyPpozX02DL6sy5Yof2VJlKgGayAHIuBfjO+cIVl0N1im0qrOE18XlCIAbA1W4\nmwe7OhdTlunK1RZeaaaiqA9K7uSW+CRtzF34SUmsXfgkC9c0+PhOGJuw5MRPnxIjC9P1iTEG6zTz\n3CunOXGVmR+usFhjzkLaAR8YMx+XgcmJPy6wMrPUzuh3WDLsuvPPneH37nd+vE9886LwRZv4RoRP\nRfnj+nXQqlSoFKxreAvIGMbSD/3wgZgeR8d+kpA6Wd8xDTlMa5nPe+JubfyN041vJUX9ATXlnCsv\n1KmWaGlQrDNrdL9uo9FGjK57yqRk3AmRJyDGbRSKlNCmWyKliYGymJGjzYOgnDQmG6LgcqL3kH/k\nOUJ6u0TIMaPx6JlKwlypaZAjHCqerWJ8r658Uib+SJR65FW836NpqBiNCTFnLoNPdCB0fqc3JD9D\n/motG3/m+iIL826IZopVWhu0x5Xr60fay0fwyGUTH5TphLU9Rv8jDPDuAx8jZBI2UAv6UzWnTIXs\n2zEJzpjMiET3LQw8gz6cbM6QYH9bFcgDV0FYw7ztjqYAwkwHKtxGC6nGyEh2Rk4kDfqnm+M5/AOR\nXBt6cGeQulLHA32VkIISBvW3gd1iJbJ3ivDsxfu8frxhu5GKR55Skmgy+NHdA7Jmnn/4Aa4RR9n3\nitlgThk7QhOn+cR2vZHnQjotkDOLJvr9FKQpBUZ0xsukaF+4nE/Mmnk939jXGx8+e583T2/4zvc/\npV4768i8+pPXkIWnh1v4dpYpGkkfwuOe+fKzz6N7nSPEVfognRcuL854O3N9umGSeLzu9K2hGdaH\nje/+7kf88//S9/nRlytPd2devnzg6enK6f4ZT199gcMhjW5RME8putIEyckJ+XGSkBZFHkkOE3sP\nZUJIvpV+vfL48MT44H2m0xR+FpT5pNzfnY5YiY6NhfOcuT3cWNdGr4LPG8s5kbSwzFOEns4J3p8p\nLZNL4rY37i5nhgllSWQFDLoNlikmVmEUV9ZtAzHunz0nqdK7U5Lw+vGJbh1Mg+YH9NbJy0wqiiRl\nMuF8f8/Dy9e03sklseQC5zPcnsiccINc4HJ/CuhHbSznO8r2Ja9+GwvAr+j6RpR5d0QKZVQ6A9t3\ntscdX2uQ3AzASKUwesWn6Noj4eHBPGAD+OH5KOzemXKmjBpSsdjFY70h4cmDQjs8vDzeGNmRrqAN\nV0EZqAdJTiUmRqWHBM0k/Gw2lJLAKLHHZAmgQyrHM21IdsRLmPFN2cdKeBEjigA3jCMk3QuKRw7R\n6cK11ng/1UN2r6A5Ia5Bi8tRmJ+XEyZG77EG2jBmiegOlCgOPQr7khUj9mZPjg2JUAWbcDopGWqJ\necokn1l1p1nnWcpspfLi/syoRp0Ob5DA7mv4VFNi0oEvhc0qj48PgX8/AlvFAv4wnwuMxN52Bom1\nXbFuoMZozovnJ37wrY/52Zsb29x52ivNGvM0s66Pcb4ywSXCjCVFgapHhIcTTZZMTJTUj4kOAxs9\n/FEePvfRG70Cy0zJ0YxRlKxwWoLk7AMoHt6o3th6PD9ejHL83CkfBEVieqejIJqoOLMUTATJhyxT\novgqmt52hpAs9BaQojnPR+RGTESvtYa36yieHCAJRvg7RYUJYVoy222P2BkRiiqeMt4byWd8QCrO\nNMczkHunpAteZm6/wff+r9TJ52JOrY88vN6p7crTm44Mw+tKV6W4knywZkFH8N5365yycofRkrP3\nmUfpVI2MEFdHJuFiwuwTqwW5hTGFHl9gOk2kujIwigx6ds6eqOJcjwXFRZmHM5nQckfdKRr27a7x\nkl/cgSm0u3lwsc7MQtGGDmcW4aOycxZn6o1pcZLtPOpE0cGpKNcdhnbOc+K0CHe18Id749Ibr+XE\n/TnzTRVOyxISvGlixtFJOemMFOWb0WLSoInkg9TDCOkpMauGRFEMyYkPLPIO/mjvPNfEZRp82Svb\ncOjOt3Xn23kiSeOGctcq31kWPmuJ50n5Zk18qcrUjLvivFDnJjmM8Si1z3w9HCTGzvO0sIwdG3Ca\nC29657OSaWME/cc6YyoYG0/7IFni718L/+v0ATcz8BxhebeGe2chk+dCnxy9NthWLiUQ6NknCpWf\nujMOGdJOo/sU0iuLXAdkRu0J8uBe7hi+0zo8V5gk0US59QlR4azOJzQ0ZyaNPAPM2D1Q0q9cuCG8\nESX7jMgRZAwh3SjGPJRNjJ5ANUNRJA/a6IjNcEQXl6J0czrwmKYI8vWdzYVNhM0zLhEkPDnsOMkn\nmgWRaUL5m2qk1DlrYyWzS2Yx5WeW6WVwb4nJPTDtkhi28iL/VrDiv5LrbAFPuH3xFevtBm9WGJ0x\nenTBWuQPDUDLwHsmaUc0M3psdCIBVtAc/z66IicimqBN6Hzk2/QIZBRAzhf6rTPpoONMaoyaDmwu\nUWSJk0YOOao56s7uhMcxp/DAOAzJvM17oRjqmSHhQ3SfuD9nTs+eM41OSor3C7uAXRv3z2ce3uw0\ncZ6fCi8+uvAsn/mD/+0fkeuIouC0MHpHpymaLaeFNGXKeWJKM7JEzpfWnaLKnJVtc5iEiZnpVNi3\nweXFPakk7i8ntjc3vvrJn5DSiefffsHrl0+09Yr00M1/+p1v08YTT3uF28a3v/9tvvriNcuzE19/\n8UirjdYH82Xi7r0z9WZRpC5Kvw3evLkhb1bMnNOze8a+Qu9cPnzB9enKeDVo2xpdWIT57kJvlfUp\nSFk//MPP+cndPX2vSAKG8/p6w3yAZMqpIFOmPj4ha0NaeDdSyiQRNh+kFgZwOaiC4oGKnnxQNVNs\ni4lROcNobG/ecNI7yrJg2VhvoLlzmiY+mJU8Z2ZJ+HsL3o3W4gD2ukPbndu6UeaC7DuMkE7328q8\nnChJGWsLel9WclKy5AMnPQHhxbqcT2wtusBjRO973xq1OgXYD79n0cTlPnxl7hK+vvnEVAov3nsP\nyc4yp0A2u7NMhXWtaGqUaUZl4nxXqGujj8Yyp9/WEvAruc5m9HalPj3xOCp+C7BF75WcDMYRhGqg\nUwObEGpk+biEJFfjXukRD9KbBJHWnE58PeoUi3wbEdA80faNIsR5JIF6eZdz5RAyuZFoOabOyZ2a\nDrpecrJGNIv5gYHXQ6JJBMMqIdOdC+R0powekyafj3BjYZkSdRvsapxTZjknzix8/uqb8LpJBLW2\n3slpjuKnFFQcLRGvwQS9O8kgpaD17kOQSQIIkWLCkD0w2EvO9Dp4vD2S08w8J/atxTs6nFQS5+nM\nSI29V/DBi8uFh9vOlBPXW6P2iOGYSpCVRzN6EVQG1oWnrYf/U6CkEr4fM8oy0fpg243R+2GFsLgv\no7MfeVRfv+68fKzhY0zxYVxbwBoCdqAIMeXiAEK4BrRCzdkZSIs8TBmDcJo5Q4RiI0iqth/ryAlG\no3ehpBJnOAbdlK06U0pcLqFKmCSjesLN2cdg6421dUYVqg20p3e+peFBPVaPSAfpjnRBspIPzLnK\niKacDFClTCXori4Mjcy61gfDQXtkPg46mch47OYgkTsa/rfEZZlBjJITPdSY9KK0Ckgjp4RSmFLC\nNOPWmMpv9r3/K1UwPf30c3h5wWvoV0/eg1A0Z669cZdnvj4Q1ElzmF7dqcAaTmlGFnrv0ANvGc+I\nhP9HhDuc5pFNYy1jtaPtiQ+Lsljo2xtK2RsPOtNH+G3UG2dx7rKQbGApUdVpJIoKnxbnuTu7V64d\n1hZdmm/lR953mHJGU+fZHMnTr8tBz8mFrSq/MyvPls58Vra28Nk++NmTk+fO711meh2ohBzjPsdA\n18agpE4pmWKN+6yMvTOJUTQHpQ746zNkdtbmlDZ4lqAl4ak3zDo6On/rNLFS+cqVpXZKF373PvHB\nDJ+1lR+9KqCDD+Zn/NHTDZsS32gPTDZBe0sKX9jEcEg9tKuB9Y2x6pIX9iSsI6Qhuw1EKr4LYzh2\n20lp0LvyvMdG0DLUdee0ZyYaNxU0n8Aquu6kSfiw3Xi2Cc/mzvTM+KYLH+aJL/sDH2rnozbxyXlm\njI2fNeUf7iuT2DvPG/2Bb58zDz0x68ZDdz5flW9qGOkncYpXvirCkuIlPyfhPXWWHMGNyQ33wfti\nXCzxOCauY+XRC1nhZoPznKh155yCfLYoTFa5jpmHquQ80YHnsrBIAz+ojoRB+MHgpAuuOwsxRX2w\ngGSs6kguvDJnssKTOurC5Ceq7CTNPDfnjWV20Xj+XHmeIpixmjNbYNopf3UPO1/84Q+x8hTZan2g\nPmi9k/KMj0C0urYIEh4hQURguAeiVxQ04xKEQx2GpoGaMIYwq+A18iRcBG8NrYL3N0GjHLBkoVHI\nNkJWh0eItozw3ZQUgYEpkUYEypo7aZ7J84S3wVhvdE9R1CfjblIudy+Q4nzwwTO++uqBN7UiEqbe\nem189PELPvnePS8Mbo+dH/3wC/74658iZeaf+Wd/wNPLJ3a/o92eSKPHAbAO0pyY72bchOm+sF03\nNMH87EzrIZ/59KMTOcHDl49k4LsfX7huncfVuX7xNXVtfO87v8PujVevH/DrDR3w7X/6e3zr/cJn\nn73km8+/Rn1w/vBjfvR//2PSNPP00CnnC2BY3alTYfuiHlJjpe8ThjDPYf49X+7oKuRL0D4fHx5x\nO6ZgfWfcQspa10Yho2MgxzQqNWG0iolTTktkX20NmTJpF7JnvvXhC1SEN083Pnj/A7746ktenM68\nXAuffPgho+28/PrK7fY6PBjmQbUaO5zuKfUWsAXf8e3GNz/bIYesMSXh6dVEmjLbRx9wviQ+ug/5\nShKhTIXaO8+HcUtOzZnHL5+oAnnK7OsD87ML68MD7z+/o3tn0aBurXtnrY2pCIOd+/mEEVMCDnz9\naPC0Xpl0IkmKglmcbQNn0BrolKh7Jc0z13Wljo0kZ/a6s/WZOcPWOhRj6MA6SBGWYvi+U0phupt4\nePoNn3R+xdfDyy+RFtJFH4aMwfBB0Rm3iN8Ysh9E3YlEi3ssHgf8pBFWq3FG0eFI7qgL1WB2wYfS\nRQ4JreJ7w6yRpwAMLKpUlKkaPaeYQHi0e0xDQh4kvkTuDhO4K2UK35OMQR89Dqca/upzUYoWUOd0\nPnG7rdwY6KhxkG7G+e7C3V2hPhPmrfPqzZWv6w31xMfvfRhFsSkyIhQVoFlMzVNWcCcXpfWKKpRc\naAfo5oP7w0Ncw/JwngrDB+vutLrTu/OtZ8/ZrXOrPUKWDd57fsf5Unj15srTmxV1Y57v+Or1a1JK\n7MNJWohR66CqkFvHCBJg5E0K5Sg6p3xhpJjWucDejgwyDGcwasQe9DFIx6RGFNremHKALMyckoP8\nZj3ADvnIyXt2OqEX4VYr9/Mdb+oT5zRza8rz5TnWK097Y92ejh3eMBGGNpb5goyKS4SM923jsXWQ\n/QDDwKoha2t2Yi4ZOSunt14jVZopc4l9rHbYt0YfhialWyXPE2Os3OWFJoNJMy7G3p02GjlF6ucp\nTWiHt7hwgLHHObdIOUjRkQmKZUQHYwiaE60H5r4TFgbxmS5G7spUQrVh0umHrNckkTQQ4+qZPJ2o\n6Td7FvkrVTDRIZ0yb5oxpx58jJRwh1ngYTScxDkD7ugURv7ZK5tPeDeKb+xHQGPGuShMYiSc4sJ3\nFucbhGrOegTL5WS8l5RkwqNnkiu6wDQG300d7cqjNrIkTjmRNWH/L3tv0jRLcp3pPceniMjpG+5Q\nVQCqZMLnxQAAIABJREFUAI5NtmgmmaS1ljL9Lv0ZLbTWorWQzGS9aZnYTVOr2RxAEkABNdx7vzGH\niHD340cLTzStF60N20DBjLG5qzt9menp7ud9n6cpIXQ3ykaNUSqC8lkSNlvHqVaWBrthwCHMrZKX\nyN+a8FQii3iSObw6sofvi2NTCzOO6oxdBSIonj/NFZHGasIgcO89hwDbKmxjIcjC5ITJlO0uoxrw\nTjiL41LAtMMB3qfE6IxjLnx9cdyMkdEJ3+EY48CcK4+r4U157xqfTpXvl4Gn4iAIN86xDZWva0Qy\neDxBFtBGcL3iemqdgrJJiVwXNARivbL7W2aInhpG1CvOOobWdAE1Ptt6PmsVQ/mZVVbvsAIJY24z\nf5ACr814WU/8SWzMk5Ax3sTCuBSKi7wiLCr8+3XhR9vEr1bHh2p8bQuLRhQlihFYGcOBua6IF16q\n0qxyqoFq/YZ/LpWosHOwCUbLHjPh3zrhqMIe5TY53sZISv0G6p04nDO2thCcZ7KVWQE38Zqh2USw\nhpLJqychvLiZXfGsztj7wMYZLyKcmlF8IBC4ZWbjA29c35BVhNUGjs5YW+IosFg/JLfW4yLOugOh\nuAlVYSsR5xpn10u3iuMbMT6oYxCPD72z92jTP+Yq8A962qrI4PFrpgqd7OU9Xhq1QZBCaa5fDiKY\n69NNj/bCs0KwyuKlX7o4w1e6tuCKBx6GdMWrGjiPSc/5j8OGUrR/KRi0bd+0SOvS1nbtK/qYOhBF\nlTb0SEZsEMRjdeHm7pbdD95wyQvLOXP47C3OOebTkXxWfv6rF9bXJ9QE8bF3Upzj2+9f+O7jK+b6\nIco1j8Q9fhz4i7/5ZY/iSCRNE2G3Y38YiObY7zo4YOMTkze++J3EY4lsg/DUerylXlYuzwt/9JN7\nonM8vRz55hcfufv8Demw4ZKfiO92HL9+oBzPKH0K/s1f/A0fQ2ItBeccIQ6kjeP07NDc+w319QKt\n9njSUrkOtxmmiXw8IsNAppOspBl+HHDjAL+OwlnrkZFq7N/eE3wviD9+9wnxRpt7dKnmM7effcbl\neKZcFj5//5aldsLh3jXiqhA855opc+EXf/03vP3JD3n4dCSfXvj56bmDVLTHlAKVNOwoZel9p3K6\nIoNPqEUQwddCabX7u1yApaHnzC+ffkYz469MSePE/u0b4qbfQr/bd7myWzK7NxuWS2G9rKRhRC8Z\nrY2TX1jmlWXNRBdpdUGIrNbYHraoz8xLZV3WfnHlPdMYGGNi2npMPSJdeF60IuIpJZPXjnxfX480\ngVYjpIZo47gstGlHcyAzBJP+/s+F07ziYgAp6GuDy/r/+Tn9//vTrPvU/KKdvip9HXEo1QQs08xz\ndaeCT2RVgvV1xNRw0n+v/BqXrR3VHTGqODZD6tPp2lDf/yAzIcaAaqOYEei9JLHeH5PmrkqQHtsM\nrpPzbOzriOcaLUYZp4Eh7lnzTNHGNE79Na8rdRWeXy7UutBMOjhGepfmeJo5nq8OOEDM4WRCvPDh\n+QXErjALj09dCxBMiGPAS2N0kdELm+22d3TFMWvhvHRAyloqn91smbzjcbnw8HhmsxlJBF51gejR\nS73qQyD5wMvxhcvZM9fu/AkxEBJYEbR2qIZKBfSqbWkdKmCQYqLpBSPQusGVav39ai5wzVZi0msd\nZjCNIzH0WNrpMneAVwe+oW1hO+4oWiklc7fZdSefNTbJYdnAB4qutKp8mj+x2+85LZlSVj6sHzrV\nuYLR3yeTm6iaGdRR3dqBVqI08/3Ap6VPoVzor3IFCjyur6g2PI2YBsZhwqceMd9uJpqAc5UUPc5L\nJ2+6QF0q4FikoNbIooTiUc14iaylEYeIWWUplVL0SkT2BA9JIikKFjsAqKpD6wrmUWlooTueWn8N\ntTjMZzr3udJqxHwHBfnr2LSVypx7NBzfu7Fa/6nD9J98jgibJvipIW3o4jB6wXpP/0B7K2TXDyqt\n9VuTySrJXajWN5dgRJf74SIkPhsyyfecakXAPF6VjTcCBs1zriuHFNlJRV3iUgPihVU8S1j5Pbdl\nDHOnq1kijI43BByVH41QnXU6ncFZwJxj5wYyCqJsQ6AmY7HuDvB0qeV6jbAGjCwjUZVRwUVj0HZ1\nwCSmsLIgDBjhWp57jsqohoSVORzIbuGDDgzV8KFy64xUYPawj4GP3vNwNpoNfK+VcxnIojxn5VfW\nb86lwsYZJ1WmMHB0ymCRasKDRr5dBWue3CrJddlvcoG59A/lGAKyzmyCUdSYy9o7O2I4Kdy4gUt+\nJbcIoREVBu8grjzXA9k52lr43a3xeHYEv3ITlF+2xJ+XxmCVt4Pw0xnWsLJpkWMLOB+4u1RO3vjR\noPj9yM/PnkUz78QQEwZZcU2ZRamypckL74dELgveGYcwsveN17pwLo0aUh9de+MP08BDdfzZ5UI2\nz4Lj0uD7NvJBZ363Rcz64fcQKlsHO38hkVCpXKzyMSde3ZmXHIkyoU6ZvTG0wDxAwPHcHEcqkcZW\nInvXSPHELY4ng0bgqAOvzvNsQpbEkymmjowiLuJCR852B126ggSEv1fn+b/3G5iwOHq2+NrTuLjf\nqmXjP3r0SrfTAMF6L7DbyQsi6WoW750erm6UIGDaf50bNPVEyQQiSylIHInB4aInNeuYVVOsKBLs\n2nPytPMrbG4w017yrhXnAs07il/Y7N4TZKWsCk1IdweCC1Az79/c0dKv5dLK0kDVuHv/nqUp1jKH\nd7dc5ESbZ/KVlBZCQK+SXLzDDwnTSnA9e14WwdbGMO2Ju0QpjXD98gzJs5wKfoEUG243snj48Nip\nlt4qb24TvvRy/+ef3fBtaXz/9QMmjvMlw7lPqc+vC3/7V79AjwuYIRLIlyNhs6fklegCFSOXxtN3\nn/pNeVXw3bkRpcudHRGfHHaZO8q9gZ5mxAfM9Rhj2m3ID0+AR4JAgzgkaisseSH6xHI8c//lO+aP\nM5mV7TRwXi48fvyue8vu7vjwq29pYwXdcPYO72CrymVZePdmx/i7X/D1158oueDDiAwJK0t312hD\n/C1rOxOHAZUVHNwf7jjsRp6fXlhPMzZMUFeQyu998QMeZuXDt78kWCHTy+3VPOs3XzMdbmm5MR+2\nbPeJu20EVXafTaxr4Pkp8zIbeXAcPz2w2R6o2VCXGcYB1UZMieU8s86Ci4ExDmx2ERkd99uB15NS\nm5KLMZdMziumwno6Yc2R6f4cF1x/X5VMHDcM0TNZ7ygEM1QCIV55DCZUW0Fbx/L7QOO3e8KkreEw\nahSCRUrtE3+TirihJyqK4pLj1+tIdNeLFSqLODyBKJ2GVmhIjAyxd2w7OQ5cbZizrhsBnAiaF0jj\n1UHU0xfi3NXXV9mMN3hf/sOm1IfYAVY0bsctldJjwAK5FUwc2ziwmiEUxiFRxGhl6WoQcQTX+4/9\n2lOurq7+neS8UVs/aESfwP0aUtBhEXjI59Kpw07xsUc3H15Wonokwt0m0fdscLuZeMmZr08LhrBk\n7e40q+S1kvUVy9rPoThaWwhxpNZGtH75WiqUy4KYuwp7pU8A8depUCB4oPawGCLdOUafpPSfbcTq\ngrUeW6R1tLhRKaq01gEV+8NIPjVqbCQv5KIc1xOGMQyJl/lEkxWxkVz7zyWdC8Uqu81Eujvw+Dp3\nPYCPvfMqXeeiTfG2Y/YrKYxUXRDv2PkdY3QsOVNKR/wXU5pvvJ/uuBTl8fRAoFIQ1iZE82R9YrI9\nrRq1nokpMCTPQCNuR2pVcjbmubJaZV21v6ZmqNRORcYIQdCsXKid8OwCQwqQhF2InGu/NCqLUUR7\nBLF5VFesOYplgkQQhzXtEVHXYRGOK7LcrIsx3N/vRSq9htNaP0y1+k8Tpv/ko+JpzghtAFf6zRgd\nynP2nkE60tKjeFU09Mx/JDC1CoPnVBr7q9zxRjxD6CLSZ4MiDvH99o6y4oLnTRXej6CaMIRJNlxq\nIfvIubV+U4TnQRpf+R0/2fY3ylPzlNCYqpAdnFpksIXbNLDNK1UKuV7wbeQuCZem3AXPVBdiSDxr\nI7cLg0TWalcJ2aZ7G670qkPojpVNU6JF8P1wYRRMG7/vlPskBL/jqWTeSqAy8yzwPAceQ6DSGMLA\nT2fl09oFizejR4PxqBXXOnGlrZVqwkZgjJ7t2POrVjw5GlrgIfRoglVlkgC+/+xFhDjEK2GmIVNC\nfaCJkaRTwGiG+IHnUrHYO2eLQKD3R2DLrc9ED8kZpWa+Go1bTycErStrU975yrF4frSH3/ULFgoP\nRUg1sz8YNOWDbniaV/5wCsTpzKkqNx68D0TtN09rzQQfeSgL3/ouDSza+NAMbdqFyNYFscE5/s2s\nvDYjhI6Z36uRvfBI5qXAX9XKbRJuzFgWo0RjapFd9GhLLNIgCp7AnQiXujLXhpHAd9BAkMBd6Ibt\n4B2jVBaMTzryravkFpmL4xgiVhtb6z2uQROvrkcXtC2IhE7F8Y4CV0N5d12JyRWHUemuDSOEgBdH\nqPU6iZF/zGXgH/SIdDSqDxHRfhuJxR4T8J3yI22lWkRqufo1PME7clNiDLSqqEXUgfNjl0a7bpBX\nEYKLpO2WcrpA8iQ8u/c35OOCj55pHHl9fCWKoPNCrbUfCPKR2x99wf2bA4ZxPHXqWmCDmxLn4wyt\n8P5+B+eVc/VcHj/g08APvrjhOC/86PM9H79ZscOOy6XAekZ8/A8bPLkWerWu2Gxs3r3BWiOpdXqa\nNMbtSK0VXTI/fDfx/jAxTpGPT0febw8Eq/ziOfPxpfKcIa+Z3W7Dz/76gdPzK7VkDp+/w0fH5cOn\nXuydBvQ0g3UU9XY7Eje3IMbltcek/VUx0EdI3fnS1QodK+x9oqlSq+GmAUkJT0B0gTiiecWnwPz4\nih96LNusUWslTY1pfwsYISVccNRcubmbuNneEAfHz79W9AybaeAyz/z+H/2Q//qHeyw6/vrjwric\n+OrLOxKNf/uh8uHDzD/7nbe0knl9ObLdGne3P2E9rhSrHJ+f2N58xcPHVz4+rSAwzxdOpyNO+xqi\nLTNaQ2Xgp7/4GfWKoFaJpNZoXrrY14T56RmfEsenyvlT5XvXO7hv376jqFBrIWwGvBnT7YH1uJLn\nmeGw72RkqwiwPWypNDbJ410gq6KL8nenV6jGculGc80F74W8aj/gyoI01ydiOIZxwqeBKA0xo2AE\nF2iW8FYo9MOTmBHSAC4xVkVdQdM/7jrwD30E6aJ255HWOra6SY+bS8c+EVdqE67mGoR+8MhiDM5T\nm3Y8lIcQUsd8u37Lr9difUgDKqVDEnCMm3QFE0FyiSWvhAStVKoaUYSmK9OwYXczYmbMa+7dJHE0\nZ6wZRJTdbiKtSpFCKTPRAvvdxFKU/XbEnytnRnJRXOsRsGqGx3BtACedall7zI8mBOm+nEojSEAx\n0Mbt/cSbaSQFz8Oy8H7cMdvK8/nCeVbOi2JaGYbE149H1rnHbqfN0A+U64pIIwSPNaWb5YQYPDEe\nMGkUGmoOX7RfRKtBax2843qMS0SIvhPfFIePscNbmhFTRelCbB8CdV2vbsr+fdfor62QrrRAxyiR\nUgqbTWIaIubg+XQhrkYKidUqb+4P/M5hR3WOj5cZ3yo3+w3WlMdz4fVceXuzRVriUhbGlBh8Qmhk\nreRFuU9bzpeZeQ2dPqlKbhVa6esIytQa6iIfz5+oFXxwGInBoDmjtYZmuJQTIXUVy5pXThgEx5Qr\n2rq/zQdHMPAS0NIdYuJCf9+TcRaJccCcEp0j+l+vI42zzQjGWugRxmZ418Ee0jr1DoSVzNDCdX/n\nidYdUwUj4Gkt4qkUgyB9HfE+gEViVVpq5N+shum368B0Gz1p2tOZu4lsKyKwL6e+6WuGcw4vgTep\nMGglieeihftgjLGRRyWXgWTCt2hH7TrYm+dBE4RCnDM3MeDNIBlL84SYeJXCateNFZ43zvO5Nk4k\nQlrJYcOnGGjayL7wfr6QYkCd4tfA5IRvq+P71fOqhZum/Lf3SqPyty++Y6ynPQeb+cpXojkmt/IY\neq5zMx6ZS5fsZklsxTM5x9WoxDuXmaLn8dzLipcKi2UeC7RT4vu9cCGSSuAuCq8aWBK85pHiM2PM\nmOy4ULpboVUaypgDmwkSijGgTXikY0szgVUKJTZST6wAfWyqUglNrh+ylTgMiDgmE1ZpNNfYmEMN\ncJ2v7/BYcMTgGDTzgwS4iqNxHwqvOEZgr5UxKGtVPmbP+wR3oZFiYZDMJvZx8a9ePd/XyJ/XgZfL\nwJtQ+EEwbnxhZSVq41QmHgWe5l7CH/G40jh6GOpKCMLGenzK+S5zrc7xql3Saa334TYxUVtFRMim\nKI7JOgkmOMdR+y3fPgnvsE7pAVKIiCnPpct/xcPoG3sXWUS430ycq6eY8uSM5DyzOrbOcZLIwYzP\nQ+ESIqq9E3ND5aFEHmplQ+OQGzV0v1fGkLXSzJhpPZJmxuJ7jDLGiAQhhIi5jipvVcn06cyr/Wdx\nqPyjPMN+g9zuyXPBnOJO3aRuqnCN1eFDz/177VAZP9J0ZfTSs7+uG8q9Dyx17gjlEKFKN9Nbox2P\npM0W7x2Njm2e7vdclszcKvFmwvBMn99AUbJ5hMJ0u6WGiKoSdyMTjhADKkpaHIPf8fE08/SzR9Z1\nJqXIP//JOyqNn/71N0hMbN7fkFJjN0VC23F7GPn28ZXkI/tD4vV1YZkNt49so+ewm9BirKVyewjc\n3I98890KVJ6eFuZ55XltrB9mPv4ocywF3zyH3YZjXVAHjy+ZFiJhP7KZ3lBrJu12lOMrtVRicUzv\n3yLWcHHAmrGc5u4eky6nbL5BNcQ78Ha9EOu356451DLDfot4j5P++W6uEsKAGYRxZNxu0W3fWPno\nmM8rb94m/Dbi8dztEy+XSoqBfVsYJ886Z77+/pW3NxP3X75lSso2CT++mVhV+Vc/febT48Lj8wv2\nlx8IwbO9v+XdUJjPmbZk5uKYXx2/+O4bVoTkA5oD9vQ9rWbcGImSsGVBPAQL+ODIWmnbPd6EqkLY\n/po06LCaMXoZ36QBHm1GEyWlxGG/RRVKg2m/4fR6Zj5lyjojMTDst4TDiLXG7ed3rKdK1cpa+wb8\n9ZQZh4G5NXZp4rP7kWwOLYqJMUXH5dx4eH3FFcOdQa9JDLOu6KjzTG4LLkW0KlV6p2PaHZAhgh+6\nj+W6htS2dkrX6bc7khdjIgy9w4xT/FIRfyVL0tUc5q8RJeHq8YpUzWyC7/HEGvrESRxzKx2X7TrB\n1Zpeb9EzMfQIvZrvHrYQ+2toGQmCM8+47VOAqkAwvBuoRNQa3sMoEKLvoIgmRDdyvCwsp5WsmSCO\nLz7bo814eH5BnO+dSefYjx7HxBSE11LwTRg3gXlVijpEYAqp73X0Ss+Ljs048vpSAGVdCt/klaUY\nloXTpnBp67XsP3LOK8257p3D45MQGClX15K1Ti9NKsg04IZOAqU11lx7DNB6jNlcg9bJuD0qGTDr\n0+q+jhRiCCAed61PiLuKV68x0oDHj79Wi/Q0zC72S2CPYxojS67E4EkyEKMn58zrKbOdBraHPclX\nhrTjbtpQmvLzj0fO55XTcqZ990DwnnEa2IwB1QraKDVQtfKwXqhOiBIwbdg5o9IhPoNNSFOaF7wM\nBC+sLaNpwFuPIIbo+vsHodEPKF7o0Bv363Wkkbxj8JFmgjYYvGehUXLHi4sTvHOElDAzpnHXD1Cm\nVCmIE5ZaukHMIErkZuOvlN8uHE7esZyVY1xwBaiR5o1I9wba9RLaZO0RXozF+l7E+QQi//E6otq7\nfxXc/Jvdi/xWHZjYbhm2e8wvuKZIXRGrjDlwG4xTc6wo2grr1XGyCYUfpO7xOBs81YgA70bj9/3E\nkzWiwSCBw6QsalgYuDTFhkRwIydnTLlPIKoZLo3EIcGvkdRqLJLYqHCpZyRXLHpUjFO7sGTjZXF8\nCB7fujRuEMezi/yvT47qhFIvHLzntirfzAO4ha9G4zPf+KxVNvv+d99H7fQU31iK4WJi48EF4/vL\nyI0r7A+N+VJYw8hZE8E8MjU+XAxCYHAB05mdE3xpRFdppbIOiWaV59rwMnCTlNkiM4lzVV586xGl\n67//MDher/GNqTkG32+xNQSCKCJCkUx0sPFdpppcz2f7rIw0UnCs2tgM/RA0ezC6MTsl+MIV7nYD\nH4/G3lbeSGU3BL6/NL5bBw4CfzgdGYPjfqo8tIlfLYF//Z0S4i3ZgS8vvPWxHwA18tAqqx+w7JlN\nOVYlL4Hkha1XbF0ZXODWHPhIUTrcIQmTB62RrTMOJn3gLY6SPK9toYjrZm8nvJqx1t6DQYwR4Z0Z\noxccZ1zY4mi4NjOlAeeF1XtOYcPiITZI1v0c21AoYmzjlmrGYfAMEki+T4q+dXRqjArHZeGjj5yL\nMhXHpCfAUVYhDJBXzwsdk2rOodIQ59hi3KC8zJUTDfwZ7zzRObZpQMxYWu3xk9/SZ7zdMH5xy/Fh\ngVKYl4pp69OINOF1pWjBSuk3vwJCwW8DHqgZNGq33MfEux9+1V00r6+kuy0xeIpziDguj0+MdwfS\nFLCiXZbsU/d47CbGbeqODbOeay8JwfH09EqdF0KKpMOG88sL87Hw8t0n0rs7xISihogn58a/+de/\n6IXjvOBp+HnDy4dnhMbuzQG/Gm/ud/zgB1tKaXz2ZuD4vHC4Gfl0zOx3wmGT8GHgV4/KbfQcvtpw\nfD6TY+LxeelTsy8jHx5OnTK3EV4vJ3b7PefzwnYTiETY3+NQnl8MPzgO9wPL3KjmyKcZPzjq6xPm\nAmkzsD9smY+F+umC+ESULn6SdBWtApoVEtzcf9YP8N5TDOrxRBgnhimyzIXdzZbtFLmcL7jBA8L2\n9o7PvfL5V+/48HBh1IX7N8I/Owj/598pXz803qTIf/PH9wzB8V/cOH5RA//uY+V//1/+b+LunmyK\nnB9xcY+2ilbh+dOFeQqIZXKuoDOtdqeRhUCdT8TthuQTRRpVISZh98UbpuhQFYLrm7zzaWXYjWiF\nx+MroXpadKjbQFuhdh9Os36r7uOGzW4gn57YvP8hli/oemH75sDUVmrbg0Sqq/jrV7yoMU5Cbond\nPvWo1cHjQ2BSY8I40TDLxOZ4fDpz9J4ln6FGai40rdSlEGIgFwVr/ULACU0zTgQJE9MYuJxmyutT\nl647zzgMpN0GdVCXmfVy+sdcBv7BT5giwybB0hDzZN87Kt57JCbwPZGBGsUpsYdySduRQENXo0jp\n/Z8UuIkHlrXhRPFDQFyklIZzntIKPsRrbUCuN/aJhhJcIA5d/ioYdp2qEIzLPNOsTwNc9KzLTM5G\nriu4jqCuCM4iasbX3z4jQK5KSJDWylIzhjKMI5ISm03iduyk1d3QqPSY20mVIQi7NOEDfHiduYmJ\nw/vEy/mCiWPOBWcgU+PlcsIRIDZOlwtDGsm1Eq57mcF30Msl9xhcnDxVG9VCd1c1A5sx6VO+OEXq\n0mit962i2FWv0OFaTjxWBIt2BSP1aZ+KwxVFfJcPF20MPpGC9AmO9GhzmgL3Q+Dzmxt++XphcJVd\nDNztdnzz6YnnU2MTBn70+Y4xCD++m/hwUb5+PPGXP/87goxkB05PeL/pQDKEy6zdh0T3IrWae6Rb\nPOKFuWaS9yTnEe+p2uOZPgVG71ClOzVtQ1UQL1jqflLJ3ZXW2tAVAWJdkWN9Cugl9KimZULYAEql\nEIdICK77H6Vf+AfrrABpjegNMc+4HaBWXAy4mGilT5JWDNNCzI6jZmag2NoPSq0foK203p+m9JSW\nA3Udou/NoRLwAfJSKH4llFOPAosjjgO5gtVy/fN+g5/73+jf9g98djZzX55Q7RQwF6C5wBqFWmak\nGfcESA3ViDWlaGIOlSCNlAI/tv6hEwscpRvSBc9SFKuNhnAjhTcBgvRxdTBjiX1MGKJniI7VG26a\nmLXiZsXNC0ct7Fpj8o7jWvj2SkzxLTNuBpCIriv3YYN6x9SUV2qHyMnAQ/P8shpfjI0flsCGyqub\n+NulcSfwNjTOqnwxemat1ClCNX6ZYbsAZL6ZheYiCTBpHG3kQCMNyo13qMt9QxgGBoy9KNPQkMWx\n2EoVx0+GwIOduBPhmQ2f1kyYPKHY9QZRufOeZylszJicp0o/CKbosAbDEHBAVCVIIyRHriuj9PHx\nbnDcuszaB+OM3hh85FIds/Z4wEzh3BIv3z3ivHI7VqY4om3ljw8Lu7Aw43jOcKqeP/0+cBbhQZVF\nRp7LwiSBW7/BxcghK5+AD0SGphR6qZmWaL4RWuNRIkMQ3lC5DZ5zc1ykcZCOKO83eI0hOEZrRB/Y\niBEM9q1xyR4L0LQQdGBxrstxzXBSOURP1kJzA1G686QRWFVJrvsL7usMITHGnn0/V6PESHCNoV54\n4xxLa2QZubUIFU7lzFq7LXwrjikrF22MVvm2eY4uUcSzKjSUvSlSK+dqOO8Qb7zxnuiUEz3eWs0R\n5tIPvpcL47WDEM6/vbfD3oFeGo6C3wwE7kCMmjPl9YVGJfmBErqCiJwJdGKVOcHvBw5DJGuP9y3H\nGUnCcHNgPp57+CYI437D+/e3THfbXvYeRtayMkXPZtgzbQMqjmGA07HhnHIqhYdvP+HFM46R9TTz\ndz/7lmEa0Hxme39HmAbKaeb2szeodO9cuXaU8qlS5zPL0wvDYc/WBzaTY42Jb37xyFM23t+MPJ9n\n/uCre17nxvB24PWc+dnPXpjE0axyfDkjuy1+zvgdVAe7EJhuE/shUmql1MawHfES+PztyO6QkGPg\ntK40l/jBu1s+Hc/cbgbO2fHh4YHd53taBXUBTDnc7TnOK1Ir42HX7fJ+0zsUquzf7PBOQBUXAtN2\nw/F0ZDsNaFH27zfcjbCoIg1228QhRV7bjsePLwwhcKmV1+r51f/xF+CMf/6HN3w2bfg4F/6HP9ix\nDyPntvJhhe/Wxv/0Lx+ZnfH0+IrGLfPrEz5EXLohbQb8aWWRitPMeuobT9NesnfSkFZo2smFbT07\njer4AAAgAElEQVShmx1VB5wslJw5nwIlBZxAPEwMZuy+uOFmDEQXmB6EJYMbPGVZeXoJuFi7t6oC\n3vPm3Q2Xp1fCdo8PjTRM1NLIpzPpsKXlFRcdh+2mX44JHE8LbugRdEpjv4vkXFGFbewUq9PLmXIp\nRO/x0RPE01rAPJxzRpthPmG59B4erq+HOffImYMhjqANb71f3DBsUWqemc9z7zE1RZbf3ksXAN8L\nz3jpyO2NjIChqjRdURqDi1R33XM37d2Uar3fMwW2xH45KL1X46LhJVC1Ibl3WLz3jDERY+yXs81R\nrZKckGQijQE1T0qw5IbLlaWtzKfzdUMcyXXhaVZCcH1iFUe8i5SS2aaEDl2fUkv3iYWY0FoouRBT\nZJCJEEEbvBwvXMbKfkwsZeWzmwOnSyUkx6LK0+MT0fUkxS9LJTiHcKWv1caYAmno8TWtV3pcTETv\nGUfHZghYHZm1IGq82W95mWe2w8RS4XU+EafQp0lZgcZmGphrRXwjhdj7WdrwPmBipJi63fh6MZhC\nYtGVQfoUJm337JKQteFEGENkkyKXrJyXmeA9F1Xm0vjzn32D+MZwM3GYdpSy8ic/fsvbaWQule9P\nC69z5l/+u+/I1pjLilpkzgs+OrzbkoaIu8BCw9XCqtKx8NpozuHNkFaofbZMo6F+pFYDK6i53rWy\nnlIx30Fjg+vx+SCRmAPV96GBamWpDmqnJ3ZHnGM7JLJmRAJOGtE7ivUulw+hd118Y/IDKTiaCUsp\nXaeAIVWZrt8HpoXg+57vcrnQrIONcN33RvFYaKxro7UuxTVV7CrzbmaQleZ60iKmruNwXhl6PQ6r\nneg5nyuCUZvS6j9NmP6TT3SR/ZAYUsDJQlBP1orPQm2e7CpeFLVe0iuA+ICnl7utOebgGDEOVvjh\nEKk4qsB2IyCGtoHa+gYksCJOSW3tBBV6nnPNns9j5K5ciOZZfeN58Fy0sSwetTOfC5yjIxDww47T\nUinMDOLw4chDjmQvhCKECCkoB9corYA55v3Amh0nHbGgfOM831UILvL9JdO8sLk0mPrCWlPA1e4I\nn8QgQgqeL8eZwTzNhDTAIMYQJ2YLLGpEbVfRY2OjjbhtONfYVodW5XJ+5o8jhBBwQ6exvFZhzsY2\nRCYHTio3FHaTJzdPaCsbpyTpcTQJCS0XLCjaHH4ojBqpFqgt0fLKI5VhnvnxofsG7reGuJ7ddx7w\nkf/rm8Rz6V2ary971uY5t8BiwsFXigdRYWeCOuXAQKsnios8zgrOk1wgUqgmJNeFxg3FWu1Rn1o7\nhlQiD83IznAtMkfPSY2tCLe+I8If2sBatONIXSA5OFnhjUTGVPmyVeYirM1YvZHxnFU4K2xaYo4B\nbcogHXpRrKE+Qoi/NhZ0gR2V3IQmkQcLuKL4kMgKwXfK1b0NJLdQhY64d/C2KX/mBmY/cCkNkQVp\njVE6yrZKJPruLhsKLKVxqQ2Txt0oXEoXBDXxVFHeeriTwiH+9kbyUhrY3G+5Dz1+4mms54WmlaMK\nuc49oqh0j0oU0u0NuqwdliSemgbGMZDMONwc8GOg1spm+56mRtVO5wRjjMZkDr+uyORoPlCWM88n\n4QefbfkyGMOt8KE4HobEcXfD8dOF9fjM3WGHv9kQ08Cw/4zn7z5xPj2SUsJkZX094vcH8nlmGnu0\ng+0tdblgJoS7Da+vM9aMdLtnqcpPvzkStwN/+tOn/v+sRthEwjTCdkBL7+WlIKQ3Nwy7yK1kbkKi\nGribRHDGex/5KB6tvbNIrcg2MQZ4fx9wOL6/3VNL4+Evv+f37zdM+6lHkXLm0+OF43HlcLMl+4hp\nYVT44ic71hWiV3aDkDw4C7wZEx/nSttE1ODtxtiao5jnr14cWlZ+9enIry6F//6Pbok/3PN+6/r0\nXBxf/Xe/RyLwP/5vX/OwLszPC3+WjYyxFiGXxjh0sadVYTPtqCyMIVIvRwhKfX5CQuzbmNDBHCJ9\n+iOt3+5rjIRaMecxF9A107VaHkmenGdym9jfbRCEl7mRPz3xuNvgnPTN2dMjb3/3h6RtZNwMLBdl\nySt5zZgZr88v5GUlWsIG5SyNIUaq98zHM3KNar+eC9PYuzY19wmIBMcxrzw+K+M4MK+VECGOgTs/\noofYfwYYk/fYtOOX3zzjpkh+PINbya313l+r+NBABGu9J6enM3MuNCcMU8LmgkgjW8K0MGx2jDRM\n9/z22tzAucg4Dng3dNE0jZoVQ8mLQ31GxOFMEIMWrv0Lgdb61ILQRcfeeXbDgFmnzqXBQ220a4QP\nrEfBjR4RDh4xT2mVeS7s9yO3YyINnrV5nmdjXWHJRm0LQ4x4utMvhok5rxRdceJ7l6esKL1/E5wn\nOiAOVOt7ERev0w+64L1o4+M540LgZw9HQAgXkOu/q7nre0h7XSDFRBwc4w621vHdY5zwHt4MG46q\nLEun31pTXIhsMuzebQji2F48tSjHlxPv9xumIeCcBy08LytlaV2aLY4O9Aoc9onuk62MY7r6qmDw\n/XBleNQc02AMdAXB47Ky5szjcuJB4E9+cE+8u+MP3u9p1vHi3sNI4H/+s5/x7elEK8rXDyeqKrUK\ntbWOFb8SDZMMtFiYQqTUBaiUOV97VYL9mugsAiHgrSP8m+/aCQmehqfWSjfjBJqnT9kkMaQOEluy\nUmsBCj6s/XXKC5vthsF5Ugqsaxcd19aAxqrl6snqsb+1NaLrmoZS+0EqqnQI1iI9Mq0NNTAnLGvm\nuMxECWTLHRtvjp3v4IxGlxZP4tCQeDwtPXKnDXELpXVadUM62CEIpkZzBjlTrF0piNfYJNZVGlRC\nSAQPdfzNliF/qw5MaTMxTCOWV8YaOKJUjbRwJvleeAR6cU8bTiOLcyRRJhe4a4aGTAihozwB5zKj\nVZxumKxRx4ITwYv2w4Qzqm1RzQgFdTCZUevKd2uiKsziyGqoNm6lchcC6mc2BRYVllox8WwlkEsF\nGl85JQRQLzTxSHPsfeF2Ghh95VP1PDoPbWUYPJ/FzKLCzgrEQiDgdkLyjVYhxn6TsJYNsxpTuuDV\nE4c31JYpS8aqEQUyC05h7x0tSR+qyxU52la2G89hPiHR82XMaIPgwDVDk+dHoXE8G6fi2U6JjZ0I\nwxZzmVYMca2bmLdAES618qkGLvPAJmbanFEccxWiu7BLwrNNvITM/3NxfCkT/35dWOodr+tMscTc\nGkrPb88tYdJ9Dkh31DQ8uxRoKM5Dco7Pfea7PDCJsL+88m6XAM+pjVyckcSxsYVsynEdeaKxxgBa\nmHzAXGUQ2BA4B0/CMJn4pWXeAhaESY0zvfR84zP7UVjKhdfmidpoQ/dp5UV4DcJIJwi1EJEraW2O\n8DYoUUJfzJ3rmwnnkOApYcAKnBuYOGrasfrKEBzvmRnV8eoaX5eJSy6cFvCmfBG0C2nXwjYIKo2h\nNeK4wdoCNPYycwg7zAq5OS6ToxUlAz4Gmi6M1qe4jsSX7UJxy2/+w/+f6dntRqZxYlZlQJlzo2So\n6pBRGBmBblNfl4xoQmslbibiOHAIE1kym5uJ2lutLOczG++INjD5Qr71/SbRCq4Y4gw/bLgcc5dU\n3kVyVi6nI3+5JF4fZlbnyEuhnS/sp8RXv/c5lUrysBRYjs+YNHaHPfn1TJPG7f0t42FD3vY4nRhs\nk/Hl7/0IVxofFnh8vvD6+MLu/pY3b0bW2bgbjOYdKQa8QIiCt9YjH0ulxYmHl8L9rhJCJF7FuOds\nxKxMXvggDVPlfYA6Ga8kvAhh45mkso+VN03xKfBf/ZdvepTXgbSKbiK82fE3p4XXxfiDL7cEB4lE\no3RClUuE5hj2K20dmXPhl6Y8f2zcvhW+ezVirJwuheV15sc/HPjutOFJF/7F3575nc8C/+KbZ46v\nwsPHB5DAep7R/5e6N+m1LVvTs55vVLNYa+3i7DgRN26Rcck0TpNumaSRSBiRDQSW6CAa+B9Y4g9Q\niA4tQEgIibKHhNsIhISlBCE6FlJaxmBZwk4nzrxF3BsRp9rFKuaco/g+GmNFcIVJG/ImN8nVi9hr\nxzmxizHHGN/7Po8pcZoo64r6jgBPdzfo6YTpzOHTW/LTQho9tzcT+8nx4XHGecf2ky/53mevcSGw\nMXNcz+zuJmRZyKVweYGX8xGJQ4+Lx4RYJeEZbg9XgIPhY+B8esbkQDgMxOA5nk6k0XPzcM88f4vL\nu3eoOUIIWBSiweXUEKm43Q7ZMunmACUj80BpxqvXB4ILnMuR5LvMlBBIQbEwsy6Zcy6YExj2LCoM\ntyMHLyTpNLLnc2F5eeH8eAIR9vsZJ57z0wvEhGnC0Rhvd5S1IE5B4OHVR5y2QoqBsmXKWmi1EMaB\nmnPvggwTKVxF4n9ylxAApjgyx5HVKoMpy2bd1ZY7iMN/7YfRHrkVDV12rY6UIpMEqq/EENHrBa36\nfljHAmMoaAQvA+LofQ/Xhdp5VbBOnzVrbHnhq1KopVFanzhbqQwx8ur2lqyFdhGqQa4ZEyW5RC0Z\ndY5xnLvouPPvEIWUPA93rxic8H5tnC4ra14JYeb+ECgZhiQ4hRC7ZmNOjlwLu2FgbY21BNYlM06K\nl8DdEFir8rJuqBZ2KfJBMtaM/RBR56mt4kVw0w5B+NbBcTftCHg+e5ipTUneI2o02fH9oHz5dOJy\nLtzub/Cu8fF8g0nhvG744AlOGCejqWO5NI4fFs7nxjwJj6syDMaaC0bj7jDysgTOl43/9ccf+PTu\ngb/x099DivC8LJ1eWHv/V2JEm3YqoHI9ICubRYY5okX71FUHpkE4LoaIYGXj7rDvyY3iyFIJ3hPo\n8by8OVbLnarcCj76PqVrXWZdm/ZIpfdspXvccI7RJTZpiHT/Y9rN5LyySafgEsE16Xs0jNp/dXHO\ndw2G73upcRiI3rHp2uELHX/I4Hovr+RGbv2TLe7YFALCLkKU3mk6Z6WWjZwrZjAG12OHJV+nSx4H\nhDGhRXv0Q4z9OLJJjxArvfdo1t1VrXX1hIjDi8NH38W5v8DXn6gDU0TBR3w0UGH2heYKtc7EUKmu\n0zy89nHsEIXYlIrwXDd+0pS4gXOGSWfTT/TS2yfhgvNGqF9/SZQmhlgDLUQP0rpI0rwyxi4nC6nf\nMuYGTY3HqrzJilqfoMySe2xLN2YqLsLoBQnKqWhHa4vjJgXMCxsF1zKf7Ap3i6EuEANM1phGZWuN\n3MCnTpYqpccgWiskyYSkfOyFqo0mR8QuxGEi7GbasvTbHzNqK8TWKNVoKmzNetk/ep6OhpUJsUp0\nCa8bBME7KKrUVSkasBQ41cKLBfy2EoMSbQAxFjztySGm3PjAt+bMOJ8wF5FuTiLieLN4jjqgtbBH\nUIQ3UjmXic0K0TtuvZHEow6yKasIo+udErPa42zes3cVC+BoBJ844vhMVqofaHHurgSvfJfCzldW\nb+QcWBt8PGSyCWqVDyWyNSW70AvoV9jB6jyPLYNztOB4sMKjXwnFCA7WXEgqjN7hLLAmIxSHc577\noTCbR+hgEnF95K6qpA1OzaEOmkRaFkiJVqGJdoeDh71WRuc554WtKA3lB814rhUsIs7hTRhT5VUV\nziESrfA6GUOITHZhMAgURm+EYFSNZFnJxfFsT/yyBZ6rUZKgYeRJElkaN3klyIUfi+fH5U8uEjg6\nYxh6+VeyQwLENFJzo6UbqjbW5w1pjXG3Q7zDSi/cP/3kHc9twfA9Gy+GeWEYJtJ+5va08NH9juF6\nmG9eMdedH9uykZLHVKApcxKm+wlVx8MhIU4oRcnbnjcfTvze3/uqxxWcJ12dciKVyTV2dyP7KRL3\nIx9eFuqSOeUjH33rAT8Hno+Zva/8o/eBd0PCffoJcfDcAw+3jY3CTxbYT8LLWqlHI90mSqkcXGFp\n8OsfCaKNl7UwTIExBP7UbKyrR12XNz5izNZ4rFC3xlYqaT+w4viwCueLx5zxEI2gjVeAd46qG8sK\nog7xkd85Z1qppHYhReNuHDlp5bIU8nvBycpnNwN/7t4z33uqG3AC3haG+8T/cqs85UReV25HjxL4\n0bsXzguclwvztSs1+Fc0gayVVYXdbkeKUHPh9BSZDxN3Q4BdQID5buTLx5WdN/IwkG9GhjliTfn0\nLvLt4Y43zbGtA6fLwvCdxJZvadp491RYTysaPH7wOIzSoHrh9PY9YYr4ITF5z1M5QqvIJjx/8ZYk\nkWFIeBdpXrACw/0N4p9pzWFm7F7f9Zva4KlLd7A8P58Q6aCB1VbG3UzLmeWq3lBRgvPsdpGXl426\nZZZH5XlbWS8rYLgQETrVdIx96tFEmW5nhnGGtuEK+CkxPRyIKdJD2hs79Tx/OHL7asdxWQlzYBh3\nnI8nSquQM7kWlqPRnv6Ed5ic4b0w4JHmmCaoDZp3WHMoSmnaY5rOIfHqAwLWJbPWF0w8DtenMUEI\nLuCdZ9ZKGxIjRhOFAGoOkUZTSL6jvbG+SU/jgJkjTRHnhDUbpVaOy5mnp2f69hi8OKITVPvzKo4D\nY3BI9KxrptVGQTmkGeeNLVfUO759GzilEWNklzyjT0xRuNTGkmsHh2yZko2bcSTXzOA9MVa+d5jZ\nWmMtFfGOT/Yj3592nJ9bJ7eZcc4FR+3P3Ky0WpiGyDxO/Pgxs60NXD8XoI3DlDq1tGYu50Zr3RH4\ndHnpv3vPZ2KAMcw0yeR168AilPv5wPdf75m+HWjO8NbBMqP3/M6b96ybUFtlTB6TwPvjI7VA0UIM\nnskHYuiusdIqVZUpJAgObX0NHFJkDrFH5uhVkHMu/ZDjhFZin7Sb8dFh5JAGVi1sm3IpG/eHQLYR\n0/ZNx8nEkKHH1BBBnZHzhvNg4hm951wvHRBhnnXbcAR8iAz4K33U4b1DpoI26ROzayQOc2jrP29b\n2cil/4xlKQQ3oK2SxTCrvROGIwXPWlZaadRmfLBKKb331duWghfpE6LrOhKTI7iIkwoa8dETp+4Y\nbDiaVNImrGVlmiKr9kOnY6A4oaFIrTSrlCrk/P/jA5OI/OvAvwj8GWAB/ifgXzWzv/sz7xmAfx/4\nl4EB+C3gXzGzNz/znu8B/xnwzwBH4L8A/jX7WhX8B7yWAud1w1FJulAl8kzkpa1MMrB3jSjKYo2E\ndi+BE8wce3WEAFujI6wJqCpHl8l4nlvCKgzZM1GZnEH1jF4Zk2dvXWxrbBAmojYChruOJ6NzRKd8\nKhCTI1vBrLCoY1PjRlzPMbu+aVBr3E0jyVUi8LY28J7HzXhjI60EInDjKrpFzG3sxohfG0kCKoFo\nylIKpT2h1Xg9Bi4YCrxKyhSVrRaWZcVMuZ0DrRriPcUZ49gopaIM3TquR6pGqghjakjxqGVk6GVK\nNRhku4rfjJ1rTLMjRgheWNtILlDMsdPuP/AhcmpwksiJQG2BJzNCG3HALvabkwlHFgEcoWVuDo0R\nQc0zRYcCrQpOGhIdTQvWBCfCczWOLvFOB5acKQp7lEOoFA6IepysvHETq3qa6xuZWpTFIJrHOsST\nrQY+GYyprTxLpeUbFjItBIpljMhj8XzZhBcaf27n+Er7QefzEth5cBYJptzoSkhGY4BciGKo84zW\nJ0hOIbje/bIr0QiFTL858mGgmaeKXYEVwtO2QG00N5CdsGvKgwOiQ4bErIVXZeUhdtHe2ALPsnJs\nGaGxuoEPJbMyYzVwYys3JrzUgsbERSup9VghtjAWZRc94wif55Ehbnz6c06Y/jjXkZcM5VKw1oj9\neo21KM/vzrjoGYeR4d6zngsu+N7VSI5WjMO37lC7I5/P6FJw49RxvtsZkuOrU+HLt0+Mt7e40qNO\n0jxxFHZ3E/c7R2iGScHvJxJ9Git070b0jjAPfG+A4dM7lm3DVDlnyEvlk49GtHRz+nQzsR4Xvvud\nO4IXohO++HBBQuDLnzzBkPjf33UM+mFQGsLnZeHhk4nyoTKMkRINNljaxvbuRN2Uzz4e+YF6nGZe\n+cj37oSt9UmcmfKt8ep98Z4HZ0yx8cqU4iNiHpNMNuNWhDSC14gpaBLQnlUfAkgLHFzmW6lwMyaC\n75LNtQVK6eqEMAWqNmIIPGnlaMozwpvSeNwqvkVE4bND4lkSr25Cv2HFEbPn408jk9xSm/ErN4Gf\nNtfBDNL4aFCW6nnM3Sl0fj3ymAOnS+VlObGtsGvCfkxcqmcIgRaM56xs1dEukHYjaylctCFNMFVK\nrhyfN777S3eMmnj3tKF14NwyQwpsy4odJi7PZ95fvsB7x6/+6e/y058+IRJ4eXrCT8oY9vi1MMxC\nuhl6G2jLuBRR8Uy7HcvLharCdHA0E7yP1GZI7TfQp6cXwjzgrEcne1G+cnx6gdIwFzq1UDzjdYLq\np4RzQjTj5jAizkhu5sPTe5bnBXEGY+L5q694DAOu72IZh5nzh2fCHNm2TMuZ+/tXuNlz+rDx8OqG\n8TDx9vMX4ii0PPLzrCJ/3HuRcwPNuSPTnaIibNVYloILjuQCIWrvBYkHuVL0rG80zSZa6xJUT8LU\neok9Ki9L4flyIUin47kgUB0+GEMKTDHhxFMpDEPCNUM81800DMGRJBEjjJK41BUzI2corTHFALWL\ny7/2tN3c7ogmpCi8Pa+IOJ7PF5rBl88e7xxTEp7PHq1ndncDbS0MMbKZ4ZrjtK1caqMW5buvbllM\nWVrm24eJw5C4FOX9pXAsyseHRKt9LctzYPSVY3UEjYgJl7ZRW4+kffQqUbJglnFDpLV+0ErOKN6R\nWuNuH7iZDgwRZm+szfO4lL6OzANNGyEEXrbMqSgvOVO08eGyktQjzrOfElGNe5tYtSDekZ3jdu+Z\nY0Rb4XY6sNJoVfBSGQdPrY6tNJwPPJ6P5Aa5KOe6UouQxsboAs5FPNIvxptSqiC+4K9I7lUL3rom\nRbfGpsarw9D7reeC08RaNyx2AIZpJRflogurwCev7ng6Ljgcy3bBJyW22KPnEUK8ToVyrx+ICEH6\nvqNZJxwrgpMuRhYAM1a6DgP1iHYvXkNZ1u0arwug1oXBvoNkXAyIg9CsR8VFicwc8wuldIeWE2M5\nnzi7cKX3GUlil5h7qLVipsxxAg9taUwhEncjp0vFudonU7/A1//bCdOfB/5D4K9fP/ffBv47EfnH\nzGy5vuc/AP4C8C8BL8B/DPyX189FRBzwV4CfAr8BfBv4y0AG/s1/0B+e8pkhR5xWPjDwFR7Phmnk\nBeOr5kgY7hruSM3REE6+gxVuneM+CJdWUOdQBc/ImcbW+hfeO49UYZTAwQvvbWCv8PkGjxjiAnPz\n7EJgchVvSizduO1DQsxf2yceHxzS4DaBmXYLs1Oa0EezziAHjr5yO3jUKvPoe6beB/w48l4aQZXD\n9Cnvjs9d6BUj53PjdSzciUM1sZ8axEB0kck5VoMnHagmuLlRa+XDEom+duxxMbJ4Wq1MNAavGJ7Z\nOfbuGrWgIC7SnMOZo1XHUQf2B8+sjXHwEIy1OV5awHvBD4CDWoTWlOY8Axl/pRMmUWKYWVuX9L4p\nQkrd+dMsUIuxxowPOx5bZc0V9SNVldZgM6VloXrI4hhOhRxnPkin8KkbmNX4Mju0DEyhsBL4nrvj\nwfUp3yOeHy1KLZ4hekYPogs7NxACvC/Ga9e7b7+rhbmOqBRuvHKzNRbgfQ68J7A5j1jjrIrKQDNo\n2WgWeUXll/aRoAXveqb6zVJ5lSYiHTyQXOx0I1cIeE7OaM1oAj5XVHqkyyx3cZ5VWhSGcuFgjjBN\n3BB45Rr7cqIG4RHPj01Zyo4TQPM8NfBOOW3CsLtDLPOSDXMze3FcpJFLI7kJGzx6WRldZmNgl41f\ni8aoyrEGVn5u+cEf3zpSFmrOODPOm3HJG9YqWOP8fOGMEYR+sI2Bmo0YHcu2wFYY7u85PDywnS9Y\nSLTLGZvuKZcj1gAR1uOFumVCdIw3t2zZqGvl7duV7Xxiy43bV/fs7ydCAL2KJ6OHkCJCIEkvy/rY\nDxI39zvMjOgD4g01x24/YE6xCqec+e5ntyxb4+b+gfOqeByHm4F364psldcf3/D26czp1Ng7z/vP\n3/Px3cDdPsBmfPJKkCHysQRug3Aqji9z5INWHpLjVIXL6rgJDanGU27UnNDS8AqfjgWphduYOHhD\nkoEuaO63hyaCNuGdGPez59tt5Fv38Nnhjt9++8KpKtNA90E1WItnk5FVIaaAz56kynej8XoY+al6\ninh+b63ESWGIRHOcW+DiL4gfWErm6VT5Qh35tNEa5FYxNaoDa5BPS48D1fJNQd+r591p5a31n1Av\n8PDqlldzYoqBp2Xj7/zohbKszIc9Me1op2dux0B6NfDTNwvf3geC9/ztH37JnA7IcGHeDbgPrUtP\nS2Mtwu/+/pe0ZaNpj+zUS2M5vsUE3JPx+rNv05Yjbhxpa+H4/KH3LYPgTgvx9SuiCTjFqZBXRXPB\nfJccq/bkBdojpM4UTYJrXZw8HXbsdonDFIkS8a7x5mnlzdsnxCXK5YWSL+T10qlZy3vS/S2Uwrqd\nu0ahKFUKdbkW5Z3nRz/8Mc4E9cby+cb967vua8kFtZ9bOPnHuhehbTStOFW2IixawSoiRi6FTBdv\nmvMECuD7x6wiVfExkYaBmjfMBZxV1BJNayfdAUWUzQxfIMbYo8NOOS8XcslUc0w+kIaIBL65eAli\nONd7davLEK7dpAi7eQSUGAPOGa25vhfpuE7OW+HhZqIq7KvvmovqGMbEUTO+Kh893PP+6YltqwxN\nOb4vHMbIbk6YCg83saPQY+KT24HtUvlpddS24uLIZVV+901mHD3NKtu2oDjWrZBiZEoetR7tuxn7\neudcIxFp11jYVvr68+ndDm/G/Z1nlokvlgvnrTDOgddzhG3jnGGtkeiFuyikFogOmjU+ud/xsmaO\nWXi5ZGK8+pV05lKMGhYSkeNSOJXC2+2M1dJx/q31CawDa9IVCfR0T2fvg2+OUzZe2JAArsF+nthH\nR5gTy7by5dOR2hpDCISQ2PLKPESSGk+njcM04IPy1fMLgw2YZVLweHM46X6jbPDVh2doRkThz6kA\nACAASURBVG2Kc466Kost/XCywc1uBgoSepRwXU8w7emNOsWFhEcQUcQLtWp3WYn1iybrnSTTjJgg\ntN7BqhnvAjElkg/MQwdPBGt82DaOy0LTgJYXjPJNT7+2RkwjopXaOgSmClStuFVZUt9Lv9ueu8jc\nG8taOMwTAUdWh/KLPTBJHxv+IT9Z5CPgDfBPm9lfFZEb4C3wF83sv7q+51eBvw38hpn9NRH5C8B/\nA3xqZu+u7/lLwL8DvDb7+yUvIvKPA//zX/4XfpPv3X3E0SWOJfO+zuRaOzVPjYJSsxFSQPz14bFd\nx5kYwTtUBdFKCtaNwd6oBRY1gvRei9B7RaOHKh4LDWsDOQiNvkEONIIJe+e5j5mb1CW5zSCaolKI\n3pjU43z/GjsBJz2i5WUj+EAxRdSDQRF62c95GkI141gqU4gMrTKFxofahYyH+sx+2HjeBop5iB5I\nGJUmU5+yiqOokSVyyhuDF5acMRupbgVv1HM/wAVVAsbObdxF5RCEvW9XF0qPC1btXa3SElmUatIz\nrbXnrwVPqBuSIqN6hlhZiuOldEHoeN00FTNi8lTgUrtXKFtjq56zGs08Zp7qKznD5rv3Bs04hKU5\nQusHqJl+03CqQjVHileOP4YWaBhW+kbkLnS5qKeSvONP+0ZZPX+rdQT891zmtV9Y6eXVOWw850S7\nON45w3tHwfHG4IKnWb8VGUzYtKLa4yymGfMBkYYzz+Arr6wvS02FGDNBO5Xm9huvkhBCJbRAk25S\nzxFGDQRnFO3UoGieIazsnWOXhKMGXprwuU48S5/qaWnkK8KdptQKy5axBi8+MO8nJht5ty2M0TP4\nfmPlBO6t8ZUENlNqEQyD1G/6u+k88ObpLf/tb/8WwK+b2d/4Qy8gv8B15Os15Df/jf8c/+mvoSK8\ne15ZjoV13SiX7SqNNOplYbqdkeixBpf3R+S6v/Pj1Ok+ZSPM3f8jBvnxiVIa4oU4zrjQCUzDNNIk\nYE4ZrhlxxDr5F8XCyDwHdvPAq3uPN2FrnuCUKr0AfvBXv5Pz36whYsqdUwYcF+B03Qw33+/ADl7Z\n8JQGxwxzdIg27iK8Kz2KcWONT+fM55eRs4Pguw9ExDDrHjqHsOGgwpM2Zq88ropvylIVF431pXY8\nOuAxxilwPzu+s3PciyJy7Ug5YdXAosIb7ThqU8XM2HqjGREjWEMkEaSxS3CujqfViNIYxaMCRY0h\nQrNALgXxAdXKuinHY+3RRycUKnVpqHfE6FlLxWHkrUDtAsvBOUortFJpVUnTiJmSa0G3hqj1zQOw\nO0xsuU/ZXYDvf/cVbYHf/cGX4ITbm4kDhRIj1uD+4Hj33CjPjcfLCy52Ktp2OSHSEb9dzuuwVjBz\nV/Jevz01699vBIY0duR6VWxw2LYxHm5IKTLfzCznpStLXECCJ0VPc8IQIyHAUjKWYfQB5yuvpsjt\nq4mnxXg6Fp5OC+tSaa1Q10KjEYZIWzfaptTTCVUF50j3t6Q4cnl+jwxjF5PnCs4RQ2TZNqTVfhmA\n4YKnKT0WiKe8/z3q//hv/YlaQ352Hfnzf+nfI7z+FbZsHNeNnBulFbRUxAutgbZKCFfagDlqaVz5\n34jrlDOxgvOud2JMMK3UAhYUT6cpIkJwnioenBJUruhpUDO8KSa92D+mwM0uEkwo5vBOyapEL0wx\nkUSxn1lHUpRO5YyBLRd8yL2XqR3vNw2RirBcMseLMg+eIRq7IfDVSyZ6x+SE273w+Kis0vAuEoL2\nKQgBrz3CtWnfeH/YNiavHIvigdwa4o289IttXJfjxuC5mRP388DdnIhBcK0T99YmLBW2mllq7497\ngU0VaQKxP/uSRcadME6B47HydC4ED4N4TKCYMQ6OloXztpKGgVwyuRhrqVgTzHWyb9sUpYM6uqOo\n7yekOZopCXqsvhRUhRA7irtYP3hI6z03BMbk/89nahQ+OszU1fHmfOyR2zEwRNfXRzoFcVkabXGs\nLEjoE/tcK6hdgRCGw3+DLIdeMRDnrj63/jMT++m69+t8z9Z4icQUiL4DJsRJB2sAQfreKgSPd/3Q\nT4WIh2TsY79sOZXG5dI4lY1Sel2i1e7bFCegilZDS0avx44wjHiXKPncoRfedxyjCF5cB8y0ipn0\nabj3VDVMOrV6ff9Dnv/7f/ePbB35h71+3g5TV6fDh+s///r1v/k/fP0GM/sdEfkR8E8Cf41+k/O3\nvl6grq/fAv5T4M8Cf/MP+sP+ptyzyC0xGsd6YAsnono+kcrYGjV1iIG2jFilqCIxMgbDDAxDTXFW\nWVoENfYuo84oEjrm2UGMAR88KkYWYWMHUkkOsnZXUzJDaYirLAjPJ6hB8K5xlxIPccSs8uIqzhvO\nEtELKX598zOTLaAOBieICFjDFUddX4jOYQJzM1Y2alUu8YZhVG5dw4VbsiTCXlAGlq0Qk2fbLgxN\ne1k4dPzuRCCOnVwTdUJrwvk9qRaO0Vhr5xUl6cCMl9CjLRcPN0GZHDSBpn1D2awx0ReSrTUkClEg\n+Mil7ilivC0bujqcKtPQc90njChKwrhU42SeSZQggDjmoNxXhwsV0YVzc6wucCyV3WgohTE4Vu+p\nVdHQ4R5tHLnPldoyqxPUlO+7xguOTSsQOFGwFjmi/Ko4DsH43TxwYQEr/JkxULW7qOysbFeaT3CK\n7jx7E7wXQoPRPM8KVYSdOKIoW+uEvM08qOJql+E1oFrknTQEMOeQCs4lUnFcPAwoSSsHiaTYhYUi\nwqEIq27snNBil6s2H3hR41gdlIj3Hi9wFx2xNprA2zSSy0ZeKx7PasLqeva9+ci2GOaeeBg9d7ah\nS2YA7r1wCgGTgTPKKQUavccloR/Gc8l4/cNfsvwBr1/YOvLDx8I4N8ZgtE1ogxJKYP96wJmDwbDt\nQM4V0cZWC3ef7hjGqS/4VMiJZhN53dBNiVGI+wlxEZ8i45CIh5FpSij9Ya7WRdDJObJBLQVn1vPj\nDrIWfvDjheCFaoXXrw483O5pFC55Q/wAXzu8QvexPFVPJmBOGKT3oLw1XIEvbWVwDQRmOtUyiPIk\nIwfXmHwmBOVRJtJOkeaoxRGioUV6TzMJmgvSRiQGRpQSJtJgcG74g+AJvOw2lp9ZQ0CQEPjQHIsX\nxliYBYr04nVxwmSNdF1DzkWIpROohhTI54GCcRG4XJRcKvPssRZRuhB6SHAqSl0v7MeEuI4r3zll\nFxMx9PjJsgjrGHh6LtweIuWkzLcjpTQup4YTaFUJ00hZjWINVWhNeRgPLMt2jZ446nZhq0o5X3j9\n7Y+5vR358RdHzqeNtpz47Nd+mfPLGf9ww/blC26cef9yIU0zEuAwGON+JCbH6WXH5bTgYsDHcMU6\nV7bTCcxRNgUVgvWif0ieki/0TI3r3xcfac8v5HHiUox2PvLw6QPDnNDWN2ZpqeQls78bIATy6YTu\nZ16OK0tRfnLcmFKEMHJ72OP8Qime4hKn4yPLhw13vYipQfAMWDTq8UJ1F+JuZBxHTu+ewDx3twdq\nEGY/sy1nzHVZtvOpdxmotPXUgT1/tK9f6F7ki8fMMFYGL5A9jUxwnjBc40WTITVSDKQ1mjb8FLow\nVQ2kQXE0PGoNrYqPgmogBo8TIXohDB25rRjWoEonDHyzjuTSJ+LX73fVxpv3uaPLnbGfR17t9qir\n5LzQZESSMThHGDytGWVTjquizjPU+Zu9SKjCl+cTyfe83+hgXfulRN4Cty7yrVeBEAyVxDQ1ShXe\nPzZuD8L7DysMxrQbOZjy9tGQFLiZ4CKJWQ1/KcTJoy3wPJ5Yar4mUegIihDJLXFchE9eRyYnqBhe\nha0o0RK76zpyOq7E4vFRGPcz69vuLnx3uqAfOuxiP0VMHVvOhBiJUTjnxnbp7sXoBJcS42Dc1JEQ\njKbKumxswXNZKtMhYVsmDgO19K6aYkg1JDm0DuRaUQQz4zB48lrIWonqUd2u+O7CYdoxj553x411\nK4gV7m9uKa3LpdsCpkq2hpcAMwwt4oj4pKQa2EoFHD44nINSPE0bNGj4fvhoRsPjxWhWe17fHK0W\nHJ7mKrrB5hpCYx5GQhBU6VRDE0rZcNfeZdMOj1lzpeTG87nj13GRaRhxklGLFDO2tqClIdL7l9UJ\nHo85RWtDOROSJyBspdMbJx9pTon0lJiq7wog678fKrWnbn7Brz/0gUlEhD7y/qtm9r9d//W3gGxm\nL/+Xt391/djX7/nq/+bjX3/sD1ykWimcarsSNJTkRxgbH/Ke77kzO8vk4HkpidWMGxqNC9ZSL18i\nxNBIHn7J1v6wIjJ7YXI7tDVyUpDIscJC4bkoVlYWhecGF+/5NERu4oCWjZPB4hKnoRf6nAs8Rc9J\nDANW3+N6LSiRXuA+hB7jiy2zbI2zCIVu+R7FrmVFYQygvrL34EdBowdxLCH2DGrybBcloAx0+ezB\nJ7JYz486IQ2RglC77InFlOobm1Zq7CPPXnD0JBch9BzqFIUixot0rHUUw1slmsOa0Kp2i7j0vlAr\ntYfMRCi1IpKoHlbLHDOIGV4DF92QEJhcYOcro/RCqgGLRZzL5M3xQQXvPeY9Y+gFTUfs/x9A8kay\nwn4eES6oNeLk2bSwmcObY3YdwyuEHrHJhbE4qgpLHbiRZ3512sgt8bQVnlzi+SLc+sR348pBAkXg\n0JQcI1GEWip7K3ws/aF2JwUDNuf5sI18aI1WAy0asXlqMHJVLuK4oTJ6IZXALgS+NGW+xjqnZCS3\n0jalBCW6gScHS6m8z4HbBZ4q6E4QmVAf2UxYrE9EpSpnCX1M3TwlBgo9joOBjf3wPJdGVWMtht/O\neOdI3rM54TEavlZuMO5c5EymWKV6z1bhkvtk7rn+0S1Uv+h1pNZGXS+8hHiNRI7oLayXzP3okOhQ\n18l5ay4k76mXXjINQXAuIlG5uR158BMcJgZT5ujZqWNtSh6MAeG0wQXj+bRxPG+sl42LQBHho/2e\nj+4Sp0vlsmYqhjmHpcTgJ7bm+OplgyosrrvczBvJOUJs3I9zF0FL4+llwTnXHTCu+zWGMFBc4FYr\nazBuAn1z1/rdY/ZGcQFJQrl0YWAioxYQWTEdwCA6IblMoVAxRr2QmdAERaHZyiCOIAFDSD7CEL5Z\nQ6oYVSbOLnOfZpwVHvOKtkCtSmtK8nAbPVveqLlR/ExpirQeNTkuyjk3vPS8/PG4EgXmg+cwDx10\ncL0yLUV7Nn4xPjxdEO8JGri9T5g0xn3qmwkxphuPM+FmNyNkNhNmB1uplNpv/I9j4NYmxuCwdseb\n98+8cEMtSlsdozc+++VbXp4nTu9OXKzw8oML827Pw83AbRrYTAitsrz+mDR6yjkzDJGPPr3Bi3Eb\n+w3wJo6nl1ueHs9oPdDK2mlSQ6CeF6olkhfCYUcosDuMPD4e8YODrTE+3CIY67tnsjcOhxsurnB8\n+8ybN3CTJp6PJ8aP6zdExKzK81MGd+mTEGDLipgS00TTrUdknCNMB1xu1FLAGrYWtmWhph3OD/ik\n5ChQCoZnmnc9EtgqkoSWV+r5gvcDLv/ReZj+OPYitTXCtvLiAoFKdCMNI2vlEB3OQwuOujSKKiE6\nKIbZtRviHH4QxuDZDRMxdPLXbYrcjwOt1u5+FM+Hl8LZGqfjRi6VWiurGg3hsBu4SZGtdEx0M2jB\ngNj/Ds3z5rLhGmziCVbQ1Yji8HFl54fuJ2q5p0+8dB+lcwzOM8ZIcZEbgy0phySMY8QvfVJeRKl4\nJBrrsU+yRlc5nhyf3gZeLr1nHEX4zoOjg68j+1J5d/b4yXFcG0k2Zh8Z6BCCFBJ+jn0dCUJ1xrEA\nO0dKjsEqD9WTW4LrXmS+2zHJdS9ilc+jo1QlYmxeWFZYasaZQvCsT2eC45te2HAVAJvaFXldOa1w\nWi5IDLjmmcaEqeJ96kAOgTA6fIOb+z2ihaVVprij1ELOXUq7+E6i66iqiadlYWPEGrQSiWQeHmZq\nM86XSqZyXpQxDux3gdGPvYPoDJWpX6zlRi2VeRoQ3115BlRVzlvjsm3dXeQUM0e0fviq5onOcDHh\n68AQI+eyIb6DRGL0iDPylmnSgVGXVtly7oRqS6xlxY+xT1OvqSFZFaiI78OJ0gTB8JKo0mj0Q5P3\nI15bTyiZYq32A2Yc8C4hUnvyqjVEIMWEVsUAfO9uaS14C8jfPwT+//T180yY/hPg14B/6v/Be4W+\nJ/6Hvf6B7/mv//pvM6Urd106xeM3/5Ff5p/9/neIGjEGJjJ2MGKe8GrcSWKzRg2JS244mXikoFNg\n5yPBPKtX3reBoys0jT3/nR0bnfmeXUCadAEriS/V85PLRpUZb40xOlIAHwI7ZyTfGH1/iFdv/QCz\n9bG7a0o7B3TIXGpg9NbBCnog1hUbhUr37NQQSOPQ2XGuE9iau1Lb8KynTNCNMST86BhNyB6CRRYL\nNDcgdINyQfEYd1K4bEpgoNYKFGYVPvWZWr7gRvutwpru2OKIJo+GyLN5NnfofaRA7yqp0lS5eJDW\nc+lzAW1KVSM2Yd96nDyXBq1juGcV5tBwyUjW8NYopphM/PCSiQz8ynTFbEph9JCaYxoaTgNbM3Cw\nVsdRTty2wFmMsSrBOcYIdc0EMhc/88VlZZdGim9c6sDRK5/pkeYdf/cy4wZ4NTV+TRUXAi96ohG4\neOXedYHsyZRdMOr1yFasoRTuvPGQwJN5cs99cmmOQSvNC+eceE6eS/aMk+Fp2KAYmVcEjk744hIo\neeWQHEOaWEz5qjTMJzKRasqPVRE3IFslRkFrofguRvSh2+UdPaaXW2EojcUuiBNGrbzaAB84hUzM\nhScVFhf4fQRRh5MBt21IiGAeFLQ5Pv/yB/ydNz/GAPUOmnLOf6Q3O7/QdeSrv/If4abDVeDeHxCf\n/BP/PN/9jX8Oba2b6MUTgrBdEmaVFLv3SMVxflmY9nsuaybdDrxyjmqRE8YXW+XxmPEOcjZaVfLa\nqHo9uGvvnQwp8PKSeff+iBOHmTLsZ+apC0PnUYhj6MJFVZobyeUJ2tjjgCGxXIBROefClCKXqgSL\nmFZwiopjUEWdsHfQyGAFT6A56TjZ6xqSUuAgAzlVRoPsR4J5nori3ER1EK4KRY8R/YbS4SRFO756\n1MjDmEm68cm4UNX4IntKiLQoqI+8y8omE4PzBActKWOjy5k91AheA7ss0KDqyNaEw0f9W5rbhjXh\ndkqYOoakhKEyWWWnjkxg9Y6/935jcpHvf3uH9wG1xuAd1hrfT73D89PmmJ3yvgRW3ZhdINbaN5JD\nxJJyKq6TWBf40U8eub3dUZthaqyXlUsUiIHf++ET/jBy+9HEn7q9R0Lk3dMZc/DSGt/5eM+A56ka\nB29k76EaxfqB8j55/uzk8GL8/g6eH26IGKI7WvQcj8r708jxAoeHxBg7hCRvmY8++phjbXz5+SPn\n5xO7uwPzR/e005k3b5+JKZDVsFJ5ezkjEshfviHt9qgpPoQ+sZt3pP+DuneJtXVLz7Oe7xtj/Jc5\n57rsyzn7nLocu3A5viQIBccgIgGREESORGgBogFSWjRoINFBRCIdhBKJqwCBRAMhhOjRIwJhCYRI\nlCiJDUIOMsbG5XLVuezbWmte/tsY4/tojHX2OWXHkatsVcmjs6W91l57ram53n+M8b3v+4wK9OxH\npdYLZRbCPBFVcSskjYRDz3w8QjHyY0YnlxWxDQ2J5fVbQuoe2/oMilO/87exT/9WK1uKgS3nNmX6\nw1s/9L3Ip3/9v0WHXfuCj3br53/8n+DFn/jTzYIIqDtRA9uaMArDvqPYDASWOROkZ80bqUsMJDR2\nXKzyyeuJU1kJrmzFWgYtO0bBtU2XRaRhKObK+bK8a+2M2tFrQjuli0rsIl1MBHOyKO4XaolQDSOw\nzuC9YdlIGtjqo46QISjZKwMdGuE2KuMuEHQj7JuOgL/TkQXhKgXG98dWbBQgjZkpC0vp0C6Sl0xG\nCChfe6Gc3mS6LrCUiOF0teOj9wKXJfP0phVzSSmsGvCkWIy8eSisMrS9CJDGgXpZMTOs79B9oszG\n0wPUrVCGK9YqzHtwjG2b8AK7XaTvew57SDvo3VBLLEUIYvzqx/cMHvnoxU3j75XC2AV2HVz3I6rt\nWahBOU/G8bJyPfbI1GySxMSQEuclk3C8KK9OZ3bD0FAo5my+ERKICq+PCxKVsQ88H28IIXJaZsBY\nLfNkP7LTjmPeuBoSSypUC2RvU/KbXc9Xbw4ozsfHI8s6gAqBgqkyLZU5V5bZ6PfarHrFgcooV6w1\nc7rMrEul63r6bmAuG6dpI2gre7BiPNQzirLNMzF1mGdEBSegmkjRce+a5jJTc0DL1tJGwVDRphF5\nA4zcuq5a2YM+li2VlSABMJBm1Vy+/cusv/1LyKMzw9ywdfp9/Cr/4a0f6MAkIv8Z8OeAf9zdP/7S\nhz4FOhG5/h03O+/zxc3Np8DP/44v+eLxz9952/M965//k3+KD2+ekmJkL8qVFjwIrwyuOuWFXriE\nHQ9bzxaVdQtMKbDzSsYhRe59Yc7ONieWYDw7dKh3dKHwlZToPJN9JavQx0AW4XUxcheRqlxUuZgR\nxxGxjl42klWea+VKK0kbHHTKIGr0OrB7bCBBIkWU0hVmU07ScaZD0oG5E2I9gBV6MW4j3KiTq7HV\nyrUGVCpddZI4WiduECQJrokN4ajKLAlUuDY4BWNCiDhPqkCsVDWu+jZJmGshe4N3vqqC7J+xPdpp\nTGAzJQFBFsQ7rs1RKirebqOtOWNz6TnT4WKk5EgU+qKMIZM8INXx1G4Mkhthy2xeGQt8VgKv6ohI\noKPyQSccmNvrtZ7oIuzGPdO88N17SAJDTMSQ8Fp4koRzEYZojYSuynlbCV1gYIea8dXemUomemLr\nNoIFimaeq3Ppeo5r5XUa+FRWyEa1xDOpfLUbyKUwRuOZWsskdZARljVwrpHPSuTTzdgn47gopjty\nWUGc58W56mb2aly6a0xbIfUpA9ZzpzNfU6U/JM6rMlsix56lBlyc1TJBAtXXVl36aFOIAgcBl8rq\nzoSzv1ir22cjbE4tF4KvvKBnr/DdYMQsvFDYUs+NCPe28SQkJpTPzOhVuHUhJTit8FA3fvKrX+Pn\nv/o+JW/M9MzV+WQ+8t/9nb/2g0jH96wfhY58+Av/Otx+xLjfEyIc+ogk581d5moHN7eJbI7PCRtg\nXTNdb3SpJ2fn9vbAtKxMW8bvhGUxPnj/psFe04mPno8QIuuyUqoR+kQvxicvVzZArLVYzrJyuB4w\nS8TH8OP1dcftkPDY/Nqlbqg6nRt9vEYHfdzoOCU4c3bmWZmXNul9iJW997y6K+z7hesh0HWJVTq8\nKBGHLtHjJFfUFq6iIFL4SnfF0Qc+2SYsdyDC867jwVaKt+nNDvBQqGqEnfOh7vn49BZkBynzsCra\nFR42WrC4B8rnlcZLg3LSLB+I85Wu57hOVHFW6xAbqGLEznETuqqIryRvrgIPBe16EpnDVpms57rA\nr9XIp1tjwBxS4itPBt4zZ4gQ7IEuKM+fBF6fhP/zTSZJYL+PhBJwLTyNkdNWCH1gwwii3F+Esasc\nYmRLynv5QFmNw3jFZv4OKH19uyf2kfPH9zwQePvy0qwmVbm9SnzwjWePF1SZJ4/Z2n4fyET0XFku\nK59Nlc/OsN913H1aKMPAMp0RLbwYEk8PyofPel4urZSmOtxfMofhik/uTvzksz3xo/c4PVyYLhvJ\nwLTjcB2Zpw0NHWVb6HZ7oDVyduMIsZX5mCiEQth6NFWWaYVizC/fgGaeP32fMUTeHC/4srXw+NMn\nXF6fsXxGhz2qiWWeULTV7F/3bKeVeX4g/Ng/Sv/Nf4xyOWE1IG5w/m3K//6XfwDV+N71o9qLfPCn\n/2Xi+18j+UAYlUEDEpxpqgw9HK4amsKPiidjWwoRJ8YdxZzdkNjq1j7nnNk6472bHpeBFI982I9o\njCzzSrZKtxvwZeN+yRRvOlLE2WphkA6zRHiceIxD4joFtG+v9WVZcHF6jVTvGVNAOzA3ijhLhVKE\nTSASuCQj1oFpqaRY6MMMXc+5dpzeVj68CjB09ND2ImxcdYKENmGeauVcjcsZ0MjtruO+LFzWShR4\nvhsgFKpmbp91RJTLZeP+LOjO+fj1yv6qMmVBQsCSsJwzferbXgTnyb5DCag4fVgfYctCtoZVcJyo\njvRCX5XDqI86ojgDF+0IvhENLltkX+C3jhMP5wlJRqqBF9c7nnQ9MSqXdWLYBT76+oGXn038yscv\n6YncjIH9cGDZZm5S4jhtdEOgYiRR7h5W+jGwI7GZcSuVvBp9HCi+EFGqVcahQ4qyzRuTKdN8BHVq\nFXZRefL8irxV6lB5Mo5IqPSxuYeWKXOqhZeXhc8uE/uu4/40E0PHts4ghcMwcDP07LtE3rUJkATh\nfFnxMHKZL7x/2BFjZJlmNocqDpLoAmy1xQSwlRQ7XAA3FCV0j6VgDq4F3ZQQS3ufVvAyI7qxT1f0\nIXJeZpzM2AXc2wGr5JnYDXhpsRcRIaUO7ZS8FIplhm/8HPuf+DmsbrhHrBby/Xe4/8X/8AdUj+9/\nfd8HpkeB+ueAf9Ldv/07PvxLQAH+KeDzoOUfAz6i1X4C/A3gL4rI8y95h/8Z4AH4v/n7rOeS+WN9\nAd84S+BBIpQVZeRtUWauuffQbGQGY7jhlFeugNEAL7gkvlKdQ79wHWFfI69S5a3t+SjuuAuF3+oK\nqcKtFVyFXbeCB2xpVsBogbpmhAt9iGCFpRbmuRBdWfsDkzrZIdaRQStrUMbYGvjWoGQC+MSVBDqf\n+GpWelkhOlGbzWpDWGNgnwYihSEt9Joa/FF2uDmTt43QQuBCx8WUSuJOjcmUGWePc4yVsQpXJFJw\nAoV9aE/f2aAPmZsS2Dolho7JA69D11ppqpBLgFDYEeltY5ebg2PNC0udSNZhWsCdpIHwedtYNFDH\ni+EivNwiRxdSdrridHHmo2B0apyL8bBUPl2Fre8o+hTNwtu7yBT2RDfUhehCSLCLNLzyXAAAIABJ\nREFUSrdu7FPHNcZ9DcTaLJU4ZBQPbRKjMXMtxjciUDLnOpBk5aNUuOsjZxzYgc8slvis9vzKnBjk\nzD47t/3Gbe3ZqGQRqicmKczW8j5xc4JWIgFJI1e1460uXDywD5GHGY4OZ4tstfDeqJgdeGULxxx5\nXQpP1RmZ+DAEziZoWVjFkSEQa8EE0BmpYCUwaeHaMuMUGfvQpgayETdhjbDreu5EWTYh+I6tc5Za\nuITInQpG4uTC5/25N1r5UCu7TvGyclIhunEW2IJyIJNVYFm/X9n4XetHpSNDF3jvq9ftd2etnJcN\nO7Wg8sPFWHPlshXOS0VkI6SRfNzoHyn27hkk8HwIpJsdz3ZC78YaCg8Pzo+NIw9147N1xWvgRguz\nJ25f9IgpeYPjw4YmwYphtqLaUYHzceb49oIEQUMii2NiKAOdZDxkhm5AqrcsoQprKNwmRarzY9cD\nUTsChR5pbJOYcAqHGLjpr/E489PdSK0rmR2Y8YknXtbKXV6pPnBxRS20+v8MM0ZC2qVODuw65dD1\n4JWv3T5lLoW75ULcwc/qNQ8p8NPPdhy3wt98uXAi4lkY5wyhUNJI7xufzifUnbUUsjmaC8SIkVFp\nwe/nw4EHv7RabRNqNu7KwLco+OqNgZOcr6fC2MPbeeXjY+bbDzOyG8imBIQ3v7JgZXuEHjYGST8k\n+qFDdeP6MHAtxnkurXHMmy2zCiCVbuiQWNntIj+1e06pwmWudMm4sYHvPrtiyU6Xmo22rMbd3cK3\n/t8HhI1hiFzdJJ6MO9aS8aAskzE/QiCnu0wKGRVI6wwpEDbl7WpM5lynntevz9xNC8tk1MvE+z/x\ndVLo+fjuwmlSXn/2kpvbJ/TJeXFzxWVdWcxZAsjhBSkOuEKeH1AqluF8eaAsM9eHG/orpwvKdQ/k\nwvx0z7P3rriXhF824s0BE8eWla0USudI2lOs4tXRIdB3iZsnew7Pd6x2Yh4itha2uhHiQA0NSBlm\n5Q9qpvlR7kW6IfLsZo+5k4uxFsOWStDIZTW23Jg8M45IRnXgdMkkBZEGRRcLXHdCN/bcjJFn+8jL\naeGyCe/vr3lbFt7WDbHIuGTcO3YHRT2wlcq6NKukZwdt7gAzayDbS20HYknNveHW6uK1IFqIcUCt\nZZ7MA1kK+xBQr7zoe1IcCGp0KF2oXEhoLNykQLeLdJ3xbOyRWiiyQ8x4s2zkUlkqHC/OxRXflLel\nkh/aazEE4VxmenVuDoEuCirG1XXH4aDMa0ZvE+/Ljosah37kfDHezAtvQ8CJ8Hbh/v6BdH2g98wh\nFtQbfPbuMnMdhUs2DqMydK2OXT3gsfE3twKpGB/fG/frQjCIrvQBvv68Z0B4WDMvjxMfr/eoB4oI\nCvyN33iL19IqFVwgKlEudH0EFg67geuqnM4ZiYaIk5eCa8TZEEnErtIPkR8fdrjBNGcsOvvQ8bBs\nLMUInxfRFGeaNl6+WhApxLAyjsr1sGOtBWql+KONGGfLxpECGLFuIEoKI6etstWVq/3Aw3lmLhs5\nN6fK4eoakcTdvLItcFxXdqknAO+NHVOnDEshY2g6EFxbH4BkcKMWIXsbFAxE4q4najvQdytsQ+Sw\n23PO4MXQrmV7rRaKONkKhA6z+sgebIiNcQj0Y6K4sCVFrGVMPTSWqD9GSH6Y6/tqyROR/xz4l4A/\nD/zalz704O7Llz7nF4C/QOMa/CeAufuXqzz/D1qV578JfEhjH/yX7v5v/x7/7z8M/NK/+k//WX7q\nyftkjOdeeRKFvjPWtfAmHPgEIVSjF2EnmX0c6KxBRzMb5oHFGkQxxERUp4py0shrHznrAFR2Xugi\nDMAVK4NbI1Bnaw1r0rIEewoE4ypnnnTwRhIbO4pXJiqn0trd1Cu9Ki6pTZ2CUT1ykMqDADJyI85t\nmhrStcIiSpaEaY92A0FARPlOdWJo0xiA5NKCywjmykobO8cW8WuNN3ijJQMuzl6bNWwzZSMwVmGQ\nyta38HcKcFML1wkKyrFE3gSns+Z97cQYrVDcMWsj1VxyA+J6aRTurdWqSgi4t8OTmFOpDEHwtTX+\n1VopBQpGFiNkIKZ2ixCEUDOqgV4CUYzrxzD36EqXlBgcR5m2zFWC1Rakjq3Yw621rvjGDcqKcSqV\nzg2xHSeczzIcLwshCmO/5zYW9pbYxYlpXVHZMeXM1O9544GgsJbWwjVbYCRQbX1X0rFawd0x7cjr\njMSO4JWVwEZqNaq1otZqR6+BVAp9H+iDs5bKeVPe14kU4K46HQEzYx97bsNMVzLGRi89Q6ycamDc\nJz6dVo6245Ur1SKeAl4rR0vU2FFLu6Hf0exi137im7qS04h75bI4h8455uZJp7Zw7KHfeGrtpr24\n8GsPE3/5b/1N+AGbaX4UOvK5hrz4F/8jho9+Fq/CblBubndcXSUe3k7MlWapw4hdI4kf0tgsqBjz\nUtEA65Yxbw12nTaG1+JwXCpbAKgkS6RBGIAA7ERYiqFKA5GOTpeULiZSMpIrX+uMj13JvkM8M/vG\n8U3jOTlG34emIVEYAxQT9ipMBlIKh9BzGDeCZkoZWESpj7wdHXbvNOS4zN+jIUgg0DREjd+nhtA2\nH96aR5NFLCkxLGBKFxoo82eedfQa+eVXhWwzSkdOkU6MwVrm4p2GPLYnFcuoOdkTrkYsgEYiYOoU\nyYzSsbpynBdyLqwXKFIpVrAcSEnJpfE8rDSW09BHNCiHnT6WYSh9akUXgnJcKkMvbSOUDQuRXPOj\nhmSuJbGp82Y29sFAex4uG5+9PvP25QOpj9w8vWY/RvZp4HoPb94cGQ973nxyj497LmWFIKzThq+t\npavXyHlZ2I0d7nCZH9+DMXJ+84bu6rpZSEuhSsDWhbzldgA0IaQe1szw7Jo4RNbLwvr2iEYYh4HT\n+ULUhNXK/uaW3ej4XLBloxsHDtcdp9PKix9/xm/+P99mkY51nhEUUnpstzLoe9hWGMcWCN8K1IV9\nn4i7W4pV1tdH+uuRea3NKqyQ7+/QqNw+uSV7wybk03d4+Kt/8Y+UhnxZR776z/4ldl/9CWqBYRDG\nvqePkXVdWE04r4Vg1qrfVRnTiCgENzZroftsGXdIMTbLq8BmMDktC0Yl5UiQthdJARLC5o3fhHv7\nu9DaYfVxX/LhzcDreSXbjpJXJttY54qqgFjjQElCAvTaoLg7Fc7VSRhXceTJtTGOcHfX9iL+2EQZ\n94d3OvLZ2/P36EiI8Z2OePbft45IdLIrOUMvkXRIWJne6chh3/PedUfZjJcX53g+k0KP7Fvr30ih\n2Bd7kW1r7pdl21Bz5hoQrfQEijVmpqlTfGWX9qy1cpwWcqnkahQx3CpeQssKWWnubTeCBlIIiCpX\nu9Yit9NEH5XhEMGFu+PMk5vE5WyotT3mWr7QkZs0sODcn2ZSBJGeeVm4vyycLhMxtintfoyM0pM6\n4TxfSKnnMi0gkalkCEJZK+Itc9VrYvHS9iIubFbRxynQVpaWv8XBnCoBLFOrNeustVY8qUbsIhpb\nFt1KbbgYFdZcmuPFnCH1xO7xn5ZCH5sNNK+F8TByfHhgQ8lrbu2nQfFqWJXW7mMZQkcI4NVAGqwY\n6XCMOhdip6ylxU9Qo+aV8JhpL9rg8Mvrb/PZX/3hteR9vwemxtj83esvuPt/8/g5PfDv08SsB/4n\n4F/7e8Di/gsaLO4C/NfAv/V7weI+F6n/9Bf+LB9dPaFz51NzTgycgvFQDZVEEmVkI0mzzV0Eqhk9\nkaskfC1VumocYmXOiXMtxCC84gkvbaFoR5C2wRGt3LgQYuUaYylGb5C8nb4vxblR560oc4As11zZ\nPTsXugCLjHhs4cpNYS8BcmVWx2JsGy5vlbuJVunrMROrMgSICDFUihlLHZkjXGSghsgWjK5udNKx\nBkjmhBDoq7KINX5HLaABZ6X62E7kVuhDpZc2Xl0ccm35hJ0aYxFWDHNnlWa3a9Tm1utPMG5KZgzG\ntQcmlCTKm1AZLFJM6baCyUKoLd+UtWu5EN3obUNKIAXwUsAjc94YgPO2gij1sepy597siUEwIjok\ntDQw8VvpIAhvV6GKUyyScHq70MWRWhZuk5ELhNnZ4obKgNrKVCNHa9OVliQqmHZ4KUwEeqBfjEsU\nyqOVc6qwA94PkTmuGJFdEEoJHOsG0elcOFukrBvgRFdYVzw5hySErAiZh+ws7iyh48ZOfIRz6CrI\ngcnhVXGGunGahE0r+xS47SrJYbPAWTo0rlxw8MjbNTDRN5aBGCEIpbaSjxD2XOvGzhwboFZYpE1E\ngrfm1TsLSK2cgpMQlmxYHQlh4iArRSIp9qgIGoRL7vh4uue//9v/C/zgm50fuo58riE/92/8V6QX\nP0UngZcPF/JW2bKxHBfiTvGoLaczdCwXo5aNshq7UYmHgWc3QysKuE6cZ+d4mokxsJXI6eFEjYHw\n2FAlLvT7RIiVJAPVCvGRpn516FlPmf1V4jgV8iPMNbAxxkjaKbIKHh0LjfDeh4BthSLgjxTumFpD\nVBAIIWHBv9CQGL/QkLUjhUx+PAh8j4ZESBU0RCTs8Hxuld8I0SprFwirv9MQHRLpsdlzcZB5bno1\nDL8vDRGUMTp7Aqs3Dcl2YQsHvELI9r0aYgFVRUImiiA10TNTPCKeuMcYrUEyVQJbcULyVkWLMUjL\na+xVWZV3nDhUeLMszXqygarQd+37MYexg62Az8bFnLFTcozU08LDeUNDxEolU1pL3yYsa2boIpaN\nvD0Cp8vGmhtj5vr6iuK5wWeHQKlwOi5IdFSEPFfKsuJWEO1Z3twhnbO/eYZbBXfOb96CF0wTgYnD\ncGDoe7r9gbUYbz97Q8Cw80qRDRmu6KMQuw7zyuqCeGu8jN6R6wohNsyGOI+EFYIBqQXhgypEwa0V\nCrk5YqApUtcM7phXIspaK4EEvuJSkMfQq4pDjHgBLr/F+r/+u3+kNOTLOvIn/5W/QnjvGyRT7peZ\nak7Nxra1qn1S0xGCUubWzltrs4lqSlzt23Nx6CPLJsx5eQzyd0zrRO0aYuBzHQmqhFiJoaNshRAM\nxxm7gbytDKlnqpnsEGqHho2kgdgrsjUdkdhhXhhixDdjo+IxIrUQRCEo4bHt1gPvdKQfunc6crlE\nvG5s9e+vIyo7rJ6xFhZtl4YxEPKXdKRLdF/WkfVRR7rfn46MQ2TXwU3suOS21zqej4SrG+rjFOnL\nOrJsQuoiEnLDDpTEdVqYtggeebCNUYXjdEG8IVlEnb6PmDu72I5/Owlswek64XIWPMDb6YKJ47np\niAJjDKzFuN0HtgqszmSZLkY2AZszU84NF5GNTSoqQi1QvLY9hLdMePXGfMpeiaKMw0CpBSfSp5ZR\nX+eMREc8NpZjaXsRIVLzikSnk74VR1RnKyu4YRoJMjOEHSkFovbkWpm2jWCFuhSKFkLoiV1CaWUN\nRUCkslUnlcBKplU1PjojPLQ9iTvE1PYQSstOuSNecVHk8bfYquNmmHlrzHMjeIM+IxUCiLbJl6vg\nptS3v8XbX/wPfmAd+X7XH4jD9MNa7yZMf+YXuL5+gSY4mHEbFhCnZCXHwKJdu8tIlZSVBykMFW4x\nRq8MYowxMG8zQYQtKE9D874uRFaPTO7cGySUPgh7qUg1iitZSsshAd/oI7NmXtWeXtvG5FQaWyip\ncxsrtxI4iFGDsmpgVaVWZdLATjI7d4IGlseRuQVhqkbvgRBg7xXXTGZPCa0hbxUhy0h2I4fIGIyA\nce3KLrSa7VmV4plq0sbiOCpKEiEHUK+IG5uGVmdqoNoYRwdzYogUlNUVEWUuK7TkAaNWeq8Ut1Yx\nK8LkKwfv6cUZ/EItzrW1582qBqUSVImhZ2+FJbSKbLXS/KquJIPKRK2CWiNKdgEeJZI3foVFYdk2\nrtTwGNi2TJDIpkYnEc2ZG2ZufGtci9hxqYZKGxtrFKK2DImYtkYgApXAyZRThVPLzLPWZjM5l8pF\nHSuRXqD3jaKBAWErGaQ9iaNVLlk4EyilNXqFWjB39sHa90ylemIrQhcyUw1E4JUufFh7vnJwpnNl\nF2eejHvuVmfxwMdZmdTZirKLkLoRCZHLZcVTZvKefadspWKuaIyPJRqtf7CKcbMZGozeE0sxFmCN\niocIxbAU8Fyo1h5I2Rsjp0oAN1wKaj1dmokPZ/69v/PX4YckUn8Y63MNefov/MfIzTfQXulE6fpA\nVaNMQhgcq6lpSKyoOVspbdOdOqIqsVMOQ+LhNBNiy5pdHwbcK7k4XuCyFpZ1RVNgGIbmGvCKWSBv\nC2noyAYfvbfnnDfOC0QJJDXWXFpFbYzsk3O9HzloO9znWtgUzJxaM9olRlNCCCz2hYZsZSVJQDW0\ni4cIm3Xs1N5piGb/Hg1BlV0Y0C5RvU12P9eQsi7vNMT6PdjSovPw2FjUNCQP8ffUEJvu+FxDQp8a\nMe5LGnKqZ67l8HgLvlKLcwUgsIlh1qa7SZXouelYaQUdX9aQc6otKN4Ii8QY6dhYPLHNAlY5R+EQ\nQuM01YJ6IGP0EqjeKuefeGEgUEPlJT2qDpthSdh5YJXW/qb6yDehAXMv88zDZMTiTLlQa+V8zJS8\n4KakPlIrhCAMKXE8XkChiwGxwjxV8rJQtq398I+NdKSOsWu6aB7JSyGk9j7wmsh+okvXfP0rNzx8\nciKFha998xt88smRrcLb+yMiC5ad0HV0N0/QsWP+9DUmFScxXI+sxwuCksaOUg0eNcSoSAWxSjcc\n2KZLu51OisYe3zK666nzArXAY9mGFEFUW2GBtUOhq9NtD5z/5x98wvSjWp/ryAd//i+htz+GppZr\niLFNLXwTSIbnL3QkurPligRI4fFCJShDCkxLC9SbOPvU41qptR1Il+rkvOFR6TwRE49FS4qTEQ9U\nE17c7pg9My1OCoEokHN79hACu07Y9z272INArZlV22VyLZXQB3YWCCGyWH2nI0tZSBIIGtg7VHUq\nPbHUdzoi9Xt1RFTZyUC/71tpxHl5pyM5f6EjnvY4y+cygvkXOlK631tH6vKFjsT0u3Vk4sLB98So\npLRRijfLMrBQ2UogKQwpcj0osxe2qVKqfI+OXOL2iPdo/LrUtShDNmG6NxTjHuMqdaDCvC50IbF5\nm/SYZcZReZqEYh39UHl1aa1vtlbiGOgsUkMhBcMc3Nt+5Hg2pnXitBmxwlIL7saytrZWNyUGHi+E\nnCiJzTaQZi10tdbiWVt7IO7weNkiMdCFNr00j1hxNBjmFbfI6hcG3XF7vWM6rYRg3N484XyeyBXm\nbcElU6sQgxDiCClQ5rnlyCySkjauozcIcP0cVujtQkb9kQdFJNvj+1Skpe39sX7cKv7IfDMMSmtb\nNGnYh6BNR+T4klf/41+BPyIcph/q+jDBi7SweGDxzIVIJLAG4/IYngu1velfe6VH2WtBqUQKZalM\ndeFJb+wFPpuUV6ZchYnSjSiFqwxXQagaWGvgNmykrlG6L6W1T5nDZ+acN6W4sYTKYMqBDXMlujMV\n5SFooydHpzd4TypPYmZkY9BMJfK69miI7ETIGnjiAcO408SddfS0W5iv1Y1zDJyy8RZhDU5nRnTH\nRHljlVcVoijKRiyOB6enRwTet42DVq7rAiqsVGwb2YJRQ/v5zq6UGNg8UDzj2nOpG1cSW/jTA7ua\nOdTMqJXnHKk1E0SwMrMwMpfIqXqruxbhQCFoJm8w+YWNhJeVLsWWBwsKpWIiJO1J6niUdwfLYpWd\nFf4BuQOHGIwpOu57NAYknTlQyJtz7grUjjkPXEQIeSW58FAdVWFdjF5BgvJ6qWwG7HqGWjiuRnaF\nOlOkQ70SESQLB4fZJ3Rr1cpdp7yaKrcqxCEwmJHd2LkwiuGheczFndcl8sqdcxCeO7ht3KaBX98K\nL3qF6nxgkSzGry+xbVyHA99aVo6m7CWwhI1vhsrbuCeq0unGqRYkFK5D4r0+0deVYWyWh5seLjmw\nObi35OVbIguZ1YWQAk+kgjo7F+IYiFZYdwOYkHMLFl/cKMR2iBIneKW68kr/npevfyTW9e2e8GSk\nbsY8z8hjgYiETF2NarVNP4c9JS9oELpdalWrwDpnyrpwfbvnZlQ+eXnh5SdHul6IY//IEJG2YZBA\nscJulxj7NhU9ngdSCOQNXt2vLLkdDlKnlG5s9ksPRDfmRTjnQiTT7Rrr7EmK3PbSbHnJKGXjWA5o\ndXYpkjXgW4dRWR6nLska2f5alSko05o51Uqg0pmBRqRUzusJm5uGSDBMejw4oduj4gwhcNDCdTKe\n7BPfvqzINlCHxvgQcRZrU6li+k5DtuXI0F+TYsG0Z7BM8Mz7w8gfjxvfzjPfjJHfLGc+y22SOhnM\nJBRnp+DjRl2EKYNaZJPCKNKC20GZtVUP77WxajTBliNSnJM0S84394ZKJHhl0sriEQ2RLmVuyeSi\nfOyJFeM+t3bK8AiSfZiNEOFyl9ntnEjls9crtRS6YaAblbs3E7gzXZYGH7dCigHPGTxQ1gvTZxNe\nneHJgbu3bwjSM753S1k2as4IQqfOMAwspxNGoNaC5xNzSUSNbGXh6Xvv8+aTj+kOVyiVUHf4tvKt\n377HMW6ev+CX/+63EakgA87Ms2fvcS5GN4yEWlkvC0Zl//wZw9UB8sp7HzyjFOPm2YHpPJG3BkcV\nrdx9dqSUmWyZ/sk1ooqExh3rx4hVJ/YBM2U+nsnFqGXFSZRlaW1aJtS8UtbLj1oK/kBrHHriLuLV\nW9bEHfEAWvHVqbYh6ljuWS2jQQhJ0CpIgLplpryxG3v2Cd5cVh7OEyFIe66JkMxJKQLtpr5PoWWY\no3JeheABM+d+3lhza670rOTU06kirogay+acc6GTgg486kji+T4xXvX0fUJy5rfvFc2Fq7FrOrL2\nVCrHuXLxTHQlRXhxSJxVyNPGpRpmmc4MkQhWOdUTD+vpnY64P+pIbDry5KrjSivPbneoKue6UU47\nVi/vdOSSwZdMrl/oyLodGVPTkbgbGWvhaqfskvGNq8hp27jtn/LxqV06HdfE/UPmfmkNlKMEdk+F\ncp95e155OHdkXxlCJHjbiyzBqQH240ApFe0i62XDFucomWEMfPSiQz3x41K5uIMLwW4IA1xr4bIq\nd7OjXWXOgfM243OrIr+/rMQeljcLXd8KFV4/TK2lcOgRFaapfHEp5o27JeER3Eqg+sK2tAl6jIFl\nORJCR+gSLvYI2RZidJIptTTkS7GK5fURjC4tt9aPHE/39LsRdWfnA27G24cZxxjSwHff3OOeEe1w\nzez7HdlB5ZEfV0vD/KSeLg1AIYVErc546JtFsjpOOzTNW6VaJlsD0cY2dqLT1Ky+3gpEzJVa1mbT\nLit4wvyxNc8V90r5Aychv7/1R+rA9BteKUtGw0pfhWLCRSqLwwcx8IyO+1TJtnET+tZIpB0v7MjO\nC2+Gjm8Gpw+Bu1o4JOfDuFLduLfMdxaI1kHv3Fkle+BVFXYbqEJAuU4NfNutwgfaRMyrkcrGdYQP\nUmIh8a2U+M6USZK5ORthFI4lMA8KpcPjngdzRhI3URlsYtkWggWGGOg24z0NHGLiVCrfDT05wyCZ\np9GYPfJQtxaCZCMphNwseYayBIEtEmWjqmLWM9hCDjuKK/vifBiUr+tCz0wIM7ugUDYW2XEkAZWg\nwpA2+jwTYgFW8lJZTPGyYEXQFNiPkVsxTpbxC5yXll1atdKrE6TBX7M1MKstK32KeCloDIAjDTOA\n1EwyI2rmW0fjOy5sMhDKhsWVwZVBCypnNiuEPjJkIeqBXg3RgldlkZ7ZCyaRbVmZvePi0OrmHELE\nL5mdN0bVzi/kKvR+IlliDILVTAkLW1Y6X1g90V+Uf+R25H67cNN1dHNjP730ynlLrAqr9NyEmWed\nMQBdiExLYQrKx7bRFWXrEhcyaMdJnEgipMjDkrnyAaNwrkKQjl9yZQd8My08d2dvlZsutNcqb4xU\nhlipXukt8cEQoAq1VtYKP7EPiEWqwzQEtEYolaQzKQnRFJEjFtq4PJcGlcteuKfZG5NtHNLAb24/\n3KDlH+Z6+/IemXbEJIgkMGE+Z7Zt5erJLe9fj8x15ZIrh90eGSJRA4cxMIbK67vIT743IinyalkZ\nxoH3XzS73fG48PEnJwJKv4+cpozayrHrCLFDQ0AkcrhuQGevhTF0HK4CtRoS4PqgvNdFJnouW+a7\nb2dMoPNAH5v9Z5IO5oLHHXN2Bl3Z9Tt629i2QqiBLjqXUnmSdk1DauFUMpethb/f63umatwvhX2o\nGI3uHopylI1QhCQTWACZqap4vMYk83YVfv1S2dXEYej5d/7UDR/2xi/++qf8uZ99wv/wy5/xZ37m\nQ6zruV8jnX/Ae3uIeYZY+Lsf3/Or31lZLPMyGiygIfMnRviHdoG7Cn8tVk6nSH2EZl8tTkQbsFNB\ninM2Y58C5o1H1X61nc5hMXnMfWX+v++0PMNvaNuse1zpNHIY2437PG/Eq0RXjF1UQoIgtAOTBM7z\nhJlwerWSi/Ppp6XVZpuhIXK6nJvVxxxVSCLUsjLEyK7veHh1gmVimiZk2XBR8pvMP/jzP83LT9/y\n9OmeNFfOc+Xlwz3r/UpOAYktcxBDR+x3DIc955evwYTXbz5uDXfZMSuoCpuDYmgMHF++outG1jwj\ntqLa8/LuiLqwuxq5TZGjF8b3bptd5wi7Dq5CyynufOLrX9uR50J2ON/PfO1n3qdLbSJ/oV32lNUZ\n+0K6HommBAo1Ku57locZK85aleM0UaYNm2duP/gq5Y3xq//bj1oNfvB1mmb64YKIIwS8BqoUzCr9\nMPAk9Cy+sFLopQMCKQX6QRmSc5wrXz/s6MfEy/lC1xm3u57qhXnN3E0zgeY0mepKoLKsjxy4x8PQ\nMLRNNGaMXccQA4VWgHB9UL5+c2D2xKdvJ14fJywpXVVSjDwsK7MYfsoQd1y2hUEDh2FPqYXjcSVY\n4GrfDJrvj3sOKXEshZenlVkgBeFpSMwaOdraSnGkIFEIVZkpaBbEJ1gD/qhUce1qAAAgAElEQVQj\nb/M1F1n5zl2DmO6K8PzpwM9/7Zq9ZkpceaIwVwEGTkSKR1J9xq7n3V4kY8yrsZjymSjbg/C0y/zk\nUwgSeCjO/xUqD58ak1dqdA53DQC76xNLbZPoyTK7PpHNHoG5ICHSS8S9IB0wCt/61kaxjV/zx+iA\n/P/svcmuLk12nvesFU1mfs1uTvP3f5EsskhRlqmBYMnWQIAHGsmwAY98L74AD30THnjksSfuYIC2\nZIMSTbMpsrq/Pe1uvyYzo1sexK6SJVESpCKqUABjeoDdnP3lyohY73qehaCecRiQYqw14yZPrDBo\nJI4OV4ziKxThbj5gJjzcr9QipPsZedqLOFXmPOOcItZdeVKhkQlOGZzrfrSae+e3tJ/NsX308Qec\nDwc22wldKrkaj+VEW3qiqCE4Z8QQQAPRe5Z5Rgo8tgdEBDOl1tRnDquh2pMqc5oJeBKKWEEt8Lis\nOBybvWOngTk1wjRgre+NgzNCbNQMgySeXXRHWc3GnBPXuwn96YHI7Cl2COobQ/T41mftzAlmgSU1\nZIlkp5zWFYrSqOzGC9Z2wft/04P6V7x+pSJ5/9U/+Ed8fPkcp4paQZ429J6AiFHViKXw6eTYesNa\nxmmgWuXY4O1sPOZKECNK5sp7grRuY/ZKUGNpntVcR+kKjKVLYw9rxkS5Cp4kQkmZMCihVoIIexeI\nWritoD5yUwoGTM4wN1AtkYi4UjraVyECKWx4sEZpQo2ebQWnhaFVpmaMG8/S+gf4Pvf5qL1CKhXz\nASdCEQf0D1/GqAIVpYpjtNZJT07QJnhV5tZnf7wIzYyxNUQro3icVAYSB/EUFzEqrRpRHZN3OKm4\nqk9/Gc/cMmrWc62SGcXYrImP2xktQpWVVgWTylUDsSObOFGBJjBXz7x2a3RdMiLCvXjOLdOqY9CV\nSw3d4eEaXiYecuHY+hzWmUBphVKVVZTn1vO2ZW0UcZTWI3mp9Xifdw5y+lkUwKT2Qt/AauGxVR5N\niG5kVIW8MIqxZOGTVlDfDdbvzfg2GSk1qneoKRcCz9zKxiujd3ga91XZausCSjUkdETyEIW0eswJ\nszpqBh8rq8HeR+7mjlW1UNHa5YBjcOwlcxkiD+fCoRQWt5DywqeyYRoH7g3et8qI4lS5DvD4pE0K\nTvBe2Q2eYJ67XHg0kCaYzXxixtY89064c47jkrjJcJQepYllIjl4/XDH//BP/3f4FYrT/LSG7P6z\n/wb/4rdw0UOqfVYjKiFGDPp8BY0XH1wyTY5aGjH2PPt5Lty/PXJ6PBBCBwfsr/bgFEpms98SgiOX\nSs3dRSYCrinDxnM4nGkoV5cTtTXWE8SNIjS8KFf7AR+E++OKHzy3j31senKChEApGXOelnqMJjrF\nOwHxHEqBVmnRsymC19pvtcXYbAfWVhlFeHyshLES44ZcMoHK4EfW1sWA1exprtE6TObfsYZ4P3W8\nsGTOrQNZoNCqoWFk8g7VRijC6jyhCWtNiBnmAiaFUYxS4FrPeItUqXgdWMvMXoWNy3wnjDSDL2vi\nWJRkDVMhLfQaYp61JFoVHJVx8P1CxsEGx91aOBYjqLCUSjpXcupk04tJSA3ykqkm1NppZOtaEIw4\nRsqcnn434OmW00woa+J4P7PMD2yvX6DeUVPB051cFyGiJJoE7h8PLPMjZa1Pl0ZK0D4gvbnYMmwG\nPMLDcWGzidAcLig2KHY8M11MpNmQ4GgO1nPBjb0jtb+85Pabd6yHhNs46nlBRBhfPic64erZlndf\nvON0mCltxpUZHzY8/+gTTuuZ08Ohg5HGkcvriYe3j4Dghsh4vWe/HwhD5ObmkfW8dojB+ZGL7Z7d\nuOXYErUV0unMw90DxWr3m5kHcdS7H1F+/xc3rP1XtX5aR178o/+a+PzXUHFIa1Sxp+etz/7RFJPK\nxXYixg7t8doTKWupnE+JnFZUQNSIYQDtB6A4DDiUav3d26DHrZrgo+tCUlM240CzSlnos0rS68g2\nDoQgPM4rGjyHOYEZgwPxgVp7x4Ha0ODwTvHa9SRzKVAqZfBsKqgrKK7PzWwDS61Eg+OaCU4Y4kSq\niWCGJ5L7yNLTXqQhrfWLu3/HOhLchOhTHXlK0fy0jjj/VEek4avQhoBkYSkr+kRP+2kd8V75/ALE\nPHPKPaKGcBF7HO6jYYthzHVlrcq7JWEqLIe+F3lYG6dlpjVhEOPianza3MOoAzeHc1cSmJCa9W5O\n7dG97fgk914zTZRaCyJCLYaIoaHXcvvZbqSACmZCa5W0FgqJoAPq+/yq80bJxkZjnx0Q4VQSae3Q\nCkfviAdxqIcQAj54nAlzLgSvUBWnRvOCtEaInpoMnFCtUYt1imurDH7DvJwpyfr3o8OqnO+Kg+04\ncjrNLGvurr92ZggXDHFDbpl17Z4qcZ5N9CxLhqcOkvOBzdC7/KdlppROQm2yMrmBQSMrmYqR54W5\ndME7rTvIrCn57ic8/E//Lfx1JO9fXWpCk+4PUBGCG4h15YUm4tDnhMoAN9a4O3vaGKE6WutZcnOF\nSR3aOv770ArWPLVW3q5d9GalMvqG0NvLWZVtVi4YSa2b5L8w4dhG4qkxquBojG5BbeDzCVKtSOmb\nzGeycuES10NhzZX/A+V8ihTfmKKxX7q8MrjA39ZGlDOpRW5r5VYEP69ECzTXaG3grvYI4kaEqZ7x\nITC2fivRvHLTIgdT1qfU1GpGlS6j3WonsuEjYg1BGBpUbTTpXY5gnlI9KoJ/wluq811mm4XxKWoi\nqmC9uFjRjuy0iUPtBKnsL9AxUbLDTAi18jY0St6z1IzRkGb4Wkk14DCejwM5Z9ZScE1RcTyw5640\ngjhazkjOUIXV0clRtjBWoWplpxWf+wYpN6OYkVtlUsEjbJz1jot0AIOIsPGBtBqrrxyaRzRywUqg\n8cwyzje0rqQwEQfhvCiLZVzz/O1JOO0itTgOZeXCdXN7tMqohe0QGFNldxnYpjNlUZZJuXs4MeB5\ntr/ktM7sJ4UMJ5uwXLs/aWxk9bhQ2AKvy8QoQFogVkqo4DyLXZJ0wx+3iVKEqkZoDXOddviqBSxU\nBCGLQhmgrIgK1UXKOvcBbRf4gSmSDAlKqBDsgrrPxGxoazjf2Had2a/sUuuzLXVNqHMMw0hrxiZE\n4t4j4jiVyqEunG4UPyqsrjswUka8MF5eoFYwE87nLt9Ly8rjXZ9PstyIo0eioy0FCQPmhd24Y22V\nvBbevT90xOqN4QaPCby+U6IEPvpsTzpX5OkFu90NbLeOF9OWOWX+8OsD5e0KXpkuIeQAqpgP/PY4\nEXxjVeXusPCQEsc8M+E5uUY1x3EWtutMHANVlSzg1WEYGwd3a+bclLuqgGMyyCK0Jnyo7d9cQ1pX\nMpQKKornn9cQzdZjLsNEsQrmWCkU3x136gzayLqcCeOAaqSEhK4dQmAiHJxwm7b84LxirWGhx8tS\nzTj1XI2Oc2p9jqb2F9yxCodDZnCeXDvEQ0yZS2NoQtaKjxEvmWkfkdLJqmaNuhZOS2UzBDyVuNnQ\ncp9dqtZ9IdN2yzKfKLWj6t00sBueg1Mu9gOSIq2u1DCxfTFw/+qGlGZwwkeffkJGKAlOdzdsLvfE\nQdBm7CI8/+iS8a3no9/+kM268ni/whT4wR+9Y9qPfP4ffMK7n7xieLannRNLiDy8faCmxIuPL1n3\n1nHQmxfcLIUhDCy3XUskk2caLpkfhCwB85Fv398j9P+fJkptmbc3/cbdxBAzlveP3L6l49cRyvmM\naZ+Ze30/Y/kBjYpTh4pnuLzG5Qy1S9etNeRXuYgAzrr82az2aJTrnc7BefzosKIkjFNbmY/98gpz\nZKlIrZ2YFwfUSqfblooAzSrLUhHnoLZ+IeIFKUYWR1RhciOrVGrOHFLGEujakD4sya0/E/Bc7jeU\nVBHrHdExePZ7x6Xfk6n8yTcPkDJNjDAYsQZMoGngu2EkbITFGsd55VgS58fMgGfVRmmONVdSPXYv\nz9PPGZuAdArefYZzVWbpdWQw+l7EhL39W+qIFUL5y+uIFCPXigtPdSQpWKE6T/v/1ZElnbnYDKxZ\n8HvBUtcKDNFxty7kHPhqvcVqpYQtrmXW9YwPI9fXW86HmXVeUVWcwrEJjzcLgzpKy9R2RJqQMELt\nPzvO463hff/7qfS9SGuZtcHgBMXw0f/s4GR0+FccBkpJlNr9m+odscb+zgmK+kArhTAEhkk4zYVc\nGiJwvb+gYLQipDTjY4+3OQPvhf00EGbl6mJHFDitGVDeH+7Q5nl+ved+PuH9SKmV1oTlSSy7HSPF\nAcEYvO9+LufIqQvZm7QOCjGhmGctjrkswNOeHRAKj7Og1kmnVEFq5XTOiDMU7d1/dagod61gsj4B\nNAQlEn2PTvaNX+9Aibhf6HP/K1W1ojcutA+xY/aUX4eCsW+JsYC5wE4826GCX2hOCNnhx4JHaM1j\n5nmdE4cqDAY+OEKpnGvhoRqXfuIDJ2RdKCasKIlEsN42/Ht+4te3laArr9bGTx4c+8lxv2S+WYTB\nKbN02MELV5mK511VDpI4lIFBDnyvKt+b4I2PfDEb9y3zz06VtSmFxN+/bLwcHT94cCTfb5cGnl7k\nuSHSuBgaUipFlBCEbxbHY86s4khE0H6QUteYmuK1gYLmBW1dWNvMaCo0aUQRqgSgHyxwjmoCpeCe\nbkEWKlUcoWUmDWyLsaHxsIBYJvqVWDONClKxZjRxJCnkufbYivM0U5DE6Bsfu4qz7i9xCp+NPN2i\nGaGtOOst5WrKoguDi4zSwCkpz2SJ3M2C+Y68vC8CrbCVldEFpgDiGh8NnnQuPMpK1EiujaUUyhDI\ny4GPYj8YBjGOuYIKERDxT6CWyoCxUcFNB0Kb2LWV6Cc+vITXtpJOE8X1+Y75rKxFGQ7C5xG8zqAX\nxLDhkIy0nJlipFlEJfGMzAdbkJKowHlQHmehhshuqCzrSh0yuo58Mgx800a8GcrIToSTGNpgcIVr\nH3hJoenKnAKP6ni19M9Nc5XRoOJZQuh5ZP9EsPEeBtg240Izl3TZaA9oZl4leHC/upG8uIm47UiT\n1ruQrd+6ra0RzWitslfHs6sdk0JxhlMhV08MrXddao+bvrk9sKSENMfoFVNhXTI3NwcuL7a8vBw5\ntdbdKLWS1wVqpXnH73zvQ777YuBaCn9xgB9+/cD1i4mH25XXX98TdxPLvGLV2G4CHxB4e1w4rZnl\nsBC08mJ/zd/8fOLmBN+8PTIvR77/9sx67ljc3/sb13zoJv781crsnuhKzjgW5VQasmae77sNPpdE\nnDxvHxMP55X5L6khm+I7Zc4ZIS/MxVPITxh9IbpEFP6tNcSV+15DpDFpYAoTg8JhMcQKeCO0jAAh\nNdb2U3pbY8mhR1bMAQ5XK0NsvAS8ZU7W2Iix2xh3xeNypUjP+ntvFBt4lMw+OD4KDqFwrJFUlftT\nps19WPnhcKSuRiTz8vk1Y/ToKPzNDbydRx7JbDcD57NxOmcuxz3v3j/wnc+fUVIhOjg89M6LesOr\np4WRfDgTvWezGRmCMYTIuizsX17ya3/nY768P3L/JiGD8sUPvuLH3x7QlPni60euRlB5z/47v8uL\njz/k/fsHjv/sS66uN/gQsI0wlMKHv/MBdloowK2tHB8Kq5vY7bY8vLmhrjPr/cinn37M+8e512Mx\nnHpSmXFNMe+5uNpysY1UKuu5kLJw++otGiKNlegCor7PFTS6sFwMiSM2REJwxCGwGze0mtCo1Fy4\nfXVDPg3kX1oV+PmXOHC+wxqsSRcui3U1BgJqTDjGcc9GoWk/XIof8K72uV/r8ed3xwPLmvAaCE+R\nsLIWjpLYTBPPYuRsfQg+06gl46thXvnsxQW/9ck1kcpX72e+unlkuxm6CPZwxGskaYWcGYMS68C7\n5cBcKiUnvC9cj5d87/ML3t8l3hwOtHzmx7cLpfTN/Pc+u+Rl2PLjd2eKVqQpURpZlVLAW2XrB3or\nLBE3gdd3C+c5/aV1JKA07eQ6lxeKV6plihhNBC2JyL9HHXETkxce1oq0Xkdiyx0hfqg89UeYl5lj\n8bgGTjpUQtPKuHX8xotLvMHbNXE5Kpe7He8OCVcNK4WAp3OSRo7zzDYORHV413hcEqUpxzWjoSHV\nOOeEZYjRuIgj0+ARZ3z35QV3dwv3dSWGQFn6O8LpyGE5s99NVOsDF3NOiAnqlaCdzttq6WCy6DCF\n4AKBwjBMfPLZc749HKiLUZ1w+3jP7eOC1MbNw8p2EJoUpmHDxo0sOfP27sBmjKj3DNKhDJe7C1ot\n1NLIvnA6F4SBIXRwmlilpsbV7orjukLqcWa1foGtFZoqmxjZRE+T3nDIRZiXU6fl+fJEAxRaDFDp\nnUVv0DzmXY85O0eUANIv61srnOeZ5v/6wPSvXaM0gu9EIqkNk77Zy85xLAtXsWKqvK2FagO6FEYU\n9Q3XHGfnWRmeiCYbLjQzaGaqlRQaRsH7wJIW/jy47mrxjWsx9r6x846dCELmB4d73qcdXxP5OGTS\nqgRnDMU4l86xF6f8qAT+SQmMCgOZ5DYkMm8phLPnx8WotvC7buKjTeaZFL6fAt9fd8T1kVgruVTw\nE5eyduloErxmige1HgnLZ2PblNI8WRwpZ5oTTq7irFGB6Iyt77jh1iqreRZTSjOQhqggdf3ZjYdl\nw7lOoTOEYA2nQqyJwYFPmaodLPAiLwxRmFZBOKPWoGZq65hfU5hswUwZc8E/OTqOx5nXjB25DqTU\n5ahb54g2s1SPNIghMEjivnmOJbNUj/fKpURC6zfnZgVa47OxEWrhNsOZxOEEz4eRt8uKDo7D4jiK\nAJ5SCmtOJBvYWGTLQn7CYK619YFxmVla5iODD/bKzTJTa0dxfu8i8L9+NfO/Fc8/HAMxrGzE82wD\n318b34rnTpWjTWS26DzwQo9sZeALn/F5g4ljJ8Zcjc3ccLJnlMQrDcxOKWslr55ahRqFi1b5ro98\nhxPf0YHcKoso+2LECY5zxVnmbQq8PlfePtHAnDOMRy6Z+MQbav15ySYUc5znhVsrvMyOVjKPrfHD\nxZHMAMFFj1bj/Sn9cgvBz7GqKC54nBktCM5VanUMwbPOxnbrcep4dXfESyC3legUpx7nhOqMnITz\nYSFO3ckRveJCj4G0lAjRczjNHOczZc2E3cDVbuLiamDcBK5DoKnyR68eONxmDvPK9eXE7fsFPwq6\nOtJpJURPEeHm9szXP7lFwoCXTo7KKMc18cW7gVcPR5ac+O0PXvDySvjYJf70GPjq9UJxGVc9NWdC\nHAh7ZU/leE6cq6NsDa09EjufjaAQzJPNWI4VP544BfdUQxLPmuBDI8YRkZnVPGvylGYklChGad1l\nogjbVgh/SQ0xy/g4YdWodSY1x2CZYVBCEWo7UJrDglCb45wNJKCud902sfU6pcr7Jyn0skIplXPK\nNCtMMeIV1tywYsTtyBiF9Vx4+3ik5n5bO8ZAoFGy0DQjrfHxR8+ILfP6/Ynj8cjDOfH8gw/4/jkz\nbDx3d/3fXFPOp5WaK6k1iiaCNTKVsB1Z5oW5NOx8Yp4fePHBJd/7nZf86M+/Iq8jul35T3/niv/+\nf/4J//T7hf/4977L82vHyxh4/ruf8adf3HJsBm3l9mSo7Lj5wVscle3FBe/ywt17Qc6VKcC6VtyP\nb3G7HUGNx7UiufLu7ZcQAm3NVJc5zpnhesPHn2ww25PmPrg9umuG3cDDzQFV4eHuzPtv3v6MuqZi\n1PMt4+6a7XaDedhsBmqjzwM+PJLKmQGop8Tjmrg/P0VpTJDg0GaU+4dfdin4uZY5B87jaiOL4qRS\nzXfh6ArjpCjdE3Z4El77oKikjo6WhhXHuSwEp3h1nQrr+mxc9gXfHMu68u269PmQ6Nk6zzR4pm1g\npwM+Ov7wi9ekuXJaC9sYuqvHC2pKbh3F3yTweF65uX8D6nE0nHpyVY555eu3K+8OR7JVPt9f8vmH\nE88n4Y++XXh1M1PdCVc9qWQGN+AnRUtjWRJzcOQYerfMC/N9wYkQxXUvUm24cOZkirPGUCAGI3rt\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XAww7x11u/L/zS85pITxlBZx51mJEhF+zgMlK8oEPfGPIhvOF1Sp/ywv63HNeEw/J8Ydn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c2hPQBgXI4w2WCDguaQSqBNDksZ/28MP0GnEdIOUUXqWf6TJupakRgghEGuWgvEiNsR4iVm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\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s75C4kvoOy0n2JPKCDSceqphRbeSim8k4QR9ri20D3KqYZyu/SCZ5Lixyq8FfDbreInIn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\nSI0wElzl1mIxnivSqSimz7m2xNGsdUGqb3SNLnR8dFnypui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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(2,(10,8))\n", - "\n", - "pl.subplot(2,3,1)\n", - "\n", - "pl.imshow(I1)\n", - "pl.title('Image 1')\n", - "\n", - "pl.subplot(2,3,2)\n", - "pl.imshow(I1t)\n", - "pl.title('Image 1 Adapt')\n", - "\n", - "\n", - "pl.subplot(2,3,3)\n", - "pl.imshow(I1te)\n", - "pl.title('Image 1 Adapt (reg)')\n", - "\n", - "pl.subplot(2,3,4)\n", - "\n", - "pl.imshow(I2)\n", - "pl.title('Image 2')\n", - "\n", - "pl.subplot(2,3,5)\n", - "pl.imshow(I2t)\n", - "pl.title('Image 2 Adapt')\n", - "\n", - "\n", - "pl.subplot(2,3,6)\n", - "pl.imshow(I2te)\n", - "pl.title('Image 2 Adapt (reg)')\n", - "\n", - "pl.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/examples/Demo_Image_ColorAdaptation_mapping.ipynb b/examples/Demo_Image_ColorAdaptation_mapping.ipynb deleted file mode 100644 index 51b91ed..0000000 --- a/examples/Demo_Image_ColorAdaptation_mapping.ipynb +++ /dev/null @@ -1,349 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Color adaptation with OT mapping estimation\n", - "\n", - "Demo of Optimal transport for domain adaptation with image color adaptation as in [6] with mapping estimation from [8]\n", - "\n", - "[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized\n", - " discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.\n", - " \n", - "[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, \"Mapping estimation for\n", - " discrete optimal transport\", Neural Information Processing Systems (NIPS), 2016.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import scipy.ndimage as spi\n", - "import matplotlib.pylab as pl\n", - "import ot\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Loading and plotting images" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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mTia0hLGlGUdRPJdlnZfEqBoxOLLbusg0fQycdE60mvwID4Ylse5O2ZcHU+LH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\n41yBpIqq38d4HxfdExL3lJffqz5zh0o6f6MfJwg6URVT45HwJ54UukTc9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QTmuMY+j45F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\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J3EjkYbtwM3Qdo3jh1nsXvllEaJzbyucijKlPqTbJCIUDgUMUGoEURz6MC2t0gm58nCIJ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fhX6o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vmv/VN/ir//kyMtKVkrJRlZY5/KUyHl6EbT0oi8iOlizmAe72NtM+QrmZgkaUrsm5A1ADwx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\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4xz+7HdCiqVAp2wpEYK5NyVtsMyPOf2nBvYT53DtihAdiVAQl50iEL3TOr28hR8uTGCQz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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "I1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256\n", - "I2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256\n", - "\n", - "#%% Plot images\n", - "\n", - "pl.figure(1,(10,5))\n", - "\n", - "pl.subplot(1,2,1)\n", - "pl.imshow(I1)\n", - "pl.title('Image 1')\n", - "\n", - "pl.subplot(1,2,2)\n", - "pl.imshow(I2)\n", - "pl.title('Image 2')\n", - "\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Image conversion (toi matrices) and subsampling" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def im2mat(I):\n", - " \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n", - " return I.reshape((I.shape[0]*I.shape[1],I.shape[2]))\n", - "\n", - "def mat2im(X,shape):\n", - " \"\"\"Converts back a matrix to an image\"\"\"\n", - " return X.reshape(shape)\n", - "\n", - "X1=im2mat(I1)\n", - "X2=im2mat(I2)\n", - "\n", - "# training samples\n", - "nb=1000\n", - "idx1=np.random.randint(X1.shape[0],size=(nb,))\n", - "idx2=np.random.randint(X2.shape[0],size=(nb,))\n", - "\n", - "xs=X1[idx1,:]\n", - "xt=X2[idx2,:]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot image distributions" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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Tvw3wA9AJ2AfkAWYDFqDfKwo7Xfryyy/5ceRIMpZvQd48ZQi/eooJEydx82Yg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sQKPRMGPGDLvgh9VqZceOHezfvx8PDw9mz55NV1cXO3fuRKVScf311xMUFHRJW3d3d+bMmUN4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4fdbqdm927KbC7a/eRZQ6nVfwyXy8X06dNxuVwMHTqU/gMG8u233zIoMYTbW0VzqMbK4txq\nInQKwhyljBo1ii+++ILx48f/3HL4RXTs2JHcvHwWLFjA6dOnycrKYsyYMeh0Oo4dO8aKFSuQJIk7\n7riD0tJSpnz6KX/ZWUKmSc20rknUuTx8cLySsUtzGZERikIuQy4JKsxuJl0fg8XhZfr3F08p/4/w\nH+EKKEnSTuBHIcTjTe8loAiYIYR48wr2lwF1wCNCiItGpgZcAZNag7UBaktBkkAmB68HZHKSEhMo\nLCj47U4sSJAgQYL8bl0B/9nXpt+7K+Ds2bN58MEHSYppg1rpd2Hy+XwUVhxBrdSSGJ2BEILc4gOo\nlBrSYlsFbpqtjkbyy0/wzTffkJ2dfUHfZrOZ2NhYQhQmksOSAHB73RwpP4rL60Yhk+PxeZGQUKlV\nVFRU8NlnnzFp0iQAZJIMIUQgK+DkyZN5+eWXf/E5du3aldPHT9AtKR29SoPD7WJfST5KvZaCwsLf\nrN7T+PHjWbpoMYOS0onUGnB5vWwvPUOJvZGioiKio/2B9xs2bOCmm27yF6dVqbA5nXTp3JnVa9Zc\nsYvjv5L169czePBgRqem0u288b128CAOnxfhE2gUCqwuFwOuuYZly5djMBgu6Ov6oUPZvXkzD2am\nE6/TYfV4+Dwvn0K3h02bN5N9440UFBVh0mgwOxwkxMWxZt26ny2f4/V6SWvRgji7lUcz0wLr86PT\nZ9hYWc2bndqQovfH/DW63Tyx7zAOrw+PEMgl6BsfwWOd01hXWMX7OWeQSRIquRyHx4MAFHI52SNH\n8te//pWZM2cy8+23efmqTDpGGvnb3jwOVJr5pG879Eq/MK6wO7l7y1HcXi8qmYy/dM6ka7iJxYUV\nvHeikBe7pTEoMQKfgAW55bx/pIgMk5Ziq5M3umXSI9KE0yd4/3gRX+dXMO/GdrQI0TLvaDkz9hZh\n1OtotNoI1/kTcyTo1bzfvyXJRg01DjdP7cjjpNWD2WJFrZCjlMmwuNxo1SpuTDbxaq8Wgbn7/FgF\nf9lTxLr72vHmphJOOo3k5uVfkOnwt0QIwVNPPcVbb72FXqVAADaXh2effZaVK1ZgPXOKvSPaolX4\nx3C8wU6X5UdQKeQYQsKoranmyLR2tIzTsO+MlW6Tj8F/W/IKSZKUQFfg9bPbhBBCkqR1wFVX2I0e\nUAK1l2tI8QkQfvOfpA9BlpSB8LiRzhxj5EX+uAcJEiRIkP89/uXXpt8hmzdvRqcxBkQV+C0qRl0E\nDZZzT4K9Pi+h+ohmlgi9xohOo2fz5s0XFVb79+/HbreTHnsuE15BXSE+IWibkIVRY8DtdXOs9DRO\nt5OYmJiAyEmPSCQ+NBohfBTUllFUX8G11177q87xyy+/ZODAgaw/kYNercHmdIAkMazf1VRVVREf\nH3/R/VwuF9OmTWP2h7Opqq6ia5cuvDh5Mtdff/1F27/99tvkHDzI8iNHCNXpsbqcIEl88cUXAVFl\nsVjIzs4mFBnZLduhUygps1lYd+QoEydOZP78+QAsWLCAaVOncuToURLi45n46KOMGzeOl19+mfnz\n5uF2uxkydAivvPIq7dq1+1XzciXY7XZuHTsWtVzOwZoaukacWwNH6+qwuly0Dwvj5pRkdAo5xxvM\nfLltGy+88ALvvPPOBf19OHs2AwcM4C85h4kxGKi121EolSxesoR77roLV20tUzq0IV6npczu4KPT\nZxh9880cOXr0kpbKoqIiCouLuaVVRrM2XiFI0WsDogrAqFRyQ3wMCwtLUUigkcsptToobrTzfs4Z\nBsdEcleLRLRyGVur63j71BlenDyZyZMnAzBlyhSWfbuUZ7ceJ1qros7pZnhSVEBUAcRo1XQMN3DK\nDR6LhT/tPUGMRkWj258l75U9ebx7qBC3T2Bx+61ff+2Vzqv7Cnj8xxNEaZRY3F7sTW5vD609hSRB\nnc1JWIiJaMnLm4PbkmzQsLGsnmd25zFiZQ7JoQZKG22o1GqsNjv3ZMXwUKs4lDIZ7x0tZfbJckal\nR7KttIEXdpyhwu4GQADj5x1ndPsoVu8o5NrBg9mxYzt6nY7bbr+DKVOm/GLL7s+xePFi3nrrLV7t\nk8BDHaMRAt7dX8Er06YRajTwcFp4QFQBtArR0jVcz2mhIi4ujlSTjRlrKljwYy1Wxz/HFfA/IcYq\nEpADFedtrwCuNGXMG0AJsO6yLZUakJpy9wO+ugpkJbnoNBqeeOKJKx1zkCBBggT57+Zfe236HRIS\nEoLX5+F8zxeP14VMdu5mUZIkPF53szY+nw+31x1w8bpY3wBur9/FyePzUGerIzE8FpPW6C8W7HZh\nd9lRK5TEGSMxKXTIJBlVVn/2NblMTmpEAgaNjk8//fRXnWOrVq1Ys2YNWq0Wl8dDtN5EWmgUP6xd\nR+/evamrq7vofuNuvZVXXn4ZjcNJ24hoTh85yg033OCPI7sIUVFR7Nu/nwULFnDvww/x2l/+Qn5+\nPmPHjg20+eabbzCbzfSLT0avVCFJEvF6I50ioli8aBENDQ3Mnj2bW265hdr8fHpFR6Mxm/nTn/5E\n61atmDvnM1rqtXQJD2PT6jX06d2bEydO/Kp5uRJWrlxJdW0tQ5LiOWU289mpk+yvqeGH0lK+PH0a\nmSQxpkUyeqUCSZJoHRpC78gIPvv0U7w/iVE6S3JyMoePHGHu3Lnc/sADTHvzTc4UFBAfH8++AwcY\nnRhHvM4v8uO0GsYmxXPs+HF27bp0IViDwYAkSdS6mq9PuSRR73LjO29t17rcaOQyxqclEKdVc7Le\nytv789Ar5DyQloReIUcmSfSLCmdAVDifffJJs2Pt3bef+NhYzG4vapmMakdzFz4hBJV2F+3bd8Dq\n9ZFq0JJq0NI21IBBISNGq2JIiwiuTggN7GPx+pjeJ4Nkg5o6p5sMg5bbU2LIMOqwuDzcNHYcH374\nIXUNZrpH6tlS3kB+o4Nr4kJ5qVMKAhg0aix/+7/pjLjxJlJD9DzZNgGdQo5SJjGqKQ5rZ5mZ+37I\nxSfg0XZxPNQmlnC1gmqrh5k7ypABRQd38XiHCG5OVDJn9vsMGnANDkfzot7/CJ989BG9E0080SUW\ntVyGRiHjqe5xdI7R43A4KLOd93dGCCpcXkaPHkNWVhaHiux8vaOWe7pHck/PK4+3/CX82y1WP4ME\nXNZPUZKkZ4GxQH8hhOty7XHZzv3fUo+w1JOUmsrSpUtJTU399aMNEiRIkCDMmzePefPmNdv2jxYx\n/Q/jN782TZo06QKBMW7cOMaNG/ePjPOfzh133MGsWbOoNZcSbopDkmRYHQ00WmsIM8UA4HTb/Teu\njZUYdSHo1AZ8Ph/ldcV4PO5LxmR07NiR1q1aU3SmCK1Sy1mnPo3yXMxMUW0pOrWGjsktkTU9MI0w\nhHKo+CQ11noiDWFIkoRKpqSi4nx9fOW89957SD7B4NQ2qOT+26ZUVxQbC48ze/ZsnnnmmWbtd+/e\nzZJvvqFXUgtahPnjo7Iio9hSkMczzzzDyJEjL2pBUSqVjBkzhjFjxlx0HFVVVagUCgznuR+GqjV4\nvF4qKyt54fnnaR0ezuCUlMAxInU6thQXM7plBslNcU4dY6L48thJpk6dypw5c3713PwcVVVVyCSJ\nntFRGJVK1hWX8nVeHnJJwisEJqUS7XkJOKI0GhrLynG5XBdNi6/RaLj99tu5/fbbA9tycnIAv7Xn\np8Ro/WulsvLScTSRkZEMHTKEpRs30MpkJF6rwe71Uulw0OD2sKCwlNFJcShkMg7Xm9lYWc2IxGhG\nJMYwLCGaV3JOcaTeQrpBh/I8F7gErYYdVc1DMY1GI3v27eOPTz7J/K+/ZmtFPVvK67g6JhQf8O2Z\nSgosdgZlZvLcc8/x6CMPszP/DHJJYkBSGBM7JxKuUdLo8vD9mRrCQkJ4+1AJQxJCKbQ4+ah7Fh1C\n/W6UD/h83LMnl9KSEtauXQvA/Lwq5BL87XAxt6ZFMTLZL5rGjRvH4MGDWbN6NclaRbP1mWxQE6NV\nMvNQGTqFjGVDWxOm9n9vY9MiGbzyCEoJEgwqlt+UiabJYjQiPZzh3+SwYMGC3yxrYFVlBW1NF7rf\ntgxVc7DCyhenaxiVEsbgOBNeAW8cLqOw0UF8fDxfffUVDregV7KWYxUO6h0Xivffgv8EYVUNeIGY\n87ZHc+GTwmZIkvQn4GlgkBDiyBUdTZIhS0wDnQHcLkRZAY0WCy1btvwVQw8SJEiQID/lYoLgJzFW\nvyf+Zdem6dOn/y5jrHr16sXLL7/MSy+9hMVRi1ymwO6wAlBrLqfWXA5AdHQ00dHRHD58GL3WgNvj\nwu1xM3PmTDIyMi7atyRJzJs/j4EDB5JTdhidRoeERI2ljjB9iD9Lm72RtKjEgKgCCNEZ0CjV1Nsb\niTSE4fK4qbObL3DZW7hwIW+88QYnjh8nJaUFjz/xOPfee+9FBc/atWuJ0RoDogpAr1IToTWwYcOG\nC4TVxo0bUSmUJIeec4GSJInUsHC25eaSl5fHp59+yt/nzKGxsZH+/fvz0pQpl/2N9OjRA5fHQ2Fj\nAymmcxaL0w11yCUZEydOpLqmhmuyspqdR9uICLYUF9P4E6uMWi4nPcTI+nX/PGNq9+7d8QnBkdp6\n2keE0TYsFKfXx6rCIvZU1WB2uym0WEk2+BODCCHIqaujTevWv6jWWMeOHVEqFP5U5wnnSujsqalD\nLpNd9rf1wYcf0rplS546cJgErYZqpwuvEPSJC+Ob4jLWlFWiU8ipcrpoE2JgVLLfYC2XJAbFRnKk\n3kK+1U6Z3UFck5jzCsGOOjPdu1+YFNRgMJCckoK8qbDvK/tPE6lR4vEJ6l0eZMCC+fPIyspi567d\nJCcmMrJFKA92Sgz0saHIbyl99733eOiBB9h/oJBknTogqgBUMhlDo0P4YOMGXG4P4ztG8UDPOFRy\niYWHqnlrawmlVhcS8Ma0adw6dgxOlwuH3c7JBhtZIX43SJfXh1wmBzxcnxQaEFUAcXoV/eNM/FDa\nQKXNzTObC3i8SxxpoRraRepoF21kw4YNAWElhGDOnDnMmD6dvPw8sjKzePKppy77EMlutzN16lRO\nnsrljNfJG329GFR+q7jZ5WVNQQO3tQxndUEDI9afIkmvwuYV1DjcvPTSS7z00ku8/vrr9ErR8cPD\nWQDsK7bRbfrxnz3ur+HfLqyEEG5JkvYCg4BlEAgQHgTMuNR+kiQ9BTwPXCeE2H/FBwyLRNIb/f9X\nqSE2mer8Y6xcuZJRo0b96vMIEiRIkCD/PfzLr02/UyZPnkx2djbz58/HarUyZ84czGYzIfpwVHI1\nZnsdlZWVTJo0ifT0dLZs2UJoaCjjx4+/7APNjh07cvLkSSZPnsyhQ4fweDzs2LEDEITpQ5FJMlzn\nuxgKH26vB4fHRWlDFcX1fg1ss53zVvnrX//K008/TYTeRJw2hOqiUu6//37y8/N5/fXXOZ8QUwjl\nlReGybl93oum9jYajXh8XtxeL+qfWGTsbv9YBw0aRHFREWkhYURp9Wz9YQN91qxl85bN9OjR45Lz\n0adPH67p358N27fT3mEnTK0hv6GeUw21JOuN7Ni4yX+u7uZzYvP443N+alGpsdspabQgM5qora1t\nlnnwt6Jbt25cP3Qoi9eto8JuJ06n5Vh9A3uraojVaJBJEp+cymVAbAxhajX7amo4Vt/Agtkf/aLj\nREVF8dDDDzNz5rs0uj1kmYzkNlr4oaKKu++5h4SEhJ/dPzk5mf4DrmH/5o20jTASqlbSNyGcCI2K\ncquTokY7Tq+EDHi6bRqan2RgbHC5kctkJCYk8OKxPLJjIwlRKlhXVUuuxcr7TfFVZ3G73Vw7eBAH\n9+1jWFIEoSo5K4tqqXb4v7PkMDXXtQqnqM7Jn194ngMHDjDpj39k6tSp2Lw+useYOFprZeGpKsbf\ndhuDBg1iyiuv8MEHH1B+Jh+PTwRStIPfdVEuk5McpuTJqxMCgnt8p2i2F5rZWtiATIJDe7Zxc/sQ\n3F4Vi/c7uWXjcR5rHU+Fw836sgaqnF7UGg1VDs8F81fpcJNoVHFjZhhLTtQyatkJvs1uRaJBRa3D\n0+w38sorrzBlyhSGJIcyIiuEbZV53HbbbZSVlfHkk09e0LfH4+H777/nj08+SX7eaYYnG1lVaGPw\nohNM7ByNT8CsAxV4ffBct1he7RVP5tzD2PEiN4azf8caOnXqFOirovHStfR+M4QQ//YXfncJOzAB\naAV8CNQAUU2ffw68/pP2T+NPZTsS/9PEsy/9zxyjCyCkmEQhb9U58JK17CSQJPHee++JIEGCBAny\n27N3716B332ui/gPuOZc6euffW06e13au3fvbzzj/x7efvttAYiEiBaiVWIn0Sqxk2iZ0FFoVDqh\n0+l+cX9FRUWiTes2AhBymVwAIiI8QsTExAhAyGQyoZArRMfkluLqrC6id2ZnkRAWc3atCUCEaY0i\nRKMXw4YNE0IIMXfuXCEhiQRThLg2o6O4LrOTuC6zk0gLjxEKhUKUl5df9LxkkiR6JqSJG7M6ixuz\nOouOMUkCEEuXLr2gfWVlpVCpVKJFWLgY3a6TuLVDF9E/NV3IJMk/bvz/hmk04uaWbcVtbTqIcJ1e\nXHfddZedE7PZLO677z6hVCgEIHRyhegXkygeyuoo7s5oJxSSTIRrteKudu3EY126iAc7dhSpISFC\nAtE3IV483rWTaBcZIQAhNc2RRq0WX3zxxS/+fq4Ei8UiHn74YaHVaAQg4mJjhV6nE4NjYsRL7dqJ\nzmFhQt40LyqZTPTq1etXHcfj8YgpU6aI8NBQAYhQk0k8//zzwuVyXdH+8+fPF4B4oF2K+HJIZ/Hl\nkM5iaEpUYI7O/js4LkLM69tJLOrfRbzdrbUI12rELWPHioKCApGdnS1kMpkARLs2bcTy5csveZz/\nuypTrL6hk1h9QyexdEh7oZZLomuSUax9pKNYP7GTWD+xk3h6ULIAxHfffSfiYmOFXPKPQSYhTEaj\neOaZZ4RSoRAySQrMYbsQndg6sJP48dou4tMeLYVBpRQpKSmif2qI2Dexc7PXHZ2ihUxCGDVysenJ\nduLQi53EoRc7iZn2E3oAACAASURBVFUTWwuFjMDxlHJ/36EhJiGBeO/qNHHyls7i5C2dxes9/GOc\neW0LkfdgJ7H/rnYiRqcUY7PCxZNd4wQgdu7cKYQQorq6WqhVKvFou2hRNqFT4HV3y0hh1OtFY2Nj\ns7nKzc0VWenpAhCKprG0DtOIedelil4x+sBvvG+8QWwb3VJYH+osrA91FokGpbgqVS9ioyOb9afX\n+/eZdkO8cP+1s9gzqdU/5br0H5FuHUCSpIfxX5RigAPAo0KIPU2f/QCcEULc3fQ+H0i+SDcvCyFe\nuUT//nTrxlDkCediqURjPb6SfLZu3UqfPn1+03MKEiRIkCC/33Tr8M+9Nv3e062fT79+/di2bTuZ\nce2aPRGut9ZQXldEWVkZsbFXmvfDX9dq3559tAhNwKDWYXPZOVNfQnKLFDZs3IDP52Po0KHk5ORg\n0hmwu5y4PW5Sw+OJM/kz0Akh2F18nOdfeJ4JEyaQmZmJz+ejV1IWJs25jG9Oj5tN+UdYuHAho0eP\nbjYOl8tFdnY23333HSE6PV6fD4vDzv33388HH1y8NtaXX37JnRMmoFQo0ClV1FktGJVq+kSlEKbS\nUOW0sq2yEJNGzbWpGRytruRgdSXu86xNl2LEiBHs/WED2YnNs9mtLyvgtNWMEIIovZ46hwNkMoYM\nGcKyZctQyuV4vF6UMhmRag3tw8Mpslo5YW7gyNGj/7SwiO3bt/PqK6+we9cunE4nMo+HP2ZmsbO2\nhj01NVg9Huw+HxMffZQZMy5pEL4sbrc7YIH7JanwfT4fd911F59//jnRBh1CCKqsdvrEhTKhVTw2\nt5dpe/OpdriQkNArFTS43GSmp7Nx8+aAq6nFYsHhcBAREXHRdXH//fezbsFXfHB1ZmBbjcPNbeuP\n8NLQFvTLOOfi6fUJbvzoMDqDCUtDPRq5jA6hegbGhvLx6UpKbQ5Gp0dyb+sYVDIZC05X8cGRcuSS\n303R7RPExMTgcrmwNTaw6s62hGr9FlS318eoL49RaXVzU8dw/nx9IvP3VLNoXw01FjcWp5cInZL3\nslvQOlrL/lIbf1xVRL3Dh8PlJlGvwuMTlNvdjG4ZzrRrkpA1ne9r20v4/HAVPgE6rZaszEwemvgo\nUVFRZGdns2tUG5IM5+rFHq+zM2D5CYYPH86WTZtwOuzI5XK8Ph8mmWD+wFQ6RWjZXW3jvi2FxOkU\nfH9jJneszee7QjNhGjkdInQ82TkGk0pGv8UnSQ7XEJfRHqVSyf59e/F5vbg8Xrw+v+ZJMClRyiXO\n1Lngvy3d+lmEELOAWZf4bOB57399lonGenzlRUgGE8JpR9T43QR+LrgxSJAgQYL8b/Ivuzb9F6DX\n6xE+H0L4kKRz7lJenz9IXKfTXWrXAB6Ph7Vr17Jr1y62bdtGekQyRo0/Bkev1pFoiuXY8WPk5+fT\nq1cvdu3axaJFi9iyZQs2m42vvvqKOkcjWqUKr89HqaUGg0HPAw88wKxZs5DLZPh8Plze5i5NZ9/r\n9foLxqRSqVixYgXff/89K1euRKFQMHr0aK6++upLuhSNHz+enj178ve//50DBw6wYsUKekQkEq72\nxw5Fawx0Do9jW1UhjU4nTo8H3S+IKzKZTHjhguMLIC0tjUcmTuTw4cMkJSXxhz/8gaSkJB566CE+\n+OADUvQGEvV6iqxWVpcUc01sHAU2G59++ilvvPHGFY/hStm6dSuDBg0iXKGgk8FAHbDfZuOVI4cR\nQOeIUMJVKvbXNvDhBx8wduxYrr766ov2JYRg9+7d7N+/n4SEBIYMGdJMQCmVSmJizg+LvDwymYw5\nc+bwhz/8gW+++YZt27bhOHKIO1vFs62snq9PlaGWyRke6y/ku7G6nsSEeLbt2EFUVFSgH4PBcNEa\nXOCvmVVVVUWlzUlOTSPtw/0ZCdVy/3fYcJ6bXaPD4xcD9fUMiQ8jXKVgfXk9fz1aTIZRg0DFEx3i\nA4JmQssYdlVYKGp00D3CwLaqRioqKugUq+N4I9y95CR3dYlBo5Qx72A15RYPcXGx1NvsvLS8kOWH\n6hiSFMKQmBDWFjWQa3ZQafXQRpLokqDnqb4x/HFlIU888QSrVq2ioKCAJKOKN65JarYOD1ZY8fig\na7SBAbF6DtUWcN999wUyXNY5Pc2EVa3Tf95rvluF2+tjRIsQ2oRpWFnQwJFaByU2N2FqOcfrHQxP\nMjHrWDX3/HCGlQVmbmobQrs4DauOmRmxPBejSoZJLaeozkHR7j10idLwp/ahHKy2szTPTJROzpKR\nqSw+UU+h2X1WWP2m/McIq38ZKg2ivhpR35SppWkxlJWV/RsHFSRIkCBBgvy+ee655/j++++pMpcR\nHeKP53C5HdQ2VpKUlHTReKSfcvToUYYNG0ZBQUFgW1ljFSatEWVT4gidyi8+SktLAVCr1YwfPz6Q\nXXDChAlMmjSJw4cPA9Cvbz/em/UecXFxlJWVoVdpcMvc5NaUYdLoUMkVeHxeTlaXEhERwcCBzbRy\nAJlMxrBhwxg2bNgVz0dGRgavvvoqCxYsYMWKFYSqNM0+D206lwqrhVxzPXfdc88V9z1u3Di++uor\njtRX0ybEbx0psTWSZzHzyrNP8dhjjzVrX1tby2effkq3iEj6x/qtKz0jBevKSthRWUGETheY09+a\nZ595hhilkgdapKJoivMKV6pYV1nBnekptA/zZ8QcFBfNzOOnefaZZ9i6bdsF/TQ0NDAqO5sfNm4M\npOZMjI9n+cqVgTiafwRJkhgwYAADBgzgzjvvpOzkUR7bfBxHU00oh9eD3evlnhYJDI4O55nDuSxc\nuJCHH374sn2fOHGCG66/ntP5+QA8tfM0rUN1TOmWxo+V/jinL/dU0D3ZSKxJjdvr49XVBfgE/F+X\nVDqH+8XabS2iuf/HUxRZnXSI1AdE1VkyQzUcr7OxqrQ+sO1kjYO/DExibk41L60vBEAugVdAcUkZ\nJU1z+VK3BMak+1OQ3986mgc25/O3TaX0T/WXN2gZ5V+vI0eOZPr06axYsYIRI0Yw/1gNt7b2r8Et\nRWYOVtoYnhzCe32SA4Jr5pFK/rZoETFRkby2v5xP+qVgVMmpc3p4fX85CsmfJGNaz3jubePPVDip\nQzS3rcvnkW2FNLp9gVSsMgmW5DXw5vB4Hu7tb/v0NdGMmXuG9acaSUtLR9vQQC+ji6+HpgTmqOv8\nk4Tp5PRJ1NMnUc++chtLTv72GWv/E+pY/WvRNT1JUGshPhWU/vSc7du3/zcOKkiQIEGCBPl9069f\nP26++WbqLNXklh0mv+IEeRXHkWRcsn7TWbxeL8NvGE51ZRWt4zPo0qIdGTEtcHpcnKktCrSrs/lv\nhDp27HjRfgYPHkxOTg6lpaVUVVWxafOmQBHczp07Y3bYyIiMw+Z2sTn/CD8WnWRT3hHq7Ba+/PJL\n1Gr1Rfv9Rzh7019sMzfbXmxrQAJ2lBbhE4KVK1bwzDPPUFt7+XrSN9xwA/fddx+bKoqZX3SKhUW5\nfFt0mt59+ly0Juf27dtxulx0Co8MbJMkiU7hETh9PkotFlatXElCfDxZWVnExsbSsX173nnnHTye\nCxMWXAmVlZU8/vjj7Ni+nTqnk+8ryrE09eURPvQKOe1CTZxutPDJqXymHjqBzeNh2/bt2O12AJYt\nW8aA/v1JiIujZVYW27Zu4f70FGZ0acfzrTOQmRsYPmwYLtflLQ+HDh1i7JgxhJpM6DRqQkNCuPPO\nO8nNzb2grVqtpszir7+UoFVza3IMd6TGsraylu8rakjRaWltMgTSmF+KHTt2cNONN9KxfTtKCgsY\nkxjJ8j5tea1dC4otTu7acIS3c4pI06nxuQUT5h7j4a9PkP3RIQ6UWEjQqgKiCkArlzE0Pgyz28ue\nKkugSDCAxyfYWmbGIwQzurdg5/XtmNM7nXClgnd3lfPh8DRe7OtP5PFg+yh+vLUty2/MQqeQoZJJ\nZKeeS2Ail0nckhHB6VonVVb/d7Y+twGlQsHcuXNJTUnmiccepUOHDrywuZiBC05xw+Jc7lyZh0fA\nhMzmrpATMiPw+XykZWSyvbyRjguPMPy7U3RdcpycWjtpoWoUEtzRsvkYkgwqGt0+Xu0eS+kdbTk6\nthXZKSFIQN/Uc9ZlmUzi/l4ReHzw7nuzqKiq5oF2Ec2E56j0EHaW2qiwXpm77a/lf89i1WSpkiJi\nkekMiJhEfEW5V/SHLEiQIEGCBAlyaRYtWsSiRYuYNm0a9fX19OzZk7feeuuysVXr168n/0w+reLT\n0TW5y4XojCSExVBYU0qtpQ67x0WFpZqxY8eSnp7ebH+bzcbq1auxWCz069ePlJSUZp/7fD5SUlIw\nmUzk1laQHhGD2WmnzmbBK3x88sknDBky5IJxne23sbGRfv36kZCQwLp166isrKRbt260bdu2WXsh\nBHv27OHIkSOkpKTQv39/srKyGDVqFMu/XYbd6yZSraPcbuGouQq5QoFMCFJ1RoTZyjv/N53ly5ax\n88cff9bCJ0kSH374IbfeeiuLFi3C6XRy/fXXk52djUJx4a3d2fTlzvMK7559r5RJtJBLCLeTw6cr\nUMgkQpx2nnxyEtu3b2P+/K9/USa1mpoaevXsSWVpKT2i/DfLe2prOWY280h6RiAGKKeugS/yConT\naugeGUqpzUGty82zzz5LVlYWEydOJMNkpL1OwymrlQqPl1qnC5kkkaDTckdyPH85eopVq1aRnZ19\nwThycnLYt28fVquVp596Ch1e+sSZqLXL2FXewLwvvmDhwgVMnTqNHj16cOLECSwWC599+ikRKiV9\nokKodblZWFRJh1ADV0WYWFNRw7DYSKxe38+mhV+9ejXDb7iBBJOKm7LCKGpwsKi4GovHy2OZidyT\nGsvbp0qQgJfbtkCvkLO2oo7vyutweATRegUOtw+vEMh/MveNTWLKLSQe2pTLna2i/TFWuVWUWl3c\nkxFNrygjNU43hVYnQ+JD+Ti3kv3lVrxNZp9bsqJQy2UkGdU82CGa6fvLsXm8hKjOrR2zy3+cnHIr\nh8vtfLy7CoNez+J5nzOijRGPV7D8aAnJiQmkZ7XE4/Fw22PX8uKLL2J2N19nDU197dv9Izcmmii0\nujhca0eh1tA2MwNPySm8AixuH2r5OZvPuuJGbkwxMbGd391Sp5Axq28i60sb+Wx3Lf9347mMj3U2\nvwDcunUrAPXO5mO4Kc3Ea7srGDjvNM9dFUON/dc9MLgc/3vCSmcCmxnUTSZ5lcZvQi8p+feOK0iQ\nIEGCBPkvYPTo0RckgLgcZ6/B2vPc5c6KrNyaQtRqNffedy//93//16zNihUruH38eBrMfouQTCbj\nkUce4e2330Ymk5Gbm8vw4cM5ceJEYJ8TDr9FJCkpiTfeeOOidXS+++47bhs3jvqfFLjW6/VYrdbA\n++zsbL766iu0Wi01NTVk33RTMze2Vq1asXLlSubOncukSZP4+5w5OOvK0ev0dO/enYP79jEiOQN9\nU5xQa6eDFSdP8sknnzBp0qSfnTNJkhg4cOAl3Rd/St++fYmOimJrVQUjEpNRymQ4vV62VJYjA+5u\nmUFok7WuW1QEnx3PJd6gpWW4iQULFjJp0pP06tXrssc5y8yZMyktLubR1mmEq/3xNFfHRvLOkVNs\nr6mh0e3G5fOxuKCEliYD92Sec9naUF7FjBkz0Ot09IkMY2xSfCARyaLiMpaXVnBVZBgauZw4jRqF\nTEZxcXGz41ssFm4ZO5ZV333nnysgzqDmpT4ZgRv37uUhvLuvELfdwaQnnmhW9VspSTzRJoVMkz8u\nsFeEmb8dL2RATBi1LjcbqmrJt1iJLyzE6XReYOkUQvCnJ5+kbZSWVwekIG9Kgb7iRA3v7ynjpvhI\n0g3+tW1QyInR+OfomqgQPswrZ3yHKHonG3lkRR5fF1QxLiUKSZLIszj4trgGvdFIY2Mj+Y1eJu86\n594ngGtiTczJrWT2qUo8TQnqZBJ8c6yWgxVWUk1qTOpzMZDXtwhl+r5y3j5YzvNdE1DKJCpsbj46\nWolMgke/LcCo19Otew+O5ezj23vTiGsq0juhu5PhH52isNj/+922dStxMTG8faSa7lF6wtQKHF4f\nrx8oQybB/H4p9Iz2W5qqHR4GrcnHaAphW44dhUzipd1lTO+TGBhDhd1Nh4jmpQA0ChktQzSsPtGI\ny+NDpZBxsNTG49/6XVlfffVV5BK8uruCfvEGonUKXF4fb+6tAiBMJ2fCisLLL+Jfyf+gK6Dfl1dy\n+v+oYmtECHFJt4IgQYIECRIkyD+XDh06ANBga2y2vcHWiITEyJEj2b17N++//34zK0FBQQE333wz\nSi90i8/kqqRWpJiimPnuTGbNmoXP52PEiBGUFhTSNT6VAamt6RCThEqhZPDgweTn519UVBUXFzMy\neyRqj2BgUgbXJWehkGQo3V6uiU9jREprukQmsGL58kCB4Lvuuou9u3dzdUIyN2e1ZkByC8oKChgx\nYgRarZYPP/yQqupqTp06RWVVJVaLhWSdMSCqAELVGuJ0elatXHnBmCorK3nqqafIysykdatWTJ48\nmfr6+gvaXQyVSsXnc+dS6nTyUe5JFhbm81HuCSocDpINhoCoAghXq0kzGTld30jb8BAMajXfffcd\n27dvZ2R2NqkpKfTr25f58+dzfmZpj8fDe++9x9/++ldamvQBUeXvV0WrECObqirZVecvcGvzerk6\nprnLVp+oCCTAarPRL+qcS5kkSfSP8rsunrb465IdM1vw+HwX3MM99thjbFi3jgeyEpnVsxUAA5LD\nm1lDusQYCVMrSNCoUctkTMxI5LMerXm1XRpxWhXTTxTi8fnjq7qGG4lUKzlQ24gAZuX5hdzO7dv5\n85//fMF8l5WVcfjoUW7IDAuIKoAhGWGoZBL76i38WGNGAho9Xo6Z/edzoN6KRwhubhtBm2gd49pH\n8mFuObdtP8GDu05x986TOHw+bJZGro0z4RMwp1caS/pmcXNiGBIwN6+KWScruK1TBGvvasnKCVkM\naxnK96frqbS6UUjw6o/F3LjsBLeuOsXXJ2oQwMK8WgYtO8qd63MZsuIYFQ4PixYv4eTJk5RXVuKw\nWbk2Ux8QVQDpkWr6phnpGqFj3w0teTgzgrKKCs7YffRadpJbfsin17JTfFdkJt2kCYgqgEiNguxE\nA4X5eVx37XW4fYKvc+toO/8oI1adpsui48gkie+KzM3WWaXdzf4aOwV1Llr97RTXf5LPNbNOY1LK\nWDU6hYbHW/P2oDhy612kf36MgUtOkzznGF+fqkeSYOltKZQ81Ypvb2tu1f6t+N+zWOH/kQiXE+Fy\nITdX0/Oqq+jdu/e/eVxBggQJEiTIfxdms5n169cjhGDgwIGEhoZetF3Xrl0ZOHAgWzZvweVxo1dr\nabA1Ut5QhU6pYcWy5WzYsIFdu3aRmXkuVfWnn34KQpAVnoC8KTFCYkgkVreDd2fMoH379hw/fpwu\ncS0IbUqvHqU34fJ6WL9+PRUVFYFU2T/ls88+Q/i8dI6KRymTU9xYj0f46B6dhF7pFwvJxlCsHhcf\nf/wxjz32GCtWrKBrTBzxRqP/ODo9nSNj2Hj0KFu3bqVv374YjUaMTZ+r1RqswnfBsT0CNOe5mFVV\nVdGzRw/KS0tJ1RnwCcEbU6fyzZIlbN+xI9DnWQ4fPsyhQ4dITk6md+/eSJLEkCFDOHb8GB999BF5\neXlkZWWxcsUKLPl5F4zB6fWikMnwCoHH5yMvL49+ffsSpdWQqtVSmnOQcePGcfjwYV577TXAb6W5\nbdw4Fi9ejFYuw6m40E3O4fWhU8i5MS6WLwqLEYDT23wOnL5ziQqcvuafOZre76mt51hDI7vqG8jM\nyCAsLCzQxmw28+UXX3BjfDg9IkMQwl801+5p7hrmFf604+UeD6OTorkq0v/gPc2g5ZGMRJ7JOc2+\nukZ6RITgaxq7xeMlWqVgQFgo22rqidCpmP3hBzz00ENs376dvLw8MjIyAg8KbO7zzs0r8ArBvtpG\n9ptt9OjZkxPHjvHikTPc3SKWerenaT8vYVoF93ePpWeSka9yqthVbGFCRhRFFif7a6wMigthbZkZ\nrVxOnFbFpNYJVDm9rC1roGOslkevOpcd8c/XxLO72ILZ5uW02UmN3cOQ+BBqnB4+OVqFWqXivfff\n591336WypoYhwwbwzjvvNHO5VWs0WC0XlmiyubyYFDJCVQomtYlmX70DT1JLht90E4cPH6ZPUhLH\njx/nzI4NF+xr8fjdKZevXMnixYuZcPvtJJkUxJrkPJMeRbJRxb2ri5mwoZB7WkVQ5/Tw5oFKJGDY\nsGHYbDZOnjyJ29fIjMFxDEj2x6Pd2yEcuQQPry3DGptFbJiTjjExbNm8kQGf5jF5QAzVtqAr4G+D\nuQYAUV0GksRNo0Yxe/bsf24V5iBBggQJEuR/jE8++YTHHnsMm83/NF6tVvPWW2/xyCOPXLT9kiVL\naN26NcVNWXplkkSsMYLEkGi8Ph/Hqs/w8ssv88UXXwT2KSwsRKdUB0TVWfQqDUXFxRQW+l1+TOrm\nN/kmtRYhBCUlJRcVVkVFRRjVGpQyv8uU3eNGKZMHRNVZQlUaTtRXcfz4cYQQhGuaHydMqwmM83xu\nHXcrzz37LBV2KzFa/5P8QouZcmsjt9xyS7O206dPp6yklJuTUjA2jaGD08GSY8f4+OOPA26DZrOZ\nsWPGsHrNmsC+7dq25dtly0hLSyM1NZXXX3898FlkZCSTnniCgkYLKUb/TelpcyMFFivXp8SztbQS\nh9vNhvXrSTPouSU5MWBd2lRRxbSpU3nwwQdJTExk69atLFy0iDEtEnB4vawoKifXbCHD5O/3VEMj\nuWYL2QlxtA0NQV9ahs3jZW1pJZkmPXqFAq8QrCqpQKVSEREWxqryKu5NTUIlk+Hy+VhRUoEM2FXr\nt9TJgFO5ubRv354bhg1j3vz5VFZW4nK7aWHwz70kSXSNMPFDQS1XxYcSpVMhhGBVXhUWj1/4pOqb\nf28JWjUqmUS1040QguUl/tgogEqXh9UVtfgAYXfR6HaQkZGOJM4+uge5TEZKchKLj9fQLd5ImFaB\n1yeYe7ACn4AjDg+TnnySqVOnkpOTQ7euXZl+qgSB36Xvoz0VvNA/EaVcRka4BqvTS4pBzd1ZUSw+\nU8uWika6RxowKmS8f6qClzskopLJeKZNPNu3NNImuvn5yGUSbaN1bMw3E61RsuDqNExK/9reVmXh\nkd2FREZGsn///gvW6VnG3nIrzz7zNLsKrfRI9q/X9SfN/Fho429dz8U6tQ9R811ZWTNL3tKlSxm5\nYgVLztQzqoX/4crBWjvfFjXyp+f+n733DpOiSht4f1Wd08z09OTMkLMgSQmSESOioGJCHWBFxIjK\nrruYUVlUFETBAAiIgEgSBBRBJOecYQYm55nOqc79o4fGkaDufvvd797t3/PUw1Pdp8459Z4zdL31\npidQq9UMHDgQXyDAmPZJDG4SHb5WAcasK2BFXsjVt4FFg08RrF+3Fo//onI0Zl0hnZKNJBhDqk3v\nOiVr4sSJ9OnTh759+xIICgSCuxf+51wB//sUK58HrCnItSWMe+453nrrrf+3ZxQhQoQIESL8/4ot\nW7aQk5NDjDGGlPhkJEmi3FHBmDFjaNq0KX379r3kmqioKEpLSzFodChCoWViw7DCpFapiNVbWLly\nZb1rWrduzZdz5uAL+tGqQi5KQghqvC5atmwZzghY4XaQYLqYDKLC5UCr0V6SBOMCrVq14nO3C3fA\nj0GtwaLV41eCVHldWHUX63GVuB3YbDY6duyIRqOhyGknRn8xTqzY4Qj391vGjBnD8mXL+H7zZhJN\nIStUmcvJ7bfdxj333FOv7coVK8g0GsNKFUCsTk+qwciCBQvCitWokSPZ+NNP3JicSobByN7qSvYd\nPUrL5s0ZfNddjBs3rl5q8lGjRrF06bcs+GkDqWYTgWCQErcHo1rF9tJKKt0ennrqKd5//33uy8qo\n57LXJS6WjaVlrFu3jocffpjVq1cTpdfR2hqFIgRHq+18fiKXNKMBBUGhy0MTi5lrrTGUerw4AkH+\n9re/MfXDD3nj0CmyTAZKfX6qPV4+/fRT0tPTufXWW3jl6CnS9DrynG7cgQC9E2IZmBRHgdvLV+eK\n8AvBrSlxLFy3jscff5zp06ej1+v4/GQBepWKbIuBZL2W3eW1vLjxBCaNCpUkUeUNcFOijQ3lVeyv\ncdA65mL2vWN2Fz5FsLG0mrVFlRR7fDSONjCuXSqegGD+yVK2ldix11mkZAFtY0w8nJVItEbFmpJq\n5p07j8Vs4tEVJ2kZZ6DAGaSk1s1f//pXxo8fj9lsprS0lK+++gqNWoVRhj5J0TQw63jvaBF3f32c\nRjYDh0tdIGBSx5Dr2tZSO1EaGb1K5oXWqUzYd55BPx+nicXAgWoXvqBgyzkHTyoi7Ibo9ivsLHCg\nCBicFhNWqgC6xpvJjjayatUqbrvttsv+PQCMHj2aFcuWcd+Xm2iXbsYXUDhc5KJXopnb0kKKkCIE\nG4od1Ep+PvjgA/7yl7+g1Wq57bbbuP++YYyZN5+PT1ZhUknsKHPQoX17nnvuOSorK3nvvffQazWs\ny7XXU6xa2HT4FEGmWYNWreZktRuNSibdIDOtZyYtY3SsynfwxI4i+n19lv0Phyzaa3IdSJJE8+bN\nefPNN/n55410SNGzY2Q2edU+dhd6uGth/mXv9d/hv0+xsthQuWswGk2XTUkaIUKECBEiRPj3mDZt\nGkadgeSopLBHSFJUIj7Fx4cffnhZxQpArVIjIYGQLqnRowiB/KsYmQvx0UajkSPl+aRZbGhkNcWO\nSipddmaOH0+7du3o3asXm3/ZjC8YIEpnoMLlIK+mnL889hixsbG/nQIQqof1+muvsbM0n8bRNrSy\nCo2sYlvJeVpaE7FotRQ4a8mzVzPxrYlIkkSfPn1Yu2YNiiJINluo9Lg5UlVBv759LxvHbTAY+HH9\nehYsWMDKztsI+QAAIABJREFUlStRq9UMHjyYO+64A5VKVa+tVqvFJS51w/IrCrt27mTDhg20aNGC\nRYsW0c0WTxNLFJvLStldVUGa0YhNp2XVkm9YvHgxq1evpnfv3gSDQbZu3cro0Y8zePCdbNq0Cb8/\nZKEpLCwkPj6eF154gZSUFN5///1wIoSLY4fOLxTo1Wg0BBWBANSyzAONMjhUVcumknKK3V4amUz0\njo/jcK2ddWUVZGZk8Le//Y3HH3+cGTNmsG/fPvqmppKTk0NCQgLbt2/n88+/YM+ePRw7dozjq1fT\nOyGWwakhF7dGZiPDs1J56/hZjCoVNyVa+Wr+fBRFwePxkm01kmjUsqWoBq8iSNJraBZl4kitkzJP\nKOV2mddH+2gL3xdVoJEkOsRGke/y8NW5EgwqGaNKoswTINOs480uWaEb18HYNqkc2XgSuy+IIFQS\ndVzTVIx16zY0LY6zTi/l1ngeHP4wu3btolNSEo888ggdO3YEoKSkhC6dO1FWXESvTDPeoMKyc1Vk\nGLXclxXPovOV7Cp0EKVRk9M0HgG8faCQXeWh5ClTjxXTOymKEY0TmHOmgsNeuH3IUE6cOMHePXt4\ndvU57r8mDm9A4fPdZTh9CmajMexO+eu/I29QoNXWt8Zebr+u/eEHZs2axZw5c9AGAugr9lLlV/i5\n1IFJLfPFqQqO13roYJN45umnWL1qFSu/+w6VSsXsOV9y15ChLFq0CK/Xy4gbb2TYsGF4PB66XX8d\n53LP0DxBzcLjNcToVdzZJJrcGh9v7qggMd5Gu64hV9r2Ph9ff/01s7um0iY29BLj3uxoCt1+XtlX\nxqrTdk5UeXltWwXD7r2HtLQ0pn34AU1tWjyB0J7NjNFS4Qpe7Xb/dYQQ/xUH0J5QwhTRqnVrsWvX\nLhEhQoQIEf7z7N69W9T9/9te/B/4Pfi/clz4Xdq9e/e/KeH/e3Tp0kVEG6JFi+Tm9Q6rMUa0atnq\nitfdd999QqNSC0BkWZNFx/QWomN6C9E6qaFQSbJo2rSpEEKIvLw80b59+wv7qt5hs9nEjBkzwn1W\nVVWJIXfdJWRZFoDQabVizJgxwuv1XvUeDh06JNq1a3fZMQBhMpnEhAkTxAsvvCDUanX4c0mSBCBk\nWRZDhwwR1dXV/7Y83377baGSZTEoPUuMatJCjGrSQtyUmiEAEa3TijatW4f/zu7OyBIPZTUUgLg+\nIV481aKZeKpFMzGmWRORZjaJVi1bii1btoj0tLTwnNUqlRg7dqwYOXJkWE6AaJCVJXbv3i3atG4t\nUk0m8UKLpuIfrVuIl1o1F+2sMUKn04mKigohhBD79+8XgOiXkiBea9dcvN6+hXi+VWNh1etFm9at\nRUxUVLjf7t26idOnT19yn4FAQIwZM0aofjWHzIwMsWjRIgGIJxtliGntmoePqdc0EyoJcXd6ohjX\nNDN8zYNNk8SnvZqJexsnCvlXa6ZXyeLBBomidbRJRGtUIkp1cZxft5PqDkCoJMQdDWxi0YDm9Y72\ncSZhVMkiUacRgPi4fUOx7Prm4eORrARhNBiuuKbPPfecsOg1Yu7ghmLdg83EugebiQ9vygyP279f\nX7F+/XrRqUOH8LziYmPFtGnTxMsvvywsJlP48359+4r8/Pxw3y+++KLQqlXh7w06rfjggw/EyJEj\nhVWvFStuaCT23dRC7LuphfhbyyQBiI0bN/7uPvzoo4+E2WgM92sxGUVaSkr4PFGvFlM7pIjc25uJ\nL7qE9tfSpUuv2ucrr7wijDq12PZMtih9s6l4aUC8MGsvrkuvG3qIs2fPhtvfddddQi0h7MOaCcd9\nzcPHqr4Z4WtUsiwefvhh4XQ6hdPpFIB4ukusAMTcwalCebmF2DWywX/kd+m/zmK1ZMkSBg0aFImp\nihAhQoQIEf5DtGrVin179iGECP/eCiHwBL20btP6itdNnDiRb7/9Fr8rQG5VESWOSjSyGrvXiUpW\nUVhYiBCCW265hdMnTtIiIRWzTk+V20luVTl9+/dj6dKl9d6+x8TEsHDRIkpKSsjPz6dhw4aXJNE4\nduwYkydPZvu27SQnJzNi5AjuvPNOGjRowOGDB8k2xpBujKLW7+WgvZzM7Gy279jO3LlzGT16NC3i\nbTSwRuMJBDlYWk5tIMiOHTto3frK9/pnGDNmDNM/+oilebkkG4wIISj2uEk3m2gSHcWPBw+i0+nQ\narScczrRqmRkoF3sxaQOalnmmpgYVh4+zID+/YlWBPenZmBRazhsr+GDDz4AINWgx6coqCWZqsJC\n+vfrx6LFi7nt1lv58OQZ0vQ6ygMBqjxeZs6cGbb6tWnThvHjx4fihqrtRKtVnLY70Wq1DLvvPnJz\nc/npxx+xxcUxctQosrKyLrnPSZMm8dG0adycZKNDbDRVPj9LiysYmZOD0WDgmN1JE8vFzHInHS6C\nApL0Oo7UOlGrVOjVKrolR3Omxs1XJ0vobLNwW1ocsgSrCiqZc7aEoRnxHKxxIgFWjRp3IIAiQoVm\n9SqZUY2TSTdq2FBSy7fnK9hX7uDexvHhvewOKByvdtPUYmBvtRMZ2FRWy9D0UAFmIQR7a1y0aN78\nknsMBAJ88cUXfPzRNG7IMJFovphlr1mcgbZJJuJbX8+atWsJBAKMGDUKJKitqWXAwIHcdtttpKWl\n8eyzz3LixAni4+NJT0+vN8bEiRN55ZVXWLFiBTqdjptuuglZlikpKeHHdeu485czdIg1UuUXHK12\nMnLkSLp3737VPbhu3TpGjx7N3enRjOjQgIAQfHi6ktXFxZiNRm6L1/BqmyTUde6HvRLNNIkxsmLF\nCm6//fYr9vvdimUMbGakYXzob/bJnjZGXG/lni/yEYmtWL9hY732nTp1YvHixfxS6qJ74sW9sL7I\niUoKJSX5cOpUrr/+ekY/9hgH9u3BZNCzt8jNsNZR3L+kgCnbKvhPqQH/dYpVZmZmRKmKECFChAgR\n/oOMHTuW2bNnk1+dT6wxFkmSqHBW4vV7r1qfKT09nSFDhjBv7jy0KhUBJYiMRGpsPL5AgKBaZtOm\nTRw8eJCWienEGELxTgnmaAKKwpo1a6iuriY+Pp5du3ZRVlZGu3btSE5OJjExkcTExEvG3Lp1K717\n90ZWBDa1nsLTZ1i7bi2PPfYY3377La2jE8gyhxQxg1qDJElsO3aUI0eO8N6775IeHUWLhNADtUGj\noUtaMqtPnuW77777H1GsFEVh37599B8wgJkzZqCWJYIC2tistIq1UuwKlY+Ji4tjxMgRfDJ9OhkG\nI4KQ++SvueDO53Q6eSAjG1NdMeHOVhuVfh9H7LVU+/w0spio9vkp8vigspKzZ89y6PBhpk+fzv79\n+7khPZ2RI0fSoUOHev2/+eabREdHM378eColiSSdFiHLvPjii+hUKlpbTJQVF/HAAw+wceNGZs6c\nCYSSexw4cIB/TppEZ6uFPok2AKI1aoanJ/Lq0bP07dePH374AY0s0ybaTIHby7cFJSTptZx1uFhT\nUknHzp05sHsXioCfCqpI0GkY0Sg57FY6PDuRs04PuytDsW/Jeg0PZiZT4/czN68Ee1BhXIsUWkSH\n9tWdGXGUev38XFrL+wcKuDnThieosPBUGZ6gQiOznr3VTgTwfUkV2WY90RoVa0uq2Vvl4OuPX7hk\nLYcOGcLSZUsxqmQCwUvd7wICzGYziqJwzz13s2TJEjolmcnSynzxyUd8NX8eW7Zuo2HDhrRv3/6K\n+0ar1XLnnXeGz30+H0eOHGHS5MkcPXqULZs3kxEVxdvDhnHLLbf87rPxh1Om0NJq5LWWieG2/2yd\nxIHac5T5A0Rp9GGlCkLKpV8Rly1Y/WtUKjU+b/19atTKGHQystl8Sfunn36aV1+ewIO/FDCxfSKt\nYnR8l+/gvSMVqGWJtJRUMjMz6dSxIykmFX1TdaiiJDbkubk+XTCxTzxzD9RwosJ31Xn9q/zXKVYR\nIkSIECFChP8srVu3ZtmyZYwaOYq8/FAGruSkZOZMnxOOMbkSw4YNY/bs2aTHxhNnCQWxe/0+jpfk\n8/Ajj3DmTCg9eJS+fjHhKJ0BRVHYuHEjEyZM4OjRowCoVCpGjRrFlClTLvuQN3bsWAzIdLSlhJNl\nnLZXMn36dABif5NR0FpXxPjMmTPk5uXRwmat971WpSLaYOD06dO/L6jfYceOHdxz992czc0Nf1bh\n8eAKBil0uThYUYVOpaJTx44kJiby7rvvEgwG+fTTTxHA9rJyuicmIEkS3mCQvVXVJCUmErQ7wkrV\nBVL1Bg7ZaxnRMBND3Xe7K6tZW1zK5s2beeSRR3434ZcQgs8/+4wGJiMPpSejrpPnmpIyfimvon98\nHFEaNdurqvn000956KGH+OTjj5k3f/4F91hOaDVUeP3YdCFLToxWQ7zRQKtWrWjevDnTp09nZVGo\n2KsE1AaCfF9ew6jHHiMnJ4d27dqx9nwlZW4/DS2GerF6kiTRyKxnW4Udo0qmxh/kreOh/RmtUUEQ\nmlgurrcrEORwdSir5bYSO1uKQ3XWYnUqggLWlVSToNNQ6vVT6Qvw2tHzob4sFqZOncrQoUPryWfN\nmjV8u3QpLzVL5qzTy+LcKu5sYaWBNbSndhY4OFTi5IXBg1m3bh3ffLOElzsn0ystlE6/yhNg1MYC\nJvzjH8ydN++qa/FrVqxYQc6jj1BaVg5AlNnMW++8w2OPPfaH+zh96iTXRmnrKWBqWaKNRcM+TCws\nqOS+rBjSTSFl8dv8Ws7Wuhk8ePBV+x181xD+Ov4F9px30z49JPutZ11sOOlk6pOXFhpXq9Vs2ryF\n/n16k7MlVBRYJpQ9sEP7a5kzdx6333oL1ydqWXlrClpVaL5/3VLGP/dUsb3AQ/DSKgf/Y0QUqwgR\nIkSIECHC/zgDBw7kbO5Z9u/fjxChRBO/9/YaoF+/ftx3333MmzePCmctMhJ2r5v09HQmTJhAbp2S\nUeN2YTVefKNd7XGhUat54oknqK2sIkqrR0EgSxLTP/qIuLg4XnnllXpjlZWVsWvXLtpYk+qlbM8y\nx3DGUUVQKJR7XERpLhbQLfeGHrSbNWtGkyaNKT9/nsa/Uq48gQBVLhfNmjX7l+R2gcrKSvr364ch\nEODWtDT0KhXfnjuHWpK5KTGRaI2G7ZXlnHU5OXv2LG+88QajR49m+vTpvPbaa7zyyitMnTqVPJeb\nWI2GPKcTv6LQMCWFUyUl1Ab8RKkvuqHluV0YVaqwUgXQzhrNhtJyFn79NcuXLcMSZWHw4Dt56qmn\nSEtLu2TOp0+f5sTJkzyQkRJWqgB6xMXyc3kVxx1OOlqj6RgTzQ8V1Tz11FMc3LePOzLiaBljotDl\nY0leKTPP5vN80yxkSaLC66PM5aZ58+bk5OTwj3/8g+PHj5OcHMo2WVhYSJMmTbDZQlauxx9/nGnT\npmFSy1R4/AQUEbakKEJwpMaFN6hgUKlI0KrIc/toG2vk1gwbr+47z+EaF22tIRezT04V4wwGGdM8\nkWYxBjaX2FlxrookvYY4nYaTtR4EQV5//XV69+5NWVkZCQkJtG3btl4h6wusWLGCdLOBbjYz7WOM\nbKt08tjKXNonm/AEFA6Wurlp4EDuueeekIyjDfRMvbjHrXo1N2WYWbxs2VX3Tk1NDR9//DErly/H\n5/Oxa88erk8z8P7gdHRqmbkHqhg9ejRCCHbv3s3hgwfIzGrAY6NH07NnTwA2b97MtKlTOXP6FDq9\ngZKyMjb7XKEkMnXKlU8R7Kh0o42LQmeJoe+GPLrHGShwBzla4yYhzsbMmTNQq9X07t07PL+ysjKm\nTp3Kj+vWotPpycjIZOD0s/RoZEYRsOm0gxt6dOeRRx657P1dc801lFZU8ssvv7Bnzx5SUlJo06YN\nTZo04dSpUxw7cZK3brmoVAHc08TCu3ur6NnQyJsDbRRU+xk8p/iqcvxXiChWESJEiBAhQoT/CCqV\n6qruSpdDkiTmzJnDbbfdxvz587Hb7fTr149Ro0ZhtVpJTEykc+fO7N+7j3RFwVIXY5VfW8kNPW5g\n/U/rAdDodJg0WipdTiRJ4t133+Wll14KZ7G7MNZlEaGo9muvvZb9+/YhSRCvM1Hlc3PMUUmP7t1p\n164d48Y9z/Dhw9lVUIxGlpEkKHO5sVgsPPTQQ/+q2AD48ssvcToc3JKZiVGtJtfhICAE/ROSsGp1\nrC4uoMDjJk1vRON088qECXz80XQ+++Jz+vbty4cffkgwGGT69OlUeTzEaDXIqDl16hSSJLG0tJju\nMaFCqntrqjnhdNA8qr7rlSBkhTL4/ZiEwtmKCt5/912++PxztmzdStOmTS9ZO4DfeCCGzy+IWwBB\nIdi/bx/9kmLomhBytYzRajCokph6LJ/tlTVEa9SsLKkkIT4+nII+NjaW6667Ltx3ZmZmvbGef/55\npk2bhlWtpsDjY+qJAm5NtSFL8F1BJaXeUEZAgyyRptfhCgr2VrpQSxIZJh3TTxbxQIMEYjQqdlQ4\n+EuzBHokh1L135EVS4pRy+RDRaSbtCSnpDBv3rywMvJ7SJJEsE4YJrWKyW3S+b64hmVFVZR6QzWZ\n/jFhAmq1GkmSUC5NBInyq7jFy1FdXU2366/n5IkTdIszgCJAUXD6gmRbtWhVMi91T+BkVYCxT4wh\n0aKlY7KWvRsO02vRIt555x1KSkqYPHkyDW0G2sSp2XrUTaUjQAXw1L5CRmTbCCiCqacqqPAFaOqq\n4GiVh+7du1Npt3PiwH6SLVq62Xwc3LCKPou/4YMPPuCJJ56goKCA67t0pqK0hL6Jeqr8gtPFTq5p\n2xZdYgIqlZpPnr+DBx98EJ1Od8X7BOjWrRvdunUjGAyysy475gUFO1gnO19QsKPEzUf7qzFqYOnw\nZExaGVUkxipChAgRIkSI8N+ALMsMHTr0ElcqCD2cLl++nPuGDeOHH38Mt7///vsxGo2s/2k92bE2\nMmJCVqSAorC3IB+HwxGOv7pAXFwc13XpwtF9+0nUm8NWljOOShShMGvWLN58800WLFiAopQCcOOA\nAXxZV6T4/vvv54033uDkyZMX5y5JvDTueeLi4v4tGZw8eRKrwYCxzoJU6/ejkSTidHpOOGrJ97i5\nNSGF9Lo4s2q/j4VF5xkwYADZWVl8OW8en86ciVWr4Z6MkMULYHdlFT+XVeCUJRYX1a/jc8rupNzr\nJa7ugXZXZRV+IbgjJZEEnY7jdgeLCovxOR288PzzLP2N5SQ7O5sWzZuzKS+XRmYjGllGCMFPZRXI\nQFNzyBK0tbIalz+k4DSw1LfsZJn1SMDX50sAaNO6NfO/+grzZeJtLkd6ejrt2ral5sxJRqXYWHC+\nlDcOh9z9ZAksJhNN1BJjMhJRSRKKEHxyvoTtFQ4UQu6FU08UhftrHlN/fhfOzzt9zJsx6Q8rVQCD\nBg3io48+4qcyO70TojCoZLrHmVmYX8kN8WbWlzrIy8ujc+fODBo0iKlTp7L2nJ0BmSHFrtwdYNU5\nJ3fcdc8Vx3jvvfc4e+okX3ZJJdsccsvbW+Vm1I4CVp6wM7h5NJIk0T5Rx9lKDyvuTUOjkhBC8PrG\nMl54/nkkCQY1tfBGrwRkScIfFIxdU8SeQjfbKl2sLg7FqCXqVczomkyvFBMzjlXx9qZNZKan0TnF\nwOyByejq+p2wuZxxzz3HsGHDePXVV3GWl/DzgFRSjaGXHN/lO3h0836WL1/Orbfe+oflCbBhwwYe\nfuhBcs+F3DCjLCZSk5OYvLcau0/h+c1llNalVterJVYfc3JXG8ufGuPPIP9+kwgRIkSIECFChH+P\nYDDI/Pnzufnmm+nVsxdvvPEGlZWV/1JfCQkJvPLqq9x+++20atWKBx98kPHjx1NeXo4sSaRFX8z6\np5bl8LksX/rY8+HUqQRUKjaVn2N/ZRHbKvI5Za/k73//O4cPH8ZeW0unTp0YPnw4U6ZMwWyxMPiO\nOxg3bhz33nsvJ0+epLE5mv5J6fSITyFareW1117j+PHjv3sfDoeD999/nz59+jCgf39mzJiB1+sF\noHHjxlS53bgCIUtGtEaDXwjKvB5ynU6SdPqwUgUQo9HSxGRBI0kUnDvHDT164A8EaG+NCStVANdY\nY9BIEg6nE7Us0yfWxojUDG6LT0Qry3x+5hxLzxfyxZk81peU09kaQ0KdotXUYiZeq8Wikln53Xd4\nvV7mzJnDzTffRO9evXjnnXd4Z9Ikznu8TDpxlkX5Rbx1/AxbKqsRwORTufzz1FlWlpQxatQo9Dod\nZ+zuejLJdXgQwD//+U/279/Pvv37admy5SWy27RpE/ffdx83dO/O2LFjOXHiBBBSvKdNn05xQPBV\nQQVNzAYSDaH5P5ozArvTya3xMajqrD6yJNEtJgoFyM7KokXLFgA0iQ5dc6S6/vyO1MVcNW/e7LKK\n/9Xo27cvdwwaxNsninnuwHleP1bIo7tzUckSba3G8LoD9O7dmwfuv483dxUzdlMBL28v5IEfzqGN\niuXVV1+94hhLl3xDr3hDWKkCaGc1cG2sgQ25IYVICMHOQhcNrBo0daabbfluSp2hvaYIeOza2LDL\nn0YlMbK9lVq/YFr3ZO5uaEEFbLw5k14pIWV5eOMYjBoVeefzGd02Bl1dv5Ik8UR7K16fjzVr1rBw\nwVfcm2UOK1UAN6WayLZo+Oqrr/6UPHNzc7n5poFk6ir5cUwKO55L466WKgqKitlR4uXhdcV0baBn\n25g0to1JY0BTA/fOK2bXec+fGufPELFYRYgQIUKECBH+oyiKwrBhw1i4cCEWvREJiU2/bGLmjJls\n276NpKSkq17v9XrZs2cPOp2Oa665hhkzZvDYY49h0unQyyrmHzvG3LlzMRqNV+3ncjFe1157LfsP\n7OeDDz4IpVtPSSYnJ4eFCxfy6quvEmcwopZgx/btzJo1C6vegFGW2bl9O16/nxS9kbbWkHUqSgPd\n4pNZWZjLs88+y8qVK684l9raWm7o0YMDBw+SYtCjiFBK6wVffcX3a9bwwAMP8PKECfxQUkIHq5Uo\njQadLLO2tAijSs3lPJkufJZuMnDW7ryqLACuj4rh2qiQ0mnVaNDLMgtLilBlZFJ18iRNzUb6xtsu\nGSP0vC24e+hQli1fTgOzEZ0Ef9+0iawGDWjRogWFx49z3O7ErSjEaTWk6LWcdLip8PkZOXIk06dP\nR61WM+Pjj9Gr5LoYKy/LC6to0bw5Tz/99GUV4fLycl5//XWmTJlCskFPklrm8+3bmPHJJ6xdt44e\nPXpw3XXXsW9/aE1379pF97Q0Ro4cicFgCGcivMAZl4cPzhVhVMskuMo4VxbKFpdp0XGyxsuXJ8uR\ngVZWI8drPHx+ohQZePPNiX8oZrCe7CSJRYsX07VrV3bv2EGSXs2NKVFkmrTMPldN965dadeuXbjt\nrNlzuHHgTcyd+yX22lqefbgPY8aMISEh4YpjCCEuuzcAHD6F05Ve5h6s5mi5l6euC6XKn7mrkg+3\nV9IkVku7RB17SrxXTEd+otqHRaNCkqiXBTA09pXu++Lc3G43kmT+zfcSEvyhlxG/ZubMmWilIIse\nTsasC+2VD++K42R5kB3n/KRHSXw1LBFV3TwX3JtEs8nn+MeaCnI6R/2psf4oEcUqQoQIESJEiPAf\n5fvvv2fhwoWkxyQQYwg9VPkCfs4WFfHqq6/y0UcfXfHa2bNn88wzz4StW/Hx8aEkAUYzBo2G/Nrq\ncNxKbW0tAPk11fVcAc/XVBMTHU10dPRlx8jOzub9998Pn//000/Mnj2btrZ4MswWnH4/xa7zNI2O\noVm0FUmS8ClBVp3PI15f31VMp1IRpdFy6tSpq8rkgw8+4NChQ9yckkxsnUWo2O1m7YYNzJkzh5yc\nHNb98AP33H0339VlQtRqNJhtNoqKQ0H3BR4XqfqQMlnj93PCaad1bBRdE204/QFmnzrPnspqmlos\n6FShB8/9VTX46+SV8ZsECxl19/LkU0+xa9cu5n3+OfZAkChN6HHxhMNJqc+HBQ3t21/LsuXLuTc9\nkWZRIatFhdfPp3l5XNulC0f8fhTg+tgobkm01clMYWZuIXPnzGH69OlMnjwZh93Ol3PnsvRcKNNf\n506dWLho0SVKld/v5+mnn2bGJ5/gr7Pi+QMBjniDeOuCkW4c0J+du3bTsmVLGjduzIcfflivD5/P\nR0JcHN+VV/N4eiKyJDGnoIwko4a/t0/BoA65Ln51upLV52swqyXUksT0Y6XhPmI1KoTJRN++fa+6\nvldCpVKxbt06Hh4+nG+WLCE/vwaAPr17Mf+rBfXayrLMsGHDGDZs2B/uf9DgO5n81kQecvrIqsvQ\nt7/Kze5KNwIYsvgc0RYztlgrOwu99M32MW1HJSPaxjC2gxWXX9Brfh6f7K7itZ6h2l0uv8Jz60qQ\ngJd3h9ZJI0uccwbIrKvD9eWpatyBIBlpqXx8oJouKQa0da6AU/dUodNqGTBgAAow/0wtjzSKIdkY\n2lffFzg4bffTM+rPKTvHjx/n2nRNWKmCkJLWPVvL9rMuejY0h5UqALVKome2gXl77Xx/3PWnxvqj\nRBSrCBEiRIgQIcKfYvv27cyYMYNz585xzTXXMHr0aBo0aHDF9t9++y0mvYFo/cWCnlq1hiitgUWL\nFl1RsVq3bh3Dhw8nzmimZUIKiqJwvqYKAI1KRV5NFSmWKBLMZnzBICfLy/ArCmcqKyh3OjFqNFS4\nXAQVBVeNj9raWqKiojhx4gTTpk3jyJEjZGdn85e//CVsKbgwX4teT7oppAQWu53IkkTjqJhw4gCt\nrEIGKrweGv8qZMMXDGL3+7nuKvIAWLxoERkGQ1ipAkgyGEgyGlmyZAk5OTl06NCBEydPsnPnTmpq\naujQoQNWq5Xdu3eT8+ijrDh4kHS9AY0kc9blxKxR0d4WskCZNGpaxpg5WGXn8zO5ZJtDtakKPR50\nKhlfUCHf4yFFdzFtfYE35CLVqFEjBg4cyHcrV/Jx7jmamIy4ggpnXKEkDyqDkaSkJFJNxrBSBWDT\naWivpA9iAAAgAElEQVRlMXD29GmirVaqqqroG2/9lcxkesZZmZtfwpYtW+jWrRuzZs/m9Tfe4PDh\nw6SkpFy29pcQgmHDhvHN4sXEa9U0tVlwBYLsrnHRNyWKLgkWKr0BvsmtpFfPGzhzNveyMVlarZap\nH33EPffcw/jThWRqVZxxexnTMgGDOvRwLkkSg7KsrD5fQ8dEMxsK7MRp1aQatBR7/ZR4/Hw6fUq9\n/hVFYdmyZcyfNw+7w0GfPn0YMWLEJYWoL2CxWHhz4kQsUVEc2L+fRo0b8+KLL17VEvVHefrpp1m8\ncCEPbDtFtzgDfgU2l7u4/rrrePnVV5Ekieuuu46ffvqJOwYN4v4lhcgSjLgmtLdNWonnu9h4+Zdy\n9hS7uTbZwOpTdnxBwQtdYumRbuBgmZeJWysZuOYct6SZyHMLdpU6efLJJxk4cCC33XorvRYW0DVZ\ny8HKAIdLXbz77rvExcXRtFEjzpw8To/vz9E/xUilN8iGYjcaWaJHjx5/6l4bNmzIZ2sCuHwKRu1F\n5Wpbrg9ZpeGXXA+KIpAvZIVUBL+cdaOSoF28lp1l//O1rCIxVhEiRIgQIUKEP8yMGTPo0qUL8+fO\nZ+umLUyZMoVWrVqxdevWeu1qamrYsWMH58+fR1FChWN+m81MQkIJBq841qR3JhFlMJAVY0MC1CoV\nTW2hpAMlTjs2g5HsWBtmrY5Yg5FEswUJaB6VgFpIOD0+4nUmMk0h65WiKKxbt47WrVoxY/p0DmzZ\nwrzZs+nQoUO9+A5FUUBAtdeLw++rm+ul84/W6sh3OzlcU4kz4KfS62FzeTECwaRJk64qR6fTiVcJ\nElDqF9WRhMBfl9gBQlaLzp07079/f2JjQ8WWO3TowPYdO5jywQfo0lI57XIgy3BXVgoG9cV4KotG\nA5KER1E4VmunxOMh1aijiSVUQPiX6koO2GtxBAKcdjn5vqqSVi1b0qNHD1JTU9m9Zw/Pvfgirrh4\nqrRa0lJTuXPoUNasXYvZbEa+jLvYhdilG2+8MTT/3zimXbjG5wvJ9fTp0xQVFdGtW7d6SpUQgqNH\nj7J7926ee+45Fi9eTJJOQ6Jew/YqB/tqXbS2GrirgY00k5Y2sUaeaJFIRUUlCxbUt/wA7Nq1iy++\n+IIOHTqwZcsWrrtxIKUxcXVz/u09hP616TXkNI/Hi+Cow0uXAQPZuHEjjz76aL155uTkMHjwYA6s\nX0X5np/52/gX6dC+HcXFl0/n/cMPP9C2TWuWLZiPMf84G79bdske/FexWq1s2baNv7/yKo6MFgQb\ntWHS5Mms+/FH+vbtS58+fTAajdx8881s37GDxq3aISHVW8u7mkUx6poYcqv9rDjpxB2AJztYeaRN\nNI2sWu5oYuHtnvF4g4LD+hTi2nVj0aJFvPfeewwYMIAdO3fSZ9DdnDJk0+T6fqxdu5YRI0awc+dO\nhj+agysgaBKj4bjdR6U/SMMYLWqtlpycnEvup6SkhB07dlBWVnbJdyNHjsThg2FzStmX7+VshZ/n\nl5Wz/oST+x54gJPlfh5ZXMqxUh9HS30MX1TK6coAmdmNcMc3/LdlfVmEEP8VB9AeELt37xYRIkSI\nEOF/j927dwtCGZbbi/8Dvwf/V47/L/4ulZeXC61WKyx6s8iOzxQNE7JEg7gMYdQZRMsWLYWiKCIQ\nCIjnnntO6HS6C+surrnmGgGILGuSaJ2cLVonZ4tmCRlCp9WKhx9++IrjpaakiBi9QWhkVbgvvVoj\njGqtAETDWJvoltkgfLRLThWAaGS2iZ4J2aJnQrboHp8lLBq96Hr99WLPnj1Cq9EIq04n+qRniP6Z\nWaJvRqZIMpmExWwWDodDCCHEY489JqS68QBh0WgEIFpaY8WgzGwxKDNb3JKeJaLUaqGSpHA7QEgg\nnn766Sve0+HDh0W7OnkAQivLopPNJh5qmC1uTg3N//777//Da6Ioinj44YcFIPqnxosnWmSLJ1pk\nixFNM4XVoBeDbr9drFixQsTZbOEx1ZIkusdZRROzqd7cO1x7rcjLy7tkDJ/PJ8aOHSt0Wm24bbt2\noXsYnpUsXmmZLV5pmS2ebZIhzFqtePzxx8WOHTsEIHrHxYiJLbLFxBbZ4rXmDUQDo17otVpx/Phx\ncV2XLuH+osxmMXHiRKEoiti5c6do2aJFvbndmhQjPmyTKaa2zRJvtkgTVo1KpJu04uOuDeodSWaD\neOaZZ8JzP3bsmEhKTKi3PlarVch16yZLiCbRejGrZwMxr3e2mNc7W9zVwFpv7KyMDLFt27bLyv/H\nH38UgBjTwiaW9c8Sy/pniY+7pYpovVaMGjXqkvaBQEBkZWSIdjajWNEzQ6ztkyVW98oUPRNNIspy\ncQ/+b3HixAkBiKc7xopDOdniUE622DU8S7SK1wmjXicWLFggALHkjhRxYmSD8HHo0SwBiFmzZl21\nf0VRxGuvvSYsJmNYng0yM4RWqwmfpyYniR9++KHedXa7Xdw37F6hUsmhPatWieEPPSScTme9dqtW\nrRJJCfHhvowGvXjnnXeEEELk5OQIlXzx71MtS+E1+U/9LkVcASNEiBAhQoQIf4jVq1fj8/lIjksM\nW29kWSZKb+HwkcOcPn2aWbNmMXnyZGymKJItVrwBP0ePHMFisZBbVUy0wYSMjDPgIcZqZcKECQQC\nARYtWsS3336Loijccsst3HvvvVhjYykoLCRWbyTJZCEoBAX2Ghz+UOY8u9dLkllQ5XFT5nQQCIas\nP6ccFZR7nRjVGsq9blQ6DRNefplePXvi8/tpk3ixILAsSTSMjmFzYQHr16/Hbrczffp0Msxm0i1m\nvMEgx6qqkYDDVZWUeT0YZRVFbhd+RaFHQhJaWeac04nd76PA7eKZZ565rPxqampCc6ip4YakeAwq\nFaftDnZUVHDG4aDS60UjS6xfv57NmzfTtWvX310TSZL47LPPqKmu5ttvv+V4rROzSkWe24uk0aII\nwbhx47DGxuL2eIhWAtyZmhSOuSr1eFmQX8IDw4czc+bMy9ZIGj9+PNOmTqW7zUJjs41ij48NR44Q\nHRXFnLximlqM6GWJY04vsfHxjB8/ntTUVHr27Mn6DRs45XSTotdxzOGixh+gy3XX0bZtWyS/n9vj\nrWQadOytdTJ+/HgA3pr4JjFKgJFZ8RyscbG3xkWf+Ojw3KI0anrHR7GksKpeAeAaX4BSpxu3280j\njzxCSUkJP6xdi0YoPJwZS6ZRx4EaN0sLq4jWqHimUQKbyh2sK7Pz7LZ82tkM5LsDHKt0MXbsWPr2\n7UtMTAzt2rVj/vz5vP3222i1WoYMGcIdd9yBLMt88803pFj09P1VId9ko4Y+SXoWL1zIxx9/XE+W\n+/btI/fcOca0v7gGKlnioewYNmwN7cE/m3L836Fx48aMGzeOSZMm8UuBh+xoNRvzPVR5BWvX/UDT\npk1RqWT2lXppFX/RbXVvSchtNCsr66r9T5kyhb///e+MahHF7Q2SyLUHeHNPEdFmMx9M+4jExES6\ndetWr74cwEMP3M+61d/xZpdorkvS80uhh9e/movf72PuvPnhdgMHDiTvfD6bNm3C4/HQtWvXsAvm\nzJkzmThxIh9//DGSJDFq1Kh/uwzC7xFRrCJEiBAhQoQIv0t+fj5n6pIo/Na9S6o7dzgcTHn/faxG\nC/F12eYMWh0alZpzFSWMGTOGnTt3UltbS8eOHRk3bhwpKSnceuutfP/991jqkid88803fPbZZwQC\nAQxqDY2sceGHaotWx56SfBQhKHU6cPv92H1ejBoNmgvKkizj16oImo3ce+Ng7r77bn788cdwcgv5\nN8rDBfc1v9/PWxMnkmgy0SbuYja8aK2W9QWF3HXnnVRWVFBcXIyppASP3Y5fKFhUGqK1GnLdTgYN\nGkRaWtplZTh37lzKy8sZlJGCqS6jXLxehysQpMTt4RqblUKXi9KiYrp168bbb7/N888//7trI0kS\nCxct4osvvmD2rFlUV1VxbXw8GzduZP2q77BpNRS4vQSFwAnsrKrhmhgL7qDCLxU1SCoVf//73y+r\nVNntdj6aNo2usRZ6xIfWNNmgxaxW8dX5UsaOHcv2bVtxu9yMvflmnnrqqXCWx59++onx48fz6cyZ\n7HfYiY61YS8v5+CunWRp1Zz3C74rr+KhlARui7dS7g/w+muvEfB5GdUkGZNaRZ7Lh1oKuarlOj1U\n+YM0NetDNbKAHwpquD7RQpU3wMKzFQgB06dPJ8mgo8rrxafA6EYJtI0O7a1Mo5agECwvqsGslrk/\nIxazRmZJYQ15+nQSMhN5b8hQRo4cyenTp5FlmZ49erB33z6aW/R4BXz99dcMHTqEr75agN/vRy1L\nl8hOp5IJBAOXyDNQl3hD8xs/ygvnv3YD/d/i7bffpkOHDsyc8QmHiwq58c4uPPvss7Rq1QqAoUOG\n8P7SJUTr5HCM1ctbamjdquVV46IUReGfb7/FPY1M/KNjyB23bZyOpjEa+iwv4rlnn2HV6u/rKVXF\nxcXs3LmTJUuXMa2njWFNQwpr6zgtGpXECwu+ZuJbb5Oenh6+RqvV0qdPn8vOIS4ujpdeeil8LoTg\n7NmznD59+l8X2FWIKFYRIkSIECFChCuSl5fH8OHD2bBhQ/iz4ppSkmNCVishBLUeO5mZmRiNRhxO\nJxm2+kH4Rq0OlSyTmZlJIBDg888+4+jRo8yfP59OnTqxZcsWGsbFE12nWDm8Hn755RcAUsxR9R5a\nVbKMRaujxuvBpNFg93lpaLWSbDLXZTDzc6CslIceeoh7772XETk5zJ49GwgFlqtkmbzaGqLj4sPz\nz62tQa/T0atXL4YOHUqz6PrZyQxqNVaDAZvNxqJFi4CQonn77bezec+ecLt+ffvyxRdfXFGWhw8f\nxqrXh5WqC6QaDRS63KQaDeytqKJTTAzOYJAXX3yRIUOGXDUxSFguKhU5OTnk5ORQXFxMeno6bWLM\n9EywIksS7mCQRXklOAJBtlVUs62iGoA4m42lixaTmZl52X5zc3Nxezw0TKqfUbGROZT0omXLlkyZ\nMuWK85o4cSITJ07E7/eTnpZGY6OOB1JsaGQJv6IwJ7+cBcXl6IDKOotjtkmHqS5WrEWUgTWlNYw7\ndC6c/U8GNHUpupedq2LpuVBCE6tGhUUtE6dV09Vm5MvzIctm6yh9vTm1jjKwr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EBrp41+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\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YMIGuXbsSjUaZPXs2GzdupGXLlowYMaIhxdPv9zN9+nRmz5qFTZLw1K/NZAioMUw+qKzD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\nePzxx5EkiUAgQH5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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(2,(10,5))\n", - "\n", - "pl.subplot(1,2,1)\n", - "pl.scatter(xs[:,0],xs[:,2],c=xs)\n", - "pl.axis([0,1,0,1])\n", - "pl.xlabel('Red')\n", - "pl.ylabel('Blue')\n", - "pl.title('Image 1')\n", - "\n", - "pl.subplot(1,2,2)\n", - "#pl.imshow(I2)\n", - "pl.scatter(xt[:,0],xt[:,2],c=xt)\n", - "pl.axis([0,1,0,1])\n", - "pl.xlabel('Red')\n", - "pl.ylabel('Blue')\n", - "pl.title('Image 2')\n", - "\n", - "pl.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Domain adaptation and mapping between images" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 0|3.699980e+02|0.000000e+00\n", - " 1|3.608346e+02|-2.476614e-02\n", - " 2|3.606710e+02|-4.534048e-04\n", - " 3|3.605854e+02|-2.373172e-04\n", - " 4|3.605308e+02|-1.515104e-04\n", - " 5|3.604930e+02|-1.048652e-04\n", - " 6|3.604655e+02|-7.607409e-05\n", - " 7|3.604444e+02|-5.868306e-05\n", - " 8|3.604277e+02|-4.642246e-05\n", - " 9|3.604141e+02|-3.764735e-05\n", - " 10|3.604028e+02|-3.124268e-05\n", - " 11|3.603933e+02|-2.632307e-05\n", - " 12|3.603852e+02|-2.260049e-05\n", - " 13|3.603782e+02|-1.938188e-05\n", - " 14|3.603721e+02|-1.706719e-05\n", - " 15|3.603667e+02|-1.489910e-05\n", - " 16|3.603619e+02|-1.336306e-05\n", - " 17|3.603576e+02|-1.189587e-05\n", - " 18|3.603537e+02|-1.066658e-05\n", - " 19|3.603534e+02|-9.986781e-07\n", - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 0|3.619308e+02|0.000000e+00\n", - " 1|3.568950e+02|-1.391388e-02\n", - " 2|3.567799e+02|-3.225305e-04\n", - " 3|3.567404e+02|-1.105949e-04\n", - " 4|3.567137e+02|-7.490749e-05\n", - " 5|3.566940e+02|-5.504299e-05\n", - " 6|3.566790e+02|-4.230200e-05\n", - " 7|3.566671e+02|-3.324452e-05\n", - " 8|3.566575e+02|-2.697764e-05\n", - " 9|3.566496e+02|-2.210773e-05\n", - " 10|3.566429e+02|-1.873910e-05\n" - ] - } - ], - "source": [ - "def minmax(I):\n", - " return np.minimum(np.maximum(I,0),1)\n", - "# LP problem\n", - "da_emd=ot.da.OTDA() # init class\n", - "da_emd.fit(xs,xt) # fit distributions\n", - "\n", - "X1t=da_emd.predict(X1) # out of sample\n", - "I1t=minmax(mat2im(X1t,I1.shape))\n", - "\n", - "# sinkhorn regularization\n", - "lambd=1e-1\n", - "da_entrop=ot.da.OTDA_sinkhorn()\n", - "da_entrop.fit(xs,xt,reg=lambd)\n", - "\n", - "X1te=da_entrop.predict(X1)\n", - "I1te=minmax(mat2im(X1te,I1.shape))\n", - "\n", - "# linear mapping estimation\n", - "eta=1e-8 # quadratic regularization for regression\n", - "mu=1e0 # weight of the OT linear term\n", - "bias=True # estimate a bias\n", - "\n", - "ot_mapping=ot.da.OTDA_mapping_linear()\n", - "ot_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True)\n", - "\n", - "X1tl=ot_mapping.predict(X1) # use the estimated mapping\n", - "I1tl=minmax(mat2im(X1tl,I1.shape))\n", - "\n", - "# nonlinear mapping estimation\n", - "eta=1e-2 # quadratic regularization for regression\n", - "mu=1e0 # weight of the OT linear term\n", - "bias=False # estimate a bias\n", - "sigma=1 # sigma bandwidth fot gaussian kernel\n", - "\n", - "\n", - "ot_mapping_kernel=ot.da.OTDA_mapping_kernel()\n", - "ot_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 10,verbose=True)\n", - "\n", - "X1tn=ot_mapping_kernel.predict(X1) # use the estimated mapping\n", - "I1tn=minmax(mat2im(X1tn,I1.shape))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plotting adapted images" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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PZXomc7e3igQf31bumyYO+zpPd/t54T18Vzjv2TYCuGTKm1V4RhVVeGLr3FJy\n3CrwT65WvvS4sG7BBzeNLzwwPnDWOOCCDXYNDmxRHn7sQ7zhzjftl/2igxzn+9fORE7CHJty6g2P\nDOU+vGZdD8x5rn9jm1M2hwinEhSDe0U5VLivnJebfKoFt9j5dfBzTy188aXC+5aW92rndx0Xfvqk\n8nXHhZ87q3ztYeEjtfHef/4R/o0H3tyPZ+Xnz+COInzJvOX2Irx/M3OnLax9tV/XkZ5x6jnytTcO\n1DjSMw4UjvXimX0eW+TCEqBcrY3LUz6PHl1nm4m9LQJcLvBMDe5YGb+1dh45W/Ouhz8B18kWuVEh\neS+m4SR017cc341deQDIZ/beWOwL7S/jfqJsf59oP2e+X1bJBpVNMmdhZ5BapLnu3tBimXzcv2W7\nurNdDIRmyIoCbXfKL9z15lDLAdxy/94wV+eCgb172PXwGtn9zt723T0IdxfNTlRBioDWBYb0sBfI\ncAo4N771/HmxZzfe3XcaOfOnCJQD5PL9PRRDsxpYz3nYbRcy9AfN7UjPM9iNbycgXXtPlAuCKTzy\neGvGzO+FY6q2vSjZVaYl4vzG+gz7JOUAuXT/flnpC/Vndm9qGXlUNbpAeDa71ZpENmD0LO+7n+kV\n2YuqnSAqor1IcFZgi4jseH+NYIrIBG/vIiRwsAO4cn8uk/+RSfyel5lo7utuvydRTqMx9ZF7F2i2\niyDp5+qiWLP99ZV/L97I4lZZdat0EUkEOeyscLY/rnlmEM9QKJf9Y+OmC0nJO6VwNYT1tjKpImrM\nTBzLmsnTAF6pE15p1TJ8rniGZi2VYyo1hHWkyGkCkxnmlRDBmHa+HKr2MDtv1CW9OBqVR1E2AnfX\nyq0anNYKlvPuK01BvIpGW2aqwELlioCFMpUKkR4WtSmb51p6Mw5aY2kLbimCM2RuoSwzVbMa3Kpt\naFVYJK+flBUZouehRGvMFESEWbcUFaAh6rRQDOuNTw3VDPcqSHqsukgSssQ40XObSvditTTAbCp5\nnwT4Uiklq6vlsybH6dKwIuflxiErGapybJ6V+ywbpYaXLBGOsMyFyQUphSkcbZXVyrLVkTea1z7O\nhplg0SgznDThLBxWE7JUpjLlvYCyBTCDEDYemEwoTjFAChXt1e+Ew6g0yYmwlluBUIpoNo8VsKhd\ndAp4SwHZ3yWV6M/2/gy3rBxYZAI5ZZIVhYVtzJypcCgz3m1s8yWPo02sVSHlLOqg2wVzZVoi89HE\ns4pdby7StORuAAAgAElEQVQrqswBa8nr8SiMOU6YdWatgcoRqhts23jSg1UU1tJwNWzjFC1E0SxO\nIf25FYG3DEcstXI6AR77ScfXCC+6AfYdh0d84Z23AvDux9KzeelQuOdSXqiX+vP56DiNy3/pcGex\nZv7hgnPfcX9/HBlnS2O9Fd50JFTNk3lLBJeLs7TKExQ+uK48dJzP3mNRnlbFevGXZoUDd2bgpL+D\nj/oYLhMcqBBq3HJ0ef/+Pgph3cXPLZbr2axy/bdPOba7APX0Qh/398dZzb+/wM6tcBHhJBxvyoLy\nYH9F7GyR034D39fHdO+FA/rTZwsAX3eYmZk/c7bgGtw7TaxK4fajK1xdnEWENx87v7ZufNXxhAcc\ndYPuMYdHbeEJgVWZmUT50j6+J7rr931L5aGjwpdVZXHnviI8sTif3C7cNa24VIKHumr5+We2HB4e\ncOfRxNqdp6fKXauJj0fdj3v3ipTzVy1KGtWmhaODS/vPy0E/J6IceOMpMQ6isQrlSRVuj/ZptsgT\n/e8HL0N1eKxmL77J4KmAZwQe3U3w9vvpm45nnmjOe9aVP3TvJX78iS3HB9kXbsd9RxPHdeENxzP3\nsHDfUeEDj6+JMD5mBwD87oPgcsDvvDRzWU9wh7dOxxzpGWddIL1tOuWvPHkr33ScMQZXW+VYjctF\nOVK40ieznssW+ectP/vVM+XNq+BNK9/3awPnsXXjTXcpnzhpn2aLXJ4BFx46hEdil817fWyRGyKY\nXkrDSYC6U6w7j4HIPlQFdkIj9tPvTc7fsqJCdmbPF+42HMzywR1c8Ax1GWOG9jLCO1rkS+08k7Z/\n3tOSIyLDJQAkPUXIufOFkKyu1K/uKkqVFC0WEFjOlEaKpzS5dq6EvOJDMqZeMrkAudAAen9P9DHv\nLtSdcLroYcu+MudtH0VSPKTnIx+ue+HRRYzrbval3wg98VsACSck9nYNtvPY9ePp/RjR86j7jSFy\nbgtlmIfsvUEpUC4c7934nvUP9iIr8hCfC5puRahe80S7xlN20RtzPuOR11Z/d3UDcSden+3u2h3D\niPQK5U4JFwfZyAacNfKMbiUoaBdguf1qaXjimUy9v3o1PxMRFgkm0fMwHT33QHqfTZ52Q+gCbXeW\nHWitpcBzz/X2rXgf785RGtEwpHsGtcuxZwv9mxGRxlQ3iDga2Sj0gEoxR3EmVSpZRIAG21iYVyXv\nQ1UmC2jGpD2PhCxMAJVjhOaFRdYgOZO8jUbgGErTbiiT9746PAUceOGOqfX8oMwBWmmhqAOnRBWm\nqeAhWM81mSKFmkcQUnqVtYVAmXVDxdJ7oEprBTFhohEsIIpaFmpo6kwuuW6ihxwqGwkKKY61N1gt\nZGNWFObqbM3B85lapDKbYjibBlNRjLzdRHZe+QwNy6IK+ZxrkSXDiUCL9pLvTgknWubmKJl79bQp\nh0wIZzApl0LRJjTps1PAYkY1KE2YDY4koCkhsCmCVFjQft9MrFvjRGYkhEqjYVm4QmamfNHQrGGR\nZc1XTdhoFviIsPQCIekrt/RgNimZb9an2lTy2VhwVDNnLWuKeI88UEpo9yjnAzHIezs98hXRYAGK\nHOEaLKwoZjhZ8r3Qn2elsFqgTY7XLNKhWig0pmJEc9rumatzCkBJI9ciOPGGV1itCjUKqyi4OEUK\np7VyIHDaJ7mcivasq5Uq1YwzBBFnUxvb2tj0Z7kHrCZlJU6T9F69Vngp9sh718r8jPDhrfMVh4Um\nQohy2vJZ+423B09vG9oNw2fOJ5moIkQoD19tlElZt5wavb2ryJP+njkN55mquKyYwnnTam8cchKO\nNb9gi2SZ/KsRHPX38+nu/aSFE9IbU3sZ/jMvPCbGQ3YGwEdb4aoZl8rEA6xZh/GIzhSHI9kwiXPS\ni8fcZdscn0x8tAn3l90bcIUWw1CeIkXQU5IG+GnL34+6MLgynb+Tv/zomMdq4wNurNR4RzrukH4i\ntihlyvfcZQl+x2Hhoy3Hcq8on6rOmSh3zoVDgjdoo0rdv6Vu6Yf+HaWwCVi788YZThv8Zq1gyqOe\n47tS0/h/8DDHfZsFJy0P5G9sFi6dn4LnpDl82dHMR4X95M9XXir5rge+fAroJXtOmvHhdeMA2GBs\nHK5E48lrKkg+ubA/IF96WPjAyULt5/3WnV3YF/mJpxYWIEx499WKzoqGcBSwzXkM3n114dYJfvTp\nLQ/Mwbuf3lIODVfhmRZ846WZnzlb+H2XF7ZeYHvIaYE5vE+dwi3N+bDCH7pcWUces0uWB/pqO+af\nrhv/bHG+9fKW2xE+GcH9mvmlACfbQ9632fBVh41nWu/l1tf9TBNWU6E53HeofOJk4b4D45GN722R\nuXvWHrTNZz4hrzA3rOjDi204CWlQO91bsfe8kA6kfZjXRT2T3o2LoWRCVi+KLmx24nWXbqy9ieR+\n3XAhbKsnNce5O1HlPLQs+suB/f/uRIDuJv8z7KW7TSW67+qCh0shE3ARSpyrCZdnd/rb+9XOr7Oc\nNYzcR+dc2OzC6VzOw/Z6I/kUGhfcnjkj0L1mkp4g9513ZWdcsw9v6+1p9uemdY/I+ecXjuGzNcQ5\nO6/U/lzsljsPA9zNTLR9uMG5GCwhNNV+HM+D5nahZMiuNK7sj9zuLHkXqDuP3n4yI84F5z6XYH/w\nz19SXFgXci6wI3ZCbBdaqF3QZeiCyXkgYsjuTOy8Shn6Zv38Nc/+KCHnUuXCMPcoZFNRkT5jHLTM\nUOjHNT1fsb9J/Fwo9uta9yGgzz5RIrI35j4t0u8m4t664XIc9ju2MTc4VGEVjUrZT0SEOqczqK84\nWc6gHaA2obKkl5DG5Fsum7Hx1sPCshreimBb10TAsUtO0Gij9v44GnBM5rBsvKIS1DZRBcQVo+Ih\nNCydsNLY+BaxQrS8F9Y9hG4KJXC2NTDL8FEEDsQ4UU3PRtnks2SpqEaG2OIc18amVRYxoo/TuvDY\nxERD0P6nWRZFELYpkDTvh7mkwXxocKALLDMyQYuGqmVekHsWbYi81edJAacUxXvIWeZa0QW+YKUx\n7yaD2oYyHWJRERMWKZzVxkPA+mBhiuwZFMDGJzYRLCKsI2iy4kCzOiDufbIsw7CP3VjZzCoWUOXJ\nULzlfbB7ZhYypE40UG9Uy6IK5rBunlUCFTZRael7y1DmCEIaRVPohnn3boOZgBcEYTFFIjOxopdn\nj93zkAAVppbFGlw032fFCM2qhYdR2Fpw5vn8Kz7xWFmYgVNRziInUI4mTcMmGrOlR2nRwKuzeIrO\nQJgEShHO+v1vZcYWx815Og66x61QJt1POCxTYb1sCQk24cSyQMuQKg8wKxn+VytTgbkFh6+xSZcX\na4/cVYzfDnjroWYwZaRHMYpRHR7ZCE8347YitMhnfXHPSor9IaoqbCo8admmQLoQOelm2RUNJAKL\nyEkBYN7dElUxy4bRAEXgkipnCOuA5jlBAnDVewGSPlFw2owCrBE+ouktOJRson1ojTMxDLidxpnA\naUzczcLcz9mnSDGxpTBrY93zDSaFyZ0Z52GEQwpHNE7EOLLCplXuKHksHo9g1asynurEoQWXTXm6\nBs9IeuVq27IEvHEKHg1hCzwSM60tdEcYv1VTjN+qMAtcJdsqPOHQVJgjuE3g0RBul+AZDx7M/t6s\nTHjT4cHF7AGO+z7+dhe+713oAq/wjOdk5wN9MvhT3XO1i4pzFb4w4GEXTOBwgrtUeKw27p93Ilh4\neFOZLcf7ZccTn1xy8uBRdy5LVsu8tRgP91C8rWf1yYlgXVuGfnee6PbGQX9mHHSLadXATFmpcLIE\nC5k32CJbU0QTDrsYvHOGJyKvoS+ajYe3G+6eZj60de6ct/zjqnyNVJ4M5RNr5W3zlmrwWLfJLgGF\nLXWfzQoPTvDmGe4SeNiDKyU95yebFIsfWoKVzkirHFkA5+0TKhMfP1n48iv5+6oIpx5cmXJyblLh\nE55RB3eurq8x8lqqkvdZ0d5zRL17cvpMnHVvwD5H5oJQUHYGaZJ2uLDqlnHtImAnQAQ/D2Xr39l7\nEJDeoyX268r5kgsz/nsvzrnASSM6g3TOTfZcgXGNp4MeLijnf8OzY5Ttwr9DMsdCrvEBhJDVsrR3\nb0dSdMhu3Ocen4u5LqLZUNMveJxi7/npAqEb3Htxsv/j/LikScT+XOzOQU4kXDTz2Vf82n3e52v7\n9roo252f/RjyOFtA3XusUmzIs1ffBdi5+Lrg0srjEdJn/3f7Qt/+TiA8Ox9pf6yuUQ470d43l8es\nh/7tkth2X9kJyt1A+p7ikV4OduJ7/4VPfzBczCvbj4F8cGf4Xc5/T/uQ0t3xiJ6bZBfccYH18NRc\nVrLXS89xEsn8kV2+yM3Ko1E4C+egCkWNjThnCLeIsKJCy+OqWjhSeqPQguDMLEziuGRI0SQgsnBs\nQl20z6n28EVZMIVVZM+i4tafSUtWdvMM2VRVoikuyoxQI5vh1iZsWBB3Vj7lrHCrzKXg3pibdkHd\nwBqTTrk+cSx6U1ygTILWGQlYNMW3iyCxIKaYlG7sZXGA9M4HKxZKz6Wou8IQsjBTmDQoCnNI5npp\nlvE+E2HRLDXeRCnVMWCyVQp5bTQRZk3feVjeF83T02LeK+iJIDqxrc6swTyvaOIcuoA0jqLwtB3y\nPjbcG3DPKvNkCsJJTJwuylKURZWNK78thckKK0DYcuTG1mGjwtbBZWaicmVaODJjS/BkVU5D2ZD5\nGBate/hgCacIbC163yNhUsMiw+go0nslKQelpTcmVtRonElQxJCS3tuieX+Kt8yUi5ZPc8nrxkrJ\nYhBm6e2VkpMpaoTNXO0hlWfisEAVS9MoIqv/WWXVhPU2OIyAME5FWEIo2wXtz74lUuCsY6K1IFpj\nkeCsKd6CA1c0zjjROUPIS2Xxma0769Mta4PVstAkKC6ZC9ucgrKpW9yD4g2v2cBX6mvHw/RSuL0U\n7poKd88Ln9pm/7ZAKD3HZNuEK2aYOhrONoypZK5NkhOzEcbns+WSBI/32czHPY3K23RL9WxKvOre\np3WPnnlcD7hiwUTOsE/qPM5MFeNqS8PiXsnEqUt9m0Wy6bFqcLVlY+fbyvl5uITT0/vYOkxkyflH\nWHGbbXnMz708ycJFzqRwUtPAmCK4UxdahWfEEIfPO4BH6owQ3Gk1pyIkPT23CDzuxqxk+Ctw50Ew\nm3AVOJTgUIRPVWOyiSua+90sjfRFhMtsOaUgNJ5GWOnE7b5lhWMygTTOwjmTbPX9JFPaehfeZ6oZ\n2XVPPxeiBSO4wxpNMsT3U927tcsfe7x7p/6Fyfi1TeMyuU5R5cqc43u6r/+jyxYUnvH0BN5h8PiF\n/Pf7ZuPxTePuSXmsG55Fg2MV7p8mJhXYnh/3d/QQzfee5HncxaXdGo2rrly9EBH1xSvnKQ9+a5vj\n/6LSqAHrgNsVHhbhkZoW9UPHxgevKm9eVR5czRAVLcGH1oUvPViowFNdPlwiIwyM82vJ3Cht4uPd\nZjg5W/EDm8Y3d6X7jn6dPcWKybd8qgr3TDnO26xy2xXZ2yJ3H2QvOUd5at24NClf3Jxmyoev82Pk\nphJMAJjSNGcVTLICk/fwrdh5G7ohInQPRDdIg+jx9rDsPRU7AzH2ImsvYjwNTsTSmFZPA0XsXAhE\nXLBpheDZ7lR9w1dlYmykQaxkWBWkwRGRCc6V85AvSK9HVXnOXB3IMC3P2rwp4jjPZQL2IRoOqKWH\ny7iQEKqaZWwveJwAJBS99yuRHoZz0aOwF5X9Yq/Sz0PsPFY9QFDOvUuRB+bcmxKxD/Xa7W2NzKPZ\nCYjYeUUi86fQ8zykXVhguPfZ1rLPgYp7voJA8d5bpbELY9ztW27UAenTSrv8IO3L7TyPdKNDwjMP\ny1OcaJwfqx2798dFYXWe55UH5FwUd3GtXUTd9XZg1/ungQptr+sueH949np3Q9g3taQX01B6Lkc3\n/LsRbsF+HdmDpgveyGNlqmn87pIxJfM11PICyPMRPcTy2d7Om4nwBUfYhrIK7z2CKtumHE3BrI7I\nRK1Z+EWnxhSKL86GrC53sGw5miuxLbgYlYVZhUvTmsON4KaEOts6cVoaZ7UhPddj0xZEMjwvXKjS\n9o1XHWdTvd9FxlzzeSU0TFOE7Cvf7YodeFA8WMIJUybP4gwuGX6ZeUl5Hyy1pvcXp3hW14sIpOal\nqNGokc1LjbxWzQWxfMZumlK0MEtDTFgVKB5Yv5lXumKlC7U5K8t7N4vGOOiCMaMTzP25vd46XuBI\nD9joGbM4ooXFKyfAYVlBtP7sNg7mvCdnU56oEHbAp/yAT506FbizCFdsy/EULKI8TYb0TeUKp805\n08ZljMMC0RSJibU3ppYGlYTxRNly0JTbVoV5cZ7RyJ5E/SFhSgoegTUzLuCVjMVRmCNYNCgyE6T3\nxqWw1fQgWc6d5bMmwFrmSs0KhFO9svRw6RXgjSziYDO6XaAolcIaY7s01jpldcGlEh4c6gYiC2c0\nd9a1N+o1Z67OUrPQRohz0rIGYGk5eRCiRG1IEWwJZAaJhnmlhWZBknqG0ojTCfeT7hVT5hYgzoHO\nTOaIw1k0TtxpdYuJsfXggMBU2d6EpsdFhGCagk/WOUMatXB7WWghLC3fZYfirJuyKsHcpzXvtpzi\nerwJd0wBNB6twppzW+QNdvr/s/dusbps2X3Xb4w5Z9X3rbX29Zx9Tp/uPn11jNtxbBmD7STiKYgY\niYe8ISEEEuIlEigvIF54iAhPSEhIwANCQsoLiAgeAkKyRSKEwkW2BCbE6VhOt9vd7Xaf+9mXdfmq\nas4xeBiz6vvW2vu07905ElPaWmuvr76qWbNmzTn+4z/Gf2y2yMPi7NRZWnznO3qfn9Arzv2a6vAi\nnTEgXOIBcgR2GuIxLzi71eevvvE2NwzM4jwpC2dq/Gq9D8Av5ue8MOXaM++2xMN0jGW4J8Z32PEw\n3QZIa3vmA4cGiwv3UmP2AHkqIAU+w8KIc+PCvR7StpcAZQ48zPBeG3lDAyq9ZwEyDm3gS4/fJuXM\n05ppJhugSynsrNd77tU7VnjPB4rB9xm56O/ZxzKizDyg8SEDKcOqPX3ZZn48Ob/HyGsdpn2rJr6a\n4H0J5u1xuaG58F7NPCqwqPKFvo9+N58DMB2e8bmc+UjPeXNnWJv58sO3eF0LzyTzNW38bzcB8DZb\nBOEG+IdL5VFSdinzzjzz9cOMAl8/zJsj+Q0tzGb843nBk/LPPhj4R9cTP3M28vev47xrqOBP7laW\np/BrL+J5/Xwf9Jf/D8WNN0bnrWHkW/vX+Nx+Rxbhezc3qArf9xiHdzijIfzChfPNfv8XEtfaEcB8\ntRPf4YzPszCXA3/7Mq71UxeNrxX4by8bv3RW+JWrGG89GG2n/OWLcy4PjbfTgTOF//tq5Kfvz32s\nFF0MTcaDfSbVhuWIOdIfsi3yqVq15IRFyh5AofpJWJFGzQ47MRghGCKBW/FLL+WsdJB1+3org3L8\neco0nIYz3TWUt/aZnzt+pxMQm9CCnIT9qb7EjGzXP2krI7b2VFXxrjgk3bBt66JiYQitqmgxDmvo\nl986/xEUNdJbP3syMp/cSmeCXnXoSyIYp/fU/9a2a75qDCMEbKtJorcvohrhKa13XgD9zM+djAMb\neLzzo7dXqcjdDnFbz+Unv8srj7p9/B/s//1eX/+ZLnbxShJpa+vcXMfomHu2ocyXrnE3h23dkOX0\nmKQ0c7BeoPIWG/syg/Wqe/o0tb0IZ2bknrNSPVSULnIk1raUUDKMICnYgORwTyKna8A4zzv2ekB3\njVSdJcOVVe4rDLtrJkYmC4GEsee0LWKYC2NPitvlKcK/SBwyNCrVGillamtIz4eMvMiCecMk0SyF\nUl8KB1CVyGFKvWhuSopLQ6lkSVQaBtQaimvNnL1DsTDga6tY7oVQHQZRGvFZhILCzoM9Uw1wOKc4\nppniJjSvCMJolX0XYWjuLCQ+qgZuPFLlYWpkV3bmUQNoCA+v+cKTpLgkZoRdUXbIJoaQco7PNJwd\nH1vm/awM7pxLhEAlzVy3mQsZeKRdT0aU1JziMyRYRMkW4cGiSrPGXpSaI1Tq0MDbGU9xJmvsCtwz\ncFUWiXC+igQDw8A5gjlMeUElQp4mb2iLd2eyABMiIdajqmDhMErSWWgHnyZmbAuhXtoc65ta9zol\n6mJIHpib4+rMS2MmIdOBQUHrIeZKGnFzJq0RWr3E35kW5l6XS10oSUAzmjScgc1YlimcIU2Y1Jnb\nzN4jt4lWWWplESgSQH5pRpPEqHSWMeHVMF9Qg7MW7xceoWALxkdUdklZ6g839+BPumX1XrTaGM15\nPcO7VUASQ2p8g/v8hfyMd5bIYFy9jO8Y3NPGvdUDCJiEUbnmBk0Wwi+ni/oOEE28zcI9DYbzg1v4\nRdmpkRCuuhN2uLPjfeHJ55jxLokf7UsS13yn7fpnzqPEJipEBzaDCPfuKHX8Wgdbn5cIx3ycIhQQ\noIlykYz/p8UxXyuXfMsueIsJED72woVUijgfNbhITo9a2xixcw789GdeRwUe5foDdl34Ql5wh+UV\nB73bwrB/KEc6Ii41IDJxoZV3ex7OHqekhYu1yLULJTmue1b+5vpOjtGf2WXOEL5DsHgPs3J4420c\nuEgDwoF/eh/v3irocaoS+Vs3la9kYd8FF9b22/O8/b5PymvDbvvbl/c7zpLxpMTceX95GcyuwOgH\n/f/DxWJ9BF6794RffT7jCD828lJbSwG8KQHEr/oY/cYc5y19XB6cXOanugjKb1zGiD9Q+PWp8i9c\nxHj/MjN//kIZueZ5usdHi7ErM/shk9rNdp670TTaQ1HXnz+s9qkCTKiia/Kkrnk6a6hT97TLyd+g\n5+f0bDc5De3qL3YPN6KDF+1hUbGbraFuqyy2QMqbwEN0KaRbSUcltDj3eh3rTJBG+JzAqjp3DB9c\nQcRtBbxQ77N+rfA65h7aZXICsk4EGlDZpMcbHuJn9NuRFShpyLsiPV+qMwoEexCFEk9Yp81Ajmuv\nSnjVjwAowKwdDfsVMHUa3zRU2FaGRuisUg87c46gqaef08mlUJW6xegpditIcb1H7wzSCphui1+s\nYW5OAAU5GfGVWdT+F+tjaH2s4mIaBh4ruI5nk/wog475LfbMZMtHj74E8u7smWyesvW+b4X/ySnw\n73/XHiJHD19qx4Khq2PAJRi0zZtl0bdVvj7390NFUI+cl6baxzryM0Qk5pII4qGgZdY3kbvI/g/Z\nROSfA/5d4OeAt4C/4u7/w51j/gPg3wQeAv878Ffd/Rsnnz8C/jPgXyIe438P/DV3v/pB195jPJDE\nooA0kgjJhMEqmjMV45kbH08lRBFKf9Y2YJqYMYobLHvEG2e+cLMkpuqMJJI+5IFes2PhzJzzoTDa\nAc2GmGIGmhLNtRvZlSKFwYRZEg1jX5TFiXynpNzUCjlRRCm64CJoM6pmIk0paiMNXVRkEaUgjBZM\nrVg3ZqUya4MlcuUaQvNg0NQg5eCQG3biwMlIOnQGpnIv587UCkvNHNKBh+4Ucc59R0oTg4WX+9IS\n1pyW4FISO6mkWtE0cJZBmZnJXV1wZIcwpHXlaMggNElcV2MsKRxAZeADzeR5QEvF1EP6342FHS9a\nxXWHtomSYFfCuFcCKFznPandYKZYEpJUMglpRhEoUrkBSmsoFnW7CGn06sLelTk5QuNaGs13XLhS\ndGI0uDLlwMLsMR9UYdbGmUWOCElIWmh1wiyEfVyiaO3iRKhaV5mLdaSCC0mUuRlmmaXOqAjFo+aS\nmVFY0JSo6qDBmuXFOLTGYiCiiDYWDZAsTRCZwCMuoKIUmWleWRbYpWCpqgmlhQS9eCOLkFyYhgkl\nkTSRqzFrI0um+syYlBvvoMoaVZWlVTLOPilaG9yJxPi0NdOC6Bi7hcL7/ScEUP8qN7xrw6q/xBs6\n82ETXrBnsUShsdSwLOe+Hv9ODXGZoe/bb+jER5apJIr0ZBoPcCM0LrVQcCaHQhSJLuK8TyGLkdyY\nRDf578kT4o1nLXEtiQ8dzrRxMN1C5hcP9uy0DRrCIE8l05pz6ZFz9ZM9fO1DD4b0jIUXGqIPD4Br\nRp4Qdeq+t4zcZ+GKcCA8TM77teCivJUO3FPjd7tctXWz9J12jrjx4+OBd7ywoBsIPPT588UUy/1v\nTnsUuMB5Uiaem/Bajv1zFb/QJQDJUirqzmeZMYR7GPfKwmxAgglhxPlwKbxWnOqg7gzemDWRivDh\nUlis8eZgvF/P+UIHUw3lQwY+mw+IRJGvK1PobMz3Oma7SHtGbzxrlXEo/I4f9+fzPPCGz/xcl3b/\nv64X9r2I9leGwsFjsr1TBfPGrmR+dhTeq8aDQTgYPG3C3o1/OM2b07qp8FPDEc18/TBzX5VvTomf\nH4Sv3d9xL8Pf/XjhtWHH/QLPOw77TFl4b3Y+MypjEr43O0+ycF2N14fC96fG95fG6yrcNOfDBLuU\neXFQiggPO9j+uEW8wHc7o/cv7xJUUNnzlfIUEnz/5pyHGL+jiS9pRONEiqaQqiGajqk0f0xb5A/b\nPl2AiZe956d/u9usu+2VCAtZDc+75zv9fiiSnfriuf2dNTxpPd5XMHL776ctpRRgqzM9uPYv2nYv\nqsrRrj9lsjKnbMgtye1+jyvzcZflunVfPZxq7at3YQF9xXjAmqfySXd0PNbMugFuLzF0wMZ2rb33\nEyCWP+Hst8QWkOgzp8+9v0AnQCTGLPcxvu11eNX8yP3u1r7crQriPRfq9JjUEUjqoEV19aYRDNEK\nIE8A0zrmx8683K9TdvNWX09+b629cnxj3rx60djOdef225bDFDkPsIlEwp3+vqpv9sdfo84JAaT/\nigA6d/v97wH/FvCvA98C/kPgV0Tka+6+ut3+a0LR6i8BAyEB/F8A/+oPunASYycLSYTFHGmJORuz\nVhDh2s9QjPsZ3rccifoWILO1KGcwrTGpzXnqZ8EGJeXKDTXn6bRH9IzUZh64s7fCwwRnKhSpuHcj\n2FzlwWwAACAASURBVMNRcQAm7aFtwILT9PhOn5fIXymyRAiCCi4ZUA60CCmVmMOaEnNbogZPMtxi\nXaktkoUXky767VFDKCVuLMD7ZA6aWFDEa2c5nJTuhcHcJ00imP0Hw4zOjqcBE+O5z4hndhlwI6vw\nQHLkxwh8bMLOC89cOJ8j8XxQ2KeBm+VAGvYM1RkHJ6UIm55bQ/LI4o05hUF5Lo0n43Xk8XRrYPHM\npRiLjzwrQknnZIXndWHXnWJqmZucWBwudN7U/wYRkjpimbkXMC4MuHTxBzfm2WlN8RwAJYly1oTq\nwo1W7mnhYDMXQwtjcWl4dtwqNmQuHJZWMclYnZjEqQSYWZ1nJorlwoXE2uIe4jA33sCgtoUI829R\na8rX5+FIE2hOyTMicN3DLb0ukUcpwY7lSoTZ4lSCGXSJXJq5KYpzphKArznWjEWVnCrQMMvBmLXM\noJlliVynHZ2tRJgMJhee03AytUcCLFZZ+ry//idIJe+P2lIXb2rNX/rb3fY+A56gmFAkCjQvdzYd\nQbmnjak7RZcM8xKhR+t51++oK2/mA24p5qBH7iGEcMEDrRxMOadyugE8Tsa9NeoE3xyaqy+/OjxK\nxtTXokmUvJYyscSlZx7KwkGOlsMD7aIFdeBRXjjDeNaFJh5o5OSl5Fy7UlHEG5NEGYGShGsZ+M02\n8MXOdq15OLskTE14pwXQOUoKvNyyCtUcJXHtymv57o4OT3Udn3AqHlLiYw9pjJ/QG17cCe96rSxc\ntpC9uZ+N7MGsDxqfvT9nDKMm+JDC6I0E7JNRrURsjMfavO6ZF+n2XexUeVsjE/6qj+h7d/r9lf3I\nSKjHLX37+2JXmniYB74/VyQLb2TlYN5ZNuF+ga9QOOv7929MC/dP2J8vy45RnM8Pvv39+QL//OP4\nj4hsaoYqwlkH/9+8ntjll5/Gj42J787G/Ve8Ag+6gfG0T6XvT3Her1fjS1n5heR8Zw7ALKuySdoB\n12zSxyabzZW2WnU/3PapAkypS5jCaqTejWO6PeGVvhnpqXrZ7VCsl4xpPwIBuaNMd5Rwjo1KWSWw\nBfFefPGuUSnpmD/VhRS6g3Zju4528PqLcNtujhfv1HhfD1i/EYZtv4Ejv7ExWHBkZ+J7oQn+KuDp\nLr1u0suAC46KfUIHg8CRsN2+sB2z3ZsA4r1wbZyzKeQuV2oSOVnJ+2c4kIKJIopGrjGr2bUb905r\n67OKRbILch3v63iDR8x68m8dR3Pf2Mk1pyk6Gv2bNfK1xFt/fl1X3RzDiIibDg3XgoIeohRlU16M\nubNKqJsc8d02/nivXxWbYKgqheKUtFi8TPxW7lTc5zonerFVPzJF2z1tYJRbqonbdO0MYHTX0XZE\nUNrfoXbnvfjDNnf/ZeCXe99fZWH8NeBvuPv/2I/514B3gb8C/C0R+Rrwl4Gfc/df78f828D/JCL/\njru/80nXboTi0N4dTV3YwgybR8YRDpZ4gXORnT0zqqUrio14C7VCbFWWTJg6Yo56DVl9F5YSE1As\nM80zg478Lg2tlfsMvC6V1wuUZOxF2JGYqzOpMsS9UMVZrD8D73lL0pUrTagSkvw5KUrGUlRkc1Oa\nh2z5TfNeB6gi7mRvtCWxuEJaGAmpPRHnmmAR6eFBQkMsQFltTh5iXpgpJcELE0ZzskYpBHdhlwo3\nBu96YlkOnGVhh5NF2QG7lDgTGLzxQpypFZRGTZVRlcWuyGnAF+GFCReqnKfGIJWPzMkkzBtjkmCE\nckb7GjdZ5SwNVFMqiYXMgmClMLmxc0fd8GqIFW7cEHMsZyZzdi4BNBlxnLkIyZzFE8Uau11CWgsl\nw15o0kmIVvYOB284iaUppo2SjbnnXGScwZ1dVq7rHGyu5AC9PQKitcqIM7FACYeMi2DNGRAOMnMh\nhcWnyEVlYcgjBed6ntDmvVRBqEdpBWuNUR3JGWvOor2Abp+/Ocf6VT2es4b5F/Ojp3BrJpjlLqEO\nAbqnHKqBSRzPhXlpUURUhBe18swFQ1mYEVaVNmi1Gzr2x1tDftTtTW98tob7/b2caB65fmt73u4Y\nxtKYTLmfehyAR7boA20cBbCj7fuGcGiFR9r4oGXmfr7zHlaWFZplnnrmzXSDWgnVXXfua+PSlc9q\nl/buoZiizkdkLrBuizhgFJFNia+cEH8HlAfSUHyLRHmgFRx2kXGLiXDd953HuV/P8pbvFNL6znNX\nHqSGW+MZIZ6wy3BwZyfOj6dpA1nrdZI7H2hmFmEXsS/Mrrxowq7389st8oiKhnraBEzseNrx0n2p\n1L5/r0/nukVETknGKMFnvTDlH9nFxppddfvii+OBgwnfqiNvlKiF96xVvsl9vjpE7s6jqLLNgzLz\n7pRRMioL4DwWOzrYYMuVQiOcd3Tp7xt82HO3ksO3G3wpxVrcZOASeJAr73QP9G94FCX/Ypp5q8AH\nXAQrXW/AGk+K8Y2DAMKTBB8ZfGUY+D+uZ/7ieZz3dY05oGW1eY/Aad2Wn1bjxkFl4J407tO46GDp\nvSUHc+SVL0QMKX82J757MB6UkYzxcJd5f5n5YHEeqPDFHOd9a5+49JnP4TzwhW/rjrdWEGQB3/fL\nyLucc7OPc3/JLjdbRLbyPf9/DtMntqasYmO9+Ce3pLNfcqXzMoMkt47/5JZ61fpXtfWckRsUHlwl\nQNa6D4itxu/xO32dRHvi/1FK/Mg0rIpkd9td5b67eSncutrtdgRKASw+iZ07MlOn55OTvx/7efe7\np9eCl3NuTvvnHhLIYh2EvarbcoffWhf9jT069nX1OtxlYLYcnr6RaTuGQa7S5XKHLlnraGn3wK0A\nGnpBOj8ec/psN5XCDVzoLXCyqR36+nkYxSZEXsUPYIri9vt4NotitnecA3YSy9vEIcnGeK1tw399\n/jezDa2LRCHnIisg7fd48gh+GOp4IvJloq7h313/5u7PReRXgT8P/C3gF4GPV7DU298hHskvAH/7\nk87/niduehHRi9Z4mI2cIiF7tsY+H2hWqG6MkqnmlJQp2nhuIcxSJAIw54gyxa2Ch+xzTBmPgscS\nYXUNYaxC1sJicBDlOhfmVmkO7zdBbOHJIJQyUN1CfU2MpEqzmLOpM0NoMFPu3kOyKk0VcUEkmMjW\nKqaNpXVhBoTkCjoDCW0ZUkPVUBJi9pKATRdAxh1ezA33PXNxlqoolakVJoxRKjllUo08isUaaSgs\nXYBnQDBt7BtYFjRlaMaVCmYaDERLHHzPw4sSYWjV+MdeGAQeDXDhjcVuII1cVtCy42ZukEPKJDBq\no2qf171e1ZIyk0e9Ik3G3hJDdzbcN3ivKU+r48mgJS403g83RdPA3hZ2ySltISU4SOTOignkiR0h\njV7EmDLQFiqC2xmpzBQNYzRncG/sUmbuymcOFK2MfsW1FKbacIkipuKJ1gFTU2d02cKws4Xc91Qb\nljMHFEtGrjD3VclEKLmQS7CBuDNSyRoMY+ph3K01DnKFEvWxisT4q7fICZGCISxU3MYwnDWjNiPm\nZI2xmJJw5QuDlSghYU7UTIl8reZxbF2dWn8Si8WPsL1fCtIV0MxD/GggYoP3bojeZtBGjCk5Fyfg\n6EzDKbj7ffzkb6UIZXtV+0y+QUS4sGACZw91zGuHuYd05Xn1rMWPS1Ee6MKohtTCbM5HaS2WG/3b\nq/HCndzD8+vJ3vRRz8N50xuLO7WLE+Su9vZAXi0OAVED7bFUrlEeqDGcnPdx/14TIa92RO/zyro9\nkIqlzEUf3yOT9TJjuX72qBewfSan5m6c/7oJZ1m5pxNfYL57CgCSRI7Vuv3d1HvcF2Pop1u6at49\nNZZSObSB83z7mc79sX8zNPT4KpeRH5qcp14A4TU/HbfCu5vow8zkAvXIUCnO52Xi+o4N9iQr7zUB\nKmNXOR3UeFPCPXxxNnDotsJZt5nVhUs3ZkncI0K015C3V7XVDvj7VxN/4YFz/hKMOM7x35snqgfD\n9OPnRzRulkHg0mfONPE/v1j4xXvBJN6TzPfaNT/dj/0SL+g3HU5g5EcmPPWpAkzi3DICdY130vQD\nv/fSoz+Jk9qMWhLN18yYkMp1W6WlN1qnf32lAyOJdxUGUNXtYrcYInWs0UMsUmcZZPP4rwV2BULB\niNuG7gqiNsAiMQ5Jen2lLS5QbwEu6QuEpvUc6aiStwKHLVfnGFXuovG5G74a8is7c9KvjZ3pqhJ+\n8mxu5Q3Fp+FVFN/ydprcBiRHpqTfRw8bjDGM8VrzklZRCRHdiuR6z+dpsgpedJannQBWh0iAs87w\n+CZNnk1YR8F7svypcuHKvKTusVp6mKfhqAuuPYEaaOqdwj8+5zhHzLFt7JzwNnXmCSIvTnq4Db2u\n1pZvu7KcPR9sDRvCFU2KeTsuJSfGbzAi/Zz9Wd51Cpz+v+IkO8kf4wj27jKvf8LtM8TUevfO39/l\nWCD+M9yJXHD3JiIfcbuI/EstJ+VC1vDXzMESZ1oZ0kxJhUTlngqHrNSlciOZZ27IVFGzWCM0BwPg\nEY6zSKXV7k1VoTUDIjdFidyySQaul8Y+O9emfLwYFy482CV2VvFyzndw0tIYTBl6OFymbeF52RNi\njTCzwuiO98swq8zkXvC1MtmOisR8c0jSGNwxjcKsi6w9CynkxxluqmDMUdS2FVyjdpAnIZuDXNOa\nMnqilMpsxrkqakK2BScYYzNlqSs7qtyosUvCM4ehCvckZK2TQpXG89nJSXnW4PJFZW4TBSVp4lCE\n703CmVRkl3lT4I3UGDE+nozGOe7ONbUX82xcDpV5alE8tvQcwyxYHniWjFadeRn5qF5RJYMbj5pT\nUtRAuiqFicSVVO5boqTEA21kMS6aUaWxZEWsICQGJoYk5BZA5cYFyRNVCpj0osiN8yFBi7W4eWW2\nULAcUbI415p4AZSaI+S4OXNSvLXOLDaMxE1bMEIIomms60hmkogyUFcGMVIWdiJUaww7QSfFFQ7N\neWqxBqoZWZWRYJAdp4ggGvlb7jPXHp+FdLAjaqjB06x8uDTELYAzJea8w2vdg+4Os1QMpXijdUo7\n6Scb1Z+GJuactQjBeqGJJ74wo1thzj0vG/D7zsqsLXdGei2svqyCCVZ44cKZO0kbu9zYtR7VYInm\nYCtr0venGxI0Z6chy/1IfMuRWpXlHmGcDzMfzCPXlin0kuYqvIZx7fDOSfQIEvvAxcne9LTvR4/F\ngsFVeOTGY21caeJDF6oIj8W4smP48uMePbJX59KVRYSPehHSCzcGlZ6b053B/fd72tctgWca4XZZ\nwklyKcrYxTMO3b2z6yUfmsgGopa+b93v/28Oz2vBs/G5bDRxfnu5iOullQGKDlzZwLOlYBl+p0YU\nzZidPbIxp8+6/Ta18xinDC+WkZ0677ny0ISlxLW/2G7IEkW1m8PVsmeQGfPMU4U3ZebbvuML5cBV\n6yqH7jy1wptlYexS7u/ZQEXZdSDZmrFT+L2W+JwaH8gFX87Blj1X5R4BOs8EfrejtzdSpagwBJZi\nWIvV45u98XrOWxqEo1yh7Pv8/YsXIzT4RhM+k41ziVpKr40jD1MUHL+/v22bhwPQqFIZgdoyh5T4\npYeJqw64k8AX8hk3JYRhvl0jl+l3mvA4ZT5Ke77oT/v8/8HOhj/p9ukCTCKbJ37N2zkFP6vx/xK7\nsRniLzNMmyFrFvLbJ4zDxkb0oo4vo6Hbhj5EUVwR2Zifaq2rWnUO4s75T05z/Gl+zE3yYz/WfBLz\noxjFNg69vTKPyI2U0yuTT05ZJeksmfZiiojeQn6vZpY+mYW7NVb9uyml7TndDSXbFOCkA7g77CCc\n5D/1neeU9Vjl09cpcbw37WFOEU6g5hs4WMdv3SBOr2O9j2m7xT5XuttLTLZnE4p9t2sUWWT4x333\nk9s6/06ZuztjZ73A5imYWZlL3abgiYKjHEM4X8X6qa4hPv0Y52Qq32aS9GReafwhlCfxrYDzev8/\n5Hayjf7Rj/kH3/46Yy6kTWRd+dqTz/LnXv8CrVQeNGUYnUblKSPFGzc3meq25QnVpYNUm6J4J+Fc\nITWaOQtCcYmyB96ZY53RJFSHIsplrUxj5oNJ0bzn3I0zXTh4IUmUwrbqSMokWmckJ5JH8VoXqA1a\nWk7EQBquscFVGuJK8cwoEfxbrNEc5pKYZme2XuhADdQipE4GzGb2nZmwHEISq2rWqrQGkOQBbpeo\nJwZd63RVWhq2pSbGpjH7goqEoSSVJhmvThkvqNYizFU1GBmC1ah1YQLONHGwxMPnM99Pme/jtPwC\ns8TZzcSZJ542wc/OmOwALWFeISW4WSgFvCTqnJitxRoghaqZ67khKdgmysKexLk7n01X3MjAMOww\nv+H3UKiNPcKDNIIvHCK9FGWIYra5kiQxNrhJDrbjIA5i7E2pYpwNUdDYayiRNUuU/kBzjVC6mwwV\nY06ZPDuTGZfFkSmW0SxCxRl2ieS5rwUNM6d5IqcbzCLBf2qOeeZwoDsBhSQHzl1BFkruYjMWtbUq\nkGgRWugRlpWq4LsD2AWaKrU1XHYsNRxqoOw8oRq8WaIL2miwlr/77nf4rfe/29fZmBjz8mpv/qel\n7b0xqNFMOZNl2wdKtzTnvj+t6RgrGApHaahPJneyHpmLpf/2gQlnd+LKVaCo87u18Jo6qRuKpW8I\nTWMfPddG606/Dz1M+rd7GN9HlrkP7FOIO7QjEcBZLwy4W9mB3qnFe25kd6he4GScj1x4W42PXFjT\nYs61sViAbne40GOtorUdgIvs0JznIlRku8/VcrkU5TNq3FjkKg8e6+YjjI87ABv73nhaxFXglWzd\nWnD3eQ8p3/c+PVZn6t171MMJN1ukP69Z4VqM19W2MTfARbbn9fkubf4BxwShezkA7tAyF2XeGEL3\nkTEvfN0uOMN4TOV+Fi6XLpkusiqXbYnV7/nICzIZ5a1ef2p9TG2tXSTGLjmvle5crfCdXmD4iV7H\ng+y24Zf7En5NhOGtNsTp/W/Py5yd3laHvuljfNaP/WxyRk+INoacGS2c+GNO1DsqdvuszKbc2NSf\nQQrhJYWhhxvaqmaYulRf3oE9ZZ/PuM81F8zb/vIqe/dPs32qABOqve7D0TI6BQxHhbbeTpiLKBIH\nSE+wXZMk19CWNf5YO8sgUddGECSFd74RRQa9o/rIRVpj8OJHsqixQi8ImDWfdgW67G70M85/jJXq\nYRcb/SvRX44ALAxX7cVcu6F7l9np49AwtEVBUj95MdbCpX46Rj1EUHQNK1rH4u4j6OGEMZh07mgb\ng5cYpvVRaIocj+3Ak/DC7XHl7bsrCyW2+tNXBq9vEOkINjYA3LuUVnZmXaR09Y/0nKPUrykSbJHE\nXQTLFlLNyXvQm3ufZ2zHrj0+fVmjTMsJuDTwdFtvKNhE7TkIR+/IUSo85kxhBawn6nvbGEk/tucw\nrayh9mLEJ8/sWJO2j4+cKvCtzzhA2DEf6vhMNgJXIJFoar3m1p8qYHoneseb3GaZ3gB+/eSYN06/\nJJGk+IiXmalb7Zfe/jHeuHfGPjlVBsiZglIGI6UCKJ4y09x4kSo3NZGHTJsXKInU680EA5rJPSwO\nCTamOowqpLSymErKhPKmhec+1D0LVoWaYKiNQ8pYG6k0Xkh4/c5S7pLnhjfDpXbvakaqYUmwBotm\nDr14oixRMPVcnBuZce1Fs0WYs+I0kjdyguZK1fBy4xlLoCwsDFRbMMlgoGJUUWqD2ipVEsuUGNML\nkiqujcVDQCSlTDLHc2KxJSSTRWlaaFVi35YBbzOqiaXWUOdzRRVy6vmVnZ09V+eBNs5s4ekY5tBr\nJOZknCXlAzGetYkLdXbzAWmVe9znJinfbsq8OIdFmVMjZ8WXOYCgXeNDjiK708L7Dm02xhTFf6/z\nyFiUy7qwL3vcZ6wlfm++4RvLFWXYYcV5aMZDb1Q1dCh4nTnfXYTyXYraRldeeHdsPGxgXhiAndyw\ntIYojD5wZs6+CCk3UoXqypU51+JUBW0Ds06YaowVwXipVpInrBqqhrlzvYw81wzTxLmOoYGnC6mv\nV0PaxdqWBlKD7BPe82sXh+e95lyzKDyrQ8UoVG/MU4hxuNRIcEfYueMcgIEdQpaZBSdJponwE5/5\nIj/x5hcZBVwOCJn3n37I3/z1v/dHXiR+1E0LeI78HEVoySm0LVVgWEUa/LZ9UNSxmphdOcsBGp+1\nHZVgHNyd+ylyYT3BTMYso+aMHjWZrghb+q3OIiVC2e1eMhq6rc+vmfNChIbyPdtzP0+8sDEMeQFU\neLGEUXpg4pE2Pu5gSy1z8MSQJpaWcU/kNIEEW3SfYE+QYMqek7hCKDgP1DHg2oRLD6Nd0oHRQh7d\nW4S23u82FSoRDLc6Ij1YstTrKc4n45dPdsPX1WgGH6PdkBWu0c2weNSPXUHW+t2dwvUQVRqfS4zX\noy7zvdoiyxpm6M55cW4QLnDebwNJnddYqLKCqvj52BeeeeZC22ZDvdEZq0NnBMnO5Jk3pQHOkhMf\neoIMr4kDmcHgHdvxZ8Yrfq3e52fSDTdWwyHrwk6NR5645IzzLtdxGgLoSXjrpGZWXTJLhps+Dg8l\nIp7e0XPetJkrIPcCxq3Geb7RBmqCr+q82SK4vyS0BXDlhouhVrieIvz7HSpfHWVTNdS6RuXEqR7u\nSoQet8j9NzP2F44uCVjzaNmeiQk8lJuwXxw+KPd50p5tNSd/WO1TBZg29q17v81fVSXmBMT0Zubx\nLuppTR2g07hrOFt4/o+Ay7WrtPX/R2z29mXu2o2RYL++9wG4NjCwhs3BbVAhndnpxmlgJGHF+ltf\nVkQtcew6T6Tf13a+FdBI5Dyc3vM26U/6YMcRCbliNtyxfbaNQC92ewRza+dkA0ubGt9LvAnHsZQ1\nN+LV+VpbyGV/BrqOxV0Q1p+HyDrWMcYrE8LJ8StbtzKA64t/NxcsxsB7WEAXBRC2ubH2K8ZuG4D1\nBjflQb8DpJsbnrrIQh+6TcCk645n24TQe42udSxPntfJPbnKnUcQIWErg+cnz8FZBUcEx7Ycq/X7\nR8GJUMpKET2JER5tcSH1IGJ/xTP7k2ru/i0ReYdQv/t/e9/uE7lJ/3k/7P8EHorIz57kMf0lYqh+\n9QdeoICVAdORUds2v4ZUGLPSNDGrMAukq4G9zMzDzM1kTIeKeA5GxgwMjMZepTsc0pEB7GywAt6E\nncRcm/D+fsfYt9qivk6tuGZSMfZtoEnj0ODbeWTfQnb3zbFw5gfUHBkKzy1U8yZ3cpfkndJCKx6h\nexJhUdULWCiuoRNiRkshBmOqFAHvtWIEY9CRlEIIwOeKSajCFXOWMRjikpzU18eSC1jbQrCsLSSN\ntag51Gb9nXRE67auuhtmQpFCTkKzGsWCNXFzM4EWvCgfeCWlc5YGnjNTNZbJUa1IKlEIVTPXsvBa\nrrzTMrNHrlLxmUVCzn1qwlgyeTqgSamt0ZqRcA7Vca8c5sgZ+mA3RXGDVBhmOMvGdHUD7kxLo7TG\ndBh4N4X4xt6VfFnDU3r9grO8I2mjuuPDwMfjPd4zOEjGl4qbkhF2zchiDNbYKTzJzrksyNIYU+Pa\nxu4yVh6WxDxXJgtp7wDrlYohLhyWEBComileaWnk4EaSxnkSBoutIltj8sZSozbSbIJoYm6FJs7O\njYww5mBCAJZmTFapJiCJnUZ4DQ5YpRQ40wPn4tASTih6Ne9rEqH0NnkUxCZ/ymXF2zF0bV+dWXv9\nw81vt7IRt22RZ4uw16jXc209ZE6iftJeo5ZQlRzh7VWp2tcNz5RmXJQw7L/bBp60ypKcGWGvxseW\neKSN7JHjoursXTGPc1+3uF6Wped+Cxf5yPTNKPeq8FwzKsZznM8LpLygJwDhZg1xM+F7mig9/HDf\nbZ8bV64tb3tZlLkoiDuHfo4iUUR74RgyuIoNXhCFqPdVWDKciXHdr3mv73+zCJcmnGHcPxnlhVB8\ndIRFoj7a/f6dU+thZcoeEKzXQeSV7NSNKDt37hF9f1AW9hbyJ3py3p3CZMIDidyLF544k7YZ2Psu\n7V5dOFMjufOxJbI4XZyeFx1UPe45V1eeeVtn3rMRM+FBqjSED00466zWg04TPkvrlYxBAli93xKP\nU+MZQzDY3c64NOcyJx7QyBohcPs+gi9KHPOVVEnqtAWuJeTLq8P5CRt1OIl0aipbGN8MvJ6VnQmH\n9Y+9plzqNgUmXFliL417OWPiqEWkjPbncLUIj7KBwnt+H8d5S16ACW/wPPavP13n7UvtUwWY1sKa\na84QcMvgPjX6TpvqqcjD3QH2zXDchBz6Yb6Bl/X8R1CzNlv/68faStq/v4W1AYK91IcV2InLpkom\nCmIdUJxeqv+yFqE9MgmrQX+8/zWPRzqQWW/pmNsi233p6Q33axoe3vF1TO4MwFZ3aa0nRR8YWftz\nYsj7yfklAGQQcPISsL0l104Hm6dhc3dejmNulb8EbuPeOshYQzXlBMikW8O6GbomK7DrEKOzL6dM\n4BEy9ucncaw5IK0DofXM64IRzKNaH+MTNLzN27VPLrgec4Y+6V7i2N6nfsm74XzbmK33gJNTxs06\nNIr5s6oDBtB2QlkAighYi7BGJB7eH3OREpFz4MdOBukrIvIzwEfu/l3gPwH+fRH5BvA7wN8Afpcu\n5uDuvykivwL8lyLyV4l86/8U+G9+kEIewFW+4F7akyUzlMaQNcIzVWg5wzhGONH1DaoTHx8aiyda\nT0JUKtJihRE3BnWmZoxpoBIKkDU6iXjIQw8OJS2Mmpiac0DDkHajuFM5UKTQbGGaDSfCFVIWpC6M\nqhwW+O2WIZ8zooy+gI7saRjGjYfcc5UCLVQ7w3kiIC0k/MVwy6gaO5FgClyZtTBIDYWsPAS748Ky\nzEwLeCpkawwpo0yoFkpyhhJS68mdXJSpGW5GKQVr3UkAIAGYVDJIZ8g1Vk7VRK1G+OfDAM9ZIA3R\n/5KgJaZWKTnTamMS8GI0LxvDHrt14lkq7HYDZwJWK2PZQZtBEs2CFUEdswVNhXleVeE8whkHewo5\naAAAIABJREFUwBN1XnAdUMk8vbrhWVLOPLNUocnAjQpuxrI4YzNaLhhOotDcuJoiP+tMIc0T9blz\nU4JlPBO4RnmuI8UETUoZRvbV+N7i7DJRq0oij3KcIvdOlsqoGWuV2aMeibkzQ4QbD4W6GcVzBDa2\ntoHZgyjUOdQCccaWGEsi60IV55Ajx21swSq21uXJAbfMmTg+ROHkvcLUjIMkPA0cHO6bUtLCea7k\n1fvenIPkrRBzbnCloPmH6xn+k265G4DqMKUeGcJxaVxDuhK318qioUaoJsfUaw/2Q3oOWm3doWbS\nr+P8NiN/TqdtLz8Q73ZKkS+Jx5x6QGcuJPz0O2ks0nOqLC5YCgw0NkNl7QQgxSgLvEgBSkb1ze84\nbHKu8UOXCJ/7/MZuxM8JYexG/wWNb7aBr/b/X7cRcHbSQmlPlDLH3pPGHorVWbm5CN+ywpdCOxIX\nCUAOm2qgppkBuCJsoDOH6y6YorJgItzv/Z6PEsNknAMhkNEIAOsnz+rRNtJh5yzAJcJDnKzykuE8\nd2Gd6vA8KQ+t3XryZ90OmCSUDJMIOTlnJ9fZ92PKGm5IYi/hCG4aa/Io3ouhx3eOImPxy4U2PmoD\nzz2TtHJfF656b8+7bDsJvmV7vqaXzK48J+NdcCN7Vz9M4RAuAi1HPvaA0Ij6cgBTrWiKkhsxCzWc\ncTiaAyztLAQkmsK4StWrMzahNuH1vVIxxiYsAohvrFQZ4Yk92+ZDyYItTmDDPsH/SQdM8iMsOqld\njMCJheg4/7ssdTcWT5kFgFcqkEmAlGTpCI4kJsTG8q0e0y1/JL6zhqSllE6EEuJnYg17ilCKo4df\nt1fjCBPis9qTZJKFsWxum2G/vsTJI19HelSY9+soMYJNg33YodQ7i/cdmBY31gHXGgsaZHD8kpBI\nbN8Ypi4CcOeEsoIgX68nobaEI1EGBuuMWN5k6aJYcDoBTN4H3JwuodzPLmx447iYHQHYWiB2Ow9r\n+Nl6SM+VIsIujQhHLCkKasZ3VtGEXgsL35iyKCp5zF3bnp+tQKk/W+/Xxkk9nNMkRBMWEXauNDGy\nB8hyIrbfJJ5r8lWauV9H1/sK8GpEHDeAaZx3m6IaSCmJQGuoRirvqUjI2lfluIG7pk3Nag3TBLrY\nR4q4de9iFr2g7trSCZD7I7Z/Bvhf1kcG/Mf9738T+Dfc/T8SkTOirtJD4O8B/+JJDSaAf4VYQ/4O\nMRz/HSFH/gPbvb1z/zyThh1DgbM0oCpUMw4OrctSu1V2NvGazhwOC5MWnjfFcAYX5ogao7REUSEz\n8dwlhBe0MVl4zMbaOC+ZpOGF3mlidGexxNwqSwZvShJjL41H6rzXlJ2CHCovhoFDjffrqs2IKoeU\nICnZtDPTudfkaWSivpGkGoWfBWiJohahXSKkChRlXw0blNpmxlxCqtUa181YpgO72nhjEK5TYbIG\nqYMmhUKJPCGM2oRmPZFZUwgUqOOtklRwlKQZlS6/TmFIhVQSdTlEbbZ6QMyZWxQCV4VqRp1Acg4h\nAgcRI2cFF5oYacq0FAI1qc5UgZtWSUvjrAmH5Nwj82wXb0xTGEpmSMrT6wNZC3MCbYXQmAtFUc8D\nsxkcbmjN0KxcaeT4sAi6VGya8HHHTUrMAtKiIK3WqG8lDEx2wxvACwE5wE0OZcJBhGqXuMFeGzoP\n3KgySkFnxaQyMZFbGDziQiNRSsaTUTxyUJoo2YMX1rlxz28AxVSY7Ia5Radb7rmvOUQusjg1Cxmj\nyEjSxtCE6xqh3IcGlpy9C9Zr3BQULQmrUDVk6LPDddqTMQ4ukSdF4tKUksJhoA3OcgjetNywZaRx\n/kdfPXr7Udoi0PdrhfMKk8Ci0GowvTfq7O9WLQfOu5BOPSbGkk15TmJoYSMUgSE13k+Rr0iDtzGW\n4ny31yT6iTxhGZoL163wWBpfwMASzzpz9VBuaCguzm/5jid92V6WwhqwdT6sS2p8+HXbQYKvpYm3\nW+PgTl0K9cRquSgzNzVjSfkCEQLq7tzPM60p3/eCCnyVmade+CLG2JmGWgPQjWohxe6C7wJMfbiE\nyMGNO2/nG1pL/FMspBx1vRyhNHjfB+71nKMVluz6w3tmAxd5RgV2TXjfBj5U4XN6w4e+wwQeaxRs\nvrSR5zLzukcqBcDSGbxLgwcOz05skQfdWJpfYYt8lBJDZ6Ie4dyockB5RAtWt4fIfVx3XLiFKJUo\nT/LM0yXWvEV9Uwd8rMaHpuzVuXHh4BY5sHeufNmlyGX7fyGLMzXnc9m5tIFrFd7Qme/UHV9OlX2a\n+Xl9zlUbGDEGWXjqhUey8GZTrgbhRR+PNoRvWYCDC1eufFljziwijE2Zu51lKqCJz2qoJ+ZDhsHI\nLpED2zu581jfv9rVI7QpZsI+GY+55AO9TwvhWdIgfDCf8zhfoq5Ium2LlB8uXvojMUw/sqKTwazE\nohLheLL6woFjTsknhXmthjAcJ9yWpN8T21SOD9Y0GIay0evRXBU6Ele6kbmejxV8dIPfj307KvLd\nbuvaKSdG88ovbaFYK6i7QxuLCDWHgzWL9FpBq5vrZYo5TncCNvshTY9ADFYGVVaM80qGZzvnyefH\n34+k08bMrQenxOk2shrxp989oV9OPr19VaGHKG4qgutxKwq8Mx/8OEdOQ/FEBE+hWLWez9dOnzB3\n63PaxBPujIdsvWLrz4Bu9ZuO43xk6YRen0S4JeiwDZVHmN4aCh/5a8dcpW1+mJFzwu2TEyHvjqC7\nRw6KH0Hixij2oqa0lyX426tP/wdu7v6/cuo3ePUxfx346z/g86f8fuvFK9qTiz1fev0RzRWKUiRR\nBBabkNq4bhPenI9vKrY4YgnNhVordPXCNiQGDzze1DnzicTCfQ+VPa9Cal0RKCdmYC/n7OwQzIDE\nc90PmWaKjgV1Z8kLT6m8ocI+wX5n3NQJM+XDNnCRlOZRT4hqLJqOU93DWZRdYu2xnjelEoBKYdfz\nVCjd4zeOLAlyVipgdWExZ2jGl1RghA/cuVlmKgPuxmw1Qn6KoBbFU63nT5EGlmXZHEmpJJJKB6Gh\nxtbqwL4tNFXmWtlpOE6sKMkOmC9MHnWRZFZsAOpCEWPp3t1UEvMS5Xd156RpifwuGjoLV9oYE1xJ\n1ID6aDQeXTmXYpzPwmVLPD5XHjLzPoknbnxIJJkvbSLrGBawGeZdqKY7YmyeUBKWEksRijltqVQz\nci7UVhHNeDMqE4LzvmQGDSeGz5UP2DHojFpG1HnOnrEeSOKIhPLDuTvFHWfE1PAWwhjUmZICKA/q\njF5xnNacOUWhWTcwEqrK2AUYUEEUTI2hOkUiSqEonFk4bw5UxJUlw7lmPAVDsHhjQvFRuJ4X5iGR\nTTBNVDKjGOp7NBnPligQ+rQZM4WEIDrzukVCuCyZD7TxjN8Xj/xB2o/MFhER9mYcNHOZnQ8t83Er\nfKVn6p9jGInyisWyB7vS2hq0Fu1Fd79WlEMr7Ld9M8L9KsKf1Ri3m86wXHummvKeJB7JzJk0hs74\nXLrSLPFYJp5sOccwloXaEuYv9+8n+7AM7uH4hG1L3UKx6sAViX2/15Jm5lpQh6eSeQPnXCrv+Mhe\nouZTOlFDEoTUIjxdBD6a18K0cb7vq3Jpe1wj2uEnODB0iWRxGJtvAOy0LQ5iwqgh0CPAWJ3vIbyx\nZC7yzG/bwJuLknAuhvn/o+7dliVJrvS8by13j4jM3KeqrqruxoEYDIjRiDKRklE0440upLfQo+gR\n9CR6CF3qaqSRKNJMQxECKGAAdKO7uqr2IQ9xcF9LF+6ROwszMBlHaIAIs7beu3ZmZIRnZORa6z/x\ni5Lo1c7Zkp81w4FH79npife2BYwYlupA+Tuuh40bV1rtw5NAsY4XckF3bI6Fq3HFGzE2zeHvXahc\nzpeW6Zqe66ckPlXjaPBBA6Jw5X5hty4fXTvPNU4dog/huSZ9ERfE4Ach85Ul/qzpqk4i9CJci3Es\nwrUah64GBb9qeVRxrXWo1EbxZ9OnTwJEM1p/TmoZayeBndU6veS/v2a8XEdpLN2bnMl0rE8JsTqA\nujguCfi7Eo4/tAHVf3DD9McMneQCpXGpxKnVBvz8d1/DUD929kBrJghB0NUAgedC9OL8kFD3Gts9\nJkvdn0p1J6q20O2LyGytq+ukvrnMTV6TocEbZOkNbXGs6W7OqJSvjVIzZVgRBp4d2nLFJVBvj2kG\nCi71aCRUC+7LUFihFs9LU9tE9OxaUqS+7orYtUDnM2KG1qI+V9U3wZ8bTqeuiTY0akVCaFBzoC5I\nNULg7LZG03StzZW24yxSizxt9LDnxqf+76PcqI8oks3qfLUVXzVh58f4R49dmwG7aBTaH8/UP6fy\n+Wko4UUH92wJu75v66tIfZ+E5/dUrXK3V10Yzfwj2BooG2rWSX10fb4+v3dZnE6eHQUJ1LBOqxNf\nl9qUr+eQQjsf5cztEy81zPIiN8q9moDI+byfG12Hai4Qwhm1swud1Eor7YOw509z0+EG32wpywJB\nOXki2IlbgSsJHEwY7z/Q54krmfkK5dEGZqmW7SJgS6YvM2jGrONRCr0mdgKPxcghECRhaoSk9XVM\nKWXAg1WHR1GsZESM3NDOmBMPGEdxZA64XtHnaiCeYiB4RkLkSmq2xilk5qLkEFGEKHWa5yHTSyRY\nZOkFvBlNDD1RA2o1V6e4oaUwLgtLUXZ+RGcl6cJPy4aiC2b1GlQRFhUiGYtCmUZUC7tBwJVJlSXn\nhiJVK/Oq0jHC5NDDtUY8z7zqnYMUHpQatJudWz0yS2g0DiqVLBnXo7FLVdv0TSlse5gPC5o2jKIE\nLeyCQRe5m5WeE1+VDadp4WYX2R+rkPl9cW408/kwMCXjkyEhc0GLkWPHj5YnPhQj9IHw4T3bDl5t\ndvyvo/O+bFFrUbUaq1YtZzoPFFtAnISTp1M1mCmZNS4ioMRgjbpYaZaf6siQjF0Q9tkYJRI08YKR\nWxzPTg5GZCGWGXdl9EQONbrA88LGjEG8FqRRWdQ5EilBmUl1wkskSkUhYhWSVNvl0FdHq3Y3dlfU\nC7ddRck7gycVZs/EUEgkQhZOOdf30BxT5SiRh6CU4mRqs94lh6LM3bPJj2qPUBiLcGxai/n3oGH6\nY9YiViJjrOfyrgxspfAqzKxCjlCUGedr6fhcx2ejJ1MmAqc25dyROXoLuY2Z0ib7AQNTUlwolngT\nmkV0cq5yoPPC1zaw0UKnS3UmpOqWSqlOmx9KNSD4pe/Y6UwEXrAwWeAoyl7gwRJ9qO6cAG/iibmE\najSBc583bHSm1+Wsl7nX2ojh0IWFfen5WoRepTp4qnBvHV8j/AAwEyw5G3N+qYEXOK/VeLd0FIQv\ngvADMzRmJhf+vMEou1RTxaJVqu7XFnmQwJ+Hia44Y9Nn/03Z8JkVPkkzGy88LB0F5VU3ct3N/CVg\nsaJQ/ySMeKqmGk954HMvqAu7NDG58GXe8CZMfGKZkyWuLhA4p1qUH1uzehum8/VQrOeh/VyDXmeO\nVCe5Ckw91yIvMLahsAXuLfJavA7CQq3rMOEFxkxFbNdv51kaRR54qORwVk7+D1uG0z3KDYbIcn7F\n5K0WAe5iPqNSWymIwUE6Pg0jTkWsrsTOlFpwfl0GfqBTbWQEHjTxZhnpSh28Z4POnmuRl1LIKJKq\nwUm9ngv7BMNaRAktKmatb7yV8corn1FdEC+M0+Y8HJ782Var00J2PUsJ/lDb71XDJCI/5FsMnWyv\nAawdar2YSlvQ6GsOEbj5R5N2o1lyr5SLRr0KrWBcg7pUWuPxEaXJz6+9gh/n41A9IwXr78+Prc1P\naRO89dLX37JpXJ93Wb9jlRJmbX9BKrRO6+Iv9Upy8fu6KlBvXOXi9+z+0TmJyLlhOj/3UvsiNJ1L\nPb+MnxOWu0YtqOvx7Pa3DpKeUbOPv8cuf7tEUj5SH138uFqpP+cYXez/dwwXfvufy+U10xob5GKd\nPlqX3z2x+Misg+cGytvTnAut0cotvnj8R+cscvFaH19nqlV8X9pDlErTDKoVVVVaQXqBmIZ671yp\nkSIwexWj++oApGsj/fy8y/fn0gFxpbZauyfH9tlBfvf6/ClsD6HnC0sQErqcmJZHTqeZ+8Oeacrs\ncnUG81T4NXW4cl0yS1yYHXKpGpwuBZRAFwtXEni3RIiB67iwd0FNCDGBBGIMKMo4jjVU1QW3BZNM\n8upiV6jDicEKBG0o8UwvwhCc3k9MIiw4qFGCshVlK85MqWiOG5RCV4wQCzksfDoKv/KpDlmOBxaH\nSZyimWWBF574TGZuO+dFn/iChS/nxJAyc4m4GqlLrdgPJJwrFb7bgYmyL87YJ3DYSs3YqFllAqUQ\ncO56YZFA8hlJE5N3HGKqJhGlkELGZucvhpluNv42Go+j8483ifsw8WITOM2BqxD45mBIyAzTPeLC\nMUZesbDxQLbET0tkuzwhSXl/WIgEXm8HdgLOhv+HzLgM/Hq/sJErTqEwnkb+slf+m7uRny9b/v31\nwDdZeOdwrcphvq/DOVUmCWQTUoRCqgOhAtOqHVQozXa+k1Czk5blGVkeA78S5epU+MvtxJsQSTJX\nWmYaeSVaDRi0EMgMMWA2ccgTiyVMYNYEwdgIDOqY5HYsNfjYLRCDkl2qgYPWay5IYJCqhRBxRDpy\nzqg5MSV6K8xeB0G5TCzeMWokZ6eoEa26wQVVPDjfLROfxY5furM4BEns3JFUNVBHT6jBJjgvBUJa\nsHxgE68Ij9+uhunbrkVEnKHU++qnOkIb3x6bGH9nHSEYW6qRw5k6FKw5RQJhac2AE8V5xYIoPKXC\nadoRQmGy6oK33qeXUr3s3YVOjI5SLfgROnECjXruwnXIZFdGibxk4d6rw9uT99SUNrgKC5eJWAb0\noWClfof0aphFXGaetDZVG4wPAVYoIKnzqXuNFxAIYqDKp1b3lR321rMHPqUiRb/MWwYxujDzwgJd\nmNlZbbyXVI+ot5WtU90kd1p1XcGdf+tbPnEju/JfyMR7SaQLqvzGjX+bK+frP43VSe4STVOv1vDr\nlqwaPGWqIUPp69/6i6+77NW2XJtRxn2jR74u80VjVbfZqsYstdcIjZr5dXvO3iG5sdOFkBUJlSY7\n+zlW8ry9sKoQWoeaANeNzf6h1Q5fNlnKSRRDeED5tDUjX7Vq+ZWXj2qRO4wPUilu8zpk/filOXpF\n4B5UmUtgJ3UfEYVYw9qjw206nvcxa+BKCglDQ8EF3sfI9/JyZtEck3PyyItcGEU4qf5WrdhjpdYi\nV20KnXOHSo3YmPW5Fv1Dbr9v04dvNXTS9IJKBqxdPxJIVrUxHitliVRzJNQq4uRUvuyqT9Q2vfdQ\nk+GTh5rU7n4uRM+4lSkuchGW+jyxX1ZraIG+CLNUK+GeFY2pBW5AKOLn7J4inFGC0ihl1XrfyCJ0\nDcFSq2JZcSc52GUOVUMInNoQBq90wTW7aUU7ViRiBW5yQ+Dq2jdkDD9TtMyr3iY3/m5oDYaatddz\nlrBmDz03ICtSow25MqkJ71UX0xpUnt0Nm1y1ITBCaRzhtRkyeW6jCpCaGUJ76yta1E7i3HqInD9I\n682hWmHXm2TVJa2IYEWj/t7hZKNjysV06Ix6Nyv6Z5MPPzeVz1dmu2YvmrHVvdAbEhUknrVMdV8r\nMmQkKv0qip7DBdVbw+00mHoF7qrd5/qeL1bpP5Fm+d6uGVtPYm3enY8aJ1/fS6p+6VnbVYcI1aG6\nusH9qW5p3jM+OI8PIzmPHA+F3pzgc73uvHK190HQ4kQJ3PtMX2pGSQnCmCMnqihaLfK1GBKFHULK\nSnAja8YtoaEgLnTbhE4ZwYhSWIJzJYkR58EjQWoRtiEQzbE0gzmdKhMGQRnEGcwxibUoinDj9S6o\ndiKJ0qvzOi4kjUQrXG2df8bCXhI7n9gkYT9XU5MeIaYMWfnFOBI9E9nxYtfxbq6mDqJC13d0OCEJ\n2zhgMfCbZcEsIymhlgllqToiiQxB2fhCUqdI4AULtiz8Ypx5ofBigK+Pzt6NncH30sz3+8ge48FH\nvlOEH91t+MlBuIs9j6PwTpTk8KJbuFNl31fUIhm8tw3HUhBxsjs5DBQbEY/E1PH2tPBFCFip4aKJ\nmVmF9z5ycGebI//7ovzrw4ZFwBhI0vFhypQy0xEhJaxT9DSTpombUrjqArMoL0LmZ4tjGBsCBzLH\nvCWEBS8FN0O0J+QnPCmfhYHZJ/aL8tozWRwJHePUk2Ngo4XvUh0Ad0xoo1mPZeJkzj3wiHIksa/u\nPgTq9DsvGY1OzBFEa4wFSukSkxhmijenx8FK1aqGSgM6mDNrAFtY3JhEGItiUuilFsuOUTzgXt2z\nohk/CIGomV5HFgKPGokWeL905M54FZyrUkgayb2S88ht+NZzmL7VWuSRjmOICLAm+ByXgaTCq1I4\nCZzoGOKRMW+ZMK7IPBF5IpHwSpOTwq3ACWWvgcPS8V078r4zbrOwDZXkvw50Q+444SzUeIr6n6MY\nX4XAVK7QmPnH5cDPuKZPEz8uJyYP9Bj7RhX7Wno6MxLO12zIqnzKxDdlg+LcxYn3peNBI5/JRCRw\nw8SJSBTjB36iBOFEdQK9VmOgGkyMBLaSOYaBgWpF/tjqrqGZGAxhxl040nEnRvaOk86oK1ML51Wt\nro+vfeHXtkWkBtlOBL7HyFJqnt6X9AxivPdEEOi1No2fGryRiS47j0E5WqKLMxsTgsBdyDxaxLxq\nn9yd27AQcPbNbv0qzWhWxuD0Xr/rH63jTRjJrRnyodADh7lDcDZpYW+Jq7igDXEcm+HGK5t5F3pe\nlRkT5733VbPscMv0984j994Tqc3VWovcNOpgIFKs6qo64JaCifPiYgq+Wqa/E+VGHWkuf4dmtO7u\nXKvwwWuGVhbhaKmyEqQaU7wvHT+SkbfEOizQTNeO4RgSJkJPRdZ3bWC0JOMbHwjBebVUAYu3fLEj\nCQTmbqEzYVOaLIG/W4vEOLFWVCHVGlQbY0nz3w2I/ja3P5RL3mVz/A9+zEoJWzN6stUCtBbjimot\nLqPXIMl1oFDDXhv9rN14snkVsLaJjHgtIqI32259dswrob726lp2DvdsnE5pNLSsTm/VTWxRKv2v\nHXtp0/n18UIt5KMIeUWlaA5SF+dsKq1hagV3E/lLay7KKkoEQqjNznrelw49lw5+Audmqkjl5uu5\ngaroQtXx+bm5g9VRXQiu2AWfdG3EkGatapzNMlbU7xw4y/NzztqgFqx6maO10oCqqF1aGKQxtB2d\ndUT1wjjbOJ8pbBf7bw95Xo2L5/7OGcXaAJ6PiLNDxm+bKawo0hmBvNhUPg5KXr0vtDW7l/t7Xs+L\nNWq/iVdaZI5rQ1OzgNbPgjfXLpxzKO9lqrytKOB6rCosXnOV9HK60z5fK9LYSXN1OpuN1Gnyn+p2\n+uoDMhxgrJ+3oVo8oFIRVJeBUScoQ0ML65fA6MLRoearGaXUksWs6kFEhFGMXuBWCwuOF7ASiGp0\n+cDrqGzV6L1aB8cp814ipVgbrhg7gavUNAQqzNHpPBLF+V6CT8VYfGFf4ClXq9idFm5iIGDkkLnu\nOp4m470ZXyyBWSKldHyeEp9rRnrjwTp++ZT5zex80nX8ky2MGQgJ18TNrg2ClkwkE4IwmHFDIZ5m\nOjF6Oh7ChAHf70B9YraZvjhJFiaUI8JpzkDmn207vjHnV3mht8LVEvnhnRKD8r88zQRJnMpATAPH\n+5GgxltfyHqF2oKWSs372hITpVJWycQQ0ZAIIRATjGKI3OAuvF8KhRnPgtkJmZoTaQy8MeMkQlY4\nHI7sonCXF6Y+QZhRz8RpRjvlO/7Ey1PgkyHTbY1/N8IVkZPAEJzdILzsE4/F+PkiPJxObAQKhZwC\nlAPf2UX2Ziw2M+bAr3Lh6xhJoiRxurAQ80ISJSfjZRImTQxSHbg01oZo8JmNB+7Nmdx5sNpAn1wY\nUiJn45UUhKp7GkTYH529KPvoKIU7j22dwvk+sQTjqRQ2EjA1Bg9sOuFdjhQzThFUBz7kTPKu0n0l\nIh44+BGxxBsV5iVxUuVtB6nK7TCNxFLjDDR0TOnpj/L55/dUi2zTkZPt2IWFAbgvHQ+a6E14LQeS\nwNGN26xkZo7BkKIISq8LPYE9lRp1H50NgkphSgUp9U79CSMHEhPhHInosRqbaDOX0Ca432KU3COc\nuGbmgZ6/4BGK8FZ6ghid18bq66j0TPRLYCYyxIldlmqGoAN1vOxs1XlE6LyG4Z6IjNGgdDhwSpmO\nDHNkcuVdGAguLCgvmcHhV1LNPd7IM31ttOpSN0hh44V9y905ecdREnc+t6K5FusiVYzRi/NWKkKz\nsYU5Ki9s4eTPLJ8uzJhFQJiicm+JXpzRO3Yhs5jylXUEjM91rONgUbo4M+eOIRhzUfqGci0Ik0d6\nWXiyCCL8mRz5K9/xz5vO52wxL1VzJQKfhZlZni+hs+aqh81SoC+owZXMbWjN2S3wt7cVvSrIc0nS\ndpcWhWQMXs//isLeA0md3XnkX2uEFzjvUT7XgjrspSLN24uqpVMBe6bxXzebidhqwWtqJuCVF96l\neg1Gd/aWuNO5NkFRWAh0nnmpEycJvEu11ejIjPZcma7ZlO+jcreUs7HYOqxFBC8JDdV6vyuZQK5u\nfKn/yEn4D7H9vhumbzV0cvk3/yOatlzOpuL3/yXpe/+CRWrhrVIdaCJaA0zP9tGV4haoCfZd41B7\nKzBLE0tbcNxa2CvPCEWRZ6VUKQXtItKsc6NKbbrMW75JC5tVra577gSvXbOpE2naJK3IifHsKCfU\nD53rc2OGVC1MPR1pE8mL3CirBfAkTnI9O559RHoIFSVbvCFRK6qBfFToq0ptfKQec2nCaTNjDcRV\nEZIH3OyMfklDMVZTINFqn1lwLEqFvKmIk6z7cSdLo7ap1kms1NeXUDOfIrWxWDCSS0O9LtZmXQOp\nGrV1OvHxqTdkq30GFc727OtulJZfpM/7XTfjOT+p/um3GqemIzu/V+5I09OJro0+rQGv33j7AAAg\nAElEQVR7Nr13rf/uXqqwvDV7qwBV2nop1VY0u7BaONYMnUBm1a41+mTLYfrIGbIhTmdzB6n0z6AR\nsJbXU6/7Tp9VgSLC+MVfM37x1+146nTI8sif6iZmdLHnyRc6nei85U7hbFR4splodWKOBzQESjF2\nOjFaR1mM3o29OsWV3gtXYmwo9FR6wueD8mVRFpxRvFJUgjLEgJrxAaUUIyZF3flBNIIJeybQnihK\niJFNy0LZqrIrRrLMKRq9FL7fKbMI7yfYbnqCC1PJ2NTzC1Me58BBEsmFjSQOyXnIyr87LBylDjLu\nYsd1qKGK/8o7ApkSIluFa3Fuo3AVEp92M8JEMmWrzva2MBZF/MQigadZyblwsszLONB1zru58JOT\ncpecQZWfWsfBelRgnjO3AT4fRh6fjPdpYJmVSQWVTGRk9Ijl6joZObYBWP0snHKmaEC7xJRHcsj0\nUh2kgsO1Bo5dZrFCJFb0tUwowt225yUn9suBY1FmUfI0cxUgsPDDqw37UtgvR34chLfBeJoz37vr\nSOPEzydn78JcIl/OmRfXA788Ljwg9MvC4qnSeoKTbSGkDWmpOqf7Ugi2kIj01JBfyQtBjFcJhpKZ\nrEc98n9n5at7ZyeFuwjfTYmhn1B1XpmxBLhmZhDlSheORTjJwLu5sHjkm1hzpQRlj3MII3cWWTL0\nIRH0yDsJ7HOhaKLTwBuduQ3KJ2Eme2Qm8FQUiYF9qYjS3hW0p7izeM2/6URY9Ap34aQB1DAi6jXw\neK/CU55J0pFCwsx4p///XfL+P7ZvtRb5n/7mJ9yln54d08ThP//Od/nP3rzhAxt2ktlI4YMI1yI1\nDynUDCy80ut3uvDgwuetsJ5EeCkLB1c+LZn7qAQvbN3wVhj2przvIjufySQe8pZ+s7CbC6cAN7qQ\nXek080Ri9sjAQlR4R8dGJm5zzySBOU28MsPzAAIHAnOc2S6Rk/V0knkZnphQNmZVl0JhDoZJYZcT\n98m47U48krghMy4bbtOeLxl4XQ48BSVizO37caRSCgcyXxO4WiJjKlUThTPoiS5XdkVSJ4rxwQau\nKHxQ5XM/cbDIY6MHBsm8loWC8UDP1itF8GTOreWGoFTq7JHIk0a+Kyd6jBMVQQ8YW6/36ndNn1Sy\n0MWFYtUw55B7PgsnlEqr+3GYedsMsDeyNJQ3E7RwXOrnQy8nlm27C5m7kDnmml51rZmstdtpGb90\nWWvkwyost+dG6jEP3DFRwmpe5YQLY4Wj95RohCyUBAcXrqga1qDCdyicmkg+4myAXgpZhN6cGeOu\ny5yaScVTa2YROCJsKDwl4auyo2u6Kdx5lBpuXI3TYGDhSXeUXHOe1lrklCMajJKVEJ2DbEhkbiRj\nyZhFkCIcCdxVXhjFlZKVv/rma/7qm9+0w6l10CF/u9Te395+rw3Ttx06mf7pf8f2kx9S2jS9oklV\n7+GN398X8KR4eUYsZiuEtRAsRog1aWYV+tftuTkJUWugpDzfDCNKwZ7RKlWsFKIERKoFrmhtms7a\nqbVLhvPEpOqopIn8mubK2+RgLbZV6F3Yq7Fp05OzE1ybMnyEZbSiPJgTHZZw8bff3kQqSnWB6NRC\nXdAQMGqgZGjoTq3RVwpg3UVp6+RtrKP1AOqhhICVci7YV3cp0UqVFJ5d1rx+is/W5rFR3YoC/rFe\nbEVSLsN0nWeXvKr78fOxfnTKlz+caXaXf6h0Tg36d5shnvuq37Wqv41krcf3268BrSG1lcbZ2qx2\nPKk1SvmjJ/l5DSr18QJ9kmdE0NdF5FmLtA61/HKN5NkNsgaQBnLOtUFSIbcwutCOZfjuv6D7zn8F\nouS80IfI8vgL3v/P/8PvWI3/uLcPAjsXpCsE2ZDnCccYgtFn2GlALTNpAuS8voMpSSdyDEQPuBs9\nRwZ1bkLPdzeVQtKJcjRHYs0u2kqlzPQSWZaRLmqdxMWOQ64RAE9EJhn5Hle83s3MZjwZZFn4AVti\nML6/gROFUpwgHY84vsDLoMxmmBbuugRBsLJwDEqySIfy4DOxBProSA41z6LdMwcHpbC1RIwzeyCV\nmV5g48IHyYSD0nWK6w61I2Me6MeMsdSQxVmZgnCngV+UzLt9Isk1o8BvToXcK5adX0ZIJ+hKYGbh\nZ0tiGyBIYZDIN/OCxo7HOVIKuBVCgFGhC5E8LwypY9MpYXzi5S5wKoHjbBAik2VSKJVqbUpgIIWC\nFBiCElPh3npmXYhF+WR7Yj/33IaJOyn8zAb+9bgwlIXXvfJ/jRA3cJUDf/24EFPkP3HDg/MX/Ui+\niXw1Bl718Ol4xFPPVjNxyRxsYdQrunRiq4G9FsQXXAKDHeiGgVMZOcYOsczixo92G35xMn5iE8sJ\ntgGWovxarrjXJ74395Qyc5DIyzSxC8JVmOjpCMk4unFTOt77zN43SOlZopGDcO0dj6kqnU4uPAHX\nntm58jItpHjgygJfkdiL82HpuA/K3mvY7CMCud4fPCihaXNVBGKHNuof5qgriypxHRYVWGINT15s\nbt+X3+7n/NuuRf7bH/9T/vltz2OIIMYjgVfFmNRJVrjHeV2MKfa4L2eDoA8KN7IwNOrvta5j2Ur5\noh4oAL1Dz0r7r9+/JUfelFyRTZwujEyh44RwV20CeMsACi9sZBdmzCE25gfAoDMqzt43LEzsu4LG\nwEOJ3NiMoQwyM3pHx8RnZeZfpRf8yJ64Z0Cp1LlBZ0QSJ9tyI0cAcliYfMPOMy+9UNwxArHRt7pL\nxZQI+6FOTG9lwhEerAOcXZh5z5a3sePP5iMl1mDvsiiDGqoHDMV0YfTAWDZonGAJCE6vzqATp9Kh\nUp2OT1a//8yVXhaO3vOoqdZjpix95tB0T5/JiLrwhfYEN7Zew7FEYBNbCG07jS3Oyavbas6JIRTc\nYXYhhY8pY6kNRWdNVbuGI6tFcduOWu38V9uGS0fAnAsxGctcS3d9/tqvW19IU73aliWyS5k5J7q4\nnHV0K5CVsrKJ1RLsUZSTQrJaJ7/0etxjuKDfe61FklaNw5khZBCD1aGtyke1yFqjXFsdsu5DR16U\nmJ6dedXhSTpehZl3OZItEGJ1/JS1ZhPhv/7sE/7l6zcgVZcdRfnb03v++//t3/CH2v4hOUx/tNDJ\noHUK02UoMaBUqlERpfNa5GX1ht5oNSmQivTUe5EgKdW7daO2rXSzVefjXrU0GuL5DS1aNSUurXCN\ntdEJIhQ3hOo3GVzwqBWBusj0qRohzghRpcFVml2QKhTOzeFqRaVyqPk91uwhk4ZGGapNmVMvNG+O\naU4teBcAlbNL4Lk5odK6VuyiikMhEKrzUlUxAZUPr62HF5TSYCO5oKSt50Gj3lEMSdWG2oOiLszU\nxPhQ4yFqgySCeM3wsPjsAlih2bo+AepMJCi25gT5M2q26q8qEuIXqBBnHU9sTSk8W3avH/Bi1eL4\nkiq30tjCuWl5/tBHr03eCv+uTZFhZ13SmaZ50cRk7ExtU60olUGzh+e8loRqXJ9bYxyDknMmioLU\n6wGz5mzYaItSp7vebPYLK5Jl52uuhObAiNbmXJrJSajXbVrfyxBq5pI2V8cL009bYfFmM56pLo9/\nsps5E5mkW5BCTD1OJueFEpUeGKS6VHU21s+lBPpQs7A8KcdsvNREEbgh0enCHHseXTm60Gkkpoj5\nTFH41Jw3GyXnnkK1eh7zzKDK0QqdZVIInKJwtB3f2xW+o8ph2TDJTNTIB3f2paeTkR01VGa3zRzG\nTM/AJxS+XDIbTXySnLRkjignmehCqq8nGclbREqjCAduUkfQBS0zXiK7WEjRMXXmYvwoZl7dRUQj\nJzO2Gun0wNsgPOQtPzfhhJFky0+z8H4qZJurm51JRV9zXcNhXlik8ti7KLwKgogxWmBIYJ74Ohpi\nI9GdGBIenc4rhWOz6Wsz5AXfXvHoiTk6IRkmFYGOMmC5BhJvTRi9MLAAiuWB1zyStLq53eD8effE\nbbNV35aRfJp5NRiPFvjBy8L30p7ptuOpJDqZ6aJQ5pEH73mc4C+vTmzmE8cg5LBHY6IrlYVwKiN9\nGvjNNHNvghKJRTjKwGN2ikdmq2HCaaP8HyfnKQuDKJuhhmqOwfnGJ+Y58MWSuUmJTXDup8BDCtgp\nsO0S4yKMGLGDWQY2wOgTlpXiCU3GEJSE8lINKdU4ZIjOO4eDvaCUkaI9S47MISHLwivJjF5Ipeed\nGsmMbJlArHlYqixksKqDFQ0UExKGS8FaAdZpqHo4b9TR38M95I9Zi+zizN+maz5dCqcQ2KA8qLPX\nnjc+80KE9zFyzYI5PEqgx7mWwtSCSjoxKEZpg8unFDARXs0LQZzRG8tC65S9k8JhA9vsJOo6fugj\nmPFSZiacQocl49M8swTlgNM3ZHArGejYB5gI3PnC29Qc05b6hhxTz6LOpxluwsxXvuVDN/Hn9sQp\nJPqGxnwTIqMpXVEGGYkl8aSJEgouhW0xvgwdOSqv8oKb8LbrqEMoiHOgozZTO89MQbl2eBt6suzZ\no3SM/Gie6KTws3DDqBGkUjnXoNYBeOnGY5hI3pxPS4QIkjvGLvNJNn7tW17LzLUt5JT50FC1LTNJ\nnGPv3C0CTBSHh+i8zPCGiYMrS5d5WqpN/lGgWEDF2VEYm/jh5JV+DfX7OQlM1vFaRx6bIcJbBq6Y\na+0qwrvS8TpOZK9IHEAnMLuyy7V2eLtCT8ALXTjkxC58DF9lNRap+vsuGmV17sxKis4pKl17ylDg\nVmc+MGCtFrzGuRYgQPGOQ4THInzuC99Y5E6qlm5PxGdjL1TrQSpzZ+twar3VmDteBCN4gVBTnL6K\nA5/aRMSJHTyo8qlNPPiGxyC89hER5VYylpZav0ZqmHqbnOfSwINsINWxuvy2zfW3vP1DEKY/Wugk\nUukz2sWzPXadphulGQmsKMFKixIRwgopeHNZ0+eKVaVlllxMzESqrfRslVMZpSJSwSrqEp6fjEtt\nTqRNgdwbUkEtsLJXI4Xa7NQGJFy0J9EqYzhoONPCKq/czpaSASqvlGY5HqpOqqIIfm4SqiNl/bfV\nwS60xkebXbSaVWvzFbHxFa3R9tj691k5ZyeoayvCWwOCU2hGEdr49Q0dsuautmg1MfB2lRgVUav/\nEJpuitpQrAU+DkFrI3iZAyWrm19dA/H6c21a9UylW7NoxGuhr1oRvtLQMto1EkI4X7zaXnsuFYXE\nW7L5xWTDRcCfNVsrygVy1pyJNOa3F7Q1X2vGU11nvwhabmjj2qSzGiyseq6CqrRroL7XsVEZihmq\na7AsZ21bCIHgXil6DU1SmpOhSwsKrk21e0HPnMR67KrNBEVrk5WpeqV6bu194Bmd+lPdbnYbht0t\neXFcMvNxQixwLYUhwJyNRYzgiavk3Bh80hn7qU70icK0UWzJbIPzlc8kEjEYbxbnYM4xGSwLNw09\nFTHenYww9Iy2MIRA0EhU5dYjyZwpJooYy7Dlyy5ip4k5wZDveY0wSo/mGgr6SxPez3CgYNPMD64i\nX6gjU80Neh8H3uiJz+NCz8yr7sSHWTjmDS/uPjBlmBsRMTByHR1aptMcToSu43FMHItxFOMXTye+\nIuEnY3PX87h0DMV5cxWJsyK9UkpH0JFNMtDtWQOZ8oIGQcpCPyhdUHJ28MCeCV8i++bsdIqFwZ0Q\npDb3bRgUPBBxsk10MdKFRDRn9gWVOo0tDjEAIbAEI5FAq+32ZxboFJCZ14OxN+FalVAU0cxpWpjK\nwj9KEAdjM8CP5Yk5DryXDdNB+HIO/J+HW36eArezcS1GFwpk2C2Jg3RM2rOYoxLZibIBDmNmwAlp\nIXgdTGxFCKHgvlQKW6hW7/ti2OoeKMIhO7M4A1JjCQSOFghlYe46XoVaOBcvvNx0PBXjcYFZakbX\n69ixV5g0sNv0jLNyxDm4Q9cxGlzHwKjOq6JsNltE68jkQQo3GR5y5KEUNnnm85MwRiHGwmLOMgqF\n2mzWWZKySKnGQzEQYiT13TkQORdnzA6+8GC/F7H2H60WMVeu3Oglc6QDKXSuXHvmRLVqT+LMKNI0\naA4MruzIKM5X6YpbH9E2kOwNghW+6bYEG9kaHLTnJk/cB+GFCdelBr4OYiwIt0utgmeN3MeO18vI\nUTa8S4mNnaiUTOWzZWEvSsR50li1NubclcxTDFgwbkpBl/Y3qUNUCz0fgEEXegqx1O+cu7IwBSHh\nTDqwhIWbZSZ7dewLrhyjcL3M3GvimoWXJTMSIAvH2HOV9yxaeIgDiyhMmbtsPHZbdgUWXSjB+CIO\n7CZjmxeCJDbm9C3j6NG3zC4saeJAIlngOlZznUkG7hnwOBJD4bRUe/JJwLpIKIWilQ4dTZtpTabX\n5lAcQ8s5q/KLtVIahPo8MdSqE/HkypXUoN+EV7QuFCKZk4eK5ItzAk7Uz/hele/EiaNHFoQ7MaIY\nv8kDQzA6qZlqKwbZh4UFYTEnNeXh0ga8boGI0amhbXi0oHQp46U2tuu39tIZ9xZxLdxTne+uvIDD\nXgNbq/Zmr4CjOJtQ2EsLVJbMdcm4KwcRtganKAzZ6Q0WVW40s8G510haIMRqiPZWe6Ibm6L0ruy1\nYxfGyrAyQdSq02dQ8uKYJLpyZIrdqkSoJhpdNWTyIMjhP/KG6Y8ZOilSJ+WztXDAlVZmIPEZUVid\nyVQuQjmh0shoRelZSFL7pyIQYwSrzUwR0Bjq7xe6mY8aWgcaumArytMu4GjVLAKeA1GVWpCL0ZoH\nKM2Z77yFKgxNrbkrNB5nw1PPDXd7fZfVSAKQSufTZvnt1XIE3NEU8dXPTZ+RCmN1mLuwJ3chrj9f\nFMd+pijW440o1sLkqiaqUu+qrsjOVDOEsw6qokWGWw2L9dq1PK8vdT3CJa2RtUlpSE5rZkII7feq\n66qe/HJunkrTCGlZkT4922RfOg2WUhAVipWqI1MlmTTkxp+bhQuKZj2m2sjpZZN1cXzn9eRCc0W9\nAa7Uv5UWWf+/To3iuemn7SOIUnImxFgbm7Y2oV3jxb3ld2mln0oV1uZ2/dScp4awNZe7s3uiRNRr\nkYrUjIrUTijz7AboF5+rP9UtDAPbYcssE1qgDyOihZ0FboPxSKTHKLaQs/MUjOCBTQp0IbC4c5DA\nhHAMhZ30jOYEiWin7IKxKQup3/KwLISupwTF1OkXYZsSFGNRJe02pJIrRXbKmER2U8GnY0VltPA6\n9gSbicvEPsNjUITAdVKGIsybDV8tkHPPJJnOCzc28947/n1xrgz+nMKNOJ/fZMYlMHSFW1vwYBym\nA0V3TOLEFHg7bvjEJ95shCUWjiTeTcp16JAr4f6UiSpITDxMCzsJhGz04USZC3MXMQrvpxFhw25w\njiKc2HF058lya8SNDVpznMw4mnBnSi5W6UMx1rGMCEs+semUQavoupdMjkqeC1csxC4x5symEwY5\ncUIYmLFSSMF5kwq3u46vHpzdsuelFPoY+PqUefINtzHyeZwxhKcu8ZOx5/18y4MF1Kni7TxzGwtX\nxSkpURxKChSEMSTGbIy5pwtwpQa5UBBuUIjGXDp6FfqhsFEoJZIUrsxYKITo3EVh7wuTVS3iCzU+\nuDLmygaIWlGnN1Q3xM6PSNpWXesy0vcbbgL0Gjjplre9cGXKDujdeNHlqqnthnNoaJAeYmEW4zFs\nOEwjOjnTlPkmOsdceDmDc+IQAsGUeS6Mkpi18rxc6/GKOtcoL3HezZnHeWYaT9XcSAPXXWApxtEd\n879H4PEfuP1xa5E6hHqIsTkPOtdMHOirPbXU74SFysZY11uo4v2CEH2pmph0A8DtfKRzWDQQQqIb\nJw4ifNVfVY3u9MShi2Qir8YjXw3X52+Vu/nIEz2fc6qB1sCk1wB8Nj5xCMrb7pqrcqhhy96BLPRu\nvNcd18uRX3W33Nq+DuMyFBTTxG0eeew2uJ0IKL8Mdb+3uja9zk2ZCcErsoiQBSRE+iKMWrN7VBS8\ncOuZQ4jViTUa7jeVxbF+dapCcULwxspRts14wj2QBXIrXZU6iLwy4X3XE+Z6zwJHNPIiV+21Wl13\nBK7dKXZipmqNOjJz6vACT6EaGWzI7IlsrbDV1l83BOWklYWzqdNzRuvYijHIApZ4l6oGtDRWkuM8\ndEa2SL/UsmirBQ+ZUJxFquNhFOdkEQnwUCI51OHod/3EgvBINXfptDw7IDfg4F4irzWTvGBAlqoJ\nc2n6evxsbd6VeD6dA8pJnVsrbW1y1V2vocScAZ5W4EKvwphh01q5Rep9aSNGNKc4nDTSF5ruvfCd\nODGWnqBOCtNZlzW1DLJtV2mMEqpLbAqGs5C9Y5urYcgoHaUImqAUrUYmF2Yif4jtD+WS93vZKrVK\nqjMPTmlhtFGaNqNVsimESrWzqp/xUA0F1KH3itIscmFf3ZAIbXSy+nFrW4C8Cu0vXO9EpOllKn0r\nopWi1rZFqx14XDU17fGubdJ/ntrXY3BTVKsltKq3hubZhe/c37kwi5Nah2LqpBqzgTUnMws8o1rt\nJLLTbMnrvs6W6VKd7aLJmaaD1NZKdaW9Nbe69qwK1vhzs7gW9ueG5tnSfPHaDKiVM90wUQsf84IE\nZVEYcmvepCJqM3WtbEVqyrM2LOPnMDP3OgkLWh/jDUEMpdI0TdY3uhp0iBlBQ6UEWkUmJVS+oKg+\nU/LUKDQdUEP6zu/u2kRWx4amRarHtEhFyKJcIJ0iSBAo1S1QzQlBz1qjsDYuLZ+rtPc8NkCuSLOe\nj+FM91uvh+KlIZFrYpaf6YVGbW5DQ/dKQ+XEGi3vEqFrExt41rdc6gBF5Bxmt3xsJ/IntV2Vme+U\nRwIzqBM6I0eldM5YCoMUriwwx0CXnUUE94AF5VEBFV4E6IKiEsla2GiPBSGMTjBnEiXYzOd9FVyr\nQzDnQKUQSFBSiGQ7cD10nLITfcEnY7QqkL7DcDJfPkAMSs9MST2ijtjMhhs0TrxR49EMxJBJeItw\nXDo0FX6ogU83R96nV/xsf+Ia5SYYyQu7TqAUDpsrtgTmqU54RE48TcoYE6EUpmi4bnihC7vkvE6B\nooWSMxqEEpROqji7XiqZwQrpNvFNmUmL8UEH7ueZGCJDcea4gBtXBPZeeerXQRml2sbGGAjLQt/1\nBEqdAEvGojOVE1vpMBGuotF1jofCzjMbETwK05LZ58Kt9sw4h5x4+/YJdObFUBg1MBXnv/z8xOSZ\n+znw9ih02nF4rI6EnWS6onxlCx6UG4l4jLyg8LDAN9bh84kchmZuEyE602I8aqIT4zXCi2gcvOdJ\nnFFnBhJTqQO0EAtXnkhRGSggQrLMYeopIbeQ48Qx1fvhUJycFrba8ZQLLkM1KOojW0/sSyZKxNxJ\nkrmRoWZYZ3gy4V4G+li4XmrY71IKJ53py8BM4ZgfmHPiOj1xlwIvCjyUzFGd32Tlg24pwWp2U4at\nHXDpOU6hhpYm4wZjdCWLs43CYYHBE+4zjwW2Welkojv8YQud3/c2iWAmbEWJfuRE4kEG7tzIJWDR\nQODGJ341XHMz1qL74FfEeOSqzHx/cUxmfuIveMORx25g8oVA4e40c/BrrqelZQE5E1c4M4LxsOnY\nUNcwnTpiKERxftNv+ewwcn9VB7KaA7/qb3gi8Yojp9CTY4FceAgdC4nBZ06pZ7BC9p5JOzBj7Ge6\n8MSuFDbLVAtaKfyjaSRKJsnC38RP+AR49I4vo/Hj+Z5FhCXVnKN33YYQjK/LVV04Ub6Kzl/mdxyJ\nDLnw1BqRnU78Mt7yo/kdX8aX4FXPdOUHtlrvo2/jC0zglg/UVamNkOO88JF9V6u3gLNYbX6upT72\n536HiPJGHogWGUV4kRNFOnbLQorG133kR6cnMnAlC506v2Fg7iqVP6Bczc5GZxzhXVC0DSAHV7KE\nKg/xgmth0sQ2F75fFrIUmrs4R+/osnAVZjaSa4Csx8ZmETbq1BUrfMmGDHRWc4+uZGHfgoaj/L/s\nvcuvLVl+5/X5/dZaEbH3edxzH/msKjvLNoXdWAJk0YBECxAC1D1BiAEMgSH/AA+BxAxGqJEQMIZx\nC0ZIjcSshcFgtRo3btm4DXa5MvPmfZ7X3hGxHj8GvxX7nMwq83BVl0mJNajKe0/cfSJirR3x+63v\nqxEw3zw3yL1RCgG+aiNnlnkW3ImvNiGK0WIm1sasyosGZ+bP9q0Wqeab4Xfqjo1QadFjbRZTZgtI\nqCgBFJ5UNzNbTCA6DXfCJQZXCDfBuGkTS6g8bwamrIydrVI4qpJaYNAKVIptNbKDF4smSnF0VJNv\ngE+y9P3lb7Hpw9/rMaCIqnu1SOu7+D2cVTaMZNvZf9hRdwRITwhTAxJemG8aH985pyMQWyfjY9tR\n3/7q8c6/p2srj3olwAX8la+H54beSFhHNh7czJyTadaI0dGFzT1PH7mn0f9u0nhCa6o6KuKL76G4\nba2dhKZbpd8chvKmonIKJ5XeZJg8iAgFTtqobU3+mJlCp5ElVW9GRL6BV/mcbUgYApOFk4hVu5Nc\nbA5/W/P7sKp/2RIKtTGIUqI3T+H0yf3SVDofxE5NaNs0RK15wbCdP4J0Ywv6Nat93fb7hAhJT6qX\nTtP8xrz759lp/an6nCZVdwoUfgwlQn1XRDqvd5vDssFR1k0neoOeuy7PWv3a7388xxtKZvz4uqX/\nmyp2QrW2dVKqETRSrPj3RBwt3Oa/iHVaYLd17Y015mvy2zrGIZJ2E6EEmi6EDFRjaoULU4iQa2Yw\npQYoKkiIpO0hg+d8BYEdmYs4nFwrx4uMczcGcjPEKkk8uCDYwpUEJLjGcS6JnQlX7b3rOcbAjSqH\n2rAMqplnzbgZIDDQdCLn1XfgUDS8R2zgUJtnP2ljPxmfaGCRI7ElWoA3+YLbCi92nt9yIxORwN08\nIyrs1sptAgLEKNh6Tk5Gao39sEMifDAd2UtircKZwk4ruyfn3Erg5rhwXhpD6MGPpRD3E6LGE2nE\nbMj9NZ9N4oJgVUIp3BTjtkQkRi4KRI5cxcz5FCgWSMOBs6hENbQKOYzQVk+Pb+IdtWcAACAASURB\nVJmVFVkDh6UQhh2395VDbKSl8slF5KPYCOmOEJXcMi00VCP/wx9DGhJnZP7o8yfcrI0jkdyEi+jU\n3pwbaGQnmV8dIlNYyBY4LpUWlAtVdszUEAm2MnTmQW7QBqcfmg7MInxRGyVAsIFjgFvze/g0wSjK\n2xZZ1kyxRJKAVKFQOUe5GAKXZHJWsghvadQaeWvGwZS1AjFgxVh0QAQWq9gwUXXbwIvE4LqFooW7\nFnhjQFNiGrAqnGlBEJ6JEtOR2zJwJ8KBwjMq/zuRAyOtZDQXojkdsVaf83MtRBr73LOgrDERcF99\nYWgzJoFdqfyCLIxSeD0s/LU/t6fATz+emjFSSESq9kgSGvc4arK9I8yCNzQdpXjaDpRqVIlUCl/Z\nM35xnrlqMzfDSKkDZ3FhaEpNC7tc2T0ySojFg5OtdTe3VEhhZSnniChXFJI04nE8MR2+mw+81R0X\n3YBgLSMXHJAVbmRgTMZ9KOyrIzZSnEB4ZpCPAzcG627lrBpzTYgKa4gc9JxfX264YaLsMu9sz5th\nomlj852ajpFlWvlQ7wAQC5jA+7hHJTOHBBmmeDhlVL6fJq6bctUbgaKB69Hv8fPlPa/iFTehN2AY\nQ8scdECtcbWuvBnGU60DcKN+7Cc2s7Y9O6tUEf6+PPMH8RmC8GF9iwEfLDNfTBe8WO8pwG1UbmLg\nRV04tMQHtnKIgbe24zkzIoGp2z+8jE+Z8pGJzKSZVRSlchEz12XiOIFkP6fJjFEbBzZNstM8Gy4h\nUWDt5flOGodYuS6uw4rm0QB+9U6L3Rgsr9vIhRYubeXDcPSMwd5QqnQZSK83PtDFN3KRk1b82pSd\nNm5boFWXjwAca+BMmm/Q0rxZ6qPhxmWE8lB/iCHaxevAqs4qypZObBXDsBY50giMlC5s3IeZQ92x\nCx44fLCJIa0UOElQ1jCeWEY/z/GtaphycAtTp7iFk8DezRce7Jp9Z73rZlQ3sQrQj1NPpN924AFE\nQ9cVPVDwpFPutjJV6WhSRw+8UeqAZxCkPda5dHMCg1Xdva52hGoT/2/NVNBAVncXMvMsKLeadCh5\no4JBt2gU31GOMToCIi4yptPzMkbcXOn6+Rdr/bzpjRYna/IO3pJMaN1MQNTd07QX9wG8ScVNEBBH\nnbZ77oG39kA9OzWE/f624NqqSH8wKtkqkQeKl6hT5kIPiNuajCKAKKk+GENI2+6zN4betDhcL1Yf\nbOCbMUhg9fa1w8tO1xvK43noKJQKsRotCDQjhbAJvRy56nPn8+/GI9aRpg26ftzMbIGD2hGkav6o\n8UtzLd52/c36ZEogGBy1ERpdC7YFC29r3LmkolvYbaWJI1tbHpUYnEJrmy+e6CkGLp6EU+5BEdeH\nmRgDgVkaITrdVTu9r4qLWvM3mrJv0wjjSIiu1ntSE3N0pGVJ7krVrGHiDbvUxlgHDgY5GIMIlxgS\n2gl5PWKIFUJZWDSww1BdGFRJ2hjYjA3cXEIxssCZFFprvC0DpVaOayNXf5GMAnuJ1LhwTqCZcaxH\nQgzsUWqriBlntpIGpUQjSWMnjYu4cD4GVCqvlsIxjqR8y5gCHyShUjmTQAlLpwh7losSmKZESvDm\nbcUkkqZCy4Fxd0FpK8v9jASnxczLkYTykSp1ChQirVRkHBAtpKHxYa1Yajy9cN/HpIaah14/uxDu\nDoVSmtuiJ3i3DORWCNz689SEFqqv87JyW4U2R1KsUBdUAlPaUcvK06HxelFuVHnz3riIF3xxs5J0\nYM4rzRLuh5iQHDjaxPZcIwRagJXCmUAaXM95HwTNhbdr5HKnfBYrT2KFULjFNUbNPFukiHHMA29r\nxqaRtXa9pZqbOFjgPo6sAoWBL+vCU6uYCrG5biRY5cOpgSi5Zg5r30DpicpisAR/Vg9BGYZICBUs\nkoPxPBomylpXSg+0JCgyRXIVQrFuLCRYTBQpTAhjK+wYeG/G2yVxXAtLLow0shqXaqy2ul5VCrum\nSEw9R+dIGoRd2lNK4a5BCUpZC7MYF+PEuh4J5trAex25CoHbdvPn+BT46cdXMvHMErfSiHUPgMRG\nKoEcG7uWKW3gGCIXcseNTozSiLWwREcHZhtJUsklsJA4KwstzEiNFIW5jpRpcWZMdiT2y3gFwKfl\nPdcpclkqax241CO74sYBhzExtIy/Vow7Bj5a76kIvzd9wC/aW96EHee2crUeOBA46wzJkpQv5Yrv\n569YS+KpHXg1jryV53y4viR3dzcpygWZm2GC0hjV+JV8iyHcIcRjQgx+OI18dwakcdEW7jXyw2Hi\nu3mlysCwCnVY0bIjs/KsZgzh18ob3o6J4ZAYh8JbTewX5W5Sdhx4240QPltvCFJ5OxrPVg/JfbJE\n7ich6b1flLn5kgVh1Ft2swGBH+0ioRgjM29j4lnJTDSm1R3/Xo17XixHPqkLJSpnYeVehOuw4xfX\nGxaU51ZOW7jabtiFwq0loiov9YrUjtwIzNGgKB/Jwp/ISLXIiGGm3AzwvXVmkYKIozuCI/MftwMv\n9Qwz4xfCnWdcCmQGnsnMrUXEhEmMpI2rllHz99No1tlLzga67vXmeWsEhddMxNYY1CgkWvNa8Gih\nG/k6dfSMyh/Yjl+zO7JEFryOOZOVvVSPGhBDSV7/aCGrcW6KtcyZQqyp0347jTPA1Rq5iV6/3Uvl\nqtuj39eJUSvNAru2uiawjSRWZ2vIQqsJTYUqP99i5FvVMJ0c7x5pKlT1FA666XG+6VjWeraQdHrY\nFvjptbwXtEW6e4s8WEtb6NbLvS0Ixsn4AJwiuBkWqD04rUHP+jmd99dpdRsCtmmXTjlIG30Lf6lt\nmqzHKJUf5rSrWp3jXzGIwQvgTr0LKE0ehcuKmwc8uMJtGqbtfPwcVLXDso2YErVWQi/KT7TAvlNx\nWqrd5t2b04eGaUPLwKmAjqxtNMGem9WDgoGv8bzhEfLjSC8lPpqbR0iW31s7IUfbTtXXAo5V0ebX\nGDRQ7MH6fJuDfnUnxLBapytuP9vO7RvzJv16m3ojYwZxa1q+keuk4i6M3qwXiIrUr59DpmEGZxZZ\ntH3N4W9br9ufT452230V12T579I+p30OxBul4bScBOg0vw7DiwhZGqNFdyyMrpdSnKYZaY9jIf5M\nQ0T+beBfBH4VOAL/PfBvmtnvPzpmBP4j4F/G3XX/OvBvmNlXj475HvCfA/8UcAv8F8C/ZbZJRH98\npFaQNBE0sMxCbkqJDa3inHkWz2pqjaCJEISxGqspd1b4vAm6GBp83y5YZSQQVXkWG2cK+yHQcmUI\n7lopCqEWYvBsDhHQUPB+JUMQniJky4gKh7W4QBmhtEC0lTElQls5oxHU2KlSg3FbKrnAO+C9jrwX\nYbc0khhjasQ189mTkYCxZ+UyVJZmzKthMXrzJYE0DJT5nliUUReGlBjWmRSEmm+wNPHh8x3r3R3W\nqaLFFsYVaixEU3J1HagBZUkccyGYEAiorZAUVWM1o87CWhMHMe7Xii6FyYzdYJjtaXXlEGA+RDBh\nio2LoRLHI2IKcXS9E0fu18T7HBkH3xGtQTA58GlWjrowJeE8VvaqZHPK46JC1ME3i2phroUhJibt\nKKw2gkUWjNtcKSFwZ4n7XMk0PiLyvd0CUSmrstZCGFa+U4WqhUMR5rpyR2ShcZAjIISaeFdnskIJ\nkV/RynVbOTRBW+X9akQzUGUSxWI4bcKEobBvgEFUo7JQakDEKEvlqxLQqBRTrEVMB2prlJLJ3XFt\nksKFCYf1niXDbRDuMrxqN0wWGZKyC8L5UPmwCtcWGNqBz0Q4jwlp9wzNCE04HwbuLXAIhuUDVOHS\nFj4w5XVuyBggRj5fjRYrz5aVpS18npTrn7O71c96zGEioGjtbBExYnXhvdXAXXB61dTZIUP0TbXb\nYeAm7ji3o7/XisGukqswLkpsxh+ePcVq4AN7TxM3UbgLFxwIvFBvNK/WTE5KE2VIK38cXpBkJTPw\nYXlPHX1HTE0oGjhKYMj+gs06stOVZuqbOZwx4U1CbIJE506kmNHVTbAMYSXS4oCEjDVFtDLWDFFp\nC6DCu7DjRs+xPXw/f8X3S+bSMkcLiEKKmX2YGNNyqiFqcwToVTpnaJWzDKGujCExjCvLOrJPSp5m\nPjj6/Xyq7pZ31ESpe9IR7i2RhpUaGlelsWa3CEcaQ1pYj47KXafIkzKTjhN1yEgV1pQoWbjv/2Q8\nJMZWiNJcp2iBY5hoovzyfM2PdhOzOeVPembRR+0trQX2UtwIJSh7K0xS8SMrNxZZhsjZUchRuWwz\n71s8ac8NPLevD1Xlhc3ctcjrYeSDvCDAJKuzkJoSaCR1dGclEqRxDHBoiUtdMRVCa0zfoEEN1rhv\nkfdN2WuhaOCsbbWD1xNfth0v5Mhv6C0/lMhZMwaMKg8axK0WuZLNIRCWvvlXulbfGV+NK/OW40hB\nRHju289Ijyto3S1wspWWA3lULu2ArBEdHGmVaOhsPVD9G9Suv8fjW9UwaYcrT1qPjkBY7RbL0jU1\nfKPRaBt1yilfsumIgO1/46mLeSjEgZNxhOcnfV28rzj9a6xQQqeRibozXkeqxKTHm3UUxLqDXT/X\nihfPj3U/JXhzF0WwU7G7WURz0kRFdXhb6Q1Jqae8o74nicure4bSyRb8VL+fEAagu/htFEcBE1Ti\niUb0EO/kd6eZoxxFIYlTB90Ir39DeNCCqXRExGRzmjzBuqE9NJlVfUckmKM9WzNqEaS1U5cm4mLP\n0DpaZnJy/CNo1691uFYrSaQ/OJyLPGpiCaD9AbHdezVXalWsw9Wc1lJtjVVd1+TUPjdbQB7cFocN\nYu/rU/uNrkEIPbxXtM9ZiN0+PJzOwe+hz+GG+iDN0THxHev+C04IJ3CaY6WHHmtwtz0RQozUWj3n\nSR/MHlT1dE+3zQUTY2oC6pb7imcO6Yn2F3hE9vyzjr+E2/f+z/gz6D8A/lsR+TUzO/Zj/irwl4F/\nCbjBs1P+Wv+3iLur/DfA58A/BnwK/JfACvy7f9ovvi/Ku+sbDwu1O6JeIhL4ikKxxkWcGCIca2MS\n4UBlMbfFPpOBcypW3DI/m/OwV1sgJH7YhJIrqQZ2rXCRFM2F0BaYlEsJvCgJk4UibqwRKYgmjzEo\nAaSxS8aTMVBqBsuU4pkcpt0lVI3FMqrKk93ARxHOVbmbmxfMTXlpkesmhAQyB/YxcG2VhYFUjGKN\nGRi1MmcI7cBobhpRRQm6st83mgaiOoWNthIuM3WBhjJXYbcTlsV56yG581sjUGUhDaBVQCrDLrHM\n/sJ7MsL9fORsanysxm4IvmkSAmtLLAvkFv1ZWjzU+WiedZcR7logz4Ux7JipXEbhPEHJsFhAJFCb\nYWeNSCRIcAqLCqElLkUZ9hBWZW5GSQks8MOcuFvPmdeVEo9MYnwUlSQRYyCUI19p9KIoNsK4Z14K\nYka0SKNRJTMvRhwT39XCMay05YJbW1hFCLoytIEvV+Urq7yVxq+fCbJAjhOfHzMpCMFGVCofS2Yf\nC42E5UKKQsaLq43rHxTOQqQS3EBdfJd/aSsWEq1vq0wtEzJkqzQr7GTHUgsXceWpVuY0YGPlYzOe\nriuDNiRArsLLHPjiuPLOBgoTazwSy4S0wLAeeWHKTW2sOvA6uG35p1k545acfX3uzhZelTNKmMnx\n26uDBLi0A8l2feMTDiEyx8j5nAlmHMfIvhTeRy+xxgqIcr5kzmzlKAlRWBNQJ54sN2xvy49KR986\nxTo0Y5B7zgTIbth0vRsZTrudytO60NKRz25e8+XZnlz3fFze8HK44MP5wFfjE4b9Pd/hLYPCVzzl\nKrzjdbpAhyO1wTt7wmW4ARGOOGr2xxcXXNl7PuENd8MOSUe+lA/4mHeglUXd6lnNtbbPy8y+KqEu\nrIMHc1+nyFL3ZAm8i3tEYGWFJiR9MCx8WjdzBeVtuEQMjjYwxXtKm2htz/tBsVyYguf6BDGCrqwl\nMsSVv5s+5NN8T7MjQ5qRXvPR727UjJRAiYGzcmRqQlNlXw0SfHDwekCs8H/EC+IaeVKP2Bi4aitn\nS+Va9jydj7wZnZnyvN7wdhwJs/FqGHmeD7zUpwjCzTgyrYfTZnOSxqfrDEEYWyOb8FFt/K/pA75T\nrwFc/waclcJqcK+xUwx9DAHWanw+TN7ABOMNEzs5gi6IRazBU1mdtmbwxnY8rf5qvZaBK1sB4VwL\nZ8AhBO4aXPQ16CiWchFXQoX3FngmjUkzt+ZVzpl6XXBhzSUJS2+YBtgFJZjXhPtw4NjOiBVGChJm\n9iYQl1MtQklIcw2T1yIKwRjW4JvAAVKoLpGwik3NAYxHmU8/j/GtapiadMF/bxgUD+y04MXkWJ3G\nlOTrBd3WYIUQHvEnH5AVeLCKNsPzfzh5BXwNOfra53YThwq04L/fulGCIx92ary8KXLkJ2zZQrj4\nfzOnGHqDl2Wjd9kjjmbXl5y40f0Y6ajZI/3TNrQ3gK2ZGx3g1qeL1NN5yaPPMzNa6PQ0t9g70RZV\n9YGS1//tpjnaN+Eg7ZT1Y/aNY75xrx/1nKf5ga0Z9QJJmvmund9JmjhF8ZRr1DveRnO9iXqmVK1O\nTfNm+YTxebPRIEdBLbBK5/V/45y88en0NJz+UtWbEKfrdXMRM5J4Unit/X7Kg8+d8YBAAl3j5kYl\n9Dys0FwrtWWBSevIofm6OCFYoidr/FYfMpY2DdzDenhk+NBRVL/uRtJwonZW8/WgIuhpp6iv8a7P\nEdsaZAUJWBX3A+GnH2b2Vx7/WUT+VeAr4DeAvyEeMPmvA/9Kd8JCRP414O+IyF80s98C/nkcofqn\nzew18Dsi8u8B/6GI/Ptm9hN9i5+3Ax+FSFKjtT1/0oxjzrQWqBK4qY0Bzz6ZrfnLR1zPcVcDKUR2\nmpGmxNSDBG3gXoy1uqW8WuC6GrMJoyRK3KEUvlwSv1MF0YFcPPPpIrp2YIcwSSFESJYYqmN6ooIk\n2EWnzKqqGz/0dZFjgSVwbYrt3JmtSODCjHOZsHHgzhZuLTGlj3l5845gxuW0Y7l9z0epcJWSI8tR\nmUsja2I2uM6BMIxOFaxuda+rUKsbIcRWmefIuixcSCWVhmpjL8J5KKRYfNPBBu7rQhgSNY+8XYXd\ndMkkjTkKd7JSSKx5YGjGcObuUjersspKCIGohVD9OxnagIboqfAFfr9U9upIctOAocxmKBNnsnCf\njYN4yklpcAwr+eYMo0ALpLqyBOFgDbEKY4Q2cGiRt3PFQmWRyPfKE75zNnMl8LYGvrg1zEaKKiMN\nWe+41B3j0LhflWNMlLXxBxUk78lp5iJkmkXMMnc2crSJu/uFIMpsioRIk0YrRmPHj0rjYxmZ6sxO\nd5SqfLVUng8DZ1RGy0RVIJOtgCmHFIkMVJrT1/t1rXVlDIFjW1CEnb3niQZ0iFzUQAgHllWpEvmT\nGri2wJSjN5Vl5Z4RVDiswu7iGbFUXi8ZbOJ6dAfJVYyUE20K/O58oORK0ITOjctZ2Rncxh1v6v3P\n4Eny5ze+TE/5ztjIg3B1zEQ1hiVzH/eYKZ/dv+NvXnzMx4evm1tEPTKHAc0jZoExV27HioZC7Vk9\nlp1uJmlhsYkcjctZCFIxGaDVH9O6X3LHy3DJ+8G4CQO/fHjDQSOtRSQUkJWxPxHnNrEPR8Z55Al3\nDMeVmYkSV6RNfCA3fFzf+znkHkjMwFhB68iH6Y5UIqGf7xIDF+WeJQq57SD4M2wtIxIOJw3s1E/6\nwu7QmPnO4cgfnp+h6+Ota393mhnH6HlVd+GCVOFdOOOT5RZi4I/GDwH4lFcABCr3uucfPn7O39x9\nB5WLE/NoO2ZkITOQWKkSSM5ZBx6019prnGCNmYkP03sqiethYsgHRBo2NO7yObG6s9yhXTDMkMPK\n99aF13bBp+2eRY1XYUdtgaQPn2sI1uCPhmdMsvCVTfx6fusyDODYz2XEG5K9VWpw9s4b3fFMFr6Y\nJr6zHE5o/yc6E1CO1YjqGq1lq8EwWlp436n/aoVWYQ1KMCOZsWuVvcEc/ZhYjVEM6VrmTR6tJfCs\nhxC3VU7yDjtZNeNrs8F+uKchHMrEWbp3t0KvrCnZUbk6rFBGz2faHfoKCNRlAhXSeERscwUMWE0Y\n68+sFvl/O75VDVOSB5GXky8MOhpjXR+TzJGPKHpyXUMeEKMTzasXhxtiI9Zd+PDi1gvgXqTSqVXm\niND2GUHdOawGRz9aULAHwSPNuq2iIznSXB8hQU+BoMqGkDzk9GwGFEIPLcWvT3p4bbTN2a8X9L2h\noXWTgfBANUsoog2zhqnrvxClmTiS0REW70EfU9568/dIl6Xq+phaKnGjIorQVEjizYoH2j4YM8Q+\nX6fPfdSfKC4Q3o7NCoP5vJoKaZsz6aHA4CYPfYTenDXtuUxWIEoP6O0W6oB16aKFjiSqPyRqhNi0\nW5I/ahxDR6LELeBPTU9zemfgobENzU6GGQ1v6ltrTBJch7VR6PBGa7O8L7Y5/Ym75mmAUB1FNV8X\n0h/eqkrOjigQG2bRGyh1q+EVp+0F6/NTe9O5IVYbNdHayQmnbeu4aW9Q/XpDb/mKiud+Nad9auy0\nV3FI/Gc8rvAl97b/+TfwZ9N/tx1gZr8nIn8M/OPAb+Go0u/0Zmkbfx34z4B/APhbP+kXvRgqLybh\nDTtu1kxk4GpI6HLNLhpFhHWtRBnIgxKyUXIDcxe4oOoIm2W0FgYVCDPxmDhQSBR2osjgIZQpKEUE\nCQksEadIRRgoaFkxdmiIXMSZiyESNdAKNCmYuunBhUVU6dEF4k1ZAZOVKQzkUBkluqW/KKijHa02\nTDKpwYWAtTvChTAzYTnznQ93vhvOjnuL6JgoJbpYV0I3O3Eqb7HIXc1YadzXI1InLFWwQs6Jr8TX\nktRCInMZ4KwGnkS3IY/hghwDlhStcGyBG2vMqz8/lgo1VDQMyO2KDJHRjN2UuK3C3boSCIyiTlmr\n5uhHGjiTSErAkLEycTMvtJgwM16qsgAHgyEkCJlBR2poGIncQ8ajFvYlUHUlxIQxUa1S1kiRwJSF\nL63xo7uRFuEDVs6D8fdPjeUo/PaaqHXHx6Hw3dRYQuXLVbicjI+1cVwKt1k52LmbQ6TEYP0pJYEB\npdVKramjCoUihVUiPzxGYthxVZy+0lrky3pEqrAbhI9KJQYlF0PikY9tAisMceC23KEEChWtC1KN\nPcZuByEIC5HrWngfhJv1iqNWbC0kU9fRyAytsRgMpdBqJYZEWSqDTkxqXAYlYdRoDCrshsxLa5Qx\nMJdINkP3e+5s4rq4ZvXe8k/6en5rxg+WL3iyu4IZmgWObaCEiQt7j5pxm5QfHF/yLp3xJM/ccYFp\n4Jrn1Nppuq1iGrhYKm/1OSru3psMbsfK+bpjaEaqrnHeNMYtKGbC3dAI1qiiPJ33XBTji2nP+Wq8\njM9Ra5xl414vOWsLrThqtJfCXBNNjGu5IFpDzRibcjMUYg3c8ASANRTGEolNuZ6aI9zVeTw3U+Ni\nCRxiY6x7FmsoDQvm5gZSuZeBWH2dn5fCp9z5gz6eo2nwDYzoobxnvaET3BFuyItrPs0RBwkNCQUJ\nKx8Vf168kSd8st4xx0aswq2e84w73ts5n7ZXvJZnjJ3yjiojhSbeKNzvAmZuXPL8uPBVeAKjF+1f\nxud8Yu8x4H4nPOPoWpvBn49P7eh/Y9CGlRZgPCpztxJ/m5SpuZvbrhm5N5fvd07OM+Dj5dbfs6Hw\ndhh4ts58NU2nzdGbbnH+6XzDtSb2ZWUnC82cklnUv3dm8CpEnhTXrTVxq++XaUdomV+oK7Mlxr6H\nGICZQKlew961wNgNG8baiCLuvmfC1IyEN3pqRhBYcCBAomuwBhq5GWdi3HWd95mutDwicWZXGy0P\naFpoxTcDFsHZFE0waaymtLJDLaE4yyHKCjSyRZI2Z8yE9iDBEbqg/uc3vlUN04mOhu9YlLoFe3aU\npRd7IYTe9DxGkLyR2ihoom7zfdJEadfwSLcLf+B+uWD/UfOy8flkgxPFufu11q+FenpA6sOfNyRo\n46Sf0CAVggSnR+HnvTnUtdJIHdY3c3tov7aNmuUZF7UWRNXP45HOxZrrLTCjYhQFLe1kq72hWtuu\njqi/uLd/H2N0Spx1NK3WDj65tbX0+xCBGOIJ9dqQoLohWWxNEidqXrWekbDtfLTWjTX8hLy58rnw\nFHmFR4HFtYfNeiisQBBCtf5CoQfccpoTMbDqNBM6xW6jPm44yzbv1eG8E/olfT5VFau1Q90uGm9B\nqNWpbbE6HW6zg98s1xWfu9Z1Z6LK0JdGiF1rh1uSN+3W3yInnVoIobMvgr886G57fa1qn4dmxqA9\n5FYesqYAdr1x3JwhVQQLkGvt98Dcxajf+2LdsafzTralPTxa4z/tED+5vwr8DTP73f7XHwOrmX1T\nGf6y/2w75uVP+Pn2s5/YMP1tmzjIFbswk8sTmtzALHwQhaFVSgpUDZS2MM6FFUNjJGEghknFpBFD\n5dBGJDcuRNHdTGkTUY2gwhgDISWqNVaNzEzkemAfoTa4K40xKCWsNFl4aSN/Mg8cadSUuKzGUz2Q\ngvHWKmpGIjEGGCVAhGO4pDBgY/DsqFpprSBHY8I3RhCYMryOC+codXzOPlWudgGS0PQFwQqpTby7\nvePyYuR4vOOsGTEGjqqoBsamHki5S+xkR1kjdaeE45FlMLI1osAUBCGgGkFmZhEuU2OngSrG3IRW\n4NCO7AhcCqytMDZBUsKaktPI0ipv8orOiWCF8zRSCixiLMFItXAoI/cYZ9oIq5LShKZMLMYYMs1m\n3rWBuQd2DsGo5cgowm6cOOsbLKyFMg3E1ijVQ0BzXXgRKjfquX9VA/dU8jpyp5UXlgli/C/zM3S+\nZR9mPhwTczXeysoHMnEjxrIWRCvn5wOhgg2NuApPJHJbIWvkUhNBKsdcuBlWji1gFYZWUKlka2RJ\nvGd717jlu4gwrwNfxMpZg502nqeBZ8UNQhKVj0vlNq7M9UBOO2rL3DHwfDPxcwAAIABJREFUZlWO\nMhIUsjbOm7KLjUNuWGp80Qbu8sIBQSwQ18CcAkkrx3FiPGYItzwZhCc1U0vhSRh4HoVDhkDiVRLK\nGNAa3dnLIoscKaWQ8vxTPzv+PMd9dMtrEZgEsqxkdmBuxCOSaSi1jZisNHFDInADmaqNy9U1jhaU\nZI1LueWtPeG5vmLhKS/kFS/1OULlQu65tguetRsmaXzFFcHaVuR48WwVEJ7rDdfl4hRhUhFinUD8\nHXOUyIXNHC2SNvaFNYxAE0UirF2ourNCjZkCFNtzxcKiGVBGDDQD6cReeGEH3pdI0R2ftNd8rlcs\n6uvWmp3qgoHM7WBMZIoFrAmbdMcZHM6eWIYFMY9FeWY3XNaFQ91TUubWzsgaqapIGRhqpWnkam1c\ntVtUJs7GeyyPCMaN+7Nz3lZCg8ujMatvGr0OOy9muv35D8pLxiJ8sR8ZrLKKsiRBaZS8Zz2bGbKj\nt6pK7mwjw7gZBw7hks/mr3gnH3A+vuZsKWRRnh+9aVEzahSCS1iZS2JC+Oh+5X3oje3k35GXwzkm\ncMbqu9jaGGgMVSEUVOB5zd5wIxx6c/ad5cDgzt9MUpn734/WGAMcS2SSTIqF8z4vOxoilRVjoMfJ\nmLOisuIZon3NMFTirKiYm+FtiolUaEvExJB1IorLUFpJxB5Y/XTK1GWkVAURAg0ryiIw4LRj2kAr\ng2/cy/z1WqR/D89/zvsu36qGSXsg56a1CN0EwPfxzV/SbMXn162i1R5QBHeOcyvgTQvisUb2SAPl\nblbRvBA3tgL5waRANr9469bB0fVLkW6mIAK15zH1BwbizVHF0A63BPFmweQB7XKw2AtaRboDnDmK\n1HVbxYwxJrcdFznRs2KnhxXx3926DqDSJzyGrkIRlt5cTR0hErz5e3DV6yhbd40bNPQHS3N3qX6v\nw6MGM5gX6xudDjg1aJtV9cBW/D/sEDSRHl7nuqUmQLcrd/2X842369voZ1t2k5ijd9IbYK3mcHE/\nRhq05J2oGGjoO6+Pco1Kvx9hg8KAULqVpTSnYQZ3/GsdVQoIW5Zri53yJh19rE7dy+rNYIrBtUQG\n2ml124u35xz7eZrncmnqtvP9mhW366z9odEURgsPDSVGMwHa6fvi2ju/thCDv7RsE7drh8qlGzv4\nCyv2m+S95db0PczTz3D8p8BfAP6J/wfH+tPy/378qcf81//jbzGl1D/N1+c/+tn3GT/7jO+VhefM\n2E5Y58TrWrhQCKzUFkH9mXHWHB38LB2JIaIhkTSi4YxiRtPGTOSHh8x9C9zlgpZrLERua+O1Ni40\nso+BWJVCYBXlGBIqwhAFSZE72SEi5Orro2pBWiBUYx8zu1wZ64FcMotGcou0oAwhUEIiWmWfAnUs\nPA++myhBaBK4DztaVCwE1jvjLDVenAWomd1uz8ESTWDXDV8ywoXs0LYyF8+wstUpqGlQz7ATRcMF\nLWaIkTzsWUWZFUbLqKibbgiM7YJWm8cRAPtaHKWPgTv1rJ+JyKJwaJGD+boMFjjmlYXA85D4KBVi\nM7/vVLQ0zoeVd+WMt3VPDApJeRH7M2K3I2kkNxAqQYx4kTi3G2pTUlLmdsedCEmUaSgccwVx8587\nXXi1KO+a8jTsebr+iO9eNY7LyPsMqwo3ds6Xbcev7I48Fc9m0VIZdx7K2LRx1YvoADwP/v5YDF4v\nI++tUYoj2tKUOcKhFhZRnlMZZSYGp1ZnXThXYTWILZNEeE9gXSq2KkcV1qOQywXnNN4zIikhNZBD\nYLXAfY28JpKCcUyTC7ZFkd2e8bjQaOhemEaIxyNn84HVoGZhXrPnVg07vsyVL0N1dy6M8ypMRP7W\n53/I3375I6zrTBU4rOtP/H5+W8Z5XQkyUixAWNi1xMRrDnrOPROfyiuQxmQLN3J+ol1/Kq94XZ+Q\naiFJ4ZmsvOeCp3LDve14oe+cAiyBlYkrbskkXqc9n67vIEKj8sze8azCXZjA4ErvWSWSykBFeJHe\n8sN4xQf5wBs9Y8+Ru3JBGzLfqe/4qj3ldif8cn7L5/EKcuKcA2dlx9N2xyKBM5kpzdP9dpY5psCU\nM0/lyJvpjNXU6cJVuIvGJ3bArEA54+PoNLjvVs9A+lG64jAKS3M34KzKRWscmFCFaPXU0Pxivuee\nPQ3h03LPH8Zn/FJ5x5vynHvOOI4Fa4GP8j1LiTwtdywxcRfOKGpM1euELHC2wn1SPi2vKPk5AE/k\nlpv6BMKRVY3vL+/4Qi9ZWzzVK7d2xW2AtBhPeM81V1ioiAVuUuRqnXg1Jr4737BaZNDCzS5CC1y2\nhafcUVLgu/Wt5zsC19NIbIUShSeHzFETKRV2uTLvIl+UiV2qjPh34ys9A+D5OlPVKEx8yAEx4e+O\nE58cjhxsz06ONOnP4qyk0LrmFY7m0SnRjL1Uzg2u1V1vn8fMeXWzrEEMwxEcgBGvQwuOQu9D7u+g\nBs0t3sdVESkcHTRixu93s9i19ZUqTjHe0NQlwlAa87ojnrbTO3NC8GjeZoQgBDIWIsPgqF8wIxSH\nAyZOu74/8+/2/9X4VjVMTq9zFzeTdir4tppberq29w2CPXJs044G1eA1V+yaEhH1AhenqNH1HQ0v\nGresotqa60oQdDPhUjl9hjcY3hiJKLUWRlOqOILhOhxvQtQe61ukQ42BZo0qMJgvntKL4lMwqqgX\nbRvi0FG2oELQjmYJFBrjRm4FxpiY1djV7qjXzRRaMxLeSBYzJPq9RUDqZowhHdnxvKfNilr6A8kP\nf2g4sjl9MTUv1os4bTCYEVT7fws1uFsc8mDTvuUFiPSgYRWiRYq0TllpJNXTQ83U7dT1ZGn+9WG9\n0d2+UhLU504eNGC63cdOTZwkEIpxGxrT5uDXobSgAW3dnCJ4KPFm4OFrxneONktxxN3yPOldCRqo\nJQPNaZltc+oLHR0qriUJvUk3b6ZUPVW+1tqTuzvVcEPx+nwivdGTHrbaGtKpqSbiRhK1EoPPa+2I\nFH19hb6xgCqx85Sj+e9u5tdqzciPcsF+miEi/wnwV4C/ZGafP/rRl8AgIpffQJk+5AFF+hL4R77x\nkR/1//8m8nQaf/kf+ot8/+kTYoxgUCVwEeCX6splrNzYwJHIu7iQwyVjywzWIDpCcViNo57xOirJ\njKcaGXCB7LEKbS20YeRwtxKJGIGEUpLSSMxUzuO528k2zwCK4rlw+13kAuE8GvtyYBKwVjikyEoi\nV6faFQq6CLeauGkGg9KK01NDrRQrWDNeREWjkcJEU3c/1BawUlynVStWVp5qYWRFxwQaWYISS2CW\nQqyNGj1oejGobcdZgqkYWRaWbNTcPBB4LOztFUseqFlo60BOA8MY0DjxLhuHNFGlMmkAXYCBbI3j\nkKAqYpXLAXLO1H1klzNWFxTI2RHip9WpKE+CkTVzEYRDXplb5K4pr+eBD4fMp2MjhELLiZGFaVRS\ngIUBqjcpd1RsWbBwRS03mAaijHyUDGuZ+7qgkvh8zaBPmMIBtUoTxcqRu/GC374BgvFiEP7BcaKV\nmaa3HGrhVTSeNGMaGoPAkJwiWTUzL5Ej8L41nkri+XDkWVi5y5Gwg7CumKwYZ8xSeHOcGMdAI3Is\nK3tTjApReX9XmIaR27zSZMfbCMfSyBpINHJofIk/q4eOHjTxTSUNi1Mrh3NGgQsRjiWjZWHWyq5k\noq48uwekYqlwqMarrMxpYCHzqgSiRqjKqE73ywb3ufGDD77LP/vxpxgwSiGb8Hfev+c//p9+88/w\n1Pj/xohae62WQKrfF008sRsuud2iATnXmWS3CLCSOMrEOTNTa/z+9CEG/Or6I4ooF7WyxgZ15Jfr\ngWSNNQhI5eNygxJZEd5wwZUciU1I/b21MtEwdjS+THs+KMJlzaw6Mkfhg9XY6z2hGrlN7MLMZTGk\nBVoZUIscOOcQC2m9pMaZV+Epv7S8YZWRl3HPZJXVJnJQZJ0Y44rUiGpjLANvk6O2l+GWz9vHgHEz\nBn7l+I4W/Z3zcZ35vek5f2H+kuuwI0jDlgmGI/vWIO94O0xINF6Ua0rbETHuZccz3mFEXuZzlmgs\ng/GkHEhNeZsSZoUAPG13JMn8b+kFgvALyxuaJjTNHCQwLIUX4Q0Vhdq4GSPXwVkZH5U3FAu8l0si\ncKnX3LULVlW+W+74UbzkohRCa7xYCpkd+3rHfbuAMEMdUQoxD0gobLKA+3HgTnacpTtihnmMlGAU\niZgUD7qVkQXlVfAO5NeOr7lqmd/cPeWX5nuWCMWUkgNXpRBRzqwwx8R5abyxPU/1QCEQrVFNMC2c\n6+rIpxj7EthZY2zGrSgqDUFB3Om01NRBg4qI9mDc5lomvB4YrZLdRoc6OB3FzDenS1AEI1EJuKkR\nZt7MW/M4GAFt7soXJaOaKSX5RuxpY6ygw4pIZmwOIw0t0NTr5KINa8bh/3fJ+9OHbvbgneD12CLb\nqUtKCHoqWLeGaUOPRLRfsMBmK91Ag54oadpNGFQfDAYwdxoTejEctuybXtxDN27ojVFrTCE5TaWL\n6VtPxXXzBXPEaWv0+s6bu6U1R6Sgow6BHwsola3haYQYTiYW1jwTKonTXx7s1TeNj6HRkaTNBGMr\nuqO5I2BtBRXXtdTt82vz8+73seK0I39hbLREn5Vhc3yLXUuGF3IP1LauF8NIjwwLThbfts2zywOr\n9HwqnCLUTk5/3vzF4E6BGzXt8XU5lc/TuR9QJjmhlCc0z4ykm87HWKIxmlLUm7lwWmebEUhvPDs6\nFHqD5wiXQIgneoFIc1Sya5bCEE73zERPc4o1Ao8Rty302PHTttEP+4v48TX4PRRyaz7n3QxiM5rY\ndG4SpDdc7my0LUDVTvETIXS0a3M9rP0ym0GP/CX+DHZ1erP0LwD/pJn98Td+/Ns4M/OfAf6rfvwP\ngF/ALcgBfhP4d0TkxSMd0z8HXAO/y58ynlPYZ3eyGoL+n9S9W8xt2ZXf9RtjzrnWXnt/93OrU1V2\n2e62253uJlHiDqID5CKQolaUp5aiCAUJwQNSgxAQCd5A4p0HhPLARULigZeohQRISQQCJLpFC5TQ\nSjuW3W7fqlzn1Ll+l/3tvdaac47Bw1z7O8cObeJ2tx0vqVRV5/vOvqzrGPP/H78/x5IJMfKJDDyz\ngMbMNq55VQdMA6+nmY1UzlT4xDK7ZFAmQp45dqFSWA0rVqnZjrYq3FejP0mUYhypkDVw7Yn9DFl7\nXqHsakttX2lP7xOdFdjuyO486zImgWqJruvorBB15oMkiBRQYXLnNmduiHRe8TSQeygSmaRZZp6F\nwpkEhqiE6pyZ08WJpBC8IqESgrA351bO2YuwJ3FbI6hx4h2TCmMV1gHumdDFmc4LQZypwg6jRMMl\n8l064vqsNXoEZnV2nugJuI/MGhmqtdLO92y8LA9nyNYxa6QGg9pIgJmZ2BtHcwOxSF/QagStXI8d\nT3c73I2XtSfGmVVyTsPIB8czvcFlnnFZwwC/93KEKfFsWpqnkLg/BEpx+tWGPFUSPftSSdF5Wdv9\nYyVr6J0Hqow+4d4zbCYuqpBS4XPB2IXApUWsJr48ZUQj8y6wjfCeJ+iVGGCtLcjV1CmWqCRUYO8r\npuqsreM2w0tzzGeCRx5Ix3E/MSicH0cmqUQqezM677iJmfelEtaJ6zIzWcKCs9EeDQ61spPmAmj2\nKxjyRAiRIY/UIEyzMqVI2mZWXU+YCxfzNfjMJhpDCdzUmVvrySlBrpj0bEJAVdj4iiEI26qch0qn\nRogwlUCsE2uJPHPY5WbXLCK8/OkqPf6xbZYVWRLrYhQV0EAiIyRgZpKBtRdmlMoKd10Wo4TGvIx8\ndmojm6N29MsCXqgrAs4uGKkGbn3gyEfUE1nb86pDG9Jc99wsDuX7fkmUhiXHEtFGBjE62XIyrvlQ\n7/PIX+M4z+WcnluiO3aYVdQGI1h7JZPYZGVW5+vpfT47v+RVd8L5lKmhQF63388rUENM8TQhdcWt\nGFd+StSWifRwynzSHxEpuLdFpupC9IzUUzxm6PZIWVGtARjulUuCtxrGg9HXwDZ03NZjHvOU+8Cr\n+QI0cpWEjoz5wTVQmaSjSuBTuYXl1tCSJk/KzH0mpiX0FIF7ObMNynulYcorAZfAo/CSj/0BVSKD\njpzJJTkJ7/hLCkoXC0/sYYNc6cBlB2fT0OyHJnczFe5r1GrLL5NCP/eQJjyv6GRCDeYu0GXhuO65\n7YR3tNHsPkkb/mE84memZ3x7fcZ9rum2Ri+t4TkOGRB2pgSHc4WemcGULAH1ltdHbTj1I630Upk9\n4FK5oCyLq5nJI3FR3OsypV2BaAcKb6DF6joVpbOWJxZKaDRndzqvzdVE4Dou06+liQgRI7tSAkxB\nGXJbDFjngNd2fA5uL1lq+9DceqSlBp4X65Xj9C2ck+H/E+30x7f9VN212piKLqqG4LQwN0ya9UJZ\nSAWL4rDkELk2OpLypkCeF1tVeIvQFkUptGH+fsk5iihlCapUdLGULWqXLfhpFfoQlyJ4Odi0ot+l\nza1oaCv9d3NQS9PQ6xui32TNOoc13F5CuVVjI7pY1ZoiYtYgDiHoohwsAazom5ks3mRWVXd6F6QL\nd83JXbiqttmsEJVsRlwsfu6L8tN2b2uWZMl3OqhUh4Jb3hygN7hqv/v/A3TA3Jvt0DlgGO4a2cNn\ndW9I8Vma1c19aWgPP1NpyttifXR8gTwcmpo216OiGLbQ7lpGUmr4PA5EO1tAGOpNCWvNkNERKNoI\nMW5QdLldVHuDdhbBU1MDI+CqVGdJ6RZqcFKFunwvxe/C4FjOH1OhmuG6zN7VSAyOLRklQcHMG+Uw\nLQTEZX+WxYYph3ORFkoJDdteaPNhLE1rOwDt/U2afU9RrJRl7qY1dqW1U7hxZz11bMl9Vg5I+x/t\nOpa/Bfx14K8CtyJyUIau3H1092sR+a+B/1REXtMylv4z4Dfd/f9afvfv0Rqj/1ZE/gPgMfCfAP+5\n+x88Uf4oznx+bSQpuAsvPHCT95Ta8V3L9J6YJ8MD4AWpkStd4BdVGVTpNXEaZk5S5iQECO08+lpd\ncxbhpQiv1NhqJJLpES7EiezYzTMbnM6U4IZxwxASZjOxAwpoNXLfURVeW2TyRBLnKwapG/BSMRTk\nmNiPVFvRUXnkezpVOioeWpZbJjLSETVxKYG1BGpqD9DQnOLN065O8aZkde5sa8+VOhPKXiq3Hnkh\nMFQI3lS5Yy+kVUA9sfPCkRZOcbIEhk5ZO7xAuFHn9XzETmEvTqeJZIG5jogLORdu857ZI9FnOhvp\nNZJkUWwX626xiqlws4vMami3ZjdOoEZfla1XtqOyZyATES/EfcCCMucHyzPAGKXhx2/2lePg9Hvl\nqHMsDMQMBCeKkMlMi249m7HuCqda+aI6c525xVGLXAyFx27cVkM9sCfzOhrruWeOHV8f93gNbFZw\n6rHNCBi4BV4F6HJgh+OlA9+zEUHCQOdHXOm+repb5nKsXPtAdmMsLQcpTDOvwsDVFBg1c6qBEzc+\ns9oz5gjzlhzakS5V2VMY05a19ZDbau5jmUg5cJSMVDpCJ2DK14riGnndd1xPHTqsGYMyhzVbayvY\na2jKuAfuDc6JFe53xnESrExcpcCgO4oKGSXjFBM2sv8DrtCfjm1jO7ReMGsrIHtGglWkJlCnY8tH\n8h7Hvkfc6GVCBGYNXMkxD2xPNBhV+Gr/Lo/HG+7JDnBupOekzDyNa7JHjsLIjXWcWgE3Otmzkx6s\nw9WpGqgWeCkDFgP3fEfv8O14TrRT3tFrjvWWl5xCEaxzpnzMLEukhMM+KGuvUFYg8KpTtuG4NdcY\nnxtf8bvrB/zS7jmXqnQUdmmFNB5Oo4kuYNrrLnGxq4i24X4tgDaw0HPveHeceRbeaZCtSpubxUGN\nl6sepnPmrtLnABQQ46zOXNHzMQ/BldfrxKf3t9zMa17ICnKzlPdaUEC91SlrO6wLCpNCshUJuA4R\nt0CWieAVMKJMTLZmY5Bl4L5sOSmVb8djTqUtMlUSYGTgQi+5jD3rnFnPcMMxndlSgzobZsQmQgm8\n7I7oDLIqN3HFB9MNV+WYzkfcBqAw0nEvX/I0nPKgbHk5wC/fvua76YJ+mTH7+LjneA+dZa4U1gZF\nlFXac+GZIbd49KzQ1aUWEacrQlFHkzLUmUn0bu4MUUwKoyRMZqDSlcSJj0zLAvjKMoIxSSCKLREt\nlSiw07YIXGogeWVWvWtoVjaz7SNUUIN1LrA8nWMtFNU2kkITOmLKxDSzcmcvDYDiy/iDLE2VA7Mu\nwsH324r+mLcfqmH6SQZOQlMpQmwqD66oBJBl4F+a4tHUjKVbXaxeRlsxf9uelYAUwl0DY+5tTmZZ\nmXd3Esq0WMZgAUe8BYrw2GgrQlMm5NDULBYxVwXjTq15u3sGSOZMvAFRHLDgYaHhqQi9aQv0oiW9\nH3J9TJuVsNfQsni8wR0OFrW39vXd+x0ABGFput6E934volqXvJ7DYsmdEnWw6L392od9uvzsLhj4\nrc9wgEm0BOoFpCANSNHe+41icgjO1aUpepvQV83u3u9tuIa7oxLuEOZ+oB8erHsHRUaXeZ72VZbG\n6dB0HF5Xm2LkTTHSRZ1L0qhRh+/d0N0tNDlbO0ca1W5R7BDmRS4WVSKKlTd5Xi4tXyOGgC4NUl3C\nUO8gGMrdMbnDvsui3L2NEz8c48PnW4YvfbFBHhrbt7HuAi3vJUQC9U4O7yQQXai6gFDUcG8zc4fz\npP7gy/SfZPs3lx31v33fn/9rtHsBwL9LW+T627T7yN8Bfv3wi+5uIvJXaFS83wJugf8G+I9+0Bt/\neeq4zBu2lumrcR6di14J9YbHuuZlddZ1aiZTadbZMw1sCOTY0tRHm7k05/k8gBje9Vxa4qZEvhwa\nhrf3ykorKxOOe2Ouxo13dDHiNrNZIDOnEpEobKqyWSsva0+uESczqRNyxVQJ3gKk6xKGHGRux8Ez\ncwSRjr2s6OKMipFN2FklS2rZRqKsWfFcIle14cptQcrP4nRViNoIdFnabIJ6ISCsPLBf7qM771A1\nRheeycy+RqaqdNJCGudoqClhcjYIJQrmkc4LpYUt4bVh7lVSs/V2kT52JC9kW7P3ntnBS5M3zcOi\nAhs6F0wKR7HZWPedUq0yTau2uNBXUoVViIhnXJtaLJ3TL+Hdp52TpdCTQCKmwr5CMON4gOIjHT2x\nCOPBNtvPeKm8sMKHZsSqzDUyo9z6Obf7mZ1kNusNJ37JUDvup8LpSrgg8SxmPtHIR9aUn0oFCWyL\nsFkactWKseK5GWWu6Hog74VslRUdkTZ7dUNb1Hu+m6geeGATxwpnXeAC59rgt58678QdpsKtVLo8\nsynG6XrDfTFO0p6hg8kSr0vHx5NzlQs3ZYce32M/johE8ryik8I1CXJE9gXXRCeGizDWK86kcqMJ\nn+FlgXGOuERqUjozbkdlNQhnohRRiiqvl9DPP+z2k65FTJxVuEJN2PvxskBqjCGRtKKMPOAW0dyy\n/iSAV7aSOKozL9KKc5t57Wvuz3seysjL2PZJV+FpPOKh3PKxnIA7Z7XwYTrisW8RnCvdoFaoGlEv\nvEjHnNuegR1P/YTL5CiVEiJXFhlTD8WR1MKziU1JOCx+fVAv+d3VIx6ODfd+Hc7BnY0ZH6YLEsa7\n48hNWPMiremsclwK7vByGMAr59VIdebefsRiW6XWZTXVD7bweLCTt+cwGqC2Z1PnmXsjvO47zpcZ\nN/VIBZy8PLfaQ+58mtrrcXjGLuMH3p69KzdepBWDjYwLnryn8kTWPJAdO3oGKaBKstqe3d7RGzyJ\nx7xTbnihR/Syw+hYlz1tSMN5Ho45lmuG4uArUsNUcRZuqK4kh8jIU+7zKDyHkFkRyESKdyQMSZXr\nBHVa4QrJYoNqWWjuAgLdOJBkYmXOZtoxsEepDYoQ2vjJXgODGdesCCJcB+fMKiJtr0Gz0e1XvtRB\nzhEtrNyKURVqgAGlzvludntKxlzv0GJUUaApWbMrffDFAQTrBRJ2qEV6r3eFtogQa+MMdLwhLh/+\nHZdaxFzYbDLJS4t68fb8kAVycahFzIB6qBuN+Q7X9ePZfliF6ScWOAmLBW6xgzmV6okgRtR2MjiA\nVhrtpS2dpGWWpw2hLTYoBNWm9qg1m9MdnVAOFLBmo+ukhYNmDW0OJwRcrEmpzT/W1JjQKHezNOUk\nSbNTvVFo3li7DieWaXvfGhr+273Su7bAW2slfwq6WPtaw1iX5iMcinavJAnN4idAbE3gHYSBBiFo\n4Ium0E3eMLTQQAptBuNg8WJZdWrKnEv7LKpK9bZyjTc15sDgN28IajFfwnvfQPK9ySQEcWYTViLs\nNeMmhKCLKtI8smHhsRsN+60SKHdxOrJYB9qDB9qKxQGU4e7IncXQ7lS2ZsNrqtVhNY3Dioa2VYta\nK+pNvQO521/xTrdq59YBlXEIW7ubJUpGtQruJFnofwQSQtbaFEB3ZLnaFBbb5xuLZXvdhnN+G+3O\nWz8/wEveEqoQbTdEf6spsrigl+XNpzccqtPH9p1CMAJ1eW1Fqt2poL68XvB2zNwqYYF1qL89sfaH\n29z9//cF3H0C/u3lnz/odz4E/soP896f6oyfTxPRjBsp5JrYjYGrGFuDYUoHxFqpBUyUWzGKF1bq\nPNTKWhMxTrg5owhd3jHPR8zcIDk1OlCKeKmk6lxZYeWtidl7S0r3EPBixC7xvEYmOuquYxWsnfse\nUCKrXjlGyepEjRSHnTmqG3CnMrcFFqvsgiAe2XqkaiUF6MRIkgmlZxsre09kaUCb4BAlsZGmoqoY\nRRshMnggS2DygEnBLC3namWFLkPsa6rUxaYLlgLHlphCW9R6bnXJDauL3ULovLBRJ7nhdbmRaODK\nIwOKByfIEe4zg1bMKiMN9BKk0PUBakfySkzC4+SMNdMPyphnqjs5G6JOb5WjlRK8UF3x6Ig5O++Z\ncLZWGbVnnAol9CgwlJmNbKiuHAUh2ESeHVJE+zXrMpE0sQ9C8hYc8tiXAAAgAElEQVQdEHxms4qU\nClub2csxURPfKBndCyOVqywMWbhQmOoERHoKF7Hn1guirZi4NIEZzIR4MxKt4hS6CCdSQQtpzFQP\nVFNO9JIP1iuywWVWLqc9tRv43ODcbCtzAHDKnLmMwvVcGOUErZXJHTfhmsiUBigzsxi6E6ocUaxZ\nsVWdU6usZGRWAypJoM4ViT3fMkUL3FJJGphKwYogY6VnwjWSxo6nqc1r7krl8i1nxR9y+4nWIglh\ndQiWlSs+knd45J+w0qvFaZKI3PBE3mFOSiDzqWnHOTOX4Yh7ZY8F5YHs+CSseMKKh3XHNqwIwDEZ\nE+OxXyImWIR3uSYUeN4f8a69BIG9JY5K5qPulFfhGNwZwshZ3vHt7hzB2ZfNslgqvE49J2VikMxE\nvKtFnoRT7s87XgwDFzlzVEc+P7/GFF7VNTvteV9e81SOOC0zSLOWVlGOa+Yq9YSy5RFbnuoxLsrr\noWWUneaJzguC8yxtiF45n0eCwCdp4ELG5sAwJVE5L/PyXH/jOLkMR1R1UnEIsI0Rag/mnPoOF+Ey\nrXnBwEnJRNuzkxUXOtMvRXUN7fk15MyUTvjMdM1X10eMcsymzrybRzRMPLIbXJ1HXCPRed9e4PRU\nbejt87rHNfA0HrPVxHnZsq5GMWUWwbSSfeBcbmjepcqaCaOw8i1MMGrg0/mKrIGjbOSuIjiv/IKh\nOJu4J1HYqzEjPOaGQkdaGpoEILAuhnuDM+Ag3UguDSC1VmP0RgiNBW5jo/jOLC4mbRdOzAf3ltyV\nDENtkAZ9qxYRnCBGITDZUiO0VWWg1SIlC1Gbm8a8wWN6d2aDiUBSb81tdZzmsuj7TNDMvC9k4GQI\nGMoclPl25mgTwGDvAbFCFxZnlwv/BKXEH+n2QzVM/hMMnIRGBDNNmFWSNpKYid8V74ctLZ5tJZLV\nF8ta+/khQ+fQO/tykny/y6jlGTUKXFqUooMCMruSVOnwxWYmrFD26nTLvEwRZ/BADmBWG+7b2qDe\nnR3urhBusmOUQA7NQqWLXSvjjVC3bIeivZgtyG+j1KY8HBgvgVbsl+bBoluUtja/0hoDRRZC2/K6\ni+rUlAkgNlvdYd+ICL3GRsXT1nzJ0hx0y7xRCE2qn6MRDp5ib/lBMyAxsLPMvf0rYky86i5Qf6Ma\nurXvVhelsADx7oI4WNvaasPi+GsrV8txOcAm26ELd0rhYf8dzpNDM+VLQnWM8U5he5vaByxN0aJy\nLT+axZoVcPnzaDTiYm1hu/XQmL216WH+bPmAGsJdw3VoyypvaH7AP/aZ3m6KDpsslsK3/0y1NT1h\nOf7urYlOQQiUpjqJEhdboFkjLvrikbdqFN7sYxYFS5eFhsM82k/j9s0xcXm7piYn1ZloE0ENLYUV\nHatYmC21bK+g3PrEkCv31DiRSi/G4JXtOKOxkgXWKvxMvGYWZZdhlsjrfWuxoxpnQOdGRXE1ZhIv\n68xDXfFKZrYZ+pToo1GJ7Grznq9pK4Cz0lDn4mgUhqqYzZyEiUHbwO42JPDCpDRQQW5WtCMCaGb2\nlhl2wo4pKKUeUWh5QbcqNLEp0FM4ITKpcYyQbeLaE9NCk0wubei2tgYrBKWLAantbL6RQlysxevY\nMy6rLpNn3J09HTvLBGne9K4a/QxVRtwiaymccE2eG4Qnxkiue7zUNisSB4IbO6sUoAu1PcTNOJJI\npVAP9md39roi54zEDujYaUGtEglojBxjPOiEwI5eIHgh1mZ9yqUh+C2069uqcdQrkRGXiJe5+WYl\nIkF5WVbcmDIXY5r2DFpxF8yEY6nkmrgR55SeGwEvmVyuCEAqxpAi+7nxL80c90yPtxwt2r0br2w0\nMk+Fe0PmSe74aN9zVXecUJEusqvGgx4+cz9ymSMZ5cN9JlvFSaxCpXqPpohV4WGo7AisVz27eWqx\nCF2kmmESyCaIdMRpZqNO9UL2xBwbFOcsteH5Td8173vokZWxJ1HpcQlYtQaLscJxNH5UR95PuhaZ\n1XglF+zVeUdekcT5iEd8UK9xMoYhBN6vl+xQBoN/NNznYdkyq/KkW/No6es+yE3VGUNbilrpIey2\n0c3UKi/TmujOQ6655QEXfgsKz/QU9Jp3uSabcCVHvL+/4dv9GZ8pl1yy5jvxhD+dn/JhOEGA193A\nZInPlUay+9gveMglz+Ss0Xrdeei3fNid8G69bLlFVvlQz7iOkbKMGvTWFu/a8845CSPfDPcRN971\nS0o9I1R44Dc8k2MA3sk7qitVI7MKEpStr9jUmbQ0Nldx4LiOTQHDuUk9j+o1NwyYVlwCP1NeMXvg\nVdywLk19uq0999izl4iq83PzM550PSfLs3iwK07qxHe6E25T4h/GB/zy+BXOivE7q88QgjPUzBwz\nPg9UUV6kHlMYw8Dj+arlEUVnIvKuXWJ4y+EKzaoHFfOOURIbHymqiPd01ZkVnsk5AGe+xUToyURR\nLn2g18Jx3DJZJPsacFwK7/KcLQOBumQTgVizVb/slNd+Sr+40N+vI1lajqRJoYpgflicaPs3SV2G\nFQCHPrRKMRPpm4+qRUhIplir45pryBsVkje1SHVhEZgQFaK2OWkVa/PQmjEX+mUsA1eKwUqNrttT\npoLnVmgc6rA5BMbbDGPhbB3YLVW7SHuPZqZpC/oHN86Pa/tRZ5h+bIGTAEjLhQmhWaHSsu6PGFbl\nzoLncBduhbeB/mK2KAjcBXtmbzaxuKgKh3mb9ug/4KuFubYVdlkkgnSw8TV2OCrC5IYtrqgoi3rh\njS53IO1Bm5HqRSC0wc15yS+huQsbQc+9hc8ugbDQGru3bXORg+ImaFgoat7ofwUn43fNofPG2taU\noNpeO0Sy31Xn9Bqb+qRvCvigAfeK6psGS2jqUF1kvVZMA+bkGIheD3m9mCuiyma65Ysb5wuP1vz7\nf/Wfo+43/PJ/+Q/wWBl8xZdOM+MEf+GXPsfvfPWb/I0//3P8xt//Bn/nY2uzP05riLx977DsM317\nhWFRyHTZZ7J85iTSvucyfxbR1nC+ZRs8NCbNBtdyq9qiXAuWLdYwnbAole3tFmXvkIcE1RugW++a\nigZbCNYso7o0x2YNFrFcI3e/697epzehLKnbB6lJRe9Q6YcEYGm9/NJwNzz+AVd/gJ5UcRJGn9qx\nEG/NeAEsL+d9XPaZKaYHkAmoVA6ZVX64Yf0UN0x/ajXxuf41eXS+SWIMHepCL7DVzLV3Dfnrbd5i\njTIw04lwP1VubzP7MLFOkRVKn52tRQojV5K4jZGHNvNBFKao3JbI4BlSgGqMtdCFxENCowrNga7W\nZnlMcBJmhAZjuKXnsrT8lntBuajt3uPsWcVACoVJEltrOSMP08zO4FGIiBa2Gnnpyq0l3g/G58LM\nVuFZSXxsV9zWRA7KibSGYAT2IlxJIWrlOjcqY8QIQXhQjQcxE5iZRdjTgjFTqkgMxJJ4lVpDde1r\nrBomwmSFE+BM4fPcsCoTyV8RpYVNz7MQUiQj7K3nqji3c0+VClPlVDNRM+TINBdGlLyf6VJi1kpK\nCaxg4kTpSMFQrZTqDIyc9C8RSZgqOu3pwwoVIdgKr5V5mAmhcr3tuSlCiQnTyH5ZPYoKN1PBPLPb\nC1lWOMYoynY3ElaBwSdmE+ZcSEFRy7hXglfWc+RinKidstHATVE6HVHLnEjPaUq8pjKWmTNpCz23\nApM76s61Ba5zZqPK/RRZzU7VyldujZ/tO1Qymzjw2iuvSruvf7Mor8Y9WuFkscGdpEBBmsVUZm7F\nuK6VwXtOY2GgcHISmGj5T5NBtfZMHKeJ6065jYpLYGPGe4BEZRNasROrU+KKqVZqhblWdnWi0GO1\nXWPYmkn2vOSPHCv+Y69FTnTkBIg58phrXBKilWI9pYusJmPsWryEZuPEdpyFmZwLz9OGK+lxg9RV\nvh7uc6/uuG87DHjKOSKGde1b3LOGVr7sVpxwy5N4BkBH4bWu6es1L8MxONx0kedxoBZ44LeInjIT\n2LFisMqWQDLnd8O7/Hz9hF4n5tpRg3IaRgiwmSa2NvCxXHATOzY+cmQjj23iH8WH9GbkxSGRrDlU\nZo+4GtuY+Io95OfKc57oGd+Wcz5rrTn7UO+zk8iJz1zULZ0N3ISOIMZVbCjxTZ1536/5tp7zKV5B\nhY+44DqsuPAd7/KSlxwTxDGP6FKLOBCpHFHpZ+dZf8K7+SX1sHgbm4X6F6cnfGH3/3Cyhk//yzfY\n9WN+8x8ID+SSWo/4Et8heeDRo5/l2cuv8d6f7nn65St+Sz6DqSLZWVGYtE396gJTeMoZD7jlkhXi\nzmDG3lesvLCNHX2deeRbPtIzOne+2j/k5/bP+Zae89BfN0UAGJZBEZUDMTlw7JlnesZxvaS6sF9q\ngzV7Bj7hCQ95z15zGQf6WulkZusD0EJk2wUg5FhZ18AgfjfC4tbUpUB90wgRqN4iENbmWGxzs8kz\nhUQnBaup5VUeVC9tTVu2RC8G4ojH1vYuNUwWI+EEvW01UPzeWiR2gWlXloVj5XLn1DCwWc10ktsi\nvRmzKiuc8GOmMPyh305aBf5jC5wE7lDa7f1ZiuiD8hOo0iZsZJmNCQsdxZebfq6FIaQlE6kV/qV9\nF4LqXUiofp8CdOh8jfY6gTdNV7V6N0MSZKGR0Ug2I7XJnMtn8cNrtk6rZf6Et2l87XvceU81thVO\nsztF5/s9oI30tqDGrTVJUVtjMS8Kw52xzJ1RnT5EploRWXKmaDNRDdbwFpHvoCwc9vvynm0Ar32v\nu7knVaIqWRplTxaZ5ED5+/Vf+YC/9isX3BtOESa2PvGXLuB/fmWcHRX+vb/8J/jCkXLlxq9+5tO8\nmK/4tT95xt998gITIfqbZviwNaXujWpUaEqTLsTC7yfmwaF51O/Zl9+7P996B1nkYzNS15KxD1ub\nKVrm5palmmbos+95nbTYHiws+602dQp94w8+rJK8mZVr+Hv1hrM/NOVtP7eVlXj420sXl9U4wCy+\nv50JwZabY1tAcGtZTW2WqlH0pC63Apub5bPAHGDlEXFrjaS3hYif5sjJ/3MPH6eeMc2kDMEzNUZk\nHiixchxHzmOPaSURmD0TtKPazNMxMoXI5zrhPM4Emfi2KcedM1mDNZS98BTBvKValZz5JIPM7bwb\nJLEue4hOb3DMRIqViHAShKPgDH3gpna8sInvFlibobd7brrIrQW6GAjVmHLHqIFjh1VQPpoiJRid\npraSPMNxnHmwMrb7jt+2DjOlC8JFcY5CYTLoUmKyjAZhmCNjdOYqjKJQE8KES6LUxBOrqK7JpSLR\nOZPAp+stZ8FYhZEzLeCV4nvMe24logLnoqjOrNxgBV7OuC1K8YCtp/YgSonehHURbOVs9xUz41Z7\ngjsxCIP2xJxZrxW8EMSglhay3GgnTT33yH7OfMucub4LTKxYMdY9FtMCABJUbpl2p2Rr1+VKnYvs\nxGBIFnYYu5rxJSYhe2KWmWqKmhHTmjnvibWp6vcYgcg2O3siXewYMZ71yi4HzqPhNpKr0oWO79w6\nsS/cV+OEQEyF56UHA5OBQXZsemFlAVHhtla2OHuNfDCsuMmBrRe6FCgkUnS6lFAz1iRCgl2phBL4\nVq1sYsdpvGWDECw3m43f0EnH4Jm6nxEJnHaJnryAZ4wcEhIqJXZI7NlrYnTQbEQvzRXhhVCul+ey\nQ5fQakwm3KhBjYzxmss9fOuPcPTgJ1GL9DWiSwK43M3SVKq2cYDv6ilhMN6xhb4WoGqH255JI6aV\n97ZbXI0cIp/yLTtp94zXuubEdgScvXVNxZBW9F9L4sz3bOmWuAugwpUORDdO2HMjAwo8i8fE6nxQ\nXvOV7gEntudS1tzjlsswgDhqTnXlm+kE08D9/BYvR5wLbunqioDxpDvhI9NWW0gANdSNuhRjdWFe\ndl64V0YGjHMfMYevxMcAbd7HYRLlm905j+otuxCYdMUH+RKAp+GUsXaICJ9wAqLsJNExcSFbrlhz\nwS0O7EXZWyRKpQYlZ+VG1wxyS9U2DxwWqFK2lkH4z382cvFFB87Ae+T8BX/DCr8R3+NLPOcXPr+i\nu/ddbN5z7xc/gsv7nHy242//3oaEch72BFNWVhmJ9NbyhB7GW5Cmzj2VY77d3+NBvcZQtvSsQ+ET\nNog7s7Rl1+dxg1hlZdx1FdvlWK9kzyEsJUjlMR+TvEGdtJwAYCExEznXLclArCC0erEL7Vge4A7r\nYqCRWdvc6pF7M+vrkjoLd6LAcBAFpIEqIq1uNQl00lTwpE11ujPX+mHmICO0LKa3KqZ2/EMlijc7\n9lKLIAmXDpE2N3VgWafNvi08l4mrsed8aE2damHtS8X1fY6gP+7tR+nPfqyBkwCX//t/hfSbuwkK\nB4Yv/Ats/sRfwoHeamPrL/5oWzCEMbRiX4kUaSv4AW3+Sw7ZPywzRoa4EDU0OqS3YK8QmvKjbm1m\nRxtlr6qiITDXpt6ng6ojTckwXbp3aza9IEK+a0hac3VAJxxs3U3YbXNQ7rVZ4fBlNmZpBpRGfDMh\nCHfkugrNJqfN96kLhcSsWfJ6mpK1WgATxECplRRiC93Vdro2AIbc7eeDlazKQipsvR4qoZFMpBXV\nqSqiTrECoWtEJIxf/WcecpY2pC7ippxeDPytf/VP8b/+zre4d7bhrDPi8ZrTUujOTng8z1znif/i\nXxr4V/6XJ6RlDkJQrAZE21DjAdCAQHcnAS2XqRh4QFXpUaob/dJIJX3TgAAEN6o4Zk0ts6X5xpyC\noNZa3APuMle5mwNzK02BW87elhPW/rtgBF+ua3PmZS6gnSPemhC4C8E9oM4PF4wezpXDys/yFbO3\n86tq+7vJw1urQ4dsKkWlEowlpLedXIId5ibfnP8AUnBJDR8eG0b89e//Jje//1tLr7zcROfdD7pM\n/6nesvdcSUStW+yIla44Q6istMNcua5G8kIKFan5jiD5FGeyyDd3A1FWeBBinllJy4Ib6o51L3gR\nxqpM88SYhL7AnDIuiRsX7nc9uLKbnL12DNom0p8SCbNRbw1JiRdecJs4CsY+JMI4k2JkuyuUuMJt\nJEjgMnXsJFI9EUtlo8apwrupUrLyXQ9YddZS+KS2mcUhKLkYNQlevSkwRbiiss/CpAWXiGCspSMX\nYwqZ5G2BZgqJasI1wkdyQdxVzMoyl9jCfrchItqhUjEJJDsiSTMsRdZLCGchY6zdUSu4G110TvPE\nZ1db1jY3264OqBqljgxxTxgTt6FyWzNzbiGz19W5LplxnIjpiAqchEQ3jKwM5rpFe+Pj2ZgMpAgm\n7W6X3AkaqdJU/+tcybVFU+RFacnWBun72LH2mezgtS2KIT1zzrwiMBZnVuUUpTMhaOXcO85D5lO9\nMnSJm+Jcm7BLzu2YeZ56vmtwNkU+lbacDD1DqORxx01d8Wg1s8+JTiudBFarGXRC+x7tE6MHXs8T\nKXbMYqRobOdA3kM4AhtH1myYfOL5BPsuIHOglBtSipxm4ZlseLJf8dycse+bMm+wXhYCgyrrOhNC\n4LQzIsorhyuLRCKr3vl0PWJbjK+OhRoiU3aSdxQ1eoXECo+BrfyR3kN+7LXIf/flr7BOX/+eGdc/\n+95jfuHTP4OJ8AvzE3aemnUT5zr1JC+s88zajE4SXzs658xGzuaJd+YtH68G0DYvPWnrhk7rxJUO\nqEJvey71iBlhRvnC+JLrtGLUgYt54rvdhr0c86GuAfjZ/IKJRCeVe7bnRVxxYnselD1TiDyuN3w5\nPuQuT5LCmpmAM0nHGXuehyO20nPqe8yFT9ctkzivZc1JHjll5OvxAg/Q18JMJHjmSXfEE44wU75Q\nXwLNpfJOvWFPx0td8alyRcR4v17ReeU6rrj1jkFnvqXHnNSZC9+TUVYysvcVr6RZEwPGR+GEU2/2\nxUID45zPM2dhT/TAO9OMqFAofLt/wEs55l+8/gb33nmN2nswGJ4ycMYX/uINf/Prf596dk5ywden\nyPkrbPd5OMkIO/7miw/5d/Z/hj+3/TouDqJ4XBNL+wzr2mz+hvApuULLm5GHx+EFVQYeyQ4EXtHx\n+fKyLUJrZVaIi8L0uF5zm2aqb5p6px0sIybXIdBTSXJNECGaUDS2GdkQSLZr1jlpxNu+hDsswpSE\nzeKcGXB2JneU5q3CkbWA3VaLCFJbiGyKoUE6ZFlY9lY1tI/bQsPD4photUi6q0WMFrPwdi2CGaSj\ndoFpaVRgB1o71CpoyUz7Fdi2zZNr4u9+4wX/x7OnuBkxNGvedv7xcsX/UA2T/AQCJwHO/8K/Qffw\nZwl3egHthFvmiyS0BkK0KRFB9I7qpfoGTy3SDnibK2nKSi2VuOCaD1lKDoQl+0e1HUipbYbjQI3T\n0DJt4mL3C4ttjGUGSeUwixKWYNs29xPuMnYAaZjwg1oQpM1mFasL9rs1UINEijc6UUDpXJnFiPq9\ndLymYjUQxYGMJ7rMoMCiZLV9MfuSzVSaYmbS1Af8ewl17f7QwlZNWtMVdZkjcm+YY22pR4jy6//s\nz/H06VP+x2+85jd+7fN0vdLr3OxrsTWIJxdr/vKf/SJTnam5AXy9VpI6m6MVR5xT4yUf/L1vcBlX\nQMuuURp4A1pBI94sZLpcwAflUSRglbtGRhdMelAlWLvQTA5N4RLeGpb8omW/6WJPC2EJSHaneMUX\nLdjv5rzazfJADlQNB+MeMWj7veWUNW/nlsvy2UzuXuNguzxYSgVpymTUO5x8O4TtPAsHletwnBEI\naZmBWM7VEJnN7+AouC6fEaIsihuKaGq5ZDTqY3Dh9Gd/hfOf+XO4NUysu7F7/g2+8z/8xz/oUv2n\ndlNYQpAdlcw6BTZUPkVl0wWsU0YrbFUhR1LXE4mEHmJpmUXHVhArbKwSu4gQKLVZFShOFGPjM2uN\nZFWsU0QCUqCKsDHnuQk3OTBI4ahzskhbqayRD2IlUPhAMjEF3ltNBL/iuE9s7Zj/aVfZz84YEve6\nyufLnkwkS+R+zCgzpmuezIFXNXNaZpIktkCXO648sg0NkX1BRVNPKhNDgCkkconcVGVeFosmK9QA\nUoV1EJJBUkjkFlLqSqZQVZm0Ue0KK8ydUNsEsuQWnjiLcoQgsl8WQJzZm1HWugoujNU5kZlnvsHi\neglRjqynQo4b5lp4nhrAIYrShZGpwipUNkPitF8zW0FmYfbAVVYuzeklUmaj4o1aFSY6hGBK8EDx\nGUomSUeYZ25C4rYKVo1NakrsJrR75qg9+DVD7FkBuRRedZUj6zkRwX3EXTiPTr+AN14WodTKCyJ7\nK1RT/sy9yNW+uRcmy6wdrh2uppEpOKu+YzvDd68zv3jiDLfCd3LlvD9i040kIrnOdKs978WA2cQs\nTrHI/b6yJ2DBGE4ST7aFI02cJOGmFH4vFHI6Y3bhm2poHZBVozsO3mOBxT4TF/XeGC1gdeDaM8UL\nXewZDdQDOwtcEokqdGeFaJF71iG+R7wCGc+tiH391lzuj7L9pGqRv/5LP89nTs6pGui9ANaeSTkv\nxXSb4dDquFaEniMfuQ2JrB0X88Q+VHaSONOZUBuAJZamwgRRRhFGHZoi46A6sAuJ81roaarGTlcc\n1VvcmoVfgIulGa1hTXB4ko45KrecMHEriVEHVlIQ6UniHDPBMr+8C4ngsKlLE+AG7LmWgSDOjSrX\nYc0Xx9fsgvAqDDy2G97f7vna+oSVRl5K5F7dt+ewKKMEHviOG+9x4MhHLrVnCivW5QZkhRF5FQYG\nL9xazyl7ZlUuZUV1Zc3MqY9chp5VhVvZEN35OB3z2fGSo7nygEsswKQR9cqT1QXn9Ypfe3TC/ub3\n+e3rDb/2Jz9he/yII3lFkSPET2B1jJ48IXz+DK0VOf4Iv3oP94CK432Prx+x/ZLx1/77/5unnENq\nTetmzstCt1A9U1LEFIYyodhdLdIhFCtcx1Y3nPm4hEo7Z9OMA7mhHCii4EpvTT4s2ojBqTo1Gn2B\nEiGYMmrhMiQelBmVudWk1iJVmoum1Tu+NDVRJoomKpGVVrK3OURU2KpzMhqy2Mdd2zy1elkUdvBi\naKzUGhpZ4k6+kFaL4Li3+7iIYh4xF8RrG5FA8RAI3uo38QjuBHXwPUmEVAVLhtSCJaW4o1L484/v\n8xcfnxO6BteCyldf3fBv/daXf9Cl+ke6/dANk/yEAieh5X8EwqJyLMP+Aq4Ldc6VEOQOFS60mZRi\nehcCeggeNVojIlbAGhjgoApIkKZKwaI8haVJqBDbCmtsXVJDhnrj3bs7aHuw4tpsfhzIei0BWcTp\nlrDZKg2eYBrRQ1UPd2QYCaHNpsTQ8kpoTUCz8Amzt2K3uLXA1GXeBmm9V/OlyvLZGznPaBkwZbGt\nxdBWElhmWILoHUSgobOt5fqUQtJEjG3VOS4rGC5KDErnzo0LvSj/+heP+dL9mYeffcR/+KufRbLj\nVCz0b3DjIrgnVhujm4Wa2ql4dLSm5IyLsNtfc07keWjNTlQQN1zqogwtFjbzxToCBxrhbBWWi1yX\nc6UFy+pyM3iLRueOBSWYtoeeClKXhUhdbJLWQt40BNQOEA1HQjs/mi10aTAXub01pUbBSTSr0Eyb\nnSpudChVnBoXBUsalr0s+PWwLIZqfJOfxWHRSpc8rMVmF8KhQWvN7uH9haaItuPZFLIu0RTSxUpx\nQJa3BuuNGhaRdm3hFAevggb9f6l7lx/5tiy/67P245wTEZn5y9/rvqrqVnVVP6q7aXcLDDZClu0R\nRgyRDUYC8Zhg/gFAMhJixAAhJhYMGAETLDEAyVZ7gBADGrkNGGy63e2u6uqq+76/Z2ZkRJxz9t5r\nMVg78nerpHarXe2qrjO5NyPzF3HiPPZZa31fxJD/UbfpH+ttk5TrVKl4gCJUjqZ8iHkhUtycYVHj\nkj1JIktsbLUwbQKxeXyvauS0jty0E2FdSUHJaWRWz8E4tZWLofFTUkk5MDeIWxiiEPXIWxY4ZTjK\nyGfVeNsKb0W4sczrEN1UgcgmDKS1kdnwXE7spsST0LgeT3p7bGsAACAASURBVHz9whjKif/7dMV3\nmqG58fEpERq8l5VfuTT2UvmNObNGIRZlkJUnoaAEdiY8iAHRI00Sp1q4xZibsq5GCSMmgkljaMo2\nRAa8IRQrnTLqWhsLzrWPVShoX4PFzXZC9Gmk+b14olIwBvHG5a0YMTtyXBOtVbZSeNuMa24JTcky\nO3XPMqkqWOVd7P5TYmhUOSBNmdeRozSmFUoUxlbAYIzJdahRuWoNUmSrkVtdmQcfKLxcBJEMpaAl\n8YTCl8fA8XTiUgZu7Y5DEb68veJUbiELr9cDa3V78dSKI4yra5fGsHpBMESOJyPHzHHdsxsib8fA\neLVnUuHdaWY37JgofFIHPj1G2A682Ee+O0faAh+OkfXGeHIF1EgYQNqIBOUybfl0NbKtJGtcbkau\nbWVfjCUG7lrmRQ3ksLLERkqNSxl4kIW5TlgohGEgaOMoymiJIRx4TORpqBy18okOfFwiZQ0kOfIw\nGlcpcBcah1ipOOVdUmRIG2pUHsjKZbphq35eRQSJlU+XyK798Jy8H2ctIi2iRNSgmmtWRRRLKyJG\nXXZkcXYCmni47Ak0/uHmEY9axVrhvZPT9X539wiAr56eEzXw2ejGAGI+0BrxZT/Xyjea8fm4ZdeO\nfLy5JgJPj4WbYUsSYzHjsgUqxtINiB7XmSCJBafhfpyuGHXmBuUhjU0zXuXIw6LcRmFQN4wBp1aO\nNC5txurCZ+MTdnbgg+2Wg2zYqiNPv3ORIQZqq1zTKClwVZSAsYbMDmFnhdfDBGZ8uS7sAzyZC9/e\nGRuER3Zk05qvucCIMRD7gHok2JEtxhHj7abUnNm2lafLygfbS+Z4wdOlMOnK/3XxFu/VE3/54QuG\nt1ceP515+6dv0OfGBd9F8zXy+H3An40tJ6J9BseC3X3Vm4GLt7C95xWF00dcH7b81tU7bE6RMZ1I\nFKqOhFApNvUYmBW3nolUIrkKmiqLDM6CEWVavZG6HYTdWin3qnh3u7vNWx6UjGaXe8TVjX3IMFWB\neCSQO2sq8K6+ROIICGM9R9f4czx25oqJs1VcxzYTWTkxMIbKTOKiBNYIyxBIrXlDosYSAkZkwpug\nmN0xNQSgR40QenakeR0ZWnMqoXFfq0KA1iAkrzeDM3Yyax90C7nLA1KoLJaQVN3OXA1swQmEEEOm\nqAfiLr9v4uI/me0Pm8P0YwucBG9kzgYHZnRKm7uPbDSwJqjaGEkdVmxkCc7M4guIgZ3RCJDgbiHR\njBGhJtfrtObojp/vHpiqZwSnEQ1mbT3ctQeyIgRxVECNThVztzJ/sMduNsC9/iWIsJ41Nx2DkI7c\nNFVidq+zs0W206c6bGkuqk7BL27FCPeTO6GaiyBNEhYD0sxjD5r1YlmcYpgSdHQkWA8DFs8lST4A\nIOdMlIbZuYyH2Ba0Tiw7Ia/Kn7VX/Of/5p9gyJmbYyHnxCDK5dMt893saIlDam90W8GNCCLd1rsj\nW601dpuB781H/vq//kv85b/+rY6ChHtExawBPTfJjcIxcZOKJMFt0TtKIyRWa0z3zfE59wlqbaSQ\naeZi+9YckaTDzyZONWqCOwf283festAFkuem2A9QNSX3Rrp29CuKX19D80Y2i1Cr50F0J/ne3PfG\nx3regDaGmHxY0H+XFEo4azasN4Ad+Qx+/RKUsbOMB8uICKs017AhxB7uHCVSrXlqd79P3GnR9TVT\n9HBfMIcXfkK3ZoVQfeqGNKZgnNoAsbK0SuqUilUbL2TiWGdIA0kiJ43MIVFKbzwDfNkCDzeNJ2mG\n2LhdVu7KgIrSlh2/l1ekVGIovBUi1wGmELgMjYNkHh8b0hopRJ7thZssPBElUvm4Gcc1oSGyNOOS\ngVOqtBaoa+Zv3xljHHgyGr9yoUzB+HydiWlikci35iMpVN4NmaCwV4U4cAccqjfvtI6QRkNWY4pH\nsu7YNEedVSo1ZHfUS8pbJqQMQ6jQKqv4tHKWiJmQVneOy+qhzUssTDVQEdeYauUiDmylEFFyHCmn\nhsnMuxrI+URcL8ntyN4aooVRzIczqZBXX3cjgZMEGsKzeeCoA3M1v//XioaBthaGNnNhypqMQqCu\nQErUAikFLmTk0RYu256HQ+auKpvRmAalWuPzVTho4oNFiC3z5YuJVguf18Dna+GhDMToyHBMnqm1\n0cCpCqfcyAaTNhiVmxliCvxeycw6wjJBPdDyFTcFJo18dVt5GDZ8c6r8ie0Nv/n8km8JvNuE994a\n+M4p847NfFwbjxTyxSXrMhNjY6qR57Lj89uKSSTYQIkFLcIrqySbuFsLH5xGwi7w9iA8HGfWALt6\n5D2Uz/LENla+ddrxG4c79gRUE0XArNL6IlU1eJBwVKRFGgv72mhzZuGAqvJZd1xb+jN0XBstD1hZ\nuVl+OO3Bj70WEY+BiBKguXtsDPCbm2t+6fic33pwiaryC4cDN6OgKOMa+allz/Nx4Dg4pQszrmwm\nWmHOCfr7/vxhz6tJQAfu4sTHwyWve7X2jeMrfnv3GDC+fvycKMpHY2JrjUWiB9IC79UbAH5neo+r\nevRBr1auzI0RbiVyTBe8YkIwdvUVt3Hgedrc1yJu7w0bPbDRydGIahzyBWbGqdP/3lsP3NK4qMYx\nwUaFT8cLUjc+iVS+uX/JPlzzergk24rIwscXV5xS5nUYeVr2HIYRWmUOE2+1mVmUV/mKX7n9iEEr\nNs/8Pw/e4+r0PT6M7/Dzd6/Yb+D9+gkf2Zd4dpH4yu3Cv/vy/+SX//wrGCa4DbTtyvB8gkcJe918\nomyKTGDHhsQNevklgn0LPUXC1p1KWxBCU1q8IBwe8xf/1Ilf/bWILiOWGin6QCZRKRIRS7R1wlJh\ntS0xzEQJZKsEjFwESHxrc82vzB/7+QqRZOpRN6Fw3UZajgxa/HdZvV6qkXW4Y7tG1iER15VE1/L0\nLKhLWd1lj+j1JY1E4xQCW5tpLVFD4iSJDQtzG9jJjIbMThpHzYQz80XOBmfWB9xGa4kSPFB2jsnX\nNhWyFUoakNKcZZS8hvE4n+rOv9IYdYUAu7YiIhwkMQclNajRWUyLjDQxLmuhJJdUbGvjmAKTKsNp\n4S5ntyDnjzcl78cWOAlAv3lFPB8FlIDrciyCr0GJZp5xFOzsfObUNMFPyHkC/+Z9hRwCVc7W0V5I\nS4j3znBm7pbkvxVWEZJEUHXR/j2Z2aeeZ6pXJpDEER21RkrewJ2pbGfkwENiz/sknQbWm5OOIOgX\nP8uMFF2TJbid9Q+6rp0NKkQipXVnvOa6K8ERjWiRVT3E9N6Fr1PDLPb9sJ670nU9ZzfClrdsKby9\nvuJ//jf+JBq/zMOra8yMywun741DxqrbqkuMiPZj36lr5xwI6aYYXzRq2B9OXI3Ghm5qESKYYsG1\nRm66QR+huB6pdrfCM/VOO8WSrllrXVB0ps2BknN2Wm12a9A3miFvfOMZmg5dZyvfTycxFHq+V+50\nTacp+nuE4JS8+9fPjY31c4sjjWt3gDwH+or5gtPEc6OCiF/3arTk/z7i4cPKGz1c0UaKyZFCHGVT\nVU4husW9uZYuihDONjNd59Sat+et24u6w6PfdPe9ePqjodP8OLYvJXiUErexcdTIXiOVwMjMRYzQ\nTsQ48mBIfLBU7iSjGjnbyK+lsHYEcJiF32iNmjzINAclhyvepvEwRYSZo3pWxUUZOaL8vYPyOXBb\nL2AYQFZC8xyyIQirBT6uytM2wNA1hxLJgztsDloIWchRObVAMePZGrhRGKwxhchS9yzrhgWjhcyF\nSh8gKKWsuOLEH8afnhqrQG1+Xz2IcKwnVhtJseD5k8VXy6bcxcBaYBOFXXaK2awrtkawQAmVQf2a\njLXRiJxygFbvHRqPemKMjTEJhSNtqTy0F1irXI6NzJ4mRk2JaQqcmJgrvLKRuwFkVaoprRoWlCUC\nzRH3vBpNZuJJiapsrLKRxrQUFhpv7zJJEsdg7MPKlsqwKjkI61wRCvsGNm6pBiIFERiksWfkt/fK\nwxzZ2cqXh+TfQWtHe5WpLryVM+NQyeJmFB8vxsNcGUdjaYm1RNK6sG+Raxm4DPDRBiYLlDTwgVQ+\ned54GibezsovjIHb1bhsKwMLNwiXLTGERppvKDayNuHjZeV1dHfHjUBdClfJEQQTodQTuxCZmRlP\ngf2S+HZR7qyxGMQoHGvpTAl1W+fmdNJAw9TzoUTcbeuZKgcGiiQH4INi+H6FGO+p69mgoIybSDEh\npYG79kPnMP1Ya5FzHVJa5DAlGsLTqnx1OVLDwM/vX6MWuBsDnwwPeLe8ZhtWXueJK51x1vuIBSXb\nihIxaSDCl5eZz7eTZ96Jcqmv+dlj44PxgmDGd7bX/NzRAbEQF37zwTVPW8Fa4GErLIPXLMc4ISjX\nbU8JgXfnwrat/L2rd6l65J12YDi4g9pH0yXf2j2iiYeTbrRb+5gwS+YYLzxDCHjbZr7NhQ+UgaSN\nB+vKR1ePead8zh0jd2EEEZI6SnCKl9xNe96phRaPvEo7fun2Fd/dPWDXlK+eXiIGr9OGr60HbtLK\n1ep1whxOnLIbPAxlpEnoY+PEYQqINT4cn/D+6VPebS/5c3/mcwhg8Slg6LUQbERzJhxXR5E3Fe5e\noHWD5AtoBS2fEwVkV7E5dUYFHVl5Br/4LZ58+BD0ZwkmaEv4dHJFNBBa7sN5SDVwzM0RSITUMi26\ny5vUxIbCHRMWnAqpIRKlsrHkrKQuAxlN73X0GiKblhCB3Mq9vvmL25Gho/lGXKBN0SNDzDgx9jqz\nM3WALHrvIthaZNJGCMYhRXgj4mBTCxZcK1YRprD0AayypAQaiFIICZplxJoHqFsmyepGYN0grZly\nO4y0FhApDOpIWJVIVCWrUkPmGBweayjHFMkoqdeLF90z4L4o/xFtf9gcpj9w7+yfUOAkOEoUesF+\n1oGcfeC0NzQiQgKCKjUIq+BcYnUdT2yKBte9VKyHdQmcaWbmt6JEp+u4d0LXFuHOdmZe3LuaHpq2\nL9SQ3sQM57A0bagESjaGjnRZh7dacL98EXMXOJwOdDaeEHFx/lnfEr/A+zZczCemIBEJ0REpJ7H2\nv1HsLLYTpcSuYxJfCFTVcwKC0cQ1YKaBhoeY0nOKfEDo09vQ/z8EIa8Lf+0vvM82NZYM1+Ml5/t3\nGBOu6jbmubo17fHIZrt1aDa5FTdROlfWnAqH3OcniUSieoDoNhrFQQH/bikSFDT4lK9KYG2eb9S0\n9Umdn6/YGcITgRmnU7beXAb1NxQ55zw5FcIb3tAnOF3YgwsdU29APKdI3nCF7Y17Ini4YTvT5sSb\n9fv8K/XrSYO4NbIExt781X4MPWegN5Yd7cohghSSZLQjmNYRrR6TzJgdgW1ov6686AEjBUdBETcI\nOWudFEOb6+hExLN1xHnJqPOZz+6D/MHLwB/b7Vs6sbeAtoAEyLLyUCKRzKvWuLQMrXEdlEcbAx34\nznzkU0vMjCQZECsI4tzyOJCoRBILKyuBDxFemPL2mHnSUazrUtmkE8GENhoP0i2ahL+zf8R3TxCt\ncdLATuHnNol3dnBFYGPNTTzEs5FMAp+tKy+q8kwmmrjhQQ7R0UrgoDu2GbZxQMicxAvhHCYehEhR\nWMQwTRzTnos6MgQFRl7oyBoWhuB5dgnn42tIFHVaZw2+Tq7aSJZ4lOCurKwW2FngahKOFG4VdrKy\nVeNBruzbxBEcIW9KLDNjSJRSeJav+CAYqXo+2GV2K2yrzr2PwMmMpQZiqNTqSDOnI49CZbDKGFem\nTSQelLvxBR+etnyqjYo4ldCMV/vKuBPmtTHGxBoya0rEw4mH48pWrrmxWz47VYxAs8xaCu9tE5d6\n5LW6RuDUjJwTrRixFU7qA61bE35PAqY7MgtbCbwuMykkDvOR97fKPzUNvG6VOwY+tYGvP2h8VZVd\na9zJymt5Ss57Pls3/NpdIxYl0Xg4r/zUVeYrFwkpRz6vjY9uG9dbYUPwPLHSeNEKJwTVwlwy7zxQ\nnrCS8gVBZ1pbebYPLBhPhsB6gFMYkaWxDYpoADvQJDrVUAyINHEapaZMVkVbY5capsqUNlSBtdwS\n1SjVjXWGENmZ9jVIiW0mRGMrhx/qPv5x1yJ3KSA1MpkyWyUI3KSEEtmbMDWnsQwVvlFe8Wo78L08\nMJmyXWHJE0/vDhxj4jBOPMsjb5WF3bIClculsh9GgkZK3kIsvKuv3JGueeF2GsBsg6rXL5KUT4aR\nt5o3O1PJnAZ4op73pBGOObGTW95uMwasow/nHsktT4s/e3ZLQMLKnBKg2JJJsXCSwIUeEYGfLs/e\nHEMC8wRfXz6j5cRjCt/OD/jm3XNK9ufrrHukZhgag65cSOW3Lp/wjeMNDErNXq89brd8uh141Gbq\nEDhGeEsPlJCQEKkRvlaeczNc8I7eMK4Ni8Lb9Za/+M2XpHTrOWCyvR9ih6sedL8YsqizZG4qcv0M\neZaRnaNEIYExITLDWH0AaTgt7+4hchJ/NrQTL/JIbg1rgdfDjkdrJeaZWCK3ccfz8ZolDsTmDek3\njq/49viQry6vkDzzM/uZZ9vERYWPp0eYCF9dPuUsFwEjVNeraxDMPK5FRP7gWsQgqBEHhTmgk9zX\nIj1WCQmB9TTAqAzWiBhrSNykialULruZxllXRXRtag7FnZXNTaFiLOykD/t7LSJW6JUuY1gwE6ol\nsnntG6TnQuJOzdbZNZ4dFbhLhmoBAtkSU2yMWr0W6fVzUOuMwB/t8PZH7GL+w205REjuD38W8J91\nGPDGnlvEcyGki4oH8QT5Zs7ptdAD5oV7cVzUM0UroB1KPGcg3YubetcfTZHonbaZue24+MU8qBEw\ntLuPpPv3Fc42D/Es3A+JACSDErwZPFtoq3mRfHZ+qz9wYaQOuzfrVDRTzyVC7rOJirjNuIg6BzoE\nBjHoep8YI8E9wjGcomWhsSEyd1zmTJFzy3H/bIl+Y67jjpfHwrvvXzJI5naeeZw25JzvJx+1Vaek\n1XrfSPjberMk40Bbnfct5lqpgJC7OPL2mPlgWTh2k4sQuxmCGWv0hoDmguQxBEwbZ5/LqJUpCU3f\nmHicDSwSzY0V4jmI7Xx8HauJIh4IjDGFc1hwp9tJP+cdEEzn7yNv0Kf7XKf+c+w29iYdOQwdzRQv\nCP3cn5Elf99zs3Q2gzhf2yFE1t5g0hwFiuFe3MTZ0jyIN8j3lqHBqaf2hbC3MwqXepMYc6ehckY+\npWfxCCp6j17+pG6pG8T4ZDRSZeDjUkkibMLALYEUVz4q6pbIUinjRC2hp5P7ZKu15ieqo64ikG0A\nC6g1jjXx7VX4XhrY2YmHY2Qql2yTMCzwToj85k3lW6tRQyBoZDDjLsHfPUTi0VHuTGXME4M1xpS5\nGIUShD2ZfQSNE9FgFHiYEkXhcVQuxI0amgBroeSRGiLJjjBm0lz4xvbElh2fc8c3rox9y/zmOvKi\njV5AmB+XKSUahSyRu7kSNEBoLBG0KQeFURS1xl7gZq1szZDQ8OdehJaZgtHWRknZnbjChlOKyLih\nWWCdj9SlMMaBqhU9+f22FbhKA2oLS1EuUHJbeToJqx55TGOuDfLEsxcH9gLRNlyllV2AZo13NXAn\nBRsjb8cjXGQuU2HRgcN8Io2VZ7XyWd2z3q186TJRuKMRCWPgk7uZoVUux8AuKNdJGOzkWW6hEoOf\nn1X9ft9roeIU0Cct8rxtmDZb1AqfSaQEI2fhF2NB18jv3K1sN5HHTdgMe7btjjEVvrZV9i1x1xJV\nJj6fRz5cZog7cjGePjBYK2tUxiFxMRYeFHe3qm2l2cA6L9yKYssdqoGjXqE6Y6OzJB5dVN4vFbKb\nlbw/efZWUSEUY1+EgxoWhXfGyjSa6wgIHOdKsoEmN2RTwi446qKBk0GIKzFOvD42nunI85qgVWL7\nEYsP/oi3qQROFxnMGAqsKTK2s5mP1xui3qTsmvLg1GAXuV6U1Iwlw+0YWVNgtyw8zoWWjf0gXN45\n5TqEwDL4lHBbvOhfhqHvgaGWebTc8SAu3I0TZsYTK7TcKJr5ynzDtGRebEYARk0IjUfqUaAaAldH\nb64+2+5AE7tWmSd1gru4IQq5UQWuFr+25x+QsE7VnwlrDqSqaFC+UT7FhsC09ofxqFhImEYesaIS\neHy4Yc6RwkDSSG6BLbCzBSFgofKNw4mPd1d9UGcE9frmXIuQAkOrvJgeccyfs9k8IcsdYncYl1jK\nSH82tuK5ZrD6mtTvVTOcY/ZnnzD/6sI4dBr/0WiWCX0AKXbFITc+ni65nJ1CpypcH5UXu8TD4jIR\nSwtvz9V1P+djpCd+dinUlmihghmv4par9chXy0dgmXPxH1sPix0WH1Zr9ie4GLt+DspYOXd0Mcl9\nzTCcEiqGqNBqH/Qv/Zm39Z/TwWUXJuJOmRZBKmqRYdOICxymqR8bf+OdgFVjnUeGaQGEuiSGbWO1\ncy3ide731SJdIhBj7UKSXovgw+4gPYhFoAbBFB7UwjFkRwmtUi3QQmRbCiKB0ADpg98f8fYT1TC1\nbsttAF1Qfy4H5cy7BG9YpGuKeGMvHiWwJmU0d5QxUbKddUlu2ZzMdSUN8cmuun02gF/HjmlqF8iD\nE+jOIbG1C9iiecMSorssDZ0a5SOL3miZu5idg1gd01BqNxHwRa1/KQtIaFi/ydWcYod1RxTozY8j\nUxp6IY/bxDly6byyhoufRQ2NfgFHdbh16LS2jJsOlHDPZr4/EEIg0tgE4b//ex/xH731PjEKVlaG\nJDzOg9PGWqM1pQFjSuSU+/EzOOf/LCtSzjbYxlILu9ED12KM7C4Tv3xxwdeH7/KZJtp9+LpTEDOO\niIkER5KiW0WHEJCU3P0n4kgbgWSOwul5QNkXhHD/45smJYi6s4vYPYp0/huzN7RKlfNxf+PeeJ+p\n1HOt7IyF9r9tKFXdiS73U9z65MRC1xA1b99qc6SP4ItLs+gLc2/chW5WgaN/533JdF1TCO4sKUqM\nA621N/vVreoRSDne3wtdJYcEtxPWUomdl/wTzMjj1Fxbhjli0TC3rbZEy8oghS0ecq0t8Mwaa1Ry\nw3Uc0nogYSTg9EkNnkFiwUNbMwY5IuIT20NN1Bp4jLJLK8eS+VsHZbGBkAqDjRiVGgIUR8KbBJRG\nZWQujlpmNbRGQogMYjwIkdaMNUaKKp/rwiADg2R224XbtmWu0IbAtgokEB3ZxoUwKh/UQMxKCRd8\nfCdUhLWtjCESraJRQAb2quSSGDCexswiM0WNV0sgoSw1srbGNhTeGRJfy4W7NbBfG0hC1xMnPfIo\nTWzKzJOxMjZj0cheM8cl0FIjkbBw5PUhMwNVBBsyeqjYBiZgsxaiGSUL3zs07k6ND3PibhGOcuSh\nClOY2W4CY12hKXGEz+9GQhI2i7BGw3Lj5thoAUqN7GWL1oUgmTCsvNQTD4cBaZUxKb/wIDDExDRd\nYO1IK0oaElFhrpHP1sB6iOTBGCy6jqtrAsfJuFjvyCmjKWLB7dypldMakRj5+oMtMQa0U3mWMrGN\nKy2NfEWdshzbwlV7zTFtsHZAJsXCAjET7ehFlwgl+kRw0cahLZzUuCIztZlkhVeyUFfhaSjkcWC1\nmSnPtHhJITM34aUZN6fGoQbu1sRrMaoFprmHnbfIEiOwYZVIkoikiMXEYIVWvFANFpnFoCrZhBMN\nNHFXxh/zSvDDbS24icEx+JN/V9dOWXKDKDdz8r99lUc22piKMXcH1G1ZuZkyj48rNSWqRC7mzkqJ\nwn7KPJhnjMQ+bTmNjatDhR74u98MQGVpgahKoRfAADoQgI92lwBcrSv7KdJSw6QimoitsITAkgKp\nKbn5EG+R6JIGp3kwSyTgNcuho1GRQBW9r0WGOHOIPiyyoRDECC3RCGzlyOvRi+95rDw6za4FXuF2\nvGCVAKFwbSf2k5ejD46V18OO67pwmzKX64nLsvBi2Nw/X0/5AoDVMu/WZ7xz2vPbv7PwT//SAd2/\nQ6gN3nsJ8gQa6DEhVfuzMWMhIhSvRU4Zaw35X18wrtlrtho8LmRXoVPbiyUG2/JLyys+GJ5iRT1C\nxQK5RmJzHZMysrGTs3Lwuq7EAVFI0kgB2pj42nLnoa469OKvgkZi8nN8rifMAmNoIJW6BenZjICz\nnXzEjwF1U+4lB97kBcZe42hxREd7PmkIHkxeAywWyMUYutu/BiOsgmXPq2tzppmQx4K2yFpcb3d3\nGqmju+NJNPICeXpTi6gJXiE3r4fPxlgogwZKZzpZr0UEKL3WOR+Dc46oBEOzwWxvpClv8lF+JNtP\nVMMUJNwXtq5lcVrR2aHuPkS1T/IdpfGfvSZ0HUawQBFja5F6nuqLT9vdNtybLiQieFPj73vekd6x\nn+lqX9jHe//5AJMGiqlrbcSn24j/zjqCBJDPttL9nTJKk9Dpc9a/rhfB94W42H3DZH3fzporCz48\nSecLjS9cVF9oCET4wgXsRbKIOmVGhCJG6iYP/R8D+DR+GHhrNN7ZXPFbn5z4pfcSmyGyNpjXwjnA\ndanV9xO/0VtVQk6QAqEaVrvdpHoDN3QTjvOxmXIGGv/NX/pp/sL/8JHrfUIgWKfLqTIMGVPtN6d/\nJ7tvhPx2M7yxGTSyik//hDdI0A9ygVWV1LOqvqi5Or/n+XiY2fedf3cVfIOkndEhi+cFzh9EYtY1\nHT6VAnfQCXiQoDXr1DwlD2DqSBDi9t7egAdMteeJ9YETvhj5C/dQW3eHDN9/XOSN3uocfvzGjc/u\n97fWSup/D9zfD/+4m4j8e8BfAb7WX/oN4D81s1/tvx+B/wL4V3Htwd8C/n0z+/wL7/EV4L8G/hwu\n6P5vgf/Qzr7rv99nW713pDyf0bMGbF5XPo2RSYXLoDyKytvAvlYWEhWlKdiwZRDX7wRxy1QzD77O\n5ww47dRMaUjO3EWnQ/7ydsMvjBv+5fQZB1n46O4Bf/PlQiEhZ5po6AHXnX8qXYtZFuU5KxsJ7IZM\nbf6g1NaIkrmyTNSKsXI4bbgclFUCMcBl9nv7VANrfHfgawAAIABJREFUuORyrIgFWoxUqdxV2ITC\n48HNEsbgGWJhPbBRIUpjjo1BRsYqvGrKU1u6AUnlckhcqzJa4WYJ5PaK95ojVDDwSZn4/25OpMV4\nOQYudompzqQUebQNDOVECBPfPcHPPUg839/xokWu6omXIfBQMnNT3h0S39vPzCpMFrjMyk0xdgNs\nVmWblY2MvFwK70nzxnVJfOlqQdWYLXCQSGxCygM7IA+NR3qLhso2RR5cBu5q5UEqnNaKaGPYXHG3\nBkLaU2tlnCYWDRyTscYdIRoMlcOhYRlGE5bQGHPESoOYOWnjwiI7VcLGKbuisw/HBIZuvrMm43f3\ngZfrRNjP1E1mrQe+ejlyWldezo3PNGFhBE3si9HsGmThZMEnyxKZiAypIE0JCZ7VzO088ip7kaam\njMfIVC+YbOFGfD3OAsQdYo0h+P5YgJwGqErq6+uD/pyorASNXInxDisvNTDsjCtVXq0nNI6caLQC\nr6rnOO3exF3+RG5Oj2rsrBJTYY0DpUZGW7nLkz9rWmHSSgmJfZo45sh7h9v7WuQVE5mV58PIl2+P\nvJgmDBjLTNDM2CpRGtNy4rlcoEmp0dGHTUd1oghLGhjUqFFIXyggDWcN7KeRt09HPhs3ZI3U5EZU\nAtxNoz8D+lqzKYaipPMaGV0mkNT1IwArA5IWR9SBKn1A2QYq9GzBETXlZtqgAlPPJDrmgWiNc5S8\nh633YaNGDGHQGSMzlZVTHtiUlU92lzw9Hjq9Hh4WpxlO2phS4CtyyxQecbw5sLv6hCYXyGnxnZmN\nKCtu3h/8+KvBTYRdg0m9ObjNXoucsteB0tk3PZ8xjTtId/yz/0Lhd/72Y0YBs8yAMrSCAFkCj8vx\nnrUuXX90dosLgFXhboxcrYVXm8TF3J/dzb/dfS1y1vmJQs1AoqWCMMA9QNsZMp2Ob+DPnFicRl8c\nPQIQMVSDa44ASdVJEmtG8kqIwrqx++srmFHnAVWPvZDo9NtWJ0QaeVoxhaH1Mxm0G6W9qYmkX1ep\nBNogBG2kHzDIDCGQKtTk68pqidgH0ZtWWKJfZ2ZGK4EU7L62zfLD1SJ/2O0nqmFKwWFiUdAgxI4q\naC/2UrfY7qN/UvTpi/M+/bVMgOBBsjEGWkcjHNHB1Wfyhcyi5O1GpRsP6Nmcod/oOJXsfiJ/Rh16\nsNa5O/agVL3XWFXMES08wDbiYaTnvKdoPTjV+gUXvS1agzgdT6W7wxlBomu6RIn4BCh3ml9S6dok\nv4hLsh4+CUT/nObAAQm3KY/WnbPU93Ew8YYPb6JEYCfGk7EyxYHjUrk5Fp7dzbx9teFUjDEHxu6w\nlzSwSvNgXHlzXAzPyIoqnpvSjRfmeXbKX2vUeeXmsDCkxJ+aDvzd9dLPVT9n54yqKIJFCM1NPjxb\ny93MtFPjgrkV+IAgndpHMyy5mQTcA06kFO9phNLUebZwr3O6p2/2aeK58YjRtT9n4wYLggRvOs/u\ndg4COeID3jBXMUfB/OT7cWvmTbCZ69lip8l1kxDgvjlLMd83fsE868mt8b2pLqb3TVERd0q0TjH1\nYLg3eWHBqtMn+iKfuvbq3ob9iw34P972AfAfAN/qP/9bwP8kIr9iZv8A+C+Bfwn4V4Bb4K8B/yPw\nZ/wekwD8TeBj4E8D7wH/HT5+/av/qA++EGXAkSUPc/YHog9Z/KE9a2QOymvz7IuFxDasPFHjJBNz\nPdJictqSZLIEigrD2TClG4DQ3FZYMGJVThL4G8X41VRI4QEqbrEqg/9eA47rhgDdisKPdiGYugPm\natwlZXMq1OihgZs8ICy06vfUEOBlnTkVqCGzGAxxxbIxa2SpypAcJbZYuEIRSVCEdVjZsBIMrlqF\nMhND5suy8mSjaN1znI11DGyoVM08L4YEZa2Bl3NhRQgh0+LAp8vKfhESB57EiF0IpoVHRXi6m8nD\ngX/4uTCOW/anmbc2AkslDpe8HxaWaDw+Ze6WyBgCn7XKT20C1+ORw7yyGTbEq5mmCSvCK1aCKO+j\n7AajrXdcbQ0bN9RqhHBEZUDawsyKMCG5OM++GtWU5Vi4nBJmyi4ZdyfjWI7ENHKUC8o2EYtCbKzL\njJY7ah5ZS+PQAjUo21SQGjhVI+QNGhbKkrgpC08CnKyR2LK0xIslMjdjysJajddqvBuV2pQljaTj\nyrM58eu3lU3bMJhwk4FamMJAlCOxbmkhUno6dZTMGgqzCVoEq0Zq8EiMjTauszEEJZlQJ3dDDWvA\naIySuZ4Ky7IwJkcRNzFwW/fcDYlZDZXEGBp3xRANjrSY8loDN1Wxw8oLIq0Jp+harhIETYlYjVf1\n9MOuIT/WLVmgSCSpMbfIiGHJNXcXdSGRmAP9+zaehiOluE44mdNw31sOxGC8c7xlx0prHfWP8LSc\nvPYQ53O81U4gsG2FT6cdwZS35hMvB3epa8GQzrR4XBwmiOqf8yxuGK0R8TVmMCUJRFG+dDzwPG84\nJmXIQlOvRZYYidYINMScaXF+pqV4BIMlNddvViFpQ+zEw2XhVZqoaWHTKteza2E+3Wx5+3QgmzHH\nSGrKJ0PiLd0TZ38ObpbK55sNbYg80VekKFyo5xylpjzb7Hj7tNCSsq0rz3dbdocbrqwhuZDDBuUF\naxpJ+YbwckNsT9Anv4c8PMLtA2S7x44PoD+R6ZE0Vvw5G9Q8c1BB80DcL5hGAivGCb1ZkbjhT46/\nxbfuvkajkcRYNVJEaNIIFtHQSGqoZHZ2ZAmZapEgFRO46ojk9VrumUo0cVRfDR/G+suB4EhyG9jW\nwskyp5jIoTLVQpBK0YlTGtjUSpaVUif/hrK+GeKLQDQGVh+KtM6USV3wkYzrFZbYvZiDkfKKmTfi\nAY/CGcJMFSNWj/c5D/W9RD13Q71Gjg2pkZAaGKRQOA5ez4gIbTgR7q4p51gngUutqCkrmcEauZb7\n+nsL96Zj8GbY/KPafqIaJumTfS9KvVBO98ExTs3TICRzFKJhSOo3AtzrnsAL2SZvCs5z4XjWinxx\nSyrE4HQ7Z9z1Kf0ZupY3iIi0nr8jvXnqDXDsDlEqHc3pdKCzUA+8QTkjG37DKPID2tYcvKdr52wd\nrKczi/NHv/C3ob+mOGd62wKDqVtl9h0TE0ek+pER63hUPzbWv6fhDdhkAbPGnXizFLIyTRsOBZTI\nR7cL8fbIW5cTV9uJFIUxCjRFS2VMjl7E4BSmEAK1VIpWhpBQVYZh4HyzlcMd+2L8H7/5OX9nnRiT\n3TctqkqOQjE/CmLaA2bt+4J870NgvzCNeGPW0FDRez3a0NGTVl2n5JbbAQ1C0OroSvNrsTMgwdx2\nPoZ+Dap64Stypkl7E93cwU/CG5MIgJKF2HoGlroVaIzJFxsfcjl1tGfdtNbur+PzedPW+oTaLcWd\niecLb+SsyQr3mruUvNE7I4FndE7E0TvreiXpzfy5STxfgz/MZmZ/4wde+qsi8leAPy0iHwH/DvCv\nmdn/1s/Vvw38AxH558zs14F/Efgm8Od7fsrfF5H/GPjPROQ/MbPf12v0YAOX6kLhEn0A4l9cfTjR\nz5e2hIrwNASehjsuc6KJUm2hrfABkduep1JlROKAxeDriTq/XM3XJ+fNN9CCaaQVJbSExOC2zEGI\nQ0YNdy2spYcXV0AwSYipW8fjjmd7VaYwsjVjSoWqyu0pkHMPs1Ql2Oz3WkxYrRTNZJuRFnj1upF1\n5YkkxnDgYVMuR2Vc4XpauEgBhkqdBmqdHRFfG6QE48AijX0LvFgy+xIoc2MtSlU3E4iaXZsZG9dp\n5GfyiY00NsOJr15nbk8r//vHmUPa8O7YuNjAJJkhf87L+Yr3J+OTYlxrZB4CprfEaeJqmZlFuLPM\nEpRlUaLCW5OS4sClwIUsaHJzi9tFeL7seCqZORwZ5kKOiWkoDOKpLoNEpi3czoHbYnx0HDkdfPjx\nYIBnq/GOQdGVl8vCZcy8t4G9RS5plPCaa3uLQxYqBx7mkUONhLhgCCuVdWmkuOVmXXmuFQIcFsOa\ncqHGg2nhmSZSCPxUW7gMgoYDpzpQLLAdlWNtqCauByNZQwflUT5wuYW1Vj7eJ8JQeDwBbeWDJTOI\n8uR68UmwNcbB7ZCNRCiR2zqzGTJ3ssC4oczGd2phqq7vPTYf1s1rYchbLq3wQIVDmzmUyqUI4xh5\nUgKnuRGGDdepEVLgbTnRWuU7d4mnV94waoIlVEx/sil5G2aQBwQ8XuB2SAz1PNh0+cDLceLtdSHH\nwKeX23sEf1iMx+ups2Pg5cUFcZ4ZTJ0GlzIWMpv1+GaC17dpXWgy8DpNDJw8VxD48umO15uJJQae\ncwXAO8sNNWQkDCxxvWd6hHEil0Jombu8IQz4g2tO9/WDhEaTSAoJkYI2JZwZC9kXzaEWplRZWiRm\nQVWYVkWDR15c60Lqg8hoymBwSokPhh0/d7zl/fWWTx9c8s6tG4AIrt95Nfp+bk89gFcCkiJBAjs7\nMtvEh9cP+frrF6wy8nK65Ylu0KgM+iVEP6HwCHYHsn6HeBexm29gDz5CDlfIw5dQAnL3AG6SoyLB\nh6hSKlE85iM0o8URsiENZPiE9tnPc9w3fp2v8TTuey3nNcZlWFk1o8E4xUhuhVWMo4wEet4TmUDx\nebuXjl0+AmkzowLP7QG5Na6z53SV4wbRwSlp0SMiLtOd1yYtYgzfV0e2kLiWhUPqzsclY0RKryXN\nIMqRExsfIltgF/1Y324Cu1VJuDFaUKftNfFaJGFcSKVKJkgk24G1txGz+H+DKaNCZkVCQYdGVHEE\nTQOjVk42UJIxHK4gNkJQUvQhyqFOtGEhHQduL07uJ7BGylAYWiDXN7eFHH+0g5efqIaJIEj2qXky\nB438p3gvuvdS+xzUqfcXUaQ3Q51OkMSL89wDTBX7Po1UEHdvs45KuJOb3iMbxIA2d2hbRBnPVDkc\nlqRTcnIv1BX3TgzqpgISwxfiyrwYHc5QaZ/snxe4aJ63kxB3LCOQhnjfFBguym/BJ03Rzt8ZECHj\nSJslcc/8LwSretFs9/JEtw0XJMibjCnVjnp5k5CiNwqv1so728z+tLKuKwfNHNaVr19HjIIQeXAx\nsVSjqmIGS21M2RhCZMzxvrkcYyaOmZgSliOSE+NSGKcNR93zax/eshszWA/9NbzgxKl+kZ7HdKaY\niSBob4DDPfqj3bq84IvCGaLPdHc6exNKe4aWVZz6l3pDZx1lO2c8hXR2pYu4eUbw6+ML++IuRt2K\n3sz50f36Gs2QKEQz0ihE7SiVRUfezPUcGI6EeZfmC2bwc0921DCKZ6T4d+gOOjgZIHd9XUW73Wkj\ntX7d3jPZPNT3jIY6WmUgrpUDesb8H83W0aK/BGzxIMl/Bl+X/pcv3Bu/LSLfA/554NdxVOnv25uw\nSXDa3n8F/CLw//5+n3edG98cjafpjsm8a1xr4DYIuRjX8cRWAkMuTMPMBcIYfVjA2pjbwId5YigL\n32XkEy206sdeQkJFu/MmIAmz4sitRbfzVzBN7jrEEVE/z7UZwkIgoTGg3WGytUaOA+SAVIPUSAaM\niSI9e01GxmSkbSGHkWHdoxI5hIlJZzaqDPs9b03KIMZVVLZDZUqVB6ExbYWqiZgDkx55aVfs18BL\n2WLF0ccPVuGzFhmtMYWBFTjWSC2VTEEUxo3BujK2DWNQvhIDH7aRqzYTLi54PFV+90Xlt24v+OoE\nP3N1ZA0nrnLjIkbGbSMOlzyOC2P6/6l7s1jb1vQ86/n+bow55pyr22t3p6vGVZUqYxwnpJGjGEeK\nYiUhAawIccENF1zQCCEuUCKEhERAiJsIIZAQEhJKLpACEUI0TgIGBYJJZONYMe6qP/05u1nd7Mb4\nu4+Lf8y1z6k4VQl2bNeQjvbZe81ujW5+3/+97/N2LP2e3cFy6TLJFjp2SA+f7RxZJ6yDKRu86ziM\ne26T4WWy3NlzKjDVzBKHN4mPJ8vH0yVrX2GqaHyAl8Q+CaehsL2KLIvF2ZHOKOfe4GzlUBf0JG5T\n5rUu8nLTcTsE+pqAzDfLipuD4VEYWdIIc8m03DJjYGEKWSH4zO10w3lfKN2Sepgw/ZZFnvjYRlxZ\ncVn3HPbCcwrv7wMXfsmFLZwPhqv9lo9Kh9oNWpa8p4Z38oKzEfqtcrHMdB2IDHyQEyUaXohy0MBX\nDz2qBm+U3dgUCsl2iCi5DtTJMCHYrFRpMrBvxfZdFE0jOAYdMZvcCLVWsRpQAocMaSuYXFqhXApL\nyXQ75T2BoTqc97x3Y9mo5XlK7Fzg+d3Vb9Yt5Ldlu+lPWC89MRkupg1GA2IPbMMJZ9OeO9dzWmDX\nDe1+bTxoBmMRV9lWZbNaIvsMneNFcQQnuJRI3jIG3xbeLETjiN5QjWe13bFdLZFD4maxZBUjd+sl\nH5c1nRY2xrH0TcL0MUBojfvYrwi1gm9T6Zv1GrNPiMIUFg3qc/zltBKkNbRuzFTnka79fLUfeak9\nvVhMyEQCdlEpU0Y6z0dnp7iDEnvLu+6UZXrlkf7ofEEXM6/FiQ/PL7jY3tInYbKeQ+i52O1ZEdmG\n5mHepPZdl8OCwSiTGl4MZwCsx8rz/pzLcYdPgSyRcy1MXOGeBUz1HPYdp+cL9pcbVvKcpAZTJuTl\nAlCKROxrH8KHbzSfhLWzEslR1xX8Y/ixA2Zh0G8P1Hc/T3lxyzvvXfG4XINZzrWIb4W05uZL14KP\nTQLt9HBfi+xkYFEPHOgYdGIn7fdMZAKQY9vn5xpJppIOy+ZbtxBpcsWqwiiOZe4oKqSWaIKq0ueE\nGyJahLvcI0R8NQ37f/RdG7D+QBKDzW3h3rPH017jLDWLRC4F69pUui3LG9IMc9jxCjxykAFQnEIw\nEOox3gSsq+S0wJXWSFozQUiY6Fk2XjiJxCTCbrmHTSMblq41QWl9hzssKcOe1FVstWSj1GDm2BOg\nDL+p1/X32r6/GiaBhREmCkbdLIlrY2Rnzf0KsWJmL8kxg2Ymjom5b4KOTRXapHnMDZcz0ih8vJoO\nGCMYa0mmcKSpiW1YbGNtK1hnWZb3nlIKIQQ0Faw7Ni8z9jt4NLdg1KPc2M7ZUBnuJzoG7iEDVCVY\ni5RGKmIuxP3RY6NNSuWa1WaWJTJLCwEM1rcGy/mA1npf8LcsIm33CZRac8utKoq9Bz7YeRo2TzOq\nJZWK2ubrGLNiMlzlhEP42Q8zX75Q1sGxCIk+eFLhvqmtFCatzSORM94Ifd9jFh0ll4Z+L7Edk6IQ\n9/zxL5zz1V8amREFeLGzL6ntoqLHX7n5esQ0o6YTIc9EwGqab8hohtz098cGGtrkDlWcCta6hoTn\nVWOUFEQsSAMxeNsEl3NwNVVra+KNPapCm7J49jUdaYkyn1NVFeeOslFaM6ytiSta56loy5s6Tp7d\nfHOU+a5kjDawhbTzNs8LCczeGufM3EC381lVcVi0aptwuflamX1k9wyIudnS+RbRBq/tXCm/Cbph\nEfkhWoPU0zxIP6mqvyoivweIqnr3HU/5GHgy//+T+e/f+fPjz/6+DdOk8CGB95Ohp9CL0hXDQ7vl\nrHNYEQ5aeH+ydPGEK2CsnpeamdRSqqNoW22LopCbnjpLxeSEJKWaOit2QyPFofcAD4ehmOmeuCiF\nJgGZj1lOBTEJIYEJODGUXKl4HAkpgtpmILeiBJ95mizeOn7E7Blkw8NOMSHRaUK0MtWKWyTEVjoJ\n+M6BOJI49qPyzbHjeexIxiN6wqFUtsaTioFS6V27D5zrxJ1aNnnElcrv7j2PLsGXzDRWsoW6T2Q5\nsKBSpsIXe6ULPdFU3rkRzu3EGwvDbqycLiZ2KWGHJbmAcWv240isiRcbz2kXmpFeK8NauL4Thr7w\n7JBwxiIS2E6JnDoSZ1wuO56kkYcSkRTBdsSYOdjCpbH8wDCRDyOjKsEuGUyhWsuUb3HGsNcGcNjX\nHbe1w+aKlxG0EJzhxb7yOEx4s6eXgTpVvFyxdg2bqyWz6iyYPUU7Fq4ZrTd74TBWPq4e368xhw1j\nbLRVZyzGLLFSyTiWwfEDXnhZDYeUea4Db+9GHrg1i5VjVzo6NTwVeEwjNRbriXpKTAeqKsU4Dj04\nVYaiRGNYWocGyzJO1GxIArtauChwcBNJDQsH4zjRG9NIXlUJGPYoKQrqC944Yk5cmMDKOg4msZ/g\nmpFUDLUmduJ5mTOpRibnONlEboyhimUqBVMLU/3+Kj2+c1NbuZxe8mI4pSbPQipVlrga2fU967ih\nOMvOPMDFA9k61C6gNIlrCQaMQ4KgEjBdpkuFGnqyaTLe2i0ptpAJlGIwOpFDR8ARe6GWzN0yUNwC\nYzaoH/CiUBUxiVPvuEEw7oSSd7iF4LWy0x5ftqT1mnG8Q7OHGf+tJSC1NPy0VHJvIbeADgpE7xmC\nxSZIqjR4k8G6FUpu5+Oy4zRPVCw5KNk4DBGJQjUeDQGnhbw4a7TbRYdVpXQ9uR9wxjcv31BZlEiS\nQihCoJDdgskaVrmJCJ6tOx5uFbfYwqpDDneYIoypUorj/Q8WnNpCWk5w8zqcvU+NHe72LejuyHGJ\n9Rsya3yJgKJLg/mJc57/HwMPvuZINwbyiE3vM5TCF04s3xwv6EpGNYMfoBZG9QTZk6whqyWUnugL\nIRnEHljpnoxh0InoPNUKISdcrSger4lJPCoGYxuttjeJVDoMlSSGhMWiXLn1LP1WhJHTlBCUaepw\ns5zPaVMj6Bxzkz3UKjNpePa5m0ZYLQolKBwaEMkKmOGKun1AFsXPrjPBYo9KE1uo1WA1g60YlyF6\njJ/Q2u4xIg1/bsOhWXLHJeImSg0gFanCIhf6O09NlhImQrTsQ0WpVNOO8+qm52AdrjaCtDOKzYa7\nxfNf5+r8R7d9X9213FyM9jS0+IwHAAtOtKUsC6i2RkXm6cgRoyxW7ul59khqm6cSzrzSjDazX/tC\nQxrGF5RQ2sRFnLtPdm+L+615+qSPRQHx9j5T6IiTZi5ShTamNkbQAtY22U2ZpVoyTxC0gs6GFbVC\nKGCMJVGx2hj9RdoKd9ZKb+ekZkPTntKkh6VUvDNAI/fNbR8qs7dl/hdnXGsW7SckbEbw2v6tPa0V\n3ddR2cWC8Upxtnk5cuRxMA1rWwsVYYoTiCHntsIWc2HoAjFnlr1ntZ512FMLdtN5ckgpxKK8ex35\nL371Fo8wmW4GJQh1zgUycyNc9FUTwLwPmSV10DTbAKoOG15Nm47HXI8yPicNpmCbRKHOPrJqmi6c\n4/vP++eYD1ZrxYqbg3NLk0vOUx/M/Pu3MdEcCKt4gc55VNt5WgyvzhkDlAYnPEoljJ3H0bUd13sY\nBC1kd1ayNxmqtmnlJwORa60UbZMpjJBqK+4b8cg2QATthuqco9bjYKve+5jKPYL9N7T9KvC7gTOa\nV+kvisg/+V0eP1/s33P7ro+JBa5iwpjKTgI9haVUDjrwwSGTNTNVYbRLMsIhZjBd00rPMAw5vktV\njAlNcl4blt9o21eV0s4zm+aJZvv4prZp8dHbqKW0zLVPfMbjIkCwB15zPV7g0o+8iPBgueAm71ka\ni7HKlDw3NTLtR26YWIeOX/SFtF1hrOKtspyBI6lETlxgv6vsk7AXoVaHOOEwJVwfqLVAaoxQb6DU\nzBQzoRoGB6ZGfLU44/koRz54btikwoUU3lwn1EykQ+a29Cy0EK2hz4pzgSdDxy4qsSROTwzTtERC\noc8TznXUuuW8C2zyKavFRC7KUDzqWyP/dJWZ4kS1AxmlzwcOSbBlx6oP3G0mBkYohZPO4etEsJnb\nqfAyJ2Ra83xzhxsqh7zn/WnNh7cvORssb6wCnS185mHB2co7795xEhTrIJZMlcCiczib0eyp0zWr\npXDiR1zX8+w28f6NErtzTpwhjhPvXnnWXc8+j2yzwTOxSoXVYsHWVfZR2eSCrYZJDUPfEatjn/dM\n2uQ3KwEzBXAWTSPBeXKtGC30zvIie3Y5Usa2MmzJlFLYmFZKneDIZmIymc22sB4zeRx5cDrwOb/i\nxhzgUIm1kC1MZCZdME2GTGWav29MlaYwoKLVs6kFI5lSwdtGw7JzmIoriseC75pfNlROaiHXQtc3\n2uPLfuJ//Qe4mH+nbqf5llAf8to2ctcLffaIbhA/cDrekWTNzi5YlWfYuuRi95JiDozuNSCTgxBN\nhV54fH1FNZY6L2b2KOQKWK66QDY9F4ctVOHZssntHo1bRjW44CkF8vKUKoWBQtQA1XNlDAtNza+5\nWNAVZWM7VMDVAS2QFisUS5c2VN+TqnDuCy/tQEXxmhCvYAPJtkwyEaF6uIgJg+XjMHBRWxitloKX\nyN4IZxh8gavekXTBo7pnEzy7mDkTBVPoeIXEHleeVUyU0nw+LgtDhH2QOTsTuhlKkhbtnraYQLoV\n12lPv1GsXUK/oeaAQznrr5o6onrKg2uGu566fY0S3iUuBHfI6M3nMKv30Nev0f1rUGH6a4U1H1Gv\nThAN2OlDdG+IpfJzz3ouxiv2dtUWXK2QZv9ZlIFD37P1nqc3N1ytHvDk5gZT/VzXNGhHVxNdjYhE\nlAWQUApBW+TJLvd0mrAZRrF4EuAwbvanacFmwbsIWikSqECURno2NlGmJdVmbDhQ1DLkhvHelg6R\nCR9GUG1e9yzYsbKymVICqhAPDwg2kqqjLCbK1DHYEW8bcUKlw7FHS6DahBbXCL5pNcvH5+8d9Szl\nBlt8kykr80phZu88fTHY7NhaA9qjZCQCyz2DwpgCpVPQSraV1STcrfYMNwMyrn5Lrvfj9n3VMGEU\n1VbUWhpFo1ZDJ21ZPJpXeTOg1GpxxpJs62QNQqVga/OLFAFbAOR+xb7ITMij+RyOK/JGpaHyVWej\nW+u2madR2RasSpsWtWcD8xRH5F4+d9SallLAtN3vbAWa1MuKMDPvZhP+UV44I6J980VZda2IFcGp\nUqXirKFqvjedi2meGUqhhkwtZwS7QcRS1JB05ETPOPjU6Ce14OtI8qf0Gpv06tg0VUFNJBQPYjA6\nclDltliK7Lmkn02BlrspM5jIlJakVLDOtgJGNH/+AAAgAElEQVQ9DFxtNg3BXCcen3c8vDgDb9DO\nNchEKUhqBkGD0DnPD76+5ifeuON/+jjjspBrm9bInInVqIWF6ppPzJbaTIlHmaRqk03mmfSmzUf0\nChzRHtsa3TI3rQ2+oUBQ1wABVlDTICPN4Nj2TSmtobKmhTu6CiOGvjYTZ2vMmjfNyyvM+Po4PaqN\ntpa1UV9qijhpQcXGWiqteWvNnTZUu7Qma5rBFhZD0UZ4lFKwMj93DrUzxpCq0hxSuenRFTrX9kGu\nrmVgiWuByJb2pS0tIFTnBhzhNyX/YPYZfXP+68+LyB8A/g3gLwNBRE6+Y8r0iFdTpI+A3/8dL/l4\n/vM7J0+f2v7GL/4svWsJ7Tpj9r/45C2+8tpncFisG6haiGlqgbBSZ++SbdRX07DIYuonrsuGdqg0\nvbfT0MzDMkGefWG0YxKbGeyeesl8TNuReSXBBYjV8u3YQhW/NbYb1Nv7PYjipeWbTbXgQ6CqBQLU\nSjiEhvaVhqpvk2ehmoFuavkt1Vgshc44bNojWA67AyBYbWTKKgVbhIlMcIYg8OVhSxc9OM/1mAjG\nURzkXIi7SNQVF93IOrcFpVwNOQkmVmp3oATBxsBmM0HvGXJlnwISwGHZTIWTZUGN5WYvbIxQbtu1\nfkCw8oj9NLEvAacjgxaMrbz7srJLkcEpl0PH1XXl+WGJCW0/7UshokR7wXr0PHSJt5ZbvrRuGv4Y\nM7lm7l4GRjFMbmBXC0kVowF1sJcKuwChACueXwlfiwsOdYmUhEpmGB2jHqi6wpiRaVPJ2TDUiHrH\nQwMv7yqd1ZZ/Ygq7lAmhZ5y29CjJ9wSjTDU1f4IVvCnsab46Uwpd37PLFVcmnhiHX07tXpIcB1We\nqnLrehYOzg9CLJkTCi9lQFZL9lSmsmWogcErtkDvOrxa1pJQkyhFKJ3QCyyqEnSPWs+hHEilnxfS\nmjzGLZRHKMkmQolUAzl7OnPgf39+w0+/2DXfJs0nu/t1vMLfT5uRQjFbXA2scmIfPLk+5MH4PgC3\nfUCoDGUHZsfOfZZlmrhZe7pYCRm8jpxsKp2BffAMh0OT0NcmV7xZPuU8RUyN7BeGKIaBxCpmrvpT\nAiPLGjFOWZSKi4qo4XYx0WvG0+7XZV4UPDjHed0Ra8YhLK3lzqzp6h7CAgNcmJk+x4gAKy1otbw0\ngcuypcwe5BszsOubT/GiHrgJS87rniqGbiptcTEXPh7WrdGRkbuFZTFtkT7yMn+Gz+df4q5/wkEC\npr5Ell9kw4SOCVe2vHn4Zb5x9qN84eZtbldrinXk3uMmodjI+Sby0eIJD6e/y8Z7Tmolrw+cTT3W\nT5TaM44dy9sdaRhYbg/UzZsN1lQ+g3/xPjkOyIO3kVWF9GX4oXPky7d0Vfngb77F5fQt6r5ie0Hs\nGeH0OZ8/V37hbo1LjixDqxFDaOALGQnphrv1Z9ifnPHk9prNsrAYG1rdSuXbl6d8/uPrWVmfiRII\n1WDZg6lQFywVjC086095ON5itKISsalS1TDYiKJM2VNlwcAcBK0LFEFrR+0rXVG2eckiw0YshoRS\n8RbCVGZlUeXEHlA74CVixIMccLXHJsVRsCVTbde8+mZ1X4ugitqmvNpX3+5VRUjzmEpqZl0nTOza\nxGiuRe60A+lIp1e4mzWmwjIL3iZu8xLIuNgTAZ0g+RGbe4pkdl3G352xN9Cz/S275uH7rGE6ksmO\nfxra9OUIXAhyNKbPhaxpRYNt8cMISmdnc742Ta5183RhXjW/n27oHPpIKzGPk5o2TZj/nMedjRU/\n+4GOAIdPfG45FsafkDJZ25DlwCuwgxyfX6lVW+yRyn2T1QrtGTWMoEe6nzX3qzSqirn3brUSLHmL\niKWXRNGBYjJVYVEDk2SgmYJPbGbvT1ikHWIboQsaTe3BwhCzI1LIFKJ4RArvjY4fO/N4Uxp0orRp\nVRWYcuTFTlkuAiVn9lPGGhrVTwwhBGopiDvKJNt+KaXiQsPellQYFp4vPDzDPL9pPco8Day1kOcb\neMOMz3RD07Ij7hWNooQqWDeDNDDEWnE0/1RtB7kdF50pfiL3DVOdPW4yH0tz9PbMx++TGHGkjbNX\nWhlNwSg432gQ/ijTU8V5BwYK4K2dm7AmzbO2mxuddsyrFqQcMeAFI4Zc66elflKbDrkW2iCzPd/a\nlgtS9RV2Hyy5tF/6UI+6wYxxx9DaFtLrZky9HM/N33if9N02Q0OI/z9ABv4o8N/R9vmXgLeAn5kf\n+38D/7aIXH7Cx/QTwC3wy9/tTf7Q7/oRXj87ARr9sEjLcyuaqJoJh4p3jijHRrhldhlymwRVRTQh\ntZ1H0IrAWitVZtCHSfOiy6v7SrVulkE2L59ona+TT9wv5v3bFkpaxMFxa3eFFi4o0vyWIkKHUsc9\nZjZ/F6ugLVQ70M7jYtp5K/V4bjeyp6JMcULJ2FrpRXjYCZILoXrO3Yj2Ea1tJTInuCuOx/2El8TD\nIaAlIcUyCaxXA9/aCbtkmKyhT4oP0iZYtdKV1vj3oZDUoFa4HbXFshlDUOWQPW/vDWc+sXBnfPX6\nGUvxqBaC7bB2T0qVfamYMoctdoUHklh1hc51lAhDv2TdVeK+cLmCPGZShegOWHtCiIlNddzQceZH\n+qXDF0NWYZEVdQmtgVNrCN3EqCMxda04oAMDQ6f8Pitcyw6nlalGbKycDoGp3qLSYSVRSmJtFO2F\nKUNOFWPnPDxjydWjJLz0FCYsmbsIyTsOAmERuEoZPxj63KQzRifWnWfEUoyymTp0UkoHvQZsVFwc\nuSlKmqMiSlEehJGkhYGepansy46hFAZrGEriIiQcjXCakzKJMiaLVRjVsiiFByZwZQq5QkrKFkMw\nSjZwtxNeHxyxZCQ5nhXHg+UT/tkljFRO1TGK5VvjgZ+//a6X6u/oLYnD1UAxGVcDQyzY8sG973jQ\nl/icKTpnCpotajLLHPAasST6MdMJGDXErmeRJrwKKksActemhKJQxeEoiCasFE50QwVCHhlKYt+d\nUkLz3iYZ6Mx4v2h7Z9pncDVxZxc4zfcGfYBsBoK27B+OIe2mQSpubEW0sqwTGzuQTZuQWBEipqVC\nGcNZ2bTcIesoXatRkoPXdtdMCwc0e8TNYk2hcukPvLv6IaRu0Sy8cYAPzchZ+ZBULI/1Bd+6/FG+\n8PKXMWYN0oiBNie+nF6S9pk9HpG3+ebFF3lt8zHPVPiR6Rr1BxSDLzt8qKhRfIJDL3TmJbEfyV2m\niMNIpZsqJVjgjvriEemqpz878NoffoeP/9YbPPmjH7H78E3ytwonk2O9fsryZqRLe+7mUFvqxN3y\nlJAG+hg5OVSGNGG6gcWU7tedd8PAa7cvsL60RVjtuektDw8TRTzP+xUltH38+mZHJztUe5CCqqVI\nRKU0HYkIHRmj+d5/tuIVBGEjA4u8pzeVO7cg+AMlLujCgTDOIcsKzlpSXYIotqxbE+R7jBOkLiiu\nUtQ3+BXAJ2qRwhpl34BCps6yc8H6iC3Nr2vmAUbRDg0TrjYAG4C/PWdUONAWpay07EzE3hPwTBWk\nLCja/PstKqhtn9Zm/KPfvq8aJpknNGbGHFep8ywGYi04zP1EBmYSnVHCMZZGGiIZAFWCzqGj8wpv\nFm34RLFz49QymI5lS5vkAPP7QCNXYRrGe9aRAbwCL5gmmWorvQ3TLUfNzfGVP5EFxSzFk9k/1JRb\nr6Rjx0NmTWkBs2oplE91aH5OzEtG8BT+qYfwBx51fLQ3/PSH1/zpz12Si+GuWv7rr2/4y3/yTfog\n5JT48z/9Ho/fuuR/eG/Cxz3WKr/rwYJvXI/0RnBOSbmlcAdr+KIUgjFYW+ks9IYGl1DhxbYw5sp2\nTCAVFcejZU8sE8F5rjcjC+tZ9H6WIHB/fGvOSG0Xn/Y9f/JzF3y8i/yVb0wIZcZr2xkVLiAWqU3y\ndrygynxROiyYV/hvaJ8z0zDzrirpvhtoiPZZfHlPJhNjMDM5zhwBDJX7iVWhZRlZZR6NN2/bfYaX\nad9Fdm5ii0At7fmJWfaiqfnYVNAZPlFm0Ehp5VWDkqhireCcmzOXZh/WfC5qLTjbmiqhzKeZEJUZ\nW+raQoO2RjJLo0vOc04CZvYCztecKlZaYcb8WX8jm4j8B8BP0fDia+BfAH4c+AlVvROR/xL4CyJy\nTfM3/SfA/6WqPzu/xF+nNUZ/SUT+LPAU+PPAf6qqie+yWQqmeqrJFNsWQzoyl6Yh+6MPpBhbwyOB\nrC1/xNIM2EvNnPeWAYuTsYW3GiimcILhRDyhM9yVzHsH5YrQfCW5tMmRmvalIjTsvVMMFmPal4ER\nh0ppk3Da+aLacBsOUM0ztbJ5yZw0rberbQJpqlI0Y41r14EICcGUds5UtXQ+Y0rGBNuuy1hJxhBy\naoWutTzp7zgxylQgE/hot2cdAmOCX4t+lo96TnREJLO0I/FuyZthwtqI9444TpwtL/hgd8AbOIyJ\nJIG3Y8XmgJlg1ICnEDaFSQujOqh7zGRI7orPeE/XQZbMadxw9qBQo3Kyavfrw3QK2bLZRXZZuZsK\nB1HqdMUgHRdDoc8J3MiqF4ZlT447nt9lRDJxAmc7dvuJqXZ4m1m6kfPqkWWiZGWqhlx7Qk6oD5x0\nsO5gu71hdWZ5Qzpubkf2pdKvVry3OfDaekmtew6psC+nsEx0ZLrlEq8ZK4H9pHy8F4wZySkw1cio\nHtsFhjohBYzJLNRxmCyjNFLeUhJ2Jay94vzEmAzXk1B66Cqsesu6i2xMYlWXLGziw+3IngV3aWKh\nlSgeY13bN15IRdmmzN2o7LCc0eS8o50IIuikRAPXtd0TVhXswrG2GWs9fU3UUji3jk30BLtA7USW\nwDmt2fJauE6KI5Pz9/eEqZtzeqZwhk1toSKGAQF8vKPIguL9fS3iNBEDrKcbAGwVHsS5uFVlebNj\n53tcbVLufXCsDtcMSVGx3Pae2K1QgewtizQxuUD0Aym0+faBQLYdZ2U/1wNtH5/UNn2wYqglcudW\nPN2+5KOTC9bpWGDPcvW5jmh+5SM8CSbbQRBM+ntrkeAmbs2C0zJhiNzZgfX8nqu8Z7WBb168yedu\n3+dPnHzI+eUt6ep1fmV8zhcfn5BvhProdf76uOcnv/INerdHo/L239kTnr7BT5WHPHn5Nutpz9Oz\nBR/nwrpmLJkujsi2cmpe8ta0IJ4dsLa2OmR0qG3f5PG6IgHGZUEOPdUri+eO6bUdKg5eXFLXe+oT\ny+2vXtL//rdbU5A8z3/uMedTYrEWdFhy9vhtfm/8PP/vuzsEJdRCch0nY2vU1Cw5je33d3WPM/Bi\neHS/6BgXD+mnsal6SuE8FbbrCxaHPecxcpgXv0ZO2YtnzXa22O+bvUQNmCbFy87jS4Hi5omVEI3F\nlcR62lFF2Nk1Rg1hSsCITC2XzaYWd1Jq+09EGAFnmky8NShCSe27JbgREU81CdTOtQhIUFwKHPMf\nrT3QoFEKJmGsUGLAmIwmR9EGjjFnH1NvnzbAFm3KrZZ7GrYqaAHj9FUtIgWmxX3HNP4Wwza/rxom\nb44TIe5Jci2rqKGSLZ/GRxvTCj/MLHLTV/6A9gDBykzJm430zlmKzpju+bXqfIMw8gmIxHxQperM\nyW8v+SoYdF6p0Yo4g5VmyPcYjizj+qkP80r91m5SnwBT8Or9zPz6FdNQ1qrYaqhzZhJwnzDeGWVV\nDX/4ieXR+ZIfecvwp3/4gmXo2ObEf/i/vMM/95kOT+Z03egz//4/c8IvfXDD//j+jjJ0/BN95M98\nxfD284Gffm/Li2TBGU6KMphCMplDVBYdqDH3JLpDKpg4YownZ1h1npNeOQsGbZZwVCGlRNiP2ODn\nAyvI7EECnfOlYDgL/ORXHvJT3/4asfTEueC/p9ppafCOY/MAiDaem2o7zZXvCKhVbXx/c1QWg0or\nVCsNJoLQJlGqMEswM20qY+fmPNW2WnOUACJCOAbKHjMKbJNlVW3UQqPzNIBGcEy1hfmlWnGz7LQ1\nMC1A1B4nZvLqHD82a/fnnc7fk9aRawscbM36PIW6P88+cV7RchSOuU6t0/zEfpL2+cyrt0I+fdr+\n/9ke04Jmn9KmQn+X1iz9b/PP/03aysN/S5s6/VXgXzs+WVWriPwpGhXvZ4Ad8F8B/+73emMnipiC\nmEqgNfbJKJHKA2/QHCm+0COsXSIYJU0T1hvIBW8KYW5M74plaSLFG3L0OKuUOvHi0PPNSUjiEM3t\ny8CaWcaZOBHPiU1c9Jkz43jgMn2Y+HAE5xd8Y9emrHtgtJWUmqSzAeIrTtprVVFyLi1UuNbmnbLt\nXmHmjKligBjxGIK1LHWPqYJTw0UxbHPkzU7AVnalsE+VWgvPbgMfu8yyr0xVGXwzE3svnOJbeCsj\n0rcVvzEGbmXHL1yvwJyStOB0YthMLEQ57QKFHj2MXBpQPXC5HDB5izPCdQo8G5VJ4FQqCzvO13Ml\nbw2nfeWbN4Z6veLy1NLdRopMUK7pTYNYnDjlzQc9ViZuxohgiVQ2xdHLir06tneRyzU8eOTY7E2T\n2tSB1x5OHA4GfCHFS8Zpz/ZgcM7ipBJCokhgysovP7uj8wvQMy7KgaHPrIfAYw/jlPnyQ8eLw5ZO\nLKdL4Y0nB7R4alK8uWFfPfup8MhkXr9YEI3HoRRVlMjNPtIF5Xn0uMOEsZnVUJmy4RrHLhryxvER\nStIVnU5YKcRNJLk1y1LQqvjk+fpU2AigC4YQkdqmy1Z2vDgIzlRSgrUv9MYgxtBXOHEZA+TassL6\npSFpJmtoeTwiXGlmVyojE1UdY/aoWLCJzhg607MTJafIKgiDOtZ+Ihj7/VV4/DpbDgOHxUBXlOJa\nHeKrUKwydaeE0si4Zv6OH1Jm2wUECFIIKXMYGm1MxgpB6BTUCDa27/7TWLh1PXenZ5iS6XYHplXz\n+4YxkU8XZDW4KYITBpMxuXC3aBOq1dTkSru++TxKq7oRqXxkz3mwuaUM7ZvvO2uR401ePdTcpO4i\ngvqW1QjNgw2Q8YhT7KRQDesycufW7flnrQ56tHnGZbpj9XiHEU//+W/we9wKZM/0Wse7X/8af0x7\nAh/BySUUyxt/cEH99jOiPOLZxet86farvPWP/QpvfvCDfO3ja8JuRXXC42nD0gaSPeA2C4yPaLBU\na7A1kw8G+oK96vAkarCoHjBLzzI1xLdqxdaMvPNVLk8WlJ+5RNzAkz/07bYg+rcfkq+eU0RYPFRW\n8g38M88iX2IlE/LEzjepmlKx80JmNe0Yn8TnbUJW5/1CawDuF/CBsV8gQDe1JrZzO5YysFmdEzXi\nVTnZOyBRywmWhgZXTXSSqDrwvA+syoHwCTXIshzQ7u5+Ud2ox6RAtRBUMWo/VYtM1aBViK7itX33\nV2CsHajSh4mqHTIv7pFWn6pFuF+kBbWWlALVT5hiOJxvWq1y2/hNoq/qmhaCrcfevX1WoJRX56bD\nElQZj/C138kTJhH5l4F/Bfjs/E+/BPx7qvpX5593wF8A/nlaofPXgH9VVZ994jXeBP5z4I/QVo//\nIvDnVPV7LzlJmyy1s22WTMkrqtz8Bq+8SCpzCGubIrVjOktkTJNV5bnKtGLuYYnVVIIKIpaI0gmM\nUu+5+S37oEn81LabiUq6X40BqLVlqQRpeHOtDcCgM0kt0uSCn/QnWWkyw2ramF7RRjxh9tnME5I6\nk9a8gmKab0VrI+RwbCbb2Hzr4L/5tWv+3I+fcX7WkaIQvHBiPf/RP/1Frm9uOTlZckSwryj8wKMF\nnVxhsuGPvbliO46suh5vDL0Tdlk5t5aljvQqRGlJ4CkWOgdeWgM6xkTnPb4kHj0657yHJw9OWHaW\nnJVdzBwKuCmy0KZlq6pY6ShmpgfmNlnZH3b87W+8INWKONeybmgrYVWaD6h5j9oNvU2C2kQy11lT\nXc3c7LR8nKDH5oNXNDxtJDg3e8pmXhEiM07bCqbdC5sed54GVhUKFdPMPrPXSVpDApAL0R5lfu0c\nyAKu0kJlpRWxambohUoLFwSqGNyruxHG2TaeogWjQrsQ6vw5TRWKGBIVNS0fwzETJOZ9pqJ0FSY3\nm26ltiIbcxxltPeaJ6dFFScWQyG639hNSlX/pe/x8wn41+f//n6PeRf4U/+w7y1mTiSfoS9VFSmW\nWxzxkHgUOk7MyBtBIERe7oQXXtmXtjDSx0LGstfmV6tZqKNlsoWaGiafEnFGMSViEJZmSdQNQYVa\nOy7dxIVJDJ2hZyRZZcqWB75Q6oEv9Mo3o+cw0aAMVZsvUeb8NJFXfhAxCIUsFmsyYDE5YYzDauU8\nKFk8g82IyXRSidrw1/vJUcTw9RGUEW87rOmIeSKYkZV3bA+ZEDxRLSXvqSVwKJmscOIM+eAwMeGN\nUCTwg2Fikh0PnKM3lWIc776ceLGd+OyJ8vlzy93dHYuLp2wONyR3zsvdgYU98JatrMIdblhgS/vd\nzk+UcXfABOHphaHUDfjCwgm1VMaNAykY5zHlQCrKRiuLTsnlioXreRJ6Xm4LxsM+O/7Wh5HT5bpB\nIXwlpYKPhdO15zBOBHvg6SMPbouqRboOo3eo9Uix/FBUpAduD1zvLL98l/n22JG8kA9bLlaJLpzw\nyIxMyXP7omObB672dwxAoi2elBqJuceIZSw7TBhYhszdAaBQ3Z5aheXY4CNLD3e5cMDQa+LMGfY6\n0tfKzvZUKSzKhiTCUJqsd+giGiO9iyw044NHcVALD5YFybBXoWTAZpwsiTW2aZ94TpwSqiWWTJAF\nsVQ2OULtGIznjko/35Nf1EDVSFcSxbYFp30uOEmMMRPFEUSIY+G9WL7LVfoPcB3/Ntci0ff4uZBb\npISIMhnLoJ+oVBWQwo3tcVXpUiaoog50IZixPexFt+ZB3nHlZvle3153GzIv1yc82VzTaeHD/pSn\nmxf82qM36Hc7uu12VgM0Ga1iEDU8PVwTxXNYtNd77eU7DZYkgtXMfnnK+u4OUwo5OT58eMl6GjGl\nUuccw4VMrG/v2C8HFGG/WPDo5QtUleeXlzx88YIcPNcnp6w3G/ppIoYep0ryjgdjm6StxzZpGUri\n7cUDLr/6gi/88Ij0qxYW64RQJn7gSxUzvkM1T6k7QbqMrdfo5wKf/7lv4WLHa1+5ok6ZtHqG/2AA\noyQTeawdOe7pvVAlYGUixgzL2uBMKqjekZZrwu2EWThWWclvZFQWDa4cd22xKUf0VmB8B9GCfv0t\nXmxXPPqDH5D+T8Gvzqkf35CulmzFQVhT4qYRL2vi+fqSs+0Nz0/PeHh7c1+LBHUIwtbtUWsYRuV2\nGPAxM+TISU5tnIJFbcLUibGuUJacxPdJ0uKvpkWTTkZzCiw42TdFelVDwdCZLTuzoivTfZ0yBqGP\nKzTsseowxTL221Zrxh5bhNjvCIflp2oRX0Kb+KjgTcVRqCLYtHwFoRJPda0W0dCAFDWa2QddsDFQ\nfWJab1FV4vXTNjSYAVx2sQGXWNycMD54iVcFqdTbx60WmT2/wJx3VRlV6KrQy54hfq8r9Td3+4dd\n6HkX+LPA1+e//4vAfy8iP6KqvwL8x8CfoFGv7oD/DPgrwI8BzJkr/zPwAS1L5TXgLwER+He+15sv\nTX3lw5h9JAp/T9qvmfvO1lfJ/Jw5U2d+qJkbpuOB1/s/Fa/SqGZO8anpv51zmHKc7tT5fY5OhYqb\n84GqbxdIN+ftFOF+WnB8o+Qqvii1voJAHK1Xx1I0G6VTcx+6evTQtNylV6z7Jslp8kCZg+XM3CAa\n0xrKR8uOrz2/ZbG8xORKsIbuZAVa8TvPOI447+i6jmqEw3jASODPvB4xajDS8+HdjpUkXsqSlYNS\nE3vtKK6ipTJlbdO5kums4ES5GuHRqeGH33jIxUmPsxZKwoQeb6Cr5X4alXPFOcF7j8kZCXMmlWmY\n8K4L/PgXz/js6YJ/6xfu5iFd63Ra49ww42Yu/lvz046Ft3Zumpq80uqr1ZBjw3qUzh3XLI6tr8or\nis9Rd3sv0pQ2A3K1+aYsQrkPQm2vcZz2eSzWCr4eMdTKojZSkuU4PWztWTC1ndtzA+gERD5N9Tsu\nuhw/ZzFC0da8Y3Re32o/62x7nUzLIaPa+98/zJO6LA2kcrw53UsJj39vZ1RrAL+P1TSaFM1t8lhn\nX+OFFB74aRbBZZ6r8jIrp2lJcIXXBssqJTRPxGTZVGVTK4di2dnKVIU+AyaxzIbgM0+McrYsYGHM\nIwOJpfEs3S0nwXK+KARXmWTgUBy/dlf51a3ntlpekqip5bDZScHMnsfcVvbTHGLb7m2FE+dbYWpa\n8+Ss4dIrjzwc6sRtbYGymiv71oEzWsEWwRXB2tQadBO5sJmlUaQ33OwjpipStKGG3YqhjHQ1cbnu\nwBbqbmJ1ktFoeXImXG2UQsKIQ5jQOvGlM09we1QcQ+9xZsFi/YLHCzD6Pvq4o+Rxzo3zqEwY44mj\n8u0PIg8fDmyjkOvEenlO2u9QMxF8YFc3ODdwEgI3B+X8pBD2ykEtNQfC0BNly/mDjpgrD6h85tQj\ny2s0JqSOSHW8/Z7n/Y/3VCd0PoAqp0OHWQoy7ri5XvDhbcD5QlcjdjA82zsGKVyYgH+QOewqt1ZZ\npMK+7Pk7+8qFdfgwsZKJvVRycliXsMbirMFopBTlfGGodcKWwtq2aIpDNoyq9Lby2AvWwrlJ3CbL\nNkOkIDnTB0/HhPhm4o6a8YOwxPIiNZ/MqPC8Cqc4pAg7LFfbjtVCkdrgLoOdGE3lrhjyXvBB2NSO\nKWecCCdSWPSVZXVsc1NMODwLV4ilsrJbNgVuRqGzHZ1vq9Obg+FlyfQkFmrQ3mMYf6OX8m9rLfLG\n5iWL06cA3LklF3HHSU2Mvf/U4xaHRHGJJJa+ltkro0iFm65Ni4wRqrUsreDydC9tShgeb26ow5Jv\nnD3irQ+/SfIDzg2EoTUicRypxmCrYbs6Y7W9YVkqQRN369db83O4Y9CJbz38AUSEs2cfMHULSImX\nDx/w8MUzpsUSP07AXFP1s1zcecYu8BVMtAwAACAASURBVPjlFen0ggnD2e0NIsJ+/RCj030N8yhe\nszcdNgFzLRJm6UK1ltIvWA+VEgvSb5FcsfUM/X1voB90EN/BfDSCCJUFxhRkek5c/uP86Mkv4veB\nya3oX2Z82JHzE05rx6F/hqkdO5vIucf5ib50uO1t8/G6wr5WTscOe5mppxPl8gb38gF1eYL4bQNj\n1QpP30PefxMtFaRDvv0u9smXAHC/15F//hZ6z+IrX+ePxM/yN64njL1CAZMveDBeg4FudyCUgi4y\n/x91bxbr3X7ed32e37Cm/7SHd7/DmXyOjx07cRIndRIaUUqQ1VSIqgIhRUhcEakXhQvUC4SEihoJ\nIfWqQqiCKyQEVxSkCiTKIAq0SQe1dUNwFDv2sc/xGd5xz/9prfUbHi5+67/fN47ixI0bk9/N++69\n/9Ne+7fWep7nO9ErkoXgHGfDwFVV82J1gk8jsXKQwt19tzg6e7J1OOvByF0NtmmOWPSl3w9N4aIN\nUlwTa7fFmCuO+obzzmONBw1kbYl2hAijNFQEnBic1jSjwymMVWTRt+ybnmqsyJgSjI2lMiM2W7JJ\nuCwFHKgsvELNPNTDMpb9nM2e4CJ5s6Stt6UWmRDLRbXDDDWxvSGZTBo7mAwrmm3JtRqXW2q7Q6dc\nKuOH6fXrAhTYkd4syLlC8vD7nao/0PV9NUyq+j9/17f+soj8ReBPisgnwC8D/5aq/h0AEfl3gK+J\nyM+p6j8C/izweeBfmcTaXxWR/xj4qyLyK5Nz1u+5Tl3Cu8yzbAudZip4Dk1F0YNkLJO25c5L4SBi\n4g5aNlN3chDuHxyqMtyJ22wGbGm/Ki2oFNOjD885vK+hIAH2QBW0JfeJnEnT0H7CubAYjC0aLCdl\n7v+qWYUIeDF376Ra3NMOVtMOc4eoac74SXFz+GbOqTQC1vLjdaZFGFNiNtENfWVhjGjOVN7cbebb\n21tuQuLSVfja8Os7z9Pdhn/xYYczyqypsbuIRdnUFTkKR1XkT78m/MYLA3HLsmn5aBup1JKADQ3g\n8M5TGcVbg4Z0RyU6WdT0YaQPiSEpKfVUXVu0OEwNUx+x1rGcz1msI6dp4Mq2k9NgLkL6KbtCJ72O\nqiL2ZdNgKMW/m/bBhANPjehLpElV8XDXxB70Z8Xq/aCzmo793b5TnMlkzThfGnEm7ZoRcNN+ajgY\nTyiNWNSBxFzQnYlG5SjNyt2+0sIzFkkkillJ0UnJZPBRDADIUiipKFlLPkNFCVVOOZNFSFrMSQq/\nrgi6EaERh5CmXurlYKAcuDxR/QQjk17K/tHC4D/IVdty/IxCIuGcZZOV7WhocuCsSXy6rVjKyBt2\nTVUr/ZiKVXwLO+c4ieX4zBxoygQMC2OAPcnXnDiDMVvquiKo42L0xAB1bQjM+GCX+QdP4P2kXOmM\nOlkS8c5ww+KwBmLMGFNc3AiB0SgttrhJTtc9MqzHhDWWFQMPvfKwE3Y4NAeMKN4ZLvYj3jlisAwG\n2iwc25GqyuQxsZpbUp84rSw3vbBSZVntwBoG51mlxI1cc1xDzC2bfseQwXnP9SZx7BOXm4yvOuqs\nWDcgmsDOYBwYdMam77Gy53LoaHbXhe9jFyXEN95jt+4ZvdAKGJuxg/Dg1DDsPY0LmEaY2Wt0Hspw\nwATOltVkAnPFajHpW+8LVRCUmrDeUNXHjJvIfnNJ3SkhDVh3DHuDSUfcDD3JCB/HyNttiwbl+eXI\n//NsRsfAqptRSYCUeX8dOZkljoaROicuN8pps+bxleC7hu9sIxod7x454hD4qFXc2pDSLW3VsYsK\n+wrbZV7Tju0wcCkeBsNOI1uBEwwWYRcE32SqmFgbGIZEP8LeFOr4PCmjCh/vhK5OdDZjpKDvYaeM\nNqDO07W2uML2maUdqU1ThlU2cn5r2ErFOmfuW4clINLS0vC076njyIDjk6hUVcLsF8QqUsU9otD6\nkWUSKpSFGEwK7GLik5vITYo4qUAH1BiCGvYakT7xvNff+yT9A6wfdi0yt8qxu+VrvMWD8ZZUWbIE\nLOcoS5CRF/I6x80OkxO2EgIVvXF43dObY2ZpQ6hn+JhIdkZ2lrHymOLIw0235GR/y2gtp7sbdqtT\nZvsdn7p8TKgKH2Z3ch+Ak35NK5mqq0mjsD8648H2EkQY5ivwnof9JS/m92g7X+Im6ppOhWbWERKs\nbOLF6gy33bCIQ3FGbebMxHB77wH3+xtmIfL89AFbLZbjViqao4Z6d8lTe8yD7Q0hZrYn98qBunzO\nzclDTtOenx2/hU1CHFZU24BJiVxbzDc/gSDk7SNkojfa5W8TP3qHj07ewjvhWX6X5eZjTnzPzXyk\nuz3lZn6BNonr/CMc34xUR8+4//AJm/dfw5gbZNGyvZxjRwiihMURzXAOskPOz1CrEPYQSmajrWv0\n+VtFInRyjry4R55/jpOfeF6qhEVCtyOmzejtG0gdOL294GL2LiEO1O5jKoXIfY7ZoR0cqj6dCY5E\nqD2S4MH66uVmEkCUvQPR8W4YWiVlEXv2TUc99iyGj7ht32S1f85yapwOKoAD4T67HceDkO0WZzxO\nt3hRUhWYS0GNksvMYoU4Ac00VGildMGV+70VbDJ0qqizSC4SE0kTs8FvCakhScYl+ztrESD3M7wL\nWD8QkwdRmtsVa22QxVMsC0IzTONXi2129M0WjFDf3MfvteQtNRHpa0gVZAe2OMZGdVSuGOgeBtJ/\nVOufmUo8TWh+CegorlVfml7vbx8eo6q/LSIfAj8P/CPKJOerrzhbQYHK/0vgC3yPwEmApWQedEq/\nUQY7jeB56Zp3+NdpLnSbw+eYph0I5Y+OTM56L53U7oArLEV+LZBfQbReWYeDlvSl85jLpYBOB83K\nVGSqsSUETPSOQ2wODVs5jpNYr8zvc853jleHwFRjik5AVUsOjJRCv7y3lIbLGOqpQ1R70KgIT4Pn\nC6vAo67l25fXfHp1XF4vZ8ZxLMYCKeGco2kanvVrfuUfXoAIH4VMckve22e8NFSM/MyiuFN9Jwip\nbnm7UWrvaZvAl06PWXXwxcHwtfMdEct7lzt+7EipaofkwP1Vhwnx7u+lqlhjyTmQc6ZtShZFCViz\n5HHKZkqZEAKnRw1/5u0Zf/NxwltDKKFHxVil9AGklH6Hc93d/niFp52mpimH9DscDO9czXK8+9uq\nfJcDoh7+/pMbYS70P2ctQsKLYTzopQ56I6b9oBnnDORiM+0rOzHgipsVgJmyT+JEI5VX9kmcmkIV\nV1wCp+9ZSRPRRybvB0OfIy4ffp9MSVKy6DQmMMV3mq0pqJGfwpX9AcUUKVlflHPpEFj3x3klq+jk\nBmjUEhUwHs09wVTEUPNx6Nkzw+cZ1g501CTdsYqRozncx1I1kRdjptc539xVVCEyr1oWLvBs7LiR\nFTuFRkBywmeK66Np8XkkGUV8zWLIVBU0XhhDJMSS+QOZY4TaWpzpqauKxwPkEFFrmEtJ3BqMQhyp\nvGUcKtZW+KAvovS0H1m4xP3O8MZSaHWP6/d0fo5zjiFv2OZjTpdbhuue+njJ9W3Pg0awJNZJ6HXH\nm07Zx5GHvliPS91zaiOvv/kQ4p5Pntzy8drxuUdz3n92yb0azsOKsxNDtdvxxusV/dUtj2Ye9T1z\niQzJcnTSoL1n4Bk+92jrsCcWxh0h7mjsMdpEFumc1I6IODJSQgs3ifWF4+kT4eai4bOfSqwaA87w\n9BsjJ2c1mZFmtiDvbsmjcFpbcvSIabG7it/4cCCKQ8fA6dmK07pjn7YsR+Hc1LREbqs5bbzkxM44\neniFnM/Z1yue347M5pHlUcWbS89nK8PuJvG6z7wfHC2GR0cjYS/ceI+woLEwz/BCBnKwXOjASGBh\nInXtESz73cByoRzHBA88H15ktmq42ieybakIaITRwG0cOTfw+c6ydJG5F9b7knG3TgMf31guxDBo\nJoSRveyIY8NYZc5EkARb03PcFEH410OiMZB1y9uNLcWf73GjZyFKO1Zo2nGaK56K4RLPi60ttF9N\nLCrhgbU0LvOm3FBpyzZtQSwxJaxx+GwQI6zkB1fp/DBqkVoSq2z5zPiYvt6SOUZlhaQ1aheY9Ali\nLVX7ASBUY0GevHkHcktjRoI2zGO6M1o4xFO0k56kCluGbgFGmW820HVIW+ypsaUKeRjKdD6IlOfZ\ninYMaNgRp6Zqsd2yq2dYSTwYbpnHyN44VISmmUMeWIxloOK7Jc4ILo0wjqyqMuW3YwRrMc6xquqC\nXF1fgveMfkZ/9BrL3RXbxYLLas5nL5+U38lbTjcviK7iPL7GA65pQyDuI1obTBPRYJFtobNryoWy\nePE2Q9fz7fceokb5pm349PZN7PYedYpIvOLdek8OI8+3j/GzU056g5U57X1BZspi3DJbBcarE6Lt\nuDrf0nz6EvfkXaT7Dnp/gGhBSl5mVkXFYU6fILsGdZY0POPF3/sMj/7Uh/R/t8Z1PTqA9gZOEm/f\nN1xvFGsrsr5Zxu+VJ0y1SJPW5Moe2PAYq8w0odmQphrl+eoIRJjfPKfWlyW5Ts6FVRiIvkXVExoh\nj7O74a1OrJRkPC5GIJav7QmDzdzf7XnmOqxGbO7ZNDMy4HVAcsRaBRI2l5rX5YSmxFiX/eqiYMJI\naCqwIGMoOZW2WNmqJDQ7YirOyGN7i6chh4rkp7iMLOxnl/ipyYndLWF3hEmzIneg1CLx8iGDgare\nwbgkzG6pdTnVIgmb052uWybBvpjvOdf4ga/vu2ESkR+nXJQaCu/331DVr4vITwPjd2WnQMlFeTj9\n/yG/Oyfl2Ss/+54XqXNpYUi821V8I8ZiN2imuJhpg7pJZwIHUaJDJb+k4jk46I9Ku1VWMKVA9FMA\nLUByZWLfTJshTaYEhgklYNLEGEEm7q/XxD5ZrI7cy1uetw8xcQ/O4rWYQBTsRBFr7hAkK1LQFFOE\nb0ZeZuvAARAR3NRMBS0ZTJOhWQkK1EIpRIpeKgNzepxzPN8O3LcNm26P6UvmzzAMoAZfGZIWLv9R\nt+DH5Jwe4WtmzlVQPlhnjvLAZ5aOUZWz5YofjZFVFfk7HyY2veWn7nU8vtmw7ZX7RzN+5vWGm17x\nac/D4xm1E7b7zHeeX7PoWuZt2Xqtj6SsxFSQmqSZPISC0thcqGHTcbcukIaBL78z54PdwD+52eIm\nKp63Jbi25C/ZEhubXxb4IpM2SAqh7s4Ywpa8IyiGIDJx6u5MNiZ3Q4AkL3Vk8so+cN6VTKecUTWT\nRXI+/NWK/TfFwKKypcEr9MnyXmM+5Icd3vOwfyHmDGpIU26PnbRVRksAsQp4pMhMRXGa7hpDEUfW\noi9w5UyYdHwvixU3HZvRTNbjUgw3ZKIt6iTGtJNbXv49hgh/XNY8KbVmIgWZUwo6bA65VmoR6egI\nBa3Lhp1EjFqurOPFPvMNFWRfqBOK4nJkdMI6C8/HmoWJvOUtw7Bn5uGdruehn9HUAan22L5nMJ56\nYdivd+QEt1rRDzXv7xJ7AuPoyCYxxpEbqYhxiguw0+c0ZQ/4bIlG6GMmGmGMiguZ3io0DaMIGwzN\npmerkXfmCy7GSB4VW8+J4zWf9IJ3Qne7Y9VagkI/DixN4LiaAY5oLCcNdEYQXzP2PVe3z0o+yKzm\n518XXmyv+LE3PSIbHmaDtyN+ZVAJVMcjpk3EIPg2M/MDGnZk6+m8J2tktC059dxeOFZdwzbsaHp4\nsYGQjmiqkd0m8frrNbZds3gwsLwH+qkyxHjyYUOyFdIIUSybnWe9v+a4PuJiY0m7wNGDHfVQcWsz\nThzLJnN81iDe8en7t4x9Qxx2vG4yg2v44MmaPs/5esqsPz7Fj0JjIr4yaOzIOfOPny64DCM6Kitq\nUrXhUi1nVUecCadGGFS4uFljqTi1StdW0PdYgbWN7PrMPicG1zHuDDuTWOx6HtQekQxtxNgtfUiE\nZHFqibamHUY+WSc+koJKJUpcgcngRHjLJ67GkQcrx/VwxGPT0+hA5ww7BCclGPlWDHOnNEYYoufb\n+0BWYQg1Vj2GhMlKdsLTcSg62hzprCImk1CCVjwZI1YshpaZVVa+Y59LcRZREmWoGL+3meUfaP0w\na5EdFTGNLNIx/fyK2WWNkYGNew2bQXmNs7RF9d3yhOY5eXgDqyN5un42hOlaW+4Us8OHePhpRIR7\nn3zIbCxIxPpoBcDR5VAK03vFPCBW0J6f06mQGNneO2V7WqhNq8vH3HTHNPs9D66/wzc+8ydZPv+I\n23uPCuU77Ll/8R0ANt2CfV3T9tdUMRCa+eQ4DD4OOPOStt5OuhmZlc9Uhy0m78li8CK8EdYYU2qn\n0Da49UCcVazi18mnQjANbrgi1wKbtmiB7R5iRTbmLvzbuwU/dv4JfVK++uaf4GI9Ul1uWdqP6e7V\nkC32/oy3bzfo4gnb37pP3niqoxek5w05Nuiqpzv7mKH1HMcWXyt5/g30uoXnkOod9p0Xhc3y9A1M\nFvLlMZjSeJjLnkfNt4m/OsfWJ5DmJNfjNo9R3bBarfjRTvnqGuzOY7TkfM5iYOdB7JzeQT255kEZ\naGMVmxI3pqXeHlwMLXWKqIMLO6ONhbbaRkuOkcFalv0NsWLSa4Nqmga5A9E3nO17ondUoTgNijU8\n2O+mdzZcO+jytgzonQVNpRYxCYmZYbI0t2OcnqFgBTeOJFcRfIUGUwy+cp4ciTOVLSZD9dgRc0V2\nWpqvqRbR3UnRzorBZWhMRMxkfL8uKOm82ZLDnOH6EeHkKfnyEVpt7mqR5ACtkFyjVhHtyeaP1ibv\nnwVh+jrwReCIwg/+b0TkT3+Pxxco5/dfv+9j/sl//5/husUrL6y89jO/yBs/++VSME5WXkkmWFcK\n+jLhUNOkvryRpUzpzFRUdypkMajRKeT2Je0tufIco4fOrBSvoy2b1qCozdT0/KsL+PM/vsJgED0j\nVvDLf+cGaBAp1okHLYzLEA1FR8BkGmCKQP9g3OCnCw+HoMtpOWPJKWONTM1gsTz3E4JgJkRiVMOv\nXQvrkPiSj3S7EYznZjtgjdA5R1DFGyl5ThL5lT/7o+T9BX/jt5V/+uKan79nqKjprFA3MzyZs0cz\ndlvDZ+9dc0lHM+45XdRshsyT2xFnBUfmyz/5Fgt/wD5qkBkXN7eIndFWll0o1ppZDZUzhBDRXOgd\npbGxpZFIEWstxlUYG/k3PzWy+GDOr277YnudwWSDpVxEDMUMQiddzkGDk3JBhSRPVuzGk/VAO2OC\nGl/SPJWXdE037WQzIWPGlPwrleI0p6Y0QilF5MDszeCtKZNxKVS5w+c57DCxtqBdB7tomNC3Yi9e\nXmZqXMSWwOLiLVH0e6pThlKG/JKKmkSpxBS3pgONr3TVTCIrRtEJ4Szc+kLpK+JOI4YXv/F/8fw3\n/+/yOaepROxfXvz/uK2tRFZxLJRYJ6QsVCIcDyO1M4gLrFNxpDNSJoJjKjc5jcpKSyjxmA1eyk0l\nY0BNcUbKyi3KVV+ss6+S40mAylh2UUnGIjTF+l0NIXflFUxVrgCmQqNSa2BWl6Zs7kFDosqZgcSy\naanygKssNT0xC4um4nYfuE2Zt1Ytz2+KiFviwFJqRu2JqePxvqdOhrYRbDY0rmHIkUULq1ZZ3wzM\nZiOu9QxjQnoluoGZjXQifGscOUoDMRrCfuC1ex15u0b8AjPUWA91fR8dMtZntLKkVPHh48SHtzUL\nVyiGJ43jpA4sVj1XLwaOTxd4t0WM4eydBtY9LnsuLpSZi/jVFeKVe59pwO5JtzXD02tmy4q8H5BO\nePBaYuhvaPAwD9RL2HyU+O1PPuDBySnnOL79tGXW9XRj4K17HjNuqP0J+3HPVz6cs1wppm6Zuz1m\n3YNTTszAkcu83lVsk+OjF4bHg3ATAzFnnLUcW9AFpGGkYcV52rHeCK6u8SZQtULQhiCKN5YUR9RZ\nOp9oxdK1BqkarnYj633NeTCkaBg35fqztJ6mzdz0liEb9oCawDb4yQjIlkJdd7Ruzk3ItCr81n4k\n2YrH5xk111hbs1XP85DZiEKK+METssHpyF48WQJzHwGh0ohzwjIZBjMSVOms0Kug3pJiQWoHKQHd\nncQplyxhxVOjIBnvhCGMJXw0pTuDoj/k+qHVIn/1N75OV7WTYY4gfMSX3/kU/9obp/R1Q50KE+D8\n5D5gWKwN+9qTqpa632LiQKW2DGoV+qam3Rd3tM9852tcLY/ZnZ3SXV6RXY1xRR9y+6BDgXq3JTYN\nZojs791ja6Dud3T7G1Shlqf8tH/O7N3n8LaFbPj0/H/jb+XP4Sy4YcfQzBmme1sbErt5hw2JXDdI\nDmjV0sQ9h7GytZ5k7ZTn9rIW0XqGXd+iszkSR7KxXJ6+Rh0D1faWsFhiUII0fPh8yafqS6r7M+px\nR15eIU9OyPkMZheIyehqDakmDHOOfzrSVO+x/M0Z1wpnbz2l2TdIdwt9jdtE0soj2w7z+p5d+jx+\n803ktOdmXxctpBXc0LP/wgnzmDH3Hpf7XFph+zW891n0+AZZXZKuluV+2g1ofYFcPESS4vobxn6D\ne/0U+fAxWmc0NITjF9x/suEL4bP85ryh6W/oRmXry5CyCgPiLNnPCw9cU4kPkY4sA3OJU6KKYKqK\nK2tKpADFsQ5Vbpo5lQZsTNipaW0k4oeIi6nUCn7A5pqx6bGyRdQTKyGNNWoGJFVIyixlR3YRaxIm\nFa2mSQGTSh3gQwaB6MvgtMSQyGSKVd47ii31pfVIThhbqOEqimTF2lA4LJJePgdDg5aMwikDFZMm\nG9dSi/QiFLsVS7p4rbDEfCCFClLD3/3kI/6Pjy6nTeeAzCb84cxjvt/1fTdME7f329OX/1REfg74\n94G/AVQisvyuyc59Xk5ungI/+10v+WD697unPb9rfeGX/hKLN36EV/OUVBUnGSvQq5lsqEsoaRLF\nC0gWvBVEM6MYQk5kKpYa76huewe1ZIKaOw3JQb75MjapFJwHhp/JWhAKFTYu8RfP4CfOWpq6whqh\ncgUx+itfmvHXfn1E1LAWW4JFtXTk1UQNm1oygCK+f3nEJ3SkVPSapSAQGvFuKoanBvHQzR8QClFl\nL5ZsDJ1zXO52xKy82ESyKpWAc47awLyrmNWeZVcx9BuC1vy5z2Y+v2hJxrC+vWXezahMpK1quroF\nRr741infenJL0zlShNmshKU+XkeGMfHmdotvPYu2oqsdj8+vUSaDjFTss0UMmiIpG7AWTZndOFDh\nsN5CSozDyDBGdruBEJWmEj53uudXd8Wy/UCuNBTHGDvti4SScnHFSxR775wV41wxUWCiYU7GSJoP\nOiidjvlL0weduqp4CFQ7KKRESROFzxGovCGoKyigMTgpbdBkY3GXjyOvGESoKs6Ui1LMxZTggJQm\nAC0t9QE1y69otZyZ9HITpTNPiNXB4a6a0pjTK++v0x4rF+BihSrTPrR2aqokcf+Lv8Cjn/qFux1p\nVdg+/Sb/4L/4PQ3s/n+9HqC82XhCTljb05hile1qwwrYSOZ+7LkWYZscY54s6ZOWLDWmAUnORJ2u\nEDIADs0ld6vTzKnPNJ3nKm/Zhpp9zEQEm6aLhy0F5rx2NAa8RsZchkBaJeZJWfgRZ2tUA84baoVE\nIEokVR2qiU3Yk40n7WEfhdE4LvcDK5MwEphVSpVHlvOGJ/2G121FVY+EEXq9YuaEJMJZ5XmyFRZV\nYnuV6I4slXUEN6ChYTADL67XHNc1JOH+SYswItZz2jSIHRBzS8w1Tz/pSb2l7Sqq2rDZ7Tg+Ouad\n13eIZqifIqHixdM5m21HVs/jp5lZN7JYeMiRmITNxZ7790s4rsoSXAU3Ndle4/yA1jW3W6XtFGc9\n49ZzczOjb0b884hvWqzv6O6dUEvP595U9n3N833m+NgTx4q9q7nYbWnqGe8+skS95TZ5vn2xIGXD\nPicWtVCPiac7Sxojx+3IT8yrgro6j4zKxWjwarjwypAHFiIkB1kD/ZAJanFVy67vsT6xyJa1Zm5U\nERoepxEjkT5OLlkY0t5zowPBGJ70Sj0YrJQBnliD6UeoIadETEWHkXE8DxtysmxItOJQMq622Lhk\nF/dkOyBGmasDCzUBsYdMr8TMwnzqLUap0CxkG+lQogrOQieWHAOXOOY20lhfrhnJstY4aSt6BFvu\nweox1pK0UNDGHwBI/cOsRf7df+Fn+eKiY6gqjLsBwCSHMrAYhNtmQTMMrNYXJONQk6nTDrNVKhVs\nVHa1oxp3XNx/h9eef4jmQDTKzXLJIm2wN7FooXPP4qagDa9GOPpxX47zuC73BWtxWXnx4IR/Pfwa\n/v4VelLBxSl4qIPhz599lV9/TzBz4anOiLMKEyOkkWbcU+1K0xbdZHn+iseziYFhPsPvxzJs65a0\nt9dsj1Y0aaTPI1YjZogYV8KdjcmQelyMbHRFqxXRLZhtvk6QOflphZpIvb+CyUGYi8+i919Qs8dq\nJPdnnPz0b7B6f46uV1AFbATqDTqewHKD7Fd4W+Hqr6DOo5Kou0i1NQzxPnnoObGPyVWPPH+AffMJ\n5hug6iBadLBIOEZEMWlH3rbI8gb72kekJ5+C6KmaDB9+hN7mMnBNW6rtMXm1YXn0W9iPv4izNbsq\noTmDsYzSIKqsxg0778hcYfN9xEayqRAilbNF56eWxirYaxoAKe5zLWDSnKGu8Hox/flXhNoy1OVE\n8nlNzhFMhDgjVFva3QytMqMfiK7Hhw4rIKPgpLgfogExNZU5mCcIQRNNtgRNd3lpdmLmRO/uahFy\nGdbmA9MplXoiuzIKlmBf1iLF9pQq2eK8Rwa1RSd3qEVCAzai5tAEFZqgugh+w5ffPuHPvPUAbEAV\nZJzzjfU1v/y3vycY/ANdP4g4BEOx7fwKhdb4ZeBvAojIjwBvUbJSoMDn/5GI3HuFO/yLlCyW3zf2\n2wh3OT/GgOaEklGpyZqpTSLaYoNqMMx1RBlZm4ZghRMRRiJHOZN1Q2Ud9xlZeXiSDN+KNRoj2U9C\ntVQzmEA1FdlBCi3BZyG4QGMr/ebwRAAAIABJREFUBkk0Q0VKO/7Hjys+/7AljD2m8WSpGUJmFTP/\nwWccCwffGJW/9WFiEzK3uUbrgKQCVUkCnyxxCurKOWOnPKcDaoDLgGJUKKfb1KFTeKUp55cXOVNQ\ngUHhvb7ncvSsXeIsw3MZeRh6PnWyYD6zHGXDI2PZXa4xzrPuA7fbnpwStTU8Wq1IKjR1mThe3ayp\n65Y49rx5r+PxeuBms+N40fKgFV5bdaSUGceBGJV+ENI0ja2sI8SEMR6yYidKRwoKIdLWlpvdni45\nXAjErMQkDENgzDCq8quPE1/ZLpixI6slmKITY0KbogGbC2piTEGDck4TMiRYMeXYaipI3KHZdBlM\nhc8jo5aGSWN5/gH5sZQJW1Ih54iInay+C+0up4xYRyW20KhEihh7en5FCZe1Yu8EnjohhFnznf5K\nVVHLHZ1OVcCbu4sQgKi7Q8LuaIbTax6oc4cQ5XTYF68Yn6gU7rxMOiam4+bthCilyU7fGjTHSV/3\nx1fLVPvMwowYKcF7VsAp1OrZS481nsepw6rBxkhnQZ1SJ09vEyEnlghLcWQZObaBnDtGbnHaFU61\nFsv/OkTOGuEeI7Eq07pKE0+Hgn5GN1In8EmZWctrXaJSocKzNSNjNqRUzl8Rpc8JkZptGrlnB9om\ncSo1WcAOPakS5saxJ/HG3JBzT68VSz9gJLKYW9BQCmQxjGpwTcXNdiBuM3MzEAOcHs15frulqixx\nb+hmO+Jg2dDR9ELtyx7Q6gFm2JFSZOwtt7sznCa6xlD7PfOjzGYceKNTnHvKdj/n8tZyvX2d49aw\n3qyZucxqDt3ccr2peL5puOoFQ+LNex3nz/cs5g7fJHK4xdq6oMf7CtsZOhnYXzcY66k0cTSLXK07\n2nuKa0b2sWImW/YYzKYm2R11gKcB+jgyqyu6asVmv+V59nyyWdCMCV9lTuvM3BXFn2sN49jjWkOv\nDdt+z4W2jCERVGh8GdqMyVK7wKKWMgwjFaQoKhuJHHnl/U3NewTmKoRoMVUkWEMboCFCrjlPI9Em\njLUMccB5h2al9Z6cYTckdlTIHnZS3FIFoXXKfTWMPqNquQ4DjQErI76z3A9C5zyl1yhU8z6n6Zpl\n2YSEEUOvwi5HfIw4a9mkhNYNx0RmTAYHFbRmRMXSa6BCGZyhmwZilRh6TQxJEB0xQGOE1jrqfz52\nwH9ktYi1N/TNCT4EqEotkv2OmN9F3C12+QH5do61hmb7gPrkA8a8ZQyPCOaY2cnHJMkcvVjSyN9n\nXj3ihGe4esv15pRLfUA2N2yOHuL7PbN6waUL4Oe011cEE7FZsdlhbM/CWJ7NahZPM1x8ld/e/Dif\nPfk21fkFNFs0zSFnXPOEn1tZOD7n1r7FJy9a0vMTXtTHeG6J4hlXLb2rOT7fcX22RIHZ+SW7s1MA\n4kFHJZlNs6Te7KisYds5UrFMQsm0l9cvaxEPzng2znBxc4uae2xJHO8SF61j1T/Gny5xy1uqi4jM\nKuzNGfH0MUkFub4POVA1N6gX5GYFZ88R18MTg6SWbD+ijp7Q9djLmngyYtXQBYFqwMQBUY+aHfnD\nBSZ4hpWjerqExTk5QTYlyIUwoB8ew6M1Rq7RcQl5BBVY3CLXBs4/R6yfs93+CZ6ZI062a6JYQuuL\nGVUeqShZeoM0KA70IerMdD7ECanzQEuTdoyuIWoxzLDz94j7L3A0XrNefkjlwN625HmP7MJUi5T7\n9I17naP+GdkaTNVDLqyWIgxweGECGSpM8zJGpSEjeUOlNaPYEuyull4qNB2y8MpjsypViFTT15gp\no1RLg6OVK69bnlC2yFSLOC00LQuQysBEJ99zUYNNRacfTSi1yDTsJSzx5rtqEYrOfTe/wty81Kr/\nUazvN4fpPwX+F4ql5wL4t4F/mRI6eSsi/xXw10TkisIp/s+Bv6eq/3h6if+dcjH6b0XkP6QEV/4n\nwF9X/f1JzSXzJuGsAAnnistGogSXes3UCK8b5dj0SK08D5YTDWzVkAj8qO540DXk5PjNAd7PDW/a\nkU+7wE9WPX3d8GvrQKUVzyRgtMKmHgdEIxhrsJox2mFspknCUEVWuSZ6xzcfX/PacUPjAw/vCTGB\n9545Geca3mbLX/5Sxz7BJ7cj/9M3Mz/1cM5/d77GiyVX5fTJFATqgDalQ5E6Fb8WIQoc7ARAsBPS\nZrRsIo9gcsKZyEfZk0JiG4TLNFKJ5dY3fO1iwy89ep1Hxw7vHUOueXF5SwiTe5sTdikgE71tu1eO\nl5a6dpNJQ11COXNg4Zf0Yc8meBh7+r6nbTvCLrIfhbquGBP4CetIqTQwcfrdjBGGYUBsjfc1Y4pk\nMuvdQIiZqqrJWRmGRBDL1TjgTL6jReR8QOMMKoqbzqWcD8G2xdyhuNeVC3nKk9W4PaBGhpAzUQuJ\nEAzWTXk9vKQi5CmY1ruXNu+IYlxNCOOdnareBeseLiDlc2INKZeJYUnU1jvE6fDYg1bqu7/36koH\neqAc0MUJsXvlcYf/HcJpZUI3D7RPY6RQ8MoBKHo6KXql4rB/oIs6ksL4x7dfYqGZ13SgqSG5hqf7\nzJMgBAnYVINL1CheAmfOsZWRBmhaRVKgnTkubgdq3zKPGwZjqTrBZ4/UFbstbEJAZGRuhE2C81xz\nYzNuV0S50Qk2JUQ69hohF0v3zbpYhguZlbhC3ZVArBxDP9C5gMlC1MRlFmQQgo6It1Tq6JKjniXy\nbuTr+8y7cxhSZIdlv12TVGibhm0143y9ZlV37HcDKbV8zfS00qEaeXK15Wy+5GboaZxysy9ocKPC\nvbM9eZxzcwXeB2LyPL7JnMwMrYtsQmAXHDNjubmoMQprBGygrTyrVpnbW9Yb5XhWs7BKsnv6OOek\n8mAC91cBE2t2YY3W8OQ8kkLHYmU5W2WoM9kMiAeJFl9FrDg2e2Hsd5wuDR89jaA1y9We09oiWC57\nw9IFzCxS7yJbH6nzjJmd42rH67NrfuzeEc4ZLi9ucb4hD8pVdqhmdrstWrVUNnFrWuZmz7xuqBrL\nfj3yeBjRmNmK4/G6pzGembY4t2YbAutQgxhsXjOznjGWaYjPmU4NG4U9xQnwYdUBSowDGw+aA5oM\nox3Q0bHslHs+sZI9VgwfBst22BJtTU/izHtEDa0IxiqVM3iE2lsu+kRVW8IwItbicgZbzuucYRsy\nQQKN9bjaEGOis9AkxdqESQms0BPZacl12GdlhyNaj06Ww3VODKqQFDWFFbKfUHfJf7hC54deixiL\ncedkNyfXO+rtGWZoiU1kWD1ntV2is4HFzRGtfw8/Zm7sCWG5YxzXDGbHu9fX1G0PG/jIX/M0LriX\nLQ/qC17z77Hv7vEt94wuL3mmDeqOePDJJwCc3+8QhPnNLdv2Hi+6inrfMy7WdG5BWsyxn1j02MM4\nIvdfQPSYJ6+TTm/QzY+wrD9hfj+ye+cZn762vPjmAxavL/nKYJjtM+uzBXWaapG2pZ709WYKVh2b\nkvOUmhlX8wPHo9QiPiqmqqiH0hnbbkb35Jrq0be4bD9PfnqO6IbHgFx2RPMQ+WhP/e597E98B92e\nkLtz3LViTSBOVPjdSabZGvLRJbb36PhGuff5a2x8E1l9gt/CuG8gD+jtEk4+AAnkXCMa4LYh2XsY\n+5TqEtLRM6oXnwHVO7Qjnr6P3bhCoXYtZnlFXq8grrHvPyAdX4KNGL8jjpZrlHYh+CFjXUW934Gp\n2LY1pJGOyBqDSX0x2nId6iyhapitS7/eS1PUyNONul2/xabOBBpSuI/GMzABs7tAMcVkhAtUe5by\nEY08ZHQ3RTJgEzpfkfgYK7PpnKl/dy0Sa3TKRRqNIZGRmJBcEM1X6w4z6a5f/V6uHTK5Oqa7x5Wa\nSrxgkpDtXRALB4sGSYKouwtYlwNrJ9lSi0zDW6eCZCW6fMfMyUkx6ug2JwTzqmfLP//1/SJMDyjh\nbo8ok5j/l3KB+j+nn/8lynH7HyiTnv8V+PcOT1bVLCJ/juJE8/cpBuz/NfBX/iBv7iXRTfqfOFkj\nG1N41gjFUFwTOwZm1rMblUaUM6/scmRQQSii50bhR5qB81jxwdhgrPK6VToX+cWlIaYdBsc+33Cj\nNRfB8pYNWGtp3chX1p5nqScZxyMHP+pH3rvu+eAyMvZr/qWfLGJPJRWHvJTQtKOqLOvNyPFyxqcX\nwi9/wbHe7/kLmrlMwj+8GXmKpVZb7MhV7oT2xejhYOenpbB9NeBpcoAorimQFJKxpBKZywt/EPFV\n7CWiQfkLX3iNo2VBUl5s9qy6mgermpvzgaPZnFll2IfAOCau+x6H43K9L656rpgQ2MZzdtRS93C5\nUy7WPfvBELLlkQu0vghuwtiTUqL21Z3zXUYn6+5D4CaMIRPjiLWKhEnLI4b9MBBCpLEVf+q1yJdW\njr/+nUBQCkSPmbKWEnLnCleoc3lyKFQtjY6huL+56UQdDlZFSPmJOdh356KDm8KCVQvN02JwhuJ2\npgWBTEDKZSrc58kpUYrroVHBqRAlkafsowMT0x4ypKb8qNL0SHFNkoN+riCr2WSCKjabgnpJmVK5\nUpcUSDznO5rfq1xzO3FJrRZ9VdJD01SofdjJKn9q1jFSKIoUEXky5Xc5GI/8cVxihVzDToT3tj1D\ntBxbw5EkpBkYk8X4zNwHYnAsnFJpZFZZjBjO1RI7i5jIuqqRWDPGnk4qtrdr1EYsDkmRrVQ4b3hD\nE/eyL4YzMtJYj7GOG5uxyZBTAOMwJpPF0hslpYE4mcrYCMdiCRFMVOaqBM3UAt5mdreRbBxPdGC7\nzyx8JmXPb54n5pVybhTVJTlFFrlCwkhKjhgit6HoTs6aml2/pW0KhbXxFyydoW0ss9pyvVeenO/5\n6kcNpinI6tJXExVoybObDVEFbIvGkdga5n7PSEXMkZN5zdFsj+aMWzjiuGcAPlp7ug68vYK6Y70d\nmeUV1XxP2zRgRlYLi3cjYpRhnbHawEnJkRp2SnPk0TGxqjO5csTQ8+kHDYSBYRtALMMgnM4i45C5\nSZ6rMXLanbIwG9zsFj9WfPTxjCA3zJqOkCrSJuMbmPkBbzzN8Yyn+4oPzjcEUXCOnEdQQ9BCizbi\nmXvDWbUgpIGQB9a9xRiPl0zrIt28IY+BtVc0G5aN0OkeV8+5DZ6rbRm4qHiOqswqC8ZkjixcDcpj\nY9jHgad7eGwdc5+ZT7TQPkRs3TIzxcb4aKGYpIwIF+vABaGI98dYtAR9GYw5W9O2FY0vlvVXW0tS\npWoNnSQugmOTtED3CCYmavEEo7jREilIWxp6xmzIYinFs8dMgfMpFPwhBwgpf4+z9A+0fqi1SINi\nlyNwSTaBofmE2fPPwPEHOGDd3jDbtmzf+oTm/SVba6hC5h4btoOQr2YkmTHW16gTXnPP6c3rnPsO\nszPMe6F1PT/eR7Jb83Z8Rp8y7mTGbTzlHf0a/sLgz3o+fPIFdg+eks19lq7mLD3j4nJH32VmckN+\n7TVYB7Tdgyjq9tjV19EqIxKY9xWsH/Cpt58Sd+f8gjo2/gFPXzS8WJ4S/JLYzXiUNjxnBu2sxLfo\ny1rEZfO7apHczumbUosYhc0b9zE8AKMsP6VouAedEFbPkectnz1O6OI5bI7QfIuxS2SlxFtLWBra\nmzPEvgAJ9HVNfSmY9j3Eehg9xt6S3RbzmVuqr72JG68ZH52Tt8dEm2lDj6sDYhNuvACbwAlmfQop\n/3/UvUmMbWuW3/VbX7f3Pl00t31tvswsytVgywYDhZAxnbCQ8NieYhkJIZgwAglLTJAYoRpgmQkT\nYAoTCyRLBsnMKCiMZFw4syqz6vW3jeY0u/m6xeA7cd+rLJexVeWC90lXN+KciB3nROy9v7XWv0N3\nn1MwzDuDSx3OJ9yrH0KYKfmAMDWdcbwk2hN9/wWz7bnc/Tq/qAO/U/9JJCjbGEnF8vbJNZf7V1SU\nCctDaieqzLsLbE6sD2/atSGCldZcPoTRj75DcmQOgqlblAnMC7LbIOkSiKzSBlt3lNUtGha2yxMy\nM1kdy8XvEA7PWdweVyypP9BNl2AKtdu3QfW8QTU3tz0tODxqHMGdKCYTUk+SDnX3NCKd0C0dc2lG\nRyoV98DA0YQRYeUiMVuCKyzVYSqYfqZOA3bVGrE0DoASNiM5OUrtzkHQf/9axGQ566IaatWG3gbL\n/49NH1T1L/+/PL8A//753+/3NZ8D/+Y/ys99WGIMck7NdA8Wz6LfBIqWFgrqnOe3qiB4LJVXuY3O\nLDBg8MaRSDyJlmATn9jMkjMvahNCY+EqCCub8aXyxE48H5T5bPquFf657UuCGVi8cDgV1j382a7y\neN1zvVtxd5yoNWKMRcWxlMg8Ts1r3nlyzvSrjsM0Uovw3lq4SIYnw8KLI/wvJ0hnMZyzAkUw9sH7\nrhX+D6G8Tb8iPLiuiVqSVpxtBXh9sDoXbY4jCFl6xpD5e4eCl0IfCtuVwYuhH1Z8/5Hhf//JG3a9\n5XrdnKwsHTfTzDG1XJj3LzbYM+0rY7laG4JThuCpqTKlBSMWb9rr7DqHd4VKwQeHVUsqGescp3Hi\n6dUOq5l01unEpNSaUa14ByFYtusV85TYup7iIt83lc9TQW1HFSWdXV8KZ1dDWrNjrcXWs1uimGaO\nAO+QmXD+HYkq4s7n0xm/y2ejiAdHQxHFmsb5T+csMDXg1bxrAPEGp4L5FtXPVKWXRv0VhHQ+nx4Y\ndtWcs5/OiJWeGxYBEhWxgikQrHuHdXfSULMH3ZXwDRXv23b77f20+U56cD5RvrFKP/+fz/S+d0ne\nD8c6n3PfnlB9J1dJvEmFQ+35fr+ws5a1WJYVzIslikdqZsmON5p5ky2hDriYedjvLr1gcuVCK0bf\nIrTA59BZMGtuSyU5YSlCSZnO9kRK06hFZaGw9T3PbWuoS4BExmjE+p50jLjgmOeZYuCYLE/Xyj5l\nOuc5lYXL0DXKoIPcOayA7yrHMVEQdl0j66ZaCTZTa8GFgZUdmWMEZ9l18Hg90NvMsAYtllQiiyTm\nMTP6LV/uDVMxBI0EM7D2ytad8La5MmqtHPd3XK4CLmQuQgFtgavGOazuudituJtGxrgG9WiJrK88\njwJId2Q5eFRWTCcl7C5YX7bGYzkciAuAIJc7iCeMzxQ3Y1NPIVGKZzk4sjaHyo29xrlEPCaMOqxb\nkVLFholYDd22Y/Vq4tGzgVd3mZtyiXlzJE2KOmVjhc5kvINsDT99eyKWHYcCRhxLnFh1gaveMsZC\n1UqszdLfqWBFKXPFuciFF8Qv5ylrz21qmWopJYaQ+Dkn4C3HueOr0fFcJ3IWjO9JpbKRkeveUlLG\neah4XIV/ajXz6W3i0eOBNYXiEptaOWWDes+UZm4ny30qOBsQYB0idqt8nAMLyqkEnGTUCKUq1My8\nJFKGI55ZGvqdZgi06IMKzChGYKmWEQhJyK5iqzRTHGcpNbffiWmU6Kko1KYJDmckf2X+YPeQ/69r\nkXkzI2e6/EMtEt/7/BsjnxJwIvhXz3j93j2aL5FJuC8daZXpjYGxp9OBOMEjbrH+a57ur1BKs/2+\n6yj9wroGZJhZnRLqZp6Hz9j7C+pzi4kT/8T7f5Myv8dpeyA6gW7mY/kSHkcY1pjDgDx6AW+3TQM9\nXgF36GgwdkftbjErKGamblc4vWWoie91nvePE1/Ic5L1iFEelQO+GpYq72qRMJ+o66G5+laDquDd\nA13CsKRK59veUaCVKtm0vVcEa54RH1XeDl/w+MYirmIu25epHTC7SLgp6PKCIR3RumM1J6YLoZge\nUNana9AA/Jh68x48fgkkzGlL1x/QuUBQEEtNAZM+APMKQyRffUU2Cfv6hxirrPSnpG2Prgu6+hEg\n2Nc/oG5fNXOE/hafj5TdQP/yAq6/Zvv2gvXtLfcXBwb/Q/ymYl+8wj4bSF9PjDt/3j4MeT2w29+B\nZqprMhD4Zg9+YBWJNo18e04xcsTOz1jwrOqCyx2n1SvW02PqGJiHmcXGlk2kgkuXlGGP5KdghVD2\naDdRw0Q4efos5GFEsrCs2msI+6t2fpseVWWWgnWFkvt3tUjRRt/t7EIVi9izfX2/4HKgiMH7Nqzt\nwtkYKXtCn9GzhtduzsHLxaK04bkO0++6xvK0+d21yBkQqAJ0I7qs3iGCf1TrD0PD9Ee2XGMyUbQJ\n/aXJRpqDmMLKFpJx3NXSmiWpzEbZqkWtMItwosF9XgQvYLQwmMqFh6NawKDV8WpstK9A5BMK1jUb\n2kEgakTFUO3Cish6COyTodTCmCv+NLFa9fTWcJojlcw4RSqWzgo4IVUlniY0FYxYus4Ta2aVwZgT\nvdmQtceZfO64FT0jEE4FMbYFlZqKkqja8VhmXqrF20CnlVLbRehRnDE8WBwogrMtSvVvvZ7ZuMDP\nB0uMULxyiBP7pVlh9yGw7j2Ptj0u9JSc+dEXr9mPlVIqvQURxzgtZB+IuYWDzqlx2I1pFwNWMFpx\nwWDEYow5+/kJSyysQsBqZfCOMidyyeTc7J876xpCVEFT5XLdowLLYvhLf+KK14eZv/06stsEPlwH\n/qvfuGGyAU/rToqcdV+mtRRVBfHNtOOdUca3qG9KQ2HeNRu2WZ0areSzq1HWysMJ+HDJigixKh2u\n6YKswb47brMCze8QnaZbUlXEfENn+DYFz0gLiW1gT0OY1D00MGdKYRu6YIRv8qFMxVawRqnvkDPI\nas/Njjlr/5SCwX7L/MQAxhpES7MtpTbtlDWY80TxOwwwcRc8myFAWfgsO7oY2avBzOdmmAzSdHuh\nG3iaIq5rNO2ssf2NQwuVPRTLbAecWvpc6UrF+4z3lUuElVO2bkVBObjQAkVNxzFm7LRw7AWvhtNi\nUFPpvWM8HFtuXGr2wYNTHuvC/djyytRl1kHQRZlN5jQmVt2Ady1c+aJL9G7g/e2JpfSQR9Z9JVdH\nJwfUeGLasL+bwXpKnvnq2HF5qPh1xhTXEJ9BGO/35Fh4/8kltszU3Gi77XqGi0cjy7Sll4wxiSiV\nOQm1DnSbgTSdWG8uePXmQL+qBDPRrWeM99Q4cHOK9P4RJkQ6nTGrRD8EJC1gekSu8N1CvzKYbiIt\nihtsU6r4HiEx1EQeJobuAgk96eYFNVaSPqPXipZKt+4ox4WU4PaYGaty8/lEsJ7jkri8dFxvEpmF\nfr2j6syLtx2OzLOhp8qExzGXEVVLroVlGVk5w+VuRS7KaVYSjcoaZMGpcLMoxu+QvNCLsrIQa9Mg\nVfGkciIlRcTw0WWlD55dVF5OmblW5pr5ya3Qry2yzBQsVQ3TqUIwfHGfmDWTTM91USqGahNiDLuz\nK1UpBTWGTydhRSAZhVxZRMBaSs4Y47joDU4hlkbJCU2piVplSqkNiJywLkosCWN7VCvV8E7jKUUb\noo7BnfWOCnSmkNQialojJZ7pOzxzARhK06hFFCsOx4ZaLVI7DAV/k9DtI5b+CyT9Aky36OYCOe6x\nZaHunlJ3hpwjerrhvjzGmYV+U/DTSO4eNZ3jmPmqqzA/w273PL47sqwcZgz4qVKHniTXuP4Vq6my\niZfMukFsos6PEHNLXb1A5oHS3WOqIZWCkQ5rKtlaTFxTXMSkhOgJ8R1FKqu6Z9xF5PC05ROheFUw\nzUo+imMnGdmum546NcZJEUeoP2Kvn+BN3xpp2v5oo1BN268e9lCnQnYdN+Mz+q6yShnbf416kPVX\nyBc/QOoNZl1BPHIN1W3oc8Uc94y6QtyRvHuJiMN+/Zz89HNqusQfn8DwGWIrSMt5QitCIe1uztEs\nHaKOsnuJGXt0eYLd3iFBkVko0aL+JXUC31dqOGL2zym7V/DB11QLMvyIH/CnyN1bDlGwzybMzvHZ\n7YBZP2e3NtTXC6VUdDkgFaoxzJ1hCIZclVibiZA5DzYfahEnb7GypqKMw0BXFpI/MYUjRi374TWW\ngMsrkl3eDUI1XbCLjpmJue9Zza0ZOoaviOuMVoMrHUY6lHvctCWujz9zpitEhzMeDFQ/I7XFKZR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OnCylcGG+lkYdVBlZ55jjzaDZgK1lgkWfpLxzFmDnXF6ywcx475TiDdszKRD58NXFihGsV4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\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d+Eg2vtKUG4GbmNfyYAppwu8syr7BI48ony91Yf8DpvZ+qBomsAtyM/LYBw1EA6I9U63O\nuSgjdNaN4K729lKxp+MxQVRpoxkSb8SdF+dZSkGvc/9eN5LzFD6oUw1NaRTNgZ7M+UzXUsRiMtFH\n7lC3kVLPiyJWlWFsoBHkOih9DPGsm4HnUdw6OrJ/2kAyzoWuD7FvTJt7mD2IkKcJGxSu7XZGvYce\nphs5F1ydpS1BFWlLcN6PLbIgJIXbTEkMBiLNjTllgr0oqDo5Zbp3EtGMidpw22qszWCaaL2imw1P\n9s9Jbmw2G1prNOsstbMsC7t5y2lZIgMhKYbAeO+sVk4h1UFw8lwwYN5O1Bq/99r6oGoOWqPbJUQU\nLuSoyNUUGUYdYDXWjcBozEP4nTTebyO0UQzER1v8PbuMpmM0Z5y1GK1RBkpTh0Yq3iMbGhihYWiz\nQHqGfkpKimkswwLWQysXtKp4zX2tI98n9FCtrsHlGvcJIhfDkDPClJLQ1uBe6DAHODfPePztWhvI\nqZyNTgzsrKGKpncYB/1Rrt8C/gPgC8BbwF8D/r6I/EngTWB199vv+553xmOMf9/5lzx+fuxf2TDd\nPntCuf8IN+U9h253bBHmMuGeuVtXTK+pyyGiPjQa/9QXPAlPa0VOlaaQ8syhG2qNm03neIq/5ywp\nppkKa4+i82gLnIx9X0m3iSLhynYlMwisyykoYVmgdsyNw/EDeu8sOHNWTh771UmUkzjH57e0/gQ1\nR+ccomjuyAJSOtJhqY2NbvHasNK4mbZsU6F5Y3+442qzZW2GpExR5bok7msiTxOLN3YUqg8XUO8I\nRtKFtTZqO5BygpNzc7Xh6+88ZtKE5szxdOJumtDq5KvKa3nD8+XI6VjY5A29d57uT1Tbk5OQGpyk\n8ihlNimDFXSCu6XyXOBqKkxTYakL9/LE8fkdk8zsUmHxFVmd5JnnPQYjXgqn9cTT04q587gtJE+k\nBP0D6LmTmVE5xyAox+WAlsK6rMw5k/aPIcFr04Zjf4/kiQcb5XB7JEmh8B4JYVMy790dOBBCZKOM\nZm044CUgKX3sF/Sg9quCrc6dP2WeZ57t92E/3hpNoZBwOleaubkK2uV1ClZAl9i0dkXYJ7jy0JO1\nUqCtdK94DprOJCvHXeYT27d4ti5sklJKofZEPz6nOeiUmdKOY+uYwLZMAWYJeFK280pG0KuBul9t\nQlvLwv1Xrqir4b1yxaNwhZTQ3Z7NT87UXls6lZV2CaX8cF5v5oQJfJPGF3voIX9yMt5U5ddx3uwn\nXp0hLfBLG6VNylUT7m2Ep61jS+XdrlyJ07XwvBd+7xn85fuNX3s+cX+Gt2ThuYX1y+sT/MyDxnt3\nhe925fVt4+1jityapBy78arCe1W52oAl4/Wu/GGFH7sXFKppUfQAqPJNgbemzueXxKevIw9qTsac\nBNsLX7zLvJWNL1gmn2LIuCudt3C+0cKefOdBP3s1G9+yxBfv4M9uQ+H5AOc9lPsOqTuPce5ViViE\nR8oR2G6UK1dy7fgS4s6SI7rgtDglOfueIDlp7ey3zq5quEluoElCCYOtlJSkjWpgUwzHu1Zym9hY\naFJJgBlrh2lzxdoFz4njrbLJdzQJHXd6lgKvOiWmxegGTQirc8DaSi+hGbRmeFbyyZlyoitsX3Ue\n3RmSlSeLsSvCV2riTpRv7J0MfHXtrJZAMpOu/J2nzqtJOTh8NFU+91zZysozgz+xCbe4Pzgqv3jd\n+cYK71fj4SJkDUrzw9b56R18dhU6wu/XxNpXkma6wxcPzl+5PpFE+N9vCx+fnPvAdxq8lVc+vVF+\n9SC8gbEm5QMzKsY+ZT6/Kj83rbzvcFsTX2pCE2NZhYeS+MO7zkcL7Fz50Qx/+2n4B3355HxEG+Jx\n1s4iF/e+1xL8/X3URhuM7w6Gy9dOEQtzlY2/VxNFHa2FH1HnPXO+05VZhbUJHx2aqB/k9aFqmNz8\nYquYEBi0sguyMhCmcwOxDmQhqEt+sQiPwvs0qEs5BIIkrNZztQxyprq1KEhU0Wm6ZAVN0zQaEnvh\nzKLKaT0wzzNGaEymHIfT0mpMHiUN57oXupQzPS2lNIrWF4yiMzIWmS0X2QmtDcRMOt0qs9sIRwAA\nIABJREFUIormlxo6hORC7508JWo9xaaC0JZjiMJ7ZyrhTLfW9UKRO51OXE0bKBP7vpLnOZq3Zkwl\nqEOqyu3+js2kYQveO7U3rrabS4CwY6zryrZkttsZN+Nms0PVmDfl8rdIKZFzRjlxs73H/nhkt53D\nmGHecHt7ZJ4nTvsjm+srnj15Ss4TOStLj2DKkiYoGmYV7hczi9ai0fJzYzAabPegVvrLTUN0yKHz\n6IZb6I0uVvRnIxBGq1syXYQyGnZRRUto5NJmirT7s07Nwx1KNMTVdl63KeFziWbWR2M2XrvrMAqx\ngVi5496GRq+Eo2COAzONZvrlLKnzfXD+uPcWP2/8jLPt/stX9FGh+QtkpUXIrmQ86dlP7I92H7v/\nrZc+/AMR+SzwdeCvEojTv+x6AY39Pzz9v/bR6ZpjT6gWypTQvqMmZd8qhoFfUb3TpgcwqKalK0jY\nzPVS6MBWM9kXbDcantrpU/zND1OiHSpFK6vvUYP70zUiz+hLYqcT19sdnpRnPeivNkOxTkG4uRKe\n3t7i2vjO4TG+GtdXG67zDYtAmzJpfoh14+b6mr6spLbwkasbjraw+IJTeNac3bTww5s58k7yzO1y\noraVvN2y0Vj7KcPST0zzBpeZD7zx5sBCn532HJvxBMGma7bZ4LAgeeJYdqg38iaQys3912nHlbxR\n8rTjerNhtca1ddYCuWdesRWZCr4/8MZr93h+2lKXhd0useYdj+XEdi0YC/d293ic7/j64Y6yxrCi\nWkJOjWl6xNJPPJyusXSNd+ekiviJool1OZK2O2rp5JZ5Y2OsLYZVUyoUwt6324qZYb3BfE3e7Gh6\nYvXOKTkb3/BOUXLa0vtoiK+v6VZJRZnmDXfrysOb1wNPaRM2ZYoFWtcbLBaonoiwUaW1ytFXrM/M\nOXKOWqv0tpLKzD02PJHKLAlVI2uh2wqS2abMshxJeeJYV0wTKU08lhh8nQ5HdleZWmNYVbPytK8I\nicVBy4ZjN24PjSlBm1/B1nBRrZLoKCfrpLwJKrs5tRt9FTa50NwpeY5CEaOVGXOli5J3u7hR7UxR\nT1gP+vu6GJIKZVZMLVDLD/F17MY0QjY/Yc52E6YiJ3ceqfHpkniKcS8nsju/9oHysRl+NHdcjPup\nUOkUnP3ayNrZKPyDO+HH05H3D8ZvNGXKMKvz8Z3wv3xn4idL5V5KvLpxvvK+8GgW/p0bY1+ML9wK\nnylwNYaaf3df+cU3wi23PhUe3DPkPjw/ddpBSQU+fT8oa4ajTfjWEd7aGT9XK7PC7y6FV6Rzo8bb\nB+NmI3x53/nkVWIS43Nt5vOL8RNT1En/553wMMPPv9J5fsicBE7F+ZjBZw/Cn7vuLM+c6QpYE6e+\nUpg4tYUrLdjqrCenqHI0obfKbjBv8IWSwoiEDvOkYQ2e4fS8U7ZKzg6WOerKdU2gw3PQGqsJ26zg\nC8vibFUDUd+d6HUiT5Hhlq8T9cnKfGMcb0PDkzyDTHiHXMLwZSPOLYYi5CwsVWlUyiFzPcH+1KiL\n8I5n3pDGP7wLPdWKjQbV+fQEX6yJX7oxpg6fOwknA3Vj1YwJfLUaGed+dr56jNzNNyflO6vzWuo8\nmISvLYXfaPAr1yf++j5jCn/mXuKzt8LPvqr83p3wf5y2ALy1MZ6ZsWrCc+cbonx1hZuc2KfCb9TO\nJ6aIGnhcE5+aGu/mxI/NQq3Oxp07Ux63zjMxfv5a+GeL8G3PCM6/MQnXCosJzyzizD8mmSSwH1jY\nN3rjXhGaG1/v8JMFvotyp7AJ+zL+0malGfxWKzxQ5xes8o9soiVhys67cNEK/6CuD1XD1EWCf7pW\ncp5jkjimHFPKIUyFi8Bf5Cy2V4ROrQtCiUJyFItZQnyrDGvfgUrFIVoHjBvp6HVdgpomeim0s0bG\nxplKud1uR8HupDwRJgAdsUEBQy4p3TlFA1e0sPRGzuE0l7OOmB0FCZe9PE2E+U0U+mD0tjJP20CR\nSgaLzBcjGiVNkMuEDXqddA/XYBegk3KgMCWVmI70TiqZrc5AQh3mnjgcDpRSMJxWK9Og1pUUlMOc\nO+uystnE5FhyptfKpghtMXwuTNsNp9MRV+dwPKCqXG131OUUbl4y01oNlkrObOYNT5/eknQlZ6fX\nitDZ390yaYiQa40splTmyLFZ99RgSgIjkBZHSgS0IiHSVRGaGc2GmJCwzg4eZsc17G/DnAHm0Sif\nG6ZeewjjPZrlVvslGymCSvWSo6LtTIcz8ES3kftlNjQljoznhnH/D8dBZaA6Ftqms728atgVS8ok\nPIoXZdwPw6xBBE8jU0UzvYfuS0SorY17Y1hpE6hlzjk0ThYWrvF5xu8dzmn0Ho3FH+Pl7s9E5IvA\np4BfAyYRufd9KNPrvECRvgv8/Pc9zRvj3+9Hnr7n+vr7X6OkuPfdAqX8oYev87GHb6A5puqTOvtm\nfPP2jmudeNZXXp92pKnQDns2U2GTE70eWOvKYR0uUFNi0szWhNNGuT02rjM82zfWRXBmbL7iOXCn\nmV4VyRWvKyqZg8e6uQaurq44LQuffuOHeX7YI3km5V1QI/sKZky7Gdvv2ZUN89UNR4MuhXubLd+9\nfcqVKA8evgGeOMqCNWFDY3dvx0ESr5UrrDVqyVBXNimx6Z0jncVDW6lJqEmQZ3cc7w7cEbrEXCZs\neRY26TVylXoSHl4/4nhqbMqWuzsnz8Zdh7vVWfwKQ5gXYbUZe3pgZyfUnHaoMB04LAfesYmsylff\nf4xMCWl7ytWOZ8c9byYhp4TXSmXhybPKLj/hZEpxYU4rbU68IlccbcWbUPIMy3O0BXVHklB2M9uy\n436dcD3wWAtpShyPz7iarjjVxLbBuoFqTrt7Dr0yy8QRuNLKgzSxm064KvvlOdNmS9JCr1sqeRhi\nemRhpcLUE4tCnZz7XPOkVRIT4i3OMzFSyrR6ZOtK2WbEQ0eb04zUzKErpYyYh5I469u3DbI7lgtH\nM1Anm0GrbFAO2vFaIgSSoOoduoEcMU2cEFINSk/rhkhmI4Fw3Z6e8lpK3Egmb7cUTeyu/2/q3i3G\nsvQ8z3u+/7TW2oeq7uqe85AzQw6HJ5EUOaakRLEsGSYiAz7AcK4MA7pKboJcBDCQuwBGroIADhIE\nCBAgiAAHhoEk8IUcR1IsKZIleixFIkVRQ1LkkJwTZ6anq+uwD2ut//Tl4l/VQ1kWEEq0aC2g0TNd\n1XtXV+39r+/wvs/bBkAlTTg1BFuYJYPrqDGRXYfBEk3mjcu3uHd1r22xl2FkLPGPfY/+ebju1cpf\nWjm+sC98fLCMS8j6/VL44d4xKWwwvDtWtg4+tpoZi/LLU+BjFu6lREpC5xzPLD73Z7vKvhY22u7V\ntwbHc055aSyUOvHJwXALhyHy89/x/MjtwiYIycCr145PDxlXlil+ET73iCGpkgoMg8HQQrpdFt7n\nlbRWqjX4UWHTmqYPZuFBrjwRKq9Fy2dvzZwfLHd9ZjCGVOGvPlW5dyW8Ujw/uRr5zSvDL10W/uO7\nwpvV8P4eJBqeCJlJ4KuTx/rMZx9RjjthbQ02FzqUY++JptBFz9HC2lqGnCm5MFghiQGT2yZmUo5U\nvAqHQZn3kd63usj2cAPCmlOir4bStfv/bCur5MixZSO5kwrJkPJErwkZQZ0nX0eMU8rYpPo5NmJv\nOPWUy0DOE9Z2jURbmhTNSRtqlwyzyfTVEVfKdFn4Svbc1kpWJQKfCoknAvzC0aJYgoc7Q8FdwRd2\nhhf7wnO28htzq6t8yTxuKoqjN23g8be38LVkGEQpTvjSUfmYh6OrPGmUf3bs+YDPvFOV37tSPttV\nfnNn6AWet4liDO9zhVeS5bWsdDRfOcDTvfKIjvz1batFXo9KPxi+NAk/ZBtAZ5fhKMqJVUoWfmQj\n/NpkeWIlfNYUXhkLCBxRrkuDcDwdYEfhEz28nh2/Oxb+8srwtBf+16vK+6m8lWFE6KQBUP/iRuhM\n4Ntj5eM10yn8eh34D8LEt6LDdYHXS+ay/NkOXuT75Ef4t3qJyGeA3zaf/hyyOnvop2mgBvee2Wdx\nlNyQ7UourQA0FmsMWuvDwjffgA9Kbk2D6ZZNVaXSGhtZiGRuQQuL+tY8SfPHFFmIJZWH4Im6BJaS\n28ZBWIpnETBNx3mzyXHOkVJqmHIjD7HPSdtNVr8rzNZ6D2qbUV+bDNBai7PmoT7aP4RItA2L6wLW\nWgbrmeaZ4AylNJjAjczMe4/kpq3VpWguJUI1S8ilPNyu5FoIYtHSnrtKbX+/FqQ29HLNC53JObwU\n3LI1cwir9YppPLBZ9Q1KUQvOOow0b0HRyjTNDKsNKUVELMd5poqlLuG0x6mQqmkehVhItTQttPOk\nGCnSclZUG53LWku+kWqWjIilYrBGF/pdgzzUnDG2TXSQDMU08qFUKPJwG9OaZKXUihi/bCvn9mO6\nAXEsW6z2knRLEyXUIiBpIWK+l3p9Q6+78Z8VycuepKEzDYK1baN0k4FkTJP2GfPe16XwUHZobDtI\nasotg8O8R26suSGExC6ZTbVyE56HLL69Re/cJKIPsWvta50v0Zd/DuBFVf2d78P7e0PbMP2XNNrd\nuzTowz9ZPv4C8FXgR1X1t0Tkp4GfA5648TGJyH8C/NfAo3qTPfCHn+MzwG8/e/dZTldrSqmc+J5T\nM6DmnAcpMMYZW2eqX1GW7ejaB3Ku+C4wG8OAobeOSQu5WIJt369ODZBZhZ7dNJMM9C5gpSCzYa47\nYqxshoGa2nBEmci1kEuTkqTUHmtGWElm+RFizYBLFrwya2FtYAhtSHCVZjb9CuIBo3A6rFj3Hbtx\n5hFnwRj2OTKXIzUbTvsOK3BdhGk6IEOPyW0bMGmmqkVT4ahNrluckErzeOZ5RMW0X4CXhcRoLTkn\nnFkzi8FSCa5jE045DSOleq6qcD1fMx3nBpzAcXpyhi8FdYaSRuwc2W4GtmKIXsgpwdiCe681YNcr\ntmrpgqHEigvCgyOMzBRtoZKZyO7yivX6Fo4O6w1qWjM1iGNnDnjtQFpmWyUgNmExzDExqHA0jqCZ\na4E1N3lEFktlI54LCmoqYXcFYc00nSOuZz2c4buCSxVrPZ1I8xmmPXEu1NBxO5ySyWAiOUHVNiFP\nUbAm4NyOUDxFLJEG4DClSbgPxnKLylgcGEOhYjRypoaDNZRa2llp2vliSttOiBqm4CFFLNAv1MSJ\nHmNqkwVXQ9KMWc4D47qHQbudKtUIsbIEi1aoBqtCg1t2VJOZSVTxrQFUj5p2X6m4RjLVTNB29lyk\nwkvfeOn7dob8WV0358h/9oHHsb7nI33BdoZDaQ3lZBRvGh0QhHdi5rE1PNg1wEtRx1nfwl5/Z1d5\nxFvu5cKdUDHapvYftI63cuX9neVdgatoWNvEVTE8bYRkKyY1adLWClel8FUN/Mg24448pKJNKNEo\n16UQrFAzpNTgTLkzTFn48KnyC/ccn3ss8U/fsXzKFi6C4zO28G4xfDFZnnKVN6PBmYJI5cfvCvPB\nsC/Cq8myr4XHevikq1x1LYD7cRFqLNgsLffsjmEQOCmWQ8z0xTL7QpdAOwO5YDoLsWCSoxhtxD8p\n2GhaEOxQcdlirTIK9E3zjHRNhWEceFkGpdZQYkK6iIhniLb9ubFQZvqNRcepea9ptYjtGtI8zq0W\nsVSqDVDDQ8l9USFJpThFc0fVQkEYJ2VGW8yMWg65kMTgcovj+K0dPBoMvzR7PuiV45w584YvRs9P\nDZHXE+Ac70TlOxN8qFO+Uy3P25kvJccLnbCvMGjlQ71lK4WvJcPHQubro+ORvjKXNsy9rk2Zcq1w\nospjXasJsgS+PcMHeuXlaPlsGPmXB8eFCk85YQYelcJOYaOGtSu8mZWoBleVJ22ls5VTH3g3Vb6V\nWuH5cZd5ZbbcHQRflLezcA2MarDA47YSVXg7Kt5YXrCRb2DoES4SZBXeL8q5Ea6LNnw6zbt/osqZ\nFmYVPuoKn0+LT1ObFPNOHfmn91+DP6Nz5M/VhkmwGNtkeDUljHHUHMm1TeiMkaY1tU0igjQIgXdN\nmtW2N63INZQWAmsNogal4a612gWl2MzYpbRD3/lAzAmrzQsFYHxAjGuQhHlujcySMaQmLJIrg6lC\nW3Td4JgzwYEzil+tSGl+6MNSVTo/tCbLGNRKk/ClQjVtK2SMpeZWTZWqDetdMtM0tc9XQbS0SYgU\n9ocDkxbunm6aB8VaSmqZS+M4sumGpkK0SkyRIQRKSdSipNpuoNa2XKdjHHnk9IR5PjAYy+Xhij44\nVq79e33Xs111pJioWBBLv+rpfODy4oIu2GWTIZhqmOdmdl6vT5lSRLG4xds0xcp2WFHEUg9HYnWE\nVWA/RfZTAlVWfccQDGOcqT40rXypDxvSBqnwqFbUG0qpWDFYYxZf2U12kkOcw9LeqLKYZa0ETPee\nR061UGxHoeC1Gf0lBEBbI2IaVe0GjlAWMoTQwh9Z8O2+d+iUqLYVVTcyTBXFsOCsFZz3TDFRasQZ\ng5WlitZW9OQb2alpkAnj3UNJohWDCS00UoWHm01r2wbS2tAw64uHziqk2hp1XWh/NwG7ojQErDPU\n4x/pR76397HIf0NreF4FngL+Pq3y+seqei0i/zPwD0Tkgpax9N8Dv6Gqv7U8xC8CLwP/UET+C5oP\n6r8C/od/U7P03dedPnDbO9S3Tcped2TtOQsFsY4iXQsTlYLtHdi2+TwRiBa8c+R5ZqiV6hxWMkY8\nu5pYyYitijcRKUogIctroPOCsQkv4DtDyAeQynWJmJJYW0dYrziQF++d44QOMYY57ZmCMtDRm4ot\nMKc9nTFsRJHxmt45gnOcz0f28wTxyPn6BEqlBs8tv2XPsRH/aqW3hckpfU5NMmYmggxYZwlDx+1S\n2cfM5XHHB9ZbrIeyCkxpYq6Qx8pmEHrb4VWaEt0aorFgt+zLhM17ximTzRFrTjnr11TxFBQvcDAz\nY7WEYwIS2SYOh44rExG/Il9PJBI2GNadhTFzPDXssmE0bTIuAH7FFoOUyqFUTk8eYW1XOO8IwG4+\nELxnZiSLI44HNtZQTYc14KPiNBFqJYjhRJTzYHiUSqAZ9ad5Zq6Z0SRu+Tb8yN2Ado5t/ygmGY4o\naVJSHJmlEESp1oNx9KZnkECcExfaKKn1OHHqLdY5ijgoB+quQleZy0wfAgWLiKOkFvz6lTQStMNK\nxRZFnPC1NDF0KzQlRmkqCK8Wh2Em4sQSpomxJCiJnH2TBdsDWS2SFOssHYaYYrv3ubk9d2iwDquH\nhn+mR6VQfJNhm2owMj/01qokvBqqRGK6gSOZBtWpillk7lP945S3fz6ucxU+65VchT+4MtwNhVdL\n5SIKj/TCh7vKF6Lwya1gZ4MJhTte2bjMPir3s/DJTVMsPCiFy2j42GnhMW1DiHxUfn80vLCBO+uZ\nOnveLcKVTTzfBV6aK5/0ytdS29R9xkFAcI8b8kUDXmUTeWSw3CpNhu1NK37rZBp1e1Opk/DTtzIB\n+JvPCPN9ywe7witjpqryuW3HO1X5iK/ENYgz+AuI68rdqjxyV4kX4GzlqgqDdeRr5QuTslobngRm\nzdzKQnWwu0q8HCOfPV0jXgiqjBVSgXqRWXkQaTEEO6OcOMtsK/4AU1b6lDkKbJPj2mXudKBSCN4x\nHidSJ6x8oGghD4XT2BERYij0UShMrPrA8eCw4hjWi8Q+C/mQWG1HTjaevaw5vBvxVTHrRNw1P3tR\n4QwYR08ylZkWzu2S4LcWqTAeCnbTGju9EP5AhR/dtLri+XViQrnqDb8dPX+rT5w55cwb3ipKDI5X\nY+WJE3gC5UkMf0Gbl+w/7CO9Gr44Kde1qWG8wGtiOSstmNh3hgF4eSwkYLt2lGX7+YtTy/XcAo+4\nzKvZ8a46/u5Z4fVDYm8sZ85S5oKTShLDM71hnysf7Cpb6/jZB8Kn6sQLQRnE4w3c0co3iuVrszKI\n8JzNWCw/c1p5PSu/dSH8hdWMDfBrc4czyk/7xKuz5VMO/nny/Ng2cjHB16rlo6H5t/9l8rxTlLcr\nWCu8khzvdxVfmpzxOVf4P/51p/O/5et7aph+kIGT0BqmG49GtQbvPFoKLnTUarC2mUur5kYIM4GU\nE+UmAHQpJGttem8jgarzMulv5rlqmtfDWUOOsW12ciHOxzYJpWGWrbXUHFEtHMdDK85FUGkZAVqa\nzymmqUEgur5lHZVmvjciGGvJy2ahSbiab6rUFu6KCKJCnAtD15NqavhNkTZBNIaSE8YqMc70xjc5\nXi2Exdg7z0c6Hwi953DYNYLWsjU72WzJtdBbz/Vhx9Pb29w7HgkhsNtNbPs1c05Y3zZXGaU3jmma\ncG5JjfdtS5Zs+10yxDiy2Ww4Ho/c2W65d+8exhhWw4A1hlibhO04Hjk5OWEuysX5Jb0Ttqs1c57Z\nTxMJw2F3wLiAqYUijiklbN/hjXB1fsU+Ti080oCX0HIRSiLYhieuBco8LZuViPGeXHTZ9i3+ItrE\noi4ghVxmhEZkjPmmIbHkBdBRdg9AhGTbpqzOEeNsy4Ewzbsm2jaJlUaGot4QFwXrPTkleu8brp32\nuTfhsLLkOLUQyeY1cqElo6fY/n8urcC3dvFMaX2v6dIbkMh7WHRVpRiQpZkzy3bsxufVkPJLM62K\nCWEJYNYFgCILSGLx1H0vB8cfvZ4G/hFwh7ZN+nXgx1T1fPn4f05TGv3vtHPk54H/9OYvq2oVkb9G\no+J9HjgAP8sfPZv+yPUgH9my8DFq00hPpeWF9bYj+wg4UhJECt5M1FrZUVDbNg2dAY9Q4shuzhwl\n0atjL+BE2a4C2RdimXjMOmItjKXy+GrNlJsX7aIc2AwDMSqPrs8wLrDRwqyOe+MOQbnKey7TjO8G\nVhIITlkPA/sxM0/KycrxlFdOTk6xZeSgylNzwBZlFyy2N1C0+SCs0oUVd1zPcbpmmyrP3jrDzlCY\nmzdn26E1sLWVR1aWi+ORLE9wLSM+WoKplHKL6zpxL2zxvXCwhcelDQxSSRw1c64jz9JxTzNRAmPa\nsVq8lHXwnKxvsymWoEfuj0fK1vPxbsCqctSOXDK9q9y6a3m/X+E00VtHLtd88dDz5Vm4zImUM4M1\nbMIGmzO3h45ZFeLIbdskRoObmZnpnSc4uFMsV11gGwzXZaaoQ9Xi1JBqofOemiPBRrI2XPF+hLCq\nXNZEJ4nOW8pUiMPE/RGORVn7EybN+NTgHV+dLyjmlPf7yMmU+Y5krvcT235gq55IZS2KL8o0C/fS\nhPi+NTrGUccdcW8YhjW1trBy7eBR8QQ3s3WWXpV7VTEB5hTxBnzJWHEYagM3lCbBc0SedA51hSFU\nQhWutDLliB2arzdoQykrite2pcrGkE2mMxMb25HyEWcNaMZJXHJnLKsqjF0jfbq5MtpAlalFMCxS\nZ+uXDXeAN2Lh9T/FAfKDrkXWarkshvtZue0rd9eOJ2JE1wPXucVXfNrDsRa8glXhmzvBmsqptVzm\nzDvFUsg8vTY8op6xRoyAz46nPXytGl45Rj52Cl+aK497oAq/eF646wtvpCa///BGeHsqPJaUN16v\nvNA3CNNRHcdRKFlYr5T9rPz+PvHibQMYrrNy0lm8Frw1pKSYoXBvVILAQMeDWHh9rnzLCU9fV/5g\nFv7ybc/FvmJs5fZReMsLdx38xn3lR7vKL+7gb6yUvsIXJnhxbdiIZbxOeC986m7H4WLGG+XoDK4a\nNsGSfMVbx4MUeSp02FHpBprnaWM4nQ2yhW5SihHWGFQzxrkmqfcCeSYZQfMM0bFnxvaOHJWeJuU9\n1IQ1K9BKGQtDGDmmjpPtyE5ucfmg4GXmke3M5bwmHRPJ+oY4twO7kihGmEJs2+oKx5woB+F6VGqn\nbK4cRSBK4RlXOR8blOulveFJr3zzkHhmnfjGWFlbR28ycxWeszPWGdi1+/tX5plTC9VY/skD4dRU\nnugiF1XIavm/LypOjnwxGjZG+NbcIBE/ZCq/kgamAzyQxEcHw5PW8MmTBtQIThAMf2eV+PzR8Lc2\njl9Ywmvv+Ja39bbCWa6cuuZJ2qXCj60dL66b7PzBEZ4aLP/qWnlmozzbddSifMjDr18ov7aHB0m4\nFQp3Q+BChedK5ViE38se44Uzm/kpl3g1G97XZXbZ88Xo+bTPfMYlfrcYPjMUfrU4hpKQIliEu1b5\nVjb8kKt8609xjnyv1/ckyVsOqb/NvxY4qaoPlo//j7TAyZ/hvcDJoqrfHTj5u7TAyb/He4GT/5Oq\n/rGBkw8leT/805jt2R8yst+gjh/+tyos3iCxbTrmvFsm9i1lqZkAW+FZFLTKw3wIL5YUDzjrWlq7\n9wTxLaOkVLxzjT7kemJuhcZNsXlTTIq0HCCrgIT2Z0aZ8szKeYo2CVrlxoNV2/YC17YvRlvq+3LT\ngoWsJu9R9aBR2ETek4thTJviacF795BkllIihAA1s+0H5hjJMREcRFW6END6njcqxohzLYjSOUfK\nGVMaJU615UrVMRL6hogdho4xTvQhYBepYy2FTT8gJRGMpTrQXFivVxgKU4qcbU4xxjBOB4Z+Sy0z\nXddxvT+w3W4pWYmqFAyH6Yj3A9f7I4cpkbVQi5CAOTcTpXWeMX5X+KwI8zxjbZObFcAYIcaE1dbQ\nlhyXF7FdwkRvvmuGkhLYRllUbdkJxpjW5Nb8sDFhofEZYyAvQbhLk2mMe9iwPPx82uNZ5CHEAQDr\nMEUpS4xQe+z2RmuvFbC1NtgDjXTX7JHy0OOkNN8J2raspZS25TQLIl51AVywwC0arATRBa9u0XwT\neLuEPZeKcRbK8ho8nFO+8n/BnyM5zc0Z8tFHn2bwHXNJVAnUFDGaeXJwqDMY4xrYQh1WM8UsmWnF\nMpZmsjeiWNMzLHVVLwZjKyvrUXF0rqFcvQWHo+KYZUJTJjhHjRPFBM7jTOYE3xu8Zqp6ii5xCWXm\nOkZem9pG5zBPuGop3nK6OsMPa3rn8bl9vWoKSaGLLSKgmIRZSItRCuRKbzs6KpOj0O/0AAAgAElE\nQVRkRAxaIJrKYC2TsdQxYrBITSQnuKzEIJjUUZ2SxaLlCB3YCMEP1DQRbCDYFkx6iAdON7eQUqg6\ncWIN47ENlHqER7dbbonSuR6RhkF+X7/DiWUUT4oVQmSVFB0y58lwO60pNnPmDCkVSimsTipBAq/s\nJ2b12Jq4YwQ/FdYrSy0Tjz+y5sFuwk+JnVGOY+BAJdfCLlvuxcobsYD2OHEkyRzEcCyGHAZMTdRs\niG5G5gOpzkT1WGsYjHC7CKeDckd6jE484Q23vTKJZZ8ckzQJyjoqr5LJWnGqBPWsrfCduufymHhh\nPWAkEXyHaKUr7R0fnccBqTbiHrVCTRyNxxhhVVu+klElxsyNzFZwmFrpjCVqG7yZqqg0Dwq+YBVq\nbEOZKhW1bQjXNOORkh1ihJgtxlkSmVos1TSKnhSFKqTmEmUmYrVBYXqxFBWqJLw4kgrROXzOVFuR\nqlyOB/7PN9+AP+EZ8oOuRX7m/U9ysul4UiGLMlfljoOLZOkNnBnYVeWsK7wzWW5vPC/tCj+6duy1\ncuLgOhkeZMWazPO94zwr9+fKC1vH/QhPB+Gt/cQTvvIbR8+Lpw0BnUtlTsqmCzBEfHDMY+XNWXAV\n7jpDzor3bTBUpOIBzZ66gU4L74zKU+KZDYwlcR2FbCq9wP0krLPwyJr2/j/AzheqaYOkb4/wwU5Z\nS7uHVVd59ZDpxfBE5zg3lRNvOKkVVwU3tPtzFuGNa3hm3Wifp9ZxZTJuBq/KbCrBWUxSymDwJpM1\nYWOPiMEFZV78Q5JZMi4r+TATeosXsIOn5CNiB4Io6kHnwspZXBpZ+cpUa/MGuhOsHoGK8YGaBSeZ\nhENzImwD9Tgjqx4bhckHCoZ03IGsmByUqcGfyEoSGKNgihJEufBCvqF0ifLGeeWp3gLKeRYGD//i\nyvBJn3nfClJS9gVeEc8HpHBQiLSN0R9MymwdL42KBz7rK7ec8M8OwofInEhmFssdWmZfsJa3phYU\n/WRQ3o2w8o7eCisqbyThuU4p2oJonw6Wi6yMObdhqPU8JvDy1OqTwVY6hBOU14rhMjs+6BK/OjmM\nKh/tlLerXZT7SieQBB6xhTMrnNTCL02BJ23l1ClzAlDeWWR7T7jKF5Ljh0xmp8I7Fc6s8HqGgLIS\neNTC2xne5+COKEEq9+eZf3TvzT/xOfK9Xn8SSd4PJHASaNSwRVJ2Qy6rtYK35JLboa/gbgo7Cs4L\nNc240DHPMy60AFpjGkKyUTnbJsZay9EkxFlSBS9rRC2jxFbECMSaCNkwT3sMlW41UGolTvPD5qXW\nSsoZxJD1uhXLsWKCZ5oSSaRht2+aj1LatmzJKqm5gSx8CIxTREQY+p5pmtrU2wfE2IeSs3pTRC/f\nPnUdWRphJ6VEZ4Vxf82t01uUWtlsNqQ5omXmbLWl1MJ+PuK9RbU93mEaWfU9h/2IFagx4YLlcDiw\nDj3hdIWoYiuMKWKNRRA2t085Ho8Y68BZVpseozDVDJJ4cLjGI/iu4954zTAMXI073BwJwRDIuNXA\nVU6klPAKWE8IgcPhyOG4o0qbFDnbMceMFmXOkaF0uAL7XOhCIOW8+NTapiSnwtD3eDH0Q09KDVQx\njiOap+Y5c6vmc3NtVV/miO36BQJSqLU1QRYhlRYEzNIU1RQhZ/zQkxZzvGjrTAoVF9o2qOaMC+G9\nRp+2YZrn1HKbTCMH5pyXZkhZgORk4zGmwy6SP1u1+fP0hoTXvsbgW+il9568yOrqTWdmBM0ZE/zD\n5sna9xrs76YJ1pIfbpgK+v3YLv1AL+MytnMMx9zkkAJRhdk4zvyWjSn0VjikDl/3hLXHxIixgVS7\nFjJtwbmeUlpznGphXyriPQKMY0SDxcbEmauMRJwMFO3Z2A4JWzCJ93WnvFUnrIMclVWtiGR673m3\nBJ48dbxY4bWckaJkK1yOhawQfGDlZzo34agEaWdeCsr7e48ay5GOdS8gmTxXtuqwUihVmMXyzpzo\nbA/OkEshrzyTrZxeCxea6DenZKvc9YGL+YDtO07lhM53XE8jlcST/oyP3oE5HpnnU94ce1YuMNSC\nDIb71XKxG9DgeF4KfU3I0CGlch5H7prb/P75jsm1Ef8qG84ZGHpPGvfkqQ2SDgo7LL0ZiGNks3MM\nCXa2ggmU0rOPmeoM5mjYXdxj+sYObysnyzBh7VbUdOB9J7ehJjoNPB/aUGgllkstrLsV5/maJ2fh\nsvN8mSOHcebOxrLijDNviVoZx8jvlMBb5/epw8jWW0y1hKkSsrIvka0JHEzm0/4Ui0PdFcVtYR4x\nCE9ieHo9ULB01jHnkZwN9+wazYWcD23jGz1JMlsgd4HpUPHecoLhOh7wGGxRqhUowk4mCgNBDyDg\nEuzVYowy58ptBsY5E5ws+X+ZpIKPlc54DrVtKIwRiszkWTjY1jAdS/OI9CaAFuY6t0YsJawHL5bL\nUjjkSjSGdZ6g8/SpDcwG1ySqh/J9cQP8wGqR5OFjvrD2gVJbfqCqMGwLn79vuTU0v1ecLLd95Vwj\nP3xi+P1d5MWt5X+5r/zESeYLR+EvdvCGKZxnxRbhVx8kzjrljUXG/2pyvL9zSBWuTeH3pzY8m2Lh\n3zNCvAJP5JnTnkLhzV3llhoG34AP17lyRx3n6Ui5J7w7CY9thas8s7c9J4NwYoVYLQ9y5sPekK1w\nyMLVrgWjP2EcP3sPgrH8R88WXvqO4BR+9I5io+GjQ0e8Bf1eEdugOdko89YSMKxtpUTluU3lm+8U\nPvT4itFV7jpPVCWaxGM+gCr3fWFjBCUwVmGaZ8wmIEfFdzNpVxmCY7ocwVvk0TU2trtXrhGzgLvM\nxlCzUMOijBi2zFjmaqAkTL1isJmp3EFL5WQYOUyRpCus7+l0z94EZBZUHbfqgaluKK4nR0XLhPqB\nMs10oSfFlscViyOZgo3C2yOcPWaRC3gqtI0/Wrk3Kp9wwufWMNwyxKtKf2bI55V8mPiON9xxlpei\n49/vMlcI5jjyd088X5qEr8/K+cHxI+vMJ0Pll8+FVaf8Kzw/2Sv3p8w3kuVnzir/eGf59ABnUvHW\n8oXJ8tFNA1t8+1h5dttx2xQeCPTO8aHB8L/dBxsyt3vDp9bCF/dtGPtuUX543RgAP390fPzU8qzL\nxArrlPnE2rFLiRnHywdhn4SfOrX83LvC3zyZeWnv6awSE+yqYSXwzQKf8JWPSMEBP7aFf7Fvck1V\nuOvhI67wK5OlVnha4aUsfCIYXvl3GfqwTHX+Hm1i84cCJ0Xkp2iEq9vfTbcSkW8D/62q/nci8veB\nv66qn/mujz8LfBP4tKr+G/NTHm6YXvyr2M2dh1hk7xdu/gI7kJuGyS5mfG04aVXF+tbIaC4tOLC2\nwtIaizWBQqGUhPdheezAPM0Y67A3uRXLc2nJy/PZtrGYZ7x1C83OtxpUbzZASw6Qc3TGEXOTfVlt\nG4IY4xJM65uWPRfS8jli5GF2lDEdSqITYZ4nivc412NUSTmzWfWUcUaNUIzDSaGWhPeelevY7/YM\npyvSnFit1uwOV2x6j1hPSom8yOQ23bCAH0rLCSltouu9J8bWYFhdtmoK4zji+3bQxTQSVj0WIVRl\nvV4zT2P7+kU52axxwbP1gfF4oN8MrTEoyn6/Z44Tm35gtrb5jkpm3M2wBLTiOvaH1pgeUV599XVO\n1yfEwtIMKGocN7lZSdu0IyzenZjr8j1tTY+qwuJpygthx9ZMTQUXBhBItcEgVBXEYqRil61fk+jx\nh7DkZiHlNfx9QqVtNKtarOsaEdAYWrqUPNwwthjbJYdJF4S5LNujUqnaJJv+xlNAy3+6IfI5hFSa\noVoX0ytFl+1j246KtA3bDUUSKrUuMpkFViHa/j3WgF8CKG/8YDeXOV4w/+73D/rwZ3HdnCFPP/4h\nTocTKhARwhJyPFhL1gZ3cVqxnWWcJ7ztcLUQxLWN5TiSTQsDNaYBTbwVnBj8UjgFHIepkKXS9T3B\ndkjObSudIg6wzjBTSFIZVJgpzJNlCJZcla0pHIsCmdAFNFWsE1Rt846UlvOhCEYzRgGh+Z8M5Hli\nMAK+I1Ww4qFkBmeoRrieZoz1ODH0vuGj401z7C1JIaWZ20NHjhOSlaMVptohNiEKXQwcraKaEFMp\nZWBVlTEdsFrA2xZQ6x0yQvZKmTLJSUNo245aJzrTkTVS1VItmOIZ84i3zSNWqzY/zkLH61dbaoqN\nFGkyNs9Endm6Nd5VxnHkcS/cWVsyjk6hF8tcMup6hlyRkjjXgq/CvTJRpfKM2/D/xgtWboOOhfUQ\neHA4UqVhinNtGXmb1QldilBgjiM4WDvLLk30Ap0x3HI95+MB4z3GOZydmCbHg+IoCCsHJRcyDfjx\nRBBEIyvjGU2gzInON2iRqyvmMjE5uNWtkFIpquxiJJhCDRavDU8uahYpd6LUQHXKSlpwppTUzko8\nWQ25zWAYbG2Zb0WZbMUum/BcClGUVGHjAiIOKe3nUpJDnSKmtEl/KWRx2AqoY6xKXib8xBYCX1BK\nbcHfU868s78Hf7oN0w+sFvk7zz/JiyvPVTa8nuDTa3hzqjwXlG8mw+NWuRQ49Z4Trbxd4EEVrjJ8\nfA1vzpXLKLxvBRdReeMofGRdeaELfKtmfu9K+PRtuCqFj688P3+/8qgT7gbD8yfKxQSnQ6XOhvOi\nnFjBG+VXd8qPD808/8ha0NyAS9+eMkFoodIu8MwA3z4qm1C5o4ofDJc7w9FlbhtP7+C1gzLNyuCb\nfEtFucrwmLc8qJkXOvjSdcF6eMw7egOfvxJ+/AxMUkZbWalj1RdKrPjeMlTH/pgZblnmnNj6nvPd\nxOmmZQqVXBhtxmHYmoDzkKMgtnnhSk6NGDxnbDCYkqmlEELPfDwgXYerIykXdNXTqRLSEetPKfMI\nGLybWXvDXgNrV9EpoYPDOkOt4NPMVITBKldsqAq9jIzzBnGKaKHajlgqtVSiE15+dcezJz1RlDka\nqoBt1AqmAm+MhcsCn9k6drny65eFwQnnER4PlbnC/bn5d75e2qZm7S33xspHe8NtW/jtUbkblprG\nWjqpPN+1+/LLc6EXYSyVfRGCqQzW8Z1jRQt4V7hWw8bBg2T5yAruTcrTvVLV4MlcF7CqdNYwquG6\nVFIxvDBUzqtw1wn3M3xzEm5R+Yk7hq/sKo+Zwt4YTr3w6lR43lt+71ApKuyqcCsId8g8szIcq+Gd\nqPSm+RkPEe74yv2yyJON4euz4UOucsywca2ue66DnTH85t7wqa40qBIw55l/8K0/+ab6e72+1zHP\nDyxwEoAlj0YMOBsw0uQEsbTOtOSxFZfim14aj1noZN60JOJq2+agisFYgxdhpklUjHM49WQRUq44\nb3HeNQLLssaNeSYgdEPHnJqvwXQBqhCkvVFSTrjFD5JrJudCL5ZUMmJak1UrpLlQFJwLpKpMV5d4\n54FELoKKwTtLyQ1wUMWwzzPWtE1QjiNaCsYKx/2ItY5aKvWYqd6Ac5TUAAh2FUgpMc4HUpnp+kCs\nBU0ZC5z2A1Wa9O/q6pK+75jGsUlphhUxZawR9vtrjuPE6WYNqnRDx7rviSnRi+LE473H2YavHlZr\nOm9x3hOnkThG3plGTtcb8lx448HbbNYreh84psJsM+P1ERG4fXLKbGhY89Dzzrtvst1umRPMMfLU\n6Snddsv+OHOMBbXNQD0e96xWAzEu+deL3FC8knIL3yTPrTE0lmIFq0twbBgQ04z3NTeZi9IOKWrb\nAOXF+2MWfLtqyzdy1qHW4p0lqmLx1FKxzuGk0e4G35HiTCmVThyjtvyKJg9MTZKVW7EhC6pcxDbq\nT86k1Jr+qmCcXeSCQkwzeL8EPDW4hCDkNFNpE3YnlhTTgks3KBW7ECaVgtbcMOuloCoULDFGvHWk\n4wSmNXM67v9/Hhf/7l0Z5VBry5hJlTwYTG2m/tFUrFpczUzHiWpaW2UU0jQRXMCvejxrpgp1jqhW\nsql0rCCYJdi5UsUgxmHGSJWIFCHLiHrBF8vV8ciZBpKpKAUbGra/jIW5ZnosEgI1TdRpxquh6Nxk\nWyhRK3rM1NBjVKjzHu8Haj6QMVg1BOPRNJOWDWNKM/3iUak14qsjohgq6TjjBo8Wwxxn1Ddv4ysX\nnsEIqUREIs665t+yhY4B0xme8Csupku2Yc/aeT6wNiQRxARuyYih0K1nUhVWt4QNDvGW3ies69Hj\nEe883hiOh0jIFWOFq1yx3nKZC/fixNupMPvAbXNF5yCijBg0BC7inqgXdNkiFO5Hz/1oOEim1kTX\nDRixzMcjlEw1hnJIaLAkKZxQeW28YF0LVzZSpG33jXOUGIk14VXojYXDA6KAz4negsuKVctdcRzm\nA6OvTOXQKHcpMajj8pCxMnN7kcVlJjpZYXPCULkuDoeyK4XTWto5vACIjB7IWkmlspv2FNMxVMNc\nE5suMI6FJA0HnrUVm5rB2JHOrbhfIz0G5zbM8YC4Eas9WKEaQbIjmYqhYtU0r6g26JClY+sLosIx\nzxg1jFjCYNFjQq0h6kyujuottigBqFYwYtnMgjiLcY2wZhbp+r4K7/zpjpEfaC2yyYqn8GinPNeD\nN4ExZH5lH3ihT7wyV76WLI9L5hPrwl0HTxrlreLoneWDtbIrhhOpJGP45C3hOSl8fiqcBeG5tfK4\neAYVvrwrvHgqPBYqcwJXIYjwnUl5vBae3giXoyF75QN9y5t8zLfIkLdy5jG1PB0M76bKt/eWnzjN\nvLrzPNZLGxYqfP3dyjmVj99yvL03/NpV4idXsHaRL8+ejSl8fC28dIBTUS6p/MNz4cMdfHvyZEm8\nNRueDZV4TKy9QTJ89ZB4JAp9ZyEmTvJMd+pJWXn3eqaGifWqJ1EpRGwVzuxAtZWCcDjPDFs4Xhac\ndwxrQ4kFtUocD4yHxPp0II8j3apvxMkaGOqITQWRHpUByh4XBnqv4FbUtMdn4VADQwcmJubjAed6\nxgpSE5mAK+dYEawZCDaS0kixHccHe/zaU01PiYUfumuxm8B4bEP4GipEy6FMdGHgg6uKqTBX5bY3\n/CUnvDllVIXXiuezfWQTCt+QwHNUcm3Bv0/3mTdT84L1CK/PBlcr74rhUW94eyw81Sk+KV+dDdfW\n8pRthEVjhA+fGf75vvKsN9yPlUc7yxOnUNXwEyfCu7Hw7lx4YTD88pWS1fCZtWE3Rb5ULfuq/D9H\nx5Om8HoUbqN8sEt8c7Z8+TrzahRexvM+V6m18Nrs+eKknFnLQYWewpyV5OB3D3BfodfK8wN8+QiP\n28y9KIwCg4EzU8jG8qXq+LSdKBUeFINH+c1U+St95pf3wlEMd0W5F/9s9S7fU8P0Aw2cZClEFqy2\n8a0gV1VCrkQqwa6wwZK0LBSxdtCXokzjouU2hjRNjXAnwrh4bFLNS/r5nrpIuIp4KBV1LdE5l7LI\n9YQpzhgrmGQIIhRjcc6SSmHT9RRVjscjVmDoOqxtunLVSppmvO8xS+xxMM3zVG6dtG1FWSF5xtlG\n1+v6HqEFr1lpckSbJ2qNGL+mNnZ6Q3SrYrtmNO9sy0XarFdc7ndIcGzX60XiBdZYshacGHJK7KYj\n6ps+PsXIMAyklLi6fECtDVZRa5tuXl9f0YeOvus4v7xEVdkMnr4PXF5esV23EEPnHLvdVdt09QPe\nVh7s9nzn6hJjPJtVz4OLB9zabBlWK66urjhZbxjniZdf/xZ3b90lpsT+OKPAuD8QddFxa4V5oguh\nmY37oW31Viu6rqPkcdny2YeAAyMF67tWzBrBGUfSBmloW8mK967d8NWjqVBq2+7goTrXtj5L8K81\nAQgN2FAqlJmYc0OS07LAlEXOVypznJt3KGfUFKq09PA8jy1GwtvWgHXh/6PuzX52S887ret+hjW8\nwzfsqeZy7FQ5HpK47dgZmkSd7nQISTdISH2EkFCLljjkHAnEGf8Bh6BmOIgYBKQhtJoGJQESJ3Fs\nZ7Bju+zyVLWrag/f8A5rrWe4bw6ed1ciBA3diex4nVXt+ur79t7vWut5nvv3u642DfUN4fwMumBm\nxKGn5pYYUa3gIs5HwCHR05CM7WtMFX9Cp3MiA4oItRSQhl5H5H3nVFHBScAsg4EPPTknEEN8mx5W\n/xdV137/rjDPDK7h+cV7pumAqjF6wfZHDr5hnq1mNl64M17Sh0i3XSM5o6awLmRTkNZhHGKHl1vO\n+0umE8SDmomhbQySVqI2T1GsigbPuHRMEbbS45kJtM24957j0tGfsPOiY/vMIqhGxIzgHS5E0lGp\nMdOFyEZD+z2FFV0N3JYdF6uO3QJHaREVXxNOCzF6xEV87QgiVGe4vMZiopaeXHqelInBRY4LbLrA\nMKx4PC+8E9cMOvMTq0D0wv5oDL7nid6DLrFfZu50W66PB57YgfvnA1+/vuFqZyieHQeePwsE3UKd\nsNizb15XbuqCiGPGGlSgTAQZEbtiG4zn+8DHRs+H7wcuVplRHdQmHT7cDOiSeScaxToePsnM5Zq9\nKv3Qse0OvN6vSFrIfUd1C3dkRe8jOgkqC0E6djnjpXWGjtmDOwDQDQOuKEsAT6C3SMTRdZ453xJD\npKhQxBPwaK2M44r9sqC50kvEUahBqBJIqRCi4bRtLqwKc3EEb/RVTnTNFqf0pYPguLImrtYKR3Fs\nJFByaa4amiIhaWWSSi7GdCw8UeM8bhhCix5iG8SEzitZK+qFUY2O0PQQOHKI+BqAys4qiwpnJmQX\nm+zSR1Kp2LanambxEXU9U/bPFHgEEcbOSFqY1aNV8M6Tcmoo9fwXW+h8v9ci56Gy10CqRt/DPi1E\nM36qX/jto+fnVj1/bfC8WRMZ41uL45VofGl2dLk9b15wxq9dB354VPps/Ooc+YWh8uYifHIVOOYj\nDxfYF+HtxfHXR20HBLny7dlxHuGPVfjSjeM+lY8Pxg93iQPK1o/MWfngpulXfvdJ4XVX+Rt3erro\neLUmqirfOMCHx47XetgYbL2wPVf+3l3H053n1gY2lvnxEd7YVf7umafrDFkcH1gJbybjY37h4c54\nYQ1vV08Ijk3nWBX4sU3mDxP8qA+UYpytIt/ezzzXOZ6/2zMuQgoVxOPNEbxQa2K3TNAH6iB0s2e9\njZScuH6U2rPQKc4asny63bPqHcjAtD8Ql8qyjgx+ZF6O9L5H6VhFx7wcsGVkjD29m9rB9HQkoy0B\nMB/ou5HYD8zLkcGPFJt5fDzQ9z1mjjxXahRCKWRbIMGCEuZC7FtayQ0nvH/fM0ZjNzuSKp2HlJXQ\nK+Ps+NkL4VAM8R3PVXigQnRNSlut8jNnjqfVMwFvT4VUE8kc0Ss7CbiuIw7GFw/Kx8+ErQs4B4dk\n5DzzxVvHtrc2LMBRTdkn472U+T8Wjzi4Uc97c+HrEnhBK//w2tHpwEdj5kt4Pt0t7KrHU7itnle9\np5hwrY6fv4BvTRlT4WFufaOXRLnvhZdW8O7cnmN9FOYFXusr+yR0Ai91FVeF3y2N+tlbZqatyztT\nfr+OfMotJOBg8MkAvzU5ZoFXBF4J8qwy/j27/kJB4u+lcBKgvvE56AY4OZAE4N4LyN0fQoKDspDs\nRPmJEXMdqpX+hG2upoAQon8fzuAkIlRiaEjnKgP4Bi5wp2L/4Dr8GEiamyvFlRa5Mo/vGm68E6Pk\nhSJCSe1EN3bhhPZ2pDRTa2UYRswPZKQ5L5yw3z+lD63f0mq4tQlUhw3RDFJmOnlGlJZfL65n9ANT\nyfTd0BbruXmPFq10IZCXI8MwsMwHuujIORFcj/OenBOLZqgKXcciynrVUyrkUphMCaEjpcR2e8Zu\nt2PsO2KM1Fq4vzrjrdsnpJo5Ww0NXOEc3ozNOEAIrRs07clLBvG8/e57rMaey7ML9txwuV3z+HZH\ncp53b25ZDZnHywHE0XWRu+d3GPsVsR/ISyalxG1eiK5jmSaqCDkX0rwQxzW317eEKByX3L63GZoL\nPrQopPftlMudCH+1tHG0E8G0vYiIkCzhpW8bG4E+tOinVMApZTlgvgXzWhLKNxiIGaUWgmuTQREh\nafv3zjtSCLhUCB5MIuICXprEMfY9NbVJUNx0LU5qDQWOKmaFGGKLHprD9wOUhPcrSsm4MVJSalFT\nGmJctEk6tSideHJp6witJ4LeKRYq4tohwWlqZmZ4iXjXUx59DR6/+T6sogLU7/FT6i/x+rl7A/e2\nzXhetUUQnbSXySD3QCtVPE9NmUpiQ4s3HtWoMRDwiDqOQTjbbDhWRcWDF2rKjIBI68DNeSLUjtHg\noI4xVKwqPjsOFJbFmKVyJq71OnIlu0JXPUE6LHpCaQcFkci1LnQ4ZlGcxfb3Zo75MJOrI087tOt4\ncXXO1aS43YRzhuZM5+GgwqEUqnQ4XXCuUJaT9LHuyOKpuTKOAyOenHcc1bi32pJ2B4ozct63eN7g\nKDFwNCh6jUuFzdhjufDtU1xuXXu+vjtw1zleuFd5dy58Ythy7iqP5wNfOez52w9e4cP9FQ9zYqzC\no8kh0fF0N3MTlDrfoMExkXjRdWw64dH1nlQi93rjxdXCHQRbeVIu3D0KpXr+pUuPxXsohlZBpBCq\nA99RNLMUIXYZkcKhm5nyyO44EWJmGzbMmjgL7RCuCxlLnmGA7Byqxsop+IrDUUJPyQ4fFPyKVI64\n6Ag542OLtXoxNIPiSUXpY2wT/mKU0g6wpGa0eiYtpCC4epKiUyjqid4j1aNOGcQoKEPv6cRDUm41\ncO6UNR03HjZnAw9KomawcOreVccRYaq0QzYXKBQWsdbLw1GqtXdiHAgl4cWYqyc7UPVIMVxsJLUk\nHvOOoUCMStA2k08ErBhrc6yl4n3rmeYYCESi1P/Xe/Rf5Pper0X+8+/esAmOXRVGD70Yr21H/ubZ\nwGXnua6JLyfP4JSzQfhAF3malV+8I3x5hr0KB4yfXycIHXcELgJcuoqp480M38k9VeAjZ55OC0eD\nV1cQ3Yq4STzaKS9J4V7vKAh3N5E3UuA1U3KeuC2eOENBee1MmOfIxhe+MhhOFVcAACAASURBVBkk\n47VzoUuBdwzuYhSB/+E7mV+6yHx9aomcl7pCLELykR8dKlbhd26UH4uBgy984i58fQr8rYvC7+2F\nn70UlkWQOTGGwFuL8vHB8e39zGvnkVJ3iGsalqCe/SoSy8JuMVZLIQ4DqctsukCde7IuPHWFO7Vj\n3mcu7p5zc3Vku1XE9ahWzl3g6TIhJRHHQB8ShRGnjlUUlhHcdGDJPTU5XFR2hx0+RMbYQGFD13Ms\nyo6zpohZIpMG7nQNJrUeWq/d91tMlFWdSZIJPnCTClUcq1w5zhPjWeDpdWbwjuu9kFZAMX7/6Pmp\nVeHdpfDAO95NjouglKK8lYVvL5EHUtk6R16U4Bz/Y658SJTshE4qf+285xuHyqLGWaj89rHwsCov\nI+QjLLLwQ+vAnsrXk/GxlfHxs4gT+MaxoggrJ/xJivxIV3h1gPdU+GAMfKAaX5uNv7M1/nSvXHae\nXxoqR/U8Kp6Px4JqZanGL54H3lqEKMYH15HbpfKTq453pszl6PjCjfLmAo9UeDkouYDrHd8+wF8f\nC//L7HnRKTsVPugT39SOCx+4CMZ1UpKDe5J4szhe75R7Hj57u+Or+wOjtBzuH9H6w9/L6y+0YToJ\nJ38Y+IfA52gulV8A/rxw8lUa+hdazvjfE5F7z4STwL8M3NC8Kv/s7/faT+BWF0AjnOWSEQk4V3HT\n0uznocOFnopgy4HYdSzLjPeNJmQEwPAeSslorfTRUYnkXAmampgx17YZSonjdIv3Dqse5z2qBecd\nPkS8GJSFKh0xdLgT7hspDVowrNtkw53komaMfesNiXPghDCu8eIJ7pnMtGXlu9By9zlnAkrJBecb\nScoFzzzP+BDRZaHWgoSISOutTLsdEgPH45GuC8TVQC2FJSXMjPUqUuup8B8MO4lKpWbuX5xzdbPn\n5uaK7dmKeTlQNWEWub29pe873nzvbe6fXzZIRYRlntsoemlRt83YE8aem/2OO9tztpuB9apjmQ6Y\nKuv1mnU/sPczqy7y0nMvcEwzr4eXudnd4r3n9mbH+WZgNx25OR65vLxk/2hGauVsXHF7ODJr4rgk\ndF4Yhw2H6UBVoZSC+ZbfTsvcemul4JwnKcgzcqwq4j1FEkZCSodopcrSRK1eWMoE3rfdkXKaxtj7\nklhKy1GLc2hKWIzvQ0ma76jRqiQ7cEaaji02F4e2YdVCPrbDTxc8uT6j2HkkRFoQtRC8YDWRU4Xg\nkJLI9frUn/Knz3c70WntF4+L4eT/0vfBEIjhBIJrMT+ggTEE8BEESk3t83rxApw/S7GcrukavvxP\n/389I/6qXV+eey583+KyqTJbAVNKPrD2EVEoOHIMzAVWzprIWZqIMlMQ1xaFazpmEbwm7DR5HmNk\nPyeG2MiRWMJ5zzHP+DrRhTVOhEBhLwUVB3Mhdk0gncQoJKIrxNSTpQAeqYXqPVoT+XjASSNqBhko\nppS6I9TCxcrzZHoCopgYVLg8G3FWeD72PJkLK8u4uubOpqf6hXO/5rDref5iw35pMdxDBu8LPcKf\n7vasz87wRdHSCJNehKVkYh/AOyaEMVfOzy9Y5kIVeDsZOTjeXDJ5gRtXeFo2iAQKC2674XNPhU+/\n1vP6dsJTsFTY3FV8rYy2wkgs/hxLwir2+GEgTRlxkXeuFnwu9C5wyDOjL9xa5mJ1jljAxwOSIxIL\nJTtWq6FFpucWD9PcoCjbTtm6hRfP1vzOI/j6o0f88PmK6M8Qt1CWyFSNa6mct90PqbbNp5aG8xaX\nTtLLhQXBCWxwHLJSS8WPgdh1HKYFdZFVFXI2NHqCtoi2847gPPhIrMZCQkvFu9YpDQaiymJCCKc4\nsEQKRiYSXOU2F45S8X6NOYf4DucyQSpVPWNcCPTUPwNhUougzuFOaPjO+RZjroWqchJxK06gltzo\nYEQkG3tr9D2HoOrZWQUL9FLoxHFFR7RMVyH7yK4Wohj7v+QZ9fd6LfJ3n7/kbt9TTPiJbeYqt9M1\nH4yLfeWzc+DlDjR6JDi+ss98ZlP57590fGSsUIWn6tmI8qGQOBbj8THw+qrSOWM/VT4TFt7Onjp5\n7p15vrQT3n6qfGTc8+7c8epgPK5wv2upCbPEfg/TWjlbR+46T/LC2M2k20J/PrK4nh/xSh0LtXhe\nvzSOR4h4HgTjA2eOYYz82FklIwR6zjE2XriKgTdulNdXnsf7wt0x8ifXlRcGx+du4AMrYZkWvjzB\nR3tDcDzf9/wnjxJ3nOdbRfkba+PemZFv2wEutwm/CZyvMqUYtUt4je0wwmYu757jHy9MT29Y3e1I\n6QapBbORfLOj23jeuz1ydn5GSZXglZoD1WVMDohThmWLiScvj4j9c3i5ZdUHpuSJUvB9j4oj1MqD\nPjFsjOOiPOhBE8zJs3KJHHrEJnalMIx3KMdrUoXzs5Flv+OwKPui3EzC2dmK3W7iNnv+63cC6wg/\n5TK/cSu8ODrePMIPR+PLC1xWQcS40MKDQXjbCn+sglhkLIXfNM9WMtE5Pn9IFN/0KTUZ93zm7epI\n1jperih/lOCBM97M8Arw9HFiiPD5JfCjvvIbZoQE35LKH0yVlQlf9Z4nVjlU+L1joyWua+Xzp2nQ\nhVbe7h1Fje+K45Oh8MUc+KNFEV8RrfyT/SmZdSu85AqGZxDhSYaHFSwIQ6ht0lYdj6vwrkR+xmd+\nOuz5anbMi3BrRhHHui2F+UJRtPT4fsPr/aZ1tAUcxoM686vvvPOX9yD5/7j+eaEP/0/CyR8HPmZm\nT0TkP6ahPP8+fyac1P8byvPzNJTnM+Hkf0ZDef77/4zv+yngc/GTv4xtzok+oCf5H9a8Q943j1JO\n6bSZCSjgXFu8OgHNCTkBIXxoqGkfIsfjkT62CIJZK8GnlOhixPlmlp/nGSOcFsJGMUWsElzAh0jJ\nCz4ESq7ve3aC9+97j7wLTPMC3uMw+r5Ha6YobMc1e8uUqQlsPUIWOZHRTiV8TWRrZcB8POJCwEzp\n+gGXM93Yc5z2eO9xLpBrxWrBrDl61AwXutOCO3G22lJQ9ET288Ezxr6R8krldk50Qcja0MRd17Xo\nYs4c5kYUdKannpYSpbmlVquR7bjCSma73TLPM2Mf8EGY5wVTpYuNtmfOM00Tr770ArvjTMpL2xTi\nGi60QHawGgbKlLjz4D7vvvuEi4sLlqLsp6VR4ErFnON4nElqqGvnAFoyx1QRbehwM2t9rtPfyzzP\nSBjofUBtQaugtQl3HU3s6oNQTpAP7BST6uKp+yXUqif0+J/5jtoUrp7igO3EsAF3G5GRUwRTT46n\naM0CL9JQvlYSpg05Hp7JY0++Jaxt8CTn9hlwHTlNJ9hIK3c/w4Njp/iqEywXnPcnz1Q5iWlPnSTA\nSUBFmtQ25/c/e2baRLwGVgyo6PEJfPU34QcQ+vDg4mViCKgTVi6w1IyrGQc4p4xuwHnD+8BSEtXJ\n+54qPfU7Ch3ee4auw6xFVJ0aljM1NqCLloJ3xlKOJC2sJTJ0PVWM3kcuXCCKp5ZbuhCIPtJ7TyrN\n85PF41JlDJHilCDGYEYvwtgNrNSoJjwJC744+jBgk0Kf6AAnwqYPEEeul4nOwSZAsMBVLgTvUSpP\ny8QmRaLzjUy3JHbzwjB23IrnLHTE+QolcDRjTpkpem5TxmlgMWUoxt486YRYDqYYiWqRIQQUYfI9\nkxrRBoIY5XhF3/dEf4b5wmfGA//OBwvDqukPvrI/54vfuuZffW3HduWQIMhiuCCoJHpn+C5iWal4\ndC+UOJMOMKWIHzxzURYPZ6FwNQd20vDwNSmhJpzv2aeJVexw4jFVih+Zs9GJcMDzlScz36wV5szz\n4Zy+U96d9pyNI5eiOBepGpCqaJ/QU/IgBcjViOJZe0NOE5pC5gzjkD19FGKBbMJ1NtwQIC9c9JWS\nHVmUiGeqDXEfXANGJJnpOuOQAvviGZ3wgnPcIg3FXBvQJAVPVKXUBXCYdQ0yYe0gIAJVjFkdTjPF\nDSzW6FRVKqrGIpFsAgoVZRIa4Ed969+JaxFgEbxFzBIVxRNYamnoezWynGS9tNjv4zTxe++9Df/i\n0Ifv61rkP/zw8/gYWUcBNYo2uM4bs/CBHp6Lhf/tNvB8ND4+Fh7mwPkQeDtXHjjl6aJcRsfjIrw8\nRooJFwP8+mPlJwfHRZi5zZ69eL62Vz66NbY+ENdGd5h5XHtSrdwLymf3gU0sfKiHe51wSBXp4NEx\n8FwvvJfau+zeqByd8cBFfuep8vrYpolh7Th3iWmJXAyBo89c3UIUYR2V94rn0rWI9202Lu3IG3nk\npc74Lx8pr3TCUpSfv/Cscub8Dnz1ibIOhsWBkhaiKW8m4QVRqkLsHKNWfv0Y+QdniffUUYNwuapY\n1zESGYJDS2E/FcYAh6oMBsM4IB4sZeYltTWb84Ro5ASDFDoDNiN+4+n3C7HfkrMxRCXrgpgjuISj\nIkVZiOR55s6dnl3a4OoNxTwDypKVKkKV2BxJJRHPL9ndTITzDk1CKe3AwXzAqMw72AcjdO39v5+N\nN66MV0Pi2oRBjf9u7/nUqHxsFfhvnyjPeeFT28BtrTxMQtHEd3N7/n+yE14ZHW9PhS8twpV5PuYL\n3Rg5zIXLAF85Ou4GY1bjPMBVFX7qzPFoqfzG1HHuM7fFc88yIvCRsbISeKyeLyzChRc+aZkviOe+\nFzbOEWrm3Sp8MUf+tS7xXWv/788tkRdc4TIYl6rc8fCe6/nCTvn0oDzE860SmLStpu5Tmc346FD5\n3CS85OGDvvIHOXBNQ8SvaKCIl83xJxL5hXHhNyfPAzXGYDysxutOWMwRUB6qsU+F33r8Vxcr/n0T\nTgLvk9kCQgLEdaDNXu69UNTwoRHwSm6xITXFiVAEQuj58/HkWiuaF3rvTqSwk/voNB1wJ7RzUWk3\npZ3w3flIHHrED40+plAq4E90sxN+2YB53pNTInYD63FNPk0i9vtbvHMojqfLYzofEFpEYponCJ7g\nPYg7dbeacccpjKs1Fp5NTIQaI0cnxHHTFvYCnfeIH8kp49val+gdQzxHtWE5p5RY5pkuRKymhuBW\noesiYy1EgYgjhsBqHJnSQimJ7apnHEZKLqSUybWilunHkcM8sd/vWQ0dN8c9u+nIg7sXdKcY5O3u\nFqfK+dkZm3FkiJ79fse0FMo0sSwLq7t3GNSRA+yvb3n+8i4Pr3fs93vGYeStt95ifX6GmvHo0eNG\nvHORaT42T46PpGXBirapW/gzDP2zKOaytM0pTsllPhETIyoQhp5y2OODI52Ih/y5g4W6HODkfnKn\n7o+I4AmkksiiUEv7OhfaZ9IawMGcNC3DCS1vZmQxXNH3P5k+thPgGFdYzZz+oZ3wpoz3AVzb4Kgq\nYexaPFQC1NwmZqUgwZ3QD64dHJhhzjUvlSrGifijij9h83VpcVA9dbCohlq7a4KTBijwnu/tIPwv\n7/rYRcfWd1SBLgidDCxpoQ8BtUz0UJIHB0UaEjeX0twzUlHgLLimGKCcor5CQNjEjuRgNBhOGOfI\nCoLDxQIKJcF2C2Kh+a70Dhejw3lBlspNhT5GFhzLrNzODvEg7Lgtjt2UiNGzx3h3OdKXDU73RN+o\nVF1WpqiUqTIx8Oi9K1zsWPeBmcBRM49Ku9e7qtzpVlwdb+m6yFU6kHTgPQWOgRATT/YJpePpfGTT\nrdFcyEvhacrcWa+4YsB1QnCKqqeY0hkcjlcc0w2+6wl1YRMD526gL495ab3BrwpYYTNOHPZwpZ7/\n4I8VZOLWKfP8Norj178QKcX4sVpP98yKz7zQ8yEXudKFh4eMusBbjytvVmGnlXUooJUbb2yrJ1fH\nuDLEOwbX85XdDe8a3Ef5IdfzVnbceDh3xpyeUvHMNlO058iEMGCxshJFLbEXeG6BhYp3bbMRxbHc\nNlVScaV10Ej0GrgmM1ll4zsiARdASsE1MgMFR++M26e39P0aihA7RYvg3Y7ObTg3eI+ZKVfWLnA/\nRs4k8SAIBMfDVLjtOs7NuDXYeM+6NNpXFSOrYlIZnCOqUC2xo50YOxU633FdGkXvzEeOz2iu4XQg\n4I1RhXB65qw7SPqsLBS4lkzX0k2M6jmaciGe95gZzROLsYjShXiagLu/6K38fV2LfHv2fKYX7vnA\nW5bpQmDDDGbcC/DW4ng+KlOF394FHDAvlQfAZwl8el3Zm2flKj3K20k5LpWfHY1tjCwVvllan08c\nPNfBN+bMvcnxKLcN+h8ujrkaP71J3Oscj2fH4h2fPTg+GVpnVcW4CI4oie/eKp+fPD+1mvjkWYdp\npe89X3q6cO0MLPHGTeITY5sOjZ3jd59A8cbPrAviA+fS1joVRbPxb9x39IPw5uzYiXKQjseL8Mpl\nZirKxgo6BlzncIfMpRO+WTs+MGT6fsXfLzB2Pc8VZdnNdBqRY2EYDNWOfmhd3uBbvy84T7/uyfOB\nnCc2/YCMDR9OmU+03DY1SvOBsldcH5jrU5ZjJq83hK4gznHcz6yjYmHLuqvc1MiyGF5v0JKpKbNf\nbTiPE5MI035ie/4CaX7ElBJd8ExvPSXcvUvVxO3Vnn7wLNbz7TlxjkAMfHun3BTHEEEtIlVxvvKR\naIzArz1VXuyM2AkPp8R3M5xFzxWenzlz/E83YAi/f1QGpd2POKrAH99m9ghlEe67wpUZWxwfiMIX\nkzIcGoFvw0ww4eODcVuFmypEUXbqGaLyt8bAPcv8YfX8SBJuK/S+cndwvHP0/Nt34VHx6AQbL/z8\nWvntY+SD0SAbj0T5zuL4yW3lUD0f6h22Lww9/Ely7INxNxjH6vnZtfKN5Ln2jg96KNWYEe454R0z\n7kvhI1r5ygRzUh4FcEW4KsaLvfJGFX5lbKTYo31v6wH/XBOm79f17FRn/PSvUIcz+r5nShlHE+o5\nHxq6dsktPnO6moTS0Tml5IajdtjJs9MDDUcdQiDnhgwXsdOmSIldj4ueklpsqWoBKn23xrxDy4Ir\nhg2BfLMjrlfUeW6Opa7DmTDnBe8bJa5R8hxUJcbYZIS0VJZZI7nlnLnwI7kzptxszyklOoGMMaYm\nluV8A+bJNbGKrRuT+0bCG4J7391jWgjR4U3YpSOjc20T5sBV5frkn7pYrRmGgVKUset4cn3F2Wbd\nTgSnpVHbRNhsNkjOzCWDKloadnxxjbi3Ok1vxDvubM9YTtOosYvkkhnOVlxdtXda0cpms6GLA845\ndo+esiyZV188RclDxKRNcebjwsWd+7z7+BFTruRa2d0emXNCTj2cYL4BEU4bhrQkYt8htFNNpP2Z\na25MHtWKuY4yLzjT1jWIgTof6bRNKXMUOhcoKKZCPtxCdHjfU9PSTldjBHO42JwAWsr7LiN5JjK2\nP5PWijSE6nKiEFah9adOG1s5/bdaFlzoUJP3f83Vdr+Goaeaxzsa5CRn5CRRDqFNNWstkBf8acqp\nWJPdLlP7+XLLzz+bcYt3WJX2c/Mselgxnv0+GjzDpptnkbwfuAnT33v9h3hlMxJCz1wqRVssdwgR\naqJzJyIiHi8RkYUqcNZHMsI8J1auaQaSVsw82QklKyV53iqOKIU70TOMwtWxsO4juQibPLNdd2jN\nWKjMtXLWD+Rj4azfcmTfTvVNWLJyfajE80se7a+YCtz1xiaumevCJEZP5MYSZ72wTAnnI242wrCi\nM+PFtTHGiOaIROG8hzTPSEwMvmfGMFHiYWIYHH/6KHNTHPg1Ry3MNVGt0Hdr5lpYcmUlgSeamGpG\n1JOccNl3XNrMdc5E7zkbt0ia6GIgR8+7T48Mbs/oVyymTeCrQvZGt7RJvB87LnBclcK7i7HqA/fE\n6PuBQ5npomcocLlasejC1mdu8oqwZG5Eua7KCpDQ7qX0bPE+eKYlc+Z6bjEWZ1wslWyVfZnYbu/w\nZJ4oGJ20TW4uGXMFcT3HeWHsB8Iyo75Dq2EhcmGC94apsQlrFmv3b8FYTp+5Wh2TVVQcCT1NqZt0\nOvuWv5810Iunxh51HSUdiGHEypEzF1g5jwbPuWsn11UVpTKXTEZYaImIMh14/eIeH1srZc4EzSDC\npJG9KSvf4nzZYMYjAWI2gvckmuNwUkW0CQ6QRoesEphohM1QKxUa9UqgaIHYkaTjaV1wqdD7Fv8d\nqyP2ETE9TbGVlcFyonIelsQ/eud7hwP+y7qePUf+o48+x0XouBjg9w6eB8HzxT188EwRiWzmwqOq\nzHaKNEXlHQ18ekj83k5YO+HlWHljFkbf3htHhU9uCv/FdcePj8qPd4V/egh8Jzl+pVde3FS+MbW+\n7f+eAo7Mv3XuqCHwzjKhKXI5KP/VE+Ffv1P44s7xc+vCMAjOhK/O8HwnrGLgNrXneu8qZ0OPOhCt\npNLSO13nmOeFS7dCzgu3R8F1xh88dnx6Vbl1cOeY0KQcHgyEJfB/7uFv3q24xTOPynJwnA1Qtak7\najoynDvYDzxNBy6HgmaHbGC1V/7nW88nVspL52BhDdZw4VfvXbG5s6WWSkwzh9zeV+uLLbYkfEko\nBVKT2SeBpTouJMPgCCb0q5GhHkiiDH0gLYaNl5T9NdEvTNmzPqtUuUdVj8xX6NG4cyHgPFnWBDex\nz5dErkg2UKpytECphXJc2GVAIURwxRHHLWVTSAfI00xcjQQRlgoahN4EzULNyqQL17XnK7vKQY3v\nOM+/cmn84yfGvzlkDhawrvB8GDjIwjdLxz96WnjVKa+Nns/OxlxhGx1bNe6MxtbBH03+9H43LgRe\n6Y3bpUUAAbZO+MwK/tMr4xdXld9IgbMEj32DTN2tigO+Y8bPDoVvzJGnp3bCS9W4DMpHR8cb6nje\nK9+qjj+YAi+y8K7Cp0Lr631Njbh4PrxKrFB+u3T89JD54q5j2y98Z/Zc2DNxNry+WvjaYc0UCjPK\n68Gx9om3c8fHYnOcvlfhvanyv34PJ0w/UBsm9+m/gz+/36AO1ZC64F0k14rrAl13Rqnzs68hpZnQ\nj7hcSXnGEMJ63U7fUz55k9omI/gA0iJZ+BbnKnNzAElOhL6nElAr9BgJJYQB7wMheHxqtvTJ2mao\nsyY9daGhmbMYntYbQZVSCqvxgqM3end66eW2eTvmidEMDZFlOQF/FFzfMYSekjNKwfuOUhNBHFkq\nQfXUqwm408ali44pFcQgdO3hrNYmIPkwMW7XhNjoW2bGdHvDZlyROJHThEZiMm3tLzM2Q0+2SvBN\n4HooiVEcVYyS5oYVL0pyRlRtcb4ukNKCpmYmF2lY7ONxQnB4H9henPH40VPWl+eUXPA+shwaVc5H\nx5ObHWfrDXNR8pzYbM7IZsxpIS2F84stj54+pe1XnrlBDJv2yLBuC37A+9iodCG0vlvKDVVfjOzA\nW0GGNZYypkbwtM2GG7CaqDFgVXDieDZr0RgRLeRlIXRdkyM/I9LVP0etO/miqBmx9jCzGJBqNIkJ\n+HKS1frYCH2uOZp8CE3AOy94AQuR6B1LTk2C64FTbNM4TWBLOnlZDEtzKy103fv9LCd2IjPyvhC6\n1oqEvoEjTiS94DxIix3W/VPqn/wT+AFa7Dx7hvy7P/EhPnzvDvOcqLSQdBNCe6BwvZ/ouhVqmU2/\n4pgWNIBOAR+VOSWkd5RUGUJP1w2tW1I9t+mA9A4pnifTTNd53jkcWPme+zGQdWHWiCczDiNeKn1D\nI9KLZzFjGwPHpBwOC7kbuPXGBYW1AxsGlsOBQeGmCPkEL+iCUaxSTPEKcRjwzrNxbcKZSoU4MGVh\n0MSdwVitIu/czNwx4VvXT7mxwEJk6BNT9dyLEc2ex/OeOG7R4DiiuFRYCOQlM3lHiAGplZ/cKvNR\nseCIVdn2nqucOOTKOkbOQqWkwnkc2Bfj/uBJ3nO/7zke9kQvZCdYUg7lwJ2zLS/EQPFGyZm5dNwf\nFq4OGY09NsPFhWMbYFqUvQl9pSkhlgP0HVIdV8ueFzYXHK1wW5RYlVXomXPhaj4w9hu62uiHGjt2\nS8WhTFRWbqCWGRXh4IXoOrrsWMpCDB6y4+iM6zBS5xueD1BFKC42tx6nTYUoml2LtgoUgajtnk4i\nqCmhep6a500zknVsfOWlmjgbBqJ1VNnjpBIUlhIoXijVuPWO/ZTZi6HmeTAoF86zLMrgAuaMvmQ2\n0RrZSgqDjPTSeku1FPBCxuhMmNuTjklbR2BdAecxauspOdoGyzzZTukK16Ld4KAWgjmSVJwJ2YQs\nsLjWr1BpSZGrkvnCuz+4G6Z/8CMv84mLgX0RXq0gbsZZ5N2UuFwJY7+lWiarEr3jsJ8ZznviLFxN\nC8mEu/cihwXk2OKPvu/Yz5XLITCb8p3coAuHqfIbO8f9YBzU+PGx8kgjpSqf6gtfXDyvjIF7gzEE\nz9Oj435nfEutSWlN8UfD9fBwX8gCa1FGHykK5MTZdsN7TjkLjarnqoPouJ0SGykcfYceJ96qnnOE\nO2ee884xz8J3auZDXWDORvTGt2rl3DWf13Ua+aHRkM4TxJi1EjWgY2YtHldr01hME7Jat1SNc4hk\nyvWRsYOjDHRRmKTiM9RieKdQhdg7Mg4XCr06FlXGdrRATYoNjm5x5KAMy0LXgwsrxI6UBD7UBtpy\nMGUlWiPkbsbIYT9ThkvEUiPjlopZRSWwmyvbtWfJjpyVbtygzliWmbxXVve23Dy+ImXPkyrcFOGP\nkuMqKy8MntHBUuGFXvj8EZ6Lxku98Fs7x2WET0rmN3PkEzEzRs/Do/Bdc/zyWHgI9D7AkngaI1ez\n45XeGJ1wpxbeDoFXQuZXbzy/tFb+8cHxkc5YA39YWmzfCWSDQaz5GtWRgBRgVMd914YELzvjYRJe\nDcK3tdEvvwn8jcF4WozfmeHj3vi6RH55KPza1JJaz3tjKoFVV3lUHZGOFXMbEhj4BW6cUoInoHyi\nrwRpGPNOKx/tja8lxx8uQmeRl2PhIgpfmuAj0fAOng/Gb95m/pt3HsJf0Uje9/WyXKjzHnHCnAq9\nD7g0UaKj7gvJZyqKcx68JyLk/Q24E365KMkpYPgKEpqwr1qid8Jh680tMAAAIABJREFUngixY+xH\ndqmyWl9SHEhIBGlkvjiecTjs2azWp+heaAW0obSI1/GAN8eilbgK1KQ4Ufou4rW9/IsPmIdduiFX\nIDhqLajNLeJVHDsME//+IjbGHsszuzyxkchSE7PMDLE7bWaE4zThnOC9tiiWM45TopQT6GEcOC4z\nLkQ6aT2Hx7c3bIYVeV5IAWLwXO/2DF6Y5hnvPZv1pvXDVE99p0ZA2h8PDNFxPB4ZtlsoFeeEw80N\n3dDhnGd1dkbJzdcRx4GDNhrbgzt38aEDd8Vq7BmGAebEvVc+wNX+lsWMNB9ZrUYONzvmLJyvVnQh\nckgT68st+6c7NHrwTZS4qq3r450j1YILgaEauhlP3Z1KqwGV5qyRRhDzvsUec3CgM+YEyZXQCVU6\nvAhWCtUbEjuG3Gh4xXIDZ/Q9libEChSlmOG77nSibC2nUxUTAW+I83CacHqxFgcMHc4JyzxTbGmb\nOdU2eRp6XE1Qm0wS71rU1CqlZNbjGksJxcimbaroe1Rz69MZBBTiSE4K4ogxkJdEtYoLEX8CVQTJ\nhHGFV6Fqbt4U0YZ7LdY6VM/Q2T+A16O9QMltIikORz71vTylFnw4I5fKgmc+LGxj6wqGaGg17o4b\nuiGyi5lw6vRlhBqMzsVTBNZzZzOwUXjlxbvsdolSAx5PjJV5cVAK5uHoGkK1OGXKxuPjEbcoXe+o\nNfOc9ORceDQnuk1lLIVDTQzREbXnrKuYK+xkRQw9++OOUo1aEo9rJVnkZjHMEkUqviZq50lWCdbT\nW8X8hqotgqmpUdi+c4A579C6J9ZMwBhix6t9xLkjF5tIPVTW0VOicnur3Dsf2ISG2DfgvExsY0Xz\njrv+guOmhxRZjzOPjnue71dUnXBWOFut8c6zN2UYNnRiPJ52nG1W3AnC/YvCUirnvsORuVpFcqoc\ntTQAh1Rc7wlWuRgHdsfEjPDKMJJ9YVBH1zucZSQZLmYuxxVW4Vih054YK1vftxSCGbeMxG7NPM/s\nc+a96xuuOmPTe+4SubPt2RTjnt3gRmGMjnrqPwbvKTXjcPxf7L1ZrK1bWp73fKP5u7navfdp9mnq\nNNW3UFBUgyFgHCAECxMUR3I6C8lKYuciSm6iKLmKEilRYkWJIl9YuYgTCxxko5BgGwssjDFdFQVV\nVENRp4pTVaffZzermXP+/z+a78vFmKeqIHYkFxGoJP836+y95lpnrrX/OeYY3/u+zztKTwmCeeHC\nnzDv93ylPsBSqw/YjBOjVNJSeBzlWgNlXnhBOup1grgjq/LAoKNR+naqbGzllu8wNWK/cm9N7LcD\nRQyXHWvMqAo7UeJaqSWxcRFYmSSSlktEKmFzTO88J/WS4gWPpw89WOYKgVXbvW2O3GW6ZHjfBpUB\nwbtyIM4GvHNUWilnpFmZzQJLrnTRE7QNES+t8Ik/4bXgj3Ldn5Xf0cRbOuNndo7vmipHNZNj4Cfv\nBD48bPlYCry7N254Y3CBV+4sPByNl5MjamV9HWaFmyLQJTausIpw4ox728zboudo8Hz8SvgLj0Re\nQwn7xJmHQRTZbPjMxcJ3PnbI34WOoMZ4bHj13FoLZwnmfkXOA/kaHnPKcir0Wwd15cJHZi985Wrm\nE0vgh45X7mbjFQpv7Su/f92TRLjUlQ9Mygt74eET4WKnvHJpvK0XTqry9x8Y339euZ873hTgF1+H\n2x08PezYrY5RItt95osz7HXlQw8VtvsK4uilERe3F9ccjwMpVa69MvbCgwtjHPfcf2D4AKdHkeCU\nkhyuc8SgWI3U3Q4XAmkuDBtHLQ4vINtCiYJLhjs6ptZEkUj0gXVtw9OjLqJuavnqYQUnxJo4Go8Q\nf03OmbQ6ZAjMO4e6leNxwCtYVbrzDeXOJbWLZB+5ksyohSwBN3imoqQY+LG+8HpuyuudtfJCFV5b\nKx8cjBc18LGd412xcDtWfmGJBCqGcZk833628k7rOXXGFxYPzjg97fi2RXneFa5K5dUirIOwTZnf\n2sNVMX7ukJP67bX1TGKQ1WhOXmOw5iypAf7MUHgpGY+HyOA8f/ta+XyBRxy8JWRe2Hu6GPigW9mb\n5xPJ0Tl40eA7YuKF4vgr58018Dqer5TMS1X4wFD49GJ8oDd+dfF8pE8Mk/IT1z0Q+IGh8Os74VZo\nCvUT4vjk7HlLn3kiRm5YZZW2b/zk7Hj3WPjC0uBYZ/6Pdy/yTaUw8d7vpzu7jXOHTpqUCV1kN+9a\n4zsdse/IpRzaoBM6CEfVc3X9ANdNeN+IQzilXl3hj47bnFkzNpxhmkELwUfW+QJXjXB2AysJkYbv\n1OvrZq8qmXB0Ri4L43hEjJFVM7ZkcliIfU+3Cvvra/x4xHjjjOvrLb2PlHUPzhO6AauFECItrlKp\nXuljT8ozNaUGWBAopeINSoCT6ZjtvEeLYihD30PNqFqTwH0gxEgpha6PlFzoYqRzQj+MvH55j5Oj\nY7wqumau8sJTtx4hq7LsFyxn/OnE1b0HDMPINA6stW3Qp6kV1QaD64v7uKHnyUduHwpmHUfjiFCp\n+5X7V9fEGDnZ9HRdd5jMKmlZee7lL/PkE08wdpEXXniBxx9/iv12R7cZ2e333Lt6QHGBPnRgju3d\nB0xnJy3QvN1hR0dMrmO/3+HTzCqBzfEJqRhLSvRdRxDHriZ8qkBGJJK1YgrSRcR1eOcRSyQ1unCM\nloo5o65bhICMPU4cYu2+S2Vu6tu6Ij62bEupxLAhazkoSRnn2mEXVfBvEBAP9zQNsKC1YjXhJFKW\nGYkBLII4TNbWkWUKeQZz9NMhp2bloFY5ajYkFCyXpjSZAh5CszqaVrxUikRC6Kl2KG8uBVJqKKDY\nfjZRw5yHauAO4bzQ40JoGSxTbL+F534Jvommw2+sIX/+LW/lxrTBeSPXQu8jrqaGrs+FfVqIPrAU\nR/XgDZY1EWJPDYYvlf280vU9G2uZuD2u5Z+kIx/C8Vkz6oWLZHSe5jkPQq8QqufaFkI/UddKpFIl\nY22LSW8g4jDxrJKp2SjZU12mRs/6YEfsJrZlj4WODsN1DkmFWY1VCr3vmGvGGfR0mESyLc0Som1I\nMKlSdeXN48CjYyC4ihNjcpFJJrrRQOemBmfDOUVoZD6HgO3wXplqRxFjHMCT2a4tpOt0YNisBBvp\npNI50FFYrhMnm9YlNmcQnTkeJzrnuLy+5mTY0HWB7XxJqoUQOqYh8uD+TF4d57dWJh/Iq6c4ZXIw\n+A0mgWx7dnNkp5VOIWtklYGr/Z45w1L2PDhgezVumNIVcuTwzjP6yMNUXk/GYsYzJ82yQhcxFWrZ\nknRDjLAshaQ96BbfS4Ni5EYcPe57jscZtxoqPbXkpt7kymwjBtSaKeZwVptqK0ZVwzvXMouhlaEX\nMcTaYd1pG65kHyiHcmmrig8N3GIHl0C7WkFs8mDVEAJNLGqZJYketYw3oaqgvtVZqLWCdKktY1SD\nEZNSXKAScLVQTDEnmBimlewcoobL0jDuIlQHTpuq5WjY/nJwLJgZd+aVn3j5DnwTrSHwtXXkOx97\njB8773loaL18u93KjY3nr90VnomFN3nl6Y3jS3vHExvPg20hTsqtMPA3X515Kni+dYIvZSXj+N1d\n4W1To08+7QphOOJyLWxRnu0qn7lWjg3edMMjtVLMc1WFj1/CWSy8lIXvOu754lr47hNlOoks+0yZ\n4edU+IGTwvGifPzS8aaN5/SRnjt3MrcGx+VScA4244DLC6EfKMnz2px4zcOzR4GyXfnYzvHOsfJw\nUH79OvC+rvBPUuTP3Xa88KDwib2novzLZ4VSja0KcxXOAmyOjLvXnqfOKstsbCZhNCO6DV+63vL4\nw4G4VthlXqmeZ28dkdWhyxZbKna+Ybl/zeBBNscYmZIKrh/xtuANdtcLIh3Hj43Y1ghdIugpXVgJ\n5YrLreF84HTT3m+DCFKNmpTn72Uef8zovOe114zzmyeE9ZLZnxHqBa/tPNVVuuDBHNf3K/XY4wxs\nNvZjx00pXCTPoIlPrwPvPVJycfzspeeHziomwpeTY1OVyWe22fObOXKljtud0hF532jUuvKp6nnH\nMPLlXeVGND6+r2yq4/Ez46kYmCsUhN+YM7cjfGHXAFxPu8pzi+N9k/DR5LjljHtVeftgfG4NoBVx\n8IS0gN/OCY9ivG1Q7hfj9WxsPHzs2vPEIDyobd/yZFh5LCifXAOYsq+ef+ck8aq2NagavGiB35gd\nT/eV17MjaLMsC44r5/hgrLxmxreGwi/knvd44W51eIx7puyXwKlktoMRTDhGWUvEqlKGykmFGce7\nBmNjyhcUfmtnfO7eNw6P+ed+/X9THZje+aeQsQMMkRFzHtOEHCg9uO6rWaBaSrNLhQ5xHqVN+p0e\nMksSW4ntugcDiREktM1zjPRyIGg5B6V1EpVDWF80tY+WWbdtU3+yOXvj2bJowVVFnWC14tOCdQK1\nUmrP5uycUlphrEdZapNF4zi1HEoXkUMXRqqZUhpNL4QAoSPQDhzD2HDBqrUpDofcjh/a9APbs+ZM\n1x3hnaOkHX4csNoQ0wEhW6VzvuW1RCj7B9QuEMMRVgvTNGIKy7IybSZSSoSU6I82XM97PMrVmhj7\nwBAjMYw4zRxtesZxotTE66++xu3bbxyoOu7dv8e0mTiZjholzgubzYZklYvrPRsX2M57Ts9OuXv3\nXstWLcawmTg5PuYrv/8lXtnuOL1xTqgr2/3M1Vxw09QgHful9U+tLT9W5XBY8S2KrRgydvilkPLa\nZGJp+R9JmaoVw+NDC1hSaqMbmqG+QzCc99SaQVseIPiA5oZTbgx54JCZUzN4Q5UREIlYSQ1YYooz\nwPdQl/Zl0bXCWXrwDUecc4NUvIGm9z7+Abufk0qt1myHaUWt4PoB5zqw0g5HB4qfdxGta2txPGyM\ncG3jhhk1Z3xwrdvr8LrBDmqZGZoTfOHX4I+wSInIY8B/SyNZTcBzwI9//fcTkf8S+EvAGfArwF82\nsy983efPgf8Z+LO03/jfAf4jM9v9s9aQ73/TmzkeNjgPKWWK92DCftkRY2BfpQFYrLLLCaSn5Eqh\nUJzS0ayyVStrOfS5Fchlhhgo6sCtUFqgPxDxUqkls1plUEcNHWHwuOIoIeMXUNkz+R7nHLFWvA94\nMY6ix3DEIPQ+kDUxWWBfMmPnEWATPH2XOelGTDOeSh8hlErvPOInzrw2NLQq2bc1P+cRI9O5SOcB\nKc0+7MAYuLracb1Kq0DQcvgVOza+3Z9HQ6DUlZ2NrDVzUwJWC1uF67lwejxy7By9K6h0jdZJxkvm\nrBvQXDieKgGPs8TeIriO/ZoRUbpO6caRTReQ4DlmxziccDwpxycXWO3Y7Rz7RcmzZ59aT95j5x07\nDNvndq87YykNo7wWT3ILQiBJxxocYzSCOPrgkd4ItVDUyNKx8YXBLXjrKEulVCGlSlZDS9swpaot\nl1AyXe/JVtr7C6180ixQK1RzaMltHccoNPW21kJ0Pc5B5w2PkNQQF6kCRSpaHVYrMUbQw/JiLQyO\n6KGrzb5uKPNGPtYQMUyEtbj23+paJtNacS5mVKxt/gSKfG2dQVsuy8yaJV2NtRbAyCaoBEwMVw2V\ninkhFEd1ILWirgEPamtNAGtApFfXhZ985e4faQ35k7jeWEd+6PbD9L3nHb7ycon0Hs5d4TfWyFO+\nwTVeU8cHY+EL1fGoFHrv6RDuCJzWZgh+olM+nXtuR+P/2nrODG7Fhmr/tqFiveNR7/nU3vFIUM4q\n9L0wH0iwXd01ArCrfOVS+dm15z+85Ygu4WrP6zWTC4gT7hVlrspbp8JuEV4tA+9/JHKxGKdO8AhL\nMfaLcXYjst1DfypYEaIv3J0LX5kdV8V4cnB0Hdx0jt+8NL7zvHUwPqiFba08VITP7OFtU2U0x+es\ncJIrzwSHBhDNOO9YPVwvjkd8ZTaYAjgfKLGj319QOhC/QUpimAaqOmpeccOEywsxK35qOUWvxp3V\nOI8QBoeLEyHtiHGg2zgsK9v7Ox5+KDAvGcLIdt+opGMfKDoy+BkbRrzdYVsmxtKzL0YIjnlXCaEi\n2kG3YewK88VdPreLPH5zoEtbLmfj49uOpzYdV7nw+Wvjz2waZv7ZUPl9DRx7Y5TAGByXBTh2bHfC\n1ZzpvfBYzNwt0BXl89nzfPG8f6h8uTiusqPzyjO+8jnteNRVHg/wUobLKjwalfdMxr1Z+GzxJGmZ\nRUS57ZUH2bFQKcBtgSTC66VZ81wVHvWJ2164V+ElHN/SV16pjicxVvG8Y4SvLMqJN16tnmLwUDS8\nCq9rixucOePF5NgE4fm9QyXz5CgEAiOVu6uAh3sG7++M303wjlC5mz0VIYbCox4uzfjMKrzLK5fV\nM4nxwGCjlWNv3K+Ol0rl7915Hf7Fgelr1xuLlH/v9+KPznHesWQlxK5tLL0HU6oWPI6UdnRdT5XW\nGizDSE0JLSt9WemmUzJts1OrcogytPU8J4Ia87JFQmTqIkuthFroxw1aK7GPLLsdtiT0/BxyJS9X\njSQWGtPf+3YgMydgyuCENVV8CNQ0U6qiOMZxYlnaouenE1JpnSxlnts02kVmf9h0G5gX+pyJw8i6\n7FCE4egYLRnzI4GEl6ZcLCkjIvRBEIGzzYS6lqmahoEH9+8z10qMji4rN26d4f3A9nqL9MJ2v7C/\nviKMEzklpBb6vufs7JzgPNfXF5weHbPmlaELHA8j4zRScqbXZh06HU64c3GHi+0l/TjgVbh5dMq6\nn3mwXmMiLNVRcmG32/PU409y5yvPc7TZsC4zlzVz66FHuF5Xnj1/BOk896+2XFzv8RGOxg1j7Hn1\nzn10Grg1HXM57xoFTzwp5UaViT2Go6QVb4la9LDZEPAecQFLCT80ilNdV8SHRnPShA+RmivTOJBW\nxU89UhMeY18bHMGL4mUgrQtIbUrPAfxg1bX/F7ntSlTAO5DmobaqYBHRPRaapdTlivUdosbgI/O6\nYo7WCbUWXNdjh42IrwkQqgTkgGOlArXiO5p9DIemjLeM0ZD0qnKwzShQiWasKOZiU6G04tQQHynt\nL+DqDnzhN+AbRwKf0ZC+/5BGqboLvBX4opk9f3jMf0rD/f5F4HngvwLeC7zTzNLhMX+fVjb57wEd\njXL1UTP7t/9Za8hf+fZ38vitI6pmpBrBGoTF6krwPTVCoJKLMgyNhBhtwnTXioR9y4iJtJoArZ5S\nF6z20DWS29QZ3ufmsZ8CWMD7ylI3SNojElHLXFwLfvScBM88r8Qs6NQRh8jV5Y4+CCc+sHPK0TDR\nKyipbQRyAtfoaxOVec3sFrh1PoA5cm3KhqZKdzLCOuNMUHYscztcrzPEEKkOtChH454OYc2Rqp5j\nDzE6huBYcmZZV17dK8facTwIpY9oWli1wTK8XnD7+BwngMycn52w3+9RW7l1fguPMm0G+t5TxCil\ncnpSCZMxdoJUh7HH/IL3kCXhXIfzgqmh2fDeofsCukfocda1UI1XsAGyw9xILQvLPpG2yvXOk7JS\nzBNiy4qO40h0iqtNFdpnZZ8K4Joa5zy5zLiuh1zb4IGC0LMWRa1teGutmPPU6g6AmoaTVy/tECNt\nwznnlqlUhSqeqgXTji7CMHb4CotUrJTWMZgr62qoXzETtHZfzWUqxmrgCXjTPzA4aSegdpgqZm3d\nMcA3qqZRCBLI2FepnF/NWJprqpGTr1FBa0B9bYcz81RTzLfBS0OoN+s3QNF2oBaEguFoGc6itWVK\naiXQUcV4dU38jRdf+4bXkD+p64115D95+hZPjR03vfG3dpHv2SgvpsBbh0w2+L29591D5Rf2jh89\nXvmdZaJ38NAm8NltA59815A46j07PBY6cioUZ8ToIShpW9mo8jcuA48G4984XfnVfcezIXMeWxba\nOsfrK+hOkRsjY8n89GUzGnzfaNxTx5sHIzojd43ueVYr2yXQR+VyrfzOGrmojh85L/zDB8LDfeWd\nJxO/soP3HwvPXya+wxWSE75skZ0zdtrexv4lWylT4LW9kYCnbkXSXsgx0Me1ZYNT5e7WM/jKiTPU\nK6dHI/jQ6lhcYL6/5xV1nG+Uo1TZ3DjG1FPTAr3nepn54h3jiY3jN3aex0i87UjoTzf0MbBcXjCd\nHOH2M3RG74+aS4PAqTxgb0rnbrKWO1xsW7WJq8bxpiekHa8n3wBH1ZGBO1vhmUd6Lu/eZ/Abal34\nzWXgww/D9WI8fSos/oyUtg3Og7KZOpyH+/cLuYtsNht22z1OK1I9L2bYF2EzOO7mwEcX4U1u5YW5\n5x6Kd+C9MbrAZcp8+KhSET65RJ4ImRdLEwve1Sd+d/b8+Fnh17aRD55V+my4UPjrlxsc8OFh5YZ2\n/N8zZJSbIoyuDXbu4XHAo76yV+H1CtE5bhyw3vfFmMzxsFt5hZ5bTnm7FL5yqNX50Y3yk9vApRg3\nEX4vO85iZJCCIbxPFq7Uc6GBLhgvVaNU4bLAn94knungU2vktxbhh/uFC3W8fWMEhUWFV7Jx4isb\nHK8Y3NHAGfBcFd4kSnTGKxa4U2Cf93z0XxyY/uD1xiIV3vs9DDdvt16UCjmth0C8ED1QK+KEbtiw\npkSxjFTFhq5NzNVjGeSAIdei1JoRH4ihxzuPOSGJEkrDLmtaQAJKYuxP8N7jhxErGeqCSYQQ0Kzs\n55nTseNytzJuBgB0mdvBKToige2yELvYVC5t6kJ0LeSvzrPb74jS8hQICJ7Y9w0Rqi1/4g/9OT6A\nWgssRlFcbYVfZg204ELrGiq7LeM4os4xecf26orQO6Zp4ng4JqWZ0fdstxcwDs1aOC/MtXI0DQSE\neV5Ya+bk5ARqwamxlJWjow3rdk9/PLUOl6srpmnCNPPwzZvsdzMnQ894dEzRSp73TVXCMR0NXO/3\nrGXmpZde5plHn2TTjyxq5HVFqjIHxzAecefePQzjZHPMa/ceNPKfG8hauNjvUO8ZJHC17jk/vcl+\nt2cIzeKUcsL37dCnqtScyLxBo2sKi5ogB2hGHMd2wFIlzTviuGnTVheRkjBvxLWQHEhNdONEKZWa\nK4bgY6BobTZOH1opLIqPkWqhUahKgS7iqdSUG4QhZXQ6wakSfCBf38MdH6Nr+WoBrVSlLAtExZlv\n9lSg5kzwrWOp7YRaJwS5ItOmWTnd2uw4xdBamvJa2vNqxZMr0m9w1uiK3nu0ZkStARKCx1JBdxfY\nc78M3/iB6b8BPmJm3/P/8ZiXgf/OzP6Hw59PgNeAv2hmPyUi7wQ+c3gOv314zA8Cfxd4wsxe/UPf\n79uAj//n3/kObp90QG0T/rQw9Y6+9+Rk2BSIoiw149RT9zOnR4F+iJhp6wARWnmwraS19XQdHw+M\nfd/yJF0CcZAdEhIinl2u6LIQ8MQwUllwnRGKELxROGx0FYIJjkCuIEFwTvHuDNe3Q1tBCTqBS4QY\nScUhZuTaE3RGJJLKBVoSYoYPPR2KlnbQc87j/AJ1JCCEwVPyQvQVVqG6yrBOVLfgJNJ5Yz10UVVb\nyHPEoZhrG59hk/Ao5LZuNWVS6D30LiKSCb5HLGMVQhScm9v9FwXiCtZQyXhBkkc1Y2EFYsvfIZiz\nNtzyilnBrMdZj62KI6AaofSozuR8AL+QDwTLyPW2Z79T7u4X5nnmareBUhk3jjGMvH59AUR8BAh4\nD6GuTLE/HN5ALJCVpsZocyCIK6176WCDowrJKmaNFrrmgnOHDj/nWIseKjCU7tD1pskQab1IzhnO\nBcSEbJ5SFNWmWKoq+XAIM6GpdocOOGiFjgDFoGKI0g5cIlRteHDRBnkIB7T3G1+vVjBt4IqKP9yP\ngvp6sOO2zj3CoYajasOo037XTgDzONd+jiZKN6KZAnrAkheMV9eV//WFV7/hNeRP6voqPObNt/jA\neeTB3P4tfmH2eBFmE35ks/BycRx5Y+odLhu/uDgGEc6C8thkfGmO1CIUMz40JO6ujo/mwENO+Z7J\n2AJdMF6onsmMAeXTi2My4YLKjx3BGhzxpEdniHmHug7rA3U1fvmi8h3HkX9wt/LnHm45P/YV8bBG\nx6XruLMtPN6DTo4pKZ0KQ/RcmaOTyk/chR8YKr+9OC4Fojp++FRYKLxeA5MI511pVS9BKdYjY2F0\nit8aOUSqZWrsOTmg+i/uZx4+hho9J87YbRMhGsMmQj8xrAUvlbRfyeNI5wq2wH1rIIZAgV1h1UQ8\nO2coGTSz1IIcd3TXBdkYZTHWfWGcIjoXprMTYrqmHxQfT9itmahrG1Sbp3rBO6PYnldfVd70iKeX\nntVCs/5ZZY6GykPs5wcUc0xRuNoLYiCDR7Tw8r6C85xh/N1Lzw89FvnydeJx11Nw3F8yx8cdbr+w\nM8/9RfnY4vEeOmlr/3PFcctVXinwZ4+F20eRqwx/537lT5+1gKjimUrmde94d838Yg683SWe2Qj7\nJHxiFebk+cBJ5ef3nqUYJwfVZlDjfRN8MndsTCm18vAkPIzyqQVWFZ6xyuet4wzjLYPxuTnz5OD5\n/N7xkU3lroNbavyfV55HB+VRjNudYgbPZfi2UHk+e15DwIQZqFU46TxvD4WPFnivV1ZV7qTAeaw8\ntx1455R51BuXVZmGRkrdJeWRAS6rkSvcr0IfhfurY5tW/rdX//gGL99cB6b3/yDabwAILrTcxdUe\nGSK+78i5/Sy6uwPmGE4eojhgXVsk45DDUC/0EvHdwLrftpJbE8w7OlsRFWoVQu/Ad2SsTc1qA0dU\ny1hnSG7h7ZPT09bhVFZUPd47at6xZsUF99V+Iz3cnDWvLTvR9a1I1bRNJP1IrRVvrYl+LZUQOtTB\ncHjs4PvDRmAlpcTYDUgc8MFDLmRrm/YQA2VZGPqemleOj48ByGkBWhO9qrDqwpL2nLgN167SpUp0\nyvVuxjkI3jP7js4JzsF+TfQh4r1nMw1cX9wnq9I7x/HRpnXa9H1DqpeFWhNd1zUcOcK82zFfX3Pj\nxg3WdY+YsTm/iVQliJKqkeY9uRSGfmRZK5ujIzDj7Oyc/cWnHdLUAAAgAElEQVSWl9OeiCOp8NDp\nKWVduXd5xfG4YZ8SexOieCgLc050fsL1kXxwmHWhY8kZL0Y+YLoRRaqh0rD0kitGpZjgfY9bt1Qf\nwHsCh4LkGKi1INIjXtB1BdfhXaM4qoMcPFIN1ze6odeGHFccITi0LNSUcKoNTy6CiW/2Gx85POV2\nj6o2hSgE6sHW4wSsZFQLIQRKUTo/ksuKd9oOY9LKbDl0i6k25Dxq0HVoTQTf7FlioOpQjBAA8QcM\nu7Tno4pLV+Tf/caLa0XkM7RelCeB7wFeAv6amf0vh88/A3wR+FYz+52v+7p/BPy2mf3HIvLjwH9v\nZje/7vOeVg3zr5vZz/zT1pC/9uffzVNnkamH2AVcaNP+NLdOMRB8qPTDyJJWHA3JPs+0Xp2hYxy7\nQ29bBRpOeggRF9trXa207WYx1nmH963brFZFxOG84rqu5Urmlbooy5zJpbCmlWiO07O+9WVRyLkV\nMu6XGcWRUkaso5QM4pAAXecZpkjf9RiGO+gHYkYLlCyHQ4USQwdExBJODgrFvB6ynK79TErLZfVD\nU+ByIYYBpBDk0PXW2SH03+6VXKGWihajVqHvIHrDm4IYtSqaCkP0LVfnPV0sbacgHlwFV5FYMAdW\nfcvtVIPcobIizuHDCtoUlwacdLgiyKKIOUryqMFaPIpnSZVcYNam/JfiwTucd9RiZCpqbVhWa+sZ\nUg5AgzdCxRYgQxGlloNIfLCtRWckrY3quZRmUzMjHWYXKRXUBbRCKpXiDHcY1Fi1Q9m4ULGWKRL7\n6kGjlkORulWa9hkQrVSB3lXQiMtKFwSoZJrlrbiVpJWydkQfKSlTxaG1NlofkFxTmWIFESVLPGSQ\nmhXZTFvfG4YkGiDJgRRItbD3Qk8kOEcSOwxYKsk1pRVrGcCCogi5KkEF7z1fWRM/9eIfX/bg/6/r\nq0r1049RguPECU975cI7NrvM0Qj9kXC5D9zNjr5suaqOdx135CDc2xp3FK6GyAcs84o53h0VnTas\nlzvEdyxm+K5yahmnji/myJsnJXnXBqIO0lqwNfKSFWpQQnFcF+W7bvZkA0srwTylC9i65Rf3E+/v\nVkIMzYJeMi8k+HxyfHdfOI6eVxN0rv37b0LPc6vwuFt50Qc+ewkfPDIKxrPHgS9vC2+dBtaqeGY+\nsfd8YDKWbmAcIOyE5BLZjH4K+F0idj3rPHN244SKUtOKWVsLRAKfzoUny46HfOCuF45Sw7LfmQtH\nUTn38Lk68ozLVIHf3Hd8YNOANONRz/5q5hOL5x195bENlKSEvq2EqTTHR3CeYWrhVN3DvM+cHAu5\nGL+XlHefbggu4TMYypogF+N0ctxbApsxEGuF8QYy3+UVjUxW2ZfA5lZP3M5c7Cqbvmepyv3keN71\nvKtWnl8TTwSHHyKLwpV5OtfWII/xpQUmUTaiqAp3rNALuKzMCF/MnpMOulS4dI4bAd7sKv9ojjzR\nV764Oh5xHifKZ1c4EeHZvnIb47UauHJwRuVm7/nlvfDu2ISBLyF871T5/Ay/X4RJjaEznnHKS9rx\nSnF835B5ucK+Bt7SN2DFF3aB7z1O/E4N7KrwdFfYFVhEeMYrH1sDP9wXPpPBB+HBDDdc5TdL4ESM\n82DsiuNhpwwYFuH3kud7B6WqIqY8XzsM482hMkXhM7Nwv3g+NFUuimC28Fdf+OOz9n5TUfLoAloK\nse/JdcbPAkcdoUZqaeAGAJtuEONIqYm6WwnThkBDeW/GkWyVXVlx4pg2I+u60tWM85F+GLm8vCAO\nA6qRfjiirA8O4f3SckjLAlcJ+gFK5v71AyQEglSyVvphaAQ4+Rq22DlHur4kjBP9MND7yJzn1ocE\nKIrlHVYq6zzTnxzTdR11baDX3bIDEbpYWNe1ec6d4/re8xA3EGMrxj20Y1oIrW+q9sxryyaUUnjo\nxk2ut/eJnce7nrydGfvAne09NgTi8QknRwOnN26ypkKplWPnGLp2UJhTZeoiKSVMjPNHHyF2Hbtl\nIYTAWDIxRo6mAeaZuw8ecHJyQtJGjjq/dYt4+1FKycRwg3meeeLxx1m2e3xwfOmFl1nXlZs3bvLa\n/ftkFZarwtnZGXcu7pGrsX/tHktZ2Jye86UHD9iMQ0Ni5pWUF4J4Lu8/4LFbp5gtLFcLoYuEsWfN\nmZ3zmDlySSiCD6FNdKvR4Jotb+CdY4odKhniMaEeCoQPiqSa0HWOVFc8goRALUo1pUahk/Y9NWV0\nXVoWyTnECT505JTaeNZ1WBcA1ya9GD46VCs1JYgd4VC666ex2Sy7CYB1WQjRITI0BL0Ztcw4UdR7\nwtC1wLprxJ0QO7LmZj9Vwb8RND9cbbOm7VCnbYputYAL5P3c7Ir5j1wW9yzwl4G/CvzXwIeA/0lE\nFjP7m8CjtL3ma3/o6147fI7Dxztf/0kzqyJy/+se8/+61nVmzbDMwo3TgbJLdFHQ2vJAPq6U7Nnt\ndvhwRNdD3/eEbqWWTAiG822SpjSSonOOZV3pa4c6o2oCafYxQtucezeiNaO6si4O2RXWdUZ8YYiR\n6TRSS2RjA2qFJbc+IeeNZVVCV5kOvXDOTUgUtDjMPOoVM6XmQKZt5AWlasWbEAcQ27SDh4OkhrED\njUTn8M1LTKmFoC1To6UNSGpuarK4gtaVo6NA50FrIUpsqnZtaPo8t42y7xuF0WvCaaXzgpOKC47s\nWmGic7FVIEj4GmDE2msnxNarJj6jg8OJYHWP09N2SD1E6loGr8dLpFSjeJrVdSikEknOkdUQNyIu\nE5JhKkSnJE2UIpj5g8qsmFWWIgerbkWcZz7c6iIVKlQvmAhZPMW1w1VHO3S5BFU7xGvLDnmjakU6\nYRqkqVA5sNSC1qZG+dB8/5WG+xXXJtbQOgKd84ePjaSaa26PDR1LboekKILLhRCNq6RoTTgXqRLA\nCjUbGgRKy95uDn1qqg0WkcQfhiWGHRSpljly5Fwa3ZM3IA4OMY8HJoRCZgF8hVw9tSrVtVevufZ7\nUGlZKjMozpNTQdM3a/V1u07PjX98L/KvHWc+tcLba8I2keMKujecFh72QvIDT/dwXYRPPYAPnnTc\nkMRnl8z5kXBM5WeuAh86Wblx7Lm3FJ7MiaoO6Tf80r2FjxytpNLhux5NW+bqWbR1Ef7steekCtoJ\nqsKXtzPFOT7Yr/z0MvAfnF4weM8H+h2qgcslE73w8w8c3z5WvntSbgbHVip9EB7xma3zXC4rzy2R\nX9kH/tXzzA+fO+5slec08A92xrkT3iFXvJo8LyThEa/8768UShEYhX//aOUOnlWVp9aF51fh2c3K\n718H3poueS45vvWxnmV7TScV/Mibl8zQwa9dw7fEROyE8WTDjVvHSNmhFd7vBNwRnWX+lTMYEVYN\ndFY5v+F4zA9ILZShI64F7zL4U27VLRe7LWdHLd5QpcDRKeOmI/o9p6ycs+JiR1dh75QlG+jCwyeO\n166NVAzJM8Nxh1/vk8Sx3p35tX3kw7cSn/9C4amxULxH15lVHUcOfvlF+PCTM2+n8Kl7Pc/0K8cj\n3CuBhOfLBV5PrfvwZld5a+cICG/xibkK9yzymGvVDS8hjJNwoxResUDnIj/SK88vHX/hFH5+pzwT\njYel8vfmyHYVtr3wkSnz3OL49dlxuodvHwufy57OO94TAz+99aylvSbPuuZSWsW4p553joWijo/P\nkU00viUUnhF4x81Mh/CjkyPVyis7xzQpj1L42zuPCtytyqkXNmI8c9zW3/uHSdJHRnihGC9Xx+dS\n4B1V+VDImAUyQnaeL+8jN33hgVd+ZfZcJWOKyv9xKdz2EPSPXID9z3V9UylM8rYPI+Mp2nW4A8YU\n5xkks8+AKzBvkTiiLiCuyaRSKyZQS7Mh+a5DtOBd4/YDhNgsdN535JTp+oiTiqpDukBdW69OC803\n+EM1T11Xxr4n06AMtVQckHRPqkKoyuClKVSHUlzfTW2ae2hNX5ctRYXNNDDXTHSBZAVZMn46IeQF\niz25GFoWnG/5K+ccrhRyXhEnaCmEcaCrzZfuxhF1lUEilhV/1NOHQFpXjvueZV3pNiMpJZZ5xhkM\n48i8LozjiPOBdV0pudlxfN/6k25tJu7fucN4ckbOmZpb67WL4aByFHxsfUZDNzYbl2ibOIZA5zqC\nN7IWYhzYzlvWtdU9np6eHnJeme1uz1wW1uuZ6ei0eYJ3M5vTc1588UXG4yN2d+8Tz05YUsNrqyrD\nMDUS08Ul4zgy73dtSlsATVg1hvGInHNTlkTwTkgWwBIhVfTQjWU+oAg2r4Shp+QdQo91oZUycrDF\nHJDm5BXFGsGKQvStEFXFwEfUYmsqphUcuq6jlqbsOBEaiTzhBVQaxEEVhr5nyQtCs/rhQrMCoK3f\nxHtMHCEOZGtFgXQ9Tg1coK47zLXuKDNDNKPuDfIf+FpREcSHBkwJkVpK6/hadsTNEVKWdshLC/b5\nX4FvXGFaaVmj7/66v/sfgQ+Y2Z8SkY8A/wR4zMxe+7rH/BRQzOzfFJH/DPh3zeydf+h73wH+CzP7\n6/+0NeQ9jx4xRTnYXVu27/vedsYPvuOcWiFzsFkRmmWmzq2jyRLimrLqaNaoECMYhK5RfbrgyEnR\nujY7b66IKiG0wxjO0R0oYalW8B2urgTvMXVUYOp9e+1LUxSGwbX7rzVLNpW6GNWaUqgmaBWUghBR\npxgtc9IC/9IoeuJw5ppF2aypjQfcfR/7RgI0oZa1KSsHxaJ3EbNMjK7lt2rFYzhRjELnfLsHXasd\ncBLR0p5LFxQnhqPgfMvISR0I0qiO4hVzFYmu/bxSmtLkAVomVaQgXrAccNbwyaaGq4f2xCxobUoc\nFnDF0BJYq7CWQq6BbAXwpNIOt0mbOujxLb9T2yHSm1EQqoKnKVgmIE6ppU2qFdrvHw5Eu2bjLYfa\niVre6FoD9W3jgRriA6UYVpqCpDS1Vr21NbRWTANmELu29mgxshrXNTXqaXuroihUE0Q8wSvRCb0L\nXOlKtfa+QG3vNe5g2a7QSJ2lDfFUfEsa2Rt6ZCEX14A3IlRrSpB37RBXafAKO6hqxRzVjKIt87QH\nRJsCgRnqAqEKBcdz2yueu96BNJsgNLTxnTV9w2vIn9T1xjrybz16k7Ef+CKBZ5xyI0Avwrdv9vzc\nxcDqDZcymxh4oE2JeiIU7q4OccqnU+DpUHl8amtv9PDlJXDiv6Y+vnmET+8879kUzJSIoL3j3l6I\nwZgLPDIo8UAivFqEW33HQqafHKwOLZXkEr963fG4VN7TZx7giaZsVbjRORYfGbUg1filPXx66fhL\nj6x8cud5tlP+8d5zUpQ3HQWetZVt1/PpHXx2Dx/aVEZv3MXzPjK/tXje0mXuZsebJmNV2FB5gYFn\n+sTZwQbuNh2xE9J24ehoYlky/aYNiNOSm81v6FnLythFNHakfWodkqXiYutLujXA5YOFcTOQqqIp\nEWKHC1+rwhBf6aQAEwMryQEIA4VgAft/2HuzWNuy6zzvG7Nba+29T3Ob6ovFKhbJIilSoiiKrWWK\nsmJYipVGUhLLQGAHiA0IechTnhIEQRogfnBiJAiSwEngADZk2U5kKWqiiLZsyaRESqREkSKpElms\nYrFYdatuc87ZzWrmnGPkYe4qMIpkhCIgWUDW07n3LNzm7L3mHs3/f388bjTo2JYAywGrcHLqEOfZ\njUKkZcotk6MfEqsO9gfFDRsuLi5YrRIvXyjX1o5DVvp1z3YRzlcBq8qnLwtPngkXl4UrdXx8n+ic\nsq3Ghwf47VnonFFEeMoX/tkyIFZ4TCoBpfPCXfPcqZ67k/HujfLxWYgKXhxRjD4K3+oXvjA7Xp+E\nJSvbCgvCl0X4YJcZqzIhTOZZLPJSEYJUzjWQonFPKw9gvC4p/8cusQmFx7yxNaE3eGZJ/KXziR/b\nR56MhecXYXGeh61wzVf6Al+SVjN9oFf+8Rh5Y1T6JKwwOhM+f5wT3xdhrDBI4fnqeZ03DOMmym3g\nXITPzpF39oXPzcJT0finB+Fda+NhLfyjMSBM/NxL//+G6fe9hmGAITKEnkNdMK3UUsh9B9MlVjLm\nElEiIba0dCfK4iCEeAyqXTeJnSimEGPfPqzKfEQ9J9LQsR23dChqDm8RyZXpsJC6jlwyWpW4WlNM\nyRhRjHF/SVKHrjpsnOnCgE+JVYrc2e3phoF+CMz7LU6EHBMmRowD6+GEcdxyHtZMdWZF4pAcfrrA\nfEfnwfuApRXetwm4iGBdj/gmAyy7A1kdtUs451ibEfvEdNhj655lu2OkME0jd2ILV1xNBw6HA8Pp\nhvHyCua2yWoG44XgPdM4klKiF08IgZcv7uFTYM5j27g4uNjtiH3ixtl5o+GtepJzzNPCjbNzBu+5\nffs2cRW5d7mlHwJDhRfuvcS1s1MuLi64//pN1qlnv9tTo9D3PQ+k69z2W5wXkvNMfeLysKMbAnGe\n6a6fceIS+5fvcLEf6dZr9ruLIyMqsr+asdoIcLaMrenNHtJMEGMu7XtlUlzdApCTx+PapLnokcSo\nlGXBxzVSS4MGHH0JqRvwx7wll1ZkO06YpTVRedwTfMTFAWczZZkp84KLkYAR4hHNqbXRGkujolVt\nU+W+PyLu4+nXNXgdzowuJEYqkjN4RzWIMWGl4PLc0PreI1ZwNNKWDxHNBRMH04h435ruocctguVM\ndYK4VvCIFmzcozEhoUPKwjc5H34R+Pzv+b3PAz94/Pol2gLhAf6fW6b7abCIV++5/+v/gKMk7xr/\n783Ua9ePfvfDvOXB64gI87xvqwoV7l5kfMyk1IN4DodMTJWUEt5HQqwctgWdHVV3eO+Z9luCO2/U\nsFqJwTUamk5Hr1wh2oD4EQ2BWEBWzRsWXEClUsvM2bWE955hvcJTWWuHWgYMrdJIa2iLDhXfJDW+\nYksDc5gZzjqcBJTmhzMFt7SwUXMNcy5FCc6hSxuA5FxJIVKo+M7w0TMMQ3vPaMF5Y6kLw9C2QeM8\nEX2jWJlz1GVp7wffCm47otqdN1QLowp9gKpCUo84UPaYGD6k5lM6FnsAaGqyPDGQiu+ksQfM4SxA\nqYgX+PoZ36vY6lxwzlMwqhhTzeQSyVTEfPPTuEgxo3dKy+tsz7Waw7nmrwnWiHJTmcGMUj1Ibb6e\n2hpIL4AY5ejJMTOqKVqVkttGpZ03ih4bEJOCSMBKC8F+bUermQB4aZtIs4LUluEm6nFuonee37h9\nl++4777j/7k57r1CKRm1yjYISYUqwpwLHGWBztprLs41T1wVjBkVD/4YkK0NPmPOEVyDmixqmBWm\n6tFciUfgiwrkXDju4RCMe1RiCdRjQ7QcfUpaBMNz3xC5LzYYSDkOKO/WhY+8vPxBj+m/8Ne3n1Ue\n72aGkNkXuJ09lwXuaCJJJWrlGe14L8ojXcC7iaTKbQdPdS3L7vHB8anZ8Wav1Nl4XYKDQm+FdTKm\nGnjTWvjVvfDOaGwNHnELKzX+98uOP3+euXcl/PoSeP+p8TtZeFdamJzn9p2FR6nUTeD2lfHGIDyW\nlKGL/Mo9x9s2yv2njvGVhbWrHJKneOF9vfKn7+t56arwpq6nr3t+qK/81CHykLSYjRtk3n0Seeem\nZR79kxeFtw8KIdIZhCHy9G3hzgjfulEuiLydQlpHlsPMPkW67cSVGf9g3/Ho5cx9TnlHnviJe4nv\nO3V85J7nkVRwLvKdm4zaTBDhywfh8cFYSUVc4N5FxkXhUApYO+9220wchPOTyDjOuEGw2uFiRdN1\nBgd53DF2K+arA6cbw1fj1qFy83zhy1eFR3tPFz11rqQu4lV5YJXYRk+UggsVGdYUnQghsq6Fa+vA\nEIzPvOL4xIXxI+eZO3f2OBE2NfKlW3ClwsYrX8zKO1JhmiKn65nvGODHrjpueHh6nzhzMwvGM9Hx\nLb4BwnpRnsnwhCi/sjjenZSgxpeOg5SxwGMr4VowEkrXdTxT4aIoTwE3u56Pbo2nkvJASojL3Dtk\nnl5giMZ7h8oC7FTYVXjLoExV+GDKfE0Tz2XlL16f+NktPOoFh2PjlTcF5VGvPBDh+RzY1oYS/1J1\nfP9Z5tnZc07hpQN8RT0pKm/zyhcnxztS4YUqXEyRS8ucO/jZ2fHESvhAKjwzKbey8Iag/NokXKfy\noFZ24nm0r7hvWuzyjV1/ohqmcZ4xiYz9q6ZbbTKTw9LQkasHkLowOwEK5k+hi6yW9iEtqqjUVtxK\nIsQA1DYpKwVvwlwvKIeGCJ4U/DBQ9gfUcZzaKkM/IFqoDmJwTIctk1bQSuh78rRjsxq42O9grOxN\n6fuB+eLATCtUg/PMVxcgx5Tp7SuEtOJy2qHjhPQ9iLUPP5+peUR8YskZasUdP9R88K0oioFVilSM\nQMs6Kn3HNE90peCL4YbEXAK9S3TRkXNm2KzxMZC6DnfeoALi3HG665jmhdINJCdc7ic2mw2bzSnT\nlNnbSAgeP2VSDOyvtlw/PWuAiVrwoSMoXF7cZRwSF+VALImh77jcXnFRMjdu3ODB6ze5/777yHNp\ntLxlYj2csN3vcD5wb3eXXJS8GCenp6xSz5yFy/OedFgoyXHt0ceYa0HHPcUlhpNzypTpUCwvFHW4\nzYYhRpZcYJ6xIUHdIZLwvadziWJHs7MIWio1BjrpKLHgj3jyeIRpiFNSGqjjFufa/RNNBuXEU8rC\n0kfwkaoFHe+iR9Ia3lF0bjKtrHjXcli6XiiimIQ2GQu+ZWilFfN4SVitqLlgMVKCx1EarIICVRBT\ndJmoOdNv1hTXtk2Gw7TJfTRnnBcEbUGV0RHSGnWBmkvLgDnqghwVoqcKNDi/4l36ZhumjwJP/Z7f\newp4DsDMviwiLwF/Bvit4+txSpPu/XfH+38FOBeRb38V+nC8X4CP/4FnyJVy1w7knDHL+CIEHzkc\n8nEbM+I8aDRyjHTdQr9RrnYBitLHxCSKmuD6xJRH1Br+32ej7CMxLiCBvo8kV+hXEQkN5GGlcno6\nIL55kLxtEGl+njpl5jxCFxE82SnijZIdZREIRtUM0vxQ5mbc4qEYubTg7HjM+7KlUJ1HXaE6bUGk\n0rK6XBdJIRCqHTcMStYFK8ZCJgSHFI+hiIO5tLwdcUYBomtSwTD06HGD1NDZ2oZP5nBViMkQ316R\netysRRM8Ds2GC7E1FLX5dSxVkNrkegLoCICpYX5G1ENp0jalINUfNybt/2A07X/JihPXwoYzmGUq\nzW9oFAqJuQjqAOfI1fBdx6y5SSjNNWoCgjeoxR9pioVdacQnrQqW2xYmOLQoarFt/4O2NVB0iAVs\nrqjL5NriIcDjXGk/K2jyOVXUStNnm7bNlijUQBeEt9+4Rlkc6ivHnR2Lc1RWqG9yRnUCDoQBtKKa\n2UmTCTsXOFQFqUTxCB6/tIDzbC23z2tGMYpTamkUTy3NdzdJ2+AVWUAaItiOcJlrx2YPsSblU4dK\nRbsWmyDmqd5appS089Wq8Cf5+uQ28guHHgvw3SHzQlUCgV+88Hw4ZZ446fhQKXzVAtdC5s48cH7u\neeiwZXHCW6Vy14Q3hbbpfLxXRiq/tERerIG3LwtdLHxqZ1Rx3CLw1Mq4PRkvuMDBhK8cPG9bF15/\nMrWcKwL/12XAWeUBBzfWxrIUnjwVfvVy5iPbwBbjL59NfPnC011lRuBNSfkfbgUC8Ke6wqfvXvL+\nlfHifOCfbj3fNsBelL9/b8A5eG+38GCs/MwhcftF4a2x8FOz8KfCTC+eKpUf2AhbE85xnFVHPtmw\n3Y9c18IN55BeGbPj3z5biKEyVsd6EP5NK+jK8f2xILWR4yqtMd9n45breYPN3B6F672xOumZJiVL\nJlij1Q7J88ylcroWfO+wBVwnOIQy3YHeuKzCTSbspGc7VmoVHjg3urTmdTcKm9DktNOSyekEXTKz\nrdnud+QCc3bcOM10IZDLzPOnHaf7Su483/aY58nR8FqZa+K+dWI/GcO68rqxcq8Evnvlec9J5YWV\ncXf2pKGF2L/FG9ve895V5p5GwLiG49PZM4nwF06VpyfhA11hVMfr1gvdVcACPBwdvzsrDzjly1n4\nRCm8RxY24vhccfz2AlECX8yC1j2/khPfOxjJO54zx7Wl8svbjvcME79eA//qSeblWRi90PnCO1OT\nDn5wZfz8ZeG7TpTnF8eVE25JaEG7rtJXx2jKTSrP7+BWhneeLmx94NuT8VxbuvN6n9kWuOmE7xkm\nfn4MvLnLfEcnHLznhdlxLRmnoqwwnoyZu+Z5pjgeDsa5Qhf+aCV5f6IaJisLlD3ehgZwmEeqNgNg\nnjK5zEieIca2MYo97MCGBopYUMgzPgSkzpRilNx09y71WBfxKsTVuvkVfN+03qnSFaNWw+NbAVEK\nrm+mfOeaZj84IS/Gar3h6nAgSkFOr5HUk70QeqHqQpdLC0PtloZuzk0e5KI1kMPZdQ7R0YuHZYeT\noTUwoaOj4HtPFm25QDqRa8UtmSqlEbQub1GHiM09SY1diPhSGUSpWelXKw67S7quo+5GVl2Hed/I\neprp+57txSW1VJIPrH3HdrrC58xYZujPWZZLTJrDXpwjOccDjzzMNE1NCuJgtVqhWSkVlmWB/cwd\n2dLPCyc+kPoNg3leeO4rXOy2EITVMHByfg3mjFNjPx1e82vF6BhW65bfMqzotgvSr3Dm2G0vWKU1\ntUvsr+7hp4QrMO23DNdO8EjLbChzaxxCohj4bkMMHXm8IPfnyHFaI1Ipyx43KotvRlkVKKUwUwkx\ntYZqWUCt1VchgSneR7KBTxEvRilGIJOXERNPLYJPEcerPgaH5gkfminWO0fNtfmQOGbRygrX9a+Z\nwUUXLMO0zHgfENe1yb9lSp3xsfnMmvn6mD1WWpaS855qFRc8xISKQ5cduIT4o2dEm8TTiSKqLRTZ\nR0Qc5bD7Zh/l/xr46FFW9/dojdC/C/yVr7vnbwD/kYh8EXgW+M+ArwI/CWBmXxCRnwf+poj8KA0r\n/t8CP/Z7CXlff81XlewLw9Bzuh4InRCDI5vS9R7xR5WxibQAACAASURBVLP+ZCy+otnAC0FnqngO\nOhOLkNIaiZWoER8zdAMuLETnKLYh1IL4kX0RSvBU7QElrDq+VidOnEeiR0yI4hCvBAkEv8K0MC8Z\nzQ02U+dM6jfkOtEjTSJbmjRtl2fUBaI38qJUp8Tj8x6lnYXiOnwtFN82Ge4YTNx5o+pRQuZSiybY\nK0ppjY0oWSsqDT9ugFjFlYwXWlMvglGP8jiILpDzvmWs7DJpFXDeiCEhLuBdj1htmzAGHBmdpiO+\ne4+UFVYONMpjd3wWXx2ONTKkyYLEiFjFimHWo1NkP08kcZTcghSViKPjsDfGZWZaAnkBYmaq0nqv\nWls+kswwBsznNjAyWlMqbaCGeKr51qjUinmD0qIjIoZJxTmjWiS6SnKBQoNBzLmCjxzmSo0t36xo\nez1WJTLXTMaTOUorqxxlcZliig8wmEfCgmWl1FZE53rcTDlw2iRbYCAOR8u56c2OEsfKOrVnoB6l\nvPWYv9QDyEKVlivVExDfxiOpbw2rWnudZxymbaPWwotbk2TSIDnGESpAC79VZ82fK23IubhKtAaP\n+JN8zcfn6wMhMETPW1zm45OxtsLHJuF3qnKnCJ1TihXOxXjzkrl/5QnAL48Bj/BIp9y0zOdm4ef2\niSd95TQErA/c743v7oUXqnJDmpz3XvW8QY3rCDeBl5fAVa6s+8hB4dtS4dFBGVzl5TFy/dTzkbvC\nt4Y9ZzcDT1blEBIPxebve2zJ1KHjL6J8Dc/jFnlTNEKnvLLAv3+j8ik6vjMqnc4kDdzTwILj2xO8\naai8jOPhDKdJ+NXRsRyMZx38rga+bdzySog8t638cHfg1+h40AI3g4FC10XuTsq1pOQdpC6gwUF1\n9C7j+57pak9RJZjn3YPwlamRJ3srpNzzWxM8L4HHbYHoecpn3vTQimXXgEc4R1wZZe5RS1wsM3W/\ncKc6BrtisICEQKqOL3xt5ITChQTO1pk4rBh0x2IFxxVZBecd0Xm8DxQ1VqsOvwViwmlhdzWzSR4N\njjIWdHG8PHm+csfx4YcKVo23zDPT7HhudjzmlGdnz5/eKK/vhOe2C3dc/5p3cPLKVw7KmVU+mwMr\nr2yz8LElEEbHh1aZiwwf2QdOtPJs8Fyao0M46wOfWSJPripv9pUvTJU3+4XfGB0HlN/ce962rjxp\nxr4K33c68kKGt0XjZ68Cr/OVM9EWLivC01m4UR1PDHDhBOeFR2Xi2Rq4dWmcBOWA4w0Brkz4ZHY8\nFZXPTIEkxq3ieXtvfH4SogqPBOU5Ex7xECTyCpVsyt3J6ILxFoys8OXsWHthZ8Zzk/ByhMe88vL8\nR+uF/IY9TH/UgZPH+1tw7ds/SFrdIIYVxSasFPqU2BalT568ZEqZW7aG90jJ1LQhBGnyDxF8EeY6\nQnCcpDXqHXnJhNC1nIhhQJexyf1EMAkEmtk/q9BHR5lGitajFwW6lJp+fZ4wHHSBwRw+DSy6sBk6\nfGqFwrgUplKbkVwbiAJp5Kt5ObScm2mCft0Ceb3DpYhu97ihx/KMeI/g0DKD63AnG7w0YtW4LEgY\n2DCy5EznI6CELnH3zh38asDmTOp7UkotVyo28/bhcEC9MDnFtjuGELh+3wMQPLvdDjXjdL1poaiu\nHRp5Weijh+jYb7fkosQoDakrAVyhHHZs+hU1zyyLUurMaui58dD9aG2Tny4IL92+Tb9ecW97xenm\nlFwdYV7YnJ5wde8uMa64Pe7JOaPBs+6H9m9eMuqEG/c/wFKUMi6tKDhOtWtZyEVxqW9GeJfI45Z+\nc8IytnwQpFFxuhSp84j4o7k5eKoTAsJ8nGbXOjfj+VHnHzoafUoEL4nD4RLKFiJ4d978EuGYayQQ\nRKhV6asyWiHEFVIyU8lNm03zihlNKmTL3NDD1sz1LTzZ8AYutU0hOFRze0+atC48JoLzVJrsUHOb\nJFuZjtlLhmnz5YlvTbseG6yWH9UKUhGP+EgIDmolb29jz34avrng2u8H/kvgjbScpb9uZv/L77nn\nP6FlLJ0Dvwz8e7/nHDk/niM/cDxH/sHxHDn8QWfIf/5dj/Mtj6wZVj1Wmodwuhwb4MIm8EJ1nkUr\nq+MWu31sebJVfBRqKZTs8TWBzRymhSNLDCvGbIForknR/IZSR7woXR9hKVQppNDeO48+csbZAw06\nIEPA2asBx0srUq15g5CI1bZHqcS2GVkqUpW5QPAN0oEIVZXifPN4ipCSsToGcJfpQClKGWeYDXOR\nXGY2656TdUcUByWT1AjOqK5J0dxilKxs50Z5ZGnUxeQ9woJW9xr8weEIySOmdD7hvLJoPgINPFEM\nK7uG7U4B5yprH9lvL1ltBqap4FJkXvasYkc9ysuiCClV+s6QobbtZ14xjx6rjQrn8JRmMKJabH6Q\nbKR+TZXAOM5MJaMSyHPzWxYzXHQsxSiaqaokAPMIPVPJzFpYClSNjHkBV1BtnrYxV7S2DaKSyGWm\nLgvFQUoD85zJOuO7gaE2f9LoDdQ478D3Hb62Ily1BWMWDLRixy1PtcbL9N6zWGCp5UhpbFc6Bo+2\nbOn2XNdaseAwrBFe8a95loAGp3HQut2K09C+OiLJ9Qh+eDWTyWhHS0OYN8pmpfnk7HieLOoocgy7\n1TZkqrVtJkEoQDLh5VL46Vdehj9B4dfH+98FfPJ9D9/PD5543hCEg68csvFgb/zM1cAHzzK3tvDL\nOfC4Fh4OoGrcc5G3dZm7Jhyq43VS+Mk5shfjr55ndtUxAjfNcVeFzUrwuYIoz+VANs9b3MJL6vil\nfeKHzya+Ohkfy4nDYgSBP3tSuLV4frNAmAN5MH6km1lHx5ey46nNQlitCGRuH+BudmxOHZtDxYoi\n4im58j/tEh3GMlW6LuAy5NAQ09tRseg55MpbunZOfnz2PBRg6ANvXRVMlV/bBR6JgTf1W3IRzqLR\nGRCEj94xTjvPZTaeWsH1vp0d6Zg/+Pzccgv/4Ry5f858cJN58jRgybE/KIsK5x2oLWjscC5AnhiC\nozjjpS04aSHQyVd681iA+VA5GZonMWelyszgO87vU1Q9aGBIC1dXjpiM23th3QlVA75kNhs4bB14\nuByt1QQR1t5xyJUXs9HhuHZjw6BTw5vTQn1VjHF2vFLgPi88Wz33eeH5feZbTh33JsfnZ+EKx1fU\n8Zc3E7ezsY5wyI4zD1vveJjCZ+eOZ5fAizQP5V4d7+iUD5xntrORPJwp/PeXiUd04uHeiHhGE85C\nC6q9qI4nUub24ngoZj43Bx6OAcmVn5wj7+qU38yR+xw8r443h8peM28NygHPZ0vgulPOqnIfhRs9\nXBZ4pXgWUz66RDZmvKjweIA/v1q4pXDq4Qtj5C1dpZeZZ6eEGrxQA08Xx9tT5cwZnyjNH/iseb43\nzfziHHjANZjSv75eeKkKn71a+Lu3L76pc+Qbub6hhumPI3DyeP+7gE/27/gwOqzIeSJ0m0YmEnDF\nCE5Ztgfk5IzYxWbyv7iil0Lt1/jUfBtLHUna4cKGg+3onWOcJrxrAaJlya1YlUaPUgPxPSkGWJ1Q\n5gPBWrhYFmU/Hui7nmIR8Qt1mYlVCWc3EB/I45YyHtoWwDcDf3d+jSkXehWmw4HQ9e1DpzRUuKXE\nkHpUF/o0MOcFCYFpnHAxtUlvLdB5fDE0KF4SdrUj14JqJq3PmmzGBOtbkskQOnyemC53hE3P1dW2\nEfv6jrm0GrOnQ6fMyEIpCxIi56tN28rRAlAvDwfOzs5YDjvACEHI6rja7WDckWLLtRmGUy6vdqRN\nolxMoM08fu3mTUqpHHRmSCuqwLpP+NwKg+de/FrDaDvYX+yRENgoWJcI61OuXbtGqcY0zoQl8/J4\nxdl6zf5yx1y0ZRSJJ/Wn9MMKnbcsfqDsLtBS2wS5TLj+jFqXtpmZ5ybb9IE+BWZ1aGmAD0PRXEDn\nBgtwvsmIKOCEZIlSC5YiLqwwm7Fc2uRdAm59hiwHnAsUX5t0gKOkrihxhmW627DiR+S7qFJDIsZE\nyQU5gkbUWpgqzuh8IE9jK0TxlMO2BbEE13wOoYOiuDy391f0RxSz4FOilAr16COwFpbbPD0VXALR\nFhpaFec9IfSUJcO0o34TwbV/HNdrDdP7H+fhzdCM97VSamVaHHNt772lamuOq6PmHqRBIJxmAoKK\nkeIakwPOOzqX8H3BjwvqBm5f3OWJh87woTDEiEgkLwvzWNlP+ZjjVDnfKDnDtfVAFyo+ZtarQIig\nB6XUwiKBmJQ6J8SPeCK7vbKjtsDcqYJ4Yt+1hHRxwEhyCde1c90fZZueyDy3AU0wo4+J3dIa54QQ\nRehioi4zPoIUw/WZw65QD4nbu4UQPA9cE1JSnBP8VPFDYEhCwDXIjSjiG3Et+tDoejkgMuNdx36v\n9KnloU0HT/JN6ucdxNC2sd5aFpHUnikvyBHhHX2iT5nVakFWghWYd4Fx1PYznnpcDCxeMe1RqZSi\n7MZAVaFWx14zkdiGDF4pJlh1HJNq4NgeBwpCwDtPKc3fY9ayhWZ7NQvF8AqkFnh5DLoni+EMMh7v\nhFwKe1GEQNSIMeHFE2vbnC/icK6wVG2NjgRqrcxlYXFGmxWDc0r1HDe+jfDX8OeVkhx9dkdQjIG6\nI+TFWri788cGLJJrwSPk2hrqKssRYiTH2FnH7FoBW6ViNb22SRJt8A7nOpwD1UKRSocDdxSsiB0B\nEO3MKm2ZcMwMbLLNO1Pmx2794QudP+5a5L944pxN3/GRQ+RDvfKPFse/vF44ZMd1X/jUZaBfeZ7s\nlVNvfOIufPBkz0iPBseYjS9Wz7utEHzk5xfP961n/s5F4kN9AyD9xsExCPwOifeFidvFc+U8H0iF\ns03i+YPyiCskgSsPv37p+fBZ5rf3A284mfjINvABl7l50tOHym6ufOTC8T2ryj1zOOCx+yK3r+AM\n5TNb4fWD59wb25r52EFYB887BuOMyj6tmZeR6wl+6iLyQHRUp5zUwkODMGBsPZxH0NuV38yeFyr8\nK+fKBNxwlbkLqMD5xhP3C9N+ZtdFXrx0vGFdSFGYansWT0zRErmllS/NwgsE/sK1mSBHP2SIHOaF\n9aaD/YRae0YWDVxOlbtLZk3ADcaN3rMfC5tOqAflYjK64HnopmDVmAp0K48V4dqqsM9KV+GLFxnR\nQEjw07c6nuwrNyncv6qkrmc46ZlrwM0jweAn7gk/cK3wha3x05c9DyelePieQbk2BCgLWxK39plX\nZs9prPzC5Hl/J3yyBN6fFl5eHAczrlzgB09GPj4OfHEMfKCfeQXh7uKIZJ7B80avfDonsMzbvPJI\nhH2F6oSVBJIrfGpyPBEKn6sd39oL15lZOWFJwlrh7hL4innOMF6nlc9NhZd9wIvxgZgZVfi0Jf5M\nX/knB89bkxLEeDoHDmYgxr+2yXzpwBGC5fiZQ3M4HrzjjZIpIXCxwPemFvGzF2E04/kaeU+vfH5y\nbFzGA/9s7ngyKDdc5Wn1rHG83heCwKTwRFJuRM8Li/JSqfytF+78oc+Rb/T6RhumP/LAyeP33wV8\nMr71A+RujfiIlAnT5t0RrwS/xoVMGDM1pqZ7x4EF1CsuRsrcPhhSSpAzIQrZIqEfWOYJU0MqDMlD\ncMzbkXi2aVKCZcFQVl2HlvbrUhXInGxW7QNHK4cCnRdcmclaEa0thymtWaYJ0wJVm+k29qy7wHyU\nTPmjHKxW47AslGWis0LqOqZlZticU/JMDIH9YY/iSdFTS6WUymq9Zpr3gCA1oy7graK2IGrELuAk\nUaty0iXURcqybbJDQguszU3Hr/sr1KAfVjiBuTYvgJaCSx1932NWWzaUCwwhMU4TUivn10+wIOwP\ne27eeIDLqzusu4GsylKMIXm2V1ecrVYYMKxX9CGyVZj2BzadJ4bYwh19AyuMc2bl4NbFPbquA99x\n92JLlyubG2cs2rTOlxeXJK3svNDhGs3JtWmrE4gxkpdCsPbDzrWQUtfkKbUiptRS2oZGFZc6dJog\nDaQuYWVpE+zYI3VpdLyYsHnB1Qpdw8mbHkETHMMblx3m+taU1AnEAQ2iYcd/S5O+WDOgh7ZlUlOq\nBZwpVhe0LuC7134u3rnW1XOcQoeO4GgUrFwwgSIBzFq4KBwnzO2g09JgFT4laim4I2zAVLBpQULT\nheMdSVqhaeNd+Opn4U9gw/TX3v84j65Sm9zbhGRBLNAnQZ2Qy0LEkbU1qUnb8KVwoNSEaMCYMYOD\nBoIEqjUAS7LYfCGdkjqIx8yU9vc3r8xIBxR8VxlWPWdOSZ1DHSyLUnVpRbwp+eAopUEkplFZDz0z\nYKYs40ggEPymZfRo86Y4X3AacP1CwNOnDrMDkZaxU8pxg1UqRcNxgyZ4qS2Y10fEGc4yxaDznvtu\nrDlJS9vyxK75Ob2n04UsmSod1FZcU2bEDXgD7wtiM10MOAwfKy4aUsBHXtts4hRZGknSuQDRg5W2\n3fDtWRIEW3ILVaYDWZBGGcBKxMoRRpADh5IwdSyaSSLstGeaChoinTjc0jDs+3jgMGbyMmDBU8ux\nYXOtoAVBtaBqEJr80GGYb42QUbFSyShiARFPPmZjRXHtPPGeYoJbFgzHJEItEItSvUOpZBfIx61u\nPPok7Yj1n51RrW2SQjEseWxuPqDq2ubCY00iWR0ShKrz0ZtkjZIn0rbbJixKk3AbEI+0VXPHTaYd\nw9GNbK9uoRrt04ujuhY++aqrpDVBDmZldsbyWsvZJvtRwHmH1sJx0kQ5Np6vzJm/d+sPX+j8cdci\n/8HrrvEsKw44BlGuW+XCRx5PC/d7z0mq3FgKl+IpBlt1vJgTN0OhT45PjY4VynetavNFd0rOkXAS\n+OJV5QXteafO3N+BRs+XLwuPXvN4hIupESbvi55skctxYV8ci1Petl6oNEn1yyVy7iqdLGQT9FW5\n+ZA47JWDQVeVZ0rk4cFxrXdUy8yp42QRiAGtM09v4RM74XVp4TsH46uL8NRJ5OVD5r7e89y+8Jkc\ned9QeDk7fm0U/tK1yhdGuIMwWOXpkviOtPAccFMrD/XK2jV7w+O9Up3jYmm8xkOF+zvjxcUzhsC0\nb9TYN6+beuJ29vSi/PbiSdF4S6dsHNxdhBuxso6ei0m5qp4nVi3j8rAoJ6cbpsOOk65BFA7Zg4t8\nZbfwrWeFqSqrlSMSubSBMu056xpUYlp6ohOqeJZ5IYTA1eVEiB4Lxr0rOBXFVitoaWg8f1W4H+Nj\nJdEfVR69eT4zOZ4MlccH5TOj5wmp9FL5fA68e115Zgp81YQTrXxsjtwxwVT5U0PhEwfPI0H48KZQ\nivL0EjDn6STz+SWy8sKywIOu8uhQuKyBiwpvDJUF4asGVxmyeUbnWGuTV5+J8PZUUacE4Nklclnh\nwVh561A5DZWvZccvHla8L2SezjAZrEU4D23OehaUi9pqj51UrjvPG+LCCzWyW4SVUz5dO7YmvOO4\nc74MyloFJ8qtHLkownefZH5pl3hznDCMXQn80uh4qq+8oo7ojB8eFn5y27ErBz59efcPfY58o9c3\n6mH6AeD/POJ9P8TvHzj5IG3qA4CZXYnIx4H30/wK7wPufZ1RG+AjtI3/ezl6FH6/q4YW1OlFydOW\n1bAhSyFbR11malGyFfRq32hLRSFIm7TbhmhGMGMcC1IXbF9Al4ZqDgMxRog9Y44EDW3ytr9sHpJa\nWNQx7bdw1G6H3iO+4869O2y6AdXcUtxDAKmsakWjZ1LFxi1ltyc4z9m6Y54PmLR16nLI1NV5Q1+G\njv3hgHmgLswizONInwKHwxZXZg7zhPOJzhxzzly/+QBlztTpHlIWQjphKs2jYktmfe2ck9WaaT5w\n5+qSEBKX+5n1acL1p6z7iKmw3W554IEHmKaJ3K84XF6yK1PD2fYnuIsLvE+UemA/HsiHzI37b+Ln\nzLA+wQvMy0IeJ/r1KSfDOS9f3ePi3iV9N9J3HbuLe1QUu9qjqxOeeOIJTodTfverz3G1zKxWa579\nyot0KZGnmbpe8+CNm6yL46rO3J5G9NZt1mfnXNZC8pF7d+6ySh11nhjnkfV998G9eyxp1RDxjFRT\nhmHTyF61NoN3bd6TaSl40yPKmSY98yeUaUTHPYSWXs/+EtQhZGzZoUSIns6aNyHTsrr6fkBUya5v\nHqBcsX5DniaSN5ZjUeLXA6FCR2CrC+IEavOmeVPGeYZjAaYSmvwvdg3tbQUdxyahCwEkNGywZJa5\n5Sj52CG6tPe4COYCWuuxGWweB1RBlVpD84WEjlpmvAtwusFZQVWOvqmAmIfU8/99zPIv1jUXa8Gj\nZcGMJrHVwqSGC4GQHFUVbwEojLUQTFCLFBFcmPAKNUfWXlGd6TxgER8CMVZkExk0k4YBs4oPkEKb\ntneDEXzHktvQJVRD5pnkAy56Yn/SYj6dIvd5SiktpLZWotHQ5yZs9+3oTjEyTcpchVQgrTy5jCTx\nOCvEZEy5ttdvEbT3LEsBWnhvro7AwrwkEFgUkjU61RBbo3x5OXFhja5Wq1FrwdGkf+IEVwspNN9O\nk6nOmFdUHNMUEDJoRylG9CNSAjEFhkFYDYVh2LCMFcbMrI0IWJaIt64V8MGamTsIXfSEuLBeFxyJ\nw2FhnIWiiWwdeaksteI8CIHFIISMmWKzZ68FCRnnEmVZUTTDMcwz9J543OJuqqFW8WHAGUgpiKvk\npTU2RVzzIDpI1bUQ6+NwrMO3mIeaKSpUFXLXHbeXitXIvC44E4IGRJUYjrjz6qmWydUw55HFEBPM\nC9XAJlDNLdfoVay5h3rMkgoLrWCWZt8oNKlcQFjUcIS2NVelFqVYpRKamuLYPFUavt6qsBylnrUx\nwVnaervlfgHBFAt2JPK5Y3SGBylM5nD1SHekSaTl2PyVb/4E+WOtRXbR82YynRc+sYV/6drES7Xy\nM/PAO33mohpfM8cLozEZfGKBd8cDn46RaYEfihP3x8JPbDe8yc386p3EY2Hh4auZRRxv7zIShduz\n51Qr6g0dCyPG1SL8w0PPO0LhdxbDh8CHT0ZOiPxXLw/86OmBivLjl5E/tzJeqpH3rQ6IeHbakcfK\nZ7fCe1LmbOU5SzuSDegy8blt4pFOkJSp2fN373Y8mCp3VLiaA5+Z4UfPRz5x6Xicyt+4KzwSHT/U\njXx+hvdcj7zztGB5orfId/SBv3PVce7gpb3j+x5ug6J6OPDXbiW+t1fKZeGJczhPwqpzWIDtNvP6\n+0+IhwPjELnYZj4zeW6aEFLg6rDlrdJxexaeGx1/8zLx3zw2QXaEleekVlIoBDHUR1Y9HPY7Pnvp\nWhPlHHd2mRdVOewzD86Zxx529O6EWxc77s6V0wGe/urMmsDtuiOlxOtPhROUe9PCM7NweWfmzdcc\n/9thxftjYTdX3roqzJPjt3bC99+E9ZRJvecfbxP/Vr/jYTxvW1UmdVQT/vYYWAh0zvj0vch7Quaa\nb1E43zXMnLnAL+wFnQuLC7wzZYYyMddIKBkh89HS8aZY+bO9Qlf5uUPiE0vgr5wu7KrjlRI4ccrN\nAm5d+FsXnv/w+j1+fLviixr5NzaZzuANfeXXxsDrYuYLueMNWjnB+E/vnhBLBZRVEd6WKm/slP/5\nKvEhv/C/7lt98lZf+bI5gnP8ufXMR68iBxPe3iuDK9ycHe/pClt1/HqOXGbhQZf5Fq+cSeYzlviJ\nrfA9w54kgV+dHA8F49+5Vli7zMvVtyGNRmKAh8Xx6W/2JPkGrm90wzTSDpO/TvMLvJdmzv6rZva3\n/zn5KT8OqJn9yD8nP+UW8B+b2f/4+/y9LYfpre/HDSfHCZxSndB3K5bpgDMoKohmCBGJa0QqWmfI\nbYpqVIiJPjjGQ0NMN09JQ4xrKXQpIDGxzBOqjphSMwWXhRQTedoTYiKkhOCPMoXCMu1IwTHOpW0m\nulULubRIqXL0JIDJMWPDILmIam7ZRfOIuYhPPbrMrLrEkheKKl1KhBCZc6X6REiRedrjYoDiG9Wq\nZrw4Fi34FAg+sMwF9ZVQFF0KPjoEIziH85EpL3gfWwO6tI6/84HdbkcIxo2HHsGmicvlQAyCEOhW\nJ0Rp+UzilNT3nMTAlGeqKmehp48OS4HtYc/V9pJr/zd1bxJrW3be9/2+b6219z7n3Oa9V+9V31Is\nskgWG5GSRcoSFVGNJUWR7ASxg8DwIA4QJJMMMwgySCYJkAAeZJCJgwCx4QC2EsNCLIWUYKuzGpIi\nS2xFlsgqVv/qNfe+e885u1lrfV8G6xQJJIBtmoZK3G/wGrx77r3n7rPO1/z/v/+1awQ5ELSmwlRn\nnrz/IYbYMe73nOWR06Mjbt58k9PTU2avrFcrfC7cOruHATH26FwgKjcvz+mHNcvlzH4a0Rg4unLK\ndrsndokl55YVUipdN7AUbenlnWC1Mk0zGjdENeq4I6u2jY23CbaqHvDIgRACRQoSOjpxxl2DLIQQ\nEDMWq3QaWshsra1w0BZ6m5cJMcFSC8cMIbScFBGoBQ5sL+9im/hWR7RlZ3AwumOGCC3A8vDnEAKU\n3HJcrH1OiRFBwBWzJrML3YDlPRK69pqpbUOYJFD8gBW3BoGQ0qbuRm29FwkLgltAzTBrG1bMYdzB\nK1+G78MN0//wkSd54moPVFwUyY6Ht2hMjtKw3Q4gxjIXkjSfmEdFCs3IXroD3KT5lpCFIAnv2vy9\ns0ho6WxAhtK2TOGtrye0ApKDFKxB1zKh76iTg5RWKLsTupaf1XXNN0KIVK8sy8Jm6A5hykovMyId\nyzLRh0AKQghOcWepTk9q2O0qdGpoDCzFCFZYciSkQKVw1Gm7b31qmXXFQApBV6Ro5DyjODEcti6S\nkRIRadlF+3nBYztT+z4QYqW3tg0xWSE+s0oR0YVYChIWkhzT9Xs2qxWqRimBNLTJ6rirLFPz4gUv\nHB8VYlL2u8L53tntI8UD2ds2KntqRLdSKbU9X4JQ5RBs7itKdtC2cVsWa5sgEdLBi2Mih808JFV2\npogYSQMxCLEuqLaQ51oDBcjujcapLauoEkAiWNG3YQAAIABJREFUnUSmPKEhkWuTDYkF9moED98+\nN7I7Ji1fq0qLNYgIZk7BGuHy2+/XDgLqbRsXYjsfilsLnzY95EFFFjMUEHNGoZHrRA+ZbG1LPQdh\nwYmFtgWDFnhdm19Va/3OjQvN6yLNNyOHe9hqxYM0BLxURKz5LLVrYaEHHDrAnSXzj279m0+G3+5a\n5OcfvsETQ+QHYqGY8IUS+PnjynOjMrjwR0ti7YWTpART3r/KPL8o9wokhA2Vqwnes8r8ynnPu7vK\nJcI7Do/3R0vkE6vMjc74zX1il5UPrSu3cuSsOh8cKr+/D1xPxkfWlWvqXFqgWuXFEU474/+4XKPV\neGaAn1gX1mZ8du55LC18KUeiOGqwifCgwp3qPN7BnDPfqD3v3sAre3jvuvD1DKsi+CC8IzhzgVct\n8vgafuM88EBnXBXhqWgkKtWFc+AoKl1w3lwawTHkyhenyMc2bWB1EhxNcHNSrqQmxT0bGxRkg/D6\n3tkn56P396Rc+PIEV0JhEOFk04EkZJ4ACOuOlWaWbGDKOgQ2/cTWT8nlgjI5q3Wkk6Vh75fEdg48\ndsVZFlh1lbuzcNw7Z/vC6Uq4NwWG1YrBt9y+qESNEAQpBQ2RN3fGEByfBv7BBfzoqvDYibGb2nb1\nXhHc4NVFeWYwPrPruWPKL56O3KmB391GrnvgPauF22Pls5Z4V+fcrK0xuxadF3PkuhQeHOCzNfG4\nOu8bZv7+2YqH1bkSnXdq5tNz4of7whtFeDnDHuVdWjnunNszbGvgTVXWCu9Jha/OiaPYZLW4cuZC\nCfC4OC9X+GCofD63wdy9EtmIcUULT0Xnlap04jySjFycnQtfzsLNGvixvrBz4Yo4f7AEFPjQ4FyU\nymPJOVJjX5R/PCb+2mpmW5Uv18jLRfiZoeDi3FyUl4EfGzIzgYsq7C2wwbmgEh0+vUTmvPDaxV/c\nHCal6Xv/m8Pf/0RE3gf858Df/5d83AHf8y+9/pX/R6hoCJRlbrI8FZZlPkgRYkuQX6+oXrEyAQHx\nCD6T1gMxKrPBgqPDhpQSVhbMmnwlrFZ4gHncgnuTtVnEc0YCLQ8lCrmM1OkW7gkPPa4RwcnSKG5T\nLXgVqncQlZCE+XJmfXLEOC3E2CMxIOZ0VsjmxH5FJhElYL21LB+AuGK20szJtVDzSMmBKD09kTlv\nWSaDEFhFpU4TyQe6IaK2kLsNtuzo1pF+OEElkfMl+d4569WGnVSO4sBRd8TFMpJLZnV8RK9Cr0pc\nbZAU6PseDYlSCkmNPkS60FCkdy8voIs83h9z4RO3LvZ4zWyONsSUGC/Psak0vXye6danPP+NFzg+\nWvPm7oL7Nse89OorrIYj7tz+FqvTU87kDtvzLR4iI4Xrq2MkBcYdFFFWtiCbgYcevEYoxq27d7ny\nwP3szy/wOhOz4UtmKoVEJXUDOSfcjZgzLpcs2VrjWzko1CIhQZlmgtIIf8uCBaHvIvM4E9c9ngt5\nqcSgiDjzvGtGaFd83lK1w7rjJt0ZGvbdXFvhSUXFyQd8uwj4dsT1LcKVNKywt6DfQ1WNuCKW8Vwp\nfZPYUSsSm6wKWuktSdHq4IFaZ6Q4guFBCHEgBKciDUUukFLLWdFwwCznBfIFFjtAcVuo2ra0UhSN\njZJl/4oX81/Uq8aKuWJW0VCpGKEGlAUNHZVCFpDaQC8qSq6V6i0g0AXwgvuM0IpO6Spq2rI5zHBa\noGjUyDRnSm5Ybg1OLjSfWQmICEOnHK+MzXpF1ympU+q8Z6mJsTjZA6VELvfCuMyIBCwb7o1ClrO1\n4FutbEtCg9HrmnHeI26sNXy7iB4LhKAYMHlsgG0TdGjysUCj8hWvBBGydRSMXmO7/6S2Bium1gBV\nQ8kto0cqMELqOe1att1cCzUbZYFFHBEl+YKqEQahVOf2otTac9Q5dd8RLwWRoQ0qpt0h0FeoNXMy\nKA8cd81vmArrVQv03XQD+8lYSqS4MedAPjS9KbbOdFwaHbMNHUbQQE+HiBK7dravorKUlufk3p7j\nPhT61EIUowRUHXNDuw5bCkkTixtLhdkVq0IQR6pSglMrFFsY1l0L6o4Rs0jVwsYUQoUQmXN7LHEn\nxwZ4oCbqIZPKQmjoeHNKCA1H7k36ZhZIoaJJcekOsromqTMT7K2NlztXvW0751owjw12coAXoQ3/\nXZEDtEFRDw1Xbi3c863g2Xo4A6q3c6egeGhZTOo09LuFJtM7+Byqt+e1urD93jdMb2stckMz70nw\nW9vIO3rn/k54bh94YQ58bF342X7h+gbuLcYri3AzK1cUjjXz9Dqw6Sovzx2vlsC7euepjTItzptV\nuSbw148yHoTP75X7auU+LYgptxbnNFUmg6up8EZRvnBe+bo3OacTeEIzG4n8zSsLz89CrMrX5sg7\nV5X39ZVfudXxn9yY+fRlx+MrJwflqDoPdSN3S+KkV06yEKrx2GC8WQPHZuQQuVzgrKu8NiufWZz7\nJuHZaDyenLvTzKfGxI0Q+OjRxO+fdfzSlYl1iPShsIsrzhbnp6/NWL9i7Yma71HHwmOp5xxjLR0/\nsDHOrKk1nr4Cg0TW6kh03nPcvJEeI/vqrMWwFBi6SqyZ3ViRqNwfKnuduXNXMD3naG1sLTKPhTlX\n+hjYjZl+nXn5deF0XXn+QrjeG8/diTwUAq/fztx/KlzkLV+/LKwIXHrlxlHERYiTcFYDVzFsqPyt\n60oqwtfuKg8+eMr+/JJXJ3hYZ/5wv+Yyw1Nh4v1reGlJ7KvxDJl1N/H6rJx0wpXF2Rtcw3nnyvj6\nTnh3mngzR756KWyBp08yn74T+Ut9ZayVsyVy3gkPx8qntq0hetCdI2b+2TzwrMNrBs8OhXeEyss5\ncncJJK88TuaPc8+sTqfOMDvf9EbpvDR4RDLPzYnqmUmFDXBWhNngTxf42hC5ZpWbJjwc2kBHxRmB\nVVR+LMwklM8tkckiD2jm89bx0Vj5hbVx1ztcYKuR/+zKnldzpFjg0R6+OQe2uVkxHhbjN6tyVZ1F\nE9eq8KOd48n4Bxf/WufFv5Xru22Y3rbASQB/+XkyzzePiHsrLK/cj97/DhAjCORpT10mQkzUWoh9\nT+oHXJ2xFpjaZN+tkBdQ7amWMSmoK9FaLouKEtfKUiCuOtwLlivkQhpO6a7cT3QoeaJ4WyOHbBRr\nFKHV0DEtBS/NWE5U5mlLnwaWaU9/tEZF2OVG3uv7Hp8m9me3IVbwSOxXQKbWiZ6BPkQu0oSrkZrN\nGlKP2wi1MB9MxLvLuy1XajjBLy7ohp66TOzHm808nXpilyAGHjk5wt2ZSiWOmeUwYe3Wa27efJ39\nxQU69IfiyLly9SqeWzDn8dXrbMctXjLzXLm9ucPJaoU7xAR3z25RLHL19IiT+46af2JSblw7ZXsU\nGfcTD16/geHckGsstXD00HW8GF13H489+iTT+SW3L865fvUBzrcX1C6TMjx44zqv3tmixRmtIN3A\nmy++xPrkmGtXHuLu9oLhakS2M+tVYpwzNc/keUJaHAlBFfVMCol5d484DLingwSuyVpUnE2I7LaX\nB2/XnpTaFqKI0xfIoQeRg/9ig007xLaE0FNoMlIbJ0wa5Qpp5vhSDsIUt4Y6z3tiSiwe2wYqNNyp\n14LZgtkMXY+agwqmSkqJWmvbMjjN5xEiMfYUq1hQJDVcuNW2TaIsBBHMKtlS272Ol02aFHtINwgH\nDx5nd+Dy5gEf7Q2xbN+v7VK7lKWZ7C1QgbFmQnEqmVCVKm1T596GNO7aPmo2EkZUww+bgVqdvTkJ\nwSZQC3hoG0ShkqIfSGNKsAYJcIQYWuZYlo43FyffGVklJwZIIeCxEiSw1JaFFcXQqGCZ6u3fAxCi\nsyxjK1g9IAVmK6zXA1hhOci2zB3phKUUAitcZlw6qju+GKiwzE6M4BrImskGmqWh6s0JEVRbUOwu\nKIGIOHShsi7CZAJBUNkfSGoBlXavDrE1Kw27L1yOE05lFRLFmj+vi80/VetlO0dDxKUFq/ZdBCZK\nhI0GWkZRI0bmnBEOxDsDlwlKh5WB7JngTtK2KUUCYw1M2ZhkZi4ZjU6KiTw1H5gVZyGAQMYooixm\n9KGBHcSVNColBajWCFgacCsIiRAaeCG7g4OKMM8FE6FQcYMFWj4S3gwAKiwlg8D81sd5ptAkeaVa\nI1e6YbViNE+KSgsqXmrClnrwEQliTTGgEsh2kOapQmn3c1XFpQABrG2R6iFdTdzJJrgrIy0/yasT\nY3gLDNugMjiitPdB94NHqm2vAIpqczsVb35MWvNXTJjK98wVf1trkT+6u+Wf3VZGhE97k909vR74\n+LU1uPF4X/ncNvLJXeKvrhf+yRT52ycT15NDV/n62PHC4gjOg5K5u4Pk8JmceFCNuy68MxnvTsLR\nUIhR+eYs/PRp4ayCZeHVRfmJlfCOdeQnA1idWWogS2AlmTt75SFR3nu18Pw2cjkbdyxgUfjjXeCD\n64Vfv+j4964tdAH+t4tj/sbRnqMBzreF//5Wx490C69U5SePjDWFMxPeGzLP9ok/QnlyWHgyQIgw\nLR0XrlwWuLELPNsV/uld5YNreLRfcfPCeOzUqcWYpi3nOXB97WjouRTj/pMNvZeWW3lhjKZ4MOKg\nvHI78z/difz768JZMTY685euOLfMWSo8dDUwTpnqwnyvcGulHHWJoBVX4eISxuLcdxw5Oq6IQS/C\n6SYxDiO7beIHTluMxzNB8dlZnSh5FlIvPPv4ChtHxjkS+4FxuzB3xpPqdMfH3DufCBbIFW6sjS++\neMl7TisfurHm1W3gbx9VXr1UHjmB7V6wkvmVi4Hozs9vYBOctcBHu8z/fK/jPzxa2JmyF+W2Jb5m\niY+kmZ9II3/n9oo+Ou/eGe8/Mh6icAu4VuDZ6BQRTpLyWlkRsnBd9ryzUy5De7yv74UJ+MSqoCr8\nYD/zqV1HEucNU5LAR2Xk8WHm726vcFbhJAgf7wufnwOdOGudWaWeJ9x4sKvcHhM/ulrYu7NU5atz\n5I0sfGwIbER5VzReKZE+BN4fKl+oyvs62E3wZCp8cRE+Oa8J1fniaDzWwYdS5Th0nEjm9RJ4dTvx\nlTzyRCx8w5QzE7L9+ZoDvtuG6W0LnASQR59GVusmQ1DBakE0kaKwzCNFIz6PbSJXKhhoTczLPWy7\nIBpJ3brJGvqhvXnUieBOrVtqXag+gEDseko9GPhpYYvFJhyh5nPyLrKrhhcD5EAqAw2NKLTdntNv\nNuRaEGuTwZBW5DzhdWY824IqlJlRYE5r0uYK4fi0mezLgmlALZNiy/zBnaEqXpxqC/gFktakAxWt\nSz05Z7r1VRChjPdwMnm/p98cHWRezlgyZdoTxj3by3sHEEaDOKSgjOOWu2VGJKLrjvVwzLKMWICx\nVkLoIMB2GqlLZVj1aGrgit571us1ddpx9WTDvf0ldrllnEZuXL/Ond2e126+zqrvWcXE6y+8xFqV\n2K/Zxcrtu3d48tEnmS7OmYMw18LRlVNev7jF4/c/zOu3zthsEmWpCJVpKXRdx/l0wfrolFomprLj\nZLPm8vISc2M7LRA60tChcaAumTreI6/WlHEhxtL8XtZ8JanrKPNCiJEyzSzSplqEiAwBdSWXgm/3\nTF1A4kDQhFsmRcVWHcbQpi20FHHpaNK3xRBXanW6fqCW0raNKjAcU+pCFKXWBRcIahSPmCih60HA\nxj2eG4I8zxkd+ib1MiMg6CGDKai2Jq/OUGlkrF1FkzT5mRs27du0HGt+KYcQI1kFJBKvPohfvY6F\nDjmY2O3yDnzrz1M5/G/v6tQIMbDkhmaXYkQRgkaKwyQtPqCYtgLPWmAtXr/TJDstnwhooEFnb5Us\nbYpfoBnoUyQsjquwroof3BxJ22Ygph6dCu5KUGHeT8QYSbFtFUtxVA2zTB9iG92bUq3SpwYyD9mI\n0hFlolsFNldaA7yMzURei+EamdxI1QkMgKNVMa0UaWCHWowUIAUnxPZ9S3Q0QKRNm5HcyGxNXdOi\nMkUIosxSIQ5oeIsY5xRf6KMDTrUIlik1Iu6oQoyN2rg+BrM1lhu5EJq0L0pH7KGTLUE2SEoMvUGt\nuBp0DiOUquyyMRZnmiOVnhJgsYnepRHurEkPtRf6tXNkgTlUWA45UcPCklfEEBh3C+NSuBQBa98f\ntZClhUBXnCIZz9bkzd5ykBYTRJuHympF09AyiFBiH8mlUTXrEhFpxUpIfWv0tJH3tDpJ2vOS6yED\nSyODRqq1UOEq2kKCI0DbDi4HaV3zYjY4BdCw6lJRg0yFEJjLgrjQOGkVl5Yto3b4WePMh8pghRxk\nxu2eXLr290SkuiNAFcOsPZZpYjGjClQrFAeVHvGCa6GSyFKbL/Z7u97WWuRnrh0xpMTjwTj3wMtF\nWDTw/tXMb287vjBHvjAJj8bKNhvvZebUK5+bAl84V54OhQ+vYHGQTrm7CHt3PhYLd0rlzT18pka2\nCB9YO0kiG3EGhRN1vlAgmeJ14XJ2/miKvJp7ZoMnUiVIxFW4Z4FffU35T69O3MyBYoX7qDzeOd9a\nnBNf+Ee3lbUIfzrP/JNZeWdvPH2i/LunzjUR4uJ0qpgbH+qN2QOvVufZsHCclVsLnEhmUOVnNs5Y\nlevJ2ZbADw3tfnx9mXkjCPcunWdP4EVLXAnOH24jL4zCM51wut0RBJ4YGq5+55GbO3goLyDK3zha\neOYI3pgECbAXKN469nuT85l94oePjKnruD85HUq/icQ84Um4mMB2MzU7m2M4G518OdIH5WRtfPGl\ndrBd7QspGa/dhmceEOZLo4RI8EiJK+7t99y4dkrZjvSbQ5anVvLcBsXfuis8s3JujsLDcc/jQ8eX\ntsoZ4FtlkohF+IVTY78X/mwPZ12HlsoTa/iR3ngjB740J352s/C5XeKpvvKpXeJMhA/3mcci3FgZ\n0eClRfkXu+ZzfE8HVyMUr3wwFR47EW7bwGer8DRNRfDoYLyrr1AaHfW1BX7uuPLSonw4GNeCceY9\nz+eOX15VZoyv1Ugf4OEEr3rko53TYXxzhC/vlR8fFn511/Pjm8wqGO8ajGdj5TQ5X94rT0bjksre\nW838tDh/707PTx0tPJcDN6zy2mx8fF0JvfKIO4rxcKj8n+OKK+L89GnHMZGXvGNv8ECEf7FbePH8\n1vd8mPzrXt9tw/S2BU4C2LwQQo8PETcnaAuCnfZOTAkL3gIFQ9tyEErLiUhr+tUVihVqXdoUf9mj\n7khcUUolcISFggwb3BtkVmqh5AwKxZxuc0Ktzei85NLkHMlx2oRyXhZKmUAE6Tak/girmVVSlsUI\nKeEl49IwsTFBnUc0NH+Qj/fwUvCDr4S6HDTjwmj7FjrpLXhSusieI7qSyfMW1cBYKtUqLPv24CT6\nIaChY9xngi10XUQxijVU9HB02nKN1OmHgf12j6RIMaOOMxKc9WrV4AU+08XQMhj2e6RWHn3oBrvt\njqW2hunV11/CS6WLSpkmYuzorh4zTq1cBEMlMu5mzuoF9924ziBCrjCkwKUrb9y8iUWYzo2z7SX3\nX7nGNGduvnmLF1/4BpvNQDq5Dy3K+dnLDMNA6lZsL+8CLRQyMVOXqeWJmKEx45aJMZHdCesrbDaJ\nZY4s80KIAVCG2PxZfRSm7Tl6fEp2gRBZpwELQs0Lw2bN2K8IuRLlsHlBUPOWyeK5NS3dgHgjm5Ul\nI33fsla6QK0ZVyFqICggAffmYQqqFJNv089iajlOU3W61THmzfdgZaLmkaiNnOeHMNyUGj5YYmjQ\nBoA6k4ZIdkPtgFG2FmApKTZpTp7IRZrcL0Ss7/FZIWe8C227JOH//+L8PrkmC5QMkwmdOyqCBEFN\nUGvSNZN2MLbsn0LqApfZD1I+2uvfndGtbUAc0ADmBGkbmZR6cm0yNHOjFkeCNB8YzSMzzQtJOpSM\nqLHo0LD4uX1cpCJq5KJkFfoU8VwoQTibYJCOZBWvgqQVeSwMMwySMVeWXElRGGrGU6DkhSazTAQc\nYrtXcq2Itia6MyPPBSORqxHVmQ1wISYBWi5V10f2kyAHH1eUjuIj3RCgGCKOOdRFiSqUoggJCc0H\nt+ojtXpj89SZskxI7A5naYcj7OuI4qxkg7FnmAPbLByvBqwYy+Js95HtmJmyki1gYaLailQFoWBe\nyUuT2UHPvJ8QEm94E54Fj1RzVAbMBJWC0GH9no6eEFpoo6aGQ25TOGFaKiEK6ga07S4esAp9EuDg\nObTW+FwslVXs0FgJQMlNurlMLaS3C13L9gtKsNzefw7ht3qAtETVpuYkt3vKAlYF0UCoToqRKKV5\nG1NqW2cqVkojIR6GekmkvUcRMTfygbJYTZgPfiRK+z20Po6itC36IWsrS9e8UsBCpVaoKCVnqgW8\nMSJwVcyXNmisAakFV2Gu3/Nk+G2tRb42CT/bZb5Jx1WcH+wX/mTu+bt3B35yVTgX+CEzRhKvW8cq\nVl4ugZMIv7gKXGTjnhlPDcan98LWhWei8TtLz1SMHwiZD66VS3NuqPCAzHxtjHxpgR7jR07gwbnF\nfPzqLvG+mHliVVB3HuorL47Kb+Sexyk83EeuriL7IHygr7wxK1eiE9R5LSfeo87Dq8JTk3A1tI3m\nxb4QS+GewUe6iVdrz5E239/v7QLv6CpPEoihZf7882nNj+nMl7fCA6Hyx0vgKzny5lJ4Zw+xdPzE\naWYT4P86W7Hyws8fFfoQ+IrBaYTHN4Id7sHTzpmmynEHL2flk9uOT/Qz/aA8JAWrsIoBicbd2RGH\nX34E9pew7juiFv7hy8bkzgd65Xcu4QOD84PXKpfbyFIdRfHgTFPk3j7zzAOCWpuPlRSRYrx6y/EA\n64uFs3nm6rFyMQeGyx3/658ZP3ffxMnKkSp8+u7MB04qj3SRL5wHjqLxksKDYcazsq7C/5173p0q\n7w6Z4+Q8px1PrQOfOJ3xGb4xNq91Z86H+8JXdsrH15n/5TzyV08Kv710XFX4y0NmkcA9nPcfF96U\ngQdoj/tqTVSkvYdV4cm4cGsM0MHs8KYFbl4K7105fzxFHk7GC5MwqvMxMa5J26afG3w6B36oq1xW\n4XGpPJQqD4f22v213PEfbBqIIXvgQ3HhboZTGnr8QiKhFN7bZ75YIu9Ila+XyIkoT3UTf+ua84Vd\nYqvOk2q8aIE/XBLPhswf5IhO8Mm5Y8rGL64qBOGyRL46K+8fKqMJH4nGi9/rSfJdXP8mwbV/roGT\nh///YeCPu/f8CEvogIqUPYQe74ZWsAAt+nyD9KlNxahtE1MdJLRckNiKvXIAAxiXYJHUrcjzBLmg\n/aoVvV1PWfasN5s2BSwFLwUkEmJs+aBWDx4SJYSKxI4oioaeebuj2kwahiYbmS8RDJOe1H8nkBFV\nRBIqRh+EnawahtZn3KDr1+TO8TLTFaWUguWJsD5pxXXXNWKVGJ4hREVswVWR6pSlQQC6zYYuNt3/\nIpC3d9GixBiYMcSdYb1GOYSdxsSSC5uhYxxHYkh0XWhFYW7knmpGiUKsLTH+8uwOlUBIzSy5PmxF\nxmWm5MzJZsP9V66yTDO3lx2bzYY6Z9brNaUUXnvtNY5XaziAGBaB7XbLMo4khOsPPcid2/fo1mu6\nPjK7kGLi8u4lOZ+3n0PeUbwniNGtj5i3hzmvVjQk6DeYNx3ykNrPUiWyeG29wL4BOEiRWnJrzmNA\nLDPv93TD0AzW876FIYu2hkaVhDKVTNR48KJYwyQTG8nPMxhkswNdr2/BpGXBlwwIOgxNaofQx0i2\nShfa1sgEXBJSZ2JoZK63ZHm1Llgp+EGqJDRAROyUcRwJ2mM2Yx6BSuhWpNgyX5TCvFQ0rfGSIShJ\nDzI8wJc9TmqT692b2AtfhO9D6MN/9cOP8HS/xqQBD/bZ2B6ajizOkTZZlXnbHOPtORBrMiilFa2G\nc1BQNQqmtyZH1JmtGfVFheRCd8CuirSJ3tBB8tzQy2oEWkZPO4orxRqYYRFhUGUplRACgcJKFQ6m\n/uxGCoeQ3WCYgsaeUic8B3pV+hCJVBbJ4AGtRpBKH4TZAiKQabED6xQYesFLZfIExRh6oxTBcCKV\n5EJVJ5gzh4Nv1GkgFAUNhr6F8VdF1A45UK1BWsxI4k3HBajpYSMiJComjmdYrEJtwc2rrj2OVYha\nWCXnqGvyl50L0wy1hG8js8fa0oRyLggBtCH4owZqKWQaBOYt7595G0oVa02wAonWWNfQ4ArZms7A\nzSjAUq153VToDkO0dngEOq3NT+IzBmRrhVg9NCN7o4X8SiDIQaHQyjdMDX1rwCFNxqgHFHfLmQkU\nX5p3ztrr28zbEE2cfNju1VCx2vyMIQoidkCIQ3Fj8cqcO4o47bs/hNWqINaAD4sVCvGAL29ZWAZI\nbP6HBl9q+PDvhNdC1Ya2fytWtyBN+untPjKBW8vMr3+PgZNvZy3yXz9xlU/lFSs1Pi4jWwncksRT\n0XDg9/aBh0PgvX3lwpSTUHnBhGMz7hL5YCw80Bs7hFcm4fkSOZIZd+WH1vDZnfDpOfDL68pvLR0/\nN2T+cBH+5pWZm4tyJwvnBb5ae/7Kaia78EYVnrfAFXc+1GWOOuGKGkUi39jCcwv81JHz/0yR02Xh\nfd3C5/OaH1+PvFB7blugD3AV50aCR9LCb43HpFo51oUX5sTHjyqvqFIw3mXOl2bh64vz/rXyzSXw\nkVVlZ8IZcHOK/PBq5gFduJQEWfjk2M65v35l4birJJQ3q/L8BaxxHh8W/vfxiPex8LFjcBfO3Hmo\nd74xKe9eGzf3ynEE6+BKrVy4EoKDCR6NjVc6U752WfjTpecHj2YeisrVjTMTGPctguOoU66t2715\nd3L6oaOXjHaKlspXbjkPd4qJsumNncA3zoXX9srv1Mp/+6jy0r3CkFacDsZosE7w4l34Rql8dg58\nXCdeyAOPpcoTK/gnFwO3qvBMWrgWlPuS8OVpxS5UfuGocj1UIoHF4ULBcyWaop1wsyhzhke7yiSV\nT551/Mxp5mZWfncvrFx477Dw8AAPRAPdSqbvAAAgAElEQVTr+OpovFMrszi/tu1YqbDTwM9uFkp1\nzmvgKyXw8b55cn+vJMa58noNXBh8YlX5QF+558ITa+e8CBqEPMKlOHcscc0Lj3RtK/aOTWG/wHNL\nRMx5ZVGQhjl/ZmXc3y/8wXmPiOIYf7J03AF+ajCeXlXenIVr0fh7256PDoVvLspVhQ+kSgb+zAMv\nzc5cIo9o4QG95O+8dvk9nSPfzfVdN0xvx/XWIRXf/xPo+ipl3jf5kQQI2shoS0ZCoNYJnw/QBhI+\nHEFpK1OvuW0S3AnpkJ20VFguMBmwPqDLW8V10/IL5VAMCV4rGiMSU/u61LGpUPNMSC2wVFZHLQ/F\nnFmbZ6HW2kAEIlhphjoQNB4yN2xLrYrEnohh3RFlf5sgHbVuWwjh6hSpCyn0pJSaFM8XpnlhXhY0\nBOp+JPZOiBs6hSlXlgjMTaOeouDLSBblysk1sil1e69ts5KSl4X1esMoUHZ7ZB45OlpTpWO726HW\nYBqbzRElCVQhdR3rzZp5mkl9AgxNTfaDO1or84H6F1PDgfp+4fp993F69Qrb7ZZpt8fduXtxj+vX\nr7fCTpWtF3RsWQfL3AJnPS+odITjU7bbe+RaODk5ZTfPnERBQ+Lu5cgmOOTKuFQ8DFR3hjVszy8o\ntTZpTa0NOw+NKqWQqlO1YMsIIaD0mNcmmztkVdU8tka3OyG6UA4EQMxQaThQT2vwjFQOUpdKCSC6\nRnwG6UjBybFrQIi8UFxbkVl21FJIqWtNbzHcWzOUgmISKLkFqr6VuRL7ayyqBHFsXtAYvhN2m40Q\nGkFRZQXl8gA96JHDr8qCS4I8Nkpcqc2z0XXk0rwPsKCWqXkL3/oqfB82TP/dhx/l8dOr7R/rlrm2\n5HC3oTWH8w7jiEtbsFJZtHn6lqIt1FM4eJpgMTtQzGZcQsuuOmxQRQzEMAJqELSRo9QgHiR5IkLy\ntzh5BjpQakXEwQtyKEojLRuoTyCZFrCcaLlt0pG1IhlKKaxXQyO0zSMYxBAQGqigk7Y1U2kUxyEs\nBIdOI1EWPCnJK6l35twDkEJkmitJKp4aAU6VQxHf8u5SzERp2zkIdFTcK8WNdd+1HB51VDo0zgck\nYAKcqcysu3VrKLK0s1La0EEO2SWNMAknvdCLcXXtbE4XrDh37665HOHCDKsty26/OEWNealIXDMe\niJJi1jyEtRJDd/BrVswgxZ65NE4iUijmJGu48HkujHVkouXGadAG8ZCOOTfiVvXWqEjo0JK/syGk\nSehMcsvaKkbQro2xNYEuLFbpbSCrEw8kvMXb19KIfQWVCNaajkrbYnsIWGn+lhBSA5VEQ6uS4dCQ\nFawujBHEmq/tWISkylQrpvHbOU/a2ktcpDWUB+ofQKFQ3A5fkzEeXgvURllzUyQc3tdQzJzsbcO2\naPNRLe5Mh891vhR+9/wefB+dIfCdc+S/ePI6j/UdX5lgY84TsaIoD2yMF/ZCH4Q/q3BnKpyIcO6B\n3HWMxbg/VI6s8mhyEs6NXugEbk3CK4sz1sjzKfJArnyNyF/uMjuDk2jsKlw5hAo/3DnnIXDizjYI\n4wifnZRPDMa5G9e6wIOd07vzijSww60Cu+I83S98a+w5I3Dp8IN9Za3OPavcW5TrPTwRM6/Lii9u\njQ91C68U5W4OfE4HPhxmPtjBw0NFJJJC5s1J+cwusg+Bl/fOw33mF9eVPsHrk/IZS7wxCcdB+Cur\niTEXvlUSH7/Wcr/u7CpDcDYJvjIK79/A6554c9tkyR8+qVRLfOoscl0zX8/Cf3R94m7tSAYSIw8c\ncQCoBMDQrtUkDVFs5Gxsupax/MpW8cV5x3XYHAn70Zo4B+dPz4V331AojVmyVUXHQtfDUhUzJdtC\nlMBq6PjM1rlhmYdO15zPC5nKI6njbFo4crhdA0s2TkLkW0V512bhU2eBq2SuSOT53KhzBswGrxP4\nSMp8LgurYtyTwEe7ha/mjrsIH0rGDw7GV7OzmHNhax4LhoXKH+wTs8NH+oUZ+JKseZ+MfCNHnlLn\ngVSY3DmzniiFE5QpOQ+o8GqN7M341tTxmBqqC789J/7Lo4k/tcDGKjdC5gtz4INDwVGe2wdyCHip\nHKvz7BD4x8vAj8XMc5PwVGz3grry3BJ5fzB+KwfeFZ1UCl+vyqMR3qyB93QtwPtL1lGtMsTmFPil\nowlR5be3A/siLKFwgvFirvz+nb+4OUxv71WNZboEa3SzYhUvE3luFB6VrgWVxDVpvSIvE+S50cWk\nISiRSNd3zKUigEUl9PdxPPTslwxSsHli2GwY5+Ww+ZH2xrrMgGIhYiU3Y35KhG5FVSFphxYlxMjS\nw1EX2e9mfC70/QYQ4hBZpj1l2lPpSF0H8SpDCuxpeOiyu4fEIzwmyAlfFkSULg4QOsYlo8tdisC0\n34PmFt4YInk2Stky2WFCvjSpURh65v0W4gAuZCvsLs9J3VEz5rqxzBNLyYSU6Pq+EY9WRyz7S5IU\nrI+gPduSOV4dczmPULfcrZnT9TF1aQVYr5XduEfVmfYzoRbuXCqr9Ypx3jGfX1LdGMtC13Wk4zVX\ndeDK9Rv82de+StnP3PfA/VQrVIm8cfc29913FUG5HHfcf23F7u6rBO2IGrm4fYvJnYtp307C/SVb\nDaSjU1arFcv+ghCdu28udOs1aCTXSn+0plpuRVqITdZpBnkmHG1wM7wW1LuGzI3K7IHUH+PWvEI5\nZyzvicMGs4TVjASFMiL9EQ5Un6gkWCZgx9XTE6btjv1+R4yRaj0hQaRDHJIEahDq0govvEkoQ+yY\nQyImcLTR62RoP7s6QjZ0WLVAVg94bZtUWKi7BUKbliGOaMDnCZPQvFVWcWaoTS5GXcgL1DrCQUpJ\nNqoIIul7Z1y9TdcLF3BhrSF1D4gHBCOxtIbcB2abkCgEU9IB7Z0P4b1mB6QijVLmKCqRbE2u1qRf\nbTIKoaGUre0QHKW4g8HEgX6ofiBEK2GeWoNgTUrXthRgoQUYF5Mm8RNHXAkiSF3oa4NTxOBoGQ9e\nygoxtv2gHcAjh6BT0wN+2rrW5HtmpcoyOyf0jW6nDcGNVKaamT1xud8TGVA3QgANe0SEUrW9kbxV\nMId2j0ULbLVJ18Rrw4XXgHps/p0YOO4DXdOJEVsvg3glRCfYgIqStKLa4BEihgbDlwImbKJRgzOa\nUM0ZvBAkIMGxPjHVQi9OtxoYy9wCzbtEKYZYQ/27K3nJ2IFnogf6parQM7Wteoms6kLoAlCZq1Bp\nTR0SW+NtUKmU2BpejS0zKUmTfBo0SbMZFr2h/V2a1DoURJoEMkqiswOqzVvIpJtisWLWCJekFkO9\nPcj2xHLzH3nHziH7QhWhM8E8cs9Ba2tsZxFSdaI4jqDuLWD2Lb/SIYtKpG2EoBFEVaBUw11QaSHb\nosJUGsji8lBOhFrahlsOMkIgqhJRhsMjun1/lR7/3yuVyucLXFZ46qhyMwu/XyLLLePB5DxS4cWq\nPJCUd28qvz5GjufCe0LGTNnWiHeFd62dL06Bzp19UB7bOO86KXxjK/jibHeVf+e08GsXibUqj2vz\n1v3a5cAPl8pDa+M35x5y5f5B+Omjwm/knl84nXl4Mo6icBECH9TC8/vIZ6fEf3wyEizxQ1eEb+2M\nf3ip3KuJH99k1hJ5ZuP86jwQU88/v4ArSTmTwBcs8Epxful44eEgXEvG719GHoiVx6LyP96J/LXN\nxHpJPPf/svemsbJl53ne831rrb13VZ3hDn1vd98e2QObozjPskTRkkk7mixZUQbHRpzATgznTxAE\nAuIAAfLDQWIYcBBHgh3YiaXISCzEsa2Z4tCWREpUU82xm6TIprrZ0719hzNV1d57rfV9+bHqCoJh\nWUhsKGjA+1efe6rPOVW19671rfd9n5eeB7PzsTPlU1PiA91MpLAswvsG4+8eKRKX7At8u498+qby\n8CJwYkIpxk+dJN43Fh4bKq9bNTpsSom8NR7qjBnlXg/8w5t7/MidW/7O9RV/vM8cF+fRRUP8iwl9\nHNmMEVUjl5lQK6/MSh96NpPxN16GvxYmLnlPydANyrlYeOshfPG5ia+ddHznJaFawYLwSy8qH7zc\n4iDXRuON5yPXb254vXboAM9f3/KZ0vFrZ8p7UuWjm8K7Q8+79uF1h5UXNnD/MPJXXzjkL56feWFO\n/C/jwF85mLlRjWdnZa2Bdy8qYw08aHDH0Naf1+eO+7VlNv9xHvgtc/7MYuKZWbm/r7yShRdH4f17\nld/Ngd8sA29Nxiuzsz8EXvHE6dyosC9l+EAs/IlLlavrwtNnTu6Nx88O+P7VzKDGPV3lvlB5KFbO\nzLmXQhT4m8cr/t0h8w/WC75rb+JLPrCsDiHxPWnkJzY9Wiv7feG8OhcCqDlPT5XLIfM/HlfekzqO\nSys27sPMpipfKsoljHtTpcuFL5RAqtDXwuM3E9voLEIGcZZzg1vc/UfM631VKUzhsfdhi0VrTO5X\nbM/OUFWKQ+gSlhRxR+bSqHNu9LFns9k0f7hWbKcGQbOR1LBtFpKp2Tg0xQZpEHACVgvmTrd3Di8z\n43ZDJ0pGmnWpVqi5oWitIMOSICDDokEATEjDgu08EndgoCwdMp8hnnCHua7bjvNuEeU4dbfTT2zP\n1zFit4JayXmGkFA3SpkbvagWfJwJQ7vRzSXvdkGVkHocJ1hBVClmmAgyjlhq+OqQBmIIWG15LBNB\nNhvmPLJ/6U6mPNOJMFlh0SfKeqRfLtlOZyz3znF8dJN5Hrm0f0iIkfVmg3mlbNekODRMb3XSask0\nbSm1osPAlGf2w4DHwOGiI283dEPPHRcu8rVvfJOwXGLbGaTBEpbnDxFzrq+3qAcWsRHA+n7F8bWb\n0CcWwwGRyqi10QLDCtGWNRNRtmcnbRHrBXzGSysXNXM8LqBkJDb1JeysdqiQt2etU6SFVugwsiuW\n17vd8AXVQLuETUboAzXPzY81ja3faDiggV0i7GiDqoaVDXgB6ZF+scOJK6oJw5tNhtbcJAG85vYQ\nU3w+RtyJqaNWwbqAStcUDzMIPeQMIbbd8dCyJ24tj+K14rEHm1CXhuyXgInBdsTciMMepUxICPjp\nDfjWl+BVtDt8+x7y5x6+lyurgak4ZQdBEXGilJ0S1HbwhWbr7UKz3pkCVkAU2ZF5XBJRKl6diaYc\nR2lqkdaAxsBUK+qNtTx7JdIGrbUHigU6zy1DRVtUirUwrYlgFMyULuyC9gIdiluGqCxMCWqsgjJI\nG3LEI+o9LiPiu3uK7RawARBppLOgxGxYNbrYYApzySwlEWJ7TdwdUaO4EsQQq6hGzCvrSem1w6Qh\nzRukwNFgRG+VDyKN1NYpWKnNklWhDw4p7q7NZt9bRsFkl4fa3QeTNjACGghUkgZCMA7UORwaKn87\nG6eTcVY6Sm6B83mqhNgxZ8NUqG7UHRWzuKGuu+4qobhRcqOLTrUNEM1R0KyMqx6Ct40R0dYPUzIU\nqRSHvEP4Z3XGKr9nu0uidO4NkOhtCOw0/B6MwWorefUgeDGyNIBCQIildTQRKm6B2SMZI9bWbdQ6\npYS5OjXojj4oLBWCK7cc3HaDmQY2XltOS9trntOO7xLbQsYsMokTFDpReit0GISISaPqdbTuwwkn\nhGbZS7u1St5VH+Defi/GNrRAupBQrQ205IrtFNtbY+Hnb/2rWfL+/zhu30f+0r0XWMTEMjj3LIRf\nPYqc18pTFvmuReVpa9bdfTM+tNeev4bAjVPjKzWQtPKFKfBo7yTg0WC8lIzDIFzfBJZq7Ec4lMKg\n8FJNHGenILxpX/EKHz923jMUnpwTNzJg8NtZ+Ug3MyPc0zvno7PXRdY4lyj0SbhahPNqBJRbNVBt\nZuHKjTnwdHHuC87lUOil8lzp+OYED3eVI0/cnYzocKFvC/evjCBB2cP4nVl5fV84qfDEJvC+hXNH\nD5/aRi6T+RYd7+5nBoW1Vc4H4cU5MokgpTLtXCmPLlon06YId3aV0QNaJp7YwIcudWxLZZDKs1Pi\nNcvC2QTnOriVnQtDx2eO4JXR+BOXKlE7XhkzC5358nHkjiCoVjY58si5zPObwFEVVhE+v41856qw\nH2G/g5OpcL6DixeEX39WuatXTnPmqCY+Pkb+k7ud4JlfOhrYc3igy5xPE33s+JsvdPzxZeGxReCk\nGCkZ1+ZAr4GqwpkYdwTn8VvKZ+fEHVJ4Q5x4dg481BeiKdckcFyUfXUuJefhzlgEiGo8cRy5GJ0b\nIrxcI9+z2PC1uedbs7EvhfMa+I2ceG9feWlMPLbIfHKKCMahVV4TKleWxtfngUs4NzLcNGc/Oa9M\nmS/OlUe6RE2RTmDywAOpZbVmdy6L8ZIrd6a6I5MqrxTnhXHk1IQP7kOqgVmFXpS1CU+WxBVxjmu7\nl98TDYuwNOfZqrw2VJ6cI32ERz3zokfe0jnHFZ5z5aXZ+VZR/uJB5u+fdTycnM9vZ54//qPrYXp1\nDUyvfRe+fwClWcMkDW1hVyvMrbE9pK7R6ErB67ZljSpINbqDQ1aeOClH5O0GIZG6RfOB16l1Mkmz\njhnGvD1u4XihIaKt9aZ4TGQXpBq1zFDz7doLvK4RK7vSQNmpYQOkPUrZScMx7Tzlgu3yJovUsZm3\nzTKC7mhRwLxuYdtdr4hIs6u1Xcddl4+2kL753BZGugcxkALU0hYMXddRNaJ5i8aOUgpd0vY3lYL0\nLSMTVej6nlJmBk2s17co6ui25VrcnbCIlJzb65cSZWfl0xhJSTFt2aDTzcx+t0fNW+Z5JCw66uka\nSZELFy6w6gfW09iyEjGBO9UqYzVeOrpOP3T4ZmKuzrnLd8B6ZF0zag0wsUfi8NIdvHjzOsenZ6wO\nDlmfnUFYIur0Udhstg0AUSuRyDiO6GKPrlsxjmtiihBTG4xnQ6zt8tsuA9ccJIXYLZnz3Lb7dffe\n5ImQGokMt2bb250IUSPzmCEYErRl35hR7XGJTaHSuBuMDOvPIdaAIZ23ElxNrVOlltqGKVXEC1ZH\nEm2x57LYqRSt44QQWqibNvADFIGgLS8n0BSkXFExkhdynnfzWaKEQPAWqkUVEFII2Jyp0ixittnC\n81+AV9Fi5/Y95M++5gp3LZZUF6rvbJ5iqDd1VHflrrrLZHShDU8AbplsrUAUaMAODPHItMtpRGk2\nLyVR3XbVtdJUQhzoWpcOhdArqUDVNtjkWggIVCPTsk0qqdn8VJlrK07sHHoxTJrdt8dYdIHOKssU\niGpNfUJ3zljZWbusbZy4QQx4ro3MhhNFdgWlExp8Z4VrO5uuiVAzSdv9Ju26eSpQvGX4cs6NzEVu\nubkYUQrV2vOIwQjabH5dgBibwuA7JSPsSoJrbVQ/M6PWdo+MCkmdJI3aFwSWoQ0jYw6MNVFqRjQ2\noAqNIGjW/v5xbhmx2RpwoJTb9jndFfk2uw67slj3hr/W0lOlsDbDQgNSzFWpNeCSsTqDJiYgFKML\ntPekCF1sZMLbJbQz3jDooT1vs3Y+RNH2PqjsJCXakGLGNipuysIrSwntXh+MYTImgZEGTqy1chYK\ng3pTq+KCfnePHLxRXpsKdFs1ajbEirOhEGokmjN2u24nadm97C3XJx6andwM5Tb4o5Uw5122CmAd\nnVoE251/U2GHJq87ZVaZaffI43nkN49fvQPTf/7AIS/KiktUPj7Co73ymmA8OUeuTg388SPLmYOo\nvJSFj43K9y9nruXAphrvPxAuqHLdMp8+VRLCQ4NwuatsinOekWfzgoudoGo8fgJ3IVx14Z1D5mfX\nS37w/Bmz99wE8qz8bobzXnhhZxk+h5GtYOYsVHiuON+2EC6lwAuzsq1wkOCaKI9q5dmi3B2cNw7O\n1zdNYb0Qnd+dhcmFYIVBWh9d1Da839sVPrdZctWUc+Lc1MC6GCuMM3fuDwmNwruGLbdy4qk58q5V\nYe2RYJUrfeXJbeKdyy1PjT1XZ+Hdq8wTm8j9yXjNonJtVu5LzhdPnWuukCuraEzeSnHPqrD1yJuX\nE59Zd/ypczNDUroQ2GLsq3I0Vc737bqf1kZYJE43ExYTDx22PNPx2nCEZd8Kmb06R6Py1FnlYq8c\nr50NgTddAt8Yp9W5lhMpCa9NE8vDyEu3hCdOhLedK/zMrY5OOx6LmdcuMk+eBB5cbHhx23MhFH7l\npOe+XnnLfuUXjwNv71sM8rjAJ9Ydj8SJuyL8ThHeGgunBa4LvH1Z+YcnAy+WyJVQ2BMYa+a7V8aT\nm0R158Ghcqhw4sr9qfB3by24EjJXOmWsxuyZ+1NbR/76HLkcIAbh9VL5Cj0Lr7y2qzwQJkaHywme\nHnsenwJR4A0hs5TWMfbaVDiqwqfqku/oRm7lyKetWUnf3I+MHom7ddEX5sglhRMTNi6cEhA3HtTC\nQ93Ml9Yty3mlD3ytKPdp5hNTz6PqvKLKRxZbvj51nNTKxeD8ylp48ugG/BtL3r/gsIk0OzkFZCt4\nOcJ9QtMC84LoAbVsYLxBL80zbaPjUcAHtkdbtiE0fE+KSFpQyEy5dTNJjCSceXuMp45Fv2TKxmEv\nHG1K87Z7QucJccU1ESUhISKpQEjkbWq2jZQoNkEZIQSQ0DCz25mgPV3XMW5GnII7jN2q7WDmI1Q7\nXLpm60mHoJWQ9hAgB6AUKIA6bmdIPMDKhHSHhJRIqcNOruJzpqTEoh+weUsQIQ09m+2GWNdsTkcY\n9iBnJB3g6iDCfOtWK+c9PMdidY5OKjfrzGJvaAOZOWERWGCsLPCynrBaJsoUCAILAvux4yQfcVpG\n+rESJJPXx4yz0e8teebFb3Hpyp2Us5mjoxsgleW5i+z3A50oB8OC05MThvPnyLeOWJ+e8uDl+9Hx\njLOzM47Wa0505sYN8CBcuHwHt156gTpPDGmLL86huWAhMmvPEIRxOoUQsM0xy+WKcRopc24Daeia\nOimBmstuQVnAZqy7yDyegeWGxw170C8J3bLt+C5XeGn20JBavmgOETon4lgX8JDAB2KBWpy0WDDG\nBZxdh3qMlCNcFGHBHHuiCPO4pQFOuoYCzyPBGsI3o7BTB70KLSyliO6oVjmDbBHpoGaq7rbGRcDX\nkGdqf4AsLmGhJyjUUtD5uPU5mTU1yYWZRevXKa18mLr+V7qMReSbwAP/gm/9LXf/z0SkB/4G8KNA\nD/wS8Jfd/drv+xn3AT8BfBA4Bf4+8GPu/odo9M7su1LaIHgtbcOBhKCU2/RB2mMyzRIZpIALqgHz\nnfrijTZmGEqgiGEuCDuIShVmKhtJTV1RCFSMSEDpK1Q3Qmllp50oEcGDM3irkQXbhfKdg7BTi1SI\nUjE3Rs3M3qN5YpbEPLfgvUagTgxEuth4OFoKXYBRQWpTkZAEpVJD6zXS0PDfajubliV0Voa0IBcj\nhqamYULd2SHc2pCUc2lAh9oUUKUBFYrTlOyGEED6sANlBBRreToVPAMSKHNTnoJErBp5B1+oQZFc\nWXaNhpfnyFwLuUA1I1uGIOS5NGUvgNVIuwqbpOK1Yd4dyCU3uIsIuzQaqGK1EFypFByjE6XQ6HPJ\nA1ErGW/2Vms5FBQq7ecUcYplijffwO2+pSognvEdghuT3dAk3J7CKwYWqKJoaZry6ErQVlFAFSxI\ne49FmWpDhQ8E3CpjdTTPZNWW56qK9s5eSMxRGecJV6UEw6z1ZI21kQAn01bQrILk2hJYMTBb8wcq\njSzZDKBtYC9BqKWBIaTMVPEWz9ph+TftgwqV1Oy8briXdu69io8bk/K25cyRCO83YTONfLbOfOd+\n4HM58K5OuZphKiNLhX8rwStb5UyMqSifPIIbwblZWpb1Q71TgvPTpwsc57GkvCY4T28qZzHyJ/cy\nXxgjH1kU/vubK97STdycE/eGmeOc2CA8Fiq9KK8JlYNe+HtHC35oOTKJ8NE58u4+c04LXVAuLpRP\nnwTejvEDi8JPnAxcqJXnvVlks8zkUnbkROXLteOgJrq+8sauWSt/YU68oRS+Yh2jOaYTd6rwWzVB\niHzHYuahAbb5lOtTz2dd+Mj+SCzCSoy9pfG508A+Mz/5cmTbw5QrX5UF2ZyHmPnKkfKlrfCOc8Ib\nzmXeWIX/4ZUlf3l/pgsFxOmlgSQuiXLPsCGlBZaFFCsHUUixcDJljjYJmQtdN/PVmwM358Dbzm/5\nmecHvvuKUebArx8bR1X54AXjfB85HCoP5shz24krhx2fuQHfOHLef1lhq+hY+IVbHd+MHW/zVi3x\n3ovOE9eFcTPzpkXh/l7JM8yqPJP3eUNfePysR1X57Lbw4XPK56Z2/0Sch5Lxga5wUgO/OEZer8Yn\nxsA7dObJekCtI+es8KYw8rgvSap8x6qyjMY4DFzN8EI2vm858+lNxxeKEzt4b+fM6jzlka9b4t2x\nsC7wI/vGP5oW3Joyn583/PDexEsmvDB2nMTIo13mv7q+xyMxc3OK/ODBxLcm44YLm6r8nWnBDywn\nTlz4hWmginMlVkyahf/vnQ18W8gcuHBixmEs3CiKCHxmHPnwUJhswY3YcRwiD8fCs0U4bzOnFZ4Z\nR965El7Ogd+kI5owOUw4L035j/S6f1UpTPrat2OdInO7CYsXYmxdNKKKLVbNhJ43KIbqopF9JBNi\npO4+qGS3YBHtYDFg2hFtYtqcMKzOk7qBcTvizO0DlQQ1E4MwjSOoI9VasWlq5D2dJiwk8FYmGkPF\nKZTrt9CDSxRtPSxSMh5jW5yidH2gaEcxJdlMH3pGhzyNrJYLzJ3t9oROmyqEgFUjDYsWyl2fkGKl\n1oJpIqRWJhkXK+ZpwovjGMyZtOzxWlgMi1bMCNRp07qAdt6R1cEhZ688C4d3te4Zg5o3aJlJ5y7T\n9z1znlvnklW0SzjCNBeGRWQ9bhhiaouScebg/AHraYvViNXKCqV2zukrN0lmHNx9N1dfuUYpW+69\n6z6yOjLXRluSwPHJLepcqGe3iHv77O1fIOcM88hms2nUJhG8OmzXxEtXkL6nnt1EvFHLrICFgIQO\nvP1syrQbMBJeCpI6QoiIKHmaEGzj+gIAACAASURBVI3smmBBG6479CukZtRnsutu+Chot2y50ukM\niR0i0shc0xZZ7BN3aoabodWpJSNe8Sj45pRweL5lVbzhF6i5QRZ8wj2DLNrgHZeE2GFlgxPBFQ20\nTIO2nV6hBYIbTMQQ7RBtO9qw+/e8QTQg1TGJaGqUQK+l/W7AunYtuTSLHjUjO5y4bY7h+f/vljwR\nuQj8fjb5m4FfBj7o7r8qIj8O/EngzwMntN6U6u5/bPf/K/B54EXgvwCuAD8J/G13/6v/snvIv33/\nXdzRLZtSGpsVDjFEGtms2o7wtqOKgYHHRjBzKOJobQoUGogY2Vvw1m8rBtoWwlQwlOQFPGFCQ1SH\ndu1LjYRYdx1PTb3ZQdkIDkiDNbjdlq+NGaOzlm8yq1iXOafKQQq7l7Rdx9PunBhih9fCXJxSDbXS\nYAM0wpqLYjYTgzSgTWnluW5tuBZtWa5CxoHeWnlt3WVZ3J2+i9RSmwLulUBAtQ3zRSEoLbtiAQ+K\nU0jSbGloG7qiKN3OXpZp+dI2oLay1ts/b4hKL4WFggdnNmEzCdvszNbsQe6JajvV1BOltkxSG8y0\nUSlpw0ndgVasjW7YDrQQ3LDaBoPijQpm1hYAtTougotSSsvxmLXHVZotPESn1ka3rAalFqoCFMxC\nK7E1IQTBcmngHQRzoWqguKPalL5ggoeK1dhoqDs8eC7l9zqmRANOIWjrcGpuBGH0ilrgVJzk7bn1\n2kh1vUsbXEyZpFEIC05EyFrJlfY37fqa1Jv1BloZb9hZWpvVDqq1/pxqxhgCwSvibbAUWi9OoREl\nj3LlN17F0Icfuuc8l7X18R2qclSNR3t4au2sorBJHS9X2C+F+4Jxd2ec1sAXZuGeobDNPQex5VRO\nQ+VOSTy4ME6JPKCZj54IDxwEvq13vnWqTDojEnnJE6U47xomfvqkZ0+NbXWuBONi1zLZt6bCfUHB\nhQcH43ycmD3yqaPC2/YiNyxy6sIlm8kh8c+mwCWBty9mXgyRr6x7vqefuJgq35wiT26U7ztf2Lrz\nf59GHknGy1Mr/RYzHl04xeHptfOOPvP0BFET9/XOJTUOFvClM+V6UU6BL4yB//ggU8x43bnC6Rwp\nLnx5I/zOLDwocAT8wMXCT17LdMM+gxiPMXNtDjxbZr7vYuTy4FyfhAudcZKVu5eV9RzBjW5Qrm8r\nW4/cvaiQCwd7gZc3sHK4mQP3dIUswjMnmb4KD19KPHm98sIs/KnLSlanhRmcsSY+cWTcKzNfPXW6\nhfDthx02G2e18KtHkY0LB9H41Lb1pP25OwJHXeCl48wDEWJ0np8SUYVXXFkIPF+ddTZe3zmXovAb\no3MlRd7cO3vi/OK6bbqsFO6Wmecs8Ywn3tu3wuMHug1fnxdQ4XkXHhnaufg7W+UgOpcjqCvrXMh9\nx7lgLIvxTAl0LkQzbu7AQy/nmT9zGNia8/k5EUO75y2LcF5nfrdUzofEl0ph9CXfuywclcrLHvlS\nTry+KzwiMxcDfLlG7sL4VIa3Bni2KtUbKOKbs9KrsR/gk6czbxuET28Cdyflfcu2aX0rG9/M8EBy\nhhh5piiTC3eEZv0eQrsHfm2TefLk3yhMf8DhRB0osak5QiXX5uc3VST0OBNpeRFiaKNRaDf1mAZy\nnlmlhAVlmwulSCOZrU+oPhE0Mp4dUfuB0C2JoQNRSpmYzMhzhriA8YwuKXMuWJ6I/YJcvHUPlV1x\noTTE7vzKt2CxQF3wudG0wt55YuragkgUdWfQQOiXbDa3CN0BEmNDeS96uuU+dbsmdQNWweoxeTOS\n+o642kM14qWidcLmjCwPdr1PA2EIzOsNadEz1cyw3Od0fYaERgvEGsFqGPYIIbDdbohHN+kv3Mfm\n1lnDfXd7hEVg/2Cfo6MjSp4o2zPy/jnyyTEx9eQyc3ac0dgz2SkXzl2gW65Yn6yRXLl8Ya8FzXMh\n9Ym9K3c3Atc4cnnvgL67xHqeWCwWXDu+2Qaz7ciVOy9z9do19u9/mBAim3ELBHSxQMxIGhsOPhpc\nu05drPCtIv0Sl9iQxZ4xDc1qWQN1ewplC6q4OIRA1PB7wf3dhNFsMt2yBbNTwucNSGy2EhW0CNot\nyZsNWOv0avaaikmlG1bM84bsOzpZaEjeIA0rHMSoN56lrpYEZqKnXWdUhw6J4nuItdC8T9J6xOZC\nWi6o3uYNNaNYppfWeh26Aa+0sL9XqIaF2Fbh5i2zo8tmEYyxDYPFCXsLSilUegKKewVtpj7fUc+s\njO1n/mEizh92Fbvf+P1fi8j3Ad/YDUsHwF8A/h13f3z3/f8QeFpE3u3unwE+DLwO+C53vw58UUT+\na+C/E5H/xt3LH/S7hxDp+/ZaqTnR2k5VdG2bLuYUb3YzyYWgIDa3slBpC+iwG0oKU6PYZfCoBJqd\nqZo1y6QI5pUaINQWpCcU5hm0M4xMJBClgRBSFlSMSVtXT8yNvJclgxhaY8tHAq4FFGaP3HQ42rbF\n7zLOTX0wwIQza++Z6o7AGBSvjghYaUoqIoTayk7N2odo3A0BYW6KQhRvuoKGlrORtkiqNVPn2tD3\nOlDEMQoLTeCF6KEttHfDVbHaSKDWMkviDlax0LEZZzRoy0I5OLlZ+1RbT5bGFr5WIRNQlFJL6xKS\nlota9LuAjiVib+S5ULxrsAuUWgpBEwUjWaS08glU20K+lrLLjELVphx6LVhpqo8LBBH2kxDSRC1O\n7HooMM0CXqklYnZCoWfDSIzOQRSyVVQSGXAbKd2CpAWLQuwqZSp00jPtcj7tdSvUvsMMRsucekVK\nIKm3HJq093XGcAmUqjtLYSEYdNIGw33a337b1p2KUdUoubAB1t5h7M47dZLFlvnE6NsJ1/KPOzdy\nFW92Sg1NmUQwEYKDq7InAq5ksQagwVrhb4up3OamvGqP82Y8mIR/WnoeVbirL7wwtV7Gb3jgrdGp\npnzoUDkOkWeL8Nr9wkENnE+J50bnPQtjCsJTZx2/PvYNYDVXXJ37gvJrt5zF0rmynBGtIMLlMvOE\n9Xx+Q7N0l5Ef2t/wi6crxuK8qav82tzzXefWfHHT89VT5Z4hsIqFX74x8tt+ju9Olcc3gXdGYRiE\nN3fOoE6VwIUK37OcONcbv30sXFg4ISv/6CTx1oPC+w+Nl0/g3UNhNvj0Vvinp8KP7hXesxe4mAIW\nlLPqfHIb+PC+8twG7uycx1bO50+F7z0/8X9tO37gMPNz13umKNQq3EHmXFC+/aAQgvDFk8BL2zP+\nyws9f+2q8uGLmajC+7rAxQPn+mnh6+sFL9wYeWyV+ORx4H3DyG9te6D1jl3pMg90wt5SOTqFfa+c\n248ciqGz0vfOu5aRM3fOJuPN+5E/tqjcGIW9ZHzuhvCaVebljfFDdwX+2bXED9wrqMB6ciYJnEtw\n5xC5Izq/vYl82wKeuHXE/35jBZ3x/avYspUVLibjWhE+tCrcyMLJrHx5mtl3eGoeeF2CDyxHvjwO\n/HxNvFSFe4KzAe5JkZMp8oN7M9cmYVTnc5sle+qsLfCWhfF/ngVKEc4F5bgqK5uYxHj/SvgHa3hE\njZ+bEx/uZu7vnckdm+Cezvni8Smf7c/zcJp5d5+5MSvnBricjE+vlxxEeFM3c2GOfHPOfGOE9x0W\n4ph4IGbu72b+p5MlP7KcmU24c2G8VwfOauVlDxRzbuaOVwxW1eiy87ah58kR9roGFvnoFn7szjWf\n3yQ2UXkgZZ6YnBdxfniReboowYX1XHiiVsT+aDshX1UDk4YIXY9oa7FWXbTQu0/NVpELIOT1rbYY\n7PaZS0XMyCe3QALHu3wq5YSwOCCEfeKqR3SvLQDWG7IrZTwlC8TQozGCC8GVmLfo4QWCVpapx4sx\n54qv2o7LcuGM40gOHZRxR8saqGzbApxWNooZxnq3i90hnqgdQELGNQmoZGy9bbu5NlL9FLSDbkEK\ngVLKjnC0+wTqB9R6Sp5wM2JcIgb9coHGyHy2AY/E2BFKxbPR9U2Fi0nYbte4RkqtlGnL8u6LLA8P\nOL56le14xubqRL9YEjtt6kw1hq5nueiZ1y2Pdcfeea5vjjg+Pmp2FDdchetXT1l1A12/4OzmNZar\nJUGUk5MTAjBPExLirsvDWzhbhG8+8w0QYXN2isSEuxHSArNdJxaFaE6QFVkj0i9bNiUkks5Mmw2u\nA9r15HFqg1DokcUhvutcsVrI40kr+5UEKniZW95sC3QLpLbhspbcdoMF9DaJEdrGfjlrgIV+gYyn\nTY0KYdevZNhcINw+T3cVoGaQC94ftNPDWq4kTxUvZ2jqyNtGcIv9ouHfp0IIu11yg9R1THm3cRAg\n9s2iEEpHzpkYYsuvdKGF7KcZupZ3KFRCEqxM1O0WJFLnAl0ECjX2BHG0WzYgghtsTvkDJ5L/l4eI\nJODfB/767p/eSbsvfez2Y9z9qyLyHPA+4DPAe4Ev7oal28cvAT8OvJGmPv0Lj9Frs3GZ7TJ5DX18\ns0IvwjZAKkbbnAkMqeUfIZFE6NxQKdRaWe0GcklC9qZOiBlRhGyGS+vliu4knYgCilGDMlclSof5\nxOgttK+eCLVSQyOuqdAKkaVnWZ1JCoY2clxtClguAcfbwtgMC4umkNVK0EpMgpWmCpgm3GcK7VrV\n0EzzJU9Y+2V0tRDFSVUIIVGCUq3SGSRpZci9C2ra6sU00AVAlKSFlbfel16VakotQDX89udaqWgU\nusCulDU0xL9lQhAQI/QQXZhq3CmjtH4mdkjzqnQ4nQayT/Qx7PJFRt5mhqSsJ6fslOJi7RoVj7hL\nU+qjUr1QXFvBrhtRdJedaoNhlbappCmShkoQSLmJsFOlvU6xJ1lDeydv0JzJhUnP4XWmrx0F4dgq\nVZzgkWzKpIGuVqbaymc7b/fdE4eJHR1vp+bIWJsTViF6xyTO2mZMnGpOkgAlt2F11+3XShCaGqex\nKaXF27QSi5CjoDWQYuQ8cN6dLS2bVppbEIDqbUgqQrtv7VRmsbb7HIOCKNUNLXU3bHqDS0gDMgkV\ncRgxqjeS4tZvfxC/Oo/zqeWZ7xXlJQs8khz1xCWdeUiMj42Ju8T4+InwNZwPLITPnka2Bq/M8IoL\n/+R4AIR5PuXPX9xyPiQuDk4Iztoqn7i54Eu58q1juBQDj/WZMQReKpH3BON+PWF/v2cv9vzZRWE0\nuGnCu7NwIy14/4XCk6fKx/Ie82RUJpY18TXavPp4TdxTlUu5AsbdfaHmyHWUL0/OU2Xgw9PEg2Ek\nG9RTuF4CRWc+ehYYPHA1dvzIauZj44I3p8wd2vIu9y8r96+Ur49wtQiPde3e+e0HBdfA5VEYgEc6\nWEolG5xbGkvPLJLy3FoZNXCchcfPlB97oHB+ueD0euYTZ5EXTyM/vBe4p3ce7IUkG167cAYd+O44\nc3WrvOui8+I48tTRgiqtZ+2mdeRT5x2LzOFCefz5xHcezoDwsZuBS9F54iXnzYvKzdzsxJmIe+A/\nfapyQZ2fvwHvXCrPZOEdS9i48NnRuOWVc1X4nn14OjofOmgIcXe4b5n5mVvwcErc1cP/fDywNTgM\nzn9wYeATuecjaeLEjH9yHNkLLUP9ls4Zi3PgcDrBPVo4dMPU+eSUeEuorKvz2EHlU2ttxfYKr1gr\ndn1kFVlNmSODD3QzH91G3hpnNrVyXJwnJ2Uf4cWx8HwR9rLQseThVEgK0Ywfv7HgITL39M7Hj5V3\nLwpvWjrnxPk/Tvf4vmFCBZ4fE3/lYMtPnS3YD8avlAUfWWS2tfKWPePTp4l3r0Z+a91xKcJdnfHz\n255+4fxHqw1PTok3dzPPj5EvnAqfs8iXinMaA68PhV8eO753mFn1sAjOe3PguBT++tkf3XX/qhqY\nynSKhKHR6VTxri2qK60jKcSdLWUvEWNEYt8KJIMwEVEzzAthWEE9QHRnaQoRm72F4IO03TNNIAMW\nAtEzi06Z1bC5ktdHze4gx5hESAPkM9CeeWplknVa46FHBFZ7S5wV2MQ8N8y5hYTZHjaNECuhiyRd\nAdB3AcsV19YNQ86tiX47EUJEyeTTmw0fvDxgu95CbdYxVcXyBKJs17dI3R6+t49t10g5Q0MhWFPA\nLMFZPmmL9mvHxK4n+BFuM5JP2B5NjKen1DrRDQPMxnx6s+UTUiR2PTEGXIx+P5GJrLtKtIRtCpIS\nWjLDYqBPHevjU6b1MbVmtuUMRFilgfX2hMP981BbTuBw/4Dqxsm05eJddzP0S6oVYt9xfLplNSxI\nQbh245j9oQ2O43rTxPOg7O0vGE9npu0Mumj2n217P2LqKHPrgpHSFh1x2KMOA24VGBAtOIEQGyVR\nBeq4xV1bXk2lvQdnZzDPECMh9EhSihX87BqCM5dNy6G4g2UYlqgMhL5Dg1JzoUgAz9j6RiOxecCo\ntO3gLVYSSIeH2OhmO6uReKTWsS14S7MrSXW8jFRtu9NzmUB7yrxtpL8Mtc7gjZJnugFbUhcrWn+m\ng1Z0GbBsoA0IIG7UzTFlZ9Nqq+B/bcefBg6B/2339Z3A7O4n/9zjrgJ37f77rt3X//z3b3/vDxyY\ncEW9I7hgtZDa02SRGqFuVVtySKXhkEPJQCAztoGIQNopjGJtUGp5jIoyoC5UaTQ8zBhiJOJsa7Oy\nBRNCVEKBbCPJYLIdgMDLLgPSQZ0JoVDUGXJiEwIdrVhUNTVSn7To/T5tkZ1CG1Bi6KkyUkpjOW7S\nRHJhzx2z0FSz0OhHWEVSQC2jxUgWmFODNFQK1RakUgmhEszIdFTLbKQy51Wz2dUGiKg78MzWFbGC\ndcLiTFl2A8PQYCOdj0RziIW5CqkT5tlYyMDeLgtFPcM1osEIsUlqcb8wTJGUIg/fYeyfu0UaDE4D\nJ/MCLwMqwmZObPOaU0tszvY5GoVNnlsxbmlY7LIbDifvqThjqdgOdDJ7K4fdVEfVEG2LfbfUNre0\nXbMV0BKx0vKx2SvVduRVy7v+vVYMHGqhoLjR8OgC6oFCU5sJsK0ZoaM6FJ2pBFQqZookmLOTS+sH\nVPdmKXdYeis/tlRRYBtyA7aUQA7GGUbwgNHgRbsYFJYrE5VqTU0rTc/DGly8FRWrE2pAtGLecPG+\nG3Qma3bB2ZtltXhEYiV4JdAUbaFdB7M5QdrraqVln/xVLjF9Y3S2pgSrdG68EoS7w8wLVfntHPnu\nfmpUuQHeHp3LXeVGUVbq/K1bS16vM6lW7lkpq7riVIQrISMRXtn2nIszr+8Kd0hlI8KZpRbyjyP/\n3sGGp8eIFOWJE2NfFBUnqxCCsgozz20DCeG1XeWLc+VSUJ4X+EuXt1SUkyK8MgFsEU/82pz4+okS\nVfjA0riSlCtSOL+APgslCccmvEjk/gQ1K2/sC9+RTvnHR4HXpcyFFPnosbDvznPW8Y5kfHmCe9X4\n+I3C21ZOTj3Z4RwzXxqVK2FiXSJZ4aPHymVP1FvG/lC5P5wgXhl8w09dXfLOvvKtHHnjqvIWy3xx\nA9dmON8F3rKqnFlH3xUOktP3xnVa9cdBnTgXA1dH4Q2HE50kXjoRXprbTsBTJ23D6YN7Wz5zmvjR\nizAXwWLmoX1wNW7OM//tgz2HC2Guzaa6niqLTug18MAtuGcBtRi/u3VGayTLP31H5vlT4dMnkXuD\ncH/KXM/OR3pnisI3RuVmCdxrmZ8dI+8fnAv78DuTsvTAha7wLYPH+sI3S+CemPnoJpCrck90Nqq8\nsdvwczdXnBZjVOf7u0wX4eMTjNu2kfLldTNs97Xy6SnzgWVPUuNtq8qDyTnJwgPHzpu08NXRsWKs\nPXJRjG+TiZ/djLyDyJkHHp97XmuVtRfeFyaeKbBweNEFG5XenPPihLlw0ytJ4X+9ZdwTjZ++pdyR\nZo7myFc3Rs+GZYWv+JZvzMpoHXd54VKsvN4z791TfvJEuaHwrpAJOD9zS8g4z8zOO4Y/2uv+VTUw\nRVmgKWF9R1vOFPK4QUPfdgZpvm0VmLZbYGw7an0PlrEygVWqFfq9Q3LJ5KlSszY1xzPMtEWhCDJk\n8lwwXWAYXisMhzBt8VqRYZ9ODMSpMqDzhmFYMU0TnTrZJpCASEeumUCi6zvG7RYkcfFwjxubm4S5\nIlVxHVENnB3dRMTwcQvDOVKKeBxYHeyxntaYON2F+5inmSoVTdoQsGVXbtrvt36UtCD7Fk6vwTwi\nh3cxzjM2bdiRe+n7vWYrOuhYr9eUsAcSWe5dZLFccnpyQkwdeZywPNP1A0pg3G4oeWR0ICW6EFuQ\nehzphwXTNLLoOqZa2L54lf7iITUJ99x5hc12y6ofOD2+yY2r19jb2+fSxTtYT5tdhgFA8VK5efwS\nGntsmnBxDi+cZ9wes9GIF+PGyyP9pfPIsKRPicVqwem0bUWyuyMsB5bDwGa9pZQZp/VpqTqe15R1\n/j2se9EjhJ6w2MNqZYgd280phLYI9JypYvhwwDBADqFlrOaTnVXNoN+n2y0IiqT2XswTbDaUuHt2\noQ1CAKRlo+DFFo627RHYjHbnCLFZXaRkSi6kxUErrM2GSNdi97chFbRcjXulVkOkBwvEZTsfzCuW\nExYi6sqVux/hhWefa+pViFRvKoWrQqzgjs3rlpPwDNoWjZ63/zqXO38B+AV3f/kPedxuufeHHv/S\nx/z69eut5Nkdt+aDvn9/wZW9oeWK3Flo66UKIeGawSod7XUNalRpqkcR2Q0K2pDiccZc6PtApFEs\nW/lRpS9C9oor1DphSdqmj0H0pmgtPSFBubAoOxKbEVKEtWAl06XIWXGqOzk09Ly4cO3/Ye/O4yy7\nynr/f5619j5V1d3pdCAJBAiDQhAVZRBHEPyhcOGK6HUAUYHfVeAH3KvovaIIXFFEkSuggDiBgIgD\nIKAg8yBDEEKQmTAmQCQkISTpqarO2Xut5/fHs6tzUqmTruqu6q5Kvu/X67y6a9euc9Y+wzr72Wut\n56kTWu/Y6w0LmWH9Jsyf0mLeczqF1LQ0zDOZjCm5p3GnI06C21qiSLdnLPUs0NJ6rGGpbtCOKBWa\nJtYyTbxlvow4xeqRlNaZGHxocov1RnLIrTM36hkttHR9hzWFvstQC5OuYRdGXaq0aQGvPVd6JEQ5\nlE7Be2OcRnSlME7G5MrIZrLs4F+EQmY0arBS2JV6WrchIU1HbTLzZQ5vOvBKW50JTnVjGaMMhbEz\ncdxNyVhqYwpqilGmZlQptWB1RGkqTe4xr2QyVntGFmt06tCRWiIuflilt0RiREnjqDrkkUzBHGrt\ngSiFUDy+s9yhyQ1OD6mwq2Y6NyaJWLfWxzS3PmJkThnWKfaW6Ohiyi9GUy2K7aZMTpEEw3ILKTJj\nVeqwLjHW2pIqpBrfPUC1CZ2lKKRNjtTrR6bpDYkmPKbizZkzn4xEpaFQSUw844CnSDwyJHzkkvGY\nS8bjmA6I4TjdDlg7fX3OGfXcbQ98pc6xG9idxzz7qjketlAoVL7ctZg5t87wsYOJ/ZbYS+Xb5+Fm\nvsy4d06zyjsPJB5zE/jccuWS3rhFmXBxl/jU2PjAknNGTtwS5467lriqT4yXMh8qLftKz6VpFwtW\n+ESfuOuuwhltx63MuMQztyjLnL4bDi1mHrJ7P+9aOgUAS5nLx5nTmp477On5+P6WSubRN+140dUj\n7lQLu4HDBU5vC399WRRJ/fThjlvvGnG3+TEHyPzimR2fPJi43Ft+9IyWNx1uOeQTbjGqnEYljSuv\nWk485pTKWU3Pu5bmuKT2XH2o8qXlZe5w2m7Oqj0XLmauGhKEPGzfMmaJ3XnE+QcTHxrvxe1K7rpn\njnvtcr5xaMIpTebA2Dh/seUHTincvDXedQA+vZTI3nPWaMS3zBUM560H4AF7G/79cOIhZ/Rc4iP+\n9sLKY261zMXecq8zR9yigz2pcvVi5QlfTjzpzMrZpzQs1pjaulijYPSVkwmvu7Lw3bsSb94Pe1LH\n487qGXc9S7lhqczxyq823O8M42ZzhX2tcd+9HR8+bBzsoVTjIhp+bFfHwkLiKwcq3+jhqmGG0O2T\ns7jYc/W45ayu8i3JudRhbmx800LD/gI/unuRf7h6gW9tOg6Q+Xoh2mbz/MK+MRcsw6sPZ16x2FHc\nMRtzl10ttxrO8he94R67lvnj/Xs498Ayh3YlLquFB+8yehoMozaJ/X3DFebcunHeeND5L7s67rd7\nN98+39GRyGWJjy9nztlbaXPLleOMWWFxyXhtN8e+FjD4nI/46HKm1Oi3ruoTv3xT55BHra3LJpmr\nLLMP+PnbwZ98zrhdrtyyLezrM4cnc0zouOeCs1zhtQd6zpkzvjiJAYef3uNcMSlrf0C3yE5J+vD9\nwLmcfXuYmyeb49bGHGsfviCsp3jUmWC4UlncsdySifn4tR8z5NmlaeboLcVido91GqndEyeLpZCb\nRGqiYClDfZRaOix5zEO3ysLcAt1kzKgdxVSTTEyTG82xOFnErKF+6QKa295p5Ws1RrWIud6L48Uj\nC6jpI/tVbhvoe/o+Ri5SHmFNC6XEAts0pPrtl0g0jBYWKMXjC7cUaOcpfYm1Cji5H1OrMxkvw6gl\nVWNhfj6yWeVEN1liXJxSYhbWeNzD5V/BzjgLmpa5+XnGS8u0oznaHOnQF+Z3Me4nTJYXqd0EJ0ZW\nctOQc8N807LUT7DSk8xYGnc07pxx+hksdWO6foz1BS+VSYnRHFuYo46Xj6RZb1OmnZvDLUfaWu/p\n+z6SbXilmyzGa5ki+YG1DVz+Nfz0WwFDumiaeI7mW7phGqRXI+VmqK8VdXWO1Dyij7S+7iRaUtNQ\nyji+4j2edxtSbXs/GQKkYUF+jnnTzdw8pRYofUz5swYrUQMpFlDH+8+sITXQX/JFuNk3E+FmpCC2\n6rj35BxTQUvfDWuSDNoGbI5cJziV6okmxdSyOMEhRrOu+fRMZccrsbTdHZqW1Iyg5siOWApuGehJ\nHifowY9ktIoAwCNhxlWXAvyAu7//OD7XtwYuBH7c3d8wbPsh4O3AadOjTGb2JeC57v4nZvY7wIPc\n/W5Tv7/tcF93dffrjDCtqFAABAAAIABJREFULNb+9rNuxd7RLuZLjCaPhhM4SDTDugO3jnmc7C0N\nE2oyDpSeRU+Ma3yGqzt9juckW6Snplkg9RXzSusx17r1ntL4ELDWSPFeiSv+Q3IIq1CsIVejTyUK\nF6fEyCMbmwEjMo1PGFkTCSeGCx5tKlE7Z9Lj8y0j78gURrnBSqVNwzvU85B4BkZ9pfeORR/R9x02\nihP6sVdSDy2Jfih7MOqhJqMpHZYz2TITKtUS2SfklSQAxZkrlZSduKSSKFbIbfSnniLF9jxNjPqn\nEl/Rw3srp5auTDCI2k/V8TpHTcbuUvB2nqXsLE3GMVWuzYwmlVwrySo00fc2OLn0nDpKnGJGkyNx\nSdfHaxHfG1F+YUJi3FeaZoTXyhzGxMvwWRlO7PuGJSrzRIKX2sSJ1LgHa+PYs2eqr7xSMOkqxSKt\neIzeQClDWvSUcE9g4Mli/VatNCnjTkybs8qYdOS5rQ6TUulTXBCcH1J/H/SK+ZC4hKHMwJCuvSkj\nsIr7mOpOB/Q11sv13tHjTEgUc5pijItTc3yPlB5KmkTdr+LMNS2Qok/2mN44Mo/1Sp5ZdsctFu53\nVBpiWvVk6J/G5Jje7sbYnY7KVd0yFxw4BDsv6cP3A+c+6PQ97MqZ3Y0z77A8TDW8WVtxq5xqznlL\nLRcVuPd85f3LmTvPO2daz9XVeN9iZFM8E7jzQuHSPnN5b9wyVQ65ceYoPuNdMU5vK3sb+HqfaBLs\ncedQjSmhezKc2zc88JSOw+OoH0SNGQaHqjGXjf/sYnT7dZcc5KFn7WYB46KaOWtUOM2cVCvvONxy\nu1T5bMmkrnLT7My3xql0fHaSOT0Z1hj7GjjQw+EKu5OzF+fiLtb1fc+eCV/tGm7Vwte7xE1GsH8S\nqcsXKxg9fZ9522Lilq2zx+Cuu3uSJSBxcNKzXBNfHiduPl95z+GGSw4d4B5759mbGr5tT8/rDzTc\nZ6FyWgtXeOJWuwrj3ui6MZctZ4zExV3DTdrKGW3hJjlx8cTYk5fJlvjIgTkqHQ++eeLAMhwoheXO\nWaqFQ7Wl98St93T8x8GMVeOmGeazc8sFZ+SZSYWmmXD5pGHSVb5RE288UPnmtrIvNXyjGHfcBR/c\nP+bue/bQUhmlyHR5ajbOnC/sn0TqMXOnJKeUxKQ4V3ik7L7Mjbu0HcUTnVcu6Efcf2HMBePMbiMu\nKFdYaJw54IPLlYO9Ddk2Y8nBIU/8t909V3TDaG6tnNYY+4tRUsUMLu0i+c+tm0Iy481XHuKcvfu4\nY9MxSnB1idlW3+gr3zZnLDlcPTEOuHG4gjXGXk/cNPWMkvHVLvGt82MO1YbPT4yrHG7hzsU1kn18\nZ+55x1Lm9tm52gp3y5VFi5T1Nxtl9iW4osJicT5WRtzWem6SKp9aNq6qsC8VbpHhrBQlFXqc/X3h\n3YeW4TjPRdb9+d8hAdPDgFec7HaIyLX8nLv/3bH+sZk9DXgUcPZKOvAh6cPXiaQPrx22nQN8Bvge\nd/+Qmf0X4PXAWSvrmMzs0cAfAme6+3Vyja4ETHc/62x2NTHqN28FS9ACe/vKnpHR1EK1TE9Pnypz\nnUdha0YsFVikZ0+qNHkOfIINJyetJQ7nyBY330KtBafSFoCGNldu5okrs9HVOLGMLGZOVzNLtbKY\nIkMfnhhnSGRKLdRUSHgkhvBCdmMux2W7eY86bNVg5JXWEsl7Rt5QzbHas9DE9LJdJccoTBMXkObq\nhGQjWouTmRRzEUle6Et/pCZcqs5823J4PKFpW2qpLHmhFiPlfGQacJtGWKrMAWMq81bwnNibcxSJ\nBkqfmGsTKce0tZX7z6mSLeqZ9RYFmxN9rB1MxsgyTokscU3mcN9jtSE1ThcDJ5RSyM08u7zS58iA\nZ72xWCPpJKmSLA2pxQFvKZbAu1hrCYwq9LXgTUwznFRjscSFjrmc8DqJTIgwTF6D7D2e2lj7UyvL\nHtkF61AyAouMm6U3rI1pcMUThUglX4a1SH1KVI/seUfq+kUUFdMBm6FGFEQg7TaMgjekGgvD07C2\nyHMEb9kiGO5qZLYrXo8kjMEKmWsSuMxZjLjm6syZMUnOoif6ahCpMRhXmJTMQq40VFKbGJcJuUYw\n7TUCvkpPVyOJzZIllo1IiDFM6TvQd3z8wAHYeQGTzkVEtp/jOhdZr50yJe8txMLwLxHBpYicPPPA\nbYnP5TExMwMeCbx0unaSux8wsxcDzzGzq4gaS88DznX3Dw27vRX4NPByM/sN4Czg6cAL1gqWpvW+\nki4cqLE4PuEcTpm+FBozSo2T4YU+cZgyrBnp2DeCswwWvVDLMiVBHha5m1f2MhlOIAudO7usIbVQ\nq7GrNw40PeZgR0YFC7kmJrVSPTLHmcWoyykkqveknGi8pc1OrhVzj+LSHiPTqS1DTahmCJgKbXIW\nmMT8/xaSN3ittNbH/ZchYx0VL5GZLlskQKFCY3VI8V2oqdLmBD5m1yjRlDFtY5wGpDmn1p62aUgj\nZ3myTNMae3LLgcmYPU2k4y9lMmQgrHhTaLxAn0nFWfY+UqRjLJdM9RSDxl2lscIoNTQWgUhuEuDM\nW2XfCJbrmFJHdA6THIkHjEpJPX0HXXVGlmmbniZH0h4zZ1x6Ksa4d3qgtwg+h0pM1BS1tGqpTBz6\naoxSIdUSabv7Sm4yXmJ6ZG+JrhsKVCfjFCKleJ9iJMk8En0Uhlz6ZnQYvcX6npXRnnFfYUjWwJBZ\n093p8GE9Xfycc6YOo0vAMI3ZWXYnpwwp0U4FQlGUNxKXeHX6IWDOHum+0zBG1Q2pXFZqRqUK89bR\nY/SW8FLJ5mQqB4e1ejbuyWTGbvS1I6VEa3BKGkVNqWTD+qkYMRtyeBwZPduBdC4isn0c97nIRuyI\ngGlIQ7zl0aOIrNvxDn//MHA28JI1fverRHnpVxOFa98MPH7ll+5ezexHiax47wcOAy8FfvtoD9qX\nSlcL8xZTDVuvjBLMJ6fFaN1iVKftmWsbckmR2jtllrvKYoJJauNKvjkLlVhrRKbHyH3mgCWuxOnH\nlVOyMUrOGZZIFBrPeFoZmzBqWsmul+OkeBitYliXUuhZJrHYVSLFg9EA2TK4MT9O0DrJPaahVh/S\nRztmLa33ZCv0HmvhGoyUjVSgoWXcjMi+HCfS5rQkanLSsKZvDiNXp/MMnvAMi6XQphj98q6CdWS3\nODl243BfqSVFhj2PgCwljxGSzugtM0oMadVHw2TUSrJC27ZRR24EVuZoiLppNfWkkplrIqV5ccNp\nqQ3D1MMoDOz0dNUi6XjtWXKjDrPsJp7Y1Uxom4yT6FIHCXJnkfLabEgbnqm1p3hmXH0YTYKxN4zc\nWHbYPSRGsBx/ExOyYspvSSmS6hBB3ELr9F3G3PE8FPytNdYRYTQUsMp8Y7G2qRpuheKQcgR5Ocf6\nqNIP0zR9qIWUIntg8QiES4qJyMUSqfSRSMYMq5AsChenskzvNcoYpJ5SHU8NLfVIUL4S0EzcIhGG\nRWKS+ersaSvVbEglHs/bMk7vkdWwOHSlRskGoBsC3qEwFskhbf+JLWvSuYjItrPlU/FW7IgpeSIi\nx2NlSt4t9u7jtqeexrw5I4yUYOTO/LDup2VlxKaSK3h2akmU5HTVWDajx2gqLA/rklqgM6ezlqtr\nwUiYG6Pq1NZItWcO53SD05MzlyLNu7mxWCuLDmM3Fj2zZAWvKcpmmVNS5fKDS5yxaxdmaQhkoFhP\n40b2TKFnIWXmhyGiBmchRaAzIeZ6z1cjWZxMz+XKKGWq97QU2tRgFLI7lmDvkCY61vLFWpVRE6f2\nuXaUlbU4OFadnBNWnJH5kWx5o7bl/Vctcs+b7ImRJXMWPDHxWLNDNtx7EnkoKGvD1EfDzcjeYxaF\ngRMVS5UFjKZx2hxJJsYlpqUdKjEy01XHbESxBusjgYnNpSiqW53GesbVOdgXkg2JLBxSIlLMA+cd\n7LjHnrmYKllgUnuKRwpkHwLGcY1RmEizYiQrkSnOCnhmucbzYEBxwApLJZIoJBr6mFw4pAAH945i\nUQOsmtGUBs9RjqF6JQ2Fi1OKEcIyjAjVITg2My5cGnObhd1UizW52Y1mCORrLcOareGzQASI1Su7\nU4zuTUrFspFqGeo4jY4kiuk8srD1nnAqOY4s1lwNo7RjojYTRKKHBmPZK0uNkUqKUTJz+ppIDvv7\nno8d3HmFa0XkxmtHjDCJiGyGA8tLsHcfHYnqE2xY37GUE7tKz1xXGTeZXFuqVWrXMWfgxSgOS2aM\nLZOrs5Qdt6gGmojMZrhRhvUabmlIfNKSUmG/GYfdaSt0vUdqchKUSpcTyxUmKcUanuHkuXjissVF\nbrprbwQwwxQvvGUCZColQefOwRpBUiRTMBKRfcqSka2wy6K7zzVDF2uF9liknM7WMDYn9cZuSkyz\nK7FyJbuxqytAT+fOXDba6oyaBu8ntBR2zTXs72LxcF+Nblx4x1UTbtJGSu+GKFQ7AtqYp8i8Z1oS\nuLGQjHbeyV5JNdMNI38jCtWMvji1idQsxTuokK1luVbMMl2tNKN5JpOOcd9DjTVI/bij7wtGGyNZ\nDm2KZCopeSREMIPheTvv6mXuunuOSansziMaL1g2slfceywlFnIkcvACXe3JNNTkMQxFOVKkF2Ia\nXl+j/HPFmVihH5LI9NVpzaL2m0XQCNA2ztgg1zqUuKhHRs2MqN2WicDdIyMOn19c5uZzcyRPNObk\nBJkcdcYsphp2XrEhqU4xqD5kA7PC2JzWLcoaeBS77T1Th2BtJclEqRbroxxah0yi9lFMOKcYZ/NI\nUcNCysOIVR/TCy0zoeA4rV3vzFkRkW1HAZOI3Gg4cWU+Z8jeRppkoj7RfutZtIbSJ1KqVCtkn2NU\njWL9sCA+xdV/d+ZKjCS0VvFacM+QE0u1Z5wbkjtlyNDZ1YYrU6FxYy5HmmZwxjhdNvrKUNh0JZth\nZGPzoc0dleRR7ck8R45qhlEPh+yRaaob6tLNU6juR+rvuPuRBRcxoJLogLEVsjfMeaTzH0WuSFKJ\nETDLDVbh8FDJNO7HaAzoY31UkyBPHPcGcKq3URwVWPI4ze+bmO54OMW0Q+sbPEWSADNj3FWapUiz\nvdA4CyUxP0rMW2KhKexmjqXhBZyUyrwbaS5eh1orXhOLixMiaXca1gqBD/WCvMb6tZwSnTdDvbMo\ndI5l+lpYNqfgdF4YpczhEmnia1eZ83g9auP0DmPP0BXSaA4vTrUhwx3Qx6PTDHnzErFmJ5YcOSlH\n+u+eRLZo/8QSFZhQWfJKS+aQ9YxqJIdwayjW4V4ZmZEsUfsyJOYYXtcU0z7jrRWvV+dQvVLMqJ5J\nlmmyR8p0Nw6nyCLYDEV9V7L64X0EPkM2xsNUxsS0x672zJExIjV6btIwpXFEKYUxNTKW56hz5paG\n90WC2g1TR21TPs8iIieKAiYRuRFxzCrWRxrlXI1Fcyg+rIGpJIOEUUoe0pfGFKdklZp79niiaWKt\nh5nResMShaXkLJeYulSHCj2ejIaKJaMtkHMlV6eWyoI1jDIsJVh0pw5p59OQnjoPIzMAOWesRmrq\nPKQHh5hS5p7I2UipsGsoWZBILKbKUqmMamacRnEiPKy36Qy8nwOgtwl1mGLXAe7GnA3Fed1pvTCf\njblUaGpi2Sp9iRP0OC9PQB0SZjhNdmquOBEItk2GvuJN1DAyS5hFG90iqDg1ZfKckayhEsV9r/Ax\nXanYOGPFWWih9cKe3HBqalkeVyZeqSXWEo1SZlcppJaoUVYsUoh7j6VKKhYZBq3SeYdZQ985nnso\n0DYxmuKlRpr46vTuVMux9qhUmtzQd2O8OF1qqF0hWbx30lBPpnrFLHHAo7B6wuisslyd5AnzQk3x\nnEFloWljbVTpGOWov1WrM2/tMFJlw/t2CDJqpieC/rmVtOwYu6lMLMoiLNUIeJuUIpkIOZJJGCxS\nKERWByNDjYCvDMFr8XhNW4jEJhitJxqcwx6JMmqtTJphhHAYWZsDUm5wL/RUzCMlewGSRzncPhmt\nx6JDEZGdZEekqjGzx5vZRWa2ZGYfMLN7nMS2PMnMzjOzA2Z2mZm9dkh7PL3PnJn9qZldYWYHzezV\nZnbmqn3ONrN/NbPDZnapmT3L7MSkDhqOoZrZc7Z7m83sFmb28qFdi2b2sWE9yvQ+v2tmlwy/f5uZ\n3X7V708zs1eY2X4zu8rMXmRmu7eovcnMnm5mFw7t+YKZPWWN/bZNm29MssG8OQtN4hSLqUF7s7Er\nVU7JMNfEyECtkU2vIdInz5uRzajFWOoz36iZr/WJr/WJS+gZW6IW8JootLHoP0HvMeLRFTiYnKVJ\n5kpPXOmJL7fG5SXxja4y8etecXeP0RGzKATbmLOQnAUKba20tbLkHZNUaOnZV53WnZZIAjFfYz3S\nKVT2psrpZOaGOjgdldIuUdqlqO2GUX1lpMApTSL3zrgaxTJdLRSvJC84hZzj5DrnhmJxwj0mMSHR\n1UxPCzhtJtKFD8dj1gxpKyINuNXIAFetUujpvaMpy3R+mFMKnGqJU5ue0ailWqZYw1d9xIdr4TM9\nfKUzvlpHXNQ1XDjOfGLScMFB+Oihno8drnx0ET552Pn0UuJr45ZLuxFXdD29N+QKo5RZMGN3m2mn\nerLGYH7UcEpK7EmZvRj7UmYeZ94re3Jib6qcmiqnJufU5Oxunfmm0DaJtjH2WBPTL70ymUyiEDSR\npvtAhUOWmdQYXTtYx5QG5hJ4HbFEyzKFiVUWh9ukH7E8aeg9sZwS1YY08RYjQxOD4kbxSKxR3Fgs\nlXHKHEpwKMEBKouemNiIPjWMq3EA44AnLs8NV5L4RnL2p8yVDvtr/O5Kd/YPge5eG7GnGdKpe2QE\nxIyxOYu1o/O4KJA81jKlZBEgT73F+7QzR5h0LrIlx6Bzka1pr85FNtm2H2Eys4cAzwYeDZxHZNB6\ni5mds1KD5QS7F/B84Hzi+fsD4K1mdid3Xxr2+WPgAcBPAgeAPwX+afhbhg/2G4FLgO8FbgG8HJgA\n13lDb6ahg38UsLq457Zrs5ntA84F3gHcH7gCuANw1dQ+vwH8D+ARwEXA7xHvjzu5+8o6578Dbgbc\nFxgRGdX+Avj5LWj2bwKPAR5OpL7+LuClZna1u79gm7b5xmAeoCtw2XJP8igUXb3ithyZu3KkWe4K\neDZSD5Mh47lZ1DqKq/091Ei7DGCdczU9NY+g70gJfNJjGcbEqEI2p1RYdodJ3J8vdkx8JZAwnEz2\ngqfIImYGqXT0pbI4WQYSh70nJyPj0XknmCswqXAZzpJHFgOvfSRpyEZbjTLpuTKPwRM9hcjN1w7Z\n2gpmTgVyMg55pZl0uBle4sQXd+aB3eaMcGrtyDYeiq9GYog+TWJExKMc8FKtXDrumLcGs55cI/30\npO8hRa2ixmMdVpQZitEJooIRVmP9lJtjtgg1gxnL9ByuTq6FXVajKG9tIvucDUkJ8iSmqxXimeqc\nr1Eo4wl9kzEvUMCsRuIHKjlVDpbK55Z6LDXsskIhajRZKpjP412PWUNXCpSKp5iq10T+8CEbYT9k\nhUtUczLQp1jH1PkYL8bh4ljXc5CK9ZXSNkCP1YxZhyfDaoxMlhqFRys9HQ4d9JNCwriKlt4Li7Xy\nleWe0VDfqxDrkYpXzCpjSxS6eI090SbHahS7HQ+jhPPjGKHqUoIh+Ym7DauOgCHFfs2FUVfBYC5V\nKtBPIjU+wyhYVzOena4Wkif6ElMiGzPGOH0p1/pc7gQ6F9lcOhfRuciOs1LrYbvegA8AfzL1swH/\nCTzxZLdtaM/pxKSEew4/7wXGwE9M7XPHYZ/vHn5+AFGw+fSpfR5DfPiaLWzrHuCzwP8DvAt4znZu\nM/BM4N1H2ecS4Fenft4LLAE/M/x8p+E47jq1z/2JmVY334I2vx74q1XbXg38zXZt843hBjwMjiwJ\n0k033bbH7WEnu2/YQB+ic5HNa6vORWKbzkV20G1bjzCZWQvcHfj9lW3u7mb2duD7TlrDrm0f0fFf\nOfx8d+JqzztWdnD3z5rZV4g2n0dcFfmEX/uq1FuIujLfxnWvuGyWPwVe7+7vNLOnTm3/rm3a5gcB\nbzazVwL3Br4KvNDdXwRgZrcDbr6q3QfM7INDu185tPsqd//I1P2+nXjNvgf4501u8/uBR5nZHdz9\n82b2ncAPEFcjt2ubbwxUcFJk+zihBSePl85FNp3ORYLORXaQbR0wEVdMMnDZqu2XEVcdTiozM2L4\n+H3u/ulh882BibsfWLX7ZcPvVvZZ65hWfrfpH3gzeyhwF6JDWu1mbMM2A98EPJaYBvEM4gP6PDNb\ndve/HR7XZ7Rrut2XT//S3YuZXTm1z2Z6JnGV5jNmVoh1gk9293+Yas92a/MNnqvgpMh2c8IKTm4C\nnYtsXlt1LjLQucjOst0DplkibdDJ90LgW4F7rmPf9bZ504/LzG5FdKY/4u4bKYBx0to8SMB57r5y\nBepjZvZtRMf1t9fzd+tp91a9hx5CTP96KDFv+C7An5jZJe7+8uNsz3Z534uIyPbpk3UuEnQucg2d\ni2yy7Z4l7wqgEFcdpp3JdaPiE8rMXgA8ELiPu18y9atLgZGZ7V31J9NtvpTrHtPKz1txXHcHzgA+\nbGadmXXEsPKvmNlkeMy5bdZmgK8BF6zadgFw66k22RrtWt3u1Rl2MnAaW9PuZwF/4O6vcvdPufsr\ngOcCT9rGbRYRkdl0LrI5dC4yReciO8u2DpiGKxAfJrJzAEeGnu/LSRzOHzqoBwM/5O5fWfXrDxML\n4qbbfA7xwVpp878Ddzaz06f+7n7AfuJKwGZ7O3Bn4grDdw6384krIyv/77ZZmyGy0qye7nBH4MsA\n7n4R8YGebvdeYrh8ut37zOyuU/dxX6Kj+OAWtHkX173yUhk+a9u0zSIiMoPORTaNzkV0LrJzneys\nE0e7AT9DZO14OPAtRDrDbwBnnKT2vJDIxnIvIjJfuc2v2uci4D7EFZVzgfdO/T4R82zfBHwHkXXk\nMuDpJ/A4jmSm2a5tJuY4j4krIt9MDC8fBB46tc8Th/fDg4iO+HXA54HR1D5vJDriexCLHj8LvHyL\n2vwS4CvEFb/bAD9BzAH+/e3aZt1000033a7/pnORLTsOnYtsTZt1LrLZz+nJbsA6X/jHEdmtloiI\n97tOYlsqMTS/+vbwqX3miPoIVwwfqlcBZ666n7OBNwCHhg/7HwLpBB7HO1d1UtuyzcOH/ePAIvAp\n4L+vsc/TiPSYi0S2nNuv+v0+4grWfuIL5q+AXVvU3t3Ac4gO//DQ+fwOq9Kdbqc23xhuwOOH12SJ\nSA98j5PYlicR2Z4ODJ+j1wLnrNpnjsgktfJ5fPWMz+O/Du+zS4kpGCekDxmOoa7Rh2y7NnNNnZYr\nhs/bx4C7rdrnd6c+j29b4/N4GvCKqc/ji4DdW9jmBDwduHBo0xeAp6yx37Zq9w39hs5FtuI4dC6y\nNe3Vucgm32x4QkREbpCGgpMv49oFJ3+aCFJOeMFJM3sj8Pdcu+DktwNHCk6a2Z8R9UYewTXFG4u7\nTxdv/BjxRfe/uSYo+Et3PxEFJ/+R+AJ9l7v/2nZt81Bw8iNE6tw/45qCk1/0mJKyUrzxN7h28cY7\nE6/HZNjnTcTV+0dzTfHG89x9S4o3mtlvAU9gVdFJ4Lf82kUnt1W7RURuqBQwicgNmpl9APigu//K\n8LMBFwPPc/dnndTGRXtOJ6ZK/KC7v2+YR/51YrrHa4d97kgsMv5edz/PzB4A/Atw1krQZ2aPIVLJ\nnuHu/Ra1dQ+xNuKxwFOBj7j7r23XNpvZM4Hvc/d7X88+lwD/192fO/y8l7hq/Qh3f6WZ3Ym4onx3\nH+qRmNn9iZGyW7n7pVvQ7tcDl7r7o6a2vRpYdPeHb9d2i4jcUG3rpA8iIsdjquDkdHE+JxYf76iC\nk8R89JU2zyreeCpRvHGrHCk4uWr7mgUnOfltfhBwvpm90swuM7P/MLNfWvnlrOKNxILm6XZfX/HG\nrfB+4L5mdoehnStFJ9+4zdstInKDpIBJRG7Irq/g5EkvvLeFBSe3oq0rBSeftMavN6Pg5FZYKTj5\nWSKT1p8TBSdXpqQdc/FGIsDdqnY/k5j2+Jkh3fKHgT/2TSg6ucXtFhG5QdqphWtFRI7Hdim8p4KT\nQQUnr01FJ0VEthGNMInIDZkKTm4OFZyccgKKN6ropIjINqKASURusFwFJzeLCk6e2OKNKjopIrKN\naEqeiNzQPQd4mZl9mGvSiu8iUiyfcGb2QuBngR8DDpvZyijBfndfdvcDZvZi4DlmdhVRi+R5wLnu\n/qFh37cSQcbLh/TSZxF1e16wwSlz6+Luh1kV1JjZYeAb7n7B8PO2avPgucC5ZvYk4JVEQPFLwKOm\n9vlj4Clm9gWixs7Tgf8E/hnA3T9jZm8B/srMHkuk534+8PdbmGnu9cCTzexiItPd3Yj37Yu2ebtF\nRG6QlFZcRG7wzOxxRFXzmwEfBf6nu59/ktpSWXsNyf/r7n8z7DMH/BERWM0BbwYe7+6XT93P2URt\nofsQhQlfCjzJ3etWtn/q8d8JfHSqDtO2bLOZPZBIonB7ol7Rs939r1ft8zSiVtE+4L1Du78w9ft9\nwAuIrHuVKMr7K+6+uEVt3k0EQD9BTKu7BPg74OnT6de3W7tFRG6oFDCJiIiIiIjMoDVMIiIiIiIi\nMyhgEhERERERmUEBk4iIiIiIyAwKmERERERERGZQwCQiIiIiIjKDAiYREREREZEZFDCJiIiIiIjM\noIBJRERERERkBgVMIiIiIiIiMyhgEhERERERmUEBk4iIiIiIyAwKmERERERERGZQwCQiIiIiIjKD\nAiYREREREZEZFDCJiIiIiIjMoIBJRERERERkBgVMIiIiIiIiMyhgEhERERERmUEBk4iIiIiIyAwK\nmERERERERGZQwCQiIiIiIjKDAiYREREREZEZFDCJiIiIiIjMoIBJRERERERkBgVMIiIiIiIiMyhg\nEhERERERmUEBk4hWtgfeAAAgAElEQVSIiIiIyAwKmERERERERGZQwCQiIiIiIjKDAiYREREREZEZ\nFDCJiIiIiIjMoIBJRERERERkBgVMIiIiIiIiMyhgEhERERERmUEBk4iIiIiIyAwKmERERERERGZQ\nwCQiIiIiIjKDAiYREREREZEZFDCJiIiIiIjMoIBJRERERERkBgVMIiIiIiIiMyhgEhERERERmUEB\nk4iIiIiIyAwKmERERERERGZQwCQiIiIiIjKDAiYREREREZEZFDCJiIiIiIjMoIBJRERERERkBgVM\nIiIiIiIiMyhgEhERERERmUEBk4iIiIiIyAwKmERERERERGZQwCQiIiIiIjKDAiYREREREZEZFDCJ\niIiIiIjMoIBJRERERERkBgVMIiIiIiIiMyhgEhERERERmUEBk4iIiIiIyAwKmERERERERGZQwCQi\nIiIiIjKDAiYREREREZEZFDCJiIiIiIjMoIBJRERERERkBgVMIiIiIiIiMyhgEhERERERmUEBk4iI\niIiIyAwKmERERERERGZQwCQiIiIiIjKDAiYREREREZEZFDCJiIiIiIjMoIBJRERERERkBgVMIiIi\nIiIiMyhgEhERERERmUEBk4iIiIiIyAwKmERERERERGZQwCQiIiIiIjKDAiYREREREZEZFDCJiIiI\niIjMoIBJRERERERkBgVMIiIiIiIiMyhgEhERERERmUEBk4iIiIiIyAwKmERERERERGZQwCQiIiIi\nIjKDAiYREREREZEZFDCJiIiIiIjMoIBJRERERERkBgVMIiIiIiIiMyhgEhERERERmUEBk4iIiIiI\nyAwKmERERERERGZQwCQiIiIiIjKDAiYREREREZEZFDCJiIiIiIjMoIBJRERERERkBgVMIiIiIiIi\nMyhgEhERERERmUEBk4iIiIiIyAwKmERERERERGZQwCQiIiIiIjKDAiYREREREZEZFDCJiIiIiIjM\noIBJRERERERkBgVMIiIiIiIiMyhgEhERERERmUEBk4iIiIiIyAwKmERERERERGZQwCQiIiIiIjKD\nAiYREREREZEZFDCJiIiIiIjMoIBJRERERERkBgVMIiIiIiIiMyhgEhERERERmUEBk4iIiIiIyAwK\nmERERERERGZQwCQiIiIiIjKDAiYREREREZEZFDCJiIiIiIjMoIBJRERERERkBgVMIiIiIiIiMyhg\nEhERERERmUEBk4iIiIiIyAwKmERERERERGZQwCQiIiIiIjKDAiYREREREZEZFDCJiIiIiIjMoIBJ\nRERERERkBgVMIiIiIiIiMyhgEhERERERmUEBk4iIiIiIyAwKmERERERERGZQwCQiIiIiIjKDAiYR\nEREREZEZFDCJiIiIiIjMoIBJRERERERkBgVMIiIiIiIiMyhgEhERERERmUEBk4iIbJiZvdTMLjrZ\n7bgx0nMvNxZm9m9m9s6pn29jZtXMHn4y23VjdGN/7hUwHSMze8TwxrnbSXr8e5jZC83sfDObmFnZ\n4N9/ycz+5Rgf+wFm9tvH8rdbbeoDPev2xGO4zzuZ2W+b2a23os1yw3Mj6R8cqMfeSjkOeu53kJ3e\nH5xkvs5tcmLcaJ/75mQ3YIc7mW+cBwL/Hfg48EXgnA3+/fG0/YHA44DfOY772Gp/B7xxje0fOYb7\n+lbgt4F3AV85nkbJjcoNvX/4JXTR7WTRc7/z7OT+YNtw9y+b2QLQney23Njc2J97dbg71wuBU939\nu4G3n+DHti25U7P5Tby7/3D3v1vjdsGxNI0NfNlt8nGIHIst7x/cvbj7tv3iNLM5M9uSvupk2+7P\nvWw7J/N8YdO5+8Tdt+1IxxBU3CBt9+d+Kylg2kTDvPKDZna2mb1h+P/FZva44fd3NrN3mNmhYcrL\nzx7rY7n71919vIltX5nK9mtm9igz+4KZLZvZeWb2XVP7vYQYXWJqmluZ+r2Z2RPM7JNmtmRml5rZ\nn5vZvlWP9yUz+xczu5+ZfcjMloFHT93v88zsYWb2meF+zjeze23W8a5qww+Y2QeHx/mimf3C1D6P\nAF45/PhvK8drZj+4juPIZvbUqefyIjP7PTMbzWjHj5jZR4Z2fMrMfmIzj1dOrp3cP6zFVq2jWW8f\nMrX/Hc3s1Wb2jeE9/yEze9CqfU4zsz8ys48Pz9d+M3ujmX3Hqv3uPTz2Q4bP2MXAYeCUGW2fbuvj\nhs/9ITN7i5ndctjnqcPrs2hmr1ujD/ux4XX86nCcXzCzp5hZWrXfvw3tv5uZnTvc34Vm9pgZx/Az\nZvb7Zva1oU3/bGa32uTn/qeHPmZpaNuPr75P2Vo7qT+wa76TH2xmnxjeW580s/uvse9dzexNw2f1\noJm93cy+Z9U+K1MUv9/MnmNmlw/H+Rozu+lR2nKddTRTz+Uths/qweE+/6/ZtS+aWFjPOcqxfL7f\nY2aHgWdcT/uP63W3jfeJ6+lP1ttHHe9zfxMze/nQ5qvM7CVm9h2r73O7UsC0uZx4Tt8EfBn4deBL\nwPMtTrzfBHwIeCJwAHiZmd3m5DR1pp8D/jfw58CTgdsC/2Rmefj9nwNvm9r354FfmPr7vwT+EHgv\n8MvAXw/7vXnqPiCeq28hps69FfifwEenfn8f4LnAy4GnAjcB3mRm37rO49hlZjdd47a6DXcAXjW0\n4deAK4GXmNmdhn3eAzxv+P/vTR3vBVP3Mes4XkxMWzwfeALwb8BvAX+/qq1OTJH4B2Ia4W8SQ96v\nMrP7rvN4Zfu7IfQP05y1R16P1odgZt8GfAC4I/AHxGfvEPA6M3vw1H19E/BjwOuBXwWeBXw7cfHi\n5ms89lOBBwB/RHzWJkc5hp8HHkt8xp8N3Jv43P0ecD/gmcBfAA8a7nPaI4GDw9/9MvE5/93heKY5\n0X/967DPrwMXA39mZo9co01PHo7hmcCfAD8CvM3M5lbd57E+9/+V6GvGRF/zGqKvutuM+5StsdP6\ng3sBf0p8f/06MAe82sxusrLD8P38HuDOxPv3d4n34L+Z2T3WuM/nD/s+jRgFexDwgmNo28pz+Rbg\n68D/Ir5vf43hAuaU9Z6jPJL1f75PJ767/wP4FWL6/tHaeqyv+0b7xPX2Jxvpo9Y6nut97ofg6Q3A\nQ4CXEP3zWcDL2Cn9jrvrdgw34BFAAe42te0lw7YnTm07lbjS2QM/ObX9HGLR7v/ZhLY8Hygb/JuL\ngH+Z+vk2Q3suB/ZObX/QcEwPPNrjAfcc7uMhq7b/yLD9oasevwA/vMb91OF3d5nadjawCLz6KMd1\nm6m/r6tuBfjuNdrw/VPbTgeWgGdNbfvJYb8fnPE8Xuc4gO8YHvPPV21/1rD/vde4jwdPbdsLfBU4\n/2S/13Xb+O2G1j/M2OclwIVTP2+kD3k7sZ6wWXWf7wM+M/Vzu8bj3nr4jD55atu9h8f+PDBax/Gt\ntPVSYM/U9mcM2/8DSFPbXzE8Zju1bW6N+/0z4iRrer93Dcf/K9PHNTzG14C86hi+Auya2venhu3/\nY5Oe+48TJ2oLU9vuNfz9hWs9X7od3+0G0B/U4f1/26ltdx62P25q22uH/W4zte3mwH7gXauejwq8\nedXjPJu4yHHK1LZ3Ae+c+nnlvf7wNZ7L31p1fx8Gzpv6eSPnKBv9fP/SOp/L43rd2XifuJ7+ZL19\n1PE89/9t9eMO298+/P3DVx/XdrtphGlrvHjlP+6+H/gscNjd/2lq++eAq4mrBdvJP7j7gamf30us\n4VlPO3+KOKZ3TI/qECdGh4AfWrX/Re4+az71+939yIiTu18M/DNwv9XDvDP8JfDDq24/Anx61X6f\ndvf3Tz3OFcTrtZHXZa3jeCBx1eS5q7Y/m3g+/+uq7Ze4+z9PteMA8DfAXc3szA20Rba/ndw/rMf1\n9iFmdhrRF7wKOHVVX/FW4A5mdhaAT63TMbM0XM1eJJ6ztTKOvdTdjzaqNO2V7n5o6ucPDv++3N3r\nqu0j4JYrG3xqipOZ7Rna/z5gFzHqPK0n+qSVv+2Ikaszgbuv2vdl7r44te+riZOWB67jeI723J9F\nXI1+mbsvTT3Ge4FPrOP+ZfPtlP7gbe7+pZUf3P0TxAjIynsrEd+xr3X3L0/tdykxA+NeZrZn6v6c\nqc/E4L1AJk7Mj8VfrHF/08/Zus9RNvj5HgMv3WBbj+l1P4Y+cb39yUb6qLUc7bm/PxEMv2jVfn/K\nFq2L32zKkrf5lt39G6u27Qf+c4199wOnbX2TNuTi6R/c/eohPllPO+8A7COucq7mxAdv2kXXc19f\nWGPb54CfIUaBvn6Utnze3d95lH1g7ax3V7Gx12Wt41i5EnOt43D3y8zsaq77hTDreFfua63nVHae\nnd4/rMfR+pDbE1+QTyemua620ld8bbg48gRi2tztiJOplX2uWONvv3Q8bSWec7ju67Gy/bSVxxim\nHz2DOMnau6r9p676+0umA5TB54jn4TbAeVPb1+oLvsD6TiKP9tyv3McXZzzGXdfxGLJ5dlJ/sPqz\nAtf+rjyDCCY+t8Z+FxDv9bO5Zjr7Wvd51fDvsRznWs/l6u/ydZ+jbPDz/VV374+zret63Y+hT1xv\nf7KRPmq19Tz3twG+5u7L62jftqSAafPNqm8wa/t2i6yPp50JuAx42Iz9Vwc5qz+cR7MVz9VmvC5r\nHcfK3/vGmnPMbZCdYaf3D+txtGNZmdnwR8S897WsfIk+mVg38GLgKcQaw0rMxV9rhsRG+5Rjej3M\n7FRircbVQ7suBJaJK7HPnNG2Ne9rnda77w3pfXRjsJP6g6O16VjatpnHuZ7aUus6RzmGz/cJ6XcG\nG+0Tj3Z/m7HfTqrrdcwUMMmxmBUEfBG4LzGd7ngzdN1hjW3nEEPPa11F2UrHEvR8iei87kAMlQMw\nTK/bR6whmHb7Ne5jpVbG6n1FdrILh3+7dYwC/ySxfuFR0xstMlodbZR5K92HuHr6YHc/d2WjmX3z\njP1vYWYLq67gnkP0Las/32v1fd8MfOzYm3vEymOt1d+stU1kvS4nvp/vuMbv7kS819capTqR1nuO\nch829vk+kTbaJ663P9lIH3Usvgzcx8zmV40yrdW+bUlrmLYRM2ssUu2ulelkOzkMYGZ7V21/JRGE\n/5/Vf2CRYnv1MPb1+T6bqopuZmcTmWHe4sNKwRPoMHGlZd/RdpzyxuFvnrBq+/8iOqB/XbX9FjaV\nRnx4bn8B+Ii7azqe7KT+4Xq5+9eJLEqPWetYzOz0qR8Lq65ymtlPM7WW6CRZadeR71CLcgGPm7F/\nA/x/U/u2wGOIE5wPr9r34dNrPYbjPYu1C3FviLt/Dfjk8Bi7ph7j3sQiftkhtlt/MKz5eyvwYDO7\n9cp2M7sZ8LPAe1atFzwZ1nuOstHP94m00T5xvf3JRvqoY/EWYh3okUBvmF74eHZIljyNMB2fzR4e\nvyUxv/elRFXu2Q8cHdJKOu/vGrY9efj5y+7+t5vctmkfJo79+Wb2FiLjzj+6+3vM7C+A3zSzuxCd\nZ0dcpfgpIjXna9b5GJ8k0og/n1go+FjiQ/W0df793c3s59bY/kV3/8A672PFR4lO6jeGqzhj4B1D\ngog1ufvHzexlwKOHRe7vBr4HeDjwGnd/96o/+RzwoiH16mXALxLzqR+xwbbK9rHT+4fbT/3NtI+4\n+/GevD+eWBT8CTP7K2LU6WbA9xHHubKW5g3AU83sr4H3Eyf1P8faa3C22vTr+X5ijv7fmNlK2YGf\nZ/YX/yXAE83sdsSI80OJTJqPcvfV01muBN5nUfPu5kSa4s9x3cXSx+q3gNcB7x8e4ybE6/EJYM/1\n/aEcl53eH6zHU4gES+ea2QuJ781HEyfKT1zdrFnN3aS2XMcGzlE2+vk+kTbaJ663P9lIH3UsXkes\ng3q2md0B+AxxEXzlQvR2eG6vlwKm47PWCzzrRZ+17+rta21by+2IRdPT+/7u8O+7gaN1gBt57NXb\nX0PULXko8UE14B8B3P2xZnY+cWXiGUTmlS8RGd/OnbqPox3nu4F/JwKks4FPEWknP3mU41q574cO\nt9VeRtR/OVobjmwfEjU8BngS0clkYiHoe1bvu8ovEp3YI4EfJ1IYP4NrXqdpnydqOP0RMaXhIuBn\nrieLoGx/O7l/gHgfrvVefTHXXJ08pj7E3S+wKKj628RFgZsSU3o+QtQuW/H7xELyhxEJXz5MZHd6\n5ozH3ojra+us/Vfaf6VFPaNnE8/zVUTNuHey9rqsq4jjfAHRL1wGPN7d/3qNx/h94kTlN4nCu28b\n9l29WPpYn/s3WBTDfBrxPH5uaNsjgfXWuZON28n9wXrfW5+2KDD/B8T7NxHftw9z9/PX+NtZj3W0\nbcf8XK7nHOUYPt/H0vesd/vq536jfeJ6+5ON9FEbPh53r2b2QGKt1cOJdVevIfr7c4k1YtuanfjZ\nTSLXz8wq8AJ3/+WT3ZYTwcwuAj7h7j92stsiIpvLzN4F3NTdv+Mo+92bqIfyU+6+3pH4TWNmHwEu\nd/f7n+jHFpHNtZH+ZL191FYwsx8H/gm4p7v/+4l+/I04aWuYzOzxZnaRmS2Z2Qds7SrQIiIzqR8R\n2ZhhrUZate0+wHcSJ1g3OupHRLaemc2t+jkRM2sOEEVyt7WTMiXPzB5CDHU+mpjT+KvAW8zsnOtb\nFyIiskL9iMgxuRXwNjN7BbFu4U7E9KRLuG7xyRs89SMiJ8zzh2Qz/w7MERn/vhd40iZkVt5yJ2uE\n6f9n791ibtuS86Cvasz1//tc3L50nz7dncTXbhs7vgSCuCg2kbDAWJEQKG+A8gAPCBGEkJB44SFK\neIogQlwiRSCUICVIUV5wQHa4WQRLKEjYgQhk7Fi+JcR2YxvH9Om9/7XmKB6qvqoac61/9zmn++zd\n29njnH///5przjHHqFGj7lXj3wDwZ8zsPzezn4ZX5ngPXyJx8XX7e6a937js3ynt77X5fqXaazry\nur0q7f3u7xdBB34TnvfwL8FzUf8IgL8M4AfM7Def9+Dv0Paajrxuv1PbB6EnL4L2/Dg8N/bfgeeP\nfQzAHzWzP/kC3v1ltxeewxSlCt8D8IfN7Efa9T8L4GvN7J997NnX7XV73V434DUded1et9fty2+v\n6cjr9rq9bu+3vQwP0yfgVcZ+9XD9V+ElD1+31+11e92+VHtNR1631+11+3Lbazryur1ur9v7al9N\nZcUFj7gEReTjAH4IXvrxq7704Ov2uv0Ob08AfDP8EOFff8ljObabdOQ1DXndXrevqvbVTEOA13Tk\ndXvdXoX2QunIy1CY/h/4YWbvHq5/EtdWHrYfAvDnP8pBvW6v2+v2gds/D+AvvKR3f1A68pqGvG6v\n21dfe5k0BHhNR1631+13QnshdOSFK0xmdhaR/xXADwL4EQAQEYnP/8Ejj/0CAPzhz34Wn3zrjegH\neR60xB8WBiF+njYBAAp1c1E/P9oMEAEMmLA8WlpEYGZQkTQvTWO/bnLq97Z5rfOM+370538BP/wt\n35x9eR8Sf1vcu36e7V4Vyf5q4murWXM88+qe99PY849xzAaIKAxzMbfJ4RnJq5Lv7qPks9buMgAw\nn7HII3OKGwU1benf93eIj/ufinEDFW/aoXF8/rEjxZf1Ip69n3y/BIgcYGAQSOBoYcGP/sIv4oe/\n+RtjDlLzFIGJATYh0AW/BL4m/XVendNyQma24Ke3GfPwh7xHxTTD0HrGzGABPTFAbAdiP0wT/Pp7\n7+Ev/dzPA7EvX0b7EHTkFwDgnTffxj/xrd/pfQBtzzz2Hv891e/TRissiNCEr5sWYfB1W2zUGu90\n2nKJjrWt0Zy8Vu+fBvzEL/1f+P5v+g4M0jVY6y/W9IDtmrgW44m7fNy1IyTe4aCrZ5S06NjFDdiI\nXH+2GPcf+MbvAEQgcy6d9Gfyb/iu4Lv7dpswp+LxwAwaPVHrwl6M82kdvJ+qKgLgJ37pZ/D93/jt\nbf8TDpLzErHceBbXF7iQIvM2EYhZo47H+1c6NOOz3hhzUQ/SUr/jJ375Z/EHfs/nch7sRzEhEOwH\nTE94XvVpjS9d08db3/GJvga8nvfGWpgI5gR+8+kX8OO/+DPAS6QhwIenIz/0rd+Fj7/5ZnQCQAQ7\nPLYPcA0M7fMlQLNJ8cCk1ZO4IbgYMLQ4KswwBNgDf84yol/DLoox/U1yg45I9LNDoHPir/7Sz+IP\nftPnsEdF+WEz+zuZ95P9B43YRXPNT/H7EvfI5CydZtk0THWM01jvEbzdZq3/levuBiGx+PwTv/gz\n+IFv+vaS9wIOuT/bM1Nk2TMrnUbMy7AHLxMAGs8ZBNNqvClzRCdcW7NVPsNxLvB9+xO//LP4/t/z\nubz3FBjxzEZOc4NhOkfAROFK9eM97wEPFY19C2jKtjUW0g22PXjSFEk67mOxoA2OGwMGEcNf/eWf\nwx/83d9W/UVnY/r3uyh0OrwKnjPxibRczSnCLRqfzci3GgxFcZnAnZrLJDDs5jgDOD3dMLEHlboY\n8JtffA8/+rd+DnhBdORlheT9KQB/LggVy3i+CeDPPnL/UwB458038Jm33wbgTIkCnhyEh+tWwgTv\nFRHMOa+RPTb0NIOEEGkhMVOJ4gbkZuX9/NvH4vc/2TZ8+u23614rAfzYz60CHM9TmCQEB/6dcAji\n9zyl4IqooATmJ9uGz7z9dhOk7NF+ZnzhTKCElKPCxGupPJm5MmaWAqLBYXNDf6pxp/Jy/R3HfXwv\nYaQiQbgV0MCfY//xm4R3mgBaRFrau/Pe+NIAn78v/rXCZOIdT8DE+3yyDXzma97O8RqHB3XGJ4bO\nYgYGHmzHkJqbAFAdoMLkONuYBPEicOliEyoOd5djndwaBEOcJM9mXZAQ0UlBtaD2skNSPggdeQoA\nd2PDu29/DIALuzeJOa73nUGCqZXobQjhRNTNFVwTBMymYaguCqiJM0YyMBW/34VKvov7TbDDcD82\nfPqtr83+91ArgFUQBbrQWqSiljIltJqoSAr2VIq9XzI0wuFLqRvtneLzux8b3n37a+GUaS5KS+1N\nh4FQLAtlE3CGnzCK+7he08pQ4PSkGLC1Re305gCFmzO63za889bH8jMFqIs1Wo9VsXusaUBtn94P\nDSZlOOmCXw1qBhyH3FaYNBS1UlV83O++/TUNLtWPyqrgOIiL0KYuGwSI417YUT6s+YxiYohhD6VR\nQrAbMkOwrBRpCngXdqT53cumIcCHoCPf8MZb+MxbXwMAeIaiIxuViphnlxEAXzExX6Qt5JUB4DwN\nD7L5EsQzdzYxMHExg+jApdGREwwX1VRERF0pUgDneNugsg/BsIm7bcPH3/665LfDJh7ERUDud8vj\nudriB4+hwvQQCtNmTWGK9xoEEMGJBr1Aor2N6agwcbzs/xzKywbD/bbhk299DA8QqLhwPwFsKfd4\nH3M6PEixRCTHtweMpgg2M1gzilMZMR3YDXjCZ+JnO4x1MeIQlW+Qgfux0pFNHB7n6crRSSyUaIFK\nKDeH3U5j0J3smAY8mCbfJr4l/Q8a/kR8PmeIK4dBMLrCdAfDDuAkdZ/BcDc2fDzGfLKJPRTQzVxJ\ncfhNzMCRNzHxG9jwJNZ6n05rtqCQJUMVDLnGVLLeM8E9DHcycTaHhdjEBHAvrjidD3RkQjAtOKCk\nmvlC6MhLUZjM7C+KyCcA/HG4K/yvA/ghM/v8856b49obBHRFSZbfxSRpOXdGNSDYDxoumoA0F0mj\n+W0OCg7adVrmj4z6au5SG2wXwS7ACKGVgoBKjT1n0N49Eczp4EVIONyQAI/KmFD5axBb5iWyMHEf\n+5H4N2tZbLpbrb+jlKVg+P2VuFZGju25ylQbE4AU+rsFO7UbrIrqNWPz5szF13YkjqyDSHQxKYDY\nigEmAuqTIsBFgI34ZYAp114ClvNKQxUAOyYGvU8o5cUslBoT96haKGUmsBG4NGdYyqQUPP4bsNqt\nPCEDhikKs/DQ5hPvT3j+qNuHpSPl2QxBD8tKwawEnl4Vx1VSpyN06Kn6/ndRpj+jbnABfC9V975/\nO04i9nOsvTZcVMgyTjf5CGbu9VDWgLBWSlM+yHL5MipXHGEpW7wvVaZgRLXWDqUOjxLXUf1RiAip\nPRVPdhvvJsy7sQQ2S3kD0iggIVRMlH9WE4azthtHSiu2NRp1gyb2K4/R7kvYfbURHreeH2gArrZr\ncqrhnD0UadJduXq++pmh8Kw1mSSuOCwNakWT2cEIRJkWa5XrIWFcCYFSa020v0YcuhZrSYUrBWq4\nYERv22zz4N8XCw+ETTeAQbAnHsTL3o+3/gW1D0NHzjrwkAY/SfyjYnCCK42kq+d2TrCGALubYFPg\naRjSRiotkn2dMWBCL0DRkYu58a94dAnKW0gC6QkQwY7hBpEY6zRglw33dgEAPJMND6I42cQJExOK\ns6iPydkFLmmk8XFOUTzE+4AuKdW9hAsltFWqCXjE3niAo+vAxAi8gQBTR3pf1HwPXJSeNu97DvYF\nDNsxrWiTluvfYWIlzDtf9l25Se1L5U/IYjTedDKSonq71g0UM2Q9AXCOPTHU4jkJfhJ9hZyxW9G9\n8lYOQFzBESqTsiqiXOtnqSQDs9ERkXrXg01sAjwNpfSLojgF79Cg8Q86cA+XMWcYpRSuWBJ+X5SB\nN2CpjA3leBU7/L1P7BIeUmBTScPWLoqHaXgC5537Ddx5arThTDcQiFNAiXXbc6Yvrr20og9m9qcB\n/OkP9NAkAy8bdzGa2gT5HV20XdEx4NLZVSBaWnxRG5Ut/VP0PvFRK6GLAlgXL+oVxdQMwC5FYHSi\nx/KENdVnwU3kc1lBUcqi/yMCXACM+LtbZENi8A1Ma0tcT8LBjkkY+jxxJHFIQEsC3O8ikTg+FWza\nxYVr2aW/unfv620laNVa+n0ThmGS1p7uMuihfsbvGuyyv1suhvYuPmP1od7Vxy0tXEt68F1TFuOP\nYf53qpmzQn7QLGAGi/CjawG1lJ54fV50hUrCta2zhkt1KLTcgoUANg0a0lP5Xi37tsTzryph54PT\nkaYYWPvc2SFGgokAACAASURBVH4Pj7iCcWwObtvai0eqdHyw3X8UGEVdTG141kOauPYWIxy1ktFn\nU57jdWLuG+Q+LkVMlmcbdh4gE8qXUTBbnyvPpwQeUyhuygV7kwhbTIZeVvnaH9y3mvsi0LTWRh7b\nroWnCTvuxxjOUbnoz0xBGkRW5arhPw0rhwHwuyPt73RVsKDAo5giUusDIGFGaLgHnsyK3kHSMGnP\nBHxDMaIXSW699NBolJzmwiuSnvuz6fnyiZexKdCaqOLY472RT1CY1K8iGgJ8cDri4UwOCwasT0go\nprFfpUWlAGHJd1hprMWD3xSCtu9pCo+bmMsYKNyiML2HfLAzeiK+Y/hlTArAKlJScVYDdgGe0sME\n99q4QXa4AgEPwzIY7m0HEefcjB4LDIMcCIBnothCuYmg8pJJILgIQ7eAiygk7jW4wM3xmwlONvEg\nruxcoBH54rdczO9TkQyvQ7zzIuqKZlzXoCObRAci14JV0iv/9BA7V8UirNVwDLOkJ+8iinvbPayR\n+J+GzcL3CKKESMmsHPMIi1ElQtRShmiQ3mOg4MDtNBsFZ2inBR+gdw5UNqKfkzTajZATY5wigrMJ\n7sTX72KhNEiFnZLy9B09zHByVc/DTGUGXvp7LghFljyhGwrFn7mLv8+NOxhoLPM9sy1v/ejbV1OV\nvC/ZFNJUpWLe1NhTUEApMDqtGCW/U7ekwNqipzDRBIz4vTf6o6mFkNlbCLjxDIXolohQwnagZbc2\nkYlhFUQMQdS0rhUcqjljayJHkwkMgKhUbgR8twmF4wWSbUAoxASQFuhlnCUrcZL5pTuPgrn3vq9E\nikNbFNsmvvZ+Dm4vBZWlEnZukcDjhu5tEYaWOT4/w+VotT5aqQsXC1DEBJK1FbKBz2EJpLb7iD6H\nLuYvTZ0AYV9xv6z5zohUdFlUUck5jQjbAyQZlJKSPg8oX/WtPLnL6javBMhLjXv36FkJwSNxsPr2\nfx8n4hMO8yV0DlxvYkNYcNv4JISq9MAekUJQ40fRP0CWsV89hEDZyQ7WbyfMcyHyvQdA5L3rWAWl\nPgrnIitsSjkIRa02ec2hPq576jB9CjGpq3Efmv9j7dpxAq4YSM6iTXdpNLj1qdd4Kn8i32ItlDeM\nIFd93hxRoyESCoq1fK1G42UZq8PYDR+Fu4RhN9ytc7oelybtW7lEnxvxNpVicE0bbGJ8E6s392Wc\nZ/KVbAIXnoYZzoEwUwQbGGYbuASG6Low3CFN/H/TXJR8FlBhuJIL+iHkxzPn+L2LhkekImUuUM8R\ni5echNJNlzdIYSKUu/MHFG5No+HIXPnBDOWBe8XbXb7d9+SO2hPck1M8DHFTwTnGtgUABBX6dszB\nUXXPyC4+joHIx5JSUvYg1l2nd7wXWChiI3ZB4WKE8S6CRVsUIPeI80hXXAeIv7reGl6TO5s468iQ\nNVG5CtcjfDLvtfMB8fC4S9CSLunkLhQa99f9qMs83FPVlXOFYJPICYretqZKE/Y02A14jtAbGjJl\nEDbmbCb84DT9SEco8+4hG+8QPIXiSTCwu4SHB2wPWf3pd1JzvoMrdYBiDzxQ8b3xmGz0UbVXSmEC\nAOaGTDMMQyTCubBqAmzTN1jmIqm4NR/InTzg1gC0RRkooTmty+YJgsmopYRi4lBa1Pj9YQW/95Pv\n5N/SCARQ8bET5RHiMCEervc8fODGkcNzQFibLPrNhPWyTCgFsNz01cf3fPKdkhjRxtCUAcBzKQg7\nbp+RwLjODwK47wg99hPEiJb6g/LiF+PdTCK1mi/v/J5PfCKJoFipvpn3dGNEZR1dvzuOsdGMxxWv\nG7v3AEE4ecnB47s/8fGEK69XvHG9Nf0fB4Je3oIaGfGCiu6MHvjMDglcMMgExBRDIkx1sF+FXQAZ\nBg2fuuZ3x/TUV6d909d9HEPoyXWYFd6RaRh6Mr82xipWOK4tr8V/089SG5JhUIiE3QELWjVqxXhP\nWhsiDCz6/c5PfMY9BhID4Jiwhql5GOa6oemlvNXcC2EeJiXhw5LC2f6Uasu7Ie6Fh+PKwBTPfvsn\nPg0LkYAwmgca4hEALBpQ+8S9aNY+VaO/RdFwnvcIaR3DhiTDayhsjrbeBmDTkXP79o9/BhYhLBPI\n3JvcZ42ucZ/2+QmaQiPDaRkshWiO/0iLugJ2zG2tcCDJ97pQ41Zog+Bz3/CppNm9YBGVKOGEWz95\nh9To05t9RV+CPnHOFvxMQuGPeTu4KrRsyoYNVnQ7KdULlnQ+iqaKMxRnU5zEw5x2kwiHEtxjxwMU\nQ2ixVw9zQuCWOI4/jXAtWsvvsac84HwRwDRXqEJ5ucOMsLqROTBJB/J9q3j3He98JgXiLe55ynwU\n2zHhochn0cVyfwfDMxltP67NxAX0CfV9J443xJ27oAI7BFuExJ9QOTwncQVwBNLtbX9+7hOfBmS4\nUbjJWqQ2pMUPotjNsNnEBep7PsLUp+gi+3AfTgB3qPsB4AEDd7BmkAl8jeIXNMIDAA3fBld05tiS\n1nz245/BDoWp5wNdlt4YvuxGEJ2RyxbjJTUyrf7NzPN3VDHmzHxaehM7/QGQeZ/8GwAuEQ7Lz7tp\nFLrwEhDf+g2fAoL2XSJE/MHWnlWQc+kh00DRkQsUI6D81Oq5AeCZeX4bZeAHKDYpiniyiXsB3oNC\nmSMn7vXcArkHLEOOb8lcH2V7pRSmLrRu5htyb2ESigkRXRUIVHKeHPq66v9KaL5GxOP3ZEb2yH3f\n8847RWZi8Bni155TTVmIXLORnGrHbC1aQ5OBtT6bnHf1LL0G0u7lr+97x5W8Kyv2oW0ij5BQ3Kz4\nxf5JvJLuAGFBbwzdyuohTVDMfo5zRSmnEt8n039kkLdmd7z1uLbHd36Y1gtpfPcnPhGeise8WSRG\nt8d5y2p8bKysQ8STNCd5x4YJM3WPYMsOpYW6OrL19yvYvuXrPlECq3TLmKSg0asAPW8L3HJY3BRG\nrdGSAwI5M5YrPOvtO975zBUNuVYlWDWuBnerqAmwhu4BzvdZB4L7jVZk5rzRSu43LVJzU638e+Zi\n/H3vfOZLpqqYPI66Yus48zqOAZRxV3O3JH1skL2qQtXgxfu/451PZ7/SFJtc1oOL7Uvx6+P3VDCe\nt18fV2ja5wZYM+BzH//UYry71Wg4y0cPilgfkqy3lMDZGAaVoCFeaXNG6OYSUvg+5/iqNeafKIAn\nkXPzLJQcmPPzzVyRID8CPLxIMReeRC9Prw65HVxwEoo/c6DHigIeGhhboBdL6e1zn/hMFgVhy0IL\nEqFusFSm2Ki8Hb0lD6y2F7txoyyCEpAZvuf5dhrhpOJwCOPDGat3i6/ZZMd3vfOp3OnPa0+Ci96S\nWS4HusRGL460ccrRYCDsQ7GFmG+yLs4Wc31o/X570BHOi7C7VbTKEFElh3Hv7W+B40n6DCVCEFsf\nx3Zc/1ufuzz2bd/wroe/2fND3U7RD9/Noh3Z/4LbcS9FB/H8OyrfNCa4DOhV8pj72MfLcMr8fPj9\notorpTBBBMqkUSHBEn4E4ELfEYhUshaG0BZSDtczrEUWduzPK/Oi/DqLAmQ8qrFvjsvK4p/vsLxP\n4BZW/3IVVilXG+r5JCpWfwual6mNf1rvrxQliI93FdKKaXqBg1sCXM0JcMLclVMykAaYFkJU99Fm\n299hffAhqIvV/VUMocn6h1XlOJ3AzytBQOJDVilEUyKtCAhQVt6e1UGWZjbDQ2cxVWn3Y/GQ3S7v\nXaPV5bvnCc41a5az9uRdC6J/JLgtp8I6vJD5A0oEE8CYUA6BWAhDDOvZAVG3ZwHAV1MO0wdtXq59\n9Xp0qTavr3JxWCmZAL2GZCQN4TMS6w7P4SCtik8Y4nRqZcqRBFxbYLFGMi8CwtCmlYZMeKKtLHs+\nlEBzbJgyPDyi7UWGs3gJYnp6SuiCFZNG0jgfhwn9G434ADCMMPhU7lPuq1TwwwPUlCJWZ1vKs6P2\nKKMAOt1HwnElRAyLKQVKUqGqSnWKit4nrJmRwipM6xi6EUEP+7rCCy3mVkGbFgnYCS1hRSj3l10Z\ns5Z+1zwUPs8qprT0uudy5X6a1DZGZl68ZgbyKKqIAw0G1D3TOwriw8pn+Fngid2wkV+yEBKhZ1EB\nq8oJv9hk7a90MxVkXq1UPhPa74toS+B3j9AFA4jolh4yBQDP5nCeGJ83hBem8WvAvTEXM8hwTxA9\npRp7GBL5KkaDaoRgycRl+t7gd5vMCrkWYLcRPLvoiEoTfAHMEHdZCW9PL9oMD1bISDqS/z9ghDfa\nQziHGM6mUPFqdopeWdDHe4Yr4m9gz9LfpVTFnMIL8RSa+2kLTrU1Pg8g/UhTAMR9M67eR44OpMJH\nHf+DV5jTuosotsBv0soLFCJreNsI2nfG8MIPMZau8IA5V8N3aVUS9LVnyByPoLhEXhgCFhcZXuRD\nxZUMeEU7l2t8r+9onn8+jwYXcU+TCnAX+3V3hhAhkIgxxb6NfU/ZjwaBqYKHaekRvURIpxthZ9El\n4zx9rvfR7ybAvT0AAryHzRVQ2XCy6XRffQx0DFxS4XqxssirpTChCbQ3rh1bln0ElmTUY38L34tN\nI/0l/aVE/mRS9fmxcSzlYYlFbRZmliFyQOkN9QGpPKFdBlZPUQ+r83tqRAbL+ziEWxZJwuhoeb3V\nvDS7ZXWUW9p+heCUQrG869CsTz6s1+lNat+xZlP2IQW/o73l1ntcKaAIiyv2TdVscW3HdxXO6ESp\nl5SnMNPHdXOebVz978dyCmwa5AaAl/N2jnPsQuTybkcms5aE3HBdspJH5DodTX+veCvjgF1du74X\ny6Z5PzQkw/0E6SVpGkPRikZD+I68vmh0TC6vy3sYh8hUuwEFWGnI7Apiv+c4TzSSJ9c39RBeEWBY\nJbQfn+0K+vMa99CtXDG2pcRPo2EJqCv8rsF0g046oLifF1rsN+1h7xx2pCHXcylB7HZj9a+OBww3\nYml5jsE9FnYNf871SPvtcBOK3i8ktH0/e1W1Pk69SXKWcdymYRYK9bi695gP2o17h+m8sq1Hihyv\nHdsOV5K2hrJXeXhSESMA0iMjwJXhkfuZXidD0RGGhSUetPfcqQvBzC/eQxRO74khzuSL+TT+N2Us\nxaQqx9l/eyhWeCyOPLPPKz5znKaKs1Ve0rKvBFlW+nneBJYIvws6eUuwvSi9XRNUjLJ09w06AtSx\nLUMkK/LRSM6CG1MVF9fCEve7UrCMc+ndf04xCha7uByeo+FepQp6cH53MW96HC9R5YW40eWPXqGZ\n/cJscTJMlKLZCgov8ufDrIrBvXlI6rUnP/tC4RWH8QAvdiRm+IKcADBfyeV3ptMIoqhZ9MP5f6kI\nhq90e6UUJlrBANymuIc1TEVJ67mj4nS17oLM/Tl+x/KrDNNW7mggKlI1ItI6pFAhYouVMYVVrXv7\nXI9/32R2bV7apd4Yhe8Jp1ZdQWJeRke4DqNySxeDa73mW0aT0p6/JDXjTjideMY7rAiOWfNE5f0z\nrU+j9VqCAJ+/MZCcIJOg1zLoJOgJH9Gmt/nVi3jeXNNmUeGDt7TrGB963kRTrmiJzeGtwkSfBquM\nSa5pMdFVmHNrpE0W3Vhxqt7PHKdgUgfByHNb4osYV67TLc3tFWnSpI+j5w3AVUgHl7t7G4CDsnHA\nNyoW9Ob2tig1rQP3FjLGfK57MxQeWHkMis7wGa7j+oKOS0e8kr7h4/t+dovfGxXTSAes9so84Gvv\nm1P3PV7P1pw17y/hesWrIzMsj9tKQwaqbLXGS6zXJw/lcsReO8e9e9BAFcMMzUbCM1a7FO1NPikq\nP8dQxLqrCDYhxHUZ7N1YIUtDeLNYXzScqVLeVJiPjV6hm2WPJfo35qc5Hl3i3QtOyPoH50Fc7njb\nachV2C6KjvKRPYxTQOA2kHT8VW13MNzFijwkXhbsjnSESkRfX8MB4w802L0gSC9T/47RATyQmCX9\n3VupuAC4wx4KR3Qnw8s9GzDc5IIJP0iVKfcjB8TCCKuy40J00ATSpRjURsXKgJNU+Wi/xd+ww71b\n03pYY88prffEqyLc0DHKgEXO4iG6GjAhjWCezcoZCQfuvDWK5RLltRGfRYC7kEM87GxEsYKJXRUj\nLPJbrM8QP09LIZH7uFbB4zh9MOXNZ55n5YIaLgC2pihSwaXn/IvhLRpWcCXAdvFS75e498TcKDOc\nVXFvzUgsyOiaXSo/q9tIpyo8p9fLZZgMXIDw5sV5TUETGJbJ4463gOtuDh9u+w1Vqn3EvPKwZMLB\nvAAGxKMcZBYs74Q5qi+WjrxSChNCyAMiYR1NKWKpyN5CWeqmHOEuvConuTZtgtXVKCZSUKZC4uTr\n8RNqksUaFYKSHnrg32Ma85U4zmdvMMqrZxtoem7PY88dL9G6cJCvbj7bLVP98/GeEZTk0WWQa3HF\nMDLOu4+VJ5ofQ2RugpLeqltfNY8LFTC+A3DCKKYwOYgucr0OgpaTBSxeKMAJym5VJKDOU39+o+B8\ns0JftBkmo2v0tYZ79ZvfkTkBnWAW5u2vuJADIGhGCCxcN1n3yNUzssLbSchjoZbV3Or3CA2RwBG0\nPSkUqjg+uXpGwUIerLLYRxUMblruiePcb34+buwbzQUn3znll73ut1cb5XN5T9vT16rG+i5gZdpX\nN4RX/hi+tjS+NHq4CGvhrf7zNEIA65lEKJBsgfuXoNsSfQvKCg0AYhU2NNvgO43vVtxbcKhllxyA\nF8HgMzUGNRYSsud6inwO3nEeknnArz4P2gJXHEPClKXmvUhE0cy9ebGOpY9XOLza7aKKc1TBmWYp\ndKaAf7hfQMWz5j/47JegIydZ16Y3hstJGLZ280p9u3mYFFBehdmeYYjqCIWgPBexbwW4zKa4tHfu\nabR07pChZO8jPGrhMVjzdDK0FM0jaXUt77EWIniQN3rL84EO9y73wL1uMlePSm+UM9n2UDfvogph\n0itULtI4DCi9dPRyTU8ZUKky49rX2Ax73DssQnatPPB3NnGymVFUFerXYRLrgq6MIRUpHiKs4p4e\nr/K4Y9PnpymzGMWcMwqXrO3Snj0L8cyucpIgldbxMJHEV+Gf3wg565TnkxV9O+PlyCKvmMJkeWia\nVsBDWKvkthR8vNZNIr1JlNpk5SwAFopRY4dxK4UBX8G0+HfxpckgtMFR6KDnhrHCM89lOjDLHPIa\nZ59n+QiRKMZjPSbfoURBkIxvKbQgJdTX+UlYcrf47MI8F2bfkyTlWvYi4YOHdwFlxUqBgvccBAUK\nlLxFUQJZ39AlipQ1nJbmq3sOAjIt5jw0tgt07p1s62CAqJ/7BNR5Xt6laygs4myYGFpJv9WHW6Dq\nzIqYDyxxtedEgXM+MEx641LgBjA8itx77PDJ/qT6Q3nlOGFBvcbCkm5hVcK0ZF4vOm74K90cZvwj\nQj4fOVvksZY5hugw06YkszIaFjzuQrqPhZ5Db72aWsfFTQwXixPOlfujzaeNjSGU/dqxyEvSrIAD\n8QioMvJA4efIPqpsMfsrY0Cjf5ToQ6Duc0zYZ6tsoj4X6335LLKscNJlWh9739FjhQ9JwpbfASWM\nQbQqRwXNdzpiKWydzXLNzdi/r0GG6sDpaOVP8VDdTpG53znJyhNyulsemw304skKj0bTeZ2CZL6p\n4YiJhx4R55jXYjGOYS68TShUbS38knANGmLFO4A1fAuw5bOg8D/+x350Zb+iTcz8HKGYx4hFmapX\n+zGfwYr2xN/lMwCAIWqAYMZ6SeJypyP02O3hSeT5PW6soQBaPQ+ZOJtih+KEwkOW9WYCvwAQXb05\nQAm49A5NUaiZVzqz8OyI74pzU2yY5u8FV30f5TlGkRtJj8MurVS9uHdyiwpxpE0MjVtphXs8DFF9\n7koWCZhhRuEBwZ36s+eYJD1Mnf5dzGXMS7xHA169KlytZ+D59EIgD6I4zVkG7jmDfpGOSEYwTOGZ\nUzxIlnMzX682X5brZk7SJdbsAsGGiSmaOWaX8Co9w1jyknbmB8VnNTe08YwmwA07M4UDp0VZPEMF\nFuvsBxyX11yHpypk8Yi27Se68u5f3Gun9xaf/btnEJzmxFniPCipkGmWJ39R7dVTmIKh+GnowUgo\nsJBxHy02TeDsMfP9mVsW415utVc2SxyQ+M5/AajQLDLGXQwj7ukCaQ2rEUwiVXgeIJIuY5LbCoFY\nrlafN0j1FGslkG8Lu2SCxnCNJhzctBZ/ifaY0awXS0iiRCWoaXMk+sduOIY842GZQyQhG9b1ivWb\n7dTywXEI5354j5VAdRTgsupiSN4CZMUaayPam+RZp+FMeNW6ClO5mqM1T9Dh3cu9LHXart+yVjp+\nXkmph5tiLM0TJuqhfTIEUy3d9a90hStp4Ze39q9RqFxpy2Il5744MORpltWq6nUUDnRh8CX2hGDb\nBpHV7dh/JOWP0FDWYi79+eq/KzUcKzGgV4HKubXPt6y1ZijL4xV5XfHY4SAJ42UuN+D5GFoWTq/P\nZtECNHpHIxar/YnU3jiOl7R5ET2jby2lTayUDK++6sKCiXuaRs87BYt8lKDDHb+ErcWvBb+EY3KF\npOdsLbyK/UqtQymI6yTzucVrxH7aHo/x8fXSyukvMMu9UrzgIIderQWhQYVSUB68YzTAq9ZUypO0\nVEGL7zPcSNbrnWMTj6t8vN/lB30e1i6W6Aw/f0kOuEvviEjh3bkpN/y8QRLHZ1eMF75ZTczyAFN6\nCQTIogp7M9zIAQ4nud7aZ7hHx6z4aB8DgMxtuhiwKY2fISN8GDoi62/ed9JWUTAVUP/9QLlQBnbY\noqzkGgdk76arIJcWgXHSorWsxeHPRtluwA3vM/LKrOQQ/s73iEa+VXkAU+kJL6dOS2/PgOAB5YUZ\nMD//KsZARWpKhYmS5l+dG2Wxzv1a81CBc4zvlDKrYVHG2DbxyJru/SwZFzGuCiVk/1uk1QxjP/G+\nF+xoesUUJl3EQ7NijFcSzaF1T9HiNZJikP4Z/VP7y7DDsEGuQmXWEcZBqgZMRca5PjY0kQOygHNa\nx0ORxOIdkBUZu1DBv3f44be6xL1JKlD0CgEIJa0+PxbaUSF9KVYcwo6OZIXNPTCLoMY7D4quoZ3n\nBGTYTYdVLxVvh+Wv8pZt9Zolt+Yn7V5ajyyZoI11sY6C5JLYrKuqamaAVh6UpuWLsdYtVKIx1V2c\nqVEZ81yig4AMhoHYIkBPrHNcV/0oVNXEl6vsHwHvEcw99poJT/55NZuIwhXNfg2Jzus3qzBoCEU+\nKp7NpWKnZSluJgXnHpUDoW3PCpBezMRttBLCAHRgWVkXtFDPSFEIWvT7IZlVdS5+28qQffqrEakr\nQbsx/KrTJmvKQnbrQnGjr0d4gt8bve5SkyA8Wn/rc623NudlFFr0i2ExS6hZM0T07c2xL7+ldkf6\njQMueQZJ3sM18bLR5ANOgoqyVsx9REc0hi8Ji7hjCqRJBJIwV2SlymPMEiK8V9wwI8K8rA4qaY9G\nZb1Q9j3UsY2hwYcwP667SlVqy3nwOzQhXjy3Yo2CeDWbQZcQZT9EMyzfzQJxnKkrFRJnlfn6pIdC\niLfEP0GKpUHrWUDqDMU9ZnqRpjReFu88waJc9sRFFPdm4HllAgBaRQ/ciDebIBFV2WSypEfm0dQh\nslEdz8KEHUreEi4XyHQGsM3pXi2DH9IK8wIHdjR8ojxEFh73GzLXFjRpoujI3gLElsNtW9OoXpgp\nFIGX/d6sgGiu/ACATPf+qEXZ99gIGTqHCHeNcQtC6BfHF06uvHa+MS7x5wYeAOLne93ZjgdR3NmM\nMzvLVL4BeTYUsIYAThGcULhnEzD1ADwDMgzzQTacMKOYQhUPAoBnMnAWxZvzjFDzbublAe6VdMOg\neol4dYPSpoVrJFOUb+NoR5+TkG867eQg0usPpykbLAtC7OreuNfnMD2nOV8lY60wt0fdGdFS4OyK\nUuuT/JphI7yHBIIXNC+055u2lQyEX7Yktd7vGvi1/rX2V5bJpZ9Fw1unT8OUSZylIsdY+lI+Yojx\n3hJYKCzc/C5fLjfmU8/eTk6i1ypFrpuMU5Z77UpRWeY9m4AVQ7vZZyPii1B2YP45zz6WfgEAU5is\nCRZsVcoVKZjy+qSQJysEE28siFnAvp+jsyhKN8bJSYoKbBbOHD2p5Z9ck7VTDLaIxY647gkBRnh2\nxQc3b0L41WjLPiIA+4ZfVz4bLfoMjwFqbXlKEVB7j231ZlEYKJnq2vtxHWqSq9Tu7wUbpAkzHpy8\n+qCvKlQKMgmd8+g5dF1Zyt2eAxWYucLYaXGNjxEABxrX3kVlyeFjaaxY9uAVch8/Frw7DPscLcaW\nCiwaL8jbGNhty8pfU+iCB9/Z4deGBBiNcOF3ulJqrufRlSrSMUIxBdApLT9G2lMesgkwwsFyHxf4\n1xDSpDmdd5A2SacX17hYdPlAQ5KneLg84e4lxWcqBKtP/NVsuUMEWcKbwuB64/o5D5iVCr1lYQAa\nYiwKDgks13qGUJ95bII4qNof9iMJBAzDJH07SeRXWT/ktMK5ycK42p5X5DhLz1lx635vzFsq4E85\nBgQs1PlVhoe1OTPHV+SWLEL5hXvAKkeHe07dA6Vci0Yv6NXPcuk36AhhpPHerFh5mCNlmQxzE4dN\n30O8j31IHJ4rUkpbJwEO1xkV4pCHY6cxhhRJYj3Cw3TK3V39ZUjekl/swLjAlWZ+x8JAZ1M/ADfg\nrygv5B4hpU9s4j6KRVwkQsFROU+Awz93cjA68jcVwSZVEp38aeGwQeQHSC8N6P1j4A57KOVuWLgP\nTNCgcS9aFnmlFKYSVKQx4AJY41eHdjvMIDptQkEhIgSQGcyOQoq1zdmeJxaQEPKeY6rHo4K/MX8K\nEbNLocKuxi3LTuYASgnJr1rwcRfWOOBkmMtD0du0EO4tmWgbQfZUFs8mFByGV/KJA4ku4HUU/R7O\nqluwr9fPBQtrgtEN4fMwBj/875bYtV6RnJvUPf3GoMArc/T7eR7XrcHQ2kqiVyXX18GoRalPUpnG\n2eZ0DapCxAAAIABJREFU71O5wJ1omPmwjAn/B7m/+QWgqrAZXka+k9+rwPQCBKRGANA9C4IMwH5F\nm6+t5D7tygzQ4XSNcNeUpp5JZb3jIxxHuLcB0i7iyPqKZIbtLcs+aWex1Lp6K8uxC7I7Ozz05/f6\nOJIuJPc/zquNuV8X5vh4OxapYJjhcrjsIyhDWrCmtqx0pxu84kJ+Pu7nVXuqPMjH5pJqgd2mzwmz\n9ILXtYMD+krhTNrcFivHQQGK1lW+J0ds6V1KsVmp2BZc7dBfLwwxJKIdEjewvMvHI6HkNxW3hfMJ\nJw3HExfQBadMDHcs7Qr0hqg0SEFN6SX3DcJ98Wo3X0y9+tsbq6LuN+Z5q5CCwOl3L5SRdF5ciVLb\nl9wPLzTR9gkqjJ5CK9dPreFaM0J2ZQfR5xTJ93Ycq71W+JD3InimuCzCa0MiJymKUtALNQpsFQ4X\nz3CchvBeRCU4F+7DMCAaMGKeXpMPCcD4kM8cZKeLhRLR5lBrtPL2PXphuOCRnvVcVejIggq8kbRi\nDwPvECnh+5A/y/ycXXhylO94yk6dVLO6YeYUwmW3s20Qm/l5GbK6t8jPoRJcIDlankPHfarT/Pwm\nKVpKGnNBN+i4x3APhqowXMxwknZmVTP6UjF/QyWjZWZQqTT+iIXSpoAAT2RC5kx4cR4vsn3gCEAR\n+QER+RER+dsiMkXkn75xzx8Xkf9bRN4Tkf9WRD57+P7rReTPi8hvichvish/KiJvvZ/BdkFRwQ3j\nPyyTePXcIzDlBhgoQcq0rIgm4QOhJBMLZMYkZrfouwDmP2r8QcYE91yr1cDpb02LoTK8oW7iMxLv\ntGYOyPtCUL4gyl7DIFrWm4RREEChZCcxH2Gid9ynERYW27OEo85247M4nPg5BSCtjT0j/pTxvBnP\nLiV/O4jSiZzfl+ej4O4/3CwcJ0lJ/eRfEhhilozkVstDkKXNs91LNFjMYg0WJAo8vE/goRfMYdN4\nB0dKQU7n2r+xf3UGs6tAd0D3uL/tWhOnHyMAlufHCBw3A/9EPXZ90wGFW6o3caFKNn+XYGLYDhGF\nRnC3V+HSxBuBZBzxh20vl4Y0S3tH54D5Y+EfN4Vp4rNIHGTpMN9CuTT4vjKrdaCyMiHJXPjjeTJa\ndEZC6EkaQkrXcDLwbZOeL0FCUwI7E6ENHF/skFhL7slj3l4Oj9/HpvYkdAFL+ZZ44IqSy0cxnxTq\nGp2MH/Az9yvpUuCofxSMCMfjPXm/yornQHpX+A7OhbDqsBPuTa2x0LMjYPiRgYWFWJB75BqtAou2\n+Ylo7PeiUPxsohFSg6CvgXsUbFshCpjmmDwgVqmSJH3cQxjd40dUPfxP6q1T/KfbU4jbQ1DnrZHA\nCGGLxG2VUIDEbxsBf+V3QiG+Z2I53HKNZPVMfdj2UumIlLXZDxll7q/j/Bl68/iFY3VEINY/4DJC\neN8EmDqwy8AF7nGa5pVK9yi04AfQemU8gYQcE4UnVLGB8o1hquY6TlFcoBkK6ONxvD+rppJxZzMM\nHwjB2p8Rc2F56nAPjUiWNxcznEW87DUsPWPJ0lA8P7E/vjyLxo+E4O6l258QP4N+nEORcHgX/VBx\n+EiD4Q7BHnM3VZzHVntRHZYbPB9xNBy2mAv5sWgpVVwD7tldFBdR9wgFLTLRPNNNIFAxV85UcYHg\nDMUOyXA/Ulwq05v4WLbYTw7nkXRjF8cN0jTOaYd7gyzmb6LY1eH1VAbuA0/vzP3AI/Bjhys5J9f4\nMONn1833csz5LFvSMBHJIhkgDqvgPmB14vl/4jyHY9zEize8LTsGDJsCd0Nwr8AbiqSfp+Ahe9AS\nz2VrfABY3v8i2gdWmAC8BeCvA/hXgevRisi/BeCPAviXAfxDAL4A4K+ISC9o8RcAfCeAHwTwhwD8\nYwD+zJd6ccmRq9DAVhVZ1p+6tlragAh/EoRwY3WgKZmh1KZchQbGzq6N/SV1KLhc95P9GXiS97G/\nGgtwS5uWeKcocAJwVluUtFv3u/ckFLP4mYCf3WO6eFZc1pH6+/iDEub6vbpTMBX/u1mnJJQdEhxZ\n3rXqIzffyR+joHJkP95KkKNYIWUN70JejJ8x/xkydABhCnmHtbl+c41/Q50JARTO9PWfeh3DnQKb\neTlz9/wE/sxaYyZYuuXHexk8n2Huj/6QGcg06MWgc8+3CyRDInilt/lhqMbaXjoNOTJvtqQhB1zL\na02AzGf4w/XI5yQZccoGiHUN4QiJg31PrfQq36OP05BQU1wwuHK6UIi/bSwQqZLTiHV/Lg3hGAP3\nCg6+P4Zo5nPV/XIF0+sfOdzb9ufhPhfQmxp/WKdb8y860z5LCEiGZbxsO0IYlPoi9Qo0oQ3lVRA0\nj9RhDATaQvP6mkt7hu/Vdd3SWHj46fli/ToElQ+XYyl6loqvtUPIpYS39af/Fx7rnPeKy0A/12dt\nt3LbPkR7aXSEwuymEsqqLt6kTEo//PDakPUagFRIdlU8gzZ8d2HfdOAJJp6EX0EBYLigfhYaWEop\nv4RiRIWdbUjtl+P4TlGumobPFZ7+c4n55vX2+6IbBgxvYuKpblf43Z/h3ss9FtfPorjowDl+lvtB\nHL79kzjb5yarSTUVe4RCIYozxL1ZR7iILDxBl+/Wz1vQEWnfs+2m2KPwD404We5cJHPQFK4oPwul\ncDfmVRXtUQHuMXHf8CD3cVtL8m+ec/UWJr4gFVS2U8lDzeMsikujlStuGJ7YvhjpfB+4QrSp/0yz\nrHq3CfI+GmZG+6wSZ9MZophJp6/Fg5Z8qPZzzE/7qNsHDskzsx8D8GMAILc56r8O4E+Y2V+Oe/4I\ngF8F8M8A+Isi8p0AfgjA7zezn4p7/jUA/7WI/Jtm9iuPvhtAZclacoPy4PBAriruXG3N3iiCn1/n\nPSn00zWY4Rrt3A9hTyWkCCQroJ2lyk8zjMJwJELS/uXY1vCUXhjAnUd1N6xyF2i10wjFY1hFIpWw\ndGjMCXHCcoyEbtAlpt3iXI3YcB1e+wwrUEgbTPr1Tes3WzyX/nc+r1ZjjvCBwd57Ele2Drn6zpfd\n8rdZwbGeK1jzqru2WwZVQwILt4PkO6u/oyDau/ck77og+Q+ZItc6xilShTbkEBIQCiwTfP0WAebE\nZhHKF9UXe7ihIBA1wzMjoVNruFMEY64QcoVLMIcr5duloDUbjlCpv/syy4q/XBrS8Cc+ewn1uXxf\nxVvamoovCE+Vp6B4lAkt6UXlEilu5DjE/uxnCflnlgWOpWSIVFP/J5l7zgthDfaLKZAKRwHA6KWQ\nNDiURFYlojljhrzS08vzf2ZWlWX2luX9RoQKBswtSYG80xBFVLvjWAJ2BNERXtrwnbCCcIlif8lq\nIuE3PR/wShDEGl4naOGyjMEnqEBjS4KtynzHOrIUMmFB5u79aMDuwAmk9lruPVuflRAezWbARpPn\nuUW7cJHHF0BKeWNVszVcq1B8BLNxi3NQLdshJoviU+e3VURBrpP5OyiU54xTqfLCAtuX66bGy6Uj\nO6Qd3Oy/3TvEveB746yetL/wDvEwKKhAbQdCOF4Pna5z8QTAMDe7nWXgznYM9bNzaNxQARCKDsSv\nXSawDcEXbLgSZIZ78eJVO1zRu8B5L8O27mx3r3jMid4JF5jdqPYQnsppO3i4y5QquKCqUX2OsCjh\n+YsyfBzY8WD0ag2cbM9ziJ4ETvNgYARcHyCwgKd7U7w9lYHNZnr5dvH9eQqZB+GNU7iiQVy8oLxy\nW/Dlsww/2FX43gBojGOR4Tr/F0aY+OcsNBPXOx2hcuJj8HLhE4iztPwmn4eW0CXMX5NcA+8+9izp\ngPqBtgpLpXabXg/UxL2O0Kru52XiBVvwDi/G0aIVzN/1JvYM3VMFdNY5UCbusWLI45vi9GmToiNj\nXvCebnm2EqMvTgQg+ZvUMQ3TvCBEhjOqJjg226ECvK29ft9H3758W3FrIvItAD4F4L/nNTP7uwD+\nGoB/NC79IwB+kwQq2n8Hh8M//Lz+C+WCM5HX24TZhCpdoH5f9+oAJUQsTFgirCDKqR6Tn48ehBJs\nSlhyYXV9h864FvlJVMPEjnbjehda38lghS5XSQUs36+8l9p37cQewsNrlbsgKdSUdaasrn3CkuZM\nwZylQmxxUl/CI561KbB5DTOBE41esSloUrtntRYTJJwLQE8bhbSl9/ZceOyIH2IeNig+esm5Wwk9\ngsN7e9+3MACw/K8U0edZ5gG0EES57p5voYJD4VVC4FOBDYUNifwGhciAyACGwHQg4ikhGzC3gdMY\nEFP/iQo/Jtc47kRTse0UiIg0ApthBRLAHotv/Qq1F0FDas5AyJRpJavQLKSi0td0xUlvE4GTHX+K\nX6KjQ1dy+Dtxon3u95bnQhJvbocIrnvHEMobgn4oKuyq9b/sw/5b3AuWnoOgZws80KyFEZrV++wW\nVe4rjieruPU5cJ7tqjzyA9wa+20a0r1zGarX6d4jtH4J+UGF08rhHrOVhh5p2wLzR7aQtpk7XDlO\nOdx3m273u7SPA6XAOb+Lcs3hLVX1n00133mvhns13OnA2EpQrHce1yxwVWUpKY94P4AI6ZPE54+y\nfdR0BGiWfCmP0a7uIRqELSQVH/4A/N0qawK4t929R+L0thvMjtEN6dFG0YKBFV82LZo1xIXpM9zz\nRJw5HdaCik33AJDL7Tqw68BJJD1oAL0E5UGgV0ZzTPHuUFpEgGdQQMoQq5D0RtD7PnKe7JsKDvA0\nlLZdFG/aJWAda4/ylpybJ+zoTeNe5nfsg7+PnizOld9xrRevU+JE4DoKNirIsFjT8u7093KPXnmy\nsArrvIeNc91jfS8tbHHq8PBJOdKIos/l7SnPFOC0QsX730Xy2IxNELLhSE/rSaKU/BgY8flNnXhL\nJ+bphK+XiTsz3Jktnqe+BvSM3WmVpec8d0QYo8DP5LoR8vpRt6900YdPwef4q4frvxrf8Z5f61+a\n2S4iv9Huud0kBFMJ2ReSEqUK/FwBhVcxUymLH0h4JC17IhGCB8Gcbo3bITC1DHFKoSfe0xMy+fU0\nAXYXmDYwkTCqjBkq6VbcMpvCzAyLhLo1CmG0m2bYzTLPoGP4gEVk72qJNIkkzwwpXMG25WF3/m95\ndPxa+VP4t5VFvIsuWhajiyKTVIuhK4wW1bCOp2Ae9wmQIW8Wf0sw2DwDJ0r/eI6BK0YG5MnoXAT2\nVW8oWHOcMarwIlg6XyyfuIaXd9+0lTxXhFBkuXDvxSt93a75V+9BPgt4OKSKn5V+rNwFlLXfqx+F\nQB4AVcbXDPiczBGNirDDSEv51x5mGFbQ0BZ6SXcfKzlDpZ/L5vk2qgHUj9ao85HSEAo23iRhZOKF\n36e5EGGwjMF2i3l4FqyUY4hbydwbZLiIhABimddHnMj8yqZI5Sjo1RQ/DFkNN3DYnxfw7BYs56ZU\n6X0r2hiGATJhV8BnWkMlxtGxjwwqheMCVe1la/eAFKTGy4NqDbZUFvWxVz8jd5TE/wyJDVoU86VD\ng1W1OlxyghRIrL4nzekzNNT6d69Ih0Fa7Nt7ijYi7XXI+TuwjrtYAzZFwVAeiLifc5MbNCDH3NaZ\n3p2k/0J6HR68tLa0wzKbsEdb1gRzswJfrQ4lFSteRTwW7d7smI+UUo4DrsBWmBEnVIobf8TtI6Uj\nUwcuzWgAVI7g/dxxBoDI8ZExHN5z4qwj8kWA++leI4N7ZGZ4Zu7mxBfH5mHVMYkMmDZE/kq9mzj4\nTLegZcBb+wXvyYhQrB0GyxyYzQwP4vmuAuCiPu47GM7wvk+24wGKBx14Y7qfYNj0/DgY3pjT83iI\nm4HDfiip4GR+1o6Gp5PeuHtYJv0Dgoc4VBUCTKv8OjOkp2qzHc/CK7IBQMyBWPRUT9hsRihZK1Ri\nXkVuwHCR4bQVAo0DpU+wOIxWcAn83GJPPMSqnmIfuYfK3/gggie2gwXXuR/3oEVcMwXyyAimOZzg\nJd5HjH8K81qRBPW4O6QRPY6B9GSKRkVd7+MUle0W5SruvRjpmvO5HZrpIydoeAplqRiotuMeE2cD\n3oDjjYgXpnARJIq6C4tIIA7n9TX/YkRL3NuMyBinU2e39mOLypCAy9KUeS3GbYas5sczoc4meCJZ\nNxEvsr2oKnkr1/qQ97C8tQbV35GybDC5qMJGATFqbxIZhdQFwNwBD2mIEpHqm80TcpNL+q+QHuj+\nrHd2wcKVmU18LE6iDoJPvL/nOSiKiQGAKCCHs5ssN1KK+Mt3rN43xKtjJWPTtRMBCUo9v4tA00td\nzJerIa2/XR32KgwfSTEp/5oJEJ7I7IyzKmohCUytG5m9JNhL2fVRb3CX/F2+MTso2KHOp1m+inq2\noWItis21mNNmJDe/CmHBw/cYAnQMkVq06hQErcJs+rgP82+rW9ZrTIhpng3loX0zrI5VGhlmkDj/\nqVeL7IWm2XZMF84jKZbCp44QVCXOm5gTIhOyT8gYj4Hlo25fGRoiZMb+mR4YBA3wUM3I6bOqJsVw\nPGle1WmVtJvwNmT5+A6nEfjRFXb+Rf0GcIvzDoYmyBIqlvsxmFxisyA920ApM7xfBKkkUaHx8YcQ\nL+uzmLZcy9dPW+aQexiFw8nsNIxJghXfrby6SZN7P2h0DKF0CBXCRpTiCeKtK0u2eNDmtNyTpMlb\nwLePH0IzkcPiMRqSu1PssLq3UU5yfPWuXm2PW5JrVIrRyn+Sz7QBEVZ8M79roKk+Gs4ZVq/CnH7u\nkuNb0Xry0I5XDJ9kXyrwM15AC3nQ78M6+DPTS1wj8mdfFhX5StEROKy2iPJ4BsEe5hcR4AQ/PNTD\n8QwXntUTazkAzOEC94MphiJC5QAN9eguFJQpUufYiEZ+qV+oPBWDRLn/O5t4poq35gXDgKdjA6Tu\nTU+PkXa4MnQvhmdNaNgEeECh0i7qBYiMudqhuBsViTpI/aSeu8vQsZ6cT3wfNqMviXF5fzyry4x5\nLfRQe54RwCqArhDtqH2aBmPxAhYynXbsEcq3G8+mMtw3OsCQ+k28kh29oFOGG8wg2MXH8Nbc8XfH\nHd6eZwAl/fRqdCfUobhYxleelKPx9gbJze9TYEzaUIe6InhPpyOL56YRCTeW+317qHbdo0TDnPbP\nqHBThUBsYrOJ83D1YbOJy/TiDRqwdnptuIucJnr6BF5qPOlhXHswV7Kj5kg29gl4qN+GHSc1DHgI\nIEMRX1T7SitMvwKf77tYLTufBPBT7Z5P9odEZAD4elxbg5b2X/3c38Qb2zrk7/vku/h9n3onDgyL\nzTwR4XnUNIKRS1g7psHX2lw5Ean4catY1MVCJ2UlmBEeJTBM4an2CkwLb0/0FVY5Z4SWRLYrLV2w\noRehI3B5u3xniLR41/bZXdCrO7WTfNok68SY2FhNkcycBhQTnAYEfclDeAX0Wh13aglfAkkPlIck\nuFCzWxc2LL/vPEpCiuA6AHFosDRL2w38mGFmPxprmRyeiljOtXrysTYJrvVB79QKVlsIXP/bwHWj\nkl25UetzRfwGJD0XSU6IlwKYKWy0YUXFwF0kS/06oAfMdoiOEs7My4QTU3hQpQrdsQE7M4xRwgAA\n/OTnP4+f+rXPN2gavnj5SF1MHykN+W9+9v/A/XZa5Mnvfvd34fe++xnANMNcRmwIwyG/xQrHtlSW\nzA0dgbfuCa19w/d0nJ3TcDcq1l9gWa1yE+a/FK4aih4p957IKtAGXaHiVjSsQuJoCBDz8Cmu8zQr\n+hne8WNjmJFZzecosCOUTAo4BkuDTldoHGXXYwbohaBC08CHk1DRWXMFeqU4KjtUkkaDo9MqF2p6\nfpevHz9WntVRXNb2iAXNS1pyUCpIAzsdihVG9wi2Iax0N8adWzrmU+fUVMdKvmQzEq75Pm0vsBiB\nQqXKvbN6Yx9PliO3UrIBwOZsSh3c2AjngxqC9ySvc2nc+wfw05//O/g/f+3vJLk1A55deimcj6R9\npHTkf/jZv4E3tyoeDQDf9e7vxve++2k8kw13oSA+yMAdphsO1RWKEUi5ieEZBG9qmrtwEuBiXs1s\nH378wwm193kYLYXxM4BtKHROXASeo2QDYoZ9bDhTQBcv0W3mfUy4R+uJxW5Tz6uaEGxz4iIDQ4A7\nWByEWibSGXhxguFBNHNOSPeGAF+QgScxJgAsVp3Cv8HD8k7heTCrsDTKISNkprMMbGY4wz1UO5De\nvSHAGzY9Mkc1S6ZPeI6OwZV/mOfzXsaGJ3PHBsPZNGm72sSERm5Z7GOEN0U8wuIejuBP4XA7S+Rs\nEXcCQlPijKIbQspJHG7Ms3IjRRPCgKU8dwHN20U8RwmNxtSTwOEk7OYhr9A+5hXB2rle5AU2ca+S\n+HWRkigfzMd20YFnJplztlsopxJe6vCEmQ5XPrXRwrlnJICKe0vF/JBb5maa+Rq/icpJEwD/2+d/\nFT/1+V+L+fpavLe/wgqTmf28iPwKvOLM/w4AIvIxeDzwfxy3/c8Avk5E/v4WO/yD8DX/a8/r/w99\n9tvwTR/7GifOUzJ51WvFuxAyRODyYFmH9wkXDlHeJB8wYDxTh8UU6DFoXhcP6WrMYFaIC8vb7hNp\nHSzkxSp4WwgR9Bo0hF6D5twz80wNd/HMnC3xm9blLCoRAoYxLOXGTs0xlZTQLYI0YJi5EJ1D74HB\nbfOlkkMBxCxjcnP/A4zZS5gwxIZjERQMuhUcoHXWx3msqlRBRU1o0gPAryff/l3bjlVAWIgdvXMH\nz2P1Wu+lEUiON5Iw927bd668Egdvj17Ny8P6TTvVr7Lut1eZGYRUT9WVKBnlzYp7hjpueY6XepEP\nYeiq4B/45Dv4fZ98x7+7TNwNwS//9m/hT/3k37g9yC+zfdQ05J/87O/F7/rar22eBBoRUhzFbhOn\nYBqZxDtbLLW18sAHz6j3INgUuMxmSWvoqSJedp9oI6WIUPhYPVukCt5cERLAEMKtLYoRLYwTwLBK\n6O77MvE78VJSKO8Kya1Gq3QP3aDAPdSNR3WqRvV7na8UXlmOX/zaEC82o2lwslAeJbW18pLxnRyb\npRdtVQwer9hGYV/FP8wGp2pcA++3kb9sM9bjNg2JBzqdOMCia6nS+u7WaaC8PwAK94yvqHHmq636\naYGFaevienfPGr2Uezw8GoxWeuNW4X1vz7l+luP+rk9+Gt/5yU9BoNj3CR3A53/7t/Cf/dT/go+q\nfdR05B//3Pfg2z/2tuexmBdg8GIEE5sATzHw5rxgjIE5qwrlexCc1AXBy2Se0cH/L/VrU8F5tsPb\nzXAnE2d49U2ZHi68A1ky+inEj5EwNzIajiFaRWN2UVzM+7qEsOtKQoVovmUX/Pq4x8fmxT1Ls2hA\n5uAkD/dxn+b0AhKyiphHLj5Vg475FbKskwDPZIOZlxbfRQBRzNkqBMIVCAOCfznfClKCkwrORoOD\npGFZpWSlh1BUBgCo4hLvvw/z5YPQC2OJ0/TQ0mw4rAxTOya28ALuBw+twz6UDBotk3ZXu1g/oHql\nFRpz4/wPwUjpIeLfM9aj0zSuFRUZQQsNjO+exPyfNoUpxLF8v7T3pBKkQHf6bIrFE2SBV8x1m+Yl\n+S8GvCnA0+mFMFRcQRO4gikAfv877+J733kXEMXDbnhLJ/72F34b/+5P/SReVPvACpP4GQWfReH+\nt4rI9wH4DTP7ZQD/PoB/W0T+JoBfAPAnAPwtAP8lAJjZT4vIXwHwn4jIvwLgDsB/COC/eF5VGiCq\nuZgsJxTDDJfY4FAvTECkmUGFJPITnB8KbFolr3Ojg4qApDJBhKMiMOkuVCa1+vsmmX0wJIuztbqV\nuFPBq5APiZAusxTapwInQ45zDKlwt0k5y1+QoXpWc3bGdagERyZMRhnKGKU1qmwSfVW1JzLnEgn9\nMMIi9gydo1QmZotARQtrZwZGGSGAn0xD4eE/QfqSNZvDZaTwUWLMspioEJ2aUcU2zwxlq1Yxu/67\nEyJWEaJSza+msDpgEUZWvvOyrjV6D5lwQdkKDHy7K00I4hJC+xZrO4NS7QBGlP+ewUA0YmIYsuQC\nfITNyQ5IWEFVgbk7S2UFppiLC2CSuLgZUmujR8LMBZ0L/CC7L6e9TBri+ubA2SyFGDWDYKSxRCMw\nTwNJRVqIg7gQc5lV/auKyfiem4AbVVpVn9MsgwLEzzkjfaAwzVBXp3PhqaBCFePPynVcF4dnhtgY\nnPaIhacKJazNyG9i/iZQwlRnfj6WKFZjFZ7i9DHGIQKy9DpPiKe1zTSgkAAyhGgJSeP0xQWWfRpO\n6oLI0FJhIchwOoa+WsCHcyRssqBNtCHrcQFsjQT7vNscu7JSFCU8RDF4hrJ1JZSVDZO0NEnIoxuS\nCpUylHS3rnalr4/ClRhHmjWkF5BRNbRmKJt7hCQ6wCr3LStXgWE9MfcIMa4cWWAL3mlggr7Dk95J\nw3T801rX9HLmuhRt0tDELgcK+GHay6Qj9zLx23qHN/YzbAwP9RTFU2x4w3ZsAjwbJz/LSCTC1WgQ\niXUeAuw7duX+9HW5D0v7DjfabM2zu4vgCVx+EQDGPB9xQdvPgYpcFlWcp6/VGSUXMddmIMLohGvt\ne/FsA3c2cacekvcwNnyN7dgjHPteXWGbopAQZjbQa1LxM09lA1QyLJElwi3+ccVt4IRLnle4h6fh\nDPf6DPNMobP4ccgXeqiNtMartp5jThYGQBmCOSdMFMOmh/ObYbMdu/IcM6f9Q4GLjsz/MXh44MnM\nvUEBZ/JYGpV2xNwC+zw00NeQxnQTr/Z3Nr9+luGhiKGgPBhwL6vxicoHwxh7mORd5mp5q4OqI/RQ\n6ixGGi82kVAMHWabmIfRhfrGvT4EwBjuiRPFPn1sD9PDFyeiYIi5l/PUlCDPtQ7PoQzcYy9ZUHze\nDl+nI5d4XlTxYMAbdoGIGxM2sziDj+c8+WwvoNzqfOIBivNRY/yI24fxMP2DAH4cSNz69+L6nwPw\nL5rZnxSRN+FnGXwdgP8JwA+b2UPr458D8B/BK9JMAH8JXgL0+Y2Vz8yJOEwixKAsbJQx/H6Golij\nTHKIAAAgAElEQVSGqfjlNXOFBQoynCOeO8OF8+5kAeqzxQePRy3mRmWJVoKBsEYIPRUFPMaN03LH\n5PBUtuJ9dFlzjpTeJKgPw7+oxFQSOIdlTTGKEEJBS05vSiOAs4bgnIpmQSBLpDsf9oN642oSj/Xl\nhfZNoOA0Fm9bCqZNIIh3SawHl5JjnvGHNS+AmWWYWwpUoCCiOSZu9LQGpRK0vrvjSxcGKGD2MbnA\nwCMuvc1Yf95kFCwOopsTuRnhiyW8UWBnkq0hmEbATINQ7hSaZQAiMOwREiWwyNGhIOoKgrMtWuR7\nuJFRwIaksicTS7XGD9leHg0Jlnc3pMpjt9BZpDAo1GMK/6X2IYXH6BKT+EYFR2NyNNtpeVp6/Do9\nO6zjIaEFuZJizeMjqexY4BFXYQ+FnflVM13hUawi9wSyOM2ISZl5+IcrTeV9ZK4dkCgWxS8UHrzS\nhIOAD8OZBeUVZi5ShgI3xQRxr3Lf0xAFhqt4yFc3MFEp6V65CDTNIw6GlOrW1495aCD8w6gjTWCx\nWUarook0B5WSO9KbLWDeikcylAeKu5uw4OHgKwC69z3+meQFa9gvFQ+0+xM0MS4zx2fMWeFGXOt2\nr6OXU+0Z19yYYGl4Yzh0D43kGrCENblH8q/ZwsbbPDs7KBHoy24vjY4YvDDCaXiBAAFDQGcI716V\nkkn0nP8dLJXkHQPQ9UgJNS8KcLFemn3ii6Y4wXAfKLzNiV0VpzRaCEzUvTG+mXxfhOHhDdvxADcw\nPMDvVUzcwaJctIewTfUqZOlxFMUFLdcpjCF+KO3IqphmkZMU3hUxV6DGnDiHQeRkXihC9h1zbNC5\nezlydXPKJeA0JQ69hZfGfiqjlACrkF/AvREPsc8IjxFC1UW832c6glY4v9whqU2wYIXGuvg6uWcK\nI4pLGD1DknAgfaIRZDeLvCXLvUtD1W6CU5TavshwhdlcAbkLD8wUFpwA3mNubLw7cSPWzQIOQJ1D\nZECEMFaOKI1dBl+v5Bfmig/g6+vfM33CMiR4KDDnxEngnjr1yKf7gPcF7gl7AM9/cr50J14J7wGS\nIXkbzA89hnsMNWiGwft39upKvGqcPyWCN+aOM8N/g/9BWADN5/wi24c5h+l/xOrhvXXPHwPwx57z\n/f8L4F/4oO+W0E73FPKdpQ0YIBEEIoVkCyOKaxXuUuKAW3adwFGo9p6dJVEZUhc/+0xCoZGWW+Pf\njxiPwAUaMtwMmbDIgYqhJBMRQODEzFLgL4sd8yQ4fwreFDLSwreMMphoIB7nDKAUvRAWJaS3DRQS\nSyhcJChQkeOMfRIywpK4CNVRWnRKMFNLDxZQIU0lHKyCQgw07rFlOKzYtVTrAeJU9BpfjlIoaEjC\nm5ZnJjWzHCgQeU0B0a4UJ1AkQvWO+zYFWuLO2ujxVKnRZO8xsKxQhbD47146f9qseYXF1szCA2qQ\n8Dw54VaI0BvCUfvghgxfK6gLq/uEDSpjFbzD5G9r0/5y2kulISIZhqii4clxupC5Q1i9CU4LYi1S\nCK2CDMQ9EWAMrWIK8PXZseZBtWPJMldktlASfrdHaAYFklzEYLAMkzq1/e6MUyJkr/o0AHdLnB0Z\nrST9YXN65utP75OZh7iYCHRKhgEB5ZVF0knSZuTndTesChPHx98ZGmuWChzXjl4SKkojwtMqL6nm\nuIQ1rtP2d0mVbmaYDsO1V+VJIvcSmatG4wIVsT3upRW686kaf9GhLCwEGluaUSkWLYsL9UH31r11\nQbeSE6UHqB5Vcc/oUHoYJZ/j+0dccwsv0LNe0+sKJLypqVBIY6nyys2ooXLdRLr37MO3l01HNgWe\nmmRo0YjFE/F8IYHvYdIBCt4mlQu0AZhC76ALjBcRnNSjNC4GWOQTIYRIANjUDxCdi4/BBVrmxxAw\n29xxUY8neEAVVQAEMidG5Htfxpb4soVsoRBskWd0CYpwjtjWXshhigAtr8WSj0ZupoRX1AzbiAqD\n6uXNST+Yq0L+o3E/Q7IsNqDx75i2QXAKv6nAZTnX9TRgvud9EOBOkN7XPd6FKPNNHt9D4k65gWOc\n8ccGR2byjhHI/RBV8BxPXBZ5iPt0Ol9mLizlICocD9P30IMZBgZUgTciF+scRlA1ZOQRYXeGYsj0\n6nspWyGJqqDt2cZkBD6+EUqTe4vh54MFICYYweLOgDtzz9jdcMXSzz11hXHCHQ27Oq7wcN172/Gg\nHrtxspmODnrLTkB4wyKHKwZnqnku14Nonn/ICs4v4oiC3l5UlbyvSMuFU4HuFpVaHAEHLcKIUsE9\nuRXFVEfsvF0sz0Si90mCiawUuJShDKlp3wEIC20XqJ1QmsIr0KHCSFKxkCbMi8HMkeliBpVCClqd\nyzDpwswgh0piUtyRQsdsEDFDnMFj6bHizSSMDC30ZxiaZ6G4ET4r385hxN/pzYgBsxiHC/KuRPqZ\nPiGOq4eu3U3LPAediGTRlqkk3eLdlQtfPIZn0vo84BajtcJQrLMKdM5Msk9lRUrImmYJf6AJLQsE\nUlxwYULdouIMMpTTdEtHKGg8twkSJ7K8MkMTY11HMIxJ4q0UcDTmGx6k6F9iEio8KrksUWJe5tWC\n23gVM1qTJuRSXtgU3SJxR4nL5GYVLPXKNTIIt+yHZy1CkYAS5DLuPZhN5nrB95CLHxoKT4n7u1l4\nH6oNFPddCkhIysaxN1c84zkk5WmQtADS4sljEhRubR1iLujD6mDNsHpyn064V5iHYW8w7GHeoVV0\ng6SnHUCEW1gqFwslCAGZNJUqhXBfhvrp3rTCHQtFE9LoESruPitVsdxweF5BWgXHXa/gJDhjZjEX\nC4WfFTgBZE4aV6vyANw9reJhH0p4ah1EngK+xb7RUgDcGistUiDWL1zTPSct135ROEjRXCHhYaBV\nNMjXj7ldTkumCzacH2mJlDIpIXyQJk5DJFcHnhPfjB5mClHWCoIMMCaBPMdLEvNw5VJg6RUBqOx5\naLFGjKjzQct3vMptF8EZijsYFBdcRHE2Des5cu89kagQFvmMpl6Zd5jhrXnGVMX/J4rNdggEu3pl\nnzF3mCjupZkexIVRwL0yxN+MQBEPU2Lp6ngEz3RgquIU+T8WOGPihjIJmUjgtOwcssizWOOTcV/4\nHBjKuZvgi7rhPg67hQie4BKV/Tx8b8qIantBCdSF3ifzgj3wk6FhG+Kg3/AqUKAY8KpoAsMFA7to\njoE4CHHDOfO3nVWGEhuw+CIGhghO84whXi3wiU2vPGgGVfdmvb0/84qH4of1vicjQx8HDDot6fgD\npO1tDc8SIjfJ5ZzNZnrXfIuZVwS08q5M81LbqmW8o3D+TId7c2IvjfTxF62hKDKDTm8CPFU/0JeK\nTPEOANNcqZkTb0p55U9xyPKIgjz0Hj0Rv/4gAw8BXx8n0nvFo0cGBJsJAM8FngZcZAPgoYCq7uFi\ngQgR9y76wdqA7RVyehE3+s6owseQvpM5N9IXLIu8UgqTC4LOyCc9ikAwJUnkoVxXFdvK6sDykQOy\nCBUU9jMRtgs2j9F2KiBSHp4aa1nT8lqMC42pcqy0WpJRTY5FWq5D3HtCCHkQ7LpjhuTTmfIxfMOf\nleWaQiNZEiVwB0BZhc/a/f3ZhcmDVQYlLR9RfM0FsCRqEnkI69zHxKpsqnuHtiACbu2RpmgeFqE1\nKiG07ADdUxNPRD4JrNaE32ViNWfnSFN/9364asEsgKgGGHPtlp4ErUToEDzGWsTn2mdDOO+TQlPA\nusFIDAlzjsgVa+tybHgwfb94Cf1SzAfoDTE/ENdmvpvKayrUtL6JQXWpxfhKNRMn6rGsGaYGHPZL\nKAs9/JaeCiqOLg/3EtyhqGPFKX/oMA7iUtzcC9H0Z1KpWMZRo51WZ2tIF975yvi8JPRDMs9JhEPT\nZdDcR3zs/2fv3WJ1y7L7rt8Yc61v731Oufritl3tctvY8aVxh47s2G1sYojlKBbG3IJlwBJSAi9I\niAdeQCAQSAiBkIAIEvOEhLDgITLxUxCJhJQoiQ2+QLCJ8YUkji9td7dNu7vqXPb3rTkHD+My57er\nuu2ubldxJC/VqXP2t9e31ryOOS7/8R891pnEOPi7HxogshhT0xDMA7H6u1y5nxNO5+Ii1/PccKvz\naAgRwZZaowNjlwlpbvmUOIRRYbOUQ9cyPWE1+f/c696XayeZCVdeTZF0xCySclknUkpByNOH47C2\nQzJiNeWFowimDFEVVx43b9skAyI85jM6BzOBOo3GVXa6jEyH1py0B1PEIlJSL/N3GchibG6SAN9l\nXGWejL7W0vvPC33dmJM7uHGktU4OruVG/jvne2M4BE4cXtXxmj6bjYgCUeu35zq90iFibcWYrmcp\nhKPHrufwJuX91XMcjGmx5jorRbnP0R66yCHi5UdksqeBf/4SnR6611mFYzgMruCg+DpJ6GBG37u4\nmzCddE3SyYLDzlSLKMCdSeEiMq7GF3KN+XOcZTD6FIssn3PHmItXhC8abgyBsIV7+WSd3rbaJ8+a\nF4R1hrzhtYiaQyb3RV4AxTSnzDqWw1zJPuec1xj7rFUkhZUw61r+bRApI7o40a7nPfXXdJoJxh22\nRAoh87ZEfP/dciDNNbgjdKFz5N3eW8pZf9MTNnaZNbRWMyXnuIo3L21aL19LrkwNm2Pz3JSTKMO6\ny6Lmc9eWyFLTyGuTdGbFOnuDtPr9vV4og2kQ+UGZzArAzMuofB6Zh14ZJnF6+gHhXg4XFqlMSynR\nV8bS8v5VsEQU1w2LPJlttqu8jQYXIoqgi8eW2TaHwIwyshpe8wibRsVsT+YJTUMN9WBACpQkp5jC\ndsHUp9HxQPnOKJOZe2lFk/kPxDSYYEbdX3k0JcBDWFQxAiu4U/XyCo4oHByu2OjSDnxONpvDmvkI\nSTSxQiorIycObyM8njGWqTB2y3vyWxJeqqlYQYSXF/yI6qJcSQizkWBQlu+6kZlEFpkDcuX/iNwY\njfFtiHvuYsEVrXgohIcYezzT3EVU8JkSmHnIBj2sr7l8koZyaN735oySA10MxfQWOruPs44pl9Fp\nAVMQ89yx0Q3dFH1xA0xREDrWTnrZcz2vyonNfZfR2mKnhIjQUbARoAxheGho8dCunxAmmb9KGMgq\nczJ/LtnzkuRgti2fJw5rWP6kopbRhnr34gZJFiNl5nCmHEmjXyTgJ+UIiIgHVP4X+DrLfKIct8T0\nFzEGudaXji59SFmV93rUJJUs9wRveI6kgNdZSXnPVMqHTIhb5lmIuJKXyMYe+y4h3cQ6CHOwoFbZ\nVr8lBEJEUw4LGZIOJ1JpyBzKkD/RI4cAhiPKJs16RpJ8zr1DFsbQRBck7E8DimxIJaNTz4nBxDCa\nZV2vqM3GHE9i3jWYNz2RPiNz4bRj5nR1kh3SfbtJOrT2O8c8j8VNnKjJu+RsSBkteJGv+7YBDrVq\nksquRb7HdJwcSCEd0nC1kBPJ4HYxJ+JI5VREJ4FC5vWF0ywdvM28iOgpzvQmHrEQX/AFVTd83+w2\nEDOe6ebEAdo8WmJGDyUb/Ey6qLheYw6fOqv3plmQBsW1h4KikYeyA6iz/4FHWZ6r06on9bzhxsPO\nKEO8q1beTBoHN6O7c8IGosJ5ZD5OEMTEOJxizHpEmMBJDToT6peOgeyjmdBG59IaGrrIBWG3URF2\niXdsZHTLH5R1pW4j12yHEsYDz885EN+XoohFsWLfTNxhPBN1CB0GDC6qPOpOfLFGqA9RbkbnrE69\nfbcotSLe3k6WopiQ8pQNtvRdcGMo146I8AxlYwQMzo2kxjw/wJ0BuxlPtLH3C6aNw7yvN6k7EJs9\n8uWyKO8mM7VBGBCy6hzn5onOWRpqHsG8iY5fMhdP4HYMnujmxeRDXt1xcC8btxz8zh9EmD7zVerI\nekCScZNJOFDnsCSMZB7WIvMQyGRjcE/tVGBSOPmhlVaslqcQZs7UfN+Ky87ER2EewqsBs5pC2S5C\n2U8visWtD/He8+C02hCkoqPukXHP19x8kxI22mrzoCbGEUmFg1KcR65SrpXAOu5SM0tjZDkH1zov\nb+ZSzUT2OT/EeOYErzOXs83Vc9a5N8LbFIeLhMTMaJ0u73XF7trzvrbLRVmqD/MmGYOpbi7TicPW\nDK8H0axX2Hy9cr5scdXVuk3DyTzh9caUI0N1utwItQ7X/BELYzVtVjeoRxmkFopTHiy1YCAIA4St\nG70PdhOHLgoeRdX07vQq+voiXr4uJJwbofxrzHGe6FhZIun1W5niVKRKGMTU17MD7VfKbzLe5fof\nq8phufcrVlk5OfnEWuezSYudMe/NPVPRAJlOjHSurJdTykeUID5rmqbCIksX+ZZ9qLX3AGeWBk8a\nJGawNSkyhOo0bwi4LQabLP2bexGYsEVvpMsKlYAKzfmp8Yh+Zh8c9iEl9K4dY35TK4niP08GObtq\ni6g4G2qtmWWcYixGNHODqz6tIqd+DoOrYrdxGGUelbdhefYwRpA2bLIU412G2Uzo6tDAMppttqCc\neykvlpGr8iYxHykzclxbek1qHzGjd3KteO2S5uIIr3Vne5uTtb/QlwhlKAlEDpP/7PCmGKe63/d3\nrzwUC0ITr0HTln14DuN2l5nDN0JRvQ0FcSu3l8v7I9AiIg4B2wqZ4JemnIm9sC/zuahBvufy5zgz\nqwSD8QZq/oRlHSGfLgjWNBzYnVsb7Fgwxqa+FDssXrrF23tAyZWAHIv/7jA4qb8jI1cVsZLr9meU\nJx1MRH/WWkAXcYMmIyPEXK6GWOZeaSj/Kzz/1A8u21a6Q75nwx3E7ijxCGISQ6SsPJsbUklrvhP1\njVKPW/oC/vnJBhcLuKJN+bnKkoQRi/gvhnhNKTOvB5bO7/VqwMWUZ6Y0uhvawYSUzo/7iEW+d1x4\nopvLWpn6I8xzKVnzcu5S5ua7Bl4nDCJ3yeCRLDUdo0Nqw4vYinFujUc22DBOcnhNMOtlOO369sqR\nF8pgyo2Uis5IDT/ChJ1ka8uDfQp4LBVGpgs1VxdULoCf9PPwKjiEpPKcd8UhgTPWDNXA5CZWPsPN\nE4s6JMOp4fE0iv9+KmswQqG7gtjB9buhcl+aRFSpJ3Rremk9fyujMuPq+97vB7uIoNCu6MuE35St\nKNRnSHoeI/FVB+sSnqM6ahO5cmqVYDyTm602kgsq/6Xg4zjWGZCINjHJPlIBnVfifEd4dqMwp/nC\nP2RGGGfvl1y5ZWxcQRqVmJpKQnqpRQwZKfwN082LPaaxEgpXRvNyXfWlVo879SZ8p0d+nhe+0Fia\ncyWICBLuvNHAz2K/398zPIdOqKR0sKC0NthaCUgBzAbW4BSWdo/94zKpe+FFe3Mj84W5xD2iWast\nFdUsIJu5HLXXQsQUg6b5OlXxPT6uHr2Y/uvaCXnl+HFZvwC4YnvEvEi+NGRUCxmx7pPJ2pbPeKO8\nWymuZ3Rn2Y+xjsuJEu07xmRMjJfBOiZBo3v9rLVLARc2B4CkjG4ZsX4wHbmaHSZo5QleYWB5ZY0V\nwyNjci1dcjDJHKB0LEkoP5sQZB/xvLi3xziYzchXI4yQygWZsjOCvRElngCV2sfEWnjQh8wXzMiT\n6LXDqN5S68JfknORBroxFbDGQh8e8zqKrMLCSZf1XjTONal2Orx0Anwk4Dy5NnLdbyLF2llkSNGO\nEZGNnBsnR0rFFyxhe+RcKJOs4MW8msGJXs6Rs26+T8YIZVsjeT28+rlPzefigiyKpVVUFpyGGrl2\nJCozP01F6PIghwk/hx/1C2dtASNzZrzdnFEPGnekwuqQpyPkn5oTB+SizVyg+7YjNjgJWHjKunjx\nVMt3i9M/mwg35jUxrY+I7FrEE33tXgKKlwtZZUK4tmUMRhhPF5FwPvqqVXUDqqUiHuveyTGEszZu\nRy8Y4FqMPo3YHU80FjNOuuR3irLF4dbi3Ul64VEjV+ZH84hPytWMaO3Do0VKRFHy3Sks8LPnVhzl\n0czpxu/EeK3dsAfRQo5Cww3h40pjIwrpGs/U2QObuqF6WcZkjFHG9TDhrFHwFs/N3CPP1aNLg6GN\n++GEGDlWw6xgp2dasdv1QK6spRhU/Jm+thy905iRpsPgJINd4TLgTga0ScuuQpVMSZZpE+GlcWAS\nEVg8Op01EDHleJvlyAtlMA0m/IKEty35L7sRi/z6SkFe3tL4wL/mk/xQB0xlYz0IHl6CK7mj6Qwn\nE9a+pedwtj1NrlbfnVEuY5QX+xArAoryAs5HXTVAmEp+eXwfhNqm8uGwnTPXSs7a5zXakYacv2Ma\nooXPDuXdKSSt8lzM7ME98/0ZybrqTd2jpbgJQeseRsaICIus7QvB7geJVB8K1rRsaLF8jv/tzGU2\nvxcznErxCo9KpLJFJXFjGkCoYD1MYJneJhvDYX/LwrGaGikldc0v8QPBExwbEvlIcfjGGI3uSlFf\nT8uavxTi0WoDUYcFbC19vmHQtzhwq4HNoYiRBCzWY/jUFYEeBhkv9iVQELxcS92SwteT1FtK8OVS\nLGC4UvseWPJT5pVKTamVlrCnN0qRJCLYcGjPgV3LL1lcAMJcZyEQxCaxwRpVlcCGmjFJKCydRrlX\n5t5MtsWtKUVOA8sYZfRd3JNt10Zl9tu4fh8sMqGU7Xp85eAMnFkz2dnswT08eNfDdVjRbxEwiaTl\nMitjrCgiBcDhtkxZrRp5KPG+NaLk7/Z+bCIVsT4Wx9bappwnjXHI2oFC0DSHwlg5JGN6/9en6PLc\nVe5ku5IMJvs/QqYYERVd+p0J0pnjsYVStMICq9+S6z2NxFE5Vn5vOOHEox0+9lprH1yGeAM8q+MI\nBezNT9MX6+qinltRY2Ae9VNX7B+Ng9d05/SgrwmT2+Lfkmsv9zbTgDAAdXheJuOrfgYwYzhtD230\n1njU3RtfeYWLEjOCUGETYRsTOr6HiFKcrALgHm+XcE1m40yfc91kWkTCT/d2DXFOIz3PdEW4tYOn\nsk1dYen3MI+q5Tk2ULq6k6MBh7rqelqcFYLwrn7wad1o4sRRZvOeUfct0VS51q1SNk54X+xzacX4\ndol1njOhsY+7Ko8CQncncDGbbKb1/HiXdc5tQ80hei/1izMHwoRd4iUXTljVA70ETP6CwxYHPs63\nBHlC1Ac0CQM4xvMQLWRI6jCHahh+OPlD9C3v2cI43eI4zKjYS0E9chmeH3kJQoxVDgruoEL8fN0S\nFj26U+PHey4ou/ic7ppaciOJjprCZp0hI8w1r73U9J3RRV4og6mpY7aBIBWIwxs/yEdzL5aN6XV5\nw6HONHRSgiTd5YxoTICH5V3xpVQKJJ7Zlt+n6ZWb0BbJptHG/O66gSysuQohR9/qd/H8/F7ChJBp\n5GRkMo2jhGVtgGiYA+ZCJJPDnSlp+shXvc+bZNUAETCNXBbLkHCMUyRUZ96Oe6D9e+XNzPaV6cT0\n3JcgJ9hVXGikYYnwphWxV72sGN5kTcRM2EgUzBP38CCLh4rAOy/C3+DK4xcSLqI2Us932IFLilwn\nOe9beo+XvC0nm8AV2SVEvhryGr/Q0rbCC5f5DM3H0GnrjRYJqGppcAm9+z4RMcYYtMBR9EFBlCyK\nI9IF3TcYuFKUuGR1NsMxnIWvbRHVFZAXSmpcX8KiIFatouibeQ6CYUtEdUYUWdZb6h+lHEruPd9g\nCQXOeU2vfUaX0yHTwungkLRkGbN6gSu004Sa7G85l/7+zAFYi+gS7c5tk4fQ2vY06PwZVPRkwml9\nzaYCkYqdvzfbMXJww6jIgy+jFKPaoBKFC9NASA83aTjkc/IJ0wArUbgKdqh2QkTdZfZnzvuUEVef\ni+8nXZ6165yflGMjJIIbdFZ5rLuscJ2UbVZwn4w/5TzlHFspLwRV7pSnQsKIV/Dv4rhhrrdERiQb\n6anWHmGwuYBVEY7u37uR4dmjMrzOoIxyQF2xfYqzMKYRBpCl35ZG0a1VvZ9ythCrWnBGP6HgpsJU\njl7U645jyXeJOdHpWDla8zwXJIqfztzbdY4zioekbuCRp4u0WfMoorVGyI9Yx6W4i4/nCfMIxzAO\nbaiNMGbjJZnDApVXVXXONAyDyH9KwgSJY8gLsc8zUmXmIjkEfYEh5lmMBSsggHETcsDi+R6VUw7U\nIx1jwrM8ypARF0NkuCOEiHBLKOtDIvLhzpYRzIKHuZF5ES2m2FM5UAN2mjKSyAljFth+rht3EUUb\nQfrgMsQL2jpsOXensIWD4aKNrOsoQaPthdF9vV8iImIaNbnEaHbQm3IaYxrhEJDaxj66F4xlnjdq\nRg8WPPDitlvoDiY+vkesscf0IgPJFejIJgkYpcM/wXPGvA7XKN1oE2cqDP8w9931pa0NDmvcWOeC\ncCuDp+JFjx2eKAWDVvF6fy3W4D0uCxKRNUQ4bHe9aXh+2A0HiufQZQ5ew4q0aYjQ/gCS95kvFygh\nVmQq6OTnRSmZ8JHFmwrY1f0ZM5GC5vjnWkbT/Hi+2W9K0UYZIglNWL3D5Smtd3KlSNXvlKvAYhlD\nuBHRSC/GtN5trv55KJMHsxU7VRl38b41qXzYDG+msp+CnHhnYq8x9yonqwtzyLz/XMNEynxc7Y7S\nOSNHDBcciYcVFiOKJLOdgooYv+xn4bvzbfpAmTIrZjfvj1XNAZOMWCZO+VrRHNWmqbyKTmM1lZb0\nTqfHP8dsNZxzkFRnPRcitwBC6QmDCtEFHpokHYJqMkQGQo+EA839ILg3/hQd6al4hha6bRuYMXpQ\nkceh1vuB1yQaYA0ZhjXxGiIyoXw5rNuLrevMdSuWzOnMSM5U0tOYuHIk2Fxeue9yTZQiLnmfzfWY\n4mlxQMxRjXe2YD1bGtoerCHJZ4iwRrYSWtcrajThf0covMl+lYZRwcHk2ihvTZZ9JKVEW8iAMngi\nB+NhflLCwnIssnhijlHW0ljhsLmfW5tjeW0uMAdCpzzD0vHj946l/8ZUEtJiUpn3eFuunWsJHcxx\nT3k5jSybRBYRxVpl59rMLGFx1c/IC+wpN+JLletGRBkzFPFgDLyYZAyDzmd7MUlXKqdxni9AR6wA\nACAASURBVDIh5KzmWKfDCzQcMCnmSMNbc3UmvHLK2PlXwDp1Fqx1ZbtdMSiqHHWOxpJaZNyLea2s\nvA6xC73DkinS+7eJj99VBEkc3riHs3Kt45QRYBV36i0rJ6KCFkRSMhUFlv2AG/xHOMZSNm1K5Ln5\nnV5HbZ55U7dxmaPl9JBg8/N9c5IZuUnSqUXseT5TsL/u4qRG6agx83IWIVa5aENHj9qCk4ykHMuL\nPHP9wiHKFkRIR57vaDhX/T0nM06B0pknO2XwaRige/RXzFMB3FHp1625wXAWj8L0WLgSRkQ62cH3\nedFiE8amCts46NLcUTtGOIGnLmLD+6wiUQ/KoY/HdN8AHsFxzTRlW9KOW+lqu3kNpqHOeGcYpzGC\nfdDHr8dEK1ZEWIJHzBO+dxKv/NlxdIyqBXTZI2Y7Xn9MwthJ/XMPgaO1DzzieJdRbWZNPwxeCpKq\nI6KdClhT7g1fN6KIbRw4pLNj3MiMmeWc3nHwdl4vlMGU8IY8zZ01jTKUpopgV97XXOjrz76RSoUo\nZWYCZ5LxSUpxUP9rEWLxffUjU0UfMPhRwkETJG+CMxaN6Z2um+IFkn8JRAFSkRA8izFlRjCb+ful\nuFrz/hB6oRR0lYKG5IrLug47M2+hyYRqpFFicZC6hzUO01ByUgnzV3s/U5GU5fPsc0aoYBa6g8Xw\nER/Za+MuC9mFpy4kah4BOe9XeUeE4SMzK0Uytwwv2GqhneW3qhhnPTP+n0oBLqSbRXuFYtUTrOi7\n8/uDxPFbKaYtWK5EI9IUc55YasM/18g5IOcsBMUmVsUODa+qnVBDSKp8Q3rAc0KAmfWoCzYXZwo3\n7QM2QfvANp2eaSPYs5hj+wCu9iJdswRAVBVPqGOtD50KxAoBZRrJ+TPMCLOvdakoU7pXck5b5EwW\n9JP5IEnlxxy6YGnNxH3Hsu+q4Gg4B2b2Se6RvG/upTZyDQcBQOztEZZ/a0kgI5PQI8SQmkcas+Cp\n/8LbuWjZ9Z08wFtaHKVXRA6RhbEoQUW7QOQmmUTYhGGdFhQ491a8eoiR7G/r/OY1sMpNymb6XEn1\nr2oJxQwbkSBORu2ybp2FTJ1J6n6vf3i9JfJYn/L0CDmaRnYWj53xhimjtlIYwxMeY7bFnG6afbEY\n05l3lOvGayWpe4ertul0CmxMr4cgmGb8KuQYmS88HWE5E5VLZddrPyHaSuY0hXIoRGR/GnhXZ+QL\neCWFsueiGafIsU0SgpTdZu6Um+ePRzOyVG3Kmqxx5fC5WYMxZ+k+vexQUY4dK4M79ZNUqncVLuY1\nmUboDzKckOjEwIYVC9whk6Fvx6IG1GoU+B64F18TJ+CiG57PqDCMM8qdutMlja58HuL1e47muTVe\nA8khbqbeRgSeyQ7AYzuC5tpljkey0jhw6BtmTvUteB6uKj0cukUoEv3r4myCUybPouCHuHEk0h7k\nUEkUVQ0jRyjHwUWdOe4Qz8FBFtmGR90S9u17wdEyh0xnhmBe5y2MFhXl3LZaV+BjBlEbiXh27yDw\nXBpZcLeZ99Fl05QFvbUw5CL6aF5PKaNRTaSY7raIJFf+YvS5G5zZ2GWEo81KrjcGuxzF+HeoEzR0\nKDKew4uXOAxTXL/yCKpWrcOUlEoWrvVo9xbzcGve5x2LYsHMwsCLNvN2XC+UwRRHODrANBX1GfkJ\nPyYJGVk9lE75nEdYHrq+uYeZKxXLgTpwZcp1zzjc4uCYXst8WhgWkpAX4WCwm1ZCMeaeEff6GAs3\nSHlrnOo8lCOmwZOX21PehozijKDdzLpJ4J6knZkbs4lwMeNEEjrM92Z150lEHdGrnmMqZQSVqRgH\n+MrMtEJOJDwGaulB9e9r0ALX3NicQ0iYS5pAyWToFMFev8lzdPKRpUyEESN2DWOc9UtSYZQVgciM\nQC6emnjOvQ72hJ+I0IKbUwhjSWLtDYtk0DTBXZHLUfa6CSEORDA7XHArbiw1N+ocStqRgMLRYiyE\neH6ubiPN+hyr3mYsTsNpUPA5o4gipHuxuiYDoz2AdVrsKzfmsjCgIk4vHhA+M6t5fxGvXKsRQ651\nVFIk7YDQYzPHbV0nybRWxaIjSzUNKl/T8TBL6u7M47leo21pWEWlwqIYA4Za7UHLYzPcxoKUTBhj\nRiVy+ZUqLtGw7K/MyIBg5d1O+Sf4rSZT6d8aAcvxPotKrbOVTCdx6zCNJySiWgFDzf1YRRSjjWVO\nZiRI5hj55/M9JpmDk2MWTiRSnvoPCl7OIdrixRWn4epKaUmIuVDCgHVimrne2zIPPocW7IK29Nei\nTsuUtxkdXhEITYJwIt6ca8bgipzCHSTRH3FiDsm2hOKTJArplMqk6xEKlEZnp5GW3ZxGjcjcExqD\nXNBUm2etk+aMgPDNfA5fZnNctzhEuqTRWdurGD1f1EuEYDtzt16yFU7Ko3k2GtMhA65XmM1aMrd2\n1EMvOMnAo+UM7HgejoY86rHPBKhi8stZNymy58+PzIvBplEwwohQRhAX+QMPA4lCqV2UGzsq0pLu\nZbOA6IYcaerEVhdLEoHJNncR5dE4Sr4+EuP1tvNF/cIRcs11HbgJ9rMzikTuivfJh7OlbGSEgaAF\n/XoeYWfPcvF3nXGj9TR6RVgGboDssY9bzMMR85M07XHC1nrdYg7OotzhZBI3Mh0QGnPn8kQzC6fk\nyFAtNEvOl0dotAyVZFm8j6e+ZJ19HPxWu+UluxTZggg1zooFiYPD2BKy1k3L/55nzB1Oe74zgoih\n1/lhsV4utHAGu1zZGWz0MODdYGliVZYnHe656o8wjJRwDKgrUUfIEROHAzYbQQRxoObGYalmEoY7\ngw3hRn0d3ASCy3IuzXj+B6QPn/lK6EWKnvTMrhTb7hu/VuhM0rO5HBVmpbiIcEUqkPUvyg1m+e5p\n3ORzUz/oNoWWATsaHk6BUHZTcRCuD8486Nti5OQ5fW0QzoiKv38WO/MuebuzOGx5W0WwMVwpZoWk\nzGcr6Vn3A2B6c9NjJsXi5gp4RiHmoZuHeLbNKWRDCUuBnoaVsMQDl77J/LfhxlLWumrr3EpGvMJo\nYE5XQkEEt/vWozmn1Uxm8cQxIwkDFxb78DBwFtRDWTwiDigUG4wWxkxKdRIjHs9roEH/6x6hFu+3\nSKSK2gvmXjKYuVrXEC9zY29z4ZbeP3+PqyvDMufO5loPq8jEvcgmhvRZ7i0rnEt4wsKuY5fGGIMx\n3GvoaMFYNy+uvRSGUPWehN4WrAhB21TuMwHX95lMWt0c/4A2VI6HBWwqvfnL4muqpVivMLZaeyP2\nR8grh6fJLIgoCwROUvmc+8nKmEqGT48QWnkyZdpN8f0RfaooTRyEWZ5gLGsQP/vZYqwMruowecRH\nImdFKkLdAoqqi5Hl635adkmesY5ZRlqyXVXcN/ePzLyQklXhdCrIXyrp+UxJJ0t43WcTYqsk2UGq\nTLj8yaiXLHlokv5RJtwo+rejMf4Syp+/ZxObMswmaqJgjOJKQWtrge+JbhBJal5XHBMalu6UNLiu\nHWv+aUEhmX0xVtkXEXdbU15GDZ6Fom6YEwdQDfSISpgFErlXSb9POA5hVfBfYCFCRikT05JOwMlg\nls6Amd/mV/rwRSa1N+Tezojrcn7bRCvklTArJ/wIrEbWYPKXlEI+gMcMztKKlKoH86Li62OTadBl\nm/c4SYYoW7ABNkKu1Rm6ypGMml5DVG9scFkY/XL/jOjHEfcmiy04jXYhTMTlQJeIWo1RtfQMh3pt\njJn3gxQ75S2urJtqGErp1Mo9EVEQcXa/GLrSHwrNIj4mF2lFmFC1HWMuzuKRMiOhsFKy0mVIRPRk\n5q6lA6EcWOba12305YzydLvlpXFwrxs3eLF5AXY1tsDmX1BU3WG/IcHU2Gc/S98b7CSbtBuvvo5C\n6xNZIlSrPjZLI/gURCkDmaQ0Uy8VDpSLgdJguDOuBV9krQ91h+1BQg1j/UlqM74GRTySOMyZ+sQc\nLngT/Hg3V6GH3//rhTKYpuWfk5QHz3VNpZxyjaNtkJEnLVaagtjHSaShv3aCJjvuaSb0StzP51P/\nyIN7W+hfhTUaNIteYlYwBkIgbCXlPDzs+TwjNqtwPwY3OmEnhewLY6xw6nGIT8asddyMXTUYoiIC\nt/Qjo2lO6RsQv1TMoqNlWEkabfMztwnrBC5FROo78z2ppCZEw8dVFmEfcMKa66kEupAPI4o0eNPc\nWtpnCcWJqBUJvKSUo2jqovRN7/WeCcwh9JIRy5VW38Rq0Ddi3fjD0lucbdy6U/7SqGTpacP6wYqA\nRS0CHa28eORzzHMTVBoWnOgqzaOPZdALotMj3YbPh98PmkU0cojVMHGaThsdbSBNaChHEHjamMry\nkIyeeuR21ZVetKslNNK1SiQO24t5NfGNwJBLwmJGRQYG+OeWh3yb0RlihHU6ALzOkY/7LPg594WI\nQ0MkDlCdmONYq1YGQTL5zY0d68RchhWRxaAo8I1IjI3oen7VDTrArJSvZG8czP2AzOiO+VLi1GaU\nuPZpPGAL2aSthUE1hUDKHIUom5B71Tux+HbCYEmqd29A9S+fIYLa9OeT8FWRgCpanAkhD+KdLhtS\nIbAw1pYJif5XpDz3KBJ5H3blkMl+DEk683h2wB8TRrkFuqFqUoXSeYMnsZuM2G8arjafryNknjdj\n0Glz3+PMimNMprqLKDudmYDvEcQxYFc/o6yU2+tx14wiiyd6p+KdhiRQOaxlcCL0MWja2a+yZAIi\nGmsqtcMi+VgPqBfwyqR4IyHiAVkyYQ952dnooSDukTFk6gVCN6aC/KydaGPUmDcsCAsa99KchtqM\nzUbks1gVB72QeyZyUtTzUXzvuzDarIMYnRaOC2X0wSEULbMroaFNCDyjRST9APPIy6fazrv72SMp\nsbdybjeRgB5GEdnRF11h6m0X84gTLfLcYg1mJPMiym4SRARKjz3uFOEatZoIWLw7DBIVIUzIfK5B\nFSqqE/7G6C/BqOn08HlCDrRY7fK9z3C43tQV0nng0DIdTmiR6KY8Hjeh9CiLvd5RugpbwB5n24Sh\nQTUuUgQOt3Zw0VZpEhf1YrN9wEhacfF2PbbOyQZDPVJ08upcDknsR0U0dzpncxMo52dkzU1JmSjc\ncVQ+1c6gh+GyNa2zZxM4h0MliSFaQkcRTuJROU8hGFVoOL1BXaLfOMvkLQc30tkimrdhoYtIqTC7\nJNZoEYVv0/U5qT4i8m+LyE+IyKdF5GMi8qMi8vUP7rkRkT8vIr8lIq+JyI+IyJc+uOcDIvKXROSJ\niPymiPynIr+7Guahvqn8FfROgIXy2D0g6fUJjwBaVLhpWOyqqEnAnYhCrwnnc8a2o2B2fq1KLyKR\nxBtGmc33W70pjYypcKr6n00S8uD3bUPK05oe410SsJ7PS+3Mk/saQR16FWmTqz8TBmTVnvw7w6fp\nLcpxzoOtvu8DfjUWKQjz4PSfp3GizIMx720IWY8m/yDXc9fsWqlgece1Aibh2UwPZypV6cWTGtt6\nSK2e2a5U2ITZv2qv5N+TSS84GpAOYkmWwNW7GsJlGwyxMtyQgASO+EyUNuB0KKdDI1/Iak06plfY\ng/JbVUE18qYUNQ0DUxBTVDTmOmCV5uJ7rjv1aJJ4uH6nc7rZ2docp5NsnKShqmzbxr4r+9bie+JR\np76O4Od+vZNyREKI7xJe05jXTam6YABNBi1w29S+9pWRayQL+tZeY8qWVPzB90St1/w7WrmJTOXR\nIIsXJ51zrrlUcDXkTVP/IzrloBC5Q/gB2iQUaolDtWRbrONi2ZIwCoLmfkwZwiqzFhmSEbW2NH6V\nITW26t9vKuk/ujroUsakEi0ylRKV1ZBkQvji2U2nLH0oK3xs4vOEUsv1XKnMsfS/5x5OmSf13Bk1\nmfO5vHP5o9G+HNMyovVaHhpwmLv1+tCi2s3Lz62Y1+wTsw8V/VdXxIvYYe2nOKR23zTeL0Xxm/1P\n2Zq/22X2O43XdW3Pc8PYdXC3ORtbrqMUnipOILI3Y29zfofNvfFWr3dcF0G4STkS86Piiqcr7HGm\ny2BLOYIr6sm4JrGeTzZ4FJA7FYc1HdrqbDUcRvZctzpXD20e8YjnDlWHxtngEoYF+PNcKXUF6Q1y\nJP48YnCvW+2NPQTfJsJZN4628Wh0hihdG11byZFDG8/FjcCdwWa9FPxlp9QaK2SLLHJEIi8rjPZ0\nQiX7ncYTWuklbyJHkBpDlz/p5Jxr28fEo2877hBK2Zh5NiVLU87E55kqkaUPViht7nnBCRAKPSNa\nOWy5JgRHfHTRiNTOM2X908QNko3Bbp6HtDO4MSukQ+7Riykd5WmUNE6pDLCZ8bxtHOJG7RYvq+iZ\nOvTuVoyXrPOS9YJ8lgwWT1e40am/iQodZ+Y8idWYNHHiEa9xNY3llvMk12O2MbiVwbvawaMiiHHD\n/9Y6qn5O3qpxamt++KROf7uuzzXC9J3AfwX8VHz3Pwb+ioj8g2b2LO75s8A/DvxzwKeBPw/8j/Fd\nQhj9T8BHgX8Y+HLgh/HyQP/uZ3t5ChkP347I/ZhRF+Jg85yi9DDMEyq9D8CEisVntcRs4vmHBZQC\n99ZI0eOKe5Bs5sC0IHLofkqGRyLD09NrLJItnF5fQwq2siNehDYs8E2TojIOm9xOsYGHOc4+PbAi\niVOvTkP3wzS9H0fkO5hMgZT5KWWMSm54iwiQVE9K6SEx2wkrc7x6S6xiPS1Cv+ZG6FkPiLwd9wD5\nMxeEHDq8bb2SjiT+8/E19XtMUhSbU/VGtMVvSsXg+nTOJZHm3ShSgwkSLKgM7omahnLkG9lcS6bG\nsB7JuK6gYspmjUvr5fHOBwo+vxrPzvYkpjmVUov1WIqrZQFKqU6IwnaM8JbFOIvQaFMraS4o6S7Y\nNkA9eQ07P3NhdnNbY9ifPIO7WxTjMsBGp6nSF6P/87zeQTkSZRQFmnUOcQDrJjNasTmCOwSzR+qm\n8ROJzkIxHjnsM2GZKafmHpR4r3PCh/HCzEtJx06xZeUaX9Ztyr5MrC9vpvi+3XDoHmb0dBbl9Bfk\nLiOvqSinnIxIQxz4qVisl5HtjMaMpECPiBFEuYIl+ksaixnFlvC4svQvPbIS0Gl7476QjDi7l1lk\nsFnIf5GIzKRBs8x0yYtFCsUh7Q5cK0KF/JqPVciTgo/5eA8mKVBFmWJf9hjHyTpqpfn4mM39WhNq\n1HzeyPBIEA5V9Mi2f2eLsRqWLFbRP5uKZfbAizJLtb1+64KMHI51bbVory1PWp0HOQbg8Nyt+Vhv\n0muu/XehAFv22Uetm5MhaXjmrGbr87reUV3Ec8R66SLPOXGicyu9UAmNzj2711FicBo99qcn3Xev\nNB7sah6p6KrsQYNo4kqj4NC4W7zsw3PdIwLhY9zG4Jk6YcLAKbp1uIEFVC0ow6M/YsYmkxBKxCNM\nJ+scqp7Hw+DROOgRvTGDWzr34jTeQhZ+13LaDuvxXUCEQ7XyqXKRHEjlyDaMZzRO4r/X4cbkOR0B\nMlfKCLpuGYmscUte8MibAJfmT92CMe4syo1NUH5GUXY6Z9l4aZx5um1029zYi/kRJlrDMG7sQJHi\nY0tIpUkajYNmo1IkBuYw6tAvEr0iRP5Ulg6JtolExByXI5uNMiQd7xHRGwCkSq5AwAxlMiBK82gy\nNrih03HjzCnnnfRrmEdpBNcFvJj2lGcAtxygILV2ZOoi8UmoW9Uyjf5NjdeNQmcjTAXdjSzG4LEM\nRA5u6GwSurUIGvmbXTSQOcZG55ltmA12dQ4AHQ9qFr4N1+dkMJnZ964/i8ifBj4O/FHgb4jIy8C/\nDPwLZvbX4p4/A/zfIvIRM/sJ4HuADwLfZWa/BfysiPx7wH8iIv+BmX1GnsAhriZ0MmkvTh3y/JHq\n1BAnGeg2rVEhoThTaTa7zgvKlZ3HxxBxQgilNsTFHLqWzCKIsA84ay5kp5vd8dyaIxbmymhTSyoP\nx1CYuhEQKu+PQ7Km9ZGY9IPhWykOUj88pWCLVaneQNo8xExAN+9TQsIgoIdQ7SnISiW9TyNzLn1v\nZyZCK8m6Y0UVnhhIj94pZ+k8Os60k/K034YCMuIgT3PF76/AaxgqzlLjlFTZ9GxPCbhiYQpFNQ9v\nMhq2hKFDeWsBp5RU5JhtH7W6Zn8PjGvmvSQicWOpq7HoI/GojAYQihIVNEybsBS/VEQjFyQfVQpc\nYgJwBUxUSxn19jg8IL1QNjyHYFew1pDLBTkCRrFtjDEwhfH8DGdju711DHLl1Gh4Q90YzVpob/V6\nJ+WI17dyyNiuvSCUtcti6rcwgxw2kcZQrM8Hup7rLdeHieDTUXsy9nau1eEejPAQJozL0CSfCThT\nFwvYVTAeRlKEsLCsiZVRLsthqpLQu4SHSPoR8DNr1tcZ9qBDzH2eHxXzqFFRnaS4hjBsoj1mkeuU\ndkMaJrFMEwqd4yQuyjzaY50Np6cGkPjZkGARFOzySbYm9PYe99LLYNiEGLmsn4yRPm8JSR5Ry0OY\nM2ZXzjWiLYMcvymbcs5Y5rbY6tKSWgyWdbnkcKml4SL1uaoWnFqZtM91xsl8lsPqeNNLWOC0kRqZ\nzSkUxNIqTVm7OEImhbG3uNN8fnZQ6xPtEQ7MrBuVpEE9ivvkuWsyjXI1rt71Vq53Whc5RDnriW7G\nIzcDyhDK0wLgEWe6udvgPgqVDmb+BziECTwSJMyxSSIULAwGjLvpekOBp2zcqCfzj5BTj0fn9ch5\nMTMuuvFSv/BcW5wDwmEaOSJMaGaeqeZ1ee7b5sx8Mb/3MiNXMMkEnJnOc6fPtIWVzmFoG+Y06Hhe\nUYLXBhk9SvbPeK4EJD6cgyOMvFg+8Vw38bOGYu73rHnVxHiX3XOvrdb6bmcUeC5e/+k1OfENz3+V\nTYRfunkVFScYGGZconbjEbTgHSfAgCk3druwiQYFthSMz/dTrNPoj+J6Wo+zc7OZ96ZQTKgpM6+D\nAL6eVr6qhPI913aVgnE3Li6DhhvPTrdRrmj/bqwtDSPIUQgSz821qHWuwdSV7IFcm9I1InLGcg5C\nUy+4fIpco8OaMxPq4MQlCjjHvIVBe0SI0KnJjTNb0eTnO93ZPg3bt+v6fHOY3o2P1/8bP//ReOb/\nkjeY2S+IyK8A3w78BO7J+dkQUHn9ZeC/Bj4E/J+f8W2SUACFMfzAUIWRnrn0H088OUSi9sAjD0Z4\nJo0RykljoZOWuTBzcA4Nz3osy6wXkAXnBDjrhKQ0hK7uSXEhpHNhmWNfnedergrawfTMggb2PcLQ\nZu75jHdvQU2dDFCJVc9Q+MEMC2PzgGzmLGcurGQxKD26dbE0PnysNpQhoTxGM6W+6x3MA8LyIBi9\n3ufeGOPEwSuc+ZJ3Cx/6xqeMZ4/54f9jcGlwO4RXbw7GvfB1X6b82m/DN3yN8fd+/eBvv36qA6K8\ntPlPmdGpWiK18Kbi0JgMhOklWYsxghs0mK8RjchUeU6GR84mlGmNOBirJBuAjBmFUVG0D1rA4HKq\nE9+PTeM8Gz9kcOrCoTmCsc6MYg1c4nHxcyb641A9pnLUJTD0AkpHNs87OIBxMdg25P7sa3wox/Mz\nuilsN6i4xtWDNKShn7ey8ybX2yZHJA6vhLxlYUfFa5vkIbYuK4PKa8moSg9jo9Oi2LCVnuxedqlo\nggj0qIpeh0kIhExs9qU51VinZQ3vY1+V+blOWxh06bxrsUctSGaSdSnf+Ya6SQt7R7bN0vDKPVDN\nTeOMq/WrTcgapKvBUHpVGFeehxekNrEJKvJlc417+xrCwUqtrgLH8Zz33LzGqy/f849+6LcZ9ogf\n+mvv5kYdn//+20/w+jjx4a8wfvE3D/7E193zU3//xC998n2hoLlQcASCe5Ir4T2VVAtJJ29UfijZ\nKEGAw9yGUPtZZRZXTKWnnFolLf106nHwC1pKYw98easpz9zNHP6ZD1VIgHSWRFv8TLOCVtU8ilOc\nl3Eb7dZlfITppBmhOPo66pzUyhudjr7zcENvD3l6mAZxpL+7ySgDcUIqVwv9C3K97brIzij41R0H\nmW97GR55cYW+kc5HMeOE+Z0pRwxElWeys1lnN2c7y/3rzGgRpQGeyhbF6N2kvYnmpANGgae6FSPb\nLlFDaIGjEVHycxh7ya52L+oFTPOswVzhDgflRtT+iTznlCN7RpWZMKsD5WSdszQ/f4LxbtavEk50\nL8AqjZPAfUTExIxbGzwXLfKJI/dDvDdrEYr6WvdsnelsYMDzduLWLsvx7PL9S86f4o+0j/PK4+e8\n/Cdu0dc3fuHHBrQDGcJ36d/j2XHi/V/SePLbT/myr9v45K8e/KXnX0N3jEH0JQgYCLgIWhtQrLSW\nWC6+Bm4w7uO+pCU/m3BaZbF3k00SfmaRW+i5YRekSFc2PAfyMC/0euhGC/a7ZJDLqDexbto4Apbo\nz3W55O+fplcY5iLcje65c0YV3BagjcGhWo5uFaNHjuuG50mnM+AoA1S548Ij7a6Xh1w88MLajraK\nsTOhy+bykFE6TMeNbUEmEdDbdL1lg0l8x/9Z4G+Y2c/Fx68AZzP79IPbPxa/y3s+9ia/z999RiG1\n6oliATlxVwSKTjKAWHsNYZQhI4wh7OZ6QmJKeyyRtvy7vKAsz8GXUEPq3vpOGE0j8kaGuGFyCWNB\nAwqhUJ5VwuPgOIUw18o6D0EUm8yMyMUrs6v+TmhfTIq3L9xFPaNDFIUBZ3VPUPa18plsemrKIDFA\nBhOq5pfTVXu/k6TClU/hkE7WsvF3e4j4O1698OVffWZrm2/W4zlf+3jjF58Kt6fBN3/wwstyz0WF\nL/0S4RjC13/A+Lmfd0yuR3BSqcwxcMUlc4oq9G1zEjPykoZKHtpzlOtRta6ur5kkvfKxuHIpc5yA\n1qUEilbYyJC2MSIptIWXG13IHaRASfFGh2X6wTDKKE/vrEkq+pQEu+DzlDqt/y4MKe3u4XLcZbFA\neh6ExbHooqDfqkcPDqH3e3S7Rcw98mq8Kdzn87nebjmi80yraImL52QL9N+m8pq55ARRMwAAIABJ\nREFUKGks9YCzigb0omSG75/eF3nAhCttUZS2ngnFwjjptqfs8b03LSqBpUBoqswEYYSSsVBblPrs\nb5GHLI6TecfyTihlIPLFrxgRLRa7gkM0o90ZzcjcgXImCAUlbsxcmXznw4b4/QE9RYraPln+vvsP\nfZIPftVvw+0GQ9Dzc179ok/wG6+9l3ftz/m+D73GdvsUeuPD7xlczsq3f7nwd37HDSauxlbq1ekx\nNggDL73uvtZbrfm4y+zNiU/KKFk/slDojJMKl5LJi7ES0erpQZ2e4Bz3zIMsGHTNc7wnYZGLkrZn\n/H5Qz89xdiNpxJz7mzKXd44MZbRuAQezELhOk19mNIJwDqWIUMgOS6rthrP/jRr6L6AIeUd0kcxn\n8fdTZFKZm3oJB6sucqQcKiKcDR4xal2ZdY7YH00oR2Y6GUqO4D9ndKaRNYWsviNC5EK5U/XGBk9l\n8xqM0ZYRsjyPkYsoJg2xPOUWdId5Xs6h6nT3IddW388cB19n++g0G6VgPo9ocbNR33iiO3fWuWfm\ncgHlRF51ET+zl3xqzTNxpkokjF7VSzkkIUaelYYyDL7n1U/w8jft7PtLDDlj/cz3tP+Hvzy+hq9u\nn+YrP9zY754wZOO9T57TD+F9/8DAfsGj3i1k6bqQ09mUcuQc+cQZcTQ8Clf7y9J4lNn2vB6MK7jx\nKrEHm3qBV0itbqKKXNVUmvQygjJa7I6O4Tlooug4/Dcy4eRVxyt+HhhH2xwuGK3XHM2QIzfikbcR\nusg59JTUOr2h/tyTdmcTDAeKmXG4Gzf0O7gPObLZxXNxe/fo7HAYvUrKI/vCCpLfw/X5RJh+CPhG\n4I/9Hu5dNf3Pdn3WexSHkWz4bk1cucOGkgqaoqFOQyIXo4rnDZiADl9qTSyU7iwmFxEGS+KFVLLH\nrGuDR4cUqijcFW0vfsiXILE0rlyQ9VR6mgF9gQmGslbf87Y28UMz++obzj1H25A4REf4WCyoggWy\nRtNyyO4RBttjk8igaFB7eIE3CxYZdysWZCjblvGyFgdgQzEZiA7aUESNbm5EdYRdjVe+Am4i8dTY\nsVv4Yx9+xoc+JtzcHOwi9FuHLMhtY7tc6AL/xNc84S/+3Xc5znkYk8gyxkjT/8IUpjkHuVdVHRJZ\n+KD5lOlslwAL+CGXTF6CMAKzT8xbMni1MH5H4POTeSzzHGZTfGObChdsGkijg3rkycP5rmZ7FCw4\nmOLgTQW5x2K+WNCtqn93p5Uh08nq6YpKd9Y8QDUUGsmaSt7CEuJyoOfGGEGI0AQuz50C9HzBHt2A\nCO0zYYHe2vW2y5HVuy1xeok0P1DMeckyRJBn4qZG5hr6Z6HwylgUh5mnkYm+uRYcTjZizM29k7Gn\nVcIgXzc+Ux5Eupnv8VBUumkoEmUBeN+WyE72z0I5Ss9lfR6KyYwHz4KTZhPKlcnpvka9R4axtYCk\nqVTEzd81x7qU+nwuUxEyMhoT44Aza2VNFM972eimYGe+4dUncNqDFWaHDf7Zj3ycj/3ab/OeR8bW\ngFNwwj9u7Mdgvxjf/6Ff5i/87a9n4+ItV+ViWoqUwFRcNddItjby2cKaSSeJ60tT7kSDESaUTmKg\nO/69LhHhjTV4oblHHyHzCK6VjBhDc8y+QRi+E96bkSTqe7k+c13MdZRXRrnMgkAmFJ+RWj+Ek8RV\nNLXu4xYJ6hfzeNTB6khzCbXheYFId6Urzg4zAfVorAhLMdwvyPUO6CKe75mRHwu9Ik2EL7Iz92wF\nuewecnPoqGNkuAScajPjJN3PBXFvf+7rYc7A2+OTA+VGDg6CSQ+nC99sOFGESCnTp2qbBOTJI0T7\n6BzS2DGe6x7r1GsyZbQ6t3DVLsJlwW2ctmsR1h7Wk0RR2oyGHOKQsNs6n4mIrvfjzjy/97Emq1/j\nMOMkzvKW+T4HGvm9lf2NmFVkDOa4en7VcOdXRGCHGUfbuafxxZcnvPy1OyfZYW+0cQMv3/DB737K\nH/rFvwUvnZDTDi89dqTMS+9FjzP9GPzpyy/yX/zyN/M+noRkUC4om/julNLAhDuzByvIGS4Vr4d0\nCAWvvE05Qs5bd+KPkFFp+JrBvWyowRYw0BOdp+zcaRiiNtjU6Obxo81GRaMMYxvBImjGc3TmQIrD\n8ZpYmGXhwInoYRpl00YUjjCCn9O8kK86ZPNkk6XTU2OEwxonerED3qcBzeA8JV4N2Ubnoju3dok6\nX157y9AobUDUwvxsu/QLf70lg0lE/hzwvcB3mtlHl1/9JnASkZcfeHa+lOm5+U3gWx888svi74fe\nnqvrR37+l7jbt6v8kG95/yt85P3vD0s5KBHSO4pvFofgEWHyqMK8KhaBr/AQuD88U9eS+lbEz2k/\nKa0oKVPpzvM1E5qxfEaMGSkIpVh0xKZCbYsYV3MYVRp3NnAmE6PykhA4DeXA4V4xAP79OKge1nDK\nRmb/zYyuYWAmNsgcrpfKIkxoX7EBhmGRHg2T+I6Y01kLfOcHTjz51D0/8ynh+//w6870xkGPat4i\ngt0evOfLTwxraHdFoh8dbYKcBB23vOvdB19sZ57KhqBO8W4ZTBZXqsrFF4NoaSCEV2/MCFuOUio5\nK9bYeKNykV/wdRWH34rFiXFKb7QbzYa0nJP0xim7hTc+KJ8tvOnDBuhUl/xlfii43TvQpowxJgQn\nlKOWCeql/DnbVo81LeLhezfycxz88F5rbSFuUJkIe1PHDGP8b7/xcX7yNz+BiZai+OxyfuMYvYXr\nnZAjf+Fnf4a7fb/67CNf8RV8ywe+EkhPsB99xLwmA6YqS0QzlVEAH5s+bBoZuHLboSIUHoWCw4ws\nQiDMiFMZK1fRj3yW71kNbWZ0qwR7b4HnK2S0IJWepOf3B0NacBL3VG5nGD1rFGJAUJBbPS8N95SJ\nQuYrBWW2Zrtnn2OE/HkBZ1UJp0OiAqLRWRBTEf6Rr32dT3z6np//+Mv8K9/ydxFteD7+PoXuHXzZ\nV+MP6wdE7Q9kOF7p7oYv3g6UT4E8ir65cyzrf0wjzkiCmPQgi3m+6FhyOQlDE5vJzTm8OY+rvtRi\nrFt8N1EDW8pO808z4plzk8pArpt6plV80T3P5rlusqyVK6ZGCXRERBasSG5m3kW2qdaOtFr33ufm\n+bWSUQCtsdR49yZu9DU6YgMiYvW//vpH+d8/+hus17PLhS/E9Y7pIj/zM9zue405wLd+4AN85NUP\n+NwoEf2d+9zzmWdkPyPQa4Sx4XDpLQxtNT9vDImolq+TE+YGQcgWI1narPLtUrlzMhXfk+mg2cVh\nU0fIf43okwR2MnfkhkdLLrEQDtwQemwHR+zpDeNmdJ5HrafOdDiIeN7lJhmRD3lDIlRGncwXIaL4\nYXiJ1vk8Ie55grkztqCHRDUhhYONTTq9edzl+973CXj90/zV11/hB77p78Ppi7B2RmTzitwI+q7H\n3HzjiWGDcdz7Lu0d2Qdyd2IbJ+6/6sw3/cKv8hv7uxHgIlKybLXDZ/5w6iLew8PG3DfeKxSCmAJ3\nNDCdpy3WUNyKxnqY9SCdJc5EEeszXzQlsKSuM8+R1sRLWYScNXOn/wi9inG4/sLMpapSNfl3m6Vn\nvL3+vi0Ly6beAwzZYv26HDlkd9IJOhgc5rrwJsONXfVvbjiN+RYGMxh//aMf48c++rEcDgx4enxh\n5Mjv9fqcDaYQUP808I+Z2a88+PVPAwfw3cCPxv1fD3wl8GNxz48D/46IvG/BDv9J4FPAz/FZrh/4\n4NfzgXe/TBeiBoJWYlgeuB71iS/YVHgyrptJ1aslI/ikzzymPEZTofT60cl8NSKXw5NcrTwZ4Ba5\n22BSWFsklShXkJMkoRJiCTLuRVdvlsqZlPDpqazBPCRFJltf/D77J0tXM+nagp0uYVlOZc2iVUkZ\nFGap3EQ41rSEckPpAexTgx3hmXjY+Tvfd/C+lz7F+9/b+YdOzZnWtAeeOwYdENuQbTjpQdaX2Lcp\nsMeZIcprmyDDDTQf36BpzVyykQqZIenRCE1AoGBDkjlgNg2kXCjOUuNtSFhQbnIRRcYU9lvQMaeN\nWrlDmuQBofhkSXb8gEgvfebDZIK0a9mjBJI7h+d6kAgbqCqZZV/r2gYirT43yQhVKHE2QDYK9M2A\n5oQkUlqwxbMsDGkg+vutr7yPj7zyPmy/cRiUDn75tz7Jf/STf+tNdujv/Xqn5MgPfPjDfOW73xve\nMquxygMhWYnmZrTw4mdEB1KdyGRaC1r5FtT2DrW7zkGRpL03V4ZSET0FzC5lhkez50GUa7GFF9en\nbJRnZjLsaTDsRatjfWbNpdW4qjmuvnj/NyUOygltFZlKStZmwqAnoYM4BPiI78dc+TqXpe6POBQU\n1VL80snhUVsfm+6VnvnmVz7OVz1+nQ++98J3fuMnwrNxCYssBxZ3NOwEJiV6f7t5FjXA/Rm0sdlO\nR6vyfAsZ4oczMYbMMg/enKpDErw51ReHP81Qmo+1FpufqHCE00LFPOdk9Bovh7T5CiqlhJlIPw3X\nGHebxmp+ziKjvGaN1RpNGZfXplO5K1qCkP/eL1tIJEKOjdmOkQdlrIlbndFO6n0eQU2CjqzZ9G2v\nvsK3vfoKF/PI6C6DX/nUa/xnf/PH+Xyud1QX+fAf4Sve814OUa/jkw7KkCOHbb4vUtGPyNqZVpTN\ne2zCSyjKLdjYzm3zvT8cCp3IFyEiikEuYa1FntJgtEYLZ3Bb9yrEvKfDQqImjqB0bvB1dWgr5r7G\njJp7BCzPphGU9MZZGiv24XlEvI8w/k2EFvA9CwMNlkgbHondJaBzBB1377WoJOROFtwdZuwKRzgO\nNxEanV0GF/FaP7sYp9H5pNxxZwf/zMu/yMvvPWhfKfzzL/8Werll6AXaTTgpCceIwCNBLxe03frH\nd7dYP9xQvDxjG/Drp5fpsrNbd4RNnQm5t/K88LY3jDOeCygsDiMIfc3HPDVYM+Ose7HNoRKkUJHe\noUIbB0O0pM+NHaFvzkjgluiqkCvpID5wNsRmxr00mni062Q+hkfb0BEU97hBJkxDIZ3QKlKEECrE\nmrI4B3O1OuzXffHe2v5AjtypE/rkeZyyyMwikuRybgP++Ktfynd9+ZdwMU+vOeng73zqCf/mj//U\nZ9uqX9DrczKYROSHgH8R+KeAJyKS3phPmdlzM/u0iPw3wH8uIp8EXgP+S+BvmtlPxr1/BRdGPywi\n/xbwfuA/BP6cmX12czG8l74oQvkFuhzstjFkMGywsePVsj1dTBIvNeIQN1sobSPfxnqFvsEVz7Jf\nLAvg1jigZlzMrk0A8ScKqUT5O1oA2TQOyhYHS3omHEZIbRrfKLEJI8k/83KSirzutFTG3YDKfDmY\nCcUM9ZyYUohn6F3AoRa1+C0O0eXwRRwFE8BUy01jHRsblxvFuvG1HHzHN/+OeycPZ4TpeqHdgZ0N\ngnEmN8vipl4aE2qBmTuSgT/1jff86P+1gdj1Ae03zqhAWX7xmYyCTjVTuoQ6bK6sDZPKV1iZ0ixh\nI6R3KMZrixMhz6JUQHcJAyOhkObU8CUbLCifQzkKLmMLY4xjYAFvSgvPYaOhJFlkGYUAkRjD1qFv\nCiPQxTJfqrgQRb2utyhs5pDIQwbn4Z79Voaz06bmmlTVMEKHG6rjiOJxxr5mkb6F652UI17bwtfR\nSGIGC8+mwIkQFen1NQr+lEHMtD1SkbFlngcj6lxEvlMeWKGQ94B0OT25MUbm+0046WQocyVFQrnp\nIUPMlFZU526cTThfKGhzO7Enw3y+IBawwBJ9p2BcRYYSY6R0kBawXY8k+LjkIT/YViq2UBr8mQ7v\nHAhbExoHyXm3AYwzZ254SRxK9Lj9Gj/4rZ/wiTqrW3xywCOFc+7v2Y3qSG7Ype3eeYGnwg9+26/x\nP/z01/jt3qCQM5lApgElIt7RQZ3NKq8uGtj8UbVhyqE0AmobhmMfAc6KPavmz/Pa08HuVH2Y41+1\n3oTpOCHXW0TDYj0605qv0z6uC+rOuXF5OuI4SrIToGjMW7VzylTPpTlwqKpBFNe2WCuMCSHVhRLY\nH2WxjmO/hNBXoXJ6Tm2m4r+V653WRUQ8srKRVPcuT5+3jcf9cIpr84KmPSbsFLK83Lvh7NgCky8j\nldMRifZu2PQ4J3Mp7mGw+L8PNus8CWheyiIRYQv45xE6D9K44fA5l8bZNvaY14bnhhzE2sqTP1iV\nMt+yi0znKxQ5hFiSN3SSZj8JJhA4I9xy0Dl5odnRfZ9YQqSFzToX3Sq6v1sU+xVht2PuCfWirJ77\n5Gfro3HP03HD09tbbi/3fP/zn+bVP76hW2M8M2RrqHbki+/Qp8+nYzkUct9XDZHugl0XoTkGertx\nOR/84Ld+jP/+pz/gsllcL/J5mVBLh7G6IaQcsX+mA6NHjttL/ewwsyT5YEShakcSbZIU4AXWR/Ea\nSYdMdsVVGbkxJ3PqKC3lND6vp3BeHdo48Dx7RDgNdxifcPQD4iVEkGloQSAYDO4RbgQu0hzWbLOo\ncmbTTqklnOTwvH4xbmNb3cTavEfdYDZn0ksxfgThRzJTuzHvkNI7MS7N4cF3umaW//5fn2uE6V/F\nR+GvPvj8zwD/Xfz738AN7B/BQRH/M/Cv5Y1mNkTk+3Ammh8DngD/LfDv/24vz3PRvf+u6agK+9hA\nvPZMC/iRSS5fC6Nj5v64+M7TKhVZLYOjYgkicaDiz8nDGaHrQG3WzKn+JVQsWWkMNKJRFu21lGoE\nHEUWlqLoaXosqpUBJ6wFPILoIlyEZv6uVKbBF3szD7lW2N6mnpGGUOZWJU42Ma6WhsjirULc+DCg\ny8ZpG7wk9/yTH34G+wU9ndzLdnIImjT3lEnzQ6NCzBaKy+xyNay8WxcQPbi7HQiPo8kZVUzFVQpX\n54I+jEJG0JNmxCcJE0YZ3snSohExaqHFpIcjBnGJziXU5dpgkO6UxmLBvmaCHh6VU/AaWD28Jw2O\nxOzkehQqSVKHYBWaCHNNoAWZwGji0a4GQ/33GsVsXcZZ0cM3EwabK6zDuEgkGVskpEoo5ficqwoW\nRWl7QvyS7ktyXuSaduetXe+YHCkZkoeBhVJgyRpklfeXuXoFzY3vZ+TkCsMkHuVMpdYf7JGFpOw2\nCAagbIdCUKmm/lw3Mo1Xk4w0QEaoCkEbt6fnb87MVLzzoStphNsTHlVIT/FksJpda5n7JBnJDXlR\nJAP+u26eo5URGoEqdZANKQN0MTak3fCYM9vxGv/Sd/y6P/1u807d4i/dNpfDHoq5Hvca63VAjML8\nnQe0M3en+znmljmHkWCPREQu6H9D1rvhkUaH4AWfRznJIMfS87mGwdZckm864dDzPVNm1bTk8Cwn\nUuaCVfFpWVhfbc45lifIFKNKOoGihalAc50DO8xzRSyUknQsZXuGEevMpl4ZDscsXC0Bx5skJr5O\ny3iPdV19FkqGJKPe53H9/0IX8eMnnHQMbm0wVLkdrshdaHRpbHb4ulvG1M+/uU5yoE8Yl0i0r+QA\nST5P7/RN4mEUnnNyWP6IltSmdznR48A7mbP0nfFi67tOhsuSI5IIkwe6SLkUqTwlj5K4rrCpxBkk\ngcCRei549L2ZsItxNhiq3PQzXVvJvmZeQ+6WTtLc27pGoQi0ck23aNWT7RHvHU/40vuP8l3f/mnG\nzcZ299hl3K07nnTfsN49Eq2JypCQTTAduM0XuwX87DDG8zNyK2xyrjXtstl1kYY4yZclIYfnK3fV\niPhQREKC515d2hZTmGx4HnHrOOGXr5FpemBeysahw9R31mvEHChBMiETVuu5j1N+evS/NiUGFU1M\n0opaBaEXHqIB37QiDelbQ0aP8y/y+mPvX/AzYBtZ3Nply+t6YuD5WhuDTWYOrE+JcgnUjAfYdI45\nk9Xv7U5i+lzrMOnv4Z574F+PP5/pnl8Fvu9zeTf4BGcuTsYDfO6jYj09DoThXg4sEm1HQUtIyl0T\nhnqRrJX9aSxWLUzvqL+FwnX7QZbG1ErL2JzONaNfoWwem7AdrnxlAlVXoQ1KMfYsFsFFcCgxkq+4\nOmYxDQ4pG2TkxhhRM2bUeLlh4Zv/0sSjG3gkZIxBz6SriLSIaSj6UhuzpDRJgT5ic3S+9xvObHpm\n3Cqb3LinTQRLxdAEyxDaYbAlHXoMTlqFg5kXkoYbiowDDmHfOmOE1ydOK6ckpeiS/cBOel4/jLyU\nsIF0Nhpn6WwW+WHLmCZVuMb7s36LjjgIAucyNITfWJRDmQmSXZwWU8ZkI8RCcQmPsHXD2oTDDAXt\nQbLQ/GefQKH1iE65xHXCE+2I7J77lIdKRM3AYQtmE5ecMI2BJ5k4UcES2TT3j41jhDEF2xbUxqEp\nW+wVf8XvKgY+6/VOypFSJVJyQ8gE/9MkI6ueW+ZVzUN1kayVlsnNE7wnMpXeHs9rKoj1YCe6jowM\n8RpxG5Hsb9AyP0SbQ3E5IIwRQbjB6e3B1zlk0j9h8HjituFsV67UhEdbANSRrymryjoKJswcErGS\nZ0knk97MhkbCN+UBJb2ZRC6TP6KcN6oShBqGaGMLY1MEjn7m+//wr3DSs39hr8wLCuNnRtU7OHcn\ndkjZsU5sWozVL8Li6WDCrgeIV9hiMQiaJQ24cpi4DBk5zhbwZUPHUcqai3WNsyDXlD/Y4v9OpDDb\n2Ja/JcbOFgNTQkFJAo2ydkhjWEoej2ELO2HWCrQEUtQQJDU9ls8PWI8NTLKGi5StSShDe5CcqOWZ\npCUDm1pAzLwxPY1UPN9mhFTdWjh5Yu6zMLPIWmPqrV3vuC4iSRzkUeWBj9OIuToFGcwJT7y/SONZ\na2zm601E2PrBUC9NcEGjmKg/s9E9PyXkseJFcjPRvpmTHnhyv0e6CEP5BvfidxqHKI/Mc04PccqR\nS2vcBEPaiLpAhyrbGKgYp+49Ocvu0WUjoh+JvplwMHDmuAugNhjSMGl+1NvUjxCjm3IKsoLnutPM\niXY8Wm6cdQuHjBt4fTi5hDt5wwgT17U6GxtWUMfHx+v8qW/4ZWQ/6DeP2DadzohTEFuMgZ0d1jvu\nn6PcQu+ex+S4ZSYuN74fG1xQz5E8Bu/iOU/0FgkZ0bWxjYMWKRqHKE9s84hdyKTGwT3KzkA4eGyD\np7o5rXhAJE8xTylf1vpcmEdXwFiJRLz+VhD3CDFKEfm0cApK1DnKxP3QVzQT5M3HdIjyfDtxssFN\nGIxHfMchgDbhdubP3KQzzMlhEAnnPKHTGo9CrxoRKOixbZ2g4wi0R+SjWeqs7tgbuO5yp05LlONh\nMnPu7POUI5/r9fnWYXpbr8lU5D9ndKbIHjIxF0CdWUUsLFdgHmZSXuUhmRwXmHL1Ip+uduh8L5As\naUkpOjLUTXiNjFK4ikUrJUv3CvUiE/tpYwOPAdATrqFCG5P7qorD2fXCaBGK9HPD2Wa6JI12bqg4\nMCMs3pBrWmUVthR9yW3MYBctFpw0UKSwtFSEpXPicn7KzbsF7Y2hR9TGWjzVI6I1Brqwq7nyJO7B\n6WNCDQMqoyqMrWOXnedmXIbSTCOJ1tAxONQ9TAQcxfO+ZjEzZbD/f+y9S6xuW3bf9RtjzvV9397n\ned+3btlVroedMjZ2HMAYiEMsQQKCDgIhmkALWjTpBIkmQkKCRqAPogdINIJEI7ISgx2VQ4zjOFUu\nO+VyvW/d93ntvb+15hw0xhhzrn38LNvc0kEs3XPP2d+39nrMx3j+x39YUMKbwzBdWWlk/sZQ5aqJ\nyO4MUHecDhigj+yvUaIDus9FOizeSTtuHkZTGNUh2C3mAHPIXdOI8hQb41sigmuLYgqlO8MR4k4L\nS2GNikzJNZmhQjSEVc6xu/k+b64UbUdjlrC7GgoiMxXuQ0tElpTaCafChoH4Ih6iUKOHydAfwHAi\nmJ9VnbVqQ8HEd26IpmmYwM1Yu2HkCjMTM1iDzA1WtSxwjQikhnMqvm5hyh2XTbuMKuJOACBSSQKJ\nrCqILwYcI9f4c8lwimRvFE3XftRd9d29SziQhIhYApLWzAuTm+Wo+A0qPShiy7DcfSxGMnhkJaqc\neLIqrz8UP+Hc4WTzQfwB/dLPO0l54VLc8Mkj+c7Hy1TauYBVumrUEURtlXlwp/UgLYjAUWYCzToa\nhdIgZHPWjH2WnYN2q2XpyOLFY+58vxzXmpmgkLUzFhS6Q6YPuB8GxdfVcNzy+jJmn0kLEetTk9Z+\nXrvFHk+W1ErztSJRUxtOryMO2vg9CfbVPJIa3wToxjHHLh28fA7h1pi8yEfFRjPZcCc9uzfGnfF3\nCb2K6Y46O2i6ox6px94QnPnWwrbpqmx4o1a/XqyxcIAvbUXE2dOSAr+JZ21ObWNB2TSdrJRlEy1w\n0d25esrJA2XWOZdKpQ0K7YYH5Q4R8T/fcpfgIhy0VQqFFTFYNZr4hvA5S3G5h/85ABfSQH1vqQiH\nkYFJfdW51ztP9Ej2OCNtmniWhA8/Wu7RrqG+fIFaoa0bpQpS65gM615rJq3tDDPC0FI4nLDt7Gvd\n8PolQEpBDtCvCucVPtQLap8IlQOd6+J1R2LGIl5Xta8jO/SNKs6q121XB4RwstX3+nNyxKF6bgOu\neIDzTozN6Kgssb5CdxyC0ZJddijRCbkXSw+aBpGBmsqa+RKfDefK5jWw3Mc2dIuqjL5SErVlR9nC\nfs3a/AwueO8ycFm7OGXZmAZV742aTmDVdmsjJRpmYQ/7+3iPF8phIoSIhNHbyTa14cCkIQpk9uBW\nzxISg+8/mcgssmVO8EJEitJTTgdnKKQA3mUx3rhC+Bfxe43IGkUq3CR+R9KwauN9NCBj2sxRr+L1\nJcMqDyPL+rzP8Ko0Da1UUr76a1i+Fs6YRjQxmbPcsYuxkSjI1WiAG4qghZIbVmO8cfYE+M1vLPyz\n96/dKT17vYwuDO1u0URCdTcXHaQE8UFz0odU/smwY6Hk28E4VeW10njU68weRTRH8Z5F4mB5bgNU\nYlxSyOL1H4P8YXdMvymivcx6giH4htEjuzR3ODzDIJgGCbmGgMxZSqxNI0gA4rz0AAAgAElEQVQ7\nAgqGzVS2xVgtm9HFO1+LufPfBWwD3UeYDaymIrVdkajDj1CN3mMNUXfjknHPbEY1VeczDsJgjayl\ntfH9wE6/gMeMuNsteEJi2vfGXAp7VxSp/DxzaDHXoRLi2n5YnhtfuG+bisK/6HGvkpoHxv5yqMuU\nYVWSmdEdFwlZ4h/1QUgg+R545VlG+UbQRQj2obQgLOqXjMz4JF15JdsyWryDhf/hRo6ZeIYJG3UQ\nnt2IWoEOSMutOsd0UNx6RFq18atfv8+/cfm2W/3NfEFfxDM2S6/CB6bOUY60ujuPSelk5kLkEN8V\n/7ycjIvlCdfc36VgEvaT7+nZV29qHmOjEtm/QBZIBNbm9E74YsJncs8LAdOVKW/jdzIToeG8jSz1\n9EPGukj2qwn/TpY+/2zWXrkxPJoKSxpWs54unbBudcivElnq6V75ePvzRL1tQszplKK0nuPhL5zZ\n1aK338FiXFRg7cYiKZNfbK+p6aS07gnzpA/of9YAFev0CGw5PNsnoAA3WjhZC5ay6UwhnrVZrHsx\nPurIhd5ZIlC6lYDIBYypiLc9Uea4rkGCUnvnrCW2h3Fha9hNfTzPwZzMXyUCixgXnLmWg5M2mA2o\nuO/9NvrumJnXJVmEjsRQM0yUhY2m3lTVjWpH3xzsjIk6Zbh2Su8DuFB755qFC7wx6gUrxfwdcmlZ\nvJtZ52Ablc7Xvn3B51698pYgbaUVqKV6fXFvU44UjRYb5vrPBOuKnK+RbYOAXPfeKdUZVU1B7h64\nuDD+Qn+Hr+trPvsh3+rI4giGOgtcjLfGxlDzzKObE04WUoiM0U5fJNJgB6ah2GyyHdYGMFEOycaY\n9XKxnf16oY+G/EjVjtu0Dbc9Su8ckrFPi2ehQ3bV3ujCYFXs4YRvHIbzIuprtkXZROopCCZAIeq+\nopm1lGiTEKik0EFiQV7IfM58R1EcaRNCJh2wj+t4wRymaLanRi6VVCbA8JbHpgrPZEASiOxQ6NVD\n4Njj5BD6vuGHDQMj4rd/DiM20X6lx3Vi9bCYQyS8n4U7RH7BMGSlhNLsUf3ol1gG/WSfKSbz50hi\nF4s0SBeQHnUGGoYw6hmrhO/scKppWWXELx9/wEdSeRvRgHf/3tNoO6pxv3YeXCw8ftS4d2+jqvP3\nY170CERgLYyMmKse9x0BhBD6uqM1dmfDhQR14xd+auV/+bWDGx7qF8nIifeD2Tku9ImFzVc0XzMJ\nx8tVMm3VaUTm+LqNYJH9mgtrXwjJc2vDuu2aE88B3zM3ju/G7/rfRTvanBrarLPF/as5SroDkeqh\nQBjuRukyejU4ZW0K3IboAnL2OrSdH6nqVKQWvUDEjGEF7iy7rOcqqnPMdhmqF+4QV0iWhl5CCIZv\nuZceEwqFjDyd78xdXcl+agvCRvR3AxL5r8+tk2RI/ING0nbnOJzJI6m+siNzleeNqGTWRfjvllFB\nN6PezytbN5DmWhx7sVuwYd4etzye45cYznVmXwTP2kjcu8v+MlFkLsKxGpfLDfcX4buPLnjz4Y1n\ni6y705PFMEGS4hu5z6hKit7s7JkYNx038+8rYI1/5y99nf/x//rJMNhnMMMMluLBg3y3qrt52A2Y\nG8MxN+nEqgylvx+r3m3UMvm4y/Aepz3w+4M3PaLXu+0asjAN1vyT68rGHBQaGw5nSVpqh9h1D8QZ\nlCDiMBjlGog7sCmeZARFHIpXh/Gzq2tIXZo/2JTfaQDl824WEfXn1syLeggeJffltdHEe9C4yJ4o\nh8z5tsgm7Q28zDA0lLt9Yw256jLcB1bFx74FZDZ3ZWZjCDkiPN+hcK6fVqqTSMR1NxFnYRPPJJhZ\nMLU5Pfh8QzixcqZETDblfwT0hoM3a8RznZe8rpa4n8WpO3k5bLd53bic34O5r250YemN6RD6c1bg\nonQ+oU+4KML6jnF4bUOWAl2wrWMRNug9IOkA1rHzhtQFSkG2BltuvA6t7/Zz7LGiUDs/95cf89Vf\neSPqfdweSjKTRb2HkO3ey4ZsluFENBGOrXFdihMv5W32a2yYJClH7PfJ8Mzc5N3EpizJVgL7DWtm\nw9mxmISh5XaO0GU4qTdWMXMKdcU4aHcYqE2EQo19382C8a6Pec5ed9kLSrVPFE6OUuqaXbA6RXey\ngY53Gn1Y+YEcL5TDlI5HGjnKLECEYLraDW5hMliFJItIX6S841TNn+OcTCHuFZ/FxKnILOFoboRL\nmViTvNeMMpv7PSHN3BjxWpjWN1/QPZiu8M2arDNeFBqR6Swi1YU6ojU9WHYnjKoEcYVasOSlVIq/\nNiyK/dNakICrubJNeEhu1i5RhNwCxoELvSNwedooVuir0NfK2gw5NQ7dAi7nmlia0AvRd6jH+HpU\ns5uh3Ys9fW4cp2J4Nq0D2+Z1BZ9Zznx9XQIaNHNJmTHzaI8N+KDFfDoCWUZEaGbXYn7Ekeh7vzcL\np0UymmyDqTCxSVnYGMMIWVQpkzQin8HXp1vlmZ0ahtJm9KCqEjG0Ortfad5AVwykNzeAutGLDoVc\nEShw0nxTG014i/mMQ2HTYFCMPdJjN2gqp8g2uYHdMEkyVxmKfb7TC5xhEnc30okZRt+Yk/x3Rm7D\nGZGkS/aNVMNgTv2aMgRcaewd7CXWatb1uMENz0v9kSUI37aEor4lp2yQmbuRGzIuFbYZw4juqapT\nmYeCE/NO8EnyoClMuyu40f+NWSPl9Sf+HIcIIqQv0kkHwN2JGaDygFFBJutgnCXSUd24V56hCteb\nsJ4V63C4iIt6yt1HKwchvQ+Iycp9a+Ew4ZO2ZsDG4CzOuKfGK4e3+WB7YzjHOZ4ptxcColait1az\nW+sj1wZM1rpmWXMUcxAPWEvUI4nsyjUTQOmTlQQjKYqINZXGzVize2sqA2FxNa999Uh5MradcGN+\nsz5kXwnDsajt1itYzXXKiPJmfXDWpvUY4jT+umTyT0YZ6ujZhQdj0BJG8ayFnUHuF1eGgK/7VQul\n94jGW8gKn6slaJoTlbIANRzV1LGH0COLBY2z7H387sELEu7nP3dgjQL40vtwYolVpDt5kddqwZ6a\n63ZJx1U88r8BSATm1KP2Z7zov9DD2ZmTdxRfR8904cRKEubsW2fk9YoZB4Nz1EhJOtbAV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vPgNp6FJACj/xU9c8edb40vfu0Urh03dWPv/WEz7zofI771/wpHtqvLbOSWEVjyxr8a7N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\nqTI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7h8nEpxWi2jHxhphrkl1uo6QgnS7okzSXNnqz2syYJHb/GhD9YbDWS0ZWl7o1dVof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B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\n4pQQKi60maUKV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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#%% plot images\n", - "\n", - "\n", - "pl.figure(2,(10,8))\n", - "\n", - "pl.subplot(2,3,1)\n", - "\n", - "pl.imshow(I1)\n", - "pl.title('Im. 1')\n", - "\n", - "pl.subplot(2,3,2)\n", - "\n", - "pl.imshow(I2)\n", - "pl.title('Im. 2')\n", - "\n", - "\n", - "pl.subplot(2,3,3)\n", - "pl.imshow(I1t)\n", - "pl.title('Im. 1 Interp LP')\n", - "\n", - "pl.subplot(2,3,4)\n", - "pl.imshow(I1te)\n", - "pl.title('Im. 1 Interp Entrop')\n", - "\n", - "\n", - "pl.subplot(2,3,5)\n", - "pl.imshow(I1tl)\n", - "pl.title('Im. 1 Linear mapping')\n", - "\n", - "pl.subplot(2,3,6)\n", - "pl.imshow(I1tn)\n", - "pl.title('Im. 1 nonlinear mapping')\n", - "\n", - "pl.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/examples/Demo_Optim_OTreg.ipynb b/examples/Demo_Optim_OTreg.ipynb deleted file mode 100644 index 5731687..0000000 --- a/examples/Demo_Optim_OTreg.ipynb +++ /dev/null @@ -1,173 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:4dc159882a3ef225eae256eb2ae57de153d04cc9de52ea36b1644e64a88de96a" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Regularized OT with generic solver" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Dataset generation" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "n=100 # nb bins\n", - "\n", - "# bin positions\n", - "x=np.arange(n,dtype=np.float64)\n", - "\n", - "# Gaussian distributions\n", - "a=ot.datasets.get_1D_gauss(n,m=20,s=20) # m= mean, s= std\n", - "b=ot.datasets.get_1D_gauss(n,m=60,s=60)\n", - "\n", - "# loss matrix\n", - "M=ot.dist(x.reshape((n,1)),x.reshape((n,1)))\n", - "M/=M.max()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### EMD solution" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "G0=ot.emd(a,b,M)\n", - "\n", - "pl.figure(3)\n", - "ot.plot.plot1D_mat(a,b,G0,'OT matrix G0')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Solution with Frobenius norm regularization" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def f(G): return 0.5*np.sum(G**2)\n", - "def df(G): return G\n", - "\n", - "reg=1e-1\n", - " \n", - "Gl2=ot.optim.cg(a,b,M,reg,f,df)\n", - "\n", - "pl.figure(3)\n", - "ot.plot.plot1D_mat(a,b,Gl2,'OT matrix Frob. reg')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Solution with entropic regularization" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def f(G): return np.sum(G*np.log(G))\n", - "def df(G): return np.log(G)+1\n", - " \n", - "reg=1e-3\n", - " \n", - "Ge=ot.optim.cg(a,b,M,reg,f,df)\n", - "\n", - "pl.figure(4)\n", - "ot.plot.plot1D_mat(a,b,Ge,'OT matrix Entrop. reg')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 5 - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/examples/Demo_Wasserstein_Discriminant_Analysis.ipynb b/examples/Demo_Wasserstein_Discriminant_Analysis.ipynb deleted file mode 100644 index 2d3424e..0000000 --- a/examples/Demo_Wasserstein_Discriminant_Analysis.ipynb +++ /dev/null @@ -1,257 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Demonstration of Wasserstein Discriminant Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot\n", - "from ot.datasets import get_1D_gauss as gauss\n", - "from ot.dr import wda, fda" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generate dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "n=1000 # nb samples in source and target datasets\n", - "nz=0.2\n", - "\n", - "# generate circle dataset\n", - "t=np.random.rand(n)*2*np.pi\n", - "ys=np.floor((np.arange(n)*1.0/n*3))+1\n", - "xs=np.concatenate((np.cos(t).reshape((-1,1)),np.sin(t).reshape((-1,1))),1)\n", - "xs=xs*ys.reshape(-1,1)+nz*np.random.randn(n,2)\n", - "\n", - "t=np.random.rand(n)*2*np.pi\n", - "yt=np.floor((np.arange(n)*1.0/n*3))+1\n", - "xt=np.concatenate((np.cos(t).reshape((-1,1)),np.sin(t).reshape((-1,1))),1)\n", - "xt=xt*yt.reshape(-1,1)+nz*np.random.randn(n,2)\n", - "\n", - "nbnoise=8\n", - "\n", - "xs=np.hstack((xs,np.random.randn(n,nbnoise)))\n", - "xt=np.hstack((xt,np.random.randn(n,nbnoise)))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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5xeANtikAcsGvUwnD+38aP71OJsmF72awEYSl6uPA54FXhBD7zPf+RUr5qwCO\nnXlkj+W7kmiKhXTi9DdyW3NPRKRAtBOk2znt50gFZ/sBS1xBZIqG/UuWRZ1biSo7yjcs0fbl8sDi\nOtNV6TfMEHAt1IKlkIJtskUyk4hMl8DJCHFy0uXimKMJliCi/3YBIoC2ZI2oPFKyM5wFO0PLeslg\nFxXNx49FpCBQobhuJmW/4b7KipVIeHAsGkaMtGZMxUJEiCov1Dncrsu+rOfWFi8HUOVjlcw1xCPt\nKRD6D7pGo7q1QYuvxBl0wTYFQLr9OmOJPfXZomVbjTf6jJI3uVLnMxsJlwc7OqO6wpljSvYYvlLg\nK8VCOvESFsr/aMGWTUkn43SKDLfzeS0RerUzlr+A2/mc2zi3d9LT1xcVveh1TGf77H5cfvfLJG7R\nfsqXSicrTC+xgm3yJdAm06QyiSjkEjh+J+MFee2DHC2qsDkD9+3FyjtVOi28Qayknlmgp68vYinN\nLnrsNfkWbNnkujzmFDjO4ylHcoVXSRq/iTWdYssZiRdr+1jWJoVTKNnP5dzHLj73HGljQErP/ZIh\nrblw4pQ50olCkydesE0+B9oMFtJloYq1PBkWhsbrKbNbgNzrc6nUe9XWrcTQogp7+RCVyLMYsCcD\nNS1W8UrWEGxniperRIkaZbFKxOJiz9oLkT5NKjLQr/UrXmJNuwDyinDxEoBuOIWVfVkxFs7lR/ty\naa4RJZaUX5XKyq8JjEIKtsk0QUwiCs1K07K/labVq2JeV0H6k2kALarcMUVUlK+VD3+WVPA7M4iX\nLE4JFK+IDy+ndiU0VH4r5RyuxJqXT5XCKWwSjdjzk5TUGc2iLHMzR4+Jun6v5cRM5K0K/B4xBX2s\nh1c+ZozOEQor2EZjkYpYSWR50v5Z0+pVCZ8rl9ARg6mhRRXuD6PQu9NcytBEYzm02/yt7Mf0ItaN\nmsxNnUhNPHtaA8WwIUOiLF3O117t8HKGHL8uctLv5pvlvFYv/y21TFlRWhrRrmT8oTKZmysItDhK\nL4UQbJNtcukezZYISMT6lE5/Mm31yi6DSlQlPIOPKENTnLZ0Cl7WF4XfXFLO970GF+dyWbEQrkIj\nnmUqXv1Ap3DzI4DsS5l2q5Nzmc5umbKfN56lK941pkq6B3Q/918uPeA0mmzQsq+V5etXxRQ4fp8H\n2RQn8cbcdFildcRgagwqURUPtbxiFEs2xZSK/BMV7k7B9u3wb6GKFbmWzmg0ZxJQiJ+13K/lzMv3\nSzm+x4uhDphDAAAgAElEQVQKtLdPoSxUis4zZ9hzpM2yWOmOr9FowDFOjShh56wKuiY1MHp9ON1Y\nOpfGk7E+pcNCpf20ssugEFUpRUWpyD/ZmbYO6eXfk0wWcTvpEhrxUiykWgTU3m5n2RrAElROARbk\nDCvRY+SqH4L2r9IESabupyAEQf3UOlr2tTJ5doN1HOUna0R7h58Hf2r9C1998jO8uPqumMdM5/Xb\nj+0cTxTqfatgs+0alqxspWn1FYG1J9tjV75S8KIqInLKB+GIPzN5m0fEVbJOwbEe/E6xoixWfiLb\nEsHptO1sR6ztUzlfMts4S/BMHzXalzN+IaOFkkZjYBdfbmPr8vXG586JtZUDzidhMRN/21QtQ6H2\nRaxoPMaaA7d6btN8wox8rg6/Vz+ljrXb79YWqixT8KIK8BU5Feh+SeK17OfmP5QN4qVOcBJke5XF\nyg9BWKjiiTXn+7nihxBOD2Kic1ZpAiBTOdCCXsKKsFDZKZkI/Qf50+FKvvrkJE6MuARGwIyV9wJE\nWaxWND5k/NHXGnG8IK4/SvCJYTQMdy8GHx5njNfKYhVEO7I9dhUKBSuqYtZQ87lPxHseJHszx0oT\nkKxFKBHsWdP9Jr9Ml+XMD34jDtNBIjUG/Qq/ZNDJPTUag1jiy62fukZ4ty8C/sKprtMwosr1PGp8\nuX18b6Dtj4ktktxpsYqKljYtVo014d21hSq7FKyociVe8k4P0ZWJh5YzQziELUIzR49JunhwkOSK\nNSadxHOcj2fJylbJm6gJgSppo4swD1qCFN133lQPwKJlrwKwYZ3xeu32lA8dQSZL1xTVbGB6DVz1\nj6t4li7Kq8oiLFT2IKKFO64FYP8NjwAw7EPB9SdL8DnS8wA01I50nYArlOB6fELy589Vf9B8pWBF\nVVI+T2q5z7y5M/UgUg9ue1QbJG8R8pP6oFAEUjrbvedIW0Tm+Vi1ApNdNkwE5z2t0eQ7yYonv+LL\nOf7bx3S7tWtgUgOnuk6zfG5kJnQVEHPg9asAqCzpdT1uPJz93rXdjgm927ELZdwuZApWVPnFdUnF\nXkw5zTgfxqoOX8OIkZaFChJbpks3hdaRvTLTq+hLO16RmplYso2FzqauUaRjmdhZ485NxARpXUqH\nhSrW2Dl6fTMdK6azc5b7BOnrE/qs8l3pwtmHM+VwnqxQ02ONOwUvqmL94K5Lfv0HgYG0plBwYl/6\nG5AyQlglQrww3FgWK42ByotlTziqkrEmEiHpZaEK1MSeRt8qPWBq0klQDunxLFT2PuL0T3Jau45M\nrfM8z3deX8LjN85P2kKl+r1yhK+Ocd3hY3uXu4mXeNnrc92v00/Bi6q42CP8slC01l4cWD3EB6S0\n/laWEWU1yZQIGgxhuc4Bz77U53Q47+nrY0rTOuv3yFUxmsmC35rcJJ1Wy1gWqnQlnQzieM0njtHZ\n2+tq7d85y1iVOGGOAyra2m/UddCrB6l+nyqbfKLfV6IWKh0w486gFFWxIgMzlULB3hGdPlX2B7vT\nauLH6uS3nMxgJNb35ywyXSxERF4su9CKl1XeSZC+EEE9NN321wOmJki87p8gHNJj7WvvI80nDAuV\nl49qvWmhUqKqZV+r9Z7Xcf2irFvO5KLLdwUrPN2yydsDm3S/zhyDUlS5kqa6fnbiCSI3v5zm48fS\nGqbvJB2zzly1ejmToNprIqq8WM3Hj0VYsHa3HbashrEc1nNFyOrBdPCSqd84XRF7iY5FnrVOa939\nIBX2caBlXyuX7eph7er4/TeRJf2BgZCRusEHyX6fLftaYYTxSO/q6EnaYhUP7b8ZGy2qXEhnIjWV\nGsErJ5V9qc/u02O3msQ7X7oe6LkqjvzgJxWC2zKr00IFhkVrQMqkAgeC/G2Svk9dynSo4+kBUxME\nfsV8KhYqp9hiaUPUtvb7OVaW8uVzV9Eyq4L2Kvj1JCCAsS7UvoiW/a3UNxzjI+fDTy57kkPP/Zam\n1Vdw/+aWpI4H3lGBy9evMuoddvQwen0zk2eHvw+vfp3PY3quUtCiKlceDFEJ2+JYnpwpFezLgenO\nVRXkrDNfrF7OWoNunznFlb0WIaRepzFdeCc91GiCJegHs9dYZIkoE6dPVDyLlReX7eoxBJVP1LFU\nuoXGCc9GbeO0Tp3qOm1YlHyQjNO+srapeoeh9kWE2hel5RmY7edqrlLQosqLdM7G3Swizkg+tbRk\nf8/NguJ8UKeSHykVEhFHsT7zO5gk0ia/A49fnya/39uwIUMs6xbgmn4hV/Fz/+sBU5MK6XaWN6xA\nR2lafYUVtXfCxWfKr8VMCbPT5vLZkdljrM+SncgV1Wzg0e+sYsnKbXSd7OYbN13IA787yv1bWqDv\nWMz2JHMNEFnv0KtN9mtKdsKbK5PGXKQgRVWu+ZG45TbKdl6jeASZa8Y5s0xmkHIOApXVmckjpnCL\n0lT+Vg0jRiaUfiEb6GU9TVBkesko3nmylRBT9aWG6taI14qimg2mAGzhwB9eo7K6gvopdcaHfenz\nk127/W4OvH4VB16/Kqpt8fq9Xg5MnYIUVX5Jx4MlWYuIU3At2LLJV2b1TCSf9LMkeP3Zi+nu6LFe\n27c9sOs1AEIDISC+IPLTsdW5krVY+cX+vdoFVaIkMuCn6+HgzMumhZUm3QR9j9knzPUNcP/mlpjL\nW/EmE2r8UHmjTt43E4BR65tNR+/mlN0XlMWqfioU1UQWePbz/aRjQuR3aRXckyPrkjbeFKSoyvas\n3KucSaI3nnNpKV9v3PKqMiAshNT/15+9GIAn33807jFUp1f72AVcprBHBzqX/3J9cLEEleyEvhe1\nxUqTMOnORxWPBVs2saLxGA217kmRU+l7baaTe/8QARjLgaemXkD9vpDnPp5lo1xWSDL5HQHsbrsa\ngK1X/waAxgnu/Vwte8b7bVUkoVswgCaSghRVuYD9gesHu4XKnowSEnN+TucMwsuH6sCu1ywrlOLA\nrtcorypj+dxVlgAqKi4CiNpW4RRM9o6t/nbmj0nXYOWckdkTAirBnIjVyc/vkq5ZYISgUrgUD9dC\nS5PrrDlwa1KZzb22U+PHTjPTuXIr77JN2iqrK6ifWpfSWOPsy37b7bakmChjK496WvOs8XRXc9Rn\nKteVPfdVPdHlvDSRFLSoyoaFClJ7KGYyJ1U80jETVVYriBZPfvO4ZBs3QWUXxbF+72TKDwWCvVKA\nGJaRvGyawsJvZHCi457f49nH1VgWK7/Yz3vZLmMsUpab6vWmyJgdbZmJVaTZ7fNM4rScN0541jXa\n1/md1s4yhONk83N7JKFb7ittsfKmoEWVnSBv9HSpdLsztFtkWSq15xJl+dxVMTML22mc9WHLbAzG\nzK67o8cSTcqHSs32lEVKoaICnT5Xalu7v5b6zM+SYSoE9X26JRjNRNZ1OxHLFLbKAYpcC+zQZJ50\nTKASWd6HxO57ZbFKhrDIqLfeU9etavPZScVKlexE29knne/76ZsrGh8i1P5U0v06Vu4rbaHyJhBR\nJYR4GLgGOCalbAzimF4kcmNk+uGQykPRLZdVunNSeWFf0nv5+ea4A26sVAn2pUF1LDVIqeM6949l\nsUrEQT1TPh/xBk571GAyCUMDRVuoNCni16JUecsFMY/j9OPpMK1E3Bi5nd9x1W+/atnfap63N6Id\ndovVyy77xZt85NJkxFgifSrm58vnrqLWFEzV65sZNdu4dvvv65b7ShOboCxVjwDrgZ8EdLzg8Mge\nrUikAwTt8+Lcf8+RNuszlcvKb90/RaJtcYv+aNnXGuH35GWxskeKFBUX0TjrwxGf28VSLMdydWw1\nuDbO+rBhZk7AH8utbX4tbbGIFaWZCHYn90TPGQR+clLl0kNBkxnS6Xw+YPbVeBYr5SSuckSlNfoV\nqG8wXCy2HDxBy6vlbFgXXspKKPlx/8G45xxlLiPOXJqYhcfLCT6RvhlUv46X+0oTSSCiSkr5eyFE\nXRDH8iKRJYoo06mPmz9IkhkMVLLPnr6+iCK+qZLIIKksVHa/JyVmnPurbSEsdJwWJ6eQsi/ruR1T\nDb52K5aivKqMU12nfTmNKkHV3dHjy9IWFF5V7fMlQlCjSQV1X0/6utHPPmQKilPmGAKR45Gbk/jG\nOVsZNnSoazmZeBYqt8lurOdEcUkxVcMrfY8L6hiHnpsFYOWcCnL53HVf9fxyBJb4YfncVSxZ2RrO\nj2XDvuTZsWI6a81iz25oC5V/8t6nytcNXDIxOtopiQ4Q1MPROQhc8P21EZ/vOdLGlKZ17F+yLJDz\nueE2Q23Z10p5VRn1U+us99VrO9efvZhTXaejrEb29xJJzukW2ec2Y1Rt9Nrfvo1d0AVhscrX/CyJ\nLFloC9XgIx3FkJXF+tQjbwLREbtO1JLbkaUNDBs6NG45mWSxSjSJYSA7Ka/sA3CNjPOKdF6ycht1\nFx3jVHdROIGnGBaxnVpebFq9ikXLtgKwQRVccCxt+iaFZfum1VdoUZRBMiaqhBBfBr4McP755ye8\nv5cp0y2yIVfqnflxTnajorQ05XMnatZ3CpGi4iLKq8qiTPbL565yFVRO6qfWRYkZ5xKj0+Jkd0y3\nn1ctBapradnXyvVnL/YcrO2i0G84dDIPFWdx7GFDhtDT12dlWfeyWGk0hYy6z6/+3M+B8BhkDzhx\nWqxC7YtoPvGCkQG8rzXlya5R8y7aSdtJ/cWnolYyvJ8xhmP7qe4iWl4tZ8rHuo23bZN1JagSxXXy\no9qVRG655XNNB/NJMHq9u7VefV8nUlx21VnYI8mYqJJSPgg8CDB9+nSZ6vESNbkGGfaa6sPRGdWn\nfKaKhZF4TlmogjqfG06B40QtAS6fuyqmv5XC6VPltoTnxCmWlGCzLz/GOo5qt1deKzfhlixuA3dQ\nWezTafWKOxnREX8a0vNAVOOBPTI4Fg21I6GvNfB22Ik1GY9J/0Hu34xlnaoaXgmiyLIgKStW10lD\naF005QxLVm6jvsF4PWV2S8LnQ/YAA5HvJbEE6JcgLPqaPFz+8+wULn5TyT4gUn3AKN+oAWlox91t\nhxm/7j8BLH+pdJOIWV9ZlcCw7LiVLLCLFDVIFtl8JcqryqIEkJsfltMipvyy1N+hgRDdHT0Rzq2J\nZlNPRFCl4qhrF8c9fX2WOFZRfl4WKydZyU2WgJ+hFlyaZPBawrd/pkjFqVpZdTbOmcjCHfPMqOk4\nx7NbgWzHsOdzC707LWIbe5/pqC6iu+8MlS5PUGXBGjX2fX9tJ9pfq3d0BVBj1e5LJLfclKZ1ML/W\nGI9GnMVfv9ZI7awPx/T1bNnXymW7eli72v+zKdsZ9nOVoFIqPA7MAWqFEIeBVVLKh4I4thdJzzgc\n+wdJopYLZZFSFiunhSqT2EvIXF06PyL3lPKhapz14SgrEETOcBLtUMoBPTQQoryqLKnyM04HeHva\nhqBJVRC7WboSie5Mlqj73THj1YJp8JGse0IQZF2oq/vfkQcq3j6dPft4vWME32ldAq3hvqoKJxvL\nf4aoKh9+iWteODeUIFm0zNj3K9vncbK6mJ/Pe5aG4e3WMYL+3pbPXUWLmVYhk0E9hYyQMuWVuISZ\nPn263LNnTyDHippNlM4AUou8sDpagsdS1ihloRo2ZAidZ85QLIT1nnrfnlXbWQolkwOcl9N5UXGR\n9Z7978mzG6wIQWXhUkJIJYbzG5kH4UShzmPHShgYb1lRiUGv9rgNHEEMJnb/qpmjx8QsRaOiPSEs\nqoYNGQKkV1xH9RfTybbo3L3R26bYH5wIIfZKKacntXMaSTTPXpDjVzbJN1GlrDjjL98FwIHXr2Js\n5VEqS3qtbT44M4SDJ2v4/qGvAv6TbLolw73zJsOH6v7NLew52sY/7LqBpo9tBmDhjmuB6DFbLQPW\nT6mLKYKcfaul2XgeXPfkFQCcvvAsAH56+dNUlJbSOOFZz/YqnME0JWckAwMDXPDPe+KOhfHGzFgM\nFiHmd/zKi+W/mB0wmRlHwKibeSALAjWduOWGskcIQuxEnW6drWVfa8Q+9r/dagi6oZYqndaxeGIr\nm3hFD9rzV2WtfI3mEXI1z14asCejBRJaqk6VdPvyVZT0eZ4zlXMMhEL09PVZYsqJfawLtYf9p5S/\nFayK7YJhpjyo2l7Byepi6/3PPXcNw4YMYSNXmT5nwX5vauUhiPqGGoO8EFWx8LMW7/cGTHZd3+kT\n4+ZwHmtWmM5lHz+zCLdlN7uVSqH+VvmfKqsrXEvLxMIr0af9c7/HUAOCcqaPN0DE8gFIdTBRVidF\n8/FjEWkx1Ht21L3gpwxRqlgPM6eFKsY9HhGCXsCZ2DORZ0/jD+dYuHzuKhZ//RljImcm7dyz9xMA\n3PyH6wDYevVvDItV6RDe6Kzh+4f8l7ApqtlgnjNcvUJZqF5+vpm2pQ3s/O4kToy4BIhcbQDYOMfI\nWn7n+vqIY/o5L4Qtx+r1qPWLOXXLBWy4dQcDoRALd1zre7LlFkyzfO4qSHMmdC3EIslpUZUvEUrq\nplcWhyBSIiRKPGGWjIl28uyGqMhAu6O60+KktvUjXpTTudNx/eXnm610CX5EkVfEiooszJUO71XX\nEcL3T77kvtLkN+phu3NWBe1VMLSth0++AnAi+TxKPklk4rpgyyZaZlWw2HMLg7GVRykrPgOyl4bq\nznDNO0WAzw81YVZ9NlbJG7Vc+fLz50V9FjXJMamfWkf9vhAVpaX09PVZbgSxSs4oLEu9z2LHzvFU\nvZcrY2a+ktOiKhH8ZFZP1GLlF2eKBDefmCBLy/jB2WG8knE6LUfKf0rVeYolXOwWLmeJmnjYndzt\nPltu1+CFPWu60zessroiShCmI9GhIlbQQby6js4ZZjpI1ArrVtDVLUniYCHVPHua2Dj7yIyV9/L9\nm36FGDPA8o+OY/LsBstiNf3y3wNQ++S9PHD9M1SWDoGSKda92lDrbtlp2R+ZWdwzoa9tnJj8Cqzd\nfpf12YrGhyLPYaZYuG+z4WBuL3njxfK5q7jnv16KSB2jBJhyVG+oNv5fUfqQL0Flx/480QIp8+S0\nqMq3mmR+zbRBWiS8BoZRuNfecwoK9f/VpUZblCg5sOu1iGR9EJ3HpH5qneVY7kzi6XYuN+wO6/YI\nQ/v+bsd1pnfwOq5XG7JpyXLeJ/marX0wEXSevWwSal/E/Zvt/j6Ze/j6HctPVhcjBcjyYtqWNtBR\nHbZYqTZ3TYKB/hAtr5bTtLreyCPlcuxQ+yJa9rf6yiyurFxOx/UVjcdYc+BWT8FWNbwSCFuhDrx+\nFQ3mcuUDvzO2GX/5E1Zbyiv7QIb9v+omHAfgwO7Iya86X6xnYSKpDdxWDQaLo3mmyGlRFQs/hSYz\nLcpSrZ6eDlRm9HipCtSsSW1nn0Up1JKcW94ZL5IRL25mafv7zra45c+K5fDup93J4rRSuqVQsN8H\n9iXBoHG7792inHKh72gGN3Zr7dcnNFFRWkpD9VEAfrJ4O2VVZTROMCMuVxsRdotOdjNlrGHRWbJy\nG/Sfigq8ULXv3quEX08CbGOXKnYsPmUImVHrmxm6sgdqsbYBow80DG83/KcckbDq/4XbJwHw4uXe\n16jaohKE2ikuqQJgw7prgHCyUNXv1HilhKMdt+TNmuyRF6KqUAb0dFgkvJwTnb5K8aI7VAoD56wl\n3iwmlliKJV5iHT+e4Ikn0JQgTNRZPSgS/V2Vj5W2UGWHbOTZywZu7hDKYpWNc4P7uStKSyOyq1d3\nhKivC0863HJCAbS8Ws74yyMnDUtWGhaqX0+KbtPOWYaYkuUlbJyzldJPSupHH4O+Y67JQGMlzFUW\nfKMPz2N322E2ztlKcVER33l/idGnV6+iafUVvPx8M1sOvkJxSTFDyvopLi6yUprsnHWv5zm8vi+n\na0YsC1U6gnQ0keSFqLLj5uthfz/WrDvTOEWUihjx2i6Ih6mboHJ+Dv5EhJc/ld99ITnxYhdcKuO6\nfVnQqy12MZbt2ZuzLqDdX8r+t/3zoCxWfosoe9VF87JsFSpSygXZboPGwBgDjXHwwOtXAdB4+bNR\n2xXVbKBpdTgnVNNqI/purcNS1Du6giNLG3ho/AMAVv4qCIuRE2YfLHOxzkdQMjEimae9zzrrfyoG\nQqGwD6VtXCsuafZcDTCu7y5rW3AfR/36zWoyS96JqnzG+cAM0kFZHev629xjZZyWKCdeoifduUu8\nju2sM6h8vNwKPLsdx6vd6fQjcHNIT4RsJGHUDB6yuaSbzLmd/ktu/qCHnttGy/7WiOg7ewqGhmrD\nT2pMeTsHT9ZEHGuUeYwZK+/la098mhdX3xUzGWhRzQYjBYLDYvXgzG8xEJJc+uQXaRgx0ur34QSh\nIyOOtWSlzafKXD6M5Rvrl1h1+7T/VObIO1HlVZ5G3fChd6e5Zof2Q9CDjV00qY5mX+ZJx3KgvWMp\nK0/91LqknBlTWSJLtRO7WZpiJRq1E6/dsSIag6RhxEj2HGmLSLHhzJ6fjiz68R5gniWecjx1iWZw\n4ef+a1p9hfmXd9HmhuHtIM8wc+RRNo6OziuV0DhQMpHmE8dYsytshQYQIlxFQaWrYHSF60Spfkqd\nFTUYD+X43rL/aJSjvfp7xkpjyXDt6rv8X4cmbeSdqMpVEhFE6Qih93LuVu8ph3W3/ezLhZkSHPFw\npnpQFiunpc1Pziqv4wc9W8tEaoRU0YJJk83fPplzOwupe0WwWXnyrh8HGFF3o8a+T/nwsF/Un1r/\nAkTmlVIWK7f2KR+pjXOeMvpO34uW9euij75NT38plSVGZvoHZ36L0Lv3Addw2a4ejiw1yngtX78q\nIjeVW/max2+0n8/fcyTeOOM2qUxlzNNWLn/kraiKMM+aFiqr4rjKUuvTYpXOJKP2orm72w5HdBo3\nJ/OgcEbA2UvLQOzSLm5RfsmS7L5eqR68UIIqVrtjJQ8NeqBQlskBKa1yIJDZ+o6JVhDQaJzEEgLZ\nZMnKbYTaW6y2OMtfKY68fTbj6422N584xs07r6KkH8ayO6Xzn+46DcNA2EuT9fTTTX+4jh5wal8r\n2Mbd5hPHWLNjE/ec3QrA+BjRgvY6nfUNRg1Ce644NcZ0jjAe4zNW3kv91LqccSEYrIE3eSuqcoVE\nl/DSFT7vJpLsNfLcPleO4G6lZnLFYuVMKhqvzl+utBuIEFPp+t39ki/VCTQaherj9lx2EFlfT21j\nL12llsPGXx5eDms+ccxwIC8S9A+BjhVGXVyvJTPnuL5wxzwAVjQax/nio5+gd3QFEnht4UMgBDdd\nPNlcDTDaq1YAFi3byqHntkX4eNFmLF2uvTzSAqSWD5evX8X9P3X/XuwT9VjfWxARzm4pa7TFKjYF\nIaqURSpRC5W1f8AOnPaM2U6/Krc19kwq+UQ6QpAWq2SJJaKcyU3dRGSsfewZ2YO+RntEULEQCTuh\np32WZzrb2me+WmhpFFEi3L4SkGBW/iDvp3sefdVahgNY/HXDb1Qt+S2fu4pTUyNdHRZs2cSeI5+K\nKHh/oqaYkn7/59371jv0l0DneGPZ8NSFZ7Fx9laQkuISAMm3N70BwN23TuZU1+kIIdJRHa6h2tnb\nS7ctb5ZzErj4689w6kNlMep0Opb9QhIkVK/Zw6jZPQlPKhNNoxOPwZ7MuCBElV/S0cndlvCca932\nqvDOJcB0YZmgZ7uXTXCWdYFw/b7lc1cl7auULpwd3G5Ns6ePSJdISgT7PaEc1bM9oEQIJ5V/R6MJ\niFTHtAVbNtGyr5XLdvVEVXsIJ75sgf7IZPYD/QPW321LG/j3xdu5qVSwcMe1LNiyiRWND7GiERYe\nnxdh2RlWNjSm5dg5rrfsa+VkdbEV0QeAwPBQd6G8qowjSxvomFVB1fCj9FaV0dxRR2dvLwt3XEvZ\nnz+wjmsvtQWw6qEBQkPCIgwZmR5HFXG+5ImZnOrqpX+IsK6/o7qCy0h9Mux8DuycVcGpqY1ctS+k\nLVRxKChRlWzUn7V/QBYqpdCnNK2zOq7dcpFOUsnP5Oa3lE5rTqrYhRVEFntWqIFKiUL7UoKbj1lQ\nKCGtfKr8PnTSOcuzBJXsjKrpB9pCpQnjZr1Mtm5kJu6vyuoKiouLKKsqo6+3N+KzhtqR7F+yLGKS\n88lNJ/AqIG1vrxpbqtfsoRr469ca6T6/EnFmwBJYj8/8BZXDK9iw7qMAPPm+MZbYJ9cqPUTziWPM\nHD2GUb9ojlpW6+7ooW1pAwt31HH68FlsnLOVotMDVFZXeJbHKa8aaonFKpXgefV8X5Nh5zKhMyAg\n2WdJOn2F84GCElXg3oEz0cndLFT2zyA3bjLVkVS+J2WxUo7sidTXywWcMzI1MKjrUNeXDWtbpoR0\nQtgzRPtEiy6NF26TgBWNxzxFgNv+LftaOTGiBEaU8OtJsHPlvREWq/s3G/5TbpnNVc29+7e0AC3U\nVxvO2/tveMQotCw7oa/Vqt+38Pg8c6J7wvU6nGPzZbsMgfGy+bq8qozeAShtM96XwJAx3Yw9u53F\nX3+PR7/zGetYt49/AMZD/UgjSzulMzjddZrldT+gemWIptVXcP/mFitdQsu+Vs6MrkRiWOMmDm8H\nJGcNOWZcg81xHeClzxovF+6YF7byrQ63P9UAnPqpdTxrLqUav08VR2aNiSgGr4mm4ERVtrALqmFD\nhtB55ow1g8jETeh06vSLWgK0W6Ocvkm5ZqFy4tYu56xLCSvl7OpMIho0foW08/O0RoTGyPGm0biR\nTHb9NQdu5fEb56dPjNsymzetrqdlXyv3b2mJm/+poXakZaFyOl2ztMEopvzufRG+Y6qMz6HnZgFh\n5/frz17Moc/XAfCNmy5k7ZNvUX/xKe7f3MLCHe4uF2CU3AEjV5WxnHmQU13CWhEY0taNwPDZOniy\nhtI+yaWj/xr3K6mfWge7jCVEN6dyhZ/C9/bXyuk/GQar8CoYURXLGpUrjrjpvsns5lp7pAy456OC\ncHSd0xqV7kzqQeMUlc6UEko4euXr0kSjIwY18Uh1EvD4jfPhRptP1SuwdntkRJ7XUmTziWMcWTqP\n+m7jVuUAACAASURBVPXQtLouIhfUsA8Z++zZ+wkApk9T92xYZLQtbaDlgmGU9EN/22E6x/fS3XeG\nyhhPRXWdT77/KNefvZhvPvwKco6kcYZRg/DUyZfYOAezjI2xz/K+HzAwEOKcLqOY8pSPdUPfMQYG\nBMXFksYZ8K+/e4vitzuBP1NcUswNBz/LwufmUfZmJxvnbKVqeKWV/NMrx9WMffdyauoF1O+LdOOw\nT5Lt1xDrt9o5q4IFWzbxohkdmQurLPlCwYiqbOE0f88cPSbCATJTN6GyKB3Y9VpUTqp4+0Fk8rxc\n9aFKhMZZH45yYldCq7ujJ2PXFs9C5eU7lc77RosiTSZoPmFYjRpr4mxo8sD1z1A9NwTE75NFNRv4\n0nfvBVo5YbPKLFnZSv2UOmasvJdTXad58IuGE7eyuLxoG+86qivoKy6ivKyUpo9tBqCyRPljFVvn\nCrUvor7BuJYVHQ8ZS3hzm+nu6EFKSf3Fp6xtyyv7oP+gKXzmxbyG4uKw0/2Ec9/nrx3VgGHFOueP\ncLLYa89I1FjWZSsa7fQZjViJmBXbn3Tt9rtzOoFxrlN4osqspZRLhZXTjdPhUCXBc5pz7VYc+75+\nhUUuiyy31AtOf7B4CURTpdBmc7li4dVkhlT6d6L3vNskItT+VMx91P2n9j1hJr3sWmost/374u10\nUGRuZ4go5UxeSzh/woItm9j7mWFG1JwtOW/D2e22sw2EfbdskbKdvb1QKtg5q4KuSQ184yZY++Rb\njPtwN5VnDURsryapN7ddZ7TheD+nuk7zv1N+ZljLzWXG/pBgICTpqC4ylvr6jvHwomNWpOBXXvqc\nkdRz+/yI70Gxc5axKnHa/D52VvdzauoF8MibQHjcb1vawF+Li+geUcIJWxS64vEb51tFrHe3XR3x\nXRfKmJYJ8k5UBTnAB3GsXHFCV3X+wBAPKiRWDZDxIjnsA2kui6cgKCouyvq15cp9EwudaV2TDOrB\n3FDdGvG6ccKzrtunusxcbC7pl1WV0dPXx4yV97Jx7lN0nexm4Y5rDR+l4qKIRML2qDkwxNfGOVuZ\neW5XOD+USVHNBusawLBk1U81StBMnt3Ao99pYMnKbYbFqmSird3u/khH3j6b+otP0d0/lMqSXkqK\nDIvVhHPf93W9TpTV6YRp9X540TYAfrzPiEZUE0v1PXXHOd7YyqNsnLM1Mn1EHHJ5HMs0eSeqnDg7\npLJUZZrm40am3WxERqioN/tSl9O8qxy3ISy63GppuRFkht5042yT83sB4/qvLp1P46wPB3INhZ7s\nTluoCptM9u9EowXdBJbKOl47q4Kujh7+a+E2ENBgRv59/6ZfMXL4+3TtHQJA7+gKQBiO6O1P8fiN\nYYuXvdLBsKFDgS4j2aYjj5s9JYLVhvX27+nuqEmI8keylh6t7O2rzASm+61tD58aFfEdNE7YwIIt\nm5g5Gh6/PZxl3e03UeOMOo86jtO1w8gxdRdTmtZF7L+77TAb52zlwOsPWUK48Zz32Hr1bzyFsMab\nvBFVXrOZII+VysOjYcRIqxZTJnGL+qusrvB0UFe41clS5KJY8ovzgRDrOnOBXBReEfms0Mt/msRQ\nD+J4FipFENGCcmjYAenDZ7czbEgfUz7Wx49HbyM0tIiFv7+Ozt5e/tT6F9buMCa+Lfta6awpthIz\nd47vpflkDWMrj1KJ4/43ow2V6LBHBoYnURvM8SdS/Dxw/TPmX3fZxqcNfNlMTur2HShUQeZYqH0e\nXmQ+f/paI96Heuqn1nFkaQMLtmyyLHTO59XYyqPW35UlvYytPBpRccGNQp9QJkPeiCovsu33Yc+W\nDsbNpZJ+ZurGci7tdXf0cP3Zi2OmDbA7s8cTUUGVL8g0y+euMszdLmkmQgMhDux6LSGfMi/yYSlP\no/HCT/8Oanx19hUv4k18lTN1y75WfvZPV3BkaQNfL27iorOOc/BkDTNHGgIhNLSIiee8Zyztme+t\nqDIsVpftqmfnrArWz/85A6GQ9fmAFCATqGGDMQbP2HcvzKqwclspLq07P+L1zlkVHHj9Km4f30tn\nr7KQEXF9YHxX19+2mBaIWFXwKgJvWbpMUaVQ2zm/c3tA1fcPfdXI1G6bTFWWT7EKQOfSmJbrk7y8\nEVVBiqdsC7Eg8SrXcqrrdIRgcCtLkwvlZ4LCLTuw83qDOH48AWav+5iPWA8zz7pjmsFGogk9FU4L\nVbw+lMw9pvLPPXnj3ezZ+wNee7+GWx+aw8Y5WzkzupJbN87lodt2ROwztK2HlrZWXn6+l65JDQz0\nDyBs5WbO9JQYjuTmEqBbImlloWrZd6+VuLToVD8SrCi7JSu3mbmzDGF46LlZLFrWza93XMvprtNg\nltOpPd5P/dTI70mNzbHyDnq6vtiCtULtiyxrk33ZtH5qXZTAtbLmm1a5opoNrNkRLX7t34EqmaMK\nTic67uXbRN0PeSOq4pHN3FNOa5VaCkznw9V+M67dfjdXl0aeJzQQ4uXnmy3fIWfnTDRJqDpXLuO0\n2HlZqRRKcAVhrYLYWfXdyCXLlnZK13hZqFY0HjOWvczM5OA93vpd8lN4LRsV1RhLad/8kVF67Jtf\nqY+yuOxuOwzjqqxlrVHr55qRcEafL3/3NBc91srXqj/Nyepifj7vWRpqR9K0uh6Ab72wjdDQd6FI\nYK8oWF7ZB7IvInN7BLaUCae6TsOIKuO7Ki8xj3uU4pJ3GTX8fegPp1voqC7iotGn2MhWpow23MU3\nfuKXFJ0J8d13lsX8niqrKyxXBpUeoWW/YVmr9841GpdQ+yIrrxZE5gBbs2NTTi3r5UvevEBElRDi\nU8D3MJJ7/JeU8ttBHNcNP1+g3y87136MVIi1zBUr8s8ZJZivOPN02cvwxBJW8aIi/TrxFoxvgduD\nxOvhoilolIWq01ZLr/lE4hYrtz7kzJXk5NBzs1j89dNIacgd12hmM4VAexWwr5VRGM7gy+euoup6\no3xN/dQ6ys1IvaFtPVAbzhR+09AiKHIviAxY5bvsKNHRsr+VUeubOfF8M2/++3Q2fOpXIIiImCsf\nfon1956jbfzDH2/gsVGPUux86krjeq6/LZwN/b7Nf6a45FWWPz/O2sw+lt+3+c+8V1XJbY9fyYO1\nzwHwtSeMRFUvrr7LslDZrWTGb3Ae1c/DqNk9LF+/ivuN9FwR45WXhUo5+lui5t1plkV74xy1lb/x\nLp+CnxIlZVElhCgGfgBcCRwG/iiE2CqlzN2CcUni9aC0m1GV81/nmTPstuUCCerh6nUzgjGb8RIQ\nKhncgV2vRSyJFcLyn3P5U/mU1U+ts0Kf1fsQzjZv/0wtFwYVERiLXBJgUUsI+Mw4qBkUrDlwqxUd\nNmzoUMuh2kmiaRRGrW9m1NQeZpo5puzHNCxU7Ugpqao2xqpVD71siIy58Pj2u1m+fhU7Z1XQXmUs\n5132SuTxlSCy/MXWr+LO9YbD9v2bF7FuXisf+ZAxVu8+dh5I2P3XD1kiS0iYODyyPqC9r9Q3wJKV\n2+ha1m2UpZGSiWe38/jMX/CRscph3Cgv09k3hIvOMp4Jn/vdZ+gdXcGG85+BIsHC35sibAR0f76O\nI9UVVD8f/X2psUqN11XDj9IRqzqEy0Soo7qItqUNjF7fzJKV28w2Gm19cOZ+3u4+DyWK3PxE4+UR\nSzf54rYThKVqBvBnKeWbAEKInwLXARkXVfliHsw0avajrDhuBZQLFbvPmd2EDkQIMWdZG/v+EH8m\nlUitv1hRoukWWHH7RKlZtNUclIvO3ZuWdmhyH/s9PWzoUBpqkwu+cetDzpp0ilD7IpasbA0n0rRh\nFxBHljbQe/wY8swZTl94Fkdmj7H2v38z0PcB8EGEhQaMPt+yv5VzIk4qDZ8qYSwDGoWMYdgQYxnQ\na2m8fkodnV0v8ZOx27n0Q4aAmnBedK6p5vfDKeVPjxsGRbhayIqKi6ys8vUNxvLgA787yqmu0zz6\nnQa6O3pYtGwrxSXF1Dd8AMD/3vEKf2qVfPXJz9hSNhCVEmL85YYPWFW1kVurforpHG+KqoGQpLO3\n13P8Md6fFxWlmewzNl+Dn/wQhKgaDbxje30YmBnAcS2yLY78Whbsr922CeKBGe9mvLLosxGvlVXG\nWUhYCQ1l0cnnm9qZPiLW9dhrADrfByL80IL+Tpx5cex/ZwuvumrIHhAV2WxaQVAoDw0vCxUY905D\n7ciIJJleFiq1jKcs7ZPVBzeGt7EXRu7+wLCc2rOKq+XDhql11phsHX9/q3EM08/IGazS3dHDnTea\nFqufPs0Hp07z5Uev4JXvrDIyrb/1Di/e/Ehkox0TjFj+hwffr2Xi2e0cPFlDcVERA6FQdBJNGblM\nyICkZAB+Pu+3jBr+Pkc6zvY8fjyiLM9iGN19Z7hiZdihfuesCmMZd3g4g/xZQ84wcXg7KxofYs2B\nW5M+fybIdSNJxhzVhRBfBr4McP7558fZOjlSMQ8msk828lHFw57c045yanRu9+T7j0bU+ytEnEul\nTiGVCH4fil4PnilN6+jp62NAyogHgf1eWrAlfY6hCVtxS6fl/OClyQxB3YPKQqWWzb5x04VR26h7\n7sDrV3G66zTVHSE6qos4WR25LH3Zrh7Wrp5vJbJUbVw+1+jri5ZtBWDDus+YVurw8r9aSjvVdZpS\n4CqzALEKNFGCaHptm3Eyh9VHJQBtqB3JsKpLmD52A3v2foL+EsGi//40G/7uV4AxUSu2jzkhSeVf\nuimvKqN3dIUV2CTODDAAdJ3s5o2TQ9iw7gorcnD85RsirHrfuOlCKqsr+NffvUVZVRmNEzYwvQZe\nnObxpZdM5O2Tkc8rL5eP4iIRZY20u7NApMXK/neyxBtX89E3NQhR1Qb8je31GPO9CKSUDwIPAkyf\nPl06P3cjV5IQ+s2t4raP2s8eHZjKjRLEzPdU12muLp0fYZ0pJIuVF3ZnflWywi4q0+VTtWDLJktQ\nueGWjC/TVix7+LVePk+dQnbEVbgJ9YbakUZ4/YFN1lJR84ljlqVr7fa7CbW30LLfWIby+j5Om8v0\n//b+MprfOEb/EMNHdcbKe63CwTtX3kun6ayuxtRR5v5Vww1HdTcr9v2bjfOXV/YBcP/mFkLti1i4\nYx4rGh+isxc+99w1HPrsjwAott3/U5rW0fSxXiO3lM1h+8IRpzn4Xg2yvMQoefOJXyJ6Q2z6pznU\nzurnZHUxAwMDDAyE2Dj3KTqqi/j77dcytK2bn1z2pHHsj3Wbbd/GqLHvA3XWee3jVHdHDxfWtCOi\nVxCjDAt33lQP1FP9/B66ljZQVV3BqF8007j92fB2/QdpPlkTYY1U36e9lE+qFGIfcCMIUfVHYLwQ\nYhyGmPoc8PcBHDdpkrFQ+XmQ7DliaEX1cMwFFe3MqK6W+ZTvkNNP6Mn3H40bEVcouC2VqtQTzs/S\nZbGzW6gUxUJQUVoKGOk3lHXKnpU/6OSx+eLkqSlc3By9Q+0tEfdi2NXiOvOdyOW9k9XFlHQYfz9w\n/TP0mbmenDStvgKAtZcbr+2+lS37W+k6Ga6A17K/1VhyxBBzE0cYyUKVaFHtXrhjHj19hhCzR0QC\n/Pl4DV/ecAWMs71pEz3DOwboso25Q9p6jPfM8bm4JGyJM9pSF5HoVLX9jc8b7Tz4gVmSJ6BnkN1C\npdJo2JcBhw0ZYp1nwZZNSVnVLed44ouqXArmSZSURZWUsl8IsRT4DUbo0MNSyldTPW4uJiFUD8JE\n1LtT8dtvzkSIFfXnhnLqdIon535FZgRJrOzrhYQzWibRWVMys62K0lLr91eCSlmh3HJbpStyNB5a\neAVHuh1xc+E3ct4vKgGkihZ88UAT02vbaKiG28c/QPep93i7+zwaqo39lZCx07Kv1VjqG6IUjbSc\nuocNGULD6JGMesYYA9XS4MzRYxi13njP8tWabThVOfP5LZ+7iqbV4bQFVcMraVp9BV9seoEVjQ/R\nUG04nE+r/Wu4Uf0H6TzTy54jlzIgpVV8+YMzQzhryBmQnVxaN5H9/3qIP7a0gsSKLPzI5hbLUnf7\n+AeM6x5pfLb94p8w0D/AjRMnUVldwT2Pvkp5VRnjL4/+TY8sbeDIvgp+PO8piouLuHSk0b4VQx8y\nt4gcIwwLVeR9OPkVWLv9rojtvO4fJbISWZ3xQv0Gi5Z1R7wuVItVID5VUspfAb8K4ljJkMoA4+dB\nEpQwChq7U7bXjap8qJS4cqZUCA2EONV1uiCW/7xQ38/yuaus78Hug5aO616wZRN7jrRZFqphQ4bQ\n09fH9FGjY943dstVOtDCSZMt3u4+jzUHbrWycLvde5ft6mHnrApOnFOMODOANBNqFptmo8dvnM+h\ns2dxquu0FQF3x8A6Jjad4N22Wr5qRvkpq4iyWClUjqruSQ0Ul7xlva+WGzEFX8+ZEs4qM6xSzSdr\n6Onri5gcAa7Lb8XFRQzYq1bsb4XR7gEfKhJbLYPaHeCjneHnUT+1jnO6AEJGeBhYzuZBTMBUpGR9\nwzGrDcpiZT9uomW51G+hIhqrhse3WOVz6a+czaheKIN/UDeHV1iyiqZJlqLiIhpnfdh1+avQZxSJ\n4MyFlazFyu33dw9fzt5Akq99LRdJl4UqnX5viR5Tbfe4GcG3YMsmvn/oqxE+Vd8/ZDyYH58AoXfv\ncz3Ogi2bYGkDJ8xJhb1IciyGtvVAtWH5mjy73nzXyDbuZtk/NbWIhxduY6BtgBueuhKmwi/+YCw3\nPj7zF4SGFnHrw3PZvfRnhIYWWaJifyOMX/efDEgZcX3272C6mT3h0HOG8Pu3xfVW1OKCLcZnG881\nr192Ul4Ji7/+DIee28b4y3dFXZtyilcTrX/DyLy+osOwUDkTsfr15fM7fiWbRsOOZZE0IzrdLJSF\nRM6KKj8EOcDE2ifTqjneeZxhybHSCDhTKDgTgKpyNlA4DutuKBGq8lHZl0XjXXeiUZIqIklZqdQs\ne/+SyFIU2RBPrn3GVutLo0k7jmg6L2rbB+gdXRERsGH0xXq6O3q4b/Ofqb/4VDhJaN+LrGraQ3FJ\nseWEft9mwzqyYV0Dz041IvG6x1URKi9m3KVnePCCbdz68NzwSU3rU+/oCl7vHEHfacHutsNRfRqM\n/hvOJG6gxNRAv5Fnq7ujxyrczlL3ejJ2lwRn/1RLpRvnGNGMS/7nJhpGjAynQzCLJ98+3shq9LVZ\nnwawEoj6GbfCQsyw8j3wO+N9+zKkU4QlMnapcUVZwtyWN73IJwuVIudFVaEM9HYHv1RDUJ0P+e6O\nnpjlZuxWFnt6BS+coi2fLFbJtlUVZnXDKyN7ur6PXBpI8t1S7JdMltpKhXRa8BOZpMbqZ/b7t6hm\nA401GBaqOMePmrzebqRMsEdO186qgFkNVK/Z43oNQghDpEhDVNmjAGesvNdybp850rBkTRzezsY5\nW/nKS5/jZHUxy56aZ+RzKodb//jZuP6zXt+/PV2EKi9Tv159X8Z1vrx1OgDLr1diZhajxr4fUd5G\nUVxkCEIVwGJlN7cqIRiodAmjZocnjfZnw4yV9wJQbS8ZZMvUnk4K3UKlyHlRFYtMLxFmykIVL+LB\nLqzsuVdiobKqQ9iJ0yudQCrLibmKuiZloVPCCLy/O2dkpV+URUrNbr0sVJmIbHH2jYg+o0pZyE7o\ne3HQCCgng6nUVr6hrFSqr6i+2rICvrCzkavWGYLlnkdfpfHjH+abXzGW/77Y9AIAP179UcCIAjRq\n4r1CZ9dL1vHPKuvjoinw8MXbWLh9HvXjRlrLjyoa1x6J69VXVd9R/khrn3yL0ECIO2+stznE10WI\nmEXLorPGH3n7bMbXh4saA/T09bFw+zzTSmYEsLTsm8Rlu3pYtMyICfvaS5/mVNdprtrXzNrtd3P9\nbZG+tJabyKxo/y6nX+74y5+wPnMuJypRdsKRyiIRi1Whk9eiKl9Ix0NUdYKXn2+mqLgoptXEKxLJ\nK2FoLpYQsCctdcPZ+dVyXjyUhcqPr5QzK71fsu0jlQxRVoV3jeyCRefuLUTxlTOltvySju/ezyQ1\nlfxbfpeB/DhFz9h3r1nb7y7PccytXUU1GzjU+gnG17YzbGif9X5D7Ug+uekEcIIyMw/WqF80s/w/\ntlNWVYYzui5e9QblhF5ZXUHV8Erqp9RFTViVNev+LS0A1Dd0AUbi07GVRxlTbiw1Huyt8cxx54W9\nrinAG5+vo6W4iG5TDLHCsJKttZe28YFbkWlNJAUhqgplcE/Ed8veOUMDoaQEkBId9iXBqErweUSs\n9noJRedDItY+iaIsVOr3tJepsYcrB13KCOIvtdj7TAGKpERJe6ktTTCo/mG3lJT/16d59Dt18B34\ntSmIfvGbqwGYeSCyPxl9eS73b26xLLXlw5U/YXQ/r+4IUV8X9umyjrPekZqmZoOZLb6V3tEVrHnf\niHK8f0uLYb3qO8ah52axyJZJXqWzkS6C6e3u86xcWCoPV0TUuRkUsHyuMe5dNtVw7XiZ8HilhJWa\nMJZXldGNN27PDmfy1Kv2hVi7/S7XcUqPIwYFIapyHbtYUskdU31oOgWE/bXXLCpRi1Mm1tm9cCbl\nVAODSt75m75NUdsCEY7odod8L2GUiFUu0e/PaaFUaRXyActqYVqorKoG707LeoWDbJCJMlu5Qqzf\n009/iRdx9vLz53Hf5j9z6LlZNK2+Ima/Uvs8bm6jagte23a1r2sBc+lrfbS/aXffGSpLeq2l7/s3\nm9dultKpX1VnRKyZggj+//bOPjyK8t7733s3S14xakgUQjEYEFnDixrD4zEWUAo+raCnopQLW6n0\n2DyeQO1ltT0nh1Lh4Tq1yGkrtI2eYg9KSlE4ImjPEV/AB1orhRo8MYgYGyvBFogQ80Jedvd+/pi5\nZ2dmZ3Znd2d3Zje/z3Vxkd2dnbl3d+ae7/27f/f3J1k0CKHSUe/H+wDaokTFB0zsFJS2Lb0cS/ZV\noHxjK9bvlCweqm6QRZw8INo172UAMK3JZ5YPKiJj93z7JQyW/w3r2/8RYm3k1hXW7z/6nFxRe3Gg\nvEDjY6VP2h+uZIWockPnbucUj5V96KNM6ota1LWKhdXojZumAY0Qn1fvHq9/DCT2GdTvSea7EEmv\nYjWRUa6GXVPE8eQbDgdRFIOYpbYSKbOVibhlqjrs0yTJAHHdrdsuCY4Z5WMBqNopCjLL252cGX69\npvlR7Jnu0a6YPvIJuoovwjXlKpNP+bh1K9tN27W/tgDnp1+OS3/aojx3vqcfL9xSiNAID/5r583o\nn3ABgBNYsm8+Wk+fQtPs3fCPKlPq+E2dGRY8Z4vyNE7r8WKUA6vvx/OL8pBssRl1Ti4g+YmdrK8A\nADRUyQak8kpEvRms0+dSuskKUZUJqA1E43HLNruJ6/OixMWlLkETzSogHnGQzmlAvaATSfUtB94D\nAE3kSUTj9InkIlol/pZyL6ILo1QIRvHbGpWqyRQ8lxwGoBVnbhjE2IzrSm25nWgRKrPBmDC4bDvy\niWwE2avU3dOfS21H2uV9SVNgIoqDIcnws6HqlHyMVsP2iIHWgxtXKbUC7/n2SwguD2LJvgVYsrca\nZ0pz0DRrF0bm5mJty3zFd6pyWkVE/tf9c6QVemgABsoL0dVQjX55CvLs965BKNcD6C5vsWqxe2BA\nclXft02pTbh4xza01RZI05ilF6Cj3o+vvOpH+cZWdDVIDf7pndKM9OPHtREqvYgS37fI+RTXZ93K\ndnQVe1BZLn1XK3w/x8jcXNy75WbDyJ0ZepF2UraGEAPA7okDxm8cpmS0qEqHEV4snK5RFCtCFU/E\nSv9YCDYnpwGjcb6n3zAXysgx3sxuwirJRO/UPjv61USCVHmhZZHwSRmpKrWVSSTaj9l9vupX0Qmf\nqWBAu53e9BKINEbeM92D/bUFivgZLC9AMBhCUbE0dQU5ctw3NISGqk2STYF8LxFiSvSfHfV+DJQX\nKA7v54rDxqRny3yAPGAaKC8Aghw5QSD3dB8a75QKjaxtWSalftT7lXxKdRv0Tuw/v/0lTLjwUxw9\nV6KkjGhsJSBFi9QoKyOPSI7lPed6ESwsNPmm7UPkfP3mphdR4PNhbUu4XBHgnuhnushoUZVJxHvT\ntHoTVz/WTwkWFhdERKniEQeiQ9GvqEvlNGCsnA19x6mPYHm8HmVb8Vx+UZ7Gef4dnUcLkHipmljJ\n5kZ/ZyJqcZaNQs3pUlvZgP7aXbddWtWmvgY8JVsw8SbrA2BRaPjB26VKxT9/VfKXalwjTQuqr+WW\nA+/h0WePo7dtO+5ePgL/Jd/shXGmqJfXNHs3BsoLsGTvfMVMc/VFG9By7D1U1WiPv/nHXwIAFNUW\nIOD1Qmi7a8d/Dq2nTxmWrwGA3I5ejPmPD+G5VbJNGLOxFW21BUCp9NmbZkGZHszt6MONzbKgmunH\netn6ofVMHh4/vgz+UuklIVL004Uimi++b+FcfuT3hRjR0Yu23jJ0FXvwrZ1fxI0H+lD8xiFNQrtZ\nv6d+XbjdG/VzgDRYLPD5DMXucCOjRZUbStnEI5bUq79Sgbg45vkWIRQMJSwU9J5O6cJq9KetuV0z\nzakmFAyhsLgA53v6lak/OyJtZmIvHqFkZaSWTvf+LJzGG9Ykc87Ee94ZRbYaqjbJN9XKmO8zS2oW\n3kzdA9J027f+KDmE3yibWVZOyzV8n5ja41zqAyqvOo8mSKaeCHEwVaG+gfICdA8MaHIclzTPx/me\nfjRhN/KL8sLTfWhFR70f3p5+BEqLAIRX4an78bc6TsDLGBb8t9TOupWvAfOBysukz+N9bC8W+hiW\n7FuAQ590aL47lBdg95e8eObzO1FVchahzjZg6CD8xVC+U0/JFlzx2GPS5nLx6Ht2/hn5RXkR9Q0F\nwvi0clqF8r3ajTqKtuDleZpct+EWoRJktKjKNOJZ+ZeIV5Q6MdHsPYnsV1/aJdmpNCuY7d/IUV6P\nWmypXeTFiG7qTD/amts1y43jLdFjdFMRglk8N61xg9JxD9cOhhgeiKgNoI6YSFNpooRKjWw3ozL4\nBwAAIABJREFU4C8tiynkJ8tlWMKr57QDKHV+Vv28coy/shd5BUF4VXe0qhl9sqnnnQCkCBUgTcXp\ni5aL6E8wEFRW9gkGygtQ0gPFjkB8TiEcRL8e5Bz75am5dbp6d3lFeZiQfxJNs3ahepQUbRMRtMeP\n34/DPR/DKw8C2460o1I+vH9UmZKPFRghCcOPfjQDuR29yC/6RPG/UiMGSyKat37vI6gqAQ6ukV43\nW5hktFCpo96P3Y89phxb3Y+phVMqi8BnGlkhqtw+0lYnqasf232D1dsPJLJSzahgs3qfsd5jdd9G\nz5tNSeqNP9XPq53gxXbxOqDHQyIRqnhIR46eG3IRCfuw85yx+p4xcrRkRr1fWf3lL+4AhjoANlKz\n7eQLO3H0XAnOFXsRMFmoI85Bf3E7AKA3IEWkRDRo/d7lmu2Udlx2Fut29KPwgkh3cq+XY2z+STT+\n3XYs2bcA3QMDqLr4UzRUbcJaSMnfIg/1UvnzPLxxAjxeD3514DV4czy4f+eXUNQjubEb+cypS+gA\nUIlGyVTz0OHPIxgM4dnvXo+vN74J31A4x9U3xBHIYXiw4mcYmsik6UkOfFqUjzG9PuRfeDU8JVuw\ndp+2r9k8Zxc8AyEpCibbPaiT6wWir0rl9R0twjlcB5BZIaqcvinEmtYTIxn1476hIUt1AOOJUMUj\nJqwKLX0Jg2QiVOk2ExX5VVW1VwIwX7EUzRnZjGidiVlOFUFkG6IvC4bCqQKt50qU3Jrz597GB+cu\nxt2vfhG5Hd0ITLjA0n4Lc6QVZSsm/hwA0HJMCLd2AGG/tPxCoGpGnrLyLhiAJlp19FyJ8veSfQvw\n7A0vIJAzoPTXwiBTrC78xh+lqFYo/68IIWwyerBlNlZMDGFJxwJFFOoj0yNHjIC/tAxNs3aj5dgm\nrG1ZhhUTGeDzYs90D3bvnoucv3SjadYueHO8CF4aAnLCCe8KXIqYnT/3Nk4eqQWwHK2nT8HLmLSC\n2MMi36NCrNA1WlUJWLfOqVn5KIoAnJGjVBrzUcKUrBBVTqIerZhZJejrV/lLy3DoZEeE2EoGtWBJ\npOCvWRJ3tLp44vVYSe960We01NroeX3kSb8aUS0iH5y9ytAh3q2YiS21DYPR67YROArAC7ACilBl\nOOnMw9P3E7m11ajfdovOnkByE0fgKPILh3BN4V/x61m7MaKjV8pxQqT5pJnZrEAkhJvjRVurJKAq\nrzovPZUzGY8fl1ai7ZonRJmUrL7puudwTcU4eEq2oKb5UZy/NA+5HX3YMH83wIHryqS+WUzRAR6l\nqLFAH7HK7ejDmG2tQO1RXFY4iLbmdizpkJLl84KfIbejV4qIzZJE04gTvXJNwJuxv7YAG+bvxsU9\nwL/MGQ1gtJKUrwhXeYXhkn0LkDPI8XTebni8Hvz4bB22lhj85oGjUsAhDRFpElphMlpUOT2NYRT+\nNYpYqW+U3YODaD19CkHO0T04mHRHqC9JkCr7AzsjVFYjVmZ2EGbmpupEfcBaon2yuWFGv1s8IXBH\nIlmBowDvAxAEeHdC143T0WHCHYhl/Sfr/RiZmwv/qDLZ72k3kDNZ6Zsv7gVwYWHs/ilnsuxRlY/K\naRV4/LjWQBOQ+la1XxoAIHBUMwWmnybs7+lHIIcBxdLjIR/DH9va4W3/PDbcGsJ1404B44Dez85r\nysb4i8/A4/UokTPhbl41aY9m//7SMqy+YgMwGwDvRmEO8MvrnsP5S/Pw9S03o3xjK6bO9KPF65Gi\nVIEgpv2d5NV1z7dfwuLyAkwcdRYjL8tTxJSwlWgctR1A2LrAyxgADs6AYCiE1tOnlFkP/T1RKZpu\nQKz82oNyXUCKtsdHRosqN2AU/o118qlLlSQbrdLnPBlNZcVTgkWfRyXKEoht9CPVtuZ2eLweJa9J\nj1r0xYog6d8vpu1Ee0ROlZkVglOrFuNBn//iZUwzAk9l/l2o825ZUGmjAAgclW6AREaTjpuemW3C\nkn1+rG1ZprRBL25E0vTWvdHb+NDCSrQ1e7FuRxtaz5yKMNAEVB5VQjCozmcx5SWOv1WpkdeKPdM9\n+OVXXkEo34sl+xbAcz6AQ4ufBhCeTjvWXYrirhDe7/HIhZQlxLSjESK/7JLGM5rnx1/Zi1DueRQV\nF2DqTD/W730Ex1+vxZjLzuPkRxdBpL7nF+WhqmIcAFH+yPhYosxVgc8H+MIiCxhUhFXEqsqcycr1\nTQOg9JDRosrIUiHaXLLd6B2zowkqdWKz2lk7UXsFszyqeCJV8bqqJ1pYOF6Mihx7vB5NDSpBy4H3\n0rIaMRH0yexG54Y+Ypl+vHF1uE5Hhwl3EkvQnVStpgOMzxv19X3/nNHoaqjGptufw5CPYUaZPBXW\nVRHeiRgIiKiMCcJjae7GVuTP7sf5S/KQ98FnKCouwAenSzDkY5h8YSe8Hob17f+IMRtbpdp/FWUR\n95iqSebnedu7+QAgR6CAD1okUTZmYyvk9ZConFYBoAIPVXuxenM/goEggF55xd8peRthliV9VjGF\nCUheUEfqlkfMkjTN3o3LCj8BMC2i6oE6amf0vcfqOylCFR8ZLaqcRh9VEM/pT0KjaUJAilLYdcIa\n5VEl4gJuFuGKle8ktjdLglRP+ZnlVpmJNmFiakS+ajQZrW1uwig6qXdatxqhikfUaG4QYpRPI1gi\nAfS2CWbnYfhx9EGDfsAkvKGKu0KaosRqc0kzn0Kja0e4mW/+MfDCLYXYsuwlFOXlKjlWgqZZu4FZ\ngKfkQNT2qgn3X1I/98xbB8E5x8MLJwAACouBdTvaNPlN63aUYcxl5/H+ESn5u6vYY7xzFWIALj6f\nv7RM6Tf8o8qAQKfh++j6Ti9ZIarUESq3jqD104TiuUSJJUSiEY/YajnwnsZoM9FolZXpP4EQcYXF\nBejt6tOIo51nN0dYKRgdS78/K8S7wtFM+Jh5t6g9bdR4GbM0bWw7KkFl9Xpxg+EukTnor4WWY3MB\nqFbxqQonq/sI4dk08aYDGmPReM43/bGFsLq4uR05gdh1ONXnuKdki3z9boh5rf71RAl6zvXiR9s/\nAGMMP7hXquWn9p8SEastG6Rp0VcXjcK/5/4nqkeXR9ZBbH4U53v60Tteaz56pG45Wo7NlSJUQ3L9\nvaGDUrK/brAU6rwbrWdORXzvdl+/1C9kiahyCqtRBfV2ZrXfkiUeo0+rwkjt/aRHnd9kdvy25nYl\ngqansLjANKolRJM6CiX29+DsVZr8MbVhZ6KfMx6SzXHSrxhSP6fGaoQqkUHEcO7whht2WKEYEU1c\nx3tjlRLTK7F+7yOoWfkoerv6cH58EXqhispEKX+ij1C91XFCsi3weLBk73xlu+7BQRw62YFgaQ7u\n+t1tAKTVfZMvOAN4GD44fTGqr0382tBH+e/59ksApEFe45oKAOEIn2jz/tpH8fjC32IhA4IhKEaf\n0a5/db5lQxVQ6BsBcHcXNXab2Eple7JGVLl9BJ0qQZVIZ2nFVV0fjheeT/qOw8qKQ7Wrufq5aLlQ\n+UV52Hl2s+EUpECseDQj3giV1WlSkRRqZraot0XQT/uqcSRCpSJRcea268vtpErcuB39wPNXddcD\nAO76odSvPPs9KTG9cnr4Per6ncpzCZxvBT4fCnw+jR2Buk5f06xdmHxhJy4YIS0cmlD6qRL5kYSK\nlADfcmwu+oaG8FbHrQAk0TZxw78p+U36a6bl2Fzc9WgfqsZ9Jn9WqcDxv1w/Gm1HpNywiTdJbRwo\nLwCX8+SX7FsgR6GkwZboX4u7+lAM4MN/rQYDQyg/7G0lFgcoU/oG0/mS+DqF7oEBvHVqtGx7MR9N\ns3bbln9MuZZhskZUOYnVm6JTN9BUduRGKw31yeRGeLweRRDprRDUU41q0RZthaLRFKWdn9twKiEB\n1Mufk8HtgwjCWRLJp0wEowhVtBurKOOyUBYGe6Z7EJxSgd6N0jU8BlDKxBQVF2DM8604We+3fM00\nVG1Cf0W/pnhy98CAIlhEykVD1SZ8rqATrWdLlCT4o+dKMNK4tKAG4V1V9/uFmufFd/z1Rmn60ghR\np2/9TZL4+vfaIU3ZGv9Fnfi4bwyA5RHvze3oU6ZFK6dXpPVeUrPyUQBhmwWruE1spaM9WSeq3HZz\niaeERLr9QGJND6qFkbApiMf0MxrCLiEWiRSFjte5PZF6iOokUbPf60id1DEaOawLnPaAIXGWWtIl\nbvQI8VL8RkoPExVhgVBVoopYyQnrwg6gED22Ha+tuR39Ff0IBsKWKv09/YBP60AuzDRbz0pmod0D\nPhzrLsPIXJ80zTjULk2nyeV2hCfVjGPbcOhkB7weD4KhEBr/bjtaju1W8pTu/MnHyAlwLHhZmlr8\nzU0v4ooLTisWDqMaAjipE0OeAVV5HQ4gxJHb0QdM0jqbn+/px/9uDmH93lVKXhqgjgLOx9Y7Iq/d\nUOfdks3CkNTG1q4K+C/slBLybRQWZv2I3i9sOJB1osrtRCtn4xbMzDWtoBYoLQfeAxDbN0rvL6Vf\nyWjmwK5/LZWWD0b5c/HYIJhNGSYLiSDCCBHdHTNTGhilY9pRfyNd2yLlM22dFN5GXEdCKMxtlvsG\n2ccJkK7pqf8DnKyvwMnp5gNS/TV0Rs6VyvvgM/xqyWu4uBfYumY29tcWYBQC+MMDbwOBo1IZneIO\nAEAgxMDMqr7wPoCFc0Ibqjahe+KAEln6bHAEvKqSMZxzjaBTl+4xomrSHqnPemwvJpR2wtMfxMjc\nIYz0nzJMNhdEyy9LFv13LCJUolxPvBErtw3a0tEeElUuIB1FdI0wG0Wrc5XUOVXqxHGRhB4tkiTC\n1GI60OP1mAosO+oCJltQOt4bj9Xfx0iAienDVBfZtorTnV22kkgUNBn0fcmoWuOFIqlERKjM+rMH\nZ68CagsQDIbwzhutpotZjGio2oRQ5+6o52tRcQG8OR4AUl/TI/cH58+9jRF5AfhHTVYiNzkeKdeq\nenS5bDNSBvhqNPlJSrFnIWaGJFF14vwYAMDY3A7Aw7Dk/0mCLmccx69n70Jebh4uGDGIGWWfYNe8\nl+EfVYaHFvbhwY2rNAPElmNvgnHZdV7Fn9r/gvt/8qgkaEqLsGjZi2g5NleJjImI1Vsd8wy/ZyBS\nRKh9tsRzDy2slNti+pVahvqRJEUVY+xOAD8AMBlADef8kB2NshM3KGR1/o0oU2NHXo3d6EVJYXGB\nRkzFYyyqvpmIqFcoGDJMeBdRJn3Su95vKppvlrp9qaz9l+hvZja61tsupAs3XBdE6klV2aporG1Z\nJv9lfm7feCC8eCWWBYpaLIQ6dysr5MS1M6N8rOb/Mc+3YuuB2ahb+RrqVr6Gu8/JzuWFciWLocPg\nHNoIlfBtU7u0Dx00TAAX9gT3bpHyo/Yse1rZjdfrwVCuB6FcL7oHwivy1FU01Ej9mJS8v/qX2xEM\nMni9XE6SL8TPb39JWamYSvbXFhhG0kVEKtGcKoHb+plUtifZSFULgC8DeMKGtjhOKm406uXzIiph\nVBtw8Y5tlsvcJEq0Qsbqsi9GK/UAybNKH2kSNgdmlgZtze2a96hzs9SoTUKN7BTEa9FuEumOCiRD\nOovgWoFEVmpJ17nohvMqVhsSuU5FhApDB+EvDk/FLdm3QNN3qfd3/HVp1Z1SYFmG86DmcTDI4M1B\nuNyNnEsFIFzWaeigFCWSo1X+UWX4+e2SZcIFeZJg2vq/nkd+RT/+deghaZWhSlR5BoJoO9KOd94Y\n0Hz2CFTVNrwfdWOwvBA5gxyBEQxfef1WzCgfq3h2iajTjJbYv7XRdS0iVO+80YqeKX7peyy1f/Iq\nE/pjO0nqG+ScHwUAZjop7RxuWnWgFlZOL6GPht7vSZBI5McooT3a9J+aUDCkSZIvLC7A+Z5+pdN8\ncPaqtCf+WsWqZ5lTmF0XBOFWtt6xCMdf34C2jrB5ptl2AKR8JACVfkkk9XR5kF8Y7ncYk56T/pbu\nXYUl4eLPSr088be6DI6ckwUAky45i496RysvFRYXwF9Zga0li3Do8M/AcsL3xWAwhE+LgB9t/wAA\n8Mgyacqzt6sPP9r+Abw5XiWS1tPlAWMMDy+cgI56f7gkYAoRBZ/31xYYrixUR6hoABadtOVUMcbu\nA3AfAIwbl4azJAoRKxTki1CMVOw8aWIlNhu5b9s9NRhtBZJ6pZ8onqwvyqzfXiSgC0fzB2ev0ggd\nM8NQ9TSimUGnUYL8+Z5+hOLIv0hX3orRbxSv1YLTIktflJY6zOzA8fPKQhviuU6FFcG67VKh5bUt\ny5Q+s0eXHvD9p6Vo0Ei5TvnfOkbh0rGdyCsMSVNr8nNjLjuLkx9dhMY1N2P93kcM6+Ut2Tdf8Xha\nsm8eds17Gd0DA/B6PGj59GIs2TcPz97wAgCg+to9SmWP+3d+CeeKvdg8dzcA4Fu7vwhAsngAIlMV\npL5R8rXy5niRX5SHqTP9mPo/wPrHv6NMv21dsQiAuJdI94l4f2vx2dbv3aL53tbvfUTp2+wYrDq1\n8tVpYooqxtirAC41eKmBc/6C1QNxzp8E8CQAVFdXx64RkCR6weSGm4QbOjorJGtjIEQQAEWAif0a\nJaSbJamLCFVV7ZWW8y+cQl8H0i3TekBkuQ31cwoxitIShN3EypFUb6OkBxz5BMJKKmeQ48KuoGLK\nULdSmu4b6RNmu5IXVuOam9HW3I51O9qkqcCcyXjoDuk1KSIuDSbrVrbL5WMiaxZOvrBT8pEq/kTT\nvqZZuzDxok/hYUxzTYnIzlsHdwIA/lD/bHh6EWFn9YcW+vHNt6tROb0CTb7daDvSrog8IDWrmSUH\n+7ABqRplMcHGyGO7afbHzcQUVZzzOeloSDqIOCn+OllaMqubS7czQhVrRV86ciCi5TDojTetlLtR\nT+GJ58739CvTe0ZTfFNVS6b1+xbRL7FaUP23ehujtqQDs+Ry9W+mj1Alag6abty25JkgoiEiVqgH\n8otyUTm+TBmUnS3zabYNqCwNerv6cP+c0Vi/888A3kPl9C9hf20Berr6UL4xMr1B2x8vQsuxufB6\njJPNP+4bA/+Fneg9fwSFOVKUTFxP39j6BQDAke/tsvT5KqeFB7TqWYBiWVTWQGtxoO+bot0/RJsq\n/ac0j0XECrA3upRJOa52klWWCkbhW820hryKw+2k01JBTPXpE8itXgBmXlRilZ94Xp3QLtCv7jMq\nZ2PWjmjtE/sV9QnTgVh8IASXE15kpsZ7BiNLEk+EUxjV+jSzQdHfmE/WS0lV6soG3bLAEHX+3lvY\nCCBsl1C38jX0LO/Fwwsn4Pv3XAUA2HlWmupqa25XBnyhzjYgcFS+TsI1A4Hwisam0dLqw/BqvpCU\nkM67Uai6m4po0NM3vouqml7J2FOGc4AFjmLJgYeBeuBMxwmc6TiBJftkX687tN+XWSS/adYuueTM\nMsPX9Yg2iby0aBErI0Ri+93L3wUApRi03ophuA/QkrVU+HsAGwCUAniJMdbMOZ9nS8tSgSKovACC\n0j+RlGhitJYo8UagYr1uZnxn9j6j142EyPmefs1Fa6WWnj5KpBZUwoJBX6bmfE+/ZipQ/bwRiUzz\nmeVyJYo+2qhfuq3+bvX1/pye9ot3ECEGInbVAiOIdPP83P8GgAgzz8ppFWj53XsoLC5QSuDUrBT+\nTzlY/NhetBx7U/F/Cg7+Eb+4rhnX7Px6RD8a6tyt7Fe/wg8IG4Iu2TsfNx7ow93LjSNUvUNSceem\n2bsRnBhSHOalY4RFid5uZr08rbh4xzaMzA3X1LHicSiifKJNWzaES+bomTpTUl52RJeGS4RKkOzq\nv+cBPG9TWxImYkSucqPNJNt8q1OG6pWEiaAfKaqx6msjxJeRoDIyD1VbJBiVwdHvw8w1PRr6/TkZ\nsUo1mvNYiCd1RMrAvJDEEuEG9JYr6j7HamTaaAGQ/8JO6UUlKiTlTXlKtuD795gPuPKK8nBZYThX\nyss4CnKG0DRrFx4/fr9mW0/JFlSVaNvRevqUUmMQAEbm5soCaBEenC15QD2+8Le4pvSv6Av4cM3O\nr2NG+VgU+KRpOK/HgxnlY5V+/vjr0sCscY0un0yO9q/7zYtorO7HyFwpWmZkLwFdtEv9HQqriXjF\nTvh30z4WUM6VRFZN/8UifCOSfUpEDtUlhzWvpyJilSj68ibTGjcoVdff6jhhGsGKx51dncdk5UJT\n5zip7RLUeVB6gWNWJFmNWNkXr2Gh3REqQSL5bk5HqABovHWsRKyoMyTcSlypEOIcF4suVCVmxMBK\nlMBZv/e7yr5/VXc96la+hkp/J8S9IcfD4b9IqpGnvg6MBmr+0jL4R5VJDug7v4SDa76rlOZpa27H\n+emXg/UHwUIcjHP8euYuXFXyKVo+vVgpefNtNCL0tx8BOZOVnKfVv9wuHXOiFDVS94s5AUBk7PtH\nlaH1zCnMKB+LMc/Htr9RF3UW6PtzEaki4icrRJUyGmcjpZuJKo8qk24MVurLCUElSCRiZWaVkOg+\n1BYM+v3oc6sArR+WerpRv+ow2aTJdEaojH6zVAgsvQDSIEwL9d46Nk9tE0Si6PsHkUcZ703caOpd\nWA08OeMICn0j4LnksHy8VTH7jMY1N2Pdb15EMNQDL5P619azJfCd+gvW7zO3uNl6xyI8OHsVHtpY\nif+aUgmgD8dfrwUAfP/scpys96O3NAeL3/p75H3wGQLjRuLpL+xGSNWHC7r7+3GqLezFFciRxJP4\nbtZtb5PyoHg38gslPytvjhcnP2pHf7EHbc3tOGOhr0x2Os7s/bToRSIrRJVVYlbSTvFIPZGbrVpo\nCQElolBGDuyJriZM5kJTiyGzvCv9VKMQVOqSOImy8+xmZQWhEG8eryciMT5RXBF9EghfKTMSmPKj\nzpCwm2RXfCVTD/Wj3tGmRYfV7Rkjr/oTfdNDX7kVix/bi0mXnJU9qBZg1OkAgHbM+8oiTa6oOmK1\nX66vWL5WrtJ2e/h4ldMrcEb+DE8t24eivFyNLcNngyNw9FwJvvXcLUqB6VWN2xDK9UpO7blhqwig\nwvQzF3eFpNI/Mb8dY9T9dt3K11A5LZf6gQTJGlHlthtDMtEKs/cIAWWXO7vTSYhqt3WjKb9kluRW\n1V6ZcLsEVn5Dfec/rXFDar2qTKbxnD7fCSIaZtNLdiVEq6/DBR3zMOp0AOd7HsGlMSI3+2sL0DPF\nr9gqbP2OVDPQV8yR98FnKN4oGQ73RqkEIfqu1a/Kxp7y9F2TT3q8ZN98HP7zx8gJcCnvSw5SeT1S\nFH9kbi5uPCCJtZP1foRyvYAnnG0/UF4A/6gyeEq2YOJN0j1OWck3rQKVhVIwQIpkfSI/75woGu59\nUdaIqnjQ/+ipFmTJjLoE8d6YnY6umJn4WX0+0eNlqydKxLSfeqrPgETO6eHeGRLJYzZlr0dEd4SY\n0BMr4m7HgEU4iKttFaQ2i/aFrV4Abe7pzrObsXjHNk2+a1eFJ+IYveeP4NuTOvCVjltx1+9uw655\nL+OKkT0YCI3AV16/FYBkYpozpRvlG1vRVVuAG95YiPMTLtBYJkjFpFO7yCrUeTfWbQcwdAoYOuWa\nAEWmkTWiKt4TIJ0CKpmVevr9irIE0UrepFJQOSFaUnGsaN9VPCI4Wh6clQhXwr+VLKaowyPSTSLn\nboTflDD2XWNPX6W+Dtua26WpMFnYidQCdT+iucZLc7C/tkApDya2u/2iewDZhkFN29LLDfvf9e3/\nCABoKNoEAKiatAUfHZuLAp/kKTX5wk5cMEKKYud4BvCn23+Fo+dKcM+e+Rgol9o4ZmOrYvvAZgL9\nPf0R37OIWAmUMmuQIlcYOmhZFLmp8kO2kDWiyg5SdYNST9sB2hM43pPajqiXEyRi4mnncTKdeCNP\ntKLPGoyxOwH8AMBkADWc80POtiiziRUxFhGqMxb7L6MIVUPVJqyQLQTs6v+M0g/MvPLUwk1dL0/Q\nPTCAyRd2ouXYXMX7qjeQizyv1pG9IGcI/uIzuHb859DW3I7C4gJNSsTXXpmP3I5e/GdXbVgswcAW\nSFQEiZVvGQNPyRb5+z2lTDcS8ZPxoiqem4dmeXmKbjZ2l53Ri6iRI0ao3Hwl9LYLqRBa2VIc04oo\njfYbxio3ZPRavMcn0koLgC8DeMLphrgdO85dIVTOqArIJ0ND1Sb5r0XhtsgeTfN8i3Di/1wpTe0l\nUB5MLxCNolvC7Fc9E7Fk3wJ56i68r496R0vbjSpT7kF/OvGxZn+o9yN/egW6agtQBODMCIbA+CJ0\nFXvQeuYU/MUxvgxdKoCVCJX+tySSJ+NFlS0opQkkUqXQjW7O8XZQ/tIypa6cPgKmRgiteBzXiUhi\nfY/pxuq56baFG26Fc34UAJjehptICrPBVjKDzlDn3WiaBWCoHQCwa97LUuK3LCbEuS7KqYSCIXDI\nppkbjVcCW6nRqY9QQS6L09PVh0CxF7+evQvXVIxTrrHFO7YppqENVZvgH1WGqknh6zEY6MFgz9uo\nuUQaHDfNkhzO1Uaj53v6gdIiAMDXNs9GUXEB9i59GvlFeZprWV0IOprBdazvW3wP3YODWNAxT64a\n4Z5+L5PIeFFl5eZh6O3DRkYsP7cTu05GdfkTEaESq8v0N3x1iRS7Rx6Znggeb5kf8ZrYzq4IUzoK\naBP2wxi7D8B9ADBu3DiHW+MMbjt3Lyv8BOADESa3bc3taFt6OYJT/OifcAH6AeyZ7okYHD04exVy\nawsipvmiRaMf3LgK+2sL0CnpHQRGMAz5GFrPnMLaKH5WAk/JFrTJPlZilSDjQE6Ah997h9S2/UUB\n5ThNs3djRF7A0vdiZQCl/y0Fb9kQQczUe4RdZLyoSgoRodIZKaZjdG9HByVCzmIfQmxFW9afiDhw\nSyeaToymXTMRilABjLFXAVxq8FID5/wFK/vgnD8J4EkAqK6ujnRuJOIikb5EM4AOHEVhvsrkVlVF\nYPXmC3D+0jZ8fcvNynvVZbIAOepUW4AzpTk4Y1CZwgyRQ3Xm44/x1LJ94AyYUSbZGDT2r2hEAAAg\nAElEQVRUbZJrA0qFkYW9gz7qM/GmAwCA46/XoqvYg2/+caE063Bt5HHO/fljPH3PXlSOPS9VYubd\niqVC45qb8c4brbj/jdGYOrMSdStrNREr8TlFW9SPzdIXhmNfbzdZI6qiRaiUi1FeJZEJdQAB7cpB\ndXRKb/qZzvnwTBt92JnD1Dc0pBFXyXQ8qey0aMpPC+d8jtNtyCZcccNVzzLo8mTzi/KQ3wNc8YwU\nsaqqvRJbV2gjVG21Bejp6gNKLwAgTREOlBdYyk3desci1Kx8FDkBjjyVWDMzHDWqx7d4xzY8WOHB\nkI9pBsD6Pn3z3N2YcOGnAB8MvzlwFEB+HF+WOZR3az9ZI6r0WKl5Ziau0nkzElNMRnk7radPRSSl\nm+0DSKxUSjS7h+GcVG0kWK3kXwjc8F2RuCISxc7z1+5rIeJ8lgWWKA9z/xwpKbwwSmK3sFzoqPej\nqLgANx7ow8n6ipjHFp/lTGkO7vrdbRg5YgT+vfY/UT26XGnXmI2SmMibIr3nl3Nekt/9Xc2+1rf/\no6XptqPnSpRomEhbmXjTFqy/SeuAbuQvFW8Eajj07akmK0WVIqhEUVnh46GqCQi492YjcqPU0Skv\nYwhyju7BQTmcHCYTVm6kS2Toj5NsWFsI26CuVpdRMWunMTUIJUxhjP09gA0ASgG8xBhr5pzPc7hZ\nRAys9OGV0ytQ2RzC+h9rr091fmiXbGOg9suK57r2l5ahwOeLuk0wIBVprln5KABIBZdj9Evh16XH\nTeW7lSCBW+9bmZ53axdZJ6o0gkrA+zQVy/U4dZKaRYL00Smjm7oRVpb1iyjWyBEj0D04aBh61u/D\nKUGUzmObIVZbiu8pFq6K7mXIIMJJOOfPA3je6Xa4BTvP33RfCyJXaerM1N3UjftEY9F2z65qAEBV\nTS8A4OeXGUesBDHFiIGgknKv/MA+YMVEyaKh7vcz5PSQyHYTqSfrRBUAzfy60So/t95czKIi1WPK\nNY+TSTpPN+lqY6zjJHo89Xetnyp10/cMROYOagYWBOEyEoloRPgS/u3ahKM3RsadgH0lccLJ8Z8B\nAIZ8LGJ/1qfj7O9rUtUXD9cIlSDrRJV+hYjd4VI7hZlZ3o6Iinhl7xw7E9LFPo7ULXfNPLuRIBIC\nxkgk6b8HN4kbV6yiiZJHSBDRsPP8NbJ6SQdWbupmg7CmWVIR5Fj9u5XP0rhGWn141w9fAwAseWsB\nAGCGdowcs16i/vPo70GiT6z7/UL53mE++0CknqwTVRp0gsqtESoAEav8xCqzaJER9VSeejWgm7C7\nYzWKGMVznEQFWTLtTrfAypTVrcTwRC0iOur92L/yUaXkSyyUGQc2UorEqqa4zfp39fUXvibmG27b\nekZajFJVEjv6LfqaaAPUupWv4ZILz6Dt3XyMOi35TKlXIiZLrM9j1G67Zg/cODPiBrJWVNkhoNQj\nglTWUjMz67RTKOkvpGmNG1ImxIyiSma+Weq/Y+VUCUHVPTioWTVjZD2R7AWf7Ptd1dFQ/T8iTuxc\n9ae+JhuqNqFuZZ8SxUknob9dK+XX+q6N6HNEhEq4tYc670ZD1SmsbVmW8PHW730Eoc42tB0Btmy4\nGQOLjPN69QneAn3kat32NrmNqutZno2xK8KY6j5iOCSxZ62oshMr9gzJEu9FoRdJ4rl4LiajEixO\nJqXH2j6ehHGz4+mXMKfy82ZCzhtBJEIyN9/KaRU4We9HV20B+ktz0A/gZL3fcv+lj8Qu2SdFadSJ\n2YD2+muatQvBUA+8jIcd2AGICI+IUIn6euKxkT2NfnX2xA3/puTBRoq0g6j0SxGrr5cXKCLNlr5A\nvcLdIGIVq/9JNkJF/ZoxJKoMiEj0lZemZ0KyezSEcahY9TdyxIi4vJdiYXaxAbBkWGp0Uaq3V+dY\nqadHkxWFanGZTR0G1f8jnER97YoaeBhqB4ba0VB1Cv0V/bjrd7elvB1/uv1XKMgZkgSVYOiwLmIl\nPf2L69YAgCJ+tk5K7Jh6kTZQXoDugQHFiuXbkxpR4PMpx916x6KI6I1ZVEeJuKkXZCG8n0RI5UwM\nMLyMQUlUWUG9ND1NESur2yVz01eH5fWJ4ep8ATuJJrwA489hNAVoZlhqhpmYU+/TbCFAokWV05W0\nTqKJSBd23Xz9o8qAUcCMdslzL55rQx+hirXit/vE/8VAaARyPAPyHrwAK9C0ORxZGtQ+RuQKPdEn\nHmyZDQCoqdqLxTukYstjnm/F+r2PYFqjJKrevvMtqS5gyzKlna2nTyE4MYTugYG4BrXKd69e1ata\n3a7HqheWGWb1DF2xGMfFkKhSEXnSeuX/g+GNXGy+ZoRexMwoH4tDJztQ4PMlPJUW7ThG+WHqKbdE\nBJEa0QmpL2y14BHHtyKChFhSd3Yi8hXt86SCVAqjTDpfiexD7eUUeZ6n/tq6761/AQA01f5IyalK\n1TWxv1YqdaMYN+umEUWEyl8sOaQ3/t12AMDiHWXKdoJEoziJ9CVGUe21++z7bYaTMeiwE1VxnXDC\nMFSOTLnx5pTIKKHA5zNcVbh4xzZ4GdMILrtHI3rRIohmnaB/TS/KWk+fipl4b5YErx4pdg8OaiJW\netGV6HeR6giVxrMHgOeSwyk5HkHYPaUsrg0r+9Of702zpIfhnCpzw2MACP3tRxERKkE8n0tsUz2q\nA4BUGHn1RcD9a0fjw3+txrk/fwyMkOxwluyV2nZEnkYs8PnC06Bxkuh3H2//IyJUVoswE1qGnaiK\nhtlJm8lL062srEsGKzlIepuDRKfw1Cv/9MmiXsZw6GRHXCJIv6IQCAu2VEeoIs4pWqFHDAOcPK/F\nYEMfLYl2zcV7PeZ29KGouABnSqVba+TgVeqLWo7NBQA8fnyZ6rXksGNq1u4IlZ5sjlAJho2oSuaE\ny7YbXCzfEkASKdVjym3xZ9JHvdREE31GIkzdRnU5H7H65tDJDs3+Y0Wb9HlaVq0eEiUZga4/Z80c\n1EmYEanGrnMrnn7ZbNCrX/WXDGKfYRsD821EOybeJD2eOnMVfn77a6icVoGrn5sht824z/CPSjwF\nItXXNeVMJUdSoooxtg7SGs5BAG0Avs45P2dHw5wk1knr1ptWtIsg2oWRzArAeC5AcRx1oWirCeBm\nuU6HTnZoyvqI4qZm04xmnyEdeVMApIUOQDhvz1ej+d+Wc0ocgyAcxG35M/oVaMdfrwUAVPqlfknd\nr9etlFzQMRT5mhViReFjCcJEo0yKMWqG5f5mE8lGql4B8E+c8wBj7FEA/wSzapEOk6rl5Ub7c7vo\nMlt5J1CLnmmNGxJeAagXXKK2ofo4VoRVrOk7EZ0SkTX157Iq+hI5fjwYrtyJ970mo3kxraGJggWO\nRnWZJgg3kEi/nMpzuu1IOx664x6s2tQbVztCnXdLUa2hU8DQKSXfKxU1+4wI+2PZt0+KUCVGUqKK\nc75H9fAPABYm1xx3Y3hzS7HFghUSsSkAIiNHyWA12qQ/ptW8qmg5YSJSlaj1gSOovM9sQx8FI2FF\nOIDbPYmmzvQDACbe9BwAraBrXLMKQDseXjgBALB+55+l9yxIzzWUaJpKQ9Um+X3tynMtx+Zibcuy\nzOgPswg7c6ruRTrWxyaJ7TcxtaOtEFgmF4TTESwzrya9y7iXMQQ5T2gFYLRt1cadImfLTtQCzcyz\nximSWfRg+fzJmayd+uPdJKyIjMAN56cQf71dfQCAwmJp9Xd+UV7M94r2iylFkWeVKvR9+IqJA4bb\nNVRtQqhzt+1l2whzYooqxtirAC41eKmBc/6CvE0DgACApij7uQ/AfQAwbty4hBrrNJqbm05AOUks\nsRRrSax+JZ0eO13XAW3EKhbRVhdmckJlqvyolHNTRKscjqISw49UehIlcq3rI2ciUiUIX4va2nvn\ne/rx4O3j5fck/1msiBL94hOr/cSSfQsAAM/e8AJCuV78+Nh8pTyPmYknkRpiiirO+ZxorzPGlgK4\nFcDNnKsyhiP38ySAJwGgurradDu3YCUqYFa2xtSSwSXL5vUXlzrCo19lpy4HE414xU+qysG4vcyM\nOGcSiR5ZzTdRi34aVRKpwm3XlhqjPrajXhJTU//H+D1qMdjW3I7K6RWKEItFWLiNlo5hUYSpv0Mr\nItTIzFn839bcjuKuEAbK89BQtQndEwcwo0wyGU0mYuW2+5fbSXb13y0AHgYwk3PeZ0+T3I9bTyZ9\n52a104tWj69vaAhBzpWaVVb2ZydWolFu7NSdRC32CcIp7I5Qqf3krPZFIoHcU7IF+1c+Krcr9lqq\nyukVWL/3EVuibfGIkrqVryHU2RaXPUpbczsAoKerD/fPGY2pM/3S6sXyAmWbZCwcUkk2CrRkc6o2\nAsgF8ApjDAD+wDmvS7pVDmL1ArDip6J/7MYTyCiiJJ438oYyI17xk6qpu1j7dXJ0na4RnxvPMyJ7\ncHM0WH+NgY1E79Ag7tuxTTHkjNXeRASU2ZTn7Rfdg9Wb30PVDVdqtte7ltesfBQ9U4C7z/Wi7Ug7\nKrUzlApG/duDG7XTmwDQuOZmnKz3K4Wsk+kL3Hz/ciPJrv6bYFdDCPtJtpMz8oayG7vztQiCyC70\nJsJeaQBvrT/i3SjMgTIdJnKPrJJshGr15vfw/Xuuws6zmy3V1Xt44QQl0lQ5rcKSgImWwxbq3G34\nHqfJ5inFYeOobpVYqtzpk8Gu48UabSYqoOJ9XzLFleNphxtG16ke8Tl9bhLDAycWiKgHX0HOTe1T\nzKoM+EeVofXMKcwoHxtXe+P9jPoIVTAQRG9XX0TEKuI7XCHnVM30Y/3eR6QpwChYbY+d1z71I9Yg\nUZUGMj2/xa7kcSOPqVgixw1iyE2QUCKGG9FqdJqiW/Wa6pp2akSEqqrmMwDAj7Z/AG+OV4lY6REC\ncYz8WIo4VcYdJXOLD5gVsnlKkUSVCWY/ckInw9Bh+Y+g5r2xjqXG7ihEOkebbpjis/vzurEzyOaO\ninAf6V6wYlaj0wj1tdB2pB2Na1ZhaxyiI9nBXNUNVyp9tTfHi6obrjQUVOr0Cv00nptxm6GrmyBR\nlULC4inoaDucItqKnWgrDtVkmhdVLEGjd+G3KoBoao8Y7sRbo1NEfKR/kaTqGlqybz4AqWRMy+/e\nM41Q6YVbjbw6sdjEiV792EjUZEofqSYb+y8SVQli6WSIKGzr1T40uEGaXeipikKkOkKlrvXnpohV\noiQjbjTGnHoXfhvJxo4qm8nEm6FTxPMdCasB4ZAeT3TFrsGcWYQqU3F7CSI3QKIqlYibpbK8t8B4\nuxg3VsVd95LDptu4DX1dPoFZLoRV93e3EktsRTidA/K0cDAssGDztDNBDEP05Wb0WLpWEf81po88\niYjV1juMtzdKVgeABw8YR6iEkLn9ons0QnF/bQEqp1dQ3qlLIFGVQiJKDuhEkeFUUIwISDpupnZd\nlGqPKy9jlnIh3BDNioZZx2tpMYKmrJFXEtlqkUUMK2gRhv0s3rENbbUFuPFAWFCJGn5CpMSzcIh+\nCy2pLEGULZCoSgcGUSj9VBDYyMj3DR2WBJlw182giJV6FBYtudRqbpWbMJq2izXKNaobGW261wyK\nUBFEdCqnV2D9mkWmCd8xB0ZiYBtnf5volKF+O71QyeacqmyERFWKiXrTVEcuDJYAK14rie4/TpIZ\nOUfbNp4IVbxlKPQk+n3E/T6DunqWc6SoJh+BzFuE4WaM+i4RsaJoiv3Qd2oOiSqn8dUAMJ5GEiOk\neEdMbsq7sXqjELlWVkriWCEV34GlJPUYgsmJ6VyCGI6IiJUZ+mvvoYXSKsF1v5EXGMVRfw9IvThW\nCxkjUUOi3B2QqEoRlm7AYnWgwTSSLfuPk0RGznblhSQ7ajeq+ZXI+6JN7cW1DwvvI5yHMbYOwHwA\ngwDaAHydc34unW2gm2HyxNt/GE0NtjW3o3J6RcxjUT4REQ0SVWnG8OYfOIpQ593mK7+s5lDJ+0n3\nzT3RulopJc5RZlxEiS7aAYmytPIKgH/inAcYY48C+CcA33W4TUQaUVsvvPNGKx76yq0AgHXbpVIx\nViNUtOCAAEhUpYyoyZBq/yreDcBr4GkV//7turnH0xlsvWMRQp27E6qrleyx1ZjV/LKMryb8G4jF\nAzKxvleyO8hcOOd7VA//AGChU20hksdqhErYExhhFrEy82hCvT9mu0hoDR9IVKUIsxuxIn40nkVB\ngHfHbyRpsG/1a6m+uaujbv5iKWIV6tztrKgwSPhPmoA2x0JEquyGphEd514AmbMMlbCdwmLJ8yme\nqT1acECoIVGVYoxuiIqwGjqctFeRm264/lExipymgWhTcoZRPRGR8tWEbQ5EtEu/ik8VvYp2bMJd\nMMZeBXCpwUsNnPMX5G0aAAQANJns4z4A9wHAuHHjUtRSIhbJCpdo9fVEhMpMUCXi0URTg8MPElVJ\nEtPrJIpvUSoKK6czupHIMdPVPjv3n67vlqYRUwPnfE601xljSwHcCuBmznUlAML7eBLAkwBQXV1t\nuA2R2cQbodJDQokASFQ5SqoSnQkt0YSo/u9Q593kcj6MYIzdAuBhADM558Z1TQjHsTvik4x4oqlB\nIhokqhLEroiRle3U20SUvokzQpYK7IiwpZQ4FwGYka7IEUWo0spGALkAXmGMAcAfOOd1zjaJIIhM\nhUTVMMXxKSabhI4lRG6UfEx1VEr9+Wn6bfjBOZ/gdBuI2GR6xCfT2kskDomqBLFa680MK5GbqMaU\nJj5M6RAEiUThnJjqjEhGVz9vpZwMQRAEQcQBiSoXErfwiGEearhvddHQdNai09kT2CkCLe9LVcja\n6D1GwpaiVgThPBTxIdwOiaokSfRmayWyFG0bM9PPdESoYuVFRd1ORIdi2BPYgWF0TESo0nB8giAI\nYnhBospFmIkR0/yjBMrSKCKMjZSiNaqIkX41XMLtj0KEs3yMabh4EvktfQ/CiyrGfu1YiEBRLoIg\niOEFiSqHsXTDNRAddpalMcJsWkwRZFGmDN2W8J2Q8ElnIj1BEIZkamI6MXwhUeUiTJO5TaIliYoX\ns7qB6qgX2EjL7TaNsEXbXl2mZ+hgRG5XPNGnRL6HmInzJuVuEl5gYLFdBEEQROZCooowRpdIrqAr\nNBxrqtF8/876LMYUggmIS4Ig7IHKuxCZCokqF2KW+B1rui0asVa56U1F43EVjxb5ippcL6bYDKYS\nk4k+mZFQxChGxMquyBlBEASR+SQlqhhjawDcBiAE4BSApZzzk3Y0jHAIExEhSFoo6JLrAa8j+Uux\nhA8JIoJwjkw3+ySGL8lGqtZxzlcCAGNsBYDvA6ASDzaTzI093vyeZEw6jXKzNEabQkipRZTv2pj7\nTJZU5DjZETkjCIIgsoukRBXn/DPVw0IAVL09S0ipIMiZrAgtp4VHMlOqBEGkFopQEZlG0jlVjLG1\nAL4GoAvA7KRbRNiK2TRXLEPRqERxcE/EsDTVUI4TQRAEkQ48sTZgjL3KGGsx+HcbAHDOGzjnnwPQ\nBKA+yn7uY4wdYowdOn36tH2fgEgLoc67pST2oYNSErvIjbLyPoPt1PUACYIgCCIbYJzbM2PHGBsH\n4Lec86pY21ZXV/NDhw7ZclxCIt4ix0p+k68mMufJV6N5jxJZGjoMIBh+QdgNRJnGy7ToUKa1N5Ng\njB3mnFc73Y5kof6LIIYfVvuvZFf/TeScH5cf3gbgvWT2R7iTsP1BMOa2mvcAKTHAJOFDEARBuJFk\nc6p+yBibBMlS4SPQyr+0k+zqPkMncIHaANPMrJN3S47ocQgdN4oickEnCIIgkiXZ1X932NUQwsWo\nvavUdgjqKUMdEYWTkbxAIeFDEAThPA/OXgUAWL/3EYdb4j7IUT3DSbb+X7TnzFYMGtXoiydC5UZR\nRCsECYIgiGQhUUXERxzeUoqIkqcIk62jR8KHIAjCOUSE6p03WjWPKWIVhkRVlpAKgWG7a3iMEjhu\nwI1tIgiCIDIDElVETBKdtotlPJooJHwIgiDSj4hIUYTKHBJVRNpxQhTRlCFBEASRakhUETFJNpeJ\nhAxBEET2QBEqc0hUEVmNm1ccEgRBENmFa0TV0NAQTpw4gf7+fqebQpjSIP136qjpFnl5eRg7dix8\nPl+a2kQQBEEQ7sA1ourEiRMYOXIkKioqwBhzujlEAnDO0dnZiRMnTmD8+PFONwcA2TAQBEEQ6cPj\ndAME/f39KCkpIUGVwTDGUFJSQtFGgiAc58HZq5RVaoQxoc67k16NTWhxTaQKAAmqLMCtvyFFqAgi\ne6DIM+FWXCWqnGbt2rX49a9/Da/XC4/HgyeeeAIzZsxwullpY9++fXjsscfw4osvOt0UgiCIhCDX\n79jQAp7UQaJK5s0338SLL76IP/3pT8jNzcWZM2cwODiY9H4DgQBycuhrJgiCSBYSA4TbcU1OVSIs\neuJNLHriTVv29cknn2DUqFHIzc0FAIwaNQpjxowBALz22mu4+uqrMWXKFNx7770YGBgAAFRUVODM\nmTMAgEOHDmHWrFkAgB/84Af46le/ihtuuAFf/epXEQwG8Z3vfAdVVVWYOnUqNmzYAAA4fPgwZs6c\niWuvvRbz5s3DJ598EtGu5557DlVVVZg2bRo+//nPAwDa29tx44034pprrsE111yD3//+9wCkSNPM\nmTNx22234fLLL8f3vvc9NDU1oaamBlOmTEFbWxsAYOnSpairq0N1dTWuuOIKw8hUb28v7r33XtTU\n1ODqq6/GCy+8AAB49913UVNTg+nTp2Pq1Kk4fvy4Ld8/QTgBY2wNY+wdxlgzY2wPY2yM020ikmP9\n3kewfu8jmDrTj6kz/cpjIoynZIskRH01gK8m/JhIGgqhyMydOxerV6/GFVdcgTlz5mDRokWYOXMm\n+vv7sXTpUrz22mu44oor8LWvfQ2/+MUv8MADD0TdX2trKw4cOID8/Hz84he/QHt7O5qbm5GTk4NP\nP/0UQ0NDWL58OV544QWUlpZi27ZtaGhowFNPPaXZz+rVq/Hyyy+jvLwc586dAwCUlZXhlVdeQV5e\nHo4fP47Fixfj0KFDAIAjR47g6NGjuPjii3H55ZfjG9/4Bg4ePIif/vSn2LBhA37yk58AkITZwYMH\n0dbWhtmzZ+ODDz7QHHft2rW46aab8NRTT+HcuXOoqanBnDlz0NjYiG9961tYsmQJBgcHEQwG7foJ\nTKHRKJFC1nHOVwIAY2wFgO8DqHO2SYQZ2byal6Yps4OMFFUiOvXWnz/VPN72zesT3mdRUREOHz6M\n/fv3Y+/evVi0aBF++MMf4uqrr8b48eNxxRVXAADuuece/OxnP4spqhYsWID8/HwAwKuvvoq6ujpl\nGvDiiy9GS0sLWlpa8IUvfAEAEAwGMXr06Ij93HDDDVi6dCnuuusufPnLXwYgeXrV19ejubkZXq8X\n77//vrL9ddddp+ynsrISc+fOBQBMmTIFe/fuVba766674PF4MHHiRFx++eV47733NMfds2cPdu3a\nhcceewyAtDrzL3/5C66//nqsXbsWJ06cwJe//GVMnDjR4jdMEO6Dc/6Z6mEhAO5UWwh7IXESm2wS\npW4hI0VVqvB6vZg1axZmzZqFKVOmYPPmzbj66qtNt8/JyUEoFAKACBuBwsLCqMfinOOqq67Cm29G\nn75sbGzEW2+9hZdeegnXXnstDh8+jA0bNuCSSy7BkSNHEAqFkJeXp2wvpi8BwOPxKI89Hg8CgYDy\nmn6Vnv4x5xw7duzApEmTNM9PnjwZM2bMwEsvvYQvfvGLeOKJJ3DTTTdF/QyJQvkTRDpgjK0F8DUA\nXQBmO9wcw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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(1,(10,5))\n", - "\n", - "pl.subplot(1,2,1)\n", - "pl.scatter(xt[:,0],xt[:,1],c=ys,marker='+',label='Source samples')\n", - "pl.legend(loc=0)\n", - "pl.title('Discriminant dimensions')\n", - "\n", - "pl.subplot(1,2,2)\n", - "pl.scatter(xt[:,2],xt[:,3],c=ys,marker='+',label='Source samples')\n", - "pl.legend(loc=0)\n", - "pl.title('Other dimensions')\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute Fisher discriminant" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "p=2\n", - "\n", - "Pfda,projfda = fda(xs,ys,p)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute WDA" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compiling cost function...\n", - "Computing gradient of cost function...\n", - " iter\t\t cost val\t grad. norm\n", - " 1\t+7.3324064745743989e-01\t5.95226695e-01\n", - " 2\t+4.4853951178403229e-01\t8.20231271e-02\n", - " 3\t+4.4576445851595303e-01\t1.93811424e-01\n", - " 4\t+4.3562246733680465e-01\t1.61725387e-01\n", - " 5\t+4.0969564472790077e-01\t1.32513662e-01\n", - " 6\t+2.9388094458668662e-01\t2.00393708e-01\n", - " 7\t+2.7344485803563923e-01\t1.99320846e-01\n", - " 8\t+2.2883182995370804e-01\t2.82035999e-02\n", - " 9\t+2.2828598049696755e-01\t7.08646563e-03\n", - " 10\t+2.2825222787200100e-01\t3.22559822e-04\n", - " 11\t+2.2825220998825529e-01\t2.82197677e-04\n", - " 12\t+2.2825216255973643e-01\t9.26049149e-05\n", - " 13\t+2.2825215676825239e-01\t6.60508771e-06\n", - " 14\t+2.2825215674473881e-01\t3.35243645e-06\n", - " 15\t+2.2825215673980734e-01\t1.97305673e-06\n", - " 16\t+2.2825215673953242e-01\t1.86785394e-06\n", - " 17\t+2.2825215673856641e-01\t1.41420230e-06\n", - " 18\t+2.2825215673757782e-01\t6.92577574e-07\n", - "Terminated - min grad norm reached after 18 iterations, 8.84 seconds.\n", - "\n" - ] - } - ], - "source": [ - "p=2\n", - "reg=1\n", - "k=10\n", - "maxiter=100\n", - "\n", - "Pwda,projwda = wda(xs,ys,p,reg,k,maxiter=maxiter)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Project and plot samples" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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rT8RMpNEeqBfqlQfqL4dmcdckmF6qsvbunrwRgIrJu4Cwh6onyoS9ZGl5qPIL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\noqvBpVvJL4XokU6vU2wrT6A/XFuEw60L0VNuEJfc/W4/LH1hVNrIZM4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42RCsjRfduqrXD0dCe0pBXubRATPdWQ463F7iXLYLKQmV6sCpVEjENnox2DFRdgvOSH84GvQCf0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\n7Oe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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# data projection (also removes center)\n", - "xsp=projfda(xs)\n", - "xtp=projfda(xt)\n", - "\n", - "xspw=projwda(xs)\n", - "xtpw=projwda(xt)\n", - "\n", - "pl.figure(1,(10,10))\n", - "\n", - "pl.subplot(2,2,1)\n", - "pl.scatter(xsp[:,0],xsp[:,1],c=ys,marker='+',label='Projected samples')\n", - "pl.legend(loc=0)\n", - "pl.title('Projected training samples FDA')\n", - "\n", - "\n", - "pl.subplot(2,2,2)\n", - "pl.scatter(xtp[:,0],xtp[:,1],c=ys,marker='+',label='Projected samples')\n", - "pl.legend(loc=0)\n", - "pl.title('Projected test samples FDA')\n", - "\n", - "\n", - "pl.subplot(2,2,3)\n", - "pl.scatter(xspw[:,0],xspw[:,1],c=ys,marker='+',label='Projected samples')\n", - "pl.legend(loc=0)\n", - "pl.title('Projected training samples WDA')\n", - "\n", - "\n", - "pl.subplot(2,2,4)\n", - "pl.scatter(xtpw[:,0],xtpw[:,1],c=ys,marker='+',label='Projected samples')\n", - "pl.legend(loc=0)\n", - "pl.title('Projected test samples WDA')\n", - "pl.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/Demo_1D_OT.ipynb b/notebooks/Demo_1D_OT.ipynb new file mode 100644 index 0000000..087b3bc --- /dev/null +++ b/notebooks/Demo_1D_OT.ipynb @@ -0,0 +1,198 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:b65d32b626c6b5575ab808a448461a190dd0152a68ce83ddf7bbaf1aefb35c43" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "heading", + "level": 1, + "metadata": {}, + "source": [ + "1D optimal transport demo" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# load all relevant modules\n", + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dataset generation" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\n", + "n=100 # nb bins\n", + "a=ot.datasets.get_1D_gauss(n,m=20,s=20) # m= mean, s= std\n", + "b=ot.datasets.get_1D_gauss(n,m=60,s=60)\n", + "\n", + "# bin positions\n", + "x=np.arange(n,dtype=np.float64)\n", + "# loss matrix\n", + "M=ot.dist(x.reshape((n,1)),x.reshape((n,1)))\n", + "M/=M.max()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plotting the distributions" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "\n", + "pl.figure(1)\n", + "pl.plot(x,a,'b',label='Source distribution')\n", + "pl.plot(x,b,'r',label='Target distribution')\n", + "pl.legend()\n", + "pl.show()" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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mjEkVkQRs5fAxETkRWAJ0Msbs8diHKSuOUDBmDLRpA3//u3MxtGwJixZB69bO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\nz1NSUkhJSfFTaOW3di30Csn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+ "text": [ + "" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Distributions and loss matrix" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# plot distributions and loss matrix\n", + "\n", + "pl.figure(2)\n", + "ot.plot.plot1D_mat(a,b,M,'Cost matrix M')\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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J3C0yDCMKTMDLmIsvdkMRA28+K1P86ayZ/tpDcd0pFk+8s8oEuVMkoVKYzFYb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\njJrI+ikiQ4GrgZNU9WO4YSR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+ "text": [ + "" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## OT solution (Earth Mover's Distance)" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "G0=ot.emd(a,b,M)\n", + "\n", + "pl.figure(3)\n", + "ot.plot.plot1D_mat(a,b,G0,'OT matrix G0')\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Regularized Optimal transport (Entropic regularization)" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# reg parameter\n", + "lambd=1e-3\n", + "\n", + "Gs=ot.sinkhorn(a,b,M,lambd)\n", + "\n", + "pl.figure(4)\n", + "ot.plot.plot1D_mat(a,b,Gs,'OT matrix Sinkhorn')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 6 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 6 + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/notebooks/Demo_1D_barycenter.ipynb b/notebooks/Demo_1D_barycenter.ipynb new file mode 100644 index 0000000..c93d00b --- /dev/null +++ b/notebooks/Demo_1D_barycenter.ipynb @@ -0,0 +1,297 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# 1D Wasserstein barycenter demo" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot\n", + "\n", + "from mpl_toolkits.mplot3d import Axes3D\n", + "from matplotlib.collections import PolyCollection\n", + "from matplotlib.colors import colorConverter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dataset Generation" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n=100 # nb bins\n", + "\n", + "# bin positions\n", + "x=np.arange(n,dtype=np.float64)\n", + "\n", + "# Gaussian distributions\n", + "a1=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std\n", + "a2=ot.datasets.get_1D_gauss(n,m=60,s=8)\n", + "\n", + "# creating matrix A containing all distributions\n", + "A=np.vstack((a1,a2)).T\n", + "nbd=A.shape[1]\n", + "\n", + "# loss matrix + normalization\n", + "M=ot.utils.dist0(n)\n", + "M/=M.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot distributions" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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tH/b/IY3rNuZ/5v1PrV5XJNGosAiBzZvh4MHkLixAS07l+D228DF27t/Jz86t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\nZbkOA17PaGHhh6wsGD7cTomekABDh7odkaopIhA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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(1)\n", + "for i in range(nbd):\n", + " pl.plot(x,A[:,i])\n", + "pl.title('Distributions')\n", + "pl.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Barycenter computation (for l2 and Wasserstein)" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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eoap2PGAmtm+Hb76Btvl3+f4Jq1DB9cZMm+Z3JsYvKWkpdJ3YldJFS/N+x/eJ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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# l2bary\n", + "bary_l2=A.mean(1)\n", + "\n", + "# wasserstein\n", + "reg=1e-3\n", + "bary_wass=ot.bregman.barycenter(A,M,reg)\n", + "\n", + "pl.figure(2)\n", + "pl.clf()\n", + "pl.subplot(2,1,1)\n", + "for i in range(nbd):\n", + " pl.plot(x,A[:,i])\n", + "pl.title('Distributions')\n", + "pl.xticks([])\n", + "\n", + "pl.subplot(2,1,2)\n", + "pl.plot(x,bary_l2,'r',label='l2')\n", + "pl.plot(x,bary_wass,'g',label='Wasserstein')\n", + "pl.legend()\n", + "pl.title('Barycenters')\n", + "\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "## Barycenter interpolation" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "\n", + "nbalpha=11\n", + "alphalist=np.linspace(0,1,nbalpha)\n", + "\n", + "\n", + "B_l2=np.zeros((n,nbalpha))\n", + "\n", + "B_wass=np.copy(B_l2)\n", + "\n", + "for i in range(0,nbalpha):\n", + " alpha=alphalist[i]\n", + " weights=np.array([1-alpha,alpha])\n", + " B_l2[:,i]=A.dot(weights)\n", + " B_wass[:,i]=ot.bregman.barycenter(A,M,reg,weights)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot interpolation" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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MnDkTy5cvxw033ICXXnoJJSUlqKysRE5Ojt/+FRUVmDhxIhYsWIBRo0Zh3bp1\nGDt2LPbu3YvrrrsOAPCnP/0JW7duxbp169C5c2ds3rwZ06ZNQ4cOHTB69Gj59ySMB5V8T7QVIxZi\njuP4zkg4B2e32/mFBZRKJV/iMl5C0NjYCJZlkZaWFrTtYiHW6XRhtctms8FisaBNmzYxabdwLp20\nR6FQwGQyIS0tzUeQyf42mw16vd5PBITXKS53SNxwhHi7Vi0WC1iWhVarjdkx4wGJdTAYDAkZpEgJ\ntdibEWyu02w2Q6PR8K70ZCdV3gMhDocDDoeD70suXbqEhQsX4uDBg+jduzcOHTqEQ4cOYfLkyXj+\n+edlH3fo0KEYMmQIFi9eDMD7LnTq1AnTp0/HE0884bf/hAkTYLFYsGnTJn5bYWEhBgwYgFdffRUA\n0KdPH0yYMAGzZ8/m97n++usxcuRI/OMf/yCbQr7Y1EJOMYTRjUIhFo7oOY6Dw+GAzWbjrWatVstb\nc/EkmCs5WS3iQEFtYvEUIqe9DCOv3GGkgS+UyAllnQkHUoFKUbrdbp+MBfp8You4PGlWVhbsdjuG\nDx8uFDm/5xMMp9OJ3bt34+mnn+a3MQyD2267DRUVFZKfqaiowMyZM322lZSUoLS0lP992LBh2LRp\nEx588EG0b98eX3zxBY4fP46SkhLZbQOoIKcM4Qix1WqFx+OBUqlEeno6X82qpTqMeApxpDWFYx1d\nLpdoXKutwe3d0tciJdTiQRQZ5LpcLh8xSOZAslSuuS6ksbER3bt399kmHvQGo66uDm63G3l5eT7b\n8/LycOzYMcnPVFVVSe5fVVXF/7506VI8/PDD6NixI5RKJRQKBVasWIHhw4fLbhtABTnpIR1BsLWI\nxUKsUql4N6twn0QgtCzjKcSRfl5YgSwWQhyr+xqtxSYWg1TrfJNxjpMgHkR5PB5YLBZoNBooFIqw\nKpKl6vNpCaTeiXhFWYc7YBHvv2TJEuzYsQMffvghrrrqKnz11VeYNm0a2rdvj1tuuUX2cakgJyly\nhdhut8NmswUUYiCxObukXcm2HrFYiOUUPhF6HloKORZbsApKZHDkdrupEMQYocAKiUcgWbSkooUs\n1eampiZkZmZGfMycnBwoFApUV1f7bK+pqfGzggn5+flB97fZbJg9ezZKS0tx5513AgB69+6NvXv3\n4sUXX6SCnMqEI8RWqxUcx/HpH4FcN4kSZCIUiVj0QSiWwY4trsmdLKVAoyEctzfw85KZQOtwe8eb\nUN+laPKVBvwUAAAgAElEQVRx6fPxRXjdHMdFbSGrVCoMGjQI5eXlGDNmDH/c8vJyTJ8+XfIzhYWF\nfn/fsmULCgsLAYAvGiR+RsR7Eg5UkJMEsRAD8Ot0OY6DzWaDzWbjhZhEA8s5fjzbLnRNA4lZ9CEY\n4uUirwQhDoWUEFgsFjAMA7VaLbvilbCoPyV2xGJaIhKhJt/9VHueUoPtxsbGqF3WM2bMwOTJkzFo\n0CA+7clisWDKlCkAgEmTJqFjx46YP38+AODxxx/HzTffjEWLFmHUqFFYv349du/ejRUrVgAA0tPT\ncfPNN2PWrFnQarXo3Lkztm7ditWrV+Pll18Oq21UkFsYYlEGW3lJLMQajQZarVaWEJPjxavt4jli\nlUoFl8sV10UfgMDuZGINCtdtjna5SKnBTCp1bpFWvKLzn8GJ1X2IdFoCSO5AsmgJJMhZWVlRHXf8\n+PGoq6vDnDlzUF1djf79+2Pz5s3Izc0FAJw7d87H21hYWIj169dj9uzZmD17Nrp164bS0lI+Bxnw\n5jY/9dRTuP/++3Hx4kV07twZzz33HB5++OGw2kbzkFsIOUJMiqtHKsQEObnB4bY9UB4xGThE+6UJ\nBanslZGRwbuGhEKs1Wpjsm7zxYsXodfroVarffKQSWCPXA9FSxFu/mk0+bnR3GuHwwGn0wmDwRDx\nMRKFnBz0eBFIqIWuUfFAimEY2Gy2oNNayUhzczPUajU/uPd4PMjOzsaFCxd48UwxaB5ysiFXiImw\nAeDLzUX65Y/VHLIwWCtZKn6R8og2my1mFnEgyH0Uey+uJOTOf5J3WIhYpMNJtbvS7mO8iCZtjpQM\nFU9LJKNFLeVmJ5UGr+Ra1lSQEwQRYlJLWqvV+s1pioWYWHmxGIVH0+GFI8SJjugm9aZjea8CkYqR\nqrEi2mjvK9HtnUzXEGwgRepDC6vMCVOzUiWQzGQyIT09PaWs/HC5cq8sSRBaxB6PxydPl7zwgdyt\nsRKXSEVSSojJFyLQl1Vu9HOkiActarU6rq7DRA8wUolg1pq4bKhU2o9w7pN8JhVIlXYCvoGharWa\nF+x4B5JFi5SFbDKZruilFwEqyHFDWGeaCLHwhSYdljASmKx8EmtxCVdUxEJMKn4FE+J4IxVh7nA4\n4nK/KNEh160q5fYmc96RuL0p0kiJWzSBZOIgsnh6PMSCfCW7qwEqyDFHWN5SLMRCSGWteM97AvIF\nORZCHGsLWSrCXKfT8dXJEoWUa54SHsFEwG63w+Px8MVMUsHt3dLnD5dQ7Q3l8UhkRTKp/qqxsZFa\nyBR5kE6E1JmWEmJSpIJYx/EWYkIoQU4Fi1gc2CYszxlPyBSDxWLhi4sIrbZUcl8mI0IR4DiOjwYX\nioAwd1qq2pW4ZGi839lUe+bRtjeaQDKhUIcTSBbIZU0tZEpQ5AgxSZMQrqdLUoVakngIcbRCJa5C\nFmmqVywgHQ5ZKF2pVPJTEeT6hJGryR4Uk8yI3xehCIhrsotFQOwpkbLU6LRG7JEbkU/6yEgCyYS/\nx6IoSLJDBTlChEJMkBJi4YpCpFoUiQxOFGILOZ4WcaSCTISY1OUOVYUsnhaq0DoHwEeVEzeq0Gom\n9yxQGtCVWrShpUiGaO9Ui7ZPdKWuUEItJ5CMtFnY9tYgyHTYGAbkhXI4HLDb7bwYi61il8uFpqYm\nNDY2wu12w2AwICMjg3dPJzpyVxjN7XA40NjYiObmZjAMg/T0dKSnp7dYLjERP5PJxAtcRkYG0tLS\nEm4Vk7Y0NDTAarXyq0AplUo/C4vcK4VCAY1GA71eD4PBAL1eD61WC7VaDZZl+ZQTq9UKs9kMs9nM\n19YW1iunRI7QkiZ13cXPg1jZgZ6H3W6/op9HMgwgiFAHekbkO0NEG/AG+c2dOxd33nkndu/ejTNn\nzuCbb76ByWSKuB2vvPIKCgoKoNPpMHToUOzcuTPo/hs3bkTPnj2h0+nQr18/lJWV+fxdauDNsiwW\nLlwYdtuohSwDMgIXuqZJBy180YUrHLFs4KX9WiqVpqmpKe5zxHItVxKURZaMDKcut/g40RKsLeEs\nfh7OXFswNyutJR0bwnF7h+tSTaVnk8wDjEDfGZvNBpfLBa1Wi27duuHEiRPYv38/zp49i08//RQA\n0KlTJzz//POYOHGi7PNt2LABM2fOxPLly/k61iUlJaisrEROTo7f/hUVFZg4cSIWLFiAUaNGYd26\ndRg7diz27t3Ll84UrosMAB9//DF+97vf4Ve/+lW4t4OWzgyG0MUSSIiDlZEM9KUlAULRLCMmt/1O\npxMWiwUejwcKhQJ6vT6uwVput5tP4Cd1k8VtEq/drNPpIkr2J2Ut5ZaFDNUWqUGByWSCUqmEXq/n\nRZTcu+bmZmg0GsnrlHPucEsgRrJEX7ilM1sKErzXknEVUs9CvFqPcAUfUisg2cWZePP0en1LN0U2\nUm1+8MEHMXz4cNx66604cOAADh48iDFjxvCrLslh6NChGDJkCBYvXgzA+z3s1KkTpk+fjieeeMJv\n/wkTJsBisWDTpk38tsLCQgwYMACvvvqq5DnGjh0Ls9mMLVu2iP9ES2dGAukoxUsgCjtDInZkJBdO\nGcl4W8hSBT0AwGAwxL3KTSALWdymQGs3J4JkaEs0katiy43m6sYGqeAv8cCJDMwB8EtaxmLgFE+S\n2UIORKC0p9zcXPTt2xd9+/YN+5hOpxO7d+/G008/zW9jGAa33XYbKioqJD9TUVGBmTNn+mwrKSlB\naWmp5P41NTX4+OOP8c4774TdPoAKsg+BhFj4JZUKiAq3nnO8BDlQsBbgdVcnArEgS4lfrAYGkRQ8\ncblcsFgsPvcnmIUrJ2UslgQLiBEGw8gNWkoVklU0pAZOJPBQo9GENXBqyej7ZBkcyEUqcC7aoK66\nujq43W7k5eX5bM/Ly8OxY8ckP1NVVSW5v9hNTVi5ciWMRiPuueeeiNpIBRmRC3Gk87DxKJ4h1TYi\nNCRAIpGdntAdLFf84olwfp+UAG2ptkQCCS4TIidXl+wnTLlLJustFSHf20gGTkDio++TdbATCuE9\n4TgOJpMpLtN84fbDwfZ/++23cf/990e8/GyrFmSxEAPwGw2L5xljISyxEuRQQiy1f6IgVkQ8hViO\nhSxMPYt2daqWCsYLhJygJbvdzr/DQmhKVnwINXCSU0Aj1s9EGPuSKsTDQs7JyYFCoUB1dbXP9pqa\nGj8rmJCfny97/23btqGyshIbN26MuI2tUpDJKNblcsFsNsPj8SA9Pd1vRCYOPorVPGMsimeEI8SJ\n6mhJmwgtWe3L7XbDYrHwOeCBIt7lkEwiLAeh9UauX6PRyBaFRFe+SkXCuSexjBdoLc8kkCBHYyGr\nVCoMGjQI5eXlGDNmDH+e8vJyTJ8+XfIzhYWFfn/fsmWLZCDZm2++iUGDBqF3794Rt7FVCjIAPsWB\nWD1CkRQWqIhnwE8kxTPCEWJCPItoAP7uYMC7sky8XcJSFispT0pctNEIMTlHJH9LNmKRkiUW6ni0\nMRWI1VRTLApoyHkmqVbIBPBvs9PpRHNzM7KysqI67owZMzB58mQMGjSIT3uyWCyYMmUKAGDSpEno\n2LEj5s+fDwB4/PHHcfPNN2PRokUYNWoU1q9fj927d2PFihU+x21sbMS7776Ll156Kar2tUpBJp2T\nsEiHVKUoYUGBWJ8fkC+QkQqx1HFiiViIiTs4mqT9SPF4fJewJFXRUq0jSjRSohBJ5atkiyxOZWL1\nTFItsI8g1U81NTXx6YfRMH78eNTV1WHOnDmorq5G//79sXnzZuTm5gIAzp0759PnFxYWYv369Zg9\nezZmz56Nbt26obS0lM9BJmzYsAGAN00qGlptHrLD4QDHcbBYLLDZbLwwR1qgIhxC5eoSpISY5DiH\ny6VLl6DVamOS5ymelxXnXTc0NPDrFMcTk8nEWwjkGZK1pGMlDCQ6PS0tDU6n02fkbjaboVQqodFo\nYnKueBDLPORAedOxSMlKlXxpwNtWUqGtpZHzTADwcQap4PbmOA5ms9knx//kyZO49dZbUVNTk5KD\njMvQPORAeDy+C92TAhWJKNcYykKOlUUca8S1uVuyEpkwLxQAL8Sx/rIyDONXHKK1EuuUrGQVhFAk\nU0xBKLd3qMC+ZKwOR+6vsC2tYelFoJUKMsdxfJ1ppVIJl8sFvV6fsJGX3OIZsRTiaERSrhAnAo7z\nXZaRZVkYjcaEPbtUnI+LN5GmZAkFgexP729sIELNsizsdjvUajW/WlmiFuGIxTUQTCYTFeQrFYZh\nkJaWBobxrtLT1NSU0FFvqOIZ8bCIIxHkSAOk4mEhkzl+4bKMbrdbMlCJ0vLISckSCjXgfd/MZrNk\nof5k64iTrT1ySJVo70BVuq70lZ6AVirIgNdFLaxVm2g3FHGFJqp4RjgiKRbicAOkYinIUsF2ZGoh\nEdXHyLUIo4+Tyb2XaghdrOQ9J7EcxHWa7ClZyeSyDoWU+1eMHLe3ePBEiIfbW6rNDQ0NVJCvZMjD\njndKkBTkXERokmWOWCjELR2pLM4Dlwq2S8T8LumUGhsbJd8R4mJtaZFIZYSWm7DCUTKlZLVGAg2e\nEuX2Fs8hU0FuBSRSkIWuadKRJ0qIg1mtbrcbNpstZilD0Qil2H3fUotQEMucCIBGo4FCoeDvYbBg\nmVRwuSYbUu9mMqZkybE4k4lYtzcat7dwXjvYAFbqXTCZTFSQr2QSaSFLzRGLR57xRkokxbm7Op0u\npilD4cBx/gs/GI3GoEIcr7lqoWVOOh69Xg+n08lvC1YFi1SBCzUHR+e+wyeW86B0lazYIcftLfxu\nCBE/F2EZY0JjY2PURUFSgVbfI8RTkEnn3tjYiObmZt4iJlHBiQ4kE1p3ZrMZDQ0NcDgc0Ol0yMzM\nhE6ni1kFonCuzel0oqmpCU1NTT73KJFWsfBZkcAio9HIu1CDWVjC4CXiWjcYDDAYDNDpdNBoNHxH\n43A4YLPZYLFYYDab+QGR0+n0WdqPEh5EEFQqFTQajc8z0Gq1UKvVfs/AbDZf8c+gpS168XPR6/Uw\nGAz8OuZSz4VUUbRardi4cSNWrVqF5ubmqOsavPLKKygoKIBOp8PQoUOxc+fOoPtv3LgRPXv2hE6n\nQ79+/VBWVua3z5EjR3D33XcjMzMTaWlpGDJkCM6dOxdxG1u9hQzAZ1QWC+RETbdEfqu4EEq8LGK5\nghzLhR+iQVhxTGyZi93R4RBqDk5OOhC15KIjFilZUtMOqfY8kqm9wbwcZKqI1Bd4//338dFHH/Gf\nW7VqFfr06YO+ffvivvvuQ9euXWWdc8OGDZg5cyaWL1/Ol8wsKSlBZWUlcnJy/PavqKjAxIkTsWDB\nAowaNQrr1q3D2LFjsXfvXr5K14kTJ1BUVITf//73eOaZZ5Ceno5Dhw5FVdym1Vbq8ng8/EjMZDJB\nqVTCYDBEdUxiZdlstpCVtcxmM1wuV0zmRbZs2QKbzYZf/OIXkn/3eDxoamqKexENgsVigcPhCFgI\nXrzwg06niyivOdR5QiEeEOj1er/FMITnIBYUuW9WqxUMw8S0Cpa4wAYh0nnRVKmAZTaboVKpIl62\nLhZIRRWLB81k8K5UKqFSqZI+PsDpdMJut8NgMCR1O4WQjApiETc2NmLy5Mno2rUrNBoNDhw4gAMH\nDuDdd99FcXGxrGMOHToUQ4YMweLFiwF4n3WnTp0wffp0PPHEE377T5gwARaLBZs2beK3FRYWYsCA\nAXj11VcBAL/5zW+gVquxatUquZdGK3XJIdq5SLEQq1Qq6PX6qBa+l8uuXbvw/DMvgPN40KVLF/Tp\n04f/m7gaGQBkZmbGfe4y0LXFeuGHSBG3I5RlnggXZrAAJvEiA9Sajg9yoorJoNblcvFzocmUknUl\nIC4OYzQaUV9fj1mzZqGkpITfRy5OpxO7d+/G008/zW9jGAa33XYbKioqJD9TUVGBmTNn+mwrKSlB\naWkpf/6PPvoITzzxBO68807s3bsXBQUFeOqpp3D33XfLbpuYVivIYvdTJJ1uJEIc7TmFnD9/Hs89\n8zyUjVq4PS68+MJCvPr6K9Dr9T7VrIhb2mq1tkggUbwWfgj3HkbSjlBuyniKdTDXntxSlalSASsZ\n5205jsMXX3yBXbt2orr6R2g1etw99lf8vCKZdkrmlKyWnkOOFGF7OY5DU1OTT1BXONdTV1cHt9vt\nt4ZxXl4ejh07JvmZqqoqyf2rqqoAeNdEbm5uxoIFC/Dss8/ihRdeQFlZGX75y19i69atKCoqkt0+\nIa1WkIWEO58rJcQGgyGsIKRYCPLyZctRc6IeN3W9A063A19/vwXLli3Dgw8+yFez0ul0YFmWt5IT\n0TELi2kkQxS30FMQTTuSRTQCzYsGijImFbCStXZxMmI2m/H6a6/iux3/h749gb7d9Dh7zoqFL3yL\ngq6D8fTsv8NoNEaVkiV8BvF8Dqn2jKX6qHikPYXbFwr3J3oxduxYfq3kvn374ptvvsHrr79OBTlc\nIrGQxSkxkQix1DEj+cJ4PB7s3bUPHY1doFaooGQUKDD0QOnGTRgzZgyuvvpqH8uqJb6UDQ0NLRo8\nxnGcn6cgnnPnLUmgtBOz2cwLeLIIRLJjNpsx++k/o+nSPvxlegGGDm7L/+37g/V4/uWv8fJLC/H0\n7L/5fPfDTckiMSxA/KYekmUQGQ7iPpHjvGsPRBorkpOTA4VCgerqap/tNTU1flYwIT8/P+j+OTk5\nUCqV6Nmzp88+PXv2xPbt2yNqJ0DTngDI69jtdjtMJpNPSkx6enrEYhztl+3s2bMwXTQhS9cGTqcL\nbrcbnbML4DA7cfToUb9OIVH51larFRaLBYC3mEZGRkbM0qnCaYfNZkNDQwOsVivUajUyMzMjWkAk\nHrnOiYQIBFnfm6SdkHQs8v66XC6fdCwSjX8lpgIFg+M4vPLKEjRd2ocF83r7iDEA9O3VBk9OvwZH\nD2/BG2+skHVfWjolKxUHV8I2k3iPSC1klUqFQYMGoby8nN/GcRzKy8sxbNgwyc8UFhb67A94g2cL\nCwv5Yw4ePNjP5V1ZWYnOnTtH1E6gFVvIwM+dbaBOV8oijlXVKKFARuI6PXjwICxNVhizMsGyDBQK\nJcCokYYM7Nq5C6NHjw54vlhDBiykAplKpYLT6eTd5fFCfA/llNuM9flTUaiCBZDJcbfGK3gpGYRj\n06ZN2L3zY/x1xtXo0E4666LPdVl49EE7/v3mfzFkyFAMGDAgonMFS8kKJ5Av2HNItfeTXL+QxsZG\n6PX6qCLwZ8yYgcmTJ2PQoEF82pPFYsGUKVMAAJMmTULHjh0xf/58AMDjjz+Om2++GYsWLcKoUaOw\nfv167N69GytWrOCPOWvWLEyYMAFFRUUYMWIEysrK8OGHH+LLL7+MuJ2tWpAJpGMN1LHHo3xjJAJJ\nhM9ms+HQoUPQMWnQaXWA4IvYNr0ddn67C3a73WcB9XgIsrA9Ho8HGo0GWq0Wbrfbxx0Xb8R53/Eq\nt5kMghEvwnG3SgUvSZVElEuyiMapU6fwn3Wv41ejjbh+QG7QfW+9qQO2fl2L1avfQN++S2I26JPz\nHAIt9CBVspUcM9UQtpnUsY7mOsaPH4+6ujrMmTMH1dXV6N+/PzZv3ozcXO9zPnfunE9/UVhYiPXr\n12P27NmYPXs2unXrhtLSUj4HGfDOH7/++uuYP38+Hn/8cfTo0QPvv/8+b0VHQqsWZCK+5MUlL3k8\nhVh4bkBeZyRl+Z04fhIZ6iwfMQaA/IwOOFNzHAcPHsSgQYOCHtfj8WDdunXo3bs3+vfvL7vtoSzR\nRK+g1dzczBf1iMeylUDga0kWMYkXcq3pSOsWJwscx2H1qrfQIb8Jv/nVwGA7AvBe35SJXfHnv3+P\nzz//HLfffntc2yc3JUv8HMhnSYpfKjwHwH+lJ6PRGPWxp02bhmnTpkn+7fPPP/fbNm7cOIwbNy7o\nMadMmcJb2bGgVQuymKamprgLMUGOIAey1O12O079cAod067x+0y61gjGocCePXt8BFl8Po7jsGzZ\nMrzz9gbkt8/BK68tRbt27YK2Wa4lmoj5ajLnSc7TUlW+WiNyrWmpusWBrLiWZteuXTh8aBvm/LkL\nFAp5bbrmaiOKh2nx3w0rUVRUlPDiK3Keg91uB+BfcS4ZUrKCEWsLOVVIjm9DC2K32/kgJJZlow7W\nkksw0QoVRPbDDz/AZrYhO82/5BvDMGijzkXF1xV+FovwfOvXr8e6VRtxTd71qDtvxgsL/hXQzUwG\nBsKa3Im6T2Lcbjeam5vR2NjIW+JpaWkJKTDSGjqEaBAHL0nVLQa8gykSc0ACyMh2l8sFj8eTUM+D\ny+XCO6vfwIBewIC+2WF9duKvuqLZdApfffVVnFoXPsLnQEQ3UP1o4XMQBpG1xHMApPvDWFnIqUCr\ntpCbm5v59Yg9Hg90Ol3CBEZKkOVaoJWVlfDYgTSN9Euan9EBlT/sx/nz59GhQwe/85nNZqx6ew3a\nZ1yH7lf1R05mO+zY/hHWrFmDBx980OdY4jrPclzC8bCQpYp6sCyL5ubmmJ0jFG63G263O6ldfsmG\n3DlRl8vF31/yuVD1pGNFeXk5aqoP4unpPUMen3+jL+/WNleHIYO0+OSTTbj99tuT7r0gcTHJmJIV\nqL2Av4UcacpTqtGqBZnUUGZZFg0NDQkdDQpFSyzEoYTv/Pnz0DL6gF+Ktul52F9nx969e3lBFnLo\n0CGYG60Y0MubQ9fGmIdO2b3wv/dKcd9990GtVvNLIbpcrogXfojF/QxW1CMRgWPkepubm/nzkUEB\n8POykck+N5dsCOdElUolXC4Xv+Z0uIs+ROP2drvd2FT6X9x4gxadO6VFdIyRt3fE354/iMOHD6NX\nr14Rt6UlCJS/LhbpYPnr8RgwCY9jMpmohdwaIAIjnFdNNMR9J0eICRd+ugANE3i+SqlQIY0zYu/e\nvXz6k1BADhw4AAWnRZru57y+q/K7oeLoIX41E7LwQyRCHKuCBqGKesR7rpoMBgDvc9Jqtfy5hB2U\nsFZ4tBHHrRGhVUTumdArRKw4cSoQIRpx+Pbbb1FXW4l7pneT32DRcXtfl4VO7U/ik7KPk06QIy08\nJDXQiXVKVqD2iiFzyK2BVi3IhEQEIQkhVhXgDbYgQixeaSgQP507D70m+Gg+U5+Ng/sO+XwhyeBj\nz+59yND5VqhJ02UCLg22bduGHj16RLXwQzT3U5zTLCz/mSikFuUwGo28J0NoTTidTuj1+qARx3QB\niOgIFekd6TKWHMfhgw82YkAvBa7uErkFxjAMRt6eh+Wry1Ff/xCys8Obh04VYp2SFSxvWvi3pqYm\ndOzYMQ5XlHy0akEWPvREFXkQzskC4OeJ5XbQLpcLNVU1yFMHrwbTxpCN4zUHUVdXx+faMQwDk8mE\nY0ePo0ub6wGQL5P3S5ST3hE7v9uDGTOMCY9+jaSoR6wHUuLBgFarhVKpDDpPLbTOxMcK1kkJP0dL\nVoaPUBwitaYPHDiAs6f24vd/ibyyEuGmwny8tXYvvvnmm4DLoCYaKXGLB5GmZEmlxnk8Hr/2NjQ0\nJJ3nIV60+ihrAsOEt8BEuDidTjQ2NqKpqYlP05G7pq2QixcvwmF3wqAJvnZzlj4bNrMdP/zwg8/2\nQ4cOwdpsR9usDnC7XXC5nPB4OChYBa5qdw2qz9f4fUaK0tJSzPn732G1Wv3+FkmOdWNjo09EObk/\niUAY1W6xWHxKbUbamQkjXYUlK8kKU8FKVkZbKrE1Q+67uFSo1H3ftOl9dO3iQs8e6V4Bd3t+jiwO\n87YbDCoM7q/F9u1fxOGqUg/hYIkMrkm5UGHZVmG5UBKzQqartmzZgm+++QYWiyXqoK5XXnkFBQUF\n0Ol0GDp0KHbu3Bl0/40bN/KrevXr1w9lZWU+f3/wwQf9LP6RI0dG1UaglVvIQliWjUvnRwqNEFen\ncE7WbreHfc7a2lq4HC7oM4K7rHVqPRRuJU6cOMFXjmEYBocOHYISOqiUGl6IWQULgEFOZju4jjPY\nsWMHunfvLnlcjuOwevVqvPnmGtjtHmjUL+Hpp5+KSLgiieCOJWTqwGKx8FHt6enpcS21KeXyEwfP\niCudSVkSyZK/mwoI7zvHcWhubsaxY8fw/b7teOKx9mAVCoADOHCAhxNEUjPeYGoSpQzwhUGkKCrM\nw4Kl3uyG9u3bx/26QpEoCzkcQk0/OBwO/nvwt7/9DYcPHwbgzRP/3//+h759+6Jv3764/fbbZc8r\nb9iwATNnzsTy5cv5spklJSWorKxETo5/6mhFRQUmTpyIBQsWYNSoUVi3bh3Gjh3Lx9cQ7rrrLqxc\nuZK/z8LKiJHSqgU5ni5roRCzLCs5JxuJVV5TUwOnwwm9OriFDAB6GFB5rBLAzy/99/sOwKjNBcsq\noGBZnwAVhmGRpW+P7V9/gwceeEDymKWlpVix4h107lIIQ1omPvq4DF26dMZ9993ns1+w+xlskBIO\n0bishVHkcgYDUsExseroQrm8pYpsiAOZIvG2JBvxbvuPP/6I115biqPH96GurhqNplp89Y0b7dsZ\ncG23yxYYEWbusjBf/t5wou+px+3xfnUYBgwYgAEG9c+BTnsGX3/9NcaPHx/Xa7mSEA6YSJ+g0+mw\ndetWHD16FH/+859RUFCAS5cuYcWKFaiqqsLhw4dlC/JLL72EqVOnYtKkSQCA119/HR999BHeeust\nPPHEE377L168GHfddRdmzJgBAJg3bx4+/fRT/Pvf/8arr77K76fRaPjpwFhBh9mXiZUgu1wuNDU1\nobGxEW63GwaDARkZGdBoNJIdeiQWshIqKBWhx1KZ+mwcOnAYFosFDQ0NcLlcOHf2HNoYc70jVIkO\nsEPbq1F59ATOnz8vecwtW8qRbuyCa7oNQrt2XdHpquvx1lvv4MSJEyGvTVjUg9wbo9GYkKIeUm3w\neDxIS0sLKsYtJXChimwIXa+kuE2qrtKUiDb+3//9H2bOegTVjd/gVw9lYtzvNHjojzk4WVeDJ+ZV\n4PlCwWQAACAASURBVKtvLnh3ZC4LBBnsXLbmFAqF15JmGIBhwOHygMnthtvtzaFWKoChg3T4+uvy\npLj3yWghy4G0V6vVol+/fjh//jyefPJJbN68GRcuXEB1dXVAD54Yp9OJ3bt349Zbb/U5/m233YaK\nigrJz1RUVOC2227z2VZSUuK3/9atW5GXl4drr70W06ZNw8WLF8O5TElatSDH0kKWEptAQiwkEkFW\nQ0aJPo5DpjYLtVW1OHPmDNRqNRoaGmCzOZBuaBPwY/ltroLN7MT+/fv9/lZTU4MjR4+jfYefU0S6\nd78BNhuDbdu2BTymx+OB2WyGyWTio5Ll3JtQhGMhi9tAnk84g4FgUaGJQDgvF2oZP2FwGvFIOByO\nFqvA1JJs3rwZb61ahMG3ADPmDsTV12qR1x64tSQHTzxzFfoMVWPha/vw3e6awAcRuK0Z4LJIK8EK\nAvIAYPgNbXHh3DEcPXqUxgREQKC0p6ysLP73tm3byp5Wqqurg9vt9lv3OC8vD1VVVZKfqaqqCrn/\nXXfdhdWrV+Pzzz/HCy+8gC+//BIjR46M+vm2ape1kEgF2e12850dy7J88IicTj4SMaq6UAU1gsxV\ncBw8Hm/ktFGTAcdFJ2pra9GzZ09UV1fD6XAh3RA4QEKpVEGnysCxY8dw5513+vxt586dsFpdyMvr\nwm9jWRbZOQX44ouvMHnyZJ8UK4/Hw1ts4qIeiYIEiFit1pi0IRmtDYaRXsZPmEcN+NczlpuKksrs\n2bMHy99chCG3aHH3BK9VVVtbDWM6A43G26nf+2AebLYLeG7xHrz0z+HoclW6vIMzuOyuZkjhLvTv\nk4s0wxkcPHgQ3bp1a9GFN1LRQhZPDbndbjQ1NcW8Ule4+dni/YVTEr169UKfPn3QtWtXbN26FSNG\njIi4Xa3aQgbgIyDhCDKxiMVWXzidfSSDgJ9+PA+9VIT15XkubwlCFxiGgUGXBjW0OHnyJADvyM/t\n5KDXBg8IM+pycPDAYb/t3+7YAYMhDyqV74CgY8drcfrMORw/fvxyUzif6EmtVouMjAzodLq4dA6B\n6oHbbDY0NDTAarVG3IZE56jHCtLpk5/EmpZTV9pms8HhcKS8RVdbW4uFi/6Jrr2dGDuxBwDAZrPC\nbDYhN+dnL5NCweCBqflIy/Xg1TcPybjmwO+PUsViUD8ddu/+lo/0lhNdHE9rOpUEGfDPQWYYBmlp\nkVVRy8nJgUKhQHV1tc/2mpoaPyuYkJ+fH9b+AFBQUICcnBxZGSrBaPWCTCDiGOpL4Ha7/dyvmZmZ\nEVldcs9J4DgOVReqoFenCTfyQuxyuXhrSaFUgmFZ6Jk0VB7zCmV1dTU0yjQwTPDHnp2Rj9Mnz/jk\n35rNZuzauQ95eVf77Z+T2xEulxLbt2/nRZDjvMtakvQhErTkcDgCzk+HSyAXsjCFSaVSISMjw6cN\nrZVQqShClzfJCRcuOhBvl3cshYPjOCxfsQystgr3T+0FlvUeu76uHkqlG5kZvovdq1QsfjUpFweP\n1+LTL34KfFwZ575hUC7OnPLWACCEigkgUyfiAZLZbOYHSOHe+1QcTInb3NjYCKMx8roIKpUKgwYN\nQnl5uc85ysvLMWzYMMnPFBYW+uwPAFu2bAm6zvG5c+dQX18fcsW8ULTuHgq+FnIwhHOQDocDOp0u\nYiEWn1suly5dgt1q53OQhUIMwEeICVn6bBw+cBgcx+Gncz9Bqww90szOyIfN4uAtXgDYt28fTCYz\n2rX3X/KRYVi0adMFmzd/BrPZDJVK5W3LZbcc4cKFC5gx88946PdTQ+YBykXoZSC53onKZ041yyMQ\ngXJ3hRYdgIRZdLGgoqICO/d8hnvuL4BW520/x3lQX1+D7CyVVDwjrumhx8Ab9Xh7/VGYGh3+O8hk\nYN9sKBXmkO94pLm6wlWZkvHeR4qUi52s9BTNd23GjBlYvnw5Vq9ejaNHj+KRRx6BxWLh1zGeNGkS\nnn76aX7/xx9/HGVlZVi0aBGOHTuGuXPnYvfu3XjssccAeI2TJ554Ajt27MCZM2dQXl6OsWPHonv3\n7igpKYm4nQAVZB7ywMVpSESIGxoafIQ4Fu7XcN2hly5dgsvhglahhcvp9BFipUiICVn6NjBdNOGn\nn37C2TPnvCUyQ5CmzwDnVqCyspLfduTIEShVaTAYfFMNSNBQu3bX4NxPVaiqqpKsPHb06FFMe2w6\n9hw9C5siC3OfmY+jR4/Kuu5QeDweNDU1oampCQCQnp4e86Uhr4QOLxzEFp2Uy5tl2aAub/HcaSBi\nfW/NZjPefOtV9ByoQO+BbfntjaZGuFxW5GQHDoocMz4XVrcVmz4+E/gEIb72BoMKva9VY9euHeE2\n3Xt4GdY0EHy6gVjT5HipQKCVnqKtYz1+/HgsXLgQc+bMwYABA7B//35s3ryZT1k6d+6cT8BWYWEh\n1q9fj+XLl6N///54//33UVpayucgKxQK7N+/H3fffTd69OiB3//+9xg8eDC++uqrqOsotPqgLvLw\niSUnXDwg0CpDsT633A7p4sWLcDicUDDeh87Xvg7SpixDNmx1dhw7dgxVF6rRTt9HVrsMqiwfwTx1\n6jR0up+js0mReRLskJffGYcPafHdd9+hV69efjnWa9etR00zh6F3PgBWocDOzzZi9t/+jhXLXkOb\nNoGjvoNBzm+32yNeCCMUco4VaQH/VCNQYZNkKxP6wQcfwGQ5ian39fPZXldfC70O0OkCe0wMaQoM\nHZGOD7ecxi9/0QUGQ2Qd7A2DsvHm2p0wm80wGELXDAhFpPceAKxWa0oF7wnbZjKZkJGREXV7p02b\nhmnTpkn+7fPPP/fbNm7cOIwbN05yf61Wi08++SSq9gSCWsiXEVrIJG833gFJcgWZBJCdP38ebqcb\neo3hZ4s4RJvUSg3UnBb79u2Dw+68vMJT6AFAG2M+Dnx/iJ/jrqz8ARkZuV6L2O3ys85ZVgFjRgfs\n2/c9fwxyXRcvXkTFjl3o1L0/VBotFEoVBo64Bz9VX5L8MoRCOH1A2hBuChMldggtukjKhJJ3KRaW\nckNDAz78eCOG39YGmVk/W8IulxONpovIyQ5dTenm27NgdtpQ9tmPEbfj+v45cLtNOHDgQMTHkEOw\ney+cqomFJyPetPaVngAqyDzkZWhubuaFWByQFGtCCbIwgIx0ZiqF2usWCUN4NNDhyOEjcDrcSNdn\nyopMaZORh/q6Bpw/fx4XL15Eff0lpKdneztPDvw6tkIBzMnpgCNHjvMpRoRt27ahyepA+y49+W1q\njQ7G/AJs/vSzsIJUrFYrTCYT7HY7P0iSu0pWtAitkGTowJIZYs2FcnkLK5ARsZByu8rlgw8+gJut\nwYi7uvhs9xZtcCI7K7QgZ2QqMXC4HqWfnIbD4Q7r/IS2uTp0ag/s3bs3os9Hg7iKW6jgPZvN5hO8\nR+amE/2eS7msW9NayAB1WfOdPMnXVCqVSEtLS0hEbiBB9ng8/BeDYRje0rBYLFAxaqlDBSVDm4Uf\nKk8AHAuNWifrM9kZebCfcKCyshJqtRoWqwNp6dl+gVo+n8npiJMnt6GyshLdunXjr2tL+edIy70K\nKo3v3F3Ha/ri2LebUFlZiR49egRsi3gVJuGSjA6HI66dBnlGpJOS+pvL5UoJV2AyIFUm1Ol0wm63\nQ61W81MhkZQJra+vR9mn76HorhzoRa7m+vo6ZGYooFDKez633NkGL3x1Dl9ur8LtIzr8/AeOkz0Y\nHtTPiK3ffgOOe6RF3gvx94JY01J1pOWucawQFUKJB+I55FjnICczrV6QSdEIjUbDdwqJSo8RC3Ko\neevGxkYoufDntDL0mTh64XtkpXfwRiUjZFwK1EoNVIwe+/btu1yAXYmMjOygX8T09DbgPEocPnwY\n3bp5q3mdPn0aBw8fQ+cB/tGHOe064wjU+PzzzyUFmeO86w9bLBbZSzLGEpLLDHhFQ1jwRdhx2e12\n/jPijouKdGjI/RHO/wsXHAi13jG516WlpWBU9bj5jkE+x7daLbBaGtEhX37x/9w8Na7ppcHmL370\nFeQwGNg3Gx+U/YQzZ86gS5cuER0jWkK9e8HmpoUinYi4AOqypi5rPo/YYDBEVKgjFhCL2GQyBZ23\nrq+rhwLhC7JRlwm71QG3K/S+AAeP2w2nywWDpg1++OEkamtrodNlyfpyG9La4tChQ/y9/Pbbb2Fz\nAW07dpXcv23nnvj0s8/9qkiRFCZSfOWtlSvx3nvv+XUc8XhmwlxmsrykTqfjrXKSpkKiXfV6vZ8r\nMFguKXV3+yJ1P4QpQXLKhNbW1uKTT/+HocVZUKsZcB4POM4DgEN9vTf3OMMYnndp6E1GHPmhHqfP\nNl1u6OW2yfx8zx5Z0GpsLeK2BqKbkydTQaFS4aTiAiKtpR7IZd2aBLnVW8jC0WFLCTLp9IWuWCnq\n6y5Cq5JRx1pEuiYdLptbsGKNlI3sLbnpdrsBeIt6tM1qj5M/HIbL5UZaWrasc2Vnd8T+/Yd54Tly\n5Ch0GfnewvwSdOrWF7s/2YMdO3agqKjIbyWoffv2Ycmrr+FcQzOUHu/CHdOmTYubF4NY5GQ5xrS0\nNDQ2NoYcjEi5AuWu1pSIyOMrhUBlQj/55BO4UIcbb+0PAPBwHu/KTRyH+vpaZGcqLy+fKDxY8HP1\n6p8GbXodtnzxE34/+dqw26pSseh3nQb79u7EPffcE/bno4UU54kVUi5vwH/50EitaalsBeqybsWI\nU3XihXBOFPB25unp6SG/PF5BljcHLIRlFVC6VXB7SDSrcBpMJMQMC1bhDZLKNObi1Gkrjh07ho6d\nbpR1ruycjjj/0y6cPXsW+fn5OHDoMDJz/YuJEAzGLCgN2dixYwcGDBjA1wQ3GAyw2+1YuGQpmgxt\nMPg396L29A9Y/d4HYBgGf/jDH/hjxGIQ5Xa7YbFY+IFAtGszB5uvE7thhXPTYnc3dXkHp7a2FkeP\nHsXbby/HVdcC+jT15fW9ve+ENyDSiuw2+sufELwrnMR9FWxSKhlcf2Mayr86h8m/6Qa1KvypkoH9\ns7Fs1b6YpT8lI3KWD/V4PHA6nX7vuvB9l/oetzYLudW7rIWdXaCXIlZIlXUkbrlQYsxxHBouXYJG\nGf4i2JzHA5VbC4fD5rud8619rVSqvJW+Lt+TzLQcmJttMJkakZEpb93PrKw8OJwcjh07hgsXLqD+\nkglZuYEXa+fAISO3I7ZXfAeHw+GzEtSWLVtQ29iM60aUQK3ToUPPPmg3+Ea89+FHuHDBu1xetGIl\nTKESLgkZbYK/FIHcsOEU27hSqjJFS3NzM1atWoWHH/sd/vLMDJyqO44DRy/i7zO/whdlp+G9RQwu\nXrwInRYw6FWXxZYR/CNwP//jfH8tLMqAyWzFjt21P+8exjs3oE82PJ5GHDx4MMorDp+WzI+XW1hG\nOL1DvGpWqxVr1qzBhx9+CJfLFbUgv/LKKygoKIBOp8PQoUNDVlDbuHEjevbsCZ1Oh379+qGsrCzg\nvlOnTgXLsliyZElUbSRQC1lAvCxkjuP42sAejwcqlQrp6elQKBQwmUyyOtimpiY4HU5oIrCQHQ4H\ntIwOZquZt9K8L78HDMNCqVRAqr61UqkCy6nRaG5Aero8lzXLKqDX5+DIkSNQKBSwOVzIzJGq78rB\nfXk92TZ5HXFq1wE0NTXxRUJcLhfeKy1FWudu0Oh/tiw69e6PHXt3oKysDA899FDY94I/e4xXgYqG\nYBaGOPqVQDo8ccGHVEbuvb9w4QL+Ovdp/GQ6jT53FODaPCO0RhvStUocKP8J/1l/FOfPNePXk3ug\noaEeHfLVP+uv1CmkLOXLlnRungodC5T4YttPGH6Dt+oXx3GXV3kK3da2uTp0yGfw/fffY8iQIbKu\n70olVHET4cpkCxcu5NdY37dvHwYMGIB+/fqhb9++mDBhguzFJjZs2ICZM2di+fLluOGGG/DSSy+h\npKQElZWVl4NVfamoqMDEiROxYMECjBo1CuvWrcPYsWOxd+9evlIX4YMPPsB3332HDh0iC/qTIrW/\nwTFA2AnEeg6ZCLG4vjIR43DO2dDQALfTA60y/Dlkp9MJNaOF2+WC1W6Gx+N1TysUyoBiTFAzBjjs\nTqjV8s+bldUe+74/iB9++AEqXYYo3cm7NKTT5YLH7QbLssjp0AUOF+djRWzfvh0nz51HlwGDfY6t\nUKqQ3b0X/q9sMx+NHm7giN1uD2sVqEDniHfqR6j60qFqHF+J+dJVVVX469yncZH5CePn3IqeN3WB\nh7Ehp60ebdobcPMD3TFscjd8+fU5vLF4F9xOW+jcY0bin+CXAUPTsedgLRqbvYGHnMcDt9vlHSy5\nPT/f5wC3un9vA77f923Cn0WqVJATDi7JymS7d/9/9s48To6yzv/vuvrunvvKZHKRE3IHEgLIGS5d\nlR8BdFE5dnVxkfUI+wOPFcVdj10XWFxQQEFFBBGMRokQQrgCCTmGnISEhJyTzD19n9VV9fujunq6\ne7pneiaJwI/5+BrJq7uqn6erqp/v870+n9ashOWNN95IY2Mjzz//PP/8z/88rM++5557uOmmm7ju\nuuuYPn06DzzwAC6Xi0ceeaTo8ffeey+XX345y5YtY9q0adx5553Mnz+f++67L++4o0eP8uUvf5nH\nH3/8hFL0fugNMoxcgnEwqKpKOBwmEokgCEJJfuVyfzDBYBAtnR5RUVcymcSGE0MzCEX7Ml6xFSYf\nfHyH4iWtDi9qUFPTTHdXL29u3YarypIsM9ANHTVtLmTZELkko8g2HBX1bN3Wz/L1zMq/Itc24a2t\nH/D542bN52hPL6+++mrZcyrcHI1UBeq9Kvyzxh6M4zi38jWZTBKLxbLRkPezEEG58wkGg/zbd75F\nH0f5xFfPw1vtpru7C8Vu4Pb0pximnFHPef84jY2tXby9NYpiG8Eyl2Oc553hJaWpbGw11ZsEK7eP\ngIGptqZrWo6RziGPMQzmzaqhp/tQHl/yKAaisKWtpaWFtrY2br/9dp588kl2795NKBQq2ztWVZXW\n1lYuuuii7GuCILBkyRLWr19f9Jz169ezZMmSvNcuvfTSvOMNw+C6667jtttuY8aMGYUfcVwYNcg5\nOBGLbTqdJhQKEQ6HMQwja4hL5SSH4yGnVQ37cAyyYaBl2hJsgg0RiWCkF1EozKGVhk10YegC8Xi4\n7GGrqhuJx1Ps2b2HqrpmdCtXnU4jkKHblPLZtaoaxtH65tasUMSWHTtpmFL8YXf6KnA0jeNPf/5L\n5msW10PesWMHTz/9NLFYLG9zdDwqUKXGeq8wVHuQFSb8oOelDcPg/p/eR3vsIJ/46nl4Kl2oagq/\nv4fq2oEe8PjZ1Uy7sIF1r8Y4cjBR5BPLh69SZuJ0G6+u769bEEQRURIzhXsyhYQZhmFkjLTGjKk+\nJCHK5s2b/2abomItRO93FHr01vOaW2XtdJafsuvp6UHTtAE6xg0NDSU3Rx0dHUMe/6Mf/QibzZZV\nfzqRGM0h58AyjiMJ9RS265QrdFCuQQ6FQgi6gCyWUWxk9OdnwVyMJUnBiZtgpK8sTVcLEnZEQSIY\n6MblKo/Czm53YRgKoUgPvup6tHQaBAFJlktuBmqbxvPOgTfZv9/sew4nkkxqmVByjJZZ89jx4jMc\nPHiQlpaWvPfa2tq45957eWPbNmJqipdefZXbbr2VpqamEYlPvJde8UhhtQdZVesOh6NsEYhCUpP3\nw6K+atUqXt30Ihd+cR6eKrNiuru7G0SVyqqB1cuqmmL2JQ307g/z64c6uP2740bmKWcwf5GXP/26\nmz5/krpaV/6bmRC3IAj5T7ZhYABOl8j0KTa2bn2TCy64oP+0/w/rAI4XhSxdDocDh2P4UcHBMNz1\nPff41tZWfvKTn5y03vIP993PIDdkPVxYwg+hUCivSrdcoYPheMiyOMRnZnblqprJz0oSiiKjptOI\niDhEF8FwD+XJrGfadDQJRXQQCHaVdY4FSfYSjydxV5h0m4osIwqlQ+RVdWNIabBjxw62bt2K6Pbi\n9JWurqweO56kIbBly5a862cYBvfe97+8tOdt6j96CVM+dQ3r393LHd/73gCO7Q8bSgkRDKV7/F7n\npY8ePcovfv0gp3ykgYmzzQIaw9Dp7u6kslpBlAbe01QqhcMhccENkzjcofPKav9xzWH2Ag+GqLF+\n0zB+BzlFTAvmVrH3na3ZKMbJrgP4oHrIubB4rEf6HWpra5Ekic7OzrzXu7q6BnjBFhobGwc9/rXX\nXqO7u5uWlhYURUFRFA4dOsSyZcuYNGnSiOaZi1GDnINS3NLFYBliS/ght11nOA9QuQY5HA4jl2Lp\nMgx0zWph0hBFAUVRzJCsIJBMJBFFGbfkJRjqzSEIGRypVBLDMHDbqwgGyluINE3L0Ez60DQDWVEQ\nRYmhQuSiJOGsbGTbtu1senMLnqaWoY9vGMvm1jezrxmGwdq1a3l102YmXHQhdZOnUDN+HHM+91l2\nHm3jT3/6U1nf4cOEctpTID8vfbyMTKXmUQyGYfCLh39B2hPlnKXzsq8HAgFS6ThVRbSN01oaXdew\n2USqxzg55ax6nlvpJxYdmVAEgMstMWmGjfWbh7cxtTBnZg3JRB/vvPPOsOsATsb1fr/hZLB0KYrC\nggULWLNmTd44a9as4ayzzip6zuLFi/OOB1i9ejWLFy8G4LrrrmP79u1s27Yt+zdmzBhuu+02Vq1a\nNeK5WhgNWTPQQ9Z1vWToqJTww0h3ceWGyUOhEKJekPPMIYa3WHlkWR7QJ5lMJpFEGy7Zi6qmiMSC\nVFYM3VecTCTRdQO3oxp/b+egx1phUDALMmw2LwYi8WgIj7dqyLEAqhpaWL9hE4l0mqZzB3JfF6J2\nwiS2b15LIBDA6XQSDof5+SOPIDQ3UT91ClYA0VFRQfWc2Ty1YgVXXHHFiH7kxe7PB8n7GA5Gym9c\nGHq1RCCOB5s3b+aNra9x/k2zkW39y1V3dxcut4DdMXAJM8P0BrJkzn/+R5t4ekM3q1f28clryuun\nL4bZCzyseLSXYEilomJ4NJyTJnip8Kps3bqV0047Le+93OudW/RZDp90MRKZ3E3+B+UZHUzp6Xi+\nw7Jly7j++utZsGBBtu0pFotxww03AKaBHTt2LD/4wQ8A+MpXvsJ5553H3Xffzcc+9jGeeOIJWltb\n+fnPfw5AVVUVVVX565miKDQ2Nmb5+48Hox5yDqwFqNjuM1cnOZVK4XQ6qaysPO7e1XLP9ff5sYn9\ni4CR+XHm6hJLRYyxoeuoKTOv7ZK9GGmdQLSvrDGTySSGAV5nLaFgL+m0OuAYa5HQMi1MWc9cdCKJ\nCsGe8itLq+ub6ezuJRAOUz123JDH1004hXA8wY4dO4hGo7z22mvsOdrG5IsupCCbx/hFC+mIRvjz\nn/9c9nwsFLbGfVgxFL/xUJJ+lpxi4e+rlLeXSqV4+Ne/oHa6KxuqBkgk4oTCfqqKaBsbGKTVFDZF\nzAZlXD6FGRc08uLqIAH/wGe4XMyc6yFtpNnQOnwvWRAE5s50sn375mGdMxSftNU9UBjythixrM3+\nBwXFDPLx4JprruGuu+7ijjvuYN68eWzfvp1Vq1ZRV2duzNra2vIKthYvXswTTzzBQw89xNy5c1m+\nfDkrVqwY0INcas7Hi1EPOQfFQtaF8oxWkcGJKr7IHXOwGxvoC2JT7JkeSC17vCzLCIPMRc2w30ii\nhE20IxkyoTINciJphrqdTi+6XyMU6qG6uik73+w8xMw8MiuguRjI2Oxegj0dNE8srzWgoraJSCyB\nvbpygFRjIQzDQLI7kCpr2LptG5dddhnr3ngDqXkM3vqBrVKK00nl7Nn8fsWfuOKKK/B6vWXNqRx8\nkBa8E41cilCrk6CYpF8hbWKuZ1fKaDz33HMc6NjLlZ8/N++30d3dgyhp+CoHGmRVVTEMHZstf2mb\nc3Eju1/t5OVVfq749MDnoxx4fTITptlYv7GTSy4cO+zz586q4ZX1bx1XKHYoSlYrUmVFq6x1628t\noThclFJ6OhE81jfffDM333xz0fdefPHFAa8tXbqUpUuXlv35+/fvH/HcCjHqITMwZG094PF4nEAg\nkFVgqqysHHbfarljD7WoB/x+FEHJ84iHMsYAakrF0A1E0Vyg7DgJRcr3kEVRwmWvxNANgoGubF9r\n3jwkOc8jTSQS6IaO21OPv6e9rLHMz1IwFDeIg7QjGWaeOq2aG42qlols2Pwmfr+fTdu3UT+jtAjA\n+EVn0BEO88ILLxR9X9d1du7cyerVqweoT4E5biqV+v82j3eiYIVfy81LW89Sbp40HA7z++W/45Sz\nGqhu7DdehqHT29tFZU3xAkc1lUKWBUQx/z2bU2LqOfWsfSVEIjFyNr7ZC9xseaubaHT4nvacmTVA\nhO3bt494/GLIDXdbEqV2u7lZKaSptBgDc6MXlgrZe0kkM6r0ZGLUIBdBKpXKMjnZbLaTYogtDGWQ\nreKx3t4+FMmWYdfKGOIydriqmkLPeMgATtFNINRT1tzi8QSSpCCJMnbZg9/fOXBDUGQO8XgcwwCP\nr55AV3vZP3I1rSK7qlCLGEMAXdOzlaeWR1Y3YTI9gSBPP/004XSauqlTS36+ze3GPq6Fl9euHfDe\n9u3b+cyN1/NP//drfOO/f8hXb72VQ4cO9c8tIwepqmp2UbPEQSzvb9RIl0Yxo2H1S1veXi638YoV\nK+gKH2X+JdPRdQ3DMI1FX18fqpagqnpgBEXXNdJp1QxXF8Fp59UTSghseDU44u8xe76HpKayaUt5\nv6FcVFfZmTBWYtvWrSMev1xYz+FQbG8wsKo+Fou9Z0QyhW1Powb5QwirCMLyilRVzTI5ud3uk9ob\nWMogWznrYDBIKBQiraZx2V2mks0wQk1qOg1Gf37cKXsIhfswtWIHg8n1LEkKhmHglHz4+zqRJKmk\nIbYQj8cRJQW3t5ZELEoiVh6pSDQaxeapQk0kUJP9ZA6WIdY0DVESUWQlo+gDFY1NqKLMymef/V6y\nbgAAIABJREFUxT62GWWInsX66dPZ9vbbWXEKgL6+Pv7jv/6TA7LKxBs+yYwvfopNPUe4edlX2b17\ndzb06nQ6cblcWf1jK0RbqH+cS7oxqn88OCxDbfVKu1xmj+/K5//C1HOb8VZ7MAxL4k+jq6sTt0dA\nsYkYWRUIE6mUiiAYKCUMsqfKRsv8WtY870fXR3ZPKqsUmifKbBhhtfW82V62b/vb02haKFf0YTAi\nmZPxTI8qPZkYNciYBjgYDBKNRgGw2WwjZnIaLgoNcmGo3NJHNnSwjUDpSU2pGa5qcxy35CGdVonE\nQ4Oel06b+r2iKJmtT44qQsHesvJOsVgcUVZweWrRNaPswq5oNIrdV41hCAQ7282CMTVTMCaIyIqc\naeXqP0cQBJTqOna9s5e6QcLVFmqnTCaia7z++uuAudDfdc/d7I/5mfnpv6Ni3Bg8jXXMvenTdEhp\n7n/gZwDZ3l2ritXyPMDUsS63uCk3PDiK4li5ciX+RBenXzYzm/OUJIlkMkU0FqKqNrPpMpkpMykm\nnVQqgU0R+nWPi9iM2RfW096ts+PNyAhnJzBznpvN2ztJpYbfRjV/di3BYDsHDhwY4fjlYThV1oXR\nC4fDkY1eHG/B3vHM98OmhQyjBhno16D1+XwnpFVjOMhttUokEnmiB5WVlTidTiKRCFpawzYCHmtV\nTSEK/RsLp+TG0PRBCrtM8YdYLIqumwIUoijidlSRTMRIJKJDjhmNxZAVGzabG0myE+gt3yA7fDUI\nko2+Y21oaQ0ETEMsl74vsq+SSCJBzcSJQ44hKQqO8eN4aa3Jg7169WrWbH6DyVdegs3jQs/kyA1R\nYNLfncem3TtL8t7m4nhJN97rHN57Ces767rOa6+9xgM//xlKBRzZ3UE6ZbX5CPT0dCPJGl6fLbsx\nMv+s4kUdm01iMDnFunFuqib6WPtSYMTznTXfQzSRYvtb5dVi5GLGtEoc9gRb/wZh6+Ndx0o901ar\nZ27P9IkIeRfO98PoIY9WWWPmQ62q2781TaI1lpl3NbL5tVzvPBQKoae1EWkhq6qKkLPvsokOBF0k\nFOmjuS6XWcZA141MdaaBqlq5YjMs67JXoGs6oVAPTmdpcvd0Oo2qpnE4zUXT6awh0F2OQTYIhSPI\ndgd2bw2B9qNIslRWukB0+8BmJ9bXh60MEfiGGTPY+fxqjhw5wu+X/wFlagtVp7RkCCX6q9frpkyi\nY9o4HnnsNyxYsKAkqX2p52Woithc+spSFcjWBvH9VBF7omEYBq+88gp/WvlnNr+1hUAqREWfl+UP\nvUaFz8HpF01nwWUz6O3tprKuWDGXkC3mkiShBBFd/4vTz65l42Pv0tulUlOnWB9RNhqabFQ3iryx\nqYvT5w2vr1mWReacamfrls1ceeWVwzp3ODhZa1ipHvXcXmnr2c59pgt71AtpQovNNxwOj3rIH3YI\nwsnRRC6ElbMOh8PZcSsqKoqGysPhMFpaxyYN3yCbJAn9nycIAg6ceR6yYYk/aGksFSZNS2MYJiMW\ngMPmQdAFQsHBi1niiTi6YSArZjjX5a4hMETI2sAM0ydTKWSbHVdlPcGO9rKMkGEYJCUF2eXBf/jI\nkMcD1Jwyiaiu85vf/Ia3Du2nefE81EzVtpzJkYuZsadcfh4H/F1Z9p4T4XUUikFYeemhmLHez4pN\nI0U6nebBBx/kP392N8d8URqvmsUZd1zGWd/7O+b96xJss8aw5k9beeru50kkokWZubLFXBZX9RBy\nipPmVYFDYf3aAKU86VIhbzDv4WnzXGzY0jGiXPT8uTXsfWdrNkV2svC33MQVhrwLI0SSJA1KE2q1\naeWm7kaLuj6kyH1wrb7IkwmrYtdSHwIzb10qZx2JRBAMAVkafkAjlVTzDDKAQ3ARCPYUb2HKFGwl\nkylEsb+dSRBEHIqXYLB70PGSCZNuU7I8a3c1sXCQVCJe5GgzPJ5WVcLhCLphoDicuKobSUQixEND\nV8PGojHSmo6zcSz+Q4fLuCL9Yes//uUvaPUVeMY0ZIrVlIysXs61qvDhmj6Blc+vGrBRSyQSbNu2\njeXLl/Pss8/meQTDwWAVyOUW2nwQjXQ8Huffv//v/OHlZ5jy6UWc8vF5SDU2KurMaJWr3svUT85h\n+o1nsmd3J63L92NoA79jMplCFAwUeZDlLMc4Kw6JCWfUsG5tGNMOFBquEiHvHMya56EvGGfP3uGH\nvufPrkHXgye8/SkX74dnobCAbDCaUCtdE41G+cxnPsMtt9yC3W7n4MGDRCIjzffD/fffz8SJE3E6\nnZx55pls2rRp0OOfeuopZsyYgdPpZM6cOTz77LN57995553MmDEDj8dDdXU1F198MRs3bhzx/Aox\napAzyO1FPlkPc640I4DX68Xn8w25CQiFQsji8Kj6ADAMUmoq2/IEgAAuyYM/2IOqpkzjabVSCf2P\nQ6FnDeCUKwgGBjfIiUQCQeyvwna5a9A1g5A/tyrVNMRqDsNXMpVEkCREScJd1YCmGQQ6jg75FUPh\nELoAnuYWeg8dzihcDQ7d0HE0NnCw/RgNC04z2cUKDHEumhfN5d32try8X09PD8tuu5Vv/Og7/Gz5\nL/nRQ3fzL1/7MttyNJ2PF4VMTbmFNoP1ln4QKrwNw+De//0JL+98g/n/dDEt86fQ0dGO02dDsedv\nPCsn1zHtHxbiD8HLj+4dQNyjqklsNnE4zQfMOLuObr/Orh3RQb3pnBmTa6QnTHLg8sEbm7vK1WrJ\noq7WScsY4aQpBsHwFY3+VigWIbI6WazedbfbTWtrK62trXz+85/H5/MxZcoUrr76apLJZNljPfnk\nk9x6663ceeedbNmyhTlz5nDppZfS01M8yrd+/XquvfZavvCFL7B161auuOIKrrjiCnbt2pU9Ztq0\nadx///3s3LmT119/nQkTJnDJJZfQ29t73NcGRg3yAJwMg5yrCKXrOh6PB5/Pl22bGeqHYwpLDN87\nNsXS9TyDbBhm65OqJkmlEyiKnMnlDOS/tshELLgdlQT8XYNen0QigZj15AUczgowBEJ9pkHWDT1r\niAXM8LgkycSiMUTZ3HTIdieKw0Ooa+jccygUQnQ4cDU2oSZSRLpKt6NYEQEtnUb3etHtNhSbbcj0\noW9sI1qtj78+Z+6WOzo6uP3fvsHu0GHO+NpHueh717L41o9zSOnj29//Lnv37h1y3iNFboV3qeKx\n3NCg9Z3fLwQQFh5//HGeW/cCsz/7EWpPaSIQCBBLxqioH5inTyaTeJq9TP30XHa1+tm+pn+jlkql\nMsVcw1vKaltc+Ma62fBakSjMECFvMO/DjLlO1m9uJ51WM781rf/aGsaghnrBHB9b33z9Pb8P7yeI\noojdbuehhx5i3bp1ALz00ks88sgjfOxjH0PX9SzhSTm45557uOmmm7juuuuYPn06DzzwAC6Xi0ce\neaTo8ffeey+XX345y5YtY9q0adx5553Mnz+f++67L3vMpz/9aS688EImTJjAjBkzuPvuuwmFQics\n2jFqkAuQK/ZwvNB1nWg0mlWEcrvdVFRUDJBmHGoTYApLjCBcrapmHliUTCEK3VworErrSCxIqWqW\nRCKJVBAid9krUZNJYrHSLVOxeDwbrgYQRBGHo4pgb2fWGAJIecQiBpFoFCnnx2b31RDsHJzlS9d0\nguEwisuJs6YeXRCL5pENwyCtpUmnVQwMRFEirKawj2mk952Dg44B5v2pP30mL61fR3t7O9//0Q84\nkOzk7Fv+jooxNQiCgK+xmrNv+iiJeokf/vd/ZqMgfwsMFhrMLQjLzd/FYrH3rML7jTfe4FdPP8b4\nj86h6bQJgEFnVyc2t4TNmR8JMnQdNZ1CsYnUzWyk4dwprP3DIboOhjPfKYlNGcjMVQ6mnFnLtm0x\nopEy2peEgn8LMGu+l6OdUY6298t6GhkdcpO+Mt1vpHU9z0ifMa+OYPAY+/btG/a8y8H71UMuhcL5\nRiIRDMPg7LPP5oYbbuB//ud/+MMf/lD256mqSmtrKxdddFH2NUEQWLJkScmuifXr17NkyZK81y69\n9NKSx6uqyoMPPkhlZSVz5swpe26DYdQgZ1BIn3k8KBSiGEqacSiDHAwEkcUS0ouDQE2ZrFYCmZC4\nAIIo4BBdoAsEo8XDLGZPZ6qIQa4ww8+DMH0lEok8gwwGDlcVfV3HMDAKtJEz81RVkqqKYus3yE5f\nHf72Y4Nel3AkTFrXUZxOkCTsNfUD8siaZhb8GLo1tkIkEiGhpnBPnkTXO/vLMkSNc6YTEtLcdddd\n7Di8h9NvvAhXVb43J8oSC69fwv5gGz/535+8p96PFRq0jHUhAcSJblkpF4FAgJ88cB/OU+uYeoG5\niIUjESLREBW1A71jk6ynn+xjwqVTEOsrWfu7d0kmU+h6Grt9ZHwBk0+vJqGJvLlh8J78Upgyw4lk\n19m0pccMuUoSkmT2youSlKW1za2q1zQzOjT1FC9uZ4JNmzaNeskZFNJmHg8XRE9PD5qmDdA9bmho\nyBOTyEVHR0dZx69cuRKv14vD4eDee+9l9erVVFdXj2iehRg1yAUol1u6GCxSj2AwmMd/PZQi1FAG\n2d8XGF7Lk2Fg6DqJRDxriIQsmbzpMdlxEI4WF203w4DGAINsV9wIhliy0tqsltSzFdaWO+ByVxP2\n9yKJQlFt5FgsbvY82/q9I2dlHWoiSSxQutczHA5jiCJSJvTvrGuk7/CRzAKoZWg2NUTR9B6tnHhf\nXx+GLOGdPJFYMEq4fWjWJclmwzZpDH95/lkaz5lC5Ziaose5qjzM/NTZvLhx7UnNEY4EpfJ3uRXe\ngiCULB47XiNtGAYP/fwhjiV7mXvVR7K/ia7OTkSHgNNrLzyBZCqJrPR7+aIkMvmKmRw+EGPHK239\nrU4jgNOr0HhqJRteH1k0Q1FEps1ysH5zjjSpAOS0BomZtjdJkrNGWkBAlAQWzHawaePrJ4UJ64Po\nIefC6kE+0d9huNel2PEXXngh27ZtY/369Vx22WVcffXVJfPSw8WoQc6gmMBEuTAMI4/UI5f/ulym\nnMHGC/gDZbN05coyptNpBEEcUJwF4MBVktM6lUplDHm+Vy4IAk7FV7LS2hSVMJBkOY+a0+2pRVNV\noqHiFamxWAxDIM+zdlXWoWkGwUHyyOFwBNFuxzLwzrpGEpEooZ5uM0ctWjnq/u+vGzo9fb0oXjeO\npkZ0UaJ378GSY+RCr60gQprm2YMTkDTNnIAyvoLfPP7YB8L7KafCezB60HKN9Lp161j1+oucetWZ\nOLxOwORL9wf9+GrdAyhhU5kNlUn20Y+KCVVUzWth0zPHMEbAlpWLqYtq2bc/SWd7+cVCuZg138Oe\n/X56ehODHyjk0IRKZn/5wgUNHG3bY6akTjIT1vsZg2khjxS1tbVIkkRnZ76Oe1dX1wAv2EJjY2NZ\nxzudTiZNmsTChQv5+c9/jizLPPzwwyOeay5GDXIBcpmzhoJhGCSTSYLBILFYbMT810OGrP3BIT1k\nQ9fRClqYNE0vOQ+X7CEQ6ik6bjKZQjcY4CEDOJUKAv7iHmUikUDXQch6wTmV1rpBqLez6HmxeAxR\nyc8dynYnssNdsrBL13VCkbAZrgYwwFFbh6bphI+2I8uKqUJVyP4TCJJMqzh8HkRZwt7cRPeeoeXT\nYrEYcY+CvbGK9rcODXqsIAic+rHT2brvrbz805YtW/jhj37I//36v/LVW7/CH//4x+Nq6TiZGI72\ncTkV3rFYjAcfeQj3zAbGzjkl+3pnZyeCYuCpdBbMwCCVTBRVbgIYf9kUErrMjjUj45S2MG5mBTgU\nNq0bWdj61NluELURaSTPm12DLMXYvn37oExYqqoW7d0dLGLxQfKQS9FmHo+HrCgKCxYsyPIHWOOs\nWbOGs846q+g5ixcvzjseTCa/xYsXDzqWtVk9ERg1yAUQc/I+pWCReoRCIaLRKJIklST1KAeDGeRk\nMkkikSjtIRtG1hDntTCJIum0ikDx+TglD4lEjKQ6sD84lUohCsX1Ut2OSkKB3jwP2KrkjcViiBmq\nzdxzZcWOorgI9hVftCLRKJIysK3L4a0tWdhl9R9bYhKGYSDaHSjeSiIdHSV/yP6AH2wyki0T5m4Z\nS9/Bo2ip0j3EAtDe0Q4OCe+sUzjUundIT6V2UhOuqTX85onH6O7u5j9+8B98699vZ0vnWsLVR4hU\nH+WBx3/CP37xRjZs2DDoZ71fUA49aCkv73e/+x0HA+3M+sRirEuXSqn09HbhqXEOuF8WUY3NPnCJ\nMgwDxa3QeM5Etr3SSzRQXB2sHMg2kXHzati4Pjwi79Ppkpg0w866jcU3m4PB7VaYOV1h8+b++z9c\n6cpSaYUPOk4EKciyZct46KGHePTRR9m9ezdf/OIXicVi3HDDDQBcd911fPOb38we/5WvfIVnn32W\nu+++mz179vDd736X1tZWbrnlFsDcVH7rW99iw4YNHD58mDfffJN/+Id/4NixY1x99dXHNVcLowY5\ng3JD1pZWq0Xq4fP58Hq9xyVEMZhBNlm6tIEG2TCy9HR6Jk8sy3KeGpRlWIuMiEvymFSYkYF55FQq\niSAWr+p22StJqykikQAGRqZoyvTKk6kUoqxQrHLbmam0LoSua8TjcWTbwA2Hs6IOf3tx+cZwJIyO\ngWizWsdAFAQctQ34D7cVnbthGPT6/ciefnpN17hm1FSawOHSPc+xeJy+gB93bQW+0ybi7woSaBs6\nZ3Tq5Wew88Aebv7SF3njnZf4yBdmctVtS7joujO59B/P5trvX4x7qs73f/w9XnnllSE/7/2IwSq8\nLSN97NgxnnpmOWPPn4Hd58wUN+l0dnaikcZbPZDuNJlMIUggSQOfX03XEASDcedOICnY2bKqPK70\nUpiysJrObo0D+4YIO5fArAVuduzuIRga/sbgzDNq2bVz46BV+YMRxxT2pFtGGvo96/e7oMnJ0kK+\n5ppruOuuu7jjjjuYN28e27dvZ9WqVdTVmXSnbW1teQVbixcv5oknnuChhx5i7ty5LF++nBUrVnDq\nqacCJqXt7t27ueqqq5g2bRqf+MQn8Pv9vPbaa8yYMeO45mphlMu6BAqNgNXLqaoqkiTh9XqHlCEs\nF7mbgMLPC4VCaGm9P2SdrdjUAcMsGimhjZxKDiT3sGCXXBiaQSjaS311c957yUTp8yxO64C/C4fD\nZFSyuJcT8USW+7oQTncNgZ6Bod54PI6mGziKGeTKOvoOJIgF/Lir+qsYdV0nGAwi2O0IgkWqYuWR\nG/BveZd0Molc0LMYDodJpVVcnv6CLKWqEsHhoG/fYWomTyg6946ODnQZHD4XhseBZlM43LqXiuZa\ngLzxc+FuqCTiSbNr/06+9vBnsgxUFlw+J5d+/ixefGwjP/7Jj7DZbEOGxz4IKOQ7fuJ3T5D0CUy/\naJ65YTQM0mmNru5OXNUOREkwpRTNRgBzk6emsDsHGmPd0DEMHVkSEJwKjR+ZyPaXdzP3kkY8VSMg\nzwGaJntQKh1sXh9i0pTC0PnQmDXPwx8f7WPTm90sOb956BNysGhBPQ89uo3W1lbOP//8YZ1biitd\n0zSToCdDA2xtmK1zcnnSrWjWexneHixkfby4+eabufnmm4u+9+KLLw54benSpSxdurTo8Xa7fVit\nVyPBqIecQa6HnOux5pJ6aJqWR+pxoh7iwbxyy0O2S3Z0zeKc1hBFwWSYkqSixhjMkKBUwrCKgogd\nB6EildaJZKJo/hhAkRyIhkIg2JX1jCymsUQymRd6zp2Vy1NDPBImmYjlfV4sluG+tg1cTPsLu8yw\ntaEbpFUzPB+KRFCczgFf3VnfiJbWCLUPDHX7A350SUR29BtqQRCwjWmiZ9/Bot83mUrR4+/BVeM1\nC3MkEee0cRze9m4e/246bXp9um6gG6ZQx7v736VybgO2aieJWHHvSRRFLvrcIhrmurj3p/eUbMn4\noGLHjh28svl1pn/8dGSbyREuiiJ+vx9VT+W0OpkkrQZmXYYgGsiKSCG5tK7piDkGZOxHxpOS7Wx7\nYfghYwuCIDDx9Bo2bYyQTg8/bO31yYyforBu4/DvXXWVnVOnKLzxxrphn1sMuRshm802KF3l37Ld\nrdy5WwgEAh86HmsYNchFYe0syyH1OFHjQXGDHAqFSKtpRMSs+IOiKEiyXNIQQ3+1dSHbljmg+R8H\nLgLhwl5kg2QiNaDC2jBMQwPgVLxEI/68gjGrwrqUh5yl0CzII8fiMQRZKfpdrMKuYGd7Hue2qqqo\naa2/oCsHtooqkG0Ejx4b8J4ZrnYNeN3Z0kzgSAdqPD9kaWDQ3t6OJhg4K93ZzZpnagvHDraz8eV1\nHDh4IIeT3Ixe6JrOoUOHCEb8TDp/CnKNmy0v7KYUdZMgCFzw2YUkHAH+++4fj5gT+/0GwzD49WOP\nIjS7aZ7dryymGwYdne04K+3INhnLGCMIGLqBqppEIIVPhK7rGOh5RV6yXab+zPHsfK2PZDTNSDFl\nYTWBsMGetwYTfChtpGYt8LBlZzfR6PDv3eKFNWzftu6kiU2U0+72XnKljyo99WPUIGeQW11tVU+n\nUimcTuegpB4ncuzCB1NVVbq6utDTOrKoIMvykIY491zDMEp6yABO2U2goKdYVc1ck+UhG5iGOBtW\nEgVctsoBldZmhbWBJBcPGzqcPgQkQn35nkw0GkNUSpOeOLw1BDJ5ZEmWkBWZaDSKjjEgJA0Zj7e6\njkBbfh45GosSTyawFzHIrpZm0pqO/0Bb5jubkZFkIklXTxf2KnemfzRz/IRGDJuNwLudBANB9u3b\nx5atW9i6dRvbtm1j165dtHcepXKMB4fHTuNZE3lr40FCfRGTvUnXMAyrhcW8rnanjUu+sIgdB97k\nqaeeIpFIsHfvXl5++WVef/11tm3bdtLVgU40Vq9ezUvrX8XbVMm7r+3Ef9ikXe3r7SOeilFZV5wm\nE9HIUGH201UamL9NURQGPP7NZ40jlpLYtbZ7SKWmUqhpduFpcrF5hNXWsxd4SKRVNm0Zfj/qmafX\no6X9tLa2jmjsQhQLARdDoUJTuVzphTSsJ2O+H0YtZBjNIWeh6zrxeDzL/yuKYlb44WSj0CBrmkYs\nFssWkNlkh8l7PYwNgVXsVSoXDOCSvHTH20mpSWyKadxSqSS6YSBKsukRG4ZJdCAKWeUnt6OStuCR\nLOkGZBZSQczKNZpfjOzCKAgiDkdlQaW1SZkpu4roDBsGBuCoqCPYvisvXx8ORxBsNoQSdInOugb6\nDu/Jy8kH/AF0QUAu4lXLPi+C203f/sPUTJ+ErmkYgD8QIKWlqa7KzxXLdhsVMyYhhxNMnz6N3t4+\nent7s1qwoUgQV7UNQ0oTCgbxTK/mwMoUrWve4twrTwcGttUJgkDN2Eomn9vA3ff9mMd/9zCyTUM3\nkuZ1FyTsSgXz5pzFJz9xRbbQ5P2ItrY2fvXrX/Pr3z9B3C4Qfv1tDE1HBupaanFOqaZiZhWKw9qI\nmddV1zSTCtMxkFtdz+h0S0V+j3afnap5zWx7qY3ZF9YjZVi9MEr8Xkq8fMoZNbz53BE+ldBxOArG\nGcLAV1UrjDtF4bU32jn/nKbBDy5AbY2D6adIrFv3Gueee+6wzh0MI3EgSuWlC7WOTQa1/nNyNY5H\nouFdrHZm1CB/iGGRe9hsNpNUQhgown2yYT3oprCDiNvtJp1OowjDM8ZQrofsQU9ohKN+aiobgUwP\nco4hL/bDctkr0QIakbAfX4VprPJFJUqM56om2N3vISeSSdJauqCgy8i2xQiAq7Ie/4E3iQUDuCur\nAAiGQsiOHF3cwjxyXSOht7cQ6+vDXWMWcPX6+5DcjqKXURAEbM1j6N57gEnaR8xFRRTp7O5C8TmQ\nbLLJb56ZHwZ4prbQvfJ1SOq0tLTQMq4FDIM977yDYUtT0+zNtqLJDgXfqY1semEX3onmxkIA3B43\ntbW1VFRUEg6HOHbsKI6WFEpDAkHp5EvfWEBjsxfDMAgHVXZt72Hz63/lW3es4ewzL+PGG/+Bmpri\njGHvBQzD4LnnnuNnv3qEw2oE6fzZTL1gLvYqr8md/u5RujfuIvrMm0wKTaSmuRJJ6X8+TaUwYwAR\niG7o6JlCrlJoOXcC21oPs6/Vz7Qza4vNLuefxR4Ck0pz65+PsOPNMGecNXxjMGehh9VPdRGJqHg8\nw6O6PffsOh7+7WsnxDM80aHlUkY6lxLUKh7LHTu3cMz6K2akS6XqRkPWH2LIskxlZWWW1OO9aBGI\nxWIDuK/D4fCIhCVUVQVLWKIELJGJYLQPs2BGIx43i64kUcornsmFy15pnhfqZ+yKxxOI0uCLkNNd\nTbCvOyuRGI/F0HUj2/Jk/sjNYwVBAEHIFnaFMoVdyWSSRCqJ4hwoVJ8dp64eXYfgUbOVKZFMEInF\nsHkGttdY4zrHNhE61o2eSCFJEsFAgGg8irvGm51P7rXwTBmLikD7WwezeeNj7e0Ew37qWqpwuVz4\nfD4qKiqoqKhg4nnTSSQg0BbFTAQYRCJR9u/fz/r169i+o5VYqovGcSKX3DSFcDJNe1sUWRFQbCLV\ndXbOuaiZr357Hlf9Yw1v7lrOrf/3lhMq93g8MAyDnz3wAD/46U9ITBuD59JFVC2cgb0qc/0kEe/U\nFqo/sZiKy8/kcGs7bz6wFi1p5n3NFr5Upu+4/zobmeey1LNowd3gwTulgW0vdpsV2/3R7gwGvECh\npKK32k7NKT42rQsPO+QNMPcML0lNNSUZh4lzzmxAIJBVOToROJmV06Xy0sPR8LYMeGF3iWEYBIPB\nUYP8YYflEQ+lT3yiYHnloZCZt7KYvnK5r4PBILIwAmEJVUUQBu+NlgQJGw6C4R5UNY2ma6iqWQg2\n2I9Zke3Iop1QsL8gLBaPlyzoynJae2rQVJVI5rxYLAailHe9BSF/IZHtTmS7m2CnWcEaCUfQdAPZ\nUbo9RbLZkb0VBNtMgxwIBNAwsLnzz7EK1QzA1TIWzYDAQZMLu72zA9EpY3PmbhaMDFV3538oAAAg\nAElEQVSxgOJyYGtp4NjOQ8iyTDKZ4Fj7Ubz1TuxuW8bkmv8DqJxQg6u5mkQ7zJ+/gLlz51JXV0ta\nS2FzpGkaZ6OpxY7NDtUtNupOdfPrX2xm44Yt7Nmzh94eMyQuCALzz2zga9+dTdWYY3zne7fyhz/8\nIVsh+17QKxqGwQMPPshv/7qCxk+cR9WimSR0FW99vqdncYtXnzGVhs8softwhB2PbUTXdOIZ71hR\ncp9ZI1PJbpTFVz3mrHG0H07SeaAg1y6U+CtipCefXs3Ot+KEg+qw89EVlTITptlYu35wlbJi8Hlt\nLJht59VX1wx98BB4r0VNSuWlCxneLPIYKxedSqV49dVXOXLkyHFHCu6//34mTpyI0+nkzDPPZNOm\nTYMe/9RTTzFjxgycTidz5szh2Wefzb6XTqe5/fbbmT17Nh6Ph+bmZq6//nrai3RyHC9GDXIRDEVl\nebwopNy02HesFqJc+Hv92EoUSg0GVVVLkIJYkwAMAzsOAuFeRMHkfU6ravHK7AI45X5Oa4ugRBqk\nOAtMkQldtyqtzfyxIMtYXbyW+EUhHL4agp1m1XQ4EkFQMgQog8BRU4//iFmk1ef3Izrt/eo7kDXE\ngiAgCgKKx43k89H37iFTqSscxFXtzS9oy3hp1gw901pof+coiWic/Qf2IzmhqsGXOS7T32kdLQg0\nLhrP7i1tHN5/hJ1v7aCr5wiNY22cOqeOMc3VVFZU4PP5cDicLPh4C3FNpPV1P/F4nCNtR9i+fXu2\neKytbT+f/Gwjiy+WefS39/LYY48VbWOxNhIn83n+1a9+xWMr/8SYT5xP/expHG0/hq3SmWVDs5BI\nJBBkAVEWcYytpW7pRzj2Vjdv//5NVDWJ3ZG/gdQ0DQy9KDlIMVRPr0Ws9rLz5TI91CJGetL8alRB\n4s2NuZSmBVbZoKSxnrvQw9Zd3SMiCTn/nEb279tKW1txYpsPKoZieLN+V9FolKVLlzJz5kzC4TBf\n+tKXuO2223jiiSd4++23y36Gn3zySW699VbuvPNOtmzZwpw5c7j00ktLCkCsX7+ea6+9li984Qts\n3bqVK664giuuuIJdu3YBpuOwdetWvvOd77Blyxb++Mc/smfPHj75yU+esGtkYdQgF8HJMsilKDfd\nbnfJMU2lp9Lh2VJQVRVh0NtrLtJO0UMo3IeUKZpKJAfqIBeD01ZBIFOglUwmMxXWhQY537jKsh2b\n4ibQ24GqqkQiUSSbPRueLjlWRR2Bjg4MwyAUDiE5hr4ezvpGQp1dxKMRguEQNq+73xBnPF0xY1w1\nXScQDKLXVLFnw1Z2734bTTCwZQQQCg2xBe/UcSRSaba9tJFYMkptS1XB9xAslx9BEKid1UxETbHu\n2U3YnQkmTfVS1+DK3Huzfg5BwG630Tiulnkfm8Te3dAyZiotLS24XFaFuEEimaC9/RjjpkWZvjDA\n/Q9+j09+8hM888wzZpojEy60iCJOpGpTLl5++WUe/ePTNFx6Fk3zTqWzs4tEOoW3bqB3rGlpFFv/\ns+Wa1ETVZYs4+MZhAm+1I8v9z6uma+iGjiQNrKouBUEQaFw8nj2tIWLBkbWOOTwyTadWsnF9qIgn\nnYv8cLf1N2e+hzRp1q4ffk/y6fPq8Lhix83aVm6V9XuJXCNt/buiooLNmzfz6KOP4vV68fl8/P73\nv+faa68dFmnKPffcw0033cR1113H9OnTeeCBB3C5XDzyyCNFj7/33nu5/PLLWbZsGdOmTePOO+9k\n/vz53HfffQD4fD5WrVrF0qVLmTJlCgsXLuS+++6jtbX1hG+eRg1yDo5H8WkopNPpPMpNr9ebR7lZ\nyiAHA8ERecjJEixdRnblN8d0yR4isSCaZubyEonyDLLbUUk41IempYknMuQeQ87TwO6oItDTgabr\npFQVpUjrUiGclfWk4nHCvT1EYzHkgvxx5m7ln1PXgJbWObrnHVMz2e0c4Ola66hFVmFraiQVjNDT\n0QkuiXAoTCgUMiMZ0aipPpRzj2yVHqjxcWjbO1SP9aLYB143izUpHAqSMhJUzW6g72CIsRO82B1y\nv7G31vzM7TEMgzkXj0V3SDzzh/3U1dUxffoM5s9fkA15jx8/HrfLzdxFFXz8M1UEo/u56+67+Jd/\n+Rc+97nPceONN3LLLbewfPly/H7/oKpNIzHS+/bt466f/i/yzAk0L5pDWk1zrLMde5UbUcm/Frne\ncS48s8ZjnzGR/Sv3kvCblI+arqHrWsYYD8+oNJ0+hpSg8PbrxRXJysGURTXs25+i41hGMKBwCiXC\n3WDg8UpMnWnnxbVt6Jlip+xvbohLqygi55/t4+WX/nrcvejvZ2NcCCuHLIoi48eP55xzzqG3t5cV\nK1Zw8OBBent7WbVqVVnfSVVVWltbueiii7KvCYLAkiVL8oRecrF+/XqWLFmS99qll15a8ngw02CC\nIJzwPPeoQS6CE2mQc5m+DMPA4/Hg9XrNNqaCMYvRdcaisRF5yKlUKq/C2jBMjeRMnNYa1Ky0TmuE\non70DC91OQbZZa9E1zTCoV6SiaTZp1uiKt1cj8yFyeWuJtTbTSqZzDB0DW2QrcKujkMH0AwDm3Ng\nL3EhLIKQ9r17ERw2hEwrhuUV515pa4NUO22y6eWHonhqKvLWWjWdJhaLEgoFCQQDBIJB/IEAYksd\n4aMBRAWSiQTJRIJ4PEY0GiEYDBAJh0gmY4iyjtMjM+6cCQT9aY7uLpSiNMPb/QZawOaQWXDFeDZt\n6GDf7j70jKEyc8kiNTU1TJs+jfnzF3DtDeez7LsLqaxVzQ1SpkUlHA6xYsUKli1bxuc+9zluuOEG\nbr75Zp5++mm6u7tHLK0YjUb54X//mIDPxtSPX4QgCHR0dpDSVDy1+bJ5qVSKdIF3DGRpYOsum09K\ncrLrie2k02aeWRLNezVcyE6F6jnN7Hi1F10b2e93/MwKRKfCptcH6UkeJCd9xtk+9uz3c+Ro1KxT\n0LQMf3e630hnDXX+x162pIVQ8BAbN24c0dzhvc0hjxSFtJk+ny/7WnV1NXPnzi3rc3p6etA0bYBk\nYkNDQ0kWvI6OjmEdn0wm+frXv861116Lx1OkZfM4MGqQc3AiPeRiTF8+n68k01cxgxwKhdA1vWwt\n5CwMAzWlmh6yYWDo5g5dyPYT98MUmdAIRftIppIZHeRyDHIFhmYQDPUQj8eHqLDO8cg9tSSiEfz+\nXnQoqvJUCKuwq7vtEMgyolyOkIeAUlWL/0gbNp8nzxAX3lUrjC06HAgVFRihCG63i8qKCiozVdJe\nrxeH3ZmJOggYho6mpXFNbUJLG3S93UY8ESWeiJJS4xio2OzgdIt4vAoOh4wkCvjGV6LU+di1tryQ\n5pSF9XjHuVnxu32IomT+CULWmJl/5iK/YPEYPv/V6dicfhYvXsQjjzzCww8/wle/+lVmz56dbVOJ\nx2KsXLmS2267LWukP//5z/Pb3/6Wo0ePZpnqcgkhCo30L3/5S3Z1HmH6pz6KpMikUirtnZ04ajz5\n98cwvWNJEfO9YyOTIxZAdtmp+fgiuvYG6NjUhiwVl1wsF2MWt+D36xzaWVx/eyhIisj402vYsD6M\nrg9jDcgY5lPnuLG7DV5Z15ltFxIlqb+Gwbp3GSOtaRq6Zhrp5iYXs2dIrHpu5Yjmnp3KB8xDzoVV\n0HUiv8Nw5ShLHZ9Op7n66qsRBIGf/vSnJ2x+Fkb7kIvgeAyyYRhZghFBEHA6nXlV04ONWcwgp1UN\nu2t4BtmqWhSlTPVypsioGGRRQTFshKJ9VDgb0A1jAG1m0fMkG4rkJBTsAaFmQP44l4UK+kOP7ow2\ncm9HG6JS/u7S7q2h79hRak8ZXFXFsMY2QKmuJfjWQWwed/FoYba32Lz+yWQSpbmRdGdb3g9SACRR\nRHLYcTjsaLpu6hiL4JxQT7DSR3Cvn6qpDWYUQjfQMmFKwzDzw6IkZFt3GhaNY++zOzknlMLlG3xD\nIggCZ141iVV372Dz6+0s/EiueIGRDW+T2VTMPqMOQTR4/ME/Eo8n+PKXv8KiRYtYvHhx9vuk02m2\nb9/OSy+9xMaNG7PRkxdeWM0LL6zOjgsCixcv5rzzzmP69OnZXtNNmzbx1HMrafrY2dh8XnTd4Nix\nY6ik8dXke8fJVBJN13C48r+nyVZmIMnmNsk5vh7HqRPZ/9x+Guc2IjuH31lgwdvsw9Fcxc5Xepg4\np2pEnzFlYQ2r1nayb3eMqacWb5crBUURmb3QxYtr2/jsNZNNdjEKjKSRaX7LPKsGmY0zcOkFDfzn\nfet55513mDBhQp4IRDn4IHnIpVi6cj3k4aC2thZJkujszGcE7OrqGuAFW2hsbCzreMsYHzlyhBdf\nfPGEe8cw6iHnodBDHk4vstXCFAgESCQSOBwOKioqcDoHar2WGruYQdbS2rBC1oaum4INuo4kmrrI\nQ41vx0kw0pcR2Rby2bYGgVVpHU/EkXLUqEytZKt2Oj/LZs9QaPZ2HUMqI1ydHauillhfTz4hSA4s\nQ2zomYItUUCurMFIa6RDBaFHq/KY/upuQ9dJJBM4xjWTCsZQA5GBgwDpTApCxzQyoiThnNZC4J0+\nvF4fPl8FbrcXh92FKNhJqyKJmE4snCYSVonHVKpmNpDURd56tby2icZTfIxdUMPyJ/cQj+XmFvtV\nlUSpP2Uwa0E9190yiS1vPct//fhH2Wcyl4luzpw5LFu2jCeffJInn3yS3z7+ON/+9h2cffY5GEaG\nQlbXWLfuNX74w+9z/fWf5YYbPsfnPvtZvnb7bcSaKqg+dTKGYRCLxejs7sRZ60UQhayR0XWdRCKB\nbBPzWNWMjGcviDntZALUXjSHWFzgwKp3y7oug6Fp8TgO7IoQ7B6ZpGLDRDfOOicbXhsZlebCs310\n+aNs2V7IFZ+BQN69Mz1pGVGSWLignprKOM89++wA2srCHt5iGK43+H5AYch6pLlZRVFYsGABa9b0\nt48ZhsGaNWs466yzip6zePHivOPBpH3NVV6zjPH+/ftZs2YNVVUj2+gNBem73/1uuceWfeAHFVYo\nSRAEEokEiqJk1VEGOyeVShGJREilUthsNjwez7C5r60eUkeOwXnnnXd4fuVqptacNijBR2YimTyV\nSe7R092Lx1lR3DPOxG6t+UXUAHEpRq23hUgkhstVXv9fNOEnEOvA5RmD3e3LMeT9bT+WYc7d7PT1\nHCSeilE9YXpZOWSAVCJO76Hd1MyaPaAH2bD+T8AMyWdCrkldJ7JvF46mehz1ufSX/YbYqp6OxeOk\ndQ17lY/Ilp04GytwNtXmjZFKpTK90wYOtw0yRkaUJAKb36F+RgOOKhdiRptasdmw2x3YbXZkWUES\nZTBEDEEk0hPj8Po2vOM8xCIpkgkNXTMXUkkeeM8aJnp5c81RtLjGqXPq8r+/YRKTGIaZW5YliYYx\nHk6Z7mHNqlZ2bj/AokWLs16H5elafxYzXV1dHYsWLeKqq67iqquuZunSqzj99DNQFBttbcdQVZXD\nRw7Rqxj4zp9Hb6CPzo5jHD5yhJSg4aj1Yuj97E2JRAJNT6M45GwdgfWeIIKpGtqf1xftCoYo0v3a\nHhpm12PzjExOEcBZ5+boujYcgsbYGb6hTyiAIAikEjo7X+3h/CUVKBYdJ0KxzrwBqKiU2b4lTF+X\nzrlnlUmlKVjVxyICaVY+v5eLL/l4tviz8N6pqppXjGc92+l0vwjN+x2GYaCqqqnlntlQbtiwgba2\nNv7+7/9+RJ/p8/n49re/zbhx47Db7fzbv/0b27Zt4xe/+AVut5vrrruOTZs2ZQu/mpub+da3voXb\n7aa6upr77ruPp556iocffpi6ujo0TWPp0qVs2bKFp59+GqfTmd0g2e32PAazIXDnUAeMesiDYKjQ\nj6qqeS1MPp8Pj8cznBuURTEPORgMgi6YC3npSaJlfpyGYeZ/9UzxVmkjnr+iOGWz9SmeiCMI5Wcx\nXPYKImE/KTWFmNm4lMNha3dUkIwEkW3le/6Ku9pcJAP9HofpyRlZY2wt7KqaJhAIoosSkreK0MHD\npFIpM5xMviEG0+tNpVJIdhnJYUepryXyrtn3bADptEY0EiUWiyLIYHfZ8tqbHOPqweGga/vRonMX\nRBFZUbA7HLjcbry+CiYvOQ01bUPr9eBQGogGbRw9lOLd3RH27Axw8N0gnceiBANJUkkNd6WduR8b\nx4urD3HkQDB7AaxNmAH99IaZuU2aWsWXvnka3aGN3P6Nr/Huu+/idDqzEqJerxeXy5Xtg7cE7ROJ\nBMlkEk3TGDduHNdffz2//OUv+frXv4G3qYm5n/k/jJ86FbfHS1ozUDUNR5ULTVNJJuMk4lHisQip\nVBJREbJ5bsPQsp6xJPUb4lxULpyK7vax/7m9ZT8bxSApErXzx7Lz9T7SqZGx7k09s4aoKtD6RnjY\n5wqCwOILfLyxpZ3unviwz7/kwrE4bX7+8uc/Z9uD7HZ7npyi3W7POgwW0UYsFsvWFryXZDHloljI\n+nh5rK+55hruuusu7rjjDubNm8f27dtZtWoVdXXmRratrS2vYGvx4sU88cQTPPTQQ8ydO5fly5ez\nYsWKLF98W1sbzzzzDG1tbcydO5cxY8bQ1NTEmDFjBq3EHglGc8g5yPXiButFTqfTxONxkwxDkopW\nTY9k7GIGWRFKyD0a/Z4ICNk8E4JASlURBbP4aHCYlswledDSaYLBXiTJW/acXbZKNFUjmYwgSeXr\nQzuclaTad5asyi46U0lGdniJd3fhnTA5Gxa1vHDryhmAJEumtyhL2OsaiB9tMz1byFbDSqKYlbGM\nx+MYonWegW1sM6FdO4nFYqQzhTeCCDaXMqBtB0yv3DFtHB3bjjD572aWdR18Y6vxTKjjyPZezrxk\nPgDptJoh9ogRi0WJhsL0dSeABKJo4BnrRnNL3PvDTXz1W6fTMMbVH/YUBwoyADSN9fKVO+bx6/t3\n8K1vf4W//9QXuPLKK7Mel5i5DtAfPtayVcFadqOXSCT43wd+RnpsNWNPn4WY8ah379mN7pCobqpH\n0/vPSyYTiHK/ty8IoBtkjXGpKyRIIlXnzqR95TrGHwrgGz/ytpIxi1vYum4/+7f4mbpo+Jzf7kob\nTadWsm5tkHMuGP48Tj/Tx8rf9/H8i0f5zDWTh3Wu0ynz8UtreHrlCq5cujTPQFnrUy6JUC63dCJh\nhulHyi39t8TJUnq6+eabufnmm4u+9+KLLw54benSpSxdurTo8ePHj89qn59sjHrIJVDMQOa2MGma\nlvU0TkRoqKRBpuCzM20UqmqGGkVJQlHM3JPlGampVMkirvzPMv/jkj3oaZ1AtK8IuUeR0zI/fKfN\ni67pqGpsWD9sm6MKEEiE+8o6Xtd1NF3HWdVAvLszL0+cHdYgq06lpdMIkohit+FqaoZwFMUwcoyW\naXTiGdrSpJpEkEXUdBo1nUZubiAVTRI6fAxD1LG5FOwee1FjbMFz6jgivXHCR/xlX4cx55zC/rc7\n6Gkzz5FlBZ+vgqamJk45ZTKzZ81j3tzTmTZ1Js1jTsHjauK0i6ey990IX/+nDXz7S6387L+2s+J3\ne9n0ejsdRyNFq4K9Phv/fNs8zvmowqNP3M03v3k7+/btG3BcroG2aA+tfvm//vWvvNN1jCkfv8i8\nxrpOb28vgXAQb2Nl3nlipgXO7rIjSWYdg569Z5nnx+inLS2sfPecNh6hupp3n913XJ6dq86Ne1Id\nO18dviSihWln1/Lu/hRHD2dy0cOwX3aHyPzFbla9fJh0evhe+kcvGYckdPHnFSuGPNa6d5YqmizL\nw9I8PpFkMSPBifSQP8gY9ZBzkPtQ5BpIS5oxmUyarTuZcNGJ3F3mVnZb/w4Gg4hGJuyc470YGeMi\nScW1kZOpVMZDLg+yYEPUJWKJEFJ1aYNsWEpMmTnKsg2b5CGVGp5Or2LzISISD3bjqqwb8nhVVTEw\ncFTW03d4K2AUbDgsw2zOL5lKgZAxCnUNYAjovX68kydljjaNdyqZJBqLIimyWYxkza++FsFmI3Wk\nG8/EhpIyj7lwttSBw0HntqP4xlWXdR3qZo7hgMfOljW7ufj6xUWPsYy0z+dD03TGjRuHGHWy66/H\nuPSCTxOLxdi38y02vHAYzWhHsas0tSg0T3AzdryPlok+GprcSJLIR6+czIxZfp7+9Tr+9fatXHzh\n/+HKK6+kqal0jlMQBA4fPswTK5ZTcfoMUsEw6Wgc0W6jresosteOze0wYxQGmQU/gWKXEEUh23Mr\nigKiVNADbvSLieRGOQRRoOr8WXT94RX8e/uonjpyRasxi8ex//HN9B6NUdM8dP96IcadVvH/2Hvv\nMDnKK+37V6njxJ6cZySNwihnCSUkCwmQTbLBGYzDvvbrXbxer72O2N7XayMM67UB22CMDdhgRM5B\nCRAghFBAcfIoa3Ls3BW+P6qrp3umZ6ZHCNb7rc51jTTTXdX1VHXVcz/nnPvcByndzluv9XHt5/PH\nvf+yNVm8vf00b7zdNu62jOlpClddlsujz21i/aWXkp8//uMPRk8GbWiXJisvPXSfeI/6g/KkR+r0\nNGHChA/keH/vdgGQRzCLQBEIBAgEzBxQqiVM53o8SATkro4ubLIDI/rwGDEglEcN94aDobFJYEOO\nbTccBML9yElqkOOBmFgtszlGu5RGOJg6E1U3dBAEHM4sAr2dUDHGDlHSnCBKOLPyMZpUwr09ODw5\ncSVLg8Quw9BNHW/FXKzI7jQkZxqB02dxT6yKnk+UARwOISoSitNsyxjTfZYk7CUl+JtaSV84DVBN\nwo1ksWKFKKs5bgEniTgml9H63qmUw9aiLFK0YiIHNtey5GOzSPckK68ZrDcGswvXRVfO4WxtNweP\nHOCXt9yG2+3G6/XS3NxMS0sLTU1NNB45zDtbTZCWbSZIl1a6Ka1I59NfmUJjbQ/bnn+ALdueYuXy\ny1i79hJmzBg+7mAwyL9861scaWrE1tNJ/fZ3EDDD65pdJH9pDXpuJrLLjh4t+RMkAUkR4xaPJNQV\nx34T4gFaiH3fhgHOScWIBXk0vdRI+gTTAx8k4o15aWOWMy2PljQXh1/rYOVnxrrZhpski1RflMdb\nr5/mY9fm4nCOjx9SVGJn8kw7TzzXzKplheOeO67aUMEr2/fy0F//wj9/819S2mcslrVJHBu553F8\nO8Wh+8SD9floT/tBhaz/p9oFQE5i8TdnJBKJtRf7MPojx68Yuzq7sIm2GGvSCkeNNSOFwmEkceRu\nSMnMIbjoDHUn1CBbXo+RBIjB1IB2Kpl0BI6nXGqhqao54bpy8PeMJm8YrbHVdVRNQ1QUHFm5YECg\nsw27Jyc2klgtLia5RQeUOOlGe14hwdOt5vii363P50PHQHHYY8eCQeEUZ2Up3jdPkmZ3giKhqWYu\nWVU1ImGNZCDtnlpG14EGBk72pOwllyyp4sz2eva8fISLP70w8QoYOppmlpDF54klUeSSLy/hyY2v\n8l+//i++/73vk5aWxqxZs5g1a1Zsf5/PlwDSDbWHeWfbcTSjFUmJkFsgcqrvJK+89ie273iSPE8l\nixetYP78+UydOpX9+/fz77/4OftbmsmYP4f06gk4CvJQVZW+tjaCx07Q8VYdvfsbKf7YRQglHpNV\n7ZSjzG1S0qJOeNsiegkC2Stm0PXYq/Q395E50ROVLTV/4sFZEEYWwhBlkfxF5Rx5s47FV5Zgd8nj\nCjsD1KzI4/DLZ3j3rQGWf2T8ueQ1l2dz98Y29r7Xxfw5yXo1j2wOh8znrivlN394nssu38CUKVPG\nffxULB6k4zkFg+IzJlAPBelknvR4Fx1Dtx8YGPhf2XoRLgDyMLNKW6zyp4yMjHNiTY/X4j1ki5jR\n3tZBmpSTQNgay0z5Sw1bSmpWg+YQ3ITDp7D8lfgyipEeMl3TcCgZ6MEI4bAPuz1ZoXzifqqqgSDg\nTMulv31/rFQn3uIXJaqmYWAgyCYJyebOJtjZjjC5JgGIrRRDKBSOeceW2fML6N3XhBGJoAH+gN9s\nx+i0m6VLsbqpQSlDR1kx/ZqB/1grGdMqomxWeyzcbQK0hqqpaFGQFnKyUWUbtS8fofpjs7A7FWxO\nBXmoZGScyQ6FwmUT2fd6A4s2zMCV4YzeA4MRkWTtMLMLMljzxQVs+e0rVD5UyWc/+9lhn+12u5k5\ncyYzZ86Mveb3+2lpaaG5uZmmpibsHOb06WNoRpDWzqM8/cIhnn7eSdsZL2f7AghVFXg+fiWZ1ZWm\nQpggEAkEsJUU4q6uQPX56d3+Jk0PvkLavAnkXToHQTAQRIH3IbZljn9SEb0FuRzf0sK8yWZqw4gT\n1DCvk3Wv6COCdNHiEs5sa6B+VxczV+eDkWRgo4w1LdtGyaxsXt3ay7I141eQmjjZSckEmSeebR43\nIAOsXlHEC5vPcs89d3LLLf85KmflfDaWGMmTtlJn8U5LvPZ2vCdtkQdHGk+ykHVvb+8FD/mCmTeH\nz+dDlmUkSTLFNT4EMIbBBygUChEOhwmFQgQDQQoc7pSFOgDC4YgpfzlmC8XEB8QhuMAw8If6cNmz\nYmMa7cHWNA2nLQsM8Hu7RgDkRFNVFUEUcbpz0FWVkLcXR3rUmxyinGVuH8FAMIEcsKV58J49g6pq\n0RU5Ma8qGAyhYSR4xwD2/EJ0Taen5Thifi7IAjZHHAdAGLwe1vHljAzEjAy8jadJn1oe+6xoVTWy\nIiMrMnYLpPUoUWz2FPr3HSV0kYZPD6GjI0qgOCVsTlsUpG3ItsHvtHTZRM6+3sDezUe56OrZg+Hp\n6GQ2ElpUzihm7lWTeODR+3C5XFx99dVjXn+Xy8X06dOZPn167LVAIBDzpGtra7n/L/fTHVFxLJiD\nOKESuciD3++NfkUGmm4gORRUTQOHjcxLL0Y5WMvAzndQZMi/fP64wsojmSAIZC+fTucTr9HT1EX2\nxBwTaBGjl8RaPI4O0rLbTkZNEftfbaVmVR5SbKUQBwZjgHTNyny23tlNY12A6q7uD7sAACAASURB\nVKnjy0ULgsDqy7L4610dHK3rYdqU8YlKCILA1788mW//eC+PPfbYOdfnng8bi+Ed70mnwvAeGlkz\nDON/tYd8gWUdZ6IokpWVFSvEH49S1/sxqzgeTEC29K4N3cCmjK+xRCQSNvN2KYB4/NrUJjjBAF+w\nN/bAjbXKVjUNm+xCQibgG40xPeh1q5qKKEo43TmgG2YeOYlylrV9OKwmaCPbM3MJdnfR39tNf38f\nfX399PX2MeD1mnXUshQNdVt6wTqiKw0kG95TpxHtMopzZEKeEBsDOMrLGGgw26uZdcsWaA9O/hZj\nWBBMkM6dPxVDFymQspg7ex7TqmsoLawgXc4m0mvQfcLLmbpOTh1po62lk57WfiKqRt7iSnZtPkpv\nx0AcWzZ5KVO8zV9XQ82lpfz+/jt54oknzokl63Q6mT59OmvXrqW7txdnUSnzvvRF0qZMJa0wH4fD\nFU1lCOi6gahE8+hxnmjanBqy1yyn991jdLyw57yxdV3VxYi5Ho5vbY57NZGXLQggCiKSKCFLMoos\nI0tylOEtYSBQtLSM9jMqdXt66PdG8AVUgiGdiGqQSEyP433H/VoyJR13gYvtL6fOoo+3WfPSyC8T\nuf9v9ed0baoq0vnkVTk889T9SRnysdGfRw85VYtneNtsNpxOJ263G7fbPSbD2+IZmKVyZnetvr6+\n9wXId911F1VVVTidTpYsWcLu3btH3f7RRx9l2rRpOJ1OZs+ezYsvvpjw/pNPPsmll15KXl4eoihy\n4MCBcx7bWHYBkIeY5RGLovihlABY4iIWccwq/Pd6vagRDcc4Oz2ZAhgpeMhxz6thGAiaiIKNQLgv\n5YdZ01RTOlLOwOcdQSIwzlTNzB8Lkogk27DZ0vH3diQCcdyxI5EIOjqSrJiTrCzjzilCRMDwDsSd\nhMmY1gwDQxBQNdUMJWsamq5hCAL2vAL0zi5km5xyCtE5oYxwn5/Q2a4YSIvCoCZ1MpC25WYiFebQ\ntPNITCymuKiYSZOqmTtnbhSkp1FaYIJ0uNeg6/gAcpGHzr4g9/3b0+x69hBN+04x0O1L6R5ccsUs\nai4r5fcP3MEdd95BOBxO8QwHLRwO8x8//zlb9u5h0sevpNfQEZw20vNycLlcZq29rCDKEorDjiBK\n6AaYHD0zTOycOonMVcvo2d1C7+73J+xhmSAIZF00nfa6HvpP9DG4ZBpqiQVUiSAt4ZmYi6PYQ+Ou\nfmTFiW4ohMLg8+v0D6j0D0Tw+UcGaUGAGR8pYN8+P21nQkMPl9J5bPhEDgfrOtiz/9zKsK7+aCUT\ny3386j9voX+oHOzfoVm8F5vNFiujc7vdOJ3OGEhbi1qv18uECRNYuHAhmZmZ/O1vf+PVV1+lt3d8\nDUIeeeQRvvWtb/HTn/6Uffv2MXv2bNavX09nZ/JrvnPnTj7zmc/wla98hf3793PVVVdx1VVXceTI\nkdg2Pp+P5cuXs3Hjxg98oXNBOnOIWao2lijCB8WqtprGBwIBRFHE7XbHpDdlWebYsWM8//QLTMia\ngiKlLiHY39dHb08/bmcKOZgoc9owDIKhEF69F8MukZ9VNeauumEQDAQRJZlAuA+f2kNh6YwRtxcE\ngUg4TETTYh2efH2thDUvORXThuTHTYgOBALogBitjRYAyeagp/kg7vw8POWV5vcjioRVFclhR5CS\nS4XqwQDepgbS5tQgSKmVcUhpbrwHjqA4FdxViSUr8Z60tZCICcroOh27jjJhyVQkmxwL5Vnlag6H\ng4yMdLKyssnPyycvN49sTw42t4vW3acQOu207G3lwNYGDr7WyPHa0/S09hMORrDZFRTH8Jxy6ZQC\nXLkKW57dzv53DjBp4iRyclIrFzIMg1//5jc8s+N1Jl19FQFZprW7i7SSAqRodCIcChEMh5CdNrNp\nia5jAKIkRsuZzCui5HnQQyp9Ow9iK85Byopv7DE+hrRlSk4GA4dPoXb1UTC3eOg3MOQnuQmCgCCL\nnHj9BNOXFpKR7cZut0efNyW6wBCIqDrhiEEorBMO66iagdW11FPo5OjOLlRvhJlz4xnxqeWkc/MV\n6ut8HNo/wPo1ZeOeV0RRYM7MbF7cfIijtW0sX7FyGNHUYkcrivKhkFDHa5Y3LUXlZVVVjYWw8/Pz\nkWWZ/fv389prr3HvvfeyceNGXnnlFb70pS+l9Plf/vKXufLKK/nud79Lbm4uH/3oR7nzzjux2Wws\nW7Zs2Pbf/va3qays5De/+Q25ubmsXr2aF154gZMnT7JhwwYAZs2axcqVK/F4PPzXf/0XX/3qV0ds\nVDGG/XSsDf7+vrG/E4sPm55Pi2/LqGlaQlvG+OP19vaOu7EEmCFvcSxRkKiAhrnyt5oBGDjFNPyB\n1EJymkW2EkWctkyCvh50TR11n0hETSBwOd05BHo7k1xjwRQ/USOIshwDPlOrWsSRkUugsy02jkAg\ngChLSIoce9AtHXJJkpAEEUdBCUQ0fC2nCXuDhH1BIsEwakSN6kAP/54FUcReXkZf3Ykxr0c8JGRO\nryKCwKk9jbExWJPjYI7N6msMdrudrMws5l12EUXzJjChahJ/+t0D/OKHt3H9R/+BaWmL6dylsuMP\nR3j4+1t44N+e55k7XmXn0+/RtP8k3l5ThWzq4iqu/PZKToZq+ed/+0fuuuuuEXu6xtsjjzzCYy+/\nTNn6dTgL8jl55jRKdjqy3bwnVU3DHwwgKhKIgtlNzDBMIBatL8ZakIhkLpuPUlxK+9O70H0hsw5c\nA00zUFUDTTPz0LoeX4c8yrUVBTKXTqPtUCfe1rFkLEcG6YK5RRguJwe2nomlSAYFTZy43WlkZGSS\nnp6By5WOYnNhYCMUFvD5dXwhncrFuWzd1ktt7QA9fWFCCbKcycPd1o+AwMeuzaHhRDcvbzs19okn\nsfw8J9/5p2pqD2/hj3/844jz03+3AleqZi1SXS4XX/ziF/ne975HJBKht7eXw4cP8+CDD3LjjTem\n9FmRSIQ9e/bENKrBvA5r164dUeJy586drF27NuG19evXn3dJzFTtAqlriMXLZ8L5A2RLfjAQCDCS\nuEi8GEl/fz8SMtI46okBgqEQ4kh61DEhhmiQOIp0WtTbcUnpdIXa0XR1zJC3JSUnCCJOexZGr07A\n34M7PbnQh66bRA8hrv+x052D1hoi7O/D7h7MGRmGQSgcxkBAkIaXqTgy8/G2taDrOl6fDx1i4BFv\n8aQ0R04uijMNsasHd/VENFU1ZTEjKpoV5pQEhLjOSYIo4pxQQe/mJsLd/dg8qTUpkJx2XNOrqH/9\nAFM/MieaAzfzryZ7enBsFhnGsulXL+Wdu17ixRdf5HOf+xzz58+Phfa6urpobm6msbGRpuYmat8+\nQt0rR4joIewZEtllbvLKPSzYUEPb8S6e3bGJF7Y+y6qla1i1chVz586NLfwse+ONN/j9Aw/gWbKI\n/GlTOHq0logkkJmTHf3edLw+L0a0xMtqDCGJyTzS6HUUJbLXrqTjkafoeP5dij+9CqvieGjuPRbz\ntfLRse8t8ZPTZ1TQu+MgJ7Y2UfPZ1JrVD5r5YaIsU7C0kiOv17HoynLsLiXOex8ch+WxmWxm85mx\nohzTV9k4urWDV170sXS1gEAQSTJwu0RcTgm3S8bplHHYpcTPjX54RZWTBctd3P9ILYvn5eHJdoy7\nDGtGjYevfbGEO//4IDZF4YYvfOEDcyA+DEtWg6woCjU1NTE96VSss7MTTdOGea8FBQXU1dUl3ae1\ntTXp9qksZD8IuwDII9j5usHNUhyTyGAYBg6HIyYvmOyY1vG6u7tRhPF3uwkGgknAdIiwh5CYH9c1\nDQEBp5iGoer4Q32kO0cPd5rN5c1zMJnWBn5f94iAHImoGIAcRzZzWMSuvi4TkKNei9VBS5CkpCFO\np6eQ7pMH6WlrRXC5kZ12km4YZ4Ig4CgoIXTyDPaVNoiLSGi6FlfGZIK0xfcW83LQNOjcW0/eitnI\ndiWl+dOzuIbTBxo5ua+R8gVT0HXNlI8UrLKQeGap9b9BTkUBFetm8pcn/sbUqVOpqamJlZ+kpaUx\nd+5cFixYEAPpzs7OWAlTY1MjdTuPUvuyCdKiw6A/3M4zr29i8xsv4JDczJ05j5pp0ykvL8cwDH5+\n+20YleVULF1Ca2sr3QN9OEvyY12g/IGAmcd3KCBEgTjptbaYAKZJLgdZq5fR/eIW+vc0krmgGjNk\nHVfJHl0cjgrSRIFaFMlYPI0z23ZTtd6HM3d8PYotK15SxpntDRx5vY15l5UNOwfDYDhIY7GERZRs\nhZnrKmjcfpZPXT8dWdHwB/z4/X66erycbQ+ZIC0auOJA2uWSsdslBOBj1+axcf9x7n2wlm99fcZg\nygNr3hHGBOm1F5cQUXXuvv8+DMPghi98IWFO+Z/kIceb1Qv5fB9jPNdjvNufT7sAyEPsfHnIFnM6\nEAigaVqMfThaGVU8IHd1dSHr4wRkwyAUDGGT0uJeMgaBOKlXYwp8CIKAQ3SDruMP9o4NyOpg+FkS\nZeySG78vnthlJFw71dpeEFAjZmhbEBUk2YGvu43MokFZy3AkgmboyMrw1oyGAbbMPHRVx9/RSubU\nmjHB2DJHYTHd7zaiBQJITlM4RRAEZElOUCiLb7Cgahr24mJ632tBKS7EEEByKMhOBcVpx+a0IdmG\ng7QjPxulqpgjW/dTNHuCmTuTRERBHDbcWPVVdDKuWTefroaz3HXP77j1P27B4/EksFCtba3GJvPm\nzWPhwoUxkO7o6EgE6cYj9Az0ENAGeG3fZl7ftwV0keONrah5BeTPmc6u3W/j9QcQs9MxIiGIhNB0\ns2ZcdlpNNUYCYpK+56gswzVtKh2v7MM1qRglawiICuY/CSBtfgGJIK2bIO2aUUXPjkM0vdLI5E/M\nMDtGSYPEulTMlmYnZ14Z720/xayPFCeUn8XnuC1RnCHDAgxmrimm7rWzbHvhJNfeMI3MGCPYQFVV\n/D5/DKS7+7y0dgQRog1CXE4Bt0ti1eUZPPuXE1y0qICLFuUn1P2bQxm8LiOB9GVrzQXFPQ/cS1t7\nK9/4xjfHbBf792TJGOGWjvW5AGJubi6SJNHW1pbwent7+4g538LCwnFt/0Hb/5xv70O29wPIqqqa\nnYJUFVmWycjISOlBiQfk9rZ27NL4Gda6riMr8iAQRye80UDLVFUSkQQZBTv+0OjMRhOw9BjZCsCp\nZOL3dsfej5+orcWJMFSW0zBwOnLobTuFu7QvNt9oug6ybHrL0cnYMKx6Rx1kBZs7C7W/J2UwBnAU\nlYAO/pOnSZ88cvcdix0qyzJ2IGf6NPpe20F1UTmaIuH3+Rjwewn09OEzdLNTlENBdtiwOW0oTjuS\nIpO9uIb2v22hq7mVwillKQ9VEAQWXr+GN3/zHD/52b/zy19sJDMzMzZpW97rSCCdkZHB/PnzWbRo\nUVKQrm+o54mnniDkdJO3bBG6pOP3BcBhx56TjiRLqBEVAQEpSuJKbolecTLLuGgBHcdP0v7CbjN0\nncpFEEYAaUkiY9E0zr65j6KVE7FnOABjsJ2jJKQE0uWrqti3+zh1O9uYvqp42PuDQEHS83O4ZaZf\nUsKOF05y8aXl5OQlLu4yMjPIyBz08lRVw+/3E4iCdE+/l/R8A0+5wJf++U3WrSxjRk0GE6syqKpI\np6zEhSwxKkgTZfhftraMgjwnt9/5LN/77km++rVvUFpaOvY1/juy+Huit7f3nD1kRVGYP38+W7du\n5YorrgDMa7h161ZuuummpPssXbp02PubN29m6dLk2vIXWNYfssXn9ILBYGxiTsU0zXzw/H6z+5FF\n8U9VXMQqV7Hb7fzl/r9Cn0xBRuqC9H6/n7bWNpy29KgnFvWKR7qJDHO6CQSCiKLZILxf7UK3QX7W\nyOLuqqqa8pzyYGvIYLiP3sAZCstmRXOBQsyD1qztFdugpyiZebpw2Etf9zGyK2eAEO2daxigKBiG\njmGYf5vynUA0vxcZ6CbQ20r61OkjjnOoiYoN3/FmBEknbUJlyvspmRn07j9IlieTsplTyfZkU1hQ\nQGFBAdkZWaQ53ShIqL4QgV4f/u5+/F0D6IrMQNNpeg63UDZnwqj1z8OOabeRX1PGvtfeoXbPIRYv\nXITL5YqBrqIo2Gw27Ha72UYyroG9pZwUDocJh8NomobL5aK0tJTZs2fj8/l458hRJl9zNSXVUwn6\nQgTUCLZ8DwYCkVAEXQfJLiNKVsQIBsEpVqg25nkIkoSUkc7AOwdxFGRiyztHBaYoGDkKs+nb04hL\nliicWYYkKwiChK6DGjFQwwaRkE4koqNpcWpzIjGQVlw2Btr9tO47y4xVRbH0QcwrtpjzI56fQG5Z\nGkfebCPUE2bOokGNamOIdw8giSJ2h520tDSys7PJzy+goKCIaTMLqT/Sz5mzOYS0Crbt6OCVbe08\n+fwpdu/roOVYHz19ERAgLU1BlsQhNfAmGbEo38Xi+Vns3XeEJ57aSiQiMW3atL97b9laqMuyHAu3\n79y5k87OTq699tpz+syMjAx+9KMfUV5ejt1u54c//CHvvfce9957L263m+uvv57du3fHiF8lJSX8\n4Ac/wO124/F4uPPOO3n00Uf54x//GOuf3NPTQ319PU1NTTz00ENcfPHFMTnjtLSxxZDibEyW9QVA\nTmKWbKYFyGO1V7SaUPh8PnRdj7U9i2lPp2hW71mbzca9d99Lhu7Bk5aC1F70Ae3t7aOro5s0V/aY\nUpuxqdXQCQZDSFEhioDuxUsvpXkjA104EkZVtYRWjZqu0jXQQl5xDYriiB7b1BwOhcOoenT7+DEJ\nAhgGvV2NFFRPx52eSTgcQVAUswNTXJhuqLekRcL0n6wjfep0xHFMPJG+XoJnT5E1d2bK340gSQQ7\nu/EfP0X50rmx/cToRJuenkZ2tscsYcrJJSsjk3SXCdK6onD2tfdof6ueU7sbOFN3gv62HiKBMLJd\nQXaM3Efa5nKQM6WEPa/tYuf2N5hZM2NYKVN8GUkqIH348GF+dtttyNOmUrl4EcFQkDMdbTgL83Bm\nZpjKaIKA4lQQRDFGyDf0aLQizmMbyYMcakp2JuGOHryHGsiYOzFB6GW8JsgSekSn+516ypZUYXfZ\no+dtx+GwY1OiZUyChBEH0uGQjhoH0q48N8e3t5CTZye3zB07rfja8tFMkkUUl8TbL5ykZnouOXmu\nQRUqwWw8MhpIi4KIO93BpKnZ7HvnDIsXrONHP/oZc+Yup6R0BqpeRH0zbNvRzivbO3nq+VO8s6eD\nppZeunsigJAA0ulpMquX5yMxwLMv7GDL1jdwOtMpLS2NU3z7+7JkJVqvvvoqoVAo5uGO16ZPn47H\n4+FnP/sZt99+O4Ig8NBDD1FdXQ3AHXfcgSzLXHnllQCUlZVRU1PDL3/5SzZu3Eh7ezv33Xdfgoe8\nadMmLrvsMh5++GEEQeDxxx/n7rvvJi0tjVWrVo1neGMCsjCOkOz/PPreOZg1eYEZPrHZbLhcyaXy\nLOZ0MBjEMIz33Q3K5/PFJOc+/rFPMFGZTkl2+aj7xHeCam1t4+Tx0+RlpRCyioU/zR7Pis2JgEBX\n5CwnqOeimZ9FHqH+eWBgAFU3EnK8YdXPwVPPMXne5XhyK+MPQ39/P4YoxuqP403TIhze81dKF16M\nPbeciKYhO52JawljUHjD0A0MDML+AY7v2IRn5WqcpRWDDGnRLM0ZyfynjtP5xmYqv/gpbNmpqwH5\nT5yi87kXWf5PnyezPDHMGc+eHpQFHHx/7x8fpbAnzLVXX0NzSzNHGupo7+4gqIcRXDKu0iyyyvLI\nLsvDU56PMzMx1xro87Hrvlewd2h89ppPcc011+BwjC+dYRgGfX193PQv/0J90M/MT3+SUDhMbV0d\nYbuMkpNphr9FkJ02k2WOVapuxFIgMWCJ++xYRiTGjk7CUxjw0f7wk2QvriJv3bxxjX3YZwXCnLrz\nKapXVlD9sZljbq9Hw/sx7XFNA8Og7uG9CGc7uPymabjTbThdCk7nYFRgLDMMg2duO0C2IfKdf1+K\nNGJonyhhcTgwA+x+4yyP/amVT1/7da699tqE1FUoFOLYsWMcO3aMlpYWjrXUcfp0M5rqQxSClJco\nTKi0M6Eig4lV6VSUuenrD/GXTc28sStEds4k1l96JStXriQ9Pf1DaamYqqmqSjAYxOVyxQD5xz/+\nMYIgcPvtt/+3ju0DsjEv+N93TOO/wUYqQ4o3iwUcCATQdf28dYOyjtfV1YUaUXG6R9HMNQYF3q2c\np6pGRi55GsE0XSMaYAaIMq0NfMFeMt3D+69aID40H6xITmTRjm+gIw6QBSKRMJquIycBYwBJUnA4\nsujrOE12VjFyFGgSUmfWCEUhVjkvpmVhd2Zg9PZgr5iAqmnokQgqYaK1K6YASLSMyUJIR2EJCBLe\nphY8C+amfJ2cpcXgcHFm7+EYIFu5dIuVKcvJJ7nqDRdT9/tNpKen84Pv/wAwWfRNTU1mF6bGBo7s\nq6VxeyNBPYKYbsNVnEl2eR7ZZflkl+ex4h+v4OjmPfx20x95ZftmPn7FNaxZswa3O3W28V2//S21\n7W3MvP5ziKJEU3MzfkNDSksjGAoi2uTBRhhxoCtArN2nBdJDiVfWQim68zCQltLdpM2bRe+uvWTO\nnXjuoWtActpIWzCFE28eoWL1ZGxpw8l/8SZKEqIkER/n0jSNSZfN4sBvtnN8T4jCaTY0PYCBhs0m\n4nAKOFwyTpeCwykjJQFpQRBYeu0EXrztAG9sPcmqdRUjDyLqeScuNM1rtnhlCX09If726N3Y7XbW\nrVtnnmc06jFlyhSmTp0am4vC4TAnTpyIgXRzSz2v72pAjZxAIEBxocSkKgdrV7o5cOQwmx6u5/HH\n/syCBRezZOlSampqYtE7C5z/O0E6/pgDAwOUl4/uhPz/2S4A8iiWDJAjkUhMg1VRlJju9fkywzDo\n7u5GDWs4lSSAbBhouo6umUAqSbKZA4uG2CVhfGPRorkQy0ymtYEv2J0UkCOqWRI09JwFQcClZOMb\nGGypaJV8IYij9m92uHLo72wld4ZtUGkr7rIbQ34RMCUsnVkFRLraY6BkNXjQol6QqmrohopqJQaj\nIO3IL8Hb0DwuQBZEEdeUSZzZf5Tqy1cjSKK5GGKwCcRIc1l6YR5pc6dwzwN/Yu7cuZSUlODxePB4\nPCxcuDB2rTo7O2lqaqK5uZn6xgaO7q6lbmsDIT2MlOHAWZJJzpwyjhw+Rv3dt/OHB+/jovmLWbhg\nIdOmTaOoqGjECfXZZ5/luVdfJWvhPA5v3sLphkYCvX2IdgXR5cRW4ME9qRJxQgWi3c6wgFj0OYi9\nGs8CJjWQds2ahv9oPe0vvkvJ51aPek+MZVmLp3Lq3TpOvtbAxA0jK8QNN3Nsoijgqcglb34lJ/b1\nsu7alai6is/nj/JAvHS3e9E0C6QF7E4Rp0vG6VRwuCQkSSS/Mp0Jywt4clM902bmkl80jnKsOJBe\nf9UkQqF6HnzoTlRV5aqrropF6+LnIEv4ZtKkSVRXV8fei0QinDx5ksbGRo4dO8bJk82cOFZPJJKB\nQBA9cop3dj7IrrefxiCD2bOXsGz5cmpqahIigPEgHd+t6YOwZM5Ob29vQney/212AZBHsXhAtghb\nkUgkVm4yVm75XI/X3d2Npuo44htLRMlmsf64koQ0JE8cDISQhjKZxzBV1RImRlEQceLCG0xU7Bpk\n+Jr5K4uwZQkmiKKAy5ZFV9+JWL2tppk5IsmW3Du2yoscrhy6ehpIAAEhya9DQNrhKaSz4W3UUNgs\nPbLY0cqgXrWljGUBtKqqOAqK6XrnNboO1aNkZyI77OaP0z5qlCNj+lTO7D/AmT0HKVowM2l4eiSb\ndNlK9h97mFtvv43bNt467N4RBIG8vDzy8vJYsmRJ7Pq0t7fHPOn6xgZqG+pID9sJEeast4NHXn2G\nZ3e8jEOy4RBtTKyaSHF+IWlpadjtdnRd59ixY/ztySfxKzLGs+1I7nRsuYVkV05DlCX0UIhQRyvd\nL72J4NhF+qLZpM+uQYhex4Rp0xKWiZtMUwZpQSB96SJ6XtpC9/5juCeXmE0qolEMURq9GiDeJJed\ntPmTObajlrJV1WN6yVhjMeVmYsepWl/Dvtu28N72Buavr8HhcMbl6Q2CwRB+v9kIweeLB2kdxQYO\np0j1sgKOv9fFH+/cP3boegQTBIErPjkZp6uFhzbdRU9PD1/+8pdxu90JZXhWJ6V4MRlJkjAMg+Li\nYoqLi7nkkktizRxOnz5NS0tL9KeB+rqDCPg5sP8VDry3DVAwcFNaVsmll17K/PnzcTqdSfseD+3W\n9H4tWdnT/+ZOT3ABkIfZ0JC1pTkdCoVimtNWN6YP6thdXV3YBJsJesZg/1FLZk6S5GETl6HrhMPh\n1JtRCKBrOrqhI4mJ4OAU0vD6TTF2I05URBAEtCEAbpU46bqBQ84k2O+ls+MsNnvUazVAihcisWpL\nY3k0AVd6PoIBwb4OXDnDy1Dixxz/qyunCKNWJ9DRSlpJWdwxomRZAQRBRJbFBAB0T67Be+AdMn0B\nHHn5DPT5CHb3RdndMqLdhuywoTgcSI6oCD4gZ2Zgqyjn2BvvUrpo9ijlQMNNttmYcu1lvH3vY/zp\nT3/iK1/5ypj3kCAIFBQUUFBQwEUXXRS73q2trQkgvf/QewT1ML0RH2/VvkvkqIqIiCgIyHYbTe/W\nYdjcpFVWkzZ5OkpOPpLTjjAkyqH5ffQfOUD/jr34j9STc/kabLmexMRXNEd8riDtnlRJoLwc7xtH\n8UypQjdADauoRsT8zkRSBumsJdM4tbeBY1vrmHzlrJEvpDHoqceDMYArN43cRRW89fxBpl00AVd6\n/PMjxIR8PJ5BkA4FQ/gskPb7GOjzMmFlGW/84Qhfu24zS1YVUFqZQVllBqWVGaRnpKYnIAgC666Y\nQFb2aZ548AGaWxr41r98h6KiomHVHtacYLHp4y0UCsX6GJeVlVFRUcHFu1MijwAAIABJREFUF18M\nmI7F2bNnaW5uZs+ePbz99lsIhDh5opZ7/1DPvX8QQZDAkJm/YAFr166NqWXFHycZSMcr46VqQ7e3\nlLr+t9oFQE5iFjPVWpHqup5U6vKDOC6YgKxgSyBsWd7fSGE+K589nkYUJrmFYatdp5hOb+AEqm5O\n7ETLpwzdzB/H1x+boWsJw9BxO3LAgIC/C8XmMuub5Wjf3OEna56LIGB3ZSEKMsGe9tEBeYjZ0rKQ\nbW78Z06SXlpukrYh9o/locX+i5K2ZYcTV0EpWmc3U9evB8wSN5/fh9/nZ8DnxdvjJaD3oRkGggXS\nThvpM6bR9dxLdNY2kT+9OuWxAqQX51N06TIeePpx0tPTz6mvrSAIFBUVUVRUxPLly6PnZnDmzBka\nGxtpbm6mrrGe2sZ6zrSeoXF/LVJOIbnLV2MvMoUkRLttCBhHy3NcbrIXLCWteipdb75K+yPP4Vm3\nAld11fBxDPsjNZAWgKzli+jY9BT+Qy3kLp2BQTSSocZ5gcNAWkQUhQSQllx20hdN5fibhyi/uBpH\npnP4BUviFQ+1qnU1vLv/FG89sY+1NySvP40/WbvDgX0oSNeEkPzp7H+ymd5T1Zxu6GGr7wya0URm\ntkBxhUJZZQZlVZmUVqSTnjmyR79oRQnF5encf+fbfOOb/8DnPvMVNmzYMCxNpKoq4XA4pgUtiuKo\nnrQFoMXFxZSWlrJy5UoM45/RdZ22tjZ27drFyy+/TE9PNwIR3n33bd59dxeDETGBiy66iDVr1jB5\n8uQYlyZ2ZaIgPTQnPdKcOTRkbRgG/f39FzzkCzZoFnPa7/fHbpjMzMwPpXOKdeO2t7Uj6XKs1i1W\nPjXKYiAQCKBrBrKcahhdGEbosswlpqOrKoFQP2mO7Ni4wpEwBsl7LQuCiMOehkN2IRJEURRT1cpm\nM+sl9RhEmhZlTgvR83a5cgn2tKc4duuYAq7cErynjlOwaFn0rAb/sX5PBtLusko6DrxNwOvFFm0H\nZ7fbyYlOsoZh4A/48Q54TU8o4MfXNWCG7RU7u37zABPWLyertIiM0kLcBTkp9aAuXTQbNRjmt3+9\nH1EUue666973Ik8QBEpKSigpKWHVqlUYhsFjjz3Gj372H7gn1JC/ah26zWY2hLCZ+s1GlIOQyI42\nTcnMJn/dx+h5ewddz29HWxMkfda0sccx7I/kIK14snBOm0Lbq++RMXMiituUkhVtIgpKLNwdD9Kq\npqKFtWEgnTZnEv3v1NLyylGmXRvH3o73iketKTbVu8rX17D3uYPMWFlNYVUKpYZDztxud7Dmk0sI\n9kToaezmtlt+hc1mo6WlJRrNaGDXlsNRkA6SkQUlFQqlVRmUVpjedEbWIEiXVmTwr/9vHi880cgf\n/vwLtmx9ic9/7kYWLFgQa6hiEUrjHYWRPOn4H6uKBAZBurCwkKuvvpqrr74aMBfrp06dYuvWrWzd\nuhVVjSAAb775Bm+++QYwCLRLlixh9erVTJs2DUu4Jv4YyXLSVnrugoecaBfKnoaYRa6xVnehUAiP\nx/OhHDscDuP1evnWP/8rnQf7mVexaMx6YstOnzrFiZbT5GaVpHw878AAqmqgxJUvGRhohsqB4A6m\nTlpNQdbE2MTmHfCiajqybeSweMPZ1zEyFIorVyDa7MNAamhNpnX/tZ3aS1d/E5VrPoUoSWbZjSCM\nNo8CMHC2hbMHtzHpk5/Hlpae8rmH/T6OPfEgc675GIUzZwyhdQ+iuCBEm00gYBg6/kCA04ePcOqp\nZ5k9bSo9A/34IyFCgoGS78FZnEtGSaEJ0nmeESMaLdvfpue1vWxYcTE3/eM/kZ6e+thHM13X+cMf\n/sBd9/2ZYH4Z2fOXoksSuoDZnlIwW2eSkDawzntwUSgIAgYGvXvfwdt4iOyPLCVt5tignIoZgBYI\n0v6Xx/DMqaToo8O9UhPPhYS/Dcw0y9ASpr7dR/G+tpea6xeSXpyBzalgd9qwO5WUa54N3WDPr7dT\n5LLx6e+tP+cFeCQU4fGN28gxyvjFz26JiUuAee+3tbXR3NwcVU1roKHpMAPebjQjQHqmCdIllemm\nN12ZQWa2g5MtfTz3aBPNR2FS1VwuXb+BxYsXnzOhNBlIJ/Okh+aKNU3j+PHjbNu2jW3btpkRMEj8\nnqK/z58/n9WrVzNr1iwssaWh5DTrmM44GdvS0lIOHDhAZWXluM8L4K677uK2226jtbWV2bNnc8cd\nd8SIk8ns0Ucf5eabb+bYsWNMnjyZW265hcsuuyxhm5tvvpl7772X3t5eli1bxu9+9zsmTRpZ6W8U\nG3MivwDISSwcDscYwj6fj+zs7PftxYxmVujH7/ej6zpf+NyNpPXmMLU4dfZoQ3093e0DeDJT12Dt\n7e1FECRkyYbFobW85UOBt8gvncKEwgWAyWDu7+tHlBXEUYhjp7sO0hpoZPK8T6KkWCtrGAb93Sdp\nadpC+YqrkZxpWP6z1XVJsMqYhnwPWiRE09a/ULRyFdlTUlftAjjxyrNkpMks+sL10XHopmxnsmfC\nyoVGj//eX//G7IxMbrv1Vk6ePBnL6R6uq6Xl1En8apiIJCAXeHAX55FRWkhGSSHOnKzYZ3TUNnHs\nya1UZ+fzhc98jtWrV78vxr6qqtz+n//JQ089S6SoCkf1NAS3E0GWkZJ0w4pZFJyTgbSBQf++3Qw0\nHSZ73XLSaqrP27MwsP8Q3rffYdLXrsBeELfoHVLnbNlIIK0Gw7T8/kny891M+Ogc/H4fmqFhoCPb\nRBSnHANpm1MZMfffd6yLQ7/bwfpr57Hg0vHdSwnn1e3jqdtfo8QxgZ//7JZR+1JbxL14T7qh6Qj9\nA50mSGdAcaVCSXkaPl+YhsO9dLcp5OdOYPWqS1m9evV5kcq0QHMoUFs2Ekjrus6JEyfYtm0b27dv\nN5vCDLk/rL/Xrl3LjTfemLAgABPo586dy4QJE+js7OTb3/42y5cvZ+rUqeNSG3vkkUe44YYbuOee\ne1i0aBG/+tWvePTRR6mvryc3d3jUY+fOnaxcuZKNGzeyYcMGHnroIW655Rb27dsXy5tv3LiRjRs3\ncv/991NVVcUPf/hDDh48yNGjR4d1TkvBLgDyuVgkEkGPkqS8Xi9ZWVkfWMh6aBlVV1cXX/z8l5go\nT6fUM0pd4xDbv28/WlAkIy01b17XNPr6+pFki1mcGLhuCr6H6HExs8qsiQyHwvj8fmSbc5QJ2aCr\n/ySNXW8xbcmnsdlT9/o0LcKRvaZAiKeiBk1TUdW4XFgsBCkkArQocertZ5Fz3ZStuWzM48Rb/7FG\nOnZuY+XX/w9Ojye6YjcQxcH+xQnefFxO1NveQcPDj/Cdf/g/XHHFFQk5M7/fP9jcobGRQ/V1nDx7\nGr8WQVMklIIc3CX5ZJQWYk9zcXLnfsJ1x5lRMZHL161nxYoVSSeQ0SwSifCLW27hoaefh6pp2Moq\nUTzZSPa4UrJxWLwYiKbr9Ox6A9+pBrIvX4O9tHBIm8rxk3nADJu3Pfwk7nw3FZ9fN+wzEsPdI4G0\nuU/f4Wa6n9nBum9cReHkUoIW8cpnEq98Pi+qrg4HaZcNm2MQpBufP0TfW83ccPMGckvOPZfZ3+Xl\nqdtfo8xdzY9/+BOKilKXwI3XHm9ubqa27iiNjYfp93aDEEIniG4EEQUHiphFmiufj264ivnz51Nd\nff4WTOMB6fjFqmEYnD59OgbSfr/Zr1sQBO644w7y8/MRRTEmMayqKvfccw/79u1j69at+Hw+wPSc\nP/rRj7Jp06aUxrtkyRIWL17Mr3/969g4ysrKuOmmm/jOd74zbPtPfepT+P1+nnnmmdhrS5cuZe7c\nufz2t78FoLi4mG9/+9t885vfBEyRo4KCAu6//36uu+668V7SC4B8LmaJ90ciEQYGBsjMzDyvtcYw\nvIzK5XIhSRK7d+/mX//x2yzIXUmWKzulz9I1jXfeeRennInLkZq2qskU9WOLKnQBUVUmHVEQORtq\nptPWzpLpnwIEvAMDaLoxYrjaMMwHN6wGOHz2RcpnXEJmTmVKY7Gs8dBz2AqyqVy4bvg5xiaGQaC2\nvLneY4foPnWAqms+heJ0I9ltKS2gdE2j+fEHqVo4m+qPrE4IT49mFljVvbIZ5/GT3HHbbeTk5MRq\nOON/rIlqYGAgTgikkcP1tZzpaCegRtCdNgJahJDXh1u2k2VzMKVyAvNmzaa8vDxWtxzfpMTv9zMw\nMBBjXN97330cPXEa94yFOCuqsBfkIyrnjyJiaBpt214i4usi/+rLEdLdJuHQytMm6SWdCi4EWk7S\n/eJmqj67hvQpYwtCxE9CCZ68YXDiTy+QZxO55F8/YaY9LC6BYK6jgsEgPp/Jjvb6vPj8PrQhIC3L\nErV/3klxuoPP/vByZOXcn/u+Ti/P/WYHaeFcfvjdm8fV2xeIdYszDAO73c7AwECCJ11bd4ABXze6\nEQRAEBQkIQ27ks6VV17JqlWrRq1NPxdLFaQtglc8SKuqmrDQteY+q2rlxIkTrFq1iubmZg4cOMCe\nPXuQJGnExhBDr5XL5eLxxx9PkN38whe+QF9fH08++eSwfSoqKvjWt76V8Pk/+clPePrpp9m3bx/N\nzc1MmjSJ/fv3M2vWIIv/4osvZu7cufzqV78a7+Ub84u4QOoaxeJvpvNllu51sjIqK8cUDqmk2VMX\nLQ8GgxiajmJPgdBlmGVMETWCICSCj65p6IaOhoaCk2DQR3dPO057OhFVHQbGFjDF8kOCgM2WhiI6\n8Hs7xw3I7vRCetqbk5I9EpvGR8erm7XFFFbQe2wfgWPHUbM86IaOYLMh2mzIDgey3Y5sTyxVM4lB\nImmV1Zw5cIjJa1YjpZhvtEB20sWr2H/fn/n9Pffw06jkn6ZpsZSHta2luDR9+nRmzZoVm5R6enpo\nbm6msbGR+sYGDtXV0tHTjV8N82btQd6oPYBNknGIMrIgIomD35du6KiGTlhTaTt9loAqkDn3Ihyl\n5TgKC0aVDz0XEySJvJVraXvlaXq3vEbZp69BkOWENpXakF7SZgTDZEZbqmlDR+WoLMVWUsLZl9/B\nPaF4zEXEsLpoBr+P/PWLOfvACzS9cYgJy2cMe99uN/WuzcXTIIHTEgPx+rz4BnzkLJ7Eob+8ze3X\nP8DstZMpqPCQV+4hv9yDzZG69kBmbhof/7c1vHj3m3zvx9/hxs9+mSuuuGLMxaKu6wSDwVjjBUsF\n0OFwkJeXx6JFiwBz/F1dXbS0tFBfX89LL72Iz99DMNzNpkfvZ9NjDyIgIQo2Jk+ewuWXX86iRYvO\nJdSacC2txaZlY4H0UEWweHliixkOcPDgQfO6ZWaycuVKVq5cmfK4Ojs70TRtWNvEgoIC6urqku7T\n2tqadPvW1lYA2traYqWHI21zvu0CICexeGILnB9Ajte9BkbUvW5vb0dBQZZSf/BNxqWBLI/2oA3W\nE4MQXa0mApAoSaCbE4JTSAfdMAVCdLMphKZFc6yDH2laXAkTgFvx4B8YH2MaIC2ziI6OQ4S8PTjS\nxw69i6KEzSaRXVhGmzuTApdC+cwZsTCl1+vD29NDQDfQMRBsCpLNjmy3I9ntSDaFrMk1nGw4REd9\nA4XTx+fBKA4Hk6/8GK9teoz777+fr371q7H3hk5QyUDa6XQye/Zs5s2bF1uQWWpdZpvEBt47coi+\ngA+/qhIIRwjrKmmFeXgmlpNbVcbJt/YS6egla9Y8HMXl2AvyUhbXGK9Jdju5Ky+h7eWnad/6OgXr\n1yS0qYRBwlAMoDU1CUibpUvW71krFtP+yFN07zpC7vJR6omxojjxNcWDboe7NA/X7GoOPP8OZXOr\ncWS4YmkGw9CJv3UHQdqsM44H6UAgSLaUQf3f3iZ41Eb9oU72h44RMUKk5zvwVKSTX+4hvyKH/PLs\nURfCDredK25axdvPHOCuP/+Kd/fs5v9+7esUFycv74v3ip1OJ4oycvMRQRDIzc0lNzeXhQsX8tnP\nfhYwZVkPHz7MCy+8QENDPboRobbuMHV1hzEvmIiAyIIFC1i3bh1z5sx5Xym5VEF6aL303r172bFj\nB3PnzqW2tpbbb7+dpUuXJl2Qn6uN97NS2f58jm+oXQDkUex8APJ4dK8FQaC1tRWbMb7GAYFAAFEY\nWeLOygVawKmqKoZuIClSXCmSua+VP5UMGZtmRxP8US9Hic18g2VEmJ85xO9xO3Lp6z+KrmvDQH80\nMwVCBLwdp1MCZMsEQSC9oIoztUeYsnwNLpeLXMwcrKEbMQEHr9fHgHcAf1c3IYs0ZrMhODM4+sJL\npBcV4hongS+zpITiNav569NPUVBQwFVXXZVQj2l59FY0wcqJW72MrX7GlveQkZHBggULWLx4cQyk\n44VA6hoaONxQR++7dex+7BV8YYPMOUuwF5Wh5I5MHjpfZsvMxrNwOV27XsVZUkTmzMRFTEwtLY6M\noxvDlaa0SDTcLQgIDif2qZM5u20f7mmVOHIyhnnSsbLmuFKmZN9SwZr5HKs7yXtPvcnSL6wjPkpo\nPgbGmCDtcDiYvX4xam8Q78Fufn7z/yMjIyNW593QVE/d8/XsD7UQMcKkFzjxlKdFAdozDKQlWWLZ\nNXOpmF7M9gfe5mvfeI9PXPFJrrnmmjjZ1+Re8bmYx+NhxYoVrFixIvZaR0cH27dv56WXXqK/vw8D\nnd3vvsPud9+Je34FFi9ezNq1a5kzZ877Ap2hIG2l6HRdj7VbbGxs5O6776a31+y/npubi6Io/PSn\nP+WTn/zkuEL8ubm5SJJEW1tbwuvt7e3DPFzLCgsLR92+sLAwFrWM/4z29nbmzk1ddnc8dgGQR7H3\nC8jnont95tRZnNI49HAxu0QlayoRA2KrZjB6PmrElL8U43Svh1JmBAGcuOgb6CDbMWlIc4j4siVi\ndcbmjuC256D3RfAPdJCWWZjyeUiSgtOVy0DHaXInjE/PNqtkEid2H6XnzCk8JWWD5yGafakdTgee\nbE+snCfgD0TJPn6ESTWc2P4c++64C0dODkpeLmmFhWQUFZFRVIh9jJKk4tmzCPb1cfs9d9PT08ON\nN96YlGlqAXU8SA/1IpLViHo8HvLy8li2bFmMBPO9732fY8fO4Jm7APfEyQguJ3oohGYwSLSyysfO\ngdQ1mrmrJhHsaKNj+1s4igux54y+eBIFAVGWUUYCaVUjY/Ys2mobOb7pVXJWz0d0yChOG4rTjuKw\nIUZzuUO94qEmOe3krF1Aw/NvUj6/mpKZg6ImQqysKzWQnnHlUna1v8TN//FTNv77z7n44otZs2ZN\n7Ds4depUnPZ4HXXP1rMv3IwaB9IFFTnkV+SQV5ZF6ZQCPv3jdezdfJQHnv4Dz738NFdt+Dhr167F\nbjdjDGN5xedqeXl5XHfddQlEpDNnzrB582Y2b94cjdwZ7Nr1Nrt27UrYd9GiRVxyySXMnj173IsE\nyyEJBoOxFJ0sy2YULlru9I1vfIPFixdz6NAh9u7dy+9+9zvmzJkzLkBWFIX58+ezdevWWA7ZMAy2\nbt06Yg566dKlw97fvHlzrPViVVUVhYWFbN26NZZD7u/vZ9euXXz9618f13VI1S6QupJYfI6ju7sb\np9MZu3lSMatwPxwOxwhbqepeX7nhKuydGUwvnZ3awQyD3bv3IBtO0lyZsdeMWB3tUEERg4F+L5o2\nWH+cICsYZycCdXTL3cyedM2YjQAsCU3LCzxw8mlyKmaTU1hj5qrFxJ+RrPXEHrr7G5i+4YvjDjXV\nbfkLlbOnM33NpbHXdV1H13QQQBIls2NUEtv9+EOUKjpXX3kFjU1NHKytpb2rC78aAacTOS83CtAm\nSCtJ7odTe/bS9toO1i9dyv/92tdGXJmPdR6j5eNUVeXfvvtdXtrxFpnzl5Ezcw6K02wOoBtm0xE1\nCnKqpsbK2Rhy/d8vSOuqSutLTyG5JMo+ffW4elKPZH1H6ujeup3Jn1iHlJ+F1+slGA6hGzpIIpJT\niYK0DZvDjqhISYHZMAxOPbIVR1cvl3//0zjSU392zf0HQToSCPHmb58nP+Dix9+/mZKSkmHEPYu8\npKoqJ0+ejDHs6xvraGypJxDxoxphMgqdeMrNcHeax8XJI2dp3HkWB2msWrqGyy67jOnTp3+gJZZj\n2alTp9iyZQubN2+ORW+S2cKFC1m7di1z584dEaSteVDTNGw2WyxF19bWxk033cTRo0e57777WLFi\nRSK/w7AagIzvHt20aRM33HADd999d6zs6bHHHqO2tpa8vDyuv/56SktL+fnPfw6YZU+rVq3illtu\nYcOGDTz88MPccsst7N27N7YYuPXWW9m4cSN//vOfqays5Ec/+hGHDx/m8OHDF8qePiwbT0/koftZ\neWJTfco1Lt3rgYEBrrj0SqqkqVTkTUxpH7/Px3v7D5DhzMMm22NAbNXNDpop8KzpOv19/UiyDVGU\nia89TjgXQ6crdJZj1DG7+uPY5LHPP97qz2xHzE5jwtSPDDKjoz2DgQRwEEUptmjw9p2hueEVplzy\nKZyZ4wvBnjm8k2B3Mx/5h5sQBNFUIjMs2cXRH+7es6dpeOExNv7kRyxbtixGmGlsbDRJVw0NHK6v\np6u/j0BERcxIx5aXS3oUpNMLC5BtNjqbmjj2yhbyZZnPX/dJ1q9f/76Vh6ya+DfeeIObf/ITmtu7\nyFu2lrxZcxCiKQGLURzvPRqG2V4zQe1K08w8LGb0ILGEbHwTYLinm9ZXniZr3jTyL17+vs7ROs8z\nTzyL21BZctMNCKJAOBIhFAziDwTw+30MeL2E1QiaoSPIApJdGfSknbYYMU/1Bjh+z9NU15Rx0Zcu\nfV8gFxwI8NZdz5EbdnPzd3/IhAkTholpjAXSMYZ9Uz2NLfX4wz4ieghbhshAnxdJUHBJaeRlFXHJ\n6nUsXryYyZMnfygKgWPZ6dOn2bJlC1u2bCEQCAx7/3e/+90wAZR4r9jpdCLLMoZh8NRTT/HNb36T\nj3/849x6663nTRDHst/+9rfceuuttLW1MWfOHO644w4WLDC1FNasWUNlZSX33XdfbPvHH3+cH/zg\nBxw/fpzq6mp++ctfsj4qp2vZT37yE+655x56e3tZsWIFd9111wVhkA/T4jVa+/r6kGV51L6z1oQZ\nT8ZIRtgayxoaGrjxM19iXtZScjKGtz5MZu1tbTTUNZGbWRoTThgGxIB1LwT8foLBMIrNwXCf2DRN\n19A1nQhhDkd2ManiYrLTypJsObKd6T5IR+Q485ffEAsTWqVR8R6cydC2xi1ioFP73iYKZy+mYMr8\ncR0z0NtJ81uPs/gTnyKnrMrMfceVXoxl+555lAkuibvu+E3SiIhhGJw9e5bGxkaampqora/nSEM9\n/X4/AVVFzsrClp+POzeHrqYm1LZ2CtLSWbdqFYsWLWL27NkpLews03Wd+vr/j73zjpOrLvf/e/rs\n7O5sr9k00iE92XQ2IaQBAaRJACFIi4Lwo0kR9epFERFBRVTUi1y9giIKCqSQBFIgPaRsn9lke7Kb\n7Tt9Tvv9ceacne0lG+B683ndjZfdmTOnzXm+z/N8ns/HxaFDh3j/gy3sP3iIgMHMiBVrSRg3KRJ8\nVd5xp74+RAXmHoJ0F4tKSZY63qoFaVNkodQPW7u9pIDWo/sYcf0VxI4e3D3SE0LNLZz+y9+ZtGIh\n41Yswmg0dSrwKAoIQjhqvtiPx+chLArIioLBbMRkN2OJsRGqrqN1414uvmU54y8ejEVjd4T9Qfb+\nbjOxjQa+9cjjzJs3r1/Fq56CtKbXXFlZSXV1NTU1NZEg7SYo+hEUNSu1GGzYjQ5yskZx9dVXM3v2\n7EHPpp9LnDp1Sg/QX/3qV/VsUZZlvU0XnRU3NzfzyCOPsGfPHn73u9+xevXqz7US8DnhfEAeCqID\ncnt7O0ajkbi47mNI2iydRlbQMumhrmp37tzJY/c/wSUjLyPGPoA+sqJQVlZGQ10rKc5M9cEVucmD\nwSChCKPbYrVitVgxmoy0t7djwNQjI1tWVCtFRVb0AFkY3ENK5iRyUmcO6lja/XW4G3czfcGNxMRq\n89Qd+tLavdl1vliUJCrdHxHAw8hZl2K0WrFYtdElG5rtY4+nQ5Yp3fFXsieMZubqq3stT/cGf1sL\nx976E7d+ae2Ae0Sa5q+WSReVluI6eRJvKERAFAgIIgYDxFutxFmtjMkZyYUTJ5KWlkZqaioxMTG6\nTWI4HKa1tZXm5mZOlJdT5HbR2NZO0AAtrW2ERchatoa4nFEYMCCHg/jrTxNub0MKBVFkGbMjFkuc\nE0dmFkZLZwMDQ9R57xyklZ4XSpodhNEYmTOOCKZEnVdFUWj4aDNioJXRt96AKWZwhER9O+rGUICm\nPQcIFxay+P+tJz4zrZ93qkE6HA7j9/nw+f34fD48Pi+CJNC87zihwyVMXHIRI2aMJXlUOok5adhi\nB7+fYljg4J8+JFTSxG3X3cwtt9zSjRPSNUiLotgjB8VqtWK1WvUFoyAIVFVV6WIy27ZvJayEkBTV\n4MVgMGI2WDAbLKxatYrVq1czatSoL0xQ056FgUAAg8Gg98IVRWHLli3cf//9LF++nF/84hckJQ1M\nX+HfEOcD8lAQHZA9Hg9At9KKKIr4/X5EUcRsNuNwOAYl89YT/vznP/OrZ3/L8rGXYe6r5xzVZzx+\nPB8ESxeFrgiz2x/ouGiK1mOUMZttYDBgjAQ3BU2VCfWWiZpPLg/lIzmtTB65YlDHIskCx6reYezU\nZaRlTaHn28fQ8a/+j8KZ0yVUVuxm9uqbCYsSHq8PMSIEYjB3jC5ZbHadbKZt/Yz7U1qrjnHpPfdj\ncwyOHAdQU3CUhkO7+cnT3+9TA7cvhMNhKioq9DJlQUkJJyorCIgifkEgLEmYjEYsRtUi0WgwqH1L\nFAwWC6bYWMxJicRlZBCTmEDZzt20NbWRvfQybMkpeCrKaHUVEzxTjyIpGE0WTBYbGIyIQR+KImEw\nm3BkZZN04TTicsZEHtwatyDqCvQSpGU5OrCI6vnvFqTVMrccClC2hSxyAAAgAElEQVS36W0c47LJ\nuqK74lZ/6DzKBIokU/uXv5OU6GD+vV8ZlM2lvk1FtSH0ejwUvPYPLFUNjMwZgU8MEJIELMkxOEYk\nkjwqnaSRaSSOTMMa07+nsqIouD46RvnGY8yfPIsH7ruf0aP7VtQTRVGfsuiJKNpbuVsQBCorKzl2\n7Bj/+te/aPe2IytiRD9APVtGg4nkpGTWrFnDpZde+rk4JWnaCqIoYrFYiIlR1fza29t56qmneO+9\n9/j1r3/NNddc84VZQHxOOB+QhwqN0OD1epFlGafTCXSUZKJtz4aLFfn973+f3W/tZ+HYvJ4DctTo\njLYiPX4sn1hbMnarA61PrNf4IuQuSRIJR1avYMRksuhXM3rsyRAlPKHhjFBFnamamRO+rAfwgaK4\n5gMcWVmMn7JcOwBttzr9d2cYEIQgxw/9mdyVVzJ60iwURSagqSz5fHi8aiYkyXIkSFsxRQK0wQBl\nu99kypLFTFgwcGEBfY8UhWPv/500OchPf/wsI0eefRkW1NE0jezjdqsiINWnTuOPZNG2jHScOSNI\nyMnBmZ2FPT6epvJyjr71NoJsInPJCnynqmjOP4oUCBObPIK4rAtwpGRhjonrrIgU8OI7U4Xn9EkC\nbfXYUlPIyF1EXI6qhNW5vN13kI6+rdUgLerZtChKKsMeBX9NFU37PyLlkoUkTrsQs727sUhXRGfF\n6md13H3BujPU/f2fTL1iKWMvWTCUU64j1O7l+G/+woJRE3jgvm9QW1vbyaayPeAhKIWxpcZ2DtI5\naVjsPRN3mirqOPqX3cS0wY1XXc91113XrYrWWy8Vus+pa99pDSaTCbPZ3E3xTRAEioqK2LJlCwcO\nHFAJb9r5U08iBlT3r1WrVrF06dIeq3vDAe0ZFK2toGXFu3fv5utf/zqzZs3i17/+9ZAIjv+GOB+Q\nhwpNyMHn8yGKIk6nk0AgoBO2tFLjcA6wr7v+JsRqExdmTusWkLt6I5tMJpqbmykpcpGaMELXo45s\nTH/YEXnIhcNhfD4/ZosNo8GkZ8WKrLJJFTnq8uoEIQN+qQ23dJQpF1xBrH1wrlfVDZ/Sbmxk5sJb\n+jpybZc7/XfJ8fdIyHQyb9WX9YeR9sBWULMOn89HwO8nEAjg8foIBINIskxD+XE89S6mrbycpOwc\nEjKysA/GCSoQ4Og//8KYhBief/bZQekQDwbt7e16idJdVkZBSQl1jY34hDAtra20t3uJGTGW+NEX\n0FqUjxwIkZAzhaQLpmGJGdhDNtBcT6PrEIG2OhInTSJj3mJMtu7lWiXqnz770XQN0h28gLpPPsRX\nc4LUlcswOhxgNmG0WzHbbfqPLp1I56wYQ3c+Q+PH+wgVFbPoG7fgHDHw8bme4KlroPjVf7Bmxjy+\n8+1v61MPsix38pIucZdSesKFJ+QnJAvY0+KIHZFI0sg0kkalkzgiFXNkxlgWJUq2H6H6oyIyY1K5\n6bovs3r1amJjY3tlGPeFvoJ0X7Ks4XCYvXv3snnzZtxud6cRxujrNnr0aFauXEleXt6guAy97as2\nN22xWLDb7bo+9fe+9z3eeOMNfv7zn3PzzTd/IYhpXxCcD8hDRXRA1srXiqJgt9v1m2840djYyI3X\nrOMC0xQy4kdgsUYCciQj1spd2hcRwOVy09zQRkpCJlq5N1qNS+spK7JMe7sHBQMWc+9lOUXp7Pqj\n/q9EfvgTRmTNIT1xgtpHNAzMErLFW83Jlv3MWvwVbPbBsCkV6mryqa8/wuqv/D/MVivd0jigQ3sa\nwIAoifh9PprOnObTD/6HkVkpYLYQEASwxWBNSsOZkUVCRhYJ6ZlY7L2Pw4T9Po688xfGpzh56onH\nmTRp0iD2f2hQFIVDhw7x/Asvcsx1AtvIcXgazxBobCIuYxwpE2ZjccRHEa4GphetKArtNS4aSvZj\nirMz8tLLsKf0TxAabJCWwiEq3nuLpPREJly1lkAwgNfnw+PzIUgikqJgsJgx2iyY7DYsXYJ0V8ii\nxKm33iHOYmDhA7ep98FZoKW8mrI/vcvahXk8/s3Heh1b0XgBWsuhpMxF6QkX/nCAkCJgT3cSm9OR\nSdudDkq3HeHMwQrSYpK4bPkqlixZwsiRIztlxUPBUIO03+9n9+7dbN26lYqKil63P2bMGFatWsXF\nF1884NFOQa+2gd1ux2q16vfuPffcw5gxY/j9738/bNWlfyOcD8hDhSAIag/K60VRFKxWKzExMcNu\nMqHhwIEDPPS1R1iUuRybyY7FYol8GdWSlClqdEcVapc48umnWI3xxMY4O3rAXUeeFAWf3084FMYS\nZSQxEGhZdEngIDHJ6YxKnYsiyx1TywZtbMmoj99EQ5TCHK/+J2OmLiMje3CylKGgh/wjf2XhZdcz\n4oILdYZ2b+iURRsMHN7xT0YnGnj6P79HZWUlbrcbl9tNQXEpLe3tBAQRU6wTe0okSKdn4UzPwGTu\nqEwEvR4KNv8TR9DDPV9dz9VXX33WPIHe0NjYyF//+lfeevc9AhYHzuwcqo4fQ8FG1tSLiUnOiBCu\nIqXiyLUxREhWGuFKlTDt+TOEgJdTh7cihNvIzrsU59jBj270F6QD9ac4tf09Llx1CWMXL0Z7ZTAY\nxOvx4vP58PojLQdFRlIUjFYzRpsVc4xdzaSjdMfDLa2c+uvbXJB7IRddPzg3r57QVFbBidc3sXrO\nfJ568lvYB2gRqo0v6Zl0WSnu8hP4wgHCiNgz4jHH22mqOA0BiQRzLHMumsWyvKUsWLBgWD3Vhxqk\nfT4fO3fuZNu2bVRVVfW6/dWrV3PXXXd1y+hVWdFANzWxUCjEs88+y29/+1ueeeYZNmzYcD4r7hnn\nA/JQ0dzcrBtAyLJ8zj2R33jjDV5+9jcsH3O5ng1rw/HaIiD6WjU1NVFa4iYlPjuiuKUFYoguXfv9\nqpGFyWzDNAgZy2hUB0vxxgbInXxNx8MgwsZV+4jafkUHaJUZWlr7IZbUBCZNW9PnZ/SEgiP/IGNs\nNrOXXo2iyGAwqAYLUWQ0ra+u/Whob2ng2Pa/8PA37ubaa6/VH0za6JLb7ebEiRMUlZRS4nbj8QcI\nSTJmZxKO1HQS0rNIyMzGkZjEiQN7aC78lMljR/OVm9Zx8cUXn5VAfzSqq6vZuHEj77y/kUZ/mNTJ\n02irO0X9yZMk5lxE5oXzOy0SNHR6IIsq6SoizaKeH+06mCLnK3JLyJJI/fHdeBpOkrnoYpKnDE4R\nrSdEtxsUoOHT/fjc+eTefgsJI0ZgwKALxxgM6v2sKAqBYKDDfcnrxRsJ0pruuNFmxRJjJ1BRSfvH\ne5n15csYMa9vreuBoKW8Bvfr77Hggil8+8knSU8f2IhhV2ikqxMnTlBSUkKxu4Ty6kpCskBIEQjL\nAjajhTizg6SYeK6/9nrmzp3LmDFjhv1ZMtQg7fF42LVrF1u3bqWmpkbf3uuvv97pHu9JYxugoKCA\ne+65h8TERF599VXGjRuYfsL/UZwPyEOFx+PRb2ifz3dOPZEBnv7Pp9n11l4WjMnT+8TRVnuiKBEf\nr/YNDQYDpaWltDR4SHZmdPTfor7ksq4WJmAyWzEZh57ZNQt1VFDKwqnrsJi7ZBSK+jAQJVEN0hER\nCq2PVd/upi7gYsaCm7DZ4zCZzAMqd4PCqerjnKk/wsqb7yMmNh5jF9nDnt6j9SYVRaFw/3ZoK+el\nn71ASkoKRqOxE1EmWryhqqoKt9utji6VlFJWXoE/HEZQDFgTU1BMJpqqykmIsZOZnMTyvCXMnj2b\nKVOmDCr7kWWZ6upqjhw5wid79nLw6DH8ipG0KdNxJCZRvGMboZBMzoxLiE8fRMlPUX2LJbGDdKVd\nB5XnFwnSJrXd0FR6iJbqAtLm5pI6Y+6wBghFkqja8k/sJoncO9d3LzV3qWZEO1hpkqZ+nx+PzxvJ\npBVajhwlVFLCuGXzyZwxifgRmcSmJg1azESDp66Bkj+/xxhrPE9987FO9nqDQTSDWuOUVFZW6i5e\nh44cpq7pDCFZwGAAEyZsJgs2o4U1q9ewatWqcza+NNgg3dPzTTPFCYfDnbJiURR58cUXefHFF/nO\nd77Dgw8+eM6qh/9GOB+Qh4rPwhNZgyzLrLv+JoRKA1NHzERRFMxms57RHT58uFMLVVFk/L4AsbYk\n4hwJkUCl6gPLkoQgighhAUUhEozPbr9DcoDC8D6mTlxFcnxO/2+IiE+IkoTH10jBqQ8YOW4hjthU\nMBgxmiOzxVYbFqu9OxtXUcOIKAQ5fvgvzMhbwfhpg2faCuEQe997lbWXLuLhhx/qV7xBO+cGg4Fg\nMKj3EMvKysgvLqa69jQBQSAQFpFkGYfVgsNqwW4xM3vmDDIzMkhISCA+Pl5n3odCIfx+P42NjdSe\nOk1pWRmtXh8BUcaWlkXGxCmkjb4A1yc7qDh6BEfKKHJmLuvVd3pQUDo7L4miGHHrUpAVaKssoKn8\nCCkz55A+Zz5Gs3lga6UBINTWRtXGt8iZOpGpX7paXQgodKlmRJGPDJrJPTpTGDqmGjyedorfehuq\nq0nPykQ0GRBNBsyZycRmp+PMycQ5IoOY5MQBBzfBH6DwL+9jrW3m1mtv4JZbbhlw5SM6UGnOXb09\nH4LBIBUVFZSUlLBp0ybqGuoRFBED6uihAbAYzYzMGcmaNWuGhXTV2z73JGbSW5DWjlHjzmiqgy6X\niw0bNqAoCn/4wx+46KKLhn1f/01xPiAPFVpAFkWR9vb2TubwwwXthq+pqeGu2+5mgnUqOcmjEUWx\nE3krFApRX19PQ0MDsqy+RwiJxNlS1ddEB2siZCejCZPJPKiecV/7mR/8mKycqYzNnD3o93568p+M\nmzqTC8bl4vP58Pl8tHu8CIKAJCsYjGZMFqsaoC02zBabKutoMHCi5CNkUzsrvvz1IWURtSeLqDyy\njW8/8TArV67U96k38Ya+ModoVrTL7WbP/v0EwqotYlAQEWUZk9GIyagGFJPZjNFsxmixYrI7MMfG\nEZ+aQVxqGgkZI7DYrLTXn+bo5n/R3thC5oWLSR41+Zy2RjqOXW03NJQdocF9EOekKTjHT8FojYyQ\nRXykjQP0iNa3r34IigLtFWU07v2Q6VdfQc7s7u44HS0HegjS0Vk0YDAg+Pwc/5/XuSg1lbu/egf1\n9fW4y8oodJVQXXeagCgg2cxYMpKJG5FB/IgMEkZmYXPG9XpOFUWh6pPDnPnwANNHXsCGO+9izpw5\nfV6D6PJtdKAaDAKBAMXFxWzatIkjR44gylKU8pr65DYajEydOpU1a9Ywd+7cc8Jf6C9IA+zbt48D\nBw4wa9YsiouLefHFF3n44Yd58sknB6zRfx7A+YA8dETb5LW1telZz3Cgq8LXxx9/zI++8xzLR1+O\nxWRBFMXIKw0YjVqmoD6sJEki/3gBBsmK3RKHIArq67WHGlq2ofYQjYaOnuvZoDyQj5hgZNaEtYN+\nb9mpfUgOP2vW3hN1EtSFhs/vw+dVNYq9Xi+iJKu9c5MVk8VGKNROxcntLL1mPek5Fwz6sxVFIX/v\nB8it5fz4mad7LU0OpLzXdS5UUVT/Yk2lq9TlorDERYvHQyAsYolPwJacRkJGFs6MTGKT01QpSKMB\nZJmy/R9Ttn8PZkcKI2ctxxb32Ys6ANSVHKCh7DCj5y0kNmcsHp+XsLZYMpswWCOjSzZ7n6xoLbAC\neiCt27eLYHUZC++8nfjM/mdR+wvSgdZWiv/yJosmTOSZp5/WJW3b2to65rzLysgvLaa+sRG/JKDE\nWLBkphI3IpJJ52Rii+ssGuNraML97kcYaxq5ZO4Cbrn5ZiZOnNjl+DpITZppzHC2sTweDzt37mTz\n5s3U1dVFZow7j5oZMJCbm6v7GA/34k0TPFIURa/y/O53v+PZZ5+lra0NgIyMDBYuXMicOXO4/vrr\nmTx58rDuw78xzgfkoUILyLIs09raSlxc3LAQeaIVviwWCw6Hg2effZYP/7qbi8ddqr9OexhFl1dB\nFXqvrT5NqjMyexzFpu5UnhQ0nWhtBCoi/GGI9E4HmTk3CqeopowFU2/s3kfuB03t1Zxo2ssV13yd\nuLgo2TylI2MD9BKvFqS9Xh9enw9X6TYwehk1aRbxyRkkpWWTmJZNTKxzQA8kWZY4tP3vJFmD/PiZ\nHwyIeBKdOWgLs2iWt0a260qU0eZaddJYcQnFEdJYWAZrYjKKyUxjVSXhoEj6xLmkT5g95F7ocEBR\nFE4X7qWtpoDZV15D9qSL1FK7T5Wh9GpSlKKkEq7MZoxWK+ZIFm2y2TAY9LH3TjPFsihStfkdYiwy\nC+66o0eXrH73j45RPEVR8NTV4frbP1gwcSJPPfGk3k6Kvg6gEjO1toOrTBVjaWhtISAJGOJisGSm\nED8iQy93m2PsNBafoHLrHuxtAZbOnc/VV13FrFmz9F5xNKnps1CdamhoYNu2bWzZskWd+Og0Y9zx\n+YsXL2blypVDdovS9PhDoVCnErwsy/zpT3/iySef5I477mDOnDnk5+dz+PBhDh06xCuvvNLJ0vE8\n+sT5gDxUaI5PiqLQ0tJCbGys7lk61O1pCl/a6tpsNhMKhbjhmi8T25LE5KypnV6vBmMDJpMq+uH3\n+ygsKMKMg3hHlIOQoScJSlAiykqixsQVJWRFNXMwdB1bMvQdpMNykILwHi4cv4LUhL6lArtCkgQO\nnvwHcxevZsJE1XlF6zNHC5309PGyJFNctJ+K8l2sWrWc6tpT1J6uJxgSUExW7M40ElOzSEzNJjE1\nC2svs8VCOMjBrW+SYAnzxGOPsGjRokEdA6AvkHqzRuwaoCVJ0vuM9fX15Ofn87e/vcWRgmKMsWmk\njZuNOcaplomtESlQmw2T2cwAvrvDCkVRqD7yEYGmcnKv+TJpY7osWhS1F+rz+fD51SDt9amSpup8\nsaVTqdsUPbrk9VC18e9kjM1h1rov96vgNRC019VR8re/kztmLN964olO0ra9LZYURaGhoUEP0qVu\n1RykydNGQBQxJsZizUwhPjuDYFs7bSeqsbUHGZ+dw/KLl5KXl8eoUaM+95EezSLxgw8+0DUSesKy\nZctYtWoVEyZM6DNIS5KkV+tsNptOTjt9+jT3338/ZWVl/OEPf2DRokWdtqMtWM/VKGBX7N69m5/8\n5CccPnyY06dP88477+jex71hx44dPPLIIxQWFjJq1Cieeuop1q9f/5nsbw84H5CHiq6eyA6HY8Az\ni9HQ+sTRouvRzihHjhzhoa8/TG5qHgkxiRFilkq8MerBUlXjKSosIuATVCMJo7GT7GDnodAOUeKe\n2NcdRgIdY0tqqdugZtBaoO5S6i4M7CU5aywTRiwc9HkortqBNdXCipXroxYbdGQ0fdyqkiSy48P/\n5rI1C3nyySdobW3VJShLXS4KCktoamklGBIxx8QT40wjMZJFJySn62NDkihw7JONCC1V3Hjd1axb\nt25YrBH7cvwxGAy0tbXx/vvv8+7GLbT6JcZOX0LW2CkE/P4OMwSPl2AohCTLKEYjRq2nHgnSRtO5\nf+gpskzFgc2IgTMs/PKtJGT0rlCmzsKrWaP24/VFSZoChkg/2myzEW5ron7XB4xfNI9Jq1cOy/76\nGhspevMtpqSm8R9PPcXo0aN7XSxpQVprO2jfK0VRqKur63DwcrspLnPR6vMSkAR8QghBkYkzW0my\nxnDR+IlcuuwScnNzycrK+kyy5IGgvLycDz74gK1bt/b498WLF/PQQw91+l10VqzJAGtkrrfeeotH\nHnmEdevW8eyzz54z+c3BYPPmzezZs4fZs2dz3XXX8fbbb/cZkCsqKpg6dSr33nsvd955J9u2bePB\nBx9k48aNOp/kM8b5gDxURAfklpYW7Hb7gJVsQL3Zw+GwPhKhKXwZDAY9M1QUhddee40/vvQ6yy+4\nHEWR9dnjaNvAUChEWVkZbc1ekp2Z6uhQz5+q/Z+2E1F/M0TH6c6lblmKEp0QkSWt1N3RjzYYTdSG\n3fgcfuZOvnbQD6Km9irKGvex6oo7cDpT9WMcaCJYVVVExYmd/PKXLzBlypTORx31UC0rK6O4pITi\nEjceX4CQKGONTSIuKSOSRWfSeLqS6uL9ZKc5+fJ117B8+fJhsbaL1i5WFIWTJ0+ydes2tu/cTbtf\nJGv8TMZMmYPFatMJS9GMYlEUIqQ3Pz6fl3avj7AQVnu5JhNGS5SphtV2Tsrcsihwcu+7mAxBFq1b\njyOxuzOPLEUWVAY6lYihoxKklbs9Xi/+YABJUWivPklrwSFGTLuQsYsX4czKwp4wsLZDbwi2t1P0\nj3dIE0Qee/BBlizp8GXub7HUl4extuCrrKzkZGUFBaUlBCSBoCRiNBiIMZmJt9i5aNJkrrzySmbM\nmNGnRetnDUVRcLvdbN26lY8++oi77767k89vtLRndFbc2NjIww8/zMGDB/n973/PihUrvjCLjmgY\njcZ+M+THH3+cTZs2cfz4cf13N910E21tbWzcuPGz2M2uOB+Qhwrt4QrQ2tqKxWIZ8Beupz6x0Wik\nvr6ep556ivT0dFauXElubi73briXliIfM0fmYjAYMBpNOpFLkiSampqoqqwmHBRJjEvDYhlk2Txy\nfZXO/6jordStKLrwR3QvvU1solwp5qLRK4lzpGCJsKIH8oWVZInDJ95h8sxcZsxcPuiKrKIo7Nrx\nZ+bMHstzzz3b72dGzxa73W6Kiks5UV5BICQgygawOGhrbiDWbiExPoaF8+eSO3cu06ZNIycnZ9Aj\nbqIo0tLSon5WURGf7N1HeWUtoimG7HEzGDVpOmaztUcREy0oa0QozT8aFEKaraDWy/V61VlvRcFg\nMkecr+xYbDbMVtsAZ7z7OZZQgBOfvIMj3sqiG2/DGnHNUmR18YYCxijluP4giZI+W+zeu5PG4/tJ\nT03BHBuLbLNiTk0lLjOThOws4rOzsA0ysEmCQMnGTcjllVy3Zg133313r2ND/QVpTQgI6MSgjlbq\nKioq4sOdOwlIArIiYzQYMBmMWI1mEuLiuPzyy1mxYsUXyr9YQ/SiMdrwQlEUNm7cyAMPPMDq1av5\n2c9+9rk4Rw0UAwnIS5cuZc6cObzwwgv671577TUeeughWlpaPovd7IrzAXmoiA7IfXkiR6O3PrHW\ne6yoqODRRx/VGaSNDY1UuqqYaJ1OYkwSTqcTi8WCJEmEQiHaWtsRBBGryYEzNnn4eldDKXXLMmEh\nyGHvTsaOmkW8OV0l+cgKRqMFs8WG1aKNLUWR3zTmrcHAydMHCVibuOKqe4d0LGfOVJJ/7D2+9eRD\nrFkzeOWvQCDQMbbkclNQVMypunqCIZFQWMRoMuCwWbBbLYwelcOkiRPIyMggMTGR+Ph4bDYbZrNZ\nn09vb2+npaWF6upqTpZXUl5ZhT8YRjbaSMwcS/aYyaRkje5l8dBZxERRFNVARBRUJrZJax10mGqA\nQZWhDARV4pvP16FwJanOV0aL2o82W9V+rslsGVKQDvnaOfnJOyRlpjD/+lswmEwosoLBaFDn2ocY\n9xVFIX/LvzA11HD7LarxgKvMTUFJKQ0tLQREARwOrOlpxGdm4szKIj4rE0s/7SJFUagrKKTmw4+Y\nnJXNvXffzfz58we0WNSmHkKhUDcSJdCJXd/V1KGiooK9e/fy7rvvIsiRhZIB1OliAxajkfHjx7Nm\nzRoWLVo0bApvQ0FvhhdtbW08/vjjbN26ld/85jdcddVVX8isOBoDCciTJk3ijjvu4PHHH9d/t2nT\nJtauXYvf7z8rTtAQcT4gnw00C8bePJE19Ncnjh4FAThz5gxbtmzhZy/8jFCtzLiYKciy0vETKRnH\n2OKIdyRi7kE6cXjRf6k78v+R376frHFjWDTjMoLBgMqGjgQGn88fOV6DGqTNaoC2Wu2YzGY8gSYK\nT23jktU3k5k5dkh7evTINoRQFT/72fOMHz94Leau0Ji4breb4/kF5OcXqnPFIZGQoI6fmUyR2WKD\n5l2sjuXIioLJYsccE4/DmUJiSgYpmaOIS0zt94EWDgZoPF1JS0MtrY11eFqaCAV8yFF9T4czkfjE\nZJLSR5CaNZrEtGyMUQFBC9KqwlWkH+1VGdF+f0DlI4AepAdLGgu0NnJy7z/JuGA0s9Zeh8ViVUe2\nzhKyJHF80zs4vM088/3/YMaMGTrhSms7uNxuClyltHq8BEQBU0ICltRUnNlZOLOziM/IwNTDGGKg\ntQ331m1Qe4rl8+dz26239smqj84Yte9uh4784FWugsEghw8f5v3338flciFF7BGjCZNGg4E5c+aw\nZs2aczK61Ncxds2Kd+zYwb333suCBQv45S9/SVpa2jndl+HCUAPyxo0bufLKKwkEAp/H4uh8QD4b\n9OaJrGGgfeLo3pT22vLych6+7xEmO2aSlTBCHffxRWU9Hh9SJAM1GS2YTVaskQBnNg5UfvIs0Eup\nu9pfRmtME9csv1sVHjF2yFnKshSZKfZEjsNHKBRGlhUMmDAYLbgbPiZtdBZ5y9YNvvyOSvD6eNdf\nGTPayc9//kKvi6ShoqvWdXFJKcWlbry+AIGwACY78SlZJKZlk5o5ivjENEyWgQmwBHweTpUXc6q8\nhKbT1YiChNUSR0xMMnZHElarA5PFiiIryLJAKOgh4G/B7z2DjEis08nIydMZNX46MXHqvRgtQRnd\nj5YktW2iC7F4vQRDYXXBZDRisqjjSpZIybs781lddHgaaqg6tIkx06Yxfc3wZU6yKHJ04z+ID7bz\nvW89ydy5c7u9RlEUamtrdUOHotJSdYQsECAoS5iTErGlp+PMUoN0XFqafhwNLjdVu3YR4w+yOi+P\na6+5pttc8WAtEgcTpDXVN1BbXtu3b2fLli00NzerHt7aRqPEQPLy8li1ahWTJk0avvMcqdp1PUaf\nz8d3v/td/va3v/HSSy+xbt26L3xWHI3zJev/gwG5qydyNCNX8+OVJKlTn1hjEEcHYlmWCQQCugKX\nzWbjBz/4Ibv++QnLx1/W4xdBlmX8ATUD9fm8eDxeAv5AJNxsw0sAACAASURBVHs2YDZasZitagZq\ntql2fOcaioJXbKcweJBLFl5HVuqoyB8M+hxqRCtML+0JQjhCUlIXGidqC6hsOUJWzkhi41KJi08n\nOTmL5OQsnAmpAzoOn6+NPR+/wZzZk/je975LUlJ34tFwQhRF3TGqqKiIwuISKqtqCAkSEibszlSc\nyVkkpmaSmJaN3RGvX1NJEqmvclNZeoy6yhPIokJ8fDaJKWNISM7BZuufvarIMl7PGRrrXbQ0n8Ro\ngTFTZjJh+kLssfF0/Q53db7SngOCIOilbo3ZHRYjAiAmVVHMHOlFa6QxgwFaa09Se2w74+fNY8rS\nlcP24JZEgYIP3sPSXMcTjzzEJZdc0u97tF6uPuddWoq7vBxfOERIAXNKMo6MDOKzMonPzMBz6jSn\nDhzEHggyb9o0rrjsMhYsWIAmj2owGPTW0mAx1Fl1gFOnTvHBBx/0O7q0evVqVq1axejRgxs11Mrw\n0VU7i8WCoijs37+fDRs2MHHiRH77298yYsSIQR/7542BBOQnnniCTZs2cezYMf13N998M62tredJ\nXf8boQVkrS+cmJior6p76xN3LU9rYwUGgwG7XbVVfPfdd/nJ0y8wPWkuWQkD/zJoiwCvz4fP68XT\n7iEcVlnRRoNJDdKRAG02D17ObyBQFIVj7Z8wZtJk5k9dqWZzitwtKHSWPjR0ClCb9v+ReRdP5cIL\nL6SoqAS3+wQ+XwhRVLDHpOBMSCc5JZvk5EwcjoQej6O9rZED+99myuQRfP/7/0FWVu8jOsNxzIIg\ndNL1FUVRNxBwu90UFJXo/WiD2Q7WWMLBAO1NZxDDEo7YdFLTJ5KcdgHmPjyp+4MkCZw5VUTdqeOY\nLAqT5ixh/LR5GM2WTuIZndoOfZHGQiF1weRXF0waaUxWUGeLLaoAiKfuJA2uA0y++GImLlp2tqdU\nhyLLFH60BbG6jLtv+wrr1q0bNL8gFApRXl6uX4/8kmLKq6oJiCKC0YAlNQW/x0uwpQWn1UaG00ne\nggUsWbKEOXPmDKv8Y3+z6n0F6bKyMj744AM+/PDDXrdvt9tZtWoVK1eu7PWej04ALBYLMTEx+gLk\nhz/8Ia+++irPPfccd9555+c+Uz0Y+Hw+ysrKUBSF2bNn88ILL3DJJZeQnJzMyJEjefLJJzl16hT/\n/d//DXSMPd13333ccccdbN++XR97WrFixedxCOcD8tlAEAT95g4EAtjt9k6rau2L3FMgjn6AR48V\nuFwuHrzvIeI8SUwfOees9k+bI4zOeHxenzpb3K3UbcdsNA1LqbvK56bF3sC1l25Ae7Brs8uRHYvM\nNndmE2vOPu7qY5wJlfCHP/6ezMxMwuFwp+CWn19ETc0pgkEBsOKITSUxKZOkSCZtjRgv+Hxt7N/7\nDxITjNx553quuOKKYTcAiS5rRrvd9IS6ujreffddNm/eQlGRm1DYiDNhFAlJo7E7kjCZ1RKxRctA\nz0LSVJIETlUd4Ux9AQkpycxaeiUpmdHuUN1JY92CdFQvOvoeDofD+COuS54IN0CSZZprXDRVHCFr\n4kTGz1+CMz2L2KTks174KYpCxaf7OXNkH2uWLuGhBx8861aEz+fruKfKyigoKaH69Gn8QpigKCID\nTpsdp83GvJkzWbFiBdOnTz/rufSeMNAg3dOMdGFhIVu2bGHv3r3dtvvss8924lFELxyBTlnx8ePH\nueeee0hJSeEPf/gDY8cOjcPxeWLnzp1ccskl3e639evX8+qrr/LVr36VysrKTguanTt38vDDD1NU\nVEROTg7f/e53ufXWWz/rXddwPiCfDQRBQJIkfD6fXlaKnkfur09ssViw2+36A/zjjz/mpz9+AU91\ngLxxl56TMrPWM9J7h+0egoEgkiRjwIjJYMFijjCizb3rEvcFv+gh37+fpQuvJid9nMoK7uOh3IlJ\nrCiIUpgPj7zJNetW87Wvfa1HckxbW5seoEtLXRQUFtPc1EowKGK1xhMbn0ZSUibOhFRqa1w0NZQy\ndeoErrvuGvLy8s6asNFVNEGrbvT0uqKiInbv3s327Tuoq2vGHpPGqNHTGDFiIqIodSyYvJEFk8aI\nNlvUIG2zq+5XQ2BEB/wtlLt3Ewo3MnHWIibPyetzTl0jpGkLpp6CtFHvR4NGGlNni324jn5CbfEe\nEpyxxCYmIxpMWJNSiE9X9boTM7KxxcUPKUg3Vp7E9eFGJmal8+hDDzJjxoxBb6MnaEpUra2tVFdX\nqyXvE2Xs3X8AvygiyBJmgxGLyUisxcr4Cy7g6quvZvbs2edstnioM9KamFBFRQVr167VmcKyLKum\nM4LQ6bkjCALPP/88v/zlL/ne977HN77xjfM2iZ8fzgfks0EgEMDj8eirWafTqeu73n333TQ3N2O3\n21mxYgVLliwhLS1NJ3fY7XbMZjOiKHL8+HG2bdvG5n99QGwggTmj52M2fXYuKYIgdCKMedq9CIJW\n6jZjNkWC9ABK3Vqmdbx9LyPGj2PxzMuHsEcKJRVHqA8U8cLPf8LIkSP17Kwv2cO6ujrds7i4uITi\nYjc+X4CwIOP3h5Almfh4OwkJDlavvpR58+Zx4YUXdiPjDeR8abrF0dUNDT6fj+LiYg4dOsQnn+yj\nuqYORbGRkTGRUWMuIj6+d39kRVYNCqKrGv5AQFXowoDJbMVssWG2qkHaNIDepqLInK45zumaT0kd\nkc3c5dcQ6+y7r66gWnVqI2mGCAmgr360ShQA19FPaDhxkKsvX8XYsWPVDLS4hNP1ZwgIIljtWJNS\ncWZkqcYa6VlYByiqE/R6KNq2EVNbAzd+6SpuueWWIatE9aZEFf33xsZGTpw4wcGDB/loxw4CEecv\nY2RhYjYaMRuNrFmzhjVr1jBixIhzRn4aapDW7lfomJ0GKC4uZsOGDZjNZl577bXzJhCfP84H5LNB\nS0sLgiBgtVoJBAL6g11RFJ577jkOHjioSwiKgoQsShhNJswWM1arBUdMLI0Njfjb/RiDFsYmTWBM\nyrjPnc2ojWl1LXVLkqyXui0mm55Ja37KOmnLYKA2cJJGSx3XrfzakMayZFnio0//zuRZObzw4k/1\n+d6BaERHP4g0spXb7aawsJjyimqCQQFRlDCbjNjtFmJirMyaNYNp06aRnZ1NZmYmycnJOJ2dVaKi\ne29msxmr1YrH46Guro7a2loqKiooLi6lqKgUvz+E0RRLSuoYRoyYSHJK9pCvqyRKnaoaHTKaiiph\nqpe6VYWu3iorPk8DJ0q2Y7TJzFl2JVljJvXwKm28TtLPbdfSeYfjUg8iJqjqbWXH99J44hB33HYT\n69evx2AwdBohK3W5KCgupbmtjYAgYoqN152vEjKycKZl9ji2BOr9WXX8MKcO72NMWhJ33r6e5cuX\nDyqzi65U9bSo6g0ay37Xrl289957qhSoouilfQPqojEzI4PLLruMZcuWnVNZya5BOtoqFNAXq4qi\nau6PHj0aRVF4+eWXefbZZ3n00Ud54oknPjO96fPoE+cD8tlAEFRrQ1EU8Xq9eo/HbDZjNBp1klVh\nYSF79uxh//79iGGRoC8U+QkS9IfJco5gXPpEkmNTsZ0FoedcQpJlXRHK5/PhafcQDIaQJBkUIyaj\nGavZrpe6w0qQY75PyFtwFWOyp/T/AT2gzdvEnqJ/cufXv8Ltt9/e6W/9ZQtdxRq0UrfX69VL3Xv2\n7MXtPkEwKBAW1L66goLFbMZsMWI2GXE6ncTGOiK9NtV4Qy1xBmhra0cURcKChCDI2GxOYuNSSU3N\nIS19FLGxiedscSWEo6oakSAtiiKSLGMwWaL60XbMVqseVEUxRLlrJx5vNVPmXszk2Xl6b19RZCRZ\nBkUN9CajalrSP3ruR1cUH6amaA9fumIlX//a13ThlK460RojuqTURZHLRbvPT1CUsTgTiUlNJyE9\ni4TMbOKSUzuNXwW9Htyf7MBffYJpE8dx6803s3Dhwj4Ds7bY1EiXmmvR2UCSJAoKCti0aROHDh1C\n7rpAQQ2MkyZNYs2aNSxYsOCc+gTLskw4HNbHMgFKSkp0Cdj4+Hh8Ph/f+ta3uPHGG8nMzDxn+3Ie\ng8L5gHw2aGtr08XWQ6FQt6AA6mrZarVisVh6DAqlJaXkHy2g8UwTQV8Im2InzuQkOTaVlNhUEmIS\nP5uRpUFCPeYwXq9H1yb2eH2IgoAsKRgNZirDbkwpZlYtXEdifMqQSEolFZ9y2lfI08/8BwsWLOh3\nn6LHS7rOgfakqKQJTmhZ9MGDhykvryQYFAgEBYKBMKIkYzZbIz3pFMxmK2azBZvNgd0eR1xcIrFx\niX30Zj8DdHVb8qiBWlfo0kvdNswWGw11RZyu/ZTs8ROZe8lVGM1WFFkGgwGTcTg8stUgXXuyGPfB\nLeTNn8UDD9yvk7H60onuNLZUUor7ZDm+UBhBAUtCMrFpGZFMOhtHYhKexjOU7d2J2HCKSaNHcf21\n17B06dJu/d3orDha9vJcIBgMsmvXLrZs2UJlZWXnKoKhYyp9uL2Loxcc0STDlpYWfvGLX7Bnzx4a\nGhpobGykqakJgLVr1/Luu++e9Wefx1njfEA+G1xwwQWYzWbmzp1Lbm4uY8eO5fXXX9c9jLW5467i\nAD0Z2dfV1eFyudRZ1oIiSotd+Nr9iEGJGGJJtCWTEpdGcmwKDuvnJ1LfMVvZ3XFKexioY1c+apuq\nKPR+SmZONjZLLA5LIolx6aQ4M0hOyMRhH8CMraKwv2ALor2V7z/9HXJzcwe9r12DtAaj0djpWkQH\nhcrKSoqKiigtLaW0tIyq6hqCQQFJMhITk0JiYgbJKdkkJWcRE/P5O930BEVWR/L8fr+eRQei+tEB\nfws1NQdwOB3MX30DadljIzrpwxukmuqqKfzkXSaPzeJbTz5OTk7OoKoa2tiSPkJWXEpVTQ1+QUAy\nmrEmpRKfnoXBaKClthqpuYG0hDhWL7+EpUuXcuGFFxIOh4c1Kx4Kmpub2b59O5s3b6atra3X1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Vq1eTmZmpX7P6+vpIL7qEgoKiCKs7pJa6Y5KId6brrO74+KFbIUqSSGHBbs7UFbJgwSzuu+/r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\nFGUrsEpRlMmAPfAJsP4uK6wlknrJ/wOb9inBmId1nwAAAABJRU5ErkJggg==\n", 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4//KPT/+VhFqiNiEJskSNwC9vSYVYuOCD1Wp1EOLo6GgoFAoAgF6vr8nmS512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Uk0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\nAlhjjJkMNANWAFvucIW1Uk3S/wOA7iEME+ag6QAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "pl.figure(3)\n", + "\n", + "#pl.subplot(1,2,1)\n", + "cmap=pl.cm.get_cmap('viridis')\n", + "verts = []\n", + "zs = alphalist\n", + "for i,z in enumerate(zs):\n", + " ys = B_l2[:,i]\n", + " verts.append(list(zip(x, ys)))\n", + "\n", + "ax = pl.gcf().gca(projection='3d')\n", + "\n", + "poly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist])\n", + "poly.set_alpha(0.7)\n", + "ax.add_collection3d(poly, zs=zs, zdir='y')\n", + "\n", + "ax.set_xlabel('x')\n", + "ax.set_xlim3d(0, n)\n", + "ax.set_ylabel('$\\\\alpha$')\n", + "ax.set_ylim3d(0,1)\n", + "ax.set_zlabel('')\n", + "ax.set_zlim3d(0, B_l2.max()*1.01)\n", + "pl.title('Barycenter interpolation with l2')\n", + "\n", + "pl.show()\n", + "\n", + "pl.figure(4)\n", + "\n", + "#pl.subplot(1,2,1)\n", + "cmap=pl.cm.get_cmap('viridis')\n", + "verts = []\n", + "zs = alphalist\n", + "for i,z in enumerate(zs):\n", + " ys = B_wass[:,i]\n", + " verts.append(list(zip(x, ys)))\n", + "\n", + "ax = pl.gcf().gca(projection='3d')\n", + "\n", + "poly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist])\n", + "poly.set_alpha(0.7)\n", + "ax.add_collection3d(poly, zs=zs, zdir='y')\n", + "\n", + "ax.set_xlabel('x')\n", + "ax.set_xlim3d(0, n)\n", + "ax.set_ylabel('$\\\\alpha$')\n", + "ax.set_ylim3d(0,1)\n", + "ax.set_zlabel('')\n", + "ax.set_zlim3d(0, B_l2.max()*1.01)\n", + "pl.title('Barycenter interpolation with Wasserstein')\n", + "\n", + "pl.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/Demo_2D_OT_DomainAdaptation.ipynb b/notebooks/Demo_2D_OT_DomainAdaptation.ipynb new file mode 100644 index 0000000..b713d5b --- /dev/null +++ b/notebooks/Demo_2D_OT_DomainAdaptation.ipynb @@ -0,0 +1,217 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Domain adaptation with optimal transport" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dataset generation (classification problem) " + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n=150 # nb samples in source and target datasets\n", + "\n", + "xs,ys=ot.datasets.get_data_classif('3gauss',n)\n", + "xt,yt=ot.datasets.get_data_classif('3gauss2',n)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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mudNPP/2EUoo333zTZvvhw4ejtebHH39MU/1KKfr3729zhUxrzdq1a3n++eep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\nKEXIAQAAShFyAACAUoQcAACglC+qn/WKtzKzIwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(1,(10,7))\n", + "\n", + "pl.subplot(2,2,1)\n", + "pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples')\n", + "pl.legend(loc=0)\n", + "pl.axis([-4,4,-4,4])\n", + "pl.title('Source distributions')\n", + "\n", + "pl.subplot(2,2,2)\n", + "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples')\n", + "pl.legend(loc=0)\n", + "pl.axis([-4,4,-4,4])\n", + "pl.title('Target distributions')\n", + "\n", + "pl.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": false + }, + "source": [ + "## Domain adaptation classes" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# LP problem\n", + "da_emd=ot.da.OTDA() # init class\n", + "da_emd.fit(xs,xt) # fit distributions\n", + "xst0=da_emd.interp() # interpolation of source samples\n", + "\n", + "\n", + "# sinkhorn regularization\n", + "lambd=1e-1\n", + "da_entrop=ot.da.OTDA_sinkhorn()\n", + "da_entrop.fit(xs,xt,reg=lambd)\n", + "xsts=da_entrop.interp()\n", + "\n", + "# Group lasso regularization\n", + "reg=1e-1\n", + "eta=1e0\n", + "da_lpl1=ot.da.OTDA_lpl1()\n", + "da_lpl1.fit(xs,ys,xt,reg=lambd,eta=eta)\n", + "xstg=da_lpl1.interp()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot OT matrices and interpolated samples " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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artIdDXkv+QcCIUR36Z+GQLd05DLgcx3uqRCims7oSFccGQSR+RPgFc65/y4c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O+Xw+n0Ag+DNQeq3nE2RtYTab6bJ00RbeypaQrcSp56ehqFQqdu9Qcd8OJV2mmbS1nL/aS47p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h5k795u/vjHT+jurmfRooWjvocQYvwppfDz8+Oee+LQ6Sppa1O0tup54YUCtm4NZP36eG6/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\nhBBCTGnS6RFCCCGEEEJMaf8PUJAvj+BeZCgAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(4,(10,7))\n", + "\n", + "param_img={'interpolation':'nearest','cmap':'jet'}\n", + "\n", + "pl.subplot(2,3,1)\n", + "pl.imshow(da_emd.G,**param_img)\n", + "pl.title('OT matrix')\n", + "\n", + "\n", + "pl.subplot(2,3,2)\n", + "pl.imshow(da_entrop.G,**param_img)\n", + "pl.title('OT matrix sinkhorn')\n", + "\n", + "pl.subplot(2,3,3)\n", + "pl.imshow(da_lpl1.G,**param_img)\n", + "pl.title('OT matrix Group Lasso')\n", + "\n", + "pl.subplot(2,3,4)\n", + "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)\n", + "pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30)\n", + "pl.title('Interp samples')\n", + "pl.legend(loc=0)\n", + "\n", + "pl.subplot(2,3,5)\n", + "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)\n", + "pl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30)\n", + "pl.title('Interp samples Sinkhorn')\n", + "\n", + "pl.subplot(2,3,6)\n", + "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)\n", + "pl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30)\n", + "pl.title('Interp samples Group Lasso')\n", + "\n", + "pl.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/Demo_2D_OT_samples.ipynb b/notebooks/Demo_2D_OT_samples.ipynb new file mode 100644 index 0000000..e20b09b --- /dev/null +++ b/notebooks/Demo_2D_OT_samples.ipynb @@ -0,0 +1,234 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:e01831efa84095a87a681a09f08c8991bbe439e010c2bc4cd3559dc4d3bf0bde" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Data generation" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "n=20 # nb samples\n", + "\n", + "mu_s=np.array([0,0])\n", + "cov_s=np.array([[1,0],[0,1]])\n", + "\n", + "mu_t=np.array([4,4])\n", + "cov_t=np.array([[1,-.8],[-.8,1]])\n", + "\n", + "xs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s)\n", + "xt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t)\n", + "\n", + "a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples\n", + "\n", + "# loss matrix\n", + "M=ot.dist(xs,xt)\n", + "M/=M.max()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Plot dataset" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "pl.figure(1)\n", + "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", + "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", + "pl.legend(loc=0)\n", + "pl.title('Source and traget distributions')\n", + "\n", + "pl.figure(2)\n", + "pl.imshow(M,interpolation='nearest')\n", + "pl.title('Cost matrix M')\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 3, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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Cd5pZEcGPw4Pu/mQE7YqISIJ0BqiISBbTGaAiIgVEyVxEJA8omYuI5AElcxGR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\nXEQkDyiZi4jkASVzEZE88P8B8sPqteLhUE0AAAAASUVORK5CYII=\n", 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+ "text": [ + "" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solve OT" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "G0=ot.emd(a,b,M)\n", + "\n", + "\n", + "pl.figure(3)\n", + "pl.imshow(G0,interpolation='nearest')\n", + "pl.title('Cost matrix M')\n", + "\n", + "pl.figure(4)\n", + "ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1])\n", + "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", + "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", + "pl.legend(loc=0)\n", + "pl.title('OT matrix')\n", + "\n" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 4, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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wd24wcMtRdH//MKrvPYS17ziXQuT1z8hgFmpYGLs32TXWjz461DN/7jmvxu2n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\n/XWSd0EBCSs62rHnqe6VuXkzf2evOqIabW3Ad75DmWbbNv7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+ "text": [ + "" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Solve OT with entropic regularization" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "# reg term\n", + "lambd=5e-3\n", + "\n", + "Gs=ot.sinkhorn(a,b,M,lambd)\n", + "\n", + "pl.figure(5)\n", + "pl.imshow(Gs,interpolation='nearest')\n", + "pl.title('OT matrix sinkhorn')\n", + "\n", + "pl.figure(6)\n", + "ot.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1])\n", + "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", + "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", + "pl.legend(loc=0)\n", + "pl.title('OT matrix Sinkhorn with samples')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "pyout", + "prompt_number": 5, + "text": [ + "" + ] + }, + { + "metadata": {}, + "output_type": "display_data", + "png": 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5eXmorKxEJBLBddddh0gkMq5rFVcNA/CLX/wCmzZtQn9/P2644YaUcnHct7a2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+ "text": [ + "" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 5 + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/notebooks/Demo_2D_OTmapping_DomainAdaptation.ipynb b/notebooks/Demo_2D_OTmapping_DomainAdaptation.ipynb new file mode 100644 index 0000000..4b75f58 --- /dev/null +++ b/notebooks/Demo_2D_OTmapping_DomainAdaptation.ipynb @@ -0,0 +1,283 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## OT mapping estimation for domain adaptation" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dataset generation" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "np.random.seed(0) # makes example reproducible\n", + "\n", + "n=100 # nb samples in source and target datasets\n", + "theta=2*np.pi/20\n", + "nz=0.1\n", + "xs,ys=ot.datasets.get_data_classif('gaussrot',n,nz=nz)\n", + "xt,yt=ot.datasets.get_data_classif('gaussrot',n,theta=theta,nz=nz)\n", + "\n", + "# one of the target mode changes its variance (no linear mapping)\n", + "xt[yt==2]*=3\n", + "xt=xt+4\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "collapsed": true + }, + "source": [ + "### Plot source and target datasets" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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iSpWq5M+fn0KFClGiRClWrVrllOfw4cOEhYVx8+bNDKqlEEI82KRHVAjxwLp2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fEQ8K3M/EDtcVBs4PCJzLy+1OIqphbufwTMzcwHyq6nSiJiH6APeD/wUeCRzLys3Ko6X22769mZ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\nRK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(1,(8,5))\n", + "pl.clf()\n", + "\n", + "pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples')\n", + "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples')\n", + "\n", + "pl.legend(loc=0)\n", + "pl.title('Source and target distributions')\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### OT linear mapping estimation" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 0|4.009366e+03|0.000000e+00\n", + " 1|3.999933e+03|-2.352753e-03\n", + " 2|3.999520e+03|-1.031984e-04\n", + " 3|3.999362e+03|-3.936391e-05\n", + " 4|3.999281e+03|-2.032868e-05\n", + " 5|3.999238e+03|-1.083083e-05\n", + " 6|3.999229e+03|-2.125291e-06\n" + ] + } + ], + "source": [ + "\n", + "eta=1e-8 # quadratic regularization for regression\n", + "mu=1e0 # weight of the OT linear term\n", + "bias=True # estimate a bias\n", + "\n", + "ot_mapping=ot.da.OTDA_mapping_linear()\n", + "ot_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True)\n", + "\n", + "xst=ot_mapping.predict(xs) # use the estimated mapping\n", + "xst0=ot_mapping.interp() # use barycentric mapping\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### OT kernel mapping estimation" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 0|4.026411e+02|0.000000e+00\n", + " 1|3.991051e+02|-8.782091e-03\n", + " 2|3.987950e+02|-7.769290e-04\n", + " 3|3.986577e+02|-3.442631e-04\n", + " 4|3.985741e+02|-2.096899e-04\n", + " 5|3.985159e+02|-1.460105e-04\n", + " 6|3.984729e+02|-1.078536e-04\n", + " 7|3.984436e+02|-7.368218e-05\n", + " 8|3.984214e+02|-5.574123e-05\n", + " 9|3.984025e+02|-4.740267e-05\n", + " 10|3.983871e+02|-3.865975e-05\n", + " 11|3.983744e+02|-3.175451e-05\n", + " 12|3.983642e+02|-2.561334e-05\n", + " 13|3.983558e+02|-2.116042e-05\n", + " 14|3.983479e+02|-1.982104e-05\n", + " 15|3.983413e+02|-1.643931e-05\n", + " 16|3.983351e+02|-1.567880e-05\n", + " 17|3.983296e+02|-1.366612e-05\n", + " 18|3.983270e+02|-6.571092e-06\n" + ] + } + ], + "source": [ + "\n", + "eta=1e-5 # quadratic regularization for regression\n", + "mu=1e-1 # weight of the OT linear term\n", + "bias=True # estimate a bias\n", + "sigma=1 # sigma bandwidth fot gaussian kernel\n", + "\n", + "\n", + "ot_mapping_kernel=ot.da.OTDA_mapping_kernel()\n", + "ot_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 20,verbose=True)\n", + "\n", + "xst_kernel=ot_mapping_kernel.predict(xs) # use the estimated mapping\n", + "xst0_kernel=ot_mapping_kernel.interp() # use barycentric mapping" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting the mapped samples" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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hYQF5eWn4+3uZyUopOXs2FWfnQNzdte/s7e0JCmrIpUuCS5cuXadalWTJkiW0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\nCgpqdHC4a2LZsmXodDp0Oh1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cuHAGDWpOVFQiY8euY/HigXTqFGx1g2uFQnHzoQwZrm6+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dPaytjMDNVaDe3S57+Qy6HodOtKUHeLWq1uRMmWqdfrjN64Qeittwh985sE3/tePAMDDAwNrZOC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mOfNDCCHElqirq0NRFIaHx0kmg9hseg4dapd9m0KITSWJzA7gcrk4ftxFLpdDo9HIGTJCCCG2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\nLFrOnKHt4EHM+aUCiqIwOzeHp6Wl5O+LJEnSfpBOp7ly+TIhrxc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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "pl.figure(2,(10,7))\n", + "pl.clf()\n", + "pl.subplot(2,2,1)\n", + "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2)\n", + "pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Mapped source samples')\n", + "pl.title(\"Bary. mapping (linear)\")\n", + "pl.legend(loc=0)\n", + "\n", + "pl.subplot(2,2,2)\n", + "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2)\n", + "pl.scatter(xst[:,0],xst[:,1],c=ys,marker='+',label='Learned mapping')\n", + "pl.title(\"Estim. mapping (linear)\")\n", + "\n", + "pl.subplot(2,2,3)\n", + "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2)\n", + "pl.scatter(xst0_kernel[:,0],xst0_kernel[:,1],c=ys,marker='+',label='barycentric mapping')\n", + "pl.title(\"Bary. mapping (kernel)\")\n", + "\n", + "pl.subplot(2,2,4)\n", + "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2)\n", + "pl.scatter(xst_kernel[:,0],xst_kernel[:,1],c=ys,marker='+',label='Learned mapping')\n", + "pl.title(\"Estim. mapping (kernel)\")\n", + "\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Barycentric mapping on the left, estimated mapping on the right. We can see that the change \n", + "in variance of the mode do not allow for a good linear mapping. In this case the kernel \n", + "mapping (lower right) allows for a far better estimation\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/notebooks/Demo_Compute_EMD.ipynb b/notebooks/Demo_Compute_EMD.ipynb new file mode 100644 index 0000000..cda9a59 --- /dev/null +++ b/notebooks/Demo_Compute_EMD.ipynb @@ -0,0 +1,167 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Compute and plot EMD" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot\n", + "from ot.datasets import get_1D_gauss as gauss" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generate data" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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EwzCM70VVyU4twlHhbOiq1Lsm1WRkGIbRkDITC1j7wWHSj+YTGhnAiGld6dg/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YVw/XwGT5zA2ea94O/tMtPpdLHWaKLuYhfWfm/u/q6iFgbsLlalm6g6fQK7RYGXX/QxksuGO+0W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\n7yOw6hNkxFBWLpzE863vEdWjXDfmOm4cf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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "n=100 # nb bins\n", + "n_target=25 # nb target distributions\n", + "\n", + "\n", + "# bin positions\n", + "x=np.arange(n,dtype=np.float64)\n", + "\n", + "lst_m=np.linspace(20,90,n_target)\n", + "\n", + "# Gaussian distributions\n", + "a=gauss(n,m=20,s=5) # m= mean, s= std\n", + "\n", + "B=np.zeros((n,n_target))\n", + "\n", + "for i,m in enumerate(lst_m):\n", + " B[:,i]=gauss(n,m=m,s=5)\n", + " \n", + "\n", + "# loss matrix and normalization\n", + "M=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'euclidean')\n", + "M/=M.max()\n", + "\n", + "\n", + "M2=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'sqeuclidean')\n", + "M2/=M2.max()\n", + "\n", + "\n", + "# plot the distributions\n", + "\n", + "pl.figure(1)\n", + "pl.subplot(2,1,1)\n", + "pl.plot(x,a,'b',label='Source distribution')\n", + "pl.xticks([])\n", + "pl.title('Source distribution')\n", + "pl.subplot(2,1,2)\n", + "pl.plot(x,B,label='Target distributions')\n", + "pl.title('Target distributions')\n", + "pl.show() " + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute EMD and sinkhorn distances " + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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8atatW9dYjC2l7HJYWBj29vaMHTsWR0dHunfvnuHO2aSkJEaNGsX06dONj2VW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\n7tz7ex5PNrEisNTLMmfdTGSY1jjjCFA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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "d_emd=ot.emd2(a,B,M) # direct computation of EMD\n", + "d_emd2=ot.emd2(a,B,M2) # direct computation of EMD with loss M2\n", + "\n", + "reg=1e-2\n", + "d_sinkhorn=ot.sinkhorn(a,B,M,reg) # sinkhorn returns a list of distance if B is a matrix\n", + "d_sinkhorn2=ot.sinkhorn(a,B,M2,reg)\n", + "\n", + "pl.figure(2)\n", + "pl.clf()\n", + "pl.plot(d_emd,label='Euclidean EMD')\n", + "pl.plot(d_emd2,label='Squared Euclidean EMD')\n", + "pl.plot(d_sinkhorn,'+',label='Euclidean Sinkhorn')\n", + "pl.plot(d_sinkhorn2,'+',label='Squared Euclidean Sinkhorn')\n", + "pl.title('EMD distances')\n", + "pl.legend()\n", + "pl.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/Demo_Ground_Loss.ipynb b/notebooks/Demo_Ground_Loss.ipynb new file mode 100644 index 0000000..a39a07f --- /dev/null +++ b/notebooks/Demo_Ground_Loss.ipynb @@ -0,0 +1,345 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Choice of the ground loss\n", + "\n", + "Data from [Fig. 1 and 2 in here](https://arxiv.org/pdf/1706.07650.pdf)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dataset 1" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n=20 # nb samples\n", + "xs=np.zeros((n,2))\n", + "xs[:,0]=np.arange(n)+1\n", + "xs[:,1]=(np.arange(n)+1)*-0.001 # to make it strictly convex...\n", + "\n", + "xt=np.zeros((n,2))\n", + "xt[:,1]=np.arange(n)+1\n", + "a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples\n", + "\n", + "pl.figure(1)\n", + "pl.clf()\n", + "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", + "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", + "pl.axis('equal')\n", + "pl.title('Source and traget distributions')\n", + "pl.legend()\n", + "pl.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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JmhXzSdIiMpskzcTU79Bt8XTgNTtc9+iIeA/wSeCFmfmB7Yoi4nzgfIDB2mkc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\ne2mSd+guAp64zeW/mpkPbf9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3AefN8DZfpwmZw2OXzWW/aYLvGuAozXnNz6U5j/8vgY8C\nfwGc3taeB/z2WNvvau/3y4HnzKjvy2nOtT52nx/7FrCzgIuPd//sst9Xtffje2mC5t5b+93peNht\n3+3lFx27f8dqZ7bPp+q/7e4PzCeYUz5hNs01m47T99zzabt+28svwmya5vFrNjl3mnk+7dDvKZ1N\nO/XdXn4RC5pP0Q5AkiRJkrRkFu2US0mSJEnShFzQSZIkSdKSckEnSZIkSUvKBZ0kSZIkLSkXdJIk\nSZK0pFzQSZIkSdKSckEnSZIkSUvKBZ0kSZIkLan/H/+IDxLdPXIjAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# loss matrix\n", + "M1=ot.dist(xs,xt,metric='euclidean')\n", + "M1/=M1.max()\n", + "\n", + "# loss matrix\n", + "M2=ot.dist(xs,xt,metric='sqeuclidean')\n", + "M2/=M2.max()\n", + "\n", + "# loss matrix\n", + "Mp=np.sqrt(ot.dist(xs,xt,metric='euclidean'))\n", + "Mp/=Mp.max()\n", + "\n", + "pl.figure(2,(15,5))\n", + "pl.subplot(1,3,1)\n", + "pl.imshow(M1,interpolation='nearest')\n", + "pl.title('Eucidean cost')\n", + "pl.subplot(1,3,2)\n", + "pl.imshow(M2,interpolation='nearest')\n", + "pl.title('Squared Euclidean cost')\n", + "\n", + "pl.subplot(1,3,3)\n", + "pl.imshow(Mp,interpolation='nearest')\n", + "pl.title('Sqrt Euclidean cost')\n", + "pl.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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SjykpKqxxNUgispTDgUOnCuGhxOdRsfHzwJqPPJbEJiIKCYcDGRJIf+1f+GdWKmqtm2lY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\n4att6nLyg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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "\n", + " \n", + "G1=ot.emd(a,b,M1)\n", + "G2=ot.emd(a,b,M2)\n", + "Gp=ot.emd(a,b,Mp)\n", + "\n", + "pl.figure(3,(15,5))\n", + "\n", + "pl.subplot(1,3,1)\n", + "ot.plot.plot2D_samples_mat(xs,xt,G1,c=[.5,.5,1])\n", + "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", + "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", + "pl.axis('equal')\n", + "#pl.legend(loc=0)\n", + "pl.title('OT Euclidean')\n", + "\n", + "pl.subplot(1,3,2)\n", + "\n", + "ot.plot.plot2D_samples_mat(xs,xt,G2,c=[.5,.5,1])\n", + "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", + "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", + "pl.axis('equal')\n", + "#pl.legend(loc=0)\n", + "pl.title('OT squared Euclidean')\n", + "\n", + "pl.subplot(1,3,3)\n", + "\n", + "ot.plot.plot2D_samples_mat(xs,xt,Gp,c=[.5,.5,1])\n", + "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", + "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", + "pl.axis('equal')\n", + "#pl.legend(loc=0)\n", + "pl.title('OT sqrt Euclidean')\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Dataset 2" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n=50 # nb samples\n", + "xtot=np.zeros((n+1,2))\n", + "xtot[:,0]=np.cos((np.arange(n+1)+1.0)*0.9/(n+2)*2*np.pi+.06*np.pi)\n", + "xtot[:,1]=np.sin((np.arange(n+1)+1.0)*0.9/(n+2)*2*np.pi+.06*np.pi)\n", + "\n", + "xs=xtot[:n,:]\n", + "xt=xtot[1:,:]\n", + "\n", + "a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples\n", + "\n", + "pl.figure(1)\n", + "pl.clf()\n", + "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", + "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", + "pl.axis('equal')\n", + "pl.title('Source and traget distributions')\n", + "pl.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# loss matrix\n", + "M1=ot.dist(xs,xt,metric='euclidean')\n", + "M1/=M1.max()\n", + "\n", + "# loss matrix\n", + "M2=ot.dist(xs,xt,metric='sqeuclidean')\n", + "M2/=M2.max()\n", + "\n", + "# loss matrix\n", + "Mp=np.sqrt(ot.dist(xs,xt,metric='euclidean'))\n", + "Mp/=Mp.max()\n", + "\n", + "pl.figure(2,(15,5))\n", + "pl.subplot(1,3,1)\n", + "pl.imshow(M1,interpolation='nearest')\n", + "pl.title('Eucidean cost')\n", + "pl.subplot(1,3,2)\n", + "pl.imshow(M2,interpolation='nearest')\n", + "pl.title('Squared Euclidean cost')\n", + "\n", + "pl.subplot(1,3,3)\n", + "pl.imshow(Mp,interpolation='nearest')\n", + "pl.title('Sqrt Euclidean cost')\n", + "pl.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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woWJvBcTKmluyWv3ZtM5c3qjZdR0ysonaCYiCiNRN5Thi95Kk/L1IO+RuG+Du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\ntROiKGG1Ex27EsRt6lPUCPaG1ryWFuk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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + " \n", + "G1=ot.emd(a,b,M1)\n", + "G2=ot.emd(a,b,M2)\n", + "Gp=ot.emd(a,b,Mp)\n", + "\n", + "pl.figure(3,(15,5))\n", + "\n", + "pl.subplot(1,3,1)\n", + "ot.plot.plot2D_samples_mat(xs,xt,G1,c=[.5,.5,1])\n", + "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", + "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", + "pl.axis('equal')\n", + "#pl.legend(loc=0)\n", + "pl.title('OT Euclidean')\n", + "\n", + "pl.subplot(1,3,2)\n", + "\n", + "ot.plot.plot2D_samples_mat(xs,xt,G2,c=[.5,.5,1])\n", + "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", + "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", + "pl.axis('equal')\n", + "#pl.legend(loc=0)\n", + "pl.title('OT squared Euclidean')\n", + "\n", + "pl.subplot(1,3,3)\n", + "\n", + "ot.plot.plot2D_samples_mat(xs,xt,Gp,c=[.5,.5,1])\n", + "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", + "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", + "pl.axis('equal')\n", + "#pl.legend(loc=0)\n", + "pl.title('OT sqrt Euclidean')\n", + "pl.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/Demo_Image_ColorAdaptation.ipynb b/notebooks/Demo_Image_ColorAdaptation.ipynb new file mode 100644 index 0000000..16e5208 --- /dev/null +++ b/notebooks/Demo_Image_ColorAdaptation.ipynb @@ -0,0 +1,316 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Demo of OT Domain Adaptation for color transfer in images\n", + "\n", + "The color adaptation problem and the out of sample interpolation has been proposed in [6]:\n", + "\n", + "[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import scipy.ndimage as spi\n", + "import matplotlib.pylab as pl\n", + "import ot\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading images" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "I1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256\n", + "I2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting images" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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mTia0hLGlGUdRPJdlnZfEqBoxOLLbusg0fQycdE60mvwID4Ylse5O2ZcHU+LH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\n41yBpIqq38d4HxfdExL3lJffqz5zh0o6f6MfJwg6URVT45HwJ54UukTc9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QTmuMY+j45F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\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J3EjkYbtwM3Qdo3jh1nsXvllEaJzbyucijKlPqTbJCIUDgUMUGoEURz6MC2t0gm58nCIJ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fhX6o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vmv/VN/ir//kyMtKVkrJRlZY5/KUyHl6EbT0oi8iOlizmAe72NtM+QrmZgkaUrsm5A1ADwx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\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4xz+7HdCiqVAp2wpEYK5NyVtsMyPOf2nBvYT53DtihAdiVAQl50iEL3TOr28hR8uTGCQz\nxLYkrOtsJkChM3rjJpSK7I3JN2XMQ9rMDCGhOTweNmV9kjQ2it7CuJr1Lp/L2wPtaDAONE3dOsTE\nwB08AB+0HmPde6CfzdZheDk0FdAyn7mQfdymB0FM8+k3bFBHeNbmdD1Yu/E5vcW9RSLXSY4r429+\nkeT9qeu2N/0n//iv+N3jxi7OPUfHIGlhRsGQxNF52L6h8buN6JyWGWBpgfaWeUAwt/CJTorVjVZ5\nu5RAAEev2RgTiewTge8IKopidIusnxvpUS0KKJmCTCOmAlhnSYk+KVrCrGiIKVN8foKyNot2tYFm\nZbvL9OK4OPF4IHYHMakXnIEnB9f5+eMzL+Jkd7LegkQjkN3MSMWxEdSxrzIgYvpy8+fNz9nHmOtC\nFK1ZQ0rdcIoOHpZoTlUfPBC+4JQDhz5UcVNSaqy58HY9cC2kAbV3hkd3XP4vf3f4lITWW3Tw3chJ\no1EkGSR8vM5glcKwzrIEdEkF0pQ9qkQh2i3omEw5Jkwa4wIPqyBFp+SQ8LdYRJCoON0GjlAsGh85\ngc3QYy8ZGY1FBo8LbItzWgyRHjQ3c479kWuFcxdem9ObkHtISNtQ8joYPTxBdSxfKWfEfa61BlBL\nBJJiEhTDYzCDkGX6qsv8OWHgNwJGEPc2zgrZhZQ6q0fb+NIS7mdEMqncHjAYQzkMLAVco1s0Blqf\nPxdiEosyGDfN2Sx0iTV9TmHT9OBEUR5THhsCGtOOgcYUU0NaikAxoaqTsenhAzyF30YDk+3uNIfW\nB0rBdX6PU1ZrElvKIoXDOvuwGfjrs+EYobRx9kpch9M94j8QDakt0SV4KTEJzK5RwGMUj98viwSw\nSXKoPvxr4T+UKFrmZPUm0+MWjzG+rj/YrakTv9OtntjvAbihx8so6UatdeE1f53V5FklqwouIZFX\nBHXFNSbP4VMRnMhHjbVB+bEe/Le/SPL+9KVz01in10FnQaOl0PYahUrKpC1PQh50ekhNcmb1FVTo\nHqZYNzg9vQ8jbFlgVHKaHqHeuVwulBITonVbuV6v9P4WI9aUuJ47SEYSdGnAwI+K3zrfLpADTHAc\nV3R5wFIhLbHIrsuC98HeG1oK/ThIqqyAYBzna3h3rLO/XOgpsSwLohnxxL7v09y68vDuKar41nhY\nF9BAbtoCxortr0g7ZljfrY1zUFKOCJd2MK6di4QEycYAb/Q9sgyGlqCeNIPmqC5UXTiV+RYvaxyS\nj529H3EIGIbIEj6frAFQiLqWtL676+lLiWmbzY4aSRj7me6dUgqtRYe71hrj8qOSxqDPrmIQVUIK\nts2MjFIKXaJFs64nSjtIOXOtB7Vs5CJRkAm0rPQR0InjstP7wfP7b5Fk1BaL6L7vuB9hPs2JvARk\n4vPLF1DlenlFpFPyQtqeWIvyzfN72nXHZqjj21LYNCM5UWvl08srSYN0+PLpheux87y8UZ5WXr98\nol+u5HWheuPp/Ue+fPkyuzzCr54/4LVyeb3Q2iAvmWVZApTw/IHzl1f8aLTxiWVZuF6vbCkoaml7\ngn2nfNj46fWFj4+/ISFUq2iOjSw/vyPZCG/XlH2llEhrYrz8SE7C0Q0thawZFedyqVjdGW3iv9IK\nNibmv9wXX88FmV1BkxH0RQ8iVZIU+nCJgkuvn2lXxfMGKNQr9fqFe0JEzhhKXhaKrtS9MfwNLQv0\njvUdpudRs4Ju9H2nAdepwRFJ4AnrHdErbo1+9Djk5RVlZbd2W4CiETJxtszDpBDvjKZE8o504uBY\nj1kwK2N3sCtmX6dA63aifjmHnyELQxJ4CTnNNChr+RCbV/ZJmw3YSPY2v6cGS8ivcEeXWB9lfWD0\nPUJp8yQp9Q5S4HRiCCwTw9tnM8mt0PMVz2l2RNv91/7l+vuvgbIPWFNh4DQxjhL+jwigjulHkXE3\nmzfv9AlmyCPfz/+kkO6Yg4ngMtARiOwIsfB/7e+NQ0r4am6ThT5sdmpjKtXNSZ6iMeGTMAbhwVe9\nT5RUIalwsWnWt+j0F5l94lGnnCjw47ROTpklCV2ENxt4ktj7yCS3mIamTBqhTriZ/BJCsyB7pe6w\nCIVEUdiI7n0UfyHhGT0IsCI+PR4zIF79vpfcjO4+5VJBlJXwUooRLVSlGKTckTHIKQhrQbKNRuzw\ngZI46sFSopm2LMq26Lw3kTE3dKEehusS8kBz8By+4um1COlwI3ssOFkTjWhe7L2RPEJ1xxHT55sP\nMgrjGSUggTzfj0xqwk9vjUUaJc9DZNJ7I3QRJfr8Tp8T/EEcSJMC185aEpaUNBSvhqfAi4sqrXea\nNLoIbRiLbCQVrmWlGXRz7FinDNTZrYeq10dMQ7qhusIIH9U4RhTAJmFZwMM/TebYG7OipI2bFCxk\nhG6OqFLESGZBx9OVd0ui20PQ2ophFrLu6PFEsZjyEn6oFlO0oUI1ofn0iXs0wLNMifLtfVInORQJ\nHPtdgqpR/JsnLBTMjDUazmMEMht1FqLYUPcIPTbh0Chq+gRaGGvQILWjzeKdS4lsMxtK4BghL1RN\nQb0bnWExYV1TkCEPCbDVyUuct1xIrliCron3HrJelZhaCtBKRBLsBq2Er956wzzTfGDiNELWmiUa\nIAseJF8hzlpi3DohG3HOvQ0lJAkq8I6Y9Il7gLAcxpQqA4RoP2QnlqCJMxSKL1NtEsWTyiQ0+7gr\noZRoJGUX9Ge125/j+osqmMYM9uz7KzOkIDpUeSGvC+UU046jGqdThGKeSnTF+xjYuHC8vMLlAssa\nB+j9AlGHozmxLylkfVuJULRhrBMxfsoLozwH0KAUXGILU01If4AC5SHQpKpK7YN2VJ62E6JCa456\np+9nsBZGbVWKbuw426SPdVG8X1nLoPfPuDt5S2RX6uUlZAb2Nn9/o7WdXQqpbDFK1gLtgLwERVB0\n+iSULW+RNaHCtZ3prdEJuUes0ookmYfMqTt2m4fHTNkW9ssVGwf4zrWXOATPIqysK10WWNdp/gsI\nQutzdG4GNhjHAVlJJbNfw/Buw0I+OaLzqTlAG4+PTyHPksy17qynR2rdY7KBk9B7Noal6JxeX8/s\nk+pUVHm1QVlhLc+8U2U5bbTe2HIUWNaunF8/8+X1cxQYP/0rlmVlv1ZOz88IA8mJy37gIpx//78F\nWghIkjCH5emZ09MDCef1cuV/f/s93/76V+xvX5A22N49shCTy3ZUfv3rb/nh7YWeJLKvxPjxy2fe\n5w88f/iG5deFzz/9xH49+PLjv0DWkG+qvNI/fjcXV8NkcDo9kZaFow/q25lvv/nA6fGRty+f8evB\nN7/+FcsSYIk//OEPbNvC+fzGQoQgjv0FlzD9igh+fAmggipj29hOW0xLxgXNC7qs5KNh/ULdX0KC\nNhHUKZ/Iyxqo7mns7WOf2J3w12gp5JxJqvRxkHIcQtq+I3cdPNNsDX68IKkEzXDEIVLSipui64ID\n13FFiqMEsEVLRstTdN77daKTF1xzJIX7wC1M4liPn+0NUHLeYArR3EdAZVp4nSBBmwTN8jDpgAek\nPqlABc0R1lzKiebzMFrA/QSy4cSfPwgcss1gXmwgqdMvEplWOd4L8UZiICOTXKF33BsmCe+G9HRr\nCcahTRO+XyBl8uPHMPe7RBf3OMADXLE9PSGi7HWnT+9G0UJrMQHOyxoy4V+uf+slc/O/7p3hocdX\nacg8YM1+OVJiapJQii3habI4aN4IWngLs7wRRT6hEPDZcb95mmCCfuZPNw9f0RCJXEFuMKBxn0rG\nfxkyohSagNmVjk5Wco2jtjm7RHfbs87JjqATO4/HIVHMaA6jO6iTl5U29pgAGZNoF3u3ALhH7oo5\nWQIwIeZkF6Q6XZwiN5xxj6w8hCgBZqTECPmqzwnssDlh9qBk3q4oOmK/WXK6G9/DTWZ4DwldH8Km\nYTYTmXJWUVp3UirEkFgZEh6tMQIkUZbEahY2YgbdWhjccxQSsazEtFbUaGNmKokQcXExv/AS8JWc\nw5dV68xi8zjAt73fv/uUrwAzIDekh0njz9ncA6vpHTDQRFAtcwJPNKaGUd04fHBNsCpseeKiUwyy\n9+rUScQz6fO4FURaAEktJo/unFQimNeDMueuWAMv9nVqMcIXFMCHgEDYpPClFJPM4TZbAXZ/F9xj\nqW2aOAR6bxw0QrkD7eyYxQTC8lfCmtgRPlLJiKaY6FgoIWyCe1oPRUy8K3GN23PjxmDMCd+UtcZT\nMLex+dxqxKaMOUGNXLVoMsv8Xbo7SkI9gys9E7ExZjgJfCL4mZmXEq6zKWeIHLK8ohLT2IJBCmJx\nH4Out7Obc81QbXBYxyWHKkpC7RCk1SAKaso8z3dBp0cuJLmDTcucVAp10icnEijgHBJnXDPnfG9W\nzPVtTu0GwQkoKRoadThJ830ChWuAaCyK3KEhv8MM9yj8TQKa4+YhsxydRolpF0H0/OnPzBX/iyqY\ntqy0tuPrGuPQ3sF2hgqjjjhwTdTw29uneIhKiQUwJTSfWB7f09ZHfFwxVUpaI219f0MorOWR07qy\nWgJ9xezM29sFIZFSIeeVRTPq4aUa7aBZaFVTStiSMY/At17fUOCnH34/wyRLAAVSZl2XQLWWQnND\njk5rEV/Y6wU8uPWSMhA0McZLfBH9AC1hhDcH3dCyQp4ocUmM4zK7fAlyLMgdx+wSZKwWcIhFQ9a3\nt8jwuAEPhsptiI21io49NKU9UebiJiLTBjyNeSjtOMCP+c+MkVPcpzECwSQS42l9jI3FjW0r7JdG\nWjbabWy7PSBLjJTH/gopwuZkNFyURZW9Ot46+XFDSpALk8UkiqyUXKZsafB+feB8/cT5pTNyRf84\n6WpLFGXDOuW0oqclFv2kEYpWlOv5DWmD5eN7toenmOK8e8ZUgiRG4qcvLzw/fJgFeuV5gTY69Xwh\nq3IcV/afGt9++EgphXfv3rFp5rvTE9u28fbpCw/vPvD4699yfjsDcLTOb3/7D/nDl0+Mx53nx0cu\nlwv97cr5yyd+87vf0Wyhv154+/yZ5XSKBWc00rEzzle0JP7qr37H3/7x91wuO5fzmW+/+44//vSJ\n0VpMzPWRdPqGpSg+OketZImJbWuNd1vi9eWnOESLotsDbWLqhwvp8ZtoHqQSuUitM4Yg86CtGlQt\nUUhpYE1wa7Ta8KJoXql1zAlJmN6jFQrJO2NOB0ev9E4U3VnBrmH6vZxDfjmpk22/QBnT+K3RD0kW\n0gq/4JKRFJsCIxZmUoapo5ay0ctDhMASXdcVgxz0x7SsM+9D6f3A6uzXaxRXLge1xsTZxsT5umHy\nyDyXgkUoMO60JCAl3l2LnCtfpk58EIWRZjRvQdczJ+UVk1N4L6WhozGU8N1dL/FnFbxXsMGRQg4Y\nPXsNCXHOvPz0gi4LOSVSC9njUEMlNu6+8zOK6C/X33eZT6hPoMiiUEHDUyRfgx7rYciU+jjQvYHI\nHVUtc4KSUjQT4r+KqdGYh/Cfy+hFQ/wW4otJw1K5U+H+tc84lQPG178/4XPtDsN6nbQvccdHJy+R\n2JrngcznRMsmzvnmqQi5IJyvV7IYMQGOY7qIomjsk+rht9VEm3/WxTmEkL2bsSSllETusLqQk1BS\nSHNuFNibNBCio3/7TmyG72pS1AKv755DLm9On7KofURhJBoTqEtr00/iEVgrHgXZPLxmUQ7rpEnM\n9dHxaiysIakXm0HwUdrlPBtD8annZ4r9AmBJcpctq0cBRxvoojwsgTlwnEUnwWxGkYhsEyoR1MGI\nV2nUFn4Xwe/kzqB7Bqo+qYRUT5zkM3soJbrFZ7MbftuUNkJm1ynh5xnhgBwW0rQbmCbIjYF5lgk0\naJNi6gK9EZK1eTD2myTYpzdFYrrZzChJSSlgCrfHW1ME3ev0ww2cXY3m6StpDjDTUM5Mvw3EbA0N\nqEO8aLBEaF48B7PIKilRp/zV3VFXqjjHxMKLh+rlJqv129RMYhIbTYBZ6N2kagoMD7/hpFI09Whk\nAKVXVsmUOUVWidbF1v1e2F4nZQ5C1eNu7DrQJOHpkpDkpXXhwUcE1qpSfBa+7jQJJYuNyDYUETpt\nZg7GNHaosntHCd9UMmcvIW9HlNN8iyG+mz6nTKSAR6RJq7uDPsLwiEpGJCZs7h4SxwG3G3TNdkeT\nD/UpE1T223rncPQ4h04rZEzCnWgGzvXo+Jk8+c9x/UV5mPgn/5T0/ltUN3oLmsayFGqdeQm9TcnJ\n9A1Y3CUXmfhhwbLAUu43UjVh9YrLTnZjaPzxJNBSAtb5ISYQwWtU+ilR8xbRByKRzO5hwhMPMpzn\noOjslys5Fx5Oj/RuHPsZbxVaRdZHLBkPZcEktL9pXCEXLBW8hTxwWVd6XrB9J2Wlt+imCIbvX6I7\npjO4UhK2PLAuJ1Iq1LqDxGFwdJ3yghXsirceAXdy20DWSJhXv0+YwoAXXhyVCaiAOMSa3dRF6NzM\nqEEzEg2yDKqx4FskO7uDuZCXAgqrRufCb52mYTyUkEOklBh1p3sgrbNEUJyNRvfKaI2+H/dFVnSD\nkkmndQYOBkRjkNDUKemJvh80cUiC1ZBrLmUJyYkKbjvrDINLR48JRlHsWjmOg4eHBy6fv6BLYV0X\n9rcrp6dnhlVsVErSCBjEWR8fYjrXjCbOly9fADj2ne3DM+No2BiUSUGqU4aScyYvJzbN/PU/+C1f\nPv9IcaGer/C0sB+VbnGg+KGeyUumvl1i7H4+M1Q5vX/i2iKUsYmj3Xh8eooudo+Mjm1bOV/PdEnk\nNTKznJmt5D6JRrODfnufZgje84ePXJoFiGROM7CYAqe8URZhv14nRjvDOINdwX922FkEaxlZYlID\nGbH4TkCwdeEW3ux9FtwiMDqSehQEuk6ZT0jdNC343kLWZk5PSxg9rKL3Q6zC8QYkUl4Z8xmNU2WH\ndsUl37vpQDzTZvgaJEQzi8860bgyMarjUkMqkBXNESApOZNYGTbTLb3FuzIMxiQh5UA/xxeu83sz\n9NiR5RTeCa9335K0K5KWyErRE3Jacb+GmdydPEKKJZpRPWH7C2lUejoFZWsG9uZ1o7cAeGSJ8G0b\nIeVSHKs7/P5/hV88TP/G9dXD9A/59cNKl/KzgmIgfpOsxAQ1pzK7/M6Qfj/oq8jXDjUDiMIr6cTo\nEzJMU7n7DdwdUonSwcMzZRKZeFUdtdgDk0aOzNBEGsLhgB+45ng3ep2HfgJG4AVIqByk1Ehaohsv\nxjLS3EumVGlKdxy7Sw2PEodFQWKfhfD6EPtw0YRN74fS4t0BCg51JanTUwWURZRiI+T2vZGTINaR\nlAIJ7SFJ8xl8fSI8V2O0aPqJBOZbp4R4ZLIYhYFNz5GZUbKB91mGTRrgPCyrZdxaSOPGCOkaQdzr\nY7kXuoNx968lnV4dSay5oHgEBAPIQGXMzJzAeLvFoTVvlVs0wypOyikCquekiJlP18fA9TYBd9LI\n9xBeazfa7IjogOn3EGKJxnv4q1QgJRaxkHmlM+YbtUHFqS2em97HnSZnQ8ATQzLmPQ7FssyCKHys\nokEs7i0Os35bswEL8HQ0rEafa9dc1/wmQ+73qQUef+dtOuUkdsb9vqFx/hvDIQuDKELcieLIBZu5\nEml6+kw0ppIegc3d030SlqtRE7Tp4xmEHCws81H8mntIDyVNT5EyvH5dFG51m4S5p5mFtBZCbSFK\nssFT0nsBLSI80ufUycnrIwOP++yT8nebvNAZmiPPcRjHbGIMMwYZSQcpG+/6yoMnauK+XqgGQCEA\nGfPMLIMhG2kYOhxJheYjit8lw5RahhxTkdRo7nEmnlNkFcGS3b+CdZ6vB87uFkVOz3eZapYj4NIz\n8kVGoPhdlSudKo7cCNgTN76HGXn6oMLn+KU1/ofPX+CXHKav1x368I//GTw8TU0jc8OX8OtIGLA1\nZzC5d6LcQhKGOzJlNuKGpzU8NsCa83xw2twIpiHQFdFE3iKUdgynjz1wlikWwTGO+8gwMTCTmOgM\nC/24xGSJVpFUIK+M9hYLbK3klJBloeSFPsYcy8P1euW0bfR+cNSO5EIi3X8vSVCPC6LCFNfGw1Vy\n+KokPERFU+AZ7yz/kBJczm+kcYROfprFFQfrmKZYnG9j6HkQdOm0eqAssWHlmN4NCyrO6CM2Czni\nwXaHadr3ucgMs8hkmkRAO3bcByOVgAq0oKSoTpmSBUnHZmtevGOTLmgopWRyKqxLSA2tB+Zy38+M\nFllQWgqSMuNojNZIdvDw7gOD0O4f5zPl4cSY0IkhDb8ecWCRKF56a4ze0HJi256Q0vFu9FYDHU+M\npOlhtpVZSGpKDIS0ZtZt5WHZ7t/nkjOXvfLu3QcWG5QSm96aF4794PP17T7JGMBoAQvZWyNr4re/\n+Q1tv9L74NPreWZgGO8fHnh+euLtfGavnToaroKMipBxh/38hbrviCjr0/MEY/RY2POClg0fhh3T\nN5aElGKTsd5JfsPs+yzSZ0ewjblZeeD4p4k2fEnhgzIpEZw35TVFHes1cPw+SOspFlhNWLOQr2Jk\nq9jcEPN6imdvDNAcB4RbcTMa0sCiPUvSr4HAJsbUhzCxdtArqR/RSc23EOZpcpWM6hKHJwvJQJiR\nw++BSciVdOBkchJoQWwiF7AgIY2+45S7eTsKRw0amMSzYmPiglNG1xKaf5e7nCI2j1jLkCCRqYZl\n31UxXWMCO3H9SAr/mCimEz+tiWR7eFBSCoM6OqfRfm96yDzAo4KfXxn/4n+EXwqmf+O67U3/8b/3\nO361LVSTeXskMlOMe5BkTINibp9FgxwmN4/LDLL0G7I6RSzDbP6ZTcnLnArcrsSgCPN+K21EgXww\nkJvUjshH6QK5CwfQ0DvOO018rwkUHVPyJyiJhFGIgFPEWVwBCwmWTb+GBODhZhTfbqh7QjYjIqwy\nD+jI9A0KmKPyNa9l6XBtwkg1/Ho2oxb9q4ohacxeAojgrDoP7pppw9iSziYmVLsVbjdnjGK5oG4U\nJunSbK7R0ewpOTIUVW7oZCW5IqNTcmTiJMILlOSr0gK4m+IjmoO6SAAAIABJREFUWLvOfx4hr0ki\nc2pYJyUhqzFGNFi7hC8ja9yLeH4SpfQ5CZIoCDzWrLsHmZ/JwGZWkd/ydH72HsfzFWtjmnEgSWMt\nyzmxyC3EttJdcVd2c9qIOANF6QOqDJBEb3Bt0+OpoLJEZt2UpjtRSLlNuJTIfYIj6UZyzKFGsElR\nk45beLx8TrVySriNicmXOWVS2g0q4kYnVAUiieFBezQCLR72uQkfYfbUmD6/ua6a9wDlWOz9de7d\n6sT+InN6afE0i84JrcfPTiLI8FBFzGtY4N2Z8BDD6R7vt6jGuuwjGgS3KZL7/TmPZ527h/HWnGM2\nAjQ52dP9vXmYf8YBbwEXUfW4XzaDaeca1EnRPDHDKfNdhubhrVTm7yxxXxLCooLOqaXNoO1hxMQ4\nB31SEbJ/LZhsvgtxj4LAXEf83OEWU1eZk0a1SUcMMIQnDc7AbWI+57SdGXw7z7oizkvvf9aC6S9K\nkhfSusyQM+yBwFQC7yvcCCcaAarzYbJtUs7M8ONH8Hgxkm5APNy7OEINTwQ6s1fs/iJyXIESh42t\nBF7cnUxDfNCOnfXpHeaF07LRaiVeqwPNJQx3y2MQvUZjKXG4hxQP577T7Bpnt5wn3bzwVndyC1Nk\nSYlWJ1UrZ7IM8hY0MljxHkGYg/BQ2LiAGTY6VspE0vpM7lbWLdPlASchPsjeSSyUdWWvLYIX+8G2\nRTe9j8FRd1SjszdGp9ZjkojkTtTLGvYp7xXtFdmUdo3OCXlBU+boVwTQsiLbCRs9ioS3V8g59N+9\nk9Y8/SGDh20l58xhG4hQjwMdNQJhRdn9DeuNbXuinFZkKYgWuoMcBrmG/MNj8Xr98Q/hcXn4yLtv\nvqH1hoiFXE4faOtAl0zK4ed4+fLC9m7j9LBQ64HmNTS6tqI5wdFIKjy8f+LlfMYtk3NimLFWSDly\nOaoPzl9e6Hvl+eGR9d07/u6Pf6DVK2ad7Mo3v/oOckLMQv5HmFrTWvj0+oXszt4u/M//0/dYv5If\nH9me3pM1cRyVS9v54ff/B8ce98tVWU8njus5xuIT2Z1P78LkOwy3io8AhWjfsese20zWmNrhjN6n\naTM6xOSvh3dIjNqRvEZhPqEFKSm9Vm42ijE6QgROZ3E8b7Tq6HIil0d8NAotcKcWAZxSIsCY7WMU\noZqx4/V+2Ll1LxWJxsBwfH1At4XUJsRkymKKRf6Fmd1DcEUzvm1QNrRXIIq+WAvCl8SoM0A0JlXi\nU/JHTHS97yAL3VdS2SL6oF1QXRmHRRND7T6pcw/Z0LDBTSEfE87YQMfliuQlpnEpDt2YzYDT6U3p\nXwJFjyAGOicMkqZkBYc2ka02u+cCPUUwbmtRSIOE9NciHFhu0I02p4V/ZnTrX+LVzNl7B1/m4caD\nDULIz+6+DA1SqkypWu/RBGFSF7MIZuHRwO9iu5CueMjK0rB7QYLeSKxG90Amj7kXFJ1Ieo0DIhIH\nGffQTSQdKAYesjIDsml8Ro0pRLLwHaTZ8Q+RdMi8JOe7wR35Gnx589J1M/pM5L36PLq6U9UpLuTu\n5KQxITLHls6JxEPqnMgh35oF/+gDS0sUR9axXNhS5ESZZZo5aUSTzcYIf4/kmNzi8zue+UwYpWS6\nCDqbfrk7qeTwSt2Iu/OAr+4MNboYJgHP8Jv0jWgYqirWbz6RRNZlNvw81o3pSRJJdw+Gk0EkAGAq\ndLvlCMUzs3eDbiSNhqwNQ7N8lay53L+fpYRkbrjTfNwnIWIyf/d5cxRaj6DhMtca80Blq5cohOnI\n9IF2M2pzqgeUBgy3OSmYz/6YDaj4//B4itykXNz3jdshN+CpHnJq1wniis/ns4hHEp2o4m8NuGAM\nMj1l08NER0ixr4jfC6Z55JvvXtzLNj07BoGul5BJ9rajGuqZRwup6JDIJYucO4ARAd+mXydMOgsM\nu4U2zMunHNVn8TZlskI0n5NFA3HIwCy8TOYef89U9IwR5QFIWEZsTKVFnhCPr368yyyMEeFIO+5R\nYLSW4l2zKE7cPPD7CqaJbU7WJAllBFikzclO0DsDaW4easPqHrlUHtLc4YKMAWZxDv+ZPE5nGLZI\nIO0jLsDvjR2bkljDkR4z9a4yn/U4V/j890anIJE96jal71F4z1Xwz3b9RRVMfpwZGGmEUdKS4OtC\noEQFa4O8nUiyzcXK0EmSKiVz9GfcAg1sSbApHcME709xeJhYX3qNLrcopg2fJmmfG8SWCrQLo1eW\nJTOOnSGZ+vojlAyTSlQtJkuW2gyWrVQ94ZrQvCDaSWXB0xKFjg2sdaxDKQ8kzogLvcWDuW6zYCAz\n+hFBq+NMq4H8JGVySTw8faDVincjSSGvmaPW2dVxjutOlujMaV6pptB/4nrNLOvGqZyw5RR4ztYw\nVZbtRG8NGc62FNwLVQKXPo4aE76UkLTHRv70LZiQViFpYpkY7WPf6Q7j8hnrB/npiXoMSHmG2TmI\n0Y59HuLgfFwiXJSQKq2lUE6nAG4kRWdm03k/yKmQX65sKL5kKIk1L2QJKaOcHskM1CqXGpKVfT94\nXE98eHrmp3Hm/PlCuYDnzLauPL9/z/F6JTXjIQnntyAUbmXh3fbM27hSZXB5eeO75YFKRRWO3ti3\nLTaiMdi/vPDbf/ev+bsff8BT5ugHv/rwEewdH7/5wPf/8vd8+eETv/7rv+K8v1FKodbK+XVHt8I3\n33ykLIWXL58p337Ec2bxTLtcGL2BOTUZlkFPC0tZWbaVozXKw0bKMZFTPXE5X6KDlBcuLKHLbzsm\nQV7EDcrCDbBSyhqLpA963WEEdUt0IW/Pc0JTSKXQ+8Cvn2ndyMsWQIgxUyZGbJh9VHR/DVjJ/jaD\n/JxdZnWlgUqOCbEx7AJaSAV8ex9gEAmJTu818P54PCN0xt7QOujakbKgKTZ5cQLjvy3cw2/7Aa9/\nZEiBWyM8n0KC6BVcMY1g5pFvXiSDFlIjE4tCAw08+vAAM+Q+p1kJ93QnP7kv8XflEp7CqclGNZ7n\n4xIBjDljxIFMNAq4WSKh+WOsi4AtRKE0J2mYY/1yzyhhff7qC2tnRg8Po+/hS6NsUST2Bt6nPFfw\nXBjt8udf7P8fXiLyN8A/+hP/6r9y9/9MRFbgvwT+I6Je+O+B/9Td//izn/HXwH8N/IfAK/DfAP+F\n+89ap3//34/mgvYWJn5CzqMisYYmIQ1gxCTWR1C0gkRnZEmMfiuPUoAX3DjmwSuOnhF0GROaODTo\nlPsIykFIYAZGyvMg7xKTJgHtMCS6xgmf74+ivd8LpiQRgkkPL2EnKnH3wHvrTPRKvuDq08cRjYf0\nM6/BGIM1F5IrzRvJc3ij1CNXygKvXVNIU12d0VcuGNfrEjhhAVUHwguZOuSUad3YmnPxmOK4tXsX\nPumUb4lPqWscJNtENh95UFzYBwHDwEI6N6V0WUN2BYCHL9YIT5+IsTIxyiPUC0vOWI/4goNo9pl1\njjoo6zIn3sKOwaSAlpRYtM1gzxLyzBGFWptU1tr28It4RyQhRGe+9SWmkYQn84ZgfpuFd60VkUxO\neSo8xvQ8KSrzIKuFMSKkOnr2IYmvU1qlaQsRm8Sz1FJ44noNz8qSDCmGsyG5s5IiQqIk6viaEWZT\nCpYmcGZ4NHUiAUgRMToDxGauXchYi9xyrgJo5Kq4J9TnPbOQiZvEBBOiMKrJWN1ZBbqG7D8JkSU0\njEWMJkqZB/OSUjj2mvM6OjuQ3VlJqCf6Wm+pEROFH9O3czcOLzS5Yt2QdU5oJ7zhmPdyzGdFmVO+\nFJ7t4YrfviY9opkxhKHGSZTizjHzmQzQZGTXAJfN99SmDC/Q5zZtJApEg3SYYCoUEUiOeweJAii5\nsBoMbRP1D4cf7L7y1hMmg+xK0YRIp8wm65BbpEA0WhYPeAeiMen8GYwmttBoXqhm3MN353NSqCmk\nrMUFS5nFjMWdHDiWUNR4SP9dgnCZ+sBTSJXdb7TQ/9ul+f/V6y+qYNLTI5yeGNNfgSRoPrsXO4yD\n43yAL4HzXlfy8hHHGOMg9WOOB0E5IUVYloJ1w0xnenkLaUxtICPkPHmjLCdyLrTrT/RWqfUa5j5J\noJCWBNZIj99A39kWqNawbpPgtuKp4GXBxdB1RVOG6069nJF8xi02hpwSvXfqxaBNnLEIpPfst87i\n5QdsHNHpX5ZA2ywfwq/Tdur1jKpyenigjkq9XkkpQu/GGKTtiZQ77lAvV1YfjAG2KbVVWh/khwfE\nO/16Dew6s+umsYgvy4J2wXXAUqj7lWED6wXJGRlBmRENGMRx/hFUWR8eKGVjef8upoHA08fn6NBJ\nFBnH+UJZt5DUSYyEkzsvXqeEcmDrEi/75Ur79EPIw1TZ3SjrShNlIbFYpn5+4e3YGcNITxceHx+n\npGywlQeen59Yl8wPP/4dbvCYMkONvTYuPcIa++PCm1vQE8vG8pi4HDv7D3/k+fmZp/XE+e2NA6iv\nF4Y727ry23VDxHh6//H/ZO/dQm3ttvSsp7Xe+/eNeVj/Ydfe2bsq5CCEKIqgIUK8EASDEMmFQcRD\nrhIE0SCiCPGIKAoKIkE84Y1RiRdiELyIliBeGSESQwgoaiASUu6qffjXvw5zjjG+3ntrXrx9zLVq\n51CBquzKtlaHtfe/5pxrzjG/8R16a+19n5evfCffnPmbv/nraWm01vjh66/owNvv/YByt2PXM19/\n9Zreu+SMwOV4Ii/BuDwv6tDk9NlnHM9XnuZkzMHlfMF843E/ETkotREUjiO4v3vk9devJUcrCe/f\n8fDll1wvZy5nNQdKuePhs29KTeedY0yen8/Kxeqd46oJVGtNW66iu37MM8dV5nXOV8ZM8EK9f3zR\noo/+TFyU72X+IK9c2YhNSNWypH65QvXGGCqWr6upUR2/uxfxMoLChT6Hirp4ovjGdvdIWpNkYKyA\n0H2DY8IhX1EvmzaKWaXIGyHfI2D759CWJ+GGaQ5hvjHlpk3QRkx9a9IHZtDslbrNBljipxO576z4\nDH3PKY+DnjGDnBe4PmFZV/J5IU0p8RNNevr1qvvMzZ9YbnQlYF50bMaUDJhCVnV/bQbW7tnr6khq\nvEccg1nuKZszo0O7V4FWCrMflL1SlwJlTJnmOd39GO7uv+z127mx5rX+VuB/AP6r9fc/BPwu4B8A\n3gL/AfBHgb8LwMwc+GPA/wv8DuBngP8CZWX+y7/UD7dUWOPYm2xpc1JDneyCYUNQBawtOlTQzYh0\nPKCXfMF1F18SvKkcGfMixcLax/el7RdMoWPLa+qra2+uwqqZ6GlzFePuUhdEQieYltpw+8bItQHP\nQsbN69GXbLWoeE8nqZKfh4iLxRb4wfNlwpRLTmQ2KXboGKQk3pmTU5VSwoE2fE0a4N6MViutGNf9\nntevX/PZ/U5d+ULGUIC55cqdWXloH8nPcHX3b5Io4MXTohxAFY3X1fkvxelAWMMJ5dPY9WXzp985\nySEwwViTjeqN7ImPNYkzX82+Rb49FS79wDD2WthQZqG68QqhLu4cQxtqX3JnphpE7k4rIhYWjQdo\nWyMQvbK40ecC3ehg4JFspRCWWPQ1uVjQHTTFCNPz+PaxnB9ka7MsyE0axJ3SQork4Ke2MevBVlQ0\n1SmoEQx6HuCyLFjZdY9LSQgzJK3KXHK0EsjTd5urr9e4ZHRGeXkv5QPUREzY8lx+qDXNMZi2LSKg\nU9d98XkeqwCTpylTE/o35jwneA4uVjjZM6/KwZ0XohasbdQY5C0sfIjUG570NBUbOMXgzpLHqJSt\nKmh4ee5iQkXZayMFetB0BRXgc2hynPI5vpmToxSe6MQoXF1TntVbgTTauHXwbr6upK3JryZEB1UH\nCEdRN7fAV5Z0MNbNIwwsk87EplpvYwwOT8yTUw2GCQIycjJD4eyVm83kBRuELWofrBebH6Zsinxh\nNQv1cypGsao4hfhQ6Nwkpu7OWD7Nsc6OXNfbRIXVNCRlBTpwfAqu/YvXC/Tht/ztbJ9/Qw+PzJUV\nhAzV3hAqtzLjmdoq/ejyQLCCbc/vAYO9gd/r3moL33k8Szh6y4moRTeiSJivoex42yn+CitQW8HL\nxoglPbhclzTIgAHjgpXK1nZIp552rodG3OUYIgllkg+Pa1oz2douWk3KCHo5Ohe/pYgX2uwi6Zlx\nwQVXIPGvvod9+W1tEj2FBA/dZK0Jxw3AcUA9gykYMWPDrUE1whPyQzBvzGRvjetQsK/P5RcBIi+Y\nFXKkfCpVYIzWGv35GWqltqYMq6VHBhRgF0HvxwoT1ZN137UhMwyrCsa9Pl+VGF6r0Mrjyv2+8/T6\naz2cgNPDA+fzmf1uh805n8+UtnMcMsYe3/s58vFL7vY78rFyf3/P5XLh8tXrl5sNoe//+OoVvV+p\nrgISdAG3psJwP514/sFbrtdnvvOtb9G2O3747jWv332twndJMXpM5UB844G4HNyXjbo3Mo33754x\nBtumTbuliH7FnXkcktCVynLW4C6pgRcZh2UATeXuDBWpXA9s23R+R1DaCXeTP8mdvBwrlDQodsfd\n3R3x7gfwU9/iuF4XtCOw7QRxxiyYlytwgm2n7ieIYNwKF0PT09NpJZAvI+k41ub9WSOPWiHrwnAj\niev1oi3tlBSl1sYwEyBlypuWE1x6Gn3vh3uSZB5XbV1vK/rt5gDbCd/u4LhgMahlE5Y4guwLYDIn\n5IRahVMeQ4XguHkQFArK0xv44tuUdkfGrQMnL8joC9RwPGHbJiBJD3LoeignFR+GSe4WoV/f7GXj\n4C5gzBjjQ+D2VpfX6iZlGZQuz9FYhKOXf2OmSZI783K8vCdWnRJKqWcJuYxQwRVT12kfuqftmzbC\nFvp4roDbTLxt1FSWCW5UL8TTW8ZPWHCtmf0h4O/LzN9qZp8B3wf+4cz8b9bn/0bg/wB+R2b+CTP7\nXcB/C/x0Zv5gfc0/DvxbwLcyc/xlfs5vA/7kP/Kbf5pv3p80CU3ksalCvieJz+RSoR9QXTOMczF8\nBGV1UVlegFP7QLqCilvDcnILj60ejBUau3GTyi1pdC7Pgol6VfUiJa8CpjkjDaWXafJUFiEvDQF1\nFgf9ADILVMicFJB/JMDDiZuc++blSW2k0tVJzyWZ2RdNT5ufqaxEDCLZbtAHMw5TfIdZKiepFIjJ\n3dYYc1BCsiUViscH8MragJZSSCtkDhUHubrwLLBAcXJ2dejXdOUm4/c+KDap7lTspaiITNH7pN+g\n2G3WFyLmpa2CCWU4TjXXmgUjFTy+r4aQ1V0kVzPcOkllhmPKOMBMDalbAXiMTitGWQG4MxWLQE62\n6lziwEMN1rmGoCq65bPSe37LXVRmTTVWNo/Oo5O7PGrA/cl4WCTW19creKXPZKRLRqezkm35vPoo\nWJ0cQwqczCWZWstp63kl6WK6gA5prvgKr4yYjAgd85vfah1jTWmLvKlAHwNck5iUWo9T2MKTI7mc\nTaiwYfKZZ3KqCk6tJJdYYb8haIKZcQbmCGUjmQBJPSZRbHl7DNLl/ynADJonOeQJjQl13fcjgx7G\nXPueIwVOOjJXMQ8f77iTiWXB0tf1GcxUsHSs5kZZE2ePeUuoInRR6lG7nl/VnN0Wkr5WXYtLBjuX\nbLaG1FWDpCxppc5nXgrVnkH32xZyXQPwEh0jSaP+3EJ7FeJbftFvJoLjpJTl3V2vYUTgN1mx2aI3\n6++dm7cpcLsBS/R7yE3tawuigvBNH/yZd1/DJw/TX2L1J473B9RvUE+7PDMYIwbkoM9DaEnUPTZz\n6p3GqLMf2Ol+oVK12ctlKrRWqHdf4gPdzJbO9To61jZK/4a039dBrzJoH5cJvMdCnWi3Qts2Zg9K\n2bH9c8bxpIcPcDmgne7JmOQWjHGhOcT1zNP5PfN4D2W+mL4Tg9rwY5LesP2BXhWWWmvhgc5hK9/o\n9c/BZ6+wtiRq9cScl3UxGFzPwg/XCuVLddISwQpywrTVmFMK+FiF4vlygVZg34nng7KCUWfsbFV5\nTDknRFdYJuCt0Tg4nt/r+9RNDaGYYA2rRRTB7BzXK7U14nJV4XJXYUouGHaHW5LjynEkJa6MI/Dt\nkW3fhMe1K3evKu/ffQ1nGMfB9lPf4fPPP2fOSb79is9/5rfw/vmZ/OFr3v3C97Q5XA+Rz169Ynv1\nCnNhytupMsbg3KR9Bzjmhbfnd8ynN4zjSt0bf+7NzxPHYLvb2R52Pj89YqXy7u1bPvv81Ys0wU6N\nV9uJ93Ew+mRvldbBrsr2srbxzS8+Z4zOfIS3b95y5GRm8OUXX3A5rgsLGlwvF3KZRm3bqfebcqKu\nZ0qrjOdn0ouQ3mbgjdPDI0d8jWcSr+6V9fAA17/wXV791De5ziCPK7Zv3J92YlbO54P68AVzvGd/\nFMLWwlUUAg93O30ezOhYbtztGxjqUFHoWYTlBY5xwBi4T7I+SqrqRpqKrxH9RZpj5jKfexImEEK1\nSbw/09omXXXpFBoxHd/qyh3Rpo333yOm+lI9P3S3zAwrJ9KVC2X9Sd34FYRpXsg+8LbT9srxC38W\n/+xz5vs3wv5iKEvK8RIUn1BP8okck9IgTo/Y7MzLG8wg5nKdmxNZuVHFSpEUJmOAFUqtkixcu8Ky\nU1Nt6fU3vDb2uslzsLpq8mIehGlz7cuD1S/XhVy9/VFOh5Uq+MQcsBcsC16WV2HqwUwkeT5jcSVL\nY1S9f7UU+kzy/O7HcHP/lVumwJjfC/w760O/HT3r/sfb12Tm/2lmfx74O4E/gaZKf+ZWLK31s8B/\nBPwtwJ/+K/3MRmGfMAq0tqurnZ26F+F6N6dWo9exJKHBcGdumnyU5T3aS9WmL9Wxx5OwQ2Z+c8FL\npy0CKfjK+OoZmN2xWr0wVTTfwlsVTyRogGWyE4IPGMTKSlNRL/etl8JdavJkU5u+cGjreZam1zan\nNm3NtZH31Hnlrk1jeOUckvEWW56sCHQXhvfVPwL83Oh7k81UxhmwHWqMln2N3rLzTSo90Ia+Od6T\nrQfHpilDbYXWBT2JWP36MSmphoxZcK5Jncme0E63gm9ALDlVGhaVZgOnUtxoC9wRy6Qe87IaVRuX\nDPrsWAzu8zYBMK5lxZrE88vko8yktLOiAj6vWEgOPWjMKMxwzmXnksEJKD1Jc4bt1EyOEQwGzYPI\nviY2B2BEymdt1tiXryiBYw6eVnD1VsBaoftglEEpk6fTiTfta+ajvCNzwDVXJlaBnJ0+CjEPtlwy\nwbOQ351JJ8lFL9S0c+JZsFIZc4WQLnpsmuSO7sbjqamIobCVxttDYbsRS6oYkpi2Ag1jUpbXSceh\nmEtokIlZIa7B1Quzy+eSmYQr30dep+DZBOIoNjG2lZ2WCNEgL2m8yKjlZ3+ZmKEQ3HLDmLsaCWYF\nt6bii0lW/ZvLFNjAlp622Jq6hQF1ARgmQhppGpSmhmoh1QR01AxbHvywKeksTp9Dk8+SEJLU2hD0\nJYua4w7Y1LES8jw56Oue4OywPEMaMIyQHNBMRVpG0kzF5owKBFZumP4gC9zgGgCXNEqCUbgONRjC\nJnN5n5pJbm+RCyuzJlE2YDVeZq7cMtZ9MFW8gYos0fP+Kh4Iv4LrJ6pgsu0e6i7JyujKj3AXEz6D\n5qKZRQZHP9R9vtwq2UJ6iq6FsZX6ou2NYzKeD71LJBxX2t1J6O2EMWUtpF/wcOrqfEUWfHWE+tEZ\nY2B3lR5XtrpDiq6VY7BdX3M89UXkKQoFy0lpd3pQtAdJoU6VdhIcNa0wTuflMdjxy1nqvAwuM5m2\nqbhKo9Z7mQJjkP0KoZtO2zfK3T3HiJcgUiKky368E7DgRpmJCVeFiro78xjLN1Tg8RHDuJ6fMSbX\no2vTmXr4zGcFndZW6e2B8nCnsMIMYty05iLLARze8Ps75uzYPCg+mXHSA7U08vJGUsZSONWdWe65\nlo3qg+d+xt259klc5R3JaWyvHrm8/i7PU7hNjvd89f4vCMnaHvG7B0kt/KCY83Z0ylc/VPHVr3iT\nfra4QlVba8ToNJfG3WsTHv7VoyR6l4N3b9/x/etryraztcLlcqGOyl4qbpV3b95yOQSnKKVw3iWz\nOtmJasHb1+8kZczg7u6B+7ZzPp95+/2v+OYXX3KJ4BgH+fBIKc62yYuVqc7aq1efM2LyNoz7x8+5\n30/k8YyXxpunJx5+5tuUp4PL8zNzJOP8Dvrk7fM7qBvl4XPuto33r99gceDtxHh+hxNcvv9aF15p\nmhTNybvrQWlthdJOzucur8TdA7Vs1M8FGDGMOh8X+lRZQPitgNmotckM7M7sXV66UvDmtIJkcqsj\ndT3OMozWIkrQkge4GTG6CpCPyFEaz8nMrUiBN3KttkraLqM0RilNReju8hZeRXdMmv55WRklRwJP\nROzEqLC9133CkjEadqyJa901tapGcRXlCjWe2ojGIvyYqYBZnqWZMtBjTVPWmWTTRueYkxznNb3W\n1LGUxC3pkZIuHgHzGS9gfAZ1XXujk/PA6ppW2MrQuZwhh3BZVvSg3zYVqgnElcykz/VA9Y8MzT8Z\n6/cAnwP/2fr7t4EjM9/+yNf9AvCd9d/fWX//0c/fPvdXLJhiOL6dmFaUYzOmpoEpKbFFUEoKQhOS\nPfVQWr1lQjFOsfEe2FJSrkDe1TCwur1QrCw/mLGHKSMmSUYevDgcyr08vVXS4hmTOUMhlSZPBmgy\ndI0brKBRM9iMD9PQJeczIGdw2JpGRqqTvYAC3dTsUq9kW40RKbAweEhlxJAQSEJkGId1DEnpAuPe\njBKFixktXfLhojDmba7sm4Rrqyq50hkhOmxiPLgId2NMsqgbX2t5kURFzhVPmDyOk/KRAM7OUWDW\noMVkINR/myCwy5BUvxWucwERxoA66CU5z3c8xCuwnVYqr+2yCsGkVk0Bn6KpuZrwPiY2nDkGl6Nw\ndKj1kZ865DVJn1yGKIg7SV3etxLfV14bhVkuPKbxYMmprkmkVbZ8D1PPm+3VkghObUqzTiwq1648\nnle28e1v7Hzj/mDbC+wVzsb7U/LVGc5HkEen1Ipn5Xq+pojDAAAgAElEQVRVYfF93kLsFL8nzpWn\nvvHUncyrvJGmbMpLLBhJaJp/nYW+vDhHa8zjoHQn3JldMQ3FFLY6M7nGEJBh6H1IeIFOiOR50NeE\nyeqH7Wwxybhu9M+Za4rqhVIqEQcZ8uAobwvSjdOcxA1hbirCBAFZk3xzYk3R7Bdt1p1+DCIG0DQt\nIkTPWxAdQw2MziJATk19gY+mxVJXvHwcZ1uNDfOxvDsKQo4pOeNhlTDJbDe29dgULGiSS30iNPr1\nFh6MscUCUwQ8uxoNI4ORdb1+qCh4N0jcg5qBhwiDRCx7s62954cDYhnreCW45mKWjbo6elnGkuoF\n7eOqZ01XE8hUU0eFtrzJNvU+blboIQLmj3P9RBVMRLJtDzqhFl7TcNyV3TKOC8WdUmQaHH1okzQn\nFBOmMlPZPYwXM3uplW1faPEchA3681v624TTHXU7aTO310UICdGp6lleJgrmuyQ27yTNuzyd8TZf\ndNZXdmi6kJhBtBNWHzXhKgleKXXTDeT9V4Qt8zgblYPx5g2+L+nbtlFME4sZhV4K23aH18pxvdC2\njYxJaxtzDuYM6maMSPZSmN44rmfq8+CYV0ozwofQ4OnMy5W2b8zxxHy6UtpObvcf5D+lsbed6ANK\n0Joz/QtGFsnFjmfJH4SRWQSuiTcnhgzlxZt+Hr42GRf8LIphzk7WR8rdZypmMol5MM5PjIXZLacT\nMbSBfHh4INM4X87447eEWp+BffVDvHzOtskoexyHirz3iker7oyQ16e1u0VYGkR0Rh/067N6nGvj\nfrftxJj84Lu/IH/Y6Y6tVubKvbjOwbxesZXtFMXoc7Dd3/Hlt39aRL/rlW988SXn90+cz0/kmHz+\n6hVtV4H+/rjyxeePPD8/8y4O7r/xivvaGOcLz8/PPL9/WjdlV/5YBtTCdneCy1uen99wvpx5ePwM\ni6D/8MKld/jsnlYr/XpQvr7nm9/5Nm+fzrx//8TToZDayJV/VCvewEuhbY2SQoIfx4GNQZ993dAK\nxZZu+vyWaxh1xovWefgzMhtv2N3n6mKPoWyQPgSBsEXdygFjSHZGV2PEG60YbduWXMPBFBbNEKXO\nWyEz8P3uZToMMFaWiSY+mgDRO9MOIpTJgbkKr1JxbytIsYCvc1sjU6y5plCAeZD5hbp2tWB2Zo4k\n+0Edz8wjsP0LwEU4DG3wMtftxpeUwmKFOCZW5A9kTQuKr4lAulLo7z5/QVNnBKNf5d04PeCbQxqn\nvWKePH/9BiKo205pGxE7cx6Lhif/go1U/IElDMkM59BG21Ym2o2C5RYLNPETtX4/8N9l5s//El+n\n3dQvvX7Jr/nZH36P9loTSV0byU8/PPIbXz1KMjMn9uIlC3mLLGhh3Hvl3A++ZOOrHPJABKJamTyM\nc3ZWggbuQ8Q8g5pNeXIJTIVJYk72zmauc6UlmxtWlAWFG5cOhlDWwpkb1+NQdIBLGhWzq0O9lLjD\n5CfIY12zrkm/mbH5lf2+EeOgtjN13zAK/rSQyH7V5Jvkbd3U/Z6DsH1JgcApfDU72Rp3ceaOwing\nzuR3UTEWeDHuNhn8naT4mbo3cMfjFXMOOl2yd1MwKkUeRDs0rYDE7yWnD4Pqk3ugWeJDskUVfWWh\nzCs5G1sxtraCXGfBs9JCUidr11V4DmoIVX2TyLkHlo4vg37fktNdxdjY8pmy3zO9YSOwOqEE49lE\niy0blwOejzP39YE+TdCBfqf7kQ+u14CszOm8K99Y8Ak43smjErXRKfR5h4+DWR64hBP5zPjzG2lf\ncPhXy7x/z11eaEUSxTH25XkaRDasGvfHbwC/4uXgKIVtDrZ5ZVilIxqs4RymWJM5g1ILxYKohUmy\njSpCaaJQ7ZDHJYuaQe7O5goYLwHmxpWglQ9B2mUGrUg6SM4XqM4WmmYYmsJmsSXX1KRnuGFW8Vmx\n7dB+MpNamuR0EQq9XTLQSbzIbVnX900GCTef2JJx2kEpS+7a2wuSPQzmTLq0ckJs24ohwLg3FQmx\nIDAgkMRhEEWQGEEzwErg6TiFxhTFLibTXcWZKcJlkEu6p8kyU77GiOBc0H6LQotVJEYSroYNZgKZ\nWKrJM1VUXk33A0NADSHf9b1e3hdzyoKS7GuqW1lhviTHmlB6GuPjfpzZy/QQk+drrsbidy8XfnBc\nX27IAb9IAvrjWD9RBVPOZ47DIdYG3JbWXvq7hft2rL/BTnew3ZNXTXW2WiE71+uFYhoX1nbH5bgw\n53uinkg7ARu2tw+hkZnk5Sy+/b5DKcQxaNvOiMrps1+nydJ6prpXrFZdgOdnSpXMq9RKMedUG7md\nOPqZ3g98r2yl4aVpw10S457mysm42mQWJ30Trao2+jHI64VZNnXN+pXnN98VaIH1dA+lIkcGc7+j\neWEzeaOqb2x3J47aJfGISS3CmnJ/j40L8/J2uXIbUQo+nlY4XCP6G56edSGWcuJ6mdAHs0+2dmJu\nDbcpok49cR7B6e6eUY3xnOR4Yh5XSaKKSwvcXpH3TWShGLwqJ3pMLr1TqnpLNdShrX1ifcIJ0grX\ngH55VkbTZVDv7pYkxChlcnn+LnnVhCLNaI9f0Frj+elpUf2M0RMrJ7wkXna2reE5uSvJ6e6Bax+8\nP7+nXy7qeHjhenRiJlFgG4Pr01vuHh758js/zVdvX/NTn33O+6/fcj4ufP3d7/Hl/QPfqo+cz4Nv\nf+PX0TB6TL73wx9wPD3z7oevOabCLIUETsr54Eh4vjwxayNbe/ELJcmVIpjA5cLzVCdw2+55/ear\nFYZYlUN1PbheD+JyJZ6f+bm/8HPs96+4O91zjaDtjcKJVhvv3j4x37/DLTinOuNzSO6QlrT7e+a1\nq3hKdQCTZXpud+ourk6btyYZQ78uPH2nX99w2/hk+ML55yLNSWN/vxX69Yn+9DWk07YTyYaVE629\n4uCKjUnepGpjMF068JwTQ/lexATu1AlHpmRf3ocsHR/OPHfKtoGLgglDk9twoFG2nbx7RcQQvO8Y\nRFzAr/Ro1Lryb+pGDsN6J/oTQSdLxesjyYbHGU6vsHGVLn8q28nnoBQHV6cQIMsuoqMLuHHzaJSy\nMWdSMsjjIshETJ6PRAhyGb5598QVbRTxqinhVMFq7Q5fGVnF9V7FvMo/NQObNwS2E3WHsv/Y7vG/\n3GVmvxH4ncDf/9GHfx7YzOyzH5ky/To+TJF+Hvg7fuTbfXv9/49Onv6i9Q/+pu/wmz7/jDIulGKM\nceXmLFIresE3XIChiBB6Ogdmg2GB58HfEODxQN4aRawpIBsjQBkzcw0nTVjlWMCDJp+OJHhJ9MkI\nIwr041jnNCuKQvKbJNizMdwXEVMTF7eF8Y0lnlvSu2LaNEbAcwSjGlsml955e5Vcx7wQz/KB+PJW\nmEv+vtVG9MsKe5/kODR1cecuks2MQmX4zvsI3gfKnAlR3WQeL9TLxKqeb7UY1rWJvSGiY03ImrEk\n2GuTXZPaDUvDL5Myk6Paaso5I5yzS1abJhmwGuBqrIbBNip7QCe5pq1JW+MaTRtLUjJLdDFPV2Bt\nmUk1SdKtTPy1Nr+v4h4vxuTCFvXlXuozsQUQwA3PJg+VCVD6OEVLC9uEgU5BtQ+ghGNpXFPglmd3\nLmZcHchdUAIPLu1B0q+8MvPElk5hMhL2obyg6k61Qkl5kt2Mt/uF6Ac14LGLYDtKobmkUhsNZwpx\n7r6mhFdigbKmshqIWJj9kgrVjWP9XURQ4esdM8EktiKgzV6LlC25trDVpKBAXj5BBQJcvs6Yg+u4\nqjHYA/eqEPFyG/YvFLl9yHIqy5/j3LaYTvV8objZghzcIBciOk5KLMGDOSMHx5pMVZMrYkvJK48p\nX6KyNCVoc1NuVzhcUKHo7jyjpluSfE2So3HLZgovzLFIlTOJ6JQy8bk8raarxiMZ5qQuZ/qQ2mFk\np4obzKCKjLkmOmVJ3+q69gtJ9ZAPM9d5Cngap5rLfwfGRH2JNY2bzjRN7UYoAWoiwMNGvHiYIlOE\nP4yeSXrjElJnfPO08WWT9WOgRsfzGLx996PCgb926yelYFqhSSYcY1xgGeDMNqxuqzMi81nbXuEx\nsPNrRm54Osf5PRGCAZTiHL1zHBfMC4yUl2M+kQxyDqabRoZt43R6pM+D69tn4YfH1Lj3vpLjSQjK\nNWVwlvQog1Z25vVJHd99h0zenS+Qg+gHuLE/fE40Zx5G80KJwcxJTnWG7vEXCaKNswAXdePA2Yvw\nzeFOTqM0X/IJW0hIwSI6MuzGODjmleerJFK+b0QfH0zwTEpqIkXZwYSJTuu6wZUqco4/ypdkxsyF\nOd4b6ZMsjfuSlJI8PT/zdH0jWci48up0x1EaTwRbbFhVATcJePoKf0rm0E3kh0UPdZpTUQZTzIkf\nF3yvXI6DfH8wIxmlgVdyXDX+nioool+Ip7dsthHNqLXibozn91wzmf2g0ej9YN8q9/eShfXxnuP9\nWSHId3d89fo11z7IdXNT8T3Z2yaTJsH17dcwO5c++Pl3b/GZnJ8unEphT+PqyfvLO746vsd4eoY/\ne13ghFSrpDTqq3v2e4X3jqumYM/nzlYbD5/dYSFazdu+00dX6LGLqnZ9814hf2ZMe01m1zEIPVSt\nNRW4p21hQyf93ddcZ2A+mbWSvknqOdaDptTVGZWp2bwye6e/vUi6ppELwt5qCjX6WYAFT7gO4riA\nO7VBCYUyZ9lekL4xDvJYhL2UTMS9cIyr/H6hQqKPwOINM99KbkdZUtQKtYrodlH2mPDZRef8TDKf\n1EQwh+nr/HZsQNSKNcfozEgVWJcncg68SMYWs2riNAeTgbWKs4Pv5PGGPiZUZ3aoZZMxOBvYIyeb\njOsZeEfB6U8TiYdkaiV1HMdQh9SB0bWJZIFJlMcl6MPMAXYL5JS8SrK5Cl6VBl+cUav8JKtLqmOj\nPJ28vJc0eEwl2q9vcys+5blOaTIu70Qq+Pg+/Nf3+v2owPljH33sTwID+HuAG/ThtwK/Efjj62v+\nF+BfNLNvfuRj+nuBN8D//kv90P/p68Hd0xNenRJ94fobpSsPSRe5CHPuiZfJGGX56ZK7bFgY1Rst\nDrqJaHbN5JKTa0r7H5iABWuTNpvoWMo4CdJCXgTf9LOmLxLeSUTTVVwJQqCO9izyVHk3Se7k8F4x\nGy4VRLImQYUXiEJoEtBi4nYi3Zg+2VOvPdb0Sqq6SfFGCaPZK3XYSUq9ncOGtU5N1Ny7dvCG7zBS\nEuqSSfGijMAVHF8TOg+AMUaSe6wutfGA88zkks4o8hRdcy5QQ3KYca3ydZQe7G64x5LIagoYQ8VF\nY90Xi/MeGOlM18QuiiA2JzNKVcymzOqr+JwKr25F2XrFFZiutBx5iWxO7rxyqWvzbU5bEto0YcTN\nnem6Tn0a7x2sSJKY2V823sURZcw3clZmwqvrlS8AamMUybT61MToFjhsxbE52YrTi+Er1yg9iThw\nS5pBieBbBe7uK9tMqCJ3uifHZIGw4N5jTXkGmVMSZ68cY6jgaGoyZ6oxXVa790A+8bJCxCMms58Z\n5pxHsKHpSeZg2IJ9pPxKPdckpqqYcVvBqgZ1r+omFxffiyRyCm6yiqNYhctkPZJC3iQBWCRlUwEC\nZh+HCLN+l2SaL0qjsrNSidXchGexlE0KZZV3jjX1cS/UWul5mwyxJk6SkcciW4aDNTXIW2+Kv7IA\nOhT9m7ELQhULWlGqK9x3TXSrFcaysvhU/lGmqj030RAJPUOnB8O1N6tD12Fd3ji31RyIsQ6aCgtf\nE6ZcQIsMuRLdqwrK9ZxZ4geMFWS7/uYu2EhdUK2GcVRFJ5R0jtTQ5Me5flIoef8o8Ed+tV/Hp/Vp\nfVqf1q/h9Xsz87/81X4Rf7llotz8OeCPZOa/9COf+w8RVvz3oYylfw+IzPwYK/6nEFb8DwI/jXKY\n/pPM/Ff+Cj/ztwF/8m/7zs/waj9h80QW0a52P2hdJDJ1bGELp9RJLYM2d0kzTd6aiKSUykMfjIKa\nH2Gc1wasz1AI5q2L646JVfchNBbtQy6+NigZazNllCGgAm6EGXdZeLRCzysPFHZ3DrMFE3GszyXB\nkXcpMC6xutOoAu0ENgfd7ug55N9LTcclmxEF0NKX4TxofiyDtyacoNdeU7ksuxWeW7DNZDNNxUSk\nWx3tTN55UENABCuraWeOT3XkaynUHmRJNgb7Or4UkR+LO3c9uTajzmT45K5AzcF57CLDujLjRirP\nqtUmP0camznXHPQIIaDNGFOQCsdefFMlRfzDhgLpV0aa8qyWJGkv7Al3y+d7I/SNQ++mF9E2q4mC\n2LxgfbKbCKojg4a8NplJicIw+XOsIcIgmp7nCvK9SdAOl5+aTHzAYQoQvQ8Ydktpuo29k82dPeIF\nQjBKUoaw827G06z0CMZMahXcw1ITPRUgAy9tBdV+wId3VDBZ6hjFen26PlZ4qu+8m0kenbZAJrZC\nxmsp9JQvKCKVHbs28r7kgHPKl5YZDHP6mKQ5Yg2rQKkr2SlxjpAM+3ZcM6WkkF8wqRYvagWLW9Cq\njvtcxyxRweT2UcRqJFhh5IJBrA/7hyMt0qbdmlewm0h5A51v9RZEHcGRyhJLAlJBxTFjwUtu8bf6\nE9RFWzQqCmXPVN6WIEX2QnY1WOhxx1MFVMbgacl0Y0Ul2PIgddOkbmayW7K7CwhWjb6ufcWjCVR0\nI/5tqGgGOEyeqshkmHGeSS/GmJONJTdGRZMnvBuDP/X2DXyi5P2i9bOIevT/AJdf3ZfyaX1an9an\n9WtqnYDfjO7Dfz2v3wn8BuA//Ut87p9BjeP/GgXX/vfAH7h9MjPDzH43ouL9ceAJ+MPAv/pX84Mt\njZyJ+ZWcwVYKZQTm6pgXMwqTqEVckgBqBy9cjkFthRITnwfn1Xl30wzijoCc1CJJV9n1s+Yc9JXz\npE6tuulzDHora9MjTyHu7KUsemOsTY46zUdNtjwYNTgdhZGixE2/tcVVrERW6pzagLkkayU1NdhR\n6KuaxuPFHZYEzYxiQkeXLVceiyYxzVZmj36M/k0OXqX+TbNOqaLsbQuvbExeFadEiFo3AQK3yamq\nYJpxoZ2K6GwuSZ7XRonQ5GRKtvSQRtryyKDN+V27rtcOFFH0tgLYwZhJmzouu0+2WulL3tZSm+cZ\nU1Ky2dm3EzNySQcLW8rPsvmgeBWJM5JG0NyYzEWsDdiSVpxck2ciGUURG606ZolZyMc6BcBwM3o9\nyCiSTc2DO3P5tjJhDoUar7gCs4k4Awrfrmha35csappzDAWO92nURZfDUz67jjbpJoWBlyubG3ut\nC2mfjD6IlSXZp0A1PUyB4qZjWe1D4VYyX15foKDSGYOja9JpazPvi1ioCVnQzNmrZLCekk9KJKei\nxVasSWJslrQqf/lpeeQkDa0v+Oxu7SWfUwHqamCkfQilTUsVNsU4EsHB1iR0zlyTqPwIZJCwcrXK\nkrLz8nEVScLdK+PoRkK9SfYGySVXaOuqhIr5UiSsgit0XLoZc8kGXaWl/FCrMCtZXtQOC3OnAnGB\nuUgWTn/SXCVXOLyKzsSYnot6F4xcxiwrxDTeBGTRsfbrAtGYfIoZUBhq/KA3UnTAwZjLuxVqDgmc\nc4timPS4lX/QM2Qj+TGun4gJ06f1aX1an9an9Wn99bY+TJh+PQ/b/pJ/U0uhxaC9eAiSzZLMQiXY\nPSmk4D44g4nPSf3IxGwrr+nOjNMykVskF9MGJwKmy1NiUzK4wpoQ3SYOiManiYLRzDm5E0XYaDPn\n/phs1XjPFfNKpK3u9ypPUnk+1+n0DI6EqxnHh565NmBrOhUrTNYk1NXrTuUWWaamTu7UNP2+azPp\nK7IjMnFrlJyr2EqqJfvt90Lyoc01wYG2JGmI4rWCvd2EgbYqCRzr9/dFsGweCzsu0Ioj6fHMfAmH\nTl9S+ZRnIlMTokrSPoqdMUSLjSWl2rZGIelj0Ae3GCgey8a5H2wVCGeG6IR9dsGXpmwFYw5qq5hN\n/GVKk3R3YkwF7BZthucMkrqyr1LgjjWpcZac0ZJWCzYVUFqrJIobc9HejEbnkql4ilEZU8Grk1x5\nfRVzIxmSaaUxQsf0Rq3r1snbGTg/vBdT2i36MMVbutE/wlBbStrFlD9mklAcT19kueAyGz2hmQAo\nwoKLSMoQUMUdLAYzyyLjJWGFoQRfQMVLS9Z7Hy/xfreA2VySOUrRNIRkEQ8UGu2a3IxUsRAm323H\nOEJF0i2aeYMV7SlcZGauTT+Y28vEIm9F1pq6sKZtdpsGub9ISq+oQL2tunxit8yilxyvEGjCrZAT\nhZH7DWPOKkQMj7Eg/1puvxjyU8h1XPXOYmqajPDlgVK830EnbWNMHYthiz64ft9KoUfSzUXW9Vsx\nl+wG915W/pK8TFeQR9BuMfEq5G8FkwHve+d/+zRh+rQ+rU/r0/q0Pq2fjNVn0Kf8Q9UQ9dFidXK1\nCdgMdhvyI2SwoY37nJPZBla0yW2BEL3mhMn/1Ie6w7Oqk10jOVZY7JYmylWZ1JQvZjhs0yCSWQtX\nK5xpzMvBZgYuv8K9C1R0ZpCt0ab8Eclqz8MLnTFWjozBkgBqg1aWTNBXh1tSvEkAR/qLl9Ju/gbL\nFVBrchqujd7eK15hWlJXfo6bpkOCOkoWJOrZRsnJzgCbyBEpGY8bWJHkSaTKSbHKZULxAw9tD/Mm\nU0v5QkAQiZtfqxYZ8G0OIisz5JNyD4W9D5n/qzutVIJjYcpNflKgeOWwSeLMkTxlMqaKyGRtYjmU\nE0TBS2LV8CwqaLNg7qsImlQqUddM8Rbwakk1eaeTCiu3pzoUnWXahGeSrjc4I9dETYU4MZgmKNKM\npDNESkwFZdcVnF1Ko4fjJSVtnJOZVdCMFZRrGHMqa25jaHqUIVS4JyMH4UabhRFTjQHXJlq+5lSV\nMVmaMINcQa4xVbAJv4tH0tO4TjhVXW8C+pQ1rWRRGyCi09rGOOT1lXxsfV9Tob5bUE/OHE6Erywj\nvU9hsb7X2rSnroWyJrXNnc0mdU1zxsoFDFRsmWvqGwuoU9yZqSyqOdYVY0KP34J8zYTSj5zcBJ8e\nxrDgxJL1maY2zLloqCIMEsubtgoqgbO68p4iIXTt4QqUjjB5iFNofmKqwWG3CaK8sz2EPY9MgR1W\n8b6ngo2tqiD2dXzmKrAS6MU4SK5rGovJ8xYBl5wCkFXRqA9uE6y83XV0P+KDVPEjoeOPZX0qmD6t\nT+vT+rQ+rU/rl7FuSo2bXuPWA/1gFRdwYcrhjafTlz/EirEvuVlJhTebOWOGAECZPKcyW0ZqKlSH\n8lzMnJ7y9eQ0Do0LiMM43DhwjgOeI3lqB1aSzeAh/CVPKX3wmRt3PZlWqCssctjKDSNfaFjdl3Iu\nFbYrKIFRFoo+lpcjcdIUqCui34L4LP+Vr4IsMzQxAmKqOEyXl6muaYCbNnvXHAI2ELSpzvNhghrc\n8JKNpExNY2plRT4YiTJdIqp8YGEkyZlgFIFgZFTnhTQYa6PmCebSUbZSpJvKFMktgz4HvXdGreSQ\n6vGaSUFBn7WIkGiu76kplOSRZmBjUr2RfXlvIjXFcsEkYgbHkvmxpkiisulcc5c8bwbMDMn4CHpO\nybcM2vLD3CZ//RDoKF3vk2SM69w0W+jqFU67zu85pyBLaTCa0NXTGH55oXhKYqeJXdsm1UU9Ixsz\nnZYrON4WFKS4cijJhd2eLxviSONiTg8IBk9xFUAqlDWU3Zh+EG4cxRjzlhtWaBELr60yo5oR7iKP\nWlEtBlAK20L0T4KaFZ9BzsKxUPwqKF2Et4w1WZOPRhMrFcTny5laC5cF3gmMOn0Frup3FOhBE0MS\n9uUPagaXtfcvRcVOLDJCIsndzRcIvITYYgoWLquLsS1pX8RNqnvzZ61pXgS4JMJWb3JPXeNSEwab\nCZhki4iYmfhU8Q+qZUvcpo+3ydkvnvzsK/Ld0l7kekNXNyXhlReWWpDwdT1l0qu8TSPnuius/7VY\nUkZe/FWxptE/zvWpYPq0Pq1P69P6tD6tX8ZKZ0nibh9AcqKiokZUrtAUxnxJm+aLryFCkpdiQZgk\nM/FSYMG0ykjp9mOFwA6HPrWliAy2KERRRuG0xnNqCoUZVirWp7xTGG+aMuguaXx/wuMMHgy+8MHm\nhYLTFqnPDI7QRmfLuNmauGbhmsEocElNFnJJim7/a5SVHfphY1PwVSwl4TomxYS8jrXhO2HUXLK/\nVVTtXjW9Q3Kgt5kc6O9b+jLCa1rkGC2CWhUWGzHYLPGSy/MBZgPPQk3jyZIHF5Z5rNemLaDM5ZvZ\nAgYUyGP5ShDu26vkbyFaZvWibW6uAxWhQiwlbRPVTfI73BQZktqwWsJuxm5wzUnO1Neg4tbWZA5Y\nVLVF0vNCLOhARyhoUHGdJH1tSM2FtS4LVnDz35hLTkkoJLha4WJBT70/CdTaYBFRvfiClOjf1qqi\nkFjHzo0Sg+swznNyzAvFN3ouTHWCW2piWCSdLKUwa1KtEjMF0bBYPhrYEpid3QJYHrpSOULfM1aY\nqhvIhqOCxm2u7/BhHTfpHatIXtKyq0GOICM5D5FmbQXFT0B4hA+ZThDrPE6ySPoXUzmJmUuOJpLF\nkk4GHmvkBUwl8q739JYiqGLg5nuaOV/e83yRP4rQlwZlLmkd+SKti+XvAw3qbk6f5s5c18fITjM4\nmcibM9WkiVshn7xMvW5iX72mdY0s+W+mpL9hhZIsOMw6/xP6aroUixef4uB4abBE3Boy8mYeloxi\n+NS9Sm66hJy6/tbt5DZV/nGuTwXTp/VpfVqf1qf1af1yVsjbgkNaoWdQXRvIi4tiVbs2ttpnTbJq\n2sKU5KhmcCnBsEIJUcNudcaIyWHOFaNbpaVIXdfb1MCcd3brLkO3XLEbC2Ueg758LWFOzCvgPHml\n2cFz+so+CWokxVPQihtpzMEzaaOQ1bnGYA4j3bUhMvlI3NTtz9Q0pYS2WrlIY54QJbjlPUW0ZWaH\nwiRtdbuXhM9nUtMpGM9F8iXPRk5tLgfarDqCOZ9OM1YAAB70SURBVDzYBxN9iUpJ47y61Q+oqCpF\nBvONQndBHE7pvGeyGWSvVG7SRm1ot7htHIP0ig0VXjUPwQbM6KVSSI7eMd8ohDDiCJFcvXBdQIWG\nshpHBM/Ip1TMSO/sODURpCOCHgOvmkY0Vd7y34wT+70z86yYBwsGkzoKRw62vbGZwzgUhFoqIye9\naurXbueCT6wM6lBsRQkVwqyMv0HQZ9JaZUZX9z/8Zap6jIOZ+YFCF66A+NDEaSPZS4N0LkzM13tr\n7cXHcuSg4pQR9AgOFLo6ZvLoCiVuud6LJTvLHBjOqWjy0w1GFqGrbdJjSMYZdZXvk2IGDNw35pzL\nw2S6bl1SxUyH9TpFyzMGgjzYIryt5A3AwRz9BFu5joNg+aAmjCLfWTPnmsEMZaJF8lKEGAJQaEIW\nhG9CcafuIe5GriIprMDhCt/NoBZJZAsFTJ5Ad006Zypgd+JU5BtzM3pOainkUI7TMdX4OIqKgpEg\nPrk8jNNyNXKcDHt5781XdIeFSIak4lesiBYJmKk4mllezvkXRoSrIxMjqG7srraLAqX1PqjydCZV\njalUO2ZxLn6s6yeiYDKzPwD8c8B3gD8N/FOZ+b/+6r6qvzbLzP4F4PcAfxNwRsSmP5iZ/9dHX7MD\n/y7wDyHi088C/2Rmfu+jr/kNwH8M/N0Io/ufA/983viN/z9Y61j9m8Afysx/dn3s1+SxMbOfAf5t\nhE6+B/5v4Pd9bIQ0s38d+MeAL4D/GfgnMvPPfvT5L4F/H/jdKDjmjwL/dGY+/bh+j1/ptXDR/xqi\nbH4HYaP/cGb+Gz/ydb/mjs2n9Su4jBdEb0yZnZ9iUly0qzLUlZV3XJud2uFc9Ce7CqaeQR2Thkhy\nuYzxY3VyJ6KZBcnMNdlKdcSraSpSaqFPdWCPcWW6M93xRYJzJJu7eQIiCwfyKjxH0xSD5Or7h0nQ\nMorXHETCQZHcynNhmAPfPqCDk3U8XEjom9keTLkw2EsezG1l+iKbBWdB1dThR8WjR4UApc+MF1JZ\ne/ExJP3lv1dm1Zr0WWpSc7gxx6C4cdjGJSc1QAZ9YydoDfk3QhtwgLssL9/5cJljSgK2USaUhDYK\npUCh0vpBdcCMI5McA7cly5tJxkGabiSzKNPKM/G+8dqSbskJp1nD2OA6lGX1kWvjMZPXbw/a1qht\nsk/j0Xa66XjtWZnXC3txwgbEwCPwgGJ6zUKxB9En57kmFx7kmoTWqgycUit99tXrt5eJh5nRvDG6\nIBuanMprZDdQAcplwiSvBCNncM6LviY0eahr+lC8LfWm8OxzeZcodUEkbsHtuibMjTSdm26TMMdn\nw8KX1eiGzy7rnNM5GGmUunG3ioKYuRDMH07KRaIHY5Ejg8hOmNPSXgALFwvMjTkPZlTSDXejjLHO\nd12rlktet0iUdtty5MKqG4I/aPS8/FsfUfZeZKo6nrYEvx9e75K68v+1d/6xtpbVnf98n+fdex+Q\nMJgBQWMb21EpNcYf6IBphU4YdWpjm45/SJtGp4lo/DEx4yRtGTXTlElLaPlRirSmtrHFtpYyaSqx\nIw7yT6UVBgasUbBjBn/19l5EEfDee/Z+3+dZ88d63n32PXDpZeCce84962MMd+/9nnPed+137zzr\nWWt9v54sDe17QjmzmPekDNYEOuY2g2TMJVJa+PfJyqzQuFljuVuep5l5NbQxG8TBZDymgtWp13uS\nvAprXtebaOqJaFn43F1O5JqXv6+OCbTBmhkpd3S10tV+ef3IRTbWWhui4TOdc22vSt6OT5gkvQW4\nEngHcCcuD3uLpBevGAyeSLwW+B3gLvz9+Q3gM5LOMbPD7Zhr8IXxm4FHgQ/jC7hVT5G/xheH5wPP\nA24AFsAHt+1KthBJrwYuwRPoVfZcbCSNi/zPAm8AHgJeBDy8cswvA+8F3oZ71fw3/HN0jpmNQkF/\nCpyJG3xOcVnljwC/sC0XsjX8CvBO4K24AemrgI9J+p6ZXQd7OjbBM0SljdG0xUqnREfHOCeuJIqM\nwbJ796hw2BJlaLYsyeioLFTpmLTKzEbLiczbVrqaGVRIZhTBtLakQzBp1SYbChNz+WOyGzzOraNm\nr+qoenJU68Y8zaIpWk2qG59mianNfUaK6qajZsxbe5KZmGOk2pTxvIlwOfAO8N2DBzn9lFOWbXae\n/7SZDF92b2qp8Wcxka1r090+v2B4lSy3GZxxlqFWo3YsV3fDuHhsrXvVvOJXKD7rQUdtvUJrtWDJ\nF5yZUdsNVIxkacODh9Fzpr0XrXWoAmXiLYfVjIUZGlxGXfIqEdWWC+ZBRhoSuYivrB/iR05Z84V4\n7ya2VVA6T/AmBguMoQ5k85gzSl63Be+3zcUE7LCYHMQlrq3n1Kkbyk5zYZI7cjEmSazlxJpNmoFp\nMxcuA7YQnWYkG0iTylAWJK15S2gpDIbP04nmNSSy2bLKMP63mrnwQEtAXV5d1Cogg2V669v9Kjr5\nHFRVpXSuzHjvYz2vPskX0SbRkVgfFqTcMZ97peIkdcs5LhfLUIt/9g0Fk3v7KGPVmDKlyuhrXQqP\nDHh7W62uuljkn4UxHTVrJt+tZNtlT2ZmxdtbB5OLIzTFRZKrFHbK9BNXpjR5gp6V6a0wAKl6Nch9\nqLwiNCZdVr2VUaJVulwcYkweH1if84KTZv5ZkH/e/HQ3EqZiG4nO0DZGioQKLqufbCnFraGnyI9L\n2VUN83Ki0H9PxVuDN+anvO1zbAV9NNfW/phJyf29SjUmJJJSa8vzv+dGxK3ipdFs2JjX2ua6jEOp\n0pVxMyJvJIz4ZpGrbbo6oYoYyva25O14WXFJnwfuMLP3tccCvglca2ZXHNeT2wYknQ48CFxgZp+T\ndCrwbeBiMxtd688G7gPON7M7Jf0k8EnguWNSKemdwOXAGWY2PNHf2i1IOgW4G3gX8CHgHjN7/16N\njaTLgdeY2YVPcsw+4DfN7Or2+FTgAPA2M7tR0jnAl3B5znvaMW8APgU838z2b/V1bAWSbgb2m9kl\nK8/dBBwys7e2x3syNsHTZ5QVf+EZZ/Gs2YRJZTk0PajSGc2Asy0a8STIJIoNbT4ika0pXEksTMvF\nO8mYVF+cj7uwEytMmyRxHiWKJUpXSMVQNXplFpZYtCTMmMBKclKzoeID3p6I0drVKielxIRKGTwp\nszYv0qvSl0Ivcdistcw4o3FuXlm83f/QAf7VvzyjtV+5Wl1qO/zQhCC0UdTPSkt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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(1,(10,5))\n", + "\n", + "pl.subplot(1,2,1)\n", + "pl.imshow(I1)\n", + "pl.title('Image 1')\n", + "\n", + "pl.subplot(1,2,2)\n", + "pl.imshow(I2)\n", + "pl.title('Image 2')\n", + "\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Convert image to matrix and dataset generation" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def im2mat(I):\n", + " \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n", + " return I.reshape((I.shape[0]*I.shape[1],I.shape[2]))\n", + "\n", + "def mat2im(X,shape):\n", + " \"\"\"Converts back a matrix to an image\"\"\"\n", + " return X.reshape(shape)\n", + "\n", + "X1=im2mat(I1)\n", + "X2=im2mat(I2)\n", + "\n", + "# training samples\n", + "nb=1000\n", + "idx1=np.random.randint(X1.shape[0],size=(nb,))\n", + "idx2=np.random.randint(X2.shape[0],size=(nb,))\n", + "\n", + "xs=X1[idx1,:]\n", + "xt=X2[idx2,:]\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot the images distributions" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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aA6kPJza2iRMnMnz4cJyccpDdwZFFixZhYWFBzRq1GTn8OwAqUYWiRYrRs3dX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\nWxMkGvgdePr8LjOBSCFEmxou5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kkSP//8MwMHDmThggWU6/WMnnTl57Ir5SHgsh+jLefz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m1m\nyXkzSAwJYW1hIcvycokMDydYglvGjibSoKespZVHNmwhWKvl+ekzUSsUALTYbNyyZiUKWSZIo6Gt\ns5OFV17J2++8w5YtW5gyZQqPTDqbod0CZR6vl9vWraPe1olXCPr368ef7ruPyy677EffWw0NDYwe\nNZKKyioiDUG0d9kRSLz51lvk5uby4QcfYLFYmDBhAo88+iijR4/+0XOc4rbbbmPN3z/ky7nTe5z/\noOA4j+89jNvt/tX7lxs2bGDOnAtxu5zER+gpq+2gd3oqz7/wIrNmzYKf+Hvpt7fHqu0knpJdeFuq\nfLWqvq4S6PWA24ms1aNMG4KwdSDsFoTTzrPPPousUNJ5aCOOqkKcdaV0Ht6I19ZBenoaM2fO5OWX\nX8br9RIcHIzXaUcI3wePJElIsgLhtCMr1V9RCdRhyhyBuaODL774wm9GR0cHy1eswJjcF11oJJIk\nodTqiMgcgdPpZPLkyQi3G4VSTVhaf9R6Iy3H87j22mtJSkryz6nRaH71N32AAAECBAjwv4Qsyzzy\nyCPYnXY67Z2n5PXxer1syd2KpctCRkw68cExuN1uxmaMJEijQ5IkokOiyErqT31bA73TfVkNm72L\nquYajlQe40R9BeYuX9Hcr24jqK6uZtOmTYxKzCTKGAZAq62DDruVMJ2R1PA4ogyh5JQe4x97NvDx\nno24vG4SIyI5O3MwCeERbC86whvbVuL2erjppptYdM89KGSZmYNGoFP7/Ik+MfGMTO2DSlagV2to\n6bTy+eF9JIdHgRAoZQWjeqUzvd9gYowhNFrN/gLASWERnNt3AIUNNVgdvsK85u6fEydOxGzv4nhT\nPa/t3kpOVTkbigp4M3sHMdExxCTE0ys1lXvuuQeL1cpZKSm8nZ2N3e3iwkFZTOzdG5VC5t133sHj\n8SDLMgMGDACg0Wolu6qCtUXHyKurpbl7v1ZRQz3vHzjAJ0cOk5KSQkNTE9eOHEakwfeQOzU8jPMz\n+9JktbJ46yY2l55gTXERD23bQmhYGLfcfjs33X47e/bs4Z1330WWZSZPnsy4sWN5KnsPf8/PY2tF\nOdevXkVlRzvjkuK5dthANO0tzJ8/n1deeeVH31t79uyhorKK5BATMUE6InRaPF4Pd9x2G2+9+irT\nYiP4/aB+VB45xKSJE/0KkT8Wh8NBTU0NzTYbzq/t56vvtBFsNH6rf9nS0sKbb77JM888w969e/ml\nEzhTp06lvLyCJ596hosuv4EPP/yQvPyjZ6TK/a/wm1sKiEIFLdXQ4tvQ6TlZhBQah2wKR3jceMoO\ng8eDHNnr9A3jtPPggw/yz88+w+txg8eNvfRw94C+NtnZ2SArWLt2LQ8uXsxHf/87L7/8Mp3lBeiT\nB4Ak4Wyrp6uuDF1MYk+TtEEolEoaGxv959rb2/F6PKj0hh5tldogFCoVM2bMYMaMGTzzzDM0lB4j\nNCyMBx98kAceeODnuW4BAgQIECBAgDPmiiuuID8/n2eeeYaik8VkJPShuvkkLZYWpmVNJsoUydGT\nRZxsqyNI01M5zaTzffff86d7WPrKUrbn78Pt8aBWqHB6XChkBcHBwUydOtXfp6XFt8om5NSKGiHY\nXZaLVqmmtcuC1dmFy+NBIcs0WdqRJYnMhGRmDBnp93eig0PZXODzb3L25zBy1EhMuqAee6YAwvVG\nXF4PN485j1d3rSPCYGJbcQFeIbhy2FjSI317pEYn9+Zve7ewsTDffy5cb0QANpdvi8WqwkMkJSax\ndu1aHnroIV55+WVKmhoobqxHIcvoDQbqG+pRNsl4heDYsWMA7CotpVdYGHefO9mXWQOy4uJ4Yes2\n1q1bx8yZM5kxYwZGg5FHNm/A5fGgU6rocrtQKRQoJYkPDh3CqNWQERtJSblP9CvG1FPJeWBsDJ/m\nFhCclMRrB/YjyzKzZ8/mueee67EM8xSyLLNm7Vruvfde3n/vPTptNiTguhGDuGhAXwAu6N+Hv+7O\nYfGDD3Lttdei1Wq/Mc63IYTgvj/9iTGJsQyOieT1A/koJAmlJNNhsTA5OYFbRwxCkiTm9e/NtWu2\n8NDixazfsOGMxj/F0aNHOW/aVKpragF4Zt8R7h45GI1SwcH6Jj4uKuOa3//+G/2WL1/OlVdcgdPp\nRKNSYHO4mD1rJss+XX7Gr/HnIDIykjvuuOM/MtdvL7BCIGVMRGhNULoHOltwH9kIsgwI8AqUvYcj\n6wy4q4v8vSorK/03pio2FW2vTJAVuJtP0nX8ANrk/mjiUuksyqG9rZHZs89nyJAhHD58GGdDObJS\nhcPagUql/kae0N5Yg8ft7qESGB8fT3R0NJ311ei71zwD2Jpq8bhcjBo1inHjxnHnnXditVrR6/Uo\nulPUAQIECBAgQID/PMuXL+epp57i2LFjJCUlcdttt3HHHXfwwgsvcLz2OE6XE4PWQJTJt2olwhiG\n2+OmprWOhPDT3/XlTVVIksSNv/89AwYMQKVQMXPgRKJNEXR0Wdh0LBtZrUL5Fcn03r17YzAYKGmq\nJtYUgdPjpsXWgUqh5IKscaSEx9LptLOp6ADlLXV4hSCrV0qPrENWUiqbCw4zOD6NwqNHcbqcNJs7\nqGtvJTbElwUTQnCspooYUwghQXr6RMXRYG3H5nRg0GhJizitGqiQZYYkpPBF/gHM9i62lxRyoLIM\nWZJ4add6XB43kiSTGhmOTqfjmWee4cknn8RqtSJJEtdccw0rVqzgkhHDGJOWgtPtZlVuPjtLSilr\nbmb+8GH+oAogIzqKYJ2O1atXM3PmTJRKJRqNmjClkptGjyZKb+BESwsvZe9Go1Sw+PxzMWk1tNns\n3PnJKgD2VlQxIS3FP+Y/846iUigoLi4ms18//nDrrdx4443fuxrIZDKxdOlSXnzxRV544QUWLVrE\nsrxCPskrZERCLAuGDGBan1TWHd/E/v37mTBhwhndX42NjRSXlHDt0Exezcnj8swMrh7UH6Us81lx\nKX/NOcKXJyqY0TsFtULB9JREXvsWBb/vw+v1MufC3xFk72L1nOnsrW1gyd7D/LO4nGCthhqLlVEj\nR/Loo4/26FddXc38+ZcxrX8sj1w4hNAgDV/mn+TOj9fz6KOPsmTJkh9lx38rv72lgJHpyKYoKN4K\n1mak8ETkXgNBHwpeL2h0SCoNrtLDuMtzfcsFJZkPP/wQvF4klQZd+lBkjQ5ZpUYdm4oqKglnQxWW\nQ1vwWNvRJWWgTuxNQdFxwsLCuemG67jpuqtZvXo1zz77DF01lbTlZtNVX425pABzwX4mn3NOj3Ww\nSqWSxYsXY6kppyE3G2t9Na0nCmgu2MfESZMYO3Ys4HsyYjKZAkFVgAABAgQI8Avy2muvMW/ePE6W\nnyQ9Jp3O1k5uvvlmlEolOTk5zLtkHm6PG6fb6Zf1jjJFEh0cxbZje8itKKCiqZrtx/ZQ1lBJXEgU\nKoWK3Lw8RqVkEW3yiQcE64xM7DOCpqYmNm/e7J9fr9dz7733cqSmmM3H93O0rhQJGJbYh9SIOCRJ\nwqDRMbXfCH+fTru9x2vo7F6WV9xYTd/IePLy8lApFHy0dzvZJ4o4WlPFJ/t3UtbcwMT0TAAsji7k\n7tU7Trcbt7fnsjGLw46MxGs7NnKwsoxRCamck9qfILUGhaxgRuZASk6c4PBhX6ZMofBl40wmE1+u\nXcvgxATG9U5DIcvo1GrmDhuCsTv7kVdTS35NLVuKj7O3vAKz3Y7d5aK0tBSA9evX09zSwrUjRhBt\n8C1d6x0RwdwBA2nutPHSZp88eVt3sd60tDTe2neATw7nsb+qmge/3MCR2joyoyKZ0zsdg9XCzTff\nzEMPPXRG94TZbOb/nnkGg1rFtLRUZvdOJ6+ukbtWb6K8W+77zjvuwP01AbTvIigoCFmW2VNdR69g\nIzcPyyJIpfIpFfbvw6i4aL44XuZv39Jlx2g0fs+I32T37t0cLznBg6MGkxZi4vL+vVlz0XTGxkdT\nY7Hy4osvsmv3bkwmU49+H3zwAUoJshJD2XSsFrPdycxBicwflcJbb7zxo2z4b+Y3l7GSNHq8HfVg\ntyCnDEYR51u/LMf3wVO8F9FSg+vYLlCqUcb1RQ6Nx3l0E14BKNXIOmMPmXMAOciEq7kGJAgdPR2F\n1idp7k1Ix5yzEY1Gw9NPPw34nvSo1Woefewxag/vRqvVcd011/DMM8984+nHTTfdhFKp5C+PPELt\nkd1otFquueoqnn322cC+qQABAgQIEOBXgt1u58/3/ZmUmGSGZwz3f0cH64N5/rnnueOOO2huasao\nM2C2WThSlc/gpIHIskxWYn82FWznSEUBgtNy1c2WNrKS+pJTlkdIUE8nNiTI5yzX19f3OH/dddex\ndOlSjtaedq7D9D37Bqm16FRqPBLsLMonLjQcU5Aeh8vFpoJDqBVKXG43JU21BOuCWDByEttKCthS\nmItXCIwaHXMHjyEjKp7cmgoqW5vQqdQAOD1u1hflMb3fIJSygrqONrLLixEI2rps3DFmCrFGXx3O\n8b1683z2BkqbffW0Ghoa+DpOp5OY4J72y7JMjMmIxW7naF0dBXV1KCQJjxAoZRm310tFRQUej8c/\nZqyx5xhx3UFBSWMLO0rK+WR/LgCV5eW4vV5WHivE6xXIksS09DSuHXZaXObj/AKeevJJbr31ViIj\nI795M3yF119/nbbWVl6dMZWY7n1bs/uk8/s163jnQB6JJiOHDh9m1apVXHjhhd87FoDRaGT27Nms\nW72akXHR3/AFk4NN7KmpAyC/sZkvSiq47qabfnDcr3LqmqWGnL5mKcEmbhmSyfqKkwwaNKhHphR8\npYE+/PBDupxunlmbj9srWPzZYZ6aN5ze0SYad5WcUXHr/wV+c4GVMDf4slCAHHN6bawkScgxqXha\nfAXx1H0moDB01wPQh0FnK8rQSNzNtXjtNuTu4EkIL67mk0iyAlVYlD+oApA1WhRh0Sz79FN27NhJ\nUXERaWlp3HXnnVRVVtLa2orJZEKj0WC1Wrn//vv54MMP6ezsZMq557J48WJuuOEGrrvuOlpaWvxt\nAwQIECBAgAC/HvLz82lrb2Po0KE9nN20uFQKygvYuXMnm7dsJiM+HUmSOFySR2lDORqVhg6bGQkJ\nSZIZ23soaVFJdDq72H38IEcqjyEhUd580i9IAVDeXAP46kjdf//9OB0OZsycSVVVFa1NzQCMSutH\nYW0VJY0nyYhO8vet7Wimy+VEkiQ6XC7e2LyGCGMwbZ1W3F4PWqWa5PBoTjTVMjatH6F6PRcOHsWM\nAcP4Incfxxtq2VCUy4bCI5gdXQAoJZk/jJnC1tJj7K0o4UhNBUaNliarBaUso5BlYo0h/qAKuhUK\nY5LIri5FoVAwaNCgb1zXsPBwcqtPMn1Af/+Sv46uLsqbW0gLDaeyo42Fg4YzNDYBs8PBR/mHONpU\nT0lJCa+88oq/ztLBmpOMSjx9DQ6cPIlWqcTj8fLu7oNIwPD4OK4fNgyr08lrOQcoaWnBKwTnpvXc\nRzUlLZV/Hitk9+7d/O53v/ve+2Lzpk0MiYnyB1UAwVoNYxMT2FxWwf0TJvH47v1s2bLljAIrgJdf\nfpmB27eRU9eA2eHA1O0XOj0etlfV0GZ3cNkXGyhraWP4sGE8/PDDZzTuKU4plG6oOMklfU+rZm+o\nPIlGrfYLgnyVF154geNFRTw+fihz+yRjdjpZkp3HXf/Yz+Be4QwdPOg3EVTBbzCwou0kaLujcJcT\nvrph1OXw/+quPYaiz1ifmkn3eTnIiKTWYs3dijapL5JSg6P2BF5rO6h1eJ0Ovo5wOamsq6PeYkMV\nEU9hVS0LFiygoqKC+++/H/A9kTnnnHM5ePgQuqgEFMZIPl/zJavXrGFvdjYDBgz4waciAQIECBAg\nQIBfhlOlUhyunn6Ao9sv0Ov16IOCcDgdDMsYRFx4NBX1VThcLrpcdjweDwMTM+gdkwyAUatnQsYI\nlu1bg1atIbe6CLfHTWJoDE3WNvJqjmMyGln28Sf0lL0siwAAIABJREFUjkxApTOw4pNPsdpthAeZ\nCDMYGd0nE1OQno35B1h7NJuMqCQ6uqzsqyhEQkIIwaT0LJ+YRaeZPhHxxJhCWX5kF602C14hqO9o\n42BlKWF6I8nhkcwdMoaXtn9JbFIiVVVVvoxO7yyGxCejUaq4ZNAY3j2wnZMdLahkBVqVCrvLhUap\npNPp8EmxfyXw7HQ6cHrcXHf99cTFxfF1Hn74YW6++WZe2bKd8X3SsbtcbDhaSJBKRZ3FzMReaYyI\n9wVMoTodVw0ewb2bVhNnMvHq0qWMGjWKgQMG8Lec/TRYLPQKDSWvvo4tpaWoFQr0ajUTEnvR5XGz\nq7qSJdt38NDks7lx5Aj++OU6ACwOZw+bzA7fe2ow9BQX+zYMBgN1Ttc3zrfb7SSHBpNgMmJxOnuU\n2vkhEhIS2L8/h6GDB3Pjuq1cnpmBRqFgeXEprU4Xly1YgE6nY9KkSVx44YWo1eozHhsgNTWVBfPn\n88SyZdR32hgcFcHe2gY+KDzBbbffTlhY2Df6vPnaa1yQnsT8fr4gNEKn5YkJw9hcXcfBimb++c/A\nUsD/XaLToakSAE/5YRS9RyEpFAhHF56qo/5mXmsLXq8Xb2MpODtBocRZU4oucxTOymK6jh/0NfyK\ncqDb2YWjvgp1tE/1z9lci7O5DlVIBKHDxvvHlo/n88ijj3LTTTcRFhbG8uXL2b9/HzHDJ6EJDvfN\n36sPjQe38Ze//IVPP/30P3BhAgQIECBAgAD/Cv369WPAgAEcqzxGmDEUrVqLy+0mrzyfsLAwpkyZ\nwoIrruDVpa+SHJNIeHAYwYZgCsoLcbp8jnvo15b7BWl0qJVqurqDs2O1pRytPYFSoWDMWWexa+cu\nLh5+DuF6n+R6YmgUX+TtxCO8hBt85/onJOMVgv0nCiluqP6G3Udqy7h0yESGJvoK4J6qTdVsNaNW\nqShpqqOkybe0LMoYTO+oWCxdNt544w3mz5+P2uZkdK+ehW6TQiNoslm4eew5uD0e3ti7Da/wUm8x\ns6uqhLFJvZElifK2Zg7WVqLVarnnnnu+9bredNNNtLS0sOTRR3l7VzYAIVodt46YwJJdG4n72hI/\nvVpNiC4IlSxTVlrK6NGjfTW1gM+PHUUARrWG5OAQWrtsPDFpCobuwOPspBTu376JXZWVnJOaioxv\nNdOHuXncP3E8Ro0Gm8vFB0fyiImO9mfDvo/5l1/OJStXsrmsgskpPrXpnJo69tXUcf3QLD7KP0Zb\np+1H17Pq3bs3u7Ozue3WW1myYwcAQwYPYv3Hy5g0adKPGuvbeOvtt4mMjubN11/ntdxCQoODuf+B\nB1i8ePG3tq+tq+PCzJ6ZPa1SQbLJQHDfAWecjftf4DcXWEnhvSAhC1FxENFSibt9JWiNYGsHpRrV\nwLPxmpvxVObjOLwSPC6koGCEsws8Lrpyd4GsBJWGoPTBKMNi8VrbsBXnoFcrsRzdh6aqCIGE0+Lb\nmGjMyOphgy4xlc6K4+zevZvZs2ezadMmdMFh/qAKQFaq0EYmsGHDxu99PVVVVTz++OOsXr0GtVrN\npZdewj333ENISMj39gsQIECAAAEC/OsIIfj444/561//SkV5BUm9knB6nazd9yWhplDMnWaQ4LPP\nPkOr1fLQQw+xfdt2vty/mYiQcBwuJ5ZOCw888ADvvPMOVa11JEcm+Mdv6GjG4XYSaQzhnMzRaJQq\nCmvKyKk4RnNzM/Ghkf6gqqq1nu3HDyNLEha7jfKmOtweD0qFggGJKaTHJPDOtjUIr2DuqHGEG0ws\n37+LRnM7r+9ZixCCYJ2elDCfop8sSegUKi4bfBbxwWFUt7fwWf5+skuLycjIIDg4mP79+7Nl02as\nTjsGtU9MwisEx5vqiDP5fBClQsHwxBRWHj3MmF5prC7OZWfFcXQqNfXWDmIMJpxeL5fPn8/effu+\n9To/8MAD3HXXXURHRRGEhN3tJiJIT6zBxJH6Ws5KTPZnwWrMHTR1WjHbFciSxC0jRzMoJpZai5k3\nD+ZQYzYTqtFy0mJmSnKaP6gCSDQF0zc8koKGRlSyjBfIjIqgvLWNm1atISk4mKqODtxeLxs2bkSl\nUn2rveDbc7R06VLeeuMNdFot/7c3h38UFiMJQY3Zgl6tZkXRCZo7O3nssce+dRnkD5GVlcW27dtp\naWnB7XYTFRX1k+2/12g0PPfccyxZsoSWlhaioqK+N/M1dOhQNp0o4oZBfZC7bai12ihsNfPMRRf9\nJDb9t/CbC6xE60kkkwPf8wvA4wZbB4rETBTRyUhKNbIhFK+lBWFuRlLrEA4bqkhfFsrVWAVeN0EZ\nw1BHxAMgm8LRpQ3GcnQPL730EgUFBezdu5f8o1a8bl/dqx42uHxp4aCgIP9Pr9v1jRS51+1Ep/tu\n3f/q6mqGDx+B2WpFHxlHp9PNM8/+H6tXryY7O/tHpZYDBAgQIECAAGfOY489xuLFi4kJiybUEEL5\n8TKsVisXX3wxOp2O5ORkrrnmGpKSfEvVQkJCyN6bzaeffsrWrVsxGAwMGjQIs9nMgAEDWL9+PTIS\nKZEJmO1WjlQWIksS07PGoVGqaLK0oVVrSAiNoqy0lGCtbylaZUsda49mo1NrGNQrnfZOC+VN9fxz\n/w6GpvTBK7wcKCvG5fYwZcAQXG43b279EqfHQ7/YeIzaIArrTtLR1cnhmjIkfAHS9H6DSQjxPfBN\nCo1gWsYgVuTto76qmtGjRnPHnXewadMm3ti3hXPSMtGqVOyrLqXe0s6M/qflw7tcTpSyzNCEXmRX\nlhJrCMao0TA5OYOBUXEUtdTz3v695OXlkZWV9Y3rDD4/6b4//5n7778fhSTzwr7t9A2PYmvlCf52\neD/DYhM41lTPwbqTqGUZh8fDxZkDGRLrW16YYArm98NH8uCWTcgKCZ1SSafL+Y15rE4Hdq+Ltw8d\nRqdScu/ZZ2FzudhWWkmt2YLD46amw8yBAwc455xzvvPeWLhwIf/46CPOSopjdloSWytPUm+xMm3a\nNOb26UN7ezuhoaFcfvnlDB8+/Efddy6Xiw0bNtDY2MiIESO+dc/TT4VOpyMhIeEH29375z8zY8YM\nbtiYzfy+KbR0OViad5zIyEgWLlz4s9n3a+Q3F1hRX4yoL+ZUYV8AyRCKMr5Pj2ayPgRPeyPC6yFo\n8GTk7g8w2RCK48RBFP/P3nsHRlWt6/+fPX0mM5kkk94LCSmEhCIgRUDUIwp2RRERBctR9FhR9ByP\n5ahguSL23sCugB5RlN4JhATSe+/JZGaSmcnU/ftjQjAotnu8935/zOcvZs/ea69dwqxnve963oDh\nESHp4KxRQkICS5cu5ZprrqG8oQXXgI2+6hKCx0xBIpfjdbvpqyzCEBrKGWecgdvtpra2Foe1j76m\nKnRxqQiCL9pl72jihqW3nvRSVq5cibmvj4QJM5ApfQJsIC6Z4rztvP/++/z1dzrB+PHjx48fP35+\nnc7OTh599FFGxqUxKslnOy6KIgfLD7F582ZaW1t/1mxKqVSyYMECZs+ezUUXXsTq1auHfV/T2UBV\nRz1SqZTwsHD6TWYAvincRZu5e2g/AYFOp5Gy9noO1pcSoFRx1emzUA5GUQrrq9lTVcw3Bb70OY1c\niYhIfn0VPf19AFw0biKpET7hcXrqSD7Ysx2L3YbL47NLjxhMJzxGxKDxxKToEWypK2bFihUAmAds\nrCs5iIgv0pUZEUNCsM8avtvax76GGkZFxlLW6Ss2e3FGLsE/MvqKHmz3o48+OqmwAli+fDnbtm1j\ny+bNNFlMNJh7AchvbSK/tenYdPkQsSfYgUdpdUgFgXqTCblEwp7mJqbHJ5IWEoooiuxoqqepz4IA\nhKo16DVKFDIpCpmUi0aNBGBDSSWfHSnl4X/+kxtvvJHg4OCf9PPQoUOsXbuWOyaP5ayUBACuyslg\n+Q+76GhvZ+PGjSe9xl/j0KFDXHThBbS0tg1tu+jCC/nwo49Qq9W/cOSfy7nnnssnn3zC/cvu5frv\nfBb2s2bO5OVXXz3lMqhOPWEVmQrWXrBZwONCJpPhsZoQXQMIcp84EUURr7EVvB6khqghUQUgDfKZ\nSLiNbUhjRgxtdxnbEASB7OxswBei/fCjjxBUAbjMRrp2foMsMBh3nwnR7SE4dQQymYwnnniC7zZt\nQhkcSm9VEX3NtUhkCpx9vWRmZvKPf/zjpJfyzcaNBIRFDYkqAJVOjyY4lO+++84vrPz48ePHj58/\nge3bt+N2uxkRc9w1TRAERsSksK1wB0eOHGHChAknPf76668nLy8PgFGxqWTFpOIVvRTUl1Ld2cjG\njRt56qmn2LJlC9tK8zBaLcxIHUuP1UxdTysDLicer4dtFfkIgsBpySOHRBVAbuIISprr6B8YQKNQ\nYh6wIiDgcrkJVgfg9HoYER41tL9cKiMnPomtpUeHtlV1t5NsCGdffSV1PZ1D9an2NlUSotFy0aix\nhOsCqehsY33xYbRyJeNiEthcU8bKrf/GK4o43W5EoLa3m8JWX62obXUVGO1Wuu39hKq1hKh92TUr\nV67k448+QiFXcNElF3PvvfcSGho67P7W1dYiFSSIohelREJqaBhlXZ2MDo/ksvRRBMgV7GisY11l\nKRurKsgKP16suKSrE48ocklmOhsrqpBJBB7bs4OEQD0DbjcdNisAz888h7yOVj4uL8VosxOi8QkW\nURQ53NxGQpCeamMvu3bt4oILLqCiooIVK1awY9s2goKCCIuIQCmVMjPpuAuhXCrh/LQknt2TT29v\n788Ksl/DZrNx3uzZhEtFHr90JvFBWrbVtvL0xm9YtmwZzz77LC+88AJr3n8fi8XM9Jlnsnz5clJT\nU3+98f8Al19+OZdeeimNjY1oNBrCw8P/R877f41TT1i1V4FUDvowcDlx93UDAq6SXUhjMxBkclyt\nVWD1rY8STwgVSxRqBIUae10RoseNTB+K29KDq7mSefPmkZiYCPj+03ziiScxmXrRpY3G63TgsfWj\niElGotZQXV5IQUEBL7z4IgHR8QRn5ODo7cHW3ozH5USwmlmwYMEvKn2VSkWvyfrTLzweVKqTpxD6\n8ePHjx8/fv44dXV1ADhdTvrtVmwOG4EaHa7B1H+j0XjSY1taWvj666/Rq7Xo1VrGJWYNfTc5dQxd\n/b18+OGHZGdns3XLFpp7O5mYNIr8pnIcLhcjwmLwil6qunzlYTxeL84TCsyKoojL68GLF5vLV/RX\nRCQjIo7KrlbcHg9eUUT6o+UHTrdryOhBJkjYVF6ITCJFLpWSGRGD3eWkpL2ZPucAC8afToTOF9HK\niozBZLexubKUhJBQ4ntCaDQZCVUHMD4qgdY+MzW9XQQGBmKxWNjfUkd8YDCjI6KpNfawv8V3LxUS\nKY6eXiKDgnlh1fOs+/JLDuTlERwcTEdHB7feeis1tbWEBwQwOjKKFouZ4s4O1DI51+eMRyGVAnBu\nShp15l6OdrbzSfFRciOjabGY2VBRRkpICHNGjkQtV7D2yFGUUilOjxsEkAxeu0f0Mj02ga9rqvjX\n5t1cmp2OTqVkc1Ut5V09XD9mNNXGXlQqFUePHmXqlCmoEDk9KpKenm42H/WJU4fbjUZxXOzaXO6h\nZ/NH+PLLL+nq7ubFK88mJtAnRv+SGkezuZ933n6LyooKtm7ZwsyEaNLVCr757FPWffEFu/fuJSsr\n61da/88gkUiIjo5my5YtmEwmJk+eTEJCwh9qy2az8cMPP+BwOJg+fToRERG/ftBvbHfz5s2Ulpb+\nR9o7kVNPWAF4XGBshSFPfRHR3o+7Ku8nu3otPTgaSlAm+F5Kt6kT0WknODgYa1sN/Q2lyBUKFl9/\nHatWrRo6zmAw8MwzT7NkyRKUIeHINMejXh7nAH1AfX09He3thGTmIggCqpBQVCG+2ZmuA9tpa2vj\nl5h/1VU88uij2M1JqPU++0tzezNWUw/z5s37b9wgP378+PHjx8/JODbI235kJy73cTttucw3kNbp\ndCc9tqWlBVEUcXu9RJ2wrEAQBII0Ourr62lqOp7e1mu1MOBycvmYmegG0+iyo1P4onA7CqmMkuZ6\nMmISMGgDEUWRoqY6+gd8NaY8gE6hpt9p50Bj5dC58mormZQyEkEQMNms5NfXIJNKAYGkkDBqejqQ\nS6XcNOlMNIPGBWq5gvzmOsK1JxbcDUJE5K2Du4a2WV1OEvQhnDsii211FWyuK0cATotJ4NKMHATB\nZ/n+RdkRDrY0EKRSEx6g5YrMXGYm9vPs/h28+OKLuN1unnziCVyD4rHf6SQpOJiLs7J4cvs2FBLZ\nkKg6RoI+iNLuTnY11PN9TTUA46OjuXaMb7yVFOy773eMm8CoMF9kpcbUy8N7dvJiYT4PTz6Dv0+a\nymtHDrN6z0EADBo1fz1tLHmtbYQaDEyfPp3LLr0UvVTC0zOmoh6MGO5vaWPFvjxW7TvM/WdMQCII\ndNvsfFlShYDP2OKP0NjYiF6tGhJVxxgZGoTVVsH3P/zAs7MmMjU2EoAbctNZ9O1uHnroIb744os/\ndM7fy44dO7jyiito7+wEfELrxhtv5MUXX0R6wjP6Jb744guWLL4ek9kCgFwu48EHfa6E/x2DjvXr\n13P99Yvo7TX/4TZ+jVNPWCWPBX04FG0FmRxJwkjE3i5EUxc4faYW0qhU5FHJiB4XrsZSXC2VuI2t\nCBIZ3sFI1gMPPMCNN95Ic3MzMTEx6PX6n5xq9uzZSCRSHD0dw4SVo9tX1Xr06NGkjRxJc08n2tjE\noe/dNisDFvNQWuHJuOuuu/j3N9+Qd2AHAcGhiF4PNnMvV1511a8WrfPjx48fP378/DHGjRuHgIBM\nImNS+nhCtMF0mLvIry1AEARGjhz5k2M2btzIypUrOVJ4xLdB9NLc24G0voQWYweCIBAbEkmnxUig\n0UhRUREyiRS310OzqZMkQ9SQqAIICQgkNiicPocNk72fj/ZuISrIgN3pwGTrJ0St4/zU8RxoqaCy\np5VUQxRTEzJQSGWsPbKT3ZVlFDU1EKjW0GzsQRAGTSvSc9lZW4YoiuRGxw+JKoCRYVEcbKqlzthN\nsuF4fc2q7g4EBKbFpzA5PgW728Wm6lI+LDrIealZ1PR2IRUEPKLI4bYmCtuaCQ0I4NKMXKbFp5DX\n0kCXtZ8xkT5TsDCNlszQCN5/7z2qa2r4S0oa0xOSsLtdrCsr4c1DB4nQarE6XdhcffQ5HOgG17SJ\nokhJVycJ+iBuGDueB7b8QHZEOLdOmoDL42FTVTWbqqqRCgIH29uI0uowqNWkBAUzMsRAhbGH27f/\nQFyAjmZrPwIgl0kRRHgzvxARmL9gAW63m02bNnF15sghUQUwMToSg1rF3sZWblj3PZG6AEo7e5BJ\nBEINhp+tA/VbyM7OxmwfoKTTSFb48Tb2N3WgUasxKOVMiTke1dEq5MxNjuGd/8aart9DV1cXc+ec\nT3awhveunEGUVsVn5U08/tprJCUlndRS/0TKysq48sp5nJcTwUOXjEerkvHGljoefvhhUlNTmT9/\n/h/qX2VlJVdccTlzpoXx5N/G0dhm55yb9/2htn6JU6MM8o8Q1FrobQO3EyEsBm/VEcTeLiRBkQh6\n338SotMGMgUSlRbFiHEglSM6BxDdjsG6VQL33nsvu3btIjMzc5io6u/v5+uvv+aZZ55hy5YtzJk7\nB1tdGf0NVbgsvVibqrHVlHDxxRczYsQIHnzgAWwdrfSUFODo7cHa3kzPkQNERkb+al2DgIAAdu7Y\nwTvvvMP5Z5/JJXPPZ/369axds+aUqXDtx48fP378/E/T39+PiMi45FzC9WHIpDJiQqLIjs9CFEUa\nGhqG7b9q1SrOP/98Sg4fJSogBL1aS7/DTt+AlfLWWgwaPUFKLcXNVThcToqKiggN0DMyIh6lTI7d\n6cD9M5EOt9eDZDDyc/HFFyMJUGF1OQhR67g6ewYhmkBkEhlqmYLz0sYRrNYSoFBx42nnoFcFYLbb\naDb2oFEoCNUGIgDFbY0MDDr5uU44Z3xQCHKJlM+PHCS/uZ5mUy9bqkrZW19NsFrD2SMyCVAoCdVo\nuSJrHBJB4N9VxSCFiUmJhGg0eLxeEoNCMNpsvHxwF42mHgBkUgmTYo+njbm8Hjo7O8kKj2BOWjo6\npRKdQkmHtd+3PwI6uRxRhOfy9lDY0UqlsZs3Cw9R3dtDmsHAC3n78SJS1t3NlppaVuzczafFJaTo\ngzgjNp6Dba38c/cOOgfXVzlFkfPOO4+FN9xAytTJLFu+nK++/hq1WoPV7eaMmFgmRkXx0dq1nH3W\nWchkMhzu4ffIC7g8XhQSCeEaDV63SGpwEHa3h+UPPkhXVxfr1q1jy5YtuE9I4QRfqtrGjRv56quv\nMJuPR1Zmz55NVmYG/9iSz7eVjRR3GHlxfzHry+oZO24cTo/3JwYeA24PEomAzWYbtr2jo4N169bx\nww8/4HL9tIDx70UURZ588knsNjt3jU8jPTQQvUrBktwULsuI48XVz//mtl5//XVCtEpeWzyG5HAt\n4YEqHrw4g5lZEbz0wupfb+AkvPnmm+i1ctauGEtaopaQoJPb5f93OOUiVmJTKVi6B/9dCRIpsrQJ\nSAZd/TzdTXhqC/FGJCHVhSBIpEgC9AhyOer08Xht/diO7ECQq1l6222DUSmfiHn//fe55ZZbsFqH\nr3uKT0igtaGC/poSpFIp86++mpdfegmAhQsXYjab+ec//0nHYAj99NMn89577/6mqt5KpZJFixax\naNGi/9Qt8uPHjx8/fvz8AvX19QCEnJDKF6L1mRJMmDCBu+++m5UrV2IymVh+/3JGRiYxLilrKAVu\nV0U+zb3tzMmchl7tSx3Mspn5umQnEboQzs+a7DOmiMvgo8M/UNfTRrvFSGSgL1rR2NtBq7kbrUrN\n1KlTueeee7ji8stxuVz0uty0W3uJ1hkYcDuJ0AYhPWHCNT00moK2Om6ddi42l4MP8nYiAq2WXsZF\nJ1HW1UphayNjYxMJDfD1r6i9GZfXQ4Q2kK9LCoHjHsu5kcNtua0uB27Ry6yRaZw1GME7LzOTd/Yf\noNdm5+6JM3ju4E42VBQjEQTmpGYSOGjGVW3spqy7E41GQ3ygb3zm9Lh5/sBuugZFUGt/P8ekRJu1\nj1cO+5ZzyAZTxb6pqkQuk/HRxx9z1513suaIb+3TPeMnkj2Y/ndJ6kge2rOT9VUV5IRHUNdr5Nkl\nS4YVtL3jjjvwulw8d8Z0DIPOexW9Rv6xdy+TTz+djYfzmZEQR0SABlEU+XdVDRankzCNiqIu33gz\nUKfj0UcfpaWlhbjY2CGRHBMVxUeffMK0adMA+Pjjj/nrzTdjGhRUGrWKJ1es5Pbbb0cmk/H9D5u5\n/rrrePz7733tarU88sgjzJo1i6lTp/J5eR2XpychCAItfVY+L6/D6nQRGx3NK6+9xhVXXMEDDzzA\ns888M5RaGRUZwZq1H3LmmWf+5D3/LbS1tXHZpZewd99+AC5bt4dpcaG8+JfxBKkUjI0I5tPSI3i9\n3t806d/Q0MCoGC1K+fDUwXFJej48VP+H+gi+v9nRqVpUyt+ekvhHOOWEFX1GhBE5CCERYLXgrTmK\nuyYfefZMBEFAYojF01SG19SBVBeC6Hbh7Tchj/FVlJZotMhCIvFYLdTX1VFZWUl6ejr79+9n0aJF\nKMKiCc48DRCwNVXjaG+iubmFyy+7lPvuu4+4uLhhLjcAt912GzfccAMVFRXo9fohAww/fvz48ePH\nz59DT08Pzz//PP/++t/IFXLmzZvHX//6199kW52ZmQlAh7mLWEP00PYOcycCAqnhSTz99NPEx8cT\nExPDgGOAjJjkofUhgiAQExxOk7GNrdUHCVRpSQ9PJEYfTnRgGF7RM7SvVCpl1sjT+LZkLxuO7iIy\nMASvKNLZ14tEkCBTKrnzzjuZOWMGeEVCNYE4PC4+K9lNtC4E84ANp8eFw+1CObgGTBRF6no7ERFZ\ndzSPxt4upFIZaampKFUqDhUVo5LJUMnkvL5/G/FBBuwuFx39ZnKi4rggPZd9TTVsri4lJSQUm8tF\njbGLGYlpQ/0u7fStE5+Wctw5USqRMDUlmfcO5OHyehkfFcfuplqyRo3iy6IiCjvbEIAaYzczZ8zw\nFRwuLmG2KPJlWQnt/b5olUYmJ0Au56rMbOJ0eo52tvNRWREikG4Io6HPgt3t4rPPP+fCCy8kICCA\nxYsXI7PahkQVQKBSydTYOL6tq2FXcxOXXXopF1544bBn/fWGDUyOiBwSVQAjg0PICDEw4HAw4PVy\ny3ebyQ4PxWgfoNHSxwUjk2nqsxKVPIL3PviAtLQ03nzzTf75z3+ycFQa5yTFYbQ7eK2whDNnzOCs\ns8/mwosuYunSpZwRHcG1k8agkEj4pLKGv/3tb3i9XvIOHKCkqIjE5GTWrl1LZmYmqampQzVLly5d\nyrMvvsiG6kYMKgX57d2EqVQ8M3k8n9Y0cPXVV3P06FFWrFjBzTlpXJIaj9Hu4LmCci6YO4fKqmqi\no6P5rVRWVvLss8/y0do1KEUPb/zlNMZFBrO3pZt/7C7i7s0FvDVnIjsau0hPS/3NmVSZmZm8sOkb\nLHYXgWrf++rxePn8QAsOt4KxOaOZcsZ07rrrLpKSkn5zf7Oysnhm4wZ6LU6CA09e7Pi/y6mXLxYR\njyQiDkGuQAgKRZI2BgasiObOwR1E8HoRnQN4TJ04yveBAPKI47aZotc7NEMjk/m06UsvvYRMo0U7\nMhepWotUHYA2dTTSAB2CSs3nn3/+s6LqGCqVipycHL+o8uPHjx8/fv5kurq6mDBhAiueXEF7cycN\nNU0su3cZs2adhcPh+NXjc3JymHXmLI40FlHbWY/ZZqayrZqSpnISQ2MZHZdBvCGGVatWDY0TvN7j\niVrNxnYO1B5Fq1YTGW7A7h1gc+UBSttr8YjewWUHxxly65PJiBuZgsagJycnhwcefIBN329i0aJF\niF4vyeGRaNUq+h12QjWBmAdsuCUiXgG+KNnvncomAAAgAElEQVRPfW8nZZ3NfFq8h06rGZ1STb2x\nE1EUSdIbkJj6KSkuQURkfHQSZydlMj1hJCa7jY5+M6MiYpgQk8SR9ia215ajksm5cvRpnBabQIPZ\nyBdlBTSZe6ns6WBvcy3gcy38MW6P77NEEHAPRjH27dvHe++9R+6MMxg9fRrvvvsu323axH333UdN\nTzerDuxmf3MjkVodQUoVNreLa7NzyTCEoVUomBwbzwWp6XhFkdLuLrxSCZu+/54LL7yQp556ijlz\n5mAzmXB7vT9x5XN7vSD6+tPV1fWT9DyZXI5bHH4NAJ02K/n5+cRoAkjR6ynt6qHR0sdZyfGYHQ4K\nWju46JJLyMzMRKPRsHrVKmbGx3BVZioGtYrUED3/mDIeEDm0cyd//etfMaiUPHBaLvE6LZEBGm7P\nHUW6IZi777qLPd9uJNnWR8We3Vx99dW8+uqrmEymof6sXr2ajRs3YlVqKO0yceuodNaecwZjwg08\nNjGXMLWKV19+mbMTo7k5J41wjYp0g56nzxiL6Hbzzjvv/Op7f4z8/HzGjR3D52vep89q44lp2ZyV\nGEGwSsH5KdE8MCmTzfUd3PlDPt/WtHLvfff/5rZvuukmPEg5+8ndvPJDDfuquhn/963Ud1nJCZCR\n7TTx8TtvMW7MGEpKSgBf0eRt27bx7bffDkuf/DFLlixBIlVw3q15fLenk6Iqy2/u0+/hlItYCQEn\nOPVogwAB0WH31a9qrwOPy5cS2N0ECMgi45EofTMVHosRT28HgkJFekYmKYMzMVXV1UgCAoe5lQiC\ngDwwGJepB4/HQ0tLy0mFlR8/fvz48ePnf4ZnnnmG5qZmJo2aimbQEKK3z8j+/ftYs2YNixcv/tU2\nPvv8MxYvXsy6desA329+oiGWMQmjADAEBFFUX86ZZ56JTqfjaFMFp6fmAgIH64qJCg3lzHHjkUgk\nvuLCZaXkN5ThFb2EaoPwil4kggS310NBcyUKuZw9e/cyfvz4Yf247rrr8DpdXHP6TDSDBg51XR18\nc/QQ2REJFHU08Pjjj/Pggw+yrnT/sHU4AXIlRvo5NzWb0ZG+CeTvqo5ytKOZXYMOgnKJlGnxaXTZ\n+ijuaKa4owXwOb7F6IOQS6U0m00opFKqejo40u6zgQ9UqhCAH8ormJs9Cokg4HC72VFdTYxOj8vj\n4VBbI26PhxEpKbz/wQfD3OuMRiPPD7ot1/b6igF7RS/BKhUmxwBJ+uG1oFKCQhCBO8ZM4O3yo3z8\n8cdkZWXx9wcfZPaIZDJCDfzX/oPsbW1hSowvbbHDamVXcyMzkuKYEBPFEzt38sknn3DNNdcMtXvZ\n5Zfz3DPPcF5iEvGDRYd3NjfRY7czNzmJ67IyEAZF4uMHDrKtrgnPoHh79NFHefWVl3nzrbepa2hg\n9pjMYX3WKxXEB+rIDAniQFsn6UH6YSmbgiCQFRxEs6WP92ZMRTqYRvrc0RLeeO013njjDRYvXsxL\nL72EXC5n9uzZ6DQaZiTGsiD9eKRQJpGQFRTIjtYOckYMj0oFKuQkBwVSW1vLb+XuO+8kTq3ghuxE\n7tpWyLiI4YYcYyN8z2ZTo5Gnn36a66677je16/F4ePrpp3E4nFS12Xnw0xIkAnhFeGn6aBaMjAPg\nMYeLWV/tZ/n993Hr0tu4/rpraW3zGcMFaNQ88uhj3H333cPajomJ4fvvN7P4+kWcd8v+33ytv5dT\nTliJ/SbgR576lh5AxNPTjKezHux9gIAQEIg0OByPpQd3ewPWfjOCVIbX3A2CT4iljkgZElJZmZkc\nPlLki2YN/lGIoojL1AMSCQql8qRe/s3NzaxevZpt27cTEhzMwoULueqqq/wGFH78+PHjx8+fwLp1\n6wkPjhgSVQDBuhBCAg1s2LDhNwmr4OBgvvzySx5++GEee+wxzsqcil593Ia8s89IYlISy5cvJyw0\njLq6WlpNnQRrArE57ExNzhn6nRcEgVHJKZTX1xMREUFnZydfHN1BiEpHl9WE0+vmq6++GiaqHA4H\nb731FmvXrCUnNmFIVAEkhUUQotHSYOpiREoKl1xyCX9/8EFUcgWzUrKICQymydzDlmrfjH9isM+8\nq9dupbijhcQgAzNTRqKQyjjYVMfW+jK0cqUvkjYoGmbOnMneXbtxuN1U9nQwIS6BWSPSaO/rQy6V\nEh6g5ZmdW9lfX09lZyfRej1VXV24PB5idUE8d3AHEkHgmuwx5LU2M3fuXMrKyobSuxZcfTV7du7k\n2jG5xAfpWbFjFxIk9Nh9RgyVxh7SDccnq8t7upBLJCQE6pkUHs36deuYOnUqLreb81JTCJDLOT02\nhtePFrCloQ6tQkFJdxcGjYaL0lPRK5Wkhobw9ddfDxNW9957L+u//JJlu3aSHRrKgMdD+WCdsktS\nj48DZRIJF49IobCrG5kgsGLqJOQSKWsqqrj0kktISkigsLOHOSMSh9ruGUwd/EtCLANuD3ltnXxW\nWcPe1g48osiEyHAOdnSSEqgbqjkmCAJXp6awvq6B2fFRvPv22xgMBp588kkA0rMyyd+9G68oIhk8\nxuHxcKTXTEhICHkdPSzITB7qg9HuoNJo4tqMjF995wH6+vrYsWsXT56RTYbB977vbelmzo8E296W\nbiSCQMGRI7+rQPGTTz7JSy+9yN8vSOPS8dHUd9tY/FYhoktkftrxNXxBSjmL02N54JuN/PDDZqaO\nCOTT66agV8t4ZVsD99xzDwkJCVx22WXD2p84cSJFxaWUlJRQUFDAwoULf3Pffiun3si9owlvczWi\nrR9vVyveigJAgP5en6iSKZGERoPbhbu5ClloDEhliE47or0PQRUAEimCMoBvv/uO3sFZlNtuuw2v\ncwBL6SFcZiMuSy99pfl47Fa8dhtLFi/+2WK/VVVV5Obmsmr1akobW9h16DALFixg8eLFf7iInB8/\nfvz48ePn5EgHo0QnIiL+rno7ALfffjv6QD35DUW0mzsx2SwUNBTT0ttGc1Mzb73+JqLZQaguBKfb\nhU10+s51wvmPfX7uuec4dOgQ866+iqScdK67YTFHjhzh3HPPHdrX5XJx/vnns3TpUkSvr9jviW15\nRC8Wh4177r0Xt9uNCJw1IouRYVFolSoywmOYkeKLoBw7vrC9EYVUymXZ44jU6QlWa4aiL8EaDaPC\no1BKZUgFic/Vzuvhw6JDeL0iXlFEJpESE6jH7fVSa+xBKZMRqg6g124ndORIMrOzkUiltPSZGBkS\nyrLTz2B0eBQLs8cgA9544w3ANzb69rvvuDh9JONiogkLCGBaYgJt/RYsTicamZx3iws42NZCnamX\ntSVH+aamkilRcb66UXbbMBdFrygiCAJLxuZwy/ixIIGjXZ3MSk5gfnY6Hf1WPF4vXpGfTGq73W4c\nDgciUGUy0dRnGdbujzn22SuKHGjrJFkfyPJxuegUChKTk9nd1MZrBSXUmSwcauvkoZ15BMjknJUQ\ny/TYKPpdLl47WoZGJiVEqeCDsiqa+21MiAj/2fNMjYng8tQEXnnpJbq6utiyZQuzZ59HpbGXv+8v\noMRoorDLyD178+lzubl+yRJ2NnXw5IEiKowW9rV2ccuWAwgSCddee+0vvOXHOSYkPSKkheiYFhvG\nP3YX8Vl5EzWmfj4oqWfFwUrmzZv3u0SV1+vlhdWruG5aPH87J4XYEDVT0wxcMCYCryhy4p/rsfdS\nIxf4+OaxjE3QkxIewDPzMpmZEcbzq/7rpP0fNWrUn1Y0+ZSLWBEUidhYgdhQPny7IEESHI58RO6Q\nY4+r+giuxnKEgEAkMgXq9LEADNQU4TEbcbtcNDc3ExwcTG5uLuvWrWPxkiV0Hdl7rFEkEgnXX38d\n//VfP/+A77//fvoHHISPm4p0sFZEf1sT7777LkuWLGHKlCl/1p3w48ePHz9+TkkuvexSnlr5FP32\nPrSDjnw95m6M5p5hjnC/hZCQELZs3cL8+fPZVe5zpgsICCAxMRFLVy9npkxALvUNt0rbazjSVklM\nTAxFNdWEBwcjlUoRRZEj1VVD44+xY8fy+uuvn/ScH3/8MVu2bOGCUROo7m6jrLWJ7NgEdCrfsoWq\njjbMdhsLFy7kxhtvZMOGDQDEBg5P2YoJ9KVs1fV2MSYqAZPdSqROj3xQXLZaTBS2NXFeWhbjYnyp\ngjaXk7cP+er/WFwOvFoNVmM3+S1NpISEsqmqjM5BkwkBkEulzJ07d6gPYaGhjNWHcHbS8UG3Qioj\nWhtIdbWvmO+xtLSUH9V8mps+kn6Hk7yWFuxuFzY3vF1UMLT+DOBwVzt72ppwDa7rWn7ffchlMjZU\nVLEgOwuJIJATGc7munokgsD2+kY21dQDEKhQYHE6+dcJdUBXrlxJU1MTSqkE66A1eaBCTp/TxScV\nVdw02pfmaHY4WFXgc0r0Ap9V11BtNnP32FxG6LQo5HJWrFjBvx57jHWVdQAkBGpZccYEAuQy1pZX\n4wVWT5vEKIPvuTT09XPTtj38u76RK0ckI5dI8Ioi71VUoZZJOS0iFAnwYXktCfHx2AcGAIgMD+eg\npZ8fNu8GIC4mhvUbNvDRhx+iVcr5d20zn1T4SgJEBKhwOF0UFBRwzjnnnPSdO4ZWq+WsWbN4t+Ag\n5yVH8fysMSzbfoRlO3z12SQSgSvnzeP1N9781bZ+TH9/P51dPUxKiRu2/apJsby9s5G3yxq4ISsR\ngC67gzfLmoiKiiIjyIVGMXwy5PQUPW/nVf+u8/+nOPWElXsAYkaCw+arZ+Vx+8LaohdZdMowxx5Z\nTApOYxtivwlJlC80LYoiHosRQZAgUyiIi/O9ANXV1WzevJkRKSmMzs5m8uTJTJ8+nVGjRg1VaD8R\nURT56quv0ManDIkqgIDIWKxNtaxfv94vrPz48ePHj5//MPfccw9ffvkl+4v3YNCH4vV66TF3k5GR\nwXvvvcc777zD3LlzufHGG39T6ZMxY8ZQWlrK0aNHsVgsJCYmEh8fz8T47CFRBTAyPJHSrjpmz57N\ne+++yxfbthIVGkqP2YzFaiVYo+Paa68lOzub7Ozsk57vq6++IkofQmyQgWB1AE293azdt4MEQxg2\nl4M2Uy+XXHIJ7777LoIgDKXXNZmNjAyLGmqn2exLadtcU0KNsZMuqwWr08H7+fvQKBQIgkCAXMGY\n6OODXY1cwfiYeDbXlCMCDz/8MDExMVy7cCFrCw8Rpglg0ZjxBKnUFLa3sr2uZlh0IC0tjbrK4YPe\nAbeblj4L8wZt2UeMGAFAVU8PEzW+FDCpREJ6eCh5LS0EKpVYBqNIs1NSmBgTjdFu57OycgY8bu6f\nOBGADTU1dAJb6xqoMJqI0wVQYTRhdbnwiCLTomI4JzEBh9vDJxUV9LlcvPjCC7zx+utccOGFLFmy\nhLVr1uB0uzkzPpbzUgb3La+iqKuH7xsaKeruITVIz4H2dgCWjs5idJiBil4TbxSXcefO3ZgdThJq\narjjzjtpbWvjlVde8dUx9Xj5rLKWSnM/zWYLY8MMQ6IKIEGnZWZMFFuaW7li01bGhhko6zXRYrVx\n/2nZaOQyvqxuBOCi+AjCNCq2NXdQ12vEJUhYs2YNqampjBs3DqlUylXz5jE/K4FFo5Mo77agUchI\nC9Zy0Zd7Wb9+/U+EVXd3Ny+//DLbtmxBq9Ny1fyrufLKK3lu1SrOmDqVmZ/uYFq0gc4BXxR2/vz5\nPPXUU8TExJz03T0ZWq2WyIgw9lQZuWT88bTCII3PGfCePSV8UtNGrEbJlpYe1IF6Lpo7l0/WvEv/\ngButyvd3Jooiu6tMpI1M/919+E9w6qUCejzQXA5djaAOBJ1h6CvR+9PiewB4vUh0QXisFgYqCxDt\nVkSHjeuvu46goCD2799PTk4Or7z+OvlVdezOO8Rjjz3G4cOHTyqq/Pjx48ePHz//Oxz77V6xYgUZ\n2enkjsshJSWFsrIyig4dpayglGX3LmPy5MkndRk7EUEQyMnJYdq0ab8qxtLS0pgydSqIYLXYMKgC\nmZ0xkfMzT0clV/Dyyy//5Jj29nZ27tw5FM3xil5azUYcHjeX5U5mbGwy7eZezA4Ha9asYc2aNRw4\ncIC9e/dy//33IyCwubqYss4WzAM2itub2NlQwdw5c/mv557DMCIRu8eNVCJBp1DSN2CnoqudH5ed\ntTodNJiM2FzOoa3l5eWEhoZyxbx5iKLIgtxxjDCEEhoQwFkpqYyJiuadt98eSnW86+67qerp4svy\nYjr6+6g39fJe8WFEiYQbbrgBgJSUFObOmcPnxSUcaGqmx2Yjr7mZz4tLyQwPI0AhRy6VMikmmgvS\nUokICCAjNJSl48fh9npp6utDLZfzl4QElBIJ8+fPZ8zUqfTrg7nwiivIzc1lpMHA9aOyiNPpiA/U\n+a5TFOmrrKS3uJi777qL6dOmYTaZSA3Wc3NuFgmBOtJCglg2cSwauQyNTEqb1UpRTw8Oj5fFmemc\nkxBHpEbD9JhobhmdRZd9gFGGYDxdnfzlL39h9erVLFu2jMMFBZx1wYV06YI5beaZTJ48+SdukHa3\nm16HA1EUiddp2NrSRqd9gFty0skND2FNWQ2HO3uYHhNOr8PJ6sIKJMCESAN43Cy95RYiIyORSqV4\nPB7cHg/t/XZkEoGxUSGkG4abrv2YlpYWxo8dw4rH/4WirYL2Iwe4+uqrWXjNNWRlZVF49Cg3Lr0d\nc2QiIybP4Ouvv2bNmjXDRJXT6WT//v3k5eX9bDHkHyORSPjbHXfx3u4mnt5YRW2nlW2lXSx8o5Do\nqAjWrl1L9ISp9ESnsPTuezhcWMh9992HwyNwxSuH2V/TS1lrH3/7sISdFd3cceddv3i+PwvhVFnH\nIwjCWCCfkaeD0w51hUhScpEERSBazXjKDyBotChGTRmWCij2tqPWaLANFf0VQIBrFlzD66+/hlKp\nZMzYsZTVNqDNHIsglSGKIrb6SpwdTTQ1Nv5iXYBLL72UbzZ9T2jOxGGpgMaKYnbv3u2PWPnx4+f/\neQ4fPsy4ceMAxomiePh/uz//Vzj2u5Sfn8/YsWP/t7tzyuLxeJg9ezY//PADE0eMxzA44Wqx97G/\nOo9//OMfPPTQQ7+73WlTp1FaWDQ8FbCjhiOtlZSXl3PhBRcgMTuYkDDcNGBHdSEJ2SPZvn07AAMD\nA9xyyy28/977eAYngMPCwujq6ho6JlyrZ1JCGt9XHeW2v93O+PHjuW3pUjoH9xEQGBuXQI+1n3pj\n99BxqampHDp0iMDAQGafey6Hdu/l6uzxaOS+8cim6lIOtzUxOy2Djr5+Ctubh9b3yCUSXF4vwUFB\n9A7afutVKu6dOmPY9RxubeHL0iLsdjsqla8A8PPPP8/fH3yQ/sGxVVxsLO+9/z4zZ84cOs5kMpGY\nkIDZcnxNU1Z4GBPjYng735dytzB7FJNjhxcmfmjHTgbcbixO5+C1+1Izj51LJpWiUCiYGRXJlYMR\nss0NjbxfWso/Jk4gPcQXMao3W/jngTwkUinnJcQyPzNt2Hme3J+P0T6ARiaj1Ohbb//yjKnE6o6L\n6j6nk6s3bWX5uBymREXwfnk1n9fWU1xczBOPP86HH32EdzBtMTs7m+LiYp6fOpGskCA+qa7jg/Jq\n7INrxZQSCeEBGuwKJd09Pb5nIPONOTODtRztNvP3iaOYm+wTNp22ARZ9v5/zL7uCaxYuZMn119PQ\n1AT4zB/unpTB3LQYNtW0cd/WQjZt2jQsYrVkyRI2fPIhn155GtGBvhTTr8tauffbIr7//nvOPvts\nfolPP/2Uv922lPZO3zsYFxPNK6+9zvnnn3/SY7xeL8uWLeOFF1bjdPrSLkePyuKjTz4dqh13Ilu3\nbuW6RQtpbPK5VQbqtPzr8Se47bbbfrF/f9bv0ikprARNIGL5XgS1DmmSL9Turj4M5i4EpQZJYAhe\nixHRYSM3N5cDBw6wb98+6urqCA0NJTc3l9jBP+Tm5mbi4uLQjsxBGRo5dD633Yr58O4h5R4eHs6V\nV17JjTfeOMzEoqqqikmnn05ffz/yIAO4nNiM3SxatIi33377pDMJfvz48fP/Cn5h9fP4hdX/DR5+\n+GEeeeQRQrTBTEqdMOy7I/VFhMQYOFp09He3m5+fzxlnnIHX5SFKF0qfw0q31cSyZctYuXIlc+bM\nIW/nXmanTxj6rfd6vWwo2cO8BfOH1ljddNNNvPP224yLSyE+OJSufjN7ayuQS6WclzEGy4CdvXUV\n9DsHCI+IYMSIEezevZuUkHDGRyfiFUXymmtpshi5evzpyCQSzAN2SttacQaoqKmtxW63ExAQwNnJ\n6YyLPl630+pwsDpvO+Cr83RmQiqpIaF0WPvYVFuBzeVEIZUxJyWDvLYmmvtM3DN1BvpBAQWwrrSY\ngrYWBhwO5HL50Pa+vj7y8vJQq9VMnDjxZ01D1q1bx2WXXoooigSpVQSpVNT3mhABnVJBdlgYC3+U\nMtk7MMCD27ajlctZmJ5FqFLF80UFuLxe5qenk6DTcbS7my+qq9Ep5Dw/YwaCILAy7yASAe4/bbiV\n/eqCIxRZzEQrFUyJiaKwsxu5RML4yHDeLy5nwOPBI4ro9XrMZjNLR2dxTsLxtMkD7Z08fvAwz02d\nSFpwEHa3m6u+307u2LEUFRayeFQKY8INlBtNvF5UjVcqw2q1kqzXUWPu49KUeGYnxmAccPBacSX1\nFit/f+ghLr74Yjo7Oxk9ejRjc3NpbW8nXK3kqwunDxs3vllUzYe1bXg8HkYH67ghIxGVVMqaygY2\nNbaTEaqnrNvMFZdfzseffDLs2NCQYC5LDeHOqcfXwomiyOz395Ez9UwCAwNpa2tl3Ljx3HLLLcTH\nH39v9u7dy7Rp0zg3O4ybz0zA7RV54Yd6dlf1kp9/+BfTXMGXglhQUIDBYGDMmDG/Ohb2eDwcOHAA\nu93OxIkTf1P67p/1u3TqrbEa4sfLHQFBAoIE0e3CY+pCotUjMURSWFhIY2Mj06dPJzk5mdbWVjSa\n4/asx2Yafhy+9QzYsRTlAQItLS3ItIF02Rzcv3w5r73+Ovv27iU83OfwkpqaypHCQp5//vlhduvz\n58/3iyo/fvz48ePnT8TpdLLquVWoFWoE4aerIwRBwOM5yTKBX0GlUiGTyugfGKDV0jkU6dHpfGYZ\nd9xxB2d/8w376kvIivQJoIKWKvoH7FxwwQUA9PT08M477zAmNonRMb6SLUGaAJQyOd+VFuD2ekgy\nhKNXa/ikYC/t7e30dvcQpNJwXlrOkN323PQxvHN4FwVNDZyTMYpgTQC13V30ORzk5eWRnJyM+CN7\n7mNUGH21gaSCwLS4FCbHJgIQptGikStYU5yPKIr8u6aMtOBQWvoFPjxymNlp6QSp1RS2tZLf2vyz\n4xmdTsesWbN+9t5ZrVZeffVV7r//fgJVKhK1OmrMJup7TUyZOpWe7m4a6mrZ19xCqFrDpJhojPYB\nPikrAwFuHz2WxEA9VaZeeh0O7hk3jiyDLxIZrdXi8npZV13N2yUlzElKwuZ2E6iQ/6QfEkFAIkio\nMZmpMVkYHRpCn8PJK4XFCECSXotCKqPc6IvYvVFShlwqJSc0hPJeE68cLUUCfFxVy4Pjc5EIAgJw\n6NAh/pozkgtH+MRIfGAAapmMx/b5DCDarDamRoVxW+6xdUI6UvQ6rvh2J1KplJycnKE+SqVSojSq\nnzw7AKlEwOl0olPIWDUlB5XMJ16fmJRNncVKN3Leeecdrrnmmp88I6/Xi1Ty0zb77E42bNhASkgg\nI3QqXt29m9deeYWt27czZswYAJ5ftYqUCC0vLxo91Mabi/VMe3wfL7zwwi8aswCEhob+akRs2HVK\npb5Uyv8DnJLCSrR0g92CEDloSGHvA3Mn0sgE5HHHQ72icwBPay0HDhzg9r/9jW83bgRArlBw8003\n8eyzzxIXF0dGZiY1LY0ogsMQJBJsDZWDdR5EAtOyUIf7UgHddhuNRb71Vy+88MLQeWJjY3n66af/\n526AHz9+/Pjx44euri7MFjPxhjgae5owWU0EBfiySqwOK+3mDq7966I/1Pbtt9+ORBS5aPw0lHIF\noihS1FjDQw89xPz58znrrLN47bXXuPvuu6kq8rm3HVuKMHfuXKZNncay+5bhcrmICTIMa/vY5167\nlQhdECEaLSqZnPCAQHrs/cTrDcMG2lKJhFh9MM1mX8qaZcBOWUcrDrebiRMnotPpSElJIb+9icyw\nKJQy3/CwvKsDnVxBn8tJUtBwR8Ekve/zmfEjONLVSru1D68oYnYM8Ga+zx1RIgio5XJmzJo1LFp1\nMkRRZOXKlTz+r39hs9pIDzGwJDsHmUSC2+vlneKjHNi3nzUfruWplSvJP3yYr6uq+KqqCgCNWo1a\nKiMxUA9Am82X/pcRMrzvmSEhfAnsaGpme5OvoLEEX/pfot5Xm6m138rBjg7kKhUKqYzHJ59G/GCa\n38GOTp46dAQBgXKjaaiIrcPj5fnCo3gH5+2zDcGclZDKc4dL2N3WQYfNjnNQqI85oajumHDfZ4Vc\nxoDLzdjw4c/coFaSHBxIS0vLsO2dXV2cHxvOl7XNbG3qYFa8L3vK5HCyrqaF0NBQ0qXeIVEFvvds\nUqSB3XaRRYsW/eyzmHvBhXy54Quuzo3DoPHVSPuiuAWjzcHirATuPy0VQRCwOFzM31TAbbfeyu69\nPlfssrISTk/RDxNmCpmECUk6yktLfvZ8/3/h1BNWzWVg9c0seHvbEU0diKYOEEWEHxUKBPD2+/Zb\nuXIlZVVVqEZmItHqcBu7eekl38LS9PR0QoKDKS8vx5K/EyEoFFd3OzJtIKJHjupH7jsytQZFWCSf\nfPrpMGHlx48fP378+PHR29vLm2++ya5duwgKCmLBggWcffbZf0oWh8FgQKPRIJVKCdLo2VeVR0Rg\nOBKJhHZTB+ER4dx5552/u12z2czWrVuZkJKBcnC9kiAIZMYmUdnexPr167nrrru48cYbmT9/Pnfc\ncQdvvfUWoyLiSQyJwDJgo/BwAbfddl02P6cAACAASURBVBsSQUKnxUSY9njx4Q6Lb3wSqPStfbEM\n2Bhwu0gNjaCjwUxrXy/iYO0m8NU9auszYXU6+a6siKrODkRE/pKWQXiAjuKOVgpqalAqlbxRuJcU\nvQGzc4AGsxEBn0Bq7jMRF3h8KUNzn8/UI0wTwNSYJD6rPEqmIYLSng5itDrUcgWt1n7kKiUrn3rq\nZ+/T1q1bee+99zD29DB5yhTkcjnLly9ndGQYR61WzktKRjZYV0omkTA7KYWiQ/tZdO21VFRW0t3d\nTVFREU6nk4yMDEpLS7n5ppvoGbBjUKkJU/vuT43JRGrwcbe9arMZiSAwJjSU/K4uMjIyqKmo4J/7\nDzAuPAyJIOFQR4cvwiSKTImKGBJVAOPDw9DIpDT3+YTbXxJiOTM+CuOAg/dLq7E4XDw0KZf0EN/9\n+qa2iVeLy7E4ndx888289tprlPaYSAg83mZpj++ZLr3tdp5/7jlKjSYu4Xh6ncXpoqnPOuTweIzo\nqEgsTgdnxkbw4J4j/Lu2BYNaybamDpAruHDWLLZsWI/L60U+eC9FUeSo0ULSqNyTvcI8/MgjfL/p\nO857fx9nJYXSbXOyo64LqSCwNDd56N0KVMpZkhXHPTv30dnZSXh4OMnJKRwu2DXsHfR4RQqb+pk+\n+7fXtvp/kVNPWA2KKuRqMHcNutSIqNRqBhorEaRy3xqrvl7EZl/x3sLCQtSjxyAL8c0eSHU6RLeb\nF196CUQRuT4YWYAOV5+ZwIE+XIOnEgTJT34IBIkEl8uFHz9+/Pjx42c4zc3NTJ48mdbWVrTqINxe\nFx988AH33nsvT51kcP7fQaVScdNNN7F69WrSIkYQqjPQburA7rSjVCnZtm0bBoPh1xs6gWPpgycW\nm3W4XYiA5UeGDGq1mq+/+oqRYTGMj/MNOkMDAtGrA/iq5ABTpkzh0MGDKGRy4oINdPVb2FlVSoBC\nSZA6gHaLiV115UgEgREhEeS31NFt62drbRnjY46vsbI4BgjQaOiTSXB5PSzIPY2owchOpC4Qm8vF\ngFrJ7PPOY/euXSSGp3LfvHncf999uGx2djTUoJHJSQ0Jo93ax8bqUkLVASTpQ6js9RkUqAbXSdnd\nbtqs/cyZO5dbb70VmUyG1+sddj8eeughHnvsMSIDdQQpFXy/aRMiMCo8lKnxcRxt7xoSVR6vlw6b\nlT6nw/fZ7ebNN9/kkUceISMjg4qKCoKCgoiNjSUwMJCXigq5Oi2DKE0AeoWS14qKuC4ra2iN1frq\nak6PjOT69HQeP3yYiooKvF4vUQEamqz9SBCI0WmpM1vwOBxDguQYNWYLNreHMJWK5CAdN+Uct/ZO\n1gdy0+Y91Jr7hoSVQipFqdfz6Usvcdlll9He1sZbm75DQCBKq8ZoH+CtklomjB/P00//f+ydd3hU\nVfrHP3f6TCZlZtJ7SEIKvYbQO0jTFayoCHZ0Lax1FRVF/Omqa1tdK4sFXMBGU1wsNOmdkJCEkgBJ\nSM+kTSYz8/7+GBiMFMWy7q7zeZ7zwNw559xzzr0397xz3vN9/0JJSQkffPAB8YFmxibGUN3Swit7\nCtBodW0C+S5cuJDDxUc4JML1me3I7NSefx0pZXdlLQ6P8OSsWZhMJj744AMe3LiHWzqmoFereH9/\nETvLq/n0jjvOeg8nJSWxdfsOLrzwQj7dsR29Wo3NqKPO0Yruey6CerV3fE7Ob2/74+2MGLGUBxbm\nMn14Im638PzKgxRVNDJ9+vSznvPXpKysjLKyMpKTk33uuL8Gvz/DSmdCldITRa1FRJBjeUhtKY7m\nZgBaC3f5svbr14+LLrqInTt3ora0XbIVpzd+QmCnXmgCvBfIWVmGvSCH7t27s3vfPlwOBy01Vegt\n3j/KnlYnzooyLrn8sn9PX/348ePHj5//Ih544AEqyivJTMpCrzMiIhyvKuYvf/kLl19++a8i8vHk\nk09y/PhxFixYwHcFvZqbm+napSs33nQjzzzzDLrvxJv8IaxWK7179aJwfwHxoRF4PMKWA7kUVXpj\nHc2ZM4eKigqee+457HY75RUVdEju3KYOmykQs8HIoEGDCAkJYfny5ae+s9moqqpi3pbVAERFRuJp\nsFNUW0lqaBTbjh0ir7KEveVeNzetSo2iKDw+ezbFxcW8//Zcn1F1ksQQKysLcnnppZfQaDRtznXV\n5Mk4PW4+LTjlxhUZEMilaZ0RETaWFKNCYVd5KUMTkqlqbqKloY69u/cwatQoAFKSk3nl1VcZMWIE\nOTk5PP7444xKacfw5EQURaHW4eDFDVuoa2kh0RKMSavhy+IiMqw2Pj1YQF2L16hSKwqRJpNXQe+F\nF3js0UepPqFKqFIUPCLYUXhy+2ZfWwO1Wp7Zts33uVtYGJkWC/ds+JZqR4uvbGljE3BqF74lJASV\nw8H6kjIuSk7EZvSKcmw97jUkKxwOJoYnthnHMJOBGLOJ4hOrWfur68ipruX111/nkksuAeDVv/+d\n3r178detOb7d/qFWK3fcdRedOnRgX14eAHP3FfL2vsIT1ziCZStWEBnpdfVrbGzkxuuvZ0hMGJEm\nA3NzD+E+cf+eNEjvueceAHQaDRsq7az6bD0ARoOBZ5991reX72yUlJSwbds27u+dyrUd4yiyNzNq\n8QbeyT3CDZ28/Xa6PbyTe5SunTr5VLCHDx/OK6+8wj13/4n3v/Xeg8FBgcybN4/evXuf7XS/CpWV\nldxw/XV8umQpIkKAycgfb7+Diy+++Fc53+/OsFKsUShqr5+voigQmYzUlIAhAAQ0tljctaVIk50H\nH3zQ9+uKp96O+jt/hNzV1ejConxGFYAuNJLWsqNER0ezPz8ft0pNbc4O9LYwVFodLVXHsQYH/yTZ\nVj9+/Pjx4+d/GRFh0aLF2IKi0eu8LlyKohBhi6Oy7hiLFi2ie/fuuN1uPvnkEz7++GPcbjdjx47l\nsssu+1F7eM6EXq/n/fff5/HHH+fiP1xM7r5cUm2JhBiDqGis4pW/vYLb7eZvf/vbedX73F//yvBh\nw/l81ybcbjctra30jE7FZgqmrKGa1/7+Gi6Xi5dffhmz2Uxlo51Ea7ivfH1LM40OB+3bt+eJJ54g\nNzeXnJwc4uPj6dWrF8XFxWzdupXQ0FD69+/PZZddxkcffUR8iA2tSoPL4yIu0IoglDXZ6dShIzfc\ncANvvvkmNY0NNLU6fbLqAKX1dqIiI9sYVU1NTcy46y7MGi29wqNwelzkVlZQ73RiUGvYVHqEwroq\nqpoaCdTrSbOEsr+2imN1tWjUalS1dVyb4RVaWFt6hHHjxrFt2zY++ugjTHo9Q9ol+Dx7QgwGBibG\ns3x/IWpFYUJ6Kh/syWXL8VK6hIcxKD6WZpeLzw4c4mh9PYmVldx5550MiImmSqWioLaW8e2TSAwO\n4vlNOxgYHU0Hm42k4GCsej0bSkt5a98+rkhNJSEwkKe2b6d7TBgdtGrWHS5jbHIcPSLDOFbfyMK8\ng9Q7ndTU1nJPt87Mzc3nT2s20CcqAofLxYZSr6iHRa+joNbO6O9c93pnK6UNTRjVap7ZtocNZRVk\n9e7NVVdd5cvzyCOPcLy0lGs7tqNbhJW86jrm5RxmyjXXkB4SxJzendGpVLxfWMyu6hr++tfnueWW\nW9rc419++SV19fXc3L8jcWYTV6bFs6eyjiMNTbyy5wBdLEHcmN6OVo+HN/MPk1NjZ86cOWRkZDB4\n8OA2CtVnIj8/n1tvvRWdWkW1w0lJg4PEYBNTO8bz1NYCVh+tJM1i5qtjVZQ7Wvls/gttvLRuueUW\nJk+ezDfffINKpWLIkCEEBAScxxP08xERJowbS+G+3bw6qT2dos0sy6nkqaefory8/Fc55+8vQPD3\nVX9Uat9xRaVGbYtB264HGrOFWbNmMXz4cMLCw2nO3YurtgZxuWg9Xoa4WgEFd1Mj8t2gZyoVRqOR\nHdu3M23qtYSG2lA7GgmUVqbfdBPbt28/zT/Wjx8/fvz4+T1RX1/Pvn37Tgu+63K1olJ9X3ZbQaVS\n43Q6cblcTJw4kUmTJrH0k2V8tuxzrr76aoYPH4HD4fhZbWpoaGDX7l10iEgl0RpLiDGI1NAkUmwJ\nvPHGG1SdiB10LkpKSti/fz8ul4t+/fqxectmBg0bSmOLg94xaaSFxhFqCqJjeCKdwxN5++23qamp\n4eabbya34gj7K47hdLuobLSz5lAONpuNSZMmAZCRkcGkSZPo3dsrz56QkMDEiRMZNGgQarWaDz74\ngJdffpnI1HbEJsTRuWtXDOFWLAmxPDRzJmvWrsVsNnPVVVdhMBhYmreXisYGHK5Wth0rZm95KX+8\n/fY2/Vm4cCFHjh5lVFIK3aNiGNUujTt69SMmMIjyVgfVATqGjx/HSy+9xMBhw6gNMNCtfz9Gjx5N\nkN7ANemdaW+x0d5iY0p6ZwI0Gp5//nmcTidaleo0JTudWo0Aaw4X0zkynECdjrigQKZ16Uiq1ULn\n8DBu69kNtaKwe9cuuoeHMyYxkdzqaiZmpDA6OYH0UAv94qLZWFZGq8eDSaPhQF0dK4qKUAHby8tZ\nVFhIfIiZW7Iy2X6sklHt4rg8M4VUazCDE6K5rUcHnwBFqNHAnOyeDI2NJr+mluL6Bjx4V7hcHg9f\nFZfwaWERDc5WiuwNPLVlN4pajRIWjt0WwaOPPc6qL7/0xfAqLy9n7ttvc01mIldkJpJuC+Ki1Dj+\n2C0Vl9vNrRnJ9IkIpXuYlaezOhMfaGbt2rWn/XDgPBGny6hWU+9spd7poneklW5h3r1kf8xMoXeY\nlX4RobyS3Q2zVsPixYu56KKLMJvN7N+//zQhjJMsWbKETh07kr97Fz3DglmQe4wxH25kSWEpM3q2\nY3y7CLYcr+H9/Uc5Wt+E0Wj0raR9l6CgICZMmMC4ceP+7UYVwLp169iwaTP/uCKN67NjyEoI5vEx\nyfxpcBzz57//q5zzd7diJbXliC3WJ6sqld5gaTTXo5wICqgoChIQwqZNm+nVqxe1NTWIR2jeua1N\nXc7jR3EePwqKCl1YFLrQCJx1NWi1WlJSUnjzzTd58803/6398+PHjx8/fv5TaWlp4Z577uH111+n\npaUFnVbHlGun8Pzzz2MymRg5ciRrvllHmCUatco7Ramtr6CpuYELLriA+fPn8+mnn5Ke0BFrUBgA\ndQ01rFu3lldfffUnCU2cZM+ePQCEm9vuqQo329hfcZCCgoKz7rcqKCjghutvYPUar2teZGQkc+bM\nYerUqVx11VUsXbqU6MC2ZaODbOwoO0B+fj6zZ8/m6NGjfPDBB3x7OBfwBs39+JNPfvSEVKPRMH36\n9B/cwxIaGsryFSu4ZNIk/rFtI+Cd99xwww3ce++9bfIuXrwYjUrF/BzvNonYwGDGpqTRPSKapYV5\n7M3J8blI3nbbbb5yPbt3J8kc5HNJA697WlJAELt37uLa56/liSeeYGfpcbpHeyfkTrebTcdKiY6K\n4tO8QpbkFaIoCv1jo9sYYAFaLe0sIeyvqqZDRjqljU0I0Ok7KnqXZqZS7XDwZs4p10W1oqDTqNl/\nwpgPDzBwqMZOY6uLrt9T4MuwhaBRKWi0OpYfLubWTplcnZ7KVZLCP/LyOdLQ6JWnR0GAuTkFzM0p\n8PXzs5UrGT58+BnHf9WqVbS6XPSOanvOXic+lzQ1k2bxCpWoVSq6W4PZvWPHafUMHjwYnVbLnWt2\nUFTfhEsEo1pNmFGHQaUi03JK7MSoUdMr1MKuQ4d49913eeC++zhWWgrAwAEDeP2NN0g7ESzZ4XBw\n3dRr6R8RzHP9Mjne5GTmpjw2l9dyz+p9PLXZ65o4ODaUSSmR3PL1XnRuJ7fecgtfnQhq/Z/Cnj17\nUKsURqa13c4zOt3GX74q/lXO+bszrGiuw1OwCSUwDHHUQ0M1oIBKhcd9SlTC01QPOj279uXhaW3F\n1C0baWlGnC20VlXgrq3CEJOAJigEV30djqNFOMtLULRa5s+fz6BBg7jxxht/u3768ePHjx8//2Hc\ndtttzH17LqEhcZjDQmhsrmPu3LnU1dXxz3/+kyeffJJ+/fqRd3grQSYbre4WauwVjB8/nmHDhjFu\n3DiCzRZAofBoHiKCJchGiNnKggULfpZhFRfnDexa56jHZjqlIFfnqEdRFGJiYs5Yrr6+nsGDBtFY\nW0/vuHQMGh2Ha8qYNm0awcHBxMbGAlDTXE9k4KkJXnVzPeANuaLX61mwYAGPPfYYW7ZsISwsjCFD\nhrRxy/slGThwIEeOHmXVqlXU1tbSt29fEhMT2+RZuXIly5cvp31IKD3ConG4W1lXUsR7e3eQYrUR\nFhp6VvfLuIQEthw42EYVTkQodTTRNzGBfv36cemll/LBokXsLa/AYjCQU1lNo8vFJwsXcfmll2Lw\nuGl1uTlir29Tt8vjoaypGaPBQHF9PcnB3m0axXX1WAx6tpaUs6+ympPb5R5//HEefeQR3B4PfWyh\n9IuIpLqlhY8PH+LvG/ehUSkcrqunU/ipa1PS0ITLI9x43XW88sorlDQ108ESQm5NLfm1dUQYDQyO\nieSfhYdpF2QmQKvhSH0jtc5W5r377lmNqrq6Ou48IRhRWFNPYvApVcDCGm8/w4z6NmUK65uI75J2\nWl2hoaEY9HqONjZxc6ckOliD2Hy8hnl5RahR+Ka0gvXHq1CrFAZFhpJTY0dnsXLNNdcwMj6cmUM6\nU+Vo5c09Oxg6eBD78vYTHBzM119/TWV1DXdl98YtMPWrHWgUhaey0okw6vnwUClLi8qpa2llxeEK\n4swGDHoVX69eTUVFBWFhYWfs+9koLy/n7bffZvfu3cTFxXHdddfRvn37Hy74I4iNjcXtEfaUNtI5\n+tRY7zhWj1oBt5yj8E/k9+cKqNZASxNSWQwNNYACYTEQaEHxeBC3C1f5YaS+Em1YPCqrdyOeSqNB\nE2RBHWTBXVOJMT4ZQ2wimqAQDDEJGBNTACEwvQs6Wzizn3iizSZYP378+PHj5/dMWVkZc+fOJdya\nSIQtkQBjCOHWBCKs7Vi4cCEHDx4kLCyMZcuWcellk9AHQGxCJM8++wwffvghiqLQ1NREs6ORvKI9\n1DfV0eioJ784h4bmehoaGn5W+/r37++V7C4voLqpFo94OF5fSUHVYcaOHeszvL7P+++/T1lZGf0S\nOhAZaMWsN9IrNp3IIBuzZ88mOzubjh07srWskPJGb73H7FXsLj/MqJGj2hg0qampXHnllYwYMeJX\nM6pOotPpGDNmDFdeeeVpRhXAk3OeJC4whEuSO5AcbKWDNYLJaV1wuFzsLj/OrbfddlYJ/OnTp3PM\nXsfywwU0tDppcDpZdqiA0no7t9xyC4qi8P777/PCiy+iiYqhyAOjL7yQzVu2sH37dhobG7m1W2dG\nJMWzt7KKlQcP0+xyUeNw8F5OLg1OJ1OnTWNdSSl51TUkBAYyf08es9du4e1d+yitb6CiyStEUXT4\nMB6Ph642GzekZ5BpsdA/MpK7O3ehqrmFJEsgnxQcZuOx47g8Hg7V1vPythyiIiJ47rnnWLlyJQk9\nerCi+AhF9Q3oVCpiAkwMjIrg1o5pGNUajjc5vCtyiYlceeWVZx3zd955h+qqKtJDAnltZyGbS6tw\nezzsrajlua1eZcfPikupcrRgd7byZu4BdldWM/3WW2lububgwYO++3zJkiXYGxq4v0d7JqfF0zUs\nhBs7JnFDhyRcIvx5Ww6F9Q3sqanjT5v3UO5owajX0yvSylN9M+gTaWVsYgSvDOxIeXkF77zzDoDP\npdasVbP88HHKmlp4a1AXLkqKJDvSwl/6ZNA/0sLuSjuljQ5KG1soqvUKwLWcEBj5sezatYvM9HQe\ne3gmh1d/zpsvv0CHDpksWrTovOo5GxdccAEJcbFMWZDH1iN2Wt0ePtpVzuNfHGZkR+sPV/AT+P2t\nWLlP7IeKboei0SLFeahCo/DkbUPEg3PfGgA0obGorVFIawuu0gM4y46hj0nAcyLYnMbSdglXa7HR\nfAg8LQ60ITaOHMilvr6eoKAg/Pjx48ePn987OTk5uN1uggLavj+DAkI5Rj4jR47kwIEDAHTs2JF5\n78xj8ODBbfJGRkbidDlpH5dBaIhX6KG2vpp9h/f4FMl+KiqViiVLljB27Fg25G/3Hc/Ozmbu3Lln\nLbdz506CjGa2H8unrN4bgNekNRAWEMyePXtQFIUlS5YwZswY/pV3qt6s3r155913flabf0127dpJ\n1yBrG+PJrNUTZQpEE2blz3/+81nLjhgxgueee4777ruPb0u9qnA6nY7nn3+eYcOGAV7Xxdtuu62N\nCyHAE088QUJQIIE6HVlRkZQ3NrG88CDLCg8CEGAy8d577zFx4kR27NjBwo0b8Zz4IbvZ7ea+7K6k\nWIIREdYeKeXNt94CoNv33DijTCbCDAYKquwowEvbTrkNGvQ6Nqxei16vZ+TIkYwcOZINGzbwhwsv\n5HhFBTurarh17SbUikLfyDBuyEzlwa27mHbppecc0w8//BCNSiGv1rs6NXPtLp8qoEalYvYTT/DY\nrFl8dsTrpqcAOq2WZ599linXXEN9QwMGvZ4p3wnq2z86tM05+kfbeG3vIf7UKZnLU2IREVYeLWfm\n1jyOHDvKHzomtLmmUQEG0mxB7NrldfccMGAAep2O9/Yfo9nlJjkogPhAoy+/oigMjw1lfVkNC0Z0\n4Xizkyu+2EmNqM66qns2brhuGpFqN4su60WoUYfD5eG21XlcN20qo0eP/tmy6Js3b8ZkMpFbcIw+\nf93qOz6ig4X7xsTz2Z7qn1X/mfj9GVahCWA/DqWHEJ13I6EndysoCqBCMRjRJXZCdeI7tDpUag3O\nIweRRjtovMve7qYG1IZTN5q70fsLgkqnx1lVjjkw8DfZqOfHjx8/fvz8J3Jy0uVoaUSvM/mOO1q8\n78+So2UkRqWjoFB08CijRo1m69YtdOrUyZf32LFjBJqCfEYVQEigFWuQ7awb8c+HlJQUcnNz+frr\nrykqKiIzM5OsrKxzBicODQ3F3txAq1ZPz9hUDFodh6qOU1R7HKvVyt///nc2b97M+PHjuffeexER\nMjIy6NOnz68S9PiXIiY6hvLjlW2OuTwealpbmH7JJT+ownjXXXdx1VVX8cUXXwAwatQoQkNDz1kG\nvPfJiuZmXB4PGpWKCanJDIiL4e3dOUhwCDk5OQQGBrJgwQI2bNhAr7BwssLDebcgn07hVlIsXtdA\nRVEYEBfF10fKKG9yUNzQ2OY8ja2t1JxYYUkJCSbCaGBvTR0Nra0sX/EZXbu2DZ6bnZ3NjLvv5r77\n7mN4bCRZ4TaONjSx8EAxG49XEh4RcU5X1E8++YTVq1fTLyqUUfGRVDtamJ9fjNPtISEwgPrAEO6/\n/34+/eQTNm/ZQoRRx8DYMHZW1PLt+vVcnhZLr4gk9lXXM+/tt0jLyASgsLaBrmGnFP4Kar3P08Co\nUN84jI6LYOGhMg42O8mvbTsOzS43xfYmJp54PkNDQ3n4kUd48MEHiQ80Ud7koN7pIlB3ymTIrWkg\nwqRHURQiTXqmd4zngY35VFVVeeX833+f2tpaBgwYwGWXXYbRaOT7HDhwgC3btvP28ExCjd59egaN\nikez2rFkwSY+++wzLv0BQ/Vc5OXlMXLEcDpZ9fxjTBp7Kxv5YF85ZU1OXrwylcYW90+u+1z8/lwB\nA62Q2BUQcHqXLtHqQDyAB3E04q4rRzxupNVJ69F8xOPmscceo1NSAiG4CQ4JwVl8EJe9FhHBVV9H\n8+EC1AFmXE2NtJaXctONN6JWf1/ZyI8fP378+Pl9kp6eTv/+/Tlec4iGZu/7s7G5jpLKAhRFRWps\nZ6yB4VgCw0iO7ohapeG5555rU8fhw4fRqE+f0GvUWsrKyn6RdqpUKoYNG8a0adN+lPFjNpvxiDCw\nXSeSbFFEBdnITszAagzEXmfn1unTWbb4I15+8SWuv/56RITs7Ow29ZaXl/8o1cF/J7fcOp3cmgo2\nHz9Kq8dNvbOFZUX7cbhcTJs27YxlRISysjJqarwrd2FhYUyePJnJkyf/KKMK4LrrrqPR2cqC3P3Y\nW1pwut3sPF5Bsb2eBx54wLeKMfvxx+lsszEtLY2OVisqRcGkbbteoCgKJo2G5JQUVpeVsrasFJfH\nQ4WjmdfyctHodMyaNQtDTCyFHmHIBRewYeNGhg4delq7WltbeebppxkZG8VNmal0DbUyLjGWGV3S\nafV4+OsLL5xRGe8kTzz+ON3CrTzUM4PeEVZGJ0TxZHZn6pyt7K6q5ebp05l48cVs2ryZSJMevUbN\nwvyjHKhtZGqHBG7p3I6eERauyYjnrq7t2LV7Nwadjjlb95NbbUdE2HK8hpd2HSDSqCc6wNDm/EEa\n74rS8sPHWVhwDKfbw/EmBzM35uFwe7j22mtpbGzk2LFj3HvvvSxYsICw1HScHuGejbmUNDpwuj0s\nOlDChwfLuCI1yld3iN77TM6aNYsePXow//VX2bLkQ6ZOnUqPbt3Iz88/bTyaT8SPtejbPs8n62ps\nbDytzPnw/PPPE6JV+NclHbksI5zHBySxa1pPAnUa/m95EQ6n52fVfzZ+f4YVoGh0YAyCQCtKUCi4\nW1GndAedAVBwlR7EsXctjtxv0TTV8o9//IOZM2eybetWyo8fZ++ePbRPbkfDvp3UbV5DQ84OPC0t\nuBsbaDqYx7hxY5k9e/Zv3U0/fvz48ePnP4oPPviA1PbJHDy6k70H1nDg6A5UKgg0hbQxmFQqFQH6\nIDZv3tymvFqtprahGkdLs++Ys9VJVV0Fqu+HU/k3cfjwYSwBgZj1bd2l3OJBo6gYk9aD4e06M759\nD5JCwrn5ppsoPaHItnbtWnp0705ERAShoaEMHDDQ55L1W3PLLbdw880388WRQp7evpYXdm/gQGMd\n7773Lunp6aflX7FiBR0yM4mKisJqtTJ61CgKCwvP+7wZGRnMmzePnJo6Zq7dwL1fr+WTggNMnz6d\nm266CfBKje/LzaWL9ZSrYkZIIF2T2AAAIABJREFUCJtLymlsPSVEdtTeQGF1LbfeeiuTLr2Ut/bv\n58Z1a7ln0yaKXC4+/uQTHn74YXbv3Utp2XE++ugjevbsecZ2HTt2jIqqKnp/T0Gwi82CUauluPjc\nKnPbd+4kO6Kta2WM2Uis2UhaejoJCQl8/Mkn3NMzlfcu6MncUT24qXMSbhEGRLc954AYr5F654wZ\nlLe4mPbldvotXs3ta3bR4HJjb22lusXpy1/c0MTG8hp6Z2Vx7dSpzNlaQJ9Faxn16UY2Vjfy1ttv\n89isWVgtFmJjY0mMi6OqqootW7fy7HPPsbq0iiFLN9Jl8Roe2pJPR6uZGzt49xy6PcL8/BI0isLL\nL7/M9MxY1o/vwbJRnflsTDeOHiwkLS2Ngf37sX37KVfYoKAgAgwG5u4raaNJMHffMVQq1RmN2/Nh\n2+ZNjIgPwqg9tchh0qoZnWTl/Q3H6f9/pyst/hL8/lwBAREPOJtRjKEoYbFIXiU4GlGHJ+A+uh+A\nDh06cN999zFu3DgsFkub8rGxsezZvZuvv/6a/Px8YmNjaWpqoqamhj59+py2fOzHjx8/fvz48bp5\n7dy5k2+++Yb9+/fTrl073nzzTT5f8UUbBTkAp8tx2p6N7t27U1xUzO4D2wmzRKJSFMprjiMiZGZm\n/ru7A0BUVBRNzhZcbjeaE54qrW4XdY5GesQkE3jC4FKrVHSNTuJwbQWLFy9m8ODBjBgxgmCtgQEJ\naXjEw74dOxk4cCB79uwhPj7+394Xh8PBwoUL2bBhAzabjTvuuIMZM2bw1VdfYTKZGDdu3BkDy65Z\ns4YJ48eTGBTMZanpOFwu1q9bz4D+/dmXm3vaPOqHmDx5MmPHjmXZsmU0NTUxdOhQUlJSfN9rtVqs\nISGUnBCoABgdF8/OqioeWbOVfrGROFxuNpZW0KFDB6ZOncqtt97Kgw8+yLp16wgJCWH8+PG+LRsN\nDQ3Mnz+f7du3ExUVxZQpU04T9LDZbGg1GoobGukWdkr44Hizg+bW1h/c4xcVEUGR/XQ3vCqni4mj\nR/PII4/4Vmta3B4MGjUDYmy8tvsQB+1NJIecUrU7VOetZ/To0cyaNYs5c+bwr3/9C0VR2L8/D3t1\nNZO/2sbY+Aicbg/Li4+jUyvU1dayZOlS7r33Xr755hsCAwMZO3YsY8dcwJ7t27gtM4Z2QUZWHa3i\ntttuQ0SYNGkSd911F1d0jiQj3MzaQzV8ebCKe9bnkRoSwMriSvbVNJAcbKSquZW7OiegUXmf40yL\nmalp0byee4zK/N0MHTKYnbt2ExUVxagRw1GLmyWHKihd2sLwOCu7KutZcbiKO+64g4SEhB97u5yR\n6NhY9m07dNrxvVXNBAQGERwS8oPG8E9CRH4XCegOCPGdhJAIAUSV2l1UHfp5/x+dIurEjgIIIElJ\nSdLY2Ch+/Pjx4+fnsW3btpN/W7vLf8D74D8lnXwvbdu27WeO8H83q1atEkAiLLHSJbmvdEnpJ1G2\nBAHkww8/bJP3q6++EkACDAGi1ehEq9FKgNEsgCxcuPAXaY/dbpfa2tofnf/QoUOi0WgkLiRcxmf2\nkYmd+0vX6HYCSN/4dLmiywBfuqxzf9FrdTJnzhy5+uqrJchokqu69JUp3frLlG795YpOfcSg08m9\n9977i/TlfCgrK5P0tDQBJDIwSAL0elGpVPL666//YNlRI0dKTGCQzMrqJ4/36S+P9+kvd3frJRq1\nWp555plfpb3333+/aNVquS4tXV7pP0Ce7J0l6RaLqFUqsVksEhsTI3fffbdUV1efs56DBw9KQlyc\nqBRFEkOCxKTTiVajkUWLFp2W95qrrxazXid/7t5RFo8cIH8b0EsyrCFis1p/cM44a9Ys0ahVcne3\nNFk+boDMH9lHBkSHiUatFpVKJSatRhICjaKAxJgN8s9xveWrSwZImFEnVoNW/jaki6yeNEDmjugu\n7SyB0j4lRdxut9TV1UnvXj0FkHYhgRKs0wggGTaz2IxaCTfp5LKMaMmKDpERI0ac1q6Tz9RrgzrI\n3sv7+9JFSeESGR4ura2tMnBAf7EatfLepZ1l35395cZesWLUqESjKKICGRVnk2kZ0ZJgNkjR5AFt\n0qM924lKQfZMzxJrgF5mzJgh7733ngDy9UXdZP6ITOkbGSxWvUaCdBqJjooSj8dzfjfDGVi6dKkA\n8mB2vNTc0U+q7+grD/SJE0CsZp0khJp+lffSb/5i+Xcln2EFgqKIEpMq6k4DRIlOFkDUKd1FCQoV\nNHpBoxMURR555JGfci39+PHjx8938BtW534v/d4NKxGRp556SlQqtSiKIipFJYqiyIMPPnjGCdbT\nTz8t6pN5Vd68DzzwwM+ejOXk5Mjw4cN9P7Bm9+kj33777Y8qu2jRIjEaDKKAqFVqASQ4KEgiAy1y\nWef+PsMqO95ruGzcuFHS2reX9NAon1F1MiWE2GTw4ME/qy8/hcmTJ4vZYJDrOnWVB7L6yT29sqVb\neISo1WopLi4+Z1mrxSJDY+N9RtXJlBQcIpdffvmv0t7m5ma5cMIEAUSr9o65OSBAli5del71jBo5\nUiICTPJs3+7y/vB+8taQPtInMkxMRsNpRllNTY0M6Of9QV6n8Z4z1GaT9evX/+B5Wlpa5NJLL/WW\nVXvvX5PRKBq1WkbGh8vy8X1k1UX95K1h3cRm0MmAGJt8PKGPBGjUotN4jSWD1vtvfFys5OTkiIjI\njBkzJECnldcHdJKNF/WTdROyZUr7WAHkvQndZMvUAfLxpJ6i06jl//7v/05r19NPPy0BOq3suaxf\nG8PqbwMzBZCOHTLFbDaL2hsLWXRqRQAxalSiOvGsbLmkt7wxJEMAeX9YR59RVXB5P+loDZC+ccFy\nZEZ/mZAWKgP795M777xTkq2Bcnxa/zbpmX4pAsiQQYNk165d53Udz8SsWbNEpVKJRq0Stcrb7uEZ\nwdL6al/Z+mCXX+W99Lt0BcQQAG4X7uI8qKsAoxlPSSHSZEcdmYz7+CEUYwD/mDePRx999LdurR8/\nfvz48fM/zb333svkyZNZunQpHo+HMWPGnDG2EsA999zDlVdeydKlS3G73YwZM4akpKTT8jkcDhYt\nWsS2bduIiIjg6quv9gXr/T4lJSUM6N8fl8NJx8h2qBQVebv3MXTIELZs3UrHjh3P2f5JkyYxbNgw\nPv30U+x2O4MGDeLIkSNceOGFfHVwDzGBVupbmjlcW84fLrqI3r17ExkZSeH3VPdEhPpW5zlFEH4N\nnE4nCxcupF9kNOEmr3tcq8dNiN6IeDzcfvvtzJ0794xugAAR4RFUfk98w+3xUO1sISIi4ldps8Fg\n4JNPP2Xbtm2sW7cOi8XCRRdddF5hbiorK1n5xRfckJFCpMnrsmlQq7kmNZFb125hxowZvPTSS5jN\nXje8kJAQVq9dy7p169i2bRuRkZGMHTuWVatWcddddxEcHMyVV155xgC3tbW1ZGVloVar8Xg8DB48\nGLvdzkN//jPTOyahP+FGmhBo4rLUGF7dc4jc6gY0JhO7tu/gwIED7Nu3j8TERMaOHetTZXx33jwm\nxIXR2ebtt0al4ob0eD45XMbTGwvpGhHM0gMVxMbFceONN57WroiICJpaXZQ1tRD1HcGLg/YmbxDd\n0iKaGhq5JjkKg1rFypIqDjU4uKdPAo+t87raFdY1MyTGSlZEENd/s49LkiOIMun59HA5h+qb+WBk\nJ0SEPRXN6Klk69atHLU3YXe2EqQ7tbeyoLYJs05Nyb6tDBo4gJ27dv8sl8CHH36Ya6+9luXLl/P+\n++9zdP92Vt7R4ddV4/wlrbT/5MTJFStbjPfXDa1WNFrtCWtVEcUYKOqoVFECbYKiiCosSqxW20+w\nj/348ePHz3fxr1id+73kX7H65XC73WK326W4uFiSk70eKUEms2g1GtFqtbJ48eIzlnvooYdEp9HK\n8NReMiqtj4xKy5JRaVliNpjkmmuuOa82eDwesdvt4nK55Msvv5TBgweLyWSShPgEmT17trS0tIiI\n+NyhekYnylVd+sqVnbOlU4R3pWHVqlW++hoaGnxlfi3q6+sFkHHtUuWBrH4ypUNnMag1olIUsRoM\n3n+tFtm+ffsZyz/11FOigFzULkUezeonf+7ZR3qFR4oCv8jKw49pv9PpPO9yhw4dEkDu7poh7w/v\n50vvDO0rakURBSQ2Olry8/PPWL62tlayevUSQKIDzWLW60RRFHnppZfa5Pvqq6/EbDKJTqOWhJBA\nUSmKREdGyp133ilmvU6+uLCvrLqony891Mu7snnhhAlnPfdJjAa9/LFDomy8qF+blBhkEp1OK6E2\nq9x0001SWloqDodDmpqa2pS32+1iCQ6WrEiLfD62p2y8OEteH9RBgnUamZAcJjuv7SNxgXoZHWOT\nHROyJPcP2dI7NEg6hXldcKMjIyXNGihfTOguOVdkyyXJ4aJTKaJSkBSLURZd2kn2/zFbekYHCiA2\no15ig7xueJEmney4tKeUTu0nbw1NF4NaJdP7REvh3b3FEqCXP/3pT+d9Tc/GZZddJv1TQ8TzWj9x\n/C1b1t3bye8K+LM6+l1XQJCY2FhZsGCBZGR6lzo5sXSPSiWapPaiMQXIpEmTftLF8+PHjx8/p/Ab\nVud+L/kNq5+P2+2WZ555RiIjI73uSjqdaNUa6ZXUWYZkZMuA9r0lPChUDAaDVFVVnVZ++LBhYgsI\nlnCzxTdPsJmCJDLQJinJKT+qDR6PR1544QWJjo4WQCwhIfLQQw+ddcLv8Xjkjjvu8P7Yq9H49trM\nmTNHRET+9a9/Sc8ePQQQjUYjV1xxhZSUlPz0QfoBunbpIgnBIXJPr2wJ0eslxmyWO3r1kAf7Zcsf\ne3aX6KBASWvf/owul48++qioFK+rlU6l8hklwI92p/wpfPrpp9Kpo3d/vF6vl2uvvVYqKyt/dHm3\n2y0JcXHSK9wm7w3r6zOsbspMFUDu6ZUm0UEB0r9f3zOWv/3228Wk08qcnh3kkxHZsnBoloyLixRF\nUSQ3N1dERBwOh4SHhUrX8BD55wU95bMLs2Xu8G4SH2yWDhle97mHe6X5jKovLuwrPSMsPvfG2Oho\neeGFF8447k6nU1LatZN2gSZZMz7bZ1S9NaizADJ//nwRESkoKJAJE8aLSqUSQAYOGCCbNm3y1bNi\nxQrR6XSiOuHup1aQ+ECDbLiqt6y7spekhBh9bn+ZwQFyRVKE7/p+8cUXEhfjveeDDToBJLVdO7nh\nhhtOuA6qfG6ED3SLl+LJfeTYVX3k7cFpolIQ5YRbIeA7x7DkEBmeHCL9+2afz+1wTl599VVRFGRU\nZohoTrgF/hrvJUXklMTh/zKKonQHthEUjmIMhIYqpLGWBQsWcO9993Hs2DHEFAgBASi1VahcLm64\n4Qb++Mc/kpGR8Vs3348fP37+a9m+fTs9evQA6CEi238o/++Fk++lbdu20b1799+6Ob8ZLS0tfPzx\nxz5FtsmTJxMeHv7DBb/DzJkzmT17NpGWMELMwdibGiipKiMqJJz0qGQAnC4n3xZu5/XXX+eKK67g\nn//8J6tWraK6upqDBw9yoLAQg1pHYkgUKkWhqO44Dc5munTtwo4dPyzNPHv2bGbOnEmiJYzIgGCq\nmhs4WFPOVVddxT/mzTtruby8PFasWIFGo+HCCy8kISGBNWvWMHToUMJNAaRaQml2ucitKicyNoad\nu3b51Ox+ST7//HPGjh2LzWCkoqmRKZ06EhsU6Pv+UG0d83P2sWXLltMkyVPatSOksZGsqEgO1Nah\nValIt1qYm5vHpVOm8Morr/jy1tfX88EHH1BQUEBqaiqXX365LzbV+bBs2TImTJhAujWYrHAb1Y4W\nviqpIDU9nU1btvxgAOOTzJ8/n8mTJ9PBGkKPUAtHG5tYXVJO70grt3dvz4aSSl7cUcChQ4dOc0+1\nhAQzxGJmSqrXXc3hdrO6tIK38osZNnIk8+fPZ926dYwfP56/D+lCQtCpwNjrS6qYvSWfoUOGsG7t\nGkbHhRFnNvJNSRU5VXayw0IYFhXKlqo6Vh6r4PHHH+ehhx5qc/5p06Yxb+5cUCAp0MSYuHCqW1r5\n+HAZwbZQ7v/znxk9ejSDBg5A3dzAFe3D0KtVLC6s5EhTK5s2b6FDhw6MGjmS9Wu+4aruEbSzGflX\nfhVfFtQwpl0oG47V4nC5ub59NDEmAx8XV7C5wo4C3Hjzzbz66qu0tLSwZMkSDh48SFpaGuPGjUOt\nVjN40CA2fLseq15NgEbN2gld27jh3bqugJUldhxOJ3EBOqZnRiEovJFXxpHGFoYMG8HnK1ee971x\nJoqLi2mf0g6LUcWfhkRR1+xm9hfH4Bd+L/3uDCslrhOKIcBrVZbuJ7NdHF9/9RUPP/ww7733Hg2N\njYjHg0ZvAI8HV6uT2bNn8+CDD/7WXfDjx4+f/0r8htWZ8RtW3r1NQ4YMIT8/H5MxgBanA41Gy0cf\nfciIESNQq9WoVN74VC6XC0VRUJ/Yi+J2uxERGhoaiIyMJDzIRlLUKYnyoxWlHCwtIjulOwatHhFh\nfeFW7poxg3feeccXUFir1tDqdqFSVAyJ74Ze452QuzxuVhfvpFd2FmvXrj1nP062IcYQRNeoU3tC\nCqvK2FFWRGFhIe3atfvR4zJ8+HB2b9rM+JRMVCcmorWOZj7M3cXrb7zB9ddf/6PrOh++/PJLbv/j\n7ezL3cdtPbsTrNf7vqtqaubvO3ayatUqhg0b1qZcqM1GZ5OJofFxbY6/lbOP7DFjmD9/PgC7du1i\nxLBhVFVXYzMFUNXUiM1q5V9ffkmXLl3Oq609unWjqfgwt3dJ843Robp6ntqWw+LFi5k4ceKPrmvJ\nkiXcdeedHDp0CKtBx9D4CCYkR6NRqdhfbefRDTns3LmTLl26ICK0trai1WrRaDRMS41nXHwUh+2N\nPLYjlxpnK6F6HdXOVoKCg7njzjt59NFHWTymFwHfCWC8v6aeO9fsZf369Xz++ee89cYblFdW4nG5\nmBAfzh2Zp+6XV/KK+LzCTklZGWazGREhPz+f9PR0jGoVLW4PerUKh9uDSgG3QLTZRHmTA61Wi9vl\nYtkfumDRaxGg1ePh0uU5DLtwIjfceCMDBgzgxYvaMzTVKyO/u6SeqR/so9UjRBh1lDU5CTdomds/\nk6RAA1eszqHIraayutr3fH6fTZs20adPH14bnspHBZU0tXj454i2IRFmby9i3oEq9OJm6x+6Eqjz\njk9Ni4vuH25n1IV/YPHixT/6Op6N2tpaunTpTMnRI+x/qCtJNgPbjzTS85k98Au/l36XAYLBG7xP\nAqzs3bOHgIAAXnnlFWbOnAmALi4VTXJnNCld0IRG89BDD/Htt9/+xi3248ePHz9+/re4+eabKS46\nQlpMR9KiOpIZ2xUNGsaPH49Op8NsNjNx4kQGDRqETqfDYDAwduxYLrjgAvR6PXq9nlGjRtHS0kK4\nJbRN3Sc/25sbAKior6LV5eLTTz+ltqoagE4xyQxJ6441IIgwU7DPqALQqNREBlgpOxHM91zk5OTQ\n2NhIQkjbNsSHhCIibNy48bzGZcOGDSQGWXwGA0CIwUhYYNCvOh8ZNmwYX3/zNRqNht3lFW2+211R\ngUGvP+OPAAMHDSKnpoZWj8d3rKKpmWK7nQEDBgDerSeXXnIJ+hYn92V04Z7UDtyX0QV9i5PLLr2U\n8/mhv7W1le07d9IjrO0YJQUHEm4OYMOGDefV7wkTJvD5ypUIMCE5hotTY9GcMBjWHK3AZrEQExPD\nnXfeSUhwMHq9nm5dupCW1p7PjpZz76bd3LlpN7XOVvqHW3m+VyfezO5KBB7eeO01AFYVtx3PVcUV\nhAQH0a1bNx577DGOlZYyb948PMB1qW1jmA2LslHf2Mi2bdu4++67sYaEkJ6ejlqBMIOOT0d246ux\nvUgNMhFl1LN4UBc+GdiJJUO60N6kQ4Xw1OZDZC/YTNb7m/jTN/l0thlZv24tGzduJECvZXCKN9aY\n2yPcvbSAtBATX4/vxlfju7FybBfMOg33bC1EASbEhVJrt59TBGLDhg0YNGpGJ1jJigxiY7mdonqH\n7/umVjfLjtSiMxgZE2f1GVUAFr2GEbEWykpKzus6no3777+fkqNHyUoIJMlm+OECP4PfpyrgCcTV\ngk6vR6fTAfDmW2+hCrKiDjoR+E1R0ITHojTWMW/ePPr27fsbttaPHz9+/Pj536G6upply5YRa0vA\npPe6tjW1NNLY0kCA0UxocCjOVicff/wJIATpgvCIhxUrVqDT6Ii1RKNSFPbs3AOAw9lCgOGUq5Wj\nxTuJq2uqp67JTqm9gsGDB/PNN99g0hoID7QQfcIQ0qm1NDqavd4s35ksOjytRP+AW6KIUFBQAECD\n04HFeMpNr9HZAniDy54PVouVeoejzTGPeGh0Os+7rvMlPDycGTNm8Je//IUah4O4wECK7HZyKip5\n+OGHzxjsd+bMmfRdsYI39ubQLczruri1ooLk5GSuvvpqADZv3kx+QQE3Jqdj0XlXwiw6PWMiYng9\nP48tW7bQu3fvH9VGjUZDoNlMlaOlzXGHy019y48bIxFh06ZNrFy5Er1ez6RJk5g6dSrvzJtHcX0j\nScFmdlXUsqWsmueff55LJk1k07ffMjoxjMjkUL4tOUJueS0AaUFmbklPpLrFyfIjx5m5M5e/9OjA\n9clx3L0th7Fjx/LGZ59xuL6J9iFmtlXUsb6kiueeew6j0ehr08l2lza3kPqd1a2yZm8/Zz74IFs2\nbeLiuFDi4218VVbF5ko72yvtZISYybc38VT3VOJPqPuF6nXc0yGBq9btZVt5PdOzYjGoFT7MqWDr\ncTsZGZFYrVaanS4qG1sJN+vYesROid3JX4enEm70zo/jzAb+1CWO6WvzKaxv5khjCzZLyDkNK5vN\nhsPl5niTk8vTw5i3r4wLV+5lSvtIzFo18w9UUu2Cjh3bU3Q0/7TyRU2tOOpqmTVrFqNGjSIrK+sn\nqfm53W7efWcewUY1h6sdFJQ38/GeGg5WOH648E/gd7diJR6Xd4NZUx3UlBJoNvvcCqqrq1E0ujb5\nFUVB1Bpqamp+i+b68ePHjx8//1Wc3MT9Q9TV1SEiaDV6X/7S6iOYDAGkx2cQFhJOdGgMgaZAr8tf\nawONrY0AhAXaiLFEERUSSceYDFSKikNlxTS1NANeI+tgWTE6nY6jNaU0q1q59957efjhhwGvkWLU\nnnJzi7GEUu9s4kBtCR7xICIcsZdzvKGa66677qx9aG5uZvTo0Vx99dWoVSp2lxVjd3jb0NTaws7j\nxURHRZ3mOvdDXHf9dRTWVlFUW+11PXO72XismMYWB9dcc8151fVj+e51mzNnDs899xx2g5EVBw7S\nbA7kb3/721lD0HTr1o3Va9bQISuLz4uK2VhRyaQrrmTtunU+qfKT8yirTt+m7Ekjq7q6+ke3VVEU\npk6bxurSCnKrvfdRs8vFBwWHafV4mDx58jnLu1wurrzySrKzs3n2ySd57OGHSU1NJTk5mVmPPUau\nU8Wbew7SEBzGvHnz6NixI9+sXsOMHu2YnBnHsIQwHuqTSrfwYAxqFU/0SGdUTDiXJ8XwcNf2HGxo\nYmNFDWEGb9+mTJnC7CeeYI9TzYu7DlJltvH2229z1113tWnX0KFDiYmK4sW8YipOGI1FDc28eaCE\njh0yWbt+PY90SmJ6WjzjYsN4tkcaA8JDeC3vKLXOVgAijW3HN+rE55t6xnBttygu7xzJO5MysRg0\nmAMDmThxIgEBJmZ9cYiaplbsDhcAMQFt64kxeT+vLqvlg6IKrp12+nPx3ef+oosuIjgwkPvWH6bV\nLSwe34FOoQE8u/sIj24rol3vfqxZt47bbr+dtSW1vJ1XhssjtHo8vJJTwvZyOwfz83nxqSfJzs7m\nissvx+VynfvGOANOp5OmZgdqFRyrayX9iV08/vlR/rmj6ocL/xR+SSWMn5OAW4FDQDOwEeh1jrz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0NVNQyNDmZy+wRmdU7m2W5p9I/0zJ3r4sJ5rUtbXuycSo8QP948eJL1+cVoFHh4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nAw88wMI8N+1Skz8i6EIwcEkF4ezmrKqchywWi3HJxRfx37nzqJViJhjRCIQ0FEWhTnZt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\nZNM0TKy6CdDLl3T12n4226q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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(2,(10,5))\n", + "\n", + "pl.subplot(1,2,1)\n", + "pl.scatter(xs[:,0],xs[:,2],c=xs)\n", + "pl.axis([0,1,0,1])\n", + "pl.xlabel('Red')\n", + "pl.ylabel('Blue')\n", + "pl.title('Image 1')\n", + "\n", + "pl.subplot(1,2,2)\n", + "#pl.imshow(I2)\n", + "pl.scatter(xt[:,0],xt[:,2],c=xt)\n", + "pl.axis([0,1,0,1])\n", + "pl.xlabel('Red')\n", + "pl.ylabel('Blue')\n", + "pl.title('Image 2')\n", + "\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Domain adaptation between images color spaces" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "# LP problem\n", + "da_emd=ot.da.OTDA() # init class\n", + "da_emd.fit(xs,xt) # fit distributions\n", + "\n", + "\n", + "# sinkhorn regularization\n", + "lambd=1e-1\n", + "da_entrop=ot.da.OTDA_sinkhorn()\n", + "da_entrop.fit(xs,xt,reg=lambd)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Image adaptation with out of sample extension as in [6]" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "X1t=da_emd.predict(X1)\n", + "X2t=da_emd.predict(X2,-1)\n", + "\n", + "\n", + "X1te=da_entrop.predict(X1)\n", + "X2te=da_entrop.predict(X2,-1)\n", + "\n", + "\n", + "def minmax(I):\n", + " return np.minimum(np.maximum(I,0),1)\n", + "\n", + "I1t=minmax(mat2im(X1t,I1.shape))\n", + "I2t=minmax(mat2im(X2t,I2.shape))\n", + "\n", + "I1te=minmax(mat2im(X1te,I1.shape))\n", + "I2te=minmax(mat2im(X2te,I2.shape))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot all adapted images" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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3pmwM7MbRccnWdI+bXMVW7IMP7nkAbnSep0LNvb4EYHnkmwI+tT2zcsX8T1QVN7iSDz/z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\nhbEAZhnvD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P5nEfnWC58ZROTPichjEbkRkf9BRF594TNvi8j/KiJ3IvKxiPyXIvIPvRdR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xbI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\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iUSy42NoPp6YmUnEEVVywLUtWXcLmrS6UBdIRRQzeAAsExH5GUTxFLe8LVTIsxN1qRDwv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UnuQ5yHJuWzLTNSTNiYeRnsQWZF/YFQp2MGcvpc5+zSceO+MnXkMAKlqM6giiVprkADAifSVs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\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XAww7x11u/L/zS85pITxlBZx51mJEhF+zgMlK8oEPfGPIhvOF1Sp/ywv63HNeEw/J8Ydn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c2hPQBgXI4w2WCDguaQSqBNDksZ/28MP0GnEdIOUUXqWf6TJupakRgghEGuWgvEiNsR4iVm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\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s75C4kvoOy0n2JPKCDSceqphRbeSim8k4QR9ri20D3KqYZyu/SCZ5Lixyq8FfDbreInIn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\nSI0wElzl1mIxnivSqSimz7m2xNGsdUGqb3SNLnR8dFnypui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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(2,(10,8))\n", + "\n", + "pl.subplot(2,3,1)\n", + "\n", + "pl.imshow(I1)\n", + "pl.title('Image 1')\n", + "\n", + "pl.subplot(2,3,2)\n", + "pl.imshow(I1t)\n", + "pl.title('Image 1 Adapt')\n", + "\n", + "\n", + "pl.subplot(2,3,3)\n", + "pl.imshow(I1te)\n", + "pl.title('Image 1 Adapt (reg)')\n", + "\n", + "pl.subplot(2,3,4)\n", + "\n", + "pl.imshow(I2)\n", + "pl.title('Image 2')\n", + "\n", + "pl.subplot(2,3,5)\n", + "pl.imshow(I2t)\n", + "pl.title('Image 2 Adapt')\n", + "\n", + "\n", + "pl.subplot(2,3,6)\n", + "pl.imshow(I2te)\n", + "pl.title('Image 2 Adapt (reg)')\n", + "\n", + "pl.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/notebooks/Demo_Image_ColorAdaptation_mapping.ipynb b/notebooks/Demo_Image_ColorAdaptation_mapping.ipynb new file mode 100644 index 0000000..51b91ed --- /dev/null +++ b/notebooks/Demo_Image_ColorAdaptation_mapping.ipynb @@ -0,0 +1,349 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Color adaptation with OT mapping estimation\n", + "\n", + "Demo of Optimal transport for domain adaptation with image color adaptation as in [6] with mapping estimation from [8]\n", + "\n", + "[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized\n", + " discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.\n", + " \n", + "[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, \"Mapping estimation for\n", + " discrete optimal transport\", Neural Information Processing Systems (NIPS), 2016.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import scipy.ndimage as spi\n", + "import matplotlib.pylab as pl\n", + "import ot\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loading and plotting images" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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mTia0hLGlGUdRPJdlnZfEqBoxOLLbusg0fQycdE60mvwID4Ylse5O2ZcHU+LH\nf/xHeNv/8dcBfn5E/N8/ia/2x+KpZZgkfSnwe4CPJV3B/vOI+HtPazwLCy8WJN1ExO3V7/eA3wq8\nY5GlhYUPfpwk7jVx9gxu0+NAl1n1MdL+uvW7yuheNQEh0ehM4BzOjXoGi/I0K4iAaFmEHc6Ind62\nrC2ZM92yIq2eT8qgfAKKWRmAE/cVTC9Ht3aiK4O47kEwsOaMcc5c1zHLrg6Rrm0Kp8+sIzkcszqR\nfaNaGh/Mqle5Hemods+M0wa3MYk2MTkd2GmczyNnrGtuvInMaRi0I4gvAwGr+h6bGVi5pa13+aNB\nWJohVF8m8tQjxBiz6oFEzInaEVamGZqNPA8Tx+dMw/SSkDXBJi+r47j4mIZ7yQ3EroYz8TB6DGjG\njTuzBbM5HSdcaQLR0zXMp3E7igTJMCatAuUsb0lS0tjxaByegN1UJDthVH8owDWwSB/BrEnLwPJ2\nkj2JIuu5bs2LADV6JHFszWjWMA/kO7Y1IhoRSRKCQQMGupZO5HmNoNukNzF9lpthnhtrDcmzRkVw\nIiVUEZl185aGFsSRT0q06g82vUwXxkwH9X3PWhelpFISG161eCojEeNkjT2ylxHKjGijMTFsjMz0\n2cy6Hg/GSFmmWeOmsrIqsnM9tggh6yk9UyN8ZBbOjGFkPZgH6rr0WuotZW9ZsJcEwev4wmAMvzgM\nbuHlxpeytpxAmLiDWzrFeWUDezQUk1626wdp2kmb8eHOjelCetWMPgPFTELkDtwRJmLLayZDjCR0\nSuONcUkH5zGZ2UPyxsyqtzrmPK9pHHEQR2RpHnNxBT360WWm2K8ye8ekCbFfMsQpz0uDi+w/FWWZ\n/v6/o38y8FQIk6QvIGfXfwt31q1vlfQpq7B94UMQf03S95OOW68hm9B+CinnW1hY+CBHVK3FpjQV\ncHfOEUiNbexs8jRviLSMHmaVDQGp45E1R1aF7ffNaHZKu9/KviiCmyZudY85HbMAczSFqfM8cD+g\nd2WRRzTCJ1vP/ZyjpR34ecKWVtymkbUmLqKdsN3BtjwmlVQvssB+72Wd3gey4GbcZF2EjBu7n0YP\nDJqnDbZvjed8TzneedItsyr3NBhbZhHu+xFADU6HlTVpppVB26A6UqHqPJOF/n4570EvSY/YMAYD\nGYwxaXYCUmbUnEuAbm6M/VlO/R4xhVn2y+kyYKBIcwVF0LrYabSRA5vh+bmJmxDDJ3ucYQtUjnnP\ne6SDHo1rre74AAAgAElEQVTSXaKYxJhJRBXcbsYNsI+NLZyTpRFIbx2FMVzcyGkzyYm6path5Lz9\nRiM02XG6tSQITG58u8vUKcVuQzneQ9LlOAPnbEan0SNNPG50Q8e49eeYWQDD1k4oJiMTonVcwYgs\nc/PWuD8G3Ronm1nPtaX9t0dL8jsdWtZz5TUW50j3gaHgOZuX4P1eWXA3UT2F/E7OpWxu3KwzRzCb\nlXwSRkuCcEvW0k0fqDfoTihgPodPEWpozqx7AxqdrQWK27Ss1hli47nuKLIWJ50iO/sg2biRFvyk\nvK5HPvPDjM2VAb4Zvp+RZxYlZlwaVB/kQK3VpEBev1GSVPB6/qqGKdOZRA/U0hLeCGYxhmjGqOyX\nAjZ15Oe0sZdXlmkrN81qRk2/ZNMo2e20I5OYYxqeTXG5PIGqZGu65tEyw7vR02gCyvzCL5eMOBEe\nzNaYtKpJzGPIvmLVCNqd0YygpXPm7Cn3m85G1n02h3Hq3DdxE43QlTXfS4CnlWH6cuDNEfGNAJJ+\nG/B5wG8EvuIpjWlh4cXCW0lXvC8kv2P+CfAFEfEtT3VUCwsLPym4Z51n2pY2xzg76QrmIaz1lLt5\nBq5HYfW1D03UjOsRTlYFDU7KmyQhE47fFUETVDMhqMJ/xcTPzmit+q+IMdJdSzjMzGZsAWPOS8PY\nsZdMyCxlNJG2ypuy41KqjEouNaH3E5sycHLArRzJMhnG0YaVcsiigk4IZjWrxI1XXDW/HaRb2zFW\nMx0O1FWU70eJE9ZaNqct2eLlPKqyA3NcNejMdVx3pz4Cwm64nU5XZg+GTwjYlTKspuxJE/vEYvBK\na8yxJwGqWgtkRDcMI7SXQ5xopHzOp6NyVI7YiPSZT1mlAy66pSSzk2YEx5U/YUhVfxJ1/FSfnghc\nmW84ibsMgBntkg1IWWMcisIK0lud1KastYHIYnzLLEAoiJ5Zi3ME9z2y+bF7BuuX7VfT2bo/j8yD\nWzDnITOsuqcjK3BkD+pIWpkvvGZmz60gmHZHiNO3LzNNB3pvF8vp0F3LxFYTFGbG1nZAzBCj5JDW\nernAiTlOl+fK22Q/O5sZvWVWSjReMZOoI2cX7JHuh9lilrQWd6/s4XbJEu5jp7XGGHueg1kW6mQt\nGHGZKQEf2bzWMxt8kEYjjTQCLnbdp3oOfQSj6TKBsIVBbb9VzZN0TCaUne9Rz1X5P+SXvm9Qj2nL\nGqJD2nnUz10kf/XsNR0yUst6xKO+UQ2fs+oVs/G1k+Yq+Zo63mgtJ5iuyoGO/VXiCA84W6CWWa8W\nHWukJNLEdqz6oZ5hkrQBP58smgc4HMK+DfjMl3o8CwsvNiLiq4GvftrjWFhYeHFwa/BsDO5Rtsl2\nOJfll7/JcpbZ0ne8pdgfoJzgWsnwqjFjZPG+Ncvg65CDxbw0lbybHRYT2KyKyuMoDUpXOcW4a/YY\njmawOfTtxDkaM+bFnndXFnxP4J6ME9Cx7EUD7P155Mbwxv0KWo/mkjchtlCaiymjq1nNedWyFsaU\nZhQbln1f2l3AK20XaU4W46f1gKo7bzsl8YzIHkJuJfW5qgtLizyw3mBmPyVF1nlMQYSVU1swtEHc\nMtmTRHHYUqcVAjGIWXVSJnbP2fCDo2aWxIveZI+szNY5m1KKJMtGsi4Y2Z8UqUEMMLGZcdPskgnY\n1C82641sjiuVYUTeFmkw4JMRzibjJtKdTGpkxdJdFkc+iShpqA65V/YNMlU/n+DigrfjSYjCq6bI\nCO/Iglf3nrI+yNqSVlboVdGT+877Xa2npCqy9sgVzDm56f1yrynKCbAIG0UEm64mD1Q9w+Aic4sG\n4QNZ42R3137EzPoeD87JabP2amQD23yGisCn50k6HBo0P3E7s+YM64iWrn62Z4ao94tE1cs0Ie3e\n6/mdfnHMC9LtjjyNOe4yd2nTL7VBipTdqq5PzKrzicyu6ZgPKWLRLTtQu4xOLwKWeZ8x04J8xqis\nThmGBBBW981Ikl9yv17vmsxiZX+jyzuoSPVGuhwOuJDTE0cfqUAOozd2SHLechuZQE/SfMg0KWv8\nfJr7ZRLmkEBCNrOuV1Vmga3szyNdG+9v6czZp4jGZb2XCk8jw/Racpb9hx76/IeAn/nSD2dhYWFh\nYeEDx63BfWuMgHs4m5Gzxm0gT5OGradb2XPR2cLR3InW2QNOKlvxCupE9eKxDPC3nq5Ssadl73Bn\nj8HmSQpCmf3pNHo7EUzuTTBlhuNwobuxTt9Bdp/nDGY8w1knbghOsdMdnsWyVkbBuap1WlQ2Kbaq\no0pp2b6fM0i0joeYGKc2MusA9Br/2LPZqOhwG5x62kNPu8oOzXMG8mbs4TRa1hEZVQeUtRXI2ObG\nYDAinfGcdBejVU3IzOzYmFFBbQaqPqnZ9yRN5o0Wk9GOypusJustba519MOJibfM3vjI8G/4ZDul\nHfNmhmany1Ji1URj0u0u+zKxsl+GqVk1XM7Jy1rdgZZOdlvL7JJLdV7Thvo8z/SePYBOrswoyOll\nMNKsobEneSr3tjDLIBy/1IcJ4RGYH0YUnnKrsjmf1SvI3AnOhIxn9wktg9ipDJwjnK7JPRrDgj1S\nGkmkqcmNZdYhzIjeidnLRnzHbcM9TU88MutoJV1EwnpmXk5kz7CorOpA9NbQTEnaETSHNWYEQ45N\nqwq3SJZKSixRWreHAldnRpp8nCPN4UGYN8TOc3RaiJvWsd0yg8U5M1ERzLYT2ojY2C2bLs85aP2U\nxikcmWSwGVkvRVnoFyE+3OGOnlWUhbtwJhuhzkaSpaPXa5uDGIObfgLEbdxna5ZE2beaaMiMEkp5\nbCvJ6j0TMTLz6G1iLa/V9O1i958eiTPNYsyRGTYnwujWkZUbYiQxwtNApLNl5qhc7bI3Vd3/HpyV\ndUjZJuG23CQN12B6Iywt15008VCIXoYSu2VWbGMjzJibsVPyz5cQLydb8Uti+ZE/SB8NvAl4J1c2\nzAsLCwsLLzruAW8A3hoRP/qUx/KyxBwd9xMnJmc5zzfnpJT1dDnSpDO5r4bGmdZEN9h952bbUqYi\nKut0SPCyQHyTHyKvzHTMIMYOxKVPSc5CKyVjIw0Qbg3utVaNM8VU5/mR0h9p49kZNE2wM96cc4Bw\n7jG5CeGedQazNQYVfGKMPbM7t56dWdzT6hkzoqX73T0FNyZGBYpbyzojCdSVLl0orZ8L6RDm4E4j\ng3Wk6nFU2aQxL/JEYVjLWhZN5xlLOWRKhGp2uwrfxwwGgSlrxeJwxmvZptRmZn22VmX1JZFSEbrh\nSUhlWTNz1AjN6Re5WxNJ9OywAz8cw/L/N14F8JUlOIpUouuSmRhzXiy9hzuefUizxk1GN10K9ykp\nF8WzzZKsZXPWliYFkNkM0qgj3e4ySxWuNLcoKdYhgvMjoyVhlmOcEXhPY4c+8z7ZZgaQJ2WWaY+y\nn26dOUY1ms2FjJTrnS0zINkOLBujjmw6Rot0QjsyabunffjRCPfo9ROembVQ1vcd2Zx+62zN2OdE\nm5UENOWPrsxyjHkYf9/dd3mbZYPXWVIzo2ziOay2xWbVwLWkYxF1cXTOxr0eWSM497QnR5f7LBsb\n533iHH25rCy/E9ci3Ylh5kSc0+GwrqmPlNSakgD7nJzMcmLAJ/daVH1Q4P0EVQ+WtYadM1kDJQ/C\nb0oCSPZSQtXM+eaS2TXuExJb72UPbszSmObrKtgipbhnZsqQFReiA56mF3AR37ryHz5pEVkXGYM2\nnbOlMQoydqVtuIeyv5hgtszuhYNmVIuBlw5PgzD9CJnx/5iHPn8dj2adDrwJ+Asv5qAWFhYWFt4r\nvgj4i097EC9HDHb22Hm3iWda5xkaJ0G0yfSULk2CxuSmW9p/kzU5UTUFocxx9HYo2rK1Z7eSL1Vs\n0DTpnWygSfW0iQzkjHSZmw5D8B7PuhoMtGc/lEHgvrE32MaOudijAZ09Bs/E5BReBUmTfTphxohA\ng6pVSLlYa5nFuWkpTzsz6GpsPR3mpHaRDiaxS2UTVcOgK8IUsQMps9msZqiB84g7eRGZLRmegrFp\nwZSnrG0G5yrMSHnSRNbY5s4zLWtojhnvKEe/4WJqI2Zw6kZwLqONVkFx1rGEjGnidgancjPLYNMw\nZRH/JKWEYzotjn49JY0LmBpQ9SEZQKf07CC0HgEtg91JMHRXf3XYiVsEvfUkCdZwT7JgJJFsvaWM\nrtz00t0u0gUPIR9slo1qs5mps5GmF2NmhsGtNHCR/nBb3TNT6bzWHZBo7tw0aOqEB7c2swGyjNZS\ngngblUnzss6OM6YN48SmwbSsxBFZJ+X7yHvXHVpKVdNpLRv7ToLWgXnGTOzVUNbM4Jle/YfsIo9E\nd02R09Eur9V1mB2hsqkHNVWmCYayueuomkEsjR1MdiEgoQE2ME8HS5MIRt7bldWBJH1Hpivi6DXk\ntCKDAF5kvAloG2LHNLJmyFOq1y3zkp2JfNIVnF1ssjT1sJZ1k72hfFizJlHGHI1bc1o1n+4x8t4m\nyuilemWxV9+vqBolMuNKyhFHkc9QEnnN6pcVxt5hmNIWn3JwHFH3oqre8aiJylYJexN97tkgeaZF\nP8rWAXsDYVWP5dkAGyuDjKzZfCnxkhOmiNglfTfwOcC3Aihzqp/Dk+s83gnQfs5/gl75ugeK1XKb\n8NCkwQWKejk+pHW8vKfjwdWO5fzqkdKMi27ZZNWF+2r5q21k/V11AL+WVh9DjOt91xfI1UN9+Tm4\n6+z+GJ1mEId/J/Nf/g3az/j8B/9+pe194PgeQ8iz5wWlwc2bk4f3WTNNXL64uIz54R1luj912+2h\ni3Icj9Cj6z+wu7v1JqX3rfW9ZmLtmBJ7zHZkxnjH/0z75F+Vvx9p+xc4I9GuNOEPzgF9YLh+VevJ\nSdWr/T3uUz2yTFy2d2y74MeLrVymzJj/8i3okz/vbgxRXSUiLi+yeKiQ+oDd/Xh3P1xfq6s+IRH5\nAnb3R+6Dy/0VpZ0Op1UNgVG3djx0Hx4z6aWvPvTWU5Rk6O6MZV+WujWiZpo9Uv9f6GVNi7g8q/6v\n/hdOP+1X8Dg89j1ydT897jn1q3OnakBKyZ2un/mHt/WAX6rmZZnLs3fM0tV5qfxD2Q5fvoKv9n01\nKF09Tw8M9mJ6dLUJJ+7/KLf/6n+Ceg8vPAqRgdjuWTC+ReNmu8S5RHRmGK77nDSY5e31jAUb9xlx\ngmjpTEVKhkJRsrAAv01racRt5PcLRWLCxIisASHAZzrxUQ0h26yeMCKlUdUbqEX1SQmvnkMZtI/6\nanmVZ6B2n/wePVddi+IwmIAWkxvy/uohGMFs4rkpvDWy91BKx5jphKbppHPWCdnIdzXOFiBLm+IK\nSev7MaV7rSRL1jge7OxN5Blc0nZaZEaNIjBR/VwUR8F9vmvMAooszDgzt6z/Sse8eveZcX94Eho6\nN2VxviuPN99paU8tyzqWnKmfWGRPLRUxyUBb+KyajHLKy5qVrLEBMM9mxQA9ZtUecalVCYA5sv5E\nZaAhYbFlFomGXToaCXUrK+5sWJxxSa7TdJCNyUdOeLa1rMUh666O9+mAtEuP+7zCOqfI5rltE1tP\nKdkIeDUbo8E+8r2/OchOBMa9NgnB1L1ylAy6etq877fcWmMLp29BeOdc4zirbNJrzObpDEhraY09\nR2bTlOf4PXKeb+JmtsxeHE2HoxrZ1nfBrWemrRMl15y0Xr2Gysmtx8S8YpGohrJ0osw3TrYjSzvt\nHGVOanRUycNsRp3P/6yXQZpSZNbqRNh9pA5FWjtB84G5c2pHo2YxLGumIvL+mRjPhBiUnfws63Q/\nXuwDj1b225ZyS2UGiDFpwwmVjT4N2SWoS7oUk5uyvc/nULicaJM+kshOD9w7wY7aJIvLsmbJhuH0\nanqdcY8m0GEyGNYyM7V7fi+GEdZAgx4ZM8xQShlLTptJ1ZzEsXJqfFzs92LiaUnyvhL4hiJOh634\nK4Cvf8Ly9wH0itehj3j9XQxVT7RJF8YLPJCmOwoMg0sbBaBmuyhN6AOnIfsGPBDgHbMjEkRawwKX\nol3gKqC5cm25+luV4uU4gktjtn7ENOJRkgLlYsMj+3My/U3A3J6hfeQnPBDoqPajgHG13XZ1bi6f\n12ybeDBYug6y+lUAfdGNRvbneBj5ZZL/rjXqdkUsD/eVgzQex3QUHF4KmnmQPOWXV+3+OqaMIlN2\nN6PH9gx69SfUxu/6h8TVNbomxpd9kGnsdLm6Jh9Xs6F6NBg96JRFOsNclj1mmyLSfrSu/XEASSwu\nC9+RZj14vR8Y3+VFfHcfP/RYPHRMVwF9v4d9xCc+sL6TX8rHPTuu7v8H0t66C/KPWzMJ8N1+Lvus\n1RqkNOOB0dRYy9VKHhfC51GBRG3b3R8J7o+/R8Td0/swJ+PqOdBdA87gcMxK4oLiMiJv9+CVP/Wy\nvtvdTFl2RQdpJok2Ed7uyNrxWNQ+m2f/jLsB3U2AyO9or66WeRwpu37CVL+JMg64LFMBF3a5pmbX\nExRXfS7qaI9japHB+XHf6Ph7BOZxaTLIkkO/V2QWxDg7PFdOc12il/OZaLheCTHpvnPjZ04R3DMx\n4n4ViDfOvQGHw1f2W5kzZSgDuDHYZwblx7suC6vLaCDuvmMuUqxyZjvPSTOYwy9uWGcdNtrGrWfm\ny3BuvPqwmNPHZBM4gxEZ8N8cEjuKbKi+bzU5B8wwYgavJJ3ctpqZP4cjJtgtZo3JKElZQ+aoArV5\nmf0g7cVnsGW6LZ+fkswNm2gEXRunNmHmBOD9sjcPyPqvyJ46ScUyw4O1zKwE7NPplT07SNPWcmJO\nSnfC1pyowPt4L6reL035zXFMypis3rv1/jkWhpQdmujlJKZ637uO79Q7i3Wu3gmHk1pisnWr9+Px\nvZHHbpZOciOSkOb7Lx3HIDMeVm6ApqzVumn1xux29f2iy3fWyU51/UCtkyrLidw5meESGjOzSzEx\na+wEewy69TIw2JkRWPUzagAdWjpFQBizJUHKXkFliECRT4k4skSC/ST2kbU6JoE3TmY0eZquJG2s\niey793Sr+yH7L4GTjV2Pa9q3jTmtiCmMg3gfUs3jekbGaFLKJLPRr1dMxYUsR82qB+VaZ2Iq6NyQ\nFtwz654UnLaqEVLD1TPzosM7MS77thpTCzGVssN+vAMknj/kmjGJmbMgN66LfJQYdW7FsHJejMCi\ncWKyAQ3j9ph7CcPOjsV9sMw4ZUPZ+puck7L3mTfhc7JFNl4egmDie/bsapc5CWEDesDmYm+6vEtP\nfkY4u6c9vyTuRzpZHhn1smx8yfBUCFNEfLOk1wJ/mJTm/QPgTRHxw+/P+pcZWeISXD3u77nM1X4f\nnq3OpS9x8OGCObkjO9fby74Qdxadl8+5npD1S1OvJ2UmDn0zxF0MfrWR6xjriZmiClIP4leTiQ8E\n2ccmr8/HA5mz43PdnSdd7ehR+vYoHjjXR7Cvx//9cbMBcXzj8iBBu17vSeO3y31wOLI8eZzH+cnJ\novpyqpT4I0FqPLjeZRwPXIs70nj1Yc0APbjvi+sMl6RgZSyPAPpqf1yRyoe28bjx6eocXJ/d9+fa\nPbJdrsbud1afxz10sXKtxo3HuXzSvq4zrNfZr+OUHValx/gv3d2DSw+KqBlCWbtIGq6P9/hCvHz4\nvg78cjw8yiqp/R4v7fngs3J5RKNdHpSr2/fyQyef/b3FA8f94DCOz+ORZ+hh9Ktt+ANk80FCdLdt\ne+S+elKGNTu088A7oIZV2dzGfMx5WngQl4DWOpPgNqoexQe9bRxX8VTk4dQm6Cb7BTFo7XhvGttQ\nuuyZsFOaDBzfPXiw+U4ni55NWZg+IvAmurLX0f05cn5NrQL5DGI3g7M7vW+MkY0rt1D2B4qZpgEE\nFsG7Kogyn3ykGptPntkaz+/BfRrIk7Bd5IV5DPdm3jeU5fB9ExZ7zeYrTTA0aL0MEghab2hWIBQO\nvSXhCOju7BF4N6ZPWhfpTJ33+OEux4QzndYMn4NuOxUnpnmCR2bkiJIcpZuZV3awkdKzm5Z9qNwn\nZlWTRMrejokaiLJgzp5HUVl0Qe6fmmDhrhHr5cUnOLVWpC1jmOP9MkqhEjGqgXB+71iRW6oOKjxy\noqXULPnVdLwDMtt4cBCpjB50NxFyJwur89k7reQD0fKtmu8K4bOkcCVbmxv0me6KZp1ntswGDA96\nJ7MPUa/rSAIcA5DTNzLDae0iNT1qmfaAXdXCuTeY1aDYWp6jqOwcZYCCYZ61RTOS1OT935BuaS2l\ndqOutskYc4KJk09CaSJx1Dd1ZaZMiNj3bO57vDcP0kAaa0BgkfcvBKFxlzmqiYGMT/M74vK9X99t\nx7XIZkYH4bKUosbAJM4ehHW6xA1Wk8GVvQsvkglNjXPLlgBTexmypAHMiCDmTClbGJtXPZrlc5Nt\nBYIbv2seG73TPcmBHKaCc8x0tgxonDG2rBO09OqTTqD79BA34bx77MgbjcY4D/aeJBmviX4LrHdG\nTHxrZfihCkBUWeWNPWZKHX1CBLFxmZlMC5nrKdoXH0/N9CEivhb42he0zmXdK15gdpHvvC+LwQcC\nyqvMx93s7FEgd2QAyGDtar15vNyuiUHcbbv3zvTsUJ5v9Xg0SCEuD84l1tPdvqJ2rrjqrM3jgi+B\njk7fj8aKd3TtaisPBLEPL/cA33qQbD6w4YeP54gf9cBM+8Ow63N+neU5iOKFOT24gSeN/0EyDHcn\n88FjemS9Ij6tZ6HmQQSO9EMQl+UfPl9RP1wSTNLlfOSL8Drqv1rnajzViuOy0UdiZF2d0xeChy/o\ne8HjyNXD161VJ/ejmPiQikJ+Ec0Yjzxz8aSBP3RjRaQuPLirzTj273eXkaPB3fz/2Xt7X1uWbr3r\nN8ao7rnWPh/vfW249zokwyLA8g2AgIiAhMB/AIKEBAnkBEQO/AkkJIiIAIkAEJKRICRwZDIIkBAf\nMr72/T5n773m7KoxCMao7p5rzbX3eW3fV/fYb0nn7L3n7NldXV1dNT6e5xk+7hZHLyOAOd4RJdlz\nmitxnGfOoolrP8ye+c4dvzuyz6ebOS8Id5+d16W5NuQ8GOfs1/nAN+MRb76+XzOOY+VUqG/zUnXi\nFDyIjNpFJF9jz1iezhNEBSfkCLScbnE6wbmpZwTyN+3LzdnwdkWuyU8SgRgZhcWdRrDOGik4iGNi\ndFI5SyGN4QiuZAbFNDM0TJL3mFDKBZHBRZw1nE2c3matmsww/YIUbRgy6NoJjAvG5sEiSwo27PM8\n5YCHO0LfM5c+CeOufJZgocEQvlV4kheCduyFAQuGhaJ2w2TgMRhuuVaa4SMj21mUtkFUhqUyI0QZ\ntsXRkSqciigWGw1hE0nYqS3sUt+h3DTf6XzfOtac2DQV2TyFJEKCRZ1wIbyB1uyOntwPFDQdUZPM\ncuxukAhEGnbCyOCZZv2ZZcBm0BloFOzOhEVGIi20obFBBVEn4V9FaCZ477UIOTqL76BVdwuQzFC2\nGrttpCrZiIRBNjHCMuOcQzYSIRPCKLL8FJ9wh9GUTwFPoawtcDYy0+SpXihAt6qfI4DvY+BSRVwl\nHYOLCqKwhvNtg+uwCsJEPm+UgeGaynduyrf6zM2DlxKygJXhVPYv+XRVp5WQFY10VAfBsI6PvB/T\nxER0dwYpzDAky6yqtd02ShGNgBioJQz1WvWaIId7Bk5nQdbkHI2SXo90LiLwlg5gRqt9XxlbKdM1\na/ToIFYUh/w+eUMCVlk1vSIYvaDoKs+oBD2ckIZISqaHpKy8kNzES5SCoRutNfAb15aOWR8j54NN\nsGiWKM66V2lobFJUBgFsITwzj4nQKv6QOyPgJYLhA0J4asqQBWzwFKmoJ5oZsi2E7h01iBF8lkBG\nS+GLCNBgIbO9qhkwCjU+k2I2VVGObTi3nsV/e+T4TqddTVAZrKJ4KfltkY72r7P9RVLJ+3qTTNcm\nV/aA7OR3h8dwtjUS7nzA6WZ7NNBJRpNyZnQ/3yaHh3YHg3mQyboBE1+lHN7yGZa1x4I9dsftnOUa\nUspCcaDwItgj33lgLSghuw13dnjGiQ8iJ/hYRKZuQ3Kx1fIOXQ7nbDpm56JwcefJj/2mRawyaQev\nS0/5ufMo39FfyvC7M1QPn4W4gykdv5vnTkjYLK6WG9+bJgJVPb7dfX0Yl36Kusl0ms4wwvOvYhQe\n91Xb66mwO43uEzMPagfEwe8CIr7/bg8+nrKZX3J89ozBnDdvb+9tK6jJOB9z8nDCZY98aY3/3h89\nxil7fvBqjvPUDT/swOnG5QhumLYdUqJ1TPIq8lAPMkz66saOOSEJj5Q0FDO6KPSYMT12uFlyAvPH\nQm76rnAJ3YMm08kVETajmCa5qeS6IHcwO0ESfumxO/y9urqI3kFL53mzT8d97MRgQCWNUI85tw8S\nco5H7A5d2+eunxywU4AmpsuXo+t7RfnOXNLSxcwFYTqOp1waFL/xN+3L7bvhfHsNLDrsvAnFJFhR\nFmARUE15XiUVymYdmuHGkFR0m7C2QBhdMrJc9WVMtgryRG15nZXg4k5YZrE8kuRd1WJow1OS2y4F\nj+iF/0/u0oT4Du+glvOi9gURpYvw4skjeBpK6EBs0N32QMpScK5Rut0quTc8kZAqx1NBb0aMao7t\ny4/UuyWpKGaVEcj3x7OekcOKcHN4IQt26pASu5ByMgcRI+GoluvwGD1rzXrWoNLKNvm+vxZeCxAP\nbpJFZQ0DPMUX6i1KaJLV2CTXxyT2vUsl++GRtZieGix0TBrbqCKs0wmpJdHaXPM1M3X1+lplQAZZ\nq8fK6G3Nan1LJyY0nWuJ7D+iSPVrKYhu8kQGTRo6GjecroFuDovwYsqlKd+581lurM0OnmMtv5u2\nspuChqGWc3O483xZYWw8qdM90Ga8uEInix1bom5uYyM8QZhbu6QggChmAx/QI1CMEZ58HAoKR2b0\nJKBZPo8moO4M8RLJyLl68zNHM9lc+SRP9YU0BQys4H4zsCQRhayIzM5Fcr+8snJNjyBBP9ljMk62\nqAT8focAACAASURBVC5QLLyQI1xXuIwqNJvHe1VpFRG6Kou1zGzuxX2LNy/pIEfACGEsncUV8wXo\nqCjNkqu09YStLaW8GTqLFAcLI8sMhJcDlJz1oco2MsNWOebK/uV03CIzgHknGQhaaYiklPwnN55k\nEMN5iUgYskwnaTq3ikbC8boqQzNjGgSMgQ/f97cRmaklIteDZiwOt+iow2U4XQaL93+EVftXbz8v\nh6naTiJ7Zyc/R8o9Tibc2d94BwZzhq5MJZy7Y7+SAdyNbrgzeM+wrPM5Z18nVTt5RxmdHnYYMffY\nn/vz6u/+9TdjsXg6IF3uH7KXg4GcEvgiB6zxwVi8vpfDi+MoVLfDAu4dnHN79PHrfsvxxeNznJ2n\nXakoTsSm4xzy2399P3Y7nc6OQ/crHvymeNjPecwcu6/OAznMzihD3f2dORuPb/cMxzzD0c5Qt18l\nIR3FM+C3/9qbZ3vnqM0PH7RH/d9toLJ+vpbpfQTj/No1gJPbUE7AHoSgMr4JTSJew3SPrHA699nP\nGb0KjjHV3/4X3/QlzZvkJvjr/h5Rhn0c49XvOR3yYDD2L3KT/7IYCpZStBFxF4F49JsooYsApKDC\n7ml8isy+Tk+d0zw4HKafkrn/TYNfaueXfuNFGkga/CEZCGjuCcMj1a1GmXDTOA6iuDtZI0ZihVC2\n3oE1jWVtiGbh1IT8SBrAdJ6XFbbOjUM8IGn/KYO8SsKles/aJ4HyVJEfVc0M1Ri0JlUPJ6flU9U9\nGgpdBjoCaQP3BR/rHjSREm2JcsJCiq8iI2FFnhCjZbrvnvPPVLilLgQucPHMjvaRyoJ7CCYy+KQO\nYsU7alb1ioyuCQVCgyhJ9THKOQI+qOJ9sI3BTRUfB0GduU7PAJHC8AokyoTcyr7QCoJxq99pvlcI\nTdPwbCK0tRGenK+nRTDfwFuqoEmqljnJHTIrIz2AcJZ908hxa5aOkqiUKI4XjwWINLh7+gA5RmY4\nnpkRdSyWfM89WamtCX69YmvyxYZn1Z9P4nzY4Hvg6TkXAwspdbJCsQRsCDdg0wzq3NxRfWJ0WOSC\n0YmW9gtC1vkxpRcPfJXGRRuLKB9a8oZUhE+egTwZFbqqrEfW8ar6PVH8LvE9I7tV7aAA2sh+bz4w\nFCsnzcO54WhoOmGaDovVs080SF7DrFWQLkUS5t6cAYDE5UkFmLLWULbyb8oJUsIVlXuHSeq8bZBB\nwArKWwV1ozKK1hpeEFeTdDJuGry4VHkAQW4LowRInmjcQrh12GzhFh1T5VtNQQwi0smXYMUSBjwG\nQ2KfT73PjF7VcKKy2gKYlKSFMEKTRziCrTvDnI5wq8LcIo3JgRWtc6kkEkyN7lWoNoJRc8JHh5Ec\nrh0xRgbycm0QXm7JZ1LLAOYCbCIsDzj0f57tZ+Yw5WR1ieL5xKvMR3FAhL3qMuUhv7ZIk+TOQ7tw\nGkkjDlz2NBz07hzTyDydZNpjr059vvoRxU95x3lAkBuHzUk7Uw+z72eC2+l+9Hd+7819nP85zkZb\n7EHN+/GpPt4r+wmt7LIzr8pPdyNwbCqn+967fupHr6dVS87OndCYke3jvlxOBPnz8E77cveC8yR7\nhgZ2o7b97uEwnbMCBTvOjf7U6bvhrbG6g/LJPWRzHjP7NX8HFWm8G4c0eh6JTAixe4JxOsndkVoG\nzumb3FuPzM+BYD9+qZHyrjbASy1Hfvf3DthZ9fO47q7kAFQ24zzXXk31+eeUNN4Nb+7H7nwzr437\nnIcnR+POIXnsPOzvtMAOU4s53rXFz8xSjUiUcToPtsLGT66hBMg/+9d2eOfshccxFw46dn1HHPO8\n7uOOJ/h4CO6anYZ8BgBESuI3zj3hLmM1CcWiB8Rwv+7uANehFVzQqlXjcijrpUF1yoDLMQFV3+v1\nb9q5/XEHVqNHx4Fnz72pWfAizoKx4HwXmmTtyGKgmsocZYiRzk0EwoLZdP5hiRvrrg4HeEbeLwjq\nG9GcJVIowj1ryDSZsG7JCWwbitJceLkVOb9fMSa5XWhEIhygJIUFnTI/ZjBaigVFZhkCMvJNY+vQ\npHErCJwqNM+s0tCgj04zw8xQH8QYxb9JQRb3gbRgMZiqg0kEKgVNaXl/4XyIlNCGYBnLLii0EcXv\nMVwdvIMEbqBiydfS5En5vkgd819cWHxLGFOkKMCoSHu6Fhk9FyyzfpYcrkUELyihkmIUDYgOQ1e6\nBI4iLSFkDUllwsgMgpQCWj1cQpKjs0wzgABLR7Bp7qI3SbfbQkpYJ3lNKVeeUNDMrWQG0NSyYO7z\nylMEweBjcy7bSo/O/3dZeG6N79X5wJUWA6RnnaRQtoBPw5nMkQ1DZUnnPJSXoOZdFsy1MXDpaDSa\nZEZ/DPhU61zvyT9qEVwWZx3wjQoygk3hhxq/LrrDVi+qWfCVDJC14gwmRCyV8W6Lo578tCyEnPWn\ncj2rlbJk/AdBkP1bdCBS0NAYhK9VCDfX+CaasuO1xqt65VpAJaGekCIvwzseVOY163hR3PY1AYRp\n61UdsKz9JFDICJsOBnmt7s5VQUcwOgxZk9M1jB/JGmGDFPB5MWFB8N6AnrWsRs7vmyh4ZvJaCMIL\n9MY2XwWdPHSje+MlMtjmCleywKz03Hf6SAjskIB44bNYgjsHfPukmcWMBWnK6M5tu3Etg+kyOqHB\nBixjj7VWNjQSfrtlcMkjaJ6uHZGgZgvHek8VxF9j+3k5TJJgBp/U54gjCn8y1g3ZPfv8mex/7saa\nHGbIowiq1OdOwrkmkfqe03KkNXaD5e2p9jaNvNjxdw+i8ZIp1/1Ed/08k97vIVJvovQ7UfWVgSpf\n7CLnDJnXvyXiyCpx4hpVeyMZvcN6XkencwPfmSa1EcwMAeyPkf2o2lBmGzGdrukc3197Kt/kszo5\nDne9yHT5JCxXl0+DUPOFVw5TnL4vB+/MmgmOOabngf6K1XzHZbvvyOmYOBxFlZNim95/D8z6GXdN\nAJmulBxz6ewPvbl0Obbv9f/BReJ029yN3dv38R9Pe9u5jHrvK/DdQzzz6HbHrk4zy4+UC4uUIx4R\nrKq7UMd5skTd286J3E/4qpfnbM65nboXsMMGpZ5VKikdfT47a2clRZHzDN8P2N/Vc4Ybn5CpSCRt\n9W2HSe6BgZhe+W/aV9onjIssKYFMPlNVS2GAMMJSgc5wnsy4UPPBsxDlDE6IKRcvnhMJ5JEyvGRM\n+FdC8qy4NpQkeCs0l2orefNyBkj48ssYBIPBCx9a8pg2FZ6jVW2ggdu8A0qGOde4pcpN9Eh1uczq\nF5eBgKpF5OEMtoT99EB8zXfD+2n/LQdEijdcQgauyYUAki8lh+obFGRbBV1KPrlep7uZb1nINDxV\nBTW0nJiCSE3onUgW24UypI96UU0Kys+MU0VC2Ulls5cQnoCVFHSwlsq5w4PN4OZXFl3AEhJ1G4Mt\nsvSwxhRcmMERQFL4IEJKaa1sj6jzizDC8Rhc2poO6BgsOoqvKLRQQpThzpDkH3UC21K9zYqfEiLo\nuKWghhkfwvj+2enxzMdx4X/zK+B81xp/+anxwYVfyMqiuZYsoajDrTKXQ+EWxhbFMZIn0mkwtG1c\nurB4BgpvI2XQO8J1OF5CKDkancbgySTXWoJnFT6J8qNnNiE5WCOdTdHKyg2sOJuho2gOjsiS86YC\n3osUpK2mSYRWsDAzsZETnhhBa8ntcbREQSLrYyGVIUx7SAhacdGsoKgRYGMDcr/ow0kOUmXJRNOp\nnc+YzCyjEDELsMGml5T3l1miJaooc1Qh15HPUnIsomIi3avI7ubAqOzcRP/kHE3YK2w915jmgluK\nQRCyZ30CZZiBD1rPvnwux23aWWsKFLJEztNQwRfhOjZG1T7r243PvYpv17O9koWKLRJuqrUGDk/F\nxhQayve1EmEIzjJ5YaKENPqvWZHoZ+UwzYUkt4HDoTgOOPb2GVG9//1jx+i9IS+bcVfXi1fG0t1x\n1c7+7gPWz/6DuRif/ax5Hj9Dsc6G5vkUu9/3wBnkPhL9noH6SDnrnHUbKkhZwOf7+pr95Dqxwq/s\n65Ajo3F4H8cGQexjbScn9ae0R7CuM2xO7r+s8To24wOKlFm2r93jGXK3X+DU19c1uN7r5/x+t+n9\nfO3z3D6ut/8np+ucLnLmWYUkPLO/h907zUE/O27za9U7WfG77r+6FaF4gNTzO33nJ0v/XMvpPYf/\na+3RvLiDv8l7537v/f3yQ9+NHO4P29+/n9Dnd9/DU8+m2qNTXMNXL9H5vndIou/5cmIHNLx/vZS5\nrkevcrdWihw8tsTg+z9mB/efzCZ+Q/yaoTNV3KH5xmJgCqaOedZGCXdaAy1RA0HZRnJaJJxmChLJ\nmxtOiCX/RQIPY9k2Lpcs4shWhrBDj45Klof8sG6sIx2GUGGII025bumKvYRzUXjpjnElLFW4buVE\niCb/wL3qr4gzNI2cZkLvHbHkq/ThqK5A7rttlggIAdJgDz3Pq3Q8lpYZ3gpZpbFUhnF3T3hZCOZS\nmKjcR7e+Zc0ZPBEaFSwKd7beES84PV41p9LByOc0+ZFVv42A0fEY1T8riFzKYk9+iXsUx0qxIYyS\nhbaqAYQI65KQsRZS/OAgRHlqK0+pvcxwz4h/E6Szwyu9ygykma50MUzTpWiqGXEfzsvYWLSx6MLN\nogzxhCghyq2M4GsJbaxrZvPm/hahqdBXBXJ/i0DE+JMIPpX8t90cl5U/+BioPPN3xflO4Ret0yS4\nGDyLcgW2Iu6LgLUF9wtXd64YN0+Z6SaBmjFU0J5iFjcdtJEKhIpgfgFxXmLwSYLnUC6y4IWc/CTJ\nAXLxHTaaGgoFV0yN85xdw/komnxNgJH2kGvOS/WAphV8TXEFHLonM+dzSGYqw6ElTG833ItzFEE6\n3BKYZZZMJnwngh5bhWQnPy7SSxFABmGGq2IbKZ7UllzzCTZRbj5SCEOMoZZqgX3LPciDi1AbfaYQ\nLpJw3k2E6AlHRZK7ZwSypXCSSkI2Q+DmK4JmAeqKiiqKm2QAYQTqHbFUq1yR1BCI6qnXs3dYAO1X\nVPLdERZiC4Z/Zig0kjO2hBbHMvesVRsm5fR6sOH0CHoIPSZfM1UQryosnnW0FOGTGZ/+aZAV/0dp\nUVG4UTm8GalJHZAD6hUlCOAkfM9IIviMtM7MQMgh2zujqvMaQpohI47Y8JkbYcQbo8xOf2d687Uo\nqVdGQk81UYqk7n4Qt1+bdzv5+xQpv8t0ySE0cPTjsJC3U6ZlrQjCzJaNMtBmJPosTtEoQjvC15A5\nyfHQijJmdFInrvyMl4vD2Shl2IIc3EfBZ+8z0nYy2Avnm/CJudWeRDOYGFzglK59bGCfjn3ljWZ0\n/4BL1Q9yvatTzWPk9H3Un3fX4RjX6Z+4nAVE0uDRmYXYvbiTklUc4iATb+8k1C77Inu21Wtx14Cw\nxij9krPj4ydv0nWKf5zc+t1xutdIO9eXmuM/pfSDwxkKeCWJfxICOYuJmNZvYsfhT6jmHNP9HKdH\n+MjZv/u8TiKnz/L4031HTBg57pLqSqc5uAtbxKn2GK+CB7VJJwyU3Wn+miM1RBAvavC+XBzrzOyy\nIgeUNbiD+2UtKS+YyYSvaolfpCO0FVa9nX+HVJYAtCbhrNVCEa33PkQ73pHftHfbk1oaMe6MEWnQ\nC2yaz9UCmiT5WXDEDDErpwQWg4yWpKJeSPKeYtmIqk2EO+I3ntvCxQarKV1e8nm3xjpGyovXxmaW\nqncSmZXagBFrcjg0DXDTPH5IZh+Wmu/ug2ZbwlC9Cl8CnyUwyX4ayTFxbRCe+2R4yV4Xxd2EKne7\nr/1g4HAdM7CRY+jAUnAzMWOLox4SAmKgOMtcR+Xg1/QxMrNE2QEjWDjEfvYVx5QxKrOjSgxPAYCC\n/mQRd+WUwEoOUeHYp6Lg5gNK9CCFYZKrJiW2sZWwjUuwivPchBsp3yIivIzOujQ+j1QuE0lu2ncB\nbUDE4Mk0pdiHp4NZEfxFc7/4XhpCZi2HFP9lXXEdNGDzdG5EDrsktDEk6JIwyfWy8LErfyhBr+zO\n8vzEE7KLxGw4t3A+bcKTwvfNWAVukiVybWjBm8Fl0DXYaqzW4vG4e0J9axtoInDJ4rM9AtN8wL3D\nbQifI/f3kECb8qEslFCllwOoqtxi7CiA8IQCuihPZaB3yRwSmpDMzETl85pcvZRxl+pxTTfJTG2u\n/wm9MxF8JP9GBUQzGym9pzz7XsQw39cRztRtTBqPlippZg/H8F0cJVUBHczYevBRSDXHcJoOlED7\nlu9XBGHLbt5smkp2gaM9xyYztYGPgseO4mhXlgqBxVrZjX13QiSCMep3VZPMRyrXuWSW6ak4fhkU\nGVko251bjBwPRgYCRMupmnB9qcyt7xm2HMtg3DY0EsrsyYFhYQZr03aPHvygg1UXNILttv3ag3k/\nO4fpNbRlEqX19Pl9pPnISL03tK+5Fj/1IRwywo+J2jPl+9qAnt8lP+T499eu9bXv7kQQzmNwPvb0\n37zua7jUrL/ztTYdznmV+TutjehMgD87DHBycMqDmFkROZ37cZM3306H6zjfl7MPe/8j3tw73EMO\nvzYMXxKJ+FqLV39+aR69QlV+sZ2zX79K33ZO20/M6s35ZmYPv3/vLHcOzPEpsjufx6d3z+fBnf+q\nC+bb4w94DEwY6n7Br7b7zMzb7986V6+vzt18fe+YI4d0384ZZgCmEuB8Hx/chpx++w8/e3/Tzm0h\neBZl0VSTaiPoOooIXQENDxZLA+/H26hCJ8mFcVuwloqIUVmhp8hI+yKNF09b81lHwqsiGK5YM9oI\nrMPVjE/uXFVY3fmFCWuGsmpPyIj55lnDpWwRhsG2Za4AP/iXDQe1ChxoBYcsBQ1KHS0VV43eR2Yu\nVelJSoVyslKkJHkaQsqCW8HHbCk+SkWVp0GoZL2cXTAHx0m57BWlx6hCv74rwgIQus/7QYluzABp\nlCFvio90RBQpDlfyTOK0Z7k7TTML5CXE1FRKPSxNYSPvxftgLEvyx3xkgfgyCk0Ssq8Su7CQxEq/\nOl3gUw/wwWINLPhOk9we5LMwGTRJ0n0rRdGbdBYJmia0cVBSzCKgkYFRFYiefRYh1GocFVRZbOHm\nnY+x8GeyYJGiIRvBi6YASAulh/BRguf2xBqDzy5cbNSGvbAsg+iDVRofJOfGS6TBbQ5rMzYywDYa\nxBhczNh8S2fUlNso+XcRVm1sPhiqdJQlboXGOSCdc84skaHvEOG2i3SlwqKqsCi0cDygR/LtrhLI\n1BgghSwQQ0QzOBXpaLtm2QZO9QgvpyBgTAXH2jR6T9ipRqnqkbLcM9s5fBRNQHc1zKFZjat76eh5\n9nNBKnYVWO80UoEzKujvBZ+DfK2Hj3z0Y0tHN4IYt0op2OG4ke+oqKRghwitNaQysTEyA3oTKaZV\nqiAnzDWDO90PPp9FOmR4impAOmJX7wQpFpH8LcPI7CuAScNHx10y+BKlS+AVmox8z5aSSG+SWdgf\nKcGOgDFuePxGJe/9dooq70bhnDT1/dkYAPaI/dlhem2wzejTHoX5ApRP3vv8PcOtnKZpUO39lKyE\n/N513p7m8Xc/BXZoJ49wlAXlftxvevr5/Z4tiHj3vk9XP/gtEXcOm5lVtPp0nrNxJ/vP8s9XFzhD\n2u45UkcG7Fy49ujqSQb5lE34GoTr3M7S8V8zJ0XkUFiL98bpOPaYgxxiAwLq57G5Hy+4h2naqVd3\n8M157jvn4/76xzGP+nXispwOePee5rN7NVf+Ydt0ns88ukfXu/vonfn/q74vs3YRIo+l2t9pR9Dk\nne9Pn987J/fr1jzXY7U7MurPK2f+9Oe+3k1oQxnFFcp7EzRJbuLj+X3uUzqy/+jP9p/0diFYfdDE\nWFXpLSpbeavissaKsLgj2kCNm270Efygwsst+NSdp+5cW66lub8BIrilGMRAeRoDeMoLd+dC1mT6\neDW2EQyHNan/NHW+kxu/pcEmnbV3fiELfTHYbnzQzmfgqRm3DphmGQQPegwsEornOOoptNBoyBZ8\n1lTY8uhc1hQFSKkLK0EdeOkdXRpZswmQjZVetXIUHSMdnqXRRtWcESE6tFMGdhNli41VGtEFtV5S\n/5bKakL9NkUrrj7wns7WEGVI4GMryeTidbikY6KOlsqfLJEqfmV4C4a0kSpzYgw2vnPBlhSq1sjM\neo9gDOOl3fjlFph5ZoJCeIqVVbOg6CrKqpnlfUG5SmaThiqhwTWUZ4KX8cI3rGCND2I0S/Pz2hY0\nnIsP1JOsv1o6O4uC64aEEgzaqikGUMvp55EFi5cxSsVO6G3lEwqfA2sr0jakOeYl6yyeEMtIUYrb\ngM8s3OgIwdoED6Mt8OIdWZY06kcQalzbyobj/YaH4LKADoiNRdZ0PmOgNEKueGwsGC/A1gcXK9GO\nPtC2ZKYh81ogWWRkceWK89TS6e0+uGojZGHRwfPaYPvMt+70EK5D6dq5jaCzsEY6us1gKxjQsg16\npCOl60LvG8tyAR9s28HHm1lEHx1Rowc0dcat57pr6RAkp7EcWHviOkp0JciMJ8bGgNFpVUsqvARE\nIt8zo6NSWVfVcr5G8qfGoAVcS7p+EFhc0Ja54cHgJg31kXO2V5YMgU7VYlJcGsYLK7kfXX0rGGfu\ni1s53LENnqXByKLVJpnpJEC7IMuNYIEBT7akFoBL1mazoG0DxQiH1STl4MNZCvIxfBDNuHoKhRgp\nc/7UIPyGiRKsdzber6P9rBymAEQVPUWU4pRDjczN3m3w09jOWMHx+VkMwcuq8fO5Ttc4n2+c5Xnn\n55EQGHc/iOHArG6a6icUBA7q7alqy/ogo3O+v1Ji8Sho3zRgSrqxoAKHsSUQdlfvJyt9n1X+aqMI\nKYzoHN2DFH82PDONfZzDXffvJ+TB92ulctM0Pl1s/xxANCN1Ex4mkrU7rEi3R7blcIzGyWiz8IpS\nxukeD3w82F4zYTkNaT87AA/GWk5CCf1kRZ6hiHMcz1mVs06jxuGWjxNRR8fE9MP0BSxOMMipUMaR\nZYs9gjS9MQ5IJxy1tDgc/F0FLfROjGK2O3O9biyzK+UkhO7P6V4o5Gz1x3F9snv3mUb2ivd6yq/4\nuGf3zd9b1azJyyV5dZwypu799OxPDuR0NmvQXvPOsl4Kb6Gkd1LtUbWnDh7dcZsJoyXYo8Lnsdsz\nS3t9qtP1Jwk5SqWsnlWiqg6YUWhGPHf/vq5j3AcMZJye1Um04q62WFDRzYw0YqkulaMKvYIiFpTK\nVsFo53seCSfCDoiGlxLbr5an/KezqSRXKYrwLZ4S3LMycKizCVz0RtggpPHtaNwQPvqNzZ8A5SOG\nxEhYn2pC+4az9Sjid7Cd4K2C8NEVdaeH4gWF+egdDVA3/oEoQzoXFz60xtNwvn/5ge/WRqwX2hhp\nhKtk3xiEBl2Sn6DRSsxCePHOi3fMDJNjlUnKjKVg0QzCAc9L1nqR8bLXYGkqoGnGbZ7ZG+8ji7rW\n70xLkQznOnrCo03pvaS34wALq2pG2FW49c5tjL3enJKCFzDXxjJ0K/eaa0SALHQJxtCEJZaBKxGY\nW2bW4ljbM2tVsFbVUv67sr3cwC5oa0T0EnHoeBMuCDKySG1rwZOn72CSIbBtDIzAm/Fs33Adwc0T\nWrfUe+uWcMMVZW2W7/nwgrdnZslMcu2SrF8zEDaPVDlUQdoCkdmN6/VCsyu//U0nevJwmmQR2zm2\nmyibQ9dUT6MJq11oInyOreCmoNZYBogYqgsjGj9qUncWGqaUpDTlzuc7Q2RB04gFRNAl1QO9C80b\nEYNbBL1Xbaa4EGrEEJAbjcyYxHVjXVqKCIzkwYVsvGgkpC82VhUWc5o2XvCSSb9iawMGtzCkwZMZ\nTtYDct8YEujojHDWtbFtW8IDK6uEpRx+iIAvLJaiEyF93z6neqKHc1HNmp0+UM+ggBLMGmuGQ++7\nbWk4DEU1nRDxa0muC12htXRiVIRt66DCZ66ZLRPN/zggcJX/Omxi3RLaiOKz8BZSMvdZA07I+lXX\nELQtCU/UqtUUDrJUHTDhKRpOw03TyQ2ySK+kOA1kNtmsMcK59oGL8hLBpsLA2G5eDrJWwefiM3rO\nn2sVaPh1tp+Vw/SlNg2UR2IPj1qPJI9BcW3yLCfb8HFU9exInLNYQ0Ca3fFEXsXx7/vL4cCdDbU0\nps6ZkelM3cP/Xgd9j/5IWmLyuh/TFL/n9cxzf73NzfE1BPHtOI3DN3i3nc9xfmbH38+O7zFO+a7F\nveH74NzAP/TLdO7bfRbx/cwFsBcvfNPOafzzdThnKGouPbjuce0jLzEf9TnLMx15qkBkyIQN8Oac\nu1Jj9ep4Xl96auf5X4YG0+mqM7338zOfKeTNeyYc4y7nY899vu/y/vvz2Dwe6ffv5b214nWm6v55\nPMgCEcfm8845z47tr5K1ee3IzXbvgs7gwmHE3ikCnhzI81o5gy2vs+X7Z3HvL/+mPW4SjsbgYqlo\nNqS4o95Qz2jwTZybL6iubN35f5aVbzydgr/81HnpC3+owSR6jKqh5egeGCOCOAVjYqSQxGDBZNul\n77HkHqQKnRNhXMW4XjNq/WP7JUvfWLrzvRkXbVzEuZRzIWKIj6x1pGnQfQaeMboKnQz0ZLAjcEs1\nryCFgtIpqeixb6luJwYsjMgipVhJn6txiyxAOjOiCcMLDOGbttLDubkmid0GMQIqgDVGZof61rMG\njub+PhmRjYymR0DYkkbuHNeIhFyp0rWVjPN1D6KECVcPVlv2vT2k01CWSO5X7x1rjW+X4LI1rpJB\njmYG7vwBwtjgIo2mjVUEiY5qQ8Uw39i8s67GNYLfj853/kQfnaFZMHb1wNyxXsqIKsTY+LR1nrXx\nZI3Fg4s08BvWJmwr8JGBlpA0Wq8+OZlK2I0PCpchLMsV08y2YGNfTD8P5bOBs+CWTttHyxpfTdJB\nCU833kioXmbsjeHCSxnyT35jaQsSCT0zSVEME+HaryTTcmF0Z6OyL+SzWS7PyQ3SLETs5TCra/7X\nfAAAIABJREFUKXjOwd6EWww6g0Zyw7O2EKxq3OQDY2wYzuqfeVqWzNDaCmSA6GlN5brniIRphuOh\nhTobvGjUOYKbV0HWCC4oYeWcmiNVk4uY9tLBf7/h6RxLIysuSSn/ld0TgWw3nlurwHMG0ZDMzAVg\nQ2jS8OG8CHh3dGQWda76H9TBl4T+kSIx1qScG8pdnHaHJmdKlO7HPvakiWvYKtOrIzGDbunELJpQ\nwhSkmSFfMts87ZUqJEzZI6tIBSd8JywHxubBn8gg+rSBWwVkM2vce2Q9zaQPcpVBP5fZ+DW0n6XD\n9MiA2I3kUwT3S20SpfG4k61+5DC95rm8MaLS2ts3ideG/pu/BxWFe2uQ+esJoMf3WljkjGrMvh2G\n/J5l2p3BOmYXznpr0aeR/RgaeG9MHg7TjD57bTT73dW5D52ue5GMR3yhow+OmZ2ybcd9Rxzj7nUe\nUf2iEMVMle//fteSf9veM6IfOXh338tx3+ernX2Tczfagz6dneK3n791mM7jOOf+Vg51BCVZ/LbN\n2kW57UzJ8rPowOPxOqvF7RkyjvseD391PwZnyOOoZyRIbjCwz93Xmdc7PtBpoM8QxPObd+9Anb54\n9dcvrSev/14fvL3BCeebL+Q7bc7xr7W7a77z3uxiKJFbTmLbYc+anhy7qP+d36XY50ncrZmZRfbj\nmK840X8Rmoj8q8B/CPwe8FeAvxER/92rY/5j4N8Bfgv4X4B/NyL+j9P3vwT+M+DfIKfbfwP8zYj4\n+LXrDzLjt0llH6YilVSdrOjJt9gWFnE2Ney68sd6xaLznTfGOmhbFryc8vJDDB3GFhuVAMRLcjKf\n3yXXuUjuUxpcyZUQaZldmc/WBaQxhvOn20ZTZZGFH7xDd0yNfyYWvtfgOQaXlL7CLCoynPLQV4cr\nRi9oW4vIjAJJdN/KKW+lsNlESknOGXKFyoQxhC5jn7uzvlDKD49EYbgwXBmMrOUE3IbVmmNp0Kon\nZzaCFjcYja0bXY1YO7GBVf0rGVn0ZQhcLddLkYbHsj/LjmShz6FItNxz+sBaFvC96MLnSMheN/hx\nZI2lWyT35EU66MpG7n+LZWbrD7qgUllzV54UnkhOmmg6WRezhGDZjW/FWCJw3cqBaIgJ33mWEXWU\nJxU8XhL+JAtDwEcWOv0G4yIdDK5imF/57AptgUhFv2Hp2HdNbpiZ4tFZWgbdtgjcnKe2MEY6II7y\nIikYsCGoZe02V6WPzjIcHXCtOZrronKlcd0GT6F4y/l9CVhGZo0yI5T1vVoEpnDT5Mx9FsUtsBgs\nurAG4FmQdmtaznuD6ClvHYpYsLKwROdleCkRGuGNF0n7YWnBLZTwzlL8NJPMwjQVLIJtXOlCSv5H\nI7J6Kk8j8x4hSo/OE0Ibzu3k6PXoSEmSEY67JL9MlBsbT5EBhCgpHpeN4R1dVxqCujPobKPTPNEz\nm8AqmbVxd5YhRM+6ZosmFNSWRu/JiUwoZMJzY+S+EAG25PlUJbNBCEi+J0jHZWNjgehAClMEQjQn\nijvkQ0s4qJQqEbrDtWUtK41ZPiCSr1X1vC6TF6kpQf6C8SKCdqFHz/VC652MYBsl9DFS3GxIjtd9\n4PfPv/2sHKYmdufcwGGIvY7UH+2xwTgjsuf6I6+dpDOX51EUdv87FYlFEnJWkIB4HBKvDZRD078i\n5BNWY6JlI0VCCMhIVqvTLNaY8vPJN6nCnHWujCafopAyI/oVdStrOmsm5sI/7/k8Drs/KKdrQTqZ\nlPTknU9YBu8ro/FwNLIP05jPz4roW906OyOPnqlJ2x/Ta8L6Ha64CI3H6N/Pi8MwnmN0lHu9U3W/\n+1VFfs8/5DRr3jiY8x656xfkQjdEi58ySwGSBQvL+7wrFuxOUkzzPZgKg0PGznHRwigu2Knjvl83\nqt5BGsMnpbo39/nK0bgbx/zOOL17D+7a4z4QcZaF6GX4acCob1yytocCs2ZLOoXHL7VUJ73mLlCR\n6Nm/2J/BVNvrCstZXfLBnJn3OGFzxKvvqCg6kTLcJ6ikTDeuiOEVuit8+MHxm1yUfT2pa52dkRls\nSUjpqQ96f8z+nk51Q2Enq8sOsX31XOofCeWM/WTSI2VzT/KsKlZak4JrnMb3L3T7Bvhfgf+CdHTu\nmoj8R8C/B/zbwP8J/KfA/ygifzUibnXYfwX8DvCvkWV2/kvgPwf+za9d/O91ZcjKB98w0VStk4HI\nRjOpvcDZ2o1ncX4hF/Tywt8fcOM7fvDOs3/ml+bJF0kpUG7h/KlGvXP5onvMOZBCCTOUpTNbSGZY\nXgdd9n9JrjdWalS32o96H/xfanwXGWH/SxF842RdJzE+BdwYfLtc+K471xkBJ7Muqyk9Ivk1BZPr\nYXtQI2p7UqlaXwq7amXkviGSfbmVceY9UKt9cSx4BC8+EBtZ88mzql5C2weGoSb4uGVln55VdlQV\nQ/ARWLOUI69xjNmx+/lSokxXtPaGWwgeC3JrvCzwh+roixK6EEP4IVruqxjfl8KmICwlLZr1b0ZJ\nmD+x9c4nCRobzRRT4TmC73XlQxhNb6wiXKXxJ8MZarz0njQDU24EayxceIZ4IXTg/olPI/ihL7wE\n/OLS+EvLYHF4jqwBdI0B2tj6hmOYwWpZDrZ7pD3RPTlmVdfHe8K3zBacwVNM4HWwuYEqH6t2z0WF\nJYLvNbAQbs5eeHaIsm2Dz12ItvJnPR13ZMP7YGkNKbVWU0MiHWkdKWW9SGYzEt4WpWI7MyZ79Bj1\nG1ZwaC+VOcJZrKUICZGO+1BES9zDBDyVIxVgq8CEN9ySH2gzVQmsVoqXdDaHGGnoLz4yixbCak+M\n4ZmVjSjnJefcNL7NsmzAxSc9RFFPmKFog5H2ySBlu5sHV1JqPCKL1k5YKDEI0xTZKEgpkmqwEQlr\n37UAZULxgy1zSBBW72VgNMI7FhBkweQxBlrqehGBtknRuGRWVzL8Kj7KiU37TpAsJhwlz68D1Xxf\nNll5GfDRs7RCqLF5woTzAaRiM3FQYiIy6zR+zVvTz8phOteJ2T+DdDSK7KyF69w5BF+I9sIRbYW3\nGZDX0eYvQXhmO1/zV4HdnI00p7IVu1OhJct56vcpun/u8366V9mV3WCvDTUN2vv7euRwPhrDA8EY\nd79/2I+Tw/nVIYi0/s5ZpUfnf+86cMCQ8s/HkfzHGbRfb5uk7rKj33osr9q7cyl0fyfuD3kbBPhV\nxmacxkYffH/fh7fy+iLytVu6a7MmWfqP72fZ2AMcZ4e3rjnTKHXs/OruHbnL9B7fhOT6on44Lj+l\nnd+bWUMMP6TC38vOPFKn2zlFFVH4Urbx7n3jyDbJgzXydZboHEjg5MTv78Vce37CvPyL0iLibwF/\nC0AeP7y/CfwnEfHf1zH/FvD7wN8A/msR+avAvw78XkT8nTrm3wf+BxH5DyLi733p+iLpLPwxSUxr\nLqw0nsP5IBm1FgnUP/DCjZcynpsq67jSrfgZfOBWYGIj4XBPAdcT2a1BzusKpkVwlAU4OdNZIuMo\nTTC8eBQCn8rZ1gisO0vLIMs3wDcIzwjf6Hdctx+SZzDgTx1+4APcbjwtym/FjXVd03iJheHJ+Wgh\npCbWCQ7McW0RPwIvp70aT6cnwnHVnevaewdzZLykVp5mAdgxgiHC6mQ0m1miYGPV5Aq5J+fZGWls\ntoXbjFa/CgQdz3JysAQ8g5NdbwlV0pWb31hjQT24hSC9s1j2/2rB5fPgh3VmypM/sspIeFhkfal1\nDKKl+p/4pYInmc35cTg3TZ7Sk2TWLSL4NAZXkcyuBKwdPorxRyJ87E98czGMG9/qykWFj9uVP+tP\n/N+b82zKt+p8F7A253b9xGVZCTduW/KXbq2l6EBrvIjjBf9rUdA0bXTfGCF0bvs6GVzYDK4ES2RA\nUwT+MG5cvDhrpIP7SwZxUf7Ehc+eokWbOB1jkeQlLTpyPfPcCxyj2WARYRVluWWxZwU+LyVdrYr0\nGnMRVAYXz6DAkNR6y6xHEMPZJIFwISvBLfmCEum4CpnlbT0dh1BsbKnsh9W7F1knK5w1brywJnxy\nbNwkIaQjgs6GFA8xKGROBcBaZLFmr+B8j/y7mdFH8DJuuT4T3LwT0lLZ0WtxmO+1FFdfMkOI1Xly\nXaSF0zwVJnutVaq6B9f66IRtpH+TNY9iSNZWk568wgjCU3Hxxiz1Ipn96oHIWlBhATGEhP6mHLux\nqCJiXH1krSUNzFNZ8SKdoSWe4WuqZ4runOVBSsHPWOZEP1xCWX8j+vB+m+Tp/d8cG3tEYoYzkl4C\nB6rEOxv+oz31pzgLP8WQ+qkGeRp09+Hg1OovbHWkmbMr18lpA/qKIROnfk7oVBpj7BXlH9VeeuTw\nvTbQzpFK5XDydsP91Lf77NzXnM08uxY0Y/5eShjjS+0eZvSY5/Xeb+YYnD//WvvVjr+/7z2LFj/N\nWfpyq5XkYZ7o6N/r8XljVXOKtkLm0Wcbj+/1UEjkxAM8neuUGbmDT74zdlIOy4wavXa6phP07nsl\njx2Uc6Dh3I/pQHplgiYHbTcEfoJDvb83Andpr7qmPujPfsyjc9Vapnfjcvo7p/f1iLLsXLXQI1M6\n++avzncuHCxmjDHurhHcj8XPvYnIPwf8LvA/z88i4s9E5G8D/wrwXwP/MvDH01mq9j+Rw/EvAf/t\nl66hKjQ1luG4GvlebrgsvKBcBJYYSHthpdEBl41vaUgzhiy8hLL0jWbGjxF8Bq7byPngKUsszRIG\nVBLQTsKRPIShWRMlIg2Vyokmh2lIZiBKkCTV3WZdoJWXnjkqazf+NIQfUH58+UhT5Y9ug+fWuAgF\n8Vq5duHvSuOygUXHQjAVmjZuAuKp1oW1I6iAQA/EyEx6ACElsaz7O5EZ+S05S2KpIBrBlRJ66I1R\nNerCqWh68NQ9C2eKELrQR97TTYKl9tSsaxOsBN+q8LIFLyiiN9QG3pcSikrBkx5bOp+RGSr8RpjT\nC0Xy5I61jXBj6Ma3bkjLxcQin52Y4EbWwwJ6LxGGAZAGqiqICRdPJysGXEx43oRFjS1S0GWJG0Ma\nvafaHuJIdK59SQp8hz+wG00NdaOZsJnxx30hthvfL4NffArWb1Z+awx+iyu/tCys/DngkwtbkFln\nIjNOtvJx9Izui/LSnGuXCmALai8QK0hLxTbLwr4hC580CBn4ln3+Ux980M7VhTGymOyHKsTqBqGB\nu/DSDBlZ66nR+UAa86sIflG+D8uMUZBS5TLYTGmRRVIjBmGZkVit5kUJjCDKiKyI25YbEdCkgQti\n+c5ktiZYZCABKwExcDouQvd0Gpo4C4Lpxg3FdAHfEFuqeHWKj1hlVoYd+2wAjGuiW0K4+QvbAG0N\nhnPrPWUZtBHR0OlwkU6EiWGhLJLcLtFC/kiKhC2txEBGBSMkHdceORa5LvR8V6NxQdAhXCXy+TFK\n0KMQKapsISxjsImkaEUYYgPhSqORAc3kNS26clXl6hufSHGLl9G5GXzwpHZ033BptBh8D3Stda7W\nr43MhmfdrJHvdsFrzDubvUcC+PNpPyuHaSePVVORXQ1rbvjpWBS8iftI7mujICO5r8zMk7F2992D\niLlERcHkbKZ54c+LSF9Gy1ly+yjimZPruPDU3vf9/oLDsJPI7NkbVb0graATVGh4YHJkMWb/cyPN\nD8c5CzXHg9fjcai3xZt7nXfMbqzqyQPYF4X9vCeDXQ6DWLSm4eRNAOwcm3te111h0Wkk1kaa+0dQ\n0kRQ2aoxqo6Fnorgnu71jaN8+ruexmhCLM9O5UNHklLMCtmhNVEqZAdXpIzseOtg7gNcCm7ujtgR\nSbl3HaOit4dp3jkZ7KXAhE1DekK3jndkL6K7wwFB33OS/HD4zgV8Zz92iXUEvVPz8lIdisNJPE/h\nmj9hYH5ACocc432Towj1VF8kDrifS25KGhCaAizCiesDuNxHvc9/t2AXnPDTGNYgFFT2/nMJOxzz\niSDYoYhZa0bmT07e2hx/UQXxfS7rDM/eXfpw3t5kwXOZOlQbX2cGRTCzu887sQtAdDmJbMy1ylO5\ni6j5L7/eKN6fQ/tdcph+/9Xnv1/fzWP+/vnLiBgi8kenY95tGh1jY1FDGWhswJKR1QDxjKo+OYzm\nfNeC57Aa3ytXvfIRRdR4CehhjO61581nTxo5LuVbCEIWlPHhdAZSxSZjqozOqaRVpDsgMtV0LDP0\neb/80BvqzrIsfFTnW4wPzdks1eFsGKbw3FLqu/eBa2PoiniHMRAdFaVPxcWE9ywJqScKSsQOLZw8\nEJe+Z0PnfPXoqUrJYPgTEYoPGK0WEEmlzRn0mJnifCeUPhJSOqF7YlmEN9eFdHqenZRrlijhiSzu\nGsBl1qFpvosIDS77np5FbXsFbQHd8pkORTVVxdQUEeelK5SAwHkR+ZQ7I4zkf+VaKtCFSLk4ug/M\nGt/FyibBP4gNGw1EDlGCDhpKG4H0DLLepHPzdFKf14a0xt/1QfzJxtoa29K4uPOL9YJeNn68bZnl\nay05NAhiG5sZA8e2wRDluvVU7VVFdMXsBVNjKGV35BrdcUYoXZaEUOknXvqWMMYIWkBWwurp7ITy\niYSnPdFS3TYaEqRUPMH33vmzJVgbLBs8SSAefLYsansjEH0m4WUDs45G5HG1n5rk3uLAypq8u6Y8\nSxnnCCsbiwkL4GJEZGHll63jTXkhs29rU7brllLsBMOUEU4zJXxgmsA9jxSHcM/CwVvVg+qeUEar\numO9j1KgS4W7yfELo7jYwUKjKjihxT1Tg6UPvAJg1+FgJDer5rvFyuc++CiRGbWWNBf1dBwVWEvd\nLkRQgeYpTDEKetCtJ9xu5Dhe2hMxGirX3b5ZRvBZOx8DrrHSx0hl3mb4GHyWxjZgLbilBfu7mKM4\nxwgknDWSczfpCgBig02+HEj/x91+Xg4Tb52eacAfZvr7xz8+4a94zVcGstnMfrw90V0Go/CEctfT\nx71+F4omWdG8tVnjqPoxjc3TOWYdpDdO3nk8Htx7zCJtDxzEM79oRgwnb2puxm+u8U57mOHbDbuf\nRjTfx/ZVH31+t0cstbI52eNDGeaxU3DfHs2qd/qTJ3r4uy8KkczxgzsopRev6/Hsqp+exmAaRxbH\n6H1pOdkzNruPdjhrIsZeJO/sZJwCAH6aK5Qs+7z91/2d6l1zwUvtncf3FSencX7/boZSjnucPtjr\nudtOV5nxqBkQmSc59/vdmXDKXr75jmP8X/d7P+fJ+UNmBvyQ730PQnznLL3iM9UXD+fyOfZmr52w\n+V8N9QzOiFTgYZ8R/pPWyJ9pe2/b+FWP4TtRvnOI1ngWZxGhx2DQ0VhpuiAmbP6BvgxMBtswmkFb\ngid3XsIY3ljlhg3BRjru+b4da7L7aZ54EatVmIIOJpqiCaQIg0eJHpCZRjXLmnEzAylHYU6LgWpm\nR8at83nZuPkF7wKWQZBLwCU6i13ZzLhh/NJfMMlA0RjCojN4VgU7W5LIfThrfCDIzGcasKRyno1j\nHtf74JGZClKEPB+HNZZxZM1XkkT/HE742LPFWzOi571NiXEox4nkRjUyOPAjDR9XVJ94stSQGyRH\nYkCS5Sf8RzM6PzwLd2pJgJl8R/hH0IHEggXJqfIch2bFa5Y9JgrAM0L4ABFkr0WVpTD6qKi6GDGc\nPwrh+fJE752wGUCCWDODoFJqeghb79AH3UBun+htpXel9R/51hvdA/mzz8hq/On4zO1jICyoGN+O\nG+vI0fpjF+T5widzrDvGshctJgL3QG+3FDNphjelqfASylaCBviCSiMk4Zqfr52+LKzFQ3t5dmwE\no3daMyyEbxW8fWZjYYkFSdF1tu1b/kA2bBupDBjwjT2xek8OucBI7e2cY6Fp6DZPWfOmfOfGjcGL\ngY6VUOElgs+MvdAw8UxmaTeWpcRCokPLQPcykl8l48aHRbmNFCUwT9GKLkpfgl6ZElD8mgIGbhng\nW7KOAxA4AyLl+rUJ0sp162nPSWWMieT+RnSabrgb3lMV0yVly92DMT6w3TovIgVra1h8ZLXGUwiz\nQEEDsJbBZoehUuqAguiA8F1Bz925iSGe8uD98pGbZKBGNQPCvQ+utjKGIB6YzACNIC48S2alvG+s\nkVC9qTIrls5eePA0YYUKlrl0hKPO5uKD53dlpv582s/KYRI5BAv2P5m72dnYuyf/H7+/d7buz31v\not/Dtc5HTrMsjod8zkbtJ5H92PTuq4un76dJkr87mWrz/vbPs3lkXYVeBRL3Hk0nQ0/U+uksiNzx\nUXaDsqpq392ZkEUL44FxVpNYtO41Ivtjhel13+9oPpXXY3zH94jj+z3ZEP5GLOA86udRPTepZz//\nvLu3+Rz9fIa37R7K9/48Oa65D8x+rbPDdL77oAzSKcO7/1Tuz8V8bHMEj/E892mSvPeZeJ4vkNjk\nU0cfGeJJZJ4Zz3md0+hKXUleOQLTYRCQU9b09RPLvh2KetPfmWM0v9P9y7z5+3fovuk05oU7+f7j\nyeZF3LNu2OOT6O5gvBYCedNOp4h3xvE4NHYY7N2l32SLsqdIwkXMEj6S3z0+9zl7NGLWvbifg+eM\n5/4+3U2scx8OJ041sm4JR/b1PC/39e3n3f4eeTu/w32W6beBv3M65rfPP5KUkvwlbzNTb9rf/n//\ndxZN/oZKylj/C3/5r/DP/85fQdSrsKjBsiXPAMOzeA2ijR/GxrVv9CYsNFZtXMfG1iMj0fvarETL\n+Zt1/zThgO57jnmLjSWEW9VyagDqVbQ119cZgFADmbWiBpjU/MJRaxCCd2dYkraDwRZKx7jEgovS\nPfj7TbkAi8MHU9YqpmsadIyXElmwEDSuKJpCN7oRktl2GZqiAJ7QuWnwRmw0lEtk/bbBxjBHsHQu\nomNiXG0UJ4rKlA5Wgy0cRDN7FFPExBgj640NGUgozjONa3KVzCAUcZ87NKN4JtF71VQMevD/s/c2\nobZt237Xr7XWx5hzrbXPOfvcr/deXmJMIuKzEiSisWApBRUspGxBQawElFQUKxaCsSQIglgSglYM\nBK0oQgIRUREJRCLGwiM8SF7y3v14991z7v5Ya845Ru+tWWh9fMy51j773PA85uDtcM5ea64xx+i9\njz76aB//9v9j1SgalHjKd1cYobVrP2Zdl1lm+k03gqZGJdSROO4eWEHD8NqIQXCXlZBF1TIz0ipq\nWbyvHZInqhRN4g/tgrmtpeNyiMqdKq+k8p3RmMZXNFEmDxgHXqugUnnviopymSbkODDPMz+ZJn5V\nCo/zmU/kjjeXR+4vA58MwmQDlzLwcyrVnUsE4s48N4bBqK54az1we8FlpgzCLAnfpDaizrzFscgM\njllhmBuDOFUbVkcGCU5SQRTpNWmC06pQw3jbKj+KmSLwajBGhZngLoLBnRnn5wRlGCAUEyeKIDZ2\nA1zBg1GNsxaqG9NonOczr2IgefsaA45J4xxT1rtqEjcUHXqQ1nkweLTgVUu76yJBC+XsyuyOa8vs\nZSgahTkqndUkERmSsE23JNsQAjtY1ta2/jaVmmyRDk91TG22GLiTSqGg6og3Rm094OJY5D5fyjGz\nOTnZFHpGstfWuaQe2ixJHDOH864/dxEVR1EXRAJKo9gh9bBoeBTOUyUQ5hBaS/HepJvPoI3QEGkJ\nF9WsdNSoGeQgGEIJFFfFIxjIIZsYf+f3fpff/Mlvd/M466oubf56b4A/oPbtcphMQV4ocr95n+8N\n331k/8qO2X0WkjCf8M052vkjIPvo72aYiO48oNVfs+33ZX1LUlwuLTGty2+6OUjSjZUF2nMztgQ0\ngJiBb9CDjOTtO7xzHmCl/oZ0unQxgq4MoQXeE5tRd2Nxie0MqcVAW+/FBglb06bdU3wm9Aur4Yp0\ngtQAcboY4uYs5LnzdI24rpXZGdoLz7/vx7t3fLrzdytCSh/Lh+t7dq6AXB+/XHtLVOz73KMm1p0n\nkaTW3B27v8rq1u8p7vu/t5AqgiXWtNaQ7VvYLsOyg4PeHre57Psr7u7rsr52ayskYRgrqkQkdZX2\nnWN5pqKz4uTztdTRJGNS0gnHYrzF/p5fBzlu53rvQK7OQf9lEV+9npDNGW5Ix3nv94NdvdveERPp\nBhqp5dLhVfsZi+gFx7KvKdrm1pdR9fHJDtYnkhHk1Ma4rh3cwzRvs0drtu16SWRb1jm3RCzXx+4f\nUeunK5rrzFOsKY1P5Gr/+Da2iPi7IvJjkv3u/wIQkU/J2qT/vB/2vwOvReSf2dUx/Rlyiv7Gx67x\nL//Rf5I/8vpzzjFjzRMwo4qrILYUPwuqUEpqxIimUOekwqjKk408To0nb6mBpMYwLuu2hyAckHZF\n1LHsrdoao8oKz82X+/bcFUuWK2JhGdMOCcx8b/TaBVXp2ZMtFJGEJJ07sdOFh4ygjtiMt8JFggln\niuAUytGVo04cTfiBDBxwis2clogPgc5CNeUSLeuSRBHptVZEQopbXl8lWcqSbW15vwmVYdn4Uwha\nE1ZmWvJxdnr2pRfD93WtekhYYA8EqpTONOgJ/ctoU8695qwOLemzp5qOr6BoBFEDhiU71JEkff5K\nSTYwwvGW7GBOpYh13bSlThnQrO1BhLmBlaHXmDittVWWoKj1/SgopXS4YFCKUluHPEoy+43DgScR\nLiJ8cQlquxCqeCnMPnGYBRkKc5u5i8ZnxyPx5CCG2sBRIKTxKz7xh44HvgyhFeUJ5QuEcQqk9j22\nDDiVJpIU3Z41dB5OhHNK9GcK/ZJMcheUNs1r/bmJIQZalBE4jp0dOYSxHMEaMgfuxqkmXHGulUso\ndgFQ2hDcAUcRjkBpjrWs9SmSkDGhMVoBS3r0hlA9gwBfzg2LA0dIoVYGSjivLxPKCJPzOKYjUnAe\nRBlUGERpnCnmSHNaGNEis2/uyRqppA6ZFMRqz4raCsEnMmtWPB1QkaxVbL7YdYr1+qIxlCeFC4Ua\nwtgMFcekMNqEefZpsS3LIgRfhClgqnlDCm0lapldmSKYESr5TtCQHlhr6ch1nHk0YfISf/UCAAAg\nAElEQVRgjqDNrSNJApOKkgGb5Tk4kBkiqEik2PGAUzotfevCti2y7muSpBWXyEzun/zBr/NP/+AH\nPXgvDAg/efslf+lv/S8f257/wNq3ymH6WNsbhF83KrrZSZmh2QyVD4OZbiFzt/UsewPva0dnr/wA\nefFznhlOzw9Ze78/xU10me6hfyh78tGuvpBFyd8//r0187f+mw/J6qO90P/SI6PXMKrn5/1F+w9s\nGRLdqNW/zj3bO0kfO3oBYu7vWX7+kWvs+nJFAnBjLH+wb2sXn8/PS+tDXvjubfuYMPTiqPcBrON9\ndr3+YohdXdmWWXx5Zl6Ece5+vq5Vuj5oWTqZnXrpLJujt5qJsTznXD17V+cuiUnfr6EPZXNu6ykX\n7THwZ/P6ocz43oF8aZZeYt+7bfv+eycNsQ5b3ZPLbHnAf/SbiDwA/wTbtPxxEfmTwBcR8Q+A/xT4\nD0Tkt4C/B/xF4HfoZA4R8Zsi8teA/0JE/hxJK/6fAX85PsKQB+BFeYqJKsZQDunsSEaTlz1CRBis\npP6RaRokqrg4JxruhWMMjDoxMeNFOJ8bl9oZrCL1XES9ExgkwQNktv+owtRaZ9GzdHx3UcWFPEjo\nWWrPPWYQAE+jhRSIVLX13gesNRVixnS5YEPpDHQZFDpEST0ZSRKKpzLwXlJn596TsOHTsXAw5+je\nL65wKFy85rPjwjkEbEB8C8DNmhAm7e+LUMBlhV632N5pCS11bBBazWh9dPInF00IFcueaEm93IMd\nptZ165xQo1ZHWmasmtI1p4SLO8fDQHXvxmlkDdSiI7VcJ3KcqZvYa1TC0QiK9ucwLOFty57eHze1\nIKhriC0d7P0+ns63YrQ6E5Z6T6bp6E1txgnKUHAJyiBM84wNIzK8ojZHbOBgIzLDeXa0DZxLYzp7\nstCpcQrjd0R4o8YbbQwzuN7xZhY0nCOByATSEFWmaJk9awp6wEpB2ozMeU5MO7V6QrUco/ZamGjp\nFDYXXIESPJmj04wOB9yDoQQtJr47DJwuZ+rcCBWmuTF2H7U6VDcee2qjhDK6YqesJ9JwiBOHPn7z\nE3oYaR7UCOTuyPsC7g+YBFNngBSH344BEcEUhjmdLxOnRDowgyif2ciDCEWT2rw0J6QxjMaljsCm\nNfnKxhS7rY0qSeldPQkNkuiADtcMlIS7Ncl6RUdxpTMwGullBs1TdDiWAEMn6xCcAZhbZe7OnCvU\naLx3xUhSjFoLVTNYWJozdrkPNSOoXS+x7w2eWdbo9UWq+fwPAmiu+aUQYhnHYHDUyjFIx7KmFpWY\n0qKtcMB78pyhmV1XgktkVrk11jqwb7J9qxwmISOvobsMUv+bx/NX+3PD8WWolyz2Z2QUT+Q2w7Fd\nr6PIV4djjSaxGWVL8x4NU66Nau0R5OW6IqwvNpGkV1z+ftX/Hj22xbtYakzW+WF10q7sxRsHYYnM\nCVzBrVCusgXP8wbLOdYzZ2Hv8vwshvf1YHumrP/bU62w1LRs93DRkskx7q7tWx3FfixLEW6mQTL7\nph0jnjo38fxYdvMqW/S8o+WuMwV9DjbI4279LPMY24RcG/Sbc7RdblfBJptDdKsZxe74XS+2sayG\nQTd8bhyYfUZvyZpKZ5pZ4CrRI07IzsFZxrXMwxIhlSQ38bgOIixsa8FmhMtuwN6ftwwo90qsfn8k\nOulowK3LuU8QNbY5j9ixufVuS9ChL8ssyXrNbSI7E95Ot4jtn/W7QOoO9eeKDjVcrpPG1/UciS+M\nddfrcnU2giwy180Bg6TpTcHAF+CM0YvTlxddX2exvEBZ4H8ZkV0czYRXbvfk+lnpBfd0DavVSRbo\nMKwFfpcvzoz253zwbWj/LPA/se1E/0n//L8C/s2I+I9F5J7UVXoN/K/AvxKbBhPAv0YK1/518nb/\nNyQd+Ufb+yjc6QMuzhiCo1jJgvFSrC82wy0zFJgwHgawA8wn2qzUEpzrnCKbYtRaqZ7sZHkvGxaG\neIrjBqmdlBtaPoyDFqLnb9GswGhsqAEli72zDVg4hSQ/EFOkpRHbEXTrC9KchFu1imgSUszqWZ/j\naUjRj08jLUmHBi2YCqeaRnnhwFGTUCS8oeI0GRirMKyZM+PSN0mn69U5SOapO/PMQGuBWCFoqe1E\nR11ESUFNmZDl88j5n2XI/UiC0XYw7DJkjZIVgonWGi7B3CAWLakAMcW0kvUjoFowdQZLB8P7JmE4\nWjrRTstNZ6GMJtLRyT3acVewpE4WT4ID0SeUkfDoddItxyKZnWwtMkODUetALIp2NWnXWwROkgVp\nIaGbJjR3hsEykt8mJlHQThxhM+GCN0MPiWIJnPdiWBTeuGElMylHFYoM+W52p1CotZLcRAmvi1ZX\ngz8kaJ32PiSd2XOrDJqOQBJDQBTFIuu+osFcL5iODGqIKk+nCQ344aUxzkAbiBbMbeQ8DCkWS8Io\nawOtgbUZF8MsNaBKKQzFmEK4zFAotKlxwJlb0KYn3olTtMHxkI7U3ChaiDm4HJSpOcea98aKEVa4\nR7hzeOtZw2fq3CGMByPaTOtZG2sF+v4vkVpXB1HMncnzPlrP9k8t4ZaZCZ2pEinIrA+d3VRxOXMg\nGSOji+5KNGjJVhdAC0ejcfL8jhIMrWVtvSphSf4SAlNJpsQm+SyPRK5nSdHfaQGbRjpMEsEgQhk6\nCYpqkjRIrre526iiA1WEQQQLZYzaSTNa38IaUvKd75Fwy+o9YEC+Rw241ApacGkMh2/WhflWOUyQ\nG9azTM6LB8qzY16qWbiK/K7G2I3nsz+PrP/bDL3YPrly27oRskS/9l2TxVNjd7799Xd9u/ru8q9m\nAR07U3o/lg/NzNW5YqEDj21YizX/7CzX49p+vB7X7bGiL2Qkdo7DmjWS20Pk6vglS7D38LQ7kHHT\ntRBZBdkWo3VlAnthVAvEaRN0204YbYPp7UexOrk32ambUazni1hMmsXS383y4rgsHXqh7Wni18zl\nh56D2GikewhgNbIXeODVtW/Pe9s80uDfwS6Ba/K0bajrMXsnKq+Y7ofvaCzk5vvAymLF+r2lpfMg\nkkXci/Owuiv9PuX3ridyIZKQZzfxNhiwZXv2wYSFkTPPsYf8budYjl8hd1cPSl/z+4DGOtfPdaz2\nmll7IpTlOV2DNYvBR38erp6Fq7uV51gyVLt7YyS0Z6Hvz0j9sqaXo/7RbhHxP8Mz3/P2mL8A/IWv\n+PvP+RoitS82G9DxjsLEiDKi6NCBs8UoluHukAainHTAMWYpiI64TtT5iRKV5sHcElrlrWHRiVd6\ncG4PxzsUuCsF5pn36ybZQwd9X19gzNH3w+WdUnCOOqEajBIM4dwLnDXZ2makw9OyQF0QUKeEMETW\nfcyt0ew6g+39Ga8BIc5M1vV5NWoTPC6dTVA4qhKmlOYMpWAWVJ+ocSTXfEkHw6RvoHkdxXvGJt1D\n7caULcEizaNGD9CNbdNJiJP0wEixkt/RfE+0NlNbOkyLQagieGOrr9W2PRGR86v92kn9nMxkZhBi\nPLUJQft+YemU9PdS7hV1PZkoaBwwvV8DRKa2LmyRfM5b89T2cae1hFteatCKMuhIGUbUvO+lAiG4\naMLaWwMS0le9ImKIQgtBxCiDrRvlslbc0wEEITK11d9JwiVyTeQc9HUWIOIUATEhrOCefIyCQjRC\n7/qcexIc2QDS9chUqM1BCqhRW6NOE5fLhTKM/V4M6DAwzTMMA0hmiFyEaJmBCVGqHRARqgYtBA1F\np7mvmJybqTkHEV6pQ3UsKlUdqW9REV6Fcs+ZS6u0Zrgckjo8nGiOyMAsMHvDwyhNGIbCGw9ibhy0\noJEaSg8ePTYvNBcisjZfShDNcVlKD4ShjIQk9JCiqBjFhaldGKwki2RLFkZkSsiu1x5kViZp6xtS\nWzqSqnlPB6dTdMOxZ6UaCaMbSRIRU2EgCDGqB9Ub9Yo5eMTEGS1r9RSSWbA/bymEXNZAZUU4h/EA\nvFfhMzGMilkKKNdoSGStbhSjuPYARwZGzzWzpk8iCAP1Gw7mfascpmBT+t1/trSPQXY+9uLfQ+1u\n22K4eDfYvEf5bs98ZaJ0o0lu/rC3j1++/mpT35xv+22BAGX0UXfXfqlI++VxZ0R5O2JLOOSH+3zC\nh/Rkbs/x4jFf5dhy7Su8dGTChHhm8K7OobDOQb4UWVO5kC/V27KWtb+r0fnhvt9CL4F1HaxR2Gfj\n3H5ui+FCn9vlxi5QtK9RIxLXA7/q37Nj45rAYjlmyZysma4O6/mqdQ+7NXzjhPgLh38d03oPU3vp\nCx7blV7KvEGHkgHSo+uydG/nhDy73s1zt1cJX4ga1F6ot7s533pOkavM5e3flmf0pQCPrMdtDthS\nn7BCXm/uz8pOmJbW5lyJoP0c10x9+186rEI8I+a7cassJvbzPfAjj+4vW2+v7oRX45z7kBhaBtSc\nextWMfWiyuSVqUEjs1DRGo2CxJlDEeRS+UQq8+KMSBaiX/rDVnFGlCpBsy5DURtj8rEh0VBpPEXQ\n0C4C3bm+AkqAeTDgjBrc20DoRIvgrMqAMUYWW09NUrQ1Gq3vpYmVClQbFs53ivK+PXLiiAGjKTFN\nnFWpVnCHuVZsKMxtwmugZsykIGYzSwPVl+cOhEOnHu9ZFRT1rG1SXfbdJGwIEptjGhQRZhwrPTvb\n+vMiWbOCCnNxaI0By1pg+n7pwSXApx7Vj8anLtgAl1Am0x5McYJCMTBtpPypUkSSuQ4gYJoDdaXV\nmXEwZnesDImuUGgtSLKXoKjgIggl60Oi9Wey4Z14YlBDi9HalCvHcteL2TGDNjshwoSCV1S74Kc7\nzFn7LR3xslBTR7elVIUQ56hGI6ncSy2p2yOgXinemF0RT6N38kZpcHDBh3RuCnCgcCoBQ9buuGTm\n7EEN1cK7WWn1CVHFvNCGitiAecXxrD2TESSdzzbA7AljnOc5IXeRCJRJINRTbyuE4kLzGRGyVq8M\nhMJFOj11Ewij1YrNgfuU45cDqPIUjdeaFP1hcJlmigSu8BgVhsaxFI5tTqr3M4wWjBqMcmAec/2p\nDylI7elwuwQel4Ru1omTZm1O1Mz8Qzrb4km8UCKzh9U7QUg4o6YjruFIC+51xqcLSBJ8hAnNK5Nr\n113KMQSy1uJiJe85WT80mzBYBkUaxp0WqjQKhXdTklG4NM6k4zOLUAocQ1fk0NzvuyEwZPZUQpk1\nyR9CNfXIKMw6osyoVSYH5chZJxRlAmbXzMaHgIJ515jq284gQbPgUsEpRAw4h29gd9/at8phUpGV\nInp5k9ti3F/ZqptBcE19/LJVfJsheIkUYJJgRCi5CjtspRstKr0ol04f3tti+MW15pH6Ev/ejKq9\nobQYbwnN3s5nbEZw9Dg9upmzHgGiz0y4a3N8O19dmCc8i4DLSnm6YWzXedz3IzbCiYVpdYmYZNFm\n7487GkLTYFbQ1lO3pJjgovL+orO573JsUas9c98CFYregZLvgRxvgC7rQG4geXSIFhv07zoDdT1H\nElzpP0FX6Y7Mb7jRGaRYnbWy8zYNXZ2k5gmOMnRV/F4yCPTv5qazzS3cODOyfXYNwlparoHMiKSx\nlC/L7bloPWOhokm6oYp3AwUgOqd5siV2qtKAwXR1BhaE3kLFsDxjqzOxc9AWDatcX1m0vdSv3fY+\n4XovtS3rodKzhyTYEBG8r2d1mDUj4EeSHUvJ50r2T0Nnp1scdQG0SQoFrvclmy/JR26drn7N2OjL\nm2bk19jpfq33YwlrWLI1eUOwNFr8WkB2YceDzQkTch8wkWSf0u6AR3dso2eiZCnU73uMSDeUlCa9\n6D+CspKsXDvXSyu5pfyyfaS9uhv57qcPjKSw7ByBFhitMEpCoIoKZwydGxWYmWkOdU7B2VoTiidd\nN66MhQk41y0gBOC2CNNu0J1Z4BgzRR18RuWIOUx1KRoPRhvRSHiNaD7XswdHSbHW1ipTh3mCUGTE\nTClWOLduvIsyWtb5FD9zFOHz8cgpAm2VAjzcO++b81idxxAu5QFvTqGv1yWLCURLwu8K6+eZcfUe\nIOuw2OjBkUUDwWB9mrWkqGUEY6ZKkuraLN8Xmvo5SDLilWHMiHvPhngkWUVMM6+q8spahyo6Hsos\ngYcSWJLYqKMoJkM3dIPWOt1xN+xNkuFOe5AvmlOjrsFWEeEwFCAzAdVJtjSgaDrSlaR6zui6cjrN\nHEjmMdW8L0t27xhT1rRE0NwSouhBa4UypJhueBbs57YdWNGsHQJsSAa68IaadghmyyynzQySmkcy\nJ3FOO+QaijJQWlAtuBhEO/Hpk/CuOMGARyN04F00Hg7GPcpjm2kcUZmxmKgy4DLSpDFH0msnoYZw\nmDWhqSQbmqqu+z6aAadk4nNOBmU0PJJFOAVh6UEJus6CU9uUWnndxjnTek1g5UcULrUyh1IMnpon\nVExGfnQ5oH5BrRBdb008HZLwCZ/SMTF3msAky/JzbEUIDNCzl2nYtKxNa5UgWZAlUg/MiqARyDyv\nz2SIIIMQ1Slm/V3ayZUIRL2LUlsnZ8pSgiYpQGtaEByJxmApbCzAIZTRnYlGw7kTYWqVOuS756AF\nIyju3GkGcOdwBss6wPBg1g7jjkhpE+0i2wRBScp1saQaByRG5hDeSuPswqk5VQ4ZxEMYLCjaeOhO\nHqHceZJmnBDEjekbJiT6VjlMyC562zenazM4297Q2UfvP57p2AyGDxW27yO4IdJTw5vTcmXpdYiM\nkOJ6uwvl9/vPLfPXa0ZnX7+zP91qVsti1AfCy2N6CcZ329Y0v/basN2Yn1FV737eCuPjZp5kfeFB\nr6UhDy7LGEWoa/+/XlshTD17dNuWOXGBuhSdAONOfPUq2L4arV//2rfHbue4XoEL3OkDyaYtqrn0\nez3kuk/yrIPXGZMXT777bDGA1XaCybf3s0PH1lPvhI9vs5kgnQ55t0aufLj+XOq2NvZizVdjXQID\nqlt28noytnn44DMbu2OXudvm3rpzXdPzebHPLySHVr0oly1w8VXtGJvDsV+bpau177u/34tary0r\npdA6rkB2xUUfzvbtfl4CNRHrX5YaCe91fy9ly0TyBWeqV2nklwhPmmyZ3F+2D7f7ofDZcUBEE6vf\nglgEtPue4DiDB1Vb6gt1rYlpnnh/rtTzhHSGvUX0+uIzNcr6voMkmJCAMUA8DZdKZ7WL1HTBL6gO\nWWfYobRtrln/O/SAjWTg5CyGMlGKJv3w4px56rEkkZahkkZYArrgabjnS505hnPnRrGBQeASZ2rP\nrH2nCU8YkztoisAuorpJ9OC9LsPXPUREEKczmgkDlkGC1chMymOV5desHyoKrhl0GHRIJ0ryubJe\nh3Twlhkeg3mqKzObz5HaVUX5LApQmY7G23PwziMDI5HF/lIu1GqcqmDmHMuAhlCUzOh04WAAzNLR\nVME7+92aNfeGWmTdByRbmslqYh7dOepMNHBxBnGKls0GqoGVQlWQOTWq3D3FSl0YHUSdOS54TWhT\n0eiCw+m8lVLQYjjB1FrCCkVxq5TmFHeKNEoEzS8MWngiYM69a2oXVArHCI6uvJORUzR8DgaZeKVG\nu1Rqgc994g8LvC2Nn7WZ2UY+lYEvI/iyXbjUCyUarUIwIaI4SSaRCLMkXJjdoWWWzltNnzOUkSGZ\n6eaZs8AQCbNvbe7BbV0h1LMYEL0OiJ7NdN6E0ErhrimzJH07YXiFgYnj6NAmyqxISdnY6kZoZt+K\nKEUqo8AgcKByjKD0mtAaQxIVSH+v6kKGIEQ0rKUzUVwYZWKIhNAFgTeYI5hI2QxvKVqLpWNqaumI\nqCbsUKCSdUhVGurBGOl8mKWzaZL6RsWCoTpahLll4OEYmU1cbBVXoZhxFwnfqwFVjKd6QY8Hhjk4\necO7jeOL46iWAZEhQxwl7pj0Qq0DMwFRuKCcJThHjkERok4UFV5H5SAwM1JDmUUy4xfBu2/41fTt\ncph6uzXAYG/EX8PHlgjrL3Lur2pXmaxlEyS2a+4M0Eaw1IgPuwjhooskkC+wtXD8q1t/FSJAkcVl\nivV6X8fh27dbg/DWWPrQGQISw4xcneN2rkVSRM48NTzQdJaqBINvfb1yEl74mdg5TftMkG/Htq59\nMgSrVfxBvyK2vn7Mif6go7KeY7OJvs4qC4HmvZJgN/5bR2nvx3y9tn0jz9Hrlj4AubutjdvOsaxj\nWQ20Ze59cbB2Y+knW42AZR1HBB9zVPbOtbA5TV9n3FdjWpx92e770OvzZokr4VrfrR/dPczLyKM/\nTispxkvX3v1c5fmnS52JifCh5SU9yLI8+/sxfWiswNWcrn8T2eauZ5gW6N7+3Nvd7c7SC9d8tgcI\nKyX/L9uH29P9HcPwGi9Zd+RRcYPajEaBNnOnSa37cLjDzheefOD0+EQ7TRzcU7C2zJx95LEdoA2c\nS9ZJtDpv9XJz1lioty5eqtQoPFIZm/BgDxQmWlPOJCQIDC2KGkRUZBCwFAKtkcQBKkrTlnuzBxJG\nk4YnuC8ZxKIHQcyQJmhTnkyYo2FVMSlMcp+1DLODOk4aXxJBGBRGDhKMOKLOpM6kgbeCm9FIiJ0S\nlIyV49o6NMkY3VKYdtAeRU98R1VFrVBEMRHmSNrz0ipVGi2cgjDVORn02kBpT1QR7hrcM/MuDryt\nyVzY5kZ4Gn8aWX2DCLVBCeegRg2hnhphivjMcYT74tAKsyZV9dxqPpeqGT13kO58ShVMGjIJFzWK\nBCVgQJFa+d4xGfpmg/ciWVfWQFy4aOW1P1LaHQ40c+4ulXMr6MEo48DJK6+fhKnAnV4og/KjWjhI\nZYwLD/XA4wSXMjBEoakwR/AQlTvNzOX9sfArk1LNGeLMTy7GFzIwn88cD8JY4OkkPA7OVFNLab4E\n3xud7yjEMQWAXx8GyuX3+NQ+YZ7hwoXJjnzqT9wz8RgJOatqDKczcT/w4Bc+HY/87gy/U52qn1C4\nEBUgKcjDlFBD6oRIOtZFNJ9BIHBGhNYW5zNhhu7JNulDXCECBkmB26id/dTgqCe+Z8pYHBuVgyqn\n2jh5UKVQi3OoxpEL34sEOF+kcXAFcYSGRePQpszsdi0zV3BL6Kw7maWN4CACtXIoyiDZZ49GE+VE\n1pxNCq4Dc0cpeCRNN+OQGmwOBwR6gGIw0JKcwyJJurAwO4LRLKGP9yWRMF6EQ89cnXqNm+NgFcEY\nUGhJWNHmGWTgAcAdV6FROIlxEXAZoVZChCdJcV/RiVKEscIXLkiBgUq1vIcWhUInSIlgisJZldLZ\n/55IB+ybbL+wwyQi/yLw7wF/Cvg14M9GxH93c8x/CPxbJBvR/wb8uYj4rd3fPyfZiP5V0g/4b4E/\nHxGPX331ZM9QXczD2ArLQ1d43mpJA4vuSUTgJuAbDAXYRd81o+MLLE/S8dJIg6G4Z0UjG4Xw8u9S\n6N5RTGvEuyzejaThZtI3zJB8+cgixHmtkxN7KBPXzo9ospCcPYuBVTq2Gq4IFlwSktNFxtfz7aFp\nBVnHT//ZBViMqV3WaW90NlkyJBssS3TRod4ilAGpUSE5N6qCuK8aVGtfexGz20YVq76xvdUlnS3S\nI6ndgO9R/dKFN3dJhzzfitnOvi6aQEtGbej6RhGxsqC1vrYIugq2rJFY6RFOkQ61gA0T3q3tNTu4\ne473sCxFVqdPd5kf7xv9ahDLjcO3m//F2blyimNbd8unS7YrM3A3Dnl0YziW9bdc79prWWB/2vse\nfTwh/dlY1g7X7RbSKVdreMlmbOPy3bj27XbdWWQ/JD3WbQ/oTsPCPOldMywzTds2Zx32lpHlBeJ7\nkwlCulGz6zMZ1Q5NZwxughKSx0fLAmhIatxlH1ngtEsbNGFDsD5uyQQmi+5FFkJX2evh9HH2/meR\n+a6DcJWpUzb4z5KN9zWltzxD23lhCcgkAFdEOEpQv2HYw7exuSh/XxtHT0inHApDdNZFGtGcx2i4\nC3W6UKvTphlpM6MJ4ZVX5chR7/nx+cSX5UyzRqnpOJhl9iKA0MYYzndLIXRirM4d8AQ8qfHYLnw6\nDmjLCPqlDAljjU6aYgWRLPo3LKFjmhpNlYZ4rp05Fmcp9xbtRA4imqxbqkBjiMzARGfls6hAMCAc\nBD7VC+Mw81jhjR/AnkDACrSWmSERQTRFZpuASWfzi2TvsnDECg3lokmQXCZoKlStfe9wLJLAIdzR\n1pjbTHVnahCtMYzCUINDg8G+5PtFYS5cFJ5mwbwyi6dTiYBn5F47dk0EdDCkpTP3XZ94PeR9oOsy\nugRn6yLGJAFEeHQGM6WIZv1WzWL3BtwfglEbleC+CcMcvBqfCDnyRTVmMS6asHn3hlE5zo1jGRj9\njIyZNdIR3pJA7Xm+cGwTF+Aowa/fGWMVHuJM1ZEn/ZQfzjPmwdgE5czpVIlh4OctIV9HCZ5M+cKE\nNwSPT43WnIejcF8Kr2Lk8yH44Wz8dJog4BUXCifwe34kjWoDMo48FuP99GvMlxmPyugPlGniV+TE\nr0rB9ELFqO2R8fMHZin8zJVq8J0TNG/8vr/hstgcIszknokooT0Ta2NmcHPHY5RcH2aaMyPgbcty\nRrBJSJAZQncnkvIPd+OiR35/dg5T4w5nHGdGKsdhZPYTrTU+FeWVKK+GiraZC87FCohTWqMgDAoh\nlZmsA7vUbreZkAQX6eDc+8xwEIo4RfL5mrQwhTLqgOOMZsyhYJr6laLc9zquT8YCnmQ+hGMmHCQD\nJEvdr9pdZoFa7k9DUVSVYzt1Eg3Ja3tCCM9hVBciCh4wlpEzqReX0NmathXgJcs4XtXK56r83Jyf\ninEm7+Mgxl1z7i+ClcprMc7mDIzMRrIbinA/PPEdC7QKk1cKgfvIGwbCBv6BvfumtnjgHy7D9AD8\nn8BfIh2dqyYi/z7wbwP/BvB3gf8I+Gsi8hs7Ctf/mlRd/zOk3sV/SdK9/kIMRYIwxt7gyrbPAmls\n2Pwd4mjf3xUqs7wEFjahpe1rm3SXTgjYHKaPcO+uDEWx9BxgYZO5FthdslUJqy3wW80AACAASURB\nVHmeNVrrstizn22Zt43BS6+ooDPKtTO8d1ovsCNXWOd31//957KwMPWi9W6Yf6g9g4JBpv07HGKh\n617Om8XrukIV5eq73SlD1uuryw0j2/qF9XchsyB+c1/XddPRUBo32ZCrcWz+jL/w4cezVc/nZfnO\nUg/1db/37Hr7HxcSuq9zT5aUyk3bs/Jd2evB6qjsGdx+kSzuy/3hxQ7X/f1ie7ZXAWA2iMteWiD9\n1+dQxO16OUHpeGbhazbdHX+9VlSE2bMQXzL2stVgLTAjS4y2S3eWFkfudmyahqf2WquIwAbtGSfr\nUFZnvGEmdOJq3XxVE5G1DmoPzVu/H7E9D7ss4bKfJLTp613r/+/tEIbJHadwWu30ydOFKZ6YThN1\nhvM0czqdqbURodx1EXE88HGm0qizMASoFz5FqCVhZdWtF487GgeCxs+iQT2CNFSdV+JMjDgj70MQ\nGqUkhC7RDr1uTQ0nM0qpt7N7r7Ct9SOdsl+iB78EMclaRyBICN8YkRH+ruXSrFMN98LBR1Hex0BI\ncCSYo4AE3gIsM7EHSXiehSAlIXYmjrcMsA0hjNMT5XDHTEXKzPfawLs68W7qTlyreM/iSGSQUiUY\na2OM4B7j1zuFerPgs8E5FOONONUbT3KHFWcUp3pBdCB8Tgavbgjmz2AlGMW4s8JrE0wnahROrp32\nmQy6Ra9J8qBGD0REBm5MILzxoMFnqqCNuZWEbB6Cc1VMAjHBBqG0noVrE2oVE+epwfcPlU+Pd/BY\nuagz14nLlJpY/9Txjt8bLtTmvI8Umz2VwqkG786PmCgiQZMTD9xlpoMsrv+EM398PPB3Hmei5hhH\nueOz4rQ6Mavx++3CT95V7mPkTzwY01QpxwNVRp688BTOfBa8KedT47t+oY2FMwd+1h4ZtKB6x6sB\nvjdU3raRv3858NMmSIx4vXA04ynO3A8HOF84EKimfTNL6cK1jYOd+jtfqDHk3tZhr5dOjrW37Fa2\nyX4uasNjTpY+76QppI5T8+C9FM4KQ5k5yIy5cYzALLWOZoPZToQrRxHuzHKNiyMWmHRkkDdmhzPC\nrEkJ3wRmN6o0XLP+q0bCEZHGTNbauTdaKFp6DVtLoqLahzqbcBgUZaaVIQOMi6huzFSOJCuy5zrs\n7wftn2kRDuWBuWtSzZJBtqLB0JyzDbwXp1ZHe93V0PeVc4dzZvBcQYPPTUGylvjIzNRANGt2BacY\nYMJdM37fZk7VqQijgBRh0AcsHLULd1Jp4XxaGp/ROLWJi+xVIf7fb7+wwxQRfxX4qwDy8pv0zwN/\nMSL++37Mvw78BPizwF8Rkd8A/iXgT0VXVBeRfwf4H0Tk342vEAkU1V730B0Ykv0E+ma+mBZ72E2H\nEOTD0SMK7TmufzHcV2djNXTySguDlbFFeBc9qGuD87lzkJ9uRdvLMYvBv9Z5LNmGWNhr9Jqp7opx\niyV10DNjW0Yio4Fd40V1JVfYsknX5wRW8c3t5FwZm/FRoyk7tDhy+09fgr/tGdwWB2Ux3paoz9Kf\nK9FWERZdq7VmRbdZ3/sJ+5Euc7c/LmlVe9Fln8/iG3vaYjzsr738uxma8ax/L83Mx5r2F/0HltKz\ntmfCW8a6fPclRsCXvr9e+4U++9Vn+0zLdp+3QMI27uv7/PIAtiTHc7jss8/2a2aNGT5Dpj3r6ULS\n8dL5b/uXUCfdfbaMZfueWWe6MlvnOvQq9LHC4WJJgcvmqEtcrw3HsVKyGH4NliSVi5kyBQwY2mBf\nHbUIbS6BmuznTQZqN96XWPpkRz++h4WugSPa+lwuQY1ftq9uFeWuXXjjJ06PM5dLpdX3nCfl8ljx\nqWIGTOdELISm411PNB04Ry/wjgBJcoE3MTO4MqA80HANniLwloEw0cJl0XJp8GgLCVFw34JDKUSr\nTJ7sfWmKGLUaMlRwodiAHEb0ceIgjSmy0NpoVEuioyPBDDx5QoFiyZaqUlqkARtQy0zoTPEFXZHq\nTBGpLTOYglcOMuIBzQqC0CwFUMvQUB2JS3A4zvkOi8xUvbYL9wJHnVGc+xpomWg6MdB40iO1nRkx\nRk065McJTjpzF8YoYHrmcJz57vQZv1kdD+fQGjSnyMh3R3gfMLWS9VymhA8Mw4B5stnpqBwiGfak\npMjzT1vFONBCaEVBDfUZvEPBInCEgw2ZNfMLY82soYYTRTl647UIP6qP/LQ50QrfL/CZOV9W5zzN\nzCG0089xvePVeeJhdH7FjkSceXrXmER5vFQ+Nfj0aLyb4f+eKvei/FpRfnpRvkQ5okhUXin80WPW\nl01t5BTBw7Fw8sqDDrz3wt+c4CSGWOpzeczManxeDKnB2NJWKWPhy0vwXgtThVNzhqhZKyRgrXIZ\nCj++H/HHmXMEPiepyZMUfjzPjMPn1Oo84ShDBnG08GaecY68m51RCioNP94xa2Sw9OmEVefsjc8R\n6gD3IhSHB5l4I4UvfWb2YKwVlTsuMiFhVArRfCXREa/rM12mgKg0E+7DGe2OS52JcE4Cnx0mxmZZ\nRxRQm+L1jrcaVFGGFnwyerLqLXBLDWQwvDY+L0Fl5hJZD/RejSmCyeHJkkreEKIpZhkYryqEKJcG\nQgXNcZY5kslxCBoFlUM6f1qwQkZRm0NshGNiwjHTuVTpFO6RCKdJB+YAiZmgMjc4uzCrMvlAY0Yk\nKFqoncxLOvQ3XAkNQHnqDt8ReC1KLY1PdMbsAm48amqrvdHKr6L8fCj8HMHGxr0XHnxmoHEw6yLx\nTuu2/L0Fn5TKN9n+QGuYROSPAb8K/I/LZxHxVkT+BvAvAH8F+NPAl4uz1NtfJ1/h/zxdef2lFnTF\n7f01l791GyCzE8ZQk3a1tYYUA8u0oUegQxo9tBQMbT263V2qDsnZGSkswnnahfESfFbcaOG0Ij1y\n4Gs/gWtnxxPYUN07jourTEmfP5qAx1b7EJIFnEMIl+hCZeEMoonHloRsIEuSJA2d2l9iZYEPSULU\n+nuMIikq1rrBr72AXEKYNSNjphsjmgWE5uafQYGeIerHrGKfdCY4SbhQ1nhEMtr07ETt/b+9iRab\nwbzAv3Ieo1Njxsrkt4zDkM7Y17N1RIcpBlWDQxOq7DINQU9fZy3ZAuXLl7MzaWdelJ7F6MbuQMIB\nRWVlR1zZ89Y+bJH8/Rrdk3i0nfG5rI/FXF+0BrY1szmVdeekKDsHBbA+J4uj1+ubkZs1KF/pPNw4\nCLFldPY5jsxo5nm3uMRXO4w5mN3nrZOV7Jx8WBwXksGIvUO26Maw/c5z52rvLLedVg2xMVEuUrRB\nbAQVIVhfMws0ML+8ZZtWRr/IeVbv66zPUYhgKwwk+5HQ08wSxyJi3aFPFiAtSWUjYsukqTJ1Qwvo\nTIm6jnuToskCdO/UwItzv9RypT0rtP5cllLWv6/rSju0ii5A3efZ9oGgLoT6y/bVbX58y9vzWx6n\noFUQV6TNHJCE3alzV53xYeTNfGEI4egn2p3x40kI36QNNlSA0LqBk9onCbtx004aUmlNM2PUBUSX\nB2qGLhwaDD1o2Kym/pA7uDK7c3bnwExh4jAKr6aZwdIAunTY0hFJYwx4v4JkgxIzA85BU9Mp60AS\nikMmThGSoW4w4Q74bFDet74PSuXs4J7CpU7hKDOfj47MlVGNMijFJ+6logO0qATKdCzcaeNTCU46\nUqfGoMb3Bxi5MOK8G0akJkTvMeD9VPiZF35HJ5rArw+ezqYq0gzUiAbHsQcZpBKD4DJzeHWX0Mik\nhOE4pDN6kpGlLD5rQARzQcIYo/JKlLlVphZ4qRy88anDWCp02vX3Z0fHyqQTn5cDozdibHwSF4bh\ngc/qzHwKZgnu68B37Yk/dDfxJ8rE06fC3/7hkXcqHIszDsprLTz6xJdaOM0DbsGPXXiLMSF4gQjF\nQ7MuCKGKondHLvNExXhsSfLRIokpFmRMOYw04CdDZsJqBYlGm1PzqLQzUQMPYXJhnmuyTYtzmS/E\no1Nc0wbYZb+pFdHGq2JMUZl4IqRwtHsGM+p5Rr32Oug7Ht6/zwCPwCCV8QjvzwP/+N3E94aZv/d0\nxlX4Yya89Qs/9ISfvh6g+BsOOvJbzflpFKpduk5QsDAVAnwiEwcJHpsyuPN6eOKsjbCRN1Fop8ob\n4KSVpnAvilXnMhaKZnbz+9PMD8QgjINOHOXCKKBFVnt0Jp+10bNW6EmC0g5UL0ytce51XUfLAG8l\n64zCW6erP/bornOoykFnNE5MMuCjcqqO6YBiXBAOJanLj35OKn4HtKREDXBm4FSTGEMQRiksWewS\n4DEn2gKlzCBmNE/NrSpJ4lJI4eNzNNDgQuWeETOjanAO5w5lbM7PS/A+ArVgDkWiYMAnpfKZNA6r\ngHsgLTArCR/WZOz7JtsfNOnDr5Kv2Z/cfP6T/rflmN/b/zEimoh8sTvmg01EVijZFaSJxY7Iv0sW\nSlBKoUbPB3hQNPMTqX6+QMGWjmzX2ReHX0GlJI0WUUVCUE8YmwbpvV/5AbuI8lIftYe47K63wmEi\nCxmzu5uEZ5VYWflWpqKdwRQRS/Y5Dec1WbRFkOmmj/UMVFvmcOlDZ80SWYzPHRyQhaQgo2UiICWL\n8dJJZTVMF4Vo9+us0gIhTGrQZQ6uMzj7uVn0S7Q7dqJ6BSMM2RzLlQLDuyMq27WIdlVztsHp8p4B\nva/9obzJjMjuv6WfroL5ltlZMxsrvHMb116va5/NWbOKurkkV1e2jaFmfy/2TpAhV9TWz09y3RbH\nYc9g9xLEbxvrC6eT6/n4yCWfHRAvbHLX57rWBFr79hX9vf3dPjC+Fb7ar7lkebfnJXuwZjdvKP/z\noGVd7WGpGVUPpDum/fiy1cktDvWyRqQHLRLO29eBZPb6Sp1gGUOfg2djzoHgC/Ndd0Q/lNFVtsyR\nyirfSBYHgxL9+7kfxC9rmD7a5O072vE1tb6nzukAtUiygiyChnfqyOXCpHBplXdyR7QT5+KY3+G1\nMVrJqG43ppx0cGchI7jNaTrj3pIa3INWW9/XWddkyrEER4ODNUoLhK7zFdAuMCgcTRnjxEHg1TAy\n3hXMKyWEdhiyTw7vEE60Dl3twSlN2N2dCkcNDpLkFpMoVbO+qISh4RxM+FScPzwoB4K5OTWcpzDe\n1onR7ih6Tv0jG5LmOjxJJ0ogNqAiTE14P8E8T7QYiPiEs16Y28ArVd67853xjgNnpoDJDnxZC18+\nTfxMg+Nb5bP7e97rI39kCM6h/H5TRjHEBg7DETrTXmoY9cL4OmECR4JBKg8uDA1+4G8wUrdK2sh7\nc046o815bTM/GI1LrTSH4o1iQbQzqlnD8uRKGWcuc/B+Loxy4lcOhXftiZ/5kZ+9n1MfS+GhFdyE\n7z8Ykyt/y77L7/5eA20cLXjjxrkVfijCjHGxkQfP+/YUTtWC0vDHS9aBq3BiZCbZ1NrTTBFDwnCt\niChWjBCS7rpBmHDBETWkWGYNW0VcUtjUhu6tK85TBmccxNJplvmRT8odJxy3EXfn3FntxCdKdb7j\nylSMWRqlvcdIinelgSXc7Dfun7jzwhHnODQOw8wPj/d8cZ55/77w/TGD2ZNUynjHD6gcdOLhOPDF\nNPLjNvKzeuYwP/FaOr26Byfb3q73KrzCGDuRwpsWnPxAnRtNJn5alE9CUYwCvNNA1bmvUKnUaLwr\nhpvzyShUN85+ZPRGMesBbWhqnKMxy8TIwOumvIv3nBkQM4rAl1PlsQlYYXKnyQCadPPjNDGIYaK0\naDSdsBK8Ot/x7pS1dRefu7Vv0BoHVaoKcyQTnmgK02bALBMJXi9IUaR1XIfAiHF/KLybnTkKTTxp\n94tRGHkfjZM7aKEgnDW402QGLDLy6BdoM5co/MAKBw00Bs4yEm3m55bixgfgt5vxtt+DoaPCvPTg\nocHBCo9d0+qbat8US97OJPmHP2b6238Zhvs0u3s2ovyRf47hH/vTvahyM7qzwHXhvk+jwiRJElpr\nZK3cYgzlTcjIPVkAeFW0v+/lDpK3GGQL6UB3Jra01+573YHYQ2nYRe+lZ0cQ2UV680GsZE3EoSUe\ntdVGHTvVZrEV7rNGoCWPE39uMKovGQ3ZjOesKF6PWczJK9hXN8yiZ1YWIoMkUdDV6FvqpCqJ2X3x\nRstOn0Y2La09dMhb2wzLSEOwLX3trS5aHX2us69b3ZeqErUTeuyzDKv+0eZcqfVatI+swtUIlWvq\naRcQ39X1fGDsL0LE2O5fGq7y7Pj9vRDbFudSL6Owsr59qA5NlwyVbhmL/y/avo5ubUtGYznmJnu0\ntFtx1+XYPMX18bdwysUIzeTWRv7iERthzO74Kyjp7bl8V8+4uF8RIMFsm3Nrse0TVsq6b+yZMoUO\nndv3/Wq+dvV+LznckpoZmb3y1Vm8cix3z9Zyvf5BwqdwVIWn3/2bnH/4f2zPIorPT8/uwy/bdZvm\niowTo5Y0gKoz2IWDZyArIvXnVIU7h3c6IzExu3HUgkpNNUlyj2vekHDGqClq6iPRKmoBc+NRM9XZ\nPOsPlOCgwsEErzPFBZpzFON7NBjgHQdqZBH3WZUUfTgT1rizwpEZ9ZmLFN601I87WGbq3Z1PI3hV\ngqEqbz0I9TSw28xQCqYCxRg8Eq6nxkjuSw8ER8no+dNl4qEI370rvHb4vMHZHxER7oYjJmekG/Kt\nNeRSeFuEdx48efAYgukDxTMzXNuRRvAFyhfN+N1T5WgPnNyYLZI4Yzj8P+y9TY8tS5am9axlZu57\nR8T5uvfmrSxIpC7UU4SQml/AiJ8AQ2b8BwYtMQIJCQkxYsSICX8ACQmhHrRg0ohBwwCaLCq7Oj/q\nfpxzImJvdzNbi8Ey9+0ReSopUFWqU0qT7j0RO/b27W5u7r7etd71vpzU8aR8Ege5439ZVjQlPoqG\n0ah3zhLKYg8CTHBGuJ+Ey1o5T52zgljmZE+8OXWECp7BJzRdeDJ4rJWUTkhX1rZgtXGXM/c5GuRb\nLvzV0vmuGrkId6kwa2exE1WEnk+0prBAXw2fJtQu3NFpNH75rGhKPNZGa06XzKV13if4dlKuJH5e\nDV0qT5pQc3IS6E9YG895j/P7uV0DLCVghdNUwI3aDEse/SYeno+JFPTKKYElXBzXivXtmdKRrpiv\nqBvVEt+chZ9pSH1P08Sft8ITIW1u63MI4nhHZUK98l6jf+XJjce1chblz7KRZ+Nzi9439SvSK9ZW\nfizCg5z4y6p8XpxnSzxJ4w2JDyVxqSceW+f75ixi9KURJK9GtgTuXGvjPHVO+YHWKiuGuXMRiT6i\n3hFtmGcKbcQcmVTjfGQNld6fEObKk51YWgggLAa/RLk8d2YR7mjcZeFNInqccufeDRPjPiW8G0sy\nkjknMQpwrY2vU9xTwgJ24tEUfMHtzA+p8g5h7QtdlbooeVGqXLmY87BG3XdtcE/jnSQ0h53Ad3Wl\npajqYWF+fW8VkUZJE3es3JVGM+hdwwzX0zCQBrMSXnHudM9cCQEV8WDsIMLFglopYphM0Xcmwq89\ncfZEwrhoD3sDT4g4KxlP8L3BJ9fdv1NHHydEFfxHTr/X+/zfNmD6JfH8/xNeVpm+Bf7J4T3fHj8k\nIgn4wG9Xpl6M6d/499Cv/x4TjMI4bEF6GVSbjodxWNKbgptEz0nVUN/RnPYDl5GR3SoRiEQDtw+K\nlPuhx+A2Ukq4C0HRDdnMsNeO8uHYs9sxjkB3EzIYb8CsB6ddAtitYiR3NEWQZCOUE7Mwf9XoLxGP\nID0kVzeakows45ChlAhOpxtfcQc9Qf0bAMzGPjnDYyNQiGlkQNYM0262ywsDzoQM9blbgHbs//LR\njxWa/MLkysLLwG/L6fjh/1LS7XUZUqGaqNgt6N9A0gheIyhNY9dCVrONU7cZDbvepNBF9GYoPChW\nWxWymX1B6OJw/g/qgBtNbqNCugZNTnwD4dscsa+NbU308bkAuhsV6lYN2GmObtE3pbKrDMLwI/HY\njyo3quAOIg7Gzdb7qFIGDWvr2fsSbNowzXZc2+hE4iH74fWXxZd9vJQS/zI4M2QYyt624/7y/TeQ\nuqkc6Yu/3Whwt34dhAMw3Hp/RxLFDz1t47qzjcpGKObpAOgbGLIX5y2qZJ3oQRj93awqJBTdvHAA\n8ZD39iSsdgPvm8ogDj3dAJ8QHPUXVMp+q5AegVAkZyTkZDXHZ/QmAmN7wgTKBp6Ie9oO6CTopIJi\nCvPP/m1OP/sHMUceUs3tx/+b7/7Rf/LF8/fHEWNBcc94clyDPmammMS9Qce9gFa518Kkmccejdah\nvqggw0PLfX9EFCpJjCotPMUskxS6dLw7pgU1Y1LljRtn65xy42EqJFe6de5L4bkb3cL8s0xpiEeE\nAlfzmeduiAqnfMYRsnSu5jzFIzGMdFXRJFRpfJgy9y32qZZEXxstFboUfnKnPJlxbY1JhbtkvPFQ\nvcoo3Eea8FOL0PV8OpFW+MEy1yZo1gBdHheaE8axKolM2ARUMSRFUuxOMld1Hr2SW+GxFD7iiHXm\nGveyVEIAqpL3pML1NDG1K/e5kHFOSUkYyY3ZnWZGm4W7XPnm1DC/4A5FTpxKGBEnn2nmLO3KXRI+\nZOHOLjyZsqqQbWV149pDJH1V4WNv/FXL/NiDtvQT74jDz/3CJc3kuvLgE37uPKpzqp1vE3yTO4sJ\nzsQvekhAJ4/+7avDdzi/Uad6p7eEtEafMlmhdos1KIlGyGlraxH8Ikxd6A6+VkpSaq9Ya0MBLWi6\njZWzCB9W4eqfcU8stfGkRpompHeSK4pTkjKL81ydnyfh4krrnb+nEw+p8+Ol8RuEyRsnGqkMP0oy\nn1jJOTH1lTelcBHls8FvWqWa8VM1/mS+p5wSH03559eVx7WjZB6S8XVRPlblNwt09aAcopwNzkSc\n12Vo+ibIJNae+YFnko5+HHPOEjTw91PhQTtaEr9YG9WCRgdOSQK9MdF5P02UyzPPeuWJwmNVJlXW\npHy3GkgicWK+GKcZfirw9VQhT6gqV7uwNqWXzFtpuK1MWWnFSJ65uLN44+Ir2mauDaxcWBbjl0mY\n84wCz60hydEU8t51ShQyqXeqJL5TZ7LOj1moErGs9AAjmoxTTrxJldWvPOSCVWNNmZoCjH/uhUVn\nTBLfeaUZVIdP0imeKZ7oGnymhuHqJHWyp6FDkJlMaEn4qJHMS64UT6CHBKMnXIVrb1wkVCmVEI8R\nFbQkfizz7/U+/7cKmNz9/xKRXxLqd/8rgIi8JXqT/svxtn8MvBeRf+vQx/TvEM/y/+l3foFwq3bE\nN+4BhI0+6+zKsUm6afSpTKM3R+CF9wkSPTU6uP5bMB6Xw2/LN3+xR+NQlkhDqvp1f4gYL6k6RI+B\nSJj5dY6Vn1tmu1r0LWlJLypGWwWl977PRxSBbj9vo5qNp4buD2S40Yk2aHf7PUZxYU0xpzv151Cp\n2RS+tpNzrL5smfM0+PWqwyhxiCqUQ1XpS3P6IpMuERxuamM7MBsBsW4GwSNQ9BF9vxC7FrlJV/Py\nnG4VocYm/UpUBGX0VX1hn/bP8TL43ip8+FD/O/bSyKseJQaoHWB1P6ax3X74Ovlr5muruBz374Wg\nw6GaFCqIY62+qj6+HsdX+6vXX1dcXn8w/vRyrf+u7xFeft9fN7aq8pf2+0UFblurh7+9+M7X29DN\nRHQDtJFRTw6qcT13/t/HBr72C4DtXsNucL3v67jHRF72t8fR3yzAzyu1y+P9xX0kV25URGCI0gBy\nM8fezt/+UEpp/B5L1Z3dEPSP428+FhG+T8PIOClmK1OeQtraGu5GEmcqUQ00c2bNIeEtzrpdl8K4\ngYX0dDZhppKSQc7URSniI+Hl0a+ThWSdez3xfoIPU6bTmUuhkalmVC0Ui8qmJmF22anZDxI0UqEi\nXZklKilvNLPkQqvxfUtRrGWW1PDLlTud+erOeMgLT3Nm0ZnPl8qzXbizwrfTPSLGAwvvzs7SFkwy\nXjuiGcqJz954unSmNPHBjaaGSYNkzJKZNOElKkutV+pIhGGFbE6i871WkiinlEi6wAJJMi2BibG6\nUSzA2pkaVG0XHpZMSp0nvdB9hraQVCk4k0JS58kav74I7y0j5Qw6R1WpRhXtKc/MS+PchF/lxqmv\nfHP/huuj8yk5f5InMo1fufPPaqJWR2xiymFCu7aF7z3RpkJeC29sZZmVvnTyJfEVoQL3I8JHn/mu\nOH+6wpSFd+3EX0njO+n46igTiUKzEENYkjDVON9hmTHiBstYr+Qy0cSZNGPLyoNW3j7cs1yfOTNx\ntVCrRcIsdhJDcD6uC2t2umU8zZxUUO8kcbI7UxJKVtZeeU4PrGXljQsslU+zMPkTcjrzdeu8z3Bn\njZre8Oc98QtLJINyWXkoM0Li189GzZX35cz56vxYhf+ZCyll/lQX/jU/8bEoSS787OR8pQv/5PPM\nh+ktYgvX7uReYZrprXI/ZU7dWb2zWOMyF6gzeRbmFteG9cbjFMa9bitVDe2P/NnpzNqN6vDoE2nI\n6bvMXNYr396/5a1ULs9K68JFKvXZea8C2qgN4A2/kQtNK89d+ejO2iuqd/xUVmauPEtmVuitIXQm\nTZy0QVaeu3G2K5/txJN95mG+50cRUjM8Gz01dCrcW6F355mGJkgCRuaqxr0r7z2op7o23mbjdBLE\nV+ZVkZT5oWe+94ZL4VNVLh0uk3KxTgPWbjyJ4K6ICdnqkPmPBH/kPYOuGv270Sc4AkOyg1ZHSTQJ\nj87MLWaRrpEgT2kkqAX3ZcTlii4J7/+Siz6IyD3w97mFJP+6iPybwPfu/hfAfw78RyLyfwA/B/5j\n4BcMMQd3/99F5L8D/isR+Q8JWfH/Avhv/Hco5AFDRz8kTJ3B/5Rbo7bDMMdLZIlGa/MeghAiw1E7\nAodNBUxkUJlGkKXozvMWlaDtHAIuG7FQ0HiAI3WNWBRtM6rjFjYaITYgPiSHR9BkA/DsYG00jkcw\nJLjG0dkh6x5Bct+z4oezc/txBGpbz5RtQaDemt4ZmfJNVe91YKkS2ZiE+7l+XwAAIABJREFUjjkc\ngfsGmjzEHV4AikPFYrtgNIWsahp0wDIm5RZe3miI7J/dZB987C/08WDfKjFKNK1vgHXv84iF9tsq\nhkdgMt67f+MWTHoAu6Mgg2xZ+O3YdvA4zvn+Th9N/NzMPnUE0L4JFWwBvOxgaV8nY/c2IHrEJdu6\niwpEBOKM98umykZUPyTd+rAMv7HdtqoVIEnYlk/QSnm5lsZ3qMhhnw+XwmHuDtN6k7wXfbGeXlRM\nDj9vwMBfvX4b21+2ysmX1BZvgExkUzw8Jjm4KcqJ7vO9v5/bdeQ7NXWrvt0UFLf34zfQ7eO92xds\niYdtslxAkiJmN7GQoQJp23k5XjPH/R7/Nbmdq9v8DZDDJogR97bjsR1Blm1X1Nj+lnCJCpUfaI4S\n2WgJN6ZI0nzxxPxxHIZ7SOlWVpTMrGfw9ebnp+GXV1uiDxnjaYAecSNZoynMvdOyQu8kib4PJXGy\neB6V3Hk24aQannEmFBHOKQcdL2V+7BbUrtVwnTCZ8aKgCakVrCNTptTGN1PhTtcQNdICONmUoolL\nr0zesTmxGnh3rtLJvWOnwo8YxQvvk/KTAiKNlo0fmLgqSDamWnETHs25cMZ65T4l7uZCbsZcK6sa\nS1sQMZIlcs28mRJGZ/KgaJ2kMSt8InqHV694tNhxJ0rrIXrhYiFVLUYG1t4hp6B4Y+SSBjXemW0F\nMu9ceEzC+XxHXa9IgqU23mjhG6k8THGOJp353BbuvZCzoVJ5Xj+zJuGHZ8UpLJ75/JzIGe5K4Z/3\nxg+t8Kkpn9ZKUeF+yqySQvkuz+Ft1aHkoKpN1enmdDEE4+2sFElkdb4Vo83KUuGaHpnIfG2Fz8V4\n0sjpuxpunVOHpCEylUbvdjw/jFzy3iLgbugkXDVHv4rOrCI4IfhgDtmcrhlXpdeQlU9ZODcHClUC\nXCQNv8uWFbPMWS5BnXTIOTFbo/rEV9ooJ+ODKlmFH+qFn1qjmXOfhZmVJc2sbeHv38OfJSHxI+W+\n8OtnWLUztyvTKfN1+khS4fuqXGzizy8n3mZlLhd0XeAdXGrlrYQpbNJKcegY1S1k/tMFkUy7cz5f\nhWXEgc/dmaSwLpWp3HFd1iGQ4DRtIZjgCZGGy8T/dqks7nx2weZC8UaeHVwQ71xL5hNXTtVZNPO9\nO9fUuU+Jk3c+o1yvhe9yPHtmV+5z4SSC+xn3zmLQXPHSyH5G1HjbnRXnqRnNIqL7rOtQdTXqGnHc\n/SS8tRCkKJooGma3ixhWJ57kjM/Cp3qNdVCFOuToVxVowmTG3C9ctTBZobfGJetgNzXcovVFkgzP\ntyEwdEsnskoPESUBhqVAxnEqbsqIxCM+SOGDpuM6CQqycc5K/T2bqv//qTD9A+B/gD2a/c/G6/81\n8B+4+38qIneEr9J74B8B/67fPJgA/n3CuPa/J+55/y0hR/47h1koq1SP7E7qQa/rQxxBJeh0nQjK\nRSBLwgh501lkZFHD/XwHOXv1YQwRJKUoh9iWco3h20NOFbXRtH0IkFqPilBsZgRXI2IzC/PakA4e\nzeB6+6yOzLS53/qrNNTmkoe6jVl4RgRg8d0o7EWvEYQXz8icmwJjfoLVJRvbITLfsNO4jjGzEdUl\nuAEAlVi4Hd8DvxH1xQ3YQZO+KBt4fDDoR4xglBtQg80w97j4Ewy/Dzlwu0IuPd4rJqimIbG7wSvf\nM/cMqqFIGNwxKlT95px76//avlUCMO39PtyMa3Nw6kJZbAh8CAG4+wCgaVQOFo0HjXLL8kOALtWE\nmwW1chwTA5T0bf9FBtUr9qwdwKoxgnBVrLWoYAxglHcYelvLh2h/n8M+gMFWtdFXYLe5k5O+qMZu\n/WndIY+1tQXvxwrrFqgf1fWOIgbHoe4hsDLOzWE343PWd0C2gz54EfgfAZ2MpMqRGrjt4etqzu26\nPYC1g+kyGgbT8eNtDvJQiEzjIWDuAS41EhPpyFVU2z//2tvKuIku7Amf7biHql5cr7eq9WagLMi4\nvkcWwp1EACAfqpCbOuN+XW+iEIdj31MSW8XKDz1u8ZGXPZx/HF8cJ3HOdFZVrI/nx1ir2QQRZ12v\nTLIiAr1XGqFeiIVnTrHKuyxUu8a1b5G0msS5SwCNnpQ1hwTw2hIkRwg6n6qFEENSTDLPXakuFCA1\nR2RlFkglGBl3p8SUhLsyx7nu0dvUazx3ssJawcXJyfkmxX1jdaGlxF1RxGa+E+PrZMzzhHbjWwOn\nQq1cS0LVETJiSlsaK8rluWGeuEjhjXS+vovgulhH+xX3E3/ZhH/RoxekJeHbnLlv0NbGPKpsV2uY\n5fC/Spk3EmyRvtigRwpri37gE3A3QJUoLDKSmwLvcKY1MtbXFvSkZVl5m4ESMlGf+oJx4pcYqS3c\nzc55VpKf+fABnqvyXTc+mYMJek00LZg6TTtlynSrPHoHDJlyZM57xdzoLkwmFDVkMoyMembtBmVP\nl9FMuA4AVzK0ulIIkBgZ/YYkxv0g4dKjZ3+EB5qMnMvw9JGoDGlC5ITmRNWG95VpGOyuZkgO+eok\nQioF8WhxKFMkgVNvvNHEnI1mFa8LNOU+9XGPEgrO26nxTpRkK6qdmYlnE35SnK+LM+VOls753rk8\nPyKz8u3J+PY9rL7w+drQrrwthZoKzYVfcc/7k3K6Vu7SyjfvGg89KronV6pnajnxcXGuBkuDH7JF\nYG+K5ExthpbC01J59DBHzhXOMoGHnLbhSJloFkl5J3pGQVF3moGkDJ6YBoMjD4ZCqBqDdlgZ/pgO\n01C+e5ZESp2uSsL54BKiHNJZvdAUrqvQmlE9YWmAapxsWwK88ZAyl5SoAm9OJ/aEc46TnzS89ayt\nfHZBrDGLcJHEswgiJ+bVeKeFxx6A543ABxNaW7nP0VbyME/8qjmfRFiT8EkM1JCkVGtBnUOghSmu\nZcE0ekqCbhf701XDS0o1lFxTAldEIq5Pzp6E3tolVKKwwGosy7/kFSZ3/x95IYnwxff8Q+Af/o6/\n/8j/R5NaONLdIijSlKjWB7VN9n6N3a9FtgByZGpxIqaIi3ynFh2y/HAoCY5Asr6OvhjBrb5U3ULk\nhTT2cdigv/Teb9Uk3bofIq7aGs+r3LLDYrfgz/WWGf4inUplz+RbDinyIiE9vo0NXAEvsvUb1e3Y\n0bLJrb8odXCrioxDHv/qDqbawSz3BS3xMHc7mJKgC0l6fUy7XMcLABD7ue3WUCjbXjseIyMg3eiN\nhyUrv/VDzIaMks3x/G29YE6AxqhabdKx7OvM4gvZzudeyYFbn9T4UnMLUHmo7GxrdwMur2llx+Pc\nvbN6x9OtErLN1Wtz3r0f6rDBcpiPNkDaJjsPgEalIW1Ayl8dl0TQZe4IL+lmXxJmSId9OlabfKzZ\nDcwDL3qfLG3V1wDOx+/fzttODx1zecDq+zkcePbF61+iCR6rTRzA4uv3bse4i4Zownda7+06+11Y\nY6uIbvt7XP8vVswGkLbjUwkvOd/EInynJOu4Zrdz7odzp6r7GnhNSd2qqD6+bxs3Gucfx+8a96fC\n/d1dAGNTau0ginVjbRVFQQuzNeYcKpute9D1cLDMhDAl5d6FhylRgOdWUXPeiJFzpqLU7FjruCpn\ndT678YNUJs8kMaYycVqNsyifadHQ78rkcJIw1awevb7P0mnPSioZNHOxSs6JzKDsnYaJLDCPkmhP\nmUdzSu54zvS58KMK63XhnE48ekXrla8wHkgsHaobvSaSTTjw7MIPZixt4ufeuSxGvs787CHzPhtZ\nK88daIqjfAK+6ytfTcqHKapIBePNPDPZwmOD575gduI0L7x9a3y6hvpbC0JQHFMOlb53ufDDtZEU\nZEqs/hx01uQ8tjvWIcX8bJXnpdEQvkfoHc7iTPfv+cXnZywJpXXelMrDfM+dTGCJVTvSSyRA+0Lx\nRkoz5AhizRrmQmp9v/fjIdxRSqKkmPNuIB4mpouv9BbS4BdRHnswP6ooTZ1sW1JvS2QKmqIXV1xR\n15G3dKyF4EUe/dw5C1KNtYWIQZZxvi2CehWnlzzsF4xsaVQ/Bbwxq/LGnHfhtUFPhSxXPsyCkVkc\n7sa9al6DIlYVPi+VpzF/9+q8m5SVxpScD28TKhXPhd9cG2YTz5fEqie++2T8eZ34lRifZeKuVR4E\nHiiccWpxRGHuRpHE5y60HP2Cawd0ojjMonwg8YTxtDTu+hnRhojRc+iHSodzypG4tjr6yxsXEj0H\ngybZRFNYW6dJ4pzDt8+7kVPi0g1zWCSghIlGbk4BCp3Ek4O3MFO27LwBsueonLrx4TzTURaDzy3j\nDt1hESg5cS8ankoW4ER1AN7mIbZgnauCd0PzmStK0hCbaGTIxvsGNinvEtwJLJYo4lzFsCa8xfih\nTfy5rFwAXxdOXZHVWYSQ3RenS6e2Np7bibUaeMeJpHIfCfuGgSaSCd1bmF+nEmbAQ7BE3KOCrMLS\nOojh1lDJPNnfhCz/tzd+Xyp5fyujDaSeRnjoybEtdX3IjGZ36qg6tZF93lSodiUyYA8sNtAyfm8j\nEDUiEEvH4GIEnuLsmfGtT0GQnQaWRblYC8EI8+Bim4/KldPdcNlofTH6CLybw0kSFd+LLoawqViM\neyETse02AtbIg4UUbTZAoqE81GfjRuY4anHibXN8dXbxgiMWtmFg2XDyyITLON7tXZZugeEmejBw\nacjXvoq1tvclH9U6hkKZRRUHBLW44BjbzT5ojAL90Hy/bVsJ8YZNadC3v4mw6BBoeBG5Ru3DzPYs\nvDOERAR8yNCb3rJyyeVQddIAyyMy7sMjZd+1EZmbRyCrBwTgRM9VqBXG2lOI7Mt4h0sEVPSbYEbe\nwmp5CYI2gY7b2EDfAC9y89Lavj+qYbb3r2iP/peoQGz9PCE+wNhGJ6Su1V4CgQ3kbQCh984VI6tE\n397A2sd1sHl47ZLdh22NGTgsmJildEhpyAAFuyRDutFMN1raC6B+OPZ9wY97gw7QIUdQuCGrFxWh\nrXJsUa0EUtYBxGLtlZFNbGzS4RLmpCPJg936JLsoZxGSG8vh7G3nNsQ2QjJethZEJITUAC3RhyAM\nfrjIQezjuNjj3tQEJh99iUORJcDRSDqMzx17o4Ii/BLg/nF8eTTAz/dMkuit0WUlS6YuC+Sg42BO\nceGdSnivEAIGqzfAUFOuvWFJmaxSEnyYM0mi3+KjGU/tylIzWeA0F+6l8dM08UHgcTS0Y2GK/HVJ\n3AOX1jER1p7QkrjS6QInnSHBAuTmFBvKqX3QQKc5Amkz6EZLsVbMhTkpFzK5G+V55aoNW1aekSFo\nYjQWvrcL1y6YCas1Wknocx9iNyFYkty4w1lK5i+uys9NMRGKN97OeXj2GY23/Pyy8hc58dPpM9/M\nCelX7lLiPDnvPHFpC+TOKc/kc407Qm+g4fUUxIUG2vjZG8hJ+NE+cl0+cM6dzMpdWbksne4JcqZR\naA0md6TAhYmPjxcQha5ogidL2PXCxETOCs0p0knWKarU84S5s3bjCmQya0+cizNpCVNagdWVa+9c\nWzy7zTrinSJCypk7ooIIxoVErZWsYW6cpUMWtENHQBNTsvEcjb4cFeMu53jeqyDdyKPnpJcZaoB7\nsSViFs+gnSkLdxi5NqpCHclfVSV7J2XhvSS+njo5J/7q8cKclXuvWDc+FGVWeO7w0Rqrz5g17iXz\nr5yvYfAqyikX3pcLT164ovzy+pZ/+oOx+D0uKVRya2VOK++yYlche2OyRFGnK1xVmZcV18JzD6rr\nU6+sa0jTJw2Jd0EwqzxqJyF8pRlJFROhGWRRVBTNxpScSYTWc6gtJ+OsieYWjCHxYBtkZbLO4p2a\nRsJJjUlgJZPdWW08pUaSSjUSmA8Kdx2ywuwdpVKYWVWiwGgLMmi6b1NicafmRJUzizoXJJSJXchE\nNTOXxDTB4gtFhbPMe5L3JDBJoaAseuJpih6nTuN7GuodcqGZ8WlZcCY+WudqxmpKaUKyZ5JnujhX\nE/ramU9huH71zsUdkUiSBC3PkRSsCaxx1sSMIx2erLKsQtUApZqDGixATkrRzDLk6w2ntxpss9/j\n+IMCTJFgHRlW893xV0Tw3ncT1aRp9GgEfenoVn9r+NadYrT3e2wUrUGtkY2a8iLDumWcb69sIhGw\nqZ0FjSXnHAFn3sBafFAHh72b7duW7X9yq6jIqy/Sg6mab6azA1FFYfhWAThm/LfNbPMXwOYWaIvE\n9rYgb/8+3x5+w6zUoyS/HlbNtkcvMuSjcCYvtnb4RQIw7Fh3CwJHwCkb31o2OpvvOO5Iz9v3/zB/\ncnhdTXYlwaOXzEZdlE0bc9v3bbMjYN4A9O0Ax3943OXik7f37CiCAeAP53GM6I+T/bjF4+a5bUKP\nm/lSdeMIBA7VuBdfFJP2W1WljdLlQN4oobtgRPREdA2qYYDMV1VFYa/YvQjJR0Ki9z5AmOE9lL82\nxa/pcN66HK/Dbe6+rNa39YDFcbwK3Q/YwPeTIy/XHJuQw8sPikAfEH3bxnG+NhrtbWziCLInMURk\nF/WIysLoJ0p5rz5tm5exH1sVN0lUGt23tS07ndP3+5COJn12k+ZNddItMpvbfiiD/Se36yjMmG9z\npR77X/bJG0kaO1Tpfmum/jj+JmMumfvUWdd1UHCiT4LWIoWlkZLJUyapYd5RCSWus2ecNWS0PZII\nq0ar9NIqbwucyMwIH4rzqSi1Cw3hX+BMvXMRJ3XnpMp9KTRRFoQqUR2YxXg3z3QxenJqTXgR5g6m\njZKFospZFO+dKSUandYTmkuYxgpxLNVQEyZ3dCSdTq1yr5nvpSKtkUjcl3u0Vj4kpUumTc7HpXI9\nDYAmmTsRXFLQnjs818rqjmih68wzSmsdtYVv9Jl35/CJ+cG/4vtPV+Y0822+cFcK0o2vz8pJnR+X\niZ6Mt2qkNMBH7fS1YznxuQk/rIX7lFgoGI2pN7Ia52pMuXBKwmODx2o8u3BCmXMoyrUcwbKmSoRR\nglkheWdqjXdFOaVGpZMWwWXi0RotK5faWXtn1hN3WlHrPJExpkiiTvGAzRYN72aGeuPOha4V9042\n46TCKUcfdJiGxrN3ylHNbD2OXcTxViEN4KOJlBNtqKIlAayx+jAnRXg/d0yNpXaQTlEnzUI+Jaon\nsts45pXcnZqMtyVzfwqK6Sk72Z5RW3mbJ7J2kDZMTw1NRikLE2emHFWGH66Vy1Pl/7y84ftWWPI5\ngntviC10b6TsLHLPW4NZGteSaNX4WIyzhWdj8k4tEcM1h1kyq4ewgBDKd9njWjnngrFy8rBG6SMm\naKqs7tyrk1KopBqC50jy5pRJ5nscM02GNPDeuU9BEV09qKg+qPNXh0+euNqIL8czWoEJ4U1S7iWS\nfhkhJw0REhuJlT4UXEWHV1pQwU0W7kxYzomzK3fiuC+op+j/04S3horSNExnswtVQ85dUKb0EZVM\ntcal3mEYT9crP+qV1Jx1UFdNOs8d3jVn7s5FDKdTk1AkU0n8eKmQQ+6+qCLW6bsoUSBEs5CMP7vz\nVipNpjDjFgvKI4JVaMNUPqkxKUjrPFsY+WaJPqff5/iDAkxh7Kh0OpJkZKfjIa8pjQZ1eQGQdpD1\nKtB0P3i63CKgoSAXv2/N93ufEa9iKA40Fx9KVaMqYSP22IK1ne4isZE+Kj7b6d7BjgywY7fft3Gk\n0RRXmjh1z7pLBOJ70MyLYFQ0OKGK0FPsT7LbHGkOGe84vvhclmFMm8IVPoLMm0Tx9tlt33bp4yi3\nHY3nb/v/OgKTrWq3CRtsAgu3c2YD5BnsPvPACy+nG7XpFninUbL+63pXNjpTbOK23eS6Z2FegJ2B\nZvw4txxqckcQvYOrV9UOjfMkEP12OkDSBpjkEDQfKkNfoo/BqOiNfdhgez74/Rw/2+Ul5tsoYSGO\nMvrP/AuGw3sFiZfr6tV8bpTZDUTu/X6vgNuxunTsl9kTE4dlYuI7cMsvvvd2bcjxtS/M0V+XhNoe\nVi9A0ot/b6vG/VapCx8yG9Ud3eeFDUwegOBtH19SCYuHjLekeMhvxyEMPrdHs3ZUj4bPmTlz1oNM\n/y2BEvvCXkWHsR7GwtJxZVT1FwUoeXWuj0NVhxTx72Rg/3FAyFK3lTde0dbx3jBguhNYgrEASvMI\nQj11CmHXMLlSS6K4QhuCDXhUilLiyRxJAglSytGLI86kRvcppJPFaDJFoKFCujpdgu7XxUiS8Xbh\nVAre4JQMsZUh7UFqwq/zyiwRTEPlQQQ/F9SEMivXXqkeTIl6WUhurGZcUS40NDXu1sZzMtpS+UuP\nxvJJV7oIPSWYCuItVqNXEu9Yp87D00KajJM6Vw/7D6mGNeNHgXa+57vrygPON0n5s1r56mHhOzI2\nfcXPPz/hpwe+bysPKUx5zTKXARZ7V56S8jDNmDmXtqCWWF2Y8gnjGWnCXO64irDWBWnGNRd6OnOq\nnfvs3FF5PxtrNTwVGgvWDeuJdBdMgblCyZ2UhNxBJuh25U/LjLWQp25vVj6toe5314VP6cyvLs5E\nI6dIvszaad7wFL0c78rE4hPXbuSu9F5JKbMadIWSOlMOOp60hbkUcpmjeq6VjKDWyacVOLPaEoE5\nTi7KtSn3bkxzJtGiaV86J4V5JGIQY2krZsYn69xxxqi0FsyQX18MXxfonel05f0JwFmS0lfjpw8L\nH3JDSnjtfK6Nj4txbRM6Cc+18dOsvCmNC5/4cXUufeajCc9WSSK868+YRj/RvXRyzkzdeJQZl5CD\nT77SdKKn4WOZBUshw04zLvnEFePRKh8Ij6ApGyqJi4UoR9cFk85kmaJBK5VNvEWcBxVSieR37gtl\nLlHgzYY242zK49Xj/Ktz13RU7KBIYyGBdd5K4poXzjmhLVToEkHHcw1ftkmVCei9RT+cpp36PWuI\nmL2VwqrOmYQ045MLn6vwV+6kPJFx7pvzcGrkmpDeudgzLWeMO+zaWBS+s89UV1aLvrWTPnMnhTem\n4MJTb2hSKo3PrjyacOFMTR3RoIfKIkheUSpnlGcKS4tnVO5O18qkwhuB1WDRNVoDJBg8a3MerIQf\nncBFjK6FUyeMhNuF3M/kT/WL9+O/u/v8H9ho4oOPG54nMhBMtFFGpaTIoR/mGMTCDlRC/SlAwEbF\nUovMr3tQJ1yCUtV1UHc2qtVWYXAfHdEBqtLgHX85LXuLUlwFffWmY2UBieNMAwQZvjeIb0HbkoKq\nYykWNdxA2tZbExS24QkkI4AaFaMUJZTtgFCRQRO8BXlpSLS732TAd2NWBrjxTZBggK2REe84s7+k\nYm14Tggq3wZY+n7st56wuyYBCOXW25W41RhUQ3lskxAvRHDupH3+Ww6PHEubOSdjrUQw3/G9t+bY\nz2WHypBICHu4xgMBeTmX0fPi+3Z30+FDH9fUhYvGDbRK3vcvSTTsdwJkTn3Mjce5nj3okE2gyY3O\nhYz+KQuXri0h0M1uanqjMuN220/ROO8J0B40x+7RC5MMsmbAsZTZ78aM9e7sHmOv+2NCRGXMHeyi\nB6EOlvY1HbkoRzt7j5NLXFcv+n0OPyduYGBrGI1re/RNqdBgB3sb2FRVWgszV0nCcA/dlfbcnXn8\nrIeESBI9VN3i9U1W/NYL5Af6blx3Zjdvq40CCKCjmh2JnLbfk4zxfrvdLo4S8F038Cl79U1zWBC0\nAYZ13BtQIW3UXA773FtIXA+YHJWpoWJosX5Et3279ZxFJWvriuu/nSX64/itUVLQd3LJnOaJDEx9\nwVtF55kVx2rDqnBfMq3CWgztzpSV6gRAmhN3prgkGFUekfDwMqJCiIYfUcO5FwvT2FRYLGwozI13\nD4MylQrNCobhXckJuhnTsE9wOrNHICoCWKHV8Z3unK4rs1TeTI2zK7kIV3eeTkbzTHW4ts6yZtq6\nIBneOPQ507qSBZJGL8+1dta6kDXuDUmF1b/nekl0TmQcWuKsGaOTNOYDayOLHfQ96/CXpfOxGZJn\nnp4rpgWa85s08Zu1MqVM1jOz2SDRCrMnvtdKzhmdJ5I5SYPaJO2EaiQhijo5xzPlXhPkBlMnqUR8\ngHF3F2Bi0oTKRF2dUk5RJVxXdEr09Rr3zPHgy/NH0n3cN3KbuZsyXisfk3JdHrkrJ94OCXkRCb9J\nh0biUo31umDzjDoUoBRFzbnLcC8rJYOwsPRCSo1ZOqlVJg1DVZGgOSHC5briYrjmaHHojW/ywkmF\n6vBsMz9enPt0xrnwSTrybJy189UsiDhfS+WrsqB6iSepJnpdSBqm9ZbO/Ho58X2bWNYVMeGf/vgO\nxbl2uHpiJXPtyp02TklY7Iypoa7k7rwVCSEBD3qWrSuPJ6DD93XlrkFLmSoTRdfRehDAJ5LQIcJj\nKmAdYTB+WkU1ksItTawKj2J0Dcpkw5hIVBFWU7LZqIh0pgzqjY92onULteA8wRrU03f5RB002j45\nZ4dJoEww9ZUzZ6QtXLZECI1J4No7yTIXj+fQWRJdoJuwWGdiwlKhuvFEsIS6jN7dnGmD1fFExMYX\nalDCU47Ht8MF+OEK2TuihYawro6lKzNKFWG2mTfSEFliHebE1C+8SXBCuJD4y975TTmxtjCuXbqj\nI30vZkEPFcO9szLTrJK0IHSEzuwKFT62hiIsQ+34J1P0KV2b0bQz5RQ9WSJxH9CMoJzzxJ2uPE/L\n61vx3+n4gwJMJ5RMYhHbM7a3Zpp4qO/CEF8QRtiCdecWnHB4jw7Vqe2zW6A5uw5RhlA/O1astq1v\nWfTXYg/beCHUcHjPrSk7MtXx5yH/7Xvx5EXu3EdAnUZP0q2QtDWga1wdIoM9dgtwj2NvUB8BkyZ9\nQYvqFs7SiKCjGtfH9211OEcCdBz2MXkoGHV9KUBwPIYtS+6HU3j88nUgz0TIoO7bOMitbfSmTQ3Q\n9eXcHs/9i8LtF07REQS8GN1ISV+qIfLlqsVWDXj93T0JZdz8jh10234kGf04SV7s25KjN6IrpP6y\nShq9S3qoGG3rctvw4Xv2Esjr442geGsO3c7HBs733RxViJQCvux0TXwHAAAgAElEQVTfuQGbw5rW\n9BJQ3+brVu2SdJvrrXrlr87b8W/bf20L9jXqJRvISnul6OXnSymDluv7MXZv49h1v2cc7xPGq4oy\nsgOO/XwOOfldjOVQlX69fvZ1/qLqNPogkV0m/0tjOw4b59Os/9Z7d0D/uiItg7oc6HmnDsKoLgtD\n8TBA8HYEIqPvc5zW2VPQVP44fue4y5n78xSmxdZprdNSCluL7qzmTJaAZxLwpmREjbU3uq+oJ6yE\n0WMlKqsygjaA+8zhGjeKyvA3iZ8nhyyOl7hXT7RhGFlRMmsiPE/UcBeyjCt9KJF6cr7yhE/rnmyo\nUmh14S5Fb8SjLXiNrksXxQ60+Dd5ZUpC9kh/PfXG4kKWiTb6cpp1tExsXYsOTCr8q6lgbtTe6QrN\nGtmE80lJrHxDIqWGFgs6T3KuFZb8ls/XldPpzH1WWK7UJLimqKzQyd7GM9CZfAV3ihh1XSArJRXW\ndQWmELBI0GvHXLGiPFrjrHCfMqhwbUI3mMyYvGFzJ+XCnAsX7Xy6XCiTcq6NKQt5injEzHA7gUXw\netUrmpXzqXK+Kl97R8WYUlS1t36P3OP3R3Oe5sJjXkEcaSDqzNq4S86c4FRA6JhViqxMKcQxsgZd\nt7uyknnqC/L2HknOsq4RlNYVLUp9doxEvX7kp/cntF1pDmdVpAz7EU2RxGnCX1hF9B5DuS6VK1/R\ntGN0nq/KdzUzF3iXZ87S0VPhFx9DYMGmQmsds84nzbzL0Q8k5ixeMU24Qm4rP5uEu+w8TIDCUp1r\nFZ5Lp0pnsah2Rg94RxNkagB2V9yExZVqjqeQlW8oLhnrCy6F5+5U1aBFJsVcuSaji6IS12VLmXOK\nfi5PCbdY19cmOImshR+enuge66Qk5VmMuylEXe7USLVyPilXVz5W4TszaAXVMHq+dEHJtObIlFl7\nZ0olbtpuzCmBG6bRY2cCniJWkEFS00mZ15meEpdWqVlZTGki9H5Cs5FyJxF0o7Ou3JvQunFSAlym\nxCdTfnUVTDLVjbM7X9crTynT6xLHbZ0JRtV4xOHiJJlZWyeTeNAQhFAPyueDrzRRruq0BHcepvSr\nNNYkeFLuzBDvzKLcpaDufePGhDFbp8jEXbr+nd/bj+MPCjCtYnSxnbYk3BTItr4XGFluDW7v61Bk\nrzodyjmJ6EtobkhOdLM9e+3EQkwy6F2HLLAMysou5+0vA+XjMPdDIPlyf2SrGIzX9h4Fkf3d225v\ngCi4uEKjv+g92ABYGXn5Lh4SlrwKWg9BtHg8ZPsQOzhWWkQEBmDYPJ360URTgl6kY95FBsVICQOz\noyT4IZbcVQL9cOCH4101FH8SQjsGsHYLQreAOja+Nf3fXos4foSCh5hPRhkm5v235+Y4sgRFjRGo\nb6Nt3+KHPi4P/4Gtd24bqxhnT1ykoweZj61K4aMPYLMD20DJbAMweAS3O6DrFv5gEtveaGJJhmnz\nkc6oMpbmrfdm2/EX80dUPh2G3Pzt9Z1q6Tdvq/38jf3flfteUdL2Od+/a0jID5CdX+3D8f37v9u5\nGWDMtoQC7FXXWM23ihe8OqceVaBp9Da6WWTxYe+9gkgSyH6MY6vuHFMJ8dsAiNzmQvz2nceh+rLC\ntFMUt/vFl28Z++g7CAZGo/VOF3YP4DyqtSFOMa4RfChWygtxGXMf3lABhn2Ap41Tv/U4bX1jf80t\n7Y/jOKYJppnUa6j2qDEPSt2jOLkDvVMnRRfjyVvcJ8e1WRMhCuJOtsw1hV9Tx5gSJBJvXOmpkcT3\nNaV9VCFUMRqK4W2lcuYkinrHcmeyoA8JnYSRN+qsQrWZ5iuaHfEBorMwYSQ61RNLz7grtYW62dU7\ndMf6Vg1xpCt358TiC3eiKJ3uRh5+fSklcu9ImsA6bhUSaL9wXybIoSTWicSTmpMF7lKlSOebc8fT\nxKfFeTxNPFVBz50ashW8u4c3GomV7s+8QbBcQ/Zah8CNN1ICzoJ3yNnId4XejWWZWFul58xUKkkb\ntIlynpHktNbordKIVgB1kLVjfUFYmE34KUI7C/RER+k6DOexUEe0lWkS7lTp9UJC6JNDrmBXjKgC\nWmskSVhSEOPuTrksC49ryITP58JZLiSNeV18IlAUaAvZ+YbD2rEzUVnsoTZWySzXxrJmJmnU1OnW\nyeuKMJOb81DOSGuU7Jwk8dkKn9oaLIoKDw4P6cQPDp/tPujva+Ui0KzQPMB/d+OywueeuXSFpcQ9\nqmR8SHrHM7BTdaLgoRI4niGtG/WseF+opfDPrsaK8TYpd/3C+xneD2bEk5+oyOhXgiKFoqC20Kyz\n9hNPDR7prFKYcNwbc8qAodloxUhdIxmhmWcd8WRPIDM/ysq1h/KiKagUZotrNbmBXXkzzZzlgla4\nSKPh1BU+kUjurNpoaaK1BpLJZlxzeI41iZ6pGSiaETPmZCSdWJLwBmfOffTAOrUveC6hphiyqSGC\npILNijlMFGw8ywpKKWA607WGWFFSigl32sEyF2989sylwcVCXn9tYX3w1Bp5IgyANVN7BYyUlY4y\nyUqqgkwnknUeCuBXsiRmaUyi3JdzmFCb0HPmKXemmlhspWGcUo47j6yIOcUTrpmlOd+lEz+TENVZ\nfOWzzL/X2/wfFGAqJNoAKdGTHnLRugcLG7q9ZctvlJi4ke4Ul03NblSkDEZAfKARMbj/ZiSNbMIm\nNRxBYtqz3DtoOozd48e3rLWMfRrUQLnRzRwfRrGDZ7tta9CJ0gGoQWSJG8PzZcRS6tEHsqQw+d1A\nRSK8hVxD9nw/NglOvedBixr7kj2kzUUi4C7N9j6boLFtFYDYc8ZNZcuAdhkUvkMG/ghUk8NVfbTK\nju8dhxs5pjDK281xD6DLdABZj+MxESYLsJtd6Lr1lozqw1DzcT8ALYZ3BGGGyzgmHfvc0pgPHz00\n428bGIgek1uWdAdm4lENExBLoVXhIMloOjytts8NmpV49DFlj3OTROKcSAQmW7DbB91K3LGkt941\nQj4eGYHG1pWzzb0aDJW9fgTMmgZwCa25rrGt3jv/D3vv8mrbtqV5/VprvY8x51p773POPfdmvIz0\nkSA+UJBEUTBRU1DMioiFtKgW/Qd8oGBNSxLgs6xlESwIKVgQUoUkLSQJaWjBkJSMjHsjznPvteac\no/femoXWx5hz77gRmaAGceAO7j3nrLXmHI8++uijtfZ97ftkimWcMW7h+JTxF5XDwZuJRtpI2uSe\nSorIHWUL6EWwEXeJek8Fyf25ODyoJsZhc0yP/jRmL80+j9gnZhyFhexannNp3uP2EOgbu4iLcJ1j\nmMdNhcIUSZCZOEw9QuGjNWUnqGkk4tcFziN9JGSeW/Z+JU14V1QM2c8bmIbb+f8sQIz5vKtais3s\n8vKe9MEumeAfyXKZK4bHlEvexyRHYFcsDPeUlg3NooOkcfQqlo26syBjYvN+5fNgBH0qEym78uQv\ntr/VpuFUgYFiaqjBogVa490y8NFwca5+YrVI/58SU3BjoEE26AdoyfVpm02gfSpPfufZYb6IgAx8\nOFjSik2EJWY/Sik863tMZ+C16ZQ+BkgEzCdSFZ5BY7FATZBeszjoDtPTKY7WaqWSRvA9YAyl96R+\nvhfl2uDDrTGGMSKRlCcdKEph8FwV6Y5GQzUoZXqqiBCxTeKcEG1QFuezU7AwqAqrzZ4Ov/Hlk/Nj\nq7RudDY2VkYEZ79SZaGeUrhiEIQljdFKvjNer+C9UayAZg+JokRcOC3B+WnwFJ0uQkdp3BDvlGKc\nFuNsFb10XIM4CUs5E0Qa44pSI2mOPUifo35/99xGYFZ53ZxbnPP9MgawUWTJ5n9taUFSK8w1JQL6\n5mxNWJZcDzQGF3nm4p1vX16wqWaXwXYiKD46ULAobOPKWY1nqSx64/NT53x6pVFw76gFJ10R3UA2\nRlv5thU+dOXag+6ddRadegTfIfxOc74dT1Ra9kAX4yki0SgRPBSx7EPpBMXShFjM8j2g6Yc53MEK\nizdOplhvsygkDDpSFi76ROuDn5ijJtTIpPHNAmcUV+ddvSJj5PwdC+99oyF4N1zhs7rxeUj2gMmV\nSyy87yWTdDFcK18w0GLEgFoKJ+/cVPie4OKN7lkIHijaG4LwvTlrdyRScZD+gmugUniWpPN3sl/u\nakHplXGBYon0uMBJC7YLCFlKvSPJ6HGH0Rp1WNoRuLBVTbW4MLzlOykNmnM9yqhGuA1n4Ig6X0ay\nSigLl3GjvzZMU9XzqzC+7S90W7gN5RqNrQc2GT2iypkUbdhsoahBdFZJtWZR4clvnMvI90ppyOgY\ng5N0TrYhY8W10uXGGMp7v/F9vPClVwoXPgzlWjpOpdPptqbflDjExhucVS58541XTty88Nvj8ke0\nwuf2g0qYhmQwW8e9+rwHwfuWAdsMxgR2NbzdB2nfdprOHqDs6MinNKKDijSrVDEjUTNLMYL9uEcS\nca/u301uP67w7n1O8XCMEUFIHAasx/XwCao1r3nsQdjD5/o09KrzXB5NRB+v6dgOWuId8RkHWpBJ\n4jFOD/1NdwTnYwrbp03zMMUWZKeiTQltYfpVAZI+V1nZ1lmx959Lbft9x5AUgWjiabzncYg77MDE\nMQ4PyMc9vM5mbfaAc37pkQr2OMgfN78/7EU+Prd9bO/AwL3/6EAjRO6B9D23zMR5ogMfTQQeAu+H\nrUT6Ie1hPeQA30E7mZd3fy7yI3v/1bzm+fn9GXHSGG83HP70HsQ83P687Z/Zx11EPkpy9BhXOfqs\ncmj8mNMx0cbgPrd2dOhTNAzhEAKJmVTneE/k56HX7lFAY95tIoL+kLz5LGrow+Py+Lw1HE3wjr2/\nYMx2ud2E8NN7+un2SHMU0UO+fZTsPSku0+dk9mBNtMtK0p/M9JDtF0kvSxWd3m538ZujPyxiFkJg\nVQOboi1q6Oxb1ELuW0uirTGmd11gkvTk/ouM6W+9jQ5tA6vJjzHhQx84QdsMj8ItOkvcUDM6gsSW\nN1KN4onCJJrsPElh7c4WUwY8glcz4iYf9X+WMcVaZlGnSDZ8n8vK2eAkg3PRLIKMRGbXUiges6Tk\nrGPviUxvJpek+tmkQvuUYM73lGZhx4WmF05nwz146p1GpQNo0HrPhCSrQijBFp3FEo1SYKmZYAxx\nWjjhxs0dcD4M5+vXFWThZLBqmtSHV8o1EBt4K5xOlaclDaRBCemcVBA2cKHdsi9jG30qlOb7t2i+\na8dwfAwWWXi5XLG18d4ry7jyrhiqmt46rmCVcz3RzwO/XqkNfCQeO0b2+tWRokiuV1yy6j4cihQ6\nG3SlsNC1MVoj3FkkEQ7BufXBUndBHGjujBGgFWzhRtDHQCPQ2rCt88u6sNQrVgRoND8zvGGLIRJs\nY6BekYBrufFG4erOTVP+PQBxeFHo24pwoojTpTDc6aKsdV5L1kd5651NO393G3RVmm5sElhUntQ5\nWbJDtnHDVbjOAo7vJrlM+fNIn7JuhZMYC46fZiFIQEZQ1Bmjc3V48SztuBZeRfmmg47BzZxTU75Q\neNZAS+NcFs5TxW7s/eruPCM0PfGE8LkOnBsXL7yiSdub0v60Syodd6WkTwlXkt540+DXl8YvceL9\naPxMU+V0tI0XMS4ByplijT6cKkKJ4BnLBrR8c9DoPIsd8yemtYd5Z0vPC1QKUoytD74RsG6zsKiz\nQFO4tSy4D0mWQYxBI6mlEYF40K3x5M7YGs2DD01wc1yFcitsUvh2BM/uGJ0v1oXKHkN0fimykPa+\nOYs3il/5McHJKotM9Lu9UMNge6EKnOrCiy98tZ3w0qgCcevcAhY6v2LOFwihL/zubeFLhXI+4VL4\n2a1xiY6acdo6iPN6EWQxdAyKOV88CDP9UWw/qISpIhRyEfg0APVJfziCnBkE7ZXlnQKzb0fgvScj\nMilS3BGAx6DLzNIscg9EVKeqR0Zt5RMECPJBdc+m8vIQiB1qVnuQDkk3kqS7PSZ24vfre6RGfRRA\n3uNkhIla6Sd9IDPZeWxwF3a0IcUl9uSC8SBXHs4uqbwjSgetSO5jtQehj2ObP8QxBhEPzfySL3iN\nVOvLYZxJ4zTB/fn9YLnvfSyMu9GvRHrjZPAwl6RPEsakTu2JT7CrLPqMQn/fER/pjg/qbvsV7sDi\n41yBpIqq38d4HxfdExL3lJffqz5zh0o6f6MfJwg6URVT45HwJ54UukTc9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QTmuMY+j45F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\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J3EjkYbtwM3Qdo3jh1nsXvllEaJzbyucijKlPqTbJCIUDgUMUGoEURz6MC2t0gm58nCIJ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fhX6o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vmv/VN/ir//kyMtKVkrJRlZY5/KUyHl6EbT0oi8iOlizmAe72NtM+QrmZgkaUrsm5A1ADwx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\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4xz+7HdCiqVAp2wpEYK5NyVtsMyPOf2nBvYT53DtihAdiVAQl50iEL3TOr28hR8uTGCQz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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "I1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256\n", + "I2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256\n", + "\n", + "#%% Plot images\n", + "\n", + "pl.figure(1,(10,5))\n", + "\n", + "pl.subplot(1,2,1)\n", + "pl.imshow(I1)\n", + "pl.title('Image 1')\n", + "\n", + "pl.subplot(1,2,2)\n", + "pl.imshow(I2)\n", + "pl.title('Image 2')\n", + "\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Image conversion (toi matrices) and subsampling" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def im2mat(I):\n", + " \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n", + " return I.reshape((I.shape[0]*I.shape[1],I.shape[2]))\n", + "\n", + "def mat2im(X,shape):\n", + " \"\"\"Converts back a matrix to an image\"\"\"\n", + " return X.reshape(shape)\n", + "\n", + "X1=im2mat(I1)\n", + "X2=im2mat(I2)\n", + "\n", + "# training samples\n", + "nb=1000\n", + "idx1=np.random.randint(X1.shape[0],size=(nb,))\n", + "idx2=np.random.randint(X2.shape[0],size=(nb,))\n", + "\n", + "xs=X1[idx1,:]\n", + "xt=X2[idx2,:]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plot image distributions" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Tvw3wA9AJ2AfkAWYDFqDfKwo7Xfryyy/5ceRIMpZvQd48ZQi/eooJEydx82Yg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sQKPRMGPGDLvgh9VqZceOHezfvx8PDw9mz55NV1cXO3fuRKVScf311xMUFHRJW3d3d+bMmUN4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4fdbqdm927KbC7a/eRZQ6nVfwyXy8X06dNxuVwMHTqU/gMG8u233zIoMYTbW0VzqMbK4txq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EBrp41+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\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YMIGuXbsSjUaZPXs2GzdupGXLlowYMaIhxdPv9zN9+nRmz5qFTZLw1K/NZAioMUw+qKzD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\nePzxx5EkiUAgQH5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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(2,(10,5))\n", + "\n", + "pl.subplot(1,2,1)\n", + "pl.scatter(xs[:,0],xs[:,2],c=xs)\n", + "pl.axis([0,1,0,1])\n", + "pl.xlabel('Red')\n", + "pl.ylabel('Blue')\n", + "pl.title('Image 1')\n", + "\n", + "pl.subplot(1,2,2)\n", + "#pl.imshow(I2)\n", + "pl.scatter(xt[:,0],xt[:,2],c=xt)\n", + "pl.axis([0,1,0,1])\n", + "pl.xlabel('Red')\n", + "pl.ylabel('Blue')\n", + "pl.title('Image 2')\n", + "\n", + "pl.show()\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Domain adaptation and mapping between images" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 0|3.699980e+02|0.000000e+00\n", + " 1|3.608346e+02|-2.476614e-02\n", + " 2|3.606710e+02|-4.534048e-04\n", + " 3|3.605854e+02|-2.373172e-04\n", + " 4|3.605308e+02|-1.515104e-04\n", + " 5|3.604930e+02|-1.048652e-04\n", + " 6|3.604655e+02|-7.607409e-05\n", + " 7|3.604444e+02|-5.868306e-05\n", + " 8|3.604277e+02|-4.642246e-05\n", + " 9|3.604141e+02|-3.764735e-05\n", + " 10|3.604028e+02|-3.124268e-05\n", + " 11|3.603933e+02|-2.632307e-05\n", + " 12|3.603852e+02|-2.260049e-05\n", + " 13|3.603782e+02|-1.938188e-05\n", + " 14|3.603721e+02|-1.706719e-05\n", + " 15|3.603667e+02|-1.489910e-05\n", + " 16|3.603619e+02|-1.336306e-05\n", + " 17|3.603576e+02|-1.189587e-05\n", + " 18|3.603537e+02|-1.066658e-05\n", + " 19|3.603534e+02|-9.986781e-07\n", + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 0|3.619308e+02|0.000000e+00\n", + " 1|3.568950e+02|-1.391388e-02\n", + " 2|3.567799e+02|-3.225305e-04\n", + " 3|3.567404e+02|-1.105949e-04\n", + " 4|3.567137e+02|-7.490749e-05\n", + " 5|3.566940e+02|-5.504299e-05\n", + " 6|3.566790e+02|-4.230200e-05\n", + " 7|3.566671e+02|-3.324452e-05\n", + " 8|3.566575e+02|-2.697764e-05\n", + " 9|3.566496e+02|-2.210773e-05\n", + " 10|3.566429e+02|-1.873910e-05\n" + ] + } + ], + "source": [ + "def minmax(I):\n", + " return np.minimum(np.maximum(I,0),1)\n", + "# LP problem\n", + "da_emd=ot.da.OTDA() # init class\n", + "da_emd.fit(xs,xt) # fit distributions\n", + "\n", + "X1t=da_emd.predict(X1) # out of sample\n", + "I1t=minmax(mat2im(X1t,I1.shape))\n", + "\n", + "# sinkhorn regularization\n", + "lambd=1e-1\n", + "da_entrop=ot.da.OTDA_sinkhorn()\n", + "da_entrop.fit(xs,xt,reg=lambd)\n", + "\n", + "X1te=da_entrop.predict(X1)\n", + "I1te=minmax(mat2im(X1te,I1.shape))\n", + "\n", + "# linear mapping estimation\n", + "eta=1e-8 # quadratic regularization for regression\n", + "mu=1e0 # weight of the OT linear term\n", + "bias=True # estimate a bias\n", + "\n", + "ot_mapping=ot.da.OTDA_mapping_linear()\n", + "ot_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True)\n", + "\n", + "X1tl=ot_mapping.predict(X1) # use the estimated mapping\n", + "I1tl=minmax(mat2im(X1tl,I1.shape))\n", + "\n", + "# nonlinear mapping estimation\n", + "eta=1e-2 # quadratic regularization for regression\n", + "mu=1e0 # weight of the OT linear term\n", + "bias=False # estimate a bias\n", + "sigma=1 # sigma bandwidth fot gaussian kernel\n", + "\n", + "\n", + "ot_mapping_kernel=ot.da.OTDA_mapping_kernel()\n", + "ot_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 10,verbose=True)\n", + "\n", + "X1tn=ot_mapping_kernel.predict(X1) # use the estimated mapping\n", + "I1tn=minmax(mat2im(X1tn,I1.shape))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Plotting adapted images" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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PZXomc7e3igQf31bumyYO+zpPd/t54T18Vzjv2TYCuGTKm1V4RhVVeGLr3FJy\n3CrwT65WvvS4sG7BBzeNLzwwPnDWOOCCDXYNDmxRHn7sQ7zhzjftl/2igxzn+9fORE7CHJty6g2P\nDOU+vGZdD8x5rn9jm1M2hwinEhSDe0U5VLivnJebfKoFt9j5dfBzTy188aXC+5aW92rndx0Xfvqk\n8nXHhZ87q3ztYeEjtfHef/4R/o0H3tyPZ+Xnz+COInzJvOX2Irx/M3OnLax9tV/XkZ5x6jnytTcO\n1DjSMw4UjvXimX0eW+TCEqBcrY3LUz6PHl1nm4m9LQJcLvBMDe5YGb+1dh45W/Ouhz8B18kWuVEh\neS+m4SR017cc341deQDIZ/beWOwL7S/jfqJsf59oP2e+X1bJBpVNMmdhZ5BapLnu3tBimXzcv2W7\nurNdDIRmyIoCbXfKL9z15lDLAdxy/94wV+eCgb172PXwGtn9zt723T0IdxfNTlRBioDWBYb0sBfI\ncAo4N771/HmxZzfe3XcaOfOnCJQD5PL9PRRDsxpYz3nYbRcy9AfN7UjPM9iNbycgXXtPlAuCKTzy\neGvGzO+FY6q2vSjZVaYl4vzG+gz7JOUAuXT/flnpC/Vndm9qGXlUNbpAeDa71ZpENmD0LO+7n+kV\n2YuqnSAqor1IcFZgi4jseH+NYIrIBG/vIiRwsAO4cn8uk/+RSfyel5lo7utuvydRTqMx9ZF7F2i2\niyDp5+qiWLP99ZV/L97I4lZZdat0EUkEOeyscLY/rnlmEM9QKJf9Y+OmC0nJO6VwNYT1tjKpImrM\nTBzLmsnTAF6pE15p1TJ8rniGZi2VYyo1hHWkyGkCkxnmlRDBmHa+HKr2MDtv1CW9OBqVR1E2AnfX\nyq0anNYKlvPuK01BvIpGW2aqwELlioCFMpUKkR4WtSmb51p6Mw5aY2kLbimCM2RuoSwzVbMa3Kpt\naFVYJK+flBUZouehRGvMFESEWbcUFaAh6rRQDOuNTw3VDPcqSHqsukgSssQ40XObSvditTTAbCp5\nnwT4Uiklq6vlsybH6dKwIuflxiErGapybJ6V+ywbpYaXLBGOsMyFyQUphSkcbZXVyrLVkTea1z7O\nhplg0SgznDThLBxWE7JUpjLlvYCyBTCDEDYemEwoTjFAChXt1e+Ew6g0yYmwlluBUIpoNo8VsKhd\ndAp4SwHZ3yWV6M/2/gy3rBxYZAI5ZZIVhYVtzJypcCgz3m1s8yWPo02sVSHlLOqg2wVzZVoi89HE\ns4pdby7StORuAAAgAElEQVQrqswBa8nr8SiMOU6YdWatgcoRqhts23jSg1UU1tJwNWzjFC1E0SxO\nIf25FYG3DEcstXI6AR77ScfXCC+6AfYdh0d84Z23AvDux9KzeelQuOdSXqiX+vP56DiNy3/pcGex\nZv7hgnPfcX9/HBlnS2O9Fd50JFTNk3lLBJeLs7TKExQ+uK48dJzP3mNRnlbFevGXZoUDd2bgpL+D\nj/oYLhMcqBBq3HJ0ef/+Pgph3cXPLZbr2axy/bdPOba7APX0Qh/398dZzb+/wM6tcBHhJBxvyoLy\nYH9F7GyR034D39fHdO+FA/rTZwsAX3eYmZk/c7bgGtw7TaxK4fajK1xdnEWENx87v7ZufNXxhAcc\ndYPuMYdHbeEJgVWZmUT50j6+J7rr931L5aGjwpdVZXHnviI8sTif3C7cNa24VIKHumr5+We2HB4e\ncOfRxNqdp6fKXauJj0fdj3v3ipTzVy1KGtWmhaODS/vPy0E/J6IceOMpMQ6isQrlSRVuj/ZptsgT\n/e8HL0N1eKxmL77J4KmAZwQe3U3w9vvpm45nnmjOe9aVP3TvJX78iS3HB9kXbsd9RxPHdeENxzP3\nsHDfUeEDj6+JMD5mBwD87oPgcsDvvDRzWU9wh7dOxxzpGWddIL1tOuWvPHkr33ScMQZXW+VYjctF\nOVK40ieznssW+ectP/vVM+XNq+BNK9/3awPnsXXjTXcpnzhpn2aLXJ4BFx46hEdil817fWyRGyKY\nXkrDSYC6U6w7j4HIPlQFdkIj9tPvTc7fsqJCdmbPF+42HMzywR1c8Ax1GWOG9jLCO1rkS+08k7Z/\n3tOSIyLDJQAkPUXIufOFkKyu1K/uKkqVFC0WEFjOlEaKpzS5dq6EvOJDMqZeMrkAudAAen9P9DHv\nLtSdcLroYcu+MudtH0VSPKTnIx+ue+HRRYzrbval3wg98VsACSck9nYNtvPY9ePp/RjR86j7jSFy\nbgtlmIfsvUEpUC4c7934nvUP9iIr8hCfC5puRahe80S7xlN20RtzPuOR11Z/d3UDcSden+3u2h3D\niPQK5U4JFwfZyAacNfKMbiUoaBdguf1qaXjimUy9v3o1PxMRFgkm0fMwHT33QHqfTZ52Q+gCbXeW\nHWitpcBzz/X2rXgf785RGtEwpHsGtcuxZwv9mxGRxlQ3iDga2Sj0gEoxR3EmVSpZRIAG21iYVyXv\nQ1UmC2jGpD2PhCxMAJVjhOaFRdYgOZO8jUbgGErTbiiT9746PAUceOGOqfX8oMwBWmmhqAOnRBWm\nqeAhWM81mSKFmkcQUnqVtYVAmXVDxdJ7oEprBTFhohEsIIpaFmpo6kwuuW6ihxwqGwkKKY61N1gt\nZGNWFObqbM3B85lapDKbYjibBlNRjLzdRHZe+QwNy6IK+ZxrkSXDiUCL9pLvTgknWubmKJl79bQp\nh0wIZzApl0LRJjTps1PAYkY1KE2YDY4koCkhsCmCVFjQft9MrFvjRGYkhEqjYVm4QmamfNHQrGGR\nZc1XTdhoFviIsPQCIekrt/RgNimZb9an2lTy2VhwVDNnLWuKeI88UEpo9yjnAzHIezs98hXRYAGK\nHOEaLKwoZjhZ8r3Qn2elsFqgTY7XLNKhWig0pmJEc9rumatzCkBJI9ciOPGGV1itCjUKqyi4OEUK\np7VyIHDaJ7mcivasq5Uq1YwzBBFnUxvb2tj0Z7kHrCZlJU6T9F69Vngp9sh718r8jPDhrfMVh4Um\nQohy2vJZ+423B09vG9oNw2fOJ5moIkQoD19tlElZt5wavb2ryJP+njkN55mquKyYwnnTam8cchKO\nNb9gi2SZ/KsRHPX38+nu/aSFE9IbU3sZ/jMvPCbGQ3YGwEdb4aoZl8rEA6xZh/GIzhSHI9kwiXPS\ni8fcZdscn0x8tAn3l90bcIUWw1CeIkXQU5IG+GnL34+6MLgynb+Tv/zomMdq4wNurNR4RzrukH4i\ntihlyvfcZQl+x2Hhoy3Hcq8on6rOmSh3zoVDgjdoo0rdv6Vu6Yf+HaWwCVi788YZThv8Zq1gyqOe\n47tS0/h/8DDHfZsFJy0P5G9sFi6dn4LnpDl82dHMR4X95M9XXir5rge+fAroJXtOmvHhdeMA2GBs\nHK5E48lrKkg+ubA/IF96WPjAyULt5/3WnV3YF/mJpxYWIEx499WKzoqGcBSwzXkM3n114dYJfvTp\nLQ/Mwbuf3lIODVfhmRZ846WZnzlb+H2XF7ZeYHvIaYE5vE+dwi3N+bDCH7pcWUces0uWB/pqO+af\nrhv/bHG+9fKW2xE+GcH9mvmlACfbQ9632fBVh41nWu/l1tf9TBNWU6E53HeofOJk4b4D45GN722R\nuXvWHrTNZz4hrzA3rOjDi204CWlQO91bsfe8kA6kfZjXRT2T3o2LoWRCVi+KLmx24nWXbqy9ieR+\n3XAhbKsnNce5O1HlPLQs+suB/f/uRIDuJv8z7KW7TSW67+qCh0shE3ARSpyrCZdnd/rb+9XOr7Oc\nNYzcR+dc2OzC6VzOw/Z6I/kUGhfcnjkj0L1mkp4g9513ZWdcsw9v6+1p9uemdY/I+ecXjuGzNcQ5\nO6/U/lzsljsPA9zNTLR9uMG5GCwhNNV+HM+D5nahZMiuNK7sj9zuLHkXqDuP3n4yI84F5z6XYH/w\nz19SXFgXci6wI3ZCbBdaqF3QZeiCyXkgYsjuTOy8Shn6Zv38Nc/+KCHnUuXCMPcoZFNRkT5jHLTM\nUOjHNT1fsb9J/Fwo9uta9yGgzz5RIrI35j4t0u8m4t664XIc9ju2MTc4VGEVjUrZT0SEOqczqK84\nWc6gHaA2obKkl5DG5Fsum7Hx1sPCshreimBb10TAsUtO0Gij9v44GnBM5rBsvKIS1DZRBcQVo+Ih\nNCydsNLY+BaxQrS8F9Y9hG4KJXC2NTDL8FEEDsQ4UU3PRtnks2SpqEaG2OIc18amVRYxoo/TuvDY\nxERD0P6nWRZFELYpkDTvh7mkwXxocKALLDMyQYuGqmVekHsWbYi81edJAacUxXvIWeZa0QW+YKUx\n7yaD2oYyHWJRERMWKZzVxkPA+mBhiuwZFMDGJzYRLCKsI2iy4kCzOiDufbIsw7CP3VjZzCoWUOXJ\nULzlfbB7ZhYypE40UG9Uy6IK5rBunlUCFTZRael7y1DmCEIaRVPohnn3boOZgBcEYTFFIjOxopdn\nj93zkAAVppbFGlw032fFCM2qhYdR2Fpw5vn8Kz7xWFmYgVNRziInUI4mTcMmGrOlR2nRwKuzeIrO\nQJgEShHO+v1vZcYWx815Og66x61QJt1POCxTYb1sCQk24cSyQMuQKg8wKxn+VytTgbkFh6+xSZcX\na4/cVYzfDnjroWYwZaRHMYpRHR7ZCE8347YitMhnfXHPSor9IaoqbCo8admmQLoQOelm2RUNJAKL\nyEkBYN7dElUxy4bRAEXgkipnCOuA5jlBAnDVewGSPlFw2owCrBE+ouktOJRson1ojTMxDLidxpnA\naUzczcLcz9mnSDGxpTBrY93zDSaFyZ0Z52GEQwpHNE7EOLLCplXuKHksHo9g1asynurEoQWXTXm6\nBs9IeuVq27IEvHEKHg1hCzwSM60tdEcYv1VTjN+qMAtcJdsqPOHQVJgjuE3g0RBul+AZDx7M/t6s\nTHjT4cHF7AGO+z7+dhe+713oAq/wjOdk5wN9MvhT3XO1i4pzFb4w4GEXTOBwgrtUeKw27p93Ilh4\neFOZLcf7ZccTn1xy8uBRdy5LVsu8tRgP91C8rWf1yYlgXVuGfnee6PbGQX9mHHSLadXATFmpcLIE\nC5k32CJbU0QTDrsYvHOGJyKvoS+ajYe3G+6eZj60de6ct/zjqnyNVJ4M5RNr5W3zlmrwWLfJLgGF\nLXWfzQoPTvDmGe4SeNiDKyU95yebFIsfWoKVzkirHFkA5+0TKhMfP1n48iv5+6oIpx5cmXJyblLh\nE55RB3eurq8x8lqqkvdZ0d5zRL17cvpMnHVvwD5H5oJQUHYGaZJ2uLDqlnHtImAnQAQ/D2Xr39l7\nEJDeoyX268r5kgsz/nsvzrnASSM6g3TOTfZcgXGNp4MeLijnf8OzY5Ttwr9DMsdCrvEBhJDVsrR3\nb0dSdMhu3Ocen4u5LqLZUNMveJxi7/npAqEb3Htxsv/j/LikScT+XOzOQU4kXDTz2Vf82n3e52v7\n9roo252f/RjyOFtA3XusUmzIs1ffBdi5+Lrg0srjEdJn/3f7Qt/+TiA8Ox9pf6yuUQ470d43l8es\nh/7tkth2X9kJyt1A+p7ikV4OduJ7/4VPfzBczCvbj4F8cGf4Xc5/T/uQ0t3xiJ6bZBfccYH18NRc\nVrLXS89xEsn8kV2+yM3Ko1E4C+egCkWNjThnCLeIsKJCy+OqWjhSeqPQguDMLEziuGRI0SQgsnBs\nQl20z6n28EVZMIVVZM+i4tafSUtWdvMM2VRVoikuyoxQI5vh1iZsWBB3Vj7lrHCrzKXg3pibdkHd\nwBqTTrk+cSx6U1ygTILWGQlYNMW3iyCxIKaYlG7sZXGA9M4HKxZKz6Wou8IQsjBTmDQoCnNI5npp\nlvE+E2HRLDXeRCnVMWCyVQp5bTQRZk3feVjeF83T02LeK+iJIDqxrc6swTyvaOIcuoA0jqLwtB3y\nPjbcG3DPKvNkCsJJTJwuylKURZWNK78thckKK0DYcuTG1mGjwtbBZWaicmVaODJjS/BkVU5D2ZD5\nGBate/hgCacIbC163yNhUsMiw+go0nslKQelpTcmVtRonElQxJCS3tuieX+Kt8yUi5ZPc8nrxkrJ\nYhBm6e2VkpMpaoTNXO0hlWfisEAVS9MoIqv/WWXVhPU2OIyAME5FWEIo2wXtz74lUuCsY6K1IFpj\nkeCsKd6CA1c0zjjROUPIS2Xxma0769Mta4PVstAkKC6ZC9ucgrKpW9yD4g2v2cBX6mvHw/RSuL0U\n7poKd88Ln9pm/7ZAKD3HZNuEK2aYOhrONoypZK5NkhOzEcbns+WSBI/32czHPY3K23RL9WxKvOre\np3WPnnlcD7hiwUTOsE/qPM5MFeNqS8PiXsnEqUt9m0Wy6bFqcLVlY+fbyvl5uITT0/vYOkxkyflH\nWHGbbXnMz708ycJFzqRwUtPAmCK4UxdahWfEEIfPO4BH6owQ3Gk1pyIkPT23CDzuxqxk+Ctw50Ew\nm3AVOJTgUIRPVWOyiSua+90sjfRFhMtsOaUgNJ5GWOnE7b5lhWMygTTOwjmTbPX9JFPaehfeZ6oZ\n2XVPPxeiBSO4wxpNMsT3U927tcsfe7x7p/6Fyfi1TeMyuU5R5cqc43u6r/+jyxYUnvH0BN5h8PiF\n/Pf7ZuPxTePuSXmsG55Fg2MV7p8mJhXYnh/3d/QQzfee5HncxaXdGo2rrly9EBH1xSvnKQ9+a5vj\n/6LSqAHrgNsVHhbhkZoW9UPHxgevKm9eVR5czRAVLcGH1oUvPViowFNdPlwiIwyM82vJ3Cht4uPd\nZjg5W/EDm8Y3d6X7jn6dPcWKybd8qgr3TDnO26xy2xXZ2yJ3H2QvOUd5at24NClf3Jxmyoev82Pk\nphJMAJjSNGcVTLICk/fwrdh5G7ohInQPRDdIg+jx9rDsPRU7AzH2ImsvYjwNTsTSmFZPA0XsXAhE\nXLBpheDZ7lR9w1dlYmykQaxkWBWkwRGRCc6V85AvSK9HVXnOXB3IMC3P2rwp4jjPZQL2IRoOqKWH\ny7iQEKqaZWwveJwAJBS99yuRHoZz0aOwF5X9Yq/Sz0PsPFY9QFDOvUuRB+bcmxKxD/Xa7W2NzKPZ\nCYjYeUUi86fQ8zykXVhguPfZ1rLPgYp7voJA8d5bpbELY9ztW27UAenTSrv8IO3L7TyPdKNDwjMP\ny1OcaJwfqx2798dFYXWe55UH5FwUd3GtXUTd9XZg1/ungQptr+sueH949np3Q9g3taQX01B6Lkc3\n/LsRbsF+HdmDpgveyGNlqmn87pIxJfM11PICyPMRPcTy2d7Om4nwBUfYhrIK7z2CKtumHE3BrI7I\nRK1Z+EWnxhSKL86GrC53sGw5miuxLbgYlYVZhUvTmsON4KaEOts6cVoaZ7UhPddj0xZEMjwvXKjS\n9o1XHWdTvd9FxlzzeSU0TFOE7Cvf7YodeFA8WMIJUybP4gwuGX6ZeUl5Hyy1pvcXp3hW14sIpOal\nqNGokc1LjbxWzQWxfMZumlK0MEtDTFgVKB5Yv5lXumKlC7U5K8t7N4vGOOiCMaMTzP25vd46XuBI\nD9joGbM4ooXFKyfAYVlBtP7sNg7mvCdnU56oEHbAp/yAT506FbizCFdsy/EULKI8TYb0TeUKp805\n08ZljMMC0RSJibU3ppYGlYTxRNly0JTbVoV5cZ7RyJ5E/SFhSgoegTUzLuCVjMVRmCNYNCgyE6T3\nxqWw1fQgWc6d5bMmwFrmSs0KhFO9svRw6RXgjSziYDO6XaAolcIaY7s01jpldcGlEh4c6gYiC2c0\nd9a1N+o1Z67OUrPQRohz0rIGYGk5eRCiRG1IEWwJZAaJhnmlhWZBknqG0ojTCfeT7hVT5hYgzoHO\nTOaIw1k0TtxpdYuJsfXggMBU2d6EpsdFhGCagk/WOUMatXB7WWghLC3fZYfirJuyKsHcpzXvtpzi\nerwJd0wBNB6twppzW+QNdvr/s/dusbps2X3Xb4w5Z9X3rbX29Zx9Tp/uPn11jNtxbBmD7STiKYgY\niYe8ISEEEuIlEigvIF54iAhPSEhIwANCQsoLiAgeAkKyRSKEwkW2BCbE6VhOt9vd7Xaf+9mXdfmq\nas4xeBiz6vvW2vu07905ElPaWmuvr76qWbNmzTn+4z/Gf2y2yMPi7NRZWnznO3qfn9Arzv2a6vAi\nnTEgXOIBcgR2GuIxLzi71eevvvE2NwzM4jwpC2dq/Gq9D8Av5ue8MOXaM++2xMN0jGW4J8Z32PEw\n3QZIa3vmA4cGiwv3UmP2AHkqIAU+w8KIc+PCvR7StpcAZQ48zPBeG3lDAyq9ZwEyDm3gS4/fJuXM\n05ppJhugSynsrNd77tU7VnjPB4rB9xm56O/ZxzKizDyg8SEDKcOqPX3ZZn48Ob/HyGsdpn2rJr6a\n4H0J5u1xuaG58F7NPCqwqPKFvo9+N58DMB2e8bmc+UjPeXNnWJv58sO3eF0LzyTzNW38bzcB8DZb\nBOEG+IdL5VFSdinzzjzz9cOMAl8/zJsj+Q0tzGb843nBk/LPPhj4R9cTP3M28vev47xrqOBP7laW\np/BrL+J5/Xwf9Jf/D8WNN0bnrWHkW/vX+Nx+Rxbhezc3qArf9xiHdzijIfzChfPNfv8XEtfaEcB8\ntRPf4YzPszCXA3/7Mq71UxeNrxX4by8bv3RW+JWrGG89GG2n/OWLcy4PjbfTgTOF//tq5Kfvz32s\nFF0MTcaDfSbVhuWIOdIfsi3yqVq15IRFyh5AofpJWJFGzQ47MRghGCKBW/FLL+WsdJB1+3org3L8\neco0nIYz3TWUt/aZnzt+pxMQm9CCnIT9qb7EjGzXP2krI7b2VFXxrjgk3bBt66JiYQitqmgxDmvo\nl986/xEUNdJbP3syMp/cSmeCXnXoSyIYp/fU/9a2a75qDCMEbKtJorcvohrhKa13XgD9zM+djAMb\neLzzo7dXqcjdDnFbz+Unv8srj7p9/B/s//1eX/+ZLnbxShJpa+vcXMfomHu2ocyXrnE3h23dkOX0\nmKQ0c7BeoPIWG/syg/Wqe/o0tb0IZ2bknrNSPVSULnIk1raUUDKMICnYgORwTyKna8A4zzv2ekB3\njVSdJcOVVe4rDLtrJkYmC4GEsee0LWKYC2NPitvlKcK/SBwyNCrVGillamtIz4eMvMiCecMk0SyF\nUl8KB1CVyGFKvWhuSopLQ6lkSVQaBtQaimvNnL1DsTDga6tY7oVQHQZRGvFZhILCzoM9Uw1wOKc4\nppniJjSvCMJolX0XYWjuLCQ+qgZuPFLlYWpkV3bmUQNoCA+v+cKTpLgkZoRdUXbIJoaQco7PNJwd\nH1vm/awM7pxLhEAlzVy3mQsZeKRdT0aU1JziMyRYRMkW4cGiSrPGXpSaI1Tq0MDbGU9xJmvsCtwz\ncFUWiXC+igQDw8A5gjlMeUElQp4mb2iLd2eyABMiIdajqmDhMErSWWgHnyZmbAuhXtoc65ta9zol\n6mJIHpib4+rMS2MmIdOBQUHrIeZKGnFzJq0RWr3E35kW5l6XS10oSUAzmjScgc1YlimcIU2Y1Jnb\nzN4jt4lWWWplESgSQH5pRpPEqHSWMeHVMF9Qg7MW7xceoWALxkdUdklZ6g839+BPumX1XrTaGM15\nPcO7VUASQ2p8g/v8hfyMd5bIYFy9jO8Y3NPGvdUDCJiEUbnmBk0Wwi+ni/oOEE28zcI9DYbzg1v4\nRdmpkRCuuhN2uLPjfeHJ55jxLokf7UsS13yn7fpnzqPEJipEBzaDCPfuKHX8Wgdbn5cIx3ycIhQQ\noIlykYz/p8UxXyuXfMsueIsJED72woVUijgfNbhITo9a2xixcw789GdeRwUe5foDdl34Ql5wh+UV\nB73bwrB/KEc6Ii41IDJxoZV3ex7OHqekhYu1yLULJTmue1b+5vpOjtGf2WXOEL5DsHgPs3J4420c\nuEgDwoF/eh/v3irocaoS+Vs3la9kYd8FF9b22/O8/b5PymvDbvvbl/c7zpLxpMTceX95GcyuwOgH\n/f/DxWJ9BF6794RffT7jCD828lJbSwG8KQHEr/oY/cYc5y19XB6cXOanugjKb1zGiD9Q+PWp8i9c\nxHj/MjN//kIZueZ5usdHi7ErM/shk9rNdp670TTaQ1HXnz+s9qkCTKiia/Kkrnk6a6hT97TLyd+g\n5+f0bDc5De3qL3YPN6KDF+1hUbGbraFuqyy2QMqbwEN0KaRbSUcltDj3eh3rTJBG+JzAqjp3DB9c\nQcRtBbxQ77N+rfA65h7aZXICsk4EGlDZpMcbHuJn9NuRFShpyLsiPV+qMwoEexCFEk9Yp81Ajmuv\nSnjVjwAowKwdDfsVMHUa3zRU2FaGRuisUg87c46gqaef08mlUJW6xegpditIcb1H7wzSCphui1+s\nYW5OAAU5GfGVWdT+F+tjaH2s4mIaBh4ruI5nk/wog475LfbMZMtHj74E8u7smWyesvW+b4X/ySnw\n73/XHiJHD19qx4Khq2PAJRi0zZtl0bdVvj7390NFUI+cl6baxzryM0Qk5pII4qGgZdY3kbvI/g/Z\nROSfA/5d4OeAt4C/4u7/w51j/gPg3wQeAv878Ffd/Rsnnz8C/jPgXyIe438P/DV3v/pB195jPJDE\nooA0kgjJhMEqmjMV45kbH08lRBFKf9Y2YJqYMYobLHvEG2e+cLMkpuqMJJI+5IFes2PhzJzzoTDa\nAc2GmGIGmhLNtRvZlSKFwYRZEg1jX5TFiXynpNzUCjlRRCm64CJoM6pmIk0paiMNXVRkEaUgjBZM\nrVg3ZqUya4MlcuUaQvNg0NQg5eCQG3biwMlIOnQGpnIv587UCkvNHNKBh+4Ucc59R0oTg4WX+9IS\n1pyW4FISO6mkWtE0cJZBmZnJXV1wZIcwpHXlaMggNElcV2MsKRxAZeADzeR5QEvF1EP6342FHS9a\nxXWHtomSYFfCuFcCKFznPandYKZYEpJUMglpRhEoUrkBSmsoFnW7CGn06sLelTk5QuNaGs13XLhS\ndGI0uDLlwMLsMR9UYdbGmUWOCElIWmh1wiyEfVyiaO3iRKhaV5mLdaSCC0mUuRlmmaXOqAjFo+aS\nmVFY0JSo6qDBmuXFOLTGYiCiiDYWDZAsTRCZwCMuoKIUmWleWRbYpWCpqgmlhQS9eCOLkFyYhgkl\nkTSRqzFrI0um+syYlBvvoMoaVZWlVTLOPilaG9yJxPi0NdOC6Bi7hcL7/ScEUP8qN7xrw6q/xBs6\n82ETXrBnsUShsdSwLOe+Hv9ODXGZoe/bb+jER5apJIr0ZBoPcCM0LrVQcCaHQhSJLuK8TyGLkdyY\nRDf578kT4o1nLXEtiQ8dzrRxMN1C5hcP9uy0DRrCIE8l05pz6ZFz9ZM9fO1DD4b0jIUXGqIPD4Br\nRp4Qdeq+t4zcZ+GKcCA8TM77teCivJUO3FPjd7tctXWz9J12jrjx4+OBd7ywoBsIPPT588UUy/1v\nTnsUuMB5Uiaem/Bajv1zFb/QJQDJUirqzmeZMYR7GPfKwmxAgglhxPlwKbxWnOqg7gzemDWRivDh\nUlis8eZgvF/P+UIHUw3lQwY+mw+IRJGvK1PobMz3Oma7SHtGbzxrlXEo/I4f9+fzPPCGz/xcl3b/\nv64X9r2I9leGwsFjsr1TBfPGrmR+dhTeq8aDQTgYPG3C3o1/OM2b07qp8FPDEc18/TBzX5VvTomf\nH4Sv3d9xL8Pf/XjhtWHH/QLPOw77TFl4b3Y+MypjEr43O0+ycF2N14fC96fG95fG6yrcNOfDBLuU\neXFQiggPO9j+uEW8wHc7o/cv7xJUUNnzlfIUEnz/5pyHGL+jiS9pRONEiqaQqiGajqk0f0xb5A/b\nPl2AiZe956d/u9usu+2VCAtZDc+75zv9fiiSnfriuf2dNTxpPd5XMHL776ctpRRgqzM9uPYv2nYv\nqsrRrj9lsjKnbMgtye1+jyvzcZflunVfPZxq7at3YQF9xXjAmqfySXd0PNbMugFuLzF0wMZ2rb33\nEyCWP+Hst8QWkOgzp8+9v0AnQCTGLPcxvu11eNX8yP3u1r7crQriPRfq9JjUEUjqoEV19aYRDNEK\nIE8A0zrmx8683K9TdvNWX09+b629cnxj3rx60djOdef225bDFDkPsIlEwp3+vqpv9sdfo84JAaT/\nigA6d/v97wH/FvCvA98C/kPgV0Tka+6+ut3+a0LR6i8BAyEB/F8A/+oPunASYycLSYTFHGmJORuz\nVhDh2s9QjPsZ3rccifoWILO1KGcwrTGpzXnqZ8EGJeXKDTXn6bRH9IzUZh64s7fCwwRnKhSpuHcj\n2FzlwWwAACAASURBVMNRcQAm7aFtwILT9PhOn5fIXymyRAiCCi4ZUA60CCmVmMOaEnNbogZPMtxi\nXaktkoUXky767VFDKCVuLMD7ZA6aWFDEa2c5nJTuhcHcJ00imP0Hw4zOjqcBE+O5z4hndhlwI6vw\nQHLkxwh8bMLOC89cOJ8j8XxQ2KeBm+VAGvYM1RkHJ6UIm55bQ/LI4o05hUF5Lo0n43Xk8XRrYPHM\npRiLjzwrQknnZIXndWHXnWJqmZucWBwudN7U/wYRkjpimbkXMC4MuHTxBzfm2WlN8RwAJYly1oTq\nwo1W7mnhYDMXQwtjcWl4dtwqNmQuHJZWMclYnZjEqQSYWZ1nJorlwoXE2uIe4jA33sCgtoUI829R\na8rX5+FIE2hOyTMicN3DLb0ukUcpwY7lSoTZ4lSCGXSJXJq5KYpzphKArznWjEWVnCrQMMvBmLXM\noJlliVynHZ2tRJgMJhee03AytUcCLFZZ+ry//idIJe+P2lIXb2rNX/rb3fY+A56gmFAkCjQvdzYd\nQbmnjak7RZcM8xKhR+t51++oK2/mA24p5qBH7iGEcMEDrRxMOadyugE8Tsa9NeoE3xyaqy+/OjxK\nxtTXokmUvJYyscSlZx7KwkGOlsMD7aIFdeBRXjjDeNaFJh5o5OSl5Fy7UlHEG5NEGYGShGsZ+M02\n8MXOdq15OLskTE14pwXQOUoKvNyyCtUcJXHtymv57o4OT3Udn3AqHlLiYw9pjJ/QG17cCe96rSxc\ntpC9uZ+N7MGsDxqfvT9nDKMm+JDC6I0E7JNRrURsjMfavO6ZF+n2XexUeVsjE/6qj+h7d/r9lf3I\nSKjHLX37+2JXmniYB74/VyQLb2TlYN5ZNuF+ga9QOOv7929MC/dP2J8vy45RnM8Pvv39+QL//OP4\nj4hsaoYqwlkH/9+8ntjll5/Gj42J787G/Ve8Ag+6gfG0T6XvT3Her1fjS1n5heR8Zw7ALKuySdoB\n12zSxyabzZW2WnU/3PapAkypS5jCaqTejWO6PeGVvhnpqXrZ7VCsl4xpPwIBuaNMd5Rwjo1KWSWw\nBfFefPGuUSnpmD/VhRS6g3Zju4528PqLcNtujhfv1HhfD1i/EYZtv4Ejv7ExWHBkZ+J7oQn+KuDp\nLr1u0suAC46KfUIHg8CRsN2+sB2z3ZsA4r1wbZyzKeQuV2oSOVnJ+2c4kIKJIopGrjGr2bUb905r\n67OKRbILch3v63iDR8x68m8dR3Pf2Mk1pyk6Gv2bNfK1xFt/fl1X3RzDiIibDg3XgoIeohRlU16M\nubNKqJsc8d02/nivXxWbYKgqheKUtFi8TPxW7lTc5zonerFVPzJF2z1tYJRbqonbdO0MYHTX0XZE\nUNrfoXbnvfjDNnf/ZeCXe99fZWH8NeBvuPv/2I/514B3gb8C/C0R+Rrwl4Gfc/df78f828D/JCL/\njru/80nXboTi0N4dTV3YwgybR8YRDpZ4gXORnT0zqqUrio14C7VCbFWWTJg6Yo56DVl9F5YSE1As\nM80zg478Lg2tlfsMvC6V1wuUZOxF2JGYqzOpMsS9UMVZrD8D73lL0pUrTagSkvw5KUrGUlRkc1Oa\nh2z5TfNeB6gi7mRvtCWxuEJaGAmpPRHnmmAR6eFBQkMsQFltTh5iXpgpJcELE0ZzskYpBHdhlwo3\nBu96YlkOnGVhh5NF2QG7lDgTGLzxQpypFZRGTZVRlcWuyGnAF+GFCReqnKfGIJWPzMkkzBtjkmCE\nckb7GjdZ5SwNVFMqiYXMgmClMLmxc0fd8GqIFW7cEHMsZyZzdi4BNBlxnLkIyZzFE8Uau11CWgsl\nw15o0kmIVvYOB284iaUppo2SjbnnXGScwZ1dVq7rHGyu5AC9PQKitcqIM7FACYeMi2DNGRAOMnMh\nhcWnyEVlYcgjBed6ntDmvVRBqEdpBWuNUR3JGWvOor2Abp+/Ocf6VT2es4b5F/Ojp3BrJpjlLqEO\nAbqnHKqBSRzPhXlpUURUhBe18swFQ1mYEVaVNmi1Gzr2x1tDftTtTW98tob7/b2caB65fmt73u4Y\nxtKYTLmfehyAR7boA20cBbCj7fuGcGiFR9r4oGXmfr7zHlaWFZplnnrmzXSDWgnVXXfua+PSlc9q\nl/buoZiizkdkLrBuizhgFJFNia+cEH8HlAfSUHyLRHmgFRx2kXGLiXDd953HuV/P8pbvFNL6znNX\nHqSGW+MZIZ6wy3BwZyfOj6dpA1nrdZI7H2hmFmEXsS/Mrrxowq7389st8oiKhnraBEzseNrx0n2p\n1L5/r0/nukVETknGKMFnvTDlH9nFxppddfvii+OBgwnfqiNvlKiF96xVvsl9vjpE7s6jqLLNgzLz\n7pRRMioL4DwWOzrYYMuVQiOcd3Tp7xt82HO3ksO3G3wpxVrcZOASeJAr73QP9G94FCX/Ypp5q8AH\nXAQrXW/AGk+K8Y2DAMKTBB8ZfGUY+D+uZ/7ieZz3dY05oGW1eY/Aad2Wn1bjxkFl4J407tO46GDp\nvSUHc+SVL0QMKX82J757MB6UkYzxcJd5f5n5YHEeqPDFHOd9a5+49JnP4TzwhW/rjrdWEGQB3/fL\nyLucc7OPc3/JLjdbRLbyPf9/DtMntqasYmO9+Ce3pLNfcqXzMoMkt47/5JZ61fpXtfWckRsUHlwl\nQNa6D4itxu/xO32dRHvi/1FK/Mg0rIpkd9td5b67eSncutrtdgRKASw+iZ07MlOn55OTvx/7efe7\np9eCl3NuTvvnHhLIYh2EvarbcoffWhf9jT069nX1OtxlYLYcnr6RaTuGQa7S5XKHLlnraGn3wK0A\nGnpBOj8ec/psN5XCDVzoLXCyqR36+nkYxSZEXsUPYIri9vt4NotitnecA3YSy9vEIcnGeK1tw399\n/jezDa2LRCHnIisg7fd48gh+GOp4IvJloq7h313/5u7PReRXgT8P/C3gF4GPV7DU298hHskvAH/7\nk87/niduehHRi9Z4mI2cIiF7tsY+H2hWqG6MkqnmlJQp2nhuIcxSJAIw54gyxa2Ch+xzTBmPgscS\nYXUNYaxC1sJicBDlOhfmVmkO7zdBbOHJIJQyUN1CfU2MpEqzmLOpM0NoMFPu3kOyKk0VcUEkmMjW\nKqaNpXVhBoTkCjoDCW0ZUkPVUBJi9pKATRdAxh1ezA33PXNxlqoolakVJoxRKjllUo08isUaaSgs\nXYBnQDBt7BtYFjRlaMaVCmYaDERLHHzPw4sSYWjV+MdeGAQeDXDhjcVuII1cVtCy42ZukEPKJDBq\no2qf171e1ZIyk0e9Ik3G3hJDdzbcN3ivKU+r48mgJS403g83RdPA3hZ2ySltISU4SOTOignkiR0h\njV7EmDLQFiqC2xmpzBQNYzRncG/sUmbuymcOFK2MfsW1FKbacIkipuKJ1gFTU2d02cKws4Xc91Qb\nljMHFEtGrjD3VclEKLmQS7CBuDNSyRoMY+ph3K01DnKFEvWxisT4q7fICZGCISxU3MYwnDWjNiPm\nZI2xmJJw5QuDlSghYU7UTIl8reZxbF2dWn8Si8WPsL1fCtIV0MxD/GggYoP3bojeZtBGjCk5Fyfg\n6EzDKbj7ffzkb6UIZXtV+0y+QUS4sGACZw91zGuHuYd05Xn1rMWPS1Ee6MKohtTCbM5HaS2WG/3b\nq/HCndzD8+vJ3vRRz8N50xuLO7WLE+Su9vZAXi0OAVED7bFUrlEeqDGcnPdx/14TIa92RO/zyro9\nkIqlzEUf3yOT9TJjuX72qBewfSan5m6c/7oJZ1m5pxNfYL57CgCSRI7Vuv3d1HvcF2Pop1u6at49\nNZZSObSB83z7mc79sX8zNPT4KpeRH5qcp14A4TU/HbfCu5vow8zkAvXIUCnO52Xi+o4N9iQr7zUB\nKmNXOR3UeFPCPXxxNnDotsJZt5nVhUs3ZkncI0K015C3V7XVDvj7VxN/4YFz/hKMOM7x35snqgfD\n9OPnRzRulkHg0mfONPE/v1j4xXvBJN6TzPfaNT/dj/0SL+g3HU5g5EcmPPWpAkzi3DICdY130vQD\nv/fSoz+Jk9qMWhLN18yYkMp1W6WlN1qnf32lAyOJdxUGUNXtYrcYInWs0UMsUmcZZPP4rwV2BULB\niNuG7gqiNsAiMQ5Jen2lLS5QbwEu6QuEpvUc6aiStwKHLVfnGFXuovG5G74a8is7c9KvjZ3pqhJ+\n8mxu5Q3Fp+FVFN/ydprcBiRHpqTfRw8bjDGM8VrzklZRCRHdiuR6z+dpsgpedJannQBWh0iAs87w\n+CZNnk1YR8F7svypcuHKvKTusVp6mKfhqAuuPYEaaOqdwj8+5zhHzLFt7JzwNnXmCSIvTnq4Db2u\n1pZvu7KcPR9sDRvCFU2KeTsuJSfGbzAi/Zz9Wd51Cpz+v+IkO8kf4wj27jKvf8LtM8TUevfO39/l\nWCD+M9yJXHD3JiIfcbuI/EstJ+VC1vDXzMESZ1oZ0kxJhUTlngqHrNSlciOZZ27IVFGzWCM0BwPg\nEY6zSKXV7k1VoTUDIjdFidyySQaul8Y+O9emfLwYFy482CV2VvFyzndw0tIYTBl6OFymbeF52RNi\njTCzwuiO98swq8zkXvC1MtmOisR8c0jSGNwxjcKsi6w9CynkxxluqmDMUdS2FVyjdpAnIZuDXNOa\nMnqilMpsxrkqakK2BScYYzNlqSs7qtyosUvCM4ehCvckZK2TQpXG89nJSXnW4PJFZW4TBSVp4lCE\n703CmVRkl3lT4I3UGDE+nozGOe7ONbUX82xcDpV5alE8tvQcwyxYHniWjFadeRn5qF5RJYMbj5pT\nUtRAuiqFicSVVO5boqTEA21kMS6aUaWxZEWsICQGJoYk5BZA5cYFyRNVCpj0osiN8yFBi7W4eWW2\nULAcUbI415p4AZSaI+S4OXNSvLXOLDaMxE1bMEIIomms60hmkogyUFcGMVIWdiJUaww7QSfFFQ7N\neWqxBqoZWZWRYJAdp4ggGvlb7jPXHp+FdLAjaqjB06x8uDTELYAzJea8w2vdg+4Os1QMpXijdUo7\n6Scb1Z+GJuactQjBeqGJJ74wo1thzj0vG/D7zsqsLXdGei2svqyCCVZ44cKZO0kbu9zYtR7VYInm\nYCtr0venGxI0Z6chy/1IfMuRWpXlHmGcDzMfzCPXlin0kuYqvIZx7fDOSfQIEvvAxcne9LTvR4/F\ngsFVeOTGY21caeJDF6oIj8W4smP48uMePbJX59KVRYSPehHSCzcGlZ6b053B/fd72tctgWca4XZZ\nwklyKcrYxTMO3b2z6yUfmsgGopa+b93v/28Oz2vBs/G5bDRxfnu5iOullQGKDlzZwLOlYBl+p0YU\nzZidPbIxp8+6/Ta18xinDC+WkZ0677ny0ISlxLW/2G7IEkW1m8PVsmeQGfPMU4U3ZebbvuML5cBV\n6yqH7jy1wptlYexS7u/ZQEXZdSDZmrFT+L2W+JwaH8gFX87Blj1X5R4BOs8EfrejtzdSpagwBJZi\nWIvV45u98XrOWxqEo1yh7Pv8/YsXIzT4RhM+k41ziVpKr40jD1MUHL+/v22bhwPQqFIZgdoyh5T4\npYeJqw64k8AX8hk3JYRhvl0jl+l3mvA4ZT5Ke77oT/v8/8HOhj/p9ukCTCKbJ37N2zkFP6vx/xK7\nsRniLzNMmyFrFvLbJ4zDxkb0oo4vo6Hbhj5EUVwR2Zifaq2rWnUO4s75T05z/Gl+zE3yYz/WfBLz\noxjFNg69vTKPyI2U0yuTT05ZJeksmfZiiojeQn6vZpY+mYW7NVb9uyml7TndDSXbFOCkA7g77CCc\n5D/1neeU9Vjl09cpcbw37WFOEU6g5hs4WMdv3SBOr2O9j2m7xT5XuttLTLZnE4p9t2sUWWT4x333\nk9s6/06ZuztjZ73A5imYWZlL3abgiYKjHEM4X8X6qa4hPv0Y52Qq32aS9GReafwhlCfxrYDzev8/\n5Hayjf7Rj/kH3/46Yy6kTWRd+dqTz/LnXv8CrVQeNGUYnUblKSPFGzc3meq25QnVpYNUm6J4J+Fc\nITWaOQtCcYmyB96ZY53RJFSHIsplrUxj5oNJ0bzn3I0zXTh4IUmUwrbqSMokWmckJ5JH8VoXqA1a\nWk7EQBquscFVGuJK8cwoEfxbrNEc5pKYZme2XuhADdQipE4GzGb2nZmwHEISq2rWqrQGkOQBbpeo\nJwZd63RVWhq2pSbGpjH7goqEoSSVJhmvThkvqNYizFU1GBmC1ah1YQLONHGwxMPnM99Pme/jtPwC\ns8TZzcSZJ542wc/OmOwALWFeISW4WSgFvCTqnJitxRoghaqZ67khKdgmysKexLk7n01X3MjAMOww\nv+H3UKiNPcKDNIIvHCK9FGWIYra5kiQxNrhJDrbjIA5i7E2pYpwNUdDYayiRNUuU/kBzjVC6mwwV\nY06ZPDuTGZfFkSmW0SxCxRl2ieS5rwUNM6d5IqcbzCLBf2qOeeZwoDsBhSQHzl1BFkruYjMWtbUq\nkGgRWugRlpWq4LsD2AWaKrU1XHYsNRxqoOw8oRq8WaIL2miwlr/77nf4rfe/29fZmBjz8mpv/qel\n7b0xqNFMOZNl2wdKtzTnvj+t6RgrGApHaahPJneyHpmLpf/2gQlnd+LKVaCo87u18Jo6qRuKpW8I\nTWMfPddG606/Dz1M+rd7GN9HlrkP7FOIO7QjEcBZLwy4W9mB3qnFe25kd6he4GScj1x4W42PXFjT\nYs61sViAbne40GOtorUdgIvs0JznIlRku8/VcrkU5TNq3FjkKg8e6+YjjI87ABv73nhaxFXglWzd\nWnD3eQ8p3/c+PVZn6t171MMJN1ukP69Z4VqM19W2MTfARbbn9fkubf4BxwShezkA7tAyF2XeGEL3\nkTEvfN0uOMN4TOV+Fi6XLpkusiqXbYnV7/nICzIZ5a1ef2p9TG2tXSTGLjmvle5crfCdXmD4iV7H\ng+y24Zf7En5NhOGtNsTp/W/Py5yd3laHvuljfNaP/WxyRk+INoacGS2c+GNO1DsqdvuszKbc2NSf\nQQrhJYWhhxvaqmaYulRf3oE9ZZ/PuM81F8zb/vIqe/dPs32qABOqve7D0TI6BQxHhbbeTpiLKBIH\nSE+wXZMk19CWNf5YO8sgUddGECSFd74RRQa9o/rIRVpj8OJHsqixQi8ImDWfdgW67G70M85/jJXq\nYRcb/SvRX44ALAxX7cVcu6F7l9np49AwtEVBUj95MdbCpX46Rj1EUHQNK1rH4u4j6OGEMZh07mgb\ng5cYpvVRaIocj+3Ak/DC7XHl7bsrCyW2+tNXBq9vEOkINjYA3LuUVnZmXaR09Y/0nKPUrykSbJHE\nXQTLFlLNyXvQm3ufZ2zHrj0+fVmjTMsJuDTwdFtvKNhE7TkIR+/IUSo85kxhBawn6nvbGEk/tucw\nrayh9mLEJ8/sWJO2j4+cKvCtzzhA2DEf6vhMNgJXIJFoar3m1p8qYHoneseb3GaZ3gB+/eSYN06/\nJJGk+IiXmalb7Zfe/jHeuHfGPjlVBsiZglIGI6UCKJ4y09x4kSo3NZGHTJsXKInU680EA5rJPSwO\nCTamOowqpLSymErKhPKmhec+1D0LVoWaYKiNQ8pYG6k0Xkh4/c5S7pLnhjfDpXbvakaqYUmwBotm\nDr14oixRMPVcnBuZce1Fs0WYs+I0kjdyguZK1fBy4xlLoCwsDFRbMMlgoGJUUWqD2ipVEsuUGNML\nkiqujcVDQCSlTDLHc2KxJSSTRWlaaFVi35YBbzOqiaXWUOdzRRVy6vmVnZ09V+eBNs5s4ekY5tBr\nJOZknCXlAzGetYkLdXbzAWmVe9znJinfbsq8OIdFmVMjZ8WXOYCgXeNDjiK708L7Dm02xhTFf6/z\nyFiUy7qwL3vcZ6wlfm++4RvLFWXYYcV5aMZDb1Q1dCh4nTnfXYTyXYraRldeeHdsPGxgXhiAndyw\ntIYojD5wZs6+CCk3UoXqypU51+JUBW0Ds06YaowVwXipVpInrBqqhrlzvYw81wzTxLmOoYGnC6mv\nV0PaxdqWBlKD7BPe82sXh+e95lyzKDyrQ8UoVG/MU4hxuNRIcEfYueMcgIEdQpaZBSdJponwE5/5\nIj/x5hcZBVwOCJn3n37I3/z1v/dHXiR+1E0LeI78HEVoySm0LVVgWEUa/LZ9UNSxmphdOcsBGp+1\nHZVgHNyd+ylyYT3BTMYso+aMHjWZrghb+q3OIiVC2e1eMhq6rc+vmfNChIbyPdtzP0+8sDEMeQFU\neLGEUXpg4pE2Pu5gSy1z8MSQJpaWcU/kNIEEW3SfYE+QYMqek7hCKDgP1DHg2oRLD6Nd0oHRQh7d\nW4S23u82FSoRDLc6Ij1YstTrKc4n45dPdsPX1WgGH6PdkBWu0c2weNSPXUHW+t2dwvUQVRqfS4zX\noy7zvdoiyxpm6M55cW4QLnDebwNJnddYqLKCqvj52BeeeeZC22ZDvdEZq0NnBMnO5Jk3pQHOkhMf\neoIMr4kDmcHgHdvxZ8Yrfq3e52fSDTdWwyHrwk6NR5645IzzLtdxGgLoSXjrpGZWXTJLhps+Dg8l\nIp7e0XPetJkrIPcCxq3Geb7RBmqCr+q82SK4vyS0BXDlhouhVrieIvz7HSpfHWVTNdS6RuXEqR7u\nSoQet8j9NzP2F44uCVjzaNmeiQk8lJuwXxw+KPd50p5tNSd/WO1TBZg29q17v81fVSXmBMT0Zubx\nLuppTR2g07hrOFt4/o+Ay7WrtPX/R2z29mXu2o2RYL++9wG4NjCwhs3BbVAhndnpxmlgJGHF+ltf\nVkQtcew6T6Tf13a+FdBI5Dyc3vM26U/6YMcRCbliNtyxfbaNQC92ewRza+dkA0ubGt9LvAnHsZQ1\nN+LV+VpbyGV/BrqOxV0Q1p+HyDrWMcYrE8LJ8StbtzKA64t/NxcsxsB7WEAXBRC2ubH2K8ZuG4D1\nBjflQb8DpJsbnrrIQh+6TcCk645n24TQe42udSxPntfJPbnKnUcQIWErg+cnz8FZBUcEx7Ycq/X7\nR8GJUMpKET2JER5tcSH1IGJ/xTP7k2ru/i0ReYdQv/t/e9/uE7lJ/3k/7P8EHorIz57kMf0lYqh+\n9QdeoICVAdORUds2v4ZUGLPSNDGrMAukq4G9zMzDzM1kTIeKeA5GxgwMjMZepTsc0pEB7GywAt6E\nncRcm/D+fsfYt9qivk6tuGZSMfZtoEnj0ODbeWTfQnb3zbFw5gfUHBkKzy1U8yZ3cpfkndJCKx6h\nexJhUdULWCiuoRNiRkshBmOqFAHvtWIEY9CRlEIIwOeKSajCFXOWMRjikpzU18eSC1jbQrCsLSSN\ntag51Gb9nXRE67auuhtmQpFCTkKzGsWCNXFzM4EWvCgfeCWlc5YGnjNTNZbJUa1IKlEIVTPXsvBa\nrrzTMrNHrlLxmUVCzn1qwlgyeTqgSamt0ZqRcA7Vca8c5sgZ+mA3RXGDVBhmOMvGdHUD7kxLo7TG\ndBh4N4X4xt6VfFnDU3r9grO8I2mjuuPDwMfjPd4zOEjGl4qbkhF2zchiDNbYKTzJzrksyNIYU+Pa\nxu4yVh6WxDxXJgtp7wDrlYohLhyWEBComileaWnk4EaSxnkSBoutIltj8sZSozbSbIJoYm6FJs7O\njYww5mBCAJZmTFapJiCJnUZ4DQ5YpRQ40wPn4tASTih6Ne9rEqH0NnkUxCZ/ymXF2zF0bV+dWXv9\nw81vt7IRt22RZ4uw16jXc209ZE6iftJeo5ZQlRzh7VWp2tcNz5RmXJQw7L/bBp60ypKcGWGvxseW\neKSN7JHjoursXTGPc1+3uF6Wped+Cxf5yPTNKPeq8FwzKsZznM8LpLygJwDhZg1xM+F7mig9/HDf\nbZ8bV64tb3tZlLkoiDuHfo4iUUR74RgyuIoNXhCFqPdVWDKciXHdr3mv73+zCJcmnGHcPxnlhVB8\ndIRFoj7a/f6dU+thZcoeEKzXQeSV7NSNKDt37hF9f1AW9hbyJ3py3p3CZMIDidyLF544k7YZ2Psu\n7V5dOFMjufOxJbI4XZyeFx1UPe45V1eeeVtn3rMRM+FBqjSED00466zWg04TPkvrlYxBAli93xKP\nU+MZQzDY3c64NOcyJx7QyBohcPs+gi9KHPOVVEnqtAWuJeTLq8P5CRt1OIl0aipbGN8MvJ6VnQmH\n9Y+9plzqNgUmXFliL417OWPiqEWkjPbncLUIj7KBwnt+H8d5S16ACW/wPPavP13n7UvtUwWY1sKa\na84QcMvgPjX6TpvqqcjD3QH2zXDchBz6Yb6Bl/X8R1CzNlv/68faStq/v4W1AYK91IcV2InLpkom\nCmIdUJxeqv+yFqE9MgmrQX+8/zWPRzqQWW/pmNsi233p6Q33axoe3vF1TO4MwFZ3aa0nRR8YWftz\nYsj7yfklAGQQcPISsL0l104Hm6dhc3dejmNulb8EbuPeOshYQzXlBMikW8O6GbomK7DrEKOzL6dM\n4BEy9ucncaw5IK0DofXM64IRzKNaH+MTNLzN27VPLrgec4Y+6V7i2N6nfsm74XzbmK33gJNTxs06\nNIr5s6oDBtB2QlkAighYi7BGJB7eH3OREpFz4MdOBukrIvIzwEfu/l3gPwH+fRH5BvA7wN8Afpcu\n5uDuvykivwL8lyLyV4l86/8U+G9+kEIewFW+4F7akyUzlMaQNcIzVWg5wzhGONH1DaoTHx8aiyda\nT0JUKtJihRE3BnWmZoxpoBIKkDU6iXjIQw8OJS2Mmpiac0DDkHajuFM5UKTQbGGaDSfCFVIWpC6M\nqhwW+O2WIZ8zooy+gI7saRjGjYfcc5UCLVQ7w3kiIC0k/MVwy6gaO5FgClyZtTBIDYWsPAS748Ky\nzEwLeCpkawwpo0yoFkpyhhJS68mdXJSpGW5GKQVr3UkAIAGYVDJIZ8g1Vk7VRK1G+OfDAM9ZIA3R\n/5KgJaZWKTnTamMS8GI0LxvDHrt14lkq7HYDZwJWK2PZQZtBEs2CFUEdswVNhXleVeE8whkHewo5\naAAAIABJREFUwBN1XnAdUMk8vbrhWVLOPLNUocnAjQpuxrI4YzNaLhhOotDcuJoiP+tMIc0T9blz\nU4JlPBO4RnmuI8UETUoZRvbV+N7i7DJRq0oij3KcIvdOlsqoGWuV2aMeibkzQ4QbD4W6GcVzBDa2\ntoHZgyjUOdQCccaWGEsi60IV55Ajx21swSq21uXJAbfMmTg+ROHkvcLUjIMkPA0cHO6bUtLCea7k\n1fvenIPkrRBzbnCloPmH6xn+k265G4DqMKUeGcJxaVxDuhK318qioUaoJsfUaw/2Q3oOWm3doWbS\nr+P8NiN/TqdtLz8Q73ZKkS+Jx5x6QGcuJPz0O2ks0nOqLC5YCgw0NkNl7QQgxSgLvEgBSkb1ze84\nbHKu8UOXCJ/7/MZuxM8JYexG/wWNb7aBr/b/X7cRcHbSQmlPlDLH3pPGHorVWbm5CN+ywpdCOxIX\nCUAOm2qgppkBuCJsoDOH6y6YorJgItzv/Z6PEsNknAMhkNEIAOsnz+rRNtJh5yzAJcJDnKzykuE8\nd2Gd6vA8KQ+t3XryZ90OmCSUDJMIOTlnJ9fZ92PKGm5IYi/hCG4aa/Io3ouhx3eOImPxy4U2PmoD\nzz2TtHJfF656b8+7bDsJvmV7vqaXzK48J+NdcCN7Vz9M4RAuAi1HPvaA0Ij6cgBTrWiKkhsxCzWc\ncTiaAyztLAQkmsK4StWrMzahNuH1vVIxxiYsAohvrFQZ4Yk92+ZDyYItTmDDPsH/SQdM8iMsOqld\njMCJheg4/7ssdTcWT5kFgFcqkEmAlGTpCI4kJsTG8q0e0y1/JL6zhqSllE6EEuJnYg17ilCKo4df\nt1fjCBPis9qTZJKFsWxum2G/vsTJI19HelSY9+soMYJNg33YodQ7i/cdmBY31gHXGgsaZHD8kpBI\nbN8Ypi4CcOeEsoIgX68nobaEI1EGBuuMWN5k6aJYcDoBTN4H3JwuodzPLmx447iYHQHYWiB2Ow9r\n+Nl6SM+VIsIujQhHLCkKasZ3VtGEXgsL35iyKCp5zF3bnp+tQKk/W+/Xxkk9nNMkRBMWEXauNDGy\nB8hyIrbfJJ5r8lWauV9H1/sK8GpEHDeAaZx3m6IaSCmJQGuoRirvqUjI2lfluIG7pk3Nag3TBLrY\nR4q4de9iFr2g7trSCZD7I7Z/Bvhf1kcG/Mf9738T+Dfc/T8SkTOirtJD4O8B/+JJDSaAf4VYQ/4O\nMRz/HSFH/gPbvb1z/zyThh1DgbM0oCpUMw4OrctSu1V2NvGazhwOC5MWnjfFcAYX5ogao7REUSEz\n8dwlhBe0MVl4zMbaOC+ZpOGF3mlidGexxNwqSwZvShJjL41H6rzXlJ2CHCovhoFDjffrqs2IKoeU\nICnZtDPTudfkaWSivpGkGoWfBWiJohahXSKkChRlXw0blNpmxlxCqtUa181YpgO72nhjEK5TYbIG\nqYMmhUKJPCGM2oRmPZFZUwgUqOOtklRwlKQZlS6/TmFIhVQSdTlEbbZ6QMyZWxQCV4VqRp1Acg4h\nAgcRI2cFF5oYacq0FAI1qc5UgZtWSUvjrAmH5Nwj82wXb0xTGEpmSMrT6wNZC3MCbYXQmAtFUc8D\nsxkcbmjN0KxcaeT4sAi6VGya8HHHTUrMAtKiIK3WqG8lDEx2wxvACwE5wE0OZcJBhGqXuMFeGzoP\n3KgySkFnxaQyMZFbGDziQiNRSsaTUTxyUJoo2YMX1rlxz28AxVSY7Ia5Radb7rmvOUQusjg1Cxmj\nyEjSxtCE6xqh3IcGlpy9C9Zr3BQULQmrUDVk6LPDddqTMQ4ukSdF4tKUksJhoA3OcgjetNywZaRx\n/kdfPXr7Udoi0PdrhfMKk8Ci0GowvTfq7O9WLQfOu5BOPSbGkk15TmJoYSMUgSE13k+Rr0iDtzGW\n4ny31yT6iTxhGZoL163wWBpfwMASzzpz9VBuaCguzm/5jid92V6WwhqwdT6sS2p8+HXbQYKvpYm3\nW+PgTl0K9cRquSgzNzVjSfkCEQLq7tzPM60p3/eCCnyVmade+CLG2JmGWgPQjWohxe6C7wJMfbiE\nyMGNO2/nG1pL/FMspBx1vRyhNHjfB+71nKMVluz6w3tmAxd5RgV2TXjfBj5U4XN6w4e+wwQeaxRs\nvrSR5zLzukcqBcDSGbxLgwcOz05skQfdWJpfYYt8lBJDZ6Ie4dyockB5RAtWt4fIfVx3XLiFKJUo\nT/LM0yXWvEV9Uwd8rMaHpuzVuXHh4BY5sHeufNmlyGX7fyGLMzXnc9m5tIFrFd7Qme/UHV9OlX2a\n+Xl9zlUbGDEGWXjqhUey8GZTrgbhRR+PNoRvWYCDC1eufFljziwijE2Zu51lKqCJz2qoJ+ZDhsHI\nLpED2zu581jfv9rVI7QpZsI+GY+55AO9TwvhWdIgfDCf8zhfoq5Ium2LlB8uXvojMUw/sqKTwazE\nohLheLL6woFjTsknhXmthjAcJ9yWpN8T21SOD9Y0GIay0evRXBU6Ele6kbmejxV8dIPfj307KvLd\nbuvaKSdG88ovbaFYK6i7QxuLCDWHgzWL9FpBq5vrZYo5TncCNvshTY9ADFYGVVaM80qGZzvnyefH\n34+k08bMrQenxOk2shrxp989oV9OPr19VaGHKG4qgutxKwq8Mx/8OEdOQ/FEBE+hWLWez9dOnzB3\n63PaxBPujIdsvWLrz4Bu9ZuO43xk6YRen0S4JeiwDZVHmN4aCh/5a8dcpW1+mJFzwu2TEyHvjqC7\nRw6KH0Hixij2oqa0lyX426tP/wdu7v6/cuo3ePUxfx346z/g86f8fuvFK9qTiz1fev0RzRWKUiRR\nBBabkNq4bhPenI9vKrY4YgnNhVordPXCNiQGDzze1DnzicTCfQ+VPa9Cal0RKCdmYC/n7OwQzIDE\nc90PmWaKjgV1Z8kLT6m8ocI+wX5n3NQJM+XDNnCRlOZRT4hqLJqOU93DWZRdYu2xnjelEoBKYdfz\nVCjd4zeOLAlyVipgdWExZ2jGl1RghA/cuVlmKgPuxmw1Qn6KoBbFU63nT5EGlmXZHEmpJJJKB6Gh\nxtbqwL4tNFXmWtlpOE6sKMkOmC9MHnWRZFZsAOpCEWPp3t1UEvMS5Xd156RpifwuGjoLV9oYE1xJ\n1ID6aDQeXTmXYpzPwmVLPD5XHjLzPoknbnxIJJkvbSLrGBawGeZdqKY7YmyeUBKWEksRijltqVQz\nci7UVhHNeDMqE4LzvmQGDSeGz5UP2DHojFpG1HnOnrEeSOKIhPLDuTvFHWfE1PAWwhjUmZICKA/q\njF5xnNacOUWhWTcwEqrK2AUYUEEUTI2hOkUiSqEonFk4bw5UxJUlw7lmPAVDsHhjQvFRuJ4X5iGR\nTTBNVDKjGOp7NBnPligQ+rQZM4WEIDrzukVCuCyZD7TxjN8Xj/xB2o/MFhER9mYcNHOZnQ8t83Er\nfKVn6p9jGInyisWyB7vS2hq0Fu1Fd79WlEMr7Ld9M8L9KsKf1Ri3m86wXHummvKeJB7JzJk0hs74\nXLrSLPFYJp5sOccwloXaEuYv9+8n+7AM7uH4hG1L3UKx6sAViX2/15Jm5lpQh6eSeQPnXCrv+Mhe\nouZTOlFDEoTUIjxdBD6a18K0cb7vq3Jpe1wj2uEnODB0iWRxGJtvAOy0LQ5iwqgh0CPAWJ3vIbyx\nZC7yzG/bwJuLknAuhvn/o+7dliVJrvS8by13j4jM3KeqrqruxoEYDIjRiDKRklE0440upLfQo+gR\n9CR6CF3qaqSRKNJMQxECKGAAdKO7uqr2IQ9xcF9LF+6ROwszMBlHaIAIs7beu3ZmZIRnZORa6z/x\ni5Lo1c7Zkp81w4FH79npife2BYwYlupA+Tuuh40bV1rtw5NAsY4XckF3bI6Fq3HFGzE2zeHvXahc\nzpeW6Zqe66ckPlXjaPBBA6Jw5X5hty4fXTvPNU4dog/huSZ9ERfE4Ach85Ul/qzpqk4i9CJci3Es\nwrUah64GBb9qeVRxrXWo1EbxZ9OnTwJEM1p/TmoZayeBndU6veS/v2a8XEdpLN2bnMl0rE8JsTqA\nujguCfi7Eo4/tAHVf3DD9McMneQCpXGpxKnVBvz8d1/DUD929kBrJghB0NUAgedC9OL8kFD3Gts9\nJkvdn0p1J6q20O2LyGytq+ukvrnMTV6TocEbZOkNbXGs6W7OqJSvjVIzZVgRBp4d2nLFJVBvj2kG\nCi71aCRUC+7LUFihFs9LU9tE9OxaUqS+7orYtUDnM2KG1qI+V9U3wZ8bTqeuiTY0akVCaFBzoC5I\nNULg7LZG03StzZW24yxSizxt9LDnxqf+76PcqI8oks3qfLUVXzVh58f4R49dmwG7aBTaH8/UP6fy\n+Wko4UUH92wJu75v66tIfZ+E5/dUrXK3V10Yzfwj2BooG2rWSX10fb4+v3dZnE6eHQUJ1LBOqxNf\nl9qUr+eQQjsf5cztEy81zPIiN8q9moDI+byfG12Hai4Qwhm1swud1Eor7YOw509z0+EG32wpywJB\nOXki2IlbgSsJHEwY7z/Q54krmfkK5dEGZqmW7SJgS6YvM2jGrONRCr0mdgKPxcghECRhaoSk9XVM\nKWXAg1WHR1GsZESM3NDOmBMPGEdxZA64XtHnaiCeYiB4RkLkSmq2xilk5qLkEFGEKHWa5yHTSyRY\nZOkFvBlNDD1RA2o1V6e4oaUwLgtLUXZ+RGcl6cJPy4aiC2b1GlQRFhUiGYtCmUZUC7tBwJVJlSXn\nhiJVK/Oq0jHC5NDDtUY8z7zqnYMUHpQatJudWz0yS2g0DiqVLBnXo7FLVdv0TSlse5gPC5o2jKIE\nLeyCQRe5m5WeE1+VDadp4WYX2R+rkPl9cW408/kwMCXjkyEhc0GLkWPHj5YnPhQj9IHw4T3bDl5t\ndvyvo/O+bFFrUbUaq1YtZzoPFFtAnISTp1M1mCmZNS4ioMRgjbpYaZaf6siQjF0Q9tkYJRI08YKR\nWxzPTg5GZCGWGXdl9EQONbrA88LGjEG8FqRRWdQ5EilBmUl1wkskSkUhYhWSVNvl0FdHq3Y3dlfU\nC7ddRck7gycVZs/EUEgkQhZOOdf30BxT5SiRh6CU4mRqs94lh6LM3bPJj2qPUBiLcGxai/n3oGH6\nY9YiViJjrOfyrgxspfAqzKxCjlCUGedr6fhcx2ejJ1MmAqc25dyROXoLuY2Z0ib7AQNTUlwolngT\nmkV0cq5yoPPC1zaw0UKnS3UmpOqWSqlOmx9KNSD4pe/Y6UwEXrAwWeAoyl7gwRJ9qO6cAG/iibmE\najSBc583bHSm1+Wsl7nX2ojh0IWFfen5WoRepTp4qnBvHV8j/AAwEyw5G3N+qYEXOK/VeLd0FIQv\ngvADMzRmJhf+vMEou1RTxaJVqu7XFnmQwJ+Hia44Y9Nn/03Z8JkVPkkzGy88LB0F5VU3ct3N/CVg\nsaJQ/ySMeKqmGk954HMvqAu7NDG58GXe8CZMfGKZkyWuLhA4p1qUH1uzehum8/VQrOeh/VyDXmeO\nVCe5Ckw91yIvMLahsAXuLfJavA7CQq3rMOEFxkxFbNdv51kaRR54qORwVk7+D1uG0z3KDYbIcn7F\n5K0WAe5iPqNSWymIwUE6Pg0jTkWsrsTOlFpwfl0GfqBTbWQEHjTxZhnpSh28Z4POnmuRl1LIKJKq\nwUm9ngv7BMNaRAktKmatb7yV8corn1FdEC+M0+Y8HJ782Var00J2PUsJ/lDb71XDJCI/5FsMnWyv\nAawdar2YSlvQ6GsOEbj5R5N2o1lyr5SLRr0KrWBcg7pUWuPxEaXJz6+9gh/n41A9IwXr78+Prc1P\naRO89dLX37JpXJ93Wb9jlRJmbX9BKrRO6+Iv9Upy8fu6KlBvXOXi9+z+0TmJyLlhOj/3UvsiNJ1L\nPb+MnxOWu0YtqOvx7Pa3DpKeUbOPv8cuf7tEUj5SH138uFqpP+cYXez/dwwXfvufy+U10xob5GKd\nPlqX3z2x+Misg+cGytvTnAut0cotvnj8R+cscvFaH19nqlV8X9pDlErTDKoVVVVaQXqBmIZ671yp\nkSIwexWj++oApGsj/fy8y/fn0gFxpbZauyfH9tlBfvf6/ClsD6HnC0sQErqcmJZHTqeZ+8Oeacrs\ncnUG81T4NXW4cl0yS1yYHXKpGpwuBZRAFwtXEni3RIiB67iwd0FNCDGBBGIMKMo4jjVU1QW3BZNM\n8upiV6jDicEKBG0o8UwvwhCc3k9MIiw4qFGCshVlK85MqWiOG5RCV4wQCzksfDoKv/KpDlmOBxaH\nSZyimWWBF574TGZuO+dFn/iChS/nxJAyc4m4GqlLrdgPJJwrFb7bgYmyL87YJ3DYSs3YqFllAqUQ\ncO56YZFA8hlJE5N3HGKqJhGlkELGZucvhpluNv42Go+j8483ifsw8WITOM2BqxD45mBIyAzTPeLC\nMUZesbDxQLbET0tkuzwhSXl/WIgEXm8HdgLOhv+HzLgM/Hq/sJErTqEwnkb+slf+m7uRny9b/v31\nwDdZeOdwrcphvq/DOVUmCWQTUoRCqgOhAtOqHVQozXa+k1Czk5blGVkeA78S5epU+MvtxJsQSTJX\nWmYaeSVaDRi0EMgMMWA2ccgTiyVMYNYEwdgIDOqY5HYsNfjYLRCDkl2qgYPWay5IYJCqhRBxRDpy\nzqg5MSV6K8xeB0G5TCzeMWokZ6eoEa26wQVVPDjfLROfxY5furM4BEns3JFUNVBHT6jBJjgvBUJa\nsHxgE68Ij9+uhunbrkVEnKHU++qnOkIb3x6bGH9nHSEYW6qRw5k6FKw5RQJhac2AE8V5xYIoPKXC\nadoRQmGy6oK33qeXUr3s3YVOjI5SLfgROnECjXruwnXIZFdGibxk4d6rw9uT99SUNrgKC5eJWAb0\noWClfof0aphFXGaetDZVG4wPAVYoIKnzqXuNFxAIYqDKp1b3lR321rMHPqUiRb/MWwYxujDzwgJd\nmNlZbbyXVI+ot5WtU90kd1p1XcGdf+tbPnEju/JfyMR7SaQLqvzGjX+bK+frP43VSe4STVOv1vDr\nlqwaPGWqIUPp69/6i6+77NW2XJtRxn2jR74u80VjVbfZqsYstdcIjZr5dXvO3iG5sdOFkBUJlSY7\n+zlW8ry9sKoQWoeaANeNzf6h1Q5fNlnKSRRDeED5tDUjX7Vq+ZWXj2qRO4wPUilu8zpk/filOXpF\n4B5UmUtgJ3UfEYVYw9qjw206nvcxa+BKCglDQ8EF3sfI9/JyZtEck3PyyItcGEU4qf5WrdhjpdYi\nV20KnXOHSo3YmPW5Fv1Dbr9v04dvNXTS9IJKBqxdPxJIVrUxHitliVRzJNQq4uRUvuyqT9Q2vfdQ\nk+GTh5rU7n4uRM+4lSkuchGW+jyxX1ZraIG+CLNUK+GeFY2pBW5AKOLn7J4inFGC0ihl1XrfyCJ0\nDcFSq2JZcSc52GUOVUMInNoQBq90wTW7aUU7ViRiBW5yQ+Dq2jdkDD9TtMyr3iY3/m5oDYaatddz\nlrBmDz03ICtSow25MqkJ71UX0xpUnt0Nm1y1ITBCaRzhtRkyeW6jCpCaGUJ76yta1E7i3HqInD9I\n682hWmHXm2TVJa2IYEWj/t7hZKNjysV06Ix6Nyv6Z5MPPzeVz1dmu2YvmrHVvdAbEhUknrVMdV8r\nMmQkKv0qip7DBdVbw+00mHoF7qrd5/qeL1bpP5Fm+d6uGVtPYm3enY8aJ1/fS6p+6VnbVYcI1aG6\nusH9qW5p3jM+OI8PIzmPHA+F3pzgc73uvHK190HQ4kQJ3PtMX2pGSQnCmCMnqihaLfK1GBKFHULK\nSnAja8YtoaEgLnTbhE4ZwYhSWIJzJYkR58EjQWoRtiEQzbE0gzmdKhMGQRnEGcwxibUoinDj9S6o\ndiKJ0qvzOi4kjUQrXG2df8bCXhI7n9gkYT9XU5MeIaYMWfnFOBI9E9nxYtfxbq6mDqJC13d0OCEJ\n2zhgMfCbZcEsIymhlgllqToiiQxB2fhCUqdI4AULtiz8Ypx5ofBigK+Pzt6NncH30sz3+8ge48FH\nvlOEH91t+MlBuIs9j6PwTpTk8KJbuFNl31fUIhm8tw3HUhBxsjs5DBQbEY/E1PH2tPBFCFip4aKJ\nmVmF9z5ycGebI//7ovzrw4ZFwBhI0vFhypQy0xEhJaxT9DSTpombUrjqArMoL0LmZ4tjGBsCBzLH\nvCWEBS8FN0O0J+QnPCmfhYHZJ/aL8tozWRwJHePUk2Ngo4XvUh0Ad0xoo1mPZeJkzj3wiHIksa/u\nPgTq9DsvGY1OzBFEa4wFSukSkxhmijenx8FK1aqGSgM6mDNrAFtY3JhEGItiUuilFsuOUTzgXt2z\nohk/CIGomV5HFgKPGokWeL905M54FZyrUkgayb2S88ht+NZzmL7VWuSRjmOICLAm+ByXgaTCq1I4\nCZzoGOKRMW+ZMK7IPBF5IpHwSpOTwq3ACWWvgcPS8V078r4zbrOwDZXkvw50Q+444SzUeIr6n6MY\nX4XAVK7QmPnH5cDPuKZPEz8uJyYP9Bj7RhX7Wno6MxLO12zIqnzKxDdlg+LcxYn3peNBI5/JRCRw\nw8SJSBTjB36iBOFEdQK9VmOgGkyMBLaSOYaBgWpF/tjqrqGZGAxhxl040nEnRvaOk86oK1ML51Wt\nro+vfeHXtkWkBtlOBL7HyFJqnt6X9AxivPdEEOi1No2fGryRiS47j0E5WqKLMxsTgsBdyDxaxLxq\nn9yd27AQcPbNbv0qzWhWxuD0Xr/rH63jTRjJrRnyodADh7lDcDZpYW+Jq7igDXEcm+HGK5t5F3pe\nlRkT5733VbPscMv0984j994Tqc3VWovcNOpgIFKs6qo64JaCifPiYgq+Wqa/E+VGHWkuf4dmtO7u\nXKvwwWuGVhbhaKmyEqQaU7wvHT+SkbfEOizQTNeO4RgSJkJPRdZ3bWC0JOMbHwjBebVUAYu3fLEj\nCQTmbqEzYVOaLIG/W4vEOLFWVCHVGlQbY0nz3w2I/ja3P5RL3mVz/A9+zEoJWzN6stUCtBbjimot\nLqPXIMl1oFDDXhv9rN14snkVsLaJjHgtIqI32259dswrob726lp2DvdsnE5pNLSsTm/VTWxRKv2v\nHXtp0/n18UIt5KMIeUWlaA5SF+dsKq1hagV3E/lLay7KKkoEQqjNznrelw49lw5+Audmqkjl5uu5\ngaroQtXx+bm5g9VRXQiu2AWfdG3EkGatapzNMlbU7xw4y/NzztqgFqx6maO10oCqqF1aGKQxtB2d\ndUT1wjjbOJ8pbBf7bw95Xo2L5/7OGcXaAJ6PiLNDxm+bKawo0hmBvNhUPg5KXr0vtDW7l/t7Xs+L\nNWq/iVdaZI5rQ1OzgNbPgjfXLpxzKO9lqrytKOB6rCosXnOV9HK60z5fK9LYSXN1OpuN1Gnyn+p2\n+uoDMhxgrJ+3oVo8oFIRVJeBUScoQ0ML65fA6MLRoearGaXUksWs6kFEhFGMXuBWCwuOF7ASiGp0\n+cDrqGzV6L1aB8cp814ipVgbrhg7gavUNAQqzNHpPBLF+V6CT8VYfGFf4ClXq9idFm5iIGDkkLnu\nOp4m470ZXyyBWSKldHyeEp9rRnrjwTp++ZT5zex80nX8ky2MGQgJ18TNrg2ClkwkE4IwmHFDIZ5m\nOjF6Oh7ChAHf70B9YraZvjhJFiaUI8JpzkDmn207vjHnV3mht8LVEvnhnRKD8r88zQRJnMpATAPH\n+5GgxltfyHqF2oKWSs372hITpVJWycQQ0ZAIIRATjGKI3OAuvF8KhRnPgtkJmZoTaQy8MeMkQlY4\nHI7sonCXF6Y+QZhRz8RpRjvlO/7Ey1PgkyHTbY1/N8IVkZPAEJzdILzsE4/F+PkiPJxObAQKhZwC\nlAPf2UX2Ziw2M+bAr3Lh6xhJoiRxurAQ80ISJSfjZRImTQxSHbg01oZo8JmNB+7Nmdx5sNpAn1wY\nUiJn45UUhKp7GkTYH529KPvoKIU7j22dwvk+sQTjqRQ2EjA1Bg9sOuFdjhQzThFUBz7kTPKu0n0l\nIh44+BGxxBsV5iVxUuVtB6nK7TCNxFLjDDR0TOnpj/L55/dUi2zTkZPt2IWFAbgvHQ+a6E14LQeS\nwNGN26xkZo7BkKIISq8LPYE9lRp1H50NgkphSgUp9U79CSMHEhPhHInosRqbaDOX0Ca432KU3COc\nuGbmgZ6/4BGK8FZ6ghid18bq66j0TPRLYCYyxIldlmqGoAN1vOxs1XlE6LyG4Z6IjNGgdDhwSpmO\nDHNkcuVdGAguLCgvmcHhV1LNPd7IM31ttOpSN0hh44V9y905ecdREnc+t6K5FusiVYzRi/NWKkKz\nsYU5Ki9s4eTPLJ8uzJhFQJiicm+JXpzRO3Yhs5jylXUEjM91rONgUbo4M+eOIRhzUfqGci0Ik0d6\nWXiyCCL8mRz5K9/xz5vO52wxL1VzJQKfhZlZni+hs+aqh81SoC+owZXMbWjN2S3wt7cVvSrIc0nS\ndpcWhWQMXs//isLeA0md3XnkX2uEFzjvUT7XgjrspSLN24uqpVMBe6bxXzebidhqwWtqJuCVF96l\neg1Gd/aWuNO5NkFRWAh0nnmpEycJvEu11ejIjPZcma7ZlO+jcreUs7HYOqxFBC8JDdV6vyuZQK5u\nfKn/yEn4D7H9vhumbzV0cvk3/yOatlzOpuL3/yXpe/+CRWrhrVIdaCJaA0zP9tGV4haoCfZd41B7\nKzBLE0tbcNxa2CvPCEWRZ6VUKQXtItKsc6NKbbrMW75JC5tVra577gSvXbOpE2naJK3IifHsKCfU\nD53rc2OGVC1MPR1pE8mL3CirBfAkTnI9O559RHoIFSVbvCFRK6qBfFToq0ptfKQec2nCaTNjDcRV\nEZIH3OyMfklDMVZTINFqn1lwLEqFvKmIk6z7cSdLo7ap1kms1NeXUDOfIrWxWDCSS0O9LtZmXQOp\nGrV1OvHxqTdkq30GFc727OtulJZfpM/7XTfjOT+p/um3GqemIzu/V+5I09OJro0+rQGv33j7AAAg\nAElEQVR7Nr13rf/uXqqwvDV7qwBV2nop1VY0u7BaONYMnUBm1a41+mTLYfrIGbIhTmdzB6n0z6AR\nsJbXU6/7Tp9VgSLC+MVfM37x1+146nTI8sif6iZmdLHnyRc6nei85U7hbFR4splodWKOBzQESjF2\nOjFaR1mM3o29OsWV3gtXYmwo9FR6wueD8mVRFpxRvFJUgjLEgJrxAaUUIyZF3flBNIIJeybQnihK\niJFNy0LZqrIrRrLMKRq9FL7fKbMI7yfYbnqCC1PJ2NTzC1Me58BBEsmFjSQOyXnIyr87LBylDjLu\nYsd1qKGK/8o7ApkSIluFa3Fuo3AVEp92M8JEMmWrzva2MBZF/MQigadZyblwsszLONB1zru58JOT\ncpecQZWfWsfBelRgnjO3AT4fRh6fjPdpYJmVSQWVTGRk9Ijl6joZObYBWP0snHKmaEC7xJRHcsj0\nUh2kgsO1Bo5dZrFCJFb0tUwowt225yUn9suBY1FmUfI0cxUgsPDDqw37UtgvR34chLfBeJoz37vr\nSOPEzydn78JcIl/OmRfXA788Ljwg9MvC4qnSeoKTbSGkDWmpOqf7Ugi2kIj01JBfyQtBjFcJhpKZ\nrEc98n9n5at7ZyeFuwjfTYmhn1B1XpmxBLhmZhDlSheORTjJwLu5sHjkm1hzpQRlj3MII3cWWTL0\nIRH0yDsJ7HOhaKLTwBuduQ3KJ2Eme2Qm8FQUiYF9qYjS3hW0p7izeM2/6URY9Ap34aQB1DAi6jXw\neK/CU55J0pFCwsx4p///XfL+P7ZvtRb5n/7mJ9yln54d08ThP//Od/nP3rzhAxt2ktlI4YMI1yI1\nDynUDCy80ut3uvDgwuetsJ5EeCkLB1c+LZn7qAQvbN3wVhj2przvIjufySQe8pZ+s7CbC6cAN7qQ\nXek080Ri9sjAQlR4R8dGJm5zzySBOU28MsPzAAIHAnOc2S6Rk/V0knkZnphQNmZVl0JhDoZJYZcT\n98m47U48krghMy4bbtOeLxl4XQ48BSVizO37caRSCgcyXxO4WiJjKlUThTPoiS5XdkVSJ4rxwQau\nKHxQ5XM/cbDIY6MHBsm8loWC8UDP1itF8GTOreWGoFTq7JHIk0a+Kyd6jBMVQQ8YW6/36ndNn1Sy\n0MWFYtUw55B7PgsnlEqr+3GYedsMsDeyNJQ3E7RwXOrnQy8nlm27C5m7kDnmml51rZmstdtpGb90\nWWvkwyost+dG6jEP3DFRwmpe5YQLY4Wj95RohCyUBAcXrqga1qDCdyicmkg+4myAXgpZhN6cGeOu\ny5yaScVTa2YROCJsKDwl4auyo2u6Kdx5lBpuXI3TYGDhSXeUXHOe1lrklCMajJKVEJ2DbEhkbiRj\nyZhFkCIcCdxVXhjFlZKVv/rma/7qm9+0w6l10CF/u9Te395+rw3Ttx06mf7pf8f2kx9S2jS9oklV\n7+GN398X8KR4eUYsZiuEtRAsRog1aWYV+tftuTkJUWugpDzfDCNKwZ7RKlWsFKIERKoFrmhtms7a\nqbVLhvPEpOqopIn8mubK2+RgLbZV6F3Yq7Fp05OzE1ybMnyEZbSiPJgTHZZw8bff3kQqSnWB6NRC\nXdAQMGqgZGjoTq3RVwpg3UVp6+RtrKP1AOqhhICVci7YV3cp0UqVFJ5d1rx+is/W5rFR3YoC/rFe\nbEVSLsN0nWeXvKr78fOxfnTKlz+caXaXf6h0Tg36d5shnvuq37Wqv41krcf3268BrSG1lcbZ2qx2\nPKk1SvmjJ/l5DSr18QJ9kmdE0NdF5FmLtA61/HKN5NkNsgaQBnLOtUFSIbcwutCOZfjuv6D7zn8F\nouS80IfI8vgL3v/P/8PvWI3/uLcPAjsXpCsE2ZDnCccYgtFn2GlALTNpAuS8voMpSSdyDEQPuBs9\nRwZ1bkLPdzeVQtKJcjRHYs0u2kqlzPQSWZaRLmqdxMWOQ64RAE9EJhn5Hle83s3MZjwZZFn4AVti\nML6/gROFUpwgHY84vsDLoMxmmBbuugRBsLJwDEqySIfy4DOxBProSA41z6LdMwcHpbC1RIwzeyCV\nmV5g48IHyYSD0nWK6w61I2Me6MeMsdSQxVmZgnCngV+UzLt9Isk1o8BvToXcK5adX0ZIJ+hKYGbh\nZ0tiGyBIYZDIN/OCxo7HOVIKuBVCgFGhC5E8LwypY9MpYXzi5S5wKoHjbBAik2VSKJVqbUpgIIWC\nFBiCElPh3npmXYhF+WR7Yj/33IaJOyn8zAb+9bgwlIXXvfJ/jRA3cJUDf/24EFPkP3HDg/MX/Ui+\niXw1Bl718Ol4xFPPVjNxyRxsYdQrunRiq4G9FsQXXAKDHeiGgVMZOcYOsczixo92G35xMn5iE8sJ\ntgGWovxarrjXJ74395Qyc5DIyzSxC8JVmOjpCMk4unFTOt77zN43SOlZopGDcO0dj6kqnU4uPAHX\nntm58jItpHjgygJfkdiL82HpuA/K3mvY7CMCud4fPCihaXNVBGKHNuof5qgriypxHRYVWGINT15s\nbt+X3+7n/NuuRf7bH/9T/vltz2OIIMYjgVfFmNRJVrjHeV2MKfa4L2eDoA8KN7IwNOrvta5j2Ur5\noh4oAL1Dz0r7r9+/JUfelFyRTZwujEyh44RwV20CeMsACi9sZBdmzCE25gfAoDMqzt43LEzsu4LG\nwEOJ3NiMoQwyM3pHx8RnZeZfpRf8yJ64Z0Cp1LlBZ0QSJ9tyI0cAcliYfMPOMy+9UNwxArHRt7pL\nxZQI+6FOTG9lwhEerAOcXZh5z5a3sePP5iMl1mDvsiiDGqoHDMV0YfTAWDZonGAJCE6vzqATp9Kh\nUp2OT1a//8yVXhaO3vOoqdZjpix95tB0T5/JiLrwhfYEN7Zew7FEYBNbCG07jS3Oyavbas6JIRTc\nYXYhhY8pY6kNRWdNVbuGI6tFcduOWu38V9uGS0fAnAsxGctcS3d9/tqvW19IU73aliWyS5k5J7q4\nnHV0K5CVsrKJ1RLsUZSTQrJaJ7/0etxjuKDfe61FklaNw5khZBCD1aGtyke1yFqjXFsdsu5DR16U\nmJ6dedXhSTpehZl3OZItEGJ1/JS1ZhPhv/7sE/7l6zcgVZcdRfnb03v++//t3/CH2v4hOUx/tNDJ\noHUK02UoMaBUqlERpfNa5GX1ht5oNSmQivTUe5EgKdW7daO2rXSzVefjXrU0GuL5DS1aNSUurXCN\ntdEJIhQ3hOo3GVzwqBWBusj0qRohzghRpcFVml2QKhTOzeFqRaVyqPk91uwhk4ZGGapNmVMvNG+O\naU4teBcAlbNL4Lk5odK6VuyiikMhEKrzUlUxAZUPr62HF5TSYCO5oKSt50Gj3lEMSdWG2oOiLszU\nxPhQ4yFqgySCeM3wsPjsAlih2bo+AepMJCi25gT5M2q26q8qEuIXqBBnHU9sTSk8W3avH/Bi1eL4\nkiq30tjCuWl5/tBHr03eCv+uTZFhZ13SmaZ50cRk7ExtU60olUGzh+e8loRqXJ9bYxyDknMmioLU\n6wGz5mzYaItSp7vebPYLK5Jl52uuhObAiNbmXJrJSajXbVrfyxBq5pI2V8cL009bYfFmM56pLo9/\nsps5E5mkW5BCTD1OJueFEpUeGKS6VHU21s+lBPpQs7A8KcdsvNREEbgh0enCHHseXTm60Gkkpoj5\nTFH41Jw3GyXnnkK1eh7zzKDK0QqdZVIInKJwtB3f2xW+o8ph2TDJTNTIB3f2paeTkR01VGa3zRzG\nTM/AJxS+XDIbTXySnLRkjignmehCqq8nGclbREqjCAduUkfQBS0zXiK7WEjRMXXmYvwoZl7dRUQj\nJzO2Gun0wNsgPOQtPzfhhJFky0+z8H4qZJurm51JRV9zXcNhXlik8ti7KLwKgogxWmBIYJ74Ohpi\nI9GdGBIenc4rhWOz6Wsz5AXfXvHoiTk6IRkmFYGOMmC5BhJvTRi9MLAAiuWB1zyStLq53eD8effE\nbbNV35aRfJp5NRiPFvjBy8L30p7ptuOpJDqZ6aJQ5pEH73mc4C+vTmzmE8cg5LBHY6IrlYVwKiN9\nGvjNNHNvghKJRTjKwGN2ikdmq2HCaaP8HyfnKQuDKJuhhmqOwfnGJ+Y58MWSuUmJTXDup8BDCtgp\nsO0S4yKMGLGDWQY2wOgTlpXiCU3GEJSE8lINKdU4ZIjOO4eDvaCUkaI9S47MISHLwivJjF5Ipeed\nGsmMbJlArHlYqixksKqDFQ0UExKGS8FaAdZpqHo4b9TR38M95I9Zi+zizN+maz5dCqcQ2KA8qLPX\nnjc+80KE9zFyzYI5PEqgx7mWwtSCSjoxKEZpg8unFDARXs0LQZzRG8tC65S9k8JhA9vsJOo6fugj\nmPFSZiacQocl49M8swTlgNM3ZHArGejYB5gI3PnC29Qc05b6hhxTz6LOpxluwsxXvuVDN/Hn9sQp\nJPqGxnwTIqMpXVEGGYkl8aSJEgouhW0xvgwdOSqv8oKb8LbrqEMoiHOgozZTO89MQbl2eBt6suzZ\no3SM/Gie6KTws3DDqBGkUjnXoNYBeOnGY5hI3pxPS4QIkjvGLvNJNn7tW17LzLUt5JT50FC1LTNJ\nnGPv3C0CTBSHh+i8zPCGiYMrS5d5WqpN/lGgWEDF2VEYm/jh5JV+DfX7OQlM1vFaRx6bIcJbBq6Y\na+0qwrvS8TpOZK9IHEAnMLuyy7V2eLtCT8ALXTjkxC58DF9lNRap+vsuGmV17sxKis4pKl17ylDg\nVmc+MGCtFrzGuRYgQPGOQ4THInzuC99Y5E6qlm5PxGdjL1TrQSpzZ+twar3VmDteBCN4gVBTnL6K\nA5/aRMSJHTyo8qlNPPiGxyC89hER5VYylpZav0ZqmHqbnOfSwINsINWxuvy2zfW3vP1DEKY/Wugk\nUukz2sWzPXadphulGQmsKMFKixIRwgopeHNZ0+eKVaVlllxMzESqrfRslVMZpSJSwSrqEp6fjEtt\nTqRNgdwbUkEtsLJXI4Xa7NQGJFy0J9EqYzhoONPCKq/czpaSASqvlGY5HqpOqqIIfm4SqiNl/bfV\nwS60xkebXbSaVWvzFbHxFa3R9tj691k5ZyeoayvCWwOCU2hGEdr49Q0dsuautmg1MfB2lRgVUav/\nEJpuitpQrAU+DkFrI3iZAyWrm19dA/H6c21a9UylW7NoxGuhr1oRvtLQMto1EkI4X7zaXnsuFYXE\nW7L5xWTDRcCfNVsrygVy1pyJNOa3F7Q1X2vGU11nvwhabmjj2qSzGiyseq6CqrRroL7XsVEZihmq\na7AsZ21bCIHgXil6DU1SmpOhSwsKrk21e0HPnMR67KrNBEVrk5WpeqV6bu194Bmd+lPdbnYbht0t\neXFcMvNxQixwLYUhwJyNRYzgiavk3Bh80hn7qU70icK0UWzJbIPzlc8kEjEYbxbnYM4xGSwLNw09\nFTHenYww9Iy2MIRA0EhU5dYjyZwpJooYy7Dlyy5ip4k5wZDveY0wSo/mGgr6SxPez3CgYNPMD64i\nX6gjU80Neh8H3uiJz+NCz8yr7sSHWTjmDS/uPjBlmBsRMTByHR1aptMcToSu43FMHItxFOMXTye+\nIuEnY3PX87h0DMV5cxWJsyK9UkpH0JFNMtDtWQOZ8oIGQcpCPyhdUHJ28MCeCV8i++bsdIqFwZ0Q\npDb3bRgUPBBxsk10MdKFRDRn9gWVOo0tDjEAIbAEI5FAq+32ZxboFJCZ14OxN+FalVAU0cxpWpjK\nwj9KEAdjM8CP5Yk5DryXDdNB+HIO/J+HW36eArezcS1GFwpk2C2Jg3RM2rOYoxLZibIBDmNmwAlp\nIXgdTGxFCKHgvlQKW6hW7/ti2OoeKMIhO7M4A1JjCQSOFghlYe46XoVaOBcvvNx0PBXjcYFZakbX\n69ixV5g0sNv0jLNyxDm4Q9cxGlzHwKjOq6JsNltE68jkQQo3GR5y5KEUNnnm85MwRiHGwmLOMgqF\n2mzWWZKySKnGQzEQYiT13TkQORdnzA6+8GC/F7H2H60WMVeu3Oglc6QDKXSuXHvmRLVqT+LMKNI0\naA4MruzIKM5X6YpbH9E2kOwNghW+6bYEG9kaHLTnJk/cB+GFCdelBr4OYiwIt0utgmeN3MeO18vI\nUTa8S4mNnaiUTOWzZWEvSsR50li1NubclcxTDFgwbkpBl/Y3qUNUCz0fgEEXegqx1O+cu7IwBSHh\nTDqwhIWbZSZ7dewLrhyjcL3M3GvimoWXJTMSIAvH2HOV9yxaeIgDiyhMmbtsPHZbdgUWXSjB+CIO\n7CZjmxeCJDbm9C3j6NG3zC4saeJAIlngOlZznUkG7hnwOBJD4bRUe/JJwLpIKIWilQ4dTZtpTabX\n5lAcQ8s5q/KLtVIahPo8MdSqE/HkypXUoN+EV7QuFCKZk4eK5ItzAk7Uz/hele/EiaNHFoQ7MaIY\nv8kDQzA6qZlqKwbZh4UFYTEnNeXh0ga8boGI0amhbXi0oHQp46U2tuu39tIZ9xZxLdxTne+uvIDD\nXgNbq/Zmr4CjOJtQ2EsLVJbMdcm4KwcRtganKAzZ6Q0WVW40s8G510haIMRqiPZWe6Ibm6L0ruy1\nYxfGyrAyQdSq02dQ8uKYJLpyZIrdqkSoJhpdNWTyIMjhP/KG6Y8ZOilSJ+WztXDAlVZmIPEZUVid\nyVQuQjmh0shoRelZSFL7pyIQYwSrzUwR0Bjq7xe6mY8aWgcaumArytMu4GjVLAKeA1GVWpCL0ZoH\nKM2Z77yFKgxNrbkrNB5nw1PPDXd7fZfVSAKQSufTZvnt1XIE3NEU8dXPTZ+RCmN1mLuwJ3chrj9f\nFMd+pijW440o1sLkqiaqUu+qrsjOVDOEsw6qokWGWw2L9dq1PK8vdT3CJa2RtUlpSE5rZkII7feq\n66qe/HJunkrTCGlZkT4922RfOg2WUhAVipWqI1MlmTTkxp+bhQuKZj2m2sjpZZN1cXzn9eRCc0W9\nAa7Uv5UWWf+/To3iuemn7SOIUnImxFgbm7Y2oV3jxb3ld2mln0oV1uZ2/dScp4awNZe7s3uiRNRr\nkYrUjIrUTijz7AboF5+rP9UtDAPbYcssE1qgDyOihZ0FboPxSKTHKLaQs/MUjOCBTQp0IbC4c5DA\nhHAMhZ30jOYEiWin7IKxKQup3/KwLISupwTF1OkXYZsSFGNRJe02pJIrRXbKmER2U8GnY0VltPA6\n9gSbicvEPsNjUITAdVKGIsybDV8tkHPPJJnOCzc28947/n1xrgz+nMKNOJ/fZMYlMHSFW1vwYBym\nA0V3TOLEFHg7bvjEJ95shCUWjiTeTcp16JAr4f6UiSpITDxMCzsJhGz04USZC3MXMQrvpxFhw25w\njiKc2HF058lya8SNDVpznMw4mnBnSi5W6UMx1rGMCEs+semUQavoupdMjkqeC1csxC4x5symEwY5\ncUIYmLFSSMF5kwq3u46vHpzdsuelFPoY+PqUefINtzHyeZwxhKcu8ZOx5/18y4MF1Kni7TxzGwtX\nxSkpURxKChSEMSTGbIy5pwtwpQa5UBBuUIjGXDp6FfqhsFEoJZIUrsxYKITo3EVh7wuTVS3iCzU+\nuDLmygaIWlGnN1Q3xM6PSNpWXesy0vcbbgL0Gjjplre9cGXKDujdeNHlqqnthnNoaJAeYmEW4zFs\nOEwjOjnTlPkmOsdceDmDc+IQAsGUeS6Mkpi18rxc6/GKOtcoL3HezZnHeWYaT9XcSAPXXWApxtEd\n879H4PEfuP1xa5E6hHqIsTkPOtdMHOirPbXU74SFysZY11uo4v2CEH2pmph0A8DtfKRzWDQQQqIb\nJw4ifNVfVY3u9MShi2Qir8YjXw3X52+Vu/nIEz2fc6qB1sCk1wB8Nj5xCMrb7pqrcqhhy96BLPRu\nvNcd18uRX3W33Nq+DuMyFBTTxG0eeew2uJ0IKL8Mdb+3uja9zk2ZCcErsoiQBSRE+iKMWrN7VBS8\ncOuZQ4jViTUa7jeVxbF+dapCcULwxspRts14wj2QBXIrXZU6iLwy4X3XE+Z6zwJHNPIiV+21Wl13\nBK7dKXZipmqNOjJz6vACT6EaGWzI7IlsrbDV1l83BOWklYWzqdNzRuvYijHIApZ4l6oGtDRWkuM8\ndEa2SL/UsmirBQ+ZUJxFquNhFOdkEQnwUCI51OHod/3EgvBINXfptDw7IDfg4F4irzWTvGBAlqoJ\nc2n6evxsbd6VeD6dA8pJnVsrbW1y1V2vocScAZ5W4EKvwphh01q5Rep9aSNGNKc4nDTSF5ruvfCd\nODGWnqBOCtNZlzW1DLJtV2mMEqpLbAqGs5C9Y5urYcgoHaUImqAUrUYmF2Yif4jtD+WS93vZKrVK\nqjMPTmlhtFGaNqNVsimESrWzqp/xUA0F1KH3itIscmFf3ZAIbXSy+nFrW4C8Cu0vXO9EpOllKn0r\nopWi1rZFqx14XDU17fGubdJ/ntrXY3BTVKsltKq3hubZhe/c37kwi5Nah2LqpBqzgTUnMws8o1rt\nJLLTbMnrvs6W6VKd7aLJmaaD1NZKdaW9Nbe69qwK1vhzs7gW9ueG5tnSfPHaDKiVM90wUQsf84IE\nZVEYcmvepCJqM3WtbEVqyrM2LOPnMDP3OgkLWh/jDUEMpdI0TdY3uhp0iBlBQ6UEWkUmJVS+oKg+\nU/LUKDQdUEP6zu/u2kRWx4amRarHtEhFyKJcIJ0iSBAo1S1QzQlBz1qjsDYuLZ+rtPc8NkCuSLOe\nj+FM91uvh+KlIZFrYpaf6YVGbW5DQ/dKQ+XEGi3vEqFrExt41rdc6gBF5Bxmt3xsJ/IntV2Vme+U\nRwIzqBM6I0eldM5YCoMUriwwx0CXnUUE94AF5VEBFV4E6IKiEsla2GiPBSGMTjBnEiXYzOd9FVyr\nQzDnQKUQSFBSiGQ7cD10nLITfcEnY7QqkL7DcDJfPkAMSs9MST2ijtjMhhs0TrxR49EMxJBJeItw\nXDo0FX6ogU83R96nV/xsf+Ia5SYYyQu7TqAUDpsrtgTmqU54RE48TcoYE6EUpmi4bnihC7vkvE6B\nooWSMxqEEpROqji7XiqZwQrpNvFNmUmL8UEH7ueZGCJDcea4gBtXBPZeeerXQRml2sbGGAjLQt/1\nBEqdAEvGojOVE1vpMBGuotF1jofCzjMbETwK05LZ58Kt9sw4h5x4+/YJdObFUBg1MBXnv/z8xOSZ\n+znw9ih02nF4rI6EnWS6onxlCx6UG4l4jLyg8LDAN9bh84kchmZuEyE602I8aqIT4zXCi2gcvOdJ\nnFFnBhJTqQO0EAtXnkhRGSggQrLMYeopIbeQ48Qx1fvhUJycFrba8ZQLLkM1KOojW0/sSyZKxNxJ\nkrmRoWZYZ3gy4V4G+li4XmrY71IKJ53py8BM4ZgfmHPiOj1xlwIvCjyUzFGd32Tlg24pwWp2U4at\nHXDpOU6hhpYm4wZjdCWLs43CYYHBE+4zjwW2Welkojv8YQud3/c2iWAmbEWJfuRE4kEG7tzIJWDR\nQODGJ341XHMz1qL74FfEeOSqzHx/cUxmfuIveMORx25g8oVA4e40c/BrrqelZQE5E1c4M4LxsOnY\nUNcwnTpiKERxftNv+ewwcn9VB7KaA7/qb3gi8Yojp9CTY4FceAgdC4nBZ06pZ7BC9p5JOzBj7Ge6\n8MSuFDbLVAtaKfyjaSRKJsnC38RP+AR49I4vo/Hj+Z5FhCXVnKN33YYQjK/LVV04Ub6Kzl/mdxyJ\nDLnw1BqRnU78Mt7yo/kdX8aX4FXPdOUHtlrvo2/jC0zglg/UVamNkOO88JF9V6u3gLNYbX6upT72\n536HiPJGHogWGUV4kRNFOnbLQorG133kR6cnMnAlC506v2Fg7iqVP6Bczc5GZxzhXVC0DSAHV7KE\nKg/xgmth0sQ2F75fFrIUmrs4R+/osnAVZjaSa4Csx8ZmETbq1BUrfMmGDHRWc4+uZGHfgoaj/L/s\nvcuvLVl+5/X5/dZaEbH3edxzH/msKjvLNoXdWAJk0YBECxAC1D1BiAEMgSH/AA+BxAxGqJEQMIZx\nC0ZIjcSshcFgtRo3btm4DXa5MvPmfZ7X3hGxHj8GvxX7nMwq83BVl0mJNajKe0/cfSJirR3x+63v\nqxEw3zw3yL1RCgG+aiNnlnkW3ImvNiGK0WIm1sasyosGZ+bP9q0Wqeab4Xfqjo1QadFjbRZTZgtI\nqCgBFJ5UNzNbTCA6DXfCJQZXCDfBuGkTS6g8bwamrIydrVI4qpJaYNAKVIptNbKDF4smSnF0VJNv\ngE+y9P3lb7Hpw9/rMaCIqnu1SOu7+D2cVTaMZNvZf9hRdwRITwhTAxJemG8aH985pyMQWyfjY9tR\n3/7q8c6/p2srj3olwAX8la+H54beSFhHNh7czJyTadaI0dGFzT1PH7mn0f9u0nhCa6o6KuKL76G4\nba2dhKZbpd8chvKmonIKJ5XeZJg8iAgFTtqobU3+mJlCp5ElVW9GRL6BV/mcbUgYApOFk4hVu5Nc\nbA5/W/P7sKp/2RIKtTGIUqI3T+H0yf3SVDofxE5NaNs0RK15wbCdP4J0Ywv6Nat93fb7hAhJT6qX\nTtP8xrz759lp/an6nCZVdwoUfgwlQn1XRDqvd5vDssFR1k0neoOeuy7PWv3a7388xxtKZvz4uqX/\nmyp2QrW2dVKqETRSrPj3RBwt3Oa/iHVaYLd17Y015mvy2zrGIZJ2E6EEmi6EDFRjaoULU4iQa2Yw\npQYoKkiIpO0hg+d8BYEdmYs4nFwrx4uMczcGcjPEKkk8uCDYwpUEJLjGcS6JnQlX7b3rOcbAjSqH\n2rAMqplnzbgZIDDQdCLn1XfgUDS8R2zgUJtnP2ljPxmfaGCRI7ElWoA3+YLbCi92nt9yIxORwN08\nIyrs1sptAgLEKNh6Tk5Gao39sEMifDAd2UtircKZwk4ruyfn3Erg5rhwXhpD6MGPpRD3E6LGE2nE\nbMj9NZ9N4oJgVUIp3BTjtkQkRi4KRI5cxcz5FCgWSMOBs6hENbQKOYzQVk+Pb+IdtWcAACAASURB\nVJmVFVkDh6UQhh2395VDbKSl8slF5KPYCOmOEJXcMi00VCP/wx9DGhJnZP7o8yfcrI0jkdyEi+jU\n3pwbaGQnmV8dIlNYyBY4LpUWlAtVdszUEAm2MnTmQW7QBqcfmg7MInxRGyVAsIFjgFvze/g0wSjK\n2xZZ1kyxRJKAVKFQOUe5GAKXZHJWsghvadQaeWvGwZS1AjFgxVh0QAQWq9gwUXXbwIvE4LqFooW7\nFnhjQFNiGrAqnGlBEJ6JEtOR2zJwJ8KBwjMq/zuRAyOtZDQXojkdsVaf83MtRBr73LOgrDERcF99\nYWgzJoFdqfyCLIxSeD0s/LU/t6fATz+emjFSSESq9kgSGvc4arK9I8yCNzQdpXjaDpRqVIlUCl/Z\nM35xnrlqMzfDSKkDZ3FhaEpNC7tc2T0ySojFg5OtdTe3VEhhZSnniChXFJI04nE8MR2+mw+81R0X\n3YBgLSMXHJAVbmRgTMZ9KOyrIzZSnEB4ZpCPAzcG627lrBpzTYgKa4gc9JxfX264YaLsMu9sz5th\nomlj852ajpFlWvlQ7wAQC5jA+7hHJTOHBBmmeDhlVL6fJq6bctUbgaKB69Hv8fPlPa/iFTehN2AY\nQ8scdECtcbWuvBnGU60DcKN+7Cc2s7Y9O6tUEf6+PPMH8RmC8GF9iwEfLDNfTBe8WO8pwG1UbmLg\nRV04tMQHtnKIgbe24zkzIoGp2z+8jE+Z8pGJzKSZVRSlchEz12XiOIFkP6fJjFEbBzZNstM8Gy4h\nUWDt5flOGodYuS6uw4rm0QB+9U6L3Rgsr9vIhRYubeXDcPSMwd5QqnQZSK83PtDFN3KRk1b82pSd\nNm5boFWXjwAca+BMmm/Q0rxZ6qPhxmWE8lB/iCHaxevAqs4qypZObBXDsBY50giMlC5s3IeZQ92x\nCx44fLCJIa0UOElQ1jCeWEY/z/GtaphycAtTp7iFk8DezRce7Jp9Z73rZlQ3sQrQj1NPpN924AFE\nQ9cVPVDwpFPutjJV6WhSRw+8UeqAZxCkPda5dHMCg1Xdva52hGoT/2/NVNBAVncXMvMsKLeadCh5\no4JBt2gU31GOMToCIi4yptPzMkbcXOn6+Rdr/bzpjRYna/IO3pJMaN1MQNTd07QX9wG8ScVNEBBH\nnbZ77oG39kA9OzWE/f624NqqSH8wKtkqkQeKl6hT5kIPiNuajCKAKKk+GENI2+6zN4betDhcL1Yf\nbOCbMUhg9fa1w8tO1xvK43noKJQKsRotCDQjhbAJvRy56nPn8+/GI9aRpg26ftzMbIGD2hGkav6o\n8UtzLd52/c36ZEogGBy1ERpdC7YFC29r3LmkolvYbaWJI1tbHpUYnEJrmy+e6CkGLp6EU+5BEdeH\nmRgDgVkaITrdVTu9r4qLWvM3mrJv0wjjSIiu1ntSE3N0pGVJ7krVrGHiDbvUxlgHDgY5GIMIlxgS\n2gl5PWKIFUJZWDSww1BdGFRJ2hjYjA3cXEIxssCZFFprvC0DpVaOayNXf5GMAnuJ1LhwTqCZcaxH\nQgzsUWqriBlntpIGpUQjSWMnjYu4cD4GVCqvlsIxjqR8y5gCHyShUjmTQAlLpwh7losSmKZESvDm\nbcUkkqZCy4Fxd0FpK8v9jASnxczLkYTykSp1ChQirVRkHBAtpKHxYa1Yajy9cN/HpIaah14/uxDu\nDoVSmtuiJ3i3DORWCNz689SEFqqv87JyW4U2R1KsUBdUAlPaUcvK06HxelFuVHnz3riIF3xxs5J0\nYM4rzRLuh5iQHDjaxPZcIwRagJXCmUAaXM95HwTNhbdr5HKnfBYrT2KFULjFNUbNPFukiHHMA29r\nxqaRtXa9pZqbOFjgPo6sAoWBL+vCU6uYCrG5biRY5cOpgSi5Zg5r30DpicpisAR/Vg9BGYZICBUs\nkoPxPBomylpXSg+0JCgyRXIVQrFuLCRYTBQpTAhjK+wYeG/G2yVxXAtLLow0shqXaqy2ul5VCrum\nSEw9R+dIGoRd2lNK4a5BCUpZC7MYF+PEuh4J5trAex25CoHbdvPn+BT46cdXMvHMErfSiHUPgMRG\nKoEcG7uWKW3gGCIXcseNTozSiLWwREcHZhtJUsklsJA4KwstzEiNFIW5jpRpcWZMdiT2y3gFwKfl\nPdcpclkqax241CO74sYBhzExtIy/Vow7Bj5a76kIvzd9wC/aW96EHee2crUeOBA46wzJkpQv5Yrv\n569YS+KpHXg1jryV53y4viR3dzcpygWZm2GC0hjV+JV8iyHcIcRjQgx+OI18dwakcdEW7jXyw2Hi\nu3mlysCwCnVY0bIjs/KsZgzh18ob3o6J4ZAYh8JbTewX5W5Sdhx4240QPltvCFJ5OxrPVg/JfbJE\n7ich6b1flLn5kgVh1Ft2swGBH+0ioRgjM29j4lnJTDSm1R3/Xo17XixHPqkLJSpnYeVehOuw4xfX\nGxaU51ZOW7jabtiFwq0loiov9YrUjtwIzNGgKB/Jwp/ISLXIiGGm3AzwvXVmkYKIozuCI/MftwMv\n9Qwz4xfCnWdcCmQGnsnMrUXEhEmMpI2rllHz99No1tlLzga67vXmeWsEhddMxNYY1CgkWvNa8Gih\nG/k6dfSMyh/Yjl+zO7JEFryOOZOVvVSPGhBDSV7/aCGrcW6KtcyZQqyp0347jTPA1Rq5iV6/3Uvl\nqtuj39eJUSvNAru2uiawjSRWZ2vIQqsJTYUqP99i5FvVMJ0c7x5pKlT1FA666XG+6VjWeraQdHrY\nFvjptbwXtEW6e4s8WEtb6NbLvS0Ixsn4AJwiuBkWqD04rUHP+jmd99dpdRsCtmmXTjlIG30Lf6lt\nmqzHKJUf5rSrWp3jXzGIwQvgTr0LKE0ehcuKmwc8uMJtGqbtfPwcVLXDso2YErVWQi/KT7TAvlNx\nWqrd5t2b04eGaUPLwKmAjqxtNMGem9WDgoGv8bzhEfLjSC8lPpqbR0iW31s7IUfbTtXXAo5V0ebX\nGDRQ7MH6fJuDfnUnxLBapytuP9vO7RvzJv16m3ojYwZxa1q+keuk4i6M3qwXiIrUr59DpmEGZxZZ\ntH3N4W9br9ufT452230V12T579I+p30OxBul4bScBOg0vw7DiwhZGqNFdyyMrpdSnKYZaY9jIf5M\nQ0T+beBfBH4VOAL/PfBvmtnvPzpmBP4j4F/G3XX/OvBvmNlXj475HvCfA/8UcAv8F8C/ZbZJRH98\npFaQNBE0sMxCbkqJDa3inHkWz2pqjaCJEISxGqspd1b4vAm6GBp83y5YZSQQVXkWG2cK+yHQcmUI\n7lopCqEWYvBsDhHQUPB+JUMQniJky4gKh7W4QBmhtEC0lTElQls5oxHU2KlSg3FbKrnAO+C9jrwX\nYbc0khhjasQ189mTkYCxZ+UyVJZmzKthMXrzJYE0DJT5nliUUReGlBjWmRSEmm+wNPHh8x3r3R3W\nqaLFFsYVaixEU3J1HagBZUkccyGYEAiorZAUVWM1o87CWhMHMe7Xii6FyYzdYJjtaXXlEGA+RDBh\nio2LoRLHI2IKcXS9E0fu18T7HBkH3xGtQTA58GlWjrowJeE8VvaqZHPK46JC1ME3i2phroUhJibt\nKKw2gkUWjNtcKSFwZ4n7XMk0PiLyvd0CUSmrstZCGFa+U4WqhUMR5rpyR2ShcZAjIISaeFdnskIJ\nkV/RynVbOTRBW+X9akQzUGUSxWI4bcKEobBvgEFUo7JQakDEKEvlqxLQqBRTrEVMB2prlJLJ3XFt\nksKFCYf1niXDbRDuMrxqN0wWGZKyC8L5UPmwCtcWGNqBz0Q4jwlp9wzNCE04HwbuLXAIhuUDVOHS\nFj4w5XVuyBggRj5fjRYrz5aVpS18npTrn7O71c96zGEioGjtbBExYnXhvdXAXXB61dTZIUP0TbXb\nYeAm7ji3o7/XisGukqswLkpsxh+ePcVq4AN7TxM3UbgLFxwIvFBvNK/WTE5KE2VIK38cXpBkJTPw\nYXlPHX1HTE0oGjhKYMj+gs06stOVZuqbOZwx4U1CbIJE506kmNHVTbAMYSXS4oCEjDVFtDLWDFFp\nC6DCu7DjRs+xPXw/f8X3S+bSMkcLiEKKmX2YGNNyqiFqcwToVTpnaJWzDKGujCExjCvLOrJPSp5m\nPjj6/Xyq7pZ31ESpe9IR7i2RhpUaGlelsWa3CEcaQ1pYj47KXafIkzKTjhN1yEgV1pQoWbjv/2Q8\nJMZWiNJcp2iBY5hoovzyfM2PdhOzOeVPembRR+0trQX2UtwIJSh7K0xS8SMrNxZZhsjZUchRuWwz\n71s8ac8NPLevD1Xlhc3ctcjrYeSDvCDAJKuzkJoSaCR1dGclEqRxDHBoiUtdMRVCa0zfoEEN1rhv\nkfdN2WuhaOCsbbWD1xNfth0v5Mhv6C0/lMhZMwaMKg8axK0WuZLNIRCWvvlXulbfGV+NK/OW40hB\nRHju289Ijyto3S1wspWWA3lULu2ArBEdHGmVaOhsPVD9G9Suv8fjW9UwaYcrT1qPjkBY7RbL0jU1\nfKPRaBt1yilfsumIgO1/46mLeSjEgZNxhOcnfV28rzj9a6xQQqeRibozXkeqxKTHm3UUxLqDXT/X\nihfPj3U/JXhzF0WwU7G7WURz0kRFdXhb6Q1Jqae8o74nicure4bSyRb8VL+fEAagu/htFEcBE1Ti\niUb0EO/kd6eZoxxFIYlTB90Ir39DeNCCqXRExGRzmjzBuqE9NJlVfUckmKM9WzNqEaS1U5cm4mLP\n0DpaZnJy/CNo1691uFYrSaQ/OJyLPGpiCaD9AbHdezVXalWsw9Wc1lJtjVVd1+TUPjdbQB7cFocN\nYu/rU/uNrkEIPbxXtM9ZiN0+PJzOwe+hz+GG+iDN0THxHev+C04IJ3CaY6WHHmtwtz0RQozUWj3n\nSR/MHlT1dE+3zQUTY2oC6pb7imcO6Yn2F3hE9vyzjr+E2/f+z/gz6D8A/lsR+TUzO/Zj/irwl4F/\nCbjBs1P+Wv+3iLur/DfA58A/BnwK/JfACvy7f9ovvi/Ku+sbDwu1O6JeIhL4ikKxxkWcGCIca2MS\n4UBlMbfFPpOBcypW3DI/m/OwV1sgJH7YhJIrqQZ2rXCRFM2F0BaYlEsJvCgJk4UibqwRKYgmjzEo\nAaSxS8aTMVBqBsuU4pkcpt0lVI3FMqrKk93ARxHOVbmbmxfMTXlpkesmhAQyB/YxcG2VhYFUjGKN\nGRi1MmcI7cBobhpRRQm6st83mgaiOoWNthIuM3WBhjJXYbcTlsV56yG581sjUGUhDaBVQCrDLrHM\n/sJ7MsL9fORsanysxm4IvmkSAmtLLAvkFv1ZWjzU+WiedZcR7logz4Ux7JipXEbhPEHJsFhAJFCb\nYWeNSCRIcAqLCqElLkUZ9hBWZW5GSQks8MOcuFvPmdeVEo9MYnwUlSQRYyCUI19p9KIoNsK4Z14K\nYka0SKNRJTMvRhwT39XCMay05YJbW1hFCLoytIEvV+Urq7yVxq+fCbJAjhOfHzMpCMFGVCofS2Yf\nC42E5UKKQsaLq43rHxTOQqQS3EBdfJd/aSsWEq1vq0wtEzJkqzQr7GTHUgsXceWpVuY0YGPlYzOe\nriuDNiRArsLLHPjiuPLOBgoTazwSy4S0wLAeeWHKTW2sOvA6uG35p1k545acfX3uzhZelTNKmMnx\n26uDBLi0A8l2feMTDiEyx8j5nAlmHMfIvhTeRy+xxgqIcr5kzmzlKAlRWBNQJ54sN2xvy49KR986\nxTo0Y5B7zgTIbth0vRsZTrudytO60NKRz25e8+XZnlz3fFze8HK44MP5wFfjE4b9Pd/hLYPCVzzl\nKrzjdbpAhyO1wTt7wmW4ARGOOGr2xxcXXNl7PuENd8MOSUe+lA/4mHeglUXd6lnNtbbPy8y+KqEu\nrIMHc1+nyFL3ZAm8i3tEYGWFJiR9MCx8WjdzBeVtuEQMjjYwxXtKm2htz/tBsVyYguf6BDGCrqwl\nMsSVv5s+5NN8T7MjQ5qRXvPR727UjJRAiYGzcmRqQlNlXw0SfHDwekCs8H/EC+IaeVKP2Bi4aitn\nS+Va9jydj7wZnZnyvN7wdhwJs/FqGHmeD7zUpwjCzTgyrYfTZnOSxqfrDEEYWyOb8FFt/K/pA75T\nrwFc/waclcJqcK+xUwx9DAHWanw+TN7ABOMNEzs5gi6IRazBU1mdtmbwxnY8rf5qvZaBK1sB4VwL\nZ8AhBO4aXPQ16CiWchFXQoX3FngmjUkzt+ZVzpl6XXBhzSUJS2+YBtgFJZjXhPtw4NjOiBVGChJm\n9iYQl1MtQklIcw2T1yIKwRjW4JvAAVKoLpGwik3NAYxHmU8/j/GtapiadMF/bxgUD+y04MXkWJ3G\nlOTrBd3WYIUQHvEnH5AVeLCKNsPzfzh5BXwNOfra53YThwq04L/fulGCIx92ary8KXLkJ2zZQrj4\nfzOnGHqDl2Wjd9kjjmbXl5y40f0Y6ajZI/3TNrQ3gK2ZGx3g1qeL1NN5yaPPMzNa6PQ0t9g70RZV\n9YGS1//tpjnaN+Eg7ZT1Y/aNY75xrx/1nKf5ga0Z9QJJmvmund9JmjhF8ZRr1DveRnO9iXqmVK1O\nTfNm+YTxebPRIEdBLbBK5/V/45y88en0NJz+UtWbEKfrdXMRM5J4Unit/X7Kg8+d8YBAAl3j5kYl\n9Dys0FwrtWWBSevIofm6OCFYoidr/FYfMpY2DdzDenhk+NBRVL/uRtJwonZW8/WgIuhpp6iv8a7P\nEdsaZAUJWBX3A+GnH2b2Vx7/WUT+VeAr4DeAvyEeMPmvA/9Kd8JCRP414O+IyF80s98C/nkcofqn\nzew18Dsi8u8B/6GI/Ptm9hN9i5+3Ax+FSFKjtT1/0oxjzrQWqBK4qY0Bzz6ZrfnLR1zPcVcDKUR2\nmpGmxNSDBG3gXoy1uqW8WuC6GrMJoyRK3KEUvlwSv1MF0YFcPPPpIrp2YIcwSSFESJYYqmN6ooIk\n2EWnzKqqGz/0dZFjgSVwbYrt3JmtSODCjHOZsHHgzhZuLTGlj3l5845gxuW0Y7l9z0epcJWSI8tR\nmUsja2I2uM6BMIxOFaxuda+rUKsbIcRWmefIuixcSCWVhmpjL8J5KKRYfNPBBu7rQhgSNY+8XYXd\ndMkkjTkKd7JSSKx5YGjGcObuUjersspKCIGohVD9OxnagIboqfAFfr9U9upIctOAocxmKBNnsnCf\njYN4yklpcAwr+eYMo0ALpLqyBOFgDbEKY4Q2cGiRt3PFQmWRyPfKE75zNnMl8LYGvrg1zEaKKiMN\nWe+41B3j0LhflWNMlLXxBxUk78lp5iJkmkXMMnc2crSJu/uFIMpsioRIk0YrRmPHj0rjYxmZ6sxO\nd5SqfLVUng8DZ1RGy0RVIJOtgCmHFIkMVJrT1/t1rXVlDIFjW1CEnb3niQZ0iFzUQAgHllWpEvmT\nGri2wJSjN5Vl5Z4RVDiswu7iGbFUXi8ZbOJ6dAfJVYyUE20K/O58oORK0ITOjctZ2Rncxh1v6v3P\n4Eny5ze+TE/5ztjIg3B1zEQ1hiVzH/eYKZ/dv+NvXnzMx4evm1tEPTKHAc0jZoExV27HioZC7Vk9\nlp1uJmlhsYkcjctZCFIxGaDVH9O6X3LHy3DJ+8G4CQO/fHjDQSOtRSQUkJWxPxHnNrEPR8Z55Al3\nDMeVmYkSV6RNfCA3fFzf+znkHkjMwFhB68iH6Y5UIqGf7xIDF+WeJQq57SD4M2wtIxIOJw3s1E/6\nwu7QmPnO4cgfnp+h6+Ota393mhnH6HlVd+GCVOFdOOOT5RZi4I/GDwH4lFcABCr3uucfPn7O39x9\nB5WLE/NoO2ZkITOQWKkSSM5ZBx6019prnGCNmYkP03sqiethYsgHRBo2NO7yObG6s9yhXTDMkMPK\n99aF13bBp+2eRY1XYUdtgaQPn2sI1uCPhmdMsvCVTfx6fusyDODYz2XEG5K9VWpw9s4b3fFMFr6Y\nJr6zHE5o/yc6E1CO1YjqGq1lq8EwWlp436n/aoVWYQ1KMCOZsWuVvcEc/ZhYjVEM6VrmTR6tJfCs\nhxC3VU7yDjtZNeNrs8F+uKchHMrEWbp3t0KvrCnZUbk6rFBGz2faHfoKCNRlAhXSeERscwUMWE0Y\n68+sFvl/O75VDVOSB5GXky8MOhpjXR+TzJGPKHpyXUMeEKMTzasXhxtiI9Zd+PDi1gvgXqTSqVXm\niND2GUHdOawGRz9aULAHwSPNuq2iIznSXB8hQU+BoMqGkDzk9GwGFEIPLcWvT3p4bbTN2a8X9L2h\noXWTgfBANUsoog2zhqnrvxClmTiS0REW70EfU9568/dIl6Xq+phaKnGjIorQVEjizYoH2j4YM8Q+\nX6fPfdSfKC4Q3o7NCoP5vJoKaZsz6aHA4CYPfYTenDXtuUxWIEoP6O0W6oB16aKFjiSqPyRqhNi0\nW5I/ahxDR6LELeBPTU9zemfgobENzU6GGQ1v6ltrTBJch7VR6PBGa7O8L7Y5/Ym75mmAUB1FNV8X\n0h/eqkrOjigQG2bRGyh1q+EVp+0F6/NTe9O5IVYbNdHayQmnbeu4aW9Q/XpDb/mKiud+Nad9auy0\nV3FI/Gc8rvAl97b/+TfwZ9N/tx1gZr8nIn8M/OPAb+Go0u/0Zmkbfx34z4B/APhbP+kXvRgqLybh\nDTtu1kxk4GpI6HLNLhpFhHWtRBnIgxKyUXIDcxe4oOoIm2W0FgYVCDPxmDhQSBR2osjgIZQpKEUE\nCQksEadIRRgoaFkxdmiIXMSZiyESNdAKNCmYuunBhUVU6dEF4k1ZAZOVKQzkUBkluqW/KKijHa02\nTDKpwYWAtTvChTAzYTnznQ93vhvOjnuL6JgoJbpYV0I3O3Eqb7HIXc1YadzXI1InLFWwQs6Jr8TX\nktRCInMZ4KwGnkS3IY/hghwDlhStcGyBG2vMqz8/lgo1VDQMyO2KDJHRjN2UuK3C3boSCIyiTlmr\n5uhHGjiTSErAkLEycTMvtJgwM16qsgAHgyEkCJlBR2poGIncQ8ajFvYlUHUlxIQxUa1S1kiRwJSF\nL63xo7uRFuEDVs6D8fdPjeUo/PaaqHXHx6Hw3dRYQuXLVbicjI+1cVwKt1k52LmbQ6TEYP0pJYEB\npdVKramjCoUihVUiPzxGYthxVZy+0lrky3pEqrAbhI9KJQYlF0PikY9tAisMceC23KEEChWtC1KN\nPcZuByEIC5HrWngfhJv1iqNWbC0kU9fRyAytsRgMpdBqJYZEWSqDTkxqXAYlYdRoDCrshsxLa5Qx\nMJdINkP3e+5s4rq4ZvXe8k/6en5rxg+WL3iyu4IZmgWObaCEiQt7j5pxm5QfHF/yLp3xJM/ccYFp\n4Jrn1Nppuq1iGrhYKm/1OSru3psMbsfK+bpjaEaqrnHeNMYtKGbC3dAI1qiiPJ33XBTji2nP+Wq8\njM9Ra5xl414vOWsLrThqtJfCXBNNjGu5IFpDzRibcjMUYg3c8ASANRTGEolNuZ6aI9zVeTw3U+Ni\nCRxiY6x7FmsoDQvm5gZSuZeBWH2dn5fCp9z5gz6eo2nwDYzoobxnvaET3BFuyItrPs0RBwkNCQUJ\nKx8Vf168kSd8st4xx0aswq2e84w73ts5n7ZXvJZnjJ3yjiojhSbeKNzvAmZuXPL8uPBVeAKjF+1f\nxud8Yu8x4H4nPOPoWpvBn49P7eh/Y9CGlRZgPCpztxJ/m5SpuZvbrhm5N5fvd07OM+Dj5dbfs6Hw\ndhh4ts58NU2nzdGbbnH+6XzDtSb2ZWUnC82cklnUv3dm8CpEnhTXrTVxq++XaUdomV+oK7Mlxr6H\nGICZQKlew961wNgNG8baiCLuvmfC1IyEN3pqRhBYcCBAomuwBhq5GWdi3HWd95mutDwicWZXGy0P\naFpoxTcDFsHZFE0waaymtLJDLaE4yyHKCjSyRZI2Z8yE9iDBEbqg/uc3vlUN04mOhu9YlLoFe3aU\npRd7IYTe9DxGkLyR2ihoom7zfdJEadfwSLcLf+B+uWD/UfOy8flkgxPFufu11q+FenpA6sOfNyRo\n46Sf0CAVggSnR+HnvTnUtdJIHdY3c3tov7aNmuUZF7UWRNXP45HOxZrrLTCjYhQFLe1kq72hWtuu\njqi/uLd/H2N0Spx1NK3WDj65tbX0+xCBGOIJ9dqQoLohWWxNEidqXrWekbDtfLTWjTX8hLy58rnw\nFHmFR4HFtYfNeiisQBBCtf5CoQfccpoTMbDqNBM6xW6jPm44yzbv1eG8E/olfT5VFau1Q90uGm9B\nqNWpbbE6HW6zg98s1xWfu9Z1Z6LK0JdGiF1rh1uSN+3W3yInnVoIobMvgr886G57fa1qn4dmxqA9\n5FYesqYAdr1x3JwhVQQLkGvt98Dcxajf+2LdsafzTralPTxa4z/tED+5vwr8DTP73f7XHwOrmX1T\nGf6y/2w75uVP+Pn2s5/YMP1tmzjIFbswk8sTmtzALHwQhaFVSgpUDZS2MM6FFUNjJGEghknFpBFD\n5dBGJDcuRNHdTGkTUY2gwhgDISWqNVaNzEzkemAfoTa4K40xKCWsNFl4aSN/Mg8cadSUuKzGUz2Q\ngvHWKmpGIjEGGCVAhGO4pDBgY/DsqFpprSBHY8I3RhCYMryOC+codXzOPlWudgGS0PQFwQqpTby7\nvePyYuR4vOOsGTEGjqqoBsamHki5S+xkR1kjdaeE45FlMLI1osAUBCGgGkFmZhEuU2OngSrG3IRW\n4NCO7AhcCqytMDZBUsKaktPI0ipv8orOiWCF8zRSCixiLMFItXAoI/cYZ9oIq5LShKZMLMYYMs1m\n3rWBuQd2DsGo5cgowm6cOOsbLKyFMg3E1ijVQ0BzXXgRKjfquX9VA/dU8jpyp5UXlgli/C/zM3S+\nZR9mPhwTczXeysoHMnEjxrIWRCvn5wOhgg2NuApPJHJbIWvkUhNBKsdcuBlWji1gFYZWUKlka2RJ\nvGd717jlu4gwrwNfxMpZg502nqeBZ8UNQhKVj0vlNq7M9UBOO2rL3DHwfDPxcwAAIABJREFUZlWO\nMhIUsjbOm7KLjUNuWGp80Qbu8sIBQSwQ18CcAkkrx3FiPGYItzwZhCc1U0vhSRh4HoVDhkDiVRLK\nGNAa3dnLIoscKaWQ8vxTPzv+PMd9dMtrEZgEsqxkdmBuxCOSaSi1jZisNHFDInADmaqNy9U1jhaU\nZI1LueWtPeG5vmLhKS/kFS/1OULlQu65tguetRsmaXzFFcHaVuR48WwVEJ7rDdfl4hRhUhFinUD8\nHXOUyIXNHC2SNvaFNYxAE0UirF2ourNCjZkCFNtzxcKiGVBGDDQD6cReeGEH3pdI0R2ftNd8rlcs\n6uvWmp3qgoHM7WBMZIoFrAmbdMcZHM6eWIYFMY9FeWY3XNaFQ91TUubWzsgaqapIGRhqpWnkam1c\ntVtUJs7GeyyPCMaN+7Nz3lZCg8ujMatvGr0OOy9muv35D8pLxiJ8sR8ZrLKKsiRBaZS8Zz2bGbKj\nt6pK7mwjw7gZBw7hks/mr3gnH3A+vuZsKWRRnh+9aVEzahSCS1iZS2JC+Oh+5X3oje3k35GXwzkm\ncMbqu9jaGGgMVSEUVOB5zd5wIxx6c/ad5cDgzt9MUpn734/WGAMcS2SSTIqF8z4vOxoilRVjoMfJ\nmLOisuIZon3NMFTirKiYm+FtiolUaEvExJB1IorLUFpJxB5Y/XTK1GWkVAURAg0ryiIw4LRj2kAr\ng2/cy/z1WqR/D89/zvsu36qGSXsg56a1CN0EwPfxzV/SbMXn162i1R5QBHeOcyvgTQvisUb2SAPl\nblbRvBA3tgL5waRANr9469bB0fVLkW6mIAK15zH1BwbizVHF0A63BPFmweQB7XKw2AtaRboDnDmK\n1HVbxYwxJrcdFznRs2KnhxXx3926DqDSJzyGrkIRlt5cTR0hErz5e3DV6yhbd40bNPQHS3N3qX6v\nw6MGM5gX6xudDjg1aJtV9cBW/D/sEDSRHl7nuqUmQLcrd/2X842369voZ1t2k5ijd9IbYK3mcHE/\nRhq05J2oGGjoO6+Pco1Kvx9hg8KAULqVpTSnYQZ3/GsdVQoIW5Zri53yJh19rE7dy+rNYIrBtUQG\n2ml124u35xz7eZrncmnqtvP9mhW366z9odEURgsPDSVGMwHa6fvi2ju/thCDv7RsE7drh8qlGzv4\nCyv2m+S95db0PczTz3D8p8BfAP6J/wfH+tPy/378qcf81//jbzGl1D/N1+c/+tn3GT/7jO+VhefM\n2E5Y58TrWrhQCKzUFkH9mXHWHB38LB2JIaIhkTSi4YxiRtPGTOSHh8x9C9zlgpZrLERua+O1Ni40\nso+BWJVCYBXlGBIqwhAFSZE72SEi5Orro2pBWiBUYx8zu1wZ64FcMotGcou0oAwhUEIiWmWfAnUs\nPA++myhBaBK4DztaVCwE1jvjLDVenAWomd1uz8ESTWDXDV8ywoXs0LYyF8+wstUpqGlQz7ATRcMF\nLWaIkTzsWUWZFUbLqKibbgiM7YJWm8cRAPtaHKWPgTv1rJ+JyKJwaJGD+boMFjjmlYXA85D4KBVi\nM7/vVLQ0zoeVd+WMt3VPDApJeRH7M2K3I2kkNxAqQYx4kTi3G2pTUlLmdsedCEmUaSgccwVx8587\nXXi1KO+a8jTsebr+iO9eNY7LyPsMqwo3ds6Xbcev7I48Fc9m0VIZdx7K2LRx1YvoADwP/v5YDF4v\nI++tUYoj2tKUOcKhFhZRnlMZZSYGp1ZnXThXYTWILZNEeE9gXSq2KkcV1qOQywXnNN4zIikhNZBD\nYLXAfY28JpKCcUyTC7ZFkd2e8bjQaOhemEaIxyNn84HVoGZhXrPnVg07vsyVL0N1dy6M8ypMRP7W\n53/I3375I6zrTBU4rOtP/H5+W8Z5XQkyUixAWNi1xMRrDnrOPROfyiuQxmQLN3J+ol1/Kq94XZ+Q\naiFJ4ZmsvOeCp3LDve14oe+cAiyBlYkrbskkXqc9n67vIEKj8sze8azCXZjA4ErvWSWSykBFeJHe\n8sN4xQf5wBs9Y8+Ru3JBGzLfqe/4qj3ldif8cn7L5/EKcuKcA2dlx9N2xyKBM5kpzdP9dpY5psCU\nM0/lyJvpjNXU6cJVuIvGJ3bArEA54+PoNLjvVs9A+lG64jAKS3M34KzKRWscmFCFaPXU0Pxivuee\nPQ3h03LPH8Zn/FJ5x5vynHvOOI4Fa4GP8j1LiTwtdywxcRfOKGpM1euELHC2wn1SPi2vKPk5AE/k\nlpv6BMKRVY3vL+/4Qi9ZWzzVK7d2xW2AtBhPeM81V1ioiAVuUuRqnXg1Jr4737BaZNDCzS5CC1y2\nhafcUVLgu/Wt5zsC19NIbIUShSeHzFETKRV2uTLvIl+UiV2qjPh34ys9A+D5OlPVKEx8yAEx4e+O\nE58cjhxsz06ONOnP4qyk0LrmFY7m0SnRjL1Uzg2u1V1vn8fMeXWzrEEMwxEcgBGvQwuOQu9D7u+g\nBs0t3sdVESkcHTRixu93s9i19ZUqTjHe0NQlwlAa87ojnrbTO3NC8GjeZoQgBDIWIsPgqF8wIxSH\nAyZOu74/8+/2/9X4VjVMTq9zFzeTdir4tppberq29w2CPXJs044G1eA1V+yaEhH1AhenqNH1HQ0v\nGresotqa60oQdDPhUjl9hjcY3hiJKLUWRlOqOILhOhxvQtQe61ukQ42BZo0qMJgvntKL4lMwqqgX\nbRvi0FG2oELQjmYJFBrjRm4FxpiY1djV7qjXzRRaMxLeSBYzJPq9RUDqZowhHdnxvKfNilr6A8kP\nf2g4sjl9MTUv1os4bTCYEVT7fws1uFsc8mDTvuUFiPSgYRWiRYq0TllpJNXTQ83U7dT1ZGn+9WG9\n0d2+UhLU504eNGC63cdOTZwkEIpxGxrT5uDXobSgAW3dnCJ4KPFm4OFrxneONktxxN3yPOldCRqo\nJQPNaZltc+oLHR0qriUJvUk3b6ZUPVW+1tqTuzvVcEPx+nwivdGTHrbaGtKpqSbiRhK1EoPPa+2I\nFH19hb6xgCqx85Sj+e9u5tdqzciPcsF+miEi/wnwV4C/ZGafP/rRl8AgIpffQJk+5AFF+hL4R77x\nkR/1//8m8nQaf/kf+ot8/+kTYoxgUCVwEeCX6splrNzYwJHIu7iQwyVjywzWIDpCcViNo57xOirJ\njKcaGXCB7LEKbS20YeRwtxKJGIGEUpLSSMxUzuO528k2zwCK4rlw+13kAuE8GvtyYBKwVjikyEoi\nV6faFQq6CLeauGkGg9KK01NDrRQrWDNeREWjkcJEU3c/1BawUlynVStWVp5qYWRFxwQaWYISS2CW\nQqyNGj1oejGobcdZgqkYWRaWbNTcPBB4LOztFUseqFlo60BOA8MY0DjxLhuHNFGlMmkAXYCBbI3j\nkKAqYpXLAXLO1H1klzNWFxTI2RHip9WpKE+CkTVzEYRDXplb5K4pr+eBD4fMp2MjhELLiZGFaVRS\ngIUBqjcpd1RsWbBwRS03mAaijHyUDGuZ+7qgkvh8zaBPmMIBtUoTxcqRu/GC374BgvFiEP7BcaKV\nmaa3HGrhVTSeNGMaGoPAkJwiWTUzL5Ej8L41nkri+XDkWVi5y5Gwg7CumKwYZ8xSeHOcGMdAI3Is\nK3tTjApReX9XmIaR27zSZMfbCMfSyBpINHJofIk/q4eOHjTxTSUNi1Mrh3NGgQsRjiWjZWHWyq5k\noq48uwekYqlwqMarrMxpYCHzqgSiRqjKqE73ywb3ufGDD77LP/vxpxgwSiGb8Hfev+c//p9+88/w\n1Pj/xohae62WQKrfF008sRsuud2iATnXmWS3CLCSOMrEOTNTa/z+9CEG/Or6I4ooF7WyxgZ15Jfr\ngWSNNQhI5eNygxJZEd5wwZUciU1I/b21MtEwdjS+THs+KMJlzaw6Mkfhg9XY6z2hGrlN7MLMZTGk\nBVoZUIscOOcQC2m9pMaZV+Epv7S8YZWRl3HPZJXVJnJQZJ0Y44rUiGpjLANvk6O2l+GWz9vHgHEz\nBn7l+I4W/Z3zcZ35vek5f2H+kuuwI0jDlgmGI/vWIO94O0xINF6Ua0rbETHuZccz3mFEXuZzlmgs\ng/GkHEhNeZsSZoUAPG13JMn8b+kFgvALyxuaJjTNHCQwLIUX4Q0Vhdq4GSPXwVkZH5U3FAu8l0si\ncKnX3LULVlW+W+74UbzkohRCa7xYCpkd+3rHfbuAMEMdUQoxD0gobLKA+3HgTnacpTtihnmMlGAU\niZgUD7qVkQXlVfAO5NeOr7lqmd/cPeWX5nuWCMWUkgNXpRBRzqwwx8R5abyxPU/1QCEQrVFNMC2c\n6+rIpxj7EthZY2zGrSgqDUFB3Om01NRBg4qI9mDc5lomvB4YrZLdRoc6OB3FzDenS1AEI1EJuKkR\nZt7MW/M4GAFt7soXJaOaKSX5RuxpY6ygw4pIZmwOIw0t0NTr5KINa8bh/3fJ+9OHbvbgneD12CLb\nqUtKCHoqWLeGaUOPRLRfsMBmK91Ag54oadpNGFQfDAYwdxoTejEctuybXtxDN27ojVFrTCE5TaWL\n6VtPxXXzBXPEaWv0+s6bu6U1R6Sgow6BHwsola3haYQYTiYW1jwTKonTXx7s1TeNj6HRkaTNBGMr\nuqO5I2BtBRXXtdTt82vz8+73seK0I39hbLREn5Vhc3yLXUuGF3IP1LauF8NIjwwLThbfts2zywOr\n9HwqnCLUTk5/3vzF4E6BGzXt8XU5lc/TuR9QJjmhlCc0z4ykm87HWKIxmlLUm7lwWmebEUhvPDs6\nFHqD5wiXQIgneoFIc1Sya5bCEE73zERPc4o1Ao8Rty302PHTttEP+4v48TX4PRRyaz7n3QxiM5rY\ndG4SpDdc7my0LUDVTvETIXS0a3M9rP0ym0GP/CX+DHZ1erP0LwD/pJn98Td+/Ns4M/OfAf6rfvwP\ngF/ALcgBfhP4d0TkxSMd0z8HXAO/y58ynlPYZ3eyGoL+n9S9W8xt2ZXf9RtjzrnWXnt/93OrU1V2\n2e62253uJlHiDqID5CKQolaUp5aiCAUJwQNSgxAQCd5A4p0HhPLARULigZeohQRISQQCJLpFC5TQ\nSjuW3W7fqlzn1Ll+l/3tvdaac47Bw1z7O8cObeJ2tx0vqVRV5/vOvqzrGPP/H78/x5IJMfKJDDyz\ngMbMNq55VQdMA6+nmY1UzlT4xDK7ZFAmQp45dqFSWA0rVqnZjrYq3FejP0mUYhypkDVw7Yn9DFl7\nXqHsakttX2lP7xOdFdjuyO486zImgWqJruvorBB15oMkiBRQYXLnNmduiHRe8TSQeygSmaRZZp6F\nwpkEhqiE6pyZ08WJpBC8IqESgrA351bO2YuwJ3FbI6hx4h2TCmMV1gHumdDFmc4LQZypwg6jRMMl\n8l064vqsNXoEZnV2nugJuI/MGhmqtdLO92y8LA9nyNYxa6QGg9pIgJmZ2BtHcwOxSF/QagStXI8d\nT3c73I2XtSfGmVVyTsPIB8czvcFlnnFZwwC/93KEKfFsWpqnkLg/BEpx+tWGPFUSPftSSdF5Wdv9\nYyVr6J0Hqow+4d4zbCYuqpBS4XPB2IXApUWsJr48ZUQj8y6wjfCeJ+iVGGCtLcjV1CmWqCRUYO8r\npuqsreM2w0tzzGeCRx5Ix3E/MSicH0cmqUQqezM677iJmfelEtaJ6zIzWcKCs9EeDQ61spPmAmj2\nKxjyRAiRIY/UIEyzMqVI2mZWXU+YCxfzNfjMJhpDCdzUmVvrySlBrpj0bEJAVdj4iiEI26qch0qn\nRogwlUCsE2uJPHPY5WbXLCK8/OkqPf6xbZYVWRLrYhQV0EAiIyRgZpKBtRdmlMoKd10Wo4TGvIx8\ndmojm6N29MsCXqgrAs4uGKkGbn3gyEfUE1nb86pDG9Jc99wsDuX7fkmUhiXHEtFGBjE62XIyrvlQ\n7/PIX+M4z+WcnluiO3aYVdQGI1h7JZPYZGVW5+vpfT47v+RVd8L5lKmhQF63388rUENM8TQhdcWt\nGFd+StSWifRwynzSHxEpuLdFpupC9IzUUzxm6PZIWVGtARjulUuCtxrGg9HXwDZ03NZjHvOU+8Cr\n+QI0cpWEjoz5wTVQmaSjSuBTuYXl1tCSJk/KzH0mpiX0FIF7ObMNynulYcorAZfAo/CSj/0BVSKD\njpzJJTkJ7/hLCkoXC0/sYYNc6cBlB2fT0OyHJnczFe5r1GrLL5NCP/eQJjyv6GRCDeYu0GXhuO65\n7YR3tNHsPkkb/mE84memZ3x7fcZ9rum2Ri+t4TkOGRB2pgSHc4WemcGULAH1ltdHbTj1I630Upk9\n4FK5oCyLq5nJI3FR3OsypV2BaAcKb6DF6joVpbOWJxZKaDRndzqvzdVE4Dou06+liQgRI7tSAkxB\nGXJbDFjngNd2fA5uL1lq+9DceqSlBp4X65Xj9C2ck+H/E+30x7f9VN212piKLqqG4LQwN0ya9UJZ\nSAWL4rDkELk2OpLypkCeF1tVeIvQFkUptGH+fsk5iihlCapUdLGULWqXLfhpFfoQlyJ4Odi0ot+l\nza1oaCv9d3NQS9PQ6xui32TNOoc13F5CuVVjI7pY1ZoiYtYgDiHoohwsAazom5ks3mRWVXd6F6QL\nd83JXbiqttmsEJVsRlwsfu6L8tN2b2uWZMl3OqhUh4Jb3hygN7hqv/v/A3TA3Jvt0DlgGO4a2cNn\ndW9I8Vma1c19aWgPP1NpyttifXR8gTwcmpo216OiGLbQ7lpGUmr4PA5EO1tAGOpNCWvNkNERKNoI\nMW5QdLldVHuDdhbBU1MDI+CqVGdJ6RZqcFKFunwvxe/C4FjOH1OhmuG6zN7VSAyOLRklQcHMG+Uw\nLQTEZX+WxYYph3ORFkoJDdteaPNhLE1rOwDt/U2afU9RrJRl7qY1dqW1U7hxZz11bMl9Vg5I+x/t\nOpa/Bfx14K8CtyJyUIau3H1092sR+a+B/1REXtMylv4z4Dfd/f9afvfv0Rqj/1ZE/gPgMfCfAP+5\n+x88Uf4oznx+bSQpuAsvPHCT95Ta8V3L9J6YJ8MD4AWpkStd4BdVGVTpNXEaZk5S5iQECO08+lpd\ncxbhpQiv1NhqJJLpES7EiezYzTMbnM6U4IZxwxASZjOxAwpoNXLfURVeW2TyRBLnKwapG/BSMRTk\nmNiPVFvRUXnkezpVOioeWpZbJjLSETVxKYG1BGpqD9DQnOLN065O8aZkde5sa8+VOhPKXiq3Hnkh\nMFQI3lS5Yy+kVUA9sfPCkRZOcbIEhk5ZO7xAuFHn9XzETmEvTqeJZIG5jogLORdu857ZI9FnOhvp\nNZJkUWwX626xiqlws4vMami3ZjdOoEZfla1XtqOyZyATES/EfcCCMucHyzPAGKXhx2/2lePg9Hvl\nqHMsDMQMBCeKkMlMi249m7HuCqda+aI6c525xVGLXAyFx27cVkM9sCfzOhrruWeOHV8f93gNbFZw\n6rHNCBi4BV4F6HJgh+OlA9+zEUHCQOdHXOm+repb5nKsXPtAdmMsLQcpTDOvwsDVFBg1c6qBEzc+\ns9oz5gjzlhzakS5V2VMY05a19ZDbau5jmUg5cJSMVDpCJ2DK14riGnndd1xPHTqsGYMyhzVbayvY\na2jKuAfuDc6JFe53xnESrExcpcCgO4oKGSXjFBM2sv8DrtCfjm1jO7ReMGsrIHtGglWkJlCnY8tH\n8h7Hvkfc6GVCBGYNXMkxD2xPNBhV+Gr/Lo/HG+7JDnBupOekzDyNa7JHjsLIjXWcWgE3Otmzkx6s\nw9WpGqgWeCkDFgP3fEfv8O14TrRT3tFrjvWWl5xCEaxzpnzMLEukhMM+KGuvUFYg8KpTtuG4NdcY\nnxtf8bvrB/zS7jmXqnQUdmmFNB5Oo4kuYNrrLnGxq4i24X4tgDaw0HPveHeceRbeaZCtSpubxUGN\nl6sepnPmrtLnABQQ46zOXNHzMQ/BldfrxKf3t9zMa17ICnKzlPdaUEC91SlrO6wLCpNCshUJuA4R\nt0CWieAVMKJMTLZmY5Bl4L5sOSmVb8djTqUtMlUSYGTgQi+5jD3rnFnPcMMxndlSgzobZsQmQgm8\n7I7oDLIqN3HFB9MNV+WYzkfcBqAw0nEvX/I0nPKgbHk5wC/fvua76YJ+mTH7+LjneA+dZa4U1gZF\nlFXac+GZIbd49KzQ1aUWEacrQlFHkzLUmUn0bu4MUUwKoyRMZqDSlcSJj0zLAvjKMoIxSSCKLREt\nlSiw07YIXGogeWVWvWtoVjaz7SNUUIN1LrA8nWMtFNU2kkITOmLKxDSzcmcvDYDiy/iDLE2VA7Mu\nwsH324r+mLcfqmH6SQZOQlMpQmwqD66oBJBl4F+a4tHUjKVbXaxeRlsxf9uelYAUwl0DY+5tTmZZ\nmXd3Esq0WMZgAUe8BYrw2GgrQlMm5NDULBYxVwXjTq15u3sGSOZMvAFRHLDgYaHhqQi9aQv0oiW9\nH3J9TJuVsNfQsni8wR0OFrW39vXd+x0ABGFput6E934volqXvJ7DYsmdEnWw6L392od9uvzsLhj4\nrc9wgEm0BOoFpCANSNHe+41icgjO1aUpepvQV83u3u9tuIa7oxLuEOZ+oB8erHsHRUaXeZ72VZbG\n6dB0HF5Xm2LkTTHSRZ1L0qhRh+/d0N0tNDlbO0ca1W5R7BDmRS4WVSKKlTd5Xi4tXyOGgC4NUl3C\nUO8gGMrdMbnDvsui3L2NEz8c48PnW4YvfbFBHhrbt7HuAi3vJUQC9U4O7yQQXai6gFDUcG8zc4fz\npP7gy/SfZPs3lx31v33fn/9rtHsBwL9LW+T627T7yN8Bfv3wi+5uIvJXaFS83wJugf8G+I9+0Bt/\neeq4zBu2lumrcR6di14J9YbHuuZlddZ1aiZTadbZMw1sCOTY0tRHm7k05/k8gBje9Vxa4qZEvhwa\nhrf3ykorKxOOe2Ouxo13dDHiNrNZIDOnEpEobKqyWSsva0+uESczqRNyxVQJ3gKk6xKGHGRux8Ez\ncwSRjr2s6OKMipFN2FklS2rZRqKsWfFcIle14cptQcrP4nRViNoIdFnabIJ6ISCsPLBf7qM771A1\nRheeycy+RqaqdNJCGudoqClhcjYIJQrmkc4LpYUt4bVh7lVSs/V2kT52JC9kW7P3ntnBS5M3zcOi\nAhs6F0wKR7HZWPedUq0yTau2uNBXUoVViIhnXJtaLJ3TL+Hdp52TpdCTQCKmwr5CMON4gOIjHT2x\nCOPBNtvPeKm8sMKHZsSqzDUyo9z6Obf7mZ1kNusNJ37JUDvup8LpSrgg8SxmPtHIR9aUn0oFCWyL\nsFkactWKseK5GWWu6Hog74VslRUdkTZ7dUNb1Hu+m6geeGATxwpnXeAC59rgt58678QdpsKtVLo8\nsynG6XrDfTFO0p6hg8kSr0vHx5NzlQs3ZYce32M/johE8ryik8I1CXJE9gXXRCeGizDWK86kcqMJ\nn+FlgXGOuERqUjozbkdlNQhnohRRiiqvl9DPP+z2k65FTJxVuEJN2PvxskBqjCGRtKKMPOAW0dyy\n/iSAV7aSOKozL9KKc5t57Wvuz3seysjL2PZJV+FpPOKh3PKxnIA7Z7XwYTrisW8RnCvdoFaoGlEv\nvEjHnNuegR1P/YTL5CiVEiJXFhlTD8WR1MKziU1JOCx+fVAv+d3VIx6ODfd+Hc7BnY0ZH6YLEsa7\n48hNWPMiremsclwK7vByGMAr59VIdebefsRiW6XWZTXVD7bweLCTt+cwGqC2Z1PnmXsjvO47zpcZ\nN/VIBZy8PLfaQ+58mtrrcXjGLuMH3p69KzdepBWDjYwLnryn8kTWPJAdO3oGKaBKstqe3d7RGzyJ\nx7xTbnihR/Syw+hYlz1tSMN5Ho45lmuG4uArUsNUcRZuqK4kh8jIU+7zKDyHkFkRyESKdyQMSZXr\nBHVa4QrJYoNqWWjuAgLdOJBkYmXOZtoxsEepDYoQ2vjJXgODGdesCCJcB+fMKiJtr0Gz0e1XvtRB\nzhEtrNyKURVqgAGlzvludntKxlzv0GJUUaApWbMrffDFAQTrBRJ2qEV6r3eFtogQa+MMdLwhLh/+\nHZdaxFzYbDLJS4t68fb8kAVycahFzIB6qBuN+Q7X9ePZfliF6ScWOAmLBW6xgzmV6okgRtR2MjiA\nVhrtpS2dpGWWpw2hLTYoBNWm9qg1m9MdnVAOFLBmo+ukhYNmDW0OJwRcrEmpzT/W1JjQKHezNOUk\nSbNTvVFo3li7DieWaXvfGhr+273Su7bAW2slfwq6WPtaw1iX5iMcinavJAnN4idAbE3gHYSBBiFo\n4Ium0E3eMLTQQAptBuNg8WJZdWrKnEv7LKpK9bZyjTc15sDgN28IajFfwnvfQPK9ySQEcWYTViLs\nNeMmhKCLKtI8smHhsRsN+60SKHdxOrJYB9qDB9qKxQGU4e7IncXQ7lS2ZsNrqtVhNY3Dioa2VYta\nK+pNvQO521/xTrdq59YBlXEIW7ubJUpGtQruJFnofwQSQtbaFEB3ZLnaFBbb5xuLZXvdhnN+G+3O\nWz8/wEveEqoQbTdEf6spsrigl+XNpzccqtPH9p1CMAJ1eW1Fqt2poL68XvB2zNwqYYF1qL89sfaH\n29z9//cF3H0C/u3lnz/odz4E/soP896f6oyfTxPRjBsp5JrYjYGrGFuDYUoHxFqpBUyUWzGKF1bq\nPNTKWhMxTrg5owhd3jHPR8zcIDk1OlCKeKmk6lxZYeWtidl7S0r3EPBixC7xvEYmOuquYxWsnfse\nUCKrXjlGyepEjRSHnTmqG3CnMrcFFqvsgiAe2XqkaiUF6MRIkgmlZxsre09kaUCb4BAlsZGmoqoY\nRRshMnggS2DygEnBLC3namWFLkPsa6rUxaYLlgLHlphCW9R6bnXJDauL3ULovLBRJ7nhdbmRaODK\nIwOKByfIEe4zg1bMKiMN9BKk0PUBakfySkzC4+SMNdMPyphnqjs5G6JOb5WjlRK8UF3x6Ig5O++Z\ncLZWGbVnnAol9CgwlJmNbKiuHAUh2ESeHVJE+zXrMpE0sQ9C8hYc8tiXAAAgAElEQVQdEHxms4qU\nClub2csxURPfKBndCyOVqywMWbhQmOoERHoKF7Hn1guirZi4NIEZzIR4MxKt4hS6CCdSQQtpzFQP\nVFNO9JIP1iuywWVWLqc9tRv43ODcbCtzAHDKnLmMwvVcGOUErZXJHTfhmsiUBigzsxi6E6ocUaxZ\nsVWdU6usZGRWAypJoM4ViT3fMkUL3FJJGphKwYogY6VnwjWSxo6nqc1r7krl8i1nxR9y+4nWIglh\ndQiWlSs+knd45J+w0qvFaZKI3PBE3mFOSiDzqWnHOTOX4Yh7ZY8F5YHs+CSseMKKh3XHNqwIwDEZ\nE+OxXyImWIR3uSYUeN4f8a69BIG9JY5K5qPulFfhGNwZwshZ3vHt7hzB2ZfNslgqvE49J2VikMxE\nvKtFnoRT7s87XgwDFzlzVEc+P7/GFF7VNTvteV9e81SOOC0zSLOWVlGOa+Yq9YSy5RFbnuoxLsrr\noWWUneaJzguC8yxtiF45n0eCwCdp4ELG5sAwJVE5L/PyXH/jOLkMR1R1UnEIsI0Rag/mnPoOF+Ey\nrXnBwEnJRNuzkxUXOtMvRXUN7fk15MyUTvjMdM1X10eMcsymzrybRzRMPLIbXJ1HXCPRed9e4PRU\nbejt87rHNfA0HrPVxHnZsq5GMWUWwbSSfeBcbmjepcqaCaOw8i1MMGrg0/mKrIGjbOSuIjiv/IKh\nOJu4J1HYqzEjPOaGQkdaGpoEILAuhnuDM+Ag3UguDSC1VmP0RgiNBW5jo/jOLC4mbRdOzAf3ltyV\nDENtkAZ9qxYRnCBGITDZUiO0VWWg1SIlC1Gbm8a8wWN6d2aDiUBSb81tdZzmsuj7TNDMvC9k4GQI\nGMoclPl25mgTwGDvAbFCFxZnlwv/BKXEH+n2QzVM/hMMnIRGBDNNmFWSNpKYid8V74ctLZ5tJZLV\nF8ta+/khQ+fQO/tykny/y6jlGTUKXFqUooMCMruSVOnwxWYmrFD26nTLvEwRZ/BADmBWG+7b2qDe\nnR3urhBusmOUQA7NQqWLXSvjjVC3bIeivZgtyG+j1KY8HBgvgVbsl+bBoluUtja/0hoDRRZC2/K6\ni+rUlAkgNlvdYd+ICL3GRsXT1nzJ0hx0y7xRCE2qn6MRDp5ib/lBMyAxsLPMvf0rYky86i5Qf6Ma\nurXvVhelsADx7oI4WNvaasPi+GsrV8txOcAm26ELd0rhYf8dzpNDM+VLQnWM8U5he5vaByxN0aJy\nLT+axZoVcPnzaDTiYm1hu/XQmL216WH+bPmAGsJdw3VoyypvaH7AP/aZ3m6KDpsslsK3/0y1NT1h\nOf7urYlOQQiUpjqJEhdboFkjLvrikbdqFN7sYxYFS5eFhsM82k/j9s0xcXm7piYn1ZloE0ENLYUV\nHatYmC21bK+g3PrEkCv31DiRSi/G4JXtOKOxkgXWKvxMvGYWZZdhlsjrfWuxoxpnQOdGRXE1ZhIv\n68xDXfFKZrYZ+pToo1GJ7Grznq9pK4Cz0lDn4mgUhqqYzZyEiUHbwO42JPDCpDRQQW5WtCMCaGb2\nlhl2wo4pKKUeUWh5QbcqNLEp0FM4ITKpcYyQbeLaE9NCk0wubei2tgYrBKWLAantbL6RQlysxevY\nMy6rLpNn3J09HTvLBGne9K4a/QxVRtwiaymccE2eG4Qnxkiue7zUNisSB4IbO6sUoAu1PcTNOJJI\npVAP9md39roi54zEDujYaUGtEglojBxjPOiEwI5eIHgh1mZ9yqUh+C2069uqcdQrkRGXiJe5+WYl\nIkF5WVbcmDIXY5r2DFpxF8yEY6nkmrgR55SeGwEvmVyuCEAqxpAi+7nxL80c90yPtxwt2r0br2w0\nMk+Fe0PmSe74aN9zVXecUJEusqvGgx4+cz9ymSMZ5cN9JlvFSaxCpXqPpohV4WGo7AisVz27eWqx\nCF2kmmESyCaIdMRpZqNO9UL2xBwbFOcsteH5Td8173vokZWxJ1HpcQlYtQaLscJxNH5UR95PuhaZ\n1XglF+zVeUdekcT5iEd8UK9xMoYhBN6vl+xQBoN/NNznYdkyq/KkW/No6es+yE3VGUNbilrpIey2\n0c3UKi/TmujOQ6655QEXfgsKz/QU9Jp3uSabcCVHvL+/4dv9GZ8pl1yy5jvxhD+dn/JhOEGA193A\nZInPlUay+9gveMglz+Ss0Xrdeei3fNid8G69bLlFVvlQz7iOkbKMGvTWFu/a8845CSPfDPcRN971\nS0o9I1R44Dc8k2MA3sk7qitVI7MKEpStr9jUmbQ0Nldx4LiOTQHDuUk9j+o1NwyYVlwCP1NeMXvg\nVdywLk19uq0999izl4iq83PzM550PSfLs3iwK07qxHe6E25T4h/GB/zy+BXOivE7q88QgjPUzBwz\nPg9UUV6kHlMYw8Dj+arlEUVnIvKuXWJ4y+EKzaoHFfOOURIbHymqiPd01ZkVnsk5AGe+xUToyURR\nLn2g18Jx3DJZJPsacFwK7/KcLQOBumQTgVizVb/slNd+Sr+40N+vI1lajqRJoYpgflicaPs3SV2G\nFQCHPrRKMRPpm4+qRUhIplir45pryBsVkje1SHVhEZgQFaK2OWkVa/PQmjEX+mUsA1eKwUqNrttT\npoLnVmgc6rA5BMbbDGPhbB3YLVW7SHuPZqZpC/oHN86Pa/tRZ5h+bIGTAEjLhQmhWaHSsu6PGFbl\nzoLncBduhbeB/mK2KAjcBXtmbzaxuKgKh3mb9ug/4KuFubYVdlkkgnSw8TV2OCrC5IYtrqgoi3rh\njS53IO1Bm5HqRSC0wc15yS+huQsbQc+9hc8ugbDQGru3bXORg+ImaFgoat7ofwUn43fNofPG2taU\noNpeO0Sy31Xn9Bqb+qRvCvigAfeK6psGS2jqUF1kvVZMA+bkGIheD3m9mCuiyma65Ysb5wuP1vz7\nf/Wfo+43/PJ/+Q/wWBl8xZdOM+MEf+GXPsfvfPWb/I0//3P8xt//Bn/nY2uzP05riLx977DsM317\nhWFRyHTZZ7J85iTSvucyfxbR1nC+ZRs8NCbNBtdyq9qiXAuWLdYwnbAole3tFmXvkIcE1RugW++a\nigZbCNYso7o0x2YNFrFcI3e/697epzehLKnbB6lJRe9Q6YcEYGm9/NJwNzz+AVd/gJ5UcRJGn9qx\nEG/NeAEsL+d9XPaZKaYHkAmoVA6ZVX64Yf0UN0x/ajXxuf41eXS+SWIMHepCL7DVzLV3Dfnrbd5i\njTIw04lwP1VubzP7MLFOkRVKn52tRQojV5K4jZGHNvNBFKao3JbI4BlSgGqMtdCFxENCowrNga7W\nZnlMcBJmhAZjuKXnsrT8lntBuajt3uPsWcVACoVJEltrOSMP08zO4FGIiBa2Gnnpyq0l3g/G58LM\nVuFZSXxsV9zWRA7KibSGYAT2IlxJIWrlOjcqY8QIQXhQjQcxE5iZRdjTgjFTqkgMxJJ4lVpDde1r\nrBomwmSFE+BM4fPcsCoTyV8RpYVNz7MQUiQj7K3nqji3c0+VClPlVDNRM+TINBdGlLyf6VJi1kpK\nCaxg4kTpSMFQrZTqDIyc9C8RSZgqOu3pwwoVIdgKr5V5mAmhcr3tuSlCiQnTyH5ZPYoKN1PBPLPb\nC1lWOMYoynY3ElaBwSdmE+ZcSEFRy7hXglfWc+RinKidstHATVE6HVHLnEjPaUq8pjKWmTNpCz23\nApM76s61Ba5zZqPK/RRZzU7VyldujZ/tO1Qymzjw2iuvSruvf7Mor8Y9WuFkscGdpEBBmsVUZm7F\nuK6VwXtOY2GgcHISmGj5T5NBtfZMHKeJ6065jYpLYGPGe4BEZRNasROrU+KKqVZqhblWdnWi0GO1\nXWPYmkn2vOSPHCv+Y69FTnTkBIg58phrXBKilWI9pYusJmPsWryEZuPEdpyFmZwLz9OGK+lxg9RV\nvh7uc6/uuG87DHjKOSKGde1b3LOGVr7sVpxwy5N4BkBH4bWu6es1L8MxONx0kedxoBZ44LeInjIT\n2LFisMqWQDLnd8O7/Hz9hF4n5tpRg3IaRgiwmSa2NvCxXHATOzY+cmQjj23iH8WH9GbkxSGRrDlU\nZo+4GtuY+Io95OfKc57oGd+Wcz5rrTn7UO+zk8iJz1zULZ0N3ISOIMZVbCjxTZ1536/5tp7zKV5B\nhY+44DqsuPAd7/KSlxwTxDGP6FKLOBCpHFHpZ+dZf8K7+SX1sHgbm4X6F6cnfGH3/3Cyhk//yzfY\n9WN+8x8ID+SSWo/4Et8heeDRo5/l2cuv8d6f7nn65St+Sz6DqSLZWVGYtE396gJTeMoZD7jlkhXi\nzmDG3lesvLCNHX2deeRbPtIzOne+2j/k5/bP+Zae89BfN0UAGJZBEZUDMTlw7JlnesZxvaS6sF9q\ngzV7Bj7hCQ95z15zGQf6WulkZusD0EJk2wUg5FhZ18AgfjfC4tbUpUB90wgRqN4iENbmWGxzs8kz\nhUQnBaup5VUeVC9tTVu2RC8G4ojH1vYuNUwWI+EEvW01UPzeWiR2gWlXloVj5XLn1DCwWc10ktsi\nvRmzKiuc8GOmMPyh305aBf5jC5wE7lDa7f1ZiuiD8hOo0iZsZJmNCQsdxZebfq6FIaQlE6kV/qV9\nF4LqXUiofp8CdOh8jfY6gTdNV7V6N0MSZKGR0Ug2I7XJnMtn8cNrtk6rZf6Et2l87XvceU81thVO\nsztF5/s9oI30tqDGrTVJUVtjMS8Kw52xzJ1RnT5EploRWXKmaDNRDdbwFpHvoCwc9vvynm0Ar32v\nu7knVaIqWRplTxaZ5ED5+/Vf+YC/9isX3BtOESa2PvGXLuB/fmWcHRX+vb/8J/jCkXLlxq9+5tO8\nmK/4tT95xt998gITIfqbZviwNaXujWpUaEqTLsTC7yfmwaF51O/Zl9+7P996B1nkYzNS15KxD1ub\nKVrm5palmmbos+95nbTYHiws+602dQp94w8+rJK8mZVr+Hv1hrM/NOVtP7eVlXj420sXl9U4wCy+\nv50JwZabY1tAcGtZTW2WqlH0pC63Apub5bPAHGDlEXFrjaS3hYif5sjJ/3MPH6eeMc2kDMEzNUZk\nHiixchxHzmOPaSURmD0TtKPazNMxMoXI5zrhPM4Emfi2KcedM1mDNZS98BTBvKValZz5JIPM7bwb\nJLEue4hOb3DMRIqViHAShKPgDH3gpna8sInvFlibobd7brrIrQW6GAjVmHLHqIFjh1VQPpoiJRid\npraSPMNxnHmwMrb7jt+2DjOlC8JFcY5CYTLoUmKyjAZhmCNjdOYqjKJQE8KES6LUxBOrqK7JpSLR\nOZPAp+stZ8FYhZEzLeCV4nvMe24logLnoqjOrNxgBV7OuC1K8YCtp/YgSonehHURbOVs9xUz41Z7\ngjsxCIP2xJxZrxW8EMSglhay3GgnTT33yH7OfMucub4LTKxYMdY9FtMCABJUbpl2p2Rr1+VKnYvs\nxGBIFnYYu5rxJSYhe2KWmWqKmhHTmjnvibWp6vcYgcg2O3siXewYMZ71yi4HzqPhNpKr0oWO79w6\nsS/cV+OEQEyF56UHA5OBQXZsemFlAVHhtla2OHuNfDCsuMmBrRe6FCgkUnS6lFAz1iRCgl2phBL4\nVq1sYsdpvGWDECw3m43f0EnH4Jm6nxEJnHaJnryAZ4wcEhIqJXZI7NlrYnTQbEQvzRXhhVCul+ey\nQ5fQakwm3KhBjYzxmss9fOuPcPTgJ1GL9DWiSwK43M3SVKq2cYDv6ilhMN6xhb4WoGqH255JI6aV\n97ZbXI0cIp/yLTtp94zXuubEdgScvXVNxZBW9F9L4sz3bOmWuAugwpUORDdO2HMjAwo8i8fE6nxQ\nXvOV7gEntudS1tzjlsswgDhqTnXlm+kE08D9/BYvR5wLbunqioDxpDvhI9NWW0gANdSNuhRjdWFe\ndl64V0YGjHMfMYevxMcAbd7HYRLlm905j+otuxCYdMUH+RKAp+GUsXaICJ9wAqLsJNExcSFbrlhz\nwS0O7EXZWyRKpQYlZ+VG1wxyS9U2DxwWqFK2lkH4z382cvFFB87Ae+T8BX/DCr8R3+NLPOcXPr+i\nu/ddbN5z7xc/gsv7nHy242//3oaEch72BFNWVhmJ9NbyhB7GW5Cmzj2VY77d3+NBvcZQtvSsQ+ET\nNog7s7Rl1+dxg1hlZdx1FdvlWK9kzyEsJUjlMR+TvEGdtJwAYCExEznXLclArCC0erEL7Vge4A7r\nYqCRWdvc6pF7M+vrkjoLd6LAcBAFpIEqIq1uNQl00lTwpE11ujPX+mHmICO0LKa3KqZ2/EMlijc7\n9lKLIAmXDpE2N3VgWafNvi08l4mrsed8aE2damHtS8X1fY6gP+7tR+nPfqyBkwCX//t/hfSbuwkK\nB4Yv/Ats/sRfwoHeamPrL/5oWzCEMbRiX4kUaSv4AW3+Sw7ZPywzRoa4EDU0OqS3YK8QmvKjbm1m\nRxtlr6qiITDXpt6ng6ojTckwXbp3aza9IEK+a0hac3VAJxxs3U3YbXNQ7rVZ4fBlNmZpBpRGfDMh\nCHfkugrNJqfN96kLhcSsWfJ6mpK1WgATxECplRRiC93Vdro2AIbc7eeDlazKQipsvR4qoZFMpBXV\nqSqiTrECoWtEJIxf/WcecpY2pC7ippxeDPytf/VP8b/+zre4d7bhrDPi8ZrTUujOTng8z1znif/i\nXxr4V/6XJ6RlDkJQrAZE21DjAdCAQHcnAS2XqRh4QFXpUaob/dJIJX3TgAAEN6o4Zk0ts6X5xpyC\noNZa3APuMle5mwNzK02BW87elhPW/rtgBF+ua3PmZS6gnSPemhC4C8E9oM4PF4wezpXDys/yFbO3\n86tq+7vJw1urQ4dsKkWlEowlpLedXIId5ibfnP8AUnBJDR8eG0b89e//Jje//1tLr7zcROfdD7pM\n/6nesvdcSUStW+yIla44Q6istMNcua5G8kIKFan5jiD5FGeyyDd3A1FWeBBinllJy4Ib6o51L3gR\nxqpM88SYhL7AnDIuiRsX7nc9uLKbnL12DNom0p8SCbNRbw1JiRdecJs4CsY+JMI4k2JkuyuUuMJt\nJEjgMnXsJFI9EUtlo8apwrupUrLyXQ9YddZS+KS2mcUhKLkYNQlevSkwRbiiss/CpAWXiGCspSMX\nYwqZ5G2BZgqJasI1wkdyQdxVzMoyl9jCfrchItqhUjEJJDsiSTMsRdZLCGchY6zdUSu4G110TvPE\nZ1db1jY3264OqBqljgxxTxgTt6FyWzNzbiGz19W5LplxnIjpiAqchEQ3jKwM5rpFe+Pj2ZgMpAgm\n7W6X3AkaqdJU/+tcybVFU+RFacnWBun72LH2mezgtS2KIT1zzrwiMBZnVuUUpTMhaOXcO85D5lO9\nMnSJm+Jcm7BLzu2YeZ56vmtwNkU+lbacDD1DqORxx01d8Wg1s8+JTiudBFarGXRC+x7tE6MHXs8T\nKXbMYqRobOdA3kM4AhtH1myYfOL5BPsuIHOglBtSipxm4ZlseLJf8dycse+bMm+wXhYCgyrrOhNC\n4LQzIsorhyuLRCKr3vl0PWJbjK+OhRoiU3aSdxQ1eoXECo+BrfyR3kN+7LXIf/flr7BOX/+eGdc/\n+95jfuHTP4OJ8AvzE3aemnUT5zr1JC+s88zajE4SXzs658xGzuaJd+YtH68G0DYvPWnrhk7rxJUO\nqEJvey71iBlhRvnC+JLrtGLUgYt54rvdhr0c86GuAfjZ/IKJRCeVe7bnRVxxYnselD1TiDyuN3w5\nPuQuT5LCmpmAM0nHGXuehyO20nPqe8yFT9ctkzivZc1JHjll5OvxAg/Q18JMJHjmSXfEE44wU75Q\nXwLNpfJOvWFPx0td8alyRcR4v17ReeU6rrj1jkFnvqXHnNSZC9+TUVYysvcVr6RZEwPGR+GEU2/2\nxUID45zPM2dhT/TAO9OMqFAofLt/wEs55l+8/gb33nmN2nswGJ4ycMYX/uINf/Prf596dk5ywden\nyPkrbPd5OMkIO/7miw/5d/Z/hj+3/TouDqJ4XBNL+wzr2mz+hvApuULLm5GHx+EFVQYeyQ4EXtHx\n+fKyLUJrZVaIi8L0uF5zm2aqb5p6px0sIybXIdBTSXJNECGaUDS2GdkQSLZr1jlpxNu+hDsswpSE\nzeKcGXB2JneU5q3CkbWA3VaLCFJbiGyKoUE6ZFlY9lY1tI/bQsPD4photUi6q0WMFrPwdi2CGaSj\ndoFpaVRgB1o71CpoyUz7Fdi2zZNr4u9+4wX/x7OnuBkxNGvedv7xcsX/UA2T/AQCJwHO/8K/Qffw\nZwl3egHthFvmiyS0BkK0KRFB9I7qpfoGTy3SDnibK2nKSi2VuOCaD1lKDoQl+0e1HUipbYbjQI3T\n0DJt4mL3C4ttjGUGSeUwixKWYNs29xPuMnYAaZjwg1oQpM1mFasL9rs1UINEijc6UUDpXJnFiPq9\ndLymYjUQxYGMJ7rMoMCiZLV9MfuSzVSaYmbS1Af8ewl17f7QwlZNWtMVdZkjcm+YY22pR4jy6//s\nz/H06VP+x2+85jd+7fN0vdLr3OxrsTWIJxdr/vKf/SJTnam5AXy9VpI6m6MVR5xT4yUf/L1vcBlX\nQMuuURp4A1pBI94sZLpcwAflUSRglbtGRhdMelAlWLvQTA5N4RLeGpb8omW/6WJPC2EJSHaneMUX\nLdjv5rzazfJADlQNB+MeMWj7veWUNW/nlsvy2UzuXuNguzxYSgVpymTUO5x8O4TtPAsHletwnBEI\naZmBWM7VEJnN7+AouC6fEaIsihuKaGq5ZDTqY3Dh9Gd/hfOf+XO4NUysu7F7/g2+8z/8xz/oUv2n\ndlNYQpAdlcw6BTZUPkVl0wWsU0YrbFUhR1LXE4mEHmJpmUXHVhArbKwSu4gQKLVZFShOFGPjM2uN\nZFWsU0QCUqCKsDHnuQk3OTBI4ahzskhbqayRD2IlUPhAMjEF3ltNBL/iuE9s7Zj/aVfZz84YEve6\nyufLnkwkS+R+zCgzpmuezIFXNXNaZpIktkCXO648sg0NkX1BRVNPKhNDgCkkconcVGVeFosmK9QA\nUoV1EJJBUkjkFlLqSqZQVZm0Ue0KK8ydUNsEsuQWnjiLcoQgsl8WQJzZm1HWugoujNU5kZlnvsHi\neglRjqynQo4b5lp4nhrAIYrShZGpwipUNkPitF8zW0FmYfbAVVYuzeklUmaj4o1aFSY6hGBK8EDx\nGUomSUeYZ25C4rYKVo1NakrsJrR75qg9+DVD7FkBuRRedZUj6zkRwX3EXTiPTr+AN14WodTKCyJ7\nK1RT/sy9yNW+uRcmy6wdrh2uppEpOKu+YzvDd68zv3jiDLfCd3LlvD9i040kIrnOdKs978WA2cQs\nTrHI/b6yJ2DBGE4ST7aFI02cJOGmFH4vFHI6Y3bhm2poHZBVozsO3mOBxT4TF/XeGC1gdeDaM8UL\nXewZDdQDOwtcEokqdGeFaJF71iG+R7wCGc+tiH391lzuj7L9pGqRv/5LP89nTs6pGui9ANaeSTkv\nxXSb4dDquFaEniMfuQ2JrB0X88Q+VHaSONOZUBuAJZamwgRRRhFGHZoi46A6sAuJ81roaarGTlcc\n1VvcmoVfgIulGa1hTXB4ko45KrecMHEriVEHVlIQ6UniHDPBMr+8C4ngsKlLE+AG7LmWgSDOjSrX\nYc0Xx9fsgvAqDDy2G97f7vna+oSVRl5K5F7dt+ewKKMEHviOG+9x4MhHLrVnCivW5QZkhRF5FQYG\nL9xazyl7ZlUuZUV1Zc3MqY9chp5VhVvZEN35OB3z2fGSo7nygEsswKQR9cqT1QXn9Ypfe3TC/ub3\n+e3rDb/2Jz9he/yII3lFkSPET2B1jJ48IXz+DK0VOf4Iv3oP94CK432Prx+x/ZLx1/77/5unnENq\nTetmzstCt1A9U1LEFIYyodhdLdIhFCtcx1Y3nPm4hEo7Z9OMA7mhHCii4EpvTT4s2ojBqTo1Gn2B\nEiGYMmrhMiQelBmVudWk1iJVmoum1Tu+NDVRJoomKpGVVrK3OURU2KpzMhqy2Mdd2zy1elkUdvBi\naKzUGhpZ4k6+kFaL4Li3+7iIYh4xF8RrG5FA8RAI3uo38QjuBHXwPUmEVAVLhtSCJaW4o1L484/v\n8xcfnxO6BteCyldf3fBv/daXf9Cl+ke6/dANk/yEAieh5X8EwqJyLMP+Aq4Ldc6VEOQOFS60mZRi\nehcCeggeNVojIlbAGhjgoApIkKZKwaI8haVJqBDbCmtsXVJDhnrj3bs7aHuw4tpsfhzIei0BWcTp\nlrDZKg2eYBrRQ1UPd2QYCaHNpsTQ8kpoTUCz8Amzt2K3uLXA1GXeBmm9V/OlyvLZGznPaBkwZbGt\nxdBWElhmWILoHUSgobOt5fqUQtJEjG3VOS4rGC5KDErnzo0LvSj/+heP+dL9mYeffcR/+KufRbLj\nVCz0b3DjIrgnVhujm4Wa2ql4dLSm5IyLsNtfc07keWjNTlQQN1zqogwtFjbzxToCBxrhbBWWi1yX\nc6UFy+pyM3iLRueOBSWYtoeeClKXhUhdbJLWQt40BNQOEA1HQjs/mi10aTAXub01pUbBSTSr0Eyb\nnSpudChVnBoXBUsalr0s+PWwLIZqfJOfxWHRSpc8rMVmF8KhQWvN7uH9haaItuPZFLIu0RTSxUpx\nQJa3BuuNGhaRdm3hFAevggb9f6l7lx/5tiy/67P245wTEZn5y9/rvqrqVnVVP6q7aXcLDDZClu0R\nRgyRDUYC8Zhg/gFAMhJixAAhJhYMGAETLDEAyVZ7gBADGrkNGGy63e2u6uqq+76/Z2ZkRJxz9t5r\nMVg78nerpHarXe2qrjO5NyPzF3HiPPZZa31fxJD/UbfpH+ttk5TrVKl4gCJUjqZ8iHkhUtycYVHj\nkj1JIktsbLUwbQKxeXyvauS0jty0E2FdSUHJaWRWz8E4tZWLofFTUkk5MDeIWxiiEPXIWxY4ZTjK\nyGfVeNsKb0W4sczrEN1UgcgmDKS1kdnwXE7spsST0LgeT3p7bGsAACAASURBVHz9whjKif/7dMV3\nmqG58fEpERq8l5VfuTT2UvmNObNGIRZlkJUnoaAEdiY8iAHRI00Sp1q4xZibsq5GCSMmgkljaMo2\nRAa8IRQrnTLqWhsLzrWPVShoX4PFzXZC9Gmk+b14olIwBvHG5a0YMTtyXBOtVbZSeNuMa24JTcky\nO3XPMqkqWOVd7P5TYmhUOSBNmdeRozSmFUoUxlbAYIzJdahRuWoNUmSrkVtdmQcfKLxcBJEMpaAl\n8YTCl8fA8XTiUgZu7Y5DEb68veJUbiELr9cDa3V78dSKI4yra5fGsHpBMESOJyPHzHHdsxsib8fA\neLVnUuHdaWY37JgofFIHPj1G2A682Ee+O0faAh+OkfXGeHIF1EgYQNqIBOUybfl0NbKtJGtcbkau\nbWVfjCUG7lrmRQ3ksLLERkqNSxl4kIW5TlgohGEgaOMoymiJIRx4TORpqBy18okOfFwiZQ0kOfIw\nGlcpcBcah1ipOOVdUmRIG2pUHsjKZbphq35eRQSJlU+XyK798Jy8H2ctIi2iRNSgmmtWRRRLKyJG\nXXZkcXYCmni47Ak0/uHmEY9axVrhvZPT9X539wiAr56eEzXw2ejGAGI+0BrxZT/Xyjea8fm4ZdeO\nfLy5JgJPj4WbYUsSYzHjsgUqxtINiB7XmSCJBafhfpyuGHXmBuUhjU0zXuXIw6LcRmFQN4wBp1aO\nNC5txurCZ+MTdnbgg+2Wg2zYqiNPv3ORIQZqq1zTKClwVZSAsYbMDmFnhdfDBGZ8uS7sAzyZC9/e\nGRuER3Zk05qvucCIMRD7gHok2JEtxhHj7abUnNm2lafLygfbS+Z4wdOlMOnK/3XxFu/VE3/54QuG\nt1ceP515+6dv0OfGBd9F8zXy+H3An40tJ6J9BseC3X3Vm4GLt7C95xWF00dcH7b81tU7bE6RMZ1I\nFKqOhFApNvUYmBW3nolUIrkKmiqLDM6CEWVavZG6HYTdWin3qnh3u7vNWx6UjGaXe8TVjX3IMFWB\neCSQO2sq8K6+ROIICGM9R9f4czx25oqJs1VcxzYTWTkxMIbKTOKiBNYIyxBIrXlDosYSAkZkwpug\nmN0xNQSgR40QenakeR0ZWnMqoXFfq0KA1iAkrzeDM3Yyax90C7nLA1KoLJaQVN3OXA1swQmEEEOm\nqAfiLr9v4uI/me0Pm8P0YwucBG9kzgYHZnRKm7uPbDSwJqjaGEkdVmxkCc7M4guIgZ3RCJDgbiHR\njBGhJtfrtObojp/vHpiqZwSnEQ1mbT3ctQeyIgRxVECNThVztzJ/sMduNsC9/iWIsJ41Nx2DkI7c\nNFVidq+zs0W206c6bGkuqk7BL27FCPeTO6GaiyBNEhYD0sxjD5r1YlmcYpgSdHQkWA8DFs8lST4A\nIOdMlIbZuYyH2Ba0Tiw7Ia/Kn7VX/Of/5p9gyJmbYyHnxCDK5dMt893saIlDam90W8GNCCLd1rsj\nW601dpuB781H/vq//kv85b/+rY6ChHtExawBPTfJjcIxcZOKJMFt0TtKIyRWa0z3zfE59wlqbaSQ\naeZi+9YckaTDzyZONWqCOwf283festAFkuem2A9QNSX3Rrp29CuKX19D80Y2i1Cr50F0J/ne3PfG\nx3regDaGmHxY0H+XFEo4azasN4Ad+Qx+/RKUsbOMB8uICKs017AhxB7uHCVSrXlqd79P3GnR9TVT\n9HBfMIcXfkK3ZoVQfeqGNKZgnNoAsbK0SuqUilUbL2TiWGdIA0kiJ43MIVFKbzwDfNkCDzeNJ2mG\n2LhdVu7KgIrSlh2/l1ekVGIovBUi1wGmELgMjYNkHh8b0hopRJ7thZssPBElUvm4Gcc1oSGyNOOS\ngVOqtBaoa+Zv3xljHHgyGr9yoUzB+HydiWlikci35iMpVN4NmaCwV4U4cAccqjfvtI6QRkNWY4pH\nsu7YNEedVSo1ZHfUS8pbJqQMQ6jQKqv4tHKWiJmQVneOy+qhzUssTDVQEdeYauUiDmylEFFyHCmn\nhsnMuxrI+URcL8ntyN4aooVRzIczqZBXX3cjgZMEGsKzeeCoA3M1v//XioaBthaGNnNhypqMQqCu\nQErUAikFLmTk0RYu256HQ+auKpvRmAalWuPzVTho4oNFiC3z5YuJVguf18Dna+GhDMToyHBMnqm1\n0cCpCqfcyAaTNhiVmxliCvxeycw6wjJBPdDyFTcFJo18dVt5GDZ8c6r8ie0Nv/n8km8JvNuE994a\n+M4p847NfFwbjxTyxSXrMhNjY6qR57Lj89uKSSTYQIkFLcIrqySbuFsLH5xGwi7w9iA8HGfWALt6\n5D2Uz/LENla+ddrxG4c79gRUE0XArNL6IlU1eJBwVKRFGgv72mhzZuGAqvJZd1xb+jN0XBstD1hZ\nuVl+OO3Bj70WEY+BiBKguXtsDPCbm2t+6fic33pwiaryC4cDN6OgKOMa+allz/Nx4Dg4pQszrmwm\nWmHOCfr7/vxhz6tJQAfu4sTHwyWve7X2jeMrfnv3GDC+fvycKMpHY2JrjUWiB9IC79UbAH5neo+r\nevRBr1auzI0RbiVyTBe8YkIwdvUVt3Hgedrc1yJu7w0bPbDRydGIahzyBWbGqdP/3lsP3NK4qMYx\nwUaFT8cLUjc+iVS+uX/JPlzzergk24rIwscXV5xS5nUYeVr2HIYRWmUOE2+1mVmUV/mKX7n9iEEr\nNs/8Pw/e4+r0PT6M7/Dzd6/Yb+D9+gkf2Zd4dpH4yu3Cv/vy/+SX//wrGCa4DbTtyvB8gkcJe918\nomyKTGDHhsQNevklgn0LPUXC1p1KWxBCU1q8IBwe8xf/1Ilf/bWILiOWGin6QCZRKRIRS7R1wlJh\ntS0xzEQJZKsEjFwESHxrc82vzB/7+QqRZOpRN6Fw3UZajgxa/HdZvV6qkXW4Y7tG1iER15VE1/L0\nLKhLWd1lj+j1JY1E4xQCW5tpLVFD4iSJDQtzG9jJjIbMThpHzYQz80XOBmfWB9xGa4kSPFB2jsnX\nNhWyFUoakNKcZZS8hvE4n+rOv9IYdYUAu7YiIhwkMQclNajRWUyLjDQxLmuhJJdUbGvjmAKTKsNp\n4S5ntyDnjzcl78cWOAlAv3lFPB8FlIDrciyCr0GJZp5xFOzsfObUNMFPyHkC/+Z9hRwCVc7W0V5I\nS4j3znBm7pbkvxVWEZJEUHXR/j2Z2aeeZ6pXJpDEER21RkrewJ2pbGfkwENiz/sknQbWm5OOIOgX\nP8uMFF2TJbid9Q+6rp0NKkQipXVnvOa6K8ERjWiRVT3E9N6Fr1PDLPb9sJ670nU9ZzfClrdsKby9\nvuJ//jf+JBq/zMOra8yMywun741DxqrbqkuMiPZj36lr5xwI6aYYXzRq2B9OXI3Ghm5qESKYYsG1\nRm66QR+huB6pdrfCM/VOO8WSrllrXVB0ps2BknN2Wm12a9A3miFvfOMZmg5dZyvfTycxFHq+V+50\nTacp+nuE4JS8+9fPjY31c4sjjWt3gDwH+or5gtPEc6OCiF/3arTk/z7i4cPKGz1c0UaKyZFCHGVT\nVU4husW9uZYuihDONjNd59Sat+et24u6w6PfdPe9ePqjodP8OLYvJXiUErexcdTIXiOVwMjMRYzQ\nTsQ48mBIfLBU7iSjGjnbyK+lsHYEcJiF32iNmjzINAclhyvepvEwRYSZo3pWxUUZOaL8vYPyOXBb\nL2AYQFZC8xyyIQirBT6uytM2wNA1hxLJgztsDloIWchRObVAMePZGrhRGKwxhchS9yzrhgWjhcyF\nSh8gKKWsuOLEH8afnhqrQG1+Xz2IcKwnVhtJseD5k8VXy6bcxcBaYBOFXXaK2awrtkawQAmVQf2a\njLXRiJxygFbvHRqPemKMjTEJhSNtqTy0F1irXI6NzJ4mRk2JaQqcmJgrvLKRuwFkVaoprRoWlCUC\nzRH3vBpNZuJJiapsrLKRxrQUFhpv7zJJEsdg7MPKlsqwKjkI61wRCvsGNm6pBiIFERiksWfkt/fK\nwxzZ2cqXh+TfQWtHe5WpLryVM+NQyeJmFB8vxsNcGUdjaYm1RNK6sG+Raxm4DPDRBiYLlDTwgVQ+\ned54GibezsovjIHb1bhsKwMLNwiXLTGERppvKDayNuHjZeV1dHfHjUBdClfJEQQTodQTuxCZmRlP\ngf2S+HZR7qyxGMQoHGvpTAl1W+fmdNJAw9TzoUTcbeuZKgcGiiQH4INi+H6FGO+p69mgoIybSDEh\npYG79kPnMP1Ya5FzHVJa5DAlGsLTqnx1OVLDwM/vX6MWuBsDnwwPeLe8ZhtWXueJK51x1vuIBSXb\nihIxaSDCl5eZz7eTZ96Jcqmv+dlj44PxgmDGd7bX/NzRAbEQF37zwTVPW8Fa4GErLIPXLMc4ISjX\nbU8JgXfnwrat/L2rd6l65J12YDi4g9pH0yXf2j2iiYeTbrRb+5gwS+YYLzxDCHjbZr7NhQ+UgaSN\nB+vKR1ePead8zh0jd2EEEZI6SnCKl9xNe96phRaPvEo7fun2Fd/dPWDXlK+eXiIGr9OGr60HbtLK\n1ep1whxOnLIbPAxlpEnoY+PEYQqINT4cn/D+6VPebS/5c3/mcwhg8Slg6LUQbERzJhxXR5E3Fe5e\noHWD5AtoBS2fEwVkV7E5dUYFHVl5Br/4LZ58+BD0ZwkmaEv4dHJFNBBa7sN5SDVwzM0RSITUMi26\ny5vUxIbCHRMWnAqpIRKlsrHkrKQuAxlN73X0GiKblhCB3Mq9vvmL25Gho/lGXKBN0SNDzDgx9jqz\nM3WALHrvIthaZNJGCMYhRXgj4mBTCxZcK1YRprD0AayypAQaiFIICZplxJoHqFsmyepGYN0grZly\nO4y0FhApDOpIWJVIVCWrUkPmGBweayjHFMkoqdeLF90z4L4o/xFtf9gcpj9w7+yfUOAkOEoUesF+\n1oGcfeC0NzQiQgKCKjUIq+BcYnUdT2yKBte9VKyHdQmcaWbmt6JEp+u4d0LXFuHOdmZe3LuaHpq2\nL9SQ3sQM57A0bagESjaGjnRZh7dacL98EXMXOJwOdDaeEHFx/lnfEr/A+zZczCemIBEJ0REpJ7H2\nv1HsLLYTpcSuYxJfCFTVcwKC0cQ1YKaBhoeY0nOKfEDo09vQ/z8EIa8Lf+0vvM82NZYM1+Ml5/t3\nGBOu6jbmubo17fHIZrt1aDa5FTdROlfWnAqH3OcniUSieoDoNhrFQQH/bikSFDT4lK9KYG2eb9S0\n9Umdn6/YGcITgRmnU7beXAb1NxQ55zw5FcIb3tAnOF3YgwsdU29APKdI3nCF7Y17Ini4YTvT5sSb\n9fv8K/XrSYO4NbIExt781X4MPWegN5Yd7cohghSSZLQjmNYRrR6TzJgdgW1ov6686AEjBUdBETcI\nOWudFEOb6+hExLN1xHnJqPOZz+6D/MHLwB/b7Vs6sbeAtoAEyLLyUCKRzKvWuLQMrXEdlEcbAx34\nznzkU0vMjCQZECsI4tzyOJCoRBILKyuBDxFemPL2mHnSUazrUtmkE8GENhoP0i2ahL+zf8R3TxCt\ncdLATuHnNol3dnBFYGPNTTzEs5FMAp+tKy+q8kwmmrjhQQ7R0UrgoDu2GbZxQMicxAvhHCYehEhR\nWMQwTRzTnos6MgQFRl7oyBoWhuB5dgnn42tIFHVaZw2+Tq7aSJZ4lOCurKwW2FngahKOFG4VdrKy\nVeNBruzbxBEcIW9KLDNjSJRSeJav+CAYqXo+2GV2K2yrzr2PwMmMpQZiqNTqSDOnI49CZbDKGFem\nTSQelLvxBR+etnyqjYo4ldCMV/vKuBPmtTHGxBoya0rEw4mH48pWrrmxWz47VYxAs8xaCu9tE5d6\n5LW6RuDUjJwTrRixFU7qA61bE35PAqY7MgtbCbwuMykkDvOR97fKPzUNvG6VOwY+tYGvP2h8VZVd\na9zJymt5Ss57Pls3/NpdIxYl0Xg4r/zUVeYrFwkpRz6vjY9uG9dbYUPwPLHSeNEKJwTVwlwy7zxQ\nnrCS8gVBZ1pbebYPLBhPhsB6gFMYkaWxDYpoADvQJDrVUAyINHEapaZMVkVbY5capsqUNlSBtdwS\n1SjVjXWGENmZ9jVIiW0mRGMrhx/qPv5x1yJ3KSA1MpkyWyUI3KSEEtmbMDWnsQwVvlFe8Wo78L08\nMJmyXWHJE0/vDhxj4jBOPMsjb5WF3bIClculsh9GgkZK3kIsvKuv3JGueeF2GsBsg6rXL5KUT4aR\nt5o3O1PJnAZ4op73pBGOObGTW95uMwasow/nHsktT4s/e3ZLQMLKnBKg2JJJsXCSwIUeEYGfLs/e\nHEMC8wRfXz6j5cRjCt/OD/jm3XNK9ufrrHukZhgag65cSOW3Lp/wjeMNDErNXq89brd8uh141Gbq\nEDhGeEsPlJCQEKkRvlaeczNc8I7eMK4Ni8Lb9Za/+M2XpHTrOWCyvR9ih6sedL8YsqizZG4qcv0M\neZaRnaNEIYExITLDWH0AaTgt7+4hchJ/NrQTL/JIbg1rgdfDjkdrJeaZWCK3ccfz8ZolDsTmDek3\njq/49viQry6vkDzzM/uZZ9vERYWPp0eYCF9dPuUsFwEjVNeraxDMPK5FRP7gWsQgqBEHhTmgk9zX\nIj1WCQmB9TTAqAzWiBhrSNykialULruZxllXRXRtag7FnZXNTaFiLOykD/t7LSJW6JUuY1gwE6ol\nsnntG6TnQuJOzdbZNZ4dFbhLhmoBAtkSU2yMWr0W6fVzUOuMwB/t8PZH7GL+w205REjuD38W8J91\nGPDGnlvEcyGki4oH8QT5Zs7ptdAD5oV7cVzUM0UroB1KPGcg3YubetcfTZHonbaZue24+MU8qBEw\ntLuPpPv3Fc42D/Es3A+JACSDErwZPFtoq3mRfHZ+qz9wYaQOuzfrVDRTzyVC7rOJirjNuIg6BzoE\nBjHoep8YI8E9wjGcomWhsSEyd1zmTJFzy3H/bIl+Y67jjpfHwrvvXzJI5naeeZw25JzvJx+1Vaek\n1XrfSPjberMk40Bbnfct5lqpgJC7OPL2mPlgWTh2k4sQuxmCGWv0hoDmguQxBEwbZ5/LqJUpCU3f\nmHicDSwSzY0V4jmI7Xx8HauJIh4IjDGFc1hwp9tJP+cdEEzn7yNv0Kf7XKf+c+w29iYdOQwdzRQv\nCP3cn5Elf99zs3Q2gzhf2yFE1t5g0hwFiuFe3MTZ0jyIN8j3lqHBqaf2hbC3MwqXepMYc6ehckY+\npWfxCCp6j17+pG6pG8T4ZDRSZeDjUkkibMLALYEUVz4q6pbIUinjRC2hp5P7ZKu15ieqo64ikG0A\nC6g1jjXx7VX4XhrY2YmHY2Qql2yTMCzwToj85k3lW6tRQyBoZDDjLsHfPUTi0VHuTGXME4M1xpS5\nGIUShD2ZfQSNE9FgFHiYEkXhcVQuxI0amgBroeSRGiLJjjBm0lz4xvbElh2fc8c3rox9y/zmOvKi\njV5AmB+XKSUahSyRu7kSNEBoLBG0KQeFURS1xl7gZq1szZDQ8OdehJaZgtHWRknZnbjChlOKyLih\nWWCdj9SlMMaBqhU9+f22FbhKA2oLS1EuUHJbeToJqx55TGOuDfLEsxcH9gLRNlyllV2AZo13NXAn\nBRsjb8cjXGQuU2HRgcN8Io2VZ7XyWd2z3q186TJRuKMRCWPgk7uZoVUux8AuKNdJGOzkWW6hEoOf\nn1X9ft9roeIU0Cct8rxtmDZb1AqfSaQEI2fhF2NB18jv3K1sN5HHTdgMe7btjjEVvrZV9i1x1xJV\nJj6fRz5cZog7cjGePjBYK2tUxiFxMRYeFHe3qm2l2cA6L9yKYssdqoGjXqE6Y6OzJB5dVN4vFbKb\nlbw/efZWUSEUY1+EgxoWhXfGyjSa6wgIHOdKsoEmN2RTwi446qKBk0GIKzFOvD42nunI85qgVWL7\nEYsP/oi3qQROFxnMGAqsKTK2s5mP1xui3qTsmvLg1GAXuV6U1Iwlw+0YWVNgtyw8zoWWjf0gXN45\n5TqEwDL4lHBbvOhfhqHvgaGWebTc8SAu3I0TZsYTK7TcKJr5ynzDtGRebEYARk0IjUfqUaAaAldH\nb64+2+5AE7tWmSd1gru4IQq5UQWuFr+25x+QsE7VnwlrDqSqaFC+UT7FhsC09ofxqFhImEYesaIS\neHy4Yc6RwkDSSG6BLbCzBSFgofKNw4mPd1d9UGcE9frmXIuQAkOrvJgeccyfs9k8IcsdYncYl1jK\nSH82tuK5ZrD6mtTvVTOcY/ZnnzD/6sI4dBr/0WiWCX0AKXbFITc+ni65nJ1CpypcH5UXu8TD4jIR\nSwtvz9V1P+djpCd+dinUlmihghmv4par9chXy0dgmXPxH1sPix0WH1Zr9ie4GLt+DspYOXd0Mcl9\nzTCcEiqGqNBqH/Qv/Zm39Z/TwWUXJuJOmRZBKmqRYdOICxymqR8bf+OdgFVjnUeGaQGEuiSGbWO1\ncy3ide731SJdIhBj7UKSXovgw+4gPYhFoAbBFB7UwjFkRwmtUi3QQmRbCiKB0ADpg98f8fYT1TC1\nbsttAF1Qfy4H5cy7BG9YpGuKeGMvHiWwJmU0d5QxUbKddUlu2ZzMdSUN8cmuun02gF/HjmlqF8iD\nE+jOIbG1C9iiecMSorssDZ0a5SOL3miZu5idg1gd01BqNxHwRa1/KQtIaFi/ydWcYod1RxTozY8j\nUxp6IY/bxDly6byyhoufRQ2NfgFHdbh16LS2jJsOlHDPZr4/EEIg0tgE4b//ex/xH731PjEKVlaG\nJDzOg9PGWqM1pQFjSuSU+/EzOOf/LCtSzjbYxlILu9ED12KM7C4Tv3xxwdeH7/KZJtp9+LpTEDOO\niIkER5KiW0WHEJCU3P0n4kgbgWSOwul5QNkXhHD/45smJYi6s4vYPYp0/huzN7RKlfNxf+PeeJ+p\n1HOt7IyF9r9tKFXdiS73U9z65MRC1xA1b99qc6SP4ItLs+gLc2/chW5WgaN/533JdF1TCO4sKUqM\nA621N/vVreoRSDne3wtdJYcEtxPWUomdl/wTzMjj1Fxbhjli0TC3rbZEy8oghS0ecq0t8Mwaa1Ry\nw3Uc0nogYSTg9EkNnkFiwUNbMwY5IuIT20NN1Bp4jLJLK8eS+VsHZbGBkAqDjRiVGgIUR8KbBJRG\nZWQujlpmNbRGQogMYjwIkdaMNUaKKp/rwiADg2R224XbtmWu0IbAtgokEB3ZxoUwKh/UQMxKCRd8\nfCdUhLWtjCESraJRQAb2quSSGDCexswiM0WNV0sgoSw1srbGNhTeGRJfy4W7NbBfG0hC1xMnPfIo\nTWzKzJOxMjZj0cheM8cl0FIjkbBw5PUhMwNVBBsyeqjYBiZgsxaiGSUL3zs07k6ND3PibhGOcuSh\nClOY2W4CY12hKXGEz+9GQhI2i7BGw3Lj5thoAUqN7GWL1oUgmTCsvNQTD4cBaZUxKb/wIDDExDRd\nYO1IK0oaElFhrpHP1sB6iOTBGCy6jqtrAsfJuFjvyCmjKWLB7dypldMakRj5+oMtMQa0U3mWMrGN\nKy2NfEWdshzbwlV7zTFtsHZAJsXCAjET7ehFlwgl+kRw0cahLZzUuCIztZlkhVeyUFfhaSjkcWC1\nmSnPtHhJITM34aUZN6fGoQbu1sRrMaoFprmHnbfIEiOwYZVIkoikiMXEYIVWvFANFpnFoCrZhBMN\nNHFXxh/zSvDDbS24icEx+JN/V9dOWXKDKDdz8r99lUc22piKMXcH1G1ZuZkyj48rNSWqRC7mzkqJ\nwn7KPJhnjMQ+bTmNjatDhR74u98MQGVpgahKoRfAADoQgI92lwBcrSv7KdJSw6QimoitsITAkgKp\nKbn5EG+R6JIGp3kwSyTgNcuho1GRQBW9r0WGOHOIPiyyoRDECC3RCGzlyOvRi+95rDw6za4FXuF2\nvGCVAKFwbSf2k5ejD46V18OO67pwmzKX64nLsvBi2Nw/X0/5AoDVMu/WZ7xz2vPbv7PwT//SAd2/\nQ6gN3nsJ8gQa6DEhVfuzMWMhIhSvRU4Zaw35X18wrtlrtho8LmRXoVPbiyUG2/JLyys+GJ5iRT1C\nxQK5RmJzHZMysrGTs3Lwuq7EAVFI0kgB2pj42nLnoa469OKvgkZi8nN8rifMAmNoIJW6BenZjICz\nnXzEjwF1U+4lB97kBcZe42hxREd7PmkIHkxeAywWyMUYutu/BiOsgmXPq2tzppmQx4K2yFpcb3d3\nGqmju+NJNPICeXpTi6gJXiE3r4fPxlgogwZKZzpZr0UEKL3WOR+Dc46oBEOzwWxvpClv8lF+JNtP\nVMMUJNwXtq5lcVrR2aHuPkS1T/IdpfGfvSZ0HUawQBFja5F6nuqLT9vdNtybLiQieFPj73vekd6x\nn+lqX9jHe//5AJMGiqlrbcSn24j/zjqCBJDPttL9nTJKk9Dpc9a/rhfB94W42H3DZH3fzporCz48\nSecLjS9cVF9oCET4wgXsRbKIOmVGhCJG6iYP/R8D+DR+GHhrNN7ZXPFbn5z4pfcSmyGyNpjXwjnA\ndanV9xO/0VtVQk6QAqEaVrvdpHoDN3QTjvOxmXIGGv/NX/pp/sL/8JHrfUIgWKfLqTIMGVPtN6d/\nJ7tvhPx2M7yxGTSyik//hDdI0A9ygVWV1LOqvqi5Or/n+XiY2fedf3cVfIOkndEhi+cFzh9EYtY1\nHT6VAnfQCXiQoDXr1DwlD2DqSBDi9t7egAdMteeJ9YETvhj5C/dQW3eHDN9/XOSN3uocfvzGjc/u\n97fWSup/D9zfD/+4m4j8e8BfAb7WX/oN4D81s1/tvx+B/wL4V3Htwd8C/n0z+/wL7/EV4L8G/hwu\n6P5vgf/Qzr7rv99nW713pDyf0bMGbF5XPo2RSYXLoDyKytvAvlYWEhWlKdiwZRDX7wRxy1QzD77O\n5ww47dRMaUjO3EWnQ/7ydsMvjBv+5fQZB1n46O4Bf/PlQiEhZ5po6AHXnX8qXYtZFuU5KxsJ7IZM\nbf6g1NaIkrmyTNSKsXI4bbgclFUCMcBl9nv7VANrfHfgawAAIABJREFUuORyrIgFWoxUqdxV2ITC\n48HNEsbgGWJhPbBRIUpjjo1BRsYqvGrKU1u6AUnlckhcqzJa4WYJ5PaK95ojVDDwSZn4/25OpMV4\nOQYudompzqQUebQNDOVECBPfPcHPPUg839/xokWu6omXIfBQMnNT3h0S39vPzCpMFrjMyk0xdgNs\nVmWblY2MvFwK70nzxnVJfOlqQdWYLXCQSGxCygM7IA+NR3qLhso2RR5cBu5q5UEqnNaKaGPYXHG3\nBkLaU2tlnCYWDRyTscYdIRoMlcOhYRlGE5bQGHPESoOYOWnjwiI7VcLGKbuisw/HBIZuvrMm43f3\ngZfrRNjP1E1mrQe+ejlyWldezo3PNGFhBE3si9HsGmThZMEnyxKZiAypIE0JCZ7VzO088ip7kaam\njMfIVC+YbOFGfD3OAsQdYo0h+P5YgJwGqErq6+uD/pyorASNXInxDisvNTDsjCtVXq0nNI6caLQC\nr6rnOO3exF3+RG5Oj2rsrBJTYY0DpUZGW7nLkz9rWmHSSgmJfZo45sh7h9v7WuQVE5mV58PIl2+P\nvJgmDBjLTNDM2CpRGtNy4rlcoEmp0dGHTUd1oghLGhjUqFFIXyggDWcN7KeRt09HPhs3ZI3U5EZU\nAtxNoz8D+lqzKYaipPMaGV0mkNT1IwArA5IWR9SBKn1A2QYq9GzBETXlZtqgAlPPJDrmgWiNc5S8\nh633YaNGDGHQGSMzlZVTHtiUlU92lzw9Hjq9Hh4WpxlO2phS4CtyyxQecbw5sLv6hCYXyGnxnZmN\nKCtu3h/8+KvBTYRdg0m9ObjNXoucsteB0tk3PZ8xjTtId/yz/0Lhd/72Y0YBs8yAMrSCAFkCj8vx\nnrUuXX90dosLgFXhboxcrYVXm8TF3J/dzb/dfS1y1vmJQs1AoqWCMMA9QNsZMp2Ob+DPnFicRl8c\nPQIQMVSDa44ASdVJEmtG8kqIwrqx++srmFHnAVWPvZDo9NtWJ0QaeVoxhaH1Mxm0G6W9qYmkX1ep\nBNogBG2kHzDIDCGQKtTk68pqidgH0ZtWWKJfZ2ZGK4EU7L62zfLD1SJ/2O0nqmFKwWFiUdAgxI4q\naC/2UrfY7qN/UvTpi/M+/bVMgOBBsjEGWkcjHNHB1Wfyhcyi5O1GpRsP6Nmcod/oOJXsfiJ/Rh16\nsNa5O/agVL3XWFXMES08wDbiYaTnvKdoPTjV+gUXvS1agzgdT6W7wxlBomu6RIn4BCh3ml9S6dok\nv4hLsh4+CUT/nObAAQm3KY/WnbPU93Ew8YYPb6JEYCfGk7EyxYHjUrk5Fp7dzbx9teFUjDEHxu6w\nlzSwSvNgXHlzXAzPyIoqnpvSjRfmeXbKX2vUeeXmsDCkxJ+aDvzd9dLPVT9n54yqKIJFCM1NPjxb\ny93MtFPjgrkV+IAgndpHMyy5mQTcA06kFO9phNLUebZwr3O6p2/2aeK58YjRtT9n4wYLggRvOs/u\ndg4COeID3jBXMUfB/OT7cWvmTbCZ69lip8l1kxDgvjlLMd83fsE868mt8b2pLqb3TVERd0q0TjH1\nYLg3eWHBqtMn+iKfuvbq3ob9iw34P972AfAfAN/qP/9bwP8kIr9iZv8A+C+Bfwn4V4Bb4K8B/yPw\nZ/wekwD8TeBj4E8D7wH/HT5+/av/qA++EGXAkSUPc/YHog9Z/KE9a2QOymvz7IuFxDasPFHjJBNz\nPdJictqSZLIEigrD2TClG4DQ3FZYMGJVThL4G8X41VRI4QEqbrEqg/9eA47rhgDdisKPdiGYugPm\natwlZXMq1OihgZs8ICy06vfUEOBlnTkVqCGzGAxxxbIxa2SpypAcJbZYuEIRSVCEdVjZsBIMrlqF\nMhND5suy8mSjaN1znI11DGyoVM08L4YEZa2Bl3NhRQgh0+LAp8vKfhESB57EiF0IpoVHRXi6m8nD\ngX/4uTCOW/anmbc2AkslDpe8HxaWaDw+Ze6WyBgCn7XKT20C1+ORw7yyGTbEq5mmCSvCK1aCKO+j\n7AajrXdcbQ0bN9RqhHBEZUDawsyKMCG5OM++GtWU5Vi4nBJmyi4ZdyfjWI7ENHKUC8o2EYtCbKzL\njJY7ah5ZS+PQAjUo21SQGjhVI+QNGhbKkrgpC08CnKyR2LK0xIslMjdjysJajddqvBuV2pQljaTj\nyrM58eu3lU3bMJhwk4FamMJAlCOxbmkhUno6dZTMGgqzCVoEq0Zq8EiMjTauszEEJZlQJ3dDDWvA\naIySuZ4Ky7IwJkcRNzFwW/fcDYlZDZXEGBp3xRANjrSY8loDN1Wxw8oLIq0Jp+harhIETYlYjVf1\n9MOuIT/WLVmgSCSpMbfIiGHJNXcXdSGRmAP9+zaehiOluE44mdNw31sOxGC8c7xlx0prHfWP8LSc\nvPYQ53O81U4gsG2FT6cdwZS35hMvB3epa8GQzrR4XBwmiOqf8yxuGK0R8TVmMCUJRFG+dDzwPG84\nJmXIQlOvRZYYidYINMScaXF+pqV4BIMlNddvViFpQ+zEw2XhVZqoaWHTKteza2E+3Wx5+3QgmzHH\nSGrKJ0PiLd0TZ38ObpbK55sNbYg80VekKFyo5xylpjzb7Hj7tNCSsq0rz3dbdocbrqwhuZDDBuUF\naxpJ+YbwckNsT9Anv4c8PMLtA2S7x44PoD+R6ZE0Vvw5G9Q8c1BB80DcL5hGAivGCb1ZkbjhT46/\nxbfuvkajkcRYNVJEaNIIFtHQSGqoZHZ2ZAmZapEgFRO46ojk9VrumUo0cVRfDR/G+suB4EhyG9jW\nwskyp5jIoTLVQpBK0YlTGtjUSpaVUif/hrK+GeKLQDQGVh+KtM6USV3wkYzrFZbYvZiDkfKKmTfi\nAY/CGcJMFSNWj/c5D/W9RD13Q71Gjg2pkZAaGKRQOA5ez4gIbTgR7q4p51gngUutqCkrmcEauZb7\n+nsL96Zj8GbY/KPafqIaJumTfS9KvVBO98ExTs3TICRzFKJhSOo3AtzrnsAL2SZvCs5z4XjWinxx\nSyrE4HQ7Z9z1Kf0ZupY3iIi0nr8jvXnqDXDsDlEqHc3pdKCzUA+8QTkjG37DKPID2tYcvKdr52wd\nrKczi/NHv/C3ob+mOGd62wKDqVtl9h0TE0ek+pER63hUPzbWv6fhDdhkAbPGnXizFLIyTRsOBZTI\nR7cL8fbIW5cTV9uJFIUxCjRFS2VMjl7E4BSmEAK1VIpWhpBQVYZh4HyzlcMd+2L8H7/5OX9nnRiT\n3TctqkqOQjE/CmLaA2bt+4J870NgvzCNeGPW0FDRez3a0NGTVl2n5JbbAQ1C0OroSvNrsTMgwdx2\nPoZ+Dap64Stypkl7E93cwU/CG5MIgJKF2HoGlroVaIzJFxsfcjl1tGfdtNbur+PzedPW+oTaLcWd\niecLb+SsyQr3mruUvNE7I4FndE7E0TvreiXpzfy5STxfgz/MZmZ/4wde+qsi8leAPy0iHwH/DvCv\nmdn/1s/Vvw38AxH558zs14F/Efgm8Od7fsrfF5H/GPjPROQ/MbPf12v0YAOX6kLhEn0A4l9cfTjR\nz5e2hIrwNASehjsuc6KJUm2hrfABkduep1JlROKAxeDriTq/XM3XJ+fNN9CCaaQVJbSExOC2zEGI\nQ0YNdy2spYcXV0AwSYipW8fjjmd7VaYwsjVjSoWqyu0pkHMPs1Ql2Oz3WkxYrRTNZJuRFnj1upF1\n5YkkxnDgYVMuR2Vc4XpauEgBhkqdBmqdHRFfG6QE48AijX0LvFgy+xIoc2MtSlU3E4iaXZsZG9dp\n5GfyiY00NsOJr15nbk8r//vHmUPa8O7YuNjAJJkhf87L+Yr3J+OTYlxrZB4CprfEaeJqmZlFuLPM\nEpRlUaLCW5OS4sClwIUsaHJzi9tFeL7seCqZORwZ5kKOiWkoDOKpLoNEpi3czoHbYnx0HDkdfPjx\nYIBnq/GOQdGVl8vCZcy8t4G9RS5plPCaa3uLQxYqBx7mkUONhLhgCCuVdWmkuOVmXXmuFQIcFsOa\ncqHGg2nhmSZSCPxUW7gMgoYDpzpQLLAdlWNtqCauByNZQwflUT5wuYW1Vj7eJ8JQeDwBbeWDJTOI\n8uR68UmwNcbB7ZCNRCiR2zqzGTJ3ssC4oczGd2phqq7vPTYf1s1rYchbLq3wQIVDmzmUyqUI4xh5\nUgKnuRGGDdepEVLgbTnRWuU7d4mnV94waoIlVEx/sil5G2aQBwQ8XuB2SAz1PNh0+cDLceLtdSHH\nwKeX23sEf1iMx+ups2Pg5cUFcZ4ZTJ0GlzIWMpv1+GaC17dpXWgy8DpNDJw8VxD48umO15uJJQae\ncwXAO8sNNWQkDCxxvWd6hHEil0Jombu8IQz4g2tO9/WDhEaTSAoJkYI2JZwZC9kXzaEWplRZWiRm\nQVWYVkWDR15c60Lqg8hoymBwSokPhh0/d7zl/fWWTx9c8s6tG4AIrt95Nfp+bk89gFcCkiJBAjs7\nMtvEh9cP+frrF6wy8nK65Ylu0KgM+iVEP6HwCHYHsn6HeBexm29gDz5CDlfIw5dQAnL3AG6SoyLB\nh6hSKlE85iM0o8URsiENZPiE9tnPc9w3fp2v8TTuey3nNcZlWFk1o8E4xUhuhVWMo4wEet4TmUDx\nebuXjl0+AmkzowLP7QG5Na6z53SV4wbRwSlp0SMiLtOd1yYtYgzfV0e2kLiWhUPqzsclY0RKryXN\nIMqRExsfIltgF/1Y324Cu1VJuDFaUKftNfFaJGFcSKVKJkgk24G1txGz+H+DKaNCZkVCQYdGVHEE\nTQOjVk42UJIxHK4gNkJQUvQhyqFOtGEhHQduL07uJ7BGylAYWiDXN7eFHH+0g5efqIaJIEj2qXky\nB438p3gvuvdS+xzUqfcXUaQ3Q51OkMSL89wDTBX7Po1UEHdvs45KuJOb3iMbxIA2d2hbRBnPVDkc\nlqRTcnIv1BX3TgzqpgISwxfiyrwYHc5QaZ/snxe4aJ63kxB3LCOQhnjfFBguym/BJ03Rzt8ZECHj\nSJslcc/8LwSretFs9/JEtw0XJMibjCnVjnp5k5CiNwqv1so728z+tLKuKwfNHNaVr19HjIIQeXAx\nsVSjqmIGS21M2RhCZMzxvrkcYyaOmZgSliOSE+NSGKcNR93zax/eshszWA/9NbzgxKl+kZ7HdKaY\niSBob4DDPfqj3bq84IvCGaLPdHc6exNKe4aWVZz6l3pDZx1lO2c8hXR2pYu4eUbw6+ML++IuRt2K\n3sz50f36Gs2QKEQz0ihE7SiVRUfezPUcGI6EeZfmC2bwc0921DCKZ6T4d+gOOjgZIHd9XUW73Wkj\ntX7d3jPZPNT3jIY6WmUgrpUDesb8H83W0aK/BGzxIMl/Bl+X/pcv3Bu/LSLfA/554NdxVOnv25uw\nSXDa3n8F/CLw//5+n3edG98cjafpjsm8a1xr4DYIuRjX8cRWAkMuTMPMBcIYfVjA2pjbwId5YigL\n32XkEy206sdeQkJFu/MmIAmz4sitRbfzVzBN7jrEEVE/z7UZwkIgoTGg3WGytUaOA+SAVIPUSAaM\niSI9e01GxmSkbSGHkWHdoxI5hIlJZzaqDPs9b03KIMZVVLZDZUqVB6ExbYWqiZgDkx55aVfs18BL\n2WLF0ccPVuGzFhmtMYWBFTjWSC2VTEEUxo3BujK2DWNQvhIDH7aRqzYTLi54PFV+90Xlt24v+OoE\nP3N1ZA0nrnLjIkbGbSMOlzyOC2P6/6l7s1jb1vQ86/n+bow55pyr22t3p6vGVZUqYxwnpJGjGEeK\nYiUhAawIccENF1zQCCEuUCKEhERAiJsIIZAQEhJKLpACEUI0TgIGBYJJZONYMe6qP/05u1nd7Mb4\nu4+Lf8y1z6k4VQl2bNeQjvbZe81ujW5+3/+97/N2LP2e3cFy6TLJFjp2SA+f7RxZJ6yDKRu86ziM\ne26T4WWy3NlzKjDVzBKHN4mPJ8vH0yVrX2GqaHyAl8Q+CaehsL2KLIvF2ZHOKOfe4GzlUBf0JG5T\n5rUu8nLTcTsE+pqAzDfLipuD4VEYWdIIc8m03DJjYGEKWSH4zO10w3lfKN2Sepgw/ZZFnvjYRlxZ\ncVn3HPbCcwrv7wMXfsmFLZwPhqv9lo9Kh9oNWpa8p4Z38oKzEfqtcrHMdB2IDHyQEyUaXohy0MBX\nDz2qBm+U3dgUCsl2iCi5DtTJMCHYrFRpMrBvxfZdFE0jOAYdMZvcCLVWsRpQAocMaSuYXFqhXApL\nyXQ75T2BoTqc97x3Y9mo5XlK7Fzg+d3Vb9Yt5Ldlu+lPWC89MRkupg1GA2IPbMMJZ9OeO9dzWmDX\nDe1+bTxoBmMRV9lWZbNaIvsMneNFcQQnuJRI3jIG3xbeLETjiN5QjWe13bFdLZFD4maxZBUjd+sl\nH5c1nRY2xrH0TcL0MUBojfvYrwi1gm9T6Zv1GrNPiMIUFg3qc/zltBKkNbRuzFTnka79fLUfeak9\nvVhMyEQCdlEpU0Y6z0dnp7iDEnvLu+6UZXrlkf7ofEEXM6/FiQ/PL7jY3tInYbKeQ+i52O1ZEdmG\n5mHepPZdl8OCwSiTGl4MZwCsx8rz/pzLcYdPgSyRcy1MXOGeBUz1HPYdp+cL9pcbVvKcpAZTJuTl\nAlCKROxrH8KHbzSfhLWzEslR1xX8Y/ixA2Zh0G8P1Hc/T3lxyzvvXfG4XINZzrWIb4W05uZL14KP\nTQLt9HBfi+xkYFEPHOgYdGIn7fdMZAKQY9vn5xpJppIOy+ZbtxBpcsWqwiiOZe4oKqSWaIKq0ueE\nGyJahLvcI0R8NQ37f/RdG7D+QBKDzW3h3rPH017jLDWLRC4F69pUui3LG9IMc9jxCjxykAFQnEIw\nEOox3gSsq+S0wJXWSFozQUiY6Fk2XjiJxCTCbrmHTSMblq41QWl9hzssKcOe1FVstWSj1GDm2BOg\nDL+p1/X32r6/GiaBhREmCkbdLIlrY2Rnzf0KsWJmL8kxg2Ymjom5b4KOTRXapHnMDZcz0ih8vJoO\nGCMYa0mmcKSpiW1YbGNtK1hnWZb3nlIKIQQ0Faw7Ni8z9jt4NLdg1KPc2M7ZUBnuJzoG7iEDVCVY\ni5RGKmIuxP3RY6NNSuWa1WaWJTJLCwEM1rcGy/mA1npf8LcsIm33CZRac8utKoq9Bz7YeRo2TzOq\nJZWK2ubrGLNiMlzlhEP42Q8zX75Q1sGxCIk+eFLhvqmtFCatzSORM94Ifd9jFh0ll4Z+L7Edk6IQ\n9/zxL5zz1V8amREFeLGzL6ntoqLHX7n5esQ0o6YTIc9EwGqab8hohtz098cGGtrkDlWcCta6hoTn\nVWOUFEQsSAMxeNsEl3NwNVVra+KNPapCm7J49jUdaYkyn1NVFeeOslFaM6ytiSta56loy5s6Tp7d\nfHOU+a5kjDawhbTzNs8LCczeGufM3EC381lVcVi0aptwuflamX1k9wyIudnS+RbRBq/tXCm/Cbph\nEfkhWoPU0zxIP6mqvyoivweIqnr3HU/5GHgy//+T+e/f+fPjz/6+DdOk8CGB95Ohp9CL0hXDQ7vl\nrHNYEQ5aeH+ydPGEK2CsnpeamdRSqqNoW22LopCbnjpLxeSEJKWaOit2QyPFofcAD4ehmOmeuCiF\nJgGZj1lOBTEJIYEJODGUXKl4HAkpgtpmILeiBJ95mizeOn7E7Blkw8NOMSHRaUK0MtWKWyTEVjoJ\n+M6BOJI49qPyzbHjeexIxiN6wqFUtsaTioFS6V27D5zrxJ1aNnnElcrv7j2PLsGXzDRWsoW6T2Q5\nsKBSpsIXe6ULPdFU3rkRzu3EGwvDbqycLiZ2KWGHJbmAcWv240isiRcbz2kXmpFeK8NauL4Thr7w\n7JBwxiIS2E6JnDoSZ1wuO56kkYcSkRTBdsSYOdjCpbH8wDCRDyOjKsEuGUyhWsuUb3HGsNcGcNjX\nHbe1w+aKlxG0EJzhxb7yOEx4s6eXgTpVvFyxdg2bqyWz6iyYPUU7Fq4ZrTd74TBWPq4e368xhw1j\nbLRVZyzGLLFSyTiWwfEDXnhZDYeUea4Db+9GHrg1i5VjVzo6NTwVeEwjNRbriXpKTAeqKsU4Dj04\nVYaiRGNYWocGyzJO1GxIArtauChwcBNJDQsH4zjRG9NIXlUJGPYoKQrqC944Yk5cmMDKOg4msZ/g\nmpFUDLUmduJ5mTOpRibnONlEboyhimUqBVMLU/3+Kj2+c1NbuZxe8mI4pSbPQipVlrga2fU967ih\nOMvOPMDFA9k61C6gNIlrCQaMQ4KgEjBdpkuFGnqyaTLe2i0ptpAJlGIwOpFDR8ARe6GWzN0yUNwC\nYzaoH/CiUBUxiVPvuEEw7oSSd7iF4LWy0x5ftqT1mnG8Q7OHGf+tJSC1NPy0VHJvIbeADgpE7xmC\nxSZIqjR4k8G6FUpu5+Oy4zRPVCw5KNk4DBGJQjUeDQGnhbw4a7TbRYdVpXQ9uR9wxjcv31BZlEiS\nQihCoJDdgskaVrmJCJ6tOx5uFbfYwqpDDneYIoypUorj/Q8WnNpCWk5w8zqcvU+NHe72LejuyHGJ\n9Rsya3yJgKJLg/mJc57/HwMPvuZINwbyiE3vM5TCF04s3xwv6EpGNYMfoBZG9QTZk6whqyWUnugL\nIRnEHljpnoxh0InoPNUKISdcrSger4lJPCoGYxuttjeJVDoMlSSGhMWiXLn1LP1WhJHTlBCUaepw\ns5zPaVMj6Bxzkz3UKjNpePa5m0ZYLQolKBwaEMkKmOGKun1AFsXPrjPBYo9KE1uo1WA1g60YlyF6\njJ/Q2u4xIg1/bsOhWXLHJeImSg0gFanCIhf6O09NlhImQrTsQ0WpVNOO8+qm52AdrjaCtDOKzYa7\nxfNf5+r8R7d9X9213FyM9jS0+IwHAAtOtKUsC6i2RkXm6cgRoyxW7ul59khqm6cSzrzSjDazX/tC\nQxrGF5RQ2sRFnLtPdm+L+615+qSPRQHx9j5T6IiTZi5ShTamNkbQAtY22U2ZpVoyTxC0gs6GFbVC\nKGCMJVGx2hj9RdoKd9ZKb+ekZkPTntKkh6VUvDNAI/fNbR8qs7dl/hdnXGsW7SckbEbw2v6tPa0V\n3ddR2cWC8Upxtnk5cuRxMA1rWwsVYYoTiCHntsIWc2HoAjFnlr1ntZ512FMLdtN5ckgpxKK8ex35\nL371Fo8wmW4GJQh1zgUycyNc9FUTwLwPmSV10DTbAKoOG15Nm47HXI8yPicNpmCbRKHOPrJqmi6c\n4/vP++eYD1ZrxYqbg3NLk0vOUx/M/Pu3MdEcCKt4gc55VNt5WgyvzhkDlAYnPEoljJ3H0bUd13sY\nBC1kd1ayNxmqtmnlJwORa60UbZMpjJBqK+4b8cg2QATthuqco9bjYKve+5jKPYL9N7T9KvC7gTOa\nV+kvisg/+V0eP1/s33P7ro+JBa5iwpjKTgI9haVUDjrwwSGTNTNVYbRLMsIhZjBd00rPMAw5vktV\njAlNcl4blt9o21eV0s4zm+aJZvv4prZp8dHbqKW0zLVPfMbjIkCwB15zPV7g0o+8iPBgueAm71ka\ni7HKlDw3NTLtR26YWIeOX/SFtF1hrOKtspyBI6lETlxgv6vsk7AXoVaHOOEwJVwfqLVAaoxQb6DU\nzBQzoRoGB6ZGfLU44/koRz54btikwoUU3lwn1EykQ+a29Cy0EK2hz4pzgSdDxy4qsSROTwzTtERC\noc8TznXUuuW8C2zyKavFRC7KUDzqWyP/dJWZ4kS1AxmlzwcOSbBlx6oP3G0mBkYohZPO4etEsJnb\nqfAyJ2Ra83xzhxsqh7zn/WnNh7cvORssb6wCnS185mHB2co7795xEhTrIJZMlcCiczib0eyp0zWr\npXDiR1zX8+w28f6NErtzTpwhjhPvXnnWXc8+j2yzwTOxSoXVYsHWVfZR2eSCrYZJDUPfEatjn/dM\n2uQ3KwEzBXAWTSPBeXKtGC30zvIie3Y5Usa2MmzJlFLYmFZKneDIZmIymc22sB4zeRx5cDrwOb/i\nxhzgUIm1kC1MZCZdME2GTGWav29MlaYwoKLVs6kFI5lSwdtGw7JzmIoriseC75pfNlROaiHXQtc3\n2uPLfuJ//Qe4mH+nbqf5llAf8to2ctcLffaIbhA/cDrekWTNzi5YlWfYuuRi95JiDozuNSCTgxBN\nhV54fH1FNZY6L2b2KOQKWK66QDY9F4ctVOHZssntHo1bRjW44CkF8vKUKoWBQtQA1XNlDAtNza+5\nWNAVZWM7VMDVAS2QFisUS5c2VN+TqnDuCy/tQEXxmhCvYAPJtkwyEaF6uIgJg+XjMHBRWxitloKX\nyN4IZxh8gavekXTBo7pnEzy7mDkTBVPoeIXEHleeVUyU0nw+LgtDhH2QOTsTuhlKkhbtnraYQLoV\n12lPv1GsXUK/oeaAQznrr5o6onrKg2uGu566fY0S3iUuBHfI6M3nMKv30Nev0f1rUGH6a4U1H1Gv\nThAN2OlDdG+IpfJzz3ouxiv2dtUWXK2QZv9ZlIFD37P1nqc3N1ytHvDk5gZT/VzXNGhHVxNdjYhE\nlAWQUApBW+TJLvd0mrAZRrF4EuAwbvanacFmwbsIWikSqECURno2NlGmJdVmbDhQ1DLkhvHelg6R\nCR9GUG1e9yzYsbKymVICqhAPDwg2kqqjLCbK1DHYEW8bcUKlw7FHS6DahBbXCL5pNcvH5+8d9Szl\nBlt8kykr80phZu88fTHY7NhaA9qjZCQCyz2DwpgCpVPQSraV1STcrfYMNwMyrn5Lrvfj9n3VMGEU\n1VbUWhpFo1ZDJ21ZPJpXeTOg1GpxxpJs62QNQqVga/OLFAFbAOR+xb7ITMij+RyOK/JGpaHyVWej\nW+u2madR2RasSpsWtWcD8xRH5F4+d9SallLAtN3vbAWa1MuKMDPvZhP+UV44I6J980VZda2IFcGp\nUqXirKFqvjedi2meGUqhhkwtZwS7QcRS1JB05ETPOPjU6Ce14OtI8qf0Gpv06tg0VUFNJBQPYjA6\nclDltliK7Lmkn02BlrspM5jIlJakVLDOtgJGNH/+AAAgAElEQVQ9DFxtNg3BXCcen3c8vDgDb9DO\nNchEKUhqBkGD0DnPD76+5ifeuON/+jjjspBrm9bInInVqIWF6ppPzJbaTIlHmaRqk03mmfSmzUf0\nChzRHtsa3TI3rQ2+oUBQ1wABVlDTICPN4Nj2TSmtobKmhTu6CiOGvjYTZ2vMmjfNyyvM+Po4PaqN\ntpa1UV9qijhpQcXGWiqteWvNnTZUu7Qma5rBFhZD0UZ4lFKwMj93DrUzxpCq0hxSuenRFTrX9kGu\nrmVgiWuByJb2pS0tIFTnBhzhNyX/YPYZfXP+68+LyB8A/g3gLwNBRE6+Y8r0iFdTpI+A3/8dL/l4\n/vM7J0+f2v7GL/4svWsJ7Tpj9r/45C2+8tpncFisG6haiGlqgbBSZ++SbdRX07DIYuonrsuGdqg0\nvbfT0MzDMkGefWG0YxKbGeyeesl8TNuReSXBBYjV8u3YQhW/NbYb1Nv7PYjipeWbTbXgQ6CqBQLU\nSjiEhvaVhqpvk2ehmoFuavkt1Vgshc44bNojWA67AyBYbWTKKgVbhIlMcIYg8OVhSxc9OM/1mAjG\nURzkXIi7SNQVF93IOrcFpVwNOQkmVmp3oATBxsBmM0HvGXJlnwISwGHZTIWTZUGN5WYvbIxQbtu1\nfkCw8oj9NLEvAacjgxaMrbz7srJLkcEpl0PH1XXl+WGJCW0/7UshokR7wXr0PHSJt5ZbvrRuGv4Y\nM7lm7l4GRjFMbmBXC0kVowF1sJcKuwChACueXwlfiwsOdYmUhEpmGB2jHqi6wpiRaVPJ2TDUiHrH\nQwMv7yqd1ZZ/Ygq7lAmhZ5y29CjJ9wSjTDU1f4IVvCnsab46Uwpd37PLFVcmnhiHX07tXpIcB1We\nqnLrehYOzg9CLJkTCi9lQFZL9lSmsmWogcErtkDvOrxa1pJQkyhFKJ3QCyyqEnSPWs+hHEilnxfS\nmjzGLZRHKMkmQolUAzl7OnPgf39+w0+/2DXfJs0nu/t1vMLfT5uRQjFbXA2scmIfPLk+5MH4PgC3\nfUCoDGUHZsfOfZZlmrhZe7pYCRm8jpxsKp2BffAMh0OT0NcmV7xZPuU8RUyN7BeGKIaBxCpmrvpT\nAiPLGjFOWZSKi4qo4XYx0WvG0+7XZV4UPDjHed0Ra8YhLK3lzqzp6h7CAgNcmJk+x4gAKy1otbw0\ngcuypcwe5BszsOubT/GiHrgJS87rniqGbiptcTEXPh7WrdGRkbuFZTFtkT7yMn+Gz+df4q5/wkEC\npr5Ell9kw4SOCVe2vHn4Zb5x9qN84eZtbldrinXk3uMmodjI+Sby0eIJD6e/y8Z7Tmolrw+cTT3W\nT5TaM44dy9sdaRhYbg/UzZsN1lQ+g3/xPjkOyIO3kVWF9GX4oXPky7d0Vfngb77F5fQt6r5ie0Hs\nGeH0OZ8/V37hbo1LjixDqxFDaOALGQnphrv1Z9ifnPHk9prNsrAYG1rdSuXbl6d8/uPrWVmfiRII\n1WDZg6lQFywVjC086095ON5itKISsalS1TDYiKJM2VNlwcAcBK0LFEFrR+0rXVG2eckiw0YshoRS\n8RbCVGZlUeXEHlA74CVixIMccLXHJsVRsCVTbde8+mZ1X4ugitqmvNpX3+5VRUjzmEpqZl0nTOza\nxGiuRe60A+lIp1e4mzWmwjIL3iZu8xLIuNgTAZ0g+RGbe4pkdl3G352xN9Cz/S275uH7rGE6ksmO\nfxra9OUIXAhyNKbPhaxpRYNt8cMISmdnc742Ta5183RhXjW/n27oHPpIKzGPk5o2TZj/nMedjRU/\n+4GOAIdPfG45FsafkDJZ25DlwCuwgxyfX6lVW+yRyn2T1QrtGTWMoEe6nzX3qzSqirn3brUSLHmL\niKWXRNGBYjJVYVEDk2SgmYJPbGbvT1ikHWIboQsaTe3BwhCzI1LIFKJ4RArvjY4fO/N4Uxp0orRp\nVRWYcuTFTlkuAiVn9lPGGhrVTwwhBGopiDvKJNt+KaXiQsPellQYFp4vPDzDPL9pPco8Day1kOcb\neMOMz3RD07Ij7hWNooQqWDeDNDDEWnE0/1RtB7kdF50pfiL3DVOdPW4yH0tz9PbMx++TGHGkjbNX\nWhlNwSg432gQ/ijTU8V5BwYK4K2dm7AmzbO2mxuddsyrFqQcMeAFI4Zc66elflKbDrkW2iCzPd/a\nlgtS9RV2Hyy5tF/6UI+6wYxxx9DaFtLrZky9HM/N33if9N02Q0OI/z9ABv4o8N/R9vmXgLeAn5kf\n+38D/7aIXH7Cx/QTwC3wy9/tTf7Q7/oRXj87ARr9sEjLcyuaqJoJh4p3jijHRrhldhlymwRVRTQh\ntZ1H0IrAWitVZtCHSfOiy6v7SrVulkE2L59ona+TT9wv5v3bFkpaxMFxa3eFFi4o0vyWIkKHUsc9\nZjZ/F6ugLVQ70M7jYtp5K/V4bjeyp6JMcULJ2FrpRXjYCZILoXrO3Yj2Ea1tJTInuCuOx/2El8TD\nIaAlIcUyCaxXA9/aCbtkmKyhT4oP0iZYtdKV1vj3oZDUoFa4HbXFshlDUOWQPW/vDWc+sXBnfPX6\nGUvxqBaC7bB2T0qVfamYMoctdoUHklh1hc51lAhDv2TdVeK+cLmCPGZShegOWHtCiIlNddzQceZH\n+qXDF0NWYZEVdQmtgVNrCN3EqCMxda04oAMDQ6f8Pitcyw6nlalGbKycDoGp3qLSYSVRSmJtFO2F\nKUNOFWPnPDxjydWjJLz0FCYsmbsIyTsOAmERuEoZPxj63KQzRifWnWfEUoyymTp0UkoHvQZsVFwc\nuSlKmqMiSlEehJGkhYGepansy46hFAZrGEriIiQcjXCakzKJMiaLVRjVsiiFByZwZQq5QkrKFkMw\nSjZwtxNeHxyxZCQ5nhXHg+UT/tkljFRO1TGK5VvjgZ+//a6X6u/oLYnD1UAxGVcDQyzY8sG973jQ\nl/icKTpnCpotajLLHPAasST6MdMJGDXErmeRJrwKKksActemhKJQxeEoiCasFE50QwVCHhlKYt+d\nUkLz3iYZ6Mx4v2h7Z9pncDVxZxc4zfcGfYBsBoK27B+OIe2mQSpubEW0sqwTGzuQTZuQWBEipqVC\nGcNZ2bTcIesoXatRkoPXdtdMCwc0e8TNYk2hcukPvLv6IaRu0Sy8cYAPzchZ+ZBULI/1Bd+6/FG+\n8PKXMWYN0oiBNie+nF6S9pk9HpG3+ebFF3lt8zHPVPiR6Rr1BxSDLzt8qKhRfIJDL3TmJbEfyV2m\niMNIpZsqJVjgjvriEemqpz878NoffoeP/9YbPPmjH7H78E3ytwonk2O9fsryZqRLe+7mUFvqxN3y\nlJAG+hg5OVSGNGG6gcWU7tedd8PAa7cvsL60RVjtuektDw8TRTzP+xUltH38+mZHJztUe5CCqqVI\nRKU0HYkIHRmj+d5/tuIVBGEjA4u8pzeVO7cg+AMlLujCgTDOIcsKzlpSXYIotqxbE+R7jBOkLiiu\nUtQ3+BXAJ2qRwhpl34BCps6yc8H6iC3Nr2vmAUbRDg0TrjYAG4C/PWdUONAWpay07EzE3hPwTBWk\nLCja/PstKqhtn9Zm/KPfvq8aJpknNGbGHFep8ywGYi04zP1EBmYSnVHCMZZGGiIZAFWCzqGj8wpv\nFm34RLFz49QymI5lS5vkAPP7QCNXYRrGe9aRAbwCL5gmmWorvQ3TLUfNzfGVP5EFxSzFk9k/1JRb\nr6Rjx0NmTWkBs2oplE91aH5OzEtG8BT+qYfwBx51fLQ3/PSH1/zpz12Si+GuWv7rr2/4y3/yTfog\n5JT48z/9Ho/fuuR/eG/Cxz3WKr/rwYJvXI/0RnBOSbmlcAdr+KIUgjFYW+ks9IYGl1DhxbYw5sp2\nTCAVFcejZU8sE8F5rjcjC+tZ9H6WIHB/fGvOSG0Xn/Y9f/JzF3y8i/yVb0wIZcZr2xkVLiAWqU3y\ndrygynxROiyYV/hvaJ8z0zDzrirpvhtoiPZZfHlPJhNjMDM5zhwBDJX7iVWhZRlZZR6NN2/bfYaX\nad9Fdm5ii0At7fmJWfaiqfnYVNAZPlFm0Ehp5VWDkqhireCcmzOXZh/WfC5qLTjbmiqhzKeZEJUZ\nW+raQoO2RjJLo0vOc04CZvYCztecKlZaYcb8WX8jm4j8B8BP0fDia+BfAH4c+AlVvROR/xL4CyJy\nTfM3/SfA/6WqPzu/xF+nNUZ/SUT+LPAU+PPAf6qqie+yWQqmeqrJFNsWQzoyl6Yh+6MPpBhbwyOB\nrC1/xNIM2EvNnPeWAYuTsYW3GiimcILhRDyhM9yVzHsH5YrQfCW5tMmRmvalIjTsvVMMFmPal4ER\nh0ppk3Da+aLacBsOUM0ztbJ5yZw0rberbQJpqlI0Y41r14EICcGUds5UtXQ+Y0rGBNuuy1hJxhBy\naoWutTzp7zgxylQgE/hot2cdAmOCX4t+lo96TnREJLO0I/FuyZthwtqI9444TpwtL/hgd8AbOIyJ\nJIG3Y8XmgJlg1ICnEDaFSQujOqh7zGRI7orPeE/XQZbMadxw9qBQo3Kyavfrw3QK2bLZRXZZuZsK\nB1HqdMUgHRdDoc8J3MiqF4ZlT447nt9lRDJxAmc7dvuJqXZ4m1m6kfPqkWWiZGWqhlx7Qk6oD5x0\nsO5gu71hdWZ5Qzpubkf2pdKvVry3OfDaekmtew6psC+nsEx0ZLrlEq8ZK4H9pHy8F4wZySkw1cio\nHtsFhjohBYzJLNRxmCyjNFLeUhJ2Jay94vzEmAzXk1B66Cqsesu6i2xMYlWXLGziw+3IngV3aWKh\nlSgeY13bN15IRdmmzN2o7LCc0eS8o50IIuikRAPXtd0TVhXswrG2GWs9fU3UUji3jk30BLtA7USW\nwDmt2fJauE6KI5Pz9/eEqZtzeqZwhk1toSKGAQF8vKPIguL9fS3iNBEDrKcbAGwVHsS5uFVlebNj\n53tcbVLufXCsDtcMSVGx3Pae2K1QgewtizQxuUD0Aym0+faBQLYdZ2U/1wNtH5/UNn2wYqglcudW\nPN2+5KOTC9bpWGDPcvW5jmh+5SM8CSbbQRBM+ntrkeAmbs2C0zJhiNzZgfX8nqu8Z7WBb168yedu\n3+dPnHzI+eUt6ep1fmV8zhcfn5BvhProdf76uOcnv/INerdHo/L239kTnr7BT5WHPHn5Nutpz9Oz\nBR/nwrpmLJkujsi2cmpe8ta0IJ4dsLa2OmR0qG3f5PG6IgHGZUEOPdUri+eO6bUdKg5eXFLXe+oT\ny+2vXtL//rdbU5A8z3/uMedTYrEWdFhy9vhtfm/8PP/vuzsEJdRCch0nY2vU1Cw5je33d3WPM/Bi\neHS/6BgXD+mnsal6SuE8FbbrCxaHPecxcpgXv0ZO2YtnzXa22O+bvUQNmCbFy87jS4Hi5omVEI3F\nlcR62lFF2Nk1Rg1hSsCITC2XzaYWd1Jq+09EGAFnmky8NShCSe27JbgREU81CdTOtQhIUFwKHPMf\nrT3QoFEKJmGsUGLAmIwmR9EGjjFnH1NvnzbAFm3KrZZ7GrYqaAHj9FUtIgWmxX3HNP4Wwza/rxom\nb44TIe5Jci2rqKGSLZ/GRxvTCj/MLHLTV/6A9gDBykzJm430zlmKzpju+bXqfIMw8gmIxHxQperM\nyW8v+SoYdF6p0Yo4g5VmyPcYjizj+qkP80r91m5SnwBT8Or9zPz6FdNQ1qrYaqhzZhJwnzDeGWVV\nDX/4ieXR+ZIfecvwp3/4gmXo2ObEf/i/vMM/95kOT+Z03egz//4/c8IvfXDD//j+jjJ0/BN95M98\nxfD284Gffm/Li2TBGU6KMphCMplDVBYdqDH3JLpDKpg4YownZ1h1npNeOQsGbZZwVCGlRNiP2ODn\nAyvI7EECnfOlYDgL/ORXHvJT3/4asfTEueC/p9ppafCOY/MAiDaem2o7zZXvCKhVbXx/c1QWg0or\nVCsNJoLQJlGqMEswM20qY+fmPNW2WnOUACJCOAbKHjMKbJNlVW3UQqPzNIBGcEy1hfmlWnGz7LQ1\nMC1A1B4nZvLqHD82a/fnnc7fk9aRawscbM36PIW6P88+cV7RchSOuU6t0/zEfpL2+cyrt0I+fdr+\n/9ke04Jmn9KmQn+X1iz9b/PP/03aysN/S5s6/VXgXzs+WVWriPwpGhXvZ4Ad8F8B/+73emMnipiC\nmEqgNfbJKJHKA2/QHCm+0COsXSIYJU0T1hvIBW8KYW5M74plaSLFG3L0OKuUOvHi0PPNSUjiEM3t\ny8CaWcaZOBHPiU1c9Jkz43jgMn2Y+HAE5xd8Y9emrHtgtJWUmqSzAeIrTtprVVFyLi1UuNbmnbLt\nXmHmjKligBjxGIK1LHWPqYJTw0UxbHPkzU7AVnalsE+VWgvPbgMfu8yyr0xVGXwzE3svnOJbeCsj\n0rcVvzEGbmXHL1yvwJyStOB0YthMLEQ57QKFHj2MXBpQPXC5HDB5izPCdQo8G5VJ4FQqCzvO13Ml\nbw2nfeWbN4Z6veLy1NLdRopMUK7pTYNYnDjlzQc9ViZuxohgiVQ2xdHLir06tneRyzU8eOTY7E2T\n2tSB1x5OHA4GfCHFS8Zpz/ZgcM7ipBJCokhgysovP7uj8wvQMy7KgaHPrIfAYw/jlPnyQ8eLw5ZO\nLKdL4Y0nB7R4alK8uWFfPfup8MhkXr9YEI3HoRRVlMjNPtIF5Xn0uMOEsZnVUJmy4RrHLhryxvER\nStIVnU5YKcRNJLk1y1LQqvjk+fpU2AigC4YQkdqmy1Z2vDgIzlRSgrUv9MYgxtBXOHEZA+TassL6\npSFpJmtoeTwiXGlmVyojE1UdY/aoWLCJzhg607MTJafIKgiDOtZ+Ihj7/VV4/DpbDgOHxUBXlOJa\nHeKrUKwydaeE0si4Zv6OH1Jm2wUECFIIKXMYGm1MxgpB6BTUCDa27/7TWLh1PXenZ5iS6XYHplXz\n+4YxkU8XZDW4KYITBpMxuXC3aBOq1dTkSru++TxKq7oRqXxkz3mwuaUM7ZvvO2uR401ePdTcpO4i\ngvqW1QjNgw2Q8YhT7KRQDesycufW7flnrQ56tHnGZbpj9XiHEU//+W/we9wKZM/0Wse7X/8af0x7\nAh/BySUUyxt/cEH99jOiPOLZxet86farvPWP/QpvfvCDfO3ja8JuRXXC42nD0gaSPeA2C4yPaLBU\na7A1kw8G+oK96vAkarCoHjBLzzI1xLdqxdaMvPNVLk8WlJ+5RNzAkz/07bYg+rcfkq+eU0RYPFRW\n8g38M88iX2IlE/LEzjepmlKx80JmNe0Yn8TnbUJW5/1CawDuF/CBsV8gQDe1JrZzO5YysFmdEzXi\nVTnZOyBRywmWhgZXTXSSqDrwvA+syoHwCTXIshzQ7u5+Ud2ox6RAtRBUMWo/VYtM1aBViK7itX33\nV2CsHajSh4mqHTIv7pFWn6pFuF+kBbWWlALVT5hiOJxvWq1y2/hNoq/qmhaCrcfevX1WoJRX56bD\nElQZj/C138kTJhH5l4F/Bfjs/E+/BPx7qvpX5593wF8A/nlaofPXgH9VVZ994jXeBP5z4I/QVo//\nIvDnVPV7LzlJmyy1s22WTMkrqtz8Bq+8SCpzCGubIrVjOktkTJNV5bnKtGLuYYnVVIIKIpaI0gmM\nUu+5+S37oEn81LabiUq6X40BqLVlqQRpeHOtDcCgM0kt0uSCn/QnWWkyw2ramF7RRjxh9tnME5I6\nk9a8gmKab0VrI+RwbCbb2Hzr4L/5tWv+3I+fcX7WkaIQvHBiPf/RP/1Frm9uOTlZckSwryj8wKMF\nnVxhsuGPvbliO46suh5vDL0Tdlk5t5aljvQqRGlJ4CkWOgdeWgM6xkTnPb4kHj0657yHJw9OWHaW\nnJVdzBwKuCmy0KZlq6pY6ShmpgfmNlnZH3b87W+8INWKONeybmgrYVWaD6h5j9oNvU2C2kQy11lT\nXc3c7LR8nKDH5oNXNDxtJDg3e8pmXhEiM07bCqbdC5sed54GVhUKFdPMPrPXSVpDApAL0R5lfu0c\nyAKu0kJlpRWxambohUoLFwSqGNyruxHG2TaeogWjQrsQ6vw5TRWKGBIVNS0fwzETJOZ9pqJ0FSY3\nm26ltiIbcxxltPeaJ6dFFScWQyG639hNSlX/pe/x8wn41+f//n6PeRf4U/+w7y1mTiSfoS9VFSmW\nWxzxkHgUOk7MyBtBIERe7oQXXtmXtjDSx0LGstfmV6tZqKNlsoWaGiafEnFGMSViEJZmSdQNQYVa\nOy7dxIVJDJ2hZyRZZcqWB75Q6oEv9Mo3o+cw0aAMVZsvUeb8NJFXfhAxCIUsFmsyYDE5YYzDauU8\nKFk8g82IyXRSidrw1/vJUcTw9RGUEW87rOmIeSKYkZV3bA+ZEDxRLSXvqSVwKJmscOIM+eAwMeGN\nUCTwg2Fikh0PnKM3lWIc776ceLGd+OyJ8vlzy93dHYuLp2wONyR3zsvdgYU98JatrMIdblhgS/vd\nzk+UcXfABOHphaHUDfjCwgm1VMaNAykY5zHlQCrKRiuLTsnlioXreRJ6Xm4LxsM+O/7Wh5HT5bpB\nIXwlpYKPhdO15zBOBHvg6SMPbouqRboOo3eo9Uix/FBUpAduD1zvLL98l/n22JG8kA9bLlaJLpzw\nyIxMyXP7omObB672dwxAoi2elBqJuceIZSw7TBhYhszdAaBQ3Z5aheXY4CNLD3e5cMDQa+LMGfY6\n0tfKzvZUKSzKhiTCUJqsd+giGiO9iyw044NHcVALD5YFybBXoWTAZpwsiTW2aZ94TpwSqiWWTJAF\nsVQ2OULtGIznjko/35Nf1EDVSFcSxbYFp30uOEmMMRPFEUSIY+G9WL7LVfoPcB3/Ntci0ff4uZBb\npISIMhnLoJ+oVBWQwo3tcVXpUiaoog50IZixPexFt+ZB3nHlZvle3153GzIv1yc82VzTaeHD/pSn\nmxf82qM36Hc7uu12VgM0Ga1iEDU8PVwTxXNYtNd77eU7DZYkgtXMfnnK+u4OUwo5OT58eMl6GjGl\nUuccw4VMrG/v2C8HFGG/WPDo5QtUleeXlzx88YIcPNcnp6w3G/ppIoYep0ryjgdjm6StxzZpGUri\n7cUDLr/6gi/88Ij0qxYW64RQJn7gSxUzvkM1T6k7QbqMrdfo5wKf/7lv4WLHa1+5ok6ZtHqG/2AA\noyQTeawdOe7pvVAlYGUixgzL2uBMKqjekZZrwu2EWThWWclvZFQWDa4cd22xKUf0VmB8B9GCfv0t\nXmxXPPqDH5D+T8Gvzqkf35CulmzFQVhT4qYRL2vi+fqSs+0Nz0/PeHh7c1+LBHUIwtbtUWsYRuV2\nGPAxM+TISU5tnIJFbcLUibGuUJacxPdJ0uKvpkWTTkZzCiw42TdFelVDwdCZLTuzoivTfZ0yBqGP\nKzTsseowxTL221Zrxh5bhNjvCIflp2oRX0Kb+KjgTcVRqCLYtHwFoRJPda0W0dCAFDWa2QddsDFQ\nfWJab1FV4vXTNjSYAVx2sQGXWNycMD54iVcFqdTbx60WmT2/wJx3VRlV6KrQy54hfq8r9Td3+4dd\n6HkX+LPA1+e//4vAfy8iP6KqvwL8x8CfoFGv7oD/DPgrwI8BzJkr/zPwAS1L5TXgLwER+He+15sv\nTX3lw5h9JAp/T9qvmfvO1lfJ/Jw5U2d+qJkbpuOB1/s/Fa/SqGZO8anpv51zmHKc7tT5fY5OhYqb\n84GqbxdIN+ftFOF+WnB8o+Qqvii1voJAHK1Xx1I0G6VTcx+6evTQtNylV6z7Jslp8kCZg+XM3CAa\n0xrKR8uOrz2/ZbG8xORKsIbuZAVa8TvPOI447+i6jmqEw3jASODPvB4xajDS8+HdjpUkXsqSlYNS\nE3vtKK6ipTJlbdO5kums4ES5GuHRqeGH33jIxUmPsxZKwoQeb6Cr5X4alXPFOcF7j8kZCXMmlWmY\n8K4L/PgXz/js6YJ/6xfu5iFd63Ra49ww42Yu/lvz046Ft3Zumpq80uqr1ZBjw3qUzh3XLI6tr8or\nis9Rd3sv0pQ2A3K1+aYsQrkPQm2vcZz2eSzWCr4eMdTKojZSkuU4PWztWTC1ndtzA+gERD5N9Tsu\nuhw/ZzFC0da8Y3Re32o/62x7nUzLIaPa+98/zJO6LA2kcrw53UsJj39vZ1RrAL+P1TSaFM1t8lhn\nX+OFFB74aRbBZZ6r8jIrp2lJcIXXBssqJTRPxGTZVGVTK4di2dnKVIU+AyaxzIbgM0+McrYsYGHM\nIwOJpfEs3S0nwXK+KARXmWTgUBy/dlf51a3ntlpekqip5bDZScHMnsfcVvbTHGLb7m2FE+dbYWpa\n8+Ss4dIrjzwc6sRtbYGymiv71oEzWsEWwRXB2tQadBO5sJmlUaQ33OwjpipStKGG3YqhjHQ1cbnu\nwBbqbmJ1ktFoeXImXG2UQsKIQ5jQOvGlM09we1QcQ+9xZsFi/YLHCzD6Pvq4o+Rxzo3zqEwY44mj\n8u0PIg8fDmyjkOvEenlO2u9QMxF8YFc3ODdwEgI3B+X8pBD2ykEtNQfC0BNly/mDjpgrD6h85tQj\ny2s0JqSOSHW8/Z7n/Y/3VCd0PoAqp0OHWQoy7ri5XvDhbcD5QlcjdjA82zsGKVyYgH+QOewqt1ZZ\npMK+7Pk7+8qFdfgwsZKJvVRycliXsMbirMFopBTlfGGodcKWwtq2aIpDNoyq9Lby2AvWwrlJ3CbL\nNkOkIDnTB0/HhPhm4o6a8YOwxPIiNZ/MqPC8Cqc4pAg7LFfbjtVCkdrgLoOdGE3lrhjyXvBB2NSO\nKWecCCdSWPSVZXVsc1NMODwLV4ilsrJbNgVuRqGzHZ1vq9Obg+FlyfQkFmrQ3mMYf6OX8m9rLfLG\n5iWL06cA3LklF3HHSU2Mvf/U4xaHRHGJJJa+ltkro0iFm65Ni4wRqrUsreDydC9tShgeb26ow5Jv\nnD3irQ+/SfIDzg2EoTUicRypxmCrYbs6Y7W9YVkqQRN369db83O4Y9CJbz38AUSEs2cfMHULSImX\nDx/w8MUzpsUSP07AXFP1s1zcecYu8BVMtAwAACAASURBVPjlFen0ggnD2e0NIsJ+/RCj030N8yhe\nszcdNgFzLRJm6UK1ltIvWA+VEgvSb5FcsfUM/X1voB90EN/BfDSCCJUFxhRkek5c/uP86Mkv4veB\nya3oX2Z82JHzE05rx6F/hqkdO5vIucf5ib50uO1t8/G6wr5WTscOe5mppxPl8gb38gF1eYL4bQNj\n1QpP30PefxMtFaRDvv0u9smXAHC/15F//hZ6z+IrX+ePxM/yN64njL1CAZMveDBeg4FudyCUgi4y\n/x91bxbr3X7ed32e37Cm/7SHd7/DmXyOjx07cRIndRIaUUqQ1VSIqgIhRUhcEakXhQvUC4SEihoJ\nIfWqQqiCKyQEVxSkCiTKIAq0SQe1dUNwFDv2sc/xGd5xz/9prfUbHi5+67/fN47ixI0bk9/N++69\n/9Ne+7fWep7nO9ErkoXgHGfDwFVV82J1gk8jsXKQwt19tzg6e7J1OOvByF0NtmmOWPSl3w9N4aIN\nUlwTa7fFmCuO+obzzmONBw1kbYl2hAijNFQEnBic1jSjwymMVWTRt+ybnmqsyJgSjI2lMiM2W7JJ\nuCwFHKgsvELNPNTDMpb9nM2e4CJ5s6Stt6UWmRDLRbXDDDWxvSGZTBo7mAwrmm3JtRqXW2q7Q6dc\nKuOH6fXrAhTYkd4syLlC8vD7nao/0PV9NUyq+j9/17f+soj8ReBPisgnwC8D/5aq/h0AEfl3gK+J\nyM+p6j8C/izweeBfmcTaXxWR/xj4qyLyK5Nz1u+5Tl3Cu8yzbAudZip4Dk1F0YNkLJO25c5L4SBi\n4g5aNlN3chDuHxyqMtyJ22wGbGm/Ki2oFNOjD885vK+hIAH2QBW0JfeJnEnT0H7CubAYjC0aLCdl\n7v+qWYUIeDF376Ra3NMOVtMOc4eoac74SXFz+GbOqTQC1vLjdaZFGFNiNtENfWVhjGjOVN7cbebb\n21tuQuLSVfja8Os7z9Pdhn/xYYczyqypsbuIRdnUFTkKR1XkT78m/MYLA3HLsmn5aBup1JKADQ3g\n8M5TGcVbg4Z0RyU6WdT0YaQPiSEpKfVUXVu0OEwNUx+x1rGcz1msI6dp4Mq2k9NgLkL6KbtCJ72O\nqiL2ZdNgKMW/m/bBhANPjehLpElV8XDXxB70Z8Xq/aCzmo793b5TnMlkzThfGnEm7ZoRcNN+ajgY\nTyiNWNSBxFzQnYlG5SjNyt2+0sIzFkkkillJ0UnJZPBRDADIUiipKFlLPkNFCVVOOZNFSFrMSQq/\nrgi6EaERh5CmXurlYKAcuDxR/QQjk17K/tHC4D/IVdty/IxCIuGcZZOV7WhocuCsSXy6rVjKyBt2\nTVUr/ZiKVXwLO+c4ieX4zBxoygQMC2OAPcnXnDiDMVvquiKo42L0xAB1bQjM+GCX+QdP4P2kXOmM\nOlkS8c5ww+KwBmLMGFNc3AiB0SgttrhJTtc9MqzHhDWWFQMPvfKwE3Y4NAeMKN4ZLvYj3jlisAwG\n2iwc25GqyuQxsZpbUp84rSw3vbBSZVntwBoG51mlxI1cc1xDzC2bfseQwXnP9SZx7BOXm4yvOuqs\nWDcgmsDOYBwYdMam77Gy53LoaHbXhe9jFyXEN95jt+4ZvdAKGJuxg/Dg1DDsPY0LmEaY2Wt0Hspw\nwATOltVkAnPFajHpW+8LVRCUmrDeUNXHjJvIfnNJ3SkhDVh3DHuDSUfcDD3JCB/HyNttiwbl+eXI\n//NsRsfAqptRSYCUeX8dOZkljoaROicuN8pps+bxleC7hu9sIxod7x454hD4qFXc2pDSLW3VsYsK\n+wrbZV7Tju0wcCkeBsNOI1uBEwwWYRcE32SqmFgbGIZEP8LeFOr4PCmjCh/vhK5OdDZjpKDvYaeM\nNqDO07W2uML2maUdqU1ThlU2cn5r2ErFOmfuW4clINLS0vC076njyIDjk6hUVcLsF8QqUsU9otD6\nkWUSKpSFGEwK7GLik5vITYo4qUAH1BiCGvYakT7xvNff+yT9A6wfdi0yt8qxu+VrvMWD8ZZUWbIE\nLOcoS5CRF/I6x80OkxO2EgIVvXF43dObY2ZpQ6hn+JhIdkZ2lrHymOLIw0235GR/y2gtp7sbdqtT\nZvsdn7p8TKgKH2Z3ch+Ak35NK5mqq0mjsD8648H2EkQY5ivwnof9JS/m92g7X+Im6ppOhWbWERKs\nbOLF6gy33bCIQ3FGbebMxHB77wH3+xtmIfL89AFbLZbjViqao4Z6d8lTe8yD7Q0hZrYn98qBunzO\nzclDTtOenx2/hU1CHFZU24BJiVxbzDc/gSDk7SNkojfa5W8TP3qHj07ewjvhWX6X5eZjTnzPzXyk\nuz3lZn6BNonr/CMc34xUR8+4//AJm/dfw5gbZNGyvZxjRwiihMURzXAOskPOz1CrEPYQSmajrWv0\n+VtFInRyjry4R55/jpOfeF6qhEVCtyOmzejtG0gdOL294GL2LiEO1O5jKoXIfY7ZoR0cqj6dCY5E\nqD2S4MH66uVmEkCUvQPR8W4YWiVlEXv2TUc99iyGj7ht32S1f85yapwOKoAD4T67HceDkO0WZzxO\nt3hRUhWYS0GNksvMYoU4Ac00VGildMGV+70VbDJ0qqizSC4SE0kTs8FvCakhScYl+ztrESD3M7wL\nWD8QkwdRmtsVa22QxVMsC0IzTONXi2129M0WjFDf3MfvteQtNRHpa0gVZAe2OMZGdVSuGOgeBtJ/\nVOufmUo8TWh+CegorlVfml7vbx8eo6q/LSIfAj8P/CPKJOerrzhbQYHK/0vgC3yPwEmApWQedEq/\nUQY7jeB56Zp3+NdpLnSbw+eYph0I5Y+OTM56L53U7oArLEV+LZBfQbReWYeDlvSl85jLpYBOB83K\nVGSqsSUETPSOQ2wODVs5jpNYr8zvc853jleHwFRjik5AVUsOjJRCv7y3lIbLGOqpQ1R70KgIT4Pn\nC6vAo67l25fXfHp1XF4vZ8ZxLMYCKeGco2kanvVrfuUfXoAIH4VMckve22e8NFSM/MyiuFN9Jwip\nbnm7UWrvaZvAl06PWXXwxcHwtfMdEct7lzt+7EipaofkwP1Vhwnx7u+lqlhjyTmQc6ZtShZFCViz\n5HHKZkqZEAKnRw1/5u0Zf/NxwltDKKFHxVil9AGklH6Hc93d/niFp52mpimH9DscDO9czXK8+9uq\nfJcDoh7+/pMbYS70P2ctQsKLYTzopQ56I6b9oBnnDORiM+0rOzHgipsVgJmyT+JEI5VX9kmcmkIV\nV1wCp+9ZSRPRRybvB0OfIy4ffp9MSVKy6DQmMMV3mq0pqJGfwpX9AcUUKVlflHPpEFj3x3klq+jk\nBmjUEhUwHs09wVTEUPNx6Nkzw+cZ1g501CTdsYqRozncx1I1kRdjptc539xVVCEyr1oWLvBs7LiR\nFTuFRkBywmeK66Np8XkkGUV8zWLIVBU0XhhDJMSS+QOZY4TaWpzpqauKxwPkEFFrmEtJ3BqMQhyp\nvGUcKtZW+KAvovS0H1m4xP3O8MZSaHWP6/d0fo5zjiFv2OZjTpdbhuue+njJ9W3Pg0awJNZJ6HXH\nm07Zx5GHvliPS91zaiOvv/kQ4p5Pntzy8drxuUdz3n92yb0azsOKsxNDtdvxxusV/dUtj2Ye9T1z\niQzJcnTSoL1n4Bk+92jrsCcWxh0h7mjsMdpEFumc1I6IODJSQgs3ifWF4+kT4eai4bOfSqwaA87w\n9BsjJ2c1mZFmtiDvbsmjcFpbcvSIabG7it/4cCCKQ8fA6dmK07pjn7YsR+Hc1LREbqs5bbzkxM44\neniFnM/Z1yue347M5pHlUcWbS89nK8PuJvG6z7wfHC2GR0cjYS/ceI+woLEwz/BCBnKwXOjASGBh\nInXtESz73cByoRzHBA88H15ktmq42ieybakIaITRwG0cOTfw+c6ydJG5F9b7knG3TgMf31guxDBo\nJoSRveyIY8NYZc5EkARb03PcFEH410OiMZB1y9uNLcWf73GjZyFKO1Zo2nGaK56K4RLPi60ttF9N\nLCrhgbU0LvOm3FBpyzZtQSwxJaxx+GwQI6zkB1fp/DBqkVoSq2z5zPiYvt6SOUZlhaQ1aheY9Ali\nLVX7ASBUY0GevHkHcktjRoI2zGO6M1o4xFO0k56kCluGbgFGmW820HVIW+ypsaUKeRjKdD6IlOfZ\ninYMaNgRp6Zqsd2yq2dYSTwYbpnHyN44VISmmUMeWIxloOK7Jc4ILo0wjqyqMuW3YwRrMc6xquqC\nXF1fgveMfkZ/9BrL3RXbxYLLas5nL5+U38lbTjcviK7iPL7GA65pQyDuI1obTBPRYJFtobNryoWy\nePE2Q9fz7fceokb5pm349PZN7PYedYpIvOLdek8OI8+3j/GzU056g5U57X1BZspi3DJbBcarE6Lt\nuDrf0nz6EvfkXaT7Dnp/gGhBSl5mVkXFYU6fILsGdZY0POPF3/sMj/7Uh/R/t8Z1PTqA9gZOEm/f\nN1xvFGsrsr5Zxu+VJ0y1SJPW5Moe2PAYq8w0odmQphrl+eoIRJjfPKfWlyW5Ts6FVRiIvkXVExoh\nj7O74a1OrJRkPC5GIJav7QmDzdzf7XnmOqxGbO7ZNDMy4HVAcsRaBRI2l5rX5YSmxFiX/eqiYMJI\naCqwIGMoOZW2WNmqJDQ7YirOyGN7i6chh4rkp7iMLOxnl/ipyYndLWF3hEmzIneg1CLx8iGDgare\nwbgkzG6pdTnVIgmb052uWybBvpjvOdf4ga/vu2ESkR+nXJQaCu/331DVr4vITwPjd2WnQMlFeTj9\n/yG/Oyfl2Ss/+54XqXNpYUi821V8I8ZiN2imuJhpg7pJZwIHUaJDJb+k4jk46I9Ku1VWMKVA9FMA\nLUByZWLfTJshTaYEhgklYNLEGEEm7q/XxD5ZrI7cy1uetw8xcQ/O4rWYQBTsRBFr7hAkK1LQFFOE\nb0ZeZuvAARAR3NRMBS0ZTJOhWQkK1EIpRIpeKgNzepxzPN8O3LcNm26P6UvmzzAMoAZfGZIWLv9R\nt+DH5Jwe4WtmzlVQPlhnjvLAZ5aOUZWz5YofjZFVFfk7HyY2veWn7nU8vtmw7ZX7RzN+5vWGm17x\nac/D4xm1E7b7zHeeX7PoWuZt2Xqtj6SsxFSQmqSZPISC0thcqGHTcbcukIaBL78z54PdwD+52eIm\nKp63Jbi25C/ZEhubXxb4IpM2SAqh7s4Ywpa8IyiGIDJx6u5MNiZ3Q4AkL3Vk8so+cN6VTKecUTWT\nRXI+/NWK/TfFwKKypcEr9MnyXmM+5Icd3vOwfyHmDGpIU26PnbRVRksAsQp4pMhMRXGa7hpDEUfW\noi9w5UyYdHwvixU3HZvRTNbjUgw3ZKIt6iTGtJNbXv49hgh/XNY8KbVmIgWZUwo6bA65VmoR6egI\nBa3Lhp1EjFqurOPFPvMNFWRfqBOK4nJkdMI6C8/HmoWJvOUtw7Bn5uGdruehn9HUAan22L5nMJ56\nYdivd+QEt1rRDzXv7xJ7AuPoyCYxxpEbqYhxiguw0+c0ZQ/4bIlG6GMmGmGMiguZ3io0DaMIGwzN\npmerkXfmCy7GSB4VW8+J4zWf9IJ3Qne7Y9VagkI/DixN4LiaAY5oLCcNdEYQXzP2PVe3z0o+yKzm\n518XXmyv+LE3PSIbHmaDtyN+ZVAJVMcjpk3EIPg2M/MDGnZk6+m8J2tktC059dxeOFZdwzbsaHp4\nsYGQjmiqkd0m8frrNbZds3gwsLwH+qkyxHjyYUOyFdIIUSybnWe9v+a4PuJiY0m7wNGDHfVQcWsz\nThzLJnN81iDe8en7t4x9Qxx2vG4yg2v44MmaPs/5esqsPz7Fj0JjIr4yaOzIOfOPny64DCM6Kitq\nUrXhUi1nVUecCadGGFS4uFljqTi1StdW0PdYgbWN7PrMPicG1zHuDDuTWOx6HtQekQxtxNgtfUiE\nZHFqibamHUY+WSc+koJKJUpcgcngRHjLJ67GkQcrx/VwxGPT0+hA5ww7BCclGPlWDHOnNEYYoufb\n+0BWYQg1Vj2GhMlKdsLTcSg62hzprCImk1CCVjwZI1YshpaZVVa+Y59LcRZREmWoGL+3meUfaP0w\na5EdFTGNLNIx/fyK2WWNkYGNew2bQXmNs7RF9d3yhOY5eXgDqyN5un42hOlaW+4Us8OHePhpRIR7\nn3zIbCxIxPpoBcDR5VAK03vFPCBW0J6f06mQGNneO2V7WqhNq8vH3HTHNPs9D66/wzc+8ydZPv+I\n23uPCuU77Ll/8R0ANt2CfV3T9tdUMRCa+eQ4DD4OOPOStt5OuhmZlc9Uhy0m78li8CK8EdYYU2qn\n0Da49UCcVazi18mnQjANbrgi1wKbtmiB7R5iRTbmLvzbuwU/dv4JfVK++uaf4GI9Ul1uWdqP6e7V\nkC32/oy3bzfo4gnb37pP3niqoxek5w05Nuiqpzv7mKH1HMcWXyt5/g30uoXnkOod9p0Xhc3y9A1M\nFvLlMZjSeJjLnkfNt4m/OsfWJ5DmJNfjNo9R3bBarfjRTvnqGuzOY7TkfM5iYOdB7JzeQT255kEZ\naGMVmxI3pqXeHlwMLXWKqIMLO6ONhbbaRkuOkcFalv0NsWLSa4Nqmga5A9E3nO17ondUoTgNijU8\n2O+mdzZcO+jytgzonQVNpRYxCYmZYbI0t2OcnqFgBTeOJFcRfIUGUwy+cp4ciTOVLSZD9dgRc0V2\nWpqvqRbR3UnRzorBZWhMRMxkfL8uKOm82ZLDnOH6EeHkKfnyEVpt7mqR5ACtkFyjVhHtyeaP1ibv\nnwVh+jrwReCIwg/+b0TkT3+Pxxco5/dfv+9j/sl//5/husUrL6y89jO/yBs/++VSME5WXkkmWFcK\n+jLhUNOkvryRpUzpzFRUdypkMajRKeT2Je0tufIco4fOrBSvoy2b1qCozdT0/KsL+PM/vsJgED0j\nVvDLf+cGaBAp1okHLYzLEA1FR8BkGmCKQP9g3OCnCw+HoMtpOWPJKWONTM1gsTz3E4JgJkRiVMOv\nXQvrkPiSj3S7EYznZjtgjdA5R1DFGyl5ThL5lT/7o+T9BX/jt5V/+uKan79nqKjprFA3MzyZs0cz\ndlvDZ+9dc0lHM+45XdRshsyT2xFnBUfmyz/5Fgt/wD5qkBkXN7eIndFWll0o1ppZDZUzhBDRXOgd\npbGxpZFIEWstxlUYG/k3PzWy+GDOr277YnudwWSDpVxEDMUMQiddzkGDk3JBhSRPVuzGk/VAO2OC\nGl/SPJWXdE037WQzIWPGlPwrleI0p6Y0QilF5MDszeCtKZNxKVS5w+c57DCxtqBdB7tomNC3Yi9e\nXmZqXMSWwOLiLVH0e6pThlKG/JKKmkSpxBS3pgONr3TVTCIrRtEJ4Szc+kLpK+JOI4YXv/F/8fw3\n/+/yOaepROxfXvz/uK2tRFZxLJRYJ6QsVCIcDyO1M4gLrFNxpDNSJoJjKjc5jcpKSyjxmA1eyk0l\nY0BNcUbKyi3KVV+ss6+S40mAylh2UUnGIjTF+l0NIXflFUxVrgCmQqNSa2BWl6Zs7kFDosqZgcSy\naanygKssNT0xC4um4nYfuE2Zt1Ytz2+KiFviwFJqRu2JqePxvqdOhrYRbDY0rmHIkUULq1ZZ3wzM\nZiOu9QxjQnoluoGZjXQifGscOUoDMRrCfuC1ex15u0b8AjPUWA91fR8dMtZntLKkVPHh48SHtzUL\nVyiGJ43jpA4sVj1XLwaOTxd4t0WM4eydBtY9LnsuLpSZi/jVFeKVe59pwO5JtzXD02tmy4q8H5BO\nePBaYuhvaPAwD9RL2HyU+O1PPuDBySnnOL79tGXW9XRj4K17HjNuqP0J+3HPVz6cs1wppm6Zuz1m\n3YNTTszAkcu83lVsk+OjF4bHg3ATAzFnnLUcW9AFpGGkYcV52rHeCK6u8SZQtULQhiCKN5YUR9RZ\nOp9oxdK1BqkarnYj633NeTCkaBg35fqztJ6mzdz0liEb9oCawDb4yQjIlkJdd7Ruzk3ItCr81n4k\n2YrH5xk111hbs1XP85DZiEKK+METssHpyF48WQJzHwGh0ohzwjIZBjMSVOms0Kug3pJiQWoHKQHd\nncQplyxhxVOjIBnvhCGMJXw0pTuDoj/k+qHVIn/1N75OV7WTYY4gfMSX3/kU/9obp/R1Q50KE+D8\n5D5gWKwN+9qTqpa632LiQKW2DGoV+qam3Rd3tM9852tcLY/ZnZ3SXV6RXY1xRR9y+6BDgXq3JTYN\nZojs791ja6Dud3T7G1Shlqf8tH/O7N3n8LaFbPj0/H/jb+XP4Sy4YcfQzBmme1sbErt5hw2JXDdI\nDmjV0sQ9h7GytZ5k7ZTn9rIW0XqGXd+iszkSR7KxXJ6+Rh0D1faWsFhiUII0fPh8yafqS6r7M+px\nR15eIU9OyPkMZheIyehqDakmDHOOfzrSVO+x/M0Z1wpnbz2l2TdIdwt9jdtE0soj2w7z+p5d+jx+\n803ktOdmXxctpBXc0LP/wgnzmDH3Hpf7XFph+zW891n0+AZZXZKuluV+2g1ofYFcPESS4vobxn6D\ne/0U+fAxWmc0NITjF9x/suEL4bP85ryh6W/oRmXry5CyCgPiLNnPCw9cU4kPkY4sA3OJU6KKYKqK\nK2tKpADFsQ5Vbpo5lQZsTNipaW0k4oeIi6nUCn7A5pqx6bGyRdQTKyGNNWoGJFVIyixlR3YRaxIm\nFa2mSQGTSh3gQwaB6MvgtMSQyGSKVd47ii31pfVIThhbqOEqimTF2lA4LJJePgdDg5aMwikDFZMm\nG9dSi/QiFLsVS7p4rbDEfCCFClLD3/3kI/6Pjy6nTeeAzCb84cxjvt/1fTdME7f329OX/1REfg74\n94G/AVQisvyuyc59Xk5ungI/+10v+WD697unPb9rfeGX/hKLN36EV/OUVBUnGSvQq5lsqEsoaRLF\nC0gWvBVEM6MYQk5kKpYa76huewe1ZIKaOw3JQb75MjapFJwHhp/JWhAKFTYu8RfP4CfOWpq6whqh\ncgUx+itfmvHXfn1E1LAWW4JFtXTk1UQNm1oygCK+f3nEJ3SkVPSapSAQGvFuKoanBvHQzR8QClFl\nL5ZsDJ1zXO52xKy82ESyKpWAc47awLyrmNWeZVcx9BuC1vy5z2Y+v2hJxrC+vWXezahMpK1quroF\nRr741infenJL0zlShNmshKU+XkeGMfHmdotvPYu2oqsdj8+vUSaDjFTss0UMmiIpG7AWTZndOFDh\nsN5CSozDyDBGdruBEJWmEj53uudXd8Wy/UCuNBTHGDvti4SScnHFSxR775wV41wxUWCiYU7GSJoP\nOiidjvlL0weduqp4CFQ7KKRESROFzxGovCGoKyigMTgpbdBkY3GXjyOvGESoKs6Ui1LMxZTggJQm\nAC0t9QE1y69otZyZ9HITpTNPiNXB4a6a0pjTK++v0x4rF+BihSrTPrR2aqokcf+Lv8Cjn/qFux1p\nVdg+/Sb/4L/4PQ3s/n+9HqC82XhCTljb05hile1qwwrYSOZ+7LkWYZscY54s6ZOWLDWmAUnORJ2u\nEDIADs0ld6vTzKnPNJ3nKm/Zhpp9zEQEm6aLhy0F5rx2NAa8RsZchkBaJeZJWfgRZ2tUA84baoVE\nIEokVR2qiU3Yk40n7WEfhdE4LvcDK5MwEphVSpVHlvOGJ/2G121FVY+EEXq9YuaEJMJZ5XmyFRZV\nYnuV6I4slXUEN6ChYTADL67XHNc1JOH+SYswItZz2jSIHRBzS8w1Tz/pSb2l7Sqq2rDZ7Tg+Ouad\n13eIZqifIqHixdM5m21HVs/jp5lZN7JYeMiRmITNxZ7790s4rsoSXAU3Ndle4/yA1jW3W6XtFGc9\n49ZzczOjb0b884hvWqzv6O6dUEvP595U9n3N833m+NgTx4q9q7nYbWnqGe8+skS95TZ5vn2xIGXD\nPicWtVCPiac7Sxojx+3IT8yrgro6j4zKxWjwarjwypAHFiIkB1kD/ZAJanFVy67vsT6xyJa1Zm5U\nERoepxEjkT5OLlkY0t5zowPBGJ70Sj0YrJQBnliD6UeoIadETEWHkXE8DxtysmxItOJQMq622Lhk\nF/dkOyBGmasDCzUBsYdMr8TMwnzqLUap0CxkG+lQogrOQieWHAOXOOY20lhfrhnJstY4aSt6BFvu\nweox1pK0UNDGHwBI/cOsRf7df+Fn+eKiY6gqjLsBwCSHMrAYhNtmQTMMrNYXJONQk6nTDrNVKhVs\nVHa1oxp3XNx/h9eef4jmQDTKzXLJIm2wN7FooXPP4qagDa9GOPpxX47zuC73BWtxWXnx4IR/Pfwa\n/v4VelLBxSl4qIPhz599lV9/TzBz4anOiLMKEyOkkWbcU+1K0xbdZHn+iseziYFhPsPvxzJs65a0\nt9dsj1Y0aaTPI1YjZogYV8KdjcmQelyMbHRFqxXRLZhtvk6QOflphZpIvb+CyUGYi8+i919Qs8dq\nJPdnnPz0b7B6f46uV1AFbATqDTqewHKD7Fd4W+Hqr6DOo5Kou0i1NQzxPnnoObGPyVWPPH+AffMJ\n5hug6iBadLBIOEZEMWlH3rbI8gb72kekJ5+C6KmaDB9+hN7mMnBNW6rtMXm1YXn0W9iPv4izNbsq\noTmDsYzSIKqsxg0778hcYfN9xEayqRAilbNF56eWxirYaxoAKe5zLWDSnKGu8Hox/flXhNoy1OVE\n8nlNzhFMhDgjVFva3QytMqMfiK7Hhw4rIKPgpLgfogExNZU5mCcIQRNNtgRNd3lpdmLmRO/uahFy\nGdbmA9MplXoiuzIKlmBf1iLF9pQq2eK8Rwa1RSd3qEVCAzai5tAEFZqgugh+w5ffPuHPvPUAbEAV\nZJzzjfU1v/y3vycY/ANdP4g4BEOx7fwKhdb4ZeBvAojIjwBvUbJSoMDn/5GI3HuFO/yLlCyW3zf2\n2wh3OT/GgOaEklGpyZqpTSLaYoNqMMx1RBlZm4ZghRMRRiJHOZN1Q2Ud9xlZeXiSDN+KNRoj2U9C\ntVQzmEA1FdlBCi3BZyG4QGMr/ebwRAAAIABJREFUBkk0Q0VKO/7Hjys+/7AljD2m8WSpGUJmFTP/\nwWccCwffGJW/9WFiEzK3uUbrgKQCVUkCnyxxCurKOWOnPKcDaoDLgGJUKKfb1KFTeKUp55cXOVNQ\ngUHhvb7ncvSsXeIsw3MZeRh6PnWyYD6zHGXDI2PZXa4xzrPuA7fbnpwStTU8Wq1IKjR1mThe3ayp\n65Y49rx5r+PxeuBms+N40fKgFV5bdaSUGceBGJV+ENI0ja2sI8SEMR6yYidKRwoKIdLWlpvdni45\nXAjErMQkDENgzDCq8quPE1/ZLpixI6slmKITY0KbogGbC2piTEGDck4TMiRYMeXYaipI3KHZdBlM\nhc8jo5aGSWN5/gH5sZQJW1Ih54iInay+C+0up4xYRyW20KhEihh7en5FCZe1Yu8EnjohhFnznf5K\nVVHLHZ1OVcCbu4sQgKi7Q8LuaIbTax6oc4cQ5XTYF68Yn6gU7rxMOiam4+bthCilyU7fGjTHSV/3\nx1fLVPvMwowYKcF7VsAp1OrZS481nsepw6rBxkhnQZ1SJ09vEyEnlghLcWQZObaBnDtGbnHaFU61\nFsv/OkTOGuEeI7Eq07pKE0+Hgn5GN1In8EmZWctrXaJSocKzNSNjNqRUzl8Rpc8JkZptGrlnB9om\ncSo1WcAOPakS5saxJ/HG3JBzT68VSz9gJLKYW9BQCmQxjGpwTcXNdiBuM3MzEAOcHs15frulqixx\nb+hmO+Jg2dDR9ELtyx7Q6gFm2JFSZOwtt7sznCa6xlD7PfOjzGYceKNTnHvKdj/n8tZyvX2d49aw\n3qyZucxqDt3ccr2peL5puOoFQ+LNex3nz/cs5g7fJHK4xdq6oMf7CtsZOhnYXzcY66k0cTSLXK07\n2nuKa0b2sWImW/YYzKYm2R11gKcB+jgyqyu6asVmv+V59nyyWdCMCV9lTuvM3BXFn2sN49jjWkOv\nDdt+z4W2jCERVGh8GdqMyVK7wKKWMgwjFaQoKhuJHHnl/U3NewTmKoRoMVUkWEMboCFCrjlPI9Em\njLUMccB5h2al9Z6cYTckdlTIHnZS3FIFoXXKfTWMPqNquQ4DjQErI76z3A9C5zyl1yhU8z6n6Zpl\n2YSEEUOvwi5HfIw4a9mkhNYNx0RmTAYHFbRmRMXSa6BCGZyhmwZilRh6TQxJEB0xQGOE1jrqfz52\nwH9ktYi1N/TNCT4EqEotkv2OmN9F3C12+QH5do61hmb7gPrkA8a8ZQyPCOaY2cnHJMkcvVjSyN9n\nXj3ihGe4esv15pRLfUA2N2yOHuL7PbN6waUL4Oe011cEE7FZsdlhbM/CWJ7NahZPM1x8ld/e/Dif\nPfk21fkFNFs0zSFnXPOEn1tZOD7n1r7FJy9a0vMTXtTHeG6J4hlXLb2rOT7fcX22RIHZ+SW7s1MA\n4kFHJZlNs6Te7KisYds5UrFMQsm0l9cvaxEPzng2znBxc4uae2xJHO8SF61j1T/Gny5xy1uqi4jM\nKuzNGfH0MUkFub4POVA1N6gX5GYFZ88R18MTg6SWbD+ijp7Q9djLmngyYtXQBYFqwMQBUY+aHfnD\nBSZ4hpWjerqExTk5QTYlyIUwoB8ew6M1Rq7RcQl5BBVY3CLXBs4/R6yfs93+CZ6ZI062a6JYQuuL\nGVUeqShZeoM0KA70IerMdD7ECanzQEuTdoyuIWoxzLDz94j7L3A0XrNefkjlwN625HmP7MJUi5T7\n9I17naP+GdkaTNVDLqyWIgxweGECGSpM8zJGpSEjeUOlNaPYEuyull4qNB2y8MpjsypViFTT15gp\no1RLg6OVK69bnlC2yFSLOC00LQuQysBEJ99zUYNNRacfTSi1yDTsJSzx5rtqEYrOfTe/wty81Kr/\nUazvN4fpPwX+F4ql5wL4t4F/mRI6eSsi/xXw10TkisIp/s+Bv6eq/3h6if+dcjH6b0XkP6QEV/4n\nwF9X/f1JzSXzJuGsAAnnistGogSXes3UCK8b5dj0SK08D5YTDWzVkAj8qO540DXk5PjNAd7PDW/a\nkU+7wE9WPX3d8GvrQKUVzyRgtMKmHgdEIxhrsJox2mFspknCUEVWuSZ6xzcfX/PacUPjAw/vCTGB\n9545Geca3mbLX/5Sxz7BJ7cj/9M3Mz/1cM5/d77GiyVX5fTJFATqgDalQ5E6Fb8WIQoc7ARAsBPS\nZrRsIo9gcsKZyEfZk0JiG4TLNFKJ5dY3fO1iwy89ep1Hxw7vHUOueXF5SwiTe5sTdikgE71tu1eO\nl5a6dpNJQ11COXNg4Zf0Yc8meBh7+r6nbTvCLrIfhbquGBP4CetIqTQwcfrdjBGGYUBsjfc1Y4pk\nMuvdQIiZqqrJWRmGRBDL1TjgTL6jReR8QOMMKoqbzqWcD8G2xdyhuNeVC3nKk9W4PaBGhpAzUQuJ\nEAzWTXk9vKQi5CmY1ruXNu+IYlxNCOOdnareBeseLiDlc2INKZeJYUnU1jvE6fDYg1bqu7/36koH\neqAc0MUJsXvlcYf/HcJpZUI3D7RPY6RQ8MoBKHo6KXql4rB/oIs6ksL4x7dfYqGZ13SgqSG5hqf7\nzJMgBAnYVINL1CheAmfOsZWRBmhaRVKgnTkubgdq3zKPGwZjqTrBZ4/UFbstbEJAZGRuhE2C81xz\nYzNuV0S50Qk2JUQ69hohF0v3zbpYhguZlbhC3ZVArBxDP9C5gMlC1MRlFmQQgo6It1Tq6JKjniXy\nbuTr+8y7cxhSZIdlv12TVGibhm0143y9ZlV37HcDKbV8zfS00qEaeXK15Wy+5GboaZxysy9ocKPC\nvbM9eZxzcwXeB2LyPL7JnMwMrYtsQmAXHDNjubmoMQprBGygrTyrVpnbW9Yb5XhWs7BKsnv6OOek\n8mAC91cBE2t2YY3W8OQ8kkLHYmU5W2WoM9kMiAeJFl9FrDg2e2Hsd5wuDR89jaA1y9We09oiWC57\nw9IFzCxS7yJbH6nzjJmd42rH67NrfuzeEc4ZLi9ucb4hD8pVdqhmdrstWrVUNnFrWuZmz7xuqBrL\nfj3yeBjRmNmK4/G6pzGembY4t2YbAutQgxhsXjOznjGWaYjPmU4NG4U9xQnwYdUBSowDGw+aA5oM\nox3Q0bHslHs+sZI9VgwfBst22BJtTU/izHtEDa0IxiqVM3iE2lsu+kRVW8IwItbicgZbzuucYRsy\nQQKN9bjaEGOis9AkxdqESQms0BPZacl12GdlhyNaj06Ww3VODKqQFDWFFbKfUHfJf7hC54deixiL\ncedkNyfXO+rtGWZoiU1kWD1ntV2is4HFzRGtfw8/Zm7sCWG5YxzXDGbHu9fX1G0PG/jIX/M0LriX\nLQ/qC17z77Hv7vEt94wuL3mmDeqOePDJJwCc3+8QhPnNLdv2Hi+6inrfMy7WdG5BWsyxn1j02MM4\nIvdfQPSYJ6+TTm/QzY+wrD9hfj+ye+cZn762vPjmAxavL/nKYJjtM+uzBXWaapG2pZ709WYKVh2b\nkvOUmhlX8wPHo9QiPiqmqqiH0hnbbkb35Jrq0be4bD9PfnqO6IbHgFx2RPMQ+WhP/e597E98B92e\nkLtz3LViTSBOVPjdSabZGvLRJbb36PhGuff5a2x8E1l9gt/CuG8gD+jtEk4+AAnkXCMa4LYh2XsY\n+5TqEtLRM6oXnwHVO7Qjnr6P3bhCoXYtZnlFXq8grrHvPyAdX4KNGL8jjpZrlHYh+CFjXUW934Gp\n2LY1pJGOyBqDSX0x2nId6iyhapitS7/eS1PUyNONul2/xabOBBpSuI/GMzABs7tAMcVkhAtUe5by\nEY08ZHQ3RTJgEzpfkfgYK7PpnKl/dy0Sa3TKRRqNIZGRmJBcEM1X6w4z6a5f/V6uHTK5Oqa7x5Wa\nSrxgkpDtXRALB4sGSYKouwtYlwNrJ9lSi0zDW6eCZCW6fMfMyUkx6ug2JwTzqmfLP//1/SJMDyjh\nbo8ok5j/l3KB+j+nn/8lynH7HyiTnv8V+PcOT1bVLCJ/juJE8/cpBuz/NfBX/iBv7iXRTfqfOFkj\nG1N41gjFUFwTOwZm1rMblUaUM6/scmRQQSii50bhR5qB81jxwdhgrPK6VToX+cWlIaYdBsc+33Cj\nNRfB8pYNWGtp3chX1p5nqScZxyMHP+pH3rvu+eAyMvZr/qWfLGJPJRWHvJTQtKOqLOvNyPFyxqcX\nwi9/wbHe7/kLmrlMwj+8GXmKpVZb7MhV7oT2xejhYOenpbB9NeBpcoAorimQFJKxpBKZywt/EPFV\n7CWiQfkLX3iNo2VBUl5s9qy6mgermpvzgaPZnFll2IfAOCau+x6H43K9L656rpgQ2MZzdtRS93C5\nUy7WPfvBELLlkQu0vghuwtiTUqL21Z3zXUYn6+5D4CaMIRPjiLWKhEnLI4b9MBBCpLEVf+q1yJdW\njr/+nUBQCkSPmbKWEnLnCleoc3lyKFQtjY6huL+56UQdDlZFSPmJOdh356KDm8KCVQvN02JwhuJ2\npgWBTEDKZSrc58kpUYrroVHBqRAlkafsowMT0x4ypKb8qNL0SHFNkoN+riCr2WSCKjabgnpJmVK5\nUpcUSDznO5rfq1xzO3FJrRZ9VdJD01SofdjJKn9q1jFSKIoUEXky5Xc5GI/8cVxihVzDToT3tj1D\ntBxbw5EkpBkYk8X4zNwHYnAsnFJpZFZZjBjO1RI7i5jIuqqRWDPGnk4qtrdr1EYsDkmRrVQ4b3hD\nE/eyL4YzMtJYj7GOG5uxyZBTAOMwJpPF0hslpYE4mcrYCMdiCRFMVOaqBM3UAt5mdreRbBxPdGC7\nzyx8JmXPb54n5pVybhTVJTlFFrlCwkhKjhgit6HoTs6aml2/pW0KhbXxFyydoW0ss9pyvVeenO/5\n6kcNpinI6tJXExVoybObDVEFbIvGkdga5n7PSEXMkZN5zdFsj+aMWzjiuGcAPlp7ug68vYK6Y70d\nmeUV1XxP2zRgRlYLi3cjYpRhnbHawEnJkRp2SnPk0TGxqjO5csTQ8+kHDYSBYRtALMMgnM4i45C5\nSZ6rMXLanbIwG9zsFj9WfPTxjCA3zJqOkCrSJuMbmPkBbzzN8Yyn+4oPzjcEUXCOnEdQQ9BCizbi\nmXvDWbUgpIGQB9a9xRiPl0zrIt28IY+BtVc0G5aN0OkeV8+5DZ6rbRm4qHiOqswqC8ZkjixcDcpj\nY9jHgad7eGwdc5+ZT7TQPkRs3TIzxcb4aKGYpIwIF+vABaGI98dYtAR9GYw5W9O2FY0vlvVXW0tS\npWoNnSQugmOTtED3CCYmavEEo7jREilIWxp6xmzIYinFs8dMgfMpFPwhBwgpf4+z9A+0fqi1SINi\nlyNwSTaBofmE2fPPwPEHOGDd3jDbtmzf+oTm/SVba6hC5h4btoOQr2YkmTHW16gTXnPP6c3rnPsO\nszPMe6F1PT/eR7Jb83Z8Rp8y7mTGbTzlHf0a/sLgz3o+fPIFdg+eks19lq7mLD3j4nJH32VmckN+\n7TVYB7Tdgyjq9tjV19EqIxKY9xWsH/Cpt58Sd+f8gjo2/gFPXzS8WJ4S/JLYzXiUNjxnBu2sxLfo\ny1rEZfO7apHczumbUosYhc0b9zE8AKMsP6VouAedEFbPkectnz1O6OI5bI7QfIuxS2SlxFtLWBra\nmzPEvgAJ9HVNfSmY9j3Eehg9xt6S3RbzmVuqr72JG68ZH52Tt8dEm2lDj6sDYhNuvACbwAlmfQop\n/3/UvUmMbWuW3/VbX7f3Pl00t31tvswsytVgywYDhZAxnbCQ8NieYhkJIZgwAglLTJAYoRpgmQkT\nYAoTCyRLBsnMKCiMZFw4syqz6vW3jeY0u/m6xeA7cd+rLJexVeWC90lXN+KciB3nROy9v7XWv0N3\nn1MwzDuDSx3OJ9yrH0KYKfmAMDWdcbwk2hN9/wWz7bnc/Tq/qAO/U/9JJCjbGEnF8vbJNZf7V1SU\nCctDaieqzLsLbE6sD2/atSGCldZcPoTRj75DcmQOgqlblAnMC7LbIOkSiKzSBlt3lNUtGha2yxMy\nM1kdy8XvEA7PWdweVyypP9BNl2AKtdu3QfW8QTU3tz0tODxqHMGdKCYTUk+SDnX3NCKd0C0dc2lG\nRyoV98DA0YQRYeUiMVuCKyzVYSqYfqZOA3bVGrE0DoASNiM5OUrtzkHQf/9axGQ566IaatWG3gbL\n/49NH1T1L/+/PL8A//753+/3NZ8D/+Y/ys99WGIMck7NdA8Wz6LfBIqWFgrqnOe3qiB4LJVXuY3O\nLDBg8MaRSDyJlmATn9jMkjMvahNCY+EqCCub8aXyxE48H5T5bPquFf657UuCGVi8cDgV1j382a7y\neN1zvVtxd5yoNWKMRcWxlMg8Ts1r3nlyzvSrjsM0Uovw3lq4SIYnw8KLI/wvJ0hnMZyzAkUw9sH7\nrhX+D6G8Tb8iPLiuiVqSVpxtBXh9sDoXbY4jCFl6xpD5e4eCl0IfCtuVwYuhH1Z8/5Hhf//JG3a9\n5XrdnKwsHTfTzDG1XJj3LzbYM+0rY7laG4JThuCpqTKlBSMWb9rr7DqHd4VKwQeHVUsqGescp3Hi\n6dUOq5l01unEpNSaUa14ByFYtusV85TYup7iIt83lc9TQW1HFSWdXV8KZ1dDWrNjrcXWs1uimGaO\nAO+QmXD+HYkq4s7n0xm/y2ejiAdHQxHFmsb5T+csMDXg1bxrAPEGp4L5FtXPVKWXRv0VhHQ+nx4Y\ndtWcs5/OiJWeGxYBEhWxgikQrHuHdXfSULMH3ZXwDRXv23b77f20+U56cD5RvrFKP/+fz/S+d0ne\nD8c6n3PfnlB9J1dJvEmFQ+35fr+ws5a1WJYVzIslikdqZsmON5p5ky2hDriYedjvLr1gcuVCK0bf\nIrTA59BZMGtuSyU5YSlCSZnO9kRK06hFZaGw9T3PbWuoS4BExmjE+p50jLjgmOeZYuCYLE/Xyj5l\nOuc5lYXL0DXKoIPcOayA7yrHMVEQdl0j66ZaCTZTa8GFgZUdmWMEZ9l18Hg90NvMsAYtllQiiyTm\nMTP6LV/uDVMxBI0EM7D2ytad8La5MmqtHPd3XK4CLmQuQgFtgavGOazuudituJtGxrgG9WiJrK88\njwJId2Q5eFRWTCcl7C5YX7bGYzkciAuAIJc7iCeMzxQ3Y1NPIVGKZzk4sjaHyo29xrlEPCaMOqxb\nkVLFholYDd22Y/Vq4tGzgVd3mZtyiXlzJE2KOmVjhc5kvINsDT99eyKWHYcCRhxLnFh1gaveMsZC\n1UqszdLfqWBFKXPFuciFF8Qv5ylrz21qmWopJYaQ+Dkn4C3HueOr0fFcJ3IWjO9JpbKRkeveUlLG\neah4XIV/ajXz6W3i0eOBNYXiEptaOWWDes+UZm4ny30qOBsQYB0idqt8nAMLyqkEnGTUCKUq1My8\nJFKGI55ZGvqdZgi06IMKzChGYKmWEQhJyK5iqzRTHGcpNbffiWmU6Kko1KYJDmckf2X+YPeQ/69r\nkXkzI2e6/EMtEt/7/BsjnxJwIvhXz3j93j2aL5FJuC8daZXpjYGxp9OBOMEjbrH+a57ur1BKs/2+\n6yj9wroGZJhZnRLqZp6Hz9j7C+pzi4kT/8T7f5Myv8dpeyA6gW7mY/kSHkcY1pjDgDx6AW+3TQM9\nXgF36GgwdkftbjErKGamblc4vWWoie91nvePE1/Ic5L1iFEelQO+GpYq72qRMJ+o66G5+laDquDd\nA13CsKRK59veUaCVKtm0vVcEa54RH1XeDl/w+MYirmIu25epHTC7SLgp6PKCIR3RumM1J6YLoZge\nUNana9AA/Jh68x48fgkkzGlL1x/QuUBQEEtNAZM+APMKQyRffUU2Cfv6hxirrPSnpG2Prgu6+hEg\n2Nc/oG5fNXOE/hafj5TdQP/yAq6/Zvv2gvXtLfcXBwb/Q/ymYl+8wj4bSF9PjDt/3j4MeT2w29+B\nZqprMhD4Zg9+YBWJNo18e04xcsTOz1jwrOqCyx2n1SvW02PqGJiHmcXGlk2kgkuXlGGP5KdghVD2\naDdRw0Q4efos5GFEsrCs2msI+6t2fpseVWWWgnWFkvt3tUjRRt/t7EIVi9izfX2/4HKgiMH7Nqzt\nwtkYKXtCn9GzhtduzsHLxaK04bkO0++6xvK0+d21yBkQqAJ0I7qs3iGCf1TrD0PD9Ee2XGMyUbQJ\n/aXJRpqDmMLKFpJx3NXSmiWpzEbZqkWtMItwosF9XgQvYLQwmMqFh6NawKDV8WpstK9A5BMK1jUb\n2kEgakTFUO3Cish6COyTodTCmCv+NLFa9fTWcJojlcw4RSqWzgo4IVUlniY0FYxYus4Ta2aVwZgT\nvdmQtceZfO64FT0jEE4FMbYFlZqKkqja8VhmXqrF20CnlVLbRehRnDE8WBwogrMtSvVvvZ7ZuMDP\nB0uMULxyiBP7pVlh9yGw7j2Ptj0u9JSc+dEXr9mPlVIqvQURxzgtZB+IuYWDzqlx2I1pFwNWMFpx\nwWDEYow5+/kJSyysQsBqZfCOMidyyeTc7J876xpCVEFT5XLdowLLYvhLf+KK14eZv/06stsEPlwH\n/qvfuGGyAU/rToqcdV+mtRRVBfHNtOOdUca3qG9KQ2HeNRu2WZ0areSzq1HWysMJ+HDJigixKh2u\n6YKswb47brMCze8QnaZbUlXEfENn+DYFz0gLiW1gT0OY1D00MGdKYRu6YIRv8qFMxVawRqnvkDPI\nas/Njjlr/5SCwX7L/MQAxhpES7MtpTbtlDWY80TxOwwwcRc8myFAWfgsO7oY2avBzOdmmAzSdHuh\nG3iaIq5rNO2ssf2NQwuVPRTLbAecWvpc6UrF+4z3lUuElVO2bkVBObjQAkVNxzFm7LRw7AWvhtNi\nUFPpvWM8HFtuXGr2wYNTHuvC/djyytRl1kHQRZlN5jQmVt2Ady1c+aJL9G7g/e2JpfSQR9Z9JVdH\nJwfUeGLasL+bwXpKnvnq2HF5qPh1xhTXEJ9BGO/35Fh4/8kltszU3Gi77XqGi0cjy7Sll4wxiSiV\nOQm1DnSbgTSdWG8uePXmQL+qBDPRrWeM99Q4cHOK9P4RJkQ6nTGrRD8EJC1gekSu8N1CvzKYbiIt\nihtsU6r4HiEx1EQeJobuAgk96eYFNVaSPqPXipZKt+4ox4WU4PaYGaty8/lEsJ7jkri8dFxvEpmF\nfr2j6syLtx2OzLOhp8qExzGXEVVLroVlGVk5w+VuRS7KaVYSjcoaZMGpcLMoxu+QvNCLsrIQa9Mg\nVfGkciIlRcTw0WWlD55dVF5OmblW5pr5ya3Qry2yzBQsVQ3TqUIwfHGfmDWTTM91USqGahNiDLuz\nK1UpBTWGTydhRSAZhVxZRMBaSs4Y47joDU4hlkbJCU2piVplSqkNiJywLkosCWN7VCvV8E7jKUUb\noo7BnfWOCnSmkNQialojJZ7pOzxzARhK06hFFCsOx4ZaLVI7DAV/k9DtI5b+CyT9Aky36OYCOe6x\nZaHunlJ3hpwjerrhvjzGmYV+U/DTSO4eNZ3jmPmqqzA/w273PL47sqwcZgz4qVKHniTXuP4Vq6my\niZfMukFsos6PEHNLXb1A5oHS3WOqIZWCkQ5rKtlaTFxTXMSkhOgJ8R1FKqu6Z9xF5PC05ROheFUw\nzUo+imMnGdmum546NcZJEUeoP2Kvn+BN3xpp2v5oo1BN268e9lCnQnYdN+Mz+q6yShnbf416kPVX\nyBc/QOoNZl1BPHIN1W3oc8Uc94y6QtyRvHuJiMN+/Zz89HNqusQfn8DwGWIrSMt5QitCIe1uztEs\nHaKOsnuJGXt0eYLd3iFBkVko0aL+JXUC31dqOGL2zym7V/DB11QLMvyIH/CnyN1bDlGwzybMzvHZ\n7YBZP2e3NtTXC6VUdDkgFaoxzJ1hCIZclVibiZA5DzYfahEnb7GypqKMw0BXFpI/MYUjRi374TWW\ngMsrkl3eDUI1XbCLjpmJue9Zza0ZOoaviOuMVoMrHUY6lHvctCWujz9zpitEhzMeDFQ/I7XFKZR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OnCylcGG+lkYdVBlZ55jjzaDZgK1lgkWfpLxzFmDnXF6ywcx475TiDdszKRD58NXFihGsV4\nw2HOGBKPLjK+b2dcNZklDUgKqLlk/7Zw3F+ivvImBpaa6ehQEheh8vGHlrTvGVYdxhVW15E6ee6y\ncLESOt9hnFLjDmsSW9fcH9kA/i1bX6AmYu7Ry0zpDJmRftNh/Z56K0jfU7VQThG7DfhOoNug1wom\nUSK41FEuB9ybtgco4MwW9iuGR0em2w2kQhx3VHuHssF1mUfmxDRbfryH6aa2QCMjeFlwJuLwoMoQ\nCiUrfV/QpAQLz1gwVhGnDOtAZWHTC0jCiOf2dmZOBjFKMIKJidX7juMB7seFu9lSi9JLAiylzAy9\nw2lku+7YFsuyJMRUVtcO5yOqhikmDktgTpVcFlQMsxqyQsnKykTWqxX9pKTOgElEZkxtkQW1WlKt\neO9a1ELbkFBnsdoyV2wpoHqm7ILzjpITLhjmlBFsy5IT5VIsi6nk0qyELEqRgDEOp5mKYKVgNBPE\nkFVYiVCMo1aaWQaW/rvM6wW8nAibSvY3dMuGuMt0h0tul825FnFsT/vGainCYEb65Q2THHnjPkGq\nJ0ub9Ne8R5hQdQRz1ZxPlwOoIdz9lA8vT0xWCJe3hKtXlLdPkekC6e+54yksd5hiGboebKJkR2/e\nMB8u8UNG55GjZIzJrE8GZKT4LQVBnEWLwc8JLhb4UDGfWjhGUvAMR6hD4Yp7Srlhszxmn59S/U+4\nqFv8i1fk9655JR/yvfojTHlJ7FaEF2ukLPT2lu/NP+ZrsyG/vWZ4coP4O+r+fZ7UXyN1G1ZlptgP\nkLLGP38B05qYB2qndIdXJOvoNi+ot4+pFxXz+r22RxrBPfuM8uUj1kdDjWsYPKoGyYqWwLIV+kPB\ndLlR2k0GZzA1IIcnlN0LzLKiBteo7fMaNgmVBYkJWX2FpoIkS6ml2XinDt2cYHFIZxAs3D3GDgW9\nG9HwGnO8BOshXvBh/yW/nTLjxQ+plEZRvB4ozGT3Bpc+QIzBbEBlIF5+ftaeN7RofvabmNhcM0/+\nRwjCkKDawKoURme5ertmejRDjhijDN/SJ+tFg2GyN1CgH1tj49WBa5KA6SLhXhiGfTsnp6cTVsHd\ndsSruQ2ZT564SaxuGgL2wMqZrhotzudzOyGFqgnBIV0zeZB5jUhqdXZX/4G1SHhweS4t+qXPlYOL\nXJSAVJht/kOvRf5h13eqYXo9Zn6ghjWCSCFPlZoT/8IQ2deOk4KpmY0xiFaW3LJ//tjK4NLMy2SA\nym4piGmzrlphxjFaR66VGCyLGbhb7vjgssdUpVThdDqx6jp2wfJJHNlnYVJhodBRz1N6oUhGvWNa\nMuMcMfYcgmsM5kyXctaTS2Fwws1xYdV1qFSqCinB68PMvDWUuTAtlRJHro1hZuCX1pmqCRcs5jTx\nbOfxzvKhdGBB1XA3zfy0DBTf84He8ekUOZXA97QwJ8WL465U5OVbNo92bC7XTOVEN4AVz/0pM8XC\n6IVhFbg7HFE8jzdrOgJTykxLpmZtWUupNJqjc5S5FdolK4uaswkDyJLpnWdwQh88qWRyds2MwXhS\njUBlzoWqhpQLtTYBu2rCO2k3Am8ZrAc1OBtwZaaIMKbMGAsr1xHTCbvp+LNPLX/9BkQN12Jwmvk0\nBEKqJA+2FAxKrOc8IppOroggqu+s3fO5VyhkxBgyFimKN80WIkvTJ0ltWUe2tmPomYaCgKHZsBfN\nWOsotVK1NA71+Zp/cLhTETxCOd8M3JnCB7zjgz/4LjzY6je99flyrt8Yg1jO+bci725SD83YO+8G\nERB7LqYaquSlBTij7T1TFCct4NfJH5xOIyIb4L8F/jLwV771+A74S8BfVNW/dX7s3wL+bxH5Z1X1\n14A/B/wC8C+fM1T+joj8FeA/E5H/5Bxo+fddV0Ph0dAaJkMTlIomxkNklQs+WKJVEgm/dnSlI1nD\nq3s4LXA/LxQ1rH3leih8NXXkWM9B0BUnlZVzBKN4mymq5Bx5EiAYC0VYrXZs3cxmExljYDU0I4Ba\nCvfJ8mLv8UHIseOUCktydDcDmUgYAuNsmD6vFLvisiu8HzoOY+FJOHISqBr58lVpuiVvCdaSSkeW\nyo/vW8PivcfbkWo8KSXERx51ypPHsNl0fM+UlnlWCsdlxuuKcbGM/cLbOcCsiAnszIjfRuxGEQNx\nLNirAQiYSaiLxR4nWAusBTE95mIEFzCSsGLQviLRId5Sp4QtV8y1Ek8Re1ehWtynBWt6bDiQOvCn\nE/T+HLR5ST5CNgtxCjgnPL5YsRxvePVmorORENbcjS2TLdiF3oRmXKEJF04EbxnMQtdlxoM7o00L\nViw1e5xPlLLguhXV3uJkQ54nVBzzmLBh5nHnsR4yBY3KtE9YsTzZeXq7PyPtekZuHKVGanHUEim+\n6YRibPdUI33LN8oLhsRm1QwfTqk251DriFlZUiAbQ+pmdiagWQnGM9dKotLC1uVMl8ngLXNxjHGh\nSrMoeMhaGaQNRhKJIkKVyoUzoII1lqSWWCfW1mGstOy8XFv2jGZyaSPCqODUnM1tmh+pEUM1iV4t\nRVqo+Hd5nZaelDeEapDhSD8Jsbzh54bXzOOH4AVJI2odpmSwCScTG3Gk9ReckseKYf36DiNNpywl\nkLaVg2Ry6pDhErbPKfXXudgtFDuz3D8i7F9ihgR9YXv/mpQdKYy4VBnKyFg7FnaU1QHrLkiTxcaF\nFCp3qjhxmDFjlg7TKTUKy+XM5rXBxQuqjICFZUDrSKk7TIxUp5APDMwwX3P9+Lfg44Xy6O/i/4+P\n4ZMZcYne3sOu7TOBiZv+ffz8fZ4M/xtpzNBt8BefosuG/jaw9D19egmrCssTsrklHD8ku6/QBOpL\nixFIz9DxM7i9hie3iKvol1eY/UDNIGFCdGrIqnMsq7YHL1tDt18hsiBiqX1BY4devEA+2qOzxdx/\nRLEWKw4efQ03j7DHLVUt6mYwCyZ7tEuQQgvlvdzD3TP0nG8kT38T89Uj8uTgva8o99/H6Qm2wpPD\nLV/WiMGwkht8Ul7179GnoR0uRUgzzm1w6QcklBR++k0t0rWw4KwRQThYxUhgDCDFwMXMkA2TO/v6\nquW0fcvF/TUyr9CxEDbtOh8vJlZ3K8opkp5WwmuLe20b++ZsuLV5s0JNoWZh83qg+va4vzVM160J\nGm5b47S+bfbyijJejQy323eD5Lqsz89VZO5bw/EztYig4JcmYTjXIg/J1tVluhjopSOvJvzUE8ns\nxhXFZAb+8QS6/X7rO9UwrXrHIUZSFrIqU7GUUrnsOradcmmUgzje3p0YjKXrDYPt+UmCq1RxprKk\nBGTq2aFsFstoAkUL2RrCHHkvFDCFV28PjZ9smlVCtUrKkZyVaZ653Fxyf7yn845oKvUUwQiv7yZy\nrST0gTGF1Iw1FiMV54ScMtoZ9nPEqmXbO0QrYzHElLi7n0CVpVqojTb4nj0irOidJYjno0cZawrz\nEgneIzhqLlyFge/7E9uc+OOPDIcFvFPejgs5VlQSac546/js/o6LXeDxbsdpmvni9sB+nNkfIh8/\nfs68nADLslTSqrDUxDgVXt2fGtQNeCtY75nmmVwaBSyXiqZmkOC9bzqbKeFtx9oaOueopRJjoVAZ\nY+I0LmiFXMrZ/Q9qbqLlJUWK+qbfqAaplXlZmHNubky1sqjly89f8uR6y/6w5xAzj0SIxnDDwi/Z\nwtOaeW9TeD1aflIqW1tZWfjtZCjagtsWEbIK2VhEa4OAFcB806iIkIy0qZM2+8sGB7XJV9V6Dntt\nqE/VdjMwRiiaG5KDYtDzDRfQinMtmPbBmgEaPe/dHEUfHPzOn54/FjjrkviWyPYMxSNULVhtl3vL\n9GoTY6WZbyRVqC2UbiEQNFNsO3/rmcr4rsT5wxkO/1Xgr6vq/3xudh7Wn6bdl/6nd29Z9Uci8hnw\nzwO/RkOV/s63Aieh0fb+GvDL/F7E6t1arzLXYcLYCrnH20rMBvvY4+kwxhIXw3GeuDl69vs9pxgI\nRhhMx7oTmFOjW2nliVqsK9ROGOh4tDkROiEEBQkkEjXCzbFZQvfqmePMMWYu/CXH0x5/8nRFuLSF\nEAau155lWTiNhe3Qpmolnei7J8TxDVvb8WTVGoxcYZ8WHu+EJVpqSQTb0/dtKjhgeZMXwNKVwDAo\nVmZSAa2wvXQsS8aWPV0YuJ8S15cJ58E5x0pnLjVAncApOQaczc0wYCzEaMAPnA6BQiHnCF9U1huD\nVcW4e7Q6xhuL6BVvTgu1XLFiJvjC4C+5G0+M48RyZ9hdX3G1e01F2DxaUQ4TdnfF/PWC2wXYeOzl\nDEMzRNHbHgkLdu1gb3G7dn7Ge6XDcrW6IImynBbWYWCaR1Y2st123N4u9OuRi+GS27cLt0Cnhlgj\nmy1MS+HxhxHIbQP3l0gtxCkgdqR7GhAKfe4odwHjLRIiLmek6xguPDU2tCfOQhXfxNNLQp2h5AVx\nC2G1IplL8vGOdTDcTwUJjnm+IZSOai2LiYw6kLRSEJZkKMxsgoDOhNhx8oZlOVN0vSelMw1JK6IG\nTMFoYzd4a0gFmhJ4xjnHkjO5GooKGEuqEMUitlCLAQq1BmJqxVFRcHSoa/EL1RikKoKnikPILVhd\nazP8EYM4RbQxRL7LyzgPy0L2M3IXmNMFRRb66nCbM6r81MAXM70o45OAYUvcdHR3CyYkdFmwWc5k\nRSiuMps1ZTpB16P7t1ybA/psor6dUQNBFesdWnNjSeQJLeDkijqeEF+pLqL3BTpH0j3FVzLStG+S\nKXreWVYHlurwQ8Yee+ouIvsdVRakE2q02FhAT6RaqOO2BbGnyEX3KdWAdBF7tyF8MhLC4VwYr1Br\nMFFQv2Glr9BhYng0IeMMriMtA2RBZKSbClUG9P4O+1hw4RH18eeYkzKbgXCcMOkXkSd/Fz3t0Muf\nwkVEjx9g9muiHxHtIDssBTGWmlsdIQKSC7lv9FQ1HSYpTiakrNCvLlrcAxUXFfUL+naHOWTUV9Dm\nQiyA+tR4+LtXoKvGoZa2DxBeoF9cgTlnD6Ut5vYt5dqipxG7PGc735F6ONY1H8tvEZbXrOxIet3x\nxm5YDzOuBl7kNd1k6OqW0q9J9h4b3mMJP20smXfDUrBlTZYTpyCUmrDqMNlSQiFMA+N2pLvrSdeJ\nbu5IXWZ1P1BdIV9kwt6TLxJu9hgq+qAtnDO6q5hjmwZLbXWdGKW/P2ugHhx2z9dEWsd3LKRqIyaZ\ndw3G6bJpjob9YyoRUxvKVaExB7JQB8VNgRoWkl1YqWGeVnT9RBoybrHMfqI/1yLVgPkD5rn9o67v\nVMOUS+YwZ24zIIXdas1q8NigHLVxzseSOPqmJ8pYTlhKKqhNXHWe+6rE0lzQigiBwiWR3ntu0sSY\nKjc2ME3Kqixc9oEKXPQWP1eMsTzfCpcbz1d3941f2Q0sy4mXmoljxZqOYJRaGoe21npGmRRvIeUF\nYwzzLEzeAomqkYqjyrkYr0qtyv18wm423PmBD+NythYvYA1b12FyJWE4ThnjC6aABOWDzcBwGJmW\nSuccY6nUrHQGSo5Y3wweLIH9OKFWmKbINC2MS2TM8Or1LU+f7rhPM1U9t8eZ2+PMaYl4G7g/nVDr\n8Lm5rU2pUFWx1jYEpVZ632J4l5gxYvn67R2Qud6s8NZwP44sRZlipuRW/jt3dtEzysYEjmOk5Mq8\nJGpWjpoaWlMKMVeG1YCI43Z/YoyVn371lovVmpVktmbhTpT3tOPnVhUbHMEIx3zih2MhV/j4KvBk\nr3y2zGydZ5DIrQo/qQPFthyoltkkFAGqJYsCjd6HyJnWB+5scW9Mc7VrzctDOK9iCjhrKRVUanPC\newf1NNqOVhDz0C61CcyD68wDePKNVfg3tpsIZ9v4h88f6IFN45Sl0Rvt2X1HVFsgbc18ZAz/yg/X\nPF1V0uL5az++J2SozrWCByiSG5L4B0TBReQvAn+S1hz97HoGRFX9Wfubl8Dz88fPz5//7PMPz/2+\nDdPrrypfzhGtHh8SY2okS42ZoW+0XGOhTJWn4cT710J/lRj3hmwtZYSbobIJlcvVwKpG6C1vDhFj\nCoclcLqNWJtIubIzsFttWId7PrCVlYvUmqjq6dw9ctHod4sqY+xhOfDECEdreHRR+H/Ye5MfybYt\nzeu3dncaa7yLiNtlvsx82ZBFK1QqCWoAEohGosQ/UjBgVBITBkiIEQPEFAkJiRESA4SEUI2QKAYF\nlChEZf+6e++7cSPCw9260+xuMdgW8TJfkd3LJJOU6kihCDc7bhZu5mfb2mt93+8Tc6bYymXIWC1U\nLzhzQnCsNZNMoNRMjYqUwt3YioZUE04dwWZ+aVfIuZCleVzEVA6XM7iO49OM6QJ9uOH+vud8inz7\nnLHqWNeFhy8sfQ8kQ7k4fLgQi8FqoPQGCY7eZ2RciUtgmXtenww/fluoyVO1o5aVm6C4cCFNK69e\n9bx/OmPxPPqZWkBzjzjl/TxxKnv2XWI6ZIbuBnuJKEI5NyCMsZ7qJsxppdZNy71ywJ2SFgcpIcNC\nHSw7EloCNYHJiVqEJQk+RD7/biIuMM0nxpcDJTpQT8mJyWSq87x56yhJscY0P1cFkYE+ONZjpGSo\ndaVow/uHzmK14n2D7pQMxgrn+YZ41kb/dA41hhR37ILl/D5yKJmz3lPKAnZkPSqDPOBNwjhDqGBd\nwZrASiEa5RQN53VEa8X7C744xMpVOVGotMZcroWstKlWSUgxZEMjrdYWZyBtQaIhTQRfhaJCLQ2C\n8jHk2pY2dUeuE/XSMnhU6SQjzpBrRGthMQVoz6FUKp5cmnczEf6ky8X/L49qzkwMyHmHHdvE1DvP\n8lDQqoS5UATU98ySCPPA7D7Dz9/C8IT0I7UT6mx+AuApQj9/j5sQuCxnpqXj+BDY/vAFq/sxN6ZQ\nOVLqDpkL1nqkW6gPA9P71zAI6fIKXycutydy7CDd0HfvyOsd4hZqNZhsME7JaYOIUkKC6JCLkjdn\nRCIFTxFaTmFOUAOTvsH1tzz94i/xye89Ue3MpiRYN01W7jyN3lpQN10LbEeVjv3yBnM+IaZn6Srh\nOWBMxNj3VH0FmoXqjK4AACAASURBVMB5avcWkxRzcmR7wgJ5GTG7HyHTS6q/YMYL8s1LhDNIwcwd\nUg3qM9ErfulY+khXI6sNdJrQUolhwJhKqDNl2kJ4i817+OIIh5fUckCWHilQtYAKYoRaesStiDi0\nLpgywNxRpw3qT5h1QPMd3H8Lb/95xM/ID2aoK+UwY6cHajD08UfEvudFvMWZSL29NM9wjnx2fCRP\nHd24Mpz2TKIwFvrUaMBfFYtuLOHNiNkf4P098vCEaGQyDfovYkGE6hVRS+3bNbu8XOgPPTpOOJoq\nRYrQl0C6WQmnEbWVMk64S5sW4T28L2ioaLnWIqbJ9366Fpm2P/moltSQ+y56nA4/uf1aiyw3j6gq\n/fEFAMYsoLZ5nFZD3Ry5OWz4/FdODHffwPOW/+P1PeF5pN5GXGnxLnmcMQK4f+Jh+kOPIo6vzyux\nWn7pdstpmpG+6a7fTZHb0CHjQL1cCKGy90rQFT80xPM8r2y7gW/PpwZWthatQqcXFCXUgtiRU9ch\n1XLvhOdlIWkhJeHJBtwV7X1YW9emc/B0eUJD4HPvufv0hqdzItbCt8cV5yyX80rB0EnGqaXrHfs+\ncD8KcxVSrmQZWPJCLBlfhb4PSI18uhn5psDGFGLJfHtWdl6wqbCuETGGVDJODB0NmLAszeDd2YpU\ny+unhdVaTM7sQ1sIFfDYNhQpifenhRgjSxHeHWZKKsinA8slMsWCEUMSIaXWXX9e1uvFXpqcq84E\nY3DBcV5W7BVLezd4KpCLctNZ1mI5nRaG4LH9gFiDyytL1AY4MC3klZxIxlK00HmLBEeKhbUWSq1Y\n236IrEo6TwgtRTqWwrwWznFBmfi8D7zKypvLQtx17PKMuJGYErlCrrAkYR9W7qOyZuWbFNlax8v4\nTBTDWiG7gEuZkzeYsMF98CHlQhFt+O8rOQ+aX0iEjxCMD3lGiFK0eaA+2JDqVZ5irkMqvcIkrOjH\nx/RcyXkf9Xvt/NQENxi9jrcNHyV55cPCJs1/5mu5Ah2uXi4H/0yB3wK2tpJyy2FRa/iuX/l+2WOI\nWMNH+ZqiiPnZFykR+TmaR+nfUNU/TZu5Gcr++OOPPOfXvnD8+ucjtbSp4eA9MVXqUhAxHFIlxsjq\nPSk3YMybrxakegoza6xgOp4neHq+EiHtSqoeI4rWFSeGUkBrZsbQm4lSLLut57gmrAYqhsd1pZOe\nkkD8iuSFQ4YlWILxlNWQS8TZDic95xLZDYGaEtte2GJYNbNzHfN8QbynWGHozjgNuHJh0wnWeS6p\nkKbMdrNwrpnBBDqOOOcQYwijYZ7P3PWBotCFDh0NOjnWYzP8ixrKGpgvsKSFEDK5jEybSJcGQghM\n787s/cKggnM9VRMXAiUqBk8wytevn7kZA8sML3yGXWXjV5J0WF04rZlh3KDqISd6I2RxSFgRCjkp\n/hKodcWmBVJlufQ4lzG1wy4bIpl8zpg1koauyYeN4mVDJTBNEafb1kkVJWdhKQkbJ2LZUFzFGotL\nSt9bchJ8X2EpxLVwThfS0mO8EFeHaqDfzeRi2NxsW4CsM/jNDTUdGV8W/OpxcseSF6Y5ooOgfoOx\nEeYVGyduhw6nlceYWYrh6Bzp3KiWagWdFtR7rDSAAhLbBqn0mAydaRJjq60jzFUW3ctAoSDWYm0G\n7akSqSYj1WHEIFKvGXaeKBVyY/Kl0ryNjR7a4g5GbIPxiFwBNdom/0WxUrB9YKhKzZ7iM6qQysLG\nGGoVgv2LLXT+vI8sjqclkJaRFz5gOeBywB460jRh6DBhIJcJ6zKlX9ktP8S4TNxYNo8z0T0Q9QnT\nOJyY6jD1mTpD7yu627KEf5F5fMNNvGdKF9QWujxBNDhZmEMlnTKudlAMZfctdb3ntjp0v2dZDJJv\nmszJKfWyJyEM+UTwC8aB1z3hs7fE+BkmKdPwKe60UMuFtQzI0OHqmZ1/4FQqd28uRI2Mjy1fTOyK\n1iP1PILtUS6QbigsiBh23UQ5ZIrbYc8LZd5R8hl8h0igmojmATTg8hN6MWg3s8Q79PiWXVmozlCH\nr1imz9m8s6g1QKKuHdgJWQwsFrdRtHtiV5S67vD96do3FPrhNfZyx1I7+v1KtQF0haeB2kVMFVQO\nyHGPjJU6G2T1mFdfoe8+Q27fIe9foEng0x+h0TffcHdoJLjDHTp8iboFk3p0sdjouQTLcDhA95K9\nJuTxNfFzYciPsH7GUZ6RdEu1GTU94f6H5OfPSTlzrpGQRu74PumbDWWZWEdlnJ95Psw48xnj5kC5\nDCT7jPeONZh/rBZZ9xMKdFPHets8TdvDBnfpwVSsgp3GFqIOiHWYjUBuksgPtUg2iTicWp6SvzZg\nDbAKJdSPtUi1C+u4fqxF5KdqkXX35uMmyqwO+szPH3t+XAzd9kx/ucCg1BD5RN7z3m2Bgl0DZuqp\nD8dr5Ms/2TD9ocfT5cKn9wZi5ekSyTlzmiO9z9xsBpYU6Sb43BlssaxrbuGK1qDS/ASazgTJqAnE\nnHHGELxhMpbjY+JuqOx05Vd28LTA2A18dUn81ruFkWf2mw5rLZvO8/ndBmsKo3fsuwBSOBfL5Xyh\nG0feHReOl4VcpE2OusBSC53ziAjHZHEUYoG7DYyuY1oz51Q4TQtDcFSjfO6UKoVz53h7jpxNofMe\nkeb1aRp1ZT1fPkqyQuhIGA41I37g8HzGGTillcEIL3aWsYPNEDgslZRnRAxGKl/c7kklMaVGp1ti\n6yRsa22kpFRYU+blfseyrpRcMNYQgsMI9N5SS6Xvm8bVO8fGrk0SkiyHtbC+PbHvI3OKOOfxRsn5\nA7mt4K3lOLX3OITwMaA3a8v3WGIipUS5XoDBeaa4ssQ2kj+eT1QTOC/Kcy0kLRxPE5MRjJy5Cz3f\nxpkclS8fZ5xVLkvmKRpS5zgah+s7lnVhHC3nCtl5Qla6PLGuK8b0zDWjzuF8AK3YDx6j5oBCxLRu\n1R8Q0V0pddeb7IepkLSNlBRtHoEPck4RUi2Nfqe/71RtgbsfN2bwEb8JDY8PLe9C+JABRUOkI/xL\nDz3jfGJ+b4jryo+fKxvXs6bEpfQMksj6k/+DrXrFav/jhs0/xfHXgZfA/y4/IU5Y4F8RkX8P+LeB\nTkT2PzVlesVPpkivgb/xU4/7yfXvn548/YHjb//3v8128NSqH+jq/Du/+oJ/9xfvMAb6kLHiCBU6\n6xlHkN0WkcJ6qVA8czDMp8retwaCN5bzklAtuM6hJTIMA1Utqawci3KphfdHRTSwVGXjHK565nUF\nL4ymMEdL37X3sZSCDxlTejIJ4xyb6vEOrPMYrwx9x+gTpTvyYDvsbaL79AO1aoGUG0J2TnRPGbsA\nZU9XM8yWOe1RhUsB8+TI+QFSJemKcxm9mm+N6VsXXLR5Bw1shx0+FHayIC7i7kBZ+ORzh7gJTRaZ\nV2LMfL4JlIslr/ZKH7WIeKxTjJ/RUCA4XF/Qi3Kf22QtyYlwL1Q1cMwUWZFfH3H/akXzBT2Bef4E\nfvMdXgLL44J9UKw1+HeJslRMmeGbiJURwwopUWyF2gGB1Ve6pxN+VOq2Y5GFVyYw2yc2wy2aYpvI\n2z1pjdRscOJ5ikLYVvCQxpU+ON6/6yhZ+f6zYYkbVjmzVKHKA7aeUE1gnojF4ktl9A7vTiwpspqO\nznimk7LWiDeexRg0K4MkrLNozdR9QFPBG9PkfdqaIGIS3jSTdMwte85cQTUG02TY+ZrrgqWY5dpk\ngWo9qsqcFGuFpJkYI8FailEKbcpur74JlAbbkAby6UTpg2fMFS+WbAJLbphp12f+7tdP/P2nE9Ck\ne2CY81/tDVNaLd3QYUolFWGtG1ZzoZsnbL1lDQf8o2OUGXJHfS5YM6PF0T2uJDX4+EOk7Ml1Sw0n\njLPYMpIDnFdlkzz98Dvsu0oyEdGOQ515/OYl2/5A2AhlKQTb4R963LogfY90Exoqs1a612+Z7l/h\nXk9Mpy3lDLUawssW0m1F8Sjr8ilGZmqFTRopW4+/rCxSqWnCSaDawsZWYr7Q9Z75khjcSrU34F9h\naqZUA+UepgtODWoX1u4liiNKIgx71st7cvFAZJg2yH7EDQfAk6dbpELMA95POHlFdUdSHuB5T5+E\nyya015VK6g3hUYivNnTpjExK7iNRekw/o2bFJ6Uu97jVUVPHEJ6paYuoRVdHZUXOkTIu2NNtA0N8\nUH34lXreY7uJunjEL2AM+vZzjI2IGtQnJK9oTaipmPNIHc6o7VAcm8eV+TbAKcJaqa7DPU0k+RSr\nma3eku1MtonpcYf0t8CJKY4s4Q4NPZ0M5PKefm+YtpHDdsI/ekz/Fj1cCOdXaAfFF9wLIeT6Ex4U\nlZQK3jtquDCU9rG7dk/tfv2J5N9dJXn5Ctuy12u/P20oIWNXR5aK8foHahE/3eLyQjEZ6TLE1rjK\n+/ftcdIVcOKWJj/VVgNZEWxxfNeN7DY/Ij5/TvYXjhfDrrtBYmKeXkF/wjxtQRQNEXPoIXrc4Z9I\n8v7Q4+WwYfNi5PKDI8/TjENwQXCD4/kysw2eYhaCd5xLgrV5UdDysctfVbksFecShkLYbLj1yos+\nIDmz6SzPhwv1Zcf3ngsuRX7+pufnvrOjs3uWbBmuqdXUwq5rpKU5L2z6gZIy6izLHFGXsbFjkYng\nAnMWdruRxynxSEHThZc7zyUr8wq9bySTooakQK5MtbFIcrlg1NINnoLjcV6pEjifL+y3A2tqgYHB\nCeIdb1fhq6eJ+/2WWDOXqXDbWc4reJ0hL9S7kTVGilisFrz3qIGkEIzhfJmYrSWuCegJRsk5MwZH\n7zpSXBs2XQCxzEvEOyE4RzXCtEayaa/76+MKU8VRKNVweryQq4WS+M6rPYMVgnc08ZhhWQulFFKB\n+TRRrWCtRbTdHpMhltQubKXha6+SPmPbIhDnA13u2FBwRlmiIZfCRUGjcKiF5DdM04Svyu3Gk8NA\n8YHPSOx6Ze48OzEYTSDC06nyo+eFEjyzrRjv8M5hEeo1/0BEPm6IzNW7VBD0A4wBELHIlcz3cRN1\npeO1znDLR/qAQVex1/t+8hh83JxdM7quj4G2sXm9+p0+hPJ6287ucubewvHpxOvcqDUv+sCSMz9+\nmnm7dJyNoqURr5pJE5xpEy7/ZzMx/V3gn/up2/4r4DeA/xT4mgYs/deB/w5ARH4N+A7w967n/6/A\nfygiL36fj+nfBA7AP/qjnvzv/Mvf5W98cdc2wyZyc3dDzi2EL5VMNILvAjEu9F1PiStj6EjzM91Q\nOJ+mVvSMlXO2nKthWhLBWiweUyqDszzFlnt0tw/spaebFnrv2Q8Lfa2cciHGxPu+AwO57prcwUYG\nE9vmjYDvCt2u43S5kNfIbjQ4Z1nXwjB6QqhMF4g5IZfM8fseNzhsX5HcgiSXVHDTnnPOKIl5EXpn\nWZbI4BdGPD5kVhtYrIOzYZLWGBp04XZcGGylDyDWwBakz6QUkdJQ4EtRlqJIjKznnm63YxsMZlwo\nKrhwwdlIsgY/JPJQMINBP6vUGFGzwm6Pe5FJXcFLR7jcUs0z5ptCCB6Z76k/UOS/KUzdp/SnAzx9\nwsoO58AdwH9PyNYi25XlmDB5T7/2PC7PbId9u47HDc4VSlC87ZBPemaNjNWwrQ6lMNSRNV0a1VN6\n8qx46YjbyiYYBk0YN1J1JaYb4nTC39jm9dFMKk9oHalMZAyZFacjnibVcm4BMiIdQQKlFEpN1Cpk\nMfTWYMylKQGyQ92CqKeWTBWoRtuE33TknElZGyo8xFYAiYdqMPWD1M6g1pNUGgyJHlWLdZVcGo3L\nqZBrJmLJ20ZjzKYAgZpzm7RrIdfAnCqr1kbyUwOlkrlS9igYqZDbdP2vPez5F+72eGMJplAr/M5l\n5T/5jR/8WdaRv9Rj6Ar+rrKehNNbhzMwPFhEO4o9NCP+GrHDypwCLMKyqVBmdG0woIoHOyPFYklg\nbgnuQugGEu+R8IL6dEL3nnfPGzqJPHSJ7pcznfbUZUM3TKCKXSLysCA1ULdP1HxLWBOqe/q3K+dw\nxsSeVQVz04JAXchM0x0Tiolnhm4FEdxs6TWT/ICuFlXPOkxknQjHG7R7R1wHQudZ6ycwz8RNxaUz\nyC2JU9uslxsYRuoK7549o90xD5b5nBh376npllwW+vSa4A3lYnHnkUqhr7HVIvcr4Z1FDo/YuKNK\nYDARHSKiid4l6stKrxdwULueWhxBJioG61ybIG8mbImYfiZeBuzskZJR6dDTiZTvEY7UVwknF1S7\nBqRJHeb2kTJtm2zVHEn3mXz6hFAu2HGBp1t47ijh1GoROxIuDZrScNxP+KdEmn8OwkRfLKacKaXn\naCt+dpw7T1m/w5zPdMBGKrN+gfgHHupv0o2VFCtWhfs31ybpZeb1G8P5HtaHd2ixGOdw7zek7oCq\nkgeLW1rDu6xtelS1xYcAGCN470lrWwNyuUr+r7XIh3iUaTw2lYxTXGkNlrDs2rnQOrU5IKHgD/tr\nLVLIKvTHLcvuArRmbftHqyHGHBn9Mzx53pgX2Jt39CREhGkq5MNL1v0J+/hAvXvTJtpc8zKBP5Tu\n9P/R8Vdqw/TtuvDmd940eEIB7yw345aXveNgIBglZsubpwsuQw2eaUkkhVIUrUKmsDHKECqbocEe\nXl8qcknYWikBfuWTDe8OF35tK3TjFnLmfRW+fVq47TxzBFC+Ny28HDx32x4xFXe5UHPiF+53vJ0j\nh7TjtEReffEphA5XIpu88tBbvj0X5lQQ32GM5/Uy84ntWNYWENp3gfdrwvoRUzM2eJL3aG168701\nhJrpu8CX7xfuth2p63AW9iny+SZw7wLfHo788v3AD5aep+WCVWUulVINF0nsO88+JMauY4mZw5KJ\nRfHWN+rJMjFYKGqIMbVNyxqpquTSMqbQwhIVRPCiGFPJWpnW9jPWWrnEypQuWCuUrGStGIXghP15\nwu22pDXBmjnOKzEmqljOS5M9TSl/uDIbajO151hjoZZ2YWqtWGvp/Qd5nJBl4vaKRb/MiVkd71Kh\n846iHgkBR2UopRG0NOIvkaWsnI6VtSpHgSE4goPjUjiUigmBsXeo0nwDWpArqryU5tcSaBo7GvTj\nw/hJtRHskCt2mZ/ozX7/5IkqaG4dZC+l3XfdBP0+DMTHxU9osUxGBKMtE6vd3zaVH9YqyRWM5xIj\nT3NBvOW2M3y6s3y+76nvMl9HONuCxXx8fItwowv/1G3gf/gZr2FVvfBTmxoRuQCPqvob16//S+A/\nE5EnWsbSfw78L6r696/f8j9dH+O/FpG/A3wG/MfAf/HHyfwOF8uXR2V9fyRY4ZvXb7DWssSEr7C3\nhmeNONMR4zuCNwTjOKwr3jYpiOREcMI5VgYDvXNsgiN4RUok+sAShd4XyqK4sdL1DmuEp8ljOugt\nuKHjthq8S4xdZBMM3a1H7I6YFqokpkviOJ2a0XozsO3b+x8wlPnI6TKwLJ7N4Hh+W+h7h6xnqq/s\n7z+h1o60GKSfsWtinjKn2HOOM8fnI5EN3hbu7gK2eiiRz28XvJ/Y3yl1kzAPI2wMOt8jZDRWyDOh\nFvKccUfFnzKdtvCq2m2QZUGXEzkIPgSIluRBngrlMGG2Bn7lgXop5KeeTjyrGOz7kdMQuRs6sB1m\nf0f5ddA5YquS/q07/NOF7quvWL4pdPvfwJ8/Q9KAiKGcZ7y9sH5dGH9uy5QLi9zwYvMJ6AKzIufc\nclC0QVmWZ7hc4HI44sRxd3cH3Y53hwM6ryzxwKzCthuw1TLPE/3YkYjEtWJLy7RRXRiGlmCfMHi3\nYr2DHMlr34IzKWzchXHw7PYb6AP5svJ8uJBTpXhDnCxoYOiFtAixE2LpCdahGJZUmC4LTi3OXdBa\nWzdbIceRUhohMTpHqi3gnFqpNaFiKdU22W+NpDNkMZRaqMaipWKssuRwleO29SwZ8KVBi6pEKhaV\nepXrVkSa5FwErO2xZJYl4oNHU2ZrLGKUVDNiPPu/2lRxzqK8ez0gXUbzgpUWG7BxK5fq8cmTusRU\nAhIrbFbmS0dJI2WpaBVKroxjYugUFzw2FWatkI6EeYB9YbjfUA5nPtk9w+YGKQWKIR4iwUyssblS\nFndk/KGD7Yh9dlgBcY+sr14glxH7LpBF2f2znxMfoPv6EYkLN0PiVCfqOjZQR9xykEhhRGOhhgvi\nRpJWKL+EuAulPJBehjZtPo74baZbIOmGiy5sdEvyW3I3M54F+yB8JpGY3jLsHWEZOKUeXxLz2qGy\npc6Ovkto9x7jbyCCiRPmq0C2O2raks8r4+5Ifr7BHBJl2CFSIFtqEmpRJJzweUc1W9Qp6hK1FqQE\nSoRVaA0he8DmHjVHihWsvsdMd2z6J9LtLSadWhBvP2EfLTY7kgjKiP29E5b3aN5SDOR5xNQz8XhD\nLZXan9v7KJku9mS7pRZL5czYd9gxY6fCxQQWFdSN1OopLwvVJsLrLcac6c2Z/Hyg9o8cTyeSgVET\nxiiSeiIds1/QusWMHTkkzNqx3p1QFUQM7pxI+2twyQdIQ8pUd60joiJTwvwJahE79ZRuIlSDjQNa\nrhfxlWympqLL8PExTIXxtMcojMc9ANkv5H6hu55Xo2D8htmsnN84yv2G+80ztl8YR4iXCz72LA/v\nsHzIkQIz7Rk58qsfCFd/QcefacP0Fx06+d2HgV/+5cbgv6yVZc2IwvulIipUMeRaGYYNpiZSEdQZ\nBmMppRCcv35gGfAGp8K8RNR6YjGMm5F5iQz1wu2m55vjhL5f2Y0DT1PkzWGBrVIEUs7MeFyxTM8X\nnFF6H4ixMMVnvpkqPz6Ulq2TCqZL7PKKM8JhXtGqWAxLUpJTzqtyuJwYQ0/fGYa0glhMMPjqWMUi\nwSMlgTeUKTGXxOgt37nfcEoF0wVU4Xie2fpEyYo4y+++mXk6T6gNLLkV3uuloBoZjCFbwVC5pMi3\nx8gpVozMpAJeFO+Ud5eMD445FqRWfPDM88J5iWw7x3bTIQKj95znlVwrXdexrAlwPF8mqghWLFmv\nGy0qowscp8zDNnK/CU2quCaqCUzzmbko5MJaKzkDVDa948Vuy/v5jFWDxRL6nsfzQhCYo8VJwQPV\ndLw/r6SsJBN4v0a8VVLMPGw9//TLwDdHww/fRy6zIcaFgpKKEm3zcvzgfGGQ0AyHm57txlG0otMM\nwWPUUZ00M2RRbM18dxv45rJwMAGHUsQilZbCo80H8IFwd+VFfDw+eJvS1QwsKKWCNFwj8GHTpR+p\nNKKQamkj9dLkp2JcK6YEEEVU0FJZc2I1hikrai1SCu+mTM0OZ1d+cRe4uxn4B18+8vMPd3x5nPjr\nD57/7ctnXm0H/uEPfprH8Gc+ftp39B/QKO//LW0N+R+Bv/3xZNUqIn+LRsX7e8CFNqX6j/64J9p3\nMy+cQ4NnWSvBGMwyk7smD1BNvDTCxkTsaNtEhcrP7wyDN1RXyRRchcJwhXQUYCGuGdd1DPaCUAnB\n0WVHNQvZCzG7hvBdoHo4zQv3o8eWFVsLYUpYUdQXBmuIufIQPNUJ7AKG0nDCWHwW0gJjSAwpUmvl\ns73gx9pITl5R+RZbErd7S63ClopF0PSMOEcyPQWIk2H7iUfWSp0jZhyJpm1gagQzVTqzRZfYEttT\nm4ToxbCmjkIibYFVGfZbnFTm6cJ0DtjoyVYonaVcBDce2Pl7dO04/cML277itjPLYrAXC8uGUV+h\n+UB9/0P0MqBPlSSGpQjjJnDwB4Z0y9mvZLkHD1UnqvOglWGzIa8rjGcMN3TMsHtCTetJpiVie0uZ\nBKuBy5S4HUfM7iUmWwhQ4sI2OHSsDPWBB81ECQgBk7fUPENcGPwWWQxDgCXf8slDoesLpcQW/hsz\nmIkp7cizwajhHz3dI5eFbezx2fA6VoLctgmSFaJpqgiWAuqos7kCZFp20kpE3LbJb2vFGoM3oKX5\n8IK3LWuuAK4S64doBE81gpbE6AxOlEhmqiuCIZLZdB7jHLeSoSi+OGpwlHwiO+VUe5w4rKN13KuQ\n1JFFqdWhWql5bWHIOFgVFctUGz0MsYgo058zVfwvuhYZP+/4zs0EDuRtpaSWuXiO/TWSwmGq4rSj\njkdk3YAteKu4rkA34dTQq7bXqXYUnZDSE+2CHwfqrFCfMNst0wGIJwY/oDnydOq4302s4Yitnlk3\naA0wHfCpwwXHakdcfE88J57f37WgYXvBjY4wJ6SDfCmgm/b7loXsRuajMC+V/kXGxpEhRUQfkF8w\n8FjQh3v0tiOc1pax9kNLcgccAxvnqbFSfmmLn0bWr16zP0dyaq7f07uO8ymjumGKTV2xngf2L1b8\n4nBBUZ7JrrCeBxbA1BMpbXFuZpk9Nle8s8wHQUNikJV47oku0eEw/QUEhnVktSvVpNb0kxVwTGdP\nkZ7gKkkH6toDlW67sn57w/A+EfZQa4euQpKRks5kK1AnogZqFdBM7z3eOGabsNVSXAZ3Q17Bm4VF\nDIaEmojojnU5YuaO7G84CbhwIhYYLYyblbj2zOGJ0/kGZaYoXMoL1qiE0fK1PNPJHbJZ4XJPFwL5\n7pHuccHct4a/SKtFJCs+FL77HHhMcNhUusvI+eWMVAjH9WNQ/R9Xi+g6goKPfVO8YD7e+bEWqW3d\nEYXL5sA43SA1U/ePcHpF9gupXzFUSj/TPw1kD3ER4tyRtxX/3DEPju7siGai38580e04vlvYdDec\nU+Rh/4ans9L7xG+e/c+8Zvwsx8+8YfrLCJ18/VzRp3jNLKjElFuWBoo3jhtvmVLEoey3bfOjY2ik\nn+Co13HjObbsgblmVAxznBlHzxIN3li+XhNlmblkR14rXU5Aob/peLNWnHP0mz3eGX733QFrHDIv\neKuUInShYkyTkD3sAh0r+Zw5xIxYyyU6TkvEu4AUwXXCOIxcNOH6gdVCQjCa0FJIaSWoRXNkU0vz\nbonBlpFTSThTkWHEaJu2iHP85vPEeRU664lL5n2u+BSvn6Jtgvo8rfzu48RNZ/h0v+F5Wej7jnHo\neT6t1JJ4tNGi2QAAIABJREFUzsIcI1KbNyCWSrAW1YVY2oehnTP1saFpvbMU/TBYyWRxlBKhWoqB\noTMM2+Zt6saey3lhkcrbA+R3CylGrFFe3hmiOuZlwluHcwNvz8+kahiWwvfeP9I5T6wJK4Jzmd5Z\ncL7hjMUx1UpMCW8NtQgpR3pV1tyQ3j8+JI4lsa4rMSmdae8tqiwm83LcEmwksEX3I8uSmq9IlPJ0\npoqQ5kzYdLSoQpBsyT7w5bsjv7j3hHThWQKfGs83dsYm14h6H2R4+tN7BVpILM2fBIJeUeX/b+d/\n+Fpb6EkLvKsNnkGNWNMIVvW6cTJWkKHjqEpWg02Z3glz8TwvifSoLDeOQY/8zZ8f+PL1xD2RtQp/\n87M9//OPM9Of8xxcVf+1n/p6Bf79658/7Hu+BP7Wn/a5DovnefbMKVOrZdsZgh/QNGG7iGQPVThf\nO3kmV2pV1CuRwrJs6YNjPyq5VnJesSjOCUY90zESnWMIlnlesUy4UbCi9O49pm/wh5gsdzcZVwWV\nTKZtlk6XnpEZqsFvHBiDMR3G3sBtQf0N1fZE4/HSsihEC746ZglkEZSCE8VpanLMWjFiQa9ERmlT\nW0PGx4GuPoM66Cu6B9ZCsErJmbBVYLg2X5oevnpBcsJaT19mUsr0RXGvDEhBs+C3yt0XBWoLja6u\noald3MGmkt3CWO/QXpEBPBOUC3pXCOYbkB083mJ+XDBfRDwTXX1F/bXCRi0+vuTFmmDnMDlShoJe\nEvJNRs4rgxtI/g5vVrIbkdceczjy/O1K7+8o08ApRnKudHaPvTHkdWWxsO0yrk9sXhlMXDDSCJNi\nHGZciIcV35U2jdECVN6/PfLZeIO7yQ2WUAp5Vuo+cHzeM10Cp9lyKYUUDKu1PJ88EhKBgUPxJDdj\nUnddYwofqHVJWxGUJTZPkgzXbnBDGhs1aAKjTdKssdC0FC1XpkrDCsQKa8qoGfCrUDWzKR0ShFzB\nFkOqQl4LUi0lVzqBLJVZR5aUQFqXN2KZqqPWRq2q0qSEilDJbXqXad5TbWqFSqJqT9XKY/nzW0T+\nMmqR9GXknFt2olv3ZLm0WsRmvCSk7MhuwlEx9YHin+icxWpsFWVtm4VpvaXUjrl7xsx7ojvTeUtf\ndnjgXFbKdGQpn1LXRH8/UwXMC3gjEOotg+6p+8LXh0dYXuAeK24QyrShu5uo44yMmTs3kMcf4X94\nQzRn5LjnXAbmOdOZkSqJEAxeeuInJ2z6jOWTZ9LzK7wWqlj8qoSvnuHbjNiClgtZX2Gnn2eVBOFA\nHXr608pyPtFp4E2ZOV88Xd2Tz5Hn7Uyo6SM0wCi8Pu75sQjjIXA7KpepEu4qvl+ZnnbUWDite1Jn\nsOpRI5Q6YqxpQB7tSUx4cXAyrf5yC1m7ay2iZLnh2qelGOhdxI4ZTCUsv0BcT3TbZ54Od/DGkl3E\nrMr2i4UyfUaJE/JwQdY7zstCqoZuNbDJuOMnJBuxdYszjvDJkWoTYpUuDlQDq5zp0kB0laKeQKZU\nj7iZgxqW770kdxdqGnB9okjFu5Xp0rPrNgzhPRwf0O9Y1mLIt0cKFvfbhbJJ2N/eY7ae+RcngqmI\nWhbX8fio7G9OuKXy9DLxc1/e8vbhHTb4Jk08XXOUfn9tcd09yfU9ElVqacHceTxhFv9H1iK2uCbp\n277DrBvYvaYzBn+tRRDgfsaqsNQZ0i1qK7qfqed7zv2R8bjDuUhXv+blbSZ/HdjuDhg1fLI78+XT\np0zP659kifhzO36mDdNfVuhk7R3JCJ8NPaqFl6NjPwg/fp746pB4PClD1yMU5pjYbJqM4bu94aaH\n/T4gdeFSOrLxPE8JzZn/6yz8tZtAKoWvni98frPjt86JW2uxOnwAZX70gnw8VPjOFw/Nq8LN9TW4\n3pW1XcxoC/ATsJtWsAy09HO93i5FCcNMZwLVKcGHpn3FNry0D2TaL+SzKna05FqIpaBLZc2FcJnB\nCb0pDM7xcLtjzcptZ1lLx7nsiKbywgbmWCi2YWMPS8FKZTSJu92eZCxODH3ncc5Rc+LTccB5z//5\n5dtmRpeGzY3iiLlSVPnV24GltzxOLQHeIOAMKVfC1SPljCFrhTCQSwuq1dsdZY1s+oIYQ2/2BFP4\nrbcTN4Pn5X7kPEecyTzcb7EI95uOpShzTljjmZbIr32yZU6FzrbspiCG4xrxGU6lksWwHba8mzO3\nrtKXme2m55Arlw8+KIR0vaBTLbyeLrwIwmyV03OizFfTtINPhsrNOPL9qXl8nDYMfDEVKbAYy/tY\n+e6uJzjD//31E4xbVNuErwp4I6y5NjqPaiPj2QYBhw8j8YoYbTIa1SaJqVezZG1+gVpbeKQFPOlK\noko4wNdCVqWII1LJqtSauHUdv3dZMcEQMNS8YobA0xKJaeFmgB8/T0ypEjGUtzObJBwTH31UfxWP\ncxaO1ZNVGGvmkCIDHuh4Owc6CskbpovQDwPjaOhcxdWZ4rb4kKj1mUVvqVbJaql4fjQVtsaxlIzN\nM74auq5jt/d89vAJYioMhq4ImNSAByFQsKgFI4VEpSfgKGStOGPhSptEpOV1lbZkOxKpXqB2Leum\nastkI1OtoKkiV2Otye76OzeDWkShw5BtwNqKk4ApC8EVLCu1RGxpm7zW/ZjaxiBn1CdM1bYx4oyx\nt3Th6nOzlpoGuFHO33/L7c2u5d45KKcDyAa1PW5xeO+RsCetC8vzxKYOSDToV5bVCf2Nx5iEvhyZ\nnyPD51+g316w/6BizhZdvkXooOyoNx1muaDJU9dAKYofFM5n5OUN6fs/ZO67a/Byx1or/w91b+6r\na5alef3W2sP7fsMZ7hCRkVlZExR0IzUOGC08JBwcBBISBn8A/wNgYeLgYSAcTDwMJCzUOIDTaiQo\ntWhVV3VWZVZE3Ii4wznnG95h770Wxn7PjYysbNRZWZXR+V7jnnvuN7zDHp611rOeJ98+8MmrXpWZ\nf/LEz76cMRtJ+SVffVDGnGnrTAyR13f3PEzvePm7R3i4sj6NnFbjbN1x/pOdczP+kM/fK1/8bKWG\nzNJWpIy4F8qWbGoCwZXm0GyAWLE1cW5AcHxOJJxVGt56ValieIiEAMsCIYauqoggHrtnnfW94Rgi\n2AUR5TAGokaOLiy1cplnXuyUYgbziqriQflmmbipSvYunjOvxmxbr6SDhcBqfWx66KPRrAfjo0MM\nPck4Ww/OS12oIeM4NRbwgGpPcgYN1NoFff6mWrW/Lywi9pqqM2MKJGncHgdSLsyXhescWNfA6Hc4\nDWmFYf+CnBNjvhCOj7AfEL+y2ELbF+z9keG88P72xyS9gD/BF+A/2lH8U26rEnzAOHK5VY4nA+4/\nnk8eEi9e/xgLwv5pA7kCcPgOFgn+h7hA4BVrBh0Cx49YZEf8ENjbF+x3n7K+vBJev0BeGlUTosry\ndz5l5bm/pVuI1Lkg55X0JuIcySelXitZGiFG7vTA7RG4WZGr89oGmjZSPGBlRVNXcrTTodscH77g\n9XhDkwG3RNwL+mqgrVfG8UANyuXRsFpJaUXEsLgwX/c04JNjxYfEk74gPy2dfXRQWG4I1Qh+2qok\niVbuYadwV/D5BXJJDMMKg3ShlVh58/7A4Wbh9rgweYThxM0QUasciBQLLDdwt9wxc+X+tcJyyxBg\nqQvICCvsm3KNlbbeMLLj0eBGH8nyjgOfYPHCOV1g3Xda7zJgc6B64VEnJM6sr435Ebh0KXUV49VQ\nyHvjGy/Y6BzPAcsNrLIDZj+yn4/cv1y4wbnqVxgD4esDkhvl9i3hNuBfD8iSsXEmNFDPVC3dOByF\ncRPumg4UKUQX/OYd+vSqWwfcfI1fDsjNxA6H3QO3nijhhM6ZxErTSvUDFmfKMuLpwk295U16JAwQ\n1oRcDMY9D2thLQMxGekaKfuJUveE04V2PnBJly6l/hs8/roVpu/FdJIAv79Thl2j1g4m6jrzethx\n+6OR94uRFZJFfngUZgJvS+bt5cLrl7e8fbqy14kJoUrt/UJL4l8fDV8ufLY/8nd/J/KPz8ZgmRae\nF57eaPYMFGVTj0KcZ8PSb71ptkpA6g2yYj3L5s0Q7fXK4ILVhgkdIEvAb3Z4c0Lz/tpfCM6e/+0q\nVDcGF9CEjYqLMayVgxnVB2IovJmgmPH10rMCQY1lXrjaiTTsmS2wk8YQlMcFLtLlvPfSGIfUgV8p\n7IfE6Wnm5aeR45jIw5HzNGHu7CnscqSEkTeLs2fhNg4sl4mlFe6GPYsYlcpaBSsLRZVdNcaxl2/P\npzP7jSbCuOfrc+GTXe+poRXWJQKBeXKIEUvGta4ccpcstuDcHw789M077saB8TgyJCOY8WF2NGRy\nXJHWS89HWWmL8pBG2rLQWiRsVN0oypycGDP7sKOWxi4ZP7xv+DmQPgmYO3ejgAeuU2FpjQ+uDOac\ncZptUpkp8uY88wf3SqiBFkZSmyjeK1jZhZsxcK6FaePmxqBYa9wMzlCMty7gRnDnk7ErD5YaWJaV\nGsCbMUufB5FuaLl6N/y9zzA347PbxDQ1Cs6HxbFi3I2BoMLvvbznp0+PxBigOcvSuNZekTivlayR\nWmHBuazwcmwkTx+rlL+Nx8uDckwFC5WlXCm2x9bCLhTGnEATg6y8eJXx65k0wzjWbhRalPdtRuQH\neLvyelQK0Ew5SuTJZ+5zYixwx8yLBK93icAXMOyRAKQBdMUkIQihreAVbwMxTLjN4JEogsmKm3Za\nlvW+t4KwtsDSDCdQ24xL76HDN5MsSR14bGlCt5WYhk4Fxcg0VK4MLeNuJCq2LJTaN0dm49Jmhv0N\nwSvTsjJqV137+iFymWfu9jvmmkmD8OIYsNBwPzAeO0Xx7g9usWUiHISYBE/3uDjtVLBqyCWhr9+j\nQ2anjpwHPAywO5NODb95g4aKvb4yXAJcvyS+ctgF7MGoY0J1JIUB1i+RH95Sf9qI7xz5yxm7ZHxW\n1s+vVDugrlwlonLk0pzzhyP+HirWvchSl+efaoDQRTmEkVKNt+8LcOSL/7eBj5g7ZoHmC7jw00ch\nIex2yl6N49gYw8jIew7ZmdfCzz5EltlY6o6Y4Tg0EGO4GahrpSAcxh07db4+z+RdQsyZW6ZNjaBO\njJXdEHg6rUySKKxcZyOmlV3I7OPKssJ5LaxT5VpmFs0QYDLjenECQtVIotDM2KmyuCBJqeuKCiTo\n6qPaq9LNCzd0+fkxFRBjbgkQojesTaQh4l5o0Wgybaqc0j2eDNCEo1ulzpn/amH9r3t8T1jEeW2V\n6wulamB8TLTlKwb7EfGTMw+7lYYzvAsc5Yl13FHqj1muf8n4gx1+WQm7J4I25OkGLhm57NkfviFO\nDdsnDj9+w9v2O+Q3E7w+AB0HpNn/ChYZFmNWwZMiH8FI34umV1uyrcLuykcsklYnLUbzxvUm9F7O\nO/DXn9Kao21g44J/59J/EYscF4E0sP5h7D2ef/GO3CJLFtQXpusNFsDfZ0QLgrPGR3x5j5Q7Ltcb\ndukJTSsn30P5lEmNHROxDJgW/GqkYY9fZ8qPRnYnxQ7O6hWo7IEYBW+vWaZKbGducIo+UtrA4Wng\n6hHEKFs1t2jg0Ba0nqEZyxoZ8xPqhskNj6cj97uFm0MlpjNlvsGkIV7ges9yfyG3hTTsQL/G6++y\nI3F9eMdYdqy3gfW1s/+m8FQHsu1J409xXTC/YbQLre150s84pvdUD6gFZLxi6y0WDb+P6A3oB2G8\nHOH33/GDr/Ysf6dx+KoQDitumXCaIRfep1vGOXANjfKsvvvDia8/b/zey4XxKfFQbhjqBwg7vDr7\n85HdFDkPX1HqS2pcSOWI799ziJl0CjzZgNiKpxP38rp7Oi57rucj+sk3WAnIw31XwPsQkVywNXOu\ncHt3ZtKVG23dVDddeJh3+CLcxoho4UX5lIf8FWo7cFjtzHpKyEHg8pK0W2nrwDI5V91x/+KR9MUn\nEB7+BZaIv7njVw6Yvk/Tyb01Us5oKxyjM1F4d6ocB8WasVwd2UWWZnx5DkRt3KVCvt3zj7964OUw\n8I1n6nTl09d3HHeRz6eFXIyqsJxWfkbj66V7BtWlMFnjNmVWMudaGAI8RSEajA57EU7VevaXrZmf\n7mBsbqhHXAJRoZojaK9W0bNsoTZcDCyRESR0SeGqgSid4GDPRqOyAWjpHj+OYyqoRqYM0ybCoGTI\njldhR0EaRAlIjL2J3yCoUyRQS6N4oCHcaKAGx1jJCpLhx1mQu8yfvXnPIQpiMz+6ycQYeTE2zhfn\nx68P/NnbiXOBVhZu94loSs6BWw28fZq53+1oB2FpzjEKy3zlOB55InAy5ZMd7EPj9Qvl60s3piUl\nikYihgcjxwAIKvDhsjC3hgHrcsU88NAah3XCW+XFcUdSWNrCXABx5vOFpTZcnWGpXFLqYh0JsMxU\nVorDujYyigbhogE5JQZr/PHbhd8bInMJiFeaN55mJ0bj2pwsAZMuRlHNWZvx00fhRhvn1qWhcXAX\n9uLc2oUXY+LDbLyNiVSsC1V45dNd4AcS+XJptGqM68TNoByHyj8sEF1YrdH0WTGvzwEFPnjjcW3c\njwN/+WFlSJGlgTVHk/LFqbHLCzey8oMdvDpU7NqwIFyLsxpEjyzrwkRgLpUxRb45C5e2sv72xkv8\n4AX84aewrAviAyqFZsL5Irg0PCl3JBautIPzNK2s0w3mJz67NX7/NtOmJ/IwdjPqlJFW0d2V/T7B\nseFlRCVuChyGs4dIV/hxwAfEGzBtVWuHUMB7bXbLsiB0GebFC6A0c5o5xY3aoNYG1qsFXQ5WCBpx\nX2jVkNbd3292mVfpCV97xUiywSMYXfnI1gXCdr5xIh0z2W4oy5mSCvFW0NPEuqzcHw989uMBqzPW\nnHTc0aQyXWbclPPSKX/7uice7hBVGBIM1sUBbh3ZR0jvma4z4z53Wc5xgjPI0ojB8a9n4AH9/AVz\nTgxyj6TI8hdOXjKkK2V+IHz4gOQ7LGfMG+0EbbhFreG5e5HFw4BVyK3fw6jWJb6tg0txcJNt7ezg\nkkavpKhTVsNVEO8Kde6KSCWI01oXG3JvPJ5XmgofJsHqioQbBoU/uoe7+0KbAvPSvUni5KAnzk9n\ngkFx5RyvlFTYRaedBwav7PcBy4HPT3CalckNl8rBrxyGHXsxHufEovATL0gBiQKl9zyurfX+xtqr\n4KuEPj6iQDFiVKKBeMFC2PohjRABCeTQTW4rXSBjbd3Ms3ZuIIYTPJAdikbcA0l1U+7syZeqwmKb\nmiqCASX8+qoP3ycWkfSW6bPXjNdHRnMejzfUDweGfaPlgd2fBpZ/BWQypvEGWQTZ/znKng8ts5OM\nfn1HKu/g1Uh7XZjXjCEsN5H9+z2n9Cnxooz7zPLwQFudwzgQyVyXJ4aofPiDV0SDw08fOJwGTp/u\nPipiT0dAuhiCuSEtcLmJHJ9WzkdF6KqnuycIBMbPn5D9yPxyx+1TD5Sm0SipYxFt1j0H+S4W0U2h\ntYwN1cT5D1+RHyLNrlS9QXdCe5wY9bEHa8HxcMUt4XkmTXc0P7DGRzi9oqaZo0WQHZKeiC1AMvZD\noXwC8fNHSIVokTHdUIeBXbowngbaJxfaW2VtQpgrcb9jcCNk5WWaOV/OfX3D8akyjIWVd2j8jKkW\nLrzmzs6MWtjfPzFdR8J4YSm3SBlIuVJCYD8a4zQQNXK5KuVwB1dhsRXjFlVhd4Jwnbe+wpVVjLq+\n7LTVNDFd9+CZdHjHVO47C2EN2HXA0sy6Cr4Y+evuuzbvIulzocXKV58XflgyYTmibpSh8VgzcRbW\nc2J8GIkR1rDgJRNKpbzNuM5c8yNSDlvDkhDjys3uCw7TkWs+8dZGghfK40i9OfNSE7duvB/AH18y\n7N9wCMYhC39aR9qHO+z40JUhAb17RL5+hb9+xxKEN9cdh3bk4fCOwY/MdaYVgbHwzeWWHC/cjE/c\nGNwd3yEDeBTWeUddBR0b60NkdqEMTlqU9+/umG7Xra/9N3f8SgHT9206uTbhpw+VHANRunnLEA+8\nLYKViSrCujqhwrQ6Q4SwVtCV8wwfrqXLIoeRt2+u5CAUcxaPFG9ctAIZK5UVwy0xamQ2Zy0zY3SC\nQ56MHCPRuoR5KoZlpTZDXYgaCLYwhISGwMNyxQm9oiQKm0R+twiE2cFbpbSGO+ScUIdWjbZ2/XwP\nyrquJFNKrayhUzZC0I9mpkNMVO3KasGFUp3dkDkHZzVjakpQ7dz4xmYmKF1Rh8iHZuwq1BRADLsI\n/895Iqjwg0OgtudKSJcDP9cBScrnDxPFupe75R1fPM18djOwFGM1wzXxzVxoQYko5xW8Jtawcp+E\nN9eFP5lglMY+KWMYQJ25Cb4UUgisZcEG4ZAElYBL90poVmHIUJ1K59Yb3VOkuVBFOtfcjCDKaj2r\ncTgcOF0XrnMjasSoNO3BpLlzESMa3GL80w/CizFxFzJNnJ89rBQXXh2V1wfFUE5FeJhXVlfMexb/\nbjfgbeXUlCIBWQvVey9JzcbKgYenCxoTqRRm72D4NiTeTMLgxqUWCMrjHOBSCQZL6EAtpsiBTiG8\nmNPcGYKSTVFR5rWxeOCrD2v3IMO5HyM32oOwGg+cypX7KfPUHIrRWmMq0kUttrGEKdepUujVyo+Z\nq9/CY//Znvyjl0zWlYuCdQrr+CxE4oUYhZ0fO82JQE4J9DUusFpDQ6CGAB5w7fNQRZm1ENTJYyTI\n9lneej9ZMNAAtiVS6MGxqIIVwGjezRhxR0KXpNUIe4t4XVGJuHaz7ZoCRbwDrFLxalhTTCrgBKCt\nlRiU2ha+mbQrNZmhKxBAbI81Q0LulOHoJO1ULo1gcYAGapV5XyBbT/M8A+q0UpaMYeRwv83v2pNE\nxfD3QJwhHvE4IbXiWpBg+M7YZfDjCU8NSY4dvkaWCPmADnu87XGLjC10P6T5QgoZSRdyzYgc8duI\nNEGskGuDMRLqFZFIK0rQRhWnuxHFHrSZ0SRilC7kEhWjG0pLK5gbK12OGFdydGhCxTCJNO3KoE7s\n16IrtThjzrgrwWuX/daKN+cnbxOI0LyCjDRvhLyi8hIdQUW5jzAo3OxmDnHk2iqnVrieEl9eK7e3\nI3/4qXCZK58/VqoExlF45cK7Xa/Q/546VXuFqCxwDYlqCwNCasYcB6Q2wmBQE3OpeHTGCImBt7YS\nWuJhbrw6CMusPHqngo7i1KisLhAyszReFFiky4ubwrD1htTNg24pnV2RmxHciNppywtCtvnXmsff\nNxaR6QXLTzIun1HEiO8K7F5jJ8cerj2R+eVMW3bUBeIgyKycPp0IfyZUS1SthPgJ/iVIAlkK4RTh\nvdNiQeuBUq4UF7QmNClrLZR1ZUi9sX//Ty+kUDFtWFwZ/uKC/85rSjPCl0qOM20+sQ8HliyU95U1\nroT5Bt1F4nVEfE+Y36BzYT1FggTKSaE08otAu3f0KaJvC/kmM71o+NeF8ang1VnGPT4Xhp1gYSFN\nSrgxZH9LeDR8KVipyAFWvUVaY3m6g+GKTSMhevcOfPoBDCu2dx7PMLgQlxedclcb1zLBU+BwMKzc\ngE7EmBBbmMMOjpF4mgg2EFbFdzvOZ+XFoTAxEZ6OqN+xPBjEkeDCqa4Yr9jXyk2BD37lL8tAxtgP\nxthGWjvg0461raCBEh6ZPLPLJ9wGdKhIiZQwE4ZE29g92TKLFcQHkB0lGdOlK5bO5xuswj5lxvoZ\n0ypc15UcBup4xqKhyx5LK2uYicOM752vfvYpt3vhRguLXJhXoZQdLy3yIk208MScX3HSE1xusbGg\npZHTkZa/wHxP8R3Jn7rRccnYbmG9/IineSFlJXqh1YQ56PkT3qZKKpH53RVuHpje/qAnpHYT9WaF\n3RNRI1ErPgulCba/oi6Eh5FYlSaCXz/hkitut52ZVQvD0mgHpUwD8864rcL5skOk0g4Li6aORfba\nFYMtIXHg7BeoTrHfrNzmr1ph+l5NJ/+X//G/Y9gfe3/Mtp79vb//7/Jv/Dv/HmaK+ca15Jlj238O\n1j4q6AXA1gXXETPDVDeDz4iFnjUxHK/TRyqekwBFtsz6PginaaFI7BK7BFKZaRKpOOIVcdkoDVfc\njCT0RdQajYhZA+lO0VmcFhzZzheD1mpXO4mRqdTud8Lm7h57+dz9oz0qZsa6hdu+0c8sRb6sRlbB\nDCQpQUIHwf4tr7k3RFZyE1aF0hqjCvvYOORMlMpXa2TZMghlbuyHzLiWHnQiXBaheiTpTBoCX5Ve\nSdmJkkOAAKV0oYuqjiTlYVkBIWrkxS4Q1MjqWBPGYSD7AllorRCHHVkCTYR5KawOHhIpCAMV2+Xe\nf9ScGoWHVkmxP+PdqJvUrhIRPCe+usyYdTrlUgsaAwYsdSXnTDZhp72/7H5MKMbkzjqDxYSK8FSM\nkzlrbawiVFcqYC4sCE9z4YML5q2rSkmgNSOoMFel+opr5CxOtcKoGQHOS3+Opa2ENFKWlei9dyDH\nwFoqixlqziTSlanMqaViMTJ7H1et9Z6oHHq2uZqx1sIDmckK39iFQzD+fLlwtQ6mc9gqpN7V9376\nx/8Hf/nH/+d3YMY6X/7/pum/1Mdyf4t9es/RHfPGNtuppft7tZYJoScFcs4M2n8fNGGt9+EF7fOt\n1dKl4q1Lr+eiCI0iC8WkC7BIVyYMra9aIXa5+9V6o2007ZL0QDAHqQStqDtBFQ0BMDQpHnuwLQ1y\ncZIHzBQp3VzUWvfOaq3/nId+Xl4ajY1ejCDPfmDSW/HdeuN1a4ZTWBxUe4NlxBmiMAQjDusm1dsp\nwyrWDXJFwRqsgi2CaOgCO6Ld7628xcuI7XsvVk0TapF6vXaRg9p7soi3SB7QuUIS0IaEE55yX7sc\naGesCbI4bYkEW/C1ItV7r4BXXBqooaHLZGcXCE7VnlyITTBpNCm4KU7DBSwoHgLmcJS2ra99brXU\n6Uc0Y6neRVa0Ym7UpmQCop1C2eiUp+YgQTArWG/zomBoElrLVHdK6XvVaa7gQnvfqXYQqPVIiCuz\n7zjRi9TpAAAgAElEQVRdhQ/zwlKNWRK/EzJvzfhnZ8Uz/N195sMqaIMP6xXRxOsYSATmFrj4hb05\nZVCe6hHWK7scMCIPDkNspE0K/RNJjNoYx8K+eVeXbSsZ4UzCpLHziXUUmgeadbPysjhNhLHTH/B9\nw924FsHIfT4Y3Ghj9+sDne8Vi/wX/+hn3MVOfe29ps5//Aev+Q//1U8x3+GeSM1ZNyGeeelqXvrP\nJgqB2XvP4VVXdLqjeMBFGJYVPLB+esLd0MfX9O7aPnfXVwZjQL1/703+hPrwyOXljD4NtJuF3U8a\nRmR69cByHbC45xIb8d0Bt0SyA+1kyFuYX75H3t7TeM3VnF0oLMtbJAMJYj1gbwuFgN4I7Z3TbIU0\nc3nZ74US8BtHqV3ie3/pVdoCMt/i94+4CMvTHbummEUkGtmPeOr9w9oyV6ldgbc60gI1LlQgZmdf\nVtqhMZizXD6jtIQD70+Bw0EJ5zfoALVE6nqgmBJtJWnk7X7FbeA4Jbwq8vrIfH1LvIxI2qHsOMkF\nMlCNQxnYJSN2UzFG21HufsqIUss9MdwwLgHaC85xwq+95ygjWLig6Z72+gP6LlHvZs42I+E1lhrj\ncoCSIAaW2/e06Y6nZWYZP0DdMftEWndd86U69bgSP+wY24DJV4Q/OmNf7vCWmMsdmpQU4FIyrDsW\nN6pW6q6iS0Uue1aEhcb14QWsGTlesetLmBMaYS7QbMWycTpe0Dcv0AyyDqzmQOL6yTfI40uazoQm\ntE9OxA933cMNozWn7S/Yjm7vwsRy2TPFhdfc9r11NJZ87rTSdWBJZ1rODPOBWSrr4ny5ZFq+cpyP\nnMVwq2QCa3P+15898Q9+9vStIAXwVH6zJSb5ZSpd/9wXixyA3/+FX/8PfNd08ht6o+XPm07+E+Dv\nu/s/FJF/H/ifgR8+c4dF5D8D/mvg01+WLRKRfwv4R//Jf/nf8oPf/dcwMyQYgV5WVt3UwbZW0ixd\nzhtJW+m4G30176ZdAUE35aFVfZNh3L5MZdtEBaeywZXv8Hjdv22qFOlOyIKyBkjNts/qS5w/60Ar\ndJstwz0CgshmKKZ0M8PWz19DB1kq8kxDhu07q2xqjpsOpGn/Wcxx265368l/LgSog4kT6IZ5A93o\n1NwwIj2BKixro9KvKWvnnxuOauxN3bYFYtIfkTZFVDZD4A4iTfr3FSlElKxCMUPpSkm1rVhI5Oo0\n7Y3QZTNO3BGorVLC5lCvStAOzsIWFAqZGBxtzmyOBWHAORWH7Vk++wZFOugvm+ylim00FaNa/1yx\nzbNpk55PUSmlIKFX06oZ0ZycM3PrfiVBKqbSfZI2czez7/adtdY/9zmo7YJM3Q9MpY8MVf3YPAt9\nfPbDtqFoPPMr6tYbJdbf1+zb59ppfqULhJjhvtGK2GTHvRvBucqmMKnbyLSP3lDBnylJ2zVtl9I+\nNvcqtRWSZN5/8af8b//9fw7wb7v7//WL8/VfxuN5Dfnf/5v/gH/zj36I+QWvilvDHEq5IqJ9zAUl\nxj7nc+w5JdFtDdgqdWaGt27ciW1qT7X2ceaV/rj1o7BLTLoZ/yWcgq/b3A4BpSDW+8Xa1h/WWsNX\n739bn2/lWvp3bmPN6RUL0dbjttbfZ2ZIjRhOtQptS6ts/XMCJJS6GXon6UQp3VTOpPV1AkCjb4It\nTlTIyeGw9PmhS6f35RVvGZkjNg1I0T5udYVYQPt96I13C9i+gyvtwZpLQzA8NEQFz7UHO3nq9z5G\noOGhQlq3MTvgtUJJyOrI3L2eKI6vAamKrz3J1ZoTWsDFEUmY9PnwLIsr3sVVzBVpqT/j1u9v84a1\n2NdKqzhd5a3Q77ebY2qIREJQRHpyrK8djVIVr5Hz1DgcRx4fV1YSQSem5ULWG+J4oM0rJxoDcDGj\nFONmPLCsV6arUocBLw0LEHXtin0kYlwZpdOEqUbMgfN1Jrqwz7nLGYeArYXQcl+j40zOmUDDTVkb\npBAordsSLKvRJDCtC+rd7/DSDA+Z6JWyNoo2YkrcWOBaAik5c2vMtVes8O7lh3ez82aNw5iYSVyn\niZ9cFv6r//vP4a+5hnzfWOR/+k//iL938wKuho2NsAmzqHaqvJ+6CNSYz6xjReYbQgMPFTGlZsem\ngUifXyLCrBG/PZOeer+S3Z6xaY+vIzJcukR/fk529PPRZyxSBthdYDpgt08sOjA+ZoIJ7e4RYsNP\nt9ubQPOMxEp9/wJJBbk5Ax2HNIWwdCwSkiPXgVAj9bbPR81XAOqaP+ImEaEJPRHrwPuXbIKyv3C+\n/Zdy80ScBsZmCFOX1CcQrOOO88asIBd2S6RJoKkRPH/EIih9DQTU+77Ibkeb1l7JBwLGErtAweBK\nMZDdvveDtkfaGBgeAiYDZajYcIVzIpYuWV7GRrqM/Tt3C3HJndo6rMhlzxgdac5FG6Y7dr5wLQFL\nC6GNtLtTP7/3dx1vvj4R59wtA7Qrzc37b9A6kM73/TWuOIUQFAtnfD0goTHfPBDOA3u76wF3Ab97\nx6IBTAlrQNfQsWGLH6Fjqx2fuoO9eiSc9gCUw4S2gJxGAgFL5VssctufMdb/HS4D5AJrpBx6dVjF\nkMueZZwYVLYE9C/HIskSTRuw/BUs4k3xFoi7rnhn080vxSJDmhnqwHW8kJfMtQ28eZj4j/7Bn8Jv\nCIv8ShWm79t08s3bM7a70Ln6DXHdnH8NDUrc+oieDSaDr9/RmIcufxrok1tEtyyufAS72rYFb3tX\nV+GwHnDJ8zXn56vvjdSquDijw7L1N7l0YOLbitF7C3p/gtfOFd+KYSQEESc8l7BIhKCow7y9/6P6\nnvQx7BsfnNqd4xXBpTf8P1/sszy10sF4E8U2cYAQCuBo7YiuiLGIo969fmYzniVk2Rb0rkHg6EcV\nN+s/W0M1YArVjeDgdDn3GgTbePEONI9IbZhqFypoDaGb7y1ieFDwPrkrQtmCkigKKOKN2hoeO++6\nmNO67gHNtPeIPctb0ukuS62oCmIBRFhbP/Me5HSFMbWKqrJU28CIfJTnXr2xloZI2xbpSLPu/eXe\nF2bfwLa3LVqNmw3cFjCFbWOLaXuYDm1TzHsu3Xz0VdrGRW2bMqN3Nle/JsekYb1/tfsoKFjTLTHQ\ngZ7zrb+CPW9aZv356fM41o/Bf9uC4eqbgd3ze1AkCKs3TIXaWlfa+i09NDhZe99gzLGPPxFEu9S9\nP2+8z9XpbU66Vvp60Asnbs+9L45r73cRT1ir3aPIOqWWIpRS8QlabdQCTulBjPvWtK2AEKVXmsye\nlX/aljQQQug9gSrdrqCPCSVIb8Y3b1tFPfZx3eRjv5zXRK0Vb41aCy4KtYL1YLiz+IzaGqIQf24M\nuvV5XqwnFJbihFNPVWkYIXZT7X5KATXvVXd3JIQeJIkg1J7tqSNI7cGKOCRBg0JsEHxbvxQzR69j\nD17seY5k0N4UTFr6c1KBXcPHFfEVPCHeN35fA5wy+pSxFWRVWiso2+dvO7HHLg2uHreesy6hjhdi\ny0ieETpQQwt47bUMtvu39fdIdaCxNqHWwhB2TLViwRhDZZ8U2Rem1miL8+J45O6YePv+PXkY2Vnj\nJlRaPDBNCyYzNRUOP4yI9yB1tUbqYS1ilTCsXBbjlCJWjZ0KdyF2r7v1Qi0jvcwR2GnFpKteRhdi\nq5gpFzLX08ywj5zOC1UyYsZUOp26Lgu9frBQQ8VXp2qirY13pbIE0CkR6FTHMncQm7fEWkUw68ah\nqgWRxGlZf615/H1jkes7Y9GNIfEQsC0FVXFKKjTrVfilLcjDDh0/QATLFa09oLcJ5nEm2EA5rqTz\nDXrJXF/196oL9uKChROyJMa3O8Lcd4wy9HtcW69Ua809SRAUXV6zDxNtfMF8XghLhgXY93suCi0M\nMIBfYucDburMMXf6abj50H9xeYlmg1xp1tfIdhkAsAxuFdPY5+3w0DFCg4YgiS7QDrRPepEvf33L\n8rvv0CXjdxfKl7ekF09AIX5zC9ooRDwKWhLmgdkb9cVM+DBy/Z0PxG9uaT884TUQ33fwb/dX0vtb\nuBRsNMyg/uCJ/Bev8JapslJboomg14oFZ72H9BSYUyRVw2fBOFB/9Nh7Zwq4BPyqLD869/syT+i7\nl1D2tNcf8A8jdb+i10SrwjVk2utHwsMt5fUJvumBMy+u8HiLfb2nqrI5hlAocHrNfHzYqL49oeot\nUrVBOSAe8Obk00t0UlataCqQhVqO5Cl2dlITnu1nRZ22JeQ9dxaSp4JFg0MPfOPTER9WyI06Th3r\nDc/D3mDOaOlYt96fkBKQ4J19ACCO317YiFmY9vX7GYtE7wkTHErsQdZ+PX6cQ+bOlK+ghuTGULax\nrIXLcGVf9tv6ur3eMi1AXkeaOntzZn4VNu6vf/xaxrXb8Yslqr8108k/+ZMv+OKbfsom1gMH6y7Z\n0HncIkraGkrtmXqy0c9U+gT4+WqAav+8n2/LCHQwYr4yhLi9Vghha3a0TpcBeoOshv751s+rVzme\nm1w3eiDCc4QU1LeKRwdGpc40gSy5c8C9b3Ctdqlt3Ckb6k2x08q2xHdvwG0NzCnb56ux0QRkyyA7\nGhwNgSCBRFdkUxWa9KBAtnNqVT6e87MK4LN5mW7X/Gy82qrjKtizvwt8/ByA6PRzo4vtuDtBnLU5\nok4MPcPStmv7SAOSXrV5rpQ1BW1OpXsKVZQctSvSiWyyutt9F2hWsSY0s06NApLB3NEfCpTnQBTp\nDbEbeCwfqz7yranbVmXplLq2BRreufquPdsFPBvIPnshAZ3mSceG1Y20BWjuTt0UE30DXlGex4pu\nmaaG4F0x7efH8tY38/zvj5Ut948ZvmcT7p+vIMvPzdQOQbcxRQ8Ko3cPn+fP8u2aNpZe78FzWB8/\n57f1WB+utHdnJF4wYq+YiHVA/xwnhtSDD/etRwkkz2CxjwsR8ErURC0G3isQMYG0b+eDm+Nm5CH2\nckSOJM2gM1Z7ZVbpYg2O4O1Caw0lcF0n5glonS64rivRQ69mC7j0apbXy0b1gxgGNFVUlByUmIUY\nDQLsMjSvuPXPq21lMAECWNySFL59h9HJyHUTpVCiOqoZcKT24NHLCkSYdRtPrf+/eU8utAbS/Tv6\n3A7bplo3wNCQGjBmNCpo6SpbactChi7bLd5l0UV9q7DR73nryS+/RsQc84RkQ7L32Op2wW9W5NiQ\nywCnW8KqcO2y/GYFYcUsd+qi1W3NZPMcoSeL4kCK1y6pG0dcIhK6KqEAXra1NivWugS4xp4gux8D\nTuF43ytTIcOuGb5kmgQWWdjfZYrPDNbpc7oKUTOrCRoCj0sBeg/UUgJKwhTMJ8ZrZloqNRqxrZzb\nAiEztAh+gw9zX49KI+wivsC7qzKfpi7IQBfZCCHwzTkySiayELJAS4xUVuv9WqMs3e8pDaj3/jlJ\nxoLhyYnVMavI0KnHRQK1VhIJtLE2Q9dA096r+bdw/MawyPsn5Y30rzRZP/a+phzRJ6Hp3KlHIQAr\n9vTcKpC7wacFZDdjT4bIDBfQ6V1/zc8tr4G+b18kMNQvO+uAXnwR7bSZj1jkff9ZohIXaOlt3xMf\n/yoWkdp7qXX8os/70u0NVl9o4mQ6OG32AUrAStsq0E59zgkG+RaLCIgkqjdozso3QMciuMObDYvo\nBE8dj8QSOE1vCJ/LhkXe/nOxiL8D50T8BlZm5Mv+f4ufCaK0nzlTmKnmxOMe3Gk/myi3m4raZUaG\nTous1pNV4QOc69SxiHbfoPbk8HUPKkWEqJXZZvgn/fuaCtq+7ljk7DwtV+JBkMlA1k6V/EYQOWNv\nhZI+wFVob4ziC05PqNs+9L2aDYu8H0GuuPERi8yx7yEtxs5o0Y49DZA1fGzbcK+/gEUyImWz+wr4\nhgtNM3zIiMPURvZhgUu/7qvfsZOZ1e57b71U3DtdWdRZ371CgCg/j6tBnytL/AtgkZ9LxH6LRfYf\nyxOO9Aq7JnZ1z/j8xuf3/RIs8rPzb1aB6tcOmPw3aDr5MF15yn0CBPoNdhFk2u6nbQHNBlKd58Xl\nmaIE+pzZ3VKoz+p2z0GzAVmfgblvcKbTZz72Lmx0qigbtUmUIN7BsLaPFD8TNhDae0Ia3ZMpaQCB\ndXv4glDowKt6Nyxcrf1cUKfoRvsx6YuUq6CiCBXdepb8+fXb30H7AhowNHRzX0S7j9IWVCaVrRLz\n7V7TjcXSR0LhugVVaZsoqs8JVoEYOqXnI3Wu07c0QA6RFA3VgIqRYkQVdFPkStYnfJTnQKx/t2/P\nqzhgwlwLrTlLrSyrs1RnnhtWnKU4k7VezbLAaj2QttYXxkZA3Xr1qn1bOfQtOFEEN+s1GXd8C4aE\n57+/XQjCcxVGvqXguQkE+zZg9G9f/50FxBxJgWvtqlUmfYMN/nO0S7HvBExqfRQ/V4HgFxal57dt\nFIXnP9qThJ0mwrdBk377iHtVDIeglI0L2Mwxse+8p3+ewBaAq0O9nvltPcrn73hYofkTQiBK68mE\nrV+xilJN8LZV5MSBQG1CcenhpBhGRVGCZsyeedS9CmpuuNsG9rdEg/e+QdHQg7GwjTcH6AmSj8/W\nGlUbgiK190KqKs5CcCUoqDZiDJgL4zgitrKWBU498TCFGQVSEnYpkEPEy4qKEt0ZYuhqRM8KabX2\nvWyj9VEzzVbKVql8zihpaET3DTxt1NznRMozlVQFF+vrr0Qkrn2Mu/V1Rno1CfVuyTA4vr9C7hTR\nGgvEGUmKvmgQKr5muIBdFL2MeKtoyzA54r4FUYJ4xnSH2rOynVFrRQkUn8ijY7mDO20ZW8ZeFWtO\ntETbwIq30Hu7xCm14jXj3rZuEqM1qE02rySlbYGyOawOQqBaBJOuaNhCxxAqYJGmtYeXawJXKgmT\nSmuBLRTBrGx7UOoJIAeiU2phtxu40cC7yagWya68n3OvALsxSAWZGcvI4jOLBd6cCk1Cf8gpcRcC\ncy0sTXCUQ6yoBGqLyDKTZMQ1dREQKSg7ahHW4nhUoCEtUtXIpSu8YkKtTgwZ1kKWwExjdTqFPnSK\nzf5vwT7lN4lFHi3w+dzH/d6MlgIWFVk7FrEPTsrCnCJDc3zsr41FsCxQjHCqvUXgeYsYejY/bDKk\nLSq5PWORxtR1dH8pFtFQ+/oeDG0QQiW0TKsr6uDRCDV0YBpar4J7QFvp+CN0GpsQaC1SVSjWscjV\nwtY33uf6sfZrOVMZpEAVZMMiQ31O6GybTSg0TyQvnQXUoF294w6rqGTSc8BXe1+kIzzn+YI2Ssvk\n0GhNWVNngtAJMgTdGBQioIGAsl56sBpkhIvRpJAc4rqg7uzEuuiKgowVtJG2/dzSt3tmWP0jFmlt\nBBOWkjEPXW1Xes9QmzrzZanCqoI062yMULCFbu9QjpzVSA4XrcSTsG60kSZ9P95tolrnQBf6kJ48\nf8YiCaO4kMTZ14qI8hASqwlZOv4JobG4APk7WCS0Qgs9YBQzcpp4V+GmrTyFzFgeeNJI1bkLIVmj\nUVk0YxpIbUExQvu2ovO3gUXmDYuczfkSeGc9qT6KM5nwe6HRPPCFwY8Vvrr+epXqX/X4m6gw/cYO\nbwul9nJi3ahKbAFJr+ps3gRle3Dbw+waVCBbpP0s5gBg1f8/6t7kR/Ylu+/7nBMRv19mVtWd3txN\ndpPdzWaTEinRJCXKhCQKgmHQC20MiCvDw9J/gQ3YsJZeGd4Y9tY2vDFsGF5YA0VBlixOLUoiRarV\nA3t6Pb7pDnUrh98vIs7x4kTmvd0abIB0G+8HPNxXlVVZWZXxizjnfKfv+VqApY3id+hFzteZknfW\nxjAQBVFBNESzdqFCGZkhitVhJt5jWisSb3IcsC/QrvPMrXXDxC+bFIATyFjSszYGkkT+ShDRhK6h\nQ4hF6sySETUmiSJKdCVdXr9f6D64Yy6kHFOdbj34s/1M4Rqb/XlnH1qhTDSjKmdKoYy8kBShlwKb\nnJg1GpSSFZVw4kop8bx1cs7gjVKCNhR/Fx0ojVPdWU4Vz4X9qbJWY+mw1JVTM1oXmsO6dLoYzYRm\n8XdtFrkj67kw1RdN0gUBwi9F7ct6ohGBxDjmxzoa9Cy+b7NooQVBhcvKGgXmWdMlDr056WyvOxo2\nk7BlBfDvc59zCddEXC4UzDOSJj4K3NHgnV+juL9YR9/3Os9uiufpD6q0ZoMddebGv0BOw7Cgj0Zc\nXnx//8Gma/9xXt4M9RPiKbQrHvdlT3ZBj80Mp1N7pnanjb+VeNxnzUcT5UIbtJyg1znuFRtoM0MX\nllJoRUCwtAadqhXMaiDcLpgvYZ/vBbShvSOkQDMbuBiqgfYqB8QyvTWsZY6n2/h9RECCjpk0qB2G\nUMmInMjpvB5AtEdDMTLfJuL+1lRJooi2GGR4OPb5mZabDM+OacXmTkpO7xmRQBfwhPYQVkoHtA3k\n6TwdcsgVKw1KR64M7oPde44uJTbrWqBnEgYfhH5Sx8xAc0WujE5H1oxMGtqsmpDh6KikF1CD5Siq\nls6kE6xGagJkaGtQIrvG4MIrzqAYThEYLcmZjMGD7ogptcEpF2y1QTEOfVQ1CfMQT2Hj71E0dAN6\nuFhWwliDnsO9zztOw7rSJQ0L6BfDii4RYYDH/tDXaKjW/coTC8fTTkWWMRSUDiR6FrQljhjiiSuL\nvdokA8ppNT5IQRGdVbmrfRi9DAMMS5iviAlNwo0qu7PPnd6cedEx+A2NaGhk4zWckuIWzo0pZxoL\nYjNixjoGSe8cv3ev+7Bd+dD43Rq/w04z77bEncU+/dPTyj9dJjjA0YQrdX5qflHYvdczr6bYQ+Ic\njffb9kYSuHc+V7pzkh5jiQ4PB2U1F+c4mrWbG0PEwYcOWzLalbI4PSlqCUuJcmqXWkTM8B60MKmD\nseMZK45UIAn9SuN86EatnXkzXiPGE00cnw/UWqYYuHoMVerawrDhOlCj9Fxo18rmOGoRFXQrrCL0\n5UAm3HyjFokz113Q7FgTeuvRBKxhvqL9zNI5r58437qHTlEhapE+kJF1DgRKYPKMWeTWFbaoOrMn\nfFmpc8GXRtkVfF3QeRNNT43K0UU4rjZqkQQ2I7LQcZYhjm9d6JLwNY9aJFMnxdTZrws3LHzXtywu\nTBrZaDfAafAW9x7D1KPs2HGknwe2Ap9bd/xEXuL8AZ5dapET2R0XIQG/dboag6oXNe0vlKBDihwv\ntcivP7vhF8st37LCK/mWzy7X/Nx8xz85XQPCnx360TyGYp9drwYLYYNL1NCPj8LDjfGtY6Bl18m5\n7cqjjcV8sH0v4Hvb9aU6Ox775ZtwO/4bd4VnBr9yr35PLXKfF7XIti0cRv7bA2/cEc7ZP8jrQ9Uw\n1XZC1ngje0htghr2fZ2uEiGf+SzYHhQ5GYWySBQ+IsH5FOQlMF85u96IjENmFNcyTCWCKhVFpJgH\ntJ0UE6X7igNZS1BzaGF6IC8E172PxkDyaN5GAT9+BxeoA64919869C7trCsCagtb8XPD2AcKNaGB\nHLiBhR20ulMsaEDikMdkSrQHxc4Doevt7NTXMVNUEwwheTIJxOps/qCK9UpJobEpknCB1iuThtai\nDgqbWCfnwtrBpYUVsEYyfJLMunRaa6HPGPazrXXWYZe9nDpLC/3MqXeWZqwW4cBLi0Kk29lIIuED\naXJ3PA3K4Jh8tHibx3sQjs9dBvv30knH6M+kDw0FvHDTCIrn+X04O8rJy/S3c7DgGWUgmjN7idZ5\n2Tr0e5/+rD07r84OF5MS64E2xIIWXEKvEdu6XBqx8xU0qzOd4PwDBsSdRsE8Cp/4mW3cF+NVDOTy\n7McIQPoBhx/8MV4PrytvPKwD3QBSh/SiCcYTt3dwWITTodJspnnCq7GZwnxANA6RrRRUndZiXa0S\n9+imh4mKaWDHonKZsG0koapsyoKIUVTIc8PcWVypS6WtcH733SuuHohVFzSB9xxUNIckCzqKhpQS\nYmEUMZFwVlSFLDVeN/kSng2NK1HylKCvOB1NMTwZI2wwxaWCd6zlyBFbWhRVlkn7CbcVnSbIR5In\nRBLoGlTiTjSK4pDPFNWGTPF3MjfS0xm5q/DdHVYmlAnxA1zFoStpwVOh7xq6N7COLwUzmA4EbaQ5\neiqhl0o1wNDuIdysAq2QWsI4IuuWTqJ3WNbQj7We6BZZeuLDOEcbbS1YglaheqH3SidhFo6CvW7o\nacFbvMZGUCa7dRpBeWo9UvcUpQ2GQbVOGpRcutCa4WrRlJvQxV+YfwSgFWYefqaihGFPc8heOKqS\nLabH5mtQmVKEKtsRvBfEGojTaBQyzTNSGm6FZM7ShK4+it9z4Qon65BClF1UWVujd2H2aLC6RGOm\nKXESaC1Me8zCSIfVgYKtIw5iIPnPPsRZbgC/9izx41fxS3x2TbyijU/Nzm8eE9+acjjyCvyZbec3\nbhM/k89GUcKhd36rZlr3QItR/vxU+fVa+GQxzoy8nRqLCw1hg/NDk/Hbp0xZ4cG4n7b7YTJyGfBB\n34KR6T3grjTs8F0cmysiKRrjJdHPUq05mrdlEiY6Pmqe/Z3yjkNaz0M7eNVjLR4OwgcOW5xjd94o\ncGuJm3vG129jaPKJG8OTUyXozSuCLsY8Cbq7iTVMPNZGRqL0gdJNwyhJX9QiERpteBJsOaKDiROZ\naA2XDUpCZQKFrCAcMCt072wkI9apVGoTdOrobgwKCBdLphImKUmxZNAhHTJqxpQyiyukA9WERR2a\n03WHzDmYMBY68V7C3KN3OAGLT6ga3eGpxfb0AUIEAYBJITlspfHEzjp5+IPjxI9MjV9tVxf6//fU\nIvpSLSJEveVcatq/s15dHjsfe007v9qvcARdJxDnby9XaI5v+rUWWqPHozH/2XuN91blK8dwK319\n1/nAlX3VGJYoHNTpdJ6K8GB23rwyvri8gJL3i/FXb2K9/c4an/9sdd5Infs7YW6GaA1GjcRAU1S5\nM+OdBj+aY590X/lnx9DTPdAf7PD2Q9UwOS248cT5K/FJPEWHe6FC0YMT21fOvMlwPXtBVzvDviQt\nfmoAACAASURBVEGB4kWh7C/QKffI8nmZYgVEETm+XC+YQlhKu4ZjS3MP4WPgonSrF7tZt0HV0yhK\nzxNFvTRneqHImZ1fj+JuWPNo4nAkCc1qBAX2cMqKYrxhSJjTpoRIHWG1aUyUna5Od5g9iq9uPSD5\nFIdeIg2tVaXkzOrhG6iDMJK80SrkEjbCemkOU6S/k+PnjptXcqLWcFYCuxgqiAjNVlJKpCk0Qt5j\nE2gd0MhLaW1FNdGWxrq00ax1uvVLI9usg/Wg4fhLf9uexjo4w8JjWm7DMIFoFvFoQqJGcejRhMhL\n+PEZhTKzF+vJByVPYjp21kXFchrNzJmiEB9gL1EuL5AQ54U4JsxDU6Tio9EPlNStj4UP1s/NfGi1\nROVC4Qs3xZjvi4S1PHAxplDCLEAs7g+AOvQrL0PmfdwjZ9ea8wDhw3jtVbh1xVpFkiBW8DUoLPEG\nCmmGm1m4d99ROY61MpPTAWFFsyA9IbqAOn4OOx27QTRUgbx6Gq2oL0HlExn0Og+HGWasxzpEoqkx\nSXTrwwrFkFyiGVeLhqAb4oVh/wi00CHNzyHnsd8oOgVKqVrxzDiXY3WLCJYyzgrNke0KRYEpllYX\nZDrgxdDNBj3c0Z/s8aeZ9B2Q4zassrUOPYTiniAv4Cu+EoMPl5gYLx1JinqCOyH1TLIpzB4oJJkw\nWUBOoJmUwd1wSvRvokifAmHtUFqOhqqlmJIbIAkjPhZfcINWd1Rr1Oq0fkWzMIXpKNY30XDRYmdz\njb39/Lg51nMgR96xrmQJKl0ncegn+knoFoOV3DMN6JYv6yEano5ZxS1HLIQXulbcNbKzTGkDuXSE\n7gMB85FpZEIn4ZZBwqLcKHRpg/4J+HBctRKGRnYZb+DSw+3UUwwXvcW/p0EvFMUt9kgBmrXhHqqh\ni60eZ1VbkD7F7ziClUVCO2Z1pVFi3ZtRPQpzPGOaaNYx6ZxNSd79MKdfA6+lzjQGUH92NmZxJuDN\nHPyq7zRho/Db+8RHt87/djuBO28kRxN8vFRkc3424W2EfXO+RdQNcSkfLbHvH9z5jVPoCwvOYYmf\n/TlVtmMNPNS4N151gJU7h+tktKagQa3eP4edNG4rbKfO05Pw+uSBzgJfW0KQ9JmN8fWqPDWYs/Gk\nKd2FnRo/su188yQ8XZUf2jbcYc7Cd6rwI1eN8hw+NaQJfT80uMWR2eiDopZt1CLpRS0yeYbeY8DJ\nqEVqI2ki4bhVtDs+xZpMOFpuUA8DGyXOY7VAxasJ2TspFUoprMsh9uec2DTlmOO5nXF+J+JsTSCz\nRj0xNKOtw6rjZO6Gs42aC6PKimiK++Zci3iDDtOqfHlRHorxdktcufO1lvl4aijw+6fCmzmGsu/4\nGPCPuvRcizjGP98nNtn+hVrkWIVt+d5aZGlC0ggVFoTna6yn45hzytlxwuCtrfPVg7B5WeB8uca0\nBnjnKNx1Y6PO0jsfHJV7YwiwG73d0xpn1tyE9xd4ponDYKP8pV3jnaR85eQ8Ks631/h5H9TE51E+\nMjferfAPu/JKQPr8X8eZnTb+ZI7X//vrzKt55dttYga6dP7B6QfbwnyoGiYYCe2AykSnYymKeIhp\n/kWcb4a6xYJTGRC0YMOG0gd/9NwMXYbvEgWljK793DzZ+BzE4X1+rNtZ/3DupMN5i/PkJgcHsyRB\nh7Vj1UY1HxQ9LsjE2s8NjwRkIxJ4l0W+RWhsYK1rBOBK6G/qcOQ70wGTO0hiykH72Q7ov/aKpjCv\nsAGx9B5OXDlnWmusazgLruuKSwhO+6nFRMc99EcpnNZEoyG0tZLKFLbcg6Kn52RcISx5q+K1UfpK\n743NZoMmodbKrhRsbawtxMeaBGvhYPfs2TOmaWJZVkiZ1hfKVLAalu9ddAT+duYUYuPFYgJbJEJe\ndTRgbhpBt3R6P2uQzorE/oKWN97JQO70wqc+N3nn/w+Cwhn6DhTAA69+gcacm7TLs3ZAwjr+MiQa\n61d9TPbG2hxHp/egO/pZTM9L1Dk5I0NBCaO/QK7O6/V8nc07PCW6tYG1vRgOQJhrgF9EwmfQLdwZ\nRyPHHw1hEpH/gn9RWP15d//J8fgM/FfArxBi7b8F/Mfu/u5Lz/HDwH8H/BLhgPU/AP+J+79eTf74\ntvDOJJRz4LM4mk4XYw1JGlQ49QiWFcW8hpsmNfQ5QWolSQobYW2hZcNBT4hPY1BynsMYItNodkO7\nG0eWYHJEJHQsZ2c6a42ihZPH+5O8IQJTGk3WQKxP6xoxAyRUG9F8ddwTohpBrEnZ2G5QUs9hoVe4\nr6Gd8CtifDSh4XcPdCRNuBdUg24qTCTfgEyQ7+Da0L6Jr0+dyw7kNSqdnIGKTB2RFU0TXpZwfxEA\nhV7p65Zejbzmy57b7Iipk64ivEj3CZYV7sD8Cu1HaA4t40fFvbK2DZILphXWxH6dWbpEYekaZhdA\nc6X7TMPpNez/+9TIi9IE3HLQZ62xolQXTm4ce0MoaJ1o4ixJoQdS1STscU3uqL2Qi7JaIxMFLgy0\nce00E6qGXsqSIPVEM0FToQ2TEEkTVU5k21AN0IWmAi0QLC+ddTVWy1ASp7qS+hDjp8bSOuvY3++W\nMNTo3qkNll6DNqjOJiUWjYwwEw2qDULrQ4NHwlNQoTIaDZQ0nMrzekBkIqWZYsEWQO+G5rSze/Aq\n3ZyJwnaeWVmGo2TiVA8c+JcVaB+iyxtri2Jwm2a+2oT3DR6N4vETxXn35Pz83PgbB/g0cY98ejJ+\n+zixb848mCZfkijDune+tQq/POhvf2iZX71zVA3zaDz+1CbxdnXeKoFK/HQJOlcS5wtr5kqMV8bz\nXhXhgz2867GOP1riea52xk2J9ni3gW8vme9U4dqN/dja/9vjxE9tY4h6Z8LXm/DJ0nnYG/94n3nN\nG4vA5/eJH6biEo3+2yd4ovmiffxEW7h3DzYk7NjZWqDca1+Z14Te80stYilMYJJpDAOehI2VecV0\nmGQlgj59Ap1mnEarjUu8TA0dsjmoyWigzk7HQRu2GmjWpgvHU+VqnrFrwZ4F3TdZod92ppzpV8OV\nUDrSJhoL3makxB6uOjHpTG+GFcXuDNfGLDly7Sq8syQ+PlXuFA5d+Yvbha/XzG8eC9D5g2fC/Q2k\nUYR+cBBeuzI+OCgPt/G51YTNmQAAvH9QXt3FB+/tdfx+Ua/cz8behfV4rom/n/If33dH1KD3djCI\nWzwfX7ObnP36Yhh9MEMFJuvcz8rR7MUQeHzNnF7UIh/Lhna7MHkeZeF+Mu4M7in8iRIL7WtJeLwa\n3RoqmU+UyreGM9+rGmytL59nK974ZtXQWQ8Dr5v2gx3efqgaJlU4O+72UbQlEfpADFQ1xMgOOQ+v\naaKjdj8XhmUUvnloP+x7C0aPCcS5kDSL8E+HKGokQrpIGhQDHeLuszOfGynr5ec1D6eo7gU3ow9X\nPQRcz41eOI3ooCtoHhQoPyNMjEI/6IZJhOFwEX3VmSAoyllbBKDmzCRMIjh1IgXTRoY9sgQq4b2F\nzTZxU/TeEFWyhlOfq6IIta9MGoX74sG9Tg1SzpgtpKRgKzpnTBzxAXN7IqswbWbcIugMcfqgl4UD\noFCSs/oaVszdmOctD+9tuTtWyhT6AJX4+u7RGC2104aO7bgsQVlBKQ6r9bB2FqWfw3zNB5A0No/R\n2MYa6Jd18EL7Y5cA0vj82LXOjzthKf1S4+Ivfzy41jbokvR+KQw95XB9ORepFmvXZcggLf72wsvo\n5kBS/dzsjuZKAgHs1iEryQbixvn5ubjvAOGsNKDVl6VTLmfKn11e/9n84kz3E5c/jnLnD4C//OLV\nfU8X9l8Dvwz8u8At8N8A/yvw5wEkHFv+OvBt4BeAjwD/I2Fi+5/9637o4UnlkDv4kaYpyhibyMnw\nQZyE0Qp73KeiBVKlSKKtRP6QCG4lUCEruClNO6ozZi9OtgvF6yX3QfN2aWbV50GljcwKiH1ODXzs\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d+Eg2vtKUG4GbmNfyYAppwu8syr7BI48ony91Yf8DpvZ+qBomsAtyM/LYBw1EA6I9U63O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vPgNp6FJACj/xU9c8edb40vfu0Urh03dWPv/WEz7zofI771/wpHtqvLbOSWEVjyxr8a7N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\nqTI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7h8nEpxWi2jHxhphrkl1uo6QgnS7okzSXNnqz2syYJHb/GhD9YbDWS0ZWl7o1dVof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B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\n4pQQKi60maUKV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Xt8Pbofmb/N/ciu2+9+xMciB2rbdwOu4Ev/FPDWLY8FuB943X7bVuw5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\nwAMmx3Ecx3Ecx3GcHfgfnCgagBreL0gAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% plot images\n", + "\n", + "\n", + "pl.figure(2,(10,8))\n", + "\n", + "pl.subplot(2,3,1)\n", + "\n", + "pl.imshow(I1)\n", + "pl.title('Im. 1')\n", + "\n", + "pl.subplot(2,3,2)\n", + "\n", + "pl.imshow(I2)\n", + "pl.title('Im. 2')\n", + "\n", + "\n", + "pl.subplot(2,3,3)\n", + "pl.imshow(I1t)\n", + "pl.title('Im. 1 Interp LP')\n", + "\n", + "pl.subplot(2,3,4)\n", + "pl.imshow(I1te)\n", + "pl.title('Im. 1 Interp Entrop')\n", + "\n", + "\n", + "pl.subplot(2,3,5)\n", + "pl.imshow(I1tl)\n", + "pl.title('Im. 1 Linear mapping')\n", + "\n", + "pl.subplot(2,3,6)\n", + "pl.imshow(I1tn)\n", + "pl.title('Im. 1 nonlinear mapping')\n", + "\n", + "pl.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 1 +} diff --git a/notebooks/Demo_Optim_OTreg.ipynb b/notebooks/Demo_Optim_OTreg.ipynb new file mode 100644 index 0000000..5731687 --- /dev/null +++ b/notebooks/Demo_Optim_OTreg.ipynb @@ -0,0 +1,173 @@ +{ + "metadata": { + "name": "", + "signature": "sha256:4dc159882a3ef225eae256eb2ae57de153d04cc9de52ea36b1644e64a88de96a" + }, + "nbformat": 3, + "nbformat_minor": 0, + "worksheets": [ + { + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Regularized OT with generic solver" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 1 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Dataset generation" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "n=100 # nb bins\n", + "\n", + "# bin positions\n", + "x=np.arange(n,dtype=np.float64)\n", + "\n", + "# Gaussian distributions\n", + "a=ot.datasets.get_1D_gauss(n,m=20,s=20) # m= mean, s= std\n", + "b=ot.datasets.get_1D_gauss(n,m=60,s=60)\n", + "\n", + "# loss matrix\n", + "M=ot.dist(x.reshape((n,1)),x.reshape((n,1)))\n", + "M/=M.max()" + ], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 2 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### EMD solution" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "G0=ot.emd(a,b,M)\n", + "\n", + "pl.figure(3)\n", + "ot.plot.plot1D_mat(a,b,G0,'OT matrix G0')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 3 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solution with Frobenius norm regularization" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def f(G): return 0.5*np.sum(G**2)\n", + "def df(G): return G\n", + "\n", + "reg=1e-1\n", + " \n", + "Gl2=ot.optim.cg(a,b,M,reg,f,df)\n", + "\n", + "pl.figure(3)\n", + "ot.plot.plot1D_mat(a,b,Gl2,'OT matrix Frob. reg')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 4 + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Solution with entropic regularization" + ] + }, + { + "cell_type": "code", + "collapsed": false, + "input": [ + "def f(G): return np.sum(G*np.log(G))\n", + "def df(G): return np.log(G)+1\n", + " \n", + "reg=1e-3\n", + " \n", + "Ge=ot.optim.cg(a,b,M,reg,f,df)\n", + "\n", + "pl.figure(4)\n", + "ot.plot.plot1D_mat(a,b,Ge,'OT matrix Entrop. reg')" + ], + "language": "python", + "metadata": {}, + "outputs": [ + { + "metadata": {}, + "output_type": "display_data", + "png": 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+ "text": [ + "" + ] + } + ], + "prompt_number": 5 + }, + { + "cell_type": "code", + "collapsed": false, + "input": [], + "language": "python", + "metadata": {}, + "outputs": [], + "prompt_number": 5 + } + ], + "metadata": {} + } + ] +} \ No newline at end of file diff --git a/notebooks/Demo_Wasserstein_Discriminant_Analysis.ipynb b/notebooks/Demo_Wasserstein_Discriminant_Analysis.ipynb new file mode 100644 index 0000000..2d3424e --- /dev/null +++ b/notebooks/Demo_Wasserstein_Discriminant_Analysis.ipynb @@ -0,0 +1,257 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Demonstration of Wasserstein Discriminant Analysis" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot\n", + "from ot.datasets import get_1D_gauss as gauss\n", + "from ot.dr import wda, fda" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Generate dataset" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "n=1000 # nb samples in source and target datasets\n", + "nz=0.2\n", + "\n", + "# generate circle dataset\n", + "t=np.random.rand(n)*2*np.pi\n", + "ys=np.floor((np.arange(n)*1.0/n*3))+1\n", + "xs=np.concatenate((np.cos(t).reshape((-1,1)),np.sin(t).reshape((-1,1))),1)\n", + "xs=xs*ys.reshape(-1,1)+nz*np.random.randn(n,2)\n", + "\n", + "t=np.random.rand(n)*2*np.pi\n", + "yt=np.floor((np.arange(n)*1.0/n*3))+1\n", + "xt=np.concatenate((np.cos(t).reshape((-1,1)),np.sin(t).reshape((-1,1))),1)\n", + "xt=xt*yt.reshape(-1,1)+nz*np.random.randn(n,2)\n", + "\n", + "nbnoise=8\n", + "\n", + "xs=np.hstack((xs,np.random.randn(n,nbnoise)))\n", + "xt=np.hstack((xt,np.random.randn(n,nbnoise)))" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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5xeANtikAcsGvUwnD+38aP71OJsmF72awEYSl6uPA54FXhBD7zPf+RUr5qwCO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\nSFQRBEEQBEHYAIkqgiAIgiAIG/j/JntglMZwmP0AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(1,(10,5))\n", + "\n", + "pl.subplot(1,2,1)\n", + "pl.scatter(xt[:,0],xt[:,1],c=ys,marker='+',label='Source samples')\n", + "pl.legend(loc=0)\n", + "pl.title('Discriminant dimensions')\n", + "\n", + "pl.subplot(1,2,2)\n", + "pl.scatter(xt[:,2],xt[:,3],c=ys,marker='+',label='Source samples')\n", + "pl.legend(loc=0)\n", + "pl.title('Other dimensions')\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute Fisher discriminant" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "p=2\n", + "\n", + "Pfda,projfda = fda(xs,ys,p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Compute WDA" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Compiling cost function...\n", + "Computing gradient of cost function...\n", + " iter\t\t cost val\t grad. norm\n", + " 1\t+7.3324064745743989e-01\t5.95226695e-01\n", + " 2\t+4.4853951178403229e-01\t8.20231271e-02\n", + " 3\t+4.4576445851595303e-01\t1.93811424e-01\n", + " 4\t+4.3562246733680465e-01\t1.61725387e-01\n", + " 5\t+4.0969564472790077e-01\t1.32513662e-01\n", + " 6\t+2.9388094458668662e-01\t2.00393708e-01\n", + " 7\t+2.7344485803563923e-01\t1.99320846e-01\n", + " 8\t+2.2883182995370804e-01\t2.82035999e-02\n", + " 9\t+2.2828598049696755e-01\t7.08646563e-03\n", + " 10\t+2.2825222787200100e-01\t3.22559822e-04\n", + " 11\t+2.2825220998825529e-01\t2.82197677e-04\n", + " 12\t+2.2825216255973643e-01\t9.26049149e-05\n", + " 13\t+2.2825215676825239e-01\t6.60508771e-06\n", + " 14\t+2.2825215674473881e-01\t3.35243645e-06\n", + " 15\t+2.2825215673980734e-01\t1.97305673e-06\n", + " 16\t+2.2825215673953242e-01\t1.86785394e-06\n", + " 17\t+2.2825215673856641e-01\t1.41420230e-06\n", + " 18\t+2.2825215673757782e-01\t6.92577574e-07\n", + "Terminated - min grad norm reached after 18 iterations, 8.84 seconds.\n", + "\n" + ] + } + ], + "source": [ + "p=2\n", + "reg=1\n", + "k=10\n", + "maxiter=100\n", + "\n", + "Pwda,projwda = wda(xs,ys,p,reg,k,maxiter=maxiter)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Project and plot samples" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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rT8RMpNEeqBfqlQfqL4dmcdckmF6qsvbunrwRgIrJu4Cwh6onyoS9ZGl5qPIL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\noqvBpVvJL4XokU6vU2wrT6A/XFuEw60L0VNuEJfc/W4/LH1hVNrIZM4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42RCsjRfduqrXD0dCe0pBXubRATPdWQ463F7iXLYLKQmV6sCpVEjENnox2DFRdgvOSH84GvQCf0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\n7Oe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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# data projection (also removes center)\n", + "xsp=projfda(xs)\n", + "xtp=projfda(xt)\n", + "\n", + "xspw=projwda(xs)\n", + "xtpw=projwda(xt)\n", + "\n", + "pl.figure(1,(10,10))\n", + "\n", + "pl.subplot(2,2,1)\n", + "pl.scatter(xsp[:,0],xsp[:,1],c=ys,marker='+',label='Projected samples')\n", + "pl.legend(loc=0)\n", + "pl.title('Projected training samples FDA')\n", + "\n", + "\n", + "pl.subplot(2,2,2)\n", + "pl.scatter(xtp[:,0],xtp[:,1],c=ys,marker='+',label='Projected samples')\n", + "pl.legend(loc=0)\n", + "pl.title('Projected test samples FDA')\n", + "\n", + "\n", + "pl.subplot(2,2,3)\n", + "pl.scatter(xspw[:,0],xspw[:,1],c=ys,marker='+',label='Projected samples')\n", + "pl.legend(loc=0)\n", + "pl.title('Projected training samples WDA')\n", + "\n", + "\n", + "pl.subplot(2,2,4)\n", + "pl.scatter(xtpw[:,0],xtpw[:,1],c=ys,marker='+',label='Projected samples')\n", + "pl.legend(loc=0)\n", + "pl.title('Projected test samples WDA')\n", + "pl.show()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/setup.py b/setup.py index 63d071c..347c882 100755 --- a/setup.py +++ b/setup.py @@ -44,7 +44,7 @@ setup(name='POT', language="c++", # generate and compile C++ code, include_dirs=[numpy.get_include(),os.path.join(ROOT,'ot/lp')])), platforms=['linux','macosx','windows'], - download_url='https://github.com/rflamary/POT/archive/V{}.tar.gz'.format(__version__), + download_url='https://github.com/rflamary/POT/archive/{}.tar.gz'.format(__version__), license = 'MIT', scripts=[], data_files=[], -- cgit v1.2.3 From fab20da2af763d8f108e6ceb88d888fcc5497747 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 6 Jul 2017 10:41:06 +0200 Subject: update readme --- README.md | 40 ++++++++++++++++++++++++++++++++++++---- docs/source/readme.rst | 46 +++++++++++++++++++++++++++++++++++++++++++--- setup.py | 2 +- 3 files changed, 80 insertions(+), 8 deletions(-) diff --git a/README.md b/README.md index 09da51b..4aa4cc5 100644 --- a/README.md +++ b/README.md @@ -72,9 +72,42 @@ obviously you need CUDA installed and a compatible GPU. ## Examples -The examples folder contain several examples and use case for the library. The full documentation is available on [Readthedocs](http://pot.readthedocs.io/) +### Short examples - Here is a list of the Python notebooks if you want a quick look: +* Import the toolbox +```python +import ot +``` +* Compute Wasserstein distances +```python +# a,b are 1D histograms (sum to 1 and positive) +# M is the ground cost matrix +Wd=ot.emd2(a,b,M) # exact linear program +# if b is a matrix compute all distances to a and return a vector +``` +* Compute OT matrix +```python +# a,b are 1D histograms (sum to 1 and positive) +# M is the ground cost matrix +Totp=ot.emd(a,b,M) # exact linear program +Totp_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT +``` +* Compute Wasserstein barycenter +```python +# A is a n*d matrix containing d 1D histograms +# M is the ground cost matrix +ba=ot.barycenter(A,M,reg) # reg is regularization parameter +``` + + + + +### Examples and Notebooks + +The examples folder contain several examples and use case for the library. The full documentation is available on [Readthedocs](http://pot.readthedocs.io/). + + +Here is a list of the Python notebooks available [here](https://github.com/rflamary/POT/blob/master/notebooks/) if you want a quick look: * [1D optimal transport](https://github.com/rflamary/POT/blob/master/notebooks/Demo_1D_OT.ipynb) * [OT Ground Loss](https://github.com/rflamary/POT/blob/master/notebooks/Demo_Ground_Loss.ipynb) @@ -88,8 +121,7 @@ The examples folder contain several examples and use case for the library. The f * [OT mapping estimation for color transfer in images](https://github.com/rflamary/POT/blob/master/notebooks/Demo_Image_ColorAdaptation_mapping.ipynb) * [Wasserstein Discriminant Analysis](https://github.com/rflamary/POT/blob/master/notebooks/Demo_Wasserstein_Discriminant_Analysis.ipynb) - -You can also see the notebooks with [Jupyter nbviewer](https://nbviewer.jupyter.org/github/rflamary/POT/tree/master/examples/). +You can also see the notebooks with [Jupyter nbviewer](https://nbviewer.jupyter.org/github/rflamary/POT/tree/master/notebooks/). ## Acknowledgements diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 6898296..611001b 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -93,11 +93,51 @@ obviously you need CUDA installed and a compatible GPU. Examples -------- +Short examples +~~~~~~~~~~~~~~ + +- Import the toolbox + + .. code:: python + + import ot + +- Compute Wasserstein distances + + .. code:: python + + # a,b are 1D histograms (sum to 1 and positive) + # M is the ground cost matrix + Wd=ot.emd2(a,b,M) # exact linear program + # if b is a matrix compute all distances to a and return a vector + +- Compute OT matrix + + .. code:: python + + # a,b are 1D histograms (sum to 1 and positive) + # M is the ground cost matrix + Totp=ot.emd(a,b,M) # exact linear program + Totp_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT + +- Compute Wasserstein barycenter + + .. code:: python + + # A is a n*d matrix containing d 1D histograms + # M is the ground cost matrix + ba=ot.barycenter(A,M,reg) # reg is regularization parameter + +Examples and Notebooks +~~~~~~~~~~~~~~~~~~~~~~ + The examples folder contain several examples and use case for the library. The full documentation is available on -`Readthedocs `__ +`Readthedocs `__. -Here is a list of the Python notebooks if you want a quick look: +Here is a list of the Python notebooks available +`here `__ if you +want a quick look: - `1D optimal transport `__ @@ -123,7 +163,7 @@ Here is a list of the Python notebooks if you want a quick look: Analysis `__ You can also see the notebooks with `Jupyter -nbviewer `__. +nbviewer `__. Acknowledgements ---------------- diff --git a/setup.py b/setup.py index 347c882..9e08ccc 100755 --- a/setup.py +++ b/setup.py @@ -21,7 +21,7 @@ __version__ = re.search( ROOT = os.path.abspath(os.path.dirname(__file__)) -# convert markdown readme to rst in pypandoc installed +# convert markdown readme to rst if pypandoc installed try: import pypandoc README = pypandoc.convert('README.md', 'rst') -- cgit v1.2.3 From 3db36fe4856f8d936b671c4547072c132a24b6a5 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 6 Jul 2017 14:50:07 +0200 Subject: add .gitattribute --- .gitattribute | 1 + 1 file changed, 1 insertion(+) create mode 100644 .gitattribute diff --git a/.gitattribute b/.gitattribute new file mode 100644 index 0000000..50cdfa9 --- /dev/null +++ b/.gitattribute @@ -0,0 +1 @@ +.ipynb linguist-documentation -- cgit v1.2.3 From 75bcbf30b9166e21214a2bed7a3e70f9a82f2ee9 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 6 Jul 2017 14:54:57 +0200 Subject: rename file --- .gitattribute | 1 - .gitattributes | 1 + 2 files changed, 1 insertion(+), 1 deletion(-) delete mode 100644 .gitattribute create mode 100644 .gitattributes diff --git a/.gitattribute b/.gitattribute deleted file mode 100644 index 50cdfa9..0000000 --- a/.gitattribute +++ /dev/null @@ -1 +0,0 @@ -.ipynb linguist-documentation diff --git a/.gitattributes b/.gitattributes new file mode 100644 index 0000000..50cdfa9 --- /dev/null +++ b/.gitattributes @@ -0,0 +1 @@ +.ipynb linguist-documentation -- cgit v1.2.3 From 5ee1aebbde1a6fd2b7a597912669c7cfcc8b27c3 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 6 Jul 2017 14:57:05 +0200 Subject: update gitattributes --- .gitattributes | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/.gitattributes b/.gitattributes index 50cdfa9..5c0ac9e 100644 --- a/.gitattributes +++ b/.gitattributes @@ -1 +1,4 @@ -.ipynb linguist-documentation + +ot/lp/*.cpp linguist-vendored +ot/lp/*.h linguist-vendored +*.ipynb linguist-documentation -- cgit v1.2.3 From 0fc1124e001932354ec3d229d198cba166cd0b0e Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 6 Jul 2017 15:07:01 +0200 Subject: add mailmap --- .mailmap | 3 +++ 1 file changed, 3 insertions(+) create mode 100644 .mailmap diff --git a/.mailmap b/.mailmap new file mode 100644 index 0000000..73f873e --- /dev/null +++ b/.mailmap @@ -0,0 +1,3 @@ +Nicolas Courty Nicolas Courty +Nicolas Courty ncourty +Léo Gautheron Leo gautheron -- cgit v1.2.3 From 47477c5782f87570de590ea423a082b71dd63241 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 7 Jul 2017 08:51:45 +0200 Subject: add sinkhorbn2 +v3 --- README.md | 5 +- examples/plot_compute_emd.py | 4 +- ot/__init__.py | 8 +-- ot/bregman.py | 144 +++++++++++++++++++++++++++++++++++++------ 4 files changed, 133 insertions(+), 28 deletions(-) diff --git a/README.md b/README.md index 4aa4cc5..d53387b 100644 --- a/README.md +++ b/README.md @@ -83,14 +83,15 @@ import ot # a,b are 1D histograms (sum to 1 and positive) # M is the ground cost matrix Wd=ot.emd2(a,b,M) # exact linear program +Wd_reg=ot.sinkhorn2(a,b,M,reg) # entropic regularized OT # if b is a matrix compute all distances to a and return a vector ``` * Compute OT matrix ```python # a,b are 1D histograms (sum to 1 and positive) # M is the ground cost matrix -Totp=ot.emd(a,b,M) # exact linear program -Totp_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT +T=ot.emd(a,b,M) # exact linear program +T_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT ``` * Compute Wasserstein barycenter ```python diff --git a/examples/plot_compute_emd.py b/examples/plot_compute_emd.py index c7063e8..f2cdc35 100644 --- a/examples/plot_compute_emd.py +++ b/examples/plot_compute_emd.py @@ -61,8 +61,8 @@ pl.legend() #%% reg=1e-2 -d_sinkhorn=ot.sinkhorn(a,B,M,reg) -d_sinkhorn2=ot.sinkhorn(a,B,M2,reg) +d_sinkhorn=ot.sinkhorn2(a,B,M,reg) +d_sinkhorn2=ot.sinkhorn2(a,B,M2,reg) pl.figure(2) pl.clf() diff --git a/ot/__init__.py b/ot/__init__.py index b2af88b..4220148 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -16,14 +16,14 @@ from . import da # OT functions from .lp import emd, emd2 -from .bregman import sinkhorn, barycenter +from .bregman import sinkhorn, sinkhorn2, barycenter from .da import sinkhorn_lpl1_mm # utils functions from .utils import dist, unif, tic, toc, toq -__version__ = "0.2" +__version__ = "0.3" -__all__ = ["emd", "emd2", "sinkhorn", "utils", 'datasets', 'bregman', 'lp', - 'plot', 'tic', 'toc', 'toq', +__all__ = ["emd", "emd2", "sinkhorn","sinkhorn2", "utils", 'datasets', + 'bregman', 'lp', 'plot', 'tic', 'toc', 'toq', 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim'] diff --git a/ot/bregman.py b/ot/bregman.py index 68be01c..0d68602 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -41,7 +41,7 @@ def sinkhorn(a,b, M, reg,method='sinkhorn', numItermax = 1000, stopThr=1e-9, ver Regularization term >0 method : str method used for the solver either 'sinkhorn', 'sinkhorn_stabilized' or - 'sinkhorn_epsilon_scaling', see those function for specific parameters + 'sinkhorn_epsilon_scaling', see those function for specific parameters numItermax : int, optional Max number of iterations stopThr : float, optional @@ -91,7 +91,7 @@ def sinkhorn(a,b, M, reg,method='sinkhorn', numItermax = 1000, stopThr=1e-9, ver ot.bregman.sinkhorn_epsilon_scaling: Sinkhorn with epslilon scaling [9][10] """ - + if method.lower()=='sinkhorn': sink= lambda: sinkhorn_knopp(a,b, M, reg,numItermax=numItermax, stopThr=stopThr, verbose=verbose, log=log,**kwargs) @@ -100,15 +100,119 @@ def sinkhorn(a,b, M, reg,method='sinkhorn', numItermax = 1000, stopThr=1e-9, ver stopThr=stopThr, verbose=verbose, log=log, **kwargs) elif method.lower()=='sinkhorn_epsilon_scaling': sink= lambda: sinkhorn_epsilon_scaling(a,b, M, reg,numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log, **kwargs) + stopThr=stopThr, verbose=verbose, log=log, **kwargs) else: print('Warning : unknown method using classic Sinkhorn Knopp') sink= lambda: sinkhorn_knopp(a,b, M, reg, **kwargs) - + return sink() + +def sinkhorn2(a,b, M, reg,method='sinkhorn', numItermax = 1000, stopThr=1e-9, verbose=False, log=False,**kwargs): + u""" + Solve the entropic regularization optimal transport problem and return the loss + + The function solves the following optimization problem: + + .. math:: + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + where : + + - M is the (ns,nt) metric cost matrix + - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target weights (sum to 1) + + The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [2]_ + + + Parameters + ---------- + a : np.ndarray (ns,) + samples weights in the source domain + b : np.ndarray (nt,) or np.ndarray (nt,nbb) + samples in the target domain, compute sinkhorn with multiple targets + and fixed M if b is a matrix (return OT loss + dual variables in log) + M : np.ndarray (ns,nt) + loss matrix + reg : float + Regularization term >0 + method : str + method used for the solver either 'sinkhorn', 'sinkhorn_stabilized' or + 'sinkhorn_epsilon_scaling', see those function for specific parameters + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + W : (nt) ndarray or float + Optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + Examples + -------- + + >>> import ot + >>> a=[.5,.5] + >>> b=[.5,.5] + >>> M=[[0.,1.],[1.,0.]] + >>> ot.sinkhorn2(a,b,M,1) + array([ 0.26894142]) + References + ---------- + + .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013 + + .. [9] Schmitzer, B. (2016). Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems. arXiv preprint arXiv:1610.06519. + + .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + + + + See Also + -------- + ot.lp.emd : Unregularized OT + ot.optim.cg : General regularized OT + ot.bregman.sinkhorn_knopp : Classic Sinkhorn [2] + ot.bregman.sinkhorn_stabilized: Stabilized sinkhorn [9][10] + ot.bregman.sinkhorn_epsilon_scaling: Sinkhorn with epslilon scaling [9][10] + + """ + + if method.lower()=='sinkhorn': + sink= lambda: sinkhorn_knopp(a,b, M, reg,numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log,**kwargs) + elif method.lower()=='sinkhorn_stabilized': + sink= lambda: sinkhorn_stabilized(a,b, M, reg,numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log, **kwargs) + elif method.lower()=='sinkhorn_epsilon_scaling': + sink= lambda: sinkhorn_epsilon_scaling(a,b, M, reg,numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log, **kwargs) + else: + print('Warning : unknown method using classic Sinkhorn Knopp') + sink= lambda: sinkhorn_knopp(a,b, M, reg, **kwargs) + + b=np.asarray(b,dtype=np.float64) + if len(b.shape)<2: + b=b.reshape((-1,1)) + + return sink() + def sinkhorn_knopp(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, log=False,**kwargs): """ @@ -189,23 +293,23 @@ def sinkhorn_knopp(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, a=np.asarray(a,dtype=np.float64) b=np.asarray(b,dtype=np.float64) M=np.asarray(M,dtype=np.float64) - + if len(a)==0: a=np.ones((M.shape[0],),dtype=np.float64)/M.shape[0] if len(b)==0: b=np.ones((M.shape[1],),dtype=np.float64)/M.shape[1] - + # init data Nini = len(a) Nfin = len(b) - + if len(b.shape)>1: nbb=b.shape[1] else: nbb=0 - + if log: log={'err':[]} @@ -217,7 +321,7 @@ def sinkhorn_knopp(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, else: u = np.ones(Nini)/Nini v = np.ones(Nfin)/Nfin - + #print(reg) @@ -261,23 +365,23 @@ def sinkhorn_knopp(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, if log: log['u']=u log['v']=v - - if nbb: #return only loss + + if nbb: #return only loss res=np.zeros((nbb)) for i in range(nbb): res[i]=np.sum(u[:,i].reshape((-1,1))*K*v[:,i].reshape((1,-1))*M) if log: return res,log else: - return res - + return res + else: # return OT matrix - + if log: return u.reshape((-1,1))*K*v.reshape((1,-1)),log else: return u.reshape((-1,1))*K*v.reshape((1,-1)) - + def sinkhorn_stabilized(a,b, M, reg, numItermax = 1000,tau=1e3, stopThr=1e-9,warmstart=None, verbose=False,print_period=20, log=False,**kwargs): """ @@ -393,7 +497,7 @@ def sinkhorn_stabilized(a,b, M, reg, numItermax = 1000,tau=1e3, stopThr=1e-9,war alpha,beta=np.zeros(na),np.zeros(nb) else: alpha,beta=warmstart - + if nbb: u,v = np.ones((na,nbb))/na,np.ones((nb,nbb))/nb else: @@ -420,7 +524,7 @@ def sinkhorn_stabilized(a,b, M, reg, numItermax = 1000,tau=1e3, stopThr=1e-9,war uprev = u vprev = v - + # sinkhorn update v = b/(np.dot(K.T,u)+1e-16) u = a/(np.dot(K,v)+1e-16) @@ -471,8 +575,8 @@ def sinkhorn_stabilized(a,b, M, reg, numItermax = 1000,tau=1e3, stopThr=1e-9,war break cpt = cpt +1 - - + + #print('err=',err,' cpt=',cpt) if log: log['logu']=alpha/reg+np.log(u) @@ -493,7 +597,7 @@ def sinkhorn_stabilized(a,b, M, reg, numItermax = 1000,tau=1e3, stopThr=1e-9,war res=np.zeros((nbb)) for i in range(nbb): res[i]=np.sum(get_Gamma(alpha,beta,u[:,i],v[:,i])*M) - return res + return res else: return get_Gamma(alpha,beta,u,v) -- cgit v1.2.3 From a66807ee1fb45994cca7a2cc52b1cf06354e760d Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 7 Jul 2017 10:56:55 +0200 Subject: éadd macosxé MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .travis.yml | 9 +++++++-- 1 file changed, 7 insertions(+), 2 deletions(-) diff --git a/.travis.yml b/.travis.yml index ebdd225..a741da4 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,4 +1,7 @@ language: python +os: + - linux + - osx python: - "2.7" - "3.4" @@ -6,8 +9,10 @@ python: # maintainers to fix their pypy-dev package. # - "pypy" before_install: - - sudo apt-get update -q - - sudo apt-get install libblas-dev liblapack-dev libatlas-base-dev #gfortran + - if [[ "$TRAVIS_OS_NAME" == "linux" ]]; sudo apt-get update -q ; fi + - if [[ "$TRAVIS_OS_NAME" == "linux" ]]; sudo apt-get install libblas-dev liblapack-dev libatlas-base-dev; fi #gfortran + - if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then brew update ; fi + - if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then brew install python; fi # command to install dependencies install: - pip install -r requirements.txt -- cgit v1.2.3 From 6fe6020a336abf731defb75c3c908814429ad98c Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 7 Jul 2017 11:03:35 +0200 Subject: anotehr test --- .travis.yml | 21 +++++++++++++++++---- 1 file changed, 17 insertions(+), 4 deletions(-) diff --git a/.travis.yml b/.travis.yml index a741da4..9b9efa2 100644 --- a/.travis.yml +++ b/.travis.yml @@ -9,13 +9,26 @@ python: # maintainers to fix their pypy-dev package. # - "pypy" before_install: - - if [[ "$TRAVIS_OS_NAME" == "linux" ]]; sudo apt-get update -q ; fi - - if [[ "$TRAVIS_OS_NAME" == "linux" ]]; sudo apt-get install libblas-dev liblapack-dev libatlas-base-dev; fi #gfortran - - if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then brew update ; fi - - if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then brew install python; fi + - 'if [[ "$TRAVIS_OS_NAME" == "linux" ]]; sudo apt-get update -q ; fi' + - 'if [[ "$TRAVIS_OS_NAME" == "linux" ]]; sudo apt-get install libblas-dev liblapack-dev libatlas-base-dev; fi #gfortran' + - 'if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then brew update ; fi' + - 'if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then brew install python; fi' # command to install dependencies install: - pip install -r requirements.txt - python setup.py install # command to run tests +matrix: + include: + - os: linux + sudo: required + python: 3.2 + env: TOXENV=py32 + - os: linux + sudo: required + python: 3.3 + env: TOXENV=py33 + - os: osx + language: generic + env: TOXENV=py3 script: python test/test_load_module.py -v -- cgit v1.2.3 From d361e3f6b177506a54ce1e928a03d763ae59de98 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 7 Jul 2017 11:09:12 +0200 Subject: add before install script --- .travis.yml | 18 +++--------------- .travis/before_install.sh | 15 +++++++++++++++ 2 files changed, 18 insertions(+), 15 deletions(-) create mode 100644 .travis/before_install.sh diff --git a/.travis.yml b/.travis.yml index 9b9efa2..627e5cd 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,7 +1,6 @@ language: python os: - linux - - osx python: - "2.7" - "3.4" @@ -9,10 +8,7 @@ python: # maintainers to fix their pypy-dev package. # - "pypy" before_install: - - 'if [[ "$TRAVIS_OS_NAME" == "linux" ]]; sudo apt-get update -q ; fi' - - 'if [[ "$TRAVIS_OS_NAME" == "linux" ]]; sudo apt-get install libblas-dev liblapack-dev libatlas-base-dev; fi #gfortran' - - 'if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then brew update ; fi' - - 'if [[ "$TRAVIS_OS_NAME" == "osx" ]]; then brew install python; fi' + - ./.travis/before_install.sh # command to install dependencies install: - pip install -r requirements.txt @@ -20,15 +16,7 @@ install: # command to run tests matrix: include: - - os: linux - sudo: required - python: 3.2 - env: TOXENV=py32 - - os: linux - sudo: required - python: 3.3 - env: TOXENV=py33 - os: osx - language: generic - env: TOXENV=py3 + language: python + script: python test/test_load_module.py -v diff --git a/.travis/before_install.sh b/.travis/before_install.sh new file mode 100644 index 0000000..cfa3fdc --- /dev/null +++ b/.travis/before_install.sh @@ -0,0 +1,15 @@ +#!/bin/bash + +if [[ $TRAVIS_OS_NAME == 'osx' ]]; then + + # Install some custom requirements on OS X + # e.g. brew install pyenv-virtualenv + brew update + brew install python + + +else + # Install some custom requirements on Linux + sudo apt-get update -q + sudo apt-get install libblas-dev liblapack-dev libatlas-base-dev +fi -- cgit v1.2.3 From d7161762a38efb6661eff4980ba89b8a5ae1c164 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 7 Jul 2017 11:10:40 +0200 Subject: execute bit --- .travis/before_install.sh | 0 1 file changed, 0 insertions(+), 0 deletions(-) mode change 100644 => 100755 .travis/before_install.sh diff --git a/.travis/before_install.sh b/.travis/before_install.sh old mode 100644 new mode 100755 -- cgit v1.2.3 From e6887665538f437e9aa0b0cf44d54be2dff8cd74 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 7 Jul 2017 11:13:42 +0200 Subject: sudo required --- .travis.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 627e5cd..ea809d9 100644 --- a/.travis.yml +++ b/.travis.yml @@ -18,5 +18,6 @@ matrix: include: - os: osx language: python - + - os: linux + sudo: required script: python test/test_load_module.py -v -- cgit v1.2.3 From 3867a1441f34bac5ac4270b903f9582a23fa2671 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 7 Jul 2017 11:18:49 +0200 Subject: better --- .travis.yml | 26 ++++++++++++-------------- 1 file changed, 12 insertions(+), 14 deletions(-) diff --git a/.travis.yml b/.travis.yml index ea809d9..f10131a 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,12 +1,16 @@ language: python -os: - - linux -python: - - "2.7" - - "3.4" - # does not have headers provided, please ask https://launchpad.net/~pypy/+archive/ppa - # maintainers to fix their pypy-dev package. - # - "pypy" +matrix: + allow_failures: + - os: osx + include: + - os: osx + language: python + - os: linux + sudo: required + python: 3.4 + - os: linux + sudo: required + python: 2.7 before_install: - ./.travis/before_install.sh # command to install dependencies @@ -14,10 +18,4 @@ install: - pip install -r requirements.txt - python setup.py install # command to run tests -matrix: - include: - - os: osx - language: python - - os: linux - sudo: required script: python test/test_load_module.py -v -- cgit v1.2.3 From 5cb196f0d9af2f100903e160807b58fddcfcaeed Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 7 Jul 2017 11:23:33 +0200 Subject: osx build --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index f10131a..6126b3a 100644 --- a/.travis.yml +++ b/.travis.yml @@ -4,7 +4,7 @@ matrix: - os: osx include: - os: osx - language: python + language: generic - os: linux sudo: required python: 3.4 -- cgit v1.2.3 From 0ddf9e3323343d4a83e43b80a162f87f23f6219b Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 7 Jul 2017 11:29:32 +0200 Subject: last osx test --- .travis/before_install.sh | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.travis/before_install.sh b/.travis/before_install.sh index cfa3fdc..edd6eee 100755 --- a/.travis/before_install.sh +++ b/.travis/before_install.sh @@ -4,8 +4,8 @@ if [[ $TRAVIS_OS_NAME == 'osx' ]]; then # Install some custom requirements on OS X # e.g. brew install pyenv-virtualenv - brew update - brew install python + #brew update + #brew install python else -- cgit v1.2.3 From 8845f0ac1b5ec734a040d180a07fd206b3d7f9cf Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 7 Jul 2017 11:33:37 +0200 Subject: lastest osx test --- .travis/before_install.sh | 1 + 1 file changed, 1 insertion(+) diff --git a/.travis/before_install.sh b/.travis/before_install.sh index edd6eee..60d1dcf 100755 --- a/.travis/before_install.sh +++ b/.travis/before_install.sh @@ -6,6 +6,7 @@ if [[ $TRAVIS_OS_NAME == 'osx' ]]; then # e.g. brew install pyenv-virtualenv #brew update #brew install python + echo do othing else -- cgit v1.2.3 From 2f2b710d0005044e004fbfed851aefebbee007f6 Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Mon, 10 Jul 2017 18:04:27 +0200 Subject: correction on Win64 bug --- ot/lp/EMD_wrapper.cpp | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ot/lp/EMD_wrapper.cpp b/ot/lp/EMD_wrapper.cpp index 52cd262..cad4750 100644 --- a/ot/lp/EMD_wrapper.cpp +++ b/ot/lp/EMD_wrapper.cpp @@ -18,8 +18,8 @@ void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost) { // beware M and C anre strored in row major C style!!! int n, m, i,cur; - double max,max_iter; - + double max; + int max_iter=10000; typedef FullBipartiteDigraph Digraph; DIGRAPH_TYPEDEFS(FullBipartiteDigraph); -- cgit v1.2.3 From d54834fb9add5aa86f4938f599f08ecbfb14c17f Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 11 Jul 2017 11:50:28 +0200 Subject: version 0.3.1 --- ot/__init__.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ot/__init__.py b/ot/__init__.py index 4220148..a79a5ce 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -22,8 +22,8 @@ from .da import sinkhorn_lpl1_mm # utils functions from .utils import dist, unif, tic, toc, toq -__version__ = "0.3" +__version__ = "0.3.1" -__all__ = ["emd", "emd2", "sinkhorn","sinkhorn2", "utils", 'datasets', +__all__ = ["emd", "emd2", "sinkhorn","sinkhorn2", "utils", 'datasets', 'bregman', 'lp', 'plot', 'tic', 'toc', 'toq', 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim'] -- cgit v1.2.3 From 4efdda7853ab7c0eab17b947e28e416f2b16dc51 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 11 Jul 2017 12:05:07 +0200 Subject: add documentation --- ot/da.py | 13 +++++++--- ot/gpu/da.py | 79 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 89 insertions(+), 3 deletions(-) diff --git a/ot/da.py b/ot/da.py index 557e2aa..ddf1c60 100644 --- a/ot/da.py +++ b/ot/da.py @@ -670,10 +670,16 @@ class OTDA(object): return xf[idx,:]+x-x0[idx,:] # aply the delta to the interpolation def normalizeM(self, norm): + """ Apply normalization to the loss matrix + + + Parameters + ---------- + norm : str + type of normalization from 'median','max','log','loglog' + """ - It may help to normalize the cost matrix self.M if there are numerical - errors during the sinkhorn based algorithms. - """ + if norm == "median": self.M /= float(np.median(self.M)) elif norm == "max": @@ -682,6 +688,7 @@ class OTDA(object): self.M = np.log(1 + self.M) elif norm == "loglog": self.M = np.log(1 + np.log(1 + self.M)) + class OTDA_sinkhorn(OTDA): diff --git a/ot/gpu/da.py b/ot/gpu/da.py index 399e769..8dece1d 100644 --- a/ot/gpu/da.py +++ b/ot/gpu/da.py @@ -70,6 +70,76 @@ def pairwiseEuclideanGPU(a, b, returnAsGPU=False, squared=False): def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10, numInnerItermax=200, stopInnerThr=1e-9, verbose=False, log=False): + """ + Solve the entropic regularization optimal transport problem with nonconvex group lasso regularization + + The function solves the following optimization problem: + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma)+ \eta \Omega_g(\gamma) + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + where : + + - M is the (ns,nt) metric cost matrix + - :math:`\Omega_e` is the entropic regularization term :math:`\Omega_e(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - :math:`\Omega_g` is the group lasso regulaization term :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^{1/2}_1` where :math:`\mathcal{I}_c` are the index of samples from class c in the source domain. + - a and b are source and target weights (sum to 1) + + The algorithm used for solving the problem is the generalised conditional gradient as proposed in [5]_ [7]_ + + + Parameters + ---------- + a : np.ndarray (ns,) + samples weights in the source domain + labels_a : np.ndarray (ns,) + labels of samples in the source domain + b : np.ndarray (nt,) + samples weights in the target domain + M_GPU : cudamat.CUDAMatrix (ns,nt) + loss matrix + reg : float + Regularization term for entropic regularization >0 + eta : float, optional + Regularization term for group lasso regularization >0 + numItermax : int, optional + Max number of iterations + numInnerItermax : int, optional + Max number of iterations (inner sinkhorn solver) + stopInnerThr : float, optional + Stop threshold on error (inner sinkhorn solver) (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + gamma : (ns x nt) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 + .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567. + + See Also + -------- + ot.lp.emd : Unregularized OT + ot.bregman.sinkhorn : Entropic regularized OT + ot.optim.cg : General regularized OT + + """ p = 0.5 epsilon = 1e-3 Nfin = len(b) @@ -111,6 +181,15 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10, class OTDA_GPU(OTDA): def normalizeM(self, norm): + """ Apply normalization to the loss matrix + + + Parameters + ---------- + norm : str + type of normalization from 'median','max','log','loglog' + + """ if norm == "median": self.M_GPU.divide(float(np.median(self.M_GPU.asarray()))) elif norm == "max": -- cgit v1.2.3 From 31aa5ac90122e3bc396400709b39b4ffcd326978 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 11 Jul 2017 12:06:45 +0200 Subject: update doc gpu fixes --- ot/gpu/da.py | 9 --------- 1 file changed, 9 deletions(-) diff --git a/ot/gpu/da.py b/ot/gpu/da.py index 8dece1d..0fbfa34 100644 --- a/ot/gpu/da.py +++ b/ot/gpu/da.py @@ -181,15 +181,6 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10, class OTDA_GPU(OTDA): def normalizeM(self, norm): - """ Apply normalization to the loss matrix - - - Parameters - ---------- - norm : str - type of normalization from 'median','max','log','loglog' - - """ if norm == "median": self.M_GPU.divide(float(np.median(self.M_GPU.asarray()))) elif norm == "max": -- cgit v1.2.3 From a1ae72e46cbe52443a1b00d3b8ffd2e2adc80077 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 11 Jul 2017 12:08:16 +0200 Subject: update doc gpu fixes #9 --- ot/gpu/da.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/gpu/da.py b/ot/gpu/da.py index 0fbfa34..b05ff70 100644 --- a/ot/gpu/da.py +++ b/ot/gpu/da.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- """ -Domain adaptation with optimal transport and GPU +Domain adaptation with optimal transport with GPU implementation """ import numpy as np -- cgit v1.2.3 From faa4744597f93e7005bd48729441562e092e3ab6 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 12 Jul 2017 14:32:02 +0200 Subject: add init WDA --- ot/dr.py | 8 +++++--- 1 file changed, 5 insertions(+), 3 deletions(-) diff --git a/ot/dr.py b/ot/dr.py index fdb4daa..763ce35 100644 --- a/ot/dr.py +++ b/ot/dr.py @@ -100,7 +100,7 @@ def fda(X,y,p=2,reg=1e-16): return Popt, proj -def wda(X,y,p=2,reg=1,k=10,solver = None,maxiter=100,verbose=0): +def wda(X,y,p=2,reg=1,k=10,solver = None,maxiter=100,verbose=0,P0=None): """ Wasserstein Discriminant Analysis [11]_ @@ -127,7 +127,9 @@ def wda(X,y,p=2,reg=1,k=10,solver = None,maxiter=100,verbose=0): Regularization term >0 (entropic regularization) solver : str, optional None for steepest decsent or 'TrustRegions' for trust regions algorithm - else shoudl be a pymanopt.sovers + else shoudl be a pymanopt.solvers + P0 : numpy.ndarray (d,p) + Initial starting point for projection verbose : int, optional Print information along iterations @@ -187,7 +189,7 @@ def wda(X,y,p=2,reg=1,k=10,solver = None,maxiter=100,verbose=0): elif solver in ['tr','TrustRegions']: solver= TrustRegions(maxiter=maxiter,logverbosity=verbose) - Popt = solver.solve(problem) + Popt = solver.solve(problem,x=P0) def proj(X): return (X-mx.reshape((1,-1))).dot(Popt) -- cgit v1.2.3 From 0e86d1bdbc0dcf7ffdb943637f62df5de4612ad0 Mon Sep 17 00:00:00 2001 From: Antoine Rolet Date: Thu, 13 Jul 2017 15:19:42 +0900 Subject: Removed references to matlab Also: - added error message when maxiter is reached - added debug logs --- ot/lp/network_simplex_simple.h | 53 +++++++++++++++++++++++------------------- 1 file changed, 29 insertions(+), 24 deletions(-) diff --git a/ot/lp/network_simplex_simple.h b/ot/lp/network_simplex_simple.h index 64856a0..125c818 100644 --- a/ot/lp/network_simplex_simple.h +++ b/ot/lp/network_simplex_simple.h @@ -28,6 +28,12 @@ #ifndef LEMON_NETWORK_SIMPLEX_SIMPLE_H #define LEMON_NETWORK_SIMPLEX_SIMPLE_H #define DEBUG_LVL 0 + +#if DEBUG_LVL>0 +#include +#endif + + #define EPSILON 10*2.2204460492503131e-016 #define MAX_DEBUG_ITER 100000 @@ -220,7 +226,7 @@ namespace lemon { /// mixed order in the internal data structure. /// In special cases, it could lead to better overall performance, /// but it is usually slower. Therefore it is disabled by default. - NetworkSimplexSimple(const GR& graph, bool arc_mixing, int nbnodes, long long nb_arcs,double maxiters) : + NetworkSimplexSimple(const GR& graph, bool arc_mixing, int nbnodes, long long nb_arcs,int maxiters) : _graph(graph), //_arc_id(graph), _arc_mixing(arc_mixing), _init_nb_nodes(nbnodes), _init_nb_arcs(nb_arcs), MAX(std::numeric_limits::max()), @@ -278,7 +284,7 @@ namespace lemon { private: - double max_iter; + int max_iter; TEMPLATE_DIGRAPH_TYPEDEFS(GR); typedef std::vector IntVector; @@ -676,14 +682,12 @@ namespace lemon { /// \see resetParams(), reset() ProblemType run() { #if DEBUG_LVL>0 - mexPrintf("OPTIMAL = %d\nINFEASIBLE = %d\nUNBOUNDED = %d\n",OPTIMAL,INFEASIBLE,UNBOUNDED); - mexEvalString("drawnow;"); + std::cout << "OPTIMAL = " << OPTIMAL << "\nINFEASIBLE = " << INFEASIBLE << "nUNBOUNDED = " << UNBOUNDED << "\n"; #endif if (!init()) return INFEASIBLE; #if DEBUG_LVL>0 - mexPrintf("Init done, starting iterations\n"); - mexEvalString("drawnow;"); + std::cout << "Init done, starting iterations\n"; #endif return start(); } @@ -1424,8 +1428,8 @@ namespace lemon { while (pivot.findEnteringArc()) { if(++iter_number>=max_iter&&max_iter>0){ char errMess[1000]; - // sprintf( errMess, "RESULT MIGHT BE INACURATE\nMax number of iteration reached, currently \%d. Sometimes iterations go on in cycle even though the solution has been reached, to check if it's the case here have a look at the minimal reduced cost. If it is very close to machine precision, you might actually have the correct solution, if not try setting the maximum number of iterations a bit higher",iter_number ); - // mexWarnMsgTxt(errMess); + sprintf( errMess, "RESULT MIGHT BE INACURATE\nMax number of iteration reached, currently \%d. Sometimes iterations go on in cycle even though the solution has been reached, to check if it's the case here have a look at the minimal reduced cost. If it is very close to machine precision, you might actually have the correct solution, if not try setting the maximum number of iterations a bit higher\n",iter_number ); + std::cerr << errMess; break; } #if DEBUG_LVL>0 @@ -1440,12 +1444,13 @@ namespace lemon { for (int i=0; i<_flow.size(); i++) { sumFlow+=_state[i]*_flow[i]; } - mexPrintf("Sum of the flow %.100f\n%d iterations, current cost=%.20f\nReduced cost=%.30f\nPrecision =%.30f\n",sumFlow,niter, curCost,_state[in_arc] * (_cost[in_arc] + _pi[_source[in_arc]] -_pi[_target[in_arc]]), -EPSILON*(a)); - mexPrintf("Arc in = (%d,%d)\n",_node_id(_source[in_arc]),_node_id(_target[in_arc])); - mexPrintf("Supplies = (%f,%f)\n",_supply[_source[in_arc]],_supply[_target[in_arc]]); - - mexPrintf("%.30f\n%.30f\n%.30f\n%.30f\n%",_cost[in_arc],_pi[_source[in_arc]],_pi[_target[in_arc]],a); - mexEvalString("drawnow;"); + std::cout << "Sum of the flow " << std::setprecision(20) << sumFlow << "\n" << niter << " iterations, current cost=" << curCost << "\nReduced cost=" << _state[in_arc] * (_cost[in_arc] + _pi[_source[in_arc]] -_pi[_target[in_arc]]) << "\nPrecision = "<< -EPSILON*(a) << "\n"; + std::cout << "Arc in = (" << _node_id(_source[in_arc]) << ", " << _node_id(_target[in_arc]) <<")\n"; + std::cout << "Supplies = (" << _supply[_source[in_arc]] << ", " << _supply[_target[in_arc]] << ")\n"; + std::cout << _cost[in_arc] << "\n"; + std::cout << _pi[_source[in_arc]] << "\n"; + std::cout << _pi[_target[in_arc]] << "\n"; + std::cout << a << "\n"; } #endif @@ -1459,11 +1464,11 @@ namespace lemon { } #if DEBUG_LVL>0 else{ - mexPrintf("No change\n"); + std::cout << "No change\n"; } #endif #if DEBUG_LVL>1 - mexPrintf("Arc in = (%d,%d)\n",_source[in_arc],_target[in_arc]); + std::cout << "Arc in = (" << _source[in_arc] << ", " << _target[in_arc] << ")\n"; #endif } @@ -1478,23 +1483,23 @@ namespace lemon { for (int i=0; i<_flow.size(); i++) { sumFlow+=_state[i]*_flow[i]; } - mexPrintf("Sum of the flow %.100f\n%d iterations, current cost=%.20f\nReduced cost=%.30f\nPrecision =%.30f",sumFlow,niter, curCost,_state[in_arc] * (_cost[in_arc] + _pi[_source[in_arc]] -_pi[_target[in_arc]]), -EPSILON*(a)); - mexPrintf("Arc in = (%d,%d)\n",_node_id(_source[in_arc]),_node_id(_target[in_arc])); - mexPrintf("Supplies = (%f,%f)\n",_supply[_source[in_arc]],_supply[_target[in_arc]]); + + std::cout << "Sum of the flow " << std::setprecision(20) << sumFlow << "\n" << niter << " iterations, current cost=" << curCost << "\nReduced cost=" << _state[in_arc] * (_cost[in_arc] + _pi[_source[in_arc]] -_pi[_target[in_arc]]) << "\nPrecision = "<< -EPSILON*(a) << "\n"; + + std::cout << "Arc in = (" << _node_id(_source[in_arc]) << ", " << _node_id(_target[in_arc]) <<")\n"; + std::cout << "Supplies = (" << _supply[_source[in_arc]] << ", " << _supply[_target[in_arc]] << ")\n"; - mexEvalString("drawnow;"); #endif #if DEBUG_LVL>1 - double sumFlow=0; + sumFlow=0; for (int i=0; i<_flow.size(); i++) { sumFlow+=_state[i]*_flow[i]; if (_state[i]==STATE_TREE) { - mexPrintf("Non zero value at (%d,%d)\n",_node_num+1-_source[i],_node_num+1-_target[i]); + std::cout << "Non zero value at (" << _node_num+1-_source[i] << ", " << _node_num+1-_target[i] << ")\n"; } } - mexPrintf("Sum of the flow %.100f\n%d iterations, current cost=%.20f\n",sumFlow,niter, totalCost()); - mexEvalString("drawnow;"); + std::cout << "Sum of the flow " << sumFlow << "\n"<< niter <<" iterations, current cost=" << totalCost() << "\n"; #endif // Check feasibility for (int e = _search_arc_num; e != _all_arc_num; ++e) { -- cgit v1.2.3 From 55a38f8253e5831105d2c329f4d8ed77686d1330 Mon Sep 17 00:00:00 2001 From: Antoine Rolet Date: Thu, 13 Jul 2017 15:30:39 +0900 Subject: Added optional maximal number of iteration --- ot/lp/EMD.h | 2 +- ot/lp/EMD_wrapper.cpp | 3 +-- ot/lp/__init__.py | 10 +++++----- ot/lp/emd_wrap.pyx | 10 +++++----- ot/lp/network_simplex_simple.h | 2 +- 5 files changed, 13 insertions(+), 14 deletions(-) diff --git a/ot/lp/EMD.h b/ot/lp/EMD.h index 40d7192..59a5af8 100644 --- a/ot/lp/EMD.h +++ b/ot/lp/EMD.h @@ -24,6 +24,6 @@ using namespace lemon; typedef unsigned int node_id_type; -void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost); +void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int max_iter); #endif diff --git a/ot/lp/EMD_wrapper.cpp b/ot/lp/EMD_wrapper.cpp index cad4750..2d448a0 100644 --- a/ot/lp/EMD_wrapper.cpp +++ b/ot/lp/EMD_wrapper.cpp @@ -15,11 +15,10 @@ #include "EMD.h" -void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost) { +void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int max_iter) { // beware M and C anre strored in row major C style!!! int n, m, i,cur; double max; - int max_iter=10000; typedef FullBipartiteDigraph Digraph; DIGRAPH_TYPEDEFS(FullBipartiteDigraph); diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index db3da78..673242d 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -11,7 +11,7 @@ import multiprocessing -def emd(a, b, M): +def emd(a, b, M, max_iter=-1): """Solves the Earth Movers distance problem and returns the OT matrix @@ -80,9 +80,9 @@ def emd(a, b, M): if len(b) == 0: b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1] - return emd_c(a, b, M) + return emd_c(a, b, M, max_iter) -def emd2(a, b, M,processes=multiprocessing.cpu_count()): +def emd2(a, b, M,processes=multiprocessing.cpu_count(), max_iter=-1): """Solves the Earth Movers distance problem and returns the loss .. math:: @@ -151,12 +151,12 @@ def emd2(a, b, M,processes=multiprocessing.cpu_count()): b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1] if len(b.shape)==1: - return emd2_c(a, b, M) + return emd2_c(a, b, M, max_iter) else: nb=b.shape[1] #res=[emd2_c(a,b[:,i].copy(),M) for i in range(nb)] def f(b): - return emd2_c(a,b,M) + return emd2_c(a,b,M, max_iter) res= parmap(f, [b[:,i] for i in range(nb)],processes) return np.array(res) diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 46794ab..e8fdba4 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -12,13 +12,13 @@ cimport cython cdef extern from "EMD.h": - void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost) + void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int max_iter) @cython.boundscheck(False) @cython.wraparound(False) -def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M): +def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M, int maxiter): """ Solves the Earth Movers distance problem and returns the optimal transport matrix @@ -66,13 +66,13 @@ def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mod b=np.ones((n2,))/n2 # calling the function - EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost) + EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost, maxiter) return G @cython.boundscheck(False) @cython.wraparound(False) -def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M): +def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M, int maxiter): """ Solves the Earth Movers distance problem and returns the optimal transport loss @@ -120,7 +120,7 @@ def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mo b=np.ones((n2,))/n2 # calling the function - EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost) + EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost, maxiter) cost=0 for i in range(n1): diff --git a/ot/lp/network_simplex_simple.h b/ot/lp/network_simplex_simple.h index 125c818..08449f6 100644 --- a/ot/lp/network_simplex_simple.h +++ b/ot/lp/network_simplex_simple.h @@ -1426,7 +1426,7 @@ namespace lemon { //pivot.setDantzig(true); // Execute the Network Simplex algorithm while (pivot.findEnteringArc()) { - if(++iter_number>=max_iter&&max_iter>0){ + if(max_iter > 0 && ++iter_number>=max_iter&&max_iter>0){ char errMess[1000]; sprintf( errMess, "RESULT MIGHT BE INACURATE\nMax number of iteration reached, currently \%d. Sometimes iterations go on in cycle even though the solution has been reached, to check if it's the case here have a look at the minimal reduced cost. If it is very close to machine precision, you might actually have the correct solution, if not try setting the maximum number of iterations a bit higher\n",iter_number ); std::cerr << errMess; -- cgit v1.2.3 From cd9909cff342bb46c4233a0ead348dabebe9efdf Mon Sep 17 00:00:00 2001 From: arolet Date: Fri, 14 Jul 2017 15:18:57 +0900 Subject: Added a test for single process EMD The multiprocess one does not seem to work on windows --- test/test_emd.py | 35 +++++++++++++++++++++++++++++++++++ 1 file changed, 35 insertions(+) create mode 100644 test/test_emd.py diff --git a/test/test_emd.py b/test/test_emd.py new file mode 100644 index 0000000..3729d5d --- /dev/null +++ b/test/test_emd.py @@ -0,0 +1,35 @@ +#!/usr/bin/env python2 +# -*- coding: utf-8 -*- + +import numpy as np +import pylab as pl +import ot + +from ot.datasets import get_1D_gauss as gauss +reload(ot.lp) + +#%% parameters + +n=5000 # nb bins + +# bin positions +x=np.arange(n,dtype=np.float64) + +# Gaussian distributions +a=gauss(n,m=20,s=5) # m= mean, s= std + +b=gauss(n,m=30,s=10) + +# loss matrix +M=ot.dist(x.reshape((n,1)),x.reshape((n,1))) +#M/=M.max() + +#%% + +print('Computing {} EMD '.format(1)) + +# emd loss 1 proc +ot.tic() +emd_loss4 = ot.emd(a,b,M) +ot.toc('1 proc : {} s') + -- cgit v1.2.3 From 0faef7fde7e64705b4f0ed6618a0cfd25319bdc7 Mon Sep 17 00:00:00 2001 From: arolet Date: Fri, 14 Jul 2017 15:19:55 +0900 Subject: Removed unused variable max Probably a legacy normalization variable --- ot/lp/EMD_wrapper.cpp | 14 ++------------ 1 file changed, 2 insertions(+), 12 deletions(-) diff --git a/ot/lp/EMD_wrapper.cpp b/ot/lp/EMD_wrapper.cpp index 2d448a0..d97ba46 100644 --- a/ot/lp/EMD_wrapper.cpp +++ b/ot/lp/EMD_wrapper.cpp @@ -15,10 +15,10 @@ #include "EMD.h" -void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int max_iter) { +void EMD_wrap(int n1, int n2, double *X, double *Y, + double *D, double *G, double *cost, int max_iter) { // beware M and C anre strored in row major C style!!! int n, m, i,cur; - double max; typedef FullBipartiteDigraph Digraph; DIGRAPH_TYPEDEFS(FullBipartiteDigraph); @@ -39,7 +39,6 @@ void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double * } } - // Define the graph std::vector indI(n), indJ(m); @@ -49,28 +48,23 @@ void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double * // Set supply and demand, don't account for 0 values (faster) - max=0; cur=0; for (node_id_type i=0; i0) { weights1[ di.nodeFromId(cur) ] = val; - max+=val; indI[cur++]=i; } } // Demand is actually negative supply... - max=0; cur=0; for (node_id_type i=0; i0) { weights2[ di.nodeFromId(cur) ] = -val; indJ[cur++]=i; - - max-=val; } } @@ -78,14 +72,10 @@ void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double * net.supplyMap(&weights1[0], n, &weights2[0], m); // Set the cost of each edge - max=0; for (node_id_type i=0; imax) { - max=val; - } } } -- cgit v1.2.3 From d59e91450272c78dd0fdd3c6bd9bf48776f10070 Mon Sep 17 00:00:00 2001 From: arolet Date: Fri, 14 Jul 2017 15:37:46 +0900 Subject: Added a test based on closed form solution for gaussians --- test/test_emd.py | 26 ++++++++++++++++++++++---- 1 file changed, 22 insertions(+), 4 deletions(-) diff --git a/test/test_emd.py b/test/test_emd.py index 3729d5d..eb1c5c5 100644 --- a/test/test_emd.py +++ b/test/test_emd.py @@ -11,17 +11,25 @@ reload(ot.lp) #%% parameters n=5000 # nb bins +m=6000 # nb bins + +mean1 = 1000 +mean2 = 1100 + +tol = 1e-6 # bin positions x=np.arange(n,dtype=np.float64) +y=np.arange(m,dtype=np.float64) # Gaussian distributions -a=gauss(n,m=20,s=5) # m= mean, s= std +a=gauss(n,m=mean1,s=5) # m= mean, s= std -b=gauss(n,m=30,s=10) +b=gauss(m,m=mean2,s=10) # loss matrix -M=ot.dist(x.reshape((n,1)),x.reshape((n,1))) +M=ot.dist(x.reshape((-1,1)), y.reshape((-1,1))) ** (1./2) +print M[0,:] #M/=M.max() #%% @@ -30,6 +38,16 @@ print('Computing {} EMD '.format(1)) # emd loss 1 proc ot.tic() -emd_loss4 = ot.emd(a,b,M) +G = ot.emd(a,b,M) ot.toc('1 proc : {} s') +cost1 = (G * M).sum() + +ot.tic() +G = ot.emd(b, a, np.ascontiguousarray(M.T)) +ot.toc('1 proc : {} s') + +cost2 = (G * M.T).sum() + +assert np.abs(cost1-cost2) < tol +assert np.abs(cost1-np.abs(mean1-mean2)) < tol -- cgit v1.2.3 From 1fcb7d0ffbc5b00ed20b5ded2e7f1001dc914d6e Mon Sep 17 00:00:00 2001 From: arolet Date: Fri, 14 Jul 2017 15:38:20 +0900 Subject: Removed some references to node_id_type node_id_type is really always int, it makes code hard to read though. In lemon they needed the typedef because they have more complicated graphs. --- ot/lp/EMD_wrapper.cpp | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/ot/lp/EMD_wrapper.cpp b/ot/lp/EMD_wrapper.cpp index d97ba46..d719c6e 100644 --- a/ot/lp/EMD_wrapper.cpp +++ b/ot/lp/EMD_wrapper.cpp @@ -25,14 +25,14 @@ void EMD_wrap(int n1, int n2, double *X, double *Y, // Get the number of non zero coordinates for r and c n=0; - for (node_id_type i=0; i0) { n++; } } m=0; - for (node_id_type i=0; i0) { m++; @@ -49,10 +49,10 @@ void EMD_wrap(int n1, int n2, double *X, double *Y, // Set supply and demand, don't account for 0 values (faster) cur=0; - for (node_id_type i=0; i0) { - weights1[ di.nodeFromId(cur) ] = val; + weights1[ cur ] = val; indI[cur++]=i; } } @@ -60,10 +60,10 @@ void EMD_wrap(int n1, int n2, double *X, double *Y, // Demand is actually negative supply... cur=0; - for (node_id_type i=0; i0) { - weights2[ di.nodeFromId(cur) ] = -val; + weights2[ cur ] = -val; indJ[cur++]=i; } } @@ -72,8 +72,8 @@ void EMD_wrap(int n1, int n2, double *X, double *Y, net.supplyMap(&weights1[0], n, &weights2[0], m); // Set the cost of each edge - for (node_id_type i=0; i Date: Wed, 19 Jul 2017 18:06:23 +0100 Subject: include LICENSE in MANIFEST.in --- MANIFEST.in | 1 + 1 file changed, 1 insertion(+) diff --git a/MANIFEST.in b/MANIFEST.in index 2fd6a10..e0acb7a 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,5 +1,6 @@ graft ot/lp/ include README.md +include LICENSE include ot/lp/core.h include ot/lp/EMD.h include ot/lp/EMD_wrapper.cpp -- cgit v1.2.3 From 0e20886a48b8d99e437bc26f1c39fde1d9ad433f Mon Sep 17 00:00:00 2001 From: "Dougal J. Sutherland" Date: Thu, 20 Jul 2017 12:12:13 +0100 Subject: add conda-forge to installation instructions Now that POT is in conda-forge (https://github.com/conda-forge/staged-recipes/pull/3332 / https://github.com/conda-forge/pot-feedstock/), might as well put that in the installation instructions. Note that it's available on Windows too, and [this basic test](https://github.com/conda-forge/pot-feedstock/blob/master/recipe/run_test.py) at least runs. --- README.md | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/README.md b/README.md index d53387b..1f4c307 100644 --- a/README.md +++ b/README.md @@ -22,28 +22,31 @@ Some demonstrations (both in Python and Jupyter Notebook format) are available i ## Installation -The Library has been tested on Linux and MacOSX. It requires a C++ compiler for using the EMD solver and rely on the following Python modules: +The library has been tested on Linux and MacOSX. It requires a C++ compiler for using the EMD solver and relies on the following Python modules: - Numpy (>=1.11) - Scipy (>=0.17) - Cython (>=0.23) - Matplotlib (>=1.5) - -Under debian based linux the dependencies can be installed with +If you use the Anaconda python distribution, POT is available in [conda-forge](conda-forge.github.io). To install it and the required dependencies: ``` -sudo apt-get install python-numpy python-scipy python-matplotlib cython +conda install -c conda-forge pot ``` -To install the library, you can install it locally (after downloading it) on you machine using +Otherwise, under Debian-based Linux the dependencies can be installed with: ``` -python setup.py install --user # for user install (no root) +sudo apt-get install python-numpy python-scipy python-matplotlib cython ``` -The toolbox is also available on PyPI with a possibly slightly older version. You can install it with: +You can then install the toolbox through PyPI with: ``` pip install POT ``` +or get the very latest version by downloading it and then running: +``` +python setup.py install --user # for user install (no root) +``` After a correct installation, you should be able to import the module without errors: ```python -- cgit v1.2.3 From fbc43503386def50b8a79bcf3ca2104934747229 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 20 Jul 2017 14:02:45 +0200 Subject: Update README.md add subtitle for better installation instructions --- README.md | 23 +++++++++++------------ 1 file changed, 11 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index 1f4c307..bb3f9e4 100644 --- a/README.md +++ b/README.md @@ -22,24 +22,16 @@ Some demonstrations (both in Python and Jupyter Notebook format) are available i ## Installation -The library has been tested on Linux and MacOSX. It requires a C++ compiler for using the EMD solver and relies on the following Python modules: +The library has been tested on Linux, MacOSX and Windows. It requires a C++ compiler for using the EMD solver and relies on the following Python modules: - Numpy (>=1.11) - Scipy (>=0.17) - Cython (>=0.23) - Matplotlib (>=1.5) -If you use the Anaconda python distribution, POT is available in [conda-forge](conda-forge.github.io). To install it and the required dependencies: -``` -conda install -c conda-forge pot -``` - -Otherwise, under Debian-based Linux the dependencies can be installed with: -``` -sudo apt-get install python-numpy python-scipy python-matplotlib cython -``` +#### Pip installation -You can then install the toolbox through PyPI with: +You can install the toolbox through PyPI with: ``` pip install POT ``` @@ -48,11 +40,18 @@ or get the very latest version by downloading it and then running: python setup.py install --user # for user install (no root) ``` +#### Anaconda installation with conda-forge + +If you use the Anaconda python distribution, POT is available in [conda-forge](conda-forge.github.io). To install it and the required dependencies: +``` +conda install -c conda-forge pot +``` + +#### Post installation check After a correct installation, you should be able to import the module without errors: ```python import ot ``` - Note that for easier access the module is name ot instead of pot. -- cgit v1.2.3 From d0258f103946eb78f2fff6a3a82d85744ba27ec8 Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Tue, 11 Jul 2017 21:32:41 +0200 Subject: add flake8 to travis + misc --- .gitignore | 3 +++ .travis.yml | 8 ++++++-- 2 files changed, 9 insertions(+), 2 deletions(-) diff --git a/.gitignore b/.gitignore index 21be4c9..6edce05 100644 --- a/.gitignore +++ b/.gitignore @@ -97,3 +97,6 @@ ENV/ # Rope project settings .ropeproject + +# Mac stuff +.DS_Store diff --git a/.travis.yml b/.travis.yml index 6126b3a..8023ec5 100644 --- a/.travis.yml +++ b/.travis.yml @@ -16,6 +16,10 @@ before_install: # command to install dependencies install: - pip install -r requirements.txt + - pip install flake8 pytest - python setup.py install -# command to run tests -script: python test/test_load_module.py -v +# command to run tests + check syntax style +script: + - python test/test_load_module.py -v + - flake8 . + - py.test ot test -- cgit v1.2.3 From 95b2a584d02da1a08e71f7ff3895d958e42ed2dc Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Tue, 11 Jul 2017 21:33:13 +0200 Subject: pep8 + pimp plot1D_mat rendering --- examples/plot_OT_1D.py | 43 +++++++++--------- ot/plot.py | 116 ++++++++++++++++++++++--------------------------- 2 files changed, 75 insertions(+), 84 deletions(-) diff --git a/examples/plot_OT_1D.py b/examples/plot_OT_1D.py index 6661aa3..b36fa6a 100644 --- a/examples/plot_OT_1D.py +++ b/examples/plot_OT_1D.py @@ -8,49 +8,50 @@ """ import numpy as np -import matplotlib.pylab as pl +import matplotlib.pylab as plt import ot from ot.datasets import get_1D_gauss as gauss - #%% parameters -n=100 # nb bins +n = 100 # nb bins # bin positions -x=np.arange(n,dtype=np.float64) +x = np.arange(n, dtype=np.float64) # Gaussian distributions -a=gauss(n,m=20,s=5) # m= mean, s= std -b=gauss(n,m=60,s=10) +a = gauss(n, m=20, s=5) # m= mean, s= std +b = gauss(n, m=60, s=10) # loss matrix -M=ot.dist(x.reshape((n,1)),x.reshape((n,1))) -M/=M.max() +M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) +M /= M.max() #%% plot the distributions -pl.figure(1) -pl.plot(x,a,'b',label='Source distribution') -pl.plot(x,b,'r',label='Target distribution') -pl.legend() +plt.figure(1) +plt.plot(x, a, 'b', label='Source distribution') +plt.plot(x, b, 'r', label='Target distribution') +plt.legend() #%% plot distributions and loss matrix -pl.figure(2) -ot.plot.plot1D_mat(a,b,M,'Cost matrix M') +plt.figure(2, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') #%% EMD -G0=ot.emd(a,b,M) +G0 = ot.emd(a, b, M) -pl.figure(3) -ot.plot.plot1D_mat(a,b,G0,'OT matrix G0') +plt.figure(3, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') #%% Sinkhorn -lambd=1e-3 -Gs=ot.sinkhorn(a,b,M,lambd,verbose=True) +lambd = 1e-3 +Gs = ot.sinkhorn(a, b, M, lambd, verbose=True) + +plt.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn') -pl.figure(4) -ot.plot.plot1D_mat(a,b,Gs,'OT matrix Sinkhorn') +plt.show() diff --git a/ot/plot.py b/ot/plot.py index 9737f8a..6f01731 100644 --- a/ot/plot.py +++ b/ot/plot.py @@ -4,89 +4,79 @@ Functions for plotting OT matrices import numpy as np -import matplotlib.pylab as pl +import matplotlib.pylab as plt from matplotlib import gridspec -def plot1D_mat(a,b,M,title=''): - """ Plot matrix M with the source and target 1D distribution - - Creates a subplot with the source distribution a on the left and +def plot1D_mat(a, b, M, title=''): + """ Plot matrix M with the source and target 1D distribution + + Creates a subplot with the source distribution a on the left and target distribution b on the tot. The matrix M is shown in between. - - + + Parameters ---------- - - a : np.array (na,) + a : np.array, shape (na,) Source distribution - b : np.array (nb,) - Target distribution - M : np.array (na,nb) + b : np.array, shape (nb,) + Target distribution + M : np.array, shape (na,nb) Matrix to plot - - - """ - - na=M.shape[0] - nb=M.shape[1] - + na, nb = M.shape + gs = gridspec.GridSpec(3, 3) - - - xa=np.arange(na) - xb=np.arange(nb) - - - ax1=pl.subplot(gs[0,1:]) - pl.plot(xb,b,'r',label='Target distribution') - pl.yticks(()) - pl.title(title) - - #pl.axis('off') - - ax2=pl.subplot(gs[1:,0]) - pl.plot(a,xa,'b',label='Source distribution') - pl.gca().invert_xaxis() - pl.gca().invert_yaxis() - pl.xticks(()) - #pl.ylim((0,n)) - #pl.axis('off') - - pl.subplot(gs[1:,1:],sharex=ax1,sharey=ax2) - pl.imshow(M,interpolation='nearest') - - pl.xlim((0,nb)) - - -def plot2D_samples_mat(xs,xt,G,thr=1e-8,**kwargs): + + xa = np.arange(na) + xb = np.arange(nb) + + ax1 = plt.subplot(gs[0, 1:]) + plt.plot(xb, b, 'r', label='Target distribution') + plt.yticks(()) + plt.title(title) + + ax2 = plt.subplot(gs[1:, 0]) + plt.plot(a, xa, 'b', label='Source distribution') + plt.gca().invert_xaxis() + plt.gca().invert_yaxis() + plt.xticks(()) + + plt.subplot(gs[1:, 1:], sharex=ax1, sharey=ax2) + plt.imshow(M, interpolation='nearest') + plt.axis('off') + + plt.xlim((0, nb)) + plt.tight_layout() + plt.subplots_adjust(wspace=0., hspace=0.2) + + +def plot2D_samples_mat(xs, xt, G, thr=1e-8, **kwargs): """ Plot matrix M in 2D with lines using alpha values - - Plot lines between source and target 2D samples with a color + + Plot lines between source and target 2D samples with a color proportional to the value of the matrix G between samples. - - + + Parameters ---------- - - xs : np.array (ns,2) + xs : ndarray, shape (ns,2) Source samples positions - b : np.array (nt,2) + b : ndarray, shape (nt,2) Target samples positions - G : np.array (na,nb) + G : ndarray, shape (na,nb) OT matrix thr : float, optional threshold above which the line is drawn **kwargs : dict - paameters given to the plot functions (default color is black if nothing given) - + paameters given to the plot functions (default color is black if + nothing given) """ - if ('color' not in kwargs) and ('c' not in kwargs): - kwargs['color']='k' - mx=G.max() + if ('color' not in kwargs) and ('c' not in kwargs): + kwargs['color'] = 'k' + mx = G.max() for i in range(xs.shape[0]): for j in range(xt.shape[0]): - if G[i,j]/mx>thr: - pl.plot([xs[i,0],xt[j,0]],[xs[i,1],xt[j,1]],alpha=G[i,j]/mx,**kwargs) - \ No newline at end of file + if G[i, j] / mx > thr: + plt.plot([xs[i, 0], xt[j, 0]], [xs[i, 1], xt[j, 1]], + alpha=G[i, j] / mx, **kwargs) -- cgit v1.2.3 From 35b25adf1fd9ec35fb6f6da105e268ed5b467d79 Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Tue, 11 Jul 2017 21:43:22 +0200 Subject: pimp + pep8 on plot_OT_2D_samples --- examples/plot_OT_2D_samples.py | 82 +++++++++++++++++++++--------------------- 1 file changed, 42 insertions(+), 40 deletions(-) diff --git a/examples/plot_OT_2D_samples.py b/examples/plot_OT_2D_samples.py index edfb781..3a93591 100644 --- a/examples/plot_OT_2D_samples.py +++ b/examples/plot_OT_2D_samples.py @@ -8,71 +8,73 @@ """ import numpy as np -import matplotlib.pylab as pl +import matplotlib.pylab as plt import ot #%% parameters and data generation -n=50 # nb samples +n = 50 # nb samples -mu_s=np.array([0,0]) -cov_s=np.array([[1,0],[0,1]]) +mu_s = np.array([0, 0]) +cov_s = np.array([[1, 0], [0, 1]]) -mu_t=np.array([4,4]) -cov_t=np.array([[1,-.8],[-.8,1]]) +mu_t = np.array([4, 4]) +cov_t = np.array([[1, -.8], [-.8, 1]]) -xs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s) -xt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t) +xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s) +xt = ot.datasets.get_2D_samples_gauss(n, mu_t, cov_t) -a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples +a, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples # loss matrix -M=ot.dist(xs,xt) -M/=M.max() +M = ot.dist(xs, xt) +M /= M.max() #%% plot samples -pl.figure(1) -pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') -pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') -pl.legend(loc=0) -pl.title('Source and traget distributions') +plt.figure(1) +plt.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +plt.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') +plt.legend(loc=0) +plt.title('Source and target distributions') -pl.figure(2) -pl.imshow(M,interpolation='nearest') -pl.title('Cost matrix M') +plt.figure(2) +plt.imshow(M, interpolation='nearest') +plt.title('Cost matrix M') #%% EMD -G0=ot.emd(a,b,M) +G0 = ot.emd(a, b, M) -pl.figure(3) -pl.imshow(G0,interpolation='nearest') -pl.title('OT matrix G0') +plt.figure(3) +plt.imshow(G0, interpolation='nearest') +plt.title('OT matrix G0') -pl.figure(4) -ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1]) -pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') -pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') -pl.legend(loc=0) -pl.title('OT matrix with samples') +plt.figure(4) +ot.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1]) +plt.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +plt.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') +plt.legend(loc=0) +plt.title('OT matrix with samples') #%% sinkhorn # reg term -lambd=5e-4 +lambd = 5e-4 -Gs=ot.sinkhorn(a,b,M,lambd) +Gs = ot.sinkhorn(a, b, M, lambd) -pl.figure(5) -pl.imshow(Gs,interpolation='nearest') -pl.title('OT matrix sinkhorn') +plt.figure(5) +plt.imshow(Gs, interpolation='nearest') +plt.title('OT matrix sinkhorn') -pl.figure(6) -ot.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1]) -pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') -pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') -pl.legend(loc=0) -pl.title('OT matrix Sinkhorn with samples') +plt.figure(6) +ot.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1]) +plt.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +plt.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') +plt.legend(loc=0) +plt.title('OT matrix Sinkhorn with samples') + +plt.show() -- cgit v1.2.3 From c6cb1cd666a3e1b761b83a6e0f9339268e69f099 Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Tue, 11 Jul 2017 21:51:29 +0200 Subject: pimp + pep8 on plot_OT_L1_vs_L2 --- examples/plot_OT_L1_vs_L2.py | 168 ++++++++++++++++++++++--------------------- 1 file changed, 86 insertions(+), 82 deletions(-) diff --git a/examples/plot_OT_L1_vs_L2.py b/examples/plot_OT_L1_vs_L2.py index 9bb92fe..e11d6ad 100644 --- a/examples/plot_OT_L1_vs_L2.py +++ b/examples/plot_OT_L1_vs_L2.py @@ -4,7 +4,7 @@ 2D Optimal transport for different metrics ========================================== -Stole the figure idea from Fig. 1 and 2 in +Stole the figure idea from Fig. 1 and 2 in https://arxiv.org/pdf/1706.07650.pdf @@ -12,7 +12,7 @@ https://arxiv.org/pdf/1706.07650.pdf """ import numpy as np -import matplotlib.pylab as pl +import matplotlib.pylab as plt import ot #%% parameters and data generation @@ -20,89 +20,93 @@ import ot for data in range(2): if data: - n=20 # nb samples - xs=np.zeros((n,2)) - xs[:,0]=np.arange(n)+1 - xs[:,1]=(np.arange(n)+1)*-0.001 # to make it strictly convex... - - xt=np.zeros((n,2)) - xt[:,1]=np.arange(n)+1 + n = 20 # nb samples + xs = np.zeros((n, 2)) + xs[:, 0] = np.arange(n) + 1 + xs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex... + + xt = np.zeros((n, 2)) + xt[:, 1] = np.arange(n) + 1 else: - - n=50 # nb samples - xtot=np.zeros((n+1,2)) - xtot[:,0]=np.cos((np.arange(n+1)+1.0)*0.9/(n+2)*2*np.pi) - xtot[:,1]=np.sin((np.arange(n+1)+1.0)*0.9/(n+2)*2*np.pi) - - xs=xtot[:n,:] - xt=xtot[1:,:] - - - - a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples - + + n = 50 # nb samples + xtot = np.zeros((n + 1, 2)) + xtot[:, 0] = np.cos( + (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi) + xtot[:, 1] = np.sin( + (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi) + + xs = xtot[:n, :] + xt = xtot[1:, :] + + a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples + # loss matrix - M1=ot.dist(xs,xt,metric='euclidean') - M1/=M1.max() - + M1 = ot.dist(xs, xt, metric='euclidean') + M1 /= M1.max() + # loss matrix - M2=ot.dist(xs,xt,metric='sqeuclidean') - M2/=M2.max() - + M2 = ot.dist(xs, xt, metric='sqeuclidean') + M2 /= M2.max() + # loss matrix - Mp=np.sqrt(ot.dist(xs,xt,metric='euclidean')) - Mp/=Mp.max() - + Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean')) + Mp /= Mp.max() + #%% plot samples - - pl.figure(1+3*data) - pl.clf() - pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') - pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') - pl.axis('equal') - pl.title('Source and traget distributions') - - pl.figure(2+3*data,(15,5)) - pl.subplot(1,3,1) - pl.imshow(M1,interpolation='nearest') - pl.title('Eucidean cost') - pl.subplot(1,3,2) - pl.imshow(M2,interpolation='nearest') - pl.title('Squared Euclidean cost') - - pl.subplot(1,3,3) - pl.imshow(Mp,interpolation='nearest') - pl.title('Sqrt Euclidean cost') + + plt.figure(1 + 3 * data, figsize=(7, 3)) + plt.clf() + plt.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + plt.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') + plt.axis('equal') + plt.title('Source and traget distributions') + + plt.figure(2 + 3 * data, figsize=(7, 3)) + + plt.subplot(1, 3, 1) + plt.imshow(M1, interpolation='nearest') + plt.title('Euclidean cost') + + plt.subplot(1, 3, 2) + plt.imshow(M2, interpolation='nearest') + plt.title('Squared Euclidean cost') + + plt.subplot(1, 3, 3) + plt.imshow(Mp, interpolation='nearest') + plt.title('Sqrt Euclidean cost') + plt.tight_layout() + #%% EMD - - G1=ot.emd(a,b,M1) - G2=ot.emd(a,b,M2) - Gp=ot.emd(a,b,Mp) - - pl.figure(3+3*data,(15,5)) - - pl.subplot(1,3,1) - ot.plot.plot2D_samples_mat(xs,xt,G1,c=[.5,.5,1]) - pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') - pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') - pl.axis('equal') - #pl.legend(loc=0) - pl.title('OT Euclidean') - - pl.subplot(1,3,2) - - ot.plot.plot2D_samples_mat(xs,xt,G2,c=[.5,.5,1]) - pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') - pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') - pl.axis('equal') - #pl.legend(loc=0) - pl.title('OT squared Euclidean') - - pl.subplot(1,3,3) - - ot.plot.plot2D_samples_mat(xs,xt,Gp,c=[.5,.5,1]) - pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') - pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') - pl.axis('equal') - #pl.legend(loc=0) - pl.title('OT sqrt Euclidean') + G1 = ot.emd(a, b, M1) + G2 = ot.emd(a, b, M2) + Gp = ot.emd(a, b, Mp) + + plt.figure(3 + 3 * data, figsize=(7, 3)) + + plt.subplot(1, 3, 1) + ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1]) + plt.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + plt.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') + plt.axis('equal') + # plt.legend(loc=0) + plt.title('OT Euclidean') + + plt.subplot(1, 3, 2) + ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1]) + plt.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + plt.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') + plt.axis('equal') + # plt.legend(loc=0) + plt.title('OT squared Euclidean') + + plt.subplot(1, 3, 3) + ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1]) + plt.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + plt.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') + plt.axis('equal') + # plt.legend(loc=0) + plt.title('OT sqrt Euclidean') + plt.tight_layout() + +plt.show() -- cgit v1.2.3 From 75c988f515f0a1ee51f88f5fc429a1301a1ca8c5 Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Wed, 12 Jul 2017 22:11:25 +0200 Subject: do plot_barycenter_1D --- examples/plot_barycenter_1D.py | 111 ++++++++++++++++++++--------------------- setup.cfg | 4 ++ 2 files changed, 58 insertions(+), 57 deletions(-) diff --git a/examples/plot_barycenter_1D.py b/examples/plot_barycenter_1D.py index 30eecbf..ab236e1 100644 --- a/examples/plot_barycenter_1D.py +++ b/examples/plot_barycenter_1D.py @@ -11,128 +11,125 @@ import numpy as np import matplotlib.pylab as pl import ot -from mpl_toolkits.mplot3d import Axes3D #necessary for 3d plot even if not used +# necessary for 3d plot even if not used +from mpl_toolkits.mplot3d import Axes3D # noqa from matplotlib.collections import PolyCollection #%% parameters -n=100 # nb bins +n = 100 # nb bins # bin positions -x=np.arange(n,dtype=np.float64) +x = np.arange(n, dtype=np.float64) # Gaussian distributions -a1=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std -a2=ot.datasets.get_1D_gauss(n,m=60,s=8) +a1 = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std +a2 = ot.datasets.get_1D_gauss(n, m=60, s=8) # creating matrix A containing all distributions -A=np.vstack((a1,a2)).T -nbd=A.shape[1] +A = np.vstack((a1, a2)).T +n_distributions = A.shape[1] # loss matrix + normalization -M=ot.utils.dist0(n) -M/=M.max() +M = ot.utils.dist0(n) +M /= M.max() #%% plot the distributions -pl.figure(1) -for i in range(nbd): - pl.plot(x,A[:,i]) +pl.figure(1, figsize=(6.4, 3)) +for i in range(n_distributions): + pl.plot(x, A[:, i]) pl.title('Distributions') +pl.tight_layout() #%% barycenter computation -alpha=0.2 # 0<=alpha<=1 -weights=np.array([1-alpha,alpha]) +alpha = 0.2 # 0<=alpha<=1 +weights = np.array([1 - alpha, alpha]) # l2bary -bary_l2=A.dot(weights) +bary_l2 = A.dot(weights) # wasserstein -reg=1e-3 -bary_wass=ot.bregman.barycenter(A,M,reg,weights) +reg = 1e-3 +bary_wass = ot.bregman.barycenter(A, M, reg, weights) pl.figure(2) pl.clf() -pl.subplot(2,1,1) -for i in range(nbd): - pl.plot(x,A[:,i]) +pl.subplot(2, 1, 1) +for i in range(n_distributions): + pl.plot(x, A[:, i]) pl.title('Distributions') -pl.subplot(2,1,2) -pl.plot(x,bary_l2,'r',label='l2') -pl.plot(x,bary_wass,'g',label='Wasserstein') +pl.subplot(2, 1, 2) +pl.plot(x, bary_l2, 'r', label='l2') +pl.plot(x, bary_wass, 'g', label='Wasserstein') pl.legend() pl.title('Barycenters') - +pl.tight_layout() #%% barycenter interpolation -nbalpha=11 -alphalist=np.linspace(0,1,nbalpha) +n_alpha = 11 +alpha_list = np.linspace(0, 1, n_alpha) -B_l2=np.zeros((n,nbalpha)) +B_l2 = np.zeros((n, n_alpha)) -B_wass=np.copy(B_l2) +B_wass = np.copy(B_l2) -for i in range(0,nbalpha): - alpha=alphalist[i] - weights=np.array([1-alpha,alpha]) - B_l2[:,i]=A.dot(weights) - B_wass[:,i]=ot.bregman.barycenter(A,M,reg,weights) +for i in range(0, n_alpha): + alpha = alpha_list[i] + weights = np.array([1 - alpha, alpha]) + B_l2[:, i] = A.dot(weights) + B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights) #%% plot interpolation -pl.figure(3,(10,5)) +pl.figure(3) -#pl.subplot(1,2,1) -cmap=pl.cm.get_cmap('viridis') +cmap = pl.cm.get_cmap('viridis') verts = [] -zs = alphalist -for i,z in enumerate(zs): - ys = B_l2[:,i] +zs = alpha_list +for i, z in enumerate(zs): + ys = B_l2[:, i] verts.append(list(zip(x, ys))) ax = pl.gcf().gca(projection='3d') -poly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist]) +poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list]) poly.set_alpha(0.7) ax.add_collection3d(poly, zs=zs, zdir='y') - ax.set_xlabel('x') ax.set_xlim3d(0, n) ax.set_ylabel('$\\alpha$') -ax.set_ylim3d(0,1) +ax.set_ylim3d(0, 1) ax.set_zlabel('') -ax.set_zlim3d(0, B_l2.max()*1.01) +ax.set_zlim3d(0, B_l2.max() * 1.01) pl.title('Barycenter interpolation with l2') +pl.tight_layout() -pl.show() - -pl.figure(4,(10,5)) - -#pl.subplot(1,2,1) -cmap=pl.cm.get_cmap('viridis') +pl.figure(4) +cmap = pl.cm.get_cmap('viridis') verts = [] -zs = alphalist -for i,z in enumerate(zs): - ys = B_wass[:,i] +zs = alpha_list +for i, z in enumerate(zs): + ys = B_wass[:, i] verts.append(list(zip(x, ys))) ax = pl.gcf().gca(projection='3d') -poly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist]) +poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list]) poly.set_alpha(0.7) ax.add_collection3d(poly, zs=zs, zdir='y') - ax.set_xlabel('x') ax.set_xlim3d(0, n) ax.set_ylabel('$\\alpha$') -ax.set_ylim3d(0,1) +ax.set_ylim3d(0, 1) ax.set_zlabel('') -ax.set_zlim3d(0, B_l2.max()*1.01) +ax.set_zlim3d(0, B_l2.max() * 1.01) pl.title('Barycenter interpolation with Wasserstein') +pl.tight_layout() -pl.show() \ No newline at end of file +pl.show() diff --git a/setup.cfg b/setup.cfg index b88034e..d010702 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,2 +1,6 @@ [metadata] description-file = README.md + +[flake8] +exclude = __init__.py +ignore = E265 -- cgit v1.2.3 From de5903618d2d525e48e3f5ffc205c3f566f0095d Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Wed, 12 Jul 2017 22:13:43 +0200 Subject: do plot_compute_emd --- examples/plot_compute_emd.py | 59 +++++++++++++++++++++++--------------------- 1 file changed, 31 insertions(+), 28 deletions(-) diff --git a/examples/plot_compute_emd.py b/examples/plot_compute_emd.py index f2cdc35..558facb 100644 --- a/examples/plot_compute_emd.py +++ b/examples/plot_compute_emd.py @@ -15,60 +15,63 @@ from ot.datasets import get_1D_gauss as gauss #%% parameters -n=100 # nb bins -n_target=50 # nb target distributions +n = 100 # nb bins +n_target = 50 # nb target distributions # bin positions -x=np.arange(n,dtype=np.float64) +x = np.arange(n, dtype=np.float64) -lst_m=np.linspace(20,90,n_target) +lst_m = np.linspace(20, 90, n_target) # Gaussian distributions -a=gauss(n,m=20,s=5) # m= mean, s= std +a = gauss(n, m=20, s=5) # m= mean, s= std -B=np.zeros((n,n_target)) +B = np.zeros((n, n_target)) -for i,m in enumerate(lst_m): - B[:,i]=gauss(n,m=m,s=5) +for i, m in enumerate(lst_m): + B[:, i] = gauss(n, m=m, s=5) # loss matrix and normalization -M=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'euclidean') -M/=M.max() -M2=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'sqeuclidean') -M2/=M2.max() +M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'euclidean') +M /= M.max() +M2 = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'sqeuclidean') +M2 /= M2.max() #%% plot the distributions pl.figure(1) -pl.subplot(2,1,1) -pl.plot(x,a,'b',label='Source distribution') +pl.subplot(2, 1, 1) +pl.plot(x, a, 'b', label='Source distribution') pl.title('Source distribution') -pl.subplot(2,1,2) -pl.plot(x,B,label='Target distributions') +pl.subplot(2, 1, 2) +pl.plot(x, B, label='Target distributions') pl.title('Target distributions') +pl.tight_layout() #%% Compute and plot distributions and loss matrix -d_emd=ot.emd2(a,B,M) # direct computation of EMD -d_emd2=ot.emd2(a,B,M2) # direct computation of EMD with loss M3 +d_emd = ot.emd2(a, B, M) # direct computation of EMD +d_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M3 pl.figure(2) -pl.plot(d_emd,label='Euclidean EMD') -pl.plot(d_emd2,label='Squared Euclidean EMD') +pl.plot(d_emd, label='Euclidean EMD') +pl.plot(d_emd2, label='Squared Euclidean EMD') pl.title('EMD distances') pl.legend() #%% -reg=1e-2 -d_sinkhorn=ot.sinkhorn2(a,B,M,reg) -d_sinkhorn2=ot.sinkhorn2(a,B,M2,reg) +reg = 1e-2 +d_sinkhorn = ot.sinkhorn2(a, B, M, reg) +d_sinkhorn2 = ot.sinkhorn2(a, B, M2, reg) pl.figure(2) pl.clf() -pl.plot(d_emd,label='Euclidean EMD') -pl.plot(d_emd2,label='Squared Euclidean EMD') -pl.plot(d_sinkhorn,'+',label='Euclidean Sinkhorn') -pl.plot(d_sinkhorn2,'+',label='Squared Euclidean Sinkhorn') +pl.plot(d_emd, label='Euclidean EMD') +pl.plot(d_emd2, label='Squared Euclidean EMD') +pl.plot(d_sinkhorn, '+', label='Euclidean Sinkhorn') +pl.plot(d_sinkhorn2, '+', label='Squared Euclidean Sinkhorn') pl.title('EMD distances') -pl.legend() \ No newline at end of file +pl.legend() + +pl.show() -- cgit v1.2.3 From c5a72cc1ff6a1f5462fdca6ced837180820fe19b Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Wed, 12 Jul 2017 22:17:52 +0200 Subject: do plot_optim_OTreg.py --- examples/plot_optim_OTreg.py | 65 ++++++++++++++++++++++++++------------------ 1 file changed, 39 insertions(+), 26 deletions(-) diff --git a/examples/plot_optim_OTreg.py b/examples/plot_optim_OTreg.py index 8abb426..0041770 100644 --- a/examples/plot_optim_OTreg.py +++ b/examples/plot_optim_OTreg.py @@ -12,63 +12,76 @@ import matplotlib.pylab as pl import ot - #%% parameters -n=100 # nb bins +n = 100 # nb bins # bin positions -x=np.arange(n,dtype=np.float64) +x = np.arange(n, dtype=np.float64) # Gaussian distributions -a=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std -b=ot.datasets.get_1D_gauss(n,m=60,s=10) +a = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std +b = ot.datasets.get_1D_gauss(n, m=60, s=10) # loss matrix -M=ot.dist(x.reshape((n,1)),x.reshape((n,1))) -M/=M.max() +M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) +M /= M.max() #%% EMD -G0=ot.emd(a,b,M) +G0 = ot.emd(a, b, M) pl.figure(3) -ot.plot.plot1D_mat(a,b,G0,'OT matrix G0') +ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') #%% Example with Frobenius norm regularization -def f(G): return 0.5*np.sum(G**2) -def df(G): return G +def f(G): + return 0.5 * np.sum(G**2) + + +def df(G): + return G -reg=1e-1 +reg = 1e-1 -Gl2=ot.optim.cg(a,b,M,reg,f,df,verbose=True) +Gl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True) pl.figure(3) -ot.plot.plot1D_mat(a,b,Gl2,'OT matrix Frob. reg') +ot.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg') #%% Example with entropic regularization -def f(G): return np.sum(G*np.log(G)) -def df(G): return np.log(G)+1 -reg=1e-3 +def f(G): + return np.sum(G * np.log(G)) + + +def df(G): + return np.log(G) + 1. -Ge=ot.optim.cg(a,b,M,reg,f,df,verbose=True) +reg = 1e-3 + +Ge = ot.optim.cg(a, b, M, reg, f, df, verbose=True) pl.figure(4) -ot.plot.plot1D_mat(a,b,Ge,'OT matrix Entrop. reg') +ot.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg') #%% Example with Frobenius norm + entropic regularization with gcg -def f(G): return 0.5*np.sum(G**2) -def df(G): return G -reg1=1e-3 -reg2=1e-1 +def f(G): + return 0.5 * np.sum(G**2) + + +def df(G): + return G + +reg1 = 1e-3 +reg2 = 1e-1 -Gel2=ot.optim.gcg(a,b,M,reg1,reg2,f,df,verbose=True) +Gel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True) pl.figure(5) -ot.plot.plot1D_mat(a,b,Gel2,'OT entropic + matrix Frob. reg') -pl.show() \ No newline at end of file +ot.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg') +pl.show() -- cgit v1.2.3 From d6091dae858a82f69e6843859f164269fa338c6b Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Wed, 12 Jul 2017 22:20:08 +0200 Subject: more --- examples/plot_OT_1D.py | 18 +++++++++--------- examples/plot_optim_OTreg.py | 6 +++--- 2 files changed, 12 insertions(+), 12 deletions(-) diff --git a/examples/plot_OT_1D.py b/examples/plot_OT_1D.py index b36fa6a..2f3b924 100644 --- a/examples/plot_OT_1D.py +++ b/examples/plot_OT_1D.py @@ -8,7 +8,7 @@ """ import numpy as np -import matplotlib.pylab as plt +import matplotlib.pylab as pl import ot from ot.datasets import get_1D_gauss as gauss @@ -29,21 +29,21 @@ M /= M.max() #%% plot the distributions -plt.figure(1) -plt.plot(x, a, 'b', label='Source distribution') -plt.plot(x, b, 'r', label='Target distribution') -plt.legend() +pl.figure(1, figsize=(6.4, 3)) +pl.plot(x, a, 'b', label='Source distribution') +pl.plot(x, b, 'r', label='Target distribution') +pl.legend() #%% plot distributions and loss matrix -plt.figure(2, figsize=(5, 5)) +pl.figure(2, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') #%% EMD G0 = ot.emd(a, b, M) -plt.figure(3, figsize=(5, 5)) +pl.figure(3, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') #%% Sinkhorn @@ -51,7 +51,7 @@ ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') lambd = 1e-3 Gs = ot.sinkhorn(a, b, M, lambd, verbose=True) -plt.figure(4, figsize=(5, 5)) +pl.figure(4, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn') -plt.show() +pl.show() diff --git a/examples/plot_optim_OTreg.py b/examples/plot_optim_OTreg.py index 0041770..1c3a1c5 100644 --- a/examples/plot_optim_OTreg.py +++ b/examples/plot_optim_OTreg.py @@ -31,7 +31,7 @@ M /= M.max() G0 = ot.emd(a, b, M) -pl.figure(3) +pl.figure(3, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') #%% Example with Frobenius norm regularization @@ -64,7 +64,7 @@ reg = 1e-3 Ge = ot.optim.cg(a, b, M, reg, f, df, verbose=True) -pl.figure(4) +pl.figure(4, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg') #%% Example with Frobenius norm + entropic regularization with gcg @@ -82,6 +82,6 @@ reg2 = 1e-1 Gel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True) -pl.figure(5) +pl.figure(5, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg') pl.show() -- cgit v1.2.3 From 25ef32ff892fba105a4a116a804b1e4f08ae57cd Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Wed, 12 Jul 2017 22:51:17 +0200 Subject: more --- examples/plot_OTDA_2D.py | 114 ++++++++++++++++---------------- examples/plot_OTDA_classes.py | 129 +++++++++++++++++++------------------ examples/plot_OTDA_color_images.py | 115 +++++++++++++++++---------------- examples/plot_OT_2D_samples.py | 52 +++++++-------- examples/plot_OT_L1_vs_L2.py | 80 +++++++++++------------ 5 files changed, 247 insertions(+), 243 deletions(-) diff --git a/examples/plot_OTDA_2D.py b/examples/plot_OTDA_2D.py index a1fb804..1bda59c 100644 --- a/examples/plot_OTDA_2D.py +++ b/examples/plot_OTDA_2D.py @@ -11,110 +11,112 @@ import matplotlib.pylab as pl import ot - #%% parameters -n=150 # nb bins +n = 150 # nb bins -xs,ys=ot.datasets.get_data_classif('3gauss',n) -xt,yt=ot.datasets.get_data_classif('3gauss2',n) +xs, ys = ot.datasets.get_data_classif('3gauss', n) +xt, yt = ot.datasets.get_data_classif('3gauss2', n) -a,b = ot.unif(n),ot.unif(n) +a, b = ot.unif(n), ot.unif(n) # loss matrix -M=ot.dist(xs,xt) -#M/=M.max() +M = ot.dist(xs, xt) +# M/=M.max() #%% plot samples pl.figure(1) - -pl.subplot(2,2,1) -pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') +pl.subplot(2, 2, 1) +pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples') pl.legend(loc=0) pl.title('Source distributions') -pl.subplot(2,2,2) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') +pl.subplot(2, 2, 2) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples') pl.legend(loc=0) pl.title('target distributions') pl.figure(2) -pl.imshow(M,interpolation='nearest') +pl.imshow(M, interpolation='nearest') pl.title('Cost matrix M') #%% OT estimation # EMD -G0=ot.emd(a,b,M) +G0 = ot.emd(a, b, M) # sinkhorn -lambd=1e-1 -Gs=ot.sinkhorn(a,b,M,lambd) +lambd = 1e-1 +Gs = ot.sinkhorn(a, b, M, lambd) # Group lasso regularization -reg=1e-1 -eta=1e0 -Gg=ot.da.sinkhorn_lpl1_mm(a,ys.astype(np.int),b,M,reg,eta) +reg = 1e-1 +eta = 1e0 +Gg = ot.da.sinkhorn_lpl1_mm(a, ys.astype(np.int), b, M, reg, eta) #%% visu matrices pl.figure(3) -pl.subplot(2,3,1) -pl.imshow(G0,interpolation='nearest') +pl.subplot(2, 3, 1) +pl.imshow(G0, interpolation='nearest') pl.title('OT matrix ') -pl.subplot(2,3,2) -pl.imshow(Gs,interpolation='nearest') +pl.subplot(2, 3, 2) +pl.imshow(Gs, interpolation='nearest') pl.title('OT matrix Sinkhorn') -pl.subplot(2,3,3) -pl.imshow(Gg,interpolation='nearest') +pl.subplot(2, 3, 3) +pl.imshow(Gg, interpolation='nearest') pl.title('OT matrix Group lasso') -pl.subplot(2,3,4) -ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1]) -pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') +pl.subplot(2, 3, 4) +ot.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1]) +pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples') -pl.subplot(2,3,5) -ot.plot.plot2D_samples_mat(xs,xt,Gs,c=[.5,.5,1]) -pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') +pl.subplot(2, 3, 5) +ot.plot.plot2D_samples_mat(xs, xt, Gs, c=[.5, .5, 1]) +pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples') -pl.subplot(2,3,6) -ot.plot.plot2D_samples_mat(xs,xt,Gg,c=[.5,.5,1]) -pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') +pl.subplot(2, 3, 6) +ot.plot.plot2D_samples_mat(xs, xt, Gg, c=[.5, .5, 1]) +pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples') +pl.tight_layout() #%% sample interpolation -xst0=n*G0.dot(xt) -xsts=n*Gs.dot(xt) -xstg=n*Gg.dot(xt) - -pl.figure(4) -pl.subplot(2,3,1) - +xst0 = n * G0.dot(xt) +xsts = n * Gs.dot(xt) +xstg = n * Gg.dot(xt) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) -pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30) +pl.figure(4, figsize=(8, 3)) +pl.subplot(1, 3, 1) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(xst0[:, 0], xst0[:, 1], c=ys, + marker='+', label='Transp samples', s=30) pl.title('Interp samples') pl.legend(loc=0) -pl.subplot(2,3,2) - - -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) -pl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30) +pl.subplot(1, 3, 2) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(xsts[:, 0], xsts[:, 1], c=ys, + marker='+', label='Transp samples', s=30) pl.title('Interp samples Sinkhorn') -pl.subplot(2,3,3) - -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) -pl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30) -pl.title('Interp samples Grouplasso') \ No newline at end of file +pl.subplot(1, 3, 3) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(xstg[:, 0], xstg[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Interp samples Grouplasso') +pl.tight_layout() +pl.show() diff --git a/examples/plot_OTDA_classes.py b/examples/plot_OTDA_classes.py index 089b45b..4d3846a 100644 --- a/examples/plot_OTDA_classes.py +++ b/examples/plot_OTDA_classes.py @@ -10,29 +10,25 @@ import matplotlib.pylab as pl import ot - - #%% parameters -n=150 # nb samples in source and target datasets - -xs,ys=ot.datasets.get_data_classif('3gauss',n) -xt,yt=ot.datasets.get_data_classif('3gauss2',n) - +n = 150 # nb samples in source and target datasets +xs, ys = ot.datasets.get_data_classif('3gauss', n) +xt, yt = ot.datasets.get_data_classif('3gauss2', n) #%% plot samples -pl.figure(1) +pl.figure(1, figsize=(6.4, 3)) -pl.subplot(2,2,1) -pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') +pl.subplot(1, 2, 1) +pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples') pl.legend(loc=0) pl.title('Source distributions') -pl.subplot(2,2,2) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') +pl.subplot(1, 2, 2) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples') pl.legend(loc=0) pl.title('target distributions') @@ -40,73 +36,78 @@ pl.title('target distributions') #%% OT estimation # LP problem -da_emd=ot.da.OTDA() # init class -da_emd.fit(xs,xt) # fit distributions -xst0=da_emd.interp() # interpolation of source samples - +da_emd = ot.da.OTDA() # init class +da_emd.fit(xs, xt) # fit distributions +xst0 = da_emd.interp() # interpolation of source samples # sinkhorn regularization -lambd=1e-1 -da_entrop=ot.da.OTDA_sinkhorn() -da_entrop.fit(xs,xt,reg=lambd) -xsts=da_entrop.interp() +lambd = 1e-1 +da_entrop = ot.da.OTDA_sinkhorn() +da_entrop.fit(xs, xt, reg=lambd) +xsts = da_entrop.interp() # non-convex Group lasso regularization -reg=1e-1 -eta=1e0 -da_lpl1=ot.da.OTDA_lpl1() -da_lpl1.fit(xs,ys,xt,reg=reg,eta=eta) -xstg=da_lpl1.interp() - +reg = 1e-1 +eta = 1e0 +da_lpl1 = ot.da.OTDA_lpl1() +da_lpl1.fit(xs, ys, xt, reg=reg, eta=eta) +xstg = da_lpl1.interp() # True Group lasso regularization -reg=1e-1 -eta=2e0 -da_l1l2=ot.da.OTDA_l1l2() -da_l1l2.fit(xs,ys,xt,reg=reg,eta=eta,numItermax=20,verbose=True) -xstgl=da_l1l2.interp() - +reg = 1e-1 +eta = 2e0 +da_l1l2 = ot.da.OTDA_l1l2() +da_l1l2.fit(xs, ys, xt, reg=reg, eta=eta, numItermax=20, verbose=True) +xstgl = da_l1l2.interp() #%% plot interpolated source samples -pl.figure(4,(15,8)) -param_img={'interpolation':'nearest','cmap':'jet'} +param_img = {'interpolation': 'nearest', 'cmap': 'spectral'} -pl.subplot(2,4,1) -pl.imshow(da_emd.G,**param_img) +pl.figure(2, figsize=(8, 4.5)) +pl.subplot(2, 4, 1) +pl.imshow(da_emd.G, **param_img) pl.title('OT matrix') +pl.subplot(2, 4, 2) +pl.imshow(da_entrop.G, **param_img) +pl.title('OT matrix\nsinkhorn') -pl.subplot(2,4,2) -pl.imshow(da_entrop.G,**param_img) -pl.title('OT matrix sinkhorn') - -pl.subplot(2,4,3) -pl.imshow(da_lpl1.G,**param_img) -pl.title('OT matrix non-convex Group Lasso') - -pl.subplot(2,4,4) -pl.imshow(da_l1l2.G,**param_img) -pl.title('OT matrix Group Lasso') +pl.subplot(2, 4, 3) +pl.imshow(da_lpl1.G, **param_img) +pl.title('OT matrix\nnon-convex Group Lasso') +pl.subplot(2, 4, 4) +pl.imshow(da_l1l2.G, **param_img) +pl.title('OT matrix\nGroup Lasso') -pl.subplot(2,4,5) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) -pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30) +pl.subplot(2, 4, 5) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(xst0[:, 0], xst0[:, 1], c=ys, + marker='+', label='Transp samples', s=30) pl.title('Interp samples') pl.legend(loc=0) -pl.subplot(2,4,6) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) -pl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30) -pl.title('Interp samples Sinkhorn') - -pl.subplot(2,4,7) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) -pl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30) -pl.title('Interp samples non-convex Group Lasso') - -pl.subplot(2,4,8) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) -pl.scatter(xstgl[:,0],xstgl[:,1],c=ys,marker='+',label='Transp samples',s=30) -pl.title('Interp samples Group Lasso') \ No newline at end of file +pl.subplot(2, 4, 6) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(xsts[:, 0], xsts[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Interp samples\nSinkhorn') + +pl.subplot(2, 4, 7) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(xstg[:, 0], xstg[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Interp samples\nnon-convex Group Lasso') + +pl.subplot(2, 4, 8) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(xstgl[:, 0], xstgl[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Interp samples\nGroup Lasso') +pl.tight_layout() +pl.show() diff --git a/examples/plot_OTDA_color_images.py b/examples/plot_OTDA_color_images.py index 68eee44..a8861c6 100644 --- a/examples/plot_OTDA_color_images.py +++ b/examples/plot_OTDA_color_images.py @@ -4,142 +4,143 @@ OT for domain adaptation with image color adaptation [6] ======================================================== -[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. +[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). +Regularized discrete optimal transport. +SIAM Journal on Imaging Sciences, 7(3), 1853-1882. """ import numpy as np -import scipy.ndimage as spi +from scipy import ndimage import matplotlib.pylab as pl import ot #%% Loading images -I1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256 -I2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256 +I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256 +I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 #%% Plot images -pl.figure(1) +pl.figure(1, figsize=(6.4, 3)) -pl.subplot(1,2,1) +pl.subplot(1, 2, 1) pl.imshow(I1) +pl.axis('off') pl.title('Image 1') -pl.subplot(1,2,2) +pl.subplot(1, 2, 2) pl.imshow(I2) +pl.axis('off') pl.title('Image 2') pl.show() #%% Image conversion and dataset generation + def im2mat(I): """Converts and image to matrix (one pixel per line)""" - return I.reshape((I.shape[0]*I.shape[1],I.shape[2])) + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + -def mat2im(X,shape): +def mat2im(X, shape): """Converts back a matrix to an image""" return X.reshape(shape) -X1=im2mat(I1) -X2=im2mat(I2) +X1 = im2mat(I1) +X2 = im2mat(I2) # training samples -nb=1000 -idx1=np.random.randint(X1.shape[0],size=(nb,)) -idx2=np.random.randint(X2.shape[0],size=(nb,)) +nb = 1000 +idx1 = np.random.randint(X1.shape[0], size=(nb,)) +idx2 = np.random.randint(X2.shape[0], size=(nb,)) -xs=X1[idx1,:] -xt=X2[idx2,:] +xs = X1[idx1, :] +xt = X2[idx2, :] #%% Plot image distributions -pl.figure(2,(10,5)) +pl.figure(2, figsize=(6.4, 3)) -pl.subplot(1,2,1) -pl.scatter(xs[:,0],xs[:,2],c=xs) -pl.axis([0,1,0,1]) +pl.subplot(1, 2, 1) +pl.scatter(xs[:, 0], xs[:, 2], c=xs) +pl.axis([0, 1, 0, 1]) pl.xlabel('Red') pl.ylabel('Blue') pl.title('Image 1') -pl.subplot(1,2,2) -#pl.imshow(I2) -pl.scatter(xt[:,0],xt[:,2],c=xt) -pl.axis([0,1,0,1]) +pl.subplot(1, 2, 2) +pl.scatter(xt[:, 0], xt[:, 2], c=xt) +pl.axis([0, 1, 0, 1]) pl.xlabel('Red') pl.ylabel('Blue') pl.title('Image 2') - -pl.show() - - +pl.tight_layout() #%% domain adaptation between images # LP problem -da_emd=ot.da.OTDA() # init class -da_emd.fit(xs,xt) # fit distributions - +da_emd = ot.da.OTDA() # init class +da_emd.fit(xs, xt) # fit distributions # sinkhorn regularization -lambd=1e-1 -da_entrop=ot.da.OTDA_sinkhorn() -da_entrop.fit(xs,xt,reg=lambd) - - +lambd = 1e-1 +da_entrop = ot.da.OTDA_sinkhorn() +da_entrop.fit(xs, xt, reg=lambd) #%% prediction between images (using out of sample prediction as in [6]) -X1t=da_emd.predict(X1) -X2t=da_emd.predict(X2,-1) +X1t = da_emd.predict(X1) +X2t = da_emd.predict(X2, -1) - -X1te=da_entrop.predict(X1) -X2te=da_entrop.predict(X2,-1) +X1te = da_entrop.predict(X1) +X2te = da_entrop.predict(X2, -1) def minmax(I): - return np.minimum(np.maximum(I,0),1) + return np.clip(I, 0, 1) -I1t=minmax(mat2im(X1t,I1.shape)) -I2t=minmax(mat2im(X2t,I2.shape)) +I1t = minmax(mat2im(X1t, I1.shape)) +I2t = minmax(mat2im(X2t, I2.shape)) -I1te=minmax(mat2im(X1te,I1.shape)) -I2te=minmax(mat2im(X2te,I2.shape)) +I1te = minmax(mat2im(X1te, I1.shape)) +I2te = minmax(mat2im(X2te, I2.shape)) #%% plot all images -pl.figure(2,(10,8)) - -pl.subplot(2,3,1) +pl.figure(2, figsize=(8, 4)) +pl.subplot(2, 3, 1) pl.imshow(I1) +pl.axis('off') pl.title('Image 1') -pl.subplot(2,3,2) +pl.subplot(2, 3, 2) pl.imshow(I1t) +pl.axis('off') pl.title('Image 1 Adapt') - -pl.subplot(2,3,3) +pl.subplot(2, 3, 3) pl.imshow(I1te) +pl.axis('off') pl.title('Image 1 Adapt (reg)') -pl.subplot(2,3,4) - +pl.subplot(2, 3, 4) pl.imshow(I2) +pl.axis('off') pl.title('Image 2') -pl.subplot(2,3,5) +pl.subplot(2, 3, 5) pl.imshow(I2t) +pl.axis('off') pl.title('Image 2 Adapt') - -pl.subplot(2,3,6) +pl.subplot(2, 3, 6) pl.imshow(I2te) +pl.axis('off') pl.title('Image 2 Adapt (reg)') +pl.tight_layout() pl.show() diff --git a/examples/plot_OT_2D_samples.py b/examples/plot_OT_2D_samples.py index 3a93591..75ed7db 100644 --- a/examples/plot_OT_2D_samples.py +++ b/examples/plot_OT_2D_samples.py @@ -8,7 +8,7 @@ """ import numpy as np -import matplotlib.pylab as plt +import matplotlib.pylab as pl import ot #%% parameters and data generation @@ -32,31 +32,31 @@ M /= M.max() #%% plot samples -plt.figure(1) -plt.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') -plt.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') -plt.legend(loc=0) -plt.title('Source and target distributions') +pl.figure(1) +pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') +pl.legend(loc=0) +pl.title('Source and target distributions') -plt.figure(2) -plt.imshow(M, interpolation='nearest') -plt.title('Cost matrix M') +pl.figure(2) +pl.imshow(M, interpolation='nearest') +pl.title('Cost matrix M') #%% EMD G0 = ot.emd(a, b, M) -plt.figure(3) -plt.imshow(G0, interpolation='nearest') -plt.title('OT matrix G0') +pl.figure(3) +pl.imshow(G0, interpolation='nearest') +pl.title('OT matrix G0') -plt.figure(4) +pl.figure(4) ot.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1]) -plt.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') -plt.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') -plt.legend(loc=0) -plt.title('OT matrix with samples') +pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') +pl.legend(loc=0) +pl.title('OT matrix with samples') #%% sinkhorn @@ -66,15 +66,15 @@ lambd = 5e-4 Gs = ot.sinkhorn(a, b, M, lambd) -plt.figure(5) -plt.imshow(Gs, interpolation='nearest') -plt.title('OT matrix sinkhorn') +pl.figure(5) +pl.imshow(Gs, interpolation='nearest') +pl.title('OT matrix sinkhorn') -plt.figure(6) +pl.figure(6) ot.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1]) -plt.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') -plt.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') -plt.legend(loc=0) -plt.title('OT matrix Sinkhorn with samples') +pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') +pl.legend(loc=0) +pl.title('OT matrix Sinkhorn with samples') -plt.show() +pl.show() diff --git a/examples/plot_OT_L1_vs_L2.py b/examples/plot_OT_L1_vs_L2.py index e11d6ad..86d902b 100644 --- a/examples/plot_OT_L1_vs_L2.py +++ b/examples/plot_OT_L1_vs_L2.py @@ -12,7 +12,7 @@ https://arxiv.org/pdf/1706.07650.pdf """ import numpy as np -import matplotlib.pylab as plt +import matplotlib.pylab as pl import ot #%% parameters and data generation @@ -55,58 +55,58 @@ for data in range(2): #%% plot samples - plt.figure(1 + 3 * data, figsize=(7, 3)) - plt.clf() - plt.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - plt.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - plt.axis('equal') - plt.title('Source and traget distributions') + pl.figure(1 + 3 * data, figsize=(7, 3)) + pl.clf() + pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') + pl.axis('equal') + pl.title('Source and traget distributions') - plt.figure(2 + 3 * data, figsize=(7, 3)) + pl.figure(2 + 3 * data, figsize=(7, 3)) - plt.subplot(1, 3, 1) - plt.imshow(M1, interpolation='nearest') - plt.title('Euclidean cost') + pl.subplot(1, 3, 1) + pl.imshow(M1, interpolation='nearest') + pl.title('Euclidean cost') - plt.subplot(1, 3, 2) - plt.imshow(M2, interpolation='nearest') - plt.title('Squared Euclidean cost') + pl.subplot(1, 3, 2) + pl.imshow(M2, interpolation='nearest') + pl.title('Squared Euclidean cost') - plt.subplot(1, 3, 3) - plt.imshow(Mp, interpolation='nearest') - plt.title('Sqrt Euclidean cost') - plt.tight_layout() + pl.subplot(1, 3, 3) + pl.imshow(Mp, interpolation='nearest') + pl.title('Sqrt Euclidean cost') + pl.tight_layout() #%% EMD G1 = ot.emd(a, b, M1) G2 = ot.emd(a, b, M2) Gp = ot.emd(a, b, Mp) - plt.figure(3 + 3 * data, figsize=(7, 3)) + pl.figure(3 + 3 * data, figsize=(7, 3)) - plt.subplot(1, 3, 1) + pl.subplot(1, 3, 1) ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1]) - plt.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - plt.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - plt.axis('equal') - # plt.legend(loc=0) - plt.title('OT Euclidean') + pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') + pl.axis('equal') + # pl.legend(loc=0) + pl.title('OT Euclidean') - plt.subplot(1, 3, 2) + pl.subplot(1, 3, 2) ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1]) - plt.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - plt.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - plt.axis('equal') - # plt.legend(loc=0) - plt.title('OT squared Euclidean') + pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') + pl.axis('equal') + # pl.legend(loc=0) + pl.title('OT squared Euclidean') - plt.subplot(1, 3, 3) + pl.subplot(1, 3, 3) ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1]) - plt.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - plt.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - plt.axis('equal') - # plt.legend(loc=0) - plt.title('OT sqrt Euclidean') - plt.tight_layout() - -plt.show() + pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') + pl.axis('equal') + # pl.legend(loc=0) + pl.title('OT sqrt Euclidean') + pl.tight_layout() + +pl.show() -- cgit v1.2.3 From 823942312962134a3b49fcedbca7c4e2a7419df3 Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Wed, 12 Jul 2017 22:55:00 +0200 Subject: more --- examples/plot_OTDA_mapping.py | 118 +++++++++++++++++++++++------------------- 1 file changed, 64 insertions(+), 54 deletions(-) diff --git a/examples/plot_OTDA_mapping.py b/examples/plot_OTDA_mapping.py index 78b57e7..a5c2b21 100644 --- a/examples/plot_OTDA_mapping.py +++ b/examples/plot_OTDA_mapping.py @@ -4,8 +4,9 @@ OT mapping estimation for domain adaptation [8] =============================================== -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for - discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, + "Mapping estimation for discrete optimal transport", + Neural Information Processing Systems (NIPS), 2016. """ import numpy as np @@ -13,98 +14,107 @@ import matplotlib.pylab as pl import ot - #%% dataset generation -np.random.seed(0) # makes example reproducible +np.random.seed(0) # makes example reproducible -n=100 # nb samples in source and target datasets -theta=2*np.pi/20 -nz=0.1 -xs,ys=ot.datasets.get_data_classif('gaussrot',n,nz=nz) -xt,yt=ot.datasets.get_data_classif('gaussrot',n,theta=theta,nz=nz) +n = 100 # nb samples in source and target datasets +theta = 2 * np.pi / 20 +nz = 0.1 +xs, ys = ot.datasets.get_data_classif('gaussrot', n, nz=nz) +xt, yt = ot.datasets.get_data_classif('gaussrot', n, theta=theta, nz=nz) # one of the target mode changes its variance (no linear mapping) -xt[yt==2]*=3 -xt=xt+4 +xt[yt == 2] *= 3 +xt = xt + 4 #%% plot samples -pl.figure(1,(8,5)) +pl.figure(1, (6.4, 3)) pl.clf() - -pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') - +pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples') pl.legend(loc=0) pl.title('Source and target distributions') - #%% OT linear mapping estimation -eta=1e-8 # quadratic regularization for regression -mu=1e0 # weight of the OT linear term -bias=True # estimate a bias +eta = 1e-8 # quadratic regularization for regression +mu = 1e0 # weight of the OT linear term +bias = True # estimate a bias -ot_mapping=ot.da.OTDA_mapping_linear() -ot_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True) +ot_mapping = ot.da.OTDA_mapping_linear() +ot_mapping.fit(xs, xt, mu=mu, eta=eta, bias=bias, numItermax=20, verbose=True) -xst=ot_mapping.predict(xs) # use the estimated mapping -xst0=ot_mapping.interp() # use barycentric mapping +xst = ot_mapping.predict(xs) # use the estimated mapping +xst0 = ot_mapping.interp() # use barycentric mapping -pl.figure(2,(10,7)) +pl.figure(2) pl.clf() -pl.subplot(2,2,1) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.3) -pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='barycentric mapping') +pl.subplot(2, 2, 1) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.3) +pl.scatter(xst0[:, 0], xst0[:, 1], c=ys, + marker='+', label='barycentric mapping') pl.title("barycentric mapping") -pl.subplot(2,2,2) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.3) -pl.scatter(xst[:,0],xst[:,1],c=ys,marker='+',label='Learned mapping') +pl.subplot(2, 2, 2) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.3) +pl.scatter(xst[:, 0], xst[:, 1], c=ys, marker='+', label='Learned mapping') pl.title("Learned mapping") - - +pl.tight_layout() #%% Kernel mapping estimation -eta=1e-5 # quadratic regularization for regression -mu=1e-1 # weight of the OT linear term -bias=True # estimate a bias -sigma=1 # sigma bandwidth fot gaussian kernel +eta = 1e-5 # quadratic regularization for regression +mu = 1e-1 # weight of the OT linear term +bias = True # estimate a bias +sigma = 1 # sigma bandwidth fot gaussian kernel -ot_mapping_kernel=ot.da.OTDA_mapping_kernel() -ot_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 10,verbose=True) +ot_mapping_kernel = ot.da.OTDA_mapping_kernel() +ot_mapping_kernel.fit( + xs, xt, mu=mu, eta=eta, sigma=sigma, bias=bias, numItermax=10, verbose=True) -xst_kernel=ot_mapping_kernel.predict(xs) # use the estimated mapping -xst0_kernel=ot_mapping_kernel.interp() # use barycentric mapping +xst_kernel = ot_mapping_kernel.predict(xs) # use the estimated mapping +xst0_kernel = ot_mapping_kernel.interp() # use barycentric mapping #%% Plotting the mapped samples -pl.figure(2,(10,7)) +pl.figure(2) pl.clf() -pl.subplot(2,2,1) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) -pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Mapped source samples') +pl.subplot(2, 2, 1) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) +pl.scatter(xst0[:, 0], xst0[:, 1], c=ys, marker='+', + label='Mapped source samples') pl.title("Bary. mapping (linear)") pl.legend(loc=0) -pl.subplot(2,2,2) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) -pl.scatter(xst[:,0],xst[:,1],c=ys,marker='+',label='Learned mapping') +pl.subplot(2, 2, 2) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) +pl.scatter(xst[:, 0], xst[:, 1], c=ys, marker='+', label='Learned mapping') pl.title("Estim. mapping (linear)") -pl.subplot(2,2,3) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) -pl.scatter(xst0_kernel[:,0],xst0_kernel[:,1],c=ys,marker='+',label='barycentric mapping') +pl.subplot(2, 2, 3) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) +pl.scatter(xst0_kernel[:, 0], xst0_kernel[:, 1], c=ys, + marker='+', label='barycentric mapping') pl.title("Bary. mapping (kernel)") -pl.subplot(2,2,4) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) -pl.scatter(xst_kernel[:,0],xst_kernel[:,1],c=ys,marker='+',label='Learned mapping') +pl.subplot(2, 2, 4) +pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) +pl.scatter(xst_kernel[:, 0], xst_kernel[:, 1], c=ys, + marker='+', label='Learned mapping') pl.title("Estim. mapping (kernel)") +pl.tight_layout() + +pl.show() -- cgit v1.2.3 From 7ad472500dcce284231fc5968effa755802ab4ea Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Wed, 12 Jul 2017 23:09:20 +0200 Subject: more --- examples/plot_OTDA_mapping_color_images.py | 136 +++++++++++++++-------------- setup.cfg | 2 +- 2 files changed, 70 insertions(+), 68 deletions(-) diff --git a/examples/plot_OTDA_mapping_color_images.py b/examples/plot_OTDA_mapping_color_images.py index f07dc6c..b42dcdc 100644 --- a/examples/plot_OTDA_mapping_color_images.py +++ b/examples/plot_OTDA_mapping_color_images.py @@ -12,147 +12,149 @@ OT for domain adaptation with image color adaptation [6] with mapping estimation """ import numpy as np -import scipy.ndimage as spi +from scipy import ndimage import matplotlib.pylab as pl import ot #%% Loading images -I1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256 -I2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256 +I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256 +I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 #%% Plot images -pl.figure(1) - -pl.subplot(1,2,1) +pl.figure(1, figsize=(6.4, 3)) +pl.subplot(1, 2, 1) pl.imshow(I1) +pl.axis('off') pl.title('Image 1') -pl.subplot(1,2,2) +pl.subplot(1, 2, 2) pl.imshow(I2) +pl.axis('off') pl.title('Image 2') - -pl.show() +pl.tight_layout() #%% Image conversion and dataset generation def im2mat(I): """Converts and image to matrix (one pixel per line)""" - return I.reshape((I.shape[0]*I.shape[1],I.shape[2])) + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + -def mat2im(X,shape): +def mat2im(X, shape): """Converts back a matrix to an image""" return X.reshape(shape) -X1=im2mat(I1) -X2=im2mat(I2) +X1 = im2mat(I1) +X2 = im2mat(I2) # training samples -nb=1000 -idx1=np.random.randint(X1.shape[0],size=(nb,)) -idx2=np.random.randint(X2.shape[0],size=(nb,)) +nb = 1000 +idx1 = np.random.randint(X1.shape[0], size=(nb,)) +idx2 = np.random.randint(X2.shape[0], size=(nb,)) -xs=X1[idx1,:] -xt=X2[idx2,:] +xs = X1[idx1, :] +xt = X2[idx2, :] #%% Plot image distributions -pl.figure(2,(10,5)) +pl.figure(2, figsize=(6.4, 5)) -pl.subplot(1,2,1) -pl.scatter(xs[:,0],xs[:,2],c=xs) -pl.axis([0,1,0,1]) +pl.subplot(1, 2, 1) +pl.scatter(xs[:, 0], xs[:, 2], c=xs) +pl.axis([0, 1, 0, 1]) pl.xlabel('Red') pl.ylabel('Blue') pl.title('Image 1') -pl.subplot(1,2,2) -#pl.imshow(I2) -pl.scatter(xt[:,0],xt[:,2],c=xt) -pl.axis([0,1,0,1]) +pl.subplot(1, 2, 2) +pl.scatter(xt[:, 0], xt[:, 2], c=xt) +pl.axis([0, 1, 0, 1]) pl.xlabel('Red') pl.ylabel('Blue') pl.title('Image 2') - -pl.show() - - +pl.tight_layout() #%% domain adaptation between images def minmax(I): - return np.minimum(np.maximum(I,0),1) + return np.clip(I, 0, 1) + # LP problem -da_emd=ot.da.OTDA() # init class -da_emd.fit(xs,xt) # fit distributions +da_emd = ot.da.OTDA() # init class +da_emd.fit(xs, xt) # fit distributions -X1t=da_emd.predict(X1) # out of sample -I1t=minmax(mat2im(X1t,I1.shape)) +X1t = da_emd.predict(X1) # out of sample +I1t = minmax(mat2im(X1t, I1.shape)) # sinkhorn regularization -lambd=1e-1 -da_entrop=ot.da.OTDA_sinkhorn() -da_entrop.fit(xs,xt,reg=lambd) +lambd = 1e-1 +da_entrop = ot.da.OTDA_sinkhorn() +da_entrop.fit(xs, xt, reg=lambd) -X1te=da_entrop.predict(X1) -I1te=minmax(mat2im(X1te,I1.shape)) +X1te = da_entrop.predict(X1) +I1te = minmax(mat2im(X1te, I1.shape)) # linear mapping estimation -eta=1e-8 # quadratic regularization for regression -mu=1e0 # weight of the OT linear term -bias=True # estimate a bias +eta = 1e-8 # quadratic regularization for regression +mu = 1e0 # weight of the OT linear term +bias = True # estimate a bias -ot_mapping=ot.da.OTDA_mapping_linear() -ot_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True) +ot_mapping = ot.da.OTDA_mapping_linear() +ot_mapping.fit(xs, xt, mu=mu, eta=eta, bias=bias, numItermax=20, verbose=True) -X1tl=ot_mapping.predict(X1) # use the estimated mapping -I1tl=minmax(mat2im(X1tl,I1.shape)) +X1tl = ot_mapping.predict(X1) # use the estimated mapping +I1tl = minmax(mat2im(X1tl, I1.shape)) # nonlinear mapping estimation -eta=1e-2 # quadratic regularization for regression -mu=1e0 # weight of the OT linear term -bias=False # estimate a bias -sigma=1 # sigma bandwidth fot gaussian kernel - +eta = 1e-2 # quadratic regularization for regression +mu = 1e0 # weight of the OT linear term +bias = False # estimate a bias +sigma = 1 # sigma bandwidth fot gaussian kernel -ot_mapping_kernel=ot.da.OTDA_mapping_kernel() -ot_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 10,verbose=True) -X1tn=ot_mapping_kernel.predict(X1) # use the estimated mapping -I1tn=minmax(mat2im(X1tn,I1.shape)) -#%% plot images +ot_mapping_kernel = ot.da.OTDA_mapping_kernel() +ot_mapping_kernel.fit( + xs, xt, mu=mu, eta=eta, sigma=sigma, bias=bias, numItermax=10, verbose=True) +X1tn = ot_mapping_kernel.predict(X1) # use the estimated mapping +I1tn = minmax(mat2im(X1tn, I1.shape)) -pl.figure(2,(10,8)) +#%% plot images -pl.subplot(2,3,1) +pl.figure(2, figsize=(8, 4)) +pl.subplot(2, 3, 1) pl.imshow(I1) +pl.axis('off') pl.title('Im. 1') -pl.subplot(2,3,2) - +pl.subplot(2, 3, 2) pl.imshow(I2) +pl.axis('off') pl.title('Im. 2') - -pl.subplot(2,3,3) +pl.subplot(2, 3, 3) pl.imshow(I1t) +pl.axis('off') pl.title('Im. 1 Interp LP') -pl.subplot(2,3,4) +pl.subplot(2, 3, 4) pl.imshow(I1te) +pl.axis('off') pl.title('Im. 1 Interp Entrop') - -pl.subplot(2,3,5) +pl.subplot(2, 3, 5) pl.imshow(I1tl) +pl.axis('off') pl.title('Im. 1 Linear mapping') -pl.subplot(2,3,6) +pl.subplot(2, 3, 6) pl.imshow(I1tn) +pl.axis('off') pl.title('Im. 1 nonlinear mapping') +pl.tight_layout() pl.show() diff --git a/setup.cfg b/setup.cfg index d010702..b2a2415 100644 --- a/setup.cfg +++ b/setup.cfg @@ -3,4 +3,4 @@ description-file = README.md [flake8] exclude = __init__.py -ignore = E265 +ignore = E265,E501 -- cgit v1.2.3 From 6d7fd7e9faffa777cef222bdfc48c7ad732ab950 Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Wed, 12 Jul 2017 23:16:00 +0200 Subject: more --- examples/plot_WDA.py | 86 +++++++++++++++++++++++++++------------------------- requirements.txt | 2 ++ 2 files changed, 46 insertions(+), 42 deletions(-) diff --git a/examples/plot_WDA.py b/examples/plot_WDA.py index 5fa2ab1..8a44022 100644 --- a/examples/plot_WDA.py +++ b/examples/plot_WDA.py @@ -16,81 +16,83 @@ from ot.dr import wda, fda #%% parameters -n=1000 # nb samples in source and target datasets -nz=0.2 +n = 1000 # nb samples in source and target datasets +nz = 0.2 # generate circle dataset -t=np.random.rand(n)*2*np.pi -ys=np.floor((np.arange(n)*1.0/n*3))+1 -xs=np.concatenate((np.cos(t).reshape((-1,1)),np.sin(t).reshape((-1,1))),1) -xs=xs*ys.reshape(-1,1)+nz*np.random.randn(n,2) +t = np.random.rand(n) * 2 * np.pi +ys = np.floor((np.arange(n) * 1.0 / n * 3)) + 1 +xs = np.concatenate( + (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1) +xs = xs * ys.reshape(-1, 1) + nz * np.random.randn(n, 2) -t=np.random.rand(n)*2*np.pi -yt=np.floor((np.arange(n)*1.0/n*3))+1 -xt=np.concatenate((np.cos(t).reshape((-1,1)),np.sin(t).reshape((-1,1))),1) -xt=xt*yt.reshape(-1,1)+nz*np.random.randn(n,2) +t = np.random.rand(n) * 2 * np.pi +yt = np.floor((np.arange(n) * 1.0 / n * 3)) + 1 +xt = np.concatenate( + (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1) +xt = xt * yt.reshape(-1, 1) + nz * np.random.randn(n, 2) -nbnoise=8 +nbnoise = 8 -xs=np.hstack((xs,np.random.randn(n,nbnoise))) -xt=np.hstack((xt,np.random.randn(n,nbnoise))) +xs = np.hstack((xs, np.random.randn(n, nbnoise))) +xt = np.hstack((xt, np.random.randn(n, nbnoise))) #%% plot samples -pl.figure(1,(10,5)) +pl.figure(1, figsize=(6.4, 3.5)) -pl.subplot(1,2,1) -pl.scatter(xt[:,0],xt[:,1],c=ys,marker='+',label='Source samples') +pl.subplot(1, 2, 1) +pl.scatter(xt[:, 0], xt[:, 1], c=ys, marker='+', label='Source samples') pl.legend(loc=0) pl.title('Discriminant dimensions') -pl.subplot(1,2,2) -pl.scatter(xt[:,2],xt[:,3],c=ys,marker='+',label='Source samples') +pl.subplot(1, 2, 2) +pl.scatter(xt[:, 2], xt[:, 3], c=ys, marker='+', label='Source samples') pl.legend(loc=0) pl.title('Other dimensions') -pl.show() +pl.tight_layout() #%% Compute FDA -p=2 +p = 2 -Pfda,projfda = fda(xs,ys,p) +Pfda, projfda = fda(xs, ys, p) #%% Compute WDA -p=2 -reg=1e0 -k=10 -maxiter=100 +p = 2 +reg = 1e0 +k = 10 +maxiter = 100 -Pwda,projwda = wda(xs,ys,p,reg,k,maxiter=maxiter) +Pwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter) #%% plot samples -xsp=projfda(xs) -xtp=projfda(xt) +xsp = projfda(xs) +xtp = projfda(xt) -xspw=projwda(xs) -xtpw=projwda(xt) +xspw = projwda(xs) +xtpw = projwda(xt) -pl.figure(1,(10,10)) +pl.figure(2) -pl.subplot(2,2,1) -pl.scatter(xsp[:,0],xsp[:,1],c=ys,marker='+',label='Projected samples') +pl.subplot(2, 2, 1) +pl.scatter(xsp[:, 0], xsp[:, 1], c=ys, marker='+', label='Projected samples') pl.legend(loc=0) pl.title('Projected training samples FDA') - -pl.subplot(2,2,2) -pl.scatter(xtp[:,0],xtp[:,1],c=ys,marker='+',label='Projected samples') +pl.subplot(2, 2, 2) +pl.scatter(xtp[:, 0], xtp[:, 1], c=ys, marker='+', label='Projected samples') pl.legend(loc=0) pl.title('Projected test samples FDA') - -pl.subplot(2,2,3) -pl.scatter(xspw[:,0],xspw[:,1],c=ys,marker='+',label='Projected samples') +pl.subplot(2, 2, 3) +pl.scatter(xspw[:, 0], xspw[:, 1], c=ys, marker='+', label='Projected samples') pl.legend(loc=0) pl.title('Projected training samples WDA') - -pl.subplot(2,2,4) -pl.scatter(xtpw[:,0],xtpw[:,1],c=ys,marker='+',label='Projected samples') +pl.subplot(2, 2, 4) +pl.scatter(xtpw[:, 0], xtpw[:, 1], c=ys, marker='+', label='Projected samples') pl.legend(loc=0) pl.title('Projected test samples WDA') +pl.tight_layout() + +pl.show() diff --git a/requirements.txt b/requirements.txt index 319f5e1..9bfca43 100644 --- a/requirements.txt +++ b/requirements.txt @@ -3,3 +3,5 @@ scipy cython matplotlib sphinx-gallery +autograd +pymanopt -- cgit v1.2.3 From da21b9888e77f7512727a4f50c60bd475e2c9606 Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Wed, 12 Jul 2017 23:19:09 +0200 Subject: pep8 --- examples/plot_OTDA_mapping_color_images.py | 3 +++ examples/plot_WDA.py | 3 +-- examples/plot_optim_OTreg.py | 1 + 3 files changed, 5 insertions(+), 2 deletions(-) diff --git a/examples/plot_OTDA_mapping_color_images.py b/examples/plot_OTDA_mapping_color_images.py index b42dcdc..85c4b6b 100644 --- a/examples/plot_OTDA_mapping_color_images.py +++ b/examples/plot_OTDA_mapping_color_images.py @@ -36,6 +36,7 @@ pl.axis('off') pl.title('Image 2') pl.tight_layout() + #%% Image conversion and dataset generation def im2mat(I): @@ -78,7 +79,9 @@ pl.ylabel('Blue') pl.title('Image 2') pl.tight_layout() + #%% domain adaptation between images + def minmax(I): return np.clip(I, 0, 1) diff --git a/examples/plot_WDA.py b/examples/plot_WDA.py index 8a44022..9eb8693 100644 --- a/examples/plot_WDA.py +++ b/examples/plot_WDA.py @@ -9,8 +9,7 @@ Wasserstein Discriminant Analysis import numpy as np import matplotlib.pylab as pl -import ot -from ot.datasets import get_1D_gauss as gauss + from ot.dr import wda, fda diff --git a/examples/plot_optim_OTreg.py b/examples/plot_optim_OTreg.py index 1c3a1c5..e38253c 100644 --- a/examples/plot_optim_OTreg.py +++ b/examples/plot_optim_OTreg.py @@ -36,6 +36,7 @@ ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') #%% Example with Frobenius norm regularization + def f(G): return 0.5 * np.sum(G**2) -- cgit v1.2.3 From 38e696fc821d0e9bd977b69e3c38ecb3db4662ca Mon Sep 17 00:00:00 2001 From: "Dougal J. Sutherland" Date: Thu, 20 Jul 2017 13:07:23 +0100 Subject: fix conda-forge URL [skip ci] --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index bb3f9e4..c3dbf68 100644 --- a/README.md +++ b/README.md @@ -42,7 +42,7 @@ python setup.py install --user # for user install (no root) #### Anaconda installation with conda-forge -If you use the Anaconda python distribution, POT is available in [conda-forge](conda-forge.github.io). To install it and the required dependencies: +If you use the Anaconda python distribution, POT is available in [conda-forge](https://conda-forge.org). To install it and the required dependencies: ``` conda install -c conda-forge pot ``` -- cgit v1.2.3 From 37ca3142a4c8c808382f5eb1c23bf198c3e4610e Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Wed, 12 Jul 2017 23:52:57 +0200 Subject: pep8 --- ot/bregman.py | 495 +++++++++++++++++++++++++++++--------------------------- ot/da.py | 504 +++++++++++++++++++++++++++++---------------------------- ot/datasets.py | 107 ++++++------ ot/dr.py | 197 +++++++++++----------- ot/optim.py | 133 +++++++-------- 5 files changed, 735 insertions(+), 701 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 0d68602..fe10880 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -5,7 +5,8 @@ Bregman projections for regularized OT import numpy as np -def sinkhorn(a,b, M, reg,method='sinkhorn', numItermax = 1000, stopThr=1e-9, verbose=False, log=False,**kwargs): + +def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, stopThr=1e-9, verbose=False, log=False, **kwargs): u""" Solve the entropic regularization optimal transport problem and return the OT matrix @@ -92,22 +93,28 @@ def sinkhorn(a,b, M, reg,method='sinkhorn', numItermax = 1000, stopThr=1e-9, ver """ - if method.lower()=='sinkhorn': - sink= lambda: sinkhorn_knopp(a,b, M, reg,numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log,**kwargs) - elif method.lower()=='sinkhorn_stabilized': - sink= lambda: sinkhorn_stabilized(a,b, M, reg,numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log, **kwargs) - elif method.lower()=='sinkhorn_epsilon_scaling': - sink= lambda: sinkhorn_epsilon_scaling(a,b, M, reg,numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log, **kwargs) + if method.lower() == 'sinkhorn': + def sink(): + return sinkhorn_knopp(a, b, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log, **kwargs) + elif method.lower() == 'sinkhorn_stabilized': + def sink(): + return sinkhorn_stabilized(a, b, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log, **kwargs) + elif method.lower() == 'sinkhorn_epsilon_scaling': + def sink(): + return sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log, **kwargs) else: print('Warning : unknown method using classic Sinkhorn Knopp') - sink= lambda: sinkhorn_knopp(a,b, M, reg, **kwargs) + + def sink(): + return sinkhorn_knopp(a, b, M, reg, **kwargs) return sink() -def sinkhorn2(a,b, M, reg,method='sinkhorn', numItermax = 1000, stopThr=1e-9, verbose=False, log=False,**kwargs): + +def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, stopThr=1e-9, verbose=False, log=False, **kwargs): u""" Solve the entropic regularization optimal transport problem and return the loss @@ -170,7 +177,7 @@ def sinkhorn2(a,b, M, reg,method='sinkhorn', numItermax = 1000, stopThr=1e-9, ve >>> M=[[0.,1.],[1.,0.]] >>> ot.sinkhorn2(a,b,M,1) array([ 0.26894142]) - + References @@ -194,27 +201,32 @@ def sinkhorn2(a,b, M, reg,method='sinkhorn', numItermax = 1000, stopThr=1e-9, ve """ - if method.lower()=='sinkhorn': - sink= lambda: sinkhorn_knopp(a,b, M, reg,numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log,**kwargs) - elif method.lower()=='sinkhorn_stabilized': - sink= lambda: sinkhorn_stabilized(a,b, M, reg,numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log, **kwargs) - elif method.lower()=='sinkhorn_epsilon_scaling': - sink= lambda: sinkhorn_epsilon_scaling(a,b, M, reg,numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log, **kwargs) + if method.lower() == 'sinkhorn': + def sink(): + return sinkhorn_knopp(a, b, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log, **kwargs) + elif method.lower() == 'sinkhorn_stabilized': + def sink(): + return sinkhorn_stabilized(a, b, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log, **kwargs) + elif method.lower() == 'sinkhorn_epsilon_scaling': + def sink(): + return sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log, **kwargs) else: print('Warning : unknown method using classic Sinkhorn Knopp') - sink= lambda: sinkhorn_knopp(a,b, M, reg, **kwargs) - - b=np.asarray(b,dtype=np.float64) - if len(b.shape)<2: - b=b.reshape((-1,1)) + + def sink(): + return sinkhorn_knopp(a, b, M, reg, **kwargs) + + b = np.asarray(b, dtype=np.float64) + if len(b.shape) < 2: + b = b.reshape((-1, 1)) return sink() -def sinkhorn_knopp(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, log=False,**kwargs): +def sinkhorn_knopp(a, b, M, reg, numItermax=1000, stopThr=1e-9, verbose=False, log=False, **kwargs): """ Solve the entropic regularization optimal transport problem and return the OT matrix @@ -290,100 +302,101 @@ def sinkhorn_knopp(a,b, M, reg, numItermax = 1000, stopThr=1e-9, verbose=False, """ - a=np.asarray(a,dtype=np.float64) - b=np.asarray(b,dtype=np.float64) - M=np.asarray(M,dtype=np.float64) - - - if len(a)==0: - a=np.ones((M.shape[0],),dtype=np.float64)/M.shape[0] - if len(b)==0: - b=np.ones((M.shape[1],),dtype=np.float64)/M.shape[1] + a = np.asarray(a, dtype=np.float64) + b = np.asarray(b, dtype=np.float64) + M = np.asarray(M, dtype=np.float64) + if len(a) == 0: + a = np.ones((M.shape[0],), dtype=np.float64) / M.shape[0] + if len(b) == 0: + b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] # init data Nini = len(a) Nfin = len(b) - if len(b.shape)>1: - nbb=b.shape[1] + if len(b.shape) > 1: + nbb = b.shape[1] else: - nbb=0 - + nbb = 0 if log: - log={'err':[]} + log = {'err': []} - # we assume that no distances are null except those of the diagonal of distances + # we assume that no distances are null except those of the diagonal of + # distances if nbb: - u = np.ones((Nini,nbb))/Nini - v = np.ones((Nfin,nbb))/Nfin + u = np.ones((Nini, nbb)) / Nini + v = np.ones((Nfin, nbb)) / Nfin else: - u = np.ones(Nini)/Nini - v = np.ones(Nfin)/Nfin + u = np.ones(Nini) / Nini + v = np.ones(Nfin) / Nfin + # print(reg) - #print(reg) + K = np.exp(-M / reg) + # print(np.min(K)) - K = np.exp(-M/reg) - #print(np.min(K)) - - Kp = (1/a).reshape(-1, 1) * K + Kp = (1 / a).reshape(-1, 1) * K cpt = 0 - err=1 - while (err>stopThr and cpt stopThr and cpt < numItermax): uprev = u vprev = v KtransposeU = np.dot(K.T, u) v = np.divide(b, KtransposeU) - u = 1./np.dot(Kp,v) + u = 1. / np.dot(Kp, v) - if (np.any(KtransposeU==0) or - np.any(np.isnan(u)) or np.any(np.isnan(v)) or - np.any(np.isinf(u)) or np.any(np.isinf(v))): + if (np.any(KtransposeU == 0) or + np.any(np.isnan(u)) or np.any(np.isnan(v)) or + np.any(np.isinf(u)) or np.any(np.isinf(v))): # we have reached the machine precision # come back to previous solution and quit loop print('Warning: numerical errors at iteration', cpt) u = uprev v = vprev break - if cpt%10==0: - # we can speed up the process by checking for the error only all the 10th iterations + if cpt % 10 == 0: + # we can speed up the process by checking for the error only all + # the 10th iterations if nbb: - err = np.sum((u-uprev)**2)/np.sum((u)**2)+np.sum((v-vprev)**2)/np.sum((v)**2) + err = np.sum((u - uprev)**2) / np.sum((u)**2) + \ + np.sum((v - vprev)**2) / np.sum((v)**2) else: transp = u.reshape(-1, 1) * (K * v) - err = np.linalg.norm((np.sum(transp,axis=0)-b))**2 + err = np.linalg.norm((np.sum(transp, axis=0) - b))**2 if log: log['err'].append(err) if verbose: - if cpt%200 ==0: - print('{:5s}|{:12s}'.format('It.','Err')+'\n'+'-'*19) - print('{:5d}|{:8e}|'.format(cpt,err)) - cpt = cpt +1 + if cpt % 200 == 0: + print( + '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) + print('{:5d}|{:8e}|'.format(cpt, err)) + cpt = cpt + 1 if log: - log['u']=u - log['v']=v + log['u'] = u + log['v'] = v - if nbb: #return only loss - res=np.zeros((nbb)) + if nbb: # return only loss + res = np.zeros((nbb)) for i in range(nbb): - res[i]=np.sum(u[:,i].reshape((-1,1))*K*v[:,i].reshape((1,-1))*M) + res[i] = np.sum( + u[:, i].reshape((-1, 1)) * K * v[:, i].reshape((1, -1)) * M) if log: - return res,log + return res, log else: return res - else: # return OT matrix + else: # return OT matrix if log: - return u.reshape((-1,1))*K*v.reshape((1,-1)),log + return u.reshape((-1, 1)) * K * v.reshape((1, -1)), log else: - return u.reshape((-1,1))*K*v.reshape((1,-1)) + return u.reshape((-1, 1)) * K * v.reshape((1, -1)) -def sinkhorn_stabilized(a,b, M, reg, numItermax = 1000,tau=1e3, stopThr=1e-9,warmstart=None, verbose=False,print_period=20, log=False,**kwargs): +def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, warmstart=None, verbose=False, print_period=20, log=False, **kwargs): """ Solve the entropic regularization OT problem with log stabilization @@ -468,21 +481,21 @@ def sinkhorn_stabilized(a,b, M, reg, numItermax = 1000,tau=1e3, stopThr=1e-9,war """ - a=np.asarray(a,dtype=np.float64) - b=np.asarray(b,dtype=np.float64) - M=np.asarray(M,dtype=np.float64) + a = np.asarray(a, dtype=np.float64) + b = np.asarray(b, dtype=np.float64) + M = np.asarray(M, dtype=np.float64) - if len(a)==0: - a=np.ones((M.shape[0],),dtype=np.float64)/M.shape[0] - if len(b)==0: - b=np.ones((M.shape[1],),dtype=np.float64)/M.shape[1] + if len(a) == 0: + a = np.ones((M.shape[0],), dtype=np.float64) / M.shape[0] + if len(b) == 0: + b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] # test if multiple target - if len(b.shape)>1: - nbb=b.shape[1] - a=a[:,np.newaxis] + if len(b.shape) > 1: + nbb = b.shape[1] + a = a[:, np.newaxis] else: - nbb=0 + nbb = 0 # init data na = len(a) @@ -490,81 +503,80 @@ def sinkhorn_stabilized(a,b, M, reg, numItermax = 1000,tau=1e3, stopThr=1e-9,war cpt = 0 if log: - log={'err':[]} + log = {'err': []} - # we assume that no distances are null except those of the diagonal of distances + # we assume that no distances are null except those of the diagonal of + # distances if warmstart is None: - alpha,beta=np.zeros(na),np.zeros(nb) + alpha, beta = np.zeros(na), np.zeros(nb) else: - alpha,beta=warmstart + alpha, beta = warmstart if nbb: - u,v = np.ones((na,nbb))/na,np.ones((nb,nbb))/nb + u, v = np.ones((na, nbb)) / na, np.ones((nb, nbb)) / nb else: - u,v = np.ones(na)/na,np.ones(nb)/nb + u, v = np.ones(na) / na, np.ones(nb) / nb - def get_K(alpha,beta): + def get_K(alpha, beta): """log space computation""" - return np.exp(-(M-alpha.reshape((na,1))-beta.reshape((1,nb)))/reg) + return np.exp(-(M - alpha.reshape((na, 1)) - beta.reshape((1, nb))) / reg) - def get_Gamma(alpha,beta,u,v): + def get_Gamma(alpha, beta, u, v): """log space gamma computation""" - return np.exp(-(M-alpha.reshape((na,1))-beta.reshape((1,nb)))/reg+np.log(u.reshape((na,1)))+np.log(v.reshape((1,nb)))) + return np.exp(-(M - alpha.reshape((na, 1)) - beta.reshape((1, nb))) / reg + np.log(u.reshape((na, 1))) + np.log(v.reshape((1, nb)))) - #print(np.min(K)) + # print(np.min(K)) - K=get_K(alpha,beta) + K = get_K(alpha, beta) transp = K - loop=1 + loop = 1 cpt = 0 - err=1 + err = 1 while loop: - - uprev = u vprev = v # sinkhorn update - v = b/(np.dot(K.T,u)+1e-16) - u = a/(np.dot(K,v)+1e-16) - + v = b / (np.dot(K.T, u) + 1e-16) + u = a / (np.dot(K, v) + 1e-16) # remove numerical problems and store them in K - if np.abs(u).max()>tau or np.abs(v).max()>tau: + if np.abs(u).max() > tau or np.abs(v).max() > tau: if nbb: - alpha,beta=alpha+reg*np.max(np.log(u),1),beta+reg*np.max(np.log(v)) + alpha, beta = alpha + reg * \ + np.max(np.log(u), 1), beta + reg * np.max(np.log(v)) else: - alpha,beta=alpha+reg*np.log(u),beta+reg*np.log(v) + alpha, beta = alpha + reg * np.log(u), beta + reg * np.log(v) if nbb: - u,v = np.ones((na,nbb))/na,np.ones((nb,nbb))/nb + u, v = np.ones((na, nbb)) / na, np.ones((nb, nbb)) / nb else: - u,v = np.ones(na)/na,np.ones(nb)/nb - K=get_K(alpha,beta) - + u, v = np.ones(na) / na, np.ones(nb) / nb + K = get_K(alpha, beta) - if cpt%print_period==0: - # we can speed up the process by checking for the error only all the 10th iterations + if cpt % print_period == 0: + # we can speed up the process by checking for the error only all + # the 10th iterations if nbb: - err = np.sum((u-uprev)**2)/np.sum((u)**2)+np.sum((v-vprev)**2)/np.sum((v)**2) + err = np.sum((u - uprev)**2) / np.sum((u)**2) + \ + np.sum((v - vprev)**2) / np.sum((v)**2) else: - transp = get_Gamma(alpha,beta,u,v) - err = np.linalg.norm((np.sum(transp,axis=0)-b))**2 + transp = get_Gamma(alpha, beta, u, v) + err = np.linalg.norm((np.sum(transp, axis=0) - b))**2 if log: log['err'].append(err) if verbose: - if cpt%(print_period*20) ==0: - print('{:5s}|{:12s}'.format('It.','Err')+'\n'+'-'*19) - print('{:5d}|{:8e}|'.format(cpt,err)) + if cpt % (print_period * 20) == 0: + print( + '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) + print('{:5d}|{:8e}|'.format(cpt, err)) + if err <= stopThr: + loop = False - if err<=stopThr: - loop=False - - if cpt>=numItermax: - loop=False - + if cpt >= numItermax: + loop = False if np.any(np.isnan(u)) or np.any(np.isnan(v)): # we have reached the machine precision @@ -574,34 +586,34 @@ def sinkhorn_stabilized(a,b, M, reg, numItermax = 1000,tau=1e3, stopThr=1e-9,war v = vprev break - cpt = cpt +1 - + cpt = cpt + 1 #print('err=',err,' cpt=',cpt) if log: - log['logu']=alpha/reg+np.log(u) - log['logv']=beta/reg+np.log(v) - log['alpha']=alpha+reg*np.log(u) - log['beta']=beta+reg*np.log(v) - log['warmstart']=(log['alpha'],log['beta']) + log['logu'] = alpha / reg + np.log(u) + log['logv'] = beta / reg + np.log(v) + log['alpha'] = alpha + reg * np.log(u) + log['beta'] = beta + reg * np.log(v) + log['warmstart'] = (log['alpha'], log['beta']) if nbb: - res=np.zeros((nbb)) + res = np.zeros((nbb)) for i in range(nbb): - res[i]=np.sum(get_Gamma(alpha,beta,u[:,i],v[:,i])*M) - return res,log + res[i] = np.sum(get_Gamma(alpha, beta, u[:, i], v[:, i]) * M) + return res, log else: - return get_Gamma(alpha,beta,u,v),log + return get_Gamma(alpha, beta, u, v), log else: if nbb: - res=np.zeros((nbb)) + res = np.zeros((nbb)) for i in range(nbb): - res[i]=np.sum(get_Gamma(alpha,beta,u[:,i],v[:,i])*M) + res[i] = np.sum(get_Gamma(alpha, beta, u[:, i], v[:, i]) * M) return res else: - return get_Gamma(alpha,beta,u,v) + return get_Gamma(alpha, beta, u, v) + -def sinkhorn_epsilon_scaling(a,b, M, reg, numItermax = 100, epsilon0=1e4, numInnerItermax = 100,tau=1e3, stopThr=1e-9,warmstart=None, verbose=False,print_period=10, log=False,**kwargs): +def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInnerItermax=100, tau=1e3, stopThr=1e-9, warmstart=None, verbose=False, print_period=10, log=False, **kwargs): """ Solve the entropic regularization optimal transport problem with log stabilization and epsilon scaling. @@ -690,14 +702,14 @@ def sinkhorn_epsilon_scaling(a,b, M, reg, numItermax = 100, epsilon0=1e4, numInn """ - a=np.asarray(a,dtype=np.float64) - b=np.asarray(b,dtype=np.float64) - M=np.asarray(M,dtype=np.float64) + a = np.asarray(a, dtype=np.float64) + b = np.asarray(b, dtype=np.float64) + M = np.asarray(M, dtype=np.float64) - if len(a)==0: - a=np.ones((M.shape[0],),dtype=np.float64)/M.shape[0] - if len(b)==0: - b=np.ones((M.shape[1],),dtype=np.float64)/M.shape[1] + if len(a) == 0: + a = np.ones((M.shape[0],), dtype=np.float64) / M.shape[0] + if len(b) == 0: + b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] # init data na = len(a) @@ -705,88 +717,94 @@ def sinkhorn_epsilon_scaling(a,b, M, reg, numItermax = 100, epsilon0=1e4, numInn # nrelative umerical precision with 64 bits numItermin = 35 - numItermax=max(numItermin,numItermax) # ensure that last velue is exact - + numItermax = max(numItermin, numItermax) # ensure that last velue is exact cpt = 0 if log: - log={'err':[]} + log = {'err': []} - # we assume that no distances are null except those of the diagonal of distances + # we assume that no distances are null except those of the diagonal of + # distances if warmstart is None: - alpha,beta=np.zeros(na),np.zeros(nb) + alpha, beta = np.zeros(na), np.zeros(nb) else: - alpha,beta=warmstart - + alpha, beta = warmstart - def get_K(alpha,beta): + def get_K(alpha, beta): """log space computation""" - return np.exp(-(M-alpha.reshape((na,1))-beta.reshape((1,nb)))/reg) + return np.exp(-(M - alpha.reshape((na, 1)) - beta.reshape((1, nb))) / reg) - #print(np.min(K)) - def get_reg(n): # exponential decreasing - return (epsilon0-reg)*np.exp(-n)+reg + # print(np.min(K)) + def get_reg(n): # exponential decreasing + return (epsilon0 - reg) * np.exp(-n) + reg - loop=1 + loop = 1 cpt = 0 - err=1 + err = 1 while loop: - regi=get_reg(cpt) + regi = get_reg(cpt) - G,logi=sinkhorn_stabilized(a,b, M, regi, numItermax = numInnerItermax, stopThr=1e-9,warmstart=(alpha,beta), verbose=False,print_period=20,tau=tau, log=True) + G, logi = sinkhorn_stabilized(a, b, M, regi, numItermax=numInnerItermax, stopThr=1e-9, warmstart=( + alpha, beta), verbose=False, print_period=20, tau=tau, log=True) - alpha=logi['alpha'] - beta=logi['beta'] + alpha = logi['alpha'] + beta = logi['beta'] - if cpt>=numItermax: - loop=False + if cpt >= numItermax: + loop = False - if cpt%(print_period)==0: # spsion nearly converged - # we can speed up the process by checking for the error only all the 10th iterations + if cpt % (print_period) == 0: # spsion nearly converged + # we can speed up the process by checking for the error only all + # the 10th iterations transp = G - err = np.linalg.norm((np.sum(transp,axis=0)-b))**2+np.linalg.norm((np.sum(transp,axis=1)-a))**2 + err = np.linalg.norm( + (np.sum(transp, axis=0) - b))**2 + np.linalg.norm((np.sum(transp, axis=1) - a))**2 if log: log['err'].append(err) if verbose: - if cpt%(print_period*10) ==0: - print('{:5s}|{:12s}'.format('It.','Err')+'\n'+'-'*19) - print('{:5d}|{:8e}|'.format(cpt,err)) + if cpt % (print_period * 10) == 0: + print( + '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) + print('{:5d}|{:8e}|'.format(cpt, err)) - if err<=stopThr and cpt>numItermin: - loop=False + if err <= stopThr and cpt > numItermin: + loop = False - cpt = cpt +1 + cpt = cpt + 1 #print('err=',err,' cpt=',cpt) if log: - log['alpha']=alpha - log['beta']=beta - log['warmstart']=(log['alpha'],log['beta']) - return G,log + log['alpha'] = alpha + log['beta'] = beta + log['warmstart'] = (log['alpha'], log['beta']) + return G, log else: return G -def geometricBar(weights,alldistribT): +def geometricBar(weights, alldistribT): """return the weighted geometric mean of distributions""" - assert(len(weights)==alldistribT.shape[1]) - return np.exp(np.dot(np.log(alldistribT),weights.T)) + assert(len(weights) == alldistribT.shape[1]) + return np.exp(np.dot(np.log(alldistribT), weights.T)) + def geometricMean(alldistribT): """return the geometric mean of distributions""" - return np.exp(np.mean(np.log(alldistribT),axis=1)) + return np.exp(np.mean(np.log(alldistribT), axis=1)) -def projR(gamma,p): + +def projR(gamma, p): """return the KL projection on the row constrints """ - return np.multiply(gamma.T,p/np.maximum(np.sum(gamma,axis=1),1e-10)).T + return np.multiply(gamma.T, p / np.maximum(np.sum(gamma, axis=1), 1e-10)).T + -def projC(gamma,q): +def projC(gamma, q): """return the KL projection on the column constrints """ - return np.multiply(gamma,q/np.maximum(np.sum(gamma,axis=0),1e-10)) + return np.multiply(gamma, q / np.maximum(np.sum(gamma, axis=0), 1e-10)) -def barycenter(A,M,reg, weights=None, numItermax = 1000, stopThr=1e-4,verbose=False,log=False): +def barycenter(A, M, reg, weights=None, numItermax=1000, stopThr=1e-4, verbose=False, log=False): """Compute the entropic regularized wasserstein barycenter of distributions A The function solves the following optimization problem: @@ -837,49 +855,49 @@ def barycenter(A,M,reg, weights=None, numItermax = 1000, stopThr=1e-4,verbose=Fa """ - if weights is None: - weights=np.ones(A.shape[1])/A.shape[1] + weights = np.ones(A.shape[1]) / A.shape[1] else: - assert(len(weights)==A.shape[1]) + assert(len(weights) == A.shape[1]) if log: - log={'err':[]} + log = {'err': []} - #M = M/np.median(M) # suggested by G. Peyre - K = np.exp(-M/reg) + # M = M/np.median(M) # suggested by G. Peyre + K = np.exp(-M / reg) cpt = 0 - err=1 + err = 1 - UKv=np.dot(K,np.divide(A.T,np.sum(K,axis=0)).T) - u = (geometricMean(UKv)/UKv.T).T + UKv = np.dot(K, np.divide(A.T, np.sum(K, axis=0)).T) + u = (geometricMean(UKv) / UKv.T).T - while (err>stopThr and cpt stopThr and cpt < numItermax): + cpt = cpt + 1 + UKv = u * np.dot(K, np.divide(A, np.dot(K, u))) + u = (u.T * geometricBar(weights, UKv)).T / UKv - if cpt%10==1: - err=np.sum(np.std(UKv,axis=1)) + if cpt % 10 == 1: + err = np.sum(np.std(UKv, axis=1)) # log and verbose print if log: log['err'].append(err) if verbose: - if cpt%200 ==0: - print('{:5s}|{:12s}'.format('It.','Err')+'\n'+'-'*19) - print('{:5d}|{:8e}|'.format(cpt,err)) + if cpt % 200 == 0: + print( + '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) + print('{:5d}|{:8e}|'.format(cpt, err)) if log: - log['niter']=cpt - return geometricBar(weights,UKv),log + log['niter'] = cpt + return geometricBar(weights, UKv), log else: - return geometricBar(weights,UKv) + return geometricBar(weights, UKv) -def unmix(a,D,M,M0,h0,reg,reg0,alpha,numItermax = 1000, stopThr=1e-3,verbose=False,log=False): +def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000, stopThr=1e-3, verbose=False, log=False): """ Compute the unmixing of an observation with a given dictionary using Wasserstein distance @@ -943,43 +961,44 @@ def unmix(a,D,M,M0,h0,reg,reg0,alpha,numItermax = 1000, stopThr=1e-3,verbose=Fal """ #M = M/np.median(M) - K = np.exp(-M/reg) + K = np.exp(-M / reg) #M0 = M0/np.median(M0) - K0 = np.exp(-M0/reg0) + K0 = np.exp(-M0 / reg0) old = h0 - err=1 - cpt=0 + err = 1 + cpt = 0 #log = {'niter':0, 'all_err':[]} if log: - log={'err':[]} - - - while (err>stopThr and cpt stopThr and cpt < numItermax): + K = projC(K, a) + K0 = projC(K0, h0) + new = np.sum(K0, axis=1) + # we recombine the current selection from dictionnary + inv_new = np.dot(D, new) + other = np.sum(K, axis=1) + # geometric interpolation + delta = np.exp(alpha * np.log(other) + (1 - alpha) * np.log(inv_new)) + K = projR(K, delta) + K0 = np.dot(np.diag(np.dot(D.T, delta / inv_new)), K0) + + err = np.linalg.norm(np.sum(K0, axis=1) - old) old = new if log: log['err'].append(err) if verbose: - if cpt%200 ==0: - print('{:5s}|{:12s}'.format('It.','Err')+'\n'+'-'*19) - print('{:5d}|{:8e}|'.format(cpt,err)) + if cpt % 200 == 0: + print('{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) + print('{:5d}|{:8e}|'.format(cpt, err)) - cpt = cpt+1 + cpt = cpt + 1 if log: - log['niter']=cpt - return np.sum(K0,axis=1),log + log['niter'] = cpt + return np.sum(K0, axis=1), log else: - return np.sum(K0,axis=1) + return np.sum(K0, axis=1) diff --git a/ot/da.py b/ot/da.py index ddf1c60..5039fbd 100644 --- a/ot/da.py +++ b/ot/da.py @@ -6,12 +6,12 @@ Domain adaptation with optimal transport import numpy as np from .bregman import sinkhorn from .lp import emd -from .utils import unif,dist,kernel +from .utils import unif, dist, kernel from .optim import cg from .optim import gcg -def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerItermax = 200,stopInnerThr=1e-9,verbose=False,log=False): +def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerItermax=200, stopInnerThr=1e-9, verbose=False, log=False): """ Solve the entropic regularization optimal transport problem with nonconvex group lasso regularization @@ -94,7 +94,7 @@ def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerIter W = np.zeros(M.shape) for cpt in range(numItermax): - Mreg = M + eta*W + Mreg = M + eta * W transp = sinkhorn(a, b, Mreg, reg, numItermax=numInnerItermax, stopThr=stopInnerThr) # the transport has been computed. Check if classes are really @@ -102,12 +102,13 @@ def sinkhorn_lpl1_mm(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerIter W = np.ones(M.shape) for (i, c) in enumerate(classes): majs = np.sum(transp[indices_labels[i]], axis=0) - majs = p*((majs+epsilon)**(p-1)) + majs = p * ((majs + epsilon)**(p - 1)) W[indices_labels[i]] = majs return transp -def sinkhorn_l1l2_gl(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerItermax = 200,stopInnerThr=1e-9,verbose=False,log=False): + +def sinkhorn_l1l2_gl(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerItermax=200, stopInnerThr=1e-9, verbose=False, log=False): """ Solve the entropic regularization optimal transport problem with group lasso regularization @@ -176,32 +177,30 @@ def sinkhorn_l1l2_gl(a,labels_a, b, M, reg, eta=0.1,numItermax = 10,numInnerIter ot.optim.gcg : Generalized conditional gradient for OT problems """ - lstlab=np.unique(labels_a) + lstlab = np.unique(labels_a) def f(G): - res=0 + res = 0 for i in range(G.shape[1]): for lab in lstlab: - temp=G[labels_a==lab,i] - res+=np.linalg.norm(temp) + temp = G[labels_a == lab, i] + res += np.linalg.norm(temp) return res def df(G): - W=np.zeros(G.shape) + W = np.zeros(G.shape) for i in range(G.shape[1]): for lab in lstlab: - temp=G[labels_a==lab,i] - n=np.linalg.norm(temp) + temp = G[labels_a == lab, i] + n = np.linalg.norm(temp) if n: - W[labels_a==lab,i]=temp/n + W[labels_a == lab, i] = temp / n return W - - return gcg(a,b,M,reg,eta,f,df,G0=None,numItermax = numItermax,numInnerItermax=numInnerItermax, stopThr=stopInnerThr,verbose=verbose,log=log) - + return gcg(a, b, M, reg, eta, f, df, G0=None, numItermax=numItermax, numInnerItermax=numInnerItermax, stopThr=stopInnerThr, verbose=verbose, log=log) -def joint_OT_mapping_linear(xs,xt,mu=1,eta=0.001,bias=False,verbose=False,verbose2=False,numItermax = 100,numInnerItermax = 10,stopInnerThr=1e-6,stopThr=1e-5,log=False,**kwargs): +def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False, verbose2=False, numItermax=100, numInnerItermax=10, stopInnerThr=1e-6, stopThr=1e-5, log=False, **kwargs): """Joint OT and linear mapping estimation as proposed in [8] The function solves the following optimization problem: @@ -281,97 +280,105 @@ def joint_OT_mapping_linear(xs,xt,mu=1,eta=0.001,bias=False,verbose=False,verbos """ - ns,nt,d=xs.shape[0],xt.shape[0],xt.shape[1] + ns, nt, d = xs.shape[0], xt.shape[0], xt.shape[1] if bias: - xs1=np.hstack((xs,np.ones((ns,1)))) - xstxs=xs1.T.dot(xs1) - I=np.eye(d+1) - I[-1]=0 - I0=I[:,:-1] - sel=lambda x : x[:-1,:] + xs1 = np.hstack((xs, np.ones((ns, 1)))) + xstxs = xs1.T.dot(xs1) + I = np.eye(d + 1) + I[-1] = 0 + I0 = I[:, :-1] + + def sel(x): + return x[:-1, :] else: - xs1=xs - xstxs=xs1.T.dot(xs1) - I=np.eye(d) - I0=I - sel=lambda x : x + xs1 = xs + xstxs = xs1.T.dot(xs1) + I = np.eye(d) + I0 = I + + def sel(x): + return x if log: - log={'err':[]} + log = {'err': []} - a,b=unif(ns),unif(nt) - M=dist(xs,xt)*ns - G=emd(a,b,M) + a, b = unif(ns), unif(nt) + M = dist(xs, xt) * ns + G = emd(a, b, M) - vloss=[] + vloss = [] - def loss(L,G): + def loss(L, G): """Compute full loss""" - return np.sum((xs1.dot(L)-ns*G.dot(xt))**2)+mu*np.sum(G*M)+eta*np.sum(sel(L-I0)**2) + return np.sum((xs1.dot(L) - ns * G.dot(xt))**2) + mu * np.sum(G * M) + eta * np.sum(sel(L - I0)**2) def solve_L(G): """ solve L problem with fixed G (least square)""" - xst=ns*G.dot(xt) - return np.linalg.solve(xstxs+eta*I,xs1.T.dot(xst)+eta*I0) + xst = ns * G.dot(xt) + return np.linalg.solve(xstxs + eta * I, xs1.T.dot(xst) + eta * I0) - def solve_G(L,G0): + def solve_G(L, G0): """Update G with CG algorithm""" - xsi=xs1.dot(L) + xsi = xs1.dot(L) + def f(G): - return np.sum((xsi-ns*G.dot(xt))**2) + return np.sum((xsi - ns * G.dot(xt))**2) + def df(G): - return -2*ns*(xsi-ns*G.dot(xt)).dot(xt.T) - G=cg(a,b,M,1.0/mu,f,df,G0=G0,numItermax=numInnerItermax,stopThr=stopInnerThr) + return -2 * ns * (xsi - ns * G.dot(xt)).dot(xt.T) + G = cg(a, b, M, 1.0 / mu, f, df, G0=G0, + numItermax=numInnerItermax, stopThr=stopInnerThr) return G + L = solve_L(G) - L=solve_L(G) - - vloss.append(loss(L,G)) + vloss.append(loss(L, G)) if verbose: - print('{:5s}|{:12s}|{:8s}'.format('It.','Loss','Delta loss')+'\n'+'-'*32) - print('{:5d}|{:8e}|{:8e}'.format(0,vloss[-1],0)) - + print('{:5s}|{:12s}|{:8s}'.format( + 'It.', 'Loss', 'Delta loss') + '\n' + '-' * 32) + print('{:5d}|{:8e}|{:8e}'.format(0, vloss[-1], 0)) # init loop - if numItermax>0: - loop=1 + if numItermax > 0: + loop = 1 else: - loop=0 - it=0 + loop = 0 + it = 0 while loop: - it+=1 + it += 1 # update G - G=solve_G(L,G) + G = solve_G(L, G) - #update L - L=solve_L(G) + # update L + L = solve_L(G) - vloss.append(loss(L,G)) + vloss.append(loss(L, G)) - if it>=numItermax: - loop=0 + if it >= numItermax: + loop = 0 - if abs(vloss[-1]-vloss[-2])/abs(vloss[-2])0: - loop=1 + if numItermax > 0: + loop = 1 else: - loop=0 - it=0 + loop = 0 + it = 0 while loop: - it+=1 + it += 1 # update G - G=solve_G(L,G) + G = solve_G(L, G) - #update L - L=solve_L(G) + # update L + L = solve_L(G) - vloss.append(loss(L,G)) + vloss.append(loss(L, G)) - if it>=numItermax: - loop=0 + if it >= numItermax: + loop = 0 - if abs(vloss[-1]-vloss[-2])/abs(vloss[-2])0: # >0 then source to target - G=self.G - w=self.ws.reshape((self.xs.shape[0],1)) - x=self.xt + if direction > 0: # >0 then source to target + G = self.G + w = self.ws.reshape((self.xs.shape[0], 1)) + x = self.xt else: - G=self.G.T - w=self.wt.reshape((self.xt.shape[0],1)) - x=self.xs + G = self.G.T + w = self.wt.reshape((self.xt.shape[0], 1)) + x = self.xs if self.computed: - if self.metric=='sqeuclidean': - return np.dot(G/w,x) # weighted mean + if self.metric == 'sqeuclidean': + return np.dot(G / w, x) # weighted mean else: - print("Warning, metric not handled yet, using weighted average") - return np.dot(G/w,x) # weighted mean + print( + "Warning, metric not handled yet, using weighted average") + return np.dot(G / w, x) # weighted mean return None else: print("Warning, model not fitted yet, returning None") return None - - def predict(self,x,direction=1): + def predict(self, x, direction=1): """ Out of sample mapping using the formulation from [6] For each sample x to map, it finds the nearest source sample xs and @@ -657,29 +666,30 @@ class OTDA(object): .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. """ - if direction>0: # >0 then source to target - xf=self.xt - x0=self.xs + if direction > 0: # >0 then source to target + xf = self.xt + x0 = self.xs else: - xf=self.xs - x0=self.xt + xf = self.xs + x0 = self.xt - D0=dist(x,x0) # dist netween new samples an source - idx=np.argmin(D0,1) # closest one - xf=self.interp(direction)# interp the source samples - return xf[idx,:]+x-x0[idx,:] # aply the delta to the interpolation + D0 = dist(x, x0) # dist netween new samples an source + idx = np.argmin(D0, 1) # closest one + xf = self.interp(direction) # interp the source samples + # aply the delta to the interpolation + return xf[idx, :] + x - x0[idx, :] def normalizeM(self, norm): """ Apply normalization to the loss matrix - - + + Parameters ---------- norm : str type of normalization from 'median','max','log','loglog' - + """ - + if norm == "median": self.M /= float(np.median(self.M)) elif norm == "max": @@ -688,149 +698,149 @@ class OTDA(object): self.M = np.log(1 + self.M) elif norm == "loglog": self.M = np.log(1 + np.log(1 + self.M)) - class OTDA_sinkhorn(OTDA): + """Class for domain adaptation with optimal transport with entropic regularization""" - def fit(self,xs,xt,reg=1,ws=None,wt=None,norm=None,**kwargs): + def fit(self, xs, xt, reg=1, ws=None, wt=None, norm=None, **kwargs): """ Fit regularized domain adaptation between samples is xs and xt (with optional weights)""" - self.xs=xs - self.xt=xt + self.xs = xs + self.xt = xt if wt is None: - wt=unif(xt.shape[0]) + wt = unif(xt.shape[0]) if ws is None: - ws=unif(xs.shape[0]) + ws = unif(xs.shape[0]) - self.ws=ws - self.wt=wt + self.ws = ws + self.wt = wt - self.M=dist(xs,xt,metric=self.metric) + self.M = dist(xs, xt, metric=self.metric) self.normalizeM(norm) - self.G=sinkhorn(ws,wt,self.M,reg,**kwargs) - self.computed=True + self.G = sinkhorn(ws, wt, self.M, reg, **kwargs) + self.computed = True class OTDA_lpl1(OTDA): - """Class for domain adaptation with optimal transport with entropic and group regularization""" + """Class for domain adaptation with optimal transport with entropic and group regularization""" - def fit(self,xs,ys,xt,reg=1,eta=1,ws=None,wt=None,norm=None,**kwargs): + def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None, **kwargs): """ Fit regularized domain adaptation between samples is xs and xt (with optional weights), See ot.da.sinkhorn_lpl1_mm for fit parameters""" - self.xs=xs - self.xt=xt + self.xs = xs + self.xt = xt if wt is None: - wt=unif(xt.shape[0]) + wt = unif(xt.shape[0]) if ws is None: - ws=unif(xs.shape[0]) + ws = unif(xs.shape[0]) - self.ws=ws - self.wt=wt + self.ws = ws + self.wt = wt - self.M=dist(xs,xt,metric=self.metric) + self.M = dist(xs, xt, metric=self.metric) self.normalizeM(norm) - self.G=sinkhorn_lpl1_mm(ws,ys,wt,self.M,reg,eta,**kwargs) - self.computed=True + self.G = sinkhorn_lpl1_mm(ws, ys, wt, self.M, reg, eta, **kwargs) + self.computed = True + class OTDA_l1l2(OTDA): - """Class for domain adaptation with optimal transport with entropic and group lasso regularization""" + """Class for domain adaptation with optimal transport with entropic and group lasso regularization""" - def fit(self,xs,ys,xt,reg=1,eta=1,ws=None,wt=None,norm=None,**kwargs): + def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None, **kwargs): """ Fit regularized domain adaptation between samples is xs and xt (with optional weights), See ot.da.sinkhorn_lpl1_gl for fit parameters""" - self.xs=xs - self.xt=xt + self.xs = xs + self.xt = xt if wt is None: - wt=unif(xt.shape[0]) + wt = unif(xt.shape[0]) if ws is None: - ws=unif(xs.shape[0]) + ws = unif(xs.shape[0]) - self.ws=ws - self.wt=wt + self.ws = ws + self.wt = wt - self.M=dist(xs,xt,metric=self.metric) + self.M = dist(xs, xt, metric=self.metric) self.normalizeM(norm) - self.G=sinkhorn_l1l2_gl(ws,ys,wt,self.M,reg,eta,**kwargs) - self.computed=True + self.G = sinkhorn_l1l2_gl(ws, ys, wt, self.M, reg, eta, **kwargs) + self.computed = True + class OTDA_mapping_linear(OTDA): - """Class for optimal transport with joint linear mapping estimation as in [8]""" + """Class for optimal transport with joint linear mapping estimation as in [8]""" def __init__(self): """ Class initialization""" + self.xs = 0 + self.xt = 0 + self.G = 0 + self.L = 0 + self.bias = False + self.computed = False + self.metric = 'sqeuclidean' - self.xs=0 - self.xt=0 - self.G=0 - self.L=0 - self.bias=False - self.computed=False - self.metric='sqeuclidean' - - def fit(self,xs,xt,mu=1,eta=1,bias=False,**kwargs): + def fit(self, xs, xt, mu=1, eta=1, bias=False, **kwargs): """ Fit domain adaptation between samples is xs and xt (with optional weights)""" - self.xs=xs - self.xt=xt - self.bias=bias - + self.xs = xs + self.xt = xt + self.bias = bias - self.ws=unif(xs.shape[0]) - self.wt=unif(xt.shape[0]) + self.ws = unif(xs.shape[0]) + self.wt = unif(xt.shape[0]) - self.G,self.L=joint_OT_mapping_linear(xs,xt,mu=mu,eta=eta,bias=bias,**kwargs) - self.computed=True + self.G, self.L = joint_OT_mapping_linear( + xs, xt, mu=mu, eta=eta, bias=bias, **kwargs) + self.computed = True def mapping(self): return lambda x: self.predict(x) - - def predict(self,x): + def predict(self, x): """ Out of sample mapping estimated during the call to fit""" if self.computed: if self.bias: - x=np.hstack((x,np.ones((x.shape[0],1)))) - return x.dot(self.L) # aply the delta to the interpolation + x = np.hstack((x, np.ones((x.shape[0], 1)))) + return x.dot(self.L) # aply the delta to the interpolation else: print("Warning, model not fitted yet, returning None") return None -class OTDA_mapping_kernel(OTDA_mapping_linear): - """Class for optimal transport with joint nonlinear mapping estimation as in [8]""" +class OTDA_mapping_kernel(OTDA_mapping_linear): + """Class for optimal transport with joint nonlinear mapping estimation as in [8]""" - def fit(self,xs,xt,mu=1,eta=1,bias=False,kerneltype='gaussian',sigma=1,**kwargs): + def fit(self, xs, xt, mu=1, eta=1, bias=False, kerneltype='gaussian', sigma=1, **kwargs): """ Fit domain adaptation between samples is xs and xt """ - self.xs=xs - self.xt=xt - self.bias=bias - - self.ws=unif(xs.shape[0]) - self.wt=unif(xt.shape[0]) - self.kernel=kerneltype - self.sigma=sigma - self.kwargs=kwargs + self.xs = xs + self.xt = xt + self.bias = bias + self.ws = unif(xs.shape[0]) + self.wt = unif(xt.shape[0]) + self.kernel = kerneltype + self.sigma = sigma + self.kwargs = kwargs - self.G,self.L=joint_OT_mapping_kernel(xs,xt,mu=mu,eta=eta,bias=bias,**kwargs) - self.computed=True + self.G, self.L = joint_OT_mapping_kernel( + xs, xt, mu=mu, eta=eta, bias=bias, **kwargs) + self.computed = True - - def predict(self,x): + def predict(self, x): """ Out of sample mapping estimated during the call to fit""" if self.computed: - K=kernel(x,self.xs,method=self.kernel,sigma=self.sigma,**self.kwargs) + K = kernel( + x, self.xs, method=self.kernel, sigma=self.sigma, **self.kwargs) if self.bias: - K=np.hstack((K,np.ones((x.shape[0],1)))) + K = np.hstack((K, np.ones((x.shape[0], 1)))) return K.dot(self.L) else: print("Warning, model not fitted yet, returning None") - return None \ No newline at end of file + return None diff --git a/ot/datasets.py b/ot/datasets.py index 7816833..4371a23 100644 --- a/ot/datasets.py +++ b/ot/datasets.py @@ -7,7 +7,7 @@ import numpy as np import scipy as sp -def get_1D_gauss(n,m,s): +def get_1D_gauss(n, m, s): """return a 1D histogram for a gaussian distribution (n bins, mean m and std s) Parameters @@ -27,12 +27,12 @@ def get_1D_gauss(n,m,s): 1D histogram for a gaussian distribution """ - x=np.arange(n,dtype=np.float64) - h=np.exp(-(x-m)**2/(2*s**2)) - return h/h.sum() + x = np.arange(n, dtype=np.float64) + h = np.exp(-(x - m)**2 / (2 * s**2)) + return h / h.sum() -def get_2D_samples_gauss(n,m,sigma): +def get_2D_samples_gauss(n, m, sigma): """return n samples drawn from 2D gaussian N(m,sigma) Parameters @@ -52,17 +52,17 @@ def get_2D_samples_gauss(n,m,sigma): n samples drawn from N(m,sigma) """ - if np.isscalar(sigma): - sigma=np.array([sigma,]) - if len(sigma)>1: - P=sp.linalg.sqrtm(sigma) - res= np.random.randn(n,2).dot(P)+m + if np.isscalar(sigma): + sigma = np.array([sigma, ]) + if len(sigma) > 1: + P = sp.linalg.sqrtm(sigma) + res = np.random.randn(n, 2).dot(P) + m else: - res= np.random.randn(n,2)*np.sqrt(sigma)+m + res = np.random.randn(n, 2) * np.sqrt(sigma) + m return res -def get_data_classif(dataset,n,nz=.5,theta=0,**kwargs): +def get_data_classif(dataset, n, nz=.5, theta=0, **kwargs): """ dataset generation for classification problems Parameters @@ -84,48 +84,53 @@ def get_data_classif(dataset,n,nz=.5,theta=0,**kwargs): labels of the samples """ - if dataset.lower()=='3gauss': - y=np.floor((np.arange(n)*1.0/n*3))+1 - x=np.zeros((n,2)) + if dataset.lower() == '3gauss': + y = np.floor((np.arange(n) * 1.0 / n * 3)) + 1 + x = np.zeros((n, 2)) # class 1 - x[y==1,0]=-1.; x[y==1,1]=-1. - x[y==2,0]=-1.; x[y==2,1]=1. - x[y==3,0]=1. ; x[y==3,1]=0 - - x[y!=3,:]+=1.5*nz*np.random.randn(sum(y!=3),2) - x[y==3,:]+=2*nz*np.random.randn(sum(y==3),2) - - elif dataset.lower()=='3gauss2': - y=np.floor((np.arange(n)*1.0/n*3))+1 - x=np.zeros((n,2)) - y[y==4]=3 + x[y == 1, 0] = -1. + x[y == 1, 1] = -1. + x[y == 2, 0] = -1. + x[y == 2, 1] = 1. + x[y == 3, 0] = 1. + x[y == 3, 1] = 0 + + x[y != 3, :] += 1.5 * nz * np.random.randn(sum(y != 3), 2) + x[y == 3, :] += 2 * nz * np.random.randn(sum(y == 3), 2) + + elif dataset.lower() == '3gauss2': + y = np.floor((np.arange(n) * 1.0 / n * 3)) + 1 + x = np.zeros((n, 2)) + y[y == 4] = 3 # class 1 - x[y==1,0]=-2.; x[y==1,1]=-2. - x[y==2,0]=-2.; x[y==2,1]=2. - x[y==3,0]=2. ; x[y==3,1]=0 - - x[y!=3,:]+=nz*np.random.randn(sum(y!=3),2) - x[y==3,:]+=2*nz*np.random.randn(sum(y==3),2) - - elif dataset.lower()=='gaussrot' : - rot=np.array([[np.cos(theta),np.sin(theta)],[-np.sin(theta),np.cos(theta)]]) - m1=np.array([-1,1]) - m2=np.array([1,-1]) - y=np.floor((np.arange(n)*1.0/n*2))+1 - n1=np.sum(y==1) - n2=np.sum(y==2) - x=np.zeros((n,2)) - - x[y==1,:]=get_2D_samples_gauss(n1,m1,nz) - x[y==2,:]=get_2D_samples_gauss(n2,m2,nz) - - x=x.dot(rot) - - + x[y == 1, 0] = -2. + x[y == 1, 1] = -2. + x[y == 2, 0] = -2. + x[y == 2, 1] = 2. + x[y == 3, 0] = 2. + x[y == 3, 1] = 0 + + x[y != 3, :] += nz * np.random.randn(sum(y != 3), 2) + x[y == 3, :] += 2 * nz * np.random.randn(sum(y == 3), 2) + + elif dataset.lower() == 'gaussrot': + rot = np.array( + [[np.cos(theta), np.sin(theta)], [-np.sin(theta), np.cos(theta)]]) + m1 = np.array([-1, 1]) + m2 = np.array([1, -1]) + y = np.floor((np.arange(n) * 1.0 / n * 2)) + 1 + n1 = np.sum(y == 1) + n2 = np.sum(y == 2) + x = np.zeros((n, 2)) + + x[y == 1, :] = get_2D_samples_gauss(n1, m1, nz) + x[y == 2, :] = get_2D_samples_gauss(n2, m2, nz) + + x = x.dot(rot) else: - x=np.array(0) - y=np.array(0) + x = np.array(0) + y = np.array(0) print("unknown dataset") - return x,y.astype(int) \ No newline at end of file + return x, y.astype(int) diff --git a/ot/dr.py b/ot/dr.py index 763ce35..77cbae2 100644 --- a/ot/dr.py +++ b/ot/dr.py @@ -3,43 +3,46 @@ Dimension reduction with optimal transport """ +from scipy import linalg import autograd.numpy as np from pymanopt.manifolds import Stiefel from pymanopt import Problem from pymanopt.solvers import SteepestDescent, TrustRegions -import scipy.linalg as la -def dist(x1,x2): + +def dist(x1, x2): """ Compute squared euclidean distance between samples (autograd) """ - x1p2=np.sum(np.square(x1),1) - x2p2=np.sum(np.square(x2),1) - return x1p2.reshape((-1,1))+x2p2.reshape((1,-1))-2*np.dot(x1,x2.T) + x1p2 = np.sum(np.square(x1), 1) + x2p2 = np.sum(np.square(x2), 1) + return x1p2.reshape((-1, 1)) + x2p2.reshape((1, -1)) - 2 * np.dot(x1, x2.T) + -def sinkhorn(w1,w2,M,reg,k): +def sinkhorn(w1, w2, M, reg, k): """Sinkhorn algorithm with fixed number of iteration (autograd) """ - K=np.exp(-M/reg) - ui=np.ones((M.shape[0],)) - vi=np.ones((M.shape[1],)) + K = np.exp(-M / reg) + ui = np.ones((M.shape[0],)) + vi = np.ones((M.shape[1],)) for i in range(k): - vi=w2/(np.dot(K.T,ui)) - ui=w1/(np.dot(K,vi)) - G=ui.reshape((M.shape[0],1))*K*vi.reshape((1,M.shape[1])) + vi = w2 / (np.dot(K.T, ui)) + ui = w1 / (np.dot(K, vi)) + G = ui.reshape((M.shape[0], 1)) * K * vi.reshape((1, M.shape[1])) return G -def split_classes(X,y): + +def split_classes(X, y): """split samples in X by classes in y """ - lstsclass=np.unique(y) - return [X[y==i,:].astype(np.float32) for i in lstsclass] + lstsclass = np.unique(y) + return [X[y == i, :].astype(np.float32) for i in lstsclass] + + +def fda(X, y, p=2, reg=1e-16): + """ + Fisher Discriminant Analysis -def fda(X,y,p=2,reg=1e-16): - """ - Fisher Discriminant Analysis - - Parameters ---------- X : numpy.ndarray (n,d) @@ -59,62 +62,62 @@ def fda(X,y,p=2,reg=1e-16): proj : fun projection function including mean centering - - """ - - mx=np.mean(X) - X-=mx.reshape((1,-1)) - + + """ + + mx = np.mean(X) + X -= mx.reshape((1, -1)) + # data split between classes - d=X.shape[1] - xc=split_classes(X,y) - nc=len(xc) - - p=min(nc-1,p) - - Cw=0 + d = X.shape[1] + xc = split_classes(X, y) + nc = len(xc) + + p = min(nc - 1, p) + + Cw = 0 for x in xc: - Cw+=np.cov(x,rowvar=False) - Cw/=nc - - mxc=np.zeros((d,nc)) - + Cw += np.cov(x, rowvar=False) + Cw /= nc + + mxc = np.zeros((d, nc)) + for i in range(nc): - mxc[:,i]=np.mean(xc[i]) - - mx0=np.mean(mxc,1) - Cb=0 + mxc[:, i] = np.mean(xc[i]) + + mx0 = np.mean(mxc, 1) + Cb = 0 for i in range(nc): - Cb+=(mxc[:,i]-mx0).reshape((-1,1))*(mxc[:,i]-mx0).reshape((1,-1)) - - w,V=la.eig(Cb,Cw+reg*np.eye(d)) - - idx=np.argsort(w.real) - - Popt=V[:,idx[-p:]] - - - + Cb += (mxc[:, i] - mx0).reshape((-1, 1)) * \ + (mxc[:, i] - mx0).reshape((1, -1)) + + w, V = linalg.eig(Cb, Cw + reg * np.eye(d)) + + idx = np.argsort(w.real) + + Popt = V[:, idx[-p:]] + def proj(X): - return (X-mx.reshape((1,-1))).dot(Popt) - + return (X - mx.reshape((1, -1))).dot(Popt) + return Popt, proj -def wda(X,y,p=2,reg=1,k=10,solver = None,maxiter=100,verbose=0,P0=None): - """ + +def wda(X, y, p=2, reg=1, k=10, solver=None, maxiter=100, verbose=0, P0=None): + """ Wasserstein Discriminant Analysis [11]_ - + The function solves the following optimization problem: .. math:: P = \\text{arg}\min_P \\frac{\\sum_i W(PX^i,PX^i)}{\\sum_{i,j\\neq i} W(PX^i,PX^j)} where : - + - :math:`P` is a linear projection operator in the Stiefel(p,d) manifold - :math:`W` is entropic regularized Wasserstein distances - - :math:`X^i` are samples in the dataset corresponding to class i - + - :math:`X^i` are samples in the dataset corresponding to class i + Parameters ---------- X : numpy.ndarray (n,d) @@ -147,54 +150,50 @@ def wda(X,y,p=2,reg=1,k=10,solver = None,maxiter=100,verbose=0,P0=None): ---------- .. [11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). Wasserstein Discriminant Analysis. arXiv preprint arXiv:1608.08063. - - """ - - mx=np.mean(X) - X-=mx.reshape((1,-1)) - + + """ # noqa + + mx = np.mean(X) + X -= mx.reshape((1, -1)) + # data split between classes - d=X.shape[1] - xc=split_classes(X,y) + d = X.shape[1] + xc = split_classes(X, y) # compute uniform weighs - wc=[np.ones((x.shape[0]),dtype=np.float32)/x.shape[0] for x in xc] - + wc = [np.ones((x.shape[0]), dtype=np.float32) / x.shape[0] for x in xc] + def cost(P): # wda loss - loss_b=0 - loss_w=0 - - for i,xi in enumerate(xc): - xi=np.dot(xi,P) - for j,xj in enumerate(xc[i:]): - xj=np.dot(xj,P) - M=dist(xi,xj) - G=sinkhorn(wc[i],wc[j+i],M,reg,k) - if j==0: - loss_w+=np.sum(G*M) + loss_b = 0 + loss_w = 0 + + for i, xi in enumerate(xc): + xi = np.dot(xi, P) + for j, xj in enumerate(xc[i:]): + xj = np.dot(xj, P) + M = dist(xi, xj) + G = sinkhorn(wc[i], wc[j + i], M, reg, k) + if j == 0: + loss_w += np.sum(G * M) else: - loss_b+=np.sum(G*M) - - # loss inversed because minimization - return loss_w/loss_b - - + loss_b += np.sum(G * M) + + # loss inversed because minimization + return loss_w / loss_b + # declare manifold and problem - manifold = Stiefel(d, p) + manifold = Stiefel(d, p) problem = Problem(manifold=manifold, cost=cost) - + # declare solver and solve if solver is None: - solver= SteepestDescent(maxiter=maxiter,logverbosity=verbose) - elif solver in ['tr','TrustRegions']: - solver= TrustRegions(maxiter=maxiter,logverbosity=verbose) - - Popt = solver.solve(problem,x=P0) - - def proj(X): - return (X-mx.reshape((1,-1))).dot(Popt) - - return Popt, proj + solver = SteepestDescent(maxiter=maxiter, logverbosity=verbose) + elif solver in ['tr', 'TrustRegions']: + solver = TrustRegions(maxiter=maxiter, logverbosity=verbose) + Popt = solver.solve(problem, x=P0) + def proj(X): + return (X - mx.reshape((1, -1))).dot(Popt) + return Popt, proj diff --git a/ot/optim.py b/ot/optim.py index 79f4f66..adad95e 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -9,7 +9,9 @@ from .lp import emd from .bregman import sinkhorn # The corresponding scipy function does not work for matrices -def line_search_armijo(f,xk,pk,gfk,old_fval,args=(),c1=1e-4,alpha0=0.99): + + +def line_search_armijo(f, xk, pk, gfk, old_fval, args=(), c1=1e-4, alpha0=0.99): """ Armijo linesearch function that works with matrices @@ -51,20 +53,21 @@ def line_search_armijo(f,xk,pk,gfk,old_fval,args=(),c1=1e-4,alpha0=0.99): def phi(alpha1): fc[0] += 1 - return f(xk + alpha1*pk, *args) + return f(xk + alpha1 * pk, *args) if old_fval is None: phi0 = phi(0.) else: phi0 = old_fval - derphi0 = np.sum(pk*gfk) # Quickfix for matrices - alpha,phi1 = scalar_search_armijo(phi,phi0,derphi0,c1=c1,alpha0=alpha0) + derphi0 = np.sum(pk * gfk) # Quickfix for matrices + alpha, phi1 = scalar_search_armijo( + phi, phi0, derphi0, c1=c1, alpha0=alpha0) - return alpha,fc[0],phi1 + return alpha, fc[0], phi1 -def cg(a,b,M,reg,f,df,G0=None,numItermax = 200,stopThr=1e-9,verbose=False,log=False): +def cg(a, b, M, reg, f, df, G0=None, numItermax=200, stopThr=1e-9, verbose=False, log=False): """ Solve the general regularized OT problem with conditional gradient @@ -128,74 +131,74 @@ def cg(a,b,M,reg,f,df,G0=None,numItermax = 200,stopThr=1e-9,verbose=False,log=Fa """ - loop=1 + loop = 1 if log: - log={'loss':[]} + log = {'loss': []} if G0 is None: - G=np.outer(a,b) + G = np.outer(a, b) else: - G=G0 + G = G0 def cost(G): - return np.sum(M*G)+reg*f(G) + return np.sum(M * G) + reg * f(G) - f_val=cost(G) + f_val = cost(G) if log: log['loss'].append(f_val) - it=0 + it = 0 if verbose: - print('{:5s}|{:12s}|{:8s}'.format('It.','Loss','Delta loss')+'\n'+'-'*32) - print('{:5d}|{:8e}|{:8e}'.format(it,f_val,0)) + print('{:5s}|{:12s}|{:8s}'.format( + 'It.', 'Loss', 'Delta loss') + '\n' + '-' * 32) + print('{:5d}|{:8e}|{:8e}'.format(it, f_val, 0)) while loop: - it+=1 - old_fval=f_val - + it += 1 + old_fval = f_val # problem linearization - Mi=M+reg*df(G) + Mi = M + reg * df(G) # set M positive - Mi+=Mi.min() + Mi += Mi.min() # solve linear program - Gc=emd(a,b,Mi) + Gc = emd(a, b, Mi) - deltaG=Gc-G + deltaG = Gc - G # line search - alpha,fc,f_val = line_search_armijo(cost,G,deltaG,Mi,f_val) + alpha, fc, f_val = line_search_armijo(cost, G, deltaG, Mi, f_val) - G=G+alpha*deltaG + G = G + alpha * deltaG # test convergence - if it>=numItermax: - loop=0 - - delta_fval=(f_val-old_fval)/abs(f_val) - if abs(delta_fval)= numItermax: + loop = 0 + delta_fval = (f_val - old_fval) / abs(f_val) + if abs(delta_fval) < stopThr: + loop = 0 if log: log['loss'].append(f_val) if verbose: - if it%20 ==0: - print('{:5s}|{:12s}|{:8s}'.format('It.','Loss','Delta loss')+'\n'+'-'*32) - print('{:5d}|{:8e}|{:8e}'.format(it,f_val,delta_fval)) - + if it % 20 == 0: + print('{:5s}|{:12s}|{:8s}'.format( + 'It.', 'Loss', 'Delta loss') + '\n' + '-' * 32) + print('{:5d}|{:8e}|{:8e}'.format(it, f_val, delta_fval)) if log: - return G,log + return G, log else: return G -def gcg(a,b,M,reg1,reg2,f,df,G0=None,numItermax = 10,numInnerItermax = 200,stopThr=1e-9,verbose=False,log=False): + +def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, numInnerItermax=200, stopThr=1e-9, verbose=False, log=False): """ Solve the general regularized OT problem with the generalized conditional gradient @@ -264,70 +267,68 @@ def gcg(a,b,M,reg1,reg2,f,df,G0=None,numItermax = 10,numInnerItermax = 200,stopT """ - loop=1 + loop = 1 if log: - log={'loss':[]} + log = {'loss': []} if G0 is None: - G=np.outer(a,b) + G = np.outer(a, b) else: - G=G0 + G = G0 def cost(G): - return np.sum(M*G)+ reg1*np.sum(G*np.log(G)) + reg2*f(G) + return np.sum(M * G) + reg1 * np.sum(G * np.log(G)) + reg2 * f(G) - f_val=cost(G) + f_val = cost(G) if log: log['loss'].append(f_val) - it=0 + it = 0 if verbose: - print('{:5s}|{:12s}|{:8s}'.format('It.','Loss','Delta loss')+'\n'+'-'*32) - print('{:5d}|{:8e}|{:8e}'.format(it,f_val,0)) + print('{:5s}|{:12s}|{:8s}'.format( + 'It.', 'Loss', 'Delta loss') + '\n' + '-' * 32) + print('{:5d}|{:8e}|{:8e}'.format(it, f_val, 0)) while loop: - it+=1 - old_fval=f_val - + it += 1 + old_fval = f_val # problem linearization - Mi=M+reg2*df(G) + Mi = M + reg2 * df(G) # solve linear program with Sinkhorn #Gc = sinkhorn_stabilized(a,b, Mi, reg1, numItermax = numInnerItermax) - Gc = sinkhorn(a,b, Mi, reg1, numItermax = numInnerItermax) + Gc = sinkhorn(a, b, Mi, reg1, numItermax=numInnerItermax) - deltaG=Gc-G + deltaG = Gc - G # line search - dcost=Mi+reg1*(1+np.log(G)) #?? - alpha,fc,f_val = line_search_armijo(cost,G,deltaG,dcost,f_val) + dcost = Mi + reg1 * (1 + np.log(G)) # ?? + alpha, fc, f_val = line_search_armijo(cost, G, deltaG, dcost, f_val) - G=G+alpha*deltaG + G = G + alpha * deltaG # test convergence - if it>=numItermax: - loop=0 - - delta_fval=(f_val-old_fval)/abs(f_val) - if abs(delta_fval)= numItermax: + loop = 0 + delta_fval = (f_val - old_fval) / abs(f_val) + if abs(delta_fval) < stopThr: + loop = 0 if log: log['loss'].append(f_val) if verbose: - if it%20 ==0: - print('{:5s}|{:12s}|{:8s}'.format('It.','Loss','Delta loss')+'\n'+'-'*32) - print('{:5d}|{:8e}|{:8e}'.format(it,f_val,delta_fval)) - + if it % 20 == 0: + print('{:5s}|{:12s}|{:8s}'.format( + 'It.', 'Loss', 'Delta loss') + '\n' + '-' * 32) + print('{:5d}|{:8e}|{:8e}'.format(it, f_val, delta_fval)) if log: - return G,log + return G, log else: return G - -- cgit v1.2.3 From 95db977e8b931277af5dadbd79eccbd5fbb8bb62 Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Wed, 12 Jul 2017 23:56:27 +0200 Subject: pep8 --- ot/gpu/bregman.py | 11 +++++---- ot/gpu/da.py | 2 +- ot/utils.py | 62 ++++++++++++++++++++++++++--------------------- test/test_emd_multi.py | 27 ++++++++++----------- test/test_gpu_sinkhorn.py | 6 +++-- test/test_load_module.py | 4 +-- 6 files changed, 61 insertions(+), 51 deletions(-) diff --git a/ot/gpu/bregman.py b/ot/gpu/bregman.py index 2c3e317..7881c65 100644 --- a/ot/gpu/bregman.py +++ b/ot/gpu/bregman.py @@ -82,7 +82,7 @@ def sinkhorn(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False, ot.lp.emd : Unregularized OT ot.optim.cg : General regularized OT - """ + """ # init data Nini = len(a) Nfin = len(b) @@ -92,11 +92,11 @@ def sinkhorn(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False, # we assume that no distances are null except those of the diagonal of # distances - u = (np.ones(Nini)/Nini).reshape((Nini, 1)) + u = (np.ones(Nini) / Nini).reshape((Nini, 1)) u_GPU = cudamat.CUDAMatrix(u) a_GPU = cudamat.CUDAMatrix(a.reshape((Nini, 1))) ones_GPU = cudamat.empty(u_GPU.shape).assign(1) - v = (np.ones(Nfin)/Nfin).reshape((Nfin, 1)) + v = (np.ones(Nfin) / Nfin).reshape((Nfin, 1)) v_GPU = cudamat.CUDAMatrix(v) b_GPU = cudamat.CUDAMatrix(b.reshape((Nfin, 1))) @@ -121,7 +121,7 @@ def sinkhorn(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False, ones_GPU.divide(Kp_GPU.dot(v_GPU), target=u_GPU) if (np.any(KtransposeU_GPU.asarray() == 0) or - not u_GPU.allfinite() or not v_GPU.allfinite()): + not u_GPU.allfinite() or not v_GPU.allfinite()): # we have reached the machine precision # come back to previous solution and quit loop print('Warning: numerical errors at iteration', cpt) @@ -142,7 +142,8 @@ def sinkhorn(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False, if verbose: if cpt % 200 == 0: - print('{:5s}|{:12s}'.format('It.', 'Err')+'\n'+'-'*19) + print( + '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) print('{:5d}|{:8e}|'.format(cpt, err)) cpt += 1 if log: diff --git a/ot/gpu/da.py b/ot/gpu/da.py index b05ff70..c66e755 100644 --- a/ot/gpu/da.py +++ b/ot/gpu/da.py @@ -167,7 +167,7 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10, tmpC_GPU = cudamat.empty((Nfin, nbRow)).assign(0) transp_GPU.transpose().select_columns(indices_labels[i], tmpC_GPU) majs_GPU = tmpC_GPU.sum(axis=1).add(epsilon) - cudamat.pow(majs_GPU, (p-1)) + cudamat.pow(majs_GPU, (p - 1)) majs_GPU.mult(p) tmpC_GPU.assign(0) diff --git a/ot/utils.py b/ot/utils.py index 7ad7637..6a43f61 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -7,31 +7,35 @@ from scipy.spatial.distance import cdist import multiprocessing import time -__time_tic_toc=time.time() +__time_tic_toc = time.time() + def tic(): """ Python implementation of Matlab tic() function """ global __time_tic_toc - __time_tic_toc=time.time() + __time_tic_toc = time.time() + def toc(message='Elapsed time : {} s'): """ Python implementation of Matlab toc() function """ - t=time.time() - print(message.format(t-__time_tic_toc)) - return t-__time_tic_toc + t = time.time() + print(message.format(t - __time_tic_toc)) + return t - __time_tic_toc + def toq(): """ Python implementation of Julia toc() function """ - t=time.time() - return t-__time_tic_toc + t = time.time() + return t - __time_tic_toc -def kernel(x1,x2,method='gaussian',sigma=1,**kwargs): +def kernel(x1, x2, method='gaussian', sigma=1, **kwargs): """Compute kernel matrix""" - if method.lower() in ['gaussian','gauss','rbf']: - K=np.exp(-dist(x1,x2)/(2*sigma**2)) + if method.lower() in ['gaussian', 'gauss', 'rbf']: + K = np.exp(-dist(x1, x2) / (2 * sigma**2)) return K + def unif(n): """ return a uniform histogram of length n (simplex) @@ -48,17 +52,19 @@ def unif(n): """ - return np.ones((n,))/n + return np.ones((n,)) / n -def clean_zeros(a,b,M): - """ Remove all components with zeros weights in a and b + +def clean_zeros(a, b, M): + """ Remove all components with zeros weights in a and b """ - M2=M[a>0,:][:,b>0].copy() # copy force c style matrix (froemd) - a2=a[a>0] - b2=b[b>0] - return a2,b2,M2 + M2 = M[a > 0, :][:, b > 0].copy() # copy force c style matrix (froemd) + a2 = a[a > 0] + b2 = b[b > 0] + return a2, b2, M2 + -def dist(x1,x2=None,metric='sqeuclidean'): +def dist(x1, x2=None, metric='sqeuclidean'): """Compute distance between samples in x1 and x2 using function scipy.spatial.distance.cdist Parameters @@ -84,12 +90,12 @@ def dist(x1,x2=None,metric='sqeuclidean'): """ if x2 is None: - x2=x1 + x2 = x1 - return cdist(x1,x2,metric=metric) + return cdist(x1, x2, metric=metric) -def dist0(n,method='lin_square'): +def dist0(n, method='lin_square'): """Compute standard cost matrices of size (n,n) for OT problems Parameters @@ -111,16 +117,17 @@ def dist0(n,method='lin_square'): """ - res=0 - if method=='lin_square': - x=np.arange(n,dtype=np.float64).reshape((n,1)) - res=dist(x,x) + res = 0 + if method == 'lin_square': + x = np.arange(n, dtype=np.float64).reshape((n, 1)) + res = dist(x, x) return res def dots(*args): """ dots function for multiple matrix multiply """ - return reduce(np.dot,args) + return reduce(np.dot, args) + def fun(f, q_in, q_out): """ Utility function for parmap with no serializing problems """ @@ -130,6 +137,7 @@ def fun(f, q_in, q_out): break q_out.put((i, f(x))) + def parmap(f, X, nprocs=multiprocessing.cpu_count()): """ paralell map for multiprocessing """ q_in = multiprocessing.Queue(1) @@ -147,4 +155,4 @@ def parmap(f, X, nprocs=multiprocessing.cpu_count()): [p.join() for p in proc] - return [x for i, x in sorted(res)] \ No newline at end of file + return [x for i, x in sorted(res)] diff --git a/test/test_emd_multi.py b/test/test_emd_multi.py index ee0a20e..99173e9 100644 --- a/test/test_emd_multi.py +++ b/test/test_emd_multi.py @@ -7,31 +7,30 @@ Created on Fri Mar 10 09:56:06 2017 """ import numpy as np -import pylab as pl -import ot +import ot from ot.datasets import get_1D_gauss as gauss -reload(ot.lp) +# reload(ot.lp) #%% parameters -n=5000 # nb bins +n = 5000 # nb bins # bin positions -x=np.arange(n,dtype=np.float64) +x = np.arange(n, dtype=np.float64) # Gaussian distributions -a=gauss(n,m=20,s=5) # m= mean, s= std +a = gauss(n, m=20, s=5) # m= mean, s= std -ls= range(20,1000,10) -nb=len(ls) -b=np.zeros((n,nb)) +ls = range(20, 1000, 10) +nb = len(ls) +b = np.zeros((n, nb)) for i in range(nb): - b[:,i]=gauss(n,m=ls[i],s=10) + b[:, i] = gauss(n, m=ls[i], s=10) # loss matrix -M=ot.dist(x.reshape((n,1)),x.reshape((n,1))) -#M/=M.max() +M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) +# M/=M.max() #%% @@ -39,10 +38,10 @@ print('Computing {} EMD '.format(nb)) # emd loss 1 proc ot.tic() -emd_loss4=ot.emd2(a,b,M,1) +emd_loss4 = ot.emd2(a, b, M, 1) ot.toc('1 proc : {} s') # emd loss multipro proc ot.tic() -emd_loss4=ot.emd2(a,b,M) +emd_loss4 = ot.emd2(a, b, M) ot.toc('multi proc : {} s') diff --git a/test/test_gpu_sinkhorn.py b/test/test_gpu_sinkhorn.py index bfa2cd2..841f062 100644 --- a/test/test_gpu_sinkhorn.py +++ b/test/test_gpu_sinkhorn.py @@ -3,8 +3,10 @@ import numpy as np import time import ot.gpu + def describeRes(r): - print("min:{:.3E}, max::{:.3E}, mean::{:.3E}, std::{:.3E}".format(np.min(r),np.max(r),np.mean(r),np.std(r))) + print("min:{:.3E}, max::{:.3E}, mean::{:.3E}, std::{:.3E}".format( + np.min(r), np.max(r), np.mean(r), np.std(r))) for n in [5000, 10000, 15000, 20000]: @@ -23,4 +25,4 @@ for n in [5000, 10000, 15000, 20000]: print("Normal sinkhorn, time: {:6.2f} sec ".format(time2 - time1)) describeRes(G1) print(" GPU sinkhorn, time: {:6.2f} sec ".format(time3 - time2)) - describeRes(G2) \ No newline at end of file + describeRes(G2) diff --git a/test/test_load_module.py b/test/test_load_module.py index a04c5df..d77261e 100644 --- a/test/test_load_module.py +++ b/test/test_load_module.py @@ -4,7 +4,7 @@ import ot import doctest # test lp solver -doctest.testmod(ot.lp,verbose=True) +doctest.testmod(ot.lp, verbose=True) # test bregman solver -doctest.testmod(ot.bregman,verbose=True) +doctest.testmod(ot.bregman, verbose=True) -- cgit v1.2.3 From f15336be3bb91b619803bfbb58e7931997c46574 Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Wed, 12 Jul 2017 23:57:11 +0200 Subject: update travis --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 8023ec5..050510b 100644 --- a/.travis.yml +++ b/.travis.yml @@ -21,5 +21,5 @@ install: # command to run tests + check syntax style script: - python test/test_load_module.py -v - - flake8 . + - flake8 examples/ ot/ test/ - py.test ot test -- cgit v1.2.3 From cd9842dc2978cba757a51c32cce0272858c9a385 Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Thu, 13 Jul 2017 00:04:49 +0200 Subject: more --- .travis.yml | 2 +- test/test_emd_multi.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.travis.yml b/.travis.yml index 050510b..8a95d7c 100644 --- a/.travis.yml +++ b/.travis.yml @@ -22,4 +22,4 @@ install: script: - python test/test_load_module.py -v - flake8 examples/ ot/ test/ - - py.test ot test + # - py.test ot test diff --git a/test/test_emd_multi.py b/test/test_emd_multi.py index 99173e9..2eef242 100644 --- a/test/test_emd_multi.py +++ b/test/test_emd_multi.py @@ -22,7 +22,7 @@ x = np.arange(n, dtype=np.float64) # Gaussian distributions a = gauss(n, m=20, s=5) # m= mean, s= std -ls = range(20, 1000, 10) +ls = np.arange(20, 1000, 10) nb = len(ls) b = np.zeros((n, nb)) for i in range(nb): -- cgit v1.2.3 From ced35d10cb9c92e3cfd98f16f2fee778a969de97 Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Thu, 20 Jul 2017 14:09:59 +0200 Subject: pt not plt --- ot/plot.py | 36 ++++++++++++++++++------------------ 1 file changed, 18 insertions(+), 18 deletions(-) diff --git a/ot/plot.py b/ot/plot.py index 6f01731..61afc9f 100644 --- a/ot/plot.py +++ b/ot/plot.py @@ -4,7 +4,7 @@ Functions for plotting OT matrices import numpy as np -import matplotlib.pylab as plt +import matplotlib.pylab as pl from matplotlib import gridspec @@ -31,24 +31,24 @@ def plot1D_mat(a, b, M, title=''): xa = np.arange(na) xb = np.arange(nb) - ax1 = plt.subplot(gs[0, 1:]) - plt.plot(xb, b, 'r', label='Target distribution') - plt.yticks(()) - plt.title(title) + ax1 = pl.subplot(gs[0, 1:]) + pl.plot(xb, b, 'r', label='Target distribution') + pl.yticks(()) + pl.title(title) - ax2 = plt.subplot(gs[1:, 0]) - plt.plot(a, xa, 'b', label='Source distribution') - plt.gca().invert_xaxis() - plt.gca().invert_yaxis() - plt.xticks(()) + ax2 = pl.subplot(gs[1:, 0]) + pl.plot(a, xa, 'b', label='Source distribution') + pl.gca().invert_xaxis() + pl.gca().invert_yaxis() + pl.xticks(()) - plt.subplot(gs[1:, 1:], sharex=ax1, sharey=ax2) - plt.imshow(M, interpolation='nearest') - plt.axis('off') + pl.subplot(gs[1:, 1:], sharex=ax1, sharey=ax2) + pl.imshow(M, interpolation='nearest') + pl.axis('off') - plt.xlim((0, nb)) - plt.tight_layout() - plt.subplots_adjust(wspace=0., hspace=0.2) + pl.xlim((0, nb)) + pl.tight_layout() + pl.subplots_adjust(wspace=0., hspace=0.2) def plot2D_samples_mat(xs, xt, G, thr=1e-8, **kwargs): @@ -78,5 +78,5 @@ def plot2D_samples_mat(xs, xt, G, thr=1e-8, **kwargs): for i in range(xs.shape[0]): for j in range(xt.shape[0]): if G[i, j] / mx > thr: - plt.plot([xs[i, 0], xt[j, 0]], [xs[i, 1], xt[j, 1]], - alpha=G[i, j] / mx, **kwargs) + pl.plot([xs[i, 0], xt[j, 0]], [xs[i, 1], xt[j, 1]], + alpha=G[i, j] / mx, **kwargs) -- cgit v1.2.3 From 20c016c5dcb310361ecfd2f18f951c9c366ef2b9 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 20 Jul 2017 14:22:40 +0200 Subject: add code of cobnduct --- CODE_OF_CONDUCT.md | 74 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 74 insertions(+) create mode 100644 CODE_OF_CONDUCT.md diff --git a/CODE_OF_CONDUCT.md b/CODE_OF_CONDUCT.md new file mode 100644 index 0000000..9c1c621 --- /dev/null +++ b/CODE_OF_CONDUCT.md @@ -0,0 +1,74 @@ +# Contributor Covenant Code of Conduct + +## Our Pledge + +In the interest of fostering an open and welcoming environment, we as +contributors and maintainers pledge to making participation in our project and +our community a harassment-free experience for everyone, regardless of age, body +size, disability, ethnicity, gender identity and expression, level of experience, +nationality, personal appearance, race, religion, or sexual identity and +orientation. + +## Our Standards + +Examples of behavior that contributes to creating a positive environment +include: + +* Using welcoming and inclusive language +* Being respectful of differing viewpoints and experiences +* Gracefully accepting constructive criticism +* Focusing on what is best for the community +* Showing empathy towards other community members + +Examples of unacceptable behavior by participants include: + +* The use of sexualized language or imagery and unwelcome sexual attention or +advances +* Trolling, insulting/derogatory comments, and personal or political attacks +* Public or private harassment +* Publishing others' private information, such as a physical or electronic + address, without explicit permission +* Other conduct which could reasonably be considered inappropriate in a + professional setting + +## Our Responsibilities + +Project maintainers are responsible for clarifying the standards of acceptable +behavior and are expected to take appropriate and fair corrective action in +response to any instances of unacceptable behavior. + +Project maintainers have the right and responsibility to remove, edit, or +reject comments, commits, code, wiki edits, issues, and other contributions +that are not aligned to this Code of Conduct, or to ban temporarily or +permanently any contributor for other behaviors that they deem inappropriate, +threatening, offensive, or harmful. + +## Scope + +This Code of Conduct applies both within project spaces and in public spaces +when an individual is representing the project or its community. Examples of +representing a project or community include using an official project e-mail +address, posting via an official social media account, or acting as an appointed +representative at an online or offline event. Representation of a project may be +further defined and clarified by project maintainers. + +## Enforcement + +Instances of abusive, harassing, or otherwise unacceptable behavior may be +reported by contacting the project team. All +complaints will be reviewed and investigated and will result in a response that +is deemed necessary and appropriate to the circumstances. The project team is +obligated to maintain confidentiality with regard to the reporter of an incident. +Further details of specific enforcement policies may be posted separately. + +Project maintainers who do not follow or enforce the Code of Conduct in good +faith may face temporary or permanent repercussions as determined by other +members of the project's leadership. + +## Attribution + +This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, +available at [http://contributor-covenant.org/version/1/4][version] + +[homepage]: http://contributor-covenant.org +[version]: http://contributor-covenant.org/version/1/4/ -- cgit v1.2.3 From 608882349326832ae72051569410ca4397e9ad8b Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 20 Jul 2017 14:37:16 +0200 Subject: add contributing code and update readme --- CONTRIBUTING.md | 204 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ README.md | 6 ++ 2 files changed, 210 insertions(+) create mode 100644 CONTRIBUTING.md diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md new file mode 100644 index 0000000..84ef29a --- /dev/null +++ b/CONTRIBUTING.md @@ -0,0 +1,204 @@ + +Contributing to POT +=================== + + +First off, thank you for considering contributing to POT. + +How to contribute +----------------- + +The preferred workflow for contributing to POT is to fork the +[main repository](https://github.com/rflamary/POT) on +GitHub, clone, and develop on a branch. Steps: + +1. Fork the [project repository](https://github.com/rflamary/POT) + by clicking on the 'Fork' button near the top right of the page. This creates + a copy of the code under your GitHub user account. For more details on + how to fork a repository see [this guide](https://help.github.com/articles/fork-a-repo/). + +2. Clone your fork of the scikit-learn repo from your GitHub account to your local disk: + + ```bash + $ git clone git@github.com:YourLogin/POT.git + $ cd POT + ``` + +3. Create a ``feature`` branch to hold your development changes: + + ```bash + $ git checkout -b my-feature + ``` + + Always use a ``feature`` branch. It's good practice to never work on the ``master`` branch! + +4. Develop the feature on your feature branch. Add changed files using ``git add`` and then ``git commit`` files: + + ```bash + $ git add modified_files + $ git commit + ``` + + to record your changes in Git, then push the changes to your GitHub account with: + + ```bash + $ git push -u origin my-feature + ``` + +5. Follow [these instructions](https://help.github.com/articles/creating-a-pull-request-from-a-fork) +to create a pull request from your fork. This will send an email to the committers. + +(If any of the above seems like magic to you, please look up the +[Git documentation](https://git-scm.com/documentation) on the web, or ask a friend or another contributor for help.) + +Pull Request Checklist +---------------------- + +We recommended that your contribution complies with the +following rules before you submit a pull request: + +- Follow the PEP8 Guidelines. + +- If your pull request addresses an issue, please use the pull request title + to describe the issue and mention the issue number in the pull request description. This will make sure a link back to the original issue is + created. + +- All public methods should have informative docstrings with sample + usage presented as doctests when appropriate. + +- Please prefix the title of your pull request with `[MRG]` (Ready for + Merge), if the contribution is complete and ready for a detailed review. + Two core developers will review your code and change the prefix of the pull + request to `[MRG + 1]` and `[MRG + 2]` on approval, making it eligible + for merging. An incomplete contribution -- where you expect to do more work before + receiving a full review -- should be prefixed `[WIP]` (to indicate a work + in progress) and changed to `[MRG]` when it matures. WIPs may be useful + to: indicate you are working on something to avoid duplicated work, + request broad review of functionality or API, or seek collaborators. + WIPs often benefit from the inclusion of a + [task list](https://github.com/blog/1375-task-lists-in-gfm-issues-pulls-comments) + in the PR description. + + +- When adding additional functionality, provide at least one + example script in the ``examples/`` folder. Have a look at other + examples for reference. Examples should demonstrate why the new + functionality is useful in practice and, if possible, compare it + to other methods available in scikit-learn. + +- Documentation and high-coverage tests are necessary for enhancements to be + accepted. Bug-fixes or new features should be provided with + [non-regression tests](https://en.wikipedia.org/wiki/Non-regression_testing). + These tests verify the correct behavior of the fix or feature. In this + manner, further modifications on the code base are granted to be consistent + with the desired behavior. + For the Bug-fixes case, at the time of the PR, this tests should fail for + the code base in master and pass for the PR code. + +- At least one paragraph of narrative documentation with links to + references in the literature (with PDF links when possible) and + the example. + +You can also check for common programming errors with the following +tools: + + +- No pyflakes warnings, check with: + + ```bash + $ pip install pyflakes + $ pyflakes path/to/module.py + ``` + +- No PEP8 warnings, check with: + + ```bash + $ pip install pep8 + $ pep8 path/to/module.py + ``` + +- AutoPEP8 can help you fix some of the easy redundant errors: + + ```bash + $ pip install autopep8 + $ autopep8 path/to/pep8.py + ``` + +Bonus points for contributions that include a performance analysis with +a benchmark script and profiling output (please report on the mailing +list or on the GitHub issue). + +Filing bugs +----------- +We use Github issues to track all bugs and feature requests; feel free to +open an issue if you have found a bug or wish to see a feature implemented. + +It is recommended to check that your issue complies with the +following rules before submitting: + +- Verify that your issue is not being currently addressed by other + [issues](https://github.com/rflamary/POT/issues?q=) + or [pull requests](https://github.com/rflamary/POT/pulls?q=). + +- Please ensure all code snippets and error messages are formatted in + appropriate code blocks. + See [Creating and highlighting code blocks](https://help.github.com/articles/creating-and-highlighting-code-blocks). + +- Please include your operating system type and version number, as well + as your Python, scikit-learn, numpy, and scipy versions. This information + can be found by running the following code snippet: + + ```python + import platform; print(platform.platform()) + import sys; print("Python", sys.version) + import numpy; print("NumPy", numpy.__version__) + import scipy; print("SciPy", scipy.__version__) + import ot; print("POT", ot.__version__) + ``` + +- Please be specific about what estimators and/or functions are involved + and the shape of the data, as appropriate; please include a + [reproducible](http://stackoverflow.com/help/mcve) code snippet + or link to a [gist](https://gist.github.com). If an exception is raised, + please provide the traceback. + +New contributor tips +-------------------- + +A great way to start contributing to scikit-learn is to pick an item +from the list of [Easy issues](https://github.com/scikit-learn/scikit-learn/issues?labels=Easy) +in the issue tracker. Resolving these issues allow you to start +contributing to the project without much prior knowledge. Your +assistance in this area will be greatly appreciated by the more +experienced developers as it helps free up their time to concentrate on +other issues. + +Documentation +------------- + +We are glad to accept any sort of documentation: function docstrings, +reStructuredText documents (like this one), tutorials, etc. +reStructuredText documents live in the source code repository under the +doc/ directory. + +You can edit the documentation using any text editor and then generate +the HTML output by typing ``make html`` from the doc/ directory. +Alternatively, ``make`` can be used to quickly generate the +documentation without the example gallery. The resulting HTML files will +be placed in ``_build/html/`` and are viewable in a web browser. See the +``README`` file in the ``doc/`` directory for more information. + +For building the documentation, you will need +[sphinx](http://sphinx.pocoo.org/), +[matplotlib](http://matplotlib.org/), and +[pillow](http://pillow.readthedocs.io/en/latest/). + +When you are writing documentation, it is important to keep a good +compromise between mathematical and algorithmic details, and give +intuition to the reader on what the algorithm does. It is best to always +start with a small paragraph with a hand-waving explanation of what the +method does to the data and a figure (coming from an example) +illustrating it. + + +This Contrubution guide is strongly inpired by the one of the [scikit-learn](https://github.com/scikit-learn/scikit-learn) team. diff --git a/README.md b/README.md index c3dbf68..6324649 100644 --- a/README.md +++ b/README.md @@ -132,6 +132,7 @@ The contributors to this library are: * [Rémi Flamary](http://remi.flamary.com/) * [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/) +* [Alexandre Gramfort](http://alexandre.gramfort.net/) * [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/) * [Michael Perrot](http://perso.univ-st-etienne.fr/pem82055/) (Mapping estimation) * [Léo Gautheron](https://github.com/aje) (GPU implementation) @@ -143,6 +144,11 @@ This toolbox benefit a lot from open source research and we would like to thank * [Antoine Rolet](https://arolet.github.io/) ( Mex file for EMD ) * [Marco Cuturi](http://marcocuturi.net/) (Sinkhorn Knopp in Matlab/Cuda) + +## Contributions and code of conduct + +Every contribution is welcome and should respect the [contribution guidelines](CONTRIBUTING.md). Each member of the project is expected to follow the [code of conduct](CODE_OF_CONDUCT.md). + ## References [1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). [Displacement interpolation using Lagrangian mass transport](https://people.csail.mit.edu/sparis/publi/2011/sigasia/Bonneel_11_Displacement_Interpolation.pdf). In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM. -- cgit v1.2.3 From 6ada23e5a672b08f28e21123c4135bc787e83b19 Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Thu, 20 Jul 2017 15:39:50 +0200 Subject: pep8 --- examples/plot_OTDA_color_images.py | 2 ++ examples/plot_OTDA_mapping_color_images.py | 2 ++ examples/plot_optim_OTreg.py | 3 +++ ot/utils.py | 6 ++++-- test/test_gpu_sinkhorn_lpl1.py | 1 + 5 files changed, 12 insertions(+), 2 deletions(-) diff --git a/examples/plot_OTDA_color_images.py b/examples/plot_OTDA_color_images.py index a8861c6..75ac5b6 100644 --- a/examples/plot_OTDA_color_images.py +++ b/examples/plot_OTDA_color_images.py @@ -48,6 +48,7 @@ def mat2im(X, shape): """Converts back a matrix to an image""" return X.reshape(shape) + X1 = im2mat(I1) X2 = im2mat(I2) @@ -102,6 +103,7 @@ X2te = da_entrop.predict(X2, -1) def minmax(I): return np.clip(I, 0, 1) + I1t = minmax(mat2im(X1t, I1.shape)) I2t = minmax(mat2im(X2t, I2.shape)) diff --git a/examples/plot_OTDA_mapping_color_images.py b/examples/plot_OTDA_mapping_color_images.py index 85c4b6b..9710461 100644 --- a/examples/plot_OTDA_mapping_color_images.py +++ b/examples/plot_OTDA_mapping_color_images.py @@ -48,6 +48,7 @@ def mat2im(X, shape): """Converts back a matrix to an image""" return X.reshape(shape) + X1 = im2mat(I1) X2 = im2mat(I2) @@ -85,6 +86,7 @@ pl.tight_layout() def minmax(I): return np.clip(I, 0, 1) + # LP problem da_emd = ot.da.OTDA() # init class da_emd.fit(xs, xt) # fit distributions diff --git a/examples/plot_optim_OTreg.py b/examples/plot_optim_OTreg.py index e38253c..276b250 100644 --- a/examples/plot_optim_OTreg.py +++ b/examples/plot_optim_OTreg.py @@ -44,6 +44,7 @@ def f(G): def df(G): return G + reg = 1e-1 Gl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True) @@ -61,6 +62,7 @@ def f(G): def df(G): return np.log(G) + 1. + reg = 1e-3 Ge = ot.optim.cg(a, b, M, reg, f, df, verbose=True) @@ -78,6 +80,7 @@ def f(G): def df(G): return G + reg1 = 1e-3 reg2 = 1e-1 diff --git a/ot/utils.py b/ot/utils.py index 6a43f61..1dee932 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -2,11 +2,13 @@ """ Various function that can be usefull """ +import multiprocessing +from functools import reduce +import time + import numpy as np from scipy.spatial.distance import cdist -import multiprocessing -import time __time_tic_toc = time.time() diff --git a/test/test_gpu_sinkhorn_lpl1.py b/test/test_gpu_sinkhorn_lpl1.py index e6cdd31..f0eb7e6 100644 --- a/test/test_gpu_sinkhorn_lpl1.py +++ b/test/test_gpu_sinkhorn_lpl1.py @@ -8,6 +8,7 @@ def describeRes(r): print("min:{:.3E}, max:{:.3E}, mean:{:.3E}, std:{:.3E}" .format(np.min(r), np.max(r), np.mean(r), np.std(r))) + for n in [5000, 10000, 15000, 20000]: print(n) a = np.random.rand(n // 4, 100) -- cgit v1.2.3 From dc3bbd4134f0e2b80e0fe72368bdcf9966f434dc Mon Sep 17 00:00:00 2001 From: arolet Date: Fri, 21 Jul 2017 12:12:21 +0900 Subject: Cleaned optimal plan and optimal cost computation --- ot/lp/EMD_wrapper.cpp | 13 ++++++------- ot/lp/emd_wrap.pyx | 5 ----- test/test_emd.py | 10 ++++++++-- 3 files changed, 14 insertions(+), 14 deletions(-) diff --git a/ot/lp/EMD_wrapper.cpp b/ot/lp/EMD_wrapper.cpp index d719c6e..cc13230 100644 --- a/ot/lp/EMD_wrapper.cpp +++ b/ot/lp/EMD_wrapper.cpp @@ -93,14 +93,13 @@ void EMD_wrap(int n1, int n2, double *X, double *Y, } } else { - for (node_id_type i=0; i a.data, b.data, M.data, G.data, &cost, maxiter) - - cost=0 - for i in range(n1): - for j in range(n2): - cost+=G[i,j]*M[i,j] return cost diff --git a/test/test_emd.py b/test/test_emd.py index eb1c5c5..4757cd1 100644 --- a/test/test_emd.py +++ b/test/test_emd.py @@ -43,11 +43,17 @@ ot.toc('1 proc : {} s') cost1 = (G * M).sum() +# emd loss 1 proc +ot.tic() +cost_emd2 = ot.emd2(a,b,M) +ot.toc('1 proc : {} s') + ot.tic() G = ot.emd(b, a, np.ascontiguousarray(M.T)) ot.toc('1 proc : {} s') cost2 = (G * M.T).sum() -assert np.abs(cost1-cost2) < tol -assert np.abs(cost1-np.abs(mean1-mean2)) < tol +assert np.abs(cost1-cost_emd2)/np.abs(cost1) < tol +assert np.abs(cost1-cost2)/np.abs(cost1) < tol +assert np.abs(cost1-np.abs(mean1-mean2))/np.abs(cost1) < tol -- cgit v1.2.3 From 88c62c39a9623e8b58ebb776a9deddc96b43b4a0 Mon Sep 17 00:00:00 2001 From: arolet Date: Fri, 21 Jul 2017 12:12:48 +0900 Subject: Added dual variables computations --- ot/lp/EMD.h | 3 ++- ot/lp/EMD_wrapper.cpp | 6 ++++-- ot/lp/__init__.py | 11 +++++++---- ot/lp/emd_wrap.pyx | 22 ++++++++++++++++------ 4 files changed, 29 insertions(+), 13 deletions(-) diff --git a/ot/lp/EMD.h b/ot/lp/EMD.h index 59a5af8..fb7feca 100644 --- a/ot/lp/EMD.h +++ b/ot/lp/EMD.h @@ -24,6 +24,7 @@ using namespace lemon; typedef unsigned int node_id_type; -void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int max_iter); +void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, + double* alpha, double* beta, double *cost, int max_iter); #endif diff --git a/ot/lp/EMD_wrapper.cpp b/ot/lp/EMD_wrapper.cpp index cc13230..6bda6a7 100644 --- a/ot/lp/EMD_wrapper.cpp +++ b/ot/lp/EMD_wrapper.cpp @@ -15,8 +15,8 @@ #include "EMD.h" -void EMD_wrap(int n1, int n2, double *X, double *Y, - double *D, double *G, double *cost, int max_iter) { +void EMD_wrap(int n1, int n2, double *X, double *Y, double *D, double *G, + double* alpha, double* beta, double *cost, int max_iter) { // beware M and C anre strored in row major C style!!! int n, m, i,cur; @@ -99,6 +99,8 @@ void EMD_wrap(int n1, int n2, double *X, double *Y, int i = di.source(a); int j = di.target(a); *(G+indI[i]*n2+indJ[j-n]) = net.flow(a); + *(alpha + indI[i]) = net.potential(i); + *(beta + indJ[j-n]) = net.potential(j); } }; diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 673242d..915a18c 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -11,7 +11,7 @@ import multiprocessing -def emd(a, b, M, max_iter=-1): +def emd(a, b, M, dual_variables=False, max_iter=-1): """Solves the Earth Movers distance problem and returns the OT matrix @@ -80,7 +80,10 @@ def emd(a, b, M, max_iter=-1): if len(b) == 0: b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1] - return emd_c(a, b, M, max_iter) + G, alpha, beta = emd_c(a, b, M, max_iter) + if dual_variables: + return G, alpha, beta + return G def emd2(a, b, M,processes=multiprocessing.cpu_count(), max_iter=-1): """Solves the Earth Movers distance problem and returns the loss @@ -151,12 +154,12 @@ def emd2(a, b, M,processes=multiprocessing.cpu_count(), max_iter=-1): b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1] if len(b.shape)==1: - return emd2_c(a, b, M, max_iter) + return emd2_c(a, b, M, max_iter)[0] else: nb=b.shape[1] #res=[emd2_c(a,b[:,i].copy(),M) for i in range(nb)] def f(b): - return emd2_c(a,b,M, max_iter) + return emd2_c(a,b,M, max_iter)[0] res= parmap(f, [b[:,i] for i in range(nb)],processes) return np.array(res) diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index c4ba125..813596f 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -12,7 +12,8 @@ cimport cython cdef extern from "EMD.h": - void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int max_iter) + void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, + double* alpha, double* beta, int max_iter) @@ -58,6 +59,8 @@ def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mod cdef float cost=0 cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([n1, n2]) + cdef np.ndarray[double, ndim=1, mode="c"] alpha=np.zeros(n1) + cdef np.ndarray[double, ndim=1, mode="c"] beta=np.zeros(n2) if not len(a): a=np.ones((n1,))/n1 @@ -65,10 +68,13 @@ def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mod if not len(b): b=np.ones((n2,))/n2 + print alpha.size + print beta.size # calling the function - EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost, maxiter) + EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, + alpha.data, beta.data, &cost, maxiter) - return G + return G, alpha, beta @cython.boundscheck(False) @cython.wraparound(False) @@ -112,15 +118,19 @@ def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mo cdef float cost=0 cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([n1, n2]) + + cdef np.ndarray[double, ndim = 1, mode = "c"] alpha = np.zeros([n1]) + cdef np.ndarray[double, ndim = 1, mode = "c"] beta = np.zeros([n2]) if not len(a): a=np.ones((n1,))/n1 if not len(b): b=np.ones((n2,))/n2 - # calling the function - EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost, maxiter) + EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, + alpha.data, beta.data, &cost, maxiter) + - return cost + return cost, alpha, beta -- cgit v1.2.3 From db2a70b1f5146d6374af57f4bea66ab95b202e77 Mon Sep 17 00:00:00 2001 From: arolet Date: Fri, 21 Jul 2017 13:33:44 +0900 Subject: Compute cost with primal --- ot/lp/EMD_wrapper.cpp | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/ot/lp/EMD_wrapper.cpp b/ot/lp/EMD_wrapper.cpp index 6bda6a7..c6cbb04 100644 --- a/ot/lp/EMD_wrapper.cpp +++ b/ot/lp/EMD_wrapper.cpp @@ -93,12 +93,14 @@ void EMD_wrap(int n1, int n2, double *X, double *Y, double *D, double *G, } } else { - *cost = net.totalCost(); + *cost = 0; Arc a; di.first(a); for (; a != INVALID; di.next(a)) { int i = di.source(a); int j = di.target(a); - *(G+indI[i]*n2+indJ[j-n]) = net.flow(a); + double flow = net.flow(a); + *cost += flow * (*(D+indI[i]*n2+indJ[j-n])); + *(G+indI[i]*n2+indJ[j-n]) = flow; *(alpha + indI[i]) = net.potential(i); *(beta + indJ[j-n]) = net.potential(j); } -- cgit v1.2.3 From c1980a414c879dd1bc1d8904fd43426326741385 Mon Sep 17 00:00:00 2001 From: arolet Date: Fri, 21 Jul 2017 13:34:09 +0900 Subject: Added and passed tests for dual variables --- ot/lp/EMD_wrapper.cpp | 2 +- ot/lp/emd_wrap.pyx | 4 ++-- test/test_emd.py | 28 +++++++++++++++++++--------- 3 files changed, 22 insertions(+), 12 deletions(-) diff --git a/ot/lp/EMD_wrapper.cpp b/ot/lp/EMD_wrapper.cpp index c6cbb04..0977e75 100644 --- a/ot/lp/EMD_wrapper.cpp +++ b/ot/lp/EMD_wrapper.cpp @@ -101,7 +101,7 @@ void EMD_wrap(int n1, int n2, double *X, double *Y, double *D, double *G, double flow = net.flow(a); *cost += flow * (*(D+indI[i]*n2+indJ[j-n])); *(G+indI[i]*n2+indJ[j-n]) = flow; - *(alpha + indI[i]) = net.potential(i); + *(alpha + indI[i]) = -net.potential(i); *(beta + indJ[j-n]) = net.potential(j); } diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 813596f..435a270 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -57,7 +57,7 @@ def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mod cdef int n1= M.shape[0] cdef int n2= M.shape[1] - cdef float cost=0 + cdef double cost=0 cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([n1, n2]) cdef np.ndarray[double, ndim=1, mode="c"] alpha=np.zeros(n1) cdef np.ndarray[double, ndim=1, mode="c"] beta=np.zeros(n2) @@ -116,7 +116,7 @@ def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mo cdef int n1= M.shape[0] cdef int n2= M.shape[1] - cdef float cost=0 + cdef double cost=0 cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([n1, n2]) cdef np.ndarray[double, ndim = 1, mode = "c"] alpha = np.zeros([n1]) diff --git a/test/test_emd.py b/test/test_emd.py index 4757cd1..3bf6fa2 100644 --- a/test/test_emd.py +++ b/test/test_emd.py @@ -2,7 +2,6 @@ # -*- coding: utf-8 -*- import numpy as np -import pylab as pl import ot from ot.datasets import get_1D_gauss as gauss @@ -16,8 +15,6 @@ m=6000 # nb bins mean1 = 1000 mean2 = 1100 -tol = 1e-6 - # bin positions x=np.arange(n,dtype=np.float64) y=np.arange(m,dtype=np.float64) @@ -38,10 +35,11 @@ print('Computing {} EMD '.format(1)) # emd loss 1 proc ot.tic() -G = ot.emd(a,b,M) +G, alpha, beta = ot.emd(a,b,M, dual_variables=True) ot.toc('1 proc : {} s') cost1 = (G * M).sum() +cost_dual = np.vdot(a, alpha) + np.vdot(b, beta) # emd loss 1 proc ot.tic() @@ -49,11 +47,23 @@ cost_emd2 = ot.emd2(a,b,M) ot.toc('1 proc : {} s') ot.tic() -G = ot.emd(b, a, np.ascontiguousarray(M.T)) +G2 = ot.emd(b, a, np.ascontiguousarray(M.T)) ot.toc('1 proc : {} s') -cost2 = (G * M.T).sum() +cost2 = (G2 * M.T).sum() + +M_reduced = M - alpha.reshape(-1,1) - beta.reshape(1, -1) + +# Check that both cost computations are equivalent +np.testing.assert_almost_equal(cost1, cost_emd2) +# Check that dual and primal cost are equal +np.testing.assert_almost_equal(cost1, cost_dual) +# Check symmetry +np.testing.assert_almost_equal(cost1, cost2) +# Check with closed-form solution for gaussians +np.testing.assert_almost_equal(cost1, np.abs(mean1-mean2)) + +[ind1, ind2] = np.nonzero(G) -assert np.abs(cost1-cost_emd2)/np.abs(cost1) < tol -assert np.abs(cost1-cost2)/np.abs(cost1) < tol -assert np.abs(cost1-np.abs(mean1-mean2))/np.abs(cost1) < tol +# Check that reduced cost is zero on transport arcs +np.testing.assert_array_almost_equal((M - alpha.reshape(-1, 1) - beta.reshape(1, -1))[ind1, ind2], np.zeros(ind1.size)) \ No newline at end of file -- cgit v1.2.3 From aaf80bbef65c1b8cee9bdec512ab81f00e8329e1 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Jul 2017 09:21:12 +0200 Subject: add slack and mailing list --- Makefile | 5 ++++- README.md | 10 ++++++++++ 2 files changed, 14 insertions(+), 1 deletion(-) diff --git a/Makefile b/Makefile index 22c1b50..c6a83c8 100644 --- a/Makefile +++ b/Makefile @@ -33,7 +33,10 @@ sremove : clean : $(PYTHON) setup.py clean - + +pep8 : + flake8 examples/ ot/ test/ + test: pytest diff --git a/README.md b/README.md index 6324649..84f5228 100644 --- a/README.md +++ b/README.md @@ -149,6 +149,16 @@ This toolbox benefit a lot from open source research and we would like to thank Every contribution is welcome and should respect the [contribution guidelines](CONTRIBUTING.md). Each member of the project is expected to follow the [code of conduct](CODE_OF_CONDUCT.md). +## Support + +You can ask questions and join the development discussion: + +* On the [POT Slack channel](pot-toolbox.slack.com) +* On the POT [mailing list](https://mail.python.org/mm3/mailman3/lists/pot.python.org/) + + +You can also post bug reports and feature requests in Github issues. Make sure to read our [guidelines](CONTRIBUTING.md) first. + ## References [1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). [Displacement interpolation using Lagrangian mass transport](https://people.csail.mit.edu/sparis/publi/2011/sigasia/Bonneel_11_Displacement_Interpolation.pdf). In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM. -- cgit v1.2.3 From 82da63f1020835a412f6174500099694a78ab6be Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Jul 2017 09:29:34 +0200 Subject: correct url --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 84f5228..7a65106 100644 --- a/README.md +++ b/README.md @@ -153,7 +153,7 @@ Every contribution is welcome and should respect the [contribution guidelines](C You can ask questions and join the development discussion: -* On the [POT Slack channel](pot-toolbox.slack.com) +* On the [POT Slack channel](https://pot-toolbox.slack.com) * On the POT [mailing list](https://mail.python.org/mm3/mailman3/lists/pot.python.org/) -- cgit v1.2.3 From 5a6b5de9b2f28c93bef1a9db2e3b94693c05ff4f Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Jul 2017 11:15:33 +0200 Subject: add proper testing --- .travis.yml | 2 +- Makefile | 13 ++++++---- docs/source/readme.rst | 52 +++++++++++++++++++++++++++++-------- test/test_emd_multi.py | 47 --------------------------------- test/test_gpu.py | 59 ++++++++++++++++++++++++++++++++++++++++++ test/test_gpu_sinkhorn.py | 28 -------------------- test/test_gpu_sinkhorn_lpl1.py | 29 --------------------- test/test_load_module.py | 10 ------- test/test_ot.py | 55 +++++++++++++++++++++++++++++++++++++++ 9 files changed, 164 insertions(+), 131 deletions(-) delete mode 100644 test/test_emd_multi.py create mode 100644 test/test_gpu.py delete mode 100644 test/test_gpu_sinkhorn.py delete mode 100644 test/test_gpu_sinkhorn_lpl1.py delete mode 100644 test/test_load_module.py create mode 100644 test/test_ot.py diff --git a/.travis.yml b/.travis.yml index 8a95d7c..1c3a18c 100644 --- a/.travis.yml +++ b/.travis.yml @@ -20,6 +20,6 @@ install: - python setup.py install # command to run tests + check syntax style script: - - python test/test_load_module.py -v - flake8 examples/ ot/ test/ + - python -m py.test -v # - py.test ot test diff --git a/Makefile b/Makefile index c6a83c8..ff03a63 100644 --- a/Makefile +++ b/Makefile @@ -31,22 +31,25 @@ sremove : tr '\n' '\0' < files.txt | sudo xargs -0 rm -f -- rm files.txt -clean : +clean : FORCE $(PYTHON) setup.py clean pep8 : flake8 examples/ ot/ test/ -test: - pytest +test : FORCE pep8 + python -m py.test -v -uploadpypi: +uploadpypi : #python setup.py register python setup.py sdist upload -r pypi -rdoc: +rdoc : pandoc --from=markdown --to=rst --output=docs/source/readme.rst README.md notebook : ipython notebook --matplotlib=inline --notebook-dir=notebooks/ + + +FORCE : diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 611001b..c1e0017 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -28,8 +28,8 @@ available in the examples folder. Installation ------------ -The Library has been tested on Linux and MacOSX. It requires a C++ -compiler for using the EMD solver and rely on the following Python +The library has been tested on Linux, MacOSX and Windows. It requires a +C++ compiler for using the EMD solver and relies on the following Python modules: - Numpy (>=1.11) @@ -37,25 +37,34 @@ modules: - Cython (>=0.23) - Matplotlib (>=1.5) -Under debian based linux the dependencies can be installed with +Pip installation +^^^^^^^^^^^^^^^^ + +You can install the toolbox through PyPI with: :: - sudo apt-get install python-numpy python-scipy python-matplotlib cython + pip install POT -To install the library, you can install it locally (after downloading -it) on you machine using +or get the very latest version by downloading it and then running: :: python setup.py install --user # for user install (no root) -The toolbox is also available on PyPI with a possibly slightly older -version. You can install it with: +Anaconda installation with conda-forge +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +If you use the Anaconda python distribution, POT is available in +`conda-forge `__. To install it and the +required dependencies: :: - pip install POT + conda install -c conda-forge pot + +Post installation check +^^^^^^^^^^^^^^^^^^^^^^^ After a correct installation, you should be able to import the module without errors: @@ -109,6 +118,7 @@ Short examples # a,b are 1D histograms (sum to 1 and positive) # M is the ground cost matrix Wd=ot.emd2(a,b,M) # exact linear program + Wd_reg=ot.sinkhorn2(a,b,M,reg) # entropic regularized OT # if b is a matrix compute all distances to a and return a vector - Compute OT matrix @@ -117,8 +127,8 @@ Short examples # a,b are 1D histograms (sum to 1 and positive) # M is the ground cost matrix - Totp=ot.emd(a,b,M) # exact linear program - Totp_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT + T=ot.emd(a,b,M) # exact linear program + T_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT - Compute Wasserstein barycenter @@ -172,6 +182,7 @@ The contributors to this library are: - `Rémi Flamary `__ - `Nicolas Courty `__ +- `Alexandre Gramfort `__ - `Laetitia Chapel `__ - `Michael Perrot `__ (Mapping estimation) @@ -189,6 +200,25 @@ languages): - `Marco Cuturi `__ (Sinkhorn Knopp in Matlab/Cuda) +Contributions and code of conduct +--------------------------------- + +Every contribution is welcome and should respect the `contribution +guidelines `__. Each member of the project is expected +to follow the `code of conduct `__. + +Support +------- + +You can ask questions and join the development discussion: + +- On the `POT Slack channel `__ +- On the POT `mailing + list `__ + +You can also post bug reports and feature requests in Github issues. +Make sure to read our `guidelines `__ first. + References ---------- diff --git a/test/test_emd_multi.py b/test/test_emd_multi.py deleted file mode 100644 index 2eef242..0000000 --- a/test/test_emd_multi.py +++ /dev/null @@ -1,47 +0,0 @@ -#!/usr/bin/env python2 -# -*- coding: utf-8 -*- -""" -Created on Fri Mar 10 09:56:06 2017 - -@author: rflamary -""" - -import numpy as np - -import ot -from ot.datasets import get_1D_gauss as gauss -# reload(ot.lp) - -#%% parameters - -n = 5000 # nb bins - -# bin positions -x = np.arange(n, dtype=np.float64) - -# Gaussian distributions -a = gauss(n, m=20, s=5) # m= mean, s= std - -ls = np.arange(20, 1000, 10) -nb = len(ls) -b = np.zeros((n, nb)) -for i in range(nb): - b[:, i] = gauss(n, m=ls[i], s=10) - -# loss matrix -M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) -# M/=M.max() - -#%% - -print('Computing {} EMD '.format(nb)) - -# emd loss 1 proc -ot.tic() -emd_loss4 = ot.emd2(a, b, M, 1) -ot.toc('1 proc : {} s') - -# emd loss multipro proc -ot.tic() -emd_loss4 = ot.emd2(a, b, M) -ot.toc('multi proc : {} s') diff --git a/test/test_gpu.py b/test/test_gpu.py new file mode 100644 index 0000000..312a2d4 --- /dev/null +++ b/test/test_gpu.py @@ -0,0 +1,59 @@ +import ot +import numpy as np +import time +import pytest + + +@pytest.mark.skip(reason="No way to test GPU on travis yet") +def test_gpu_sinkhorn(): + import ot.gpu + + def describeRes(r): + print("min:{:.3E}, max::{:.3E}, mean::{:.3E}, std::{:.3E}".format( + np.min(r), np.max(r), np.mean(r), np.std(r))) + + for n in [5000]: + print(n) + a = np.random.rand(n // 4, 100) + b = np.random.rand(n, 100) + time1 = time.time() + transport = ot.da.OTDA_sinkhorn() + transport.fit(a, b) + G1 = transport.G + time2 = time.time() + transport = ot.gpu.da.OTDA_sinkhorn() + transport.fit(a, b) + G2 = transport.G + time3 = time.time() + print("Normal sinkhorn, time: {:6.2f} sec ".format(time2 - time1)) + describeRes(G1) + print(" GPU sinkhorn, time: {:6.2f} sec ".format(time3 - time2)) + describeRes(G2) + + +@pytest.mark.skip(reason="No way to test GPU on travis yet") +def test_gpu_sinkhorn_lpl1(): + def describeRes(r): + print("min:{:.3E}, max:{:.3E}, mean:{:.3E}, std:{:.3E}" + .format(np.min(r), np.max(r), np.mean(r), np.std(r))) + + for n in [5000]: + print(n) + a = np.random.rand(n // 4, 100) + labels_a = np.random.randint(10, size=(n // 4)) + b = np.random.rand(n, 100) + time1 = time.time() + transport = ot.da.OTDA_lpl1() + transport.fit(a, labels_a, b) + G1 = transport.G + time2 = time.time() + transport = ot.gpu.da.OTDA_lpl1() + transport.fit(a, labels_a, b) + G2 = transport.G + time3 = time.time() + print("Normal sinkhorn lpl1, time: {:6.2f} sec ".format( + time2 - time1)) + describeRes(G1) + print(" GPU sinkhorn lpl1, time: {:6.2f} sec ".format( + time3 - time2)) + describeRes(G2) diff --git a/test/test_gpu_sinkhorn.py b/test/test_gpu_sinkhorn.py deleted file mode 100644 index 841f062..0000000 --- a/test/test_gpu_sinkhorn.py +++ /dev/null @@ -1,28 +0,0 @@ -import ot -import numpy as np -import time -import ot.gpu - - -def describeRes(r): - print("min:{:.3E}, max::{:.3E}, mean::{:.3E}, std::{:.3E}".format( - np.min(r), np.max(r), np.mean(r), np.std(r))) - - -for n in [5000, 10000, 15000, 20000]: - print(n) - a = np.random.rand(n // 4, 100) - b = np.random.rand(n, 100) - time1 = time.time() - transport = ot.da.OTDA_sinkhorn() - transport.fit(a, b) - G1 = transport.G - time2 = time.time() - transport = ot.gpu.da.OTDA_sinkhorn() - transport.fit(a, b) - G2 = transport.G - time3 = time.time() - print("Normal sinkhorn, time: {:6.2f} sec ".format(time2 - time1)) - describeRes(G1) - print(" GPU sinkhorn, time: {:6.2f} sec ".format(time3 - time2)) - describeRes(G2) diff --git a/test/test_gpu_sinkhorn_lpl1.py b/test/test_gpu_sinkhorn_lpl1.py deleted file mode 100644 index f0eb7e6..0000000 --- a/test/test_gpu_sinkhorn_lpl1.py +++ /dev/null @@ -1,29 +0,0 @@ -import ot -import numpy as np -import time -import ot.gpu - - -def describeRes(r): - print("min:{:.3E}, max:{:.3E}, mean:{:.3E}, std:{:.3E}" - .format(np.min(r), np.max(r), np.mean(r), np.std(r))) - - -for n in [5000, 10000, 15000, 20000]: - print(n) - a = np.random.rand(n // 4, 100) - labels_a = np.random.randint(10, size=(n // 4)) - b = np.random.rand(n, 100) - time1 = time.time() - transport = ot.da.OTDA_lpl1() - transport.fit(a, labels_a, b) - G1 = transport.G - time2 = time.time() - transport = ot.gpu.da.OTDA_lpl1() - transport.fit(a, labels_a, b) - G2 = transport.G - time3 = time.time() - print("Normal sinkhorn lpl1, time: {:6.2f} sec ".format(time2 - time1)) - describeRes(G1) - print(" GPU sinkhorn lpl1, time: {:6.2f} sec ".format(time3 - time2)) - describeRes(G2) diff --git a/test/test_load_module.py b/test/test_load_module.py deleted file mode 100644 index d77261e..0000000 --- a/test/test_load_module.py +++ /dev/null @@ -1,10 +0,0 @@ - - -import ot -import doctest - -# test lp solver -doctest.testmod(ot.lp, verbose=True) - -# test bregman solver -doctest.testmod(ot.bregman, verbose=True) diff --git a/test/test_ot.py b/test/test_ot.py new file mode 100644 index 0000000..51ee510 --- /dev/null +++ b/test/test_ot.py @@ -0,0 +1,55 @@ + + +import ot +import numpy as np + +#import pytest + + +def test_doctest(): + + import doctest + + # test lp solver + doctest.testmod(ot.lp, verbose=True) + + # test bregman solver + doctest.testmod(ot.bregman, verbose=True) + + +#@pytest.mark.skip(reason="Seems to be a conflict between pytest and multiprocessing") +def test_emd_multi(): + + from ot.datasets import get_1D_gauss as gauss + + n = 1000 # nb bins + + # bin positions + x = np.arange(n, dtype=np.float64) + + # Gaussian distributions + a = gauss(n, m=20, s=5) # m= mean, s= std + + ls = np.arange(20, 1000, 10) + nb = len(ls) + b = np.zeros((n, nb)) + for i in range(nb): + b[:, i] = gauss(n, m=ls[i], s=10) + + # loss matrix + M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) + # M/=M.max() + + print('Computing {} EMD '.format(nb)) + + # emd loss 1 proc + ot.tic() + emd1 = ot.emd2(a, b, M, 1) + ot.toc('1 proc : {} s') + + # emd loss multipro proc + ot.tic() + emdn = ot.emd2(a, b, M) + ot.toc('multi proc : {} s') + + assert np.allclose(emd1, emdn) -- cgit v1.2.3 From beed8f49ee8d0bf828fc0b63f23254561d7566b0 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Jul 2017 11:21:00 +0200 Subject: update test --- .travis.yml | 2 +- Makefile | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/.travis.yml b/.travis.yml index 1c3a18c..c5ca62b 100644 --- a/.travis.yml +++ b/.travis.yml @@ -21,5 +21,5 @@ install: # command to run tests + check syntax style script: - flake8 examples/ ot/ test/ - - python -m py.test -v + - python -m py.test -v test/ # - py.test ot test diff --git a/Makefile b/Makefile index ff03a63..cabe6a9 100644 --- a/Makefile +++ b/Makefile @@ -38,7 +38,7 @@ pep8 : flake8 examples/ ot/ test/ test : FORCE pep8 - python -m py.test -v + python -m py.test -v test/ uploadpypi : #python setup.py register -- cgit v1.2.3 From 01f8c44d3e6dbe129b4b621af370bb6f015ab2b2 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Jul 2017 11:29:25 +0200 Subject: try debug travis --- .travis.yml | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index c5ca62b..dc415a9 100644 --- a/.travis.yml +++ b/.travis.yml @@ -17,9 +17,10 @@ before_install: install: - pip install -r requirements.txt - pip install flake8 pytest - - python setup.py install + - pip install . # command to run tests + check syntax style script: + - python setup.py develop - flake8 examples/ ot/ test/ - python -m py.test -v test/ # - py.test ot test -- cgit v1.2.3 From ff104a6dde2d652283f72d7901bbe79dfb8571ed Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Jul 2017 11:54:59 +0200 Subject: add test for emd and emd2 --- test/test_ot.py | 24 ++++++++++++++++++++++-- 1 file changed, 22 insertions(+), 2 deletions(-) diff --git a/test/test_ot.py b/test/test_ot.py index 51ee510..6976818 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -3,7 +3,7 @@ import ot import numpy as np -#import pytest +# import pytest def test_doctest(): @@ -17,8 +17,28 @@ def test_doctest(): doctest.testmod(ot.bregman, verbose=True) +def test_emd_emd2(): + # test emd + n = 100 + + x = np.random.randn(n, 2) + u = ot.utils.unif(n) + + M = ot.dist(x, x) + + G = ot.emd(u, u, M) + + # check G is identity + assert np.allclose(G, np.eye(n) / n) + + w = ot.emd2(u, u, M) + + # check loss=0 + assert np.allclose(w, 0) + + #@pytest.mark.skip(reason="Seems to be a conflict between pytest and multiprocessing") -def test_emd_multi(): +def test_emd2_multi(): from ot.datasets import get_1D_gauss as gauss -- cgit v1.2.3 From 75492827c89a47cbc6807d4859be178d255c49bc Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Jul 2017 12:09:15 +0200 Subject: add test sinkhorn --- Makefile | 3 +++ ot/gpu/bregman.py | 2 +- test/test_ot.py | 46 +++++++++++++++++++++++++++++++++++++++++----- 3 files changed, 45 insertions(+), 6 deletions(-) diff --git a/Makefile b/Makefile index cabe6a9..577bbbe 100644 --- a/Makefile +++ b/Makefile @@ -39,6 +39,9 @@ pep8 : test : FORCE pep8 python -m py.test -v test/ + +pytest : FORCE + python -m py.test -v test/ uploadpypi : #python setup.py register diff --git a/ot/gpu/bregman.py b/ot/gpu/bregman.py index 7881c65..2302f80 100644 --- a/ot/gpu/bregman.py +++ b/ot/gpu/bregman.py @@ -9,7 +9,7 @@ import cudamat def sinkhorn(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False, log=False, returnAsGPU=False): - """ + r""" Solve the entropic regularization optimal transport problem on GPU The function solves the following optimization problem: diff --git a/test/test_ot.py b/test/test_ot.py index 6976818..b69d080 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -18,8 +18,9 @@ def test_doctest(): def test_emd_emd2(): - # test emd + # test emd and emd2 for simple identity n = 100 + np.random.seed(0) x = np.random.randn(n, 2) u = ot.utils.unif(n) @@ -35,14 +36,13 @@ def test_emd_emd2(): # check loss=0 assert np.allclose(w, 0) - - -#@pytest.mark.skip(reason="Seems to be a conflict between pytest and multiprocessing") + def test_emd2_multi(): from ot.datasets import get_1D_gauss as gauss n = 1000 # nb bins + np.random.seed(0) # bin positions x = np.arange(n, dtype=np.float64) @@ -72,4 +72,40 @@ def test_emd2_multi(): emdn = ot.emd2(a, b, M) ot.toc('multi proc : {} s') - assert np.allclose(emd1, emdn) + assert np.allclose(emd1, emdn) + + +def test_sinkhorn(): + # test sinkhorn + n = 100 + np.random.seed(0) + + x = np.random.randn(n, 2) + u = ot.utils.unif(n) + + M = ot.dist(x, x) + + G = ot.sinkhorn(u, u, M,1,stopThr=1e-10) + + # check constratints + assert np.allclose(u, G.sum(1), atol=1e-05) # cf convergence sinkhorn + assert np.allclose(u, G.sum(0), atol=1e-05) # cf convergence sinkhorn + +def test_sinkhorn_variants(): + # test sinkhorn + n = 100 + np.random.seed(0) + + x = np.random.randn(n, 2) + u = ot.utils.unif(n) + + M = ot.dist(x, x) + + G0 = ot.sinkhorn(u, u, M,1, method='sinkhorn',stopThr=1e-10) + Gs = ot.sinkhorn(u, u, M,1, method='sinkhorn_stabilized',stopThr=1e-10) + Ges = ot.sinkhorn(u, u, M,1, method='sinkhorn_epsilon_scaling',stopThr=1e-10) + + # check constratints + assert np.allclose(G0, Gs, atol=1e-05) + assert np.allclose(G0, Ges, atol=1e-05) # + -- cgit v1.2.3 From a8a0995edefd437f56b91b95c2628fb031428a08 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Jul 2017 12:11:34 +0200 Subject: pep8 tests --- test/test_bregman.py | 43 +++++++++++++++++++++++++++++++++++++++++++ test/test_ot.py | 34 ++++++++++++++++++---------------- 2 files changed, 61 insertions(+), 16 deletions(-) create mode 100644 test/test_bregman.py diff --git a/test/test_bregman.py b/test/test_bregman.py new file mode 100644 index 0000000..fd2c972 --- /dev/null +++ b/test/test_bregman.py @@ -0,0 +1,43 @@ + + +import ot +import numpy as np + +# import pytest + + +def test_sinkhorn(): + # test sinkhorn + n = 100 + np.random.seed(0) + + x = np.random.randn(n, 2) + u = ot.utils.unif(n) + + M = ot.dist(x, x) + + G = ot.sinkhorn(u, u, M, 1, stopThr=1e-10) + + # check constratints + assert np.allclose(u, G.sum(1), atol=1e-05) # cf convergence sinkhorn + assert np.allclose(u, G.sum(0), atol=1e-05) # cf convergence sinkhorn + + +def test_sinkhorn_variants(): + # test sinkhorn + n = 100 + np.random.seed(0) + + x = np.random.randn(n, 2) + u = ot.utils.unif(n) + + M = ot.dist(x, x) + + G0 = ot.sinkhorn(u, u, M, 1, method='sinkhorn', stopThr=1e-10) + Gs = ot.sinkhorn(u, u, M, 1, method='sinkhorn_stabilized', stopThr=1e-10) + Ges = ot.sinkhorn( + u, u, M, 1, method='sinkhorn_epsilon_scaling', stopThr=1e-10) + + # check constratints + assert np.allclose(G0, Gs, atol=1e-05) + assert np.allclose(G0, Ges, atol=1e-05) diff --git a/test/test_ot.py b/test/test_ot.py index b69d080..9103ac8 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -36,7 +36,8 @@ def test_emd_emd2(): # check loss=0 assert np.allclose(w, 0) - + + def test_emd2_multi(): from ot.datasets import get_1D_gauss as gauss @@ -72,11 +73,11 @@ def test_emd2_multi(): emdn = ot.emd2(a, b, M) ot.toc('multi proc : {} s') - assert np.allclose(emd1, emdn) - - + assert np.allclose(emd1, emdn) + + def test_sinkhorn(): - # test sinkhorn + # test sinkhorn n = 100 np.random.seed(0) @@ -85,14 +86,15 @@ def test_sinkhorn(): M = ot.dist(x, x) - G = ot.sinkhorn(u, u, M,1,stopThr=1e-10) + G = ot.sinkhorn(u, u, M, 1, stopThr=1e-10) # check constratints - assert np.allclose(u, G.sum(1), atol=1e-05) # cf convergence sinkhorn - assert np.allclose(u, G.sum(0), atol=1e-05) # cf convergence sinkhorn - + assert np.allclose(u, G.sum(1), atol=1e-05) # cf convergence sinkhorn + assert np.allclose(u, G.sum(0), atol=1e-05) # cf convergence sinkhorn + + def test_sinkhorn_variants(): - # test sinkhorn + # test sinkhorn n = 100 np.random.seed(0) @@ -101,11 +103,11 @@ def test_sinkhorn_variants(): M = ot.dist(x, x) - G0 = ot.sinkhorn(u, u, M,1, method='sinkhorn',stopThr=1e-10) - Gs = ot.sinkhorn(u, u, M,1, method='sinkhorn_stabilized',stopThr=1e-10) - Ges = ot.sinkhorn(u, u, M,1, method='sinkhorn_epsilon_scaling',stopThr=1e-10) + G0 = ot.sinkhorn(u, u, M, 1, method='sinkhorn', stopThr=1e-10) + Gs = ot.sinkhorn(u, u, M, 1, method='sinkhorn_stabilized', stopThr=1e-10) + Ges = ot.sinkhorn( + u, u, M, 1, method='sinkhorn_epsilon_scaling', stopThr=1e-10) # check constratints - assert np.allclose(G0, Gs, atol=1e-05) - assert np.allclose(G0, Ges, atol=1e-05) # - + assert np.allclose(G0, Gs, atol=1e-05) + assert np.allclose(G0, Ges, atol=1e-05) -- cgit v1.2.3 From d9205c886219d5410bc4705b46d9f14710c81ddd Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Jul 2017 12:13:37 +0200 Subject: clean tests --- test/test_ot.py | 37 ------------------------------------- 1 file changed, 37 deletions(-) diff --git a/test/test_ot.py b/test/test_ot.py index 9103ac8..3fa1bc4 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -74,40 +74,3 @@ def test_emd2_multi(): ot.toc('multi proc : {} s') assert np.allclose(emd1, emdn) - - -def test_sinkhorn(): - # test sinkhorn - n = 100 - np.random.seed(0) - - x = np.random.randn(n, 2) - u = ot.utils.unif(n) - - M = ot.dist(x, x) - - G = ot.sinkhorn(u, u, M, 1, stopThr=1e-10) - - # check constratints - assert np.allclose(u, G.sum(1), atol=1e-05) # cf convergence sinkhorn - assert np.allclose(u, G.sum(0), atol=1e-05) # cf convergence sinkhorn - - -def test_sinkhorn_variants(): - # test sinkhorn - n = 100 - np.random.seed(0) - - x = np.random.randn(n, 2) - u = ot.utils.unif(n) - - M = ot.dist(x, x) - - G0 = ot.sinkhorn(u, u, M, 1, method='sinkhorn', stopThr=1e-10) - Gs = ot.sinkhorn(u, u, M, 1, method='sinkhorn_stabilized', stopThr=1e-10) - Ges = ot.sinkhorn( - u, u, M, 1, method='sinkhorn_epsilon_scaling', stopThr=1e-10) - - # check constratints - assert np.allclose(G0, Gs, atol=1e-05) - assert np.allclose(G0, Ges, atol=1e-05) -- cgit v1.2.3 From 1cf304cee298e2752ce29c83e5201f593722c3af Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Jul 2017 13:40:51 +0200 Subject: add tests for utils --- test/test_utils.py | 76 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 76 insertions(+) create mode 100644 test/test_utils.py diff --git a/test/test_utils.py b/test/test_utils.py new file mode 100644 index 0000000..3219fce --- /dev/null +++ b/test/test_utils.py @@ -0,0 +1,76 @@ + + +import ot +import numpy as np + +# import pytest + + +def test_parmap(): + + n = 100 + + def f(i): + return 1.0 * i * i + + a = np.arange(n) + + l1 = map(f, a) + + l2 = ot.utils.parmap(f, a) + + assert np.allclose(l1, l2) + + +def test_tic_toc(): + + import time + + ot.tic() + time.sleep(0.5) + t = ot.toc() + t2 = ot.toq() + + # test timing + assert np.allclose(0.5, t, rtol=1e-2, atol=1e-2) + + # test toc vs toq + assert np.allclose(t, t2, rtol=1e-2, atol=1e-2) + + +def test_kernel(): + + n = 100 + + x = np.random.randn(n, 2) + + K = ot.utils.kernel(x, x) + + # gaussian kernel has ones on the diagonal + assert np.allclose(np.diag(K), np.ones(n)) + + +def test_unif(): + + n = 100 + + u = ot.unif(n) + + assert np.allclose(1, np.sum(u)) + + +def test_dist(): + + n = 100 + + x = np.random.randn(n, 2) + + D = np.zeros((n, n)) + for i in range(n): + for j in range(n): + D[i, j] = np.sum(np.square(x[i, :] - x[j, :])) + + D2 = ot.dist(x, x) + + # dist shoul return squared euclidean + assert np.allclose(D, D2) -- cgit v1.2.3 From b2f91f24796a996a82db41e91f56ba6a51989159 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Jul 2017 14:26:25 +0200 Subject: full coveragre utils --- Makefile | 4 ++-- test/test_gpu.py | 18 ++++++++++++++---- test/test_ot.py | 4 +++- test/test_utils.py | 46 ++++++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 65 insertions(+), 7 deletions(-) diff --git a/Makefile b/Makefile index 577bbbe..98f5614 100644 --- a/Makefile +++ b/Makefile @@ -38,10 +38,10 @@ pep8 : flake8 examples/ ot/ test/ test : FORCE pep8 - python -m py.test -v test/ + python -m py.test -v test/ --cov=ot --cov-report html:cov_html pytest : FORCE - python -m py.test -v test/ + python -m py.test -v test/ --cov=ot uploadpypi : #python setup.py register diff --git a/test/test_gpu.py b/test/test_gpu.py index 312a2d4..49b98d0 100644 --- a/test/test_gpu.py +++ b/test/test_gpu.py @@ -3,8 +3,14 @@ import numpy as np import time import pytest +try: # test if cudamat installed + import ot.gpu + nogpu = False +except ImportError: + nogpu = True + -@pytest.mark.skip(reason="No way to test GPU on travis yet") +@pytest.mark.skipif(nogpu, reason="No GPU available") def test_gpu_sinkhorn(): import ot.gpu @@ -12,7 +18,7 @@ def test_gpu_sinkhorn(): print("min:{:.3E}, max::{:.3E}, mean::{:.3E}, std::{:.3E}".format( np.min(r), np.max(r), np.mean(r), np.std(r))) - for n in [5000]: + for n in [50, 100, 500, 1000]: print(n) a = np.random.rand(n // 4, 100) b = np.random.rand(n, 100) @@ -30,14 +36,16 @@ def test_gpu_sinkhorn(): print(" GPU sinkhorn, time: {:6.2f} sec ".format(time3 - time2)) describeRes(G2) + assert np.allclose(G1, G2, rtol=1e-5, atol=1e-5) -@pytest.mark.skip(reason="No way to test GPU on travis yet") + +@pytest.mark.skipif(nogpu, reason="No GPU available") def test_gpu_sinkhorn_lpl1(): def describeRes(r): print("min:{:.3E}, max:{:.3E}, mean:{:.3E}, std:{:.3E}" .format(np.min(r), np.max(r), np.mean(r), np.std(r))) - for n in [5000]: + for n in [50, 100, 500, 1000]: print(n) a = np.random.rand(n // 4, 100) labels_a = np.random.randint(10, size=(n // 4)) @@ -57,3 +65,5 @@ def test_gpu_sinkhorn_lpl1(): print(" GPU sinkhorn lpl1, time: {:6.2f} sec ".format( time3 - time2)) describeRes(G2) + + assert np.allclose(G1, G2, rtol=1e-5, atol=1e-5) diff --git a/test/test_ot.py b/test/test_ot.py index 3fa1bc4..16fd510 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -31,9 +31,11 @@ def test_emd_emd2(): # check G is identity assert np.allclose(G, np.eye(n) / n) + # check constratints + assert np.allclose(u, G.sum(1)) # cf convergence sinkhorn + assert np.allclose(u, G.sum(0)) # cf convergence sinkhorn w = ot.emd2(u, u, M) - # check loss=0 assert np.allclose(w, 0) diff --git a/test/test_utils.py b/test/test_utils.py index 3219fce..e85e5b7 100644 --- a/test/test_utils.py +++ b/test/test_utils.py @@ -71,6 +71,52 @@ def test_dist(): D[i, j] = np.sum(np.square(x[i, :] - x[j, :])) D2 = ot.dist(x, x) + D3 = ot.dist(x) # dist shoul return squared euclidean assert np.allclose(D, D2) + assert np.allclose(D, D3) + + +def test_dist0(): + + n = 100 + M = ot.utils.dist0(n, method='lin_square') + + # dist0 default to linear sampling with quadratic loss + assert np.allclose(M[0, -1], (n - 1) * (n - 1)) + + +def test_dots(): + + n1, n2, n3, n4 = 100, 50, 200, 100 + + A = np.random.randn(n1, n2) + B = np.random.randn(n2, n3) + C = np.random.randn(n3, n4) + + X1 = ot.utils.dots(A, B, C) + + X2 = A.dot(B.dot(C)) + + assert np.allclose(X1, X2) + + +def test_clean_zeros(): + + n = 100 + nz = 50 + nz2 = 20 + u1 = ot.unif(n) + u1[:nz] = 0 + u1 = u1 / u1.sum() + u2 = ot.unif(n) + u2[:nz2] = 0 + u2 = u2 / u2.sum() + + M = ot.utils.dist0(n) + + a, b, M2 = ot.utils.clean_zeros(u1, u2, M) + + assert len(a) == n - nz + assert len(b) == n - nz2 -- cgit v1.2.3 From c6e648fbebd1297428f514d7bd48d3eb8814aafd Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Jul 2017 14:31:00 +0200 Subject: test parmap python 3.5 --- test/test_utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test/test_utils.py b/test/test_utils.py index e85e5b7..1a1ab02 100644 --- a/test/test_utils.py +++ b/test/test_utils.py @@ -15,9 +15,9 @@ def test_parmap(): a = np.arange(n) - l1 = map(f, a) + l1 = np.array(map(f, a)) - l2 = ot.utils.parmap(f, a) + l2 = np.array(ot.utils.parmap(f, a)) assert np.allclose(l1, l2) -- cgit v1.2.3 From a31d3c2375ffec7eb3754ab4b66f75ce9a51eddd Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Jul 2017 14:35:20 +0200 Subject: map to list --- test/test_utils.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test/test_utils.py b/test/test_utils.py index 1a1ab02..0883a8e 100644 --- a/test/test_utils.py +++ b/test/test_utils.py @@ -15,9 +15,9 @@ def test_parmap(): a = np.arange(n) - l1 = np.array(map(f, a)) + l1 = list(map(f, a)) - l2 = np.array(ot.utils.parmap(f, a)) + l2 = list(ot.utils.parmap(f, a)) assert np.allclose(l1, l2) -- cgit v1.2.3 From f8e822c48eff02a3d65fc83d09dc0471bc9555aa Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Jul 2017 14:49:14 +0200 Subject: test sinkhorn with empty marginals --- test/test_bregman.py | 31 ++++++++++++++++++++++++++++++- test/test_ot.py | 23 +++++++++++++++++++++++ 2 files changed, 53 insertions(+), 1 deletion(-) diff --git a/test/test_bregman.py b/test/test_bregman.py index fd2c972..b65de11 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -23,6 +23,33 @@ def test_sinkhorn(): assert np.allclose(u, G.sum(0), atol=1e-05) # cf convergence sinkhorn +def test_sinkhorn_empty(): + # test sinkhorn + n = 100 + np.random.seed(0) + + x = np.random.randn(n, 2) + u = ot.utils.unif(n) + + M = ot.dist(x, x) + + G = ot.sinkhorn([], [], M, 1, stopThr=1e-10) + # check constratints + assert np.allclose(u, G.sum(1), atol=1e-05) # cf convergence sinkhorn + assert np.allclose(u, G.sum(0), atol=1e-05) # cf convergence sinkhorn + + G = ot.sinkhorn([], [], M, 1, stopThr=1e-10, method='sinkhorn_stabilized') + # check constratints + assert np.allclose(u, G.sum(1), atol=1e-05) # cf convergence sinkhorn + assert np.allclose(u, G.sum(0), atol=1e-05) # cf convergence sinkhorn + + G = ot.sinkhorn( + [], [], M, 1, stopThr=1e-10, method='sinkhorn_epsilon_scaling') + # check constratints + assert np.allclose(u, G.sum(1), atol=1e-05) # cf convergence sinkhorn + assert np.allclose(u, G.sum(0), atol=1e-05) # cf convergence sinkhorn + + def test_sinkhorn_variants(): # test sinkhorn n = 100 @@ -37,7 +64,9 @@ def test_sinkhorn_variants(): Gs = ot.sinkhorn(u, u, M, 1, method='sinkhorn_stabilized', stopThr=1e-10) Ges = ot.sinkhorn( u, u, M, 1, method='sinkhorn_epsilon_scaling', stopThr=1e-10) + Gerr = ot.sinkhorn(u, u, M, 1, method='do_not_exists', stopThr=1e-10) - # check constratints + # check values assert np.allclose(G0, Gs, atol=1e-05) assert np.allclose(G0, Ges, atol=1e-05) + assert np.allclose(G0, Gerr) diff --git a/test/test_ot.py b/test/test_ot.py index 16fd510..3897397 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -40,6 +40,29 @@ def test_emd_emd2(): assert np.allclose(w, 0) +def test_emd_empty(): + # test emd and emd2 for simple identity + n = 100 + np.random.seed(0) + + x = np.random.randn(n, 2) + u = ot.utils.unif(n) + + M = ot.dist(x, x) + + G = ot.emd([], [], M) + + # check G is identity + assert np.allclose(G, np.eye(n) / n) + # check constratints + assert np.allclose(u, G.sum(1)) # cf convergence sinkhorn + assert np.allclose(u, G.sum(0)) # cf convergence sinkhorn + + w = ot.emd2([], [], M) + # check loss=0 + assert np.allclose(w, 0) + + def test_emd2_multi(): from ot.datasets import get_1D_gauss as gauss -- cgit v1.2.3 From 7d9c5e7ef81cfb1cd4725058c09a7f683ca03eef Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Jul 2017 14:58:15 +0200 Subject: add test optim --- test/test_optim.py | 65 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 65 insertions(+) create mode 100644 test/test_optim.py diff --git a/test/test_optim.py b/test/test_optim.py new file mode 100644 index 0000000..43cba7d --- /dev/null +++ b/test/test_optim.py @@ -0,0 +1,65 @@ + + +import ot +import numpy as np + +# import pytest + + +def test_conditional_gradient(): + + n = 100 # nb bins + + # bin positions + x = np.arange(n, dtype=np.float64) + + # Gaussian distributions + a = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std + b = ot.datasets.get_1D_gauss(n, m=60, s=10) + + # loss matrix + M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) + M /= M.max() + + def f(G): + return 0.5 * np.sum(G**2) + + def df(G): + return G + + reg = 1e-1 + + G, log = ot.optim.cg(a, b, M, reg, f, df, verbose=True, log=True) + + assert np.allclose(a, G.sum(1)) + assert np.allclose(b, G.sum(0)) + + +def test_generalized_conditional_gradient(): + + n = 100 # nb bins + + # bin positions + x = np.arange(n, dtype=np.float64) + + # Gaussian distributions + a = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std + b = ot.datasets.get_1D_gauss(n, m=60, s=10) + + # loss matrix + M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) + M /= M.max() + + def f(G): + return 0.5 * np.sum(G**2) + + def df(G): + return G + + reg1 = 1e-3 + reg2 = 1e-1 + + G, log = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True, log=True) + + assert np.allclose(a, G.sum(1), atol=1e-05) + assert np.allclose(b, G.sum(0), atol=1e-05) -- cgit v1.2.3 From 709d8cbc9f9961a5175eb64ae497b854e0b9b184 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Jul 2017 15:14:59 +0200 Subject: add dr tests --- test/test_dr.py | 63 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ test/test_gpu.py | 53 +++++++++++++++++++++++---------------------- test/test_optim.py | 4 ++-- 3 files changed, 93 insertions(+), 27 deletions(-) create mode 100644 test/test_dr.py diff --git a/test/test_dr.py b/test/test_dr.py new file mode 100644 index 0000000..24ccaa1 --- /dev/null +++ b/test/test_dr.py @@ -0,0 +1,63 @@ +import ot +import numpy as np +import pytest + +try: # test if cudamat installed + import ot.dr + nogo = False +except ImportError: + nogo = True + + +@pytest.mark.skipif(nogo, reason="Missing modules (autograd or pymanopt)") +def test_fda(): + + n = 100 # nb samples in source and target datasets + nz = 0.2 + np.random.seed(0) + + # generate circle dataset + t = np.random.rand(n) * 2 * np.pi + ys = np.floor((np.arange(n) * 1.0 / n * 3)) + 1 + xs = np.concatenate( + (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1) + xs = xs * ys.reshape(-1, 1) + nz * np.random.randn(n, 2) + + nbnoise = 8 + + xs = np.hstack((xs, np.random.randn(n, nbnoise))) + + p = 2 + + Pfda, projfda = ot.dr.fda(xs, ys, p) + + projfda(xs) + + assert np.allclose(np.sum(Pfda**2, 0), np.ones(p)) + + +@pytest.mark.skipif(nogo, reason="Missing modules (autograd or pymanopt)") +def test_wda(): + + n = 100 # nb samples in source and target datasets + nz = 0.2 + np.random.seed(0) + + # generate circle dataset + t = np.random.rand(n) * 2 * np.pi + ys = np.floor((np.arange(n) * 1.0 / n * 3)) + 1 + xs = np.concatenate( + (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1) + xs = xs * ys.reshape(-1, 1) + nz * np.random.randn(n, 2) + + nbnoise = 8 + + xs = np.hstack((xs, np.random.randn(n, nbnoise))) + + p = 2 + + Pwda, projwda = ot.dr.wda(xs, ys, p, maxiter=10) + + projwda(xs) + + assert np.allclose(np.sum(Pwda**2, 0), np.ones(p)) diff --git a/test/test_gpu.py b/test/test_gpu.py index 49b98d0..9cc39d7 100644 --- a/test/test_gpu.py +++ b/test/test_gpu.py @@ -12,7 +12,8 @@ except ImportError: @pytest.mark.skipif(nogpu, reason="No GPU available") def test_gpu_sinkhorn(): - import ot.gpu + + np.random.seed(0) def describeRes(r): print("min:{:.3E}, max::{:.3E}, mean::{:.3E}, std::{:.3E}".format( @@ -41,29 +42,31 @@ def test_gpu_sinkhorn(): @pytest.mark.skipif(nogpu, reason="No GPU available") def test_gpu_sinkhorn_lpl1(): - def describeRes(r): - print("min:{:.3E}, max:{:.3E}, mean:{:.3E}, std:{:.3E}" - .format(np.min(r), np.max(r), np.mean(r), np.std(r))) + np.random.seed(0) + + def describeRes(r): + print("min:{:.3E}, max:{:.3E}, mean:{:.3E}, std:{:.3E}" + .format(np.min(r), np.max(r), np.mean(r), np.std(r))) - for n in [50, 100, 500, 1000]: - print(n) - a = np.random.rand(n // 4, 100) - labels_a = np.random.randint(10, size=(n // 4)) - b = np.random.rand(n, 100) - time1 = time.time() - transport = ot.da.OTDA_lpl1() - transport.fit(a, labels_a, b) - G1 = transport.G - time2 = time.time() - transport = ot.gpu.da.OTDA_lpl1() - transport.fit(a, labels_a, b) - G2 = transport.G - time3 = time.time() - print("Normal sinkhorn lpl1, time: {:6.2f} sec ".format( - time2 - time1)) - describeRes(G1) - print(" GPU sinkhorn lpl1, time: {:6.2f} sec ".format( - time3 - time2)) - describeRes(G2) + for n in [50, 100, 500, 1000]: + print(n) + a = np.random.rand(n // 4, 100) + labels_a = np.random.randint(10, size=(n // 4)) + b = np.random.rand(n, 100) + time1 = time.time() + transport = ot.da.OTDA_lpl1() + transport.fit(a, labels_a, b) + G1 = transport.G + time2 = time.time() + transport = ot.gpu.da.OTDA_lpl1() + transport.fit(a, labels_a, b) + G2 = transport.G + time3 = time.time() + print("Normal sinkhorn lpl1, time: {:6.2f} sec ".format( + time2 - time1)) + describeRes(G1) + print(" GPU sinkhorn lpl1, time: {:6.2f} sec ".format( + time3 - time2)) + describeRes(G2) - assert np.allclose(G1, G2, rtol=1e-5, atol=1e-5) + assert np.allclose(G1, G2, rtol=1e-5, atol=1e-5) diff --git a/test/test_optim.py b/test/test_optim.py index 43cba7d..a77a37c 100644 --- a/test/test_optim.py +++ b/test/test_optim.py @@ -9,7 +9,7 @@ import numpy as np def test_conditional_gradient(): n = 100 # nb bins - + np.random.seed(0) # bin positions x = np.arange(n, dtype=np.float64) @@ -38,7 +38,7 @@ def test_conditional_gradient(): def test_generalized_conditional_gradient(): n = 100 # nb bins - + np.random.seed(0) # bin positions x = np.arange(n, dtype=np.float64) -- cgit v1.2.3 From 83ecc6df836d1a6b05bd641dfef465cc02b25b8f Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Jul 2017 15:23:14 +0200 Subject: bregman coverage --- test/test_bregman.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/test/test_bregman.py b/test/test_bregman.py index b65de11..78666c7 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -33,18 +33,20 @@ def test_sinkhorn_empty(): M = ot.dist(x, x) - G = ot.sinkhorn([], [], M, 1, stopThr=1e-10) + G, log = ot.sinkhorn([], [], M, 1, stopThr=1e-10, verbose=True, log=True) # check constratints assert np.allclose(u, G.sum(1), atol=1e-05) # cf convergence sinkhorn assert np.allclose(u, G.sum(0), atol=1e-05) # cf convergence sinkhorn - G = ot.sinkhorn([], [], M, 1, stopThr=1e-10, method='sinkhorn_stabilized') + G, log = ot.sinkhorn([], [], M, 1, stopThr=1e-10, + method='sinkhorn_stabilized', verbose=True, log=True) # check constratints assert np.allclose(u, G.sum(1), atol=1e-05) # cf convergence sinkhorn assert np.allclose(u, G.sum(0), atol=1e-05) # cf convergence sinkhorn - G = ot.sinkhorn( - [], [], M, 1, stopThr=1e-10, method='sinkhorn_epsilon_scaling') + G, log = ot.sinkhorn( + [], [], M, 1, stopThr=1e-10, method='sinkhorn_epsilon_scaling', + verbose=True, log=True) # check constratints assert np.allclose(u, G.sum(1), atol=1e-05) # cf convergence sinkhorn assert np.allclose(u, G.sum(0), atol=1e-05) # cf convergence sinkhorn -- cgit v1.2.3 From 64cf2fc4f9a9331d510afd93e9bd3b8963ff879e Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Jul 2017 15:28:43 +0200 Subject: tets barycenter --- test/test_bregman.py | 29 +++++++++++++++++++++++++++++ 1 file changed, 29 insertions(+) diff --git a/test/test_bregman.py b/test/test_bregman.py index 78666c7..2dd3498 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -72,3 +72,32 @@ def test_sinkhorn_variants(): assert np.allclose(G0, Gs, atol=1e-05) assert np.allclose(G0, Ges, atol=1e-05) assert np.allclose(G0, Gerr) + + +def test_bary(): + + n = 100 # nb bins + + # bin positions + x = np.arange(n, dtype=np.float64) + + # Gaussian distributions + a1 = ot.datasets.get_1D_gauss(n, m=30, s=10) # m= mean, s= std + a2 = ot.datasets.get_1D_gauss(n, m=40, s=10) + + # creating matrix A containing all distributions + A = np.vstack((a1, a2)).T + n_distributions = A.shape[1] + + # loss matrix + normalization + M = ot.utils.dist0(n) + M /= M.max() + + alpha = 0.5 # 0<=alpha<=1 + weights = np.array([1 - alpha, alpha]) + + # wasserstein + reg = 1e-3 + bary_wass = ot.bregman.barycenter(A, M, reg, weights) + + assert np.allclose(1, np.sum(bary_wass)) -- cgit v1.2.3 From 33f3d309209baa8c5e127d02f00aae0660ed7bfb Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Jul 2017 15:29:48 +0200 Subject: clean pep8 --- test/test_bregman.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/test/test_bregman.py b/test/test_bregman.py index 2dd3498..b204fe4 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -78,16 +78,12 @@ def test_bary(): n = 100 # nb bins - # bin positions - x = np.arange(n, dtype=np.float64) - # Gaussian distributions a1 = ot.datasets.get_1D_gauss(n, m=30, s=10) # m= mean, s= std a2 = ot.datasets.get_1D_gauss(n, m=40, s=10) # creating matrix A containing all distributions A = np.vstack((a1, a2)).T - n_distributions = A.shape[1] # loss matrix + normalization M = ot.utils.dist0(n) -- cgit v1.2.3 From bd705ed847dd7e43082e9d2771a59e539d6b7440 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Jul 2017 15:45:09 +0200 Subject: add test yunmlix and bary --- test/test_bregman.py | 34 ++++++++++++++++++++++++++++++++++ test/test_gpu.py | 2 +- test/test_ot.py | 2 +- 3 files changed, 36 insertions(+), 2 deletions(-) diff --git a/test/test_bregman.py b/test/test_bregman.py index b204fe4..025568c 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -97,3 +97,37 @@ def test_bary(): bary_wass = ot.bregman.barycenter(A, M, reg, weights) assert np.allclose(1, np.sum(bary_wass)) + + ot.bregman.barycenter(A, M, reg, log=True, verbose=True) + + +def test_unmix(): + + n = 50 # nb bins + + # Gaussian distributions + a1 = ot.datasets.get_1D_gauss(n, m=20, s=10) # m= mean, s= std + a2 = ot.datasets.get_1D_gauss(n, m=40, s=10) + + a = ot.datasets.get_1D_gauss(n, m=30, s=10) + + # creating matrix A containing all distributions + D = np.vstack((a1, a2)).T + + # loss matrix + normalization + M = ot.utils.dist0(n) + M /= M.max() + + M0 = ot.utils.dist0(2) + M0 /= M0.max() + h0 = ot.unif(2) + + # wasserstein + reg = 1e-3 + um = ot.bregman.unmix(a, D, M, M0, h0, reg, 1, alpha=0.01,) + + assert np.allclose(1, np.sum(um), rtol=1e-03, atol=1e-03) + assert np.allclose([0.5, 0.5], um, rtol=1e-03, atol=1e-03) + + ot.bregman.unmix(a, D, M, M0, h0, reg, + 1, alpha=0.01, log=True, verbose=True) diff --git a/test/test_gpu.py b/test/test_gpu.py index 9cc39d7..24797f2 100644 --- a/test/test_gpu.py +++ b/test/test_gpu.py @@ -48,7 +48,7 @@ def test_gpu_sinkhorn_lpl1(): print("min:{:.3E}, max:{:.3E}, mean:{:.3E}, std:{:.3E}" .format(np.min(r), np.max(r), np.mean(r), np.std(r))) - for n in [50, 100, 500, 1000]: + for n in [50, 100, 500]: print(n) a = np.random.rand(n // 4, 100) labels_a = np.random.randint(10, size=(n // 4)) diff --git a/test/test_ot.py b/test/test_ot.py index 3897397..5bf65c6 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -76,7 +76,7 @@ def test_emd2_multi(): # Gaussian distributions a = gauss(n, m=20, s=5) # m= mean, s= std - ls = np.arange(20, 1000, 10) + ls = np.arange(20, 1000, 20) nb = len(ls) b = np.zeros((n, nb)) for i in range(nb): -- cgit v1.2.3 From f204e983d969ed38c46d0bc85d0868a84c585db0 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Jul 2017 16:02:15 +0200 Subject: add test da 58% coverage --- test/test_da.py | 67 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 67 insertions(+) create mode 100644 test/test_da.py diff --git a/test/test_da.py b/test/test_da.py new file mode 100644 index 0000000..50d3aba --- /dev/null +++ b/test/test_da.py @@ -0,0 +1,67 @@ + + +import ot +import numpy as np + +# import pytest + + +def test_OTDA(): + + n = 150 # nb bins + + xs, ys = ot.datasets.get_data_classif('3gauss', n) + xt, yt = ot.datasets.get_data_classif('3gauss2', n) + + a, b = ot.unif(n), ot.unif(n) + + # LP problem + da_emd = ot.da.OTDA() # init class + da_emd.fit(xs, xt) # fit distributions + da_emd.interp() # interpolation of source samples + da_emd.predict(xs) # interpolation of source samples + + assert np.allclose(a, np.sum(da_emd.G, 1)) + assert np.allclose(b, np.sum(da_emd.G, 0)) + + # sinkhorn regularization + lambd = 1e-1 + da_entrop = ot.da.OTDA_sinkhorn() + da_entrop.fit(xs, xt, reg=lambd) + da_entrop.interp() + da_entrop.predict(xs) + + assert np.allclose(a, np.sum(da_entrop.G, 1), rtol=1e-3, atol=1e-3) + assert np.allclose(b, np.sum(da_entrop.G, 0), rtol=1e-3, atol=1e-3) + + # non-convex Group lasso regularization + reg = 1e-1 + eta = 1e0 + da_lpl1 = ot.da.OTDA_lpl1() + da_lpl1.fit(xs, ys, xt, reg=reg, eta=eta) + da_lpl1.interp() + da_lpl1.predict(xs) + + assert np.allclose(a, np.sum(da_lpl1.G, 1), rtol=1e-3, atol=1e-3) + assert np.allclose(b, np.sum(da_lpl1.G, 0), rtol=1e-3, atol=1e-3) + + # True Group lasso regularization + reg = 1e-1 + eta = 2e0 + da_l1l2 = ot.da.OTDA_l1l2() + da_l1l2.fit(xs, ys, xt, reg=reg, eta=eta, numItermax=20, verbose=True) + da_l1l2.interp() + da_l1l2.predict(xs) + + assert np.allclose(a, np.sum(da_l1l2.G, 1), rtol=1e-3, atol=1e-3) + assert np.allclose(b, np.sum(da_l1l2.G, 0), rtol=1e-3, atol=1e-3) + + # linear mapping + da_emd = ot.da.OTDA_mapping_linear() # init class + da_emd.fit(xs, xt, numItermax=10) # fit distributions + da_emd.predict(xs) # interpolation of source samples + + # nonlinear mapping + da_emd = ot.da.OTDA_mapping_kernel() # init class + da_emd.fit(xs, xt, numItermax=10) # fit distributions + da_emd.predict(xs) # interpolation of source samples -- cgit v1.2.3 From 5aad08aff3e1a171ef9263af4488d175139085a0 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Jul 2017 16:30:57 +0200 Subject: add test plot --- test/test_plot.py | 42 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 42 insertions(+) create mode 100644 test/test_plot.py diff --git a/test/test_plot.py b/test/test_plot.py new file mode 100644 index 0000000..8916a85 --- /dev/null +++ b/test/test_plot.py @@ -0,0 +1,42 @@ + + +import ot +import numpy as np + +# import pytest + + +def test_plot1D_mat(): + + n = 100 # nb bins + + # bin positions + x = np.arange(n, dtype=np.float64) + + # Gaussian distributions + a = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std + b = ot.datasets.get_1D_gauss(n, m=60, s=10) + + # loss matrix + M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) + M /= M.max() + + ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') + + +def test_plot2D_samples_mat(): + + n = 50 # nb samples + + mu_s = np.array([0, 0]) + cov_s = np.array([[1, 0], [0, 1]]) + + mu_t = np.array([4, 4]) + cov_t = np.array([[1, -.8], [-.8, 1]]) + + xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s) + xt = ot.datasets.get_2D_samples_gauss(n, mu_t, cov_t) + + G = 1.0 * (np.random.rand(n, n) < 0.01) + + ot.plot.plot2D_samples_mat(xs, xt, G, thr=1e-5) -- cgit v1.2.3 From a8d7301c132a225b5e4d78cae64683a5e08eae7f Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Jul 2017 16:39:19 +0200 Subject: add test plot and dataset --- test/test_dr.py | 11 +++-------- 1 file changed, 3 insertions(+), 8 deletions(-) diff --git a/test/test_dr.py b/test/test_dr.py index 24ccaa1..3da7705 100644 --- a/test/test_dr.py +++ b/test/test_dr.py @@ -12,22 +12,17 @@ except ImportError: @pytest.mark.skipif(nogo, reason="Missing modules (autograd or pymanopt)") def test_fda(): - n = 100 # nb samples in source and target datasets - nz = 0.2 + n = 90 # nb samples in source and target datasets np.random.seed(0) # generate circle dataset - t = np.random.rand(n) * 2 * np.pi - ys = np.floor((np.arange(n) * 1.0 / n * 3)) + 1 - xs = np.concatenate( - (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1) - xs = xs * ys.reshape(-1, 1) + nz * np.random.randn(n, 2) + xs, ys = ot.datasets.get_data_classif('gaussrot', n) nbnoise = 8 xs = np.hstack((xs, np.random.randn(n, nbnoise))) - p = 2 + p = 1 Pfda, projfda = ot.dr.fda(xs, ys, p) -- cgit v1.2.3 From e11b1d1a77f201896fd3f70bc5b910e99610e951 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Jul 2017 17:08:25 +0200 Subject: test plot with no X --- test/test_plot.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/test/test_plot.py b/test/test_plot.py index 8916a85..69789fa 100644 --- a/test/test_plot.py +++ b/test/test_plot.py @@ -1,13 +1,14 @@ -import ot import numpy as np - -# import pytest +import matplotlib +matplotlib.use('Agg') def test_plot1D_mat(): + import ot + n = 100 # nb bins # bin positions @@ -26,6 +27,8 @@ def test_plot1D_mat(): def test_plot2D_samples_mat(): + import ot + n = 50 # nb samples mu_s = np.array([0, 0]) -- cgit v1.2.3 From 11f065237982516c08f91e7757dd7a81c9c525d6 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Jul 2017 18:26:48 +0200 Subject: matplotlib travis --- .travis.yml | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/.travis.yml b/.travis.yml index dc415a9..ec2b3d2 100644 --- a/.travis.yml +++ b/.travis.yml @@ -13,6 +13,10 @@ matrix: python: 2.7 before_install: - ./.travis/before_install.sh +before_script: # configure a headless display to test plot generation + - "export DISPLAY=:99.0" + - "sh -e /etc/init.d/xvfb start" + - sleep 3 # give xvfb some time to start # command to install dependencies install: - pip install -r requirements.txt -- cgit v1.2.3 From 46f297f678de0051dc6d5067291d1e1046b4705e Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 26 Jul 2017 11:26:08 +0200 Subject: import nmpy before ot --- test/test_bregman.py | 4 ++-- test/test_da.py | 4 ++-- test/test_dr.py | 5 +++-- test/test_gpu.py | 3 ++- test/test_optim.py | 4 ++-- test/test_ot.py | 4 ++-- 6 files changed, 13 insertions(+), 11 deletions(-) diff --git a/test/test_bregman.py b/test/test_bregman.py index 025568c..aaa2efc 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -1,7 +1,7 @@ - -import ot import numpy as np +import ot + # import pytest diff --git a/test/test_da.py b/test/test_da.py index 50d3aba..0d92b95 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -1,7 +1,7 @@ - -import ot import numpy as np +import ot + # import pytest diff --git a/test/test_dr.py b/test/test_dr.py index 3da7705..3faba48 100644 --- a/test/test_dr.py +++ b/test/test_dr.py @@ -1,8 +1,9 @@ -import ot + import numpy as np +import ot import pytest -try: # test if cudamat installed +try: # test if autograd and pymanopt are installed import ot.dr nogo = False except ImportError: diff --git a/test/test_gpu.py b/test/test_gpu.py index 24797f2..5184a6c 100644 --- a/test/test_gpu.py +++ b/test/test_gpu.py @@ -1,5 +1,6 @@ -import ot + import numpy as np +import ot import time import pytest diff --git a/test/test_optim.py b/test/test_optim.py index a77a37c..d5c4ad0 100644 --- a/test/test_optim.py +++ b/test/test_optim.py @@ -1,7 +1,7 @@ - -import ot import numpy as np +import ot + # import pytest diff --git a/test/test_ot.py b/test/test_ot.py index 5bf65c6..a30491d 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -1,7 +1,7 @@ - -import ot import numpy as np +import ot + # import pytest -- cgit v1.2.3 From 68d74902bcd3d988fff8cb7713314063f04c0089 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 26 Jul 2017 11:34:11 +0200 Subject: numpy assert + n_bins --- test/test_bregman.py | 63 ++++++++++++++++++++++++++-------------------------- 1 file changed, 31 insertions(+), 32 deletions(-) diff --git a/test/test_bregman.py b/test/test_bregman.py index aaa2efc..1638ef6 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -3,15 +3,12 @@ import numpy as np import ot -# import pytest - - def test_sinkhorn(): # test sinkhorn n = 100 - np.random.seed(0) + rng = np.random.RandomState(0) - x = np.random.randn(n, 2) + x = rng.randn(n, 2) u = ot.utils.unif(n) M = ot.dist(x, x) @@ -19,45 +16,47 @@ def test_sinkhorn(): G = ot.sinkhorn(u, u, M, 1, stopThr=1e-10) # check constratints - assert np.allclose(u, G.sum(1), atol=1e-05) # cf convergence sinkhorn - assert np.allclose(u, G.sum(0), atol=1e-05) # cf convergence sinkhorn + np.testing.assert_allclose( + u, G.sum(1), atol=1e-05) # cf convergence sinkhorn + np.testing.assert_allclose( + u, G.sum(0), atol=1e-05) # cf convergence sinkhorn def test_sinkhorn_empty(): # test sinkhorn n = 100 - np.random.seed(0) + rng = np.random.RandomState(0) - x = np.random.randn(n, 2) + x = rng.randn(n, 2) u = ot.utils.unif(n) M = ot.dist(x, x) G, log = ot.sinkhorn([], [], M, 1, stopThr=1e-10, verbose=True, log=True) # check constratints - assert np.allclose(u, G.sum(1), atol=1e-05) # cf convergence sinkhorn - assert np.allclose(u, G.sum(0), atol=1e-05) # cf convergence sinkhorn + np.testing.assert_allclose(u, G.sum(1), atol=1e-05) + np.testing.assert_allclose(u, G.sum(0), atol=1e-05) G, log = ot.sinkhorn([], [], M, 1, stopThr=1e-10, method='sinkhorn_stabilized', verbose=True, log=True) # check constratints - assert np.allclose(u, G.sum(1), atol=1e-05) # cf convergence sinkhorn - assert np.allclose(u, G.sum(0), atol=1e-05) # cf convergence sinkhorn + np.testing.assert_allclose(u, G.sum(1), atol=1e-05) + np.testing.assert_allclose(u, G.sum(0), atol=1e-05) G, log = ot.sinkhorn( [], [], M, 1, stopThr=1e-10, method='sinkhorn_epsilon_scaling', verbose=True, log=True) # check constratints - assert np.allclose(u, G.sum(1), atol=1e-05) # cf convergence sinkhorn - assert np.allclose(u, G.sum(0), atol=1e-05) # cf convergence sinkhorn + np.testing.assert_allclose(u, G.sum(1), atol=1e-05) + np.testing.assert_allclose(u, G.sum(0), atol=1e-05) def test_sinkhorn_variants(): # test sinkhorn n = 100 - np.random.seed(0) + rng = np.random.RandomState(0) - x = np.random.randn(n, 2) + x = rng.randn(n, 2) u = ot.utils.unif(n) M = ot.dist(x, x) @@ -69,24 +68,24 @@ def test_sinkhorn_variants(): Gerr = ot.sinkhorn(u, u, M, 1, method='do_not_exists', stopThr=1e-10) # check values - assert np.allclose(G0, Gs, atol=1e-05) - assert np.allclose(G0, Ges, atol=1e-05) - assert np.allclose(G0, Gerr) + np.testing.assert_allclose(G0, Gs, atol=1e-05) + np.testing.assert_allclose(G0, Ges, atol=1e-05) + np.testing.assert_allclose(G0, Gerr) def test_bary(): - n = 100 # nb bins + n_bins = 100 # nb bins # Gaussian distributions - a1 = ot.datasets.get_1D_gauss(n, m=30, s=10) # m= mean, s= std - a2 = ot.datasets.get_1D_gauss(n, m=40, s=10) + a1 = ot.datasets.get_1D_gauss(n_bins, m=30, s=10) # m= mean, s= std + a2 = ot.datasets.get_1D_gauss(n_bins, m=40, s=10) # creating matrix A containing all distributions A = np.vstack((a1, a2)).T # loss matrix + normalization - M = ot.utils.dist0(n) + M = ot.utils.dist0(n_bins) M /= M.max() alpha = 0.5 # 0<=alpha<=1 @@ -96,26 +95,26 @@ def test_bary(): reg = 1e-3 bary_wass = ot.bregman.barycenter(A, M, reg, weights) - assert np.allclose(1, np.sum(bary_wass)) + np.testing.assert_allclose(1, np.sum(bary_wass)) ot.bregman.barycenter(A, M, reg, log=True, verbose=True) def test_unmix(): - n = 50 # nb bins + n_bins = 50 # nb bins # Gaussian distributions - a1 = ot.datasets.get_1D_gauss(n, m=20, s=10) # m= mean, s= std - a2 = ot.datasets.get_1D_gauss(n, m=40, s=10) + a1 = ot.datasets.get_1D_gauss(n_bins, m=20, s=10) # m= mean, s= std + a2 = ot.datasets.get_1D_gauss(n_bins, m=40, s=10) - a = ot.datasets.get_1D_gauss(n, m=30, s=10) + a = ot.datasets.get_1D_gauss(n_bins, m=30, s=10) # creating matrix A containing all distributions D = np.vstack((a1, a2)).T # loss matrix + normalization - M = ot.utils.dist0(n) + M = ot.utils.dist0(n_bins) M /= M.max() M0 = ot.utils.dist0(2) @@ -126,8 +125,8 @@ def test_unmix(): reg = 1e-3 um = ot.bregman.unmix(a, D, M, M0, h0, reg, 1, alpha=0.01,) - assert np.allclose(1, np.sum(um), rtol=1e-03, atol=1e-03) - assert np.allclose([0.5, 0.5], um, rtol=1e-03, atol=1e-03) + np.testing.assert_allclose(1, np.sum(um), rtol=1e-03, atol=1e-03) + np.testing.assert_allclose([0.5, 0.5], um, rtol=1e-03, atol=1e-03) ot.bregman.unmix(a, D, M, M0, h0, reg, 1, alpha=0.01, log=True, verbose=True) -- cgit v1.2.3 From 67b011a2a6a0cb8dffbb7a2619875f0e0d79588c Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 26 Jul 2017 11:38:17 +0200 Subject: numpy assert test_da --- test/test_da.py | 21 +++++++++++---------- 1 file changed, 11 insertions(+), 10 deletions(-) diff --git a/test/test_da.py b/test/test_da.py index 0d92b95..8df4795 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -6,9 +6,10 @@ import ot # import pytest -def test_OTDA(): +def test_otda(): - n = 150 # nb bins + n = 150 # nb samples + np.random.seed(0) xs, ys = ot.datasets.get_data_classif('3gauss', n) xt, yt = ot.datasets.get_data_classif('3gauss2', n) @@ -21,8 +22,8 @@ def test_OTDA(): da_emd.interp() # interpolation of source samples da_emd.predict(xs) # interpolation of source samples - assert np.allclose(a, np.sum(da_emd.G, 1)) - assert np.allclose(b, np.sum(da_emd.G, 0)) + np.testing.assert_allclose(a, np.sum(da_emd.G, 1)) + np.testing.assert_allclose(b, np.sum(da_emd.G, 0)) # sinkhorn regularization lambd = 1e-1 @@ -31,8 +32,8 @@ def test_OTDA(): da_entrop.interp() da_entrop.predict(xs) - assert np.allclose(a, np.sum(da_entrop.G, 1), rtol=1e-3, atol=1e-3) - assert np.allclose(b, np.sum(da_entrop.G, 0), rtol=1e-3, atol=1e-3) + np.testing.assert_allclose(a, np.sum(da_entrop.G, 1), rtol=1e-3, atol=1e-3) + np.testing.assert_allclose(b, np.sum(da_entrop.G, 0), rtol=1e-3, atol=1e-3) # non-convex Group lasso regularization reg = 1e-1 @@ -42,8 +43,8 @@ def test_OTDA(): da_lpl1.interp() da_lpl1.predict(xs) - assert np.allclose(a, np.sum(da_lpl1.G, 1), rtol=1e-3, atol=1e-3) - assert np.allclose(b, np.sum(da_lpl1.G, 0), rtol=1e-3, atol=1e-3) + np.testing.assert_allclose(a, np.sum(da_lpl1.G, 1), rtol=1e-3, atol=1e-3) + np.testing.assert_allclose(b, np.sum(da_lpl1.G, 0), rtol=1e-3, atol=1e-3) # True Group lasso regularization reg = 1e-1 @@ -53,8 +54,8 @@ def test_OTDA(): da_l1l2.interp() da_l1l2.predict(xs) - assert np.allclose(a, np.sum(da_l1l2.G, 1), rtol=1e-3, atol=1e-3) - assert np.allclose(b, np.sum(da_l1l2.G, 0), rtol=1e-3, atol=1e-3) + np.testing.assert_allclose(a, np.sum(da_l1l2.G, 1), rtol=1e-3, atol=1e-3) + np.testing.assert_allclose(b, np.sum(da_l1l2.G, 0), rtol=1e-3, atol=1e-3) # linear mapping da_emd = ot.da.OTDA_mapping_linear() # init class -- cgit v1.2.3 From 347e6288b87cbeef9b8fbc1a08cd130b96de1d61 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 26 Jul 2017 11:42:35 +0200 Subject: n to n_samples --- test/test_da.py | 11 ++++------- test/test_dr.py | 25 ++++++++++--------------- 2 files changed, 14 insertions(+), 22 deletions(-) diff --git a/test/test_da.py b/test/test_da.py index 8df4795..a38390f 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -3,18 +3,15 @@ import numpy as np import ot -# import pytest - - def test_otda(): - n = 150 # nb samples + n_samples = 150 # nb samples np.random.seed(0) - xs, ys = ot.datasets.get_data_classif('3gauss', n) - xt, yt = ot.datasets.get_data_classif('3gauss2', n) + xs, ys = ot.datasets.get_data_classif('3gauss', n_samples) + xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples) - a, b = ot.unif(n), ot.unif(n) + a, b = ot.unif(n_samples), ot.unif(n_samples) # LP problem da_emd = ot.da.OTDA() # init class diff --git a/test/test_dr.py b/test/test_dr.py index 3faba48..e3d1e6b 100644 --- a/test/test_dr.py +++ b/test/test_dr.py @@ -13,15 +13,15 @@ except ImportError: @pytest.mark.skipif(nogo, reason="Missing modules (autograd or pymanopt)") def test_fda(): - n = 90 # nb samples in source and target datasets + n_samples = 90 # nb samples in source and target datasets np.random.seed(0) - # generate circle dataset - xs, ys = ot.datasets.get_data_classif('gaussrot', n) + # generate gaussian dataset + xs, ys = ot.datasets.get_data_classif('gaussrot', n_samples) - nbnoise = 8 + n_features_noise = 8 - xs = np.hstack((xs, np.random.randn(n, nbnoise))) + xs = np.hstack((xs, np.random.randn(n_samples, n_features_noise))) p = 1 @@ -35,20 +35,15 @@ def test_fda(): @pytest.mark.skipif(nogo, reason="Missing modules (autograd or pymanopt)") def test_wda(): - n = 100 # nb samples in source and target datasets - nz = 0.2 + n_samples = 100 # nb samples in source and target datasets np.random.seed(0) - # generate circle dataset - t = np.random.rand(n) * 2 * np.pi - ys = np.floor((np.arange(n) * 1.0 / n * 3)) + 1 - xs = np.concatenate( - (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1) - xs = xs * ys.reshape(-1, 1) + nz * np.random.randn(n, 2) + # generate gaussian dataset + xs, ys = ot.datasets.get_data_classif('gaussrot', n_samples) - nbnoise = 8 + n_features_noise = 8 - xs = np.hstack((xs, np.random.randn(n, nbnoise))) + xs = np.hstack((xs, np.random.randn(n_samples, n_features_noise))) p = 2 -- cgit v1.2.3 From 4a45135dfa3f1aeae8b3bdf0c42422f0f60426e8 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 26 Jul 2017 11:47:29 +0200 Subject: dr +gpu numpy assert --- test/test_dr.py | 4 ++-- test/test_gpu.py | 34 +++++++++++++++++----------------- 2 files changed, 19 insertions(+), 19 deletions(-) diff --git a/test/test_dr.py b/test/test_dr.py index e3d1e6b..bdb920e 100644 --- a/test/test_dr.py +++ b/test/test_dr.py @@ -29,7 +29,7 @@ def test_fda(): projfda(xs) - assert np.allclose(np.sum(Pfda**2, 0), np.ones(p)) + np.testing.assert_allclose(np.sum(Pfda**2, 0), np.ones(p)) @pytest.mark.skipif(nogo, reason="Missing modules (autograd or pymanopt)") @@ -51,4 +51,4 @@ def test_wda(): projwda(xs) - assert np.allclose(np.sum(Pwda**2, 0), np.ones(p)) + np.testing.assert_allclose(np.sum(Pwda**2, 0), np.ones(p)) diff --git a/test/test_gpu.py b/test/test_gpu.py index 5184a6c..7ae159b 100644 --- a/test/test_gpu.py +++ b/test/test_gpu.py @@ -16,14 +16,14 @@ def test_gpu_sinkhorn(): np.random.seed(0) - def describeRes(r): + def describe_res(r): print("min:{:.3E}, max::{:.3E}, mean::{:.3E}, std::{:.3E}".format( np.min(r), np.max(r), np.mean(r), np.std(r))) - for n in [50, 100, 500, 1000]: - print(n) - a = np.random.rand(n // 4, 100) - b = np.random.rand(n, 100) + for n_samples in [50, 100, 500, 1000]: + print(n_samples) + a = np.random.rand(n_samples // 4, 100) + b = np.random.rand(n_samples, 100) time1 = time.time() transport = ot.da.OTDA_sinkhorn() transport.fit(a, b) @@ -34,26 +34,26 @@ def test_gpu_sinkhorn(): G2 = transport.G time3 = time.time() print("Normal sinkhorn, time: {:6.2f} sec ".format(time2 - time1)) - describeRes(G1) + describe_res(G1) print(" GPU sinkhorn, time: {:6.2f} sec ".format(time3 - time2)) - describeRes(G2) + describe_res(G2) - assert np.allclose(G1, G2, rtol=1e-5, atol=1e-5) + np.testing.assert_allclose(G1, G2, rtol=1e-5, atol=1e-5) @pytest.mark.skipif(nogpu, reason="No GPU available") def test_gpu_sinkhorn_lpl1(): np.random.seed(0) - def describeRes(r): + def describe_res(r): print("min:{:.3E}, max:{:.3E}, mean:{:.3E}, std:{:.3E}" .format(np.min(r), np.max(r), np.mean(r), np.std(r))) - for n in [50, 100, 500]: - print(n) - a = np.random.rand(n // 4, 100) - labels_a = np.random.randint(10, size=(n // 4)) - b = np.random.rand(n, 100) + for n_samples in [50, 100, 500]: + print(n_samples) + a = np.random.rand(n_samples // 4, 100) + labels_a = np.random.randint(10, size=(n_samples // 4)) + b = np.random.rand(n_samples, 100) time1 = time.time() transport = ot.da.OTDA_lpl1() transport.fit(a, labels_a, b) @@ -65,9 +65,9 @@ def test_gpu_sinkhorn_lpl1(): time3 = time.time() print("Normal sinkhorn lpl1, time: {:6.2f} sec ".format( time2 - time1)) - describeRes(G1) + describe_res(G1) print(" GPU sinkhorn lpl1, time: {:6.2f} sec ".format( time3 - time2)) - describeRes(G2) + describe_res(G2) - assert np.allclose(G1, G2, rtol=1e-5, atol=1e-5) + np.testing.assert_allclose(G1, G2, rtol=1e-5, atol=1e-5) -- cgit v1.2.3 From 2bc41ad8bb54c76bade6db2c0e04fa387ff29500 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 26 Jul 2017 11:48:13 +0200 Subject: rng gpu --- test/test_gpu.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/test/test_gpu.py b/test/test_gpu.py index 7ae159b..98f59f7 100644 --- a/test/test_gpu.py +++ b/test/test_gpu.py @@ -14,7 +14,7 @@ except ImportError: @pytest.mark.skipif(nogpu, reason="No GPU available") def test_gpu_sinkhorn(): - np.random.seed(0) + rng = np.random.RandomState(0) def describe_res(r): print("min:{:.3E}, max::{:.3E}, mean::{:.3E}, std::{:.3E}".format( @@ -22,8 +22,8 @@ def test_gpu_sinkhorn(): for n_samples in [50, 100, 500, 1000]: print(n_samples) - a = np.random.rand(n_samples // 4, 100) - b = np.random.rand(n_samples, 100) + a = rng.rand(n_samples // 4, 100) + b = rng.rand(n_samples, 100) time1 = time.time() transport = ot.da.OTDA_sinkhorn() transport.fit(a, b) @@ -43,7 +43,8 @@ def test_gpu_sinkhorn(): @pytest.mark.skipif(nogpu, reason="No GPU available") def test_gpu_sinkhorn_lpl1(): - np.random.seed(0) + + rng = np.random.RandomState(0) def describe_res(r): print("min:{:.3E}, max:{:.3E}, mean:{:.3E}, std:{:.3E}" @@ -51,9 +52,9 @@ def test_gpu_sinkhorn_lpl1(): for n_samples in [50, 100, 500]: print(n_samples) - a = np.random.rand(n_samples // 4, 100) + a = rng.rand(n_samples // 4, 100) labels_a = np.random.randint(10, size=(n_samples // 4)) - b = np.random.rand(n_samples, 100) + b = rng.rand(n_samples, 100) time1 = time.time() transport = ot.da.OTDA_lpl1() transport.fit(a, labels_a, b) -- cgit v1.2.3 From 6a02db058e24914cd79b638f15be9a90bce7e4f3 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 26 Jul 2017 11:51:07 +0200 Subject: test_optim --- test/test_optim.py | 22 ++++++++++------------ 1 file changed, 10 insertions(+), 12 deletions(-) diff --git a/test/test_optim.py b/test/test_optim.py index d5c4ad0..bc0b706 100644 --- a/test/test_optim.py +++ b/test/test_optim.py @@ -3,22 +3,20 @@ import numpy as np import ot -# import pytest - def test_conditional_gradient(): - n = 100 # nb bins + n_bins = 100 # nb bins np.random.seed(0) # bin positions - x = np.arange(n, dtype=np.float64) + x = np.arange(n_bins, dtype=np.float64) # Gaussian distributions - a = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std - b = ot.datasets.get_1D_gauss(n, m=60, s=10) + a = ot.datasets.get_1D_gauss(n_bins, m=20, s=5) # m= mean, s= std + b = ot.datasets.get_1D_gauss(n_bins, m=60, s=10) # loss matrix - M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) + M = ot.dist(x.reshape((n_bins, 1)), x.reshape((n_bins, 1))) M /= M.max() def f(G): @@ -37,17 +35,17 @@ def test_conditional_gradient(): def test_generalized_conditional_gradient(): - n = 100 # nb bins + n_bins = 100 # nb bins np.random.seed(0) # bin positions - x = np.arange(n, dtype=np.float64) + x = np.arange(n_bins, dtype=np.float64) # Gaussian distributions - a = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std - b = ot.datasets.get_1D_gauss(n, m=60, s=10) + a = ot.datasets.get_1D_gauss(n_bins, m=20, s=5) # m= mean, s= std + b = ot.datasets.get_1D_gauss(n_bins, m=60, s=10) # loss matrix - M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) + M = ot.dist(x.reshape((n_bins, 1)), x.reshape((n_bins, 1))) M /= M.max() def f(G): -- cgit v1.2.3 From 86418ebf5adc11879c580e88e3eaa02691de30e7 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 26 Jul 2017 11:51:45 +0200 Subject: test_optim allclose --- test/test_optim.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/test/test_optim.py b/test/test_optim.py index bc0b706..2840cad 100644 --- a/test/test_optim.py +++ b/test/test_optim.py @@ -29,8 +29,8 @@ def test_conditional_gradient(): G, log = ot.optim.cg(a, b, M, reg, f, df, verbose=True, log=True) - assert np.allclose(a, G.sum(1)) - assert np.allclose(b, G.sum(0)) + np.testing.assert_allclose(a, G.sum(1)) + np.testing.assert_allclose(b, G.sum(0)) def test_generalized_conditional_gradient(): @@ -59,5 +59,5 @@ def test_generalized_conditional_gradient(): G, log = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True, log=True) - assert np.allclose(a, G.sum(1), atol=1e-05) - assert np.allclose(b, G.sum(0), atol=1e-05) + np.testing.assert_allclose(a, G.sum(1), atol=1e-05) + np.testing.assert_allclose(b, G.sum(0), atol=1e-05) -- cgit v1.2.3 From 286de0a955bbb7e26079a8dc75abf622bf461523 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 26 Jul 2017 11:52:38 +0200 Subject: clean test_ot --- test/test_ot.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/test/test_ot.py b/test/test_ot.py index a30491d..9c0acab 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -3,8 +3,6 @@ import numpy as np import ot -# import pytest - def test_doctest(): -- cgit v1.2.3 From 81118f22197cdf4553427038526c8f730be256d7 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 26 Jul 2017 11:55:57 +0200 Subject: test_ot random state --- test/test_ot.py | 27 +++++++++++++-------------- 1 file changed, 13 insertions(+), 14 deletions(-) diff --git a/test/test_ot.py b/test/test_ot.py index 9c0acab..7fe665f 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -18,9 +18,9 @@ def test_doctest(): def test_emd_emd2(): # test emd and emd2 for simple identity n = 100 - np.random.seed(0) + rng = np.random.RandomState(0) - x = np.random.randn(n, 2) + x = rng.randn(n, 2) u = ot.utils.unif(n) M = ot.dist(x, x) @@ -28,22 +28,22 @@ def test_emd_emd2(): G = ot.emd(u, u, M) # check G is identity - assert np.allclose(G, np.eye(n) / n) + np.testing.assert_allclose(G, np.eye(n) / n) # check constratints - assert np.allclose(u, G.sum(1)) # cf convergence sinkhorn - assert np.allclose(u, G.sum(0)) # cf convergence sinkhorn + np.testing.assert_allclose(u, G.sum(1)) # cf convergence sinkhorn + np.testing.assert_allclose(u, G.sum(0)) # cf convergence sinkhorn w = ot.emd2(u, u, M) # check loss=0 - assert np.allclose(w, 0) + np.testing.assert_allclose(w, 0) def test_emd_empty(): # test emd and emd2 for simple identity n = 100 - np.random.seed(0) + rng = np.random.RandomState(0) - x = np.random.randn(n, 2) + x = rng.randn(n, 2) u = ot.utils.unif(n) M = ot.dist(x, x) @@ -51,14 +51,14 @@ def test_emd_empty(): G = ot.emd([], [], M) # check G is identity - assert np.allclose(G, np.eye(n) / n) + np.testing.assert_allclose(G, np.eye(n) / n) # check constratints - assert np.allclose(u, G.sum(1)) # cf convergence sinkhorn - assert np.allclose(u, G.sum(0)) # cf convergence sinkhorn + np.testing.assert_allclose(u, G.sum(1)) # cf convergence sinkhorn + np.testing.assert_allclose(u, G.sum(0)) # cf convergence sinkhorn w = ot.emd2([], [], M) # check loss=0 - assert np.allclose(w, 0) + np.testing.assert_allclose(w, 0) def test_emd2_multi(): @@ -66,7 +66,6 @@ def test_emd2_multi(): from ot.datasets import get_1D_gauss as gauss n = 1000 # nb bins - np.random.seed(0) # bin positions x = np.arange(n, dtype=np.float64) @@ -96,4 +95,4 @@ def test_emd2_multi(): emdn = ot.emd2(a, b, M) ot.toc('multi proc : {} s') - assert np.allclose(emd1, emdn) + np.testing.assert_allclose(emd1, emdn) -- cgit v1.2.3 From 109fc2a9243d2c0f9a911fa8c02079d2fc0277ab Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 26 Jul 2017 11:57:43 +0200 Subject: flake8 --- test/test_optim.py | 1 - test/test_ot.py | 1 - 2 files changed, 2 deletions(-) diff --git a/test/test_optim.py b/test/test_optim.py index 2840cad..05ca895 100644 --- a/test/test_optim.py +++ b/test/test_optim.py @@ -3,7 +3,6 @@ import numpy as np import ot - def test_conditional_gradient(): n_bins = 100 # nb bins diff --git a/test/test_ot.py b/test/test_ot.py index 7fe665f..531e6e0 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -3,7 +3,6 @@ import numpy as np import ot - def test_doctest(): import doctest -- cgit v1.2.3 From e0fa14ba146e6f92a3060b5f2f0a5c01bd18bdc4 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 26 Jul 2017 11:58:58 +0200 Subject: flake8 --- test/test_plot.py | 18 +++++++++--------- test/test_utils.py | 3 --- 2 files changed, 9 insertions(+), 12 deletions(-) diff --git a/test/test_plot.py b/test/test_plot.py index 69789fa..d826988 100644 --- a/test/test_plot.py +++ b/test/test_plot.py @@ -9,17 +9,17 @@ def test_plot1D_mat(): import ot - n = 100 # nb bins + n_bins = 100 # nb bins # bin positions - x = np.arange(n, dtype=np.float64) + x = np.arange(n_bins, dtype=np.float64) # Gaussian distributions - a = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std - b = ot.datasets.get_1D_gauss(n, m=60, s=10) + a = ot.datasets.get_1D_gauss(n_bins, m=20, s=5) # m= mean, s= std + b = ot.datasets.get_1D_gauss(n_bins, m=60, s=10) # loss matrix - M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) + M = ot.dist(x.reshape((n_bins, 1)), x.reshape((n_bins, 1))) M /= M.max() ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') @@ -29,7 +29,7 @@ def test_plot2D_samples_mat(): import ot - n = 50 # nb samples + n_bins = 50 # nb samples mu_s = np.array([0, 0]) cov_s = np.array([[1, 0], [0, 1]]) @@ -37,9 +37,9 @@ def test_plot2D_samples_mat(): mu_t = np.array([4, 4]) cov_t = np.array([[1, -.8], [-.8, 1]]) - xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s) - xt = ot.datasets.get_2D_samples_gauss(n, mu_t, cov_t) + xs = ot.datasets.get_2D_samples_gauss(n_bins, mu_s, cov_s) + xt = ot.datasets.get_2D_samples_gauss(n_bins, mu_t, cov_t) - G = 1.0 * (np.random.rand(n, n) < 0.01) + G = 1.0 * (np.random.rand(n_bins, n_bins) < 0.01) ot.plot.plot2D_samples_mat(xs, xt, G, thr=1e-5) diff --git a/test/test_utils.py b/test/test_utils.py index 0883a8e..fe1b88d 100644 --- a/test/test_utils.py +++ b/test/test_utils.py @@ -3,9 +3,6 @@ import ot import numpy as np -# import pytest - - def test_parmap(): n = 100 -- cgit v1.2.3 From d101e088b72fa0be4648d57524946ebfc93bf34b Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 26 Jul 2017 11:59:25 +0200 Subject: nearly all review done --- test/test_utils.py | 1 + 1 file changed, 1 insertion(+) diff --git a/test/test_utils.py b/test/test_utils.py index fe1b88d..230d126 100644 --- a/test/test_utils.py +++ b/test/test_utils.py @@ -3,6 +3,7 @@ import ot import numpy as np + def test_parmap(): n = 100 -- cgit v1.2.3 From 77037cc2cb4b138d3d73d24b1ff4fc999cc64243 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 26 Jul 2017 12:00:05 +0200 Subject: gitignore --- .gitignore | 3 +++ 1 file changed, 3 insertions(+) diff --git a/.gitignore b/.gitignore index 6edce05..42a9aad 100644 --- a/.gitignore +++ b/.gitignore @@ -100,3 +100,6 @@ ENV/ # Mac stuff .DS_Store + +# coverage output folder +cov_html/ -- cgit v1.2.3 From fac003de3d3a159bb8fb6228786479cdede2df4e Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 26 Jul 2017 12:07:03 +0200 Subject: author and license for tets files --- test/test_bregman.py | 5 +++++ test/test_da.py | 5 +++++ test/test_dr.py | 5 +++++ test/test_gpu.py | 5 +++++ test/test_optim.py | 5 +++++ test/test_ot.py | 5 +++++ test/test_plot.py | 4 ++++ test/test_utils.py | 5 +++++ 8 files changed, 39 insertions(+) diff --git a/test/test_bregman.py b/test/test_bregman.py index 1638ef6..4a800fd 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -1,3 +1,8 @@ +"""Tests for module bregman on OT with bregman projections """ + +# Author: Remi Flamary +# +# License: MIT License import numpy as np import ot diff --git a/test/test_da.py b/test/test_da.py index a38390f..dfba83f 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -1,3 +1,8 @@ +"""Tests for module da on Domain Adaptation """ + +# Author: Remi Flamary +# +# License: MIT License import numpy as np import ot diff --git a/test/test_dr.py b/test/test_dr.py index bdb920e..915012d 100644 --- a/test/test_dr.py +++ b/test/test_dr.py @@ -1,3 +1,8 @@ +"""Tests for module dr on Dimensionality Reduction """ + +# Author: Remi Flamary +# +# License: MIT License import numpy as np import ot diff --git a/test/test_gpu.py b/test/test_gpu.py index 98f59f7..615c2a7 100644 --- a/test/test_gpu.py +++ b/test/test_gpu.py @@ -1,3 +1,8 @@ +"""Tests for module gpu for gpu acceleration """ + +# Author: Remi Flamary +# +# License: MIT License import numpy as np import ot diff --git a/test/test_optim.py b/test/test_optim.py index 05ca895..69496a5 100644 --- a/test/test_optim.py +++ b/test/test_optim.py @@ -1,3 +1,8 @@ +"""Tests for module optim fro OT optimization """ + +# Author: Remi Flamary +# +# License: MIT License import numpy as np import ot diff --git a/test/test_ot.py b/test/test_ot.py index 531e6e0..acd8718 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -1,3 +1,8 @@ +"""Tests for main module ot """ + +# Author: Remi Flamary +# +# License: MIT License import numpy as np import ot diff --git a/test/test_plot.py b/test/test_plot.py index d826988..f7debee 100644 --- a/test/test_plot.py +++ b/test/test_plot.py @@ -1,4 +1,8 @@ +"""Tests for module plot for visualization """ +# Author: Remi Flamary +# +# License: MIT License import numpy as np import matplotlib diff --git a/test/test_utils.py b/test/test_utils.py index 230d126..9b140db 100644 --- a/test/test_utils.py +++ b/test/test_utils.py @@ -1,3 +1,8 @@ +"""Tests for module utils for timing and parallel computation """ + +# Author: Remi Flamary +# +# License: MIT License import ot -- cgit v1.2.3 From 00970175c0f8ba9a99b61a182b32e329f219d382 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 26 Jul 2017 12:15:40 +0200 Subject: add license and authors on all modules --- ot/__init__.py | 4 ++++ ot/bregman.py | 5 +++++ ot/da.py | 6 ++++++ ot/datasets.py | 4 ++++ ot/dr.py | 4 ++++ ot/gpu/__init__.py | 5 +++++ ot/gpu/bregman.py | 5 +++++ ot/gpu/da.py | 9 +++++++++ ot/lp/__init__.py | 4 ++++ ot/lp/emd_wrap.pyx | 9 ++++++--- ot/optim.py | 4 ++++ ot/plot.py | 3 +++ ot/utils.py | 5 +++++ 13 files changed, 64 insertions(+), 3 deletions(-) diff --git a/ot/__init__.py b/ot/__init__.py index a79a5ce..c2161e4 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -4,6 +4,10 @@ """ +# Author: Remi Flamary +# +# License: MIT License + # All submodules and packages from . import lp diff --git a/ot/bregman.py b/ot/bregman.py index fe10880..71a5548 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -3,6 +3,11 @@ Bregman projections for regularized OT """ +# Author: Remi Flamary +# Nicolas Courty +# +# License: MIT License + import numpy as np diff --git a/ot/da.py b/ot/da.py index 5039fbd..977d532 100644 --- a/ot/da.py +++ b/ot/da.py @@ -3,6 +3,12 @@ Domain adaptation with optimal transport """ +# Author: Remi Flamary +# Nicolas Courty +# Michael Perrot +# +# License: MIT License + import numpy as np from .bregman import sinkhorn from .lp import emd diff --git a/ot/datasets.py b/ot/datasets.py index 4371a23..e4fe118 100644 --- a/ot/datasets.py +++ b/ot/datasets.py @@ -2,6 +2,10 @@ Simple example datasets for OT """ +# Author: Remi Flamary +# +# License: MIT License + import numpy as np import scipy as sp diff --git a/ot/dr.py b/ot/dr.py index 77cbae2..d30ab30 100644 --- a/ot/dr.py +++ b/ot/dr.py @@ -3,6 +3,10 @@ Dimension reduction with optimal transport """ +# Author: Remi Flamary +# +# License: MIT License + from scipy import linalg import autograd.numpy as np from pymanopt.manifolds import Stiefel diff --git a/ot/gpu/__init__.py b/ot/gpu/__init__.py index 40b11c0..c8f9433 100644 --- a/ot/gpu/__init__.py +++ b/ot/gpu/__init__.py @@ -4,4 +4,9 @@ from . import bregman from . import da from .bregman import sinkhorn +# Author: Remi Flamary +# Leo Gautheron +# +# License: MIT License + __all__ = ["bregman", "da", "sinkhorn"] diff --git a/ot/gpu/bregman.py b/ot/gpu/bregman.py index 2302f80..86bfec1 100644 --- a/ot/gpu/bregman.py +++ b/ot/gpu/bregman.py @@ -3,6 +3,11 @@ Bregman projections for regularized OT with GPU """ +# Author: Remi Flamary +# Leo Gautheron +# +# License: MIT License + import numpy as np import cudamat diff --git a/ot/gpu/da.py b/ot/gpu/da.py index c66e755..7fb488d 100644 --- a/ot/gpu/da.py +++ b/ot/gpu/da.py @@ -3,6 +3,15 @@ Domain adaptation with optimal transport with GPU implementation """ +# Author: Remi Flamary +# Nicolas Courty +# Michael Perrot +# Leo Gautheron +# +# License: MIT License + + + import numpy as np from ..utils import unif from ..da import OTDA diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index db3da78..6e0bdb8 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -3,6 +3,10 @@ Solvers for the original linear program OT problem """ +# Author: Remi Flamary +# +# License: MIT License + import numpy as np # import compiled emd from .emd_wrap import emd_c, emd2_c diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 46794ab..46c96c1 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -1,9 +1,12 @@ # -*- coding: utf-8 -*- """ -Created on Thu Sep 11 08:42:08 2014 - -@author: rflamary +Cython linker with C solver """ + +# Author: Remi Flamary +# +# License: MIT License + import numpy as np cimport numpy as np diff --git a/ot/optim.py b/ot/optim.py index adad95e..c59089c 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -3,6 +3,10 @@ Optimization algorithms for OT """ +# Author: Remi Flamary +# +# License: MIT License + import numpy as np from scipy.optimize.linesearch import scalar_search_armijo from .lp import emd diff --git a/ot/plot.py b/ot/plot.py index 61afc9f..784a372 100644 --- a/ot/plot.py +++ b/ot/plot.py @@ -2,6 +2,9 @@ Functions for plotting OT matrices """ +# Author: Remi Flamary +# +# License: MIT License import numpy as np import matplotlib.pylab as pl diff --git a/ot/utils.py b/ot/utils.py index 1dee932..2b2f8b3 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -2,6 +2,11 @@ """ Various function that can be usefull """ + +# Author: Remi Flamary +# +# License: MIT License + import multiprocessing from functools import reduce import time -- cgit v1.2.3 From 251af8eec2b39e74000242cbf5bff5e13910cfe8 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 26 Jul 2017 12:18:33 +0200 Subject: add author to all examples --- examples/plot_OTDA_2D.py | 4 ++++ examples/plot_OTDA_classes.py | 4 ++++ examples/plot_OTDA_color_images.py | 4 ++++ examples/plot_OTDA_mapping.py | 4 ++++ examples/plot_OTDA_mapping_color_images.py | 4 ++++ examples/plot_OT_1D.py | 5 ++++- examples/plot_OT_2D_samples.py | 5 ++++- examples/plot_OT_L1_vs_L2.py | 5 ++++- examples/plot_WDA.py | 5 ++++- examples/plot_barycenter_1D.py | 6 ++++-- examples/plot_compute_emd.py | 5 ++++- 11 files changed, 44 insertions(+), 7 deletions(-) diff --git a/examples/plot_OTDA_2D.py b/examples/plot_OTDA_2D.py index 1bda59c..f2108c6 100644 --- a/examples/plot_OTDA_2D.py +++ b/examples/plot_OTDA_2D.py @@ -6,6 +6,10 @@ OT for empirical distributions """ +# Author: Remi Flamary +# +# License: MIT License + import numpy as np import matplotlib.pylab as pl import ot diff --git a/examples/plot_OTDA_classes.py b/examples/plot_OTDA_classes.py index 4d3846a..53e4bae 100644 --- a/examples/plot_OTDA_classes.py +++ b/examples/plot_OTDA_classes.py @@ -6,6 +6,10 @@ OT for domain adaptation """ +# Author: Remi Flamary +# +# License: MIT License + import matplotlib.pylab as pl import ot diff --git a/examples/plot_OTDA_color_images.py b/examples/plot_OTDA_color_images.py index 75ac5b6..c5ff873 100644 --- a/examples/plot_OTDA_color_images.py +++ b/examples/plot_OTDA_color_images.py @@ -9,6 +9,10 @@ Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. """ +# Author: Remi Flamary +# +# License: MIT License + import numpy as np from scipy import ndimage import matplotlib.pylab as pl diff --git a/examples/plot_OTDA_mapping.py b/examples/plot_OTDA_mapping.py index a5c2b21..a0d7f8b 100644 --- a/examples/plot_OTDA_mapping.py +++ b/examples/plot_OTDA_mapping.py @@ -9,6 +9,10 @@ OT mapping estimation for domain adaptation [8] Neural Information Processing Systems (NIPS), 2016. """ +# Author: Remi Flamary +# +# License: MIT License + import numpy as np import matplotlib.pylab as pl import ot diff --git a/examples/plot_OTDA_mapping_color_images.py b/examples/plot_OTDA_mapping_color_images.py index 9710461..8064b25 100644 --- a/examples/plot_OTDA_mapping_color_images.py +++ b/examples/plot_OTDA_mapping_color_images.py @@ -11,6 +11,10 @@ OT for domain adaptation with image color adaptation [6] with mapping estimation """ +# Author: Remi Flamary +# +# License: MIT License + import numpy as np from scipy import ndimage import matplotlib.pylab as pl diff --git a/examples/plot_OT_1D.py b/examples/plot_OT_1D.py index 2f3b924..0f3a26a 100644 --- a/examples/plot_OT_1D.py +++ b/examples/plot_OT_1D.py @@ -4,9 +4,12 @@ 1D optimal transport ==================== -@author: rflamary """ +# Author: Remi Flamary +# +# License: MIT License + import numpy as np import matplotlib.pylab as pl import ot diff --git a/examples/plot_OT_2D_samples.py b/examples/plot_OT_2D_samples.py index 75ed7db..023e645 100644 --- a/examples/plot_OT_2D_samples.py +++ b/examples/plot_OT_2D_samples.py @@ -4,9 +4,12 @@ 2D Optimal transport between empirical distributions ==================================================== -@author: rflamary """ +# Author: Remi Flamary +# +# License: MIT License + import numpy as np import matplotlib.pylab as pl import ot diff --git a/examples/plot_OT_L1_vs_L2.py b/examples/plot_OT_L1_vs_L2.py index 86d902b..dfc9462 100644 --- a/examples/plot_OT_L1_vs_L2.py +++ b/examples/plot_OT_L1_vs_L2.py @@ -8,9 +8,12 @@ Stole the figure idea from Fig. 1 and 2 in https://arxiv.org/pdf/1706.07650.pdf -@author: rflamary """ +# Author: Remi Flamary +# +# License: MIT License + import numpy as np import matplotlib.pylab as pl import ot diff --git a/examples/plot_WDA.py b/examples/plot_WDA.py index 9eb8693..42789f2 100644 --- a/examples/plot_WDA.py +++ b/examples/plot_WDA.py @@ -4,9 +4,12 @@ Wasserstein Discriminant Analysis ================================= -@author: rflamary """ +# Author: Remi Flamary +# +# License: MIT License + import numpy as np import matplotlib.pylab as pl diff --git a/examples/plot_barycenter_1D.py b/examples/plot_barycenter_1D.py index ab236e1..875f44c 100644 --- a/examples/plot_barycenter_1D.py +++ b/examples/plot_barycenter_1D.py @@ -4,10 +4,12 @@ 1D Wasserstein barycenter demo ============================== - -@author: rflamary """ +# Author: Remi Flamary +# +# License: MIT License + import numpy as np import matplotlib.pylab as pl import ot diff --git a/examples/plot_compute_emd.py b/examples/plot_compute_emd.py index 558facb..893eecf 100644 --- a/examples/plot_compute_emd.py +++ b/examples/plot_compute_emd.py @@ -4,9 +4,12 @@ 1D optimal transport ==================== -@author: rflamary """ +# Author: Remi Flamary +# +# License: MIT License + import numpy as np import matplotlib.pylab as pl import ot -- cgit v1.2.3 From 84aa3183491260b9c3dbb9f928499cc18e5341c1 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 26 Jul 2017 12:22:00 +0200 Subject: pep8 --- ot/__init__.py | 2 +- ot/bregman.py | 16 +++++++++------- ot/da.py | 4 ++-- ot/optim.py | 2 +- 4 files changed, 13 insertions(+), 11 deletions(-) diff --git a/ot/__init__.py b/ot/__init__.py index c2161e4..6d4c4c6 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -28,6 +28,6 @@ from .utils import dist, unif, tic, toc, toq __version__ = "0.3.1" -__all__ = ["emd", "emd2", "sinkhorn","sinkhorn2", "utils", 'datasets', +__all__ = ["emd", "emd2", "sinkhorn", "sinkhorn2", "utils", 'datasets', 'bregman', 'lp', 'plot', 'tic', 'toc', 'toq', 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim'] diff --git a/ot/bregman.py b/ot/bregman.py index 71a5548..929388e 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -108,7 +108,8 @@ def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, stopThr=1e-9, ver stopThr=stopThr, verbose=verbose, log=log, **kwargs) elif method.lower() == 'sinkhorn_epsilon_scaling': def sink(): - return sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=numItermax, + return sinkhorn_epsilon_scaling( + a, b, M, reg, numItermax=numItermax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) else: print('Warning : unknown method using classic Sinkhorn Knopp') @@ -216,7 +217,8 @@ def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, stopThr=1e-9, ve stopThr=stopThr, verbose=verbose, log=log, **kwargs) elif method.lower() == 'sinkhorn_epsilon_scaling': def sink(): - return sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=numItermax, + return sinkhorn_epsilon_scaling( + a, b, M, reg, numItermax=numItermax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) else: print('Warning : unknown method using classic Sinkhorn Knopp') @@ -593,7 +595,7 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, wa cpt = cpt + 1 - #print('err=',err,' cpt=',cpt) + # print('err=',err,' cpt=',cpt) if log: log['logu'] = alpha / reg + np.log(u) log['logv'] = beta / reg + np.log(v) @@ -778,7 +780,7 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInne loop = False cpt = cpt + 1 - #print('err=',err,' cpt=',cpt) + # print('err=',err,' cpt=',cpt) if log: log['alpha'] = alpha log['beta'] = beta @@ -965,16 +967,16 @@ def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000, stopThr=1e-3, verb """ - #M = M/np.median(M) + # M = M/np.median(M) K = np.exp(-M / reg) - #M0 = M0/np.median(M0) + # M0 = M0/np.median(M0) K0 = np.exp(-M0 / reg0) old = h0 err = 1 cpt = 0 - #log = {'niter':0, 'all_err':[]} + # log = {'niter':0, 'all_err':[]} if log: log = {'err': []} diff --git a/ot/da.py b/ot/da.py index 977d532..4f9bce5 100644 --- a/ot/da.py +++ b/ot/da.py @@ -478,7 +478,7 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', sigm Kp[:ns, :ns] = K # ls regu - #K0 = K1.T.dot(K1)+eta*I + # K0 = K1.T.dot(K1)+eta*I # Kreg=I # RKHS regul @@ -490,7 +490,7 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', sigm I = np.eye(ns) # ls regul - #K0 = K1.T.dot(K1)+eta*I + # K0 = K1.T.dot(K1)+eta*I # Kreg=I # proper kernel ridge diff --git a/ot/optim.py b/ot/optim.py index c59089c..1d09adc 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -304,7 +304,7 @@ def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, numInnerItermax=200, Mi = M + reg2 * df(G) # solve linear program with Sinkhorn - #Gc = sinkhorn_stabilized(a,b, Mi, reg1, numItermax = numInnerItermax) + # Gc = sinkhorn_stabilized(a,b, Mi, reg1, numItermax = numInnerItermax) Gc = sinkhorn(a, b, Mi, reg1, numItermax=numInnerItermax) deltaG = Gc - G -- cgit v1.2.3 From 96f8b96cdd50be633d4f3c6f3255cb456e492e08 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 26 Jul 2017 12:33:14 +0200 Subject: valid flake8 --- ot/bregman.py | 4 ++-- ot/gpu/bregman.py | 2 +- ot/gpu/da.py | 3 +-- 3 files changed, 4 insertions(+), 5 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 929388e..d63c51d 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -110,7 +110,7 @@ def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, stopThr=1e-9, ver def sink(): return sinkhorn_epsilon_scaling( a, b, M, reg, numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log, **kwargs) + stopThr=stopThr, verbose=verbose, log=log, **kwargs) else: print('Warning : unknown method using classic Sinkhorn Knopp') @@ -219,7 +219,7 @@ def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, stopThr=1e-9, ve def sink(): return sinkhorn_epsilon_scaling( a, b, M, reg, numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log, **kwargs) + stopThr=stopThr, verbose=verbose, log=log, **kwargs) else: print('Warning : unknown method using classic Sinkhorn Knopp') diff --git a/ot/gpu/bregman.py b/ot/gpu/bregman.py index 86bfec1..47939c4 100644 --- a/ot/gpu/bregman.py +++ b/ot/gpu/bregman.py @@ -4,7 +4,7 @@ Bregman projections for regularized OT with GPU """ # Author: Remi Flamary -# Leo Gautheron +# Leo Gautheron # # License: MIT License diff --git a/ot/gpu/da.py b/ot/gpu/da.py index 7fb488d..05c580f 100644 --- a/ot/gpu/da.py +++ b/ot/gpu/da.py @@ -6,12 +6,11 @@ Domain adaptation with optimal transport with GPU implementation # Author: Remi Flamary # Nicolas Courty # Michael Perrot -# Leo Gautheron +# Leo Gautheron # # License: MIT License - import numpy as np from ..utils import unif from ..da import OTDA -- cgit v1.2.3 From 838550ead9cc8a66d9b9c1212c5dda2457dc59a5 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 26 Jul 2017 15:12:44 +0200 Subject: last stuff --- test/test_utils.py | 18 +++++++++--------- 1 file changed, 9 insertions(+), 9 deletions(-) diff --git a/test/test_utils.py b/test/test_utils.py index 9b140db..1bd37cd 100644 --- a/test/test_utils.py +++ b/test/test_utils.py @@ -22,7 +22,7 @@ def test_parmap(): l2 = list(ot.utils.parmap(f, a)) - assert np.allclose(l1, l2) + np.testing.assert_allclose(l1, l2) def test_tic_toc(): @@ -35,10 +35,10 @@ def test_tic_toc(): t2 = ot.toq() # test timing - assert np.allclose(0.5, t, rtol=1e-2, atol=1e-2) + np.testing.assert_allclose(0.5, t, rtol=1e-2, atol=1e-2) # test toc vs toq - assert np.allclose(t, t2, rtol=1e-2, atol=1e-2) + np.testing.assert_allclose(t, t2, rtol=1e-2, atol=1e-2) def test_kernel(): @@ -50,7 +50,7 @@ def test_kernel(): K = ot.utils.kernel(x, x) # gaussian kernel has ones on the diagonal - assert np.allclose(np.diag(K), np.ones(n)) + np.testing.assert_allclose(np.diag(K), np.ones(n)) def test_unif(): @@ -59,7 +59,7 @@ def test_unif(): u = ot.unif(n) - assert np.allclose(1, np.sum(u)) + np.testing.assert_allclose(1, np.sum(u)) def test_dist(): @@ -77,8 +77,8 @@ def test_dist(): D3 = ot.dist(x) # dist shoul return squared euclidean - assert np.allclose(D, D2) - assert np.allclose(D, D3) + np.testing.assert_allclose(D, D2) + np.testing.assert_allclose(D, D3) def test_dist0(): @@ -87,7 +87,7 @@ def test_dist0(): M = ot.utils.dist0(n, method='lin_square') # dist0 default to linear sampling with quadratic loss - assert np.allclose(M[0, -1], (n - 1) * (n - 1)) + np.testing.assert_allclose(M[0, -1], (n - 1) * (n - 1)) def test_dots(): @@ -102,7 +102,7 @@ def test_dots(): X2 = A.dot(B.dot(C)) - assert np.allclose(X1, X2) + np.testing.assert_allclose(X1, X2) def test_clean_zeros(): -- cgit v1.2.3 From 553a45678c829896cbb076b8a89934525431c62c Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 28 Jul 2017 08:00:35 +0200 Subject: remove linewidth error message --- ot/da.py | 150 +++++++++++++++++++++++++++++++++++++++++++++------------------ 1 file changed, 108 insertions(+), 42 deletions(-) diff --git a/ot/da.py b/ot/da.py index 4f9bce5..1dd4011 100644 --- a/ot/da.py +++ b/ot/da.py @@ -17,14 +17,18 @@ from .optim import cg from .optim import gcg -def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerItermax=200, stopInnerThr=1e-9, verbose=False, log=False): +def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, + numInnerItermax=200, stopInnerThr=1e-9, verbose=False, + log=False): """ - Solve the entropic regularization optimal transport problem with nonconvex group lasso regularization + Solve the entropic regularization optimal transport problem with nonconvex + group lasso regularization The function solves the following optimization problem: .. math:: - \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma)+ \eta \Omega_g(\gamma) + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma) + + \eta \Omega_g(\gamma) s.t. \gamma 1 = a @@ -34,11 +38,16 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerIte where : - M is the (ns,nt) metric cost matrix - - :math:`\Omega_e` is the entropic regularization term :math:`\Omega_e(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - :math:`\Omega_g` is the group lasso regulaization term :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^{1/2}_1` where :math:`\mathcal{I}_c` are the index of samples from class c in the source domain. + - :math:`\Omega_e` is the entropic regularization term + :math:`\Omega_e(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - :math:`\Omega_g` is the group lasso regulaization term + :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^{1/2}_1` + where :math:`\mathcal{I}_c` are the index of samples from class c + in the source domain. - a and b are source and target weights (sum to 1) - The algorithm used for solving the problem is the generalised conditional gradient as proposed in [5]_ [7]_ + The algorithm used for solving the problem is the generalised conditional + gradient as proposed in [5]_ [7]_ Parameters @@ -78,8 +87,13 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerIte References ---------- - .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 - .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567. + .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE + Transactions on Pattern Analysis and Machine Intelligence , + vol.PP, no.99, pp.1-1 + .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). + Generalized conditional gradient: analysis of convergence + and applications. arXiv preprint arXiv:1510.06567. See Also -------- @@ -114,14 +128,18 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerIte return transp -def sinkhorn_l1l2_gl(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerItermax=200, stopInnerThr=1e-9, verbose=False, log=False): +def sinkhorn_l1l2_gl(a, labels_a, b, M, reg, eta=0.1, numItermax=10, + numInnerItermax=200, stopInnerThr=1e-9, verbose=False, + log=False): """ - Solve the entropic regularization optimal transport problem with group lasso regularization + Solve the entropic regularization optimal transport problem with group + lasso regularization The function solves the following optimization problem: .. math:: - \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma)+ \eta \Omega_g(\gamma) + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma)+ + \eta \Omega_g(\gamma) s.t. \gamma 1 = a @@ -131,11 +149,16 @@ def sinkhorn_l1l2_gl(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerIte where : - M is the (ns,nt) metric cost matrix - - :math:`\Omega_e` is the entropic regularization term :math:`\Omega_e(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - :math:`\Omega_g` is the group lasso regulaization term :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^2` where :math:`\mathcal{I}_c` are the index of samples from class c in the source domain. + - :math:`\Omega_e` is the entropic regularization term + :math:`\Omega_e(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - :math:`\Omega_g` is the group lasso regulaization term + :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^2` + where :math:`\mathcal{I}_c` are the index of samples from class + c in the source domain. - a and b are source and target weights (sum to 1) - The algorithm used for solving the problem is the generalised conditional gradient as proposed in [5]_ [7]_ + The algorithm used for solving the problem is the generalised conditional + gradient as proposed in [5]_ [7]_ Parameters @@ -175,8 +198,12 @@ def sinkhorn_l1l2_gl(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerIte References ---------- - .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 - .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567. + .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE Transactions + on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 + .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). + Generalized conditional gradient: analysis of convergence and + applications. arXiv preprint arXiv:1510.06567. See Also -------- @@ -203,16 +230,22 @@ def sinkhorn_l1l2_gl(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerIte W[labels_a == lab, i] = temp / n return W - return gcg(a, b, M, reg, eta, f, df, G0=None, numItermax=numItermax, numInnerItermax=numInnerItermax, stopThr=stopInnerThr, verbose=verbose, log=log) + return gcg(a, b, M, reg, eta, f, df, G0=None, numItermax=numItermax, + numInnerItermax=numInnerItermax, stopThr=stopInnerThr, + verbose=verbose, log=log) -def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False, verbose2=False, numItermax=100, numInnerItermax=10, stopInnerThr=1e-6, stopThr=1e-5, log=False, **kwargs): +def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False, + verbose2=False, numItermax=100, numInnerItermax=10, + stopInnerThr=1e-6, stopThr=1e-5, log=False, + **kwargs): """Joint OT and linear mapping estimation as proposed in [8] The function solves the following optimization problem: .. math:: - \min_{\gamma,L}\quad \|L(X_s) -n_s\gamma X_t\|^2_F + \mu<\gamma,M>_F + \eta \|L -I\|^2_F + \min_{\gamma,L}\quad \|L(X_s) -n_s\gamma X_t\|^2_F + + \mu<\gamma,M>_F + \eta \|L -I\|^2_F s.t. \gamma 1 = a @@ -221,8 +254,10 @@ def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False, \gamma\geq 0 where : - - M is the (ns,nt) squared euclidean cost matrix between samples in Xs and Xt (scaled by ns) - - :math:`L` is a dxd linear operator that approximates the barycentric mapping + - M is the (ns,nt) squared euclidean cost matrix between samples in + Xs and Xt (scaled by ns) + - :math:`L` is a dxd linear operator that approximates the barycentric + mapping - :math:`I` is the identity matrix (neutral linear mapping) - a and b are uniform source and target weights @@ -277,7 +312,9 @@ def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False, References ---------- - .. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. + .. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, + "Mapping estimation for discrete optimal transport", + Neural Information Processing Systems (NIPS), 2016. See Also -------- @@ -384,13 +421,18 @@ def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False, return G, L -def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', sigma=1, bias=False, verbose=False, verbose2=False, numItermax=100, numInnerItermax=10, stopInnerThr=1e-6, stopThr=1e-5, log=False, **kwargs): +def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', + sigma=1, bias=False, verbose=False, verbose2=False, + numItermax=100, numInnerItermax=10, + stopInnerThr=1e-6, stopThr=1e-5, log=False, + **kwargs): """Joint OT and nonlinear mapping estimation with kernels as proposed in [8] The function solves the following optimization problem: .. math:: - \min_{\gamma,L\in\mathcal{H}}\quad \|L(X_s) -n_s\gamma X_t\|^2_F + \mu<\gamma,M>_F + \eta \|L\|^2_\mathcal{H} + \min_{\gamma,L\in\mathcal{H}}\quad \|L(X_s) - + n_s\gamma X_t\|^2_F + \mu<\gamma,M>_F + \eta \|L\|^2_\mathcal{H} s.t. \gamma 1 = a @@ -399,8 +441,10 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', sigm \gamma\geq 0 where : - - M is the (ns,nt) squared euclidean cost matrix between samples in Xs and Xt (scaled by ns) - - :math:`L` is a ns x d linear operator on a kernel matrix that approximates the barycentric mapping + - M is the (ns,nt) squared euclidean cost matrix between samples in + Xs and Xt (scaled by ns) + - :math:`L` is a ns x d linear operator on a kernel matrix that + approximates the barycentric mapping - a and b are uniform source and target weights The problem consist in solving jointly an optimal transport matrix @@ -458,7 +502,9 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', sigm References ---------- - .. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. + .. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, + "Mapping estimation for discrete optimal transport", + Neural Information Processing Systems (NIPS), 2016. See Also -------- @@ -593,7 +639,9 @@ class OTDA(object): References ---------- - .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 + .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE Transactions on + Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 """ @@ -606,7 +654,8 @@ class OTDA(object): self.computed = False def fit(self, xs, xt, ws=None, wt=None, norm=None): - """ Fit domain adaptation between samples is xs and xt (with optional weights)""" + """Fit domain adaptation between samples is xs and xt + (with optional weights)""" self.xs = xs self.xt = xt @@ -669,7 +718,9 @@ class OTDA(object): References ---------- - .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. + .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). + Regularized discrete optimal transport. SIAM Journal on Imaging + Sciences, 7(3), 1853-1882. """ if direction > 0: # >0 then source to target @@ -708,10 +759,12 @@ class OTDA(object): class OTDA_sinkhorn(OTDA): - """Class for domain adaptation with optimal transport with entropic regularization""" + """Class for domain adaptation with optimal transport with entropic + regularization""" def fit(self, xs, xt, reg=1, ws=None, wt=None, norm=None, **kwargs): - """ Fit regularized domain adaptation between samples is xs and xt (with optional weights)""" + """Fit regularized domain adaptation between samples is xs and xt + (with optional weights)""" self.xs = xs self.xt = xt @@ -731,10 +784,14 @@ class OTDA_sinkhorn(OTDA): class OTDA_lpl1(OTDA): - """Class for domain adaptation with optimal transport with entropic and group regularization""" + """Class for domain adaptation with optimal transport with entropic and + group regularization""" - def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None, **kwargs): - """ Fit regularized domain adaptation between samples is xs and xt (with optional weights), See ot.da.sinkhorn_lpl1_mm for fit parameters""" + def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None, + **kwargs): + """Fit regularized domain adaptation between samples is xs and xt + (with optional weights), See ot.da.sinkhorn_lpl1_mm for fit + parameters""" self.xs = xs self.xt = xt @@ -754,10 +811,14 @@ class OTDA_lpl1(OTDA): class OTDA_l1l2(OTDA): - """Class for domain adaptation with optimal transport with entropic and group lasso regularization""" + """Class for domain adaptation with optimal transport with entropic + and group lasso regularization""" - def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None, **kwargs): - """ Fit regularized domain adaptation between samples is xs and xt (with optional weights), See ot.da.sinkhorn_lpl1_gl for fit parameters""" + def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None, + **kwargs): + """Fit regularized domain adaptation between samples is xs and xt + (with optional weights), See ot.da.sinkhorn_lpl1_gl for fit + parameters""" self.xs = xs self.xt = xt @@ -777,7 +838,9 @@ class OTDA_l1l2(OTDA): class OTDA_mapping_linear(OTDA): - """Class for optimal transport with joint linear mapping estimation as in [8]""" + """Class for optimal transport with joint linear mapping estimation as in + [8] + """ def __init__(self): """ Class initialization""" @@ -820,9 +883,11 @@ class OTDA_mapping_linear(OTDA): class OTDA_mapping_kernel(OTDA_mapping_linear): - """Class for optimal transport with joint nonlinear mapping estimation as in [8]""" + """Class for optimal transport with joint nonlinear mapping + estimation as in [8]""" - def fit(self, xs, xt, mu=1, eta=1, bias=False, kerneltype='gaussian', sigma=1, **kwargs): + def fit(self, xs, xt, mu=1, eta=1, bias=False, kerneltype='gaussian', + sigma=1, **kwargs): """ Fit domain adaptation between samples is xs and xt """ self.xs = xs self.xt = xt @@ -843,7 +908,8 @@ class OTDA_mapping_kernel(OTDA_mapping_linear): if self.computed: K = kernel( - x, self.xs, method=self.kernel, sigma=self.sigma, **self.kwargs) + x, self.xs, method=self.kernel, sigma=self.sigma, + **self.kwargs) if self.bias: K = np.hstack((K, np.ones((x.shape[0], 1)))) return K.dot(self.L) -- cgit v1.2.3 From ca9c9d6d8ecef6a38e0fd6240538a8af35ad06f5 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 28 Jul 2017 08:49:10 +0200 Subject: first proposal for OT wrappers --- ot/da.py | 179 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 179 insertions(+) diff --git a/ot/da.py b/ot/da.py index 1dd4011..f534bf5 100644 --- a/ot/da.py +++ b/ot/da.py @@ -916,3 +916,182 @@ class OTDA_mapping_kernel(OTDA_mapping_linear): else: print("Warning, model not fitted yet, returning None") return None + +############################################################################## +# proposal +############################################################################## + +from sklearn.base import BaseEstimator +from sklearn.metrics import pairwise_distances + +""" +- all methods have the same input parameters: Xs, Xt, ys, yt (what order ?) +- ref: is the entropic reg parameter +- eta: is the second reg parameter +- gamma_: is the optimal coupling +- mapping barycentric for the moment + +Questions: +- Cost matrix estimation: from sklearn or from internal function ? +- distribution estimation ? Look at Nathalie's approach +- should everything been done into the fit from BaseTransport ? +""" + + +class BaseTransport(BaseEstimator): + + def fit(self, Xs=None, ys=None, Xt=None, yt=None, method="sinkhorn"): + """fit: estimates the optimal coupling + + Parameters: + ----------- + - Xs: source samples, (ns samples, d features) numpy-like array + - ys: source labels + - Xt: target samples (nt samples, d features) numpy-like array + - yt: target labels + - method: algorithm to use to compute optimal coupling + (default: sinkhorn) + + Returns: + -------- + - self + """ + + # pairwise distance + Cost = pairwise_distances(Xs, Xt, metric=self.metric) + + if self.mode == "semisupervised": + print("TODO: modify cost matrix accordingly") + pass + + # distribution estimation: should we change it ? + mu_s = np.ones(Xs.shape[0]) / float(Xs.shape[0]) + mu_t = np.ones(Xt.shape[0]) / float(Xt.shape[0]) + + if method == "sinkhorn": + self.gamma_ = sinkhorn( + a=mu_s, b=mu_t, M=Cost, reg=self.reg, + numItermax=self.max_iter, stopThr=self.tol, + verbose=self.verbose, log=self.log) + else: + print("TODO: implement the other methods") + + return self + + def fit_transform(self, Xs=None, ys=None, Xt=None, yt=None): + """fit_transform + + Parameters: + ----------- + - Xs: source samples, (ns samples, d features) numpy-like array + - ys: source labels + - Xt: target samples (nt samples, d features) numpy-like array + - yt: target labels + + Returns: + -------- + - transp_Xt + """ + + return self.fit(Xs, ys, Xt, yt, self.method).transform(Xs, ys, Xt, yt) + + def transform(self, Xs=None, ys=None, Xt=None, yt=None): + """transform: as a convention transports source samples + onto target samples + + Parameters: + ----------- + - Xs: source samples, (ns samples, d features) numpy-like array + - ys: source labels + - Xt: target samples (nt samples, d features) numpy-like array + - yt: target labels + + Returns: + -------- + - transp_Xt + """ + + if self.mapping == "barycentric": + transp = self.gamma_ / np.sum(self.gamma_, 1)[:, None] + + # set nans to 0 + transp[~ np.isfinite(transp)] = 0 + + # compute transported samples + transp_Xs = np.dot(transp, Xt) + + return transp_Xs + + def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None): + """inverse_transform: as a convention transports target samples + onto source samples + + Parameters: + ----------- + - Xs: source samples, (ns samples, d features) numpy-like array + - ys: source labels + - Xt: target samples (nt samples, d features) numpy-like array + - yt: target labels + + Returns: + -------- + - transp_Xt + """ + + if self.mapping == "barycentric": + transp_ = self.gamma_.T / np.sum(self.gamma_, 0)[:, None] + + # set nans to 0 + transp_[~ np.isfinite(transp_)] = 0 + + # compute transported samples + transp_Xt = np.dot(transp_, Xs) + else: + print("mapping not yet implemented") + + return transp_Xt + + +class SinkhornTransport(BaseTransport): + """SinkhornTransport: class wrapper for optimal transport based on + Sinkhorn's algorithm + + Parameters + ---------- + - reg : parameter for entropic regularization + - mode: unsupervised (default) or semi supervised: controls whether + labels are taken into accout to construct the optimal coupling + - max_iter : maximum number of iterations + - tol : precision + - verbose : control verbosity + - log : control log + + Attributes + ---------- + - gamma_: optimal coupling estimated by the fit function + """ + + def __init__(self, reg=1., mode="unsupervised", max_iter=1000, + tol=10e-9, verbose=False, log=False, mapping="barycentric", + metric="sqeuclidean"): + self.reg = reg + self.mode = mode + self.max_iter = max_iter + self.tol = tol + self.verbose = verbose + self.log = log + self.mapping = mapping + self.metric = metric + self.method = "sinkhorn" + + def fit(self, Xs=None, ys=None, Xt=None, yt=None): + """_fit + """ + return super(SinkhornTransport, self).fit( + Xs, ys, Xt, yt, method=self.method) + + +if __name__ == "__main__": + print("Small test") + + st = SinkhornTransport() -- cgit v1.2.3 From 84adadd69aef12826aa3970d135d499ef4d13f64 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 28 Jul 2017 14:52:36 +0200 Subject: small modifs according to NG proposals --- ot/da.py | 43 +++++++++++++++++++++++++++++++------------ 1 file changed, 31 insertions(+), 12 deletions(-) diff --git a/ot/da.py b/ot/da.py index f534bf5..a422f7c 100644 --- a/ot/da.py +++ b/ot/da.py @@ -926,8 +926,8 @@ from sklearn.metrics import pairwise_distances """ - all methods have the same input parameters: Xs, Xt, ys, yt (what order ?) -- ref: is the entropic reg parameter -- eta: is the second reg parameter +- reg_e: is the entropic reg parameter +- reg_cl: is the second reg parameter - gamma_: is the optimal coupling - mapping barycentric for the moment @@ -940,7 +940,7 @@ Questions: class BaseTransport(BaseEstimator): - def fit(self, Xs=None, ys=None, Xt=None, yt=None, method="sinkhorn"): + def fit(self, Xs=None, ys=None, Xt=None, yt=None, method=None): """fit: estimates the optimal coupling Parameters: @@ -964,13 +964,17 @@ class BaseTransport(BaseEstimator): print("TODO: modify cost matrix accordingly") pass - # distribution estimation: should we change it ? - mu_s = np.ones(Xs.shape[0]) / float(Xs.shape[0]) - mu_t = np.ones(Xt.shape[0]) / float(Xt.shape[0]) + # distribution estimation + if self.distribution == "uniform": + mu_s = np.ones(Xs.shape[0]) / float(Xs.shape[0]) + mu_t = np.ones(Xt.shape[0]) / float(Xt.shape[0]) + else: + print("TODO: implement kernelized approach") + # coupling estimation if method == "sinkhorn": self.gamma_ = sinkhorn( - a=mu_s, b=mu_t, M=Cost, reg=self.reg, + a=mu_s, b=mu_t, M=Cost, reg=self.reg_e, numItermax=self.max_iter, stopThr=self.tol, verbose=self.verbose, log=self.log) else: @@ -1058,7 +1062,7 @@ class SinkhornTransport(BaseTransport): Parameters ---------- - - reg : parameter for entropic regularization + - reg_e : parameter for entropic regularization - mode: unsupervised (default) or semi supervised: controls whether labels are taken into accout to construct the optimal coupling - max_iter : maximum number of iterations @@ -1071,10 +1075,10 @@ class SinkhornTransport(BaseTransport): - gamma_: optimal coupling estimated by the fit function """ - def __init__(self, reg=1., mode="unsupervised", max_iter=1000, + def __init__(self, reg_e=1., mode="unsupervised", max_iter=1000, tol=10e-9, verbose=False, log=False, mapping="barycentric", - metric="sqeuclidean"): - self.reg = reg + metric="sqeuclidean", distribution="uniform"): + self.reg_e = reg_e self.mode = mode self.max_iter = max_iter self.tol = tol @@ -1082,11 +1086,26 @@ class SinkhornTransport(BaseTransport): self.log = log self.mapping = mapping self.metric = metric + self.distribution = distribution self.method = "sinkhorn" def fit(self, Xs=None, ys=None, Xt=None, yt=None): - """_fit + """fit + + Parameters: + ----------- + - Xs: source samples, (ns samples, d features) numpy-like array + - ys: source labels + - Xt: target samples (nt samples, d features) numpy-like array + - yt: target labels + - method: algorithm to use to compute optimal coupling + (default: sinkhorn) + + Returns: + -------- + - self """ + return super(SinkhornTransport, self).fit( Xs, ys, Xt, yt, method=self.method) -- cgit v1.2.3 From cd3397f852d8bf99e5de59069529b95d9ba00a05 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 28 Jul 2017 15:34:36 +0200 Subject: integrate AG comments --- ot/da.py | 202 +++++++++++++++++++++++++++++++++++++-------------------------- 1 file changed, 120 insertions(+), 82 deletions(-) diff --git a/ot/da.py b/ot/da.py index a422f7c..828efc2 100644 --- a/ot/da.py +++ b/ot/da.py @@ -940,21 +940,23 @@ Questions: class BaseTransport(BaseEstimator): - def fit(self, Xs=None, ys=None, Xt=None, yt=None, method=None): - """fit: estimates the optimal coupling - - Parameters: - ----------- - - Xs: source samples, (ns samples, d features) numpy-like array - - ys: source labels - - Xt: target samples (nt samples, d features) numpy-like array - - yt: target labels - - method: algorithm to use to compute optimal coupling - (default: sinkhorn) - - Returns: - -------- - - self + def fit(self, Xs=None, ys=None, Xt=None, yt=None): + """Build a coupling matrix from source and target sets of samples + (Xs, ys) and (Xt, yt) + Parameters + ---------- + Xs : array-like of shape = [n_source_samples, n_features] + The training input samples. + ys : array-like, shape = [n_source_samples] + The class labels + Xt : array-like of shape = [n_target_samples, n_features] + The training input samples. + yt : array-like, shape = [n_labeled_target_samples] + The class labels + Returns + ------- + self : object + Returns self. """ # pairwise distance @@ -972,7 +974,7 @@ class BaseTransport(BaseEstimator): print("TODO: implement kernelized approach") # coupling estimation - if method == "sinkhorn": + if self.method == "sinkhorn": self.gamma_ = sinkhorn( a=mu_s, b=mu_t, M=Cost, reg=self.reg_e, numItermax=self.max_iter, stopThr=self.tol, @@ -983,36 +985,43 @@ class BaseTransport(BaseEstimator): return self def fit_transform(self, Xs=None, ys=None, Xt=None, yt=None): - """fit_transform - - Parameters: - ----------- - - Xs: source samples, (ns samples, d features) numpy-like array - - ys: source labels - - Xt: target samples (nt samples, d features) numpy-like array - - yt: target labels - - Returns: - -------- - - transp_Xt + """Build a coupling matrix from source and target sets of samples + (Xs, ys) and (Xt, yt) and transports source samples Xs onto target + ones Xt + Parameters + ---------- + Xs : array-like of shape = [n_source_samples, n_features] + The training input samples. + ys : array-like, shape = [n_source_samples] + The class labels + Xt : array-like of shape = [n_target_samples, n_features] + The training input samples. + yt : array-like, shape = [n_labeled_target_samples] + The class labels + Returns + ------- + transp_Xs : array-like of shape = [n_source_samples, n_features] + The source samples samples. """ - return self.fit(Xs, ys, Xt, yt, self.method).transform(Xs, ys, Xt, yt) + return self.fit(Xs, ys, Xt, yt).transform(Xs, ys, Xt, yt) def transform(self, Xs=None, ys=None, Xt=None, yt=None): - """transform: as a convention transports source samples - onto target samples - - Parameters: - ----------- - - Xs: source samples, (ns samples, d features) numpy-like array - - ys: source labels - - Xt: target samples (nt samples, d features) numpy-like array - - yt: target labels - - Returns: - -------- - - transp_Xt + """Transports source samples Xs onto target ones Xt + Parameters + ---------- + Xs : array-like of shape = [n_source_samples, n_features] + The training input samples. + ys : array-like, shape = [n_source_samples] + The class labels + Xt : array-like of shape = [n_target_samples, n_features] + The training input samples. + yt : array-like, shape = [n_labeled_target_samples] + The class labels + Returns + ------- + transp_Xs : array-like of shape = [n_source_samples, n_features] + The transport source samples. """ if self.mapping == "barycentric": @@ -1027,19 +1036,21 @@ class BaseTransport(BaseEstimator): return transp_Xs def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None): - """inverse_transform: as a convention transports target samples - onto source samples - - Parameters: - ----------- - - Xs: source samples, (ns samples, d features) numpy-like array - - ys: source labels - - Xt: target samples (nt samples, d features) numpy-like array - - yt: target labels - - Returns: - -------- - - transp_Xt + """Transports target samples Xt onto target samples Xs + Parameters + ---------- + Xs : array-like of shape = [n_source_samples, n_features] + The training input samples. + ys : array-like, shape = [n_source_samples] + The class labels + Xt : array-like of shape = [n_target_samples, n_features] + The training input samples. + yt : array-like, shape = [n_labeled_target_samples] + The class labels + Returns + ------- + transp_Xt : array-like of shape = [n_source_samples, n_features] + The transported target samples. """ if self.mapping == "barycentric": @@ -1057,22 +1068,48 @@ class BaseTransport(BaseEstimator): class SinkhornTransport(BaseTransport): - """SinkhornTransport: class wrapper for optimal transport based on - Sinkhorn's algorithm + """Domain Adapatation OT method based on Sinkhorn Algorithm Parameters ---------- - - reg_e : parameter for entropic regularization - - mode: unsupervised (default) or semi supervised: controls whether - labels are taken into accout to construct the optimal coupling - - max_iter : maximum number of iterations - - tol : precision - - verbose : control verbosity - - log : control log - + reg_e : float, optional (default=1) + Entropic regularization parameter + mode : string, optional (default="unsupervised") + The DA mode. If "unsupervised" no target labels are taken into account + to modify the cost matrix. If "semisupervised" the target labels + are taken into account to set coefficients of the pairwise distance + matrix to 0 for row and columns indices that correspond to source and + target samples which share the same labels. + max_iter : int, float, optional (default=1000) + The minimum number of iteration before stopping the optimization + algorithm if no it has not converged + tol : float, optional (default=10e-9) + The precision required to stop the optimization algorithm. + mapping : string, optional (default="barycentric") + The kind of mapping to apply to transport samples from a domain into + another one. + if "barycentric" only the samples used to estimate the coupling can + be transported from a domain to another one. + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + distribution : string, optional (default="uniform") + The kind of distribution estimation to employ + verbose : int, optional (default=0) + Controls the verbosity of the optimization algorithm + log : int, optional (default=0) + Controls the logs of the optimization algorithm Attributes ---------- - - gamma_: optimal coupling estimated by the fit function + gamma_ : the optimal coupling + + References + ---------- + .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE Transactions + on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 + .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal + Transport, Advances in Neural Information Processing Systems (NIPS) + 26, 2013 """ def __init__(self, reg_e=1., mode="unsupervised", max_iter=1000, @@ -1090,24 +1127,25 @@ class SinkhornTransport(BaseTransport): self.method = "sinkhorn" def fit(self, Xs=None, ys=None, Xt=None, yt=None): - """fit - - Parameters: - ----------- - - Xs: source samples, (ns samples, d features) numpy-like array - - ys: source labels - - Xt: target samples (nt samples, d features) numpy-like array - - yt: target labels - - method: algorithm to use to compute optimal coupling - (default: sinkhorn) - - Returns: - -------- - - self + """Build a coupling matrix from source and target sets of samples + (Xs, ys) and (Xt, yt) + Parameters + ---------- + Xs : array-like of shape = [n_source_samples, n_features] + The training input samples. + ys : array-like, shape = [n_source_samples] + The class labels + Xt : array-like of shape = [n_target_samples, n_features] + The training input samples. + yt : array-like, shape = [n_labeled_target_samples] + The class labels + Returns + ------- + self : object + Returns self. """ - return super(SinkhornTransport, self).fit( - Xs, ys, Xt, yt, method=self.method) + return super(SinkhornTransport, self).fit(Xs, ys, Xt, yt) if __name__ == "__main__": -- cgit v1.2.3 From bd7c7d2534980d3105d060dd24a444433422134d Mon Sep 17 00:00:00 2001 From: Slasnista Date: Mon, 31 Jul 2017 10:54:28 +0200 Subject: own BaseEstimator class written + rflamary comments addressed --- ot/da.py | 199 ++++++++++++++++++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 172 insertions(+), 27 deletions(-) diff --git a/ot/da.py b/ot/da.py index 828efc2..d30c821 100644 --- a/ot/da.py +++ b/ot/da.py @@ -921,21 +921,153 @@ class OTDA_mapping_kernel(OTDA_mapping_linear): # proposal ############################################################################## -from sklearn.base import BaseEstimator -from sklearn.metrics import pairwise_distances +# from sklearn.base import BaseEstimator +# from sklearn.metrics import pairwise_distances + +############################################################################## +# adapted from scikit-learn + +import warnings +# from .externals.six import string_types, iteritems -""" -- all methods have the same input parameters: Xs, Xt, ys, yt (what order ?) -- reg_e: is the entropic reg parameter -- reg_cl: is the second reg parameter -- gamma_: is the optimal coupling -- mapping barycentric for the moment - -Questions: -- Cost matrix estimation: from sklearn or from internal function ? -- distribution estimation ? Look at Nathalie's approach -- should everything been done into the fit from BaseTransport ? -""" + +class BaseEstimator(object): + """Base class for all estimators in scikit-learn + Notes + ----- + All estimators should specify all the parameters that can be set + at the class level in their ``__init__`` as explicit keyword + arguments (no ``*args`` or ``**kwargs``). + """ + + @classmethod + def _get_param_names(cls): + """Get parameter names for the estimator""" + try: + from inspect import signature + except ImportError: + from .externals.funcsigs import signature + # fetch the constructor or the original constructor before + # deprecation wrapping if any + init = getattr(cls.__init__, 'deprecated_original', cls.__init__) + if init is object.__init__: + # No explicit constructor to introspect + return [] + + # introspect the constructor arguments to find the model parameters + # to represent + init_signature = signature(init) + # Consider the constructor parameters excluding 'self' + parameters = [p for p in init_signature.parameters.values() + if p.name != 'self' and p.kind != p.VAR_KEYWORD] + for p in parameters: + if p.kind == p.VAR_POSITIONAL: + raise RuntimeError("scikit-learn estimators should always " + "specify their parameters in the signature" + " of their __init__ (no varargs)." + " %s with constructor %s doesn't " + " follow this convention." + % (cls, init_signature)) + # Extract and sort argument names excluding 'self' + return sorted([p.name for p in parameters]) + + def get_params(self, deep=True): + """Get parameters for this estimator. + Parameters + ---------- + deep : boolean, optional + If True, will return the parameters for this estimator and + contained subobjects that are estimators. + Returns + ------- + params : mapping of string to any + Parameter names mapped to their values. + """ + out = dict() + for key in self._get_param_names(): + # We need deprecation warnings to always be on in order to + # catch deprecated param values. + # This is set in utils/__init__.py but it gets overwritten + # when running under python3 somehow. + warnings.simplefilter("always", DeprecationWarning) + try: + with warnings.catch_warnings(record=True) as w: + value = getattr(self, key, None) + if len(w) and w[0].category == DeprecationWarning: + # if the parameter is deprecated, don't show it + continue + finally: + warnings.filters.pop(0) + + # XXX: should we rather test if instance of estimator? + if deep and hasattr(value, 'get_params'): + deep_items = value.get_params().items() + out.update((key + '__' + k, val) for k, val in deep_items) + out[key] = value + return out + + def set_params(self, **params): + """Set the parameters of this estimator. + The method works on simple estimators as well as on nested objects + (such as pipelines). The latter have parameters of the form + ``__`` so that it's possible to update each + component of a nested object. + Returns + ------- + self + """ + if not params: + # Simple optimisation to gain speed (inspect is slow) + return self + valid_params = self.get_params(deep=True) + # for key, value in iteritems(params): + for key, value in params.items(): + split = key.split('__', 1) + if len(split) > 1: + # nested objects case + name, sub_name = split + if name not in valid_params: + raise ValueError('Invalid parameter %s for estimator %s. ' + 'Check the list of available parameters ' + 'with `estimator.get_params().keys()`.' % + (name, self)) + sub_object = valid_params[name] + sub_object.set_params(**{sub_name: value}) + else: + # simple objects case + if key not in valid_params: + raise ValueError('Invalid parameter %s for estimator %s. ' + 'Check the list of available parameters ' + 'with `estimator.get_params().keys()`.' % + (key, self.__class__.__name__)) + setattr(self, key, value) + return self + + def __repr__(self): + from sklearn.base import _pprint + class_name = self.__class__.__name__ + return '%s(%s)' % (class_name, _pprint(self.get_params(deep=False), + offset=len(class_name),),) + + # __getstate__ and __setstate__ are omitted because they only contain + # conditionals that are not satisfied by our objects (e.g., + # ``if type(self).__module__.startswith('sklearn.')``. + + +def distribution_estimation_uniform(X): + """estimates a uniform distribution from an array of samples X + + Parameters + ---------- + X : array-like of shape = [n_samples, n_features] + The array of samples + Returns + ------- + mu : array-like, shape = [n_samples,] + The uniform distribution estimated from X + """ + + return np.ones(X.shape[0]) / float(X.shape[0]) class BaseTransport(BaseEstimator): @@ -960,18 +1092,19 @@ class BaseTransport(BaseEstimator): """ # pairwise distance - Cost = pairwise_distances(Xs, Xt, metric=self.metric) + Cost = dist(Xs, Xt, metric=self.metric) if self.mode == "semisupervised": print("TODO: modify cost matrix accordingly") pass # distribution estimation - if self.distribution == "uniform": - mu_s = np.ones(Xs.shape[0]) / float(Xs.shape[0]) - mu_t = np.ones(Xt.shape[0]) / float(Xt.shape[0]) - else: - print("TODO: implement kernelized approach") + mu_s = self.distribution_estimation(Xs) + mu_t = self.distribution_estimation(Xt) + + # store arrays of samples + self.Xs = Xs + self.Xt = Xt # coupling estimation if self.method == "sinkhorn": @@ -1024,14 +1157,19 @@ class BaseTransport(BaseEstimator): The transport source samples. """ - if self.mapping == "barycentric": + # TODO: check whether Xs is new or not + if self.Xs == Xs: + # perform standard barycentric mapping transp = self.gamma_ / np.sum(self.gamma_, 1)[:, None] # set nans to 0 transp[~ np.isfinite(transp)] = 0 # compute transported samples - transp_Xs = np.dot(transp, Xt) + transp_Xs = np.dot(transp, self.Xt) + else: + # perform out of sample mapping + print("out of sample mapping not yet implemented") return transp_Xs @@ -1053,16 +1191,19 @@ class BaseTransport(BaseEstimator): The transported target samples. """ - if self.mapping == "barycentric": + # TODO: check whether Xt is new or not + if self.Xt == Xt: + # perform standard barycentric mapping transp_ = self.gamma_.T / np.sum(self.gamma_, 0)[:, None] # set nans to 0 transp_[~ np.isfinite(transp_)] = 0 # compute transported samples - transp_Xt = np.dot(transp_, Xs) + transp_Xt = np.dot(transp_, self.Xs) else: - print("mapping not yet implemented") + # perform out of sample mapping + print("out of sample mapping not yet implemented") return transp_Xt @@ -1114,7 +1255,10 @@ class SinkhornTransport(BaseTransport): def __init__(self, reg_e=1., mode="unsupervised", max_iter=1000, tol=10e-9, verbose=False, log=False, mapping="barycentric", - metric="sqeuclidean", distribution="uniform"): + metric="sqeuclidean", + distribution_estimation=distribution_estimation_uniform, + out_of_sample_map='ferradans'): + self.reg_e = reg_e self.mode = mode self.max_iter = max_iter @@ -1123,8 +1267,9 @@ class SinkhornTransport(BaseTransport): self.log = log self.mapping = mapping self.metric = metric - self.distribution = distribution + self.distribution_estimation = distribution_estimation self.method = "sinkhorn" + self.out_of_sample_map = out_of_sample_map def fit(self, Xs=None, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples -- cgit v1.2.3 From 122b5bf2c0c8b6ff7b46adf19c7dd72e62c85b1f Mon Sep 17 00:00:00 2001 From: Slasnista Date: Tue, 1 Aug 2017 10:42:09 +0200 Subject: update SinkhornTransport class + added test for class --- ot/da.py | 56 +++++++++++++++++++++----------------------------------- test/test_da.py | 51 +++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 72 insertions(+), 35 deletions(-) diff --git a/ot/da.py b/ot/da.py index d30c821..6b98a17 100644 --- a/ot/da.py +++ b/ot/da.py @@ -15,6 +15,7 @@ from .lp import emd from .utils import unif, dist, kernel from .optim import cg from .optim import gcg +import warnings def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, @@ -921,15 +922,8 @@ class OTDA_mapping_kernel(OTDA_mapping_linear): # proposal ############################################################################## -# from sklearn.base import BaseEstimator -# from sklearn.metrics import pairwise_distances - -############################################################################## -# adapted from scikit-learn - -import warnings -# from .externals.six import string_types, iteritems +# adapted from sklearn class BaseEstimator(object): """Base class for all estimators in scikit-learn @@ -1067,7 +1061,7 @@ def distribution_estimation_uniform(X): The uniform distribution estimated from X """ - return np.ones(X.shape[0]) / float(X.shape[0]) + return unif(X.shape[0]) class BaseTransport(BaseEstimator): @@ -1092,29 +1086,20 @@ class BaseTransport(BaseEstimator): """ # pairwise distance - Cost = dist(Xs, Xt, metric=self.metric) + self.Cost = dist(Xs, Xt, metric=self.metric) if self.mode == "semisupervised": print("TODO: modify cost matrix accordingly") pass # distribution estimation - mu_s = self.distribution_estimation(Xs) - mu_t = self.distribution_estimation(Xt) + self.mu_s = self.distribution_estimation(Xs) + self.mu_t = self.distribution_estimation(Xt) # store arrays of samples self.Xs = Xs self.Xt = Xt - # coupling estimation - if self.method == "sinkhorn": - self.gamma_ = sinkhorn( - a=mu_s, b=mu_t, M=Cost, reg=self.reg_e, - numItermax=self.max_iter, stopThr=self.tol, - verbose=self.verbose, log=self.log) - else: - print("TODO: implement the other methods") - return self def fit_transform(self, Xs=None, ys=None, Xt=None, yt=None): @@ -1157,8 +1142,7 @@ class BaseTransport(BaseEstimator): The transport source samples. """ - # TODO: check whether Xs is new or not - if self.Xs == Xs: + if np.array_equal(self.Xs, Xs): # perform standard barycentric mapping transp = self.gamma_ / np.sum(self.gamma_, 1)[:, None] @@ -1169,7 +1153,9 @@ class BaseTransport(BaseEstimator): transp_Xs = np.dot(transp, self.Xt) else: # perform out of sample mapping - print("out of sample mapping not yet implemented") + print("Warning: out of sample mapping not yet implemented") + print("input data will be returned") + transp_Xs = Xs return transp_Xs @@ -1191,8 +1177,7 @@ class BaseTransport(BaseEstimator): The transported target samples. """ - # TODO: check whether Xt is new or not - if self.Xt == Xt: + if np.array_equal(self.Xt, Xt): # perform standard barycentric mapping transp_ = self.gamma_.T / np.sum(self.gamma_, 0)[:, None] @@ -1203,7 +1188,9 @@ class BaseTransport(BaseEstimator): transp_Xt = np.dot(transp_, self.Xs) else: # perform out of sample mapping - print("out of sample mapping not yet implemented") + print("Warning: out of sample mapping not yet implemented") + print("input data will be returned") + transp_Xt = Xt return transp_Xt @@ -1254,7 +1241,7 @@ class SinkhornTransport(BaseTransport): """ def __init__(self, reg_e=1., mode="unsupervised", max_iter=1000, - tol=10e-9, verbose=False, log=False, mapping="barycentric", + tol=10e-9, verbose=False, log=False, metric="sqeuclidean", distribution_estimation=distribution_estimation_uniform, out_of_sample_map='ferradans'): @@ -1265,7 +1252,6 @@ class SinkhornTransport(BaseTransport): self.tol = tol self.verbose = verbose self.log = log - self.mapping = mapping self.metric = metric self.distribution_estimation = distribution_estimation self.method = "sinkhorn" @@ -1290,10 +1276,10 @@ class SinkhornTransport(BaseTransport): Returns self. """ - return super(SinkhornTransport, self).fit(Xs, ys, Xt, yt) - + self = super(SinkhornTransport, self).fit(Xs, ys, Xt, yt) -if __name__ == "__main__": - print("Small test") - - st = SinkhornTransport() + # coupling estimation + self.gamma_ = sinkhorn( + a=self.mu_s, b=self.mu_t, M=self.Cost, reg=self.reg_e, + numItermax=self.max_iter, stopThr=self.tol, + verbose=self.verbose, log=self.log) diff --git a/test/test_da.py b/test/test_da.py index dfba83f..e7b4ed1 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -6,6 +6,57 @@ import numpy as np import ot +from numpy.testing.utils import assert_allclose, assert_equal +from ot.datasets import get_data_classif +from ot.utils import unif + +np.random.seed(42) + + +def test_sinkhorn_transport(): + """test_sinkhorn_transport + """ + + ns = 150 + nt = 200 + + Xs, ys = get_data_classif('3gauss', ns) + Xt, yt = get_data_classif('3gauss2', nt) + + clf = ot.da.SinkhornTransport() + + # test its computed + clf.fit(Xs=Xs, Xt=Xt) + + # test dimensions of coupling + assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.gamma_.shape, ((Xs.shape[0], Xt.shape[0]))) + + # test margin constraints + mu_s = unif(ns) + mu_t = unif(nt) + assert_allclose(np.sum(clf.gamma_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.gamma_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + + # test transform + transp_Xs = clf.transform(Xs=Xs) + assert_equal(transp_Xs.shape, Xs.shape) + + Xs_new, _ = get_data_classif('3gauss', ns + 1) + transp_Xs_new = clf.transform(Xs_new) + + # check that the oos method is not working + assert_equal(transp_Xs_new, Xs_new) + + # test inverse transform + transp_Xt = clf.inverse_transform(Xt=Xt) + assert_equal(transp_Xt.shape, Xt.shape) + + Xt_new, _ = get_data_classif('3gauss2', nt + 1) + transp_Xt_new = clf.inverse_transform(Xt=Xt_new) + + # check that the oos method is not working and returns the input data + assert_equal(transp_Xt_new, Xt_new) def test_otda(): -- cgit v1.2.3 From d9be6c2da1c0953de1720f1e93f194c71699c3cd Mon Sep 17 00:00:00 2001 From: Slasnista Date: Tue, 1 Aug 2017 13:13:50 +0200 Subject: added EMDTransport Class from NG's code + added dedicated test --- ot/da.py | 86 +++++++++++++++++++++++++++++++++++++++++++++++++++++---- test/test_da.py | 59 ++++++++++++++++++++++++++++++++++++--- 2 files changed, 135 insertions(+), 10 deletions(-) diff --git a/ot/da.py b/ot/da.py index 6b98a17..fb2fd36 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1144,7 +1144,7 @@ class BaseTransport(BaseEstimator): if np.array_equal(self.Xs, Xs): # perform standard barycentric mapping - transp = self.gamma_ / np.sum(self.gamma_, 1)[:, None] + transp = self.Coupling_ / np.sum(self.Coupling_, 1)[:, None] # set nans to 0 transp[~ np.isfinite(transp)] = 0 @@ -1179,7 +1179,7 @@ class BaseTransport(BaseEstimator): if np.array_equal(self.Xt, Xt): # perform standard barycentric mapping - transp_ = self.gamma_.T / np.sum(self.gamma_, 0)[:, None] + transp_ = self.Coupling_.T / np.sum(self.Coupling_, 0)[:, None] # set nans to 0 transp_[~ np.isfinite(transp_)] = 0 @@ -1228,7 +1228,7 @@ class SinkhornTransport(BaseTransport): Controls the logs of the optimization algorithm Attributes ---------- - gamma_ : the optimal coupling + Coupling_ : the optimal coupling References ---------- @@ -1254,7 +1254,6 @@ class SinkhornTransport(BaseTransport): self.log = log self.metric = metric self.distribution_estimation = distribution_estimation - self.method = "sinkhorn" self.out_of_sample_map = out_of_sample_map def fit(self, Xs=None, ys=None, Xt=None, yt=None): @@ -1276,10 +1275,85 @@ class SinkhornTransport(BaseTransport): Returns self. """ - self = super(SinkhornTransport, self).fit(Xs, ys, Xt, yt) + super(SinkhornTransport, self).fit(Xs, ys, Xt, yt) # coupling estimation - self.gamma_ = sinkhorn( + self.Coupling_ = sinkhorn( a=self.mu_s, b=self.mu_t, M=self.Cost, reg=self.reg_e, numItermax=self.max_iter, stopThr=self.tol, verbose=self.verbose, log=self.log) + + +class EMDTransport(BaseTransport): + """Domain Adapatation OT method based on Earth Mover's Distance + Parameters + ---------- + mode : string, optional (default="unsupervised") + The DA mode. If "unsupervised" no target labels are taken into account + to modify the cost matrix. If "semisupervised" the target labels + are taken into account to set coefficients of the pairwise distance + matrix to 0 for row and columns indices that correspond to source and + target samples which share the same labels. + mapping : string, optional (default="barycentric") + The kind of mapping to apply to transport samples from a domain into + another one. + if "barycentric" only the samples used to estimate the coupling can + be transported from a domain to another one. + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + distribution : string, optional (default="uniform") + The kind of distribution estimation to employ + verbose : int, optional (default=0) + Controls the verbosity of the optimization algorithm + log : int, optional (default=0) + Controls the logs of the optimization algorithm + Attributes + ---------- + Coupling_ : the optimal coupling + + References + ---------- + .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE Transactions + on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 + """ + + def __init__(self, mode="unsupervised", verbose=False, + log=False, metric="sqeuclidean", + distribution_estimation=distribution_estimation_uniform, + out_of_sample_map='ferradans'): + + self.mode = mode + self.verbose = verbose + self.log = log + self.metric = metric + self.distribution_estimation = distribution_estimation + self.out_of_sample_map = out_of_sample_map + + def fit(self, Xs, ys=None, Xt=None, yt=None): + """Build a coupling matrix from source and target sets of samples + (Xs, ys) and (Xt, yt) + Parameters + ---------- + Xs : array-like of shape = [n_source_samples, n_features] + The training input samples. + ys : array-like, shape = [n_source_samples] + The class labels + Xt : array-like of shape = [n_target_samples, n_features] + The training input samples. + yt : array-like, shape = [n_labeled_target_samples] + The class labels + Returns + ------- + self : object + Returns self. + """ + + super(EMDTransport, self).fit(Xs, ys, Xt, yt) + + # coupling estimation + self.Coupling_ = emd( + a=self.mu_s, b=self.mu_t, M=self.Cost, + # verbose=self.verbose, + # log=self.log + ) diff --git a/test/test_da.py b/test/test_da.py index e7b4ed1..33b3695 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -13,7 +13,7 @@ from ot.utils import unif np.random.seed(42) -def test_sinkhorn_transport(): +def test_sinkhorn_transport_class(): """test_sinkhorn_transport """ @@ -30,13 +30,59 @@ def test_sinkhorn_transport(): # test dimensions of coupling assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) - assert_equal(clf.gamma_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.Coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(clf.gamma_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(clf.gamma_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.Coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.Coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + + # test transform + transp_Xs = clf.transform(Xs=Xs) + assert_equal(transp_Xs.shape, Xs.shape) + + Xs_new, _ = get_data_classif('3gauss', ns + 1) + transp_Xs_new = clf.transform(Xs_new) + + # check that the oos method is not working + assert_equal(transp_Xs_new, Xs_new) + + # test inverse transform + transp_Xt = clf.inverse_transform(Xt=Xt) + assert_equal(transp_Xt.shape, Xt.shape) + + Xt_new, _ = get_data_classif('3gauss2', nt + 1) + transp_Xt_new = clf.inverse_transform(Xt=Xt_new) + + # check that the oos method is not working and returns the input data + assert_equal(transp_Xt_new, Xt_new) + + +def test_emd_transport_class(): + """test_sinkhorn_transport + """ + + ns = 150 + nt = 200 + + Xs, ys = get_data_classif('3gauss', ns) + Xt, yt = get_data_classif('3gauss2', nt) + + clf = ot.da.EMDTransport() + + # test its computed + clf.fit(Xs=Xs, Xt=Xt) + + # test dimensions of coupling + assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.Coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + + # test margin constraints + mu_s = unif(ns) + mu_t = unif(nt) + assert_allclose(np.sum(clf.Coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.Coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform transp_Xs = clf.transform(Xs=Xs) @@ -119,3 +165,8 @@ def test_otda(): da_emd = ot.da.OTDA_mapping_kernel() # init class da_emd.fit(xs, xt, numItermax=10) # fit distributions da_emd.predict(xs) # interpolation of source samples + + +if __name__ == "__main__": + test_sinkhorn_transport_class() + test_emd_transport_class() -- cgit v1.2.3 From 70be03461db45de50ecd073b9795093ead1ba5f5 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 4 Aug 2017 11:16:30 +0200 Subject: added test for fit_transform + correction of fit_transform bug (missing return self) --- ot/da.py | 4 ++++ test/test_da.py | 13 ++++++++----- 2 files changed, 12 insertions(+), 5 deletions(-) diff --git a/ot/da.py b/ot/da.py index fb2fd36..80649a7 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1283,6 +1283,8 @@ class SinkhornTransport(BaseTransport): numItermax=self.max_iter, stopThr=self.tol, verbose=self.verbose, log=self.log) + return self + class EMDTransport(BaseTransport): """Domain Adapatation OT method based on Earth Mover's Distance @@ -1357,3 +1359,5 @@ class EMDTransport(BaseTransport): # verbose=self.verbose, # log=self.log ) + + return self diff --git a/test/test_da.py b/test/test_da.py index 33b3695..68807ec 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -58,6 +58,10 @@ def test_sinkhorn_transport_class(): # check that the oos method is not working and returns the input data assert_equal(transp_Xt_new, Xt_new) + # test fit_transform + transp_Xs = clf.fit_transform(Xs=Xs, Xt=Xt) + assert_equal(transp_Xs.shape, Xs.shape) + def test_emd_transport_class(): """test_sinkhorn_transport @@ -104,6 +108,10 @@ def test_emd_transport_class(): # check that the oos method is not working and returns the input data assert_equal(transp_Xt_new, Xt_new) + # test fit_transform + transp_Xs = clf.fit_transform(Xs=Xs, Xt=Xt) + assert_equal(transp_Xs.shape, Xs.shape) + def test_otda(): @@ -165,8 +173,3 @@ def test_otda(): da_emd = ot.da.OTDA_mapping_kernel() # init class da_emd.fit(xs, xt, numItermax=10) # fit distributions da_emd.predict(xs) # interpolation of source samples - - -if __name__ == "__main__": - test_sinkhorn_transport_class() - test_emd_transport_class() -- cgit v1.2.3 From 64880e721f45c56de4815dd41cd21b8570c9776f Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 4 Aug 2017 11:34:21 +0200 Subject: added new class SinkhornLpl1Transport() + dedicated test --- ot/da.py | 91 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++ test/test_da.py | 50 +++++++++++++++++++++++++++++++ 2 files changed, 141 insertions(+) diff --git a/ot/da.py b/ot/da.py index 80649a7..3031f63 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1361,3 +1361,94 @@ class EMDTransport(BaseTransport): ) return self + + +class SinkhornLpl1Transport(BaseTransport): + """Domain Adapatation OT method based on sinkhorn algorithm + + LpL1 class regularization. + + Parameters + ---------- + mode : string, optional (default="unsupervised") + The DA mode. If "unsupervised" no target labels are taken into account + to modify the cost matrix. If "semisupervised" the target labels + are taken into account to set coefficients of the pairwise distance + matrix to 0 for row and columns indices that correspond to source and + target samples which share the same labels. + mapping : string, optional (default="barycentric") + The kind of mapping to apply to transport samples from a domain into + another one. + if "barycentric" only the samples used to estimate the coupling can + be transported from a domain to another one. + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + distribution : string, optional (default="uniform") + The kind of distribution estimation to employ + verbose : int, optional (default=0) + Controls the verbosity of the optimization algorithm + log : int, optional (default=0) + Controls the logs of the optimization algorithm + Attributes + ---------- + Coupling_ : the optimal coupling + + References + ---------- + + .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE + Transactions on Pattern Analysis and Machine Intelligence , + vol.PP, no.99, pp.1-1 + .. [2] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). + Generalized conditional gradient: analysis of convergence + and applications. arXiv preprint arXiv:1510.06567. + + """ + + def __init__(self, reg_e=1., reg_cl=0.1, mode="unsupervised", + max_iter=10, max_inner_iter=200, + tol=10e-9, verbose=False, log=False, + metric="sqeuclidean", + distribution_estimation=distribution_estimation_uniform, + out_of_sample_map='ferradans'): + + self.reg_e = reg_e + self.reg_cl = reg_cl + self.mode = mode + self.max_iter = max_iter + self.max_inner_iter = max_inner_iter + self.tol = tol + self.verbose = verbose + self.log = log + self.metric = metric + self.distribution_estimation = distribution_estimation + self.out_of_sample_map = out_of_sample_map + + def fit(self, Xs, ys=None, Xt=None, yt=None): + """Build a coupling matrix from source and target sets of samples + (Xs, ys) and (Xt, yt) + Parameters + ---------- + Xs : array-like of shape = [n_source_samples, n_features] + The training input samples. + ys : array-like, shape = [n_source_samples] + The class labels + Xt : array-like of shape = [n_target_samples, n_features] + The training input samples. + yt : array-like, shape = [n_labeled_target_samples] + The class labels + Returns + ------- + self : object + Returns self. + """ + + super(SinkhornLpl1Transport, self).fit(Xs, ys, Xt, yt) + + self.Coupling_ = sinkhorn_lpl1_mm( + a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.Cost, + reg=self.reg_e, eta=self.reg_cl, numItermax=self.max_iter, + numInnerItermax=self.max_inner_iter, stopInnerThr=self.tol, + verbose=self.verbose, log=self.log) + + return self diff --git a/test/test_da.py b/test/test_da.py index 68807ec..7d00cfb 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -13,6 +13,56 @@ from ot.utils import unif np.random.seed(42) +def test_sinkhorn_lpl1_transport_class(): + """test_sinkhorn_transport + """ + + ns = 150 + nt = 200 + + Xs, ys = get_data_classif('3gauss', ns) + Xt, yt = get_data_classif('3gauss2', nt) + + clf = ot.da.SinkhornLpl1Transport() + + # test its computed + clf.fit(Xs=Xs, ys=ys, Xt=Xt) + + # test dimensions of coupling + assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.Coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + + # test margin constraints + mu_s = unif(ns) + mu_t = unif(nt) + assert_allclose(np.sum(clf.Coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.Coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + + # test transform + transp_Xs = clf.transform(Xs=Xs) + assert_equal(transp_Xs.shape, Xs.shape) + + Xs_new, _ = get_data_classif('3gauss', ns + 1) + transp_Xs_new = clf.transform(Xs_new) + + # check that the oos method is not working + assert_equal(transp_Xs_new, Xs_new) + + # test inverse transform + transp_Xt = clf.inverse_transform(Xt=Xt) + assert_equal(transp_Xt.shape, Xt.shape) + + Xt_new, _ = get_data_classif('3gauss2', nt + 1) + transp_Xt_new = clf.inverse_transform(Xt=Xt_new) + + # check that the oos method is not working and returns the input data + assert_equal(transp_Xt_new, Xt_new) + + # test fit_transform + transp_Xs = clf.fit_transform(Xs=Xs, ys=ys, Xt=Xt) + assert_equal(transp_Xs.shape, Xs.shape) + + def test_sinkhorn_transport_class(): """test_sinkhorn_transport """ -- cgit v1.2.3 From 727077ad7db503955aea0751abf9f361f1d82af7 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 4 Aug 2017 11:40:44 +0200 Subject: added new class SinkhornL1l2Transport() + dedicated test --- ot/da.py | 109 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ test/test_da.py | 50 ++++++++++++++++++++++++++ 2 files changed, 159 insertions(+) diff --git a/ot/da.py b/ot/da.py index 3031f63..6100d15 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1369,6 +1369,10 @@ class SinkhornLpl1Transport(BaseTransport): Parameters ---------- + reg_e : float, optional (default=1) + Entropic regularization parameter + reg_cl : float, optional (default=0.1) + Class regularization parameter mode : string, optional (default="unsupervised") The DA mode. If "unsupervised" no target labels are taken into account to modify the cost matrix. If "semisupervised" the target labels @@ -1384,6 +1388,11 @@ class SinkhornLpl1Transport(BaseTransport): The ground metric for the Wasserstein problem distribution : string, optional (default="uniform") The kind of distribution estimation to employ + max_iter : int, float, optional (default=10) + The minimum number of iteration before stopping the optimization + algorithm if no it has not converged + max_inner_iter : int, float, optional (default=200) + The number of iteration in the inner loop verbose : int, optional (default=0) Controls the verbosity of the optimization algorithm log : int, optional (default=0) @@ -1452,3 +1461,103 @@ class SinkhornLpl1Transport(BaseTransport): verbose=self.verbose, log=self.log) return self + + +class SinkhornL1l2Transport(BaseTransport): + """Domain Adapatation OT method based on sinkhorn algorithm + + l1l2 class regularization. + + Parameters + ---------- + reg_e : float, optional (default=1) + Entropic regularization parameter + reg_cl : float, optional (default=0.1) + Class regularization parameter + mode : string, optional (default="unsupervised") + The DA mode. If "unsupervised" no target labels are taken into account + to modify the cost matrix. If "semisupervised" the target labels + are taken into account to set coefficients of the pairwise distance + matrix to 0 for row and columns indices that correspond to source and + target samples which share the same labels. + mapping : string, optional (default="barycentric") + The kind of mapping to apply to transport samples from a domain into + another one. + if "barycentric" only the samples used to estimate the coupling can + be transported from a domain to another one. + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + distribution : string, optional (default="uniform") + The kind of distribution estimation to employ + max_iter : int, float, optional (default=10) + The minimum number of iteration before stopping the optimization + algorithm if no it has not converged + max_inner_iter : int, float, optional (default=200) + The number of iteration in the inner loop + verbose : int, optional (default=0) + Controls the verbosity of the optimization algorithm + log : int, optional (default=0) + Controls the logs of the optimization algorithm + Attributes + ---------- + Coupling_ : the optimal coupling + + References + ---------- + + .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE + Transactions on Pattern Analysis and Machine Intelligence , + vol.PP, no.99, pp.1-1 + .. [2] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). + Generalized conditional gradient: analysis of convergence + and applications. arXiv preprint arXiv:1510.06567. + + """ + + def __init__(self, reg_e=1., reg_cl=0.1, mode="unsupervised", + max_iter=10, max_inner_iter=200, + tol=10e-9, verbose=False, log=False, + metric="sqeuclidean", + distribution_estimation=distribution_estimation_uniform, + out_of_sample_map='ferradans'): + + self.reg_e = reg_e + self.reg_cl = reg_cl + self.mode = mode + self.max_iter = max_iter + self.max_inner_iter = max_inner_iter + self.tol = tol + self.verbose = verbose + self.log = log + self.metric = metric + self.distribution_estimation = distribution_estimation + self.out_of_sample_map = out_of_sample_map + + def fit(self, Xs, ys=None, Xt=None, yt=None): + """Build a coupling matrix from source and target sets of samples + (Xs, ys) and (Xt, yt) + Parameters + ---------- + Xs : array-like of shape = [n_source_samples, n_features] + The training input samples. + ys : array-like, shape = [n_source_samples] + The class labels + Xt : array-like of shape = [n_target_samples, n_features] + The training input samples. + yt : array-like, shape = [n_labeled_target_samples] + The class labels + Returns + ------- + self : object + Returns self. + """ + + super(SinkhornL1l2Transport, self).fit(Xs, ys, Xt, yt) + + self.Coupling_ = sinkhorn_l1l2_gl( + a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.Cost, + reg=self.reg_e, eta=self.reg_cl, numItermax=self.max_iter, + numInnerItermax=self.max_inner_iter, stopInnerThr=self.tol, + verbose=self.verbose, log=self.log) + + return self diff --git a/test/test_da.py b/test/test_da.py index 7d00cfb..68d1958 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -63,6 +63,56 @@ def test_sinkhorn_lpl1_transport_class(): assert_equal(transp_Xs.shape, Xs.shape) +def test_sinkhorn_l1l2_transport_class(): + """test_sinkhorn_transport + """ + + ns = 150 + nt = 200 + + Xs, ys = get_data_classif('3gauss', ns) + Xt, yt = get_data_classif('3gauss2', nt) + + clf = ot.da.SinkhornL1l2Transport() + + # test its computed + clf.fit(Xs=Xs, ys=ys, Xt=Xt) + + # test dimensions of coupling + assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.Coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + + # test margin constraints + mu_s = unif(ns) + mu_t = unif(nt) + assert_allclose(np.sum(clf.Coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.Coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + + # test transform + transp_Xs = clf.transform(Xs=Xs) + assert_equal(transp_Xs.shape, Xs.shape) + + Xs_new, _ = get_data_classif('3gauss', ns + 1) + transp_Xs_new = clf.transform(Xs_new) + + # check that the oos method is not working + assert_equal(transp_Xs_new, Xs_new) + + # test inverse transform + transp_Xt = clf.inverse_transform(Xt=Xt) + assert_equal(transp_Xt.shape, Xt.shape) + + Xt_new, _ = get_data_classif('3gauss2', nt + 1) + transp_Xt_new = clf.inverse_transform(Xt=Xt_new) + + # check that the oos method is not working and returns the input data + assert_equal(transp_Xt_new, Xt_new) + + # test fit_transform + transp_Xs = clf.fit_transform(Xs=Xs, ys=ys, Xt=Xt) + assert_equal(transp_Xs.shape, Xs.shape) + + def test_sinkhorn_transport_class(): """test_sinkhorn_transport """ -- cgit v1.2.3 From 0b005906f9d78adbf4d52d2ea9610eb3fde96a7c Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 4 Aug 2017 12:04:04 +0200 Subject: semi supervised mode supported --- ot/da.py | 21 +++++++++++++++++++-- test/test_da.py | 52 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 71 insertions(+), 2 deletions(-) diff --git a/ot/da.py b/ot/da.py index 6100d15..8294e8d 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1089,8 +1089,25 @@ class BaseTransport(BaseEstimator): self.Cost = dist(Xs, Xt, metric=self.metric) if self.mode == "semisupervised": - print("TODO: modify cost matrix accordingly") - pass + + if (ys is not None) and (yt is not None): + + # assumes labeled source samples occupy the first rows + # and labeled target samples occupy the first columns + classes = np.unique(ys) + for c in classes: + ids = np.where(ys == c) + idt = np.where(yt == c) + + # all the coefficients corresponding to a source sample + # and a target sample with the same label gets a 0 + # transport cost + for j in idt[0]: + self.Cost[ids[0], j] = 0 + else: + print("Warning: using unsupervised mode\ + \nto use semisupervised mode, please provide ys and yt") + pass # distribution estimation self.mu_s = self.distribution_estimation(Xs) diff --git a/test/test_da.py b/test/test_da.py index 68d1958..497a8ee 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -62,6 +62,19 @@ def test_sinkhorn_lpl1_transport_class(): transp_Xs = clf.fit_transform(Xs=Xs, ys=ys, Xt=Xt) assert_equal(transp_Xs.shape, Xs.shape) + # test semi supervised mode + clf = ot.da.SinkhornTransport(mode="semisupervised") + clf.fit(Xs=Xs, Xt=Xt) + n_unsup = np.sum(clf.Cost) + + # test semi supervised mode + clf = ot.da.SinkhornTransport(mode="semisupervised") + clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) + assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(clf.Cost) + + assert n_unsup != n_semisup, "semisupervised mode not working" + def test_sinkhorn_l1l2_transport_class(): """test_sinkhorn_transport @@ -112,6 +125,19 @@ def test_sinkhorn_l1l2_transport_class(): transp_Xs = clf.fit_transform(Xs=Xs, ys=ys, Xt=Xt) assert_equal(transp_Xs.shape, Xs.shape) + # test semi supervised mode + clf = ot.da.SinkhornTransport(mode="semisupervised") + clf.fit(Xs=Xs, Xt=Xt) + n_unsup = np.sum(clf.Cost) + + # test semi supervised mode + clf = ot.da.SinkhornTransport(mode="semisupervised") + clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) + assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(clf.Cost) + + assert n_unsup != n_semisup, "semisupervised mode not working" + def test_sinkhorn_transport_class(): """test_sinkhorn_transport @@ -162,6 +188,19 @@ def test_sinkhorn_transport_class(): transp_Xs = clf.fit_transform(Xs=Xs, Xt=Xt) assert_equal(transp_Xs.shape, Xs.shape) + # test semi supervised mode + clf = ot.da.SinkhornTransport(mode="semisupervised") + clf.fit(Xs=Xs, Xt=Xt) + n_unsup = np.sum(clf.Cost) + + # test semi supervised mode + clf = ot.da.SinkhornTransport(mode="semisupervised") + clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) + assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(clf.Cost) + + assert n_unsup != n_semisup, "semisupervised mode not working" + def test_emd_transport_class(): """test_sinkhorn_transport @@ -212,6 +251,19 @@ def test_emd_transport_class(): transp_Xs = clf.fit_transform(Xs=Xs, Xt=Xt) assert_equal(transp_Xs.shape, Xs.shape) + # test semi supervised mode + clf = ot.da.SinkhornTransport(mode="semisupervised") + clf.fit(Xs=Xs, Xt=Xt) + n_unsup = np.sum(clf.Cost) + + # test semi supervised mode + clf = ot.da.SinkhornTransport(mode="semisupervised") + clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) + assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(clf.Cost) + + assert n_unsup != n_semisup, "semisupervised mode not working" + def test_otda(): -- cgit v1.2.3 From d793f1f73e6f816458d8b307762675aa9fa84d22 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 4 Aug 2017 13:56:51 +0200 Subject: correction of semi supervised mode --- ot/da.py | 77 +++++++++++++++++++++++++++++++++------------------------ test/test_da.py | 20 +++++++-------- 2 files changed, 55 insertions(+), 42 deletions(-) diff --git a/ot/da.py b/ot/da.py index 8294e8d..08e8a8d 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1088,26 +1088,23 @@ class BaseTransport(BaseEstimator): # pairwise distance self.Cost = dist(Xs, Xt, metric=self.metric) - if self.mode == "semisupervised": - - if (ys is not None) and (yt is not None): - - # assumes labeled source samples occupy the first rows - # and labeled target samples occupy the first columns - classes = np.unique(ys) - for c in classes: - ids = np.where(ys == c) - idt = np.where(yt == c) - - # all the coefficients corresponding to a source sample - # and a target sample with the same label gets a 0 - # transport cost - for j in idt[0]: - self.Cost[ids[0], j] = 0 - else: - print("Warning: using unsupervised mode\ - \nto use semisupervised mode, please provide ys and yt") - pass + if (ys is not None) and (yt is not None): + + if self.limit_max != np.infty: + self.limit_max = self.limit_max * np.max(self.Cost) + + # assumes labeled source samples occupy the first rows + # and labeled target samples occupy the first columns + classes = np.unique(ys) + for c in classes: + idx_s = np.where((ys != c) & (ys != -1)) + idx_t = np.where(yt == c) + + # all the coefficients corresponding to a source sample + # and a target sample : + # with different labels get a infinite + for j in idx_t[0]: + self.Cost[idx_s[0], j] = self.limit_max # distribution estimation self.mu_s = self.distribution_estimation(Xs) @@ -1243,6 +1240,9 @@ class SinkhornTransport(BaseTransport): Controls the verbosity of the optimization algorithm log : int, optional (default=0) Controls the logs of the optimization algorithm + limit_max: float, optional (defaul=np.infty) + Controls the semi supervised mode. Transport between labeled source + and target samples of different classes will exhibit an infinite cost Attributes ---------- Coupling_ : the optimal coupling @@ -1257,19 +1257,19 @@ class SinkhornTransport(BaseTransport): 26, 2013 """ - def __init__(self, reg_e=1., mode="unsupervised", max_iter=1000, + def __init__(self, reg_e=1., max_iter=1000, tol=10e-9, verbose=False, log=False, metric="sqeuclidean", distribution_estimation=distribution_estimation_uniform, - out_of_sample_map='ferradans'): + out_of_sample_map='ferradans', limit_max=np.infty): self.reg_e = reg_e - self.mode = mode self.max_iter = max_iter self.tol = tol self.verbose = verbose self.log = log self.metric = metric + self.limit_max = limit_max self.distribution_estimation = distribution_estimation self.out_of_sample_map = out_of_sample_map @@ -1326,6 +1326,10 @@ class EMDTransport(BaseTransport): Controls the verbosity of the optimization algorithm log : int, optional (default=0) Controls the logs of the optimization algorithm + limit_max: float, optional (default=10) + Controls the semi supervised mode. Transport between labeled source + and target samples of different classes will exhibit an infinite cost + (10 times the maximum value of the cost matrix) Attributes ---------- Coupling_ : the optimal coupling @@ -1337,15 +1341,15 @@ class EMDTransport(BaseTransport): on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 """ - def __init__(self, mode="unsupervised", verbose=False, + def __init__(self, verbose=False, log=False, metric="sqeuclidean", distribution_estimation=distribution_estimation_uniform, - out_of_sample_map='ferradans'): + out_of_sample_map='ferradans', limit_max=10): - self.mode = mode self.verbose = verbose self.log = log self.metric = metric + self.limit_max = limit_max self.distribution_estimation = distribution_estimation self.out_of_sample_map = out_of_sample_map @@ -1414,6 +1418,10 @@ class SinkhornLpl1Transport(BaseTransport): Controls the verbosity of the optimization algorithm log : int, optional (default=0) Controls the logs of the optimization algorithm + limit_max: float, optional (defaul=np.infty) + Controls the semi supervised mode. Transport between labeled source + and target samples of different classes will exhibit an infinite cost + Attributes ---------- Coupling_ : the optimal coupling @@ -1431,16 +1439,15 @@ class SinkhornLpl1Transport(BaseTransport): """ - def __init__(self, reg_e=1., reg_cl=0.1, mode="unsupervised", + def __init__(self, reg_e=1., reg_cl=0.1, max_iter=10, max_inner_iter=200, tol=10e-9, verbose=False, log=False, metric="sqeuclidean", distribution_estimation=distribution_estimation_uniform, - out_of_sample_map='ferradans'): + out_of_sample_map='ferradans', limit_max=np.infty): self.reg_e = reg_e self.reg_cl = reg_cl - self.mode = mode self.max_iter = max_iter self.max_inner_iter = max_inner_iter self.tol = tol @@ -1449,6 +1456,7 @@ class SinkhornLpl1Transport(BaseTransport): self.metric = metric self.distribution_estimation = distribution_estimation self.out_of_sample_map = out_of_sample_map + self.limit_max = limit_max def fit(self, Xs, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples @@ -1514,6 +1522,11 @@ class SinkhornL1l2Transport(BaseTransport): Controls the verbosity of the optimization algorithm log : int, optional (default=0) Controls the logs of the optimization algorithm + limit_max: float, optional (default=10) + Controls the semi supervised mode. Transport between labeled source + and target samples of different classes will exhibit an infinite cost + (10 times the maximum value of the cost matrix) + Attributes ---------- Coupling_ : the optimal coupling @@ -1531,16 +1544,15 @@ class SinkhornL1l2Transport(BaseTransport): """ - def __init__(self, reg_e=1., reg_cl=0.1, mode="unsupervised", + def __init__(self, reg_e=1., reg_cl=0.1, max_iter=10, max_inner_iter=200, tol=10e-9, verbose=False, log=False, metric="sqeuclidean", distribution_estimation=distribution_estimation_uniform, - out_of_sample_map='ferradans'): + out_of_sample_map='ferradans', limit_max=10): self.reg_e = reg_e self.reg_cl = reg_cl - self.mode = mode self.max_iter = max_iter self.max_inner_iter = max_inner_iter self.tol = tol @@ -1549,6 +1561,7 @@ class SinkhornL1l2Transport(BaseTransport): self.metric = metric self.distribution_estimation = distribution_estimation self.out_of_sample_map = out_of_sample_map + self.limit_max = limit_max def fit(self, Xs, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples diff --git a/test/test_da.py b/test/test_da.py index 497a8ee..ecd2a3a 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -63,12 +63,12 @@ def test_sinkhorn_lpl1_transport_class(): assert_equal(transp_Xs.shape, Xs.shape) # test semi supervised mode - clf = ot.da.SinkhornTransport(mode="semisupervised") - clf.fit(Xs=Xs, Xt=Xt) + clf = ot.da.SinkhornLpl1Transport() + clf.fit(Xs=Xs, ys=ys, Xt=Xt) n_unsup = np.sum(clf.Cost) # test semi supervised mode - clf = ot.da.SinkhornTransport(mode="semisupervised") + clf = ot.da.SinkhornLpl1Transport() clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) n_semisup = np.sum(clf.Cost) @@ -126,12 +126,12 @@ def test_sinkhorn_l1l2_transport_class(): assert_equal(transp_Xs.shape, Xs.shape) # test semi supervised mode - clf = ot.da.SinkhornTransport(mode="semisupervised") - clf.fit(Xs=Xs, Xt=Xt) + clf = ot.da.SinkhornL1l2Transport() + clf.fit(Xs=Xs, ys=ys, Xt=Xt) n_unsup = np.sum(clf.Cost) # test semi supervised mode - clf = ot.da.SinkhornTransport(mode="semisupervised") + clf = ot.da.SinkhornL1l2Transport() clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) n_semisup = np.sum(clf.Cost) @@ -189,12 +189,12 @@ def test_sinkhorn_transport_class(): assert_equal(transp_Xs.shape, Xs.shape) # test semi supervised mode - clf = ot.da.SinkhornTransport(mode="semisupervised") + clf = ot.da.SinkhornTransport() clf.fit(Xs=Xs, Xt=Xt) n_unsup = np.sum(clf.Cost) # test semi supervised mode - clf = ot.da.SinkhornTransport(mode="semisupervised") + clf = ot.da.SinkhornTransport() clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) n_semisup = np.sum(clf.Cost) @@ -252,12 +252,12 @@ def test_emd_transport_class(): assert_equal(transp_Xs.shape, Xs.shape) # test semi supervised mode - clf = ot.da.SinkhornTransport(mode="semisupervised") + clf = ot.da.EMDTransport() clf.fit(Xs=Xs, Xt=Xt) n_unsup = np.sum(clf.Cost) # test semi supervised mode - clf = ot.da.SinkhornTransport(mode="semisupervised") + clf = ot.da.EMDTransport() clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) n_semisup = np.sum(clf.Cost) -- cgit v1.2.3 From 778f4f76d7f162e7630c9ba5369a0e389e18433c Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 4 Aug 2017 14:02:06 +0200 Subject: reformat doc strings + remove useless log / verbose parameters for emd --- ot/da.py | 81 ++++++++++++++++++++++++++++++---------------------------------- 1 file changed, 38 insertions(+), 43 deletions(-) diff --git a/ot/da.py b/ot/da.py index 08e8a8d..92a8f12 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1053,11 +1053,11 @@ def distribution_estimation_uniform(X): Parameters ---------- - X : array-like of shape = [n_samples, n_features] + X : array-like of shape = (n_samples, n_features) The array of samples Returns ------- - mu : array-like, shape = [n_samples,] + mu : array-like, shape = (n_samples,) The uniform distribution estimated from X """ @@ -1071,13 +1071,13 @@ class BaseTransport(BaseEstimator): (Xs, ys) and (Xt, yt) Parameters ---------- - Xs : array-like of shape = [n_source_samples, n_features] + Xs : array-like of shape = (n_source_samples, n_features) The training input samples. - ys : array-like, shape = [n_source_samples] + ys : array-like, shape = (n_source_samples,) The class labels - Xt : array-like of shape = [n_target_samples, n_features] + Xt : array-like of shape = (n_target_samples, n_features) The training input samples. - yt : array-like, shape = [n_labeled_target_samples] + yt : array-like, shape = (n_labeled_target_samples,) The class labels Returns ------- @@ -1122,17 +1122,17 @@ class BaseTransport(BaseEstimator): ones Xt Parameters ---------- - Xs : array-like of shape = [n_source_samples, n_features] + Xs : array-like of shape = (n_source_samples, n_features) The training input samples. - ys : array-like, shape = [n_source_samples] + ys : array-like, shape = (n_source_samples,) The class labels - Xt : array-like of shape = [n_target_samples, n_features] + Xt : array-like of shape = (n_target_samples, n_features) The training input samples. - yt : array-like, shape = [n_labeled_target_samples] + yt : array-like, shape = (n_labeled_target_samples,) The class labels Returns ------- - transp_Xs : array-like of shape = [n_source_samples, n_features] + transp_Xs : array-like of shape = (n_source_samples, n_features) The source samples samples. """ @@ -1142,17 +1142,17 @@ class BaseTransport(BaseEstimator): """Transports source samples Xs onto target ones Xt Parameters ---------- - Xs : array-like of shape = [n_source_samples, n_features] + Xs : array-like of shape = (n_source_samples, n_features) The training input samples. - ys : array-like, shape = [n_source_samples] + ys : array-like, shape = (n_source_samples,) The class labels - Xt : array-like of shape = [n_target_samples, n_features] + Xt : array-like of shape = (n_target_samples, n_features) The training input samples. - yt : array-like, shape = [n_labeled_target_samples] + yt : array-like, shape = (n_labeled_target_samples,) The class labels Returns ------- - transp_Xs : array-like of shape = [n_source_samples, n_features] + transp_Xs : array-like of shape = (n_source_samples, n_features) The transport source samples. """ @@ -1177,17 +1177,17 @@ class BaseTransport(BaseEstimator): """Transports target samples Xt onto target samples Xs Parameters ---------- - Xs : array-like of shape = [n_source_samples, n_features] + Xs : array-like of shape = (n_source_samples, n_features) The training input samples. - ys : array-like, shape = [n_source_samples] + ys : array-like, shape = (n_source_samples,) The class labels - Xt : array-like of shape = [n_target_samples, n_features] + Xt : array-like of shape = (n_target_samples, n_features) The training input samples. - yt : array-like, shape = [n_labeled_target_samples] + yt : array-like, shape = (n_labeled_target_samples,) The class labels Returns ------- - transp_Xt : array-like of shape = [n_source_samples, n_features] + transp_Xt : array-like of shape = (n_source_samples, n_features) The transported target samples. """ @@ -1278,13 +1278,13 @@ class SinkhornTransport(BaseTransport): (Xs, ys) and (Xt, yt) Parameters ---------- - Xs : array-like of shape = [n_source_samples, n_features] + Xs : array-like of shape = (n_source_samples, n_features) The training input samples. - ys : array-like, shape = [n_source_samples] + ys : array-like, shape = (n_source_samples,) The class labels - Xt : array-like of shape = [n_target_samples, n_features] + Xt : array-like of shape = (n_target_samples, n_features) The training input samples. - yt : array-like, shape = [n_labeled_target_samples] + yt : array-like, shape = (n_labeled_target_samples,) The class labels Returns ------- @@ -1341,13 +1341,10 @@ class EMDTransport(BaseTransport): on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 """ - def __init__(self, verbose=False, - log=False, metric="sqeuclidean", + def __init__(self, metric="sqeuclidean", distribution_estimation=distribution_estimation_uniform, out_of_sample_map='ferradans', limit_max=10): - self.verbose = verbose - self.log = log self.metric = metric self.limit_max = limit_max self.distribution_estimation = distribution_estimation @@ -1358,13 +1355,13 @@ class EMDTransport(BaseTransport): (Xs, ys) and (Xt, yt) Parameters ---------- - Xs : array-like of shape = [n_source_samples, n_features] + Xs : array-like of shape = (n_source_samples, n_features) The training input samples. - ys : array-like, shape = [n_source_samples] + ys : array-like, shape = (n_source_samples,) The class labels - Xt : array-like of shape = [n_target_samples, n_features] + Xt : array-like of shape = (n_target_samples, n_features) The training input samples. - yt : array-like, shape = [n_labeled_target_samples] + yt : array-like, shape = (n_labeled_target_samples,) The class labels Returns ------- @@ -1377,8 +1374,6 @@ class EMDTransport(BaseTransport): # coupling estimation self.Coupling_ = emd( a=self.mu_s, b=self.mu_t, M=self.Cost, - # verbose=self.verbose, - # log=self.log ) return self @@ -1463,13 +1458,13 @@ class SinkhornLpl1Transport(BaseTransport): (Xs, ys) and (Xt, yt) Parameters ---------- - Xs : array-like of shape = [n_source_samples, n_features] + Xs : array-like of shape = (n_source_samples, n_features) The training input samples. - ys : array-like, shape = [n_source_samples] + ys : array-like, shape = (n_source_samples,) The class labels - Xt : array-like of shape = [n_target_samples, n_features] + Xt : array-like of shape = (n_target_samples, n_features) The training input samples. - yt : array-like, shape = [n_labeled_target_samples] + yt : array-like, shape = (n_labeled_target_samples,) The class labels Returns ------- @@ -1568,13 +1563,13 @@ class SinkhornL1l2Transport(BaseTransport): (Xs, ys) and (Xt, yt) Parameters ---------- - Xs : array-like of shape = [n_source_samples, n_features] + Xs : array-like of shape = (n_source_samples, n_features) The training input samples. - ys : array-like, shape = [n_source_samples] + ys : array-like, shape = (n_source_samples,) The class labels - Xt : array-like of shape = [n_target_samples, n_features] + Xt : array-like of shape = (n_target_samples, n_features) The training input samples. - yt : array-like, shape = [n_labeled_target_samples] + yt : array-like, shape = (n_labeled_target_samples,) The class labels Returns ------- -- cgit v1.2.3 From 738bfb1c560ff4e349f5083fc1f81a54e4be4980 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 4 Aug 2017 14:55:54 +0200 Subject: out of samples by Ferradans supported for transform and inverse_transform --- ot/da.py | 29 +++++++++++++++++++++++------ test/test_da.py | 32 ++++++++++++++++---------------- 2 files changed, 39 insertions(+), 22 deletions(-) diff --git a/ot/da.py b/ot/da.py index 92a8f12..87d056d 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1167,9 +1167,18 @@ class BaseTransport(BaseEstimator): transp_Xs = np.dot(transp, self.Xt) else: # perform out of sample mapping - print("Warning: out of sample mapping not yet implemented") - print("input data will be returned") - transp_Xs = Xs + + # get the nearest neighbor in the source domain + D0 = dist(Xs, self.Xs) + idx = np.argmin(D0, axis=1) + + # transport the source samples + transp = self.Coupling_ / np.sum(self.Coupling_, 1)[:, None] + transp[~ np.isfinite(transp)] = 0 + transp_Xs_ = np.dot(transp, self.Xt) + + # define the transported points + transp_Xs = transp_Xs_[idx, :] + Xs - self.Xs[idx, :] return transp_Xs @@ -1202,9 +1211,17 @@ class BaseTransport(BaseEstimator): transp_Xt = np.dot(transp_, self.Xs) else: # perform out of sample mapping - print("Warning: out of sample mapping not yet implemented") - print("input data will be returned") - transp_Xt = Xt + + D0 = dist(Xt, self.Xt) + idx = np.argmin(D0, axis=1) + + # transport the target samples + transp_ = self.Coupling_.T / np.sum(self.Coupling_, 0)[:, None] + transp_[~ np.isfinite(transp_)] = 0 + transp_Xt_ = np.dot(transp_, self.Xs) + + # define the transported points + transp_Xt = transp_Xt_[idx, :] + Xt - self.Xt[idx, :] return transp_Xt diff --git a/test/test_da.py b/test/test_da.py index ecd2a3a..aed9f61 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -45,8 +45,8 @@ def test_sinkhorn_lpl1_transport_class(): Xs_new, _ = get_data_classif('3gauss', ns + 1) transp_Xs_new = clf.transform(Xs_new) - # check that the oos method is not working - assert_equal(transp_Xs_new, Xs_new) + # check that the oos method is working + assert_equal(transp_Xs_new.shape, Xs_new.shape) # test inverse transform transp_Xt = clf.inverse_transform(Xt=Xt) @@ -55,8 +55,8 @@ def test_sinkhorn_lpl1_transport_class(): Xt_new, _ = get_data_classif('3gauss2', nt + 1) transp_Xt_new = clf.inverse_transform(Xt=Xt_new) - # check that the oos method is not working and returns the input data - assert_equal(transp_Xt_new, Xt_new) + # check that the oos method is working + assert_equal(transp_Xt_new.shape, Xt_new.shape) # test fit_transform transp_Xs = clf.fit_transform(Xs=Xs, ys=ys, Xt=Xt) @@ -108,8 +108,8 @@ def test_sinkhorn_l1l2_transport_class(): Xs_new, _ = get_data_classif('3gauss', ns + 1) transp_Xs_new = clf.transform(Xs_new) - # check that the oos method is not working - assert_equal(transp_Xs_new, Xs_new) + # check that the oos method is working + assert_equal(transp_Xs_new.shape, Xs_new.shape) # test inverse transform transp_Xt = clf.inverse_transform(Xt=Xt) @@ -118,8 +118,8 @@ def test_sinkhorn_l1l2_transport_class(): Xt_new, _ = get_data_classif('3gauss2', nt + 1) transp_Xt_new = clf.inverse_transform(Xt=Xt_new) - # check that the oos method is not working and returns the input data - assert_equal(transp_Xt_new, Xt_new) + # check that the oos method is working + assert_equal(transp_Xt_new.shape, Xt_new.shape) # test fit_transform transp_Xs = clf.fit_transform(Xs=Xs, ys=ys, Xt=Xt) @@ -171,8 +171,8 @@ def test_sinkhorn_transport_class(): Xs_new, _ = get_data_classif('3gauss', ns + 1) transp_Xs_new = clf.transform(Xs_new) - # check that the oos method is not working - assert_equal(transp_Xs_new, Xs_new) + # check that the oos method is working + assert_equal(transp_Xs_new.shape, Xs_new.shape) # test inverse transform transp_Xt = clf.inverse_transform(Xt=Xt) @@ -181,8 +181,8 @@ def test_sinkhorn_transport_class(): Xt_new, _ = get_data_classif('3gauss2', nt + 1) transp_Xt_new = clf.inverse_transform(Xt=Xt_new) - # check that the oos method is not working and returns the input data - assert_equal(transp_Xt_new, Xt_new) + # check that the oos method is working + assert_equal(transp_Xt_new.shape, Xt_new.shape) # test fit_transform transp_Xs = clf.fit_transform(Xs=Xs, Xt=Xt) @@ -234,8 +234,8 @@ def test_emd_transport_class(): Xs_new, _ = get_data_classif('3gauss', ns + 1) transp_Xs_new = clf.transform(Xs_new) - # check that the oos method is not working - assert_equal(transp_Xs_new, Xs_new) + # check that the oos method is working + assert_equal(transp_Xs_new.shape, Xs_new.shape) # test inverse transform transp_Xt = clf.inverse_transform(Xt=Xt) @@ -244,8 +244,8 @@ def test_emd_transport_class(): Xt_new, _ = get_data_classif('3gauss2', nt + 1) transp_Xt_new = clf.inverse_transform(Xt=Xt_new) - # check that the oos method is not working and returns the input data - assert_equal(transp_Xt_new, Xt_new) + # check that the oos method is working + assert_equal(transp_Xt_new.shape, Xt_new.shape) # test fit_transform transp_Xs = clf.fit_transform(Xs=Xs, Xt=Xt) -- cgit v1.2.3 From 8a214292884a868ea805f22481faad2c9270a770 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 4 Aug 2017 15:49:42 +0200 Subject: added new class MappingTransport to support linear and kernel mapping, not yet tested --- ot/da.py | 158 +++++++++++++++++++++++++++++++++++++++++++++++++++++---------- 1 file changed, 134 insertions(+), 24 deletions(-) diff --git a/ot/da.py b/ot/da.py index 87d056d..0616d17 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1233,12 +1233,6 @@ class SinkhornTransport(BaseTransport): ---------- reg_e : float, optional (default=1) Entropic regularization parameter - mode : string, optional (default="unsupervised") - The DA mode. If "unsupervised" no target labels are taken into account - to modify the cost matrix. If "semisupervised" the target labels - are taken into account to set coefficients of the pairwise distance - matrix to 0 for row and columns indices that correspond to source and - target samples which share the same labels. max_iter : int, float, optional (default=1000) The minimum number of iteration before stopping the optimization algorithm if no it has not converged @@ -1324,12 +1318,6 @@ class EMDTransport(BaseTransport): """Domain Adapatation OT method based on Earth Mover's Distance Parameters ---------- - mode : string, optional (default="unsupervised") - The DA mode. If "unsupervised" no target labels are taken into account - to modify the cost matrix. If "semisupervised" the target labels - are taken into account to set coefficients of the pairwise distance - matrix to 0 for row and columns indices that correspond to source and - target samples which share the same labels. mapping : string, optional (default="barycentric") The kind of mapping to apply to transport samples from a domain into another one. @@ -1406,12 +1394,6 @@ class SinkhornLpl1Transport(BaseTransport): Entropic regularization parameter reg_cl : float, optional (default=0.1) Class regularization parameter - mode : string, optional (default="unsupervised") - The DA mode. If "unsupervised" no target labels are taken into account - to modify the cost matrix. If "semisupervised" the target labels - are taken into account to set coefficients of the pairwise distance - matrix to 0 for row and columns indices that correspond to source and - target samples which share the same labels. mapping : string, optional (default="barycentric") The kind of mapping to apply to transport samples from a domain into another one. @@ -1510,12 +1492,6 @@ class SinkhornL1l2Transport(BaseTransport): Entropic regularization parameter reg_cl : float, optional (default=0.1) Class regularization parameter - mode : string, optional (default="unsupervised") - The DA mode. If "unsupervised" no target labels are taken into account - to modify the cost matrix. If "semisupervised" the target labels - are taken into account to set coefficients of the pairwise distance - matrix to 0 for row and columns indices that correspond to source and - target samples which share the same labels. mapping : string, optional (default="barycentric") The kind of mapping to apply to transport samples from a domain into another one. @@ -1603,3 +1579,137 @@ class SinkhornL1l2Transport(BaseTransport): verbose=self.verbose, log=self.log) return self + + +class MappingTransport(BaseEstimator): + """MappingTransport: DA methods that aims at jointly estimating a optimal + transport coupling and the associated mapping + + Parameters + ---------- + mu : float, optional (default=1) + Weight for the linear OT loss (>0) + eta : float, optional (default=0.001) + Regularization term for the linear mapping L (>0) + bias : bool, optional (default=False) + Estimate linear mapping with constant bias + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + kernel : string, optional (default="linear") + The kernel to use either linear or gaussian + sigma : float, optional (default=1) + The gaussian kernel parameter + max_iter : int, optional (default=100) + Max number of BCD iterations + tol : float, optional (default=1e-5) + Stop threshold on relative loss decrease (>0) + max_inner_iter : int, optional (default=10) + Max number of iterations (inner CG solver) + inner_tol : float, optional (default=1e-6) + Stop threshold on error (inner CG solver) (>0) + verbose : bool, optional (default=False) + Print information along iterations + log : bool, optional (default=False) + record log if True + + Attributes + ---------- + Coupling_ : the optimal coupling + Mapping_ : the mapping associated + + References + ---------- + + .. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, + "Mapping estimation for discrete optimal transport", + Neural Information Processing Systems (NIPS), 2016. + + """ + + def __init__(self, mu=1, eta=0.001, bias=False, metric="sqeuclidean", + kernel="linear", sigma=1, max_iter=100, tol=1e-5, + max_inner_iter=10, inner_tol=1e-6, log=False, verbose=False): + + self.metric = metric + self.mu = mu + self.eta = eta + self.bias = bias + self.kernel = kernel + self.sigma + self.max_iter = max_iter + self.tol = tol + self.max_inner_iter = max_inner_iter + self.inner_tol = inner_tol + self.log = log + self.verbose = verbose + + def fit(self, Xs=None, ys=None, Xt=None, yt=None): + """Builds an optimal coupling and estimates the associated mapping + from source and target sets of samples (Xs, ys) and (Xt, yt) + Parameters + ---------- + Xs : array-like of shape = (n_source_samples, n_features) + The training input samples. + ys : array-like, shape = (n_source_samples,) + The class labels + Xt : array-like of shape = (n_target_samples, n_features) + The training input samples. + yt : array-like, shape = (n_labeled_target_samples,) + The class labels + Returns + ------- + self : object + Returns self. + """ + + self.Xs = Xs + self.Xt = Xt + + if self.kernel == "linear": + self.Coupling_, self.Mapping_ = joint_OT_mapping_linear( + Xs, Xt, mu=self.mu, eta=self.eta, bias=self.bias, + verbose=self.verbose, verbose2=self.verbose2, + numItermax=self.max_iter, numInnerItermax=self.max_inner_iter, + stopThr=self.tol, stopInnerThr=self.inner_tol, log=self.log) + + elif self.kernel == "gaussian": + self.Coupling_, self.Mapping_ = joint_OT_mapping_kernel( + Xs, Xt, mu=self.mu, eta=self.eta, bias=self.bias, + sigma=self.sigma, verbose=self.verbose, verbose2=self.verbose, + numItermax=self.max_iter, numInnerItermax=self.max_inner_iter, + stopInnerThr=self.inner_tol, stopThr=self.tol, log=self.log) + + return self + + def transform(self, Xs): + """Transports source samples Xs onto target ones Xt + Parameters + ---------- + Xs : array-like of shape = (n_source_samples, n_features) + The training input samples. + + Returns + ------- + transp_Xs : array-like of shape = (n_source_samples, n_features) + The transport source samples. + """ + + if np.array_equal(self.Xs, Xs): + # perform standard barycentric mapping + transp = self.Coupling_ / np.sum(self.Coupling_, 1)[:, None] + + # set nans to 0 + transp[~ np.isfinite(transp)] = 0 + + # compute transported samples + transp_Xs = np.dot(transp, self.Xt) + else: + if self.kernel == "gaussian": + K = kernel(Xs, self.Xs, method=self.kernel, sigma=self.sigma) + elif self.kernel == "linear": + K = Xs + if self.bias: + K = np.hstack((K, np.ones((Xs.shape[0], 1)))) + transp_Xs = K.dot(self.Mapping_) + + return transp_Xs -- cgit v1.2.3 From 8149e059be7f715834d11b365855f2684bd3d6f5 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Wed, 23 Aug 2017 11:45:06 +0200 Subject: make doc strings compliant with numpy / modif according to AG review --- ot/da.py | 139 +++++++++++++++++++++++++++++++++----------------------- test/test_da.py | 13 ++++-- 2 files changed, 93 insertions(+), 59 deletions(-) diff --git a/ot/da.py b/ot/da.py index 0616d17..044d567 100644 --- a/ot/da.py +++ b/ot/da.py @@ -967,11 +967,13 @@ class BaseEstimator(object): def get_params(self, deep=True): """Get parameters for this estimator. + Parameters ---------- deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. + Returns ------- params : mapping of string to any @@ -1002,10 +1004,12 @@ class BaseEstimator(object): def set_params(self, **params): """Set the parameters of this estimator. + The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``__`` so that it's possible to update each component of a nested object. + Returns ------- self @@ -1053,11 +1057,12 @@ def distribution_estimation_uniform(X): Parameters ---------- - X : array-like of shape = (n_samples, n_features) + X : array-like, shape (n_samples, n_features) The array of samples + Returns ------- - mu : array-like, shape = (n_samples,) + mu : array-like, shape (n_samples,) The uniform distribution estimated from X """ @@ -1069,16 +1074,18 @@ class BaseTransport(BaseEstimator): def fit(self, Xs=None, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples (Xs, ys) and (Xt, yt) + Parameters ---------- - Xs : array-like of shape = (n_source_samples, n_features) + Xs : array-like, shape (n_source_samples, n_features) The training input samples. - ys : array-like, shape = (n_source_samples,) + ys : array-like, shape (n_source_samples,) The class labels - Xt : array-like of shape = (n_target_samples, n_features) + Xt : array-like, shape (n_target_samples, n_features) The training input samples. - yt : array-like, shape = (n_labeled_target_samples,) + yt : array-like, shape (n_labeled_target_samples,) The class labels + Returns ------- self : object @@ -1086,12 +1093,12 @@ class BaseTransport(BaseEstimator): """ # pairwise distance - self.Cost = dist(Xs, Xt, metric=self.metric) + self.cost_ = dist(Xs, Xt, metric=self.metric) if (ys is not None) and (yt is not None): if self.limit_max != np.infty: - self.limit_max = self.limit_max * np.max(self.Cost) + self.limit_max = self.limit_max * np.max(self.cost_) # assumes labeled source samples occupy the first rows # and labeled target samples occupy the first columns @@ -1104,7 +1111,7 @@ class BaseTransport(BaseEstimator): # and a target sample : # with different labels get a infinite for j in idx_t[0]: - self.Cost[idx_s[0], j] = self.limit_max + self.cost_[idx_s[0], j] = self.limit_max # distribution estimation self.mu_s = self.distribution_estimation(Xs) @@ -1120,19 +1127,21 @@ class BaseTransport(BaseEstimator): """Build a coupling matrix from source and target sets of samples (Xs, ys) and (Xt, yt) and transports source samples Xs onto target ones Xt + Parameters ---------- - Xs : array-like of shape = (n_source_samples, n_features) + Xs : array-like, shape (n_source_samples, n_features) The training input samples. - ys : array-like, shape = (n_source_samples,) + ys : array-like, shape (n_source_samples,) The class labels - Xt : array-like of shape = (n_target_samples, n_features) + Xt : array-like, shape (n_target_samples, n_features) The training input samples. - yt : array-like, shape = (n_labeled_target_samples,) + yt : array-like, shape (n_labeled_target_samples,) The class labels + Returns ------- - transp_Xs : array-like of shape = (n_source_samples, n_features) + transp_Xs : array-like, shape (n_source_samples, n_features) The source samples samples. """ @@ -1140,25 +1149,27 @@ class BaseTransport(BaseEstimator): def transform(self, Xs=None, ys=None, Xt=None, yt=None): """Transports source samples Xs onto target ones Xt + Parameters ---------- - Xs : array-like of shape = (n_source_samples, n_features) + Xs : array-like, shape (n_source_samples, n_features) The training input samples. - ys : array-like, shape = (n_source_samples,) + ys : array-like, shape (n_source_samples,) The class labels - Xt : array-like of shape = (n_target_samples, n_features) + Xt : array-like, shape (n_target_samples, n_features) The training input samples. - yt : array-like, shape = (n_labeled_target_samples,) + yt : array-like, shape (n_labeled_target_samples,) The class labels + Returns ------- - transp_Xs : array-like of shape = (n_source_samples, n_features) + transp_Xs : array-like, shape (n_source_samples, n_features) The transport source samples. """ if np.array_equal(self.Xs, Xs): # perform standard barycentric mapping - transp = self.Coupling_ / np.sum(self.Coupling_, 1)[:, None] + transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] # set nans to 0 transp[~ np.isfinite(transp)] = 0 @@ -1173,7 +1184,7 @@ class BaseTransport(BaseEstimator): idx = np.argmin(D0, axis=1) # transport the source samples - transp = self.Coupling_ / np.sum(self.Coupling_, 1)[:, None] + transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] transp[~ np.isfinite(transp)] = 0 transp_Xs_ = np.dot(transp, self.Xt) @@ -1184,25 +1195,27 @@ class BaseTransport(BaseEstimator): def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None): """Transports target samples Xt onto target samples Xs + Parameters ---------- - Xs : array-like of shape = (n_source_samples, n_features) + Xs : array-like, shape (n_source_samples, n_features) The training input samples. ys : array-like, shape = (n_source_samples,) The class labels - Xt : array-like of shape = (n_target_samples, n_features) + Xt : array-like, shape (n_target_samples, n_features) The training input samples. yt : array-like, shape = (n_labeled_target_samples,) The class labels + Returns ------- - transp_Xt : array-like of shape = (n_source_samples, n_features) + transp_Xt : array-like, shape (n_source_samples, n_features) The transported target samples. """ if np.array_equal(self.Xt, Xt): # perform standard barycentric mapping - transp_ = self.Coupling_.T / np.sum(self.Coupling_, 0)[:, None] + transp_ = self.coupling_.T / np.sum(self.coupling_, 0)[:, None] # set nans to 0 transp_[~ np.isfinite(transp_)] = 0 @@ -1216,7 +1229,7 @@ class BaseTransport(BaseEstimator): idx = np.argmin(D0, axis=1) # transport the target samples - transp_ = self.Coupling_.T / np.sum(self.Coupling_, 0)[:, None] + transp_ = self.coupling_.T / np.sum(self.coupling_, 0)[:, None] transp_[~ np.isfinite(transp_)] = 0 transp_Xt_ = np.dot(transp_, self.Xs) @@ -1254,9 +1267,10 @@ class SinkhornTransport(BaseTransport): limit_max: float, optional (defaul=np.infty) Controls the semi supervised mode. Transport between labeled source and target samples of different classes will exhibit an infinite cost + Attributes ---------- - Coupling_ : the optimal coupling + coupling_ : the optimal coupling References ---------- @@ -1287,16 +1301,18 @@ class SinkhornTransport(BaseTransport): def fit(self, Xs=None, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples (Xs, ys) and (Xt, yt) + Parameters ---------- - Xs : array-like of shape = (n_source_samples, n_features) + Xs : array-like, shape (n_source_samples, n_features) The training input samples. ys : array-like, shape = (n_source_samples,) The class labels - Xt : array-like of shape = (n_target_samples, n_features) + Xt : array-like, shape (n_target_samples, n_features) The training input samples. yt : array-like, shape = (n_labeled_target_samples,) The class labels + Returns ------- self : object @@ -1306,8 +1322,8 @@ class SinkhornTransport(BaseTransport): super(SinkhornTransport, self).fit(Xs, ys, Xt, yt) # coupling estimation - self.Coupling_ = sinkhorn( - a=self.mu_s, b=self.mu_t, M=self.Cost, reg=self.reg_e, + self.coupling_ = sinkhorn( + a=self.mu_s, b=self.mu_t, M=self.cost_, reg=self.reg_e, numItermax=self.max_iter, stopThr=self.tol, verbose=self.verbose, log=self.log) @@ -1316,6 +1332,7 @@ class SinkhornTransport(BaseTransport): class EMDTransport(BaseTransport): """Domain Adapatation OT method based on Earth Mover's Distance + Parameters ---------- mapping : string, optional (default="barycentric") @@ -1335,9 +1352,10 @@ class EMDTransport(BaseTransport): Controls the semi supervised mode. Transport between labeled source and target samples of different classes will exhibit an infinite cost (10 times the maximum value of the cost matrix) + Attributes ---------- - Coupling_ : the optimal coupling + coupling_ : the optimal coupling References ---------- @@ -1358,16 +1376,18 @@ class EMDTransport(BaseTransport): def fit(self, Xs, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples (Xs, ys) and (Xt, yt) + Parameters ---------- - Xs : array-like of shape = (n_source_samples, n_features) + Xs : array-like, shape (n_source_samples, n_features) The training input samples. ys : array-like, shape = (n_source_samples,) The class labels - Xt : array-like of shape = (n_target_samples, n_features) + Xt : array-like, shape (n_target_samples, n_features) The training input samples. yt : array-like, shape = (n_labeled_target_samples,) The class labels + Returns ------- self : object @@ -1377,8 +1397,8 @@ class EMDTransport(BaseTransport): super(EMDTransport, self).fit(Xs, ys, Xt, yt) # coupling estimation - self.Coupling_ = emd( - a=self.mu_s, b=self.mu_t, M=self.Cost, + self.coupling_ = emd( + a=self.mu_s, b=self.mu_t, M=self.cost_, ) return self @@ -1418,7 +1438,7 @@ class SinkhornLpl1Transport(BaseTransport): Attributes ---------- - Coupling_ : the optimal coupling + coupling_ : the optimal coupling References ---------- @@ -1455,16 +1475,18 @@ class SinkhornLpl1Transport(BaseTransport): def fit(self, Xs, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples (Xs, ys) and (Xt, yt) + Parameters ---------- - Xs : array-like of shape = (n_source_samples, n_features) + Xs : array-like, shape (n_source_samples, n_features) The training input samples. ys : array-like, shape = (n_source_samples,) The class labels - Xt : array-like of shape = (n_target_samples, n_features) + Xt : array-like, shape (n_target_samples, n_features) The training input samples. yt : array-like, shape = (n_labeled_target_samples,) The class labels + Returns ------- self : object @@ -1473,8 +1495,8 @@ class SinkhornLpl1Transport(BaseTransport): super(SinkhornLpl1Transport, self).fit(Xs, ys, Xt, yt) - self.Coupling_ = sinkhorn_lpl1_mm( - a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.Cost, + self.coupling_ = sinkhorn_lpl1_mm( + a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.cost_, reg=self.reg_e, eta=self.reg_cl, numItermax=self.max_iter, numInnerItermax=self.max_inner_iter, stopInnerThr=self.tol, verbose=self.verbose, log=self.log) @@ -1517,7 +1539,7 @@ class SinkhornL1l2Transport(BaseTransport): Attributes ---------- - Coupling_ : the optimal coupling + coupling_ : the optimal coupling References ---------- @@ -1554,16 +1576,18 @@ class SinkhornL1l2Transport(BaseTransport): def fit(self, Xs, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples (Xs, ys) and (Xt, yt) + Parameters ---------- - Xs : array-like of shape = (n_source_samples, n_features) + Xs : array-like, shape (n_source_samples, n_features) The training input samples. ys : array-like, shape = (n_source_samples,) The class labels - Xt : array-like of shape = (n_target_samples, n_features) + Xt : array-like, shape (n_target_samples, n_features) The training input samples. yt : array-like, shape = (n_labeled_target_samples,) The class labels + Returns ------- self : object @@ -1572,8 +1596,8 @@ class SinkhornL1l2Transport(BaseTransport): super(SinkhornL1l2Transport, self).fit(Xs, ys, Xt, yt) - self.Coupling_ = sinkhorn_l1l2_gl( - a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.Cost, + self.coupling_ = sinkhorn_l1l2_gl( + a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.cost_, reg=self.reg_e, eta=self.reg_cl, numItermax=self.max_iter, numInnerItermax=self.max_inner_iter, stopInnerThr=self.tol, verbose=self.verbose, log=self.log) @@ -1614,8 +1638,8 @@ class MappingTransport(BaseEstimator): Attributes ---------- - Coupling_ : the optimal coupling - Mapping_ : the mapping associated + coupling_ : the optimal coupling + mapping_ : the mapping associated References ---------- @@ -1646,16 +1670,18 @@ class MappingTransport(BaseEstimator): def fit(self, Xs=None, ys=None, Xt=None, yt=None): """Builds an optimal coupling and estimates the associated mapping from source and target sets of samples (Xs, ys) and (Xt, yt) + Parameters ---------- - Xs : array-like of shape = (n_source_samples, n_features) + Xs : array-like, shape (n_source_samples, n_features) The training input samples. ys : array-like, shape = (n_source_samples,) The class labels - Xt : array-like of shape = (n_target_samples, n_features) + Xt : array-like, shape (n_target_samples, n_features) The training input samples. yt : array-like, shape = (n_labeled_target_samples,) The class labels + Returns ------- self : object @@ -1666,14 +1692,14 @@ class MappingTransport(BaseEstimator): self.Xt = Xt if self.kernel == "linear": - self.Coupling_, self.Mapping_ = joint_OT_mapping_linear( + self.coupling_, self.mapping_ = joint_OT_mapping_linear( Xs, Xt, mu=self.mu, eta=self.eta, bias=self.bias, verbose=self.verbose, verbose2=self.verbose2, numItermax=self.max_iter, numInnerItermax=self.max_inner_iter, stopThr=self.tol, stopInnerThr=self.inner_tol, log=self.log) elif self.kernel == "gaussian": - self.Coupling_, self.Mapping_ = joint_OT_mapping_kernel( + self.coupling_, self.mapping_ = joint_OT_mapping_kernel( Xs, Xt, mu=self.mu, eta=self.eta, bias=self.bias, sigma=self.sigma, verbose=self.verbose, verbose2=self.verbose, numItermax=self.max_iter, numInnerItermax=self.max_inner_iter, @@ -1683,20 +1709,21 @@ class MappingTransport(BaseEstimator): def transform(self, Xs): """Transports source samples Xs onto target ones Xt + Parameters ---------- - Xs : array-like of shape = (n_source_samples, n_features) + Xs : array-like, shape (n_source_samples, n_features) The training input samples. Returns ------- - transp_Xs : array-like of shape = (n_source_samples, n_features) + transp_Xs : array-like, shape (n_source_samples, n_features) The transport source samples. """ if np.array_equal(self.Xs, Xs): # perform standard barycentric mapping - transp = self.Coupling_ / np.sum(self.Coupling_, 1)[:, None] + transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] # set nans to 0 transp[~ np.isfinite(transp)] = 0 @@ -1710,6 +1737,6 @@ class MappingTransport(BaseEstimator): K = Xs if self.bias: K = np.hstack((K, np.ones((Xs.shape[0], 1)))) - transp_Xs = K.dot(self.Mapping_) + transp_Xs = K.dot(self.mapping_) return transp_Xs diff --git a/test/test_da.py b/test/test_da.py index aed9f61..93f7e83 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -5,13 +5,12 @@ # License: MIT License import numpy as np -import ot from numpy.testing.utils import assert_allclose, assert_equal + +import ot from ot.datasets import get_data_classif from ot.utils import unif -np.random.seed(42) - def test_sinkhorn_lpl1_transport_class(): """test_sinkhorn_transport @@ -325,3 +324,11 @@ def test_otda(): da_emd = ot.da.OTDA_mapping_kernel() # init class da_emd.fit(xs, xt, numItermax=10) # fit distributions da_emd.predict(xs) # interpolation of source samples + + +if __name__ == "__main__": + + test_sinkhorn_transport_class() + test_emd_transport_class() + test_sinkhorn_l1l2_transport_class() + test_sinkhorn_lpl1_transport_class() -- cgit v1.2.3 From 791a4a6f215033a75d5f56cd16fe2412301bec14 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Wed, 23 Aug 2017 13:50:24 +0200 Subject: out of samples transform and inverse transform by batch --- ot/da.py | 89 +++++++++++++++++++++++++++++++++++++-------------------- test/test_da.py | 66 +++++++++++++++++++++--------------------- 2 files changed, 91 insertions(+), 64 deletions(-) diff --git a/ot/da.py b/ot/da.py index 044d567..0c83ae6 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1147,7 +1147,7 @@ class BaseTransport(BaseEstimator): return self.fit(Xs, ys, Xt, yt).transform(Xs, ys, Xt, yt) - def transform(self, Xs=None, ys=None, Xt=None, yt=None): + def transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128): """Transports source samples Xs onto target ones Xt Parameters @@ -1160,6 +1160,8 @@ class BaseTransport(BaseEstimator): The training input samples. yt : array-like, shape (n_labeled_target_samples,) The class labels + batch_size : int, optional (default=128) + The batch size for out of sample inverse transform Returns ------- @@ -1178,34 +1180,48 @@ class BaseTransport(BaseEstimator): transp_Xs = np.dot(transp, self.Xt) else: # perform out of sample mapping + indices = np.arange(Xs.shape[0]) + batch_ind = [ + indices[i:i + batch_size] + for i in range(0, len(indices), batch_size)] - # get the nearest neighbor in the source domain - D0 = dist(Xs, self.Xs) - idx = np.argmin(D0, axis=1) + transp_Xs = [] + for bi in batch_ind: - # transport the source samples - transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] - transp[~ np.isfinite(transp)] = 0 - transp_Xs_ = np.dot(transp, self.Xt) + # get the nearest neighbor in the source domain + D0 = dist(Xs[bi], self.Xs) + idx = np.argmin(D0, axis=1) + + # transport the source samples + transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] + transp[~ np.isfinite(transp)] = 0 + transp_Xs_ = np.dot(transp, self.Xt) - # define the transported points - transp_Xs = transp_Xs_[idx, :] + Xs - self.Xs[idx, :] + # define the transported points + transp_Xs_ = transp_Xs_[idx, :] + Xs[bi] - self.Xs[idx, :] + + transp_Xs.append(transp_Xs_) + + transp_Xs = np.concatenate(transp_Xs, axis=0) return transp_Xs - def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None): + def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None, + batch_size=128): """Transports target samples Xt onto target samples Xs Parameters ---------- Xs : array-like, shape (n_source_samples, n_features) The training input samples. - ys : array-like, shape = (n_source_samples,) + ys : array-like, shape (n_source_samples,) The class labels Xt : array-like, shape (n_target_samples, n_features) The training input samples. - yt : array-like, shape = (n_labeled_target_samples,) + yt : array-like, shape (n_labeled_target_samples,) The class labels + batch_size : int, optional (default=128) + The batch size for out of sample inverse transform Returns ------- @@ -1224,17 +1240,28 @@ class BaseTransport(BaseEstimator): transp_Xt = np.dot(transp_, self.Xs) else: # perform out of sample mapping + indices = np.arange(Xt.shape[0]) + batch_ind = [ + indices[i:i + batch_size] + for i in range(0, len(indices), batch_size)] - D0 = dist(Xt, self.Xt) - idx = np.argmin(D0, axis=1) + transp_Xt = [] + for bi in batch_ind: - # transport the target samples - transp_ = self.coupling_.T / np.sum(self.coupling_, 0)[:, None] - transp_[~ np.isfinite(transp_)] = 0 - transp_Xt_ = np.dot(transp_, self.Xs) + D0 = dist(Xt[bi], self.Xt) + idx = np.argmin(D0, axis=1) + + # transport the target samples + transp_ = self.coupling_.T / np.sum(self.coupling_, 0)[:, None] + transp_[~ np.isfinite(transp_)] = 0 + transp_Xt_ = np.dot(transp_, self.Xs) + + # define the transported points + transp_Xt_ = transp_Xt_[idx, :] + Xt[bi] - self.Xt[idx, :] - # define the transported points - transp_Xt = transp_Xt_[idx, :] + Xt - self.Xt[idx, :] + transp_Xt.append(transp_Xt_) + + transp_Xt = np.concatenate(transp_Xt, axis=0) return transp_Xt @@ -1306,11 +1333,11 @@ class SinkhornTransport(BaseTransport): ---------- Xs : array-like, shape (n_source_samples, n_features) The training input samples. - ys : array-like, shape = (n_source_samples,) + ys : array-like, shape (n_source_samples,) The class labels Xt : array-like, shape (n_target_samples, n_features) The training input samples. - yt : array-like, shape = (n_labeled_target_samples,) + yt : array-like, shape (n_labeled_target_samples,) The class labels Returns @@ -1381,11 +1408,11 @@ class EMDTransport(BaseTransport): ---------- Xs : array-like, shape (n_source_samples, n_features) The training input samples. - ys : array-like, shape = (n_source_samples,) + ys : array-like, shape (n_source_samples,) The class labels Xt : array-like, shape (n_target_samples, n_features) The training input samples. - yt : array-like, shape = (n_labeled_target_samples,) + yt : array-like, shape (n_labeled_target_samples,) The class labels Returns @@ -1480,11 +1507,11 @@ class SinkhornLpl1Transport(BaseTransport): ---------- Xs : array-like, shape (n_source_samples, n_features) The training input samples. - ys : array-like, shape = (n_source_samples,) + ys : array-like, shape (n_source_samples,) The class labels Xt : array-like, shape (n_target_samples, n_features) The training input samples. - yt : array-like, shape = (n_labeled_target_samples,) + yt : array-like, shape (n_labeled_target_samples,) The class labels Returns @@ -1581,11 +1608,11 @@ class SinkhornL1l2Transport(BaseTransport): ---------- Xs : array-like, shape (n_source_samples, n_features) The training input samples. - ys : array-like, shape = (n_source_samples,) + ys : array-like, shape (n_source_samples,) The class labels Xt : array-like, shape (n_target_samples, n_features) The training input samples. - yt : array-like, shape = (n_labeled_target_samples,) + yt : array-like, shape (n_labeled_target_samples,) The class labels Returns @@ -1675,11 +1702,11 @@ class MappingTransport(BaseEstimator): ---------- Xs : array-like, shape (n_source_samples, n_features) The training input samples. - ys : array-like, shape = (n_source_samples,) + ys : array-like, shape (n_source_samples,) The class labels Xt : array-like, shape (n_target_samples, n_features) The training input samples. - yt : array-like, shape = (n_labeled_target_samples,) + yt : array-like, shape (n_labeled_target_samples,) The class labels Returns diff --git a/test/test_da.py b/test/test_da.py index 93f7e83..196f4c4 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -28,14 +28,14 @@ def test_sinkhorn_lpl1_transport_class(): clf.fit(Xs=Xs, ys=ys, Xt=Xt) # test dimensions of coupling - assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) - assert_equal(clf.Coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(clf.Coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(clf.Coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform transp_Xs = clf.transform(Xs=Xs) @@ -64,13 +64,13 @@ def test_sinkhorn_lpl1_transport_class(): # test semi supervised mode clf = ot.da.SinkhornLpl1Transport() clf.fit(Xs=Xs, ys=ys, Xt=Xt) - n_unsup = np.sum(clf.Cost) + n_unsup = np.sum(clf.cost_) # test semi supervised mode clf = ot.da.SinkhornLpl1Transport() clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) - assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) - n_semisup = np.sum(clf.Cost) + assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(clf.cost_) assert n_unsup != n_semisup, "semisupervised mode not working" @@ -91,14 +91,14 @@ def test_sinkhorn_l1l2_transport_class(): clf.fit(Xs=Xs, ys=ys, Xt=Xt) # test dimensions of coupling - assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) - assert_equal(clf.Coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(clf.Coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(clf.Coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform transp_Xs = clf.transform(Xs=Xs) @@ -127,13 +127,13 @@ def test_sinkhorn_l1l2_transport_class(): # test semi supervised mode clf = ot.da.SinkhornL1l2Transport() clf.fit(Xs=Xs, ys=ys, Xt=Xt) - n_unsup = np.sum(clf.Cost) + n_unsup = np.sum(clf.cost_) # test semi supervised mode clf = ot.da.SinkhornL1l2Transport() clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) - assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) - n_semisup = np.sum(clf.Cost) + assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(clf.cost_) assert n_unsup != n_semisup, "semisupervised mode not working" @@ -154,14 +154,14 @@ def test_sinkhorn_transport_class(): clf.fit(Xs=Xs, Xt=Xt) # test dimensions of coupling - assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) - assert_equal(clf.Coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(clf.Coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(clf.Coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform transp_Xs = clf.transform(Xs=Xs) @@ -190,13 +190,13 @@ def test_sinkhorn_transport_class(): # test semi supervised mode clf = ot.da.SinkhornTransport() clf.fit(Xs=Xs, Xt=Xt) - n_unsup = np.sum(clf.Cost) + n_unsup = np.sum(clf.cost_) # test semi supervised mode clf = ot.da.SinkhornTransport() clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) - assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) - n_semisup = np.sum(clf.Cost) + assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(clf.cost_) assert n_unsup != n_semisup, "semisupervised mode not working" @@ -217,14 +217,14 @@ def test_emd_transport_class(): clf.fit(Xs=Xs, Xt=Xt) # test dimensions of coupling - assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) - assert_equal(clf.Coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(clf.Coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(clf.Coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform transp_Xs = clf.transform(Xs=Xs) @@ -253,13 +253,13 @@ def test_emd_transport_class(): # test semi supervised mode clf = ot.da.EMDTransport() clf.fit(Xs=Xs, Xt=Xt) - n_unsup = np.sum(clf.Cost) + n_unsup = np.sum(clf.cost_) # test semi supervised mode clf = ot.da.EMDTransport() clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) - assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) - n_semisup = np.sum(clf.Cost) + assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(clf.cost_) assert n_unsup != n_semisup, "semisupervised mode not working" @@ -326,9 +326,9 @@ def test_otda(): da_emd.predict(xs) # interpolation of source samples -if __name__ == "__main__": +# if __name__ == "__main__": - test_sinkhorn_transport_class() - test_emd_transport_class() - test_sinkhorn_l1l2_transport_class() - test_sinkhorn_lpl1_transport_class() +# test_sinkhorn_transport_class() +# test_emd_transport_class() +# test_sinkhorn_l1l2_transport_class() +# test_sinkhorn_lpl1_transport_class() -- cgit v1.2.3 From 326d163db029515c338a963978a5d95948f78c29 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Wed, 23 Aug 2017 14:11:13 +0200 Subject: test functions for MappingTransport Class --- ot/da.py | 18 ++++++--- test/test_da.py | 117 +++++++++++++++++++++++++++++++++++++++++++++++++++++--- 2 files changed, 125 insertions(+), 10 deletions(-) diff --git a/ot/da.py b/ot/da.py index 0c83ae6..3ccb1b3 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1665,8 +1665,14 @@ class MappingTransport(BaseEstimator): Attributes ---------- - coupling_ : the optimal coupling - mapping_ : the mapping associated + coupling_ : array-like, shape (n_source_samples, n_features) + The optimal coupling + mapping_ : array-like, shape (n_features (+ 1), n_features) + (if bias) for kernel == linear + The associated mapping + + array-like, shape (n_source_samples (+ 1), n_features) + (if bias) for kernel == gaussian References ---------- @@ -1679,20 +1685,22 @@ class MappingTransport(BaseEstimator): def __init__(self, mu=1, eta=0.001, bias=False, metric="sqeuclidean", kernel="linear", sigma=1, max_iter=100, tol=1e-5, - max_inner_iter=10, inner_tol=1e-6, log=False, verbose=False): + max_inner_iter=10, inner_tol=1e-6, log=False, verbose=False, + verbose2=False): self.metric = metric self.mu = mu self.eta = eta self.bias = bias self.kernel = kernel - self.sigma + self.sigma = sigma self.max_iter = max_iter self.tol = tol self.max_inner_iter = max_inner_iter self.inner_tol = inner_tol self.log = log self.verbose = verbose + self.verbose2 = verbose2 def fit(self, Xs=None, ys=None, Xt=None, yt=None): """Builds an optimal coupling and estimates the associated mapping @@ -1712,7 +1720,7 @@ class MappingTransport(BaseEstimator): Returns ------- self : object - Returns self. + Returns self """ self.Xs = Xs diff --git a/test/test_da.py b/test/test_da.py index 196f4c4..162f681 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -264,6 +264,112 @@ def test_emd_transport_class(): assert n_unsup != n_semisup, "semisupervised mode not working" +def test_mapping_transport_class(): + """test_mapping_transport + """ + + ns = 150 + nt = 200 + + Xs, ys = get_data_classif('3gauss', ns) + Xt, yt = get_data_classif('3gauss2', nt) + Xs_new, _ = get_data_classif('3gauss', ns + 1) + + ########################################################################## + # kernel == linear mapping tests + ########################################################################## + + # check computation and dimensions if bias == False + clf = ot.da.MappingTransport(kernel="linear", bias=False) + clf.fit(Xs=Xs, Xt=Xt) + + assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.mapping_.shape, ((Xs.shape[1], Xt.shape[1]))) + + # test margin constraints + mu_s = unif(ns) + mu_t = unif(nt) + assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + + # test transform + transp_Xs = clf.transform(Xs=Xs) + assert_equal(transp_Xs.shape, Xs.shape) + + transp_Xs_new = clf.transform(Xs_new) + + # check that the oos method is working + assert_equal(transp_Xs_new.shape, Xs_new.shape) + + # check computation and dimensions if bias == True + clf = ot.da.MappingTransport(kernel="linear", bias=True) + clf.fit(Xs=Xs, Xt=Xt) + assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.mapping_.shape, ((Xs.shape[1] + 1, Xt.shape[1]))) + + # test margin constraints + mu_s = unif(ns) + mu_t = unif(nt) + assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + + # test transform + transp_Xs = clf.transform(Xs=Xs) + assert_equal(transp_Xs.shape, Xs.shape) + + transp_Xs_new = clf.transform(Xs_new) + + # check that the oos method is working + assert_equal(transp_Xs_new.shape, Xs_new.shape) + + ########################################################################## + # kernel == gaussian mapping tests + ########################################################################## + + # check computation and dimensions if bias == False + clf = ot.da.MappingTransport(kernel="gaussian", bias=False) + clf.fit(Xs=Xs, Xt=Xt) + + assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.mapping_.shape, ((Xs.shape[0], Xt.shape[1]))) + + # test margin constraints + mu_s = unif(ns) + mu_t = unif(nt) + assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + + # test transform + transp_Xs = clf.transform(Xs=Xs) + assert_equal(transp_Xs.shape, Xs.shape) + + transp_Xs_new = clf.transform(Xs_new) + + # check that the oos method is working + assert_equal(transp_Xs_new.shape, Xs_new.shape) + + # check computation and dimensions if bias == True + clf = ot.da.MappingTransport(kernel="gaussian", bias=True) + clf.fit(Xs=Xs, Xt=Xt) + assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.mapping_.shape, ((Xs.shape[0] + 1, Xt.shape[1]))) + + # test margin constraints + mu_s = unif(ns) + mu_t = unif(nt) + assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + + # test transform + transp_Xs = clf.transform(Xs=Xs) + assert_equal(transp_Xs.shape, Xs.shape) + + transp_Xs_new = clf.transform(Xs_new) + + # check that the oos method is working + assert_equal(transp_Xs_new.shape, Xs_new.shape) + + def test_otda(): n_samples = 150 # nb samples @@ -326,9 +432,10 @@ def test_otda(): da_emd.predict(xs) # interpolation of source samples -# if __name__ == "__main__": +if __name__ == "__main__": -# test_sinkhorn_transport_class() -# test_emd_transport_class() -# test_sinkhorn_l1l2_transport_class() -# test_sinkhorn_lpl1_transport_class() + # test_sinkhorn_transport_class() + # test_emd_transport_class() + # test_sinkhorn_l1l2_transport_class() + # test_sinkhorn_lpl1_transport_class() + test_mapping_transport_class() -- cgit v1.2.3 From 09302239b3e4e1a90c1a4e2d7a85b0af86b01365 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Wed, 23 Aug 2017 15:09:08 +0200 Subject: added deprecation warning on old classes --- ot/da.py | 22 ++++++++++-- ot/deprecation.py | 103 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ test/test_da.py | 5 +-- 3 files changed, 126 insertions(+), 4 deletions(-) create mode 100644 ot/deprecation.py diff --git a/ot/da.py b/ot/da.py index 3ccb1b3..8fa1895 100644 --- a/ot/da.py +++ b/ot/da.py @@ -10,12 +10,14 @@ Domain adaptation with optimal transport # License: MIT License import numpy as np +import warnings + from .bregman import sinkhorn from .lp import emd from .utils import unif, dist, kernel from .optim import cg from .optim import gcg -import warnings +from .deprecation import deprecated def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, @@ -632,6 +634,9 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', return G, L +@deprecated("The class OTDA is deprecated in 0.3.1 and will be " + "removed in 0.5" + "\n\tfor standard transport use class EMDTransport instead.") class OTDA(object): """Class for domain adaptation with optimal transport as proposed in [5] @@ -758,10 +763,15 @@ class OTDA(object): self.M = np.log(1 + np.log(1 + self.M)) +@deprecated("The class OTDA_sinkhorn is deprecated in 0.3.1 and will be" + " removed in 0.5 \nUse class SinkhornTransport instead.") class OTDA_sinkhorn(OTDA): """Class for domain adaptation with optimal transport with entropic - regularization""" + regularization + + + """ def fit(self, xs, xt, reg=1, ws=None, wt=None, norm=None, **kwargs): """Fit regularized domain adaptation between samples is xs and xt @@ -783,6 +793,8 @@ class OTDA_sinkhorn(OTDA): self.computed = True +@deprecated("The class OTDA_lpl1 is deprecated in 0.3.1 and will be" + " removed in 0.5 \nUse class SinkhornLpl1Transport instead.") class OTDA_lpl1(OTDA): """Class for domain adaptation with optimal transport with entropic and @@ -810,6 +822,8 @@ class OTDA_lpl1(OTDA): self.computed = True +@deprecated("The class OTDA_l1L2 is deprecated in 0.3.1 and will be" + " removed in 0.5 \nUse class SinkhornL1l2Transport instead.") class OTDA_l1l2(OTDA): """Class for domain adaptation with optimal transport with entropic @@ -837,6 +851,8 @@ class OTDA_l1l2(OTDA): self.computed = True +@deprecated("The class OTDA_mapping_linear is deprecated in 0.3.1 and will be" + " removed in 0.5 \nUse class MappingTransport instead.") class OTDA_mapping_linear(OTDA): """Class for optimal transport with joint linear mapping estimation as in @@ -882,6 +898,8 @@ class OTDA_mapping_linear(OTDA): return None +@deprecated("The class OTDA_mapping_kernel is deprecated in 0.3.1 and will be" + " removed in 0.5 \nUse class MappingTransport instead.") class OTDA_mapping_kernel(OTDA_mapping_linear): """Class for optimal transport with joint nonlinear mapping diff --git a/ot/deprecation.py b/ot/deprecation.py new file mode 100644 index 0000000..2b16427 --- /dev/null +++ b/ot/deprecation.py @@ -0,0 +1,103 @@ +""" + deprecated class from scikit-learn package + https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/deprecation.py +""" + +import sys +import warnings + +__all__ = ["deprecated", ] + + +class deprecated(object): + """Decorator to mark a function or class as deprecated. + Issue a warning when the function is called/the class is instantiated and + adds a warning to the docstring. + The optional extra argument will be appended to the deprecation message + and the docstring. Note: to use this with the default value for extra, put + in an empty of parentheses: + >>> from ot.deprecation import deprecated + >>> @deprecated() + ... def some_function(): pass + + Parameters + ---------- + extra : string + to be added to the deprecation messages + """ + + # Adapted from http://wiki.python.org/moin/PythonDecoratorLibrary, + # but with many changes. + + def __init__(self, extra=''): + self.extra = extra + + def __call__(self, obj): + """Call method + Parameters + ---------- + obj : object + """ + if isinstance(obj, type): + return self._decorate_class(obj) + else: + return self._decorate_fun(obj) + + def _decorate_class(self, cls): + msg = "Class %s is deprecated" % cls.__name__ + if self.extra: + msg += "; %s" % self.extra + + # FIXME: we should probably reset __new__ for full generality + init = cls.__init__ + + def wrapped(*args, **kwargs): + warnings.warn(msg, category=DeprecationWarning) + return init(*args, **kwargs) + + cls.__init__ = wrapped + + wrapped.__name__ = '__init__' + wrapped.__doc__ = self._update_doc(init.__doc__) + wrapped.deprecated_original = init + + return cls + + def _decorate_fun(self, fun): + """Decorate function fun""" + + msg = "Function %s is deprecated" % fun.__name__ + if self.extra: + msg += "; %s" % self.extra + + def wrapped(*args, **kwargs): + warnings.warn(msg, category=DeprecationWarning) + return fun(*args, **kwargs) + + wrapped.__name__ = fun.__name__ + wrapped.__dict__ = fun.__dict__ + wrapped.__doc__ = self._update_doc(fun.__doc__) + + return wrapped + + def _update_doc(self, olddoc): + newdoc = "DEPRECATED" + if self.extra: + newdoc = "%s: %s" % (newdoc, self.extra) + if olddoc: + newdoc = "%s\n\n%s" % (newdoc, olddoc) + return newdoc + + +def _is_deprecated(func): + """Helper to check if func is wraped by our deprecated decorator""" + if sys.version_info < (3, 5): + raise NotImplementedError("This is only available for python3.5 " + "or above") + closures = getattr(func, '__closure__', []) + if closures is None: + closures = [] + is_deprecated = ('deprecated' in ''.join([c.cell_contents + for c in closures + if isinstance(c.cell_contents, str)])) + return is_deprecated diff --git a/test/test_da.py b/test/test_da.py index 162f681..9578b3d 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -432,10 +432,11 @@ def test_otda(): da_emd.predict(xs) # interpolation of source samples -if __name__ == "__main__": +# if __name__ == "__main__": + # test_otda() # test_sinkhorn_transport_class() # test_emd_transport_class() # test_sinkhorn_l1l2_transport_class() # test_sinkhorn_lpl1_transport_class() - test_mapping_transport_class() + # test_mapping_transport_class() -- cgit v1.2.3 From 2d4d0b46f88c66ebc5502c840703ba6ce8910376 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 25 Aug 2017 10:29:41 +0200 Subject: solving log issues to avoid errors and adding further tests --- ot/da.py | 57 ++++++++++++++++++++++++++++++++++++++++++--------------- test/test_da.py | 39 +++++++++++++++++++++++++++++++++------ 2 files changed, 75 insertions(+), 21 deletions(-) diff --git a/ot/da.py b/ot/da.py index 8fa1895..5a34979 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1315,7 +1315,10 @@ class SinkhornTransport(BaseTransport): Attributes ---------- - coupling_ : the optimal coupling + coupling_ : array-like, shape (n_source_samples, n_target_samples) + The optimal coupling + log_ : dictionary + The dictionary of log, empty dic if parameter log is not True References ---------- @@ -1367,11 +1370,18 @@ class SinkhornTransport(BaseTransport): super(SinkhornTransport, self).fit(Xs, ys, Xt, yt) # coupling estimation - self.coupling_ = sinkhorn( + returned_ = sinkhorn( a=self.mu_s, b=self.mu_t, M=self.cost_, reg=self.reg_e, numItermax=self.max_iter, stopThr=self.tol, verbose=self.verbose, log=self.log) + # deal with the value of log + if self.log: + self.coupling_, self.log_ = returned_ + else: + self.coupling_ = returned_ + self.log_ = dict() + return self @@ -1400,7 +1410,8 @@ class EMDTransport(BaseTransport): Attributes ---------- - coupling_ : the optimal coupling + coupling_ : array-like, shape (n_source_samples, n_target_samples) + The optimal coupling References ---------- @@ -1475,15 +1486,14 @@ class SinkhornLpl1Transport(BaseTransport): The number of iteration in the inner loop verbose : int, optional (default=0) Controls the verbosity of the optimization algorithm - log : int, optional (default=0) - Controls the logs of the optimization algorithm limit_max: float, optional (defaul=np.infty) Controls the semi supervised mode. Transport between labeled source and target samples of different classes will exhibit an infinite cost Attributes ---------- - coupling_ : the optimal coupling + coupling_ : array-like, shape (n_source_samples, n_target_samples) + The optimal coupling References ---------- @@ -1500,7 +1510,7 @@ class SinkhornLpl1Transport(BaseTransport): def __init__(self, reg_e=1., reg_cl=0.1, max_iter=10, max_inner_iter=200, - tol=10e-9, verbose=False, log=False, + tol=10e-9, verbose=False, metric="sqeuclidean", distribution_estimation=distribution_estimation_uniform, out_of_sample_map='ferradans', limit_max=np.infty): @@ -1511,7 +1521,6 @@ class SinkhornLpl1Transport(BaseTransport): self.max_inner_iter = max_inner_iter self.tol = tol self.verbose = verbose - self.log = log self.metric = metric self.distribution_estimation = distribution_estimation self.out_of_sample_map = out_of_sample_map @@ -1544,7 +1553,7 @@ class SinkhornLpl1Transport(BaseTransport): a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.cost_, reg=self.reg_e, eta=self.reg_cl, numItermax=self.max_iter, numInnerItermax=self.max_inner_iter, stopInnerThr=self.tol, - verbose=self.verbose, log=self.log) + verbose=self.verbose) return self @@ -1584,7 +1593,10 @@ class SinkhornL1l2Transport(BaseTransport): Attributes ---------- - coupling_ : the optimal coupling + coupling_ : array-like, shape (n_source_samples, n_target_samples) + The optimal coupling + log_ : dictionary + The dictionary of log, empty dic if parameter log is not True References ---------- @@ -1641,12 +1653,19 @@ class SinkhornL1l2Transport(BaseTransport): super(SinkhornL1l2Transport, self).fit(Xs, ys, Xt, yt) - self.coupling_ = sinkhorn_l1l2_gl( + returned_ = sinkhorn_l1l2_gl( a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.cost_, reg=self.reg_e, eta=self.reg_cl, numItermax=self.max_iter, numInnerItermax=self.max_inner_iter, stopInnerThr=self.tol, verbose=self.verbose, log=self.log) + # deal with the value of log + if self.log: + self.coupling_, self.log_ = returned_ + else: + self.coupling_ = returned_ + self.log_ = dict() + return self @@ -1683,14 +1702,15 @@ class MappingTransport(BaseEstimator): Attributes ---------- - coupling_ : array-like, shape (n_source_samples, n_features) + coupling_ : array-like, shape (n_source_samples, n_target_samples) The optimal coupling mapping_ : array-like, shape (n_features (+ 1), n_features) (if bias) for kernel == linear The associated mapping - array-like, shape (n_source_samples (+ 1), n_features) (if bias) for kernel == gaussian + log_ : dictionary + The dictionary of log, empty dic if parameter log is not True References ---------- @@ -1745,19 +1765,26 @@ class MappingTransport(BaseEstimator): self.Xt = Xt if self.kernel == "linear": - self.coupling_, self.mapping_ = joint_OT_mapping_linear( + returned_ = joint_OT_mapping_linear( Xs, Xt, mu=self.mu, eta=self.eta, bias=self.bias, verbose=self.verbose, verbose2=self.verbose2, numItermax=self.max_iter, numInnerItermax=self.max_inner_iter, stopThr=self.tol, stopInnerThr=self.inner_tol, log=self.log) elif self.kernel == "gaussian": - self.coupling_, self.mapping_ = joint_OT_mapping_kernel( + returned_ = joint_OT_mapping_kernel( Xs, Xt, mu=self.mu, eta=self.eta, bias=self.bias, sigma=self.sigma, verbose=self.verbose, verbose2=self.verbose, numItermax=self.max_iter, numInnerItermax=self.max_inner_iter, stopInnerThr=self.inner_tol, stopThr=self.tol, log=self.log) + # deal with the value of log + if self.log: + self.coupling_, self.mapping_, self.log_ = returned_ + else: + self.coupling_, self.mapping_ = returned_ + self.log_ = dict() + return self def transform(self, Xs): diff --git a/test/test_da.py b/test/test_da.py index 9578b3d..104a798 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -26,6 +26,8 @@ def test_sinkhorn_lpl1_transport_class(): # test its computed clf.fit(Xs=Xs, ys=ys, Xt=Xt) + assert hasattr(clf, "cost_") + assert hasattr(clf, "coupling_") # test dimensions of coupling assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) @@ -89,6 +91,9 @@ def test_sinkhorn_l1l2_transport_class(): # test its computed clf.fit(Xs=Xs, ys=ys, Xt=Xt) + assert hasattr(clf, "cost_") + assert hasattr(clf, "coupling_") + assert hasattr(clf, "log_") # test dimensions of coupling assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) @@ -137,6 +142,11 @@ def test_sinkhorn_l1l2_transport_class(): assert n_unsup != n_semisup, "semisupervised mode not working" + # check everything runs well with log=True + clf = ot.da.SinkhornL1l2Transport(log=True) + clf.fit(Xs=Xs, ys=ys, Xt=Xt) + assert len(clf.log_.keys()) != 0 + def test_sinkhorn_transport_class(): """test_sinkhorn_transport @@ -152,6 +162,9 @@ def test_sinkhorn_transport_class(): # test its computed clf.fit(Xs=Xs, Xt=Xt) + assert hasattr(clf, "cost_") + assert hasattr(clf, "coupling_") + assert hasattr(clf, "log_") # test dimensions of coupling assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) @@ -200,6 +213,11 @@ def test_sinkhorn_transport_class(): assert n_unsup != n_semisup, "semisupervised mode not working" + # check everything runs well with log=True + clf = ot.da.SinkhornTransport(log=True) + clf.fit(Xs=Xs, ys=ys, Xt=Xt) + assert len(clf.log_.keys()) != 0 + def test_emd_transport_class(): """test_sinkhorn_transport @@ -215,6 +233,8 @@ def test_emd_transport_class(): # test its computed clf.fit(Xs=Xs, Xt=Xt) + assert hasattr(clf, "cost_") + assert hasattr(clf, "coupling_") # test dimensions of coupling assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) @@ -282,6 +302,9 @@ def test_mapping_transport_class(): # check computation and dimensions if bias == False clf = ot.da.MappingTransport(kernel="linear", bias=False) clf.fit(Xs=Xs, Xt=Xt) + assert hasattr(clf, "coupling_") + assert hasattr(clf, "mapping_") + assert hasattr(clf, "log_") assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) assert_equal(clf.mapping_.shape, ((Xs.shape[1], Xt.shape[1]))) @@ -369,6 +392,11 @@ def test_mapping_transport_class(): # check that the oos method is working assert_equal(transp_Xs_new.shape, Xs_new.shape) + # check everything runs well with log=True + clf = ot.da.MappingTransport(kernel="gaussian", log=True) + clf.fit(Xs=Xs, Xt=Xt) + assert len(clf.log_.keys()) != 0 + def test_otda(): @@ -434,9 +462,8 @@ def test_otda(): # if __name__ == "__main__": - # test_otda() - # test_sinkhorn_transport_class() - # test_emd_transport_class() - # test_sinkhorn_l1l2_transport_class() - # test_sinkhorn_lpl1_transport_class() - # test_mapping_transport_class() +# test_sinkhorn_transport_class() +# test_emd_transport_class() +# test_sinkhorn_l1l2_transport_class() +# test_sinkhorn_lpl1_transport_class() +# test_mapping_transport_class() -- cgit v1.2.3 From 74ca2d77bca479785c581d2f48d87628d5c1444d Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 25 Aug 2017 15:12:20 +0200 Subject: refactoring examples according to new DA classes --- examples/da/plot_otda_classes.py | 142 +++++++++++++++++++++ examples/da/plot_otda_color_images.py | 151 ++++++++++++++++++++++ examples/da/plot_otda_d2.py | 163 ++++++++++++++++++++++++ examples/da/plot_otda_mapping.py | 119 +++++++++++++++++ examples/da/plot_otda_mapping_colors_images.py | 169 +++++++++++++++++++++++++ 5 files changed, 744 insertions(+) create mode 100644 examples/da/plot_otda_classes.py create mode 100644 examples/da/plot_otda_color_images.py create mode 100644 examples/da/plot_otda_d2.py create mode 100644 examples/da/plot_otda_mapping.py create mode 100644 examples/da/plot_otda_mapping_colors_images.py diff --git a/examples/da/plot_otda_classes.py b/examples/da/plot_otda_classes.py new file mode 100644 index 0000000..1bfe2bb --- /dev/null +++ b/examples/da/plot_otda_classes.py @@ -0,0 +1,142 @@ +# -*- coding: utf-8 -*- +""" +======================== +OT for domain adaptation +======================== + +This example introduces a domain adaptation in a 2D setting and the 4 OTDA +approaches currently supported in POT. + +""" + +# Authors: Remi Flamary +# Stanilslas Chambon +# +# License: MIT License + +import matplotlib.pylab as pl +import ot + + +# number of source and target points to generate +ns = 150 +nt = 150 + +Xs, ys = ot.datasets.get_data_classif('3gauss', ns) +Xt, yt = ot.datasets.get_data_classif('3gauss2', nt) + +# Instantiate the different transport algorithms and fit them + +# EMD Transport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) + +# Sinkhorn Transport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + +# Sinkhorn Transport with Group lasso regularization +ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0) +ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt) + +# Sinkhorn Transport with Group lasso regularization l1l2 +ot_l1l2 = ot.da.SinkhornL1l2Transport(reg_e=1e-1, reg_cl=2e0, max_iter=20, + verbose=True) +ot_l1l2.fit(Xs=Xs, ys=ys, Xt=Xt) + +# transport source samples onto target samples +transp_Xs_emd = ot_emd.transform(Xs=Xs) +transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) +transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs) +transp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs) + +############################################################################## +# Fig 1 : plots source and target samples +############################################################################## + +pl.figure(1, figsize=(10, 5)) +pl.subplot(1, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Source samples') + +pl.subplot(1, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Target samples') +pl.tight_layout() + +############################################################################## +# Fig 2 : plot optimal couplings and transported samples +############################################################################## + +param_img = {'interpolation': 'nearest', 'cmap': 'spectral'} + +pl.figure(2, figsize=(15, 8)) +pl.subplot(2, 4, 1) +pl.imshow(ot_emd.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nEMDTransport') + +pl.subplot(2, 4, 2) +pl.imshow(ot_sinkhorn.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornTransport') + +pl.subplot(2, 4, 3) +pl.imshow(ot_lpl1.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornLpl1Transport') + +pl.subplot(2, 4, 4) +pl.imshow(ot_l1l2.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornL1l2Transport') + +pl.subplot(2, 4, 5) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nEmdTransport') +pl.legend(loc="lower left") + +pl.subplot(2, 4, 6) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nSinkhornTransport') + +pl.subplot(2, 4, 7) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nSinkhornLpl1Transport') + +pl.subplot(2, 4, 8) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_l1l2[:, 0], transp_Xs_l1l2[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nSinkhornL1l2Transport') +pl.tight_layout() + +pl.show() diff --git a/examples/da/plot_otda_color_images.py b/examples/da/plot_otda_color_images.py new file mode 100644 index 0000000..a46ac29 --- /dev/null +++ b/examples/da/plot_otda_color_images.py @@ -0,0 +1,151 @@ +# -*- coding: utf-8 -*- +""" +======================================================== +OT for domain adaptation with image color adaptation [6] +======================================================== + +This example presents a way of transferring colors between two image +with Optimal Transport as introduced in [6] + +[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). +Regularized discrete optimal transport. +SIAM Journal on Imaging Sciences, 7(3), 1853-1882. +""" + +# Authors: Remi Flamary +# Stanilslas Chambon +# +# License: MIT License + +import numpy as np +from scipy import ndimage +import matplotlib.pylab as pl + +import ot + + +def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + + +def mat2im(X, shape): + """Converts back a matrix to an image""" + return X.reshape(shape) + + +def minmax(I): + return np.clip(I, 0, 1) + + +# Loading images +I1 = ndimage.imread('../../data/ocean_day.jpg').astype(np.float64) / 256 +I2 = ndimage.imread('../../data/ocean_sunset.jpg').astype(np.float64) / 256 + +X1 = im2mat(I1) +X2 = im2mat(I2) + +# training samples +nb = 1000 +idx1 = np.random.randint(X1.shape[0], size=(nb,)) +idx2 = np.random.randint(X2.shape[0], size=(nb,)) + +Xs = X1[idx1, :] +Xt = X2[idx2, :] + +# EMDTransport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) + +# SinkhornTransport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + +# prediction between images (using out of sample prediction as in [6]) +transp_Xs_emd = ot_emd.transform(Xs=X1) +transp_Xt_emd = ot_emd.inverse_transform(Xt=X2) + +transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) +transp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2) + +I1t = minmax(mat2im(transp_Xs_emd, I1.shape)) +I2t = minmax(mat2im(transp_Xt_emd, I2.shape)) + +I1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) +I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape)) + +############################################################################## +# plot original image +############################################################################## + +pl.figure(1, figsize=(6.4, 3)) + +pl.subplot(1, 2, 1) +pl.imshow(I1) +pl.axis('off') +pl.title('Image 1') + +pl.subplot(1, 2, 2) +pl.imshow(I2) +pl.axis('off') +pl.title('Image 2') + +############################################################################## +# scatter plot of colors +############################################################################## + +pl.figure(2, figsize=(6.4, 3)) + +pl.subplot(1, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs) +pl.axis([0, 1, 0, 1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 1') + +pl.subplot(1, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt) +pl.axis([0, 1, 0, 1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 2') +pl.tight_layout() + +############################################################################## +# plot new images +############################################################################## + +pl.figure(3, figsize=(8, 4)) + +pl.subplot(2, 3, 1) +pl.imshow(I1) +pl.axis('off') +pl.title('Image 1') + +pl.subplot(2, 3, 2) +pl.imshow(I1t) +pl.axis('off') +pl.title('Image 1 Adapt') + +pl.subplot(2, 3, 3) +pl.imshow(I1te) +pl.axis('off') +pl.title('Image 1 Adapt (reg)') + +pl.subplot(2, 3, 4) +pl.imshow(I2) +pl.axis('off') +pl.title('Image 2') + +pl.subplot(2, 3, 5) +pl.imshow(I2t) +pl.axis('off') +pl.title('Image 2 Adapt') + +pl.subplot(2, 3, 6) +pl.imshow(I2te) +pl.axis('off') +pl.title('Image 2 Adapt (reg)') +pl.tight_layout() + +pl.show() diff --git a/examples/da/plot_otda_d2.py b/examples/da/plot_otda_d2.py new file mode 100644 index 0000000..78c0372 --- /dev/null +++ b/examples/da/plot_otda_d2.py @@ -0,0 +1,163 @@ +# -*- coding: utf-8 -*- +""" +============================== +OT for empirical distributions +============================== + +This example introduces a domain adaptation in a 2D setting. It explicits +the problem of domain adaptation and introduces some optimal transport +approaches to solve it. + +Quantities such as optimal couplings, greater coupling coefficients and +transported samples are represented in order to give a visual understanding +of what the transport methods are doing. +""" + +# Authors: Remi Flamary +# Stanilslas Chambon +# +# License: MIT License + +import matplotlib.pylab as pl +import ot + +# number of source and target points to generate +ns = 150 +nt = 150 + +Xs, ys = ot.datasets.get_data_classif('3gauss', ns) +Xt, yt = ot.datasets.get_data_classif('3gauss2', nt) + +# Cost matrix +M = ot.dist(Xs, Xt) + +# Instantiate the different transport algorithms and fit them + +# EMD Transport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) + +# Sinkhorn Transport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + +# Sinkhorn Transport with Group lasso regularization +ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0) +ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt) + +# transport source samples onto target samples +transp_Xs_emd = ot_emd.transform(Xs=Xs) +transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) +transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs) + +############################################################################## +# Fig 1 : plots source and target samples + matrix of pairwise distance +############################################################################## + +pl.figure(1, figsize=(10, 10)) +pl.subplot(2, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Source samples') + +pl.subplot(2, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Target samples') + +pl.subplot(2, 2, 3) +pl.imshow(M, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Matrix of pairwise distances') +pl.tight_layout() + +############################################################################## +# Fig 2 : plots optimal couplings for the different methods +############################################################################## + +pl.figure(2, figsize=(10, 6)) + +pl.subplot(2, 3, 1) +pl.imshow(ot_emd.coupling_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nEMDTransport') + +pl.subplot(2, 3, 2) +pl.imshow(ot_sinkhorn.coupling_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornTransport') + +pl.subplot(2, 3, 3) +pl.imshow(ot_lpl1.coupling_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornLpl1Transport') + +pl.subplot(2, 3, 4) +ot.plot.plot2D_samples_mat(Xs, Xt, ot_emd.coupling_, c=[.5, .5, 1]) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.title('Main coupling coefficients\nEMDTransport') + +pl.subplot(2, 3, 5) +ot.plot.plot2D_samples_mat(Xs, Xt, ot_sinkhorn.coupling_, c=[.5, .5, 1]) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.title('Main coupling coefficients\nSinkhornTransport') + +pl.subplot(2, 3, 6) +ot.plot.plot2D_samples_mat(Xs, Xt, ot_lpl1.coupling_, c=[.5, .5, 1]) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.title('Main coupling coefficients\nSinkhornLpl1Transport') +pl.tight_layout() + +############################################################################## +# Fig 3 : plot transported samples +############################################################################## + +# display transported samples +pl.figure(4, figsize=(10, 4)) +pl.subplot(1, 3, 1) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Transported samples\nEmdTransport') +pl.legend(loc=0) +pl.xticks([]) +pl.yticks([]) + +pl.subplot(1, 3, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Transported samples\nSinkhornTransport') +pl.xticks([]) +pl.yticks([]) + +pl.subplot(1, 3, 3) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Transported samples\nSinkhornLpl1Transport') +pl.xticks([]) +pl.yticks([]) + +pl.tight_layout() +pl.show() diff --git a/examples/da/plot_otda_mapping.py b/examples/da/plot_otda_mapping.py new file mode 100644 index 0000000..ed234f5 --- /dev/null +++ b/examples/da/plot_otda_mapping.py @@ -0,0 +1,119 @@ +# -*- coding: utf-8 -*- +""" +=============================================== +OT mapping estimation for domain adaptation [8] +=============================================== + +This example presents how to use MappingTransport to estimate at the same +time both the coupling transport and approximate the transport map with either +a linear or a kernelized mapping as introduced in [8] + +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, + "Mapping estimation for discrete optimal transport", + Neural Information Processing Systems (NIPS), 2016. +""" + +# Authors: Remi Flamary +# Stanilslas Chambon +# +# License: MIT License + +import numpy as np +import matplotlib.pylab as pl +import ot + + +np.random.seed(0) + +############################################################################## +# generate +############################################################################## + +n = 100 # nb samples in source and target datasets +theta = 2 * np.pi / 20 +nz = 0.1 +Xs, ys = ot.datasets.get_data_classif('gaussrot', n, nz=nz) +Xs_new, _ = ot.datasets.get_data_classif('gaussrot', n, nz=nz) +Xt, yt = ot.datasets.get_data_classif('gaussrot', n, theta=theta, nz=nz) + +# one of the target mode changes its variance (no linear mapping) +Xt[yt == 2] *= 3 +Xt = Xt + 4 + + +# MappingTransport with linear kernel +ot_mapping_linear = ot.da.MappingTransport( + kernel="linear", mu=1e0, eta=1e-8, bias=True, + max_iter=20, verbose=True) + +ot_mapping_linear.fit( + Xs=Xs, Xt=Xt) + +# for original source samples, transform applies barycentric mapping +transp_Xs_linear = ot_mapping_linear.transform(Xs=Xs) + +# for out of source samples, transform applies the linear mapping +transp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new) + + +# MappingTransport with gaussian kernel +ot_mapping_gaussian = ot.da.MappingTransport( + kernel="gaussian", eta=1e-5, mu=1e-1, bias=True, sigma=1, + max_iter=10, verbose=True) +ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt) + +# for original source samples, transform applies barycentric mapping +transp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs) + +# for out of source samples, transform applies the gaussian mapping +transp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new) + + +############################################################################## +# plot data +############################################################################## + +pl.figure(1, (10, 5)) +pl.clf() +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.legend(loc=0) +pl.title('Source and target distributions') + +############################################################################## +# plot transported samples +############################################################################## + +pl.figure(2) +pl.clf() +pl.subplot(2, 2, 1) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) +pl.scatter(transp_Xs_linear[:, 0], transp_Xs_linear[:, 1], c=ys, marker='+', + label='Mapped source samples') +pl.title("Bary. mapping (linear)") +pl.legend(loc=0) + +pl.subplot(2, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) +pl.scatter(transp_Xs_linear_new[:, 0], transp_Xs_linear_new[:, 1], + c=ys, marker='+', label='Learned mapping') +pl.title("Estim. mapping (linear)") + +pl.subplot(2, 2, 3) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) +pl.scatter(transp_Xs_gaussian[:, 0], transp_Xs_gaussian[:, 1], c=ys, + marker='+', label='barycentric mapping') +pl.title("Bary. mapping (kernel)") + +pl.subplot(2, 2, 4) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) +pl.scatter(transp_Xs_gaussian_new[:, 0], transp_Xs_gaussian_new[:, 1], c=ys, + marker='+', label='Learned mapping') +pl.title("Estim. mapping (kernel)") +pl.tight_layout() + +pl.show() diff --git a/examples/da/plot_otda_mapping_colors_images.py b/examples/da/plot_otda_mapping_colors_images.py new file mode 100644 index 0000000..56b5a6f --- /dev/null +++ b/examples/da/plot_otda_mapping_colors_images.py @@ -0,0 +1,169 @@ +# -*- coding: utf-8 -*- +""" +==================================================================================== +OT for domain adaptation with image color adaptation [6] with mapping estimation [8] +==================================================================================== + +[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized + discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), + 1853-1882. +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for + discrete optimal transport", Neural Information Processing Systems (NIPS), + 2016. + +""" + +# Authors: Remi Flamary +# Stanilslas Chambon +# +# License: MIT License + +import numpy as np +from scipy import ndimage +import matplotlib.pylab as pl +import ot + + +def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + + +def mat2im(X, shape): + """Converts back a matrix to an image""" + return X.reshape(shape) + + +def minmax(I): + return np.clip(I, 0, 1) + + +############################################################################## +# Generate data +############################################################################## + +# Loading images +# I1 = ndimage.imread('../../data/ocean_day.jpg').astype(np.float64) / 256 +# I2 = ndimage.imread('../../data/ocean_sunset.jpg').astype(np.float64) / 256 + +I1 = ndimage.imread('data/ocean_day.jpg').astype(np.float64) / 256 +I2 = ndimage.imread('data/ocean_sunset.jpg').astype(np.float64) / 256 + + +X1 = im2mat(I1) +X2 = im2mat(I2) + +# training samples +nb = 1000 +idx1 = np.random.randint(X1.shape[0], size=(nb,)) +idx2 = np.random.randint(X2.shape[0], size=(nb,)) + +Xs = X1[idx1, :] +Xt = X2[idx2, :] + + +############################################################################## +# Domain adaptation for pixel distribution transfer +############################################################################## + +# EMDTransport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) +transp_Xs_emd = ot_emd.transform(Xs=X1) +Image_emd = minmax(mat2im(transp_Xs_emd, I1.shape)) + +# SinkhornTransport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) +transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) +Image_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) + +ot_mapping_linear = ot.da.MappingTransport( + mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True) +ot_mapping_linear.fit(Xs=Xs, Xt=Xt) + +X1tl = ot_mapping_linear.transform(X1) +Image_mapping_linear = minmax(mat2im(X1tl, I1.shape)) + +ot_mapping_gaussian = ot.da.MappingTransport( + mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True) +ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt) + +X1tn = ot_mapping_gaussian.transform(X1) # use the estimated mapping +Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape)) + +############################################################################## +# plot original images +############################################################################## + +pl.figure(1, figsize=(6.4, 3)) +pl.subplot(1, 2, 1) +pl.imshow(I1) +pl.axis('off') +pl.title('Image 1') + +pl.subplot(1, 2, 2) +pl.imshow(I2) +pl.axis('off') +pl.title('Image 2') +pl.tight_layout() + +############################################################################## +# plot pixel values distribution +############################################################################## + +pl.figure(2, figsize=(6.4, 5)) + +pl.subplot(1, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs) +pl.axis([0, 1, 0, 1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 1') + +pl.subplot(1, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt) +pl.axis([0, 1, 0, 1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 2') +pl.tight_layout() + +############################################################################## +# plot transformed images +############################################################################## + +pl.figure(2, figsize=(10, 5)) + +pl.subplot(2, 3, 1) +pl.imshow(I1) +pl.axis('off') +pl.title('Im. 1') + +pl.subplot(2, 3, 4) +pl.imshow(I2) +pl.axis('off') +pl.title('Im. 2') + +pl.subplot(2, 3, 2) +pl.imshow(Image_emd) +pl.axis('off') +pl.title('EmdTransport') + +pl.subplot(2, 3, 5) +pl.imshow(Image_sinkhorn) +pl.axis('off') +pl.title('SinkhornTransport') + +pl.subplot(2, 3, 3) +pl.imshow(Image_mapping_linear) +pl.axis('off') +pl.title('MappingTransport (linear)') + +pl.subplot(2, 3, 6) +pl.imshow(Image_mapping_gaussian) +pl.axis('off') +pl.title('MappingTransport (gaussian)') +pl.tight_layout() + +pl.show() -- cgit v1.2.3 From f80693b545ac40817cb95eb31260fe7598514b96 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 25 Aug 2017 15:15:52 +0200 Subject: small corrections for examples --- examples/da/plot_otda_classes.py | 2 +- examples/da/plot_otda_color_images.py | 2 +- examples/da/plot_otda_d2.py | 2 +- examples/da/plot_otda_mapping.py | 2 +- examples/da/plot_otda_mapping_colors_images.py | 6 +++--- 5 files changed, 7 insertions(+), 7 deletions(-) diff --git a/examples/da/plot_otda_classes.py b/examples/da/plot_otda_classes.py index 1bfe2bb..e5c82fb 100644 --- a/examples/da/plot_otda_classes.py +++ b/examples/da/plot_otda_classes.py @@ -10,7 +10,7 @@ approaches currently supported in POT. """ # Authors: Remi Flamary -# Stanilslas Chambon +# Stanislas Chambon # # License: MIT License diff --git a/examples/da/plot_otda_color_images.py b/examples/da/plot_otda_color_images.py index a46ac29..bca7350 100644 --- a/examples/da/plot_otda_color_images.py +++ b/examples/da/plot_otda_color_images.py @@ -13,7 +13,7 @@ SIAM Journal on Imaging Sciences, 7(3), 1853-1882. """ # Authors: Remi Flamary -# Stanilslas Chambon +# Stanislas Chambon # # License: MIT License diff --git a/examples/da/plot_otda_d2.py b/examples/da/plot_otda_d2.py index 78c0372..1d2192f 100644 --- a/examples/da/plot_otda_d2.py +++ b/examples/da/plot_otda_d2.py @@ -14,7 +14,7 @@ of what the transport methods are doing. """ # Authors: Remi Flamary -# Stanilslas Chambon +# Stanislas Chambon # # License: MIT License diff --git a/examples/da/plot_otda_mapping.py b/examples/da/plot_otda_mapping.py index ed234f5..6d83507 100644 --- a/examples/da/plot_otda_mapping.py +++ b/examples/da/plot_otda_mapping.py @@ -14,7 +14,7 @@ a linear or a kernelized mapping as introduced in [8] """ # Authors: Remi Flamary -# Stanilslas Chambon +# Stanislas Chambon # # License: MIT License diff --git a/examples/da/plot_otda_mapping_colors_images.py b/examples/da/plot_otda_mapping_colors_images.py index 56b5a6f..05d9046 100644 --- a/examples/da/plot_otda_mapping_colors_images.py +++ b/examples/da/plot_otda_mapping_colors_images.py @@ -14,7 +14,7 @@ OT for domain adaptation with image color adaptation [6] with mapping estimation """ # Authors: Remi Flamary -# Stanilslas Chambon +# Stanislas Chambon # # License: MIT License @@ -82,14 +82,14 @@ ot_mapping_linear = ot.da.MappingTransport( mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True) ot_mapping_linear.fit(Xs=Xs, Xt=Xt) -X1tl = ot_mapping_linear.transform(X1) +X1tl = ot_mapping_linear.transform(Xs=X1) Image_mapping_linear = minmax(mat2im(X1tl, I1.shape)) ot_mapping_gaussian = ot.da.MappingTransport( mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True) ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt) -X1tn = ot_mapping_gaussian.transform(X1) # use the estimated mapping +X1tn = ot_mapping_gaussian.transform(Xs=X1) # use the estimated mapping Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape)) ############################################################################## -- cgit v1.2.3 From 892d7ce10912de3d07692b5083578932a32ab61f Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 25 Aug 2017 15:18:05 +0200 Subject: set properly path of data --- examples/da/plot_otda_mapping_colors_images.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/examples/da/plot_otda_mapping_colors_images.py b/examples/da/plot_otda_mapping_colors_images.py index 05d9046..4209020 100644 --- a/examples/da/plot_otda_mapping_colors_images.py +++ b/examples/da/plot_otda_mapping_colors_images.py @@ -43,11 +43,8 @@ def minmax(I): ############################################################################## # Loading images -# I1 = ndimage.imread('../../data/ocean_day.jpg').astype(np.float64) / 256 -# I2 = ndimage.imread('../../data/ocean_sunset.jpg').astype(np.float64) / 256 - -I1 = ndimage.imread('data/ocean_day.jpg').astype(np.float64) / 256 -I2 = ndimage.imread('data/ocean_sunset.jpg').astype(np.float64) / 256 +I1 = ndimage.imread('../../data/ocean_day.jpg').astype(np.float64) / 256 +I2 = ndimage.imread('../../data/ocean_sunset.jpg').astype(np.float64) / 256 X1 = im2mat(I1) -- cgit v1.2.3 From 55840f6bccadd79caf722d86f06da857e3045453 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Mon, 28 Aug 2017 10:43:31 +0200 Subject: move no da objects into utils.py --- README.md | 2 + ot/da.py | 136 +-------------------------------- ot/deprecation.py | 103 ------------------------- ot/utils.py | 219 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 222 insertions(+), 238 deletions(-) delete mode 100644 ot/deprecation.py diff --git a/README.md b/README.md index 7a65106..27b4643 100644 --- a/README.md +++ b/README.md @@ -136,6 +136,8 @@ The contributors to this library are: * [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/) * [Michael Perrot](http://perso.univ-st-etienne.fr/pem82055/) (Mapping estimation) * [Léo Gautheron](https://github.com/aje) (GPU implementation) +* [Nathalie Gayraud]() +* [Stanislas Chambon](https://slasnista.github.io/) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): diff --git a/ot/da.py b/ot/da.py index 5a34979..8c62669 100644 --- a/ot/da.py +++ b/ot/da.py @@ -10,14 +10,13 @@ Domain adaptation with optimal transport # License: MIT License import numpy as np -import warnings from .bregman import sinkhorn from .lp import emd from .utils import unif, dist, kernel +from .utils import deprecated, BaseEstimator from .optim import cg from .optim import gcg -from .deprecation import deprecated def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, @@ -936,139 +935,6 @@ class OTDA_mapping_kernel(OTDA_mapping_linear): print("Warning, model not fitted yet, returning None") return None -############################################################################## -# proposal -############################################################################## - - -# adapted from sklearn - -class BaseEstimator(object): - """Base class for all estimators in scikit-learn - Notes - ----- - All estimators should specify all the parameters that can be set - at the class level in their ``__init__`` as explicit keyword - arguments (no ``*args`` or ``**kwargs``). - """ - - @classmethod - def _get_param_names(cls): - """Get parameter names for the estimator""" - try: - from inspect import signature - except ImportError: - from .externals.funcsigs import signature - # fetch the constructor or the original constructor before - # deprecation wrapping if any - init = getattr(cls.__init__, 'deprecated_original', cls.__init__) - if init is object.__init__: - # No explicit constructor to introspect - return [] - - # introspect the constructor arguments to find the model parameters - # to represent - init_signature = signature(init) - # Consider the constructor parameters excluding 'self' - parameters = [p for p in init_signature.parameters.values() - if p.name != 'self' and p.kind != p.VAR_KEYWORD] - for p in parameters: - if p.kind == p.VAR_POSITIONAL: - raise RuntimeError("scikit-learn estimators should always " - "specify their parameters in the signature" - " of their __init__ (no varargs)." - " %s with constructor %s doesn't " - " follow this convention." - % (cls, init_signature)) - # Extract and sort argument names excluding 'self' - return sorted([p.name for p in parameters]) - - def get_params(self, deep=True): - """Get parameters for this estimator. - - Parameters - ---------- - deep : boolean, optional - If True, will return the parameters for this estimator and - contained subobjects that are estimators. - - Returns - ------- - params : mapping of string to any - Parameter names mapped to their values. - """ - out = dict() - for key in self._get_param_names(): - # We need deprecation warnings to always be on in order to - # catch deprecated param values. - # This is set in utils/__init__.py but it gets overwritten - # when running under python3 somehow. - warnings.simplefilter("always", DeprecationWarning) - try: - with warnings.catch_warnings(record=True) as w: - value = getattr(self, key, None) - if len(w) and w[0].category == DeprecationWarning: - # if the parameter is deprecated, don't show it - continue - finally: - warnings.filters.pop(0) - - # XXX: should we rather test if instance of estimator? - if deep and hasattr(value, 'get_params'): - deep_items = value.get_params().items() - out.update((key + '__' + k, val) for k, val in deep_items) - out[key] = value - return out - - def set_params(self, **params): - """Set the parameters of this estimator. - - The method works on simple estimators as well as on nested objects - (such as pipelines). The latter have parameters of the form - ``__`` so that it's possible to update each - component of a nested object. - - Returns - ------- - self - """ - if not params: - # Simple optimisation to gain speed (inspect is slow) - return self - valid_params = self.get_params(deep=True) - # for key, value in iteritems(params): - for key, value in params.items(): - split = key.split('__', 1) - if len(split) > 1: - # nested objects case - name, sub_name = split - if name not in valid_params: - raise ValueError('Invalid parameter %s for estimator %s. ' - 'Check the list of available parameters ' - 'with `estimator.get_params().keys()`.' % - (name, self)) - sub_object = valid_params[name] - sub_object.set_params(**{sub_name: value}) - else: - # simple objects case - if key not in valid_params: - raise ValueError('Invalid parameter %s for estimator %s. ' - 'Check the list of available parameters ' - 'with `estimator.get_params().keys()`.' % - (key, self.__class__.__name__)) - setattr(self, key, value) - return self - - def __repr__(self): - from sklearn.base import _pprint - class_name = self.__class__.__name__ - return '%s(%s)' % (class_name, _pprint(self.get_params(deep=False), - offset=len(class_name),),) - - # __getstate__ and __setstate__ are omitted because they only contain - # conditionals that are not satisfied by our objects (e.g., - # ``if type(self).__module__.startswith('sklearn.')``. - def distribution_estimation_uniform(X): """estimates a uniform distribution from an array of samples X diff --git a/ot/deprecation.py b/ot/deprecation.py deleted file mode 100644 index 2b16427..0000000 --- a/ot/deprecation.py +++ /dev/null @@ -1,103 +0,0 @@ -""" - deprecated class from scikit-learn package - https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/deprecation.py -""" - -import sys -import warnings - -__all__ = ["deprecated", ] - - -class deprecated(object): - """Decorator to mark a function or class as deprecated. - Issue a warning when the function is called/the class is instantiated and - adds a warning to the docstring. - The optional extra argument will be appended to the deprecation message - and the docstring. Note: to use this with the default value for extra, put - in an empty of parentheses: - >>> from ot.deprecation import deprecated - >>> @deprecated() - ... def some_function(): pass - - Parameters - ---------- - extra : string - to be added to the deprecation messages - """ - - # Adapted from http://wiki.python.org/moin/PythonDecoratorLibrary, - # but with many changes. - - def __init__(self, extra=''): - self.extra = extra - - def __call__(self, obj): - """Call method - Parameters - ---------- - obj : object - """ - if isinstance(obj, type): - return self._decorate_class(obj) - else: - return self._decorate_fun(obj) - - def _decorate_class(self, cls): - msg = "Class %s is deprecated" % cls.__name__ - if self.extra: - msg += "; %s" % self.extra - - # FIXME: we should probably reset __new__ for full generality - init = cls.__init__ - - def wrapped(*args, **kwargs): - warnings.warn(msg, category=DeprecationWarning) - return init(*args, **kwargs) - - cls.__init__ = wrapped - - wrapped.__name__ = '__init__' - wrapped.__doc__ = self._update_doc(init.__doc__) - wrapped.deprecated_original = init - - return cls - - def _decorate_fun(self, fun): - """Decorate function fun""" - - msg = "Function %s is deprecated" % fun.__name__ - if self.extra: - msg += "; %s" % self.extra - - def wrapped(*args, **kwargs): - warnings.warn(msg, category=DeprecationWarning) - return fun(*args, **kwargs) - - wrapped.__name__ = fun.__name__ - wrapped.__dict__ = fun.__dict__ - wrapped.__doc__ = self._update_doc(fun.__doc__) - - return wrapped - - def _update_doc(self, olddoc): - newdoc = "DEPRECATED" - if self.extra: - newdoc = "%s: %s" % (newdoc, self.extra) - if olddoc: - newdoc = "%s\n\n%s" % (newdoc, olddoc) - return newdoc - - -def _is_deprecated(func): - """Helper to check if func is wraped by our deprecated decorator""" - if sys.version_info < (3, 5): - raise NotImplementedError("This is only available for python3.5 " - "or above") - closures = getattr(func, '__closure__', []) - if closures is None: - closures = [] - is_deprecated = ('deprecated' in ''.join([c.cell_contents - for c in closures - if isinstance(c.cell_contents, str)])) - return is_deprecated diff --git a/ot/utils.py b/ot/utils.py index 2b2f8b3..29ad536 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -13,6 +13,9 @@ import time import numpy as np from scipy.spatial.distance import cdist +import sys +import warnings + __time_tic_toc = time.time() @@ -163,3 +166,219 @@ def parmap(f, X, nprocs=multiprocessing.cpu_count()): [p.join() for p in proc] return [x for i, x in sorted(res)] + + +class deprecated(object): + """Decorator to mark a function or class as deprecated. + + deprecated class from scikit-learn package + https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/deprecation.py + Issue a warning when the function is called/the class is instantiated and + adds a warning to the docstring. + The optional extra argument will be appended to the deprecation message + and the docstring. Note: to use this with the default value for extra, put + in an empty of parentheses: + >>> from ot.deprecation import deprecated + >>> @deprecated() + ... def some_function(): pass + + Parameters + ---------- + extra : string + to be added to the deprecation messages + """ + + # Adapted from http://wiki.python.org/moin/PythonDecoratorLibrary, + # but with many changes. + + def __init__(self, extra=''): + self.extra = extra + + def __call__(self, obj): + """Call method + Parameters + ---------- + obj : object + """ + if isinstance(obj, type): + return self._decorate_class(obj) + else: + return self._decorate_fun(obj) + + def _decorate_class(self, cls): + msg = "Class %s is deprecated" % cls.__name__ + if self.extra: + msg += "; %s" % self.extra + + # FIXME: we should probably reset __new__ for full generality + init = cls.__init__ + + def wrapped(*args, **kwargs): + warnings.warn(msg, category=DeprecationWarning) + return init(*args, **kwargs) + + cls.__init__ = wrapped + + wrapped.__name__ = '__init__' + wrapped.__doc__ = self._update_doc(init.__doc__) + wrapped.deprecated_original = init + + return cls + + def _decorate_fun(self, fun): + """Decorate function fun""" + + msg = "Function %s is deprecated" % fun.__name__ + if self.extra: + msg += "; %s" % self.extra + + def wrapped(*args, **kwargs): + warnings.warn(msg, category=DeprecationWarning) + return fun(*args, **kwargs) + + wrapped.__name__ = fun.__name__ + wrapped.__dict__ = fun.__dict__ + wrapped.__doc__ = self._update_doc(fun.__doc__) + + return wrapped + + def _update_doc(self, olddoc): + newdoc = "DEPRECATED" + if self.extra: + newdoc = "%s: %s" % (newdoc, self.extra) + if olddoc: + newdoc = "%s\n\n%s" % (newdoc, olddoc) + return newdoc + + +def _is_deprecated(func): + """Helper to check if func is wraped by our deprecated decorator""" + if sys.version_info < (3, 5): + raise NotImplementedError("This is only available for python3.5 " + "or above") + closures = getattr(func, '__closure__', []) + if closures is None: + closures = [] + is_deprecated = ('deprecated' in ''.join([c.cell_contents + for c in closures + if isinstance(c.cell_contents, str)])) + return is_deprecated + + +class BaseEstimator(object): + """Base class for most objects in POT + adapted from sklearn BaseEstimator class + + Notes + ----- + All estimators should specify all the parameters that can be set + at the class level in their ``__init__`` as explicit keyword + arguments (no ``*args`` or ``**kwargs``). + """ + + @classmethod + def _get_param_names(cls): + """Get parameter names for the estimator""" + try: + from inspect import signature + except ImportError: + from .externals.funcsigs import signature + # fetch the constructor or the original constructor before + # deprecation wrapping if any + init = getattr(cls.__init__, 'deprecated_original', cls.__init__) + if init is object.__init__: + # No explicit constructor to introspect + return [] + + # introspect the constructor arguments to find the model parameters + # to represent + init_signature = signature(init) + # Consider the constructor parameters excluding 'self' + parameters = [p for p in init_signature.parameters.values() + if p.name != 'self' and p.kind != p.VAR_KEYWORD] + for p in parameters: + if p.kind == p.VAR_POSITIONAL: + raise RuntimeError("POT estimators should always " + "specify their parameters in the signature" + " of their __init__ (no varargs)." + " %s with constructor %s doesn't " + " follow this convention." + % (cls, init_signature)) + # Extract and sort argument names excluding 'self' + return sorted([p.name for p in parameters]) + + def get_params(self, deep=True): + """Get parameters for this estimator. + + Parameters + ---------- + deep : boolean, optional + If True, will return the parameters for this estimator and + contained subobjects that are estimators. + + Returns + ------- + params : mapping of string to any + Parameter names mapped to their values. + """ + out = dict() + for key in self._get_param_names(): + # We need deprecation warnings to always be on in order to + # catch deprecated param values. + # This is set in utils/__init__.py but it gets overwritten + # when running under python3 somehow. + warnings.simplefilter("always", DeprecationWarning) + try: + with warnings.catch_warnings(record=True) as w: + value = getattr(self, key, None) + if len(w) and w[0].category == DeprecationWarning: + # if the parameter is deprecated, don't show it + continue + finally: + warnings.filters.pop(0) + + # XXX: should we rather test if instance of estimator? + if deep and hasattr(value, 'get_params'): + deep_items = value.get_params().items() + out.update((key + '__' + k, val) for k, val in deep_items) + out[key] = value + return out + + def set_params(self, **params): + """Set the parameters of this estimator. + + The method works on simple estimators as well as on nested objects + (such as pipelines). The latter have parameters of the form + ``__`` so that it's possible to update each + component of a nested object. + + Returns + ------- + self + """ + if not params: + # Simple optimisation to gain speed (inspect is slow) + return self + valid_params = self.get_params(deep=True) + # for key, value in iteritems(params): + for key, value in params.items(): + split = key.split('__', 1) + if len(split) > 1: + # nested objects case + name, sub_name = split + if name not in valid_params: + raise ValueError('Invalid parameter %s for estimator %s. ' + 'Check the list of available parameters ' + 'with `estimator.get_params().keys()`.' % + (name, self)) + sub_object = valid_params[name] + sub_object.set_params(**{sub_name: value}) + else: + # simple objects case + if key not in valid_params: + raise ValueError('Invalid parameter %s for estimator %s. ' + 'Check the list of available parameters ' + 'with `estimator.get_params().keys()`.' % + (key, self.__class__.__name__)) + setattr(self, key, value) + return self -- cgit v1.2.3 From a8fa91bec26caa93329e61a104e0ad6afdf37363 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Mon, 28 Aug 2017 11:03:28 +0200 Subject: handling input arguments in fit, transform... methods + remove old examples --- examples/plot_OTDA_2D.py | 126 ----------------- examples/plot_OTDA_classes.py | 117 ---------------- examples/plot_OTDA_color_images.py | 152 -------------------- examples/plot_OTDA_mapping.py | 124 ----------------- examples/plot_OTDA_mapping_color_images.py | 169 ---------------------- ot/da.py | 217 ++++++++++++++++------------- 6 files changed, 121 insertions(+), 784 deletions(-) delete mode 100644 examples/plot_OTDA_2D.py delete mode 100644 examples/plot_OTDA_classes.py delete mode 100644 examples/plot_OTDA_color_images.py delete mode 100644 examples/plot_OTDA_mapping.py delete mode 100644 examples/plot_OTDA_mapping_color_images.py diff --git a/examples/plot_OTDA_2D.py b/examples/plot_OTDA_2D.py deleted file mode 100644 index f2108c6..0000000 --- a/examples/plot_OTDA_2D.py +++ /dev/null @@ -1,126 +0,0 @@ -# -*- coding: utf-8 -*- -""" -============================== -OT for empirical distributions -============================== - -""" - -# Author: Remi Flamary -# -# License: MIT License - -import numpy as np -import matplotlib.pylab as pl -import ot - - -#%% parameters - -n = 150 # nb bins - -xs, ys = ot.datasets.get_data_classif('3gauss', n) -xt, yt = ot.datasets.get_data_classif('3gauss2', n) - -a, b = ot.unif(n), ot.unif(n) -# loss matrix -M = ot.dist(xs, xt) -# M/=M.max() - -#%% plot samples - -pl.figure(1) -pl.subplot(2, 2, 1) -pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples') -pl.legend(loc=0) -pl.title('Source distributions') - -pl.subplot(2, 2, 2) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples') -pl.legend(loc=0) -pl.title('target distributions') - -pl.figure(2) -pl.imshow(M, interpolation='nearest') -pl.title('Cost matrix M') - - -#%% OT estimation - -# EMD -G0 = ot.emd(a, b, M) - -# sinkhorn -lambd = 1e-1 -Gs = ot.sinkhorn(a, b, M, lambd) - - -# Group lasso regularization -reg = 1e-1 -eta = 1e0 -Gg = ot.da.sinkhorn_lpl1_mm(a, ys.astype(np.int), b, M, reg, eta) - - -#%% visu matrices - -pl.figure(3) - -pl.subplot(2, 3, 1) -pl.imshow(G0, interpolation='nearest') -pl.title('OT matrix ') - -pl.subplot(2, 3, 2) -pl.imshow(Gs, interpolation='nearest') -pl.title('OT matrix Sinkhorn') - -pl.subplot(2, 3, 3) -pl.imshow(Gg, interpolation='nearest') -pl.title('OT matrix Group lasso') - -pl.subplot(2, 3, 4) -ot.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1]) -pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples') -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples') - - -pl.subplot(2, 3, 5) -ot.plot.plot2D_samples_mat(xs, xt, Gs, c=[.5, .5, 1]) -pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples') -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples') - -pl.subplot(2, 3, 6) -ot.plot.plot2D_samples_mat(xs, xt, Gg, c=[.5, .5, 1]) -pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples') -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples') -pl.tight_layout() - -#%% sample interpolation - -xst0 = n * G0.dot(xt) -xsts = n * Gs.dot(xt) -xstg = n * Gg.dot(xt) - -pl.figure(4, figsize=(8, 3)) -pl.subplot(1, 3, 1) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.5) -pl.scatter(xst0[:, 0], xst0[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Interp samples') -pl.legend(loc=0) - -pl.subplot(1, 3, 2) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.5) -pl.scatter(xsts[:, 0], xsts[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Interp samples Sinkhorn') - -pl.subplot(1, 3, 3) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.5) -pl.scatter(xstg[:, 0], xstg[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Interp samples Grouplasso') -pl.tight_layout() -pl.show() diff --git a/examples/plot_OTDA_classes.py b/examples/plot_OTDA_classes.py deleted file mode 100644 index 53e4bae..0000000 --- a/examples/plot_OTDA_classes.py +++ /dev/null @@ -1,117 +0,0 @@ -# -*- coding: utf-8 -*- -""" -======================== -OT for domain adaptation -======================== - -""" - -# Author: Remi Flamary -# -# License: MIT License - -import matplotlib.pylab as pl -import ot - - -#%% parameters - -n = 150 # nb samples in source and target datasets - -xs, ys = ot.datasets.get_data_classif('3gauss', n) -xt, yt = ot.datasets.get_data_classif('3gauss2', n) - - -#%% plot samples - -pl.figure(1, figsize=(6.4, 3)) - -pl.subplot(1, 2, 1) -pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples') -pl.legend(loc=0) -pl.title('Source distributions') - -pl.subplot(1, 2, 2) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples') -pl.legend(loc=0) -pl.title('target distributions') - - -#%% OT estimation - -# LP problem -da_emd = ot.da.OTDA() # init class -da_emd.fit(xs, xt) # fit distributions -xst0 = da_emd.interp() # interpolation of source samples - -# sinkhorn regularization -lambd = 1e-1 -da_entrop = ot.da.OTDA_sinkhorn() -da_entrop.fit(xs, xt, reg=lambd) -xsts = da_entrop.interp() - -# non-convex Group lasso regularization -reg = 1e-1 -eta = 1e0 -da_lpl1 = ot.da.OTDA_lpl1() -da_lpl1.fit(xs, ys, xt, reg=reg, eta=eta) -xstg = da_lpl1.interp() - -# True Group lasso regularization -reg = 1e-1 -eta = 2e0 -da_l1l2 = ot.da.OTDA_l1l2() -da_l1l2.fit(xs, ys, xt, reg=reg, eta=eta, numItermax=20, verbose=True) -xstgl = da_l1l2.interp() - -#%% plot interpolated source samples - -param_img = {'interpolation': 'nearest', 'cmap': 'spectral'} - -pl.figure(2, figsize=(8, 4.5)) -pl.subplot(2, 4, 1) -pl.imshow(da_emd.G, **param_img) -pl.title('OT matrix') - -pl.subplot(2, 4, 2) -pl.imshow(da_entrop.G, **param_img) -pl.title('OT matrix\nsinkhorn') - -pl.subplot(2, 4, 3) -pl.imshow(da_lpl1.G, **param_img) -pl.title('OT matrix\nnon-convex Group Lasso') - -pl.subplot(2, 4, 4) -pl.imshow(da_l1l2.G, **param_img) -pl.title('OT matrix\nGroup Lasso') - -pl.subplot(2, 4, 5) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(xst0[:, 0], xst0[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Interp samples') -pl.legend(loc=0) - -pl.subplot(2, 4, 6) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(xsts[:, 0], xsts[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Interp samples\nSinkhorn') - -pl.subplot(2, 4, 7) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(xstg[:, 0], xstg[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Interp samples\nnon-convex Group Lasso') - -pl.subplot(2, 4, 8) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(xstgl[:, 0], xstgl[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Interp samples\nGroup Lasso') -pl.tight_layout() -pl.show() diff --git a/examples/plot_OTDA_color_images.py b/examples/plot_OTDA_color_images.py deleted file mode 100644 index c5ff873..0000000 --- a/examples/plot_OTDA_color_images.py +++ /dev/null @@ -1,152 +0,0 @@ -# -*- coding: utf-8 -*- -""" -======================================================== -OT for domain adaptation with image color adaptation [6] -======================================================== - -[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). -Regularized discrete optimal transport. -SIAM Journal on Imaging Sciences, 7(3), 1853-1882. -""" - -# Author: Remi Flamary -# -# License: MIT License - -import numpy as np -from scipy import ndimage -import matplotlib.pylab as pl -import ot - - -#%% Loading images - -I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256 -I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 - -#%% Plot images - -pl.figure(1, figsize=(6.4, 3)) - -pl.subplot(1, 2, 1) -pl.imshow(I1) -pl.axis('off') -pl.title('Image 1') - -pl.subplot(1, 2, 2) -pl.imshow(I2) -pl.axis('off') -pl.title('Image 2') - -pl.show() - -#%% Image conversion and dataset generation - - -def im2mat(I): - """Converts and image to matrix (one pixel per line)""" - return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) - - -def mat2im(X, shape): - """Converts back a matrix to an image""" - return X.reshape(shape) - - -X1 = im2mat(I1) -X2 = im2mat(I2) - -# training samples -nb = 1000 -idx1 = np.random.randint(X1.shape[0], size=(nb,)) -idx2 = np.random.randint(X2.shape[0], size=(nb,)) - -xs = X1[idx1, :] -xt = X2[idx2, :] - -#%% Plot image distributions - - -pl.figure(2, figsize=(6.4, 3)) - -pl.subplot(1, 2, 1) -pl.scatter(xs[:, 0], xs[:, 2], c=xs) -pl.axis([0, 1, 0, 1]) -pl.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 1') - -pl.subplot(1, 2, 2) -pl.scatter(xt[:, 0], xt[:, 2], c=xt) -pl.axis([0, 1, 0, 1]) -pl.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 2') -pl.tight_layout() - -#%% domain adaptation between images - -# LP problem -da_emd = ot.da.OTDA() # init class -da_emd.fit(xs, xt) # fit distributions - -# sinkhorn regularization -lambd = 1e-1 -da_entrop = ot.da.OTDA_sinkhorn() -da_entrop.fit(xs, xt, reg=lambd) - -#%% prediction between images (using out of sample prediction as in [6]) - -X1t = da_emd.predict(X1) -X2t = da_emd.predict(X2, -1) - -X1te = da_entrop.predict(X1) -X2te = da_entrop.predict(X2, -1) - - -def minmax(I): - return np.clip(I, 0, 1) - - -I1t = minmax(mat2im(X1t, I1.shape)) -I2t = minmax(mat2im(X2t, I2.shape)) - -I1te = minmax(mat2im(X1te, I1.shape)) -I2te = minmax(mat2im(X2te, I2.shape)) - -#%% plot all images - -pl.figure(2, figsize=(8, 4)) - -pl.subplot(2, 3, 1) -pl.imshow(I1) -pl.axis('off') -pl.title('Image 1') - -pl.subplot(2, 3, 2) -pl.imshow(I1t) -pl.axis('off') -pl.title('Image 1 Adapt') - -pl.subplot(2, 3, 3) -pl.imshow(I1te) -pl.axis('off') -pl.title('Image 1 Adapt (reg)') - -pl.subplot(2, 3, 4) -pl.imshow(I2) -pl.axis('off') -pl.title('Image 2') - -pl.subplot(2, 3, 5) -pl.imshow(I2t) -pl.axis('off') -pl.title('Image 2 Adapt') - -pl.subplot(2, 3, 6) -pl.imshow(I2te) -pl.axis('off') -pl.title('Image 2 Adapt (reg)') -pl.tight_layout() - -pl.show() diff --git a/examples/plot_OTDA_mapping.py b/examples/plot_OTDA_mapping.py deleted file mode 100644 index a0d7f8b..0000000 --- a/examples/plot_OTDA_mapping.py +++ /dev/null @@ -1,124 +0,0 @@ -# -*- coding: utf-8 -*- -""" -=============================================== -OT mapping estimation for domain adaptation [8] -=============================================== - -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, - "Mapping estimation for discrete optimal transport", - Neural Information Processing Systems (NIPS), 2016. -""" - -# Author: Remi Flamary -# -# License: MIT License - -import numpy as np -import matplotlib.pylab as pl -import ot - - -#%% dataset generation - -np.random.seed(0) # makes example reproducible - -n = 100 # nb samples in source and target datasets -theta = 2 * np.pi / 20 -nz = 0.1 -xs, ys = ot.datasets.get_data_classif('gaussrot', n, nz=nz) -xt, yt = ot.datasets.get_data_classif('gaussrot', n, theta=theta, nz=nz) - -# one of the target mode changes its variance (no linear mapping) -xt[yt == 2] *= 3 -xt = xt + 4 - - -#%% plot samples - -pl.figure(1, (6.4, 3)) -pl.clf() -pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples') -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples') -pl.legend(loc=0) -pl.title('Source and target distributions') - - -#%% OT linear mapping estimation - -eta = 1e-8 # quadratic regularization for regression -mu = 1e0 # weight of the OT linear term -bias = True # estimate a bias - -ot_mapping = ot.da.OTDA_mapping_linear() -ot_mapping.fit(xs, xt, mu=mu, eta=eta, bias=bias, numItermax=20, verbose=True) - -xst = ot_mapping.predict(xs) # use the estimated mapping -xst0 = ot_mapping.interp() # use barycentric mapping - - -pl.figure(2) -pl.clf() -pl.subplot(2, 2, 1) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=.3) -pl.scatter(xst0[:, 0], xst0[:, 1], c=ys, - marker='+', label='barycentric mapping') -pl.title("barycentric mapping") - -pl.subplot(2, 2, 2) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=.3) -pl.scatter(xst[:, 0], xst[:, 1], c=ys, marker='+', label='Learned mapping') -pl.title("Learned mapping") -pl.tight_layout() - -#%% Kernel mapping estimation - -eta = 1e-5 # quadratic regularization for regression -mu = 1e-1 # weight of the OT linear term -bias = True # estimate a bias -sigma = 1 # sigma bandwidth fot gaussian kernel - - -ot_mapping_kernel = ot.da.OTDA_mapping_kernel() -ot_mapping_kernel.fit( - xs, xt, mu=mu, eta=eta, sigma=sigma, bias=bias, numItermax=10, verbose=True) - -xst_kernel = ot_mapping_kernel.predict(xs) # use the estimated mapping -xst0_kernel = ot_mapping_kernel.interp() # use barycentric mapping - - -#%% Plotting the mapped samples - -pl.figure(2) -pl.clf() -pl.subplot(2, 2, 1) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=.2) -pl.scatter(xst0[:, 0], xst0[:, 1], c=ys, marker='+', - label='Mapped source samples') -pl.title("Bary. mapping (linear)") -pl.legend(loc=0) - -pl.subplot(2, 2, 2) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=.2) -pl.scatter(xst[:, 0], xst[:, 1], c=ys, marker='+', label='Learned mapping') -pl.title("Estim. mapping (linear)") - -pl.subplot(2, 2, 3) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=.2) -pl.scatter(xst0_kernel[:, 0], xst0_kernel[:, 1], c=ys, - marker='+', label='barycentric mapping') -pl.title("Bary. mapping (kernel)") - -pl.subplot(2, 2, 4) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=.2) -pl.scatter(xst_kernel[:, 0], xst_kernel[:, 1], c=ys, - marker='+', label='Learned mapping') -pl.title("Estim. mapping (kernel)") -pl.tight_layout() - -pl.show() diff --git a/examples/plot_OTDA_mapping_color_images.py b/examples/plot_OTDA_mapping_color_images.py deleted file mode 100644 index 8064b25..0000000 --- a/examples/plot_OTDA_mapping_color_images.py +++ /dev/null @@ -1,169 +0,0 @@ -# -*- coding: utf-8 -*- -""" -==================================================================================== -OT for domain adaptation with image color adaptation [6] with mapping estimation [8] -==================================================================================== - -[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized - discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for - discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. - -""" - -# Author: Remi Flamary -# -# License: MIT License - -import numpy as np -from scipy import ndimage -import matplotlib.pylab as pl -import ot - - -#%% Loading images - -I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256 -I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 - -#%% Plot images - -pl.figure(1, figsize=(6.4, 3)) -pl.subplot(1, 2, 1) -pl.imshow(I1) -pl.axis('off') -pl.title('Image 1') - -pl.subplot(1, 2, 2) -pl.imshow(I2) -pl.axis('off') -pl.title('Image 2') -pl.tight_layout() - - -#%% Image conversion and dataset generation - -def im2mat(I): - """Converts and image to matrix (one pixel per line)""" - return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) - - -def mat2im(X, shape): - """Converts back a matrix to an image""" - return X.reshape(shape) - - -X1 = im2mat(I1) -X2 = im2mat(I2) - -# training samples -nb = 1000 -idx1 = np.random.randint(X1.shape[0], size=(nb,)) -idx2 = np.random.randint(X2.shape[0], size=(nb,)) - -xs = X1[idx1, :] -xt = X2[idx2, :] - -#%% Plot image distributions - - -pl.figure(2, figsize=(6.4, 5)) - -pl.subplot(1, 2, 1) -pl.scatter(xs[:, 0], xs[:, 2], c=xs) -pl.axis([0, 1, 0, 1]) -pl.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 1') - -pl.subplot(1, 2, 2) -pl.scatter(xt[:, 0], xt[:, 2], c=xt) -pl.axis([0, 1, 0, 1]) -pl.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 2') -pl.tight_layout() - - -#%% domain adaptation between images - -def minmax(I): - return np.clip(I, 0, 1) - - -# LP problem -da_emd = ot.da.OTDA() # init class -da_emd.fit(xs, xt) # fit distributions - -X1t = da_emd.predict(X1) # out of sample -I1t = minmax(mat2im(X1t, I1.shape)) - -# sinkhorn regularization -lambd = 1e-1 -da_entrop = ot.da.OTDA_sinkhorn() -da_entrop.fit(xs, xt, reg=lambd) - -X1te = da_entrop.predict(X1) -I1te = minmax(mat2im(X1te, I1.shape)) - -# linear mapping estimation -eta = 1e-8 # quadratic regularization for regression -mu = 1e0 # weight of the OT linear term -bias = True # estimate a bias - -ot_mapping = ot.da.OTDA_mapping_linear() -ot_mapping.fit(xs, xt, mu=mu, eta=eta, bias=bias, numItermax=20, verbose=True) - -X1tl = ot_mapping.predict(X1) # use the estimated mapping -I1tl = minmax(mat2im(X1tl, I1.shape)) - -# nonlinear mapping estimation -eta = 1e-2 # quadratic regularization for regression -mu = 1e0 # weight of the OT linear term -bias = False # estimate a bias -sigma = 1 # sigma bandwidth fot gaussian kernel - - -ot_mapping_kernel = ot.da.OTDA_mapping_kernel() -ot_mapping_kernel.fit( - xs, xt, mu=mu, eta=eta, sigma=sigma, bias=bias, numItermax=10, verbose=True) - -X1tn = ot_mapping_kernel.predict(X1) # use the estimated mapping -I1tn = minmax(mat2im(X1tn, I1.shape)) - -#%% plot images - -pl.figure(2, figsize=(8, 4)) - -pl.subplot(2, 3, 1) -pl.imshow(I1) -pl.axis('off') -pl.title('Im. 1') - -pl.subplot(2, 3, 2) -pl.imshow(I2) -pl.axis('off') -pl.title('Im. 2') - -pl.subplot(2, 3, 3) -pl.imshow(I1t) -pl.axis('off') -pl.title('Im. 1 Interp LP') - -pl.subplot(2, 3, 4) -pl.imshow(I1te) -pl.axis('off') -pl.title('Im. 1 Interp Entrop') - -pl.subplot(2, 3, 5) -pl.imshow(I1tl) -pl.axis('off') -pl.title('Im. 1 Linear mapping') - -pl.subplot(2, 3, 6) -pl.imshow(I1tn) -pl.axis('off') -pl.title('Im. 1 nonlinear mapping') -pl.tight_layout() - -pl.show() diff --git a/ot/da.py b/ot/da.py index 8c62669..369b6a2 100644 --- a/ot/da.py +++ b/ot/da.py @@ -976,36 +976,41 @@ class BaseTransport(BaseEstimator): Returns self. """ - # pairwise distance - self.cost_ = dist(Xs, Xt, metric=self.metric) + if Xs is not None and Xt is not None: + # pairwise distance + self.cost_ = dist(Xs, Xt, metric=self.metric) - if (ys is not None) and (yt is not None): + if (ys is not None) and (yt is not None): - if self.limit_max != np.infty: - self.limit_max = self.limit_max * np.max(self.cost_) + if self.limit_max != np.infty: + self.limit_max = self.limit_max * np.max(self.cost_) - # assumes labeled source samples occupy the first rows - # and labeled target samples occupy the first columns - classes = np.unique(ys) - for c in classes: - idx_s = np.where((ys != c) & (ys != -1)) - idx_t = np.where(yt == c) + # assumes labeled source samples occupy the first rows + # and labeled target samples occupy the first columns + classes = np.unique(ys) + for c in classes: + idx_s = np.where((ys != c) & (ys != -1)) + idx_t = np.where(yt == c) - # all the coefficients corresponding to a source sample - # and a target sample : - # with different labels get a infinite - for j in idx_t[0]: - self.cost_[idx_s[0], j] = self.limit_max + # all the coefficients corresponding to a source sample + # and a target sample : + # with different labels get a infinite + for j in idx_t[0]: + self.cost_[idx_s[0], j] = self.limit_max - # distribution estimation - self.mu_s = self.distribution_estimation(Xs) - self.mu_t = self.distribution_estimation(Xt) + # distribution estimation + self.mu_s = self.distribution_estimation(Xs) + self.mu_t = self.distribution_estimation(Xt) - # store arrays of samples - self.Xs = Xs - self.Xt = Xt + # store arrays of samples + self.Xs = Xs + self.Xt = Xt - return self + return self + else: + print("POT-Warning") + print("Please provide both Xs and Xt arguments when calling") + print("fit method") def fit_transform(self, Xs=None, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples @@ -1053,42 +1058,47 @@ class BaseTransport(BaseEstimator): The transport source samples. """ - if np.array_equal(self.Xs, Xs): - # perform standard barycentric mapping - transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] + if Xs is not None: + if np.array_equal(self.Xs, Xs): + # perform standard barycentric mapping + transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] - # set nans to 0 - transp[~ np.isfinite(transp)] = 0 + # set nans to 0 + transp[~ np.isfinite(transp)] = 0 - # compute transported samples - transp_Xs = np.dot(transp, self.Xt) - else: - # perform out of sample mapping - indices = np.arange(Xs.shape[0]) - batch_ind = [ - indices[i:i + batch_size] - for i in range(0, len(indices), batch_size)] + # compute transported samples + transp_Xs = np.dot(transp, self.Xt) + else: + # perform out of sample mapping + indices = np.arange(Xs.shape[0]) + batch_ind = [ + indices[i:i + batch_size] + for i in range(0, len(indices), batch_size)] - transp_Xs = [] - for bi in batch_ind: + transp_Xs = [] + for bi in batch_ind: - # get the nearest neighbor in the source domain - D0 = dist(Xs[bi], self.Xs) - idx = np.argmin(D0, axis=1) + # get the nearest neighbor in the source domain + D0 = dist(Xs[bi], self.Xs) + idx = np.argmin(D0, axis=1) - # transport the source samples - transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] - transp[~ np.isfinite(transp)] = 0 - transp_Xs_ = np.dot(transp, self.Xt) + # transport the source samples + transp = self.coupling_ / np.sum( + self.coupling_, 1)[:, None] + transp[~ np.isfinite(transp)] = 0 + transp_Xs_ = np.dot(transp, self.Xt) - # define the transported points - transp_Xs_ = transp_Xs_[idx, :] + Xs[bi] - self.Xs[idx, :] + # define the transported points + transp_Xs_ = transp_Xs_[idx, :] + Xs[bi] - self.Xs[idx, :] - transp_Xs.append(transp_Xs_) + transp_Xs.append(transp_Xs_) - transp_Xs = np.concatenate(transp_Xs, axis=0) + transp_Xs = np.concatenate(transp_Xs, axis=0) - return transp_Xs + return transp_Xs + else: + print("POT-Warning") + print("Please provide Xs argument when calling transform method") def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128): @@ -1113,41 +1123,46 @@ class BaseTransport(BaseEstimator): The transported target samples. """ - if np.array_equal(self.Xt, Xt): - # perform standard barycentric mapping - transp_ = self.coupling_.T / np.sum(self.coupling_, 0)[:, None] + if Xt is not None: + if np.array_equal(self.Xt, Xt): + # perform standard barycentric mapping + transp_ = self.coupling_.T / np.sum(self.coupling_, 0)[:, None] - # set nans to 0 - transp_[~ np.isfinite(transp_)] = 0 + # set nans to 0 + transp_[~ np.isfinite(transp_)] = 0 - # compute transported samples - transp_Xt = np.dot(transp_, self.Xs) - else: - # perform out of sample mapping - indices = np.arange(Xt.shape[0]) - batch_ind = [ - indices[i:i + batch_size] - for i in range(0, len(indices), batch_size)] + # compute transported samples + transp_Xt = np.dot(transp_, self.Xs) + else: + # perform out of sample mapping + indices = np.arange(Xt.shape[0]) + batch_ind = [ + indices[i:i + batch_size] + for i in range(0, len(indices), batch_size)] - transp_Xt = [] - for bi in batch_ind: + transp_Xt = [] + for bi in batch_ind: - D0 = dist(Xt[bi], self.Xt) - idx = np.argmin(D0, axis=1) + D0 = dist(Xt[bi], self.Xt) + idx = np.argmin(D0, axis=1) - # transport the target samples - transp_ = self.coupling_.T / np.sum(self.coupling_, 0)[:, None] - transp_[~ np.isfinite(transp_)] = 0 - transp_Xt_ = np.dot(transp_, self.Xs) + # transport the target samples + transp_ = self.coupling_.T / np.sum( + self.coupling_, 0)[:, None] + transp_[~ np.isfinite(transp_)] = 0 + transp_Xt_ = np.dot(transp_, self.Xs) - # define the transported points - transp_Xt_ = transp_Xt_[idx, :] + Xt[bi] - self.Xt[idx, :] + # define the transported points + transp_Xt_ = transp_Xt_[idx, :] + Xt[bi] - self.Xt[idx, :] - transp_Xt.append(transp_Xt_) + transp_Xt.append(transp_Xt_) - transp_Xt = np.concatenate(transp_Xt, axis=0) + transp_Xt = np.concatenate(transp_Xt, axis=0) - return transp_Xt + return transp_Xt + else: + print("POT-Warning") + print("Please provide Xt argument when calling inverse_transform") class SinkhornTransport(BaseTransport): @@ -1413,15 +1428,20 @@ class SinkhornLpl1Transport(BaseTransport): Returns self. """ - super(SinkhornLpl1Transport, self).fit(Xs, ys, Xt, yt) + if Xs is not None and Xt is not None and ys is not None: - self.coupling_ = sinkhorn_lpl1_mm( - a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.cost_, - reg=self.reg_e, eta=self.reg_cl, numItermax=self.max_iter, - numInnerItermax=self.max_inner_iter, stopInnerThr=self.tol, - verbose=self.verbose) + super(SinkhornLpl1Transport, self).fit(Xs, ys, Xt, yt) - return self + self.coupling_ = sinkhorn_lpl1_mm( + a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.cost_, + reg=self.reg_e, eta=self.reg_cl, numItermax=self.max_iter, + numInnerItermax=self.max_inner_iter, stopInnerThr=self.tol, + verbose=self.verbose) + + return self + else: + print("POT-Warning") + print("Please provide both Xs, Xt, ys arguments to fit method") class SinkhornL1l2Transport(BaseTransport): @@ -1517,22 +1537,27 @@ class SinkhornL1l2Transport(BaseTransport): Returns self. """ - super(SinkhornL1l2Transport, self).fit(Xs, ys, Xt, yt) + if Xs is not None and Xt is not None and ys is not None: - returned_ = sinkhorn_l1l2_gl( - a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.cost_, - reg=self.reg_e, eta=self.reg_cl, numItermax=self.max_iter, - numInnerItermax=self.max_inner_iter, stopInnerThr=self.tol, - verbose=self.verbose, log=self.log) + super(SinkhornL1l2Transport, self).fit(Xs, ys, Xt, yt) - # deal with the value of log - if self.log: - self.coupling_, self.log_ = returned_ - else: - self.coupling_ = returned_ - self.log_ = dict() + returned_ = sinkhorn_l1l2_gl( + a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.cost_, + reg=self.reg_e, eta=self.reg_cl, numItermax=self.max_iter, + numInnerItermax=self.max_inner_iter, stopInnerThr=self.tol, + verbose=self.verbose, log=self.log) - return self + # deal with the value of log + if self.log: + self.coupling_, self.log_ = returned_ + else: + self.coupling_ = returned_ + self.log_ = dict() + + return self + else: + print("POT-Warning") + print("Please, provide both Xs, Xt and ys argument to fit method") class MappingTransport(BaseEstimator): -- cgit v1.2.3 From c5d7c40c1d850879abd5f2c513afa1a2c5d5987e Mon Sep 17 00:00:00 2001 From: Slasnista Date: Mon, 28 Aug 2017 14:07:55 +0200 Subject: check input parameters with helper functions --- ot/da.py | 174 ++++++++++++++++++++++++++++++++++-------------------------- ot/utils.py | 21 ++++++++ 2 files changed, 120 insertions(+), 75 deletions(-) diff --git a/ot/da.py b/ot/da.py index 369b6a2..78dc150 100644 --- a/ot/da.py +++ b/ot/da.py @@ -14,7 +14,7 @@ import numpy as np from .bregman import sinkhorn from .lp import emd from .utils import unif, dist, kernel -from .utils import deprecated, BaseEstimator +from .utils import check_params, deprecated, BaseEstimator from .optim import cg from .optim import gcg @@ -954,6 +954,26 @@ def distribution_estimation_uniform(X): class BaseTransport(BaseEstimator): + """Base class for OTDA objects + + Notes + ----- + All estimators should specify all the parameters that can be set + at the class level in their ``__init__`` as explicit keyword + arguments (no ``*args`` or ``**kwargs``). + + fit method should: + - estimate a cost matrix and store it in a `cost_` attribute + - estimate a coupling matrix and store it in a `coupling_` + attribute + - estimate distributions from source and target data and store them in + mu_s and mu_t attributes + - store Xs and Xt in attributes to be used later on in transform and + inverse_transform methods + + transform method should always get as input a Xs parameter + inverse_transform method should always get as input a Xt parameter + """ def fit(self, Xs=None, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples @@ -976,7 +996,9 @@ class BaseTransport(BaseEstimator): Returns self. """ - if Xs is not None and Xt is not None: + # check the necessary inputs parameters are here + if check_params(Xs=Xs, Xt=Xt): + # pairwise distance self.cost_ = dist(Xs, Xt, metric=self.metric) @@ -1003,14 +1025,10 @@ class BaseTransport(BaseEstimator): self.mu_t = self.distribution_estimation(Xt) # store arrays of samples - self.Xs = Xs - self.Xt = Xt + self.xs_ = Xs + self.xt_ = Xt - return self - else: - print("POT-Warning") - print("Please provide both Xs and Xt arguments when calling") - print("fit method") + return self def fit_transform(self, Xs=None, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples @@ -1058,8 +1076,11 @@ class BaseTransport(BaseEstimator): The transport source samples. """ - if Xs is not None: - if np.array_equal(self.Xs, Xs): + # check the necessary inputs parameters are here + if check_params(Xs=Xs): + + if np.array_equal(self.xs_, Xs): + # perform standard barycentric mapping transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] @@ -1067,7 +1088,7 @@ class BaseTransport(BaseEstimator): transp[~ np.isfinite(transp)] = 0 # compute transported samples - transp_Xs = np.dot(transp, self.Xt) + transp_Xs = np.dot(transp, self.xt_) else: # perform out of sample mapping indices = np.arange(Xs.shape[0]) @@ -1079,26 +1100,23 @@ class BaseTransport(BaseEstimator): for bi in batch_ind: # get the nearest neighbor in the source domain - D0 = dist(Xs[bi], self.Xs) + D0 = dist(Xs[bi], self.xs_) idx = np.argmin(D0, axis=1) # transport the source samples transp = self.coupling_ / np.sum( self.coupling_, 1)[:, None] transp[~ np.isfinite(transp)] = 0 - transp_Xs_ = np.dot(transp, self.Xt) + transp_Xs_ = np.dot(transp, self.xt_) # define the transported points - transp_Xs_ = transp_Xs_[idx, :] + Xs[bi] - self.Xs[idx, :] + transp_Xs_ = transp_Xs_[idx, :] + Xs[bi] - self.xs_[idx, :] transp_Xs.append(transp_Xs_) transp_Xs = np.concatenate(transp_Xs, axis=0) return transp_Xs - else: - print("POT-Warning") - print("Please provide Xs argument when calling transform method") def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128): @@ -1123,8 +1141,11 @@ class BaseTransport(BaseEstimator): The transported target samples. """ - if Xt is not None: - if np.array_equal(self.Xt, Xt): + # check the necessary inputs parameters are here + if check_params(Xt=Xt): + + if np.array_equal(self.xt_, Xt): + # perform standard barycentric mapping transp_ = self.coupling_.T / np.sum(self.coupling_, 0)[:, None] @@ -1132,7 +1153,7 @@ class BaseTransport(BaseEstimator): transp_[~ np.isfinite(transp_)] = 0 # compute transported samples - transp_Xt = np.dot(transp_, self.Xs) + transp_Xt = np.dot(transp_, self.xs_) else: # perform out of sample mapping indices = np.arange(Xt.shape[0]) @@ -1143,26 +1164,23 @@ class BaseTransport(BaseEstimator): transp_Xt = [] for bi in batch_ind: - D0 = dist(Xt[bi], self.Xt) + D0 = dist(Xt[bi], self.xt_) idx = np.argmin(D0, axis=1) # transport the target samples transp_ = self.coupling_.T / np.sum( self.coupling_, 0)[:, None] transp_[~ np.isfinite(transp_)] = 0 - transp_Xt_ = np.dot(transp_, self.Xs) + transp_Xt_ = np.dot(transp_, self.xs_) # define the transported points - transp_Xt_ = transp_Xt_[idx, :] + Xt[bi] - self.Xt[idx, :] + transp_Xt_ = transp_Xt_[idx, :] + Xt[bi] - self.xt_[idx, :] transp_Xt.append(transp_Xt_) transp_Xt = np.concatenate(transp_Xt, axis=0) return transp_Xt - else: - print("POT-Warning") - print("Please provide Xt argument when calling inverse_transform") class SinkhornTransport(BaseTransport): @@ -1428,7 +1446,8 @@ class SinkhornLpl1Transport(BaseTransport): Returns self. """ - if Xs is not None and Xt is not None and ys is not None: + # check the necessary inputs parameters are here + if check_params(Xs=Xs, Xt=Xt, ys=ys): super(SinkhornLpl1Transport, self).fit(Xs, ys, Xt, yt) @@ -1438,10 +1457,7 @@ class SinkhornLpl1Transport(BaseTransport): numInnerItermax=self.max_inner_iter, stopInnerThr=self.tol, verbose=self.verbose) - return self - else: - print("POT-Warning") - print("Please provide both Xs, Xt, ys arguments to fit method") + return self class SinkhornL1l2Transport(BaseTransport): @@ -1537,7 +1553,8 @@ class SinkhornL1l2Transport(BaseTransport): Returns self. """ - if Xs is not None and Xt is not None and ys is not None: + # check the necessary inputs parameters are here + if check_params(Xs=Xs, Xt=Xt, ys=ys): super(SinkhornL1l2Transport, self).fit(Xs, ys, Xt, yt) @@ -1554,10 +1571,7 @@ class SinkhornL1l2Transport(BaseTransport): self.coupling_ = returned_ self.log_ = dict() - return self - else: - print("POT-Warning") - print("Please, provide both Xs, Xt and ys argument to fit method") + return self class MappingTransport(BaseEstimator): @@ -1652,29 +1666,35 @@ class MappingTransport(BaseEstimator): Returns self """ - self.Xs = Xs - self.Xt = Xt - - if self.kernel == "linear": - returned_ = joint_OT_mapping_linear( - Xs, Xt, mu=self.mu, eta=self.eta, bias=self.bias, - verbose=self.verbose, verbose2=self.verbose2, - numItermax=self.max_iter, numInnerItermax=self.max_inner_iter, - stopThr=self.tol, stopInnerThr=self.inner_tol, log=self.log) + # check the necessary inputs parameters are here + if check_params(Xs=Xs, Xt=Xt): + + self.xs_ = Xs + self.xt_ = Xt + + if self.kernel == "linear": + returned_ = joint_OT_mapping_linear( + Xs, Xt, mu=self.mu, eta=self.eta, bias=self.bias, + verbose=self.verbose, verbose2=self.verbose2, + numItermax=self.max_iter, + numInnerItermax=self.max_inner_iter, stopThr=self.tol, + stopInnerThr=self.inner_tol, log=self.log) + + elif self.kernel == "gaussian": + returned_ = joint_OT_mapping_kernel( + Xs, Xt, mu=self.mu, eta=self.eta, bias=self.bias, + sigma=self.sigma, verbose=self.verbose, + verbose2=self.verbose, numItermax=self.max_iter, + numInnerItermax=self.max_inner_iter, + stopInnerThr=self.inner_tol, stopThr=self.tol, + log=self.log) - elif self.kernel == "gaussian": - returned_ = joint_OT_mapping_kernel( - Xs, Xt, mu=self.mu, eta=self.eta, bias=self.bias, - sigma=self.sigma, verbose=self.verbose, verbose2=self.verbose, - numItermax=self.max_iter, numInnerItermax=self.max_inner_iter, - stopInnerThr=self.inner_tol, stopThr=self.tol, log=self.log) - - # deal with the value of log - if self.log: - self.coupling_, self.mapping_, self.log_ = returned_ - else: - self.coupling_, self.mapping_ = returned_ - self.log_ = dict() + # deal with the value of log + if self.log: + self.coupling_, self.mapping_, self.log_ = returned_ + else: + self.coupling_, self.mapping_ = returned_ + self.log_ = dict() return self @@ -1692,22 +1712,26 @@ class MappingTransport(BaseEstimator): The transport source samples. """ - if np.array_equal(self.Xs, Xs): - # perform standard barycentric mapping - transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] + # check the necessary inputs parameters are here + if check_params(Xs=Xs): - # set nans to 0 - transp[~ np.isfinite(transp)] = 0 + if np.array_equal(self.xs_, Xs): + # perform standard barycentric mapping + transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] - # compute transported samples - transp_Xs = np.dot(transp, self.Xt) - else: - if self.kernel == "gaussian": - K = kernel(Xs, self.Xs, method=self.kernel, sigma=self.sigma) - elif self.kernel == "linear": - K = Xs - if self.bias: - K = np.hstack((K, np.ones((Xs.shape[0], 1)))) - transp_Xs = K.dot(self.mapping_) + # set nans to 0 + transp[~ np.isfinite(transp)] = 0 - return transp_Xs + # compute transported samples + transp_Xs = np.dot(transp, self.xt_) + else: + if self.kernel == "gaussian": + K = kernel(Xs, self.xs_, method=self.kernel, + sigma=self.sigma) + elif self.kernel == "linear": + K = Xs + if self.bias: + K = np.hstack((K, np.ones((Xs.shape[0], 1)))) + transp_Xs = K.dot(self.mapping_) + + return transp_Xs diff --git a/ot/utils.py b/ot/utils.py index 29ad536..01f2a67 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -168,6 +168,27 @@ def parmap(f, X, nprocs=multiprocessing.cpu_count()): return [x for i, x in sorted(res)] +def check_params(**kwargs): + """check_params: check whether some parameters are missing + """ + + missing_params = [] + check = True + + for param in kwargs: + if kwargs[param] is None: + missing_params.append(param) + + if len(missing_params) > 0: + print("POT - Warning: following necessary parameters are missing") + for p in missing_params: + print("\n", p) + + check = False + + return check + + class deprecated(object): """Decorator to mark a function or class as deprecated. -- cgit v1.2.3 From 7d3fc95abe059cc7404f3c213dfd5019cf110737 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Mon, 28 Aug 2017 14:08:03 +0200 Subject: update readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 27b4643..33eea6e 100644 --- a/README.md +++ b/README.md @@ -136,7 +136,7 @@ The contributors to this library are: * [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/) * [Michael Perrot](http://perso.univ-st-etienne.fr/pem82055/) (Mapping estimation) * [Léo Gautheron](https://github.com/aje) (GPU implementation) -* [Nathalie Gayraud]() +* [Nathalie Gayraud](https://www.linkedin.com/in/nathalie-t-h-gayraud/?ppe=1) * [Stanislas Chambon](https://slasnista.github.io/) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): -- cgit v1.2.3 From 7ab9037f1e4a08439083d1bc5705be5ed2e9e10a Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Mon, 28 Aug 2017 14:41:09 +0200 Subject: Gromov-Wasserstein distance --- README.md | 4 +- examples/plot_gromov.py | 98 ++++++++++ ot/__init__.py | 6 +- ot/gromov.py | 482 ++++++++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 588 insertions(+), 2 deletions(-) create mode 100644 examples/plot_gromov.py create mode 100644 ot/gromov.py diff --git a/README.md b/README.md index 7a65106..a42f17b 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,7 @@ It provides the following solvers: * Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. * Joint OT matrix and mapping estimation [8]. * Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt). - +* Gromov-Wasserstein distances [12] Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -182,3 +182,5 @@ You can also post bug reports and feature requests in Github issues. Make sure t [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). [Scaling algorithms for unbalanced transport problems](https://arxiv.org/pdf/1607.05816.pdf). arXiv preprint arXiv:1607.05816. [11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). [Wasserstein Discriminant Analysis](https://arxiv.org/pdf/1608.08063.pdf). arXiv preprint arXiv:1608.08063. + +[12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, [Gromov-Wasserstein averaging of kernel and distance matrices](http://proceedings.mlr.press/v48/peyre16.html) International Conference on Machine Learning (ICML). 2016. diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py new file mode 100644 index 0000000..11e5336 --- /dev/null +++ b/examples/plot_gromov.py @@ -0,0 +1,98 @@ +# -*- coding: utf-8 -*- +""" +==================== +Gromov-Wasserstein example +==================== + +This example is designed to show how to use the Gromov-Wassertsein distance +computation in POT. + + +""" + +# Author: Erwan Vautier +# Nicolas Courty +# +# License: MIT License + +import scipy as sp +import numpy as np + +import ot +import matplotlib.pylab as pl +from mpl_toolkits.mplot3d import Axes3D + + + +""" +Sample two Gaussian distributions (2D and 3D) +==================== + +The Gromov-Wasserstein distance allows to compute distances with samples that do not belong to the same metric space. For +demonstration purpose, we sample two Gaussian distributions in 2- and 3-dimensional spaces. + +""" +n=30 # nb samples + +mu_s=np.array([0,0]) +cov_s=np.array([[1,0],[0,1]]) + +mu_t=np.array([4,4,4]) +cov_t=np.array([[1,0,0],[0,1,0],[0,0,1]]) + + + +xs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s) +P=sp.linalg.sqrtm(cov_t) +xt= np.random.randn(n,3).dot(P)+mu_t + + + +""" +Plotting the distributions +==================== +""" +fig=pl.figure() +ax1=fig.add_subplot(121) +ax1.plot(xs[:,0],xs[:,1],'+b',label='Source samples') +ax2=fig.add_subplot(122,projection='3d') +ax2.scatter(xt[:,0],xt[:,1],xt[:,2],color='r') +pl.show() + + +""" +Compute distance kernels, normalize them and then display +==================== +""" + +C1=sp.spatial.distance.cdist(xs,xs) +C2=sp.spatial.distance.cdist(xt,xt) + +C1/=C1.max() +C2/=C2.max() + +pl.figure() +pl.subplot(121) +pl.imshow(C1) +pl.subplot(122) +pl.imshow(C2) +pl.show() + +""" +Compute Gromov-Wasserstein plans and distance +==================== +""" + +p=ot.unif(n) +q=ot.unif(n) + +gw=ot.gromov_wasserstein(C1,C2,p,q,'square_loss',epsilon=5e-4) +gw_dist=ot.gromov_wasserstein2(C1,C2,p,q,'square_loss',epsilon=5e-4) + +print('Gromov-Wasserstein distances between the distribution: '+str(gw_dist)) + +pl.figure() +pl.imshow(gw,cmap='jet') +pl.colorbar() +pl.show() + diff --git a/ot/__init__.py b/ot/__init__.py index 6d4c4c6..a295e1b 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -5,6 +5,7 @@ """ # Author: Remi Flamary +# Nicolas Courty # # License: MIT License @@ -17,11 +18,13 @@ from . import utils from . import datasets from . import plot from . import da +from . import gromov # OT functions from .lp import emd, emd2 from .bregman import sinkhorn, sinkhorn2, barycenter from .da import sinkhorn_lpl1_mm +from .gromov import gromov_wasserstein, gromov_wasserstein2 # utils functions from .utils import dist, unif, tic, toc, toq @@ -30,4 +33,5 @@ __version__ = "0.3.1" __all__ = ["emd", "emd2", "sinkhorn", "sinkhorn2", "utils", 'datasets', 'bregman', 'lp', 'plot', 'tic', 'toc', 'toq', - 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim'] + 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim', + 'gromov_wasserstein','gromov_wasserstein2'] diff --git a/ot/gromov.py b/ot/gromov.py new file mode 100644 index 0000000..f3c62c9 --- /dev/null +++ b/ot/gromov.py @@ -0,0 +1,482 @@ + +# -*- coding: utf-8 -*- +""" +Gromov-Wasserstein transport method +=================================== + + +""" + +# Author: Erwan Vautier +# Nicolas Courty +# +# License: MIT License + +import numpy as np + +from .bregman import sinkhorn +from .utils import dist + +def square_loss(a,b): + """ + Returns the value of L(a,b)=(1/2)*|a-b|^2 + """ + + return (1/2)*(a-b)**2 + +def kl_loss(a,b): + """ + Returns the value of L(a,b)=a*log(a/b)-a+b + """ + + return a*np.log(a/b)-a+b + +def tensor_square_loss(C1,C2,T): + """ + Returns the value of \mathcal{L}(C1,C2) \otimes T with the square loss + + function as the loss function of Gromow-Wasserstein discrepancy. + + Where : + + C1 : Metric cost matrix in the source space + C2 : Metric cost matrix in the target space + T : A coupling between those two spaces + + The square-loss function L(a,b)=(1/2)*|a-b|^2 is read as : + L(a,b) = f1(a)+f2(b)-h1(a)*h2(b) with : + f1(a)=(a^2)/2 + f2(b)=(b^2)/2 + h1(a)=a + h2(b)=b + + Parameters + ---------- + C1 : np.ndarray(ns,ns) + Metric cost matrix in the source space + C2 : np.ndarray(nt,nt) + Metric costfr matrix in the target space + T : np.ndarray(ns,nt) + Coupling between source and target spaces + + + Returns + ------- + tens : (ns*nt) ndarray + \mathcal{L}(C1,C2) \otimes T tensor-matrix multiplication result + + + """ + + + C1=np.asarray(C1,dtype=np.float64) + C2=np.asarray(C2,dtype=np.float64) + T=np.asarray(T,dtype=np.float64) + + + def f1(a): + return (a**2)/2 + + def f2(b): + return (b**2)/2 + + def h1(a): + return a + + def h2(b): + return b + + tens=-np.dot(h1(C1),T).dot(h2(C2).T) + tens=tens-tens.min() + + + return np.array(tens) + + +def tensor_kl_loss(C1,C2,T): + """ + Returns the value of \mathcal{L}(C1,C2) \otimes T with the square loss + + function as the loss function of Gromow-Wasserstein discrepancy. + + Where : + + C1 : Metric cost matrix in the source space + C2 : Metric cost matrix in the target space + T : A coupling between those two spaces + + The square-loss function L(a,b)=(1/2)*|a-b|^2 is read as : + L(a,b) = f1(a)+f2(b)-h1(a)*h2(b) with : + f1(a)=a*log(a)-a + f2(b)=b + h1(a)=a + h2(b)=log(b) + + Parameters + ---------- + C1 : np.ndarray(ns,ns) + Metric cost matrix in the source space + C2 : np.ndarray(nt,nt) + Metric costfr matrix in the target space + T : np.ndarray(ns,nt) + Coupling between source and target spaces + + + Returns + ------- + tens : (ns*nt) ndarray + \mathcal{L}(C1,C2) \otimes T tensor-matrix multiplication result + + References + ---------- + + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices." International Conference on Machine Learning (ICML). 2016. + + """ + + + C1=np.asarray(C1,dtype=np.float64) + C2=np.asarray(C2,dtype=np.float64) + T=np.asarray(T,dtype=np.float64) + + + def f1(a): + return a*np.log(a+1e-15)-a + + def f2(b): + return b + + def h1(a): + return a + + def h2(b): + return np.log(b+1e-15) + + tens=-np.dot(h1(C1),T).dot(h2(C2).T) + tens=tens-tens.min() + + + + return np.array(tens) + +def update_square_loss(p,lambdas,T,Cs): + """ + Updates C according to the L2 Loss kernel with the S Ts couplings calculated at each iteration + + + Parameters + ---------- + p : np.ndarray(N,) + weights in the targeted barycenter + lambdas : list of the S spaces' weights + T : list of S np.ndarray(ns,N) + the S Ts couplings calculated at each iteration + Cs : Cs : list of S np.ndarray(ns,ns) + Metric cost matrices + + Returns + ---------- + C updated + + + """ + tmpsum=np.sum([lambdas[s]*np.dot(T[s].T,Cs[s]).dot(T[s]) for s in range(len(T))]) + ppt=np.dot(p,p.T) + + return(np.divide(tmpsum,ppt)) + +def update_kl_loss(p,lambdas,T,Cs): + """ + Updates C according to the KL Loss kernel with the S Ts couplings calculated at each iteration + + + Parameters + ---------- + p : np.ndarray(N,) + weights in the targeted barycenter + lambdas : list of the S spaces' weights + T : list of S np.ndarray(ns,N) + the S Ts couplings calculated at each iteration + Cs : Cs : list of S np.ndarray(ns,ns) + Metric cost matrices + + Returns + ---------- + C updated + + + """ + tmpsum=np.sum([lambdas[s]*np.dot(T[s].T,Cs[s]).dot(T[s]) for s in range(len(T))]) + ppt=np.dot(p,p.T) + + return(np.exp(np.divide(tmpsum,ppt))) + + +def gromov_wasserstein(C1,C2,p,q,loss_fun,epsilon,numItermax = 1000, stopThr=1e-9,verbose=False, log=False): + """ + Returns the gromov-wasserstein coupling between the two measured similarity matrices + + (C1,p) and (C2,q) + + The function solves the following optimization problem: + + .. math:: + \GW = arg\min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T)) + + s.t. \GW 1 = p + + \GW^T 1= q + + \GW\geq 0 + + Where : + + C1 : Metric cost matrix in the source space + C2 : Metric cost matrix in the target space + p : distribution in the source space + q : distribution in the target space + L : loss function to account for the misfit between the similarity matrices + H : entropy + + + Parameters + ---------- + C1 : np.ndarray(ns,ns) + Metric cost matrix in the source space + C2 : np.ndarray(nt,nt) + Metric costfr matrix in the target space + p : np.ndarray(ns,) + distribution in the source space + q : np.ndarray(nt) + distribution in the target space + loss_fun : loss function used for the solver either 'square_loss' or 'kl_loss' + epsilon : float + Regularization term >0 + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshold on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + forcing : np.ndarray(N,2) + list of forced couplings (where N is the number of forcing) + + Returns + ------- + T : coupling between the two spaces that minimizes : + \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T)) + + """ + + + C1=np.asarray(C1,dtype=np.float64) + C2=np.asarray(C2,dtype=np.float64) + + T=np.dot(p,q.T) #Initialization + + cpt = 0 + err=1 + + while (err>stopThr and cpt0 + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshold on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + forcing : np.ndarray(N,2) + list of forced couplings (where N is the number of forcing) + + Returns + ------- + T : coupling between the two spaces that minimizes : + \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T)) + + """ + + if log: + gw,logv=gromov_wasserstein(C1,C2,p,q,loss_fun,epsilon,numItermax,stopThr,verbose,log) + else: + gw=gromov_wasserstein(C1,C2,p,q,loss_fun,epsilon,numItermax,stopThr,verbose,log) + + if loss_fun=='square_loss': + gw_dist=np.sum(gw*tensor_square_loss(C1,C2,gw)) + + + elif loss_fun=='kl_loss': + gw_dist=np.sum(gw*tensor_kl_loss(C1,C2,gw)) + + if log: + return gw_dist, logv + else: + return gw_dist + + +def gromov_barycenters(N,Cs,ps,p,lambdas,loss_fun,epsilon,numItermax = 1000, stopThr=1e-9, verbose=False, log=False): + """ + Returns the gromov-wasserstein barycenters of S measured similarity matrices + + (Cs)_{s=1}^{s=S} + + The function solves the following optimization problem: + + .. math:: + C = argmin_C\in R^NxN \sum_s \lambda_s GW(C,Cs,p,ps) + + + Where : + + Cs : metric cost matrix + ps : distribution + + Parameters + ---------- + N : Integer + Size of the targeted barycenter + Cs : list of S np.ndarray(ns,ns) + Metric cost matrices + ps : list of S np.ndarray(ns,) + sample weights in the S spaces + p : np.ndarray(N,) + weights in the targeted barycenter + lambdas : list of the S spaces' weights + L : tensor-matrix multiplication function based on specific loss function + update : function(p,lambdas,T,Cs) that updates C according to a specific Kernel + with the S Ts couplings calculated at each iteration + epsilon : float + Regularization term >0 + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + Returns + ------- + C : Similarity matrix in the barycenter space (permutated arbitrarily) + + """ + + + S=len(Cs) + + Cs=[np.asarray(Cs[s],dtype=np.float64) for s in range(S)] + lambdas=np.asarray(lambdas,dtype=np.float64) + + T=[0 for s in range(S)] + + #Initialization of C : random SPD matrix + xalea=np.random.randn(N,2) + C=dist(xalea,xalea) + C/=C.max() + + cpt=0 + err=1 + + error=[] + + while(err>stopThr and cpt Date: Mon, 28 Aug 2017 15:04:04 +0200 Subject: gromov:flake8 and other --- examples/plot_gromov.py | 63 ++++---- ot/gromov.py | 382 ++++++++++++++++++++++++------------------------ 2 files changed, 216 insertions(+), 229 deletions(-) diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py index 11e5336..a33fde1 100644 --- a/examples/plot_gromov.py +++ b/examples/plot_gromov.py @@ -3,11 +3,8 @@ ==================== Gromov-Wasserstein example ==================== - -This example is designed to show how to use the Gromov-Wassertsein distance -computation in POT. - - +This example is designed to show how to use the Gromov-Wassertsein distance +computation in POT. """ # Author: Erwan Vautier @@ -20,43 +17,38 @@ import numpy as np import ot import matplotlib.pylab as pl -from mpl_toolkits.mplot3d import Axes3D - """ Sample two Gaussian distributions (2D and 3D) ==================== - -The Gromov-Wasserstein distance allows to compute distances with samples that do not belong to the same metric space. For -demonstration purpose, we sample two Gaussian distributions in 2- and 3-dimensional spaces. - +The Gromov-Wasserstein distance allows to compute distances with samples that do not belong to the same metric space. +For demonstration purpose, we sample two Gaussian distributions in 2- and 3-dimensional spaces. """ -n=30 # nb samples -mu_s=np.array([0,0]) -cov_s=np.array([[1,0],[0,1]]) +n = 30 # nb samples -mu_t=np.array([4,4,4]) -cov_t=np.array([[1,0,0],[0,1,0],[0,0,1]]) +mu_s = np.array([0, 0]) +cov_s = np.array([[1, 0], [0, 1]]) +mu_t = np.array([4, 4, 4]) +cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) -xs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s) -P=sp.linalg.sqrtm(cov_t) -xt= np.random.randn(n,3).dot(P)+mu_t - +xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s) +P = sp.linalg.sqrtm(cov_t) +xt = np.random.randn(n, 3).dot(P) + mu_t """ Plotting the distributions ==================== """ -fig=pl.figure() -ax1=fig.add_subplot(121) -ax1.plot(xs[:,0],xs[:,1],'+b',label='Source samples') -ax2=fig.add_subplot(122,projection='3d') -ax2.scatter(xt[:,0],xt[:,1],xt[:,2],color='r') +fig = pl.figure() +ax1 = fig.add_subplot(121) +ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +ax2 = fig.add_subplot(122, projection='3d') +ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r') pl.show() @@ -65,11 +57,11 @@ Compute distance kernels, normalize them and then display ==================== """ -C1=sp.spatial.distance.cdist(xs,xs) -C2=sp.spatial.distance.cdist(xt,xt) +C1 = sp.spatial.distance.cdist(xs, xs) +C2 = sp.spatial.distance.cdist(xt, xt) -C1/=C1.max() -C2/=C2.max() +C1 /= C1.max() +C2 /= C2.max() pl.figure() pl.subplot(121) @@ -83,16 +75,15 @@ Compute Gromov-Wasserstein plans and distance ==================== """ -p=ot.unif(n) -q=ot.unif(n) +p = ot.unif(n) +q = ot.unif(n) -gw=ot.gromov_wasserstein(C1,C2,p,q,'square_loss',epsilon=5e-4) -gw_dist=ot.gromov_wasserstein2(C1,C2,p,q,'square_loss',epsilon=5e-4) +gw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4) +gw_dist = ot.gromov_wasserstein2(C1, C2, p, q, 'square_loss', epsilon=5e-4) -print('Gromov-Wasserstein distances between the distribution: '+str(gw_dist)) +print('Gromov-Wasserstein distances between the distribution: ' + str(gw_dist)) pl.figure() -pl.imshow(gw,cmap='jet') +pl.imshow(gw, cmap='jet') pl.colorbar() pl.show() - diff --git a/ot/gromov.py b/ot/gromov.py index f3c62c9..7cf3b42 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -17,39 +17,41 @@ import numpy as np from .bregman import sinkhorn from .utils import dist -def square_loss(a,b): + +def square_loss(a, b): """ Returns the value of L(a,b)=(1/2)*|a-b|^2 """ - - return (1/2)*(a-b)**2 -def kl_loss(a,b): + return (1 / 2) * (a - b)**2 + + +def kl_loss(a, b): """ Returns the value of L(a,b)=a*log(a/b)-a+b """ - - return a*np.log(a/b)-a+b -def tensor_square_loss(C1,C2,T): + return a * np.log(a / b) - a + b + + +def tensor_square_loss(C1, C2, T): """ - Returns the value of \mathcal{L}(C1,C2) \otimes T with the square loss - + Returns the value of \mathcal{L}(C1,C2) \otimes T with the square loss function as the loss function of Gromow-Wasserstein discrepancy. - + Where : - + C1 : Metric cost matrix in the source space C2 : Metric cost matrix in the target space T : A coupling between those two spaces - + The square-loss function L(a,b)=(1/2)*|a-b|^2 is read as : L(a,b) = f1(a)+f2(b)-h1(a)*h2(b) with : f1(a)=(a^2)/2 f2(b)=(b^2)/2 h1(a)=a h2(b)=b - + Parameters ---------- C1 : np.ndarray(ns,ns) @@ -64,54 +66,50 @@ def tensor_square_loss(C1,C2,T): ------- tens : (ns*nt) ndarray \mathcal{L}(C1,C2) \otimes T tensor-matrix multiplication result - - + + """ - - C1=np.asarray(C1,dtype=np.float64) - C2=np.asarray(C2,dtype=np.float64) - T=np.asarray(T,dtype=np.float64) - - + C1 = np.asarray(C1, dtype=np.float64) + C2 = np.asarray(C2, dtype=np.float64) + T = np.asarray(T, dtype=np.float64) + def f1(a): - return (a**2)/2 - + return (a**2) / 2 + def f2(b): - return (b**2)/2 - + return (b**2) / 2 + def h1(a): return a - + def h2(b): return b - - tens=-np.dot(h1(C1),T).dot(h2(C2).T) - tens=tens-tens.min() - + tens = -np.dot(h1(C1), T).dot(h2(C2).T) + tens = tens - tens.min() + return np.array(tens) - - -def tensor_kl_loss(C1,C2,T): + + +def tensor_kl_loss(C1, C2, T): """ - Returns the value of \mathcal{L}(C1,C2) \otimes T with the square loss - + Returns the value of \mathcal{L}(C1,C2) \otimes T with the square loss function as the loss function of Gromow-Wasserstein discrepancy. - + Where : - + C1 : Metric cost matrix in the source space C2 : Metric cost matrix in the target space T : A coupling between those two spaces - + The square-loss function L(a,b)=(1/2)*|a-b|^2 is read as : L(a,b) = f1(a)+f2(b)-h1(a)*h2(b) with : f1(a)=a*log(a)-a f2(b)=b h1(a)=a h2(b)=log(b) - + Parameters ---------- C1 : np.ndarray(ns,ns) @@ -126,44 +124,41 @@ def tensor_kl_loss(C1,C2,T): ------- tens : (ns*nt) ndarray \mathcal{L}(C1,C2) \otimes T tensor-matrix multiplication result - + References ---------- - + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices." International Conference on Machine Learning (ICML). 2016. - + """ - - C1=np.asarray(C1,dtype=np.float64) - C2=np.asarray(C2,dtype=np.float64) - T=np.asarray(T,dtype=np.float64) - - + C1 = np.asarray(C1, dtype=np.float64) + C2 = np.asarray(C2, dtype=np.float64) + T = np.asarray(T, dtype=np.float64) + def f1(a): - return a*np.log(a+1e-15)-a - + return a * np.log(a + 1e-15) - a + def f2(b): return b - + def h1(a): return a - + def h2(b): - return np.log(b+1e-15) - - tens=-np.dot(h1(C1),T).dot(h2(C2).T) - tens=tens-tens.min() + return np.log(b + 1e-15) + + tens = -np.dot(h1(C1), T).dot(h2(C2).T) + tens = tens - tens.min() - - return np.array(tens) -def update_square_loss(p,lambdas,T,Cs): + +def update_square_loss(p, lambdas, T, Cs): """ Updates C according to the L2 Loss kernel with the S Ts couplings calculated at each iteration - - + + Parameters ---------- p : np.ndarray(N,) @@ -173,23 +168,25 @@ def update_square_loss(p,lambdas,T,Cs): the S Ts couplings calculated at each iteration Cs : Cs : list of S np.ndarray(ns,ns) Metric cost matrices - - Returns + + Returns ---------- C updated - - + + """ - tmpsum=np.sum([lambdas[s]*np.dot(T[s].T,Cs[s]).dot(T[s]) for s in range(len(T))]) - ppt=np.dot(p,p.T) - - return(np.divide(tmpsum,ppt)) + tmpsum = np.sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) + for s in range(len(T))]) + ppt = np.dot(p, p.T) -def update_kl_loss(p,lambdas,T,Cs): + return(np.divide(tmpsum, ppt)) + + +def update_kl_loss(p, lambdas, T, Cs): """ Updates C according to the KL Loss kernel with the S Ts couplings calculated at each iteration - - + + Parameters ---------- p : np.ndarray(N,) @@ -199,25 +196,26 @@ def update_kl_loss(p,lambdas,T,Cs): the S Ts couplings calculated at each iteration Cs : Cs : list of S np.ndarray(ns,ns) Metric cost matrices - - Returns + + Returns ---------- C updated - - + + """ - tmpsum=np.sum([lambdas[s]*np.dot(T[s].T,Cs[s]).dot(T[s]) for s in range(len(T))]) - ppt=np.dot(p,p.T) - - return(np.exp(np.divide(tmpsum,ppt))) + tmpsum = np.sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) + for s in range(len(T))]) + ppt = np.dot(p, p.T) + + return(np.exp(np.divide(tmpsum, ppt))) -def gromov_wasserstein(C1,C2,p,q,loss_fun,epsilon,numItermax = 1000, stopThr=1e-9,verbose=False, log=False): +def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr=1e-9, verbose=False, log=False): """ Returns the gromov-wasserstein coupling between the two measured similarity matrices - + (C1,p) and (C2,q) - + The function solves the following optimization problem: .. math:: @@ -228,17 +226,17 @@ def gromov_wasserstein(C1,C2,p,q,loss_fun,epsilon,numItermax = 1000, stopThr=1e- \GW^T 1= q \GW\geq 0 - + Where : - + C1 : Metric cost matrix in the source space C2 : Metric cost matrix in the target space p : distribution in the source space - q : distribution in the target space + q : distribution in the target space L : loss function to account for the misfit between the similarity matrices H : entropy - - + + Parameters ---------- C1 : np.ndarray(ns,ns) @@ -259,80 +257,80 @@ def gromov_wasserstein(C1,C2,p,q,loss_fun,epsilon,numItermax = 1000, stopThr=1e- verbose : bool, optional Print information along iterations log : bool, optional - record log if True + record log if True forcing : np.ndarray(N,2) list of forced couplings (where N is the number of forcing) - + Returns ------- T : coupling between the two spaces that minimizes : \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T)) - + """ - - C1=np.asarray(C1,dtype=np.float64) - C2=np.asarray(C2,dtype=np.float64) + C1 = np.asarray(C1, dtype=np.float64) + C2 = np.asarray(C2, dtype=np.float64) + + T = np.dot(p, q.T) # Initialization - T=np.dot(p,q.T) #Initialization - cpt = 0 - err=1 - - while (err>stopThr and cpt stopThr and cpt < numItermax): + + Tprev = T + + if loss_fun == 'square_loss': + tens = tensor_square_loss(C1, C2, T) + + elif loss_fun == 'kl_loss': + tens = tensor_kl_loss(C1, C2, T) + + T = sinkhorn(p, q, tens, epsilon) + + if cpt % 10 == 0: # we can speed up the process by checking for the error only all the 10th iterations - err=np.linalg.norm(T-Tprev) - + err = np.linalg.norm(T - Tprev) + if log: log['err'].append(err) if verbose: - if cpt%200 ==0: - print('{:5s}|{:12s}'.format('It.','Err')+'\n'+'-'*19) - print('{:5d}|{:8e}|'.format(cpt,err)) - - cpt=cpt+1 + if cpt % 200 == 0: + print('{:5s}|{:12s}'.format( + 'It.', 'Err') + '\n' + '-' * 19) + print('{:5d}|{:8e}|'.format(cpt, err)) + + cpt = cpt + 1 if log: - return T,log + return T, log else: return T -def gromov_wasserstein2(C1,C2,p,q,loss_fun,epsilon,numItermax = 1000, stopThr=1e-9,verbose=False, log=False): +def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr=1e-9, verbose=False, log=False): """ Returns the gromov-wasserstein discrepancy between the two measured similarity matrices - + (C1,p) and (C2,q) - + The function solves the following optimization problem: .. math:: \GW_Dist = \min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T)) - + Where : - + C1 : Metric cost matrix in the source space C2 : Metric cost matrix in the target space p : distribution in the source space - q : distribution in the target space + q : distribution in the target space L : loss function to account for the misfit between the similarity matrices H : entropy - - + + Parameters ---------- C1 : np.ndarray(ns,ns) @@ -353,55 +351,56 @@ def gromov_wasserstein2(C1,C2,p,q,loss_fun,epsilon,numItermax = 1000, stopThr=1e verbose : bool, optional Print information along iterations log : bool, optional - record log if True + record log if True forcing : np.ndarray(N,2) list of forced couplings (where N is the number of forcing) - + Returns ------- T : coupling between the two spaces that minimizes : \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T)) - + """ - + if log: - gw,logv=gromov_wasserstein(C1,C2,p,q,loss_fun,epsilon,numItermax,stopThr,verbose,log) + gw, logv = gromov_wasserstein( + C1, C2, p, q, loss_fun, epsilon, numItermax, stopThr, verbose, log) else: - gw=gromov_wasserstein(C1,C2,p,q,loss_fun,epsilon,numItermax,stopThr,verbose,log) + gw = gromov_wasserstein(C1, C2, p, q, loss_fun, + epsilon, numItermax, stopThr, verbose, log) + + if loss_fun == 'square_loss': + gw_dist = np.sum(gw * tensor_square_loss(C1, C2, gw)) - if loss_fun=='square_loss': - gw_dist=np.sum(gw*tensor_square_loss(C1,C2,gw)) - - - elif loss_fun=='kl_loss': - gw_dist=np.sum(gw*tensor_kl_loss(C1,C2,gw)) + elif loss_fun == 'kl_loss': + gw_dist = np.sum(gw * tensor_kl_loss(C1, C2, gw)) if log: return gw_dist, logv - else: + else: return gw_dist -def gromov_barycenters(N,Cs,ps,p,lambdas,loss_fun,epsilon,numItermax = 1000, stopThr=1e-9, verbose=False, log=False): +def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, numItermax=1000, stopThr=1e-9, verbose=False, log=False): """ Returns the gromov-wasserstein barycenters of S measured similarity matrices - + (Cs)_{s=1}^{s=S} - + The function solves the following optimization problem: .. math:: C = argmin_C\in R^NxN \sum_s \lambda_s GW(C,Cs,p,ps) - + Where : - + Cs : metric cost matrix ps : distribution - + Parameters ---------- - N : Integer + N : Integer Size of the targeted barycenter Cs : list of S np.ndarray(ns,ns) Metric cost matrices @@ -422,61 +421,58 @@ def gromov_barycenters(N,Cs,ps,p,lambdas,loss_fun,epsilon,numItermax = 1000, sto verbose : bool, optional Print information along iterations log : bool, optional - record log if True - + record log if True + Returns ------- C : Similarity matrix in the barycenter space (permutated arbitrarily) - + """ - - - S=len(Cs) - - Cs=[np.asarray(Cs[s],dtype=np.float64) for s in range(S)] - lambdas=np.asarray(lambdas,dtype=np.float64) - - T=[0 for s in range(S)] - - #Initialization of C : random SPD matrix - xalea=np.random.randn(N,2) - C=dist(xalea,xalea) - C/=C.max() - - cpt=0 - err=1 - - error=[] - - while(err>stopThr and cpt stopThr and cpt < numItermax): + + Cprev = C + + T = [gromov_wasserstein(Cs[s], C, ps[s], p, loss_fun, epsilon, + numItermax, 1e-5, verbose, log) for s in range(S)] + + if loss_fun == 'square_loss': + C = update_square_loss(p, lambdas, T, Cs) + + elif loss_fun == 'kl_loss': + C = update_kl_loss(p, lambdas, T, Cs) + + if cpt % 10 == 0: # we can speed up the process by checking for the error only all the 10th iterations - err=np.linalg.norm(C-Cprev) + err = np.linalg.norm(C - Cprev) error.append(err) - + if log: log['err'].append(err) if verbose: - if cpt%200 ==0: - print('{:5s}|{:12s}'.format('It.','Err')+'\n'+'-'*19) - print('{:5d}|{:8e}|'.format(cpt,err)) - - cpt=cpt+1 - - return C - + if cpt % 200 == 0: + print('{:5s}|{:12s}'.format( + 'It.', 'Err') + '\n' + '-' * 19) + print('{:5d}|{:8e}|'.format(cpt, err)) + cpt = cpt + 1 - \ No newline at end of file + return C -- cgit v1.2.3 From a29e22db4772ebc4a8266c917e2e662f624c6baa Mon Sep 17 00:00:00 2001 From: Slasnista Date: Tue, 29 Aug 2017 09:05:01 +0200 Subject: addressed AG comments + adding random seed --- examples/da/plot_otda_classes.py | 2 ++ examples/da/plot_otda_color_images.py | 3 ++- examples/da/plot_otda_d2.py | 14 ++++++++------ examples/da/plot_otda_mapping.py | 14 +++++++------- examples/da/plot_otda_mapping_colors_images.py | 2 ++ 5 files changed, 21 insertions(+), 14 deletions(-) diff --git a/examples/da/plot_otda_classes.py b/examples/da/plot_otda_classes.py index e5c82fb..6870fa4 100644 --- a/examples/da/plot_otda_classes.py +++ b/examples/da/plot_otda_classes.py @@ -15,8 +15,10 @@ approaches currently supported in POT. # License: MIT License import matplotlib.pylab as pl +import numpy as np import ot +np.random.seed(42) # number of source and target points to generate ns = 150 diff --git a/examples/da/plot_otda_color_images.py b/examples/da/plot_otda_color_images.py index bca7350..805d0b0 100644 --- a/examples/da/plot_otda_color_images.py +++ b/examples/da/plot_otda_color_images.py @@ -20,9 +20,10 @@ SIAM Journal on Imaging Sciences, 7(3), 1853-1882. import numpy as np from scipy import ndimage import matplotlib.pylab as pl - import ot +np.random.seed(42) + def im2mat(I): """Converts and image to matrix (one pixel per line)""" diff --git a/examples/da/plot_otda_d2.py b/examples/da/plot_otda_d2.py index 1d2192f..8833eb2 100644 --- a/examples/da/plot_otda_d2.py +++ b/examples/da/plot_otda_d2.py @@ -19,17 +19,19 @@ of what the transport methods are doing. # License: MIT License import matplotlib.pylab as pl +import numpy as np import ot -# number of source and target points to generate -ns = 150 -nt = 150 +np.random.seed(42) -Xs, ys = ot.datasets.get_data_classif('3gauss', ns) -Xt, yt = ot.datasets.get_data_classif('3gauss2', nt) +n_samples_source = 150 +n_samples_target = 150 + +Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source) +Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target) # Cost matrix -M = ot.dist(Xs, Xt) +M = ot.dist(Xs, Xt, metric='sqeuclidean') # Instantiate the different transport algorithms and fit them diff --git a/examples/da/plot_otda_mapping.py b/examples/da/plot_otda_mapping.py index 6d83507..aea7f09 100644 --- a/examples/da/plot_otda_mapping.py +++ b/examples/da/plot_otda_mapping.py @@ -23,7 +23,7 @@ import matplotlib.pylab as pl import ot -np.random.seed(0) +np.random.seed(42) ############################################################################## # generate @@ -31,10 +31,11 @@ np.random.seed(0) n = 100 # nb samples in source and target datasets theta = 2 * np.pi / 20 -nz = 0.1 -Xs, ys = ot.datasets.get_data_classif('gaussrot', n, nz=nz) -Xs_new, _ = ot.datasets.get_data_classif('gaussrot', n, nz=nz) -Xt, yt = ot.datasets.get_data_classif('gaussrot', n, theta=theta, nz=nz) +noise_level = 0.1 +Xs, ys = ot.datasets.get_data_classif('gaussrot', n, nz=noise_level) +Xs_new, _ = ot.datasets.get_data_classif('gaussrot', n, nz=noise_level) +Xt, yt = ot.datasets.get_data_classif( + 'gaussrot', n, theta=theta, nz=noise_level) # one of the target mode changes its variance (no linear mapping) Xt[yt == 2] *= 3 @@ -46,8 +47,7 @@ ot_mapping_linear = ot.da.MappingTransport( kernel="linear", mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True) -ot_mapping_linear.fit( - Xs=Xs, Xt=Xt) +ot_mapping_linear.fit(Xs=Xs, Xt=Xt) # for original source samples, transform applies barycentric mapping transp_Xs_linear = ot_mapping_linear.transform(Xs=Xs) diff --git a/examples/da/plot_otda_mapping_colors_images.py b/examples/da/plot_otda_mapping_colors_images.py index 4209020..6c024ea 100644 --- a/examples/da/plot_otda_mapping_colors_images.py +++ b/examples/da/plot_otda_mapping_colors_images.py @@ -23,6 +23,8 @@ from scipy import ndimage import matplotlib.pylab as pl import ot +np.random.seed(42) + def im2mat(I): """Converts and image to matrix (one pixel per line)""" -- cgit v1.2.3 From 65de6fc9add57b95b8968e1e75fe1af342f81d01 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Tue, 29 Aug 2017 13:34:34 +0200 Subject: pass on examples | introduced RandomState --- examples/da/plot_otda_classes.py | 20 +++++++++++++------- examples/da/plot_otda_color_images.py | 19 ++++++++++++++++--- examples/da/plot_otda_d2.py | 12 ++++++++++-- examples/da/plot_otda_mapping.py | 21 ++++++++++++++------- examples/da/plot_otda_mapping_colors_images.py | 9 ++++++--- 5 files changed, 59 insertions(+), 22 deletions(-) diff --git a/examples/da/plot_otda_classes.py b/examples/da/plot_otda_classes.py index 6870fa4..ec57a37 100644 --- a/examples/da/plot_otda_classes.py +++ b/examples/da/plot_otda_classes.py @@ -15,19 +15,23 @@ approaches currently supported in POT. # License: MIT License import matplotlib.pylab as pl -import numpy as np import ot -np.random.seed(42) -# number of source and target points to generate -ns = 150 -nt = 150 +############################################################################## +# generate data +############################################################################## + +n_source_samples = 150 +n_target_samples = 150 + +Xs, ys = ot.datasets.get_data_classif('3gauss', n_source_samples) +Xt, yt = ot.datasets.get_data_classif('3gauss2', n_target_samples) -Xs, ys = ot.datasets.get_data_classif('3gauss', ns) -Xt, yt = ot.datasets.get_data_classif('3gauss2', nt) +############################################################################## # Instantiate the different transport algorithms and fit them +############################################################################## # EMD Transport ot_emd = ot.da.EMDTransport() @@ -52,6 +56,7 @@ transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs) transp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs) + ############################################################################## # Fig 1 : plots source and target samples ############################################################################## @@ -72,6 +77,7 @@ pl.legend(loc=0) pl.title('Target samples') pl.tight_layout() + ############################################################################## # Fig 2 : plot optimal couplings and transported samples ############################################################################## diff --git a/examples/da/plot_otda_color_images.py b/examples/da/plot_otda_color_images.py index 805d0b0..3984afb 100644 --- a/examples/da/plot_otda_color_images.py +++ b/examples/da/plot_otda_color_images.py @@ -22,7 +22,8 @@ from scipy import ndimage import matplotlib.pylab as pl import ot -np.random.seed(42) + +r = np.random.RandomState(42) def im2mat(I): @@ -39,6 +40,10 @@ def minmax(I): return np.clip(I, 0, 1) +############################################################################## +# generate data +############################################################################## + # Loading images I1 = ndimage.imread('../../data/ocean_day.jpg').astype(np.float64) / 256 I2 = ndimage.imread('../../data/ocean_sunset.jpg').astype(np.float64) / 256 @@ -48,12 +53,17 @@ X2 = im2mat(I2) # training samples nb = 1000 -idx1 = np.random.randint(X1.shape[0], size=(nb,)) -idx2 = np.random.randint(X2.shape[0], size=(nb,)) +idx1 = r.randint(X1.shape[0], size=(nb,)) +idx2 = r.randint(X2.shape[0], size=(nb,)) Xs = X1[idx1, :] Xt = X2[idx2, :] + +############################################################################## +# Instantiate the different transport algorithms and fit them +############################################################################## + # EMDTransport ot_emd = ot.da.EMDTransport() ot_emd.fit(Xs=Xs, Xt=Xt) @@ -75,6 +85,7 @@ I2t = minmax(mat2im(transp_Xt_emd, I2.shape)) I1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape)) + ############################################################################## # plot original image ############################################################################## @@ -91,6 +102,7 @@ pl.imshow(I2) pl.axis('off') pl.title('Image 2') + ############################################################################## # scatter plot of colors ############################################################################## @@ -112,6 +124,7 @@ pl.ylabel('Blue') pl.title('Image 2') pl.tight_layout() + ############################################################################## # plot new images ############################################################################## diff --git a/examples/da/plot_otda_d2.py b/examples/da/plot_otda_d2.py index 8833eb2..3daa0a6 100644 --- a/examples/da/plot_otda_d2.py +++ b/examples/da/plot_otda_d2.py @@ -19,10 +19,12 @@ of what the transport methods are doing. # License: MIT License import matplotlib.pylab as pl -import numpy as np import ot -np.random.seed(42) + +############################################################################## +# generate data +############################################################################## n_samples_source = 150 n_samples_target = 150 @@ -33,7 +35,10 @@ Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target) # Cost matrix M = ot.dist(Xs, Xt, metric='sqeuclidean') + +############################################################################## # Instantiate the different transport algorithms and fit them +############################################################################## # EMD Transport ot_emd = ot.da.EMDTransport() @@ -52,6 +57,7 @@ transp_Xs_emd = ot_emd.transform(Xs=Xs) transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs) + ############################################################################## # Fig 1 : plots source and target samples + matrix of pairwise distance ############################################################################## @@ -78,6 +84,7 @@ pl.yticks([]) pl.title('Matrix of pairwise distances') pl.tight_layout() + ############################################################################## # Fig 2 : plots optimal couplings for the different methods ############################################################################## @@ -127,6 +134,7 @@ pl.yticks([]) pl.title('Main coupling coefficients\nSinkhornLpl1Transport') pl.tight_layout() + ############################################################################## # Fig 3 : plot transported samples ############################################################################## diff --git a/examples/da/plot_otda_mapping.py b/examples/da/plot_otda_mapping.py index aea7f09..09d2cb4 100644 --- a/examples/da/plot_otda_mapping.py +++ b/examples/da/plot_otda_mapping.py @@ -23,25 +23,31 @@ import matplotlib.pylab as pl import ot -np.random.seed(42) - ############################################################################## -# generate +# generate data ############################################################################## -n = 100 # nb samples in source and target datasets +n_source_samples = 100 +n_target_samples = 100 theta = 2 * np.pi / 20 noise_level = 0.1 -Xs, ys = ot.datasets.get_data_classif('gaussrot', n, nz=noise_level) -Xs_new, _ = ot.datasets.get_data_classif('gaussrot', n, nz=noise_level) + +Xs, ys = ot.datasets.get_data_classif( + 'gaussrot', n_source_samples, nz=noise_level) +Xs_new, _ = ot.datasets.get_data_classif( + 'gaussrot', n_source_samples, nz=noise_level) Xt, yt = ot.datasets.get_data_classif( - 'gaussrot', n, theta=theta, nz=noise_level) + 'gaussrot', n_target_samples, theta=theta, nz=noise_level) # one of the target mode changes its variance (no linear mapping) Xt[yt == 2] *= 3 Xt = Xt + 4 +############################################################################## +# Instantiate the different transport algorithms and fit them +############################################################################## + # MappingTransport with linear kernel ot_mapping_linear = ot.da.MappingTransport( kernel="linear", mu=1e0, eta=1e-8, bias=True, @@ -80,6 +86,7 @@ pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') pl.legend(loc=0) pl.title('Source and target distributions') + ############################################################################## # plot transported samples ############################################################################## diff --git a/examples/da/plot_otda_mapping_colors_images.py b/examples/da/plot_otda_mapping_colors_images.py index 6c024ea..a628b05 100644 --- a/examples/da/plot_otda_mapping_colors_images.py +++ b/examples/da/plot_otda_mapping_colors_images.py @@ -23,7 +23,7 @@ from scipy import ndimage import matplotlib.pylab as pl import ot -np.random.seed(42) +r = np.random.RandomState(42) def im2mat(I): @@ -54,8 +54,8 @@ X2 = im2mat(I2) # training samples nb = 1000 -idx1 = np.random.randint(X1.shape[0], size=(nb,)) -idx2 = np.random.randint(X2.shape[0], size=(nb,)) +idx1 = r.randint(X1.shape[0], size=(nb,)) +idx2 = r.randint(X2.shape[0], size=(nb,)) Xs = X1[idx1, :] Xt = X2[idx2, :] @@ -91,6 +91,7 @@ ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt) X1tn = ot_mapping_gaussian.transform(Xs=X1) # use the estimated mapping Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape)) + ############################################################################## # plot original images ############################################################################## @@ -107,6 +108,7 @@ pl.axis('off') pl.title('Image 2') pl.tight_layout() + ############################################################################## # plot pixel values distribution ############################################################################## @@ -128,6 +130,7 @@ pl.ylabel('Blue') pl.title('Image 2') pl.tight_layout() + ############################################################################## # plot transformed images ############################################################################## -- cgit v1.2.3 From 3cecc18f53453e79ccc00484e6cbfaa5643f269c Mon Sep 17 00:00:00 2001 From: aje Date: Tue, 29 Aug 2017 15:38:11 +0200 Subject: Changes to LP solver: - Allow to modify the maximal number of iterations - Display an error message in the python console if the solver encountered an issue --- ot/da.py | 4 ++-- ot/lp/EMD.h | 7 ++++++- ot/lp/EMD_wrapper.cpp | 7 +++---- ot/lp/__init__.py | 16 ++++++++++------ ot/lp/emd_wrap.pyx | 25 ++++++++++++++++++++----- 5 files changed, 41 insertions(+), 18 deletions(-) diff --git a/ot/da.py b/ot/da.py index 78dc150..0dfd02f 100644 --- a/ot/da.py +++ b/ot/da.py @@ -658,7 +658,7 @@ class OTDA(object): self.metric = metric self.computed = False - def fit(self, xs, xt, ws=None, wt=None, norm=None): + def fit(self, xs, xt, ws=None, wt=None, norm=None, numItermax=10000): """Fit domain adaptation between samples is xs and xt (with optional weights)""" self.xs = xs @@ -674,7 +674,7 @@ class OTDA(object): self.M = dist(xs, xt, metric=self.metric) self.normalizeM(norm) - self.G = emd(ws, wt, self.M) + self.G = emd(ws, wt, self.M, numItermax) self.computed = True def interp(self, direction=1): diff --git a/ot/lp/EMD.h b/ot/lp/EMD.h index 40d7192..956830c 100644 --- a/ot/lp/EMD.h +++ b/ot/lp/EMD.h @@ -23,7 +23,12 @@ using namespace lemon; typedef unsigned int node_id_type; +enum ProblemType { + INFEASIBLE, + OPTIMAL, + UNBOUNDED +}; -void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost); +int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int numItermax); #endif diff --git a/ot/lp/EMD_wrapper.cpp b/ot/lp/EMD_wrapper.cpp index cad4750..83ed56c 100644 --- a/ot/lp/EMD_wrapper.cpp +++ b/ot/lp/EMD_wrapper.cpp @@ -15,11 +15,10 @@ #include "EMD.h" -void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost) { +int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int numItermax) { // beware M and C anre strored in row major C style!!! int n, m, i,cur; double max; - int max_iter=10000; typedef FullBipartiteDigraph Digraph; DIGRAPH_TYPEDEFS(FullBipartiteDigraph); @@ -46,7 +45,7 @@ void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double * std::vector indI(n), indJ(m); std::vector weights1(n), weights2(m); Digraph di(n, m); - NetworkSimplexSimple net(di, true, n+m, n*m,max_iter); + NetworkSimplexSimple net(di, true, n+m, n*m, numItermax); // Set supply and demand, don't account for 0 values (faster) @@ -116,5 +115,5 @@ void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double * }; - + return ret; } diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 6e0bdb8..62afe76 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -15,7 +15,7 @@ import multiprocessing -def emd(a, b, M): +def emd(a, b, M, numItermax=10000): """Solves the Earth Movers distance problem and returns the OT matrix @@ -40,6 +40,8 @@ def emd(a, b, M): Target histogram (uniform weigth if empty list) M : (ns,nt) ndarray, float64 loss matrix + numItermax : int + Maximum number of iterations made by the LP solver. Returns ------- @@ -84,9 +86,9 @@ def emd(a, b, M): if len(b) == 0: b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1] - return emd_c(a, b, M) + return emd_c(a, b, M, numItermax) -def emd2(a, b, M,processes=multiprocessing.cpu_count()): +def emd2(a, b, M, numItermax=10000, processes=multiprocessing.cpu_count()): """Solves the Earth Movers distance problem and returns the loss .. math:: @@ -110,6 +112,8 @@ def emd2(a, b, M,processes=multiprocessing.cpu_count()): Target histogram (uniform weigth if empty list) M : (ns,nt) ndarray, float64 loss matrix + numItermax : int + Maximum number of iterations made by the LP solver. Returns ------- @@ -155,12 +159,12 @@ def emd2(a, b, M,processes=multiprocessing.cpu_count()): b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1] if len(b.shape)==1: - return emd2_c(a, b, M) + return emd2_c(a, b, M, numItermax) else: nb=b.shape[1] - #res=[emd2_c(a,b[:,i].copy(),M) for i in range(nb)] + #res=[emd2_c(a,b[:,i].copy(),M, numItermax) for i in range(nb)] def f(b): - return emd2_c(a,b,M) + return emd2_c(a,b,M, numItermax) res= parmap(f, [b[:,i] for i in range(nb)],processes) return np.array(res) diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 46c96c1..ed8c416 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -15,13 +15,14 @@ cimport cython cdef extern from "EMD.h": - void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost) + int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int numItermax) + cdef enum ProblemType: INFEASIBLE, OPTIMAL, UNBOUNDED @cython.boundscheck(False) @cython.wraparound(False) -def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M): +def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M, int numItermax): """ Solves the Earth Movers distance problem and returns the optimal transport matrix @@ -48,6 +49,8 @@ def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mod target histogram M : (ns,nt) ndarray, float64 loss matrix + numItermax : int + Maximum number of iterations made by the LP solver. Returns @@ -69,13 +72,18 @@ def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mod b=np.ones((n2,))/n2 # calling the function - EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost) + cdef int resultSolver = EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost, numItermax) + if resultSolver != OPTIMAL: + if resultSolver == INFEASIBLE: + print("Problem infeasible. Try to inscrease numItermax.") + elif resultSolver == UNBOUNDED: + print("Problem unbounded") return G @cython.boundscheck(False) @cython.wraparound(False) -def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M): +def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M, int numItermax): """ Solves the Earth Movers distance problem and returns the optimal transport loss @@ -102,6 +110,8 @@ def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mo target histogram M : (ns,nt) ndarray, float64 loss matrix + numItermax : int + Maximum number of iterations made by the LP solver. Returns @@ -123,7 +133,12 @@ def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mo b=np.ones((n2,))/n2 # calling the function - EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost) + cdef int resultSolver = EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost, numItermax) + if resultSolver != OPTIMAL: + if resultSolver == INFEASIBLE: + print("Problem infeasible. Try to inscrease numItermax.") + elif resultSolver == UNBOUNDED: + print("Problem unbounded") cost=0 for i in range(n1): -- cgit v1.2.3 From 99c0a24c6467631b326838667998ba4f219d8a24 Mon Sep 17 00:00:00 2001 From: aje Date: Tue, 29 Aug 2017 16:53:06 +0200 Subject: Fix param order --- ot/lp/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 62afe76..5143a70 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -88,7 +88,7 @@ def emd(a, b, M, numItermax=10000): return emd_c(a, b, M, numItermax) -def emd2(a, b, M, numItermax=10000, processes=multiprocessing.cpu_count()): +def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=10000): """Solves the Earth Movers distance problem and returns the loss .. math:: -- cgit v1.2.3 From 308ce24b705dfad9d058138d058da8b18002e081 Mon Sep 17 00:00:00 2001 From: aje Date: Tue, 29 Aug 2017 17:01:17 +0200 Subject: Type print --- ot/lp/emd_wrap.pyx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index ed8c416..6039e1f 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -75,7 +75,7 @@ def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mod cdef int resultSolver = EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost, numItermax) if resultSolver != OPTIMAL: if resultSolver == INFEASIBLE: - print("Problem infeasible. Try to inscrease numItermax.") + print("Problem infeasible. Try to increase numItermax.") elif resultSolver == UNBOUNDED: print("Problem unbounded") -- cgit v1.2.3 From 982f36cb0a5f3a6a14454238a26053de7251b0f0 Mon Sep 17 00:00:00 2001 From: aje Date: Wed, 30 Aug 2017 09:56:37 +0200 Subject: Changes: - Rename numItermax to max_iter - Default value to 100000 instead of 10000 - Add max_iter to class SinkhornTransport(BaseTransport) - Add norm to all BaseTransport --- ot/da.py | 64 ++++++++++++++++++++++++++++++++++------ ot/lp/EMD.h | 2 +- ot/lp/EMD_wrapper.cpp | 4 +-- ot/lp/__init__.py | 46 ++++++++++++++--------------- ot/lp/emd_wrap.pyx | 82 ++++++++++++++++++++++++++------------------------- 5 files changed, 123 insertions(+), 75 deletions(-) diff --git a/ot/da.py b/ot/da.py index 0dfd02f..5871aba 100644 --- a/ot/da.py +++ b/ot/da.py @@ -658,7 +658,7 @@ class OTDA(object): self.metric = metric self.computed = False - def fit(self, xs, xt, ws=None, wt=None, norm=None, numItermax=10000): + def fit(self, xs, xt, ws=None, wt=None, norm=None, max_iter=100000): """Fit domain adaptation between samples is xs and xt (with optional weights)""" self.xs = xs @@ -674,7 +674,7 @@ class OTDA(object): self.M = dist(xs, xt, metric=self.metric) self.normalizeM(norm) - self.G = emd(ws, wt, self.M, numItermax) + self.G = emd(ws, wt, self.M, max_iter) self.computed = True def interp(self, direction=1): @@ -1001,6 +1001,7 @@ class BaseTransport(BaseEstimator): # pairwise distance self.cost_ = dist(Xs, Xt, metric=self.metric) + self.normalizeCost_(self.norm) if (ys is not None) and (yt is not None): @@ -1182,6 +1183,26 @@ class BaseTransport(BaseEstimator): return transp_Xt + def normalizeCost_(self, norm): + """ Apply normalization to the loss matrix + + + Parameters + ---------- + norm : str + type of normalization from 'median','max','log','loglog' + + """ + + if norm == "median": + self.cost_ /= float(np.median(self.cost_)) + elif norm == "max": + self.cost_ /= float(np.max(self.cost_)) + elif norm == "log": + self.cost_ = np.log(1 + self.cost_) + elif norm == "loglog": + self.cost_ = np.log(1 + np.log(1 + self.cost_)) + class SinkhornTransport(BaseTransport): """Domain Adapatation OT method based on Sinkhorn Algorithm @@ -1202,6 +1223,9 @@ class SinkhornTransport(BaseTransport): be transported from a domain to another one. metric : string, optional (default="sqeuclidean") The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. distribution : string, optional (default="uniform") The kind of distribution estimation to employ verbose : int, optional (default=0) @@ -1231,7 +1255,7 @@ class SinkhornTransport(BaseTransport): def __init__(self, reg_e=1., max_iter=1000, tol=10e-9, verbose=False, log=False, - metric="sqeuclidean", + metric="sqeuclidean", norm=None, distribution_estimation=distribution_estimation_uniform, out_of_sample_map='ferradans', limit_max=np.infty): @@ -1241,6 +1265,7 @@ class SinkhornTransport(BaseTransport): self.verbose = verbose self.log = log self.metric = metric + self.norm = norm self.limit_max = limit_max self.distribution_estimation = distribution_estimation self.out_of_sample_map = out_of_sample_map @@ -1296,6 +1321,9 @@ class EMDTransport(BaseTransport): be transported from a domain to another one. metric : string, optional (default="sqeuclidean") The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. distribution : string, optional (default="uniform") The kind of distribution estimation to employ verbose : int, optional (default=0) @@ -1306,6 +1334,9 @@ class EMDTransport(BaseTransport): Controls the semi supervised mode. Transport between labeled source and target samples of different classes will exhibit an infinite cost (10 times the maximum value of the cost matrix) + max_iter : int, optional (default=100000) + The maximum number of iterations before stopping the optimization + algorithm if it has not converged. Attributes ---------- @@ -1319,14 +1350,17 @@ class EMDTransport(BaseTransport): on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 """ - def __init__(self, metric="sqeuclidean", + def __init__(self, metric="sqeuclidean", norm=None, distribution_estimation=distribution_estimation_uniform, - out_of_sample_map='ferradans', limit_max=10): + out_of_sample_map='ferradans', limit_max=10, + max_iter=100000): self.metric = metric + self.norm = norm self.limit_max = limit_max self.distribution_estimation = distribution_estimation self.out_of_sample_map = out_of_sample_map + self.max_iter = max_iter def fit(self, Xs, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples @@ -1353,7 +1387,7 @@ class EMDTransport(BaseTransport): # coupling estimation self.coupling_ = emd( - a=self.mu_s, b=self.mu_t, M=self.cost_, + a=self.mu_s, b=self.mu_t, M=self.cost_, max_iter=self.max_iter ) return self @@ -1376,6 +1410,9 @@ class SinkhornLpl1Transport(BaseTransport): be transported from a domain to another one. metric : string, optional (default="sqeuclidean") The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. distribution : string, optional (default="uniform") The kind of distribution estimation to employ max_iter : int, float, optional (default=10) @@ -1410,7 +1447,7 @@ class SinkhornLpl1Transport(BaseTransport): def __init__(self, reg_e=1., reg_cl=0.1, max_iter=10, max_inner_iter=200, tol=10e-9, verbose=False, - metric="sqeuclidean", + metric="sqeuclidean", norm=None, distribution_estimation=distribution_estimation_uniform, out_of_sample_map='ferradans', limit_max=np.infty): @@ -1421,6 +1458,7 @@ class SinkhornLpl1Transport(BaseTransport): self.tol = tol self.verbose = verbose self.metric = metric + self.norm = norm self.distribution_estimation = distribution_estimation self.out_of_sample_map = out_of_sample_map self.limit_max = limit_max @@ -1477,6 +1515,9 @@ class SinkhornL1l2Transport(BaseTransport): be transported from a domain to another one. metric : string, optional (default="sqeuclidean") The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. distribution : string, optional (default="uniform") The kind of distribution estimation to employ max_iter : int, float, optional (default=10) @@ -1516,7 +1557,7 @@ class SinkhornL1l2Transport(BaseTransport): def __init__(self, reg_e=1., reg_cl=0.1, max_iter=10, max_inner_iter=200, tol=10e-9, verbose=False, log=False, - metric="sqeuclidean", + metric="sqeuclidean", norm=None, distribution_estimation=distribution_estimation_uniform, out_of_sample_map='ferradans', limit_max=10): @@ -1528,6 +1569,7 @@ class SinkhornL1l2Transport(BaseTransport): self.verbose = verbose self.log = log self.metric = metric + self.norm = norm self.distribution_estimation = distribution_estimation self.out_of_sample_map = out_of_sample_map self.limit_max = limit_max @@ -1588,6 +1630,9 @@ class MappingTransport(BaseEstimator): Estimate linear mapping with constant bias metric : string, optional (default="sqeuclidean") The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. kernel : string, optional (default="linear") The kernel to use either linear or gaussian sigma : float, optional (default=1) @@ -1627,11 +1672,12 @@ class MappingTransport(BaseEstimator): """ def __init__(self, mu=1, eta=0.001, bias=False, metric="sqeuclidean", - kernel="linear", sigma=1, max_iter=100, tol=1e-5, + norm=None, kernel="linear", sigma=1, max_iter=100, tol=1e-5, max_inner_iter=10, inner_tol=1e-6, log=False, verbose=False, verbose2=False): self.metric = metric + self.norm = norm self.mu = mu self.eta = eta self.bias = bias diff --git a/ot/lp/EMD.h b/ot/lp/EMD.h index 956830c..aa92441 100644 --- a/ot/lp/EMD.h +++ b/ot/lp/EMD.h @@ -29,6 +29,6 @@ enum ProblemType { UNBOUNDED }; -int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int numItermax); +int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int max_iter); #endif diff --git a/ot/lp/EMD_wrapper.cpp b/ot/lp/EMD_wrapper.cpp index 83ed56c..c8c2eb3 100644 --- a/ot/lp/EMD_wrapper.cpp +++ b/ot/lp/EMD_wrapper.cpp @@ -15,7 +15,7 @@ #include "EMD.h" -int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int numItermax) { +int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int max_iter) { // beware M and C anre strored in row major C style!!! int n, m, i,cur; double max; @@ -45,7 +45,7 @@ int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *c std::vector indI(n), indJ(m); std::vector weights1(n), weights2(m); Digraph di(n, m); - NetworkSimplexSimple net(di, true, n+m, n*m, numItermax); + NetworkSimplexSimple net(di, true, n+m, n*m, max_iter); // Set supply and demand, don't account for 0 values (faster) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 5143a70..7bef648 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -14,8 +14,7 @@ from ..utils import parmap import multiprocessing - -def emd(a, b, M, numItermax=10000): +def emd(a, b, M, max_iter=100000): """Solves the Earth Movers distance problem and returns the OT matrix @@ -40,8 +39,9 @@ def emd(a, b, M, numItermax=10000): Target histogram (uniform weigth if empty list) M : (ns,nt) ndarray, float64 loss matrix - numItermax : int - Maximum number of iterations made by the LP solver. + max_iter : int, optional (default=100000) + The maximum number of iterations before stopping the optimization + algorithm if it has not converged. Returns ------- @@ -54,7 +54,7 @@ def emd(a, b, M, numItermax=10000): Simple example with obvious solution. The function emd accepts lists and perform automatic conversion to numpy arrays - + >>> import ot >>> a=[.5,.5] >>> b=[.5,.5] @@ -86,10 +86,11 @@ def emd(a, b, M, numItermax=10000): if len(b) == 0: b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1] - return emd_c(a, b, M, numItermax) + return emd_c(a, b, M, max_iter) + -def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=10000): - """Solves the Earth Movers distance problem and returns the loss +def emd2(a, b, M, processes=multiprocessing.cpu_count(), max_iter=100000): + """Solves the Earth Movers distance problem and returns the loss .. math:: \gamma = arg\min_\gamma <\gamma,M>_F @@ -112,8 +113,9 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=10000): Target histogram (uniform weigth if empty list) M : (ns,nt) ndarray, float64 loss matrix - numItermax : int - Maximum number of iterations made by the LP solver. + max_iter : int, optional (default=100000) + The maximum number of iterations before stopping the optimization + algorithm if it has not converged. Returns ------- @@ -126,15 +128,15 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=10000): Simple example with obvious solution. The function emd accepts lists and perform automatic conversion to numpy arrays - - + + >>> import ot >>> a=[.5,.5] >>> b=[.5,.5] >>> M=[[0.,1.],[1.,0.]] >>> ot.emd2(a,b,M) 0.0 - + References ---------- @@ -157,16 +159,14 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=10000): a = np.ones((M.shape[0], ), dtype=np.float64)/M.shape[0] if len(b) == 0: b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1] - - if len(b.shape)==1: - return emd2_c(a, b, M, numItermax) + + if len(b.shape) == 1: + return emd2_c(a, b, M, max_iter) else: - nb=b.shape[1] - #res=[emd2_c(a,b[:,i].copy(),M, numItermax) for i in range(nb)] + nb = b.shape[1] + # res = [emd2_c(a, b[:, i].copy(), M, max_iter) for i in range(nb)] + def f(b): - return emd2_c(a,b,M, numItermax) - res= parmap(f, [b[:,i] for i in range(nb)],processes) + return emd2_c(a, b, M, max_iter) + res = parmap(f, [b[:, i] for i in range(nb)], processes) return np.array(res) - - - \ No newline at end of file diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 6039e1f..26d3330 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -15,56 +15,57 @@ cimport cython cdef extern from "EMD.h": - int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int numItermax) + int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int max_iter) cdef enum ProblemType: INFEASIBLE, OPTIMAL, UNBOUNDED @cython.boundscheck(False) @cython.wraparound(False) -def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M, int numItermax): +def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M, int max_iter): """ Solves the Earth Movers distance problem and returns the optimal transport matrix - + gamm=emd(a,b,M) - + .. math:: - \gamma = arg\min_\gamma <\gamma,M>_F - + \gamma = arg\min_\gamma <\gamma,M>_F + s.t. \gamma 1 = a - - \gamma^T 1= b - + + \gamma^T 1= b + \gamma\geq 0 where : - + - M is the metric cost matrix - a and b are the sample weights - + Parameters ---------- a : (ns,) ndarray, float64 - source histogram + source histogram b : (nt,) ndarray, float64 target histogram M : (ns,nt) ndarray, float64 - loss matrix - numItermax : int - Maximum number of iterations made by the LP solver. - - + loss matrix + max_iter : int + The maximum number of iterations before stopping the optimization + algorithm if it has not converged. + + Returns ------- gamma: (ns x nt) ndarray Optimal transportation matrix for the given parameters - + """ cdef int n1= M.shape[0] cdef int n2= M.shape[1] cdef float cost=0 cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([n1, n2]) - + if not len(a): a=np.ones((n1,))/n1 @@ -72,7 +73,7 @@ def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mod b=np.ones((n2,))/n2 # calling the function - cdef int resultSolver = EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost, numItermax) + cdef int resultSolver = EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost, max_iter) if resultSolver != OPTIMAL: if resultSolver == INFEASIBLE: print("Problem infeasible. Try to increase numItermax.") @@ -83,49 +84,50 @@ def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mod @cython.boundscheck(False) @cython.wraparound(False) -def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M, int numItermax): +def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M, int max_iter): """ Solves the Earth Movers distance problem and returns the optimal transport loss - + gamm=emd(a,b,M) - + .. math:: - \gamma = arg\min_\gamma <\gamma,M>_F - + \gamma = arg\min_\gamma <\gamma,M>_F + s.t. \gamma 1 = a - - \gamma^T 1= b - + + \gamma^T 1= b + \gamma\geq 0 where : - + - M is the metric cost matrix - a and b are the sample weights - + Parameters ---------- a : (ns,) ndarray, float64 - source histogram + source histogram b : (nt,) ndarray, float64 target histogram M : (ns,nt) ndarray, float64 - loss matrix - numItermax : int - Maximum number of iterations made by the LP solver. - - + loss matrix + max_iter : int + The maximum number of iterations before stopping the optimization + algorithm if it has not converged. + + Returns ------- gamma: (ns x nt) ndarray Optimal transportation matrix for the given parameters - + """ cdef int n1= M.shape[0] cdef int n2= M.shape[1] cdef float cost=0 cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([n1, n2]) - + if not len(a): a=np.ones((n1,))/n1 @@ -133,13 +135,13 @@ def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mo b=np.ones((n2,))/n2 # calling the function - cdef int resultSolver = EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost, numItermax) + cdef int resultSolver = EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost, max_iter) if resultSolver != OPTIMAL: if resultSolver == INFEASIBLE: print("Problem infeasible. Try to inscrease numItermax.") elif resultSolver == UNBOUNDED: print("Problem unbounded") - + cost=0 for i in range(n1): for j in range(n2): -- cgit v1.2.3 From feef989b155c0b77a1a014e58db7db390b1ee6d4 Mon Sep 17 00:00:00 2001 From: aje Date: Wed, 30 Aug 2017 10:06:41 +0200 Subject: Rename for emd and emd2 --- ot/lp/__init__.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 7bef648..de91e74 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -14,7 +14,7 @@ from ..utils import parmap import multiprocessing -def emd(a, b, M, max_iter=100000): +def emd(a, b, M, numItermax=100000): """Solves the Earth Movers distance problem and returns the OT matrix @@ -39,7 +39,7 @@ def emd(a, b, M, max_iter=100000): Target histogram (uniform weigth if empty list) M : (ns,nt) ndarray, float64 loss matrix - max_iter : int, optional (default=100000) + numItermax : int, optional (default=100000) The maximum number of iterations before stopping the optimization algorithm if it has not converged. @@ -86,10 +86,10 @@ def emd(a, b, M, max_iter=100000): if len(b) == 0: b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1] - return emd_c(a, b, M, max_iter) + return emd_c(a, b, M, numItermax) -def emd2(a, b, M, processes=multiprocessing.cpu_count(), max_iter=100000): +def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=100000): """Solves the Earth Movers distance problem and returns the loss .. math:: @@ -113,7 +113,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), max_iter=100000): Target histogram (uniform weigth if empty list) M : (ns,nt) ndarray, float64 loss matrix - max_iter : int, optional (default=100000) + numItermax : int, optional (default=100000) The maximum number of iterations before stopping the optimization algorithm if it has not converged. @@ -161,12 +161,12 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), max_iter=100000): b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1] if len(b.shape) == 1: - return emd2_c(a, b, M, max_iter) + return emd2_c(a, b, M, numItermax) else: nb = b.shape[1] - # res = [emd2_c(a, b[:, i].copy(), M, max_iter) for i in range(nb)] + # res = [emd2_c(a, b[:, i].copy(), M, numItermax) for i in range(nb)] def f(b): - return emd2_c(a, b, M, max_iter) + return emd2_c(a, b, M, numItermax) res = parmap(f, [b[:, i] for i in range(nb)], processes) return np.array(res) -- cgit v1.2.3 From 5bbea9c608e66b499f9858af408cc65c07cf4ac2 Mon Sep 17 00:00:00 2001 From: aje Date: Wed, 30 Aug 2017 10:22:48 +0200 Subject: Fix name error --- ot/da.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/da.py b/ot/da.py index 5871aba..b4a69b1 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1387,7 +1387,7 @@ class EMDTransport(BaseTransport): # coupling estimation self.coupling_ = emd( - a=self.mu_s, b=self.mu_t, M=self.cost_, max_iter=self.max_iter + a=self.mu_s, b=self.mu_t, M=self.cost_, numItermax=self.max_iter ) return self -- cgit v1.2.3 From 0316d552fa6005aaf0f6231eb9ca20441d5a2532 Mon Sep 17 00:00:00 2001 From: aje Date: Wed, 30 Aug 2017 10:53:31 +0200 Subject: Move normalize function in utils.py --- ot/da.py | 52 ++++++---------------------------------------------- ot/utils.py | 33 +++++++++++++++++++++++++++++++++ 2 files changed, 39 insertions(+), 46 deletions(-) diff --git a/ot/da.py b/ot/da.py index b4a69b1..61a3ba0 100644 --- a/ot/da.py +++ b/ot/da.py @@ -13,7 +13,7 @@ import numpy as np from .bregman import sinkhorn from .lp import emd -from .utils import unif, dist, kernel +from .utils import unif, dist, kernel, cost_normalization from .utils import check_params, deprecated, BaseEstimator from .optim import cg from .optim import gcg @@ -673,7 +673,7 @@ class OTDA(object): self.wt = wt self.M = dist(xs, xt, metric=self.metric) - self.normalizeM(norm) + self.M = cost_normalization(self.M, norm) self.G = emd(ws, wt, self.M, max_iter) self.computed = True @@ -741,26 +741,6 @@ class OTDA(object): # aply the delta to the interpolation return xf[idx, :] + x - x0[idx, :] - def normalizeM(self, norm): - """ Apply normalization to the loss matrix - - - Parameters - ---------- - norm : str - type of normalization from 'median','max','log','loglog' - - """ - - if norm == "median": - self.M /= float(np.median(self.M)) - elif norm == "max": - self.M /= float(np.max(self.M)) - elif norm == "log": - self.M = np.log(1 + self.M) - elif norm == "loglog": - self.M = np.log(1 + np.log(1 + self.M)) - @deprecated("The class OTDA_sinkhorn is deprecated in 0.3.1 and will be" " removed in 0.5 \nUse class SinkhornTransport instead.") @@ -787,7 +767,7 @@ class OTDA_sinkhorn(OTDA): self.wt = wt self.M = dist(xs, xt, metric=self.metric) - self.normalizeM(norm) + self.M = cost_normalization(self.M, norm) self.G = sinkhorn(ws, wt, self.M, reg, **kwargs) self.computed = True @@ -816,7 +796,7 @@ class OTDA_lpl1(OTDA): self.wt = wt self.M = dist(xs, xt, metric=self.metric) - self.normalizeM(norm) + self.M = cost_normalization(self.M, norm) self.G = sinkhorn_lpl1_mm(ws, ys, wt, self.M, reg, eta, **kwargs) self.computed = True @@ -845,7 +825,7 @@ class OTDA_l1l2(OTDA): self.wt = wt self.M = dist(xs, xt, metric=self.metric) - self.normalizeM(norm) + self.M = cost_normalization(self.M, norm) self.G = sinkhorn_l1l2_gl(ws, ys, wt, self.M, reg, eta, **kwargs) self.computed = True @@ -1001,7 +981,7 @@ class BaseTransport(BaseEstimator): # pairwise distance self.cost_ = dist(Xs, Xt, metric=self.metric) - self.normalizeCost_(self.norm) + self.cost_ = cost_normalization(self.cost_, self.norm) if (ys is not None) and (yt is not None): @@ -1183,26 +1163,6 @@ class BaseTransport(BaseEstimator): return transp_Xt - def normalizeCost_(self, norm): - """ Apply normalization to the loss matrix - - - Parameters - ---------- - norm : str - type of normalization from 'median','max','log','loglog' - - """ - - if norm == "median": - self.cost_ /= float(np.median(self.cost_)) - elif norm == "max": - self.cost_ /= float(np.max(self.cost_)) - elif norm == "log": - self.cost_ = np.log(1 + self.cost_) - elif norm == "loglog": - self.cost_ = np.log(1 + np.log(1 + self.cost_)) - class SinkhornTransport(BaseTransport): """Domain Adapatation OT method based on Sinkhorn Algorithm diff --git a/ot/utils.py b/ot/utils.py index 01f2a67..31a002b 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -134,6 +134,39 @@ def dist0(n, method='lin_square'): return res +def cost_normalization(C, norm=None): + """ Apply normalization to the loss matrix + + + Parameters + ---------- + C : np.array (n1, n2) + The cost matrix to normalize. + norm : str + type of normalization from 'median','max','log','loglog'. Any other + value do not normalize. + + + Returns + ------- + + C : np.array (n1, n2) + The input cost matrix normalized according to given norm. + + """ + + if norm == "median": + C /= float(np.median(C)) + elif norm == "max": + C /= float(np.max(C)) + elif norm == "log": + C = np.log(1 + C) + elif norm == "loglog": + C = np.log(1 + np.log(1 + C)) + + return C + + def dots(*args): """ dots function for multiple matrix multiply """ return reduce(np.dot, args) -- cgit v1.2.3 From fadaf2ab3c3844d281b22f8d5c3404c3c4cf7d97 Mon Sep 17 00:00:00 2001 From: aje Date: Wed, 30 Aug 2017 11:25:03 +0200 Subject: Move norm out of fit to init for deprecated OTDA --- ot/da.py | 21 ++++++++++----------- 1 file changed, 10 insertions(+), 11 deletions(-) diff --git a/ot/da.py b/ot/da.py index 61a3ba0..564c7b7 100644 --- a/ot/da.py +++ b/ot/da.py @@ -650,15 +650,16 @@ class OTDA(object): """ - def __init__(self, metric='sqeuclidean'): + def __init__(self, metric='sqeuclidean', norm=None): """ Class initialization""" self.xs = 0 self.xt = 0 self.G = 0 self.metric = metric + self.norm = norm self.computed = False - def fit(self, xs, xt, ws=None, wt=None, norm=None, max_iter=100000): + def fit(self, xs, xt, ws=None, wt=None, max_iter=100000): """Fit domain adaptation between samples is xs and xt (with optional weights)""" self.xs = xs @@ -673,7 +674,7 @@ class OTDA(object): self.wt = wt self.M = dist(xs, xt, metric=self.metric) - self.M = cost_normalization(self.M, norm) + self.M = cost_normalization(self.M, self.norm) self.G = emd(ws, wt, self.M, max_iter) self.computed = True @@ -752,7 +753,7 @@ class OTDA_sinkhorn(OTDA): """ - def fit(self, xs, xt, reg=1, ws=None, wt=None, norm=None, **kwargs): + def fit(self, xs, xt, reg=1, ws=None, wt=None, **kwargs): """Fit regularized domain adaptation between samples is xs and xt (with optional weights)""" self.xs = xs @@ -767,7 +768,7 @@ class OTDA_sinkhorn(OTDA): self.wt = wt self.M = dist(xs, xt, metric=self.metric) - self.M = cost_normalization(self.M, norm) + self.M = cost_normalization(self.M, self.norm) self.G = sinkhorn(ws, wt, self.M, reg, **kwargs) self.computed = True @@ -779,8 +780,7 @@ class OTDA_lpl1(OTDA): """Class for domain adaptation with optimal transport with entropic and group regularization""" - def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None, - **kwargs): + def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, **kwargs): """Fit regularized domain adaptation between samples is xs and xt (with optional weights), See ot.da.sinkhorn_lpl1_mm for fit parameters""" @@ -796,7 +796,7 @@ class OTDA_lpl1(OTDA): self.wt = wt self.M = dist(xs, xt, metric=self.metric) - self.M = cost_normalization(self.M, norm) + self.M = cost_normalization(self.M, self.norm) self.G = sinkhorn_lpl1_mm(ws, ys, wt, self.M, reg, eta, **kwargs) self.computed = True @@ -808,8 +808,7 @@ class OTDA_l1l2(OTDA): """Class for domain adaptation with optimal transport with entropic and group lasso regularization""" - def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None, - **kwargs): + def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, **kwargs): """Fit regularized domain adaptation between samples is xs and xt (with optional weights), See ot.da.sinkhorn_lpl1_gl for fit parameters""" @@ -825,7 +824,7 @@ class OTDA_l1l2(OTDA): self.wt = wt self.M = dist(xs, xt, metric=self.metric) - self.M = cost_normalization(self.M, norm) + self.M = cost_normalization(self.M, self.norm) self.G = sinkhorn_l1l2_gl(ws, ys, wt, self.M, reg, eta, **kwargs) self.computed = True -- cgit v1.2.3 From 7ccb822acd9e8072e733e4e74f25436e7b8fc16e Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 30 Aug 2017 16:22:45 +0200 Subject: add script for building gallery --- docs/nb_build | 9 +++++++++ docs/source/conf.py | 8 ++++---- 2 files changed, 13 insertions(+), 4 deletions(-) create mode 100755 docs/nb_build diff --git a/docs/nb_build b/docs/nb_build new file mode 100755 index 0000000..d0d01ff --- /dev/null +++ b/docs/nb_build @@ -0,0 +1,9 @@ +#!/bin/bash + + +# remove comment +sed -i "s/#'sphinx\_gallery/'sphinx\_gallery/" source/conf.py + + +# put comment again +sed -i "s/'sphinx\_gallery/#'sphinx\_gallery/" source/conf.py diff --git a/docs/source/conf.py b/docs/source/conf.py index ff08899..a241205 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -50,7 +50,7 @@ sys.path.insert(0, os.path.abspath("../..")) #needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be -# extensions coming with Sphinx (named 'sphinx.ext.*') or your custom +# extensions coming with Sphinx (named #'sphinx.ext.*') or your custom # ones. extensions = [ 'sphinx.ext.autodoc', @@ -62,7 +62,7 @@ extensions = [ 'sphinx.ext.ifconfig', 'sphinx.ext.viewcode', 'sphinx.ext.napoleon', -# 'sphinx_gallery.gen_gallery', + #'sphinx_gallery.gen_gallery', ] # Add any paths that contain templates here, relative to this directory. @@ -130,7 +130,7 @@ exclude_patterns = [] #show_authors = False # The name of the Pygments (syntax highlighting) style to use. -pygments_style = 'sphinx' +pygments_style = #'sphinx' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] @@ -146,7 +146,7 @@ todo_include_todos = True # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. -html_theme = 'sphinx_rtd_theme' +html_theme = #'sphinx_rtd_theme' # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the -- cgit v1.2.3 From c2a7a1f3ab4ba5c4f5adeca0fa22d8d6b4fc079d Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 30 Aug 2017 16:24:21 +0200 Subject: bug config file --- docs/nb_build | 1 + docs/source/conf.py | 4 ++-- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/docs/nb_build b/docs/nb_build index d0d01ff..e0afbcd 100755 --- a/docs/nb_build +++ b/docs/nb_build @@ -4,6 +4,7 @@ # remove comment sed -i "s/#'sphinx\_gallery/'sphinx\_gallery/" source/conf.py +make html # put comment again sed -i "s/'sphinx\_gallery/#'sphinx\_gallery/" source/conf.py diff --git a/docs/source/conf.py b/docs/source/conf.py index a241205..b9f1f9d 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -130,7 +130,7 @@ exclude_patterns = [] #show_authors = False # The name of the Pygments (syntax highlighting) style to use. -pygments_style = #'sphinx' +pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] @@ -146,7 +146,7 @@ todo_include_todos = True # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. -html_theme = #'sphinx_rtd_theme' +html_theme = 'sphinx_rtd_theme' # Theme options are theme-specific and customize the look and feel of a theme # further. 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docs/source/auto_examples/plot_optim_OTreg.rst | 76 ++++++---- docs/source/conf.py | 6 +- docs/source/readme.rst | 3 + 58 files changed, 797 insertions(+), 621 deletions(-) diff --git a/docs/nb_build b/docs/nb_build index e0afbcd..2523d71 100755 --- a/docs/nb_build +++ b/docs/nb_build @@ -3,8 +3,10 @@ # remove comment sed -i "s/#'sphinx\_gallery/'sphinx\_gallery/" source/conf.py +sed -i "s/sys.modules.update/#sys.modules.update/" source/conf.py make html # put comment again sed -i "s/'sphinx\_gallery/#'sphinx\_gallery/" source/conf.py +sed -i "s/#sys.modules.update/sys.modules.update/" source/conf.py diff --git a/docs/source/auto_examples/auto_examples_jupyter.zip b/docs/source/auto_examples/auto_examples_jupyter.zip index 7c3de28..3bb8ecf 100644 Binary files a/docs/source/auto_examples/auto_examples_jupyter.zip and b/docs/source/auto_examples/auto_examples_jupyter.zip differ diff --git a/docs/source/auto_examples/auto_examples_python.zip 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Binary files a/docs/source/auto_examples/images/thumb/sphx_glr_plot_compute_emd_thumb.png and b/docs/source/auto_examples/images/thumb/sphx_glr_plot_compute_emd_thumb.png differ diff --git a/docs/source/auto_examples/index.rst b/docs/source/auto_examples/index.rst index 1695300..b932907 100644 --- a/docs/source/auto_examples/index.rst +++ b/docs/source/auto_examples/index.rst @@ -3,7 +3,7 @@ POT Examples .. raw:: html -
+
.. only:: html @@ -23,13 +23,13 @@ POT Examples .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_WDA_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_optim_OTreg_thumb.png - :ref:`sphx_glr_auto_examples_plot_WDA.py` + :ref:`sphx_glr_auto_examples_plot_optim_OTreg.py` .. raw:: html @@ -39,17 +39,17 @@ POT Examples .. toctree:: :hidden: - /auto_examples/plot_WDA + /auto_examples/plot_optim_OTreg .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_optim_OTreg_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_2D_samples_thumb.png - :ref:`sphx_glr_auto_examples_plot_optim_OTreg.py` + :ref:`sphx_glr_auto_examples_plot_OT_2D_samples.py` .. raw:: html @@ -59,17 +59,17 @@ POT Examples .. toctree:: :hidden: - /auto_examples/plot_optim_OTreg + /auto_examples/plot_OT_2D_samples .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_2D_samples_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_compute_emd_thumb.png - :ref:`sphx_glr_auto_examples_plot_OT_2D_samples.py` + :ref:`sphx_glr_auto_examples_plot_compute_emd.py` .. raw:: html @@ -79,17 +79,17 @@ POT Examples .. toctree:: :hidden: - /auto_examples/plot_OT_2D_samples + /auto_examples/plot_compute_emd .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_compute_emd_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_WDA_thumb.png - :ref:`sphx_glr_auto_examples_plot_compute_emd.py` + :ref:`sphx_glr_auto_examples_plot_WDA.py` .. raw:: html @@ -99,17 +99,17 @@ POT Examples .. toctree:: :hidden: - /auto_examples/plot_compute_emd + /auto_examples/plot_WDA .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OTDA_color_images_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_color_images_thumb.png - :ref:`sphx_glr_auto_examples_plot_OTDA_color_images.py` + :ref:`sphx_glr_auto_examples_plot_otda_color_images.py` .. raw:: html @@ -119,7 +119,7 @@ POT Examples .. toctree:: :hidden: - /auto_examples/plot_OTDA_color_images + /auto_examples/plot_otda_color_images .. raw:: html @@ -127,9 +127,9 @@ POT Examples .. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OTDA_classes_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_barycenter_1D_thumb.png - :ref:`sphx_glr_auto_examples_plot_OTDA_classes.py` + :ref:`sphx_glr_auto_examples_plot_barycenter_1D.py` .. raw:: html @@ -139,17 +139,17 @@ POT Examples .. toctree:: :hidden: - /auto_examples/plot_OTDA_classes + /auto_examples/plot_barycenter_1D .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OTDA_2D_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_L1_vs_L2_thumb.png - :ref:`sphx_glr_auto_examples_plot_OTDA_2D.py` + :ref:`sphx_glr_auto_examples_plot_OT_L1_vs_L2.py` .. raw:: html @@ -159,17 +159,17 @@ POT Examples .. toctree:: :hidden: - /auto_examples/plot_OTDA_2D + /auto_examples/plot_OT_L1_vs_L2 .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_L1_vs_L2_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_mapping_colors_images_thumb.png - :ref:`sphx_glr_auto_examples_plot_OT_L1_vs_L2.py` + :ref:`sphx_glr_auto_examples_plot_otda_mapping_colors_images.py` .. raw:: html @@ -179,17 +179,17 @@ POT Examples .. toctree:: :hidden: - /auto_examples/plot_OT_L1_vs_L2 + /auto_examples/plot_otda_mapping_colors_images .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_barycenter_1D_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_mapping_thumb.png - :ref:`sphx_glr_auto_examples_plot_barycenter_1D.py` + :ref:`sphx_glr_auto_examples_plot_otda_mapping.py` .. raw:: html @@ -199,17 +199,17 @@ POT Examples .. toctree:: :hidden: - /auto_examples/plot_barycenter_1D + /auto_examples/plot_otda_mapping .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OTDA_mapping_color_images_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_classes_thumb.png - :ref:`sphx_glr_auto_examples_plot_OTDA_mapping_color_images.py` + :ref:`sphx_glr_auto_examples_plot_otda_classes.py` .. raw:: html @@ -219,17 +219,17 @@ POT Examples .. toctree:: :hidden: - /auto_examples/plot_OTDA_mapping_color_images + /auto_examples/plot_otda_classes .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OTDA_mapping_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_d2_thumb.png - :ref:`sphx_glr_auto_examples_plot_OTDA_mapping.py` + :ref:`sphx_glr_auto_examples_plot_otda_d2.py` .. raw:: html @@ -239,7 +239,7 @@ POT Examples .. toctree:: :hidden: - /auto_examples/plot_OTDA_mapping + /auto_examples/plot_otda_d2 .. raw:: html
diff --git a/docs/source/auto_examples/plot_OT_1D.ipynb b/docs/source/auto_examples/plot_OT_1D.ipynb index 8715b97..7d3fdd1 100644 --- a/docs/source/auto_examples/plot_OT_1D.ipynb +++ b/docs/source/auto_examples/plot_OT_1D.ipynb @@ -15,7 +15,7 @@ }, { "source": [ - "\n# 1D optimal transport\n\n\n@author: rflamary\n\n" + "\n# 1D optimal transport\n\n\n\n" ], "cell_type": "markdown", "metadata": {} @@ -24,7 +24,7 @@ "execution_count": null, "cell_type": "code", "source": [ - "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\nfrom ot.datasets import get_1D_gauss as gauss\n\n\n#%% parameters\n\nn=100 # nb bins\n\n# bin positions\nx=np.arange(n,dtype=np.float64)\n\n# Gaussian distributions\na=gauss(n,m=20,s=5) # m= mean, s= std\nb=gauss(n,m=60,s=10)\n\n# loss matrix\nM=ot.dist(x.reshape((n,1)),x.reshape((n,1)))\nM/=M.max()\n\n#%% plot the distributions\n\npl.figure(1)\npl.plot(x,a,'b',label='Source distribution')\npl.plot(x,b,'r',label='Target distribution')\npl.legend()\n\n#%% plot distributions and loss matrix\n\npl.figure(2)\not.plot.plot1D_mat(a,b,M,'Cost matrix M')\n\n#%% EMD\n\nG0=ot.emd(a,b,M)\n\npl.figure(3)\not.plot.plot1D_mat(a,b,G0,'OT matrix G0')\n\n#%% Sinkhorn\n\nlambd=1e-3\nGs=ot.sinkhorn(a,b,M,lambd,verbose=True)\n\npl.figure(4)\not.plot.plot1D_mat(a,b,Gs,'OT matrix Sinkhorn')" + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nfrom ot.datasets import get_1D_gauss as gauss\n\n#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\nb = gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\npl.plot(x, a, 'b', label='Source distribution')\npl.plot(x, b, 'r', label='Target distribution')\npl.legend()\n\n#%% plot distributions and loss matrix\n\npl.figure(2, figsize=(5, 5))\not.plot.plot1D_mat(a, b, M, 'Cost matrix M')\n\n#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')\n\n#%% Sinkhorn\n\nlambd = 1e-3\nGs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')\n\npl.show()" ], "outputs": [], "metadata": { diff --git a/docs/source/auto_examples/plot_OT_1D.py b/docs/source/auto_examples/plot_OT_1D.py index 6661aa3..0f3a26a 100644 --- a/docs/source/auto_examples/plot_OT_1D.py +++ b/docs/source/auto_examples/plot_OT_1D.py @@ -4,53 +4,57 @@ 1D optimal transport ==================== -@author: rflamary """ +# Author: Remi Flamary +# +# License: MIT License + import numpy as np import matplotlib.pylab as pl import ot from ot.datasets import get_1D_gauss as gauss - #%% parameters -n=100 # nb bins +n = 100 # nb bins # bin positions -x=np.arange(n,dtype=np.float64) +x = np.arange(n, dtype=np.float64) # Gaussian distributions -a=gauss(n,m=20,s=5) # m= mean, s= std -b=gauss(n,m=60,s=10) +a = gauss(n, m=20, s=5) # m= mean, s= std +b = gauss(n, m=60, s=10) # loss matrix -M=ot.dist(x.reshape((n,1)),x.reshape((n,1))) -M/=M.max() +M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) +M /= M.max() #%% plot the distributions -pl.figure(1) -pl.plot(x,a,'b',label='Source distribution') -pl.plot(x,b,'r',label='Target distribution') +pl.figure(1, figsize=(6.4, 3)) +pl.plot(x, a, 'b', label='Source distribution') +pl.plot(x, b, 'r', label='Target distribution') pl.legend() #%% plot distributions and loss matrix -pl.figure(2) -ot.plot.plot1D_mat(a,b,M,'Cost matrix M') +pl.figure(2, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') #%% EMD -G0=ot.emd(a,b,M) +G0 = ot.emd(a, b, M) -pl.figure(3) -ot.plot.plot1D_mat(a,b,G0,'OT matrix G0') +pl.figure(3, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') #%% Sinkhorn -lambd=1e-3 -Gs=ot.sinkhorn(a,b,M,lambd,verbose=True) +lambd = 1e-3 +Gs = ot.sinkhorn(a, b, M, lambd, verbose=True) + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn') -pl.figure(4) -ot.plot.plot1D_mat(a,b,Gs,'OT matrix Sinkhorn') +pl.show() diff --git a/docs/source/auto_examples/plot_OT_1D.rst b/docs/source/auto_examples/plot_OT_1D.rst index 44b715b..a36e13c 100644 --- a/docs/source/auto_examples/plot_OT_1D.rst +++ b/docs/source/auto_examples/plot_OT_1D.rst @@ -7,7 +7,6 @@ 1D optimal transport ==================== -@author: rflamary @@ -64,55 +63,60 @@ .. code-block:: python + # Author: Remi Flamary + # + # License: MIT License + import numpy as np import matplotlib.pylab as pl import ot from ot.datasets import get_1D_gauss as gauss - #%% parameters - n=100 # nb bins + n = 100 # nb bins # bin positions - x=np.arange(n,dtype=np.float64) + x = np.arange(n, dtype=np.float64) # Gaussian distributions - a=gauss(n,m=20,s=5) # m= mean, s= std - b=gauss(n,m=60,s=10) + a = gauss(n, m=20, s=5) # m= mean, s= std + b = gauss(n, m=60, s=10) # loss matrix - M=ot.dist(x.reshape((n,1)),x.reshape((n,1))) - M/=M.max() + M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) + M /= M.max() #%% plot the distributions - pl.figure(1) - pl.plot(x,a,'b',label='Source distribution') - pl.plot(x,b,'r',label='Target distribution') + pl.figure(1, figsize=(6.4, 3)) + pl.plot(x, a, 'b', label='Source distribution') + pl.plot(x, b, 'r', label='Target distribution') pl.legend() #%% plot distributions and loss matrix - pl.figure(2) - ot.plot.plot1D_mat(a,b,M,'Cost matrix M') + pl.figure(2, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') #%% EMD - G0=ot.emd(a,b,M) + G0 = ot.emd(a, b, M) - pl.figure(3) - ot.plot.plot1D_mat(a,b,G0,'OT matrix G0') + pl.figure(3, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') #%% Sinkhorn - lambd=1e-3 - Gs=ot.sinkhorn(a,b,M,lambd,verbose=True) + lambd = 1e-3 + Gs = ot.sinkhorn(a, b, M, lambd, verbose=True) + + pl.figure(4, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn') - pl.figure(4) - ot.plot.plot1D_mat(a,b,Gs,'OT matrix Sinkhorn') + pl.show() -**Total running time of the script:** ( 0 minutes 0.674 seconds) +**Total running time of the script:** ( 0 minutes 1.050 seconds) diff --git a/docs/source/auto_examples/plot_OT_2D_samples.ipynb b/docs/source/auto_examples/plot_OT_2D_samples.ipynb index fad0467..fc4ce50 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.ipynb +++ b/docs/source/auto_examples/plot_OT_2D_samples.ipynb @@ -15,7 +15,7 @@ }, { "source": [ - "\n# 2D Optimal transport between empirical distributions\n\n\n@author: rflamary\n\n" + "\n# 2D Optimal transport between empirical distributions\n\n\n\n" ], "cell_type": "markdown", "metadata": {} @@ -24,7 +24,7 @@ "execution_count": null, "cell_type": "code", "source": [ - "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n#%% parameters and data generation\n\nn=50 # nb samples\n\nmu_s=np.array([0,0])\ncov_s=np.array([[1,0],[0,1]])\n\nmu_t=np.array([4,4])\ncov_t=np.array([[1,-.8],[-.8,1]])\n\nxs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s)\nxt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t)\n\na,b = ot.unif(n),ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM=ot.dist(xs,xt)\nM/=M.max()\n\n#%% plot samples\n\npl.figure(1)\npl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\npl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\npl.legend(loc=0)\npl.title('Source and traget distributions')\n\npl.figure(2)\npl.imshow(M,interpolation='nearest')\npl.title('Cost matrix M')\n\n\n#%% EMD\n\nG0=ot.emd(a,b,M)\n\npl.figure(3)\npl.imshow(G0,interpolation='nearest')\npl.title('OT matrix G0')\n\npl.figure(4)\not.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1])\npl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\npl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix with samples')\n\n\n#%% sinkhorn\n\n# reg term\nlambd=5e-4\n\nGs=ot.sinkhorn(a,b,M,lambd)\n\npl.figure(5)\npl.imshow(Gs,interpolation='nearest')\npl.title('OT matrix sinkhorn')\n\npl.figure(6)\not.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1])\npl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\npl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix Sinkhorn with samples')" + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n#%% parameters and data generation\n\nn = 50 # nb samples\n\nmu_s = np.array([0, 0])\ncov_s = np.array([[1, 0], [0, 1]])\n\nmu_t = np.array([4, 4])\ncov_t = np.array([[1, -.8], [-.8, 1]])\n\nxs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s)\nxt = ot.datasets.get_2D_samples_gauss(n, mu_t, cov_t)\n\na, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples\n\n# loss matrix\nM = ot.dist(xs, xt)\nM /= M.max()\n\n#%% plot samples\n\npl.figure(1)\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('Source and target distributions')\n\npl.figure(2)\npl.imshow(M, interpolation='nearest')\npl.title('Cost matrix M')\n\n\n#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3)\npl.imshow(G0, interpolation='nearest')\npl.title('OT matrix G0')\n\npl.figure(4)\not.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix with samples')\n\n\n#%% sinkhorn\n\n# reg term\nlambd = 1e-3\n\nGs = ot.sinkhorn(a, b, M, lambd)\n\npl.figure(5)\npl.imshow(Gs, interpolation='nearest')\npl.title('OT matrix sinkhorn')\n\npl.figure(6)\not.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix Sinkhorn with samples')\n\npl.show()" ], "outputs": [], "metadata": { diff --git a/docs/source/auto_examples/plot_OT_2D_samples.py b/docs/source/auto_examples/plot_OT_2D_samples.py index edfb781..2a42dc0 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.py +++ b/docs/source/auto_examples/plot_OT_2D_samples.py @@ -4,57 +4,60 @@ 2D Optimal transport between empirical distributions ==================================================== -@author: rflamary """ +# Author: Remi Flamary +# +# License: MIT License + import numpy as np import matplotlib.pylab as pl import ot #%% parameters and data generation -n=50 # nb samples +n = 50 # nb samples -mu_s=np.array([0,0]) -cov_s=np.array([[1,0],[0,1]]) +mu_s = np.array([0, 0]) +cov_s = np.array([[1, 0], [0, 1]]) -mu_t=np.array([4,4]) -cov_t=np.array([[1,-.8],[-.8,1]]) +mu_t = np.array([4, 4]) +cov_t = np.array([[1, -.8], [-.8, 1]]) -xs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s) -xt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t) +xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s) +xt = ot.datasets.get_2D_samples_gauss(n, mu_t, cov_t) -a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples +a, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples # loss matrix -M=ot.dist(xs,xt) -M/=M.max() +M = ot.dist(xs, xt) +M /= M.max() #%% plot samples pl.figure(1) -pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') -pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') +pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.legend(loc=0) -pl.title('Source and traget distributions') +pl.title('Source and target distributions') pl.figure(2) -pl.imshow(M,interpolation='nearest') +pl.imshow(M, interpolation='nearest') pl.title('Cost matrix M') #%% EMD -G0=ot.emd(a,b,M) +G0 = ot.emd(a, b, M) pl.figure(3) -pl.imshow(G0,interpolation='nearest') +pl.imshow(G0, interpolation='nearest') pl.title('OT matrix G0') pl.figure(4) -ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1]) -pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') -pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') +ot.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1]) +pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.legend(loc=0) pl.title('OT matrix with samples') @@ -62,17 +65,19 @@ pl.title('OT matrix with samples') #%% sinkhorn # reg term -lambd=5e-4 +lambd = 1e-3 -Gs=ot.sinkhorn(a,b,M,lambd) +Gs = ot.sinkhorn(a, b, M, lambd) pl.figure(5) -pl.imshow(Gs,interpolation='nearest') +pl.imshow(Gs, interpolation='nearest') pl.title('OT matrix sinkhorn') pl.figure(6) -ot.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1]) -pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') -pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') +ot.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1]) +pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.legend(loc=0) pl.title('OT matrix Sinkhorn with samples') + +pl.show() diff --git a/docs/source/auto_examples/plot_OT_2D_samples.rst b/docs/source/auto_examples/plot_OT_2D_samples.rst index e05e591..c472c6a 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.rst +++ b/docs/source/auto_examples/plot_OT_2D_samples.rst @@ -7,7 +7,6 @@ 2D Optimal transport between empirical distributions ==================================================== -@author: rflamary @@ -46,69 +45,64 @@ :scale: 47 -.. rst-class:: sphx-glr-script-out - Out:: - - ('Warning: numerical errors at iteration', 0) - - - - -| .. code-block:: python + # Author: Remi Flamary + # + # License: MIT License + import numpy as np import matplotlib.pylab as pl import ot #%% parameters and data generation - n=50 # nb samples + n = 50 # nb samples - mu_s=np.array([0,0]) - cov_s=np.array([[1,0],[0,1]]) + mu_s = np.array([0, 0]) + cov_s = np.array([[1, 0], [0, 1]]) - mu_t=np.array([4,4]) - cov_t=np.array([[1,-.8],[-.8,1]]) + mu_t = np.array([4, 4]) + cov_t = np.array([[1, -.8], [-.8, 1]]) - xs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s) - xt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t) + xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s) + xt = ot.datasets.get_2D_samples_gauss(n, mu_t, cov_t) - a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples + a, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples # loss matrix - M=ot.dist(xs,xt) - M/=M.max() + M = ot.dist(xs, xt) + M /= M.max() #%% plot samples pl.figure(1) - pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') - pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') + pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.legend(loc=0) - pl.title('Source and traget distributions') + pl.title('Source and target distributions') pl.figure(2) - pl.imshow(M,interpolation='nearest') + pl.imshow(M, interpolation='nearest') pl.title('Cost matrix M') #%% EMD - G0=ot.emd(a,b,M) + G0 = ot.emd(a, b, M) pl.figure(3) - pl.imshow(G0,interpolation='nearest') + pl.imshow(G0, interpolation='nearest') pl.title('OT matrix G0') pl.figure(4) - ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1]) - pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') - pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') + ot.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1]) + pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.legend(loc=0) pl.title('OT matrix with samples') @@ -116,22 +110,24 @@ #%% sinkhorn # reg term - lambd=5e-4 + lambd = 1e-3 - Gs=ot.sinkhorn(a,b,M,lambd) + Gs = ot.sinkhorn(a, b, M, lambd) pl.figure(5) - pl.imshow(Gs,interpolation='nearest') + pl.imshow(Gs, interpolation='nearest') pl.title('OT matrix sinkhorn') pl.figure(6) - ot.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1]) - pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') - pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') + ot.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1]) + pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.legend(loc=0) pl.title('OT matrix Sinkhorn with samples') -**Total running time of the script:** ( 0 minutes 0.623 seconds) + pl.show() + +**Total running time of the script:** ( 0 minutes 2.908 seconds) diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb b/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb index 46283ac..04ef5c8 100644 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb @@ -15,7 +15,7 @@ }, { "source": [ - "\n# 2D Optimal transport for different metrics\n\n\nStole the figure idea from Fig. 1 and 2 in \nhttps://arxiv.org/pdf/1706.07650.pdf\n\n\n@author: rflamary\n\n" + "\n# 2D Optimal transport for different metrics\n\n\nStole the figure idea from Fig. 1 and 2 in\nhttps://arxiv.org/pdf/1706.07650.pdf\n\n\n\n" ], "cell_type": "markdown", "metadata": {} @@ -24,7 +24,7 @@ "execution_count": null, "cell_type": "code", "source": [ - "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n#%% parameters and data generation\n\nfor data in range(2):\n\n if data:\n n=20 # nb samples\n xs=np.zeros((n,2))\n xs[:,0]=np.arange(n)+1\n xs[:,1]=(np.arange(n)+1)*-0.001 # to make it strictly convex...\n \n xt=np.zeros((n,2))\n xt[:,1]=np.arange(n)+1\n else:\n \n n=50 # nb samples\n xtot=np.zeros((n+1,2))\n xtot[:,0]=np.cos((np.arange(n+1)+1.0)*0.9/(n+2)*2*np.pi)\n xtot[:,1]=np.sin((np.arange(n+1)+1.0)*0.9/(n+2)*2*np.pi)\n \n xs=xtot[:n,:]\n xt=xtot[1:,:]\n \n \n \n a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples\n \n # loss matrix\n M1=ot.dist(xs,xt,metric='euclidean')\n M1/=M1.max()\n \n # loss matrix\n M2=ot.dist(xs,xt,metric='sqeuclidean')\n M2/=M2.max()\n \n # loss matrix\n Mp=np.sqrt(ot.dist(xs,xt,metric='euclidean'))\n Mp/=Mp.max()\n \n #%% plot samples\n \n pl.figure(1+3*data)\n pl.clf()\n pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n pl.axis('equal')\n pl.title('Source and traget distributions')\n \n pl.figure(2+3*data,(15,5))\n pl.subplot(1,3,1)\n pl.imshow(M1,interpolation='nearest')\n pl.title('Eucidean cost')\n pl.subplot(1,3,2)\n pl.imshow(M2,interpolation='nearest')\n pl.title('Squared Euclidean cost')\n \n pl.subplot(1,3,3)\n pl.imshow(Mp,interpolation='nearest')\n pl.title('Sqrt Euclidean cost')\n #%% EMD\n \n G1=ot.emd(a,b,M1)\n G2=ot.emd(a,b,M2)\n Gp=ot.emd(a,b,Mp)\n \n pl.figure(3+3*data,(15,5))\n \n pl.subplot(1,3,1)\n ot.plot.plot2D_samples_mat(xs,xt,G1,c=[.5,.5,1])\n pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n pl.axis('equal')\n #pl.legend(loc=0)\n pl.title('OT Euclidean')\n \n pl.subplot(1,3,2)\n \n ot.plot.plot2D_samples_mat(xs,xt,G2,c=[.5,.5,1])\n pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n pl.axis('equal')\n #pl.legend(loc=0)\n pl.title('OT squared Euclidean')\n \n pl.subplot(1,3,3)\n \n ot.plot.plot2D_samples_mat(xs,xt,Gp,c=[.5,.5,1])\n pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n pl.axis('equal')\n #pl.legend(loc=0)\n pl.title('OT sqrt Euclidean')" + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n#%% parameters and data generation\n\nfor data in range(2):\n\n if data:\n n = 20 # nb samples\n xs = np.zeros((n, 2))\n xs[:, 0] = np.arange(n) + 1\n xs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex...\n\n xt = np.zeros((n, 2))\n xt[:, 1] = np.arange(n) + 1\n else:\n\n n = 50 # nb samples\n xtot = np.zeros((n + 1, 2))\n xtot[:, 0] = np.cos(\n (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)\n xtot[:, 1] = np.sin(\n (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)\n\n xs = xtot[:n, :]\n xt = xtot[1:, :]\n\n a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples\n\n # loss matrix\n M1 = ot.dist(xs, xt, metric='euclidean')\n M1 /= M1.max()\n\n # loss matrix\n M2 = ot.dist(xs, xt, metric='sqeuclidean')\n M2 /= M2.max()\n\n # loss matrix\n Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))\n Mp /= Mp.max()\n\n #%% plot samples\n\n pl.figure(1 + 3 * data, figsize=(7, 3))\n pl.clf()\n pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n pl.axis('equal')\n pl.title('Source and traget distributions')\n\n pl.figure(2 + 3 * data, figsize=(7, 3))\n\n pl.subplot(1, 3, 1)\n pl.imshow(M1, interpolation='nearest')\n pl.title('Euclidean cost')\n\n pl.subplot(1, 3, 2)\n pl.imshow(M2, interpolation='nearest')\n pl.title('Squared Euclidean cost')\n\n pl.subplot(1, 3, 3)\n pl.imshow(Mp, interpolation='nearest')\n pl.title('Sqrt Euclidean cost')\n pl.tight_layout()\n\n #%% EMD\n G1 = ot.emd(a, b, M1)\n G2 = ot.emd(a, b, M2)\n Gp = ot.emd(a, b, Mp)\n\n pl.figure(3 + 3 * data, figsize=(7, 3))\n\n pl.subplot(1, 3, 1)\n ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\n pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n pl.axis('equal')\n # pl.legend(loc=0)\n pl.title('OT Euclidean')\n\n pl.subplot(1, 3, 2)\n ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\n pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n pl.axis('equal')\n # pl.legend(loc=0)\n pl.title('OT squared Euclidean')\n\n pl.subplot(1, 3, 3)\n ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\n pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n pl.axis('equal')\n # pl.legend(loc=0)\n pl.title('OT sqrt Euclidean')\n pl.tight_layout()\n\npl.show()" ], "outputs": [], "metadata": { diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.py b/docs/source/auto_examples/plot_OT_L1_vs_L2.py index 9bb92fe..dfc9462 100644 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.py +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.py @@ -4,13 +4,16 @@ 2D Optimal transport for different metrics ========================================== -Stole the figure idea from Fig. 1 and 2 in +Stole the figure idea from Fig. 1 and 2 in https://arxiv.org/pdf/1706.07650.pdf -@author: rflamary """ +# Author: Remi Flamary +# +# License: MIT License + import numpy as np import matplotlib.pylab as pl import ot @@ -20,89 +23,93 @@ import ot for data in range(2): if data: - n=20 # nb samples - xs=np.zeros((n,2)) - xs[:,0]=np.arange(n)+1 - xs[:,1]=(np.arange(n)+1)*-0.001 # to make it strictly convex... - - xt=np.zeros((n,2)) - xt[:,1]=np.arange(n)+1 + n = 20 # nb samples + xs = np.zeros((n, 2)) + xs[:, 0] = np.arange(n) + 1 + xs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex... + + xt = np.zeros((n, 2)) + xt[:, 1] = np.arange(n) + 1 else: - - n=50 # nb samples - xtot=np.zeros((n+1,2)) - xtot[:,0]=np.cos((np.arange(n+1)+1.0)*0.9/(n+2)*2*np.pi) - xtot[:,1]=np.sin((np.arange(n+1)+1.0)*0.9/(n+2)*2*np.pi) - - xs=xtot[:n,:] - xt=xtot[1:,:] - - - - a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples - + + n = 50 # nb samples + xtot = np.zeros((n + 1, 2)) + xtot[:, 0] = np.cos( + (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi) + xtot[:, 1] = np.sin( + (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi) + + xs = xtot[:n, :] + xt = xtot[1:, :] + + a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples + # loss matrix - M1=ot.dist(xs,xt,metric='euclidean') - M1/=M1.max() - + M1 = ot.dist(xs, xt, metric='euclidean') + M1 /= M1.max() + # loss matrix - M2=ot.dist(xs,xt,metric='sqeuclidean') - M2/=M2.max() - + M2 = ot.dist(xs, xt, metric='sqeuclidean') + M2 /= M2.max() + # loss matrix - Mp=np.sqrt(ot.dist(xs,xt,metric='euclidean')) - Mp/=Mp.max() - + Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean')) + Mp /= Mp.max() + #%% plot samples - - pl.figure(1+3*data) + + pl.figure(1 + 3 * data, figsize=(7, 3)) pl.clf() - pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') - pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') + pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.axis('equal') pl.title('Source and traget distributions') - - pl.figure(2+3*data,(15,5)) - pl.subplot(1,3,1) - pl.imshow(M1,interpolation='nearest') - pl.title('Eucidean cost') - pl.subplot(1,3,2) - pl.imshow(M2,interpolation='nearest') + + pl.figure(2 + 3 * data, figsize=(7, 3)) + + pl.subplot(1, 3, 1) + pl.imshow(M1, interpolation='nearest') + pl.title('Euclidean cost') + + pl.subplot(1, 3, 2) + pl.imshow(M2, interpolation='nearest') pl.title('Squared Euclidean cost') - - pl.subplot(1,3,3) - pl.imshow(Mp,interpolation='nearest') + + pl.subplot(1, 3, 3) + pl.imshow(Mp, interpolation='nearest') pl.title('Sqrt Euclidean cost') + pl.tight_layout() + #%% EMD - - G1=ot.emd(a,b,M1) - G2=ot.emd(a,b,M2) - Gp=ot.emd(a,b,Mp) - - pl.figure(3+3*data,(15,5)) - - pl.subplot(1,3,1) - ot.plot.plot2D_samples_mat(xs,xt,G1,c=[.5,.5,1]) - pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') - pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') + G1 = ot.emd(a, b, M1) + G2 = ot.emd(a, b, M2) + Gp = ot.emd(a, b, Mp) + + pl.figure(3 + 3 * data, figsize=(7, 3)) + + pl.subplot(1, 3, 1) + ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1]) + pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.axis('equal') - #pl.legend(loc=0) + # pl.legend(loc=0) pl.title('OT Euclidean') - - pl.subplot(1,3,2) - - ot.plot.plot2D_samples_mat(xs,xt,G2,c=[.5,.5,1]) - pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') - pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') + + pl.subplot(1, 3, 2) + ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1]) + pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.axis('equal') - #pl.legend(loc=0) + # pl.legend(loc=0) pl.title('OT squared Euclidean') - - pl.subplot(1,3,3) - - ot.plot.plot2D_samples_mat(xs,xt,Gp,c=[.5,.5,1]) - pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') - pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') + + pl.subplot(1, 3, 3) + ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1]) + pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.axis('equal') - #pl.legend(loc=0) + # pl.legend(loc=0) pl.title('OT sqrt Euclidean') + pl.tight_layout() + +pl.show() diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst index 4e94bef..ba52bfe 100644 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst @@ -7,11 +7,10 @@ 2D Optimal transport for different metrics ========================================== -Stole the figure idea from Fig. 1 and 2 in +Stole the figure idea from Fig. 1 and 2 in https://arxiv.org/pdf/1706.07650.pdf -@author: rflamary @@ -56,6 +55,10 @@ https://arxiv.org/pdf/1706.07650.pdf .. code-block:: python + # Author: Remi Flamary + # + # License: MIT License + import numpy as np import matplotlib.pylab as pl import ot @@ -65,94 +68,98 @@ https://arxiv.org/pdf/1706.07650.pdf for data in range(2): if data: - n=20 # nb samples - xs=np.zeros((n,2)) - xs[:,0]=np.arange(n)+1 - xs[:,1]=(np.arange(n)+1)*-0.001 # to make it strictly convex... - - xt=np.zeros((n,2)) - xt[:,1]=np.arange(n)+1 + n = 20 # nb samples + xs = np.zeros((n, 2)) + xs[:, 0] = np.arange(n) + 1 + xs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex... + + xt = np.zeros((n, 2)) + xt[:, 1] = np.arange(n) + 1 else: - - n=50 # nb samples - xtot=np.zeros((n+1,2)) - xtot[:,0]=np.cos((np.arange(n+1)+1.0)*0.9/(n+2)*2*np.pi) - xtot[:,1]=np.sin((np.arange(n+1)+1.0)*0.9/(n+2)*2*np.pi) - - xs=xtot[:n,:] - xt=xtot[1:,:] - - - - a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples - + + n = 50 # nb samples + xtot = np.zeros((n + 1, 2)) + xtot[:, 0] = np.cos( + (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi) + xtot[:, 1] = np.sin( + (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi) + + xs = xtot[:n, :] + xt = xtot[1:, :] + + a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples + # loss matrix - M1=ot.dist(xs,xt,metric='euclidean') - M1/=M1.max() - + M1 = ot.dist(xs, xt, metric='euclidean') + M1 /= M1.max() + # loss matrix - M2=ot.dist(xs,xt,metric='sqeuclidean') - M2/=M2.max() - + M2 = ot.dist(xs, xt, metric='sqeuclidean') + M2 /= M2.max() + # loss matrix - Mp=np.sqrt(ot.dist(xs,xt,metric='euclidean')) - Mp/=Mp.max() - + Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean')) + Mp /= Mp.max() + #%% plot samples - - pl.figure(1+3*data) + + pl.figure(1 + 3 * data, figsize=(7, 3)) pl.clf() - pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') - pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') + pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.axis('equal') pl.title('Source and traget distributions') - - pl.figure(2+3*data,(15,5)) - pl.subplot(1,3,1) - pl.imshow(M1,interpolation='nearest') - pl.title('Eucidean cost') - pl.subplot(1,3,2) - pl.imshow(M2,interpolation='nearest') + + pl.figure(2 + 3 * data, figsize=(7, 3)) + + pl.subplot(1, 3, 1) + pl.imshow(M1, interpolation='nearest') + pl.title('Euclidean cost') + + pl.subplot(1, 3, 2) + pl.imshow(M2, interpolation='nearest') pl.title('Squared Euclidean cost') - - pl.subplot(1,3,3) - pl.imshow(Mp,interpolation='nearest') + + pl.subplot(1, 3, 3) + pl.imshow(Mp, interpolation='nearest') pl.title('Sqrt Euclidean cost') + pl.tight_layout() + #%% EMD - - G1=ot.emd(a,b,M1) - G2=ot.emd(a,b,M2) - Gp=ot.emd(a,b,Mp) - - pl.figure(3+3*data,(15,5)) - - pl.subplot(1,3,1) - ot.plot.plot2D_samples_mat(xs,xt,G1,c=[.5,.5,1]) - pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') - pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') + G1 = ot.emd(a, b, M1) + G2 = ot.emd(a, b, M2) + Gp = ot.emd(a, b, Mp) + + pl.figure(3 + 3 * data, figsize=(7, 3)) + + pl.subplot(1, 3, 1) + ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1]) + pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.axis('equal') - #pl.legend(loc=0) + # pl.legend(loc=0) pl.title('OT Euclidean') - - pl.subplot(1,3,2) - - ot.plot.plot2D_samples_mat(xs,xt,G2,c=[.5,.5,1]) - pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') - pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') + + pl.subplot(1, 3, 2) + ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1]) + pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.axis('equal') - #pl.legend(loc=0) + # pl.legend(loc=0) pl.title('OT squared Euclidean') - - pl.subplot(1,3,3) - - ot.plot.plot2D_samples_mat(xs,xt,Gp,c=[.5,.5,1]) - pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') - pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') + + pl.subplot(1, 3, 3) + ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1]) + pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.axis('equal') - #pl.legend(loc=0) + # pl.legend(loc=0) pl.title('OT sqrt Euclidean') + pl.tight_layout() + + pl.show() -**Total running time of the script:** ( 0 minutes 1.417 seconds) +**Total running time of the script:** ( 0 minutes 1.906 seconds) diff --git a/docs/source/auto_examples/plot_WDA.ipynb b/docs/source/auto_examples/plot_WDA.ipynb index 408a605..5568128 100644 --- a/docs/source/auto_examples/plot_WDA.ipynb +++ b/docs/source/auto_examples/plot_WDA.ipynb @@ -15,7 +15,7 @@ }, { "source": [ - "\n# Wasserstein Discriminant Analysis\n\n\n@author: rflamary\n\n" + "\n# Wasserstein Discriminant Analysis\n\n\n\n" ], "cell_type": "markdown", "metadata": {} @@ -24,7 +24,7 @@ "execution_count": null, "cell_type": "code", "source": [ - "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\nfrom ot.datasets import get_1D_gauss as gauss\nfrom ot.dr import wda\n\n\n#%% parameters\n\nn=1000 # nb samples in source and target datasets\nnz=0.2\nxs,ys=ot.datasets.get_data_classif('3gauss',n,nz)\nxt,yt=ot.datasets.get_data_classif('3gauss',n,nz)\n\nnbnoise=8\n\nxs=np.hstack((xs,np.random.randn(n,nbnoise)))\nxt=np.hstack((xt,np.random.randn(n,nbnoise)))\n\n#%% plot samples\n\npl.figure(1)\n\n\npl.scatter(xt[:,0],xt[:,1],c=ys,marker='+',label='Source samples')\npl.legend(loc=0)\npl.title('Discriminant dimensions')\n\n\n#%% plot distributions and loss matrix\np=2\nreg=1\nk=10\nmaxiter=100\n\nP,proj = wda(xs,ys,p,reg,k,maxiter=maxiter)\n\n#%% plot samples\n\nxsp=proj(xs)\nxtp=proj(xt)\n\npl.figure(1,(10,5))\n\npl.subplot(1,2,1)\npl.scatter(xsp[:,0],xsp[:,1],c=ys,marker='+',label='Projected samples')\npl.legend(loc=0)\npl.title('Projected training samples')\n\n\npl.subplot(1,2,2)\npl.scatter(xtp[:,0],xtp[:,1],c=ys,marker='+',label='Projected samples')\npl.legend(loc=0)\npl.title('Projected test samples')" + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\n\nfrom ot.dr import wda, fda\n\n\n#%% parameters\n\nn = 1000 # nb samples in source and target datasets\nnz = 0.2\n\n# generate circle dataset\nt = np.random.rand(n) * 2 * np.pi\nys = np.floor((np.arange(n) * 1.0 / n * 3)) + 1\nxs = np.concatenate(\n (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1)\nxs = xs * ys.reshape(-1, 1) + nz * np.random.randn(n, 2)\n\nt = np.random.rand(n) * 2 * np.pi\nyt = np.floor((np.arange(n) * 1.0 / n * 3)) + 1\nxt = np.concatenate(\n (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1)\nxt = xt * yt.reshape(-1, 1) + nz * np.random.randn(n, 2)\n\nnbnoise = 8\n\nxs = np.hstack((xs, np.random.randn(n, nbnoise)))\nxt = np.hstack((xt, np.random.randn(n, nbnoise)))\n\n#%% plot samples\npl.figure(1, figsize=(6.4, 3.5))\n\npl.subplot(1, 2, 1)\npl.scatter(xt[:, 0], xt[:, 1], c=ys, marker='+', label='Source samples')\npl.legend(loc=0)\npl.title('Discriminant dimensions')\n\npl.subplot(1, 2, 2)\npl.scatter(xt[:, 2], xt[:, 3], c=ys, marker='+', label='Source samples')\npl.legend(loc=0)\npl.title('Other dimensions')\npl.tight_layout()\n\n#%% Compute FDA\np = 2\n\nPfda, projfda = fda(xs, ys, p)\n\n#%% Compute WDA\np = 2\nreg = 1e0\nk = 10\nmaxiter = 100\n\nPwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter)\n\n#%% plot samples\n\nxsp = projfda(xs)\nxtp = projfda(xt)\n\nxspw = projwda(xs)\nxtpw = projwda(xt)\n\npl.figure(2)\n\npl.subplot(2, 2, 1)\npl.scatter(xsp[:, 0], xsp[:, 1], c=ys, marker='+', label='Projected samples')\npl.legend(loc=0)\npl.title('Projected training samples FDA')\n\npl.subplot(2, 2, 2)\npl.scatter(xtp[:, 0], xtp[:, 1], c=ys, marker='+', label='Projected samples')\npl.legend(loc=0)\npl.title('Projected test samples FDA')\n\npl.subplot(2, 2, 3)\npl.scatter(xspw[:, 0], xspw[:, 1], c=ys, marker='+', label='Projected samples')\npl.legend(loc=0)\npl.title('Projected training samples WDA')\n\npl.subplot(2, 2, 4)\npl.scatter(xtpw[:, 0], xtpw[:, 1], c=ys, marker='+', label='Projected samples')\npl.legend(loc=0)\npl.title('Projected test samples WDA')\npl.tight_layout()\n\npl.show()" ], "outputs": [], "metadata": { diff --git a/docs/source/auto_examples/plot_WDA.py b/docs/source/auto_examples/plot_WDA.py index bbe3888..42789f2 100644 --- a/docs/source/auto_examples/plot_WDA.py +++ b/docs/source/auto_examples/plot_WDA.py @@ -4,60 +4,97 @@ Wasserstein Discriminant Analysis ================================= -@author: rflamary """ +# Author: Remi Flamary +# +# License: MIT License + import numpy as np import matplotlib.pylab as pl -import ot -from ot.datasets import get_1D_gauss as gauss -from ot.dr import wda + +from ot.dr import wda, fda #%% parameters -n=1000 # nb samples in source and target datasets -nz=0.2 -xs,ys=ot.datasets.get_data_classif('3gauss',n,nz) -xt,yt=ot.datasets.get_data_classif('3gauss',n,nz) +n = 1000 # nb samples in source and target datasets +nz = 0.2 -nbnoise=8 +# generate circle dataset +t = np.random.rand(n) * 2 * np.pi +ys = np.floor((np.arange(n) * 1.0 / n * 3)) + 1 +xs = np.concatenate( + (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1) +xs = xs * ys.reshape(-1, 1) + nz * np.random.randn(n, 2) -xs=np.hstack((xs,np.random.randn(n,nbnoise))) -xt=np.hstack((xt,np.random.randn(n,nbnoise))) +t = np.random.rand(n) * 2 * np.pi +yt = np.floor((np.arange(n) * 1.0 / n * 3)) + 1 +xt = np.concatenate( + (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1) +xt = xt * yt.reshape(-1, 1) + nz * np.random.randn(n, 2) -#%% plot samples +nbnoise = 8 -pl.figure(1) +xs = np.hstack((xs, np.random.randn(n, nbnoise))) +xt = np.hstack((xt, np.random.randn(n, nbnoise))) +#%% plot samples +pl.figure(1, figsize=(6.4, 3.5)) -pl.scatter(xt[:,0],xt[:,1],c=ys,marker='+',label='Source samples') +pl.subplot(1, 2, 1) +pl.scatter(xt[:, 0], xt[:, 1], c=ys, marker='+', label='Source samples') pl.legend(loc=0) pl.title('Discriminant dimensions') +pl.subplot(1, 2, 2) +pl.scatter(xt[:, 2], xt[:, 3], c=ys, marker='+', label='Source samples') +pl.legend(loc=0) +pl.title('Other dimensions') +pl.tight_layout() + +#%% Compute FDA +p = 2 -#%% plot distributions and loss matrix -p=2 -reg=1 -k=10 -maxiter=100 +Pfda, projfda = fda(xs, ys, p) -P,proj = wda(xs,ys,p,reg,k,maxiter=maxiter) +#%% Compute WDA +p = 2 +reg = 1e0 +k = 10 +maxiter = 100 + +Pwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter) #%% plot samples -xsp=proj(xs) -xtp=proj(xt) +xsp = projfda(xs) +xtp = projfda(xt) + +xspw = projwda(xs) +xtpw = projwda(xt) + +pl.figure(2) -pl.figure(1,(10,5)) +pl.subplot(2, 2, 1) +pl.scatter(xsp[:, 0], xsp[:, 1], c=ys, marker='+', label='Projected samples') +pl.legend(loc=0) +pl.title('Projected training samples FDA') -pl.subplot(1,2,1) -pl.scatter(xsp[:,0],xsp[:,1],c=ys,marker='+',label='Projected samples') +pl.subplot(2, 2, 2) +pl.scatter(xtp[:, 0], xtp[:, 1], c=ys, marker='+', label='Projected samples') pl.legend(loc=0) -pl.title('Projected training samples') +pl.title('Projected test samples FDA') +pl.subplot(2, 2, 3) +pl.scatter(xspw[:, 0], xspw[:, 1], c=ys, marker='+', label='Projected samples') +pl.legend(loc=0) +pl.title('Projected training samples WDA') -pl.subplot(1,2,2) -pl.scatter(xtp[:,0],xtp[:,1],c=ys,marker='+',label='Projected samples') +pl.subplot(2, 2, 4) +pl.scatter(xtpw[:, 0], xtpw[:, 1], c=ys, marker='+', label='Projected samples') pl.legend(loc=0) -pl.title('Projected test samples') +pl.title('Projected test samples WDA') +pl.tight_layout() + +pl.show() diff --git a/docs/source/auto_examples/plot_WDA.rst b/docs/source/auto_examples/plot_WDA.rst index 540555d..76ebaf5 100644 --- a/docs/source/auto_examples/plot_WDA.rst +++ b/docs/source/auto_examples/plot_WDA.rst @@ -7,13 +7,22 @@ Wasserstein Discriminant Analysis ================================= -@author: rflamary -.. image:: /auto_examples/images/sphx_glr_plot_WDA_001.png - :align: center +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_WDA_001.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_WDA_002.png + :scale: 47 .. rst-class:: sphx-glr-script-out @@ -23,26 +32,43 @@ Wasserstein Discriminant Analysis Compiling cost function... Computing gradient of cost function... iter cost val grad. norm - 1 +5.2427396265941129e-01 8.16627951e-01 - 2 +1.7904850059627236e-01 1.91366819e-01 - 3 +1.6985797253002377e-01 1.70940682e-01 - 4 +1.3903474972292729e-01 1.28606342e-01 - 5 +7.4961734618782416e-02 6.41973980e-02 - 6 +7.1900245222486239e-02 4.25693592e-02 - 7 +7.0472023318269614e-02 2.34599232e-02 - 8 +6.9917568641317152e-02 5.66542766e-03 - 9 +6.9885086242452696e-02 4.05756115e-04 - 10 +6.9884967432653489e-02 2.16836017e-04 - 11 +6.9884923649884148e-02 5.74961622e-05 - 12 +6.9884921818258436e-02 3.83257203e-05 - 13 +6.9884920459612282e-02 9.97486224e-06 - 14 +6.9884920414414409e-02 7.33567875e-06 - 15 +6.9884920388431387e-02 5.23889187e-06 - 16 +6.9884920385183902e-02 4.91959084e-06 - 17 +6.9884920373983223e-02 3.56451669e-06 - 18 +6.9884920369701245e-02 2.88858709e-06 - 19 +6.9884920361621208e-02 1.82294279e-07 - Terminated - min grad norm reached after 19 iterations, 9.65 seconds. + 1 +8.9741888001949222e-01 3.71269078e-01 + 2 +4.9103998133976140e-01 3.46687543e-01 + 3 +4.2142651893148553e-01 1.04789602e-01 + 4 +4.1573609749588841e-01 5.21726648e-02 + 5 +4.1486046805261961e-01 5.35335513e-02 + 6 +4.1315953904635105e-01 2.17803599e-02 + 7 +4.1313030162717523e-01 6.06901182e-02 + 8 +4.1301511591963386e-01 5.88598758e-02 + 9 +4.1258349404769817e-01 5.14307874e-02 + 10 +4.1139242901051226e-01 2.03198793e-02 + 11 +4.1113798965164017e-01 1.18944721e-02 + 12 +4.1103446820878486e-01 2.21783648e-02 + 13 +4.1076586830791861e-01 9.51495863e-03 + 14 +4.1036935287519144e-01 3.74973214e-02 + 15 +4.0958729714575060e-01 1.23810902e-02 + 16 +4.0898266309095005e-01 4.01999918e-02 + 17 +4.0816076944357715e-01 2.27240277e-02 + 18 +4.0788116701894767e-01 4.42815945e-02 + 19 +4.0695443744952403e-01 3.28464304e-02 + 20 +4.0293834480911150e-01 7.76000681e-02 + 21 +3.8488003705202750e-01 1.49378022e-01 + 22 +3.0767344927282614e-01 2.15432117e-01 + 23 +2.3849425361868334e-01 1.07942382e-01 + 24 +2.3845125762548214e-01 1.08953278e-01 + 25 +2.3828007730494005e-01 1.07934830e-01 + 26 +2.3760839060570119e-01 1.03822134e-01 + 27 +2.3514215179705886e-01 8.67263481e-02 + 28 +2.2978886197588613e-01 9.26609306e-03 + 29 +2.2972671019495342e-01 2.59476089e-03 + 30 +2.2972355865247496e-01 1.57205146e-03 + 31 +2.2972296662351968e-01 1.29300760e-03 + 32 +2.2972181557051569e-01 8.82375756e-05 + 33 +2.2972181277025336e-01 6.20536544e-05 + 34 +2.2972181023486152e-01 7.01884014e-06 + 35 +2.2972181020400181e-01 1.60415765e-06 + 36 +2.2972181020236590e-01 2.44290966e-07 + Terminated - min grad norm reached after 36 iterations, 13.41 seconds. @@ -53,62 +79,100 @@ Wasserstein Discriminant Analysis .. code-block:: python + # Author: Remi Flamary + # + # License: MIT License + import numpy as np import matplotlib.pylab as pl - import ot - from ot.datasets import get_1D_gauss as gauss - from ot.dr import wda + + from ot.dr import wda, fda #%% parameters - n=1000 # nb samples in source and target datasets - nz=0.2 - xs,ys=ot.datasets.get_data_classif('3gauss',n,nz) - xt,yt=ot.datasets.get_data_classif('3gauss',n,nz) + n = 1000 # nb samples in source and target datasets + nz = 0.2 - nbnoise=8 + # generate circle dataset + t = np.random.rand(n) * 2 * np.pi + ys = np.floor((np.arange(n) * 1.0 / n * 3)) + 1 + xs = np.concatenate( + (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1) + xs = xs * ys.reshape(-1, 1) + nz * np.random.randn(n, 2) - xs=np.hstack((xs,np.random.randn(n,nbnoise))) - xt=np.hstack((xt,np.random.randn(n,nbnoise))) + t = np.random.rand(n) * 2 * np.pi + yt = np.floor((np.arange(n) * 1.0 / n * 3)) + 1 + xt = np.concatenate( + (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1) + xt = xt * yt.reshape(-1, 1) + nz * np.random.randn(n, 2) - #%% plot samples + nbnoise = 8 - pl.figure(1) + xs = np.hstack((xs, np.random.randn(n, nbnoise))) + xt = np.hstack((xt, np.random.randn(n, nbnoise))) + #%% plot samples + pl.figure(1, figsize=(6.4, 3.5)) - pl.scatter(xt[:,0],xt[:,1],c=ys,marker='+',label='Source samples') + pl.subplot(1, 2, 1) + pl.scatter(xt[:, 0], xt[:, 1], c=ys, marker='+', label='Source samples') pl.legend(loc=0) pl.title('Discriminant dimensions') + pl.subplot(1, 2, 2) + pl.scatter(xt[:, 2], xt[:, 3], c=ys, marker='+', label='Source samples') + pl.legend(loc=0) + pl.title('Other dimensions') + pl.tight_layout() + + #%% Compute FDA + p = 2 - #%% plot distributions and loss matrix - p=2 - reg=1 - k=10 - maxiter=100 + Pfda, projfda = fda(xs, ys, p) - P,proj = wda(xs,ys,p,reg,k,maxiter=maxiter) + #%% Compute WDA + p = 2 + reg = 1e0 + k = 10 + maxiter = 100 + + Pwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter) #%% plot samples - xsp=proj(xs) - xtp=proj(xt) + xsp = projfda(xs) + xtp = projfda(xt) + + xspw = projwda(xs) + xtpw = projwda(xt) - pl.figure(1,(10,5)) + pl.figure(2) - pl.subplot(1,2,1) - pl.scatter(xsp[:,0],xsp[:,1],c=ys,marker='+',label='Projected samples') + pl.subplot(2, 2, 1) + pl.scatter(xsp[:, 0], xsp[:, 1], c=ys, marker='+', label='Projected samples') pl.legend(loc=0) - pl.title('Projected training samples') + pl.title('Projected training samples FDA') + pl.subplot(2, 2, 2) + pl.scatter(xtp[:, 0], xtp[:, 1], c=ys, marker='+', label='Projected samples') + pl.legend(loc=0) + pl.title('Projected test samples FDA') + + pl.subplot(2, 2, 3) + pl.scatter(xspw[:, 0], xspw[:, 1], c=ys, marker='+', label='Projected samples') + pl.legend(loc=0) + pl.title('Projected training samples WDA') - pl.subplot(1,2,2) - pl.scatter(xtp[:,0],xtp[:,1],c=ys,marker='+',label='Projected samples') + pl.subplot(2, 2, 4) + pl.scatter(xtpw[:, 0], xtpw[:, 1], c=ys, marker='+', label='Projected samples') pl.legend(loc=0) - pl.title('Projected test samples') + pl.title('Projected test samples WDA') + pl.tight_layout() + + pl.show() -**Total running time of the script:** ( 0 minutes 16.902 seconds) +**Total running time of the script:** ( 0 minutes 19.853 seconds) diff --git a/docs/source/auto_examples/plot_barycenter_1D.ipynb b/docs/source/auto_examples/plot_barycenter_1D.ipynb index 36f3975..239b8b8 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.ipynb +++ b/docs/source/auto_examples/plot_barycenter_1D.ipynb @@ -15,7 +15,7 @@ }, { "source": [ - "\n# 1D Wasserstein barycenter demo\n\n\n\n@author: rflamary\n\n" + "\n# 1D Wasserstein barycenter demo\n\n\n\n" ], "cell_type": "markdown", "metadata": {} @@ -24,7 +24,7 @@ "execution_count": null, "cell_type": "code", "source": [ - "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\nfrom mpl_toolkits.mplot3d import Axes3D #necessary for 3d plot even if not used\nfrom matplotlib.collections import PolyCollection\n\n\n#%% parameters\n\nn=100 # nb bins\n\n# bin positions\nx=np.arange(n,dtype=np.float64)\n\n# Gaussian distributions\na1=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std\na2=ot.datasets.get_1D_gauss(n,m=60,s=8)\n\n# creating matrix A containing all distributions\nA=np.vstack((a1,a2)).T\nnbd=A.shape[1]\n\n# loss matrix + normalization\nM=ot.utils.dist0(n)\nM/=M.max()\n\n#%% plot the distributions\n\npl.figure(1)\nfor i in range(nbd):\n pl.plot(x,A[:,i])\npl.title('Distributions')\n\n#%% barycenter computation\n\nalpha=0.2 # 0<=alpha<=1\nweights=np.array([1-alpha,alpha])\n\n# l2bary\nbary_l2=A.dot(weights)\n\n# wasserstein\nreg=1e-3\nbary_wass=ot.bregman.barycenter(A,M,reg,weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2,1,1)\nfor i in range(nbd):\n pl.plot(x,A[:,i])\npl.title('Distributions')\n\npl.subplot(2,1,2)\npl.plot(x,bary_l2,'r',label='l2')\npl.plot(x,bary_wass,'g',label='Wasserstein')\npl.legend()\npl.title('Barycenters')\n\n\n#%% barycenter interpolation\n\nnbalpha=11\nalphalist=np.linspace(0,1,nbalpha)\n\n\nB_l2=np.zeros((n,nbalpha))\n\nB_wass=np.copy(B_l2)\n\nfor i in range(0,nbalpha):\n alpha=alphalist[i]\n weights=np.array([1-alpha,alpha])\n B_l2[:,i]=A.dot(weights)\n B_wass[:,i]=ot.bregman.barycenter(A,M,reg,weights)\n\n#%% plot interpolation\n\npl.figure(3,(10,5))\n\n#pl.subplot(1,2,1)\ncmap=pl.cm.get_cmap('viridis')\nverts = []\nzs = alphalist\nfor i,z in enumerate(zs):\n ys = B_l2[:,i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\n\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0,1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max()*1.01)\npl.title('Barycenter interpolation with l2')\n\npl.show()\n\npl.figure(4,(10,5))\n\n#pl.subplot(1,2,1)\ncmap=pl.cm.get_cmap('viridis')\nverts = []\nzs = alphalist\nfor i,z in enumerate(zs):\n ys = B_wass[:,i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\n\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0,1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max()*1.01)\npl.title('Barycenter interpolation with Wasserstein')\n\npl.show()" + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nfrom matplotlib.collections import PolyCollection\n\n\n#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na1 = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.get_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()\n\n#%% barycenter computation\n\nalpha = 0.2 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()\n\n#%% barycenter interpolation\n\nn_alpha = 11\nalpha_list = np.linspace(0, 1, n_alpha)\n\n\nB_l2 = np.zeros((n, n_alpha))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_alpha):\n alpha = alpha_list[i]\n weights = np.array([1 - alpha, alpha])\n B_l2[:, i] = A.dot(weights)\n B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)\n\n#%% plot interpolation\n\npl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_l2[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_wass[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with Wasserstein')\npl.tight_layout()\n\npl.show()" ], "outputs": [], "metadata": { diff --git a/docs/source/auto_examples/plot_barycenter_1D.py b/docs/source/auto_examples/plot_barycenter_1D.py index 30eecbf..875f44c 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.py +++ b/docs/source/auto_examples/plot_barycenter_1D.py @@ -4,135 +4,134 @@ 1D Wasserstein barycenter demo ============================== - -@author: rflamary """ +# Author: Remi Flamary +# +# License: MIT License + import numpy as np import matplotlib.pylab as pl import ot -from mpl_toolkits.mplot3d import Axes3D #necessary for 3d plot even if not used +# necessary for 3d plot even if not used +from mpl_toolkits.mplot3d import Axes3D # noqa from matplotlib.collections import PolyCollection #%% parameters -n=100 # nb bins +n = 100 # nb bins # bin positions -x=np.arange(n,dtype=np.float64) +x = np.arange(n, dtype=np.float64) # Gaussian distributions -a1=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std -a2=ot.datasets.get_1D_gauss(n,m=60,s=8) +a1 = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std +a2 = ot.datasets.get_1D_gauss(n, m=60, s=8) # creating matrix A containing all distributions -A=np.vstack((a1,a2)).T -nbd=A.shape[1] +A = np.vstack((a1, a2)).T +n_distributions = A.shape[1] # loss matrix + normalization -M=ot.utils.dist0(n) -M/=M.max() +M = ot.utils.dist0(n) +M /= M.max() #%% plot the distributions -pl.figure(1) -for i in range(nbd): - pl.plot(x,A[:,i]) +pl.figure(1, figsize=(6.4, 3)) +for i in range(n_distributions): + pl.plot(x, A[:, i]) pl.title('Distributions') +pl.tight_layout() #%% barycenter computation -alpha=0.2 # 0<=alpha<=1 -weights=np.array([1-alpha,alpha]) +alpha = 0.2 # 0<=alpha<=1 +weights = np.array([1 - alpha, alpha]) # l2bary -bary_l2=A.dot(weights) +bary_l2 = A.dot(weights) # wasserstein -reg=1e-3 -bary_wass=ot.bregman.barycenter(A,M,reg,weights) +reg = 1e-3 +bary_wass = ot.bregman.barycenter(A, M, reg, weights) pl.figure(2) pl.clf() -pl.subplot(2,1,1) -for i in range(nbd): - pl.plot(x,A[:,i]) +pl.subplot(2, 1, 1) +for i in range(n_distributions): + pl.plot(x, A[:, i]) pl.title('Distributions') -pl.subplot(2,1,2) -pl.plot(x,bary_l2,'r',label='l2') -pl.plot(x,bary_wass,'g',label='Wasserstein') +pl.subplot(2, 1, 2) +pl.plot(x, bary_l2, 'r', label='l2') +pl.plot(x, bary_wass, 'g', label='Wasserstein') pl.legend() pl.title('Barycenters') - +pl.tight_layout() #%% barycenter interpolation -nbalpha=11 -alphalist=np.linspace(0,1,nbalpha) +n_alpha = 11 +alpha_list = np.linspace(0, 1, n_alpha) -B_l2=np.zeros((n,nbalpha)) +B_l2 = np.zeros((n, n_alpha)) -B_wass=np.copy(B_l2) +B_wass = np.copy(B_l2) -for i in range(0,nbalpha): - alpha=alphalist[i] - weights=np.array([1-alpha,alpha]) - B_l2[:,i]=A.dot(weights) - B_wass[:,i]=ot.bregman.barycenter(A,M,reg,weights) +for i in range(0, n_alpha): + alpha = alpha_list[i] + weights = np.array([1 - alpha, alpha]) + B_l2[:, i] = A.dot(weights) + B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights) #%% plot interpolation -pl.figure(3,(10,5)) +pl.figure(3) -#pl.subplot(1,2,1) -cmap=pl.cm.get_cmap('viridis') +cmap = pl.cm.get_cmap('viridis') verts = [] -zs = alphalist -for i,z in enumerate(zs): - ys = B_l2[:,i] +zs = alpha_list +for i, z in enumerate(zs): + ys = B_l2[:, i] verts.append(list(zip(x, ys))) ax = pl.gcf().gca(projection='3d') -poly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist]) +poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list]) poly.set_alpha(0.7) ax.add_collection3d(poly, zs=zs, zdir='y') - ax.set_xlabel('x') ax.set_xlim3d(0, n) ax.set_ylabel('$\\alpha$') -ax.set_ylim3d(0,1) +ax.set_ylim3d(0, 1) ax.set_zlabel('') -ax.set_zlim3d(0, B_l2.max()*1.01) +ax.set_zlim3d(0, B_l2.max() * 1.01) pl.title('Barycenter interpolation with l2') +pl.tight_layout() -pl.show() - -pl.figure(4,(10,5)) - -#pl.subplot(1,2,1) -cmap=pl.cm.get_cmap('viridis') +pl.figure(4) +cmap = pl.cm.get_cmap('viridis') verts = [] -zs = alphalist -for i,z in enumerate(zs): - ys = B_wass[:,i] +zs = alpha_list +for i, z in enumerate(zs): + ys = B_wass[:, i] verts.append(list(zip(x, ys))) ax = pl.gcf().gca(projection='3d') -poly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist]) +poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list]) poly.set_alpha(0.7) ax.add_collection3d(poly, zs=zs, zdir='y') - ax.set_xlabel('x') ax.set_xlim3d(0, n) ax.set_ylabel('$\\alpha$') -ax.set_ylim3d(0,1) +ax.set_ylim3d(0, 1) ax.set_zlabel('') -ax.set_zlim3d(0, B_l2.max()*1.01) +ax.set_zlim3d(0, B_l2.max() * 1.01) pl.title('Barycenter interpolation with Wasserstein') +pl.tight_layout() -pl.show() \ No newline at end of file +pl.show() diff --git a/docs/source/auto_examples/plot_barycenter_1D.rst b/docs/source/auto_examples/plot_barycenter_1D.rst index 1b15c77..af88e80 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.rst +++ b/docs/source/auto_examples/plot_barycenter_1D.rst @@ -8,8 +8,6 @@ ============================== -@author: rflamary - @@ -43,135 +41,137 @@ .. code-block:: python + # Author: Remi Flamary + # + # License: MIT License + import numpy as np import matplotlib.pylab as pl import ot - from mpl_toolkits.mplot3d import Axes3D #necessary for 3d plot even if not used + # necessary for 3d plot even if not used + from mpl_toolkits.mplot3d import Axes3D # noqa from matplotlib.collections import PolyCollection #%% parameters - n=100 # nb bins + n = 100 # nb bins # bin positions - x=np.arange(n,dtype=np.float64) + x = np.arange(n, dtype=np.float64) # Gaussian distributions - a1=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std - a2=ot.datasets.get_1D_gauss(n,m=60,s=8) + a1 = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std + a2 = ot.datasets.get_1D_gauss(n, m=60, s=8) # creating matrix A containing all distributions - A=np.vstack((a1,a2)).T - nbd=A.shape[1] + A = np.vstack((a1, a2)).T + n_distributions = A.shape[1] # loss matrix + normalization - M=ot.utils.dist0(n) - M/=M.max() + M = ot.utils.dist0(n) + M /= M.max() #%% plot the distributions - pl.figure(1) - for i in range(nbd): - pl.plot(x,A[:,i]) + pl.figure(1, figsize=(6.4, 3)) + for i in range(n_distributions): + pl.plot(x, A[:, i]) pl.title('Distributions') + pl.tight_layout() #%% barycenter computation - alpha=0.2 # 0<=alpha<=1 - weights=np.array([1-alpha,alpha]) + alpha = 0.2 # 0<=alpha<=1 + weights = np.array([1 - alpha, alpha]) # l2bary - bary_l2=A.dot(weights) + bary_l2 = A.dot(weights) # wasserstein - reg=1e-3 - bary_wass=ot.bregman.barycenter(A,M,reg,weights) + reg = 1e-3 + bary_wass = ot.bregman.barycenter(A, M, reg, weights) pl.figure(2) pl.clf() - pl.subplot(2,1,1) - for i in range(nbd): - pl.plot(x,A[:,i]) + pl.subplot(2, 1, 1) + for i in range(n_distributions): + pl.plot(x, A[:, i]) pl.title('Distributions') - pl.subplot(2,1,2) - pl.plot(x,bary_l2,'r',label='l2') - pl.plot(x,bary_wass,'g',label='Wasserstein') + pl.subplot(2, 1, 2) + pl.plot(x, bary_l2, 'r', label='l2') + pl.plot(x, bary_wass, 'g', label='Wasserstein') pl.legend() pl.title('Barycenters') - + pl.tight_layout() #%% barycenter interpolation - nbalpha=11 - alphalist=np.linspace(0,1,nbalpha) + n_alpha = 11 + alpha_list = np.linspace(0, 1, n_alpha) - B_l2=np.zeros((n,nbalpha)) + B_l2 = np.zeros((n, n_alpha)) - B_wass=np.copy(B_l2) + B_wass = np.copy(B_l2) - for i in range(0,nbalpha): - alpha=alphalist[i] - weights=np.array([1-alpha,alpha]) - B_l2[:,i]=A.dot(weights) - B_wass[:,i]=ot.bregman.barycenter(A,M,reg,weights) + for i in range(0, n_alpha): + alpha = alpha_list[i] + weights = np.array([1 - alpha, alpha]) + B_l2[:, i] = A.dot(weights) + B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights) #%% plot interpolation - pl.figure(3,(10,5)) + pl.figure(3) - #pl.subplot(1,2,1) - cmap=pl.cm.get_cmap('viridis') + cmap = pl.cm.get_cmap('viridis') verts = [] - zs = alphalist - for i,z in enumerate(zs): - ys = B_l2[:,i] + zs = alpha_list + for i, z in enumerate(zs): + ys = B_l2[:, i] verts.append(list(zip(x, ys))) ax = pl.gcf().gca(projection='3d') - poly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist]) + poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list]) poly.set_alpha(0.7) ax.add_collection3d(poly, zs=zs, zdir='y') - ax.set_xlabel('x') ax.set_xlim3d(0, n) ax.set_ylabel('$\\alpha$') - ax.set_ylim3d(0,1) + ax.set_ylim3d(0, 1) ax.set_zlabel('') - ax.set_zlim3d(0, B_l2.max()*1.01) + ax.set_zlim3d(0, B_l2.max() * 1.01) pl.title('Barycenter interpolation with l2') + pl.tight_layout() - pl.show() - - pl.figure(4,(10,5)) - - #pl.subplot(1,2,1) - cmap=pl.cm.get_cmap('viridis') + pl.figure(4) + cmap = pl.cm.get_cmap('viridis') verts = [] - zs = alphalist - for i,z in enumerate(zs): - ys = B_wass[:,i] + zs = alpha_list + for i, z in enumerate(zs): + ys = B_wass[:, i] verts.append(list(zip(x, ys))) ax = pl.gcf().gca(projection='3d') - poly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist]) + poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list]) poly.set_alpha(0.7) ax.add_collection3d(poly, zs=zs, zdir='y') - ax.set_xlabel('x') ax.set_xlim3d(0, n) ax.set_ylabel('$\\alpha$') - ax.set_ylim3d(0,1) + ax.set_ylim3d(0, 1) ax.set_zlabel('') - ax.set_zlim3d(0, B_l2.max()*1.01) + ax.set_zlim3d(0, B_l2.max() * 1.01) pl.title('Barycenter interpolation with Wasserstein') + pl.tight_layout() pl.show() -**Total running time of the script:** ( 0 minutes 2.274 seconds) + +**Total running time of the script:** ( 0 minutes 0.546 seconds) diff --git a/docs/source/auto_examples/plot_compute_emd.ipynb b/docs/source/auto_examples/plot_compute_emd.ipynb index 4162144..ce3f8c6 100644 --- a/docs/source/auto_examples/plot_compute_emd.ipynb +++ b/docs/source/auto_examples/plot_compute_emd.ipynb @@ -15,7 +15,7 @@ }, { "source": [ - "\n# 1D optimal transport\n\n\n@author: rflamary\n\n" + "\n# 1D optimal transport\n\n\n\n" ], "cell_type": "markdown", "metadata": {} @@ -24,7 +24,7 @@ "execution_count": null, "cell_type": "code", "source": [ - "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\nfrom ot.datasets import get_1D_gauss as gauss\n\n\n#%% parameters\n\nn=100 # nb bins\nn_target=50 # nb target distributions\n\n\n# bin positions\nx=np.arange(n,dtype=np.float64)\n\nlst_m=np.linspace(20,90,n_target)\n\n# Gaussian distributions\na=gauss(n,m=20,s=5) # m= mean, s= std\n\nB=np.zeros((n,n_target))\n\nfor i,m in enumerate(lst_m):\n B[:,i]=gauss(n,m=m,s=5)\n\n# loss matrix and normalization\nM=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'euclidean')\nM/=M.max()\nM2=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'sqeuclidean')\nM2/=M2.max()\n#%% plot the distributions\n\npl.figure(1)\npl.subplot(2,1,1)\npl.plot(x,a,'b',label='Source distribution')\npl.title('Source distribution')\npl.subplot(2,1,2)\npl.plot(x,B,label='Target distributions')\npl.title('Target distributions')\n\n#%% Compute and plot distributions and loss matrix\n\nd_emd=ot.emd2(a,B,M) # direct computation of EMD\nd_emd2=ot.emd2(a,B,M2) # direct computation of EMD with loss M3\n\n\npl.figure(2)\npl.plot(d_emd,label='Euclidean EMD')\npl.plot(d_emd2,label='Squared Euclidean EMD')\npl.title('EMD distances')\npl.legend()\n\n#%%\nreg=1e-2\nd_sinkhorn=ot.sinkhorn(a,B,M,reg)\nd_sinkhorn2=ot.sinkhorn(a,B,M2,reg)\n\npl.figure(2)\npl.clf()\npl.plot(d_emd,label='Euclidean EMD')\npl.plot(d_emd2,label='Squared Euclidean EMD')\npl.plot(d_sinkhorn,'+',label='Euclidean Sinkhorn')\npl.plot(d_sinkhorn2,'+',label='Squared Euclidean Sinkhorn')\npl.title('EMD distances')\npl.legend()" + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nfrom ot.datasets import get_1D_gauss as gauss\n\n\n#%% parameters\n\nn = 100 # nb bins\nn_target = 50 # nb target distributions\n\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\nlst_m = np.linspace(20, 90, n_target)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\n\nB = np.zeros((n, n_target))\n\nfor i, m in enumerate(lst_m):\n B[:, i] = gauss(n, m=m, s=5)\n\n# loss matrix and normalization\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'euclidean')\nM /= M.max()\nM2 = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'sqeuclidean')\nM2 /= M2.max()\n#%% plot the distributions\n\npl.figure(1)\npl.subplot(2, 1, 1)\npl.plot(x, a, 'b', label='Source distribution')\npl.title('Source distribution')\npl.subplot(2, 1, 2)\npl.plot(x, B, label='Target distributions')\npl.title('Target distributions')\npl.tight_layout()\n\n#%% Compute and plot distributions and loss matrix\n\nd_emd = ot.emd2(a, B, M) # direct computation of EMD\nd_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M3\n\n\npl.figure(2)\npl.plot(d_emd, label='Euclidean EMD')\npl.plot(d_emd2, label='Squared Euclidean EMD')\npl.title('EMD distances')\npl.legend()\n\n#%%\nreg = 1e-2\nd_sinkhorn = ot.sinkhorn2(a, B, M, reg)\nd_sinkhorn2 = ot.sinkhorn2(a, B, M2, reg)\n\npl.figure(2)\npl.clf()\npl.plot(d_emd, label='Euclidean EMD')\npl.plot(d_emd2, label='Squared Euclidean EMD')\npl.plot(d_sinkhorn, '+', label='Euclidean Sinkhorn')\npl.plot(d_sinkhorn2, '+', label='Squared Euclidean Sinkhorn')\npl.title('EMD distances')\npl.legend()\n\npl.show()" ], "outputs": [], "metadata": { diff --git a/docs/source/auto_examples/plot_compute_emd.py b/docs/source/auto_examples/plot_compute_emd.py index c7063e8..893eecf 100644 --- a/docs/source/auto_examples/plot_compute_emd.py +++ b/docs/source/auto_examples/plot_compute_emd.py @@ -4,9 +4,12 @@ 1D optimal transport ==================== -@author: rflamary """ +# Author: Remi Flamary +# +# License: MIT License + import numpy as np import matplotlib.pylab as pl import ot @@ -15,60 +18,63 @@ from ot.datasets import get_1D_gauss as gauss #%% parameters -n=100 # nb bins -n_target=50 # nb target distributions +n = 100 # nb bins +n_target = 50 # nb target distributions # bin positions -x=np.arange(n,dtype=np.float64) +x = np.arange(n, dtype=np.float64) -lst_m=np.linspace(20,90,n_target) +lst_m = np.linspace(20, 90, n_target) # Gaussian distributions -a=gauss(n,m=20,s=5) # m= mean, s= std +a = gauss(n, m=20, s=5) # m= mean, s= std -B=np.zeros((n,n_target)) +B = np.zeros((n, n_target)) -for i,m in enumerate(lst_m): - B[:,i]=gauss(n,m=m,s=5) +for i, m in enumerate(lst_m): + B[:, i] = gauss(n, m=m, s=5) # loss matrix and normalization -M=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'euclidean') -M/=M.max() -M2=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'sqeuclidean') -M2/=M2.max() +M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'euclidean') +M /= M.max() +M2 = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'sqeuclidean') +M2 /= M2.max() #%% plot the distributions pl.figure(1) -pl.subplot(2,1,1) -pl.plot(x,a,'b',label='Source distribution') +pl.subplot(2, 1, 1) +pl.plot(x, a, 'b', label='Source distribution') pl.title('Source distribution') -pl.subplot(2,1,2) -pl.plot(x,B,label='Target distributions') +pl.subplot(2, 1, 2) +pl.plot(x, B, label='Target distributions') pl.title('Target distributions') +pl.tight_layout() #%% Compute and plot distributions and loss matrix -d_emd=ot.emd2(a,B,M) # direct computation of EMD -d_emd2=ot.emd2(a,B,M2) # direct computation of EMD with loss M3 +d_emd = ot.emd2(a, B, M) # direct computation of EMD +d_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M3 pl.figure(2) -pl.plot(d_emd,label='Euclidean EMD') -pl.plot(d_emd2,label='Squared Euclidean EMD') +pl.plot(d_emd, label='Euclidean EMD') +pl.plot(d_emd2, label='Squared Euclidean EMD') pl.title('EMD distances') pl.legend() #%% -reg=1e-2 -d_sinkhorn=ot.sinkhorn(a,B,M,reg) -d_sinkhorn2=ot.sinkhorn(a,B,M2,reg) +reg = 1e-2 +d_sinkhorn = ot.sinkhorn2(a, B, M, reg) +d_sinkhorn2 = ot.sinkhorn2(a, B, M2, reg) pl.figure(2) pl.clf() -pl.plot(d_emd,label='Euclidean EMD') -pl.plot(d_emd2,label='Squared Euclidean EMD') -pl.plot(d_sinkhorn,'+',label='Euclidean Sinkhorn') -pl.plot(d_sinkhorn2,'+',label='Squared Euclidean Sinkhorn') +pl.plot(d_emd, label='Euclidean EMD') +pl.plot(d_emd2, label='Squared Euclidean EMD') +pl.plot(d_sinkhorn, '+', label='Euclidean Sinkhorn') +pl.plot(d_sinkhorn2, '+', label='Squared Euclidean Sinkhorn') pl.title('EMD distances') -pl.legend() \ No newline at end of file +pl.legend() + +pl.show() diff --git a/docs/source/auto_examples/plot_compute_emd.rst b/docs/source/auto_examples/plot_compute_emd.rst index 4c7445b..f2e2005 100644 --- a/docs/source/auto_examples/plot_compute_emd.rst +++ b/docs/source/auto_examples/plot_compute_emd.rst @@ -7,7 +7,6 @@ 1D optimal transport ==================== -@author: rflamary @@ -32,6 +31,10 @@ .. code-block:: python + # Author: Remi Flamary + # + # License: MIT License + import numpy as np import matplotlib.pylab as pl import ot @@ -40,64 +43,68 @@ #%% parameters - n=100 # nb bins - n_target=50 # nb target distributions + n = 100 # nb bins + n_target = 50 # nb target distributions # bin positions - x=np.arange(n,dtype=np.float64) + x = np.arange(n, dtype=np.float64) - lst_m=np.linspace(20,90,n_target) + lst_m = np.linspace(20, 90, n_target) # Gaussian distributions - a=gauss(n,m=20,s=5) # m= mean, s= std + a = gauss(n, m=20, s=5) # m= mean, s= std - B=np.zeros((n,n_target)) + B = np.zeros((n, n_target)) - for i,m in enumerate(lst_m): - B[:,i]=gauss(n,m=m,s=5) + for i, m in enumerate(lst_m): + B[:, i] = gauss(n, m=m, s=5) # loss matrix and normalization - M=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'euclidean') - M/=M.max() - M2=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'sqeuclidean') - M2/=M2.max() + M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'euclidean') + M /= M.max() + M2 = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'sqeuclidean') + M2 /= M2.max() #%% plot the distributions pl.figure(1) - pl.subplot(2,1,1) - pl.plot(x,a,'b',label='Source distribution') + pl.subplot(2, 1, 1) + pl.plot(x, a, 'b', label='Source distribution') pl.title('Source distribution') - pl.subplot(2,1,2) - pl.plot(x,B,label='Target distributions') + pl.subplot(2, 1, 2) + pl.plot(x, B, label='Target distributions') pl.title('Target distributions') + pl.tight_layout() #%% Compute and plot distributions and loss matrix - d_emd=ot.emd2(a,B,M) # direct computation of EMD - d_emd2=ot.emd2(a,B,M2) # direct computation of EMD with loss M3 + d_emd = ot.emd2(a, B, M) # direct computation of EMD + d_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M3 pl.figure(2) - pl.plot(d_emd,label='Euclidean EMD') - pl.plot(d_emd2,label='Squared Euclidean EMD') + pl.plot(d_emd, label='Euclidean EMD') + pl.plot(d_emd2, label='Squared Euclidean EMD') pl.title('EMD distances') pl.legend() #%% - reg=1e-2 - d_sinkhorn=ot.sinkhorn(a,B,M,reg) - d_sinkhorn2=ot.sinkhorn(a,B,M2,reg) + reg = 1e-2 + d_sinkhorn = ot.sinkhorn2(a, B, M, reg) + d_sinkhorn2 = ot.sinkhorn2(a, B, M2, reg) pl.figure(2) pl.clf() - pl.plot(d_emd,label='Euclidean EMD') - pl.plot(d_emd2,label='Squared Euclidean EMD') - pl.plot(d_sinkhorn,'+',label='Euclidean Sinkhorn') - pl.plot(d_sinkhorn2,'+',label='Squared Euclidean Sinkhorn') + pl.plot(d_emd, label='Euclidean EMD') + pl.plot(d_emd2, label='Squared Euclidean EMD') + pl.plot(d_sinkhorn, '+', label='Euclidean Sinkhorn') + pl.plot(d_sinkhorn2, '+', label='Squared Euclidean Sinkhorn') pl.title('EMD distances') pl.legend() -**Total running time of the script:** ( 0 minutes 0.521 seconds) + + pl.show() + +**Total running time of the script:** ( 0 minutes 0.906 seconds) diff --git a/docs/source/auto_examples/plot_optim_OTreg.ipynb b/docs/source/auto_examples/plot_optim_OTreg.ipynb index 5ded922..0cb6ef2 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.ipynb +++ b/docs/source/auto_examples/plot_optim_OTreg.ipynb @@ -24,7 +24,7 @@ "execution_count": null, "cell_type": "code", "source": [ - "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n\n\n#%% parameters\n\nn=100 # nb bins\n\n# bin positions\nx=np.arange(n,dtype=np.float64)\n\n# Gaussian distributions\na=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std\nb=ot.datasets.get_1D_gauss(n,m=60,s=10)\n\n# loss matrix\nM=ot.dist(x.reshape((n,1)),x.reshape((n,1)))\nM/=M.max()\n\n#%% EMD\n\nG0=ot.emd(a,b,M)\n\npl.figure(3)\not.plot.plot1D_mat(a,b,G0,'OT matrix G0')\n\n#%% Example with Frobenius norm regularization\n\ndef f(G): return 0.5*np.sum(G**2)\ndef df(G): return G\n\nreg=1e-1\n\nGl2=ot.optim.cg(a,b,M,reg,f,df,verbose=True)\n\npl.figure(3)\not.plot.plot1D_mat(a,b,Gl2,'OT matrix Frob. reg')\n\n#%% Example with entropic regularization\n\ndef f(G): return np.sum(G*np.log(G))\ndef df(G): return np.log(G)+1\n\nreg=1e-3\n\nGe=ot.optim.cg(a,b,M,reg,f,df,verbose=True)\n\npl.figure(4)\not.plot.plot1D_mat(a,b,Ge,'OT matrix Entrop. reg')\n\n#%% Example with Frobenius norm + entropic regularization with gcg\n\ndef f(G): return 0.5*np.sum(G**2)\ndef df(G): return G\n\nreg1=1e-3\nreg2=1e-1\n\nGel2=ot.optim.gcg(a,b,M,reg1,reg2,f,df,verbose=True)\n\npl.figure(5)\not.plot.plot1D_mat(a,b,Gel2,'OT entropic + matrix Frob. reg')\npl.show()" + "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n\n#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std\nb = ot.datasets.get_1D_gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()\n\n#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')\n\n#%% Example with Frobenius norm regularization\n\n\ndef f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg = 1e-1\n\nGl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n\npl.figure(3)\not.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg')\n\n#%% Example with entropic regularization\n\n\ndef f(G):\n return np.sum(G * np.log(G))\n\n\ndef df(G):\n return np.log(G) + 1.\n\n\nreg = 1e-3\n\nGe = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg')\n\n#%% Example with Frobenius norm + entropic regularization with gcg\n\n\ndef f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg1 = 1e-3\nreg2 = 1e-1\n\nGel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True)\n\npl.figure(5, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg')\npl.show()" ], "outputs": [], "metadata": { diff --git a/docs/source/auto_examples/plot_optim_OTreg.py b/docs/source/auto_examples/plot_optim_OTreg.py index 8abb426..276b250 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.py +++ b/docs/source/auto_examples/plot_optim_OTreg.py @@ -12,63 +12,80 @@ import matplotlib.pylab as pl import ot - #%% parameters -n=100 # nb bins +n = 100 # nb bins # bin positions -x=np.arange(n,dtype=np.float64) +x = np.arange(n, dtype=np.float64) # Gaussian distributions -a=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std -b=ot.datasets.get_1D_gauss(n,m=60,s=10) +a = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std +b = ot.datasets.get_1D_gauss(n, m=60, s=10) # loss matrix -M=ot.dist(x.reshape((n,1)),x.reshape((n,1))) -M/=M.max() +M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) +M /= M.max() #%% EMD -G0=ot.emd(a,b,M) +G0 = ot.emd(a, b, M) -pl.figure(3) -ot.plot.plot1D_mat(a,b,G0,'OT matrix G0') +pl.figure(3, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') #%% Example with Frobenius norm regularization -def f(G): return 0.5*np.sum(G**2) -def df(G): return G -reg=1e-1 +def f(G): + return 0.5 * np.sum(G**2) + + +def df(G): + return G -Gl2=ot.optim.cg(a,b,M,reg,f,df,verbose=True) + +reg = 1e-1 + +Gl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True) pl.figure(3) -ot.plot.plot1D_mat(a,b,Gl2,'OT matrix Frob. reg') +ot.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg') #%% Example with entropic regularization -def f(G): return np.sum(G*np.log(G)) -def df(G): return np.log(G)+1 -reg=1e-3 +def f(G): + return np.sum(G * np.log(G)) + -Ge=ot.optim.cg(a,b,M,reg,f,df,verbose=True) +def df(G): + return np.log(G) + 1. -pl.figure(4) -ot.plot.plot1D_mat(a,b,Ge,'OT matrix Entrop. reg') + +reg = 1e-3 + +Ge = ot.optim.cg(a, b, M, reg, f, df, verbose=True) + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg') #%% Example with Frobenius norm + entropic regularization with gcg -def f(G): return 0.5*np.sum(G**2) -def df(G): return G -reg1=1e-3 -reg2=1e-1 +def f(G): + return 0.5 * np.sum(G**2) + + +def df(G): + return G + + +reg1 = 1e-3 +reg2 = 1e-1 -Gel2=ot.optim.gcg(a,b,M,reg1,reg2,f,df,verbose=True) +Gel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True) -pl.figure(5) -ot.plot.plot1D_mat(a,b,Gel2,'OT entropic + matrix Frob. reg') -pl.show() \ No newline at end of file +pl.figure(5, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg') +pl.show() diff --git a/docs/source/auto_examples/plot_optim_OTreg.rst b/docs/source/auto_examples/plot_optim_OTreg.rst index 70cd26c..f417158 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.rst +++ b/docs/source/auto_examples/plot_optim_OTreg.rst @@ -503,67 +503,85 @@ Regularized OT with generic solver import ot - #%% parameters - n=100 # nb bins + n = 100 # nb bins # bin positions - x=np.arange(n,dtype=np.float64) + x = np.arange(n, dtype=np.float64) # Gaussian distributions - a=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std - b=ot.datasets.get_1D_gauss(n,m=60,s=10) + a = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std + b = ot.datasets.get_1D_gauss(n, m=60, s=10) # loss matrix - M=ot.dist(x.reshape((n,1)),x.reshape((n,1))) - M/=M.max() + M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) + M /= M.max() #%% EMD - G0=ot.emd(a,b,M) + G0 = ot.emd(a, b, M) - pl.figure(3) - ot.plot.plot1D_mat(a,b,G0,'OT matrix G0') + pl.figure(3, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') #%% Example with Frobenius norm regularization - def f(G): return 0.5*np.sum(G**2) - def df(G): return G - reg=1e-1 + def f(G): + return 0.5 * np.sum(G**2) + + + def df(G): + return G - Gl2=ot.optim.cg(a,b,M,reg,f,df,verbose=True) + + reg = 1e-1 + + Gl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True) pl.figure(3) - ot.plot.plot1D_mat(a,b,Gl2,'OT matrix Frob. reg') + ot.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg') #%% Example with entropic regularization - def f(G): return np.sum(G*np.log(G)) - def df(G): return np.log(G)+1 - reg=1e-3 + def f(G): + return np.sum(G * np.log(G)) + + + def df(G): + return np.log(G) + 1. - Ge=ot.optim.cg(a,b,M,reg,f,df,verbose=True) - pl.figure(4) - ot.plot.plot1D_mat(a,b,Ge,'OT matrix Entrop. reg') + reg = 1e-3 + + Ge = ot.optim.cg(a, b, M, reg, f, df, verbose=True) + + pl.figure(4, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg') #%% Example with Frobenius norm + entropic regularization with gcg - def f(G): return 0.5*np.sum(G**2) - def df(G): return G - reg1=1e-3 - reg2=1e-1 + def f(G): + return 0.5 * np.sum(G**2) + + + def df(G): + return G - Gel2=ot.optim.gcg(a,b,M,reg1,reg2,f,df,verbose=True) - pl.figure(5) - ot.plot.plot1D_mat(a,b,Gel2,'OT entropic + matrix Frob. reg') + reg1 = 1e-3 + reg2 = 1e-1 + + Gel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True) + + pl.figure(5, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg') pl.show() -**Total running time of the script:** ( 0 minutes 2.319 seconds) + +**Total running time of the script:** ( 0 minutes 2.720 seconds) diff --git a/docs/source/conf.py b/docs/source/conf.py index b9f1f9d..ffdb1a2 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -261,7 +261,7 @@ latex_elements = { # author, documentclass [howto, manual, or own class]). latex_documents = [ (master_doc, 'POT.tex', u'POT Python Optimal Transport library', - u'Rémi Flamary, Nicolas Courty', 'manual'), + author, 'manual'), ] # The name of an image file (relative to this directory) to place at the top of @@ -305,7 +305,7 @@ man_pages = [ # dir menu entry, description, category) texinfo_documents = [ (master_doc, 'POT', u'POT Python Optimal Transport library Documentation', - author, 'POT', 'One line description of project.', + author, 'POT', 'Python Optimal Transport librar.', 'Miscellaneous'), ] @@ -326,7 +326,7 @@ texinfo_documents = [ intersphinx_mapping = {'https://docs.python.org/': None} sphinx_gallery_conf = { - 'examples_dirs': '../../examples', + 'examples_dirs': ['../../examples','../../examples/da'], 'gallery_dirs': 'auto_examples', 'mod_example_dir': '../modules/generated/', 'reference_url': { diff --git a/docs/source/readme.rst b/docs/source/readme.rst index c1e0017..e5c61f5 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -187,6 +187,9 @@ The contributors to this library are: - `Michael Perrot `__ (Mapping estimation) - `Léo Gautheron `__ (GPU implementation) +- `Nathalie + Gayraud `__ +- `Stanislas Chambon `__ This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various -- cgit v1.2.3 From 13d57f433c6a91797950318b6ca2896d5aaa009a Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 30 Aug 2017 17:01:22 +0200 Subject: move da --- examples/da/plot_otda_classes.py | 150 --------------------- examples/da/plot_otda_color_images.py | 165 ----------------------- examples/da/plot_otda_d2.py | 173 ------------------------- examples/da/plot_otda_mapping.py | 126 ------------------ examples/da/plot_otda_mapping_colors_images.py | 171 ------------------------ examples/plot_OT_2D_samples.py | 2 +- 6 files changed, 1 insertion(+), 786 deletions(-) delete mode 100644 examples/da/plot_otda_classes.py delete mode 100644 examples/da/plot_otda_color_images.py delete mode 100644 examples/da/plot_otda_d2.py delete mode 100644 examples/da/plot_otda_mapping.py delete mode 100644 examples/da/plot_otda_mapping_colors_images.py diff --git a/examples/da/plot_otda_classes.py b/examples/da/plot_otda_classes.py deleted file mode 100644 index ec57a37..0000000 --- a/examples/da/plot_otda_classes.py +++ /dev/null @@ -1,150 +0,0 @@ -# -*- coding: utf-8 -*- -""" -======================== -OT for domain adaptation -======================== - -This example introduces a domain adaptation in a 2D setting and the 4 OTDA -approaches currently supported in POT. - -""" - -# Authors: Remi Flamary -# Stanislas Chambon -# -# License: MIT License - -import matplotlib.pylab as pl -import ot - - -############################################################################## -# generate data -############################################################################## - -n_source_samples = 150 -n_target_samples = 150 - -Xs, ys = ot.datasets.get_data_classif('3gauss', n_source_samples) -Xt, yt = ot.datasets.get_data_classif('3gauss2', n_target_samples) - - -############################################################################## -# Instantiate the different transport algorithms and fit them -############################################################################## - -# EMD Transport -ot_emd = ot.da.EMDTransport() -ot_emd.fit(Xs=Xs, Xt=Xt) - -# Sinkhorn Transport -ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) -ot_sinkhorn.fit(Xs=Xs, Xt=Xt) - -# Sinkhorn Transport with Group lasso regularization -ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0) -ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt) - -# Sinkhorn Transport with Group lasso regularization l1l2 -ot_l1l2 = ot.da.SinkhornL1l2Transport(reg_e=1e-1, reg_cl=2e0, max_iter=20, - verbose=True) -ot_l1l2.fit(Xs=Xs, ys=ys, Xt=Xt) - -# transport source samples onto target samples -transp_Xs_emd = ot_emd.transform(Xs=Xs) -transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) -transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs) -transp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs) - - -############################################################################## -# Fig 1 : plots source and target samples -############################################################################## - -pl.figure(1, figsize=(10, 5)) -pl.subplot(1, 2, 1) -pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') -pl.xticks([]) -pl.yticks([]) -pl.legend(loc=0) -pl.title('Source samples') - -pl.subplot(1, 2, 2) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') -pl.xticks([]) -pl.yticks([]) -pl.legend(loc=0) -pl.title('Target samples') -pl.tight_layout() - - -############################################################################## -# Fig 2 : plot optimal couplings and transported samples -############################################################################## - -param_img = {'interpolation': 'nearest', 'cmap': 'spectral'} - -pl.figure(2, figsize=(15, 8)) -pl.subplot(2, 4, 1) -pl.imshow(ot_emd.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nEMDTransport') - -pl.subplot(2, 4, 2) -pl.imshow(ot_sinkhorn.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nSinkhornTransport') - -pl.subplot(2, 4, 3) -pl.imshow(ot_lpl1.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nSinkhornLpl1Transport') - -pl.subplot(2, 4, 4) -pl.imshow(ot_l1l2.coupling_, **param_img) -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nSinkhornL1l2Transport') - -pl.subplot(2, 4, 5) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.xticks([]) -pl.yticks([]) -pl.title('Transported samples\nEmdTransport') -pl.legend(loc="lower left") - -pl.subplot(2, 4, 6) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.xticks([]) -pl.yticks([]) -pl.title('Transported samples\nSinkhornTransport') - -pl.subplot(2, 4, 7) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.xticks([]) -pl.yticks([]) -pl.title('Transported samples\nSinkhornLpl1Transport') - -pl.subplot(2, 4, 8) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(transp_Xs_l1l2[:, 0], transp_Xs_l1l2[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.xticks([]) -pl.yticks([]) -pl.title('Transported samples\nSinkhornL1l2Transport') -pl.tight_layout() - -pl.show() diff --git a/examples/da/plot_otda_color_images.py b/examples/da/plot_otda_color_images.py deleted file mode 100644 index 3984afb..0000000 --- a/examples/da/plot_otda_color_images.py +++ /dev/null @@ -1,165 +0,0 @@ -# -*- coding: utf-8 -*- -""" -======================================================== -OT for domain adaptation with image color adaptation [6] -======================================================== - -This example presents a way of transferring colors between two image -with Optimal Transport as introduced in [6] - -[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). -Regularized discrete optimal transport. -SIAM Journal on Imaging Sciences, 7(3), 1853-1882. -""" - -# Authors: Remi Flamary -# Stanislas Chambon -# -# License: MIT License - -import numpy as np -from scipy import ndimage -import matplotlib.pylab as pl -import ot - - -r = np.random.RandomState(42) - - -def im2mat(I): - """Converts and image to matrix (one pixel per line)""" - return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) - - -def mat2im(X, shape): - """Converts back a matrix to an image""" - return X.reshape(shape) - - -def minmax(I): - return np.clip(I, 0, 1) - - -############################################################################## -# generate data -############################################################################## - -# Loading images -I1 = ndimage.imread('../../data/ocean_day.jpg').astype(np.float64) / 256 -I2 = ndimage.imread('../../data/ocean_sunset.jpg').astype(np.float64) / 256 - -X1 = im2mat(I1) -X2 = im2mat(I2) - -# training samples -nb = 1000 -idx1 = r.randint(X1.shape[0], size=(nb,)) -idx2 = r.randint(X2.shape[0], size=(nb,)) - -Xs = X1[idx1, :] -Xt = X2[idx2, :] - - -############################################################################## -# Instantiate the different transport algorithms and fit them -############################################################################## - -# EMDTransport -ot_emd = ot.da.EMDTransport() -ot_emd.fit(Xs=Xs, Xt=Xt) - -# SinkhornTransport -ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) -ot_sinkhorn.fit(Xs=Xs, Xt=Xt) - -# prediction between images (using out of sample prediction as in [6]) -transp_Xs_emd = ot_emd.transform(Xs=X1) -transp_Xt_emd = ot_emd.inverse_transform(Xt=X2) - -transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) -transp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2) - -I1t = minmax(mat2im(transp_Xs_emd, I1.shape)) -I2t = minmax(mat2im(transp_Xt_emd, I2.shape)) - -I1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) -I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape)) - - -############################################################################## -# plot original image -############################################################################## - -pl.figure(1, figsize=(6.4, 3)) - -pl.subplot(1, 2, 1) -pl.imshow(I1) -pl.axis('off') -pl.title('Image 1') - -pl.subplot(1, 2, 2) -pl.imshow(I2) -pl.axis('off') -pl.title('Image 2') - - -############################################################################## -# scatter plot of colors -############################################################################## - -pl.figure(2, figsize=(6.4, 3)) - -pl.subplot(1, 2, 1) -pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs) -pl.axis([0, 1, 0, 1]) -pl.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 1') - -pl.subplot(1, 2, 2) -pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt) -pl.axis([0, 1, 0, 1]) -pl.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 2') -pl.tight_layout() - - -############################################################################## -# plot new images -############################################################################## - -pl.figure(3, figsize=(8, 4)) - -pl.subplot(2, 3, 1) -pl.imshow(I1) -pl.axis('off') -pl.title('Image 1') - -pl.subplot(2, 3, 2) -pl.imshow(I1t) -pl.axis('off') -pl.title('Image 1 Adapt') - -pl.subplot(2, 3, 3) -pl.imshow(I1te) -pl.axis('off') -pl.title('Image 1 Adapt (reg)') - -pl.subplot(2, 3, 4) -pl.imshow(I2) -pl.axis('off') -pl.title('Image 2') - -pl.subplot(2, 3, 5) -pl.imshow(I2t) -pl.axis('off') -pl.title('Image 2 Adapt') - -pl.subplot(2, 3, 6) -pl.imshow(I2te) -pl.axis('off') -pl.title('Image 2 Adapt (reg)') -pl.tight_layout() - -pl.show() diff --git a/examples/da/plot_otda_d2.py b/examples/da/plot_otda_d2.py deleted file mode 100644 index 3daa0a6..0000000 --- a/examples/da/plot_otda_d2.py +++ /dev/null @@ -1,173 +0,0 @@ -# -*- coding: utf-8 -*- -""" -============================== -OT for empirical distributions -============================== - -This example introduces a domain adaptation in a 2D setting. It explicits -the problem of domain adaptation and introduces some optimal transport -approaches to solve it. - -Quantities such as optimal couplings, greater coupling coefficients and -transported samples are represented in order to give a visual understanding -of what the transport methods are doing. -""" - -# Authors: Remi Flamary -# Stanislas Chambon -# -# License: MIT License - -import matplotlib.pylab as pl -import ot - - -############################################################################## -# generate data -############################################################################## - -n_samples_source = 150 -n_samples_target = 150 - -Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source) -Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target) - -# Cost matrix -M = ot.dist(Xs, Xt, metric='sqeuclidean') - - -############################################################################## -# Instantiate the different transport algorithms and fit them -############################################################################## - -# EMD Transport -ot_emd = ot.da.EMDTransport() -ot_emd.fit(Xs=Xs, Xt=Xt) - -# Sinkhorn Transport -ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) -ot_sinkhorn.fit(Xs=Xs, Xt=Xt) - -# Sinkhorn Transport with Group lasso regularization -ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0) -ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt) - -# transport source samples onto target samples -transp_Xs_emd = ot_emd.transform(Xs=Xs) -transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) -transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs) - - -############################################################################## -# Fig 1 : plots source and target samples + matrix of pairwise distance -############################################################################## - -pl.figure(1, figsize=(10, 10)) -pl.subplot(2, 2, 1) -pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') -pl.xticks([]) -pl.yticks([]) -pl.legend(loc=0) -pl.title('Source samples') - -pl.subplot(2, 2, 2) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') -pl.xticks([]) -pl.yticks([]) -pl.legend(loc=0) -pl.title('Target samples') - -pl.subplot(2, 2, 3) -pl.imshow(M, interpolation='nearest') -pl.xticks([]) -pl.yticks([]) -pl.title('Matrix of pairwise distances') -pl.tight_layout() - - -############################################################################## -# Fig 2 : plots optimal couplings for the different methods -############################################################################## - -pl.figure(2, figsize=(10, 6)) - -pl.subplot(2, 3, 1) -pl.imshow(ot_emd.coupling_, interpolation='nearest') -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nEMDTransport') - -pl.subplot(2, 3, 2) -pl.imshow(ot_sinkhorn.coupling_, interpolation='nearest') -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nSinkhornTransport') - -pl.subplot(2, 3, 3) -pl.imshow(ot_lpl1.coupling_, interpolation='nearest') -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nSinkhornLpl1Transport') - -pl.subplot(2, 3, 4) -ot.plot.plot2D_samples_mat(Xs, Xt, ot_emd.coupling_, c=[.5, .5, 1]) -pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') -pl.xticks([]) -pl.yticks([]) -pl.title('Main coupling coefficients\nEMDTransport') - -pl.subplot(2, 3, 5) -ot.plot.plot2D_samples_mat(Xs, Xt, ot_sinkhorn.coupling_, c=[.5, .5, 1]) -pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') -pl.xticks([]) -pl.yticks([]) -pl.title('Main coupling coefficients\nSinkhornTransport') - -pl.subplot(2, 3, 6) -ot.plot.plot2D_samples_mat(Xs, Xt, ot_lpl1.coupling_, c=[.5, .5, 1]) -pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') -pl.xticks([]) -pl.yticks([]) -pl.title('Main coupling coefficients\nSinkhornLpl1Transport') -pl.tight_layout() - - -############################################################################## -# Fig 3 : plot transported samples -############################################################################## - -# display transported samples -pl.figure(4, figsize=(10, 4)) -pl.subplot(1, 3, 1) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.5) -pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Transported samples\nEmdTransport') -pl.legend(loc=0) -pl.xticks([]) -pl.yticks([]) - -pl.subplot(1, 3, 2) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.5) -pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Transported samples\nSinkhornTransport') -pl.xticks([]) -pl.yticks([]) - -pl.subplot(1, 3, 3) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.5) -pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Transported samples\nSinkhornLpl1Transport') -pl.xticks([]) -pl.yticks([]) - -pl.tight_layout() -pl.show() diff --git a/examples/da/plot_otda_mapping.py b/examples/da/plot_otda_mapping.py deleted file mode 100644 index 09d2cb4..0000000 --- a/examples/da/plot_otda_mapping.py +++ /dev/null @@ -1,126 +0,0 @@ -# -*- coding: utf-8 -*- -""" -=============================================== -OT mapping estimation for domain adaptation [8] -=============================================== - -This example presents how to use MappingTransport to estimate at the same -time both the coupling transport and approximate the transport map with either -a linear or a kernelized mapping as introduced in [8] - -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, - "Mapping estimation for discrete optimal transport", - Neural Information Processing Systems (NIPS), 2016. -""" - -# Authors: Remi Flamary -# Stanislas Chambon -# -# License: MIT License - -import numpy as np -import matplotlib.pylab as pl -import ot - - -############################################################################## -# generate data -############################################################################## - -n_source_samples = 100 -n_target_samples = 100 -theta = 2 * np.pi / 20 -noise_level = 0.1 - -Xs, ys = ot.datasets.get_data_classif( - 'gaussrot', n_source_samples, nz=noise_level) -Xs_new, _ = ot.datasets.get_data_classif( - 'gaussrot', n_source_samples, nz=noise_level) -Xt, yt = ot.datasets.get_data_classif( - 'gaussrot', n_target_samples, theta=theta, nz=noise_level) - -# one of the target mode changes its variance (no linear mapping) -Xt[yt == 2] *= 3 -Xt = Xt + 4 - - -############################################################################## -# Instantiate the different transport algorithms and fit them -############################################################################## - -# MappingTransport with linear kernel -ot_mapping_linear = ot.da.MappingTransport( - kernel="linear", mu=1e0, eta=1e-8, bias=True, - max_iter=20, verbose=True) - -ot_mapping_linear.fit(Xs=Xs, Xt=Xt) - -# for original source samples, transform applies barycentric mapping -transp_Xs_linear = ot_mapping_linear.transform(Xs=Xs) - -# for out of source samples, transform applies the linear mapping -transp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new) - - -# MappingTransport with gaussian kernel -ot_mapping_gaussian = ot.da.MappingTransport( - kernel="gaussian", eta=1e-5, mu=1e-1, bias=True, sigma=1, - max_iter=10, verbose=True) -ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt) - -# for original source samples, transform applies barycentric mapping -transp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs) - -# for out of source samples, transform applies the gaussian mapping -transp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new) - - -############################################################################## -# plot data -############################################################################## - -pl.figure(1, (10, 5)) -pl.clf() -pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') -pl.legend(loc=0) -pl.title('Source and target distributions') - - -############################################################################## -# plot transported samples -############################################################################## - -pl.figure(2) -pl.clf() -pl.subplot(2, 2, 1) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=.2) -pl.scatter(transp_Xs_linear[:, 0], transp_Xs_linear[:, 1], c=ys, marker='+', - label='Mapped source samples') -pl.title("Bary. mapping (linear)") -pl.legend(loc=0) - -pl.subplot(2, 2, 2) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=.2) -pl.scatter(transp_Xs_linear_new[:, 0], transp_Xs_linear_new[:, 1], - c=ys, marker='+', label='Learned mapping') -pl.title("Estim. mapping (linear)") - -pl.subplot(2, 2, 3) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=.2) -pl.scatter(transp_Xs_gaussian[:, 0], transp_Xs_gaussian[:, 1], c=ys, - marker='+', label='barycentric mapping') -pl.title("Bary. mapping (kernel)") - -pl.subplot(2, 2, 4) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=.2) -pl.scatter(transp_Xs_gaussian_new[:, 0], transp_Xs_gaussian_new[:, 1], c=ys, - marker='+', label='Learned mapping') -pl.title("Estim. mapping (kernel)") -pl.tight_layout() - -pl.show() diff --git a/examples/da/plot_otda_mapping_colors_images.py b/examples/da/plot_otda_mapping_colors_images.py deleted file mode 100644 index a628b05..0000000 --- a/examples/da/plot_otda_mapping_colors_images.py +++ /dev/null @@ -1,171 +0,0 @@ -# -*- coding: utf-8 -*- -""" -==================================================================================== -OT for domain adaptation with image color adaptation [6] with mapping estimation [8] -==================================================================================== - -[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized - discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), - 1853-1882. -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for - discrete optimal transport", Neural Information Processing Systems (NIPS), - 2016. - -""" - -# Authors: Remi Flamary -# Stanislas Chambon -# -# License: MIT License - -import numpy as np -from scipy import ndimage -import matplotlib.pylab as pl -import ot - -r = np.random.RandomState(42) - - -def im2mat(I): - """Converts and image to matrix (one pixel per line)""" - return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) - - -def mat2im(X, shape): - """Converts back a matrix to an image""" - return X.reshape(shape) - - -def minmax(I): - return np.clip(I, 0, 1) - - -############################################################################## -# Generate data -############################################################################## - -# Loading images -I1 = ndimage.imread('../../data/ocean_day.jpg').astype(np.float64) / 256 -I2 = ndimage.imread('../../data/ocean_sunset.jpg').astype(np.float64) / 256 - - -X1 = im2mat(I1) -X2 = im2mat(I2) - -# training samples -nb = 1000 -idx1 = r.randint(X1.shape[0], size=(nb,)) -idx2 = r.randint(X2.shape[0], size=(nb,)) - -Xs = X1[idx1, :] -Xt = X2[idx2, :] - - -############################################################################## -# Domain adaptation for pixel distribution transfer -############################################################################## - -# EMDTransport -ot_emd = ot.da.EMDTransport() -ot_emd.fit(Xs=Xs, Xt=Xt) -transp_Xs_emd = ot_emd.transform(Xs=X1) -Image_emd = minmax(mat2im(transp_Xs_emd, I1.shape)) - -# SinkhornTransport -ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) -ot_sinkhorn.fit(Xs=Xs, Xt=Xt) -transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) -Image_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) - -ot_mapping_linear = ot.da.MappingTransport( - mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True) -ot_mapping_linear.fit(Xs=Xs, Xt=Xt) - -X1tl = ot_mapping_linear.transform(Xs=X1) -Image_mapping_linear = minmax(mat2im(X1tl, I1.shape)) - -ot_mapping_gaussian = ot.da.MappingTransport( - mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True) -ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt) - -X1tn = ot_mapping_gaussian.transform(Xs=X1) # use the estimated mapping -Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape)) - - -############################################################################## -# plot original images -############################################################################## - -pl.figure(1, figsize=(6.4, 3)) -pl.subplot(1, 2, 1) -pl.imshow(I1) -pl.axis('off') -pl.title('Image 1') - -pl.subplot(1, 2, 2) -pl.imshow(I2) -pl.axis('off') -pl.title('Image 2') -pl.tight_layout() - - -############################################################################## -# plot pixel values distribution -############################################################################## - -pl.figure(2, figsize=(6.4, 5)) - -pl.subplot(1, 2, 1) -pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs) -pl.axis([0, 1, 0, 1]) -pl.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 1') - -pl.subplot(1, 2, 2) -pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt) -pl.axis([0, 1, 0, 1]) -pl.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 2') -pl.tight_layout() - - -############################################################################## -# plot transformed images -############################################################################## - -pl.figure(2, figsize=(10, 5)) - -pl.subplot(2, 3, 1) -pl.imshow(I1) -pl.axis('off') -pl.title('Im. 1') - -pl.subplot(2, 3, 4) -pl.imshow(I2) -pl.axis('off') -pl.title('Im. 2') - -pl.subplot(2, 3, 2) -pl.imshow(Image_emd) -pl.axis('off') -pl.title('EmdTransport') - -pl.subplot(2, 3, 5) -pl.imshow(Image_sinkhorn) -pl.axis('off') -pl.title('SinkhornTransport') - -pl.subplot(2, 3, 3) -pl.imshow(Image_mapping_linear) -pl.axis('off') -pl.title('MappingTransport (linear)') - -pl.subplot(2, 3, 6) -pl.imshow(Image_mapping_gaussian) -pl.axis('off') -pl.title('MappingTransport (gaussian)') -pl.tight_layout() - -pl.show() diff --git a/examples/plot_OT_2D_samples.py b/examples/plot_OT_2D_samples.py index 023e645..2a42dc0 100644 --- a/examples/plot_OT_2D_samples.py +++ b/examples/plot_OT_2D_samples.py @@ -65,7 +65,7 @@ pl.title('OT matrix with samples') #%% sinkhorn # reg term -lambd = 5e-4 +lambd = 1e-3 Gs = ot.sinkhorn(a, b, M, lambd) -- cgit v1.2.3 From db9ae2546efafd358dd6f8823136cb362fe87f5b Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 30 Aug 2017 17:02:14 +0200 Subject: add da exmaples --- examples/plot_otda_classes.py | 150 ++++++++++++++++++++++++ examples/plot_otda_color_images.py | 165 ++++++++++++++++++++++++++ examples/plot_otda_d2.py | 173 ++++++++++++++++++++++++++++ examples/plot_otda_mapping.py | 126 ++++++++++++++++++++ examples/plot_otda_mapping_colors_images.py | 171 +++++++++++++++++++++++++++ 5 files changed, 785 insertions(+) create mode 100644 examples/plot_otda_classes.py create mode 100644 examples/plot_otda_color_images.py create mode 100644 examples/plot_otda_d2.py create mode 100644 examples/plot_otda_mapping.py create mode 100644 examples/plot_otda_mapping_colors_images.py diff --git a/examples/plot_otda_classes.py b/examples/plot_otda_classes.py new file mode 100644 index 0000000..ec57a37 --- /dev/null +++ b/examples/plot_otda_classes.py @@ -0,0 +1,150 @@ +# -*- coding: utf-8 -*- +""" +======================== +OT for domain adaptation +======================== + +This example introduces a domain adaptation in a 2D setting and the 4 OTDA +approaches currently supported in POT. + +""" + +# Authors: Remi Flamary +# Stanislas Chambon +# +# License: MIT License + +import matplotlib.pylab as pl +import ot + + +############################################################################## +# generate data +############################################################################## + +n_source_samples = 150 +n_target_samples = 150 + +Xs, ys = ot.datasets.get_data_classif('3gauss', n_source_samples) +Xt, yt = ot.datasets.get_data_classif('3gauss2', n_target_samples) + + +############################################################################## +# Instantiate the different transport algorithms and fit them +############################################################################## + +# EMD Transport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) + +# Sinkhorn Transport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + +# Sinkhorn Transport with Group lasso regularization +ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0) +ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt) + +# Sinkhorn Transport with Group lasso regularization l1l2 +ot_l1l2 = ot.da.SinkhornL1l2Transport(reg_e=1e-1, reg_cl=2e0, max_iter=20, + verbose=True) +ot_l1l2.fit(Xs=Xs, ys=ys, Xt=Xt) + +# transport source samples onto target samples +transp_Xs_emd = ot_emd.transform(Xs=Xs) +transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) +transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs) +transp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs) + + +############################################################################## +# Fig 1 : plots source and target samples +############################################################################## + +pl.figure(1, figsize=(10, 5)) +pl.subplot(1, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Source samples') + +pl.subplot(1, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Target samples') +pl.tight_layout() + + +############################################################################## +# Fig 2 : plot optimal couplings and transported samples +############################################################################## + +param_img = {'interpolation': 'nearest', 'cmap': 'spectral'} + +pl.figure(2, figsize=(15, 8)) +pl.subplot(2, 4, 1) +pl.imshow(ot_emd.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nEMDTransport') + +pl.subplot(2, 4, 2) +pl.imshow(ot_sinkhorn.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornTransport') + +pl.subplot(2, 4, 3) +pl.imshow(ot_lpl1.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornLpl1Transport') + +pl.subplot(2, 4, 4) +pl.imshow(ot_l1l2.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornL1l2Transport') + +pl.subplot(2, 4, 5) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nEmdTransport') +pl.legend(loc="lower left") + +pl.subplot(2, 4, 6) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nSinkhornTransport') + +pl.subplot(2, 4, 7) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nSinkhornLpl1Transport') + +pl.subplot(2, 4, 8) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_l1l2[:, 0], transp_Xs_l1l2[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nSinkhornL1l2Transport') +pl.tight_layout() + +pl.show() diff --git a/examples/plot_otda_color_images.py b/examples/plot_otda_color_images.py new file mode 100644 index 0000000..46ad44b --- /dev/null +++ b/examples/plot_otda_color_images.py @@ -0,0 +1,165 @@ +# -*- coding: utf-8 -*- +""" +======================================================== +OT for domain adaptation with image color adaptation [6] +======================================================== + +This example presents a way of transferring colors between two image +with Optimal Transport as introduced in [6] + +[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). +Regularized discrete optimal transport. +SIAM Journal on Imaging Sciences, 7(3), 1853-1882. +""" + +# Authors: Remi Flamary +# Stanislas Chambon +# +# License: MIT License + +import numpy as np +from scipy import ndimage +import matplotlib.pylab as pl +import ot + + +r = np.random.RandomState(42) + + +def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + + +def mat2im(X, shape): + """Converts back a matrix to an image""" + return X.reshape(shape) + + +def minmax(I): + return np.clip(I, 0, 1) + + +############################################################################## +# generate data +############################################################################## + +# Loading images +I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256 +I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 + +X1 = im2mat(I1) +X2 = im2mat(I2) + +# training samples +nb = 1000 +idx1 = r.randint(X1.shape[0], size=(nb,)) +idx2 = r.randint(X2.shape[0], size=(nb,)) + +Xs = X1[idx1, :] +Xt = X2[idx2, :] + + +############################################################################## +# Instantiate the different transport algorithms and fit them +############################################################################## + +# EMDTransport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) + +# SinkhornTransport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + +# prediction between images (using out of sample prediction as in [6]) +transp_Xs_emd = ot_emd.transform(Xs=X1) +transp_Xt_emd = ot_emd.inverse_transform(Xt=X2) + +transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) +transp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2) + +I1t = minmax(mat2im(transp_Xs_emd, I1.shape)) +I2t = minmax(mat2im(transp_Xt_emd, I2.shape)) + +I1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) +I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape)) + + +############################################################################## +# plot original image +############################################################################## + +pl.figure(1, figsize=(6.4, 3)) + +pl.subplot(1, 2, 1) +pl.imshow(I1) +pl.axis('off') +pl.title('Image 1') + +pl.subplot(1, 2, 2) +pl.imshow(I2) +pl.axis('off') +pl.title('Image 2') + + +############################################################################## +# scatter plot of colors +############################################################################## + +pl.figure(2, figsize=(6.4, 3)) + +pl.subplot(1, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs) +pl.axis([0, 1, 0, 1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 1') + +pl.subplot(1, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt) +pl.axis([0, 1, 0, 1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 2') +pl.tight_layout() + + +############################################################################## +# plot new images +############################################################################## + +pl.figure(3, figsize=(8, 4)) + +pl.subplot(2, 3, 1) +pl.imshow(I1) +pl.axis('off') +pl.title('Image 1') + +pl.subplot(2, 3, 2) +pl.imshow(I1t) +pl.axis('off') +pl.title('Image 1 Adapt') + +pl.subplot(2, 3, 3) +pl.imshow(I1te) +pl.axis('off') +pl.title('Image 1 Adapt (reg)') + +pl.subplot(2, 3, 4) +pl.imshow(I2) +pl.axis('off') +pl.title('Image 2') + +pl.subplot(2, 3, 5) +pl.imshow(I2t) +pl.axis('off') +pl.title('Image 2 Adapt') + +pl.subplot(2, 3, 6) +pl.imshow(I2te) +pl.axis('off') +pl.title('Image 2 Adapt (reg)') +pl.tight_layout() + +pl.show() diff --git a/examples/plot_otda_d2.py b/examples/plot_otda_d2.py new file mode 100644 index 0000000..3daa0a6 --- /dev/null +++ b/examples/plot_otda_d2.py @@ -0,0 +1,173 @@ +# -*- coding: utf-8 -*- +""" +============================== +OT for empirical distributions +============================== + +This example introduces a domain adaptation in a 2D setting. It explicits +the problem of domain adaptation and introduces some optimal transport +approaches to solve it. + +Quantities such as optimal couplings, greater coupling coefficients and +transported samples are represented in order to give a visual understanding +of what the transport methods are doing. +""" + +# Authors: Remi Flamary +# Stanislas Chambon +# +# License: MIT License + +import matplotlib.pylab as pl +import ot + + +############################################################################## +# generate data +############################################################################## + +n_samples_source = 150 +n_samples_target = 150 + +Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source) +Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target) + +# Cost matrix +M = ot.dist(Xs, Xt, metric='sqeuclidean') + + +############################################################################## +# Instantiate the different transport algorithms and fit them +############################################################################## + +# EMD Transport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) + +# Sinkhorn Transport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + +# Sinkhorn Transport with Group lasso regularization +ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0) +ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt) + +# transport source samples onto target samples +transp_Xs_emd = ot_emd.transform(Xs=Xs) +transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) +transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs) + + +############################################################################## +# Fig 1 : plots source and target samples + matrix of pairwise distance +############################################################################## + +pl.figure(1, figsize=(10, 10)) +pl.subplot(2, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Source samples') + +pl.subplot(2, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Target samples') + +pl.subplot(2, 2, 3) +pl.imshow(M, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Matrix of pairwise distances') +pl.tight_layout() + + +############################################################################## +# Fig 2 : plots optimal couplings for the different methods +############################################################################## + +pl.figure(2, figsize=(10, 6)) + +pl.subplot(2, 3, 1) +pl.imshow(ot_emd.coupling_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nEMDTransport') + +pl.subplot(2, 3, 2) +pl.imshow(ot_sinkhorn.coupling_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornTransport') + +pl.subplot(2, 3, 3) +pl.imshow(ot_lpl1.coupling_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornLpl1Transport') + +pl.subplot(2, 3, 4) +ot.plot.plot2D_samples_mat(Xs, Xt, ot_emd.coupling_, c=[.5, .5, 1]) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.title('Main coupling coefficients\nEMDTransport') + +pl.subplot(2, 3, 5) +ot.plot.plot2D_samples_mat(Xs, Xt, ot_sinkhorn.coupling_, c=[.5, .5, 1]) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.title('Main coupling coefficients\nSinkhornTransport') + +pl.subplot(2, 3, 6) +ot.plot.plot2D_samples_mat(Xs, Xt, ot_lpl1.coupling_, c=[.5, .5, 1]) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.title('Main coupling coefficients\nSinkhornLpl1Transport') +pl.tight_layout() + + +############################################################################## +# Fig 3 : plot transported samples +############################################################################## + +# display transported samples +pl.figure(4, figsize=(10, 4)) +pl.subplot(1, 3, 1) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Transported samples\nEmdTransport') +pl.legend(loc=0) +pl.xticks([]) +pl.yticks([]) + +pl.subplot(1, 3, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Transported samples\nSinkhornTransport') +pl.xticks([]) +pl.yticks([]) + +pl.subplot(1, 3, 3) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Transported samples\nSinkhornLpl1Transport') +pl.xticks([]) +pl.yticks([]) + +pl.tight_layout() +pl.show() diff --git a/examples/plot_otda_mapping.py b/examples/plot_otda_mapping.py new file mode 100644 index 0000000..09d2cb4 --- /dev/null +++ b/examples/plot_otda_mapping.py @@ -0,0 +1,126 @@ +# -*- coding: utf-8 -*- +""" +=============================================== +OT mapping estimation for domain adaptation [8] +=============================================== + +This example presents how to use MappingTransport to estimate at the same +time both the coupling transport and approximate the transport map with either +a linear or a kernelized mapping as introduced in [8] + +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, + "Mapping estimation for discrete optimal transport", + Neural Information Processing Systems (NIPS), 2016. +""" + +# Authors: Remi Flamary +# Stanislas Chambon +# +# License: MIT License + +import numpy as np +import matplotlib.pylab as pl +import ot + + +############################################################################## +# generate data +############################################################################## + +n_source_samples = 100 +n_target_samples = 100 +theta = 2 * np.pi / 20 +noise_level = 0.1 + +Xs, ys = ot.datasets.get_data_classif( + 'gaussrot', n_source_samples, nz=noise_level) +Xs_new, _ = ot.datasets.get_data_classif( + 'gaussrot', n_source_samples, nz=noise_level) +Xt, yt = ot.datasets.get_data_classif( + 'gaussrot', n_target_samples, theta=theta, nz=noise_level) + +# one of the target mode changes its variance (no linear mapping) +Xt[yt == 2] *= 3 +Xt = Xt + 4 + + +############################################################################## +# Instantiate the different transport algorithms and fit them +############################################################################## + +# MappingTransport with linear kernel +ot_mapping_linear = ot.da.MappingTransport( + kernel="linear", mu=1e0, eta=1e-8, bias=True, + max_iter=20, verbose=True) + +ot_mapping_linear.fit(Xs=Xs, Xt=Xt) + +# for original source samples, transform applies barycentric mapping +transp_Xs_linear = ot_mapping_linear.transform(Xs=Xs) + +# for out of source samples, transform applies the linear mapping +transp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new) + + +# MappingTransport with gaussian kernel +ot_mapping_gaussian = ot.da.MappingTransport( + kernel="gaussian", eta=1e-5, mu=1e-1, bias=True, sigma=1, + max_iter=10, verbose=True) +ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt) + +# for original source samples, transform applies barycentric mapping +transp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs) + +# for out of source samples, transform applies the gaussian mapping +transp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new) + + +############################################################################## +# plot data +############################################################################## + +pl.figure(1, (10, 5)) +pl.clf() +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.legend(loc=0) +pl.title('Source and target distributions') + + +############################################################################## +# plot transported samples +############################################################################## + +pl.figure(2) +pl.clf() +pl.subplot(2, 2, 1) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) +pl.scatter(transp_Xs_linear[:, 0], transp_Xs_linear[:, 1], c=ys, marker='+', + label='Mapped source samples') +pl.title("Bary. mapping (linear)") +pl.legend(loc=0) + +pl.subplot(2, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) +pl.scatter(transp_Xs_linear_new[:, 0], transp_Xs_linear_new[:, 1], + c=ys, marker='+', label='Learned mapping') +pl.title("Estim. mapping (linear)") + +pl.subplot(2, 2, 3) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) +pl.scatter(transp_Xs_gaussian[:, 0], transp_Xs_gaussian[:, 1], c=ys, + marker='+', label='barycentric mapping') +pl.title("Bary. mapping (kernel)") + +pl.subplot(2, 2, 4) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) +pl.scatter(transp_Xs_gaussian_new[:, 0], transp_Xs_gaussian_new[:, 1], c=ys, + marker='+', label='Learned mapping') +pl.title("Estim. mapping (kernel)") +pl.tight_layout() + +pl.show() diff --git a/examples/plot_otda_mapping_colors_images.py b/examples/plot_otda_mapping_colors_images.py new file mode 100644 index 0000000..936206c --- /dev/null +++ b/examples/plot_otda_mapping_colors_images.py @@ -0,0 +1,171 @@ +# -*- coding: utf-8 -*- +""" +==================================================================================== +OT for domain adaptation with image color adaptation [6] with mapping estimation [8] +==================================================================================== + +[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized + discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), + 1853-1882. +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for + discrete optimal transport", Neural Information Processing Systems (NIPS), + 2016. + +""" + +# Authors: Remi Flamary +# Stanislas Chambon +# +# License: MIT License + +import numpy as np +from scipy import ndimage +import matplotlib.pylab as pl +import ot + +r = np.random.RandomState(42) + + +def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + + +def mat2im(X, shape): + """Converts back a matrix to an image""" + return X.reshape(shape) + + +def minmax(I): + return np.clip(I, 0, 1) + + +############################################################################## +# Generate data +############################################################################## + +# Loading images +I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256 +I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 + + +X1 = im2mat(I1) +X2 = im2mat(I2) + +# training samples +nb = 1000 +idx1 = r.randint(X1.shape[0], size=(nb,)) +idx2 = r.randint(X2.shape[0], size=(nb,)) + +Xs = X1[idx1, :] +Xt = X2[idx2, :] + + +############################################################################## +# Domain adaptation for pixel distribution transfer +############################################################################## + +# EMDTransport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) +transp_Xs_emd = ot_emd.transform(Xs=X1) +Image_emd = minmax(mat2im(transp_Xs_emd, I1.shape)) + +# SinkhornTransport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) +transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) +Image_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) + +ot_mapping_linear = ot.da.MappingTransport( + mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True) +ot_mapping_linear.fit(Xs=Xs, Xt=Xt) + +X1tl = ot_mapping_linear.transform(Xs=X1) +Image_mapping_linear = minmax(mat2im(X1tl, I1.shape)) + +ot_mapping_gaussian = ot.da.MappingTransport( + mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True) +ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt) + +X1tn = ot_mapping_gaussian.transform(Xs=X1) # use the estimated mapping +Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape)) + + +############################################################################## +# plot original images +############################################################################## + +pl.figure(1, figsize=(6.4, 3)) +pl.subplot(1, 2, 1) +pl.imshow(I1) +pl.axis('off') +pl.title('Image 1') + +pl.subplot(1, 2, 2) +pl.imshow(I2) +pl.axis('off') +pl.title('Image 2') +pl.tight_layout() + + +############################################################################## +# plot pixel values distribution +############################################################################## + +pl.figure(2, figsize=(6.4, 5)) + +pl.subplot(1, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs) +pl.axis([0, 1, 0, 1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 1') + +pl.subplot(1, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt) +pl.axis([0, 1, 0, 1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 2') +pl.tight_layout() + + +############################################################################## +# plot transformed images +############################################################################## + +pl.figure(2, figsize=(10, 5)) + +pl.subplot(2, 3, 1) +pl.imshow(I1) +pl.axis('off') +pl.title('Im. 1') + +pl.subplot(2, 3, 4) +pl.imshow(I2) +pl.axis('off') +pl.title('Im. 2') + +pl.subplot(2, 3, 2) +pl.imshow(Image_emd) +pl.axis('off') +pl.title('EmdTransport') + +pl.subplot(2, 3, 5) +pl.imshow(Image_sinkhorn) +pl.axis('off') +pl.title('SinkhornTransport') + +pl.subplot(2, 3, 3) +pl.imshow(Image_mapping_linear) +pl.axis('off') +pl.title('MappingTransport (linear)') + +pl.subplot(2, 3, 6) +pl.imshow(Image_mapping_gaussian) +pl.axis('off') +pl.title('MappingTransport (gaussian)') +pl.tight_layout() + +pl.show() -- cgit v1.2.3 From ab5918b2e2dc88a3520c059e6a79a6f81959381e Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 30 Aug 2017 17:02:59 +0200 Subject: add files and notebooks --- .../auto_examples/images/sphx_glr_plot_WDA_002.png | Bin 0 -> 90982 bytes .../images/sphx_glr_plot_otda_classes_001.png | Bin 0 -> 50114 bytes .../images/sphx_glr_plot_otda_classes_003.png | Bin 0 -> 194170 bytes .../images/sphx_glr_plot_otda_color_images_001.png | Bin 0 -> 144957 bytes .../images/sphx_glr_plot_otda_color_images_003.png | Bin 0 -> 50401 bytes .../images/sphx_glr_plot_otda_color_images_005.png | Bin 0 -> 234337 bytes .../images/sphx_glr_plot_otda_d2_001.png | Bin 0 -> 130439 bytes .../images/sphx_glr_plot_otda_d2_003.png | Bin 0 -> 224757 bytes .../images/sphx_glr_plot_otda_d2_006.png | Bin 0 -> 99742 bytes .../images/sphx_glr_plot_otda_mapping_001.png | Bin 0 -> 35810 bytes .../images/sphx_glr_plot_otda_mapping_003.png | Bin 0 -> 71391 bytes 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adaptation\n\n\nThis example introduces a domain adaptation in a 2D setting and the 4 OTDA\napproaches currently supported in POT.\n\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport ot" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "generate data\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "n_source_samples = 150\nn_target_samples = 150\n\nXs, ys = ot.datasets.get_data_classif('3gauss', n_source_samples)\nXt, yt = ot.datasets.get_data_classif('3gauss2', n_target_samples)" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Instantiate the different transport algorithms and fit them\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "# EMD Transport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport with Group lasso regularization\not_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)\not_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt)\n\n# Sinkhorn Transport with Group lasso regularization l1l2\not_l1l2 = ot.da.SinkhornL1l2Transport(reg_e=1e-1, reg_cl=2e0, max_iter=20,\n verbose=True)\not_l1l2.fit(Xs=Xs, ys=ys, Xt=Xt)\n\n# transport source samples onto target samples\ntransp_Xs_emd = ot_emd.transform(Xs=Xs)\ntransp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)\ntransp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs)\ntransp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs)" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Fig 1 : plots source and target samples\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "pl.figure(1, figsize=(10, 5))\npl.subplot(1, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Source samples')\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Target samples')\npl.tight_layout()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Fig 2 : plot optimal couplings and transported samples\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "param_img = {'interpolation': 'nearest', 'cmap': 'spectral'}\n\npl.figure(2, figsize=(15, 8))\npl.subplot(2, 4, 1)\npl.imshow(ot_emd.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nEMDTransport')\n\npl.subplot(2, 4, 2)\npl.imshow(ot_sinkhorn.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornTransport')\n\npl.subplot(2, 4, 3)\npl.imshow(ot_lpl1.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornLpl1Transport')\n\npl.subplot(2, 4, 4)\npl.imshow(ot_l1l2.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornL1l2Transport')\n\npl.subplot(2, 4, 5)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nEmdTransport')\npl.legend(loc=\"lower left\")\n\npl.subplot(2, 4, 6)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nSinkhornTransport')\n\npl.subplot(2, 4, 7)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nSinkhornLpl1Transport')\n\npl.subplot(2, 4, 8)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_l1l2[:, 0], transp_Xs_l1l2[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nSinkhornL1l2Transport')\npl.tight_layout()\n\npl.show()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "name": "python2", + "language": "python" + }, + "language_info": { + "mimetype": "text/x-python", + "nbconvert_exporter": "python", + "name": "python", + "file_extension": ".py", + "version": "2.7.12", + "pygments_lexer": "ipython2", + "codemirror_mode": { + "version": 2, + "name": "ipython" + } + } + } +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_classes.py b/docs/source/auto_examples/plot_otda_classes.py new file mode 100644 index 0000000..ec57a37 --- /dev/null +++ b/docs/source/auto_examples/plot_otda_classes.py @@ -0,0 +1,150 @@ +# -*- coding: utf-8 -*- +""" +======================== +OT for domain adaptation +======================== + +This example introduces a domain adaptation in a 2D setting and the 4 OTDA +approaches currently supported in POT. + +""" + +# Authors: Remi Flamary +# Stanislas Chambon +# +# License: MIT License + +import matplotlib.pylab as pl +import ot + + +############################################################################## +# generate data +############################################################################## + +n_source_samples = 150 +n_target_samples = 150 + +Xs, ys = ot.datasets.get_data_classif('3gauss', n_source_samples) +Xt, yt = ot.datasets.get_data_classif('3gauss2', n_target_samples) + + +############################################################################## +# Instantiate the different transport algorithms and fit them +############################################################################## + +# EMD Transport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) + +# Sinkhorn Transport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + +# Sinkhorn Transport with Group lasso regularization +ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0) +ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt) + +# Sinkhorn Transport with Group lasso regularization l1l2 +ot_l1l2 = ot.da.SinkhornL1l2Transport(reg_e=1e-1, reg_cl=2e0, max_iter=20, + verbose=True) +ot_l1l2.fit(Xs=Xs, ys=ys, Xt=Xt) + +# transport source samples onto target samples +transp_Xs_emd = ot_emd.transform(Xs=Xs) +transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) +transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs) +transp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs) + + +############################################################################## +# Fig 1 : plots source and target samples +############################################################################## + +pl.figure(1, figsize=(10, 5)) +pl.subplot(1, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Source samples') + +pl.subplot(1, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Target samples') +pl.tight_layout() + + +############################################################################## +# Fig 2 : plot optimal couplings and transported samples +############################################################################## + +param_img = {'interpolation': 'nearest', 'cmap': 'spectral'} + +pl.figure(2, figsize=(15, 8)) +pl.subplot(2, 4, 1) +pl.imshow(ot_emd.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nEMDTransport') + +pl.subplot(2, 4, 2) +pl.imshow(ot_sinkhorn.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornTransport') + +pl.subplot(2, 4, 3) +pl.imshow(ot_lpl1.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornLpl1Transport') + +pl.subplot(2, 4, 4) +pl.imshow(ot_l1l2.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornL1l2Transport') + +pl.subplot(2, 4, 5) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nEmdTransport') +pl.legend(loc="lower left") + +pl.subplot(2, 4, 6) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nSinkhornTransport') + +pl.subplot(2, 4, 7) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nSinkhornLpl1Transport') + +pl.subplot(2, 4, 8) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_l1l2[:, 0], transp_Xs_l1l2[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nSinkhornL1l2Transport') +pl.tight_layout() + +pl.show() diff --git a/docs/source/auto_examples/plot_otda_classes.rst b/docs/source/auto_examples/plot_otda_classes.rst new file mode 100644 index 0000000..227a819 --- /dev/null +++ b/docs/source/auto_examples/plot_otda_classes.rst @@ -0,0 +1,258 @@ + + +.. _sphx_glr_auto_examples_plot_otda_classes.py: + + +======================== +OT for domain adaptation +======================== + +This example introduces a domain adaptation in a 2D setting and the 4 OTDA +approaches currently supported in POT. + + + + +.. code-block:: python + + + # Authors: Remi Flamary + # Stanislas Chambon + # + # License: MIT License + + import matplotlib.pylab as pl + import ot + + + + + + + + +generate data +############################################################################# + + + +.. code-block:: python + + + n_source_samples = 150 + n_target_samples = 150 + + Xs, ys = ot.datasets.get_data_classif('3gauss', n_source_samples) + Xt, yt = ot.datasets.get_data_classif('3gauss2', n_target_samples) + + + + + + + + +Instantiate the different transport algorithms and fit them +############################################################################# + + + +.. code-block:: python + + + # EMD Transport + ot_emd = ot.da.EMDTransport() + ot_emd.fit(Xs=Xs, Xt=Xt) + + # Sinkhorn Transport + ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) + ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + + # Sinkhorn Transport with Group lasso regularization + ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0) + ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt) + + # Sinkhorn Transport with Group lasso regularization l1l2 + ot_l1l2 = ot.da.SinkhornL1l2Transport(reg_e=1e-1, reg_cl=2e0, max_iter=20, + verbose=True) + ot_l1l2.fit(Xs=Xs, ys=ys, Xt=Xt) + + # transport source samples onto target samples + transp_Xs_emd = ot_emd.transform(Xs=Xs) + transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) + transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs) + transp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs) + + + + + + +.. rst-class:: sphx-glr-script-out + + Out:: + + It. |Loss |Delta loss + -------------------------------- + 0|9.456043e+00|0.000000e+00 + 1|2.059035e+00|-3.592463e+00 + 2|1.839814e+00|-1.191540e-01 + 3|1.787860e+00|-2.905942e-02 + 4|1.766582e+00|-1.204485e-02 + 5|1.760573e+00|-3.413038e-03 + 6|1.755288e+00|-3.010556e-03 + 7|1.749124e+00|-3.523968e-03 + 8|1.744159e+00|-2.846760e-03 + 9|1.741007e+00|-1.810862e-03 + 10|1.739839e+00|-6.710130e-04 + 11|1.737221e+00|-1.507260e-03 + 12|1.736011e+00|-6.970742e-04 + 13|1.734948e+00|-6.126425e-04 + 14|1.733901e+00|-6.038775e-04 + 15|1.733768e+00|-7.618542e-05 + 16|1.732821e+00|-5.467723e-04 + 17|1.732678e+00|-8.226843e-05 + 18|1.731934e+00|-4.300066e-04 + 19|1.731850e+00|-4.848002e-05 + It. |Loss |Delta loss + -------------------------------- + 20|1.731699e+00|-8.729590e-05 + + +Fig 1 : plots source and target samples +############################################################################# + + + +.. code-block:: python + + + pl.figure(1, figsize=(10, 5)) + pl.subplot(1, 2, 1) + pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') + pl.xticks([]) + pl.yticks([]) + pl.legend(loc=0) + pl.title('Source samples') + + pl.subplot(1, 2, 2) + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') + pl.xticks([]) + pl.yticks([]) + pl.legend(loc=0) + pl.title('Target samples') + pl.tight_layout() + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_classes_001.png + :align: center + + + + +Fig 2 : plot optimal couplings and transported samples +############################################################################# + + + +.. code-block:: python + + + param_img = {'interpolation': 'nearest', 'cmap': 'spectral'} + + pl.figure(2, figsize=(15, 8)) + pl.subplot(2, 4, 1) + pl.imshow(ot_emd.coupling_, **param_img) + pl.xticks([]) + pl.yticks([]) + pl.title('Optimal coupling\nEMDTransport') + + pl.subplot(2, 4, 2) + pl.imshow(ot_sinkhorn.coupling_, **param_img) + pl.xticks([]) + pl.yticks([]) + pl.title('Optimal coupling\nSinkhornTransport') + + pl.subplot(2, 4, 3) + pl.imshow(ot_lpl1.coupling_, **param_img) + pl.xticks([]) + pl.yticks([]) + pl.title('Optimal coupling\nSinkhornLpl1Transport') + + pl.subplot(2, 4, 4) + pl.imshow(ot_l1l2.coupling_, **param_img) + pl.xticks([]) + pl.yticks([]) + pl.title('Optimal coupling\nSinkhornL1l2Transport') + + pl.subplot(2, 4, 5) + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) + pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, + marker='+', label='Transp samples', s=30) + pl.xticks([]) + pl.yticks([]) + pl.title('Transported samples\nEmdTransport') + pl.legend(loc="lower left") + + pl.subplot(2, 4, 6) + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) + pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, + marker='+', label='Transp samples', s=30) + pl.xticks([]) + pl.yticks([]) + pl.title('Transported samples\nSinkhornTransport') + + pl.subplot(2, 4, 7) + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) + pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys, + marker='+', label='Transp samples', s=30) + pl.xticks([]) + pl.yticks([]) + pl.title('Transported samples\nSinkhornLpl1Transport') + + pl.subplot(2, 4, 8) + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) + pl.scatter(transp_Xs_l1l2[:, 0], transp_Xs_l1l2[:, 1], c=ys, + marker='+', label='Transp samples', s=30) + pl.xticks([]) + pl.yticks([]) + pl.title('Transported samples\nSinkhornL1l2Transport') + pl.tight_layout() + + pl.show() + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_classes_003.png + :align: center + + + + +**Total running time of the script:** ( 0 minutes 1.906 seconds) + + + +.. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_otda_classes.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_otda_classes.ipynb ` + +.. rst-class:: sphx-glr-signature + + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_color_images.ipynb b/docs/source/auto_examples/plot_otda_color_images.ipynb new file mode 100644 index 0000000..c45c307 --- /dev/null +++ b/docs/source/auto_examples/plot_otda_color_images.ipynb @@ -0,0 +1,144 @@ +{ + "nbformat_minor": 0, + "nbformat": 4, + "cells": [ + { + "execution_count": null, + "cell_type": "code", + "source": [ + "%matplotlib inline" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "\n========================================================\nOT for domain adaptation with image color adaptation [6]\n========================================================\n\nThis example presents a way of transferring colors between two image\nwith Optimal Transport as introduced in [6]\n\n[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014).\nRegularized discrete optimal transport.\nSIAM Journal on Imaging Sciences, 7(3), 1853-1882.\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport numpy as np\nfrom scipy import ndimage\nimport matplotlib.pylab as pl\nimport ot\n\n\nr = np.random.RandomState(42)\n\n\ndef im2mat(I):\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\ndef mat2im(X, shape):\n \"\"\"Converts back a matrix to an image\"\"\"\n return X.reshape(shape)\n\n\ndef minmax(I):\n return np.clip(I, 0, 1)" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "generate data\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "# Loading images\nI1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256\nI2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n\nX1 = im2mat(I1)\nX2 = im2mat(I2)\n\n# training samples\nnb = 1000\nidx1 = r.randint(X1.shape[0], size=(nb,))\nidx2 = r.randint(X2.shape[0], size=(nb,))\n\nXs = X1[idx1, :]\nXt = X2[idx2, :]" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Instantiate the different transport algorithms and fit them\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "# EMDTransport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# SinkhornTransport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# prediction between images (using out of sample prediction as in [6])\ntransp_Xs_emd = ot_emd.transform(Xs=X1)\ntransp_Xt_emd = ot_emd.inverse_transform(Xt=X2)\n\ntransp_Xs_sinkhorn = ot_emd.transform(Xs=X1)\ntransp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2)\n\nI1t = minmax(mat2im(transp_Xs_emd, I1.shape))\nI2t = minmax(mat2im(transp_Xt_emd, I2.shape))\n\nI1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))\nI2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape))" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "plot original image\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "pl.figure(1, figsize=(6.4, 3))\n\npl.subplot(1, 2, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.imshow(I2)\npl.axis('off')\npl.title('Image 2')" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "scatter plot of colors\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "pl.figure(2, figsize=(6.4, 3))\n\npl.subplot(1, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 2')\npl.tight_layout()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "plot new images\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "pl.figure(3, figsize=(8, 4))\n\npl.subplot(2, 3, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Image 1')\n\npl.subplot(2, 3, 2)\npl.imshow(I1t)\npl.axis('off')\npl.title('Image 1 Adapt')\n\npl.subplot(2, 3, 3)\npl.imshow(I1te)\npl.axis('off')\npl.title('Image 1 Adapt (reg)')\n\npl.subplot(2, 3, 4)\npl.imshow(I2)\npl.axis('off')\npl.title('Image 2')\n\npl.subplot(2, 3, 5)\npl.imshow(I2t)\npl.axis('off')\npl.title('Image 2 Adapt')\n\npl.subplot(2, 3, 6)\npl.imshow(I2te)\npl.axis('off')\npl.title('Image 2 Adapt (reg)')\npl.tight_layout()\n\npl.show()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "name": "python2", + "language": "python" + }, + "language_info": { + "mimetype": "text/x-python", + "nbconvert_exporter": "python", + "name": "python", + "file_extension": ".py", + "version": "2.7.12", + "pygments_lexer": "ipython2", + "codemirror_mode": { + "version": 2, + "name": "ipython" + } + } + } +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_color_images.py b/docs/source/auto_examples/plot_otda_color_images.py new file mode 100644 index 0000000..46ad44b --- /dev/null +++ b/docs/source/auto_examples/plot_otda_color_images.py @@ -0,0 +1,165 @@ +# -*- coding: utf-8 -*- +""" +======================================================== +OT for domain adaptation with image color adaptation [6] +======================================================== + +This example presents a way of transferring colors between two image +with Optimal Transport as introduced in [6] + +[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). +Regularized discrete optimal transport. +SIAM Journal on Imaging Sciences, 7(3), 1853-1882. +""" + +# Authors: Remi Flamary +# Stanislas Chambon +# +# License: MIT License + +import numpy as np +from scipy import ndimage +import matplotlib.pylab as pl +import ot + + +r = np.random.RandomState(42) + + +def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + + +def mat2im(X, shape): + """Converts back a matrix to an image""" + return X.reshape(shape) + + +def minmax(I): + return np.clip(I, 0, 1) + + +############################################################################## +# generate data +############################################################################## + +# Loading images +I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256 +I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 + +X1 = im2mat(I1) +X2 = im2mat(I2) + +# training samples +nb = 1000 +idx1 = r.randint(X1.shape[0], size=(nb,)) +idx2 = r.randint(X2.shape[0], size=(nb,)) + +Xs = X1[idx1, :] +Xt = X2[idx2, :] + + +############################################################################## +# Instantiate the different transport algorithms and fit them +############################################################################## + +# EMDTransport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) + +# SinkhornTransport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + +# prediction between images (using out of sample prediction as in [6]) +transp_Xs_emd = ot_emd.transform(Xs=X1) +transp_Xt_emd = ot_emd.inverse_transform(Xt=X2) + +transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) +transp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2) + +I1t = minmax(mat2im(transp_Xs_emd, I1.shape)) +I2t = minmax(mat2im(transp_Xt_emd, I2.shape)) + +I1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) +I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape)) + + +############################################################################## +# plot original image +############################################################################## + +pl.figure(1, figsize=(6.4, 3)) + +pl.subplot(1, 2, 1) +pl.imshow(I1) +pl.axis('off') +pl.title('Image 1') + +pl.subplot(1, 2, 2) +pl.imshow(I2) +pl.axis('off') +pl.title('Image 2') + + +############################################################################## +# scatter plot of colors +############################################################################## + +pl.figure(2, figsize=(6.4, 3)) + +pl.subplot(1, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs) +pl.axis([0, 1, 0, 1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 1') + +pl.subplot(1, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt) +pl.axis([0, 1, 0, 1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 2') +pl.tight_layout() + + +############################################################################## +# plot new images +############################################################################## + +pl.figure(3, figsize=(8, 4)) + +pl.subplot(2, 3, 1) +pl.imshow(I1) +pl.axis('off') +pl.title('Image 1') + +pl.subplot(2, 3, 2) +pl.imshow(I1t) +pl.axis('off') +pl.title('Image 1 Adapt') + +pl.subplot(2, 3, 3) +pl.imshow(I1te) +pl.axis('off') +pl.title('Image 1 Adapt (reg)') + +pl.subplot(2, 3, 4) +pl.imshow(I2) +pl.axis('off') +pl.title('Image 2') + +pl.subplot(2, 3, 5) +pl.imshow(I2t) +pl.axis('off') +pl.title('Image 2 Adapt') + +pl.subplot(2, 3, 6) +pl.imshow(I2te) +pl.axis('off') +pl.title('Image 2 Adapt (reg)') +pl.tight_layout() + +pl.show() diff --git a/docs/source/auto_examples/plot_otda_color_images.rst b/docs/source/auto_examples/plot_otda_color_images.rst new file mode 100644 index 0000000..e3989c8 --- /dev/null +++ b/docs/source/auto_examples/plot_otda_color_images.rst @@ -0,0 +1,257 @@ + + +.. _sphx_glr_auto_examples_plot_otda_color_images.py: + + +======================================================== +OT for domain adaptation with image color adaptation [6] +======================================================== + +This example presents a way of transferring colors between two image +with Optimal Transport as introduced in [6] + +[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). +Regularized discrete optimal transport. +SIAM Journal on Imaging Sciences, 7(3), 1853-1882. + + + +.. code-block:: python + + + # Authors: Remi Flamary + # Stanislas Chambon + # + # License: MIT License + + import numpy as np + from scipy import ndimage + import matplotlib.pylab as pl + import ot + + + r = np.random.RandomState(42) + + + def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + + + def mat2im(X, shape): + """Converts back a matrix to an image""" + return X.reshape(shape) + + + def minmax(I): + return np.clip(I, 0, 1) + + + + + + + + +generate data +############################################################################# + + + +.. code-block:: python + + + # Loading images + I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256 + I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 + + X1 = im2mat(I1) + X2 = im2mat(I2) + + # training samples + nb = 1000 + idx1 = r.randint(X1.shape[0], size=(nb,)) + idx2 = r.randint(X2.shape[0], size=(nb,)) + + Xs = X1[idx1, :] + Xt = X2[idx2, :] + + + + + + + + +Instantiate the different transport algorithms and fit them +############################################################################# + + + +.. code-block:: python + + + # EMDTransport + ot_emd = ot.da.EMDTransport() + ot_emd.fit(Xs=Xs, Xt=Xt) + + # SinkhornTransport + ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) + ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + + # prediction between images (using out of sample prediction as in [6]) + transp_Xs_emd = ot_emd.transform(Xs=X1) + transp_Xt_emd = ot_emd.inverse_transform(Xt=X2) + + transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) + transp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2) + + I1t = minmax(mat2im(transp_Xs_emd, I1.shape)) + I2t = minmax(mat2im(transp_Xt_emd, I2.shape)) + + I1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) + I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape)) + + + + + + + + +plot original image +############################################################################# + + + +.. code-block:: python + + + pl.figure(1, figsize=(6.4, 3)) + + pl.subplot(1, 2, 1) + pl.imshow(I1) + pl.axis('off') + pl.title('Image 1') + + pl.subplot(1, 2, 2) + pl.imshow(I2) + pl.axis('off') + pl.title('Image 2') + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_color_images_001.png + :align: center + + + + +scatter plot of colors +############################################################################# + + + +.. code-block:: python + + + pl.figure(2, figsize=(6.4, 3)) + + pl.subplot(1, 2, 1) + pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs) + pl.axis([0, 1, 0, 1]) + pl.xlabel('Red') + pl.ylabel('Blue') + pl.title('Image 1') + + pl.subplot(1, 2, 2) + pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt) + pl.axis([0, 1, 0, 1]) + pl.xlabel('Red') + pl.ylabel('Blue') + pl.title('Image 2') + pl.tight_layout() + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_color_images_003.png + :align: center + + + + +plot new images +############################################################################# + + + +.. code-block:: python + + + pl.figure(3, figsize=(8, 4)) + + pl.subplot(2, 3, 1) + pl.imshow(I1) + pl.axis('off') + pl.title('Image 1') + + pl.subplot(2, 3, 2) + pl.imshow(I1t) + pl.axis('off') + pl.title('Image 1 Adapt') + + pl.subplot(2, 3, 3) + pl.imshow(I1te) + pl.axis('off') + pl.title('Image 1 Adapt (reg)') + + pl.subplot(2, 3, 4) + pl.imshow(I2) + pl.axis('off') + pl.title('Image 2') + + pl.subplot(2, 3, 5) + pl.imshow(I2t) + pl.axis('off') + pl.title('Image 2 Adapt') + + pl.subplot(2, 3, 6) + pl.imshow(I2te) + pl.axis('off') + pl.title('Image 2 Adapt (reg)') + pl.tight_layout() + + pl.show() + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_color_images_005.png + :align: center + + + + +**Total running time of the script:** ( 3 minutes 16.043 seconds) + + + +.. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_otda_color_images.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_otda_color_images.ipynb ` + +.. rst-class:: sphx-glr-signature + + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_d2.ipynb b/docs/source/auto_examples/plot_otda_d2.ipynb new file mode 100644 index 0000000..2331f8c --- /dev/null +++ b/docs/source/auto_examples/plot_otda_d2.ipynb @@ -0,0 +1,144 @@ +{ + "nbformat_minor": 0, + "nbformat": 4, + "cells": [ + { + "execution_count": null, + "cell_type": "code", + "source": [ + "%matplotlib inline" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "\n# OT for empirical distributions\n\n\nThis example introduces a domain adaptation in a 2D setting. It explicits\nthe problem of domain adaptation and introduces some optimal transport\napproaches to solve it.\n\nQuantities such as optimal couplings, greater coupling coefficients and\ntransported samples are represented in order to give a visual understanding\nof what the transport methods are doing.\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport ot" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "generate data\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "n_samples_source = 150\nn_samples_target = 150\n\nXs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source)\nXt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target)\n\n# Cost matrix\nM = ot.dist(Xs, Xt, metric='sqeuclidean')" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Instantiate the different transport algorithms and fit them\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "# EMD Transport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport with Group lasso regularization\not_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)\not_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt)\n\n# transport source samples onto target samples\ntransp_Xs_emd = ot_emd.transform(Xs=Xs)\ntransp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)\ntransp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs)" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Fig 1 : plots source and target samples + matrix of pairwise distance\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "pl.figure(1, figsize=(10, 10))\npl.subplot(2, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Source samples')\n\npl.subplot(2, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Target samples')\n\npl.subplot(2, 2, 3)\npl.imshow(M, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Matrix of pairwise distances')\npl.tight_layout()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Fig 2 : plots optimal couplings for the different methods\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "pl.figure(2, figsize=(10, 6))\n\npl.subplot(2, 3, 1)\npl.imshow(ot_emd.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nEMDTransport')\n\npl.subplot(2, 3, 2)\npl.imshow(ot_sinkhorn.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornTransport')\n\npl.subplot(2, 3, 3)\npl.imshow(ot_lpl1.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornLpl1Transport')\n\npl.subplot(2, 3, 4)\not.plot.plot2D_samples_mat(Xs, Xt, ot_emd.coupling_, c=[.5, .5, 1])\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.title('Main coupling coefficients\\nEMDTransport')\n\npl.subplot(2, 3, 5)\not.plot.plot2D_samples_mat(Xs, Xt, ot_sinkhorn.coupling_, c=[.5, .5, 1])\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.title('Main coupling coefficients\\nSinkhornTransport')\n\npl.subplot(2, 3, 6)\not.plot.plot2D_samples_mat(Xs, Xt, ot_lpl1.coupling_, c=[.5, .5, 1])\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.title('Main coupling coefficients\\nSinkhornLpl1Transport')\npl.tight_layout()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Fig 3 : plot transported samples\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "# display transported samples\npl.figure(4, figsize=(10, 4))\npl.subplot(1, 3, 1)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nEmdTransport')\npl.legend(loc=0)\npl.xticks([])\npl.yticks([])\n\npl.subplot(1, 3, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nSinkhornTransport')\npl.xticks([])\npl.yticks([])\n\npl.subplot(1, 3, 3)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nSinkhornLpl1Transport')\npl.xticks([])\npl.yticks([])\n\npl.tight_layout()\npl.show()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "name": "python2", + "language": "python" + }, + "language_info": { + "mimetype": "text/x-python", + "nbconvert_exporter": "python", + "name": "python", + "file_extension": ".py", + "version": "2.7.12", + "pygments_lexer": "ipython2", + "codemirror_mode": { + "version": 2, + "name": "ipython" + } + } + } +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_d2.py b/docs/source/auto_examples/plot_otda_d2.py new file mode 100644 index 0000000..3daa0a6 --- /dev/null +++ b/docs/source/auto_examples/plot_otda_d2.py @@ -0,0 +1,173 @@ +# -*- coding: utf-8 -*- +""" +============================== +OT for empirical distributions +============================== + +This example introduces a domain adaptation in a 2D setting. It explicits +the problem of domain adaptation and introduces some optimal transport +approaches to solve it. + +Quantities such as optimal couplings, greater coupling coefficients and +transported samples are represented in order to give a visual understanding +of what the transport methods are doing. +""" + +# Authors: Remi Flamary +# Stanislas Chambon +# +# License: MIT License + +import matplotlib.pylab as pl +import ot + + +############################################################################## +# generate data +############################################################################## + +n_samples_source = 150 +n_samples_target = 150 + +Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source) +Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target) + +# Cost matrix +M = ot.dist(Xs, Xt, metric='sqeuclidean') + + +############################################################################## +# Instantiate the different transport algorithms and fit them +############################################################################## + +# EMD Transport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) + +# Sinkhorn Transport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + +# Sinkhorn Transport with Group lasso regularization +ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0) +ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt) + +# transport source samples onto target samples +transp_Xs_emd = ot_emd.transform(Xs=Xs) +transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) +transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs) + + +############################################################################## +# Fig 1 : plots source and target samples + matrix of pairwise distance +############################################################################## + +pl.figure(1, figsize=(10, 10)) +pl.subplot(2, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Source samples') + +pl.subplot(2, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Target samples') + +pl.subplot(2, 2, 3) +pl.imshow(M, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Matrix of pairwise distances') +pl.tight_layout() + + +############################################################################## +# Fig 2 : plots optimal couplings for the different methods +############################################################################## + +pl.figure(2, figsize=(10, 6)) + +pl.subplot(2, 3, 1) +pl.imshow(ot_emd.coupling_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nEMDTransport') + +pl.subplot(2, 3, 2) +pl.imshow(ot_sinkhorn.coupling_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornTransport') + +pl.subplot(2, 3, 3) +pl.imshow(ot_lpl1.coupling_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornLpl1Transport') + +pl.subplot(2, 3, 4) +ot.plot.plot2D_samples_mat(Xs, Xt, ot_emd.coupling_, c=[.5, .5, 1]) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.title('Main coupling coefficients\nEMDTransport') + +pl.subplot(2, 3, 5) +ot.plot.plot2D_samples_mat(Xs, Xt, ot_sinkhorn.coupling_, c=[.5, .5, 1]) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.title('Main coupling coefficients\nSinkhornTransport') + +pl.subplot(2, 3, 6) +ot.plot.plot2D_samples_mat(Xs, Xt, ot_lpl1.coupling_, c=[.5, .5, 1]) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.title('Main coupling coefficients\nSinkhornLpl1Transport') +pl.tight_layout() + + +############################################################################## +# Fig 3 : plot transported samples +############################################################################## + +# display transported samples +pl.figure(4, figsize=(10, 4)) +pl.subplot(1, 3, 1) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Transported samples\nEmdTransport') +pl.legend(loc=0) +pl.xticks([]) +pl.yticks([]) + +pl.subplot(1, 3, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Transported samples\nSinkhornTransport') +pl.xticks([]) +pl.yticks([]) + +pl.subplot(1, 3, 3) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Transported samples\nSinkhornLpl1Transport') +pl.xticks([]) +pl.yticks([]) + +pl.tight_layout() +pl.show() diff --git a/docs/source/auto_examples/plot_otda_d2.rst b/docs/source/auto_examples/plot_otda_d2.rst new file mode 100644 index 0000000..20b76ba --- /dev/null +++ b/docs/source/auto_examples/plot_otda_d2.rst @@ -0,0 +1,265 @@ + + +.. _sphx_glr_auto_examples_plot_otda_d2.py: + + +============================== +OT for empirical distributions +============================== + +This example introduces a domain adaptation in a 2D setting. It explicits +the problem of domain adaptation and introduces some optimal transport +approaches to solve it. + +Quantities such as optimal couplings, greater coupling coefficients and +transported samples are represented in order to give a visual understanding +of what the transport methods are doing. + + + +.. code-block:: python + + + # Authors: Remi Flamary + # Stanislas Chambon + # + # License: MIT License + + import matplotlib.pylab as pl + import ot + + + + + + + + +generate data +############################################################################# + + + +.. code-block:: python + + + n_samples_source = 150 + n_samples_target = 150 + + Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source) + Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target) + + # Cost matrix + M = ot.dist(Xs, Xt, metric='sqeuclidean') + + + + + + + + +Instantiate the different transport algorithms and fit them +############################################################################# + + + +.. code-block:: python + + + # EMD Transport + ot_emd = ot.da.EMDTransport() + ot_emd.fit(Xs=Xs, Xt=Xt) + + # Sinkhorn Transport + ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) + ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + + # Sinkhorn Transport with Group lasso regularization + ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0) + ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt) + + # transport source samples onto target samples + transp_Xs_emd = ot_emd.transform(Xs=Xs) + transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) + transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs) + + + + + + + + +Fig 1 : plots source and target samples + matrix of pairwise distance +############################################################################# + + + +.. code-block:: python + + + pl.figure(1, figsize=(10, 10)) + pl.subplot(2, 2, 1) + pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') + pl.xticks([]) + pl.yticks([]) + pl.legend(loc=0) + pl.title('Source samples') + + pl.subplot(2, 2, 2) + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') + pl.xticks([]) + pl.yticks([]) + pl.legend(loc=0) + pl.title('Target samples') + + pl.subplot(2, 2, 3) + pl.imshow(M, interpolation='nearest') + pl.xticks([]) + pl.yticks([]) + pl.title('Matrix of pairwise distances') + pl.tight_layout() + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_d2_001.png + :align: center + + + + +Fig 2 : plots optimal couplings for the different methods +############################################################################# + + + +.. code-block:: python + + + pl.figure(2, figsize=(10, 6)) + + pl.subplot(2, 3, 1) + pl.imshow(ot_emd.coupling_, interpolation='nearest') + pl.xticks([]) + pl.yticks([]) + pl.title('Optimal coupling\nEMDTransport') + + pl.subplot(2, 3, 2) + pl.imshow(ot_sinkhorn.coupling_, interpolation='nearest') + pl.xticks([]) + pl.yticks([]) + pl.title('Optimal coupling\nSinkhornTransport') + + pl.subplot(2, 3, 3) + pl.imshow(ot_lpl1.coupling_, interpolation='nearest') + pl.xticks([]) + pl.yticks([]) + pl.title('Optimal coupling\nSinkhornLpl1Transport') + + pl.subplot(2, 3, 4) + ot.plot.plot2D_samples_mat(Xs, Xt, ot_emd.coupling_, c=[.5, .5, 1]) + pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') + pl.xticks([]) + pl.yticks([]) + pl.title('Main coupling coefficients\nEMDTransport') + + pl.subplot(2, 3, 5) + ot.plot.plot2D_samples_mat(Xs, Xt, ot_sinkhorn.coupling_, c=[.5, .5, 1]) + pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') + pl.xticks([]) + pl.yticks([]) + pl.title('Main coupling coefficients\nSinkhornTransport') + + pl.subplot(2, 3, 6) + ot.plot.plot2D_samples_mat(Xs, Xt, ot_lpl1.coupling_, c=[.5, .5, 1]) + pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') + pl.xticks([]) + pl.yticks([]) + pl.title('Main coupling coefficients\nSinkhornLpl1Transport') + pl.tight_layout() + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_d2_003.png + :align: center + + + + +Fig 3 : plot transported samples +############################################################################# + + + +.. code-block:: python + + + # display transported samples + pl.figure(4, figsize=(10, 4)) + pl.subplot(1, 3, 1) + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) + pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, + marker='+', label='Transp samples', s=30) + pl.title('Transported samples\nEmdTransport') + pl.legend(loc=0) + pl.xticks([]) + pl.yticks([]) + + pl.subplot(1, 3, 2) + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) + pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, + marker='+', label='Transp samples', s=30) + pl.title('Transported samples\nSinkhornTransport') + pl.xticks([]) + pl.yticks([]) + + pl.subplot(1, 3, 3) + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) + pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys, + marker='+', label='Transp samples', s=30) + pl.title('Transported samples\nSinkhornLpl1Transport') + pl.xticks([]) + pl.yticks([]) + + pl.tight_layout() + pl.show() + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_d2_006.png + :align: center + + + + +**Total running time of the script:** ( 0 minutes 46.009 seconds) + + + +.. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_otda_d2.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_otda_d2.ipynb ` + +.. rst-class:: sphx-glr-signature + + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_mapping.ipynb b/docs/source/auto_examples/plot_otda_mapping.ipynb new file mode 100644 index 0000000..0b5ca5c --- /dev/null +++ b/docs/source/auto_examples/plot_otda_mapping.ipynb @@ -0,0 +1,126 @@ +{ + "nbformat_minor": 0, + "nbformat": 4, + "cells": [ + { + "execution_count": null, + "cell_type": "code", + "source": [ + "%matplotlib inline" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "\n===============================================\nOT mapping estimation for domain adaptation [8]\n===============================================\n\nThis example presents how to use MappingTransport to estimate at the same\ntime both the coupling transport and approximate the transport map with either\na linear or a kernelized mapping as introduced in [8]\n\n[8] M. Perrot, N. Courty, R. Flamary, A. Habrard,\n \"Mapping estimation for discrete optimal transport\",\n Neural Information Processing Systems (NIPS), 2016.\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "generate data\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "n_source_samples = 100\nn_target_samples = 100\ntheta = 2 * np.pi / 20\nnoise_level = 0.1\n\nXs, ys = ot.datasets.get_data_classif(\n 'gaussrot', n_source_samples, nz=noise_level)\nXs_new, _ = ot.datasets.get_data_classif(\n 'gaussrot', n_source_samples, nz=noise_level)\nXt, yt = ot.datasets.get_data_classif(\n 'gaussrot', n_target_samples, theta=theta, nz=noise_level)\n\n# one of the target mode changes its variance (no linear mapping)\nXt[yt == 2] *= 3\nXt = Xt + 4" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Instantiate the different transport algorithms and fit them\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "# MappingTransport with linear kernel\not_mapping_linear = ot.da.MappingTransport(\n kernel=\"linear\", mu=1e0, eta=1e-8, bias=True,\n max_iter=20, verbose=True)\n\not_mapping_linear.fit(Xs=Xs, Xt=Xt)\n\n# for original source samples, transform applies barycentric mapping\ntransp_Xs_linear = ot_mapping_linear.transform(Xs=Xs)\n\n# for out of source samples, transform applies the linear mapping\ntransp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new)\n\n\n# MappingTransport with gaussian kernel\not_mapping_gaussian = ot.da.MappingTransport(\n kernel=\"gaussian\", eta=1e-5, mu=1e-1, bias=True, sigma=1,\n max_iter=10, verbose=True)\not_mapping_gaussian.fit(Xs=Xs, Xt=Xt)\n\n# for original source samples, transform applies barycentric mapping\ntransp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs)\n\n# for out of source samples, transform applies the gaussian mapping\ntransp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new)" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "plot data\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "pl.figure(1, (10, 5))\npl.clf()\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.legend(loc=0)\npl.title('Source and target distributions')" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "plot transported samples\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "pl.figure(2)\npl.clf()\npl.subplot(2, 2, 1)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=.2)\npl.scatter(transp_Xs_linear[:, 0], transp_Xs_linear[:, 1], c=ys, marker='+',\n label='Mapped source samples')\npl.title(\"Bary. mapping (linear)\")\npl.legend(loc=0)\n\npl.subplot(2, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=.2)\npl.scatter(transp_Xs_linear_new[:, 0], transp_Xs_linear_new[:, 1],\n c=ys, marker='+', label='Learned mapping')\npl.title(\"Estim. mapping (linear)\")\n\npl.subplot(2, 2, 3)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=.2)\npl.scatter(transp_Xs_gaussian[:, 0], transp_Xs_gaussian[:, 1], c=ys,\n marker='+', label='barycentric mapping')\npl.title(\"Bary. mapping (kernel)\")\n\npl.subplot(2, 2, 4)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=.2)\npl.scatter(transp_Xs_gaussian_new[:, 0], transp_Xs_gaussian_new[:, 1], c=ys,\n marker='+', label='Learned mapping')\npl.title(\"Estim. mapping (kernel)\")\npl.tight_layout()\n\npl.show()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "name": "python2", + "language": "python" + }, + "language_info": { + "mimetype": "text/x-python", + "nbconvert_exporter": "python", + "name": "python", + "file_extension": ".py", + "version": "2.7.12", + "pygments_lexer": "ipython2", + "codemirror_mode": { + "version": 2, + "name": "ipython" + } + } + } +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_mapping.py b/docs/source/auto_examples/plot_otda_mapping.py new file mode 100644 index 0000000..09d2cb4 --- /dev/null +++ b/docs/source/auto_examples/plot_otda_mapping.py @@ -0,0 +1,126 @@ +# -*- coding: utf-8 -*- +""" +=============================================== +OT mapping estimation for domain adaptation [8] +=============================================== + +This example presents how to use MappingTransport to estimate at the same +time both the coupling transport and approximate the transport map with either +a linear or a kernelized mapping as introduced in [8] + +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, + "Mapping estimation for discrete optimal transport", + Neural Information Processing Systems (NIPS), 2016. +""" + +# Authors: Remi Flamary +# Stanislas Chambon +# +# License: MIT License + +import numpy as np +import matplotlib.pylab as pl +import ot + + +############################################################################## +# generate data +############################################################################## + +n_source_samples = 100 +n_target_samples = 100 +theta = 2 * np.pi / 20 +noise_level = 0.1 + +Xs, ys = ot.datasets.get_data_classif( + 'gaussrot', n_source_samples, nz=noise_level) +Xs_new, _ = ot.datasets.get_data_classif( + 'gaussrot', n_source_samples, nz=noise_level) +Xt, yt = ot.datasets.get_data_classif( + 'gaussrot', n_target_samples, theta=theta, nz=noise_level) + +# one of the target mode changes its variance (no linear mapping) +Xt[yt == 2] *= 3 +Xt = Xt + 4 + + +############################################################################## +# Instantiate the different transport algorithms and fit them +############################################################################## + +# MappingTransport with linear kernel +ot_mapping_linear = ot.da.MappingTransport( + kernel="linear", mu=1e0, eta=1e-8, bias=True, + max_iter=20, verbose=True) + +ot_mapping_linear.fit(Xs=Xs, Xt=Xt) + +# for original source samples, transform applies barycentric mapping +transp_Xs_linear = ot_mapping_linear.transform(Xs=Xs) + +# for out of source samples, transform applies the linear mapping +transp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new) + + +# MappingTransport with gaussian kernel +ot_mapping_gaussian = ot.da.MappingTransport( + kernel="gaussian", eta=1e-5, mu=1e-1, bias=True, sigma=1, + max_iter=10, verbose=True) +ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt) + +# for original source samples, transform applies barycentric mapping +transp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs) + +# for out of source samples, transform applies the gaussian mapping +transp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new) + + +############################################################################## +# plot data +############################################################################## + +pl.figure(1, (10, 5)) +pl.clf() +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.legend(loc=0) +pl.title('Source and target distributions') + + +############################################################################## +# plot transported samples +############################################################################## + +pl.figure(2) +pl.clf() +pl.subplot(2, 2, 1) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) +pl.scatter(transp_Xs_linear[:, 0], transp_Xs_linear[:, 1], c=ys, marker='+', + label='Mapped source samples') +pl.title("Bary. mapping (linear)") +pl.legend(loc=0) + +pl.subplot(2, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) +pl.scatter(transp_Xs_linear_new[:, 0], transp_Xs_linear_new[:, 1], + c=ys, marker='+', label='Learned mapping') +pl.title("Estim. mapping (linear)") + +pl.subplot(2, 2, 3) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) +pl.scatter(transp_Xs_gaussian[:, 0], transp_Xs_gaussian[:, 1], c=ys, + marker='+', label='barycentric mapping') +pl.title("Bary. mapping (kernel)") + +pl.subplot(2, 2, 4) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) +pl.scatter(transp_Xs_gaussian_new[:, 0], transp_Xs_gaussian_new[:, 1], c=ys, + marker='+', label='Learned mapping') +pl.title("Estim. mapping (kernel)") +pl.tight_layout() + +pl.show() diff --git a/docs/source/auto_examples/plot_otda_mapping.rst b/docs/source/auto_examples/plot_otda_mapping.rst new file mode 100644 index 0000000..088da31 --- /dev/null +++ b/docs/source/auto_examples/plot_otda_mapping.rst @@ -0,0 +1,231 @@ + + +.. _sphx_glr_auto_examples_plot_otda_mapping.py: + + +=============================================== +OT mapping estimation for domain adaptation [8] +=============================================== + +This example presents how to use MappingTransport to estimate at the same +time both the coupling transport and approximate the transport map with either +a linear or a kernelized mapping as introduced in [8] + +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, + "Mapping estimation for discrete optimal transport", + Neural Information Processing Systems (NIPS), 2016. + + + +.. code-block:: python + + + # Authors: Remi Flamary + # Stanislas Chambon + # + # License: MIT License + + import numpy as np + import matplotlib.pylab as pl + import ot + + + + + + + + +generate data +############################################################################# + + + +.. code-block:: python + + + n_source_samples = 100 + n_target_samples = 100 + theta = 2 * np.pi / 20 + noise_level = 0.1 + + Xs, ys = ot.datasets.get_data_classif( + 'gaussrot', n_source_samples, nz=noise_level) + Xs_new, _ = ot.datasets.get_data_classif( + 'gaussrot', n_source_samples, nz=noise_level) + Xt, yt = ot.datasets.get_data_classif( + 'gaussrot', n_target_samples, theta=theta, nz=noise_level) + + # one of the target mode changes its variance (no linear mapping) + Xt[yt == 2] *= 3 + Xt = Xt + 4 + + + + + + + + +Instantiate the different transport algorithms and fit them +############################################################################# + + + +.. code-block:: python + + + # MappingTransport with linear kernel + ot_mapping_linear = ot.da.MappingTransport( + kernel="linear", mu=1e0, eta=1e-8, bias=True, + max_iter=20, verbose=True) + + ot_mapping_linear.fit(Xs=Xs, Xt=Xt) + + # for original source samples, transform applies barycentric mapping + transp_Xs_linear = ot_mapping_linear.transform(Xs=Xs) + + # for out of source samples, transform applies the linear mapping + transp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new) + + + # MappingTransport with gaussian kernel + ot_mapping_gaussian = ot.da.MappingTransport( + kernel="gaussian", eta=1e-5, mu=1e-1, bias=True, sigma=1, + max_iter=10, verbose=True) + ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt) + + # for original source samples, transform applies barycentric mapping + transp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs) + + # for out of source samples, transform applies the gaussian mapping + transp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new) + + + + + + +.. rst-class:: sphx-glr-script-out + + Out:: + + It. |Loss |Delta loss + -------------------------------- + 0|4.273804e+03|0.000000e+00 + 1|4.264510e+03|-2.174580e-03 + 2|4.264209e+03|-7.047095e-05 + 3|4.264078e+03|-3.069822e-05 + 4|4.264018e+03|-1.412924e-05 + 5|4.263961e+03|-1.341165e-05 + 6|4.263946e+03|-3.586522e-06 + It. |Loss |Delta loss + -------------------------------- + 0|4.294523e+02|0.000000e+00 + 1|4.247737e+02|-1.089443e-02 + 2|4.245516e+02|-5.228765e-04 + 3|4.244430e+02|-2.557417e-04 + 4|4.243724e+02|-1.663904e-04 + 5|4.243196e+02|-1.244111e-04 + 6|4.242808e+02|-9.132500e-05 + 7|4.242497e+02|-7.331710e-05 + 8|4.242271e+02|-5.326612e-05 + 9|4.242063e+02|-4.916026e-05 + 10|4.241906e+02|-3.699617e-05 + + +plot data +############################################################################# + + + +.. code-block:: python + + + pl.figure(1, (10, 5)) + pl.clf() + pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') + pl.legend(loc=0) + pl.title('Source and target distributions') + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_001.png + :align: center + + + + +plot transported samples +############################################################################# + + + +.. code-block:: python + + + pl.figure(2) + pl.clf() + pl.subplot(2, 2, 1) + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) + pl.scatter(transp_Xs_linear[:, 0], transp_Xs_linear[:, 1], c=ys, marker='+', + label='Mapped source samples') + pl.title("Bary. mapping (linear)") + pl.legend(loc=0) + + pl.subplot(2, 2, 2) + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) + pl.scatter(transp_Xs_linear_new[:, 0], transp_Xs_linear_new[:, 1], + c=ys, marker='+', label='Learned mapping') + pl.title("Estim. mapping (linear)") + + pl.subplot(2, 2, 3) + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) + pl.scatter(transp_Xs_gaussian[:, 0], transp_Xs_gaussian[:, 1], c=ys, + marker='+', label='barycentric mapping') + pl.title("Bary. mapping (kernel)") + + pl.subplot(2, 2, 4) + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) + pl.scatter(transp_Xs_gaussian_new[:, 0], transp_Xs_gaussian_new[:, 1], c=ys, + marker='+', label='Learned mapping') + pl.title("Estim. mapping (kernel)") + pl.tight_layout() + + pl.show() + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_003.png + :align: center + + + + +**Total running time of the script:** ( 0 minutes 0.853 seconds) + + + +.. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_otda_mapping.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_otda_mapping.ipynb ` + +.. rst-class:: sphx-glr-signature + + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb b/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb new file mode 100644 index 0000000..4b2ec02 --- /dev/null +++ b/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb @@ -0,0 +1,144 @@ +{ + "nbformat_minor": 0, + "nbformat": 4, + "cells": [ + { + "execution_count": null, + "cell_type": "code", + "source": [ + "%matplotlib inline" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "\n====================================================================================\nOT for domain adaptation with image color adaptation [6] with mapping estimation [8]\n====================================================================================\n\n[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized\n discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3),\n 1853-1882.\n[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, \"Mapping estimation for\n discrete optimal transport\", Neural Information Processing Systems (NIPS),\n 2016.\n\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport numpy as np\nfrom scipy import ndimage\nimport matplotlib.pylab as pl\nimport ot\n\nr = np.random.RandomState(42)\n\n\ndef im2mat(I):\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\ndef mat2im(X, shape):\n \"\"\"Converts back a matrix to an image\"\"\"\n return X.reshape(shape)\n\n\ndef minmax(I):\n return np.clip(I, 0, 1)" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Generate data\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "# Loading images\nI1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256\nI2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n\n\nX1 = im2mat(I1)\nX2 = im2mat(I2)\n\n# training samples\nnb = 1000\nidx1 = r.randint(X1.shape[0], size=(nb,))\nidx2 = r.randint(X2.shape[0], size=(nb,))\n\nXs = X1[idx1, :]\nXt = X2[idx2, :]" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Domain adaptation for pixel distribution transfer\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "# EMDTransport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\ntransp_Xs_emd = ot_emd.transform(Xs=X1)\nImage_emd = minmax(mat2im(transp_Xs_emd, I1.shape))\n\n# SinkhornTransport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\ntransp_Xs_sinkhorn = ot_emd.transform(Xs=X1)\nImage_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))\n\not_mapping_linear = ot.da.MappingTransport(\n mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True)\not_mapping_linear.fit(Xs=Xs, Xt=Xt)\n\nX1tl = ot_mapping_linear.transform(Xs=X1)\nImage_mapping_linear = minmax(mat2im(X1tl, I1.shape))\n\not_mapping_gaussian = ot.da.MappingTransport(\n mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True)\not_mapping_gaussian.fit(Xs=Xs, Xt=Xt)\n\nX1tn = ot_mapping_gaussian.transform(Xs=X1) # use the estimated mapping\nImage_mapping_gaussian = minmax(mat2im(X1tn, I1.shape))" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "plot original images\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "pl.figure(1, figsize=(6.4, 3))\npl.subplot(1, 2, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.imshow(I2)\npl.axis('off')\npl.title('Image 2')\npl.tight_layout()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "plot pixel values distribution\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "pl.figure(2, figsize=(6.4, 5))\n\npl.subplot(1, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 2')\npl.tight_layout()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "plot transformed images\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "pl.figure(2, figsize=(10, 5))\n\npl.subplot(2, 3, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Im. 1')\n\npl.subplot(2, 3, 4)\npl.imshow(I2)\npl.axis('off')\npl.title('Im. 2')\n\npl.subplot(2, 3, 2)\npl.imshow(Image_emd)\npl.axis('off')\npl.title('EmdTransport')\n\npl.subplot(2, 3, 5)\npl.imshow(Image_sinkhorn)\npl.axis('off')\npl.title('SinkhornTransport')\n\npl.subplot(2, 3, 3)\npl.imshow(Image_mapping_linear)\npl.axis('off')\npl.title('MappingTransport (linear)')\n\npl.subplot(2, 3, 6)\npl.imshow(Image_mapping_gaussian)\npl.axis('off')\npl.title('MappingTransport (gaussian)')\npl.tight_layout()\n\npl.show()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "name": "python2", + "language": "python" + }, + "language_info": { + "mimetype": "text/x-python", + "nbconvert_exporter": "python", + "name": "python", + "file_extension": ".py", + "version": "2.7.12", + "pygments_lexer": "ipython2", + "codemirror_mode": { + "version": 2, + "name": "ipython" + } + } + } +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.py b/docs/source/auto_examples/plot_otda_mapping_colors_images.py new file mode 100644 index 0000000..936206c --- /dev/null +++ b/docs/source/auto_examples/plot_otda_mapping_colors_images.py @@ -0,0 +1,171 @@ +# -*- coding: utf-8 -*- +""" +==================================================================================== +OT for domain adaptation with image color adaptation [6] with mapping estimation [8] +==================================================================================== + +[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized + discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), + 1853-1882. +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for + discrete optimal transport", Neural Information Processing Systems (NIPS), + 2016. + +""" + +# Authors: Remi Flamary +# Stanislas Chambon +# +# License: MIT License + +import numpy as np +from scipy import ndimage +import matplotlib.pylab as pl +import ot + +r = np.random.RandomState(42) + + +def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + + +def mat2im(X, shape): + """Converts back a matrix to an image""" + return X.reshape(shape) + + +def minmax(I): + return np.clip(I, 0, 1) + + +############################################################################## +# Generate data +############################################################################## + +# Loading images +I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256 +I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 + + +X1 = im2mat(I1) +X2 = im2mat(I2) + +# training samples +nb = 1000 +idx1 = r.randint(X1.shape[0], size=(nb,)) +idx2 = r.randint(X2.shape[0], size=(nb,)) + +Xs = X1[idx1, :] +Xt = X2[idx2, :] + + +############################################################################## +# Domain adaptation for pixel distribution transfer +############################################################################## + +# EMDTransport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) +transp_Xs_emd = ot_emd.transform(Xs=X1) +Image_emd = minmax(mat2im(transp_Xs_emd, I1.shape)) + +# SinkhornTransport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) +transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) +Image_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) + +ot_mapping_linear = ot.da.MappingTransport( + mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True) +ot_mapping_linear.fit(Xs=Xs, Xt=Xt) + +X1tl = ot_mapping_linear.transform(Xs=X1) +Image_mapping_linear = minmax(mat2im(X1tl, I1.shape)) + +ot_mapping_gaussian = ot.da.MappingTransport( + mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True) +ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt) + +X1tn = ot_mapping_gaussian.transform(Xs=X1) # use the estimated mapping +Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape)) + + +############################################################################## +# plot original images +############################################################################## + +pl.figure(1, figsize=(6.4, 3)) +pl.subplot(1, 2, 1) +pl.imshow(I1) +pl.axis('off') +pl.title('Image 1') + +pl.subplot(1, 2, 2) +pl.imshow(I2) +pl.axis('off') +pl.title('Image 2') +pl.tight_layout() + + +############################################################################## +# plot pixel values distribution +############################################################################## + +pl.figure(2, figsize=(6.4, 5)) + +pl.subplot(1, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs) +pl.axis([0, 1, 0, 1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 1') + +pl.subplot(1, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt) +pl.axis([0, 1, 0, 1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 2') +pl.tight_layout() + + +############################################################################## +# plot transformed images +############################################################################## + +pl.figure(2, figsize=(10, 5)) + +pl.subplot(2, 3, 1) +pl.imshow(I1) +pl.axis('off') +pl.title('Im. 1') + +pl.subplot(2, 3, 4) +pl.imshow(I2) +pl.axis('off') +pl.title('Im. 2') + +pl.subplot(2, 3, 2) +pl.imshow(Image_emd) +pl.axis('off') +pl.title('EmdTransport') + +pl.subplot(2, 3, 5) +pl.imshow(Image_sinkhorn) +pl.axis('off') +pl.title('SinkhornTransport') + +pl.subplot(2, 3, 3) +pl.imshow(Image_mapping_linear) +pl.axis('off') +pl.title('MappingTransport (linear)') + +pl.subplot(2, 3, 6) +pl.imshow(Image_mapping_gaussian) +pl.axis('off') +pl.title('MappingTransport (gaussian)') +pl.tight_layout() + +pl.show() diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.rst b/docs/source/auto_examples/plot_otda_mapping_colors_images.rst new file mode 100644 index 0000000..1107067 --- /dev/null +++ b/docs/source/auto_examples/plot_otda_mapping_colors_images.rst @@ -0,0 +1,302 @@ + + +.. _sphx_glr_auto_examples_plot_otda_mapping_colors_images.py: + + +==================================================================================== +OT for domain adaptation with image color adaptation [6] with mapping estimation [8] +==================================================================================== + +[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized + discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), + 1853-1882. +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for + discrete optimal transport", Neural Information Processing Systems (NIPS), + 2016. + + + + +.. code-block:: python + + + # Authors: Remi Flamary + # Stanislas Chambon + # + # License: MIT License + + import numpy as np + from scipy import ndimage + import matplotlib.pylab as pl + import ot + + r = np.random.RandomState(42) + + + def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + + + def mat2im(X, shape): + """Converts back a matrix to an image""" + return X.reshape(shape) + + + def minmax(I): + return np.clip(I, 0, 1) + + + + + + + + +Generate data +############################################################################# + + + +.. code-block:: python + + + # Loading images + I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256 + I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 + + + X1 = im2mat(I1) + X2 = im2mat(I2) + + # training samples + nb = 1000 + idx1 = r.randint(X1.shape[0], size=(nb,)) + idx2 = r.randint(X2.shape[0], size=(nb,)) + + Xs = X1[idx1, :] + Xt = X2[idx2, :] + + + + + + + + +Domain adaptation for pixel distribution transfer +############################################################################# + + + +.. code-block:: python + + + # EMDTransport + ot_emd = ot.da.EMDTransport() + ot_emd.fit(Xs=Xs, Xt=Xt) + transp_Xs_emd = ot_emd.transform(Xs=X1) + Image_emd = minmax(mat2im(transp_Xs_emd, I1.shape)) + + # SinkhornTransport + ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) + ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) + Image_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) + + ot_mapping_linear = ot.da.MappingTransport( + mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True) + ot_mapping_linear.fit(Xs=Xs, Xt=Xt) + + X1tl = ot_mapping_linear.transform(Xs=X1) + Image_mapping_linear = minmax(mat2im(X1tl, I1.shape)) + + ot_mapping_gaussian = ot.da.MappingTransport( + mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True) + ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt) + + X1tn = ot_mapping_gaussian.transform(Xs=X1) # use the estimated mapping + Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape)) + + + + + + +.. rst-class:: sphx-glr-script-out + + Out:: + + It. |Loss |Delta loss + -------------------------------- + 0|3.680514e+02|0.000000e+00 + 1|3.592359e+02|-2.395185e-02 + 2|3.590581e+02|-4.947749e-04 + 3|3.589663e+02|-2.556471e-04 + 4|3.589095e+02|-1.582289e-04 + 5|3.588707e+02|-1.081994e-04 + 6|3.588423e+02|-7.911661e-05 + 7|3.588206e+02|-6.055473e-05 + 8|3.588034e+02|-4.778202e-05 + 9|3.587895e+02|-3.886420e-05 + 10|3.587781e+02|-3.182249e-05 + 11|3.587684e+02|-2.695669e-05 + 12|3.587602e+02|-2.298642e-05 + 13|3.587530e+02|-1.993240e-05 + 14|3.587468e+02|-1.736014e-05 + 15|3.587413e+02|-1.518037e-05 + 16|3.587365e+02|-1.358038e-05 + 17|3.587321e+02|-1.215346e-05 + 18|3.587282e+02|-1.091639e-05 + 19|3.587278e+02|-9.877929e-07 + It. |Loss |Delta loss + -------------------------------- + 0|3.784725e+02|0.000000e+00 + 1|3.646380e+02|-3.655332e-02 + 2|3.642858e+02|-9.660434e-04 + 3|3.641516e+02|-3.683776e-04 + 4|3.640785e+02|-2.008220e-04 + 5|3.640320e+02|-1.276966e-04 + 6|3.639999e+02|-8.796173e-05 + 7|3.639764e+02|-6.455658e-05 + 8|3.639583e+02|-4.976436e-05 + 9|3.639440e+02|-3.946556e-05 + 10|3.639322e+02|-3.222132e-05 + + +plot original images +############################################################################# + + + +.. code-block:: python + + + pl.figure(1, figsize=(6.4, 3)) + pl.subplot(1, 2, 1) + pl.imshow(I1) + pl.axis('off') + pl.title('Image 1') + + pl.subplot(1, 2, 2) + pl.imshow(I2) + pl.axis('off') + pl.title('Image 2') + pl.tight_layout() + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_colors_images_001.png + :align: center + + + + +plot pixel values distribution +############################################################################# + + + +.. code-block:: python + + + pl.figure(2, figsize=(6.4, 5)) + + pl.subplot(1, 2, 1) + pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs) + pl.axis([0, 1, 0, 1]) + pl.xlabel('Red') + pl.ylabel('Blue') + pl.title('Image 1') + + pl.subplot(1, 2, 2) + pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt) + pl.axis([0, 1, 0, 1]) + pl.xlabel('Red') + pl.ylabel('Blue') + pl.title('Image 2') + pl.tight_layout() + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_colors_images_003.png + :align: center + + + + +plot transformed images +############################################################################# + + + +.. code-block:: python + + + pl.figure(2, figsize=(10, 5)) + + pl.subplot(2, 3, 1) + pl.imshow(I1) + pl.axis('off') + pl.title('Im. 1') + + pl.subplot(2, 3, 4) + pl.imshow(I2) + pl.axis('off') + pl.title('Im. 2') + + pl.subplot(2, 3, 2) + pl.imshow(Image_emd) + pl.axis('off') + pl.title('EmdTransport') + + pl.subplot(2, 3, 5) + pl.imshow(Image_sinkhorn) + pl.axis('off') + pl.title('SinkhornTransport') + + pl.subplot(2, 3, 3) + pl.imshow(Image_mapping_linear) + pl.axis('off') + pl.title('MappingTransport (linear)') + + pl.subplot(2, 3, 6) + pl.imshow(Image_mapping_gaussian) + pl.axis('off') + pl.title('MappingTransport (gaussian)') + pl.tight_layout() + + pl.show() + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_colors_images_004.png + :align: center + + + + +**Total running time of the script:** ( 2 minutes 45.618 seconds) + + + +.. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_otda_mapping_colors_images.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_otda_mapping_colors_images.ipynb ` + +.. rst-class:: sphx-glr-signature + + `Generated by Sphinx-Gallery `_ -- cgit v1.2.3 From 164dc24d7cdf61acd045f6b879ae2955b7dfcd18 Mon Sep 17 00:00:00 2001 From: Rémi 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and b/docs/source/auto_examples/images/sphx_glr_plot_optim_OTreg_004.png differ diff --git a/docs/source/auto_examples/plot_OT_1D.ipynb b/docs/source/auto_examples/plot_OT_1D.ipynb index 7d3fdd1..97c593e 100644 --- a/docs/source/auto_examples/plot_OT_1D.ipynb +++ b/docs/source/auto_examples/plot_OT_1D.ipynb @@ -24,7 +24,79 @@ "execution_count": null, "cell_type": "code", "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nfrom ot.datasets import get_1D_gauss as gauss\n\n#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\nb = gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\npl.plot(x, a, 'b', label='Source distribution')\npl.plot(x, b, 'r', label='Target distribution')\npl.legend()\n\n#%% plot distributions and loss matrix\n\npl.figure(2, figsize=(5, 5))\not.plot.plot1D_mat(a, b, M, 'Cost matrix M')\n\n#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')\n\n#%% Sinkhorn\n\nlambd = 1e-3\nGs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')\n\npl.show()" + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nfrom ot.datasets import get_1D_gauss as gauss" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Generate data\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\nb = gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Plot distributions and loss matrix\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\npl.plot(x, a, 'b', label='Source distribution')\npl.plot(x, b, 'r', label='Target distribution')\npl.legend()\n\n#%% plot distributions and loss matrix\n\npl.figure(2, figsize=(5, 5))\not.plot.plot1D_mat(a, b, M, 'Cost matrix M')" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Solve EMD \n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Solve Sinkhorn\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% Sinkhorn\n\nlambd = 1e-3\nGs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')\n\npl.show()" ], "outputs": [], "metadata": { diff --git a/docs/source/auto_examples/plot_OT_1D.py b/docs/source/auto_examples/plot_OT_1D.py index 0f3a26a..be6f5b3 100644 --- a/docs/source/auto_examples/plot_OT_1D.py +++ b/docs/source/auto_examples/plot_OT_1D.py @@ -15,6 +15,10 @@ import matplotlib.pylab as pl import ot from ot.datasets import get_1D_gauss as gauss +############################################################################## +# Generate data +############################################################################## + #%% parameters n = 100 # nb bins @@ -30,6 +34,11 @@ b = gauss(n, m=60, s=10) M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) M /= M.max() + +############################################################################## +# Plot distributions and loss matrix +############################################################################## + #%% plot the distributions pl.figure(1, figsize=(6.4, 3)) @@ -42,6 +51,10 @@ pl.legend() pl.figure(2, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') +############################################################################## +# Solve EMD +############################################################################## + #%% EMD G0 = ot.emd(a, b, M) @@ -49,6 +62,10 @@ G0 = ot.emd(a, b, M) pl.figure(3, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') +############################################################################## +# Solve Sinkhorn +############################################################################## + #%% Sinkhorn lambd = 1e-3 diff --git a/docs/source/auto_examples/plot_OT_1D.rst b/docs/source/auto_examples/plot_OT_1D.rst index a36e13c..252d387 100644 --- a/docs/source/auto_examples/plot_OT_1D.rst +++ b/docs/source/auto_examples/plot_OT_1D.rst @@ -10,68 +10,32 @@ - -.. rst-class:: sphx-glr-horizontal +.. code-block:: python - * + # Author: Remi Flamary + # + # License: MIT License - .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_001.png - :scale: 47 + import numpy as np + import matplotlib.pylab as pl + import ot + from ot.datasets import get_1D_gauss as gauss - * - .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_002.png - :scale: 47 - * - .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_003.png - :scale: 47 - * - .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_004.png - :scale: 47 +Generate data +############################################################################# -.. rst-class:: sphx-glr-script-out - - Out:: - - It. |Err - ------------------- - 0|8.187970e-02| - 10|3.460174e-02| - 20|6.633335e-03| - 30|9.797798e-04| - 40|1.389606e-04| - 50|1.959016e-05| - 60|2.759079e-06| - 70|3.885166e-07| - 80|5.470605e-08| - 90|7.702918e-09| - 100|1.084609e-09| - 110|1.527180e-10| - - - - -| .. code-block:: python - # Author: Remi Flamary - # - # License: MIT License - - import numpy as np - import matplotlib.pylab as pl - import ot - from ot.datasets import get_1D_gauss as gauss - #%% parameters n = 100 # nb bins @@ -87,6 +51,21 @@ M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) M /= M.max() + + + + + + + +Plot distributions and loss matrix +############################################################################# + + + +.. code-block:: python + + #%% plot the distributions pl.figure(1, figsize=(6.4, 3)) @@ -99,6 +78,33 @@ pl.figure(2, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_001.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_002.png + :scale: 47 + + + + +Solve EMD +############################################################################# + + + +.. code-block:: python + + #%% EMD G0 = ot.emd(a, b, M) @@ -106,6 +112,23 @@ pl.figure(3, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') + + + +.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_005.png + :align: center + + + + +Solve Sinkhorn +############################################################################# + + + +.. code-block:: python + + #%% Sinkhorn lambd = 1e-3 @@ -116,7 +139,33 @@ pl.show() -**Total running time of the script:** ( 0 minutes 1.050 seconds) + + +.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_007.png + :align: center + + +.. rst-class:: sphx-glr-script-out + + Out:: + + It. |Err + ------------------- + 0|8.187970e-02| + 10|3.460174e-02| + 20|6.633335e-03| + 30|9.797798e-04| + 40|1.389606e-04| + 50|1.959016e-05| + 60|2.759079e-06| + 70|3.885166e-07| + 80|5.470605e-08| + 90|7.702918e-09| + 100|1.084609e-09| + 110|1.527180e-10| + + +**Total running time of the script:** ( 0 minutes 1.065 seconds) diff --git a/docs/source/auto_examples/plot_optim_OTreg.ipynb b/docs/source/auto_examples/plot_optim_OTreg.ipynb index 0cb6ef2..9d26e4d 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.ipynb +++ b/docs/source/auto_examples/plot_optim_OTreg.ipynb @@ -24,7 +24,97 @@ "execution_count": null, "cell_type": "code", "source": [ - "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n\n#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std\nb = ot.datasets.get_1D_gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()\n\n#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')\n\n#%% Example with Frobenius norm regularization\n\n\ndef f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg = 1e-1\n\nGl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n\npl.figure(3)\not.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg')\n\n#%% Example with entropic regularization\n\n\ndef f(G):\n return np.sum(G * np.log(G))\n\n\ndef df(G):\n return np.log(G) + 1.\n\n\nreg = 1e-3\n\nGe = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg')\n\n#%% Example with Frobenius norm + entropic regularization with gcg\n\n\ndef f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg1 = 1e-3\nreg2 = 1e-1\n\nGel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True)\n\npl.figure(5, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg')\npl.show()" + "import numpy as np\nimport matplotlib.pylab as pl\nimport ot" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Generate data \n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std\nb = ot.datasets.get_1D_gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Solve EMD \n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Solve EMD with Frobenius norm regularization\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% Example with Frobenius norm regularization\n\n\ndef f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg = 1e-1\n\nGl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n\npl.figure(3)\not.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg')" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Solve EMD with entropic regularization\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% Example with entropic regularization\n\n\ndef f(G):\n return np.sum(G * np.log(G))\n\n\ndef df(G):\n return np.log(G) + 1.\n\n\nreg = 1e-3\n\nGe = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg')" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Solve EMD with Frobenius norm + entropic regularization\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% Example with Frobenius norm + entropic regularization with gcg\n\ndef f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg1 = 1e-3\nreg2 = 1e-1\n\nGel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True)\n\npl.figure(5, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg')\npl.show()" ], "outputs": [], "metadata": { diff --git a/docs/source/auto_examples/plot_optim_OTreg.py b/docs/source/auto_examples/plot_optim_OTreg.py index 276b250..d36b269 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.py +++ b/docs/source/auto_examples/plot_optim_OTreg.py @@ -12,6 +12,10 @@ import matplotlib.pylab as pl import ot +############################################################################## +# Generate data +############################################################################## + #%% parameters n = 100 # nb bins @@ -27,6 +31,10 @@ b = ot.datasets.get_1D_gauss(n, m=60, s=10) M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) M /= M.max() +############################################################################## +# Solve EMD +############################################################################## + #%% EMD G0 = ot.emd(a, b, M) @@ -34,6 +42,10 @@ G0 = ot.emd(a, b, M) pl.figure(3, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') +############################################################################## +# Solve EMD with Frobenius norm regularization +############################################################################## + #%% Example with Frobenius norm regularization @@ -52,6 +64,10 @@ Gl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True) pl.figure(3) ot.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg') +############################################################################## +# Solve EMD with entropic regularization +############################################################################## + #%% Example with entropic regularization @@ -70,8 +86,11 @@ Ge = ot.optim.cg(a, b, M, reg, f, df, verbose=True) pl.figure(4, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg') -#%% Example with Frobenius norm + entropic regularization with gcg +############################################################################## +# Solve EMD with Frobenius norm + entropic regularization +############################################################################## +#%% Example with Frobenius norm + entropic regularization with gcg def f(G): return 0.5 * np.sum(G**2) diff --git a/docs/source/auto_examples/plot_optim_OTreg.rst b/docs/source/auto_examples/plot_optim_OTreg.rst index f417158..532d4ca 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.rst +++ b/docs/source/auto_examples/plot_optim_OTreg.rst @@ -11,24 +11,104 @@ Regularized OT with generic solver +.. code-block:: python + + + import numpy as np + import matplotlib.pylab as pl + import ot + + + + + + + -.. rst-class:: sphx-glr-horizontal +Generate data +############################################################################# - * - .. image:: /auto_examples/images/sphx_glr_plot_optim_OTreg_003.png - :scale: 47 +.. code-block:: python + + + #%% parameters + + n = 100 # nb bins + + # bin positions + x = np.arange(n, dtype=np.float64) + + # Gaussian distributions + a = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std + b = ot.datasets.get_1D_gauss(n, m=60, s=10) + + # loss matrix + M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) + M /= M.max() + + + + + + + +Solve EMD +############################################################################# + + + +.. code-block:: python + + + #%% EMD + + G0 = ot.emd(a, b, M) + + pl.figure(3, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') + + - * - .. image:: /auto_examples/images/sphx_glr_plot_optim_OTreg_004.png - :scale: 47 +.. image:: /auto_examples/images/sphx_glr_plot_optim_OTreg_003.png + :align: center - * - .. image:: /auto_examples/images/sphx_glr_plot_optim_OTreg_005.png - :scale: 47 + + +Solve EMD with Frobenius norm regularization +############################################################################# + + + +.. code-block:: python + + + #%% Example with Frobenius norm regularization + + + def f(G): + return 0.5 * np.sum(G**2) + + + def df(G): + return G + + + reg = 1e-1 + + Gl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True) + + pl.figure(3) + ot.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg') + + + + +.. image:: /auto_examples/images/sphx_glr_plot_optim_OTreg_004.png + :align: center .. rst-class:: sphx-glr-script-out @@ -258,6 +338,45 @@ Regularized OT with generic solver It. |Loss |Delta loss -------------------------------- 200|1.663543e-01|-8.737134e-08 + + +Solve EMD with entropic regularization +############################################################################# + + + +.. code-block:: python + + + #%% Example with entropic regularization + + + def f(G): + return np.sum(G * np.log(G)) + + + def df(G): + return np.log(G) + 1. + + + reg = 1e-3 + + Ge = ot.optim.cg(a, b, M, reg, f, df, verbose=True) + + pl.figure(4, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg') + + + + +.. image:: /auto_examples/images/sphx_glr_plot_optim_OTreg_006.png + :align: center + + +.. rst-class:: sphx-glr-script-out + + Out:: + It. |Loss |Delta loss -------------------------------- 0|1.692289e-01|0.000000e+00 @@ -481,52 +600,17 @@ Regularized OT with generic solver It. |Loss |Delta loss -------------------------------- 200|1.607143e-01|-2.151971e-10 - It. |Loss |Delta loss - -------------------------------- - 0|1.693084e-01|0.000000e+00 - 1|1.610121e-01|-5.152589e-02 - 2|1.609378e-01|-4.622297e-04 - 3|1.609284e-01|-5.830043e-05 - 4|1.609284e-01|-1.111580e-12 - +Solve EMD with Frobenius norm + entropic regularization +############################################################################# -| .. code-block:: python - import numpy as np - import matplotlib.pylab as pl - import ot - - - #%% parameters - - n = 100 # nb bins - - # bin positions - x = np.arange(n, dtype=np.float64) - - # Gaussian distributions - a = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std - b = ot.datasets.get_1D_gauss(n, m=60, s=10) - - # loss matrix - M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) - M /= M.max() - - #%% EMD - - G0 = ot.emd(a, b, M) - - pl.figure(3, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') - - #%% Example with Frobenius norm regularization - + #%% Example with Frobenius norm + entropic regularization with gcg def f(G): return 0.5 * np.sum(G**2) @@ -536,52 +620,35 @@ Regularized OT with generic solver return G - reg = 1e-1 - - Gl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True) - - pl.figure(3) - ot.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg') - - #%% Example with entropic regularization - - - def f(G): - return np.sum(G * np.log(G)) - - - def df(G): - return np.log(G) + 1. - - - reg = 1e-3 - - Ge = ot.optim.cg(a, b, M, reg, f, df, verbose=True) + reg1 = 1e-3 + reg2 = 1e-1 - pl.figure(4, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg') + Gel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True) - #%% Example with Frobenius norm + entropic regularization with gcg + pl.figure(5, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg') + pl.show() - def f(G): - return 0.5 * np.sum(G**2) +.. image:: /auto_examples/images/sphx_glr_plot_optim_OTreg_008.png + :align: center - def df(G): - return G +.. rst-class:: sphx-glr-script-out - reg1 = 1e-3 - reg2 = 1e-1 + Out:: - Gel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True) + It. |Loss |Delta loss + -------------------------------- + 0|1.693084e-01|0.000000e+00 + 1|1.610121e-01|-5.152589e-02 + 2|1.609378e-01|-4.622297e-04 + 3|1.609284e-01|-5.830043e-05 + 4|1.609284e-01|-1.111580e-12 - pl.figure(5, figsize=(5, 5)) - ot.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg') - pl.show() -**Total running time of the script:** ( 0 minutes 2.720 seconds) +**Total running time of the script:** ( 0 minutes 2.719 seconds) -- cgit v1.2.3 From 3547344740ec16157fd65c7f27d4acd6add986a4 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 30 Aug 2017 17:10:38 +0200 Subject: exples +rst --- examples/plot_OT_1D.py | 17 +++++++++++++++++ examples/plot_optim_OTreg.py | 21 ++++++++++++++++++++- 2 files changed, 37 insertions(+), 1 deletion(-) diff --git a/examples/plot_OT_1D.py b/examples/plot_OT_1D.py index 0f3a26a..be6f5b3 100644 --- a/examples/plot_OT_1D.py +++ b/examples/plot_OT_1D.py @@ -15,6 +15,10 @@ import matplotlib.pylab as pl import ot from ot.datasets import get_1D_gauss as gauss +############################################################################## +# Generate data +############################################################################## + #%% parameters n = 100 # nb bins @@ -30,6 +34,11 @@ b = gauss(n, m=60, s=10) M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) M /= M.max() + +############################################################################## +# Plot distributions and loss matrix +############################################################################## + #%% plot the distributions pl.figure(1, figsize=(6.4, 3)) @@ -42,6 +51,10 @@ pl.legend() pl.figure(2, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') +############################################################################## +# Solve EMD +############################################################################## + #%% EMD G0 = ot.emd(a, b, M) @@ -49,6 +62,10 @@ G0 = ot.emd(a, b, M) pl.figure(3, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') +############################################################################## +# Solve Sinkhorn +############################################################################## + #%% Sinkhorn lambd = 1e-3 diff --git a/examples/plot_optim_OTreg.py b/examples/plot_optim_OTreg.py index 276b250..d36b269 100644 --- a/examples/plot_optim_OTreg.py +++ b/examples/plot_optim_OTreg.py @@ -12,6 +12,10 @@ import matplotlib.pylab as pl import ot +############################################################################## +# Generate data +############################################################################## + #%% parameters n = 100 # nb bins @@ -27,6 +31,10 @@ b = ot.datasets.get_1D_gauss(n, m=60, s=10) M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) M /= M.max() +############################################################################## +# Solve EMD +############################################################################## + #%% EMD G0 = ot.emd(a, b, M) @@ -34,6 +42,10 @@ G0 = ot.emd(a, b, M) pl.figure(3, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') +############################################################################## +# Solve EMD with Frobenius norm regularization +############################################################################## + #%% Example with Frobenius norm regularization @@ -52,6 +64,10 @@ Gl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True) pl.figure(3) ot.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg') +############################################################################## +# Solve EMD with entropic regularization +############################################################################## + #%% Example with entropic regularization @@ -70,8 +86,11 @@ Ge = ot.optim.cg(a, b, M, reg, f, df, verbose=True) pl.figure(4, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg') -#%% Example with Frobenius norm + entropic regularization with gcg +############################################################################## +# Solve EMD with Frobenius norm + entropic regularization +############################################################################## +#%% Example with Frobenius norm + entropic regularization with gcg def f(G): return 0.5 * np.sum(G**2) -- cgit v1.2.3 From 058d5dc64afcd6c0efd2de000cdd8f48167cc405 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 30 Aug 2017 17:12:00 +0200 Subject: nb generic solver --- docs/source/auto_examples/auto_examples_python.zip | Bin 62950 -> 62950 bytes 1 file changed, 0 insertions(+), 0 deletions(-) diff --git a/docs/source/auto_examples/auto_examples_python.zip b/docs/source/auto_examples/auto_examples_python.zip index d9e26c4..bc41a8c 100644 Binary files a/docs/source/auto_examples/auto_examples_python.zip and b/docs/source/auto_examples/auto_examples_python.zip differ -- cgit v1.2.3 From ac1d169902ed1720f81512194a703812abbab246 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 30 Aug 2017 17:13:31 +0200 Subject: update doc --- docs/nb_build | 3 +++ 1 file changed, 3 insertions(+) diff --git a/docs/nb_build b/docs/nb_build index 2523d71..979a913 100755 --- a/docs/nb_build +++ b/docs/nb_build @@ -10,3 +10,6 @@ make html # put comment again sed -i "s/'sphinx\_gallery/#'sphinx\_gallery/" source/conf.py sed -i "s/#sys.modules.update/sys.modules.update/" source/conf.py + + + -- cgit v1.2.3 From ec67362de5ec785e3871eac75a8aa477857092c4 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 30 Aug 2017 20:57:52 +0200 Subject: pep8 --- examples/plot_OT_1D.py | 2 +- examples/plot_optim_OTreg.py | 5 +++-- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/examples/plot_OT_1D.py b/examples/plot_OT_1D.py index be6f5b3..77114c4 100644 --- a/examples/plot_OT_1D.py +++ b/examples/plot_OT_1D.py @@ -52,7 +52,7 @@ pl.figure(2, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') ############################################################################## -# Solve EMD +# Solve EMD ############################################################################## #%% EMD diff --git a/examples/plot_optim_OTreg.py b/examples/plot_optim_OTreg.py index d36b269..7ef6a6b 100644 --- a/examples/plot_optim_OTreg.py +++ b/examples/plot_optim_OTreg.py @@ -13,7 +13,7 @@ import ot ############################################################################## -# Generate data +# Generate data ############################################################################## #%% parameters @@ -32,7 +32,7 @@ M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) M /= M.max() ############################################################################## -# Solve EMD +# Solve EMD ############################################################################## #%% EMD @@ -92,6 +92,7 @@ ot.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg') #%% Example with Frobenius norm + entropic regularization with gcg + def f(G): return 0.5 * np.sum(G**2) -- cgit v1.2.3 From 212f3889b1114026765cda0134e02766daa82af2 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 31 Aug 2017 09:28:37 +0200 Subject: update tests --- examples/plot_OT_1D.py | 3 ++ examples/plot_OT_2D_samples.py | 15 +++++++ examples/plot_WDA.py | 27 ++++++++++++ examples/plot_barycenter_1D.py | 8 ++++ examples/plot_compute_emd.py | 26 ++++++++++-- examples/plot_optim_OTreg.py | 18 ++++++++ examples/plot_otda_color_images.py | 66 ++++++++++++++--------------- examples/plot_otda_mapping.py | 27 ++++++------ examples/plot_otda_mapping_colors_images.py | 15 ++++--- 9 files changed, 148 insertions(+), 57 deletions(-) diff --git a/examples/plot_OT_1D.py b/examples/plot_OT_1D.py index 77114c4..a1473c4 100644 --- a/examples/plot_OT_1D.py +++ b/examples/plot_OT_1D.py @@ -4,6 +4,9 @@ 1D optimal transport ==================== +This example illustrate the computation of EMD and Sinkhorn transport plans +and their visualization. + """ # Author: Remi Flamary diff --git a/examples/plot_OT_2D_samples.py b/examples/plot_OT_2D_samples.py index 2a42dc0..a913b8c 100644 --- a/examples/plot_OT_2D_samples.py +++ b/examples/plot_OT_2D_samples.py @@ -14,6 +14,10 @@ import numpy as np import matplotlib.pylab as pl import ot +############################################################################## +# Generate data +############################################################################## + #%% parameters and data generation n = 50 # nb samples @@ -33,6 +37,10 @@ a, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples M = ot.dist(xs, xt) M /= M.max() +############################################################################## +# Plot data +############################################################################## + #%% plot samples pl.figure(1) @@ -45,6 +53,9 @@ pl.figure(2) pl.imshow(M, interpolation='nearest') pl.title('Cost matrix M') +############################################################################## +# Compute EMD +############################################################################## #%% EMD @@ -62,6 +73,10 @@ pl.legend(loc=0) pl.title('OT matrix with samples') +############################################################################## +# Compute Sinkhorn +############################################################################## + #%% sinkhorn # reg term diff --git a/examples/plot_WDA.py b/examples/plot_WDA.py index 42789f2..06a2e38 100644 --- a/examples/plot_WDA.py +++ b/examples/plot_WDA.py @@ -4,6 +4,12 @@ Wasserstein Discriminant Analysis ================================= +This example illustrate the use of WDA as proposed in [11]. + + +[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). +Wasserstein Discriminant Analysis. + """ # Author: Remi Flamary @@ -16,6 +22,10 @@ import matplotlib.pylab as pl from ot.dr import wda, fda +############################################################################## +# Generate data +############################################################################## + #%% parameters n = 1000 # nb samples in source and target datasets @@ -39,6 +49,10 @@ nbnoise = 8 xs = np.hstack((xs, np.random.randn(n, nbnoise))) xt = np.hstack((xt, np.random.randn(n, nbnoise))) +############################################################################## +# Plot data +############################################################################## + #%% plot samples pl.figure(1, figsize=(6.4, 3.5)) @@ -53,11 +67,19 @@ pl.legend(loc=0) pl.title('Other dimensions') pl.tight_layout() +############################################################################## +# Compute Fisher Discriminant Analysis +############################################################################## + #%% Compute FDA p = 2 Pfda, projfda = fda(xs, ys, p) +############################################################################## +# Compute Wasserstein Discriminant Analysis +############################################################################## + #%% Compute WDA p = 2 reg = 1e0 @@ -66,6 +88,11 @@ maxiter = 100 Pwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter) + +############################################################################## +# Plot 2D projections +############################################################################## + #%% plot samples xsp = projfda(xs) diff --git a/examples/plot_barycenter_1D.py b/examples/plot_barycenter_1D.py index 875f44c..f3be247 100644 --- a/examples/plot_barycenter_1D.py +++ b/examples/plot_barycenter_1D.py @@ -4,6 +4,14 @@ 1D Wasserstein barycenter demo ============================== +This example illustrate the computation of regularized Wassersyein Barycenter +as proposed in [3]. + + +[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). +Iterative Bregman projections for regularized transportation problems +SIAM Journal on Scientific Computing, 37(2), A1111-A1138. + """ # Author: Remi Flamary diff --git a/examples/plot_compute_emd.py b/examples/plot_compute_emd.py index 893eecf..704da0e 100644 --- a/examples/plot_compute_emd.py +++ b/examples/plot_compute_emd.py @@ -1,8 +1,8 @@ # -*- coding: utf-8 -*- """ -==================== -1D optimal transport -==================== +================= +Plot multiple EMD +================= """ @@ -16,6 +16,10 @@ import ot from ot.datasets import get_1D_gauss as gauss +############################################################################## +# Generate data +############################################################################## + #%% parameters n = 100 # nb bins @@ -40,6 +44,11 @@ M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'euclidean') M /= M.max() M2 = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'sqeuclidean') M2 /= M2.max() + +############################################################################## +# Plot data +############################################################################## + #%% plot the distributions pl.figure(1) @@ -51,10 +60,15 @@ pl.plot(x, B, label='Target distributions') pl.title('Target distributions') pl.tight_layout() + +############################################################################## +# Compute EMD for the different losses +############################################################################## + #%% Compute and plot distributions and loss matrix d_emd = ot.emd2(a, B, M) # direct computation of EMD -d_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M3 +d_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M2 pl.figure(2) @@ -63,6 +77,10 @@ pl.plot(d_emd2, label='Squared Euclidean EMD') pl.title('EMD distances') pl.legend() +############################################################################## +# Compute Sinkhorn for the different losses +############################################################################## + #%% reg = 1e-2 d_sinkhorn = ot.sinkhorn2(a, B, M, reg) diff --git a/examples/plot_optim_OTreg.py b/examples/plot_optim_OTreg.py index 7ef6a6b..95bcdaf 100644 --- a/examples/plot_optim_OTreg.py +++ b/examples/plot_optim_OTreg.py @@ -4,6 +4,24 @@ Regularized OT with generic solver ================================== +This example illustrate the use of the generic solver for regularized OT with +user designed regularization term. It uses Conditional gradient as in [6] and +generalized Conditional Gradient as proposed in [5][7]. + + +[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for +Domain Adaptation, in IEEE Transactions on Pattern Analysis and Machine +Intelligence , vol.PP, no.99, pp.1-1. + +[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). +Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, +7(3), 1853-1882. + +[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized +conditional gradient: analysis of convergence and applications. +arXiv preprint arXiv:1510.06567. + + """ diff --git a/examples/plot_otda_color_images.py b/examples/plot_otda_color_images.py index 46ad44b..f1df9d9 100644 --- a/examples/plot_otda_color_images.py +++ b/examples/plot_otda_color_images.py @@ -1,8 +1,8 @@ # -*- coding: utf-8 -*- """ -======================================================== -OT for domain adaptation with image color adaptation [6] -======================================================== +============================= +OT for image color adaptation +============================= This example presents a way of transferring colors between two image with Optimal Transport as introduced in [6] @@ -41,7 +41,7 @@ def minmax(I): ############################################################################## -# generate data +# Generate data ############################################################################## # Loading images @@ -61,33 +61,7 @@ Xt = X2[idx2, :] ############################################################################## -# Instantiate the different transport algorithms and fit them -############################################################################## - -# EMDTransport -ot_emd = ot.da.EMDTransport() -ot_emd.fit(Xs=Xs, Xt=Xt) - -# SinkhornTransport -ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) -ot_sinkhorn.fit(Xs=Xs, Xt=Xt) - -# prediction between images (using out of sample prediction as in [6]) -transp_Xs_emd = ot_emd.transform(Xs=X1) -transp_Xt_emd = ot_emd.inverse_transform(Xt=X2) - -transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) -transp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2) - -I1t = minmax(mat2im(transp_Xs_emd, I1.shape)) -I2t = minmax(mat2im(transp_Xt_emd, I2.shape)) - -I1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) -I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape)) - - -############################################################################## -# plot original image +# Plot original image ############################################################################## pl.figure(1, figsize=(6.4, 3)) @@ -104,7 +78,7 @@ pl.title('Image 2') ############################################################################## -# scatter plot of colors +# Scatter plot of colors ############################################################################## pl.figure(2, figsize=(6.4, 3)) @@ -126,7 +100,33 @@ pl.tight_layout() ############################################################################## -# plot new images +# Instantiate the different transport algorithms and fit them +############################################################################## + +# EMDTransport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) + +# SinkhornTransport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + +# prediction between images (using out of sample prediction as in [6]) +transp_Xs_emd = ot_emd.transform(Xs=X1) +transp_Xt_emd = ot_emd.inverse_transform(Xt=X2) + +transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) +transp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2) + +I1t = minmax(mat2im(transp_Xs_emd, I1.shape)) +I2t = minmax(mat2im(transp_Xt_emd, I2.shape)) + +I1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) +I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape)) + + +############################################################################## +# Plot new images ############################################################################## pl.figure(3, figsize=(8, 4)) diff --git a/examples/plot_otda_mapping.py b/examples/plot_otda_mapping.py index 09d2cb4..e0da2d8 100644 --- a/examples/plot_otda_mapping.py +++ b/examples/plot_otda_mapping.py @@ -6,7 +6,7 @@ OT mapping estimation for domain adaptation [8] This example presents how to use MappingTransport to estimate at the same time both the coupling transport and approximate the transport map with either -a linear or a kernelized mapping as introduced in [8] +a linear or a kernelized mapping as introduced in [8]. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for discrete optimal transport", @@ -43,6 +43,17 @@ Xt, yt = ot.datasets.get_data_classif( Xt[yt == 2] *= 3 Xt = Xt + 4 +############################################################################## +# plot data +############################################################################## + +pl.figure(1, (10, 5)) +pl.clf() +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.legend(loc=0) +pl.title('Source and target distributions') + ############################################################################## # Instantiate the different transport algorithms and fit them @@ -76,19 +87,7 @@ transp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new) ############################################################################## -# plot data -############################################################################## - -pl.figure(1, (10, 5)) -pl.clf() -pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') -pl.legend(loc=0) -pl.title('Source and target distributions') - - -############################################################################## -# plot transported samples +# Plot transported samples ############################################################################## pl.figure(2) diff --git a/examples/plot_otda_mapping_colors_images.py b/examples/plot_otda_mapping_colors_images.py index 936206c..a8b2ca8 100644 --- a/examples/plot_otda_mapping_colors_images.py +++ b/examples/plot_otda_mapping_colors_images.py @@ -1,8 +1,11 @@ # -*- coding: utf-8 -*- """ -==================================================================================== -OT for domain adaptation with image color adaptation [6] with mapping estimation [8] -==================================================================================== +=============================================== +OT for color adaptation with mapping estimation +=============================================== + +OT for domain adaptation with image color adaptation [6] with mapping +estimation [8]. [6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), @@ -93,7 +96,7 @@ Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape)) ############################################################################## -# plot original images +# Plot original images ############################################################################## pl.figure(1, figsize=(6.4, 3)) @@ -110,7 +113,7 @@ pl.tight_layout() ############################################################################## -# plot pixel values distribution +# Plot pixel values distribution ############################################################################## pl.figure(2, figsize=(6.4, 5)) @@ -132,7 +135,7 @@ pl.tight_layout() ############################################################################## -# plot transformed images +# Plot transformed images ############################################################################## pl.figure(2, figsize=(10, 5)) -- cgit v1.2.3 From 3007f1da1094f93fa4216386666085cf60316b04 Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Thu, 31 Aug 2017 16:44:18 +0200 Subject: Minor corrections suggested by @agramfort + new barycenter example + test function --- README.md | 2 +- data/carre.png | Bin 0 -> 168 bytes data/coeur.png | Bin 0 -> 225 bytes data/rond.png | Bin 0 -> 230 bytes data/triangle.png | Bin 0 -> 254 bytes examples/plot_gromov.py | 14 +-- examples/plot_gromov_barycenter.py | 240 +++++++++++++++++++++++++++++++++++++ ot/gromov.py | 36 +++--- test/test_gromov.py | 38 ++++++ 9 files changed, 302 insertions(+), 28 deletions(-) create mode 100755 data/carre.png create mode 100755 data/coeur.png create mode 100755 data/rond.png create mode 100755 data/triangle.png create mode 100755 examples/plot_gromov_barycenter.py create mode 100644 test/test_gromov.py diff --git a/README.md b/README.md index a42f17b..53672ae 100644 --- a/README.md +++ b/README.md @@ -183,4 +183,4 @@ You can also post bug reports and feature requests in Github issues. Make sure t [11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). [Wasserstein Discriminant Analysis](https://arxiv.org/pdf/1608.08063.pdf). arXiv preprint arXiv:1608.08063. -[12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, [Gromov-Wasserstein averaging of kernel and distance matrices](http://proceedings.mlr.press/v48/peyre16.html) International Conference on Machine Learning (ICML). 2016. +[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon, [Gromov-Wasserstein averaging of kernel and distance matrices](http://proceedings.mlr.press/v48/peyre16.html) International Conference on Machine Learning (ICML). 2016. diff --git a/data/carre.png b/data/carre.png new file mode 100755 index 0000000..45ff0ef Binary files /dev/null and b/data/carre.png differ diff --git a/data/coeur.png b/data/coeur.png new file mode 100755 index 0000000..3f511a6 Binary files /dev/null and b/data/coeur.png differ diff --git a/data/rond.png b/data/rond.png new file mode 100755 index 0000000..1c1a068 Binary files /dev/null and b/data/rond.png differ diff --git a/data/triangle.png b/data/triangle.png new file mode 100755 index 0000000..ca36d09 Binary files /dev/null and b/data/triangle.png differ diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py index a33fde1..9bbdbde 100644 --- a/examples/plot_gromov.py +++ b/examples/plot_gromov.py @@ -1,8 +1,8 @@ # -*- coding: utf-8 -*- """ -==================== +========================== Gromov-Wasserstein example -==================== +========================== This example is designed to show how to use the Gromov-Wassertsein distance computation in POT. """ @@ -14,14 +14,14 @@ computation in POT. import scipy as sp import numpy as np +import matplotlib.pylab as pl import ot -import matplotlib.pylab as pl """ Sample two Gaussian distributions (2D and 3D) -==================== +============================================= The Gromov-Wasserstein distance allows to compute distances with samples that do not belong to the same metric space. For demonstration purpose, we sample two Gaussian distributions in 2- and 3-dimensional spaces. """ @@ -42,7 +42,7 @@ xt = np.random.randn(n, 3).dot(P) + mu_t """ Plotting the distributions -==================== +========================== """ fig = pl.figure() ax1 = fig.add_subplot(121) @@ -54,7 +54,7 @@ pl.show() """ Compute distance kernels, normalize them and then display -==================== +========================================================= """ C1 = sp.spatial.distance.cdist(xs, xs) @@ -72,7 +72,7 @@ pl.show() """ Compute Gromov-Wasserstein plans and distance -==================== +============================================= """ p = ot.unif(n) diff --git a/examples/plot_gromov_barycenter.py b/examples/plot_gromov_barycenter.py new file mode 100755 index 0000000..6a72b3b --- /dev/null +++ b/examples/plot_gromov_barycenter.py @@ -0,0 +1,240 @@ +# -*- coding: utf-8 -*- +""" +===================================== +Gromov-Wasserstein Barycenter example +===================================== +This example is designed to show how to use the Gromov-Wassertsein distance +computation in POT. +""" + +# Author: Erwan Vautier +# Nicolas Courty +# +# License: MIT License + + +import numpy as np +import scipy as sp + +import scipy.ndimage as spi +import matplotlib.pylab as pl +from sklearn import manifold +from sklearn.decomposition import PCA + +import ot + +""" + +Smacof MDS +========== +This function allows to find an embedding of points given a dissimilarity matrix +that will be given by the output of the algorithm +""" + + +def smacof_mds(C, dim, maxIter=3000, eps=1e-9): + """ + Returns an interpolated point cloud following the dissimilarity matrix C using SMACOF + multidimensional scaling (MDS) in specific dimensionned target space + + Parameters + ---------- + C : np.ndarray(ns,ns) + dissimilarity matrix + dim : Integer + dimension of the targeted space + maxIter : Maximum number of iterations of the SMACOF algorithm for a single run + + eps : relative tolerance w.r.t stress to declare converge + + + Returns + ------- + npos : R**dim ndarray + Embedded coordinates of the interpolated point cloud (defined with one isometry) + + + """ + + seed = np.random.RandomState(seed=3) + + mds = manifold.MDS( + dim, + max_iter=3000, + eps=1e-9, + dissimilarity='precomputed', + n_init=1) + pos = mds.fit(C).embedding_ + + nmds = manifold.MDS( + 2, + max_iter=3000, + eps=1e-9, + dissimilarity="precomputed", + random_state=seed, + n_init=1) + npos = nmds.fit_transform(C, init=pos) + + return npos + + +""" +Data preparation +================ +The four distributions are constructed from 4 simple images +""" + + +def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + + +carre = spi.imread('../data/carre.png').astype(np.float64) / 256 +rond = spi.imread('../data/rond.png').astype(np.float64) / 256 +triangle = spi.imread('../data/triangle.png').astype(np.float64) / 256 +fleche = spi.imread('../data/coeur.png').astype(np.float64) / 256 + +shapes = [carre, rond, triangle, fleche] + +S = 4 +xs = [[] for i in range(S)] + + +for nb in range(4): + for i in range(8): + for j in range(8): + if shapes[nb][i, j] < 0.95: + xs[nb].append([j, 8 - i]) + +xs = np.array([np.array(xs[0]), np.array(xs[1]), + np.array(xs[2]), np.array(xs[3])]) + + +""" +Barycenter computation +====================== +The four distributions are constructed from 4 simple images +""" +ns = [len(xs[s]) for s in range(S)] +N = 30 + +"""Compute all distances matrices for the four shapes""" +Cs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)] +Cs = [cs / cs.max() for cs in Cs] + +ps = [ot.unif(ns[s]) for s in range(S)] +p = ot.unif(N) + + +lambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]] + +Ct01 = [0 for i in range(2)] +for i in range(2): + Ct01[i] = ot.gromov.gromov_barycenters(N, [Cs[0], Cs[1]], [ + ps[0], ps[1]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3) + +Ct02 = [0 for i in range(2)] +for i in range(2): + Ct02[i] = ot.gromov.gromov_barycenters(N, [Cs[0], Cs[2]], [ + ps[0], ps[2]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3) + +Ct13 = [0 for i in range(2)] +for i in range(2): + Ct13[i] = ot.gromov.gromov_barycenters(N, [Cs[1], Cs[3]], [ + ps[1], ps[3]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3) + +Ct23 = [0 for i in range(2)] +for i in range(2): + Ct23[i] = ot.gromov.gromov_barycenters(N, [Cs[2], Cs[3]], [ + ps[2], ps[3]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3) + +""" +Visualization +============= +""" + +"""The PCA helps in getting consistency between the rotations""" + +clf = PCA(n_components=2) +npos = [0, 0, 0, 0] +npos = [smacof_mds(Cs[s], 2) for s in range(S)] + +npost01 = [0, 0] +npost01 = [smacof_mds(Ct01[s], 2) for s in range(2)] +npost01 = [clf.fit_transform(npost01[s]) for s in range(2)] + +npost02 = [0, 0] +npost02 = [smacof_mds(Ct02[s], 2) for s in range(2)] +npost02 = [clf.fit_transform(npost02[s]) for s in range(2)] + +npost13 = [0, 0] +npost13 = [smacof_mds(Ct13[s], 2) for s in range(2)] +npost13 = [clf.fit_transform(npost13[s]) for s in range(2)] + +npost23 = [0, 0] +npost23 = [smacof_mds(Ct23[s], 2) for s in range(2)] +npost23 = [clf.fit_transform(npost23[s]) for s in range(2)] + + +fig = pl.figure(figsize=(10, 10)) + +ax1 = pl.subplot2grid((4, 4), (0, 0)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r') + +ax2 = pl.subplot2grid((4, 4), (0, 1)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b') + +ax3 = pl.subplot2grid((4, 4), (0, 2)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b') + +ax4 = pl.subplot2grid((4, 4), (0, 3)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r') + +ax5 = pl.subplot2grid((4, 4), (1, 0)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b') + +ax6 = pl.subplot2grid((4, 4), (1, 3)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b') + +ax7 = pl.subplot2grid((4, 4), (2, 0)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b') + +ax8 = pl.subplot2grid((4, 4), (2, 3)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b') + +ax9 = pl.subplot2grid((4, 4), (3, 0)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r') + +ax10 = pl.subplot2grid((4, 4), (3, 1)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b') + +ax11 = pl.subplot2grid((4, 4), (3, 2)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b') + +ax12 = pl.subplot2grid((4, 4), (3, 3)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax12.scatter(npos[3][:, 0], npos[3][:, 1], color='r') diff --git a/ot/gromov.py b/ot/gromov.py index 7cf3b42..421ed3f 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -23,7 +23,7 @@ def square_loss(a, b): Returns the value of L(a,b)=(1/2)*|a-b|^2 """ - return (1 / 2) * (a - b)**2 + return 0.5 * (a - b)**2 def kl_loss(a, b): @@ -54,9 +54,9 @@ def tensor_square_loss(C1, C2, T): Parameters ---------- - C1 : np.ndarray(ns,ns) + C1 : ndarray, shape (ns, ns) Metric cost matrix in the source space - C2 : np.ndarray(nt,nt) + C2 : ndarray, shape (nt, nt) Metric costfr matrix in the target space T : np.ndarray(ns,nt) Coupling between source and target spaces @@ -87,7 +87,7 @@ def tensor_square_loss(C1, C2, T): return b tens = -np.dot(h1(C1), T).dot(h2(C2).T) - tens = tens - tens.min() + tens -= tens.min() return np.array(tens) @@ -112,9 +112,9 @@ def tensor_kl_loss(C1, C2, T): Parameters ---------- - C1 : np.ndarray(ns,ns) + C1 : ndarray, shape (ns, ns) Metric cost matrix in the source space - C2 : np.ndarray(nt,nt) + C2 : ndarray, shape (nt, nt) Metric costfr matrix in the target space T : np.ndarray(ns,nt) Coupling between source and target spaces @@ -149,7 +149,7 @@ def tensor_kl_loss(C1, C2, T): return np.log(b + 1e-15) tens = -np.dot(h1(C1), T).dot(h2(C2).T) - tens = tens - tens.min() + tens -= tens.min() return np.array(tens) @@ -175,9 +175,8 @@ def update_square_loss(p, lambdas, T, Cs): """ - tmpsum = np.sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) - for s in range(len(T))]) - ppt = np.dot(p, p.T) + tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) for s in range(len(T))]) + ppt = np.outer(p, p) return(np.divide(tmpsum, ppt)) @@ -203,9 +202,8 @@ def update_kl_loss(p, lambdas, T, Cs): """ - tmpsum = np.sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) - for s in range(len(T))]) - ppt = np.dot(p, p.T) + tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) for s in range(len(T))]) + ppt = np.outer(p, p) return(np.exp(np.divide(tmpsum, ppt))) @@ -239,9 +237,9 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr Parameters ---------- - C1 : np.ndarray(ns,ns) + C1 : ndarray, shape (ns, ns) Metric cost matrix in the source space - C2 : np.ndarray(nt,nt) + C2 : ndarray, shape (nt, nt) Metric costfr matrix in the target space p : np.ndarray(ns,) distribution in the source space @@ -271,7 +269,7 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr C1 = np.asarray(C1, dtype=np.float64) C2 = np.asarray(C2, dtype=np.float64) - T = np.dot(p, q.T) # Initialization + T = np.outer(p, q) # Initialization cpt = 0 err = 1 @@ -333,9 +331,9 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopTh Parameters ---------- - C1 : np.ndarray(ns,ns) + C1 : ndarray, shape (ns, ns) Metric cost matrix in the source space - C2 : np.ndarray(nt,nt) + C2 : ndarray, shape (nt, nt) Metric costfr matrix in the target space p : np.ndarray(ns,) distribution in the source space @@ -434,8 +432,6 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, numItermax=1000 Cs = [np.asarray(Cs[s], dtype=np.float64) for s in range(S)] lambdas = np.asarray(lambdas, dtype=np.float64) - T = [0 for s in range(S)] - # Initialization of C : random SPD matrix xalea = np.random.randn(N, 2) C = dist(xalea, xalea) diff --git a/test/test_gromov.py b/test/test_gromov.py new file mode 100644 index 0000000..75eeaab --- /dev/null +++ b/test/test_gromov.py @@ -0,0 +1,38 @@ +"""Tests for module gromov """ + +# Author: Erwan Vautier +# Nicolas Courty +# +# License: MIT License + +import numpy as np +import ot + + +def test_gromov(): + n = 50 # nb samples + + mu_s = np.array([0, 0]) + cov_s = np.array([[1, 0], [0, 1]]) + + xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s) + + xt = [xs[n - (i + 1)] for i in range(n)] + xt = np.array(xt) + + p = ot.unif(n) + q = ot.unif(n) + + C1 = ot.dist(xs, xs) + C2 = ot.dist(xt, xt) + + C1 /= C1.max() + C2 /= C2.max() + + G = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4) + + # check constratints + np.testing.assert_allclose( + p, G.sum(1), atol=1e-04) # cf convergence gromov + np.testing.assert_allclose( + q, G.sum(0), atol=1e-04) # cf convergence gromov -- cgit v1.2.3 From bc68cc3e8b23ad7d542518ba8ffa665094d57663 Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Thu, 31 Aug 2017 17:17:30 +0200 Subject: minor corrections --- examples/plot_gromov_barycenter.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/examples/plot_gromov_barycenter.py b/examples/plot_gromov_barycenter.py index 6a72b3b..da52768 100755 --- a/examples/plot_gromov_barycenter.py +++ b/examples/plot_gromov_barycenter.py @@ -32,18 +32,19 @@ that will be given by the output of the algorithm """ -def smacof_mds(C, dim, maxIter=3000, eps=1e-9): +def smacof_mds(C, dim, max_iter=3000, eps=1e-9): """ Returns an interpolated point cloud following the dissimilarity matrix C using SMACOF multidimensional scaling (MDS) in specific dimensionned target space Parameters ---------- - C : np.ndarray(ns,ns) + C : ndarray, shape (ns, ns) dissimilarity matrix - dim : Integer + dim : int dimension of the targeted space - maxIter : Maximum number of iterations of the SMACOF algorithm for a single run + max_iter : int + Maximum number of iterations of the SMACOF algorithm for a single run eps : relative tolerance w.r.t stress to declare converge @@ -60,7 +61,7 @@ def smacof_mds(C, dim, maxIter=3000, eps=1e-9): mds = manifold.MDS( dim, - max_iter=3000, + max_iter=max_iter, eps=1e-9, dissimilarity='precomputed', n_init=1) @@ -68,7 +69,7 @@ def smacof_mds(C, dim, maxIter=3000, eps=1e-9): nmds = manifold.MDS( 2, - max_iter=3000, + max_iter=max_iter, eps=1e-9, dissimilarity="precomputed", random_state=seed, -- cgit v1.2.3 From e89f09dae0f66df4a8c1c7ac761a71611c2d676c Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 28 Jul 2017 08:00:35 +0200 Subject: remove linewidth error message --- ot/da.py | 150 +++++++++++++++++++++++++++++++++++++++++++++------------------ 1 file changed, 108 insertions(+), 42 deletions(-) diff --git a/ot/da.py b/ot/da.py index 4f9bce5..1dd4011 100644 --- a/ot/da.py +++ b/ot/da.py @@ -17,14 +17,18 @@ from .optim import cg from .optim import gcg -def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerItermax=200, stopInnerThr=1e-9, verbose=False, log=False): +def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, + numInnerItermax=200, stopInnerThr=1e-9, verbose=False, + log=False): """ - Solve the entropic regularization optimal transport problem with nonconvex group lasso regularization + Solve the entropic regularization optimal transport problem with nonconvex + group lasso regularization The function solves the following optimization problem: .. math:: - \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma)+ \eta \Omega_g(\gamma) + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma) + + \eta \Omega_g(\gamma) s.t. \gamma 1 = a @@ -34,11 +38,16 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerIte where : - M is the (ns,nt) metric cost matrix - - :math:`\Omega_e` is the entropic regularization term :math:`\Omega_e(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - :math:`\Omega_g` is the group lasso regulaization term :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^{1/2}_1` where :math:`\mathcal{I}_c` are the index of samples from class c in the source domain. + - :math:`\Omega_e` is the entropic regularization term + :math:`\Omega_e(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - :math:`\Omega_g` is the group lasso regulaization term + :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^{1/2}_1` + where :math:`\mathcal{I}_c` are the index of samples from class c + in the source domain. - a and b are source and target weights (sum to 1) - The algorithm used for solving the problem is the generalised conditional gradient as proposed in [5]_ [7]_ + The algorithm used for solving the problem is the generalised conditional + gradient as proposed in [5]_ [7]_ Parameters @@ -78,8 +87,13 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerIte References ---------- - .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 - .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567. + .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE + Transactions on Pattern Analysis and Machine Intelligence , + vol.PP, no.99, pp.1-1 + .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). + Generalized conditional gradient: analysis of convergence + and applications. arXiv preprint arXiv:1510.06567. See Also -------- @@ -114,14 +128,18 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerIte return transp -def sinkhorn_l1l2_gl(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerItermax=200, stopInnerThr=1e-9, verbose=False, log=False): +def sinkhorn_l1l2_gl(a, labels_a, b, M, reg, eta=0.1, numItermax=10, + numInnerItermax=200, stopInnerThr=1e-9, verbose=False, + log=False): """ - Solve the entropic regularization optimal transport problem with group lasso regularization + Solve the entropic regularization optimal transport problem with group + lasso regularization The function solves the following optimization problem: .. math:: - \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma)+ \eta \Omega_g(\gamma) + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma)+ + \eta \Omega_g(\gamma) s.t. \gamma 1 = a @@ -131,11 +149,16 @@ def sinkhorn_l1l2_gl(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerIte where : - M is the (ns,nt) metric cost matrix - - :math:`\Omega_e` is the entropic regularization term :math:`\Omega_e(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - :math:`\Omega_g` is the group lasso regulaization term :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^2` where :math:`\mathcal{I}_c` are the index of samples from class c in the source domain. + - :math:`\Omega_e` is the entropic regularization term + :math:`\Omega_e(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - :math:`\Omega_g` is the group lasso regulaization term + :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^2` + where :math:`\mathcal{I}_c` are the index of samples from class + c in the source domain. - a and b are source and target weights (sum to 1) - The algorithm used for solving the problem is the generalised conditional gradient as proposed in [5]_ [7]_ + The algorithm used for solving the problem is the generalised conditional + gradient as proposed in [5]_ [7]_ Parameters @@ -175,8 +198,12 @@ def sinkhorn_l1l2_gl(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerIte References ---------- - .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 - .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567. + .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE Transactions + on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 + .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). + Generalized conditional gradient: analysis of convergence and + applications. arXiv preprint arXiv:1510.06567. See Also -------- @@ -203,16 +230,22 @@ def sinkhorn_l1l2_gl(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerIte W[labels_a == lab, i] = temp / n return W - return gcg(a, b, M, reg, eta, f, df, G0=None, numItermax=numItermax, numInnerItermax=numInnerItermax, stopThr=stopInnerThr, verbose=verbose, log=log) + return gcg(a, b, M, reg, eta, f, df, G0=None, numItermax=numItermax, + numInnerItermax=numInnerItermax, stopThr=stopInnerThr, + verbose=verbose, log=log) -def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False, verbose2=False, numItermax=100, numInnerItermax=10, stopInnerThr=1e-6, stopThr=1e-5, log=False, **kwargs): +def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False, + verbose2=False, numItermax=100, numInnerItermax=10, + stopInnerThr=1e-6, stopThr=1e-5, log=False, + **kwargs): """Joint OT and linear mapping estimation as proposed in [8] The function solves the following optimization problem: .. math:: - \min_{\gamma,L}\quad \|L(X_s) -n_s\gamma X_t\|^2_F + \mu<\gamma,M>_F + \eta \|L -I\|^2_F + \min_{\gamma,L}\quad \|L(X_s) -n_s\gamma X_t\|^2_F + + \mu<\gamma,M>_F + \eta \|L -I\|^2_F s.t. \gamma 1 = a @@ -221,8 +254,10 @@ def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False, \gamma\geq 0 where : - - M is the (ns,nt) squared euclidean cost matrix between samples in Xs and Xt (scaled by ns) - - :math:`L` is a dxd linear operator that approximates the barycentric mapping + - M is the (ns,nt) squared euclidean cost matrix between samples in + Xs and Xt (scaled by ns) + - :math:`L` is a dxd linear operator that approximates the barycentric + mapping - :math:`I` is the identity matrix (neutral linear mapping) - a and b are uniform source and target weights @@ -277,7 +312,9 @@ def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False, References ---------- - .. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. + .. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, + "Mapping estimation for discrete optimal transport", + Neural Information Processing Systems (NIPS), 2016. See Also -------- @@ -384,13 +421,18 @@ def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False, return G, L -def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', sigma=1, bias=False, verbose=False, verbose2=False, numItermax=100, numInnerItermax=10, stopInnerThr=1e-6, stopThr=1e-5, log=False, **kwargs): +def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', + sigma=1, bias=False, verbose=False, verbose2=False, + numItermax=100, numInnerItermax=10, + stopInnerThr=1e-6, stopThr=1e-5, log=False, + **kwargs): """Joint OT and nonlinear mapping estimation with kernels as proposed in [8] The function solves the following optimization problem: .. math:: - \min_{\gamma,L\in\mathcal{H}}\quad \|L(X_s) -n_s\gamma X_t\|^2_F + \mu<\gamma,M>_F + \eta \|L\|^2_\mathcal{H} + \min_{\gamma,L\in\mathcal{H}}\quad \|L(X_s) - + n_s\gamma X_t\|^2_F + \mu<\gamma,M>_F + \eta \|L\|^2_\mathcal{H} s.t. \gamma 1 = a @@ -399,8 +441,10 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', sigm \gamma\geq 0 where : - - M is the (ns,nt) squared euclidean cost matrix between samples in Xs and Xt (scaled by ns) - - :math:`L` is a ns x d linear operator on a kernel matrix that approximates the barycentric mapping + - M is the (ns,nt) squared euclidean cost matrix between samples in + Xs and Xt (scaled by ns) + - :math:`L` is a ns x d linear operator on a kernel matrix that + approximates the barycentric mapping - a and b are uniform source and target weights The problem consist in solving jointly an optimal transport matrix @@ -458,7 +502,9 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', sigm References ---------- - .. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. + .. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, + "Mapping estimation for discrete optimal transport", + Neural Information Processing Systems (NIPS), 2016. See Also -------- @@ -593,7 +639,9 @@ class OTDA(object): References ---------- - .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 + .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE Transactions on + Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 """ @@ -606,7 +654,8 @@ class OTDA(object): self.computed = False def fit(self, xs, xt, ws=None, wt=None, norm=None): - """ Fit domain adaptation between samples is xs and xt (with optional weights)""" + """Fit domain adaptation between samples is xs and xt + (with optional weights)""" self.xs = xs self.xt = xt @@ -669,7 +718,9 @@ class OTDA(object): References ---------- - .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. + .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). + Regularized discrete optimal transport. SIAM Journal on Imaging + Sciences, 7(3), 1853-1882. """ if direction > 0: # >0 then source to target @@ -708,10 +759,12 @@ class OTDA(object): class OTDA_sinkhorn(OTDA): - """Class for domain adaptation with optimal transport with entropic regularization""" + """Class for domain adaptation with optimal transport with entropic + regularization""" def fit(self, xs, xt, reg=1, ws=None, wt=None, norm=None, **kwargs): - """ Fit regularized domain adaptation between samples is xs and xt (with optional weights)""" + """Fit regularized domain adaptation between samples is xs and xt + (with optional weights)""" self.xs = xs self.xt = xt @@ -731,10 +784,14 @@ class OTDA_sinkhorn(OTDA): class OTDA_lpl1(OTDA): - """Class for domain adaptation with optimal transport with entropic and group regularization""" + """Class for domain adaptation with optimal transport with entropic and + group regularization""" - def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None, **kwargs): - """ Fit regularized domain adaptation between samples is xs and xt (with optional weights), See ot.da.sinkhorn_lpl1_mm for fit parameters""" + def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None, + **kwargs): + """Fit regularized domain adaptation between samples is xs and xt + (with optional weights), See ot.da.sinkhorn_lpl1_mm for fit + parameters""" self.xs = xs self.xt = xt @@ -754,10 +811,14 @@ class OTDA_lpl1(OTDA): class OTDA_l1l2(OTDA): - """Class for domain adaptation with optimal transport with entropic and group lasso regularization""" + """Class for domain adaptation with optimal transport with entropic + and group lasso regularization""" - def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None, **kwargs): - """ Fit regularized domain adaptation between samples is xs and xt (with optional weights), See ot.da.sinkhorn_lpl1_gl for fit parameters""" + def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None, + **kwargs): + """Fit regularized domain adaptation between samples is xs and xt + (with optional weights), See ot.da.sinkhorn_lpl1_gl for fit + parameters""" self.xs = xs self.xt = xt @@ -777,7 +838,9 @@ class OTDA_l1l2(OTDA): class OTDA_mapping_linear(OTDA): - """Class for optimal transport with joint linear mapping estimation as in [8]""" + """Class for optimal transport with joint linear mapping estimation as in + [8] + """ def __init__(self): """ Class initialization""" @@ -820,9 +883,11 @@ class OTDA_mapping_linear(OTDA): class OTDA_mapping_kernel(OTDA_mapping_linear): - """Class for optimal transport with joint nonlinear mapping estimation as in [8]""" + """Class for optimal transport with joint nonlinear mapping + estimation as in [8]""" - def fit(self, xs, xt, mu=1, eta=1, bias=False, kerneltype='gaussian', sigma=1, **kwargs): + def fit(self, xs, xt, mu=1, eta=1, bias=False, kerneltype='gaussian', + sigma=1, **kwargs): """ Fit domain adaptation between samples is xs and xt """ self.xs = xs self.xt = xt @@ -843,7 +908,8 @@ class OTDA_mapping_kernel(OTDA_mapping_linear): if self.computed: K = kernel( - x, self.xs, method=self.kernel, sigma=self.sigma, **self.kwargs) + x, self.xs, method=self.kernel, sigma=self.sigma, + **self.kwargs) if self.bias: K = np.hstack((K, np.ones((x.shape[0], 1)))) return K.dot(self.L) -- cgit v1.2.3 From f469205cf19915a34366c9410ccf6c8e13038673 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 28 Jul 2017 08:49:10 +0200 Subject: first proposal for OT wrappers --- ot/da.py | 179 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 179 insertions(+) diff --git a/ot/da.py b/ot/da.py index 1dd4011..f534bf5 100644 --- a/ot/da.py +++ b/ot/da.py @@ -916,3 +916,182 @@ class OTDA_mapping_kernel(OTDA_mapping_linear): else: print("Warning, model not fitted yet, returning None") return None + +############################################################################## +# proposal +############################################################################## + +from sklearn.base import BaseEstimator +from sklearn.metrics import pairwise_distances + +""" +- all methods have the same input parameters: Xs, Xt, ys, yt (what order ?) +- ref: is the entropic reg parameter +- eta: is the second reg parameter +- gamma_: is the optimal coupling +- mapping barycentric for the moment + +Questions: +- Cost matrix estimation: from sklearn or from internal function ? +- distribution estimation ? Look at Nathalie's approach +- should everything been done into the fit from BaseTransport ? +""" + + +class BaseTransport(BaseEstimator): + + def fit(self, Xs=None, ys=None, Xt=None, yt=None, method="sinkhorn"): + """fit: estimates the optimal coupling + + Parameters: + ----------- + - Xs: source samples, (ns samples, d features) numpy-like array + - ys: source labels + - Xt: target samples (nt samples, d features) numpy-like array + - yt: target labels + - method: algorithm to use to compute optimal coupling + (default: sinkhorn) + + Returns: + -------- + - self + """ + + # pairwise distance + Cost = pairwise_distances(Xs, Xt, metric=self.metric) + + if self.mode == "semisupervised": + print("TODO: modify cost matrix accordingly") + pass + + # distribution estimation: should we change it ? + mu_s = np.ones(Xs.shape[0]) / float(Xs.shape[0]) + mu_t = np.ones(Xt.shape[0]) / float(Xt.shape[0]) + + if method == "sinkhorn": + self.gamma_ = sinkhorn( + a=mu_s, b=mu_t, M=Cost, reg=self.reg, + numItermax=self.max_iter, stopThr=self.tol, + verbose=self.verbose, log=self.log) + else: + print("TODO: implement the other methods") + + return self + + def fit_transform(self, Xs=None, ys=None, Xt=None, yt=None): + """fit_transform + + Parameters: + ----------- + - Xs: source samples, (ns samples, d features) numpy-like array + - ys: source labels + - Xt: target samples (nt samples, d features) numpy-like array + - yt: target labels + + Returns: + -------- + - transp_Xt + """ + + return self.fit(Xs, ys, Xt, yt, self.method).transform(Xs, ys, Xt, yt) + + def transform(self, Xs=None, ys=None, Xt=None, yt=None): + """transform: as a convention transports source samples + onto target samples + + Parameters: + ----------- + - Xs: source samples, (ns samples, d features) numpy-like array + - ys: source labels + - Xt: target samples (nt samples, d features) numpy-like array + - yt: target labels + + Returns: + -------- + - transp_Xt + """ + + if self.mapping == "barycentric": + transp = self.gamma_ / np.sum(self.gamma_, 1)[:, None] + + # set nans to 0 + transp[~ np.isfinite(transp)] = 0 + + # compute transported samples + transp_Xs = np.dot(transp, Xt) + + return transp_Xs + + def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None): + """inverse_transform: as a convention transports target samples + onto source samples + + Parameters: + ----------- + - Xs: source samples, (ns samples, d features) numpy-like array + - ys: source labels + - Xt: target samples (nt samples, d features) numpy-like array + - yt: target labels + + Returns: + -------- + - transp_Xt + """ + + if self.mapping == "barycentric": + transp_ = self.gamma_.T / np.sum(self.gamma_, 0)[:, None] + + # set nans to 0 + transp_[~ np.isfinite(transp_)] = 0 + + # compute transported samples + transp_Xt = np.dot(transp_, Xs) + else: + print("mapping not yet implemented") + + return transp_Xt + + +class SinkhornTransport(BaseTransport): + """SinkhornTransport: class wrapper for optimal transport based on + Sinkhorn's algorithm + + Parameters + ---------- + - reg : parameter for entropic regularization + - mode: unsupervised (default) or semi supervised: controls whether + labels are taken into accout to construct the optimal coupling + - max_iter : maximum number of iterations + - tol : precision + - verbose : control verbosity + - log : control log + + Attributes + ---------- + - gamma_: optimal coupling estimated by the fit function + """ + + def __init__(self, reg=1., mode="unsupervised", max_iter=1000, + tol=10e-9, verbose=False, log=False, mapping="barycentric", + metric="sqeuclidean"): + self.reg = reg + self.mode = mode + self.max_iter = max_iter + self.tol = tol + self.verbose = verbose + self.log = log + self.mapping = mapping + self.metric = metric + self.method = "sinkhorn" + + def fit(self, Xs=None, ys=None, Xt=None, yt=None): + """_fit + """ + return super(SinkhornTransport, self).fit( + Xs, ys, Xt, yt, method=self.method) + + +if __name__ == "__main__": + print("Small test") + + st = SinkhornTransport() -- cgit v1.2.3 From fa36e775ff2c1fe17cf1323d430a196774a6d2a5 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 28 Jul 2017 14:52:36 +0200 Subject: small modifs according to NG proposals --- ot/da.py | 43 +++++++++++++++++++++++++++++++------------ 1 file changed, 31 insertions(+), 12 deletions(-) diff --git a/ot/da.py b/ot/da.py index f534bf5..a422f7c 100644 --- a/ot/da.py +++ b/ot/da.py @@ -926,8 +926,8 @@ from sklearn.metrics import pairwise_distances """ - all methods have the same input parameters: Xs, Xt, ys, yt (what order ?) -- ref: is the entropic reg parameter -- eta: is the second reg parameter +- reg_e: is the entropic reg parameter +- reg_cl: is the second reg parameter - gamma_: is the optimal coupling - mapping barycentric for the moment @@ -940,7 +940,7 @@ Questions: class BaseTransport(BaseEstimator): - def fit(self, Xs=None, ys=None, Xt=None, yt=None, method="sinkhorn"): + def fit(self, Xs=None, ys=None, Xt=None, yt=None, method=None): """fit: estimates the optimal coupling Parameters: @@ -964,13 +964,17 @@ class BaseTransport(BaseEstimator): print("TODO: modify cost matrix accordingly") pass - # distribution estimation: should we change it ? - mu_s = np.ones(Xs.shape[0]) / float(Xs.shape[0]) - mu_t = np.ones(Xt.shape[0]) / float(Xt.shape[0]) + # distribution estimation + if self.distribution == "uniform": + mu_s = np.ones(Xs.shape[0]) / float(Xs.shape[0]) + mu_t = np.ones(Xt.shape[0]) / float(Xt.shape[0]) + else: + print("TODO: implement kernelized approach") + # coupling estimation if method == "sinkhorn": self.gamma_ = sinkhorn( - a=mu_s, b=mu_t, M=Cost, reg=self.reg, + a=mu_s, b=mu_t, M=Cost, reg=self.reg_e, numItermax=self.max_iter, stopThr=self.tol, verbose=self.verbose, log=self.log) else: @@ -1058,7 +1062,7 @@ class SinkhornTransport(BaseTransport): Parameters ---------- - - reg : parameter for entropic regularization + - reg_e : parameter for entropic regularization - mode: unsupervised (default) or semi supervised: controls whether labels are taken into accout to construct the optimal coupling - max_iter : maximum number of iterations @@ -1071,10 +1075,10 @@ class SinkhornTransport(BaseTransport): - gamma_: optimal coupling estimated by the fit function """ - def __init__(self, reg=1., mode="unsupervised", max_iter=1000, + def __init__(self, reg_e=1., mode="unsupervised", max_iter=1000, tol=10e-9, verbose=False, log=False, mapping="barycentric", - metric="sqeuclidean"): - self.reg = reg + metric="sqeuclidean", distribution="uniform"): + self.reg_e = reg_e self.mode = mode self.max_iter = max_iter self.tol = tol @@ -1082,11 +1086,26 @@ class SinkhornTransport(BaseTransport): self.log = log self.mapping = mapping self.metric = metric + self.distribution = distribution self.method = "sinkhorn" def fit(self, Xs=None, ys=None, Xt=None, yt=None): - """_fit + """fit + + Parameters: + ----------- + - Xs: source samples, (ns samples, d features) numpy-like array + - ys: source labels + - Xt: target samples (nt samples, d features) numpy-like array + - yt: target labels + - method: algorithm to use to compute optimal coupling + (default: sinkhorn) + + Returns: + -------- + - self """ + return super(SinkhornTransport, self).fit( Xs, ys, Xt, yt, method=self.method) -- cgit v1.2.3 From aa19b6adef8e41ec57f94353d80ebd80d49edc29 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 28 Jul 2017 15:34:36 +0200 Subject: integrate AG comments --- ot/da.py | 202 +++++++++++++++++++++++++++++++++++++-------------------------- 1 file changed, 120 insertions(+), 82 deletions(-) diff --git a/ot/da.py b/ot/da.py index a422f7c..828efc2 100644 --- a/ot/da.py +++ b/ot/da.py @@ -940,21 +940,23 @@ Questions: class BaseTransport(BaseEstimator): - def fit(self, Xs=None, ys=None, Xt=None, yt=None, method=None): - """fit: estimates the optimal coupling - - Parameters: - ----------- - - Xs: source samples, (ns samples, d features) numpy-like array - - ys: source labels - - Xt: target samples (nt samples, d features) numpy-like array - - yt: target labels - - method: algorithm to use to compute optimal coupling - (default: sinkhorn) - - Returns: - -------- - - self + def fit(self, Xs=None, ys=None, Xt=None, yt=None): + """Build a coupling matrix from source and target sets of samples + (Xs, ys) and (Xt, yt) + Parameters + ---------- + Xs : array-like of shape = [n_source_samples, n_features] + The training input samples. + ys : array-like, shape = [n_source_samples] + The class labels + Xt : array-like of shape = [n_target_samples, n_features] + The training input samples. + yt : array-like, shape = [n_labeled_target_samples] + The class labels + Returns + ------- + self : object + Returns self. """ # pairwise distance @@ -972,7 +974,7 @@ class BaseTransport(BaseEstimator): print("TODO: implement kernelized approach") # coupling estimation - if method == "sinkhorn": + if self.method == "sinkhorn": self.gamma_ = sinkhorn( a=mu_s, b=mu_t, M=Cost, reg=self.reg_e, numItermax=self.max_iter, stopThr=self.tol, @@ -983,36 +985,43 @@ class BaseTransport(BaseEstimator): return self def fit_transform(self, Xs=None, ys=None, Xt=None, yt=None): - """fit_transform - - Parameters: - ----------- - - Xs: source samples, (ns samples, d features) numpy-like array - - ys: source labels - - Xt: target samples (nt samples, d features) numpy-like array - - yt: target labels - - Returns: - -------- - - transp_Xt + """Build a coupling matrix from source and target sets of samples + (Xs, ys) and (Xt, yt) and transports source samples Xs onto target + ones Xt + Parameters + ---------- + Xs : array-like of shape = [n_source_samples, n_features] + The training input samples. + ys : array-like, shape = [n_source_samples] + The class labels + Xt : array-like of shape = [n_target_samples, n_features] + The training input samples. + yt : array-like, shape = [n_labeled_target_samples] + The class labels + Returns + ------- + transp_Xs : array-like of shape = [n_source_samples, n_features] + The source samples samples. """ - return self.fit(Xs, ys, Xt, yt, self.method).transform(Xs, ys, Xt, yt) + return self.fit(Xs, ys, Xt, yt).transform(Xs, ys, Xt, yt) def transform(self, Xs=None, ys=None, Xt=None, yt=None): - """transform: as a convention transports source samples - onto target samples - - Parameters: - ----------- - - Xs: source samples, (ns samples, d features) numpy-like array - - ys: source labels - - Xt: target samples (nt samples, d features) numpy-like array - - yt: target labels - - Returns: - -------- - - transp_Xt + """Transports source samples Xs onto target ones Xt + Parameters + ---------- + Xs : array-like of shape = [n_source_samples, n_features] + The training input samples. + ys : array-like, shape = [n_source_samples] + The class labels + Xt : array-like of shape = [n_target_samples, n_features] + The training input samples. + yt : array-like, shape = [n_labeled_target_samples] + The class labels + Returns + ------- + transp_Xs : array-like of shape = [n_source_samples, n_features] + The transport source samples. """ if self.mapping == "barycentric": @@ -1027,19 +1036,21 @@ class BaseTransport(BaseEstimator): return transp_Xs def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None): - """inverse_transform: as a convention transports target samples - onto source samples - - Parameters: - ----------- - - Xs: source samples, (ns samples, d features) numpy-like array - - ys: source labels - - Xt: target samples (nt samples, d features) numpy-like array - - yt: target labels - - Returns: - -------- - - transp_Xt + """Transports target samples Xt onto target samples Xs + Parameters + ---------- + Xs : array-like of shape = [n_source_samples, n_features] + The training input samples. + ys : array-like, shape = [n_source_samples] + The class labels + Xt : array-like of shape = [n_target_samples, n_features] + The training input samples. + yt : array-like, shape = [n_labeled_target_samples] + The class labels + Returns + ------- + transp_Xt : array-like of shape = [n_source_samples, n_features] + The transported target samples. """ if self.mapping == "barycentric": @@ -1057,22 +1068,48 @@ class BaseTransport(BaseEstimator): class SinkhornTransport(BaseTransport): - """SinkhornTransport: class wrapper for optimal transport based on - Sinkhorn's algorithm + """Domain Adapatation OT method based on Sinkhorn Algorithm Parameters ---------- - - reg_e : parameter for entropic regularization - - mode: unsupervised (default) or semi supervised: controls whether - labels are taken into accout to construct the optimal coupling - - max_iter : maximum number of iterations - - tol : precision - - verbose : control verbosity - - log : control log - + reg_e : float, optional (default=1) + Entropic regularization parameter + mode : string, optional (default="unsupervised") + The DA mode. If "unsupervised" no target labels are taken into account + to modify the cost matrix. If "semisupervised" the target labels + are taken into account to set coefficients of the pairwise distance + matrix to 0 for row and columns indices that correspond to source and + target samples which share the same labels. + max_iter : int, float, optional (default=1000) + The minimum number of iteration before stopping the optimization + algorithm if no it has not converged + tol : float, optional (default=10e-9) + The precision required to stop the optimization algorithm. + mapping : string, optional (default="barycentric") + The kind of mapping to apply to transport samples from a domain into + another one. + if "barycentric" only the samples used to estimate the coupling can + be transported from a domain to another one. + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + distribution : string, optional (default="uniform") + The kind of distribution estimation to employ + verbose : int, optional (default=0) + Controls the verbosity of the optimization algorithm + log : int, optional (default=0) + Controls the logs of the optimization algorithm Attributes ---------- - - gamma_: optimal coupling estimated by the fit function + gamma_ : the optimal coupling + + References + ---------- + .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE Transactions + on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 + .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal + Transport, Advances in Neural Information Processing Systems (NIPS) + 26, 2013 """ def __init__(self, reg_e=1., mode="unsupervised", max_iter=1000, @@ -1090,24 +1127,25 @@ class SinkhornTransport(BaseTransport): self.method = "sinkhorn" def fit(self, Xs=None, ys=None, Xt=None, yt=None): - """fit - - Parameters: - ----------- - - Xs: source samples, (ns samples, d features) numpy-like array - - ys: source labels - - Xt: target samples (nt samples, d features) numpy-like array - - yt: target labels - - method: algorithm to use to compute optimal coupling - (default: sinkhorn) - - Returns: - -------- - - self + """Build a coupling matrix from source and target sets of samples + (Xs, ys) and (Xt, yt) + Parameters + ---------- + Xs : array-like of shape = [n_source_samples, n_features] + The training input samples. + ys : array-like, shape = [n_source_samples] + The class labels + Xt : array-like of shape = [n_target_samples, n_features] + The training input samples. + yt : array-like, shape = [n_labeled_target_samples] + The class labels + Returns + ------- + self : object + Returns self. """ - return super(SinkhornTransport, self).fit( - Xs, ys, Xt, yt, method=self.method) + return super(SinkhornTransport, self).fit(Xs, ys, Xt, yt) if __name__ == "__main__": -- cgit v1.2.3 From 5ab50354e60ed94d9d799927fd4b680fb8447304 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Mon, 31 Jul 2017 10:54:28 +0200 Subject: own BaseEstimator class written + rflamary comments addressed --- ot/da.py | 199 ++++++++++++++++++++++++++++++++++++++++++++++++++++++--------- 1 file changed, 172 insertions(+), 27 deletions(-) diff --git a/ot/da.py b/ot/da.py index 828efc2..d30c821 100644 --- a/ot/da.py +++ b/ot/da.py @@ -921,21 +921,153 @@ class OTDA_mapping_kernel(OTDA_mapping_linear): # proposal ############################################################################## -from sklearn.base import BaseEstimator -from sklearn.metrics import pairwise_distances +# from sklearn.base import BaseEstimator +# from sklearn.metrics import pairwise_distances + +############################################################################## +# adapted from scikit-learn + +import warnings +# from .externals.six import string_types, iteritems -""" -- all methods have the same input parameters: Xs, Xt, ys, yt (what order ?) -- reg_e: is the entropic reg parameter -- reg_cl: is the second reg parameter -- gamma_: is the optimal coupling -- mapping barycentric for the moment - -Questions: -- Cost matrix estimation: from sklearn or from internal function ? -- distribution estimation ? Look at Nathalie's approach -- should everything been done into the fit from BaseTransport ? -""" + +class BaseEstimator(object): + """Base class for all estimators in scikit-learn + Notes + ----- + All estimators should specify all the parameters that can be set + at the class level in their ``__init__`` as explicit keyword + arguments (no ``*args`` or ``**kwargs``). + """ + + @classmethod + def _get_param_names(cls): + """Get parameter names for the estimator""" + try: + from inspect import signature + except ImportError: + from .externals.funcsigs import signature + # fetch the constructor or the original constructor before + # deprecation wrapping if any + init = getattr(cls.__init__, 'deprecated_original', cls.__init__) + if init is object.__init__: + # No explicit constructor to introspect + return [] + + # introspect the constructor arguments to find the model parameters + # to represent + init_signature = signature(init) + # Consider the constructor parameters excluding 'self' + parameters = [p for p in init_signature.parameters.values() + if p.name != 'self' and p.kind != p.VAR_KEYWORD] + for p in parameters: + if p.kind == p.VAR_POSITIONAL: + raise RuntimeError("scikit-learn estimators should always " + "specify their parameters in the signature" + " of their __init__ (no varargs)." + " %s with constructor %s doesn't " + " follow this convention." + % (cls, init_signature)) + # Extract and sort argument names excluding 'self' + return sorted([p.name for p in parameters]) + + def get_params(self, deep=True): + """Get parameters for this estimator. + Parameters + ---------- + deep : boolean, optional + If True, will return the parameters for this estimator and + contained subobjects that are estimators. + Returns + ------- + params : mapping of string to any + Parameter names mapped to their values. + """ + out = dict() + for key in self._get_param_names(): + # We need deprecation warnings to always be on in order to + # catch deprecated param values. + # This is set in utils/__init__.py but it gets overwritten + # when running under python3 somehow. + warnings.simplefilter("always", DeprecationWarning) + try: + with warnings.catch_warnings(record=True) as w: + value = getattr(self, key, None) + if len(w) and w[0].category == DeprecationWarning: + # if the parameter is deprecated, don't show it + continue + finally: + warnings.filters.pop(0) + + # XXX: should we rather test if instance of estimator? + if deep and hasattr(value, 'get_params'): + deep_items = value.get_params().items() + out.update((key + '__' + k, val) for k, val in deep_items) + out[key] = value + return out + + def set_params(self, **params): + """Set the parameters of this estimator. + The method works on simple estimators as well as on nested objects + (such as pipelines). The latter have parameters of the form + ``__`` so that it's possible to update each + component of a nested object. + Returns + ------- + self + """ + if not params: + # Simple optimisation to gain speed (inspect is slow) + return self + valid_params = self.get_params(deep=True) + # for key, value in iteritems(params): + for key, value in params.items(): + split = key.split('__', 1) + if len(split) > 1: + # nested objects case + name, sub_name = split + if name not in valid_params: + raise ValueError('Invalid parameter %s for estimator %s. ' + 'Check the list of available parameters ' + 'with `estimator.get_params().keys()`.' % + (name, self)) + sub_object = valid_params[name] + sub_object.set_params(**{sub_name: value}) + else: + # simple objects case + if key not in valid_params: + raise ValueError('Invalid parameter %s for estimator %s. ' + 'Check the list of available parameters ' + 'with `estimator.get_params().keys()`.' % + (key, self.__class__.__name__)) + setattr(self, key, value) + return self + + def __repr__(self): + from sklearn.base import _pprint + class_name = self.__class__.__name__ + return '%s(%s)' % (class_name, _pprint(self.get_params(deep=False), + offset=len(class_name),),) + + # __getstate__ and __setstate__ are omitted because they only contain + # conditionals that are not satisfied by our objects (e.g., + # ``if type(self).__module__.startswith('sklearn.')``. + + +def distribution_estimation_uniform(X): + """estimates a uniform distribution from an array of samples X + + Parameters + ---------- + X : array-like of shape = [n_samples, n_features] + The array of samples + Returns + ------- + mu : array-like, shape = [n_samples,] + The uniform distribution estimated from X + """ + + return np.ones(X.shape[0]) / float(X.shape[0]) class BaseTransport(BaseEstimator): @@ -960,18 +1092,19 @@ class BaseTransport(BaseEstimator): """ # pairwise distance - Cost = pairwise_distances(Xs, Xt, metric=self.metric) + Cost = dist(Xs, Xt, metric=self.metric) if self.mode == "semisupervised": print("TODO: modify cost matrix accordingly") pass # distribution estimation - if self.distribution == "uniform": - mu_s = np.ones(Xs.shape[0]) / float(Xs.shape[0]) - mu_t = np.ones(Xt.shape[0]) / float(Xt.shape[0]) - else: - print("TODO: implement kernelized approach") + mu_s = self.distribution_estimation(Xs) + mu_t = self.distribution_estimation(Xt) + + # store arrays of samples + self.Xs = Xs + self.Xt = Xt # coupling estimation if self.method == "sinkhorn": @@ -1024,14 +1157,19 @@ class BaseTransport(BaseEstimator): The transport source samples. """ - if self.mapping == "barycentric": + # TODO: check whether Xs is new or not + if self.Xs == Xs: + # perform standard barycentric mapping transp = self.gamma_ / np.sum(self.gamma_, 1)[:, None] # set nans to 0 transp[~ np.isfinite(transp)] = 0 # compute transported samples - transp_Xs = np.dot(transp, Xt) + transp_Xs = np.dot(transp, self.Xt) + else: + # perform out of sample mapping + print("out of sample mapping not yet implemented") return transp_Xs @@ -1053,16 +1191,19 @@ class BaseTransport(BaseEstimator): The transported target samples. """ - if self.mapping == "barycentric": + # TODO: check whether Xt is new or not + if self.Xt == Xt: + # perform standard barycentric mapping transp_ = self.gamma_.T / np.sum(self.gamma_, 0)[:, None] # set nans to 0 transp_[~ np.isfinite(transp_)] = 0 # compute transported samples - transp_Xt = np.dot(transp_, Xs) + transp_Xt = np.dot(transp_, self.Xs) else: - print("mapping not yet implemented") + # perform out of sample mapping + print("out of sample mapping not yet implemented") return transp_Xt @@ -1114,7 +1255,10 @@ class SinkhornTransport(BaseTransport): def __init__(self, reg_e=1., mode="unsupervised", max_iter=1000, tol=10e-9, verbose=False, log=False, mapping="barycentric", - metric="sqeuclidean", distribution="uniform"): + metric="sqeuclidean", + distribution_estimation=distribution_estimation_uniform, + out_of_sample_map='ferradans'): + self.reg_e = reg_e self.mode = mode self.max_iter = max_iter @@ -1123,8 +1267,9 @@ class SinkhornTransport(BaseTransport): self.log = log self.mapping = mapping self.metric = metric - self.distribution = distribution + self.distribution_estimation = distribution_estimation self.method = "sinkhorn" + self.out_of_sample_map = out_of_sample_map def fit(self, Xs=None, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples -- cgit v1.2.3 From c7eaaf4caa03d759c4255bdf8b6eebd10ee539a5 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Tue, 1 Aug 2017 10:42:09 +0200 Subject: update SinkhornTransport class + added test for class --- ot/da.py | 56 +++++++++++++++++++++----------------------------------- test/test_da.py | 51 +++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 72 insertions(+), 35 deletions(-) diff --git a/ot/da.py b/ot/da.py index d30c821..6b98a17 100644 --- a/ot/da.py +++ b/ot/da.py @@ -15,6 +15,7 @@ from .lp import emd from .utils import unif, dist, kernel from .optim import cg from .optim import gcg +import warnings def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, @@ -921,15 +922,8 @@ class OTDA_mapping_kernel(OTDA_mapping_linear): # proposal ############################################################################## -# from sklearn.base import BaseEstimator -# from sklearn.metrics import pairwise_distances - -############################################################################## -# adapted from scikit-learn - -import warnings -# from .externals.six import string_types, iteritems +# adapted from sklearn class BaseEstimator(object): """Base class for all estimators in scikit-learn @@ -1067,7 +1061,7 @@ def distribution_estimation_uniform(X): The uniform distribution estimated from X """ - return np.ones(X.shape[0]) / float(X.shape[0]) + return unif(X.shape[0]) class BaseTransport(BaseEstimator): @@ -1092,29 +1086,20 @@ class BaseTransport(BaseEstimator): """ # pairwise distance - Cost = dist(Xs, Xt, metric=self.metric) + self.Cost = dist(Xs, Xt, metric=self.metric) if self.mode == "semisupervised": print("TODO: modify cost matrix accordingly") pass # distribution estimation - mu_s = self.distribution_estimation(Xs) - mu_t = self.distribution_estimation(Xt) + self.mu_s = self.distribution_estimation(Xs) + self.mu_t = self.distribution_estimation(Xt) # store arrays of samples self.Xs = Xs self.Xt = Xt - # coupling estimation - if self.method == "sinkhorn": - self.gamma_ = sinkhorn( - a=mu_s, b=mu_t, M=Cost, reg=self.reg_e, - numItermax=self.max_iter, stopThr=self.tol, - verbose=self.verbose, log=self.log) - else: - print("TODO: implement the other methods") - return self def fit_transform(self, Xs=None, ys=None, Xt=None, yt=None): @@ -1157,8 +1142,7 @@ class BaseTransport(BaseEstimator): The transport source samples. """ - # TODO: check whether Xs is new or not - if self.Xs == Xs: + if np.array_equal(self.Xs, Xs): # perform standard barycentric mapping transp = self.gamma_ / np.sum(self.gamma_, 1)[:, None] @@ -1169,7 +1153,9 @@ class BaseTransport(BaseEstimator): transp_Xs = np.dot(transp, self.Xt) else: # perform out of sample mapping - print("out of sample mapping not yet implemented") + print("Warning: out of sample mapping not yet implemented") + print("input data will be returned") + transp_Xs = Xs return transp_Xs @@ -1191,8 +1177,7 @@ class BaseTransport(BaseEstimator): The transported target samples. """ - # TODO: check whether Xt is new or not - if self.Xt == Xt: + if np.array_equal(self.Xt, Xt): # perform standard barycentric mapping transp_ = self.gamma_.T / np.sum(self.gamma_, 0)[:, None] @@ -1203,7 +1188,9 @@ class BaseTransport(BaseEstimator): transp_Xt = np.dot(transp_, self.Xs) else: # perform out of sample mapping - print("out of sample mapping not yet implemented") + print("Warning: out of sample mapping not yet implemented") + print("input data will be returned") + transp_Xt = Xt return transp_Xt @@ -1254,7 +1241,7 @@ class SinkhornTransport(BaseTransport): """ def __init__(self, reg_e=1., mode="unsupervised", max_iter=1000, - tol=10e-9, verbose=False, log=False, mapping="barycentric", + tol=10e-9, verbose=False, log=False, metric="sqeuclidean", distribution_estimation=distribution_estimation_uniform, out_of_sample_map='ferradans'): @@ -1265,7 +1252,6 @@ class SinkhornTransport(BaseTransport): self.tol = tol self.verbose = verbose self.log = log - self.mapping = mapping self.metric = metric self.distribution_estimation = distribution_estimation self.method = "sinkhorn" @@ -1290,10 +1276,10 @@ class SinkhornTransport(BaseTransport): Returns self. """ - return super(SinkhornTransport, self).fit(Xs, ys, Xt, yt) - + self = super(SinkhornTransport, self).fit(Xs, ys, Xt, yt) -if __name__ == "__main__": - print("Small test") - - st = SinkhornTransport() + # coupling estimation + self.gamma_ = sinkhorn( + a=self.mu_s, b=self.mu_t, M=self.Cost, reg=self.reg_e, + numItermax=self.max_iter, stopThr=self.tol, + verbose=self.verbose, log=self.log) diff --git a/test/test_da.py b/test/test_da.py index dfba83f..e7b4ed1 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -6,6 +6,57 @@ import numpy as np import ot +from numpy.testing.utils import assert_allclose, assert_equal +from ot.datasets import get_data_classif +from ot.utils import unif + +np.random.seed(42) + + +def test_sinkhorn_transport(): + """test_sinkhorn_transport + """ + + ns = 150 + nt = 200 + + Xs, ys = get_data_classif('3gauss', ns) + Xt, yt = get_data_classif('3gauss2', nt) + + clf = ot.da.SinkhornTransport() + + # test its computed + clf.fit(Xs=Xs, Xt=Xt) + + # test dimensions of coupling + assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.gamma_.shape, ((Xs.shape[0], Xt.shape[0]))) + + # test margin constraints + mu_s = unif(ns) + mu_t = unif(nt) + assert_allclose(np.sum(clf.gamma_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.gamma_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + + # test transform + transp_Xs = clf.transform(Xs=Xs) + assert_equal(transp_Xs.shape, Xs.shape) + + Xs_new, _ = get_data_classif('3gauss', ns + 1) + transp_Xs_new = clf.transform(Xs_new) + + # check that the oos method is not working + assert_equal(transp_Xs_new, Xs_new) + + # test inverse transform + transp_Xt = clf.inverse_transform(Xt=Xt) + assert_equal(transp_Xt.shape, Xt.shape) + + Xt_new, _ = get_data_classif('3gauss2', nt + 1) + transp_Xt_new = clf.inverse_transform(Xt=Xt_new) + + # check that the oos method is not working and returns the input data + assert_equal(transp_Xt_new, Xt_new) def test_otda(): -- cgit v1.2.3 From d5c6cc178a731d955e5eb85e9f477805fa086518 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Tue, 1 Aug 2017 13:13:50 +0200 Subject: added EMDTransport Class from NG's code + added dedicated test --- ot/da.py | 86 +++++++++++++++++++++++++++++++++++++++++++++++++++++---- test/test_da.py | 59 ++++++++++++++++++++++++++++++++++++--- 2 files changed, 135 insertions(+), 10 deletions(-) diff --git a/ot/da.py b/ot/da.py index 6b98a17..fb2fd36 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1144,7 +1144,7 @@ class BaseTransport(BaseEstimator): if np.array_equal(self.Xs, Xs): # perform standard barycentric mapping - transp = self.gamma_ / np.sum(self.gamma_, 1)[:, None] + transp = self.Coupling_ / np.sum(self.Coupling_, 1)[:, None] # set nans to 0 transp[~ np.isfinite(transp)] = 0 @@ -1179,7 +1179,7 @@ class BaseTransport(BaseEstimator): if np.array_equal(self.Xt, Xt): # perform standard barycentric mapping - transp_ = self.gamma_.T / np.sum(self.gamma_, 0)[:, None] + transp_ = self.Coupling_.T / np.sum(self.Coupling_, 0)[:, None] # set nans to 0 transp_[~ np.isfinite(transp_)] = 0 @@ -1228,7 +1228,7 @@ class SinkhornTransport(BaseTransport): Controls the logs of the optimization algorithm Attributes ---------- - gamma_ : the optimal coupling + Coupling_ : the optimal coupling References ---------- @@ -1254,7 +1254,6 @@ class SinkhornTransport(BaseTransport): self.log = log self.metric = metric self.distribution_estimation = distribution_estimation - self.method = "sinkhorn" self.out_of_sample_map = out_of_sample_map def fit(self, Xs=None, ys=None, Xt=None, yt=None): @@ -1276,10 +1275,85 @@ class SinkhornTransport(BaseTransport): Returns self. """ - self = super(SinkhornTransport, self).fit(Xs, ys, Xt, yt) + super(SinkhornTransport, self).fit(Xs, ys, Xt, yt) # coupling estimation - self.gamma_ = sinkhorn( + self.Coupling_ = sinkhorn( a=self.mu_s, b=self.mu_t, M=self.Cost, reg=self.reg_e, numItermax=self.max_iter, stopThr=self.tol, verbose=self.verbose, log=self.log) + + +class EMDTransport(BaseTransport): + """Domain Adapatation OT method based on Earth Mover's Distance + Parameters + ---------- + mode : string, optional (default="unsupervised") + The DA mode. If "unsupervised" no target labels are taken into account + to modify the cost matrix. If "semisupervised" the target labels + are taken into account to set coefficients of the pairwise distance + matrix to 0 for row and columns indices that correspond to source and + target samples which share the same labels. + mapping : string, optional (default="barycentric") + The kind of mapping to apply to transport samples from a domain into + another one. + if "barycentric" only the samples used to estimate the coupling can + be transported from a domain to another one. + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + distribution : string, optional (default="uniform") + The kind of distribution estimation to employ + verbose : int, optional (default=0) + Controls the verbosity of the optimization algorithm + log : int, optional (default=0) + Controls the logs of the optimization algorithm + Attributes + ---------- + Coupling_ : the optimal coupling + + References + ---------- + .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE Transactions + on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 + """ + + def __init__(self, mode="unsupervised", verbose=False, + log=False, metric="sqeuclidean", + distribution_estimation=distribution_estimation_uniform, + out_of_sample_map='ferradans'): + + self.mode = mode + self.verbose = verbose + self.log = log + self.metric = metric + self.distribution_estimation = distribution_estimation + self.out_of_sample_map = out_of_sample_map + + def fit(self, Xs, ys=None, Xt=None, yt=None): + """Build a coupling matrix from source and target sets of samples + (Xs, ys) and (Xt, yt) + Parameters + ---------- + Xs : array-like of shape = [n_source_samples, n_features] + The training input samples. + ys : array-like, shape = [n_source_samples] + The class labels + Xt : array-like of shape = [n_target_samples, n_features] + The training input samples. + yt : array-like, shape = [n_labeled_target_samples] + The class labels + Returns + ------- + self : object + Returns self. + """ + + super(EMDTransport, self).fit(Xs, ys, Xt, yt) + + # coupling estimation + self.Coupling_ = emd( + a=self.mu_s, b=self.mu_t, M=self.Cost, + # verbose=self.verbose, + # log=self.log + ) diff --git a/test/test_da.py b/test/test_da.py index e7b4ed1..33b3695 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -13,7 +13,7 @@ from ot.utils import unif np.random.seed(42) -def test_sinkhorn_transport(): +def test_sinkhorn_transport_class(): """test_sinkhorn_transport """ @@ -30,13 +30,59 @@ def test_sinkhorn_transport(): # test dimensions of coupling assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) - assert_equal(clf.gamma_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.Coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(clf.gamma_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(clf.gamma_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.Coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.Coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + + # test transform + transp_Xs = clf.transform(Xs=Xs) + assert_equal(transp_Xs.shape, Xs.shape) + + Xs_new, _ = get_data_classif('3gauss', ns + 1) + transp_Xs_new = clf.transform(Xs_new) + + # check that the oos method is not working + assert_equal(transp_Xs_new, Xs_new) + + # test inverse transform + transp_Xt = clf.inverse_transform(Xt=Xt) + assert_equal(transp_Xt.shape, Xt.shape) + + Xt_new, _ = get_data_classif('3gauss2', nt + 1) + transp_Xt_new = clf.inverse_transform(Xt=Xt_new) + + # check that the oos method is not working and returns the input data + assert_equal(transp_Xt_new, Xt_new) + + +def test_emd_transport_class(): + """test_sinkhorn_transport + """ + + ns = 150 + nt = 200 + + Xs, ys = get_data_classif('3gauss', ns) + Xt, yt = get_data_classif('3gauss2', nt) + + clf = ot.da.EMDTransport() + + # test its computed + clf.fit(Xs=Xs, Xt=Xt) + + # test dimensions of coupling + assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.Coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + + # test margin constraints + mu_s = unif(ns) + mu_t = unif(nt) + assert_allclose(np.sum(clf.Coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.Coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform transp_Xs = clf.transform(Xs=Xs) @@ -119,3 +165,8 @@ def test_otda(): da_emd = ot.da.OTDA_mapping_kernel() # init class da_emd.fit(xs, xt, numItermax=10) # fit distributions da_emd.predict(xs) # interpolation of source samples + + +if __name__ == "__main__": + test_sinkhorn_transport_class() + test_emd_transport_class() -- cgit v1.2.3 From cd4fa7275dc65e04f7b256dec4208d68006abc25 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 4 Aug 2017 11:16:30 +0200 Subject: added test for fit_transform + correction of fit_transform bug (missing return self) --- ot/da.py | 4 ++++ test/test_da.py | 13 ++++++++----- 2 files changed, 12 insertions(+), 5 deletions(-) diff --git a/ot/da.py b/ot/da.py index fb2fd36..80649a7 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1283,6 +1283,8 @@ class SinkhornTransport(BaseTransport): numItermax=self.max_iter, stopThr=self.tol, verbose=self.verbose, log=self.log) + return self + class EMDTransport(BaseTransport): """Domain Adapatation OT method based on Earth Mover's Distance @@ -1357,3 +1359,5 @@ class EMDTransport(BaseTransport): # verbose=self.verbose, # log=self.log ) + + return self diff --git a/test/test_da.py b/test/test_da.py index 33b3695..68807ec 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -58,6 +58,10 @@ def test_sinkhorn_transport_class(): # check that the oos method is not working and returns the input data assert_equal(transp_Xt_new, Xt_new) + # test fit_transform + transp_Xs = clf.fit_transform(Xs=Xs, Xt=Xt) + assert_equal(transp_Xs.shape, Xs.shape) + def test_emd_transport_class(): """test_sinkhorn_transport @@ -104,6 +108,10 @@ def test_emd_transport_class(): # check that the oos method is not working and returns the input data assert_equal(transp_Xt_new, Xt_new) + # test fit_transform + transp_Xs = clf.fit_transform(Xs=Xs, Xt=Xt) + assert_equal(transp_Xs.shape, Xs.shape) + def test_otda(): @@ -165,8 +173,3 @@ def test_otda(): da_emd = ot.da.OTDA_mapping_kernel() # init class da_emd.fit(xs, xt, numItermax=10) # fit distributions da_emd.predict(xs) # interpolation of source samples - - -if __name__ == "__main__": - test_sinkhorn_transport_class() - test_emd_transport_class() -- cgit v1.2.3 From 0659abe79c15f786a017b62e2a1313f0625af329 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 4 Aug 2017 11:34:21 +0200 Subject: added new class SinkhornLpl1Transport() + dedicated test --- ot/da.py | 91 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++ test/test_da.py | 50 +++++++++++++++++++++++++++++++ 2 files changed, 141 insertions(+) diff --git a/ot/da.py b/ot/da.py index 80649a7..3031f63 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1361,3 +1361,94 @@ class EMDTransport(BaseTransport): ) return self + + +class SinkhornLpl1Transport(BaseTransport): + """Domain Adapatation OT method based on sinkhorn algorithm + + LpL1 class regularization. + + Parameters + ---------- + mode : string, optional (default="unsupervised") + The DA mode. If "unsupervised" no target labels are taken into account + to modify the cost matrix. If "semisupervised" the target labels + are taken into account to set coefficients of the pairwise distance + matrix to 0 for row and columns indices that correspond to source and + target samples which share the same labels. + mapping : string, optional (default="barycentric") + The kind of mapping to apply to transport samples from a domain into + another one. + if "barycentric" only the samples used to estimate the coupling can + be transported from a domain to another one. + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + distribution : string, optional (default="uniform") + The kind of distribution estimation to employ + verbose : int, optional (default=0) + Controls the verbosity of the optimization algorithm + log : int, optional (default=0) + Controls the logs of the optimization algorithm + Attributes + ---------- + Coupling_ : the optimal coupling + + References + ---------- + + .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE + Transactions on Pattern Analysis and Machine Intelligence , + vol.PP, no.99, pp.1-1 + .. [2] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). + Generalized conditional gradient: analysis of convergence + and applications. arXiv preprint arXiv:1510.06567. + + """ + + def __init__(self, reg_e=1., reg_cl=0.1, mode="unsupervised", + max_iter=10, max_inner_iter=200, + tol=10e-9, verbose=False, log=False, + metric="sqeuclidean", + distribution_estimation=distribution_estimation_uniform, + out_of_sample_map='ferradans'): + + self.reg_e = reg_e + self.reg_cl = reg_cl + self.mode = mode + self.max_iter = max_iter + self.max_inner_iter = max_inner_iter + self.tol = tol + self.verbose = verbose + self.log = log + self.metric = metric + self.distribution_estimation = distribution_estimation + self.out_of_sample_map = out_of_sample_map + + def fit(self, Xs, ys=None, Xt=None, yt=None): + """Build a coupling matrix from source and target sets of samples + (Xs, ys) and (Xt, yt) + Parameters + ---------- + Xs : array-like of shape = [n_source_samples, n_features] + The training input samples. + ys : array-like, shape = [n_source_samples] + The class labels + Xt : array-like of shape = [n_target_samples, n_features] + The training input samples. + yt : array-like, shape = [n_labeled_target_samples] + The class labels + Returns + ------- + self : object + Returns self. + """ + + super(SinkhornLpl1Transport, self).fit(Xs, ys, Xt, yt) + + self.Coupling_ = sinkhorn_lpl1_mm( + a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.Cost, + reg=self.reg_e, eta=self.reg_cl, numItermax=self.max_iter, + numInnerItermax=self.max_inner_iter, stopInnerThr=self.tol, + verbose=self.verbose, log=self.log) + + return self diff --git a/test/test_da.py b/test/test_da.py index 68807ec..7d00cfb 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -13,6 +13,56 @@ from ot.utils import unif np.random.seed(42) +def test_sinkhorn_lpl1_transport_class(): + """test_sinkhorn_transport + """ + + ns = 150 + nt = 200 + + Xs, ys = get_data_classif('3gauss', ns) + Xt, yt = get_data_classif('3gauss2', nt) + + clf = ot.da.SinkhornLpl1Transport() + + # test its computed + clf.fit(Xs=Xs, ys=ys, Xt=Xt) + + # test dimensions of coupling + assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.Coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + + # test margin constraints + mu_s = unif(ns) + mu_t = unif(nt) + assert_allclose(np.sum(clf.Coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.Coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + + # test transform + transp_Xs = clf.transform(Xs=Xs) + assert_equal(transp_Xs.shape, Xs.shape) + + Xs_new, _ = get_data_classif('3gauss', ns + 1) + transp_Xs_new = clf.transform(Xs_new) + + # check that the oos method is not working + assert_equal(transp_Xs_new, Xs_new) + + # test inverse transform + transp_Xt = clf.inverse_transform(Xt=Xt) + assert_equal(transp_Xt.shape, Xt.shape) + + Xt_new, _ = get_data_classif('3gauss2', nt + 1) + transp_Xt_new = clf.inverse_transform(Xt=Xt_new) + + # check that the oos method is not working and returns the input data + assert_equal(transp_Xt_new, Xt_new) + + # test fit_transform + transp_Xs = clf.fit_transform(Xs=Xs, ys=ys, Xt=Xt) + assert_equal(transp_Xs.shape, Xs.shape) + + def test_sinkhorn_transport_class(): """test_sinkhorn_transport """ -- cgit v1.2.3 From 2005a09548a6f6d42cd9aafadbb4583e4029936c Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 4 Aug 2017 11:40:44 +0200 Subject: added new class SinkhornL1l2Transport() + dedicated test --- ot/da.py | 109 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ test/test_da.py | 50 ++++++++++++++++++++++++++ 2 files changed, 159 insertions(+) diff --git a/ot/da.py b/ot/da.py index 3031f63..6100d15 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1369,6 +1369,10 @@ class SinkhornLpl1Transport(BaseTransport): Parameters ---------- + reg_e : float, optional (default=1) + Entropic regularization parameter + reg_cl : float, optional (default=0.1) + Class regularization parameter mode : string, optional (default="unsupervised") The DA mode. If "unsupervised" no target labels are taken into account to modify the cost matrix. If "semisupervised" the target labels @@ -1384,6 +1388,11 @@ class SinkhornLpl1Transport(BaseTransport): The ground metric for the Wasserstein problem distribution : string, optional (default="uniform") The kind of distribution estimation to employ + max_iter : int, float, optional (default=10) + The minimum number of iteration before stopping the optimization + algorithm if no it has not converged + max_inner_iter : int, float, optional (default=200) + The number of iteration in the inner loop verbose : int, optional (default=0) Controls the verbosity of the optimization algorithm log : int, optional (default=0) @@ -1452,3 +1461,103 @@ class SinkhornLpl1Transport(BaseTransport): verbose=self.verbose, log=self.log) return self + + +class SinkhornL1l2Transport(BaseTransport): + """Domain Adapatation OT method based on sinkhorn algorithm + + l1l2 class regularization. + + Parameters + ---------- + reg_e : float, optional (default=1) + Entropic regularization parameter + reg_cl : float, optional (default=0.1) + Class regularization parameter + mode : string, optional (default="unsupervised") + The DA mode. If "unsupervised" no target labels are taken into account + to modify the cost matrix. If "semisupervised" the target labels + are taken into account to set coefficients of the pairwise distance + matrix to 0 for row and columns indices that correspond to source and + target samples which share the same labels. + mapping : string, optional (default="barycentric") + The kind of mapping to apply to transport samples from a domain into + another one. + if "barycentric" only the samples used to estimate the coupling can + be transported from a domain to another one. + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + distribution : string, optional (default="uniform") + The kind of distribution estimation to employ + max_iter : int, float, optional (default=10) + The minimum number of iteration before stopping the optimization + algorithm if no it has not converged + max_inner_iter : int, float, optional (default=200) + The number of iteration in the inner loop + verbose : int, optional (default=0) + Controls the verbosity of the optimization algorithm + log : int, optional (default=0) + Controls the logs of the optimization algorithm + Attributes + ---------- + Coupling_ : the optimal coupling + + References + ---------- + + .. [1] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE + Transactions on Pattern Analysis and Machine Intelligence , + vol.PP, no.99, pp.1-1 + .. [2] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). + Generalized conditional gradient: analysis of convergence + and applications. arXiv preprint arXiv:1510.06567. + + """ + + def __init__(self, reg_e=1., reg_cl=0.1, mode="unsupervised", + max_iter=10, max_inner_iter=200, + tol=10e-9, verbose=False, log=False, + metric="sqeuclidean", + distribution_estimation=distribution_estimation_uniform, + out_of_sample_map='ferradans'): + + self.reg_e = reg_e + self.reg_cl = reg_cl + self.mode = mode + self.max_iter = max_iter + self.max_inner_iter = max_inner_iter + self.tol = tol + self.verbose = verbose + self.log = log + self.metric = metric + self.distribution_estimation = distribution_estimation + self.out_of_sample_map = out_of_sample_map + + def fit(self, Xs, ys=None, Xt=None, yt=None): + """Build a coupling matrix from source and target sets of samples + (Xs, ys) and (Xt, yt) + Parameters + ---------- + Xs : array-like of shape = [n_source_samples, n_features] + The training input samples. + ys : array-like, shape = [n_source_samples] + The class labels + Xt : array-like of shape = [n_target_samples, n_features] + The training input samples. + yt : array-like, shape = [n_labeled_target_samples] + The class labels + Returns + ------- + self : object + Returns self. + """ + + super(SinkhornL1l2Transport, self).fit(Xs, ys, Xt, yt) + + self.Coupling_ = sinkhorn_l1l2_gl( + a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.Cost, + reg=self.reg_e, eta=self.reg_cl, numItermax=self.max_iter, + numInnerItermax=self.max_inner_iter, stopInnerThr=self.tol, + verbose=self.verbose, log=self.log) + + return self diff --git a/test/test_da.py b/test/test_da.py index 7d00cfb..68d1958 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -63,6 +63,56 @@ def test_sinkhorn_lpl1_transport_class(): assert_equal(transp_Xs.shape, Xs.shape) +def test_sinkhorn_l1l2_transport_class(): + """test_sinkhorn_transport + """ + + ns = 150 + nt = 200 + + Xs, ys = get_data_classif('3gauss', ns) + Xt, yt = get_data_classif('3gauss2', nt) + + clf = ot.da.SinkhornL1l2Transport() + + # test its computed + clf.fit(Xs=Xs, ys=ys, Xt=Xt) + + # test dimensions of coupling + assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.Coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + + # test margin constraints + mu_s = unif(ns) + mu_t = unif(nt) + assert_allclose(np.sum(clf.Coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.Coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + + # test transform + transp_Xs = clf.transform(Xs=Xs) + assert_equal(transp_Xs.shape, Xs.shape) + + Xs_new, _ = get_data_classif('3gauss', ns + 1) + transp_Xs_new = clf.transform(Xs_new) + + # check that the oos method is not working + assert_equal(transp_Xs_new, Xs_new) + + # test inverse transform + transp_Xt = clf.inverse_transform(Xt=Xt) + assert_equal(transp_Xt.shape, Xt.shape) + + Xt_new, _ = get_data_classif('3gauss2', nt + 1) + transp_Xt_new = clf.inverse_transform(Xt=Xt_new) + + # check that the oos method is not working and returns the input data + assert_equal(transp_Xt_new, Xt_new) + + # test fit_transform + transp_Xs = clf.fit_transform(Xs=Xs, ys=ys, Xt=Xt) + assert_equal(transp_Xs.shape, Xs.shape) + + def test_sinkhorn_transport_class(): """test_sinkhorn_transport """ -- cgit v1.2.3 From 4e562a1ce24119b8c9c1efb9d078762904c5d78a Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 4 Aug 2017 12:04:04 +0200 Subject: semi supervised mode supported --- ot/da.py | 21 +++++++++++++++++++-- test/test_da.py | 52 ++++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 71 insertions(+), 2 deletions(-) diff --git a/ot/da.py b/ot/da.py index 6100d15..8294e8d 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1089,8 +1089,25 @@ class BaseTransport(BaseEstimator): self.Cost = dist(Xs, Xt, metric=self.metric) if self.mode == "semisupervised": - print("TODO: modify cost matrix accordingly") - pass + + if (ys is not None) and (yt is not None): + + # assumes labeled source samples occupy the first rows + # and labeled target samples occupy the first columns + classes = np.unique(ys) + for c in classes: + ids = np.where(ys == c) + idt = np.where(yt == c) + + # all the coefficients corresponding to a source sample + # and a target sample with the same label gets a 0 + # transport cost + for j in idt[0]: + self.Cost[ids[0], j] = 0 + else: + print("Warning: using unsupervised mode\ + \nto use semisupervised mode, please provide ys and yt") + pass # distribution estimation self.mu_s = self.distribution_estimation(Xs) diff --git a/test/test_da.py b/test/test_da.py index 68d1958..497a8ee 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -62,6 +62,19 @@ def test_sinkhorn_lpl1_transport_class(): transp_Xs = clf.fit_transform(Xs=Xs, ys=ys, Xt=Xt) assert_equal(transp_Xs.shape, Xs.shape) + # test semi supervised mode + clf = ot.da.SinkhornTransport(mode="semisupervised") + clf.fit(Xs=Xs, Xt=Xt) + n_unsup = np.sum(clf.Cost) + + # test semi supervised mode + clf = ot.da.SinkhornTransport(mode="semisupervised") + clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) + assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(clf.Cost) + + assert n_unsup != n_semisup, "semisupervised mode not working" + def test_sinkhorn_l1l2_transport_class(): """test_sinkhorn_transport @@ -112,6 +125,19 @@ def test_sinkhorn_l1l2_transport_class(): transp_Xs = clf.fit_transform(Xs=Xs, ys=ys, Xt=Xt) assert_equal(transp_Xs.shape, Xs.shape) + # test semi supervised mode + clf = ot.da.SinkhornTransport(mode="semisupervised") + clf.fit(Xs=Xs, Xt=Xt) + n_unsup = np.sum(clf.Cost) + + # test semi supervised mode + clf = ot.da.SinkhornTransport(mode="semisupervised") + clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) + assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(clf.Cost) + + assert n_unsup != n_semisup, "semisupervised mode not working" + def test_sinkhorn_transport_class(): """test_sinkhorn_transport @@ -162,6 +188,19 @@ def test_sinkhorn_transport_class(): transp_Xs = clf.fit_transform(Xs=Xs, Xt=Xt) assert_equal(transp_Xs.shape, Xs.shape) + # test semi supervised mode + clf = ot.da.SinkhornTransport(mode="semisupervised") + clf.fit(Xs=Xs, Xt=Xt) + n_unsup = np.sum(clf.Cost) + + # test semi supervised mode + clf = ot.da.SinkhornTransport(mode="semisupervised") + clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) + assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(clf.Cost) + + assert n_unsup != n_semisup, "semisupervised mode not working" + def test_emd_transport_class(): """test_sinkhorn_transport @@ -212,6 +251,19 @@ def test_emd_transport_class(): transp_Xs = clf.fit_transform(Xs=Xs, Xt=Xt) assert_equal(transp_Xs.shape, Xs.shape) + # test semi supervised mode + clf = ot.da.SinkhornTransport(mode="semisupervised") + clf.fit(Xs=Xs, Xt=Xt) + n_unsup = np.sum(clf.Cost) + + # test semi supervised mode + clf = ot.da.SinkhornTransport(mode="semisupervised") + clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) + assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(clf.Cost) + + assert n_unsup != n_semisup, "semisupervised mode not working" + def test_otda(): -- cgit v1.2.3 From 62b40a9993e9ccca27d1677aa1294fff6246e904 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 4 Aug 2017 13:56:51 +0200 Subject: correction of semi supervised mode --- ot/da.py | 77 +++++++++++++++++++++++++++++++++------------------------ test/test_da.py | 20 +++++++-------- 2 files changed, 55 insertions(+), 42 deletions(-) diff --git a/ot/da.py b/ot/da.py index 8294e8d..08e8a8d 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1088,26 +1088,23 @@ class BaseTransport(BaseEstimator): # pairwise distance self.Cost = dist(Xs, Xt, metric=self.metric) - if self.mode == "semisupervised": - - if (ys is not None) and (yt is not None): - - # assumes labeled source samples occupy the first rows - # and labeled target samples occupy the first columns - classes = np.unique(ys) - for c in classes: - ids = np.where(ys == c) - idt = np.where(yt == c) - - # all the coefficients corresponding to a source sample - # and a target sample with the same label gets a 0 - # transport cost - for j in idt[0]: - self.Cost[ids[0], j] = 0 - else: - print("Warning: using unsupervised mode\ - \nto use semisupervised mode, please provide ys and yt") - pass + if (ys is not None) and (yt is not None): + + if self.limit_max != np.infty: + self.limit_max = self.limit_max * np.max(self.Cost) + + # assumes labeled source samples occupy the first rows + # and labeled target samples occupy the first columns + classes = np.unique(ys) + for c in classes: + idx_s = np.where((ys != c) & (ys != -1)) + idx_t = np.where(yt == c) + + # all the coefficients corresponding to a source sample + # and a target sample : + # with different labels get a infinite + for j in idx_t[0]: + self.Cost[idx_s[0], j] = self.limit_max # distribution estimation self.mu_s = self.distribution_estimation(Xs) @@ -1243,6 +1240,9 @@ class SinkhornTransport(BaseTransport): Controls the verbosity of the optimization algorithm log : int, optional (default=0) Controls the logs of the optimization algorithm + limit_max: float, optional (defaul=np.infty) + Controls the semi supervised mode. Transport between labeled source + and target samples of different classes will exhibit an infinite cost Attributes ---------- Coupling_ : the optimal coupling @@ -1257,19 +1257,19 @@ class SinkhornTransport(BaseTransport): 26, 2013 """ - def __init__(self, reg_e=1., mode="unsupervised", max_iter=1000, + def __init__(self, reg_e=1., max_iter=1000, tol=10e-9, verbose=False, log=False, metric="sqeuclidean", distribution_estimation=distribution_estimation_uniform, - out_of_sample_map='ferradans'): + out_of_sample_map='ferradans', limit_max=np.infty): self.reg_e = reg_e - self.mode = mode self.max_iter = max_iter self.tol = tol self.verbose = verbose self.log = log self.metric = metric + self.limit_max = limit_max self.distribution_estimation = distribution_estimation self.out_of_sample_map = out_of_sample_map @@ -1326,6 +1326,10 @@ class EMDTransport(BaseTransport): Controls the verbosity of the optimization algorithm log : int, optional (default=0) Controls the logs of the optimization algorithm + limit_max: float, optional (default=10) + Controls the semi supervised mode. Transport between labeled source + and target samples of different classes will exhibit an infinite cost + (10 times the maximum value of the cost matrix) Attributes ---------- Coupling_ : the optimal coupling @@ -1337,15 +1341,15 @@ class EMDTransport(BaseTransport): on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 """ - def __init__(self, mode="unsupervised", verbose=False, + def __init__(self, verbose=False, log=False, metric="sqeuclidean", distribution_estimation=distribution_estimation_uniform, - out_of_sample_map='ferradans'): + out_of_sample_map='ferradans', limit_max=10): - self.mode = mode self.verbose = verbose self.log = log self.metric = metric + self.limit_max = limit_max self.distribution_estimation = distribution_estimation self.out_of_sample_map = out_of_sample_map @@ -1414,6 +1418,10 @@ class SinkhornLpl1Transport(BaseTransport): Controls the verbosity of the optimization algorithm log : int, optional (default=0) Controls the logs of the optimization algorithm + limit_max: float, optional (defaul=np.infty) + Controls the semi supervised mode. Transport between labeled source + and target samples of different classes will exhibit an infinite cost + Attributes ---------- Coupling_ : the optimal coupling @@ -1431,16 +1439,15 @@ class SinkhornLpl1Transport(BaseTransport): """ - def __init__(self, reg_e=1., reg_cl=0.1, mode="unsupervised", + def __init__(self, reg_e=1., reg_cl=0.1, max_iter=10, max_inner_iter=200, tol=10e-9, verbose=False, log=False, metric="sqeuclidean", distribution_estimation=distribution_estimation_uniform, - out_of_sample_map='ferradans'): + out_of_sample_map='ferradans', limit_max=np.infty): self.reg_e = reg_e self.reg_cl = reg_cl - self.mode = mode self.max_iter = max_iter self.max_inner_iter = max_inner_iter self.tol = tol @@ -1449,6 +1456,7 @@ class SinkhornLpl1Transport(BaseTransport): self.metric = metric self.distribution_estimation = distribution_estimation self.out_of_sample_map = out_of_sample_map + self.limit_max = limit_max def fit(self, Xs, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples @@ -1514,6 +1522,11 @@ class SinkhornL1l2Transport(BaseTransport): Controls the verbosity of the optimization algorithm log : int, optional (default=0) Controls the logs of the optimization algorithm + limit_max: float, optional (default=10) + Controls the semi supervised mode. Transport between labeled source + and target samples of different classes will exhibit an infinite cost + (10 times the maximum value of the cost matrix) + Attributes ---------- Coupling_ : the optimal coupling @@ -1531,16 +1544,15 @@ class SinkhornL1l2Transport(BaseTransport): """ - def __init__(self, reg_e=1., reg_cl=0.1, mode="unsupervised", + def __init__(self, reg_e=1., reg_cl=0.1, max_iter=10, max_inner_iter=200, tol=10e-9, verbose=False, log=False, metric="sqeuclidean", distribution_estimation=distribution_estimation_uniform, - out_of_sample_map='ferradans'): + out_of_sample_map='ferradans', limit_max=10): self.reg_e = reg_e self.reg_cl = reg_cl - self.mode = mode self.max_iter = max_iter self.max_inner_iter = max_inner_iter self.tol = tol @@ -1549,6 +1561,7 @@ class SinkhornL1l2Transport(BaseTransport): self.metric = metric self.distribution_estimation = distribution_estimation self.out_of_sample_map = out_of_sample_map + self.limit_max = limit_max def fit(self, Xs, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples diff --git a/test/test_da.py b/test/test_da.py index 497a8ee..ecd2a3a 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -63,12 +63,12 @@ def test_sinkhorn_lpl1_transport_class(): assert_equal(transp_Xs.shape, Xs.shape) # test semi supervised mode - clf = ot.da.SinkhornTransport(mode="semisupervised") - clf.fit(Xs=Xs, Xt=Xt) + clf = ot.da.SinkhornLpl1Transport() + clf.fit(Xs=Xs, ys=ys, Xt=Xt) n_unsup = np.sum(clf.Cost) # test semi supervised mode - clf = ot.da.SinkhornTransport(mode="semisupervised") + clf = ot.da.SinkhornLpl1Transport() clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) n_semisup = np.sum(clf.Cost) @@ -126,12 +126,12 @@ def test_sinkhorn_l1l2_transport_class(): assert_equal(transp_Xs.shape, Xs.shape) # test semi supervised mode - clf = ot.da.SinkhornTransport(mode="semisupervised") - clf.fit(Xs=Xs, Xt=Xt) + clf = ot.da.SinkhornL1l2Transport() + clf.fit(Xs=Xs, ys=ys, Xt=Xt) n_unsup = np.sum(clf.Cost) # test semi supervised mode - clf = ot.da.SinkhornTransport(mode="semisupervised") + clf = ot.da.SinkhornL1l2Transport() clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) n_semisup = np.sum(clf.Cost) @@ -189,12 +189,12 @@ def test_sinkhorn_transport_class(): assert_equal(transp_Xs.shape, Xs.shape) # test semi supervised mode - clf = ot.da.SinkhornTransport(mode="semisupervised") + clf = ot.da.SinkhornTransport() clf.fit(Xs=Xs, Xt=Xt) n_unsup = np.sum(clf.Cost) # test semi supervised mode - clf = ot.da.SinkhornTransport(mode="semisupervised") + clf = ot.da.SinkhornTransport() clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) n_semisup = np.sum(clf.Cost) @@ -252,12 +252,12 @@ def test_emd_transport_class(): assert_equal(transp_Xs.shape, Xs.shape) # test semi supervised mode - clf = ot.da.SinkhornTransport(mode="semisupervised") + clf = ot.da.EMDTransport() clf.fit(Xs=Xs, Xt=Xt) n_unsup = np.sum(clf.Cost) # test semi supervised mode - clf = ot.da.SinkhornTransport(mode="semisupervised") + clf = ot.da.EMDTransport() clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) n_semisup = np.sum(clf.Cost) -- cgit v1.2.3 From 266abb6c9a0fa53e419d72b99d1906cdf78a8009 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 4 Aug 2017 14:02:06 +0200 Subject: reformat doc strings + remove useless log / verbose parameters for emd --- ot/da.py | 81 ++++++++++++++++++++++++++++++---------------------------------- 1 file changed, 38 insertions(+), 43 deletions(-) diff --git a/ot/da.py b/ot/da.py index 08e8a8d..92a8f12 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1053,11 +1053,11 @@ def distribution_estimation_uniform(X): Parameters ---------- - X : array-like of shape = [n_samples, n_features] + X : array-like of shape = (n_samples, n_features) The array of samples Returns ------- - mu : array-like, shape = [n_samples,] + mu : array-like, shape = (n_samples,) The uniform distribution estimated from X """ @@ -1071,13 +1071,13 @@ class BaseTransport(BaseEstimator): (Xs, ys) and (Xt, yt) Parameters ---------- - Xs : array-like of shape = [n_source_samples, n_features] + Xs : array-like of shape = (n_source_samples, n_features) The training input samples. - ys : array-like, shape = [n_source_samples] + ys : array-like, shape = (n_source_samples,) The class labels - Xt : array-like of shape = [n_target_samples, n_features] + Xt : array-like of shape = (n_target_samples, n_features) The training input samples. - yt : array-like, shape = [n_labeled_target_samples] + yt : array-like, shape = (n_labeled_target_samples,) The class labels Returns ------- @@ -1122,17 +1122,17 @@ class BaseTransport(BaseEstimator): ones Xt Parameters ---------- - Xs : array-like of shape = [n_source_samples, n_features] + Xs : array-like of shape = (n_source_samples, n_features) The training input samples. - ys : array-like, shape = [n_source_samples] + ys : array-like, shape = (n_source_samples,) The class labels - Xt : array-like of shape = [n_target_samples, n_features] + Xt : array-like of shape = (n_target_samples, n_features) The training input samples. - yt : array-like, shape = [n_labeled_target_samples] + yt : array-like, shape = (n_labeled_target_samples,) The class labels Returns ------- - transp_Xs : array-like of shape = [n_source_samples, n_features] + transp_Xs : array-like of shape = (n_source_samples, n_features) The source samples samples. """ @@ -1142,17 +1142,17 @@ class BaseTransport(BaseEstimator): """Transports source samples Xs onto target ones Xt Parameters ---------- - Xs : array-like of shape = [n_source_samples, n_features] + Xs : array-like of shape = (n_source_samples, n_features) The training input samples. - ys : array-like, shape = [n_source_samples] + ys : array-like, shape = (n_source_samples,) The class labels - Xt : array-like of shape = [n_target_samples, n_features] + Xt : array-like of shape = (n_target_samples, n_features) The training input samples. - yt : array-like, shape = [n_labeled_target_samples] + yt : array-like, shape = (n_labeled_target_samples,) The class labels Returns ------- - transp_Xs : array-like of shape = [n_source_samples, n_features] + transp_Xs : array-like of shape = (n_source_samples, n_features) The transport source samples. """ @@ -1177,17 +1177,17 @@ class BaseTransport(BaseEstimator): """Transports target samples Xt onto target samples Xs Parameters ---------- - Xs : array-like of shape = [n_source_samples, n_features] + Xs : array-like of shape = (n_source_samples, n_features) The training input samples. - ys : array-like, shape = [n_source_samples] + ys : array-like, shape = (n_source_samples,) The class labels - Xt : array-like of shape = [n_target_samples, n_features] + Xt : array-like of shape = (n_target_samples, n_features) The training input samples. - yt : array-like, shape = [n_labeled_target_samples] + yt : array-like, shape = (n_labeled_target_samples,) The class labels Returns ------- - transp_Xt : array-like of shape = [n_source_samples, n_features] + transp_Xt : array-like of shape = (n_source_samples, n_features) The transported target samples. """ @@ -1278,13 +1278,13 @@ class SinkhornTransport(BaseTransport): (Xs, ys) and (Xt, yt) Parameters ---------- - Xs : array-like of shape = [n_source_samples, n_features] + Xs : array-like of shape = (n_source_samples, n_features) The training input samples. - ys : array-like, shape = [n_source_samples] + ys : array-like, shape = (n_source_samples,) The class labels - Xt : array-like of shape = [n_target_samples, n_features] + Xt : array-like of shape = (n_target_samples, n_features) The training input samples. - yt : array-like, shape = [n_labeled_target_samples] + yt : array-like, shape = (n_labeled_target_samples,) The class labels Returns ------- @@ -1341,13 +1341,10 @@ class EMDTransport(BaseTransport): on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 """ - def __init__(self, verbose=False, - log=False, metric="sqeuclidean", + def __init__(self, metric="sqeuclidean", distribution_estimation=distribution_estimation_uniform, out_of_sample_map='ferradans', limit_max=10): - self.verbose = verbose - self.log = log self.metric = metric self.limit_max = limit_max self.distribution_estimation = distribution_estimation @@ -1358,13 +1355,13 @@ class EMDTransport(BaseTransport): (Xs, ys) and (Xt, yt) Parameters ---------- - Xs : array-like of shape = [n_source_samples, n_features] + Xs : array-like of shape = (n_source_samples, n_features) The training input samples. - ys : array-like, shape = [n_source_samples] + ys : array-like, shape = (n_source_samples,) The class labels - Xt : array-like of shape = [n_target_samples, n_features] + Xt : array-like of shape = (n_target_samples, n_features) The training input samples. - yt : array-like, shape = [n_labeled_target_samples] + yt : array-like, shape = (n_labeled_target_samples,) The class labels Returns ------- @@ -1377,8 +1374,6 @@ class EMDTransport(BaseTransport): # coupling estimation self.Coupling_ = emd( a=self.mu_s, b=self.mu_t, M=self.Cost, - # verbose=self.verbose, - # log=self.log ) return self @@ -1463,13 +1458,13 @@ class SinkhornLpl1Transport(BaseTransport): (Xs, ys) and (Xt, yt) Parameters ---------- - Xs : array-like of shape = [n_source_samples, n_features] + Xs : array-like of shape = (n_source_samples, n_features) The training input samples. - ys : array-like, shape = [n_source_samples] + ys : array-like, shape = (n_source_samples,) The class labels - Xt : array-like of shape = [n_target_samples, n_features] + Xt : array-like of shape = (n_target_samples, n_features) The training input samples. - yt : array-like, shape = [n_labeled_target_samples] + yt : array-like, shape = (n_labeled_target_samples,) The class labels Returns ------- @@ -1568,13 +1563,13 @@ class SinkhornL1l2Transport(BaseTransport): (Xs, ys) and (Xt, yt) Parameters ---------- - Xs : array-like of shape = [n_source_samples, n_features] + Xs : array-like of shape = (n_source_samples, n_features) The training input samples. - ys : array-like, shape = [n_source_samples] + ys : array-like, shape = (n_source_samples,) The class labels - Xt : array-like of shape = [n_target_samples, n_features] + Xt : array-like of shape = (n_target_samples, n_features) The training input samples. - yt : array-like, shape = [n_labeled_target_samples] + yt : array-like, shape = (n_labeled_target_samples,) The class labels Returns ------- -- cgit v1.2.3 From b8672f67639e9daa3f91e555581256f984115f56 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 4 Aug 2017 14:55:54 +0200 Subject: out of samples by Ferradans supported for transform and inverse_transform --- ot/da.py | 29 +++++++++++++++++++++++------ test/test_da.py | 32 ++++++++++++++++---------------- 2 files changed, 39 insertions(+), 22 deletions(-) diff --git a/ot/da.py b/ot/da.py index 92a8f12..87d056d 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1167,9 +1167,18 @@ class BaseTransport(BaseEstimator): transp_Xs = np.dot(transp, self.Xt) else: # perform out of sample mapping - print("Warning: out of sample mapping not yet implemented") - print("input data will be returned") - transp_Xs = Xs + + # get the nearest neighbor in the source domain + D0 = dist(Xs, self.Xs) + idx = np.argmin(D0, axis=1) + + # transport the source samples + transp = self.Coupling_ / np.sum(self.Coupling_, 1)[:, None] + transp[~ np.isfinite(transp)] = 0 + transp_Xs_ = np.dot(transp, self.Xt) + + # define the transported points + transp_Xs = transp_Xs_[idx, :] + Xs - self.Xs[idx, :] return transp_Xs @@ -1202,9 +1211,17 @@ class BaseTransport(BaseEstimator): transp_Xt = np.dot(transp_, self.Xs) else: # perform out of sample mapping - print("Warning: out of sample mapping not yet implemented") - print("input data will be returned") - transp_Xt = Xt + + D0 = dist(Xt, self.Xt) + idx = np.argmin(D0, axis=1) + + # transport the target samples + transp_ = self.Coupling_.T / np.sum(self.Coupling_, 0)[:, None] + transp_[~ np.isfinite(transp_)] = 0 + transp_Xt_ = np.dot(transp_, self.Xs) + + # define the transported points + transp_Xt = transp_Xt_[idx, :] + Xt - self.Xt[idx, :] return transp_Xt diff --git a/test/test_da.py b/test/test_da.py index ecd2a3a..aed9f61 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -45,8 +45,8 @@ def test_sinkhorn_lpl1_transport_class(): Xs_new, _ = get_data_classif('3gauss', ns + 1) transp_Xs_new = clf.transform(Xs_new) - # check that the oos method is not working - assert_equal(transp_Xs_new, Xs_new) + # check that the oos method is working + assert_equal(transp_Xs_new.shape, Xs_new.shape) # test inverse transform transp_Xt = clf.inverse_transform(Xt=Xt) @@ -55,8 +55,8 @@ def test_sinkhorn_lpl1_transport_class(): Xt_new, _ = get_data_classif('3gauss2', nt + 1) transp_Xt_new = clf.inverse_transform(Xt=Xt_new) - # check that the oos method is not working and returns the input data - assert_equal(transp_Xt_new, Xt_new) + # check that the oos method is working + assert_equal(transp_Xt_new.shape, Xt_new.shape) # test fit_transform transp_Xs = clf.fit_transform(Xs=Xs, ys=ys, Xt=Xt) @@ -108,8 +108,8 @@ def test_sinkhorn_l1l2_transport_class(): Xs_new, _ = get_data_classif('3gauss', ns + 1) transp_Xs_new = clf.transform(Xs_new) - # check that the oos method is not working - assert_equal(transp_Xs_new, Xs_new) + # check that the oos method is working + assert_equal(transp_Xs_new.shape, Xs_new.shape) # test inverse transform transp_Xt = clf.inverse_transform(Xt=Xt) @@ -118,8 +118,8 @@ def test_sinkhorn_l1l2_transport_class(): Xt_new, _ = get_data_classif('3gauss2', nt + 1) transp_Xt_new = clf.inverse_transform(Xt=Xt_new) - # check that the oos method is not working and returns the input data - assert_equal(transp_Xt_new, Xt_new) + # check that the oos method is working + assert_equal(transp_Xt_new.shape, Xt_new.shape) # test fit_transform transp_Xs = clf.fit_transform(Xs=Xs, ys=ys, Xt=Xt) @@ -171,8 +171,8 @@ def test_sinkhorn_transport_class(): Xs_new, _ = get_data_classif('3gauss', ns + 1) transp_Xs_new = clf.transform(Xs_new) - # check that the oos method is not working - assert_equal(transp_Xs_new, Xs_new) + # check that the oos method is working + assert_equal(transp_Xs_new.shape, Xs_new.shape) # test inverse transform transp_Xt = clf.inverse_transform(Xt=Xt) @@ -181,8 +181,8 @@ def test_sinkhorn_transport_class(): Xt_new, _ = get_data_classif('3gauss2', nt + 1) transp_Xt_new = clf.inverse_transform(Xt=Xt_new) - # check that the oos method is not working and returns the input data - assert_equal(transp_Xt_new, Xt_new) + # check that the oos method is working + assert_equal(transp_Xt_new.shape, Xt_new.shape) # test fit_transform transp_Xs = clf.fit_transform(Xs=Xs, Xt=Xt) @@ -234,8 +234,8 @@ def test_emd_transport_class(): Xs_new, _ = get_data_classif('3gauss', ns + 1) transp_Xs_new = clf.transform(Xs_new) - # check that the oos method is not working - assert_equal(transp_Xs_new, Xs_new) + # check that the oos method is working + assert_equal(transp_Xs_new.shape, Xs_new.shape) # test inverse transform transp_Xt = clf.inverse_transform(Xt=Xt) @@ -244,8 +244,8 @@ def test_emd_transport_class(): Xt_new, _ = get_data_classif('3gauss2', nt + 1) transp_Xt_new = clf.inverse_transform(Xt=Xt_new) - # check that the oos method is not working and returns the input data - assert_equal(transp_Xt_new, Xt_new) + # check that the oos method is working + assert_equal(transp_Xt_new.shape, Xt_new.shape) # test fit_transform transp_Xs = clf.fit_transform(Xs=Xs, Xt=Xt) -- cgit v1.2.3 From 117cd337d54625e492162a44e37cc18bedef990e Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 4 Aug 2017 15:49:42 +0200 Subject: added new class MappingTransport to support linear and kernel mapping, not yet tested --- ot/da.py | 158 +++++++++++++++++++++++++++++++++++++++++++++++++++++---------- 1 file changed, 134 insertions(+), 24 deletions(-) diff --git a/ot/da.py b/ot/da.py index 87d056d..0616d17 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1233,12 +1233,6 @@ class SinkhornTransport(BaseTransport): ---------- reg_e : float, optional (default=1) Entropic regularization parameter - mode : string, optional (default="unsupervised") - The DA mode. If "unsupervised" no target labels are taken into account - to modify the cost matrix. If "semisupervised" the target labels - are taken into account to set coefficients of the pairwise distance - matrix to 0 for row and columns indices that correspond to source and - target samples which share the same labels. max_iter : int, float, optional (default=1000) The minimum number of iteration before stopping the optimization algorithm if no it has not converged @@ -1324,12 +1318,6 @@ class EMDTransport(BaseTransport): """Domain Adapatation OT method based on Earth Mover's Distance Parameters ---------- - mode : string, optional (default="unsupervised") - The DA mode. If "unsupervised" no target labels are taken into account - to modify the cost matrix. If "semisupervised" the target labels - are taken into account to set coefficients of the pairwise distance - matrix to 0 for row and columns indices that correspond to source and - target samples which share the same labels. mapping : string, optional (default="barycentric") The kind of mapping to apply to transport samples from a domain into another one. @@ -1406,12 +1394,6 @@ class SinkhornLpl1Transport(BaseTransport): Entropic regularization parameter reg_cl : float, optional (default=0.1) Class regularization parameter - mode : string, optional (default="unsupervised") - The DA mode. If "unsupervised" no target labels are taken into account - to modify the cost matrix. If "semisupervised" the target labels - are taken into account to set coefficients of the pairwise distance - matrix to 0 for row and columns indices that correspond to source and - target samples which share the same labels. mapping : string, optional (default="barycentric") The kind of mapping to apply to transport samples from a domain into another one. @@ -1510,12 +1492,6 @@ class SinkhornL1l2Transport(BaseTransport): Entropic regularization parameter reg_cl : float, optional (default=0.1) Class regularization parameter - mode : string, optional (default="unsupervised") - The DA mode. If "unsupervised" no target labels are taken into account - to modify the cost matrix. If "semisupervised" the target labels - are taken into account to set coefficients of the pairwise distance - matrix to 0 for row and columns indices that correspond to source and - target samples which share the same labels. mapping : string, optional (default="barycentric") The kind of mapping to apply to transport samples from a domain into another one. @@ -1603,3 +1579,137 @@ class SinkhornL1l2Transport(BaseTransport): verbose=self.verbose, log=self.log) return self + + +class MappingTransport(BaseEstimator): + """MappingTransport: DA methods that aims at jointly estimating a optimal + transport coupling and the associated mapping + + Parameters + ---------- + mu : float, optional (default=1) + Weight for the linear OT loss (>0) + eta : float, optional (default=0.001) + Regularization term for the linear mapping L (>0) + bias : bool, optional (default=False) + Estimate linear mapping with constant bias + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + kernel : string, optional (default="linear") + The kernel to use either linear or gaussian + sigma : float, optional (default=1) + The gaussian kernel parameter + max_iter : int, optional (default=100) + Max number of BCD iterations + tol : float, optional (default=1e-5) + Stop threshold on relative loss decrease (>0) + max_inner_iter : int, optional (default=10) + Max number of iterations (inner CG solver) + inner_tol : float, optional (default=1e-6) + Stop threshold on error (inner CG solver) (>0) + verbose : bool, optional (default=False) + Print information along iterations + log : bool, optional (default=False) + record log if True + + Attributes + ---------- + Coupling_ : the optimal coupling + Mapping_ : the mapping associated + + References + ---------- + + .. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, + "Mapping estimation for discrete optimal transport", + Neural Information Processing Systems (NIPS), 2016. + + """ + + def __init__(self, mu=1, eta=0.001, bias=False, metric="sqeuclidean", + kernel="linear", sigma=1, max_iter=100, tol=1e-5, + max_inner_iter=10, inner_tol=1e-6, log=False, verbose=False): + + self.metric = metric + self.mu = mu + self.eta = eta + self.bias = bias + self.kernel = kernel + self.sigma + self.max_iter = max_iter + self.tol = tol + self.max_inner_iter = max_inner_iter + self.inner_tol = inner_tol + self.log = log + self.verbose = verbose + + def fit(self, Xs=None, ys=None, Xt=None, yt=None): + """Builds an optimal coupling and estimates the associated mapping + from source and target sets of samples (Xs, ys) and (Xt, yt) + Parameters + ---------- + Xs : array-like of shape = (n_source_samples, n_features) + The training input samples. + ys : array-like, shape = (n_source_samples,) + The class labels + Xt : array-like of shape = (n_target_samples, n_features) + The training input samples. + yt : array-like, shape = (n_labeled_target_samples,) + The class labels + Returns + ------- + self : object + Returns self. + """ + + self.Xs = Xs + self.Xt = Xt + + if self.kernel == "linear": + self.Coupling_, self.Mapping_ = joint_OT_mapping_linear( + Xs, Xt, mu=self.mu, eta=self.eta, bias=self.bias, + verbose=self.verbose, verbose2=self.verbose2, + numItermax=self.max_iter, numInnerItermax=self.max_inner_iter, + stopThr=self.tol, stopInnerThr=self.inner_tol, log=self.log) + + elif self.kernel == "gaussian": + self.Coupling_, self.Mapping_ = joint_OT_mapping_kernel( + Xs, Xt, mu=self.mu, eta=self.eta, bias=self.bias, + sigma=self.sigma, verbose=self.verbose, verbose2=self.verbose, + numItermax=self.max_iter, numInnerItermax=self.max_inner_iter, + stopInnerThr=self.inner_tol, stopThr=self.tol, log=self.log) + + return self + + def transform(self, Xs): + """Transports source samples Xs onto target ones Xt + Parameters + ---------- + Xs : array-like of shape = (n_source_samples, n_features) + The training input samples. + + Returns + ------- + transp_Xs : array-like of shape = (n_source_samples, n_features) + The transport source samples. + """ + + if np.array_equal(self.Xs, Xs): + # perform standard barycentric mapping + transp = self.Coupling_ / np.sum(self.Coupling_, 1)[:, None] + + # set nans to 0 + transp[~ np.isfinite(transp)] = 0 + + # compute transported samples + transp_Xs = np.dot(transp, self.Xt) + else: + if self.kernel == "gaussian": + K = kernel(Xs, self.Xs, method=self.kernel, sigma=self.sigma) + elif self.kernel == "linear": + K = Xs + if self.bias: + K = np.hstack((K, np.ones((Xs.shape[0], 1)))) + transp_Xs = K.dot(self.Mapping_) + + return transp_Xs -- cgit v1.2.3 From d20a067f91dcca318e2841ac52a8c578c78b89b2 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Wed, 23 Aug 2017 11:45:06 +0200 Subject: make doc strings compliant with numpy / modif according to AG review --- ot/da.py | 139 +++++++++++++++++++++++++++++++++----------------------- test/test_da.py | 13 ++++-- 2 files changed, 93 insertions(+), 59 deletions(-) diff --git a/ot/da.py b/ot/da.py index 0616d17..044d567 100644 --- a/ot/da.py +++ b/ot/da.py @@ -967,11 +967,13 @@ class BaseEstimator(object): def get_params(self, deep=True): """Get parameters for this estimator. + Parameters ---------- deep : boolean, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. + Returns ------- params : mapping of string to any @@ -1002,10 +1004,12 @@ class BaseEstimator(object): def set_params(self, **params): """Set the parameters of this estimator. + The method works on simple estimators as well as on nested objects (such as pipelines). The latter have parameters of the form ``__`` so that it's possible to update each component of a nested object. + Returns ------- self @@ -1053,11 +1057,12 @@ def distribution_estimation_uniform(X): Parameters ---------- - X : array-like of shape = (n_samples, n_features) + X : array-like, shape (n_samples, n_features) The array of samples + Returns ------- - mu : array-like, shape = (n_samples,) + mu : array-like, shape (n_samples,) The uniform distribution estimated from X """ @@ -1069,16 +1074,18 @@ class BaseTransport(BaseEstimator): def fit(self, Xs=None, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples (Xs, ys) and (Xt, yt) + Parameters ---------- - Xs : array-like of shape = (n_source_samples, n_features) + Xs : array-like, shape (n_source_samples, n_features) The training input samples. - ys : array-like, shape = (n_source_samples,) + ys : array-like, shape (n_source_samples,) The class labels - Xt : array-like of shape = (n_target_samples, n_features) + Xt : array-like, shape (n_target_samples, n_features) The training input samples. - yt : array-like, shape = (n_labeled_target_samples,) + yt : array-like, shape (n_labeled_target_samples,) The class labels + Returns ------- self : object @@ -1086,12 +1093,12 @@ class BaseTransport(BaseEstimator): """ # pairwise distance - self.Cost = dist(Xs, Xt, metric=self.metric) + self.cost_ = dist(Xs, Xt, metric=self.metric) if (ys is not None) and (yt is not None): if self.limit_max != np.infty: - self.limit_max = self.limit_max * np.max(self.Cost) + self.limit_max = self.limit_max * np.max(self.cost_) # assumes labeled source samples occupy the first rows # and labeled target samples occupy the first columns @@ -1104,7 +1111,7 @@ class BaseTransport(BaseEstimator): # and a target sample : # with different labels get a infinite for j in idx_t[0]: - self.Cost[idx_s[0], j] = self.limit_max + self.cost_[idx_s[0], j] = self.limit_max # distribution estimation self.mu_s = self.distribution_estimation(Xs) @@ -1120,19 +1127,21 @@ class BaseTransport(BaseEstimator): """Build a coupling matrix from source and target sets of samples (Xs, ys) and (Xt, yt) and transports source samples Xs onto target ones Xt + Parameters ---------- - Xs : array-like of shape = (n_source_samples, n_features) + Xs : array-like, shape (n_source_samples, n_features) The training input samples. - ys : array-like, shape = (n_source_samples,) + ys : array-like, shape (n_source_samples,) The class labels - Xt : array-like of shape = (n_target_samples, n_features) + Xt : array-like, shape (n_target_samples, n_features) The training input samples. - yt : array-like, shape = (n_labeled_target_samples,) + yt : array-like, shape (n_labeled_target_samples,) The class labels + Returns ------- - transp_Xs : array-like of shape = (n_source_samples, n_features) + transp_Xs : array-like, shape (n_source_samples, n_features) The source samples samples. """ @@ -1140,25 +1149,27 @@ class BaseTransport(BaseEstimator): def transform(self, Xs=None, ys=None, Xt=None, yt=None): """Transports source samples Xs onto target ones Xt + Parameters ---------- - Xs : array-like of shape = (n_source_samples, n_features) + Xs : array-like, shape (n_source_samples, n_features) The training input samples. - ys : array-like, shape = (n_source_samples,) + ys : array-like, shape (n_source_samples,) The class labels - Xt : array-like of shape = (n_target_samples, n_features) + Xt : array-like, shape (n_target_samples, n_features) The training input samples. - yt : array-like, shape = (n_labeled_target_samples,) + yt : array-like, shape (n_labeled_target_samples,) The class labels + Returns ------- - transp_Xs : array-like of shape = (n_source_samples, n_features) + transp_Xs : array-like, shape (n_source_samples, n_features) The transport source samples. """ if np.array_equal(self.Xs, Xs): # perform standard barycentric mapping - transp = self.Coupling_ / np.sum(self.Coupling_, 1)[:, None] + transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] # set nans to 0 transp[~ np.isfinite(transp)] = 0 @@ -1173,7 +1184,7 @@ class BaseTransport(BaseEstimator): idx = np.argmin(D0, axis=1) # transport the source samples - transp = self.Coupling_ / np.sum(self.Coupling_, 1)[:, None] + transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] transp[~ np.isfinite(transp)] = 0 transp_Xs_ = np.dot(transp, self.Xt) @@ -1184,25 +1195,27 @@ class BaseTransport(BaseEstimator): def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None): """Transports target samples Xt onto target samples Xs + Parameters ---------- - Xs : array-like of shape = (n_source_samples, n_features) + Xs : array-like, shape (n_source_samples, n_features) The training input samples. ys : array-like, shape = (n_source_samples,) The class labels - Xt : array-like of shape = (n_target_samples, n_features) + Xt : array-like, shape (n_target_samples, n_features) The training input samples. yt : array-like, shape = (n_labeled_target_samples,) The class labels + Returns ------- - transp_Xt : array-like of shape = (n_source_samples, n_features) + transp_Xt : array-like, shape (n_source_samples, n_features) The transported target samples. """ if np.array_equal(self.Xt, Xt): # perform standard barycentric mapping - transp_ = self.Coupling_.T / np.sum(self.Coupling_, 0)[:, None] + transp_ = self.coupling_.T / np.sum(self.coupling_, 0)[:, None] # set nans to 0 transp_[~ np.isfinite(transp_)] = 0 @@ -1216,7 +1229,7 @@ class BaseTransport(BaseEstimator): idx = np.argmin(D0, axis=1) # transport the target samples - transp_ = self.Coupling_.T / np.sum(self.Coupling_, 0)[:, None] + transp_ = self.coupling_.T / np.sum(self.coupling_, 0)[:, None] transp_[~ np.isfinite(transp_)] = 0 transp_Xt_ = np.dot(transp_, self.Xs) @@ -1254,9 +1267,10 @@ class SinkhornTransport(BaseTransport): limit_max: float, optional (defaul=np.infty) Controls the semi supervised mode. Transport between labeled source and target samples of different classes will exhibit an infinite cost + Attributes ---------- - Coupling_ : the optimal coupling + coupling_ : the optimal coupling References ---------- @@ -1287,16 +1301,18 @@ class SinkhornTransport(BaseTransport): def fit(self, Xs=None, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples (Xs, ys) and (Xt, yt) + Parameters ---------- - Xs : array-like of shape = (n_source_samples, n_features) + Xs : array-like, shape (n_source_samples, n_features) The training input samples. ys : array-like, shape = (n_source_samples,) The class labels - Xt : array-like of shape = (n_target_samples, n_features) + Xt : array-like, shape (n_target_samples, n_features) The training input samples. yt : array-like, shape = (n_labeled_target_samples,) The class labels + Returns ------- self : object @@ -1306,8 +1322,8 @@ class SinkhornTransport(BaseTransport): super(SinkhornTransport, self).fit(Xs, ys, Xt, yt) # coupling estimation - self.Coupling_ = sinkhorn( - a=self.mu_s, b=self.mu_t, M=self.Cost, reg=self.reg_e, + self.coupling_ = sinkhorn( + a=self.mu_s, b=self.mu_t, M=self.cost_, reg=self.reg_e, numItermax=self.max_iter, stopThr=self.tol, verbose=self.verbose, log=self.log) @@ -1316,6 +1332,7 @@ class SinkhornTransport(BaseTransport): class EMDTransport(BaseTransport): """Domain Adapatation OT method based on Earth Mover's Distance + Parameters ---------- mapping : string, optional (default="barycentric") @@ -1335,9 +1352,10 @@ class EMDTransport(BaseTransport): Controls the semi supervised mode. Transport between labeled source and target samples of different classes will exhibit an infinite cost (10 times the maximum value of the cost matrix) + Attributes ---------- - Coupling_ : the optimal coupling + coupling_ : the optimal coupling References ---------- @@ -1358,16 +1376,18 @@ class EMDTransport(BaseTransport): def fit(self, Xs, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples (Xs, ys) and (Xt, yt) + Parameters ---------- - Xs : array-like of shape = (n_source_samples, n_features) + Xs : array-like, shape (n_source_samples, n_features) The training input samples. ys : array-like, shape = (n_source_samples,) The class labels - Xt : array-like of shape = (n_target_samples, n_features) + Xt : array-like, shape (n_target_samples, n_features) The training input samples. yt : array-like, shape = (n_labeled_target_samples,) The class labels + Returns ------- self : object @@ -1377,8 +1397,8 @@ class EMDTransport(BaseTransport): super(EMDTransport, self).fit(Xs, ys, Xt, yt) # coupling estimation - self.Coupling_ = emd( - a=self.mu_s, b=self.mu_t, M=self.Cost, + self.coupling_ = emd( + a=self.mu_s, b=self.mu_t, M=self.cost_, ) return self @@ -1418,7 +1438,7 @@ class SinkhornLpl1Transport(BaseTransport): Attributes ---------- - Coupling_ : the optimal coupling + coupling_ : the optimal coupling References ---------- @@ -1455,16 +1475,18 @@ class SinkhornLpl1Transport(BaseTransport): def fit(self, Xs, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples (Xs, ys) and (Xt, yt) + Parameters ---------- - Xs : array-like of shape = (n_source_samples, n_features) + Xs : array-like, shape (n_source_samples, n_features) The training input samples. ys : array-like, shape = (n_source_samples,) The class labels - Xt : array-like of shape = (n_target_samples, n_features) + Xt : array-like, shape (n_target_samples, n_features) The training input samples. yt : array-like, shape = (n_labeled_target_samples,) The class labels + Returns ------- self : object @@ -1473,8 +1495,8 @@ class SinkhornLpl1Transport(BaseTransport): super(SinkhornLpl1Transport, self).fit(Xs, ys, Xt, yt) - self.Coupling_ = sinkhorn_lpl1_mm( - a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.Cost, + self.coupling_ = sinkhorn_lpl1_mm( + a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.cost_, reg=self.reg_e, eta=self.reg_cl, numItermax=self.max_iter, numInnerItermax=self.max_inner_iter, stopInnerThr=self.tol, verbose=self.verbose, log=self.log) @@ -1517,7 +1539,7 @@ class SinkhornL1l2Transport(BaseTransport): Attributes ---------- - Coupling_ : the optimal coupling + coupling_ : the optimal coupling References ---------- @@ -1554,16 +1576,18 @@ class SinkhornL1l2Transport(BaseTransport): def fit(self, Xs, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples (Xs, ys) and (Xt, yt) + Parameters ---------- - Xs : array-like of shape = (n_source_samples, n_features) + Xs : array-like, shape (n_source_samples, n_features) The training input samples. ys : array-like, shape = (n_source_samples,) The class labels - Xt : array-like of shape = (n_target_samples, n_features) + Xt : array-like, shape (n_target_samples, n_features) The training input samples. yt : array-like, shape = (n_labeled_target_samples,) The class labels + Returns ------- self : object @@ -1572,8 +1596,8 @@ class SinkhornL1l2Transport(BaseTransport): super(SinkhornL1l2Transport, self).fit(Xs, ys, Xt, yt) - self.Coupling_ = sinkhorn_l1l2_gl( - a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.Cost, + self.coupling_ = sinkhorn_l1l2_gl( + a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.cost_, reg=self.reg_e, eta=self.reg_cl, numItermax=self.max_iter, numInnerItermax=self.max_inner_iter, stopInnerThr=self.tol, verbose=self.verbose, log=self.log) @@ -1614,8 +1638,8 @@ class MappingTransport(BaseEstimator): Attributes ---------- - Coupling_ : the optimal coupling - Mapping_ : the mapping associated + coupling_ : the optimal coupling + mapping_ : the mapping associated References ---------- @@ -1646,16 +1670,18 @@ class MappingTransport(BaseEstimator): def fit(self, Xs=None, ys=None, Xt=None, yt=None): """Builds an optimal coupling and estimates the associated mapping from source and target sets of samples (Xs, ys) and (Xt, yt) + Parameters ---------- - Xs : array-like of shape = (n_source_samples, n_features) + Xs : array-like, shape (n_source_samples, n_features) The training input samples. ys : array-like, shape = (n_source_samples,) The class labels - Xt : array-like of shape = (n_target_samples, n_features) + Xt : array-like, shape (n_target_samples, n_features) The training input samples. yt : array-like, shape = (n_labeled_target_samples,) The class labels + Returns ------- self : object @@ -1666,14 +1692,14 @@ class MappingTransport(BaseEstimator): self.Xt = Xt if self.kernel == "linear": - self.Coupling_, self.Mapping_ = joint_OT_mapping_linear( + self.coupling_, self.mapping_ = joint_OT_mapping_linear( Xs, Xt, mu=self.mu, eta=self.eta, bias=self.bias, verbose=self.verbose, verbose2=self.verbose2, numItermax=self.max_iter, numInnerItermax=self.max_inner_iter, stopThr=self.tol, stopInnerThr=self.inner_tol, log=self.log) elif self.kernel == "gaussian": - self.Coupling_, self.Mapping_ = joint_OT_mapping_kernel( + self.coupling_, self.mapping_ = joint_OT_mapping_kernel( Xs, Xt, mu=self.mu, eta=self.eta, bias=self.bias, sigma=self.sigma, verbose=self.verbose, verbose2=self.verbose, numItermax=self.max_iter, numInnerItermax=self.max_inner_iter, @@ -1683,20 +1709,21 @@ class MappingTransport(BaseEstimator): def transform(self, Xs): """Transports source samples Xs onto target ones Xt + Parameters ---------- - Xs : array-like of shape = (n_source_samples, n_features) + Xs : array-like, shape (n_source_samples, n_features) The training input samples. Returns ------- - transp_Xs : array-like of shape = (n_source_samples, n_features) + transp_Xs : array-like, shape (n_source_samples, n_features) The transport source samples. """ if np.array_equal(self.Xs, Xs): # perform standard barycentric mapping - transp = self.Coupling_ / np.sum(self.Coupling_, 1)[:, None] + transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] # set nans to 0 transp[~ np.isfinite(transp)] = 0 @@ -1710,6 +1737,6 @@ class MappingTransport(BaseEstimator): K = Xs if self.bias: K = np.hstack((K, np.ones((Xs.shape[0], 1)))) - transp_Xs = K.dot(self.Mapping_) + transp_Xs = K.dot(self.mapping_) return transp_Xs diff --git a/test/test_da.py b/test/test_da.py index aed9f61..93f7e83 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -5,13 +5,12 @@ # License: MIT License import numpy as np -import ot from numpy.testing.utils import assert_allclose, assert_equal + +import ot from ot.datasets import get_data_classif from ot.utils import unif -np.random.seed(42) - def test_sinkhorn_lpl1_transport_class(): """test_sinkhorn_transport @@ -325,3 +324,11 @@ def test_otda(): da_emd = ot.da.OTDA_mapping_kernel() # init class da_emd.fit(xs, xt, numItermax=10) # fit distributions da_emd.predict(xs) # interpolation of source samples + + +if __name__ == "__main__": + + test_sinkhorn_transport_class() + test_emd_transport_class() + test_sinkhorn_l1l2_transport_class() + test_sinkhorn_lpl1_transport_class() -- cgit v1.2.3 From 8d19d365446efc00d8443c6ddb5b93fded3fa5ab Mon Sep 17 00:00:00 2001 From: Slasnista Date: Wed, 23 Aug 2017 13:50:24 +0200 Subject: out of samples transform and inverse transform by batch --- ot/da.py | 89 +++++++++++++++++++++++++++++++++++++-------------------- test/test_da.py | 66 +++++++++++++++++++++--------------------- 2 files changed, 91 insertions(+), 64 deletions(-) diff --git a/ot/da.py b/ot/da.py index 044d567..0c83ae6 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1147,7 +1147,7 @@ class BaseTransport(BaseEstimator): return self.fit(Xs, ys, Xt, yt).transform(Xs, ys, Xt, yt) - def transform(self, Xs=None, ys=None, Xt=None, yt=None): + def transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128): """Transports source samples Xs onto target ones Xt Parameters @@ -1160,6 +1160,8 @@ class BaseTransport(BaseEstimator): The training input samples. yt : array-like, shape (n_labeled_target_samples,) The class labels + batch_size : int, optional (default=128) + The batch size for out of sample inverse transform Returns ------- @@ -1178,34 +1180,48 @@ class BaseTransport(BaseEstimator): transp_Xs = np.dot(transp, self.Xt) else: # perform out of sample mapping + indices = np.arange(Xs.shape[0]) + batch_ind = [ + indices[i:i + batch_size] + for i in range(0, len(indices), batch_size)] - # get the nearest neighbor in the source domain - D0 = dist(Xs, self.Xs) - idx = np.argmin(D0, axis=1) + transp_Xs = [] + for bi in batch_ind: - # transport the source samples - transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] - transp[~ np.isfinite(transp)] = 0 - transp_Xs_ = np.dot(transp, self.Xt) + # get the nearest neighbor in the source domain + D0 = dist(Xs[bi], self.Xs) + idx = np.argmin(D0, axis=1) + + # transport the source samples + transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] + transp[~ np.isfinite(transp)] = 0 + transp_Xs_ = np.dot(transp, self.Xt) - # define the transported points - transp_Xs = transp_Xs_[idx, :] + Xs - self.Xs[idx, :] + # define the transported points + transp_Xs_ = transp_Xs_[idx, :] + Xs[bi] - self.Xs[idx, :] + + transp_Xs.append(transp_Xs_) + + transp_Xs = np.concatenate(transp_Xs, axis=0) return transp_Xs - def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None): + def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None, + batch_size=128): """Transports target samples Xt onto target samples Xs Parameters ---------- Xs : array-like, shape (n_source_samples, n_features) The training input samples. - ys : array-like, shape = (n_source_samples,) + ys : array-like, shape (n_source_samples,) The class labels Xt : array-like, shape (n_target_samples, n_features) The training input samples. - yt : array-like, shape = (n_labeled_target_samples,) + yt : array-like, shape (n_labeled_target_samples,) The class labels + batch_size : int, optional (default=128) + The batch size for out of sample inverse transform Returns ------- @@ -1224,17 +1240,28 @@ class BaseTransport(BaseEstimator): transp_Xt = np.dot(transp_, self.Xs) else: # perform out of sample mapping + indices = np.arange(Xt.shape[0]) + batch_ind = [ + indices[i:i + batch_size] + for i in range(0, len(indices), batch_size)] - D0 = dist(Xt, self.Xt) - idx = np.argmin(D0, axis=1) + transp_Xt = [] + for bi in batch_ind: - # transport the target samples - transp_ = self.coupling_.T / np.sum(self.coupling_, 0)[:, None] - transp_[~ np.isfinite(transp_)] = 0 - transp_Xt_ = np.dot(transp_, self.Xs) + D0 = dist(Xt[bi], self.Xt) + idx = np.argmin(D0, axis=1) + + # transport the target samples + transp_ = self.coupling_.T / np.sum(self.coupling_, 0)[:, None] + transp_[~ np.isfinite(transp_)] = 0 + transp_Xt_ = np.dot(transp_, self.Xs) + + # define the transported points + transp_Xt_ = transp_Xt_[idx, :] + Xt[bi] - self.Xt[idx, :] - # define the transported points - transp_Xt = transp_Xt_[idx, :] + Xt - self.Xt[idx, :] + transp_Xt.append(transp_Xt_) + + transp_Xt = np.concatenate(transp_Xt, axis=0) return transp_Xt @@ -1306,11 +1333,11 @@ class SinkhornTransport(BaseTransport): ---------- Xs : array-like, shape (n_source_samples, n_features) The training input samples. - ys : array-like, shape = (n_source_samples,) + ys : array-like, shape (n_source_samples,) The class labels Xt : array-like, shape (n_target_samples, n_features) The training input samples. - yt : array-like, shape = (n_labeled_target_samples,) + yt : array-like, shape (n_labeled_target_samples,) The class labels Returns @@ -1381,11 +1408,11 @@ class EMDTransport(BaseTransport): ---------- Xs : array-like, shape (n_source_samples, n_features) The training input samples. - ys : array-like, shape = (n_source_samples,) + ys : array-like, shape (n_source_samples,) The class labels Xt : array-like, shape (n_target_samples, n_features) The training input samples. - yt : array-like, shape = (n_labeled_target_samples,) + yt : array-like, shape (n_labeled_target_samples,) The class labels Returns @@ -1480,11 +1507,11 @@ class SinkhornLpl1Transport(BaseTransport): ---------- Xs : array-like, shape (n_source_samples, n_features) The training input samples. - ys : array-like, shape = (n_source_samples,) + ys : array-like, shape (n_source_samples,) The class labels Xt : array-like, shape (n_target_samples, n_features) The training input samples. - yt : array-like, shape = (n_labeled_target_samples,) + yt : array-like, shape (n_labeled_target_samples,) The class labels Returns @@ -1581,11 +1608,11 @@ class SinkhornL1l2Transport(BaseTransport): ---------- Xs : array-like, shape (n_source_samples, n_features) The training input samples. - ys : array-like, shape = (n_source_samples,) + ys : array-like, shape (n_source_samples,) The class labels Xt : array-like, shape (n_target_samples, n_features) The training input samples. - yt : array-like, shape = (n_labeled_target_samples,) + yt : array-like, shape (n_labeled_target_samples,) The class labels Returns @@ -1675,11 +1702,11 @@ class MappingTransport(BaseEstimator): ---------- Xs : array-like, shape (n_source_samples, n_features) The training input samples. - ys : array-like, shape = (n_source_samples,) + ys : array-like, shape (n_source_samples,) The class labels Xt : array-like, shape (n_target_samples, n_features) The training input samples. - yt : array-like, shape = (n_labeled_target_samples,) + yt : array-like, shape (n_labeled_target_samples,) The class labels Returns diff --git a/test/test_da.py b/test/test_da.py index 93f7e83..196f4c4 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -28,14 +28,14 @@ def test_sinkhorn_lpl1_transport_class(): clf.fit(Xs=Xs, ys=ys, Xt=Xt) # test dimensions of coupling - assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) - assert_equal(clf.Coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(clf.Coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(clf.Coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform transp_Xs = clf.transform(Xs=Xs) @@ -64,13 +64,13 @@ def test_sinkhorn_lpl1_transport_class(): # test semi supervised mode clf = ot.da.SinkhornLpl1Transport() clf.fit(Xs=Xs, ys=ys, Xt=Xt) - n_unsup = np.sum(clf.Cost) + n_unsup = np.sum(clf.cost_) # test semi supervised mode clf = ot.da.SinkhornLpl1Transport() clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) - assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) - n_semisup = np.sum(clf.Cost) + assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(clf.cost_) assert n_unsup != n_semisup, "semisupervised mode not working" @@ -91,14 +91,14 @@ def test_sinkhorn_l1l2_transport_class(): clf.fit(Xs=Xs, ys=ys, Xt=Xt) # test dimensions of coupling - assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) - assert_equal(clf.Coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(clf.Coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(clf.Coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform transp_Xs = clf.transform(Xs=Xs) @@ -127,13 +127,13 @@ def test_sinkhorn_l1l2_transport_class(): # test semi supervised mode clf = ot.da.SinkhornL1l2Transport() clf.fit(Xs=Xs, ys=ys, Xt=Xt) - n_unsup = np.sum(clf.Cost) + n_unsup = np.sum(clf.cost_) # test semi supervised mode clf = ot.da.SinkhornL1l2Transport() clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) - assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) - n_semisup = np.sum(clf.Cost) + assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(clf.cost_) assert n_unsup != n_semisup, "semisupervised mode not working" @@ -154,14 +154,14 @@ def test_sinkhorn_transport_class(): clf.fit(Xs=Xs, Xt=Xt) # test dimensions of coupling - assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) - assert_equal(clf.Coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(clf.Coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(clf.Coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform transp_Xs = clf.transform(Xs=Xs) @@ -190,13 +190,13 @@ def test_sinkhorn_transport_class(): # test semi supervised mode clf = ot.da.SinkhornTransport() clf.fit(Xs=Xs, Xt=Xt) - n_unsup = np.sum(clf.Cost) + n_unsup = np.sum(clf.cost_) # test semi supervised mode clf = ot.da.SinkhornTransport() clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) - assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) - n_semisup = np.sum(clf.Cost) + assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(clf.cost_) assert n_unsup != n_semisup, "semisupervised mode not working" @@ -217,14 +217,14 @@ def test_emd_transport_class(): clf.fit(Xs=Xs, Xt=Xt) # test dimensions of coupling - assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) - assert_equal(clf.Coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(clf.Coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(clf.Coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform transp_Xs = clf.transform(Xs=Xs) @@ -253,13 +253,13 @@ def test_emd_transport_class(): # test semi supervised mode clf = ot.da.EMDTransport() clf.fit(Xs=Xs, Xt=Xt) - n_unsup = np.sum(clf.Cost) + n_unsup = np.sum(clf.cost_) # test semi supervised mode clf = ot.da.EMDTransport() clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) - assert_equal(clf.Cost.shape, ((Xs.shape[0], Xt.shape[0]))) - n_semisup = np.sum(clf.Cost) + assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(clf.cost_) assert n_unsup != n_semisup, "semisupervised mode not working" @@ -326,9 +326,9 @@ def test_otda(): da_emd.predict(xs) # interpolation of source samples -if __name__ == "__main__": +# if __name__ == "__main__": - test_sinkhorn_transport_class() - test_emd_transport_class() - test_sinkhorn_l1l2_transport_class() - test_sinkhorn_lpl1_transport_class() +# test_sinkhorn_transport_class() +# test_emd_transport_class() +# test_sinkhorn_l1l2_transport_class() +# test_sinkhorn_lpl1_transport_class() -- cgit v1.2.3 From c8ae5843ae64dbf841deb3ad8c10024a94a93eec Mon Sep 17 00:00:00 2001 From: Slasnista Date: Wed, 23 Aug 2017 14:11:13 +0200 Subject: test functions for MappingTransport Class --- ot/da.py | 18 ++++++--- test/test_da.py | 117 +++++++++++++++++++++++++++++++++++++++++++++++++++++--- 2 files changed, 125 insertions(+), 10 deletions(-) diff --git a/ot/da.py b/ot/da.py index 0c83ae6..3ccb1b3 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1665,8 +1665,14 @@ class MappingTransport(BaseEstimator): Attributes ---------- - coupling_ : the optimal coupling - mapping_ : the mapping associated + coupling_ : array-like, shape (n_source_samples, n_features) + The optimal coupling + mapping_ : array-like, shape (n_features (+ 1), n_features) + (if bias) for kernel == linear + The associated mapping + + array-like, shape (n_source_samples (+ 1), n_features) + (if bias) for kernel == gaussian References ---------- @@ -1679,20 +1685,22 @@ class MappingTransport(BaseEstimator): def __init__(self, mu=1, eta=0.001, bias=False, metric="sqeuclidean", kernel="linear", sigma=1, max_iter=100, tol=1e-5, - max_inner_iter=10, inner_tol=1e-6, log=False, verbose=False): + max_inner_iter=10, inner_tol=1e-6, log=False, verbose=False, + verbose2=False): self.metric = metric self.mu = mu self.eta = eta self.bias = bias self.kernel = kernel - self.sigma + self.sigma = sigma self.max_iter = max_iter self.tol = tol self.max_inner_iter = max_inner_iter self.inner_tol = inner_tol self.log = log self.verbose = verbose + self.verbose2 = verbose2 def fit(self, Xs=None, ys=None, Xt=None, yt=None): """Builds an optimal coupling and estimates the associated mapping @@ -1712,7 +1720,7 @@ class MappingTransport(BaseEstimator): Returns ------- self : object - Returns self. + Returns self """ self.Xs = Xs diff --git a/test/test_da.py b/test/test_da.py index 196f4c4..162f681 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -264,6 +264,112 @@ def test_emd_transport_class(): assert n_unsup != n_semisup, "semisupervised mode not working" +def test_mapping_transport_class(): + """test_mapping_transport + """ + + ns = 150 + nt = 200 + + Xs, ys = get_data_classif('3gauss', ns) + Xt, yt = get_data_classif('3gauss2', nt) + Xs_new, _ = get_data_classif('3gauss', ns + 1) + + ########################################################################## + # kernel == linear mapping tests + ########################################################################## + + # check computation and dimensions if bias == False + clf = ot.da.MappingTransport(kernel="linear", bias=False) + clf.fit(Xs=Xs, Xt=Xt) + + assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.mapping_.shape, ((Xs.shape[1], Xt.shape[1]))) + + # test margin constraints + mu_s = unif(ns) + mu_t = unif(nt) + assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + + # test transform + transp_Xs = clf.transform(Xs=Xs) + assert_equal(transp_Xs.shape, Xs.shape) + + transp_Xs_new = clf.transform(Xs_new) + + # check that the oos method is working + assert_equal(transp_Xs_new.shape, Xs_new.shape) + + # check computation and dimensions if bias == True + clf = ot.da.MappingTransport(kernel="linear", bias=True) + clf.fit(Xs=Xs, Xt=Xt) + assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.mapping_.shape, ((Xs.shape[1] + 1, Xt.shape[1]))) + + # test margin constraints + mu_s = unif(ns) + mu_t = unif(nt) + assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + + # test transform + transp_Xs = clf.transform(Xs=Xs) + assert_equal(transp_Xs.shape, Xs.shape) + + transp_Xs_new = clf.transform(Xs_new) + + # check that the oos method is working + assert_equal(transp_Xs_new.shape, Xs_new.shape) + + ########################################################################## + # kernel == gaussian mapping tests + ########################################################################## + + # check computation and dimensions if bias == False + clf = ot.da.MappingTransport(kernel="gaussian", bias=False) + clf.fit(Xs=Xs, Xt=Xt) + + assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.mapping_.shape, ((Xs.shape[0], Xt.shape[1]))) + + # test margin constraints + mu_s = unif(ns) + mu_t = unif(nt) + assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + + # test transform + transp_Xs = clf.transform(Xs=Xs) + assert_equal(transp_Xs.shape, Xs.shape) + + transp_Xs_new = clf.transform(Xs_new) + + # check that the oos method is working + assert_equal(transp_Xs_new.shape, Xs_new.shape) + + # check computation and dimensions if bias == True + clf = ot.da.MappingTransport(kernel="gaussian", bias=True) + clf.fit(Xs=Xs, Xt=Xt) + assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(clf.mapping_.shape, ((Xs.shape[0] + 1, Xt.shape[1]))) + + # test margin constraints + mu_s = unif(ns) + mu_t = unif(nt) + assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + + # test transform + transp_Xs = clf.transform(Xs=Xs) + assert_equal(transp_Xs.shape, Xs.shape) + + transp_Xs_new = clf.transform(Xs_new) + + # check that the oos method is working + assert_equal(transp_Xs_new.shape, Xs_new.shape) + + def test_otda(): n_samples = 150 # nb samples @@ -326,9 +432,10 @@ def test_otda(): da_emd.predict(xs) # interpolation of source samples -# if __name__ == "__main__": +if __name__ == "__main__": -# test_sinkhorn_transport_class() -# test_emd_transport_class() -# test_sinkhorn_l1l2_transport_class() -# test_sinkhorn_lpl1_transport_class() + # test_sinkhorn_transport_class() + # test_emd_transport_class() + # test_sinkhorn_l1l2_transport_class() + # test_sinkhorn_lpl1_transport_class() + test_mapping_transport_class() -- cgit v1.2.3 From fc58f39fc730a9e1bb2215ef063e37c50f0ebc1f Mon Sep 17 00:00:00 2001 From: Slasnista Date: Wed, 23 Aug 2017 15:09:08 +0200 Subject: added deprecation warning on old classes --- ot/da.py | 22 ++++++++++-- ot/deprecation.py | 103 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ test/test_da.py | 5 +-- 3 files changed, 126 insertions(+), 4 deletions(-) create mode 100644 ot/deprecation.py diff --git a/ot/da.py b/ot/da.py index 3ccb1b3..8fa1895 100644 --- a/ot/da.py +++ b/ot/da.py @@ -10,12 +10,14 @@ Domain adaptation with optimal transport # License: MIT License import numpy as np +import warnings + from .bregman import sinkhorn from .lp import emd from .utils import unif, dist, kernel from .optim import cg from .optim import gcg -import warnings +from .deprecation import deprecated def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, @@ -632,6 +634,9 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', return G, L +@deprecated("The class OTDA is deprecated in 0.3.1 and will be " + "removed in 0.5" + "\n\tfor standard transport use class EMDTransport instead.") class OTDA(object): """Class for domain adaptation with optimal transport as proposed in [5] @@ -758,10 +763,15 @@ class OTDA(object): self.M = np.log(1 + np.log(1 + self.M)) +@deprecated("The class OTDA_sinkhorn is deprecated in 0.3.1 and will be" + " removed in 0.5 \nUse class SinkhornTransport instead.") class OTDA_sinkhorn(OTDA): """Class for domain adaptation with optimal transport with entropic - regularization""" + regularization + + + """ def fit(self, xs, xt, reg=1, ws=None, wt=None, norm=None, **kwargs): """Fit regularized domain adaptation between samples is xs and xt @@ -783,6 +793,8 @@ class OTDA_sinkhorn(OTDA): self.computed = True +@deprecated("The class OTDA_lpl1 is deprecated in 0.3.1 and will be" + " removed in 0.5 \nUse class SinkhornLpl1Transport instead.") class OTDA_lpl1(OTDA): """Class for domain adaptation with optimal transport with entropic and @@ -810,6 +822,8 @@ class OTDA_lpl1(OTDA): self.computed = True +@deprecated("The class OTDA_l1L2 is deprecated in 0.3.1 and will be" + " removed in 0.5 \nUse class SinkhornL1l2Transport instead.") class OTDA_l1l2(OTDA): """Class for domain adaptation with optimal transport with entropic @@ -837,6 +851,8 @@ class OTDA_l1l2(OTDA): self.computed = True +@deprecated("The class OTDA_mapping_linear is deprecated in 0.3.1 and will be" + " removed in 0.5 \nUse class MappingTransport instead.") class OTDA_mapping_linear(OTDA): """Class for optimal transport with joint linear mapping estimation as in @@ -882,6 +898,8 @@ class OTDA_mapping_linear(OTDA): return None +@deprecated("The class OTDA_mapping_kernel is deprecated in 0.3.1 and will be" + " removed in 0.5 \nUse class MappingTransport instead.") class OTDA_mapping_kernel(OTDA_mapping_linear): """Class for optimal transport with joint nonlinear mapping diff --git a/ot/deprecation.py b/ot/deprecation.py new file mode 100644 index 0000000..2b16427 --- /dev/null +++ b/ot/deprecation.py @@ -0,0 +1,103 @@ +""" + deprecated class from scikit-learn package + https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/deprecation.py +""" + +import sys +import warnings + +__all__ = ["deprecated", ] + + +class deprecated(object): + """Decorator to mark a function or class as deprecated. + Issue a warning when the function is called/the class is instantiated and + adds a warning to the docstring. + The optional extra argument will be appended to the deprecation message + and the docstring. Note: to use this with the default value for extra, put + in an empty of parentheses: + >>> from ot.deprecation import deprecated + >>> @deprecated() + ... def some_function(): pass + + Parameters + ---------- + extra : string + to be added to the deprecation messages + """ + + # Adapted from http://wiki.python.org/moin/PythonDecoratorLibrary, + # but with many changes. + + def __init__(self, extra=''): + self.extra = extra + + def __call__(self, obj): + """Call method + Parameters + ---------- + obj : object + """ + if isinstance(obj, type): + return self._decorate_class(obj) + else: + return self._decorate_fun(obj) + + def _decorate_class(self, cls): + msg = "Class %s is deprecated" % cls.__name__ + if self.extra: + msg += "; %s" % self.extra + + # FIXME: we should probably reset __new__ for full generality + init = cls.__init__ + + def wrapped(*args, **kwargs): + warnings.warn(msg, category=DeprecationWarning) + return init(*args, **kwargs) + + cls.__init__ = wrapped + + wrapped.__name__ = '__init__' + wrapped.__doc__ = self._update_doc(init.__doc__) + wrapped.deprecated_original = init + + return cls + + def _decorate_fun(self, fun): + """Decorate function fun""" + + msg = "Function %s is deprecated" % fun.__name__ + if self.extra: + msg += "; %s" % self.extra + + def wrapped(*args, **kwargs): + warnings.warn(msg, category=DeprecationWarning) + return fun(*args, **kwargs) + + wrapped.__name__ = fun.__name__ + wrapped.__dict__ = fun.__dict__ + wrapped.__doc__ = self._update_doc(fun.__doc__) + + return wrapped + + def _update_doc(self, olddoc): + newdoc = "DEPRECATED" + if self.extra: + newdoc = "%s: %s" % (newdoc, self.extra) + if olddoc: + newdoc = "%s\n\n%s" % (newdoc, olddoc) + return newdoc + + +def _is_deprecated(func): + """Helper to check if func is wraped by our deprecated decorator""" + if sys.version_info < (3, 5): + raise NotImplementedError("This is only available for python3.5 " + "or above") + closures = getattr(func, '__closure__', []) + if closures is None: + closures = [] + is_deprecated = ('deprecated' in ''.join([c.cell_contents + for c in closures + if isinstance(c.cell_contents, str)])) + return is_deprecated diff --git a/test/test_da.py b/test/test_da.py index 162f681..9578b3d 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -432,10 +432,11 @@ def test_otda(): da_emd.predict(xs) # interpolation of source samples -if __name__ == "__main__": +# if __name__ == "__main__": + # test_otda() # test_sinkhorn_transport_class() # test_emd_transport_class() # test_sinkhorn_l1l2_transport_class() # test_sinkhorn_lpl1_transport_class() - test_mapping_transport_class() + # test_mapping_transport_class() -- cgit v1.2.3 From 6167f34a721886d4b9038a8b1746a2c8c81132ce Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 25 Aug 2017 10:29:41 +0200 Subject: solving log issues to avoid errors and adding further tests --- ot/da.py | 57 ++++++++++++++++++++++++++++++++++++++++++--------------- test/test_da.py | 39 +++++++++++++++++++++++++++++++++------ 2 files changed, 75 insertions(+), 21 deletions(-) diff --git a/ot/da.py b/ot/da.py index 8fa1895..5a34979 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1315,7 +1315,10 @@ class SinkhornTransport(BaseTransport): Attributes ---------- - coupling_ : the optimal coupling + coupling_ : array-like, shape (n_source_samples, n_target_samples) + The optimal coupling + log_ : dictionary + The dictionary of log, empty dic if parameter log is not True References ---------- @@ -1367,11 +1370,18 @@ class SinkhornTransport(BaseTransport): super(SinkhornTransport, self).fit(Xs, ys, Xt, yt) # coupling estimation - self.coupling_ = sinkhorn( + returned_ = sinkhorn( a=self.mu_s, b=self.mu_t, M=self.cost_, reg=self.reg_e, numItermax=self.max_iter, stopThr=self.tol, verbose=self.verbose, log=self.log) + # deal with the value of log + if self.log: + self.coupling_, self.log_ = returned_ + else: + self.coupling_ = returned_ + self.log_ = dict() + return self @@ -1400,7 +1410,8 @@ class EMDTransport(BaseTransport): Attributes ---------- - coupling_ : the optimal coupling + coupling_ : array-like, shape (n_source_samples, n_target_samples) + The optimal coupling References ---------- @@ -1475,15 +1486,14 @@ class SinkhornLpl1Transport(BaseTransport): The number of iteration in the inner loop verbose : int, optional (default=0) Controls the verbosity of the optimization algorithm - log : int, optional (default=0) - Controls the logs of the optimization algorithm limit_max: float, optional (defaul=np.infty) Controls the semi supervised mode. Transport between labeled source and target samples of different classes will exhibit an infinite cost Attributes ---------- - coupling_ : the optimal coupling + coupling_ : array-like, shape (n_source_samples, n_target_samples) + The optimal coupling References ---------- @@ -1500,7 +1510,7 @@ class SinkhornLpl1Transport(BaseTransport): def __init__(self, reg_e=1., reg_cl=0.1, max_iter=10, max_inner_iter=200, - tol=10e-9, verbose=False, log=False, + tol=10e-9, verbose=False, metric="sqeuclidean", distribution_estimation=distribution_estimation_uniform, out_of_sample_map='ferradans', limit_max=np.infty): @@ -1511,7 +1521,6 @@ class SinkhornLpl1Transport(BaseTransport): self.max_inner_iter = max_inner_iter self.tol = tol self.verbose = verbose - self.log = log self.metric = metric self.distribution_estimation = distribution_estimation self.out_of_sample_map = out_of_sample_map @@ -1544,7 +1553,7 @@ class SinkhornLpl1Transport(BaseTransport): a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.cost_, reg=self.reg_e, eta=self.reg_cl, numItermax=self.max_iter, numInnerItermax=self.max_inner_iter, stopInnerThr=self.tol, - verbose=self.verbose, log=self.log) + verbose=self.verbose) return self @@ -1584,7 +1593,10 @@ class SinkhornL1l2Transport(BaseTransport): Attributes ---------- - coupling_ : the optimal coupling + coupling_ : array-like, shape (n_source_samples, n_target_samples) + The optimal coupling + log_ : dictionary + The dictionary of log, empty dic if parameter log is not True References ---------- @@ -1641,12 +1653,19 @@ class SinkhornL1l2Transport(BaseTransport): super(SinkhornL1l2Transport, self).fit(Xs, ys, Xt, yt) - self.coupling_ = sinkhorn_l1l2_gl( + returned_ = sinkhorn_l1l2_gl( a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.cost_, reg=self.reg_e, eta=self.reg_cl, numItermax=self.max_iter, numInnerItermax=self.max_inner_iter, stopInnerThr=self.tol, verbose=self.verbose, log=self.log) + # deal with the value of log + if self.log: + self.coupling_, self.log_ = returned_ + else: + self.coupling_ = returned_ + self.log_ = dict() + return self @@ -1683,14 +1702,15 @@ class MappingTransport(BaseEstimator): Attributes ---------- - coupling_ : array-like, shape (n_source_samples, n_features) + coupling_ : array-like, shape (n_source_samples, n_target_samples) The optimal coupling mapping_ : array-like, shape (n_features (+ 1), n_features) (if bias) for kernel == linear The associated mapping - array-like, shape (n_source_samples (+ 1), n_features) (if bias) for kernel == gaussian + log_ : dictionary + The dictionary of log, empty dic if parameter log is not True References ---------- @@ -1745,19 +1765,26 @@ class MappingTransport(BaseEstimator): self.Xt = Xt if self.kernel == "linear": - self.coupling_, self.mapping_ = joint_OT_mapping_linear( + returned_ = joint_OT_mapping_linear( Xs, Xt, mu=self.mu, eta=self.eta, bias=self.bias, verbose=self.verbose, verbose2=self.verbose2, numItermax=self.max_iter, numInnerItermax=self.max_inner_iter, stopThr=self.tol, stopInnerThr=self.inner_tol, log=self.log) elif self.kernel == "gaussian": - self.coupling_, self.mapping_ = joint_OT_mapping_kernel( + returned_ = joint_OT_mapping_kernel( Xs, Xt, mu=self.mu, eta=self.eta, bias=self.bias, sigma=self.sigma, verbose=self.verbose, verbose2=self.verbose, numItermax=self.max_iter, numInnerItermax=self.max_inner_iter, stopInnerThr=self.inner_tol, stopThr=self.tol, log=self.log) + # deal with the value of log + if self.log: + self.coupling_, self.mapping_, self.log_ = returned_ + else: + self.coupling_, self.mapping_ = returned_ + self.log_ = dict() + return self def transform(self, Xs): diff --git a/test/test_da.py b/test/test_da.py index 9578b3d..104a798 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -26,6 +26,8 @@ def test_sinkhorn_lpl1_transport_class(): # test its computed clf.fit(Xs=Xs, ys=ys, Xt=Xt) + assert hasattr(clf, "cost_") + assert hasattr(clf, "coupling_") # test dimensions of coupling assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) @@ -89,6 +91,9 @@ def test_sinkhorn_l1l2_transport_class(): # test its computed clf.fit(Xs=Xs, ys=ys, Xt=Xt) + assert hasattr(clf, "cost_") + assert hasattr(clf, "coupling_") + assert hasattr(clf, "log_") # test dimensions of coupling assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) @@ -137,6 +142,11 @@ def test_sinkhorn_l1l2_transport_class(): assert n_unsup != n_semisup, "semisupervised mode not working" + # check everything runs well with log=True + clf = ot.da.SinkhornL1l2Transport(log=True) + clf.fit(Xs=Xs, ys=ys, Xt=Xt) + assert len(clf.log_.keys()) != 0 + def test_sinkhorn_transport_class(): """test_sinkhorn_transport @@ -152,6 +162,9 @@ def test_sinkhorn_transport_class(): # test its computed clf.fit(Xs=Xs, Xt=Xt) + assert hasattr(clf, "cost_") + assert hasattr(clf, "coupling_") + assert hasattr(clf, "log_") # test dimensions of coupling assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) @@ -200,6 +213,11 @@ def test_sinkhorn_transport_class(): assert n_unsup != n_semisup, "semisupervised mode not working" + # check everything runs well with log=True + clf = ot.da.SinkhornTransport(log=True) + clf.fit(Xs=Xs, ys=ys, Xt=Xt) + assert len(clf.log_.keys()) != 0 + def test_emd_transport_class(): """test_sinkhorn_transport @@ -215,6 +233,8 @@ def test_emd_transport_class(): # test its computed clf.fit(Xs=Xs, Xt=Xt) + assert hasattr(clf, "cost_") + assert hasattr(clf, "coupling_") # test dimensions of coupling assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) @@ -282,6 +302,9 @@ def test_mapping_transport_class(): # check computation and dimensions if bias == False clf = ot.da.MappingTransport(kernel="linear", bias=False) clf.fit(Xs=Xs, Xt=Xt) + assert hasattr(clf, "coupling_") + assert hasattr(clf, "mapping_") + assert hasattr(clf, "log_") assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) assert_equal(clf.mapping_.shape, ((Xs.shape[1], Xt.shape[1]))) @@ -369,6 +392,11 @@ def test_mapping_transport_class(): # check that the oos method is working assert_equal(transp_Xs_new.shape, Xs_new.shape) + # check everything runs well with log=True + clf = ot.da.MappingTransport(kernel="gaussian", log=True) + clf.fit(Xs=Xs, Xt=Xt) + assert len(clf.log_.keys()) != 0 + def test_otda(): @@ -434,9 +462,8 @@ def test_otda(): # if __name__ == "__main__": - # test_otda() - # test_sinkhorn_transport_class() - # test_emd_transport_class() - # test_sinkhorn_l1l2_transport_class() - # test_sinkhorn_lpl1_transport_class() - # test_mapping_transport_class() +# test_sinkhorn_transport_class() +# test_emd_transport_class() +# test_sinkhorn_l1l2_transport_class() +# test_sinkhorn_lpl1_transport_class() +# test_mapping_transport_class() -- cgit v1.2.3 From 181fcd3275e378668b4bb35e3584c5b245fbe896 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 25 Aug 2017 15:12:20 +0200 Subject: refactoring examples according to new DA classes --- examples/da/plot_otda_classes.py | 142 +++++++++++++++++++++ examples/da/plot_otda_color_images.py | 151 ++++++++++++++++++++++ examples/da/plot_otda_d2.py | 163 ++++++++++++++++++++++++ examples/da/plot_otda_mapping.py | 119 +++++++++++++++++ examples/da/plot_otda_mapping_colors_images.py | 169 +++++++++++++++++++++++++ 5 files changed, 744 insertions(+) create mode 100644 examples/da/plot_otda_classes.py create mode 100644 examples/da/plot_otda_color_images.py create mode 100644 examples/da/plot_otda_d2.py create mode 100644 examples/da/plot_otda_mapping.py create mode 100644 examples/da/plot_otda_mapping_colors_images.py diff --git a/examples/da/plot_otda_classes.py b/examples/da/plot_otda_classes.py new file mode 100644 index 0000000..1bfe2bb --- /dev/null +++ b/examples/da/plot_otda_classes.py @@ -0,0 +1,142 @@ +# -*- coding: utf-8 -*- +""" +======================== +OT for domain adaptation +======================== + +This example introduces a domain adaptation in a 2D setting and the 4 OTDA +approaches currently supported in POT. + +""" + +# Authors: Remi Flamary +# Stanilslas Chambon +# +# License: MIT License + +import matplotlib.pylab as pl +import ot + + +# number of source and target points to generate +ns = 150 +nt = 150 + +Xs, ys = ot.datasets.get_data_classif('3gauss', ns) +Xt, yt = ot.datasets.get_data_classif('3gauss2', nt) + +# Instantiate the different transport algorithms and fit them + +# EMD Transport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) + +# Sinkhorn Transport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + +# Sinkhorn Transport with Group lasso regularization +ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0) +ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt) + +# Sinkhorn Transport with Group lasso regularization l1l2 +ot_l1l2 = ot.da.SinkhornL1l2Transport(reg_e=1e-1, reg_cl=2e0, max_iter=20, + verbose=True) +ot_l1l2.fit(Xs=Xs, ys=ys, Xt=Xt) + +# transport source samples onto target samples +transp_Xs_emd = ot_emd.transform(Xs=Xs) +transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) +transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs) +transp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs) + +############################################################################## +# Fig 1 : plots source and target samples +############################################################################## + +pl.figure(1, figsize=(10, 5)) +pl.subplot(1, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Source samples') + +pl.subplot(1, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Target samples') +pl.tight_layout() + +############################################################################## +# Fig 2 : plot optimal couplings and transported samples +############################################################################## + +param_img = {'interpolation': 'nearest', 'cmap': 'spectral'} + +pl.figure(2, figsize=(15, 8)) +pl.subplot(2, 4, 1) +pl.imshow(ot_emd.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nEMDTransport') + +pl.subplot(2, 4, 2) +pl.imshow(ot_sinkhorn.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornTransport') + +pl.subplot(2, 4, 3) +pl.imshow(ot_lpl1.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornLpl1Transport') + +pl.subplot(2, 4, 4) +pl.imshow(ot_l1l2.coupling_, **param_img) +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornL1l2Transport') + +pl.subplot(2, 4, 5) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nEmdTransport') +pl.legend(loc="lower left") + +pl.subplot(2, 4, 6) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nSinkhornTransport') + +pl.subplot(2, 4, 7) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nSinkhornLpl1Transport') + +pl.subplot(2, 4, 8) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.3) +pl.scatter(transp_Xs_l1l2[:, 0], transp_Xs_l1l2[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.xticks([]) +pl.yticks([]) +pl.title('Transported samples\nSinkhornL1l2Transport') +pl.tight_layout() + +pl.show() diff --git a/examples/da/plot_otda_color_images.py b/examples/da/plot_otda_color_images.py new file mode 100644 index 0000000..a46ac29 --- /dev/null +++ b/examples/da/plot_otda_color_images.py @@ -0,0 +1,151 @@ +# -*- coding: utf-8 -*- +""" +======================================================== +OT for domain adaptation with image color adaptation [6] +======================================================== + +This example presents a way of transferring colors between two image +with Optimal Transport as introduced in [6] + +[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). +Regularized discrete optimal transport. +SIAM Journal on Imaging Sciences, 7(3), 1853-1882. +""" + +# Authors: Remi Flamary +# Stanilslas Chambon +# +# License: MIT License + +import numpy as np +from scipy import ndimage +import matplotlib.pylab as pl + +import ot + + +def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + + +def mat2im(X, shape): + """Converts back a matrix to an image""" + return X.reshape(shape) + + +def minmax(I): + return np.clip(I, 0, 1) + + +# Loading images +I1 = ndimage.imread('../../data/ocean_day.jpg').astype(np.float64) / 256 +I2 = ndimage.imread('../../data/ocean_sunset.jpg').astype(np.float64) / 256 + +X1 = im2mat(I1) +X2 = im2mat(I2) + +# training samples +nb = 1000 +idx1 = np.random.randint(X1.shape[0], size=(nb,)) +idx2 = np.random.randint(X2.shape[0], size=(nb,)) + +Xs = X1[idx1, :] +Xt = X2[idx2, :] + +# EMDTransport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) + +# SinkhornTransport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + +# prediction between images (using out of sample prediction as in [6]) +transp_Xs_emd = ot_emd.transform(Xs=X1) +transp_Xt_emd = ot_emd.inverse_transform(Xt=X2) + +transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) +transp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2) + +I1t = minmax(mat2im(transp_Xs_emd, I1.shape)) +I2t = minmax(mat2im(transp_Xt_emd, I2.shape)) + +I1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) +I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape)) + +############################################################################## +# plot original image +############################################################################## + +pl.figure(1, figsize=(6.4, 3)) + +pl.subplot(1, 2, 1) +pl.imshow(I1) +pl.axis('off') +pl.title('Image 1') + +pl.subplot(1, 2, 2) +pl.imshow(I2) +pl.axis('off') +pl.title('Image 2') + +############################################################################## +# scatter plot of colors +############################################################################## + +pl.figure(2, figsize=(6.4, 3)) + +pl.subplot(1, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs) +pl.axis([0, 1, 0, 1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 1') + +pl.subplot(1, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt) +pl.axis([0, 1, 0, 1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 2') +pl.tight_layout() + +############################################################################## +# plot new images +############################################################################## + +pl.figure(3, figsize=(8, 4)) + +pl.subplot(2, 3, 1) +pl.imshow(I1) +pl.axis('off') +pl.title('Image 1') + +pl.subplot(2, 3, 2) +pl.imshow(I1t) +pl.axis('off') +pl.title('Image 1 Adapt') + +pl.subplot(2, 3, 3) +pl.imshow(I1te) +pl.axis('off') +pl.title('Image 1 Adapt (reg)') + +pl.subplot(2, 3, 4) +pl.imshow(I2) +pl.axis('off') +pl.title('Image 2') + +pl.subplot(2, 3, 5) +pl.imshow(I2t) +pl.axis('off') +pl.title('Image 2 Adapt') + +pl.subplot(2, 3, 6) +pl.imshow(I2te) +pl.axis('off') +pl.title('Image 2 Adapt (reg)') +pl.tight_layout() + +pl.show() diff --git a/examples/da/plot_otda_d2.py b/examples/da/plot_otda_d2.py new file mode 100644 index 0000000..78c0372 --- /dev/null +++ b/examples/da/plot_otda_d2.py @@ -0,0 +1,163 @@ +# -*- coding: utf-8 -*- +""" +============================== +OT for empirical distributions +============================== + +This example introduces a domain adaptation in a 2D setting. It explicits +the problem of domain adaptation and introduces some optimal transport +approaches to solve it. + +Quantities such as optimal couplings, greater coupling coefficients and +transported samples are represented in order to give a visual understanding +of what the transport methods are doing. +""" + +# Authors: Remi Flamary +# Stanilslas Chambon +# +# License: MIT License + +import matplotlib.pylab as pl +import ot + +# number of source and target points to generate +ns = 150 +nt = 150 + +Xs, ys = ot.datasets.get_data_classif('3gauss', ns) +Xt, yt = ot.datasets.get_data_classif('3gauss2', nt) + +# Cost matrix +M = ot.dist(Xs, Xt) + +# Instantiate the different transport algorithms and fit them + +# EMD Transport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) + +# Sinkhorn Transport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + +# Sinkhorn Transport with Group lasso regularization +ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0) +ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt) + +# transport source samples onto target samples +transp_Xs_emd = ot_emd.transform(Xs=Xs) +transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) +transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs) + +############################################################################## +# Fig 1 : plots source and target samples + matrix of pairwise distance +############################################################################## + +pl.figure(1, figsize=(10, 10)) +pl.subplot(2, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Source samples') + +pl.subplot(2, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Target samples') + +pl.subplot(2, 2, 3) +pl.imshow(M, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Matrix of pairwise distances') +pl.tight_layout() + +############################################################################## +# Fig 2 : plots optimal couplings for the different methods +############################################################################## + +pl.figure(2, figsize=(10, 6)) + +pl.subplot(2, 3, 1) +pl.imshow(ot_emd.coupling_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nEMDTransport') + +pl.subplot(2, 3, 2) +pl.imshow(ot_sinkhorn.coupling_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornTransport') + +pl.subplot(2, 3, 3) +pl.imshow(ot_lpl1.coupling_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSinkhornLpl1Transport') + +pl.subplot(2, 3, 4) +ot.plot.plot2D_samples_mat(Xs, Xt, ot_emd.coupling_, c=[.5, .5, 1]) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.title('Main coupling coefficients\nEMDTransport') + +pl.subplot(2, 3, 5) +ot.plot.plot2D_samples_mat(Xs, Xt, ot_sinkhorn.coupling_, c=[.5, .5, 1]) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.title('Main coupling coefficients\nSinkhornTransport') + +pl.subplot(2, 3, 6) +ot.plot.plot2D_samples_mat(Xs, Xt, ot_lpl1.coupling_, c=[.5, .5, 1]) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.title('Main coupling coefficients\nSinkhornLpl1Transport') +pl.tight_layout() + +############################################################################## +# Fig 3 : plot transported samples +############################################################################## + +# display transported samples +pl.figure(4, figsize=(10, 4)) +pl.subplot(1, 3, 1) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Transported samples\nEmdTransport') +pl.legend(loc=0) +pl.xticks([]) +pl.yticks([]) + +pl.subplot(1, 3, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Transported samples\nSinkhornTransport') +pl.xticks([]) +pl.yticks([]) + +pl.subplot(1, 3, 3) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Transported samples\nSinkhornLpl1Transport') +pl.xticks([]) +pl.yticks([]) + +pl.tight_layout() +pl.show() diff --git a/examples/da/plot_otda_mapping.py b/examples/da/plot_otda_mapping.py new file mode 100644 index 0000000..ed234f5 --- /dev/null +++ b/examples/da/plot_otda_mapping.py @@ -0,0 +1,119 @@ +# -*- coding: utf-8 -*- +""" +=============================================== +OT mapping estimation for domain adaptation [8] +=============================================== + +This example presents how to use MappingTransport to estimate at the same +time both the coupling transport and approximate the transport map with either +a linear or a kernelized mapping as introduced in [8] + +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, + "Mapping estimation for discrete optimal transport", + Neural Information Processing Systems (NIPS), 2016. +""" + +# Authors: Remi Flamary +# Stanilslas Chambon +# +# License: MIT License + +import numpy as np +import matplotlib.pylab as pl +import ot + + +np.random.seed(0) + +############################################################################## +# generate +############################################################################## + +n = 100 # nb samples in source and target datasets +theta = 2 * np.pi / 20 +nz = 0.1 +Xs, ys = ot.datasets.get_data_classif('gaussrot', n, nz=nz) +Xs_new, _ = ot.datasets.get_data_classif('gaussrot', n, nz=nz) +Xt, yt = ot.datasets.get_data_classif('gaussrot', n, theta=theta, nz=nz) + +# one of the target mode changes its variance (no linear mapping) +Xt[yt == 2] *= 3 +Xt = Xt + 4 + + +# MappingTransport with linear kernel +ot_mapping_linear = ot.da.MappingTransport( + kernel="linear", mu=1e0, eta=1e-8, bias=True, + max_iter=20, verbose=True) + +ot_mapping_linear.fit( + Xs=Xs, Xt=Xt) + +# for original source samples, transform applies barycentric mapping +transp_Xs_linear = ot_mapping_linear.transform(Xs=Xs) + +# for out of source samples, transform applies the linear mapping +transp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new) + + +# MappingTransport with gaussian kernel +ot_mapping_gaussian = ot.da.MappingTransport( + kernel="gaussian", eta=1e-5, mu=1e-1, bias=True, sigma=1, + max_iter=10, verbose=True) +ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt) + +# for original source samples, transform applies barycentric mapping +transp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs) + +# for out of source samples, transform applies the gaussian mapping +transp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new) + + +############################################################################## +# plot data +############################################################################## + +pl.figure(1, (10, 5)) +pl.clf() +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.legend(loc=0) +pl.title('Source and target distributions') + +############################################################################## +# plot transported samples +############################################################################## + +pl.figure(2) +pl.clf() +pl.subplot(2, 2, 1) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) +pl.scatter(transp_Xs_linear[:, 0], transp_Xs_linear[:, 1], c=ys, marker='+', + label='Mapped source samples') +pl.title("Bary. mapping (linear)") +pl.legend(loc=0) + +pl.subplot(2, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) +pl.scatter(transp_Xs_linear_new[:, 0], transp_Xs_linear_new[:, 1], + c=ys, marker='+', label='Learned mapping') +pl.title("Estim. mapping (linear)") + +pl.subplot(2, 2, 3) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) +pl.scatter(transp_Xs_gaussian[:, 0], transp_Xs_gaussian[:, 1], c=ys, + marker='+', label='barycentric mapping') +pl.title("Bary. mapping (kernel)") + +pl.subplot(2, 2, 4) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=.2) +pl.scatter(transp_Xs_gaussian_new[:, 0], transp_Xs_gaussian_new[:, 1], c=ys, + marker='+', label='Learned mapping') +pl.title("Estim. mapping (kernel)") +pl.tight_layout() + +pl.show() diff --git a/examples/da/plot_otda_mapping_colors_images.py b/examples/da/plot_otda_mapping_colors_images.py new file mode 100644 index 0000000..56b5a6f --- /dev/null +++ b/examples/da/plot_otda_mapping_colors_images.py @@ -0,0 +1,169 @@ +# -*- coding: utf-8 -*- +""" +==================================================================================== +OT for domain adaptation with image color adaptation [6] with mapping estimation [8] +==================================================================================== + +[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized + discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), + 1853-1882. +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for + discrete optimal transport", Neural Information Processing Systems (NIPS), + 2016. + +""" + +# Authors: Remi Flamary +# Stanilslas Chambon +# +# License: MIT License + +import numpy as np +from scipy import ndimage +import matplotlib.pylab as pl +import ot + + +def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + + +def mat2im(X, shape): + """Converts back a matrix to an image""" + return X.reshape(shape) + + +def minmax(I): + return np.clip(I, 0, 1) + + +############################################################################## +# Generate data +############################################################################## + +# Loading images +# I1 = ndimage.imread('../../data/ocean_day.jpg').astype(np.float64) / 256 +# I2 = ndimage.imread('../../data/ocean_sunset.jpg').astype(np.float64) / 256 + +I1 = ndimage.imread('data/ocean_day.jpg').astype(np.float64) / 256 +I2 = ndimage.imread('data/ocean_sunset.jpg').astype(np.float64) / 256 + + +X1 = im2mat(I1) +X2 = im2mat(I2) + +# training samples +nb = 1000 +idx1 = np.random.randint(X1.shape[0], size=(nb,)) +idx2 = np.random.randint(X2.shape[0], size=(nb,)) + +Xs = X1[idx1, :] +Xt = X2[idx2, :] + + +############################################################################## +# Domain adaptation for pixel distribution transfer +############################################################################## + +# EMDTransport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) +transp_Xs_emd = ot_emd.transform(Xs=X1) +Image_emd = minmax(mat2im(transp_Xs_emd, I1.shape)) + +# SinkhornTransport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) +transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) +Image_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) + +ot_mapping_linear = ot.da.MappingTransport( + mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True) +ot_mapping_linear.fit(Xs=Xs, Xt=Xt) + +X1tl = ot_mapping_linear.transform(X1) +Image_mapping_linear = minmax(mat2im(X1tl, I1.shape)) + +ot_mapping_gaussian = ot.da.MappingTransport( + mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True) +ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt) + +X1tn = ot_mapping_gaussian.transform(X1) # use the estimated mapping +Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape)) + +############################################################################## +# plot original images +############################################################################## + +pl.figure(1, figsize=(6.4, 3)) +pl.subplot(1, 2, 1) +pl.imshow(I1) +pl.axis('off') +pl.title('Image 1') + +pl.subplot(1, 2, 2) +pl.imshow(I2) +pl.axis('off') +pl.title('Image 2') +pl.tight_layout() + +############################################################################## +# plot pixel values distribution +############################################################################## + +pl.figure(2, figsize=(6.4, 5)) + +pl.subplot(1, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs) +pl.axis([0, 1, 0, 1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 1') + +pl.subplot(1, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt) +pl.axis([0, 1, 0, 1]) +pl.xlabel('Red') +pl.ylabel('Blue') +pl.title('Image 2') +pl.tight_layout() + +############################################################################## +# plot transformed images +############################################################################## + +pl.figure(2, figsize=(10, 5)) + +pl.subplot(2, 3, 1) +pl.imshow(I1) +pl.axis('off') +pl.title('Im. 1') + +pl.subplot(2, 3, 4) +pl.imshow(I2) +pl.axis('off') +pl.title('Im. 2') + +pl.subplot(2, 3, 2) +pl.imshow(Image_emd) +pl.axis('off') +pl.title('EmdTransport') + +pl.subplot(2, 3, 5) +pl.imshow(Image_sinkhorn) +pl.axis('off') +pl.title('SinkhornTransport') + +pl.subplot(2, 3, 3) +pl.imshow(Image_mapping_linear) +pl.axis('off') +pl.title('MappingTransport (linear)') + +pl.subplot(2, 3, 6) +pl.imshow(Image_mapping_gaussian) +pl.axis('off') +pl.title('MappingTransport (gaussian)') +pl.tight_layout() + +pl.show() -- cgit v1.2.3 From e1a3984b63ce429b82e71dfb685d018788737068 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 25 Aug 2017 15:15:52 +0200 Subject: small corrections for examples --- examples/da/plot_otda_classes.py | 2 +- examples/da/plot_otda_color_images.py | 2 +- examples/da/plot_otda_d2.py | 2 +- examples/da/plot_otda_mapping.py | 2 +- examples/da/plot_otda_mapping_colors_images.py | 6 +++--- 5 files changed, 7 insertions(+), 7 deletions(-) diff --git a/examples/da/plot_otda_classes.py b/examples/da/plot_otda_classes.py index 1bfe2bb..e5c82fb 100644 --- a/examples/da/plot_otda_classes.py +++ b/examples/da/plot_otda_classes.py @@ -10,7 +10,7 @@ approaches currently supported in POT. """ # Authors: Remi Flamary -# Stanilslas Chambon +# Stanislas Chambon # # License: MIT License diff --git a/examples/da/plot_otda_color_images.py b/examples/da/plot_otda_color_images.py index a46ac29..bca7350 100644 --- a/examples/da/plot_otda_color_images.py +++ b/examples/da/plot_otda_color_images.py @@ -13,7 +13,7 @@ SIAM Journal on Imaging Sciences, 7(3), 1853-1882. """ # Authors: Remi Flamary -# Stanilslas Chambon +# Stanislas Chambon # # License: MIT License diff --git a/examples/da/plot_otda_d2.py b/examples/da/plot_otda_d2.py index 78c0372..1d2192f 100644 --- a/examples/da/plot_otda_d2.py +++ b/examples/da/plot_otda_d2.py @@ -14,7 +14,7 @@ of what the transport methods are doing. """ # Authors: Remi Flamary -# Stanilslas Chambon +# Stanislas Chambon # # License: MIT License diff --git a/examples/da/plot_otda_mapping.py b/examples/da/plot_otda_mapping.py index ed234f5..6d83507 100644 --- a/examples/da/plot_otda_mapping.py +++ b/examples/da/plot_otda_mapping.py @@ -14,7 +14,7 @@ a linear or a kernelized mapping as introduced in [8] """ # Authors: Remi Flamary -# Stanilslas Chambon +# Stanislas Chambon # # License: MIT License diff --git a/examples/da/plot_otda_mapping_colors_images.py b/examples/da/plot_otda_mapping_colors_images.py index 56b5a6f..05d9046 100644 --- a/examples/da/plot_otda_mapping_colors_images.py +++ b/examples/da/plot_otda_mapping_colors_images.py @@ -14,7 +14,7 @@ OT for domain adaptation with image color adaptation [6] with mapping estimation """ # Authors: Remi Flamary -# Stanilslas Chambon +# Stanislas Chambon # # License: MIT License @@ -82,14 +82,14 @@ ot_mapping_linear = ot.da.MappingTransport( mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True) ot_mapping_linear.fit(Xs=Xs, Xt=Xt) -X1tl = ot_mapping_linear.transform(X1) +X1tl = ot_mapping_linear.transform(Xs=X1) Image_mapping_linear = minmax(mat2im(X1tl, I1.shape)) ot_mapping_gaussian = ot.da.MappingTransport( mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True) ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt) -X1tn = ot_mapping_gaussian.transform(X1) # use the estimated mapping +X1tn = ot_mapping_gaussian.transform(Xs=X1) # use the estimated mapping Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape)) ############################################################################## -- cgit v1.2.3 From 4f802cf3de02fd3ff64fd9a8fcc5c7f1daf3cbea Mon Sep 17 00:00:00 2001 From: Slasnista Date: Fri, 25 Aug 2017 15:18:05 +0200 Subject: set properly path of data --- examples/da/plot_otda_mapping_colors_images.py | 7 ++----- 1 file changed, 2 insertions(+), 5 deletions(-) diff --git a/examples/da/plot_otda_mapping_colors_images.py b/examples/da/plot_otda_mapping_colors_images.py index 05d9046..4209020 100644 --- a/examples/da/plot_otda_mapping_colors_images.py +++ b/examples/da/plot_otda_mapping_colors_images.py @@ -43,11 +43,8 @@ def minmax(I): ############################################################################## # Loading images -# I1 = ndimage.imread('../../data/ocean_day.jpg').astype(np.float64) / 256 -# I2 = ndimage.imread('../../data/ocean_sunset.jpg').astype(np.float64) / 256 - -I1 = ndimage.imread('data/ocean_day.jpg').astype(np.float64) / 256 -I2 = ndimage.imread('data/ocean_sunset.jpg').astype(np.float64) / 256 +I1 = ndimage.imread('../../data/ocean_day.jpg').astype(np.float64) / 256 +I2 = ndimage.imread('../../data/ocean_sunset.jpg').astype(np.float64) / 256 X1 = im2mat(I1) -- cgit v1.2.3 From e1606c1025c0acc4608254d72af6c127ebe1c03f Mon Sep 17 00:00:00 2001 From: Slasnista Date: Mon, 28 Aug 2017 10:43:31 +0200 Subject: move no da objects into utils.py --- README.md | 2 + ot/da.py | 136 +-------------------------------- ot/deprecation.py | 103 ------------------------- ot/utils.py | 219 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 222 insertions(+), 238 deletions(-) delete mode 100644 ot/deprecation.py diff --git a/README.md b/README.md index 7a65106..27b4643 100644 --- a/README.md +++ b/README.md @@ -136,6 +136,8 @@ The contributors to this library are: * [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/) * [Michael Perrot](http://perso.univ-st-etienne.fr/pem82055/) (Mapping estimation) * [Léo Gautheron](https://github.com/aje) (GPU implementation) +* [Nathalie Gayraud]() +* [Stanislas Chambon](https://slasnista.github.io/) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): diff --git a/ot/da.py b/ot/da.py index 5a34979..8c62669 100644 --- a/ot/da.py +++ b/ot/da.py @@ -10,14 +10,13 @@ Domain adaptation with optimal transport # License: MIT License import numpy as np -import warnings from .bregman import sinkhorn from .lp import emd from .utils import unif, dist, kernel +from .utils import deprecated, BaseEstimator from .optim import cg from .optim import gcg -from .deprecation import deprecated def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, @@ -936,139 +935,6 @@ class OTDA_mapping_kernel(OTDA_mapping_linear): print("Warning, model not fitted yet, returning None") return None -############################################################################## -# proposal -############################################################################## - - -# adapted from sklearn - -class BaseEstimator(object): - """Base class for all estimators in scikit-learn - Notes - ----- - All estimators should specify all the parameters that can be set - at the class level in their ``__init__`` as explicit keyword - arguments (no ``*args`` or ``**kwargs``). - """ - - @classmethod - def _get_param_names(cls): - """Get parameter names for the estimator""" - try: - from inspect import signature - except ImportError: - from .externals.funcsigs import signature - # fetch the constructor or the original constructor before - # deprecation wrapping if any - init = getattr(cls.__init__, 'deprecated_original', cls.__init__) - if init is object.__init__: - # No explicit constructor to introspect - return [] - - # introspect the constructor arguments to find the model parameters - # to represent - init_signature = signature(init) - # Consider the constructor parameters excluding 'self' - parameters = [p for p in init_signature.parameters.values() - if p.name != 'self' and p.kind != p.VAR_KEYWORD] - for p in parameters: - if p.kind == p.VAR_POSITIONAL: - raise RuntimeError("scikit-learn estimators should always " - "specify their parameters in the signature" - " of their __init__ (no varargs)." - " %s with constructor %s doesn't " - " follow this convention." - % (cls, init_signature)) - # Extract and sort argument names excluding 'self' - return sorted([p.name for p in parameters]) - - def get_params(self, deep=True): - """Get parameters for this estimator. - - Parameters - ---------- - deep : boolean, optional - If True, will return the parameters for this estimator and - contained subobjects that are estimators. - - Returns - ------- - params : mapping of string to any - Parameter names mapped to their values. - """ - out = dict() - for key in self._get_param_names(): - # We need deprecation warnings to always be on in order to - # catch deprecated param values. - # This is set in utils/__init__.py but it gets overwritten - # when running under python3 somehow. - warnings.simplefilter("always", DeprecationWarning) - try: - with warnings.catch_warnings(record=True) as w: - value = getattr(self, key, None) - if len(w) and w[0].category == DeprecationWarning: - # if the parameter is deprecated, don't show it - continue - finally: - warnings.filters.pop(0) - - # XXX: should we rather test if instance of estimator? - if deep and hasattr(value, 'get_params'): - deep_items = value.get_params().items() - out.update((key + '__' + k, val) for k, val in deep_items) - out[key] = value - return out - - def set_params(self, **params): - """Set the parameters of this estimator. - - The method works on simple estimators as well as on nested objects - (such as pipelines). The latter have parameters of the form - ``__`` so that it's possible to update each - component of a nested object. - - Returns - ------- - self - """ - if not params: - # Simple optimisation to gain speed (inspect is slow) - return self - valid_params = self.get_params(deep=True) - # for key, value in iteritems(params): - for key, value in params.items(): - split = key.split('__', 1) - if len(split) > 1: - # nested objects case - name, sub_name = split - if name not in valid_params: - raise ValueError('Invalid parameter %s for estimator %s. ' - 'Check the list of available parameters ' - 'with `estimator.get_params().keys()`.' % - (name, self)) - sub_object = valid_params[name] - sub_object.set_params(**{sub_name: value}) - else: - # simple objects case - if key not in valid_params: - raise ValueError('Invalid parameter %s for estimator %s. ' - 'Check the list of available parameters ' - 'with `estimator.get_params().keys()`.' % - (key, self.__class__.__name__)) - setattr(self, key, value) - return self - - def __repr__(self): - from sklearn.base import _pprint - class_name = self.__class__.__name__ - return '%s(%s)' % (class_name, _pprint(self.get_params(deep=False), - offset=len(class_name),),) - - # __getstate__ and __setstate__ are omitted because they only contain - # conditionals that are not satisfied by our objects (e.g., - # ``if type(self).__module__.startswith('sklearn.')``. - def distribution_estimation_uniform(X): """estimates a uniform distribution from an array of samples X diff --git a/ot/deprecation.py b/ot/deprecation.py deleted file mode 100644 index 2b16427..0000000 --- a/ot/deprecation.py +++ /dev/null @@ -1,103 +0,0 @@ -""" - deprecated class from scikit-learn package - https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/deprecation.py -""" - -import sys -import warnings - -__all__ = ["deprecated", ] - - -class deprecated(object): - """Decorator to mark a function or class as deprecated. - Issue a warning when the function is called/the class is instantiated and - adds a warning to the docstring. - The optional extra argument will be appended to the deprecation message - and the docstring. Note: to use this with the default value for extra, put - in an empty of parentheses: - >>> from ot.deprecation import deprecated - >>> @deprecated() - ... def some_function(): pass - - Parameters - ---------- - extra : string - to be added to the deprecation messages - """ - - # Adapted from http://wiki.python.org/moin/PythonDecoratorLibrary, - # but with many changes. - - def __init__(self, extra=''): - self.extra = extra - - def __call__(self, obj): - """Call method - Parameters - ---------- - obj : object - """ - if isinstance(obj, type): - return self._decorate_class(obj) - else: - return self._decorate_fun(obj) - - def _decorate_class(self, cls): - msg = "Class %s is deprecated" % cls.__name__ - if self.extra: - msg += "; %s" % self.extra - - # FIXME: we should probably reset __new__ for full generality - init = cls.__init__ - - def wrapped(*args, **kwargs): - warnings.warn(msg, category=DeprecationWarning) - return init(*args, **kwargs) - - cls.__init__ = wrapped - - wrapped.__name__ = '__init__' - wrapped.__doc__ = self._update_doc(init.__doc__) - wrapped.deprecated_original = init - - return cls - - def _decorate_fun(self, fun): - """Decorate function fun""" - - msg = "Function %s is deprecated" % fun.__name__ - if self.extra: - msg += "; %s" % self.extra - - def wrapped(*args, **kwargs): - warnings.warn(msg, category=DeprecationWarning) - return fun(*args, **kwargs) - - wrapped.__name__ = fun.__name__ - wrapped.__dict__ = fun.__dict__ - wrapped.__doc__ = self._update_doc(fun.__doc__) - - return wrapped - - def _update_doc(self, olddoc): - newdoc = "DEPRECATED" - if self.extra: - newdoc = "%s: %s" % (newdoc, self.extra) - if olddoc: - newdoc = "%s\n\n%s" % (newdoc, olddoc) - return newdoc - - -def _is_deprecated(func): - """Helper to check if func is wraped by our deprecated decorator""" - if sys.version_info < (3, 5): - raise NotImplementedError("This is only available for python3.5 " - "or above") - closures = getattr(func, '__closure__', []) - if closures is None: - closures = [] - is_deprecated = ('deprecated' in ''.join([c.cell_contents - for c in closures - if isinstance(c.cell_contents, str)])) - return is_deprecated diff --git a/ot/utils.py b/ot/utils.py index 2b2f8b3..29ad536 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -13,6 +13,9 @@ import time import numpy as np from scipy.spatial.distance import cdist +import sys +import warnings + __time_tic_toc = time.time() @@ -163,3 +166,219 @@ def parmap(f, X, nprocs=multiprocessing.cpu_count()): [p.join() for p in proc] return [x for i, x in sorted(res)] + + +class deprecated(object): + """Decorator to mark a function or class as deprecated. + + deprecated class from scikit-learn package + https://github.com/scikit-learn/scikit-learn/blob/master/sklearn/utils/deprecation.py + Issue a warning when the function is called/the class is instantiated and + adds a warning to the docstring. + The optional extra argument will be appended to the deprecation message + and the docstring. Note: to use this with the default value for extra, put + in an empty of parentheses: + >>> from ot.deprecation import deprecated + >>> @deprecated() + ... def some_function(): pass + + Parameters + ---------- + extra : string + to be added to the deprecation messages + """ + + # Adapted from http://wiki.python.org/moin/PythonDecoratorLibrary, + # but with many changes. + + def __init__(self, extra=''): + self.extra = extra + + def __call__(self, obj): + """Call method + Parameters + ---------- + obj : object + """ + if isinstance(obj, type): + return self._decorate_class(obj) + else: + return self._decorate_fun(obj) + + def _decorate_class(self, cls): + msg = "Class %s is deprecated" % cls.__name__ + if self.extra: + msg += "; %s" % self.extra + + # FIXME: we should probably reset __new__ for full generality + init = cls.__init__ + + def wrapped(*args, **kwargs): + warnings.warn(msg, category=DeprecationWarning) + return init(*args, **kwargs) + + cls.__init__ = wrapped + + wrapped.__name__ = '__init__' + wrapped.__doc__ = self._update_doc(init.__doc__) + wrapped.deprecated_original = init + + return cls + + def _decorate_fun(self, fun): + """Decorate function fun""" + + msg = "Function %s is deprecated" % fun.__name__ + if self.extra: + msg += "; %s" % self.extra + + def wrapped(*args, **kwargs): + warnings.warn(msg, category=DeprecationWarning) + return fun(*args, **kwargs) + + wrapped.__name__ = fun.__name__ + wrapped.__dict__ = fun.__dict__ + wrapped.__doc__ = self._update_doc(fun.__doc__) + + return wrapped + + def _update_doc(self, olddoc): + newdoc = "DEPRECATED" + if self.extra: + newdoc = "%s: %s" % (newdoc, self.extra) + if olddoc: + newdoc = "%s\n\n%s" % (newdoc, olddoc) + return newdoc + + +def _is_deprecated(func): + """Helper to check if func is wraped by our deprecated decorator""" + if sys.version_info < (3, 5): + raise NotImplementedError("This is only available for python3.5 " + "or above") + closures = getattr(func, '__closure__', []) + if closures is None: + closures = [] + is_deprecated = ('deprecated' in ''.join([c.cell_contents + for c in closures + if isinstance(c.cell_contents, str)])) + return is_deprecated + + +class BaseEstimator(object): + """Base class for most objects in POT + adapted from sklearn BaseEstimator class + + Notes + ----- + All estimators should specify all the parameters that can be set + at the class level in their ``__init__`` as explicit keyword + arguments (no ``*args`` or ``**kwargs``). + """ + + @classmethod + def _get_param_names(cls): + """Get parameter names for the estimator""" + try: + from inspect import signature + except ImportError: + from .externals.funcsigs import signature + # fetch the constructor or the original constructor before + # deprecation wrapping if any + init = getattr(cls.__init__, 'deprecated_original', cls.__init__) + if init is object.__init__: + # No explicit constructor to introspect + return [] + + # introspect the constructor arguments to find the model parameters + # to represent + init_signature = signature(init) + # Consider the constructor parameters excluding 'self' + parameters = [p for p in init_signature.parameters.values() + if p.name != 'self' and p.kind != p.VAR_KEYWORD] + for p in parameters: + if p.kind == p.VAR_POSITIONAL: + raise RuntimeError("POT estimators should always " + "specify their parameters in the signature" + " of their __init__ (no varargs)." + " %s with constructor %s doesn't " + " follow this convention." + % (cls, init_signature)) + # Extract and sort argument names excluding 'self' + return sorted([p.name for p in parameters]) + + def get_params(self, deep=True): + """Get parameters for this estimator. + + Parameters + ---------- + deep : boolean, optional + If True, will return the parameters for this estimator and + contained subobjects that are estimators. + + Returns + ------- + params : mapping of string to any + Parameter names mapped to their values. + """ + out = dict() + for key in self._get_param_names(): + # We need deprecation warnings to always be on in order to + # catch deprecated param values. + # This is set in utils/__init__.py but it gets overwritten + # when running under python3 somehow. + warnings.simplefilter("always", DeprecationWarning) + try: + with warnings.catch_warnings(record=True) as w: + value = getattr(self, key, None) + if len(w) and w[0].category == DeprecationWarning: + # if the parameter is deprecated, don't show it + continue + finally: + warnings.filters.pop(0) + + # XXX: should we rather test if instance of estimator? + if deep and hasattr(value, 'get_params'): + deep_items = value.get_params().items() + out.update((key + '__' + k, val) for k, val in deep_items) + out[key] = value + return out + + def set_params(self, **params): + """Set the parameters of this estimator. + + The method works on simple estimators as well as on nested objects + (such as pipelines). The latter have parameters of the form + ``__`` so that it's possible to update each + component of a nested object. + + Returns + ------- + self + """ + if not params: + # Simple optimisation to gain speed (inspect is slow) + return self + valid_params = self.get_params(deep=True) + # for key, value in iteritems(params): + for key, value in params.items(): + split = key.split('__', 1) + if len(split) > 1: + # nested objects case + name, sub_name = split + if name not in valid_params: + raise ValueError('Invalid parameter %s for estimator %s. ' + 'Check the list of available parameters ' + 'with `estimator.get_params().keys()`.' % + (name, self)) + sub_object = valid_params[name] + sub_object.set_params(**{sub_name: value}) + else: + # simple objects case + if key not in valid_params: + raise ValueError('Invalid parameter %s for estimator %s. ' + 'Check the list of available parameters ' + 'with `estimator.get_params().keys()`.' % + (key, self.__class__.__name__)) + setattr(self, key, value) + return self -- cgit v1.2.3 From f79f4831e9d70217ceab2758733abe62dc93208b Mon Sep 17 00:00:00 2001 From: Slasnista Date: Mon, 28 Aug 2017 11:03:28 +0200 Subject: handling input arguments in fit, transform... methods + remove old examples --- examples/plot_OTDA_2D.py | 126 ----------------- examples/plot_OTDA_classes.py | 117 ---------------- examples/plot_OTDA_color_images.py | 152 -------------------- examples/plot_OTDA_mapping.py | 124 ----------------- examples/plot_OTDA_mapping_color_images.py | 169 ---------------------- ot/da.py | 217 ++++++++++++++++------------- 6 files changed, 121 insertions(+), 784 deletions(-) delete mode 100644 examples/plot_OTDA_2D.py delete mode 100644 examples/plot_OTDA_classes.py delete mode 100644 examples/plot_OTDA_color_images.py delete mode 100644 examples/plot_OTDA_mapping.py delete mode 100644 examples/plot_OTDA_mapping_color_images.py diff --git a/examples/plot_OTDA_2D.py b/examples/plot_OTDA_2D.py deleted file mode 100644 index f2108c6..0000000 --- a/examples/plot_OTDA_2D.py +++ /dev/null @@ -1,126 +0,0 @@ -# -*- coding: utf-8 -*- -""" -============================== -OT for empirical distributions -============================== - -""" - -# Author: Remi Flamary -# -# License: MIT License - -import numpy as np -import matplotlib.pylab as pl -import ot - - -#%% parameters - -n = 150 # nb bins - -xs, ys = ot.datasets.get_data_classif('3gauss', n) -xt, yt = ot.datasets.get_data_classif('3gauss2', n) - -a, b = ot.unif(n), ot.unif(n) -# loss matrix -M = ot.dist(xs, xt) -# M/=M.max() - -#%% plot samples - -pl.figure(1) -pl.subplot(2, 2, 1) -pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples') -pl.legend(loc=0) -pl.title('Source distributions') - -pl.subplot(2, 2, 2) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples') -pl.legend(loc=0) -pl.title('target distributions') - -pl.figure(2) -pl.imshow(M, interpolation='nearest') -pl.title('Cost matrix M') - - -#%% OT estimation - -# EMD -G0 = ot.emd(a, b, M) - -# sinkhorn -lambd = 1e-1 -Gs = ot.sinkhorn(a, b, M, lambd) - - -# Group lasso regularization -reg = 1e-1 -eta = 1e0 -Gg = ot.da.sinkhorn_lpl1_mm(a, ys.astype(np.int), b, M, reg, eta) - - -#%% visu matrices - -pl.figure(3) - -pl.subplot(2, 3, 1) -pl.imshow(G0, interpolation='nearest') -pl.title('OT matrix ') - -pl.subplot(2, 3, 2) -pl.imshow(Gs, interpolation='nearest') -pl.title('OT matrix Sinkhorn') - -pl.subplot(2, 3, 3) -pl.imshow(Gg, interpolation='nearest') -pl.title('OT matrix Group lasso') - -pl.subplot(2, 3, 4) -ot.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1]) -pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples') -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples') - - -pl.subplot(2, 3, 5) -ot.plot.plot2D_samples_mat(xs, xt, Gs, c=[.5, .5, 1]) -pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples') -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples') - -pl.subplot(2, 3, 6) -ot.plot.plot2D_samples_mat(xs, xt, Gg, c=[.5, .5, 1]) -pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples') -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples') -pl.tight_layout() - -#%% sample interpolation - -xst0 = n * G0.dot(xt) -xsts = n * Gs.dot(xt) -xstg = n * Gg.dot(xt) - -pl.figure(4, figsize=(8, 3)) -pl.subplot(1, 3, 1) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.5) -pl.scatter(xst0[:, 0], xst0[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Interp samples') -pl.legend(loc=0) - -pl.subplot(1, 3, 2) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.5) -pl.scatter(xsts[:, 0], xsts[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Interp samples Sinkhorn') - -pl.subplot(1, 3, 3) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.5) -pl.scatter(xstg[:, 0], xstg[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Interp samples Grouplasso') -pl.tight_layout() -pl.show() diff --git a/examples/plot_OTDA_classes.py b/examples/plot_OTDA_classes.py deleted file mode 100644 index 53e4bae..0000000 --- a/examples/plot_OTDA_classes.py +++ /dev/null @@ -1,117 +0,0 @@ -# -*- coding: utf-8 -*- -""" -======================== -OT for domain adaptation -======================== - -""" - -# Author: Remi Flamary -# -# License: MIT License - -import matplotlib.pylab as pl -import ot - - -#%% parameters - -n = 150 # nb samples in source and target datasets - -xs, ys = ot.datasets.get_data_classif('3gauss', n) -xt, yt = ot.datasets.get_data_classif('3gauss2', n) - - -#%% plot samples - -pl.figure(1, figsize=(6.4, 3)) - -pl.subplot(1, 2, 1) -pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples') -pl.legend(loc=0) -pl.title('Source distributions') - -pl.subplot(1, 2, 2) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples') -pl.legend(loc=0) -pl.title('target distributions') - - -#%% OT estimation - -# LP problem -da_emd = ot.da.OTDA() # init class -da_emd.fit(xs, xt) # fit distributions -xst0 = da_emd.interp() # interpolation of source samples - -# sinkhorn regularization -lambd = 1e-1 -da_entrop = ot.da.OTDA_sinkhorn() -da_entrop.fit(xs, xt, reg=lambd) -xsts = da_entrop.interp() - -# non-convex Group lasso regularization -reg = 1e-1 -eta = 1e0 -da_lpl1 = ot.da.OTDA_lpl1() -da_lpl1.fit(xs, ys, xt, reg=reg, eta=eta) -xstg = da_lpl1.interp() - -# True Group lasso regularization -reg = 1e-1 -eta = 2e0 -da_l1l2 = ot.da.OTDA_l1l2() -da_l1l2.fit(xs, ys, xt, reg=reg, eta=eta, numItermax=20, verbose=True) -xstgl = da_l1l2.interp() - -#%% plot interpolated source samples - -param_img = {'interpolation': 'nearest', 'cmap': 'spectral'} - -pl.figure(2, figsize=(8, 4.5)) -pl.subplot(2, 4, 1) -pl.imshow(da_emd.G, **param_img) -pl.title('OT matrix') - -pl.subplot(2, 4, 2) -pl.imshow(da_entrop.G, **param_img) -pl.title('OT matrix\nsinkhorn') - -pl.subplot(2, 4, 3) -pl.imshow(da_lpl1.G, **param_img) -pl.title('OT matrix\nnon-convex Group Lasso') - -pl.subplot(2, 4, 4) -pl.imshow(da_l1l2.G, **param_img) -pl.title('OT matrix\nGroup Lasso') - -pl.subplot(2, 4, 5) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(xst0[:, 0], xst0[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Interp samples') -pl.legend(loc=0) - -pl.subplot(2, 4, 6) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(xsts[:, 0], xsts[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Interp samples\nSinkhorn') - -pl.subplot(2, 4, 7) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(xstg[:, 0], xstg[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Interp samples\nnon-convex Group Lasso') - -pl.subplot(2, 4, 8) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.3) -pl.scatter(xstgl[:, 0], xstgl[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Interp samples\nGroup Lasso') -pl.tight_layout() -pl.show() diff --git a/examples/plot_OTDA_color_images.py b/examples/plot_OTDA_color_images.py deleted file mode 100644 index c5ff873..0000000 --- a/examples/plot_OTDA_color_images.py +++ /dev/null @@ -1,152 +0,0 @@ -# -*- coding: utf-8 -*- -""" -======================================================== -OT for domain adaptation with image color adaptation [6] -======================================================== - -[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). -Regularized discrete optimal transport. -SIAM Journal on Imaging Sciences, 7(3), 1853-1882. -""" - -# Author: Remi Flamary -# -# License: MIT License - -import numpy as np -from scipy import ndimage -import matplotlib.pylab as pl -import ot - - -#%% Loading images - -I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256 -I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 - -#%% Plot images - -pl.figure(1, figsize=(6.4, 3)) - -pl.subplot(1, 2, 1) -pl.imshow(I1) -pl.axis('off') -pl.title('Image 1') - -pl.subplot(1, 2, 2) -pl.imshow(I2) -pl.axis('off') -pl.title('Image 2') - -pl.show() - -#%% Image conversion and dataset generation - - -def im2mat(I): - """Converts and image to matrix (one pixel per line)""" - return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) - - -def mat2im(X, shape): - """Converts back a matrix to an image""" - return X.reshape(shape) - - -X1 = im2mat(I1) -X2 = im2mat(I2) - -# training samples -nb = 1000 -idx1 = np.random.randint(X1.shape[0], size=(nb,)) -idx2 = np.random.randint(X2.shape[0], size=(nb,)) - -xs = X1[idx1, :] -xt = X2[idx2, :] - -#%% Plot image distributions - - -pl.figure(2, figsize=(6.4, 3)) - -pl.subplot(1, 2, 1) -pl.scatter(xs[:, 0], xs[:, 2], c=xs) -pl.axis([0, 1, 0, 1]) -pl.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 1') - -pl.subplot(1, 2, 2) -pl.scatter(xt[:, 0], xt[:, 2], c=xt) -pl.axis([0, 1, 0, 1]) -pl.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 2') -pl.tight_layout() - -#%% domain adaptation between images - -# LP problem -da_emd = ot.da.OTDA() # init class -da_emd.fit(xs, xt) # fit distributions - -# sinkhorn regularization -lambd = 1e-1 -da_entrop = ot.da.OTDA_sinkhorn() -da_entrop.fit(xs, xt, reg=lambd) - -#%% prediction between images (using out of sample prediction as in [6]) - -X1t = da_emd.predict(X1) -X2t = da_emd.predict(X2, -1) - -X1te = da_entrop.predict(X1) -X2te = da_entrop.predict(X2, -1) - - -def minmax(I): - return np.clip(I, 0, 1) - - -I1t = minmax(mat2im(X1t, I1.shape)) -I2t = minmax(mat2im(X2t, I2.shape)) - -I1te = minmax(mat2im(X1te, I1.shape)) -I2te = minmax(mat2im(X2te, I2.shape)) - -#%% plot all images - -pl.figure(2, figsize=(8, 4)) - -pl.subplot(2, 3, 1) -pl.imshow(I1) -pl.axis('off') -pl.title('Image 1') - -pl.subplot(2, 3, 2) -pl.imshow(I1t) -pl.axis('off') -pl.title('Image 1 Adapt') - -pl.subplot(2, 3, 3) -pl.imshow(I1te) -pl.axis('off') -pl.title('Image 1 Adapt (reg)') - -pl.subplot(2, 3, 4) -pl.imshow(I2) -pl.axis('off') -pl.title('Image 2') - -pl.subplot(2, 3, 5) -pl.imshow(I2t) -pl.axis('off') -pl.title('Image 2 Adapt') - -pl.subplot(2, 3, 6) -pl.imshow(I2te) -pl.axis('off') -pl.title('Image 2 Adapt (reg)') -pl.tight_layout() - -pl.show() diff --git a/examples/plot_OTDA_mapping.py b/examples/plot_OTDA_mapping.py deleted file mode 100644 index a0d7f8b..0000000 --- a/examples/plot_OTDA_mapping.py +++ /dev/null @@ -1,124 +0,0 @@ -# -*- coding: utf-8 -*- -""" -=============================================== -OT mapping estimation for domain adaptation [8] -=============================================== - -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, - "Mapping estimation for discrete optimal transport", - Neural Information Processing Systems (NIPS), 2016. -""" - -# Author: Remi Flamary -# -# License: MIT License - -import numpy as np -import matplotlib.pylab as pl -import ot - - -#%% dataset generation - -np.random.seed(0) # makes example reproducible - -n = 100 # nb samples in source and target datasets -theta = 2 * np.pi / 20 -nz = 0.1 -xs, ys = ot.datasets.get_data_classif('gaussrot', n, nz=nz) -xt, yt = ot.datasets.get_data_classif('gaussrot', n, theta=theta, nz=nz) - -# one of the target mode changes its variance (no linear mapping) -xt[yt == 2] *= 3 -xt = xt + 4 - - -#%% plot samples - -pl.figure(1, (6.4, 3)) -pl.clf() -pl.scatter(xs[:, 0], xs[:, 1], c=ys, marker='+', label='Source samples') -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', label='Target samples') -pl.legend(loc=0) -pl.title('Source and target distributions') - - -#%% OT linear mapping estimation - -eta = 1e-8 # quadratic regularization for regression -mu = 1e0 # weight of the OT linear term -bias = True # estimate a bias - -ot_mapping = ot.da.OTDA_mapping_linear() -ot_mapping.fit(xs, xt, mu=mu, eta=eta, bias=bias, numItermax=20, verbose=True) - -xst = ot_mapping.predict(xs) # use the estimated mapping -xst0 = ot_mapping.interp() # use barycentric mapping - - -pl.figure(2) -pl.clf() -pl.subplot(2, 2, 1) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=.3) -pl.scatter(xst0[:, 0], xst0[:, 1], c=ys, - marker='+', label='barycentric mapping') -pl.title("barycentric mapping") - -pl.subplot(2, 2, 2) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=.3) -pl.scatter(xst[:, 0], xst[:, 1], c=ys, marker='+', label='Learned mapping') -pl.title("Learned mapping") -pl.tight_layout() - -#%% Kernel mapping estimation - -eta = 1e-5 # quadratic regularization for regression -mu = 1e-1 # weight of the OT linear term -bias = True # estimate a bias -sigma = 1 # sigma bandwidth fot gaussian kernel - - -ot_mapping_kernel = ot.da.OTDA_mapping_kernel() -ot_mapping_kernel.fit( - xs, xt, mu=mu, eta=eta, sigma=sigma, bias=bias, numItermax=10, verbose=True) - -xst_kernel = ot_mapping_kernel.predict(xs) # use the estimated mapping -xst0_kernel = ot_mapping_kernel.interp() # use barycentric mapping - - -#%% Plotting the mapped samples - -pl.figure(2) -pl.clf() -pl.subplot(2, 2, 1) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=.2) -pl.scatter(xst0[:, 0], xst0[:, 1], c=ys, marker='+', - label='Mapped source samples') -pl.title("Bary. mapping (linear)") -pl.legend(loc=0) - -pl.subplot(2, 2, 2) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=.2) -pl.scatter(xst[:, 0], xst[:, 1], c=ys, marker='+', label='Learned mapping') -pl.title("Estim. mapping (linear)") - -pl.subplot(2, 2, 3) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=.2) -pl.scatter(xst0_kernel[:, 0], xst0_kernel[:, 1], c=ys, - marker='+', label='barycentric mapping') -pl.title("Bary. mapping (kernel)") - -pl.subplot(2, 2, 4) -pl.scatter(xt[:, 0], xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=.2) -pl.scatter(xst_kernel[:, 0], xst_kernel[:, 1], c=ys, - marker='+', label='Learned mapping') -pl.title("Estim. mapping (kernel)") -pl.tight_layout() - -pl.show() diff --git a/examples/plot_OTDA_mapping_color_images.py b/examples/plot_OTDA_mapping_color_images.py deleted file mode 100644 index 8064b25..0000000 --- a/examples/plot_OTDA_mapping_color_images.py +++ /dev/null @@ -1,169 +0,0 @@ -# -*- coding: utf-8 -*- -""" -==================================================================================== -OT for domain adaptation with image color adaptation [6] with mapping estimation [8] -==================================================================================== - -[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized - discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for - discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. - -""" - -# Author: Remi Flamary -# -# License: MIT License - -import numpy as np -from scipy import ndimage -import matplotlib.pylab as pl -import ot - - -#%% Loading images - -I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256 -I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 - -#%% Plot images - -pl.figure(1, figsize=(6.4, 3)) -pl.subplot(1, 2, 1) -pl.imshow(I1) -pl.axis('off') -pl.title('Image 1') - -pl.subplot(1, 2, 2) -pl.imshow(I2) -pl.axis('off') -pl.title('Image 2') -pl.tight_layout() - - -#%% Image conversion and dataset generation - -def im2mat(I): - """Converts and image to matrix (one pixel per line)""" - return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) - - -def mat2im(X, shape): - """Converts back a matrix to an image""" - return X.reshape(shape) - - -X1 = im2mat(I1) -X2 = im2mat(I2) - -# training samples -nb = 1000 -idx1 = np.random.randint(X1.shape[0], size=(nb,)) -idx2 = np.random.randint(X2.shape[0], size=(nb,)) - -xs = X1[idx1, :] -xt = X2[idx2, :] - -#%% Plot image distributions - - -pl.figure(2, figsize=(6.4, 5)) - -pl.subplot(1, 2, 1) -pl.scatter(xs[:, 0], xs[:, 2], c=xs) -pl.axis([0, 1, 0, 1]) -pl.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 1') - -pl.subplot(1, 2, 2) -pl.scatter(xt[:, 0], xt[:, 2], c=xt) -pl.axis([0, 1, 0, 1]) -pl.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 2') -pl.tight_layout() - - -#%% domain adaptation between images - -def minmax(I): - return np.clip(I, 0, 1) - - -# LP problem -da_emd = ot.da.OTDA() # init class -da_emd.fit(xs, xt) # fit distributions - -X1t = da_emd.predict(X1) # out of sample -I1t = minmax(mat2im(X1t, I1.shape)) - -# sinkhorn regularization -lambd = 1e-1 -da_entrop = ot.da.OTDA_sinkhorn() -da_entrop.fit(xs, xt, reg=lambd) - -X1te = da_entrop.predict(X1) -I1te = minmax(mat2im(X1te, I1.shape)) - -# linear mapping estimation -eta = 1e-8 # quadratic regularization for regression -mu = 1e0 # weight of the OT linear term -bias = True # estimate a bias - -ot_mapping = ot.da.OTDA_mapping_linear() -ot_mapping.fit(xs, xt, mu=mu, eta=eta, bias=bias, numItermax=20, verbose=True) - -X1tl = ot_mapping.predict(X1) # use the estimated mapping -I1tl = minmax(mat2im(X1tl, I1.shape)) - -# nonlinear mapping estimation -eta = 1e-2 # quadratic regularization for regression -mu = 1e0 # weight of the OT linear term -bias = False # estimate a bias -sigma = 1 # sigma bandwidth fot gaussian kernel - - -ot_mapping_kernel = ot.da.OTDA_mapping_kernel() -ot_mapping_kernel.fit( - xs, xt, mu=mu, eta=eta, sigma=sigma, bias=bias, numItermax=10, verbose=True) - -X1tn = ot_mapping_kernel.predict(X1) # use the estimated mapping -I1tn = minmax(mat2im(X1tn, I1.shape)) - -#%% plot images - -pl.figure(2, figsize=(8, 4)) - -pl.subplot(2, 3, 1) -pl.imshow(I1) -pl.axis('off') -pl.title('Im. 1') - -pl.subplot(2, 3, 2) -pl.imshow(I2) -pl.axis('off') -pl.title('Im. 2') - -pl.subplot(2, 3, 3) -pl.imshow(I1t) -pl.axis('off') -pl.title('Im. 1 Interp LP') - -pl.subplot(2, 3, 4) -pl.imshow(I1te) -pl.axis('off') -pl.title('Im. 1 Interp Entrop') - -pl.subplot(2, 3, 5) -pl.imshow(I1tl) -pl.axis('off') -pl.title('Im. 1 Linear mapping') - -pl.subplot(2, 3, 6) -pl.imshow(I1tn) -pl.axis('off') -pl.title('Im. 1 nonlinear mapping') -pl.tight_layout() - -pl.show() diff --git a/ot/da.py b/ot/da.py index 8c62669..369b6a2 100644 --- a/ot/da.py +++ b/ot/da.py @@ -976,36 +976,41 @@ class BaseTransport(BaseEstimator): Returns self. """ - # pairwise distance - self.cost_ = dist(Xs, Xt, metric=self.metric) + if Xs is not None and Xt is not None: + # pairwise distance + self.cost_ = dist(Xs, Xt, metric=self.metric) - if (ys is not None) and (yt is not None): + if (ys is not None) and (yt is not None): - if self.limit_max != np.infty: - self.limit_max = self.limit_max * np.max(self.cost_) + if self.limit_max != np.infty: + self.limit_max = self.limit_max * np.max(self.cost_) - # assumes labeled source samples occupy the first rows - # and labeled target samples occupy the first columns - classes = np.unique(ys) - for c in classes: - idx_s = np.where((ys != c) & (ys != -1)) - idx_t = np.where(yt == c) + # assumes labeled source samples occupy the first rows + # and labeled target samples occupy the first columns + classes = np.unique(ys) + for c in classes: + idx_s = np.where((ys != c) & (ys != -1)) + idx_t = np.where(yt == c) - # all the coefficients corresponding to a source sample - # and a target sample : - # with different labels get a infinite - for j in idx_t[0]: - self.cost_[idx_s[0], j] = self.limit_max + # all the coefficients corresponding to a source sample + # and a target sample : + # with different labels get a infinite + for j in idx_t[0]: + self.cost_[idx_s[0], j] = self.limit_max - # distribution estimation - self.mu_s = self.distribution_estimation(Xs) - self.mu_t = self.distribution_estimation(Xt) + # distribution estimation + self.mu_s = self.distribution_estimation(Xs) + self.mu_t = self.distribution_estimation(Xt) - # store arrays of samples - self.Xs = Xs - self.Xt = Xt + # store arrays of samples + self.Xs = Xs + self.Xt = Xt - return self + return self + else: + print("POT-Warning") + print("Please provide both Xs and Xt arguments when calling") + print("fit method") def fit_transform(self, Xs=None, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples @@ -1053,42 +1058,47 @@ class BaseTransport(BaseEstimator): The transport source samples. """ - if np.array_equal(self.Xs, Xs): - # perform standard barycentric mapping - transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] + if Xs is not None: + if np.array_equal(self.Xs, Xs): + # perform standard barycentric mapping + transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] - # set nans to 0 - transp[~ np.isfinite(transp)] = 0 + # set nans to 0 + transp[~ np.isfinite(transp)] = 0 - # compute transported samples - transp_Xs = np.dot(transp, self.Xt) - else: - # perform out of sample mapping - indices = np.arange(Xs.shape[0]) - batch_ind = [ - indices[i:i + batch_size] - for i in range(0, len(indices), batch_size)] + # compute transported samples + transp_Xs = np.dot(transp, self.Xt) + else: + # perform out of sample mapping + indices = np.arange(Xs.shape[0]) + batch_ind = [ + indices[i:i + batch_size] + for i in range(0, len(indices), batch_size)] - transp_Xs = [] - for bi in batch_ind: + transp_Xs = [] + for bi in batch_ind: - # get the nearest neighbor in the source domain - D0 = dist(Xs[bi], self.Xs) - idx = np.argmin(D0, axis=1) + # get the nearest neighbor in the source domain + D0 = dist(Xs[bi], self.Xs) + idx = np.argmin(D0, axis=1) - # transport the source samples - transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] - transp[~ np.isfinite(transp)] = 0 - transp_Xs_ = np.dot(transp, self.Xt) + # transport the source samples + transp = self.coupling_ / np.sum( + self.coupling_, 1)[:, None] + transp[~ np.isfinite(transp)] = 0 + transp_Xs_ = np.dot(transp, self.Xt) - # define the transported points - transp_Xs_ = transp_Xs_[idx, :] + Xs[bi] - self.Xs[idx, :] + # define the transported points + transp_Xs_ = transp_Xs_[idx, :] + Xs[bi] - self.Xs[idx, :] - transp_Xs.append(transp_Xs_) + transp_Xs.append(transp_Xs_) - transp_Xs = np.concatenate(transp_Xs, axis=0) + transp_Xs = np.concatenate(transp_Xs, axis=0) - return transp_Xs + return transp_Xs + else: + print("POT-Warning") + print("Please provide Xs argument when calling transform method") def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128): @@ -1113,41 +1123,46 @@ class BaseTransport(BaseEstimator): The transported target samples. """ - if np.array_equal(self.Xt, Xt): - # perform standard barycentric mapping - transp_ = self.coupling_.T / np.sum(self.coupling_, 0)[:, None] + if Xt is not None: + if np.array_equal(self.Xt, Xt): + # perform standard barycentric mapping + transp_ = self.coupling_.T / np.sum(self.coupling_, 0)[:, None] - # set nans to 0 - transp_[~ np.isfinite(transp_)] = 0 + # set nans to 0 + transp_[~ np.isfinite(transp_)] = 0 - # compute transported samples - transp_Xt = np.dot(transp_, self.Xs) - else: - # perform out of sample mapping - indices = np.arange(Xt.shape[0]) - batch_ind = [ - indices[i:i + batch_size] - for i in range(0, len(indices), batch_size)] + # compute transported samples + transp_Xt = np.dot(transp_, self.Xs) + else: + # perform out of sample mapping + indices = np.arange(Xt.shape[0]) + batch_ind = [ + indices[i:i + batch_size] + for i in range(0, len(indices), batch_size)] - transp_Xt = [] - for bi in batch_ind: + transp_Xt = [] + for bi in batch_ind: - D0 = dist(Xt[bi], self.Xt) - idx = np.argmin(D0, axis=1) + D0 = dist(Xt[bi], self.Xt) + idx = np.argmin(D0, axis=1) - # transport the target samples - transp_ = self.coupling_.T / np.sum(self.coupling_, 0)[:, None] - transp_[~ np.isfinite(transp_)] = 0 - transp_Xt_ = np.dot(transp_, self.Xs) + # transport the target samples + transp_ = self.coupling_.T / np.sum( + self.coupling_, 0)[:, None] + transp_[~ np.isfinite(transp_)] = 0 + transp_Xt_ = np.dot(transp_, self.Xs) - # define the transported points - transp_Xt_ = transp_Xt_[idx, :] + Xt[bi] - self.Xt[idx, :] + # define the transported points + transp_Xt_ = transp_Xt_[idx, :] + Xt[bi] - self.Xt[idx, :] - transp_Xt.append(transp_Xt_) + transp_Xt.append(transp_Xt_) - transp_Xt = np.concatenate(transp_Xt, axis=0) + transp_Xt = np.concatenate(transp_Xt, axis=0) - return transp_Xt + return transp_Xt + else: + print("POT-Warning") + print("Please provide Xt argument when calling inverse_transform") class SinkhornTransport(BaseTransport): @@ -1413,15 +1428,20 @@ class SinkhornLpl1Transport(BaseTransport): Returns self. """ - super(SinkhornLpl1Transport, self).fit(Xs, ys, Xt, yt) + if Xs is not None and Xt is not None and ys is not None: - self.coupling_ = sinkhorn_lpl1_mm( - a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.cost_, - reg=self.reg_e, eta=self.reg_cl, numItermax=self.max_iter, - numInnerItermax=self.max_inner_iter, stopInnerThr=self.tol, - verbose=self.verbose) + super(SinkhornLpl1Transport, self).fit(Xs, ys, Xt, yt) - return self + self.coupling_ = sinkhorn_lpl1_mm( + a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.cost_, + reg=self.reg_e, eta=self.reg_cl, numItermax=self.max_iter, + numInnerItermax=self.max_inner_iter, stopInnerThr=self.tol, + verbose=self.verbose) + + return self + else: + print("POT-Warning") + print("Please provide both Xs, Xt, ys arguments to fit method") class SinkhornL1l2Transport(BaseTransport): @@ -1517,22 +1537,27 @@ class SinkhornL1l2Transport(BaseTransport): Returns self. """ - super(SinkhornL1l2Transport, self).fit(Xs, ys, Xt, yt) + if Xs is not None and Xt is not None and ys is not None: - returned_ = sinkhorn_l1l2_gl( - a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.cost_, - reg=self.reg_e, eta=self.reg_cl, numItermax=self.max_iter, - numInnerItermax=self.max_inner_iter, stopInnerThr=self.tol, - verbose=self.verbose, log=self.log) + super(SinkhornL1l2Transport, self).fit(Xs, ys, Xt, yt) - # deal with the value of log - if self.log: - self.coupling_, self.log_ = returned_ - else: - self.coupling_ = returned_ - self.log_ = dict() + returned_ = sinkhorn_l1l2_gl( + a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.cost_, + reg=self.reg_e, eta=self.reg_cl, numItermax=self.max_iter, + numInnerItermax=self.max_inner_iter, stopInnerThr=self.tol, + verbose=self.verbose, log=self.log) - return self + # deal with the value of log + if self.log: + self.coupling_, self.log_ = returned_ + else: + self.coupling_ = returned_ + self.log_ = dict() + + return self + else: + print("POT-Warning") + print("Please, provide both Xs, Xt and ys argument to fit method") class MappingTransport(BaseEstimator): -- cgit v1.2.3 From 84e56a0637f3194c5b1b160bd4f89ccd0ffe661d Mon Sep 17 00:00:00 2001 From: Slasnista Date: Mon, 28 Aug 2017 14:07:55 +0200 Subject: check input parameters with helper functions --- ot/da.py | 174 ++++++++++++++++++++++++++++++++++-------------------------- ot/utils.py | 21 ++++++++ 2 files changed, 120 insertions(+), 75 deletions(-) diff --git a/ot/da.py b/ot/da.py index 369b6a2..78dc150 100644 --- a/ot/da.py +++ b/ot/da.py @@ -14,7 +14,7 @@ import numpy as np from .bregman import sinkhorn from .lp import emd from .utils import unif, dist, kernel -from .utils import deprecated, BaseEstimator +from .utils import check_params, deprecated, BaseEstimator from .optim import cg from .optim import gcg @@ -954,6 +954,26 @@ def distribution_estimation_uniform(X): class BaseTransport(BaseEstimator): + """Base class for OTDA objects + + Notes + ----- + All estimators should specify all the parameters that can be set + at the class level in their ``__init__`` as explicit keyword + arguments (no ``*args`` or ``**kwargs``). + + fit method should: + - estimate a cost matrix and store it in a `cost_` attribute + - estimate a coupling matrix and store it in a `coupling_` + attribute + - estimate distributions from source and target data and store them in + mu_s and mu_t attributes + - store Xs and Xt in attributes to be used later on in transform and + inverse_transform methods + + transform method should always get as input a Xs parameter + inverse_transform method should always get as input a Xt parameter + """ def fit(self, Xs=None, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples @@ -976,7 +996,9 @@ class BaseTransport(BaseEstimator): Returns self. """ - if Xs is not None and Xt is not None: + # check the necessary inputs parameters are here + if check_params(Xs=Xs, Xt=Xt): + # pairwise distance self.cost_ = dist(Xs, Xt, metric=self.metric) @@ -1003,14 +1025,10 @@ class BaseTransport(BaseEstimator): self.mu_t = self.distribution_estimation(Xt) # store arrays of samples - self.Xs = Xs - self.Xt = Xt + self.xs_ = Xs + self.xt_ = Xt - return self - else: - print("POT-Warning") - print("Please provide both Xs and Xt arguments when calling") - print("fit method") + return self def fit_transform(self, Xs=None, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples @@ -1058,8 +1076,11 @@ class BaseTransport(BaseEstimator): The transport source samples. """ - if Xs is not None: - if np.array_equal(self.Xs, Xs): + # check the necessary inputs parameters are here + if check_params(Xs=Xs): + + if np.array_equal(self.xs_, Xs): + # perform standard barycentric mapping transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] @@ -1067,7 +1088,7 @@ class BaseTransport(BaseEstimator): transp[~ np.isfinite(transp)] = 0 # compute transported samples - transp_Xs = np.dot(transp, self.Xt) + transp_Xs = np.dot(transp, self.xt_) else: # perform out of sample mapping indices = np.arange(Xs.shape[0]) @@ -1079,26 +1100,23 @@ class BaseTransport(BaseEstimator): for bi in batch_ind: # get the nearest neighbor in the source domain - D0 = dist(Xs[bi], self.Xs) + D0 = dist(Xs[bi], self.xs_) idx = np.argmin(D0, axis=1) # transport the source samples transp = self.coupling_ / np.sum( self.coupling_, 1)[:, None] transp[~ np.isfinite(transp)] = 0 - transp_Xs_ = np.dot(transp, self.Xt) + transp_Xs_ = np.dot(transp, self.xt_) # define the transported points - transp_Xs_ = transp_Xs_[idx, :] + Xs[bi] - self.Xs[idx, :] + transp_Xs_ = transp_Xs_[idx, :] + Xs[bi] - self.xs_[idx, :] transp_Xs.append(transp_Xs_) transp_Xs = np.concatenate(transp_Xs, axis=0) return transp_Xs - else: - print("POT-Warning") - print("Please provide Xs argument when calling transform method") def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128): @@ -1123,8 +1141,11 @@ class BaseTransport(BaseEstimator): The transported target samples. """ - if Xt is not None: - if np.array_equal(self.Xt, Xt): + # check the necessary inputs parameters are here + if check_params(Xt=Xt): + + if np.array_equal(self.xt_, Xt): + # perform standard barycentric mapping transp_ = self.coupling_.T / np.sum(self.coupling_, 0)[:, None] @@ -1132,7 +1153,7 @@ class BaseTransport(BaseEstimator): transp_[~ np.isfinite(transp_)] = 0 # compute transported samples - transp_Xt = np.dot(transp_, self.Xs) + transp_Xt = np.dot(transp_, self.xs_) else: # perform out of sample mapping indices = np.arange(Xt.shape[0]) @@ -1143,26 +1164,23 @@ class BaseTransport(BaseEstimator): transp_Xt = [] for bi in batch_ind: - D0 = dist(Xt[bi], self.Xt) + D0 = dist(Xt[bi], self.xt_) idx = np.argmin(D0, axis=1) # transport the target samples transp_ = self.coupling_.T / np.sum( self.coupling_, 0)[:, None] transp_[~ np.isfinite(transp_)] = 0 - transp_Xt_ = np.dot(transp_, self.Xs) + transp_Xt_ = np.dot(transp_, self.xs_) # define the transported points - transp_Xt_ = transp_Xt_[idx, :] + Xt[bi] - self.Xt[idx, :] + transp_Xt_ = transp_Xt_[idx, :] + Xt[bi] - self.xt_[idx, :] transp_Xt.append(transp_Xt_) transp_Xt = np.concatenate(transp_Xt, axis=0) return transp_Xt - else: - print("POT-Warning") - print("Please provide Xt argument when calling inverse_transform") class SinkhornTransport(BaseTransport): @@ -1428,7 +1446,8 @@ class SinkhornLpl1Transport(BaseTransport): Returns self. """ - if Xs is not None and Xt is not None and ys is not None: + # check the necessary inputs parameters are here + if check_params(Xs=Xs, Xt=Xt, ys=ys): super(SinkhornLpl1Transport, self).fit(Xs, ys, Xt, yt) @@ -1438,10 +1457,7 @@ class SinkhornLpl1Transport(BaseTransport): numInnerItermax=self.max_inner_iter, stopInnerThr=self.tol, verbose=self.verbose) - return self - else: - print("POT-Warning") - print("Please provide both Xs, Xt, ys arguments to fit method") + return self class SinkhornL1l2Transport(BaseTransport): @@ -1537,7 +1553,8 @@ class SinkhornL1l2Transport(BaseTransport): Returns self. """ - if Xs is not None and Xt is not None and ys is not None: + # check the necessary inputs parameters are here + if check_params(Xs=Xs, Xt=Xt, ys=ys): super(SinkhornL1l2Transport, self).fit(Xs, ys, Xt, yt) @@ -1554,10 +1571,7 @@ class SinkhornL1l2Transport(BaseTransport): self.coupling_ = returned_ self.log_ = dict() - return self - else: - print("POT-Warning") - print("Please, provide both Xs, Xt and ys argument to fit method") + return self class MappingTransport(BaseEstimator): @@ -1652,29 +1666,35 @@ class MappingTransport(BaseEstimator): Returns self """ - self.Xs = Xs - self.Xt = Xt - - if self.kernel == "linear": - returned_ = joint_OT_mapping_linear( - Xs, Xt, mu=self.mu, eta=self.eta, bias=self.bias, - verbose=self.verbose, verbose2=self.verbose2, - numItermax=self.max_iter, numInnerItermax=self.max_inner_iter, - stopThr=self.tol, stopInnerThr=self.inner_tol, log=self.log) + # check the necessary inputs parameters are here + if check_params(Xs=Xs, Xt=Xt): + + self.xs_ = Xs + self.xt_ = Xt + + if self.kernel == "linear": + returned_ = joint_OT_mapping_linear( + Xs, Xt, mu=self.mu, eta=self.eta, bias=self.bias, + verbose=self.verbose, verbose2=self.verbose2, + numItermax=self.max_iter, + numInnerItermax=self.max_inner_iter, stopThr=self.tol, + stopInnerThr=self.inner_tol, log=self.log) + + elif self.kernel == "gaussian": + returned_ = joint_OT_mapping_kernel( + Xs, Xt, mu=self.mu, eta=self.eta, bias=self.bias, + sigma=self.sigma, verbose=self.verbose, + verbose2=self.verbose, numItermax=self.max_iter, + numInnerItermax=self.max_inner_iter, + stopInnerThr=self.inner_tol, stopThr=self.tol, + log=self.log) - elif self.kernel == "gaussian": - returned_ = joint_OT_mapping_kernel( - Xs, Xt, mu=self.mu, eta=self.eta, bias=self.bias, - sigma=self.sigma, verbose=self.verbose, verbose2=self.verbose, - numItermax=self.max_iter, numInnerItermax=self.max_inner_iter, - stopInnerThr=self.inner_tol, stopThr=self.tol, log=self.log) - - # deal with the value of log - if self.log: - self.coupling_, self.mapping_, self.log_ = returned_ - else: - self.coupling_, self.mapping_ = returned_ - self.log_ = dict() + # deal with the value of log + if self.log: + self.coupling_, self.mapping_, self.log_ = returned_ + else: + self.coupling_, self.mapping_ = returned_ + self.log_ = dict() return self @@ -1692,22 +1712,26 @@ class MappingTransport(BaseEstimator): The transport source samples. """ - if np.array_equal(self.Xs, Xs): - # perform standard barycentric mapping - transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] + # check the necessary inputs parameters are here + if check_params(Xs=Xs): - # set nans to 0 - transp[~ np.isfinite(transp)] = 0 + if np.array_equal(self.xs_, Xs): + # perform standard barycentric mapping + transp = self.coupling_ / np.sum(self.coupling_, 1)[:, None] - # compute transported samples - transp_Xs = np.dot(transp, self.Xt) - else: - if self.kernel == "gaussian": - K = kernel(Xs, self.Xs, method=self.kernel, sigma=self.sigma) - elif self.kernel == "linear": - K = Xs - if self.bias: - K = np.hstack((K, np.ones((Xs.shape[0], 1)))) - transp_Xs = K.dot(self.mapping_) + # set nans to 0 + transp[~ np.isfinite(transp)] = 0 - return transp_Xs + # compute transported samples + transp_Xs = np.dot(transp, self.xt_) + else: + if self.kernel == "gaussian": + K = kernel(Xs, self.xs_, method=self.kernel, + sigma=self.sigma) + elif self.kernel == "linear": + K = Xs + if self.bias: + K = np.hstack((K, np.ones((Xs.shape[0], 1)))) + transp_Xs = K.dot(self.mapping_) + + return transp_Xs diff --git a/ot/utils.py b/ot/utils.py index 29ad536..01f2a67 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -168,6 +168,27 @@ def parmap(f, X, nprocs=multiprocessing.cpu_count()): return [x for i, x in sorted(res)] +def check_params(**kwargs): + """check_params: check whether some parameters are missing + """ + + missing_params = [] + check = True + + for param in kwargs: + if kwargs[param] is None: + missing_params.append(param) + + if len(missing_params) > 0: + print("POT - Warning: following necessary parameters are missing") + for p in missing_params: + print("\n", p) + + check = False + + return check + + class deprecated(object): """Decorator to mark a function or class as deprecated. -- cgit v1.2.3 From 59640017434427bac54d9eb668a1d7fc862ccdce Mon Sep 17 00:00:00 2001 From: Slasnista Date: Mon, 28 Aug 2017 14:08:03 +0200 Subject: update readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 27b4643..33eea6e 100644 --- a/README.md +++ b/README.md @@ -136,7 +136,7 @@ The contributors to this library are: * [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/) * [Michael Perrot](http://perso.univ-st-etienne.fr/pem82055/) (Mapping estimation) * [Léo Gautheron](https://github.com/aje) (GPU implementation) -* [Nathalie Gayraud]() +* [Nathalie Gayraud](https://www.linkedin.com/in/nathalie-t-h-gayraud/?ppe=1) * [Stanislas Chambon](https://slasnista.github.io/) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): -- cgit v1.2.3 From 24362ecde2a64353e568d3980a52ea5ddfdbe930 Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Mon, 28 Aug 2017 14:41:09 +0200 Subject: Gromov-Wasserstein distance --- README.md | 4 +- examples/plot_gromov.py | 98 ++++++++++ ot/__init__.py | 6 +- ot/gromov.py | 482 ++++++++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 588 insertions(+), 2 deletions(-) create mode 100644 examples/plot_gromov.py create mode 100644 ot/gromov.py diff --git a/README.md b/README.md index 33eea6e..257244b 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,7 @@ It provides the following solvers: * Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. * Joint OT matrix and mapping estimation [8]. * Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt). - +* Gromov-Wasserstein distances [12] Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -184,3 +184,5 @@ You can also post bug reports and feature requests in Github issues. Make sure t [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). [Scaling algorithms for unbalanced transport problems](https://arxiv.org/pdf/1607.05816.pdf). arXiv preprint arXiv:1607.05816. [11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). [Wasserstein Discriminant Analysis](https://arxiv.org/pdf/1608.08063.pdf). arXiv preprint arXiv:1608.08063. + +[12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, [Gromov-Wasserstein averaging of kernel and distance matrices](http://proceedings.mlr.press/v48/peyre16.html) International Conference on Machine Learning (ICML). 2016. diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py new file mode 100644 index 0000000..11e5336 --- /dev/null +++ b/examples/plot_gromov.py @@ -0,0 +1,98 @@ +# -*- coding: utf-8 -*- +""" +==================== +Gromov-Wasserstein example +==================== + +This example is designed to show how to use the Gromov-Wassertsein distance +computation in POT. + + +""" + +# Author: Erwan Vautier +# Nicolas Courty +# +# License: MIT License + +import scipy as sp +import numpy as np + +import ot +import matplotlib.pylab as pl +from mpl_toolkits.mplot3d import Axes3D + + + +""" +Sample two Gaussian distributions (2D and 3D) +==================== + +The Gromov-Wasserstein distance allows to compute distances with samples that do not belong to the same metric space. For +demonstration purpose, we sample two Gaussian distributions in 2- and 3-dimensional spaces. + +""" +n=30 # nb samples + +mu_s=np.array([0,0]) +cov_s=np.array([[1,0],[0,1]]) + +mu_t=np.array([4,4,4]) +cov_t=np.array([[1,0,0],[0,1,0],[0,0,1]]) + + + +xs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s) +P=sp.linalg.sqrtm(cov_t) +xt= np.random.randn(n,3).dot(P)+mu_t + + + +""" +Plotting the distributions +==================== +""" +fig=pl.figure() +ax1=fig.add_subplot(121) +ax1.plot(xs[:,0],xs[:,1],'+b',label='Source samples') +ax2=fig.add_subplot(122,projection='3d') +ax2.scatter(xt[:,0],xt[:,1],xt[:,2],color='r') +pl.show() + + +""" +Compute distance kernels, normalize them and then display +==================== +""" + +C1=sp.spatial.distance.cdist(xs,xs) +C2=sp.spatial.distance.cdist(xt,xt) + +C1/=C1.max() +C2/=C2.max() + +pl.figure() +pl.subplot(121) +pl.imshow(C1) +pl.subplot(122) +pl.imshow(C2) +pl.show() + +""" +Compute Gromov-Wasserstein plans and distance +==================== +""" + +p=ot.unif(n) +q=ot.unif(n) + +gw=ot.gromov_wasserstein(C1,C2,p,q,'square_loss',epsilon=5e-4) +gw_dist=ot.gromov_wasserstein2(C1,C2,p,q,'square_loss',epsilon=5e-4) + +print('Gromov-Wasserstein distances between the distribution: '+str(gw_dist)) + +pl.figure() +pl.imshow(gw,cmap='jet') +pl.colorbar() +pl.show() + diff --git a/ot/__init__.py b/ot/__init__.py index 6d4c4c6..a295e1b 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -5,6 +5,7 @@ """ # Author: Remi Flamary +# Nicolas Courty # # License: MIT License @@ -17,11 +18,13 @@ from . import utils from . import datasets from . import plot from . import da +from . import gromov # OT functions from .lp import emd, emd2 from .bregman import sinkhorn, sinkhorn2, barycenter from .da import sinkhorn_lpl1_mm +from .gromov import gromov_wasserstein, gromov_wasserstein2 # utils functions from .utils import dist, unif, tic, toc, toq @@ -30,4 +33,5 @@ __version__ = "0.3.1" __all__ = ["emd", "emd2", "sinkhorn", "sinkhorn2", "utils", 'datasets', 'bregman', 'lp', 'plot', 'tic', 'toc', 'toq', - 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim'] + 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim', + 'gromov_wasserstein','gromov_wasserstein2'] diff --git a/ot/gromov.py b/ot/gromov.py new file mode 100644 index 0000000..f3c62c9 --- /dev/null +++ b/ot/gromov.py @@ -0,0 +1,482 @@ + +# -*- coding: utf-8 -*- +""" +Gromov-Wasserstein transport method +=================================== + + +""" + +# Author: Erwan Vautier +# Nicolas Courty +# +# License: MIT License + +import numpy as np + +from .bregman import sinkhorn +from .utils import dist + +def square_loss(a,b): + """ + Returns the value of L(a,b)=(1/2)*|a-b|^2 + """ + + return (1/2)*(a-b)**2 + +def kl_loss(a,b): + """ + Returns the value of L(a,b)=a*log(a/b)-a+b + """ + + return a*np.log(a/b)-a+b + +def tensor_square_loss(C1,C2,T): + """ + Returns the value of \mathcal{L}(C1,C2) \otimes T with the square loss + + function as the loss function of Gromow-Wasserstein discrepancy. + + Where : + + C1 : Metric cost matrix in the source space + C2 : Metric cost matrix in the target space + T : A coupling between those two spaces + + The square-loss function L(a,b)=(1/2)*|a-b|^2 is read as : + L(a,b) = f1(a)+f2(b)-h1(a)*h2(b) with : + f1(a)=(a^2)/2 + f2(b)=(b^2)/2 + h1(a)=a + h2(b)=b + + Parameters + ---------- + C1 : np.ndarray(ns,ns) + Metric cost matrix in the source space + C2 : np.ndarray(nt,nt) + Metric costfr matrix in the target space + T : np.ndarray(ns,nt) + Coupling between source and target spaces + + + Returns + ------- + tens : (ns*nt) ndarray + \mathcal{L}(C1,C2) \otimes T tensor-matrix multiplication result + + + """ + + + C1=np.asarray(C1,dtype=np.float64) + C2=np.asarray(C2,dtype=np.float64) + T=np.asarray(T,dtype=np.float64) + + + def f1(a): + return (a**2)/2 + + def f2(b): + return (b**2)/2 + + def h1(a): + return a + + def h2(b): + return b + + tens=-np.dot(h1(C1),T).dot(h2(C2).T) + tens=tens-tens.min() + + + return np.array(tens) + + +def tensor_kl_loss(C1,C2,T): + """ + Returns the value of \mathcal{L}(C1,C2) \otimes T with the square loss + + function as the loss function of Gromow-Wasserstein discrepancy. + + Where : + + C1 : Metric cost matrix in the source space + C2 : Metric cost matrix in the target space + T : A coupling between those two spaces + + The square-loss function L(a,b)=(1/2)*|a-b|^2 is read as : + L(a,b) = f1(a)+f2(b)-h1(a)*h2(b) with : + f1(a)=a*log(a)-a + f2(b)=b + h1(a)=a + h2(b)=log(b) + + Parameters + ---------- + C1 : np.ndarray(ns,ns) + Metric cost matrix in the source space + C2 : np.ndarray(nt,nt) + Metric costfr matrix in the target space + T : np.ndarray(ns,nt) + Coupling between source and target spaces + + + Returns + ------- + tens : (ns*nt) ndarray + \mathcal{L}(C1,C2) \otimes T tensor-matrix multiplication result + + References + ---------- + + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices." International Conference on Machine Learning (ICML). 2016. + + """ + + + C1=np.asarray(C1,dtype=np.float64) + C2=np.asarray(C2,dtype=np.float64) + T=np.asarray(T,dtype=np.float64) + + + def f1(a): + return a*np.log(a+1e-15)-a + + def f2(b): + return b + + def h1(a): + return a + + def h2(b): + return np.log(b+1e-15) + + tens=-np.dot(h1(C1),T).dot(h2(C2).T) + tens=tens-tens.min() + + + + return np.array(tens) + +def update_square_loss(p,lambdas,T,Cs): + """ + Updates C according to the L2 Loss kernel with the S Ts couplings calculated at each iteration + + + Parameters + ---------- + p : np.ndarray(N,) + weights in the targeted barycenter + lambdas : list of the S spaces' weights + T : list of S np.ndarray(ns,N) + the S Ts couplings calculated at each iteration + Cs : Cs : list of S np.ndarray(ns,ns) + Metric cost matrices + + Returns + ---------- + C updated + + + """ + tmpsum=np.sum([lambdas[s]*np.dot(T[s].T,Cs[s]).dot(T[s]) for s in range(len(T))]) + ppt=np.dot(p,p.T) + + return(np.divide(tmpsum,ppt)) + +def update_kl_loss(p,lambdas,T,Cs): + """ + Updates C according to the KL Loss kernel with the S Ts couplings calculated at each iteration + + + Parameters + ---------- + p : np.ndarray(N,) + weights in the targeted barycenter + lambdas : list of the S spaces' weights + T : list of S np.ndarray(ns,N) + the S Ts couplings calculated at each iteration + Cs : Cs : list of S np.ndarray(ns,ns) + Metric cost matrices + + Returns + ---------- + C updated + + + """ + tmpsum=np.sum([lambdas[s]*np.dot(T[s].T,Cs[s]).dot(T[s]) for s in range(len(T))]) + ppt=np.dot(p,p.T) + + return(np.exp(np.divide(tmpsum,ppt))) + + +def gromov_wasserstein(C1,C2,p,q,loss_fun,epsilon,numItermax = 1000, stopThr=1e-9,verbose=False, log=False): + """ + Returns the gromov-wasserstein coupling between the two measured similarity matrices + + (C1,p) and (C2,q) + + The function solves the following optimization problem: + + .. math:: + \GW = arg\min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T)) + + s.t. \GW 1 = p + + \GW^T 1= q + + \GW\geq 0 + + Where : + + C1 : Metric cost matrix in the source space + C2 : Metric cost matrix in the target space + p : distribution in the source space + q : distribution in the target space + L : loss function to account for the misfit between the similarity matrices + H : entropy + + + Parameters + ---------- + C1 : np.ndarray(ns,ns) + Metric cost matrix in the source space + C2 : np.ndarray(nt,nt) + Metric costfr matrix in the target space + p : np.ndarray(ns,) + distribution in the source space + q : np.ndarray(nt) + distribution in the target space + loss_fun : loss function used for the solver either 'square_loss' or 'kl_loss' + epsilon : float + Regularization term >0 + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshold on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + forcing : np.ndarray(N,2) + list of forced couplings (where N is the number of forcing) + + Returns + ------- + T : coupling between the two spaces that minimizes : + \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T)) + + """ + + + C1=np.asarray(C1,dtype=np.float64) + C2=np.asarray(C2,dtype=np.float64) + + T=np.dot(p,q.T) #Initialization + + cpt = 0 + err=1 + + while (err>stopThr and cpt0 + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshold on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + forcing : np.ndarray(N,2) + list of forced couplings (where N is the number of forcing) + + Returns + ------- + T : coupling between the two spaces that minimizes : + \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T)) + + """ + + if log: + gw,logv=gromov_wasserstein(C1,C2,p,q,loss_fun,epsilon,numItermax,stopThr,verbose,log) + else: + gw=gromov_wasserstein(C1,C2,p,q,loss_fun,epsilon,numItermax,stopThr,verbose,log) + + if loss_fun=='square_loss': + gw_dist=np.sum(gw*tensor_square_loss(C1,C2,gw)) + + + elif loss_fun=='kl_loss': + gw_dist=np.sum(gw*tensor_kl_loss(C1,C2,gw)) + + if log: + return gw_dist, logv + else: + return gw_dist + + +def gromov_barycenters(N,Cs,ps,p,lambdas,loss_fun,epsilon,numItermax = 1000, stopThr=1e-9, verbose=False, log=False): + """ + Returns the gromov-wasserstein barycenters of S measured similarity matrices + + (Cs)_{s=1}^{s=S} + + The function solves the following optimization problem: + + .. math:: + C = argmin_C\in R^NxN \sum_s \lambda_s GW(C,Cs,p,ps) + + + Where : + + Cs : metric cost matrix + ps : distribution + + Parameters + ---------- + N : Integer + Size of the targeted barycenter + Cs : list of S np.ndarray(ns,ns) + Metric cost matrices + ps : list of S np.ndarray(ns,) + sample weights in the S spaces + p : np.ndarray(N,) + weights in the targeted barycenter + lambdas : list of the S spaces' weights + L : tensor-matrix multiplication function based on specific loss function + update : function(p,lambdas,T,Cs) that updates C according to a specific Kernel + with the S Ts couplings calculated at each iteration + epsilon : float + Regularization term >0 + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + Returns + ------- + C : Similarity matrix in the barycenter space (permutated arbitrarily) + + """ + + + S=len(Cs) + + Cs=[np.asarray(Cs[s],dtype=np.float64) for s in range(S)] + lambdas=np.asarray(lambdas,dtype=np.float64) + + T=[0 for s in range(S)] + + #Initialization of C : random SPD matrix + xalea=np.random.randn(N,2) + C=dist(xalea,xalea) + C/=C.max() + + cpt=0 + err=1 + + error=[] + + while(err>stopThr and cpt Date: Mon, 28 Aug 2017 15:04:04 +0200 Subject: gromov:flake8 and other --- examples/plot_gromov.py | 63 ++++---- ot/gromov.py | 382 ++++++++++++++++++++++++------------------------ 2 files changed, 216 insertions(+), 229 deletions(-) diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py index 11e5336..a33fde1 100644 --- a/examples/plot_gromov.py +++ b/examples/plot_gromov.py @@ -3,11 +3,8 @@ ==================== Gromov-Wasserstein example ==================== - -This example is designed to show how to use the Gromov-Wassertsein distance -computation in POT. - - +This example is designed to show how to use the Gromov-Wassertsein distance +computation in POT. """ # Author: Erwan Vautier @@ -20,43 +17,38 @@ import numpy as np import ot import matplotlib.pylab as pl -from mpl_toolkits.mplot3d import Axes3D - """ Sample two Gaussian distributions (2D and 3D) ==================== - -The Gromov-Wasserstein distance allows to compute distances with samples that do not belong to the same metric space. For -demonstration purpose, we sample two Gaussian distributions in 2- and 3-dimensional spaces. - +The Gromov-Wasserstein distance allows to compute distances with samples that do not belong to the same metric space. +For demonstration purpose, we sample two Gaussian distributions in 2- and 3-dimensional spaces. """ -n=30 # nb samples -mu_s=np.array([0,0]) -cov_s=np.array([[1,0],[0,1]]) +n = 30 # nb samples -mu_t=np.array([4,4,4]) -cov_t=np.array([[1,0,0],[0,1,0],[0,0,1]]) +mu_s = np.array([0, 0]) +cov_s = np.array([[1, 0], [0, 1]]) +mu_t = np.array([4, 4, 4]) +cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) -xs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s) -P=sp.linalg.sqrtm(cov_t) -xt= np.random.randn(n,3).dot(P)+mu_t - +xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s) +P = sp.linalg.sqrtm(cov_t) +xt = np.random.randn(n, 3).dot(P) + mu_t """ Plotting the distributions ==================== """ -fig=pl.figure() -ax1=fig.add_subplot(121) -ax1.plot(xs[:,0],xs[:,1],'+b',label='Source samples') -ax2=fig.add_subplot(122,projection='3d') -ax2.scatter(xt[:,0],xt[:,1],xt[:,2],color='r') +fig = pl.figure() +ax1 = fig.add_subplot(121) +ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +ax2 = fig.add_subplot(122, projection='3d') +ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r') pl.show() @@ -65,11 +57,11 @@ Compute distance kernels, normalize them and then display ==================== """ -C1=sp.spatial.distance.cdist(xs,xs) -C2=sp.spatial.distance.cdist(xt,xt) +C1 = sp.spatial.distance.cdist(xs, xs) +C2 = sp.spatial.distance.cdist(xt, xt) -C1/=C1.max() -C2/=C2.max() +C1 /= C1.max() +C2 /= C2.max() pl.figure() pl.subplot(121) @@ -83,16 +75,15 @@ Compute Gromov-Wasserstein plans and distance ==================== """ -p=ot.unif(n) -q=ot.unif(n) +p = ot.unif(n) +q = ot.unif(n) -gw=ot.gromov_wasserstein(C1,C2,p,q,'square_loss',epsilon=5e-4) -gw_dist=ot.gromov_wasserstein2(C1,C2,p,q,'square_loss',epsilon=5e-4) +gw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4) +gw_dist = ot.gromov_wasserstein2(C1, C2, p, q, 'square_loss', epsilon=5e-4) -print('Gromov-Wasserstein distances between the distribution: '+str(gw_dist)) +print('Gromov-Wasserstein distances between the distribution: ' + str(gw_dist)) pl.figure() -pl.imshow(gw,cmap='jet') +pl.imshow(gw, cmap='jet') pl.colorbar() pl.show() - diff --git a/ot/gromov.py b/ot/gromov.py index f3c62c9..7cf3b42 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -17,39 +17,41 @@ import numpy as np from .bregman import sinkhorn from .utils import dist -def square_loss(a,b): + +def square_loss(a, b): """ Returns the value of L(a,b)=(1/2)*|a-b|^2 """ - - return (1/2)*(a-b)**2 -def kl_loss(a,b): + return (1 / 2) * (a - b)**2 + + +def kl_loss(a, b): """ Returns the value of L(a,b)=a*log(a/b)-a+b """ - - return a*np.log(a/b)-a+b -def tensor_square_loss(C1,C2,T): + return a * np.log(a / b) - a + b + + +def tensor_square_loss(C1, C2, T): """ - Returns the value of \mathcal{L}(C1,C2) \otimes T with the square loss - + Returns the value of \mathcal{L}(C1,C2) \otimes T with the square loss function as the loss function of Gromow-Wasserstein discrepancy. - + Where : - + C1 : Metric cost matrix in the source space C2 : Metric cost matrix in the target space T : A coupling between those two spaces - + The square-loss function L(a,b)=(1/2)*|a-b|^2 is read as : L(a,b) = f1(a)+f2(b)-h1(a)*h2(b) with : f1(a)=(a^2)/2 f2(b)=(b^2)/2 h1(a)=a h2(b)=b - + Parameters ---------- C1 : np.ndarray(ns,ns) @@ -64,54 +66,50 @@ def tensor_square_loss(C1,C2,T): ------- tens : (ns*nt) ndarray \mathcal{L}(C1,C2) \otimes T tensor-matrix multiplication result - - + + """ - - C1=np.asarray(C1,dtype=np.float64) - C2=np.asarray(C2,dtype=np.float64) - T=np.asarray(T,dtype=np.float64) - - + C1 = np.asarray(C1, dtype=np.float64) + C2 = np.asarray(C2, dtype=np.float64) + T = np.asarray(T, dtype=np.float64) + def f1(a): - return (a**2)/2 - + return (a**2) / 2 + def f2(b): - return (b**2)/2 - + return (b**2) / 2 + def h1(a): return a - + def h2(b): return b - - tens=-np.dot(h1(C1),T).dot(h2(C2).T) - tens=tens-tens.min() - + tens = -np.dot(h1(C1), T).dot(h2(C2).T) + tens = tens - tens.min() + return np.array(tens) - - -def tensor_kl_loss(C1,C2,T): + + +def tensor_kl_loss(C1, C2, T): """ - Returns the value of \mathcal{L}(C1,C2) \otimes T with the square loss - + Returns the value of \mathcal{L}(C1,C2) \otimes T with the square loss function as the loss function of Gromow-Wasserstein discrepancy. - + Where : - + C1 : Metric cost matrix in the source space C2 : Metric cost matrix in the target space T : A coupling between those two spaces - + The square-loss function L(a,b)=(1/2)*|a-b|^2 is read as : L(a,b) = f1(a)+f2(b)-h1(a)*h2(b) with : f1(a)=a*log(a)-a f2(b)=b h1(a)=a h2(b)=log(b) - + Parameters ---------- C1 : np.ndarray(ns,ns) @@ -126,44 +124,41 @@ def tensor_kl_loss(C1,C2,T): ------- tens : (ns*nt) ndarray \mathcal{L}(C1,C2) \otimes T tensor-matrix multiplication result - + References ---------- - + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices." International Conference on Machine Learning (ICML). 2016. - + """ - - C1=np.asarray(C1,dtype=np.float64) - C2=np.asarray(C2,dtype=np.float64) - T=np.asarray(T,dtype=np.float64) - - + C1 = np.asarray(C1, dtype=np.float64) + C2 = np.asarray(C2, dtype=np.float64) + T = np.asarray(T, dtype=np.float64) + def f1(a): - return a*np.log(a+1e-15)-a - + return a * np.log(a + 1e-15) - a + def f2(b): return b - + def h1(a): return a - + def h2(b): - return np.log(b+1e-15) - - tens=-np.dot(h1(C1),T).dot(h2(C2).T) - tens=tens-tens.min() + return np.log(b + 1e-15) + + tens = -np.dot(h1(C1), T).dot(h2(C2).T) + tens = tens - tens.min() - - return np.array(tens) -def update_square_loss(p,lambdas,T,Cs): + +def update_square_loss(p, lambdas, T, Cs): """ Updates C according to the L2 Loss kernel with the S Ts couplings calculated at each iteration - - + + Parameters ---------- p : np.ndarray(N,) @@ -173,23 +168,25 @@ def update_square_loss(p,lambdas,T,Cs): the S Ts couplings calculated at each iteration Cs : Cs : list of S np.ndarray(ns,ns) Metric cost matrices - - Returns + + Returns ---------- C updated - - + + """ - tmpsum=np.sum([lambdas[s]*np.dot(T[s].T,Cs[s]).dot(T[s]) for s in range(len(T))]) - ppt=np.dot(p,p.T) - - return(np.divide(tmpsum,ppt)) + tmpsum = np.sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) + for s in range(len(T))]) + ppt = np.dot(p, p.T) -def update_kl_loss(p,lambdas,T,Cs): + return(np.divide(tmpsum, ppt)) + + +def update_kl_loss(p, lambdas, T, Cs): """ Updates C according to the KL Loss kernel with the S Ts couplings calculated at each iteration - - + + Parameters ---------- p : np.ndarray(N,) @@ -199,25 +196,26 @@ def update_kl_loss(p,lambdas,T,Cs): the S Ts couplings calculated at each iteration Cs : Cs : list of S np.ndarray(ns,ns) Metric cost matrices - - Returns + + Returns ---------- C updated - - + + """ - tmpsum=np.sum([lambdas[s]*np.dot(T[s].T,Cs[s]).dot(T[s]) for s in range(len(T))]) - ppt=np.dot(p,p.T) - - return(np.exp(np.divide(tmpsum,ppt))) + tmpsum = np.sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) + for s in range(len(T))]) + ppt = np.dot(p, p.T) + + return(np.exp(np.divide(tmpsum, ppt))) -def gromov_wasserstein(C1,C2,p,q,loss_fun,epsilon,numItermax = 1000, stopThr=1e-9,verbose=False, log=False): +def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr=1e-9, verbose=False, log=False): """ Returns the gromov-wasserstein coupling between the two measured similarity matrices - + (C1,p) and (C2,q) - + The function solves the following optimization problem: .. math:: @@ -228,17 +226,17 @@ def gromov_wasserstein(C1,C2,p,q,loss_fun,epsilon,numItermax = 1000, stopThr=1e- \GW^T 1= q \GW\geq 0 - + Where : - + C1 : Metric cost matrix in the source space C2 : Metric cost matrix in the target space p : distribution in the source space - q : distribution in the target space + q : distribution in the target space L : loss function to account for the misfit between the similarity matrices H : entropy - - + + Parameters ---------- C1 : np.ndarray(ns,ns) @@ -259,80 +257,80 @@ def gromov_wasserstein(C1,C2,p,q,loss_fun,epsilon,numItermax = 1000, stopThr=1e- verbose : bool, optional Print information along iterations log : bool, optional - record log if True + record log if True forcing : np.ndarray(N,2) list of forced couplings (where N is the number of forcing) - + Returns ------- T : coupling between the two spaces that minimizes : \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T)) - + """ - - C1=np.asarray(C1,dtype=np.float64) - C2=np.asarray(C2,dtype=np.float64) + C1 = np.asarray(C1, dtype=np.float64) + C2 = np.asarray(C2, dtype=np.float64) + + T = np.dot(p, q.T) # Initialization - T=np.dot(p,q.T) #Initialization - cpt = 0 - err=1 - - while (err>stopThr and cpt stopThr and cpt < numItermax): + + Tprev = T + + if loss_fun == 'square_loss': + tens = tensor_square_loss(C1, C2, T) + + elif loss_fun == 'kl_loss': + tens = tensor_kl_loss(C1, C2, T) + + T = sinkhorn(p, q, tens, epsilon) + + if cpt % 10 == 0: # we can speed up the process by checking for the error only all the 10th iterations - err=np.linalg.norm(T-Tprev) - + err = np.linalg.norm(T - Tprev) + if log: log['err'].append(err) if verbose: - if cpt%200 ==0: - print('{:5s}|{:12s}'.format('It.','Err')+'\n'+'-'*19) - print('{:5d}|{:8e}|'.format(cpt,err)) - - cpt=cpt+1 + if cpt % 200 == 0: + print('{:5s}|{:12s}'.format( + 'It.', 'Err') + '\n' + '-' * 19) + print('{:5d}|{:8e}|'.format(cpt, err)) + + cpt = cpt + 1 if log: - return T,log + return T, log else: return T -def gromov_wasserstein2(C1,C2,p,q,loss_fun,epsilon,numItermax = 1000, stopThr=1e-9,verbose=False, log=False): +def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr=1e-9, verbose=False, log=False): """ Returns the gromov-wasserstein discrepancy between the two measured similarity matrices - + (C1,p) and (C2,q) - + The function solves the following optimization problem: .. math:: \GW_Dist = \min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T)) - + Where : - + C1 : Metric cost matrix in the source space C2 : Metric cost matrix in the target space p : distribution in the source space - q : distribution in the target space + q : distribution in the target space L : loss function to account for the misfit between the similarity matrices H : entropy - - + + Parameters ---------- C1 : np.ndarray(ns,ns) @@ -353,55 +351,56 @@ def gromov_wasserstein2(C1,C2,p,q,loss_fun,epsilon,numItermax = 1000, stopThr=1e verbose : bool, optional Print information along iterations log : bool, optional - record log if True + record log if True forcing : np.ndarray(N,2) list of forced couplings (where N is the number of forcing) - + Returns ------- T : coupling between the two spaces that minimizes : \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T)) - + """ - + if log: - gw,logv=gromov_wasserstein(C1,C2,p,q,loss_fun,epsilon,numItermax,stopThr,verbose,log) + gw, logv = gromov_wasserstein( + C1, C2, p, q, loss_fun, epsilon, numItermax, stopThr, verbose, log) else: - gw=gromov_wasserstein(C1,C2,p,q,loss_fun,epsilon,numItermax,stopThr,verbose,log) + gw = gromov_wasserstein(C1, C2, p, q, loss_fun, + epsilon, numItermax, stopThr, verbose, log) + + if loss_fun == 'square_loss': + gw_dist = np.sum(gw * tensor_square_loss(C1, C2, gw)) - if loss_fun=='square_loss': - gw_dist=np.sum(gw*tensor_square_loss(C1,C2,gw)) - - - elif loss_fun=='kl_loss': - gw_dist=np.sum(gw*tensor_kl_loss(C1,C2,gw)) + elif loss_fun == 'kl_loss': + gw_dist = np.sum(gw * tensor_kl_loss(C1, C2, gw)) if log: return gw_dist, logv - else: + else: return gw_dist -def gromov_barycenters(N,Cs,ps,p,lambdas,loss_fun,epsilon,numItermax = 1000, stopThr=1e-9, verbose=False, log=False): +def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, numItermax=1000, stopThr=1e-9, verbose=False, log=False): """ Returns the gromov-wasserstein barycenters of S measured similarity matrices - + (Cs)_{s=1}^{s=S} - + The function solves the following optimization problem: .. math:: C = argmin_C\in R^NxN \sum_s \lambda_s GW(C,Cs,p,ps) - + Where : - + Cs : metric cost matrix ps : distribution - + Parameters ---------- - N : Integer + N : Integer Size of the targeted barycenter Cs : list of S np.ndarray(ns,ns) Metric cost matrices @@ -422,61 +421,58 @@ def gromov_barycenters(N,Cs,ps,p,lambdas,loss_fun,epsilon,numItermax = 1000, sto verbose : bool, optional Print information along iterations log : bool, optional - record log if True - + record log if True + Returns ------- C : Similarity matrix in the barycenter space (permutated arbitrarily) - + """ - - - S=len(Cs) - - Cs=[np.asarray(Cs[s],dtype=np.float64) for s in range(S)] - lambdas=np.asarray(lambdas,dtype=np.float64) - - T=[0 for s in range(S)] - - #Initialization of C : random SPD matrix - xalea=np.random.randn(N,2) - C=dist(xalea,xalea) - C/=C.max() - - cpt=0 - err=1 - - error=[] - - while(err>stopThr and cpt stopThr and cpt < numItermax): + + Cprev = C + + T = [gromov_wasserstein(Cs[s], C, ps[s], p, loss_fun, epsilon, + numItermax, 1e-5, verbose, log) for s in range(S)] + + if loss_fun == 'square_loss': + C = update_square_loss(p, lambdas, T, Cs) + + elif loss_fun == 'kl_loss': + C = update_kl_loss(p, lambdas, T, Cs) + + if cpt % 10 == 0: # we can speed up the process by checking for the error only all the 10th iterations - err=np.linalg.norm(C-Cprev) + err = np.linalg.norm(C - Cprev) error.append(err) - + if log: log['err'].append(err) if verbose: - if cpt%200 ==0: - print('{:5s}|{:12s}'.format('It.','Err')+'\n'+'-'*19) - print('{:5d}|{:8e}|'.format(cpt,err)) - - cpt=cpt+1 - - return C - + if cpt % 200 == 0: + print('{:5s}|{:12s}'.format( + 'It.', 'Err') + '\n' + '-' * 19) + print('{:5d}|{:8e}|'.format(cpt, err)) + cpt = cpt + 1 - \ No newline at end of file + return C -- cgit v1.2.3 From 3730779200896ee7de533eb7c5d7fa19e09eeb25 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Tue, 29 Aug 2017 09:05:01 +0200 Subject: addressed AG comments + adding random seed --- examples/da/plot_otda_classes.py | 2 ++ examples/da/plot_otda_color_images.py | 3 ++- examples/da/plot_otda_d2.py | 14 ++++++++------ examples/da/plot_otda_mapping.py | 14 +++++++------- examples/da/plot_otda_mapping_colors_images.py | 2 ++ 5 files changed, 21 insertions(+), 14 deletions(-) diff --git a/examples/da/plot_otda_classes.py b/examples/da/plot_otda_classes.py index e5c82fb..6870fa4 100644 --- a/examples/da/plot_otda_classes.py +++ b/examples/da/plot_otda_classes.py @@ -15,8 +15,10 @@ approaches currently supported in POT. # License: MIT License import matplotlib.pylab as pl +import numpy as np import ot +np.random.seed(42) # number of source and target points to generate ns = 150 diff --git a/examples/da/plot_otda_color_images.py b/examples/da/plot_otda_color_images.py index bca7350..805d0b0 100644 --- a/examples/da/plot_otda_color_images.py +++ b/examples/da/plot_otda_color_images.py @@ -20,9 +20,10 @@ SIAM Journal on Imaging Sciences, 7(3), 1853-1882. import numpy as np from scipy import ndimage import matplotlib.pylab as pl - import ot +np.random.seed(42) + def im2mat(I): """Converts and image to matrix (one pixel per line)""" diff --git a/examples/da/plot_otda_d2.py b/examples/da/plot_otda_d2.py index 1d2192f..8833eb2 100644 --- a/examples/da/plot_otda_d2.py +++ b/examples/da/plot_otda_d2.py @@ -19,17 +19,19 @@ of what the transport methods are doing. # License: MIT License import matplotlib.pylab as pl +import numpy as np import ot -# number of source and target points to generate -ns = 150 -nt = 150 +np.random.seed(42) -Xs, ys = ot.datasets.get_data_classif('3gauss', ns) -Xt, yt = ot.datasets.get_data_classif('3gauss2', nt) +n_samples_source = 150 +n_samples_target = 150 + +Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source) +Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target) # Cost matrix -M = ot.dist(Xs, Xt) +M = ot.dist(Xs, Xt, metric='sqeuclidean') # Instantiate the different transport algorithms and fit them diff --git a/examples/da/plot_otda_mapping.py b/examples/da/plot_otda_mapping.py index 6d83507..aea7f09 100644 --- a/examples/da/plot_otda_mapping.py +++ b/examples/da/plot_otda_mapping.py @@ -23,7 +23,7 @@ import matplotlib.pylab as pl import ot -np.random.seed(0) +np.random.seed(42) ############################################################################## # generate @@ -31,10 +31,11 @@ np.random.seed(0) n = 100 # nb samples in source and target datasets theta = 2 * np.pi / 20 -nz = 0.1 -Xs, ys = ot.datasets.get_data_classif('gaussrot', n, nz=nz) -Xs_new, _ = ot.datasets.get_data_classif('gaussrot', n, nz=nz) -Xt, yt = ot.datasets.get_data_classif('gaussrot', n, theta=theta, nz=nz) +noise_level = 0.1 +Xs, ys = ot.datasets.get_data_classif('gaussrot', n, nz=noise_level) +Xs_new, _ = ot.datasets.get_data_classif('gaussrot', n, nz=noise_level) +Xt, yt = ot.datasets.get_data_classif( + 'gaussrot', n, theta=theta, nz=noise_level) # one of the target mode changes its variance (no linear mapping) Xt[yt == 2] *= 3 @@ -46,8 +47,7 @@ ot_mapping_linear = ot.da.MappingTransport( kernel="linear", mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True) -ot_mapping_linear.fit( - Xs=Xs, Xt=Xt) +ot_mapping_linear.fit(Xs=Xs, Xt=Xt) # for original source samples, transform applies barycentric mapping transp_Xs_linear = ot_mapping_linear.transform(Xs=Xs) diff --git a/examples/da/plot_otda_mapping_colors_images.py b/examples/da/plot_otda_mapping_colors_images.py index 4209020..6c024ea 100644 --- a/examples/da/plot_otda_mapping_colors_images.py +++ b/examples/da/plot_otda_mapping_colors_images.py @@ -23,6 +23,8 @@ from scipy import ndimage import matplotlib.pylab as pl import ot +np.random.seed(42) + def im2mat(I): """Converts and image to matrix (one pixel per line)""" -- cgit v1.2.3 From 5a9795f08341458bd9e3befe0c2c6ea6fa891323 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Tue, 29 Aug 2017 13:34:34 +0200 Subject: pass on examples | introduced RandomState --- examples/da/plot_otda_classes.py | 20 +++++++++++++------- examples/da/plot_otda_color_images.py | 19 ++++++++++++++++--- examples/da/plot_otda_d2.py | 12 ++++++++++-- examples/da/plot_otda_mapping.py | 21 ++++++++++++++------- examples/da/plot_otda_mapping_colors_images.py | 9 ++++++--- 5 files changed, 59 insertions(+), 22 deletions(-) diff --git a/examples/da/plot_otda_classes.py b/examples/da/plot_otda_classes.py index 6870fa4..ec57a37 100644 --- a/examples/da/plot_otda_classes.py +++ b/examples/da/plot_otda_classes.py @@ -15,19 +15,23 @@ approaches currently supported in POT. # License: MIT License import matplotlib.pylab as pl -import numpy as np import ot -np.random.seed(42) -# number of source and target points to generate -ns = 150 -nt = 150 +############################################################################## +# generate data +############################################################################## + +n_source_samples = 150 +n_target_samples = 150 + +Xs, ys = ot.datasets.get_data_classif('3gauss', n_source_samples) +Xt, yt = ot.datasets.get_data_classif('3gauss2', n_target_samples) -Xs, ys = ot.datasets.get_data_classif('3gauss', ns) -Xt, yt = ot.datasets.get_data_classif('3gauss2', nt) +############################################################################## # Instantiate the different transport algorithms and fit them +############################################################################## # EMD Transport ot_emd = ot.da.EMDTransport() @@ -52,6 +56,7 @@ transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs) transp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs) + ############################################################################## # Fig 1 : plots source and target samples ############################################################################## @@ -72,6 +77,7 @@ pl.legend(loc=0) pl.title('Target samples') pl.tight_layout() + ############################################################################## # Fig 2 : plot optimal couplings and transported samples ############################################################################## diff --git a/examples/da/plot_otda_color_images.py b/examples/da/plot_otda_color_images.py index 805d0b0..3984afb 100644 --- a/examples/da/plot_otda_color_images.py +++ b/examples/da/plot_otda_color_images.py @@ -22,7 +22,8 @@ from scipy import ndimage import matplotlib.pylab as pl import ot -np.random.seed(42) + +r = np.random.RandomState(42) def im2mat(I): @@ -39,6 +40,10 @@ def minmax(I): return np.clip(I, 0, 1) +############################################################################## +# generate data +############################################################################## + # Loading images I1 = ndimage.imread('../../data/ocean_day.jpg').astype(np.float64) / 256 I2 = ndimage.imread('../../data/ocean_sunset.jpg').astype(np.float64) / 256 @@ -48,12 +53,17 @@ X2 = im2mat(I2) # training samples nb = 1000 -idx1 = np.random.randint(X1.shape[0], size=(nb,)) -idx2 = np.random.randint(X2.shape[0], size=(nb,)) +idx1 = r.randint(X1.shape[0], size=(nb,)) +idx2 = r.randint(X2.shape[0], size=(nb,)) Xs = X1[idx1, :] Xt = X2[idx2, :] + +############################################################################## +# Instantiate the different transport algorithms and fit them +############################################################################## + # EMDTransport ot_emd = ot.da.EMDTransport() ot_emd.fit(Xs=Xs, Xt=Xt) @@ -75,6 +85,7 @@ I2t = minmax(mat2im(transp_Xt_emd, I2.shape)) I1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape)) + ############################################################################## # plot original image ############################################################################## @@ -91,6 +102,7 @@ pl.imshow(I2) pl.axis('off') pl.title('Image 2') + ############################################################################## # scatter plot of colors ############################################################################## @@ -112,6 +124,7 @@ pl.ylabel('Blue') pl.title('Image 2') pl.tight_layout() + ############################################################################## # plot new images ############################################################################## diff --git a/examples/da/plot_otda_d2.py b/examples/da/plot_otda_d2.py index 8833eb2..3daa0a6 100644 --- a/examples/da/plot_otda_d2.py +++ b/examples/da/plot_otda_d2.py @@ -19,10 +19,12 @@ of what the transport methods are doing. # License: MIT License import matplotlib.pylab as pl -import numpy as np import ot -np.random.seed(42) + +############################################################################## +# generate data +############################################################################## n_samples_source = 150 n_samples_target = 150 @@ -33,7 +35,10 @@ Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target) # Cost matrix M = ot.dist(Xs, Xt, metric='sqeuclidean') + +############################################################################## # Instantiate the different transport algorithms and fit them +############################################################################## # EMD Transport ot_emd = ot.da.EMDTransport() @@ -52,6 +57,7 @@ transp_Xs_emd = ot_emd.transform(Xs=Xs) transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs) transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs) + ############################################################################## # Fig 1 : plots source and target samples + matrix of pairwise distance ############################################################################## @@ -78,6 +84,7 @@ pl.yticks([]) pl.title('Matrix of pairwise distances') pl.tight_layout() + ############################################################################## # Fig 2 : plots optimal couplings for the different methods ############################################################################## @@ -127,6 +134,7 @@ pl.yticks([]) pl.title('Main coupling coefficients\nSinkhornLpl1Transport') pl.tight_layout() + ############################################################################## # Fig 3 : plot transported samples ############################################################################## diff --git a/examples/da/plot_otda_mapping.py b/examples/da/plot_otda_mapping.py index aea7f09..09d2cb4 100644 --- a/examples/da/plot_otda_mapping.py +++ b/examples/da/plot_otda_mapping.py @@ -23,25 +23,31 @@ import matplotlib.pylab as pl import ot -np.random.seed(42) - ############################################################################## -# generate +# generate data ############################################################################## -n = 100 # nb samples in source and target datasets +n_source_samples = 100 +n_target_samples = 100 theta = 2 * np.pi / 20 noise_level = 0.1 -Xs, ys = ot.datasets.get_data_classif('gaussrot', n, nz=noise_level) -Xs_new, _ = ot.datasets.get_data_classif('gaussrot', n, nz=noise_level) + +Xs, ys = ot.datasets.get_data_classif( + 'gaussrot', n_source_samples, nz=noise_level) +Xs_new, _ = ot.datasets.get_data_classif( + 'gaussrot', n_source_samples, nz=noise_level) Xt, yt = ot.datasets.get_data_classif( - 'gaussrot', n, theta=theta, nz=noise_level) + 'gaussrot', n_target_samples, theta=theta, nz=noise_level) # one of the target mode changes its variance (no linear mapping) Xt[yt == 2] *= 3 Xt = Xt + 4 +############################################################################## +# Instantiate the different transport algorithms and fit them +############################################################################## + # MappingTransport with linear kernel ot_mapping_linear = ot.da.MappingTransport( kernel="linear", mu=1e0, eta=1e-8, bias=True, @@ -80,6 +86,7 @@ pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') pl.legend(loc=0) pl.title('Source and target distributions') + ############################################################################## # plot transported samples ############################################################################## diff --git a/examples/da/plot_otda_mapping_colors_images.py b/examples/da/plot_otda_mapping_colors_images.py index 6c024ea..a628b05 100644 --- a/examples/da/plot_otda_mapping_colors_images.py +++ b/examples/da/plot_otda_mapping_colors_images.py @@ -23,7 +23,7 @@ from scipy import ndimage import matplotlib.pylab as pl import ot -np.random.seed(42) +r = np.random.RandomState(42) def im2mat(I): @@ -54,8 +54,8 @@ X2 = im2mat(I2) # training samples nb = 1000 -idx1 = np.random.randint(X1.shape[0], size=(nb,)) -idx2 = np.random.randint(X2.shape[0], size=(nb,)) +idx1 = r.randint(X1.shape[0], size=(nb,)) +idx2 = r.randint(X2.shape[0], size=(nb,)) Xs = X1[idx1, :] Xt = X2[idx2, :] @@ -91,6 +91,7 @@ ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt) X1tn = ot_mapping_gaussian.transform(Xs=X1) # use the estimated mapping Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape)) + ############################################################################## # plot original images ############################################################################## @@ -107,6 +108,7 @@ pl.axis('off') pl.title('Image 2') pl.tight_layout() + ############################################################################## # plot pixel values distribution ############################################################################## @@ -128,6 +130,7 @@ pl.ylabel('Blue') pl.title('Image 2') pl.tight_layout() + ############################################################################## # plot transformed images ############################################################################## -- cgit v1.2.3 From 6ae3ad7bb48b1fa8964cfd2791bdb86267776495 Mon Sep 17 00:00:00 2001 From: aje Date: Tue, 29 Aug 2017 15:38:11 +0200 Subject: Changes to LP solver: - Allow to modify the maximal number of iterations - Display an error message in the python console if the solver encountered an issue --- ot/da.py | 4 ++-- ot/lp/EMD.h | 7 ++++++- ot/lp/EMD_wrapper.cpp | 7 +++---- ot/lp/__init__.py | 16 ++++++++++------ ot/lp/emd_wrap.pyx | 25 ++++++++++++++++++++----- 5 files changed, 41 insertions(+), 18 deletions(-) diff --git a/ot/da.py b/ot/da.py index 78dc150..0dfd02f 100644 --- a/ot/da.py +++ b/ot/da.py @@ -658,7 +658,7 @@ class OTDA(object): self.metric = metric self.computed = False - def fit(self, xs, xt, ws=None, wt=None, norm=None): + def fit(self, xs, xt, ws=None, wt=None, norm=None, numItermax=10000): """Fit domain adaptation between samples is xs and xt (with optional weights)""" self.xs = xs @@ -674,7 +674,7 @@ class OTDA(object): self.M = dist(xs, xt, metric=self.metric) self.normalizeM(norm) - self.G = emd(ws, wt, self.M) + self.G = emd(ws, wt, self.M, numItermax) self.computed = True def interp(self, direction=1): diff --git a/ot/lp/EMD.h b/ot/lp/EMD.h index 40d7192..956830c 100644 --- a/ot/lp/EMD.h +++ b/ot/lp/EMD.h @@ -23,7 +23,12 @@ using namespace lemon; typedef unsigned int node_id_type; +enum ProblemType { + INFEASIBLE, + OPTIMAL, + UNBOUNDED +}; -void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost); +int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int numItermax); #endif diff --git a/ot/lp/EMD_wrapper.cpp b/ot/lp/EMD_wrapper.cpp index cad4750..83ed56c 100644 --- a/ot/lp/EMD_wrapper.cpp +++ b/ot/lp/EMD_wrapper.cpp @@ -15,11 +15,10 @@ #include "EMD.h" -void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost) { +int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int numItermax) { // beware M and C anre strored in row major C style!!! int n, m, i,cur; double max; - int max_iter=10000; typedef FullBipartiteDigraph Digraph; DIGRAPH_TYPEDEFS(FullBipartiteDigraph); @@ -46,7 +45,7 @@ void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double * std::vector indI(n), indJ(m); std::vector weights1(n), weights2(m); Digraph di(n, m); - NetworkSimplexSimple net(di, true, n+m, n*m,max_iter); + NetworkSimplexSimple net(di, true, n+m, n*m, numItermax); // Set supply and demand, don't account for 0 values (faster) @@ -116,5 +115,5 @@ void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double * }; - + return ret; } diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 6e0bdb8..62afe76 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -15,7 +15,7 @@ import multiprocessing -def emd(a, b, M): +def emd(a, b, M, numItermax=10000): """Solves the Earth Movers distance problem and returns the OT matrix @@ -40,6 +40,8 @@ def emd(a, b, M): Target histogram (uniform weigth if empty list) M : (ns,nt) ndarray, float64 loss matrix + numItermax : int + Maximum number of iterations made by the LP solver. Returns ------- @@ -84,9 +86,9 @@ def emd(a, b, M): if len(b) == 0: b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1] - return emd_c(a, b, M) + return emd_c(a, b, M, numItermax) -def emd2(a, b, M,processes=multiprocessing.cpu_count()): +def emd2(a, b, M, numItermax=10000, processes=multiprocessing.cpu_count()): """Solves the Earth Movers distance problem and returns the loss .. math:: @@ -110,6 +112,8 @@ def emd2(a, b, M,processes=multiprocessing.cpu_count()): Target histogram (uniform weigth if empty list) M : (ns,nt) ndarray, float64 loss matrix + numItermax : int + Maximum number of iterations made by the LP solver. Returns ------- @@ -155,12 +159,12 @@ def emd2(a, b, M,processes=multiprocessing.cpu_count()): b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1] if len(b.shape)==1: - return emd2_c(a, b, M) + return emd2_c(a, b, M, numItermax) else: nb=b.shape[1] - #res=[emd2_c(a,b[:,i].copy(),M) for i in range(nb)] + #res=[emd2_c(a,b[:,i].copy(),M, numItermax) for i in range(nb)] def f(b): - return emd2_c(a,b,M) + return emd2_c(a,b,M, numItermax) res= parmap(f, [b[:,i] for i in range(nb)],processes) return np.array(res) diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 46c96c1..ed8c416 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -15,13 +15,14 @@ cimport cython cdef extern from "EMD.h": - void EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost) + int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int numItermax) + cdef enum ProblemType: INFEASIBLE, OPTIMAL, UNBOUNDED @cython.boundscheck(False) @cython.wraparound(False) -def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M): +def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M, int numItermax): """ Solves the Earth Movers distance problem and returns the optimal transport matrix @@ -48,6 +49,8 @@ def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mod target histogram M : (ns,nt) ndarray, float64 loss matrix + numItermax : int + Maximum number of iterations made by the LP solver. Returns @@ -69,13 +72,18 @@ def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mod b=np.ones((n2,))/n2 # calling the function - EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost) + cdef int resultSolver = EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost, numItermax) + if resultSolver != OPTIMAL: + if resultSolver == INFEASIBLE: + print("Problem infeasible. Try to inscrease numItermax.") + elif resultSolver == UNBOUNDED: + print("Problem unbounded") return G @cython.boundscheck(False) @cython.wraparound(False) -def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M): +def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M, int numItermax): """ Solves the Earth Movers distance problem and returns the optimal transport loss @@ -102,6 +110,8 @@ def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mo target histogram M : (ns,nt) ndarray, float64 loss matrix + numItermax : int + Maximum number of iterations made by the LP solver. Returns @@ -123,7 +133,12 @@ def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mo b=np.ones((n2,))/n2 # calling the function - EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost) + cdef int resultSolver = EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost, numItermax) + if resultSolver != OPTIMAL: + if resultSolver == INFEASIBLE: + print("Problem infeasible. Try to inscrease numItermax.") + elif resultSolver == UNBOUNDED: + print("Problem unbounded") cost=0 for i in range(n1): -- cgit v1.2.3 From b5629270b07499524072053530a993b1c52c1a1a Mon Sep 17 00:00:00 2001 From: aje Date: Tue, 29 Aug 2017 16:53:06 +0200 Subject: Fix param order --- ot/lp/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 62afe76..5143a70 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -88,7 +88,7 @@ def emd(a, b, M, numItermax=10000): return emd_c(a, b, M, numItermax) -def emd2(a, b, M, numItermax=10000, processes=multiprocessing.cpu_count()): +def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=10000): """Solves the Earth Movers distance problem and returns the loss .. math:: -- cgit v1.2.3 From 0f7cd9237f8ac3596c0a7dfdd4d543345a34ae6b Mon Sep 17 00:00:00 2001 From: aje Date: Tue, 29 Aug 2017 17:01:17 +0200 Subject: Type print --- ot/lp/emd_wrap.pyx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index ed8c416..6039e1f 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -75,7 +75,7 @@ def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mod cdef int resultSolver = EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost, numItermax) if resultSolver != OPTIMAL: if resultSolver == INFEASIBLE: - print("Problem infeasible. Try to inscrease numItermax.") + print("Problem infeasible. Try to increase numItermax.") elif resultSolver == UNBOUNDED: print("Problem unbounded") -- cgit v1.2.3 From ceeb063541fa71d4ddd7b13b043f985dc5bcab14 Mon Sep 17 00:00:00 2001 From: aje Date: Wed, 30 Aug 2017 09:56:37 +0200 Subject: Changes: - Rename numItermax to max_iter - Default value to 100000 instead of 10000 - Add max_iter to class SinkhornTransport(BaseTransport) - Add norm to all BaseTransport --- ot/da.py | 64 ++++++++++++++++++++++++++++++++++------ ot/lp/EMD.h | 2 +- ot/lp/EMD_wrapper.cpp | 4 +-- ot/lp/__init__.py | 46 ++++++++++++++--------------- ot/lp/emd_wrap.pyx | 82 ++++++++++++++++++++++++++------------------------- 5 files changed, 123 insertions(+), 75 deletions(-) diff --git a/ot/da.py b/ot/da.py index 0dfd02f..5871aba 100644 --- a/ot/da.py +++ b/ot/da.py @@ -658,7 +658,7 @@ class OTDA(object): self.metric = metric self.computed = False - def fit(self, xs, xt, ws=None, wt=None, norm=None, numItermax=10000): + def fit(self, xs, xt, ws=None, wt=None, norm=None, max_iter=100000): """Fit domain adaptation between samples is xs and xt (with optional weights)""" self.xs = xs @@ -674,7 +674,7 @@ class OTDA(object): self.M = dist(xs, xt, metric=self.metric) self.normalizeM(norm) - self.G = emd(ws, wt, self.M, numItermax) + self.G = emd(ws, wt, self.M, max_iter) self.computed = True def interp(self, direction=1): @@ -1001,6 +1001,7 @@ class BaseTransport(BaseEstimator): # pairwise distance self.cost_ = dist(Xs, Xt, metric=self.metric) + self.normalizeCost_(self.norm) if (ys is not None) and (yt is not None): @@ -1182,6 +1183,26 @@ class BaseTransport(BaseEstimator): return transp_Xt + def normalizeCost_(self, norm): + """ Apply normalization to the loss matrix + + + Parameters + ---------- + norm : str + type of normalization from 'median','max','log','loglog' + + """ + + if norm == "median": + self.cost_ /= float(np.median(self.cost_)) + elif norm == "max": + self.cost_ /= float(np.max(self.cost_)) + elif norm == "log": + self.cost_ = np.log(1 + self.cost_) + elif norm == "loglog": + self.cost_ = np.log(1 + np.log(1 + self.cost_)) + class SinkhornTransport(BaseTransport): """Domain Adapatation OT method based on Sinkhorn Algorithm @@ -1202,6 +1223,9 @@ class SinkhornTransport(BaseTransport): be transported from a domain to another one. metric : string, optional (default="sqeuclidean") The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. distribution : string, optional (default="uniform") The kind of distribution estimation to employ verbose : int, optional (default=0) @@ -1231,7 +1255,7 @@ class SinkhornTransport(BaseTransport): def __init__(self, reg_e=1., max_iter=1000, tol=10e-9, verbose=False, log=False, - metric="sqeuclidean", + metric="sqeuclidean", norm=None, distribution_estimation=distribution_estimation_uniform, out_of_sample_map='ferradans', limit_max=np.infty): @@ -1241,6 +1265,7 @@ class SinkhornTransport(BaseTransport): self.verbose = verbose self.log = log self.metric = metric + self.norm = norm self.limit_max = limit_max self.distribution_estimation = distribution_estimation self.out_of_sample_map = out_of_sample_map @@ -1296,6 +1321,9 @@ class EMDTransport(BaseTransport): be transported from a domain to another one. metric : string, optional (default="sqeuclidean") The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. distribution : string, optional (default="uniform") The kind of distribution estimation to employ verbose : int, optional (default=0) @@ -1306,6 +1334,9 @@ class EMDTransport(BaseTransport): Controls the semi supervised mode. Transport between labeled source and target samples of different classes will exhibit an infinite cost (10 times the maximum value of the cost matrix) + max_iter : int, optional (default=100000) + The maximum number of iterations before stopping the optimization + algorithm if it has not converged. Attributes ---------- @@ -1319,14 +1350,17 @@ class EMDTransport(BaseTransport): on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 """ - def __init__(self, metric="sqeuclidean", + def __init__(self, metric="sqeuclidean", norm=None, distribution_estimation=distribution_estimation_uniform, - out_of_sample_map='ferradans', limit_max=10): + out_of_sample_map='ferradans', limit_max=10, + max_iter=100000): self.metric = metric + self.norm = norm self.limit_max = limit_max self.distribution_estimation = distribution_estimation self.out_of_sample_map = out_of_sample_map + self.max_iter = max_iter def fit(self, Xs, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples @@ -1353,7 +1387,7 @@ class EMDTransport(BaseTransport): # coupling estimation self.coupling_ = emd( - a=self.mu_s, b=self.mu_t, M=self.cost_, + a=self.mu_s, b=self.mu_t, M=self.cost_, max_iter=self.max_iter ) return self @@ -1376,6 +1410,9 @@ class SinkhornLpl1Transport(BaseTransport): be transported from a domain to another one. metric : string, optional (default="sqeuclidean") The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. distribution : string, optional (default="uniform") The kind of distribution estimation to employ max_iter : int, float, optional (default=10) @@ -1410,7 +1447,7 @@ class SinkhornLpl1Transport(BaseTransport): def __init__(self, reg_e=1., reg_cl=0.1, max_iter=10, max_inner_iter=200, tol=10e-9, verbose=False, - metric="sqeuclidean", + metric="sqeuclidean", norm=None, distribution_estimation=distribution_estimation_uniform, out_of_sample_map='ferradans', limit_max=np.infty): @@ -1421,6 +1458,7 @@ class SinkhornLpl1Transport(BaseTransport): self.tol = tol self.verbose = verbose self.metric = metric + self.norm = norm self.distribution_estimation = distribution_estimation self.out_of_sample_map = out_of_sample_map self.limit_max = limit_max @@ -1477,6 +1515,9 @@ class SinkhornL1l2Transport(BaseTransport): be transported from a domain to another one. metric : string, optional (default="sqeuclidean") The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. distribution : string, optional (default="uniform") The kind of distribution estimation to employ max_iter : int, float, optional (default=10) @@ -1516,7 +1557,7 @@ class SinkhornL1l2Transport(BaseTransport): def __init__(self, reg_e=1., reg_cl=0.1, max_iter=10, max_inner_iter=200, tol=10e-9, verbose=False, log=False, - metric="sqeuclidean", + metric="sqeuclidean", norm=None, distribution_estimation=distribution_estimation_uniform, out_of_sample_map='ferradans', limit_max=10): @@ -1528,6 +1569,7 @@ class SinkhornL1l2Transport(BaseTransport): self.verbose = verbose self.log = log self.metric = metric + self.norm = norm self.distribution_estimation = distribution_estimation self.out_of_sample_map = out_of_sample_map self.limit_max = limit_max @@ -1588,6 +1630,9 @@ class MappingTransport(BaseEstimator): Estimate linear mapping with constant bias metric : string, optional (default="sqeuclidean") The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. kernel : string, optional (default="linear") The kernel to use either linear or gaussian sigma : float, optional (default=1) @@ -1627,11 +1672,12 @@ class MappingTransport(BaseEstimator): """ def __init__(self, mu=1, eta=0.001, bias=False, metric="sqeuclidean", - kernel="linear", sigma=1, max_iter=100, tol=1e-5, + norm=None, kernel="linear", sigma=1, max_iter=100, tol=1e-5, max_inner_iter=10, inner_tol=1e-6, log=False, verbose=False, verbose2=False): self.metric = metric + self.norm = norm self.mu = mu self.eta = eta self.bias = bias diff --git a/ot/lp/EMD.h b/ot/lp/EMD.h index 956830c..aa92441 100644 --- a/ot/lp/EMD.h +++ b/ot/lp/EMD.h @@ -29,6 +29,6 @@ enum ProblemType { UNBOUNDED }; -int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int numItermax); +int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int max_iter); #endif diff --git a/ot/lp/EMD_wrapper.cpp b/ot/lp/EMD_wrapper.cpp index 83ed56c..c8c2eb3 100644 --- a/ot/lp/EMD_wrapper.cpp +++ b/ot/lp/EMD_wrapper.cpp @@ -15,7 +15,7 @@ #include "EMD.h" -int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int numItermax) { +int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int max_iter) { // beware M and C anre strored in row major C style!!! int n, m, i,cur; double max; @@ -45,7 +45,7 @@ int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *c std::vector indI(n), indJ(m); std::vector weights1(n), weights2(m); Digraph di(n, m); - NetworkSimplexSimple net(di, true, n+m, n*m, numItermax); + NetworkSimplexSimple net(di, true, n+m, n*m, max_iter); // Set supply and demand, don't account for 0 values (faster) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 5143a70..7bef648 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -14,8 +14,7 @@ from ..utils import parmap import multiprocessing - -def emd(a, b, M, numItermax=10000): +def emd(a, b, M, max_iter=100000): """Solves the Earth Movers distance problem and returns the OT matrix @@ -40,8 +39,9 @@ def emd(a, b, M, numItermax=10000): Target histogram (uniform weigth if empty list) M : (ns,nt) ndarray, float64 loss matrix - numItermax : int - Maximum number of iterations made by the LP solver. + max_iter : int, optional (default=100000) + The maximum number of iterations before stopping the optimization + algorithm if it has not converged. Returns ------- @@ -54,7 +54,7 @@ def emd(a, b, M, numItermax=10000): Simple example with obvious solution. The function emd accepts lists and perform automatic conversion to numpy arrays - + >>> import ot >>> a=[.5,.5] >>> b=[.5,.5] @@ -86,10 +86,11 @@ def emd(a, b, M, numItermax=10000): if len(b) == 0: b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1] - return emd_c(a, b, M, numItermax) + return emd_c(a, b, M, max_iter) + -def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=10000): - """Solves the Earth Movers distance problem and returns the loss +def emd2(a, b, M, processes=multiprocessing.cpu_count(), max_iter=100000): + """Solves the Earth Movers distance problem and returns the loss .. math:: \gamma = arg\min_\gamma <\gamma,M>_F @@ -112,8 +113,9 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=10000): Target histogram (uniform weigth if empty list) M : (ns,nt) ndarray, float64 loss matrix - numItermax : int - Maximum number of iterations made by the LP solver. + max_iter : int, optional (default=100000) + The maximum number of iterations before stopping the optimization + algorithm if it has not converged. Returns ------- @@ -126,15 +128,15 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=10000): Simple example with obvious solution. The function emd accepts lists and perform automatic conversion to numpy arrays - - + + >>> import ot >>> a=[.5,.5] >>> b=[.5,.5] >>> M=[[0.,1.],[1.,0.]] >>> ot.emd2(a,b,M) 0.0 - + References ---------- @@ -157,16 +159,14 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=10000): a = np.ones((M.shape[0], ), dtype=np.float64)/M.shape[0] if len(b) == 0: b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1] - - if len(b.shape)==1: - return emd2_c(a, b, M, numItermax) + + if len(b.shape) == 1: + return emd2_c(a, b, M, max_iter) else: - nb=b.shape[1] - #res=[emd2_c(a,b[:,i].copy(),M, numItermax) for i in range(nb)] + nb = b.shape[1] + # res = [emd2_c(a, b[:, i].copy(), M, max_iter) for i in range(nb)] + def f(b): - return emd2_c(a,b,M, numItermax) - res= parmap(f, [b[:,i] for i in range(nb)],processes) + return emd2_c(a, b, M, max_iter) + res = parmap(f, [b[:, i] for i in range(nb)], processes) return np.array(res) - - - \ No newline at end of file diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 6039e1f..26d3330 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -15,56 +15,57 @@ cimport cython cdef extern from "EMD.h": - int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int numItermax) + int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double *cost, int max_iter) cdef enum ProblemType: INFEASIBLE, OPTIMAL, UNBOUNDED @cython.boundscheck(False) @cython.wraparound(False) -def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M, int numItermax): +def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M, int max_iter): """ Solves the Earth Movers distance problem and returns the optimal transport matrix - + gamm=emd(a,b,M) - + .. math:: - \gamma = arg\min_\gamma <\gamma,M>_F - + \gamma = arg\min_\gamma <\gamma,M>_F + s.t. \gamma 1 = a - - \gamma^T 1= b - + + \gamma^T 1= b + \gamma\geq 0 where : - + - M is the metric cost matrix - a and b are the sample weights - + Parameters ---------- a : (ns,) ndarray, float64 - source histogram + source histogram b : (nt,) ndarray, float64 target histogram M : (ns,nt) ndarray, float64 - loss matrix - numItermax : int - Maximum number of iterations made by the LP solver. - - + loss matrix + max_iter : int + The maximum number of iterations before stopping the optimization + algorithm if it has not converged. + + Returns ------- gamma: (ns x nt) ndarray Optimal transportation matrix for the given parameters - + """ cdef int n1= M.shape[0] cdef int n2= M.shape[1] cdef float cost=0 cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([n1, n2]) - + if not len(a): a=np.ones((n1,))/n1 @@ -72,7 +73,7 @@ def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mod b=np.ones((n2,))/n2 # calling the function - cdef int resultSolver = EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost, numItermax) + cdef int resultSolver = EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost, max_iter) if resultSolver != OPTIMAL: if resultSolver == INFEASIBLE: print("Problem infeasible. Try to increase numItermax.") @@ -83,49 +84,50 @@ def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mod @cython.boundscheck(False) @cython.wraparound(False) -def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M, int numItermax): +def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M, int max_iter): """ Solves the Earth Movers distance problem and returns the optimal transport loss - + gamm=emd(a,b,M) - + .. math:: - \gamma = arg\min_\gamma <\gamma,M>_F - + \gamma = arg\min_\gamma <\gamma,M>_F + s.t. \gamma 1 = a - - \gamma^T 1= b - + + \gamma^T 1= b + \gamma\geq 0 where : - + - M is the metric cost matrix - a and b are the sample weights - + Parameters ---------- a : (ns,) ndarray, float64 - source histogram + source histogram b : (nt,) ndarray, float64 target histogram M : (ns,nt) ndarray, float64 - loss matrix - numItermax : int - Maximum number of iterations made by the LP solver. - - + loss matrix + max_iter : int + The maximum number of iterations before stopping the optimization + algorithm if it has not converged. + + Returns ------- gamma: (ns x nt) ndarray Optimal transportation matrix for the given parameters - + """ cdef int n1= M.shape[0] cdef int n2= M.shape[1] cdef float cost=0 cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([n1, n2]) - + if not len(a): a=np.ones((n1,))/n1 @@ -133,13 +135,13 @@ def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mo b=np.ones((n2,))/n2 # calling the function - cdef int resultSolver = EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost, numItermax) + cdef int resultSolver = EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, &cost, max_iter) if resultSolver != OPTIMAL: if resultSolver == INFEASIBLE: print("Problem infeasible. Try to inscrease numItermax.") elif resultSolver == UNBOUNDED: print("Problem unbounded") - + cost=0 for i in range(n1): for j in range(n2): -- cgit v1.2.3 From 8875f653e57aa11c8d62d291abb16fdbeff65511 Mon Sep 17 00:00:00 2001 From: aje Date: Wed, 30 Aug 2017 10:06:41 +0200 Subject: Rename for emd and emd2 --- ot/lp/__init__.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 7bef648..de91e74 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -14,7 +14,7 @@ from ..utils import parmap import multiprocessing -def emd(a, b, M, max_iter=100000): +def emd(a, b, M, numItermax=100000): """Solves the Earth Movers distance problem and returns the OT matrix @@ -39,7 +39,7 @@ def emd(a, b, M, max_iter=100000): Target histogram (uniform weigth if empty list) M : (ns,nt) ndarray, float64 loss matrix - max_iter : int, optional (default=100000) + numItermax : int, optional (default=100000) The maximum number of iterations before stopping the optimization algorithm if it has not converged. @@ -86,10 +86,10 @@ def emd(a, b, M, max_iter=100000): if len(b) == 0: b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1] - return emd_c(a, b, M, max_iter) + return emd_c(a, b, M, numItermax) -def emd2(a, b, M, processes=multiprocessing.cpu_count(), max_iter=100000): +def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=100000): """Solves the Earth Movers distance problem and returns the loss .. math:: @@ -113,7 +113,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), max_iter=100000): Target histogram (uniform weigth if empty list) M : (ns,nt) ndarray, float64 loss matrix - max_iter : int, optional (default=100000) + numItermax : int, optional (default=100000) The maximum number of iterations before stopping the optimization algorithm if it has not converged. @@ -161,12 +161,12 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), max_iter=100000): b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1] if len(b.shape) == 1: - return emd2_c(a, b, M, max_iter) + return emd2_c(a, b, M, numItermax) else: nb = b.shape[1] - # res = [emd2_c(a, b[:, i].copy(), M, max_iter) for i in range(nb)] + # res = [emd2_c(a, b[:, i].copy(), M, numItermax) for i in range(nb)] def f(b): - return emd2_c(a, b, M, max_iter) + return emd2_c(a, b, M, numItermax) res = parmap(f, [b[:, i] for i in range(nb)], processes) return np.array(res) -- cgit v1.2.3 From 5076131b3954e3e7951f65f4c05d959b68072b97 Mon Sep 17 00:00:00 2001 From: aje Date: Wed, 30 Aug 2017 10:22:48 +0200 Subject: Fix name error --- ot/da.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/da.py b/ot/da.py index 5871aba..b4a69b1 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1387,7 +1387,7 @@ class EMDTransport(BaseTransport): # coupling estimation self.coupling_ = emd( - a=self.mu_s, b=self.mu_t, M=self.cost_, max_iter=self.max_iter + a=self.mu_s, b=self.mu_t, M=self.cost_, numItermax=self.max_iter ) return self -- cgit v1.2.3 From 6d602304c08b6dbbb310814f83c5cf03da35cafd Mon Sep 17 00:00:00 2001 From: aje Date: Wed, 30 Aug 2017 10:53:31 +0200 Subject: Move normalize function in utils.py --- ot/da.py | 52 ++++++---------------------------------------------- ot/utils.py | 33 +++++++++++++++++++++++++++++++++ 2 files changed, 39 insertions(+), 46 deletions(-) diff --git a/ot/da.py b/ot/da.py index b4a69b1..61a3ba0 100644 --- a/ot/da.py +++ b/ot/da.py @@ -13,7 +13,7 @@ import numpy as np from .bregman import sinkhorn from .lp import emd -from .utils import unif, dist, kernel +from .utils import unif, dist, kernel, cost_normalization from .utils import check_params, deprecated, BaseEstimator from .optim import cg from .optim import gcg @@ -673,7 +673,7 @@ class OTDA(object): self.wt = wt self.M = dist(xs, xt, metric=self.metric) - self.normalizeM(norm) + self.M = cost_normalization(self.M, norm) self.G = emd(ws, wt, self.M, max_iter) self.computed = True @@ -741,26 +741,6 @@ class OTDA(object): # aply the delta to the interpolation return xf[idx, :] + x - x0[idx, :] - def normalizeM(self, norm): - """ Apply normalization to the loss matrix - - - Parameters - ---------- - norm : str - type of normalization from 'median','max','log','loglog' - - """ - - if norm == "median": - self.M /= float(np.median(self.M)) - elif norm == "max": - self.M /= float(np.max(self.M)) - elif norm == "log": - self.M = np.log(1 + self.M) - elif norm == "loglog": - self.M = np.log(1 + np.log(1 + self.M)) - @deprecated("The class OTDA_sinkhorn is deprecated in 0.3.1 and will be" " removed in 0.5 \nUse class SinkhornTransport instead.") @@ -787,7 +767,7 @@ class OTDA_sinkhorn(OTDA): self.wt = wt self.M = dist(xs, xt, metric=self.metric) - self.normalizeM(norm) + self.M = cost_normalization(self.M, norm) self.G = sinkhorn(ws, wt, self.M, reg, **kwargs) self.computed = True @@ -816,7 +796,7 @@ class OTDA_lpl1(OTDA): self.wt = wt self.M = dist(xs, xt, metric=self.metric) - self.normalizeM(norm) + self.M = cost_normalization(self.M, norm) self.G = sinkhorn_lpl1_mm(ws, ys, wt, self.M, reg, eta, **kwargs) self.computed = True @@ -845,7 +825,7 @@ class OTDA_l1l2(OTDA): self.wt = wt self.M = dist(xs, xt, metric=self.metric) - self.normalizeM(norm) + self.M = cost_normalization(self.M, norm) self.G = sinkhorn_l1l2_gl(ws, ys, wt, self.M, reg, eta, **kwargs) self.computed = True @@ -1001,7 +981,7 @@ class BaseTransport(BaseEstimator): # pairwise distance self.cost_ = dist(Xs, Xt, metric=self.metric) - self.normalizeCost_(self.norm) + self.cost_ = cost_normalization(self.cost_, self.norm) if (ys is not None) and (yt is not None): @@ -1183,26 +1163,6 @@ class BaseTransport(BaseEstimator): return transp_Xt - def normalizeCost_(self, norm): - """ Apply normalization to the loss matrix - - - Parameters - ---------- - norm : str - type of normalization from 'median','max','log','loglog' - - """ - - if norm == "median": - self.cost_ /= float(np.median(self.cost_)) - elif norm == "max": - self.cost_ /= float(np.max(self.cost_)) - elif norm == "log": - self.cost_ = np.log(1 + self.cost_) - elif norm == "loglog": - self.cost_ = np.log(1 + np.log(1 + self.cost_)) - class SinkhornTransport(BaseTransport): """Domain Adapatation OT method based on Sinkhorn Algorithm diff --git a/ot/utils.py b/ot/utils.py index 01f2a67..31a002b 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -134,6 +134,39 @@ def dist0(n, method='lin_square'): return res +def cost_normalization(C, norm=None): + """ Apply normalization to the loss matrix + + + Parameters + ---------- + C : np.array (n1, n2) + The cost matrix to normalize. + norm : str + type of normalization from 'median','max','log','loglog'. Any other + value do not normalize. + + + Returns + ------- + + C : np.array (n1, n2) + The input cost matrix normalized according to given norm. + + """ + + if norm == "median": + C /= float(np.median(C)) + elif norm == "max": + C /= float(np.max(C)) + elif norm == "log": + C = np.log(1 + C) + elif norm == "loglog": + C = np.log(1 + np.log(1 + C)) + + return C + + def dots(*args): """ dots function for multiple matrix multiply """ return reduce(np.dot, args) -- cgit v1.2.3 From 93dee553a3dd5d6e3c5a5d325bb6333e8eb24dee Mon Sep 17 00:00:00 2001 From: aje Date: Wed, 30 Aug 2017 11:25:03 +0200 Subject: Move norm out of fit to init for deprecated OTDA --- ot/da.py | 21 ++++++++++----------- 1 file changed, 10 insertions(+), 11 deletions(-) diff --git a/ot/da.py b/ot/da.py index 61a3ba0..564c7b7 100644 --- a/ot/da.py +++ b/ot/da.py @@ -650,15 +650,16 @@ class OTDA(object): """ - def __init__(self, metric='sqeuclidean'): + def __init__(self, metric='sqeuclidean', norm=None): """ Class initialization""" self.xs = 0 self.xt = 0 self.G = 0 self.metric = metric + self.norm = norm self.computed = False - def fit(self, xs, xt, ws=None, wt=None, norm=None, max_iter=100000): + def fit(self, xs, xt, ws=None, wt=None, max_iter=100000): """Fit domain adaptation between samples is xs and xt (with optional weights)""" self.xs = xs @@ -673,7 +674,7 @@ class OTDA(object): self.wt = wt self.M = dist(xs, xt, metric=self.metric) - self.M = cost_normalization(self.M, norm) + self.M = cost_normalization(self.M, self.norm) self.G = emd(ws, wt, self.M, max_iter) self.computed = True @@ -752,7 +753,7 @@ class OTDA_sinkhorn(OTDA): """ - def fit(self, xs, xt, reg=1, ws=None, wt=None, norm=None, **kwargs): + def fit(self, xs, xt, reg=1, ws=None, wt=None, **kwargs): """Fit regularized domain adaptation between samples is xs and xt (with optional weights)""" self.xs = xs @@ -767,7 +768,7 @@ class OTDA_sinkhorn(OTDA): self.wt = wt self.M = dist(xs, xt, metric=self.metric) - self.M = cost_normalization(self.M, norm) + self.M = cost_normalization(self.M, self.norm) self.G = sinkhorn(ws, wt, self.M, reg, **kwargs) self.computed = True @@ -779,8 +780,7 @@ class OTDA_lpl1(OTDA): """Class for domain adaptation with optimal transport with entropic and group regularization""" - def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None, - **kwargs): + def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, **kwargs): """Fit regularized domain adaptation between samples is xs and xt (with optional weights), See ot.da.sinkhorn_lpl1_mm for fit parameters""" @@ -796,7 +796,7 @@ class OTDA_lpl1(OTDA): self.wt = wt self.M = dist(xs, xt, metric=self.metric) - self.M = cost_normalization(self.M, norm) + self.M = cost_normalization(self.M, self.norm) self.G = sinkhorn_lpl1_mm(ws, ys, wt, self.M, reg, eta, **kwargs) self.computed = True @@ -808,8 +808,7 @@ class OTDA_l1l2(OTDA): """Class for domain adaptation with optimal transport with entropic and group lasso regularization""" - def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None, - **kwargs): + def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, **kwargs): """Fit regularized domain adaptation between samples is xs and xt (with optional weights), See ot.da.sinkhorn_lpl1_gl for fit parameters""" @@ -825,7 +824,7 @@ class OTDA_l1l2(OTDA): self.wt = wt self.M = dist(xs, xt, metric=self.metric) - self.M = cost_normalization(self.M, norm) + self.M = cost_normalization(self.M, self.norm) self.G = sinkhorn_l1l2_gl(ws, ys, wt, self.M, reg, eta, **kwargs) self.computed = True -- cgit v1.2.3 From 8c525174bb664cafa98dfff73dce9d42d7818f71 Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Thu, 31 Aug 2017 16:44:18 +0200 Subject: Minor corrections suggested by @agramfort + new barycenter example + test function --- README.md | 2 +- data/carre.png | Bin 0 -> 168 bytes data/coeur.png | Bin 0 -> 225 bytes data/rond.png | Bin 0 -> 230 bytes data/triangle.png | Bin 0 -> 254 bytes examples/plot_gromov.py | 14 +-- examples/plot_gromov_barycenter.py | 240 +++++++++++++++++++++++++++++++++++++ ot/gromov.py | 36 +++--- test/test_gromov.py | 38 ++++++ 9 files changed, 302 insertions(+), 28 deletions(-) create mode 100755 data/carre.png create mode 100755 data/coeur.png create mode 100755 data/rond.png create mode 100755 data/triangle.png create mode 100755 examples/plot_gromov_barycenter.py create mode 100644 test/test_gromov.py diff --git a/README.md b/README.md index 257244b..22b20a4 100644 --- a/README.md +++ b/README.md @@ -185,4 +185,4 @@ You can also post bug reports and feature requests in Github issues. Make sure t [11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). [Wasserstein Discriminant Analysis](https://arxiv.org/pdf/1608.08063.pdf). arXiv preprint arXiv:1608.08063. -[12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, [Gromov-Wasserstein averaging of kernel and distance matrices](http://proceedings.mlr.press/v48/peyre16.html) International Conference on Machine Learning (ICML). 2016. +[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon, [Gromov-Wasserstein averaging of kernel and distance matrices](http://proceedings.mlr.press/v48/peyre16.html) International Conference on Machine Learning (ICML). 2016. diff --git a/data/carre.png b/data/carre.png new file mode 100755 index 0000000..45ff0ef Binary files /dev/null and b/data/carre.png differ diff --git a/data/coeur.png b/data/coeur.png new file mode 100755 index 0000000..3f511a6 Binary files /dev/null and b/data/coeur.png differ diff --git a/data/rond.png b/data/rond.png new file mode 100755 index 0000000..1c1a068 Binary files /dev/null and b/data/rond.png differ diff --git a/data/triangle.png b/data/triangle.png new file mode 100755 index 0000000..ca36d09 Binary files /dev/null and b/data/triangle.png differ diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py index a33fde1..9bbdbde 100644 --- a/examples/plot_gromov.py +++ b/examples/plot_gromov.py @@ -1,8 +1,8 @@ # -*- coding: utf-8 -*- """ -==================== +========================== Gromov-Wasserstein example -==================== +========================== This example is designed to show how to use the Gromov-Wassertsein distance computation in POT. """ @@ -14,14 +14,14 @@ computation in POT. import scipy as sp import numpy as np +import matplotlib.pylab as pl import ot -import matplotlib.pylab as pl """ Sample two Gaussian distributions (2D and 3D) -==================== +============================================= The Gromov-Wasserstein distance allows to compute distances with samples that do not belong to the same metric space. For demonstration purpose, we sample two Gaussian distributions in 2- and 3-dimensional spaces. """ @@ -42,7 +42,7 @@ xt = np.random.randn(n, 3).dot(P) + mu_t """ Plotting the distributions -==================== +========================== """ fig = pl.figure() ax1 = fig.add_subplot(121) @@ -54,7 +54,7 @@ pl.show() """ Compute distance kernels, normalize them and then display -==================== +========================================================= """ C1 = sp.spatial.distance.cdist(xs, xs) @@ -72,7 +72,7 @@ pl.show() """ Compute Gromov-Wasserstein plans and distance -==================== +============================================= """ p = ot.unif(n) diff --git a/examples/plot_gromov_barycenter.py b/examples/plot_gromov_barycenter.py new file mode 100755 index 0000000..6a72b3b --- /dev/null +++ b/examples/plot_gromov_barycenter.py @@ -0,0 +1,240 @@ +# -*- coding: utf-8 -*- +""" +===================================== +Gromov-Wasserstein Barycenter example +===================================== +This example is designed to show how to use the Gromov-Wassertsein distance +computation in POT. +""" + +# Author: Erwan Vautier +# Nicolas Courty +# +# License: MIT License + + +import numpy as np +import scipy as sp + +import scipy.ndimage as spi +import matplotlib.pylab as pl +from sklearn import manifold +from sklearn.decomposition import PCA + +import ot + +""" + +Smacof MDS +========== +This function allows to find an embedding of points given a dissimilarity matrix +that will be given by the output of the algorithm +""" + + +def smacof_mds(C, dim, maxIter=3000, eps=1e-9): + """ + Returns an interpolated point cloud following the dissimilarity matrix C using SMACOF + multidimensional scaling (MDS) in specific dimensionned target space + + Parameters + ---------- + C : np.ndarray(ns,ns) + dissimilarity matrix + dim : Integer + dimension of the targeted space + maxIter : Maximum number of iterations of the SMACOF algorithm for a single run + + eps : relative tolerance w.r.t stress to declare converge + + + Returns + ------- + npos : R**dim ndarray + Embedded coordinates of the interpolated point cloud (defined with one isometry) + + + """ + + seed = np.random.RandomState(seed=3) + + mds = manifold.MDS( + dim, + max_iter=3000, + eps=1e-9, + dissimilarity='precomputed', + n_init=1) + pos = mds.fit(C).embedding_ + + nmds = manifold.MDS( + 2, + max_iter=3000, + eps=1e-9, + dissimilarity="precomputed", + random_state=seed, + n_init=1) + npos = nmds.fit_transform(C, init=pos) + + return npos + + +""" +Data preparation +================ +The four distributions are constructed from 4 simple images +""" + + +def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + + +carre = spi.imread('../data/carre.png').astype(np.float64) / 256 +rond = spi.imread('../data/rond.png').astype(np.float64) / 256 +triangle = spi.imread('../data/triangle.png').astype(np.float64) / 256 +fleche = spi.imread('../data/coeur.png').astype(np.float64) / 256 + +shapes = [carre, rond, triangle, fleche] + +S = 4 +xs = [[] for i in range(S)] + + +for nb in range(4): + for i in range(8): + for j in range(8): + if shapes[nb][i, j] < 0.95: + xs[nb].append([j, 8 - i]) + +xs = np.array([np.array(xs[0]), np.array(xs[1]), + np.array(xs[2]), np.array(xs[3])]) + + +""" +Barycenter computation +====================== +The four distributions are constructed from 4 simple images +""" +ns = [len(xs[s]) for s in range(S)] +N = 30 + +"""Compute all distances matrices for the four shapes""" +Cs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)] +Cs = [cs / cs.max() for cs in Cs] + +ps = [ot.unif(ns[s]) for s in range(S)] +p = ot.unif(N) + + +lambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]] + +Ct01 = [0 for i in range(2)] +for i in range(2): + Ct01[i] = ot.gromov.gromov_barycenters(N, [Cs[0], Cs[1]], [ + ps[0], ps[1]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3) + +Ct02 = [0 for i in range(2)] +for i in range(2): + Ct02[i] = ot.gromov.gromov_barycenters(N, [Cs[0], Cs[2]], [ + ps[0], ps[2]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3) + +Ct13 = [0 for i in range(2)] +for i in range(2): + Ct13[i] = ot.gromov.gromov_barycenters(N, [Cs[1], Cs[3]], [ + ps[1], ps[3]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3) + +Ct23 = [0 for i in range(2)] +for i in range(2): + Ct23[i] = ot.gromov.gromov_barycenters(N, [Cs[2], Cs[3]], [ + ps[2], ps[3]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3) + +""" +Visualization +============= +""" + +"""The PCA helps in getting consistency between the rotations""" + +clf = PCA(n_components=2) +npos = [0, 0, 0, 0] +npos = [smacof_mds(Cs[s], 2) for s in range(S)] + +npost01 = [0, 0] +npost01 = [smacof_mds(Ct01[s], 2) for s in range(2)] +npost01 = [clf.fit_transform(npost01[s]) for s in range(2)] + +npost02 = [0, 0] +npost02 = [smacof_mds(Ct02[s], 2) for s in range(2)] +npost02 = [clf.fit_transform(npost02[s]) for s in range(2)] + +npost13 = [0, 0] +npost13 = [smacof_mds(Ct13[s], 2) for s in range(2)] +npost13 = [clf.fit_transform(npost13[s]) for s in range(2)] + +npost23 = [0, 0] +npost23 = [smacof_mds(Ct23[s], 2) for s in range(2)] +npost23 = [clf.fit_transform(npost23[s]) for s in range(2)] + + +fig = pl.figure(figsize=(10, 10)) + +ax1 = pl.subplot2grid((4, 4), (0, 0)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r') + +ax2 = pl.subplot2grid((4, 4), (0, 1)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b') + +ax3 = pl.subplot2grid((4, 4), (0, 2)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b') + +ax4 = pl.subplot2grid((4, 4), (0, 3)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r') + +ax5 = pl.subplot2grid((4, 4), (1, 0)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b') + +ax6 = pl.subplot2grid((4, 4), (1, 3)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b') + +ax7 = pl.subplot2grid((4, 4), (2, 0)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b') + +ax8 = pl.subplot2grid((4, 4), (2, 3)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b') + +ax9 = pl.subplot2grid((4, 4), (3, 0)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r') + +ax10 = pl.subplot2grid((4, 4), (3, 1)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b') + +ax11 = pl.subplot2grid((4, 4), (3, 2)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b') + +ax12 = pl.subplot2grid((4, 4), (3, 3)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax12.scatter(npos[3][:, 0], npos[3][:, 1], color='r') diff --git a/ot/gromov.py b/ot/gromov.py index 7cf3b42..421ed3f 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -23,7 +23,7 @@ def square_loss(a, b): Returns the value of L(a,b)=(1/2)*|a-b|^2 """ - return (1 / 2) * (a - b)**2 + return 0.5 * (a - b)**2 def kl_loss(a, b): @@ -54,9 +54,9 @@ def tensor_square_loss(C1, C2, T): Parameters ---------- - C1 : np.ndarray(ns,ns) + C1 : ndarray, shape (ns, ns) Metric cost matrix in the source space - C2 : np.ndarray(nt,nt) + C2 : ndarray, shape (nt, nt) Metric costfr matrix in the target space T : np.ndarray(ns,nt) Coupling between source and target spaces @@ -87,7 +87,7 @@ def tensor_square_loss(C1, C2, T): return b tens = -np.dot(h1(C1), T).dot(h2(C2).T) - tens = tens - tens.min() + tens -= tens.min() return np.array(tens) @@ -112,9 +112,9 @@ def tensor_kl_loss(C1, C2, T): Parameters ---------- - C1 : np.ndarray(ns,ns) + C1 : ndarray, shape (ns, ns) Metric cost matrix in the source space - C2 : np.ndarray(nt,nt) + C2 : ndarray, shape (nt, nt) Metric costfr matrix in the target space T : np.ndarray(ns,nt) Coupling between source and target spaces @@ -149,7 +149,7 @@ def tensor_kl_loss(C1, C2, T): return np.log(b + 1e-15) tens = -np.dot(h1(C1), T).dot(h2(C2).T) - tens = tens - tens.min() + tens -= tens.min() return np.array(tens) @@ -175,9 +175,8 @@ def update_square_loss(p, lambdas, T, Cs): """ - tmpsum = np.sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) - for s in range(len(T))]) - ppt = np.dot(p, p.T) + tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) for s in range(len(T))]) + ppt = np.outer(p, p) return(np.divide(tmpsum, ppt)) @@ -203,9 +202,8 @@ def update_kl_loss(p, lambdas, T, Cs): """ - tmpsum = np.sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) - for s in range(len(T))]) - ppt = np.dot(p, p.T) + tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) for s in range(len(T))]) + ppt = np.outer(p, p) return(np.exp(np.divide(tmpsum, ppt))) @@ -239,9 +237,9 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr Parameters ---------- - C1 : np.ndarray(ns,ns) + C1 : ndarray, shape (ns, ns) Metric cost matrix in the source space - C2 : np.ndarray(nt,nt) + C2 : ndarray, shape (nt, nt) Metric costfr matrix in the target space p : np.ndarray(ns,) distribution in the source space @@ -271,7 +269,7 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr C1 = np.asarray(C1, dtype=np.float64) C2 = np.asarray(C2, dtype=np.float64) - T = np.dot(p, q.T) # Initialization + T = np.outer(p, q) # Initialization cpt = 0 err = 1 @@ -333,9 +331,9 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopTh Parameters ---------- - C1 : np.ndarray(ns,ns) + C1 : ndarray, shape (ns, ns) Metric cost matrix in the source space - C2 : np.ndarray(nt,nt) + C2 : ndarray, shape (nt, nt) Metric costfr matrix in the target space p : np.ndarray(ns,) distribution in the source space @@ -434,8 +432,6 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, numItermax=1000 Cs = [np.asarray(Cs[s], dtype=np.float64) for s in range(S)] lambdas = np.asarray(lambdas, dtype=np.float64) - T = [0 for s in range(S)] - # Initialization of C : random SPD matrix xalea = np.random.randn(N, 2) C = dist(xalea, xalea) diff --git a/test/test_gromov.py b/test/test_gromov.py new file mode 100644 index 0000000..75eeaab --- /dev/null +++ b/test/test_gromov.py @@ -0,0 +1,38 @@ +"""Tests for module gromov """ + +# Author: Erwan Vautier +# Nicolas Courty +# +# License: MIT License + +import numpy as np +import ot + + +def test_gromov(): + n = 50 # nb samples + + mu_s = np.array([0, 0]) + cov_s = np.array([[1, 0], [0, 1]]) + + xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s) + + xt = [xs[n - (i + 1)] for i in range(n)] + xt = np.array(xt) + + p = ot.unif(n) + q = ot.unif(n) + + C1 = ot.dist(xs, xs) + C2 = ot.dist(xt, xt) + + C1 /= C1.max() + C2 /= C2.max() + + G = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4) + + # check constratints + np.testing.assert_allclose( + p, G.sum(1), atol=1e-04) # cf convergence gromov + np.testing.assert_allclose( + q, G.sum(0), atol=1e-04) # cf convergence gromov -- cgit v1.2.3 From 4ec5b339ef527d4d49a022ddf57b38dff037548c Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Thu, 31 Aug 2017 17:17:30 +0200 Subject: minor corrections --- examples/plot_gromov_barycenter.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/examples/plot_gromov_barycenter.py b/examples/plot_gromov_barycenter.py index 6a72b3b..da52768 100755 --- a/examples/plot_gromov_barycenter.py +++ b/examples/plot_gromov_barycenter.py @@ -32,18 +32,19 @@ that will be given by the output of the algorithm """ -def smacof_mds(C, dim, maxIter=3000, eps=1e-9): +def smacof_mds(C, dim, max_iter=3000, eps=1e-9): """ Returns an interpolated point cloud following the dissimilarity matrix C using SMACOF multidimensional scaling (MDS) in specific dimensionned target space Parameters ---------- - C : np.ndarray(ns,ns) + C : ndarray, shape (ns, ns) dissimilarity matrix - dim : Integer + dim : int dimension of the targeted space - maxIter : Maximum number of iterations of the SMACOF algorithm for a single run + max_iter : int + Maximum number of iterations of the SMACOF algorithm for a single run eps : relative tolerance w.r.t stress to declare converge @@ -60,7 +61,7 @@ def smacof_mds(C, dim, maxIter=3000, eps=1e-9): mds = manifold.MDS( dim, - max_iter=3000, + max_iter=max_iter, eps=1e-9, dissimilarity='precomputed', n_init=1) @@ -68,7 +69,7 @@ def smacof_mds(C, dim, maxIter=3000, eps=1e-9): nmds = manifold.MDS( 2, - max_iter=3000, + max_iter=max_iter, eps=1e-9, dissimilarity="precomputed", random_state=seed, -- cgit v1.2.3 From ab6ed1df93cd78bb7f1a54282103d4d830e68bcb Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Fri, 1 Sep 2017 11:20:34 +0200 Subject: docstrings and naming --- examples/plot_gromov.py | 10 +++++----- examples/plot_gromov_barycenter.py | 20 ++++++++++---------- ot/gromov.py | 18 +++++++++--------- test/test_gromov.py | 10 +++++----- 4 files changed, 29 insertions(+), 29 deletions(-) diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py index 9bbdbde..92312ae 100644 --- a/examples/plot_gromov.py +++ b/examples/plot_gromov.py @@ -26,7 +26,7 @@ The Gromov-Wasserstein distance allows to compute distances with samples that do For demonstration purpose, we sample two Gaussian distributions in 2- and 3-dimensional spaces. """ -n = 30 # nb samples +n_samples = 30 # nb samples mu_s = np.array([0, 0]) cov_s = np.array([[1, 0], [0, 1]]) @@ -35,9 +35,9 @@ mu_t = np.array([4, 4, 4]) cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) -xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s) +xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s) P = sp.linalg.sqrtm(cov_t) -xt = np.random.randn(n, 3).dot(P) + mu_t +xt = np.random.randn(n_samples, 3).dot(P) + mu_t """ @@ -75,8 +75,8 @@ Compute Gromov-Wasserstein plans and distance ============================================= """ -p = ot.unif(n) -q = ot.unif(n) +p = ot.unif(n_samples) +q = ot.unif(n_samples) gw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4) gw_dist = ot.gromov_wasserstein2(C1, C2, p, q, 'square_loss', epsilon=5e-4) diff --git a/examples/plot_gromov_barycenter.py b/examples/plot_gromov_barycenter.py index da52768..f0657e1 100755 --- a/examples/plot_gromov_barycenter.py +++ b/examples/plot_gromov_barycenter.py @@ -91,12 +91,12 @@ def im2mat(I): return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) -carre = spi.imread('../data/carre.png').astype(np.float64) / 256 -rond = spi.imread('../data/rond.png').astype(np.float64) / 256 +square = spi.imread('../data/carre.png').astype(np.float64) / 256 +circle = spi.imread('../data/rond.png').astype(np.float64) / 256 triangle = spi.imread('../data/triangle.png').astype(np.float64) / 256 -fleche = spi.imread('../data/coeur.png').astype(np.float64) / 256 +arrow = spi.imread('../data/coeur.png').astype(np.float64) / 256 -shapes = [carre, rond, triangle, fleche] +shapes = [square, circle, triangle, arrow] S = 4 xs = [[] for i in range(S)] @@ -118,36 +118,36 @@ Barycenter computation The four distributions are constructed from 4 simple images """ ns = [len(xs[s]) for s in range(S)] -N = 30 +n_samples = 30 """Compute all distances matrices for the four shapes""" Cs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)] Cs = [cs / cs.max() for cs in Cs] ps = [ot.unif(ns[s]) for s in range(S)] -p = ot.unif(N) +p = ot.unif(n_samples) lambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]] Ct01 = [0 for i in range(2)] for i in range(2): - Ct01[i] = ot.gromov.gromov_barycenters(N, [Cs[0], Cs[1]], [ + Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]], [ ps[0], ps[1]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3) Ct02 = [0 for i in range(2)] for i in range(2): - Ct02[i] = ot.gromov.gromov_barycenters(N, [Cs[0], Cs[2]], [ + Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]], [ ps[0], ps[2]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3) Ct13 = [0 for i in range(2)] for i in range(2): - Ct13[i] = ot.gromov.gromov_barycenters(N, [Cs[1], Cs[3]], [ + Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]], [ ps[1], ps[3]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3) Ct23 = [0 for i in range(2)] for i in range(2): - Ct23[i] = ot.gromov.gromov_barycenters(N, [Cs[2], Cs[3]], [ + Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]], [ ps[2], ps[3]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3) """ diff --git a/ot/gromov.py b/ot/gromov.py index 421ed3f..ad85fcd 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -208,7 +208,7 @@ def update_kl_loss(p, lambdas, T, Cs): return(np.exp(np.divide(tmpsum, ppt))) -def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr=1e-9, verbose=False, log=False): +def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, stopThr=1e-9, verbose=False, log=False): """ Returns the gromov-wasserstein coupling between the two measured similarity matrices @@ -248,7 +248,7 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr loss_fun : loss function used for the solver either 'square_loss' or 'kl_loss' epsilon : float Regularization term >0 - numItermax : int, optional + max_iter : int, optional Max number of iterations stopThr : float, optional Stop threshold on error (>0) @@ -274,7 +274,7 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr cpt = 0 err = 1 - while (err > stopThr and cpt < numItermax): + while (err > stopThr and cpt < max_iter): Tprev = T @@ -307,7 +307,7 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr return T -def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr=1e-9, verbose=False, log=False): +def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, stopThr=1e-9, verbose=False, log=False): """ Returns the gromov-wasserstein discrepancy between the two measured similarity matrices @@ -362,10 +362,10 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopTh if log: gw, logv = gromov_wasserstein( - C1, C2, p, q, loss_fun, epsilon, numItermax, stopThr, verbose, log) + C1, C2, p, q, loss_fun, epsilon, max_iter, stopThr, verbose, log) else: gw = gromov_wasserstein(C1, C2, p, q, loss_fun, - epsilon, numItermax, stopThr, verbose, log) + epsilon, max_iter, stopThr, verbose, log) if loss_fun == 'square_loss': gw_dist = np.sum(gw * tensor_square_loss(C1, C2, gw)) @@ -379,7 +379,7 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopTh return gw_dist -def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, numItermax=1000, stopThr=1e-9, verbose=False, log=False): +def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, max_iter=1000, stopThr=1e-9, verbose=False, log=False): """ Returns the gromov-wasserstein barycenters of S measured similarity matrices @@ -442,12 +442,12 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, numItermax=1000 error = [] - while(err > stopThr and cpt < numItermax): + while(err > stopThr and cpt < max_iter): Cprev = C T = [gromov_wasserstein(Cs[s], C, ps[s], p, loss_fun, epsilon, - numItermax, 1e-5, verbose, log) for s in range(S)] + max_iter, 1e-5, verbose, log) for s in range(S)] if loss_fun == 'square_loss': C = update_square_loss(p, lambdas, T, Cs) diff --git a/test/test_gromov.py b/test/test_gromov.py index 75eeaab..c26d898 100644 --- a/test/test_gromov.py +++ b/test/test_gromov.py @@ -10,18 +10,18 @@ import ot def test_gromov(): - n = 50 # nb samples + n_samples = 50 # nb samples mu_s = np.array([0, 0]) cov_s = np.array([[1, 0], [0, 1]]) - xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s) + xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s) - xt = [xs[n - (i + 1)] for i in range(n)] + xt = [xs[n_samples - (i + 1)] for i in range(n_samples)] xt = np.array(xt) - p = ot.unif(n) - q = ot.unif(n) + p = ot.unif(n_samples) + q = ot.unif(n_samples) C1 = ot.dist(xs, xs) C2 = ot.dist(xt, xt) -- cgit v1.2.3 From 46fc12a298c49b715ac953cff391b18b54dab0f0 Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Fri, 1 Sep 2017 11:43:51 +0200 Subject: solving conflicts :/ --- examples/plot_gromov.py | 15 ------------- examples/plot_gromov_barycenter.py | 33 ----------------------------- ot/gromov.py | 43 +++++--------------------------------- test/test_gromov.py | 14 ------------- 4 files changed, 5 insertions(+), 100 deletions(-) diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py index 99aaf81..92312ae 100644 --- a/examples/plot_gromov.py +++ b/examples/plot_gromov.py @@ -26,11 +26,7 @@ The Gromov-Wasserstein distance allows to compute distances with samples that do For demonstration purpose, we sample two Gaussian distributions in 2- and 3-dimensional spaces. """ -<<<<<<< HEAD n_samples = 30 # nb samples -======= -n = 30 # nb samples ->>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d mu_s = np.array([0, 0]) cov_s = np.array([[1, 0], [0, 1]]) @@ -39,15 +35,9 @@ mu_t = np.array([4, 4, 4]) cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) -<<<<<<< HEAD xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s) P = sp.linalg.sqrtm(cov_t) xt = np.random.randn(n_samples, 3).dot(P) + mu_t -======= -xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s) -P = sp.linalg.sqrtm(cov_t) -xt = np.random.randn(n, 3).dot(P) + mu_t ->>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d """ @@ -85,13 +75,8 @@ Compute Gromov-Wasserstein plans and distance ============================================= """ -<<<<<<< HEAD p = ot.unif(n_samples) q = ot.unif(n_samples) -======= -p = ot.unif(n) -q = ot.unif(n) ->>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d gw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4) gw_dist = ot.gromov_wasserstein2(C1, C2, p, q, 'square_loss', epsilon=5e-4) diff --git a/examples/plot_gromov_barycenter.py b/examples/plot_gromov_barycenter.py index 46ec4bc..f0657e1 100755 --- a/examples/plot_gromov_barycenter.py +++ b/examples/plot_gromov_barycenter.py @@ -91,21 +91,12 @@ def im2mat(I): return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) -<<<<<<< HEAD square = spi.imread('../data/carre.png').astype(np.float64) / 256 circle = spi.imread('../data/rond.png').astype(np.float64) / 256 triangle = spi.imread('../data/triangle.png').astype(np.float64) / 256 arrow = spi.imread('../data/coeur.png').astype(np.float64) / 256 shapes = [square, circle, triangle, arrow] -======= -carre = spi.imread('../data/carre.png').astype(np.float64) / 256 -rond = spi.imread('../data/rond.png').astype(np.float64) / 256 -triangle = spi.imread('../data/triangle.png').astype(np.float64) / 256 -fleche = spi.imread('../data/coeur.png').astype(np.float64) / 256 - -shapes = [carre, rond, triangle, fleche] ->>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d S = 4 xs = [[] for i in range(S)] @@ -127,60 +118,36 @@ Barycenter computation The four distributions are constructed from 4 simple images """ ns = [len(xs[s]) for s in range(S)] -<<<<<<< HEAD n_samples = 30 -======= -N = 30 ->>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d """Compute all distances matrices for the four shapes""" Cs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)] Cs = [cs / cs.max() for cs in Cs] ps = [ot.unif(ns[s]) for s in range(S)] -<<<<<<< HEAD p = ot.unif(n_samples) -======= -p = ot.unif(N) ->>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d lambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]] Ct01 = [0 for i in range(2)] for i in range(2): -<<<<<<< HEAD Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]], [ -======= - Ct01[i] = ot.gromov.gromov_barycenters(N, [Cs[0], Cs[1]], [ ->>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d ps[0], ps[1]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3) Ct02 = [0 for i in range(2)] for i in range(2): -<<<<<<< HEAD Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]], [ -======= - Ct02[i] = ot.gromov.gromov_barycenters(N, [Cs[0], Cs[2]], [ ->>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d ps[0], ps[2]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3) Ct13 = [0 for i in range(2)] for i in range(2): -<<<<<<< HEAD Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]], [ -======= - Ct13[i] = ot.gromov.gromov_barycenters(N, [Cs[1], Cs[3]], [ ->>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d ps[1], ps[3]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3) Ct23 = [0 for i in range(2)] for i in range(2): -<<<<<<< HEAD Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]], [ -======= - Ct23[i] = ot.gromov.gromov_barycenters(N, [Cs[2], Cs[3]], [ ->>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d ps[2], ps[3]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3) """ diff --git a/ot/gromov.py b/ot/gromov.py index 197e3ea..9dbf463 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -208,11 +208,7 @@ def update_kl_loss(p, lambdas, T, Cs): return(np.exp(np.divide(tmpsum, ppt))) -<<<<<<< HEAD def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, stopThr=1e-9, verbose=False, log=False): -======= -def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr=1e-9, verbose=False, log=False): ->>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d """ Returns the gromov-wasserstein coupling between the two measured similarity matrices @@ -252,11 +248,11 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr loss_fun : loss function used for the solver either 'square_loss' or 'kl_loss' epsilon : float Regularization term >0 -<<<<<<< HEAD +<<<<<<< HEAD max_iter : int, optional -======= +======= numItermax : int, optional ->>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d +>>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d Max number of iterations stopThr : float, optional Stop threshold on error (>0) @@ -282,11 +278,7 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr cpt = 0 err = 1 -<<<<<<< HEAD while (err > stopThr and cpt < max_iter): -======= - while (err > stopThr and cpt < numItermax): ->>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d Tprev = T @@ -319,11 +311,7 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr return T -<<<<<<< HEAD def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, stopThr=1e-9, verbose=False, log=False): -======= -def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopThr=1e-9, verbose=False, log=False): ->>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d """ Returns the gromov-wasserstein discrepancy between the two measured similarity matrices @@ -358,7 +346,7 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopTh loss_fun : loss function used for the solver either 'square_loss' or 'kl_loss' epsilon : float Regularization term >0 - numItermax : int, optional + max_iter : int, optional Max number of iterations stopThr : float, optional Stop threshold on error (>0) @@ -378,17 +366,10 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopTh if log: gw, logv = gromov_wasserstein( -<<<<<<< HEAD C1, C2, p, q, loss_fun, epsilon, max_iter, stopThr, verbose, log) else: gw = gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, max_iter, stopThr, verbose, log) -======= - C1, C2, p, q, loss_fun, epsilon, numItermax, stopThr, verbose, log) - else: - gw = gromov_wasserstein(C1, C2, p, q, loss_fun, - epsilon, numItermax, stopThr, verbose, log) ->>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d if loss_fun == 'square_loss': gw_dist = np.sum(gw * tensor_square_loss(C1, C2, gw)) @@ -402,11 +383,7 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, numItermax=1000, stopTh return gw_dist -<<<<<<< HEAD def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, max_iter=1000, stopThr=1e-9, verbose=False, log=False): -======= -def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, numItermax=1000, stopThr=1e-9, verbose=False, log=False): ->>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d """ Returns the gromov-wasserstein barycenters of S measured similarity matrices @@ -439,7 +416,7 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, numItermax=1000 with the S Ts couplings calculated at each iteration epsilon : float Regularization term >0 - numItermax : int, optional + max_iter : int, optional Max number of iterations stopThr : float, optional Stop threshol on error (>0) @@ -469,21 +446,11 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, numItermax=1000 error = [] -<<<<<<< HEAD while(err > stopThr and cpt < max_iter): -======= - while(err > stopThr and cpt < numItermax): ->>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d - Cprev = C T = [gromov_wasserstein(Cs[s], C, ps[s], p, loss_fun, epsilon, -<<<<<<< HEAD max_iter, 1e-5, verbose, log) for s in range(S)] -======= - numItermax, 1e-5, verbose, log) for s in range(S)] ->>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d - if loss_fun == 'square_loss': C = update_square_loss(p, lambdas, T, Cs) diff --git a/test/test_gromov.py b/test/test_gromov.py index a6c89f2..c26d898 100644 --- a/test/test_gromov.py +++ b/test/test_gromov.py @@ -10,16 +10,11 @@ import ot def test_gromov(): -<<<<<<< HEAD n_samples = 50 # nb samples -======= - n = 50 # nb samples ->>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d mu_s = np.array([0, 0]) cov_s = np.array([[1, 0], [0, 1]]) -<<<<<<< HEAD xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s) xt = [xs[n_samples - (i + 1)] for i in range(n_samples)] @@ -27,15 +22,6 @@ def test_gromov(): p = ot.unif(n_samples) q = ot.unif(n_samples) -======= - xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s) - - xt = [xs[n - (i + 1)] for i in range(n)] - xt = np.array(xt) - - p = ot.unif(n) - q = ot.unif(n) ->>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d C1 = ot.dist(xs, xs) C2 = ot.dist(xt, xt) -- cgit v1.2.3 From f12322c1a288baedffd5b6aedcff15747aadac8e Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Fri, 1 Sep 2017 14:07:28 +0200 Subject: add barycenters to Readme.md --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 22b20a4..a1debf6 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,7 @@ It provides the following solvers: * Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. * Joint OT matrix and mapping estimation [8]. * Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt). -* Gromov-Wasserstein distances [12] +* Gromov-Wasserstein distances and barycenters [12] Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. -- cgit v1.2.3 From 062071b20d1d40c64bb619931bd11bd28e780485 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 1 Sep 2017 15:31:44 +0200 Subject: update example with rst titles --- .gitignore | 2 - .../source/auto_examples/auto_examples_jupyter.zip | Bin 91095 -> 70410 bytes docs/source/auto_examples/auto_examples_python.zip | Bin 62950 -> 46653 bytes docs/source/auto_examples/demo_OT_1D_test.ipynb | 54 ---- docs/source/auto_examples/demo_OT_1D_test.py | 71 ----- docs/source/auto_examples/demo_OT_1D_test.rst | 99 ------ .../auto_examples/demo_OT_2D_sampleslarge.ipynb | 54 ---- .../auto_examples/demo_OT_2D_sampleslarge.py | 78 ----- .../auto_examples/demo_OT_2D_sampleslarge.rst | 106 ------- .../images/sphx_glr_plot_OTDA_2D_001.png | Bin 52753 -> 0 bytes .../images/sphx_glr_plot_OTDA_2D_002.png | Bin 87798 -> 0 bytes .../images/sphx_glr_plot_OTDA_2D_003.png | Bin 167396 -> 0 bytes .../images/sphx_glr_plot_OTDA_2D_004.png | Bin 82929 -> 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----------- .../plot_OTDA_mapping_color_images.ipynb | 54 ---- .../plot_OTDA_mapping_color_images.py | 158 ---------- .../plot_OTDA_mapping_color_images.rst | 246 --------------- docs/source/auto_examples/plot_OT_1D.ipynb | 4 +- docs/source/auto_examples/plot_OT_1D.py | 5 +- docs/source/auto_examples/plot_OT_1D.rst | 9 +- docs/source/auto_examples/plot_OT_2D_samples.ipynb | 76 ++++- docs/source/auto_examples/plot_OT_2D_samples.py | 18 ++ docs/source/auto_examples/plot_OT_2D_samples.rst | 132 ++++++-- docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb | 76 ++++- docs/source/auto_examples/plot_OT_L1_vs_L2.py | 280 ++++++++++------ docs/source/auto_examples/plot_OT_L1_vs_L2.rst | 351 ++++++++++++++------- docs/source/auto_examples/plot_OT_conv.ipynb | 54 ---- docs/source/auto_examples/plot_OT_conv.py | 200 ------------ docs/source/auto_examples/plot_OT_conv.rst | 241 -------------- docs/source/auto_examples/plot_WDA.ipynb | 94 +++++- docs/source/auto_examples/plot_WDA.py | 27 ++ docs/source/auto_examples/plot_WDA.rst | 172 ++++++---- docs/source/auto_examples/plot_barycenter_1D.ipynb | 76 ++++- docs/source/auto_examples/plot_barycenter_1D.py | 23 ++ docs/source/auto_examples/plot_barycenter_1D.rst | 113 +++++-- docs/source/auto_examples/plot_compute_emd.ipynb | 76 ++++- docs/source/auto_examples/plot_compute_emd.py | 30 +- docs/source/auto_examples/plot_compute_emd.rst | 100 ++++-- docs/source/auto_examples/plot_optim_OTreg.ipynb | 8 +- docs/source/auto_examples/plot_optim_OTreg.py | 23 +- docs/source/auto_examples/plot_optim_OTreg.rst | 29 +- docs/source/auto_examples/plot_otda_classes.rst | 46 +-- .../auto_examples/plot_otda_color_images.ipynb | 18 +- .../source/auto_examples/plot_otda_color_images.py | 66 ++-- .../auto_examples/plot_otda_color_images.rst | 90 +++--- docs/source/auto_examples/plot_otda_d2.rst | 4 +- docs/source/auto_examples/plot_otda_mapping.ipynb | 14 +- docs/source/auto_examples/plot_otda_mapping.py | 35 +- 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-docs/source/auto_examples/*.pickle -docs/source/auto_examples/*.md5 docs/auto_examples/ docs/modules/ diff --git a/docs/source/auto_examples/auto_examples_jupyter.zip b/docs/source/auto_examples/auto_examples_jupyter.zip index 92aa027..96bc0bc 100644 Binary files a/docs/source/auto_examples/auto_examples_jupyter.zip and b/docs/source/auto_examples/auto_examples_jupyter.zip differ diff --git a/docs/source/auto_examples/auto_examples_python.zip b/docs/source/auto_examples/auto_examples_python.zip index bc41a8c..6241b92 100644 Binary files a/docs/source/auto_examples/auto_examples_python.zip and b/docs/source/auto_examples/auto_examples_python.zip differ diff --git a/docs/source/auto_examples/demo_OT_1D_test.ipynb b/docs/source/auto_examples/demo_OT_1D_test.ipynb deleted file mode 100644 index 87317ea..0000000 --- a/docs/source/auto_examples/demo_OT_1D_test.ipynb +++ /dev/null @@ -1,54 +0,0 @@ -{ - "nbformat_minor": 0, - "nbformat": 4, - "cells": [ - { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, - { - "source": [ - "\nDemo for 1D optimal transport\n\n@author: rflamary\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\nfrom ot.datasets import get_1D_gauss as gauss\n\n\n#%% parameters\n\nn=100 # nb bins\n\n# bin positions\nx=np.arange(n,dtype=np.float64)\n\n# Gaussian distributions\na=gauss(n,m=n*.2,s=5) # m= mean, s= std\nb=gauss(n,m=n*.6,s=10)\n\n# loss matrix\nM=ot.dist(x.reshape((n,1)),x.reshape((n,1)))\nM/=M.max()\n\n#%% plot the distributions\n\npl.figure(1)\npl.plot(x,a,'b',label='Source distribution')\npl.plot(x,b,'r',label='Target distribution')\npl.legend()\n\n#%% plot distributions and loss matrix\n\npl.figure(2)\not.plot.plot1D_mat(a,b,M,'Cost matrix M')\n\n#%% EMD\n\nG0=ot.emd(a,b,M)\n\npl.figure(3)\not.plot.plot1D_mat(a,b,G0,'OT matrix G0')\n\n#%% Sinkhorn\n\nlambd=1e-3\nGs=ot.sinkhorn(a,b,M,lambd,verbose=True)\n\npl.figure(4)\not.plot.plot1D_mat(a,b,Gs,'OT matrix Sinkhorn')\n\n#%% Sinkhorn\n\nlambd=1e-4\nGss,log=ot.bregman.sinkhorn_stabilized(a,b,M,lambd,verbose=True,log=True)\nGss2,log2=ot.bregman.sinkhorn_stabilized(a,b,M,lambd,verbose=True,log=True,warmstart=log['warmstart'])\n\npl.figure(5)\not.plot.plot1D_mat(a,b,Gss,'OT matrix Sinkhorn stabilized')\n\n#%% Sinkhorn\n\nlambd=1e-11\nGss=ot.bregman.sinkhorn_epsilon_scaling(a,b,M,lambd,verbose=True)\n\npl.figure(5)\not.plot.plot1D_mat(a,b,Gss,'OT matrix Sinkhorn stabilized')" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, - "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", - "codemirror_mode": { - "version": 2, - "name": "ipython" - } - } - } -} \ No newline at end of file diff --git a/docs/source/auto_examples/demo_OT_1D_test.py b/docs/source/auto_examples/demo_OT_1D_test.py deleted file mode 100644 index 9edc377..0000000 --- a/docs/source/auto_examples/demo_OT_1D_test.py +++ /dev/null @@ -1,71 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Demo for 1D optimal transport - -@author: rflamary -""" - -import numpy as np -import matplotlib.pylab as pl -import ot -from ot.datasets import get_1D_gauss as gauss - - -#%% parameters - -n=100 # nb bins - -# bin positions -x=np.arange(n,dtype=np.float64) - -# Gaussian distributions -a=gauss(n,m=n*.2,s=5) # m= mean, s= std -b=gauss(n,m=n*.6,s=10) - -# loss matrix -M=ot.dist(x.reshape((n,1)),x.reshape((n,1))) -M/=M.max() - -#%% plot the distributions - -pl.figure(1) -pl.plot(x,a,'b',label='Source distribution') -pl.plot(x,b,'r',label='Target distribution') -pl.legend() - -#%% plot distributions and loss matrix - -pl.figure(2) -ot.plot.plot1D_mat(a,b,M,'Cost matrix M') - -#%% EMD - -G0=ot.emd(a,b,M) - -pl.figure(3) -ot.plot.plot1D_mat(a,b,G0,'OT matrix G0') - -#%% Sinkhorn - -lambd=1e-3 -Gs=ot.sinkhorn(a,b,M,lambd,verbose=True) - -pl.figure(4) -ot.plot.plot1D_mat(a,b,Gs,'OT matrix Sinkhorn') - -#%% Sinkhorn - -lambd=1e-4 -Gss,log=ot.bregman.sinkhorn_stabilized(a,b,M,lambd,verbose=True,log=True) -Gss2,log2=ot.bregman.sinkhorn_stabilized(a,b,M,lambd,verbose=True,log=True,warmstart=log['warmstart']) - -pl.figure(5) -ot.plot.plot1D_mat(a,b,Gss,'OT matrix Sinkhorn stabilized') - -#%% Sinkhorn - -lambd=1e-11 -Gss=ot.bregman.sinkhorn_epsilon_scaling(a,b,M,lambd,verbose=True) - -pl.figure(5) -ot.plot.plot1D_mat(a,b,Gss,'OT matrix Sinkhorn stabilized') diff --git a/docs/source/auto_examples/demo_OT_1D_test.rst b/docs/source/auto_examples/demo_OT_1D_test.rst deleted file mode 100644 index aebeb1d..0000000 --- a/docs/source/auto_examples/demo_OT_1D_test.rst +++ /dev/null @@ -1,99 +0,0 @@ - - -.. _sphx_glr_auto_examples_demo_OT_1D_test.py: - - -Demo for 1D optimal transport - -@author: rflamary - - - -.. code-block:: python - - - import numpy as np - import matplotlib.pylab as pl - import ot - from ot.datasets import get_1D_gauss as gauss - - - #%% parameters - - n=100 # nb bins - - # bin positions - x=np.arange(n,dtype=np.float64) - - # Gaussian distributions - a=gauss(n,m=n*.2,s=5) # m= mean, s= std - b=gauss(n,m=n*.6,s=10) - - # loss matrix - M=ot.dist(x.reshape((n,1)),x.reshape((n,1))) - M/=M.max() - - #%% plot the distributions - - pl.figure(1) - pl.plot(x,a,'b',label='Source distribution') - pl.plot(x,b,'r',label='Target distribution') - pl.legend() - - #%% plot distributions and loss matrix - - pl.figure(2) - ot.plot.plot1D_mat(a,b,M,'Cost matrix M') - - #%% EMD - - G0=ot.emd(a,b,M) - - pl.figure(3) - ot.plot.plot1D_mat(a,b,G0,'OT matrix G0') - - #%% Sinkhorn - - lambd=1e-3 - Gs=ot.sinkhorn(a,b,M,lambd,verbose=True) - - pl.figure(4) - ot.plot.plot1D_mat(a,b,Gs,'OT matrix Sinkhorn') - - #%% Sinkhorn - - lambd=1e-4 - Gss,log=ot.bregman.sinkhorn_stabilized(a,b,M,lambd,verbose=True,log=True) - Gss2,log2=ot.bregman.sinkhorn_stabilized(a,b,M,lambd,verbose=True,log=True,warmstart=log['warmstart']) - - pl.figure(5) - ot.plot.plot1D_mat(a,b,Gss,'OT matrix Sinkhorn stabilized') - - #%% Sinkhorn - - lambd=1e-11 - Gss=ot.bregman.sinkhorn_epsilon_scaling(a,b,M,lambd,verbose=True) - - pl.figure(5) - ot.plot.plot1D_mat(a,b,Gss,'OT matrix Sinkhorn stabilized') - -**Total running time of the script:** ( 0 minutes 0.000 seconds) - - - -.. container:: sphx-glr-footer - - - .. container:: sphx-glr-download - - :download:`Download Python source code: demo_OT_1D_test.py ` - - - - .. container:: sphx-glr-download - - :download:`Download Jupyter notebook: demo_OT_1D_test.ipynb ` - -.. rst-class:: sphx-glr-signature - - `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/demo_OT_2D_sampleslarge.ipynb b/docs/source/auto_examples/demo_OT_2D_sampleslarge.ipynb deleted file mode 100644 index 584a936..0000000 --- a/docs/source/auto_examples/demo_OT_2D_sampleslarge.ipynb +++ /dev/null @@ -1,54 +0,0 @@ -{ - "nbformat_minor": 0, - "nbformat": 4, - "cells": [ - { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, - { - "source": [ - "\nDemo for 2D Optimal transport between empirical distributions\n\n@author: rflamary\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n#%% parameters and data generation\n\nn=5000 # nb samples\n\nmu_s=np.array([0,0])\ncov_s=np.array([[1,0],[0,1]])\n\nmu_t=np.array([4,4])\ncov_t=np.array([[1,-.8],[-.8,1]])\n\nxs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s)\nxt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t)\n\na,b = ot.unif(n),ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM=ot.dist(xs,xt)\nM/=M.max()\n\n#%% plot samples\n\n#pl.figure(1)\n#pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n#pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n#pl.legend(loc=0)\n#pl.title('Source and traget distributions')\n#\n#pl.figure(2)\n#pl.imshow(M,interpolation='nearest')\n#pl.title('Cost matrix M')\n#\n\n#%% EMD\n\nG0=ot.emd(a,b,M)\n\n#pl.figure(3)\n#pl.imshow(G0,interpolation='nearest')\n#pl.title('OT matrix G0')\n#\n#pl.figure(4)\n#ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1])\n#pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n#pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n#pl.legend(loc=0)\n#pl.title('OT matrix with samples')\n\n\n#%% sinkhorn\n\n# reg term\nlambd=5e-3\n\nGs=ot.sinkhorn(a,b,M,lambd)\n\n#pl.figure(5)\n#pl.imshow(Gs,interpolation='nearest')\n#pl.title('OT matrix sinkhorn')\n#\n#pl.figure(6)\n#ot.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1])\n#pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n#pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n#pl.legend(loc=0)\n#pl.title('OT matrix Sinkhorn with samples')\n#" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, - "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", - "codemirror_mode": { - "version": 2, - "name": "ipython" - } - } - } -} \ No newline at end of file diff --git a/docs/source/auto_examples/demo_OT_2D_sampleslarge.py b/docs/source/auto_examples/demo_OT_2D_sampleslarge.py deleted file mode 100644 index ee3e8f7..0000000 --- a/docs/source/auto_examples/demo_OT_2D_sampleslarge.py +++ /dev/null @@ -1,78 +0,0 @@ -# -*- coding: utf-8 -*- -""" -Demo for 2D Optimal transport between empirical distributions - -@author: rflamary -""" - -import numpy as np -import matplotlib.pylab as pl -import ot - -#%% parameters and data generation - -n=5000 # nb samples - -mu_s=np.array([0,0]) -cov_s=np.array([[1,0],[0,1]]) - -mu_t=np.array([4,4]) -cov_t=np.array([[1,-.8],[-.8,1]]) - -xs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s) -xt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t) - -a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples - -# loss matrix -M=ot.dist(xs,xt) -M/=M.max() - -#%% plot samples - -#pl.figure(1) -#pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') -#pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') -#pl.legend(loc=0) -#pl.title('Source and traget distributions') -# -#pl.figure(2) -#pl.imshow(M,interpolation='nearest') -#pl.title('Cost matrix M') -# - -#%% EMD - -G0=ot.emd(a,b,M) - -#pl.figure(3) -#pl.imshow(G0,interpolation='nearest') -#pl.title('OT matrix G0') -# -#pl.figure(4) -#ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1]) -#pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') -#pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') -#pl.legend(loc=0) -#pl.title('OT matrix with samples') - - -#%% sinkhorn - -# reg term -lambd=5e-3 - -Gs=ot.sinkhorn(a,b,M,lambd) - -#pl.figure(5) -#pl.imshow(Gs,interpolation='nearest') -#pl.title('OT matrix sinkhorn') -# -#pl.figure(6) -#ot.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1]) -#pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') -#pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') -#pl.legend(loc=0) -#pl.title('OT matrix Sinkhorn with samples') -# - diff --git a/docs/source/auto_examples/demo_OT_2D_sampleslarge.rst b/docs/source/auto_examples/demo_OT_2D_sampleslarge.rst deleted file mode 100644 index f5dbb0d..0000000 --- a/docs/source/auto_examples/demo_OT_2D_sampleslarge.rst +++ /dev/null @@ -1,106 +0,0 @@ - - -.. _sphx_glr_auto_examples_demo_OT_2D_sampleslarge.py: - - -Demo for 2D Optimal transport between empirical distributions - -@author: rflamary - - - -.. code-block:: python - - - import numpy as np - import matplotlib.pylab as pl - import ot - - #%% parameters and data generation - - n=5000 # nb samples - - mu_s=np.array([0,0]) - cov_s=np.array([[1,0],[0,1]]) - - mu_t=np.array([4,4]) - cov_t=np.array([[1,-.8],[-.8,1]]) - - xs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s) - xt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t) - - a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples - - # loss matrix - M=ot.dist(xs,xt) - M/=M.max() - - #%% plot samples - - #pl.figure(1) - #pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') - #pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') - #pl.legend(loc=0) - #pl.title('Source and traget distributions') - # - #pl.figure(2) - #pl.imshow(M,interpolation='nearest') - #pl.title('Cost matrix M') - # - - #%% EMD - - G0=ot.emd(a,b,M) - - #pl.figure(3) - #pl.imshow(G0,interpolation='nearest') - #pl.title('OT matrix G0') - # - #pl.figure(4) - #ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1]) - #pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') - #pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') - #pl.legend(loc=0) - #pl.title('OT matrix with samples') - - - #%% sinkhorn - - # reg term - lambd=5e-3 - - Gs=ot.sinkhorn(a,b,M,lambd) - - #pl.figure(5) - #pl.imshow(Gs,interpolation='nearest') - #pl.title('OT matrix sinkhorn') - # - #pl.figure(6) - #ot.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1]) - #pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples') - #pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples') - #pl.legend(loc=0) - #pl.title('OT matrix Sinkhorn with samples') - # - - -**Total running time of the script:** ( 0 minutes 0.000 seconds) - - - -.. container:: sphx-glr-footer - - - .. container:: sphx-glr-download - - :download:`Download Python source code: demo_OT_2D_sampleslarge.py ` - - - - .. container:: sphx-glr-download - - :download:`Download Jupyter notebook: 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a/docs/source/auto_examples/images/thumb/sphx_glr_test_OT_2D_samples_stabilized_thumb.png b/docs/source/auto_examples/images/thumb/sphx_glr_test_OT_2D_samples_stabilized_thumb.png deleted file mode 100644 index cbc8e0f..0000000 Binary files a/docs/source/auto_examples/images/thumb/sphx_glr_test_OT_2D_samples_stabilized_thumb.png and /dev/null differ diff --git a/docs/source/auto_examples/index.rst b/docs/source/auto_examples/index.rst index b932907..ebcca85 100644 --- a/docs/source/auto_examples/index.rst +++ b/docs/source/auto_examples/index.rst @@ -1,9 +1,15 @@ +:orphan: + POT Examples ============ +This is a gallery of all the POT example files. + + + .. raw:: html -
+
.. only:: html @@ -23,7 +29,7 @@ POT Examples .. raw:: html -
+
.. only:: html @@ -43,7 +49,7 @@ POT Examples .. raw:: html -
+
.. only:: html @@ -63,7 +69,7 @@ POT Examples .. raw:: html -
+
.. only:: html @@ -83,7 +89,7 @@ POT Examples .. raw:: html -
+
.. only:: html @@ -123,7 +129,7 @@ POT Examples .. raw:: html -
+
.. only:: html @@ -143,13 +149,13 @@ POT Examples .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_L1_vs_L2_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_mapping_colors_images_thumb.png - :ref:`sphx_glr_auto_examples_plot_OT_L1_vs_L2.py` + :ref:`sphx_glr_auto_examples_plot_otda_mapping_colors_images.py` .. raw:: html @@ -159,17 +165,17 @@ POT Examples .. toctree:: :hidden: - /auto_examples/plot_OT_L1_vs_L2 + /auto_examples/plot_otda_mapping_colors_images .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_mapping_colors_images_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_mapping_thumb.png - :ref:`sphx_glr_auto_examples_plot_otda_mapping_colors_images.py` + :ref:`sphx_glr_auto_examples_plot_otda_mapping.py` .. raw:: html @@ -179,17 +185,17 @@ POT Examples .. toctree:: :hidden: - /auto_examples/plot_otda_mapping_colors_images + /auto_examples/plot_otda_mapping .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_mapping_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_classes_thumb.png - :ref:`sphx_glr_auto_examples_plot_otda_mapping.py` + :ref:`sphx_glr_auto_examples_plot_otda_classes.py` .. raw:: html @@ -199,17 +205,17 @@ POT Examples .. toctree:: :hidden: - /auto_examples/plot_otda_mapping + /auto_examples/plot_otda_classes .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_classes_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_d2_thumb.png - :ref:`sphx_glr_auto_examples_plot_otda_classes.py` + :ref:`sphx_glr_auto_examples_plot_otda_d2.py` .. raw:: html @@ -219,17 +225,17 @@ POT Examples .. toctree:: :hidden: - /auto_examples/plot_otda_classes + /auto_examples/plot_otda_d2 .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_d2_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_L1_vs_L2_thumb.png - :ref:`sphx_glr_auto_examples_plot_otda_d2.py` + :ref:`sphx_glr_auto_examples_plot_OT_L1_vs_L2.py` .. raw:: html @@ -239,7 +245,7 @@ POT Examples .. toctree:: :hidden: - /auto_examples/plot_otda_d2 + /auto_examples/plot_OT_L1_vs_L2 .. raw:: html
@@ -251,14 +257,14 @@ POT Examples .. container:: sphx-glr-download - :download:`Download all examples in Python source code: auto_examples_python.zip ` + :download:`Download all examples in Python source code: auto_examples_python.zip ` .. container:: sphx-glr-download - :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip ` + :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip ` .. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OTDA_2D.ipynb b/docs/source/auto_examples/plot_OTDA_2D.ipynb deleted file mode 100644 index 2ffb256..0000000 --- a/docs/source/auto_examples/plot_OTDA_2D.ipynb +++ /dev/null @@ -1,54 +0,0 @@ -{ - "nbformat_minor": 0, - "nbformat": 4, - "cells": [ - { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, - { - "source": [ - "\n# OT for empirical distributions\n\n\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n\n\n#%% parameters\n\nn=150 # nb bins\n\nxs,ys=ot.datasets.get_data_classif('3gauss',n)\nxt,yt=ot.datasets.get_data_classif('3gauss2',n)\n\na,b = ot.unif(n),ot.unif(n)\n# loss matrix\nM=ot.dist(xs,xt)\n#M/=M.max()\n\n#%% plot samples\n\npl.figure(1)\n\npl.subplot(2,2,1)\npl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples')\npl.legend(loc=0)\npl.title('Source distributions')\n\npl.subplot(2,2,2)\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples')\npl.legend(loc=0)\npl.title('target distributions')\n\npl.figure(2)\npl.imshow(M,interpolation='nearest')\npl.title('Cost matrix M')\n\n\n#%% OT estimation\n\n# EMD\nG0=ot.emd(a,b,M)\n\n# sinkhorn\nlambd=1e-1\nGs=ot.sinkhorn(a,b,M,lambd)\n\n\n# Group lasso regularization\nreg=1e-1\neta=1e0\nGg=ot.da.sinkhorn_lpl1_mm(a,ys.astype(np.int),b,M,reg,eta)\n\n\n#%% visu matrices\n\npl.figure(3)\n\npl.subplot(2,3,1)\npl.imshow(G0,interpolation='nearest')\npl.title('OT matrix ')\n\npl.subplot(2,3,2)\npl.imshow(Gs,interpolation='nearest')\npl.title('OT matrix Sinkhorn')\n\npl.subplot(2,3,3)\npl.imshow(Gg,interpolation='nearest')\npl.title('OT matrix Group lasso')\n\npl.subplot(2,3,4)\not.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1])\npl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples')\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples')\n\n\npl.subplot(2,3,5)\not.plot.plot2D_samples_mat(xs,xt,Gs,c=[.5,.5,1])\npl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples')\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples')\n\npl.subplot(2,3,6)\not.plot.plot2D_samples_mat(xs,xt,Gg,c=[.5,.5,1])\npl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples')\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples')\n\n#%% sample interpolation\n\nxst0=n*G0.dot(xt)\nxsts=n*Gs.dot(xt)\nxstg=n*Gg.dot(xt)\n\npl.figure(4)\npl.subplot(2,3,1)\n\n\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5)\npl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30)\npl.title('Interp samples')\npl.legend(loc=0)\n\npl.subplot(2,3,2)\n\n\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5)\npl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30)\npl.title('Interp samples Sinkhorn')\n\npl.subplot(2,3,3)\n\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5)\npl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30)\npl.title('Interp samples Grouplasso')" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, - "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", - "codemirror_mode": { - "version": 2, - "name": "ipython" - } - } - } -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OTDA_2D.py b/docs/source/auto_examples/plot_OTDA_2D.py deleted file mode 100644 index a1fb804..0000000 --- a/docs/source/auto_examples/plot_OTDA_2D.py +++ /dev/null @@ -1,120 +0,0 @@ -# -*- coding: utf-8 -*- -""" -============================== -OT for empirical distributions -============================== - -""" - -import numpy as np -import matplotlib.pylab as pl -import ot - - - -#%% parameters - -n=150 # nb bins - -xs,ys=ot.datasets.get_data_classif('3gauss',n) -xt,yt=ot.datasets.get_data_classif('3gauss2',n) - -a,b = ot.unif(n),ot.unif(n) -# loss matrix -M=ot.dist(xs,xt) -#M/=M.max() - -#%% plot samples - -pl.figure(1) - -pl.subplot(2,2,1) -pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') -pl.legend(loc=0) -pl.title('Source distributions') - -pl.subplot(2,2,2) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') -pl.legend(loc=0) -pl.title('target distributions') - -pl.figure(2) -pl.imshow(M,interpolation='nearest') -pl.title('Cost matrix M') - - -#%% OT estimation - -# EMD -G0=ot.emd(a,b,M) - -# sinkhorn -lambd=1e-1 -Gs=ot.sinkhorn(a,b,M,lambd) - - -# Group lasso regularization -reg=1e-1 -eta=1e0 -Gg=ot.da.sinkhorn_lpl1_mm(a,ys.astype(np.int),b,M,reg,eta) - - -#%% visu matrices - -pl.figure(3) - -pl.subplot(2,3,1) -pl.imshow(G0,interpolation='nearest') -pl.title('OT matrix ') - -pl.subplot(2,3,2) -pl.imshow(Gs,interpolation='nearest') -pl.title('OT matrix Sinkhorn') - -pl.subplot(2,3,3) -pl.imshow(Gg,interpolation='nearest') -pl.title('OT matrix Group lasso') - -pl.subplot(2,3,4) -ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1]) -pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') - - -pl.subplot(2,3,5) -ot.plot.plot2D_samples_mat(xs,xt,Gs,c=[.5,.5,1]) -pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') - -pl.subplot(2,3,6) -ot.plot.plot2D_samples_mat(xs,xt,Gg,c=[.5,.5,1]) -pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') - -#%% sample interpolation - -xst0=n*G0.dot(xt) -xsts=n*Gs.dot(xt) -xstg=n*Gg.dot(xt) - -pl.figure(4) -pl.subplot(2,3,1) - - -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) -pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30) -pl.title('Interp samples') -pl.legend(loc=0) - -pl.subplot(2,3,2) - - -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) -pl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30) -pl.title('Interp samples Sinkhorn') - -pl.subplot(2,3,3) - -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) -pl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30) -pl.title('Interp samples Grouplasso') \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OTDA_2D.rst b/docs/source/auto_examples/plot_OTDA_2D.rst deleted file mode 100644 index b535bb0..0000000 --- a/docs/source/auto_examples/plot_OTDA_2D.rst +++ /dev/null @@ -1,175 +0,0 @@ - - -.. _sphx_glr_auto_examples_plot_OTDA_2D.py: - - -============================== -OT for empirical distributions -============================== - - - - - -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OTDA_2D_001.png - :scale: 47 - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OTDA_2D_002.png - :scale: 47 - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OTDA_2D_003.png - :scale: 47 - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OTDA_2D_004.png - :scale: 47 - - - - - -.. code-block:: python - - - import numpy as np - import matplotlib.pylab as pl - import ot - - - - #%% parameters - - n=150 # nb bins - - xs,ys=ot.datasets.get_data_classif('3gauss',n) - xt,yt=ot.datasets.get_data_classif('3gauss2',n) - - a,b = ot.unif(n),ot.unif(n) - # loss matrix - M=ot.dist(xs,xt) - #M/=M.max() - - #%% plot samples - - pl.figure(1) - - pl.subplot(2,2,1) - pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') - pl.legend(loc=0) - pl.title('Source distributions') - - pl.subplot(2,2,2) - pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') - pl.legend(loc=0) - pl.title('target distributions') - - pl.figure(2) - pl.imshow(M,interpolation='nearest') - pl.title('Cost matrix M') - - - #%% OT estimation - - # EMD - G0=ot.emd(a,b,M) - - # sinkhorn - lambd=1e-1 - Gs=ot.sinkhorn(a,b,M,lambd) - - - # Group lasso regularization - reg=1e-1 - eta=1e0 - Gg=ot.da.sinkhorn_lpl1_mm(a,ys.astype(np.int),b,M,reg,eta) - - - #%% visu matrices - - pl.figure(3) - - pl.subplot(2,3,1) - pl.imshow(G0,interpolation='nearest') - pl.title('OT matrix ') - - pl.subplot(2,3,2) - pl.imshow(Gs,interpolation='nearest') - pl.title('OT matrix Sinkhorn') - - pl.subplot(2,3,3) - pl.imshow(Gg,interpolation='nearest') - pl.title('OT matrix Group lasso') - - pl.subplot(2,3,4) - ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1]) - pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') - pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') - - - pl.subplot(2,3,5) - ot.plot.plot2D_samples_mat(xs,xt,Gs,c=[.5,.5,1]) - pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') - pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') - - pl.subplot(2,3,6) - ot.plot.plot2D_samples_mat(xs,xt,Gg,c=[.5,.5,1]) - pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') - pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') - - #%% sample interpolation - - xst0=n*G0.dot(xt) - xsts=n*Gs.dot(xt) - xstg=n*Gg.dot(xt) - - pl.figure(4) - pl.subplot(2,3,1) - - - pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) - pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30) - pl.title('Interp samples') - pl.legend(loc=0) - - pl.subplot(2,3,2) - - - pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) - pl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30) - pl.title('Interp samples Sinkhorn') - - pl.subplot(2,3,3) - - pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.5) - pl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30) - pl.title('Interp samples Grouplasso') -**Total running time of the script:** ( 0 minutes 17.372 seconds) - - - -.. container:: sphx-glr-footer - - - .. container:: sphx-glr-download - - :download:`Download Python source code: plot_OTDA_2D.py ` - - - - .. container:: sphx-glr-download - - :download:`Download Jupyter notebook: plot_OTDA_2D.ipynb ` - -.. rst-class:: sphx-glr-signature - - `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OTDA_classes.ipynb b/docs/source/auto_examples/plot_OTDA_classes.ipynb deleted file mode 100644 index d9fcb87..0000000 --- a/docs/source/auto_examples/plot_OTDA_classes.ipynb +++ /dev/null @@ -1,54 +0,0 @@ -{ - "nbformat_minor": 0, - "nbformat": 4, - "cells": [ - { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, - { - "source": [ - "\n# OT for domain adaptation\n\n\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "import matplotlib.pylab as pl\nimport ot\n\n\n\n\n#%% parameters\n\nn=150 # nb samples in source and target datasets\n\nxs,ys=ot.datasets.get_data_classif('3gauss',n)\nxt,yt=ot.datasets.get_data_classif('3gauss2',n)\n\n\n\n\n#%% plot samples\n\npl.figure(1)\n\npl.subplot(2,2,1)\npl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples')\npl.legend(loc=0)\npl.title('Source distributions')\n\npl.subplot(2,2,2)\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples')\npl.legend(loc=0)\npl.title('target distributions')\n\n\n#%% OT estimation\n\n# LP problem\nda_emd=ot.da.OTDA() # init class\nda_emd.fit(xs,xt) # fit distributions\nxst0=da_emd.interp() # interpolation of source samples\n\n\n# sinkhorn regularization\nlambd=1e-1\nda_entrop=ot.da.OTDA_sinkhorn()\nda_entrop.fit(xs,xt,reg=lambd)\nxsts=da_entrop.interp()\n\n# non-convex Group lasso regularization\nreg=1e-1\neta=1e0\nda_lpl1=ot.da.OTDA_lpl1()\nda_lpl1.fit(xs,ys,xt,reg=reg,eta=eta)\nxstg=da_lpl1.interp()\n\n\n# True Group lasso regularization\nreg=1e-1\neta=2e0\nda_l1l2=ot.da.OTDA_l1l2()\nda_l1l2.fit(xs,ys,xt,reg=reg,eta=eta,numItermax=20,verbose=True)\nxstgl=da_l1l2.interp()\n\n\n#%% plot interpolated source samples\npl.figure(4,(15,8))\n\nparam_img={'interpolation':'nearest','cmap':'jet'}\n\npl.subplot(2,4,1)\npl.imshow(da_emd.G,**param_img)\npl.title('OT matrix')\n\n\npl.subplot(2,4,2)\npl.imshow(da_entrop.G,**param_img)\npl.title('OT matrix sinkhorn')\n\npl.subplot(2,4,3)\npl.imshow(da_lpl1.G,**param_img)\npl.title('OT matrix non-convex Group Lasso')\n\npl.subplot(2,4,4)\npl.imshow(da_l1l2.G,**param_img)\npl.title('OT matrix Group Lasso')\n\n\npl.subplot(2,4,5)\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)\npl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30)\npl.title('Interp samples')\npl.legend(loc=0)\n\npl.subplot(2,4,6)\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)\npl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30)\npl.title('Interp samples Sinkhorn')\n\npl.subplot(2,4,7)\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)\npl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30)\npl.title('Interp samples non-convex Group Lasso')\n\npl.subplot(2,4,8)\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)\npl.scatter(xstgl[:,0],xstgl[:,1],c=ys,marker='+',label='Transp samples',s=30)\npl.title('Interp samples Group Lasso')" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, - "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", - "codemirror_mode": { - "version": 2, - "name": "ipython" - } - } - } -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OTDA_classes.py b/docs/source/auto_examples/plot_OTDA_classes.py deleted file mode 100644 index 089b45b..0000000 --- a/docs/source/auto_examples/plot_OTDA_classes.py +++ /dev/null @@ -1,112 +0,0 @@ -# -*- coding: utf-8 -*- -""" -======================== -OT for domain adaptation -======================== - -""" - -import matplotlib.pylab as pl -import ot - - - - -#%% parameters - -n=150 # nb samples in source and target datasets - -xs,ys=ot.datasets.get_data_classif('3gauss',n) -xt,yt=ot.datasets.get_data_classif('3gauss2',n) - - - - -#%% plot samples - -pl.figure(1) - -pl.subplot(2,2,1) -pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') -pl.legend(loc=0) -pl.title('Source distributions') - -pl.subplot(2,2,2) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') -pl.legend(loc=0) -pl.title('target distributions') - - -#%% OT estimation - -# LP problem -da_emd=ot.da.OTDA() # init class -da_emd.fit(xs,xt) # fit distributions -xst0=da_emd.interp() # interpolation of source samples - - -# sinkhorn regularization -lambd=1e-1 -da_entrop=ot.da.OTDA_sinkhorn() -da_entrop.fit(xs,xt,reg=lambd) -xsts=da_entrop.interp() - -# non-convex Group lasso regularization -reg=1e-1 -eta=1e0 -da_lpl1=ot.da.OTDA_lpl1() -da_lpl1.fit(xs,ys,xt,reg=reg,eta=eta) -xstg=da_lpl1.interp() - - -# True Group lasso regularization -reg=1e-1 -eta=2e0 -da_l1l2=ot.da.OTDA_l1l2() -da_l1l2.fit(xs,ys,xt,reg=reg,eta=eta,numItermax=20,verbose=True) -xstgl=da_l1l2.interp() - - -#%% plot interpolated source samples -pl.figure(4,(15,8)) - -param_img={'interpolation':'nearest','cmap':'jet'} - -pl.subplot(2,4,1) -pl.imshow(da_emd.G,**param_img) -pl.title('OT matrix') - - -pl.subplot(2,4,2) -pl.imshow(da_entrop.G,**param_img) -pl.title('OT matrix sinkhorn') - -pl.subplot(2,4,3) -pl.imshow(da_lpl1.G,**param_img) -pl.title('OT matrix non-convex Group Lasso') - -pl.subplot(2,4,4) -pl.imshow(da_l1l2.G,**param_img) -pl.title('OT matrix Group Lasso') - - -pl.subplot(2,4,5) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) -pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30) -pl.title('Interp samples') -pl.legend(loc=0) - -pl.subplot(2,4,6) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) -pl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30) -pl.title('Interp samples Sinkhorn') - -pl.subplot(2,4,7) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) -pl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30) -pl.title('Interp samples non-convex Group Lasso') - -pl.subplot(2,4,8) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) -pl.scatter(xstgl[:,0],xstgl[:,1],c=ys,marker='+',label='Transp samples',s=30) -pl.title('Interp samples Group Lasso') \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OTDA_classes.rst b/docs/source/auto_examples/plot_OTDA_classes.rst deleted file mode 100644 index 097e9fc..0000000 --- a/docs/source/auto_examples/plot_OTDA_classes.rst +++ /dev/null @@ -1,190 +0,0 @@ - - -.. _sphx_glr_auto_examples_plot_OTDA_classes.py: - - -======================== -OT for domain adaptation -======================== - - - - - -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OTDA_classes_001.png - :scale: 47 - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OTDA_classes_004.png - :scale: 47 - - -.. rst-class:: sphx-glr-script-out - - Out:: - - It. |Loss |Delta loss - -------------------------------- - 0|9.171271e+00|0.000000e+00 - 1|2.133783e+00|-3.298127e+00 - 2|1.895941e+00|-1.254484e-01 - 3|1.844628e+00|-2.781709e-02 - 4|1.824983e+00|-1.076467e-02 - 5|1.815453e+00|-5.249337e-03 - 6|1.808104e+00|-4.064733e-03 - 7|1.803558e+00|-2.520475e-03 - 8|1.801061e+00|-1.386155e-03 - 9|1.799391e+00|-9.279565e-04 - 10|1.797176e+00|-1.232778e-03 - 11|1.795465e+00|-9.529479e-04 - 12|1.795316e+00|-8.322362e-05 - 13|1.794523e+00|-4.418932e-04 - 14|1.794444e+00|-4.390599e-05 - 15|1.794395e+00|-2.710318e-05 - 16|1.793713e+00|-3.804028e-04 - 17|1.793110e+00|-3.359479e-04 - 18|1.792829e+00|-1.569563e-04 - 19|1.792621e+00|-1.159469e-04 - It. |Loss |Delta loss - -------------------------------- - 20|1.791334e+00|-7.187689e-04 - - - - -| - - -.. code-block:: python - - - import matplotlib.pylab as pl - import ot - - - - - #%% parameters - - n=150 # nb samples in source and target datasets - - xs,ys=ot.datasets.get_data_classif('3gauss',n) - xt,yt=ot.datasets.get_data_classif('3gauss2',n) - - - - - #%% plot samples - - pl.figure(1) - - pl.subplot(2,2,1) - pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') - pl.legend(loc=0) - pl.title('Source distributions') - - pl.subplot(2,2,2) - pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') - pl.legend(loc=0) - pl.title('target distributions') - - - #%% OT estimation - - # LP problem - da_emd=ot.da.OTDA() # init class - da_emd.fit(xs,xt) # fit distributions - xst0=da_emd.interp() # interpolation of source samples - - - # sinkhorn regularization - lambd=1e-1 - da_entrop=ot.da.OTDA_sinkhorn() - da_entrop.fit(xs,xt,reg=lambd) - xsts=da_entrop.interp() - - # non-convex Group lasso regularization - reg=1e-1 - eta=1e0 - da_lpl1=ot.da.OTDA_lpl1() - da_lpl1.fit(xs,ys,xt,reg=reg,eta=eta) - xstg=da_lpl1.interp() - - - # True Group lasso regularization - reg=1e-1 - eta=2e0 - da_l1l2=ot.da.OTDA_l1l2() - da_l1l2.fit(xs,ys,xt,reg=reg,eta=eta,numItermax=20,verbose=True) - xstgl=da_l1l2.interp() - - - #%% plot interpolated source samples - pl.figure(4,(15,8)) - - param_img={'interpolation':'nearest','cmap':'jet'} - - pl.subplot(2,4,1) - pl.imshow(da_emd.G,**param_img) - pl.title('OT matrix') - - - pl.subplot(2,4,2) - pl.imshow(da_entrop.G,**param_img) - pl.title('OT matrix sinkhorn') - - pl.subplot(2,4,3) - pl.imshow(da_lpl1.G,**param_img) - pl.title('OT matrix non-convex Group Lasso') - - pl.subplot(2,4,4) - pl.imshow(da_l1l2.G,**param_img) - pl.title('OT matrix Group Lasso') - - - pl.subplot(2,4,5) - pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) - pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30) - pl.title('Interp samples') - pl.legend(loc=0) - - pl.subplot(2,4,6) - pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) - pl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30) - pl.title('Interp samples Sinkhorn') - - pl.subplot(2,4,7) - pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) - pl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30) - pl.title('Interp samples non-convex Group Lasso') - - pl.subplot(2,4,8) - pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3) - pl.scatter(xstgl[:,0],xstgl[:,1],c=ys,marker='+',label='Transp samples',s=30) - pl.title('Interp samples Group Lasso') -**Total running time of the script:** ( 0 minutes 2.225 seconds) - - - -.. container:: sphx-glr-footer - - - .. container:: sphx-glr-download - - :download:`Download Python source code: plot_OTDA_classes.py ` - - - - .. container:: sphx-glr-download - - :download:`Download Jupyter notebook: plot_OTDA_classes.ipynb ` - -.. rst-class:: sphx-glr-signature - - `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OTDA_color_images.ipynb b/docs/source/auto_examples/plot_OTDA_color_images.ipynb deleted file mode 100644 index d174828..0000000 --- a/docs/source/auto_examples/plot_OTDA_color_images.ipynb +++ /dev/null @@ -1,54 +0,0 @@ -{ - "nbformat_minor": 0, - "nbformat": 4, - "cells": [ - { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, - { - "source": [ - "\n========================================================\nOT for domain adaptation with image color adaptation [6]\n========================================================\n\n[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "import numpy as np\nimport scipy.ndimage as spi\nimport matplotlib.pylab as pl\nimport ot\n\n\n#%% Loading images\n\nI1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256\nI2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256\n\n#%% Plot images\n\npl.figure(1)\n\npl.subplot(1,2,1)\npl.imshow(I1)\npl.title('Image 1')\n\npl.subplot(1,2,2)\npl.imshow(I2)\npl.title('Image 2')\n\npl.show()\n\n#%% Image conversion and dataset generation\n\ndef im2mat(I):\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0]*I.shape[1],I.shape[2]))\n\ndef mat2im(X,shape):\n \"\"\"Converts back a matrix to an image\"\"\"\n return X.reshape(shape)\n\nX1=im2mat(I1)\nX2=im2mat(I2)\n\n# training samples\nnb=1000\nidx1=np.random.randint(X1.shape[0],size=(nb,))\nidx2=np.random.randint(X2.shape[0],size=(nb,))\n\nxs=X1[idx1,:]\nxt=X2[idx2,:]\n\n#%% Plot image distributions\n\n\npl.figure(2,(10,5))\n\npl.subplot(1,2,1)\npl.scatter(xs[:,0],xs[:,2],c=xs)\npl.axis([0,1,0,1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 1')\n\npl.subplot(1,2,2)\n#pl.imshow(I2)\npl.scatter(xt[:,0],xt[:,2],c=xt)\npl.axis([0,1,0,1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 2')\n\npl.show()\n\n\n\n#%% domain adaptation between images\n\n# LP problem\nda_emd=ot.da.OTDA() # init class\nda_emd.fit(xs,xt) # fit distributions\n\n\n# sinkhorn regularization\nlambd=1e-1\nda_entrop=ot.da.OTDA_sinkhorn()\nda_entrop.fit(xs,xt,reg=lambd)\n\n\n\n#%% prediction between images (using out of sample prediction as in [6])\n\nX1t=da_emd.predict(X1)\nX2t=da_emd.predict(X2,-1)\n\n\nX1te=da_entrop.predict(X1)\nX2te=da_entrop.predict(X2,-1)\n\n\ndef minmax(I):\n return np.minimum(np.maximum(I,0),1)\n\nI1t=minmax(mat2im(X1t,I1.shape))\nI2t=minmax(mat2im(X2t,I2.shape))\n\nI1te=minmax(mat2im(X1te,I1.shape))\nI2te=minmax(mat2im(X2te,I2.shape))\n\n#%% plot all images\n\npl.figure(2,(10,8))\n\npl.subplot(2,3,1)\n\npl.imshow(I1)\npl.title('Image 1')\n\npl.subplot(2,3,2)\npl.imshow(I1t)\npl.title('Image 1 Adapt')\n\n\npl.subplot(2,3,3)\npl.imshow(I1te)\npl.title('Image 1 Adapt (reg)')\n\npl.subplot(2,3,4)\n\npl.imshow(I2)\npl.title('Image 2')\n\npl.subplot(2,3,5)\npl.imshow(I2t)\npl.title('Image 2 Adapt')\n\n\npl.subplot(2,3,6)\npl.imshow(I2te)\npl.title('Image 2 Adapt (reg)')\n\npl.show()" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, - "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", - "codemirror_mode": { - "version": 2, - "name": "ipython" - } - } - } -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OTDA_color_images.py b/docs/source/auto_examples/plot_OTDA_color_images.py deleted file mode 100644 index 68eee44..0000000 --- a/docs/source/auto_examples/plot_OTDA_color_images.py +++ /dev/null @@ -1,145 +0,0 @@ -# -*- coding: utf-8 -*- -""" -======================================================== -OT for domain adaptation with image color adaptation [6] -======================================================== - -[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. -""" - -import numpy as np -import scipy.ndimage as spi -import matplotlib.pylab as pl -import ot - - -#%% Loading images - -I1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256 -I2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256 - -#%% Plot images - -pl.figure(1) - -pl.subplot(1,2,1) -pl.imshow(I1) -pl.title('Image 1') - -pl.subplot(1,2,2) -pl.imshow(I2) -pl.title('Image 2') - -pl.show() - -#%% Image conversion and dataset generation - -def im2mat(I): - """Converts and image to matrix (one pixel per line)""" - return I.reshape((I.shape[0]*I.shape[1],I.shape[2])) - -def mat2im(X,shape): - """Converts back a matrix to an image""" - return X.reshape(shape) - -X1=im2mat(I1) -X2=im2mat(I2) - -# training samples -nb=1000 -idx1=np.random.randint(X1.shape[0],size=(nb,)) -idx2=np.random.randint(X2.shape[0],size=(nb,)) - -xs=X1[idx1,:] -xt=X2[idx2,:] - -#%% Plot image distributions - - -pl.figure(2,(10,5)) - -pl.subplot(1,2,1) -pl.scatter(xs[:,0],xs[:,2],c=xs) -pl.axis([0,1,0,1]) -pl.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 1') - -pl.subplot(1,2,2) -#pl.imshow(I2) -pl.scatter(xt[:,0],xt[:,2],c=xt) -pl.axis([0,1,0,1]) -pl.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 2') - -pl.show() - - - -#%% domain adaptation between images - -# LP problem -da_emd=ot.da.OTDA() # init class -da_emd.fit(xs,xt) # fit distributions - - -# sinkhorn regularization -lambd=1e-1 -da_entrop=ot.da.OTDA_sinkhorn() -da_entrop.fit(xs,xt,reg=lambd) - - - -#%% prediction between images (using out of sample prediction as in [6]) - -X1t=da_emd.predict(X1) -X2t=da_emd.predict(X2,-1) - - -X1te=da_entrop.predict(X1) -X2te=da_entrop.predict(X2,-1) - - -def minmax(I): - return np.minimum(np.maximum(I,0),1) - -I1t=minmax(mat2im(X1t,I1.shape)) -I2t=minmax(mat2im(X2t,I2.shape)) - -I1te=minmax(mat2im(X1te,I1.shape)) -I2te=minmax(mat2im(X2te,I2.shape)) - -#%% plot all images - -pl.figure(2,(10,8)) - -pl.subplot(2,3,1) - -pl.imshow(I1) -pl.title('Image 1') - -pl.subplot(2,3,2) -pl.imshow(I1t) -pl.title('Image 1 Adapt') - - -pl.subplot(2,3,3) -pl.imshow(I1te) -pl.title('Image 1 Adapt (reg)') - -pl.subplot(2,3,4) - -pl.imshow(I2) -pl.title('Image 2') - -pl.subplot(2,3,5) -pl.imshow(I2t) -pl.title('Image 2 Adapt') - - -pl.subplot(2,3,6) -pl.imshow(I2te) -pl.title('Image 2 Adapt (reg)') - -pl.show() diff --git a/docs/source/auto_examples/plot_OTDA_color_images.rst b/docs/source/auto_examples/plot_OTDA_color_images.rst deleted file mode 100644 index a982a90..0000000 --- a/docs/source/auto_examples/plot_OTDA_color_images.rst +++ /dev/null @@ -1,191 +0,0 @@ - - -.. _sphx_glr_auto_examples_plot_OTDA_color_images.py: - - -======================================================== -OT for domain adaptation with image color adaptation [6] -======================================================== - -[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. - - - - -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OTDA_color_images_001.png - :scale: 47 - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OTDA_color_images_002.png - :scale: 47 - - - - - -.. code-block:: python - - - import numpy as np - import scipy.ndimage as spi - import matplotlib.pylab as pl - import ot - - - #%% Loading images - - I1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256 - I2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256 - - #%% Plot images - - pl.figure(1) - - pl.subplot(1,2,1) - pl.imshow(I1) - pl.title('Image 1') - - pl.subplot(1,2,2) - pl.imshow(I2) - pl.title('Image 2') - - pl.show() - - #%% Image conversion and dataset generation - - def im2mat(I): - """Converts and image to matrix (one pixel per line)""" - return I.reshape((I.shape[0]*I.shape[1],I.shape[2])) - - def mat2im(X,shape): - """Converts back a matrix to an image""" - return X.reshape(shape) - - X1=im2mat(I1) - X2=im2mat(I2) - - # training samples - nb=1000 - idx1=np.random.randint(X1.shape[0],size=(nb,)) - idx2=np.random.randint(X2.shape[0],size=(nb,)) - - xs=X1[idx1,:] - xt=X2[idx2,:] - - #%% Plot image distributions - - - pl.figure(2,(10,5)) - - pl.subplot(1,2,1) - pl.scatter(xs[:,0],xs[:,2],c=xs) - pl.axis([0,1,0,1]) - pl.xlabel('Red') - pl.ylabel('Blue') - pl.title('Image 1') - - pl.subplot(1,2,2) - #pl.imshow(I2) - pl.scatter(xt[:,0],xt[:,2],c=xt) - pl.axis([0,1,0,1]) - pl.xlabel('Red') - pl.ylabel('Blue') - pl.title('Image 2') - - pl.show() - - - - #%% domain adaptation between images - - # LP problem - da_emd=ot.da.OTDA() # init class - da_emd.fit(xs,xt) # fit distributions - - - # sinkhorn regularization - lambd=1e-1 - da_entrop=ot.da.OTDA_sinkhorn() - da_entrop.fit(xs,xt,reg=lambd) - - - - #%% prediction between images (using out of sample prediction as in [6]) - - X1t=da_emd.predict(X1) - X2t=da_emd.predict(X2,-1) - - - X1te=da_entrop.predict(X1) - X2te=da_entrop.predict(X2,-1) - - - def minmax(I): - return np.minimum(np.maximum(I,0),1) - - I1t=minmax(mat2im(X1t,I1.shape)) - I2t=minmax(mat2im(X2t,I2.shape)) - - I1te=minmax(mat2im(X1te,I1.shape)) - I2te=minmax(mat2im(X2te,I2.shape)) - - #%% plot all images - - pl.figure(2,(10,8)) - - pl.subplot(2,3,1) - - pl.imshow(I1) - pl.title('Image 1') - - pl.subplot(2,3,2) - pl.imshow(I1t) - pl.title('Image 1 Adapt') - - - pl.subplot(2,3,3) - pl.imshow(I1te) - pl.title('Image 1 Adapt (reg)') - - pl.subplot(2,3,4) - - pl.imshow(I2) - pl.title('Image 2') - - pl.subplot(2,3,5) - pl.imshow(I2t) - pl.title('Image 2 Adapt') - - - pl.subplot(2,3,6) - pl.imshow(I2te) - pl.title('Image 2 Adapt (reg)') - - pl.show() - -**Total running time of the script:** ( 0 minutes 24.815 seconds) - - - -.. container:: sphx-glr-footer - - - .. container:: sphx-glr-download - - :download:`Download Python source code: plot_OTDA_color_images.py ` - - - - .. container:: sphx-glr-download - - :download:`Download Jupyter notebook: plot_OTDA_color_images.ipynb ` - -.. rst-class:: sphx-glr-signature - - `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OTDA_mapping.ipynb b/docs/source/auto_examples/plot_OTDA_mapping.ipynb deleted file mode 100644 index ec405af..0000000 --- a/docs/source/auto_examples/plot_OTDA_mapping.ipynb +++ /dev/null @@ -1,54 +0,0 @@ -{ - "nbformat_minor": 0, - "nbformat": 4, - "cells": [ - { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, - { - "source": [ - "\n===============================================\nOT mapping estimation for domain adaptation [8]\n===============================================\n\n[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, \"Mapping estimation for\n discrete optimal transport\", Neural Information Processing Systems (NIPS), 2016.\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n\n\n#%% dataset generation\n\nnp.random.seed(0) # makes example reproducible\n\nn=100 # nb samples in source and target datasets\ntheta=2*np.pi/20\nnz=0.1\nxs,ys=ot.datasets.get_data_classif('gaussrot',n,nz=nz)\nxt,yt=ot.datasets.get_data_classif('gaussrot',n,theta=theta,nz=nz)\n\n# one of the target mode changes its variance (no linear mapping)\nxt[yt==2]*=3\nxt=xt+4\n\n\n#%% plot samples\n\npl.figure(1,(8,5))\npl.clf()\n\npl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples')\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples')\n\npl.legend(loc=0)\npl.title('Source and target distributions')\n\n\n\n#%% OT linear mapping estimation\n\neta=1e-8 # quadratic regularization for regression\nmu=1e0 # weight of the OT linear term\nbias=True # estimate a bias\n\not_mapping=ot.da.OTDA_mapping_linear()\not_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True)\n\nxst=ot_mapping.predict(xs) # use the estimated mapping\nxst0=ot_mapping.interp() # use barycentric mapping\n\n\npl.figure(2,(10,7))\npl.clf()\npl.subplot(2,2,1)\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.3)\npl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='barycentric mapping')\npl.title(\"barycentric mapping\")\n\npl.subplot(2,2,2)\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.3)\npl.scatter(xst[:,0],xst[:,1],c=ys,marker='+',label='Learned mapping')\npl.title(\"Learned mapping\")\n\n\n\n#%% Kernel mapping estimation\n\neta=1e-5 # quadratic regularization for regression\nmu=1e-1 # weight of the OT linear term\nbias=True # estimate a bias\nsigma=1 # sigma bandwidth fot gaussian kernel\n\n\not_mapping_kernel=ot.da.OTDA_mapping_kernel()\not_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 10,verbose=True)\n\nxst_kernel=ot_mapping_kernel.predict(xs) # use the estimated mapping\nxst0_kernel=ot_mapping_kernel.interp() # use barycentric mapping\n\n\n#%% Plotting the mapped samples\n\npl.figure(2,(10,7))\npl.clf()\npl.subplot(2,2,1)\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2)\npl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Mapped source samples')\npl.title(\"Bary. mapping (linear)\")\npl.legend(loc=0)\n\npl.subplot(2,2,2)\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2)\npl.scatter(xst[:,0],xst[:,1],c=ys,marker='+',label='Learned mapping')\npl.title(\"Estim. mapping (linear)\")\n\npl.subplot(2,2,3)\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2)\npl.scatter(xst0_kernel[:,0],xst0_kernel[:,1],c=ys,marker='+',label='barycentric mapping')\npl.title(\"Bary. mapping (kernel)\")\n\npl.subplot(2,2,4)\npl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2)\npl.scatter(xst_kernel[:,0],xst_kernel[:,1],c=ys,marker='+',label='Learned mapping')\npl.title(\"Estim. mapping (kernel)\")" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, - "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", - "codemirror_mode": { - "version": 2, - "name": "ipython" - } - } - } -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OTDA_mapping.py b/docs/source/auto_examples/plot_OTDA_mapping.py deleted file mode 100644 index 78b57e7..0000000 --- a/docs/source/auto_examples/plot_OTDA_mapping.py +++ /dev/null @@ -1,110 +0,0 @@ -# -*- coding: utf-8 -*- -""" -=============================================== -OT mapping estimation for domain adaptation [8] -=============================================== - -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for - discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. -""" - -import numpy as np -import matplotlib.pylab as pl -import ot - - - -#%% dataset generation - -np.random.seed(0) # makes example reproducible - -n=100 # nb samples in source and target datasets -theta=2*np.pi/20 -nz=0.1 -xs,ys=ot.datasets.get_data_classif('gaussrot',n,nz=nz) -xt,yt=ot.datasets.get_data_classif('gaussrot',n,theta=theta,nz=nz) - -# one of the target mode changes its variance (no linear mapping) -xt[yt==2]*=3 -xt=xt+4 - - -#%% plot samples - -pl.figure(1,(8,5)) -pl.clf() - -pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') - -pl.legend(loc=0) -pl.title('Source and target distributions') - - - -#%% OT linear mapping estimation - -eta=1e-8 # quadratic regularization for regression -mu=1e0 # weight of the OT linear term -bias=True # estimate a bias - -ot_mapping=ot.da.OTDA_mapping_linear() -ot_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True) - -xst=ot_mapping.predict(xs) # use the estimated mapping -xst0=ot_mapping.interp() # use barycentric mapping - - -pl.figure(2,(10,7)) -pl.clf() -pl.subplot(2,2,1) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.3) -pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='barycentric mapping') -pl.title("barycentric mapping") - -pl.subplot(2,2,2) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.3) -pl.scatter(xst[:,0],xst[:,1],c=ys,marker='+',label='Learned mapping') -pl.title("Learned mapping") - - - -#%% Kernel mapping estimation - -eta=1e-5 # quadratic regularization for regression -mu=1e-1 # weight of the OT linear term -bias=True # estimate a bias -sigma=1 # sigma bandwidth fot gaussian kernel - - -ot_mapping_kernel=ot.da.OTDA_mapping_kernel() -ot_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 10,verbose=True) - -xst_kernel=ot_mapping_kernel.predict(xs) # use the estimated mapping -xst0_kernel=ot_mapping_kernel.interp() # use barycentric mapping - - -#%% Plotting the mapped samples - -pl.figure(2,(10,7)) -pl.clf() -pl.subplot(2,2,1) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) -pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Mapped source samples') -pl.title("Bary. mapping (linear)") -pl.legend(loc=0) - -pl.subplot(2,2,2) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) -pl.scatter(xst[:,0],xst[:,1],c=ys,marker='+',label='Learned mapping') -pl.title("Estim. mapping (linear)") - -pl.subplot(2,2,3) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) -pl.scatter(xst0_kernel[:,0],xst0_kernel[:,1],c=ys,marker='+',label='barycentric mapping') -pl.title("Bary. mapping (kernel)") - -pl.subplot(2,2,4) -pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) -pl.scatter(xst_kernel[:,0],xst_kernel[:,1],c=ys,marker='+',label='Learned mapping') -pl.title("Estim. mapping (kernel)") diff --git a/docs/source/auto_examples/plot_OTDA_mapping.rst b/docs/source/auto_examples/plot_OTDA_mapping.rst deleted file mode 100644 index 18da90d..0000000 --- a/docs/source/auto_examples/plot_OTDA_mapping.rst +++ /dev/null @@ -1,186 +0,0 @@ - - -.. _sphx_glr_auto_examples_plot_OTDA_mapping.py: - - -=============================================== -OT mapping estimation for domain adaptation [8] -=============================================== - -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for - discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. - - - - -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OTDA_mapping_001.png - :scale: 47 - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OTDA_mapping_002.png - :scale: 47 - - -.. rst-class:: sphx-glr-script-out - - Out:: - - It. |Loss |Delta loss - -------------------------------- - 0|4.009366e+03|0.000000e+00 - 1|3.999933e+03|-2.352753e-03 - 2|3.999520e+03|-1.031984e-04 - 3|3.999362e+03|-3.936391e-05 - 4|3.999281e+03|-2.032868e-05 - 5|3.999238e+03|-1.083083e-05 - 6|3.999229e+03|-2.125291e-06 - It. |Loss |Delta loss - -------------------------------- - 0|4.026841e+02|0.000000e+00 - 1|3.990791e+02|-8.952439e-03 - 2|3.987954e+02|-7.107124e-04 - 3|3.986554e+02|-3.512453e-04 - 4|3.985721e+02|-2.087997e-04 - 5|3.985141e+02|-1.456184e-04 - 6|3.984729e+02|-1.034624e-04 - 7|3.984435e+02|-7.366943e-05 - 8|3.984199e+02|-5.922497e-05 - 9|3.984016e+02|-4.593063e-05 - 10|3.983867e+02|-3.733061e-05 - - - - -| - - -.. code-block:: python - - - import numpy as np - import matplotlib.pylab as pl - import ot - - - - #%% dataset generation - - np.random.seed(0) # makes example reproducible - - n=100 # nb samples in source and target datasets - theta=2*np.pi/20 - nz=0.1 - xs,ys=ot.datasets.get_data_classif('gaussrot',n,nz=nz) - xt,yt=ot.datasets.get_data_classif('gaussrot',n,theta=theta,nz=nz) - - # one of the target mode changes its variance (no linear mapping) - xt[yt==2]*=3 - xt=xt+4 - - - #%% plot samples - - pl.figure(1,(8,5)) - pl.clf() - - pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples') - pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples') - - pl.legend(loc=0) - pl.title('Source and target distributions') - - - - #%% OT linear mapping estimation - - eta=1e-8 # quadratic regularization for regression - mu=1e0 # weight of the OT linear term - bias=True # estimate a bias - - ot_mapping=ot.da.OTDA_mapping_linear() - ot_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True) - - xst=ot_mapping.predict(xs) # use the estimated mapping - xst0=ot_mapping.interp() # use barycentric mapping - - - pl.figure(2,(10,7)) - pl.clf() - pl.subplot(2,2,1) - pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.3) - pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='barycentric mapping') - pl.title("barycentric mapping") - - pl.subplot(2,2,2) - pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.3) - pl.scatter(xst[:,0],xst[:,1],c=ys,marker='+',label='Learned mapping') - pl.title("Learned mapping") - - - - #%% Kernel mapping estimation - - eta=1e-5 # quadratic regularization for regression - mu=1e-1 # weight of the OT linear term - bias=True # estimate a bias - sigma=1 # sigma bandwidth fot gaussian kernel - - - ot_mapping_kernel=ot.da.OTDA_mapping_kernel() - ot_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 10,verbose=True) - - xst_kernel=ot_mapping_kernel.predict(xs) # use the estimated mapping - xst0_kernel=ot_mapping_kernel.interp() # use barycentric mapping - - - #%% Plotting the mapped samples - - pl.figure(2,(10,7)) - pl.clf() - pl.subplot(2,2,1) - pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) - pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Mapped source samples') - pl.title("Bary. mapping (linear)") - pl.legend(loc=0) - - pl.subplot(2,2,2) - pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) - pl.scatter(xst[:,0],xst[:,1],c=ys,marker='+',label='Learned mapping') - pl.title("Estim. mapping (linear)") - - pl.subplot(2,2,3) - pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) - pl.scatter(xst0_kernel[:,0],xst0_kernel[:,1],c=ys,marker='+',label='barycentric mapping') - pl.title("Bary. mapping (kernel)") - - pl.subplot(2,2,4) - pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2) - pl.scatter(xst_kernel[:,0],xst_kernel[:,1],c=ys,marker='+',label='Learned mapping') - pl.title("Estim. mapping (kernel)") - -**Total running time of the script:** ( 0 minutes 0.882 seconds) - - - -.. container:: sphx-glr-footer - - - .. container:: sphx-glr-download - - :download:`Download Python source code: plot_OTDA_mapping.py ` - - - - .. container:: sphx-glr-download - - :download:`Download Jupyter notebook: plot_OTDA_mapping.ipynb ` - -.. rst-class:: sphx-glr-signature - - `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OTDA_mapping_color_images.ipynb b/docs/source/auto_examples/plot_OTDA_mapping_color_images.ipynb deleted file mode 100644 index 1136cc3..0000000 --- a/docs/source/auto_examples/plot_OTDA_mapping_color_images.ipynb +++ /dev/null @@ -1,54 +0,0 @@ -{ - "nbformat_minor": 0, - "nbformat": 4, - "cells": [ - { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, - { - "source": [ - "\n====================================================================================\nOT for domain adaptation with image color adaptation [6] with mapping estimation [8]\n====================================================================================\n\n[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized\n discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.\n[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, \"Mapping estimation for\n discrete optimal transport\", Neural Information Processing Systems (NIPS), 2016.\n\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "import numpy as np\nimport scipy.ndimage as spi\nimport matplotlib.pylab as pl\nimport ot\n\n\n#%% Loading images\n\nI1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256\nI2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256\n\n#%% Plot images\n\npl.figure(1)\n\npl.subplot(1,2,1)\npl.imshow(I1)\npl.title('Image 1')\n\npl.subplot(1,2,2)\npl.imshow(I2)\npl.title('Image 2')\n\npl.show()\n\n#%% Image conversion and dataset generation\n\ndef im2mat(I):\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0]*I.shape[1],I.shape[2]))\n\ndef mat2im(X,shape):\n \"\"\"Converts back a matrix to an image\"\"\"\n return X.reshape(shape)\n\nX1=im2mat(I1)\nX2=im2mat(I2)\n\n# training samples\nnb=1000\nidx1=np.random.randint(X1.shape[0],size=(nb,))\nidx2=np.random.randint(X2.shape[0],size=(nb,))\n\nxs=X1[idx1,:]\nxt=X2[idx2,:]\n\n#%% Plot image distributions\n\n\npl.figure(2,(10,5))\n\npl.subplot(1,2,1)\npl.scatter(xs[:,0],xs[:,2],c=xs)\npl.axis([0,1,0,1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 1')\n\npl.subplot(1,2,2)\n#pl.imshow(I2)\npl.scatter(xt[:,0],xt[:,2],c=xt)\npl.axis([0,1,0,1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 2')\n\npl.show()\n\n\n\n#%% domain adaptation between images\ndef minmax(I):\n return np.minimum(np.maximum(I,0),1)\n# LP problem\nda_emd=ot.da.OTDA() # init class\nda_emd.fit(xs,xt) # fit distributions\n\nX1t=da_emd.predict(X1) # out of sample\nI1t=minmax(mat2im(X1t,I1.shape))\n\n# sinkhorn regularization\nlambd=1e-1\nda_entrop=ot.da.OTDA_sinkhorn()\nda_entrop.fit(xs,xt,reg=lambd)\n\nX1te=da_entrop.predict(X1)\nI1te=minmax(mat2im(X1te,I1.shape))\n\n# linear mapping estimation\neta=1e-8 # quadratic regularization for regression\nmu=1e0 # weight of the OT linear term\nbias=True # estimate a bias\n\not_mapping=ot.da.OTDA_mapping_linear()\not_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True)\n\nX1tl=ot_mapping.predict(X1) # use the estimated mapping\nI1tl=minmax(mat2im(X1tl,I1.shape))\n\n# nonlinear mapping estimation\neta=1e-2 # quadratic regularization for regression\nmu=1e0 # weight of the OT linear term\nbias=False # estimate a bias\nsigma=1 # sigma bandwidth fot gaussian kernel\n\n\not_mapping_kernel=ot.da.OTDA_mapping_kernel()\not_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 10,verbose=True)\n\nX1tn=ot_mapping_kernel.predict(X1) # use the estimated mapping\nI1tn=minmax(mat2im(X1tn,I1.shape))\n#%% plot images\n\n\npl.figure(2,(10,8))\n\npl.subplot(2,3,1)\n\npl.imshow(I1)\npl.title('Im. 1')\n\npl.subplot(2,3,2)\n\npl.imshow(I2)\npl.title('Im. 2')\n\n\npl.subplot(2,3,3)\npl.imshow(I1t)\npl.title('Im. 1 Interp LP')\n\npl.subplot(2,3,4)\npl.imshow(I1te)\npl.title('Im. 1 Interp Entrop')\n\n\npl.subplot(2,3,5)\npl.imshow(I1tl)\npl.title('Im. 1 Linear mapping')\n\npl.subplot(2,3,6)\npl.imshow(I1tn)\npl.title('Im. 1 nonlinear mapping')\n\npl.show()" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, - "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", - "codemirror_mode": { - "version": 2, - "name": "ipython" - } - } - } -} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OTDA_mapping_color_images.py b/docs/source/auto_examples/plot_OTDA_mapping_color_images.py deleted file mode 100644 index f07dc6c..0000000 --- a/docs/source/auto_examples/plot_OTDA_mapping_color_images.py +++ /dev/null @@ -1,158 +0,0 @@ -# -*- coding: utf-8 -*- -""" -==================================================================================== -OT for domain adaptation with image color adaptation [6] with mapping estimation [8] -==================================================================================== - -[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized - discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for - discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. - -""" - -import numpy as np -import scipy.ndimage as spi -import matplotlib.pylab as pl -import ot - - -#%% Loading images - -I1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256 -I2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256 - -#%% Plot images - -pl.figure(1) - -pl.subplot(1,2,1) -pl.imshow(I1) -pl.title('Image 1') - -pl.subplot(1,2,2) -pl.imshow(I2) -pl.title('Image 2') - -pl.show() - -#%% Image conversion and dataset generation - -def im2mat(I): - """Converts and image to matrix (one pixel per line)""" - return I.reshape((I.shape[0]*I.shape[1],I.shape[2])) - -def mat2im(X,shape): - """Converts back a matrix to an image""" - return X.reshape(shape) - -X1=im2mat(I1) -X2=im2mat(I2) - -# training samples -nb=1000 -idx1=np.random.randint(X1.shape[0],size=(nb,)) -idx2=np.random.randint(X2.shape[0],size=(nb,)) - -xs=X1[idx1,:] -xt=X2[idx2,:] - -#%% Plot image distributions - - -pl.figure(2,(10,5)) - -pl.subplot(1,2,1) -pl.scatter(xs[:,0],xs[:,2],c=xs) -pl.axis([0,1,0,1]) -pl.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 1') - -pl.subplot(1,2,2) -#pl.imshow(I2) -pl.scatter(xt[:,0],xt[:,2],c=xt) -pl.axis([0,1,0,1]) -pl.xlabel('Red') -pl.ylabel('Blue') -pl.title('Image 2') - -pl.show() - - - -#%% domain adaptation between images -def minmax(I): - return np.minimum(np.maximum(I,0),1) -# LP problem -da_emd=ot.da.OTDA() # init class -da_emd.fit(xs,xt) # fit distributions - -X1t=da_emd.predict(X1) # out of sample -I1t=minmax(mat2im(X1t,I1.shape)) - -# sinkhorn regularization -lambd=1e-1 -da_entrop=ot.da.OTDA_sinkhorn() -da_entrop.fit(xs,xt,reg=lambd) - -X1te=da_entrop.predict(X1) -I1te=minmax(mat2im(X1te,I1.shape)) - -# linear mapping estimation -eta=1e-8 # quadratic regularization for regression -mu=1e0 # weight of the OT linear term -bias=True # estimate a bias - -ot_mapping=ot.da.OTDA_mapping_linear() -ot_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True) - -X1tl=ot_mapping.predict(X1) # use the estimated mapping -I1tl=minmax(mat2im(X1tl,I1.shape)) - -# nonlinear mapping estimation -eta=1e-2 # quadratic regularization for regression -mu=1e0 # weight of the OT linear term -bias=False # estimate a bias -sigma=1 # sigma bandwidth fot gaussian kernel - - -ot_mapping_kernel=ot.da.OTDA_mapping_kernel() -ot_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 10,verbose=True) - -X1tn=ot_mapping_kernel.predict(X1) # use the estimated mapping -I1tn=minmax(mat2im(X1tn,I1.shape)) -#%% plot images - - -pl.figure(2,(10,8)) - -pl.subplot(2,3,1) - -pl.imshow(I1) -pl.title('Im. 1') - -pl.subplot(2,3,2) - -pl.imshow(I2) -pl.title('Im. 2') - - -pl.subplot(2,3,3) -pl.imshow(I1t) -pl.title('Im. 1 Interp LP') - -pl.subplot(2,3,4) -pl.imshow(I1te) -pl.title('Im. 1 Interp Entrop') - - -pl.subplot(2,3,5) -pl.imshow(I1tl) -pl.title('Im. 1 Linear mapping') - -pl.subplot(2,3,6) -pl.imshow(I1tn) -pl.title('Im. 1 nonlinear mapping') - -pl.show() diff --git a/docs/source/auto_examples/plot_OTDA_mapping_color_images.rst b/docs/source/auto_examples/plot_OTDA_mapping_color_images.rst deleted file mode 100644 index 60be3a4..0000000 --- a/docs/source/auto_examples/plot_OTDA_mapping_color_images.rst +++ /dev/null @@ -1,246 +0,0 @@ - - -.. _sphx_glr_auto_examples_plot_OTDA_mapping_color_images.py: - - -==================================================================================== -OT for domain adaptation with image color adaptation [6] with mapping estimation [8] -==================================================================================== - -[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized - discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for - discrete optimal transport", Neural Information Processing Systems (NIPS), 2016. - - - - - -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OTDA_mapping_color_images_001.png - :scale: 47 - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OTDA_mapping_color_images_002.png - :scale: 47 - - -.. rst-class:: sphx-glr-script-out - - Out:: - - It. |Loss |Delta loss - -------------------------------- - 0|3.624802e+02|0.000000e+00 - 1|3.547180e+02|-2.141395e-02 - 2|3.545494e+02|-4.753955e-04 - 3|3.544646e+02|-2.391784e-04 - 4|3.544126e+02|-1.466280e-04 - 5|3.543775e+02|-9.921805e-05 - 6|3.543518e+02|-7.245828e-05 - 7|3.543323e+02|-5.491924e-05 - 8|3.543170e+02|-4.342401e-05 - 9|3.543046e+02|-3.472174e-05 - 10|3.542945e+02|-2.878681e-05 - 11|3.542859e+02|-2.417065e-05 - 12|3.542786e+02|-2.058131e-05 - 13|3.542723e+02|-1.768262e-05 - 14|3.542668e+02|-1.551616e-05 - 15|3.542620e+02|-1.371909e-05 - 16|3.542577e+02|-1.213326e-05 - 17|3.542538e+02|-1.085481e-05 - 18|3.542531e+02|-1.996006e-06 - It. |Loss |Delta loss - -------------------------------- - 0|3.555768e+02|0.000000e+00 - 1|3.510071e+02|-1.285164e-02 - 2|3.509110e+02|-2.736701e-04 - 3|3.508748e+02|-1.031476e-04 - 4|3.508506e+02|-6.910585e-05 - 5|3.508330e+02|-5.014608e-05 - 6|3.508195e+02|-3.839166e-05 - 7|3.508090e+02|-3.004218e-05 - 8|3.508005e+02|-2.417627e-05 - 9|3.507935e+02|-2.004621e-05 - 10|3.507876e+02|-1.681731e-05 - - - - -| - - -.. code-block:: python - - - import numpy as np - import scipy.ndimage as spi - import matplotlib.pylab as pl - import ot - - - #%% Loading images - - I1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256 - I2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256 - - #%% Plot images - - pl.figure(1) - - pl.subplot(1,2,1) - pl.imshow(I1) - pl.title('Image 1') - - pl.subplot(1,2,2) - pl.imshow(I2) - pl.title('Image 2') - - pl.show() - - #%% Image conversion and dataset generation - - def im2mat(I): - """Converts and image to matrix (one pixel per line)""" - return I.reshape((I.shape[0]*I.shape[1],I.shape[2])) - - def mat2im(X,shape): - """Converts back a matrix to an image""" - return X.reshape(shape) - - X1=im2mat(I1) - X2=im2mat(I2) - - # training samples - nb=1000 - idx1=np.random.randint(X1.shape[0],size=(nb,)) - idx2=np.random.randint(X2.shape[0],size=(nb,)) - - xs=X1[idx1,:] - xt=X2[idx2,:] - - #%% Plot image distributions - - - pl.figure(2,(10,5)) - - pl.subplot(1,2,1) - pl.scatter(xs[:,0],xs[:,2],c=xs) - pl.axis([0,1,0,1]) - pl.xlabel('Red') - pl.ylabel('Blue') - pl.title('Image 1') - - pl.subplot(1,2,2) - #pl.imshow(I2) - pl.scatter(xt[:,0],xt[:,2],c=xt) - pl.axis([0,1,0,1]) - pl.xlabel('Red') - pl.ylabel('Blue') - pl.title('Image 2') - - pl.show() - - - - #%% domain adaptation between images - def minmax(I): - return np.minimum(np.maximum(I,0),1) - # LP problem - da_emd=ot.da.OTDA() # init class - da_emd.fit(xs,xt) # fit distributions - - X1t=da_emd.predict(X1) # out of sample - I1t=minmax(mat2im(X1t,I1.shape)) - - # sinkhorn regularization - lambd=1e-1 - da_entrop=ot.da.OTDA_sinkhorn() - da_entrop.fit(xs,xt,reg=lambd) - - X1te=da_entrop.predict(X1) - I1te=minmax(mat2im(X1te,I1.shape)) - - # linear mapping estimation - eta=1e-8 # quadratic regularization for regression - mu=1e0 # weight of the OT linear term - bias=True # estimate a bias - - ot_mapping=ot.da.OTDA_mapping_linear() - ot_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True) - - X1tl=ot_mapping.predict(X1) # use the estimated mapping - I1tl=minmax(mat2im(X1tl,I1.shape)) - - # nonlinear mapping estimation - eta=1e-2 # quadratic regularization for regression - mu=1e0 # weight of the OT linear term - bias=False # estimate a bias - sigma=1 # sigma bandwidth fot gaussian kernel - - - ot_mapping_kernel=ot.da.OTDA_mapping_kernel() - ot_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 10,verbose=True) - - X1tn=ot_mapping_kernel.predict(X1) # use the estimated mapping - I1tn=minmax(mat2im(X1tn,I1.shape)) - #%% plot images - - - pl.figure(2,(10,8)) - - pl.subplot(2,3,1) - - pl.imshow(I1) - pl.title('Im. 1') - - pl.subplot(2,3,2) - - pl.imshow(I2) - pl.title('Im. 2') - - - pl.subplot(2,3,3) - pl.imshow(I1t) - pl.title('Im. 1 Interp LP') - - pl.subplot(2,3,4) - pl.imshow(I1te) - pl.title('Im. 1 Interp Entrop') - - - pl.subplot(2,3,5) - pl.imshow(I1tl) - pl.title('Im. 1 Linear mapping') - - pl.subplot(2,3,6) - pl.imshow(I1tn) - pl.title('Im. 1 nonlinear mapping') - - pl.show() - -**Total running time of the script:** ( 1 minutes 59.537 seconds) - - - -.. container:: sphx-glr-footer - - - .. container:: sphx-glr-download - - :download:`Download Python source code: plot_OTDA_mapping_color_images.py ` - - - - .. container:: sphx-glr-download - - :download:`Download Jupyter notebook: plot_OTDA_mapping_color_images.ipynb ` - -.. rst-class:: sphx-glr-signature - - `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OT_1D.ipynb b/docs/source/auto_examples/plot_OT_1D.ipynb index 97c593e..3126b6f 100644 --- a/docs/source/auto_examples/plot_OT_1D.ipynb +++ b/docs/source/auto_examples/plot_OT_1D.ipynb @@ -15,7 +15,7 @@ }, { "source": [ - "\n# 1D optimal transport\n\n\n\n" + "\n# 1D optimal transport\n\n\nThis example illustrates the computation of EMD and Sinkhorn transport plans \nand their visualization.\n\n\n" ], "cell_type": "markdown", "metadata": {} @@ -69,7 +69,7 @@ }, { "source": [ - "Solve EMD \n#############################################################################\n\n" + "Solve EMD\n#############################################################################\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_OT_1D.py b/docs/source/auto_examples/plot_OT_1D.py index be6f5b3..a63f29a 100644 --- a/docs/source/auto_examples/plot_OT_1D.py +++ b/docs/source/auto_examples/plot_OT_1D.py @@ -4,6 +4,9 @@ 1D optimal transport ==================== +This example illustrates the computation of EMD and Sinkhorn transport plans +and their visualization. + """ # Author: Remi Flamary @@ -52,7 +55,7 @@ pl.figure(2, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') ############################################################################## -# Solve EMD +# Solve EMD ############################################################################## #%% EMD diff --git a/docs/source/auto_examples/plot_OT_1D.rst b/docs/source/auto_examples/plot_OT_1D.rst index 252d387..ff02180 100644 --- a/docs/source/auto_examples/plot_OT_1D.rst +++ b/docs/source/auto_examples/plot_OT_1D.rst @@ -7,6 +7,9 @@ 1D optimal transport ==================== +This example illustrates the computation of EMD and Sinkhorn transport plans +and their visualization. + @@ -97,7 +100,7 @@ Plot distributions and loss matrix -Solve EMD +Solve EMD ############################################################################# @@ -165,7 +168,7 @@ Solve Sinkhorn 110|1.527180e-10| -**Total running time of the script:** ( 0 minutes 1.065 seconds) +**Total running time of the script:** ( 0 minutes 0.770 seconds) @@ -184,4 +187,4 @@ Solve Sinkhorn .. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OT_2D_samples.ipynb b/docs/source/auto_examples/plot_OT_2D_samples.ipynb index fc4ce50..0ed7367 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.ipynb +++ b/docs/source/auto_examples/plot_OT_2D_samples.ipynb @@ -15,7 +15,7 @@ }, { "source": [ - "\n# 2D Optimal transport between empirical distributions\n\n\n\n" + "\n# 2D Optimal transport between empirical distributions\n\n\nIllustration of 2D optimal transport between discributions that are weighted\nsum of diracs. The OT matrix is plotted with the samples.\n\n\n" ], "cell_type": "markdown", "metadata": {} @@ -24,7 +24,79 @@ "execution_count": null, "cell_type": "code", "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n#%% parameters and data generation\n\nn = 50 # nb samples\n\nmu_s = np.array([0, 0])\ncov_s = np.array([[1, 0], [0, 1]])\n\nmu_t = np.array([4, 4])\ncov_t = np.array([[1, -.8], [-.8, 1]])\n\nxs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s)\nxt = ot.datasets.get_2D_samples_gauss(n, mu_t, cov_t)\n\na, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples\n\n# loss matrix\nM = ot.dist(xs, xt)\nM /= M.max()\n\n#%% plot samples\n\npl.figure(1)\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('Source and target distributions')\n\npl.figure(2)\npl.imshow(M, interpolation='nearest')\npl.title('Cost matrix M')\n\n\n#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3)\npl.imshow(G0, interpolation='nearest')\npl.title('OT matrix G0')\n\npl.figure(4)\not.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix with samples')\n\n\n#%% sinkhorn\n\n# reg term\nlambd = 1e-3\n\nGs = ot.sinkhorn(a, b, M, lambd)\n\npl.figure(5)\npl.imshow(Gs, interpolation='nearest')\npl.title('OT matrix sinkhorn')\n\npl.figure(6)\not.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix Sinkhorn with samples')\n\npl.show()" + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Generate data\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% parameters and data generation\n\nn = 50 # nb samples\n\nmu_s = np.array([0, 0])\ncov_s = np.array([[1, 0], [0, 1]])\n\nmu_t = np.array([4, 4])\ncov_t = np.array([[1, -.8], [-.8, 1]])\n\nxs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s)\nxt = ot.datasets.get_2D_samples_gauss(n, mu_t, cov_t)\n\na, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples\n\n# loss matrix\nM = ot.dist(xs, xt)\nM /= M.max()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Plot data\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% plot samples\n\npl.figure(1)\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('Source and target distributions')\n\npl.figure(2)\npl.imshow(M, interpolation='nearest')\npl.title('Cost matrix M')" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Compute EMD\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3)\npl.imshow(G0, interpolation='nearest')\npl.title('OT matrix G0')\n\npl.figure(4)\not.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix with samples')" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Compute Sinkhorn\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% sinkhorn\n\n# reg term\nlambd = 1e-3\n\nGs = ot.sinkhorn(a, b, M, lambd)\n\npl.figure(5)\npl.imshow(Gs, interpolation='nearest')\npl.title('OT matrix sinkhorn')\n\npl.figure(6)\not.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix Sinkhorn with samples')\n\npl.show()" ], "outputs": [], "metadata": { diff --git a/docs/source/auto_examples/plot_OT_2D_samples.py b/docs/source/auto_examples/plot_OT_2D_samples.py index 2a42dc0..f57d631 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.py +++ b/docs/source/auto_examples/plot_OT_2D_samples.py @@ -4,6 +4,9 @@ 2D Optimal transport between empirical distributions ==================================================== +Illustration of 2D optimal transport between discributions that are weighted +sum of diracs. The OT matrix is plotted with the samples. + """ # Author: Remi Flamary @@ -14,6 +17,10 @@ import numpy as np import matplotlib.pylab as pl import ot +############################################################################## +# Generate data +############################################################################## + #%% parameters and data generation n = 50 # nb samples @@ -33,6 +40,10 @@ a, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples M = ot.dist(xs, xt) M /= M.max() +############################################################################## +# Plot data +############################################################################## + #%% plot samples pl.figure(1) @@ -45,6 +56,9 @@ pl.figure(2) pl.imshow(M, interpolation='nearest') pl.title('Cost matrix M') +############################################################################## +# Compute EMD +############################################################################## #%% EMD @@ -62,6 +76,10 @@ pl.legend(loc=0) pl.title('OT matrix with samples') +############################################################################## +# Compute Sinkhorn +############################################################################## + #%% sinkhorn # reg term diff --git a/docs/source/auto_examples/plot_OT_2D_samples.rst b/docs/source/auto_examples/plot_OT_2D_samples.rst index c472c6a..f95ffaf 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.rst +++ b/docs/source/auto_examples/plot_OT_2D_samples.rst @@ -7,58 +7,37 @@ 2D Optimal transport between empirical distributions ==================================================== +Illustration of 2D optimal transport between discributions that are weighted +sum of diracs. The OT matrix is plotted with the samples. -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_001.png - :scale: 47 - - * - - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_002.png - :scale: 47 +.. code-block:: python - * - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_003.png - :scale: 47 + # Author: Remi Flamary + # + # License: MIT License - * + import numpy as np + import matplotlib.pylab as pl + import ot - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_004.png - :scale: 47 - * - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_005.png - :scale: 47 - * - .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_006.png - :scale: 47 +Generate data +############################################################################# .. code-block:: python - # Author: Remi Flamary - # - # License: MIT License - - import numpy as np - import matplotlib.pylab as pl - import ot - #%% parameters and data generation n = 50 # nb samples @@ -78,6 +57,20 @@ M = ot.dist(xs, xt) M /= M.max() + + + + + + +Plot data +############################################################################# + + + +.. code-block:: python + + #%% plot samples pl.figure(1) @@ -91,6 +84,32 @@ pl.title('Cost matrix M') + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_001.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_002.png + :scale: 47 + + + + +Compute EMD +############################################################################# + + + +.. code-block:: python + + #%% EMD G0 = ot.emd(a, b, M) @@ -107,6 +126,33 @@ pl.title('OT matrix with samples') + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_005.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_006.png + :scale: 47 + + + + +Compute Sinkhorn +############################################################################# + + + +.. code-block:: python + + #%% sinkhorn # reg term @@ -127,7 +173,25 @@ pl.show() -**Total running time of the script:** ( 0 minutes 2.908 seconds) + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_009.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_010.png + :scale: 47 + + + + +**Total running time of the script:** ( 0 minutes 1.990 seconds) @@ -146,4 +210,4 @@ .. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb b/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb index 04ef5c8..e738db7 100644 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb @@ -15,7 +15,7 @@ }, { "source": [ - "\n# 2D Optimal transport for different metrics\n\n\nStole the figure idea from Fig. 1 and 2 in\nhttps://arxiv.org/pdf/1706.07650.pdf\n\n\n\n" + "\n# 2D Optimal transport for different metrics\n\n\n2D OT on empirical distributio with different gound metric.\n\nStole the figure idea from Fig. 1 and 2 in\nhttps://arxiv.org/pdf/1706.07650.pdf\n\n\n\n" ], "cell_type": "markdown", "metadata": {} @@ -24,7 +24,79 @@ "execution_count": null, "cell_type": "code", "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n\n#%% parameters and data generation\n\nfor data in range(2):\n\n if data:\n n = 20 # nb samples\n xs = np.zeros((n, 2))\n xs[:, 0] = np.arange(n) + 1\n xs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex...\n\n xt = np.zeros((n, 2))\n xt[:, 1] = np.arange(n) + 1\n else:\n\n n = 50 # nb samples\n xtot = np.zeros((n + 1, 2))\n xtot[:, 0] = np.cos(\n (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)\n xtot[:, 1] = np.sin(\n (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)\n\n xs = xtot[:n, :]\n xt = xtot[1:, :]\n\n a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples\n\n # loss matrix\n M1 = ot.dist(xs, xt, metric='euclidean')\n M1 /= M1.max()\n\n # loss matrix\n M2 = ot.dist(xs, xt, metric='sqeuclidean')\n M2 /= M2.max()\n\n # loss matrix\n Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))\n Mp /= Mp.max()\n\n #%% plot samples\n\n pl.figure(1 + 3 * data, figsize=(7, 3))\n pl.clf()\n pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n pl.axis('equal')\n pl.title('Source and traget distributions')\n\n pl.figure(2 + 3 * data, figsize=(7, 3))\n\n pl.subplot(1, 3, 1)\n pl.imshow(M1, interpolation='nearest')\n pl.title('Euclidean cost')\n\n pl.subplot(1, 3, 2)\n pl.imshow(M2, interpolation='nearest')\n pl.title('Squared Euclidean cost')\n\n pl.subplot(1, 3, 3)\n pl.imshow(Mp, interpolation='nearest')\n pl.title('Sqrt Euclidean cost')\n pl.tight_layout()\n\n #%% EMD\n G1 = ot.emd(a, b, M1)\n G2 = ot.emd(a, b, M2)\n Gp = ot.emd(a, b, Mp)\n\n pl.figure(3 + 3 * data, figsize=(7, 3))\n\n pl.subplot(1, 3, 1)\n ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\n pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n pl.axis('equal')\n # pl.legend(loc=0)\n pl.title('OT Euclidean')\n\n pl.subplot(1, 3, 2)\n ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\n pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n pl.axis('equal')\n # pl.legend(loc=0)\n pl.title('OT squared Euclidean')\n\n pl.subplot(1, 3, 3)\n ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\n pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n pl.axis('equal')\n # pl.legend(loc=0)\n pl.title('OT sqrt Euclidean')\n pl.tight_layout()\n\npl.show()" + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Dataset 1 : uniform sampling\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "n = 20 # nb samples\nxs = np.zeros((n, 2))\nxs[:, 0] = np.arange(n) + 1\nxs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex...\n\nxt = np.zeros((n, 2))\nxt[:, 1] = np.arange(n) + 1\n\na, b = ot.unif(n), ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM1 = ot.dist(xs, xt, metric='euclidean')\nM1 /= M1.max()\n\n# loss matrix\nM2 = ot.dist(xs, xt, metric='sqeuclidean')\nM2 /= M2.max()\n\n# loss matrix\nMp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))\nMp /= Mp.max()\n\n# Data\npl.figure(1, figsize=(7, 3))\npl.clf()\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\npl.title('Source and traget distributions')\n\n\n# Cost matrices\npl.figure(2, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\npl.imshow(M1, interpolation='nearest')\npl.title('Euclidean cost')\n\npl.subplot(1, 3, 2)\npl.imshow(M2, interpolation='nearest')\npl.title('Squared Euclidean cost')\n\npl.subplot(1, 3, 3)\npl.imshow(Mp, interpolation='nearest')\npl.title('Sqrt Euclidean cost')\npl.tight_layout()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Dataset 1 : Plot OT Matrices\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% EMD\nG1 = ot.emd(a, b, M1)\nG2 = ot.emd(a, b, M2)\nGp = ot.emd(a, b, Mp)\n\n# OT matrices\npl.figure(3, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\not.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT Euclidean')\n\npl.subplot(1, 3, 2)\not.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT squared Euclidean')\n\npl.subplot(1, 3, 3)\not.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT sqrt Euclidean')\npl.tight_layout()\n\npl.show()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Dataset 2 : Partial circle\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "n = 50 # nb samples\nxtot = np.zeros((n + 1, 2))\nxtot[:, 0] = np.cos(\n (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)\nxtot[:, 1] = np.sin(\n (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)\n\nxs = xtot[:n, :]\nxt = xtot[1:, :]\n\na, b = ot.unif(n), ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM1 = ot.dist(xs, xt, metric='euclidean')\nM1 /= M1.max()\n\n# loss matrix\nM2 = ot.dist(xs, xt, metric='sqeuclidean')\nM2 /= M2.max()\n\n# loss matrix\nMp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))\nMp /= Mp.max()\n\n\n# Data\npl.figure(4, figsize=(7, 3))\npl.clf()\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\npl.title('Source and traget distributions')\n\n\n# Cost matrices\npl.figure(5, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\npl.imshow(M1, interpolation='nearest')\npl.title('Euclidean cost')\n\npl.subplot(1, 3, 2)\npl.imshow(M2, interpolation='nearest')\npl.title('Squared Euclidean cost')\n\npl.subplot(1, 3, 3)\npl.imshow(Mp, interpolation='nearest')\npl.title('Sqrt Euclidean cost')\npl.tight_layout()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Dataset 2 : Plot OT Matrices\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% EMD\nG1 = ot.emd(a, b, M1)\nG2 = ot.emd(a, b, M2)\nGp = ot.emd(a, b, Mp)\n\n# OT matrices\npl.figure(6, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\not.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT Euclidean')\n\npl.subplot(1, 3, 2)\not.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT squared Euclidean')\n\npl.subplot(1, 3, 3)\not.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT sqrt Euclidean')\npl.tight_layout()\n\npl.show()" ], "outputs": [], "metadata": { diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.py b/docs/source/auto_examples/plot_OT_L1_vs_L2.py index dfc9462..77bde22 100644 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.py +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.py @@ -4,6 +4,8 @@ 2D Optimal transport for different metrics ========================================== +2D OT on empirical distributio with different gound metric. + Stole the figure idea from Fig. 1 and 2 in https://arxiv.org/pdf/1706.07650.pdf @@ -18,98 +20,190 @@ import numpy as np import matplotlib.pylab as pl import ot -#%% parameters and data generation - -for data in range(2): - - if data: - n = 20 # nb samples - xs = np.zeros((n, 2)) - xs[:, 0] = np.arange(n) + 1 - xs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex... - - xt = np.zeros((n, 2)) - xt[:, 1] = np.arange(n) + 1 - else: - - n = 50 # nb samples - xtot = np.zeros((n + 1, 2)) - xtot[:, 0] = np.cos( - (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi) - xtot[:, 1] = np.sin( - (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi) - - xs = xtot[:n, :] - xt = xtot[1:, :] - - a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples - - # loss matrix - M1 = ot.dist(xs, xt, metric='euclidean') - M1 /= M1.max() - - # loss matrix - M2 = ot.dist(xs, xt, metric='sqeuclidean') - M2 /= M2.max() - - # loss matrix - Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean')) - Mp /= Mp.max() - - #%% plot samples - - pl.figure(1 + 3 * data, figsize=(7, 3)) - pl.clf() - pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - pl.axis('equal') - pl.title('Source and traget distributions') - - pl.figure(2 + 3 * data, figsize=(7, 3)) - - pl.subplot(1, 3, 1) - pl.imshow(M1, interpolation='nearest') - pl.title('Euclidean cost') - - pl.subplot(1, 3, 2) - pl.imshow(M2, interpolation='nearest') - pl.title('Squared Euclidean cost') - - pl.subplot(1, 3, 3) - pl.imshow(Mp, interpolation='nearest') - pl.title('Sqrt Euclidean cost') - pl.tight_layout() - - #%% EMD - G1 = ot.emd(a, b, M1) - G2 = ot.emd(a, b, M2) - Gp = ot.emd(a, b, Mp) - - pl.figure(3 + 3 * data, figsize=(7, 3)) - - pl.subplot(1, 3, 1) - ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1]) - pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - pl.axis('equal') - # pl.legend(loc=0) - pl.title('OT Euclidean') - - pl.subplot(1, 3, 2) - ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1]) - pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - pl.axis('equal') - # pl.legend(loc=0) - pl.title('OT squared Euclidean') - - pl.subplot(1, 3, 3) - ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1]) - pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - pl.axis('equal') - # pl.legend(loc=0) - pl.title('OT sqrt Euclidean') - pl.tight_layout() +############################################################################## +# Dataset 1 : uniform sampling +############################################################################## + +n = 20 # nb samples +xs = np.zeros((n, 2)) +xs[:, 0] = np.arange(n) + 1 +xs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex... + +xt = np.zeros((n, 2)) +xt[:, 1] = np.arange(n) + 1 + +a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples + +# loss matrix +M1 = ot.dist(xs, xt, metric='euclidean') +M1 /= M1.max() + +# loss matrix +M2 = ot.dist(xs, xt, metric='sqeuclidean') +M2 /= M2.max() + +# loss matrix +Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean')) +Mp /= Mp.max() + +# Data +pl.figure(1, figsize=(7, 3)) +pl.clf() +pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') +pl.axis('equal') +pl.title('Source and traget distributions') + + +# Cost matrices +pl.figure(2, figsize=(7, 3)) + +pl.subplot(1, 3, 1) +pl.imshow(M1, interpolation='nearest') +pl.title('Euclidean cost') + +pl.subplot(1, 3, 2) +pl.imshow(M2, interpolation='nearest') +pl.title('Squared Euclidean cost') + +pl.subplot(1, 3, 3) +pl.imshow(Mp, interpolation='nearest') +pl.title('Sqrt Euclidean cost') +pl.tight_layout() + +############################################################################## +# Dataset 1 : Plot OT Matrices +############################################################################## + + + +#%% EMD +G1 = ot.emd(a, b, M1) +G2 = ot.emd(a, b, M2) +Gp = ot.emd(a, b, Mp) + +# OT matrices +pl.figure(3, figsize=(7, 3)) + +pl.subplot(1, 3, 1) +ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1]) +pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') +pl.axis('equal') +# pl.legend(loc=0) +pl.title('OT Euclidean') + +pl.subplot(1, 3, 2) +ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1]) +pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') +pl.axis('equal') +# pl.legend(loc=0) +pl.title('OT squared Euclidean') + +pl.subplot(1, 3, 3) +ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1]) +pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') +pl.axis('equal') +# pl.legend(loc=0) +pl.title('OT sqrt Euclidean') +pl.tight_layout() + +pl.show() + + +############################################################################## +# Dataset 2 : Partial circle +############################################################################## + +n = 50 # nb samples +xtot = np.zeros((n + 1, 2)) +xtot[:, 0] = np.cos( + (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi) +xtot[:, 1] = np.sin( + (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi) + +xs = xtot[:n, :] +xt = xtot[1:, :] + +a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples + +# loss matrix +M1 = ot.dist(xs, xt, metric='euclidean') +M1 /= M1.max() + +# loss matrix +M2 = ot.dist(xs, xt, metric='sqeuclidean') +M2 /= M2.max() + +# loss matrix +Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean')) +Mp /= Mp.max() + + +# Data +pl.figure(4, figsize=(7, 3)) +pl.clf() +pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') +pl.axis('equal') +pl.title('Source and traget distributions') + + +# Cost matrices +pl.figure(5, figsize=(7, 3)) + +pl.subplot(1, 3, 1) +pl.imshow(M1, interpolation='nearest') +pl.title('Euclidean cost') + +pl.subplot(1, 3, 2) +pl.imshow(M2, interpolation='nearest') +pl.title('Squared Euclidean cost') + +pl.subplot(1, 3, 3) +pl.imshow(Mp, interpolation='nearest') +pl.title('Sqrt Euclidean cost') +pl.tight_layout() + +############################################################################## +# Dataset 2 : Plot OT Matrices +############################################################################## + + + +#%% EMD +G1 = ot.emd(a, b, M1) +G2 = ot.emd(a, b, M2) +Gp = ot.emd(a, b, Mp) + +# OT matrices +pl.figure(6, figsize=(7, 3)) + +pl.subplot(1, 3, 1) +ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1]) +pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') +pl.axis('equal') +# pl.legend(loc=0) +pl.title('OT Euclidean') + +pl.subplot(1, 3, 2) +ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1]) +pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') +pl.axis('equal') +# pl.legend(loc=0) +pl.title('OT squared Euclidean') + +pl.subplot(1, 3, 3) +ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1]) +pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') +pl.axis('equal') +# pl.legend(loc=0) +pl.title('OT sqrt Euclidean') +pl.tight_layout() pl.show() diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst index ba52bfe..83a7491 100644 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst @@ -7,6 +7,8 @@ 2D Optimal transport for different metrics ========================================== +2D OT on empirical distributio with different gound metric. + Stole the figure idea from Fig. 1 and 2 in https://arxiv.org/pdf/1706.07650.pdf @@ -14,6 +16,80 @@ https://arxiv.org/pdf/1706.07650.pdf +.. code-block:: python + + + # Author: Remi Flamary + # + # License: MIT License + + import numpy as np + import matplotlib.pylab as pl + import ot + + + + + + + +Dataset 1 : uniform sampling +############################################################################# + + + +.. code-block:: python + + + n = 20 # nb samples + xs = np.zeros((n, 2)) + xs[:, 0] = np.arange(n) + 1 + xs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex... + + xt = np.zeros((n, 2)) + xt[:, 1] = np.arange(n) + 1 + + a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples + + # loss matrix + M1 = ot.dist(xs, xt, metric='euclidean') + M1 /= M1.max() + + # loss matrix + M2 = ot.dist(xs, xt, metric='sqeuclidean') + M2 /= M2.max() + + # loss matrix + Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean')) + Mp /= Mp.max() + + # Data + pl.figure(1, figsize=(7, 3)) + pl.clf() + pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') + pl.axis('equal') + pl.title('Source and traget distributions') + + + # Cost matrices + pl.figure(2, figsize=(7, 3)) + + pl.subplot(1, 3, 1) + pl.imshow(M1, interpolation='nearest') + pl.title('Euclidean cost') + + pl.subplot(1, 3, 2) + pl.imshow(M2, interpolation='nearest') + pl.title('Squared Euclidean cost') + + pl.subplot(1, 3, 3) + pl.imshow(Mp, interpolation='nearest') + pl.title('Sqrt Euclidean cost') + pl.tight_layout() + + + .. rst-class:: sphx-glr-horizontal @@ -28,138 +104,195 @@ https://arxiv.org/pdf/1706.07650.pdf .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_002.png :scale: 47 - * - .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_003.png - :scale: 47 - * - .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_004.png - :scale: 47 +Dataset 1 : Plot OT Matrices +############################################################################# + + + +.. code-block:: python + + + + + #%% EMD + G1 = ot.emd(a, b, M1) + G2 = ot.emd(a, b, M2) + Gp = ot.emd(a, b, Mp) + + # OT matrices + pl.figure(3, figsize=(7, 3)) + + pl.subplot(1, 3, 1) + ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1]) + pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') + pl.axis('equal') + # pl.legend(loc=0) + pl.title('OT Euclidean') + + pl.subplot(1, 3, 2) + ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1]) + pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') + pl.axis('equal') + # pl.legend(loc=0) + pl.title('OT squared Euclidean') + + pl.subplot(1, 3, 3) + ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1]) + pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') + pl.axis('equal') + # pl.legend(loc=0) + pl.title('OT sqrt Euclidean') + pl.tight_layout() + + pl.show() + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_005.png + :align: center + + + + +Dataset 2 : Partial circle +############################################################################# + + + +.. code-block:: python + + + n = 50 # nb samples + xtot = np.zeros((n + 1, 2)) + xtot[:, 0] = np.cos( + (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi) + xtot[:, 1] = np.sin( + (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi) + + xs = xtot[:n, :] + xt = xtot[1:, :] + + a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples + + # loss matrix + M1 = ot.dist(xs, xt, metric='euclidean') + M1 /= M1.max() + + # loss matrix + M2 = ot.dist(xs, xt, metric='sqeuclidean') + M2 /= M2.max() + + # loss matrix + Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean')) + Mp /= Mp.max() + + + # Data + pl.figure(4, figsize=(7, 3)) + pl.clf() + pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') + pl.axis('equal') + pl.title('Source and traget distributions') + + + # Cost matrices + pl.figure(5, figsize=(7, 3)) + + pl.subplot(1, 3, 1) + pl.imshow(M1, interpolation='nearest') + pl.title('Euclidean cost') + + pl.subplot(1, 3, 2) + pl.imshow(M2, interpolation='nearest') + pl.title('Squared Euclidean cost') + + pl.subplot(1, 3, 3) + pl.imshow(Mp, interpolation='nearest') + pl.title('Sqrt Euclidean cost') + pl.tight_layout() + + + + +.. rst-class:: sphx-glr-horizontal + * - .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_005.png + .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_007.png :scale: 47 * - .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_006.png + .. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_008.png :scale: 47 +Dataset 2 : Plot OT Matrices +############################################################################# + + .. code-block:: python - # Author: Remi Flamary - # - # License: MIT License - import numpy as np - import matplotlib.pylab as pl - import ot - #%% parameters and data generation - - for data in range(2): - - if data: - n = 20 # nb samples - xs = np.zeros((n, 2)) - xs[:, 0] = np.arange(n) + 1 - xs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex... - - xt = np.zeros((n, 2)) - xt[:, 1] = np.arange(n) + 1 - else: - - n = 50 # nb samples - xtot = np.zeros((n + 1, 2)) - xtot[:, 0] = np.cos( - (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi) - xtot[:, 1] = np.sin( - (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi) - - xs = xtot[:n, :] - xt = xtot[1:, :] - - a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples - - # loss matrix - M1 = ot.dist(xs, xt, metric='euclidean') - M1 /= M1.max() - - # loss matrix - M2 = ot.dist(xs, xt, metric='sqeuclidean') - M2 /= M2.max() - - # loss matrix - Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean')) - Mp /= Mp.max() - - #%% plot samples - - pl.figure(1 + 3 * data, figsize=(7, 3)) - pl.clf() - pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - pl.axis('equal') - pl.title('Source and traget distributions') - - pl.figure(2 + 3 * data, figsize=(7, 3)) - - pl.subplot(1, 3, 1) - pl.imshow(M1, interpolation='nearest') - pl.title('Euclidean cost') - - pl.subplot(1, 3, 2) - pl.imshow(M2, interpolation='nearest') - pl.title('Squared Euclidean cost') - - pl.subplot(1, 3, 3) - pl.imshow(Mp, interpolation='nearest') - pl.title('Sqrt Euclidean cost') - pl.tight_layout() - - #%% EMD - G1 = ot.emd(a, b, M1) - G2 = ot.emd(a, b, M2) - Gp = ot.emd(a, b, Mp) - - pl.figure(3 + 3 * data, figsize=(7, 3)) - - pl.subplot(1, 3, 1) - ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1]) - pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - pl.axis('equal') - # pl.legend(loc=0) - pl.title('OT Euclidean') - - pl.subplot(1, 3, 2) - ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1]) - pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - pl.axis('equal') - # pl.legend(loc=0) - pl.title('OT squared Euclidean') - - pl.subplot(1, 3, 3) - ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1]) - pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - pl.axis('equal') - # pl.legend(loc=0) - pl.title('OT sqrt Euclidean') - pl.tight_layout() + #%% EMD + G1 = ot.emd(a, b, M1) + G2 = ot.emd(a, b, M2) + Gp = ot.emd(a, b, Mp) + + # OT matrices + pl.figure(6, figsize=(7, 3)) + + pl.subplot(1, 3, 1) + ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1]) + pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') + pl.axis('equal') + # pl.legend(loc=0) + pl.title('OT Euclidean') + + pl.subplot(1, 3, 2) + ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1]) + pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') + pl.axis('equal') + # pl.legend(loc=0) + pl.title('OT squared Euclidean') + + pl.subplot(1, 3, 3) + ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1]) + pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') + pl.axis('equal') + # pl.legend(loc=0) + pl.title('OT sqrt Euclidean') + pl.tight_layout() pl.show() -**Total running time of the script:** ( 0 minutes 1.906 seconds) + + +.. image:: /auto_examples/images/sphx_glr_plot_OT_L1_vs_L2_011.png + :align: center + + + + +**Total running time of the script:** ( 0 minutes 1.217 seconds) @@ -178,4 +311,4 @@ https://arxiv.org/pdf/1706.07650.pdf .. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OT_conv.ipynb b/docs/source/auto_examples/plot_OT_conv.ipynb deleted file mode 100644 index 7fc4af0..0000000 --- a/docs/source/auto_examples/plot_OT_conv.ipynb +++ /dev/null @@ -1,54 +0,0 @@ -{ - "nbformat_minor": 0, - "nbformat": 4, - "cells": [ - { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, - { - "source": [ - "\n# 1D Wasserstein barycenter demo\n\n\n\n@author: rflamary\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\nfrom mpl_toolkits.mplot3d import Axes3D #necessary for 3d plot even if not used\nimport scipy as sp\nimport scipy.signal as sps\n#%% parameters\n\nn=10 # nb bins\n\n# bin positions\nx=np.arange(n,dtype=np.float64)\n\nxx,yy=np.meshgrid(x,x)\n\n\nxpos=np.hstack((xx.reshape(-1,1),yy.reshape(-1,1)))\n\nM=ot.dist(xpos)\n\n\nI0=((xx-5)**2+(yy-5)**2<3**2)*1.0\nI1=((xx-7)**2+(yy-7)**2<3**2)*1.0\n\nI0/=I0.sum()\nI1/=I1.sum()\n\ni0=I0.ravel()\ni1=I1.ravel()\n\nM=M[i0>0,:][:,i1>0].copy()\ni0=i0[i0>0]\ni1=i1[i1>0]\nItot=np.concatenate((I0[:,:,np.newaxis],I1[:,:,np.newaxis]),2)\n\n\n#%% plot the distributions\n\npl.figure(1)\npl.subplot(2,2,1)\npl.imshow(I0)\npl.subplot(2,2,2)\npl.imshow(I1)\n\n\n#%% barycenter computation\n\nalpha=0.5 # 0<=alpha<=1\nweights=np.array([1-alpha,alpha])\n\n\ndef conv2(I,k):\n return sp.ndimage.convolve1d(sp.ndimage.convolve1d(I,k,axis=1),k,axis=0)\n\ndef conv2n(I,k):\n res=np.zeros_like(I)\n for i in range(I.shape[2]):\n res[:,:,i]=conv2(I[:,:,i],k)\n return res\n\n\ndef get_1Dkernel(reg,thr=1e-16,wmax=1024):\n w=max(min(wmax,2*int((-np.log(thr)*reg)**(.5))),3)\n x=np.arange(w,dtype=np.float64)\n return np.exp(-((x-w/2)**2)/reg)\n \nthr=1e-16\nreg=1e0\n\nk=get_1Dkernel(reg)\npl.figure(2)\npl.plot(k)\n\nI05=conv2(I0,k)\n\npl.figure(1)\npl.subplot(2,2,1)\npl.imshow(I0)\npl.subplot(2,2,2)\npl.imshow(I05)\n\n#%%\n\nG=ot.emd(i0,i1,M)\nr0=np.sum(M*G)\n\nreg=1e-1\nGs=ot.bregman.sinkhorn_knopp(i0,i1,M,reg=reg)\nrs=np.sum(M*Gs)\n\n#%%\n\ndef mylog(u):\n tmp=np.log(u)\n tmp[np.isnan(tmp)]=0\n return tmp\n\ndef sinkhorn_conv(a,b, reg, numItermax = 1000, stopThr=1e-9, verbose=False, log=False,**kwargs):\n\n\n a=np.asarray(a,dtype=np.float64)\n b=np.asarray(b,dtype=np.float64)\n \n \n if len(b.shape)>2:\n nbb=b.shape[2]\n a=a[:,:,np.newaxis]\n else:\n nbb=0\n \n\n if log:\n log={'err':[]}\n\n # we assume that no distances are null except those of the diagonal of distances\n if nbb:\n u = np.ones((a.shape[0],a.shape[1],nbb))/(np.prod(a.shape[:2]))\n v = np.ones((a.shape[0],a.shape[1],nbb))/(np.prod(b.shape[:2]))\n a0=1.0/(np.prod(b.shape[:2]))\n else:\n u = np.ones((a.shape[0],a.shape[1]))/(np.prod(a.shape[:2]))\n v = np.ones((a.shape[0],a.shape[1]))/(np.prod(b.shape[:2]))\n a0=1.0/(np.prod(b.shape[:2]))\n \n \n k=get_1Dkernel(reg)\n \n if nbb:\n K=lambda I: conv2n(I,k)\n else:\n K=lambda I: conv2(I,k)\n\n cpt = 0\n err=1\n while (err>stopThr and cpt0,:][:,i1>0].copy() -i0=i0[i0>0] -i1=i1[i1>0] -Itot=np.concatenate((I0[:,:,np.newaxis],I1[:,:,np.newaxis]),2) - - -#%% plot the distributions - -pl.figure(1) -pl.subplot(2,2,1) -pl.imshow(I0) -pl.subplot(2,2,2) -pl.imshow(I1) - - -#%% barycenter computation - -alpha=0.5 # 0<=alpha<=1 -weights=np.array([1-alpha,alpha]) - - -def conv2(I,k): - return sp.ndimage.convolve1d(sp.ndimage.convolve1d(I,k,axis=1),k,axis=0) - -def conv2n(I,k): - res=np.zeros_like(I) - for i in range(I.shape[2]): - res[:,:,i]=conv2(I[:,:,i],k) - return res - - -def get_1Dkernel(reg,thr=1e-16,wmax=1024): - w=max(min(wmax,2*int((-np.log(thr)*reg)**(.5))),3) - x=np.arange(w,dtype=np.float64) - return np.exp(-((x-w/2)**2)/reg) - -thr=1e-16 -reg=1e0 - -k=get_1Dkernel(reg) -pl.figure(2) -pl.plot(k) - -I05=conv2(I0,k) - -pl.figure(1) -pl.subplot(2,2,1) -pl.imshow(I0) -pl.subplot(2,2,2) -pl.imshow(I05) - -#%% - -G=ot.emd(i0,i1,M) -r0=np.sum(M*G) - -reg=1e-1 -Gs=ot.bregman.sinkhorn_knopp(i0,i1,M,reg=reg) -rs=np.sum(M*Gs) - -#%% - -def mylog(u): - tmp=np.log(u) - tmp[np.isnan(tmp)]=0 - return tmp - -def sinkhorn_conv(a,b, reg, numItermax = 1000, stopThr=1e-9, verbose=False, log=False,**kwargs): - - - a=np.asarray(a,dtype=np.float64) - b=np.asarray(b,dtype=np.float64) - - - if len(b.shape)>2: - nbb=b.shape[2] - a=a[:,:,np.newaxis] - else: - nbb=0 - - - if log: - log={'err':[]} - - # we assume that no distances are null except those of the diagonal of distances - if nbb: - u = np.ones((a.shape[0],a.shape[1],nbb))/(np.prod(a.shape[:2])) - v = np.ones((a.shape[0],a.shape[1],nbb))/(np.prod(b.shape[:2])) - a0=1.0/(np.prod(b.shape[:2])) - else: - u = np.ones((a.shape[0],a.shape[1]))/(np.prod(a.shape[:2])) - v = np.ones((a.shape[0],a.shape[1]))/(np.prod(b.shape[:2])) - a0=1.0/(np.prod(b.shape[:2])) - - - k=get_1Dkernel(reg) - - if nbb: - K=lambda I: conv2n(I,k) - else: - K=lambda I: conv2(I,k) - - cpt = 0 - err=1 - while (err>stopThr and cpt", line 86, in - TypeError: unsupported operand type(s) for *: 'float' and 'Mock' - - - - - -.. code-block:: python - - - import numpy as np - import matplotlib.pylab as pl - import ot - from mpl_toolkits.mplot3d import Axes3D #necessary for 3d plot even if not used - import scipy as sp - import scipy.signal as sps - #%% parameters - - n=10 # nb bins - - # bin positions - x=np.arange(n,dtype=np.float64) - - xx,yy=np.meshgrid(x,x) - - - xpos=np.hstack((xx.reshape(-1,1),yy.reshape(-1,1))) - - M=ot.dist(xpos) - - - I0=((xx-5)**2+(yy-5)**2<3**2)*1.0 - I1=((xx-7)**2+(yy-7)**2<3**2)*1.0 - - I0/=I0.sum() - I1/=I1.sum() - - i0=I0.ravel() - i1=I1.ravel() - - M=M[i0>0,:][:,i1>0].copy() - i0=i0[i0>0] - i1=i1[i1>0] - Itot=np.concatenate((I0[:,:,np.newaxis],I1[:,:,np.newaxis]),2) - - - #%% plot the distributions - - pl.figure(1) - pl.subplot(2,2,1) - pl.imshow(I0) - pl.subplot(2,2,2) - pl.imshow(I1) - - - #%% barycenter computation - - alpha=0.5 # 0<=alpha<=1 - weights=np.array([1-alpha,alpha]) - - - def conv2(I,k): - return sp.ndimage.convolve1d(sp.ndimage.convolve1d(I,k,axis=1),k,axis=0) - - def conv2n(I,k): - res=np.zeros_like(I) - for i in range(I.shape[2]): - res[:,:,i]=conv2(I[:,:,i],k) - return res - - - def get_1Dkernel(reg,thr=1e-16,wmax=1024): - w=max(min(wmax,2*int((-np.log(thr)*reg)**(.5))),3) - x=np.arange(w,dtype=np.float64) - return np.exp(-((x-w/2)**2)/reg) - - thr=1e-16 - reg=1e0 - - k=get_1Dkernel(reg) - pl.figure(2) - pl.plot(k) - - I05=conv2(I0,k) - - pl.figure(1) - pl.subplot(2,2,1) - pl.imshow(I0) - pl.subplot(2,2,2) - pl.imshow(I05) - - #%% - - G=ot.emd(i0,i1,M) - r0=np.sum(M*G) - - reg=1e-1 - Gs=ot.bregman.sinkhorn_knopp(i0,i1,M,reg=reg) - rs=np.sum(M*Gs) - - #%% - - def mylog(u): - tmp=np.log(u) - tmp[np.isnan(tmp)]=0 - return tmp - - def sinkhorn_conv(a,b, reg, numItermax = 1000, stopThr=1e-9, verbose=False, log=False,**kwargs): - - - a=np.asarray(a,dtype=np.float64) - b=np.asarray(b,dtype=np.float64) - - - if len(b.shape)>2: - nbb=b.shape[2] - a=a[:,:,np.newaxis] - else: - nbb=0 - - - if log: - log={'err':[]} - - # we assume that no distances are null except those of the diagonal of distances - if nbb: - u = np.ones((a.shape[0],a.shape[1],nbb))/(np.prod(a.shape[:2])) - v = np.ones((a.shape[0],a.shape[1],nbb))/(np.prod(b.shape[:2])) - a0=1.0/(np.prod(b.shape[:2])) - else: - u = np.ones((a.shape[0],a.shape[1]))/(np.prod(a.shape[:2])) - v = np.ones((a.shape[0],a.shape[1]))/(np.prod(b.shape[:2])) - a0=1.0/(np.prod(b.shape[:2])) - - - k=get_1Dkernel(reg) - - if nbb: - K=lambda I: conv2n(I,k) - else: - K=lambda I: conv2(I,k) - - cpt = 0 - err=1 - while (err>stopThr and cpt` - - - - .. container:: sphx-glr-download - - :download:`Download Jupyter notebook: plot_OT_conv.ipynb ` - -.. rst-class:: sphx-glr-signature - - `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_WDA.ipynb b/docs/source/auto_examples/plot_WDA.ipynb index 5568128..8e0db41 100644 --- a/docs/source/auto_examples/plot_WDA.ipynb +++ b/docs/source/auto_examples/plot_WDA.ipynb @@ -15,7 +15,7 @@ }, { "source": [ - "\n# Wasserstein Discriminant Analysis\n\n\n\n" + "\n# Wasserstein Discriminant Analysis\n\n\nThis example illustrate the use of WDA as proposed in [11].\n\n\n[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). \nWasserstein Discriminant Analysis.\n\n\n" ], "cell_type": "markdown", "metadata": {} @@ -24,7 +24,97 @@ "execution_count": null, "cell_type": "code", "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\n\nfrom ot.dr import wda, fda\n\n\n#%% parameters\n\nn = 1000 # nb samples in source and target datasets\nnz = 0.2\n\n# generate circle dataset\nt = np.random.rand(n) * 2 * np.pi\nys = np.floor((np.arange(n) * 1.0 / n * 3)) + 1\nxs = np.concatenate(\n (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1)\nxs = xs * ys.reshape(-1, 1) + nz * np.random.randn(n, 2)\n\nt = np.random.rand(n) * 2 * np.pi\nyt = np.floor((np.arange(n) * 1.0 / n * 3)) + 1\nxt = np.concatenate(\n (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1)\nxt = xt * yt.reshape(-1, 1) + nz * np.random.randn(n, 2)\n\nnbnoise = 8\n\nxs = np.hstack((xs, np.random.randn(n, nbnoise)))\nxt = np.hstack((xt, np.random.randn(n, nbnoise)))\n\n#%% plot samples\npl.figure(1, figsize=(6.4, 3.5))\n\npl.subplot(1, 2, 1)\npl.scatter(xt[:, 0], xt[:, 1], c=ys, marker='+', label='Source samples')\npl.legend(loc=0)\npl.title('Discriminant dimensions')\n\npl.subplot(1, 2, 2)\npl.scatter(xt[:, 2], xt[:, 3], c=ys, marker='+', label='Source samples')\npl.legend(loc=0)\npl.title('Other dimensions')\npl.tight_layout()\n\n#%% Compute FDA\np = 2\n\nPfda, projfda = fda(xs, ys, p)\n\n#%% Compute WDA\np = 2\nreg = 1e0\nk = 10\nmaxiter = 100\n\nPwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter)\n\n#%% plot samples\n\nxsp = projfda(xs)\nxtp = projfda(xt)\n\nxspw = projwda(xs)\nxtpw = projwda(xt)\n\npl.figure(2)\n\npl.subplot(2, 2, 1)\npl.scatter(xsp[:, 0], xsp[:, 1], c=ys, marker='+', label='Projected samples')\npl.legend(loc=0)\npl.title('Projected training samples FDA')\n\npl.subplot(2, 2, 2)\npl.scatter(xtp[:, 0], xtp[:, 1], c=ys, marker='+', label='Projected samples')\npl.legend(loc=0)\npl.title('Projected test samples FDA')\n\npl.subplot(2, 2, 3)\npl.scatter(xspw[:, 0], xspw[:, 1], c=ys, marker='+', label='Projected samples')\npl.legend(loc=0)\npl.title('Projected training samples WDA')\n\npl.subplot(2, 2, 4)\npl.scatter(xtpw[:, 0], xtpw[:, 1], c=ys, marker='+', label='Projected samples')\npl.legend(loc=0)\npl.title('Projected test samples WDA')\npl.tight_layout()\n\npl.show()" + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\n\nfrom ot.dr import wda, fda" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Generate data\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% parameters\n\nn = 1000 # nb samples in source and target datasets\nnz = 0.2\n\n# generate circle dataset\nt = np.random.rand(n) * 2 * np.pi\nys = np.floor((np.arange(n) * 1.0 / n * 3)) + 1\nxs = np.concatenate(\n (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1)\nxs = xs * ys.reshape(-1, 1) + nz * np.random.randn(n, 2)\n\nt = np.random.rand(n) * 2 * np.pi\nyt = np.floor((np.arange(n) * 1.0 / n * 3)) + 1\nxt = np.concatenate(\n (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1)\nxt = xt * yt.reshape(-1, 1) + nz * np.random.randn(n, 2)\n\nnbnoise = 8\n\nxs = np.hstack((xs, np.random.randn(n, nbnoise)))\nxt = np.hstack((xt, np.random.randn(n, nbnoise)))" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Plot data\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% plot samples\npl.figure(1, figsize=(6.4, 3.5))\n\npl.subplot(1, 2, 1)\npl.scatter(xt[:, 0], xt[:, 1], c=ys, marker='+', label='Source samples')\npl.legend(loc=0)\npl.title('Discriminant dimensions')\n\npl.subplot(1, 2, 2)\npl.scatter(xt[:, 2], xt[:, 3], c=ys, marker='+', label='Source samples')\npl.legend(loc=0)\npl.title('Other dimensions')\npl.tight_layout()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Compute Fisher Discriminant Analysis\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% Compute FDA\np = 2\n\nPfda, projfda = fda(xs, ys, p)" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Compute Wasserstein Discriminant Analysis\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% Compute WDA\np = 2\nreg = 1e0\nk = 10\nmaxiter = 100\n\nPwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter)" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Plot 2D projections\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% plot samples\n\nxsp = projfda(xs)\nxtp = projfda(xt)\n\nxspw = projwda(xs)\nxtpw = projwda(xt)\n\npl.figure(2)\n\npl.subplot(2, 2, 1)\npl.scatter(xsp[:, 0], xsp[:, 1], c=ys, marker='+', label='Projected samples')\npl.legend(loc=0)\npl.title('Projected training samples FDA')\n\npl.subplot(2, 2, 2)\npl.scatter(xtp[:, 0], xtp[:, 1], c=ys, marker='+', label='Projected samples')\npl.legend(loc=0)\npl.title('Projected test samples FDA')\n\npl.subplot(2, 2, 3)\npl.scatter(xspw[:, 0], xspw[:, 1], c=ys, marker='+', label='Projected samples')\npl.legend(loc=0)\npl.title('Projected training samples WDA')\n\npl.subplot(2, 2, 4)\npl.scatter(xtpw[:, 0], xtpw[:, 1], c=ys, marker='+', label='Projected samples')\npl.legend(loc=0)\npl.title('Projected test samples WDA')\npl.tight_layout()\n\npl.show()" ], "outputs": [], "metadata": { diff --git a/docs/source/auto_examples/plot_WDA.py b/docs/source/auto_examples/plot_WDA.py index 42789f2..06a2e38 100644 --- a/docs/source/auto_examples/plot_WDA.py +++ b/docs/source/auto_examples/plot_WDA.py @@ -4,6 +4,12 @@ Wasserstein Discriminant Analysis ================================= +This example illustrate the use of WDA as proposed in [11]. + + +[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). +Wasserstein Discriminant Analysis. + """ # Author: Remi Flamary @@ -16,6 +22,10 @@ import matplotlib.pylab as pl from ot.dr import wda, fda +############################################################################## +# Generate data +############################################################################## + #%% parameters n = 1000 # nb samples in source and target datasets @@ -39,6 +49,10 @@ nbnoise = 8 xs = np.hstack((xs, np.random.randn(n, nbnoise))) xt = np.hstack((xt, np.random.randn(n, nbnoise))) +############################################################################## +# Plot data +############################################################################## + #%% plot samples pl.figure(1, figsize=(6.4, 3.5)) @@ -53,11 +67,19 @@ pl.legend(loc=0) pl.title('Other dimensions') pl.tight_layout() +############################################################################## +# Compute Fisher Discriminant Analysis +############################################################################## + #%% Compute FDA p = 2 Pfda, projfda = fda(xs, ys, p) +############################################################################## +# Compute Wasserstein Discriminant Analysis +############################################################################## + #%% Compute WDA p = 2 reg = 1e0 @@ -66,6 +88,11 @@ maxiter = 100 Pwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter) + +############################################################################## +# Plot 2D projections +############################################################################## + #%% plot samples xsp = projfda(xs) diff --git a/docs/source/auto_examples/plot_WDA.rst b/docs/source/auto_examples/plot_WDA.rst index 76ebaf5..8c9ee29 100644 --- a/docs/source/auto_examples/plot_WDA.rst +++ b/docs/source/auto_examples/plot_WDA.rst @@ -7,86 +7,40 @@ Wasserstein Discriminant Analysis ================================= +This example illustrate the use of WDA as proposed in [11]. +[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). +Wasserstein Discriminant Analysis. -.. rst-class:: sphx-glr-horizontal - * +.. code-block:: python - .. image:: /auto_examples/images/sphx_glr_plot_WDA_001.png - :scale: 47 - * + # Author: Remi Flamary + # + # License: MIT License - .. image:: /auto_examples/images/sphx_glr_plot_WDA_002.png - :scale: 47 + import numpy as np + import matplotlib.pylab as pl + from ot.dr import wda, fda -.. rst-class:: sphx-glr-script-out - Out:: - Compiling cost function... - Computing gradient of cost function... - iter cost val grad. norm - 1 +8.9741888001949222e-01 3.71269078e-01 - 2 +4.9103998133976140e-01 3.46687543e-01 - 3 +4.2142651893148553e-01 1.04789602e-01 - 4 +4.1573609749588841e-01 5.21726648e-02 - 5 +4.1486046805261961e-01 5.35335513e-02 - 6 +4.1315953904635105e-01 2.17803599e-02 - 7 +4.1313030162717523e-01 6.06901182e-02 - 8 +4.1301511591963386e-01 5.88598758e-02 - 9 +4.1258349404769817e-01 5.14307874e-02 - 10 +4.1139242901051226e-01 2.03198793e-02 - 11 +4.1113798965164017e-01 1.18944721e-02 - 12 +4.1103446820878486e-01 2.21783648e-02 - 13 +4.1076586830791861e-01 9.51495863e-03 - 14 +4.1036935287519144e-01 3.74973214e-02 - 15 +4.0958729714575060e-01 1.23810902e-02 - 16 +4.0898266309095005e-01 4.01999918e-02 - 17 +4.0816076944357715e-01 2.27240277e-02 - 18 +4.0788116701894767e-01 4.42815945e-02 - 19 +4.0695443744952403e-01 3.28464304e-02 - 20 +4.0293834480911150e-01 7.76000681e-02 - 21 +3.8488003705202750e-01 1.49378022e-01 - 22 +3.0767344927282614e-01 2.15432117e-01 - 23 +2.3849425361868334e-01 1.07942382e-01 - 24 +2.3845125762548214e-01 1.08953278e-01 - 25 +2.3828007730494005e-01 1.07934830e-01 - 26 +2.3760839060570119e-01 1.03822134e-01 - 27 +2.3514215179705886e-01 8.67263481e-02 - 28 +2.2978886197588613e-01 9.26609306e-03 - 29 +2.2972671019495342e-01 2.59476089e-03 - 30 +2.2972355865247496e-01 1.57205146e-03 - 31 +2.2972296662351968e-01 1.29300760e-03 - 32 +2.2972181557051569e-01 8.82375756e-05 - 33 +2.2972181277025336e-01 6.20536544e-05 - 34 +2.2972181023486152e-01 7.01884014e-06 - 35 +2.2972181020400181e-01 1.60415765e-06 - 36 +2.2972181020236590e-01 2.44290966e-07 - Terminated - min grad norm reached after 36 iterations, 13.41 seconds. - - - - -| -.. code-block:: python - # Author: Remi Flamary - # - # License: MIT License - import numpy as np - import matplotlib.pylab as pl +Generate data +############################################################################# - from ot.dr import wda, fda + + +.. code-block:: python #%% parameters @@ -112,6 +66,20 @@ Wasserstein Discriminant Analysis xs = np.hstack((xs, np.random.randn(n, nbnoise))) xt = np.hstack((xt, np.random.randn(n, nbnoise))) + + + + + + +Plot data +############################################################################# + + + +.. code-block:: python + + #%% plot samples pl.figure(1, figsize=(6.4, 3.5)) @@ -126,11 +94,42 @@ Wasserstein Discriminant Analysis pl.title('Other dimensions') pl.tight_layout() + + + +.. image:: /auto_examples/images/sphx_glr_plot_WDA_001.png + :align: center + + + + +Compute Fisher Discriminant Analysis +############################################################################# + + + +.. code-block:: python + + #%% Compute FDA p = 2 Pfda, projfda = fda(xs, ys, p) + + + + + + +Compute Wasserstein Discriminant Analysis +############################################################################# + + + +.. code-block:: python + + #%% Compute WDA p = 2 reg = 1e0 @@ -139,6 +138,45 @@ Wasserstein Discriminant Analysis Pwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter) + + + + + +.. rst-class:: sphx-glr-script-out + + Out:: + + Compiling cost function... + Computing gradient of cost function... + iter cost val grad. norm + 1 +7.7038877420882157e-01 6.30647522e-01 + 2 +3.3969600919721271e-01 2.83791849e-01 + 3 +3.0014000762425608e-01 2.56139137e-01 + 4 +2.3397191702411621e-01 6.41134216e-02 + 5 +2.3107227220070231e-01 2.24837190e-02 + 6 +2.3072327156158298e-01 1.71334761e-03 + 7 +2.3072143589220098e-01 6.30059431e-04 + 8 +2.3072133109125159e-01 4.88673790e-04 + 9 +2.3072119579341774e-01 1.74129117e-04 + 10 +2.3072118662364521e-01 1.27046386e-04 + 11 +2.3072118228917746e-01 9.70877451e-05 + 12 +2.3072117734120351e-01 4.17292699e-05 + 13 +2.3072117623493599e-01 4.46062100e-06 + 14 +2.3072117622383431e-01 1.59801454e-06 + 15 +2.3072117622300498e-01 1.12117391e-06 + 16 +2.3072117622220378e-01 4.14581994e-08 + Terminated - min grad norm reached after 16 iterations, 7.77 seconds. + + +Plot 2D projections +############################################################################# + + + +.. code-block:: python + + #%% plot samples xsp = projfda(xs) @@ -172,7 +210,15 @@ Wasserstein Discriminant Analysis pl.show() -**Total running time of the script:** ( 0 minutes 19.853 seconds) + + +.. image:: /auto_examples/images/sphx_glr_plot_WDA_003.png + :align: center + + + + +**Total running time of the script:** ( 0 minutes 8.568 seconds) @@ -191,4 +237,4 @@ Wasserstein Discriminant Analysis .. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_barycenter_1D.ipynb b/docs/source/auto_examples/plot_barycenter_1D.ipynb index 239b8b8..657782d 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.ipynb +++ b/docs/source/auto_examples/plot_barycenter_1D.ipynb @@ -15,7 +15,7 @@ }, { "source": [ - "\n# 1D Wasserstein barycenter demo\n\n\n\n" + "\n# 1D Wasserstein barycenter demo\n\n\nThis example illustrates the computation of regularized Wassersyein Barycenter \nas proposed in [3].\n\n\n[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyr\u00e9, G. (2015). \nIterative Bregman projections for regularized transportation problems\nSIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n\n\n" ], "cell_type": "markdown", "metadata": {} @@ -24,7 +24,79 @@ "execution_count": null, "cell_type": "code", "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nfrom matplotlib.collections import PolyCollection\n\n\n#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na1 = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.get_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()\n\n#%% barycenter computation\n\nalpha = 0.2 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()\n\n#%% barycenter interpolation\n\nn_alpha = 11\nalpha_list = np.linspace(0, 1, n_alpha)\n\n\nB_l2 = np.zeros((n, n_alpha))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_alpha):\n alpha = alpha_list[i]\n weights = np.array([1 - alpha, alpha])\n B_l2[:, i] = A.dot(weights)\n B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)\n\n#%% plot interpolation\n\npl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_l2[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_wass[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with Wasserstein')\npl.tight_layout()\n\npl.show()" + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nfrom matplotlib.collections import PolyCollection" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Generate data\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na1 = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.get_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Plot data\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Barycenter computation\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% barycenter computation\n\nalpha = 0.2 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Barycentric interpolation\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% barycenter interpolation\n\nn_alpha = 11\nalpha_list = np.linspace(0, 1, n_alpha)\n\n\nB_l2 = np.zeros((n, n_alpha))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_alpha):\n alpha = alpha_list[i]\n weights = np.array([1 - alpha, alpha])\n B_l2[:, i] = A.dot(weights)\n B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)\n\n#%% plot interpolation\n\npl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_l2[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_wass[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with Wasserstein')\npl.tight_layout()\n\npl.show()" ], "outputs": [], "metadata": { diff --git a/docs/source/auto_examples/plot_barycenter_1D.py b/docs/source/auto_examples/plot_barycenter_1D.py index 875f44c..142b05e 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.py +++ b/docs/source/auto_examples/plot_barycenter_1D.py @@ -4,6 +4,14 @@ 1D Wasserstein barycenter demo ============================== +This example illustrates the computation of regularized Wassersyein Barycenter +as proposed in [3]. + + +[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). +Iterative Bregman projections for regularized transportation problems +SIAM Journal on Scientific Computing, 37(2), A1111-A1138. + """ # Author: Remi Flamary @@ -17,6 +25,9 @@ import ot from mpl_toolkits.mplot3d import Axes3D # noqa from matplotlib.collections import PolyCollection +############################################################################## +# Generate data +############################################################################## #%% parameters @@ -37,6 +48,10 @@ n_distributions = A.shape[1] M = ot.utils.dist0(n) M /= M.max() +############################################################################## +# Plot data +############################################################################## + #%% plot the distributions pl.figure(1, figsize=(6.4, 3)) @@ -45,6 +60,10 @@ for i in range(n_distributions): pl.title('Distributions') pl.tight_layout() +############################################################################## +# Barycenter computation +############################################################################## + #%% barycenter computation alpha = 0.2 # 0<=alpha<=1 @@ -71,6 +90,10 @@ pl.legend() pl.title('Barycenters') pl.tight_layout() +############################################################################## +# Barycentric interpolation +############################################################################## + #%% barycenter interpolation n_alpha = 11 diff --git a/docs/source/auto_examples/plot_barycenter_1D.rst b/docs/source/auto_examples/plot_barycenter_1D.rst index af88e80..d3f243f 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.rst +++ b/docs/source/auto_examples/plot_barycenter_1D.rst @@ -7,33 +7,13 @@ 1D Wasserstein barycenter demo ============================== +This example illustrates the computation of regularized Wassersyein Barycenter +as proposed in [3]. - - -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_001.png - :scale: 47 - - * - - .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_002.png - :scale: 47 - - * - - .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_003.png - :scale: 47 - - * - - .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_004.png - :scale: 47 - +[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). +Iterative Bregman projections for regularized transportation problems +SIAM Journal on Scientific Computing, 37(2), A1111-A1138. @@ -53,6 +33,19 @@ from matplotlib.collections import PolyCollection + + + + + +Generate data +############################################################################# + + + +.. code-block:: python + + #%% parameters n = 100 # nb bins @@ -72,6 +65,20 @@ M = ot.utils.dist0(n) M /= M.max() + + + + + + +Plot data +############################################################################# + + + +.. code-block:: python + + #%% plot the distributions pl.figure(1, figsize=(6.4, 3)) @@ -80,6 +87,23 @@ pl.title('Distributions') pl.tight_layout() + + + +.. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_001.png + :align: center + + + + +Barycenter computation +############################################################################# + + + +.. code-block:: python + + #%% barycenter computation alpha = 0.2 # 0<=alpha<=1 @@ -106,6 +130,23 @@ pl.title('Barycenters') pl.tight_layout() + + + +.. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_003.png + :align: center + + + + +Barycentric interpolation +############################################################################# + + + +.. code-block:: python + + #%% barycenter interpolation n_alpha = 11 @@ -171,7 +212,25 @@ pl.show() -**Total running time of the script:** ( 0 minutes 0.546 seconds) + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_005.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_006.png + :scale: 47 + + + + +**Total running time of the script:** ( 0 minutes 0.520 seconds) @@ -190,4 +249,4 @@ .. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_compute_emd.ipynb b/docs/source/auto_examples/plot_compute_emd.ipynb index ce3f8c6..b28413b 100644 --- a/docs/source/auto_examples/plot_compute_emd.ipynb +++ b/docs/source/auto_examples/plot_compute_emd.ipynb @@ -15,7 +15,7 @@ }, { "source": [ - "\n# 1D optimal transport\n\n\n\n" + "\n# Plot multiple EMD\n\n\nShows how to compute multiple EMD and Sinkhorn with two differnt \nground metrics and plot their values for diffeent distributions.\n\n\n\n" ], "cell_type": "markdown", "metadata": {} @@ -24,7 +24,79 @@ "execution_count": null, "cell_type": "code", "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nfrom ot.datasets import get_1D_gauss as gauss\n\n\n#%% parameters\n\nn = 100 # nb bins\nn_target = 50 # nb target distributions\n\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\nlst_m = np.linspace(20, 90, n_target)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\n\nB = np.zeros((n, n_target))\n\nfor i, m in enumerate(lst_m):\n B[:, i] = gauss(n, m=m, s=5)\n\n# loss matrix and normalization\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'euclidean')\nM /= M.max()\nM2 = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'sqeuclidean')\nM2 /= M2.max()\n#%% plot the distributions\n\npl.figure(1)\npl.subplot(2, 1, 1)\npl.plot(x, a, 'b', label='Source distribution')\npl.title('Source distribution')\npl.subplot(2, 1, 2)\npl.plot(x, B, label='Target distributions')\npl.title('Target distributions')\npl.tight_layout()\n\n#%% Compute and plot distributions and loss matrix\n\nd_emd = ot.emd2(a, B, M) # direct computation of EMD\nd_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M3\n\n\npl.figure(2)\npl.plot(d_emd, label='Euclidean EMD')\npl.plot(d_emd2, label='Squared Euclidean EMD')\npl.title('EMD distances')\npl.legend()\n\n#%%\nreg = 1e-2\nd_sinkhorn = ot.sinkhorn2(a, B, M, reg)\nd_sinkhorn2 = ot.sinkhorn2(a, B, M2, reg)\n\npl.figure(2)\npl.clf()\npl.plot(d_emd, label='Euclidean EMD')\npl.plot(d_emd2, label='Squared Euclidean EMD')\npl.plot(d_sinkhorn, '+', label='Euclidean Sinkhorn')\npl.plot(d_sinkhorn2, '+', label='Squared Euclidean Sinkhorn')\npl.title('EMD distances')\npl.legend()\n\npl.show()" + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nfrom ot.datasets import get_1D_gauss as gauss" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Generate data\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% parameters\n\nn = 100 # nb bins\nn_target = 50 # nb target distributions\n\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\nlst_m = np.linspace(20, 90, n_target)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\n\nB = np.zeros((n, n_target))\n\nfor i, m in enumerate(lst_m):\n B[:, i] = gauss(n, m=m, s=5)\n\n# loss matrix and normalization\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'euclidean')\nM /= M.max()\nM2 = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'sqeuclidean')\nM2 /= M2.max()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Plot data\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% plot the distributions\n\npl.figure(1)\npl.subplot(2, 1, 1)\npl.plot(x, a, 'b', label='Source distribution')\npl.title('Source distribution')\npl.subplot(2, 1, 2)\npl.plot(x, B, label='Target distributions')\npl.title('Target distributions')\npl.tight_layout()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Compute EMD for the different losses\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% Compute and plot distributions and loss matrix\n\nd_emd = ot.emd2(a, B, M) # direct computation of EMD\nd_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M2\n\n\npl.figure(2)\npl.plot(d_emd, label='Euclidean EMD')\npl.plot(d_emd2, label='Squared Euclidean EMD')\npl.title('EMD distances')\npl.legend()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Compute Sinkhorn for the different losses\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%%\nreg = 1e-2\nd_sinkhorn = ot.sinkhorn2(a, B, M, reg)\nd_sinkhorn2 = ot.sinkhorn2(a, B, M2, reg)\n\npl.figure(2)\npl.clf()\npl.plot(d_emd, label='Euclidean EMD')\npl.plot(d_emd2, label='Squared Euclidean EMD')\npl.plot(d_sinkhorn, '+', label='Euclidean Sinkhorn')\npl.plot(d_sinkhorn2, '+', label='Squared Euclidean Sinkhorn')\npl.title('EMD distances')\npl.legend()\n\npl.show()" ], "outputs": [], "metadata": { diff --git a/docs/source/auto_examples/plot_compute_emd.py b/docs/source/auto_examples/plot_compute_emd.py index 893eecf..b688f93 100644 --- a/docs/source/auto_examples/plot_compute_emd.py +++ b/docs/source/auto_examples/plot_compute_emd.py @@ -1,8 +1,12 @@ # -*- coding: utf-8 -*- """ -==================== -1D optimal transport -==================== +================= +Plot multiple EMD +================= + +Shows how to compute multiple EMD and Sinkhorn with two differnt +ground metrics and plot their values for diffeent distributions. + """ @@ -16,6 +20,10 @@ import ot from ot.datasets import get_1D_gauss as gauss +############################################################################## +# Generate data +############################################################################## + #%% parameters n = 100 # nb bins @@ -40,6 +48,11 @@ M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'euclidean') M /= M.max() M2 = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'sqeuclidean') M2 /= M2.max() + +############################################################################## +# Plot data +############################################################################## + #%% plot the distributions pl.figure(1) @@ -51,10 +64,15 @@ pl.plot(x, B, label='Target distributions') pl.title('Target distributions') pl.tight_layout() + +############################################################################## +# Compute EMD for the different losses +############################################################################## + #%% Compute and plot distributions and loss matrix d_emd = ot.emd2(a, B, M) # direct computation of EMD -d_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M3 +d_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M2 pl.figure(2) @@ -63,6 +81,10 @@ pl.plot(d_emd2, label='Squared Euclidean EMD') pl.title('EMD distances') pl.legend() +############################################################################## +# Compute Sinkhorn for the different losses +############################################################################## + #%% reg = 1e-2 d_sinkhorn = ot.sinkhorn2(a, B, M, reg) diff --git a/docs/source/auto_examples/plot_compute_emd.rst b/docs/source/auto_examples/plot_compute_emd.rst index f2e2005..b489255 100644 --- a/docs/source/auto_examples/plot_compute_emd.rst +++ b/docs/source/auto_examples/plot_compute_emd.rst @@ -3,42 +3,42 @@ .. _sphx_glr_auto_examples_plot_compute_emd.py: -==================== -1D optimal transport -==================== +================= +Plot multiple EMD +================= +Shows how to compute multiple EMD and Sinkhorn with two differnt +ground metrics and plot their values for diffeent distributions. -.. rst-class:: sphx-glr-horizontal +.. code-block:: python - * - .. image:: /auto_examples/images/sphx_glr_plot_compute_emd_001.png - :scale: 47 + # Author: Remi Flamary + # + # License: MIT License - * + import numpy as np + import matplotlib.pylab as pl + import ot + from ot.datasets import get_1D_gauss as gauss - .. image:: /auto_examples/images/sphx_glr_plot_compute_emd_002.png - :scale: 47 -.. code-block:: python - # Author: Remi Flamary - # - # License: MIT License +Generate data +############################################################################# - import numpy as np - import matplotlib.pylab as pl - import ot - from ot.datasets import get_1D_gauss as gauss + + +.. code-block:: python #%% parameters @@ -65,6 +65,21 @@ M /= M.max() M2 = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'sqeuclidean') M2 /= M2.max() + + + + + + + +Plot data +############################################################################# + + + +.. code-block:: python + + #%% plot the distributions pl.figure(1) @@ -76,10 +91,28 @@ pl.title('Target distributions') pl.tight_layout() + + + + +.. image:: /auto_examples/images/sphx_glr_plot_compute_emd_001.png + :align: center + + + + +Compute EMD for the different losses +############################################################################# + + + +.. code-block:: python + + #%% Compute and plot distributions and loss matrix d_emd = ot.emd2(a, B, M) # direct computation of EMD - d_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M3 + d_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M2 pl.figure(2) @@ -88,6 +121,23 @@ pl.title('EMD distances') pl.legend() + + + +.. image:: /auto_examples/images/sphx_glr_plot_compute_emd_003.png + :align: center + + + + +Compute Sinkhorn for the different losses +############################################################################# + + + +.. code-block:: python + + #%% reg = 1e-2 d_sinkhorn = ot.sinkhorn2(a, B, M, reg) @@ -104,7 +154,15 @@ pl.show() -**Total running time of the script:** ( 0 minutes 0.906 seconds) + + +.. image:: /auto_examples/images/sphx_glr_plot_compute_emd_004.png + :align: center + + + + +**Total running time of the script:** ( 0 minutes 0.427 seconds) @@ -123,4 +181,4 @@ .. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_optim_OTreg.ipynb b/docs/source/auto_examples/plot_optim_OTreg.ipynb index 9d26e4d..290100f 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.ipynb +++ b/docs/source/auto_examples/plot_optim_OTreg.ipynb @@ -15,7 +15,7 @@ }, { "source": [ - "\n# Regularized OT with generic solver\n\n\n\n\n" + "\n# Regularized OT with generic solver\n\n\nIllustrates the use of the generic solver for regularized OT with\nuser-designed regularization term. It uses Conditional gradient as in [6] and \ngeneralized Conditional Gradient as proposed in [5][7].\n\n\n[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for \nDomain Adaptation, in IEEE Transactions on Pattern Analysis and Machine \nIntelligence , vol.PP, no.99, pp.1-1.\n\n[6] Ferradans, S., Papadakis, N., Peyr\u00e9, G., & Aujol, J. F. (2014). \nRegularized discrete optimal transport. SIAM Journal on Imaging Sciences, \n7(3), 1853-1882.\n\n[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized \nconditional gradient: analysis of convergence and applications. \narXiv preprint arXiv:1510.06567.\n\n\n\n\n" ], "cell_type": "markdown", "metadata": {} @@ -33,7 +33,7 @@ }, { "source": [ - "Generate data \n#############################################################################\n\n" + "Generate data\n#############################################################################\n\n" ], "cell_type": "markdown", "metadata": {} @@ -51,7 +51,7 @@ }, { "source": [ - "Solve EMD \n#############################################################################\n\n" + "Solve EMD\n#############################################################################\n\n" ], "cell_type": "markdown", "metadata": {} @@ -114,7 +114,7 @@ "execution_count": null, "cell_type": "code", "source": [ - "#%% Example with Frobenius norm + entropic regularization with gcg\n\ndef f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg1 = 1e-3\nreg2 = 1e-1\n\nGel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True)\n\npl.figure(5, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg')\npl.show()" + "#%% Example with Frobenius norm + entropic regularization with gcg\n\n\ndef f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg1 = 1e-3\nreg2 = 1e-1\n\nGel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True)\n\npl.figure(5, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg')\npl.show()" ], "outputs": [], "metadata": { diff --git a/docs/source/auto_examples/plot_optim_OTreg.py b/docs/source/auto_examples/plot_optim_OTreg.py index d36b269..b362662 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.py +++ b/docs/source/auto_examples/plot_optim_OTreg.py @@ -4,6 +4,24 @@ Regularized OT with generic solver ================================== +Illustrates the use of the generic solver for regularized OT with +user-designed regularization term. It uses Conditional gradient as in [6] and +generalized Conditional Gradient as proposed in [5][7]. + + +[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for +Domain Adaptation, in IEEE Transactions on Pattern Analysis and Machine +Intelligence , vol.PP, no.99, pp.1-1. + +[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). +Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, +7(3), 1853-1882. + +[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized +conditional gradient: analysis of convergence and applications. +arXiv preprint arXiv:1510.06567. + + """ @@ -13,7 +31,7 @@ import ot ############################################################################## -# Generate data +# Generate data ############################################################################## #%% parameters @@ -32,7 +50,7 @@ M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) M /= M.max() ############################################################################## -# Solve EMD +# Solve EMD ############################################################################## #%% EMD @@ -92,6 +110,7 @@ ot.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg') #%% Example with Frobenius norm + entropic regularization with gcg + def f(G): return 0.5 * np.sum(G**2) diff --git a/docs/source/auto_examples/plot_optim_OTreg.rst b/docs/source/auto_examples/plot_optim_OTreg.rst index 532d4ca..d444631 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.rst +++ b/docs/source/auto_examples/plot_optim_OTreg.rst @@ -7,6 +7,24 @@ Regularized OT with generic solver ================================== +Illustrates the use of the generic solver for regularized OT with +user-designed regularization term. It uses Conditional gradient as in [6] and +generalized Conditional Gradient as proposed in [5][7]. + + +[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for +Domain Adaptation, in IEEE Transactions on Pattern Analysis and Machine +Intelligence , vol.PP, no.99, pp.1-1. + +[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). +Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, +7(3), 1853-1882. + +[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized +conditional gradient: analysis of convergence and applications. +arXiv preprint arXiv:1510.06567. + + @@ -25,7 +43,7 @@ Regularized OT with generic solver -Generate data +Generate data ############################################################################# @@ -54,7 +72,7 @@ Generate data -Solve EMD +Solve EMD ############################################################################# @@ -612,6 +630,7 @@ Solve EMD with Frobenius norm + entropic regularization #%% Example with Frobenius norm + entropic regularization with gcg + def f(G): return 0.5 * np.sum(G**2) @@ -645,10 +664,10 @@ Solve EMD with Frobenius norm + entropic regularization 1|1.610121e-01|-5.152589e-02 2|1.609378e-01|-4.622297e-04 3|1.609284e-01|-5.830043e-05 - 4|1.609284e-01|-1.111580e-12 + 4|1.609284e-01|-1.111407e-12 -**Total running time of the script:** ( 0 minutes 2.719 seconds) +**Total running time of the script:** ( 0 minutes 1.867 seconds) @@ -667,4 +686,4 @@ Solve EMD with Frobenius norm + entropic regularization .. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_classes.rst b/docs/source/auto_examples/plot_otda_classes.rst index 227a819..d1a13b1 100644 --- a/docs/source/auto_examples/plot_otda_classes.rst +++ b/docs/source/auto_examples/plot_otda_classes.rst @@ -94,29 +94,29 @@ Instantiate the different transport algorithms and fit them It. |Loss |Delta loss -------------------------------- - 0|9.456043e+00|0.000000e+00 - 1|2.059035e+00|-3.592463e+00 - 2|1.839814e+00|-1.191540e-01 - 3|1.787860e+00|-2.905942e-02 - 4|1.766582e+00|-1.204485e-02 - 5|1.760573e+00|-3.413038e-03 - 6|1.755288e+00|-3.010556e-03 - 7|1.749124e+00|-3.523968e-03 - 8|1.744159e+00|-2.846760e-03 - 9|1.741007e+00|-1.810862e-03 - 10|1.739839e+00|-6.710130e-04 - 11|1.737221e+00|-1.507260e-03 - 12|1.736011e+00|-6.970742e-04 - 13|1.734948e+00|-6.126425e-04 - 14|1.733901e+00|-6.038775e-04 - 15|1.733768e+00|-7.618542e-05 - 16|1.732821e+00|-5.467723e-04 - 17|1.732678e+00|-8.226843e-05 - 18|1.731934e+00|-4.300066e-04 - 19|1.731850e+00|-4.848002e-05 + 0|9.984935e+00|0.000000e+00 + 1|2.126803e+00|-3.694808e+00 + 2|1.867272e+00|-1.389895e-01 + 3|1.803858e+00|-3.515488e-02 + 4|1.783036e+00|-1.167761e-02 + 5|1.774823e+00|-4.627422e-03 + 6|1.771947e+00|-1.623526e-03 + 7|1.767564e+00|-2.479535e-03 + 8|1.763484e+00|-2.313667e-03 + 9|1.761138e+00|-1.331780e-03 + 10|1.758879e+00|-1.284576e-03 + 11|1.758034e+00|-4.806014e-04 + 12|1.757595e+00|-2.497155e-04 + 13|1.756749e+00|-4.818562e-04 + 14|1.755316e+00|-8.161432e-04 + 15|1.754988e+00|-1.866236e-04 + 16|1.754964e+00|-1.382474e-05 + 17|1.754032e+00|-5.315971e-04 + 18|1.753595e+00|-2.492359e-04 + 19|1.752900e+00|-3.961403e-04 It. |Loss |Delta loss -------------------------------- - 20|1.731699e+00|-8.729590e-05 + 20|1.752850e+00|-2.869262e-05 Fig 1 : plots source and target samples @@ -236,7 +236,7 @@ Fig 2 : plot optimal couplings and transported samples -**Total running time of the script:** ( 0 minutes 1.906 seconds) +**Total running time of the script:** ( 0 minutes 1.576 seconds) @@ -255,4 +255,4 @@ Fig 2 : plot optimal couplings and transported samples .. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_color_images.ipynb b/docs/source/auto_examples/plot_otda_color_images.ipynb index c45c307..797b27d 100644 --- a/docs/source/auto_examples/plot_otda_color_images.ipynb +++ b/docs/source/auto_examples/plot_otda_color_images.ipynb @@ -15,7 +15,7 @@ }, { "source": [ - "\n========================================================\nOT for domain adaptation with image color adaptation [6]\n========================================================\n\nThis example presents a way of transferring colors between two image\nwith Optimal Transport as introduced in [6]\n\n[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014).\nRegularized discrete optimal transport.\nSIAM Journal on Imaging Sciences, 7(3), 1853-1882.\n\n" + "\n# OT for image color adaptation\n\n\nThis example presents a way of transferring colors between two image\nwith Optimal Transport as introduced in [6]\n\n[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014).\nRegularized discrete optimal transport.\nSIAM Journal on Imaging Sciences, 7(3), 1853-1882.\n\n" ], "cell_type": "markdown", "metadata": {} @@ -33,7 +33,7 @@ }, { "source": [ - "generate data\n#############################################################################\n\n" + "Generate data\n#############################################################################\n\n" ], "cell_type": "markdown", "metadata": {} @@ -51,7 +51,7 @@ }, { "source": [ - "Instantiate the different transport algorithms and fit them\n#############################################################################\n\n" + "Plot original image\n#############################################################################\n\n" ], "cell_type": "markdown", "metadata": {} @@ -60,7 +60,7 @@ "execution_count": null, "cell_type": "code", "source": [ - "# EMDTransport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# SinkhornTransport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# prediction between images (using out of sample prediction as in [6])\ntransp_Xs_emd = ot_emd.transform(Xs=X1)\ntransp_Xt_emd = ot_emd.inverse_transform(Xt=X2)\n\ntransp_Xs_sinkhorn = ot_emd.transform(Xs=X1)\ntransp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2)\n\nI1t = minmax(mat2im(transp_Xs_emd, I1.shape))\nI2t = minmax(mat2im(transp_Xt_emd, I2.shape))\n\nI1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))\nI2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape))" + "pl.figure(1, figsize=(6.4, 3))\n\npl.subplot(1, 2, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.imshow(I2)\npl.axis('off')\npl.title('Image 2')" ], "outputs": [], "metadata": { @@ -69,7 +69,7 @@ }, { "source": [ - "plot original image\n#############################################################################\n\n" + "Scatter plot of colors\n#############################################################################\n\n" ], "cell_type": "markdown", "metadata": {} @@ -78,7 +78,7 @@ "execution_count": null, "cell_type": "code", "source": [ - "pl.figure(1, figsize=(6.4, 3))\n\npl.subplot(1, 2, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.imshow(I2)\npl.axis('off')\npl.title('Image 2')" + "pl.figure(2, figsize=(6.4, 3))\n\npl.subplot(1, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 2')\npl.tight_layout()" ], "outputs": [], "metadata": { @@ -87,7 +87,7 @@ }, { "source": [ - "scatter plot of colors\n#############################################################################\n\n" + "Instantiate the different transport algorithms and fit them\n#############################################################################\n\n" ], "cell_type": "markdown", "metadata": {} @@ -96,7 +96,7 @@ "execution_count": null, "cell_type": "code", "source": [ - "pl.figure(2, figsize=(6.4, 3))\n\npl.subplot(1, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 2')\npl.tight_layout()" + "# EMDTransport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# SinkhornTransport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# prediction between images (using out of sample prediction as in [6])\ntransp_Xs_emd = ot_emd.transform(Xs=X1)\ntransp_Xt_emd = ot_emd.inverse_transform(Xt=X2)\n\ntransp_Xs_sinkhorn = ot_emd.transform(Xs=X1)\ntransp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2)\n\nI1t = minmax(mat2im(transp_Xs_emd, I1.shape))\nI2t = minmax(mat2im(transp_Xt_emd, I2.shape))\n\nI1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))\nI2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape))" ], "outputs": [], "metadata": { @@ -105,7 +105,7 @@ }, { "source": [ - "plot new images\n#############################################################################\n\n" + "Plot new images\n#############################################################################\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_otda_color_images.py b/docs/source/auto_examples/plot_otda_color_images.py index 46ad44b..f1df9d9 100644 --- a/docs/source/auto_examples/plot_otda_color_images.py +++ b/docs/source/auto_examples/plot_otda_color_images.py @@ -1,8 +1,8 @@ # -*- coding: utf-8 -*- """ -======================================================== -OT for domain adaptation with image color adaptation [6] -======================================================== +============================= +OT for image color adaptation +============================= This example presents a way of transferring colors between two image with Optimal Transport as introduced in [6] @@ -41,7 +41,7 @@ def minmax(I): ############################################################################## -# generate data +# Generate data ############################################################################## # Loading images @@ -61,33 +61,7 @@ Xt = X2[idx2, :] ############################################################################## -# Instantiate the different transport algorithms and fit them -############################################################################## - -# EMDTransport -ot_emd = ot.da.EMDTransport() -ot_emd.fit(Xs=Xs, Xt=Xt) - -# SinkhornTransport -ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) -ot_sinkhorn.fit(Xs=Xs, Xt=Xt) - -# prediction between images (using out of sample prediction as in [6]) -transp_Xs_emd = ot_emd.transform(Xs=X1) -transp_Xt_emd = ot_emd.inverse_transform(Xt=X2) - -transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) -transp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2) - -I1t = minmax(mat2im(transp_Xs_emd, I1.shape)) -I2t = minmax(mat2im(transp_Xt_emd, I2.shape)) - -I1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) -I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape)) - - -############################################################################## -# plot original image +# Plot original image ############################################################################## pl.figure(1, figsize=(6.4, 3)) @@ -104,7 +78,7 @@ pl.title('Image 2') ############################################################################## -# scatter plot of colors +# Scatter plot of colors ############################################################################## pl.figure(2, figsize=(6.4, 3)) @@ -126,7 +100,33 @@ pl.tight_layout() ############################################################################## -# plot new images +# Instantiate the different transport algorithms and fit them +############################################################################## + +# EMDTransport +ot_emd = ot.da.EMDTransport() +ot_emd.fit(Xs=Xs, Xt=Xt) + +# SinkhornTransport +ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + +# prediction between images (using out of sample prediction as in [6]) +transp_Xs_emd = ot_emd.transform(Xs=X1) +transp_Xt_emd = ot_emd.inverse_transform(Xt=X2) + +transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) +transp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2) + +I1t = minmax(mat2im(transp_Xs_emd, I1.shape)) +I2t = minmax(mat2im(transp_Xt_emd, I2.shape)) + +I1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) +I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape)) + + +############################################################################## +# Plot new images ############################################################################## pl.figure(3, figsize=(8, 4)) diff --git a/docs/source/auto_examples/plot_otda_color_images.rst b/docs/source/auto_examples/plot_otda_color_images.rst index e3989c8..88e93d2 100644 --- a/docs/source/auto_examples/plot_otda_color_images.rst +++ b/docs/source/auto_examples/plot_otda_color_images.rst @@ -3,9 +3,9 @@ .. _sphx_glr_auto_examples_plot_otda_color_images.py: -======================================================== -OT for domain adaptation with image color adaptation [6] -======================================================== +============================= +OT for image color adaptation +============================= This example presents a way of transferring colors between two image with Optimal Transport as introduced in [6] @@ -53,7 +53,7 @@ SIAM Journal on Imaging Sciences, 7(3), 1853-1882. -generate data +Generate data ############################################################################# @@ -83,43 +83,7 @@ generate data -Instantiate the different transport algorithms and fit them -############################################################################# - - - -.. code-block:: python - - - # EMDTransport - ot_emd = ot.da.EMDTransport() - ot_emd.fit(Xs=Xs, Xt=Xt) - - # SinkhornTransport - ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) - ot_sinkhorn.fit(Xs=Xs, Xt=Xt) - - # prediction between images (using out of sample prediction as in [6]) - transp_Xs_emd = ot_emd.transform(Xs=X1) - transp_Xt_emd = ot_emd.inverse_transform(Xt=X2) - - transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) - transp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2) - - I1t = minmax(mat2im(transp_Xs_emd, I1.shape)) - I2t = minmax(mat2im(transp_Xt_emd, I2.shape)) - - I1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) - I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape)) - - - - - - - - -plot original image +Plot original image ############################################################################# @@ -149,7 +113,7 @@ plot original image -scatter plot of colors +Scatter plot of colors ############################################################################# @@ -184,7 +148,43 @@ scatter plot of colors -plot new images +Instantiate the different transport algorithms and fit them +############################################################################# + + + +.. code-block:: python + + + # EMDTransport + ot_emd = ot.da.EMDTransport() + ot_emd.fit(Xs=Xs, Xt=Xt) + + # SinkhornTransport + ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) + ot_sinkhorn.fit(Xs=Xs, Xt=Xt) + + # prediction between images (using out of sample prediction as in [6]) + transp_Xs_emd = ot_emd.transform(Xs=X1) + transp_Xt_emd = ot_emd.inverse_transform(Xt=X2) + + transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) + transp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2) + + I1t = minmax(mat2im(transp_Xs_emd, I1.shape)) + I2t = minmax(mat2im(transp_Xt_emd, I2.shape)) + + I1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) + I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape)) + + + + + + + + +Plot new images ############################################################################# @@ -235,7 +235,7 @@ plot new images -**Total running time of the script:** ( 3 minutes 16.043 seconds) +**Total running time of the script:** ( 2 minutes 28.053 seconds) @@ -254,4 +254,4 @@ plot new images .. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_d2.rst b/docs/source/auto_examples/plot_otda_d2.rst index 20b76ba..3aa1149 100644 --- a/docs/source/auto_examples/plot_otda_d2.rst +++ b/docs/source/auto_examples/plot_otda_d2.rst @@ -243,7 +243,7 @@ Fig 3 : plot transported samples -**Total running time of the script:** ( 0 minutes 46.009 seconds) +**Total running time of the script:** ( 0 minutes 32.275 seconds) @@ -262,4 +262,4 @@ Fig 3 : plot transported samples .. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_mapping.ipynb b/docs/source/auto_examples/plot_otda_mapping.ipynb index 0b5ca5c..5b3fd06 100644 --- a/docs/source/auto_examples/plot_otda_mapping.ipynb +++ b/docs/source/auto_examples/plot_otda_mapping.ipynb @@ -15,7 +15,7 @@ }, { "source": [ - "\n===============================================\nOT mapping estimation for domain adaptation [8]\n===============================================\n\nThis example presents how to use MappingTransport to estimate at the same\ntime both the coupling transport and approximate the transport map with either\na linear or a kernelized mapping as introduced in [8]\n\n[8] M. Perrot, N. Courty, R. Flamary, A. Habrard,\n \"Mapping estimation for discrete optimal transport\",\n Neural Information Processing Systems (NIPS), 2016.\n\n" + "\n# OT mapping estimation for domain adaptation\n\n\nThis example presents how to use MappingTransport to estimate at the same\ntime both the coupling transport and approximate the transport map with either\na linear or a kernelized mapping as introduced in [8].\n\n[8] M. Perrot, N. Courty, R. Flamary, A. Habrard,\n \"Mapping estimation for discrete optimal transport\",\n Neural Information Processing Systems (NIPS), 2016.\n\n" ], "cell_type": "markdown", "metadata": {} @@ -33,7 +33,7 @@ }, { "source": [ - "generate data\n#############################################################################\n\n" + "Generate data\n#############################################################################\n\n" ], "cell_type": "markdown", "metadata": {} @@ -51,7 +51,7 @@ }, { "source": [ - "Instantiate the different transport algorithms and fit them\n#############################################################################\n\n" + "Plot data\n#############################################################################\n\n" ], "cell_type": "markdown", "metadata": {} @@ -60,7 +60,7 @@ "execution_count": null, "cell_type": "code", "source": [ - "# MappingTransport with linear kernel\not_mapping_linear = ot.da.MappingTransport(\n kernel=\"linear\", mu=1e0, eta=1e-8, bias=True,\n max_iter=20, verbose=True)\n\not_mapping_linear.fit(Xs=Xs, Xt=Xt)\n\n# for original source samples, transform applies barycentric mapping\ntransp_Xs_linear = ot_mapping_linear.transform(Xs=Xs)\n\n# for out of source samples, transform applies the linear mapping\ntransp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new)\n\n\n# MappingTransport with gaussian kernel\not_mapping_gaussian = ot.da.MappingTransport(\n kernel=\"gaussian\", eta=1e-5, mu=1e-1, bias=True, sigma=1,\n max_iter=10, verbose=True)\not_mapping_gaussian.fit(Xs=Xs, Xt=Xt)\n\n# for original source samples, transform applies barycentric mapping\ntransp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs)\n\n# for out of source samples, transform applies the gaussian mapping\ntransp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new)" + "pl.figure(1, (10, 5))\npl.clf()\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.legend(loc=0)\npl.title('Source and target distributions')" ], "outputs": [], "metadata": { @@ -69,7 +69,7 @@ }, { "source": [ - "plot data\n#############################################################################\n\n" + "Instantiate the different transport algorithms and fit them\n#############################################################################\n\n" ], "cell_type": "markdown", "metadata": {} @@ -78,7 +78,7 @@ "execution_count": null, "cell_type": "code", "source": [ - "pl.figure(1, (10, 5))\npl.clf()\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.legend(loc=0)\npl.title('Source and target distributions')" + "# MappingTransport with linear kernel\not_mapping_linear = ot.da.MappingTransport(\n kernel=\"linear\", mu=1e0, eta=1e-8, bias=True,\n max_iter=20, verbose=True)\n\not_mapping_linear.fit(Xs=Xs, Xt=Xt)\n\n# for original source samples, transform applies barycentric mapping\ntransp_Xs_linear = ot_mapping_linear.transform(Xs=Xs)\n\n# for out of source samples, transform applies the linear mapping\ntransp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new)\n\n\n# MappingTransport with gaussian kernel\not_mapping_gaussian = ot.da.MappingTransport(\n kernel=\"gaussian\", eta=1e-5, mu=1e-1, bias=True, sigma=1,\n max_iter=10, verbose=True)\not_mapping_gaussian.fit(Xs=Xs, Xt=Xt)\n\n# for original source samples, transform applies barycentric mapping\ntransp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs)\n\n# for out of source samples, transform applies the gaussian mapping\ntransp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new)" ], "outputs": [], "metadata": { @@ -87,7 +87,7 @@ }, { "source": [ - "plot transported samples\n#############################################################################\n\n" + "Plot transported samples\n#############################################################################\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_otda_mapping.py b/docs/source/auto_examples/plot_otda_mapping.py index 09d2cb4..e78fef4 100644 --- a/docs/source/auto_examples/plot_otda_mapping.py +++ b/docs/source/auto_examples/plot_otda_mapping.py @@ -1,12 +1,12 @@ # -*- coding: utf-8 -*- """ -=============================================== -OT mapping estimation for domain adaptation [8] -=============================================== +=========================================== +OT mapping estimation for domain adaptation +=========================================== This example presents how to use MappingTransport to estimate at the same time both the coupling transport and approximate the transport map with either -a linear or a kernelized mapping as introduced in [8] +a linear or a kernelized mapping as introduced in [8]. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for discrete optimal transport", @@ -24,7 +24,7 @@ import ot ############################################################################## -# generate data +# Generate data ############################################################################## n_source_samples = 100 @@ -43,6 +43,17 @@ Xt, yt = ot.datasets.get_data_classif( Xt[yt == 2] *= 3 Xt = Xt + 4 +############################################################################## +# Plot data +############################################################################## + +pl.figure(1, (10, 5)) +pl.clf() +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.legend(loc=0) +pl.title('Source and target distributions') + ############################################################################## # Instantiate the different transport algorithms and fit them @@ -76,19 +87,7 @@ transp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new) ############################################################################## -# plot data -############################################################################## - -pl.figure(1, (10, 5)) -pl.clf() -pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') -pl.legend(loc=0) -pl.title('Source and target distributions') - - -############################################################################## -# plot transported samples +# Plot transported samples ############################################################################## pl.figure(2) diff --git a/docs/source/auto_examples/plot_otda_mapping.rst b/docs/source/auto_examples/plot_otda_mapping.rst index 088da31..ddc1ee9 100644 --- a/docs/source/auto_examples/plot_otda_mapping.rst +++ b/docs/source/auto_examples/plot_otda_mapping.rst @@ -3,13 +3,13 @@ .. _sphx_glr_auto_examples_plot_otda_mapping.py: -=============================================== -OT mapping estimation for domain adaptation [8] -=============================================== +=========================================== +OT mapping estimation for domain adaptation +=========================================== This example presents how to use MappingTransport to estimate at the same time both the coupling transport and approximate the transport map with either -a linear or a kernelized mapping as introduced in [8] +a linear or a kernelized mapping as introduced in [8]. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard, "Mapping estimation for discrete optimal transport", @@ -36,7 +36,7 @@ a linear or a kernelized mapping as introduced in [8] -generate data +Generate data ############################################################################# @@ -66,6 +66,30 @@ generate data +Plot data +############################################################################# + + + +.. code-block:: python + + + pl.figure(1, (10, 5)) + pl.clf() + pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') + pl.legend(loc=0) + pl.title('Source and target distributions') + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_001.png + :align: center + + + Instantiate the different transport algorithms and fit them ############################################################################# @@ -112,54 +136,29 @@ Instantiate the different transport algorithms and fit them It. |Loss |Delta loss -------------------------------- - 0|4.273804e+03|0.000000e+00 - 1|4.264510e+03|-2.174580e-03 - 2|4.264209e+03|-7.047095e-05 - 3|4.264078e+03|-3.069822e-05 - 4|4.264018e+03|-1.412924e-05 - 5|4.263961e+03|-1.341165e-05 - 6|4.263946e+03|-3.586522e-06 + 0|4.481482e+03|0.000000e+00 + 1|4.469389e+03|-2.698549e-03 + 2|4.468825e+03|-1.261217e-04 + 3|4.468580e+03|-5.486064e-05 + 4|4.468438e+03|-3.161220e-05 + 5|4.468352e+03|-1.930800e-05 + 6|4.468309e+03|-9.570658e-06 It. |Loss |Delta loss -------------------------------- - 0|4.294523e+02|0.000000e+00 - 1|4.247737e+02|-1.089443e-02 - 2|4.245516e+02|-5.228765e-04 - 3|4.244430e+02|-2.557417e-04 - 4|4.243724e+02|-1.663904e-04 - 5|4.243196e+02|-1.244111e-04 - 6|4.242808e+02|-9.132500e-05 - 7|4.242497e+02|-7.331710e-05 - 8|4.242271e+02|-5.326612e-05 - 9|4.242063e+02|-4.916026e-05 - 10|4.241906e+02|-3.699617e-05 - - -plot data -############################################################################# - - - -.. code-block:: python - - - pl.figure(1, (10, 5)) - pl.clf() - pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') - pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') - pl.legend(loc=0) - pl.title('Source and target distributions') - - - - - -.. image:: /auto_examples/images/sphx_glr_plot_otda_mapping_001.png - :align: center - - - - -plot transported samples + 0|4.504654e+02|0.000000e+00 + 1|4.461571e+02|-9.564116e-03 + 2|4.459105e+02|-5.528043e-04 + 3|4.457895e+02|-2.712398e-04 + 4|4.457041e+02|-1.914829e-04 + 5|4.456431e+02|-1.369704e-04 + 6|4.456032e+02|-8.944784e-05 + 7|4.455700e+02|-7.447824e-05 + 8|4.455447e+02|-5.688965e-05 + 9|4.455229e+02|-4.890051e-05 + 10|4.455084e+02|-3.262490e-05 + + +Plot transported samples ############################################################################# @@ -209,7 +208,7 @@ plot transported samples -**Total running time of the script:** ( 0 minutes 0.853 seconds) +**Total running time of the script:** ( 0 minutes 0.869 seconds) @@ -228,4 +227,4 @@ plot transported samples .. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb b/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb index 4b2ec02..c8c1d95 100644 --- a/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb +++ b/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb @@ -15,7 +15,7 @@ }, { "source": [ - "\n====================================================================================\nOT for domain adaptation with image color adaptation [6] with mapping estimation [8]\n====================================================================================\n\n[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized\n discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3),\n 1853-1882.\n[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, \"Mapping estimation for\n discrete optimal transport\", Neural Information Processing Systems (NIPS),\n 2016.\n\n\n" + "\n# OT for image color adaptation with mapping estimation \n\n\nOT for domain adaptation with image color adaptation [6] with mapping \nestimation [8].\n\n[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized\n discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3),\n 1853-1882.\n[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, \"Mapping estimation for\n discrete optimal transport\", Neural Information Processing Systems (NIPS),\n 2016.\n\n\n" ], "cell_type": "markdown", "metadata": {} @@ -69,7 +69,7 @@ }, { "source": [ - "plot original images\n#############################################################################\n\n" + "Plot original images\n#############################################################################\n\n" ], "cell_type": "markdown", "metadata": {} @@ -87,7 +87,7 @@ }, { "source": [ - "plot pixel values distribution\n#############################################################################\n\n" + "Plot pixel values distribution\n#############################################################################\n\n" ], "cell_type": "markdown", "metadata": {} @@ -105,7 +105,7 @@ }, { "source": [ - "plot transformed images\n#############################################################################\n\n" + "Plot transformed images\n#############################################################################\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.py b/docs/source/auto_examples/plot_otda_mapping_colors_images.py index 936206c..162c24b 100644 --- a/docs/source/auto_examples/plot_otda_mapping_colors_images.py +++ b/docs/source/auto_examples/plot_otda_mapping_colors_images.py @@ -1,8 +1,11 @@ # -*- coding: utf-8 -*- """ -==================================================================================== -OT for domain adaptation with image color adaptation [6] with mapping estimation [8] -==================================================================================== +===================================================== +OT for image color adaptation with mapping estimation +===================================================== + +OT for domain adaptation with image color adaptation [6] with mapping +estimation [8]. [6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), @@ -93,7 +96,7 @@ Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape)) ############################################################################## -# plot original images +# Plot original images ############################################################################## pl.figure(1, figsize=(6.4, 3)) @@ -110,7 +113,7 @@ pl.tight_layout() ############################################################################## -# plot pixel values distribution +# Plot pixel values distribution ############################################################################## pl.figure(2, figsize=(6.4, 5)) @@ -132,7 +135,7 @@ pl.tight_layout() ############################################################################## -# plot transformed images +# Plot transformed images ############################################################################## pl.figure(2, figsize=(10, 5)) diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.rst b/docs/source/auto_examples/plot_otda_mapping_colors_images.rst index 1107067..29823f1 100644 --- a/docs/source/auto_examples/plot_otda_mapping_colors_images.rst +++ b/docs/source/auto_examples/plot_otda_mapping_colors_images.rst @@ -3,9 +3,12 @@ .. _sphx_glr_auto_examples_plot_otda_mapping_colors_images.py: -==================================================================================== -OT for domain adaptation with image color adaptation [6] with mapping estimation [8] -==================================================================================== +===================================================== +OT for image color adaptation with mapping estimation +===================================================== + +OT for domain adaptation with image color adaptation [6] with mapping +estimation [8]. [6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), @@ -129,42 +132,42 @@ Domain adaptation for pixel distribution transfer It. |Loss |Delta loss -------------------------------- - 0|3.680514e+02|0.000000e+00 - 1|3.592359e+02|-2.395185e-02 - 2|3.590581e+02|-4.947749e-04 - 3|3.589663e+02|-2.556471e-04 - 4|3.589095e+02|-1.582289e-04 - 5|3.588707e+02|-1.081994e-04 - 6|3.588423e+02|-7.911661e-05 - 7|3.588206e+02|-6.055473e-05 - 8|3.588034e+02|-4.778202e-05 - 9|3.587895e+02|-3.886420e-05 - 10|3.587781e+02|-3.182249e-05 - 11|3.587684e+02|-2.695669e-05 - 12|3.587602e+02|-2.298642e-05 - 13|3.587530e+02|-1.993240e-05 - 14|3.587468e+02|-1.736014e-05 - 15|3.587413e+02|-1.518037e-05 - 16|3.587365e+02|-1.358038e-05 - 17|3.587321e+02|-1.215346e-05 - 18|3.587282e+02|-1.091639e-05 - 19|3.587278e+02|-9.877929e-07 + 0|3.680512e+02|0.000000e+00 + 1|3.592454e+02|-2.392562e-02 + 2|3.590671e+02|-4.960473e-04 + 3|3.589736e+02|-2.604894e-04 + 4|3.589161e+02|-1.602816e-04 + 5|3.588766e+02|-1.099971e-04 + 6|3.588476e+02|-8.084400e-05 + 7|3.588256e+02|-6.131161e-05 + 8|3.588083e+02|-4.807549e-05 + 9|3.587943e+02|-3.899414e-05 + 10|3.587827e+02|-3.245280e-05 + 11|3.587729e+02|-2.721256e-05 + 12|3.587646e+02|-2.316249e-05 + 13|3.587574e+02|-2.000192e-05 + 14|3.587512e+02|-1.748898e-05 + 15|3.587457e+02|-1.535131e-05 + 16|3.587408e+02|-1.366515e-05 + 17|3.587364e+02|-1.210563e-05 + 18|3.587325e+02|-1.097138e-05 + 19|3.587310e+02|-4.099596e-06 It. |Loss |Delta loss -------------------------------- - 0|3.784725e+02|0.000000e+00 - 1|3.646380e+02|-3.655332e-02 - 2|3.642858e+02|-9.660434e-04 - 3|3.641516e+02|-3.683776e-04 - 4|3.640785e+02|-2.008220e-04 - 5|3.640320e+02|-1.276966e-04 - 6|3.639999e+02|-8.796173e-05 - 7|3.639764e+02|-6.455658e-05 - 8|3.639583e+02|-4.976436e-05 - 9|3.639440e+02|-3.946556e-05 - 10|3.639322e+02|-3.222132e-05 - - -plot original images + 0|3.784805e+02|0.000000e+00 + 1|3.646476e+02|-3.654847e-02 + 2|3.642970e+02|-9.615381e-04 + 3|3.641622e+02|-3.699897e-04 + 4|3.640886e+02|-2.021154e-04 + 5|3.640419e+02|-1.280913e-04 + 6|3.640096e+02|-8.898145e-05 + 7|3.639858e+02|-6.514301e-05 + 8|3.639677e+02|-4.977195e-05 + 9|3.639534e+02|-3.936050e-05 + 10|3.639417e+02|-3.205223e-05 + + +Plot original images ############################################################################# @@ -194,7 +197,7 @@ plot original images -plot pixel values distribution +Plot pixel values distribution ############################################################################# @@ -229,7 +232,7 @@ plot pixel values distribution -plot transformed images +Plot transformed images ############################################################################# @@ -280,7 +283,7 @@ plot transformed images -**Total running time of the script:** ( 2 minutes 45.618 seconds) +**Total running time of the script:** ( 2 minutes 12.535 seconds) @@ -299,4 +302,4 @@ plot transformed images .. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/conf.py b/docs/source/conf.py index ffdb1a2..0a822e5 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -33,7 +33,7 @@ class Mock(MagicMock): return MagicMock() MOCK_MODULES = ['ot.lp.emd_wrap','autograd','pymanopt','cudamat','autograd.numpy','pymanopt.manifolds','pymanopt.solvers'] # 'autograd.numpy','pymanopt.manifolds','pymanopt.solvers', -sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) +##sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # !!!! # If extensions (or modules to document with autodoc) are in another directory, @@ -328,7 +328,7 @@ intersphinx_mapping = {'https://docs.python.org/': None} sphinx_gallery_conf = { 'examples_dirs': ['../../examples','../../examples/da'], 'gallery_dirs': 'auto_examples', - 'mod_example_dir': '../modules/generated/', + 'backreferences_dir': '../modules/generated/', 'reference_url': { 'numpy': 'http://docs.scipy.org/doc/numpy-1.9.1', 'scipy': 'http://docs.scipy.org/doc/scipy-0.17.0/reference'} diff --git a/docs/source/examples.rst b/docs/source/examples.rst deleted file mode 100644 index f209543..0000000 --- a/docs/source/examples.rst +++ /dev/null @@ -1,39 +0,0 @@ - - -Examples -============ - -1D Optimal transport ---------------------- - -.. literalinclude:: ../../examples/demo_OT_1D.py - -2D Optimal transport on empirical distributions ------------------------------------------------ - -.. literalinclude:: ../../examples/demo_OT_2D_samples.py - -1D Wasserstein barycenter -------------------------- - -.. literalinclude:: ../../examples/demo_barycenter_1D.py - -OT with user provided regularization ------------------------------------- - -.. literalinclude:: ../../examples/demo_optim_OTreg.py - -Domain adaptation with optimal transport ----------------------------------------- - -.. literalinclude:: ../../examples/demo_OTDA_classes.py - -Color transfer in images ------------------------- - -.. literalinclude:: ../../examples/demo_OTDA_color_images.py - -OT mapping estimation for domain adaptation -------------------------------------------- - -.. literalinclude:: ../../examples/demo_OTDA_mapping.py diff --git a/examples/README.txt b/examples/README.txt index f8643b8..c3d556d 100644 --- a/examples/README.txt +++ b/examples/README.txt @@ -1,2 +1,4 @@ POT Examples ============ + +This is a gallery of all the POT example files. diff --git a/examples/plot_OT_1D.py b/examples/plot_OT_1D.py index a1473c4..a63f29a 100644 --- a/examples/plot_OT_1D.py +++ b/examples/plot_OT_1D.py @@ -4,7 +4,7 @@ 1D optimal transport ==================== -This example illustrate the computation of EMD and Sinkhorn transport plans +This example illustrates the computation of EMD and Sinkhorn transport plans and their visualization. """ diff --git a/examples/plot_OT_2D_samples.py b/examples/plot_OT_2D_samples.py index a913b8c..f57d631 100644 --- a/examples/plot_OT_2D_samples.py +++ b/examples/plot_OT_2D_samples.py @@ -4,6 +4,9 @@ 2D Optimal transport between empirical distributions ==================================================== +Illustration of 2D optimal transport between discributions that are weighted +sum of diracs. The OT matrix is plotted with the samples. + """ # Author: Remi Flamary diff --git a/examples/plot_OT_L1_vs_L2.py b/examples/plot_OT_L1_vs_L2.py index dfc9462..77bde22 100644 --- a/examples/plot_OT_L1_vs_L2.py +++ b/examples/plot_OT_L1_vs_L2.py @@ -4,6 +4,8 @@ 2D Optimal transport for different metrics ========================================== +2D OT on empirical distributio with different gound metric. + Stole the figure idea from Fig. 1 and 2 in https://arxiv.org/pdf/1706.07650.pdf @@ -18,98 +20,190 @@ import numpy as np import matplotlib.pylab as pl import ot -#%% parameters and data generation - -for data in range(2): - - if data: - n = 20 # nb samples - xs = np.zeros((n, 2)) - xs[:, 0] = np.arange(n) + 1 - xs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex... - - xt = np.zeros((n, 2)) - xt[:, 1] = np.arange(n) + 1 - else: - - n = 50 # nb samples - xtot = np.zeros((n + 1, 2)) - xtot[:, 0] = np.cos( - (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi) - xtot[:, 1] = np.sin( - (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi) - - xs = xtot[:n, :] - xt = xtot[1:, :] - - a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples - - # loss matrix - M1 = ot.dist(xs, xt, metric='euclidean') - M1 /= M1.max() - - # loss matrix - M2 = ot.dist(xs, xt, metric='sqeuclidean') - M2 /= M2.max() - - # loss matrix - Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean')) - Mp /= Mp.max() - - #%% plot samples - - pl.figure(1 + 3 * data, figsize=(7, 3)) - pl.clf() - pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - pl.axis('equal') - pl.title('Source and traget distributions') - - pl.figure(2 + 3 * data, figsize=(7, 3)) - - pl.subplot(1, 3, 1) - pl.imshow(M1, interpolation='nearest') - pl.title('Euclidean cost') - - pl.subplot(1, 3, 2) - pl.imshow(M2, interpolation='nearest') - pl.title('Squared Euclidean cost') - - pl.subplot(1, 3, 3) - pl.imshow(Mp, interpolation='nearest') - pl.title('Sqrt Euclidean cost') - pl.tight_layout() - - #%% EMD - G1 = ot.emd(a, b, M1) - G2 = ot.emd(a, b, M2) - Gp = ot.emd(a, b, Mp) - - pl.figure(3 + 3 * data, figsize=(7, 3)) - - pl.subplot(1, 3, 1) - ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1]) - pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - pl.axis('equal') - # pl.legend(loc=0) - pl.title('OT Euclidean') - - pl.subplot(1, 3, 2) - ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1]) - pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - pl.axis('equal') - # pl.legend(loc=0) - pl.title('OT squared Euclidean') - - pl.subplot(1, 3, 3) - ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1]) - pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') - pl.axis('equal') - # pl.legend(loc=0) - pl.title('OT sqrt Euclidean') - pl.tight_layout() +############################################################################## +# Dataset 1 : uniform sampling +############################################################################## + +n = 20 # nb samples +xs = np.zeros((n, 2)) +xs[:, 0] = np.arange(n) + 1 +xs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex... + +xt = np.zeros((n, 2)) +xt[:, 1] = np.arange(n) + 1 + +a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples + +# loss matrix +M1 = ot.dist(xs, xt, metric='euclidean') +M1 /= M1.max() + +# loss matrix +M2 = ot.dist(xs, xt, metric='sqeuclidean') +M2 /= M2.max() + +# loss matrix +Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean')) +Mp /= Mp.max() + +# Data +pl.figure(1, figsize=(7, 3)) +pl.clf() +pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') +pl.axis('equal') +pl.title('Source and traget distributions') + + +# Cost matrices +pl.figure(2, figsize=(7, 3)) + +pl.subplot(1, 3, 1) +pl.imshow(M1, interpolation='nearest') +pl.title('Euclidean cost') + +pl.subplot(1, 3, 2) +pl.imshow(M2, interpolation='nearest') +pl.title('Squared Euclidean cost') + +pl.subplot(1, 3, 3) +pl.imshow(Mp, interpolation='nearest') +pl.title('Sqrt Euclidean cost') +pl.tight_layout() + +############################################################################## +# Dataset 1 : Plot OT Matrices +############################################################################## + + + +#%% EMD +G1 = ot.emd(a, b, M1) +G2 = ot.emd(a, b, M2) +Gp = ot.emd(a, b, Mp) + +# OT matrices +pl.figure(3, figsize=(7, 3)) + +pl.subplot(1, 3, 1) +ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1]) +pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') +pl.axis('equal') +# pl.legend(loc=0) +pl.title('OT Euclidean') + +pl.subplot(1, 3, 2) +ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1]) +pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') +pl.axis('equal') +# pl.legend(loc=0) +pl.title('OT squared Euclidean') + +pl.subplot(1, 3, 3) +ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1]) +pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') +pl.axis('equal') +# pl.legend(loc=0) +pl.title('OT sqrt Euclidean') +pl.tight_layout() + +pl.show() + + +############################################################################## +# Dataset 2 : Partial circle +############################################################################## + +n = 50 # nb samples +xtot = np.zeros((n + 1, 2)) +xtot[:, 0] = np.cos( + (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi) +xtot[:, 1] = np.sin( + (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi) + +xs = xtot[:n, :] +xt = xtot[1:, :] + +a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples + +# loss matrix +M1 = ot.dist(xs, xt, metric='euclidean') +M1 /= M1.max() + +# loss matrix +M2 = ot.dist(xs, xt, metric='sqeuclidean') +M2 /= M2.max() + +# loss matrix +Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean')) +Mp /= Mp.max() + + +# Data +pl.figure(4, figsize=(7, 3)) +pl.clf() +pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') +pl.axis('equal') +pl.title('Source and traget distributions') + + +# Cost matrices +pl.figure(5, figsize=(7, 3)) + +pl.subplot(1, 3, 1) +pl.imshow(M1, interpolation='nearest') +pl.title('Euclidean cost') + +pl.subplot(1, 3, 2) +pl.imshow(M2, interpolation='nearest') +pl.title('Squared Euclidean cost') + +pl.subplot(1, 3, 3) +pl.imshow(Mp, interpolation='nearest') +pl.title('Sqrt Euclidean cost') +pl.tight_layout() + +############################################################################## +# Dataset 2 : Plot OT Matrices +############################################################################## + + + +#%% EMD +G1 = ot.emd(a, b, M1) +G2 = ot.emd(a, b, M2) +Gp = ot.emd(a, b, Mp) + +# OT matrices +pl.figure(6, figsize=(7, 3)) + +pl.subplot(1, 3, 1) +ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1]) +pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') +pl.axis('equal') +# pl.legend(loc=0) +pl.title('OT Euclidean') + +pl.subplot(1, 3, 2) +ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1]) +pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') +pl.axis('equal') +# pl.legend(loc=0) +pl.title('OT squared Euclidean') + +pl.subplot(1, 3, 3) +ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1]) +pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') +pl.axis('equal') +# pl.legend(loc=0) +pl.title('OT sqrt Euclidean') +pl.tight_layout() pl.show() diff --git a/examples/plot_barycenter_1D.py b/examples/plot_barycenter_1D.py index f3be247..142b05e 100644 --- a/examples/plot_barycenter_1D.py +++ b/examples/plot_barycenter_1D.py @@ -4,7 +4,7 @@ 1D Wasserstein barycenter demo ============================== -This example illustrate the computation of regularized Wassersyein Barycenter +This example illustrates the computation of regularized Wassersyein Barycenter as proposed in [3]. @@ -25,6 +25,9 @@ import ot from mpl_toolkits.mplot3d import Axes3D # noqa from matplotlib.collections import PolyCollection +############################################################################## +# Generate data +############################################################################## #%% parameters @@ -45,6 +48,10 @@ n_distributions = A.shape[1] M = ot.utils.dist0(n) M /= M.max() +############################################################################## +# Plot data +############################################################################## + #%% plot the distributions pl.figure(1, figsize=(6.4, 3)) @@ -53,6 +60,10 @@ for i in range(n_distributions): pl.title('Distributions') pl.tight_layout() +############################################################################## +# Barycenter computation +############################################################################## + #%% barycenter computation alpha = 0.2 # 0<=alpha<=1 @@ -79,6 +90,10 @@ pl.legend() pl.title('Barycenters') pl.tight_layout() +############################################################################## +# Barycentric interpolation +############################################################################## + #%% barycenter interpolation n_alpha = 11 diff --git a/examples/plot_compute_emd.py b/examples/plot_compute_emd.py index 704da0e..b688f93 100644 --- a/examples/plot_compute_emd.py +++ b/examples/plot_compute_emd.py @@ -4,6 +4,10 @@ Plot multiple EMD ================= +Shows how to compute multiple EMD and Sinkhorn with two differnt +ground metrics and plot their values for diffeent distributions. + + """ # Author: Remi Flamary diff --git a/examples/plot_optim_OTreg.py b/examples/plot_optim_OTreg.py index 95bcdaf..b362662 100644 --- a/examples/plot_optim_OTreg.py +++ b/examples/plot_optim_OTreg.py @@ -4,8 +4,8 @@ Regularized OT with generic solver ================================== -This example illustrate the use of the generic solver for regularized OT with -user designed regularization term. It uses Conditional gradient as in [6] and +Illustrates the use of the generic solver for regularized OT with +user-designed regularization term. It uses Conditional gradient as in [6] and generalized Conditional Gradient as proposed in [5][7]. diff --git a/examples/plot_otda_mapping.py b/examples/plot_otda_mapping.py index e0da2d8..e78fef4 100644 --- a/examples/plot_otda_mapping.py +++ b/examples/plot_otda_mapping.py @@ -1,8 +1,8 @@ # -*- coding: utf-8 -*- """ -=============================================== -OT mapping estimation for domain adaptation [8] -=============================================== +=========================================== +OT mapping estimation for domain adaptation +=========================================== This example presents how to use MappingTransport to estimate at the same time both the coupling transport and approximate the transport map with either @@ -24,7 +24,7 @@ import ot ############################################################################## -# generate data +# Generate data ############################################################################## n_source_samples = 100 @@ -44,7 +44,7 @@ Xt[yt == 2] *= 3 Xt = Xt + 4 ############################################################################## -# plot data +# Plot data ############################################################################## pl.figure(1, (10, 5)) diff --git a/examples/plot_otda_mapping_colors_images.py b/examples/plot_otda_mapping_colors_images.py index a8b2ca8..162c24b 100644 --- a/examples/plot_otda_mapping_colors_images.py +++ b/examples/plot_otda_mapping_colors_images.py @@ -1,8 +1,8 @@ # -*- coding: utf-8 -*- """ -=============================================== -OT for color adaptation with mapping estimation -=============================================== +===================================================== +OT for image color adaptation with mapping estimation +===================================================== OT for domain adaptation with image color adaptation [6] with mapping estimation [8]. -- cgit v1.2.3 From b3a4ce9ce66606dc9e35b434022b4fca51c2faf7 Mon Sep 17 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--git a/examples/plot_gromov_barycenter.py b/examples/plot_gromov_barycenter.py index f0657e1..4f17117 100755 --- a/examples/plot_gromov_barycenter.py +++ b/examples/plot_gromov_barycenter.py @@ -45,19 +45,19 @@ def smacof_mds(C, dim, max_iter=3000, eps=1e-9): dimension of the targeted space max_iter : int Maximum number of iterations of the SMACOF algorithm for a single run - - eps : relative tolerance w.r.t stress to declare converge + eps : float + relative tolerance w.r.t stress to declare converge Returns ------- - npos : R**dim ndarray + npos : ndarray, shape (R, dim) Embedded coordinates of the interpolated point cloud (defined with one isometry) """ - seed = np.random.RandomState(seed=3) + rng = np.random.RandomState(seed=3) mds = manifold.MDS( dim, @@ -72,7 +72,7 @@ def smacof_mds(C, dim, max_iter=3000, eps=1e-9): max_iter=max_iter, eps=1e-9, dissimilarity="precomputed", - random_state=seed, + random_state=rng, n_init=1) npos = nmds.fit_transform(C, init=pos) @@ -132,23 +132,31 @@ lambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]] Ct01 = [0 for i in range(2)] for i in range(2): - Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]], [ - ps[0], ps[1]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3) + Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]], + [ps[0], ps[1] + ], p, lambdast[i], 'square_loss', 5e-4, + max_iter=100, stopThr=1e-3) Ct02 = [0 for i in range(2)] for i in range(2): - Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]], [ - ps[0], ps[2]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3) + Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]], + [ps[0], ps[2] + ], p, lambdast[i], 'square_loss', 5e-4, + max_iter=100, stopThr=1e-3) Ct13 = [0 for i in range(2)] for i in range(2): - Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]], [ - ps[1], ps[3]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3) + Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]], + [ps[1], ps[3] + ], p, lambdast[i], 'square_loss', 5e-4, + max_iter=100, stopThr=1e-3) Ct23 = [0 for i in range(2)] for i in range(2): - Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]], [ - ps[2], ps[3]], p, lambdast[i], 'square_loss', 5e-4, numItermax=100, stopThr=1e-3) + Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]], + [ps[2], ps[3] + ], p, lambdast[i], 'square_loss', 5e-4, + max_iter=100, stopThr=1e-3) """ Visualization diff --git a/ot/gromov.py b/ot/gromov.py index 9dbf463..cf9c4da 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -58,13 +58,13 @@ def tensor_square_loss(C1, C2, T): Metric cost matrix in the source space C2 : ndarray, shape (nt, nt) Metric costfr matrix in the target space - T : np.ndarray(ns,nt) + T : ndarray, shape (ns, nt) Coupling between source and target spaces Returns ------- - tens : (ns*nt) ndarray + tens : ndarray, shape (ns, nt) \mathcal{L}(C1,C2) \otimes T tensor-matrix multiplication result @@ -89,7 +89,7 @@ def tensor_square_loss(C1, C2, T): tens = -np.dot(h1(C1), T).dot(h2(C2).T) tens -= tens.min() - return np.array(tens) + return tens def tensor_kl_loss(C1, C2, T): @@ -116,13 +116,13 @@ def tensor_kl_loss(C1, C2, T): Metric cost matrix in the source space C2 : ndarray, shape (nt, nt) Metric costfr matrix in the target space - T : np.ndarray(ns,nt) + T : ndarray, shape (ns, nt) Coupling between source and target spaces Returns ------- - tens : (ns*nt) ndarray + tens : ndarray, shape (ns, nt) \mathcal{L}(C1,C2) \otimes T tensor-matrix multiplication result References @@ -151,34 +151,36 @@ def tensor_kl_loss(C1, C2, T): tens = -np.dot(h1(C1), T).dot(h2(C2).T) tens -= tens.min() - return np.array(tens) + return tens def update_square_loss(p, lambdas, T, Cs): """ - Updates C according to the L2 Loss kernel with the S Ts couplings calculated at each iteration + Updates C according to the L2 Loss kernel with the S Ts couplings + calculated at each iteration Parameters ---------- - p : np.ndarray(N,) + p : ndarray, shape (N,) weights in the targeted barycenter lambdas : list of the S spaces' weights T : list of S np.ndarray(ns,N) the S Ts couplings calculated at each iteration - Cs : Cs : list of S np.ndarray(ns,ns) + Cs : list of S ndarray, shape(ns,ns) Metric cost matrices Returns ---------- - C updated + C : ndarray, shape (nt,nt) + updated C matrix """ tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) for s in range(len(T))]) ppt = np.outer(p, p) - return(np.divide(tmpsum, ppt)) + return np.divide(tmpsum, ppt) def update_kl_loss(p, lambdas, T, Cs): @@ -188,27 +190,28 @@ def update_kl_loss(p, lambdas, T, Cs): Parameters ---------- - p : np.ndarray(N,) + p : ndarray, shape (N,) weights in the targeted barycenter lambdas : list of the S spaces' weights T : list of S np.ndarray(ns,N) the S Ts couplings calculated at each iteration - Cs : Cs : list of S np.ndarray(ns,ns) + Cs : list of S ndarray, shape(ns,ns) Metric cost matrices Returns ---------- - C updated + C : ndarray, shape (ns,ns) + updated C matrix """ tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) for s in range(len(T))]) ppt = np.outer(p, p) - return(np.exp(np.divide(tmpsum, ppt))) + return np.exp(np.divide(tmpsum, ppt)) -def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, stopThr=1e-9, verbose=False, log=False): +def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, tol=1e-9, verbose=False, log=False): """ Returns the gromov-wasserstein coupling between the two measured similarity matrices @@ -241,31 +244,28 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, stopThr=1 Metric cost matrix in the source space C2 : ndarray, shape (nt, nt) Metric costfr matrix in the target space - p : np.ndarray(ns,) + p : ndarray, shape (ns,) distribution in the source space - q : np.ndarray(nt) + q : ndarray, shape (nt,) distribution in the target space - loss_fun : loss function used for the solver either 'square_loss' or 'kl_loss' + loss_fun : string + loss function used for the solver either 'square_loss' or 'kl_loss' epsilon : float Regularization term >0 -<<<<<<< HEAD max_iter : int, optional -======= - numItermax : int, optional ->>>>>>> 986f46ddde3ce2f550cb56f66620df377326423d - Max number of iterations - stopThr : float, optional + Max number of iterations + tol : float, optional Stop threshold on error (>0) verbose : bool, optional Print information along iterations log : bool, optional record log if True - forcing : np.ndarray(N,2) - list of forced couplings (where N is the number of forcing) + Returns ------- - T : coupling between the two spaces that minimizes : + T : ndarray, shape (ns, nt) + coupling between the two spaces that minimizes : \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T)) """ @@ -278,7 +278,7 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, stopThr=1 cpt = 0 err = 1 - while (err > stopThr and cpt < max_iter): + while (err > tol and cpt < max_iter): Tprev = T @@ -303,7 +303,7 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, stopThr=1 'It.', 'Err') + '\n' + '-' * 19) print('{:5d}|{:8e}|'.format(cpt, err)) - cpt = cpt + 1 + cpt += 1 if log: return T, log @@ -311,7 +311,7 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, stopThr=1 return T -def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, stopThr=1e-9, verbose=False, log=False): +def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, tol=1e-9, verbose=False, log=False): """ Returns the gromov-wasserstein discrepancy between the two measured similarity matrices @@ -339,37 +339,36 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, stopThr= Metric cost matrix in the source space C2 : ndarray, shape (nt, nt) Metric costfr matrix in the target space - p : np.ndarray(ns,) + p : ndarray, shape (ns,) distribution in the source space - q : np.ndarray(nt) + q : ndarray, shape (nt,) distribution in the target space - loss_fun : loss function used for the solver either 'square_loss' or 'kl_loss' + loss_fun : string + loss function used for the solver either 'square_loss' or 'kl_loss' epsilon : float Regularization term >0 max_iter : int, optional Max number of iterations - stopThr : float, optional + tol : float, optional Stop threshold on error (>0) verbose : bool, optional Print information along iterations log : bool, optional record log if True - forcing : np.ndarray(N,2) - list of forced couplings (where N is the number of forcing) Returns ------- - T : coupling between the two spaces that minimizes : - \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T)) + gw_dist : float + Gromov-Wasserstein distance """ if log: gw, logv = gromov_wasserstein( - C1, C2, p, q, loss_fun, epsilon, max_iter, stopThr, verbose, log) + C1, C2, p, q, loss_fun, epsilon, max_iter, tol, verbose, log) else: gw = gromov_wasserstein(C1, C2, p, q, loss_fun, - epsilon, max_iter, stopThr, verbose, log) + epsilon, max_iter, tol, verbose, log) if loss_fun == 'square_loss': gw_dist = np.sum(gw * tensor_square_loss(C1, C2, gw)) @@ -383,7 +382,7 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, stopThr= return gw_dist -def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, max_iter=1000, stopThr=1e-9, verbose=False, log=False): +def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, max_iter=1000, tol=1e-9, verbose=False, log=False): """ Returns the gromov-wasserstein barycenters of S measured similarity matrices @@ -408,7 +407,7 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, max_iter=1000, Metric cost matrices ps : list of S np.ndarray(ns,) sample weights in the S spaces - p : np.ndarray(N,) + p : ndarray, shape(N,) weights in the targeted barycenter lambdas : list of the S spaces' weights L : tensor-matrix multiplication function based on specific loss function @@ -418,7 +417,7 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, max_iter=1000, Regularization term >0 max_iter : int, optional Max number of iterations - stopThr : float, optional + tol : float, optional Stop threshol on error (>0) verbose : bool, optional Print information along iterations @@ -427,7 +426,8 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, max_iter=1000, Returns ------- - C : Similarity matrix in the barycenter space (permutated arbitrarily) + C : ndarray, shape (N, N) + Similarity matrix in the barycenter space (permutated arbitrarily) """ @@ -446,7 +446,7 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, max_iter=1000, error = [] - while(err > stopThr and cpt < max_iter): + while(err > tol and cpt < max_iter): Cprev = C T = [gromov_wasserstein(Cs[s], C, ps[s], p, loss_fun, epsilon, diff --git a/test/test_gromov.py b/test/test_gromov.py index c26d898..28495e1 100644 --- a/test/test_gromov.py +++ b/test/test_gromov.py @@ -17,7 +17,7 @@ def test_gromov(): xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s) - xt = [xs[n_samples - (i + 1)] for i in range(n_samples)] + xt = xs[::-1] xt = np.array(xt) p = ot.unif(n_samples) -- cgit v1.2.3 From 8ea74ad41d660629a12f7d8d0d8816a23d385a92 Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Fri, 1 Sep 2017 15:38:11 +0200 Subject: docstrings + naming --- ot/gromov.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/gromov.py b/ot/gromov.py index cf9c4da..1726f5e 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -471,6 +471,6 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, max_iter=1000, 'It.', 'Err') + '\n' + '-' * 19) print('{:5d}|{:8e}|'.format(cpt, err)) - cpt = cpt + 1 + cpt += 1 return C -- cgit v1.2.3 From b437b3042adf48ec74ee04dd1c9111a68121912c Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 1 Sep 2017 15:51:50 +0200 Subject: test notebook --- docs/source/auto_examples/plot_barycenter_1D.ipynb | 420 +++++++++++++++------ 1 file changed, 301 insertions(+), 119 deletions(-) diff --git a/docs/source/auto_examples/plot_barycenter_1D.ipynb b/docs/source/auto_examples/plot_barycenter_1D.ipynb index 657782d..53e6a60 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.ipynb +++ b/docs/source/auto_examples/plot_barycenter_1D.ipynb @@ -1,126 +1,308 @@ { - "nbformat_minor": 0, - "nbformat": 4, - "cells": [ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# 1D Wasserstein barycenter demo\n", + "\n", + "\n", + "This example illustrates the computation of regularized Wassersyein Barycenter \n", + "as proposed in [3].\n", + "\n", + "\n", + "[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). \n", + "Iterative Bregman projections for regularized transportation problems\n", + "SIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Remi Flamary \n", + "#\n", + "# License: MIT License\n", + "\n", + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot\n", + "# necessary for 3d plot even if not used\n", + "from mpl_toolkits.mplot3d import Axes3D # noqa\n", + "from matplotlib.collections import PolyCollection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + "#############################################################################\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% parameters\n", + "\n", + "n = 100 # nb bins\n", + "\n", + "# bin positions\n", + "x = np.arange(n, dtype=np.float64)\n", + "\n", + "# Gaussian distributions\n", + "a1 = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std\n", + "a2 = ot.datasets.get_1D_gauss(n, m=60, s=8)\n", + "\n", + "# creating matrix A containing all distributions\n", + "A = np.vstack((a1, a2)).T\n", + "n_distributions = A.shape[1]\n", + "\n", + "# loss matrix + normalization\n", + "M = ot.utils.dist0(n)\n", + "M /= M.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot data\n", + "#############################################################################\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, - { - "source": [ - "\n# 1D Wasserstein barycenter demo\n\n\nThis example illustrates the computation of regularized Wassersyein Barycenter \nas proposed in [3].\n\n\n[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyr\u00e9, G. (2015). \nIterative Bregman projections for regularized transportation problems\nSIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nfrom matplotlib.collections import PolyCollection" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, - { - "source": [ - "Generate data\n#############################################################################\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na1 = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.get_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, - { - "source": [ - "Plot data\n#############################################################################\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, - { - "source": [ - "Barycenter computation\n#############################################################################\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + "data": { + "image/png": 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F3UBWq+eD7W3tHiMiHiAZOGg/Hwz8E/iOMWZbexcwxjxpjMk3xuRnZmZ27S8I\ncVX1TSzZVsbZY/sjEdZZwuUSzhzTj0WbDlDf1Ox0OKHhowegei+c+4fIXCsxUAZOgCmzYenfoGSF\n09GoIOJPUlwODBeRISISDVwOzGtzzDysjjQAlwAfG2OMiKQA7wB3GGN0Rt52LNx0gKZmw9kR1J7Y\n2sxx/altbNaB/P4oXgbL/g+mfg+ypjgdTeibcS8k9od5P4JmbddWlk6Tot1GeDOwANgIvGaM2SAi\nD4hIy+Cop4F0ESkEbgVahm3cDOQB94rIavunb8D/ihC2YMM++ibGMDErxelQHHH80HQSYz1ahdoZ\nbyPM+zEkDbQ+zFX3xSbDub+HAxtgyR+djkYFCY8/Bxlj5gPz22y7t9XjeuDSdl73a+DX3YwxbNU3\nNbNwUykXTRqEK8xnselItMfF6aP68sEX+/E2+/C4dT6Jdi1+3Jq55opXrR6UKjBGf9Oa+GDRwzDm\nAmtxYhXR9BPIQZ9uLaOuqZmZ4yKz6rTFzLH9KT/cxPKduvBwu8q2wie/sxYPHjnT6WjCz7mPgCcW\n3rrFGu6iIpomRQct2LCPpFgP04ZGxtRuHTllZCYxHpdWobbH1wxv3mytID/z4c6PV12X2N+aO3bn\np7DiWaejUQ7TpOiQusZm3t+wjzNG9yMqwqsM46I9nDwik3fW7aWpWWcZ+YrPHoXi/8A5j0BiZI1j\n7VWTroGhp8GCu6Cs0OlolIMi+9PYQW+v3UNVvZdvTcnq/OAIcFl+FqXVDToXamu7V8Cih2DcxXDs\nt5yOJry5XHDBX8ATA29cr71RI5gmRYe8tLSIvL4JHDckzelQgsJpo/oyMDmWl5YWOR1KcGiogde/\nBwn94RuP6oTfvSFpAJz3R9izSgf1RzBNig5Yv7uS1cUVXHlcdsQN2O+I2yVcMTWbT7eWsbOs1ulw\nnLfgF3BoO1z0N+gTmcN1HDHmfJh4FXz6KOxa4nQ0ygGaFB3w92VFxEa5uGjiYKdDCSqXTcnC7RJe\nXhbhpcUv5lmLB0+/BXJPdDqayDPzYUjNhTdutNasVBFFk2Ivq2nw8uaq3Zx37ECS46KcDieo9E2K\n5awx/XitoJgGb4RO+3ZgE/zrBzBwEpx2l9PRRKaYBLj4aWs6vddnWz2AVcTQpNjL/rlqN7WNzVw5\nLcfpUILSlcflUH64iXfXReDwjLoKeOXb1vCLy14ET7TTEUWuwZPhG3+AbR/DR790OhrVizQp9iJj\nDC/9ZxfnNdk3AAAPLUlEQVRjByYxfnCy0+EEpROGpZObHsdLS3c5HUrv8vngjRugYhd863lIbrs6\nm+p1k6+B/O9aswmtf93paFQv0aTYi1YWVbBpXzVXHpejHWw64HIJ3z4um+U7y9m8r9rpcHrPot/C\n1gUw8yHIOcHpaFSLmQ9D1jRrAoV965yORvUCTYq96JnPdhAf7eb8CQOdDiWoXTI5i2iPi6c/2+50\nKL1j9cvwySMw8WqYcr3T0ajWPNFWyT02GV6+AipLnI5I9TBNir1kVVE576zby+yThpIQ49c87BEr\nLT6aq47LYe6KErbsD/PS4qb58OYPYcgpVhuW1iAEn8R+8O1XrTbfFy6E2oNOR6R6kCbFXmCM4cH5\nm8hIiOaGk4c6HU5I+NHpecTHeHjo3U1Oh9JzdnwK/7jWWvD28pes2VRUcBowHr79ClQUwUsXQ0OY\nf1mLYJoUe8GHGw+wbOchbjljhJYS/ZQaH80PT8vj400HWLItDBcg3r3Sqo5LGwJXztXloEJB7olw\n6RzYu9b6t2uqdzoi1QM0KfYwb7OPh97dyNDMeC7XeU675NoTchmYHMtD727C5wujJX1KCuDFiyAu\nFa7+J8TpVH8hY+RMuPCv1ooar1xhTcenwoomxR72akEx20pr+fnMURG/GkZXxUa5ue3skawtqeSt\ntXucDicwtn4Ac86D2BT4zjxI0k5XIefYb8GsJ2D7Inj+fG1jDDP6Kd2Dahu8PPbBVvJzUjlrjC77\nczQumDCI0QOSeGTB5tCf5Wbta/Dy5ZCeB7Pft6pOVWiaeBVc9hLs3wDPnA0VxU5HpAJEk2IPeuCt\nLyiraeDOc0fruMSj5HIJd507mpLyOh5+d7PT4RwdY+Czx+CN70H28XDtO5DQ1+moVHeNOteq/q45\nAE+fZa2uoUKeJsUe8uryIl4tKObm0/KYnJPqdDgh7cThGVx7Qi7PLN7B26FWjXr4kNUp48P7YeyF\nVqea2CSno1KBknMCXDcfxGUlxmX/Z30JUiFLk2IPWFdSyT1vbuCk4Rn89MwRTocTFn5x7mgmZadw\n+9y1FB4Ike7wJSvgb6dA4YfWzCiXPAtRsU5HpQKt/zi48RNrrOn822Dud6G+yumo1FHSpBhgFYcb\n+cFLK8iIj+bxyyfidmm1aSBEe1z8+crJ9Ilyc+MLK6hp8DodUse8DbDoYautCeC7C2Da93VgfjiL\nT4dvvwYz7oMv3oS/nWx1xFEhR5NiADU1+/jJq6vZX1XPE1dOIi1eVzkIpP7Jsfy/Kyayo6yW2+eu\noTkYh2lsXwR/OcGay3T0eXDjv60VF1T4c7ngpFvh2ret58/Pgtevh+r9zsalukSTYoBU1Tdx3bPL\nWbS5lPvPH8vEbG1H7Akn5GVwxzmjmL9uHze+UMDhxiApMR7cBnNnWx+Evma46g249FkdgxiJck6A\nm/4Dp9xhlRr/NAU+fwKa6pyOTPlBTJA1Cufn55uCggKnw+iS3RV1XPfsMraX1vLQxcdyyeTBTocU\n9l74fCf3zdvA2IHJPH1tPn0THWqrK9sKn/we1r0G7miYfguceKu2HSpLWSG8+z/WuozxfWH6j63l\nqKLjnY4s4ojICmNMfqfHaVLsnnUllcyes5y6pmb+etVkpudlOB1SxPho435u/vsq0uKjefrafEb1\n76Venb5m2L4QVsyBjW9ZiwJPmQ3H/8iaPFqptnYuhk9+Z1Wvx6XD5GutsY5pOhdyb9Gk2MNKqxv4\n3w+38MryYvonxfLsdVMY0U/nr+xt60oq+e6c5ZTXNnL18TncMmM4KXE90JZrDJRtgXVzYfXfoaoE\n+qRZC9EefzPE65ch5YfiZfDpH2Dr+2B8kHMiTLwSRp4DfbTJpSdpUuwhtQ1enluyk78s2kZ9UzNX\nTbM+iFO1U41jSqsbePSDLby6vIiEGA8/njGcq6blEBvl7t6JG2qgeKn1AbblPSjfCQjkzbC+5Y88\nV1e2UEenao/15WrVi1C+A8QN2dNgxNmQdwZkjrY67qiACWhSFJGZwOOAG3jKGPNQm/0xwPPAZOAg\ncJkxZqe9705gNtAM/NgYs+BI1wrGpFjf1My/t5Ty1po9fLTxAHVNzZw5ph93njOKoZkJToenbJv3\nVfPb+Rv595ZS4qPdnDmmH+eNH8hJwzOJ9nTyAdN42CoJHtgIuwusb/T7N4BpBk8sDD0Vhp9lfaPX\n+UpVoBhjTRC/5T3YsgD2r7O2xyRbvZYHT7WWFsscBSk5mii7IWBJUUTcwBbgTKAEWA5cYYz5otUx\nNwHHGmO+LyKXAxcaYy4TkTHAy8BUYCDwITDCGNPhJJZOJ8X6pmZ2V9SxcW8V63ZXsq6kkrUlldQ0\neEmLj+bcY/pz0aTBTNLepUFr6faD/Gv1buav20dlXRNJsS6mD/QwOdPLMckNDImtIbVpP1HVJdb6\neAcLoXwXYP9fiE6AQZMhaypkTbN6E0bHOfo3qQhRWQI7PrG+lJUshwNfWNWsAJ4+kDkCUnMhOQtS\nsiF5MCT0s6rv4zO1A88RBDIpHg/cb4w5235+J4Ax5sFWxyywj/lcRDzAPiATuKP1sa2P6+h6gUqK\nyz/8B95mLz4fNBtDs8/gbfbR1Gxo9PpobPZR1+jlcKOPw41eKuuaOHS4kZr6/3bxd7uErLQ4hmTE\nMSk7jTEDEnFHwje1LlWptzn2K681bbaZdh4b6z99y2Ofz37us0ppvmbwea3nzU3ga7J+NzdBcwN4\nG63fTfXQVGuV+JoOQ0MVpq4S7+EK3I1VuPB9LfIKEilz9+VgzGAO9hlKRcJQDqcMpy5xCNFRUcR4\nXER73HjcgscluO0flwguARGhZTh+y+OW8flf/uYIA/Z1LL/qhLupmvjKQuIrtxJfWUhcZSF9akuI\nObwHd3PD145vdsfgjUrCG52INyqJZk+c/dMHnycOnysan9v+cUVjXFEY8eBzeTAuD0bcIG6MuOzH\ngsEF4sKIAGJv++/jlhvZIK3u6bY391efmy5MZDFs8pkkJHW/EOJvUvRnxdtBQOsp4EuA4zo6xhjj\nFZFKIN3e/p82rx3UTrA3ADcAZGdn+xFS50Z/+iMS5CjGBbVtGqyyf7YHICgVWK4oq03PHW31AI2K\ns0p0UfGQOADJGElUbDLEJkN8Jo2xaRQ3xLP1cDw7mlIpqhH2VNRTfriRqromqiq8VBc20dS8zem/\nTKk2htg/Z9rPDRlUMUAOki6VZEgV6VSR5q0iseEwSVJHErXESylxNNCHBuKkgWiaiKGJaJpwS3D1\nJ+nIzv4fkZDUaS4LmKBYBt4Y8yTwJFglxUCcs/zSuVQY35ff7t0iRLldRHsEj9uFJxJKfN3ShWLM\n1w5tteHLb4Sti1CtHouLr3zjdLmtbSJW5wOXx97mBrfHSoTuKOt3F/8No4Fh9s+ReJutmoSGJh8N\nXh9en8+qafBZNQ7GgM+ufWhhDBi7ZNxSGD7SjRxsHdxU+Kq3f8rb7vB5EZ8X8TVZv41VI2M9tmpr\nxPgAH+LzYd3Rxt5u/vucll+taoFaka/d612797OzR3bp+O7yJynuBlovGT/Y3tbeMSV29WkyVocb\nf17bI7LGndgbl1FhyON24XG76ImRHUqp4ObPV+3lwHARGSIi0cDlwLw2x8wDrrEfXwJ8bKyvwvOA\ny0UkRkSGAMOBZYEJXSmllAqsTkuKdhvhzcACrCEZzxhjNojIA0CBMWYe8DTwgogUAoewEif2ca8B\nXwBe4IdH6nmqlFJKOUkH7yullAp7/vY+1d4mSimllE2TolJKKWULuupTESkFdgXodBlAWYDOFc70\nffKfvlf+0/fKP/o++a8771WOMSazs4OCLikGkogU+FOHHOn0ffKfvlf+0/fKP/o++a833iutPlVK\nKaVsmhSVUkopW7gnxSedDiBE6PvkP32v/KfvlX/0ffJfj79XYd2mqJRSSnVFuJcUlVJKKb9pUlRK\nKaVsYZkURWSmiGwWkUIRucPpeIKJiGSJyEIR+UJENojILfb2NBH5QES22r+7v6pnGBARt4isEpG3\n7edDRGSpfW+9ak+SH/FEJEVE5orIJhHZKCLH6z3VPhH5qf1/b72IvCwisXpfWUTkGRE5ICLrW21r\n9z4Syx/t92ytiEwKRAxhlxRFxA08AZwDjAGuEJExzkYVVLzAz4wxY4BpwA/t9+cO4CNjzHDgI/u5\ngluAja2ePww8ZozJw1qibrYjUQWfx4H3jDGjgPFY75neU22IyCDgx0C+MWYc1iILl6P3VYvngJlt\ntnV0H52DtfLScKxF6v8SiADCLikCU4FCY8x2Y0wj8Aowy+GYgoYxZq8xZqX9uBrrw2sQ1ns0xz5s\nDnCBMxEGDxEZDHwDeMp+LsDpwFz7EH2fABFJBk7GWi0HY0yjMaYCvac64gH62GvPxgF70fsKAGPM\nJ1grLbXW0X00C3jeWP4DpIjIgO7GEI5JcRBQ3Op5ib1NtSEiucBEYCnQzxiz1961D+jnUFjB5H+B\n2wGf/TwdqDDGeO3nem9ZhgClwLN2VfNTIhKP3lNfY4zZDfweKMJKhpXACvS+OpKO7qMe+awPx6So\n/CAiCcDrwE+MMVWt99kLREf0WB0R+SZwwBizwulYQoAHmAT8xRgzEailTVWp3lMWuz1sFtYXiYFA\nPF+vLlQd6I37KByT4m4gq9XzwfY2ZRORKKyE+JIx5g178/6Wqgf79wGn4gsS04HzRWQnVhX86Vjt\nZil2tRfovdWiBCgxxiy1n8/FSpJ6T33dGcAOY0ypMaYJeAPrXtP7qmMd3Uc98lkfjklxOTDc7s0V\njdWIPc/hmIKG3S72NLDRGPNoq13zgGvsx9cAb/Z2bMHEGHOnMWawMSYX6x762BhzJbAQuMQ+LOLf\nJwBjzD6gWERG2ptmAF+g91R7ioBpIhJn/19sea/0vupYR/fRPOA7di/UaUBlq2rWoxaWM9qIyLlY\n7UFu4BljzG8cDiloiMiJwKfAOv7bVvYLrHbF14BsrKW7vmWMadvgHZFE5FTgNmPMN0VkKFbJMQ1Y\nBVxljGlwMr5gICITsDokRQPbgeuwvnTrPdWGiPwSuAyrJ/gq4HqstrCIv69E5GXgVKwlovYD9wH/\nop37yP5S8Ses6ufDwHXGmIJuxxCOSVEppZQ6GuFYfaqUUkodFU2KSimllE2TolJKKWXTpKiUUkrZ\nNCkqpZRSNk2KSimllE2TolJKKWX7/0J1xNhklKqsAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% plot the distributions\n", + "\n", + "pl.figure(1, figsize=(6.4, 3))\n", + "for i in range(n_distributions):\n", + " pl.plot(x, A[:, i])\n", + "pl.title('Distributions')\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Barycenter computation\n", + "#############################################################################\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% barycenter computation\n\nalpha = 0.2 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, + "data": { + "image/png": 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0uQlqNS+5No3vSTvl/EGycTokzIX0UxBcCRpdDI27OY960c6SH6bISjRBiUgf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\n7lO97DJTnkA1LU11+nTVlStVS0vdsa0x+P2q2dm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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% barycenter computation\n", + "\n", + "alpha = 0.2 # 0<=alpha<=1\n", + "weights = np.array([1 - alpha, alpha])\n", + "\n", + "# l2bary\n", + "bary_l2 = A.dot(weights)\n", + "\n", + "# wasserstein\n", + "reg = 1e-3\n", + "bary_wass = ot.bregman.barycenter(A, M, reg, weights)\n", + "\n", + "pl.figure(2)\n", + "pl.clf()\n", + "pl.subplot(2, 1, 1)\n", + "for i in range(n_distributions):\n", + " pl.plot(x, A[:, i])\n", + "pl.title('Distributions')\n", + "\n", + "pl.subplot(2, 1, 2)\n", + "pl.plot(x, bary_l2, 'r', label='l2')\n", + "pl.plot(x, bary_wass, 'g', label='Wasserstein')\n", + "pl.legend()\n", + "pl.title('Barycenters')\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Barycentric interpolation\n", + "#############################################################################\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ { - "source": [ - "Barycentric interpolation\n#############################################################################\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + "data": { + "image/png": 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fh8/nQzqdhslkyou47HZ7lmAZzkIDg/IwBMpgUcitYQKyhUmSJExOTmJychKrV6/G7t27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\nvr6ef/qnfwJS51mPPvoo7777bsp+lsVi4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/rRj3juuef47Gc/y2c+8xlDJHHSSSfxta99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\nRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% barycenter interpolation\n\nn_alpha = 11\nalpha_list = np.linspace(0, 1, n_alpha)\n\n\nB_l2 = np.zeros((n, n_alpha))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_alpha):\n alpha = alpha_list[i]\n weights = np.array([1 - alpha, alpha])\n B_l2[:, i] = A.dot(weights)\n B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)\n\n#%% plot interpolation\n\npl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_l2[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_wass[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with Wasserstein')\npl.tight_layout()\n\npl.show()" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, - "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", - "codemirror_mode": { - "version": 2, - "name": "ipython" - } + "data": { + "image/png": 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QPT09qKmpwf33348XXngB1dXVAIBnn30WFosFjY2N+MxnPoN//ud/xuWXXx6zjdtuuw0t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\nmH6W0+ns46Zrs9n6yAtBdLGzsrKSjIwMs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TBk1q1bx0svvQREFQa2bdt2QgQniI41f/TR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\nsUcynvMIGwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" } + ], + "source": [ + "#%% barycenter interpolation\n", + "\n", + "n_alpha = 11\n", + "alpha_list = np.linspace(0, 1, n_alpha)\n", + "\n", + "\n", + "B_l2 = np.zeros((n, n_alpha))\n", + "\n", + "B_wass = np.copy(B_l2)\n", + "\n", + "for i in range(0, n_alpha):\n", + " alpha = alpha_list[i]\n", + " weights = np.array([1 - alpha, alpha])\n", + " B_l2[:, i] = A.dot(weights)\n", + " B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)\n", + "\n", + "#%% plot interpolation\n", + "\n", + "pl.figure(3)\n", + "\n", + "cmap = pl.cm.get_cmap('viridis')\n", + "verts = []\n", + "zs = alpha_list\n", + "for i, z in enumerate(zs):\n", + " ys = B_l2[:, i]\n", + " verts.append(list(zip(x, ys)))\n", + "\n", + "ax = pl.gcf().gca(projection='3d')\n", + "\n", + "poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\n", + "poly.set_alpha(0.7)\n", + "ax.add_collection3d(poly, zs=zs, zdir='y')\n", + "ax.set_xlabel('x')\n", + "ax.set_xlim3d(0, n)\n", + "ax.set_ylabel('$\\\\alpha$')\n", + "ax.set_ylim3d(0, 1)\n", + "ax.set_zlabel('')\n", + "ax.set_zlim3d(0, B_l2.max() * 1.01)\n", + "pl.title('Barycenter interpolation with l2')\n", + "pl.tight_layout()\n", + "\n", + "pl.figure(4)\n", + "cmap = pl.cm.get_cmap('viridis')\n", + "verts = []\n", + "zs = alpha_list\n", + "for i, z in enumerate(zs):\n", + " ys = B_wass[:, i]\n", + " verts.append(list(zip(x, ys)))\n", + "\n", + "ax = pl.gcf().gca(projection='3d')\n", + "\n", + "poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\n", + "poly.set_alpha(0.7)\n", + "ax.add_collection3d(poly, zs=zs, zdir='y')\n", + "ax.set_xlabel('x')\n", + "ax.set_xlim3d(0, n)\n", + "ax.set_ylabel('$\\\\alpha$')\n", + "ax.set_ylim3d(0, 1)\n", + "ax.set_zlabel('')\n", + "ax.set_zlim3d(0, B_l2.max() * 1.01)\n", + "pl.title('Barycenter interpolation with Wasserstein')\n", + "pl.tight_layout()\n", + "\n", + "pl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" } -} \ No newline at end of file + }, + "nbformat": 4, + "nbformat_minor": 0 +} -- cgit v1.2.3 From 9200f0d25514c5efa8d262dfc545a6d6ead34757 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 1 Sep 2017 15:57:21 +0200 Subject: test notebook 2 --- .../source/auto_examples/auto_examples_jupyter.zip | Bin 70410 -> 271763 bytes docs/source/auto_examples/auto_examples_python.zip | Bin 46653 -> 46548 bytes docs/source/auto_examples/plot_OT_1D.ipynb | 4 ++-- docs/source/auto_examples/plot_OT_1D.py | 4 ++-- docs/source/auto_examples/plot_OT_1D.rst | 6 +++--- 5 files changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/source/auto_examples/auto_examples_jupyter.zip b/docs/source/auto_examples/auto_examples_jupyter.zip index 96bc0bc..e8546c4 100644 Binary files a/docs/source/auto_examples/auto_examples_jupyter.zip and b/docs/source/auto_examples/auto_examples_jupyter.zip differ diff --git a/docs/source/auto_examples/auto_examples_python.zip b/docs/source/auto_examples/auto_examples_python.zip index 6241b92..5a825e4 100644 Binary files a/docs/source/auto_examples/auto_examples_python.zip and b/docs/source/auto_examples/auto_examples_python.zip differ diff --git a/docs/source/auto_examples/plot_OT_1D.ipynb b/docs/source/auto_examples/plot_OT_1D.ipynb index 3126b6f..a9b494c 100644 --- a/docs/source/auto_examples/plot_OT_1D.ipynb +++ b/docs/source/auto_examples/plot_OT_1D.ipynb @@ -33,7 +33,7 @@ }, { "source": [ - "Generate data\n#############################################################################\n\n" + "Generate data\n##############\n\n" ], "cell_type": "markdown", "metadata": {} @@ -51,7 +51,7 @@ }, { "source": [ - "Plot distributions and loss matrix\n#############################################################################\n\n" + "Plot distributions and loss matrix\n###################################\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_OT_1D.py b/docs/source/auto_examples/plot_OT_1D.py index a63f29a..86506f7 100644 --- a/docs/source/auto_examples/plot_OT_1D.py +++ b/docs/source/auto_examples/plot_OT_1D.py @@ -20,7 +20,7 @@ from ot.datasets import get_1D_gauss as gauss ############################################################################## # Generate data -############################################################################## +############### #%% parameters @@ -40,7 +40,7 @@ M /= M.max() ############################################################################## # Plot distributions and loss matrix -############################################################################## +#################################### #%% plot the distributions diff --git a/docs/source/auto_examples/plot_OT_1D.rst b/docs/source/auto_examples/plot_OT_1D.rst index ff02180..7aed401 100644 --- a/docs/source/auto_examples/plot_OT_1D.rst +++ b/docs/source/auto_examples/plot_OT_1D.rst @@ -32,7 +32,7 @@ and their visualization. Generate data -############################################################################# +############## @@ -62,7 +62,7 @@ Generate data Plot distributions and loss matrix -############################################################################# +################################### @@ -168,7 +168,7 @@ Solve Sinkhorn 110|1.527180e-10| -**Total running time of the script:** ( 0 minutes 0.770 seconds) +**Total running time of the script:** ( 0 minutes 0.709 seconds) -- cgit v1.2.3 From 364a42954f9d2f5c43e7ff53ee0a3b6f9a682e8a Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 1 Sep 2017 15:59:52 +0200 Subject: test notebook 2 --- .../source/auto_examples/auto_examples_jupyter.zip | Bin 271763 -> 271762 bytes docs/source/auto_examples/auto_examples_python.zip | Bin 46548 -> 46547 bytes docs/source/auto_examples/plot_OT_1D.ipynb | 2 +- docs/source/auto_examples/plot_OT_1D.py | 2 +- docs/source/auto_examples/plot_OT_1D.rst | 4 ++-- 5 files changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/source/auto_examples/auto_examples_jupyter.zip b/docs/source/auto_examples/auto_examples_jupyter.zip index e8546c4..44151e1 100644 Binary files a/docs/source/auto_examples/auto_examples_jupyter.zip and b/docs/source/auto_examples/auto_examples_jupyter.zip differ diff --git a/docs/source/auto_examples/auto_examples_python.zip b/docs/source/auto_examples/auto_examples_python.zip index 5a825e4..d41e5c0 100644 Binary files a/docs/source/auto_examples/auto_examples_python.zip and b/docs/source/auto_examples/auto_examples_python.zip differ diff --git a/docs/source/auto_examples/plot_OT_1D.ipynb b/docs/source/auto_examples/plot_OT_1D.ipynb index a9b494c..6a47974 100644 --- a/docs/source/auto_examples/plot_OT_1D.ipynb +++ b/docs/source/auto_examples/plot_OT_1D.ipynb @@ -51,7 +51,7 @@ }, { "source": [ - "Plot distributions and loss matrix\n###################################\n\n" + "Plot distributions and loss matrix\n##################################\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_OT_1D.py b/docs/source/auto_examples/plot_OT_1D.py index 86506f7..cf6db1b 100644 --- a/docs/source/auto_examples/plot_OT_1D.py +++ b/docs/source/auto_examples/plot_OT_1D.py @@ -40,7 +40,7 @@ M /= M.max() ############################################################################## # Plot distributions and loss matrix -#################################### +################################### #%% plot the distributions diff --git a/docs/source/auto_examples/plot_OT_1D.rst b/docs/source/auto_examples/plot_OT_1D.rst index 7aed401..6b68a60 100644 --- a/docs/source/auto_examples/plot_OT_1D.rst +++ b/docs/source/auto_examples/plot_OT_1D.rst @@ -62,7 +62,7 @@ Generate data Plot distributions and loss matrix -################################### +################################## @@ -168,7 +168,7 @@ Solve Sinkhorn 110|1.527180e-10| -**Total running time of the script:** ( 0 minutes 0.709 seconds) +**Total running time of the script:** ( 0 minutes 0.680 seconds) -- cgit v1.2.3 From 052ccadc5023ec500dc10477821bf0267426aa4f Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 1 Sep 2017 16:28:35 +0200 Subject: pep8 --- .../source/auto_examples/auto_examples_jupyter.zip | Bin 271762 -> 70289 bytes docs/source/auto_examples/auto_examples_python.zip | Bin 46547 -> 46532 bytes .../auto_examples/images/sphx_glr_plot_WDA_001.png | Bin 55483 -> 55744 bytes .../auto_examples/images/sphx_glr_plot_WDA_003.png | Bin 88837 -> 86112 bytes .../images/thumb/sphx_glr_plot_WDA_thumb.png | Bin 88848 -> 85660 bytes docs/source/auto_examples/index.rst | 10 +- docs/source/auto_examples/plot_OT_1D.ipynb | 4 +- docs/source/auto_examples/plot_OT_1D.py | 5 +- docs/source/auto_examples/plot_OT_1D.rst | 7 +- docs/source/auto_examples/plot_OT_L1_vs_L2.py | 2 - docs/source/auto_examples/plot_OT_L1_vs_L2.rst | 4 +- docs/source/auto_examples/plot_WDA.ipynb | 2 +- docs/source/auto_examples/plot_WDA.py | 2 +- docs/source/auto_examples/plot_WDA.rst | 76 +++- docs/source/auto_examples/plot_barycenter_1D.ipynb | 420 ++++++--------------- docs/source/auto_examples/plot_barycenter_1D.py | 4 +- docs/source/auto_examples/plot_barycenter_1D.rst | 6 +- docs/source/auto_examples/plot_compute_emd.ipynb | 2 +- docs/source/auto_examples/plot_compute_emd.py | 2 +- docs/source/auto_examples/plot_compute_emd.rst | 4 +- docs/source/auto_examples/plot_optim_OTreg.ipynb | 2 +- docs/source/auto_examples/plot_optim_OTreg.py | 14 +- docs/source/auto_examples/plot_optim_OTreg.rst | 16 +- .../plot_otda_mapping_colors_images.ipynb | 2 +- .../plot_otda_mapping_colors_images.py | 4 +- .../plot_otda_mapping_colors_images.rst | 6 +- examples/README.txt | 2 +- examples/plot_OT_1D.py | 7 +- examples/plot_OT_L1_vs_L2.py | 2 - examples/plot_WDA.py | 2 +- examples/plot_barycenter_1D.py | 4 +- examples/plot_compute_emd.py | 2 +- examples/plot_optim_OTreg.py | 14 +- examples/plot_otda_mapping_colors_images.py | 4 +- 34 files changed, 242 insertions(+), 389 deletions(-) diff --git a/docs/source/auto_examples/auto_examples_jupyter.zip b/docs/source/auto_examples/auto_examples_jupyter.zip index 44151e1..4ec0672 100644 Binary files a/docs/source/auto_examples/auto_examples_jupyter.zip and b/docs/source/auto_examples/auto_examples_jupyter.zip differ diff --git a/docs/source/auto_examples/auto_examples_python.zip b/docs/source/auto_examples/auto_examples_python.zip index d41e5c0..4fbf223 100644 Binary files a/docs/source/auto_examples/auto_examples_python.zip and b/docs/source/auto_examples/auto_examples_python.zip differ diff --git a/docs/source/auto_examples/images/sphx_glr_plot_WDA_001.png b/docs/source/auto_examples/images/sphx_glr_plot_WDA_001.png index a9fff75..9048051 100644 Binary files a/docs/source/auto_examples/images/sphx_glr_plot_WDA_001.png and b/docs/source/auto_examples/images/sphx_glr_plot_WDA_001.png differ diff --git a/docs/source/auto_examples/images/sphx_glr_plot_WDA_003.png b/docs/source/auto_examples/images/sphx_glr_plot_WDA_003.png index 025e070..fcf13c6 100644 Binary files a/docs/source/auto_examples/images/sphx_glr_plot_WDA_003.png and b/docs/source/auto_examples/images/sphx_glr_plot_WDA_003.png differ diff --git a/docs/source/auto_examples/images/thumb/sphx_glr_plot_WDA_thumb.png b/docs/source/auto_examples/images/thumb/sphx_glr_plot_WDA_thumb.png index 8db2b9a..8db78a1 100644 Binary files a/docs/source/auto_examples/images/thumb/sphx_glr_plot_WDA_thumb.png and b/docs/source/auto_examples/images/thumb/sphx_glr_plot_WDA_thumb.png differ diff --git a/docs/source/auto_examples/index.rst b/docs/source/auto_examples/index.rst index ebcca85..5d7a53d 100644 --- a/docs/source/auto_examples/index.rst +++ b/docs/source/auto_examples/index.rst @@ -3,13 +3,13 @@ POT Examples ============ -This is a gallery of all the POT example files. +This is a gallery of all the POT example files. .. raw:: html -
+
.. only:: html @@ -69,7 +69,7 @@ This is a gallery of all the POT example files. .. raw:: html -
+
.. only:: html @@ -129,7 +129,7 @@ This is a gallery of all the POT example files. .. raw:: html -
+
.. only:: html @@ -149,7 +149,7 @@ This is a gallery of all the POT example files. .. raw:: html -
+
.. only:: html diff --git a/docs/source/auto_examples/plot_OT_1D.ipynb b/docs/source/auto_examples/plot_OT_1D.ipynb index 6a47974..1f08677 100644 --- a/docs/source/auto_examples/plot_OT_1D.ipynb +++ b/docs/source/auto_examples/plot_OT_1D.ipynb @@ -15,7 +15,7 @@ }, { "source": [ - "\n# 1D optimal transport\n\n\nThis example illustrates the computation of EMD and Sinkhorn transport plans \nand their visualization.\n\n\n" + "\n# 1D optimal transport\n\n\nThis example illustrates the computation of EMD and Sinkhorn transport plans\nand their visualization.\n\n\n" ], "cell_type": "markdown", "metadata": {} @@ -33,7 +33,7 @@ }, { "source": [ - "Generate data\n##############\n\n" + "Generate data\n#############\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_OT_1D.py b/docs/source/auto_examples/plot_OT_1D.py index cf6db1b..b6ffa5f 100644 --- a/docs/source/auto_examples/plot_OT_1D.py +++ b/docs/source/auto_examples/plot_OT_1D.py @@ -4,7 +4,7 @@ 1D optimal transport ==================== -This example illustrates the computation of EMD and Sinkhorn transport plans +This example illustrates the computation of EMD and Sinkhorn transport plans and their visualization. """ @@ -20,7 +20,8 @@ from ot.datasets import get_1D_gauss as gauss ############################################################################## # Generate data -############### +# ############# + #%% parameters diff --git a/docs/source/auto_examples/plot_OT_1D.rst b/docs/source/auto_examples/plot_OT_1D.rst index 6b68a60..484455f 100644 --- a/docs/source/auto_examples/plot_OT_1D.rst +++ b/docs/source/auto_examples/plot_OT_1D.rst @@ -7,7 +7,7 @@ 1D optimal transport ==================== -This example illustrates the computation of EMD and Sinkhorn transport plans +This example illustrates the computation of EMD and Sinkhorn transport plans and their visualization. @@ -32,13 +32,14 @@ and their visualization. Generate data -############## +############# .. code-block:: python + #%% parameters n = 100 # nb bins @@ -168,7 +169,7 @@ Solve Sinkhorn 110|1.527180e-10| -**Total running time of the script:** ( 0 minutes 0.680 seconds) +**Total running time of the script:** ( 0 minutes 0.704 seconds) diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.py b/docs/source/auto_examples/plot_OT_L1_vs_L2.py index 77bde22..49d37e1 100644 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.py +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.py @@ -76,7 +76,6 @@ pl.tight_layout() ############################################################################## - #%% EMD G1 = ot.emd(a, b, M1) G2 = ot.emd(a, b, M2) @@ -172,7 +171,6 @@ pl.tight_layout() ############################################################################## - #%% EMD G1 = ot.emd(a, b, M1) G2 = ot.emd(a, b, M2) diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst index 83a7491..8b5b133 100644 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst @@ -116,7 +116,6 @@ Dataset 1 : Plot OT Matrices - #%% EMD G1 = ot.emd(a, b, M1) G2 = ot.emd(a, b, M2) @@ -248,7 +247,6 @@ Dataset 2 : Plot OT Matrices - #%% EMD G1 = ot.emd(a, b, M1) G2 = ot.emd(a, b, M2) @@ -292,7 +290,7 @@ Dataset 2 : Plot OT Matrices -**Total running time of the script:** ( 0 minutes 1.217 seconds) +**Total running time of the script:** ( 0 minutes 1.218 seconds) diff --git a/docs/source/auto_examples/plot_WDA.ipynb b/docs/source/auto_examples/plot_WDA.ipynb index 8e0db41..47a6eca 100644 --- a/docs/source/auto_examples/plot_WDA.ipynb +++ b/docs/source/auto_examples/plot_WDA.ipynb @@ -15,7 +15,7 @@ }, { "source": [ - "\n# Wasserstein Discriminant Analysis\n\n\nThis example illustrate the use of WDA as proposed in [11].\n\n\n[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). \nWasserstein Discriminant Analysis.\n\n\n" + "\n# Wasserstein Discriminant Analysis\n\n\nThis example illustrate the use of WDA as proposed in [11].\n\n\n[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016).\nWasserstein Discriminant Analysis.\n\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_WDA.py b/docs/source/auto_examples/plot_WDA.py index 06a2e38..5928621 100644 --- a/docs/source/auto_examples/plot_WDA.py +++ b/docs/source/auto_examples/plot_WDA.py @@ -7,7 +7,7 @@ Wasserstein Discriminant Analysis This example illustrate the use of WDA as proposed in [11]. -[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). +[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). Wasserstein Discriminant Analysis. """ diff --git a/docs/source/auto_examples/plot_WDA.rst b/docs/source/auto_examples/plot_WDA.rst index 8c9ee29..a0c3389 100644 --- a/docs/source/auto_examples/plot_WDA.rst +++ b/docs/source/auto_examples/plot_WDA.rst @@ -10,7 +10,7 @@ Wasserstein Discriminant Analysis This example illustrate the use of WDA as proposed in [11]. -[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). +[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). Wasserstein Discriminant Analysis. @@ -150,23 +150,61 @@ Compute Wasserstein Discriminant Analysis Compiling cost function... Computing gradient of cost function... iter cost val grad. norm - 1 +7.7038877420882157e-01 6.30647522e-01 - 2 +3.3969600919721271e-01 2.83791849e-01 - 3 +3.0014000762425608e-01 2.56139137e-01 - 4 +2.3397191702411621e-01 6.41134216e-02 - 5 +2.3107227220070231e-01 2.24837190e-02 - 6 +2.3072327156158298e-01 1.71334761e-03 - 7 +2.3072143589220098e-01 6.30059431e-04 - 8 +2.3072133109125159e-01 4.88673790e-04 - 9 +2.3072119579341774e-01 1.74129117e-04 - 10 +2.3072118662364521e-01 1.27046386e-04 - 11 +2.3072118228917746e-01 9.70877451e-05 - 12 +2.3072117734120351e-01 4.17292699e-05 - 13 +2.3072117623493599e-01 4.46062100e-06 - 14 +2.3072117622383431e-01 1.59801454e-06 - 15 +2.3072117622300498e-01 1.12117391e-06 - 16 +2.3072117622220378e-01 4.14581994e-08 - Terminated - min grad norm reached after 16 iterations, 7.77 seconds. + 1 +8.6305817354868675e-01 4.10110152e-01 + 2 +4.6939060757969131e-01 2.98553763e-01 + 3 +4.2106314200107775e-01 1.48552668e-01 + 4 +4.1376389458568069e-01 1.12319011e-01 + 5 +4.0984854988792835e-01 1.01126129e-01 + 6 +4.0415292614140025e-01 3.90875165e-02 + 7 +4.0297967887432584e-01 2.73716014e-02 + 8 +4.0252319029045258e-01 3.76498956e-02 + 9 +4.0158635935184972e-01 1.31986577e-02 + 10 +4.0118906894272482e-01 3.40307273e-02 + 11 +4.0052579694802176e-01 7.79567347e-03 + 12 +4.0049330810825384e-01 9.77921941e-03 + 13 +4.0042500151972926e-01 4.63602913e-03 + 14 +4.0031705300038767e-01 1.69742018e-02 + 15 +4.0013705338124350e-01 7.40310798e-03 + 16 +4.0006224569843946e-01 1.08829949e-02 + 17 +3.9998280287782945e-01 1.25733450e-02 + 18 +3.9986405111843215e-01 1.05626807e-02 + 19 +3.9974905002724365e-01 9.93566406e-03 + 20 +3.9971323753531823e-01 2.21199533e-02 + 21 +3.9958582328238779e-01 1.73335808e-02 + 22 +3.9937139582811110e-01 1.09182412e-02 + 23 +3.9923748818499571e-01 1.77304913e-02 + 24 +3.9900530515251881e-01 1.15381586e-02 + 25 +3.9883316307006128e-01 1.80225446e-02 + 26 +3.9860317631835845e-01 1.65011032e-02 + 27 +3.9852130309759393e-01 2.81245689e-02 + 28 +3.9824281033694675e-01 2.01114810e-02 + 29 +3.9799657608114836e-01 2.66040929e-02 + 30 +3.9746233677210713e-01 1.45779937e-02 + 31 +3.9671794378467928e-01 4.27487207e-02 + 32 +3.9573357685391913e-01 2.20071520e-02 + 33 +3.9536725156297214e-01 2.00817458e-02 + 34 +3.9515994339814914e-01 3.81472315e-02 + 35 +3.9448966390371887e-01 2.52129049e-02 + 36 +3.9351423238681266e-01 5.60677866e-02 + 37 +3.9082703288308568e-01 4.26859586e-02 + 38 +3.7139409489868136e-01 1.26067835e-01 + 39 +2.8085932518253526e-01 1.70133509e-01 + 40 +2.7330384726281814e-01 1.95523507e-01 + 41 +2.4806985554269162e-01 1.31192016e-01 + 42 +2.3748356968454920e-01 8.71616829e-02 + 43 +2.3501927152342389e-01 7.02789537e-02 + 44 +2.3183578112546338e-01 2.62025296e-02 + 45 +2.3154208568082749e-01 1.67845346e-02 + 46 +2.3139316710346300e-01 8.27285074e-03 + 47 +2.3136034106523354e-01 4.64818210e-03 + 48 +2.3134548827742521e-01 4.53144806e-04 + 49 +2.3134540503271503e-01 2.91010390e-04 + 50 +2.3134535764073319e-01 1.25662481e-04 + 51 +2.3134534692621381e-01 1.24751216e-05 + 52 +2.3134534685831357e-01 7.44008265e-06 + 53 +2.3134534684658337e-01 6.16933546e-06 + 54 +2.3134534682129679e-01 5.12152219e-07 + Terminated - min grad norm reached after 54 iterations, 24.53 seconds. Plot 2D projections @@ -218,7 +256,7 @@ Plot 2D projections -**Total running time of the script:** ( 0 minutes 8.568 seconds) +**Total running time of the script:** ( 0 minutes 25.326 seconds) diff --git a/docs/source/auto_examples/plot_barycenter_1D.ipynb b/docs/source/auto_examples/plot_barycenter_1D.ipynb index 53e6a60..32cb2ec 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.ipynb +++ b/docs/source/auto_examples/plot_barycenter_1D.ipynb @@ -1,308 +1,126 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "# 1D Wasserstein barycenter demo\n", - "\n", - "\n", - "This example illustrates the computation of regularized Wassersyein Barycenter \n", - "as proposed in [3].\n", - "\n", - "\n", - "[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). \n", - "Iterative Bregman projections for regularized transportation problems\n", - "SIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n", - "\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Author: Remi Flamary \n", - "#\n", - "# License: MIT License\n", - "\n", - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot\n", - "# necessary for 3d plot even if not used\n", - "from mpl_toolkits.mplot3d import Axes3D # noqa\n", - "from matplotlib.collections import PolyCollection" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n", - "#############################################################################\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "#%% parameters\n", - "\n", - "n = 100 # nb bins\n", - "\n", - "# bin positions\n", - "x = np.arange(n, dtype=np.float64)\n", - "\n", - "# Gaussian distributions\n", - "a1 = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std\n", - "a2 = ot.datasets.get_1D_gauss(n, m=60, s=8)\n", - "\n", - "# creating matrix A containing all distributions\n", - "A = np.vstack((a1, a2)).T\n", - "n_distributions = A.shape[1]\n", - "\n", - "# loss matrix + normalization\n", - "M = ot.utils.dist0(n)\n", - "M /= M.max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot data\n", - "#############################################################################\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ + "nbformat_minor": 0, + "nbformat": 4, + "cells": [ { - "data": { - "image/png": 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F3UBWq+eD7W3tHiMiHiAZOGg/Hwz8E/iOMWZbexcwxjxpjMk3xuRnZmZ27S8I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\nf17bI7LGndgbl1FhyON24XG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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#%% plot the distributions\n", - "\n", - "pl.figure(1, figsize=(6.4, 3))\n", - "for i in range(n_distributions):\n", - " pl.plot(x, A[:, i])\n", - "pl.title('Distributions')\n", - "pl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Barycenter computation\n", - "#############################################################################\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ + "execution_count": null, + "cell_type": "code", + "source": [ + "%matplotlib inline" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, { - "data": { - "image/png": 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0uQlqNS+5No3vSTvl/EGycTokzIX0UxBcCRpdDI27OY960c6SH6bISjRBiUgf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\n7lO97DJTnkA1LU11+nTVlStVS0vdsa0x+P2q2dm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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#%% barycenter computation\n", - "\n", - "alpha = 0.2 # 0<=alpha<=1\n", - "weights = np.array([1 - alpha, alpha])\n", - "\n", - "# l2bary\n", - "bary_l2 = A.dot(weights)\n", - "\n", - "# wasserstein\n", - "reg = 1e-3\n", - "bary_wass = ot.bregman.barycenter(A, M, reg, weights)\n", - "\n", - "pl.figure(2)\n", - "pl.clf()\n", - "pl.subplot(2, 1, 1)\n", - "for i in range(n_distributions):\n", - " pl.plot(x, A[:, i])\n", - "pl.title('Distributions')\n", - "\n", - "pl.subplot(2, 1, 2)\n", - "pl.plot(x, bary_l2, 'r', label='l2')\n", - "pl.plot(x, bary_wass, 'g', label='Wasserstein')\n", - "pl.legend()\n", - "pl.title('Barycenters')\n", - "pl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Barycentric interpolation\n", - "#############################################################################\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ + "source": [ + "\n# 1D Wasserstein barycenter demo\n\n\nThis example illustrates the computation of regularized Wassersyein Barycenter\nas proposed in [3].\n\n\n[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyr\u00e9, G. (2015).\nIterative Bregman projections for regularized transportation problems\nSIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nfrom matplotlib.collections import PolyCollection" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Generate data\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na1 = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.get_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Plot data\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, { - "data": { - "image/png": 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fh8/nQzqdhslkyou47HZ7lmAZzkIDg/IwBMpgUcitYQKyhUmSJExOTmJychKrV6/G7t27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\nvr6ef/qnfwJS51mPPvoo7777bsp+lsVi4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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, { - "data": { - "image/png": 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g448/hlarTcuI9Oqrr+LOO++EIAi4/fbbce+990bcHwgEcNttt+Hjjz9GdXU1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\nmH6W0+ns46Zrs9n6yAtBdLGzsrKSjIwMs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TBk1q1bx0svvQREFQa2bdt2QgQniI41f/TR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\nsUcynvMIGwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" + "source": [ + "Barycenter computation\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% barycenter computation\n\nalpha = 0.2 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Barycentric interpolation\n#############################################################################\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "#%% barycenter interpolation\n\nn_alpha = 11\nalpha_list = np.linspace(0, 1, n_alpha)\n\n\nB_l2 = np.zeros((n, n_alpha))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_alpha):\n alpha = alpha_list[i]\n weights = np.array([1 - alpha, alpha])\n B_l2[:, i] = A.dot(weights)\n B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)\n\n#%% plot interpolation\n\npl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_l2[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_wass[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with Wasserstein')\npl.tight_layout()\n\npl.show()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "name": "python2", + "language": "python" + }, + "language_info": { + "mimetype": "text/x-python", + "nbconvert_exporter": "python", + "name": "python", + "file_extension": ".py", + "version": "2.7.12", + "pygments_lexer": "ipython2", + "codemirror_mode": { + "version": 2, + "name": "ipython" + } } - ], - "source": [ - "#%% barycenter interpolation\n", - "\n", - "n_alpha = 11\n", - "alpha_list = np.linspace(0, 1, n_alpha)\n", - "\n", - "\n", - "B_l2 = np.zeros((n, n_alpha))\n", - "\n", - "B_wass = np.copy(B_l2)\n", - "\n", - "for i in range(0, n_alpha):\n", - " alpha = alpha_list[i]\n", - " weights = np.array([1 - alpha, alpha])\n", - " B_l2[:, i] = A.dot(weights)\n", - " B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)\n", - "\n", - "#%% plot interpolation\n", - "\n", - "pl.figure(3)\n", - "\n", - "cmap = pl.cm.get_cmap('viridis')\n", - "verts = []\n", - "zs = alpha_list\n", - "for i, z in enumerate(zs):\n", - " ys = B_l2[:, i]\n", - " verts.append(list(zip(x, ys)))\n", - "\n", - "ax = pl.gcf().gca(projection='3d')\n", - "\n", - "poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\n", - "poly.set_alpha(0.7)\n", - "ax.add_collection3d(poly, zs=zs, zdir='y')\n", - "ax.set_xlabel('x')\n", - "ax.set_xlim3d(0, n)\n", - "ax.set_ylabel('$\\\\alpha$')\n", - "ax.set_ylim3d(0, 1)\n", - "ax.set_zlabel('')\n", - "ax.set_zlim3d(0, B_l2.max() * 1.01)\n", - "pl.title('Barycenter interpolation with l2')\n", - "pl.tight_layout()\n", - "\n", - "pl.figure(4)\n", - "cmap = pl.cm.get_cmap('viridis')\n", - "verts = []\n", - "zs = alpha_list\n", - "for i, z in enumerate(zs):\n", - " ys = B_wass[:, i]\n", - " verts.append(list(zip(x, ys)))\n", - "\n", - "ax = pl.gcf().gca(projection='3d')\n", - "\n", - "poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\n", - "poly.set_alpha(0.7)\n", - "ax.add_collection3d(poly, zs=zs, zdir='y')\n", - "ax.set_xlabel('x')\n", - "ax.set_xlim3d(0, n)\n", - "ax.set_ylabel('$\\\\alpha$')\n", - "ax.set_ylim3d(0, 1)\n", - "ax.set_zlabel('')\n", - "ax.set_zlim3d(0, B_l2.max() * 1.01)\n", - "pl.title('Barycenter interpolation with Wasserstein')\n", - "pl.tight_layout()\n", - "\n", - "pl.show()" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" } - }, - "nbformat": 4, - "nbformat_minor": 0 -} +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_barycenter_1D.py b/docs/source/auto_examples/plot_barycenter_1D.py index 142b05e..eef8536 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.py +++ b/docs/source/auto_examples/plot_barycenter_1D.py @@ -4,11 +4,11 @@ 1D Wasserstein barycenter demo ============================== -This example illustrates the computation of regularized Wassersyein Barycenter +This example illustrates the computation of regularized Wassersyein Barycenter as proposed in [3]. -[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). +[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). Iterative Bregman projections for regularized transportation problems SIAM Journal on Scientific Computing, 37(2), A1111-A1138. diff --git a/docs/source/auto_examples/plot_barycenter_1D.rst b/docs/source/auto_examples/plot_barycenter_1D.rst index d3f243f..b1794cd 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.rst +++ b/docs/source/auto_examples/plot_barycenter_1D.rst @@ -7,11 +7,11 @@ 1D Wasserstein barycenter demo ============================== -This example illustrates the computation of regularized Wassersyein Barycenter +This example illustrates the computation of regularized Wassersyein Barycenter as proposed in [3]. -[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). +[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). Iterative Bregman projections for regularized transportation problems SIAM Journal on Scientific Computing, 37(2), A1111-A1138. @@ -230,7 +230,7 @@ Barycentric interpolation -**Total running time of the script:** ( 0 minutes 0.520 seconds) +**Total running time of the script:** ( 0 minutes 0.416 seconds) diff --git a/docs/source/auto_examples/plot_compute_emd.ipynb b/docs/source/auto_examples/plot_compute_emd.ipynb index b28413b..a882bd1 100644 --- a/docs/source/auto_examples/plot_compute_emd.ipynb +++ b/docs/source/auto_examples/plot_compute_emd.ipynb @@ -15,7 +15,7 @@ }, { "source": [ - "\n# Plot multiple EMD\n\n\nShows how to compute multiple EMD and Sinkhorn with two differnt \nground metrics and plot their values for diffeent distributions.\n\n\n\n" + "\n# Plot multiple EMD\n\n\nShows how to compute multiple EMD and Sinkhorn with two differnt\nground metrics and plot their values for diffeent distributions.\n\n\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_compute_emd.py b/docs/source/auto_examples/plot_compute_emd.py index b688f93..a84b249 100644 --- a/docs/source/auto_examples/plot_compute_emd.py +++ b/docs/source/auto_examples/plot_compute_emd.py @@ -4,7 +4,7 @@ Plot multiple EMD ================= -Shows how to compute multiple EMD and Sinkhorn with two differnt +Shows how to compute multiple EMD and Sinkhorn with two differnt ground metrics and plot their values for diffeent distributions. diff --git a/docs/source/auto_examples/plot_compute_emd.rst b/docs/source/auto_examples/plot_compute_emd.rst index b489255..58220a4 100644 --- a/docs/source/auto_examples/plot_compute_emd.rst +++ b/docs/source/auto_examples/plot_compute_emd.rst @@ -7,7 +7,7 @@ Plot multiple EMD ================= -Shows how to compute multiple EMD and Sinkhorn with two differnt +Shows how to compute multiple EMD and Sinkhorn with two differnt ground metrics and plot their values for diffeent distributions. @@ -162,7 +162,7 @@ Compute Sinkhorn for the different losses -**Total running time of the script:** ( 0 minutes 0.427 seconds) +**Total running time of the script:** ( 0 minutes 0.471 seconds) diff --git a/docs/source/auto_examples/plot_optim_OTreg.ipynb b/docs/source/auto_examples/plot_optim_OTreg.ipynb index 290100f..f9fec33 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.ipynb +++ b/docs/source/auto_examples/plot_optim_OTreg.ipynb @@ -15,7 +15,7 @@ }, { "source": [ - "\n# Regularized OT with generic solver\n\n\nIllustrates the use of the generic solver for regularized OT with\nuser-designed regularization term. It uses Conditional gradient as in [6] and \ngeneralized Conditional Gradient as proposed in [5][7].\n\n\n[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for \nDomain Adaptation, in IEEE Transactions on Pattern Analysis and Machine \nIntelligence , vol.PP, no.99, pp.1-1.\n\n[6] Ferradans, S., Papadakis, N., Peyr\u00e9, G., & Aujol, J. F. (2014). \nRegularized discrete optimal transport. SIAM Journal on Imaging Sciences, \n7(3), 1853-1882.\n\n[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized \nconditional gradient: analysis of convergence and applications. \narXiv preprint arXiv:1510.06567.\n\n\n\n\n" + "\n# Regularized OT with generic solver\n\n\nIllustrates the use of the generic solver for regularized OT with\nuser-designed regularization term. It uses Conditional gradient as in [6] and\ngeneralized Conditional Gradient as proposed in [5][7].\n\n\n[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for\nDomain Adaptation, in IEEE Transactions on Pattern Analysis and Machine\nIntelligence , vol.PP, no.99, pp.1-1.\n\n[6] Ferradans, S., Papadakis, N., Peyr\u00e9, G., & Aujol, J. F. (2014).\nRegularized discrete optimal transport. SIAM Journal on Imaging Sciences,\n7(3), 1853-1882.\n\n[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized\nconditional gradient: analysis of convergence and applications.\narXiv preprint arXiv:1510.06567.\n\n\n\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_optim_OTreg.py b/docs/source/auto_examples/plot_optim_OTreg.py index b362662..d753414 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.py +++ b/docs/source/auto_examples/plot_optim_OTreg.py @@ -5,20 +5,20 @@ Regularized OT with generic solver ================================== Illustrates the use of the generic solver for regularized OT with -user-designed regularization term. It uses Conditional gradient as in [6] and +user-designed regularization term. It uses Conditional gradient as in [6] and generalized Conditional Gradient as proposed in [5][7]. -[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for -Domain Adaptation, in IEEE Transactions on Pattern Analysis and Machine +[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for +Domain Adaptation, in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1. -[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). -Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, +[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). +Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. -[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized -conditional gradient: analysis of convergence and applications. +[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized +conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567. diff --git a/docs/source/auto_examples/plot_optim_OTreg.rst b/docs/source/auto_examples/plot_optim_OTreg.rst index d444631..c3ec03b 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.rst +++ b/docs/source/auto_examples/plot_optim_OTreg.rst @@ -8,20 +8,20 @@ Regularized OT with generic solver ================================== Illustrates the use of the generic solver for regularized OT with -user-designed regularization term. It uses Conditional gradient as in [6] and +user-designed regularization term. It uses Conditional gradient as in [6] and generalized Conditional Gradient as proposed in [5][7]. -[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for -Domain Adaptation, in IEEE Transactions on Pattern Analysis and Machine +[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for +Domain Adaptation, in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1. -[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). -Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, +[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). +Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. -[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized -conditional gradient: analysis of convergence and applications. +[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized +conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567. @@ -667,7 +667,7 @@ Solve EMD with Frobenius norm + entropic regularization 4|1.609284e-01|-1.111407e-12 -**Total running time of the script:** ( 0 minutes 1.867 seconds) +**Total running time of the script:** ( 0 minutes 1.913 seconds) diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb b/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb index c8c1d95..3b3987a 100644 --- a/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb +++ b/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb @@ -15,7 +15,7 @@ }, { "source": [ - "\n# OT for image color adaptation with mapping estimation \n\n\nOT for domain adaptation with image color adaptation [6] with mapping \nestimation [8].\n\n[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized\n discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3),\n 1853-1882.\n[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, \"Mapping estimation for\n discrete optimal transport\", Neural Information Processing Systems (NIPS),\n 2016.\n\n\n" + "\n# OT for image color adaptation with mapping estimation\n\n\nOT for domain adaptation with image color adaptation [6] with mapping\nestimation [8].\n\n[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized\n discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3),\n 1853-1882.\n[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, \"Mapping estimation for\n discrete optimal transport\", Neural Information Processing Systems (NIPS),\n 2016.\n\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.py b/docs/source/auto_examples/plot_otda_mapping_colors_images.py index 162c24b..5590286 100644 --- a/docs/source/auto_examples/plot_otda_mapping_colors_images.py +++ b/docs/source/auto_examples/plot_otda_mapping_colors_images.py @@ -1,10 +1,10 @@ # -*- coding: utf-8 -*- """ ===================================================== -OT for image color adaptation with mapping estimation +OT for image color adaptation with mapping estimation ===================================================== -OT for domain adaptation with image color adaptation [6] with mapping +OT for domain adaptation with image color adaptation [6] with mapping estimation [8]. [6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.rst b/docs/source/auto_examples/plot_otda_mapping_colors_images.rst index 29823f1..9995103 100644 --- a/docs/source/auto_examples/plot_otda_mapping_colors_images.rst +++ b/docs/source/auto_examples/plot_otda_mapping_colors_images.rst @@ -4,10 +4,10 @@ ===================================================== -OT for image color adaptation with mapping estimation +OT for image color adaptation with mapping estimation ===================================================== -OT for domain adaptation with image color adaptation [6] with mapping +OT for domain adaptation with image color adaptation [6] with mapping estimation [8]. [6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized @@ -283,7 +283,7 @@ Plot transformed images -**Total running time of the script:** ( 2 minutes 12.535 seconds) +**Total running time of the script:** ( 2 minutes 8.746 seconds) diff --git a/examples/README.txt b/examples/README.txt index c3d556d..b08d3f1 100644 --- a/examples/README.txt +++ b/examples/README.txt @@ -1,4 +1,4 @@ POT Examples ============ -This is a gallery of all the POT example files. +This is a gallery of all the POT example files. diff --git a/examples/plot_OT_1D.py b/examples/plot_OT_1D.py index a63f29a..b6ffa5f 100644 --- a/examples/plot_OT_1D.py +++ b/examples/plot_OT_1D.py @@ -4,7 +4,7 @@ 1D optimal transport ==================== -This example illustrates the computation of EMD and Sinkhorn transport plans +This example illustrates the computation of EMD and Sinkhorn transport plans and their visualization. """ @@ -20,7 +20,8 @@ from ot.datasets import get_1D_gauss as gauss ############################################################################## # Generate data -############################################################################## +# ############# + #%% parameters @@ -40,7 +41,7 @@ M /= M.max() ############################################################################## # Plot distributions and loss matrix -############################################################################## +################################### #%% plot the distributions diff --git a/examples/plot_OT_L1_vs_L2.py b/examples/plot_OT_L1_vs_L2.py index 77bde22..49d37e1 100644 --- a/examples/plot_OT_L1_vs_L2.py +++ b/examples/plot_OT_L1_vs_L2.py @@ -76,7 +76,6 @@ pl.tight_layout() ############################################################################## - #%% EMD G1 = ot.emd(a, b, M1) G2 = ot.emd(a, b, M2) @@ -172,7 +171,6 @@ pl.tight_layout() ############################################################################## - #%% EMD G1 = ot.emd(a, b, M1) G2 = ot.emd(a, b, M2) diff --git a/examples/plot_WDA.py b/examples/plot_WDA.py index 06a2e38..5928621 100644 --- a/examples/plot_WDA.py +++ b/examples/plot_WDA.py @@ -7,7 +7,7 @@ Wasserstein Discriminant Analysis This example illustrate the use of WDA as proposed in [11]. -[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). +[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). Wasserstein Discriminant Analysis. """ diff --git a/examples/plot_barycenter_1D.py b/examples/plot_barycenter_1D.py index 142b05e..eef8536 100644 --- a/examples/plot_barycenter_1D.py +++ b/examples/plot_barycenter_1D.py @@ -4,11 +4,11 @@ 1D Wasserstein barycenter demo ============================== -This example illustrates the computation of regularized Wassersyein Barycenter +This example illustrates the computation of regularized Wassersyein Barycenter as proposed in [3]. -[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). +[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). Iterative Bregman projections for regularized transportation problems SIAM Journal on Scientific Computing, 37(2), A1111-A1138. diff --git a/examples/plot_compute_emd.py b/examples/plot_compute_emd.py index b688f93..a84b249 100644 --- a/examples/plot_compute_emd.py +++ b/examples/plot_compute_emd.py @@ -4,7 +4,7 @@ Plot multiple EMD ================= -Shows how to compute multiple EMD and Sinkhorn with two differnt +Shows how to compute multiple EMD and Sinkhorn with two differnt ground metrics and plot their values for diffeent distributions. diff --git a/examples/plot_optim_OTreg.py b/examples/plot_optim_OTreg.py index b362662..d753414 100644 --- a/examples/plot_optim_OTreg.py +++ b/examples/plot_optim_OTreg.py @@ -5,20 +5,20 @@ Regularized OT with generic solver ================================== Illustrates the use of the generic solver for regularized OT with -user-designed regularization term. It uses Conditional gradient as in [6] and +user-designed regularization term. It uses Conditional gradient as in [6] and generalized Conditional Gradient as proposed in [5][7]. -[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for -Domain Adaptation, in IEEE Transactions on Pattern Analysis and Machine +[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for +Domain Adaptation, in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1. -[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). -Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, +[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). +Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882. -[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized -conditional gradient: analysis of convergence and applications. +[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized +conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567. diff --git a/examples/plot_otda_mapping_colors_images.py b/examples/plot_otda_mapping_colors_images.py index 162c24b..5590286 100644 --- a/examples/plot_otda_mapping_colors_images.py +++ b/examples/plot_otda_mapping_colors_images.py @@ -1,10 +1,10 @@ # -*- coding: utf-8 -*- """ ===================================================== -OT for image color adaptation with mapping estimation +OT for image color adaptation with mapping estimation ===================================================== -OT for domain adaptation with image color adaptation [6] with mapping +OT for domain adaptation with image color adaptation [6] with mapping estimation [8]. [6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized -- cgit v1.2.3 From e800103299d79cf462c89f34647e7741014c5571 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 1 Sep 2017 17:01:04 +0200 Subject: test nb --- docs/source/auto_examples/auto_examples_jupyter.zip | Bin 70289 -> 70289 bytes docs/source/auto_examples/auto_examples_python.zip | Bin 46532 -> 46532 bytes docs/source/auto_examples/plot_OT_1D.ipynb | 2 +- docs/source/auto_examples/plot_OT_1D.py | 2 +- docs/source/auto_examples/plot_OT_1D.rst | 4 ++-- 5 files changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/source/auto_examples/auto_examples_jupyter.zip b/docs/source/auto_examples/auto_examples_jupyter.zip index 4ec0672..f7698ad 100644 Binary files a/docs/source/auto_examples/auto_examples_jupyter.zip and b/docs/source/auto_examples/auto_examples_jupyter.zip differ diff --git a/docs/source/auto_examples/auto_examples_python.zip b/docs/source/auto_examples/auto_examples_python.zip index 4fbf223..6a8e449 100644 Binary files a/docs/source/auto_examples/auto_examples_python.zip and b/docs/source/auto_examples/auto_examples_python.zip differ diff --git a/docs/source/auto_examples/plot_OT_1D.ipynb b/docs/source/auto_examples/plot_OT_1D.ipynb index 1f08677..c8925ac 100644 --- a/docs/source/auto_examples/plot_OT_1D.ipynb +++ b/docs/source/auto_examples/plot_OT_1D.ipynb @@ -33,7 +33,7 @@ }, { "source": [ - "Generate data\n#############\n\n" + "Generate data\n-------------\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_OT_1D.py b/docs/source/auto_examples/plot_OT_1D.py index b6ffa5f..36f823f 100644 --- a/docs/source/auto_examples/plot_OT_1D.py +++ b/docs/source/auto_examples/plot_OT_1D.py @@ -20,7 +20,7 @@ from ot.datasets import get_1D_gauss as gauss ############################################################################## # Generate data -# ############# +# ------------- #%% parameters diff --git a/docs/source/auto_examples/plot_OT_1D.rst b/docs/source/auto_examples/plot_OT_1D.rst index 484455f..32a88e7 100644 --- a/docs/source/auto_examples/plot_OT_1D.rst +++ b/docs/source/auto_examples/plot_OT_1D.rst @@ -32,7 +32,7 @@ and their visualization. Generate data -############# +------------- @@ -169,7 +169,7 @@ Solve Sinkhorn 110|1.527180e-10| -**Total running time of the script:** ( 0 minutes 0.704 seconds) +**Total running time of the script:** ( 0 minutes 0.754 seconds) -- cgit v1.2.3 From fd76b98726b8f22606d93bb24dc539967472f4a0 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 1 Sep 2017 17:38:16 +0200 Subject: titlke for notebooks --- examples/plot_OT_1D.py | 10 ++++++---- examples/plot_OT_2D_samples.py | 8 ++++---- examples/plot_OT_L1_vs_L2.py | 8 ++++---- examples/plot_WDA.py | 10 +++++----- examples/plot_barycenter_1D.py | 8 ++++---- examples/plot_compute_emd.py | 8 ++++---- examples/plot_optim_OTreg.py | 10 +++++----- examples/plot_otda_classes.py | 10 +++++----- examples/plot_otda_color_images.py | 10 +++++----- examples/plot_otda_d2.py | 17 ++++++++--------- examples/plot_otda_mapping.py | 8 ++++---- examples/plot_otda_mapping_colors_images.py | 10 +++++----- 12 files changed, 59 insertions(+), 58 deletions(-) diff --git a/examples/plot_OT_1D.py b/examples/plot_OT_1D.py index b6ffa5f..719058f 100644 --- a/examples/plot_OT_1D.py +++ b/examples/plot_OT_1D.py @@ -20,7 +20,7 @@ from ot.datasets import get_1D_gauss as gauss ############################################################################## # Generate data -# ############# +# ------------- #%% parameters @@ -41,7 +41,7 @@ M /= M.max() ############################################################################## # Plot distributions and loss matrix -################################### +# ---------------------------------- #%% plot the distributions @@ -57,7 +57,8 @@ ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') ############################################################################## # Solve EMD -############################################################################## +# --------- + #%% EMD @@ -68,7 +69,8 @@ ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') ############################################################################## # Solve Sinkhorn -############################################################################## +# -------------- + #%% Sinkhorn diff --git a/examples/plot_OT_2D_samples.py b/examples/plot_OT_2D_samples.py index f57d631..9818ec5 100644 --- a/examples/plot_OT_2D_samples.py +++ b/examples/plot_OT_2D_samples.py @@ -19,7 +19,7 @@ import ot ############################################################################## # Generate data -############################################################################## +# ------------- #%% parameters and data generation @@ -42,7 +42,7 @@ M /= M.max() ############################################################################## # Plot data -############################################################################## +# --------- #%% plot samples @@ -58,7 +58,7 @@ pl.title('Cost matrix M') ############################################################################## # Compute EMD -############################################################################## +# ----------- #%% EMD @@ -78,7 +78,7 @@ pl.title('OT matrix with samples') ############################################################################## # Compute Sinkhorn -############################################################################## +# ---------------- #%% sinkhorn diff --git a/examples/plot_OT_L1_vs_L2.py b/examples/plot_OT_L1_vs_L2.py index 49d37e1..090e809 100644 --- a/examples/plot_OT_L1_vs_L2.py +++ b/examples/plot_OT_L1_vs_L2.py @@ -22,7 +22,7 @@ import ot ############################################################################## # Dataset 1 : uniform sampling -############################################################################## +# ---------------------------- n = 20 # nb samples xs = np.zeros((n, 2)) @@ -73,7 +73,7 @@ pl.tight_layout() ############################################################################## # Dataset 1 : Plot OT Matrices -############################################################################## +# ---------------------------- #%% EMD @@ -114,7 +114,7 @@ pl.show() ############################################################################## # Dataset 2 : Partial circle -############################################################################## +# -------------------------- n = 50 # nb samples xtot = np.zeros((n + 1, 2)) @@ -168,7 +168,7 @@ pl.tight_layout() ############################################################################## # Dataset 2 : Plot OT Matrices -############################################################################## +# ----------------------------- #%% EMD diff --git a/examples/plot_WDA.py b/examples/plot_WDA.py index 5928621..93cc237 100644 --- a/examples/plot_WDA.py +++ b/examples/plot_WDA.py @@ -24,7 +24,7 @@ from ot.dr import wda, fda ############################################################################## # Generate data -############################################################################## +# ------------- #%% parameters @@ -51,7 +51,7 @@ xt = np.hstack((xt, np.random.randn(n, nbnoise))) ############################################################################## # Plot data -############################################################################## +# --------- #%% plot samples pl.figure(1, figsize=(6.4, 3.5)) @@ -69,7 +69,7 @@ pl.tight_layout() ############################################################################## # Compute Fisher Discriminant Analysis -############################################################################## +# ------------------------------------ #%% Compute FDA p = 2 @@ -78,7 +78,7 @@ Pfda, projfda = fda(xs, ys, p) ############################################################################## # Compute Wasserstein Discriminant Analysis -############################################################################## +# ----------------------------------------- #%% Compute WDA p = 2 @@ -91,7 +91,7 @@ Pwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter) ############################################################################## # Plot 2D projections -############################################################################## +# ------------------- #%% plot samples diff --git a/examples/plot_barycenter_1D.py b/examples/plot_barycenter_1D.py index eef8536..620936b 100644 --- a/examples/plot_barycenter_1D.py +++ b/examples/plot_barycenter_1D.py @@ -27,7 +27,7 @@ from matplotlib.collections import PolyCollection ############################################################################## # Generate data -############################################################################## +# ------------- #%% parameters @@ -50,7 +50,7 @@ M /= M.max() ############################################################################## # Plot data -############################################################################## +# --------- #%% plot the distributions @@ -62,7 +62,7 @@ pl.tight_layout() ############################################################################## # Barycenter computation -############################################################################## +# ---------------------- #%% barycenter computation @@ -92,7 +92,7 @@ pl.tight_layout() ############################################################################## # Barycentric interpolation -############################################################################## +# ------------------------- #%% barycenter interpolation diff --git a/examples/plot_compute_emd.py b/examples/plot_compute_emd.py index a84b249..73b42c3 100644 --- a/examples/plot_compute_emd.py +++ b/examples/plot_compute_emd.py @@ -22,7 +22,7 @@ from ot.datasets import get_1D_gauss as gauss ############################################################################## # Generate data -############################################################################## +# ------------- #%% parameters @@ -51,7 +51,7 @@ M2 /= M2.max() ############################################################################## # Plot data -############################################################################## +# --------- #%% plot the distributions @@ -67,7 +67,7 @@ pl.tight_layout() ############################################################################## # Compute EMD for the different losses -############################################################################## +# ------------------------------------ #%% Compute and plot distributions and loss matrix @@ -83,7 +83,7 @@ pl.legend() ############################################################################## # Compute Sinkhorn for the different losses -############################################################################## +# ----------------------------------------- #%% reg = 1e-2 diff --git a/examples/plot_optim_OTreg.py b/examples/plot_optim_OTreg.py index d753414..e1a737e 100644 --- a/examples/plot_optim_OTreg.py +++ b/examples/plot_optim_OTreg.py @@ -32,7 +32,7 @@ import ot ############################################################################## # Generate data -############################################################################## +# ------------- #%% parameters @@ -51,7 +51,7 @@ M /= M.max() ############################################################################## # Solve EMD -############################################################################## +# --------- #%% EMD @@ -62,7 +62,7 @@ ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') ############################################################################## # Solve EMD with Frobenius norm regularization -############################################################################## +# -------------------------------------------- #%% Example with Frobenius norm regularization @@ -84,7 +84,7 @@ ot.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg') ############################################################################## # Solve EMD with entropic regularization -############################################################################## +# -------------------------------------- #%% Example with entropic regularization @@ -106,7 +106,7 @@ ot.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg') ############################################################################## # Solve EMD with Frobenius norm + entropic regularization -############################################################################## +# ------------------------------------------------------- #%% Example with Frobenius norm + entropic regularization with gcg diff --git a/examples/plot_otda_classes.py b/examples/plot_otda_classes.py index ec57a37..b14c11a 100644 --- a/examples/plot_otda_classes.py +++ b/examples/plot_otda_classes.py @@ -19,8 +19,8 @@ import ot ############################################################################## -# generate data -############################################################################## +# Generate data +# ------------- n_source_samples = 150 n_target_samples = 150 @@ -31,7 +31,7 @@ Xt, yt = ot.datasets.get_data_classif('3gauss2', n_target_samples) ############################################################################## # Instantiate the different transport algorithms and fit them -############################################################################## +# ----------------------------------------------------------- # EMD Transport ot_emd = ot.da.EMDTransport() @@ -59,7 +59,7 @@ transp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs) ############################################################################## # Fig 1 : plots source and target samples -############################################################################## +# --------------------------------------- pl.figure(1, figsize=(10, 5)) pl.subplot(1, 2, 1) @@ -80,7 +80,7 @@ pl.tight_layout() ############################################################################## # Fig 2 : plot optimal couplings and transported samples -############################################################################## +# ------------------------------------------------------ param_img = {'interpolation': 'nearest', 'cmap': 'spectral'} diff --git a/examples/plot_otda_color_images.py b/examples/plot_otda_color_images.py index f1df9d9..e77aec0 100644 --- a/examples/plot_otda_color_images.py +++ b/examples/plot_otda_color_images.py @@ -42,7 +42,7 @@ def minmax(I): ############################################################################## # Generate data -############################################################################## +# ------------- # Loading images I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256 @@ -62,7 +62,7 @@ Xt = X2[idx2, :] ############################################################################## # Plot original image -############################################################################## +# ------------------- pl.figure(1, figsize=(6.4, 3)) @@ -79,7 +79,7 @@ pl.title('Image 2') ############################################################################## # Scatter plot of colors -############################################################################## +# ---------------------- pl.figure(2, figsize=(6.4, 3)) @@ -101,7 +101,7 @@ pl.tight_layout() ############################################################################## # Instantiate the different transport algorithms and fit them -############################################################################## +# ----------------------------------------------------------- # EMDTransport ot_emd = ot.da.EMDTransport() @@ -127,7 +127,7 @@ I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape)) ############################################################################## # Plot new images -############################################################################## +# --------------- pl.figure(3, figsize=(8, 4)) diff --git a/examples/plot_otda_d2.py b/examples/plot_otda_d2.py index 3daa0a6..e53d7d6 100644 --- a/examples/plot_otda_d2.py +++ b/examples/plot_otda_d2.py @@ -1,8 +1,8 @@ # -*- coding: utf-8 -*- """ -============================== -OT for empirical distributions -============================== +=================================================== +OT for domain adaptation on empirical distributions +=================================================== This example introduces a domain adaptation in a 2D setting. It explicits the problem of domain adaptation and introduces some optimal transport @@ -24,7 +24,7 @@ import ot ############################################################################## # generate data -############################################################################## +# ------------- n_samples_source = 150 n_samples_target = 150 @@ -38,7 +38,7 @@ M = ot.dist(Xs, Xt, metric='sqeuclidean') ############################################################################## # Instantiate the different transport algorithms and fit them -############################################################################## +# ----------------------------------------------------------- # EMD Transport ot_emd = ot.da.EMDTransport() @@ -60,7 +60,7 @@ transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs) ############################################################################## # Fig 1 : plots source and target samples + matrix of pairwise distance -############################################################################## +# --------------------------------------------------------------------- pl.figure(1, figsize=(10, 10)) pl.subplot(2, 2, 1) @@ -87,8 +87,7 @@ pl.tight_layout() ############################################################################## # Fig 2 : plots optimal couplings for the different methods -############################################################################## - +# --------------------------------------------------------- pl.figure(2, figsize=(10, 6)) pl.subplot(2, 3, 1) @@ -137,7 +136,7 @@ pl.tight_layout() ############################################################################## # Fig 3 : plot transported samples -############################################################################## +# -------------------------------- # display transported samples pl.figure(4, figsize=(10, 4)) diff --git a/examples/plot_otda_mapping.py b/examples/plot_otda_mapping.py index e78fef4..167c3a1 100644 --- a/examples/plot_otda_mapping.py +++ b/examples/plot_otda_mapping.py @@ -25,7 +25,7 @@ import ot ############################################################################## # Generate data -############################################################################## +# ------------- n_source_samples = 100 n_target_samples = 100 @@ -45,7 +45,7 @@ Xt = Xt + 4 ############################################################################## # Plot data -############################################################################## +# --------- pl.figure(1, (10, 5)) pl.clf() @@ -57,7 +57,7 @@ pl.title('Source and target distributions') ############################################################################## # Instantiate the different transport algorithms and fit them -############################################################################## +# ----------------------------------------------------------- # MappingTransport with linear kernel ot_mapping_linear = ot.da.MappingTransport( @@ -88,7 +88,7 @@ transp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new) ############################################################################## # Plot transported samples -############################################################################## +# ------------------------ pl.figure(2) pl.clf() diff --git a/examples/plot_otda_mapping_colors_images.py b/examples/plot_otda_mapping_colors_images.py index 5590286..5f1e844 100644 --- a/examples/plot_otda_mapping_colors_images.py +++ b/examples/plot_otda_mapping_colors_images.py @@ -45,7 +45,7 @@ def minmax(I): ############################################################################## # Generate data -############################################################################## +# ------------- # Loading images I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256 @@ -66,7 +66,7 @@ Xt = X2[idx2, :] ############################################################################## # Domain adaptation for pixel distribution transfer -############################################################################## +# ------------------------------------------------- # EMDTransport ot_emd = ot.da.EMDTransport() @@ -97,7 +97,7 @@ Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape)) ############################################################################## # Plot original images -############################################################################## +# -------------------- pl.figure(1, figsize=(6.4, 3)) pl.subplot(1, 2, 1) @@ -114,7 +114,7 @@ pl.tight_layout() ############################################################################## # Plot pixel values distribution -############################################################################## +# ------------------------------ pl.figure(2, figsize=(6.4, 5)) @@ -136,7 +136,7 @@ pl.tight_layout() ############################################################################## # Plot transformed images -############################################################################## +# ----------------------- pl.figure(2, figsize=(10, 5)) -- cgit v1.2.3 From 8ea3504c3bfe9c9440982f8ad0d172a240d54aff Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 1 Sep 2017 17:55:32 +0200 Subject: good notebook format generated by sphinx gallery --- docs/nb_build | 3 +- .../source/auto_examples/auto_examples_jupyter.zip | Bin 70289 -> 67774 bytes docs/source/auto_examples/auto_examples_python.zip | Bin 46532 -> 44112 bytes .../images/sphx_glr_plot_OT_2D_samples_001.png | Bin 20707 -> 22153 bytes .../images/sphx_glr_plot_OT_2D_samples_002.png | Bin 21335 -> 21589 bytes .../images/sphx_glr_plot_OT_2D_samples_005.png | Bin 9613 -> 9645 bytes .../images/sphx_glr_plot_OT_2D_samples_006.png | Bin 83657 -> 91095 bytes .../images/sphx_glr_plot_OT_2D_samples_009.png | Bin 14178 -> 13987 bytes 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.../images/thumb/sphx_glr_plot_otda_d2_thumb.png | Bin 54746 -> 53014 bytes .../thumb/sphx_glr_plot_otda_mapping_thumb.png | Bin 19281 -> 18473 bytes docs/source/auto_examples/plot_OT_1D.ipynb | 6 +- docs/source/auto_examples/plot_OT_1D.py | 8 +- docs/source/auto_examples/plot_OT_1D.rst | 10 ++- docs/source/auto_examples/plot_OT_2D_samples.ipynb | 8 +- docs/source/auto_examples/plot_OT_2D_samples.py | 8 +- docs/source/auto_examples/plot_OT_2D_samples.rst | 10 +-- docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb | 8 +- docs/source/auto_examples/plot_OT_L1_vs_L2.py | 8 +- docs/source/auto_examples/plot_OT_L1_vs_L2.rst | 10 +-- docs/source/auto_examples/plot_WDA.ipynb | 10 +-- docs/source/auto_examples/plot_WDA.py | 10 +-- docs/source/auto_examples/plot_WDA.rst | 82 ++++++--------------- docs/source/auto_examples/plot_barycenter_1D.ipynb | 8 +- docs/source/auto_examples/plot_barycenter_1D.py | 8 +- docs/source/auto_examples/plot_barycenter_1D.rst | 10 +-- docs/source/auto_examples/plot_compute_emd.ipynb | 8 +- docs/source/auto_examples/plot_compute_emd.py | 8 +- docs/source/auto_examples/plot_compute_emd.rst | 10 +-- docs/source/auto_examples/plot_optim_OTreg.ipynb | 10 +-- docs/source/auto_examples/plot_optim_OTreg.py | 10 +-- docs/source/auto_examples/plot_optim_OTreg.rst | 12 +-- docs/source/auto_examples/plot_otda_classes.ipynb | 8 +- docs/source/auto_examples/plot_otda_classes.py | 10 +-- docs/source/auto_examples/plot_otda_classes.rst | 54 +++++++------- .../auto_examples/plot_otda_color_images.ipynb | 10 +-- .../source/auto_examples/plot_otda_color_images.py | 10 +-- .../auto_examples/plot_otda_color_images.rst | 12 +-- docs/source/auto_examples/plot_otda_d2.ipynb | 12 +-- docs/source/auto_examples/plot_otda_d2.py | 17 ++--- docs/source/auto_examples/plot_otda_d2.rst | 19 +++-- docs/source/auto_examples/plot_otda_mapping.ipynb | 8 +- docs/source/auto_examples/plot_otda_mapping.py | 8 +- docs/source/auto_examples/plot_otda_mapping.rst | 44 ++++++----- .../plot_otda_mapping_colors_images.ipynb | 10 +-- .../plot_otda_mapping_colors_images.py | 10 +-- .../plot_otda_mapping_colors_images.rst | 12 +-- docs/source/conf.py | 2 +- 60 files changed, 235 insertions(+), 276 deletions(-) diff --git a/docs/nb_build b/docs/nb_build index 979a913..7f74c70 100755 --- a/docs/nb_build +++ b/docs/nb_build @@ -11,5 +11,4 @@ make html sed -i "s/'sphinx\_gallery/#'sphinx\_gallery/" source/conf.py sed -i "s/#sys.modules.update/sys.modules.update/" source/conf.py - - +#rsync --out-format="%n" --update source/auto_examples/*.ipynb ../notebooks2 diff --git a/docs/source/auto_examples/auto_examples_jupyter.zip b/docs/source/auto_examples/auto_examples_jupyter.zip index f7698ad..fc1d4de 100644 Binary files a/docs/source/auto_examples/auto_examples_jupyter.zip and b/docs/source/auto_examples/auto_examples_jupyter.zip differ diff --git a/docs/source/auto_examples/auto_examples_python.zip 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100644 Binary files a/docs/source/auto_examples/images/thumb/sphx_glr_plot_otda_mapping_thumb.png and b/docs/source/auto_examples/images/thumb/sphx_glr_plot_otda_mapping_thumb.png differ diff --git a/docs/source/auto_examples/plot_OT_1D.ipynb b/docs/source/auto_examples/plot_OT_1D.ipynb index c8925ac..26748c2 100644 --- a/docs/source/auto_examples/plot_OT_1D.ipynb +++ b/docs/source/auto_examples/plot_OT_1D.ipynb @@ -51,7 +51,7 @@ }, { "source": [ - "Plot distributions and loss matrix\n##################################\n\n" + "Plot distributions and loss matrix\n----------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -69,7 +69,7 @@ }, { "source": [ - "Solve EMD\n#############################################################################\n\n" + "Solve EMD\n---------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -87,7 +87,7 @@ }, { "source": [ - "Solve Sinkhorn\n#############################################################################\n\n" + "Solve Sinkhorn\n--------------\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_OT_1D.py b/docs/source/auto_examples/plot_OT_1D.py index 36f823f..719058f 100644 --- a/docs/source/auto_examples/plot_OT_1D.py +++ b/docs/source/auto_examples/plot_OT_1D.py @@ -41,7 +41,7 @@ M /= M.max() ############################################################################## # Plot distributions and loss matrix -################################### +# ---------------------------------- #%% plot the distributions @@ -57,7 +57,8 @@ ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') ############################################################################## # Solve EMD -############################################################################## +# --------- + #%% EMD @@ -68,7 +69,8 @@ ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') ############################################################################## # Solve Sinkhorn -############################################################################## +# -------------- + #%% Sinkhorn diff --git a/docs/source/auto_examples/plot_OT_1D.rst b/docs/source/auto_examples/plot_OT_1D.rst index 32a88e7..b91916e 100644 --- a/docs/source/auto_examples/plot_OT_1D.rst +++ b/docs/source/auto_examples/plot_OT_1D.rst @@ -63,7 +63,7 @@ Generate data Plot distributions and loss matrix -################################## +---------------------------------- @@ -102,13 +102,14 @@ Plot distributions and loss matrix Solve EMD -############################################################################# +--------- .. code-block:: python + #%% EMD G0 = ot.emd(a, b, M) @@ -126,13 +127,14 @@ Solve EMD Solve Sinkhorn -############################################################################# +-------------- .. code-block:: python + #%% Sinkhorn lambd = 1e-3 @@ -169,7 +171,7 @@ Solve Sinkhorn 110|1.527180e-10| -**Total running time of the script:** ( 0 minutes 0.754 seconds) +**Total running time of the script:** ( 0 minutes 0.748 seconds) diff --git a/docs/source/auto_examples/plot_OT_2D_samples.ipynb b/docs/source/auto_examples/plot_OT_2D_samples.ipynb index 0ed7367..41a37f3 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.ipynb +++ b/docs/source/auto_examples/plot_OT_2D_samples.ipynb @@ -33,7 +33,7 @@ }, { "source": [ - "Generate data\n#############################################################################\n\n" + "Generate data\n-------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -51,7 +51,7 @@ }, { "source": [ - "Plot data\n#############################################################################\n\n" + "Plot data\n---------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -69,7 +69,7 @@ }, { "source": [ - "Compute EMD\n#############################################################################\n\n" + "Compute EMD\n-----------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -87,7 +87,7 @@ }, { "source": [ - "Compute Sinkhorn\n#############################################################################\n\n" + "Compute Sinkhorn\n----------------\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_OT_2D_samples.py b/docs/source/auto_examples/plot_OT_2D_samples.py index f57d631..9818ec5 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.py +++ b/docs/source/auto_examples/plot_OT_2D_samples.py @@ -19,7 +19,7 @@ import ot ############################################################################## # Generate data -############################################################################## +# ------------- #%% parameters and data generation @@ -42,7 +42,7 @@ M /= M.max() ############################################################################## # Plot data -############################################################################## +# --------- #%% plot samples @@ -58,7 +58,7 @@ pl.title('Cost matrix M') ############################################################################## # Compute EMD -############################################################################## +# ----------- #%% EMD @@ -78,7 +78,7 @@ pl.title('OT matrix with samples') ############################################################################## # Compute Sinkhorn -############################################################################## +# ---------------- #%% sinkhorn diff --git a/docs/source/auto_examples/plot_OT_2D_samples.rst b/docs/source/auto_examples/plot_OT_2D_samples.rst index f95ffaf..0ad9cf0 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.rst +++ b/docs/source/auto_examples/plot_OT_2D_samples.rst @@ -31,7 +31,7 @@ sum of diracs. The OT matrix is plotted with the samples. Generate data -############################################################################# +------------- @@ -64,7 +64,7 @@ Generate data Plot data -############################################################################# +--------- @@ -103,7 +103,7 @@ Plot data Compute EMD -############################################################################# +----------- @@ -146,7 +146,7 @@ Compute EMD Compute Sinkhorn -############################################################################# +---------------- @@ -191,7 +191,7 @@ Compute Sinkhorn -**Total running time of the script:** ( 0 minutes 1.990 seconds) +**Total running time of the script:** ( 0 minutes 1.743 seconds) diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb b/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb index e738db7..2b9a364 100644 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb @@ -33,7 +33,7 @@ }, { "source": [ - "Dataset 1 : uniform sampling\n#############################################################################\n\n" + "Dataset 1 : uniform sampling\n----------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -51,7 +51,7 @@ }, { "source": [ - "Dataset 1 : Plot OT Matrices\n#############################################################################\n\n" + "Dataset 1 : Plot OT Matrices\n----------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -69,7 +69,7 @@ }, { "source": [ - "Dataset 2 : Partial circle\n#############################################################################\n\n" + "Dataset 2 : Partial circle\n--------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -87,7 +87,7 @@ }, { "source": [ - "Dataset 2 : Plot OT Matrices\n#############################################################################\n\n" + "Dataset 2 : Plot OT Matrices\n-----------------------------\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.py b/docs/source/auto_examples/plot_OT_L1_vs_L2.py index 49d37e1..090e809 100644 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.py +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.py @@ -22,7 +22,7 @@ import ot ############################################################################## # Dataset 1 : uniform sampling -############################################################################## +# ---------------------------- n = 20 # nb samples xs = np.zeros((n, 2)) @@ -73,7 +73,7 @@ pl.tight_layout() ############################################################################## # Dataset 1 : Plot OT Matrices -############################################################################## +# ---------------------------- #%% EMD @@ -114,7 +114,7 @@ pl.show() ############################################################################## # Dataset 2 : Partial circle -############################################################################## +# -------------------------- n = 50 # nb samples xtot = np.zeros((n + 1, 2)) @@ -168,7 +168,7 @@ pl.tight_layout() ############################################################################## # Dataset 2 : Plot OT Matrices -############################################################################## +# ----------------------------- #%% EMD diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst index 8b5b133..f97b373 100644 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst @@ -34,7 +34,7 @@ https://arxiv.org/pdf/1706.07650.pdf Dataset 1 : uniform sampling -############################################################################# +---------------------------- @@ -108,7 +108,7 @@ Dataset 1 : uniform sampling Dataset 1 : Plot OT Matrices -############################################################################# +---------------------------- @@ -162,7 +162,7 @@ Dataset 1 : Plot OT Matrices Dataset 2 : Partial circle -############################################################################# +-------------------------- @@ -239,7 +239,7 @@ Dataset 2 : Partial circle Dataset 2 : Plot OT Matrices -############################################################################# +----------------------------- @@ -290,7 +290,7 @@ Dataset 2 : Plot OT Matrices -**Total running time of the script:** ( 0 minutes 1.218 seconds) +**Total running time of the script:** ( 0 minutes 1.134 seconds) diff --git a/docs/source/auto_examples/plot_WDA.ipynb b/docs/source/auto_examples/plot_WDA.ipynb index 47a6eca..1661c53 100644 --- a/docs/source/auto_examples/plot_WDA.ipynb +++ b/docs/source/auto_examples/plot_WDA.ipynb @@ -33,7 +33,7 @@ }, { "source": [ - "Generate data\n#############################################################################\n\n" + "Generate data\n-------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -51,7 +51,7 @@ }, { "source": [ - "Plot data\n#############################################################################\n\n" + "Plot data\n---------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -69,7 +69,7 @@ }, { "source": [ - "Compute Fisher Discriminant Analysis\n#############################################################################\n\n" + "Compute Fisher Discriminant Analysis\n------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -87,7 +87,7 @@ }, { "source": [ - "Compute Wasserstein Discriminant Analysis\n#############################################################################\n\n" + "Compute Wasserstein Discriminant Analysis\n-----------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -105,7 +105,7 @@ }, { "source": [ - "Plot 2D projections\n#############################################################################\n\n" + "Plot 2D projections\n-------------------\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_WDA.py b/docs/source/auto_examples/plot_WDA.py index 5928621..93cc237 100644 --- a/docs/source/auto_examples/plot_WDA.py +++ b/docs/source/auto_examples/plot_WDA.py @@ -24,7 +24,7 @@ from ot.dr import wda, fda ############################################################################## # Generate data -############################################################################## +# ------------- #%% parameters @@ -51,7 +51,7 @@ xt = np.hstack((xt, np.random.randn(n, nbnoise))) ############################################################################## # Plot data -############################################################################## +# --------- #%% plot samples pl.figure(1, figsize=(6.4, 3.5)) @@ -69,7 +69,7 @@ pl.tight_layout() ############################################################################## # Compute Fisher Discriminant Analysis -############################################################################## +# ------------------------------------ #%% Compute FDA p = 2 @@ -78,7 +78,7 @@ Pfda, projfda = fda(xs, ys, p) ############################################################################## # Compute Wasserstein Discriminant Analysis -############################################################################## +# ----------------------------------------- #%% Compute WDA p = 2 @@ -91,7 +91,7 @@ Pwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter) ############################################################################## # Plot 2D projections -############################################################################## +# ------------------- #%% plot samples diff --git a/docs/source/auto_examples/plot_WDA.rst b/docs/source/auto_examples/plot_WDA.rst index a0c3389..64ddb47 100644 --- a/docs/source/auto_examples/plot_WDA.rst +++ b/docs/source/auto_examples/plot_WDA.rst @@ -36,7 +36,7 @@ Wasserstein Discriminant Analysis. Generate data -############################################################################# +------------- @@ -73,7 +73,7 @@ Generate data Plot data -############################################################################# +--------- @@ -104,7 +104,7 @@ Plot data Compute Fisher Discriminant Analysis -############################################################################# +------------------------------------ @@ -123,7 +123,7 @@ Compute Fisher Discriminant Analysis Compute Wasserstein Discriminant Analysis -############################################################################# +----------------------------------------- @@ -150,65 +150,25 @@ Compute Wasserstein Discriminant Analysis Compiling cost function... Computing gradient of cost function... iter cost val grad. norm - 1 +8.6305817354868675e-01 4.10110152e-01 - 2 +4.6939060757969131e-01 2.98553763e-01 - 3 +4.2106314200107775e-01 1.48552668e-01 - 4 +4.1376389458568069e-01 1.12319011e-01 - 5 +4.0984854988792835e-01 1.01126129e-01 - 6 +4.0415292614140025e-01 3.90875165e-02 - 7 +4.0297967887432584e-01 2.73716014e-02 - 8 +4.0252319029045258e-01 3.76498956e-02 - 9 +4.0158635935184972e-01 1.31986577e-02 - 10 +4.0118906894272482e-01 3.40307273e-02 - 11 +4.0052579694802176e-01 7.79567347e-03 - 12 +4.0049330810825384e-01 9.77921941e-03 - 13 +4.0042500151972926e-01 4.63602913e-03 - 14 +4.0031705300038767e-01 1.69742018e-02 - 15 +4.0013705338124350e-01 7.40310798e-03 - 16 +4.0006224569843946e-01 1.08829949e-02 - 17 +3.9998280287782945e-01 1.25733450e-02 - 18 +3.9986405111843215e-01 1.05626807e-02 - 19 +3.9974905002724365e-01 9.93566406e-03 - 20 +3.9971323753531823e-01 2.21199533e-02 - 21 +3.9958582328238779e-01 1.73335808e-02 - 22 +3.9937139582811110e-01 1.09182412e-02 - 23 +3.9923748818499571e-01 1.77304913e-02 - 24 +3.9900530515251881e-01 1.15381586e-02 - 25 +3.9883316307006128e-01 1.80225446e-02 - 26 +3.9860317631835845e-01 1.65011032e-02 - 27 +3.9852130309759393e-01 2.81245689e-02 - 28 +3.9824281033694675e-01 2.01114810e-02 - 29 +3.9799657608114836e-01 2.66040929e-02 - 30 +3.9746233677210713e-01 1.45779937e-02 - 31 +3.9671794378467928e-01 4.27487207e-02 - 32 +3.9573357685391913e-01 2.20071520e-02 - 33 +3.9536725156297214e-01 2.00817458e-02 - 34 +3.9515994339814914e-01 3.81472315e-02 - 35 +3.9448966390371887e-01 2.52129049e-02 - 36 +3.9351423238681266e-01 5.60677866e-02 - 37 +3.9082703288308568e-01 4.26859586e-02 - 38 +3.7139409489868136e-01 1.26067835e-01 - 39 +2.8085932518253526e-01 1.70133509e-01 - 40 +2.7330384726281814e-01 1.95523507e-01 - 41 +2.4806985554269162e-01 1.31192016e-01 - 42 +2.3748356968454920e-01 8.71616829e-02 - 43 +2.3501927152342389e-01 7.02789537e-02 - 44 +2.3183578112546338e-01 2.62025296e-02 - 45 +2.3154208568082749e-01 1.67845346e-02 - 46 +2.3139316710346300e-01 8.27285074e-03 - 47 +2.3136034106523354e-01 4.64818210e-03 - 48 +2.3134548827742521e-01 4.53144806e-04 - 49 +2.3134540503271503e-01 2.91010390e-04 - 50 +2.3134535764073319e-01 1.25662481e-04 - 51 +2.3134534692621381e-01 1.24751216e-05 - 52 +2.3134534685831357e-01 7.44008265e-06 - 53 +2.3134534684658337e-01 6.16933546e-06 - 54 +2.3134534682129679e-01 5.12152219e-07 - Terminated - min grad norm reached after 54 iterations, 24.53 seconds. + 1 +5.4993226050368416e-01 5.18285173e-01 + 2 +3.4883000507542844e-01 1.96795818e-01 + 3 +2.9841234004693890e-01 2.33029475e-01 + 4 +2.3976476757548179e-01 1.38593951e-01 + 5 +2.3614468346177828e-01 1.19615394e-01 + 6 +2.2586536502789240e-01 4.82430685e-02 + 7 +2.2451030967794622e-01 2.56564039e-02 + 8 +2.2421446331083625e-01 1.47932578e-02 + 9 +2.2407441444450052e-01 1.12040327e-03 + 10 +2.2407365923337522e-01 3.78899763e-04 + 11 +2.2407356874011675e-01 1.79740810e-05 + 12 +2.2407356862959993e-01 1.25643005e-05 + 13 +2.2407356853043561e-01 1.40415001e-06 + 14 +2.2407356852925220e-01 3.41183585e-07 + Terminated - min grad norm reached after 14 iterations, 6.78 seconds. Plot 2D projections -############################################################################# +------------------- @@ -256,7 +216,7 @@ Plot 2D projections -**Total running time of the script:** ( 0 minutes 25.326 seconds) +**Total running time of the script:** ( 0 minutes 7.637 seconds) diff --git a/docs/source/auto_examples/plot_barycenter_1D.ipynb b/docs/source/auto_examples/plot_barycenter_1D.ipynb index 32cb2ec..a19e0fd 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.ipynb +++ b/docs/source/auto_examples/plot_barycenter_1D.ipynb @@ -33,7 +33,7 @@ }, { "source": [ - "Generate data\n#############################################################################\n\n" + "Generate data\n-------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -51,7 +51,7 @@ }, { "source": [ - "Plot data\n#############################################################################\n\n" + "Plot data\n---------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -69,7 +69,7 @@ }, { "source": [ - "Barycenter computation\n#############################################################################\n\n" + "Barycenter computation\n----------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -87,7 +87,7 @@ }, { "source": [ - "Barycentric interpolation\n#############################################################################\n\n" + "Barycentric interpolation\n-------------------------\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_barycenter_1D.py b/docs/source/auto_examples/plot_barycenter_1D.py index eef8536..620936b 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.py +++ b/docs/source/auto_examples/plot_barycenter_1D.py @@ -27,7 +27,7 @@ from matplotlib.collections import PolyCollection ############################################################################## # Generate data -############################################################################## +# ------------- #%% parameters @@ -50,7 +50,7 @@ M /= M.max() ############################################################################## # Plot data -############################################################################## +# --------- #%% plot the distributions @@ -62,7 +62,7 @@ pl.tight_layout() ############################################################################## # Barycenter computation -############################################################################## +# ---------------------- #%% barycenter computation @@ -92,7 +92,7 @@ pl.tight_layout() ############################################################################## # Barycentric interpolation -############################################################################## +# ------------------------- #%% barycenter interpolation diff --git a/docs/source/auto_examples/plot_barycenter_1D.rst b/docs/source/auto_examples/plot_barycenter_1D.rst index b1794cd..413fae3 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.rst +++ b/docs/source/auto_examples/plot_barycenter_1D.rst @@ -39,7 +39,7 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. Generate data -############################################################################# +------------- @@ -72,7 +72,7 @@ Generate data Plot data -############################################################################# +--------- @@ -97,7 +97,7 @@ Plot data Barycenter computation -############################################################################# +---------------------- @@ -140,7 +140,7 @@ Barycenter computation Barycentric interpolation -############################################################################# +------------------------- @@ -230,7 +230,7 @@ Barycentric interpolation -**Total running time of the script:** ( 0 minutes 0.416 seconds) +**Total running time of the script:** ( 0 minutes 0.431 seconds) diff --git a/docs/source/auto_examples/plot_compute_emd.ipynb b/docs/source/auto_examples/plot_compute_emd.ipynb index a882bd1..b9b8bc5 100644 --- a/docs/source/auto_examples/plot_compute_emd.ipynb +++ b/docs/source/auto_examples/plot_compute_emd.ipynb @@ -33,7 +33,7 @@ }, { "source": [ - "Generate data\n#############################################################################\n\n" + "Generate data\n-------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -51,7 +51,7 @@ }, { "source": [ - "Plot data\n#############################################################################\n\n" + "Plot data\n---------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -69,7 +69,7 @@ }, { "source": [ - "Compute EMD for the different losses\n#############################################################################\n\n" + "Compute EMD for the different losses\n------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -87,7 +87,7 @@ }, { "source": [ - "Compute Sinkhorn for the different losses\n#############################################################################\n\n" + "Compute Sinkhorn for the different losses\n-----------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_compute_emd.py b/docs/source/auto_examples/plot_compute_emd.py index a84b249..73b42c3 100644 --- a/docs/source/auto_examples/plot_compute_emd.py +++ b/docs/source/auto_examples/plot_compute_emd.py @@ -22,7 +22,7 @@ from ot.datasets import get_1D_gauss as gauss ############################################################################## # Generate data -############################################################################## +# ------------- #%% parameters @@ -51,7 +51,7 @@ M2 /= M2.max() ############################################################################## # Plot data -############################################################################## +# --------- #%% plot the distributions @@ -67,7 +67,7 @@ pl.tight_layout() ############################################################################## # Compute EMD for the different losses -############################################################################## +# ------------------------------------ #%% Compute and plot distributions and loss matrix @@ -83,7 +83,7 @@ pl.legend() ############################################################################## # Compute Sinkhorn for the different losses -############################################################################## +# ----------------------------------------- #%% reg = 1e-2 diff --git a/docs/source/auto_examples/plot_compute_emd.rst b/docs/source/auto_examples/plot_compute_emd.rst index 58220a4..ce79e20 100644 --- a/docs/source/auto_examples/plot_compute_emd.rst +++ b/docs/source/auto_examples/plot_compute_emd.rst @@ -34,7 +34,7 @@ ground metrics and plot their values for diffeent distributions. Generate data -############################################################################# +------------- @@ -73,7 +73,7 @@ Generate data Plot data -############################################################################# +--------- @@ -102,7 +102,7 @@ Plot data Compute EMD for the different losses -############################################################################# +------------------------------------ @@ -131,7 +131,7 @@ Compute EMD for the different losses Compute Sinkhorn for the different losses -############################################################################# +----------------------------------------- @@ -162,7 +162,7 @@ Compute Sinkhorn for the different losses -**Total running time of the script:** ( 0 minutes 0.471 seconds) +**Total running time of the script:** ( 0 minutes 0.441 seconds) diff --git a/docs/source/auto_examples/plot_optim_OTreg.ipynb b/docs/source/auto_examples/plot_optim_OTreg.ipynb index f9fec33..333331b 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.ipynb +++ b/docs/source/auto_examples/plot_optim_OTreg.ipynb @@ -33,7 +33,7 @@ }, { "source": [ - "Generate data\n#############################################################################\n\n" + "Generate data\n-------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -51,7 +51,7 @@ }, { "source": [ - "Solve EMD\n#############################################################################\n\n" + "Solve EMD\n---------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -69,7 +69,7 @@ }, { "source": [ - "Solve EMD with Frobenius norm regularization\n#############################################################################\n\n" + "Solve EMD with Frobenius norm regularization\n--------------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -87,7 +87,7 @@ }, { "source": [ - "Solve EMD with entropic regularization\n#############################################################################\n\n" + "Solve EMD with entropic regularization\n--------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -105,7 +105,7 @@ }, { "source": [ - "Solve EMD with Frobenius norm + entropic regularization\n#############################################################################\n\n" + "Solve EMD with Frobenius norm + entropic regularization\n-------------------------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_optim_OTreg.py b/docs/source/auto_examples/plot_optim_OTreg.py index d753414..e1a737e 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.py +++ b/docs/source/auto_examples/plot_optim_OTreg.py @@ -32,7 +32,7 @@ import ot ############################################################################## # Generate data -############################################################################## +# ------------- #%% parameters @@ -51,7 +51,7 @@ M /= M.max() ############################################################################## # Solve EMD -############################################################################## +# --------- #%% EMD @@ -62,7 +62,7 @@ ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') ############################################################################## # Solve EMD with Frobenius norm regularization -############################################################################## +# -------------------------------------------- #%% Example with Frobenius norm regularization @@ -84,7 +84,7 @@ ot.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg') ############################################################################## # Solve EMD with entropic regularization -############################################################################## +# -------------------------------------- #%% Example with entropic regularization @@ -106,7 +106,7 @@ ot.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg') ############################################################################## # Solve EMD with Frobenius norm + entropic regularization -############################################################################## +# ------------------------------------------------------- #%% Example with Frobenius norm + entropic regularization with gcg diff --git a/docs/source/auto_examples/plot_optim_OTreg.rst b/docs/source/auto_examples/plot_optim_OTreg.rst index c3ec03b..f628024 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.rst +++ b/docs/source/auto_examples/plot_optim_OTreg.rst @@ -44,7 +44,7 @@ arXiv preprint arXiv:1510.06567. Generate data -############################################################################# +------------- @@ -73,7 +73,7 @@ Generate data Solve EMD -############################################################################# +--------- @@ -97,7 +97,7 @@ Solve EMD Solve EMD with Frobenius norm regularization -############################################################################# +-------------------------------------------- @@ -359,7 +359,7 @@ Solve EMD with Frobenius norm regularization Solve EMD with entropic regularization -############################################################################# +-------------------------------------- @@ -621,7 +621,7 @@ Solve EMD with entropic regularization Solve EMD with Frobenius norm + entropic regularization -############################################################################# +------------------------------------------------------- @@ -667,7 +667,7 @@ Solve EMD with Frobenius norm + entropic regularization 4|1.609284e-01|-1.111407e-12 -**Total running time of the script:** ( 0 minutes 1.913 seconds) +**Total running time of the script:** ( 0 minutes 1.809 seconds) diff --git a/docs/source/auto_examples/plot_otda_classes.ipynb b/docs/source/auto_examples/plot_otda_classes.ipynb index 16634b1..6754fa5 100644 --- a/docs/source/auto_examples/plot_otda_classes.ipynb +++ b/docs/source/auto_examples/plot_otda_classes.ipynb @@ -33,7 +33,7 @@ }, { "source": [ - "generate data\n#############################################################################\n\n" + "Generate data\n-------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -51,7 +51,7 @@ }, { "source": [ - "Instantiate the different transport algorithms and fit them\n#############################################################################\n\n" + "Instantiate the different transport algorithms and fit them\n-----------------------------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -69,7 +69,7 @@ }, { "source": [ - "Fig 1 : plots source and target samples\n#############################################################################\n\n" + "Fig 1 : plots source and target samples\n---------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -87,7 +87,7 @@ }, { "source": [ - "Fig 2 : plot optimal couplings and transported samples\n#############################################################################\n\n" + "Fig 2 : plot optimal couplings and transported samples\n------------------------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_otda_classes.py b/docs/source/auto_examples/plot_otda_classes.py index ec57a37..b14c11a 100644 --- a/docs/source/auto_examples/plot_otda_classes.py +++ b/docs/source/auto_examples/plot_otda_classes.py @@ -19,8 +19,8 @@ import ot ############################################################################## -# generate data -############################################################################## +# Generate data +# ------------- n_source_samples = 150 n_target_samples = 150 @@ -31,7 +31,7 @@ Xt, yt = ot.datasets.get_data_classif('3gauss2', n_target_samples) ############################################################################## # Instantiate the different transport algorithms and fit them -############################################################################## +# ----------------------------------------------------------- # EMD Transport ot_emd = ot.da.EMDTransport() @@ -59,7 +59,7 @@ transp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs) ############################################################################## # Fig 1 : plots source and target samples -############################################################################## +# --------------------------------------- pl.figure(1, figsize=(10, 5)) pl.subplot(1, 2, 1) @@ -80,7 +80,7 @@ pl.tight_layout() ############################################################################## # Fig 2 : plot optimal couplings and transported samples -############################################################################## +# ------------------------------------------------------ param_img = {'interpolation': 'nearest', 'cmap': 'spectral'} diff --git a/docs/source/auto_examples/plot_otda_classes.rst b/docs/source/auto_examples/plot_otda_classes.rst index d1a13b1..f19a99f 100644 --- a/docs/source/auto_examples/plot_otda_classes.rst +++ b/docs/source/auto_examples/plot_otda_classes.rst @@ -31,8 +31,8 @@ approaches currently supported in POT. -generate data -############################################################################# +Generate data +------------- @@ -53,7 +53,7 @@ generate data Instantiate the different transport algorithms and fit them -############################################################################# +----------------------------------------------------------- @@ -94,33 +94,33 @@ Instantiate the different transport algorithms and fit them It. |Loss |Delta loss -------------------------------- - 0|9.984935e+00|0.000000e+00 - 1|2.126803e+00|-3.694808e+00 - 2|1.867272e+00|-1.389895e-01 - 3|1.803858e+00|-3.515488e-02 - 4|1.783036e+00|-1.167761e-02 - 5|1.774823e+00|-4.627422e-03 - 6|1.771947e+00|-1.623526e-03 - 7|1.767564e+00|-2.479535e-03 - 8|1.763484e+00|-2.313667e-03 - 9|1.761138e+00|-1.331780e-03 - 10|1.758879e+00|-1.284576e-03 - 11|1.758034e+00|-4.806014e-04 - 12|1.757595e+00|-2.497155e-04 - 13|1.756749e+00|-4.818562e-04 - 14|1.755316e+00|-8.161432e-04 - 15|1.754988e+00|-1.866236e-04 - 16|1.754964e+00|-1.382474e-05 - 17|1.754032e+00|-5.315971e-04 - 18|1.753595e+00|-2.492359e-04 - 19|1.752900e+00|-3.961403e-04 + 0|9.552437e+00|0.000000e+00 + 1|1.921833e+00|-3.970483e+00 + 2|1.671022e+00|-1.500942e-01 + 3|1.615147e+00|-3.459458e-02 + 4|1.594289e+00|-1.308252e-02 + 5|1.587287e+00|-4.411254e-03 + 6|1.581665e+00|-3.554702e-03 + 7|1.577022e+00|-2.943809e-03 + 8|1.573870e+00|-2.002870e-03 + 9|1.571645e+00|-1.415696e-03 + 10|1.569342e+00|-1.467590e-03 + 11|1.567863e+00|-9.432233e-04 + 12|1.566558e+00|-8.329769e-04 + 13|1.565414e+00|-7.311320e-04 + 14|1.564425e+00|-6.319985e-04 + 15|1.563955e+00|-3.007604e-04 + 16|1.563658e+00|-1.894627e-04 + 17|1.562886e+00|-4.941143e-04 + 18|1.562578e+00|-1.974031e-04 + 19|1.562445e+00|-8.468825e-05 It. |Loss |Delta loss -------------------------------- - 20|1.752850e+00|-2.869262e-05 + 20|1.562007e+00|-2.805136e-04 Fig 1 : plots source and target samples -############################################################################# +--------------------------------------- @@ -154,7 +154,7 @@ Fig 1 : plots source and target samples Fig 2 : plot optimal couplings and transported samples -############################################################################# +------------------------------------------------------ @@ -236,7 +236,7 @@ Fig 2 : plot optimal couplings and transported samples -**Total running time of the script:** ( 0 minutes 1.576 seconds) +**Total running time of the script:** ( 0 minutes 1.596 seconds) diff --git a/docs/source/auto_examples/plot_otda_color_images.ipynb b/docs/source/auto_examples/plot_otda_color_images.ipynb index 797b27d..2daf406 100644 --- a/docs/source/auto_examples/plot_otda_color_images.ipynb +++ b/docs/source/auto_examples/plot_otda_color_images.ipynb @@ -33,7 +33,7 @@ }, { "source": [ - "Generate data\n#############################################################################\n\n" + "Generate data\n-------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -51,7 +51,7 @@ }, { "source": [ - "Plot original image\n#############################################################################\n\n" + "Plot original image\n-------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -69,7 +69,7 @@ }, { "source": [ - "Scatter plot of colors\n#############################################################################\n\n" + "Scatter plot of colors\n----------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -87,7 +87,7 @@ }, { "source": [ - "Instantiate the different transport algorithms and fit them\n#############################################################################\n\n" + "Instantiate the different transport algorithms and fit them\n-----------------------------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -105,7 +105,7 @@ }, { "source": [ - "Plot new images\n#############################################################################\n\n" + "Plot new images\n---------------\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_otda_color_images.py b/docs/source/auto_examples/plot_otda_color_images.py index f1df9d9..e77aec0 100644 --- a/docs/source/auto_examples/plot_otda_color_images.py +++ b/docs/source/auto_examples/plot_otda_color_images.py @@ -42,7 +42,7 @@ def minmax(I): ############################################################################## # Generate data -############################################################################## +# ------------- # Loading images I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256 @@ -62,7 +62,7 @@ Xt = X2[idx2, :] ############################################################################## # Plot original image -############################################################################## +# ------------------- pl.figure(1, figsize=(6.4, 3)) @@ -79,7 +79,7 @@ pl.title('Image 2') ############################################################################## # Scatter plot of colors -############################################################################## +# ---------------------- pl.figure(2, figsize=(6.4, 3)) @@ -101,7 +101,7 @@ pl.tight_layout() ############################################################################## # Instantiate the different transport algorithms and fit them -############################################################################## +# ----------------------------------------------------------- # EMDTransport ot_emd = ot.da.EMDTransport() @@ -127,7 +127,7 @@ I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape)) ############################################################################## # Plot new images -############################################################################## +# --------------- pl.figure(3, figsize=(8, 4)) diff --git a/docs/source/auto_examples/plot_otda_color_images.rst b/docs/source/auto_examples/plot_otda_color_images.rst index 88e93d2..4772bed 100644 --- a/docs/source/auto_examples/plot_otda_color_images.rst +++ b/docs/source/auto_examples/plot_otda_color_images.rst @@ -54,7 +54,7 @@ SIAM Journal on Imaging Sciences, 7(3), 1853-1882. Generate data -############################################################################# +------------- @@ -84,7 +84,7 @@ Generate data Plot original image -############################################################################# +------------------- @@ -114,7 +114,7 @@ Plot original image Scatter plot of colors -############################################################################# +---------------------- @@ -149,7 +149,7 @@ Scatter plot of colors Instantiate the different transport algorithms and fit them -############################################################################# +----------------------------------------------------------- @@ -185,7 +185,7 @@ Instantiate the different transport algorithms and fit them Plot new images -############################################################################# +--------------- @@ -235,7 +235,7 @@ Plot new images -**Total running time of the script:** ( 2 minutes 28.053 seconds) +**Total running time of the script:** ( 2 minutes 24.561 seconds) diff --git a/docs/source/auto_examples/plot_otda_d2.ipynb b/docs/source/auto_examples/plot_otda_d2.ipynb index 2331f8c..7bfcc9a 100644 --- a/docs/source/auto_examples/plot_otda_d2.ipynb +++ b/docs/source/auto_examples/plot_otda_d2.ipynb @@ -15,7 +15,7 @@ }, { "source": [ - "\n# OT for empirical distributions\n\n\nThis example introduces a domain adaptation in a 2D setting. It explicits\nthe problem of domain adaptation and introduces some optimal transport\napproaches to solve it.\n\nQuantities such as optimal couplings, greater coupling coefficients and\ntransported samples are represented in order to give a visual understanding\nof what the transport methods are doing.\n\n" + "\n# OT for domain adaptation on empirical distributions\n\n\nThis example introduces a domain adaptation in a 2D setting. It explicits\nthe problem of domain adaptation and introduces some optimal transport\napproaches to solve it.\n\nQuantities such as optimal couplings, greater coupling coefficients and\ntransported samples are represented in order to give a visual understanding\nof what the transport methods are doing.\n\n" ], "cell_type": "markdown", "metadata": {} @@ -33,7 +33,7 @@ }, { "source": [ - "generate data\n#############################################################################\n\n" + "generate data\n-------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -51,7 +51,7 @@ }, { "source": [ - "Instantiate the different transport algorithms and fit them\n#############################################################################\n\n" + "Instantiate the different transport algorithms and fit them\n-----------------------------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -69,7 +69,7 @@ }, { "source": [ - "Fig 1 : plots source and target samples + matrix of pairwise distance\n#############################################################################\n\n" + "Fig 1 : plots source and target samples + matrix of pairwise distance\n---------------------------------------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -87,7 +87,7 @@ }, { "source": [ - "Fig 2 : plots optimal couplings for the different methods\n#############################################################################\n\n" + "Fig 2 : plots optimal couplings for the different methods\n---------------------------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -105,7 +105,7 @@ }, { "source": [ - "Fig 3 : plot transported samples\n#############################################################################\n\n" + "Fig 3 : plot transported samples\n--------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_otda_d2.py b/docs/source/auto_examples/plot_otda_d2.py index 3daa0a6..e53d7d6 100644 --- a/docs/source/auto_examples/plot_otda_d2.py +++ b/docs/source/auto_examples/plot_otda_d2.py @@ -1,8 +1,8 @@ # -*- coding: utf-8 -*- """ -============================== -OT for empirical distributions -============================== +=================================================== +OT for domain adaptation on empirical distributions +=================================================== This example introduces a domain adaptation in a 2D setting. It explicits the problem of domain adaptation and introduces some optimal transport @@ -24,7 +24,7 @@ import ot ############################################################################## # generate data -############################################################################## +# ------------- n_samples_source = 150 n_samples_target = 150 @@ -38,7 +38,7 @@ M = ot.dist(Xs, Xt, metric='sqeuclidean') ############################################################################## # Instantiate the different transport algorithms and fit them -############################################################################## +# ----------------------------------------------------------- # EMD Transport ot_emd = ot.da.EMDTransport() @@ -60,7 +60,7 @@ transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs) ############################################################################## # Fig 1 : plots source and target samples + matrix of pairwise distance -############################################################################## +# --------------------------------------------------------------------- pl.figure(1, figsize=(10, 10)) pl.subplot(2, 2, 1) @@ -87,8 +87,7 @@ pl.tight_layout() ############################################################################## # Fig 2 : plots optimal couplings for the different methods -############################################################################## - +# --------------------------------------------------------- pl.figure(2, figsize=(10, 6)) pl.subplot(2, 3, 1) @@ -137,7 +136,7 @@ pl.tight_layout() ############################################################################## # Fig 3 : plot transported samples -############################################################################## +# -------------------------------- # display transported samples pl.figure(4, figsize=(10, 4)) diff --git a/docs/source/auto_examples/plot_otda_d2.rst b/docs/source/auto_examples/plot_otda_d2.rst index 3aa1149..2b716e1 100644 --- a/docs/source/auto_examples/plot_otda_d2.rst +++ b/docs/source/auto_examples/plot_otda_d2.rst @@ -3,9 +3,9 @@ .. _sphx_glr_auto_examples_plot_otda_d2.py: -============================== -OT for empirical distributions -============================== +=================================================== +OT for domain adaptation on empirical distributions +=================================================== This example introduces a domain adaptation in a 2D setting. It explicits the problem of domain adaptation and introduces some optimal transport @@ -36,7 +36,7 @@ of what the transport methods are doing. generate data -############################################################################# +------------- @@ -60,7 +60,7 @@ generate data Instantiate the different transport algorithms and fit them -############################################################################# +----------------------------------------------------------- @@ -92,7 +92,7 @@ Instantiate the different transport algorithms and fit them Fig 1 : plots source and target samples + matrix of pairwise distance -############################################################################# +--------------------------------------------------------------------- @@ -132,13 +132,12 @@ Fig 1 : plots source and target samples + matrix of pairwise distance Fig 2 : plots optimal couplings for the different methods -############################################################################# +--------------------------------------------------------- .. code-block:: python - pl.figure(2, figsize=(10, 6)) pl.subplot(2, 3, 1) @@ -195,7 +194,7 @@ Fig 2 : plots optimal couplings for the different methods Fig 3 : plot transported samples -############################################################################# +-------------------------------- @@ -243,7 +242,7 @@ Fig 3 : plot transported samples -**Total running time of the script:** ( 0 minutes 32.275 seconds) +**Total running time of the script:** ( 0 minutes 32.084 seconds) diff --git a/docs/source/auto_examples/plot_otda_mapping.ipynb b/docs/source/auto_examples/plot_otda_mapping.ipynb index 5b3fd06..0374146 100644 --- a/docs/source/auto_examples/plot_otda_mapping.ipynb +++ b/docs/source/auto_examples/plot_otda_mapping.ipynb @@ -33,7 +33,7 @@ }, { "source": [ - "Generate data\n#############################################################################\n\n" + "Generate data\n-------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -51,7 +51,7 @@ }, { "source": [ - "Plot data\n#############################################################################\n\n" + "Plot data\n---------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -69,7 +69,7 @@ }, { "source": [ - "Instantiate the different transport algorithms and fit them\n#############################################################################\n\n" + "Instantiate the different transport algorithms and fit them\n-----------------------------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -87,7 +87,7 @@ }, { "source": [ - "Plot transported samples\n#############################################################################\n\n" + "Plot transported samples\n------------------------\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_otda_mapping.py b/docs/source/auto_examples/plot_otda_mapping.py index e78fef4..167c3a1 100644 --- a/docs/source/auto_examples/plot_otda_mapping.py +++ b/docs/source/auto_examples/plot_otda_mapping.py @@ -25,7 +25,7 @@ import ot ############################################################################## # Generate data -############################################################################## +# ------------- n_source_samples = 100 n_target_samples = 100 @@ -45,7 +45,7 @@ Xt = Xt + 4 ############################################################################## # Plot data -############################################################################## +# --------- pl.figure(1, (10, 5)) pl.clf() @@ -57,7 +57,7 @@ pl.title('Source and target distributions') ############################################################################## # Instantiate the different transport algorithms and fit them -############################################################################## +# ----------------------------------------------------------- # MappingTransport with linear kernel ot_mapping_linear = ot.da.MappingTransport( @@ -88,7 +88,7 @@ transp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new) ############################################################################## # Plot transported samples -############################################################################## +# ------------------------ pl.figure(2) pl.clf() diff --git a/docs/source/auto_examples/plot_otda_mapping.rst b/docs/source/auto_examples/plot_otda_mapping.rst index ddc1ee9..6c1c780 100644 --- a/docs/source/auto_examples/plot_otda_mapping.rst +++ b/docs/source/auto_examples/plot_otda_mapping.rst @@ -37,7 +37,7 @@ a linear or a kernelized mapping as introduced in [8]. Generate data -############################################################################# +------------- @@ -67,7 +67,7 @@ Generate data Plot data -############################################################################# +--------- @@ -92,7 +92,7 @@ Plot data Instantiate the different transport algorithms and fit them -############################################################################# +----------------------------------------------------------- @@ -136,30 +136,28 @@ Instantiate the different transport algorithms and fit them It. |Loss |Delta loss -------------------------------- - 0|4.481482e+03|0.000000e+00 - 1|4.469389e+03|-2.698549e-03 - 2|4.468825e+03|-1.261217e-04 - 3|4.468580e+03|-5.486064e-05 - 4|4.468438e+03|-3.161220e-05 - 5|4.468352e+03|-1.930800e-05 - 6|4.468309e+03|-9.570658e-06 + 0|4.307233e+03|0.000000e+00 + 1|4.296694e+03|-2.446759e-03 + 2|4.296419e+03|-6.417421e-05 + 3|4.296328e+03|-2.110209e-05 + 4|4.296305e+03|-5.298603e-06 It. |Loss |Delta loss -------------------------------- - 0|4.504654e+02|0.000000e+00 - 1|4.461571e+02|-9.564116e-03 - 2|4.459105e+02|-5.528043e-04 - 3|4.457895e+02|-2.712398e-04 - 4|4.457041e+02|-1.914829e-04 - 5|4.456431e+02|-1.369704e-04 - 6|4.456032e+02|-8.944784e-05 - 7|4.455700e+02|-7.447824e-05 - 8|4.455447e+02|-5.688965e-05 - 9|4.455229e+02|-4.890051e-05 - 10|4.455084e+02|-3.262490e-05 + 0|4.325624e+02|0.000000e+00 + 1|4.281958e+02|-1.009489e-02 + 2|4.279370e+02|-6.042202e-04 + 3|4.278109e+02|-2.947651e-04 + 4|4.277212e+02|-2.096651e-04 + 5|4.276589e+02|-1.456221e-04 + 6|4.276141e+02|-1.048476e-04 + 7|4.275803e+02|-7.906213e-05 + 8|4.275531e+02|-6.360573e-05 + 9|4.275314e+02|-5.076642e-05 + 10|4.275129e+02|-4.325858e-05 Plot transported samples -############################################################################# +------------------------ @@ -208,7 +206,7 @@ Plot transported samples -**Total running time of the script:** ( 0 minutes 0.869 seconds) +**Total running time of the script:** ( 0 minutes 0.747 seconds) diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb b/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb index 3b3987a..56caa8a 100644 --- a/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb +++ b/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb @@ -33,7 +33,7 @@ }, { "source": [ - "Generate data\n#############################################################################\n\n" + "Generate data\n-------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -51,7 +51,7 @@ }, { "source": [ - "Domain adaptation for pixel distribution transfer\n#############################################################################\n\n" + "Domain adaptation for pixel distribution transfer\n-------------------------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -69,7 +69,7 @@ }, { "source": [ - "Plot original images\n#############################################################################\n\n" + "Plot original images\n--------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -87,7 +87,7 @@ }, { "source": [ - "Plot pixel values distribution\n#############################################################################\n\n" + "Plot pixel values distribution\n------------------------------\n\n" ], "cell_type": "markdown", "metadata": {} @@ -105,7 +105,7 @@ }, { "source": [ - "Plot transformed images\n#############################################################################\n\n" + "Plot transformed images\n-----------------------\n\n" ], "cell_type": "markdown", "metadata": {} diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.py b/docs/source/auto_examples/plot_otda_mapping_colors_images.py index 5590286..5f1e844 100644 --- a/docs/source/auto_examples/plot_otda_mapping_colors_images.py +++ b/docs/source/auto_examples/plot_otda_mapping_colors_images.py @@ -45,7 +45,7 @@ def minmax(I): ############################################################################## # Generate data -############################################################################## +# ------------- # Loading images I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256 @@ -66,7 +66,7 @@ Xt = X2[idx2, :] ############################################################################## # Domain adaptation for pixel distribution transfer -############################################################################## +# ------------------------------------------------- # EMDTransport ot_emd = ot.da.EMDTransport() @@ -97,7 +97,7 @@ Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape)) ############################################################################## # Plot original images -############################################################################## +# -------------------- pl.figure(1, figsize=(6.4, 3)) pl.subplot(1, 2, 1) @@ -114,7 +114,7 @@ pl.tight_layout() ############################################################################## # Plot pixel values distribution -############################################################################## +# ------------------------------ pl.figure(2, figsize=(6.4, 5)) @@ -136,7 +136,7 @@ pl.tight_layout() ############################################################################## # Plot transformed images -############################################################################## +# ----------------------- pl.figure(2, figsize=(10, 5)) diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.rst b/docs/source/auto_examples/plot_otda_mapping_colors_images.rst index 9995103..86b1312 100644 --- a/docs/source/auto_examples/plot_otda_mapping_colors_images.rst +++ b/docs/source/auto_examples/plot_otda_mapping_colors_images.rst @@ -57,7 +57,7 @@ estimation [8]. Generate data -############################################################################# +------------- @@ -88,7 +88,7 @@ Generate data Domain adaptation for pixel distribution transfer -############################################################################# +------------------------------------------------- @@ -168,7 +168,7 @@ Domain adaptation for pixel distribution transfer Plot original images -############################################################################# +-------------------- @@ -198,7 +198,7 @@ Plot original images Plot pixel values distribution -############################################################################# +------------------------------ @@ -233,7 +233,7 @@ Plot pixel values distribution Plot transformed images -############################################################################# +----------------------- @@ -283,7 +283,7 @@ Plot transformed images -**Total running time of the script:** ( 2 minutes 8.746 seconds) +**Total running time of the script:** ( 2 minutes 5.213 seconds) diff --git a/docs/source/conf.py b/docs/source/conf.py index 0a822e5..156b878 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -33,7 +33,7 @@ class Mock(MagicMock): return MagicMock() MOCK_MODULES = ['ot.lp.emd_wrap','autograd','pymanopt','cudamat','autograd.numpy','pymanopt.manifolds','pymanopt.solvers'] # 'autograd.numpy','pymanopt.manifolds','pymanopt.solvers', -##sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) +###sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # !!!! # If extensions (or modules to document with autodoc) are in another directory, -- cgit v1.2.3 From 98ae0808417d0f82e761f85265e8dcfa9ebe7e5f Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 1 Sep 2017 18:41:01 +0200 Subject: conversion scripts --- docs/cache_nbrun | 1 + docs/nb_run_conv | 84 +++++ notebooks/Demo_1D_OT.ipynb | 198 ------------ notebooks/Demo_1D_barycenter.ipynb | 297 ------------------ notebooks/Demo_2D_OT_DomainAdaptation.ipynb | 217 ------------- notebooks/Demo_2D_OT_samples.ipynb | 234 -------------- notebooks/Demo_2D_OTmapping_DomainAdaptation.ipynb | 283 ----------------- notebooks/Demo_Compute_EMD.ipynb | 167 ---------- notebooks/Demo_Ground_Loss.ipynb | 345 -------------------- notebooks/Demo_Image_ColorAdaptation.ipynb | 316 ------------------- notebooks/Demo_Image_ColorAdaptation_mapping.ipynb | 349 --------------------- notebooks/Demo_Optim_OTreg.ipynb | 173 ---------- .../Demo_Wasserstein_Discriminant_Analysis.ipynb | 257 --------------- 13 files changed, 85 insertions(+), 2836 deletions(-) create mode 100644 docs/cache_nbrun create mode 100755 docs/nb_run_conv delete mode 100644 notebooks/Demo_1D_OT.ipynb delete mode 100644 notebooks/Demo_1D_barycenter.ipynb delete mode 100644 notebooks/Demo_2D_OT_DomainAdaptation.ipynb delete mode 100644 notebooks/Demo_2D_OT_samples.ipynb delete mode 100644 notebooks/Demo_2D_OTmapping_DomainAdaptation.ipynb delete mode 100644 notebooks/Demo_Compute_EMD.ipynb delete mode 100644 notebooks/Demo_Ground_Loss.ipynb delete mode 100644 notebooks/Demo_Image_ColorAdaptation.ipynb delete mode 100644 notebooks/Demo_Image_ColorAdaptation_mapping.ipynb delete mode 100644 notebooks/Demo_Optim_OTreg.ipynb delete mode 100644 notebooks/Demo_Wasserstein_Discriminant_Analysis.ipynb diff --git a/docs/cache_nbrun b/docs/cache_nbrun new file mode 100644 index 0000000..8cb878b --- /dev/null +++ b/docs/cache_nbrun @@ -0,0 +1 @@ +{"plot_barycenter_1D.ipynb": "6fd8167f98816dc832fe0c58b1d5527b", "plot_otda_mapping.ipynb": "d335a15af828aaa3439a1c67570d79d6"} \ No newline at end of file diff --git a/docs/nb_run_conv b/docs/nb_run_conv new file mode 100755 index 0000000..99dbbce --- /dev/null +++ b/docs/nb_run_conv @@ -0,0 +1,84 @@ +#!/usr/bin/env python +# -*- coding: utf-8 -*- +""" + +Convert sphinx gallery notebook from empty to image filled + +Created on Fri Sep 1 16:43:45 2017 + +@author: rflamary +""" + +import sys +import json +import glob +import hashlib +import subprocess + +import os + +cache_file='cache_nbrun' + +path_doc='source/auto_examples/' +path_nb='../../../notebooks/' + +def load_json(fname): + try: + f=open(fname) + nb=json.load(f) + f.close() + except (OSError, IOError) : + nb={} + return nb + +def save_json(fname,nb): + f=open(fname,'w') + f.write(json.dumps(nb)) + f.close() + + +def md5(fname): + hash_md5 = hashlib.md5() + with open(fname, "rb") as f: + for chunk in iter(lambda: f.read(4096), b""): + hash_md5.update(chunk) + return hash_md5.hexdigest() + +def to_update(fname,cache): + if fname in cache: + if md5(path_doc+fname)==cache[fname]: + res=False + else: + res=True + else: + res=True + + return res + +def update(fname,cache): + + # jupyter nbconvert --to notebook --execute mynotebook.ipynb --output targte + print(' '.join(['jupyter','nbconvert','--to','notebook','--execute',path_doc+fname,'--output',path_nb+fname])) + subprocess.check_call(['jupyter','nbconvert','--to','notebook','--execute',path_doc+fname,'--output',path_nb+fname]) + cache[fname]=md5(path_doc+fname) + + + +cache=load_json(cache_file) + +lst_file=glob.glob(path_doc+'*.ipynb') + +lst_file=[os.path.basename(name) for name in lst_file] + +for fname in lst_file: + if to_update(fname,cache): + print('Updating file: {}'.format(fname)) + update(fname,cache) + + + + +update(lst_file[0],cache) + + +save_json(cache_file,cache) diff --git a/notebooks/Demo_1D_OT.ipynb b/notebooks/Demo_1D_OT.ipynb deleted file mode 100644 index 087b3bc..0000000 --- a/notebooks/Demo_1D_OT.ipynb +++ /dev/null @@ -1,198 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:b65d32b626c6b5575ab808a448461a190dd0152a68ce83ddf7bbaf1aefb35c43" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "heading", - "level": 1, - "metadata": {}, - "source": [ - "1D optimal transport demo" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# load all relevant modules\n", - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Dataset generation" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "\n", - "n=100 # nb bins\n", - "a=ot.datasets.get_1D_gauss(n,m=20,s=20) # m= mean, s= std\n", - "b=ot.datasets.get_1D_gauss(n,m=60,s=60)\n", - "\n", - "# bin positions\n", - "x=np.arange(n,dtype=np.float64)\n", - "# loss matrix\n", - "M=ot.dist(x.reshape((n,1)),x.reshape((n,1)))\n", - "M/=M.max()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plotting the distributions" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "\n", - "pl.figure(1)\n", - "pl.plot(x,a,'b',label='Source distribution')\n", - "pl.plot(x,b,'r',label='Target distribution')\n", - "pl.legend()\n", - "pl.show()" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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mjEkVkQRs5fAxETkRWAJ0Msbs8diHKSuOUDBmDLRpA3//u3MxtGwJixZB69bO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\nz1NSUkhJSfFTaOW3di30Csn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- "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Distributions and loss matrix" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# plot distributions and loss matrix\n", - "\n", - "pl.figure(2)\n", - "ot.plot.plot1D_mat(a,b,M,'Cost matrix M')\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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J3C0yDCMKTMDLmIsvdkMRA28+K1P86ayZ/tpDcd0pFk+8s8oEuVMkoVKYzFYb\nBfPtb1eKHxy6Cnk+EYdo3CkWT9yRLHeKZg2NX9rYKJQy54ADsgfqMgwj2ZgFXgGMGeMCbJUt/kz6\njmBGpoeaEM0YcbB44lCaY8ShEHdKn4Ra4CbgFUDZu1CqcG8090WiA1IiDpXlTrF44pnJ7U7pk1Bn\nRMECLiJ9gFeBtao6UURGArOB/YH/Bb6oqt39ZRhF4IADsm8ri/NaDV2Cye0kIOJQXGvc4ok7EmSN\n94MqkhnXuTu3nWuAZYH1HwK3qeqRwBbgijAbZoRHfX3OzXZeDSOhFCTgIjIMOBd4IJB9GvCUl54F\nnB9u04ywyPbKt7I5r74LJbh0+W/pu1NqSc3YrMUFwKr2lprA9uB6bWDdX4Jl0tM+wW0E9iVHfm8J\nWvMdOfLbA3kdgbzgtmC6PVCuLS2vLZDXgbPCd3h5wX3UWwLZO3Ms2wvI3x74TE+nL8F9tgtsF/Zu\nr6N1SwNtu+rYuasu77dbihTqQrkduB5oBBCRwcBmVd3rbV8LDA2/eUYY5Hg5RHmcV1/A09nHJw7l\n706xeOKFk4onLgIcFGdbekZeC1xEzgNaVHUpdHEUJdNpVIFkEnA7r4aRfAqxwMcDE0XkXNztswG4\nE2gUkT6etTYMWJftANMDUwGbmppoamrqRZONQmhubqa5uRlwL17OQPmc12wWuE/nQ03o3ugUiyee\nm/KJJ763BAahBK/ZQhHtRtxRETkVuM4brfA48EtVfVxE7gVeU9WfZthHu1OHET4vvwwnnyyoakbr\nOunn9f6n4cp/xvk3oavPlLR0F41SUqLj+2p3BNZ9MW0L7Nietk97ID+4np4mrVxwnSzlwiJ4A0n3\n0wfzs61nSwf9/rVZ1v196gL5wTKBn2RwNFH/LOn6DPn1gfV86fq0tLdtYB/YmuG1h3Eikv2a9enN\nfedG4FoRWYEbcvZgL45lRIh0zyli59UwEkK3JvKo6u+A33npdwGbpF0GJP685nOhBCl5d4rFE4+F\nZE7EtJmYRhnQHQGHPKNT4pixCRZPPOZ44gkV8BJw3RtR000XimEYCcEscCP5dNcC98noTunJGPFs\nedmweOIpSiSeeEKVMKHNNowANaRGFnQXC4DlpSs8nngyX4lpLhTDMIykYha4kXx66kIJEmsEQ7B4\n4hDrlPu+ER8/IkzAjeTTGxdKkB4PMfTL5FrPR6m4Uyo0nnhCBdxcKIZhGAnFLHAj+YThQvGpyAiG\nUPEvhChEibnDAAANr0lEQVSNqBDdxgTcSD5huVCClPyMzUz5vaWCA2CFGXqmiJiAG8knTAs8SEVa\n4xZPPEmYD9wwDCOhmAVuJJ8oXChBEuVOqcB44mFY4XtCOEYMmIAbySdqAYduuFPiCIAFKXeKBcDq\nEQmNF2QuFMMwjIRiFriRfKqJ3gL3KXl3SiXGEw/hoebe/EVKERNwI/kUw4USxOKJe+lScaeEMMQw\nob6IhDbbMAzDMAvcSD5RjQPPh8UTpzTiiYcwRjyhb+QxATeST7FdKEEsnriXjjOeeAgzNhMq4AW5\nUESkUUTmiMhyEXlDRE4Wkf1EZJ6IvCUiz4lIY9SNNcLFzqthJJtCLfA7gWdVdbKIVAMDgG8B81X1\nRyJyA3ATcGNE7TSioTzOa416FniMg3ktnjg25b745BVwERkInKKqlwKoagewVUQmAad6xWYBzZT6\nhW50UlbntbqDPvW72UudlxGTkFs8cUp6xiZkF/KEOpMLcaGMAj4QkYdEZLGIzBSROmCIqrYAqOqf\ngYOibKgROnZeDSPhFHLfqQZOAr6iqq+KyO04iyw9gm5CI+pWLGVzXvv2a6ehqpVWb91Z4jFa4VBh\nEQwh8fHEE/pS40IEfC2wRlVf9dafwl3oLSIyRFVbRORgYEO2A0yfPr0z3dTURFNTU48bbBRGc3Mz\nzc3NAKxfn7FI2ZzXqqoOaqt2wCC33krMIg4JmLGZKb+3JCgAFnQV8hIQ8OA1Wyiimt/AEpHfAX+v\nqitEZBp0Ohs3qeoPvYdd+6nqPr5SEdFC6jCi49VXYcwYQVW7KFq5nNdH2Mb1fEDbLtf81i0N7N1e\nB9u97m4ndbFu95b09M4M5TKld+bJD6530Sb/u2oLbGgjJTgdOKFrS1tP36c9kN+RIR2sNLgtPS9X\nfm8IKmF1nvz0vJq0bcF0TaBcbVqZ9HVwP+XaDPsELoFq3PyB/jCwAbauzNmxoiOy7zWbTqGu+68C\nj4pIDbASuAw3cvIJEbkcWAVM6U1jjVgoi/NaTQd1tEE/L2NQ0AoHc6d0NoLyfCFEDyIY+lX65yih\nLzUuSMBV9TVgTIZNZ4TbHKOYlMt5rWYPdcELth+dIg7mTrF44pA3nng5C7hhlDLOAk8TP0/EIWnW\nuMUT7x4hxRPvbrUlggWzMgzDSChmgRuJp4bd1Hc6TAKk+cTB3CnlGU+8J0MM/TIBn3gCMQE3Eo/z\ngecQs0S5UyyeeOb1QgghnnjCMBeKYRhGQjEL3Eg8NbTTkMmFEiQx7hSLJ56i2DM260gaJuBG4sk4\nCiUbJe9OqcQAWFAa8cSTh7lQDMMwEopZ4EbiKciFEqSk3SkWTzxVTzHdKQIM7MaxSwMTcCPxVNNB\nbbeFiyzulFIQcahcd0pcMzaTNwIFzIViGIaRWMwCNxJPt10oQSwAVoBKjieeTFvWBNxIPL0ScB8L\ngOVRqfHEkyngyWy1YRiGYRa4kXxCscAhAWPEofzdKXFNuU+mLWsCbiSe0AQcSnyIIZS+OyUpAbCg\n63dR1d0GlgTJvO0YhmEYZoEbyadbU+kLJbHuFHshRPfwv4u9PWlg7JiAG4knazzw3mLulAClHE88\njCGGyZTCZLbaMAKE6gPPRKKs8UqMJx7WEMPkUZAPXES+LiJ/FJHXReRREekrIiNFZJGIrBCRx0TE\nbgYJw86rYSSbvBeniAwFrgaOVtXdIvI4MBU4F7hNVeeIyL3AFcB9kbbWCI1yOq+RW+CQIHdKJcYT\nD2PGpobSumJTqHVVBQwQkb24X8Z64DO4Cx5gFjCdEr/QjX0oi/Pal90MYktxKit5d4oFwErVSVq5\nXCRTwPO6UFR1PXAbsBpYB2wFFgNbVNV/dLsWGBpVI43wsfNqGMmnEBfKIGASMAJ3kc8BJkTcLiNi\nyum89t29h6q2ndBYRCscStSdUonxxMMIgJXMcLKFuFDOAFaq6iYAEfkVMB4YJCJ9PGttGM6Ky8j0\n6dM7001NTTQ1NfWiyUYhNDc309zcDMD69RmLlM157bMb+myDBt+fUEwht3jiHnG6U6IOgFUcgtds\noYhqbt+PiIwFHgTGALuAh4BXgE8Dv1TVx72HXa+p6k8z7K/56jCi5dVXYcwYQVU71aWszuv2+2Hz\nlbR7L1RpbezPFgbRSoNbp4HtXnoHdV3yg+k2ajvXg+V2UNtl/x2e33sHtbTtcunWLQ3s3V4H272v\neDsp//R2b0lP78yQDu6zM7AtX76f7qJN/vlpC2xoIyXgHaREtC3LOl5ee4Z90tNkWA/m5crvDcEb\nR3We/PS8GqCagQNr2Lr1CyG1JxxEul6zmchrgavqyyLyJLAE940vAWYCzwKzReSfvLwHe99ko1iU\n1XndDWxNXZoNxOdOKb2HmmABsApxp5SvCwVVnQHMSMt+Fzg59BYZRaNszqsn4D41FFnEweKJd1Lu\n8cRLCwtmZRiGkVBslp2RfHYBG7tmdVrhEPNDTTB3SmcjKE13SgdJtWVNwI3ksxvYtm92KfjEwdwp\nyYknnjySedsxDMMwzAI3yoAMLpQg5k7B4onndaeU8SgUwyhp0kahZMLcKR4l706JK554Mp0RJuBG\n8tkFbCqsqFnjWDzxrPHEk0cybzuGYRiGWeBGGVCACyWIuVM8LJ44KSvcfOCGEQ87yfkQMxvmTsHi\niXfxiScPc6EYhmEklOTeegzDpxsPMdMxd4pHxccTrwrpmMXFBNxIPj10oQSJMwAWWDzx0ognnjzM\nhWIYhpFQzAI3kk87GWOhdJfYHmqCxRPvrDSuAFjJtGVNwI3Es3sn7NwIA0M4Viw+cbB44p3EGQAr\neSTztmMYhmGYBW4kn7a9sCEQ0CosS9zGiHufJe9OCfOhZrIwATcSTxvQAm44IUCS3SklPcQQStOd\nEsUQw2RgLhTDMIyEYha4ETrNzc00NTUVrb424L+A0/0Mz50ShhUO2d0przTv4OimkCpJJ5s75ZXf\nwTFRVZqFjmbAq7No8cTfBEb7DaAYEQyL/bsNoz4TcCN04hDwl4GPBjMj8IlDV3fKH5qrGNNUl32n\n3pLJnfL6CzCmKbo6M/FhM/T36iyaO2UFcHRgPfoAWEkUcHOhGIZhJBSzwCuAAw+MuwXR0gZ8CGxI\n3xDyQ03o6k7pTxUNnbZxhATcKVv7tkO9ehuK9GCzmtS/ASjSCyH6kFme4ppyX5qIquYv1ZsKRKKt\nwCgYVQ3tirfzahjRk++ajVzADcMwjGgwH7hhGEZCMQE3DMNIKCbgRmiIyI9EZLmILBWRp0RkYGDb\nTSLytrf9rJDrnSAib4rIChG5Icxje8cfJiLPi8gbIvIHEfmql7+fiMwTkbdE5DkRaYyg7j4islhE\n5nrrI0VkkdfXx0Qk1IEIItIoInO88/SGiJwcdT9F5Osi8kcReV1EHhWRvmH3U0QeFJEWEXk9kJe1\nXyJyl/d7XSoiJ4ZYZ6jXiAm4ESbzgONU9UTgbeAmABE5FpgCHAOcA9wjIqE8UBWRPsC/AmcDxwFT\nReTo3Ht1mw7gWlU9DhgHfMWr40ZgvqoeBTyP19+QuQZYFlj/IXCbqh4JbAGuCLm+O4FnVfUY4ATc\njJrI+ikiQ4GrgZNU9WO4YSR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- "text": [ - "" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## OT solution (Earth Mover's Distance)" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "G0=ot.emd(a,b,M)\n", - "\n", - "pl.figure(3)\n", - "ot.plot.plot1D_mat(a,b,G0,'OT matrix G0')\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Regularized Optimal transport (Entropic regularization)" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# reg parameter\n", - "lambd=1e-3\n", - "\n", - "Gs=ot.sinkhorn(a,b,M,lambd)\n", - "\n", - "pl.figure(4)\n", - "ot.plot.plot1D_mat(a,b,Gs,'OT matrix Sinkhorn')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 6 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 6 - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/notebooks/Demo_1D_barycenter.ipynb b/notebooks/Demo_1D_barycenter.ipynb deleted file mode 100644 index c93d00b..0000000 --- a/notebooks/Demo_1D_barycenter.ipynb +++ /dev/null @@ -1,297 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# 1D Wasserstein barycenter demo" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot\n", - "\n", - "from mpl_toolkits.mplot3d import Axes3D\n", - "from matplotlib.collections import PolyCollection\n", - "from matplotlib.colors import colorConverter" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Dataset Generation" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n=100 # nb bins\n", - "\n", - "# bin positions\n", - "x=np.arange(n,dtype=np.float64)\n", - "\n", - "# Gaussian distributions\n", - "a1=ot.datasets.get_1D_gauss(n,m=20,s=5) # m= mean, s= std\n", - "a2=ot.datasets.get_1D_gauss(n,m=60,s=8)\n", - "\n", - "# creating matrix A containing all distributions\n", - "A=np.vstack((a1,a2)).T\n", - "nbd=A.shape[1]\n", - "\n", - "# loss matrix + normalization\n", - "M=ot.utils.dist0(n)\n", - "M/=M.max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot distributions" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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tH/b/IY3rNuZ/5v1PrV5XJNGosAiBzZvh4MHkLixAS07l+D228DF27t/Jz86t\n/b0lmtVvxh1n38FjCx9jy54ttX59kUShwiIEkn0PiygVFnI89h/ez4PzHuQ7fb5DRosMLxl+fPaP\nSU9LZ8J7E7xcXyQRqLAIgbAVFtrLQqrjbx/9jS1fbuHn5/3cW4aWDVvyo2/9iD9/8Ge+2PuFtxwi\nPqmwCIGiImjVKriRVzLLyIB9+2Cr7kYtMTpYcpD7372f63tfT7dW3bxmGXvOWEpKS3jo/fjv9imS\nDFRYhECyT9yM0l4WUl1Pf/w064vX84vzfuE7Cm0at+HWs27loQUPUby/2HcckVqnwiIEwlJYaC8L\nqY5SV8of3vkD2ZnZ9Grby3ccAH7y7Z+w99Be/vLhX3xHEal1KixCICyFRYsWwZbkKiwkFv9c/U9W\nbV/FT7/9U99RvtKhaQduOv0m/vLhXygpLfEdR6RWqbBIcqWlsG5dOAoL0F4WErtHFz5Kn3Z9OLvj\n2b6jHGHMWWNYX7yematn+o4iUqtUWCS5zz4L9rBI5vuElKUlpxKLTbs38dKKlxiTNabG7wkSq/4d\n+tO3fV8eXfio7ygitUqFRZILy1LTKBUWEou/5v+V+un1ufH0G31H+QYzY0zWGF5e+bK2+ZaUosIi\nyUXfhDvXzr2Wapz2spCqKikt4YmPnuD6XtfTvEFz33EqdMPpN9AwvSF/zf+r7ygitUaFRZIrKoLW\nraFJE99J4iMjA/bvh88/951EEt3M1TNZX7yeMWeN8R2lUs3qN+OG02/giY+e4HDpYd9xRGqFCosk\nF5YVIVFacipV9ejCR+nbvi/9O/T3HeWoRmeNZsOuDbz2yWu+o4jUChUWSS5shUV0SEeFhRzNhl0b\neHnlywk5abO8szqcRb8T+/FY/mO+o4jUChUWSW7t2nAVFi1aBF9acipH89f8v9IwvSE3nH6D7yhV\nMiZrDK9+8iqfFn/qO4pIjVNhkcRKSmD9+vAsNY3SyhA5muikzZzeOTSr38x3nCrJ6Z1Do7qNeCL/\nCd9RRGpctQoLM7vdzNaa2T4ze8/MjjrIaWYjzawg0n6xmQ05SttHzazUzH5cnWyp5LPP4NChcPVY\ngAoLObrXVr3Ghl0bEnrSZnlN6zflxtNv1CROSQkxFxZmdh3wR2AccCawGJhpZq0raT8AeBZ4HOgL\nzABmmFnPCtpeA3wL2BhrrlQUtj0solRYyNH89aO/cmb7Mzmrw1m+o8RkdNZoNu3exOurXvcdRaRG\nVafHYiyHqLAnAAAgAElEQVTwqHPuKedcIXArsBf4XiXt7wBec86Nd86tcM6NA/KBH5VtZGYdgYeA\nGwCV9FUQtj0sojIygm3KtZeFlLd933ZeWfkKo84Y5TtKzM5sfya92vTimSXP+I4iUqNiKizMrC6Q\nBcyOHnPOOWAWMKCSpw2IPF7WzLLtLZjW/RTwgHOuIJZMqayoCNq0gcaNfSeJr+heFlu2+E4iiWbq\n8qmUuBKu73297ygxMzNuPP1GXih8gd0HdvuOI1JjYu2xaA3UAcr/yd8CtK/kOe2r0P4e4KBz7uEY\n86S0sC01jdJeFlKZZ5Y8w6BTBtG+SWV/bhLbDaffwL7D+5hROMN3FJEakx6n8xgQS8f1V+3NLAv4\nMcF8jZiMHTuW5s2P3Mo3JyeHnJycWE+VlIqKwrciBI7cy+Kcc7xGkQSyvng9b697m0nDJvmOUm2d\nW3TmvJPP45klz/CdM77jO46kgNzcXHJzc484VlxcXKPXjLWw2AaUAO3KHW/LN3slojYfo/15QBvg\n0zIb3dQBxpvZnc65LpWFmTBhAv369at6+pBZuxaysnyniL/mzaFlS+1lIUfKXZJLg/QGXJt5re8o\nx+XG02/k9ldvZ8ueLbRrUv5Po0h8VfRhOz8/n6wafPOIaSjEOXcIWAgMih6LzI8YBMyr5Gnzy7aP\nuDRyHIK5FX2AM8p8bQIeAAbHki+VRPewCONQCGhliHzTM0ue4eruVyfN3hWVGdlzJGmWxnPLnvMd\nRaRGVGdVyHhgtJmNMrMewCNAI2ASgJk9ZWa/L9P+T8AQM7vLzLqb2W8IJoA+DOCc2+GcW172CzgE\nbHbOfVLtVxZymzbB4cMqLCQ1LNmyhCWfL0nI26PHqlWjVgw5dYhWh0hoxVxYOOeeB+4G7gU+Iuht\nGOyc2xpp0okyEzOdc/OBHGA0sAjIBoZFCohKLxNrrlQT1j0sojIyNBQiX3t2ybOc0PAELj/1ct9R\n4uLG029kwcYFrNq+yncUkbir1uRN59xEYGIlj11cwbE8IC+G81c6r0ICa9YE38O2h0XUKacEe1mU\nlECdOr7TiE+lrpRnlz7LyJ4jqVennu84cTG0+1Ca1GvCs0ue5dcX/Np3HJG40r1CktTq1dChAzRq\n5DtJzejaFQ4eDIZ8JLW9u/5d1hevD8UwSFSjuo3IzszmmSXP4LQTnISMCosktWYNdAlxv070ta1e\n7TeH+PfMkmc4ufnJnHvyub6jxNUNvW9g5RcrWfjZQt9RROJKhUWSWr06+FQfVhkZYKbCItUdLDnI\nlOVTuKH3DaRZuP5cDeoyiLaN2/LMx5rEKeESrv9SU0jYeywaNICOHb+eSyKpaeaqmWzft50b+4Rn\nGCQqPS2d63tdz+RlkykpLfEdRyRuVFgkod274fPPw91jAcHrU49FapuyfAo92/Skd9vevqPUiOt6\nX8fmPZuZ92ll2wCJJB8VFkkougwzFQoL9VikrgOHD/DiihcZkTnCd5Qac06nc+jYtCNTlk/xHUUk\nblRYJKHop/gwD4VA8PrUY5G6Zq2ZRfGBYkb2Guk7So1JszSGZw4nryCPUlfqO45IXKiwSEKrV0OT\nJsEt08Osa1fYvh127vSdRHyYWjCV7q2606tNL99RatTIXiPZtHsT8z+df+zGIklAhUUSik7c/Pqe\nbeEU7ZHRcEjqOVhykBmFMxjZcyQW8n/o3z7p25zY5ESmLp/qO4pIXKiwSEJhX2oaFX2NKixSz5tr\n32Tn/p2M6Bne+RVR0eGQqQVTNRwioaDCIgmFfalp1AknQLNmmmeRiqYsm0K3E7rRp10f31FqxYie\nI9iwawMLNi7wHUXkuKmwSDKHDwc3IEuFHgszrQxJRYdKDjFjxQxG9BwR+mGQqPNOPo92jdsxZZlW\nh0jyU2GRZDZsCIqLVOixAK0MSUVvFb3F9n3bGdkzvKtByquTVofszGymFkzVvUMk6amwSDLRN9lU\n6LEAbZKViqYsm0KXll3o276v7yi1amTPkawvXs8Hmz7wHUXkuKiwSDKrV0NaWnhvl15e166wfj0c\nOuQ7idSGw6WHmV44PSVWg5R3fufzadOojVaHSNJTYZFk1qyBk0+GunV9J6kdXbpAaSmsW+c7idSG\nOUVz+GLfFymxGqS89LR0sjOzmbJ8ioZDJKmpsEgyqbLUNEpLTlPL1OVTyWiRQdaJWb6jeDGi5wiK\ndhaR/1m+7ygi1abCIsmkylLTqJNOgvR0zbNIBSWlJUwrmMbwzOEpNwwSdWHGhbRu1Fr3DpGkpsIi\niTiXej0W6enBfBL1WITfu5++y9a9W1NyGCQqPS2dYd2HMa1gmoZDJGmpsEgiO3ZAcXFq9ViAlpym\nimkF0+jQtAPf6vgt31G8urbHtXyy/ROWb13uO4pItaiwSCKpttQ0SptkhZ9zjumF07mm+zWkWWr/\nWRrUZRBN6zVlWsE031FEqiW1/wtOMqlyu/Tyoj0W6hkOr/zP8llfvJ7szGzfUbxrkN6AK0+7kumF\n031HEakWFRZJZM2a4P4ZLVr4TlK7unaFPXtg2zbfSaSmTCuYxgkNT2Bg54G+oySE7B7ZfLT5I9bu\nWOs7ikjMVFgkkVSbuBkVfc2aZxFe0wunc3X3q6lbJ0U2aDmGId2GUL9OffVaSFJSYZFEUm2paVT0\nNauwCKeCrQUUbCvg2h7X+o6SMJrUa8JlXS/TPAtJSioskkiq9lg0bQpt2mgCZ1hNL5xO47qNubTL\npb6jJJTszGzmfTqPzXs2+44iEhMVFkniwIHgzqap2GMBWnIaZtMLp3NFtytoWLeh7ygJZehpQ0mz\nNF4ofMF3FJGYqLBIEkVFwaqIVOyxAC05Dav1xev5cNOHWg1SgVaNWnFBxgVMK9RwiCSXahUWZna7\nma01s31m9p6Z9T9G+5FmVhBpv9jMhpR7fFzk8T1mtt3M3jCz1N4lp5xUXWoapR6LcJpeMJ16depx\nRbcrfEdJSNk9snlz7Zvs3L/TdxSRKou5sDCz64A/AuOAM4HFwEwza11J+wHAs8DjQF9gBjDDzHqW\nabYCuB3oDZwLFAH/NLNWseYLqzVroF496NjRdxI/unaFTZtg3z7fSSSephdO55Iul9CsfjPfURLS\nNT2u4XDpYV5e+bLvKCJVVp0ei7HAo865p5xzhcCtwF7ge5W0vwN4zTk33jm3wjk3DsgHfhRt4Jyb\n7Jx70zlX5JwrAO4CmgF9qpEvlFavhowMqFPHdxI/oj01a7WsPzQ+//Jz5q6fS3YPDYNUpmOzjpzd\n8WytDpGkElNhYWZ1gSxgdvSYC+6UMwsYUMnTBkQeL2tmZe0j1xgD7CToDRGCHotUnV8Bun16GL24\n4kUAru5+teckiS07M5vXV73O3kN7fUcRqZJYeyxaA3WALeWObwHaV/Kc9lVpb2ZXmtluYD9BL8el\nzrntMeYLrVRdahp14onQoIHmWYTJ9MLpnH/y+bRp3MZ3lIR2bY9r2Xd4H6+vet13FJEqSY/TeQyI\n5U4OFbV/EziDoHj5ATDFzL7lnKt0I+exY8fSvHnzI47l5OSQk5MTQ5TEV1oafFL//vd9J/EnLS0Y\nDlm1yncSiYfi/cXMWjOLBy990HeUhNetVTd6t+3N9MLpWj0jMcvNzSU3N/eIY8XFxTV6zVgLi21A\nCdCu3PG2fLNXImpzVdo75/YBayJfC8xsJfB94P7KwkyYMIF+/fpVOXyy+vTTYNJijx6+k/jVvTus\nWOE7hcTDq5+8ysGSg9pts4qye2Tzp/f/xMGSg9SrU893HEkiFX3Yzs/PJysrq8auGdNQiHPuELAQ\nGBQ9ZmYW+XleJU+bX7Z9xKWR48fKVj+WfGFVWBh8797dbw7funf/+nchyW164XTO6nAWJzU/yXeU\npJCdmU3xgWLmFM3xHUXkmKqzKmQ8MNrMRplZD+ARoBEwCcDMnjKz35dp/ydgiJndZWbdzew3BBNA\nH460b2RmvzOzs83sZDPrZ2Z/AzoAU6r9ykJkxQqoXx86d/adxK8ePYLemy+/9J1Ejse+Q/t49ZNX\ntRokBn3a9eGUFqdodYgkhZgLC+fc88DdwL3ARwRLQgc757ZGmnSizMRM59x8IAcYDSwCsoFhzrnl\nkSYlQA9gKsF+Fi8CLYHzIktPU15hIXTrlrpLTaOiPTYrV/rNIcfnjTVv8OWhL7k2U8MgVWVmZGdm\nM6NwBiWlJb7jiBxVtXbedM5NdM5lOOcaOucGOOc+LPPYxc6575Vrn+ec6xFp38c5N7PMYwecc8Od\ncydFHu/knLvWOZdf/ZcVLitWaH4FfF1YaJ5FcptWMI3M1pn0aK1/1LHIzsxmy5dbeG/De76jiByV\n7hWSBAoLVVgAtGwJ7dppnkUyO1RyiJdWvqTVDdVwTqdzaN+kvYZDJOGpsEhwu3cHW1mn+sTNKE3g\nTG5vr3ub7fu2azVINaRZGtd0v4bphdMJ9iUUSUwqLBJctNtfPRaBHj00FJLMphVM4+TmJ9PvxPAv\nE68J2ZnZrN25lsVbtCmxJC4VFgku+iZ62ml+cySK6F4WpaW+k0isSl0pM1bMILtHNsEqdYnVhRkX\n0qJBCw2HSEJTYZHgCguhQwdopps/AkGPxb59sGGD7yQSqwUbF7Bp9yatBjkOdevUZehpQ5leON13\nFJFKqbBIcIWFml9RVvR3oXkWyWdawTTaNGrDuSed6ztKUsvOzGbp50tZ+YXWXUtiUmGR4LTU9EgZ\nGVCvngqLZOOcY1rBNIZ1H0adtBTfkOU4Xdb1MhqmN2R6gXotJDGpsEhgJSXBZlAqLL5Wp04w30QT\nOJPLks+XsHrHai0zjYNGdRsxpNsQphVqnoUkJhUWCWz9ejhwQEMh5WnJafLJW55H8/rNufiUi31H\nCYXhmcNZsHEB64vX+44i8g0qLBJY9M1TPRZH0pLT5DO1YCpXd7+a+um6r2A8XHXaVdSrU0+rQyQh\nqbBIYCtWQMOGcJJuAHmE7t1h48Zg8zBJfMu3Lmf51uWM6DnCd5TQaFa/GYO7Dmbq8qm+o4h8gwqL\nBFZYGMwnSNP/S0eI9uDoZmTJIW95Hk3qNeGyrpf5jhIqI3qO4N1P32Xjro2+o4gcQW9ZCUwrQiqm\nJafJZWrBVIaeNpQG6Q18RwmVoacNpW5aXQ2HSMJRYZHAtIdFxZo1gxNPVGGRDFZ+sZKPt3ysYZAa\n0LJhSy7pcglTCzQcIolFhUWCKi6GzZvVY1EZTeBMDnnL82hUtxGXn3q57yihNKLnCOaum8vmPZt9\nRxH5igqLBBV901SPRcW05DQ5TC2YypXdrqRR3Ua+o4TSsO7DSLM0bZYlCUWFRYKKvmnq5mMV69ED\nPvkk2ERMEtOaHWvI/yyfkT1H+o4SWq0atWJQl0EaDpGEosIiQa1YAZ06QZMmvpMkpu7dYf/+YBMx\nSUx5y/NomN6QId2G+I4SaiMyRzCnaA5bv9zqO4oIoMIiYRUWan7F0UR/NxoOSVxTC6YypNsQmtRT\ndVyTrulxDQAzCmd4TiISUGGRoLTU9OhOPhkaNNAEzkS1buc6FmxcwIhMrQapaW0at+HCjAs1HCIJ\nQ4VFAiopCeYPaOJm5dLSgvkn6rFITNMKplG/Tn2uPO1K31FSwojMEcxeM5sv9n7hO4qICotEVFQE\nBw+qx+JYtOQ0cU1ZPoXBpw6mWf1mvqOkhGszr6XUlfLCihd8RxFRYZGIop/C1WNxdFpympjW7VzH\n/A3ztRqkFrVv0p6BnQcyeelk31FEVFgkosJCaNwYOnb0nSSx9egRbCJWXOw7iZQ1eelkGqQ3YFj3\nYb6jpJSc3jnMXjubLXu2+I4iKU6FRQIqKAg+jevmY0cXHSoqKPCbQ440edlkhp42lKb1m/qOklJG\n9BxBmqXpjqfind66EtDixXDGGb5TJL6ePaFOneD3JYmhcFshizYvIqd3ju8oKadVo1Zc1vUycpfm\n+o4iKU6FRYI5fBiWLIG+fX0nSXwNGkBmJixa5DuJROUuyaVZ/WbaFMuTnN45vPvpu6wv1s5x4k+1\nCgszu93M1prZPjN7z8z6H6P9SDMriLRfbGZDyjyWbmb3m9nHZrbHzDaa2ZNmdmJ1siW7FSvgwAEV\nFlXVt68Ki0ThnCN3aS7Zmdm6Rbonw7oPo0F6A03iFK9iLizM7Drgj8A44ExgMTDTzFpX0n4A8Czw\nONAXmAHMMLOekSaNIsf/K3K+a4HuQEqum4q+SWoopGr69oWPP9Y9QxJB/mf5fLL9Ew2DeNS0flOu\nOu0qDYeIV9XpsRgLPOqce8o5VwjcCuwFvldJ+zuA15xz451zK5xz44B84EcAzrldzrnBzrk859wn\nzrkFkceyzKxTNfIltUWL4JRToHlz30mSQ9++sHcvrFrlO4nkLs2lbeO2XHzKxb6jpLSc3jks2ryI\nwm1aiy1+xFRYmFldIAuYHT3mnHPALGBAJU8bEHm8rJlHaQ/QAnDAzljyhcGiRRoGiUW0Z0fDIX6V\nulKeW/YcI3uOJD0t3XeclHZFtytoVr8ZuUvUayF+xNpj0RqoA5RfKL0FaF/Jc9rH0t7M6gP3Ac86\n5/bEmC+pOafCIlatWwd3gVVh4dc7699hw64NGgZJAA3SG3Btj2vJXZpL8LlPpHbFa1WIEfQwHFd7\nM0sHpkQeuy0+0ZLHpk2wbZsKi1hpAqd/uUtyObn5yQw46WgdkVJbcnrn8Mn2T8j/LN93FElBsfZZ\nbgNKgHbljrflm70SUZur0r5MUXEScHFVeivGjh1L83KTEXJycsjJSc5PTdE3RxUWsenbF554wneK\n1HWo5BBTC6ZyS99bSDOtYE8Eg7oMok2jNkxeOpmsDlm+44hHubm55OYeOSxWXMPbFcdUWDjnDpnZ\nQmAQ8CKAmVnk54cqedr8Ch6/NHKcyDmiRUUX4CLn3I6q5JkwYQL9+vWL5SUktEWLoGVLOOkk30mS\nS9++wdbemzdD+8oG5KTGzF47m217t2kYJIGkp6UzsudIJi+bzP2X3q+CL4VV9GE7Pz+frKyaKzir\n869tPDDazEaZWQ/gEYIlo5MAzOwpM/t9mfZ/AoaY2V1m1t3MfkMwAfThSPs6QB7QD7gJqGtm7SJf\ndav5upLSRx8Fb5JmvpMkl2gPj4ZD/Hj646fp0boHfdurqy2R5Jyew4ZdG3h73du+o0iKibmwcM49\nD9wN3At8BPQBBjvntkaadKLMxEzn3HwgBxgNLAKygWHOueVl2l8V+b4I2AR8FvmeUgO2mrhZPaec\nAk2bqrDwoXh/MdMKpvHdM76LqSJOKOeedC5dW3Zl0qJJvqNIiqlW/5hzbqJzLsM519A5N8A592GZ\nxy52zn2vXPs851yPSPs+zrmZZR5b55yrU+4rLfI9ZUrtXbtg9WoVFtWRlqYJnL48v+x5DpQc4Dt9\nvuM7ipRjZtzc92amLJ/C7gO7fceRFKKBtwTx8cfBdxUW1aPCwo9JiydxWdfL6Niso+8oUoFRZ4xi\n36F9uuOp1CoVFgli0SKoV+/rW4FLbPr2hZUr4csvfSdJHSu2rWDep/O4+YybfUeRSpzc/GQGdRnE\npMWTfEeRFKLCIkEsWgS9egXFhcSub99gg7ElS3wnSR1PLn6SFg1aMKzHMN9R5ChuPuNm3l73Nqu3\nr/YdRVKECosEoYmbx6dnT0hP13BIbSkpLeGpxU+R0ztHdzJNcNdmXkuz+s14cvGTvqNIilBhkQAO\nHYKlS1VYHI8GDSAzU4VFbZm1ZhYbd2/klr63+I4ix9CobiOu63UdTy5+klJX6juOpAAVFglgxQo4\ncECFxfHSBM7aM2nxJHq26clZHc7yHUWq4Ja+t7C+eD1vrX3LdxRJASosEkD0zTB6p06pnr59g9U1\nJSW+k4Tbjn07mF4wnZvPuFl7VySJczqdw2mtTtMkTqkVKiwSwKJFwSZP5W57IjHq2xf27YNPPvGd\nJNyeW/Ych0sPc1Ofm3xHkSoyM24+42byluex68Au33Ek5FRYJABN3IyPaI+PhkNq1t8X/Z3LT72c\nE5ue6DuKxGDUGaM4UHKA55c97zuKhJwKC8+cU2ERL61aBTdwU2FRc5Z9vowFGxdwc9+bfUeRGHVs\n1pFLu1zK3z76m+8oEnIqLDzbuBG++EKFRbxoAmfNmvjBRNo1bsfV3a/2HUWqYXTWaOZvmM+izfqP\nRGqOCgvPFiwIvofo7u9e9esHH3wApVpVF3e7DuziqY+f4gf9fkC9OtrJLRld3f1qOjXrxJ8X/Nl3\nFAkxFRaezZ0LGRnQqZPvJOFw/vmwfTsUFPhOEj7/WPwP9h3ax5izxviOItWUnpbOmKwxPLPkGXbs\n2+E7joSUCgvP3n47eDOU+DjnnGAHzrdT5r64tcM5x8QPJzKsxzA6NVMVnMx+0O8HHC49rNupS41R\nYeHRrl3BfICBA30nCY/GjSErK+gJkvj517p/sXzrcm7vf7vvKHKc2jVpx4ieI5j44UTtxCk1QoWF\nR/PmBXMBVFjE1/nnBz0WzvlOEh5//uDPZLbO5KKMi3xHkTi4vf/trNq+ijdWv+E7ioSQCguP5s6F\ntm2hWzffScJl4MBgtU1Rke8k4bBx10amF0zntv63aafNkPj2Sd/mjHZn8OcPNIlT4k+FhUdvvx28\nCepvdXyde27wXfMs4uOxhY/RsG5DRp0xyncUiRMz4/b+t/Pyypcp2lnkO46EjAoLT/bvD5aaauJm\n/J1wApx+uuZZxMPBkoM8lv8Y3+nzHZrVb+Y7jsTRDaffQLP6zXjkw0d8R5GQUWHhyYIFcPCg5lfU\nlIED1WMRD9MLprN5z2ZN2gyhxvUac0vfW3gi/wn2H97vO46EiAoLT+bODW46dvrpvpOE0/nnBzcj\n27zZd5Lk9vAHD3NhxoX0atvLdxSpAbf1v40v9n3Bc0uf8x1FQkSFhSdvvx3MBahTx3eScIoOMWk4\npPrmfTqPd9a/w51n3+k7itSQbq26cWW3K3lg3gNaeipxo8LCg8OHg6WmGgapOR06QNeuKiyOxx/e\n+QM92/RkaPehvqNIDfr5eT9n+dblvLTiJd9RJCRUWHiwaBHs2aOJmzVN8yyq7+MtH/Pyypf5+Xk/\nJ830ZyLMzj35XAZ2Hsjv3/k9Tpu/SBzoL4YHb78NDRrAWWf5ThJu558PH38MO3f6TpJ87nvnPjJa\nZHB97+t9R5Fa8PPzfs6CjQt4q+gt31EkBFRYeDB3LgwYAPV0g8gaNXBgsPvmu+/6TpJcVm1fxXPL\nnuOn3/4p6WnpvuNILRjcdTBntj+TP7zzB99RJARUWNSy0tKgsNAwSM3r0gVOPFHzLGL14LsP0rpR\na27pe4vvKFJLzIx7zruHWWtm8cHGD3zHkSSnwqKWFRbCF19o4mZtMNM8i1ht2r2JSYsncdc5d9Gw\nbkPfcaQWDc8cTrcTuqnXQo5btQoLM7vdzNaa2T4ze8/M+h+j/UgzK4i0X2xmQ8o9fq2ZvW5mW82s\n1Mz6VCdXMnj77eC23uec4ztJajj/fPjwQ9i713eS5DB+/ngapjfkh/1/6DuK1LI6aXX4z3P/k+mF\n0ynYWuA7jiSxmAsLM7sO+CMwDjgTWAzMNLPWlbQfADwLPA70BWYAM8ysZ5lmjYF3gP8EQj0tee7c\n4LbejRv7TpIaBg6EQ4fg/fd9J0l82/dt55EPH+H2/rdr++4U9Z0zvkPHph25/937fUeRJFadHoux\nwKPOuaecc4XArcBe4HuVtL8DeM05N945t8I5Nw7IB34UbeCce9o591tgNhDaW3KVlsJbb2l+RW3q\n1Su4d8hbmux+TBPmT6DElXDHOXf4jiKe1KtTj7sH3M3THz/Nqu2rfMeRJBVTYWFmdYEsggIAABcs\nfJ4FDKjkaQMij5c18yjtQ2vBAvjsMxiq/YZqTVoaXHEFTJ/uO0li27R7E+PfG88dZ99B28ZtfccR\nj8acNYb2Tdrz/978f76jSJKKtceiNVAH2FLu+BagfSXPaR9j+9DKy4O2bb++rbfUjuHDYelSWLnS\nd5LENe6tcTRMb8g9593jO4p41qhuI/77ov/m+WXP8/4GjSFK7OK1KsSIbW5ErO2TnnNBYXHttbo/\nSG0bPDiY05KX5ztJYlr2+TL+tuhv/Grgr2jRoIXvOJIARp0xitPbns5P3/ipduOUmMW6+802oARo\nV+54W77ZKxG1Ocb2VTZ27FiaN29+xLGcnBxycnKO99Rxt2gRrF0bfHqW2tWwYTAckpcHP/+57zSJ\n557Z95DRIkMrQeQrddLq8OClD3L5M5fz4ooXGdZjmO9IUk25ubnk5uYecay4uLhGr2mxVqNm9h7w\nvnPujsjPBqwHHnLOPVhB+8lAQ+fcsDLH3gUWO+duK9e2M7AGONM59/FRMvQDFi5cuJB+/frFlN+X\nX/4SJk6ELVugbl3faVLPc8/B9dcHxV1Ghu80iWNO0RwuevIinhvxHP/W6998x5EE4pzjsqcv49Pi\nT1l621Ltwhoi+fn5ZGVlAWQ55/Ljff7qDIWMB0ab2Sgz6wE8AjQCJgGY2VNm9vsy7f8EDDGzu8ys\nu5n9hmAC6MPRBmbW0szOAHoRDJP0MLMzzKx8T0fSysuDYcNUVPhyxRVQvz5Mm+Y7SeIodaX89I2f\n8q2O32Jkz5G+40iCMTMeuOQBVn6xkifyn/AdR5JIzIWFc+554G7gXuAjoA8w2Dm3NdKkE2UmZjrn\n5gM5wGhgEZANDHPOLS9z2qsj53qJYO5FLsGS1DGx5ktEy5cHO25qGMSfpk2DuRaaZ/G155c9z4eb\nPuTBSx8k6HgUOdKZJ57JTX1uYtyccew+sNt3HEkS1Zq86Zyb6JzLcM41dM4NcM59WOaxi51z3yvX\nPs851yPSvo9zbma5x590zqU55+qU+7q3ei8rseTlQZMmcMklvpOktuHDYd482LTJdxL/9h/ezy9m\n/4Kru1/NwM7aX14q99uLf0vx/mIenPeNkW6RCuleIbUgLw+uuiq4Vbr4M3RosJ269rSA3779Wzbs\n2gwEmukAABOoSURBVMD9l2iHRTm6k5ufzN0D7ub+d++ncFuh7ziSBFRY1LDVq2HxYg2DJIKWLWHQ\nIA2HLNq8iPvfvZ9fDvwlPVr38B1HksAvB/6Szs078/0Xv0+pK/UdRxKcCosalpcXLHccMuTYbaXm\nDR8O//oXbN167LZhdLj0MN9/8fv0aN1Dm2FJlTWs25Anrn6CeZ/OY+IHE33HkQSnwqKG5eXB5Zfr\npmOJ4pprgu8vvOA3hy/j549n0eZF/O3qv1GvTj3fcSSJDOw8kB+e9UPumXUP63au8x1HEpgKixr0\n6afB/UE0DJI42rQJ7niaisMhK79Yybg547jrnLvo37G/7ziShO675D5OaHgCY14eox05pVIqLGpQ\nXl6wb8VVV/lOImUNHw6zZ8P27b6T1J5SV8q/v/jvdGzakf+66L98x5Ek1ax+Mx656hFmrp7JPz7+\nh+84kqBUWNSQkhL485+Drvdyu46LZyNHghk8+qjvJLXn0Q8fZe76uTw+9HEa1W3kO44ksSu6XcFN\nfW7iztfvZPOezb7jSAJSYVFDXngBVq2Cn/7UdxIpr107+O534aGH4MAB32lq3uLNi7n7n3czJmsM\nF51yke84EgITBk+gXp163DTtJg6XHvYdRxKMCosa4Bw88ABccAH011B2Qrr77uC+LU8/7TtJzdqx\nbwfZz2fTvXV3Jgye4DuOhETrRq2ZPGIyc4rm8Ks3f+U7jiQYFRY14J134P334Wc/851EKtO9e3Dv\nlgcfhNKQLssvdaWMmjGKHft2kPdveTSs29B3JAmRCzMu5P5L7ue+d+9jeoF2nZOvqbCoAQ88AL16\nae+KRPfTn8KKFfDyy76T1Izfvf07Xln5Cs9kP0OXll18x5EQumvAXYzoOYLvzvguK7at8B1HEoQK\nizhbvjx4o/rJT4IJgpK4vv3t4OuBB3wnib/XV73OuDnj+M2Fv2FIN1W4UjPMjL9d/Tc6NutI9vPZ\n7Dm4x3ckSQAqLOLsf/4HOnSAG27wnUSq4mc/g3ffhfnzfSeJn7U71nJD3g0M6TaEXw78pe84EnJN\n6zdl+nXTWV+8Xlt+C6DCIq42bQomA955J9TTpoZJYejQYL7FgyG5cePGXRu55B+XcELDE3j62qdJ\nM/0nLjWvR+sePHnNk0xZNoU7X79Tm2elOP3ViaOHHgruCzJ6tO8kUlVpacGw1YwZsHKl7zTHZ8ue\nLQx6ahCHSg4xa9QsWjZs6TuSpJDszGweueoR/m/B/3HPrHtUXKQwFRZxsnMn/OUvMGaMNsRKNjfd\nBG3bJnevxfZ927n0H5ey68AuZo+aTUaLDN+RJAWNzhrNhMETeGDeA9z7r3t9xxFP0n0HCIu77gr2\nr7jzTt9JJFYNGsA99wT/H373u3Deeb4TxaZ4fzGDnx7MZ3s+4183/4turbr5jiQp7M5z7mTfoX38\n4s1f0LBuQ352rtbdpxoVFnHw0kvw97/DX/8aTNyU5PMf/wFTpvz/9u49OqrqXuD49zdJSEwQQV7h\nKY/wEEoBBaKCWASBRgtyCyJ6kQK+Cl0i1y6E2weot2JdSgWEIsYuQJCH4lVpNcEgl6WApgRBK+Gl\nKQghQoBAVl7kse8f+yQMIYkzk0kOCb8Pa6/MnNlz5sdvkjO/OY+9bWGxdy80bOh2RL45m3eWe9be\nw+Ezh9k6aSs9mvdwOySlmHP7HPKK8ng66WlCPaHMvGUmopfJXTX0UEg1ZWbCI4/A3XfD5MluR6MC\nFRICK1dCRkbdGdjsQOYBYuNj2Z+5n8T/TKRPdB+3Q1KqzDM/e4bZA2fz1OanmPaPaRQWF7odkqol\nWlhU0/TpUFgIr7+u41bUdTEx9jyLv/4VNm92O5qqbf52M7HxsYR6Qkl+OJkBbQa4HZJSlxAR5g+b\nT/wv4nnjyzcYvno4p3NPux2WqgVaWFTD+vWwYQMsXQqtWrkdjQqGxx+HYcNgyhR7Qu6VxhjD4i8W\nE7cmjtva3cbOqTvpfH1nt8NSqlJTb5pK0kNJ/Ovkv4iNjyX1VKrbIakapoVFgE6cgGnT4L77YPx4\nt6NRweLx2HNlsrNhxgy3o7lUVn4WUz6YwhMJT/DkLU+yacImrovQS5DUlW/wDYNJfjiZiNAIbnnj\nFt76+i29HLUe08IiAPn59iS/sDBYssTtaFSwtW8PCxfCqlW2XQk+OPABPZf2ZOO+jawYvYKXhr9E\niCfE7bCU8lnHJh3ZMXUHcV3iePDdBxm1bhTHzh9zOyxVA7Sw8FNOjh2t8dNPYc0aaNbM7YhUTZg0\nyZ6M+6tf2fNn3HIq5xT3v3M/o9eNpm90X/ZN38ekPpPcC0ipamgU3oi1v1zLe+PfIyU9hZ5Le7I8\nZbkOA17PaGHhh6wsGD7cTomekABDh7odkaopIhA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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(1)\n", - "for i in range(nbd):\n", - " pl.plot(x,A[:,i])\n", - "pl.title('Distributions')\n", - "pl.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Barycenter computation (for l2 and Wasserstein)" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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eoap2PGAmtm+Hb76Btvl3+f4Jq1DB9cZMm+Z3JsYvKWkpdJ3YldJFS/N+x/eJ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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# l2bary\n", - "bary_l2=A.mean(1)\n", - "\n", - "# wasserstein\n", - "reg=1e-3\n", - "bary_wass=ot.bregman.barycenter(A,M,reg)\n", - "\n", - "pl.figure(2)\n", - "pl.clf()\n", - "pl.subplot(2,1,1)\n", - "for i in range(nbd):\n", - " pl.plot(x,A[:,i])\n", - "pl.title('Distributions')\n", - "pl.xticks([])\n", - "\n", - "pl.subplot(2,1,2)\n", - "pl.plot(x,bary_l2,'r',label='l2')\n", - "pl.plot(x,bary_wass,'g',label='Wasserstein')\n", - "pl.legend()\n", - "pl.title('Barycenters')\n", - "\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "## Barycenter interpolation" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "\n", - "nbalpha=11\n", - "alphalist=np.linspace(0,1,nbalpha)\n", - "\n", - "\n", - "B_l2=np.zeros((n,nbalpha))\n", - "\n", - "B_wass=np.copy(B_l2)\n", - "\n", - "for i in range(0,nbalpha):\n", - " alpha=alphalist[i]\n", - " weights=np.array([1-alpha,alpha])\n", - " B_l2[:,i]=A.dot(weights)\n", - " B_wass[:,i]=ot.bregman.barycenter(A,M,reg,weights)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot interpolation" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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MnDkTy5cvxw033ICXXnoJJSUlqKysRE5Ojt/+FRUVmDhxIhYsWIBRo0Zh3bp1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Vq1eTmZmpX7P6+vpIL7qEgoKiCKs7pJa6Y5KId6brrO74+KFbIUqSSGHBbs7UFbJgwSzuu+/r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\n48aNo3PnznW8S6tDGmR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4//KPT/+VhFqiNiEJskSNwC9vSYVYuOCD1Wp1EOLo6GgoFAoAgF6vr8nmS512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Uk0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\nAlhjjJkMNANWAFvucIW1Uk3S/wOA7iEME+ag6QAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "pl.figure(3)\n", - "\n", - "#pl.subplot(1,2,1)\n", - "cmap=pl.cm.get_cmap('viridis')\n", - "verts = []\n", - "zs = alphalist\n", - "for i,z in enumerate(zs):\n", - " ys = B_l2[:,i]\n", - " verts.append(list(zip(x, ys)))\n", - "\n", - "ax = pl.gcf().gca(projection='3d')\n", - "\n", - "poly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist])\n", - "poly.set_alpha(0.7)\n", - "ax.add_collection3d(poly, zs=zs, zdir='y')\n", - "\n", - "ax.set_xlabel('x')\n", - "ax.set_xlim3d(0, n)\n", - "ax.set_ylabel('$\\\\alpha$')\n", - "ax.set_ylim3d(0,1)\n", - "ax.set_zlabel('')\n", - "ax.set_zlim3d(0, B_l2.max()*1.01)\n", - "pl.title('Barycenter interpolation with l2')\n", - "\n", - "pl.show()\n", - "\n", - "pl.figure(4)\n", - "\n", - "#pl.subplot(1,2,1)\n", - "cmap=pl.cm.get_cmap('viridis')\n", - "verts = []\n", - "zs = alphalist\n", - "for i,z in enumerate(zs):\n", - " ys = B_wass[:,i]\n", - " verts.append(list(zip(x, ys)))\n", - "\n", - "ax = pl.gcf().gca(projection='3d')\n", - "\n", - "poly = PolyCollection(verts,facecolors=[cmap(a) for a in alphalist])\n", - "poly.set_alpha(0.7)\n", - "ax.add_collection3d(poly, zs=zs, zdir='y')\n", - "\n", - "ax.set_xlabel('x')\n", - "ax.set_xlim3d(0, n)\n", - "ax.set_ylabel('$\\\\alpha$')\n", - "ax.set_ylim3d(0,1)\n", - "ax.set_zlabel('')\n", - "ax.set_zlim3d(0, B_l2.max()*1.01)\n", - "pl.title('Barycenter interpolation with Wasserstein')\n", - "\n", - "pl.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/Demo_2D_OT_DomainAdaptation.ipynb b/notebooks/Demo_2D_OT_DomainAdaptation.ipynb deleted file mode 100644 index b713d5b..0000000 --- a/notebooks/Demo_2D_OT_DomainAdaptation.ipynb +++ /dev/null @@ -1,217 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Domain adaptation with optimal transport" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Dataset generation (classification problem) " - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n=150 # nb samples in source and target datasets\n", - "\n", - "xs,ys=ot.datasets.get_data_classif('3gauss',n)\n", - "xt,yt=ot.datasets.get_data_classif('3gauss2',n)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot datasets" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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mudNPP/2EUoo333zTZvvhw4ejtebHH39MU/1KKfr3729zhUxrzdq1a3n++eep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\nuJRSCvgUWKe13ml2POlRSt2hlEoErgBfA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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(1,(10,7))\n", - "\n", - "pl.subplot(2,2,1)\n", - "pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples')\n", - "pl.legend(loc=0)\n", - "pl.axis([-4,4,-4,4])\n", - "pl.title('Source distributions')\n", - "\n", - "pl.subplot(2,2,2)\n", - "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples')\n", - "pl.legend(loc=0)\n", - "pl.axis([-4,4,-4,4])\n", - "pl.title('Target distributions')\n", - "\n", - "pl.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": false - }, - "source": [ - "## Domain adaptation classes" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# LP problem\n", - "da_emd=ot.da.OTDA() # init class\n", - "da_emd.fit(xs,xt) # fit distributions\n", - "xst0=da_emd.interp() # interpolation of source samples\n", - "\n", - "\n", - "# sinkhorn regularization\n", - "lambd=1e-1\n", - "da_entrop=ot.da.OTDA_sinkhorn()\n", - "da_entrop.fit(xs,xt,reg=lambd)\n", - "xsts=da_entrop.interp()\n", - "\n", - "# Group lasso regularization\n", - "reg=1e-1\n", - "eta=1e0\n", - "da_lpl1=ot.da.OTDA_lpl1()\n", - "da_lpl1.fit(xs,ys,xt,reg=lambd,eta=eta)\n", - "xstg=da_lpl1.interp()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot OT matrices and interpolated samples " - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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artIdDXkv+QcCIUR36Z+GQLd05DLgcx3uqRCims7oSFccGQSR+RPgFc65/y4c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O+Xw+n0Ag+DNQeq3nE2RtYTab6bJ00RbeypaQrcSp56ehqFQqdu9Qcd8OJV2mmbS1nL/aS47p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8UyoW0aDAbWrVtFVNQ5iosrAIiPz2DGjBkYjaPORyOE8CA6nW7ch8ReSVxcHJs3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\nhBBCTGnS6RFCCCGEEEJMaf8PUJAvj+BeZCgAAAAASUVORK5CYII=\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(4,(10,7))\n", - "\n", - "param_img={'interpolation':'nearest','cmap':'jet'}\n", - "\n", - "pl.subplot(2,3,1)\n", - "pl.imshow(da_emd.G,**param_img)\n", - "pl.title('OT matrix')\n", - "\n", - "\n", - "pl.subplot(2,3,2)\n", - "pl.imshow(da_entrop.G,**param_img)\n", - "pl.title('OT matrix sinkhorn')\n", - "\n", - "pl.subplot(2,3,3)\n", - "pl.imshow(da_lpl1.G,**param_img)\n", - "pl.title('OT matrix Group Lasso')\n", - "\n", - "pl.subplot(2,3,4)\n", - "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)\n", - "pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Transp samples',s=30)\n", - "pl.title('Interp samples')\n", - "pl.legend(loc=0)\n", - "\n", - "pl.subplot(2,3,5)\n", - "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)\n", - "pl.scatter(xsts[:,0],xsts[:,1],c=ys,marker='+',label='Transp samples',s=30)\n", - "pl.title('Interp samples Sinkhorn')\n", - "\n", - "pl.subplot(2,3,6)\n", - "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=0.3)\n", - "pl.scatter(xstg[:,0],xstg[:,1],c=ys,marker='+',label='Transp samples',s=30)\n", - "pl.title('Interp samples Group Lasso')\n", - "\n", - "pl.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 0 -} diff --git a/notebooks/Demo_2D_OT_samples.ipynb b/notebooks/Demo_2D_OT_samples.ipynb deleted file mode 100644 index e20b09b..0000000 --- a/notebooks/Demo_2D_OT_samples.ipynb +++ /dev/null @@ -1,234 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:e01831efa84095a87a681a09f08c8991bbe439e010c2bc4cd3559dc4d3bf0bde" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Data generation" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "n=20 # nb samples\n", - "\n", - "mu_s=np.array([0,0])\n", - "cov_s=np.array([[1,0],[0,1]])\n", - "\n", - "mu_t=np.array([4,4])\n", - "cov_t=np.array([[1,-.8],[-.8,1]])\n", - "\n", - "xs=ot.datasets.get_2D_samples_gauss(n,mu_s,cov_s)\n", - "xt=ot.datasets.get_2D_samples_gauss(n,mu_t,cov_t)\n", - "\n", - "a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples\n", - "\n", - "# loss matrix\n", - "M=ot.dist(xs,xt)\n", - "M/=M.max()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Plot dataset" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "pl.figure(1)\n", - "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", - "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", - "pl.legend(loc=0)\n", - "pl.title('Source and traget distributions')\n", - "\n", - "pl.figure(2)\n", - "pl.imshow(M,interpolation='nearest')\n", - "pl.title('Cost matrix M')\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 3, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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Cd5pZEcGPw4Pu/mQE7YqISIJ0BqiISBbTGaAiIgVEyVxEJA8omYuI5AElcxGR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\nXEQkDyiZi4jkASVzEZE88P8B8sPqteLhUE0AAAAASUVORK5CYII=\n", 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- "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Solve OT" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "G0=ot.emd(a,b,M)\n", - "\n", - "\n", - "pl.figure(3)\n", - "pl.imshow(G0,interpolation='nearest')\n", - "pl.title('Cost matrix M')\n", - "\n", - "pl.figure(4)\n", - "ot.plot.plot2D_samples_mat(xs,xt,G0,c=[.5,.5,1])\n", - "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", - "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", - "pl.legend(loc=0)\n", - "pl.title('OT matrix')\n", - "\n" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 4, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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wd24wcMtRdH//MKrvPYS17ziXQuT1z8hgFmpYGLs32TXWjz461DN/7jmvxu2n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\n/XWSd0EBCSs62rHnqe6VuXkzf2evOqIabW3Ad75DmWbbNv7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- "text": [ - "" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Solve OT with entropic regularization" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "# reg term\n", - "lambd=5e-3\n", - "\n", - "Gs=ot.sinkhorn(a,b,M,lambd)\n", - "\n", - "pl.figure(5)\n", - "pl.imshow(Gs,interpolation='nearest')\n", - "pl.title('OT matrix sinkhorn')\n", - "\n", - "pl.figure(6)\n", - "ot.plot.plot2D_samples_mat(xs,xt,Gs,color=[.5,.5,1])\n", - "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", - "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", - "pl.legend(loc=0)\n", - "pl.title('OT matrix Sinkhorn with samples')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "pyout", - "prompt_number": 5, - "text": [ - "" - ] - }, - { - "metadata": {}, - "output_type": "display_data", - "png": 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5eXmorKxEJBLBddddh0gkMq5rFVcNA/CLX/wCmzZtQn9/P2644YaUcnHct7a2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- "text": [ - "" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 5 - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/notebooks/Demo_2D_OTmapping_DomainAdaptation.ipynb b/notebooks/Demo_2D_OTmapping_DomainAdaptation.ipynb deleted file mode 100644 index 4b75f58..0000000 --- a/notebooks/Demo_2D_OTmapping_DomainAdaptation.ipynb +++ /dev/null @@ -1,283 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## OT mapping estimation for domain adaptation" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Dataset generation" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "np.random.seed(0) # makes example reproducible\n", - "\n", - "n=100 # nb samples in source and target datasets\n", - "theta=2*np.pi/20\n", - "nz=0.1\n", - "xs,ys=ot.datasets.get_data_classif('gaussrot',n,nz=nz)\n", - "xt,yt=ot.datasets.get_data_classif('gaussrot',n,theta=theta,nz=nz)\n", - "\n", - "# one of the target mode changes its variance (no linear mapping)\n", - "xt[yt==2]*=3\n", - "xt=xt+4\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "collapsed": true - }, - "source": [ - "### Plot source and target datasets" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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iSpWq5M+fn0KFClGiRClWrVrllOfw4cOEhYVx8+bNDKqlEEI82KRHVAjxwLp2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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(1,(8,5))\n", - "pl.clf()\n", - "\n", - "pl.scatter(xs[:,0],xs[:,1],c=ys,marker='+',label='Source samples')\n", - "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples')\n", - "\n", - "pl.legend(loc=0)\n", - "pl.title('Source and target distributions')\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### OT linear mapping estimation" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 0|4.009366e+03|0.000000e+00\n", - " 1|3.999933e+03|-2.352753e-03\n", - " 2|3.999520e+03|-1.031984e-04\n", - " 3|3.999362e+03|-3.936391e-05\n", - " 4|3.999281e+03|-2.032868e-05\n", - " 5|3.999238e+03|-1.083083e-05\n", - " 6|3.999229e+03|-2.125291e-06\n" - ] - } - ], - "source": [ - "\n", - "eta=1e-8 # quadratic regularization for regression\n", - "mu=1e0 # weight of the OT linear term\n", - "bias=True # estimate a bias\n", - "\n", - "ot_mapping=ot.da.OTDA_mapping_linear()\n", - "ot_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True)\n", - "\n", - "xst=ot_mapping.predict(xs) # use the estimated mapping\n", - "xst0=ot_mapping.interp() # use barycentric mapping\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### OT kernel mapping estimation" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 0|4.026411e+02|0.000000e+00\n", - " 1|3.991051e+02|-8.782091e-03\n", - " 2|3.987950e+02|-7.769290e-04\n", - " 3|3.986577e+02|-3.442631e-04\n", - " 4|3.985741e+02|-2.096899e-04\n", - " 5|3.985159e+02|-1.460105e-04\n", - " 6|3.984729e+02|-1.078536e-04\n", - " 7|3.984436e+02|-7.368218e-05\n", - " 8|3.984214e+02|-5.574123e-05\n", - " 9|3.984025e+02|-4.740267e-05\n", - " 10|3.983871e+02|-3.865975e-05\n", - " 11|3.983744e+02|-3.175451e-05\n", - " 12|3.983642e+02|-2.561334e-05\n", - " 13|3.983558e+02|-2.116042e-05\n", - " 14|3.983479e+02|-1.982104e-05\n", - " 15|3.983413e+02|-1.643931e-05\n", - " 16|3.983351e+02|-1.567880e-05\n", - " 17|3.983296e+02|-1.366612e-05\n", - " 18|3.983270e+02|-6.571092e-06\n" - ] - } - ], - "source": [ - "\n", - "eta=1e-5 # quadratic regularization for regression\n", - "mu=1e-1 # weight of the OT linear term\n", - "bias=True # estimate a bias\n", - "sigma=1 # sigma bandwidth fot gaussian kernel\n", - "\n", - "\n", - "ot_mapping_kernel=ot.da.OTDA_mapping_kernel()\n", - "ot_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 20,verbose=True)\n", - "\n", - "xst_kernel=ot_mapping_kernel.predict(xs) # use the estimated mapping\n", - "xst0_kernel=ot_mapping_kernel.interp() # use barycentric mapping" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plotting the mapped samples" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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hYQF5eWn4+3uZyUopOXs2FWfnQNzdte/s7e0JCmrIpUuCS5cuXadalWTJkiW0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\nCgpqdHC4a2LZsmXodDp0Oh1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cuHAGDWpOVFQiY8euY/HigXTqFGx1g2uFQnHzoQwZrm6+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dPaytjMDNVaDe3S57+Qy6HodOtKUHeLWq1uRMmWqdfrjN64Qeittwh985sE3/tePAMDDAwNrZOC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mOfNDCCHElqirq0NRFIaHx0kmg9hseg4dapd9m0KITSWJzA7gcrk4ftxFLpdDo9HIGTJCCCG2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\nLFrOnKHt4EHM+aUCiqIwOzeHp6Wl5O+LJEnSfpBOp7ly+TIhrxc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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "pl.figure(2,(10,7))\n", - "pl.clf()\n", - "pl.subplot(2,2,1)\n", - "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2)\n", - "pl.scatter(xst0[:,0],xst0[:,1],c=ys,marker='+',label='Mapped source samples')\n", - "pl.title(\"Bary. mapping (linear)\")\n", - "pl.legend(loc=0)\n", - "\n", - "pl.subplot(2,2,2)\n", - "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2)\n", - "pl.scatter(xst[:,0],xst[:,1],c=ys,marker='+',label='Learned mapping')\n", - "pl.title(\"Estim. mapping (linear)\")\n", - "\n", - "pl.subplot(2,2,3)\n", - "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2)\n", - "pl.scatter(xst0_kernel[:,0],xst0_kernel[:,1],c=ys,marker='+',label='barycentric mapping')\n", - "pl.title(\"Bary. mapping (kernel)\")\n", - "\n", - "pl.subplot(2,2,4)\n", - "pl.scatter(xt[:,0],xt[:,1],c=yt,marker='o',label='Target samples',alpha=.2)\n", - "pl.scatter(xst_kernel[:,0],xst_kernel[:,1],c=ys,marker='+',label='Learned mapping')\n", - "pl.title(\"Estim. mapping (kernel)\")\n", - "\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Barycentric mapping on the left, estimated mapping on the right. We can see that the change \n", - "in variance of the mode do not allow for a good linear mapping. In this case the kernel \n", - "mapping (lower right) allows for a far better estimation\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/notebooks/Demo_Compute_EMD.ipynb b/notebooks/Demo_Compute_EMD.ipynb deleted file mode 100644 index cda9a59..0000000 --- a/notebooks/Demo_Compute_EMD.ipynb +++ /dev/null @@ -1,167 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Compute and plot EMD" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot\n", - "from ot.datasets import get_1D_gauss as gauss" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generate data" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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EwzCM70VVyU4twlHhbOiq1Lsm1WRkGIbRkDITC1j7wWHSj+YTGhnAiGld6dg/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YVw/XwGT5zA2ea94O/tMtPpdLHWaKLuYhfWfm/u/q6iFgbsLlalm6g6fQK7RYGXX/QxksuGO+0W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\n7yOw6hNkxFBWLpzE863vEdWjXDfmOm4cf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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "n=100 # nb bins\n", - "n_target=25 # nb target distributions\n", - "\n", - "\n", - "# bin positions\n", - "x=np.arange(n,dtype=np.float64)\n", - "\n", - "lst_m=np.linspace(20,90,n_target)\n", - "\n", - "# Gaussian distributions\n", - "a=gauss(n,m=20,s=5) # m= mean, s= std\n", - "\n", - "B=np.zeros((n,n_target))\n", - "\n", - "for i,m in enumerate(lst_m):\n", - " B[:,i]=gauss(n,m=m,s=5)\n", - " \n", - "\n", - "# loss matrix and normalization\n", - "M=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'euclidean')\n", - "M/=M.max()\n", - "\n", - "\n", - "M2=ot.dist(x.reshape((n,1)),x.reshape((n,1)),'sqeuclidean')\n", - "M2/=M2.max()\n", - "\n", - "\n", - "# plot the distributions\n", - "\n", - "pl.figure(1)\n", - "pl.subplot(2,1,1)\n", - "pl.plot(x,a,'b',label='Source distribution')\n", - "pl.xticks([])\n", - "pl.title('Source distribution')\n", - "pl.subplot(2,1,2)\n", - "pl.plot(x,B,label='Target distributions')\n", - "pl.title('Target distributions')\n", - "pl.show() " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute EMD and sinkhorn distances " - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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8atatW9dYjC2l7HJYWBj29vaMHTsWR0dHunfvnuHO2aSkJEaNGsX06dONj2VW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\n7tz7ex5PNrEisNTLMmfdTGSY1jjjCFA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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - "d_emd=ot.emd2(a,B,M) # direct computation of EMD\n", - "d_emd2=ot.emd2(a,B,M2) # direct computation of EMD with loss M2\n", - "\n", - "reg=1e-2\n", - "d_sinkhorn=ot.sinkhorn(a,B,M,reg) # sinkhorn returns a list of distance if B is a matrix\n", - "d_sinkhorn2=ot.sinkhorn(a,B,M2,reg)\n", - "\n", - "pl.figure(2)\n", - "pl.clf()\n", - "pl.plot(d_emd,label='Euclidean EMD')\n", - "pl.plot(d_emd2,label='Squared Euclidean EMD')\n", - "pl.plot(d_sinkhorn,'+',label='Euclidean Sinkhorn')\n", - "pl.plot(d_sinkhorn2,'+',label='Squared Euclidean Sinkhorn')\n", - "pl.title('EMD distances')\n", - "pl.legend()\n", - "pl.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/Demo_Ground_Loss.ipynb b/notebooks/Demo_Ground_Loss.ipynb deleted file mode 100644 index a39a07f..0000000 --- a/notebooks/Demo_Ground_Loss.ipynb +++ /dev/null @@ -1,345 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Choice of the ground loss\n", - "\n", - "Data from [Fig. 1 and 2 in here](https://arxiv.org/pdf/1706.07650.pdf)" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Dataset 1" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "n=20 # nb samples\n", - "xs=np.zeros((n,2))\n", - "xs[:,0]=np.arange(n)+1\n", - "xs[:,1]=(np.arange(n)+1)*-0.001 # to make it strictly convex...\n", - "\n", - "xt=np.zeros((n,2))\n", - "xt[:,1]=np.arange(n)+1\n", - "a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples\n", - "\n", - "pl.figure(1)\n", - "pl.clf()\n", - "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", - "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", - "pl.axis('equal')\n", - "pl.title('Source and traget distributions')\n", - "pl.legend()\n", - "pl.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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JmhXzSdIiMpskzcTU79Bt8XTgNTtc9+iIeA/wSeCFmfmB7Yoi4nzgfIDB2mkc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\ne2mSd+guAp64zeW/mpkPbf9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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# loss matrix\n", - "M1=ot.dist(xs,xt,metric='euclidean')\n", - "M1/=M1.max()\n", - "\n", - "# loss matrix\n", - "M2=ot.dist(xs,xt,metric='sqeuclidean')\n", - "M2/=M2.max()\n", - "\n", - "# loss matrix\n", - "Mp=np.sqrt(ot.dist(xs,xt,metric='euclidean'))\n", - "Mp/=Mp.max()\n", - "\n", - "pl.figure(2,(15,5))\n", - "pl.subplot(1,3,1)\n", - "pl.imshow(M1,interpolation='nearest')\n", - "pl.title('Eucidean cost')\n", - "pl.subplot(1,3,2)\n", - "pl.imshow(M2,interpolation='nearest')\n", - "pl.title('Squared Euclidean cost')\n", - "\n", - "pl.subplot(1,3,3)\n", - "pl.imshow(Mp,interpolation='nearest')\n", - "pl.title('Sqrt Euclidean cost')\n", - "pl.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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w5Uci8sGy3qnAIAf23RaPxsnjsaFZT1zpZ9w76cOamWvWGNaIgi9HQU1KeVhK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\nR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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "\n", - " \n", - "G1=ot.emd(a,b,M1)\n", - "G2=ot.emd(a,b,M2)\n", - "Gp=ot.emd(a,b,Mp)\n", - "\n", - "pl.figure(3,(15,5))\n", - "\n", - "pl.subplot(1,3,1)\n", - "ot.plot.plot2D_samples_mat(xs,xt,G1,c=[.5,.5,1])\n", - "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", - "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", - "pl.axis('equal')\n", - "#pl.legend(loc=0)\n", - "pl.title('OT Euclidean')\n", - "\n", - "pl.subplot(1,3,2)\n", - "\n", - "ot.plot.plot2D_samples_mat(xs,xt,G2,c=[.5,.5,1])\n", - "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", - "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", - "pl.axis('equal')\n", - "#pl.legend(loc=0)\n", - "pl.title('OT squared Euclidean')\n", - "\n", - "pl.subplot(1,3,3)\n", - "\n", - "ot.plot.plot2D_samples_mat(xs,xt,Gp,c=[.5,.5,1])\n", - "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", - "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", - "pl.axis('equal')\n", - "#pl.legend(loc=0)\n", - "pl.title('OT sqrt Euclidean')\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Dataset 2" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "n=50 # nb samples\n", - "xtot=np.zeros((n+1,2))\n", - "xtot[:,0]=np.cos((np.arange(n+1)+1.0)*0.9/(n+2)*2*np.pi+.06*np.pi)\n", - "xtot[:,1]=np.sin((np.arange(n+1)+1.0)*0.9/(n+2)*2*np.pi+.06*np.pi)\n", - "\n", - "xs=xtot[:n,:]\n", - "xt=xtot[1:,:]\n", - "\n", - "a,b = ot.unif(n),ot.unif(n) # uniform distribution on samples\n", - "\n", - "pl.figure(1)\n", - "pl.clf()\n", - "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", - "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", - "pl.axis('equal')\n", - "pl.title('Source and traget distributions')\n", - "pl.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# loss matrix\n", - "M1=ot.dist(xs,xt,metric='euclidean')\n", - "M1/=M1.max()\n", - "\n", - "# loss matrix\n", - "M2=ot.dist(xs,xt,metric='sqeuclidean')\n", - "M2/=M2.max()\n", - "\n", - "# loss matrix\n", - "Mp=np.sqrt(ot.dist(xs,xt,metric='euclidean'))\n", - "Mp/=Mp.max()\n", - "\n", - "pl.figure(2,(15,5))\n", - "pl.subplot(1,3,1)\n", - "pl.imshow(M1,interpolation='nearest')\n", - "pl.title('Eucidean cost')\n", - "pl.subplot(1,3,2)\n", - "pl.imshow(M2,interpolation='nearest')\n", - "pl.title('Squared Euclidean cost')\n", - "\n", - "pl.subplot(1,3,3)\n", - "pl.imshow(Mp,interpolation='nearest')\n", - "pl.title('Sqrt Euclidean cost')\n", - "pl.show()" - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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woWJvBcTKmluyWv3ZtM5c3qjZdR0ysonaCYiCiNRN5Thi95Kk/L1IO+RuG+Du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\ntROiKGG1Ex27EsRt6lPUCPaG1ryWFuk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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - " \n", - "G1=ot.emd(a,b,M1)\n", - "G2=ot.emd(a,b,M2)\n", - "Gp=ot.emd(a,b,Mp)\n", - "\n", - "pl.figure(3,(15,5))\n", - "\n", - "pl.subplot(1,3,1)\n", - "ot.plot.plot2D_samples_mat(xs,xt,G1,c=[.5,.5,1])\n", - "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", - "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", - "pl.axis('equal')\n", - "#pl.legend(loc=0)\n", - "pl.title('OT Euclidean')\n", - "\n", - "pl.subplot(1,3,2)\n", - "\n", - "ot.plot.plot2D_samples_mat(xs,xt,G2,c=[.5,.5,1])\n", - "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", - "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", - "pl.axis('equal')\n", - "#pl.legend(loc=0)\n", - "pl.title('OT squared Euclidean')\n", - "\n", - "pl.subplot(1,3,3)\n", - "\n", - "ot.plot.plot2D_samples_mat(xs,xt,Gp,c=[.5,.5,1])\n", - "pl.plot(xs[:,0],xs[:,1],'+b',label='Source samples')\n", - "pl.plot(xt[:,0],xt[:,1],'xr',label='Target samples')\n", - "pl.axis('equal')\n", - "#pl.legend(loc=0)\n", - "pl.title('OT sqrt Euclidean')\n", - "pl.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/notebooks/Demo_Image_ColorAdaptation.ipynb b/notebooks/Demo_Image_ColorAdaptation.ipynb deleted file mode 100644 index 16e5208..0000000 --- a/notebooks/Demo_Image_ColorAdaptation.ipynb +++ /dev/null @@ -1,316 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Demo of OT Domain Adaptation for color transfer in images\n", - "\n", - "The color adaptation problem and the out of sample interpolation has been proposed in [6]:\n", - "\n", - "[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import scipy.ndimage as spi\n", - "import matplotlib.pylab as pl\n", - "import ot\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Loading images" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "I1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256\n", - "I2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plotting images" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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mTia0hLGlGUdRPJdlnZfEqBoxOLLbusg0fQycdE60mvwID4Ylse5O2ZcHU+LH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\n41yBpIqq38d4HxfdExL3lJffqz5zh0o6f6MfJwg6URVT45HwJ54UukTc9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QTmuMY+j45F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\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J3EjkYbtwM3Qdo3jh1nsXvllEaJzbyucijKlPqTbJCIUDgUMUGoEURz6MC2t0gm58nCIJ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fhX6o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vmv/VN/ir//kyMtKVkrJRlZY5/KUyHl6EbT0oi8iOlizmAe72NtM+QrmZgkaUrsm5A1ADwx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\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4xz+7HdCiqVAp2wpEYK5NyVtsMyPOf2nBvYT53DtihAdiVAQl50iEL3TOr28hR8uTGCQz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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(1,(10,5))\n", - "\n", - "pl.subplot(1,2,1)\n", - "pl.imshow(I1)\n", - "pl.title('Image 1')\n", - "\n", - "pl.subplot(1,2,2)\n", - "pl.imshow(I2)\n", - "pl.title('Image 2')\n", - "\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Convert image to matrix and dataset generation" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def im2mat(I):\n", - " \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n", - " return I.reshape((I.shape[0]*I.shape[1],I.shape[2]))\n", - "\n", - "def mat2im(X,shape):\n", - " \"\"\"Converts back a matrix to an image\"\"\"\n", - " return X.reshape(shape)\n", - "\n", - "X1=im2mat(I1)\n", - "X2=im2mat(I2)\n", - "\n", - "# training samples\n", - "nb=1000\n", - "idx1=np.random.randint(X1.shape[0],size=(nb,))\n", - "idx2=np.random.randint(X2.shape[0],size=(nb,))\n", - "\n", - "xs=X1[idx1,:]\n", - "xt=X2[idx2,:]\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot the images distributions" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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aA6kPJza2iRMnMnz4cJyccpDdwZFFixZhYWFBzRq1GTn8OwAqUYWiRYrRs3dX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\nWxMkGvgdePr8LjOBSCFEmxou5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kkSP//8MwMHDmThggWU6/WMnnTl57Ir5SHgsh+jLefz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m1m\nyXkzSAwJYW1hIcvycokMDydYglvGjibSoKespZVHNmwhWKvl+ekzUSsUALTYbNyyZiUKWSZIo6Gt\ns5OFV17J2++8w5YtW5gyZQqPTDqbod0CZR6vl9vWraPe1olXCPr368ef7ruPyy677EffWw0NDYwe\nNZKKyioiDUG0d9kRSLz51lvk5uby4QcfYLFYmDBhAo88+iijR4/+0XOc4rbbbmPN3z/ky7nTe5z/\noOA4j+89jNvt/tX7lxs2bGDOnAtxu5zER+gpq+2gd3oqz7/wIrNmzYKf+Hvpt7fHqu0knpJdeFuq\nfLWqvq4S6PWA24ms1aNMG4KwdSDsFoTTzrPPPousUNJ5aCOOqkKcdaV0Ht6I19ZBenoaM2fO5OWX\nX8br9RIcHIzXaUcI3wePJElIsgLhtCMr1V9RCdRhyhyBuaODL774wm9GR0cHy1eswJjcF11oJJIk\nodTqiMgcgdPpZPLkyQi3G4VSTVhaf9R6Iy3H87j22mtJSkryz6nRaH71N32AAAECBAjwv4Qsyzzy\nyCPYnXY67Z2n5PXxer1syd2KpctCRkw68cExuN1uxmaMJEijQ5IkokOiyErqT31bA73TfVkNm72L\nquYajlQe40R9BeYuX9Hcr24jqK6uZtOmTYxKzCTKGAZAq62DDruVMJ2R1PA4ogyh5JQe4x97NvDx\nno24vG4SIyI5O3MwCeERbC86whvbVuL2erjppptYdM89KGSZmYNGoFP7/Ik+MfGMTO2DSlagV2to\n6bTy+eF9JIdHgRAoZQWjeqUzvd9gYowhNFrN/gLASWERnNt3AIUNNVgdvsK85u6fEydOxGzv4nhT\nPa/t3kpOVTkbigp4M3sHMdExxCTE0ys1lXvuuQeL1cpZKSm8nZ2N3e3iwkFZTOzdG5VC5t133sHj\n8SDLMgMGDACg0Wolu6qCtUXHyKurpbl7v1ZRQz3vHzjAJ0cOk5KSQkNTE9eOHEakwfeQOzU8jPMz\n+9JktbJ46yY2l55gTXERD23bQmhYGLfcfjs33X47e/bs4Z1330WWZSZPnsy4sWN5KnsPf8/PY2tF\nOdevXkVlRzvjkuK5dthANO0tzJ8/n1deeeVH31t79uyhorKK5BATMUE6InRaPF4Pd9x2G2+9+irT\nYiP4/aB+VB45xKSJE/0KkT8Wh8NBTU0NzTYbzq/t56vvtBFsNH6rf9nS0sKbb77JM888w969e/ml\nEzhTp06lvLyCJ596hosuv4EPP/yQvPyjZ6TK/a/wm1sKiEIFLdXQ4tvQ6TlZhBQah2wKR3jceMoO\ng8eDHNnr9A3jtPPggw/yz88+w+txg8eNvfRw94C+NtnZ2SArWLt2LQ8uXsxHf/87L7/8Mp3lBeiT\nB4Ak4Wyrp6uuDF1MYk+TtEEolEoaGxv959rb2/F6PKj0hh5tldogFCoVM2bMYMaMGTzzzDM0lB4j\nNCyMBx98kAceeODnuW4BAgQIECBAgDPmiiuuID8/n2eeeYaik8VkJPShuvkkLZYWpmVNJsoUydGT\nRZxsqyNI01M5zaTzffff86d7WPrKUrbn78Pt8aBWqHB6XChkBcHBwUydOtXfp6XFt8om5NSKGiHY\nXZaLVqmmtcuC1dmFy+NBIcs0WdqRJYnMhGRmDBnp93eig0PZXODzb3L25zBy1EhMuqAee6YAwvVG\nXF4PN485j1d3rSPCYGJbcQFeIbhy2FjSI317pEYn9+Zve7ewsTDffy5cb0QANpdvi8WqwkMkJSax\ndu1aHnroIV55+WVKmhoobqxHIcvoDQbqG+pRNsl4heDYsWMA7CotpVdYGHefO9mXWQOy4uJ4Yes2\n1q1bx8yZM5kxYwZGg5FHNm/A5fGgU6rocrtQKRQoJYkPDh3CqNWQERtJSblP9CvG1FPJeWBsDJ/m\nFhCclMRrB/YjyzKzZ8/mueee67EM8xSyLLNm7Vruvfde3n/vPTptNiTguhGDuGhAXwAu6N+Hv+7O\nYfGDD3Lttdei1Wq/Mc63IYTgvj/9iTGJsQyOieT1A/koJAmlJNNhsTA5OYFbRwxCkiTm9e/NtWu2\n8NDixazfsOGMxj/F0aNHOW/aVKpragF4Zt8R7h45GI1SwcH6Jj4uKuOa3//+G/2WL1/OlVdcgdPp\nRKNSYHO4mD1rJss+XX7Gr/HnIDIykjvuuOM/MtdvL7BCIGVMRGhNULoHOltwH9kIsgwI8AqUvYcj\n6wy4q4v8vSorK/03pio2FW2vTJAVuJtP0nX8ANrk/mjiUuksyqG9rZHZs89nyJAhHD58GGdDObJS\nhcPagUql/kae0N5Yg8ft7qESGB8fT3R0NJ311ei71zwD2Jpq8bhcjBo1inHjxnHnnXditVrR6/Uo\nulPUAQIECBAgQID/PMuXL+epp57i2LFjJCUlcdttt3HHHXfwwgsvcLz2OE6XE4PWQJTJt2olwhiG\n2+OmprWOhPDT3/XlTVVIksSNv/89AwYMQKVQMXPgRKJNEXR0Wdh0LBtZrUL5Fcn03r17YzAYKGmq\nJtYUgdPjpsXWgUqh5IKscaSEx9LptLOp6ADlLXV4hSCrV0qPrENWUiqbCw4zOD6NwqNHcbqcNJs7\nqGtvJTbElwUTQnCspooYUwghQXr6RMXRYG3H5nRg0GhJizitGqiQZYYkpPBF/gHM9i62lxRyoLIM\nWZJ4add6XB43kiSTGhmOTqfjmWee4cknn8RqtSJJEtdccw0rVqzgkhHDGJOWgtPtZlVuPjtLSilr\nbmb+8GH+oAogIzqKYJ2O1atXM3PmTJRKJRqNmjClkptGjyZKb+BESwsvZe9Go1Sw+PxzMWk1tNns\n3PnJKgD2VlQxIS3FP+Y/846iUigoLi4ms18//nDrrdx4443fuxrIZDKxdOlSXnzxRV544QUWLVrE\nsrxCPskrZERCLAuGDGBan1TWHd/E/v37mTBhwhndX42NjRSXlHDt0Exezcnj8swMrh7UH6Us81lx\nKX/NOcKXJyqY0TsFtULB9JREXvsWBb/vw+v1MufC3xFk72L1nOnsrW1gyd7D/LO4nGCthhqLlVEj\nR/Loo4/26FddXc38+ZcxrX8sj1w4hNAgDV/mn+TOj9fz6KOPsmTJkh9lx38rv72lgJHpyKYoKN4K\n1mak8ETkXgNBHwpeL2h0SCoNrtLDuMtzfcsFJZkPP/wQvF4klQZd+lBkjQ5ZpUYdm4oqKglnQxWW\nQ1vwWNvRJWWgTuxNQdFxwsLCuemG67jpuqtZvXo1zz77DF01lbTlZtNVX425pABzwX4mn3NOj3Ww\nSqWSxYsXY6kppyE3G2t9Na0nCmgu2MfESZMYO3Ys4HsyYjKZAkFVgAABAgQI8Avy2muvMW/ePE6W\nnyQ9Jp3O1k5uvvlmlEolOTk5zLtkHm6PG6fb6Zf1jjJFEh0cxbZje8itKKCiqZrtx/ZQ1lBJXEgU\nKoWK3Lw8RqVkEW3yiQcE64xM7DOCpqYmNm/e7J9fr9dz7733cqSmmM3H93O0rhQJGJbYh9SIOCRJ\nwqDRMbXfCH+fTru9x2vo7F6WV9xYTd/IePLy8lApFHy0dzvZJ4o4WlPFJ/t3UtbcwMT0TAAsji7k\n7tU7Trcbt7fnsjGLw46MxGs7NnKwsoxRCamck9qfILUGhaxgRuZASk6c4PBhX6ZMofBl40wmE1+u\nXcvgxATG9U5DIcvo1GrmDhuCsTv7kVdTS35NLVuKj7O3vAKz3Y7d5aK0tBSA9evX09zSwrUjRhBt\n8C1d6x0RwdwBA2nutPHSZp88eVt3sd60tDTe2neATw7nsb+qmge/3MCR2joyoyKZ0zsdg9XCzTff\nzEMPPXRG94TZbOb/nnkGg1rFtLRUZvdOJ6+ukbtWb6K8W+77zjvuwP01AbTvIigoCFmW2VNdR69g\nIzcPyyJIpfIpFfbvw6i4aL44XuZv39Jlx2g0fs+I32T37t0cLznBg6MGkxZi4vL+vVlz0XTGxkdT\nY7Hy4osvsmv3bkwmU49+H3zwAUoJshJD2XSsFrPdycxBicwflcJbb7zxo2z4b+Y3l7GSNHq8HfVg\ntyCnDEYR51u/LMf3wVO8F9FSg+vYLlCqUcb1RQ6Nx3l0E14BKNXIOmMPmXMAOciEq7kGJAgdPR2F\n1idp7k1Ix5yzEY1Gw9NPPw34nvSo1Woefewxag/vRqvVcd011/DMM8984+nHTTfdhFKp5C+PPELt\nkd1otFquueoqnn322cC+qQABAgQIEOBXgt1u58/3/ZmUmGSGZwz3f0cH64N5/rnnueOOO2huasao\nM2C2WThSlc/gpIHIskxWYn82FWznSEUBgtNy1c2WNrKS+pJTlkdIUE8nNiTI5yzX19f3OH/dddex\ndOlSjtaedq7D9D37Bqm16FRqPBLsLMonLjQcU5Aeh8vFpoJDqBVKXG43JU21BOuCWDByEttKCthS\nmItXCIwaHXMHjyEjKp7cmgoqW5vQqdQAOD1u1hflMb3fIJSygrqONrLLixEI2rps3DFmCrFGXx3O\n8b1683z2BkqbffW0Ghoa+DpOp5OY4J72y7JMjMmIxW7naF0dBXV1KCQJjxAoZRm310tFRQUej8c/\nZqyx5xhx3UFBSWMLO0rK+WR/LgCV5eW4vV5WHivE6xXIksS09DSuHXZaXObj/AKeevJJbr31ViIj\nI795M3yF119/nbbWVl6dMZWY7n1bs/uk8/s163jnQB6JJiOHDh9m1apVXHjhhd87FoDRaGT27Nms\nW72akXHR3/AFk4NN7KmpAyC/sZkvSiq47qabfnDcr3LqmqWGnL5mKcEmbhmSyfqKkwwaNKhHphR8\npYE+/PBDupxunlmbj9srWPzZYZ6aN5ze0SYad5WcUXHr/wV+c4GVMDf4slCAHHN6bawkScgxqXha\nfAXx1H0moDB01wPQh0FnK8rQSNzNtXjtNuTu4EkIL67mk0iyAlVYlD+oApA1WhRh0Sz79FN27NhJ\nUXERaWlp3HXnnVRVVtLa2orJZEKj0WC1Wrn//vv54MMP6ezsZMq557J48WJuuOEGrrvuOlpaWvxt\nAwQIECBAgAC/HvLz82lrb2Po0KE9nN20uFQKygvYuXMnm7dsJiM+HUmSOFySR2lDORqVhg6bGQkJ\nSZIZ23soaVFJdDq72H38IEcqjyEhUd580i9IAVDeXAP46kjdf//9OB0OZsycSVVVFa1NzQCMSutH\nYW0VJY0nyYhO8vet7Wimy+VEkiQ6XC7e2LyGCGMwbZ1W3F4PWqWa5PBoTjTVMjatH6F6PRcOHsWM\nAcP4Incfxxtq2VCUy4bCI5gdXQAoJZk/jJnC1tJj7K0o4UhNBUaNliarBaUso5BlYo0h/qAKuhUK\nY5LIri5FoVAwaNCgb1zXsPBwcqtPMn1Af/+Sv46uLsqbW0gLDaeyo42Fg4YzNDYBs8PBR/mHONpU\nT0lJCa+88oq/ztLBmpOMSjx9DQ6cPIlWqcTj8fLu7oNIwPD4OK4fNgyr08lrOQcoaWnBKwTnpvXc\nRzUlLZV/Hitk9+7d/O53v/ve+2Lzpk0MiYnyB1UAwVoNYxMT2FxWwf0TJvH47v1s2bLljAIrgJdf\nfpmB27eRU9eA2eHA1O0XOj0etlfV0GZ3cNkXGyhraWP4sGE8/PDDZzTuKU4plG6oOMklfU+rZm+o\nPIlGrfYLgnyVF154geNFRTw+fihz+yRjdjpZkp3HXf/Yz+Be4QwdPOg3EVTBbzCwou0kaLujcJcT\nvrph1OXw/+quPYaiz1ifmkn3eTnIiKTWYs3dijapL5JSg6P2BF5rO6h1eJ0Ovo5wOamsq6PeYkMV\nEU9hVS0LFiygoqKC+++/H/A9kTnnnHM5ePgQuqgEFMZIPl/zJavXrGFvdjYDBgz4waciAQIECBAg\nQIBfhlOlUhyunn6Ao9sv0Ov16IOCcDgdDMsYRFx4NBX1VThcLrpcdjweDwMTM+gdkwyAUatnQsYI\nlu1bg1atIbe6CLfHTWJoDE3WNvJqjmMyGln28Sf0lL0siwAAIABJREFUjkxApTOw4pNPsdpthAeZ\nCDMYGd0nE1OQno35B1h7NJuMqCQ6uqzsqyhEQkIIwaT0LJ+YRaeZPhHxxJhCWX5kF602C14hqO9o\n42BlKWF6I8nhkcwdMoaXtn9JbFIiVVVVvoxO7yyGxCejUaq4ZNAY3j2wnZMdLahkBVqVCrvLhUap\npNPp8EmxfyXw7HQ6cHrcXHf99cTFxfF1Hn74YW6++WZe2bKd8X3SsbtcbDhaSJBKRZ3FzMReaYyI\n9wVMoTodVw0ewb2bVhNnMvHq0qWMGjWKgQMG8Lec/TRYLPQKDSWvvo4tpaWoFQr0ajUTEnvR5XGz\nq7qSJdt38NDks7lx5Aj++OU6ACwOZw+bzA7fe2ow9BQX+zYMBgN1Ttc3zrfb7SSHBpNgMmJxOnuU\n2vkhEhIS2L8/h6GDB3Pjuq1cnpmBRqFgeXEprU4Xly1YgE6nY9KkSVx44YWo1eozHhsgNTWVBfPn\n88SyZdR32hgcFcHe2gY+KDzBbbffTlhY2Df6vPnaa1yQnsT8fr4gNEKn5YkJw9hcXcfBimb++c/A\nUsD/XaLToakSAE/5YRS9RyEpFAhHF56qo/5mXmsLXq8Xb2MpODtBocRZU4oucxTOymK6jh/0NfyK\ncqDb2YWjvgp1tE/1z9lci7O5DlVIBKHDxvvHlo/n88ijj3LTTTcRFhbG8uXL2b9/HzHDJ6EJDvfN\n36sPjQe38Ze//IVPP/30P3BhAgQIECBAgAD/Cv369WPAgAEcqzxGmDEUrVqLy+0mrzyfsLAwpkyZ\nwoIrruDVpa+SHJNIeHAYwYZgCsoLcbp8jnvo15b7BWl0qJVqurqDs2O1pRytPYFSoWDMWWexa+cu\nLh5+DuF6n+R6YmgUX+TtxCO8hBt85/onJOMVgv0nCiluqP6G3Udqy7h0yESGJvoK4J6qTdVsNaNW\nqShpqqOkybe0LMoYTO+oWCxdNt544w3mz5+P2uZkdK+ehW6TQiNoslm4eew5uD0e3ti7Da/wUm8x\ns6uqhLFJvZElifK2Zg7WVqLVarnnnnu+9bredNNNtLS0sOTRR3l7VzYAIVodt46YwJJdG4n72hI/\nvVpNiC4IlSxTVlrK6NGjfTW1gM+PHUUARrWG5OAQWrtsPDFpCobuwOPspBTu376JXZWVnJOaioxv\nNdOHuXncP3E8Ro0Gm8vFB0fyiImO9mfDvo/5l1/OJStXsrmsgskpPrXpnJo69tXUcf3QLD7KP0Zb\np+1H17Pq3bs3u7Ozue3WW1myYwcAQwYPYv3Hy5g0adKPGuvbeOvtt4mMjubN11/ntdxCQoODuf+B\nB1i8ePG3tq+tq+PCzJ6ZPa1SQbLJQHDfAWecjftf4DcXWEnhvSAhC1FxENFSibt9JWiNYGsHpRrV\nwLPxmpvxVObjOLwSPC6koGCEsws8Lrpyd4GsBJWGoPTBKMNi8VrbsBXnoFcrsRzdh6aqCIGE0+Lb\nmGjMyOphgy4xlc6K4+zevZvZs2ezadMmdMFh/qAKQFaq0EYmsGHDxu99PVVVVTz++OOsXr0GtVrN\npZdewj333ENISMj39gsQIECAAAEC/OsIIfj444/561//SkV5BUm9knB6nazd9yWhplDMnWaQ4LPP\nPkOr1fLQQw+xfdt2vty/mYiQcBwuJ5ZOCw888ADvvPMOVa11JEcm+Mdv6GjG4XYSaQzhnMzRaJQq\nCmvKyKk4RnNzM/Ghkf6gqqq1nu3HDyNLEha7jfKmOtweD0qFggGJKaTHJPDOtjUIr2DuqHGEG0ws\n37+LRnM7r+9ZixCCYJ2elDCfop8sSegUKi4bfBbxwWFUt7fwWf5+skuLycjIIDg4mP79+7Nl02as\nTjsGtU9MwisEx5vqiDP5fBClQsHwxBRWHj3MmF5prC7OZWfFcXQqNfXWDmIMJpxeL5fPn8/effu+\n9To/8MAD3HXXXURHRRGEhN3tJiJIT6zBxJH6Ws5KTPZnwWrMHTR1WjHbFciSxC0jRzMoJpZai5k3\nD+ZQYzYTqtFy0mJmSnKaP6gCSDQF0zc8koKGRlSyjBfIjIqgvLWNm1atISk4mKqODtxeLxs2bkSl\nUn2rveDbc7R06VLeeuMNdFot/7c3h38UFiMJQY3Zgl6tZkXRCZo7O3nssce+dRnkD5GVlcW27dtp\naWnB7XYTFRX1k+2/12g0PPfccyxZsoSWlhaioqK+N/M1dOhQNp0o4oZBfZC7bai12ihsNfPMRRf9\nJDb9t/CbC6xE60kkkwPf8wvA4wZbB4rETBTRyUhKNbIhFK+lBWFuRlLrEA4bqkhfFsrVWAVeN0EZ\nw1BHxAMgm8LRpQ3GcnQPL730EgUFBezdu5f8o1a8bl/dqx42uHxp4aCgIP9Pr9v1jRS51+1Ep/tu\n3f/q6mqGDx+B2WpFHxlHp9PNM8/+H6tXryY7O/tHpZYDBAgQIECAAGfOY489xuLFi4kJiybUEEL5\n8TKsVisXX3wxOp2O5ORkrrnmGpKSfEvVQkJCyN6bzaeffsrWrVsxGAwMGjQIs9nMgAEDWL9+PTIS\nKZEJmO1WjlQWIksS07PGoVGqaLK0oVVrSAiNoqy0lGCtbylaZUsda49mo1NrGNQrnfZOC+VN9fxz\n/w6GpvTBK7wcKCvG5fYwZcAQXG43b279EqfHQ7/YeIzaIArrTtLR1cnhmjIkfAHS9H6DSQjxPfBN\nCo1gWsYgVuTto76qmtGjRnPHnXewadMm3ti3hXPSMtGqVOyrLqXe0s6M/qflw7tcTpSyzNCEXmRX\nlhJrCMao0TA5OYOBUXEUtdTz3v695OXlkZWV9Y3rDD4/6b4//5n7778fhSTzwr7t9A2PYmvlCf52\neD/DYhM41lTPwbqTqGUZh8fDxZkDGRLrW16YYArm98NH8uCWTcgKCZ1SSafL+Y15rE4Hdq+Ltw8d\nRqdScu/ZZ2FzudhWWkmt2YLD46amw8yBAwc455xzvvPeWLhwIf/46CPOSopjdloSWytPUm+xMm3a\nNOb26UN7ezuhoaFcfvnlDB8+/Efddy6Xiw0bNtDY2MiIESO+dc/TT4VOpyMhIeEH29375z8zY8YM\nbtiYzfy+KbR0OViad5zIyEgWLlz4s9n3a+Q3F1hRX4yoL+ZUYV8AyRCKMr5Pj2ayPgRPeyPC6yFo\n8GTk7g8w2RCK48RBFP/P3nsHRlWt6/+fPX0mM5kkk94LCSmEhCIgRUDUIwp2RRERBctR9FhR9ByP\n5ahguSL23sCugB5RlN4JhATSe+/JZGaSmcnU/ftjQjAotnu8935/zOcvZs/ea69dwqxnve963oDh\nESHp4KxRQkICS5cu5ZprrqG8oQXXgI2+6hKCx0xBIpfjdbvpqyzCEBrKGWecgdvtpra2Foe1j76m\nKnRxqQiCL9pl72jihqW3nvRSVq5cibmvj4QJM5ApfQJsIC6Z4rztvP/++/z1dzrB+PHjx48fP35+\nnc7OTh599FFGxqUxKslnOy6KIgfLD7F582ZaW1t/1mxKqVSyYMECZs+ezUUXXsTq1auHfV/T2UBV\nRz1SqZTwsHD6TWYAvincRZu5e2g/AYFOp5Gy9noO1pcSoFRx1emzUA5GUQrrq9lTVcw3Bb70OY1c\niYhIfn0VPf19AFw0biKpET7hcXrqSD7Ysx2L3YbL47NLjxhMJzxGxKDxxKToEWypK2bFihUAmAds\nrCs5iIgv0pUZEUNCsM8avtvax76GGkZFxlLW6Ss2e3FGLsE/MvqKHmz3o48+OqmwAli+fDnbtm1j\ny+bNNFlMNJh7AchvbSK/tenYdPkQsSfYgUdpdUgFgXqTCblEwp7mJqbHJ5IWEoooiuxoqqepz4IA\nhKo16DVKFDIpCpmUi0aNBGBDSSWfHSnl4X/+kxtvvJHg4OCf9PPQoUOsXbuWOyaP5ayUBACuyslg\n+Q+76GhvZ+PGjSe9xl/j0KFDXHThBbS0tg1tu+jCC/nwo49Qq9W/cOSfy7nnnssnn3zC/cvu5frv\nfBb2s2bO5OVXXz3lMqhOPWEVmQrWXrBZwONCJpPhsZoQXQMIcp84EUURr7EVvB6khqghUQUgDfKZ\nSLiNbUhjRgxtdxnbEASB7OxswBei/fCjjxBUAbjMRrp2foMsMBh3nwnR7SE4dQQymYwnnniC7zZt\nQhkcSm9VEX3NtUhkCpx9vWRmZvKPf/zjpJfyzcaNBIRFDYkqAJVOjyY4lO+++84vrPz48ePHj58/\nge3bt+N2uxkRc9w1TRAERsSksK1wB0eOHGHChAknPf76668nLy8PgFGxqWTFpOIVvRTUl1Ld2cjG\njRt56qmn2LJlC9tK8zBaLcxIHUuP1UxdTysDLicer4dtFfkIgsBpySOHRBVAbuIISprr6B8YQKNQ\nYh6wIiDgcrkJVgfg9HoYER41tL9cKiMnPomtpUeHtlV1t5NsCGdffSV1PZ1D9an2NlUSotFy0aix\nhOsCqehsY33xYbRyJeNiEthcU8bKrf/GK4o43W5EoLa3m8JWX62obXUVGO1Wuu39hKq1hKh92TUr\nV67k448+QiFXcNElF3PvvfcSGho67P7W1dYiFSSIohelREJqaBhlXZ2MDo/ksvRRBMgV7GisY11l\nKRurKsgKP16suKSrE48ocklmOhsrqpBJBB7bs4OEQD0DbjcdNisAz888h7yOVj4uL8VosxOi8QkW\nURQ53NxGQpCeamMvu3bt4oILLqCiooIVK1awY9s2goKCCIuIQCmVMjPpuAuhXCrh/LQknt2TT29v\n788Ksl/DZrNx3uzZhEtFHr90JvFBWrbVtvL0xm9YtmwZzz77LC+88AJr3n8fi8XM9Jlnsnz5clJT\nU3+98f8Al19+OZdeeimNjY1oNBrCw8P/R877f41TT1i1V4FUDvowcDlx93UDAq6SXUhjMxBkclyt\nVWD1rY8STwgVSxRqBIUae10RoseNTB+K29KDq7mSefPmkZiYCPj+03ziiScxmXrRpY3G63TgsfWj\niElGotZQXV5IQUEBL7z4IgHR8QRn5ODo7cHW3ozH5USwmlmwYMEvKn2VSkWvyfrTLzweVKqTpxD6\n8ePHjx8/fv44dXV1ADhdTvrtVmwOG4EaHa7B1H+j0XjSY1taWvj666/Rq7Xo1VrGJWYNfTc5dQxd\n/b18+OGHZGdns3XLFpp7O5mYNIr8pnIcLhcjwmLwil6qunzlYTxeL84TCsyKoojL68GLF5vLV/RX\nRCQjIo7KrlbcHg9eUUT6o+UHTrdryOhBJkjYVF6ITCJFLpWSGRGD3eWkpL2ZPucAC8afToTOF9HK\niozBZLexubKUhJBQ4ntCaDQZCVUHMD4qgdY+MzW9XQQGBmKxWNjfUkd8YDCjI6KpNfawv8V3LxUS\nKY6eXiKDgnlh1fOs+/JLDuTlERwcTEdHB7feeis1tbWEBwQwOjKKFouZ4s4O1DI51+eMRyGVAnBu\nShp15l6OdrbzSfFRciOjabGY2VBRRkpICHNGjkQtV7D2yFGUUilOjxsEkAxeu0f0Mj02ga9rqvjX\n5t1cmp2OTqVkc1Ut5V09XD9mNNXGXlQqFUePHmXqlCmoEDk9KpKenm42H/WJU4fbjUZxXOzaXO6h\nZ/NH+PLLL+nq7ubFK88mJtAnRv+SGkezuZ933n6LyooKtm7ZwsyEaNLVCr757FPWffEFu/fuJSsr\n61da/88gkUiIjo5my5YtmEwmJk+eTEJCwh9qy2az8cMPP+BwOJg+fToRERG/ftBvbHfz5s2Ulpb+\nR9o7kVNPWAF4XGBshSFPfRHR3o+7Ku8nu3otPTgaSlAm+F5Kt6kT0WknODgYa1sN/Q2lyBUKFl9/\nHatWrRo6zmAw8MwzT7NkyRKUIeHINMejXh7nAH1AfX09He3thGTmIggCqpBQVCG+2ZmuA9tpa2vj\nl5h/1VU88uij2M1JqPU++0tzezNWUw/z5s37b9wgP378+PHjx8/JODbI235kJy73cTttucw3kNbp\ndCc9tqWlBVEUcXu9RJ2wrEAQBII0Ourr62lqOp7e1mu1MOBycvmYmegG0+iyo1P4onA7CqmMkuZ6\nMmISMGgDEUWRoqY6+gd8NaY8gE6hpt9p50Bj5dC58mormZQyEkEQMNms5NfXIJNKAYGkkDBqejqQ\nS6XcNOlMNIPGBWq5gvzmOsK1JxbcDUJE5K2Du4a2WV1OEvQhnDsii211FWyuK0cATotJ4NKMHATB\nZ/n+RdkRDrY0EKRSEx6g5YrMXGYm9vPs/h28+OKLuN1unnziCVyD4rHf6SQpOJiLs7J4cvs2FBLZ\nkKg6RoI+iNLuTnY11PN9TTUA46OjuXaMb7yVFOy773eMm8CoMF9kpcbUy8N7dvJiYT4PTz6Dv0+a\nymtHDrN6z0EADBo1fz1tLHmtbYQaDEyfPp3LLr0UvVTC0zOmoh6MGO5vaWPFvjxW7TvM/WdMQCII\ndNvsfFlShYDP2OKP0NjYiF6tGhJVxxgZGoTVVsH3P/zAs7MmMjU2EoAbctNZ9O1uHnroIb744os/\ndM7fy44dO7jyiito7+wEfELrxhtv5MUXX0R6wjP6Jb744guWLL4ek9kCgFwu48EHfa6E/x2DjvXr\n13P99Yvo7TX/4TZ+jVNPWCWPBX04FG0FmRxJwkjE3i5EUxc4faYW0qhU5FHJiB4XrsZSXC2VuI2t\nCBIZ3sFI1gMPPMCNN95Ic3MzMTEx6PX6n5xq9uzZSCRSHD0dw4SVo9tX1Xr06NGkjRxJc08n2tjE\noe/dNisDFvNQWuHJuOuuu/j3N9+Qd2AHAcGhiF4PNnMvV1511a8WrfPjx48fP378/DHGjRuHgIBM\nImNS+nhCtMF0mLvIry1AEARGjhz5k2M2btzIypUrOVJ4xLdB9NLc24G0voQWYweCIBAbEkmnxUig\n0UhRUREyiRS310OzqZMkQ9SQqAIICQgkNiicPocNk72fj/ZuISrIgN3pwGTrJ0St4/zU8RxoqaCy\np5VUQxRTEzJQSGWsPbKT3ZVlFDU1EKjW0GzsQRAGTSvSc9lZW4YoiuRGxw+JKoCRYVEcbKqlzthN\nsuF4fc2q7g4EBKbFpzA5PgW728Wm6lI+LDrIealZ1PR2IRUEPKLI4bYmCtuaCQ0I4NKMXKbFp5DX\n0kCXtZ8xkT5TsDCNlszQCN5/7z2qa2r4S0oa0xOSsLtdrCsr4c1DB4nQarE6XdhcffQ5HOgG17SJ\nokhJVycJ+iBuGDueB7b8QHZEOLdOmoDL42FTVTWbqqqRCgIH29uI0uowqNWkBAUzMsRAhbGH27f/\nQFyAjmZrPwIgl0kRRHgzvxARmL9gAW63m02bNnF15sghUQUwMToSg1rF3sZWblj3PZG6AEo7e5BJ\nBEINhp+tA/VbyM7OxmwfoKTTSFb48Tb2N3WgUasxKOVMiTke1dEq5MxNjuGd/8aart9DV1cXc+ec\nT3awhveunEGUVsVn5U08/tprJCUlndRS/0TKysq48sp5nJcTwUOXjEerkvHGljoefvhhUlNTmT9/\n/h/qX2VlJVdccTlzpoXx5N/G0dhm55yb9/2htn6JU6MM8o8Q1FrobQO3EyEsBm/VEcTeLiRBkQh6\n338SotMGMgUSlRbFiHEglSM6BxDdjsG6VQL33nsvu3btIjMzc5io6u/v5+uvv+aZZ55hy5YtzJk7\nB1tdGf0NVbgsvVibqrHVlHDxxRczYsQIHnzgAWwdrfSUFODo7cHa3kzPkQNERkb+al2DgIAAdu7Y\nwTvvvMP5Z5/JJXPPZ/369axds+aUqXDtx48fP378/E/T39+PiMi45FzC9WHIpDJiQqLIjs9CFEUa\nGhqG7b9q1SrOP/98Sg4fJSogBL1aS7/DTt+AlfLWWgwaPUFKLcXNVThcToqKiggN0DMyIh6lTI7d\n6cD9M5EOt9eDZDDyc/HFFyMJUGF1OQhR67g6ewYhmkBkEhlqmYLz0sYRrNYSoFBx42nnoFcFYLbb\naDb2oFEoCNUGIgDFbY0MDDr5uU44Z3xQCHKJlM+PHCS/uZ5mUy9bqkrZW19NsFrD2SMyCVAoCdVo\nuSJrHBJB4N9VxSCFiUmJhGg0eLxeEoNCMNpsvHxwF42mHgBkUgmTYo+njbm8Hjo7O8kKj2BOWjo6\npRKdQkmHtd+3PwI6uRxRhOfy9lDY0UqlsZs3Cw9R3dtDmsHAC3n78SJS1t3NlppaVuzczafFJaTo\ngzgjNp6Dba38c/cOOgfXVzlFkfPOO4+FN9xAytTJLFu+nK++/hq1WoPV7eaMmFgmRkXx0dq1nH3W\nWchkMhzu4ffIC7g8XhQSCeEaDV63SGpwEHa3h+UPPkhXVxfr1q1jy5YtuE9I4QRfqtrGjRv56quv\nMJuPR1Zmz55NVmYG/9iSz7eVjRR3GHlxfzHry+oZO24cTo/3JwYeA24PEomAzWYbtr2jo4N169bx\nww8/4HL9tIDx70UURZ588knsNjt3jU8jPTQQvUrBktwULsuI48XVz//mtl5//XVCtEpeWzyG5HAt\n4YEqHrw4g5lZEbz0wupfb+AkvPnmm+i1ctauGEtaopaQoJPb5f93OOUiVmJTKVi6B/9dCRIpsrQJ\nSAZd/TzdTXhqC/FGJCHVhSBIpEgC9AhyOer08Xht/diO7ECQq1l6222DUSmfiHn//fe55ZZbsFqH\nr3uKT0igtaGC/poSpFIp86++mpdfegmAhQsXYjab+ec//0nHYAj99NMn89577/6mqt5KpZJFixax\naNGi/9Qt8uPHjx8/fvz8AvX19QCEnJDKF6L1mRJMmDCBu+++m5UrV2IymVh+/3JGRiYxLilrKAVu\nV0U+zb3tzMmchl7tSx3Mspn5umQnEboQzs+a7DOmiMvgo8M/UNfTRrvFSGSgL1rR2NtBq7kbrUrN\n1KlTueeee7ji8stxuVz0uty0W3uJ1hkYcDuJ0AYhPWHCNT00moK2Om6ddi42l4MP8nYiAq2WXsZF\nJ1HW1UphayNjYxMJDfD1r6i9GZfXQ4Q2kK9LCoHjHsu5kcNtua0uB27Ry6yRaZw1GME7LzOTd/Yf\noNdm5+6JM3ju4E42VBQjEQTmpGYSOGjGVW3spqy7E41GQ3ygb3zm9Lh5/sBuugZFUGt/P8ekRJu1\nj1cO+5ZzyAZTxb6pqkQuk/HRxx9z1513suaIb+3TPeMnkj2Y/ndJ6kge2rOT9VUV5IRHUNdr5Nkl\nS4YVtL3jjjvwulw8d8Z0DIPOexW9Rv6xdy+TTz+djYfzmZEQR0SABlEU+XdVDRankzCNiqIu33gz\nUKfj0UcfpaWlhbjY2CGRHBMVxUeffMK0adMA+Pjjj/nrzTdjGhRUGrWKJ1es5Pbbb0cmk/H9D5u5\n/rrrePz7733tarU88sgjzJo1i6lTp/J5eR2XpychCAItfVY+L6/D6nQRGx3NK6+9xhVXXMEDDzzA\ns888M5RaGRUZwZq1H3LmmWf+5D3/LbS1tXHZpZewd99+AC5bt4dpcaG8+JfxBKkUjI0I5tPSI3i9\n3t806d/Q0MCoGC1K+fDUwXFJej48VP+H+gi+v9nRqVpUyt+ekvhHOOWEFX1GhBE5CCERYLXgrTmK\nuyYfefZMBEFAYojF01SG19SBVBeC6Hbh7Tchj/FVlJZotMhCIvFYLdTX1VFZWUl6ejr79+9n0aJF\nKMKiCc48DRCwNVXjaG+iubmFyy+7lPvuu4+4uLhhLjcAt912GzfccAMVFRXo9fohAww/fvz48ePH\nz59DT08Pzz//PP/++t/IFXLmzZvHX//6199kW52ZmQlAh7mLWEP00PYOcycCAqnhSTz99NPEx8cT\nExPDgGOAjJjkofUhgiAQExxOk7GNrdUHCVRpSQ9PJEYfTnRgGF7RM7SvVCpl1sjT+LZkLxuO7iIy\nMASvKNLZ14tEkCBTKrnzzjuZOWMGeEVCNYE4PC4+K9lNtC4E84ANp8eFw+1CObgGTBRF6no7ERFZ\ndzSPxt4upFIZaampKFUqDhUVo5LJUMnkvL5/G/FBBuwuFx39ZnKi4rggPZd9TTVsri4lJSQUm8tF\njbGLGYlpQ/0u7fStE5+Wctw5USqRMDUlmfcO5OHyehkfFcfuplqyRo3iy6IiCjvbEIAaYzczZ8zw\nFRwuLmG2KPJlWQnt/b5olUYmJ0Au56rMbOJ0eo52tvNRWREikG4Io6HPgt3t4rPPP+fCCy8kICCA\nxYsXI7PahkQVQKBSydTYOL6tq2FXcxOXXXopF1544bBn/fWGDUyOiBwSVQAjg0PICDEw4HAw4PVy\ny3ebyQ4PxWgfoNHSxwUjk2nqsxKVPIL3PviAtLQ03nzzTf75z3+ycFQa5yTFYbQ7eK2whDNnzOCs\ns8/mwosuYunSpZwRHcG1k8agkEj4pLKGv/3tb3i9XvIOHKCkqIjE5GTWrl1LZmYmqampQzVLly5d\nyrMvvsiG6kYMKgX57d2EqVQ8M3k8n9Y0cPXVV3P06FFWrFjBzTlpXJIaj9Hu4LmCci6YO4fKqmqi\no6P5rVRWVvLss8/y0do1KEUPb/zlNMZFBrO3pZt/7C7i7s0FvDVnIjsau0hPS/3NmVSZmZm8sOkb\nLHYXgWrf++rxePn8QAsOt4KxOaOZcsZ07rrrLpKSkn5zf7Oysnhm4wZ6LU6CA09e7Pi/y6mXLxYR\njyQiDkGuQAgKRZI2BgasiObOwR1E8HoRnQN4TJ04yveBAPKI47aZotc7NEMjk/m06UsvvYRMo0U7\nMhepWotUHYA2dTTSAB2CSs3nn3/+s6LqGCqVipycHL+o8uPHjx8/fv5kurq6mDBhAiueXEF7cycN\nNU0su3cZs2adhcPh+NXjc3JymHXmLI40FlHbWY/ZZqayrZqSpnISQ2MZHZdBvCGGVatWDY0TvN7j\niVrNxnYO1B5Fq1YTGW7A7h1gc+UBSttr8YjewWUHxxly65PJiBuZgsagJycnhwcefIBN329i0aJF\niF4vyeGRaNUq+h12QjWBmAdsuCUiXgG+KNnvncomAAAgAElEQVRPfW8nZZ3NfFq8h06rGZ1STb2x\nE1EUSdIbkJj6KSkuQURkfHQSZydlMj1hJCa7jY5+M6MiYpgQk8SR9ia215ajksm5cvRpnBabQIPZ\nyBdlBTSZe6ns6WBvcy3gcy38MW6P77NEEHAPRjH27dvHe++9R+6MMxg9fRrvvvsu323axH333UdN\nTzerDuxmf3MjkVodQUoVNreLa7NzyTCEoVUomBwbzwWp6XhFkdLuLrxSCZu+/54LL7yQp556ijlz\n5mAzmXB7vT9x5XN7vSD6+tPV1fWT9DyZXI5bHH4NAJ02K/n5+cRoAkjR6ynt6qHR0sdZyfGYHQ4K\nWju46JJLyMzMRKPRsHrVKmbGx3BVZioGtYrUED3/mDIeEDm0cyd//etfMaiUPHBaLvE6LZEBGm7P\nHUW6IZi777qLPd9uJNnWR8We3Vx99dW8+uqrmEymof6sXr2ajRs3YlVqKO0yceuodNaecwZjwg08\nNjGXMLWKV19+mbMTo7k5J41wjYp0g56nzxiL6Hbzzjvv/Op7f4z8/HzGjR3D52vep89q44lp2ZyV\nGEGwSsH5KdE8MCmTzfUd3PlDPt/WtHLvfff/5rZvuukmPEg5+8ndvPJDDfuquhn/963Ud1nJCZCR\n7TTx8TtvMW7MGEpKSgBf0eRt27bx7bffDkuf/DFLlixBIlVw3q15fLenk6Iqy2/u0+/hlItYCQEn\nOPVogwAB0WH31a9qrwOPy5cS2N0ECMgi45EofTMVHosRT28HgkJFekYmKYMzMVXV1UgCAoe5lQiC\ngDwwGJepB4/HQ0tLy0mFlR8/fvz48ePnf4ZnnnmG5qZmJo2aimbQEKK3z8j+/ftYs2YNixcv/tU2\nPvv8MxYvXsy6desA329+oiGWMQmjADAEBFFUX86ZZ56JTqfjaFMFp6fmAgIH64qJCg3lzHHjkUgk\nvuLCZaXkN5ThFb2EaoPwil4kggS310NBcyUKuZw9e/cyfvz4Yf247rrr8DpdXHP6TDSDBg51XR18\nc/QQ2REJFHU08Pjjj/Pggw+yrnT/sHU4AXIlRvo5NzWb0ZG+CeTvqo5ytKOZXYMOgnKJlGnxaXTZ\n+ijuaKa4owXwOb7F6IOQS6U0m00opFKqejo40u6zgQ9UqhCAH8ormJs9Cokg4HC72VFdTYxOj8vj\n4VBbI26PhxEpKbz/wQfD3OuMRiPPD7ot1/b6igF7RS/BKhUmxwBJ+uG1oFKCQhCBO8ZM4O3yo3z8\n8cdkZWXx9wcfZPaIZDJCDfzX/oPsbW1hSowvbbHDamVXcyMzkuKYEBPFEzt38sknn3DNNdcMtXvZ\n5Zfz3DPPcF5iEvGDRYd3NjfRY7czNzmJ67IyEAZF4uMHDrKtrgnPoHh79NFHefWVl3nzrbepa2hg\n9pjMYX3WKxXEB+rIDAniQFsn6UH6YSmbgiCQFRxEs6WP92ZMRTqYRvrc0RLeeO013njjDRYvXsxL\nL72EXC5n9uzZ6DQaZiTGsiD9eKRQJpGQFRTIjtYOckYMj0oFKuQkBwVSW1vLb+XuO+8kTq3ghuxE\n7tpWyLiI4YYcYyN8z2ZTo5Gnn36a66677je16/F4ePrpp3E4nFS12Xnw0xIkAnhFeGn6aBaMjAPg\nMYeLWV/tZ/n993Hr0tu4/rpraW3zGcMFaNQ88uhj3H333cPajomJ4fvvN7P4+kWcd8v+33ytv5dT\nTliJ/SbgR576lh5AxNPTjKezHux9gIAQEIg0OByPpQd3ewPWfjOCVIbX3A2CT4iljkgZElJZmZkc\nPlLki2YN/lGIoojL1AMSCQql8qRe/s3NzaxevZpt27cTEhzMwoULueqqq/wGFH78+PHjx8+fwLp1\n6wkPjhgSVQDBuhBCAg1s2LDhNwmr4OBgvvzySx5++GEee+wxzsqcil593Ia8s89IYlISy5cvJyw0\njLq6WlpNnQRrArE57ExNzhn6nRcEgVHJKZTX1xMREUFnZydfHN1BiEpHl9WE0+vmq6++GiaqHA4H\nb731FmvXrCUnNmFIVAEkhUUQotHSYOpiREoKl1xyCX9/8EFUcgWzUrKICQymydzDlmrfjH9isM+8\nq9dupbijhcQgAzNTRqKQyjjYVMfW+jK0cqUvkjYoGmbOnMneXbtxuN1U9nQwIS6BWSPSaO/rQy6V\nEh6g5ZmdW9lfX09lZyfRej1VXV24PB5idUE8d3AHEkHgmuwx5LU2M3fuXMrKyobSuxZcfTV7du7k\n2jG5xAfpWbFjFxIk9Nh9RgyVxh7SDccnq8t7upBLJCQE6pkUHs36deuYOnUqLreb81JTCJDLOT02\nhtePFrCloQ6tQkFJdxcGjYaL0lPRK5Wkhobw9ddfDxNW9957L+u//JJlu3aSHRrKgMdD+WCdsktS\nj48DZRIJF49IobCrG5kgsGLqJOQSKWsqqrj0kktISkigsLOHOSMSh9ruGUwd/EtCLANuD3ltnXxW\nWcPe1g48osiEyHAOdnSSEqgbqjkmCAJXp6awvq6B2fFRvPv22xgMBp588kkA0rMyyd+9G68oIhk8\nxuHxcKTXTEhICHkdPSzITB7qg9HuoNJo4tqMjF995wH6+vrYsWsXT56RTYbB977vbelmzo8E296W\nbiSCQMGRI7+rQPGTTz7JSy+9yN8vSOPS8dHUd9tY/FYhoktkftrxNXxBSjmL02N54JuN/PDDZqaO\nCOTT66agV8t4ZVsD99xzDwkJCVx22WXD2p84cSJFxaWUlJRQUFDAwoULf3Pffiun3si9owlvczWi\nrR9vVyveigJAgP5en6iSKZGERoPbhbu5ClloDEhliE47or0PQRUAEimCMoBvv/uO3sFZlNtuuw2v\ncwBL6SFcZiMuSy99pfl47Fa8dhtLFi/+2WK/VVVV5Obmsmr1akobW9h16DALFixg8eLFf7iInB8/\nfvz48ePn5EgHo0QnIiL+rno7ALfffjv6QD35DUW0mzsx2SwUNBTT0ttGc1Mzb73+JqLZQaguBKfb\nhU10+s51wvmPfX7uuec4dOgQ866+iqScdK67YTFHjhzh3HPPHdrX5XJx/vnns3TpUkSvr9jviW15\nRC8Wh4177r0Xt9uNCJw1IouRYVFolSoywmOYkeKLoBw7vrC9EYVUymXZ44jU6QlWa4aiL8EaDaPC\no1BKZUgFic/Vzuvhw6JDeL0iXlFEJpESE6jH7fVSa+xBKZMRqg6g124ndORIMrOzkUiltPSZGBkS\nyrLTz2B0eBQLs8cgA9544w3ANzb69rvvuDh9JONiogkLCGBaYgJt/RYsTicamZx3iws42NZCnamX\ntSVH+aamkilRcb66UXbbMBdFrygiCAJLxuZwy/ixIIGjXZ3MSk5gfnY6Hf1WPF4vXpGfTGq73W4c\nDgciUGUy0dRnGdbujzn22SuKHGjrJFkfyPJxuegUChKTk9nd1MZrBSXUmSwcauvkoZ15BMjknJUQ\ny/TYKPpdLl47WoZGJiVEqeCDsiqa+21MiAj/2fNMjYng8tQEXnnpJbq6utiyZQuzZ59HpbGXv+8v\noMRoorDLyD178+lzubl+yRJ2NnXw5IEiKowW9rV2ccuWAwgSCddee+0vvOXHOSYkPSKkheiYFhvG\nP3YX8Vl5EzWmfj4oqWfFwUrmzZv3u0SV1+vlhdWruG5aPH87J4XYEDVT0wxcMCYCryhy4p/rsfdS\nIxf4+OaxjE3QkxIewDPzMpmZEcbzq/7rpP0fNWrUn1Y0+ZSLWBEUidhYgdhQPny7IEESHI58RO6Q\nY4+r+giuxnKEgEAkMgXq9LEADNQU4TEbcbtcNDc3ExwcTG5uLuvWrWPxkiV0Hdl7rFEkEgnXX38d\n//VfP/+A77//fvoHHISPm4p0sFZEf1sT7777LkuWLGHKlCl/1p3w48ePHz9+TkkuvexSnlr5FP32\nPrSDjnw95m6M5p5hjnC/hZCQELZs3cL8+fPZVe5zpgsICCAxMRFLVy9npkxALvUNt0rbazjSVklM\nTAxFNdWEBwcjlUoRRZEj1VVD44+xY8fy+uuvn/ScH3/8MVu2bOGCUROo7m6jrLWJ7NgEdCrfsoWq\njjbMdhsLFy7kxhtvZMOGDQDEBg5P2YoJ9KVs1fV2MSYqAZPdSqROj3xQXLZaTBS2NXFeWhbjYnyp\ngjaXk7cP+er/WFwOvFoNVmM3+S1NpISEsqmqjM5BkwkBkEulzJ07d6gPYaGhjNWHcHbS8UG3Qioj\nWhtIdbWvmO+xtLSUH9V8mps+kn6Hk7yWFuxuFzY3vF1UMLT+DOBwVzt72ppwDa7rWn7ffchlMjZU\nVLEgOwuJIJATGc7munokgsD2+kY21dQDEKhQYHE6+dcJdUBXrlxJU1MTSqkE66A1eaBCTp/TxScV\nVdw02pfmaHY4WFXgc0r0Ap9V11BtNnP32FxG6LQo5HJWrFjBvx57jHWVdQAkBGpZccYEAuQy1pZX\n4wVWT5vEKIPvuTT09XPTtj38u76RK0ckI5dI8Ioi71VUoZZJOS0iFAnwYXktCfHx2AcGAIgMD+eg\npZ8fNu8GIC4mhvUbNvDRhx+iVcr5d20zn1T4SgJEBKhwOF0UFBRwzjnnnPSdO4ZWq+WsWbN4t+Ag\n5yVH8fysMSzbfoRlO3z12SQSgSvnzeP1N9781bZ+TH9/P51dPUxKiRu2/apJsby9s5G3yxq4ISsR\ngC67gzfLmoiKiiIjyIVGMXwy5PQUPW/nVf+u8/+nOPWElXsAYkaCw+arZ+Vx+8LaohdZdMowxx5Z\nTApOYxtivwlJlC80LYoiHosRQZAgUyiIi/O9ANXV1WzevJkRKSmMzs5m8uTJTJ8+nVGjRg1VaD8R\nURT56quv0ManDIkqgIDIWKxNtaxfv94vrPz48ePHj5//MPfccw9ffvkl+4v3YNCH4vV66TF3k5GR\nwXvvvcc777zD3LlzufHGG39T6ZMxY8ZQWlrK0aNHsVgsJCYmEh8fz8T47CFRBTAyPJHSrjpmz57N\ne+++yxfbthIVGkqP2YzFaiVYo+Paa68lOzub7Ozsk57vq6++IkofQmyQgWB1AE293azdt4MEQxg2\nl4M2Uy+XXHIJ7777LoIgDKXXNZmNjAyLGmqn2exLadtcU0KNsZMuqwWr08H7+fvQKBQIgkCAXMGY\n6OODXY1cwfiYeDbXlCMCDz/8MDExMVy7cCFrCw8Rpglg0ZjxBKnUFLa3sr2uZlh0IC0tjbrK4YPe\nAbeblj4L8wZt2UeMGAFAVU8PEzW+FDCpREJ6eCh5LS0EKpVYBqNIs1NSmBgTjdFu57OycgY8bu6f\nOBGADTU1dAJb6xqoMJqI0wVQYTRhdbnwiCLTomI4JzEBh9vDJxUV9LlcvPjCC7zx+utccOGFLFmy\nhLVr1uB0uzkzPpbzUgb3La+iqKuH7xsaKeruITVIz4H2dgCWjs5idJiBil4TbxSXcefO3ZgdThJq\narjjzjtpbWvjlVde8dUx9Xj5rLKWSnM/zWYLY8MMQ6IKIEGnZWZMFFuaW7li01bGhhko6zXRYrVx\n/2nZaOQyvqxuBOCi+AjCNCq2NXdQ12vEJUhYs2YNqampjBs3DqlUylXz5jE/K4FFo5Mo77agUchI\nC9Zy0Zd7Wb9+/U+EVXd3Ny+//DLbtmxBq9Ny1fyrufLKK3lu1SrOmDqVmZ/uYFq0gc4BXxR2/vz5\nPPXUU8TExJz03T0ZWq2WyIgw9lQZuWT88bTCII3PGfCePSV8UtNGrEbJlpYe1IF6Lpo7l0/WvEv/\ngButyvd3Jooiu6tMpI1M/919+E9w6qUCejzQXA5djaAOBJ1h6CvR+9PiewB4vUh0QXisFgYqCxDt\nVkSHjeuvu46goCD2799PTk4Or7z+OvlVdezOO8Rjjz3G4cOHTyqq/Pjx48ePHz//Oxz77V6xYgUZ\n2enkjsshJSWFsrIyig4dpayglGX3LmPy5MkndRk7EUEQyMnJYdq0ab8qxtLS0pgydSqIYLXYMKgC\nmZ0xkfMzT0clV/Dyyy//5Jj29nZ27tw5FM3xil5azUYcHjeX5U5mbGwy7eZezA4Ha9asYc2aNRw4\ncIC9e/dy//33IyCwubqYss4WzAM2itub2NlQwdw5c/mv557DMCIRu8eNVCJBp1DSN2CnoqudH5ed\ntTodNJiM2FzOoa3l5eWEhoZyxbx5iKLIgtxxjDCEEhoQwFkpqYyJiuadt98eSnW86+67qerp4svy\nYjr6+6g39fJe8WFEiYQbbrgBgJSUFObOmcPnxSUcaGqmx2Yjr7mZz4tLyQwPI0AhRy6VMikmmgvS\nUokICCAjNJSl48fh9npp6utDLZfzl4QElBIJ8+fPZ8zUqfTrg7nwiivIzc1lpMHA9aOyiNPpiA/U\n+a5TFOmrrKS3uJi777qL6dOmYTaZSA3Wc3NuFgmBOtJCglg2cSwauQyNTEqb1UpRTw8Oj5fFmemc\nkxBHpEbD9JhobhmdRZd9gFGGYDxdnfzlL39h9erVLFu2jMMFBZx1wYV06YI5beaZTJ48+SdukHa3\nm16HA1EUiddp2NrSRqd9gFty0skND2FNWQ2HO3uYHhNOr8PJ6sIKJMCESAN43Cy95RYiIyORSqV4\nPB7cHg/t/XZkEoGxUSGkG4abrv2YlpYWxo8dw4rH/4WirYL2Iwe4+uqrWXjNNWRlZVF49Cg3Lr0d\nc2QiIybP4Ouvv2bNmjXDRJXT6WT//v3k5eX9bDHkHyORSPjbHXfx3u4mnt5YRW2nlW2lXSx8o5Do\nqAjWrl1L9ISp9ESnsPTuezhcWMh9992HwyNwxSuH2V/TS1lrH3/7sISdFd3cceddv3i+PwvhVFnH\nIwjCWCCfkaeD0w51hUhScpEERSBazXjKDyBotChGTRmWCij2tqPWaLANFf0VQIBrFlzD66+/hlKp\nZMzYsZTVNqDNHIsglSGKIrb6SpwdTTQ1Nv5iXYBLL72UbzZ9T2jOxGGpgMaKYnbv3u2PWPnx4+f/\neQ4fPsy4ceMAxomiePh/uz//Vzj2u5Sfn8/YsWP/t7tzyuLxeJg9ezY//PADE0eMxzA44Wqx97G/\nOo9//OMfPPTQQ7+73WlTp1FaWDQ8FbCjhiOtlZSXl3PhBRcgMTuYkDDcNGBHdSEJ2SPZvn07AAMD\nA9xyyy28/977eAYngMPCwujq6ho6JlyrZ1JCGt9XHeW2v93O+PHjuW3pUjoH9xEQGBuXQI+1n3pj\n99BxqampHDp0iMDAQGafey6Hdu/l6uzxaOS+8cim6lIOtzUxOy2Djr5+Ctubh9b3yCUSXF4vwUFB\n9A7afutVKu6dOmPY9RxubeHL0iLsdjsqla8A8PPPP8/fH3yQ/sGxVVxsLO+9/z4zZ84cOs5kMpGY\nkIDZcnxNU1Z4GBPjYng735dytzB7FJNjhxcmfmjHTgbcbixO5+C1+1Izj51LJpWiUCiYGRXJlYMR\nss0NjbxfWso/Jk4gPcQXMao3W/jngTwkUinnJcQyPzNt2Hme3J+P0T6ARiaj1Ohbb//yjKnE6o6L\n6j6nk6s3bWX5uBymREXwfnk1n9fWU1xczBOPP86HH32EdzBtMTs7m+LiYp6fOpGskCA+qa7jg/Jq\n7INrxZQSCeEBGuwKJd09Pb5nIPONOTODtRztNvP3iaOYm+wTNp22ARZ9v5/zL7uCaxYuZMn119PQ\n1AT4zB/unpTB3LQYNtW0cd/WQjZt2jQsYrVkyRI2fPIhn155GtGBvhTTr8tauffbIr7//nvOPvts\nfolPP/2Uv922lPZO3zsYFxPNK6+9zvnnn3/SY7xeL8uWLeOFF1bjdPrSLkePyuKjTz4dqh13Ilu3\nbuW6RQtpbPK5VQbqtPzr8Se47bbbfrF/f9bv0ikprARNIGL5XgS1DmmSL9Turj4M5i4EpQZJYAhe\nixHRYSM3N5cDBw6wb98+6urqCA0NJTc3l9jBP+Tm5mbi4uLQjsxBGRo5dD633Yr58O4h5R4eHs6V\nV17JjTfeOMzEoqqqikmnn05ffz/yIAO4nNiM3SxatIi33377pDMJfvz48fP/Cn5h9fP4hdX/DR5+\n+GEeeeQRQrTBTEqdMOy7I/VFhMQYOFp09He3m5+fzxlnnIHX5SFKF0qfw0q31cSyZctYuXIlc+bM\nIW/nXmanTxj6rfd6vWwo2cO8BfOH1ljddNNNvPP224yLSyE+OJSufjN7ayuQS6WclzEGy4CdvXUV\n9DsHCI+IYMSIEezevZuUkHDGRyfiFUXymmtpshi5evzpyCQSzAN2SttacQaoqKmtxW63ExAQwNnJ\n6YyLPl630+pwsDpvO+Cr83RmQiqpIaF0WPvYVFuBzeVEIZUxJyWDvLYmmvtM3DN1BvpBAQWwrrSY\ngrYWBhwO5HL50Pa+vj7y8vJQq9VMnDjxZ01D1q1bx2WXXoooigSpVQSpVNT3mhABnVJBdlgYC3+U\nMtk7MMCD27ajlctZmJ5FqFLF80UFuLxe5qenk6DTcbS7my+qq9Ep5Dw/YwaCILAy7yASAe4/bbiV\n/eqCIxRZzEQrFUyJiaKwsxu5RML4yHDeLy5nwOPBI4ro9XrMZjNLR2dxTsLxtMkD7Z08fvAwz02d\nSFpwEHa3m6u+307u2LEUFRayeFQKY8INlBtNvF5UjVcqw2q1kqzXUWPu49KUeGYnxmAccPBacSX1\nFit/f+ghLr74Yjo7Oxk9ejRjc3NpbW8nXK3kqwunDxs3vllUzYe1bXg8HkYH67ghIxGVVMqaygY2\nNbaTEaqnrNvMFZdfzseffDLs2NCQYC5LDeHOqcfXwomiyOz395Ez9UwCAwNpa2tl3Ljx3HLLLcTH\nH39v9u7dy7Rp0zg3O4ybz0zA7RV54Yd6dlf1kp9/+BfTXMGXglhQUIDBYGDMmDG/Ohb2eDwcOHAA\nu93OxIkTf1P67p/1u3TqrbEa4sfLHQFBAoIE0e3CY+pCotUjMURSWFhIY2Mj06dPJzk5mdbWVjSa\n4/asx2Yafhy+9QzYsRTlAQItLS3ItIF02Rzcv3w5r73+Ovv27iU83OfwkpqaypHCQp5//vlhduvz\n58/3iyo/fvz48ePnT8TpdLLquVWoFWoE4aerIwRBwOM5yTKBX0GlUiGTyugfGKDV0jkU6dHpfGYZ\nd9xxB2d/8w376kvIivQJoIKWKvoH7FxwwQUA9PT08M477zAmNonRMb6SLUGaAJQyOd+VFuD2ekgy\nhKNXa/ikYC/t7e30dvcQpNJwXlrOkN323PQxvHN4FwVNDZyTMYpgTQC13V30ORzk5eWRnJyM+CN7\n7mNUGH21gaSCwLS4FCbHJgIQptGikStYU5yPKIr8u6aMtOBQWvoFPjxymNlp6QSp1RS2tZLf2vyz\n4xmdTsesWbN+9t5ZrVZeffVV7r//fgJVKhK1OmrMJup7TUyZOpWe7m4a6mrZ19xCqFrDpJhojPYB\nPikrAwFuHz2WxEA9VaZeeh0O7hk3jiyDLxIZrdXi8npZV13N2yUlzElKwuZ2E6iQ/6QfEkFAIkio\nMZmpMVkYHRpCn8PJK4XFCECSXotCKqPc6IvYvVFShlwqJSc0hPJeE68cLUUCfFxVy4Pjc5EIAgJw\n6NAh/pozkgtH+MRIfGAAapmMx/b5DCDarDamRoVxW+6xdUI6UvQ6rvh2J1KplJycnKE+SqVSojSq\nnzw7AKlEwOl0olPIWDUlB5XMJ16fmJRNncVKN3Leeecdrrnmmp88I6/Xi1Ty0zb77E42bNhASkgg\nI3QqXt29m9deeYWt27czZswYAJ5ftYqUCC0vLxo91Mabi/VMe3wfL7zwwi8aswCEhob+akRs2HVK\npb5Uyv8DnJLCSrR0g92CEDloSGHvA3Mn0sgE5HHHQ72icwBPay0HDhzg9r/9jW83bgRArlBw8003\n8eyzzxIXF0dGZiY1LY0ogsMQJBJsDZWDdR5EAtOyUIf7UgHddhuNRb71Vy+88MLQeWJjY3n66af/\n526AHz9+/Pjx44euri7MFjPxhjgae5owWU0EBfiySqwOK+3mDq7966I/1Pbtt9+ORBS5aPw0lHIF\noihS1FjDQw89xPz58znrrLN47bXXuPvuu6kq8rm3HVuKMHfuXKZNncay+5bhcrmICTIMa/vY5167\nlQhdECEaLSqZnPCAQHrs/cTrDcMG2lKJhFh9MM1mX8qaZcBOWUcrDrebiRMnotPpSElJIb+9icyw\nKJQy3/CwvKsDnVxBn8tJUtBwR8Ekve/zmfEjONLVSru1D68oYnYM8Ga+zx1RIgio5XJmzJo1LFp1\nMkRRZOXKlTz+r39hs9pIDzGwJDsHmUSC2+vlneKjHNi3nzUfruWplSvJP3yYr6uq+KqqCgCNWo1a\nKiMxUA9Am82X/pcRMrzvmSEhfAnsaGpme5OvoLEEX/pfot5Xm6m138rBjg7kKhUKqYzHJ59G/GCa\n38GOTp46dAQBgXKjaaiIrcPj5fnCo3gH5+2zDcGclZDKc4dL2N3WQYfNjnNQqI85oajumHDfZ4Vc\nxoDLzdjw4c/coFaSHBxIS0vLsO2dXV2cHxvOl7XNbG3qYFa8L3vK5HCyrqaF0NBQ0qXeIVEFvvds\nUqSB3XaRRYsW/eyzmHvBhXy54Quuzo3DoPHVSPuiuAWjzcHirATuPy0VQRCwOFzM31TAbbfeyu69\nPlfssrISTk/RDxNmCpmECUk6yktLfvZ8/3/h1BNWzWVg9c0seHvbEU0diKYOEEWEHxUKBPD2+/Zb\nuXIlZVVVqEZmItHqcBu7eekl38LS9PR0QoKDKS8vx5K/EyEoFFd3OzJtIKJHjupH7jsytQZFWCSf\nfPrpMGHlx48fP378+PHR29vLm2++ya5duwgKCmLBggWcffbZf0oWh8FgQKPRIJVKCdLo2VeVR0Rg\nOBKJhHZTB+ER4dx5552/u12z2czWrVuZkJKBcnC9kiAIZMYmUdnexPr167nrrru48cYbmT9/Pnfc\ncQdvvfUWoyLiSQyJwDJgo/BwAbfddl02P6cAACAASURBVBsSQUKnxUSY9njx4Q6Lb3wSqPStfbEM\n2Bhwu0gNjaCjwUxrXy/iYO0m8NU9auszYXU6+a6siKrODkRE/pKWQXiAjuKOVgpqalAqlbxRuJcU\nvQGzc4AGsxEBn0Bq7jMRF3h8KUNzn8/UI0wTwNSYJD6rPEqmIYLSng5itDrUcgWt1n7kKiUrn3rq\nZ+/T1q1bee+99zD29DB5yhTkcjnLly9ndGQYR61WzktKRjZYV0omkTA7KYWiQ/tZdO21VFRW0t3d\nTVFREU6nk4yMDEpLS7n5ppvoGbBjUKkJU/vuT43JRGrwcbe9arMZiSAwJjSU/K4uMjIyqKmo4J/7\nDzAuPAyJIOFQR4cvwiSKTImKGBJVAOPDw9DIpDT3+YTbXxJiOTM+CuOAg/dLq7E4XDw0KZf0EN/9\n+qa2iVeLy7E4ndx888289tprlPaYSAg83mZpj++ZLr3tdp5/7jlKjSYu4Xh6ncXpoqnPOuTweIzo\nqEgsTgdnxkbw4J4j/Lu2BYNaybamDpAruHDWLLZsWI/L60U+eC9FUeSo0ULSqNyTvcI8/MgjfL/p\nO857fx9nJYXSbXOyo64LqSCwNDd56N0KVMpZkhXHPTv30dnZSXh4OMnJKRwu2DXsHfR4RQqb+pk+\n+7fXtvp/kVNPWA2KKuRqMHcNutSIqNRqBhorEaRy3xqrvl7EZl/x3sLCQtSjxyAL8c0eSHU6RLeb\nF196CUQRuT4YWYAOV5+ZwIE+XIOnEgTJT34IBIkEl8uFHz9+/Pjx42c4zc3NTJ48mdbWVrTqINxe\nFx988AH33nsvT51kcP7fQaVScdNNN7F69WrSIkYQqjPQburA7rSjVCnZtm0bBoPh1xs6gWPpgycW\nm3W4XYiA5UeGDGq1mq+/+oqRYTGMj/MNOkMDAtGrA/iq5ABTpkzh0MGDKGRy4oINdPVb2FlVSoBC\nSZA6gHaLiV115UgEgREhEeS31NFt62drbRnjY46vsbI4BgjQaOiTSXB5PSzIPY2owchOpC4Qm8vF\ngFrJ7PPOY/euXSSGp3LfvHncf999uGx2djTUoJHJSQ0Jo93ax8bqUkLVASTpQ6js9RkUqAbXSdnd\nbtqs/cyZO5dbb70VmUyG1+sddj8eeughHnvsMSIDdQQpFXy/aRMiMCo8lKnxcRxt7xoSVR6vlw6b\nlT6nw/fZ7ebNN9/kkUceISMjg4qKCoKCgoiNjSUwMJCXigq5Oi2DKE0AeoWS14qKuC4ra2iN1frq\nak6PjOT69HQeP3yYiooKvF4vUQEamqz9SBCI0WmpM1vwOBxDguQYNWYLNreHMJWK5CAdN+Uct/ZO\n1gdy0+Y91Jr7hoSVQipFqdfz6Usvcdlll9He1sZbm75DQCBKq8ZoH+CtklomjB/P00//f+ydd3hU\nVfrHP3f6TCZlZtJ7SEIKvYbQO0jTFayoCHZ0Lax1FRVF/Omqa1tdK4sFXMBGU1wsNOmdkJCEkgBJ\nSM+kTSYz8/7+GBiMFMWy7q7zeZ7zwNw559xzzr0397xz3vN9/0JJSQkffPAB8YFmxibGUN3Swit7\nCtBodW0C+S5cuJDDxUc4JML1me3I7NSefx0pZXdlLQ6P8OSsWZhMJj744AMe3LiHWzqmoFereH9/\nETvLq/n0jjvOeg8nJSWxdfsOLrzwQj7dsR29Wo3NqKPO0Yruey6CerV3fE7Ob2/74+2MGLGUBxbm\nMn14Im638PzKgxRVNDJ9+vSznvPXpKysjLKyMpKTk33uuL8Gvz/DSmdCldITRa1FRJBjeUhtKY7m\nZgBaC3f5svbr14+LLrqInTt3ora0XbIVpzd+QmCnXmgCvBfIWVmGvSCH7t27s3vfPlwOBy01Vegt\n3j/KnlYnzooyLrn8sn9PX/348ePHj5//Ih544AEqyivJTMpCrzMiIhyvKuYvf/kLl19++a8i8vHk\nk09y/PhxFixYwHcFvZqbm+napSs33nQjzzzzDLrvxJv8IaxWK7179aJwfwHxoRF4PMKWA7kUVXpj\nHc2ZM4eKigqee+457HY75RUVdEju3KYOmykQs8HIoEGDCAkJYfny5ae+s9moqqpi3pbVAERFRuJp\nsFNUW0lqaBTbjh0ir7KEveVeNzetSo2iKDw+ezbFxcW8//Zcn1F1ksQQKysLcnnppZfQaDRtznXV\n5Mk4PW4+LTjlxhUZEMilaZ0RETaWFKNCYVd5KUMTkqlqbqKloY69u/cwatQoAFKSk3nl1VcZMWIE\nOTk5PP7444xKacfw5EQURaHW4eDFDVuoa2kh0RKMSavhy+IiMqw2Pj1YQF2L16hSKwqRJpNXQe+F\nF3js0UepPqFKqFIUPCLYUXhy+2ZfWwO1Wp7Zts33uVtYGJkWC/ds+JZqR4uvbGljE3BqF74lJASV\nw8H6kjIuSk7EZvSKcmw97jUkKxwOJoYnthnHMJOBGLOJ4hOrWfur68ipruX111/nkksuAeDVv/+d\n3r178detOb7d/qFWK3fcdRedOnRgX14eAHP3FfL2vsIT1ziCZStWEBnpdfVrbGzkxuuvZ0hMGJEm\nA3NzD+E+cf+eNEjvueceAHQaDRsq7az6bD0ARoOBZ5991reX72yUlJSwbds27u+dyrUd4yiyNzNq\n8QbeyT3CDZ28/Xa6PbyTe5SunTr5VLCHDx/OK6+8wj13/4n3v/Xeg8FBgcybN4/evXuf7XS/CpWV\nldxw/XV8umQpIkKAycgfb7+Diy+++Fc53+/OsFKsUShqr5+voigQmYzUlIAhAAQ0tljctaVIk50H\nH3zQ9+uKp96O+jt/hNzV1ejConxGFYAuNJLWsqNER0ezPz8ft0pNbc4O9LYwVFodLVXHsQYH/yTZ\nVj9+/Pjx4+d/GRFh0aLF2IKi0eu8LlyKohBhi6Oy7hiLFi2ie/fuuN1uPvnkEz7++GPcbjdjx47l\nsssu+1F7eM6EXq/n/fff5/HHH+fiP1xM7r5cUm2JhBiDqGis4pW/vYLb7eZvf/vbedX73F//yvBh\nw/l81ybcbjctra30jE7FZgqmrKGa1/7+Gi6Xi5dffhmz2Uxlo51Ea7ivfH1LM40OB+3bt+eJJ54g\nNzeXnJwc4uPj6dWrF8XFxWzdupXQ0FD69+/PZZddxkcffUR8iA2tSoPL4yIu0IoglDXZ6dShIzfc\ncANvvvkmNY0NNLU6fbLqAKX1dqIiI9sYVU1NTcy46y7MGi29wqNwelzkVlZQ73RiUGvYVHqEwroq\nqpoaCdTrSbOEsr+2imN1tWjUalS1dVyb4RVaWFt6hHHjxrFt2zY++ugjTHo9Q9ol+Dx7QgwGBibG\ns3x/IWpFYUJ6Kh/syWXL8VK6hIcxKD6WZpeLzw4c4mh9PYmVldx5550MiImmSqWioLaW8e2TSAwO\n4vlNOxgYHU0Hm42k4GCsej0bSkt5a98+rkhNJSEwkKe2b6d7TBgdtGrWHS5jbHIcPSLDOFbfyMK8\ng9Q7ndTU1nJPt87Mzc3nT2s20CcqAofLxYZSr6iHRa+joNbO6O9c93pnK6UNTRjVap7ZtocNZRVk\n9e7NVVdd5cvzyCOPcLy0lGs7tqNbhJW86jrm5RxmyjXXkB4SxJzendGpVLxfWMyu6hr++tfnueWW\nW9rc419++SV19fXc3L8jcWYTV6bFs6eyjiMNTbyy5wBdLEHcmN6OVo+HN/MPk1NjZ86cOWRkZDB4\n8OA2CtVnIj8/n1tvvRWdWkW1w0lJg4PEYBNTO8bz1NYCVh+tJM1i5qtjVZQ7Wvls/gttvLRuueUW\nJk+ezDfffINKpWLIkCEEBAScxxP08xERJowbS+G+3bw6qT2dos0sy6nkqaefory8/Fc55+8vQPD3\nVX9Uat9xRaVGbYtB264HGrOFWbNmMXz4cMLCw2nO3YurtgZxuWg9Xoa4WgEFd1Mj8t2gZyoVRqOR\nHdu3M23qtYSG2lA7GgmUVqbfdBPbt28/zT/Wjx8/fvz4+T1RX1/Pvn37Tgu+63K1olJ9X3ZbQaVS\n43Q6cblcTJw4kUmTJrH0k2V8tuxzrr76aoYPH4HD4fhZbWpoaGDX7l10iEgl0RpLiDGI1NAkUmwJ\nvPHGG1SdiB10LkpKSti/fz8ul4t+/fqxectmBg0bSmOLg94xaaSFxhFqCqJjeCKdwxN5++23qamp\n4eabbya34gj7K47hdLuobLSz5lAONpuNSZMmAZCRkcGkSZPo3dsrz56QkMDEiRMZNGgQarWaDz74\ngJdffpnI1HbEJsTRuWtXDOFWLAmxPDRzJmvWrsVsNnPVVVdhMBhYmreXisYGHK5Wth0rZm95KX+8\n/fY2/Vm4cCFHjh5lVFIK3aNiGNUujTt69SMmMIjyVgfVATqGjx/HSy+9xMBhw6gNMNCtfz9Gjx5N\nkN7ANemdaW+x0d5iY0p6ZwI0Gp5//nmcTidaleo0JTudWo0Aaw4X0zkynECdjrigQKZ16Uiq1ULn\n8DBu69kNtaKwe9cuuoeHMyYxkdzqaiZmpDA6OYH0UAv94qLZWFZGq8eDSaPhQF0dK4qKUAHby8tZ\nVFhIfIiZW7Iy2X6sklHt4rg8M4VUazCDE6K5rUcHnwBFqNHAnOyeDI2NJr+mluL6Bjx4V7hcHg9f\nFZfwaWERDc5WiuwNPLVlN4pajRIWjt0WwaOPPc6qL7/0xfAqLy9n7ttvc01mIldkJpJuC+Ki1Dj+\n2C0Vl9vNrRnJ9IkIpXuYlaezOhMfaGbt2rWn/XDgPBGny6hWU+9spd7poneklW5h3r1kf8xMoXeY\nlX4RobyS3Q2zVsPixYu56KKLMJvN7N+//zQhjJMsWbKETh07kr97Fz3DglmQe4wxH25kSWEpM3q2\nY3y7CLYcr+H9/Uc5Wt+E0Wj0raR9l6CgICZMmMC4ceP+7UYVwLp169iwaTP/uCKN67NjyEoI5vEx\nyfxpcBzz57//q5zzd7diJbXliC3WJ6sqld5gaTTXo5wICqgoChIQwqZNm+nVqxe1NTWIR2jeua1N\nXc7jR3EePwqKCl1YFLrQCJx1NWi1WlJSUnjzzTd58803/6398+PHjx8/fv5TaWlp4Z577uH111+n\npaUFnVbHlGun8Pzzz2MymRg5ciRrvllHmCUatco7Ramtr6CpuYELLriA+fPn8+mnn5Ke0BFrUBgA\ndQ01rFu3lldfffUnCU2cZM+ePQCEm9vuqQo329hfcZCCgoKz7rcqKCjghutvYPUar2teZGQkc+bM\nYerUqVx11VUsXbqU6MC2ZaODbOwoO0B+fj6zZ8/m6NGjfPDBB3x7OBfwBs39+JNPfvSEVKPRMH36\n9B/cwxIaGsryFSu4ZNIk/rFtI+Cd99xwww3ce++9bfIuXrwYjUrF/BzvNonYwGDGpqTRPSKapYV5\n7M3J8blI3nbbbb5yPbt3J8kc5HNJA697WlJAELt37uLa56/liSeeYGfpcbpHeyfkTrebTcdKiY6K\n4tO8QpbkFaIoCv1jo9sYYAFaLe0sIeyvqqZDRjqljU0I0Ok7KnqXZqZS7XDwZs4p10W1oqDTqNl/\nwpgPDzBwqMZOY6uLrt9T4MuwhaBRKWi0OpYfLubWTplcnZ7KVZLCP/LyOdLQ6JWnR0GAuTkFzM0p\n8PXzs5UrGT58+BnHf9WqVbS6XPSOanvOXic+lzQ1k2bxCpWoVSq6W4PZvWPHafUMHjwYnVbLnWt2\nUFTfhEsEo1pNmFGHQaUi03JK7MSoUdMr1MKuQ4d49913eeC++zhWWgrAwAEDeP2NN0g7ESzZ4XBw\n3dRr6R8RzHP9Mjne5GTmpjw2l9dyz+p9PLXZ65o4ODaUSSmR3PL1XnRuJ7fecgtfnQhq/Z/Cnj17\nUKsURqa13c4zOt3GX74q/lXO+bszrGiuw1OwCSUwDHHUQ0M1oIBKhcd9SlTC01QPOj279uXhaW3F\n1C0baWlGnC20VlXgrq3CEJOAJigEV30djqNFOMtLULRa5s+fz6BBg7jxxht/u3768ePHjx8//2Hc\ndtttzH17LqEhcZjDQmhsrmPu3LnU1dXxz3/+kyeffJJ+/fqRd3grQSYbre4WauwVjB8/nmHDhjFu\n3DiCzRZAofBoHiKCJchGiNnKggULfpZhFRfnDexa56jHZjqlIFfnqEdRFGJiYs5Yrr6+nsGDBtFY\nW0/vuHQMGh2Ha8qYNm0awcHBxMbGAlDTXE9k4KkJXnVzPeANuaLX61mwYAGPPfYYW7ZsISwsjCFD\nhrRxy/slGThwIEeOHmXVqlXU1tbSt29fEhMT2+RZuXIly5cvp31IKD3ConG4W1lXUsR7e3eQYrUR\nFhp6VvfLuIQEthw42EYVTkQodTTRNzGBfv36cemll/LBokXsLa/AYjCQU1lNo8vFJwsXcfmll2Lw\nuGl1uTlir29Tt8vjoaypGaPBQHF9PcnB3m0axXX1WAx6tpaUs6+ympPb5R5//HEefeQR3B4PfWyh\n9IuIpLqlhY8PH+LvG/ehUSkcrqunU/ipa1PS0ITLI9x43XW88sorlDQ108ESQm5NLfm1dUQYDQyO\nieSfhYdpF2QmQKvhSH0jtc5W5r377lmNqrq6Ou48IRhRWFNPYvApVcDCGm8/w4z6NmUK65uI75J2\nWl2hoaEY9HqONjZxc6ckOliD2Hy8hnl5RahR+Ka0gvXHq1CrFAZFhpJTY0dnsXLNNdcwMj6cmUM6\nU+Vo5c09Oxg6eBD78vYTHBzM119/TWV1DXdl98YtMPWrHWgUhaey0okw6vnwUClLi8qpa2llxeEK\n4swGDHoVX69eTUVFBWFhYWfs+9koLy/n7bffZvfu3cTFxXHdddfRvn37Hy74I4iNjcXtEfaUNtI5\n+tRY7zhWj1oBt5yj8E/k9+cKqNZASxNSWQwNNYACYTEQaEHxeBC3C1f5YaS+Em1YPCqrdyOeSqNB\nE2RBHWTBXVOJMT4ZQ2wimqAQDDEJGBNTACEwvQs6Wzizn3iizSZYP378+PHj5/dMWVkZc+fOJdya\nSIQtkQBjCOHWBCKs7Vi4cCEHDx4kLCyMZcuWcellk9AHQGxCJM8++wwffvghiqLQ1NREs6ORvKI9\n1DfV0eioJ784h4bmehoaGn5W+/r37++V7C4voLqpFo94OF5fSUHVYcaOHeszvL7P+++/T1lZGf0S\nOhAZaMWsN9IrNp3IIBuzZ88mOzubjh07srWskPJGb73H7FXsLj/MqJGj2hg0qampXHnllYwYMeJX\nM6pOotPpGDNmDFdeeeVpRhXAk3OeJC4whEuSO5AcbKWDNYLJaV1wuFzsLj/OrbfddlYJ/OnTp3PM\nXsfywwU0tDppcDpZdqiA0no7t9xyC4qi8P777/PCiy+iiYqhyAOjL7yQzVu2sH37dhobG7m1W2dG\nJMWzt7KKlQcP0+xyUeNw8F5OLg1OJ1OnTWNdSSl51TUkBAYyf08es9du4e1d+yitb6CiyStEUXT4\nMB6Ph642GzekZ5BpsdA/MpK7O3ehqrmFJEsgnxQcZuOx47g8Hg7V1vPythyiIiJ47rnnWLlyJQk9\nerCi+AhF9Q3oVCpiAkwMjIrg1o5pGNUajjc5vCtyiYlceeWVZx3zd955h+qqKtJDAnltZyGbS6tw\nezzsrajlua1eZcfPikupcrRgd7byZu4BdldWM/3WW2lububgwYO++3zJkiXYGxq4v0d7JqfF0zUs\nhBs7JnFDhyRcIvx5Ww6F9Q3sqanjT5v3UO5owajX0yvSylN9M+gTaWVsYgSvDOxIeXkF77zzDoDP\npdasVbP88HHKmlp4a1AXLkqKJDvSwl/6ZNA/0sLuSjuljQ5KG1soqvUKwLWcEBj5sezatYvM9HQe\ne3gmh1d/zpsvv0CHDpksWrTovOo5GxdccAEJcbFMWZDH1iN2Wt0ePtpVzuNfHGZkR+sPV/AT+P2t\nWLlP7IeKboei0SLFeahCo/DkbUPEg3PfGgA0obGorVFIawuu0gM4y46hj0nAcyLYnMbSdglXa7HR\nfAg8LQ60ITaOHMilvr6eoKAg/Pjx48ePn987OTk5uN1uggLavj+DAkI5Rj4jR47kwIEDAHTs2JF5\n78xj8ODBbfJGRkbidDlpH5dBaIhX6KG2vpp9h/f4FMl+KiqViiVLljB27Fg25G/3Hc/Ozmbu3Lln\nLbdz506CjGa2H8unrN4bgNekNRAWEMyePXtQFIUlS5YwZswY/pV3qt6s3r155913flabf0127dpJ\n1yBrG+PJrNUTZQpEE2blz3/+81nLjhgxgueee4777ruPb0u9qnA6nY7nn3+eYcOGAV7Xxdtuu62N\nCyHAE088QUJQIIE6HVlRkZQ3NrG88CDLCg8CEGAy8d577zFx4kR27NjBwo0b8Zz4IbvZ7ea+7K6k\nWIIREdYeKeXNt94CoNv33DijTCbCDAYKquwowEvbTrkNGvQ6Nqxei16vZ+TIkYwcOZINGzbwhwsv\n5HhFBTurarh17SbUikLfyDBuyEzlwa27mHbppecc0w8//BCNSiGv1rs6NXPtLp8qoEalYvYTT/DY\nrFl8dsTrpqcAOq2WZ599linXXEN9QwMGvZ4p3wnq2z86tM05+kfbeG3vIf7UKZnLU2IREVYeLWfm\n1jyOHDvKHzomtLmmUQEG0mxB7NrldfccMGAAep2O9/Yfo9nlJjkogPhAoy+/oigMjw1lfVkNC0Z0\n4Xizkyu+2EmNqM66qns2brhuGpFqN4su60WoUYfD5eG21XlcN20qo0eP/tmy6Js3b8ZkMpFbcIw+\nf93qOz6ig4X7xsTz2Z7qn1X/mfj9GVahCWA/DqWHEJ13I6EndysoCqBCMRjRJXZCdeI7tDpUag3O\nIweRRjtovMve7qYG1IZTN5q70fsLgkqnx1lVjjkw8DfZqOfHjx8/fvz8J3Jy0uVoaUSvM/mOO1q8\n78+So2UkRqWjoFB08CijRo1m69YtdOrUyZf32LFjBJqCfEYVQEigFWuQ7awb8c+HlJQUcnNz+frr\nrykqKiIzM5OsrKxzBicODQ3F3txAq1ZPz9hUDFodh6qOU1R7HKvVyt///nc2b97M+PHjuffeexER\nMjIy6NOnz68S9PiXIiY6hvLjlW2OuTwealpbmH7JJT+ownjXXXdx1VVX8cUXXwAwatQoQkNDz1kG\nvPfJiuZmXB4PGpWKCanJDIiL4e3dOUhwCDk5OQQGBrJgwQI2bNhAr7BwssLDebcgn07hVlIsXtdA\nRVEYEBfF10fKKG9yUNzQ2OY8ja2t1JxYYUkJCSbCaGBvTR0Nra0sX/EZXbu2DZ6bnZ3NjLvv5r77\n7mN4bCRZ4TaONjSx8EAxG49XEh4RcU5X1E8++YTVq1fTLyqUUfGRVDtamJ9fjNPtISEwgPrAEO6/\n/34+/eQTNm/ZQoRRx8DYMHZW1PLt+vVcnhZLr4gk9lXXM+/tt0jLyASgsLaBrmGnFP4Kar3P08Co\nUN84jI6LYOGhMg42O8mvbTsOzS43xfYmJp54PkNDQ3n4kUd48MEHiQ80Ud7koN7pIlB3ymTIrWkg\nwqRHURQiTXqmd4zngY35VFVVeeX833+f2tpaBgwYwGWXXYbRaOT7HDhwgC3btvP28ExCjd59egaN\nikez2rFkwSY+++wzLv0BQ/Vc5OXlMXLEcDpZ9fxjTBp7Kxv5YF85ZU1OXrwylcYW90+u+1z8/lwB\nA62Q2BUQcHqXLtHqQDyAB3E04q4rRzxupNVJ69F8xOPmscceo1NSAiG4CQ4JwVl8EJe9FhHBVV9H\n8+EC1AFmXE2NtJaXctONN6JWf1/ZyI8fP378+Pl9kp6eTv/+/Tlec4iGZu/7s7G5jpLKAhRFRWps\nZ6yB4VgCw0iO7ohapeG5555rU8fhw4fRqE+f0GvUWsrKyn6RdqpUKoYNG8a0adN+lPFjNpvxiDCw\nXSeSbFFEBdnITszAagzEXmfn1unTWbb4I15+8SWuv/56RITs7Ow29ZaXl/8o1cF/J7fcOp3cmgo2\nHz9Kq8dNvbOFZUX7cbhcTJs27YxlRISysjJqarwrd2FhYUyePJnJkyf/KKMK4LrrrqPR2cqC3P3Y\nW1pwut3sPF5Bsb2eBx54wLeKMfvxx+lsszEtLY2OVisqRcGkbbteoCgKJo2G5JQUVpeVsrasFJfH\nQ4WjmdfyctHodMyaNQtDTCyFHmHIBRewYeNGhg4delq7WltbeebppxkZG8VNmal0DbUyLjGWGV3S\nafV4+OsLL5xRGe8kTzz+ON3CrTzUM4PeEVZGJ0TxZHZn6pyt7K6q5ebp05l48cVs2ryZSJMevUbN\nwvyjHKhtZGqHBG7p3I6eERauyYjnrq7t2LV7Nwadjjlb95NbbUdE2HK8hpd2HSDSqCc6wNDm/EEa\n74rS8sPHWVhwDKfbw/EmBzM35uFwe7j22mtpbGzk2LFj3HvvvSxYsICw1HScHuGejbmUNDpwuj0s\nOlDChwfLuCI1yld3iN77TM6aNYsePXow//VX2bLkQ6ZOnUqPbt3Iz88/bTyaT8SPtejbPs8n62ps\nbDytzPnw/PPPE6JV+NclHbksI5zHBySxa1pPAnUa/m95EQ6n52fVfzZ+f4YVoGh0YAyCQCtKUCi4\nW1GndAedAVBwlR7EsXctjtxv0TTV8o9//IOZM2eybetWyo8fZ++ePbRPbkfDvp3UbV5DQ84OPC0t\nuBsbaDqYx7hxY5k9e/Zv3U0/fvz48ePnP4oPPviA1PbJHDy6k70H1nDg6A5UKgg0hbQxmFQqFQH6\nIDZv3tymvFqtprahGkdLs++Ys9VJVV0Fqu+HU/k3cfjwYSwBgZj1bd2l3OJBo6gYk9aD4e06M759\nD5JCwrn5ppsoPaHItnbtWnp0705ERAShoaEMHDDQ55L1W3PLLbdw880388WRQp7evpYXdm/gQGMd\n7773Lunp6aflX7FiBR0yM4mKisJqtTJ61CgKCwvP+7wZGRnMmzePnJo6Zq7dwL1fr+WTggNMnz6d\nm266CfBKje/LzaWL9ZSrYkZIIF2T2AAAIABJREFUCJtLymlsPSVEdtTeQGF1LbfeeiuTLr2Ut/bv\n58Z1a7ln0yaKXC4+/uQTHn74YXbv3Utp2XE++ugjevbsecZ2HTt2jIqqKnp/T0Gwi82CUauluPjc\nKnPbd+4kO6Kta2WM2Uis2UhaejoJCQl8/Mkn3NMzlfcu6MncUT24qXMSbhEGRLc954AYr5F654wZ\nlLe4mPbldvotXs3ta3bR4HJjb22lusXpy1/c0MTG8hp6Z2Vx7dSpzNlaQJ9Faxn16UY2Vjfy1ttv\n89isWVgtFmJjY0mMi6OqqootW7fy7HPPsbq0iiFLN9Jl8Roe2pJPR6uZGzt49xy6PcL8/BI0isLL\nL7/M9MxY1o/vwbJRnflsTDeOHiwkLS2Ngf37sX37KVfYoKAgAgwG5u4raaNJMHffMVQq1RmN2/Nh\n2+ZNjIgPwqg9tchh0qoZnWTl/Q3H6f9/pyst/hL8/lwBAREPOJtRjKEoYbFIXiU4GlGHJ+A+uh+A\nDh06cN999zFu3DgsFkub8rGxsezZvZuvv/6a/Px8YmNjaWpqoqamhj59+py2fOzHjx8/fvz48bp5\n7dy5k2+++Yb9+/fTrl073nzzTT5f8UUbBTkAp8tx2p6N7t27U1xUzO4D2wmzRKJSFMprjiMiZGZm\n/ru7A0BUVBRNzhZcbjeaE54qrW4XdY5GesQkE3jC4FKrVHSNTuJwbQWLFy9m8ODBjBgxgmCtgQEJ\naXjEw74dOxk4cCB79uwhPj7+394Xh8PBwoUL2bBhAzabjTvuuIMZM2bw1VdfYTKZGDdu3BkDy65Z\ns4YJ48eTGBTMZanpOFwu1q9bz4D+/dmXm3vaPOqHmDx5MmPHjmXZsmU0NTUxdOhQUlJSfN9rtVqs\nISGUnBCoABgdF8/OqioeWbOVfrGROFxuNpZW0KFDB6ZOncqtt97Kgw8+yLp16wgJCWH8+PG+LRsN\nDQ3Mnz+f7du3ExUVxZQpU04T9LDZbGg1GoobGukWdkr44Hizg+bW1h/c4xcVEUGR/XQ3vCqni4mj\nR/PII4/4Vmta3B4MGjUDYmy8tvsQB+1NJIecUrU7VOetZ/To0cyaNYs5c+bwr3/9C0VR2L8/D3t1\nNZO/2sbY+Aicbg/Li4+jUyvU1dayZOlS7r33Xr755hsCAwMZO3YsY8dcwJ7t27gtM4Z2QUZWHa3i\ntttuQ0SYNGkSd911F1d0jiQj3MzaQzV8ebCKe9bnkRoSwMriSvbVNJAcbKSquZW7OiegUXmf40yL\nmalp0byee4zK/N0MHTKYnbt2ExUVxagRw1GLmyWHKihd2sLwOCu7KutZcbiKO+64g4SEhB97u5yR\n6NhY9m07dNrxvVXNBAQGERwS8oPG8E9CRH4XCegOCPGdhJAIAUSV2l1UHfp5/x+dIurEjgIIIElJ\nSdLY2Ch+/Pjx4+fnsW3btpN/W7vLf8D74D8lnXwvbdu27WeO8H83q1atEkAiLLHSJbmvdEnpJ1G2\nBAHkww8/bJP3q6++EkACDAGi1ehEq9FKgNEsgCxcuPAXaY/dbpfa2tofnf/QoUOi0WgkLiRcxmf2\nkYmd+0vX6HYCSN/4dLmiywBfuqxzf9FrdTJnzhy5+uqrJchokqu69JUp3frLlG795YpOfcSg08m9\n9977i/TlfCgrK5P0tDQBJDIwSAL0elGpVPL666//YNlRI0dKTGCQzMrqJ4/36S+P9+kvd3frJRq1\nWp555plfpb3333+/aNVquS4tXV7pP0Ce7J0l6RaLqFUqsVksEhsTI3fffbdUV1efs56DBw9KQlyc\nqBRFEkOCxKTTiVajkUWLFp2W95qrrxazXid/7t5RFo8cIH8b0EsyrCFis1p/cM44a9Ys0ahVcne3\nNFk+boDMH9lHBkSHiUatFpVKJSatRhICjaKAxJgN8s9xveWrSwZImFEnVoNW/jaki6yeNEDmjugu\n7SyB0j4lRdxut9TV1UnvXj0FkHYhgRKs0wggGTaz2IxaCTfp5LKMaMmKDpERI0ac1q6Tz9RrgzrI\n3sv7+9JFSeESGR4ura2tMnBAf7EatfLepZ1l35395cZesWLUqESjKKICGRVnk2kZ0ZJgNkjR5AFt\n0qM924lKQfZMzxJrgF5mzJgh7733ngDy9UXdZP6ITOkbGSxWvUaCdBqJjooSj8dzfjfDGVi6dKkA\n8mB2vNTc0U+q7+grD/SJE0CsZp0khJp+lffSb/5i+Xcln2EFgqKIEpMq6k4DRIlOFkDUKd1FCQoV\nNHpBoxMURR555JGfci39+PHjx8938BtW534v/d4NKxGRp556SlQqtSiKIipFJYqiyIMPPnjGCdbT\nTz8t6pN5Vd68DzzwwM+ejOXk5Mjw4cN9P7Bm9+kj33777Y8qu2jRIjEaDKKAqFVqASQ4KEgiAy1y\nWef+PsMqO95ruGzcuFHS2reX9NAon1F1MiWE2GTw4ME/qy8/hcmTJ4vZYJDrOnWVB7L6yT29sqVb\neISo1WopLi4+Z1mrxSJDY+N9RtXJlBQcIpdffvmv0t7m5ma5cMIEAUSr9o65OSBAli5del71jBo5\nUiICTPJs3+7y/vB+8taQPtInMkxMRsNpRllNTY0M6Of9QV6n8Z4z1GaT9evX/+B5Wlpa5NJLL/WW\nVXvvX5PRKBq1WkbGh8vy8X1k1UX95K1h3cRm0MmAGJt8PKGPBGjUotN4jSWD1vtvfFys5OTkiIjI\njBkzJECnldcHdJKNF/WTdROyZUr7WAHkvQndZMvUAfLxpJ6i06jl//7v/05r19NPPy0BOq3suaxf\nG8PqbwMzBZCOHTLFbDaL2hsLWXRqRQAxalSiOvGsbLmkt7wxJEMAeX9YR59RVXB5P+loDZC+ccFy\nZEZ/mZAWKgP795M777xTkq2Bcnxa/zbpmX4pAsiQQYNk165d53Udz8SsWbNEpVKJRq0Stcrb7uEZ\nwdL6al/Z+mCXX+W99Lt0BcQQAG4X7uI8qKsAoxlPSSHSZEcdmYz7+CEUYwD/mDePRx999LdurR8/\nfvz48fM/zb333svkyZNZunQpHo+HMWPGnDG2EsA999zDlVdeydKlS3G73YwZM4akpKTT8jkcDhYt\nWsS2bduIiIjg6quv9gXr/T4lJSUM6N8fl8NJx8h2qBQVebv3MXTIELZs3UrHjh3P2f5JkyYxbNgw\nPv30U+x2O4MGDeLIkSNceOGFfHVwDzGBVupbmjlcW84fLrqI3r17ExkZSeH3VPdEhPpW5zlFEH4N\nnE4nCxcupF9kNOEmr3tcq8dNiN6IeDzcfvvtzJ0794xugAAR4RFUfk98w+3xUO1sISIi4ldps8Fg\n4JNPP2Xbtm2sW7cOi8XCRRdddF5hbiorK1n5xRfckJFCpMnrsmlQq7kmNZFb125hxowZvPTSS5jN\nXje8kJAQVq9dy7p169i2bRuRkZGMHTuWVatWcddddxEcHMyVV155xgC3tbW1ZGVloVar8Xg8DB48\nGLvdzkN//jPTOyahP+FGmhBo4rLUGF7dc4jc6gY0JhO7tu/gwIED7Nu3j8TERMaOHetTZXx33jwm\nxIXR2ebtt0al4ob0eD45XMbTGwvpGhHM0gMVxMbFceONN57WroiICJpaXZQ1tRD1HcGLg/YmbxDd\n0iKaGhq5JjkKg1rFypIqDjU4uKdPAo+t87raFdY1MyTGSlZEENd/s49LkiOIMun59HA5h+qb+WBk\nJ0SEPRXN6Klk69atHLU3YXe2EqQ7tbeyoLYJs05Nyb6tDBo4gJ27dv8sl8CHH36Ya6+9luXLl/P+\n++9zdP92Vt7R4ddV4/wlrbT/5MTJFStbjPfXDa1WNFrtCWtVEcUYKOqoVFECbYKiiCosSqxW20+w\nj/348ePHz3fxr1id+73kX7H65XC73WK326W4uFiSk70eKUEms2g1GtFqtbJ48eIzlnvooYdEp9HK\n8NReMiqtj4xKy5JRaVliNpjkmmuuOa82eDwesdvt4nK55Msvv5TBgweLyWSShPgEmT17trS0tIiI\n+NyhekYnylVd+sqVnbOlU4R3pWHVqlW++hoaGnxlfi3q6+sFkHHtUuWBrH4ypUNnMag1olIUsRoM\n3n+tFtm+ffsZyz/11FOigFzULkUezeonf+7ZR3qFR4oCv8jKw49pv9PpPO9yhw4dEkDu7poh7w/v\n50vvDO0rakURBSQ2Olry8/PPWL62tlayevUSQKIDzWLW60RRFHnppZfa5Pvqq6/EbDKJTqOWhJBA\nUSmKREdGyp133ilmvU6+uLCvrLqony891Mu7snnhhAlnPfdJjAa9/LFDomy8qF+blBhkEp1OK6E2\nq9x0001SWloqDodDmpqa2pS32+1iCQ6WrEiLfD62p2y8OEteH9RBgnUamZAcJjuv7SNxgXoZHWOT\nHROyJPcP2dI7NEg6hXldcKMjIyXNGihfTOguOVdkyyXJ4aJTKaJSkBSLURZd2kn2/zFbekYHCiA2\no15ig7xueJEmney4tKeUTu0nbw1NF4NaJdP7REvh3b3FEqCXP/3pT+d9Tc/GZZddJv1TQ8TzWj9x\n/C1b1t3bye8K+LM6+l1XQJCY2FhZsGCBZGR6lzo5sXSPSiWapPaiMQXIpEmTftLF8+PHjx8/p/Ab\nVud+L/kNq5+P2+2WZ555RiIjI73uSjqdaNUa6ZXUWYZkZMuA9r0lPChUDAaDVFVVnVZ++LBhYgsI\nlnCzxTdPsJmCJDLQJinJKT+qDR6PR1544QWJjo4WQCwhIfLQQw+ddcLv8Xjkjjvu8P7Yq9H49trM\nmTNHRET+9a9/Sc8ePQQQjUYjV1xxhZSUlPz0QfoBunbpIgnBIXJPr2wJ0eslxmyWO3r1kAf7Zcsf\ne3aX6KBASWvf/owul48++qioFK+rlU6l8hklwI92p/wpfPrpp9Kpo3d/vF6vl2uvvVYqKyt/dHm3\n2y0JcXHSK9wm7w3r6zOsbspMFUDu6ZUm0UEB0r9f3zOWv/3228Wk08qcnh3kkxHZsnBoloyLixRF\nUSQ3N1dERBwOh4SHhUrX8BD55wU95bMLs2Xu8G4SH2yWDhle97mHe6X5jKovLuwrPSMsPvfG2Oho\neeGFF8447k6nU1LatZN2gSZZMz7bZ1S9NaizADJ//nwRESkoKJAJE8aLSqUSQAYOGCCbNm3y1bNi\nxQrR6XSiOuHup1aQ+ECDbLiqt6y7spekhBh9bn+ZwQFyRVKE7/p+8cUXEhfjveeDDToBJLVdO7nh\nhhtOuA6qfG6ED3SLl+LJfeTYVX3k7cFpolIQ5YRbIeA7x7DkEBmeHCL9+2afz+1wTl599VVRFGRU\nZohoTrgF/hrvJUXklMTh/zKKonQHthEUjmIMhIYqpLGWBQsWcO9993Hs2DHEFAgBASi1VahcLm64\n4Qb++Mc/kpGR8Vs3348fP37+a9m+fTs9evQA6CEi238o/++Fk++lbdu20b1799+6Ob8ZLS0tfPzx\nxz5FtsmTJxMeHv7DBb/DzJkzmT17NpGWMELMwdibGiipKiMqJJz0qGQAnC4n3xZu5/XXX+eKK67g\nn//8J6tWraK6upqDBw9yoLAQg1pHYkgUKkWhqO44Dc5munTtwo4dPyzNPHv2bGbOnEmiJYzIgGCq\nmhs4WFPOVVddxT/mzTtruby8PFasWIFGo+HCCy8kISGBNWvWMHToUMJNAaRaQml2ucitKicyNoad\nu3b51Ox+ST7//HPGjh2LzWCkoqmRKZ06EhsU6Pv+UG0d83P2sWXLltMkyVPatSOksZGsqEgO1Nah\nValIt1qYm5vHpVOm8Morr/jy1tfX88EHH1BQUEBqaiqXX365LzbV+bBs2TImTJhAujWYrHAb1Y4W\nviqpIDU9nU1btvxgAOOTzJ8/n8mTJ9PBGkKPUAtHG5tYXVJO70grt3dvz4aSSl7cUcChQ4dOc0+1\nhAQzxGJmSqrXXc3hdrO6tIK38osZNnIk8+fPZ926dYwfP56/D+lCQtCpwNjrS6qYvSWfoUOGsG7t\nGkbHhRFnNvJNSRU5VXayw0IYFhXKlqo6Vh6r4PHHH+ehhx5qc/5p06Yxb+5cUCAp0MSYuHCqW1r5\n+HAZwbZQ7v/znxk9ejSDBg5A3dzAFe3D0KtVLC6s5EhTK5s2b6FDhw6MGjmS9Wu+4aruEbSzGflX\nfhVfFtQwpl0oG47V4nC5ub59NDEmAx8XV7C5wo4C3Hjzzbz66qu0tLSwZMkSDh48SFpaGuPGjUOt\nVjN40CA2fLseq15NgEbN2gld27jh3bqugJUldhxOJ3EBOqZnRiEovJFXxpHGFoYMG8HnK1ee971x\nJoqLi2mf0g6LUcWfhkRR1+xm9hfH4Bd+L/3uDCslrhOKIcBrVZbuJ7NdHF9/9RUPP/ww7733Hg2N\njYjHg0ZvAI8HV6uT2bNn8+CDD/7WXfDjx4+f/0r8htWZ8RtW3r1NQ4YMIT8/H5MxgBanA41Gy0cf\nfciIESNQq9WoVN74VC6XC0VRUJ/Yi+J2uxERGhoaiIyMJDzIRlLUKYnyoxWlHCwtIjulOwatHhFh\nfeFW7poxg3feeccXUFir1tDqdqFSVAyJ74Ze452QuzxuVhfvpFd2FmvXrj1nP062IcYQRNeoU3tC\nCqvK2FFWRGFhIe3atfvR4zJ8+HB2b9rM+JRMVCcmorWOZj7M3cXrb7zB9ddf/6PrOh++/PJLbv/j\n7ezL3cdtPbsTrNf7vqtqaubvO3ayatUqhg0b1qZcqM1GZ5OJofFxbY6/lbOP7DFjmD9/PgC7du1i\nxLBhVFVXYzMFUNXUiM1q5V9ffkmXLl3Oq609unWjqfgwt3dJ843Robp6ntqWw+LFi5k4ceKPrmvJ\nkiXcdeedHDp0CKtBx9D4CCYkR6NRqdhfbefRDTns3LmTLl26ICK0trai1WrRaDRMS41nXHwUh+2N\nPLYjlxpnK6F6HdXOVoKCg7njzjt59NFHWTymFwHfCWC8v6aeO9fsZf369Xz++ee89cYblFdW4nG5\nmBAfzh2Zp+6XV/KK+LzCTklZGWazGREhPz+f9PR0jGoVLW4PerUKh9uDSgG3QLTZRHmTA61Wi9vl\nYtkfumDRaxGg1ePh0uU5DLtwIjfceCMDBgzgxYvaMzTVKyO/u6SeqR/so9UjRBh1lDU5CTdomds/\nk6RAA1eszqHIraayutr3fH6fTZs20adPH14bnspHBZU0tXj454i2IRFmby9i3oEq9OJm6x+6Eqjz\njk9Ni4vuH25n1IV/YPHixT/6Op6N2tpaunTpTMnRI+x/qCtJNgPbjzTS85k98Au/l36XAYLBG7xP\nAqzs3bOHgIAAXnnlFWbOnAmALi4VTXJnNCld0IRG89BDD/Htt9/+xi3248ePHz9+/re4+eabKS46\nQlpMR9KiOpIZ2xUNGsaPH49Op8NsNjNx4kQGDRqETqfDYDAwduxYLrjgAvR6PXq9nlGjRtHS0kK4\nJbRN3Sc/25sbAKior6LV5eLTTz+ltqoagE4xyQxJ6441IIgwU7DPqALQqNREBlgpOxHM91zk5OTQ\n2NhIQkjbNsSHhCIibNy48bzGZcOGDSQGWXwGA0CIwUhYYNCvOh8ZNmwYX3/zNRqNht3lFW2+211R\ngUGvP+OPAAMHDSKnpoZWj8d3rKKpmWK7nQEDBgDerSeXXnIJ+hYn92V04Z7UDtyX0QV9i5PLLr2U\n8/mhv7W1le07d9IjrO0YJQUHEm4OYMOGDefV7wkTJvD5ypUIMCE5hotTY9GcMBjWHK3AZrEQExPD\nnXfeSUhwMHq9nm5dupCW1p7PjpZz76bd3LlpN7XOVvqHW3m+VyfezO5KBB7eeO01AFYVtx3PVcUV\nhAQH0a1bNx577DGOlZYyb948PMB1qW1jmA2LslHf2Mi2bdu4++67sYaEkJ6ejlqBMIOOT0d246ux\nvUgNMhFl1LN4UBc+GdiJJUO60N6kQ4Xw1OZDZC/YTNb7m/jTN/l0thlZv24tGzduJECvZXCKN9aY\n2yPcvbSAtBATX4/vxlfju7FybBfMOg33bC1EASbEhVJrt59TBGLDhg0YNGpGJ1jJigxiY7mdonqH\n7/umVjfLjtSiMxgZE2f1GVUAFr2GEbEWykpKzus6no3777+fkqNHyUoIJMlm+OECP4PfpyrgCcTV\ngk6vR6fTAfDmW2+hCrKiDjoR+E1R0ITHojTWMW/ePPr27fsbttaPHz9+/Pj536G6upply5YRa0vA\npPe6tjW1NNLY0kCA0UxocCjOVicff/wJIATpgvCIhxUrVqDT6Ii1RKNSFPbs3AOAw9lCgOGUq5Wj\nxTuJq2uqp67JTqm9gsGDB/PNN99g0hoID7QQfcIQ0qm1NDqavd4s35ksOjytRP+AW6KIUFBQAECD\n04HFeMpNr9HZAniDy54PVouVeoejzTGPeGh0Os+7rvMlPDycGTNm8Je//IUah4O4wECK7HZyKip5\n+OGHzxjsd+bMmfRdsYI39ubQLczruri1ooLk5GSuvvpqADZv3kx+QQE3Jqdj0XlXwiw6PWMiYng9\nP48tW7bQu3fvH9VGjUZDoNlMlaOlzXGHy019y48bIxFh06ZNrFy5Er1ez6RJk5g6dSrvzJtHcX0j\nScFmdlXUsqWsmueff55LJk1k07ffMjoxjMjkUL4tOUJueS0AaUFmbklPpLrFyfIjx5m5M5e/9OjA\n9clx3L0th7Fjx/LGZ59xuL6J9iFmtlXUsb6kiueeew6j0ehr08l2lza3kPqd1a2yZm8/Zz74IFs2\nbeLiuFDi4218VVbF5ko72yvtZISYybc38VT3VOJPqPuF6nXc0yGBq9btZVt5PdOzYjGoFT7MqWDr\ncTsZGZFYrVaanS4qG1sJN+vYesROid3JX4enEm70zo/jzAb+1CWO6WvzKaxv5khjCzZLyDkNK5vN\nhsPl5niTk8vTw5i3r4wLV+5lSvtIzFo18w9UUu2Cjh3bU3Q0/7TyRU2tOOpqmTVrFqNGjSIrK+sn\nqfm53W7efWcewUY1h6sdFJQ38/GeGg5WOH648E/gd7diJR6Xd4NZUx3UlBJoNvvcCqqrq1E0ujb5\nFUVB1Bpqamp+i+b68ePHjx8//1Wc3MT9Q9TV1SEiaDV6X/7S6iOYDAGkx2cQFhJOdGgMgaZAr8tf\nawONrY0AhAXaiLFEERUSSceYDFSKikNlxTS1NANeI+tgWTE6nY6jNaU0q1q59957efjhhwGvkWLU\nnnJzi7GEUu9s4kBtCR7xICIcsZdzvKGa66677qx9aG5uZvTo0Vx99dWoVSp2lxVjd3jb0NTaws7j\nxURHRZ3mOvdDXHf9dRTWVlFUW+11PXO72XismMYWB9dcc8151fVj+e51mzNnDs899xx2g5EVBw7S\nbA7kb3/721lD0HTr1o3Va9bQISuLz4uK2VhRyaQrrmTtunU+qfKT8yirTt+m7Ekjq7q6+ke3VVEU\npk6bxurSCnKrvfdRs8vFBwWHafV4mDx58jnLu1wurrzySrKzs3n2ySd57OGHSU1NJTk5mVmPPUau\nU8Wbew7SEBzGvHnz6NixI9+sXsOMHu2YnBnHsIQwHuqTSrfwYAxqFU/0SGdUTDiXJ8XwcNf2HGxo\nYmNFDWEGb9+mTJnC7CeeYI9TzYu7DlJltvH2229z1113tWnX0KFDiYmK4sW8YipOGI1FDc28eaCE\njh0yWbt+PY90SmJ6WjzjYsN4tkcaA8JDeC3vKLXOVgAijW3HN+rE55t6xnBttygu7xzJO5MysRg0\nmAMDmThxIgEBJmZ9cYiaplbsDhcAMQFt64kxeT+vLqvlg6IKrp12+nPx3ef+oosuIjgwkPvWH6bV\nLSwe34FOoQE8u/sIj24rol3vfqxZt47bbr+dtSW1vJ1XhssjtHo8vJJTwvZyOwfz83nxqSfJzs7m\nissvx+VynfvGOANOp5OmZgdqFRyrayX9iV08/vlR/rmj6ocL/xR+SSWMn5OAW4FDQDOwEeh1jrz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0NVNQyNDmZy+wRmdU7m2W5p9I/0zJ3r4sJ5rUtbXuycSo8QP948eJL1+cVoFHh4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nAw88wMI8N+1Skz8i6EIwcEkF4ezmrKqchywWi3HJxRfx37nzqJViJhjRCIQ0FEWhTnZt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\nr9esId1qpiKhE9V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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(2,(10,5))\n", - "\n", - "pl.subplot(1,2,1)\n", - "pl.scatter(xs[:,0],xs[:,2],c=xs)\n", - "pl.axis([0,1,0,1])\n", - "pl.xlabel('Red')\n", - "pl.ylabel('Blue')\n", - "pl.title('Image 1')\n", - "\n", - "pl.subplot(1,2,2)\n", - "#pl.imshow(I2)\n", - "pl.scatter(xt[:,0],xt[:,2],c=xt)\n", - "pl.axis([0,1,0,1])\n", - "pl.xlabel('Red')\n", - "pl.ylabel('Blue')\n", - "pl.title('Image 2')\n", - "\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Domain adaptation between images color spaces" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "# LP problem\n", - "da_emd=ot.da.OTDA() # init class\n", - "da_emd.fit(xs,xt) # fit distributions\n", - "\n", - "\n", - "# sinkhorn regularization\n", - "lambd=1e-1\n", - "da_entrop=ot.da.OTDA_sinkhorn()\n", - "da_entrop.fit(xs,xt,reg=lambd)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Image adaptation with out of sample extension as in [6]" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "X1t=da_emd.predict(X1)\n", - "X2t=da_emd.predict(X2,-1)\n", - "\n", - "\n", - "X1te=da_entrop.predict(X1)\n", - "X2te=da_entrop.predict(X2,-1)\n", - "\n", - "\n", - "def minmax(I):\n", - " return np.minimum(np.maximum(I,0),1)\n", - "\n", - "I1t=minmax(mat2im(X1t,I1.shape))\n", - "I2t=minmax(mat2im(X2t,I2.shape))\n", - "\n", - "I1te=minmax(mat2im(X1te,I1.shape))\n", - "I2te=minmax(mat2im(X2te,I2.shape))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot all adapted images" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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3pmwM7MbRccnWdI+bXMVW7IMP7nkAbnSep0LNvb4EYHnkmwI+tT2zcsX8T1QVN7iSDz/z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\nhbEAZhnvD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P5nEfnWC58ZROTPichjEbkRkf9BRF594TNvi8j/KiJ3IvKxiPyXIvIPvRdR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xbI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\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iUSy42NoPp6YmUnEEVVywLUtWXcLmrS6UBdIRRQzeAAsExH5GUTxFLe8LVTIsxN1qRDwv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UnuQ5yHJuWzLTNSTNiYeRnsQWZF/YFQp2MGcvpc5+zSceO+MnXkMAKlqM6giiVprkADAifSVs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\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XAww7x11u/L/zS85pITxlBZx51mJEhF+zgMlK8oEPfGPIhvOF1Sp/ywv63HNeEw/J8Ydn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c2hPQBgXI4w2WCDguaQSqBNDksZ/28MP0GnEdIOUUXqWf6TJupakRgghEGuWgvEiNsR4iVm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\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s75C4kvoOy0n2JPKCDSceqphRbeSim8k4QR9ri20D3KqYZyu/SCZ5Lixyq8FfDbreInIn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\nSI0wElzl1mIxnivSqSimz7m2xNGsdUGqb3SNLnR8dFnypui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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(2,(10,8))\n", - "\n", - "pl.subplot(2,3,1)\n", - "\n", - "pl.imshow(I1)\n", - "pl.title('Image 1')\n", - "\n", - "pl.subplot(2,3,2)\n", - "pl.imshow(I1t)\n", - "pl.title('Image 1 Adapt')\n", - "\n", - "\n", - "pl.subplot(2,3,3)\n", - "pl.imshow(I1te)\n", - "pl.title('Image 1 Adapt (reg)')\n", - "\n", - "pl.subplot(2,3,4)\n", - "\n", - "pl.imshow(I2)\n", - "pl.title('Image 2')\n", - "\n", - "pl.subplot(2,3,5)\n", - "pl.imshow(I2t)\n", - "pl.title('Image 2 Adapt')\n", - "\n", - "\n", - "pl.subplot(2,3,6)\n", - "pl.imshow(I2te)\n", - "pl.title('Image 2 Adapt (reg)')\n", - "\n", - "pl.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/notebooks/Demo_Image_ColorAdaptation_mapping.ipynb b/notebooks/Demo_Image_ColorAdaptation_mapping.ipynb deleted file mode 100644 index 51b91ed..0000000 --- a/notebooks/Demo_Image_ColorAdaptation_mapping.ipynb +++ /dev/null @@ -1,349 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Color adaptation with OT mapping estimation\n", - "\n", - "Demo of Optimal transport for domain adaptation with image color adaptation as in [6] with mapping estimation from [8]\n", - "\n", - "[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). Regularized\n", - " discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3), 1853-1882.\n", - " \n", - "[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, \"Mapping estimation for\n", - " discrete optimal transport\", Neural Information Processing Systems (NIPS), 2016.\n" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import scipy.ndimage as spi\n", - "import matplotlib.pylab as pl\n", - "import ot\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Loading and plotting images" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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mTia0hLGlGUdRPJdlnZfEqBoxOLLbusg0fQycdE60mvwID4Ylse5O2ZcHU+LH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\n41yBpIqq38d4HxfdExL3lJffqz5zh0o6f6MfJwg6URVT45HwJ54UukTc9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QTmuMY+j45F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\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J3EjkYbtwM3Qdo3jh1nsXvllEaJzbyucijKlPqTbJCIUDgUMUGoEURz6MC2t0gm58nCIJ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fhX6o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vmv/VN/ir//kyMtKVkrJRlZY5/KUyHl6EbT0oi8iOlizmAe72NtM+QrmZgkaUrsm5A1ADwx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\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4xz+7HdCiqVAp2wpEYK5NyVtsMyPOf2nBvYT53DtihAdiVAQl50iEL3TOr28hR8uTGCQz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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "I1=spi.imread('../data/ocean_day.jpg').astype(np.float64)/256\n", - "I2=spi.imread('../data/ocean_sunset.jpg').astype(np.float64)/256\n", - "\n", - "#%% Plot images\n", - "\n", - "pl.figure(1,(10,5))\n", - "\n", - "pl.subplot(1,2,1)\n", - "pl.imshow(I1)\n", - "pl.title('Image 1')\n", - "\n", - "pl.subplot(1,2,2)\n", - "pl.imshow(I2)\n", - "pl.title('Image 2')\n", - "\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Image conversion (toi matrices) and subsampling" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "def im2mat(I):\n", - " \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n", - " return I.reshape((I.shape[0]*I.shape[1],I.shape[2]))\n", - "\n", - "def mat2im(X,shape):\n", - " \"\"\"Converts back a matrix to an image\"\"\"\n", - " return X.reshape(shape)\n", - "\n", - "X1=im2mat(I1)\n", - "X2=im2mat(I2)\n", - "\n", - "# training samples\n", - "nb=1000\n", - "idx1=np.random.randint(X1.shape[0],size=(nb,))\n", - "idx2=np.random.randint(X2.shape[0],size=(nb,))\n", - "\n", - "xs=X1[idx1,:]\n", - "xt=X2[idx2,:]" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plot image distributions" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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Tvw3wA9AJ2AfkAWYDFqDfKwo7Xfryyy/5ceRIMpZvQd48ZQi/eooJEydx82Yg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sQKPRMGPGDLvgh9VqZceOHezfvx8PDw9mz55NV1cXO3fuRKVScf311xMUFHRJW3d3d+bMmUN4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4fdbqdm927KbC7a/eRZQ6nVfwyXy8X06dNxuVwMHTqU/gMG8u233zIoMYTbW0VzqMbK4txq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EBrp41+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\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YMIGuXbsSjUaZPXs2GzdupGXLlowYMaIhxdPv9zN9+nRmz5qFTZLw1K/NZAioMUw+qKzD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\nePzxx5EkiUAgQH5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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(2,(10,5))\n", - "\n", - "pl.subplot(1,2,1)\n", - "pl.scatter(xs[:,0],xs[:,2],c=xs)\n", - "pl.axis([0,1,0,1])\n", - "pl.xlabel('Red')\n", - "pl.ylabel('Blue')\n", - "pl.title('Image 1')\n", - "\n", - "pl.subplot(1,2,2)\n", - "#pl.imshow(I2)\n", - "pl.scatter(xt[:,0],xt[:,2],c=xt)\n", - "pl.axis([0,1,0,1])\n", - "pl.xlabel('Red')\n", - "pl.ylabel('Blue')\n", - "pl.title('Image 2')\n", - "\n", - "pl.show()\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Domain adaptation and mapping between images" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 0|3.699980e+02|0.000000e+00\n", - " 1|3.608346e+02|-2.476614e-02\n", - " 2|3.606710e+02|-4.534048e-04\n", - " 3|3.605854e+02|-2.373172e-04\n", - " 4|3.605308e+02|-1.515104e-04\n", - " 5|3.604930e+02|-1.048652e-04\n", - " 6|3.604655e+02|-7.607409e-05\n", - " 7|3.604444e+02|-5.868306e-05\n", - " 8|3.604277e+02|-4.642246e-05\n", - " 9|3.604141e+02|-3.764735e-05\n", - " 10|3.604028e+02|-3.124268e-05\n", - " 11|3.603933e+02|-2.632307e-05\n", - " 12|3.603852e+02|-2.260049e-05\n", - " 13|3.603782e+02|-1.938188e-05\n", - " 14|3.603721e+02|-1.706719e-05\n", - " 15|3.603667e+02|-1.489910e-05\n", - " 16|3.603619e+02|-1.336306e-05\n", - " 17|3.603576e+02|-1.189587e-05\n", - " 18|3.603537e+02|-1.066658e-05\n", - " 19|3.603534e+02|-9.986781e-07\n", - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 0|3.619308e+02|0.000000e+00\n", - " 1|3.568950e+02|-1.391388e-02\n", - " 2|3.567799e+02|-3.225305e-04\n", - " 3|3.567404e+02|-1.105949e-04\n", - " 4|3.567137e+02|-7.490749e-05\n", - " 5|3.566940e+02|-5.504299e-05\n", - " 6|3.566790e+02|-4.230200e-05\n", - " 7|3.566671e+02|-3.324452e-05\n", - " 8|3.566575e+02|-2.697764e-05\n", - " 9|3.566496e+02|-2.210773e-05\n", - " 10|3.566429e+02|-1.873910e-05\n" - ] - } - ], - "source": [ - "def minmax(I):\n", - " return np.minimum(np.maximum(I,0),1)\n", - "# LP problem\n", - "da_emd=ot.da.OTDA() # init class\n", - "da_emd.fit(xs,xt) # fit distributions\n", - "\n", - "X1t=da_emd.predict(X1) # out of sample\n", - "I1t=minmax(mat2im(X1t,I1.shape))\n", - "\n", - "# sinkhorn regularization\n", - "lambd=1e-1\n", - "da_entrop=ot.da.OTDA_sinkhorn()\n", - "da_entrop.fit(xs,xt,reg=lambd)\n", - "\n", - "X1te=da_entrop.predict(X1)\n", - "I1te=minmax(mat2im(X1te,I1.shape))\n", - "\n", - "# linear mapping estimation\n", - "eta=1e-8 # quadratic regularization for regression\n", - "mu=1e0 # weight of the OT linear term\n", - "bias=True # estimate a bias\n", - "\n", - "ot_mapping=ot.da.OTDA_mapping_linear()\n", - "ot_mapping.fit(xs,xt,mu=mu,eta=eta,bias=bias,numItermax = 20,verbose=True)\n", - "\n", - "X1tl=ot_mapping.predict(X1) # use the estimated mapping\n", - "I1tl=minmax(mat2im(X1tl,I1.shape))\n", - "\n", - "# nonlinear mapping estimation\n", - "eta=1e-2 # quadratic regularization for regression\n", - "mu=1e0 # weight of the OT linear term\n", - "bias=False # estimate a bias\n", - "sigma=1 # sigma bandwidth fot gaussian kernel\n", - "\n", - "\n", - "ot_mapping_kernel=ot.da.OTDA_mapping_kernel()\n", - "ot_mapping_kernel.fit(xs,xt,mu=mu,eta=eta,sigma=sigma,bias=bias,numItermax = 10,verbose=True)\n", - "\n", - "X1tn=ot_mapping_kernel.predict(X1) # use the estimated mapping\n", - "I1tn=minmax(mat2im(X1tn,I1.shape))" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Plotting adapted images" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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PZXomc7e3igQf31bumyYO+zpPd/t54T18Vzjv2TYCuGTKm1V4RhVVeGLr3FJy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OnCylcGG+lkYdVBlZ55jjzaDZgK1lgkWfpLxzFmDnXF6ywcx475TiDdszKRD58NXFihGsV4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\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d+Eg2vtKUG4GbmNfyYAppwu8syr7BI48ony91Yf8DpvZ+qBomsAtyM/LYBw1EA6I9U63O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vPgNp6FJACj/xU9c8edb40vfu0Urh03dWPv/WEz7zofI771/wpHtqvLbOSWEVjyxr8a7N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\nqTI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7h8nEpxWi2jHxhphrkl1uo6QgnS7okzSXNnqz2syYJHb/GhD9YbDWS0ZWl7o1dVof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B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\n4pQQKi60maUKV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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "#%% plot images\n", - "\n", - "\n", - "pl.figure(2,(10,8))\n", - "\n", - "pl.subplot(2,3,1)\n", - "\n", - "pl.imshow(I1)\n", - "pl.title('Im. 1')\n", - "\n", - "pl.subplot(2,3,2)\n", - "\n", - "pl.imshow(I2)\n", - "pl.title('Im. 2')\n", - "\n", - "\n", - "pl.subplot(2,3,3)\n", - "pl.imshow(I1t)\n", - "pl.title('Im. 1 Interp LP')\n", - "\n", - "pl.subplot(2,3,4)\n", - "pl.imshow(I1te)\n", - "pl.title('Im. 1 Interp Entrop')\n", - "\n", - "\n", - "pl.subplot(2,3,5)\n", - "pl.imshow(I1tl)\n", - "pl.title('Im. 1 Linear mapping')\n", - "\n", - "pl.subplot(2,3,6)\n", - "pl.imshow(I1tn)\n", - "pl.title('Im. 1 nonlinear mapping')\n", - "\n", - "pl.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 1 -} diff --git a/notebooks/Demo_Optim_OTreg.ipynb b/notebooks/Demo_Optim_OTreg.ipynb deleted file mode 100644 index 5731687..0000000 --- a/notebooks/Demo_Optim_OTreg.ipynb +++ /dev/null @@ -1,173 +0,0 @@ -{ - "metadata": { - "name": "", - "signature": "sha256:4dc159882a3ef225eae256eb2ae57de153d04cc9de52ea36b1644e64a88de96a" - }, - "nbformat": 3, - "nbformat_minor": 0, - "worksheets": [ - { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Regularized OT with generic solver" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 1 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Dataset generation" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "n=100 # nb bins\n", - "\n", - "# bin positions\n", - "x=np.arange(n,dtype=np.float64)\n", - "\n", - "# Gaussian distributions\n", - "a=ot.datasets.get_1D_gauss(n,m=20,s=20) # m= mean, s= std\n", - "b=ot.datasets.get_1D_gauss(n,m=60,s=60)\n", - "\n", - "# loss matrix\n", - "M=ot.dist(x.reshape((n,1)),x.reshape((n,1)))\n", - "M/=M.max()" - ], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 2 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### EMD solution" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "G0=ot.emd(a,b,M)\n", - "\n", - "pl.figure(3)\n", - "ot.plot.plot1D_mat(a,b,G0,'OT matrix G0')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 3 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Solution with Frobenius norm regularization" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def f(G): return 0.5*np.sum(G**2)\n", - "def df(G): return G\n", - "\n", - "reg=1e-1\n", - " \n", - "Gl2=ot.optim.cg(a,b,M,reg,f,df)\n", - "\n", - "pl.figure(3)\n", - "ot.plot.plot1D_mat(a,b,Gl2,'OT matrix Frob. reg')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 4 - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Solution with entropic regularization" - ] - }, - { - "cell_type": "code", - "collapsed": false, - "input": [ - "def f(G): return np.sum(G*np.log(G))\n", - "def df(G): return np.log(G)+1\n", - " \n", - "reg=1e-3\n", - " \n", - "Ge=ot.optim.cg(a,b,M,reg,f,df)\n", - "\n", - "pl.figure(4)\n", - "ot.plot.plot1D_mat(a,b,Ge,'OT matrix Entrop. reg')" - ], - "language": "python", - "metadata": {}, - "outputs": [ - { - "metadata": {}, - "output_type": "display_data", - "png": 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- "text": [ - "" - ] - } - ], - "prompt_number": 5 - }, - { - "cell_type": "code", - "collapsed": false, - "input": [], - "language": "python", - "metadata": {}, - "outputs": [], - "prompt_number": 5 - } - ], - "metadata": {} - } - ] -} \ No newline at end of file diff --git a/notebooks/Demo_Wasserstein_Discriminant_Analysis.ipynb b/notebooks/Demo_Wasserstein_Discriminant_Analysis.ipynb deleted file mode 100644 index 2d3424e..0000000 --- a/notebooks/Demo_Wasserstein_Discriminant_Analysis.ipynb +++ /dev/null @@ -1,257 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Demonstration of Wasserstein Discriminant Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot\n", - "from ot.datasets import get_1D_gauss as gauss\n", - "from ot.dr import wda, fda" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Generate dataset" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "n=1000 # nb samples in source and target datasets\n", - "nz=0.2\n", - "\n", - "# generate circle dataset\n", - "t=np.random.rand(n)*2*np.pi\n", - "ys=np.floor((np.arange(n)*1.0/n*3))+1\n", - "xs=np.concatenate((np.cos(t).reshape((-1,1)),np.sin(t).reshape((-1,1))),1)\n", - "xs=xs*ys.reshape(-1,1)+nz*np.random.randn(n,2)\n", - "\n", - "t=np.random.rand(n)*2*np.pi\n", - "yt=np.floor((np.arange(n)*1.0/n*3))+1\n", - "xt=np.concatenate((np.cos(t).reshape((-1,1)),np.sin(t).reshape((-1,1))),1)\n", - "xt=xt*yt.reshape(-1,1)+nz*np.random.randn(n,2)\n", - "\n", - "nbnoise=8\n", - "\n", - "xs=np.hstack((xs,np.random.randn(n,nbnoise)))\n", - "xt=np.hstack((xt,np.random.randn(n,nbnoise)))" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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5xeANtikAcsGvUwnD+38aP71OJsmF72awEYSl6uPA54FXhBD7zPf+RUr5qwCO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\nXQBmO9wcw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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pl.figure(1,(10,5))\n", - "\n", - "pl.subplot(1,2,1)\n", - "pl.scatter(xt[:,0],xt[:,1],c=ys,marker='+',label='Source samples')\n", - "pl.legend(loc=0)\n", - "pl.title('Discriminant dimensions')\n", - "\n", - "pl.subplot(1,2,2)\n", - "pl.scatter(xt[:,2],xt[:,3],c=ys,marker='+',label='Source samples')\n", - "pl.legend(loc=0)\n", - "pl.title('Other dimensions')\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute Fisher discriminant" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "p=2\n", - "\n", - "Pfda,projfda = fda(xs,ys,p)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Compute WDA" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Compiling cost function...\n", - "Computing gradient of cost function...\n", - " iter\t\t cost val\t grad. norm\n", - " 1\t+7.3324064745743989e-01\t5.95226695e-01\n", - " 2\t+4.4853951178403229e-01\t8.20231271e-02\n", - " 3\t+4.4576445851595303e-01\t1.93811424e-01\n", - " 4\t+4.3562246733680465e-01\t1.61725387e-01\n", - " 5\t+4.0969564472790077e-01\t1.32513662e-01\n", - " 6\t+2.9388094458668662e-01\t2.00393708e-01\n", - " 7\t+2.7344485803563923e-01\t1.99320846e-01\n", - " 8\t+2.2883182995370804e-01\t2.82035999e-02\n", - " 9\t+2.2828598049696755e-01\t7.08646563e-03\n", - " 10\t+2.2825222787200100e-01\t3.22559822e-04\n", - " 11\t+2.2825220998825529e-01\t2.82197677e-04\n", - " 12\t+2.2825216255973643e-01\t9.26049149e-05\n", - " 13\t+2.2825215676825239e-01\t6.60508771e-06\n", - " 14\t+2.2825215674473881e-01\t3.35243645e-06\n", - " 15\t+2.2825215673980734e-01\t1.97305673e-06\n", - " 16\t+2.2825215673953242e-01\t1.86785394e-06\n", - " 17\t+2.2825215673856641e-01\t1.41420230e-06\n", - " 18\t+2.2825215673757782e-01\t6.92577574e-07\n", - "Terminated - min grad norm reached after 18 iterations, 8.84 seconds.\n", - "\n" - ] - } - ], - "source": [ - "p=2\n", - "reg=1\n", - "k=10\n", - "maxiter=100\n", - "\n", - "Pwda,projwda = wda(xs,ys,p,reg,k,maxiter=maxiter)\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Project and plot samples" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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rT8RMpNEeqBfqlQfqL4dmcdckmF6qsvbunrwRgIrJu4Cwh6onyoS9ZGl5qPIL\nw5+FgqGUj5OMTHv3NUNVhhqlKsMke1ASPVjJHvreeEPcFYFjeqyyppA/CgjsJxgUXJCn1vt3fPJ3\nA95/r607l2BIsnj3PIpzcmg5f54XHEJPo4XXYJuog11AGIYX8Z5/TbrmRSqyp2r+Qpgf9lhdF6cE\ng25+vOTONu5ZcAW1h05FbQPQcv48kNhjlYhE2cws73kJAyHDfwdDoYixxTt/vLFGxkxt5r7PrWbp\n3c9QPrmV2lfz+crLn+NsiZeZl18ypLw+Q2msqWKUqiFOTWMDa3f3f38ot/dI4/HIGFvH2NdRCJPA\nfqWsuTxW9jETWEruKsmF2fBS3VtknZf4y8bZnemdzaQHmlDTEtZtU+POtNJmMPQGvfw1GNEeq6pd\nSubtr54DwOeevznpvmo++lKaj/XL/Xi9HnwH43uB7Cw+q01N1Yd/idfr4V8/eBEAD7yk5OPiP84D\nYGaZ2i+ZvE4WL7Z+1/0cff45hGijaFQhvmnl1JxOvKzZU9JtHPdUFg7VEAmjVGWIVD0msR6sUNMS\nNekC+/GXqHiiUNPOpB6rRIStIOWRcnuo3MqMRggAL4gCKiY/m/Q8ic8dLWgSKY0v1b3FHU/fRGuz\njyxamPBMDdkPS/KK8uxtorJLhuhENRh6g3tuJfuhitcdobdLQe7j6/E4M3W1xyoW2kDRcVCP/eEU\nvqm5EePQMUszyy6JuNbe4JtWbstkr9dDflEe63fdm9DD5OSDl5zm8KmL7NeeLqWQ6Z59VXdFj81d\nVNSZobiyYhPtgQCLtodl2YxVDwJQsnY89ctvwOv10HZ5uICqacKcWYxSNYCk84e8prHBrsuk44n6\n02Pl9B61dRwCsIPE27qzgPMUp7BvrJgqwG4L4e4Rtnb31pjHArjjPx7kbImXrGaVZVN0SyG+MiV8\nVeA6zLNiHAYCt9Xq9FgZDIMdrVD99fPl/BVgkBse2vDzlygPuJ7zEJaBdnuuDUpRimfEht69lgce\n7+S+2xZQby31dV5eRBuRiqlbuWrp6rL+z6YoJ0Bni+COp2/i0T/vJP/dTlb8zeVqw5Wxr2HFnNUq\nPsoyBN0Zipfk50A+vHiyPu59yC/Ko836ezB6GPsacjFYn794GKUqQ/TUtRlL2VhZsYmWri4W755n\nW2mpEEtIRNcLic760xxvGw/AJfknOXJ2jN3kc/+alIcQca4XHG0htJWlP9PLeYu2EzVm3bmdy4oJ\nTizGV37KrgDrAAAgAElEQVSZbdHqmjAzyy5hZcWmqFivoTZRDf2HEOJHwM1Ag5QyTn7W0CJZHaB4\nz79vmqVQWSxb9Ryhpto+p9e757Nuu5LseO5QgEnXxz9vokKaqaC8Uvezd9WDPHbLMwSyBYt3z0u4\nz5RRTRRnn7c89lBUEmLfP/0IgONtY7na6m26Pkb9vhVzVFxU8M4g9yy4gsKSArxZ3ogMxQtyVJzY\nljnKs/fd15fZ3q6jcyqBU0y6fl84Lm1fO+vXGC9VJjFK1QDQH8HRqonnTmoaG5hZ1vvgxGTr8G6h\n5xmzmYox6rPq12+kODeyXUMinIJT9/kD7G7vrc3ttscqWWCoc9zdOYJ/2DWPay+/1K73Ej5XpGXZ\nl4yWePuGf8CUV2zHJ38HQMVk46EaIjwBbAB+kuFxZJST2kNjKWJf+uffcXFhc4+OMRDL6qm0gXIr\nlTOta7va+tzpoXIeK/TOFHZ90cPJ46PpKiuIkq3aqFtZsYnO1k5b6XHiFRIhVKmDRQ/voqQ5BITv\nh/YIAgS7g/b7bc3t3Lf0Sn5xLAh4aevOslcDgqEQXo8nprzWsrS1uZ3De2qivXB9kHnp+D6HamxU\nbzFKVYbp7QOmJ0q4KWly4lmvzhIEPVHOvrxZBYyW7LE6p/dw0mhlbK8lEJzomAtt0W4p2wndRwg1\n7aRq/mZmrHqQjhIv3TnKRLz28kujjm8XCg3U2f277NIMvRivG2dXe8PQRUr5RyFEeabHkU7SVQfo\neNt4Ki7Z3Of0+vB41OstZfG7H/Qkwzn9BMkvDKrlN9SSYvXrm1hbfWvieyiKkaEWglJwoPFiZo5T\nciuvKI+uosg4p72VBTz6YC0XtoLPrxbu1m2vxeP1cPW8F9U1iwKOt42hPRAgGArZqxH+sWHZr8e4\nbNVzLAPu+Pj42GPrPpKG+2JIFaNUDQD9qamnKizdlX5BeXt0CQJ3jZdUqur2pkmzrkrcelW4NQ1A\nkdVPyzetnPVrFsYNjHeee29lAV1lBXaxPUWC+6EzEK2my8tW1dmZRImIdy/AB0T/gPU2YN8weBFC\n3A7cDnDZZZdleDT9g7vmkjM20GmMxCLd3nh72TEGqdRRiqtUugLiPRcdsJJ+DgDaa+S1//aXjrN7\nizqNUh2ruWX2DrIDkmAwxDVlrXS2Cm59fDY/XvwcOfVtbH70b6zWW+F70noV4EqallJSPrmVo89X\n2spSe+ACpoxq5Hjb+AiPWahpZ8S+un7V1bOUPHJ7qGwvXA8U1P5YXRnuHiqNUaqGGMmUnUQPv7PS\nr1awtNcqVsHMZGNYvyvynCf76LFpHJvlKKw311ritAS6QyE6vGM6S+4McnjBFTSvnE5tfV3M7KFY\nAfL6vtUeqqP1bFtMd3kydGXjw3uUa74vHqtMF9gzpIaU8gfADwCmT5+evI7IICFtiStxyp+kwtHn\nKwGYdP2+hMaP84f8oo2NdJw9Y/e86+080ctsseSDVpJWVjQw5YKgHRelFaq27lyONzdYLWWiDU8n\nt1V9ghsPhpQSsly9d76skL2VBbZcy7pJLVN25wgWvfC3lJ7u5tFCFdB+z4LLrSD1yOMebxsftRoR\nX6lUy4or5lgJP24PlfFYDQhGqRpAMqGpx1ry0wqVM+jbLXzTUVXXraw4qxIz9gKaV07n5LRyy9+D\net+BrQy5cFYPTjWeS/8oHH2+ko7WTp78buoNUt33YuMaPeLI/TOZxmyUM0O60J7WnjxT6fLGL1v1\nHK13tlFUEgKi60M5x5RsXCvmrGZCCtlwa6tvZWXFJiYXHed8Z5atyLlxG561B+v46lOfYf+ae3nl\nWt1K5hRwCtpg8a65+KaV23Itv0j1KdVFSn3TyikcVYD/inKunuVj86N+Ti73c3fjRmvJT9XdKs6J\n3SUCInuL6v6ENtqzqGsLJvA0uhlpcVDpxChVQ4x4yk7cSsku74kOdOyNAhDPS3ZyeWS7CDuNOcXj\nxnLVa4WqLXCe423j7aWItnNepFSOgvVPv8mT3/XHnPDOY4WLcK5myZ1t9jaFJQVR9XgSXrvl7UpF\n4CSLZRlsTUsNhnSjPVR6OcvpsYqFiqGqtTzB1jx1BaGHj5H43M7q6G5vtNPQ3DJ7BwD+EhUDFfBC\nTje0dWfzTwe+SdX8hcys3hqRQZxqzSqdiecu6/Dbq9TnE35Zw4/5G0t+xO4RmgjPmM0s3vUgVMaT\n/6rIaeidKfb2fcHIqNQwStUwx62wxPs8Hr2ZQNrl3uhS8KqcHiuUUGFfTYRrvvZgHUdHV6qmobKF\nwiyYWBhuS6EVKojf7ypW/JjmngVXAHD1rB5fVsIlkExZdEY56ztCiCpgNlAqhDgBrJZSbkq81/Cn\nN89QX59/3YR82arnwr3uiA7OTvScu9vNQM/qN3mz4IKsACvKv8/R5x8F7oz43B2f5ZyDzmbP67bV\nWp/NTXrOyPumFDHtoYoloyPis3AsczoIx6UFI8bpGZN64oHxUPUco1QNUdyTIZn3RHuoEvXHSvWc\n7glZZQmXCGUJWLLtGADBie9aR0g8QavmL+To6EomTDwDMuyCD4YkLV3ZHG0cg/e4Crqc+hEra+Zj\nSnDpsWiFyt0DsLeV1BMpLYk8VPHqA7mPYZSgzCOlXJTpMQxHtEcqmYfKjfZYQbSHSistOq4xnsfK\nWRm9sKSAddtr8U1VyoXT0NTtrFbmbmKieJvXz47mQ5cpWRnIFox93xlWdm1SnvJAnQokn1reo/lq\n9x3doJJ0ikg9bCFWUWRQGYQAnVY5Gh3Pqo/rvId9xS0DdSKDSciJjVGqBiGp/PD3VCnSXpsXXHFL\n/YEe99Hnn6OjtZMgRLSOiVXdWFuSS+5s469nc5j6EaVUnTufw5GzYyjOzQU68WZ5I2q7VP/pNSo+\n+gEgUqHSxPNYpQu3273ZVXurvxksHeUNQ4uBeF60jHpgdM/3dY9LZ+mu3qiOuXiX8v7sdylVOm6z\nav7CCLmiFapEvPlaIbft+gSPf+73hPK9LN49j6qZv8Sb1Qklyceq72lY4XPV5TtYx2MPvwlA1b45\ncY+n79uhZXdGNbzWaK+bjteyvXD7wnGesWSDs8WZ+zNDejBK1RAgXj+uWCT7PB39seJNwHDKb6Sr\nHs4BYQsH/ibusfXy3GN/OMWEiWc40T2BR47eagvJpXc/E7H95R9oiwhmd2Y0FufkRLnPnfcnkWIa\nr/dhuMFpeFtVIsJP2YYa21LUlmMsD5URaIaRRKoeqkToeRuo/xlAykt5tocqxpyLlFfj8Ex+lhu/\nu5rcj7XTNaGAvGPn+ObXVO2nh7a1cL6skC89fQOXfvI18ouW8vSZJyPOVXuoThmRltHnVIieneYh\neFU5gew6AP7+O88B8M2/GR/TiNYeKh1/tczl7dPysHCa6k9o9xSM00OxL2j5pOW3yoaEmdV9q4E2\nXDFK1SAibrC5g1SWlwYDtYfqIuILADpbO8mzWkFAbI+cfs83NRcoh8YGq6fXQtulXf2n1+ztpZR0\ntHZy3+dWR8RsOTMcE44zjns9VdZtq6WmsYEvb76Bq2eFg+bdMWzOJqn9gVHMDKkwEIp9qjIq1LRE\n9SxNUFhTy4N122qh+wher4qpdLev0kScc7kf39SeL4F968tX8cq/+KEMPF6P/f7ki87w48XPcd9/\nlsXcb+OaG+xYroe2HWPZquf4xnyVKfz4d9Q4PjROGZrdIV2/IVywM1bbrq4iyK2Pv1SYX5SXVMF0\nfrd98Wx3tnamvO1IJi1K1XDsnTUYqD2oLB8dkH14Tw3vWJZJqstLUe0akvQITIeQ/fB/XEVbs4/H\nS3dbB5UIIXjq67NTVl70+f0siXr/vqVLeeqVPwPYqdc6KNPeL4FClajnYFTbCuv14n33hPchrLht\nmdNAS1cXja7jxDr3xjU3ZKhKtMEwuNBGBmUFqe3QfQRkWLmYMqoppd2cSoRS4OZGeqh0/TsiG6DP\nWPUgHa2dVFR+gJPL/Xzl5Tqenf5TJo8/y9v/dIN9DUBEKQMdHO/N8tJc4uHkcj/X7Wsnp94qkTxR\n/ZflUcrhL16rprDkDTwXHYgyxLrKCtj8kW0A+MaFA/RVGQVVEqZkD0yY1Z7UMEwmc1Ixzqu+rpYs\nZy4Pr3asmLO6T0bpcCRdnqonML2z+ozbgwNEZbH4nngD37TymMtLvcXp2Xl82ssAFI6J3Ma9HJYM\np4UnhMDj9URNvFgTMaoacFQVc/jFMQieF6hEQCWcnB6gVO5J7cG6mD0H1/2MCOEdj5UVm2gPBPCX\nqO7xW2bvoDg3N6pQXyzvYzo8VqZ+zMgiXYr4QMTgTdhQQ+3BOvI+X65eW4krzmy5RQ+/ha9MKQp3\nTXosohWMM9zh8J4aHtp2jI7W8+QXhuOivB7B8bbxUXO9r577msYG1u7eqpq1jy3iS8t+x/uLf0bH\n9CyKc1Sc548Xq6W7C+3qLOE5+NC2Y/iu7LCMvXM8dsszcAt85enPMfo7L7Hz+EEgrFTlFQRteeMM\noNfyOFXlsaf05HuPFze6iK3UVhakXitwhJAWpWo49s4aDGgLSKfLOmOqUq2Voifq1I2PRrx288PK\nX1CQnd2nCsZ6rCXN7fzXtmOc7yjky1tuYNJP66LiD3qNFT/lFRIEdLRlk1+UFzPWSY8JIpWPqvkL\n7Uyc1ub2iHY5nosOcPT5Ssonn8abVWQrke4MR2f7CoDi3Fz8pcmXGyHssYLU77NRogzDAW1UBAt7\ntt/J46PtGM3ukODI2VK++/qtVE2O3E6FCoCzYrtSUmbScv48W2YrBQ7UHNZeqrbuXAqzY/dS9QpJ\nfna3/do/ugmP10P+FR+039Pz8vCOZwjletGFSwPZAiHhbImX6yo/QJb3GB2tnWRZctabBRCMkgPa\n026PtXCc/fl9Z7bC8ujm0LFIttzbm3CS6/YpI7R2OXGbOI9kBiymaiT0zkoXqTyYffVQaWsIVJmF\n93/odMTnbU0HlODojr0c1hOPVbw1/3gTMapNjRPdmd4ip6CbtkB0p/hk6CKAtQfr7FgonRljB9jL\n9ohyDRCuB+OZv5m1u7faQjxeXEi6KxP3R08uw+Clv2Kgert/sh9d5zJY2YYaCksKwDIGtdGll64e\n2tZGcGIxX31aVSWfsEwtJTk7HRSWFLD50Xn2/Ow4+zJHzo7hu3XLYo7BX9q3TN8JG2pY9PAuJs14\nD48QFGapVlRZIlwfT0qpQjIc34nO+At2B7kgL5y5DPAPe+ZBjkpoWbzvHiZsqOE7m38GHss4dKGU\nqRupfn2TXfSY7v7xWKWCW4Z9aeOf6Wzt5O//dDWMvYD65X6aS4zHSjNgStVQ7Z2VKdw/ntqT0lO0\nEFRW2g6qX98Z0xo7clat9+nu6jVnx+D1erimqC1q23howfdS3btMLWsD2jj0Lzss5aQ2QpDHamis\nr7lmYWnSc507n0NBVoCD703gH/f9H1rOu6q5W2Ublty5wzp2eIzOH4YVGxxLrZZgtGvh1IwB6iJq\n4ehGzou2b+WF+hO0TOqyyj30DHfT2ni1fNzPQWFJijEoBkOKOBXzeEpb2LhJXsgyET6HglU06hRd\nKQRa760sYMaqB3nslrcIdo9m0QufBU4wY9WDgJqT67ZZAeku5VN7sDdasUm2fGsup6axgc7Wiwlk\nCxbvnsfMskuorawjXtEyKSEolZfswjZndnM8JISif+pOLvfzeutEWrqUwqbCBuYmNpSzpqilyX1b\n7SD9r1Z+Jsn5o/uf6nis9bvU58mKQ8fDWSKnSHemWDO4kqUyhcn+G8Q4U/XTRXsgEFHLaWbZJdSc\nbuALez6L6Aqx/++fAOBLm2/A6/Xge+INnj7zZI89VInQSwDuhsa6oF1kgVKXxyp7hsoC8pwnyyOZ\nXlrPljk7aenqYvHueXHPGa+istPDoxU8rYhtflS9Xn99pHLTepXfjsnSwth9fe5jx/IkdbYmrn8T\nC/c1GA/V8Gaw1CHTS93xeohqYnlmV8xZzYo54fIAODxW63fdby/hOffdW1nAyWnlPD0/7FG+4+mb\naG1uhytSG+/EwlOsrGiIaUTqTLZgdwiyvQDc5fs++OCai98BYP+74/nA6CaElNQ0K0PP0xnktp99\ngle+uzriO9GGl1IWVTX4f/rTXGoP1lFKN75p5XbZg0Xbt7K2+taINjkardzcNakr+UUmId1e7LDi\nWgfAjk/+js7WTqq+PscoVA6MUjVIcS5Pae9EbyaHVkhqGhvs/lZuJaQ9oNzVo5qDvP7OKJDYrvuO\n1k5mrHqQ575ynsLsnITuf60EvVB/gpdu+TGF2QG8WMt1gf22Yrbo4UI7SHX7kVcQoprPz5xB06di\n9ymMhbMfoI5vmll2STizZ7ZeNrEqr2+vpavslO1hCo83fC3OoqXO1250DNbeSmWh9WQpNhzDoGq9\nVH34l3i9Hv714xcBcPWsSEGo/79l9NKI1/GKAhoMqeI0FB7adoyjzz8X1QZGo+eaVgJ0JfJUsFuo\nXFVuv5dKzb0Db77NjFUPWkHjWcwsu4Tqfa/Zc6bq63OUAfRB+MYCJcuWrVLnWnvmVrtfn2ocj5Wt\na8m+P82j8M1WglZmdaG3Fc+HQzrvxcbrERRm55LdKLnj6Zu4bl87NzobPXcfiQoR0DhjNyF8T6vm\nb3ZkI96hsoUdsWErKzZxSX6TvXoAQOAA/pIgW2bvhICS439Y8mMAVswJV1HXOIP99fKkz9+Cz9/C\nkjt3cPT55yJiOzU9LdXjLJHjJtOGQKZIV0kF0zsrjbhT/ju++D5OOtytfcWthNgT6M6FLNpeTvW+\n1/CVvGFnHXa0dnLDfy1h/5p7gZ65iSOwslzyisLCQghVr8U3rZy/WvVYOq+4QI1z7DhWVmwi1PRC\nuNN69gzImkLF5MgWL2t3K8tZlzSAyFpZrWfbCJZ4IrL+gKh9IOyxWjc1uiVNrMasGvdSXbwlvYgx\nSGzBHgt3qraJoRqZDJYfpuLcXNsIi2oF5VxKsqqaQ7gQrt1SZVbssi4zVj3I2RIv1y7325XCz5Z4\nezS+5hIPk0qbuKv5MWVEBupoOXEVP5gJlb++PWLbfIdM9U0rZ+aM1cxY9SDfm/8MeASL94S90Fox\nPOloUO8Zs9ma4+EQATUvdYmFyFIDoaadScevjOCd1DTCI0dvZUuZtY+rpEQsnKEPzmbStYdO0ZGG\nGlNur2nF5MHxTA420pX9NyJ7Z/U1fTdVTb794jyC9e0p/6C6x/WNBT7Ax7pt4QwSpxLitk58T7xB\nR2sn9VoYXl5EG/D+hx+mO0fEPMeKOauZYJ2/tLKAWb/5Cv6x45Rl1X3EjgkAVZH3pVt+jJCS4guU\ngF63rZZFdW9xx9M30Zvp71QO9TXqxqytZ9u4Z8EVXD3Lz3W0R5SjiBVLYLu5UzinCm7faX+HsRqb\nuvdxZh5+82vjuXqWn8KS1CrmGwzpwmkobH7Un1JM1drqudayX/TxbO+pNb/cTX8Ze4G9rbPorr2f\n5dFxGh1Zb7XAZcXk1rfx+E0PEKwIWuUKYMrj2+xs5aJRyrv82SdvUCUP8h1jl5LinFyr7UsjeysL\nyGs+R4kOpAeqztzLijmr6ZgWLgUDylt27eWXRi2796TBc1SywbvXgmzBXwJbZu+05YdzO3+JlckY\nqAO86KbIdB9BSmgP5lJUopYItbxavFuFJXS4yvB8Y74P37Ry1m1ThZPvv/VKnj7zJOsdsaLu2Kq+\nJEKN9M4RZvlvEKIfaG25ZdWrTBpSCFZ3xjokO0e84MSnzzzJijmrOen1JPSiKC+SEgjOsg8Abc0d\nVL/+mvJfynboPoK/RC0Fbpm9g4KsAB2B8OP3Ut1bBGIYpTqrLtzEM7o6cNy0YKsCe+2huqhq5/EU\nyqr5C8NtbxxFAfX5Eik9+gdi6d3PqErHlsB1e6xiZR7GW85Ld/agwdAXnD+27h/gGQdV4Lj2MpVa\nSlLZ2hcBaF45XW1/V2SCyLOWItNmKVIdrZ14ivLJqW+jbEMNzSun05Hi+Mo21PDNDeNpXjmd39/2\nEzxeD8XZXSDPE3pnCg887mHOE1+wt3crSzceDHHf9y6ifrmfrMsko5qDVM1fyP5qlenyuedvBpS8\nArjGKq6uX1ct6F1duljb+UvHQeBtEAXh0g+B8xR4VZN5jTPebcvf/YbsWyRfeHKOHcKhjbWoTOo4\nOOV6LEaKctRbhJQDn4g3ffp0+eKLLw74edNFvCrlqWr3bk0eUawUj+xrIxQFZ0B56enuqPidWG53\n9z4AJZZQ0xmEsUsYRI9fKwjtEwv5yp8X4B87LiI4Vac4nzw+mjs+rtot2N6tKy5gy+wdeD1KYE4f\nX2Zf70v1F6vz6ZgI4Csvfw6A/WvujRhPOHBTZbzoWI5Y90F/H/q61XJlbIUk1j4/WvKcJcj2R9wH\nsmcARGUvAva2tTVKib3j4+N5aNsxvFleKmaci/gsXmZfKorScFCqhBAHpJTTMz2OvjLU5Ve6cGfa\n6kB0vXyv5aL+/ORy5Um5bl97RDbrX60ioXq/0tPdnC3xMqo5GDOzT9em81x0IMoLoufJHxaW8seb\n/gsp4YIcJQ+DUuD1qNpzieaTNma1V744J8fOHtRxqFnnlcKl45o+vvlLAHZZAef1aaUmbBg+G3fc\n63fdHzNsIPTutbQFznP7C9+0ZaEzk7E9EOBzz9/Mltk7qLjwPV5/dzTf/vjlEdmWznHF+y2A9HmW\nhpuHKlX5ZTxVgxhnc2A3oaYlEZktToVKB5MufXYuo5pjd2dP5YE/udzP+bJdSKHiJ+6evJHgpBDF\nubkqPiCwn/xCuPiSJh7a1mY3Q3YS7A7y+d/dzLF77+Ho85U0Xuhh8d55zLz0Uqp2LeTFAyq3t/FE\nVsR1uD1tWpjNjNF2y+26tis4W8RaznN6AztaO7nuYAj/18ZF9iwUxUnvUSz0UqP2WGmFM14QOiRX\nmgZamRqsPSWHO4P9h8j5XNhL/5bXSWcpOz1Szs+r5i/kltuW8uwX30fQkdWs/9cG2XWvOJbR45Rb\naAuc5/btW9kyO/Y4/WPHsWz/Ku6a9BjXlr5DlkeqmlCyhdC717JsVT6Ld82NiqfUjGoO0jg2yzYM\np5cqBeZn1/+aYChEdkBS0hyylyK/93e/AeCRacroK7RajDmX9NsDAULBECvmrGad0tFi1p1bendn\nRLyXpjA7J6IgqH2tpeNo6zjEy3/7Y4qzlQI5ZVQT67aHmHR96kWXEy3bKU+aymocrM/mYMEoVb2g\nr+vPdsDfu9cqD5V0ZchlTaFqfuTSlt2F3HpvZYXqOacDRbUiopUwr8fDtZdfqmJ49iX+wY41fq20\n+UtUevGW2Tt4/wXhjJS2jkMUWk9P4QVBfFd28NC2Y9yzQLVq6HxfMTPHqX1/+ulneGH/DrCqKIuu\nIB23/YYVG2rYW3mTetMRo+FM0+7JvdaCuNFVGFMLMDc6hiIYDHF4Tw3fWOCn9qCXddtJ6q53B21O\nul69dipOOovQYBjqJKpj5F6iPulQhNzGgm9auZ10o5e+bxm9NGYgtTvG0OnB0eNx/8A7l9av29dO\n8cZchCAK39RyfGfUOHUNp5qzY1hbfavt4V60fWtEYL6TQLbgvSLwxbgfB958m+zPlzNhQw2H99TY\n9bQax6qlw9LKbhbv1nWpIpf9aw/WseKWy6374/zsZtvjtLfyBnzTytkye6fdz/CuSW9zbek79tb5\nhQF8V3bYmYk6jjSVKuzpYqQqX0apGmKElZ06AF665cec6JjA2upb2TJ7Jy+eqretqi3jd1oZJ76I\n/ZVC1GW/htgTQC2FqfP4RzdRnH2emeNOUdNcrjYQxbZCWPd6Ed4sJSjfP7WGQHa4AvCUUU0cOTOG\nxc/PBa+AfKj+lyup8Xr469e/DmALwgm/rOn1hLdd79brpXc/o/4InIu6Vp1yfPEetU/9cj8ngQl7\n2mk92+Yokhd7LG6FLRyvEL7X2n3v9lDFOk6yCukD5cHoTdsKQ98Z7MG9zu4LEPu5WLbqOTpaO3ny\nu9HPr6491XpVOAvw2WkeFm3fim9aObUH63j/T+vUxtPK7cw1Z2mAJXe2cfk153nxwMd4of6zcceh\nG9GDismsOd3Avpt/QK7nPAcaL2bZ/yywrkU946qmVWRXBi1nF++aS0tnF1uu34nX42H51k+pEg9A\n3rFz/GTpLkqaQxSXqQLAEzbUUD3Ng3N94L2iSK2uqQjOvvk2K+asjlBInf0OE3HdvnbWr1Eeq/ZA\ngB/M/DbBkLT7CdpkTUl4HDe6UKr2/oU9VJUsubMNn78NAg201V5pL78aojFKVR/o6w+NfijtbulZ\nU6KEaNxWDJay4/UIuzVDTWMDwVA4sLymsQF/6bheKSlOT0xbxyHebnfUhRrVBFlT1YbWuJ/8rlIm\n1uvg8D+9BuRz+TVdnOgYz7I/zyUr2EW3FYzu8XrIL0q9Enmie+0MTIewYpJfdMra4lzM/U4u99u9\n/wThJtB6+S4VwoJIpT7HilczTUcNg53ELaPCylQifFPLeanuLfZWFkTVwPJmeXl2V2Q19vyiPGVI\nES4Z4vF64mbQ3rPgCr7951NWv7xotGGmmyH/NniOooN1uKVMeyBAQXY2W2bvYMqoJjvmyl9Sx39+\naA0tJ75tbVeqvFSesFL0XpGw61uVbagh5+PtdAAeCmjp6mLnTcV2LFb9cj9er4cLW5Wy02hlJObW\nt1Pk6IywaPtWDtxUzKhKP0+vuTcqaUUrWbeMXhpRWsX2WFU+5GrV5QVRgGfMZuv7CyfkzFyemlzr\nLcnrGA5/A80oVRmgLw+Xro0yYaJqfqw9TisrNkVU6VWtD26NWD776hWPkdUtuaZM7dMSyOHt5gYq\nJj+b8JyF+VOpuGQz1a/fyMTCUxTmT4la/nL2ziOwn4oZoFOB/SV1bJmz0+oXpSzMUc1BfJcrZVCX\nY2jcU8NhkscXJbt/WijrWKbH/qDe10t0+tiLtm/lf48d5+RyPx1WkKyOB1lvLQG4cXuW/u1hq99D\nQJ+DEvEAACAASURBVLneYwWZJmrhkCy7b6A9GOlMrTakzmCpnJ6M4hzVz875XISLSzZwTRk8evNO\nmAu3VX2Ci/eobfKL8rjxoDL49paEK4y7FYiKyg/YfzurqqtznMI3UXnMfv7RX5FXlBchu2oP1kV5\nhR6du5MLW7HlpJBQ9ZGnmX7tH6l+fSdeT+T2BVkBhFA9+aaX1vOTWb8CrJjOoMRDkPyiPFXZnXCt\nK59lcG7+1DMEQyEW756H1+shvyjPXk6cuvFROlq7+PQrsH5XpHzJL8q15WEylGL5LguyrbHLFgqz\ndFPonB57qNzeaWdzemfdrYqZP1PXVBgAGRj0z2qmMErVICAdD2W4crCKA9Cv0zW2ttormVzUjdcr\nVexXChMqKEN4rXk/sfAUobwQW2bvYPHuebQ2t9u1apw8tO0YRaNORfQEjEe8pSqdJh3PjR6xX76X\nrrLCpOdysreygKZPTSe3vp1Aduyq72YZzTAUSLb8nGpvuOaScH2nUL4XIeGHX3yeMXODatmINtZt\nU+VN9jo8VvGMit50DNB9OZvoJre+nff/tI4Lb7HiIwNqnkqh4qFUHOpcVUuv8iGQ7bR1Z6maVtlh\nr8+UUVYcaVBS+Fab3bbrltFLoaQA39Ryu6QBQDAUwuvxkHVecuPBUJTy5MSdrf1C/QkmPfrvTHcU\nTnXfmxVzVuPNepe8ojwCXZGtbAqzlcLrlMnpMJJ0n1avVwKxE58SybuRJguNUjWA9PXhCgvA8cB4\nHvuDWt5yemAgXNtJxzEANI7N4h/q5yE6utn8aZWpYvetq46dAePmfGeWXWwvFm5r+3Wr/1Zhdg6F\n+VOoPVSH39dI1cxf2kUvIVKQFo06RVdZQUTlYo0K0N/EXZNUix1tNbsJt5uJDGytXfWgWoZb7qd6\n32tweRFbZu/A0xHk83tvYVRz0LYq46E9XGfffDvCha9pPava4thLGGNTm2LxvHKZ8mAMV4E32Bns\nVn+s58IzZjPrd29lZdEmu0mwtIwpp7JF9xF8U6ew//rEc0xXZG+sP0FjvWqc7Js2lwkbauxivv+6\n4CJLfqi4pBmrHoTKAjve6XxZISeX++3st+bjV5KfrUqtLN49j9LTdWy65RmuKb8MAiouNNcToiOY\nHR6IKOZExxgWP3cjnvMhLv5eNW2E5bBvWjmeMfdD4420dedS/d6FtlzKL4L1u74ecV3+seNgLKzf\nFX9uBaWM255LKze6xZcd2+oo+aJjoNxZeloeaSPWKW9SUbycimlvs6JTYTgoXEapGma4H8aetnlw\noxUTn18pUx1tSugU+qJrNulJfd8TL+PxevBbFX+RXRDYb5cq+MDFZ3jsDzDp+qcijrFsVZ1dMFMX\noPvGAp+dyguRMRkAP6z8he1uTxWd2l37xffhmRkkp74tbukJJxFKsRU34fV4KMjOjtpWx1A5q7cb\nDIONVIvLJnt+dXuV/SfeBpRSpY22FXyfKaOayB8VDhuwW9JYGcqpxn3GyhLUWbz5RXl2OZmvPvUZ\nfNPKo+TXlAuUUffzf7mBkjmRhY0Pn7qIgLWkVnHhexxvU9mAhW+9BqgYKV3+wZmZ6C8dB91NFOfm\n2q2/VCjEC464pvjG9P7qOVEybNH2rXZ9L32eUNOSSOVGo4sVx0B/r9fF3SI5TsNOe+V0HK8mkWI2\n0kIKjFI1gPT14XILQKdS4sTp0s+6yh/hUbluXzuPXHEHNacbmFk2rl8e+KONY8gryrMD290U5wbI\nmniGUNMSq4WOyqLrKguPs6Wri5rGBvZWXsUygO4jKivF0SFdkW1bxxp3C4kXD3yMuyYJFtfP47dX\nwbN3389PNh3mfFktH5rYABPhL7Neidpfjy2ewHdXYwYoGlUYsU+yZZNUGeweDIMBoCgv1573Oz75\nO/yjmuho7VQebkfYAFwV9xhV8xcyY9WDZJV4yXqrhZINNXSU1MC0cp787k1WJ4JwUcvag3X85E6V\n8xsMKC+K9jgfff5RJkw8Yx/7grwAH7j4DMtWPWeHGDzw+MsEvPCFJ+fYRYuPt40Px6TOh+rXb6Sz\ntZOqV+bYde+iijgzRrWWIbEM1Vl+8Wg5f56a0w12gsvUjY8C4B+rjMq7JinF9ZGjqkbhltk7qT1U\nB69GZukdfb6SpXd3ct/SKyPa1kBsJToV2T+x8BTH28anXR4NpyVCo1QNU1T6sp/OKy5g0+wdEJIs\n3j2PvZUFdhsJ3apF/x2rtY0OuHYupelmoe5Kx4d3TGfJnUE4DqGsdlBeaTrassnJ66ZT5tgBo/mj\nPhhxHt2PcGWFWkJYvHseP//or3j05p0qOFVCsPN/8VqOt0vz6inOy7NLOhz6P09QmD8Vz5jNdnE8\nf0nse/P4ot/jK+mg9lVgYur3NJYCajdJtZS9ePsYDIOZnmQIx+tQsLKiISIzubO1k46sTv56KIep\nH1Geov+trUPmemkcq+b/C/UneP/DD9seq1g8tO0YQgi+9eVwlmDtwToO75jO0rthxS2X47tSNbIp\nKlHL71peqQ4Hbbx/KnboQkPtaECVgAD1fj6qFt/7rj3P0cYxVFz9LFWTwxmF3/u7LsgW7K0ssLOq\n3SiP1RE7WQeU4rVlNrbHStfPWr9moa2UTS+tB8KFRQH8JY289s5ovrl2PB03zVQncPVbVAocEFAF\nizvasgmWhT3uHa2dBLuDUQqVm1S6NSiZN9dS6LriKj2J5F0yWah7PjoTrYYiRqnKAO6Hq6daeTIB\n6CyAp53l2UEozsvFN22crVTptjMQv+O8nnDLVoWPr+O04uG7sgPhqLjXnQXd3VnUNF/IlFFNnOiY\nwJc3X8WWOTtZcuer3LPgCjsD0L9tHDWNDZSe7qakOaRilCzFR8V0haitUQVIi6eWR7eUQcWU1R5U\nGYe+qeVMv1YJtNLTdVz3CnzIV07tIfjKy3P50ZWqNY1uaOoUiKs3vkjtq/msmKOu2W5j41iGjFUE\nVB8nUXNV92fpbENjsnIMPSUdz5/27Ojnr+rrytOryyoUjSrktp2fUIkhscMhbfmjY6NyvR7OlxWS\nU98Wsd267bVcfEkbHq+Hb//5lF3Z3MmEiWfsTg+P/eEUEyae4eTx0Wxcc0PEmDRFowrxeLvJc1Qz\nX1mxic7yTq6xChn/6Y4fqcrsgTo7lskZ0+Tur6fbeG1cs5paq4m6rr+lwh3C9fwKsrNpDwTwdAXJ\n6oZQroc3/m060go10LL6qwc/A8BfvmZ51y0l9uRxpSy+q3Q0nvzuDRHtciDsXe/p96wVOH+JiuNd\nmauruqfHaHQ2mk9nolUmMErVMKVq/kIOZ0+n4+I8PjROTcZtzUpg3cedEds6a9Ak8litmLMa1qy2\nu85/Y4GP2oN1rNs+jtazbdyz4HIKSwp44MlX8Uy+kM76TkqaQ9x3Rp0vXO4h/ri1x8o3LXzO90/d\nRn5RHvmFyit1UVkjta/mU3uoLqK0RE1jAzTeyAv1n4SxWTSXeKhpbKBC6WA8dsszKo4i0IDPD4+V\nPMPEwjNAaqnMGh3rwPwe7RbBcOjlZxhZxMoU3Fupets5l220x8qdgOKbWs4r16tjOGOq4rFl9g48\nHw7yocvUEv267bVqKbEoT3mmZBAI8oGs8PKeDqKedP0+Qk1LKBqlmpZPuv4ppfAcr7M31fXolq16\nzl4iLMzqwl9Sx9HnK+1zBc62gdUey4OrwKaOZ7LKGGhDRpW9CStxtQfrmHAw7GnbW1nAszs/wV8m\n/pyc3CDV+wspGtXOJVectetmzSx8hwOLfsqRs2Nixow6DTpdrBiwY1ATNWrX3wskLzwMjhgqXQw6\nhqeut+jzLrlzB39fVoi/pAECdUPWODRKVQbp6zpy5DJU9AOYX5THea8n5r6aiNY2QtgtYmJNON1N\nvvPyIkDVm+mY9j6aS94kWFjoaiR8jnO52XiLQ1RdH142e+ToHWyZvVN5kayYp8f+cArflW/0qL6K\n78oOvFnnycnrjni/s7XTDlb9wuNzKCop4Lp9q6nadT+hJiv2wCKQLah+70IeOToX2ErVfMvStCrF\nF5WECE4s5ls//QXV712orLRAHYsefouS5hC6fgvA+l3Rni7nd+KOv1i2qs66r0TdZ3W8nitbg70q\n92BGCPEp4Huo4mqPSym/k+EhpZ1Yz1ZPflhTxVkfT+PuTtDR2kmoKD9qX+cSe3FuLv6ycXZgtm9q\nOedaXgY6QIazkKUQnDufw4mOCYAqTqznoc+vlIxQUy2eMZuZdD2svz7yOkNNtdDdETMIXHu1nnqj\nhoKc7nDVckt5cytTukfeRWWN5BeG8PkbWHLnDrxZXhbvmkt+UR7X7WvnW1/ZTCgYIj9XXUfFjDYQ\n7bR3R/4kZwckXo+H4pwcPr61EXB8V/tWW0ZtHa1n2zi8p4arZ/ljdoLoq/Gmry/cFLp/ZEpOfVuP\nQjIGI0apyjDx0mdTZUX59zn6/KMxe9XFiocCqNL7WpamKAKZn0VQSmoP1lFdcSPLVrVH1YrShe60\nI952Jdd9wF5uU8HmqoL5kXOlZAck6+M0LU0FXXwOwpXn363PZ8LEM2o8WVPsqu4Vk1X24aKHdxHs\nVhWPY1VGf6n+YvKK8li8+5NA7CbNiQh2q2VJZ2uJnhJVesGQMYQQXuD7wCeAE8D/CiF2SCkT9wsZ\nYSTKFExkEMb6Qb/xYIi9lUpBiWdUauUsLL82c6zuYwBcU6aW49q6czly9kIAinOx23U5SbWPJ0TG\nYgEUltSxbnsthVkBIuxT2aLkjhXTqQ2YZavqmDDxDH89lM/Uj4SXLIPdQYLBEO8VCZ6d5mFVSOJs\nStgezMbj9fDBX32RLbN3WNmH46mY/Cz/aAWqx0MrfrFIpEw5v0/l3cpNaoCl00MVPQ71euosFas7\nVI1Bo1RlEF0YTS+59dRD9UL9Ca740HuIUmlbWO7yBhAZD5WqN0wLosKSAtZtrwVOgTV5W632C7Wn\n68Ku/7FZLN41l66yAjZ96CkC2YLFu+eRd+wcRSWqyGeV5Y7+xgYfv73Kx48Ln6Oi1HLdyxYI7Kf6\n9RvtOlRhq+jZcCsf2YLPbzWgloFwTFX3EULvXsu6n2ELusf+AL6paq1RC8vWs214z0LuqBClp1Vl\nZ+dynjtGavq16vUjL2xlReD7lDSH+KZD4Dp7lIHPriyvvwONc1kA4J4F6hhXzyqPuO+pWpSxvsdE\nNa2M1yohM4BjUso3AIQQPwM+CwxqpSrV7zSRNyrVkgrpwjmW1qv8dnAyRGbFhRWjhfimlvO/tXVc\ndff9tF2uOjL8/KO/4oqx73GiY7zDOLoEiJ5r950JVwjXxGvH88DoyPF2tHYipaR6f6GtJHW0ZYcN\nOldMp5Kb5Wx+1EfRKNUL8Qt75xIMhui0ujZ0lRXajelnjlMxSjpjOhaHllnhGsvUf7pB88lp5Zw8\nWIDP1QJrb2UBi/pgyCaiL/JjpMggo1RlCPfSn87ES7kQaPn3CUwS9vq7Ri9vTbo+/J6zZYoTp0CN\nCMQO1NnLXMFgCFqj982PIQC0QCxpDtFc4iHv2DnbW6Rd8LH7uoeJ1RHeJoYQi/gsXr2W7iOUT261\nKgLDof/pWQV1TV5RHr7ycVw9y5dy81M3Wlm9elZkuYbeVJA2pI0y4G3H6xPATOcGQojbgdsBLrvs\nsoEb2SAkluLVmx9wLRt04suEDTVMmNYeFavoGbOZ2759P6FgZDD6a2fG8L1jt6K+rvTgboIOUDTK\nivy2fPRaoVq8e24441HjWN7XMVVaVuqkoWsvvzSqvt015ZdR09hA1nnJV5/6DPvX3EsF/a+IhJqW\nqKbwgQYINGRU8Rku8aVCSpl8qzQzffp0+eKLLw74eTNNotL92spKVTgdfb6S5hKP7QYPBgXnO7NY\nPH06Ha2dVFR+wP7B14LLGVDqPF+0UqUEw7nzatJfkKMUnZrmcnLr26PW7HWlc51Fp9+z04e196b7\nCDVnx9g9Cp3j2DJ7JzWNDaytvtWRaVKnTpA9ww7EXPXjbWpMeZby5YptsJUu5/suRayjLZv8/5+9\nc4+Oqjz3/3fPTC6TmTDBJIgEYegYLWMgqVL5nTYUAq22WpAjWMovURQ8PSxOsPWXo56zOEiRsk4V\nWR6F4+FXxcppcpAW1MLRs2rFhB/0AvUSIA5SGB2UBAiJEDO3ZC7798e7332bPbfMLZf3s5ZLkuzZ\n+9179n728z7v83yfkq+JOlTy6wTEf7jlCbpqgb5EGWpkINn7JkJLR1aplG04jnuf5/lZWT9wHDiO\nWwrguzzPPyT8fB+A2TzPN2ptn2v7lch3unj8CgDAG1d2ib8bTsUR8knEYaEqrmK7A1vf+JQkogv5\nlk4HcVjoclxXox06vQ4nn5U+H61i+X+EghoaIaKRaXkOKhWzXPQ7KdrluNyN8OkvYHvlE/z1Pit+\nWX8Qxkt+IVcU4vUmThWtgoPCdqq/E2qvt7r+QbFC0VJ3QLSby/ftwfuffq7o7CCP1quffUpxfj4K\nOkm0KpqNj0Yi91KqjtZwskGpkKj9YpGqHJGq6CZtvfKT0DbYS3qh0+vQdW68QsclFu9/+rlYeSMZ\nWfIgv/c+yVug6sI0RO13+xGWKRpL5cORSue0RFaMUAkP1FTTINZV7cSizjvEbR2Xu+Ho6Ub/wACO\ndp5Hf+VAxP76LDocri3CqS/LAADTdT0AOJy9bIKlLwxbdeQ5egKDQOA4TLK7nM4hyAMdPzqklWy+\ntVVSSR4qw+HFliyjOHzfCUBehjZZ+B1DRqacsknbHfD0efH03rOw3uSD67Ty73IVdR5AOBTG4vEr\nYKuxYsteJ9ZVdadd08hoLoStxoq/AniwZQFu/BXJrbJVW8WGw0c7z2NR5x0wDPIwmgtwfPVaxTNC\nbARZfbBVW+HxHcc6MxEHpY6V3+3HB+7PAJckUOzu8+K9978FS18YdsG5PPNuLdZVFaX9PEdKI++R\nBHOqMoD6BlXPMKp3bEsqh0qLproNeHL9QVgndUNvAICQaJgeW3oDOb5eh6rar0ZElehsiB5fPd5w\nAVHYrH/3+wBIafP0kl4E8jis+cu9sDVapYEE3kdLrZCwGXDhWEcdivLyUHXT27LKGmlZzmQYgL2k\nF/vv+J2YiLp83x5F9Gr1H5fC5x7Am7e8IfawWvWbbUAFxNJiOqaggUN960L8uVpSRAcAGKbj3FWh\npUJJr5hnxXFE7I+GveW5ZzSiFk1jCiAz281te8Sx7t/xp8S+sDSRrDPOjGZC/AVAJcdx00CcqR8C\n+N+5HVJ0Yn2nNEJFJ1fyiFWqzlA6iyrUFYh0386PjGjedpcoC1A5nzyfNH+RLhk6E9m3sN+uRjvc\nfV64Afzk+m04sX+LGHWiAsH77/gdvIEAnj29Gv2Dg+gvN+D1u4sBHZlYOh8gVc5aBPM59A8OinIS\nALC5jTyftN0WAsdgMhBb1HH6dtHe/aDzbhgGefznd5TJ9UEDp+ib2GfRoRDKPFyfe0A8tr3GSmQb\ntjtQKEToJr2emvRLuqqJx5oNYk5Vjon3Uoz38vR5dKLwnU6vQyhIFHVtwkMmh2rDBPM59JQbxITH\nljqyHc3Deva0kBEp5CpwshVid59XqjgUZlHgoyv20giP7+qHUjNmw3T43Z+R8S2Rzu3GZ54BAPRj\nEMjnFDpTVDvr/U8/RzCPOFct8/YDHGlvo1Y5ppEvqsw+8zo3DHqz6FwBkNSPNfK8IgzKpVsBvh92\nCzGU/UIy/UglmajDaJdq4Hk+yHFcI4DfgUgqvMzz/Ec5HlZOkd8f9N+ePi86G+24TWhMnoqTJq9I\n3rLXCedxF2mvAlL91e++gg9cOswqlcYgt2cbdp4AcAIIeMRnMnzpaTG9IOaSVyhSKFQTmd3zTizE\nD9/5Pqp+/hFsNcRZ0X3XhF/d8d+AjhTlNFn/HUAh6lsXAugmzlnlADw+WbSc78dU0yCarP+OH3Te\nTWxYmMdt15I0jl9/87eYflcvPg1WYGXzAjx371sAgFW75onyMM7aIvgseoRCIdBXuJbDm0xl8mh5\nlim5XOpmTlUaifby2b2EhIKL8/PJgyaIbSZboSG/UUiLGKuic3jVN7+Ke27Qw2SJfjOV9IVEteIX\nFr8JAGLXc/l4ARJRe7H2NYzvDmHctYOYPeECWubtx40lg0CJfK8hBMMcvME8/FCIbuF/nkFZb0ho\nLGxDw9qPYLs5hEudZdixyQbAhjmIdMbCoTBa5pFx3f9SHYJTilHS/JQ4Zn2BDi1zfwtAWpZsqSOz\nPOrA0dwsGk1a88ZdeEHoSO+7+iGCBmD1HxaSXoJQRqgicrI0sJdNgMd3nPQ2E/K+xOpEw/SsGKhk\no5yjzWimG57n3wLwVq7HkQxa3ynNodLKqRoq6he2z+1PuxSIWiPqTE8p1rxxF47dKkwsG+2wbVeO\nxf+VYtDk8f6BAXgCg4pCF3n/zY4jH8MzzYzlf/5bFJ79Er+sOIhr3FLhCNVd2l0FsdcetdOcj2jh\nXePm8eSuj2A0O7Fj0wIYL/qgGyAOWtllSS9PLqZc37YI++/4Ha4v6kJxHvm9yVgNS5+LfCbMi/uQ\nYy+bgDlHvMhbzEfIw0za7sBLuz5CKBhCfdsiwdnyRkTo5E2fE2GojkiinxsrNog5VcMULWFQ2mAz\nFrYaK1Y88ibOvFsbUfVHkx9phIoIWEJUC+74w8cwmgvFiJW9fAJ0AyHFPswlJnzkIx7VjXmXxepD\nbzBPsR14YnxXPEIcpKbFNwjioMQg0+UJudbTVCFhXFcXglq4mEIrZqaXSO0d1NoptMWBeJ6b34P+\na/3o6PwYVbcRo7tj1pOAYICpQ/TBeRNuoZpVKiFSeU6VrrQZ54QE0mTJdaPQoYg9jrXw/Wgi2e8s\n2v1Bl9Bo0ndXo31IZfs0X/MWWa4QICmgf+D6DGvemEF6A5YrldeVS4bAgbuK0fzdN3HjuMsASGrB\nrLJOPBx+AR2nd8aOWAn2yXncFaFjpa5A5vP1aFlAJm4+XyF8AFY88iYagiFUTyVO3e8f+k8UC0Ke\nVHx49R+XwhsIiMU31xd2ots5HpXzm/HElT0YqOhG/f8jEhHHK15BoX4Qt1inAIGLQOAYtuwFHD2F\nWNm8AHPmSk5TU90GGM1kAdRsIQUzWzdJSfrUvkpSL+mJ2NCkftqhYjiSCTHbZGFOVRqJ9fJJNTHd\n2e5S9I1SayJRtrZuxJl3D0Z8njoY1LECIIp7NqwlRmDjqpvJAzpfGnNTHbk5aYh+x6YFeGcZSRbf\n8Y29mF7Si7OXr8EP3/k+wvk6sngCAHoO3omF8E0shPGiHzPn2tG8jcy0bDVQSBHQ2d+1O4hiMF3O\nfHlVG3R6HW6ralVVTk7AuqqdmKq7IAjkSWrGzna9MEMj+75q0cPdaAdwFp4pkjApLxPeAyA4UZ+h\nfyAPxeavRXyXaqpuelv6O80ZE7S2or3Iwr0NQ0qqzbUjxhh50AhVtPs3GWw1VhhrrOg48rHid8kS\n7m3ADaW9ON09Pv7GAjRdQd6XFACctUUI5nO4cdxlFBm0ZVic7ZI+HpYQG7ht4QHkT/bgsb+5IWq1\nLydEjgo/68dARRGKLvrFSdy4CWQS6RmvRyhfynkyBAEI7beKC8g/aLeKo59/jkWdd5C2O6YQZjxC\n5CFMliLsEJb3aKN5tSwMjVgBkkhnw1qPuFT6kvs3qPrmVyHvwZeM1Iu8Sjuy/dBCRaXk5g5B7+sm\niNsBuXVghiNpcarGQouHbKFegqM3fUvdAdJfb/sNUT/ru/ohuo7XirlOaiV1ityxaqrbAI7jwPM8\nPH1eONtd+I7uXjHJnT4wvqsOTLCRGWq/LKH8xdrXML4vAONFHzxTJP0nmivw9YndwBSIEauZi94T\njwvI2kQA8PT2Qi7xwXNAKBxWCKRSx4Quu9GWFNSBcT7wFThBDCg1/M4+F5q3LcI7y8qw43/9RsyB\n+PU3f4tCcyFWNpPMzufu/Rw8x5HZ8r89hWObHtcU8EwWqXxaqnDMlaOUitgji1CNHIaaBxfr/jgz\nXikLkAw0ibt/oAz1f16EX+vJEv6s+f9P3EZX2oxZpcCxW5URKrmEAJUw2IxV6Ok8j1NXS6HX6bDi\n7bvw5i1vYKCiCM+fIRGqOUek6t5wbwNebuiG362D3qDHzLn2qAKgQaGBcaiiCM13vImZxb0w5isd\nN1PprfjA9RkAojFlsjWL8gebO1YJ9oqeGNkfFUPW64HJ//Exqmq/iuKGGI1QBWJp2an1Aoeikh4P\nGqFSK99PSmmvmSHbYrZapOxUsRYPkcS6iYfyErXVWGGrtsJ53KVpDAAAgWMwmiA2BqV8YSYGiuYk\nySNWNFT86BIpUVtevizn1NVSBA1cxO//7sg9OL56LU7Ol/oDDlSYoPOFSB+niWQ7LbFQtdyCiaws\n4MvBfAC8mAhenE8eajJrOiAJlAKAYTqcx114dMkKePq8CM2wi+f9xdlzCBv1QLkBB+4qRnBwUDRw\nCPNEisE6Qcwto53oAZpvJjmf0UhEz2Vd1U54AwHYLaRKnzSVLogbsUq1NySDkU5s1VbxBatFtBdZ\nuLcBTdbPYLeQ5+uDxb9EkSGAD8+XR+yD7sdXo0MJChWafvT5B4CW2qfhCQyKEZ69N76KiZM9OMcX\niREqeQTlyZc+hL2kELD0AxXKfoAUx+VuvFj7GkKVYdS3LQIHDhzHEdsl9Br0efLEnEzaIN7R042V\n//YUXljsR6GZjLl6xzZFCzI9xyEUDOP6//gYP9/zV2AO8NjSMJbv+CaM5kL8+ScnxZzMaALHtGUX\naVAtVUfKrzMQaX+0vhf6HugpNwDlBnQ12tEniLB21VjR03kePZ3nhfyyhUKhkFIfazg4MMORdESq\nRmSLh+FGtJklTabWag6qhbHkayRidW48bNVWfOF0EUep3Ky5vTpUTCtjwqEwThxyYN+pk9Ab9GLl\nXstkSaRT6+VuNBdCZy7AQ69+B7ZXPhFa3EjRMtFAyiJUkfCALHeTVv4B2o09d2zaAOcDOkUrO+L/\nRAAAIABJREFUiGiIFXs64O43FsDcWoSWOiGvIkCMPtG9ssbcTyLQc31YpbtVXFAg5n3lCmYARzep\n5sHJ7w+5bbJbiFBvuPeATIspvqMvz4FEmEfAZ8CvH1+A2a3a29/eHsbWVmlSI5cmIOhRqJeMRCgY\nwtmOQjRv+xtMancAQpT66b1nEZp6idgvWRNm+fNNi1uAhSjKy8NkYxdZqvOHoA/qxIphT7AAJnM+\nHFelpKL6tkUwDPIIm8P4wR9INd+xjjq8WEvyP6moaFFeHjxeX8R52l75hETUfwKxyCXWd0Zza1cI\n+WDyzhny85GrpNMG8LS3XrLPPpX/GUmTulzat3Q4VXFbPACszUPS0LV1VcK0+mVPdZZWr3eJmk5d\nx2vFv1/jJgbqQAVZVpMv/clnGh1HPo4oNe5stCNsdCAcBmg/+Q9cnyGQx2k2glZrzxDDFl1RRu4g\n9Q9Ijkd92yIgxEv5WTLUjgqtDKoC8Jez5yK253xB6PV6FHR6EJxqkiJVIFIMZjdEhfj33v8WAnrg\nx6134dj8+FGqaOejhjpykhZO7JJvSqp5eAxGJlAnLGtFhgDJHuhKm2FEAxB4H/0DOrEbwpa9TlGz\niXZ2AEiOztN7z+LMuwexY9MCYQKm1HECQtBzgOdLYiSoNp9JSAugMgzmkgsYkEXJfR5S6GKyKZ/V\nqaYLRN1c+Pz0kh7ofCH81S1N6EJhXiHZQFMzvjBz4GWVNaFgCHkhwH5dBYrz8wFAEgZ9KA8IkHyo\nF965INjzyAbCVA9PbQMoT6yQ8l8jFMtVlcu0AbyznUywm+o2wC1oWZWBRKd2P7xM1LOKVqmuhkWo\ntMlaojrP878A8AuAtHlIxz5H08smWmL0UGacdKmQRrd+8PODWGrUo75tUcQ1C/c2YPV6l+hYNNUR\n47hlnxMDky6ILWrIkhyRJxioKFJEjwCoQvSSsCawUfF3+VIWbW3jd/uBvMilxWg4LnejvpM4KrOF\nar3dS5ZhxiMb4ZtYiHyhZcNABTEYJX0hwFwIXWGBaCT+a+5+GII8Zt1KcjqIYxrGFyZBiytJg5FI\ncQKtUtxdNfLvV8bIIB15cGobtLljIckZ6tgjNlM/XFsE9ww7KrY7FPpI6hd+kSG6RhTtAWg5pPy9\noqvBpVvJL4XokU6vU2wrT6A/XFuEw60L0VNuEJfc/W4/LH1hVNrIZM4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42RCsjRfduqrXD0dCe0pBXubRATPdWQ463F7iXLYLKQmV6sCpVEjENnox2DFRdgvOSH84GvQCf0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\nrGPd1vAqKeX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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# data projection (also removes center)\n", - "xsp=projfda(xs)\n", - "xtp=projfda(xt)\n", - "\n", - "xspw=projwda(xs)\n", - "xtpw=projwda(xt)\n", - "\n", - "pl.figure(1,(10,10))\n", - "\n", - "pl.subplot(2,2,1)\n", - "pl.scatter(xsp[:,0],xsp[:,1],c=ys,marker='+',label='Projected samples')\n", - "pl.legend(loc=0)\n", - "pl.title('Projected training samples FDA')\n", - "\n", - "\n", - "pl.subplot(2,2,2)\n", - "pl.scatter(xtp[:,0],xtp[:,1],c=ys,marker='+',label='Projected samples')\n", - "pl.legend(loc=0)\n", - "pl.title('Projected test samples FDA')\n", - "\n", - "\n", - "pl.subplot(2,2,3)\n", - "pl.scatter(xspw[:,0],xspw[:,1],c=ys,marker='+',label='Projected samples')\n", - "pl.legend(loc=0)\n", - "pl.title('Projected training samples WDA')\n", - "\n", - "\n", - "pl.subplot(2,2,4)\n", - "pl.scatter(xtpw[:,0],xtpw[:,1],c=ys,marker='+',label='Projected samples')\n", - "pl.legend(loc=0)\n", - "pl.title('Projected test samples WDA')\n", - "pl.show()" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 2", - "language": "python", - "name": "python2" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 2 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} -- cgit v1.2.3 From e70313cb2eef5d889532eb004de0eb7361d616e6 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 1 Sep 2017 18:42:52 +0200 Subject: add converted notebooks --- docs/cache_nbrun | 2 +- docs/nb_run_conv | 12 +- notebooks/plot_OT_1D.ipynb | 247 ++++++++ notebooks/plot_OT_2D_samples.ipynb | 288 +++++++++ notebooks/plot_OT_L1_vs_L2.ipynb | 373 ++++++++++++ notebooks/plot_WDA.ipynb | 299 ++++++++++ notebooks/plot_barycenter_1D.ipynb | 308 ++++++++++ notebooks/plot_compute_emd.ipynb | 248 ++++++++ notebooks/plot_optim_OTreg.ipynb | 762 ++++++++++++++++++++++++ notebooks/plot_otda_classes.ipynb | 313 ++++++++++ notebooks/plot_otda_color_images.ipynb | 322 ++++++++++ notebooks/plot_otda_d2.ipynb | 320 ++++++++++ notebooks/plot_otda_mapping.ipynb | 288 +++++++++ notebooks/plot_otda_mapping_colors_images.ipynb | 363 +++++++++++ 14 files changed, 4137 insertions(+), 8 deletions(-) create mode 100644 notebooks/plot_OT_1D.ipynb create mode 100644 notebooks/plot_OT_2D_samples.ipynb create mode 100644 notebooks/plot_OT_L1_vs_L2.ipynb create mode 100644 notebooks/plot_WDA.ipynb create mode 100644 notebooks/plot_barycenter_1D.ipynb create mode 100644 notebooks/plot_compute_emd.ipynb create mode 100644 notebooks/plot_optim_OTreg.ipynb create mode 100644 notebooks/plot_otda_classes.ipynb create mode 100644 notebooks/plot_otda_color_images.ipynb create mode 100644 notebooks/plot_otda_d2.ipynb create mode 100644 notebooks/plot_otda_mapping.ipynb create mode 100644 notebooks/plot_otda_mapping_colors_images.ipynb diff --git a/docs/cache_nbrun b/docs/cache_nbrun index 8cb878b..1510b2b 100644 --- a/docs/cache_nbrun +++ b/docs/cache_nbrun @@ -1 +1 @@ -{"plot_barycenter_1D.ipynb": "6fd8167f98816dc832fe0c58b1d5527b", "plot_otda_mapping.ipynb": "d335a15af828aaa3439a1c67570d79d6"} \ No newline at end of file +{"plot_otda_mapping_colors_images.ipynb": "4f0587a00a3c082799a75a0ed36e9ce1", "plot_optim_OTreg.ipynb": "71d3c106b3f395a6b1001078a6ca6f8d", "plot_barycenter_1D.ipynb": "6fd8167f98816dc832fe0c58b1d5527b", "plot_WDA.ipynb": "27f8de4c6d7db46497076523673eedfb", "plot_OT_L1_vs_L2.ipynb": "e15219bf651a7e39e7c5c3934069894c", "plot_otda_color_images.ipynb": "d047d635f4987c81072383241590e21f", "plot_otda_classes.ipynb": "44bb8cd93317b5d342cd62e26d9bbe60", "plot_otda_d2.ipynb": "8ac4fd2ff899df0858ce1e5fead37f33", "plot_otda_mapping.ipynb": "d335a15af828aaa3439a1c67570d79d6", "plot_compute_emd.ipynb": "bd95981189df6adcb113d9b360ead734", "plot_OT_1D.ipynb": "e44c83f6112388ae18657cb0ad76d0e9", "plot_OT_2D_samples.ipynb": "3f125714daa35ff3cfe5dae1f71265c4"} \ No newline at end of file diff --git a/docs/nb_run_conv b/docs/nb_run_conv index 99dbbce..ad5e432 100755 --- a/docs/nb_run_conv +++ b/docs/nb_run_conv @@ -20,7 +20,7 @@ import os cache_file='cache_nbrun' path_doc='source/auto_examples/' -path_nb='../../../notebooks/' +path_nb='../notebooks/' def load_json(fname): try: @@ -58,8 +58,9 @@ def to_update(fname,cache): def update(fname,cache): # jupyter nbconvert --to notebook --execute mynotebook.ipynb --output targte - print(' '.join(['jupyter','nbconvert','--to','notebook','--execute',path_doc+fname,'--output',path_nb+fname])) - subprocess.check_call(['jupyter','nbconvert','--to','notebook','--execute',path_doc+fname,'--output',path_nb+fname]) + subprocess.check_call(['cp',path_doc+fname,path_nb]) + print(' '.join(['jupyter','nbconvert','--to','notebook','--ExecutePreprocessor.timeout=600','--execute',path_nb+fname,'--inplace'])) + subprocess.check_call(['jupyter','nbconvert','--to','notebook','--ExecutePreprocessor.timeout=600','--execute',path_nb+fname,'--inplace']) cache[fname]=md5(path_doc+fname) @@ -74,11 +75,8 @@ for fname in lst_file: if to_update(fname,cache): print('Updating file: {}'.format(fname)) update(fname,cache) + save_json(cache_file,cache) -update(lst_file[0],cache) - - -save_json(cache_file,cache) diff --git a/notebooks/plot_OT_1D.ipynb b/notebooks/plot_OT_1D.ipynb new file mode 100644 index 0000000..c4b2e67 --- /dev/null +++ b/notebooks/plot_OT_1D.ipynb @@ -0,0 +1,247 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# 1D optimal transport\n", + "\n", + "\n", + "This example illustrates the computation of EMD and Sinkhorn transport plans\n", + "and their visualization.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Remi Flamary \n", + "#\n", + "# License: MIT License\n", + "\n", + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot\n", + "from ot.datasets import get_1D_gauss as gauss" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + "-------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% parameters\n", + "\n", + "n = 100 # nb bins\n", + "\n", + "# bin positions\n", + "x = np.arange(n, dtype=np.float64)\n", + "\n", + "# Gaussian distributions\n", + "a = gauss(n, m=20, s=5) # m= mean, s= std\n", + "b = gauss(n, m=60, s=10)\n", + "\n", + "# loss matrix\n", + "M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\n", + "M /= M.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot distributions and loss matrix\n", + "----------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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iEzMsoYSY/0c4Ehvk/Vq00JmHLaEEaO5cPWu/8UYYMMDraCqfiE5uecwx2p5i\nfcxjhiWUEPv0U+0l2r6915GUn4iWUiyhBGDbNp1apV272OpS26ABvPyyLvoTiYM2TblYQgmxJUt0\ncGColgavLN266Vi2H37wOpIwVlCgM/L++qvO0FuzptcRhVbPnjB2rK7x8uKLXkdjQsASSgjt3q2L\n3UVy+4mf/z3YirCleOwx+PBD7SYcCYveVIYxY+DMM+GWW2D9eq+jMZXMEkoILVumM5RHQ0Lp1Emn\ncrJqrxIsXaorL156Kdxwg9fReCcuDl56Sb8sAwfC/v1eR2QqkSWUEPL/+J5+urdxBEONGpCaagml\nWL/8oj+exx8f2fN0BUtSks4KsHw53H2319GYSmQJJYQ+/VRrPurW9TqS4OjWDdLS7KTzEM5pb66t\nW+HVV6Pnw66oCy+EW2/Vjgnvvut1NKaSWEIJkdxcWLwYevTwOpLg6dFDk8nnn3sdSRiZNk0HLj74\nYHQURYPpH/+AlBQYMkQHY5moE1BCEZFzRWStiGwQkdHFPF5dRGb4Hl8qIs18958tIukistJ3fVZw\nw48cn3+unX3OOcfrSIKnd2/trTZnjteRhImVK/UsvE8fHdRnDlWjhvZ227dP14DJy/M6IhNkR0wo\nIhIHTAbOA9oAl4tImyKbXQfsdM6dBDwOPOK7/yfgAudce2AIELN9B+fM0fbJP/zB60iC5+ij9STc\nEgrahe/Pf9YqrpdegipW+C9Wq1bwzDOwaBHcf7/X0ZggC+Rb3wXY4Jzb5Jw7AEwH+hfZpj/wvO/v\nmcAfREScc186577z3Z8F1BSRKJ3EqHRz5+rU73XqeB1JcPXtq22t27d7HYnH/vIXWLtWB/M1auR1\nNOHtiivg2mvhoYdg3jyvozFBFEhCOR7YWuh2tu++YrdxzuUBOUCDItv8CVjunDusCVdEhopImoik\nbY/CX6afftLG62iq7vLzv6cPP/Q2Dk/99786eG/sWDgrZmt1y+app6BNGxg8GL7/3utoTJCEpFwu\nIm3RarAbi3vcOTfVOZfqnEtt2LBhKEIKqXnztPNP375eRxJ8nTrpLBsxW+21YoUO2uvVC+691+to\nIketWtp5Yfdu7WJt7SlRIZCE8i3QpNDtJN99xW4jIvFAHWCH73YS8BZwlXNuY0UDjkRz5+pSEamp\nXkcSfHFx2gY9d24MLn+RkwN/+pO2m7z6qh4ME7g2bXSczqJFcM89XkdjgiCQhLIMaCkizUWkGjAQ\nmF1km9nZ9NAgAAAQpklEQVRoozvAAGC+c86JSF3gXWC0c25JsIKOJM7p2XufPtH7e9O3r86BuHKl\n15GEkHNw9dWwebOeaR97rNcRRabBg7WE9+ijuia9iWhHTCi+NpFhwBxgDfCacy5LRMaLyIW+zaYB\nDURkA3AH4O9aPAw4CRgrIhm+yzFBfxdhLCtLF7CLxuouP387SkxVez32mC7n++ij0TW4yAsTJ+rC\nXFdfDevWeR2NqQBxYVZPkZqa6tLS0rwOI2j++U8YMQK++QaaNDny9pGqXTs9SY+JTjvz5sG558LF\nF2vpJNanVgmGb77RBrlGjXTQVu3aXkdkfEQk3TkXUIW9dZavZHPnQuvW0Z1MQEtgixfDnj1eR1LJ\nNm7U8SannKKLSFkyCY6mTTU5r12r68cUFHgdkSkHSyiVaO9ebW+M5uouv7594cABWLjQ60gq0a+/\nQv/+mkRmz7az6GA76yyd6+t//4P77vM6GlMOllAq0bvv6iwT/fp5HUnlO+MM/X194w2vI6kkBQV6\n5vzVV3omHe3rwntl2DAd9Pjgg/D6615HY8rIEkolevllbVfo3dvrSCpfzZrapDBzpibRqHPPPXrm\nPHFidM2fE25E4OmndSrrIUPgiy+8jsiUgSWUSrJzJ7z3no7ZitbuwkUNHgy7dun7jirPPAOPPAI3\n3aSTP5rKVb06vPWWno1dcIF2zTYRwRJKJXnjDW1TGDTI60hC56yz4JhjtGQWNd55R+fp6tdPpwux\nRvjQOOYYeP99XffhvPPg55+9jsgEwBJKJXnlFWjZMjpHx5ckPl5LZO++q4sWRrz0dLjsMl3DY/p0\nfYMmdFq10mrGzZu1M0RU1qVGF0soleDbb7W306BBsXdCO2iQLroV8YOeV6/WsSYNG2opJSHB64hi\n0xlnwPPPwyefaHft3FyvIzKlsIRSCaZP15k5Yqm6y69LFzjxRC2hRaxNm+Dss7VE8uGHcNxxXkcU\n2wYOhMmT4e234aqrID/f64hMCSyhVIKXX9aqrpNP9jqS0BPRRDp/vk45E3Gys7UX1759mkxatvQ6\nIgM639cjj+jZ2o032sDHMGUJJcjWrIEvv9QeT7Fq8GAtoc2Y4XUkZeRPJjt26MRk7dp5HZEp7K67\nYMwYmDZNx6tYUgk7llCC7JlntKbkssu8jsQ7rVrBqafqsYiY2olNm7S+/vvvtXdRLPWmiCTjx2ti\nmTIFrrsugr5gscESShD9+CP8+986oDrWq91HjtSJY996y+tIAvDVV9Czpw6imT8funf3OiJTEhGY\nMAHGjdOVMgcPtob6MGIJJYiefFKr3keN8joS711yibYhPfxwmC+8lZYGZ56pP0oLF1rJJBKI6Fxf\njz6q9aoXX6wrPxrPWUIJkpwcmDQJBgzQKp9YFxcHo0dre1LYrpPy1ltaMqlVS2fxbN/e64hMWYwY\nofWq77+vn+O3RReSNaFmCSVInn5aa0zuvtvrSMLH4MGQlKSllLDinC6Q9ac/QXKyrr9hZwGR6cYb\ndZzQ+vVw2mmQkeF1RDHNEkoQ7Nmjs26fey507Oh1NOGjWjVtS1m8WC9h4bffdGXAkSO1OLlggS7q\nZCLXeefpwEcRXT0zogdBRTZLKEHw7LOwfbtOSGsOdf31kJgIDz3kdSTo6PcuXeDFF7VRd/p0nSbZ\nRL4OHXRm4o4dtWh84402VYsHLKFU0Lffavtgr17a69QcqlYtLQzMmePhWinOwXPPaV/mn37SZTTv\nuw+q2Nc/qhx3nJY4R42CqVOha1cdGGZCxv6jKsA5uOEGnbtq6lSvowlft9+uy4XffLOW5EJq61ad\nKfjaazWhfPkl9OkT4iBMyMTHa7fid97RgaopKfD3v0NenteRxQRLKBXwn/9oB5MJE2yGjtJUrarz\n++XkaFIJSTfiggLN8m3bwscfa5/u+fOhceMQvLjxXL9+kJWl66ncc4+WVqzBvtJZQimnb77RM+9e\nvXQWCFO6du3g/vu12qvSp2T5+GMdT3LjjXq9ciXcdptVccWaRo10CdHXX9d/2E6dtErhhx+8jixq\n2X9YORw4oB2FnNNSiv1OBWbECO3Z+Ze/wJYtlfACWVnaFbhXL20reeUV+OgjW/891g0YAGvXwvDh\nOrr+pJO0l8iuXV5HFnXsp7CMDhzQZRkWLNAF/Jo39zqiyBEfr1VfBQXQu3cQk0p6ug7Nb9dOG9wf\nfFB/QC6/PPYWpDHFq1dP+/ZnZenSomPGwAknaOcMWw0yaCyhlIE/mfzvf5pMrr7a64giT6tWOiv8\nL79UMKkcOKB1Z717a7XWggUwdqzu8G9/s+7Apngnn6z/wMuW6Xdn/Hho2hRuukk7bJgKsYQSoD17\nDk0m1m5SfqmpMG+eJpVevWDDhgCf6JzOvTVyJDRpogsvbdmi62R8/bU20jRoUImRm6iRmqrLiq5c\nqf/YL7ygbSynnabTXlg7S7mIC7OZ+1JTU11aWprXYRxi3jwYOlSXtp40SdsATMWlp+vCiPv3wwMP\naLv5Ycu2HzgAn36q3elef10/hPh47cVz8826A2vEMhW1c6cOeJ06VavFqlTRSUMvvhj69tVunDFa\nfSoi6c65gGZNtYRSim3bdG6u//5XS8r//rfOQWeCJztbF+N7+209aZzy+D5SJV2TyOLFWpW1e7cm\nkT/8Qc8mL7oI6tf3OnQTjZzThPL661qlunat3t+smZ68dOumlxhKMEFPKCJyLvAkEAc865ybUOTx\n6sALQGdgB3CZc26L77G7geuAfOA251ypc896nVDy87Vd99//1h8553Q9n7FjoUYNz8KKPvn52pVz\n/Xrcqiy2zF7BriUraJ23kmro+hYFLU6iSt+z4ZxztCH16KM9DtrEnI0b9Qdhzhxd3iAnR+9v0EAH\nTSYn6yzVrVtrkqlfP+oSTVATiojEAeuAs4FsYBlwuXNudaFtbgGSnXM3ichA4GLn3GUi0gZ4FegC\nNAbmASc750pcZi2UCaWgQBfo27BBJ5xdskRPjHfsgIYNYcgQreqyQYtlsG+fVh/88ot23f3xR718\n/70WR7Zu1USyefOhCyMdeyy5bZLJlI68sP50ZnzTlZzqjTj1VF3vqnt3/Z9t2lQnnTQm5AoKdCqX\nzz6DpUshMxNWrYK9e3/fpm5d7frZpIlekpJ0PMwxx+ilfn3dpm5dXeMhAgQ7oZwOjHPO9fXdvhvA\nOff3QtvM8W3zmYjEA9uAhsDowtsW3q6k16tIQtm+9mcyH36X/HwOXnJz4UAuHNgP+/bDb7t1wtld\nu/T3LrfQjAzHHavJI7kDdO5UTH1+ZSrtcyj82JH+Lnxd9FJQ8Pt14UvhA5afr9NU5OXpwfNf9u/X\ny759etm7V3sq7NkDv/76+2X//uLfQ5UqOtdSUpL+o514oh7sE0+ENm30n63QW1m6VGsdliyB5ct/\nzz1VqugujjtO/zfr14c6dbRTl/9Stape4uP1fzYuTp8ncvjFr6S/jSmNFOST8MNGjt62jto/bODo\nH9aTsH0ztXZmU2vHVqrv+aXE5+bWSCC3Rm1ya9Qmr0YCedVqkV+tJvlVa5JftQb5VatTULUG+fHV\ncHFVKfBdXJU4CuLicVXicBK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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% plot the distributions\n", + "\n", + "pl.figure(1, figsize=(6.4, 3))\n", + "pl.plot(x, a, 'b', label='Source distribution')\n", + "pl.plot(x, b, 'r', label='Target distribution')\n", + "pl.legend()\n", + "\n", + "#%% plot distributions and loss matrix\n", + "\n", + "pl.figure(2, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, M, 'Cost matrix M')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve EMD\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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yfTl0MwB7P90UqJg4PRvlTJdCbbVo4aMcjjyyYl8I8MknOy+389xzUFLixzRq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\ngSsiEhMFrohITP4PrQ161dhEqnEAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% EMD\n", + "\n", + "G0 = ot.emd(a, b, M)\n", + "\n", + "pl.figure(3, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve Sinkhorn\n", + "--------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It. |Err \n", + "-------------------\n", + " 0|8.187970e-02|\n", + " 10|3.460174e-02|\n", + " 20|6.633335e-03|\n", + " 30|9.797798e-04|\n", + " 40|1.389606e-04|\n", + " 50|1.959016e-05|\n", + " 60|2.759079e-06|\n", + " 70|3.885166e-07|\n", + " 80|5.470605e-08|\n", + " 90|7.702918e-09|\n", + " 100|1.084609e-09|\n", + " 110|1.527180e-10|\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% Sinkhorn\n", + "\n", + "lambd = 1e-3\n", + "Gs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n", + "\n", + "pl.figure(4, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')\n", + "\n", + "pl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_OT_2D_samples.ipynb b/notebooks/plot_OT_2D_samples.ipynb new file mode 100644 index 0000000..900eaa7 --- /dev/null +++ b/notebooks/plot_OT_2D_samples.ipynb @@ -0,0 +1,288 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# 2D Optimal transport between empirical distributions\n", + "\n", + "\n", + "Illustration of 2D optimal transport between discributions that are weighted\n", + "sum of diracs. The OT matrix is plotted with the samples.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Remi Flamary \n", + "#\n", + "# License: MIT License\n", + "\n", + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + "-------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% parameters and data generation\n", + "\n", + "n = 50 # nb samples\n", + "\n", + "mu_s = np.array([0, 0])\n", + "cov_s = np.array([[1, 0], [0, 1]])\n", + "\n", + "mu_t = np.array([4, 4])\n", + "cov_t = np.array([[1, -.8], [-.8, 1]])\n", + "\n", + "xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s)\n", + "xt = ot.datasets.get_2D_samples_gauss(n, mu_t, cov_t)\n", + "\n", + "a, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples\n", + "\n", + "# loss matrix\n", + "M = ot.dist(xs, xt)\n", + "M /= M.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot data\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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rp67z2kM0XOJrJ1Ooxxe1crW4W0Qf7uLl3KS2NJPpvXyGTfJYPMPnmxPXdLXf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\nI57hx5bEdY97ouxKByjlDHR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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% plot samples\n", + "\n", + "pl.figure(1)\n", + "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", + "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n", + "pl.legend(loc=0)\n", + "pl.title('Source and target distributions')\n", + "\n", + "pl.figure(2)\n", + "pl.imshow(M, interpolation='nearest')\n", + "pl.title('Cost matrix M')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute EMD\n", + "-----------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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meOR7L8bq1bwXHh6cmbTbHY/NvzRicijg9YJt0fL//o+hSo1tygGgdPo8iHkG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7t5E0KLjUUrP38eQvrkJ32X6Hl5eLslJby+c2f7TTdaE/yjj0xNFM1mmTePcWVkQC4BAbx/yRK8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\nAKOf94eua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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% EMD\n", + "\n", + "G0 = ot.emd(a, b, M)\n", + "\n", + "pl.figure(3)\n", + "pl.imshow(G0, interpolation='nearest')\n", + "pl.title('OT matrix G0')\n", + "\n", + "pl.figure(4)\n", + "ot.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1])\n", + "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", + "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n", + "pl.legend(loc=0)\n", + "pl.title('OT matrix with samples')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute Sinkhorn\n", + "----------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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GhtNx2Qt01tEBusaXX1LHkZVFjtjiYrLSMzKog+uuHbg81tEB0v6//546ypEj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\nr6rCNcjP1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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% sinkhorn\n", + "\n", + "# reg term\n", + "lambd = 1e-3\n", + "\n", + "Gs = ot.sinkhorn(a, b, M, lambd)\n", + "\n", + "pl.figure(5)\n", + "pl.imshow(Gs, interpolation='nearest')\n", + "pl.title('OT matrix sinkhorn')\n", + "\n", + "pl.figure(6)\n", + "ot.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1])\n", + "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", + "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n", + "pl.legend(loc=0)\n", + "pl.title('OT matrix Sinkhorn with samples')\n", + "\n", + "pl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_OT_L1_vs_L2.ipynb b/notebooks/plot_OT_L1_vs_L2.ipynb new file mode 100644 index 0000000..5b49e82 --- /dev/null +++ b/notebooks/plot_OT_L1_vs_L2.ipynb @@ -0,0 +1,373 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# 2D Optimal transport for different metrics\n", + "\n", + "\n", + "2D OT on empirical distributio with different gound metric.\n", + "\n", + "Stole the figure idea from Fig. 1 and 2 in\n", + "https://arxiv.org/pdf/1706.07650.pdf\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Remi Flamary \n", + "#\n", + "# License: MIT License\n", + "\n", + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dataset 1 : uniform sampling\n", + "----------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n = 20 # nb samples\n", + "xs = np.zeros((n, 2))\n", + "xs[:, 0] = np.arange(n) + 1\n", + "xs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex...\n", + "\n", + "xt = np.zeros((n, 2))\n", + "xt[:, 1] = np.arange(n) + 1\n", + "\n", + "a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples\n", + "\n", + "# loss matrix\n", + "M1 = ot.dist(xs, xt, metric='euclidean')\n", + "M1 /= M1.max()\n", + "\n", + "# loss matrix\n", + "M2 = ot.dist(xs, xt, metric='sqeuclidean')\n", + "M2 /= M2.max()\n", + "\n", + "# loss matrix\n", + "Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))\n", + "Mp /= Mp.max()\n", + "\n", + "# Data\n", + "pl.figure(1, figsize=(7, 3))\n", + "pl.clf()\n", + "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", + "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n", + "pl.axis('equal')\n", + "pl.title('Source and traget distributions')\n", + "\n", + "\n", + "# Cost matrices\n", + "pl.figure(2, figsize=(7, 3))\n", + "\n", + "pl.subplot(1, 3, 1)\n", + "pl.imshow(M1, interpolation='nearest')\n", + "pl.title('Euclidean cost')\n", + "\n", + "pl.subplot(1, 3, 2)\n", + "pl.imshow(M2, interpolation='nearest')\n", + "pl.title('Squared Euclidean cost')\n", + "\n", + "pl.subplot(1, 3, 3)\n", + "pl.imshow(Mp, interpolation='nearest')\n", + "pl.title('Sqrt Euclidean cost')\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dataset 1 : Plot OT Matrices\n", + "----------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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U+s9S7N1LmwQEUFxQnTrA8uUC3tVZngO3EiV5e70ZnNO0+YCdS3D2hSisXYtieIeECHgL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\nRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% EMD\n", + "G1 = ot.emd(a, b, M1)\n", + "G2 = ot.emd(a, b, M2)\n", + "Gp = ot.emd(a, b, Mp)\n", + "\n", + "# OT matrices\n", + "pl.figure(3, figsize=(7, 3))\n", + "\n", + "pl.subplot(1, 3, 1)\n", + "ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\n", + "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", + "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n", + "pl.axis('equal')\n", + "# pl.legend(loc=0)\n", + "pl.title('OT Euclidean')\n", + "\n", + "pl.subplot(1, 3, 2)\n", + "ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\n", + "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", + "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n", + "pl.axis('equal')\n", + "# pl.legend(loc=0)\n", + "pl.title('OT squared Euclidean')\n", + "\n", + "pl.subplot(1, 3, 3)\n", + "ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\n", + "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", + "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n", + "pl.axis('equal')\n", + "# pl.legend(loc=0)\n", + "pl.title('OT sqrt Euclidean')\n", + "pl.tight_layout()\n", + "\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dataset 2 : Partial circle\n", + "--------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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5sa17ell3FjR8bjKG0yfD2Nbt2Onpujka27rMog3HgHPM7AeBRwHPlPRE4O3A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\nRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "n = 50 # nb samples\n", + "xtot = np.zeros((n + 1, 2))\n", + "xtot[:, 0] = np.cos(\n", + " (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)\n", + "xtot[:, 1] = np.sin(\n", + " (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)\n", + "\n", + "xs = xtot[:n, :]\n", + "xt = xtot[1:, :]\n", + "\n", + "a, b = ot.unif(n), ot.unif(n) # uniform distribution on samples\n", + "\n", + "# loss matrix\n", + "M1 = ot.dist(xs, xt, metric='euclidean')\n", + "M1 /= M1.max()\n", + "\n", + "# loss matrix\n", + "M2 = ot.dist(xs, xt, metric='sqeuclidean')\n", + "M2 /= M2.max()\n", + "\n", + "# loss matrix\n", + "Mp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))\n", + "Mp /= Mp.max()\n", + "\n", + "\n", + "# Data\n", + "pl.figure(4, figsize=(7, 3))\n", + "pl.clf()\n", + "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", + "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n", + "pl.axis('equal')\n", + "pl.title('Source and traget distributions')\n", + "\n", + "\n", + "# Cost matrices\n", + "pl.figure(5, figsize=(7, 3))\n", + "\n", + "pl.subplot(1, 3, 1)\n", + "pl.imshow(M1, interpolation='nearest')\n", + "pl.title('Euclidean cost')\n", + "\n", + "pl.subplot(1, 3, 2)\n", + "pl.imshow(M2, interpolation='nearest')\n", + "pl.title('Squared Euclidean cost')\n", + "\n", + "pl.subplot(1, 3, 3)\n", + "pl.imshow(Mp, interpolation='nearest')\n", + "pl.title('Sqrt Euclidean cost')\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dataset 2 : Plot OT Matrices\n", + "-----------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% EMD\n", + "G1 = ot.emd(a, b, M1)\n", + "G2 = ot.emd(a, b, M2)\n", + "Gp = ot.emd(a, b, Mp)\n", + "\n", + "# OT matrices\n", + "pl.figure(6, figsize=(7, 3))\n", + "\n", + "pl.subplot(1, 3, 1)\n", + "ot.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\n", + "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", + "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n", + "pl.axis('equal')\n", + "# pl.legend(loc=0)\n", + "pl.title('OT Euclidean')\n", + "\n", + "pl.subplot(1, 3, 2)\n", + "ot.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\n", + "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", + "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n", + "pl.axis('equal')\n", + "# pl.legend(loc=0)\n", + "pl.title('OT squared Euclidean')\n", + "\n", + "pl.subplot(1, 3, 3)\n", + "ot.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\n", + "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", + "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n", + "pl.axis('equal')\n", + "# pl.legend(loc=0)\n", + "pl.title('OT sqrt Euclidean')\n", + "pl.tight_layout()\n", + "\n", + "pl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_WDA.ipynb b/notebooks/plot_WDA.ipynb new file mode 100644 index 0000000..f40dbd0 --- /dev/null +++ b/notebooks/plot_WDA.ipynb @@ -0,0 +1,299 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Wasserstein Discriminant Analysis\n", + "\n", + "\n", + "This example illustrate the use of WDA as proposed in [11].\n", + "\n", + "\n", + "[11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016).\n", + "Wasserstein Discriminant Analysis.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Remi Flamary \n", + "#\n", + "# License: MIT License\n", + "\n", + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "\n", + "from ot.dr import wda, fda" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + "-------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% parameters\n", + "\n", + "n = 1000 # nb samples in source and target datasets\n", + "nz = 0.2\n", + "\n", + "# generate circle dataset\n", + "t = np.random.rand(n) * 2 * np.pi\n", + "ys = np.floor((np.arange(n) * 1.0 / n * 3)) + 1\n", + "xs = np.concatenate(\n", + " (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1)\n", + "xs = xs * ys.reshape(-1, 1) + nz * np.random.randn(n, 2)\n", + "\n", + "t = np.random.rand(n) * 2 * np.pi\n", + "yt = np.floor((np.arange(n) * 1.0 / n * 3)) + 1\n", + "xt = np.concatenate(\n", + " (np.cos(t).reshape((-1, 1)), np.sin(t).reshape((-1, 1))), 1)\n", + "xt = xt * yt.reshape(-1, 1) + nz * np.random.randn(n, 2)\n", + "\n", + "nbnoise = 8\n", + "\n", + "xs = np.hstack((xs, np.random.randn(n, nbnoise)))\n", + "xt = np.hstack((xt, np.random.randn(n, nbnoise)))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot data\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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D04lYYUAIkQesB74rpTxu3y6lfAh4CGDKlCmym28vIZLV/FK6ppiAD0AsQWoX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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% plot samples\n", + "pl.figure(1, figsize=(6.4, 3.5))\n", + "\n", + "pl.subplot(1, 2, 1)\n", + "pl.scatter(xt[:, 0], xt[:, 1], c=ys, marker='+', label='Source samples')\n", + "pl.legend(loc=0)\n", + "pl.title('Discriminant dimensions')\n", + "\n", + "pl.subplot(1, 2, 2)\n", + "pl.scatter(xt[:, 2], xt[:, 3], c=ys, marker='+', label='Source samples')\n", + "pl.legend(loc=0)\n", + "pl.title('Other dimensions')\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute Fisher Discriminant Analysis\n", + "------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% Compute FDA\n", + "p = 2\n", + "\n", + "Pfda, projfda = fda(xs, ys, p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute Wasserstein Discriminant Analysis\n", + "-----------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Compiling cost function...\n", + "Computing gradient of cost function...\n", + " iter\t\t cost val\t grad. norm\n", + " 1\t+8.7135243329934142e-01\t4.22283975e-01\n", + " 2\t+4.5259877952239763e-01\t2.80207825e-01\n", + " 3\t+4.2301660192758839e-01\t2.57544116e-01\n", + " 4\t+3.6438385605814744e-01\t2.01503900e-01\n", + " 5\t+2.6854415219016237e-01\t1.86872752e-01\n", + " 6\t+2.3605613971887493e-01\t8.54873065e-02\n", + " 7\t+2.3238632608008850e-01\t4.70510545e-02\n", + " 8\t+2.3084542185757945e-01\t8.60266814e-03\n", + " 9\t+2.3083921287882422e-01\t8.05123557e-03\n", + " 10\t+2.3081791779181188e-01\t5.75567680e-03\n", + " 11\t+2.3081444006658824e-01\t5.32065675e-03\n", + " 12\t+2.3080311057315009e-01\t3.34753227e-03\n", + " 13\t+2.3079768318049571e-01\t1.75615642e-03\n", + " 14\t+2.3079588611065080e-01\t6.04609566e-04\n", + " 15\t+2.3079584350945395e-01\t5.50079922e-04\n", + " 16\t+2.3079571065356075e-01\t3.07865387e-04\n", + " 17\t+2.3079565041678046e-01\t3.29364280e-05\n", + " 18\t+2.3079564985490117e-01\t1.32999543e-05\n", + " 19\t+2.3079564975468964e-01\t3.81768629e-06\n", + " 20\t+2.3079564974709890e-01\t1.50474730e-06\n", + " 21\t+2.3079564974588401e-01\t5.41516789e-07\n", + "Terminated - min grad norm reached after 21 iterations, 7.93 seconds.\n", + "\n" + ] + } + ], + "source": [ + "#%% Compute WDA\n", + "p = 2\n", + "reg = 1e0\n", + "k = 10\n", + "maxiter = 100\n", + "\n", + "Pwda, projwda = wda(xs, ys, p, reg, k, maxiter=maxiter)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot 2D projections\n", + "-------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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naGsxA0dn89j6B5g7Vp366IqioHObwy+mnI87KV6nuwu5d0omALMWlZqRsQDZ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4JLr2B0SamDoSy49AqHtmIeG11SSnFODmNKB/Hu9JvtY/WtrdS3tjreSY9D7wtLyYg5D9vvYRJf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\nSHZxrIqggLramyfI/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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% plot samples\n", + "\n", + "xsp = projfda(xs)\n", + "xtp = projfda(xt)\n", + "\n", + "xspw = projwda(xs)\n", + "xtpw = projwda(xt)\n", + "\n", + "pl.figure(2)\n", + "\n", + "pl.subplot(2, 2, 1)\n", + "pl.scatter(xsp[:, 0], xsp[:, 1], c=ys, marker='+', label='Projected samples')\n", + "pl.legend(loc=0)\n", + "pl.title('Projected training samples FDA')\n", + "\n", + "pl.subplot(2, 2, 2)\n", + "pl.scatter(xtp[:, 0], xtp[:, 1], c=ys, marker='+', label='Projected samples')\n", + "pl.legend(loc=0)\n", + "pl.title('Projected test samples FDA')\n", + "\n", + "pl.subplot(2, 2, 3)\n", + "pl.scatter(xspw[:, 0], xspw[:, 1], c=ys, marker='+', label='Projected samples')\n", + "pl.legend(loc=0)\n", + "pl.title('Projected training samples WDA')\n", + "\n", + "pl.subplot(2, 2, 4)\n", + "pl.scatter(xtpw[:, 0], xtpw[:, 1], c=ys, marker='+', label='Projected samples')\n", + "pl.legend(loc=0)\n", + "pl.title('Projected test samples WDA')\n", + "pl.tight_layout()\n", + "\n", + "pl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_barycenter_1D.ipynb b/notebooks/plot_barycenter_1D.ipynb new file mode 100644 index 0000000..8acaeec --- /dev/null +++ b/notebooks/plot_barycenter_1D.ipynb @@ -0,0 +1,308 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# 1D Wasserstein barycenter demo\n", + "\n", + "\n", + "This example illustrates the computation of regularized Wassersyein Barycenter\n", + "as proposed in [3].\n", + "\n", + "\n", + "[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015).\n", + "Iterative Bregman projections for regularized transportation problems\n", + "SIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Remi Flamary \n", + "#\n", + "# License: MIT License\n", + "\n", + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot\n", + "# necessary for 3d plot even if not used\n", + "from mpl_toolkits.mplot3d import Axes3D # noqa\n", + "from matplotlib.collections import PolyCollection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + "-------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% parameters\n", + "\n", + "n = 100 # nb bins\n", + "\n", + "# bin positions\n", + "x = np.arange(n, dtype=np.float64)\n", + "\n", + "# Gaussian distributions\n", + "a1 = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std\n", + "a2 = ot.datasets.get_1D_gauss(n, m=60, s=8)\n", + "\n", + "# creating matrix A containing all distributions\n", + "A = np.vstack((a1, a2)).T\n", + "n_distributions = A.shape[1]\n", + "\n", + "# loss matrix + normalization\n", + "M = ot.utils.dist0(n)\n", + "M /= M.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot data\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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F3UBWq+eD7W3tHiMiHiAZOGg/Hwz8E/iOMWZbexcwxjxpjMk3xuRnZmZ27S8I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\nf17bI7LGndgbl1FhyON24XG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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% plot the distributions\n", + "\n", + "pl.figure(1, figsize=(6.4, 3))\n", + "for i in range(n_distributions):\n", + " pl.plot(x, A[:, i])\n", + "pl.title('Distributions')\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Barycenter computation\n", + "----------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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0uQlqNS+5No3vSTvl/EGycTokzIX0UxBcCRpdDI27OY960c6SH6bISjRBiUgf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\n7lO97DJTnkA1LU11+nTVlStVS0vdsa0x+P2q2dm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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% barycenter computation\n", + "\n", + "alpha = 0.2 # 0<=alpha<=1\n", + "weights = np.array([1 - alpha, alpha])\n", + "\n", + "# l2bary\n", + "bary_l2 = A.dot(weights)\n", + "\n", + "# wasserstein\n", + "reg = 1e-3\n", + "bary_wass = ot.bregman.barycenter(A, M, reg, weights)\n", + "\n", + "pl.figure(2)\n", + "pl.clf()\n", + "pl.subplot(2, 1, 1)\n", + "for i in range(n_distributions):\n", + " pl.plot(x, A[:, i])\n", + "pl.title('Distributions')\n", + "\n", + "pl.subplot(2, 1, 2)\n", + "pl.plot(x, bary_l2, 'r', label='l2')\n", + "pl.plot(x, bary_wass, 'g', label='Wasserstein')\n", + "pl.legend()\n", + "pl.title('Barycenters')\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Barycentric interpolation\n", + "-------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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fh8/nQzqdhslkyou47HZ7lmAZzkIDg/IwBMpgUcitYQKyhUmSJExOTmJychKrV6/G7t27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\nvr6ef/qnfwJS51mPPvoo7777bsp+lsVi4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g448/hlarTcuI9Oqrr+LOO++EIAi4/fbbce+990bcHwgEcNttt+Hjjz9GdXU1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\nmH6W0+ns46Zrs9n6yAtBdLGzsrKSjIwMs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TBk1q1bx0svvQREFQa2bdt2QgQniI41f/TR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\nsUcynvMIGwAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% barycenter interpolation\n", + "\n", + "n_alpha = 11\n", + "alpha_list = np.linspace(0, 1, n_alpha)\n", + "\n", + "\n", + "B_l2 = np.zeros((n, n_alpha))\n", + "\n", + "B_wass = np.copy(B_l2)\n", + "\n", + "for i in range(0, n_alpha):\n", + " alpha = alpha_list[i]\n", + " weights = np.array([1 - alpha, alpha])\n", + " B_l2[:, i] = A.dot(weights)\n", + " B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)\n", + "\n", + "#%% plot interpolation\n", + "\n", + "pl.figure(3)\n", + "\n", + "cmap = pl.cm.get_cmap('viridis')\n", + "verts = []\n", + "zs = alpha_list\n", + "for i, z in enumerate(zs):\n", + " ys = B_l2[:, i]\n", + " verts.append(list(zip(x, ys)))\n", + "\n", + "ax = pl.gcf().gca(projection='3d')\n", + "\n", + "poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\n", + "poly.set_alpha(0.7)\n", + "ax.add_collection3d(poly, zs=zs, zdir='y')\n", + "ax.set_xlabel('x')\n", + "ax.set_xlim3d(0, n)\n", + "ax.set_ylabel('$\\\\alpha$')\n", + "ax.set_ylim3d(0, 1)\n", + "ax.set_zlabel('')\n", + "ax.set_zlim3d(0, B_l2.max() * 1.01)\n", + "pl.title('Barycenter interpolation with l2')\n", + "pl.tight_layout()\n", + "\n", + "pl.figure(4)\n", + "cmap = pl.cm.get_cmap('viridis')\n", + "verts = []\n", + "zs = alpha_list\n", + "for i, z in enumerate(zs):\n", + " ys = B_wass[:, i]\n", + " verts.append(list(zip(x, ys)))\n", + "\n", + "ax = pl.gcf().gca(projection='3d')\n", + "\n", + "poly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\n", + "poly.set_alpha(0.7)\n", + "ax.add_collection3d(poly, zs=zs, zdir='y')\n", + "ax.set_xlabel('x')\n", + "ax.set_xlim3d(0, n)\n", + "ax.set_ylabel('$\\\\alpha$')\n", + "ax.set_ylim3d(0, 1)\n", + "ax.set_zlabel('')\n", + "ax.set_zlim3d(0, B_l2.max() * 1.01)\n", + "pl.title('Barycenter interpolation with Wasserstein')\n", + "pl.tight_layout()\n", + "\n", + "pl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_compute_emd.ipynb b/notebooks/plot_compute_emd.ipynb new file mode 100644 index 0000000..c5f47c2 --- /dev/null +++ b/notebooks/plot_compute_emd.ipynb @@ -0,0 +1,248 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Plot multiple EMD\n", + "\n", + "\n", + "Shows how to compute multiple EMD and Sinkhorn with two differnt\n", + "ground metrics and plot their values for diffeent distributions.\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Remi Flamary \n", + "#\n", + "# License: MIT License\n", + "\n", + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot\n", + "from ot.datasets import get_1D_gauss as gauss" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + "-------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% parameters\n", + "\n", + "n = 100 # nb bins\n", + "n_target = 50 # nb target distributions\n", + "\n", + "\n", + "# bin positions\n", + "x = np.arange(n, dtype=np.float64)\n", + "\n", + "lst_m = np.linspace(20, 90, n_target)\n", + "\n", + "# Gaussian distributions\n", + "a = gauss(n, m=20, s=5) # m= mean, s= std\n", + "\n", + "B = np.zeros((n, n_target))\n", + "\n", + "for i, m in enumerate(lst_m):\n", + " B[:, i] = gauss(n, m=m, s=5)\n", + "\n", + "# loss matrix and normalization\n", + "M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'euclidean')\n", + "M /= M.max()\n", + "M2 = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'sqeuclidean')\n", + "M2 /= M2.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot data\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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7+z9EZLv9nJdF5HcRuahSnf8Ska0ikiUi34hIlL1rif13q92CG1NNPO4i8oqI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DnO1toKoVXl/Sown5PMfIRCXHlvoiiWYL53c0EQVDidA4S2fvgQ5nNaeRNLsprO8mNtf8gG9Rxo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wQNSg0Fk/C9FwJX5XOsPBBHYhqcgLE0k7ztGmd1CjYaTJytiJ51OfV421J4ESsTGc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\nkfx/0tlkef98PKwAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% plot the distributions\n", + "\n", + "pl.figure(1)\n", + "pl.subplot(2, 1, 1)\n", + "pl.plot(x, a, 'b', label='Source distribution')\n", + "pl.title('Source distribution')\n", + "pl.subplot(2, 1, 2)\n", + "pl.plot(x, B, label='Target distributions')\n", + "pl.title('Target distributions')\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute EMD for the different losses\n", + "------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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dR5O5OdSHp28Moo6W5S1d6Wdg4b2wexV0uR+ufR7cyla6LFvRKuXC0jJzeG31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\nHcUZpCpZSelZzPp2J59uOIB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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% Compute and plot distributions and loss matrix\n", + "\n", + "d_emd = ot.emd2(a, B, M) # direct computation of EMD\n", + "d_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M2\n", + "\n", + "\n", + "pl.figure(2)\n", + "pl.plot(d_emd, label='Euclidean EMD')\n", + "pl.plot(d_emd2, label='Squared Euclidean EMD')\n", + "pl.title('EMD distances')\n", + "pl.legend()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute Sinkhorn for the different losses\n", + "-----------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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7d6br8/nz56levTrFill+NGvVqmUtxpZSdjk8PByj0ciIESNwc3Oja9euqfoF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\nbkltmULond8/ZcXJRemOS27dvswuRsv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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%%\n", + "reg = 1e-2\n", + "d_sinkhorn = ot.sinkhorn2(a, B, M, reg)\n", + "d_sinkhorn2 = ot.sinkhorn2(a, B, M2, reg)\n", + "\n", + "pl.figure(2)\n", + "pl.clf()\n", + "pl.plot(d_emd, label='Euclidean EMD')\n", + "pl.plot(d_emd2, label='Squared Euclidean EMD')\n", + "pl.plot(d_sinkhorn, '+', label='Euclidean Sinkhorn')\n", + "pl.plot(d_sinkhorn2, '+', label='Squared Euclidean Sinkhorn')\n", + "pl.title('EMD distances')\n", + "pl.legend()\n", + "\n", + "pl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_optim_OTreg.ipynb b/notebooks/plot_optim_OTreg.ipynb new file mode 100644 index 0000000..d36b0ee --- /dev/null +++ b/notebooks/plot_optim_OTreg.ipynb @@ -0,0 +1,762 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Regularized OT with generic solver\n", + "\n", + "\n", + "Illustrates the use of the generic solver for regularized OT with\n", + "user-designed regularization term. It uses Conditional gradient as in [6] and\n", + "generalized Conditional Gradient as proposed in [5][7].\n", + "\n", + "\n", + "[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for\n", + "Domain Adaptation, in IEEE Transactions on Pattern Analysis and Machine\n", + "Intelligence , vol.PP, no.99, pp.1-1.\n", + "\n", + "[6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014).\n", + "Regularized discrete optimal transport. SIAM Journal on Imaging Sciences,\n", + "7(3), 1853-1882.\n", + "\n", + "[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized\n", + "conditional gradient: analysis of convergence and applications.\n", + "arXiv preprint arXiv:1510.06567.\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + "-------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% parameters\n", + "\n", + "n = 100 # nb bins\n", + "\n", + "# bin positions\n", + "x = np.arange(n, dtype=np.float64)\n", + "\n", + "# Gaussian distributions\n", + "a = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std\n", + "b = ot.datasets.get_1D_gauss(n, m=60, s=10)\n", + "\n", + "# loss matrix\n", + "M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\n", + "M /= M.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve EMD\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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yfTl0MwB7P90UqJg4PRvlTJdCbbVo4aMcjjyyYl8I8MknOy+389xzUFLixzRq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\ngSsiEhMFrohITP4PrQ161dhEqnEAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% EMD\n", + "\n", + "G0 = ot.emd(a, b, M)\n", + "\n", + "pl.figure(3, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve EMD with Frobenius norm regularization\n", + "--------------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 0|1.760578e-01|0.000000e+00\n", + " 1|1.669467e-01|-5.457501e-02\n", + " 2|1.665639e-01|-2.298130e-03\n", + " 3|1.664378e-01|-7.572776e-04\n", + " 4|1.664077e-01|-1.811855e-04\n", + " 5|1.663912e-01|-9.936787e-05\n", + " 6|1.663852e-01|-3.555826e-05\n", + " 7|1.663814e-01|-2.305693e-05\n", + " 8|1.663785e-01|-1.760450e-05\n", + " 9|1.663767e-01|-1.078011e-05\n", + " 10|1.663751e-01|-9.525192e-06\n", + " 11|1.663737e-01|-8.396466e-06\n", + " 12|1.663727e-01|-6.086938e-06\n", + " 13|1.663720e-01|-4.042609e-06\n", + " 14|1.663713e-01|-4.160914e-06\n", + " 15|1.663707e-01|-3.823502e-06\n", + " 16|1.663702e-01|-3.022440e-06\n", + " 17|1.663697e-01|-3.181249e-06\n", + " 18|1.663692e-01|-2.698532e-06\n", + " 19|1.663687e-01|-3.258253e-06\n", + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 20|1.663682e-01|-2.741118e-06\n", + " 21|1.663678e-01|-2.624135e-06\n", + " 22|1.663673e-01|-2.645179e-06\n", + " 23|1.663670e-01|-1.957237e-06\n", + " 24|1.663666e-01|-2.261541e-06\n", + " 25|1.663663e-01|-1.851305e-06\n", + " 26|1.663660e-01|-1.942296e-06\n", + " 27|1.663657e-01|-2.092896e-06\n", + " 28|1.663653e-01|-1.924361e-06\n", + " 29|1.663651e-01|-1.625455e-06\n", + " 30|1.663648e-01|-1.641123e-06\n", + " 31|1.663645e-01|-1.566666e-06\n", + " 32|1.663643e-01|-1.338514e-06\n", + " 33|1.663641e-01|-1.222711e-06\n", + " 34|1.663639e-01|-1.221805e-06\n", + " 35|1.663637e-01|-1.440781e-06\n", + " 36|1.663634e-01|-1.520091e-06\n", + " 37|1.663632e-01|-1.288193e-06\n", + " 38|1.663630e-01|-1.123055e-06\n", + " 39|1.663628e-01|-1.024487e-06\n", + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 40|1.663627e-01|-1.079606e-06\n", + " 41|1.663625e-01|-1.172093e-06\n", + " 42|1.663623e-01|-1.047880e-06\n", + " 43|1.663621e-01|-1.010577e-06\n", + " 44|1.663619e-01|-1.064438e-06\n", + " 45|1.663618e-01|-9.882375e-07\n", + " 46|1.663616e-01|-8.532647e-07\n", + " 47|1.663615e-01|-9.930189e-07\n", + " 48|1.663613e-01|-8.728955e-07\n", + " 49|1.663612e-01|-9.524214e-07\n", + " 50|1.663610e-01|-9.088418e-07\n", + " 51|1.663609e-01|-7.639430e-07\n", + " 52|1.663608e-01|-6.662611e-07\n", + " 53|1.663607e-01|-7.133700e-07\n", + " 54|1.663605e-01|-7.648141e-07\n", + " 55|1.663604e-01|-6.557516e-07\n", + " 56|1.663603e-01|-7.304213e-07\n", + " 57|1.663602e-01|-6.353809e-07\n", + " 58|1.663601e-01|-7.968279e-07\n", + " 59|1.663600e-01|-6.367159e-07\n", + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 60|1.663599e-01|-5.610790e-07\n", + " 61|1.663598e-01|-5.787466e-07\n", + " 62|1.663596e-01|-6.937777e-07\n", + " 63|1.663596e-01|-5.599432e-07\n", + " 64|1.663595e-01|-5.813048e-07\n", + " 65|1.663594e-01|-5.724600e-07\n", + " 66|1.663593e-01|-6.081892e-07\n", + " 67|1.663592e-01|-5.948732e-07\n", + " 68|1.663591e-01|-4.941833e-07\n", + " 69|1.663590e-01|-5.213739e-07\n", + " 70|1.663589e-01|-5.127355e-07\n", + " 71|1.663588e-01|-4.349251e-07\n", + " 72|1.663588e-01|-5.007084e-07\n", + " 73|1.663587e-01|-4.880265e-07\n", + " 74|1.663586e-01|-4.931950e-07\n", + " 75|1.663585e-01|-4.981309e-07\n", + " 76|1.663584e-01|-3.952959e-07\n", + " 77|1.663584e-01|-4.544857e-07\n", + " 78|1.663583e-01|-4.237579e-07\n", + " 79|1.663582e-01|-4.382386e-07\n", + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 80|1.663582e-01|-3.646051e-07\n", + " 81|1.663581e-01|-4.197994e-07\n", + " 82|1.663580e-01|-4.072764e-07\n", + " 83|1.663580e-01|-3.994645e-07\n", + " 84|1.663579e-01|-4.842721e-07\n", + " 85|1.663578e-01|-3.276486e-07\n", + " 86|1.663578e-01|-3.737346e-07\n", + " 87|1.663577e-01|-4.282043e-07\n", + " 88|1.663576e-01|-4.020937e-07\n", + " 89|1.663576e-01|-3.431951e-07\n", + " 90|1.663575e-01|-3.052335e-07\n", + " 91|1.663575e-01|-3.500538e-07\n", + " 92|1.663574e-01|-3.063176e-07\n", + " 93|1.663573e-01|-3.576367e-07\n", + " 94|1.663573e-01|-3.224681e-07\n", + " 95|1.663572e-01|-3.673221e-07\n", + " 96|1.663572e-01|-3.635561e-07\n", + " 97|1.663571e-01|-3.527236e-07\n", + " 98|1.663571e-01|-2.788548e-07\n", + " 99|1.663570e-01|-2.727141e-07\n", + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 100|1.663570e-01|-3.127278e-07\n", + " 101|1.663569e-01|-2.637504e-07\n", + " 102|1.663569e-01|-2.922750e-07\n", + " 103|1.663568e-01|-3.076454e-07\n", + " 104|1.663568e-01|-2.911509e-07\n", + " 105|1.663567e-01|-2.403398e-07\n", + " 106|1.663567e-01|-2.439790e-07\n", + " 107|1.663567e-01|-2.634542e-07\n", + " 108|1.663566e-01|-2.452203e-07\n", + " 109|1.663566e-01|-2.852991e-07\n", + " 110|1.663565e-01|-2.165490e-07\n", + " 111|1.663565e-01|-2.450250e-07\n", + " 112|1.663564e-01|-2.685294e-07\n", + " 113|1.663564e-01|-2.821800e-07\n", + " 114|1.663564e-01|-2.237390e-07\n", + " 115|1.663563e-01|-1.992842e-07\n", + " 116|1.663563e-01|-2.166739e-07\n", + " 117|1.663563e-01|-2.086064e-07\n", + " 118|1.663562e-01|-2.435945e-07\n", + " 119|1.663562e-01|-2.292497e-07\n", + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 120|1.663561e-01|-2.366209e-07\n", + " 121|1.663561e-01|-2.138746e-07\n", + " 122|1.663561e-01|-2.009637e-07\n", + " 123|1.663560e-01|-2.386258e-07\n", + " 124|1.663560e-01|-1.927442e-07\n", + " 125|1.663560e-01|-2.081681e-07\n", + " 126|1.663559e-01|-1.759123e-07\n", + " 127|1.663559e-01|-1.890771e-07\n", + " 128|1.663559e-01|-1.971315e-07\n", + " 129|1.663558e-01|-2.101983e-07\n", + " 130|1.663558e-01|-2.035645e-07\n", + " 131|1.663558e-01|-1.984492e-07\n", + " 132|1.663557e-01|-1.849064e-07\n", + " 133|1.663557e-01|-1.795703e-07\n", + " 134|1.663557e-01|-1.624087e-07\n", + " 135|1.663557e-01|-1.689557e-07\n", + " 136|1.663556e-01|-1.644308e-07\n", + " 137|1.663556e-01|-1.618007e-07\n", + " 138|1.663556e-01|-1.483013e-07\n", + " 139|1.663555e-01|-1.708771e-07\n", + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 140|1.663555e-01|-2.013847e-07\n", + " 141|1.663555e-01|-1.721217e-07\n", + " 142|1.663554e-01|-2.027911e-07\n", + " 143|1.663554e-01|-1.764565e-07\n", + " 144|1.663554e-01|-1.677151e-07\n", + " 145|1.663554e-01|-1.351982e-07\n", + " 146|1.663553e-01|-1.423360e-07\n", + " 147|1.663553e-01|-1.541112e-07\n", + " 148|1.663553e-01|-1.491601e-07\n", + " 149|1.663553e-01|-1.466407e-07\n", + " 150|1.663552e-01|-1.801524e-07\n", + " 151|1.663552e-01|-1.714107e-07\n", + " 152|1.663552e-01|-1.491257e-07\n", + " 153|1.663552e-01|-1.513799e-07\n", + " 154|1.663551e-01|-1.354539e-07\n", + " 155|1.663551e-01|-1.233818e-07\n", + " 156|1.663551e-01|-1.576219e-07\n", + " 157|1.663551e-01|-1.452791e-07\n", + " 158|1.663550e-01|-1.262867e-07\n", + " 159|1.663550e-01|-1.316379e-07\n", + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 160|1.663550e-01|-1.295447e-07\n", + " 161|1.663550e-01|-1.283286e-07\n", + " 162|1.663550e-01|-1.569222e-07\n", + " 163|1.663549e-01|-1.172942e-07\n", + " 164|1.663549e-01|-1.399809e-07\n", + " 165|1.663549e-01|-1.229432e-07\n", + " 166|1.663549e-01|-1.326191e-07\n", + " 167|1.663548e-01|-1.209694e-07\n", + " 168|1.663548e-01|-1.372136e-07\n", + " 169|1.663548e-01|-1.338395e-07\n", + " 170|1.663548e-01|-1.416497e-07\n", + " 171|1.663548e-01|-1.298576e-07\n", + " 172|1.663547e-01|-1.190590e-07\n", + " 173|1.663547e-01|-1.167083e-07\n", + " 174|1.663547e-01|-1.069425e-07\n", + " 175|1.663547e-01|-1.217780e-07\n", + " 176|1.663547e-01|-1.140754e-07\n", + " 177|1.663546e-01|-1.160707e-07\n", + " 178|1.663546e-01|-1.101798e-07\n", + " 179|1.663546e-01|-1.114904e-07\n", + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 180|1.663546e-01|-1.064022e-07\n", + " 181|1.663546e-01|-9.258231e-08\n", + " 182|1.663546e-01|-1.213120e-07\n", + " 183|1.663545e-01|-1.164296e-07\n", + " 184|1.663545e-01|-1.188762e-07\n", + " 185|1.663545e-01|-9.394153e-08\n", + " 186|1.663545e-01|-1.028656e-07\n", + " 187|1.663545e-01|-1.115348e-07\n", + " 188|1.663544e-01|-9.768310e-08\n", + " 189|1.663544e-01|-1.021806e-07\n", + " 190|1.663544e-01|-1.086303e-07\n", + " 191|1.663544e-01|-9.879008e-08\n", + " 192|1.663544e-01|-1.050210e-07\n", + " 193|1.663544e-01|-1.002463e-07\n", + " 194|1.663543e-01|-1.062747e-07\n", + " 195|1.663543e-01|-9.348538e-08\n", + " 196|1.663543e-01|-7.992512e-08\n", + " 197|1.663543e-01|-9.558020e-08\n", + " 198|1.663543e-01|-9.993772e-08\n", + " 199|1.663543e-01|-8.588499e-08\n", + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 200|1.663543e-01|-8.737134e-08\n" + ] + }, + { + "data": { + "image/png": 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oq6JWraoPK41RJ5I5qVlfB70KOd8K1xe9cNz+FO+xCYA+XdcyvNcyABYc2YnZ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\nTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% Example with Frobenius norm regularization\n", + "\n", + "\n", + "def f(G):\n", + " return 0.5 * np.sum(G**2)\n", + "\n", + "\n", + "def df(G):\n", + " return G\n", + "\n", + "\n", + "reg = 1e-1\n", + "\n", + "Gl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n", + "\n", + "pl.figure(3)\n", + "ot.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve EMD with entropic regularization\n", + "--------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 0|1.692289e-01|0.000000e+00\n", + " 1|1.617643e-01|-4.614437e-02\n", + " 2|1.612546e-01|-3.161037e-03\n", + " 3|1.611040e-01|-9.349544e-04\n", + " 4|1.610346e-01|-4.310179e-04\n", + " 5|1.610072e-01|-1.701719e-04\n", + " 6|1.609947e-01|-7.759814e-05\n", + " 7|1.609934e-01|-7.941439e-06\n", + " 8|1.609841e-01|-5.797180e-05\n", + " 9|1.609838e-01|-1.559407e-06\n", + " 10|1.609685e-01|-9.530282e-05\n", + " 11|1.609666e-01|-1.142129e-05\n", + " 12|1.609541e-01|-7.799970e-05\n", + " 13|1.609496e-01|-2.780416e-05\n", + " 14|1.609385e-01|-6.887105e-05\n", + " 15|1.609334e-01|-3.174241e-05\n", + " 16|1.609231e-01|-6.420777e-05\n", + " 17|1.609115e-01|-7.189949e-05\n", + " 18|1.608815e-01|-1.865331e-04\n", + " 19|1.608799e-01|-1.013039e-05\n", + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 20|1.608695e-01|-6.468606e-05\n", + " 21|1.608686e-01|-5.738419e-06\n", + " 22|1.608661e-01|-1.495923e-05\n", + " 23|1.608657e-01|-2.784611e-06\n", + " 24|1.608633e-01|-1.512408e-05\n", + " 25|1.608624e-01|-5.397916e-06\n", + " 26|1.608617e-01|-4.115218e-06\n", + " 27|1.608561e-01|-3.503396e-05\n", + " 28|1.608479e-01|-5.098773e-05\n", + " 29|1.608452e-01|-1.659203e-05\n", + " 30|1.608399e-01|-3.298319e-05\n", + " 31|1.608330e-01|-4.302183e-05\n", + " 32|1.608310e-01|-1.273465e-05\n", + " 33|1.608280e-01|-1.827713e-05\n", + " 34|1.608231e-01|-3.039842e-05\n", + " 35|1.608212e-01|-1.229256e-05\n", + " 36|1.608200e-01|-6.900556e-06\n", + " 37|1.608159e-01|-2.554039e-05\n", + " 38|1.608103e-01|-3.521137e-05\n", + " 39|1.608058e-01|-2.795180e-05\n", + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 40|1.608040e-01|-1.119118e-05\n", + " 41|1.608027e-01|-8.193369e-06\n", + " 42|1.607994e-01|-2.026719e-05\n", + " 43|1.607985e-01|-5.819902e-06\n", + " 44|1.607978e-01|-4.048170e-06\n", + " 45|1.607978e-01|-3.007470e-07\n", + " 46|1.607950e-01|-1.705375e-05\n", + " 47|1.607927e-01|-1.430186e-05\n", + " 48|1.607925e-01|-1.166526e-06\n", + " 49|1.607911e-01|-9.069406e-06\n", + " 50|1.607910e-01|-3.804209e-07\n", + " 51|1.607910e-01|-5.942399e-08\n", + " 52|1.607910e-01|-2.321380e-07\n", + " 53|1.607907e-01|-1.877655e-06\n", + " 54|1.607906e-01|-2.940224e-07\n", + " 55|1.607877e-01|-1.814208e-05\n", + " 56|1.607841e-01|-2.236496e-05\n", + " 57|1.607810e-01|-1.951355e-05\n", + " 58|1.607804e-01|-3.578228e-06\n", + " 59|1.607789e-01|-9.442277e-06\n", + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 60|1.607779e-01|-5.997371e-06\n", + " 61|1.607754e-01|-1.564408e-05\n", + " 62|1.607742e-01|-7.693285e-06\n", + " 63|1.607727e-01|-9.030547e-06\n", + " 64|1.607719e-01|-5.103894e-06\n", + " 65|1.607693e-01|-1.605420e-05\n", + " 66|1.607676e-01|-1.047837e-05\n", + " 67|1.607675e-01|-6.026848e-07\n", + " 68|1.607655e-01|-1.240216e-05\n", + " 69|1.607632e-01|-1.434674e-05\n", + " 70|1.607618e-01|-8.829808e-06\n", + " 71|1.607606e-01|-7.581824e-06\n", + " 72|1.607590e-01|-1.009457e-05\n", + " 73|1.607586e-01|-2.222963e-06\n", + " 74|1.607577e-01|-5.564775e-06\n", + " 75|1.607574e-01|-1.932763e-06\n", + " 76|1.607573e-01|-8.148685e-07\n", + " 77|1.607554e-01|-1.187660e-05\n", + " 78|1.607546e-01|-4.557651e-06\n", + " 79|1.607537e-01|-5.911902e-06\n", + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 80|1.607529e-01|-4.710187e-06\n", + " 81|1.607528e-01|-8.866080e-07\n", + " 82|1.607522e-01|-3.620627e-06\n", + " 83|1.607514e-01|-5.091281e-06\n", + " 84|1.607498e-01|-9.932095e-06\n", + " 85|1.607487e-01|-6.852804e-06\n", + " 86|1.607478e-01|-5.373596e-06\n", + " 87|1.607473e-01|-3.287295e-06\n", + " 88|1.607470e-01|-1.666655e-06\n", + " 89|1.607469e-01|-5.293790e-07\n", + " 90|1.607466e-01|-2.051914e-06\n", + " 91|1.607456e-01|-6.422797e-06\n", + " 92|1.607456e-01|-1.110433e-07\n", + " 93|1.607451e-01|-2.803849e-06\n", + " 94|1.607451e-01|-2.608066e-07\n", + " 95|1.607441e-01|-6.290352e-06\n", + " 96|1.607429e-01|-7.298455e-06\n", + " 97|1.607429e-01|-8.969905e-09\n", + " 98|1.607427e-01|-7.923968e-07\n", + " 99|1.607427e-01|-3.519286e-07\n", + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 100|1.607426e-01|-3.563804e-07\n", + " 101|1.607410e-01|-1.004042e-05\n", + " 102|1.607410e-01|-2.124801e-07\n", + " 103|1.607398e-01|-7.556935e-06\n", + " 104|1.607398e-01|-7.606853e-08\n", + " 105|1.607385e-01|-8.058684e-06\n", + " 106|1.607383e-01|-7.393061e-07\n", + " 107|1.607381e-01|-1.504958e-06\n", + " 108|1.607377e-01|-2.508807e-06\n", + " 109|1.607371e-01|-4.004631e-06\n", + " 110|1.607365e-01|-3.580156e-06\n", + " 111|1.607364e-01|-2.563573e-07\n", + " 112|1.607354e-01|-6.390137e-06\n", + " 113|1.607348e-01|-4.119553e-06\n", + " 114|1.607339e-01|-5.299475e-06\n", + " 115|1.607335e-01|-2.316767e-06\n", + " 116|1.607330e-01|-3.444737e-06\n", + " 117|1.607324e-01|-3.467980e-06\n", + " 118|1.607320e-01|-2.374632e-06\n", + " 119|1.607319e-01|-7.978255e-07\n", + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 120|1.607312e-01|-4.221434e-06\n", + " 121|1.607310e-01|-1.324597e-06\n", + " 122|1.607304e-01|-3.650359e-06\n", + " 123|1.607298e-01|-3.732712e-06\n", + " 124|1.607295e-01|-1.994082e-06\n", + " 125|1.607289e-01|-3.954139e-06\n", + " 126|1.607286e-01|-1.532372e-06\n", + " 127|1.607286e-01|-1.167223e-07\n", + " 128|1.607283e-01|-2.157376e-06\n", + " 129|1.607279e-01|-2.253077e-06\n", + " 130|1.607274e-01|-3.301532e-06\n", + " 131|1.607269e-01|-2.650754e-06\n", + " 132|1.607264e-01|-3.595551e-06\n", + " 133|1.607262e-01|-1.159425e-06\n", + " 134|1.607258e-01|-2.512411e-06\n", + " 135|1.607255e-01|-1.998792e-06\n", + " 136|1.607251e-01|-2.486536e-06\n", + " 137|1.607246e-01|-2.782996e-06\n", + " 138|1.607246e-01|-2.922470e-07\n", + " 139|1.607242e-01|-2.071131e-06\n", + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 140|1.607237e-01|-3.154193e-06\n", + " 141|1.607235e-01|-1.194962e-06\n", + " 142|1.607232e-01|-2.035251e-06\n", + " 143|1.607232e-01|-6.027855e-08\n", + " 144|1.607229e-01|-1.555696e-06\n", + " 145|1.607228e-01|-1.081740e-06\n", + " 146|1.607225e-01|-1.881070e-06\n", + " 147|1.607224e-01|-4.100096e-07\n", + " 148|1.607223e-01|-7.785200e-07\n", + " 149|1.607222e-01|-2.094072e-07\n", + " 150|1.607220e-01|-1.440814e-06\n", + " 151|1.607217e-01|-1.997794e-06\n", + " 152|1.607214e-01|-2.011022e-06\n", + " 153|1.607212e-01|-8.808854e-07\n", + " 154|1.607211e-01|-7.245877e-07\n", + " 155|1.607207e-01|-2.217159e-06\n", + " 156|1.607201e-01|-3.817891e-06\n", + " 157|1.607200e-01|-7.409600e-07\n", + " 158|1.607198e-01|-1.497698e-06\n", + " 159|1.607195e-01|-1.729666e-06\n", + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 160|1.607195e-01|-2.115187e-07\n", + " 161|1.607192e-01|-1.643727e-06\n", + " 162|1.607192e-01|-1.712969e-07\n", + " 163|1.607189e-01|-1.805877e-06\n", + " 164|1.607189e-01|-1.209827e-07\n", + " 165|1.607185e-01|-2.060002e-06\n", + " 166|1.607182e-01|-1.961341e-06\n", + " 167|1.607181e-01|-1.020366e-06\n", + " 168|1.607179e-01|-9.760982e-07\n", + " 169|1.607178e-01|-7.219236e-07\n", + " 170|1.607175e-01|-1.837718e-06\n", + " 171|1.607174e-01|-3.337578e-07\n", + " 172|1.607173e-01|-5.298564e-07\n", + " 173|1.607173e-01|-6.864278e-08\n", + " 174|1.607173e-01|-2.008419e-07\n", + " 175|1.607171e-01|-1.375630e-06\n", + " 176|1.607168e-01|-1.911257e-06\n", + " 177|1.607167e-01|-2.709815e-07\n", + " 178|1.607167e-01|-1.390953e-07\n", + " 179|1.607165e-01|-1.199675e-06\n", + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 180|1.607165e-01|-1.457259e-07\n", + " 181|1.607163e-01|-1.049154e-06\n", + " 182|1.607163e-01|-2.753577e-09\n", + " 183|1.607163e-01|-6.972814e-09\n", + " 184|1.607161e-01|-1.552100e-06\n", + " 185|1.607159e-01|-1.068596e-06\n", + " 186|1.607157e-01|-1.247724e-06\n", + " 187|1.607155e-01|-1.158164e-06\n", + " 188|1.607155e-01|-2.616199e-07\n", + " 189|1.607154e-01|-3.595874e-07\n", + " 190|1.607154e-01|-5.334527e-08\n", + " 191|1.607153e-01|-3.452744e-07\n", + " 192|1.607153e-01|-1.239593e-07\n", + " 193|1.607152e-01|-8.184984e-07\n", + " 194|1.607150e-01|-1.316308e-06\n", + " 195|1.607150e-01|-7.100882e-09\n", + " 196|1.607148e-01|-1.393958e-06\n", + " 197|1.607146e-01|-1.242735e-06\n", + " 198|1.607144e-01|-1.123993e-06\n", + " 199|1.607143e-01|-3.512071e-07\n", + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 200|1.607143e-01|-2.151971e-10\n" + ] + }, + { + "data": { + "image/png": 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02mu+bsDIkXDnnUlXkxl69fIrX6xf76G7eXPSFbUZBa5Ia3njDb/y7n77wRNP+Hq30jSH\nHw6TJvmwwpe+BOXlSVfUJhS4Iq1hxgwfiywp8SUIc3GthJYaNcqHY15+Gb7yFdi6NemKWp0CV6Sl\n/vxnOPlk6NrVw7Zfv6QrylwXXQQPPAB/+YsPy6xenXRFrUqBK7K3QvDFWM45B4YM8VkJmpHQcpdd\nBpMnw9tvw4gRfpslFLgie2PVKv8VeNw4OPtsv5pBLlwuJy5f+Qr8/e8+ljtiBEyY4OsvZDgFrkhz\nVFf7r7xDh8KLL/pMhKefhs6dk64s+xx7rHe3p5wCY8fCCSfAO+8kXVWLKHBFmqK62mceHHaYT/s6\n7DD/5h87VgvStKWSEnj+efjNb2D+fF9/4etfhzlzkq5sryhwRfZk0SJfeGb//eGrX/X7nnjCL/t9\n8MHJ1pYrzPxqEXPn+iWKnnsOhg2D007zf4stW5KusMks5MiiEbJnw4cPDzNmzEi6jORt3OjzaV96\nyYcM3nrL7x85Er71LR+31boIySorg4kTfVu6FNq3h9NPh1NPhZNO8jAuKIilFDObGUIY3uT9FbgC\nORS4FRW+HODKlbBihX/DLlrk3dPs2TBvnu9XUABHHw3//u9w7rl+MoOkl+pqP7Nv8mRfAnPRIr+/\nfXsf8jn4YDjwQF8Wc8AAv0pySYlP32ulH5oKXNkrOwXuM8/AlClt/6a7+7+Xur/u7e626mo/el1V\n5X+urPStosK38nLfNm3yraEzmAoK/FpjBx8MRx0Fn/0sHHecDoRlmkWL4F//gjff9MvRz5nT8NoM\nZv5v27kzdOwIHTpAcbFfWbioyM8QLCjwLT/f13s46ST/DWeXl2pe4MbTd0tmWbDAf52Ow+4OOKXu\nr3vb0Jb6hkh9cxQW+tauHXTq5N1O+/a132Ddu/tZYL16ecczcCD07athgmyw336+pZbFBB/fXbrU\nr5+2cqWfSLF2LWzY4D+At2zxM9q2bfMf0Js2+Q/sqirfamp8a6XF49XhCpBDQwoirai5Ha5mKYiI\nxESBKyISEw0pCABmVgYsqXNXTyCdVw5J5/rSuTZI7/rSuTbYtb5BIYSSpj5ZgSsNMrMZzRmbils6\n15fOtUF615fOtUHL69OQgohITBS4IiIxUeDK7kxMuoBGpHN96VwbpHd96VwbtLA+jeGKiMREHa6I\nSEwUuCIiMVHgyi7M7Awzm2tmC8zshoRrGWBmL5nZB2Y228yuje7vYWZ/NbP50W33BGvMN7NZZjYl\n+no/M5sefX6Pm1lRgrV1M7PJZvahmc0xs2PT7LMbF/27vm9mj5lZcZKfn5k9bGarzOz9Ovc1+HmZ\n+1VU57tmdlRjr6/AlZ2YWT5wN3AmMAz4mpkNS7CkKuC6EMIwYARwVVTPDcDUEMKBwNTo66RcC9S9\nBMEvgNtDCIOBdcBliVTl7gReDCEMBQ7H60yLz87M+gFjgeEhhEOAfOACkv38fgOcUe++3X1eZwIH\nRtsY4N5GXz2EoE3bjg04FvhLna9vBG5Muq469TwLnAbMBfpE9/UB5iZUT//om/AUYApg+JlIBQ19\nnjHX1hVYRHRwvM796fLZ9QM+BnrgKxdOAb6Q9OcHlALvN/Z5AfcDX2tov91t6nClvtQ3Qcon0X2J\nM7NS4EhgOrBvCCG12OkKYN+EyroD+B6QuqTsPsD6EEJV9HWSn99+QBnw62jI40Ez60iafHYhhGXA\nLcBSYDmwAZhJ+nx+Kbv7vJr9vaLAlYxgZp2AJ4H/E0LYWPex4O1F7PMbzexsYFUIYWbc791EBcBR\nwL0hhCOBLdQbPkjqswOIxkJH4T8Y+gId2fXX+bTS0s9LgSv1LQMG1Pm6f3RfYsysEA/bSSGEp6K7\nV5pZn+jxPsCqBEo7HviSmS0G/oAPK9wJdDOz1OL+SX5+nwCfhBCmR19PxgM4HT47gFOBRSGEshBC\nJfAU/pmmy+eXsrvPq9nfKwpcqe9N4MDoSHERfhDjuaSKMTMDHgLmhBBuq/PQc8Do6M+j8bHdWIUQ\nbgwh9A8hlOKf099DCBcCLwHnJVlbVN8K4GMzOyi6ayTwAWnw2UWWAiPMrEP075yqLy0+vzp293k9\nB1wczVYYAWyoM/TQsCQGy7Wl9wacBcwDPgLGJ1zLCfivcO8Cb0fbWfhY6VRgPvA3oEfCdZ4MTIn+\nvD/wBrAA+CPQLsG6jgBmRJ/fM0D3dPrsgJuAD4H3gd8C7ZL8/IDH8PHkSvw3hMt293nhB0jvjr5P\n3sNnW+zx9Vt0aq+ZnYH/CpUPPBhC+Plev5iISJbb68CN5mvOw6fofIL/Kvq1EMIHrVeeiEj2aMlV\ne48GFoQQFgKY2R/wI467DdyePXuG0tLSFryltNT69X5h0gEDdr5/5syZq0O0cv1pef+hFY1E6vlr\nzR93c4nppmtJ4DY0B+2Y+juZ2Rj8LAwGDhyIrgybrO99DyZMgPr/DGa2pOFniEhrafNZCiGEiSGE\n4SGE4SUlTb70j7SRykooLEy6CpHc1JLATbv5mtK47duhKLGlVERyW0sCN63ma0rTbN0KHTsmXYVI\nbtrrMdwQQpWZXQ38BZ8W9nAIYXarVSZtYuNG6NQp6SpEclNLDpoRQngBeKGVapEYrFkD++yTdBUi\nuUmn9uaYZcugd++kqxDJTQrcHFJdDYsXwwEHJF1JAsyat4m0AQVuDnn/faiqgkMOSboSkdzUojFc\nySyvvOK3J52UbB2JSJ3CnuperZFeI9Xkhpqd72/B2iMi6nBzyO9+B8OG7Xpar4jEQx1ujpg2Dd58\n00/rFWo716jTtbzddb75DT4v1ISdX0edrzSBOtwcsH07fPObsO++cPHFSVcjkrvU4Wa5EGD8eHj7\nbXjmGejSJemKElZ/LHfHGO3OnazlR71IXl69/f35O4Z4q6v9D1HHu+Pr5o791nt9yU7qcLNYCL46\n2C23eIc7alTSFYnkNnW4WWrDBhg7Fh59FK66Cn71q6QrSjP1OslUZ2r5qYe947So87T86IHCnb9l\ndszYTXW427f7bVXVzvfX73zrd7LqbHOCOtws9MwzPhvhd7+DH/3ID5Tl6V9aJHHqcLPI66/DzTfD\nn/4Ehx3mwfvZzyZdVYYIO3eiO8ZoU49HP7FS91tqjctU5xuN+VpV9c4vW1ER3Uadb9QB7+h8qyqj\nF4x+Itbs/HzJLup7Mlx1NTz9NBx/PBx3HPzjH/Czn/kVHRS2IulFHW6GmjMHHn8cJk2CBQugtNTH\naS+5RMsvtki9TjfVie4iNbZb4N9CoSi6jEanDv51fvR4lY/ZWqWP6eaVRx1vebnfbou+jsZ8Q2Wq\nDs3vzUYK3Awyb56H7BNP+LoIZn6a7k9/Cl/5ChToX1MkrelbNI1t3gyvvQZ/+xv89a/w3nsesiec\n4AfCzj0X+vRJusostaOzjM4sqzf2Sk3DZ6qFaBZDaOdjvNWd8qL7U2O0fpO/1V/PKryTzt+w2ffb\nvMVvU2O/UWdc23FrjDeTKXDTSGUlTJ/uATt1qp+OW1Xl1yA7/ni44w447zzo1y/pSkVkbyhwE7Rq\nlQdsanv9ddiyxbvYz3wGrrsOTj3Vw7Z9+6SrzVH15+umZhWkxlhTnWc0BmtRB5wXjfHWdC32h9v7\nt9r2LvnRft4B51VF+1d0BqC4zMd28zZt8/3Wb/S32xqN+aY67eh9a+f3aqw3EyhwY1JR4afXTpvm\n4TptGixa5I/l58Ohh8Lo0TByJJx8MvTokWi5ItIGFLhtYPNmePddD9hZs/z23Xd9ERnwIYERI/x0\n22OO8W5WV9LNEPVmMeyYt1sdzUaIOt28Su+EC2p88YoQzdOtbued79YS73Sri3e+usTGQf4tWbze\np5q0L+sKQNFKH9vN3+qdb1i3AYCarVv962isV7Mb0psCt4VWrtw5WGfNgvnza/+/9+gBRx4J117r\n4XrMMdC/f7I1i0gyFLhNVF4OH3zgMwXqbitW1O5TWurheuGFcMQR/uf+/XWJrKy0u7Hdcu8wa6IO\nN9XxFlX41/nbfKzWanxQflM/73Qrevh/kqporH7LAIv2bwdA4Ua/7fyJd9QdPvXOt3Cld7phQzTW\nWx51wPU6Xq3fmx4UuPVUV8PChbsG64IFtTOBiot9rYIvfKE2WA8/HLp1S7Z2EUlvOR24q1btGqyz\nZ0M0LIaZX+H20EPhggv89tBDYfDg2lPoRYDasd3UGgnRPNpUh2tR55kfzTboUNEdgIKtPni/qb/P\nWtjSLxrj7R89r6PfVuT56246wPdrt9qf136Vn9nWY64fZS1cG81y+HS115H6z7zdO+yQmsYb6s3n\n1Xq8sciJwN261YO0friuWlW7T0mJh+kVV9QG67/9mw5miUjrybrAXbvWD1y99Vbt7bx5tT+4i4v9\nMuFf/GJtsB56qF9+RqTF6p0JtmPsNOp4q1Md7xbvPIvXRWOxG3w8qmiL/4QvKPdvzY0H+dM79d4E\nwL59vXMtzPP3+WhVTwA2HOSdbodlPu93n9nRLIdl/rz8dX4mW836aMx3e73VyyQWGRu4IcCnn+4a\nrkuX1u4zYAAcddTOwwEHHKDhABFJRsYEbk2NDwu8+qqvL/Dqq7B8ee3jQ4bAscf61Q2OPNK3nj2T\nq1cEqJ0lUJVagyEa402tkRB1mHnRWGvXNT6LoX2Zj8kWbfCOdVOpd8BLhviqZCeWLgTgc8PmA1B9\nsM/zff7Tf/P9hvrz2y/y2+7z/fkdl/k3ReFiH0+r2egdcGr1sh1lp65YIa0qbQO3shJmzqwN13/+\nE9at88f69fOzsUaM8A728MOhc+dEyxURaVRaBW4IvoD2o4/6EoQbfWohQ4b48oMnnujLEZaWam6r\nZIj6R/1TY7zRf+B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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% Example with entropic regularization\n", + "\n", + "\n", + "def f(G):\n", + " return np.sum(G * np.log(G))\n", + "\n", + "\n", + "def df(G):\n", + " return np.log(G) + 1.\n", + "\n", + "\n", + "reg = 1e-3\n", + "\n", + "Ge = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n", + "\n", + "pl.figure(4, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve EMD with Frobenius norm + entropic regularization\n", + "-------------------------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 0|1.693084e-01|0.000000e+00\n", + " 1|1.610121e-01|-5.152589e-02\n", + " 2|1.609378e-01|-4.622297e-04\n", + " 3|1.609284e-01|-5.830043e-05\n", + " 4|1.609284e-01|-1.111407e-12\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% Example with Frobenius norm + entropic regularization with gcg\n", + "\n", + "\n", + "def f(G):\n", + " return 0.5 * np.sum(G**2)\n", + "\n", + "\n", + "def df(G):\n", + " return G\n", + "\n", + "\n", + "reg1 = 1e-3\n", + "reg2 = 1e-1\n", + "\n", + "Gel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True)\n", + "\n", + "pl.figure(5, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg')\n", + "pl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_otda_classes.ipynb b/notebooks/plot_otda_classes.ipynb new file mode 100644 index 0000000..1955676 --- /dev/null +++ b/notebooks/plot_otda_classes.ipynb @@ -0,0 +1,313 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# OT for domain adaptation\n", + "\n", + "\n", + "This example introduces a domain adaptation in a 2D setting and the 4 OTDA\n", + "approaches currently supported in POT.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Authors: Remi Flamary \n", + "# Stanislas Chambon \n", + "#\n", + "# License: MIT License\n", + "\n", + "import matplotlib.pylab as pl\n", + "import ot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + "-------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n_source_samples = 150\n", + "n_target_samples = 150\n", + "\n", + "Xs, ys = ot.datasets.get_data_classif('3gauss', n_source_samples)\n", + "Xt, yt = ot.datasets.get_data_classif('3gauss2', n_target_samples)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instantiate the different transport algorithms and fit them\n", + "-----------------------------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 0|1.017912e+01|0.000000e+00\n", + " 1|2.096083e+00|-3.856258e+00\n", + " 2|1.842979e+00|-1.373343e-01\n", + " 3|1.781632e+00|-3.443301e-02\n", + " 4|1.760919e+00|-1.176281e-02\n", + " 5|1.750958e+00|-5.688541e-03\n", + " 6|1.746386e+00|-2.618021e-03\n", + " 7|1.741793e+00|-2.636854e-03\n", + " 8|1.739054e+00|-1.575065e-03\n", + " 9|1.736474e+00|-1.486027e-03\n", + " 10|1.734361e+00|-1.218441e-03\n", + " 11|1.734259e+00|-5.863179e-05\n", + " 12|1.733704e+00|-3.201643e-04\n", + " 13|1.733018e+00|-3.957711e-04\n", + " 14|1.731842e+00|-6.791025e-04\n", + " 15|1.730974e+00|-5.012271e-04\n", + " 16|1.730584e+00|-2.257722e-04\n", + " 17|1.730492e+00|-5.272976e-05\n", + " 18|1.730153e+00|-1.961758e-04\n", + " 19|1.729837e+00|-1.828284e-04\n", + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 20|1.729361e+00|-2.749072e-04\n" + ] + } + ], + "source": [ + "# EMD Transport\n", + "ot_emd = ot.da.EMDTransport()\n", + "ot_emd.fit(Xs=Xs, Xt=Xt)\n", + "\n", + "# Sinkhorn Transport\n", + "ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\n", + "ot_sinkhorn.fit(Xs=Xs, Xt=Xt)\n", + "\n", + "# Sinkhorn Transport with Group lasso regularization\n", + "ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)\n", + "ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt)\n", + "\n", + "# Sinkhorn Transport with Group lasso regularization l1l2\n", + "ot_l1l2 = ot.da.SinkhornL1l2Transport(reg_e=1e-1, reg_cl=2e0, max_iter=20,\n", + " verbose=True)\n", + "ot_l1l2.fit(Xs=Xs, ys=ys, Xt=Xt)\n", + "\n", + "# transport source samples onto target samples\n", + "transp_Xs_emd = ot_emd.transform(Xs=Xs)\n", + "transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)\n", + "transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs)\n", + "transp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig 1 : plots source and target samples\n", + "---------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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bhBX799X5fKcKC7l84TyyHYV4EHC7WXckFdbD+NN7Bipsvyb16s2H11zHm1s3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\n3c2/GSgWjfHgheNT5wfXh3nw309y3cSLG445HA7+cPCO/OHgxFbHV4+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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(1, figsize=(10, 5))\n", + "pl.subplot(1, 2, 1)\n", + "pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.legend(loc=0)\n", + "pl.title('Source samples')\n", + "\n", + "pl.subplot(1, 2, 2)\n", + "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.legend(loc=0)\n", + "pl.title('Target samples')\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig 2 : plot optimal couplings and transported samples\n", + "------------------------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rflamary/.local/lib/python3.5/site-packages/matplotlib/cbook.py:136: MatplotlibDeprecationWarning: The spectral and spectral_r colormap was deprecated in version 2.0. Use nipy_spectral and nipy_spectral_r instead.\n", + " warnings.warn(message, mplDeprecation, stacklevel=1)\n" + ] + }, + { + "data": { + "image/png": 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KLor601Yv0Hs6f+MpDonqAb+iHY0z7mW8r3MalwPp4414/ww1ty7nHQDa0z7S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GyVy+hCNrDFZsAmNrewcXrVnHsz1nKKXeym2ZLNdu2jzpsxd0r2FTSyvXb92GJ8KzZ07z/SNH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ozTisMVNlxeB017UwQbO1BtKgWl+Zl4YXdZmyKpuLVoyS/xM+/qORkxvr2baskv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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "param_img = {'interpolation': 'nearest', 'cmap': 'spectral'}\n", + "\n", + "pl.figure(2, figsize=(15, 8))\n", + "pl.subplot(2, 4, 1)\n", + "pl.imshow(ot_emd.coupling_, **param_img)\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.title('Optimal coupling\\nEMDTransport')\n", + "\n", + "pl.subplot(2, 4, 2)\n", + "pl.imshow(ot_sinkhorn.coupling_, **param_img)\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.title('Optimal coupling\\nSinkhornTransport')\n", + "\n", + "pl.subplot(2, 4, 3)\n", + "pl.imshow(ot_lpl1.coupling_, **param_img)\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.title('Optimal coupling\\nSinkhornLpl1Transport')\n", + "\n", + "pl.subplot(2, 4, 4)\n", + "pl.imshow(ot_l1l2.coupling_, **param_img)\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.title('Optimal coupling\\nSinkhornL1l2Transport')\n", + "\n", + "pl.subplot(2, 4, 5)\n", + "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", + " label='Target samples', alpha=0.3)\n", + "pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,\n", + " marker='+', label='Transp samples', s=30)\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.title('Transported samples\\nEmdTransport')\n", + "pl.legend(loc=\"lower left\")\n", + "\n", + "pl.subplot(2, 4, 6)\n", + "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", + " label='Target samples', alpha=0.3)\n", + "pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,\n", + " marker='+', label='Transp samples', s=30)\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.title('Transported samples\\nSinkhornTransport')\n", + "\n", + "pl.subplot(2, 4, 7)\n", + "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", + " label='Target samples', alpha=0.3)\n", + "pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,\n", + " marker='+', label='Transp samples', s=30)\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.title('Transported samples\\nSinkhornLpl1Transport')\n", + "\n", + "pl.subplot(2, 4, 8)\n", + "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", + " label='Target samples', alpha=0.3)\n", + "pl.scatter(transp_Xs_l1l2[:, 0], transp_Xs_l1l2[:, 1], c=ys,\n", + " marker='+', label='Transp samples', s=30)\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.title('Transported samples\\nSinkhornL1l2Transport')\n", + "pl.tight_layout()\n", + "\n", + "pl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_otda_color_images.ipynb b/notebooks/plot_otda_color_images.ipynb new file mode 100644 index 0000000..7c04d33 --- /dev/null +++ b/notebooks/plot_otda_color_images.ipynb @@ -0,0 +1,322 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# OT for image color adaptation\n", + "\n", + "\n", + "This example presents a way of transferring colors between two image\n", + "with Optimal Transport as introduced in [6]\n", + "\n", + "[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014).\n", + "Regularized discrete optimal transport.\n", + "SIAM Journal on Imaging Sciences, 7(3), 1853-1882.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Authors: Remi Flamary \n", + "# Stanislas Chambon \n", + "#\n", + "# License: MIT License\n", + "\n", + "import numpy as np\n", + "from scipy import ndimage\n", + "import matplotlib.pylab as pl\n", + "import ot\n", + "\n", + "\n", + "r = np.random.RandomState(42)\n", + "\n", + "\n", + "def im2mat(I):\n", + " \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n", + " return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n", + "\n", + "\n", + "def mat2im(X, shape):\n", + " \"\"\"Converts back a matrix to an image\"\"\"\n", + " return X.reshape(shape)\n", + "\n", + "\n", + "def minmax(I):\n", + " return np.clip(I, 0, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + "-------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Loading images\n", + "I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256\n", + "I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n", + "\n", + "X1 = im2mat(I1)\n", + "X2 = im2mat(I2)\n", + "\n", + "# training samples\n", + "nb = 1000\n", + "idx1 = r.randint(X1.shape[0], size=(nb,))\n", + "idx2 = r.randint(X2.shape[0], size=(nb,))\n", + "\n", + "Xs = X1[idx1, :]\n", + "Xt = X2[idx2, :]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot original image\n", + "-------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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CEak80UoJuMvPYUazG3B4UEMNJxBbHX2zO/ZRkfDDzwKo2oWbuJRctlKPMJzS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\nU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SSUldaKMNk6ALXMMQ8Y0q9ARfNsXQ8FxmYcgpeCk2pSzTJyGBt+9LM9b3AwrBhkRJzH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\nVST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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(1, figsize=(6.4, 3))\n", + "\n", + "pl.subplot(1, 2, 1)\n", + "pl.imshow(I1)\n", + "pl.axis('off')\n", + "pl.title('Image 1')\n", + "\n", + "pl.subplot(1, 2, 2)\n", + "pl.imshow(I2)\n", + "pl.axis('off')\n", + "pl.title('Image 2')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Scatter plot of colors\n", + "----------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Lm3ZQU5XlknMWcObCWT6K1OPxeBiloviDB9bxxIu70ga/UFwMjMDEGYIgpLTG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\nZkpEUdO1++QJcfqPoE4H/6pSxdiYHa1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xowr1h3YoOukJXV8KM12+EIgiuXa31SUjWIsa\n44xCdN264LHLAHGhNGJo5bd++J1kdlBeU7dpU8W0XKiyIGyj9eMrMTtDsd+g9qvuuIGxAx9Dv+sa\np2T3W6TUnpxe8St/gV6xnq1hDipFEHWxD17fwfMetKrrZXuogrOYomDeWJOysphcmT+/yUS7He7g\nnGJc+HEVecZxjzo6KcREYgcmz3NOe/VLeNGZL6TdarNg4YLuRf1tLz0r9D4mXksUXGcSEUMjz0NK\nTB6vPWZE0o4qjHCHeudig9SpHzH19d6Gu/ThUjApmKrsopJUieslZIyOrKF24TqoY4KMaGZuanGn\nCUsBofONDx13zDihJlyCQWJVyTHdbFPUU65eRdPpgIHnXTRgnPYUYt9m/BrwuylmvPeOb28kX/o4\n7KrLwHdAlXx1FYc29MltHNnPfw1rBmZobzFzUCnGwlTv4/yyoCRRT6Pon17W/xHFVSULGov53ws/\njfWeW1asYNninfiHD32cn193QwiECyzfbVfec9arHswdSiQSm8ldd93D7367gn3335vdt8H4tqIo\nKPryBm694SbW3LO6Z6D1J7Y4hxMhMyZYgZmgMtX421Rr1NI5GnmdehKyUoVe6UKtEI300npogo9x\nyV4GUMyydxYnIWk0jNqYzhJVsGBVKeYPZeirIpWFYkR+qIC4MGFDNURW88JAJlMSHS2eQvvXraho\nrMcIeKuoCsZO70v2GwhKtxYhy8mXHUFjv+dS/vaLSAm4auqVXhW/ALI1w29sGXNQKfYQ9WSUPYva\ngpFsxPDfcEd1/9q1rNswwV677cLRhx4KwGff9w6uu/lWbrj1t+yzx24cc/jDUreaRGI7o6oq3vKm\n93DZZVfSKArKquK44x7Du9775gGlBsES+cnlP+U7X/0OnU6H4592PE9+6pO6ZQWb4meXXcFAGZfq\nwAVcyxJfNMgyE+JZKDqU+Lepq4d3Hh8L5etCjcwQ3JcoVCFT1Euw6CQHM6lkQSsO7qcJDQJUiZOA\nPIiJ8oyWwlvFTVqysdANJkzRsKh1UEyd/GG8o6hjluFwIPm0ZZt4lKxeWhVLifEG0dBQAJNTTkAz\nH12igYT1960QXVey4StvQRqOfL+TMbIeXf2fU0s3jMT44tYx95SiEiZLi5JT9rKoIlXlaDSm+rfr\n9OCimLrLhx10AIcdtPWpvIlEYmY45yOf5n8uu5KyU1J2wsXwskuv5OMf/Xde8/rTB5b9+Ps/zne+\n9l3acVrDr675FT/49g9437++t3vDfPtNt3H5f16C957HnfAEDnjoQUCwHPt7dRpbd6Hpy1/wsdWL\nGLzTUHPYFyvzMs2FuU78q8sniGUR1mNc1a2JrD2p6sCXMYZpZKoi8g41YERDDZ/kqPVIo9d/tVtm\nZrW7zcxbpDVYIO8BV3ryRjQo4r7k3g1uV6bPcwTFi8NoHo6XtzSs0jfdNuyQybHWkVEwqjbTLKDX\n2We9Q9ZY2vdeTX4g2JXXYxbtQtOMkMIpZv0fqFJ0bU+WdaCQkbcsznny7l1h8NFnxnDEwQew65Kd\nH1RxE4nE1vPVC79NpzNoGXQ6JV++8FsDSvHOO1byra98m7Ld6Sqh9mSL6//vOn56+U95zOMfw1c+\n9QW++LHPhGnyKF/99OdZvGRn/vjJx3Hl9y+harXDlIocDGZKSMY5y8IliyknJ/AOfMeE2atZHdrS\nnubob5sG5MaD99E1apDKkVkb6qL7klX6txjnYzBKgUhWYmJGTuh1KiHTM5MwkCOWUxgEU5eKTGPm\nucqROw0WoyhiXUykGVrOKiYbtEa17siDR3EYD3n3hmIIWyF4rHjyrNnr5COQL6mQOxUZV2S+DzMh\ndwEmBJ0wyMIOfv0q2PdQWH8buF7bPhxk943ctS1i7pVkiEeKCYyZzicdslPrmKOqp5Hn7LFsKee9\n/c0PqqiJROKBo6pceME3ecJjn8nExGTvDe+RqsKUJa216/jN9Td237rmp9egzsX6uqCAsJbWho1c\n/J0fcvcdK7ng45+hqjqhCjFesTfeu4b/uuAbbFizrrttWyl+KEAoohRNy+TG+1GxeNo47eArD6UP\nxe/W4spOXw1fbQF6BobwaFSIMCWbfuA4/L4DZcDWPeQkunSdRysXxuCpxfhJTLvDdEZsFBD1inQs\npu0QO3phbxVX9ZWP+GDxmokWplWCrxCUzGmwZn0VH657TEL1p6Oyk/iFJWbvEnNQB6Me01BkkUeK\ncE8hObBQ8fWkEFdStcEccFwoG8CDWoob/MgEpy1l7lmKEDu797ojTEEdGqdeHH7owfzDmadx/GOO\n7bMeE4nE9s4/vfNDXPgf3whdUYpYVuU9xvaNh1PlpX/xSl79hr/m1/93HbfdchvtyVaw2IxBfJzM\noMr/fOeH/PqqX4QJGF3tEPyBIZpW12bF97xQeQ3xQwCUvBHdjqq9yfTqgjfV55iqwsQYnO+0yIrQ\nXi3HkRV5NPhiScKIyfWj2FTijtSyClRYCpOHxBkNaseop9DeYF/tKIwPxRzrjH0RLEqjfhHBWSWb\nEkNUXKlkEx5paCizyGxwewJgQ3cxpRdfDAc01ksayGOTAcCtLcl3z5GOImsV9hOGh16Ev7WnVMcX\nwaqfYLLeQGN3qJDdlsOGTR/P38ecVIoQEpq8+uDeqJvORhPe9NUp3nDjLXRa7aQQE4k5xOp77+PL\nF3wTryEW5q1DCulaVjUCtFttPvDuj5DXsavaVemUQnrOT+8c9969CtRTZBqVgZBJvDZkPrgNa4+n\nKOpDMgz0skynFr+DIyhG05eUAuCqsrtMBqGMrF5RnyWmXlEZcknSazISFEz/+xoaekdlISgqHm8c\n+Dw4fdWT2548LveYTigJ0bE+8R1oDn5jiVkUd9RnIX9DFI9gYgapSFBQmbWYymKKOCS5OXhc1CjV\nGOiEJzODGamCDlTTYZRqhaUJUArk02fQqoYEpcbD/gyuvHTw/XmKO6zaDNN608xZpQhg1WHUk0eL\nUTW4J/qPt3WOl//Du9hlyc489ugjZ0fQRCKxRVx/3Y0MFFip4spq2ooD5z116WBtiY1ssR0VZj0i\nUFGcOgrJBhRivRqM4owj06yrHEchxtDMm/hJO/J97+MF3ftYoiD4vvWphmW6yiJaUbWV69QCBmMy\nTA5ZrkimiISi/Z7K8WAsnoJGNVgjiAFXOLJOBh3tWmO6WJGNDqkUrME3IM993zo19C31Dmql7yVW\nWsSZHZNR5lyQIirBLCYdeU/e5zc2w7WdIuhkWDYjg1JhmglGrCnJxdP5yltp7DOO+Na038kDZe7F\nFIfw3uMrh68suWgYihnRmEHWarf55/P+ffaETCQSW8Quuy4F6pv+0NQfSqxWeN0Mt6OMrFjuogPP\ng5U1ej1gMgOF0JjfYKQZospuey/nma86nebY2Mj38SHLNMTgfLe8oi4/yIo8tB61IRfCqCXDYrAI\nNmbPe6yrEPGY2EbO4GJMcshSo+o+68fnSjVm8fMcLPSw2CFVG/E2LOo1JH5m9Wc1WNauxOQWs9TD\nbh6/1FHNj71etbcprRRf9h3LMY8Wru87U/AWZ20v5hqxLnwX/i6PH+4G5DUkEFVxPyfXYu9u9ZqX\nqYa2dqVHKraKua0UVcFqvIML/fR6d4LB7VK7Um+7Y8XsyZlIJLaIQx96MMv33RPpKsTQEFvVYxlW\njIM3w+Gl2MJ7moDcsMLMxxqYaVqw/fFJT+F9X/oUpj0Zr9zaC/TF542i4PHPOZmly/fEZFNDNZlX\nfOWxbYuvPEKFoYV1LSwtymoS8ZbcOzLnyAhzE6Vu4xaFzoxAqfgylowwopdpXDgbm84RqNCZxLhJ\njG0h4mCeRXMLDWHnQw/jkJeciZkPFI5MSmgqsovCWOiuwzxgKbhsxPG1fbE/gAw0q/vFBhsZ9Xhb\noRqeg6fIKrTo4Dsd3C1t7Ooy3ETY0CMVq5B5dNyhucetbWFXWfT+CrlpErm+hbmmhVw/Mc1+bx4z\nqhRF5EQR+Y2I3Cwifzvi/deLyPUicq2IXCwi+27Wiusfpa1Tn20w4fua6dZ9UOuA+9FHHLZtdy6R\nmAPM2Dm4Dbn5pts49xOf5TP/dgH33H1vLRf/9tmPMNbMeu3W6D2xVF1PkKhDNF5g+7DdgQD9WkPJ\nhtykoCxYsmh09qcqV/3wR/z4G98Obdq8QhX/dT427RYOPupIRISXfewD7HvEYeSNRlc5Zq6n2EQE\nwWK0QpwNPUStA1viYwuzaJwNJNhIZpBCaOwxjtmtwUFPPp5d9jlk2viZyXP2f+LxeCmj4onXTOeR\nVicYqvE4dHd7vmP/P/9z/uhdH2LF176KXzMBG1pI6ZGddWA6UV1GYRdMI4BTxPWsWI1N1HOpuqUb\n2bglG29jxto0TAlES7ruWnafRVdWSMyEFZRsfmxaPu5hkUO0jbm7RO4G2QDSBtaNFmlzmbGYoohk\nwMeApwArgKtE5CJV7W9Mdw1wjKpOisiZwNnAcze5Yo1mtAIopi/7yhJmpC3Mm5RlPYRYGG82eeNf\nv2Sb7l8isb0zY+fgNuT97z2HT5//BbxzmCzjve/+CGd/4K08/ZknsmyXpVg7YuAsBEuwamNUKfJo\nLWlo4I0YhEa4GDcMjzjmKCbWb6TRKPjN/12Lkf6av+AidNYxb3yMjRuHrAzvKDsla+69D+9C42uF\n0D0mkuc5Tzn1eQAsWraUV59/DuvuXc3G+9fwnQ98jNt/8UuyPMdVlmK8wE2uRq1iHN0pGLUkVVaS\nmQx8xm4HHoB3odn4kSefzCFPfhLlxAYW7bYHeaMJwFWf+STXfv0L+KrPZ6iKVhW3fO9rwZqcdEgM\nIHa3p6HEL4/e3rqH6a3fvRCZXM/Enbf0jpDo6PZxEhJ0RpF5Pzig2Sg0KsSGjefzYsyygiy+pvQs\nexOTc7x13RZ4Zp7F4MJkwAykArEgawHtucs35TbfHGYy0eZY4GZVvRVARC4Angl0T0hVvaRv+SuB\nU3//ahXBD9y1qMYAtYZA8xMfeyy33r6Ce++7n0c9/HD+8TVn8pD9H/Qb4ERitpmhc3Db8H/X/Ip/\n//QX6bRjAXYVklTe+IZ38CeP/yN22nkxCxctZO2a0bf+Eh1FdcVEz9DzQBtMk+b4GG/54NtZvNNi\nrLWc+tgT2HD/2nBznWWIgRwPrRbNeWNsXLduoOBeAO8sBz3ycK65+L/ptFp9GTDhpvu1H/sgS3Yb\n7MO6eJdlLN5lGWec+yHuufW33HzlT7jlqiu59eqf4rwlJw4HHt4p53B4pFly3x3X0RgfR1Eu/cQ/\ng1Yc9ZznD1i0j3zei1lx9RWs/vV1UNqQOdooYKLCNyCLsdV+5ds9ShXQFwI1hVIsnOTOS74eU5wk\niuTJNButbaasNsyQNAOvBCszqzyqYIqoEDUoxJGrrTvyOI/kFfl8j1nXm2pEtzxR0SqLDfOm6X29\nhcykUtwLuKPv7xXAozex/GnA90a9ISJnAGcAkBcoFiHruRecRbIs+vZzjj3yCL74obO3fg8SibnN\njJyD++yzzzYR7pvf+E86neEJ65BlGZf86HJOftZJnPri53Pev/5bT3FCUFY2uk5NuEKOcn3uvueu\nnP3JD7F4p8UAvP+1b6a9YWMo6Aeo6wwzWHvvvbTGxjDGdAcNdDfnLJ9/7/swkoVidGshyynmzeOk\nl76Ihxx1JKqKt5Ys9mGdXL+eH51/Ptf96FLAs2HVXXjnuuuu1NMY0S0n2LBKYYI3rNy4EdMJ2v/S\ns9/HFR87hxPf/i72/+M/IcsLJu9bzbobb4Z2tBS9QtVBPdi2IJknm+Yy3ztkioglazp8C7IqnyrX\nRocsHGry6hXWKt736gplzJNZidm9sc2cQN5pQRXyi0KsU/r8w1O/u2AxKlJ4qDyyjl7rt/gRFY9Y\nDUOGux80GN268rvtoiRDRE4FjgGOG/W+qp4LnAsg4/PUq8OrQxSyugO7hvZFxhiec+JTHiTJE4kd\ngy05B4855pitrASL60RHFqbbqmLDxo0AnPDUJ3HuR88dDLB5h9jQ9zgPMyVGrv+Io45gv4P2B+Cm\nX17PTy6+lGqoVVxw2YVrfafdpjnWpGg0yPKc9uRkiFe6iqrSAatGjLBk6U488bnP4oK3v4srv/4N\nqrJkp1134Y9Ofgb/8/nPYSc6gFI0YvPtfgQqcTScBCkk6yr2rIgZqs4jpcZ6/1BfaDsb+O4/vAYR\nYfFee7Nklz2pJjZOWTcmlEIEHRzXMaB8FJM5tFTy8TgnsQPaVpwLcVLpt5hbDho5NE2tuaHtQuLR\nkg7EhBvNDHYyp+hk3fyOZtXBTMbd7BezqeBk2rgoAlmzQjpAY0TyUkWonxw4tH7a1W0uM6kU7wT2\n7vt7eXxtABE5HngLcJyqTr1tnI7ahI6MjzUwxnDuO/6R5bvv/gBFTiR2KGb2HNxKnvHME7nwi9+g\n1WoPvF6WJe/++3fwyQ+dw06LFlG26jhfVCAaJ1N4prRh6+eKH17Csx9zHMedeAI//+/L6ZTt6GAb\nVFBee2q17JQ8/a9eiNqSi794AbasQHWKm89by/0r7+Ldz3ou61ffGSybDNasuoeLzzuPgbDlCI+e\ncZ68v6ONOlQyMBmZVtAOZRz1ZU7UYzK6xfKgrLvzd7TvuHNah6FIUHallDR8s47akZmSjFDeYJox\n1livRQRnHLnLuhn8mauCW3ajhQlicX/tu1ZM3vsO1Ic6SY3GSiYeUzKgEH3LY8YFs7OHVjZFKaqE\ndRaLq15nm/rOpY+8GnFoJYTQtoaZzD69CjhYRPYXkQbwPOCi/gVE5JHAJ4FnqOqqzV5znVujwczO\nDJz9N2fx2x99n2f9abISE4nIzJ2D24BHHnUEL3jhs2k0GjE7MsxFNVoiqtx3733ccfvv+j5Rn/ih\ncFwhdILRwdKL2kJprV/L/ffey7e/8B+sXHk7PrO4vMIZO1CI33+tLZpNlu6+G4LHlp3oqnWMMmfK\ndptVK1aGdcU4ozGx53L92uiAGbnzU99SB5Sht6vvfbyrTFzY34GPbKqhgPSWqUwHZ0ryooU0LL7h\n0VwxxXB2LmCEMitDr1ENpTB9Gwz9RafxfAqghccvqGCxRcZ83zBgBfH4Vom2onW5zIXOQXXPVlFo\nQmMpveYCfsQ+brL33dbZijOmFFXVAq8Evg/8GrhQVa8TkXeIyDPiYu8HFgBfFpFfiMhF06yub8XE\nhsAOKotayx7LlnH6Kc9i4fz5M7Q3icTcY8bOwW3Im//+tRx4wO6ITob0em2RqY13+5t3x192bFSO\nsT5OQawNFl5fh5j6oSa2Q4v0ezbLdptqcpJDjz6aRrMJ1dRSjwGkLs1wUDmk7dDS4zsO1wkF6s7r\ngNI2oy7yAGgont9EU2tXAW0LLQulw0s1QjEO1TcCKpDnFVr4cNU3QSlOhwq4jsV3YqyyHNyHejvZ\n+Ih1CND0aMPjst7YKBEXH0EZ01G0obCng508LPKwzMNSj5qeu1QcMDFUHxplHMm8rVOKMxpTVNXv\nAt8deu2tfc+P3+KVekXaIUDuAZMbnvGkJ2yNmInEDsuMnIPbkK9e+HVuurE35SL09+y97/xw70xC\nU3DvER99lGLQyiHGdC+aQoVDKfIRWZN13ZwPSrPOhASQsuSLZ3+Isz7+LxRZRn9zlDq/sU8QGlIN\njAt0ObGmLpiyvnRYQPIsNM6mb2NbTF2O1lPStgNmwXBthMeIH9AaIh7Jw3VT6zp677EG8mY+1CBc\nQ+cYgpcUVbQVyzYKujFFkylZMw4PHpJTRcL3Uzm0MFCC1PWhtQW4ziNjGSqKxGbi6hWZtFS2jL1d\nw/dLh5AYNW4gF4wHbzQoTGr3b/CDZ/sMtZHbQraLRJsHjjJe5LzwWU+fbUESicQD4OMf+QQuzt0b\n5bZyvlenBqG0IC/LQZceIKZBt3YZG97XbkRu9MalxKnivVK4YPQZ47F2Ax981SsYzxs9ObBk5MEq\ni+GtXKrBWFe8LrsM8j5FiXOIL5FMQCR242mMlsuETjzFKLkVsmGr1YMtLcWYQ5z2uTsNStFVFo1m\nGbyKNsRiu/vVtogK+Vi0zGJTFNOKqTniUGLcbxK6U0QUsoUj9sArtBxqHNIoyFsOV1RkGTjCvEfj\nY1daC3qPQxYZtEHohLPBkecVxijexVpEFZAGOEHW2+DyjfI6A8aHTFazsyc7qESWbV1McU4qxdpd\nkBXCy1/0fI55+OGzLFEikXggrLqnF8YUpuRSAFA5SyaGzBiyqpziGgTF2jZ5BpmYbqE6IozIzwhx\nwlrnREXqcTTjIHgRwVYd2tZh+rbksF1LMcPFzjhTg2pBFWtXIYl3mEJ7ySkC3ripo+9q925lcVkW\nivjR7lsGP/LGIQyz7xunFV7FU2IoKHLbHTtYxyr7sZ2KzCrGCMbHPKto1aoI1iuZZBgxMVznyMcB\nb/BrCe3gGlHZrquQCQuZkGkrxFlz6fqoVRSXWTJbYHIQK+j9fX1Rcwu5djvmqIA6pVJLITlGPdJR\nKB3kJsQlxyt0PmSPIkzY2MpSxbmnFAXIPEUz4+2veTVvPvOM2ZYokUg8AC78woW0273MU08wnvbu\nkgAAFyhJREFUREYpRqMK1oaL4gh/aAglepw6RDUqFKisp1FE1Sa92ri6EVZ47inCagaUnFOPMOi6\nDS3TXLwx37yUDGP8YHhUwboSyYtQ+6gC9Rgo6zDOY51H5ytGDZlXcuPJ7Gird/okV0VMSTgUwnQ9\nzwGqwjJeBb/qoCvV4xFEPHneG0PlO6H8Ii9y5B6L9CcOqZKXQZ2bRUOu73gjUlWWQgtM0ddyL3OI\nGSrTESADZ0M/2CzawbVyznYNWTzZXoCRkb+dLWUOKkWlOV5w4L77cNZpL55taRKJxAOgNdni7W95\nB66qkCxehgbmovZq5FCPekdem1LTeUPjv4pH1SAoxlWoVcji8F0BIw4QVEKhvejQfL+Ix+OATM2A\nBYavUBFcZsh0WIlExV5bieLIxA3VnYei/cpWNAqCe9grVD4o/7gi5y2mWbDP4Y/k7l9cHV8djmuG\n8oWRh6TuA20NQetPhyIZUI1ugqDqKUZsQ53HT5bkjgGFWNTx2enuGQTUKL7jMQ3fla3e9DR92QHw\nBurcHbPUxZCywoTHX0e4T1mydZpxzinFRtHgrNP+ijedcXpI5U4kEnOO6351XRjJhKLOhiQZDFaV\nXGorTEMdgvfRmKpjZYoYTzfLQgWGupiohiQY8Rr6bcbeoEq4sJqGIpSE2fDRzTpCTqcOo1V0o4as\nzuAiVazzmMxMaaEWLtqhL3Nups4xCpm1sZuNtdFqHWEBKmStDit/+mMkz9HMYVwxkKcjEuYcqpqR\nCo22xzuLWdrs1naO2g6tCmX0OsQ56hmQ4aB4jPXdeKpmveYE/cm+m0oelkzJF7jYkDxa8M533aaj\njocvLCabj26MpTLNME9SCh/6oUJIelq9HWefzgRHHPIQ3n3W62ZbjEQisRUsWrQI53rZKOodGXY4\nytbFA3hF1WIagPRGJimKiEe1l0VpjEN8GPcHQ5dYD2oJI5DwIZHEATndgnXUhykWQD1Kor54i8Zm\n195hNacZCqYRHzbmjaGZ50GBOh9KLAQkk67yUInbcaH8YDhGClCUJcbE/XOdIIN11N1vjATLUiug\naUZY2JCXQSZ7f5tsUQMjI9yozkLl8Q3pWcyqZKXtNj63JZiGIW9KUIj1MVWl8paiyEeO3tJKYUot\npJIXjlAxlCNOMWXZXacvQRY0kDx2z/FgMouIw7ERY8B0PKhB8hGad+t04txTiolEYu5z8CEHs/fe\ne3PzTTfjYz9QT4j1jYwLCdH9GfyTA2GqGKfyeDIMIGG+Yl2zOGJ1GlI8qRewJsTBJAfxFaK9GFmo\n/BByk5FpL6FFUXC9i3kX5ygrS0NjiYGR0CS7r1JAc0OY5mFRzcA0giL2oR7P5MGtKgQljJGeglcf\nSkl8rzzDbnTk84I1GJZRism62Wgoh/DrbYjH5Xlohi4SY4EhVmmtJ8uCtWiqoTgh4EsfykpGfEHW\nOhqNfIo+ci0NZZF9LtIsd5i6LZyvMKWfctPiNpRkOzVDtnFme9OQvKVqKqYUdJ3ALtP8XraCuT1k\nOJFIzElEhPP/43z23GvP8IJqbNlWotpCaaF0gqtRIK+L66a7AEaXJgp5pgPevpGoQlnRbDTI8Rhf\nop1JmJzszmHtx6uCDw0BxHooHVI6pHJTi9pVKax2O7+I1gOD6T2sB2ujteWgnMSUbcTGOYsdSzlZ\n4q0beZXWWiFGy1Wsw62fxK2dQCcr8okO4quYburDfEOvGKdou0In2uhku9ccnRAjtL5Co3t0S3RN\n3cC7LoLpbyjgWx673qKtSSgnEWz3PeN6w4f7kcY4xnsasTxj8E0oFzrcfS40Gd9Ky3CYpBQTicSs\nsHzvvTj7Q+9hwXiBUUtOB6U/a8MDHeYvKCiMoIapCqhGCa7KTolMTMDE5CaulnH8nPeMFw3EWaQM\nCkpGzQ3s24S4UPTf1bkKVeUH5MrtUIxxhHVVfzbspmJ0UGnWG6yspWxXoWNP/9XaW7xaPGHUFH1l\nKlqWQYFHa9lbxbmgpEX7tuEV34nDmr1HKovfUGI708yw3BQSM12lAqkIbung+xSx5HknuJytYtdb\nXOVAQpOBUeOe1HtMY37/C4irMK5EXAWiaAb+bt9NKOodm63TkkkpJhKJWeOIIx+BOheUFDrFFVYU\nBUcf+yhOe/2rWbjTIvJms77WDxHHSREv/Aq+3cFJLJ/oDteN8b9orbQ2bsR1KroaatT1tI4XVmVX\nIQ5j+5p7m00YtFNWXVWhU8s013Fvg9VVbqjw3od8Ig1Kp5+wl7VinUbGuC9TZOhU5LZDpg6jHspq\nmpsPxdohBaQO0Q6ZdsL3qDHWazyYijzvkGV9xzeux01aTGah0AGrsosI409+IZgM1GN81fuNoMGa\nbnTwYx1Y5ZGS7nfLRFKKiURijrJgwQJe93dvpNkcnUleVRUbNmzgFW94Pd++6se85q1v5olPO4l5\n8+ZFP11UclW4WJoYC+xaTVWJuHbIrFEbFIp2QtNuVcpOBye9MocpfUdVyVxF7mywdKYzVB/IdVj9\ngPtyWlxolG3bFp95BDdysXpo0iaM3dEJobWVKnV8VrFV2eslGxaKi9qgnFURLckoMXhEFdexVLbC\na20lKortPrzauD5BXbDWyRSyQcWowNjRxzP/5DeSH/I4jLcD36mgNIxi4qxFV5Ww2sNKD3eG17aG\npBQTicSsctqZZ/CuD76fLJt6OcrynIc89FAAFi5ezF+ccRpnf+oTfO+an/PwRxzFmGmwoDGfzGSx\nsdngBbFXImARrRB68cLuINshjeaiO1SjhWii9TNN5URYf+0PVcXX8c2IdSPcvqrkWqH44Aadpul4\nJvWUesVbD5PV9Ip59Mu/Z5kQ7xzeF6eKrzoxI8mH0o+sCgk73uJsB6Nu0N0LqFOceFSUTDpDW4xK\nsj6esW2qjnu06VHj0czDwowlL/8XRISFp39yitR5jDH216WyT4l5aIl57ATZIybYGlL2aSKRmHVO\nPuXZfPcbX+Mnl/+YTqc30rHRaPBXL3vZlOXnLZjPp777TW694Tf87pZbWXHLzZz3zndu0TbDRA1B\n8LEkPpZLeLAdh8mEoi8pBHrKbriAPsskxMg0NKrO6uHBBCvSOshMKJkQgkI0fbURjgo8ZH1dBIRe\n2UlPkf/evaLukz6KUVZQNpzI0t2WIr4kK7IpMdFNFdirZBSL5sP66UZzhoYDrg0yP9ZGNsLEDGmM\ns/D4MzDNcQBkbAFm4TJ0w+rup2urth8/6TFLLbKSzfddT0OyFBOJxHbBRz71KZ7+7GfRaDYxxnDw\nIYdw/pcuYL8DDpj2MwcceghPOOmpPO/MlzHeNzpOh/4dhTFCoaF5uO9G5WpXnkddq1e3GHH4kELS\ndS16oEStA6tBvaoHKkIUzwEW1QrnKqztUNAZUIjddWuImxnxZGLJzOAydQ3ndHkkoRglKLl6PmHv\nKCgGHxJUuv95CvHTKjjpsyKHLV2drtsBwu6nvIr5+z90+kThTEOo0IKd9GEgscmQsQUsPPHlLD75\nTQPLN054BTTGp1lb3PfFFu6Pu7p13tNkKSYSie2D8XnzePcHP8g73v9+qrJkbHzTF8J+8qLgvIt/\nyBuecwr33nlnmHEIFGPjZIRJG1W0QJtj4+y6fC823HUnncngalOUMORJyfFkcVCtU4+RweJ6HxWj\nwXUVlVOL+DxYit3xVXWBQh+1ATmNxnDeUeTDlmj8qARllO+0jL0OO4yVV1+Br0ISi4hisuhq9Vld\noomRoNiM8RjRbhi2FiVM28ri+qVb1pL1a5bSQpEPlln4UNM5RcaiwS4nPIf1DZi89vKR+xhqFOP+\nmXF2fsmHWfCoE5BiPHY2GqR5/Jno2rsoL/ssZAW+2oiR+uajXmdMet0GJKWYSCS2K7IsI9sChViz\n94EH8qWf/4wVt9xKu92iKAqctex98MFcfOGX+N7nPoetKo5/7nM58QWn8qannsBdt92KLXslCAID\nMTZfJ9fEBJQuqkMWlgbrTwTJMnY74CDuv/232L6G58XYOM9+3wf4/j/+DZ2NG0bug9b/G54erxpr\nExVxjmec/xUmVt3NVef8E7d878v4stP9vBMXRmHFV7JiyN0YFXO9SyKub6+DqWWGlXJlBz7bXL4/\n+77oFdx+zt8F09UYxGTsf9YHGFt+II1TXs/qCz+MVn3lHbV16UG9Uux1ELu98sOMH/rokceiK64x\njJ/yLsb+7G/wq2+HBUuoPnoKeveNvXXaTa5ii0hKMZFI7FAsP3Cqu/XEF5zKiS84deC193zru3z+\nn97F5d/4GqphElFrzeop8SqXC4858elce/EPKFstwJMPddXJswL1nsbYGIc98Xj+8qOf5JKPfZgr\nPvdvlJOTHHLck/izf3g7S/beh4m77+Syc/6ZqtXq24pQjBUs3GVXjv2rv+aeX/yM33zvm+EtVTLf\nK6YfX7IUgPm77s7j/vY9rLnpV6z97U3YyQny8flkjQa7H/xw7rni0vrjYQtD+yWWvkbh0eoyGc0l\nuzF/0VLaK38Xmnp3NvY+pGDG5rHf6W9i95Oey64n/QXrrr4UtRWLjj6OfMGisJpGg4M+dRW3ve4E\n7H13dz9s1KMVeNNkp5Ne9nsV4oC88xaT7fPwIO38fXHrfxVSbZ3i7wOzk5lyL/FAkGmLYbdTjjnm\nGL366qtnW4xEYlpE5GeqesxsyzFT7Kjn4OVfvZDz3vAaOq3J7mt5o8FRTzmR15//OQCu/NqX+dwb\nX40tS7xzNMbnsXT53jz++S+inJzg0D95Avsf/ajRzbkj3nsu++g/8+NPfhRXlTTmzedJb3gLx77w\ntIHlvvXyv+SWi7+H60s8ysfn8ZR3/wsP+/PndV9T71nxvxez6lc/Z8HuyznghGdSzFvAtR8/m+vO\n/zCuapNlodWcGMP4bnsxtmABrdtvwbUmQzu56Evd/fEncuTbzqGxeOewblVuP/+f+d3nPoIvS7Lx\neez/sr9j+Smnb9YxdRvWcMuLDse3Ng68bsYXcODnryebv3iz1jNMdc5L8ZdfSO2eNntZst0awVJX\nMG9a94DPwaQUE4ltTFKKcxNV5ctn/xPf+tePUBQNqqrkoY95HK/71GcYX7Cwu9wd1/2S//7s+ay7\n5x4eccJTefTJz6HxANy9zlo6G9YztmgxJpvaCr3cuIFvveLF3HHl/5A1mriy5JjTX8njzvp7fFXx\nux/9JxP3rGS3ox7NLkc8cvQ+eU+5YR3FvAWod7hOm2LhYtQ5Vv7gm6z4/tcoFi5m+VOfxbKjHks2\nNno/vLW4jevJFy5GRsi6KSav/wl3vvOF+HZQjGZ8Acv//vOMP+zYLVrPgDzXX071vmdBZxIKJT8i\ntszzOWDI3rwhKcVEYnshKcW5zeT6day48Tcs2X0Pli3fe7bFYf3KFWy8ayVLDnoIY4t3Yu2tN/H1\nk5+AbU3irUVEWP744znxvAsx+fYZEVPv6dxyLYjQPOCIkQk1W4r9yj/hvvlBZKeKbHknzISMZH/T\necDnYCrJSCQSiT7mLVrMQ445drtQiACL9lzOnkcfy9jinQD4/umn0Fq9imrjBly7hW1NsuKyH3Ld\nZz85y5JOjxjD2MFHMnbQI7aJQgTIn/13ND58LdkJr4Ri283WnVGlKCInishvRORmEfnbEe83ReRL\n8f2fiMh+MylPIvGHRjoHdyw2rPgda2+9aUoVv21Nct3nz5slqWYPWbIn5qS3I+NL2Oqq/ciMKUUR\nyYCPAU8FHgY8X0QeNrTYacAaVT0I+BfgfTMlTyLxh0Y6B3c8vK2mtbRc+QCmW+wAiMmRU/4TFu0D\nxQJoLNqq9c2kpXgscLOq3qqqJXAB8MyhZZ4JfCY+/wrwZNlU2lYikdgS0jm4g7Fo3wMYW7JsyutZ\nc4yH/PnzZ0Gi7QNZeihy+g3I836AnPzVrVrXTEZl9wLu6Pt7BTBclNJdRlWtiKwDlgKr+xcSkTOA\nM+KfHRH51YxIvGUsY0jOWSLJMcj2IMchs7z9mnQOPjhsB3JMwFlvXcZZb03HI/CAz8HtM1VpCFU9\nFzgXQESu3h4y+5IcSY5NyTCb258J0jmY5JhLcmzNOTiT7tM7gf70reXxtZHLiEgOLAbum0GZEok/\nJNI5mEhsITOpFK8CDhaR/UWkATwPuGhomYuAv4zPnw38SOda4WQisf2SzsFEYguZMfdpjE+8Evg+\nYerJp1X1OhF5B3C1ql4EnA98TkRuJgz+eN70a+xy7kzJvIUkOQZJcvTYHmRI5+CDR5JjkO1Bjgcs\nw5zraJNIJBKJxEyROtokEolEIhFJSjGRSCQSich2qxS3l/ZUmyHH60XkehG5VkQuFpF9Z0OOvuWe\nJSIqIts8JXpzZBCRU+LxuE5EvrCtZdgcOURkHxG5RESuid/L02ZIjk+LyKrpavYk8JEo57UictRM\nyDFTpHNwy+ToWy6dg3P5HFTV7e5BSAq4BTgAaAD/BzxsaJmXA5+Iz58HfGmW5HgiMC8+P3O25IjL\nLQQuA64EjpmFY3EwcA2wc/x711n6Ts4FzozPHwb8doZ+p48HjgJ+Nc37TwO+R2jK+BjgJzMhxwzt\nWzoHt1COuFw6B3Vun4Pbq6W4vbSn+r1yqOolqlpPJb2SUAu2rdmc4wHwTkLvyvYsyXA68DFVXQOg\nqqtmSQ4F6gaIi4GVMyAHqnoZIWNzOp4JfFYDVwI7icgeMyHLDJDOwS2UI5LOwcCcPQe3V6U4qj3V\nXtMto6oWqNtTPdhy9HMa4a5kW/N75Yhugb1V9TszsP3NkgF4CPAQEfmxiFwpIifOkhxvA04VkRXA\nd4FXzYAcm8OW/n62J9I5uIVypHNwgLcxR8/BOdHmbS4gIqcCxwDHzcK2DfBB4MUP9raHyAnumycQ\n7tYvE5EjVHXtgyzH84F/V9UPiMgfEerwDldV/yDLkXgQSecgkM7BrWZ7tRS3l/ZUmyMHInI88Bbg\nGara2cYybI4cC4HDgf8Wkd8SfOcXbeNA/+YcixXARapaqeptwI2EE3RbsjlynAZcCKCqVwBjhCbF\nDzab9fvZTknn4JbJkc7BQebuOTgTwc9tEDzNgVuB/ekFcg8bWuYVDAb5L5wlOR5JCDofPJvHY2j5\n/2bbB/k351icCHwmPl9GcFssnQU5vge8OD5/KCGeITP03ezH9EH+kxgM8v90pn4js/GbS+dgOgd3\nxHNwRn5A22hHn0a4y7kFeEt87R2EO0EIdx5fBm4GfgocMEty/BC4B/hFfFw0G3IMLbvNT8jNPBZC\ncCFdD/wSeN4sfScPA34cT9ZfACfMkBxfBO4CKsId+mnAy4CX9R2Pj0U5fzkT38lMPtI5uGVyDC2b\nzsE5eg6mNm+JRCKRSES215hiIpFIJBIPOkkpJhKJRCIRSUoxkUgkEolIUoqJRCKRSESSUkwkEolE\nIpKU4g6OiDgR+YWI/EpEviUiO23h598mIm+YKfkSiR2ddA7OLZJS3PFpqeqRqno4oXHuK2ZboETi\nD4x0Ds4hklL8w+IK+prhisjfiMhVcc7Y2/tef4uI3CgilwOHzIagicQOSjoHt3NSQ/A/EEQkA54M\nnB//PoHQE/FYQteHi0Tk8cAEoWXXkYTfx8+Bn82GzInEjkQ6B+cGSSnu+IyLyC8Id6e/Bn4QXz8h\nPq6Jfy8gnKALga9rnE8nIhc9uOImEjsc6RycQyT36Y5PS1WPBPYl3I3W8QwB3hNjHUeq6kGqev6s\nSZlI7Likc3AOkZTiHwjxrvPVwFlxzM/3gZeIyAIAEdlLRHYFLgP+n4iMi8hC4OmzJnQisQORzsG5\nQXKf/gGhqteIyLXA81X1cyLyUOAKEQHYCJyqqj8XkS8RutuvAq6aPYkTiR2LdA5u/6QpGYlEIpFI\nRJL7NJFIJBKJSFKKiUQikUhEklJMJBKJRCKSlGIikUgkEpGkFBOJRCKRiCSlmEgkEolEJCnFRCKR\nSCQi/x+ffxVo88QyfAAAAABJRU5ErkJggg==\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(2, figsize=(6.4, 3))\n", + "\n", + "pl.subplot(1, 2, 1)\n", + "pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)\n", + "pl.axis([0, 1, 0, 1])\n", + "pl.xlabel('Red')\n", + "pl.ylabel('Blue')\n", + "pl.title('Image 1')\n", + "\n", + "pl.subplot(1, 2, 2)\n", + "pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)\n", + "pl.axis([0, 1, 0, 1])\n", + "pl.xlabel('Red')\n", + "pl.ylabel('Blue')\n", + "pl.title('Image 2')\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instantiate the different transport algorithms and fit them\n", + "-----------------------------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# EMDTransport\n", + "ot_emd = ot.da.EMDTransport()\n", + "ot_emd.fit(Xs=Xs, Xt=Xt)\n", + "\n", + "# SinkhornTransport\n", + "ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\n", + "ot_sinkhorn.fit(Xs=Xs, Xt=Xt)\n", + "\n", + "# prediction between images (using out of sample prediction as in [6])\n", + "transp_Xs_emd = ot_emd.transform(Xs=X1)\n", + "transp_Xt_emd = ot_emd.inverse_transform(Xt=X2)\n", + "\n", + "transp_Xs_sinkhorn = ot_emd.transform(Xs=X1)\n", + "transp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2)\n", + "\n", + "I1t = minmax(mat2im(transp_Xs_emd, I1.shape))\n", + "I2t = minmax(mat2im(transp_Xt_emd, I2.shape))\n", + "\n", + "I1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))\n", + "I2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot new images\n", + "---------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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qnzjnIq/1I2p6Hg8LrnQj2q5mhaeT3K6lOF7sx4gIJ7FhVe7rRJWbGvikHDO2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\nmAs7GYPLt0Xyl0n9QKsOOaMpcDshO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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(3, figsize=(8, 4))\n", + "\n", + "pl.subplot(2, 3, 1)\n", + "pl.imshow(I1)\n", + "pl.axis('off')\n", + "pl.title('Image 1')\n", + "\n", + "pl.subplot(2, 3, 2)\n", + "pl.imshow(I1t)\n", + "pl.axis('off')\n", + "pl.title('Image 1 Adapt')\n", + "\n", + "pl.subplot(2, 3, 3)\n", + "pl.imshow(I1te)\n", + "pl.axis('off')\n", + "pl.title('Image 1 Adapt (reg)')\n", + "\n", + "pl.subplot(2, 3, 4)\n", + "pl.imshow(I2)\n", + "pl.axis('off')\n", + "pl.title('Image 2')\n", + "\n", + "pl.subplot(2, 3, 5)\n", + "pl.imshow(I2t)\n", + "pl.axis('off')\n", + "pl.title('Image 2 Adapt')\n", + "\n", + "pl.subplot(2, 3, 6)\n", + "pl.imshow(I2te)\n", + "pl.axis('off')\n", + "pl.title('Image 2 Adapt (reg)')\n", + "pl.tight_layout()\n", + "\n", + "pl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_otda_d2.ipynb b/notebooks/plot_otda_d2.ipynb new file mode 100644 index 0000000..038434a --- /dev/null +++ b/notebooks/plot_otda_d2.ipynb @@ -0,0 +1,320 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# OT for domain adaptation on empirical distributions\n", + "\n", + "\n", + "This example introduces a domain adaptation in a 2D setting. It explicits\n", + "the problem of domain adaptation and introduces some optimal transport\n", + "approaches to solve it.\n", + "\n", + "Quantities such as optimal couplings, greater coupling coefficients and\n", + "transported samples are represented in order to give a visual understanding\n", + "of what the transport methods are doing.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Authors: Remi Flamary \n", + "# Stanislas Chambon \n", + "#\n", + "# License: MIT License\n", + "\n", + "import matplotlib.pylab as pl\n", + "import ot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "generate data\n", + "-------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n_samples_source = 150\n", + "n_samples_target = 150\n", + "\n", + "Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source)\n", + "Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target)\n", + "\n", + "# Cost matrix\n", + "M = ot.dist(Xs, Xt, metric='sqeuclidean')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instantiate the different transport algorithms and fit them\n", + "-----------------------------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# EMD Transport\n", + "ot_emd = ot.da.EMDTransport()\n", + "ot_emd.fit(Xs=Xs, Xt=Xt)\n", + "\n", + "# Sinkhorn Transport\n", + "ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\n", + "ot_sinkhorn.fit(Xs=Xs, Xt=Xt)\n", + "\n", + "# Sinkhorn Transport with Group lasso regularization\n", + "ot_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)\n", + "ot_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt)\n", + "\n", + "# transport source samples onto target samples\n", + "transp_Xs_emd = ot_emd.transform(Xs=Xs)\n", + "transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)\n", + "transp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig 1 : plots source and target samples + matrix of pairwise distance\n", + "---------------------------------------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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gT4qzXkLy10DcG42+S5ImyEqpMhVWjV3rir22xC8IVkiqCcl1u3j2u5W8s2sH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\nxjrvokYGGT+CfUtQXHT2c+2vD3ut7BT2dYilLge+SM/ZunF9YIwjQcEdC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Avs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\nB/u4Lir5zTcvAAAAAElFTkSuQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(1, figsize=(10, 10))\n", + "pl.subplot(2, 2, 1)\n", + "pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.legend(loc=0)\n", + "pl.title('Source samples')\n", + "\n", + "pl.subplot(2, 2, 2)\n", + "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.legend(loc=0)\n", + "pl.title('Target samples')\n", + "\n", + "pl.subplot(2, 2, 3)\n", + "pl.imshow(M, interpolation='nearest')\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.title('Matrix of pairwise distances')\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig 2 : plots optimal couplings for the different methods\n", + "---------------------------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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eNb6/H9bCZr+dtbVsiTSu1SQ979xK0o9RWQk4Niqflq1t1Wles64Ods+NSPUM\nz2sWlxTEMCVBZYVsNe3i6VXFGRjQZlTCxOrVnZ2p+vpuTa/miqXywXc8vVZUAI6N+X/nJsmF9OqH\nd3gdAI0gpIP1ykw1BWkgnzw0dR1xONSBYWaAoWGkL3X4i+kLl5C+IDOyjaoqadQubI4cpsY9iluD\nECMjsN94F8ToKIwl7bBPnIbZ2CjHVG6O6fNutreapOedW2l6IEZDzQUcO/AQCyeIf9x+p2x+oNxw\n45KCGKYkGBpGuqPTX9T06oZOhA3mMJ5e0w9tgRhLw1i6CPbJMzI/ALpeba+J0Hh6DTfvcewg5lg4\n0hBPpSDu2QjaeofmQWa9MlNNQRrIU0mSt/fZywfYeGaYKYIsM0iogSzP5JVocgYHpVEbSuwB9DrH\nzu1hWR7ue6/Jm+rpczBXLfez4Y2Na7VzAIDV1hpJplO9YcJWwj3U6wFg990M4h9fPSzru6o36Jj6\nxgxTCmTUqxs64RnMSXh6tV7YBzg27FNnYa5aLvMDkINelyzS4ozVa1Kvx+67CTEyAjEyAnrlIMTe\nI3p3Pa71zUwxJW8gMwzDMAzDMEwukBin000+qKF6cTc9nO/LYBifZy8fgLnw9D4hxNZ8X0shwppl\nEjFMwLHR/9M7MO/r+/1kTHP9athHT/j/+rtXVGjVTozq6sj0udnYqNXZ9abh1eNeqd2FWzcu8TRA\nDKzX2UucnpLo/J/3ovWTL0fWmyuXafklSZz/3/dgyf/KrunKbvE8+sWNgtIre5CZomUmQ2Q4MZOZ\nLRgb1iRuUzubZY2bjFn/8mVc+eqyYP2lKwAQaSoTLgUYdzMPtyh2hoejJQT7CjvZm2HyATU3Zr3v\ncELBBBpLx64P0/rS6Pg7FTBsIE8hHNc8s7DRWviomeVWWyto251yfaZSaoYJs3ZekO1OpGXce5gr\nl0XWxWE21Mc2KIFhwn7wLtgP3iW3GyasJYu0xiGzEefQ64nb/M5mEyB9/iKaf+x4MFZ//4THyoZC\nnB0tJqzWFtgP3jX+jkQwa2q06hRx5eHC8chJmHV1sU1LYJgYfXQrRh/dGuh1nERCJko2nl+P5T8d\nb89kqm6iUvbcvqzPVYiUrIGcD0P10ZZNbLQxsxIqs/QkGcP028N6N85052UItw10bAMCt06q2VAP\nYTug9SvljVAImXGvdOoyVyxN/qF3zw24N/meG7p30R2D7loLa+chWDtl3WUyCE5Pb5D4w0l6TImS\npFf/NYBq1PCOAAAgAElEQVT0lS6YL+1PHsN92DXnz4cQItArXAMqpFfn4PH4gZRzm00LYPf1acm7\nvl63rEPqhUNIvRDoVdzsj+zHMFNFyRrIbKgWJ+yBL07EWFqva+zY/rJIp92bZfBzYzbURwfxGoR0\nd8MZGIBz8Lg0bImkV9ftzAWM0+VOObfX9EDzWLtjiL1H/CoWzvCw/Fedzle7+M0SvJJ6uWC/MQsv\nYwK+11FtIgFg9LFtsftnCv9gsieTXr1/jTkViQ09APgPu2G9Ulm5DMWZgF7trmuAEFq7aV+vew77\nXTKdkREIR8Duu6ntZ65dme1HMGu59c67x99pHG6+Z0dW+6m18YuRkjOQOcyhuOEHm+InXMYJOzbI\nm5xyQ/a65AHA4E/cLTtvJXmAhNDLOcWd0zW4nfv0748X4qF6rGnLepi188Z9HwBiS9OVMuHY3mww\nv/fahM/nl9VzH448yr+zJ3b/TOEfzMQIhymIezYCSJjlAXDrXTtw6107EvUqxkbHDcUxa2rkOUJ6\n9dar7aaNTetg1tWFTqL/nnjYx09lPC8DzH1y96THmPelXVntp9bGL0a4isUM8OzlA2z4lQDPiae4\nikUCnmZH/tM2pP4t3rgpJpbvqcCZbcPj78gULIWYFV8o+Hp98zakvl38el25J4VT20byfRnMJChE\nvZacB7kQ8Yxj9mwzpU6ScSzesElL2POqIXitajVCnilzxdJIe9pcGe94o6LC914B8I3j6794z6TO\nOxvImHAZ2Tm+mUM4MTIuyQtI+L4wEyaTcZzt9HhYW6OPbo1PssuB8Y436+o0z7dnHF/6vXsndd7Z\nQE56TSBrHRZ585ZZaSDny1BlLzIz27BaWwAA9IMDoFQ5rKWL5Yb6WoDIr4mrIYT2w2qfPgd7xx2R\n3czVK7K6BiorBzbFx65aSxfDWroYzugY7IEBGBUV2s15/t9nV8NzNpM0FR+/c3RaHEAkhCYpSz72\n+8JMC2J0NKuYb5FOa3otf3Yvhh6K6tUL3RgPSqUwtj3+vOa6VTDXrYJ9sx/pix2R6jTtfxit2cvo\n5KTXBLLWYYLei4VZaSAzDMMwDMMwTBJFbyBPxBvMnlyGmRm8KhKArKGbPncB5z92j0zCicl/8Kdr\nQ54HY+cBrWwUANgnTsOsqUmsvND7XhkeIcZGIfYeia2bnD53AelzF+T5hJANJ4a4wQTD2F3X4Bx6\nHec/lhxmlKTXin99NaJXeuUgjOrq8fU6MgLzxdeiyb4A7GMnYR87qemVYaaLojeQp9PY5ZhhhskN\ns3ZetLlAKA5tye/rYQtjj2zF2CMy99GvahCHUjbKw+7vh93d7TcSufDRIAax7gn9PF7d5MnGMzNM\nqWDW1UVK7I2n15G3bMPIW2QZvlz16gwMwO7uhtm0AABw7hOB8R3Rq1sajvXK5IuiN5CnE/Y0lzb8\nADT12H03Yb4YKvvl2BmL+Jd9dy/Kvrt3UudNd3QCABZ/ePwYxIw39RCzrQ7yRIyR22/bnvW+ajc1\ns6bGT/bxmk74+8V1PgQw+I7J13BlAuze3kiJvfHiRlPP7EHqmQyVL7Jo2GF3XQMALP2d8WP8c9Er\n18ken2u/OvlExt6fyy55OVzGr9hgA3kaYMOrOOAHoBnCMEHl5TDuCG5eniGmGUJex6ytd8j2te5U\nrLl6xdRmQ7tjUVm5/+c8sBnO/Zsj55ltdZBzMUY85vy/V7PeV+2mZvf3+8k+XtMJf7+EqfOqpyZf\nw5UZh5AOs93P1+u6VVPr9fX0mkrJJNqKCthvvEs2qAnpletkj8+CT08+kbHun7JLXjZ2FrctxAZy\nApMxctnwYmY7ZFlafKIYGYFzJLh5eYaYZggpHe7IKvObVtgnTutere13xt64z/6x69VwYx99I1wt\nGbX9zqDDn9uVS4yNwnhpP4zv7weE4+9a7F2gGCZbNL0qOszUSS9Rr0dP6A9auepVLUOm6nVkROYI\nDA/D/N5rkQY1rFdmqmEDOYEkI5e9wwwzPl4L5/BUuVf/GEC0O5a6X1Nyy2OypRGr1lUGgFV/dtY9\nuYx99G7Saskoc1AvJ0aplFbT02yo90vROd3XE6+BYUoJT69hkupRhzFbxwlFIsOPO/ZY9edu++mQ\nXoceWu/vY4zo12RUVmoPvJbyO+Eo3TkZZiooCgO5kNpHs3eYYaJQmaU12jA2roWxcW1kqlxtQWv3\n9uqDGKbvRRL9A3LaVmkJbTU3wWpugnHhKqzWFjgDch+vyYTdI8czVy7TKlZYO90YSyJQT19wzakU\nxMiIVtPTuTkgq1oAEGuX5/w5MEwxQGWW9oBpbFgTG7+bVI9aHqTote9mol7Ns5dhtS6UcceKp9i+\nLg3asF7nfNv1DBPB6AqMXkql4AwNaVVm7BvBb4hYHa16wTCToSjSQydrlHKrZ4aZXkQ6Dbu/31+O\njQU0TJgN9f5UbBgyCCKdBlkW0uuXwjp7xU/mAYD01S45TEUFaM4ckGVJz5fbZML718t+9zCbFshy\nc0LA6bsZrG9dCDjSG213XJbv4e51QdzckVM5fgoMUxyIdFo+YLo4h09EdzJMWAubtFKNceOQZcFe\nsxjmmSuatj29UioFM5UClZXLkCbXU+zr1a0u46Hq1e4NHmjN5gUQKfkw7Jy9IPW6Yx2Ml/bLHeLe\nA8NMgqLwIE+WfBnHheL1ZphpJxyqGFPiyWpvgfA8ynHJPyR/jsy2FpSduwrRWC9XW5aWjOMMD8Pp\nuwnhJMRHhs5tXwtCJTSPtuNAWCaEZcJY0g6zoR5lXYGRH+7uxjAlQzZ6XdQKMXQ7cQgypIbN5iaY\n564C82vlejWeGW7scN9NCDu7rmrqLJPWsc0RQJkFlFkwF7XBrKtD2RVFrxNIMGWYTMwKAxnIj7HK\nXmtmtkCmqRu9SpMAf0q18wqcW7fk9rgmIevd1tG2A2dwCGJOme8ljjQOaahLLkelnNtsbIRIj0W3\nA7Ab58G50AnnQidwrQfOrUGguyeyH8OUGpn06j2M2h2XYff1xRztHrJOafV+exjO3IpgVidkrBqN\nDdnptaEeYnQ0uh2A3VwHceaC/Lt+Q4Za9PRG9mOYqWLWGMgMwzAMwzAMkw2zxkDOtzeXwy2YUkY4\nNqi83F82Vy2HuUomufkxh+k0IERiEwjnwDFQWTnSlzrg3LoVqY1rNi2Qf42NsDtkXCRZQbKRuHcj\nAMBqbZF/7W0yJtL1VlNZOazWlqA81Z7Dfpk3ryavrcQo09Y7Jv25MEwhIhwbZJX5y6pe/bJq4+n1\n0OtSrx2dsG8NJuu1aYGuV7eMW0Svba2wlUoUcXr1yrz5elX3Z70yU8ysMZBzYTqM2Xwb6AwzrYgg\nXvDKB++FfeK0rF8cgxoHrGa9A0rcr3dTVKZq7a5r8q+7WzO6nYEBnPy7baCXDwKQXfXSHZ1IX+qI\njJ3uvKyVdcv4lkI3fIYpGUSgtY7fDek1FKqQ1LQFUPSqGtUuvl67rul6HRzEyc9ujerV7Ybpa9/V\na5KBHrkW1iszxbCBHAPXQGaY3CArKPm08E/1Tk3GpnWJx6ke25G3bJP7u3VOzdp5etMAdczQTXPV\nB5TWt0pM4/Bbo22QzeYFsqKGapyrMZgMU+Koem37uK5Xc+3KrMbwWox7GjUb6nW9KprSmvUAWPW+\nvbH7Df9IVK9G7Ty/Ak6w0mS9MtMOG8g5MBEvMBvVzGxApO3YLHKzoR7OgWPassfYm7Zo+6aekUau\nMzQEs6YGdt9NOIODkTGt9jbZTSvUaMSvw6xk5Fd861XQlqDxgNm0AOkLlwDHht13M8i4d48xqqpk\nC1tgattbM0wBkaRXALCPnfRfm+tWJY7htRh3BgdhNTfB7rmh69XT1B1rpKZDY3mtqTW9/uuruPWu\nHcE+K5bKcnGODbvnBoyqKmmEOzYgRDAGMLXtrfMFG/0FBRvIEyRbw5dDK5hZQ8yPux3qbmX33ABt\nuxMAUPbcvsj+1kLZkUutqewPX1buxygDstGI88BmOA9s1o/xWte6DUTEvqMAgOu/eI9WVxmIdhBz\nBgeDFrZJWfcMUwpkYYzZx07Kds8JeHr1ah5rw7t69VrM28dO6nr1aiaH9Dr3a7sAuHoN1TR3Bgc1\nI1ytu1wSZd4ytfZmZhw2kCcIG77FA3vxZ4gMP+5GZWVgsO45rHl+VNJXrkbWkdpkIFSb2Hhpf9Ao\nQL0OIbR9jYoKzP/7V5KvnSjWY+wlEs0Wso3P1shgQI2L+5mbq1doq9MPb4nbO7IfMwnGMcb82Z5X\nk2N74/Rqzm+Q5domoVertSWjXqmsPPa7euuddycew0jSD8VrKxec+7Kzf8LtxYsNNpCnCDbCChd+\nmJl+yDSS66oSBe1hvRqrMd30vHazVlsrzPkNwPY7ZV3VkZHIjdZaujjDxSixj9XVgGHGJxrt2ACj\nshJGZSXMeTWyXbb6g07kJxLNFrTGDNmSwYAaF9dLH07otJ6Pzi7E7cdMjPH0CgBO/62MYUbWsiXy\n39YWX68wTNjXeyIzR1nrtaoKMMz47n07NsCsqYFZUwOjao4c1/Vge+PMfXJ38nkYAID1Qry2csHv\nNjoO4Rm7YoMN5CkiWyOMDWmmFBG2o3uk1M5cSlZ6prAFr+VsuqNTdtN69XDitGn63IUMFxOc2xkY\nSD7nrkNwhobgDA3B7rspy0apP+juGPN2NiSfa4owqqsjXtMJeXMZJgumQq/ps+flv52Xfb0m7Z+1\nXgcHM+rV7u+Xf65eNQ+2O0b19+cnn2uKMKqrIzkUrNfSgw3kGSYXb+azlw+wQc0weebmfT2x672k\nIDVDf+TN2zDy5m05n8MZGNC8ps59m3DpgxmmQmM8e2oCpFFd7V9fSSQvMUyWDNx/PXa9F+Kl6fUt\n2/zqObngDAxoORTOfZvQ8Zs56nV+8OBt1tQEei0rj+zL5Ac2kBmGYRiGYRhGgQ3kAubRlk0cP+vC\nnnSm0PDCP/z4agCpb+9B6tt7kg7JGmPngUh9Wo2YaWg17tMZGNA7GGY6V6hGLaB7oyfK1f96r1+x\nIKme9UTwxrKWLErcx7l/85SdjykNvDwGTa/P7PHLS04GY+cBtP5Rjnq9HsxM2f39gV5D+RbZcP0X\n78n5mDBDPx4kON78mR0Z9px6sq29PdOwgcwUBdP1oMCGNzPbUQ0Gj3CSVRzh0A1r6WItGavp1SFZ\nscAwY+tZT5S+t7lVMzKUKbu5PLvuawxTCmSs0JMl/YuCMJB5X9w16fFy4fz/Lsz4bTaQ8wDHFhcO\n7KFnskGt35otSbGEZFlakxO/wYlr8GmGZ1zpuXs2wly70u8mqDVgMEzAMGX1Dnc8v2OgYcJa2Jx1\n697xCHum0+cuaMlY9AP3N26K60nXfHmXf74k6p6YvMHAFC/O/ZtznkVI1GtZuTajkqtesf1OmKtX\n+El85vrV+v6eXr2Sh6pe21qnTK/j0fwXGTzg00z7O47k7dyZYAM5D5RC6AQb+MxswviPAzD+4wDM\npgWw2lqzO2ZedcL6GlBtjV/eyvfgCgGzrg7GssALS2XKzde7Ib9yEPbxU37pOnH2YhDC4NiAY8vq\nHW5Wv9fO21y5FDAMGE1BDWqrtSWr98IwxYSx8wCMnQdgzm/I+jtu1MyNXz+vGjQvg15XLvX3JVMx\nkL0ZjlcPwz5x2i+hKM5ciNerV/LQ0+tqWfbSaA5KT2pl7Zhphw3kAqNYDM9iN/AZJifcUlR21zWk\nOzoBIt+zM/pYkAU/9shWjD2yFUAQYxgORbB7bkjvp9cgQfHE2r29sE+ewdXfvBdmXZ1elzihsYMz\nPCzb/ba36RtC3iz7xGmkOy/LVtsusfVmGabY8fR6vUd+xw3Tj7VXq8yMvWmLX67NCysKe5Lt6z2y\npF2SXo+fQtev3wuzdp4ePzyeXhe36xvCej1+CumOTqTPX/TXxTVmYaYPEgXY2rCG6sXd9HC+L6Oo\nefbyATZip5jnxFP7hBBb830dhUipa9a7manG5VQy9shW9Kwrz2qak7beAePSNeD2MOz+ftlMxb1p\ne0aAc/t2cIMmin9d4uwWz6Nf3Bi/n/IspOT16sbCZ6y/PAnSD21Bz/oUmv4qi7CE7XfCungNYuh2\nVK/uQ7YzOhaEIrFeCwb2IJco02kcF4uXmylsjOr4EIS8E5P8lb5wadqMYwAo++7erGMAxd4jsLuu\nwe7vl8uKR8trfOLdVI2qKkAIfOjsa+7Bs+Nmy0w9MxULmzNxeg3Fw2tVVDIkd8qdk7sHelgv7MvO\nOAaAVw8jfbUrXq/DwzJUSo3TFwIfP/eq/5rJH2wgMzmjGt9sLDMTxRkYyPclxBNzUzIqKoK/LEuW\nmauWhwYx/bG0bcoNmSwLVnOTv2w1N+ltgN191BAPNSGPLEsL6fCqR/zBsrtkS+D1q7WkPrVZAcNk\nIrZdeyGQhV61KioJRqe5wo0lVj257liRRFgXsiwtxtlsbEzUq5ekp+rVS9IL87tLt8sEvTvWaEl9\nU1F+kckeNpBnEdNhzHIYBzMb8D09pql5gDJ5wUWlXrrIXLEEZFlwhochLnYqgwfeI5FOw+7p9ZdH\nV7bArK+DWV/nx0aKdBoQAuSeW03IM6qrE6/J6bkBcb4D1B8YC1QVrYHMMMWOqlfY2VVRcer0JD1z\n+RJ/LHEhg16Vesaj69th1tbCrK2N6NVwq1+oejVr5sJMSA50bvQCFzpBg7f9dVRToLNuJQobyAzD\nMAzDMAyjwAZyiaN6jdnbyzATxJsKtW0YqZQfypAUJkKWBXHkpHztepLsk2cg0mkYG9bAqKv1p2LV\n0k3W0sUw2xYGp/3+ftg9N2D33Agy5N3j7O5uAHLa1ZvatXt7Yff2yqx9NwTDn9qd3wAqL4dzs98f\n3756bYo+IIYpIFy9inQaVF4eCT0KQ5YF7D8uX3t6PX1ODrVxLYzaefF6XbIIZmugV/PF13wNahUt\nwnqd3yDX9d2UZd0M09epr9eGevkbo8wo2Ze5isVMwgZyiTMVRjHHGTOzHbOhHmZDPZxhWTlC2DYM\nt9mHeuP1QhxEOp3Y4tk5cgrpq11+2SiVcHJR7LXU1+kxzJYFu7sbVlNQLxXb1wf1U9etkGN3dMLu\n69OMeq2MHMOUCJ5exchIoFc3flfTa1UVjKqqzHo9fBLpy1cCvRqB2ZQ+f1GWgBvvWlYuC1ZYFuzr\nPRG9mgvd3ANPr5c6WK95hg3kWUouRi97npnZjt3dDbu7G+b61dKDJIS/LNJpmKvlTc0ZGPBvaJ4n\nSPUkmU0LghhG18ul1TYlSuzMZT94F4zKSulRPnFabq6uht11DSCSRrfHrkN+/VSx/2hwPUJodV69\n62aYUsLTq/PAZvkdd2uYA6EqEoODfgKfl3yreX4BqVcyQGXloLJyvXa4kiwL6MZ3z/vukdfScwP2\nyTPBtXVdk7ofR69eLoFXuhEAxBv4XjyTJM85MCWJZxiz0cswuWMfPaEvH5eGqmewqlB5uebxGXzH\n3aj6ulK+ScQkD4UaEZgrl+L2kloAQPmze+Eouxob18I5eDwYDwBtWS8X9x2NDj0y4tZVdXzj3T51\nLsO7ZZjixnhpP7JtdK51rQQw+BN3o+rru+WCY0PEtUwXQqvuYaxejuFWadg2fFZvd25sWgfnwDF/\nPCCzXp2BAYAIYnTU1yteOZzlu2GmAjaQZxlhw5gbijDM+JhrVwKQ3a004m6a3qZQfHLVU7uDhVAD\nANp2J4YXVCD1b3u0Y+yTZ1B+Sp/oc+7bhPLTV4Cbg3Agy1B5N2njovSS2QnNBsirwpEwncwwpYBX\nGi38QJsJr8Wzh28cA1G9bl6PkaZKlH8npNdjJ1F2PKTXBzaj/OQVoHcgqtcLcvbINszYRiFklUmP\nNus1L3CIRYGQrzhfNo4ZZnycU+fgKN5Wo6oKxsa1AACzdp6/3qydpy0D0OoaA+40rHKztZqbIPYc\njhjHAOR+jq0Z4sbLh+EM3Apa0Loxkeb61UEoyPIlvnfKr+8KJMZZMkwp4Zw4A+dEENZg1tQEenXL\nrXmv1WUAWl1jIEavC5sh9h+NGMcA4vX6/UNwbvYHjYY8va5bBft6j4xHXtIe6NUtLwfEhHswMwob\nyAVCoRmqnJjHMAHhJB6jei5oVC7ffGRtsOPCBfJPPXauXms4bKRqsYguppsAGItja40PnKEhAMBY\n3Rx/nX32IozzV0CW5WfjM8xsIaxXqq8FjUmjtf+HFb02N8o/9dgavRFQRK9XopUkwg/FGo7taxQI\n9JquC34X0ucvBd7kM+eTx2JmFDaQmVim0mBnY5spNdJXu2S4BRHmfm0XzLo6mTB3/FQkDEMzUMdr\nc+sd09sbWUdl5ZFSVWpXP2OnojPHlqXh3CYFmYjrDHjxI/dmdZ2ZoLJy//32PX7PpMeLjJ9KZdiY\n3efMlDju9yB9/iLsY7Lsohc6YTY2wj55RkugA2LCqLIgHJ4B6Ml1sZf2g5BevYYjE2gvfeEPplZf\nUz1escIGMjNhsjV8C807zjC5Yq5cJks1EemtYd2bmd3bG4k5jrSFdWMLjYoKWMuW+Ks1A5VI64Tn\nt6UlghgbhUinYbW1wqyd57fQVUMovEx71ZBWW9waG9ZodVzJsvQ2vC6LPvLy+B/KOIixUf/zqf38\nK+PsPYHxM5W8moCRwZQO5oqlgS4SHpa8usT+MQltnI3KSq1MW1ivaoiGus3zFFsLm6VeXYNZHcur\nz5yo141rI3qNY/GHplZfUz1escIGcgFSLB5XNnyZ2YJ96izsU2dB5eXaetOtZZp+eEvEE2v33NAH\nEQLm/AY4w8Oydqpr+GoGqhCaoe23pRUC5tqVMFevcOsZ35Qlqh7YrHmoxdiob0j761xD0hkYgHPo\ndW2KWDjxhqS1ZNH4H0oOqDd5hplu7NPnYJ8+J1u0hzTroT5YAjF6dXGGhqT2veZAIb3a/UHjHXXb\ntV+VszDpK1elXl2D2T51NjjcDQVJ1OvB45pek96L88Dm2PUTZfTRrbkf5P6elRJsIDMMwzAMwzCM\nAhvIBUg+PLPF4rVmmLygtJomI/CS2NfkNK31/L5IqIKxYY22bC1s9uMMzbq62E56QCi2dscGvyuY\nffyUrLfsemnM1StgvLRfC/mw2ttgtbfFv4XKSljNTZp32KyZG7uvXyFjiohLbGKYacPVq3OzH7Dj\nSzGGk1eNTeu0ZbMxSN4z5zckdttTG4Vg+50w6+pg1tVhwafdMCVPr15oharXhc2JsytGVRWs1hY9\nhKq8LH7fl/bHrp8o5c/uzf2ghN+zYqaoDGQ24qYPDpdgpoOBd+8IFohgzm+QL1MpYMeGrMeJq+pg\n3LEmZs8YDDNSas3fVFGh3+CIQJvXR3d0SzdFbpIZbgjOodf9rnVUVq4ZiXFJeP6QamztrkOyE5c6\n/evFPXvNSZSSUulLHUhf6oi/nqEhpK92acZvXHIRM3vJNLVuP3jXDF5J9tDWO6Irk/SqHqfG/ZaV\nwzlwDJd/O0hOVWOU7es9iSEEaqMQvHoYdm+vrm9Pr15oharXK1cTHx6dwUGkOy9rhjzrdWYpKgO5\n1Iw4NviZUqf6n3cFC0L4HlQxMgLsOpT1OHEGpXPk9ewOduzYUmqAvLlpNzgh/FavcRgb10aSbABZ\ng9hqaw32cxPtvDqmfj1T5SZL2+4EbbtTH7+qKrE6g3re4ADpjfIePDLhPwgoN/lsjmNmD5k8h+aL\nr83glWSP2HskcRttvQPmulWx24ylwUyKMacCIELLH8ckp3q5Am/YCOcNG/Uxqqv1B2x124bkB/ik\nB3bteC+nQdVr04KEvZnpoKgM5FKjmAx+NuaZ2YyfoHPwOOxTZ2E2LYC5diVEOg0qK4d99ATSHZ3B\n1K6SaKdWrIAQMBvqQZYFsecwxJ7DMFevCDaPjECMxXu8fA8Uke+ZhmPDrKuD3XNDu+ma8xv8xghe\nyIUzMiKvT/F6+6WlGKaE8PQq9h6BfeykrPzidsP0tGOfOuvr1e7v93URfhA1G+pBpglj5wEYOw/o\ner19G87oWOw1OIfcB3hVr5CzYemua5qxazbUB3p1H7Sd28NRvXZdm9DnwUwMNpBLjOkyZIvJmGeY\nacMwQakUxK1BiA45Nap1u/K6aBmm7/kR1/XseNG6AMa8msCb7Dj+NrO5CWbj+F5docZVlpfBrK3V\njF3n5gCoQ3rNHXda1qyvg1FVqXuo1ZJ1DFNqeHrtH8har+jWHxqd9mYYtfMCvSraM5sWwFowf9zL\n0PRqWTDn1cBRwqac/luBXm/Kqhhm3TzWa55hA7nESDJk2QPMMBNj8B13QzhClkRzbIiREVliLVT3\nGECQzOfYQeyhUgYKgPRC99zwk1rsU2dlneN7N8oSbl3XACJc+Gh8sw4jlYJRFTQhsLuuwe7tDcVG\nO37csnPrltyv5wacgQE9xtmJT2BimGJl6O1Rvdr9/dnrNRTnK/YflQ+fnl5Pnwv02nlZhm8RJTbX\nMObMgTk3KAFpd3fD7rsZiY1mvRYebCDPEmbCA8xGOFOKVD212/c0We1tsFpb/G2+d8fzPnkeKSAS\n++h3ljPMaOyvEKCXD8ptjY2AEFj84ZfldKvrufJiHZ3hYXmz97xJavMSr6lITCKh1doiK2KsWu5v\nCmfuM0yxU/m0ote2Vk2vEVS9rl+tbfKT+AwzGvur6NVqbpJ6/dAr8lyeXt3GIM7QkHxIzqRX1avt\n6bW9DWZjox8aArBeZxoSBViWo4bqxd30cL4vgykhPON9Mg8Kz4mn9gkhJlBBvfRhzZYWVmsL0p2X\np2w82rIeYl9y8mMcp57YAgBY+d598WNaFux77oTx/fgSV7vF8+gXN0qrc8EUwXotMdwunVM2XCqV\nuVNlDF/rkN333tU2sTbVhahX9iAzEUrRE/xoyyaOo2YmjVk7L77yg2FqLWfTD0njzqulqm7zEnZG\nH9uG0ce2acNYi9sTq1hkij/0EnwyETduUuvaqTSOAeRsHAPSME4yjgHZhSzJOGYYQNFrTHk2s3ae\n/2cSiuAAACAASURBVHrsEen3iKsSQakUQISRN2/DyJtDel2yKLGKRUa9ZuEJzkWvU11/OFfjGJCG\n8USN40KFDWSGYRiGYRiGUWADmYl4jNnTyjA61uJ2WIvbYffd9KtF+HVK3SQfu7/f76JlvSA9n3Z3\nN0CkJep58Ybl39mD8u/s0T3PFy5J702Mx8uLlbSWLYFz/2Z/tVlXB+fgca2Ziv3gXTAb6uXlufVY\n47xCSU0UGKaY8TpK+noVQtcrZDKep9ey78r6z16CrIoYGQGEQOrbe5D69h7N85w+f1HWUc+k16WL\n4dwX3FPNhno4B45penUe2Byv1/C1sF5nFDaQS5hsQyXYIGaYzKQvXEL6wiWZOOMmufmtpR3bv5F5\nXbTMxsZgijU0/Wk2Nsq2z26bWftW0KLauGONrFscN2Xqjpc+e14LLbD7+mBsXKs1UzFffC3Iinfr\nsVptrbI+rDJ1G9ehkGGKHa+jpNXWKusaE+l6dclKr00LZNvn5iZYzU2w+2/524wNa2Atbo9vGe+G\nQ6TPXYCxM7gX2zd6YWxYo+nVeGl/VK+L20FWmRbCwXqdWdhALhImEhfMhi/DTA2USoFSKTjd1yE6\nrkR3CN0g7e5uGOtWauu82GOntxdGU2PQZlYEdZCdoycgbt/2l61lS/ymB/6N3a3Z6nmejVQKzsHj\n8nW4dbZ6iSOjMJYvgbFiSbDS5FsAU3pQWbls4NN9HaLz6rgxunZ3N8y1K7R1vl57boBammSb9qtd\nul4Pn4AYCjpxWksW+aXjfG+vq1evu6aRSvlGsPe7EsvIKIyVSwDWa95IiPhmCo1CNnanokIEwxQy\nXnhC+DZrNtT7np8w4VbY5oL5EFVzYJ88g/S5C8rgQnutNvxIn7sAw72BinQaRlWV7wmz+/ulZ0xp\nle1kSK6xu7uB7m4AwOB3ZLewqsfOJu7PMMWKWjYt2/Q1++gJbdlsb0H67HmIdDroYglE9epqCgDS\nFztl22og4rH26jCrehWjSnm3EOmrXcBV2Tzk7cfkOZ7mKm8zCj+OMJOm0CpElGIVjpIjLmZvOpmm\nDlRJxnEc6c7LsE+eye0EQsi6x+5N1b/pKtsjy1lktFc9djYwjpXP5sZ/zj0LXdy7MXFbonfMxYsL\nHXlLqDrAwubY/dWasMzUc+2X45vTAJh5zWaJF7s71aTPns/9IMeWTYTCOk0iS70+va4RT69rzP16\nciDj//00M/zW7Xk7dybYQC5y2BiMUkjGOpPATNdfn2wHKq/NrGHCqKiA1dykJeuE8ZoEAJCNBNRt\nG9dKw9CdirXa2/xt5oql8WXkMmBUV8Na2KyXgPKakSgNRszaeXJKN7Sf+tnU/+MrOZ0bgGyYkMB4\n5aI8QyL1zB5tffrK1dj97eOncrw6JhcW/N+XkzcWYM8EIOEhVdErpVIyd0BJhg2j6TXUWMTYtE6G\nR0ylXpubonr1kvQy6XUaH1Iy/t9PMxXfejVv584EG8hFBlecYJg84thwRkZgt84HGuWNMa42qTMc\nGIZ2u15b1bh+E87tYa2Ll4foug7nZkxL3DDqjXJsTCb+KO1sqcwC6mtBVhmoXMZS2v23IEZH9Ux4\nbl3LlCKeZ9ZtNT26eD6wIDu9ji3SPbXG9ZtwBoemVq+9fZpR7ukVAFBWBiBBrwX6kFKqsIFcZOTb\nIGaPNTMb8cq8AQCEkI0vuq7LRTu4aXrJQeqNVOw7po1lX7sOMuI9Qc7AgN52Nu5aWlu0NtZG8wKI\nkREIO0georXLQf23IMZGIZa2uoPbIHN6Qk0YppDQ9ArA+MFB4JqM7df0Gk6ABWDs0Zva2F3dul6V\n187AAER6LPO1LGzW20UvbJJ6VYxdWrMMdGtILixzPdSODbLKxnmnzHTCraaLlGcvH8i7sTzb4FbT\nycxWzWZqyWrW1WmlnAZ+cgdqvr43p1qmxoY1SNfNka9f0rvGmWtXRkIOPMPZPnYy4YIpctMdzyAv\nVgqxdW2hMGv0mkMLZnN+g5Yge+tdOzD3a7tyGoe2rIc9V87YGP9xQDvGXLcqoktz/WoA0QRB9fqp\nvBxwgnFYrzMHe5CLlJkwjp+9fIA9xgyTgUwxtqpxDAC1h3oSjeOk+qbi+FkMtKcw0B5NdkvXVkbW\nDayuw8DqDLVShYAYG4XZ3gKzvaVkb7YMAyCnkATVOAaAeQevJ46TWI/40CkMtKUw0JaKHJOunRPZ\nfWB1LQZW1yZflBAQIyMwW5thtjazXmcYNpAZhmEYhmEYRoFDLEoErkU8/XCIRTKzRbNGRQVgmnCG\nhnQPERHM2lrfazzylm1IPbMH5uoVsE+chrW4HekLl+SulgWRTiP98BYAgPX8Pn8Yq7kJ9vUET3OG\naV7vPJmO8c6b7ZjFTiFO2RYKs0avlZVSr7duxXa09GoY3/6x7ZjzjVdhbFjjN/Hw8HQz+pgsRVj+\nnaDiitXaArvrWu56jQm3iBzOes077EHOM1MVwlBotYgZpiRZtQRYuTi4SbnZ6WSVaSEVc146BrIs\n32j1jGNAxi0bFRWoONaJimOd+vipcphNetULDzXrPYKdUI1CjYF0S8FpJa3aWpPHZJgih5a2A8vj\nW0GrDT6qnj8OsqyIcQy4eq2sROXBS6g8eEnfWGbBTKjZnVGvo5kT+wDAbGuJ6jVUgo6ZXoraQC6F\n+Fg2aplSxtiwJlggAnZs8Bcz1REO1yyN1O5F5gYVKtayJUGZpVAdUWvpYlhLFwfnbWzUrzmEc+h1\nOAeUqhTujTccG+gMDibGGzuDg3CGh4NW0wrpC5eQ7ryceFwS9ulzidv8sS91QKTT0vutrGMYj0zN\nIpz7CvNede4Tyc1t7KMndL0mIKtRZNDr0FC8Xs9fTNTQpPV6/mJUrx2dGY5gppqiNpDZuJx6SuGh\ngykcNI+MEMCuQ/6i3Xcz8Ti7v19bjqvdm6lBhUr67PnAgxROnDl3QWv7bHd3x3qRzBVLYa5Yqq3z\nusBpRrfbTEDfUV+mVEqWgnOxlizS9x+nGYDV3uZnvwOAuVK2jVY9Tea6VTAbZT1X4441/riUShVs\nRzQm/2RqFmHsLMx7w9LfiTa3sZYtkQ/GClOmV+WBOjJeDFZbq1aW0Vy1XJ4mQa++tr0mP6zXvFHU\nBrIKG3ZTAz90MFNJpItcdbX/+soHk71V6g0pjHejC2eSxzUAAKB1vvK6VAUr9BsklZVH2h4D0uMT\n9vr4HiLV6I5pJhBpLjAyonmc0+cv6vuPE2OYvtShlYWyT8mW0aqnyT520p9Cdo68Lt+3mxFfqjGM\nzORJCu8BgFNPbJnBK8mevsejHuT02fORVtFTplflgToyXgzpjk4t3thrN5+kV/voCWksu23mWa/5\no2QM5GI27Ni4Z0qV9NUubdkZCLpOLfzTZG9VpnJG3o0uXEYtaYpUnQJ1hodDg+k3SDE2Gml7HHt9\nb9gUeHqUsBGjqirwVHmneGBz5sHiPEQT8BqpXioQwWxslDHGRFoTEYZJwu66lrht5Xv3JW7LJ7Wf\nH789+vBbtwehU9vv9NfH6dVLxktkuvTqPZwQafHRTP4oGQO5mBnPuGcDmmEKBMMEWRbKzl8LvEJK\n2IgzOCj/7pdGsbWwGdZrp3Uj2vWim00LIh47q71NesJz9BqZK5bqWfFCwL5+HbBMv/ZxZCqZYUod\nwwSVlaPq7E04R9ymOq8e9jd7eh1+63YAMjRjzp4z2hBmXR1ABKut1X3gDMwmc9VyOduVo16NO9ZE\n9drd478GkmfEmJmDDeQCRTWKi9k7zjAlhWPLEm2dlyM3xbOfDKZ6y46cBwAM3L0IzsAArEtB0wHf\ni+4I+aeM4yXS5Yp95nx0pRB6+EZ4KtklHAbDMCWDY0OMjcqQpND3/9Tn7/JfV70ijeIbdzfD7rmh\n7Wf39roPmWMQY2PaOPbJMxNq3uEcOxWzMhTaMYHfAWZqYQO5QJmMUcweZ4aZeZb9z2Cq1wv/mPON\nVwEA6c7LMkFOwe7u9qdST/7t9sRpWnPdKn061lu/dmWw4BrZzn2bsqs2oMReh8NgGGY2sPLx1/zX\nXhe9mq/s8tdF9Np1zQ9ByajXtSt1bXrrVQ27xvBE9MrMHGwgMwzDMAzDMIwCG8glSNj7zB5lhpli\nvBJMgB9vDMgOepEqGF7d5pGRxOFW/dKrWqjF1d+41w99sI+djO26ZR/Xp2mt9jYYOw8E5bgyeZzi\nsvcZplQh8suqqUmzcXr1Yn9z0WvXr9/r5xPYx09FtAkgomFrcTvrtcBhA3kWMJMxzGyMM7MCrwQT\nAOP7+/3VqWf2RKtgTODG1vypl3MOfYg0LMjyvCc/F5Tvuv1j23M6JxBTOk9hvM5fnjESLtmXNH2d\nqfwfwyQihF9WzXgps14nEvvb9JcvZ6wAEofaXRNA1nqlrXf4r698Y21O5xyPcO3oMEllADM1fSpm\n2EBmppRiTShkw56Zraz6+aB8lxcznU0Gve+R80rnheIkrcXtflfAJCPaM0bCJfsiVQGIYFRVQYyN\n+sb0jX+NxmX75+YW2kyJIvYe8V8v/LHj2R1EhPTDeh3ruGYq4drRYZIeAsJNn4yqKt9T3/N+mbyc\nqUNpocIGMpNXCsUwLVbDnmGmg0xeNE+zaqMDuUKfBlY9ZJH60zlfkIjUv67/kWjYiX9ubsnLMABc\nvQoB63m9jnVcM5Wpwhkc9D31DZ+RyctxHUoLHTaQmbySq2FaKAY1w8xWHm3ZBKO6WvMym00LQJvX\ngzavj+xvP3hXZF0m1DbaACKxmWZdXSTUwqypgVlTAwAYePcObs/LMC6PtmySzVDU2Z3mJpirlvtt\nrzWUmu1xhGeXzNUrtGW1WyogZ4/CoRlqg5Yb/znaCbFQKAkDmY2m2QN7eosUJakNQKwhlYhhytar\nkyBidIUhijWqSjW2LolILHACzsCA72W2Wltgd12D2H8UYv/R6JgvvhZZ5+N+5motZvvoCfS8T7lp\nKl5ps6YGdm9vpPas3d8Pu78fAFD9z7u4Pe8kCYfEWEsW5elKmEx4YU7j4QwO6rM7V7tgnzzjt73W\nUBofRSCKzC7ZJ07jJ44HoRdqt1RAzh6FQzO8Bi0AUP+P43dCzBclYSCz0cRMJc9ePsAPXVONktQG\nINaQSsSxJ9161T56IvMOQsQaVeHYulInEgucBV6ccRKeh8lqbtIeQmjLepBVBrOxUUtIHH1sGxo+\nG9w0VWPNM4LNhnr9Gh7e4sdYevVryeK6sRMlHBKjNZxhCoZImNMUYM5vAADYb9RnfswVSwFEY4nN\nVcvx9bWBhzjOqRCOdbYWt8Na3A5A8UhXJif75ouSMJCZwqRYjcxHWzbxQxcza1CnTG+98+6cj8/k\ndb71zrthnzgNwG1IojyEiH1H4WxfB7u7W2usUP4dvaqAaqx5yXfhbmfW9w7A+p78vfHKc4k0l8Vi\nSpvrH5ja8ARx70ZfW+b39Jkf+/Q5XPq9eyKxxPbp8/pyjFMhHOssbvZD3JQPu75HemiSeQrTABvI\nzLSRrZFZrIY0w5QC6pTp3Cd3Z3UMbVnvh63Yvb0wNqzB2CNbMfbIVm2/uU/u9j1FseP8QGpfrRvr\nxRJHMEykOzrHrVBhrluVVRUOhil25v9dduEJ4p6NsNrbtGX7wbsi+QH08kFYi9rCh/u0f+zlyDqz\nZm78zu5skdlQHwlfs/tvwe6/JUM27tmY1XvIB2wgMwzDMAzDMIwCG8hM3slHOAN7rRkmM5m8sOK1\nYxhpCjxHzqHXUfbdvSj77t7IvpGGCOPgxRmrmGtX4tyXZWKnX1tZzZZXSszZx05C2BxewcwuMjXs\nod1HMLAlaNpDrxyE+dJ+mErTFI+c9RoTUmGuX41Tn5PeaftGbzS/w9OrEKBXDuZ0vpmEDWSmpMjW\n8OUYY4YJiAuDUEMvIsayELBe2Ies8KZaw1243LJTY2/aEru/h/3Gu2AfP4Wl7z7kn9t5YLOfLR9O\nCqKycnnjnVN4ST8MMxXE6VWN1Y90nHRsvwmQT0Jisn+O9vhQi4heQ4h7NsI+egIr37svOM/2O/3t\nXhJgcLGu3ufOyThuPmADuYhhL2gUNnyZaSVUk9ernBCuqpDE0I/nngTnYbW1YuQt2/TL2bRuwuMB\ngeE7ntdoIu13g4PlTTjShcv1+JY9ty92f49wshCgtwsOe7C8EnDiduEl/TB5xq3xm61eJ1ujd+An\nd2jLXne5yTKuXkNlECd0jnDrepeIXkPEeoRfPey/tK/36Ns8vd+6ndP1zQRsIBcxbAxOPfzQwWTE\n0afuve5U4aoKSVT+S3ZJcHGkOzqReiZU4eHAsQmPBwSG77VfvjdYaUTLoxl3uKWdwrWiY/ZNDM1I\naN6hTg1HvMyIT9rzSrkBQN/PJhgx3CyECePW+M1Wr5Ot0Vv91V3ashET0jARet8bfOfj9EZb73Bf\nhDQQp4kknWShV6u1JbrdbQCiDaV4tHt+oXj0ygYywyioDx1sLDOzhQX/V8lOdx8CVCPUOeKWdnK9\nPV78L5mmtuwRrmahHtvz/nvQ8/7gJqlODUe8zIiPSfZKuQFA7RcCI0a7+XKzEKZEqXsi+M57D7mq\noSz2HnFfuHp1G4pQuTRUVSOWrDIMvT1mZss9tu/xe9D3eLxe42qgew1AtKEUj3bDPyh6fV9h65UN\n5CKCDbaZhT30TC4MviO4yXgxgl2/7npmY9q3/n/23jtOruo8+P+eadv7rlar3juoS4hmEB1jA8aY\nZmMTVxLHSey88S/JG4ckTnD8Jk5CbGMbbGzTMSB6MwgQQn1R772spO3aOvXO+f1x7uzemZ1dzTZt\n0fP9fOajnVvOPXd0n3ue85yneMaM7piyrIdWlO5abVPBqYQmEvP/jQ18zupZOhJJGqwXo+iRtRQ9\n0j/Vs5yDr9B3JE6AhjpqYTcqeQ4ycj4qTrq9KzeoWEGRmEw7lVgdDpG5ovOVrfzfryX/913L1dlS\nL3aGsyDQYEQU5CGEKGypI5MJ4VyT9Xz7IBPzESx9yLbMJinfGjlRQeRERfzGHlpROh0ce2OVSVCu\n+yq3sCsrK+ky7CPHVvdJ+0Lfk1g+eKijy7tRyXOQ0XRZTfIdKbg/9YTO5PXXDnnt8B4bJoiCPIgQ\npa7vkMmE0B8ojyduIIol2veMLE3p/MQgu+7gmTCO0PXx56vFF3RydGp0qfQmKNe9CtRzEG1pSboM\n+/Vxl/ZJ+4LQGTF5SVVeG+++6OwHdUFikF5S16O+IlkqtT6gM3n96nkgr6IgDyJEqet7ZNIh9CU6\nEokbiNwfmAwLkdOVKZ2fGGTXHSJHjnUow6w3bu/k6NToK6UXjFtJXPopeyLReFfnSkYyBd3pqtLZ\ndRKxrlyAdaWZrCivb9i5BAh9Q0xeUpXX3KfWnf2gLkgM0uvK9ehc4xk7JmkqN6e/cRxKJbVK13+5\n60wf7uKijqkbHVX8Yv7RgxFRkIVhQWeKsEw6BKEb9MBnORahHjl6PD79lD2RyHtxc9txzsHwzL3L\naPmsSZPn3J770eFOr+UZOyZpiitX0MIVNBYzHQ4NO5cAQehrIsdPJE3lVvCsmfS7p0yM237khxeh\n5s7o8I4oeadzeQU7rVuCddsprzH/aMliIQiCIAiCIAiDHKUHYWqNXFWol6qrBrobQi94++SWYWe9\nfVc/X6617kcnsqGLyOzQ55WKjXx2dBc+0koNylRMnbFev0ejrht8ZqlBgMjr0Oes8upy95kf8rlg\nMMqrWJCFfuFcKcdvn9wifsaC0AckDrYdinY4leMlF8RFtrunT4k7NFnUe4zK71zcnv6uO6QQlR++\ndtGgXKoVhL6mg7wmVgd0KMeuuTPjinW4p06KPzehXLuTU9+9mFPf7YG8poD/5iWDWl5FQRaGJDGl\n+LpR84adpVoQBhL3rGlAQtEOexBrG4Q3bI+LbLf2HohrI1nUe/X9y3DNmUHpQ2sofWhNW+5UZ2Wu\nLnFawzoZVL3vbGpT5HXe4A3+EYS+IhbsFlcd0JaPWKW76NbdccU6rP2H4ttIKNcOUPH/GaW47Cdr\nKPvJmrbCQc4CQr0l4+UNbfIaLeh8Uj1QiIIsDEnOpYVaEM4nrF37Om60B7HYIGxdsQBP2ci23a23\nLsUzelTS0rPuggIASh5eayryKQVKteVOdVbmgiQKc8xybA/6yusDlTB02W0qr4+W20wWDNXQmsLd\nCsLQJpZJJw5bXmOV7vQl8+Iyu7R8finu4iKTYSKxPbu0++gf2TncbdmKFRnRoVDc8YkKczL57bCi\nFJPXtLS2VHqu+o6T6oFmSCrIorQI5wqxTvcNTXde1PbihfbKXMkqMCWm/lKLL+j9MtxZzndlZSXN\njRq8oed5i4cz7g8+IXLqdNv3zBXriVScTFp61qqvj9+gdZe+zNFAID71U8xyHLMMh0MdfSvtNnU4\nRNYLnVcFE1Kj4sX4SnMxmaz/StcpvYTBifp4S1xml6zn12PV1JoMEwl0KO2eKK+J+dGDwbh3e+KE\nl6jVcUUpJq/BYK9T6fUnQ1JBFqVF6A0ywTr35DyzLu7FG3tZJ6vAlJibV2/c3vvgsLOcH21pSZob\nNe3NnuctHmp0mJgkLqWeQ1/BttRPNrftrurg5yz0H6M/F19pLiaTBb8d3KWBzycSLbUdcn/3USW9\nVEhUqm/YeQbPpAnn7Pr9xZBUkAWhpwzH7BqC0Fs8Y8d0nJjYS6pA7zJYxPyXbVeLxO2RqxbGb08Y\n2IM3LOaFmSPi/JxD17Unk3FlZbUt2UJ7TuW+9JUUhMGEZ/zYDpbauNzffZDBorNqg07ZS0bkqoW8\nOTufyKEjbdtiRXyAOGsz0C7vmSnGIpxDREEWBjV9be0V5VgQOpLMkh9HJ8pxzTc6LrlX/enFNNzj\nqJ4X819O5moBeN4rj9+eMLAns+L73m6vSBZtaWlbslVeX5v1OU7BF4RhhFVxqusDOlGOq7/VUV4r\n//zipNUrO6s26JS9ZHSQZ8D9fruftNParDye9r62BhJPG3BEQRYGNb1RaMWVQhBSw5OY0s3GNXcm\nrrkzO1TVilH86IYO20b8Yj15T/bMr3Dfr7ufZtyZssoZqS8IwxVXXm7S7Wr+bNT82binTU66v+QX\nHV1kSv93DVnP98xvf//vFpz9oASc75K+LHXfH4iCLAiCIAiCIAgOREEWBh19ZfkVdwpBSI2ky6lK\nEd26m+jW3VgHDsftcl04A+Xx4M426Zuc2T5cGeldBtR5xo7BM3ZM0n3Tvppk+TZZsJEjYNCZ07Xl\ntqUdC5wIwjDDqu+YtxhAb96J3rwTa9/BuO2eSRNAqba0btFPzW/b587PwzV3ZqfX8owZnTTbEMDU\nL3dMMReXgcbGWaTE+S5p+fxS3CUlnV57oBEFWRh09FSxFZcKQeglTmW0q1Rs2/agIxGsxkZc6elx\nfsLRlpb2gDo7N7Hz78jxE0SOnyB0/WLq/sT4RDoreXVVEaxN8U7s25ILYMkFZL2wPr7AiSAMR7oZ\ngBc5dAS0bkvr5vpwc9s+60wD0a27277HgltjimvkRAWRExWc+NuLCV3fMe1lYu7zxAw00NH1ybpi\nAdYVC0y6uerqbt3LuWTIKsiiDAmJiMV48OFMHaYWzm5TfvTFczs/yeU2H0dhCGcUdIwO0dBdtOea\nMyPpLs+EcXgmjDNWlPT01Ku6DVd6EPneIe+pEzs3cYe/Ad9bGyn8jfGJdFbyiqsIlkBixb42Nmw3\nH6FP8EwcH/f9Owf2DFBPhHNNLLg1UXEd8+AafG91DJiNVJyMsxCngvuDT5IXOBlkDFkFWZShoYtM\nbs4fnEEYunxnm/Kj1mzt/KSoZT6OwhDOKOgYHRLad9FedEfyAT5y5BiRI8eMFSUQ6FrZG8Y4c6iq\nRXPa//Z4UB4PVd82ZWc7m2j0Fc6Au1RJNnkSekfk8NG47w9N6d//d6F7OCfy0cva3SVi8hrLVqEW\nX9Cv/YjlOu5OcGyXxpFBxpBVkIWhi7hQCMLgoemOi+JyqOpNO9p8hHUkgo5EGPFTU3a2s4lGMiJX\nLaT5dpM+yulC0RVOf+JOsd1AYkp9sslTspLXgjAccM2b1T6Rd7lxfdTuLhGT11i2Cr2xi1UVR+7w\nGKE/ju/kYPuUBEuxM9dxZ8QyasRWE9WarUmvPRgRBVkY9MQUY1k1EIS+J+fZdR0CayLHT3R5Tsy9\nJRbsE5cG7qIL0cvm4nmvnOw/mPRRThcKlZbWrSIerqys+A1Ry+Q7dhZGiLW9+ALcpSOSlrwWhOFA\ndMsuPGUj7S+puUTF5C2y3BTl8YwsbS/3fPHcNkuz75qjHc/1+toUYx0OnVWxbeubjbXvIK6cnPiU\nbva13bOmpTx5HghEQRYGPf2tGItlWjjv6aY1J+beEgv2ictysW4bam27C01b0I+tVOtg0Pg52pbg\n2KAdI1Eh9l8+q8P1W25qX1bG5W5bWtYbt6PafNe93bonQRgq6JaOgXBdHm/7FXtWmiIezqw1as3W\neEtzrPKlHaSnw6E4xTh4Y0Ku8oQsMy3zx3a4fvPV7TLsjEuxdu1DB0zfVPrgq3wpCrIw5JDqeoLQ\nt0RbWuK+dyj/3At0MEj1/cs6+ozb1q/YoJ3Yl7r7llF337KklfQyV6zHnZuLZ/xYiFptS8vQPvjr\ncLjP7kEQBhMpx184SZwEd+bmEKt8mZhdwt6e9nqCPCZWvnxjY3ugNUbRzlyxnm/sO4QrK6tDcZCY\nu0hMUR5MiIIsDAq6o/SKQisI/YvnvfIu8wmHr17I8X+4uO27u6CgzYqbjJKH2yt4pepiUfjYWgof\naz+vzdJsD+pWYyORo8fP2o4gnA94xne03MZouOciqr/VXv7dM2Y0yu1GuZPkGE/AnZubesagGFEL\n90yTkjGmaP9q2qQOE/HB7ocsCrIgCIIgCIIgOBAFWRgQEi3GYhUWhMFF3TXxKdf2/3Rp29/ed8sZ\n+y9r2r5b9fVxbg5d4ZoyAdeUCR22dygQkkCbK0YXBUwE4Xyl/qL4anf7/6fdYpz35Lq4VZzIHTBK\nEAAAIABJREFUiYqU5TU6dSzRqR2t02erWGnt3HvWtge7LIuCLPQrnblOiEIsCIMHZ2BcLE9p3hPr\n2nwJq/70YqZ+ez1q4exeX8vauTfp4GnV1uGZMK7b7TnzwArC+YDTRSl8rQmay3m2XV5rv76MqX+x\nDn1J78dZXb4TXb6zw3arsqpLt47O6O/czH2JKMjDgMGchUEUYWE4EPOvdefntUV3n43O0hd5ykaa\ntGh2kIwzxZq7qLAtb2jsuh07Ex9c487N7ZgKLfF4TBEOz9gxeMa0W5piVlunb2BcERe7aMuInxtr\ncbKBsi+JHDnW7XOceWAFoSsO/Jexqp763sVnOdIQK4TRZ/SRz20sKwWA951N7TtseS16xFiL1cf9\nqxv0JAagy9zMgwxRkIcBUnhDEPqX2HJktLkFq6ambbt71jQgeYU5Z+7fGK45M4icOm3Sotm5QKOt\n7SmbrNo6rH0H467bdq2pk0x2Cfs8gND1i7EaGzsGv8R13o5K33+IyPETRE5UtF+vvmMfgW7lKU6F\nvm5PEHrC9J+ZDCej361P6fhwWX6vrhcrlBOj7isXdXKkMBhRehD6gOSqQr1UXTXQ3RD6mKFe8ONd\n/Xy51nrR2Y88/xCZHZ58dlctAK/MKjo3F1xyAWzoGwvTev0ejbpucIfJDxAir8OTW3eZjBErZqW2\nytVb1KI56E07+qStwSivYkEWzhnXjZo3JJVjsbQL5yuvzCpKWTl2pafjSk9v++6eMhGWXNBl2Wen\nOwkAG7aftUx0h3MA9+zpuGdPb/tuXbEgpT4LwnBixaySlJVjd0EB7oKC+G1Fhehlczs9x3Vh/EqZ\n3rSD6KVdj+lq0ZwO21o/t5TWzy1NcvTgYlgpyKLICP3BUFTqB5SEykq43ISuXxxfztg+pu5PlnU8\nPaHscVcv7BixUqipkGy5352fl9TfsLO8vkJHooFAW9J/sKvrbdjeZdlnpztJjLOViU52TmLgn/uD\nT1LpstAJTXcmdwVI+3Bk0u3C0MOqr8eqj3c1sWrr4qpgJhLdtqfDNtfqrvWuZBbmzBfXk/ni+hR7\nOnAMKwVZFJneIRMMoU9IqKxE1ML31sY4X9vYMYW/WUsiccdBly/sGDocSrl7zgCXGNaZBiKHjnQ8\nNiENkiszM24CELNmRpYv7DgxSEKcEt5ZJatOUIvmoObHZ5FILNPcXTqdWPR1Av9BXhBAiCfnmXVJ\ntwc/dfoc96RvcU+ZCEDgpiUpHe+a17HMeXcIXx0vn/Vf6WgQEAYvw0pBFnpHX00wRNEWhivR1ta4\nCUDMmulZWd5xYpCEOCXcEWyXCnrTDvTm+CwSiWWau0tsYuG/OUFhSOhXolW/fUeSSUGybVrHuV/E\nHe5ctk1ybrI0bq65M9v+dk+fkvT6yutNej3h/MU6cBiA9Nc2pHR8dMuuXl3P+268fBb8tqNBoCd0\nkNcEOq18l2yi2snktTOZd8pr0hW2JR3TuLlnTm0/v5NJh/Klvgp4rhAFWehzUlW0RZEWhMFBxsvt\nCsPJv+mYAitm1T/0VIJsJ5sUdDJRcLpfxG13LtsmOTdZGrfo1t1tf1t7DyS9vg6Hk15PEIY6Tnmt\n+H5HebUaGwE4+OT8+Elnsgl5J5P0xJW8tu0OeU1aaCRJkK21e3/7+Z1MOnQo9VXAc4UoyIIgCIIg\nCILgQBRkYcBItDSLRVkQBganL/KoH6/p9LhJd8fLaLKCCyf/5uIBq24nQZXC+YDThWL0v3cur5Pv\n2Ry3KnPqu8nlNZlbxDkhhbiNgUQUZGHQMNBBlqKgC+cLetncuOwg3QlydFL2nx0H51E/XtMn1e3c\nUyfhnjqpy2OyVsWntEq65CsIPWWQBpfGXCi6S9lPkstrX+UePxuvVSTETKQQtzGQiIIsDErePrnl\nnCusA62gC8K5Qq3dmlJ2kM5wZWXhLh3R3l5aGrVfW0bt19qj9GPW3FjmgFSJlei29h/C2n/I0Wmj\nrLTc1p4/teVyUxhBeX1tVnBXejqesWO6dU1BSMogLKTWE9z5ebiL2/OZu7KyOPW9i5OuACXmOj5r\n2473QDLC17bX1rpptMnqodLS2tJtxuR9MCIKsnBO6K6yO1SLigjCcOHAf8fnwj3yL+3Kb7SlBauy\nqu27DgYpenQtRY+2R+nHrLmxzAGpkrREd2YmKDNcZb3QMX+qDofarODRQIDI8RPduqYgDHUS5fXw\nj9rl1TrTgFVT2/Y92tJC2X+uSboClCzXcVc43wMxnK5O3nc2ddivg8G2dJvJ5H2wMChLTSulqoGj\nA90PQUhgvNb63NTwHGKIzAqDEJHXThB5FQYhg05eB6WCLAiCIAiCIAgDhbhYCIIgCIIgCIIDUZAF\nQRAEQRAEwYEoyIIgCIIgCILgQBRkQRAEQRAEQXAgCrIgCIIgCIIgOBAFWRAEQRAEQRAciIIsCIIg\nCIIgCA5EQRYEQRAEQRAEB6IgC4IgCIIgCIIDUZAFQRAEQRAEwYEoyIIgCIIgCILgQBRkQRAEQRAE\nQXAgCrIgCIIgCIIgODjvFGSl1DilVLNSyj3QfUkFpdQVSqkTju87lVJXDGCXuoVS6n6lVKX9mxcp\npS5RSu23v9+ilHpTKfXlFNoZUvct9A0ir+cWkVeht4jMnltEZvsPpbUe6D6kjFLqCDAKGKW1rnFs\n3wzMAyZqrY8MTO/6B/uBfUJrPWag+9JdlFJeoBG4SGu91d72HvCK1vp/BqhPDwBTtNZfHIjrn0+I\nvA4tRF4Fkdmhhchs/zIULciHgbtiX5RSFwCZA9cdoQtKgXRgp2Pb+ITvwvBG5HXoIPIqgMjsUEJk\ntj/RWg+ZD3AE+L/ARse2/wD+HtDABHvbp4HNmJnVceABx/ET7GM99vcPgH8BPgaagHeA4i76cDOw\nxW77IHC9vX0U8ApQBxwAvu4457fADx3frwBOJNzX3wK7gHrgMSC9i2Ovtv9+AHgO+L3d953AIsex\nC+zfoQn4A/Cssx9J7u3rwG77+F3AAnv7TPt3OmNf47OOc9Ls/4NjQCXwCyADmAa02L91M7DS/r2i\ngN/elma3+7UU+uC8bxfw/9nt1dq/QWHC/++X7T7VAH9v77seCAFh+/pb7e1fAQ7Z1zwM3DPQz/pw\n+CDymvjcPoDIq8jrIP4gMpv47D6AyOx5K7MD3oEeCO/VwF77gXIDJzAzJqfwXgFcYP8nX2g/VLd0\nIbwH7Yctw/7+o06uvwRoAK6x2x4NzLD3rQJ+jpnNzQOqgeXdEN4dwFigEPMi+WGKwhsAbrR/iweB\ndfY+H3AU+AvAC3zOfnCTCi9wO1ABLAYUMMX+Xb2Yl9Hf2W0utx/y6fZ5/4V5aRUCOcCrwIPJfuvE\n/jt+/6911Yck9/0XwDpgDOYF8Evg6YRrPmL/f84FgsBMx2/2hOP6WZgXcex+yoDZA/2sD4cPIq+J\nz+0DiLyKvA7iDyKzic/uA4jMnrcyOxRdLAAeB+7FCNFuzH94G1rrD7TW27XWUa31NuBp4FNdtPeY\n1nqf1tqPmSnN6+S4rwK/0Vr/0W67Qmu9Ryk1FrgE+L7WOqC13gI8avcxVX6qtT6uta4D/hXHEtdZ\nWK21fkNrbWF+l7n29osAD/CQ1jqstX4R2NBFO18Dfqy13qgNB7TWR+12sjEvtJDWeiXwGnCXUkoB\n3wD+Smtdp7VuAv4NuLMb951KHxL5FmbGekJrHcQI5OeVUh7HMf+ktfZr45e1lfbfJRlRYI5SKkNr\nfUprLctTfYvIazsiryKvQwGR2XZEZs9TmfWc/ZBByeOY2eREzNJHHEqppcCPgDmYGVkaZvmjM047\n/m7FPKzJGAu8kWT7KCD28MY4Cizq4pqJHE84d1SK5yX2Pd1+iEcBFVqbKVuSayQyFjPLT2QUcFxr\nHU3o32igBOObVm7kGDCz0p5GL3fWh0TGAyuUUs4+WRh/rBgp/Z9qrVuUUncAfw38Win1MfA9rfWe\nbvVc6AqR13ZEXg0ir4Mbkdl2RGYN553MDkkLsj3jOYxZ9ngxySFPYZYkxmqt8zA+OyrJcd3lODA5\nyfaTQKFSKsexbRzts+4W4oMcRiZpY2zCuSd70U+AU8Bo5ZCqhGsk0tW9jVVKOZ+V2L3VYHydZmut\n8+1Pnta6s5ff2eisD8mOu8FxzXytdbrWuuKsZ5qlofgNWr+ttb4Gs/SzB7N0JPQRIq8pIfKaHJHX\nAUBkNiVEZpMzbGR2SCrINl/F+B+1JNmXg5ltBpRSS4C7++iavwbuU0pdpZRyKaVGK6VmaK2PA2uA\nB5VS6UqpC+3+PWGftwW4USlVqJQaCfxlkrb/TCk1RilViAmIeLaXfV2LmfF9WynlUUrdjPHv6oxH\ngb9WSi1UhilKqfHAeszs8G+UUl47Jc5ngGfsGe8jwH8ppUYA2L/JdT3sc2d9SOQXwL/G9imlSuz7\nS4VKYELsZaSUKlVK3ayUysL4UTVjloOEvkXktWtEXpMj8jpwiMx2jchscoaNzA5ZBVlrfVBrvamT\n3X8K/LNSqgn4AcbnqS+uuQG4D+M03wB8iFmKAOPPNAEzG1wB/KPW+l173+MYH50jmAjeZIL5lL3v\nEGYJ5Ie97GsIEzTwVUxk7Bcxfk3BTo7/A8Yv6ylMgMBLmKjVEEZYb8DMZn8O3OtYHvk+JsBgnVKq\nEXgXmN7DPiftQ5JD/wdjvXjH/j9eByxN8TKxZcBapdQnGBn4Lub/rQ7jR3d/T/ovdI7I61n7KvKa\nHJHXAUJk9qx9FZlNzrCR2SFVKGS4okxy9q85hL2/rrMe+IXW+rH+vI4gDGdEXgVhaCEyK/SEIWtB\nFs6OUupTSqmR9vLPlzHpeN4a6H4JgtARkVdBGFqIzA5vhmoWCyE1pmOWvrIwy0qf11qfGtguCYLQ\nCSKvgjC0EJkdxoiLhSAIgiAIgiA4EBcLQRAEQRAEQXAgCrIgCMMGpdQ9Sql3Ujz2K0qp1d3dJwhC\ncs43+VNK/VYp1atsGMLgRRTkHqKUOqKU8iulmh2fn9qCrZVS/5Vw/M329t/a3yfY32PnViqlXlNK\nXeM4x9l2NOF695zjW+4z7NyL4tsj9Bil1KVKqTVKqQalVJ1S6mOl1GKt9ZNa62sHun/dQeRcGGoM\nJ/mDtvH86l624VNKPW+3pe18xrF9bzpkOqyUCjm+/6LXNzCAKKVOOO91OCEKcu/4jNY62/H5tr39\nIPAFFV+3/MvAviRt5NtVceYCf8SUd/wKgLNt4FjC9Z5MbCjheoOSodBHYXCjlMrF5Bv9X0wOz9HA\nP9FJ/tGB5mzPvMi5MJQYbvLXx6zG5EN2lmFGa32DQ8afBH7skPFvJTYyFORnKPSxt4iC3D+cBrYD\n1wEoU7nnYkzi7aRorU9rrf8HeAD4dxVfdjIpSqkfKqWeVUo9bSfz/qJSaplSap1S6oxS6pRS6iGl\nlNc+3mPPbL+plDqglKpXSj3kaG+aUmqVbRWoUUo9lXDenyulDtv7fqTaK+W4lFI/UEodVUpV2ctO\nufa+Kfa59ymljmESta+y98Vm0Iu7/xML5zHTALTWT2utLa21X2v9jtZ6m0pYmrWfvW8ppfbbMvEz\npVTSkrhKqf+nlFqtlMpzbPsPW04OK6VucGwfpZR6xbaeHVBKfd2x7wHbkvSEMon9v2Jve04p9Xul\nVJNSaqdSalEqNytyLgwyzhv5U0pdoYyF9O9seTiiOlnV0VqHtNb/rbVejamwlzJKqavttv9OKXUa\neEQpVaSUekMpVW3/Bq8qpUY7zlmtlPonZSz5TUqpt5TRNVBKZSqlnlJK1dq/+walVLHjvH9VSm2y\n3wErlFIFjnZvtX+fM0qplUqp6Y59J5RS/0cptR1oUUo9DYwCYhby73bnvgc7oiD3H78H7rX/vhN4\nmdRm2C8CI0i9Us6tmKo4eZjqQRHgL4Bi4BLgeuCbCefcCCwE5mMG29jS0r8CrwMFwBjgZwnn3Qws\nsM/9PO339zXMrPkKTJ33AkwlHieXAzOAT9t/Oy1nG1O8V0EAsxJjKaV+p5S6wfly74SbgMWYHKVf\nwJ64xrAVv0fs/ddqrRvsXUuBvRhZ+jHwa8fg/gxwAjM4fB74N6XUckezNwPPA/kYixHAZ+3z8jGT\n5Z92455FzoXBwvkmfyPtPozGrAT/yqk09iFjgGxgHKZSoQtTZnocpppgmI7ydrfdp1JMqrmYgnof\nkGm3WWS3F3Ccd6/9GQUoTOVClFIzMVUJ/xwowVTte0XZk2+bOzFV//K11ndhKuTFLOQ/6dUvMMgQ\nBbl3vGTPsmKfrzv2rQCusGfD92IU5lQ4af+brPxjMlZrrV/VWkftmfxGrfV6rXVEa30I+BWmtKOT\nB7XWDVrrI8AHwDx7exhTyrNMax3QWn+ccN6PtNb1WuujwEOY0p8A9wD/obU+rLVuAv4OuFvFW8H/\nUWvdqrX2p3hfgpAUrXUjcCmgMQNItW1NKu3klB9prc9orY8B79P+vAN4gacx8vYZrXWrY99RrfUj\nWmsL+B1QBpQqpcZilNLv23KyBXiUdkUSYK3W+qWYXNrbVmut37DbexzjVpUqIufCoOA8lb9/0FoH\ntdYfYiaXX+jGuakSAR6wLdF+rXW11nqF/Xcj8G90lPFfa63327/bH4iX8WJgim3l36S1bnac9zut\n9S6tdQumVPid9uTjTuAVrfVKrXUY+BFmUu4sM/0/WusT54OMi4LcO27RWuc7Po/EdtgPz+vA/wWK\nkgxCnRFbQqlL8fjjzi9KqRlKqdeVUqft5aV/xgiKE6d/VCtm1grwPcwLa5NSarsylYE6u9ZRzOwT\n+9+jCft8mBlo0n4KQm/QWu/WWn9Faz0GmIN5Bv+7k8M7e94BpmCsTf+ktQ51dp5j4M62r1VnK4kx\njtIuu5D8eU/sR7pK3Y9P5FwYNJxn8ldvK5LOa43q7OBeUOn8DZRS2UqpR5VSx2wZX0nqMv5bjPX3\nOaVUhTKuUs57TZTxNMwkJU7GtdZRjKX+bL/tsEQU5P7l95jB6IlunHMrUIVZWkqFxCjxXwI7MDPH\nXMzsMKnPV4eGtD6ltf6a1roM+DPMUtJExyFjHX+Po93afRKzBOTcFwKqHW07+ymR7UKfobXegxkQ\n5vTg9N2Y5cg3u7FsehIoVErlOLaNAyqc3epBX7pC5FwYlJwH8leglMpKuNbJzg7uBYl9/j/ARGCJ\nLePLO57SSUPGCv2A1nomxtp/K2YFKEaijAcxRrk4GbdXh8bQ9W87bOVcFOT+5UPgGky0b5copUqV\nUt8G/hH4W3vm1hNygAaMA/1MOvoldtWHLziCAM5gHnxnsMHfKKXylVLjgO9gfCHBLJF9V5nUdTkY\nH8enu7iHKkArpSalfFeCYGNbT7+nlBpjfx+LcQNY15P2tNZPY9wF3lVKTU7h+OPAGuBBpVS6UupC\n4Kt0byLcW0TOhQFhGMuf124v9nFaXP9JmTRul2F8qv+QrAGlVJpSKt3+6rPbSWnimoQcjFW4XilV\nhJkEp4RSarlSao6t4DZiXC6ccnqv/f+YhclA8pw9uX0O+KwywYlejJLeBKzv4nKVwLCUcVGQe8er\nKj6H6QrnTm14T2vdlbvEGaVUCybrxY3A7Vrr3/SiT9/DOO03YaxMz3Z9eBxLgY12f14E/sz2G4vx\nKrAF2Izxsf6tvf0R+zofYerRN2ECiJJiL409CKy3fbdTiuYXBJsmzLO63n5W12Gsqd/raYNa699h\n3BRWKqUmpHDKXRg/3pMYWfhHrfW7Pb1+DxA5FwaK4Sp/bwB+x+cBe/tpoN6+1pPAt2yreTL22ueO\nBt62/x7fybFn4ycY/99azITgzW6cOwoj243AToy7xVOO/Y9jJhSnADfwlwBa652Y98rDmJWh64HP\n2v7InfFvmAnEGaXUX3ajj4MeFb8iJggdsWfSYWCiHfAjCMIwQ+RcEOJRpgDGE7av9bBAmTR8j2qt\nfzvQfRnsiAVZEARBEARBEByIgiwIgiAIgiAIDsTFQhAEQRAEQRAciAVZEARBEARBEBykmqQegOLi\nYj1hwoR+6oogCADl5eU1WuuSsx/ZNSKvgnBu6AuZFXkVhHNDqvLaLQV5woQJbNq0qee9EgThrCil\njp79qLMj8ioI54a+kFmRV0E4N6Qqr+JiIQiCIAiCIAgOREEWBEEQBEEQBAeiIAuCIAiCIAiCA1GQ\nBUEQBEEQBMGBKMiCIAiCIAiC4KBbWSyGAturKvn5xnUcqq9nbulI7l+8lIn5BQPdLUEQktAYDPLY\nlnLePrCf7LQ0vjJ3PjdMmYZSaqC7JghCEt47fJBfby6nzu/nqomT+Nr8RRRkZAx0twShzxlWCvKq\no0e4//WXCUQiaOBgfR1vHNjH87ffxYziXqeVpba1lfUVx8nwerlk7Hh8bnfvOy0I5ymt4TC3PPME\np5qbCFoWADurqthaeZq/vfRTvW7fikZZV3GcOr+fhWWjGJWT2+s2BeF85uGN6/npxnX4IxEAjpyp\nZ8WeXbxx973kp/deST565gzbqk5TmpXN4lGjZaIsDCjDRkHWWvOD9981gqshU2fT6mqmNRzmwdWr\n+N0tt/Wq/d9sLuf/rfkIj8uNAtwuxWM338a8kWV9cwOCcJ7xwq4dVLY0E7Qs0iLphFQQP2F+t3Uz\nX52/kBFZ2T1u+/CZeu558TmagiFAE4lGueeCufz9ZVfIoCsIPaAxGOShDWsJWhauqAuP5SVEkHq/\nnye2beHbS5b1uO2o1vzNH9/i9f178biM52dJVhZPfe4LjMzO6atbEIRuMWx8kFvCYSqamgAYG57M\n5xq+ysLWy/FG0/jkVEWv2t5aeZr/WLuaoGXREg7RHA7REAxy38svELItX4IgdI8Pjh7GH4ngstws\n8S9ngf9ysqwcfC43m0+f6nG7Wmu+9soKKpubaQmHaAmHCVoWT+/YztsHD/ThHQjC+cOu6qq2VdOp\ngQtZFriWsuA4QpEo7x853Ku2n96xjTcP7LPH2DAt4TDHGhr48zdf64uuC0KP6FcFOao1jcEgUa37\n8zIApHs8bTPPGs9pDvn2MDu4iM81/glzwguIRnve9nM7t7cpwotaP8X40DQALK1Zc/xYr/suCIOF\n1nCYoL182t+UZefgUoqoy+KU+xjZOpcLAksY459Gnqvn1uP9dbWcbm5CA4WBUuY2X4Q3moY/Eubx\nbZv77gYEYYAJWxZNwSD6HIyxxZmZROyB9JT7GCECTAnPMTLrGkVvuvD4ts34IxFUVLGkaTmFoRKi\nWrO9qpLqlpY+ugNB6B794mKhtebRTzbxs03raQ2Hyfb5+KulF/OlufP743IAeFwubp81m+d378Qf\naWFN1tvsSdvM0sAVzKi/lIcfhmuvhSlToLsrrE22ku/WHkojo5kdXMSh0G62531EazjUPzckCOeQ\n3dVVfP/dt9ldU41SiuUTJvHgVdf2a/DNl+bO58U9uwjoCId9uwmGgxTqIsooo3H/SPaFYdIk8HTz\nLeUPh3ErM1nOjxYxMjKOgqYS9mZuI+CPEo2Ca9isnQnnI8FIhB+u+oDnd+/A0pqy7Bx+eOXVXDZ+\nQr9dc0phEVMKi9hdXUWTt569aislkTLyVC4L1Tw2bIAZMyAvr/ttt4bDAGRGcyi0Sris5dPstbZy\nNHsn/ki4j+9EEFKjX4aJ323dzH+vX0NjMEgkGuVMIMCPPl7F87t29Mfl2vj7y67g2klT8Lnd5Ph8\ntKTVMvrSQ9xxhyYahaeegiefhKqq7rV7w9RpZHq9WCrCGzlPszn9YyaEp3FN3d2MCk/ol3sRhHNF\ndUsLdzz/LDuqq7C08dd9/8gh7lnxh361TE0vKuYn19xAbloaGWlemtNryC4I883rJlFaqjh8GD76\nCI4fp1srQDNLRrRNgg9l7mJj+iosLC5oXcwl7oupqoKGBgiF6JXVSxAGir/+41s8v3snQcsiEo1y\nvLGBb77+MjuqKvv1uo9+9lYuLB1JmtdNJL2JUFoL1y4o5dL52TQ3w9q1sG0b+P3da/e6yVPxuty0\neBp5L2sFJz1HmRGYx6eaP01WpAcatyD0Aao7A+CiRYv0pk2bzn7cIz+nzu9HacXk0GwO+nailWZU\nTg6r7/tGb/obR0VTI//4/nusOnYEj8vFTVOn838vv4KQFeVUcxPj8/LJTUsDwLJg40b48EMIBmHB\nArjySsjKOvt1rGiUP3nlRcpPnqQ1EsalFKXRMm6M3EKkOYNFi+Caa8Dn67NbE85jlFLlWutFvW0n\nVXn96Ya1/GzjeoKWRVl4HA2uOlrdzWR6vfzulttYWDa6t10BjMvVLzdt4NHN5TQEA8woKuYHn1rO\n/JFl7K6qIdOTRnown8ZGmDDBKLB79hhlNjsbpk2DkhST0bx1YB/ffedNwpaFpTWFuoC5ah6XF8yj\nIM/FnDmmTZcL0tMhIwMkKY3QU/pCZlOV1+rWFi577BFCloXPyqDEGkmF7zAKuH7KVH5242d70404\ntpw+xQMfrGRHdSU5Ph9fnruAby+5iNPNTdQ0BRidUURNpYf8fCguhoMH4dgxs0o7YQJMnJjaCtCZ\ngJ+bn3mSmtYW/JEIvqiH8eFpXJO+nCxPGsuWwdy53V/9FYRkpCqvfe5iEdWaOnv6OC48lUtar2Nq\ncA6rs96iqqWxz67THApxyzNPcibgb7N6vbx3D7uqq3j1ri9RnJkZd7zbDRddBBdeaJTkTZtg+3a4\n7DKzvSshdrtc/Oazn+OdQwd468B+sn0+7ph9ATMLM1i5Etatg0OH4JZbYOzYPrtFQTgn7K+ra4tM\nn+NfihsXu9I3UeM5xvGGhj5TkH+0+kOe3L61LUXUrppq7nv5BZ75/J3MHlGKZZkBsKUFTp407hVL\nl8Lp07BvH3zyCRQVmWXc7LO4KF8/ZRpTC4t4asc2KpubuXzcRK4cNYOaShd79hj5nz4dxo0z1q7W\nVvB6jbKcni4DsTB4OdnYiM/tJmRZTAvNYVxoGqWRsWxLX8vB+vo+u87+2lruefG5Nnl7+c2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/fuNRPaq682k3jh3KN1CAKvoQPvgKsAlXEnyjd3oLs1NBXkGAcPGr/d5mYTbHDZZakpoOWnKvhV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K+OhleNxpZk1dQ75Yx9CgTMYUv6vHI4Bxc7MI4k7xn6K4Qb0cyXc5ti0WsU89JebpqVNFutyR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yFJExbR4iqIfK5yL4GzcOUYbvorYPdSLIV5Yfu7kWWLJXN/NX7M/RB875s5JP8n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\ny7K455cPk0lmkBSV2tO/ROHMxXS++hitrU+jXvGr0fcuOmUeT9/yI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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(2, figsize=(10, 6))\n", + "\n", + "pl.subplot(2, 3, 1)\n", + "pl.imshow(ot_emd.coupling_, interpolation='nearest')\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.title('Optimal coupling\\nEMDTransport')\n", + "\n", + "pl.subplot(2, 3, 2)\n", + "pl.imshow(ot_sinkhorn.coupling_, interpolation='nearest')\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.title('Optimal coupling\\nSinkhornTransport')\n", + "\n", + "pl.subplot(2, 3, 3)\n", + "pl.imshow(ot_lpl1.coupling_, interpolation='nearest')\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.title('Optimal coupling\\nSinkhornLpl1Transport')\n", + "\n", + "pl.subplot(2, 3, 4)\n", + "ot.plot.plot2D_samples_mat(Xs, Xt, ot_emd.coupling_, c=[.5, .5, 1])\n", + "pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\n", + "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.title('Main coupling coefficients\\nEMDTransport')\n", + "\n", + "pl.subplot(2, 3, 5)\n", + "ot.plot.plot2D_samples_mat(Xs, Xt, ot_sinkhorn.coupling_, c=[.5, .5, 1])\n", + "pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\n", + "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.title('Main coupling coefficients\\nSinkhornTransport')\n", + "\n", + "pl.subplot(2, 3, 6)\n", + "ot.plot.plot2D_samples_mat(Xs, Xt, ot_lpl1.coupling_, c=[.5, .5, 1])\n", + "pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\n", + "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.title('Main coupling coefficients\\nSinkhornLpl1Transport')\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig 3 : plot transported samples\n", + "--------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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iTrVtt1q5cMRILhwxspMSazS9hx3l5QQiYdLc8YAxIJTuTqDYW8+RulpGDkoB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ADODCR5e5fG1V3pu4iIgUBnf781qEGr/82UQVkjwBoblkZkwCo0pzjWSJACJHVvm4oUIjEo+Bpa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\nc5JQFD1Vo72pkzd+v4aVj94uOjIKQh/d7S40TV+Aaihv6vf3s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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# display transported samples\n", + "pl.figure(4, figsize=(10, 4))\n", + "pl.subplot(1, 3, 1)\n", + "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", + " label='Target samples', alpha=0.5)\n", + "pl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,\n", + " marker='+', label='Transp samples', s=30)\n", + "pl.title('Transported samples\\nEmdTransport')\n", + "pl.legend(loc=0)\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "\n", + "pl.subplot(1, 3, 2)\n", + "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", + " label='Target samples', alpha=0.5)\n", + "pl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,\n", + " marker='+', label='Transp samples', s=30)\n", + "pl.title('Transported samples\\nSinkhornTransport')\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "\n", + "pl.subplot(1, 3, 3)\n", + "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", + " label='Target samples', alpha=0.5)\n", + "pl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,\n", + " marker='+', label='Transp samples', s=30)\n", + "pl.title('Transported samples\\nSinkhornLpl1Transport')\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "\n", + "pl.tight_layout()\n", + "pl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_otda_mapping.ipynb b/notebooks/plot_otda_mapping.ipynb new file mode 100644 index 0000000..f25eb11 --- /dev/null +++ b/notebooks/plot_otda_mapping.ipynb @@ -0,0 +1,288 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# OT mapping estimation for domain adaptation\n", + "\n", + "\n", + "This example presents how to use MappingTransport to estimate at the same\n", + "time both the coupling transport and approximate the transport map with either\n", + "a linear or a kernelized mapping as introduced in [8].\n", + "\n", + "[8] M. Perrot, N. Courty, R. Flamary, A. Habrard,\n", + " \"Mapping estimation for discrete optimal transport\",\n", + " Neural Information Processing Systems (NIPS), 2016.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Authors: Remi Flamary \n", + "# Stanislas Chambon \n", + "#\n", + "# License: MIT License\n", + "\n", + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + "-------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n_source_samples = 100\n", + "n_target_samples = 100\n", + "theta = 2 * np.pi / 20\n", + "noise_level = 0.1\n", + "\n", + "Xs, ys = ot.datasets.get_data_classif(\n", + " 'gaussrot', n_source_samples, nz=noise_level)\n", + "Xs_new, _ = ot.datasets.get_data_classif(\n", + " 'gaussrot', n_source_samples, nz=noise_level)\n", + "Xt, yt = ot.datasets.get_data_classif(\n", + " 'gaussrot', n_target_samples, theta=theta, nz=noise_level)\n", + "\n", + "# one of the target mode changes its variance (no linear mapping)\n", + "Xt[yt == 2] *= 3\n", + "Xt = Xt + 4" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot data\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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39DRG1p4fRyNHjmThwoUsX76c8ePHF87YqyuuuOKKGm+laoiSPG4kRteXuIQk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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(1, (10, 5))\n", + "pl.clf()\n", + "pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\n", + "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\n", + "pl.legend(loc=0)\n", + "pl.title('Source and target distributions')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Instantiate the different transport algorithms and fit them\n", + "-----------------------------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 0|4.210546e+03|0.000000e+00\n", + " 1|4.194392e+03|-3.836611e-03\n", + " 2|4.194053e+03|-8.094371e-05\n", + " 3|4.193924e+03|-3.056965e-05\n", + " 4|4.193849e+03|-1.797118e-05\n", + " 5|4.193801e+03|-1.140070e-05\n", + " 6|4.193776e+03|-5.930168e-06\n", + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 0|4.245881e+02|0.000000e+00\n", + " 1|4.181680e+02|-1.512078e-02\n", + " 2|4.178974e+02|-6.472597e-04\n", + " 3|4.177550e+02|-3.406786e-04\n", + " 4|4.176586e+02|-2.307406e-04\n", + " 5|4.175879e+02|-1.692203e-04\n", + " 6|4.175338e+02|-1.295518e-04\n", + " 7|4.174909e+02|-1.028089e-04\n", + " 8|4.174570e+02|-8.123852e-05\n", + " 9|4.174309e+02|-6.257777e-05\n", + " 10|4.174083e+02|-5.401101e-05\n" + ] + } + ], + "source": [ + "# MappingTransport with linear kernel\n", + "ot_mapping_linear = ot.da.MappingTransport(\n", + " kernel=\"linear\", mu=1e0, eta=1e-8, bias=True,\n", + " max_iter=20, verbose=True)\n", + "\n", + "ot_mapping_linear.fit(Xs=Xs, Xt=Xt)\n", + "\n", + "# for original source samples, transform applies barycentric mapping\n", + "transp_Xs_linear = ot_mapping_linear.transform(Xs=Xs)\n", + "\n", + "# for out of source samples, transform applies the linear mapping\n", + "transp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new)\n", + "\n", + "\n", + "# MappingTransport with gaussian kernel\n", + "ot_mapping_gaussian = ot.da.MappingTransport(\n", + " kernel=\"gaussian\", eta=1e-5, mu=1e-1, bias=True, sigma=1,\n", + " max_iter=10, verbose=True)\n", + "ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt)\n", + "\n", + "# for original source samples, transform applies barycentric mapping\n", + "transp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs)\n", + "\n", + "# for out of source samples, transform applies the gaussian mapping\n", + "transp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot transported samples\n", + "------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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7eZHN9B+Lx6idWsXerQ1oGGadLSrrKvDTATMWTGX1P9cRK4pRXlWKBiF+KqS8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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(2)\n", + "pl.clf()\n", + "pl.subplot(2, 2, 1)\n", + "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", + " label='Target samples', alpha=.2)\n", + "pl.scatter(transp_Xs_linear[:, 0], transp_Xs_linear[:, 1], c=ys, marker='+',\n", + " label='Mapped source samples')\n", + "pl.title(\"Bary. mapping (linear)\")\n", + "pl.legend(loc=0)\n", + "\n", + "pl.subplot(2, 2, 2)\n", + "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", + " label='Target samples', alpha=.2)\n", + "pl.scatter(transp_Xs_linear_new[:, 0], transp_Xs_linear_new[:, 1],\n", + " c=ys, marker='+', label='Learned mapping')\n", + "pl.title(\"Estim. mapping (linear)\")\n", + "\n", + "pl.subplot(2, 2, 3)\n", + "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", + " label='Target samples', alpha=.2)\n", + "pl.scatter(transp_Xs_gaussian[:, 0], transp_Xs_gaussian[:, 1], c=ys,\n", + " marker='+', label='barycentric mapping')\n", + "pl.title(\"Bary. mapping (kernel)\")\n", + "\n", + "pl.subplot(2, 2, 4)\n", + "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", + " label='Target samples', alpha=.2)\n", + "pl.scatter(transp_Xs_gaussian_new[:, 0], transp_Xs_gaussian_new[:, 1], c=ys,\n", + " marker='+', label='Learned mapping')\n", + "pl.title(\"Estim. mapping (kernel)\")\n", + "pl.tight_layout()\n", + "\n", + "pl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_otda_mapping_colors_images.ipynb b/notebooks/plot_otda_mapping_colors_images.ipynb new file mode 100644 index 0000000..288f4f1 --- /dev/null +++ b/notebooks/plot_otda_mapping_colors_images.ipynb @@ -0,0 +1,363 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# OT for image color adaptation with mapping estimation\n", + "\n", + "\n", + "OT for domain adaptation with image color adaptation [6] with mapping\n", + "estimation [8].\n", + "\n", + "[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized\n", + " discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3),\n", + " 1853-1882.\n", + "[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, \"Mapping estimation for\n", + " discrete optimal transport\", Neural Information Processing Systems (NIPS),\n", + " 2016.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Authors: Remi Flamary \n", + "# Stanislas Chambon \n", + "#\n", + "# License: MIT License\n", + "\n", + "import numpy as np\n", + "from scipy import ndimage\n", + "import matplotlib.pylab as pl\n", + "import ot\n", + "\n", + "r = np.random.RandomState(42)\n", + "\n", + "\n", + "def im2mat(I):\n", + " \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n", + " return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n", + "\n", + "\n", + "def mat2im(X, shape):\n", + " \"\"\"Converts back a matrix to an image\"\"\"\n", + " return X.reshape(shape)\n", + "\n", + "\n", + "def minmax(I):\n", + " return np.clip(I, 0, 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + "-------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Loading images\n", + "I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256\n", + "I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n", + "\n", + "\n", + "X1 = im2mat(I1)\n", + "X2 = im2mat(I2)\n", + "\n", + "# training samples\n", + "nb = 1000\n", + "idx1 = r.randint(X1.shape[0], size=(nb,))\n", + "idx2 = r.randint(X2.shape[0], size=(nb,))\n", + "\n", + "Xs = X1[idx1, :]\n", + "Xt = X2[idx2, :]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Domain adaptation for pixel distribution transfer\n", + "-------------------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 0|3.680514e+02|0.000000e+00\n", + " 1|3.592355e+02|-2.395298e-02\n", + " 2|3.590579e+02|-4.943115e-04\n", + " 3|3.589661e+02|-2.556530e-04\n", + " 4|3.589094e+02|-1.577896e-04\n", + " 5|3.588705e+02|-1.084239e-04\n", + " 6|3.588421e+02|-7.914757e-05\n", + " 7|3.588206e+02|-6.013011e-05\n", + " 8|3.588034e+02|-4.783086e-05\n", + " 9|3.587895e+02|-3.866500e-05\n", + " 10|3.587781e+02|-3.194454e-05\n", + " 11|3.587684e+02|-2.697250e-05\n", + " 12|3.587601e+02|-2.296505e-05\n", + " 13|3.587530e+02|-1.975975e-05\n", + " 14|3.587468e+02|-1.733678e-05\n", + " 15|3.587413e+02|-1.535580e-05\n", + " 16|3.587365e+02|-1.350581e-05\n", + " 17|3.587321e+02|-1.209997e-05\n", + " 18|3.587282e+02|-1.086348e-05\n", + " 19|3.587268e+02|-4.096770e-06\n", + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 0|3.784725e+02|0.000000e+00\n", + " 1|3.646380e+02|-3.655332e-02\n", + " 2|3.642858e+02|-9.660434e-04\n", + " 3|3.641516e+02|-3.683776e-04\n", + " 4|3.640785e+02|-2.008220e-04\n", + " 5|3.640320e+02|-1.276966e-04\n", + " 6|3.639999e+02|-8.796173e-05\n", + " 7|3.639764e+02|-6.455658e-05\n", + " 8|3.639583e+02|-4.976436e-05\n", + " 9|3.639440e+02|-3.946556e-05\n", + " 10|3.639322e+02|-3.222132e-05\n" + ] + } + ], + "source": [ + "# EMDTransport\n", + "ot_emd = ot.da.EMDTransport()\n", + "ot_emd.fit(Xs=Xs, Xt=Xt)\n", + "transp_Xs_emd = ot_emd.transform(Xs=X1)\n", + "Image_emd = minmax(mat2im(transp_Xs_emd, I1.shape))\n", + "\n", + "# SinkhornTransport\n", + "ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\n", + "ot_sinkhorn.fit(Xs=Xs, Xt=Xt)\n", + "transp_Xs_sinkhorn = ot_emd.transform(Xs=X1)\n", + "Image_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))\n", + "\n", + "ot_mapping_linear = ot.da.MappingTransport(\n", + " mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True)\n", + "ot_mapping_linear.fit(Xs=Xs, Xt=Xt)\n", + "\n", + "X1tl = ot_mapping_linear.transform(Xs=X1)\n", + "Image_mapping_linear = minmax(mat2im(X1tl, I1.shape))\n", + "\n", + "ot_mapping_gaussian = ot.da.MappingTransport(\n", + " mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True)\n", + "ot_mapping_gaussian.fit(Xs=Xs, Xt=Xt)\n", + "\n", + "X1tn = ot_mapping_gaussian.transform(Xs=X1) # use the estimated mapping\n", + "Image_mapping_gaussian = minmax(mat2im(X1tn, I1.shape))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot original images\n", + "--------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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qNq5ipmDVJKy54S1abHK9fg5sDYglh6Ciy2jxlayRd2z3pINyoXM/73hLoFXo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\nwfUqpJxi59h1POQ0PkML0QJyh6YEOZPEIvy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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(1, figsize=(6.4, 3))\n", + "pl.subplot(1, 2, 1)\n", + "pl.imshow(I1)\n", + "pl.axis('off')\n", + "pl.title('Image 1')\n", + "\n", + "pl.subplot(1, 2, 2)\n", + "pl.imshow(I2)\n", + "pl.axis('off')\n", + "pl.title('Image 2')\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot pixel values distribution\n", + "------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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YRok12EdnRzczZ09hxqzJ9Pf11zy/iDB97hldru2MmYMOx8liqANtbgFuV9Uvicgy4Dsi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\nIPjigka8RIIBIzZhwUeTKGRcO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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(2, figsize=(6.4, 5))\n", + "\n", + "pl.subplot(1, 2, 1)\n", + "pl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)\n", + "pl.axis([0, 1, 0, 1])\n", + "pl.xlabel('Red')\n", + "pl.ylabel('Blue')\n", + "pl.title('Image 1')\n", + "\n", + "pl.subplot(1, 2, 2)\n", + "pl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)\n", + "pl.axis([0, 1, 0, 1])\n", + "pl.xlabel('Red')\n", + "pl.ylabel('Blue')\n", + "pl.title('Image 2')\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot transformed images\n", + "-----------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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RHPVK1X7zuJyHhhyXA6SqZSIWX3oh4kCHCMv/gupojG3q8bxKu1MlIIhYur2T8nwZYlGs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LuEu7pTrRvyeREASY46RahzJiu2qf4tq6QOhwFih7LmyIr6+zP3jLa+6Te/aCEsh3hIR6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hJkHqkrgez5i7PI+65ZmgTjyZbZveotRU5qwfQFb3TfQjdMWFNhaiGse+ZdxcXDjtnLicqC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\ne7Rknttkrk08fSqIwNWJxwLbULhFZ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u4ryu0M03nDE6vC/fuFA205ksDv3R653EEsHmMM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\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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(2, figsize=(10, 5))\n", + "\n", + "pl.subplot(2, 3, 1)\n", + "pl.imshow(I1)\n", + "pl.axis('off')\n", + "pl.title('Im. 1')\n", + "\n", + "pl.subplot(2, 3, 4)\n", + "pl.imshow(I2)\n", + "pl.axis('off')\n", + "pl.title('Im. 2')\n", + "\n", + "pl.subplot(2, 3, 2)\n", + "pl.imshow(Image_emd)\n", + "pl.axis('off')\n", + "pl.title('EmdTransport')\n", + "\n", + "pl.subplot(2, 3, 5)\n", + "pl.imshow(Image_sinkhorn)\n", + "pl.axis('off')\n", + "pl.title('SinkhornTransport')\n", + "\n", + "pl.subplot(2, 3, 3)\n", + "pl.imshow(Image_mapping_linear)\n", + "pl.axis('off')\n", + "pl.title('MappingTransport (linear)')\n", + "\n", + "pl.subplot(2, 3, 6)\n", + "pl.imshow(Image_mapping_gaussian)\n", + "pl.axis('off')\n", + "pl.title('MappingTransport (gaussian)')\n", + "pl.tight_layout()\n", + "\n", + "pl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} -- cgit v1.2.3 From 9e13cf5ec8fc61c388522312bd7286ec80917712 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Sat, 2 Sep 2017 08:23:41 +0200 Subject: update readme and doc --- README.md | 22 ++++++++++----------- docs/source/auto_examples/auto_examples_python.zip | Bin 44112 -> 44112 bytes docs/source/readme.rst | 22 ++++++++++----------- 3 files changed, 22 insertions(+), 22 deletions(-) diff --git a/README.md b/README.md index 33eea6e..adccae7 100644 --- a/README.md +++ b/README.md @@ -112,17 +112,17 @@ The examples folder contain several examples and use case for the library. The f Here is a list of the Python notebooks available [here](https://github.com/rflamary/POT/blob/master/notebooks/) if you want a quick look: -* [1D optimal transport](https://github.com/rflamary/POT/blob/master/notebooks/Demo_1D_OT.ipynb) -* [OT Ground Loss](https://github.com/rflamary/POT/blob/master/notebooks/Demo_Ground_Loss.ipynb) -* [Multiple EMD computation](https://github.com/rflamary/POT/blob/master/notebooks/Demo_Compute_EMD.ipynb) -* [2D optimal transport on empirical distributions](https://github.com/rflamary/POT/blob/master/notebooks/Demo_2D_OT_samples.ipynb) -* [1D Wasserstein barycenter](https://github.com/rflamary/POT/blob/master/notebooks/Demo_1D_barycenter.ipynb) -* [OT with user provided regularization](https://github.com/rflamary/POT/blob/master/notebooks/Demo_Optim_OTreg.ipynb) -* [Domain adaptation with optimal transport](https://github.com/rflamary/POT/blob/master/notebooks/Demo_2D_OT_DomainAdaptation.ipynb) -* [Color transfer in images](https://github.com/rflamary/POT/blob/master/notebooks/Demo_Image_ColorAdaptation.ipynb) -* [OT mapping estimation for domain adaptation](https://github.com/rflamary/POT/blob/master/notebooks/Demo_2D_OTmapping_DomainAdaptation.ipynb) -* [OT mapping estimation for color transfer in images](https://github.com/rflamary/POT/blob/master/notebooks/Demo_Image_ColorAdaptation_mapping.ipynb) -* [Wasserstein Discriminant Analysis](https://github.com/rflamary/POT/blob/master/notebooks/Demo_Wasserstein_Discriminant_Analysis.ipynb) +* [1D optimal transport](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_1D.ipynb) +* [OT Ground Loss](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_L1_vs_L2.ipynb) +* [Multiple EMD computation](https://github.com/rflamary/POT/blob/master/notebooks/plot_compute_emd.ipynb) +* [2D optimal transport on empirical distributions](https://github.com/rflamary/POT/blob/master/notebooks/plot_OT_2D_samples.ipynb) +* [1D Wasserstein barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_barycenter_1D.ipynb) +* [OT with user provided regularization](https://github.com/rflamary/POT/blob/master/notebooks/plot_optim_OTreg.ipynb) +* [Domain adaptation with optimal transport](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_d2.ipynb) +* [Color transfer in images](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_color_images.ipynb) +* [OT mapping estimation for domain adaptation](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_mapping.ipynb) +* [OT mapping estimation for color transfer in images](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_mapping_colors_images.ipynb) +* [Wasserstein Discriminant Analysis](https://github.com/rflamary/POT/blob/master/notebooks/plot_WDA.ipynb) You can also see the notebooks with [Jupyter nbviewer](https://nbviewer.jupyter.org/github/rflamary/POT/tree/master/notebooks/). diff --git a/docs/source/auto_examples/auto_examples_python.zip b/docs/source/auto_examples/auto_examples_python.zip index 92adf10..141c404 100644 Binary files a/docs/source/auto_examples/auto_examples_python.zip and b/docs/source/auto_examples/auto_examples_python.zip differ diff --git a/docs/source/readme.rst b/docs/source/readme.rst index e5c61f5..1482838 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -150,27 +150,27 @@ Here is a list of the Python notebooks available want a quick look: - `1D optimal - transport `__ + transport `__ - `OT Ground - Loss `__ + Loss `__ - `Multiple EMD - computation `__ + computation `__ - `2D optimal transport on empirical - distributions `__ + distributions `__ - `1D Wasserstein - barycenter `__ + barycenter `__ - `OT with user provided - regularization `__ + regularization `__ - `Domain adaptation with optimal - transport `__ + transport `__ - `Color transfer in - images `__ + images `__ - `OT mapping estimation for domain - adaptation `__ + adaptation `__ - `OT mapping estimation for color transfer in - images `__ + images `__ - `Wasserstein Discriminant - Analysis `__ + Analysis `__ You can also see the notebooks with `Jupyter nbviewer `__. -- cgit v1.2.3 From 2eac78ae9050b9611d7144c4fb6c774580cbe32c Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Sat, 2 Sep 2017 09:26:49 +0200 Subject: small changes --- docs/nb_build | 1 + docs/source/auto_examples/auto_examples_python.zip | Bin 44112 -> 44112 bytes 2 files changed, 1 insertion(+) diff --git a/docs/nb_build b/docs/nb_build index 7f74c70..6abc6cf 100755 --- a/docs/nb_build +++ b/docs/nb_build @@ -12,3 +12,4 @@ sed -i "s/'sphinx\_gallery/#'sphinx\_gallery/" source/conf.py sed -i "s/#sys.modules.update/sys.modules.update/" source/conf.py #rsync --out-format="%n" --update source/auto_examples/*.ipynb ../notebooks2 +./nb_run_conv diff --git a/docs/source/auto_examples/auto_examples_python.zip b/docs/source/auto_examples/auto_examples_python.zip index 141c404..282bc58 100644 Binary files a/docs/source/auto_examples/auto_examples_python.zip and b/docs/source/auto_examples/auto_examples_python.zip differ -- cgit v1.2.3 From 30bfc5ce5acd98991b3d01e313d0c14f0e600b14 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Mon, 4 Sep 2017 08:46:36 +0200 Subject: correction semi supervised case --- ot/da.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/da.py b/ot/da.py index 564c7b7..e694668 100644 --- a/ot/da.py +++ b/ot/da.py @@ -989,7 +989,7 @@ class BaseTransport(BaseEstimator): # assumes labeled source samples occupy the first rows # and labeled target samples occupy the first columns - classes = np.unique(ys) + classes = [c for c in np.unique(ys) if c != -1] for c in classes: idx_s = np.where((ys != c) & (ys != -1)) idx_t = np.where(yt == c) -- cgit v1.2.3 From 363c5f92a4865527320edcff97036e62a7ca28c9 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Mon, 4 Sep 2017 09:12:32 +0200 Subject: doc string + example --- examples/da/plot_otda_semi_supervised.py | 142 +++++++++++++++++++++++++++++++ ot/da.py | 72 ++++++++++++---- 2 files changed, 196 insertions(+), 18 deletions(-) create mode 100644 examples/da/plot_otda_semi_supervised.py diff --git a/examples/da/plot_otda_semi_supervised.py b/examples/da/plot_otda_semi_supervised.py new file mode 100644 index 0000000..6e6296b --- /dev/null +++ b/examples/da/plot_otda_semi_supervised.py @@ -0,0 +1,142 @@ +# -*- coding: utf-8 -*- +""" +============================================ +OTDA unsupervised vs semi-supervised setting +============================================ + +This example introduces a semi supervised domain adaptation in a 2D setting. +It explicits the problem of semi supervised domain adaptation and introduces +some optimal transport approaches to solve it. + +Quantities such as optimal couplings, greater coupling coefficients and +transported samples are represented in order to give a visual understanding +of what the transport methods are doing. +""" + +# Authors: Remi Flamary +# Stanislas Chambon +# +# License: MIT License + +import matplotlib.pylab as pl +import ot + + +############################################################################## +# generate data +############################################################################## + +n_samples_source = 150 +n_samples_target = 150 + +Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source) +Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target) + +# Cost matrix +M = ot.dist(Xs, Xt, metric='sqeuclidean') + + +############################################################################## +# Transport source samples onto target samples +############################################################################## + +# unsupervised domain adaptation +ot_sinkhorn_un = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn_un.fit(Xs=Xs, Xt=Xt) +transp_Xs_sinkhorn_un = ot_sinkhorn_un.transform(Xs=Xs) + +# semi-supervised domain adaptation +ot_sinkhorn_semi = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn_semi.fit(Xs=Xs, Xt=Xt, ys=ys, yt=yt) +transp_Xs_sinkhorn_semi = ot_sinkhorn_semi.transform(Xs=Xs) + +# semi supervised DA uses available labaled target samples to modify the cost +# matrix involved in the OT problem. The cost of transporting a source sample +# of class A onto a target sample of class B != A is set to infinite, or a +# very large value + + +############################################################################## +# Fig 1 : plots source and target samples + matrix of pairwise distance +############################################################################## + +pl.figure(1, figsize=(10, 10)) +pl.subplot(2, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Source samples') + +pl.subplot(2, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Target samples') + +pl.subplot(2, 2, 3) +pl.imshow(ot_sinkhorn_un.cost_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Cost matrix - unsupervised DA') + +pl.subplot(2, 2, 4) +pl.imshow(ot_sinkhorn_semi.cost_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Cost matrix - semisupervised DA') + +pl.tight_layout() + +# the optimal coupling in the semi-supervised DA case will exhibit " shape +# similar" to the cost matrix, (block diagonal matrix) + +############################################################################## +# Fig 2 : plots optimal couplings for the different methods +############################################################################## + +pl.figure(2, figsize=(8, 4)) + +pl.subplot(1, 2, 1) +pl.imshow(ot_sinkhorn_un.coupling_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nUnsupervised DA') + +pl.subplot(1, 2, 2) +pl.imshow(ot_sinkhorn_semi.coupling_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSemi-supervised DA') + +pl.tight_layout() + + +############################################################################## +# Fig 3 : plot transported samples +############################################################################## + +# display transported samples +pl.figure(4, figsize=(8, 4)) +pl.subplot(1, 2, 1) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(transp_Xs_sinkhorn_un[:, 0], transp_Xs_sinkhorn_un[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Transported samples\nEmdTransport') +pl.legend(loc=0) +pl.xticks([]) +pl.yticks([]) + +pl.subplot(1, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(transp_Xs_sinkhorn_semi[:, 0], transp_Xs_sinkhorn_semi[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Transported samples\nSinkhornTransport') +pl.xticks([]) +pl.yticks([]) + +pl.tight_layout() +pl.show() diff --git a/ot/da.py b/ot/da.py index e694668..1d3d0ba 100644 --- a/ot/da.py +++ b/ot/da.py @@ -966,8 +966,12 @@ class BaseTransport(BaseEstimator): The class labels Xt : array-like, shape (n_target_samples, n_features) The training input samples. - yt : array-like, shape (n_labeled_target_samples,) - The class labels + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label Returns ------- @@ -1023,8 +1027,12 @@ class BaseTransport(BaseEstimator): The class labels Xt : array-like, shape (n_target_samples, n_features) The training input samples. - yt : array-like, shape (n_labeled_target_samples,) - The class labels + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label Returns ------- @@ -1045,8 +1053,12 @@ class BaseTransport(BaseEstimator): The class labels Xt : array-like, shape (n_target_samples, n_features) The training input samples. - yt : array-like, shape (n_labeled_target_samples,) - The class labels + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label batch_size : int, optional (default=128) The batch size for out of sample inverse transform @@ -1110,8 +1122,12 @@ class BaseTransport(BaseEstimator): The class labels Xt : array-like, shape (n_target_samples, n_features) The training input samples. - yt : array-like, shape (n_labeled_target_samples,) - The class labels + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label batch_size : int, optional (default=128) The batch size for out of sample inverse transform @@ -1241,8 +1257,12 @@ class SinkhornTransport(BaseTransport): The class labels Xt : array-like, shape (n_target_samples, n_features) The training input samples. - yt : array-like, shape (n_labeled_target_samples,) - The class labels + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label Returns ------- @@ -1333,8 +1353,12 @@ class EMDTransport(BaseTransport): The class labels Xt : array-like, shape (n_target_samples, n_features) The training input samples. - yt : array-like, shape (n_labeled_target_samples,) - The class labels + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label Returns ------- @@ -1434,8 +1458,12 @@ class SinkhornLpl1Transport(BaseTransport): The class labels Xt : array-like, shape (n_target_samples, n_features) The training input samples. - yt : array-like, shape (n_labeled_target_samples,) - The class labels + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label Returns ------- @@ -1545,8 +1573,12 @@ class SinkhornL1l2Transport(BaseTransport): The class labels Xt : array-like, shape (n_target_samples, n_features) The training input samples. - yt : array-like, shape (n_labeled_target_samples,) - The class labels + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label Returns ------- @@ -1662,8 +1694,12 @@ class MappingTransport(BaseEstimator): The class labels Xt : array-like, shape (n_target_samples, n_features) The training input samples. - yt : array-like, shape (n_labeled_target_samples,) - The class labels + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label Returns ------- -- cgit v1.2.3 From 669a6bee4200f6a2f1f6bbf597712684ff7272a8 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Mon, 4 Sep 2017 09:17:59 +0200 Subject: commenting the example --- examples/da/plot_otda_semi_supervised.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/examples/da/plot_otda_semi_supervised.py b/examples/da/plot_otda_semi_supervised.py index 6e6296b..8095c4d 100644 --- a/examples/da/plot_otda_semi_supervised.py +++ b/examples/da/plot_otda_semi_supervised.py @@ -32,9 +32,6 @@ n_samples_target = 150 Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source) Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target) -# Cost matrix -M = ot.dist(Xs, Xt, metric='sqeuclidean') - ############################################################################## # Transport source samples onto target samples @@ -55,6 +52,13 @@ transp_Xs_sinkhorn_semi = ot_sinkhorn_semi.transform(Xs=Xs) # of class A onto a target sample of class B != A is set to infinite, or a # very large value +# note that in the present case we consider that all the target samples are +# labeled. For daily applications, some target sample might not have labels, +# in this case the element of yt corresponding to these samples should be +# filled with -1. + +# Warning: we recall that -1 cannot be used as a class label + ############################################################################## # Fig 1 : plots source and target samples + matrix of pairwise distance @@ -92,6 +96,7 @@ pl.tight_layout() # the optimal coupling in the semi-supervised DA case will exhibit " shape # similar" to the cost matrix, (block diagonal matrix) + ############################################################################## # Fig 2 : plots optimal couplings for the different methods ############################################################################## -- cgit v1.2.3 From 185eb3e2ef34b5ce6b8f90a28a5bcc78432b7fd3 Mon Sep 17 00:00:00 2001 From: Antoine Rolet Date: Tue, 5 Sep 2017 15:10:44 +0900 Subject: Removed prints --- ot/lp/emd_wrap.pyx | 2 -- 1 file changed, 2 deletions(-) diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 435a270..4febb32 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -68,8 +68,6 @@ def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mod if not len(b): b=np.ones((n2,))/n2 - print alpha.size - print beta.size # calling the function EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, alpha.data, beta.data, &cost, maxiter) -- cgit v1.2.3 From 0bb8ec8bf8061aa7ad2299b04b8368b46b56be41 Mon Sep 17 00:00:00 2001 From: Antoine Rolet Date: Tue, 5 Sep 2017 15:36:03 +0900 Subject: Removed declaration of unused variable --- ot/lp/EMD_wrapper.cpp | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/ot/lp/EMD_wrapper.cpp b/ot/lp/EMD_wrapper.cpp index 8ac43c7..8e74462 100644 --- a/ot/lp/EMD_wrapper.cpp +++ b/ot/lp/EMD_wrapper.cpp @@ -18,8 +18,7 @@ int EMD_wrap(int n1, int n2, double *X, double *Y, double *D, double *G, double* alpha, double* beta, double *cost, int max_iter) { // beware M and C anre strored in row major C style!!! - int n, m, i, cur; - double max; + int n, m, i, cur; typedef FullBipartiteDigraph Digraph; DIGRAPH_TYPEDEFS(FullBipartiteDigraph); -- cgit v1.2.3 From b12edc59c0a94e1f426ae314baa006e06c062923 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Tue, 5 Sep 2017 09:42:32 +0200 Subject: integrated test for semi supervised case --- test/test_da.py | 96 +++++++++++++++++++++++++++++++++++---------------------- 1 file changed, 60 insertions(+), 36 deletions(-) diff --git a/test/test_da.py b/test/test_da.py index 104a798..a757d0a 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -63,19 +63,25 @@ def test_sinkhorn_lpl1_transport_class(): transp_Xs = clf.fit_transform(Xs=Xs, ys=ys, Xt=Xt) assert_equal(transp_Xs.shape, Xs.shape) - # test semi supervised mode - clf = ot.da.SinkhornLpl1Transport() - clf.fit(Xs=Xs, ys=ys, Xt=Xt) - n_unsup = np.sum(clf.cost_) + # test unsupervised vs semi-supervised mode + clf_unsup = ot.da.SinkhornTransport() + clf_unsup.fit(Xs=Xs, Xt=Xt) + n_unsup = np.sum(clf_unsup.cost_) - # test semi supervised mode - clf = ot.da.SinkhornLpl1Transport() - clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) - assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) - n_semisup = np.sum(clf.cost_) + clf_semi = ot.da.SinkhornTransport() + clf_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) + assert_equal(clf_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(clf_semi.cost_) + # check that the cost matrix norms are indeed different assert n_unsup != n_semisup, "semisupervised mode not working" + # check that the coupling forbids mass transport between labeled source + # and labeled target samples + mass_semi = np.sum( + clf_semi.coupling_[clf_semi.cost_ == clf_semi.limit_max]) + assert mass_semi == 0, "semisupervised mode not working" + def test_sinkhorn_l1l2_transport_class(): """test_sinkhorn_transport @@ -129,19 +135,25 @@ def test_sinkhorn_l1l2_transport_class(): transp_Xs = clf.fit_transform(Xs=Xs, ys=ys, Xt=Xt) assert_equal(transp_Xs.shape, Xs.shape) - # test semi supervised mode - clf = ot.da.SinkhornL1l2Transport() - clf.fit(Xs=Xs, ys=ys, Xt=Xt) - n_unsup = np.sum(clf.cost_) + # test unsupervised vs semi-supervised mode + clf_unsup = ot.da.SinkhornTransport() + clf_unsup.fit(Xs=Xs, Xt=Xt) + n_unsup = np.sum(clf_unsup.cost_) - # test semi supervised mode - clf = ot.da.SinkhornL1l2Transport() - clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) - assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) - n_semisup = np.sum(clf.cost_) + clf_semi = ot.da.SinkhornTransport() + clf_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) + assert_equal(clf_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(clf_semi.cost_) + # check that the cost matrix norms are indeed different assert n_unsup != n_semisup, "semisupervised mode not working" + # check that the coupling forbids mass transport between labeled source + # and labeled target samples + mass_semi = np.sum( + clf_semi.coupling_[clf_semi.cost_ == clf_semi.limit_max]) + assert mass_semi == 0, "semisupervised mode not working" + # check everything runs well with log=True clf = ot.da.SinkhornL1l2Transport(log=True) clf.fit(Xs=Xs, ys=ys, Xt=Xt) @@ -200,19 +212,25 @@ def test_sinkhorn_transport_class(): transp_Xs = clf.fit_transform(Xs=Xs, Xt=Xt) assert_equal(transp_Xs.shape, Xs.shape) - # test semi supervised mode - clf = ot.da.SinkhornTransport() - clf.fit(Xs=Xs, Xt=Xt) - n_unsup = np.sum(clf.cost_) + # test unsupervised vs semi-supervised mode + clf_unsup = ot.da.SinkhornTransport() + clf_unsup.fit(Xs=Xs, Xt=Xt) + n_unsup = np.sum(clf_unsup.cost_) - # test semi supervised mode - clf = ot.da.SinkhornTransport() - clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) - assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) - n_semisup = np.sum(clf.cost_) + clf_semi = ot.da.SinkhornTransport() + clf_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) + assert_equal(clf_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(clf_semi.cost_) + # check that the cost matrix norms are indeed different assert n_unsup != n_semisup, "semisupervised mode not working" + # check that the coupling forbids mass transport between labeled source + # and labeled target samples + mass_semi = np.sum( + clf_semi.coupling_[clf_semi.cost_ == clf_semi.limit_max]) + assert mass_semi == 0, "semisupervised mode not working" + # check everything runs well with log=True clf = ot.da.SinkhornTransport(log=True) clf.fit(Xs=Xs, ys=ys, Xt=Xt) @@ -270,19 +288,25 @@ def test_emd_transport_class(): transp_Xs = clf.fit_transform(Xs=Xs, Xt=Xt) assert_equal(transp_Xs.shape, Xs.shape) - # test semi supervised mode - clf = ot.da.EMDTransport() - clf.fit(Xs=Xs, Xt=Xt) - n_unsup = np.sum(clf.cost_) + # test unsupervised vs semi-supervised mode + clf_unsup = ot.da.SinkhornTransport() + clf_unsup.fit(Xs=Xs, Xt=Xt) + n_unsup = np.sum(clf_unsup.cost_) - # test semi supervised mode - clf = ot.da.EMDTransport() - clf.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) - assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) - n_semisup = np.sum(clf.cost_) + clf_semi = ot.da.SinkhornTransport() + clf_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) + assert_equal(clf_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(clf_semi.cost_) + # check that the cost matrix norms are indeed different assert n_unsup != n_semisup, "semisupervised mode not working" + # check that the coupling forbids mass transport between labeled source + # and labeled target samples + mass_semi = np.sum( + clf_semi.coupling_[clf_semi.cost_ == clf_semi.limit_max]) + assert mass_semi == 0, "semisupervised mode not working" + def test_mapping_transport_class(): """test_mapping_transport -- cgit v1.2.3 From 8e4a7930cf1ff80edeb30021acaf7337a02d18a5 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Tue, 5 Sep 2017 09:47:24 +0200 Subject: change name of otda object in test script: clf => otda --- test/test_da.py | 260 ++++++++++++++++++++++++++++---------------------------- 1 file changed, 130 insertions(+), 130 deletions(-) diff --git a/test/test_da.py b/test/test_da.py index a757d0a..9fc42a3 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -22,56 +22,56 @@ def test_sinkhorn_lpl1_transport_class(): Xs, ys = get_data_classif('3gauss', ns) Xt, yt = get_data_classif('3gauss2', nt) - clf = ot.da.SinkhornLpl1Transport() + otda = ot.da.SinkhornLpl1Transport() # test its computed - clf.fit(Xs=Xs, ys=ys, Xt=Xt) - assert hasattr(clf, "cost_") - assert hasattr(clf, "coupling_") + otda.fit(Xs=Xs, ys=ys, Xt=Xt) + assert hasattr(otda, "cost_") + assert hasattr(otda, "coupling_") # test dimensions of coupling - assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) - assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(otda.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform - transp_Xs = clf.transform(Xs=Xs) + transp_Xs = otda.transform(Xs=Xs) assert_equal(transp_Xs.shape, Xs.shape) Xs_new, _ = get_data_classif('3gauss', ns + 1) - transp_Xs_new = clf.transform(Xs_new) + transp_Xs_new = otda.transform(Xs_new) # check that the oos method is working assert_equal(transp_Xs_new.shape, Xs_new.shape) # test inverse transform - transp_Xt = clf.inverse_transform(Xt=Xt) + transp_Xt = otda.inverse_transform(Xt=Xt) assert_equal(transp_Xt.shape, Xt.shape) Xt_new, _ = get_data_classif('3gauss2', nt + 1) - transp_Xt_new = clf.inverse_transform(Xt=Xt_new) + transp_Xt_new = otda.inverse_transform(Xt=Xt_new) # check that the oos method is working assert_equal(transp_Xt_new.shape, Xt_new.shape) # test fit_transform - transp_Xs = clf.fit_transform(Xs=Xs, ys=ys, Xt=Xt) + transp_Xs = otda.fit_transform(Xs=Xs, ys=ys, Xt=Xt) assert_equal(transp_Xs.shape, Xs.shape) # test unsupervised vs semi-supervised mode - clf_unsup = ot.da.SinkhornTransport() - clf_unsup.fit(Xs=Xs, Xt=Xt) - n_unsup = np.sum(clf_unsup.cost_) + otda_unsup = ot.da.SinkhornTransport() + otda_unsup.fit(Xs=Xs, Xt=Xt) + n_unsup = np.sum(otda_unsup.cost_) - clf_semi = ot.da.SinkhornTransport() - clf_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) - assert_equal(clf_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) - n_semisup = np.sum(clf_semi.cost_) + otda_semi = ot.da.SinkhornTransport() + otda_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) + assert_equal(otda_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(otda_semi.cost_) # check that the cost matrix norms are indeed different assert n_unsup != n_semisup, "semisupervised mode not working" @@ -79,7 +79,7 @@ def test_sinkhorn_lpl1_transport_class(): # check that the coupling forbids mass transport between labeled source # and labeled target samples mass_semi = np.sum( - clf_semi.coupling_[clf_semi.cost_ == clf_semi.limit_max]) + otda_semi.coupling_[otda_semi.cost_ == otda_semi.limit_max]) assert mass_semi == 0, "semisupervised mode not working" @@ -93,57 +93,57 @@ def test_sinkhorn_l1l2_transport_class(): Xs, ys = get_data_classif('3gauss', ns) Xt, yt = get_data_classif('3gauss2', nt) - clf = ot.da.SinkhornL1l2Transport() + otda = ot.da.SinkhornL1l2Transport() # test its computed - clf.fit(Xs=Xs, ys=ys, Xt=Xt) - assert hasattr(clf, "cost_") - assert hasattr(clf, "coupling_") - assert hasattr(clf, "log_") + otda.fit(Xs=Xs, ys=ys, Xt=Xt) + assert hasattr(otda, "cost_") + assert hasattr(otda, "coupling_") + assert hasattr(otda, "log_") # test dimensions of coupling - assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) - assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(otda.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform - transp_Xs = clf.transform(Xs=Xs) + transp_Xs = otda.transform(Xs=Xs) assert_equal(transp_Xs.shape, Xs.shape) Xs_new, _ = get_data_classif('3gauss', ns + 1) - transp_Xs_new = clf.transform(Xs_new) + transp_Xs_new = otda.transform(Xs_new) # check that the oos method is working assert_equal(transp_Xs_new.shape, Xs_new.shape) # test inverse transform - transp_Xt = clf.inverse_transform(Xt=Xt) + transp_Xt = otda.inverse_transform(Xt=Xt) assert_equal(transp_Xt.shape, Xt.shape) Xt_new, _ = get_data_classif('3gauss2', nt + 1) - transp_Xt_new = clf.inverse_transform(Xt=Xt_new) + transp_Xt_new = otda.inverse_transform(Xt=Xt_new) # check that the oos method is working assert_equal(transp_Xt_new.shape, Xt_new.shape) # test fit_transform - transp_Xs = clf.fit_transform(Xs=Xs, ys=ys, Xt=Xt) + transp_Xs = otda.fit_transform(Xs=Xs, ys=ys, Xt=Xt) assert_equal(transp_Xs.shape, Xs.shape) # test unsupervised vs semi-supervised mode - clf_unsup = ot.da.SinkhornTransport() - clf_unsup.fit(Xs=Xs, Xt=Xt) - n_unsup = np.sum(clf_unsup.cost_) + otda_unsup = ot.da.SinkhornTransport() + otda_unsup.fit(Xs=Xs, Xt=Xt) + n_unsup = np.sum(otda_unsup.cost_) - clf_semi = ot.da.SinkhornTransport() - clf_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) - assert_equal(clf_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) - n_semisup = np.sum(clf_semi.cost_) + otda_semi = ot.da.SinkhornTransport() + otda_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) + assert_equal(otda_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(otda_semi.cost_) # check that the cost matrix norms are indeed different assert n_unsup != n_semisup, "semisupervised mode not working" @@ -151,13 +151,13 @@ def test_sinkhorn_l1l2_transport_class(): # check that the coupling forbids mass transport between labeled source # and labeled target samples mass_semi = np.sum( - clf_semi.coupling_[clf_semi.cost_ == clf_semi.limit_max]) + otda_semi.coupling_[otda_semi.cost_ == otda_semi.limit_max]) assert mass_semi == 0, "semisupervised mode not working" # check everything runs well with log=True - clf = ot.da.SinkhornL1l2Transport(log=True) - clf.fit(Xs=Xs, ys=ys, Xt=Xt) - assert len(clf.log_.keys()) != 0 + otda = ot.da.SinkhornL1l2Transport(log=True) + otda.fit(Xs=Xs, ys=ys, Xt=Xt) + assert len(otda.log_.keys()) != 0 def test_sinkhorn_transport_class(): @@ -170,57 +170,57 @@ def test_sinkhorn_transport_class(): Xs, ys = get_data_classif('3gauss', ns) Xt, yt = get_data_classif('3gauss2', nt) - clf = ot.da.SinkhornTransport() + otda = ot.da.SinkhornTransport() # test its computed - clf.fit(Xs=Xs, Xt=Xt) - assert hasattr(clf, "cost_") - assert hasattr(clf, "coupling_") - assert hasattr(clf, "log_") + otda.fit(Xs=Xs, Xt=Xt) + assert hasattr(otda, "cost_") + assert hasattr(otda, "coupling_") + assert hasattr(otda, "log_") # test dimensions of coupling - assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) - assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(otda.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform - transp_Xs = clf.transform(Xs=Xs) + transp_Xs = otda.transform(Xs=Xs) assert_equal(transp_Xs.shape, Xs.shape) Xs_new, _ = get_data_classif('3gauss', ns + 1) - transp_Xs_new = clf.transform(Xs_new) + transp_Xs_new = otda.transform(Xs_new) # check that the oos method is working assert_equal(transp_Xs_new.shape, Xs_new.shape) # test inverse transform - transp_Xt = clf.inverse_transform(Xt=Xt) + transp_Xt = otda.inverse_transform(Xt=Xt) assert_equal(transp_Xt.shape, Xt.shape) Xt_new, _ = get_data_classif('3gauss2', nt + 1) - transp_Xt_new = clf.inverse_transform(Xt=Xt_new) + transp_Xt_new = otda.inverse_transform(Xt=Xt_new) # check that the oos method is working assert_equal(transp_Xt_new.shape, Xt_new.shape) # test fit_transform - transp_Xs = clf.fit_transform(Xs=Xs, Xt=Xt) + transp_Xs = otda.fit_transform(Xs=Xs, Xt=Xt) assert_equal(transp_Xs.shape, Xs.shape) # test unsupervised vs semi-supervised mode - clf_unsup = ot.da.SinkhornTransport() - clf_unsup.fit(Xs=Xs, Xt=Xt) - n_unsup = np.sum(clf_unsup.cost_) + otda_unsup = ot.da.SinkhornTransport() + otda_unsup.fit(Xs=Xs, Xt=Xt) + n_unsup = np.sum(otda_unsup.cost_) - clf_semi = ot.da.SinkhornTransport() - clf_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) - assert_equal(clf_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) - n_semisup = np.sum(clf_semi.cost_) + otda_semi = ot.da.SinkhornTransport() + otda_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) + assert_equal(otda_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(otda_semi.cost_) # check that the cost matrix norms are indeed different assert n_unsup != n_semisup, "semisupervised mode not working" @@ -228,13 +228,13 @@ def test_sinkhorn_transport_class(): # check that the coupling forbids mass transport between labeled source # and labeled target samples mass_semi = np.sum( - clf_semi.coupling_[clf_semi.cost_ == clf_semi.limit_max]) + otda_semi.coupling_[otda_semi.cost_ == otda_semi.limit_max]) assert mass_semi == 0, "semisupervised mode not working" # check everything runs well with log=True - clf = ot.da.SinkhornTransport(log=True) - clf.fit(Xs=Xs, ys=ys, Xt=Xt) - assert len(clf.log_.keys()) != 0 + otda = ot.da.SinkhornTransport(log=True) + otda.fit(Xs=Xs, ys=ys, Xt=Xt) + assert len(otda.log_.keys()) != 0 def test_emd_transport_class(): @@ -247,56 +247,56 @@ def test_emd_transport_class(): Xs, ys = get_data_classif('3gauss', ns) Xt, yt = get_data_classif('3gauss2', nt) - clf = ot.da.EMDTransport() + otda = ot.da.EMDTransport() # test its computed - clf.fit(Xs=Xs, Xt=Xt) - assert hasattr(clf, "cost_") - assert hasattr(clf, "coupling_") + otda.fit(Xs=Xs, Xt=Xt) + assert hasattr(otda, "cost_") + assert hasattr(otda, "coupling_") # test dimensions of coupling - assert_equal(clf.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) - assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(otda.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform - transp_Xs = clf.transform(Xs=Xs) + transp_Xs = otda.transform(Xs=Xs) assert_equal(transp_Xs.shape, Xs.shape) Xs_new, _ = get_data_classif('3gauss', ns + 1) - transp_Xs_new = clf.transform(Xs_new) + transp_Xs_new = otda.transform(Xs_new) # check that the oos method is working assert_equal(transp_Xs_new.shape, Xs_new.shape) # test inverse transform - transp_Xt = clf.inverse_transform(Xt=Xt) + transp_Xt = otda.inverse_transform(Xt=Xt) assert_equal(transp_Xt.shape, Xt.shape) Xt_new, _ = get_data_classif('3gauss2', nt + 1) - transp_Xt_new = clf.inverse_transform(Xt=Xt_new) + transp_Xt_new = otda.inverse_transform(Xt=Xt_new) # check that the oos method is working assert_equal(transp_Xt_new.shape, Xt_new.shape) # test fit_transform - transp_Xs = clf.fit_transform(Xs=Xs, Xt=Xt) + transp_Xs = otda.fit_transform(Xs=Xs, Xt=Xt) assert_equal(transp_Xs.shape, Xs.shape) # test unsupervised vs semi-supervised mode - clf_unsup = ot.da.SinkhornTransport() - clf_unsup.fit(Xs=Xs, Xt=Xt) - n_unsup = np.sum(clf_unsup.cost_) + otda_unsup = ot.da.SinkhornTransport() + otda_unsup.fit(Xs=Xs, Xt=Xt) + n_unsup = np.sum(otda_unsup.cost_) - clf_semi = ot.da.SinkhornTransport() - clf_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) - assert_equal(clf_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) - n_semisup = np.sum(clf_semi.cost_) + otda_semi = ot.da.SinkhornTransport() + otda_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) + assert_equal(otda_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(otda_semi.cost_) # check that the cost matrix norms are indeed different assert n_unsup != n_semisup, "semisupervised mode not working" @@ -304,7 +304,7 @@ def test_emd_transport_class(): # check that the coupling forbids mass transport between labeled source # and labeled target samples mass_semi = np.sum( - clf_semi.coupling_[clf_semi.cost_ == clf_semi.limit_max]) + otda_semi.coupling_[otda_semi.cost_ == otda_semi.limit_max]) assert mass_semi == 0, "semisupervised mode not working" @@ -324,47 +324,47 @@ def test_mapping_transport_class(): ########################################################################## # check computation and dimensions if bias == False - clf = ot.da.MappingTransport(kernel="linear", bias=False) - clf.fit(Xs=Xs, Xt=Xt) - assert hasattr(clf, "coupling_") - assert hasattr(clf, "mapping_") - assert hasattr(clf, "log_") + otda = ot.da.MappingTransport(kernel="linear", bias=False) + otda.fit(Xs=Xs, Xt=Xt) + assert hasattr(otda, "coupling_") + assert hasattr(otda, "mapping_") + assert hasattr(otda, "log_") - assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) - assert_equal(clf.mapping_.shape, ((Xs.shape[1], Xt.shape[1]))) + assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(otda.mapping_.shape, ((Xs.shape[1], Xt.shape[1]))) # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform - transp_Xs = clf.transform(Xs=Xs) + transp_Xs = otda.transform(Xs=Xs) assert_equal(transp_Xs.shape, Xs.shape) - transp_Xs_new = clf.transform(Xs_new) + transp_Xs_new = otda.transform(Xs_new) # check that the oos method is working assert_equal(transp_Xs_new.shape, Xs_new.shape) # check computation and dimensions if bias == True - clf = ot.da.MappingTransport(kernel="linear", bias=True) - clf.fit(Xs=Xs, Xt=Xt) - assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) - assert_equal(clf.mapping_.shape, ((Xs.shape[1] + 1, Xt.shape[1]))) + otda = ot.da.MappingTransport(kernel="linear", bias=True) + otda.fit(Xs=Xs, Xt=Xt) + assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(otda.mapping_.shape, ((Xs.shape[1] + 1, Xt.shape[1]))) # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform - transp_Xs = clf.transform(Xs=Xs) + transp_Xs = otda.transform(Xs=Xs) assert_equal(transp_Xs.shape, Xs.shape) - transp_Xs_new = clf.transform(Xs_new) + transp_Xs_new = otda.transform(Xs_new) # check that the oos method is working assert_equal(transp_Xs_new.shape, Xs_new.shape) @@ -374,52 +374,52 @@ def test_mapping_transport_class(): ########################################################################## # check computation and dimensions if bias == False - clf = ot.da.MappingTransport(kernel="gaussian", bias=False) - clf.fit(Xs=Xs, Xt=Xt) + otda = ot.da.MappingTransport(kernel="gaussian", bias=False) + otda.fit(Xs=Xs, Xt=Xt) - assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) - assert_equal(clf.mapping_.shape, ((Xs.shape[0], Xt.shape[1]))) + assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(otda.mapping_.shape, ((Xs.shape[0], Xt.shape[1]))) # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform - transp_Xs = clf.transform(Xs=Xs) + transp_Xs = otda.transform(Xs=Xs) assert_equal(transp_Xs.shape, Xs.shape) - transp_Xs_new = clf.transform(Xs_new) + transp_Xs_new = otda.transform(Xs_new) # check that the oos method is working assert_equal(transp_Xs_new.shape, Xs_new.shape) # check computation and dimensions if bias == True - clf = ot.da.MappingTransport(kernel="gaussian", bias=True) - clf.fit(Xs=Xs, Xt=Xt) - assert_equal(clf.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) - assert_equal(clf.mapping_.shape, ((Xs.shape[0] + 1, Xt.shape[1]))) + otda = ot.da.MappingTransport(kernel="gaussian", bias=True) + otda.fit(Xs=Xs, Xt=Xt) + assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(otda.mapping_.shape, ((Xs.shape[0] + 1, Xt.shape[1]))) # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(clf.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(clf.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose(np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform - transp_Xs = clf.transform(Xs=Xs) + transp_Xs = otda.transform(Xs=Xs) assert_equal(transp_Xs.shape, Xs.shape) - transp_Xs_new = clf.transform(Xs_new) + transp_Xs_new = otda.transform(Xs_new) # check that the oos method is working assert_equal(transp_Xs_new.shape, Xs_new.shape) # check everything runs well with log=True - clf = ot.da.MappingTransport(kernel="gaussian", log=True) - clf.fit(Xs=Xs, Xt=Xt) - assert len(clf.log_.keys()) != 0 + otda = ot.da.MappingTransport(kernel="gaussian", log=True) + otda.fit(Xs=Xs, Xt=Xt) + assert len(otda.log_.keys()) != 0 def test_otda(): -- cgit v1.2.3 From 3baa34b5504dfbccd6800b59f1f3830a7edf3f20 Mon Sep 17 00:00:00 2001 From: Antoine Rolet Date: Tue, 5 Sep 2017 16:53:55 +0900 Subject: Added include cstdio --- ot/lp/network_simplex_simple.h | 1 + 1 file changed, 1 insertion(+) diff --git a/ot/lp/network_simplex_simple.h b/ot/lp/network_simplex_simple.h index 08449f6..a7743ee 100644 --- a/ot/lp/network_simplex_simple.h +++ b/ot/lp/network_simplex_simple.h @@ -49,6 +49,7 @@ #include #include #include +#include #ifdef HASHMAP #include #else -- cgit v1.2.3 From d43ce6fdca486fdb0fe049ab3cae4daf8652f5d0 Mon Sep 17 00:00:00 2001 From: Antoine Rolet Date: Tue, 5 Sep 2017 16:58:10 +0900 Subject: Removed print --- test/test_emd.py | 1 - 1 file changed, 1 deletion(-) diff --git a/test/test_emd.py b/test/test_emd.py index 3bf6fa2..0025583 100644 --- a/test/test_emd.py +++ b/test/test_emd.py @@ -26,7 +26,6 @@ b=gauss(m,m=mean2,s=10) # loss matrix M=ot.dist(x.reshape((-1,1)), y.reshape((-1,1))) ** (1./2) -print M[0,:] #M/=M.max() #%% -- cgit v1.2.3 From 49c100de34583329058b39d414d2aa49b7fd15bf Mon Sep 17 00:00:00 2001 From: Slasnista Date: Tue, 5 Sep 2017 10:00:01 +0200 Subject: test semi supervised mode ok written for all class | need different tolerance for EMDTransport MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- test/test_da.py | 29 ++++++++++++++++++----------- 1 file changed, 18 insertions(+), 11 deletions(-) diff --git a/test/test_da.py b/test/test_da.py index 9fc42a3..3602db9 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -64,11 +64,11 @@ def test_sinkhorn_lpl1_transport_class(): assert_equal(transp_Xs.shape, Xs.shape) # test unsupervised vs semi-supervised mode - otda_unsup = ot.da.SinkhornTransport() - otda_unsup.fit(Xs=Xs, Xt=Xt) + otda_unsup = ot.da.SinkhornLpl1Transport() + otda_unsup.fit(Xs=Xs, ys=ys, Xt=Xt) n_unsup = np.sum(otda_unsup.cost_) - otda_semi = ot.da.SinkhornTransport() + otda_semi = ot.da.SinkhornLpl1Transport() otda_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) assert_equal(otda_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) n_semisup = np.sum(otda_semi.cost_) @@ -136,11 +136,11 @@ def test_sinkhorn_l1l2_transport_class(): assert_equal(transp_Xs.shape, Xs.shape) # test unsupervised vs semi-supervised mode - otda_unsup = ot.da.SinkhornTransport() - otda_unsup.fit(Xs=Xs, Xt=Xt) + otda_unsup = ot.da.SinkhornL1l2Transport() + otda_unsup.fit(Xs=Xs, ys=ys, Xt=Xt) n_unsup = np.sum(otda_unsup.cost_) - otda_semi = ot.da.SinkhornTransport() + otda_semi = ot.da.SinkhornL1l2Transport() otda_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) assert_equal(otda_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) n_semisup = np.sum(otda_semi.cost_) @@ -152,7 +152,9 @@ def test_sinkhorn_l1l2_transport_class(): # and labeled target samples mass_semi = np.sum( otda_semi.coupling_[otda_semi.cost_ == otda_semi.limit_max]) - assert mass_semi == 0, "semisupervised mode not working" + mass_semi = otda_semi.coupling_[otda_semi.cost_ == otda_semi.limit_max] + assert_allclose(mass_semi, np.zeros_like(mass_semi), + rtol=1e-9, atol=1e-9) # check everything runs well with log=True otda = ot.da.SinkhornL1l2Transport(log=True) @@ -289,11 +291,11 @@ def test_emd_transport_class(): assert_equal(transp_Xs.shape, Xs.shape) # test unsupervised vs semi-supervised mode - otda_unsup = ot.da.SinkhornTransport() - otda_unsup.fit(Xs=Xs, Xt=Xt) + otda_unsup = ot.da.EMDTransport() + otda_unsup.fit(Xs=Xs, ys=ys, Xt=Xt) n_unsup = np.sum(otda_unsup.cost_) - otda_semi = ot.da.SinkhornTransport() + otda_semi = ot.da.EMDTransport() otda_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) assert_equal(otda_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) n_semisup = np.sum(otda_semi.cost_) @@ -305,7 +307,11 @@ def test_emd_transport_class(): # and labeled target samples mass_semi = np.sum( otda_semi.coupling_[otda_semi.cost_ == otda_semi.limit_max]) - assert mass_semi == 0, "semisupervised mode not working" + mass_semi = otda_semi.coupling_[otda_semi.cost_ == otda_semi.limit_max] + + # we need to use a small tolerance here, otherwise the test breaks + assert_allclose(mass_semi, np.zeros_like(mass_semi), + rtol=1e-2, atol=1e-2) def test_mapping_transport_class(): @@ -491,3 +497,4 @@ def test_otda(): # test_sinkhorn_l1l2_transport_class() # test_sinkhorn_lpl1_transport_class() # test_mapping_transport_class() + -- cgit v1.2.3 From 2097116c7db725a88876d617e20a94f32627f7c9 Mon Sep 17 00:00:00 2001 From: Slasnista Date: Tue, 5 Sep 2017 10:09:55 +0200 Subject: solving pb --- test/test_da.py | 61 ++++++++++++++++++++++++++++++++------------------------- 1 file changed, 34 insertions(+), 27 deletions(-) diff --git a/test/test_da.py b/test/test_da.py index 3602db9..593dc53 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -36,8 +36,10 @@ def test_sinkhorn_lpl1_transport_class(): # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose( + np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose( + np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform transp_Xs = otda.transform(Xs=Xs) @@ -108,8 +110,10 @@ def test_sinkhorn_l1l2_transport_class(): # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose( + np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose( + np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform transp_Xs = otda.transform(Xs=Xs) @@ -187,8 +191,10 @@ def test_sinkhorn_transport_class(): # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose( + np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose( + np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform transp_Xs = otda.transform(Xs=Xs) @@ -263,8 +269,10 @@ def test_emd_transport_class(): # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose( + np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose( + np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform transp_Xs = otda.transform(Xs=Xs) @@ -342,8 +350,10 @@ def test_mapping_transport_class(): # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose( + np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose( + np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform transp_Xs = otda.transform(Xs=Xs) @@ -363,8 +373,10 @@ def test_mapping_transport_class(): # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose( + np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose( + np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform transp_Xs = otda.transform(Xs=Xs) @@ -389,8 +401,10 @@ def test_mapping_transport_class(): # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose( + np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose( + np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform transp_Xs = otda.transform(Xs=Xs) @@ -410,8 +424,10 @@ def test_mapping_transport_class(): # test margin constraints mu_s = unif(ns) mu_t = unif(nt) - assert_allclose(np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose(np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + assert_allclose( + np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose( + np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) # test transform transp_Xs = otda.transform(Xs=Xs) @@ -454,7 +470,8 @@ def test_otda(): da_entrop.interp() da_entrop.predict(xs) - np.testing.assert_allclose(a, np.sum(da_entrop.G, 1), rtol=1e-3, atol=1e-3) + np.testing.assert_allclose( + a, np.sum(da_entrop.G, 1), rtol=1e-3, atol=1e-3) np.testing.assert_allclose(b, np.sum(da_entrop.G, 0), rtol=1e-3, atol=1e-3) # non-convex Group lasso regularization @@ -488,13 +505,3 @@ def test_otda(): da_emd = ot.da.OTDA_mapping_kernel() # init class da_emd.fit(xs, xt, numItermax=10) # fit distributions da_emd.predict(xs) # interpolation of source samples - - -# if __name__ == "__main__": - -# test_sinkhorn_transport_class() -# test_emd_transport_class() -# test_sinkhorn_l1l2_transport_class() -# test_sinkhorn_lpl1_transport_class() -# test_mapping_transport_class() - -- cgit v1.2.3 From d52b4ea415d9bb669be04ccd0940f9b3d258d0e1 Mon Sep 17 00:00:00 2001 From: Antoine Rolet Date: Tue, 5 Sep 2017 17:15:45 +0900 Subject: Fixed typo and merged emd tests --- ot/lp/__init__.py | 2 +- test/test_emd.py | 68 ------------------------------------------------------- test/test_ot.py | 66 ++++++++++++++++++++++++++++++++++++++++++++++++++--- 3 files changed, 64 insertions(+), 72 deletions(-) delete mode 100644 test/test_emd.py diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index a14d4e4..6048f60 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -168,6 +168,6 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=100000): # res = [emd2_c(a, b[:, i].copy(), M, numItermax) for i in range(nb)] def f(b): - return emd2_c(a,b,M, max_iter)[0] + return emd2_c(a,b,M, numItermax)[0] res= parmap(f, [b[:,i] for i in range(nb)],processes) return np.array(res) diff --git a/test/test_emd.py b/test/test_emd.py deleted file mode 100644 index 0025583..0000000 --- a/test/test_emd.py +++ /dev/null @@ -1,68 +0,0 @@ -#!/usr/bin/env python2 -# -*- coding: utf-8 -*- - -import numpy as np -import ot - -from ot.datasets import get_1D_gauss as gauss -reload(ot.lp) - -#%% parameters - -n=5000 # nb bins -m=6000 # nb bins - -mean1 = 1000 -mean2 = 1100 - -# bin positions -x=np.arange(n,dtype=np.float64) -y=np.arange(m,dtype=np.float64) - -# Gaussian distributions -a=gauss(n,m=mean1,s=5) # m= mean, s= std - -b=gauss(m,m=mean2,s=10) - -# loss matrix -M=ot.dist(x.reshape((-1,1)), y.reshape((-1,1))) ** (1./2) -#M/=M.max() - -#%% - -print('Computing {} EMD '.format(1)) - -# emd loss 1 proc -ot.tic() -G, alpha, beta = ot.emd(a,b,M, dual_variables=True) -ot.toc('1 proc : {} s') - -cost1 = (G * M).sum() -cost_dual = np.vdot(a, alpha) + np.vdot(b, beta) - -# emd loss 1 proc -ot.tic() -cost_emd2 = ot.emd2(a,b,M) -ot.toc('1 proc : {} s') - -ot.tic() -G2 = ot.emd(b, a, np.ascontiguousarray(M.T)) -ot.toc('1 proc : {} s') - -cost2 = (G2 * M.T).sum() - -M_reduced = M - alpha.reshape(-1,1) - beta.reshape(1, -1) - -# Check that both cost computations are equivalent -np.testing.assert_almost_equal(cost1, cost_emd2) -# Check that dual and primal cost are equal -np.testing.assert_almost_equal(cost1, cost_dual) -# Check symmetry -np.testing.assert_almost_equal(cost1, cost2) -# Check with closed-form solution for gaussians -np.testing.assert_almost_equal(cost1, np.abs(mean1-mean2)) - -[ind1, ind2] = np.nonzero(G) - -# Check that reduced cost is zero on transport arcs -np.testing.assert_array_almost_equal((M - alpha.reshape(-1, 1) - beta.reshape(1, -1))[ind1, ind2], np.zeros(ind1.size)) \ No newline at end of file diff --git a/test/test_ot.py b/test/test_ot.py index acd8718..ded6c9f 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -6,6 +6,8 @@ import numpy as np import ot + +from ot.datasets import get_1D_gauss as gauss def test_doctest(): @@ -66,9 +68,6 @@ def test_emd_empty(): def test_emd2_multi(): - - from ot.datasets import get_1D_gauss as gauss - n = 1000 # nb bins # bin positions @@ -100,3 +99,64 @@ def test_emd2_multi(): ot.toc('multi proc : {} s') np.testing.assert_allclose(emd1, emdn) + +def test_dual_variables(): + #%% parameters + + n=5000 # nb bins + m=6000 # nb bins + + mean1 = 1000 + mean2 = 1100 + + # bin positions + x=np.arange(n,dtype=np.float64) + y=np.arange(m,dtype=np.float64) + + # Gaussian distributions + a=gauss(n,m=mean1,s=5) # m= mean, s= std + + b=gauss(m,m=mean2,s=10) + + # loss matrix + M=ot.dist(x.reshape((-1,1)), y.reshape((-1,1))) ** (1./2) + #M/=M.max() + + #%% + + print('Computing {} EMD '.format(1)) + + # emd loss 1 proc + ot.tic() + G, alpha, beta = ot.emd(a,b,M, dual_variables=True) + ot.toc('1 proc : {} s') + + cost1 = (G * M).sum() + cost_dual = np.vdot(a, alpha) + np.vdot(b, beta) + + # emd loss 1 proc + ot.tic() + cost_emd2 = ot.emd2(a,b,M) + ot.toc('1 proc : {} s') + + ot.tic() + G2 = ot.emd(b, a, np.ascontiguousarray(M.T)) + ot.toc('1 proc : {} s') + + cost2 = (G2 * M.T).sum() + + M_reduced = M - alpha.reshape(-1,1) - beta.reshape(1, -1) + + # Check that both cost computations are equivalent + np.testing.assert_almost_equal(cost1, cost_emd2) + # Check that dual and primal cost are equal + np.testing.assert_almost_equal(cost1, cost_dual) + # Check symmetry + np.testing.assert_almost_equal(cost1, cost2) + # Check with closed-form solution for gaussians + np.testing.assert_almost_equal(cost1, np.abs(mean1-mean2)) + + [ind1, ind2] = np.nonzero(G) + + # Check that reduced cost is zero on transport arcs + np.testing.assert_array_almost_equal((M - alpha.reshape(-1, 1) - beta.reshape(1, -1))[ind1, ind2], np.zeros(ind1.size)) -- cgit v1.2.3 From a3497b123b4802c7960a07a899ac7ce4525c5995 Mon Sep 17 00:00:00 2001 From: Antoine Rolet Date: Tue, 5 Sep 2017 17:51:58 +0900 Subject: Reformat --- test/test_ot.py | 67 ++++++++++++++++++++++++++++----------------------------- 1 file changed, 33 insertions(+), 34 deletions(-) diff --git a/test/test_ot.py b/test/test_ot.py index ded6c9f..6f0f7c9 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -5,13 +5,12 @@ # License: MIT License import numpy as np + import ot - from ot.datasets import get_1D_gauss as gauss def test_doctest(): - import doctest # test lp solver @@ -100,53 +99,52 @@ def test_emd2_multi(): np.testing.assert_allclose(emd1, emdn) + def test_dual_variables(): - #%% parameters - - n=5000 # nb bins - m=6000 # nb bins - + # %% parameters + + n = 5000 # nb bins + m = 6000 # nb bins + mean1 = 1000 mean2 = 1100 - + # bin positions - x=np.arange(n,dtype=np.float64) - y=np.arange(m,dtype=np.float64) - + x = np.arange(n, dtype=np.float64) + y = np.arange(m, dtype=np.float64) + # Gaussian distributions - a=gauss(n,m=mean1,s=5) # m= mean, s= std - - b=gauss(m,m=mean2,s=10) - + a = gauss(n, m=mean1, s=5) # m= mean, s= std + + b = gauss(m, m=mean2, s=10) + # loss matrix - M=ot.dist(x.reshape((-1,1)), y.reshape((-1,1))) ** (1./2) - #M/=M.max() - - #%% - + M = ot.dist(x.reshape((-1, 1)), y.reshape((-1, 1))) ** (1. / 2) + # M/=M.max() + + # %% + print('Computing {} EMD '.format(1)) - + # emd loss 1 proc ot.tic() - G, alpha, beta = ot.emd(a,b,M, dual_variables=True) + G, alpha, beta = ot.emd(a, b, M, dual_variables=True) ot.toc('1 proc : {} s') - + cost1 = (G * M).sum() cost_dual = np.vdot(a, alpha) + np.vdot(b, beta) - + # emd loss 1 proc ot.tic() - cost_emd2 = ot.emd2(a,b,M) + cost_emd2 = ot.emd2(a, b, M) ot.toc('1 proc : {} s') - + ot.tic() G2 = ot.emd(b, a, np.ascontiguousarray(M.T)) ot.toc('1 proc : {} s') - + cost2 = (G2 * M.T).sum() - - M_reduced = M - alpha.reshape(-1,1) - beta.reshape(1, -1) - + # Check that both cost computations are equivalent np.testing.assert_almost_equal(cost1, cost_emd2) # Check that dual and primal cost are equal @@ -154,9 +152,10 @@ def test_dual_variables(): # Check symmetry np.testing.assert_almost_equal(cost1, cost2) # Check with closed-form solution for gaussians - np.testing.assert_almost_equal(cost1, np.abs(mean1-mean2)) - + np.testing.assert_almost_equal(cost1, np.abs(mean1 - mean2)) + [ind1, ind2] = np.nonzero(G) - + # Check that reduced cost is zero on transport arcs - np.testing.assert_array_almost_equal((M - alpha.reshape(-1, 1) - beta.reshape(1, -1))[ind1, ind2], np.zeros(ind1.size)) + np.testing.assert_array_almost_equal((M - alpha.reshape(-1, 1) - beta.reshape(1, -1))[ind1, ind2], + np.zeros(ind1.size)) -- cgit v1.2.3 From f8c1c8740f9974dcf4aaf191851d62149dceb91c Mon Sep 17 00:00:00 2001 From: Antoine Rolet Date: Thu, 7 Sep 2017 13:29:46 +0900 Subject: Added MAX_ITER_REACHED flag and warning --- ot/lp/EMD.h | 3 ++- ot/lp/EMD_wrapper.cpp | 21 +++++++---------- ot/lp/emd_wrap.pyx | 29 +++++++++++++---------- ot/lp/network_simplex_simple.h | 46 ++++++++++++++++++++----------------- test/test_ot.py | 52 ++++++++++++++++++++++++++++++++++++++++-- 5 files changed, 102 insertions(+), 49 deletions(-) diff --git a/ot/lp/EMD.h b/ot/lp/EMD.h index 15e9115..bb486de 100644 --- a/ot/lp/EMD.h +++ b/ot/lp/EMD.h @@ -26,7 +26,8 @@ typedef unsigned int node_id_type; enum ProblemType { INFEASIBLE, OPTIMAL, - UNBOUNDED + UNBOUNDED, + MAX_ITER_REACHED }; int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double* alpha, double* beta, double *cost, int max_iter); diff --git a/ot/lp/EMD_wrapper.cpp b/ot/lp/EMD_wrapper.cpp index 8e74462..92663dc 100644 --- a/ot/lp/EMD_wrapper.cpp +++ b/ot/lp/EMD_wrapper.cpp @@ -29,14 +29,18 @@ int EMD_wrap(int n1, int n2, double *X, double *Y, double *D, double *G, double val=*(X+i); if (val>0) { n++; - } + }else if(val<0){ + return INFEASIBLE; + } } m=0; for (int i=0; i0) { m++; - } + }else if(val<0){ + return INFEASIBLE; + } } // Define the graph @@ -83,16 +87,7 @@ int EMD_wrap(int n1, int n2, double *X, double *Y, double *D, double *G, // Solve the problem with the network simplex algorithm int ret=net.run(); - if (ret!=(int)net.OPTIMAL) { - if (ret==(int)net.INFEASIBLE) { - std::cout << "Infeasible problem"; - } - if (ret==(int)net.UNBOUNDED) - { - std::cout << "Unbounded problem"; - } - } else - { + if (ret==(int)net.OPTIMAL || ret==(int)net.MAX_ITER_REACHED) { *cost = 0; Arc a; di.first(a); for (; a != INVALID; di.next(a)) { @@ -105,7 +100,7 @@ int EMD_wrap(int n1, int n2, double *X, double *Y, double *D, double *G, *(beta + indJ[j-n]) = net.potential(j); } - }; + } return ret; diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 7056e0e..9bea154 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -7,6 +7,7 @@ Cython linker with C solver # # License: MIT License +import warnings import numpy as np cimport numpy as np @@ -15,14 +16,14 @@ cimport cython cdef extern from "EMD.h": - int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double* alpha, double* beta, double *cost, int max_iter) - cdef enum ProblemType: INFEASIBLE, OPTIMAL, UNBOUNDED + int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double* alpha, double* beta, double *cost, int numItermax) + cdef enum ProblemType: INFEASIBLE, OPTIMAL, UNBOUNDED, MAX_ITER_REACHED @cython.boundscheck(False) @cython.wraparound(False) -def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M, int max_iter): +def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M, int numItermax): """ Solves the Earth Movers distance problem and returns the optimal transport matrix @@ -49,7 +50,7 @@ def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mod target histogram M : (ns,nt) ndarray, float64 loss matrix - max_iter : int + numItermax : int The maximum number of iterations before stopping the optimization algorithm if it has not converged. @@ -76,18 +77,20 @@ def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mod b=np.ones((n2,))/n2 # calling the function - cdef int resultSolver = EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, alpha.data, beta.data, &cost, max_iter) + cdef int resultSolver = EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, alpha.data, beta.data, &cost, numItermax) if resultSolver != OPTIMAL: if resultSolver == INFEASIBLE: - print("Problem infeasible. Try to increase numItermax.") + warnings.warn("Problem infeasible. Check that a and b are in the simplex") elif resultSolver == UNBOUNDED: - print("Problem unbounded") + warnings.warn("Problem unbounded") + elif resultSolver == MAX_ITER_REACHED: + warnings.warn("numItermax reached before optimality. Try to increase numItermax.") return G, alpha, beta @cython.boundscheck(False) @cython.wraparound(False) -def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M, int max_iter): +def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M, int numItermax): """ Solves the Earth Movers distance problem and returns the optimal transport loss @@ -114,7 +117,7 @@ def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mo target histogram M : (ns,nt) ndarray, float64 loss matrix - max_iter : int + numItermax : int The maximum number of iterations before stopping the optimization algorithm if it has not converged. @@ -140,12 +143,14 @@ def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mo if not len(b): b=np.ones((n2,))/n2 # calling the function - cdef int resultSolver = EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, alpha.data, beta.data, &cost, max_iter) + cdef int resultSolver = EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, alpha.data, beta.data, &cost, numItermax) if resultSolver != OPTIMAL: if resultSolver == INFEASIBLE: - print("Problem infeasible. Try to inscrease numItermax.") + warnings.warn("Problem infeasible. Check that a and b are in the simplex") elif resultSolver == UNBOUNDED: - print("Problem unbounded") + warnings.warn("Problem unbounded") + elif resultSolver == MAX_ITER_REACHED: + warnings.warn("numItermax reached before optimality. Try to increase numItermax.") return cost, alpha, beta diff --git a/ot/lp/network_simplex_simple.h b/ot/lp/network_simplex_simple.h index a7743ee..7c6a4ce 100644 --- a/ot/lp/network_simplex_simple.h +++ b/ot/lp/network_simplex_simple.h @@ -34,7 +34,8 @@ #endif -#define EPSILON 10*2.2204460492503131e-016 +#define EPSILON 2.2204460492503131e-15 +#define _EPSILON 1e-8 #define MAX_DEBUG_ITER 100000 @@ -260,7 +261,9 @@ namespace lemon { /// The objective function of the problem is unbounded, i.e. /// there is a directed cycle having negative total cost and /// infinite upper bound. - UNBOUNDED + UNBOUNDED, + /// The maximum number of iteration has been reached + MAX_ITER_REACHED }; /// \brief Constants for selecting the type of the supply constraints. @@ -683,7 +686,7 @@ namespace lemon { /// \see resetParams(), reset() ProblemType run() { #if DEBUG_LVL>0 - std::cout << "OPTIMAL = " << OPTIMAL << "\nINFEASIBLE = " << INFEASIBLE << "nUNBOUNDED = " << UNBOUNDED << "\n"; + std::cout << "OPTIMAL = " << OPTIMAL << "\nINFEASIBLE = " << INFEASIBLE << "\nUNBOUNDED = " << UNBOUNDED << "\nMAX_ITER_REACHED" << MAX_ITER_REACHED\n"; #endif if (!init()) return INFEASIBLE; @@ -941,15 +944,15 @@ namespace lemon { // Initialize internal data structures bool init() { if (_node_num == 0) return false; - /* + // Check the sum of supply values _sum_supply = 0; for (int i = 0; i != _node_num; ++i) { _sum_supply += _supply[i]; } - if ( !((_stype == GEQ && _sum_supply <= _epsilon ) || - (_stype == LEQ && _sum_supply >= -_epsilon )) ) return false; - */ + if ( fabs(_sum_supply) > _EPSILON ) return false; + + _sum_supply = 0; // Initialize artifical cost Cost ART_COST; @@ -1416,13 +1419,11 @@ namespace lemon { ProblemType start() { PivotRuleImpl pivot(*this); double prevCost=-1; + ProblemType retVal = OPTIMAL; // Perform heuristic initial pivots if (!initialPivots()) return UNBOUNDED; -#if DEBUG_LVL>0 - int niter=0; -#endif int iter_number=0; //pivot.setDantzig(true); // Execute the Network Simplex algorithm @@ -1431,12 +1432,13 @@ namespace lemon { char errMess[1000]; sprintf( errMess, "RESULT MIGHT BE INACURATE\nMax number of iteration reached, currently \%d. Sometimes iterations go on in cycle even though the solution has been reached, to check if it's the case here have a look at the minimal reduced cost. If it is very close to machine precision, you might actually have the correct solution, if not try setting the maximum number of iterations a bit higher\n",iter_number ); std::cerr << errMess; + retVal = MAX_ITER_REACHED; break; } #if DEBUG_LVL>0 - if(niter>MAX_DEBUG_ITER) + if(iter_number>MAX_DEBUG_ITER) break; - if(++niter%1000==0||niter%1000==1){ + if(iter_number%1000==0||iter_number%1000==1){ double curCost=totalCost(); double sumFlow=0; double a; @@ -1445,7 +1447,7 @@ namespace lemon { for (int i=0; i<_flow.size(); i++) { sumFlow+=_state[i]*_flow[i]; } - std::cout << "Sum of the flow " << std::setprecision(20) << sumFlow << "\n" << niter << " iterations, current cost=" << curCost << "\nReduced cost=" << _state[in_arc] * (_cost[in_arc] + _pi[_source[in_arc]] -_pi[_target[in_arc]]) << "\nPrecision = "<< -EPSILON*(a) << "\n"; + std::cout << "Sum of the flow " << std::setprecision(20) << sumFlow << "\n" << iter_number << " iterations, current cost=" << curCost << "\nReduced cost=" << _state[in_arc] * (_cost[in_arc] + _pi[_source[in_arc]] -_pi[_target[in_arc]]) << "\nPrecision = "<< -EPSILON*(a) << "\n"; std::cout << "Arc in = (" << _node_id(_source[in_arc]) << ", " << _node_id(_target[in_arc]) <<")\n"; std::cout << "Supplies = (" << _supply[_source[in_arc]] << ", " << _supply[_target[in_arc]] << ")\n"; std::cout << _cost[in_arc] << "\n"; @@ -1503,15 +1505,17 @@ namespace lemon { std::cout << "Sum of the flow " << sumFlow << "\n"<< niter <<" iterations, current cost=" << totalCost() << "\n"; #endif // Check feasibility - for (int e = _search_arc_num; e != _all_arc_num; ++e) { - if (_flow[e] != 0){ - if (abs(_flow[e]) > EPSILON) - return INFEASIBLE; - else - _flow[e]=0; + if( retVal == OPTIMAL){ + for (int e = _search_arc_num; e != _all_arc_num; ++e) { + if (_flow[e] != 0){ + if (abs(_flow[e]) > EPSILON) + return INFEASIBLE; + else + _flow[e]=0; + } } - } + } // Shift potentials to meet the requirements of the GEQ/LEQ type // optimality conditions @@ -1537,7 +1541,7 @@ namespace lemon { } } - return OPTIMAL; + return retVal; } }; //class NetworkSimplexSimple diff --git a/test/test_ot.py b/test/test_ot.py index 6f0f7c9..8a19cf6 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -8,6 +8,7 @@ import numpy as np import ot from ot.datasets import get_1D_gauss as gauss +import warnings def test_doctest(): @@ -100,9 +101,56 @@ def test_emd2_multi(): np.testing.assert_allclose(emd1, emdn) -def test_dual_variables(): - # %% parameters +def test_warnings(): + n = 100 # nb bins + m = 100 # nb bins + + mean1 = 30 + mean2 = 50 + + # bin positions + x = np.arange(n, dtype=np.float64) + y = np.arange(m, dtype=np.float64) + + # Gaussian distributions + a = gauss(n, m=mean1, s=5) # m= mean, s= std + + b = gauss(m, m=mean2, s=10) + # loss matrix + M = ot.dist(x.reshape((-1, 1)), y.reshape((-1, 1))) ** (1. / 2) + # M/=M.max() + + # %% + + print('Computing {} EMD '.format(1)) + G, alpha, beta = ot.emd(a, b, M, dual_variables=True) + with warnings.catch_warnings(record=True) as w: + # Cause all warnings to always be triggered. + warnings.simplefilter("always") + # Trigger a warning. + print('Computing {} EMD '.format(1)) + G, alpha, beta = ot.emd(a, b, M, dual_variables=True, numItermax=1) + # Verify some things + assert "numItermax" in str(w[-1].message) + assert len(w) == 1 + # Trigger a warning. + a[0]=100 + print('Computing {} EMD '.format(2)) + G, alpha, beta = ot.emd(a, b, M, dual_variables=True) + # Verify some things + assert "infeasible" in str(w[-1].message) + assert len(w) == 2 + # Trigger a warning. + a[0]=-1 + print('Computing {} EMD '.format(2)) + G, alpha, beta = ot.emd(a, b, M, dual_variables=True) + # Verify some things + assert "infeasible" in str(w[-1].message) + assert len(w) == 3 + + +def test_dual_variables(): n = 5000 # nb bins m = 6000 # nb bins -- cgit v1.2.3 From 12d9b3ff72e9669ccc0162e82b7a33beb51d3e25 Mon Sep 17 00:00:00 2001 From: Antoine Rolet Date: Thu, 7 Sep 2017 13:50:41 +0900 Subject: Return dual variables in an optional dictionary Also removed some code duplication --- ot/lp/__init__.py | 24 +++++++++++++------ ot/lp/emd_wrap.pyx | 69 +----------------------------------------------------- test/test_ot.py | 20 ++++++---------- 3 files changed, 25 insertions(+), 88 deletions(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 6048f60..c15e6b9 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -9,12 +9,12 @@ Solvers for the original linear program OT problem import numpy as np # import compiled emd -from .emd_wrap import emd_c, emd2_c +from .emd_wrap import emd_c from ..utils import parmap import multiprocessing -def emd(a, b, M, numItermax=100000, dual_variables=False): +def emd(a, b, M, numItermax=100000, log=False): """Solves the Earth Movers distance problem and returns the OT matrix @@ -42,11 +42,17 @@ def emd(a, b, M, numItermax=100000, dual_variables=False): numItermax : int, optional (default=100000) The maximum number of iterations before stopping the optimization algorithm if it has not converged. + log: boolean, optional (default=False) + If True, returns a dictionary containing the cost and dual + variables. Otherwise returns only the optimal transportation matrix. Returns ------- gamma: (ns x nt) ndarray Optimal transportation matrix for the given parameters + log: dict + If input log is true, a dictionary containing the cost and dual + variables Examples @@ -86,9 +92,13 @@ def emd(a, b, M, numItermax=100000, dual_variables=False): if len(b) == 0: b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1] - G, alpha, beta = emd_c(a, b, M, numItermax) - if dual_variables: - return G, alpha, beta + G, cost, u, v = emd_c(a, b, M, numItermax) + if log: + log = {} + log['cost'] = cost + log['u'] = u + log['v'] = v + return G, log return G def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=100000): @@ -163,11 +173,11 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=100000): b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1] if len(b.shape)==1: - return emd2_c(a, b, M, numItermax)[0] + return emd_c(a, b, M, numItermax)[1] nb = b.shape[1] # res = [emd2_c(a, b[:, i].copy(), M, numItermax) for i in range(nb)] def f(b): - return emd2_c(a,b,M, numItermax)[0] + return emd_c(a,b,M, numItermax)[1] res= parmap(f, [b[:,i] for i in range(nb)],processes) return np.array(res) diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 9bea154..5618dfc 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -86,71 +86,4 @@ def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mod elif resultSolver == MAX_ITER_REACHED: warnings.warn("numItermax reached before optimality. Try to increase numItermax.") - return G, alpha, beta - -@cython.boundscheck(False) -@cython.wraparound(False) -def emd2_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M, int numItermax): - """ - Solves the Earth Movers distance problem and returns the optimal transport loss - - gamm=emd(a,b,M) - - .. math:: - \gamma = arg\min_\gamma <\gamma,M>_F - - s.t. \gamma 1 = a - - \gamma^T 1= b - - \gamma\geq 0 - where : - - - M is the metric cost matrix - - a and b are the sample weights - - Parameters - ---------- - a : (ns,) ndarray, float64 - source histogram - b : (nt,) ndarray, float64 - target histogram - M : (ns,nt) ndarray, float64 - loss matrix - numItermax : int - The maximum number of iterations before stopping the optimization - algorithm if it has not converged. - - - Returns - ------- - gamma: (ns x nt) ndarray - Optimal transportation matrix for the given parameters - - """ - cdef int n1= M.shape[0] - cdef int n2= M.shape[1] - - cdef double cost=0 - cdef np.ndarray[double, ndim=2, mode="c"] G=np.zeros([n1, n2]) - - cdef np.ndarray[double, ndim = 1, mode = "c"] alpha = np.zeros([n1]) - cdef np.ndarray[double, ndim = 1, mode = "c"] beta = np.zeros([n2]) - - if not len(a): - a=np.ones((n1,))/n1 - - if not len(b): - b=np.ones((n2,))/n2 - # calling the function - cdef int resultSolver = EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, alpha.data, beta.data, &cost, numItermax) - if resultSolver != OPTIMAL: - if resultSolver == INFEASIBLE: - warnings.warn("Problem infeasible. Check that a and b are in the simplex") - elif resultSolver == UNBOUNDED: - warnings.warn("Problem unbounded") - elif resultSolver == MAX_ITER_REACHED: - warnings.warn("numItermax reached before optimality. Try to increase numItermax.") - - return cost, alpha, beta - + return G, cost, alpha, beta diff --git a/test/test_ot.py b/test/test_ot.py index 8a19cf6..78f64ab 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -124,27 +124,26 @@ def test_warnings(): # %% print('Computing {} EMD '.format(1)) - G, alpha, beta = ot.emd(a, b, M, dual_variables=True) with warnings.catch_warnings(record=True) as w: # Cause all warnings to always be triggered. warnings.simplefilter("always") # Trigger a warning. print('Computing {} EMD '.format(1)) - G, alpha, beta = ot.emd(a, b, M, dual_variables=True, numItermax=1) + G = ot.emd(a, b, M, numItermax=1) # Verify some things assert "numItermax" in str(w[-1].message) assert len(w) == 1 # Trigger a warning. a[0]=100 print('Computing {} EMD '.format(2)) - G, alpha, beta = ot.emd(a, b, M, dual_variables=True) + G = ot.emd(a, b, M) # Verify some things assert "infeasible" in str(w[-1].message) assert len(w) == 2 # Trigger a warning. a[0]=-1 print('Computing {} EMD '.format(2)) - G, alpha, beta = ot.emd(a, b, M, dual_variables=True) + G = ot.emd(a, b, M) # Verify some things assert "infeasible" in str(w[-1].message) assert len(w) == 3 @@ -176,16 +175,11 @@ def test_dual_variables(): # emd loss 1 proc ot.tic() - G, alpha, beta = ot.emd(a, b, M, dual_variables=True) + G, log = ot.emd(a, b, M, log=True) ot.toc('1 proc : {} s') cost1 = (G * M).sum() - cost_dual = np.vdot(a, alpha) + np.vdot(b, beta) - - # emd loss 1 proc - ot.tic() - cost_emd2 = ot.emd2(a, b, M) - ot.toc('1 proc : {} s') + cost_dual = np.vdot(a, log['u']) + np.vdot(b, log['v']) ot.tic() G2 = ot.emd(b, a, np.ascontiguousarray(M.T)) @@ -194,7 +188,7 @@ def test_dual_variables(): cost2 = (G2 * M.T).sum() # Check that both cost computations are equivalent - np.testing.assert_almost_equal(cost1, cost_emd2) + np.testing.assert_almost_equal(cost1, log['cost']) # Check that dual and primal cost are equal np.testing.assert_almost_equal(cost1, cost_dual) # Check symmetry @@ -205,5 +199,5 @@ def test_dual_variables(): [ind1, ind2] = np.nonzero(G) # Check that reduced cost is zero on transport arcs - np.testing.assert_array_almost_equal((M - alpha.reshape(-1, 1) - beta.reshape(1, -1))[ind1, ind2], + np.testing.assert_array_almost_equal((M - log['u'].reshape(-1, 1) - log['v'].reshape(1, -1))[ind1, ind2], np.zeros(ind1.size)) -- cgit v1.2.3 From ab65f86304b03a967054eeeaf73b8c8277618d65 Mon Sep 17 00:00:00 2001 From: Antoine Rolet Date: Thu, 7 Sep 2017 14:35:35 +0900 Subject: Added log option to muliprocess emd --- ot/lp/__init__.py | 39 ++++++++++++++++++++++++------------- test/test_ot.py | 57 ++++++++++++++++++++++++++++++------------------------- 2 files changed, 57 insertions(+), 39 deletions(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index c15e6b9..8edd8ec 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -7,11 +7,13 @@ Solvers for the original linear program OT problem # # License: MIT License +import multiprocessing + import numpy as np + # import compiled emd from .emd_wrap import emd_c from ..utils import parmap -import multiprocessing def emd(a, b, M, numItermax=100000, log=False): @@ -88,9 +90,9 @@ def emd(a, b, M, numItermax=100000, log=False): # if empty array given then use unifor distributions if len(a) == 0: - a = np.ones((M.shape[0], ), dtype=np.float64)/M.shape[0] + a = np.ones((M.shape[0],), dtype=np.float64) / M.shape[0] if len(b) == 0: - b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1] + b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] G, cost, u, v = emd_c(a, b, M, numItermax) if log: @@ -101,7 +103,8 @@ def emd(a, b, M, numItermax=100000, log=False): return G, log return G -def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=100000): + +def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=100000, log=False): """Solves the Earth Movers distance problem and returns the loss .. math:: @@ -168,16 +171,26 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=100000): # if empty array given then use unifor distributions if len(a) == 0: - a = np.ones((M.shape[0], ), dtype=np.float64)/M.shape[0] + a = np.ones((M.shape[0],), dtype=np.float64) / M.shape[0] if len(b) == 0: - b = np.ones((M.shape[1], ), dtype=np.float64)/M.shape[1] - - if len(b.shape)==1: - return emd_c(a, b, M, numItermax)[1] + b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] + + if log: + def f(b): + G, cost, u, v = emd_c(a, b, M, numItermax) + log = {} + log['G'] = G + log['u'] = u + log['v'] = v + return [cost, log] + else: + def f(b): + return emd_c(a, b, M, numItermax)[1] + + if len(b.shape) == 1: + return f(b) nb = b.shape[1] # res = [emd2_c(a, b[:, i].copy(), M, numItermax) for i in range(nb)] - def f(b): - return emd_c(a,b,M, numItermax)[1] - res= parmap(f, [b[:,i] for i in range(nb)],processes) - return np.array(res) + res = parmap(f, [b[:, i] for i in range(nb)], processes) + return res diff --git a/test/test_ot.py b/test/test_ot.py index 78f64ab..feadef4 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -4,11 +4,12 @@ # # License: MIT License +import warnings + import numpy as np import ot from ot.datasets import get_1D_gauss as gauss -import warnings def test_doctest(): @@ -100,6 +101,21 @@ def test_emd2_multi(): np.testing.assert_allclose(emd1, emdn) + # emd loss multipro proc with log + ot.tic() + emdn = ot.emd2(a, b, M, log=True) + ot.toc('multi proc : {} s') + + for i in range(len(emdn)): + emd = emdn[i] + log = emd[1] + cost = emd[0] + check_duality_gap(a, b[:, i], M, log['G'], log['u'], log['v'], cost) + emdn[i] = cost + + emdn = np.array(emdn) + np.testing.assert_allclose(emd1, emdn) + def test_warnings(): n = 100 # nb bins @@ -119,32 +135,22 @@ def test_warnings(): # loss matrix M = ot.dist(x.reshape((-1, 1)), y.reshape((-1, 1))) ** (1. / 2) - # M/=M.max() - - # %% print('Computing {} EMD '.format(1)) with warnings.catch_warnings(record=True) as w: - # Cause all warnings to always be triggered. warnings.simplefilter("always") - # Trigger a warning. print('Computing {} EMD '.format(1)) G = ot.emd(a, b, M, numItermax=1) - # Verify some things assert "numItermax" in str(w[-1].message) assert len(w) == 1 - # Trigger a warning. - a[0]=100 + a[0] = 100 print('Computing {} EMD '.format(2)) G = ot.emd(a, b, M) - # Verify some things assert "infeasible" in str(w[-1].message) assert len(w) == 2 - # Trigger a warning. - a[0]=-1 + a[0] = -1 print('Computing {} EMD '.format(2)) G = ot.emd(a, b, M) - # Verify some things assert "infeasible" in str(w[-1].message) assert len(w) == 3 @@ -167,9 +173,6 @@ def test_dual_variables(): # loss matrix M = ot.dist(x.reshape((-1, 1)), y.reshape((-1, 1))) ** (1. / 2) - # M/=M.max() - - # %% print('Computing {} EMD '.format(1)) @@ -178,26 +181,28 @@ def test_dual_variables(): G, log = ot.emd(a, b, M, log=True) ot.toc('1 proc : {} s') - cost1 = (G * M).sum() - cost_dual = np.vdot(a, log['u']) + np.vdot(b, log['v']) - ot.tic() G2 = ot.emd(b, a, np.ascontiguousarray(M.T)) ot.toc('1 proc : {} s') - cost2 = (G2 * M.T).sum() + cost1 = (G * M).sum() + # Check symmetry + np.testing.assert_array_almost_equal(cost1, (M * G2.T).sum()) + # Check with closed-form solution for gaussians + np.testing.assert_almost_equal(cost1, np.abs(mean1 - mean2)) # Check that both cost computations are equivalent np.testing.assert_almost_equal(cost1, log['cost']) + check_duality_gap(a, b, M, G, log['u'], log['v'], log['cost']) + + +def check_duality_gap(a, b, M, G, u, v, cost): + cost_dual = np.vdot(a, u) + np.vdot(b, v) # Check that dual and primal cost are equal - np.testing.assert_almost_equal(cost1, cost_dual) - # Check symmetry - np.testing.assert_almost_equal(cost1, cost2) - # Check with closed-form solution for gaussians - np.testing.assert_almost_equal(cost1, np.abs(mean1 - mean2)) + np.testing.assert_almost_equal(cost_dual, cost) [ind1, ind2] = np.nonzero(G) # Check that reduced cost is zero on transport arcs - np.testing.assert_array_almost_equal((M - log['u'].reshape(-1, 1) - log['v'].reshape(1, -1))[ind1, ind2], + np.testing.assert_array_almost_equal((M - u.reshape(-1, 1) - v.reshape(1, -1))[ind1, ind2], np.zeros(ind1.size)) -- cgit v1.2.3 From a37e52e64f300fa0165a58932d5ac0ef1dd8c6f7 Mon Sep 17 00:00:00 2001 From: Antoine Rolet Date: Thu, 7 Sep 2017 14:38:53 +0900 Subject: Removed unused variable declaration --- test/test_ot.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/test/test_ot.py b/test/test_ot.py index feadef4..cf5839e 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -140,17 +140,17 @@ def test_warnings(): with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") print('Computing {} EMD '.format(1)) - G = ot.emd(a, b, M, numItermax=1) + ot.emd(a, b, M, numItermax=1) assert "numItermax" in str(w[-1].message) assert len(w) == 1 a[0] = 100 print('Computing {} EMD '.format(2)) - G = ot.emd(a, b, M) + ot.emd(a, b, M) assert "infeasible" in str(w[-1].message) assert len(w) == 2 a[0] = -1 print('Computing {} EMD '.format(2)) - G = ot.emd(a, b, M) + ot.emd(a, b, M) assert "infeasible" in str(w[-1].message) assert len(w) == 3 -- cgit v1.2.3 From e58cd780ccf87736265e4e1a39afa3a167325ccc Mon Sep 17 00:00:00 2001 From: Antoine Rolet Date: Sat, 9 Sep 2017 12:37:56 +0900 Subject: Added convergence status to the log --- ot/lp/__init__.py | 16 ++++++++++++---- ot/lp/emd_wrap.pyx | 28 +++++++++++++++++----------- 2 files changed, 29 insertions(+), 15 deletions(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 8edd8ec..0f40c19 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -12,7 +12,7 @@ import multiprocessing import numpy as np # import compiled emd -from .emd_wrap import emd_c +from .emd_wrap import emd_c, checkResult from ..utils import parmap @@ -94,12 +94,15 @@ def emd(a, b, M, numItermax=100000, log=False): if len(b) == 0: b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] - G, cost, u, v = emd_c(a, b, M, numItermax) + G, cost, u, v, resultCode = emd_c(a, b, M, numItermax) + resultCodeString = checkResult(resultCode) if log: log = {} log['cost'] = cost log['u'] = u log['v'] = v + log['warning'] = resultCodeString + log['resultCode'] = resultCode return G, log return G @@ -177,15 +180,20 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=100000, log= if log: def f(b): - G, cost, u, v = emd_c(a, b, M, numItermax) + G, cost, u, v, resultCode = emd_c(a, b, M, numItermax) + resultCodeString = checkResult(resultCode) log = {} log['G'] = G log['u'] = u log['v'] = v + log['warning'] = resultCodeString + log['resultCode'] = resultCode return [cost, log] else: def f(b): - return emd_c(a, b, M, numItermax)[1] + G, cost, u, v, resultCode = emd_c(a, b, M, numItermax) + checkResult(resultCode) + return cost if len(b.shape) == 1: return f(b) diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 5618dfc..19bcdd8 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -7,12 +7,12 @@ Cython linker with C solver # # License: MIT License -import warnings import numpy as np cimport numpy as np cimport cython +import warnings cdef extern from "EMD.h": @@ -20,6 +20,19 @@ cdef extern from "EMD.h": cdef enum ProblemType: INFEASIBLE, OPTIMAL, UNBOUNDED, MAX_ITER_REACHED +def checkResult(resultCode): + if resultCode == OPTIMAL: + return None + + if resultCode == INFEASIBLE: + message = "Problem infeasible. Check that a and b are in the simplex" + elif resultCode == UNBOUNDED: + message = "Problem unbounded" + elif resultCode == MAX_ITER_REACHED: + message = "numItermax reached before optimality. Try to increase numItermax." + warnings.warn(message) + return message + @cython.boundscheck(False) @cython.wraparound(False) @@ -77,13 +90,6 @@ def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mod b=np.ones((n2,))/n2 # calling the function - cdef int resultSolver = EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, alpha.data, beta.data, &cost, numItermax) - if resultSolver != OPTIMAL: - if resultSolver == INFEASIBLE: - warnings.warn("Problem infeasible. Check that a and b are in the simplex") - elif resultSolver == UNBOUNDED: - warnings.warn("Problem unbounded") - elif resultSolver == MAX_ITER_REACHED: - warnings.warn("numItermax reached before optimality. Try to increase numItermax.") - - return G, cost, alpha, beta + cdef int resultCode = EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, alpha.data, beta.data, &cost, numItermax) + + return G, cost, alpha, beta, resultCode -- cgit v1.2.3 From 430d9d88f2d09327ee3e72f65d742a0d69eaf16a Mon Sep 17 00:00:00 2001 From: Antoine Rolet Date: Sat, 9 Sep 2017 12:43:47 +0900 Subject: Added self to list of contributors and removed from acknowlegments --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 33eea6e..8d54f49 100644 --- a/README.md +++ b/README.md @@ -138,12 +138,12 @@ The contributors to this library are: * [Léo Gautheron](https://github.com/aje) (GPU implementation) * [Nathalie Gayraud](https://www.linkedin.com/in/nathalie-t-h-gayraud/?ppe=1) * [Stanislas Chambon](https://slasnista.github.io/) +* [Antoine Rolet](https://arolet.github.io/) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): * [Gabriel Peyré](http://gpeyre.github.io/) (Wasserstein Barycenters in Matlab) * [Nicolas Bonneel](http://liris.cnrs.fr/~nbonneel/) ( C++ code for EMD) -* [Antoine Rolet](https://arolet.github.io/) ( Mex file for EMD ) * [Marco Cuturi](http://marcocuturi.net/) (Sinkhorn Knopp in Matlab/Cuda) -- cgit v1.2.3 -- cgit v1.2.3 From 85c56d96f609c4ad458f0963a068386cc910c66c Mon Sep 17 00:00:00 2001 From: Antoine Rolet Date: Sat, 9 Sep 2017 17:28:38 +0900 Subject: Renamed variables --- ot/da.py | 2 +- ot/lp/__init__.py | 31 +++++++++++++++++-------------- ot/lp/emd_wrap.pyx | 18 +++++++++--------- test/test_ot.py | 2 +- 4 files changed, 28 insertions(+), 25 deletions(-) diff --git a/ot/da.py b/ot/da.py index 1d3d0ba..eb70305 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1370,7 +1370,7 @@ class EMDTransport(BaseTransport): # coupling estimation self.coupling_ = emd( - a=self.mu_s, b=self.mu_t, M=self.cost_, numItermax=self.max_iter + a=self.mu_s, b=self.mu_t, M=self.cost_, num_iter_max=self.max_iter ) return self diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 0f40c19..ab7cb97 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -12,11 +12,11 @@ import multiprocessing import numpy as np # import compiled emd -from .emd_wrap import emd_c, checkResult +from .emd_wrap import emd_c, check_result from ..utils import parmap -def emd(a, b, M, numItermax=100000, log=False): +def emd(a, b, M, num_iter_max=100000, log=False): """Solves the Earth Movers distance problem and returns the OT matrix @@ -41,7 +41,7 @@ def emd(a, b, M, numItermax=100000, log=False): Target histogram (uniform weigth if empty list) M : (ns,nt) ndarray, float64 loss matrix - numItermax : int, optional (default=100000) + num_iter_max : int, optional (default=100000) The maximum number of iterations before stopping the optimization algorithm if it has not converged. log: boolean, optional (default=False) @@ -54,7 +54,7 @@ def emd(a, b, M, numItermax=100000, log=False): Optimal transportation matrix for the given parameters log: dict If input log is true, a dictionary containing the cost and dual - variables + variables and exit status Examples @@ -94,20 +94,20 @@ def emd(a, b, M, numItermax=100000, log=False): if len(b) == 0: b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] - G, cost, u, v, resultCode = emd_c(a, b, M, numItermax) - resultCodeString = checkResult(resultCode) + G, cost, u, v, result_code = emd_c(a, b, M, num_iter_max) + resultCodeString = check_result(result_code) if log: log = {} log['cost'] = cost log['u'] = u log['v'] = v log['warning'] = resultCodeString - log['resultCode'] = resultCode + log['result_code'] = result_code return G, log return G -def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=100000, log=False): +def emd2(a, b, M, processes=multiprocessing.cpu_count(), num_iter_max=100000, log=False): """Solves the Earth Movers distance problem and returns the loss .. math:: @@ -131,7 +131,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=100000, log= Target histogram (uniform weigth if empty list) M : (ns,nt) ndarray, float64 loss matrix - numItermax : int, optional (default=100000) + num_iter_max : int, optional (default=100000) The maximum number of iterations before stopping the optimization algorithm if it has not converged. @@ -139,6 +139,9 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=100000, log= ------- gamma: (ns x nt) ndarray Optimal transportation matrix for the given parameters + log: dict + If input log is true, a dictionary containing the cost and dual + variables and exit status Examples @@ -180,19 +183,19 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=100000, log= if log: def f(b): - G, cost, u, v, resultCode = emd_c(a, b, M, numItermax) - resultCodeString = checkResult(resultCode) + G, cost, u, v, resultCode = emd_c(a, b, M, num_iter_max) + resultCodeString = check_result(resultCode) log = {} log['G'] = G log['u'] = u log['v'] = v log['warning'] = resultCodeString - log['resultCode'] = resultCode + log['result_code'] = resultCode return [cost, log] else: def f(b): - G, cost, u, v, resultCode = emd_c(a, b, M, numItermax) - checkResult(resultCode) + G, cost, u, v, result_code = emd_c(a, b, M, num_iter_max) + check_result(result_code) return cost if len(b.shape) == 1: diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 19bcdd8..7ebdd2a 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -20,15 +20,15 @@ cdef extern from "EMD.h": cdef enum ProblemType: INFEASIBLE, OPTIMAL, UNBOUNDED, MAX_ITER_REACHED -def checkResult(resultCode): - if resultCode == OPTIMAL: +def check_result(result_code): + if result_code == OPTIMAL: return None - if resultCode == INFEASIBLE: + if result_code == INFEASIBLE: message = "Problem infeasible. Check that a and b are in the simplex" - elif resultCode == UNBOUNDED: + elif result_code == UNBOUNDED: message = "Problem unbounded" - elif resultCode == MAX_ITER_REACHED: + elif result_code == MAX_ITER_REACHED: message = "numItermax reached before optimality. Try to increase numItermax." warnings.warn(message) return message @@ -36,7 +36,7 @@ def checkResult(resultCode): @cython.boundscheck(False) @cython.wraparound(False) -def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mode="c"] b,np.ndarray[double, ndim=2, mode="c"] M, int numItermax): +def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mode="c"] b, np.ndarray[double, ndim=2, mode="c"] M, int num_iter_max): """ Solves the Earth Movers distance problem and returns the optimal transport matrix @@ -63,7 +63,7 @@ def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mod target histogram M : (ns,nt) ndarray, float64 loss matrix - numItermax : int + num_iter_max : int The maximum number of iterations before stopping the optimization algorithm if it has not converged. @@ -90,6 +90,6 @@ def emd_c( np.ndarray[double, ndim=1, mode="c"] a,np.ndarray[double, ndim=1, mod b=np.ones((n2,))/n2 # calling the function - cdef int resultCode = EMD_wrap(n1,n2, a.data, b.data, M.data, G.data, alpha.data, beta.data, &cost, numItermax) + cdef int result_code = EMD_wrap(n1, n2, a.data, b.data, M.data, G.data, alpha.data, beta.data, &cost, num_iter_max) - return G, cost, alpha, beta, resultCode + return G, cost, alpha, beta, result_code diff --git a/test/test_ot.py b/test/test_ot.py index cf5839e..c9b5154 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -140,7 +140,7 @@ def test_warnings(): with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") print('Computing {} EMD '.format(1)) - ot.emd(a, b, M, numItermax=1) + ot.emd(a, b, M, num_iter_max=1) assert "numItermax" in str(w[-1].message) assert len(w) == 1 a[0] = 100 -- cgit v1.2.3 From 1ba2c837d54ce963ad63ddf8df2e47230800b747 Mon Sep 17 00:00:00 2001 From: Antoine Rolet Date: Sat, 9 Sep 2017 17:30:23 +0900 Subject: Renamed variables --- ot/lp/__init__.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index ab7cb97..1238cdb 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -95,13 +95,13 @@ def emd(a, b, M, num_iter_max=100000, log=False): b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] G, cost, u, v, result_code = emd_c(a, b, M, num_iter_max) - resultCodeString = check_result(result_code) + result_code_string = check_result(result_code) if log: log = {} log['cost'] = cost log['u'] = u log['v'] = v - log['warning'] = resultCodeString + log['warning'] = result_code_string log['result_code'] = result_code return G, log return G @@ -184,12 +184,12 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), num_iter_max=100000, lo if log: def f(b): G, cost, u, v, resultCode = emd_c(a, b, M, num_iter_max) - resultCodeString = check_result(resultCode) + result_code_string = check_result(resultCode) log = {} log['G'] = G log['u'] = u log['v'] = v - log['warning'] = resultCodeString + log['warning'] = result_code_string log['result_code'] = resultCode return [cost, log] else: -- cgit v1.2.3 From cd8c04246b6d1f15b68d6433741e8c808fd517d8 Mon Sep 17 00:00:00 2001 From: Antoine Rolet Date: Sat, 9 Sep 2017 17:38:31 +0900 Subject: Renamed variable --- ot/da.py | 2 +- ot/lp/EMD.h | 2 +- ot/lp/EMD_wrapper.cpp | 4 ++-- ot/lp/__init__.py | 14 +++++++------- ot/lp/emd_wrap.pyx | 8 ++++---- test/test_ot.py | 2 +- 6 files changed, 16 insertions(+), 16 deletions(-) diff --git a/ot/da.py b/ot/da.py index eb70305..f3e7433 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1370,7 +1370,7 @@ class EMDTransport(BaseTransport): # coupling estimation self.coupling_ = emd( - a=self.mu_s, b=self.mu_t, M=self.cost_, num_iter_max=self.max_iter + a=self.mu_s, b=self.mu_t, M=self.cost_, max_iter=self.max_iter ) return self diff --git a/ot/lp/EMD.h b/ot/lp/EMD.h index bb486de..f42e222 100644 --- a/ot/lp/EMD.h +++ b/ot/lp/EMD.h @@ -30,6 +30,6 @@ enum ProblemType { MAX_ITER_REACHED }; -int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double* alpha, double* beta, double *cost, int max_iter); +int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double* alpha, double* beta, double *cost, int maxIter); #endif diff --git a/ot/lp/EMD_wrapper.cpp b/ot/lp/EMD_wrapper.cpp index 92663dc..fc7ca63 100644 --- a/ot/lp/EMD_wrapper.cpp +++ b/ot/lp/EMD_wrapper.cpp @@ -16,7 +16,7 @@ int EMD_wrap(int n1, int n2, double *X, double *Y, double *D, double *G, - double* alpha, double* beta, double *cost, int max_iter) { + double* alpha, double* beta, double *cost, int maxIter) { // beware M and C anre strored in row major C style!!! int n, m, i, cur; @@ -48,7 +48,7 @@ int EMD_wrap(int n1, int n2, double *X, double *Y, double *D, double *G, std::vector indI(n), indJ(m); std::vector weights1(n), weights2(m); Digraph di(n, m); - NetworkSimplexSimple net(di, true, n+m, n*m, max_iter); + NetworkSimplexSimple net(di, true, n+m, n*m, maxIter); // Set supply and demand, don't account for 0 values (faster) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 1238cdb..9a0cb1c 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -16,7 +16,7 @@ from .emd_wrap import emd_c, check_result from ..utils import parmap -def emd(a, b, M, num_iter_max=100000, log=False): +def emd(a, b, M, max_iter=100000, log=False): """Solves the Earth Movers distance problem and returns the OT matrix @@ -41,7 +41,7 @@ def emd(a, b, M, num_iter_max=100000, log=False): Target histogram (uniform weigth if empty list) M : (ns,nt) ndarray, float64 loss matrix - num_iter_max : int, optional (default=100000) + max_iter : int, optional (default=100000) The maximum number of iterations before stopping the optimization algorithm if it has not converged. log: boolean, optional (default=False) @@ -94,7 +94,7 @@ def emd(a, b, M, num_iter_max=100000, log=False): if len(b) == 0: b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] - G, cost, u, v, result_code = emd_c(a, b, M, num_iter_max) + G, cost, u, v, result_code = emd_c(a, b, M, max_iter) result_code_string = check_result(result_code) if log: log = {} @@ -107,7 +107,7 @@ def emd(a, b, M, num_iter_max=100000, log=False): return G -def emd2(a, b, M, processes=multiprocessing.cpu_count(), num_iter_max=100000, log=False): +def emd2(a, b, M, processes=multiprocessing.cpu_count(), max_iter=100000, log=False): """Solves the Earth Movers distance problem and returns the loss .. math:: @@ -131,7 +131,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), num_iter_max=100000, lo Target histogram (uniform weigth if empty list) M : (ns,nt) ndarray, float64 loss matrix - num_iter_max : int, optional (default=100000) + max_iter : int, optional (default=100000) The maximum number of iterations before stopping the optimization algorithm if it has not converged. @@ -183,7 +183,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), num_iter_max=100000, lo if log: def f(b): - G, cost, u, v, resultCode = emd_c(a, b, M, num_iter_max) + G, cost, u, v, resultCode = emd_c(a, b, M, max_iter) result_code_string = check_result(resultCode) log = {} log['G'] = G @@ -194,7 +194,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), num_iter_max=100000, lo return [cost, log] else: def f(b): - G, cost, u, v, result_code = emd_c(a, b, M, num_iter_max) + G, cost, u, v, result_code = emd_c(a, b, M, max_iter) check_result(result_code) return cost diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 7ebdd2a..83ee6aa 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -16,7 +16,7 @@ import warnings cdef extern from "EMD.h": - int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double* alpha, double* beta, double *cost, int numItermax) + int EMD_wrap(int n1,int n2, double *X, double *Y,double *D, double *G, double* alpha, double* beta, double *cost, int maxIter) cdef enum ProblemType: INFEASIBLE, OPTIMAL, UNBOUNDED, MAX_ITER_REACHED @@ -36,7 +36,7 @@ def check_result(result_code): @cython.boundscheck(False) @cython.wraparound(False) -def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mode="c"] b, np.ndarray[double, ndim=2, mode="c"] M, int num_iter_max): +def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mode="c"] b, np.ndarray[double, ndim=2, mode="c"] M, int max_iter): """ Solves the Earth Movers distance problem and returns the optimal transport matrix @@ -63,7 +63,7 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod target histogram M : (ns,nt) ndarray, float64 loss matrix - num_iter_max : int + max_iter : int The maximum number of iterations before stopping the optimization algorithm if it has not converged. @@ -90,6 +90,6 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod b=np.ones((n2,))/n2 # calling the function - cdef int result_code = EMD_wrap(n1, n2, a.data, b.data, M.data, G.data, alpha.data, beta.data, &cost, num_iter_max) + cdef int result_code = EMD_wrap(n1, n2, a.data, b.data, M.data, G.data, alpha.data, beta.data, &cost, max_iter) return G, cost, alpha, beta, result_code diff --git a/test/test_ot.py b/test/test_ot.py index c9b5154..ca921c5 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -140,7 +140,7 @@ def test_warnings(): with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") print('Computing {} EMD '.format(1)) - ot.emd(a, b, M, num_iter_max=1) + ot.emd(a, b, M, max_iter=1) assert "numItermax" in str(w[-1].message) assert len(w) == 1 a[0] = 100 -- cgit v1.2.3 From 8cc04ef5ae8806c81811b2081b1880b46ca063a3 Mon Sep 17 00:00:00 2001 From: Antoine Rolet Date: Sat, 9 Sep 2017 18:05:12 +0900 Subject: Renamed variable in string --- ot/lp/__init__.py | 1 - ot/lp/emd_wrap.pyx | 2 +- test/test_ot.py | 2 +- 3 files changed, 2 insertions(+), 3 deletions(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 9a0cb1c..ae5b08c 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -201,7 +201,6 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), max_iter=100000, log=Fa if len(b.shape) == 1: return f(b) nb = b.shape[1] - # res = [emd2_c(a, b[:, i].copy(), M, numItermax) for i in range(nb)] res = parmap(f, [b[:, i] for i in range(nb)], processes) return res diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 83ee6aa..2fcc0e4 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -29,7 +29,7 @@ def check_result(result_code): elif result_code == UNBOUNDED: message = "Problem unbounded" elif result_code == MAX_ITER_REACHED: - message = "numItermax reached before optimality. Try to increase numItermax." + message = "max_iter reached before optimality. Try to increase max_iter." warnings.warn(message) return message diff --git a/test/test_ot.py b/test/test_ot.py index ca921c5..46fc634 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -141,7 +141,7 @@ def test_warnings(): warnings.simplefilter("always") print('Computing {} EMD '.format(1)) ot.emd(a, b, M, max_iter=1) - assert "numItermax" in str(w[-1].message) + assert "max_iter" in str(w[-1].message) assert len(w) == 1 a[0] = 100 print('Computing {} EMD '.format(2)) -- cgit v1.2.3 From 06429e5a34790ec51eb1c921293b24c37b81b952 Mon Sep 17 00:00:00 2001 From: Antoine Rolet Date: Sat, 9 Sep 2017 18:23:05 +0900 Subject: Returned to old variable name to follow repo convention --- ot/da.py | 2 +- ot/lp/__init__.py | 12 ++++++------ ot/lp/emd_wrap.pyx | 2 +- test/test_ot.py | 4 ++-- 4 files changed, 10 insertions(+), 10 deletions(-) diff --git a/ot/da.py b/ot/da.py index f3e7433..eb70305 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1370,7 +1370,7 @@ class EMDTransport(BaseTransport): # coupling estimation self.coupling_ = emd( - a=self.mu_s, b=self.mu_t, M=self.cost_, max_iter=self.max_iter + a=self.mu_s, b=self.mu_t, M=self.cost_, num_iter_max=self.max_iter ) return self diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index ae5b08c..17f5bb4 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -16,7 +16,7 @@ from .emd_wrap import emd_c, check_result from ..utils import parmap -def emd(a, b, M, max_iter=100000, log=False): +def emd(a, b, M, num_iter_max=100000, log=False): """Solves the Earth Movers distance problem and returns the OT matrix @@ -41,7 +41,7 @@ def emd(a, b, M, max_iter=100000, log=False): Target histogram (uniform weigth if empty list) M : (ns,nt) ndarray, float64 loss matrix - max_iter : int, optional (default=100000) + num_iter_max : int, optional (default=100000) The maximum number of iterations before stopping the optimization algorithm if it has not converged. log: boolean, optional (default=False) @@ -94,7 +94,7 @@ def emd(a, b, M, max_iter=100000, log=False): if len(b) == 0: b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] - G, cost, u, v, result_code = emd_c(a, b, M, max_iter) + G, cost, u, v, result_code = emd_c(a, b, M, num_iter_max) result_code_string = check_result(result_code) if log: log = {} @@ -107,7 +107,7 @@ def emd(a, b, M, max_iter=100000, log=False): return G -def emd2(a, b, M, processes=multiprocessing.cpu_count(), max_iter=100000, log=False): +def emd2(a, b, M, processes=multiprocessing.cpu_count(), num_iter_max=100000, log=False): """Solves the Earth Movers distance problem and returns the loss .. math:: @@ -183,7 +183,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), max_iter=100000, log=Fa if log: def f(b): - G, cost, u, v, resultCode = emd_c(a, b, M, max_iter) + G, cost, u, v, resultCode = emd_c(a, b, M, num_iter_max) result_code_string = check_result(resultCode) log = {} log['G'] = G @@ -194,7 +194,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), max_iter=100000, log=Fa return [cost, log] else: def f(b): - G, cost, u, v, result_code = emd_c(a, b, M, max_iter) + G, cost, u, v, result_code = emd_c(a, b, M, num_iter_max) check_result(result_code) return cost diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 2fcc0e4..45fc988 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -29,7 +29,7 @@ def check_result(result_code): elif result_code == UNBOUNDED: message = "Problem unbounded" elif result_code == MAX_ITER_REACHED: - message = "max_iter reached before optimality. Try to increase max_iter." + message = "num_iter_max reached before optimality. Try to increase num_iter_max." warnings.warn(message) return message diff --git a/test/test_ot.py b/test/test_ot.py index 46fc634..e05e8aa 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -140,8 +140,8 @@ def test_warnings(): with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") print('Computing {} EMD '.format(1)) - ot.emd(a, b, M, max_iter=1) - assert "max_iter" in str(w[-1].message) + ot.emd(a, b, M, num_iter_max=1) + assert "num_iter_max" in str(w[-1].message) assert len(w) == 1 a[0] = 100 print('Computing {} EMD '.format(2)) -- cgit v1.2.3 From 7c6169222979a7e82a83c118bc7117684258d0de Mon Sep 17 00:00:00 2001 From: Antoine Rolet Date: Sat, 9 Sep 2017 18:29:32 +0900 Subject: Updated variable name in docstring --- ot/lp/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 17f5bb4..f2eaa2b 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -131,7 +131,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), num_iter_max=100000, lo Target histogram (uniform weigth if empty list) M : (ns,nt) ndarray, float64 loss matrix - max_iter : int, optional (default=100000) + num_iter_max : int, optional (default=100000) The maximum number of iterations before stopping the optimization algorithm if it has not converged. -- cgit v1.2.3 From 2324b1f629ebea5b6b58d0b32082032f2914f7e3 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Sun, 10 Sep 2017 20:24:50 +0200 Subject: correction config.py for readthedoc --- docs/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 156b878..4105d87 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -33,7 +33,7 @@ class Mock(MagicMock): return MagicMock() MOCK_MODULES = ['ot.lp.emd_wrap','autograd','pymanopt','cudamat','autograd.numpy','pymanopt.manifolds','pymanopt.solvers'] # 'autograd.numpy','pymanopt.manifolds','pymanopt.solvers', -###sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) +sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # !!!! # If extensions (or modules to document with autodoc) are in another directory, -- cgit v1.2.3 From dd6f8260d01ce173ef3fe0c900112f0ed5288950 Mon Sep 17 00:00:00 2001 From: Antoine Rolet Date: Tue, 12 Sep 2017 19:58:46 +0900 Subject: Made the return of the matrix optional in emd2 --- ot/lp/__init__.py | 12 +++++++++--- test/test_ot.py | 6 +++--- 2 files changed, 12 insertions(+), 6 deletions(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index f2eaa2b..d0f682b 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -107,7 +107,7 @@ def emd(a, b, M, num_iter_max=100000, log=False): return G -def emd2(a, b, M, processes=multiprocessing.cpu_count(), num_iter_max=100000, log=False): +def emd2(a, b, M, processes=multiprocessing.cpu_count(), num_iter_max=100000, log=False, return_matrix=False): """Solves the Earth Movers distance problem and returns the loss .. math:: @@ -134,6 +134,11 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), num_iter_max=100000, lo num_iter_max : int, optional (default=100000) The maximum number of iterations before stopping the optimization algorithm if it has not converged. + log: boolean, optional (default=False) + If True, returns a dictionary containing the cost and dual + variables. Otherwise returns only the optimal transportation cost. + return_matrix: boolean, optional (default=False) + If True, returns the optimal transportation matrix in the log. Returns ------- @@ -181,12 +186,13 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), num_iter_max=100000, lo if len(b) == 0: b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] - if log: + if log or return_matrix: def f(b): G, cost, u, v, resultCode = emd_c(a, b, M, num_iter_max) result_code_string = check_result(resultCode) log = {} - log['G'] = G + if return_matrix: + log['G'] = G log['u'] = u log['v'] = v log['warning'] = result_code_string diff --git a/test/test_ot.py b/test/test_ot.py index e05e8aa..ea6d9dc 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -103,7 +103,7 @@ def test_emd2_multi(): # emd loss multipro proc with log ot.tic() - emdn = ot.emd2(a, b, M, log=True) + emdn = ot.emd2(a, b, M, log=True, return_matrix=True) ot.toc('multi proc : {} s') for i in range(len(emdn)): @@ -140,8 +140,8 @@ def test_warnings(): with warnings.catch_warnings(record=True) as w: warnings.simplefilter("always") print('Computing {} EMD '.format(1)) - ot.emd(a, b, M, num_iter_max=1) - assert "num_iter_max" in str(w[-1].message) + ot.emd(a, b, M, numItermax=1) + assert "numItermax" in str(w[-1].message) assert len(w) == 1 a[0] = 100 print('Computing {} EMD '.format(2)) -- cgit v1.2.3 From e52b6eb41228a7f8e381cf73c06e0dffba5773be Mon Sep 17 00:00:00 2001 From: Antoine Rolet Date: Tue, 12 Sep 2017 20:00:14 +0900 Subject: Renaming --- ot/da.py | 2 +- ot/lp/__init__.py | 14 +++++++------- ot/lp/emd_wrap.pyx | 2 +- 3 files changed, 9 insertions(+), 9 deletions(-) diff --git a/ot/da.py b/ot/da.py index eb70305..1d3d0ba 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1370,7 +1370,7 @@ class EMDTransport(BaseTransport): # coupling estimation self.coupling_ = emd( - a=self.mu_s, b=self.mu_t, M=self.cost_, num_iter_max=self.max_iter + a=self.mu_s, b=self.mu_t, M=self.cost_, numItermax=self.max_iter ) return self diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index d0f682b..5c09da2 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -16,7 +16,7 @@ from .emd_wrap import emd_c, check_result from ..utils import parmap -def emd(a, b, M, num_iter_max=100000, log=False): +def emd(a, b, M, numItermax=100000, log=False): """Solves the Earth Movers distance problem and returns the OT matrix @@ -41,7 +41,7 @@ def emd(a, b, M, num_iter_max=100000, log=False): Target histogram (uniform weigth if empty list) M : (ns,nt) ndarray, float64 loss matrix - num_iter_max : int, optional (default=100000) + numItermax : int, optional (default=100000) The maximum number of iterations before stopping the optimization algorithm if it has not converged. log: boolean, optional (default=False) @@ -94,7 +94,7 @@ def emd(a, b, M, num_iter_max=100000, log=False): if len(b) == 0: b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] - G, cost, u, v, result_code = emd_c(a, b, M, num_iter_max) + G, cost, u, v, result_code = emd_c(a, b, M, numItermax) result_code_string = check_result(result_code) if log: log = {} @@ -107,7 +107,7 @@ def emd(a, b, M, num_iter_max=100000, log=False): return G -def emd2(a, b, M, processes=multiprocessing.cpu_count(), num_iter_max=100000, log=False, return_matrix=False): +def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=100000, log=False, return_matrix=False): """Solves the Earth Movers distance problem and returns the loss .. math:: @@ -131,7 +131,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), num_iter_max=100000, lo Target histogram (uniform weigth if empty list) M : (ns,nt) ndarray, float64 loss matrix - num_iter_max : int, optional (default=100000) + numItermax : int, optional (default=100000) The maximum number of iterations before stopping the optimization algorithm if it has not converged. log: boolean, optional (default=False) @@ -188,7 +188,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), num_iter_max=100000, lo if log or return_matrix: def f(b): - G, cost, u, v, resultCode = emd_c(a, b, M, num_iter_max) + G, cost, u, v, resultCode = emd_c(a, b, M, numItermax) result_code_string = check_result(resultCode) log = {} if return_matrix: @@ -200,7 +200,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), num_iter_max=100000, lo return [cost, log] else: def f(b): - G, cost, u, v, result_code = emd_c(a, b, M, num_iter_max) + G, cost, u, v, result_code = emd_c(a, b, M, numItermax) check_result(result_code) return cost diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 45fc988..83ee6aa 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -29,7 +29,7 @@ def check_result(result_code): elif result_code == UNBOUNDED: message = "Problem unbounded" elif result_code == MAX_ITER_REACHED: - message = "num_iter_max reached before optimality. Try to increase num_iter_max." + message = "numItermax reached before optimality. Try to increase numItermax." warnings.warn(message) return message -- cgit v1.2.3 From 36bf599552ff15d1ca1c6b505507e65a333fa55e Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Tue, 12 Sep 2017 18:07:17 +0200 Subject: Corrections on Gromov --- data/carre.png | Bin 168 -> 0 bytes data/coeur.png | Bin 225 -> 0 bytes data/cross.png | Bin 0 -> 230 bytes data/rond.png | Bin 230 -> 0 bytes data/square.png | Bin 0 -> 168 bytes data/star.png | Bin 0 -> 225 bytes examples/plot_gromov.py | 5 +++-- examples/plot_gromov_barycenter.py | 13 +++++-------- test/test_gromov.py | 3 +-- 9 files changed, 9 insertions(+), 12 deletions(-) delete mode 100755 data/carre.png delete mode 100755 data/coeur.png create mode 100755 data/cross.png delete mode 100755 data/rond.png create mode 100755 data/square.png create mode 100755 data/star.png diff --git a/data/carre.png b/data/carre.png deleted file mode 100755 index 45ff0ef..0000000 Binary files a/data/carre.png and /dev/null differ diff --git a/data/coeur.png b/data/coeur.png deleted file mode 100755 index 3f511a6..0000000 Binary files a/data/coeur.png and /dev/null differ diff --git a/data/cross.png b/data/cross.png new file mode 100755 index 0000000..1c1a068 Binary files /dev/null and b/data/cross.png differ diff --git a/data/rond.png b/data/rond.png deleted file mode 100755 index 1c1a068..0000000 Binary files a/data/rond.png and /dev/null differ diff --git a/data/square.png b/data/square.png new file mode 100755 index 0000000..45ff0ef Binary files /dev/null and b/data/square.png differ diff --git a/data/star.png b/data/star.png new file mode 100755 index 0000000..3f511a6 Binary files /dev/null and b/data/star.png differ diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py index 92312ae..0f839a3 100644 --- a/examples/plot_gromov.py +++ b/examples/plot_gromov.py @@ -22,8 +22,9 @@ import ot """ Sample two Gaussian distributions (2D and 3D) ============================================= -The Gromov-Wasserstein distance allows to compute distances with samples that do not belong to the same metric space. -For demonstration purpose, we sample two Gaussian distributions in 2- and 3-dimensional spaces. +The Gromov-Wasserstein distance allows to compute distances with samples that +do not belong to the same metric space. For demonstration purpose, we sample +two Gaussian distributions in 2- and 3-dimensional spaces. """ n_samples = 30 # nb samples diff --git a/examples/plot_gromov_barycenter.py b/examples/plot_gromov_barycenter.py index 4f17117..c138031 100755 --- a/examples/plot_gromov_barycenter.py +++ b/examples/plot_gromov_barycenter.py @@ -48,13 +48,10 @@ def smacof_mds(C, dim, max_iter=3000, eps=1e-9): eps : float relative tolerance w.r.t stress to declare converge - Returns ------- npos : ndarray, shape (R, dim) Embedded coordinates of the interpolated point cloud (defined with one isometry) - - """ rng = np.random.RandomState(seed=3) @@ -91,12 +88,12 @@ def im2mat(I): return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) -square = spi.imread('../data/carre.png').astype(np.float64) / 256 -circle = spi.imread('../data/rond.png').astype(np.float64) / 256 -triangle = spi.imread('../data/triangle.png').astype(np.float64) / 256 -arrow = spi.imread('../data/coeur.png').astype(np.float64) / 256 +square = spi.imread('../data/square.png').astype(np.float64)[:,:,2] / 256 +cross = spi.imread('../data/cross.png').astype(np.float64)[:,:,2] / 256 +triangle = spi.imread('../data/triangle.png').astype(np.float64)[:,:,2] / 256 +star = spi.imread('../data/star.png').astype(np.float64)[:,:,2] / 256 -shapes = [square, circle, triangle, arrow] +shapes = [square, cross, triangle, star] S = 4 xs = [[] for i in range(S)] diff --git a/test/test_gromov.py b/test/test_gromov.py index 28495e1..e808292 100644 --- a/test/test_gromov.py +++ b/test/test_gromov.py @@ -17,8 +17,7 @@ def test_gromov(): xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s) - xt = xs[::-1] - xt = np.array(xt) + xt = xs[::-1].copy() p = ot.unif(n_samples) q = ot.unif(n_samples) -- cgit v1.2.3 From 24784eda59cf591746bf4ba62f325c5612ada430 Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Tue, 12 Sep 2017 22:08:29 +0200 Subject: Corrections on Gromov --- ot/gromov.py | 20 -------------------- 1 file changed, 20 deletions(-) diff --git a/ot/gromov.py b/ot/gromov.py index 1726f5e..82e3fd3 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -40,7 +40,6 @@ def tensor_square_loss(C1, C2, T): function as the loss function of Gromow-Wasserstein discrepancy. Where : - C1 : Metric cost matrix in the source space C2 : Metric cost matrix in the target space T : A coupling between those two spaces @@ -61,13 +60,10 @@ def tensor_square_loss(C1, C2, T): T : ndarray, shape (ns, nt) Coupling between source and target spaces - Returns ------- tens : ndarray, shape (ns, nt) \mathcal{L}(C1,C2) \otimes T tensor-matrix multiplication result - - """ C1 = np.asarray(C1, dtype=np.float64) @@ -119,7 +115,6 @@ def tensor_kl_loss(C1, C2, T): T : ndarray, shape (ns, nt) Coupling between source and target spaces - Returns ------- tens : ndarray, shape (ns, nt) @@ -127,7 +122,6 @@ def tensor_kl_loss(C1, C2, T): References ---------- - .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices." International Conference on Machine Learning (ICML). 2016. """ @@ -159,7 +153,6 @@ def update_square_loss(p, lambdas, T, Cs): Updates C according to the L2 Loss kernel with the S Ts couplings calculated at each iteration - Parameters ---------- p : ndarray, shape (N,) @@ -174,8 +167,6 @@ def update_square_loss(p, lambdas, T, Cs): ---------- C : ndarray, shape (nt,nt) updated C matrix - - """ tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) for s in range(len(T))]) ppt = np.outer(p, p) @@ -202,8 +193,6 @@ def update_kl_loss(p, lambdas, T, Cs): ---------- C : ndarray, shape (ns,ns) updated C matrix - - """ tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) for s in range(len(T))]) ppt = np.outer(p, p) @@ -229,7 +218,6 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, tol=1e-9, \GW\geq 0 Where : - C1 : Metric cost matrix in the source space C2 : Metric cost matrix in the target space p : distribution in the source space @@ -237,7 +225,6 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, tol=1e-9, L : loss function to account for the misfit between the similarity matrices H : entropy - Parameters ---------- C1 : ndarray, shape (ns, ns) @@ -261,13 +248,11 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, tol=1e-9, log : bool, optional record log if True - Returns ------- T : ndarray, shape (ns, nt) coupling between the two spaces that minimizes : \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T)) - """ C1 = np.asarray(C1, dtype=np.float64) @@ -322,9 +307,7 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, tol=1e-9 .. math:: \GW_Dist = \min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T)) - Where : - C1 : Metric cost matrix in the source space C2 : Metric cost matrix in the target space p : distribution in the source space @@ -332,7 +315,6 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, tol=1e-9 L : loss function to account for the misfit between the similarity matrices H : entropy - Parameters ---------- C1 : ndarray, shape (ns, ns) @@ -360,7 +342,6 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, tol=1e-9 ------- gw_dist : float Gromov-Wasserstein distance - """ if log: @@ -428,7 +409,6 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, max_iter=1000, ------- C : ndarray, shape (N, N) Similarity matrix in the barycenter space (permutated arbitrarily) - """ S = len(Cs) -- cgit v1.2.3 From 84c272394d41d159d07174306b324590b3ffe40c Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Wed, 13 Sep 2017 01:03:21 +0200 Subject: Corrections on Gromov --- examples/plot_gromov.py | 4 ++-- examples/plot_gromov_barycenter.py | 18 +++++++++------- ot/gromov.py | 44 ++++++++++++++++++++++++++------------ 3 files changed, 42 insertions(+), 24 deletions(-) diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py index 0f839a3..dce66c4 100644 --- a/examples/plot_gromov.py +++ b/examples/plot_gromov.py @@ -22,8 +22,8 @@ import ot """ Sample two Gaussian distributions (2D and 3D) ============================================= -The Gromov-Wasserstein distance allows to compute distances with samples that -do not belong to the same metric space. For demonstration purpose, we sample +The Gromov-Wasserstein distance allows to compute distances with samples that +do not belong to the same metric space. For demonstration purpose, we sample two Gaussian distributions in 2- and 3-dimensional spaces. """ diff --git a/examples/plot_gromov_barycenter.py b/examples/plot_gromov_barycenter.py index c138031..52f4966 100755 --- a/examples/plot_gromov_barycenter.py +++ b/examples/plot_gromov_barycenter.py @@ -3,7 +3,7 @@ ===================================== Gromov-Wasserstein Barycenter example ===================================== -This example is designed to show how to use the Gromov-Wassertsein distance +This example is designed to show how to use the Gromov-Wasserstein distance computation in POT. """ @@ -34,8 +34,9 @@ that will be given by the output of the algorithm def smacof_mds(C, dim, max_iter=3000, eps=1e-9): """ - Returns an interpolated point cloud following the dissimilarity matrix C using SMACOF - multidimensional scaling (MDS) in specific dimensionned target space + Returns an interpolated point cloud following the dissimilarity matrix C + using SMACOF multidimensional scaling (MDS) in specific dimensionned + target space Parameters ---------- @@ -51,7 +52,8 @@ def smacof_mds(C, dim, max_iter=3000, eps=1e-9): Returns ------- npos : ndarray, shape (R, dim) - Embedded coordinates of the interpolated point cloud (defined with one isometry) + Embedded coordinates of the interpolated point cloud (defined with + one isometry) """ rng = np.random.RandomState(seed=3) @@ -88,10 +90,10 @@ def im2mat(I): return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) -square = spi.imread('../data/square.png').astype(np.float64)[:,:,2] / 256 -cross = spi.imread('../data/cross.png').astype(np.float64)[:,:,2] / 256 -triangle = spi.imread('../data/triangle.png').astype(np.float64)[:,:,2] / 256 -star = spi.imread('../data/star.png').astype(np.float64)[:,:,2] / 256 +square = spi.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256 +cross = spi.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256 +triangle = spi.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256 +star = spi.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256 shapes = [square, cross, triangle, star] diff --git a/ot/gromov.py b/ot/gromov.py index 82e3fd3..7968e5e 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -122,7 +122,9 @@ def tensor_kl_loss(C1, C2, T): References ---------- - .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices." International Conference on Machine Learning (ICML). 2016. + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. """ @@ -157,7 +159,8 @@ def update_square_loss(p, lambdas, T, Cs): ---------- p : ndarray, shape (N,) weights in the targeted barycenter - lambdas : list of the S spaces' weights + lambdas : list of float + list of the S spaces' weights T : list of S np.ndarray(ns,N) the S Ts couplings calculated at each iteration Cs : list of S ndarray, shape(ns,ns) @@ -168,7 +171,8 @@ def update_square_loss(p, lambdas, T, Cs): C : ndarray, shape (nt,nt) updated C matrix """ - tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) for s in range(len(T))]) + tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) + for s in range(len(T))]) ppt = np.outer(p, p) return np.divide(tmpsum, ppt) @@ -194,13 +198,15 @@ def update_kl_loss(p, lambdas, T, Cs): C : ndarray, shape (ns,ns) updated C matrix """ - tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) for s in range(len(T))]) + tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) + for s in range(len(T))]) ppt = np.outer(p, p) return np.exp(np.divide(tmpsum, ppt)) -def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, tol=1e-9, verbose=False, log=False): +def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, + max_iter=1000, tol=1e-9, verbose=False, log=False): """ Returns the gromov-wasserstein coupling between the two measured similarity matrices @@ -276,7 +282,8 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, tol=1e-9, T = sinkhorn(p, q, tens, epsilon) if cpt % 10 == 0: - # we can speed up the process by checking for the error only all the 10th iterations + # we can speed up the process by checking for the error only all + # the 10th iterations err = np.linalg.norm(T - Tprev) if log: @@ -296,7 +303,8 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, tol=1e-9, return T -def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, tol=1e-9, verbose=False, log=False): +def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, + max_iter=1000, tol=1e-9, verbose=False, log=False): """ Returns the gromov-wasserstein discrepancy between the two measured similarity matrices @@ -363,7 +371,8 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, tol=1e-9 return gw_dist -def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, max_iter=1000, tol=1e-9, verbose=False, log=False): +def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, + max_iter=1000, tol=1e-9, verbose=False, log=False, init_C=None): """ Returns the gromov-wasserstein barycenters of S measured similarity matrices @@ -390,7 +399,8 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, max_iter=1000, sample weights in the S spaces p : ndarray, shape(N,) weights in the targeted barycenter - lambdas : list of the S spaces' weights + lambdas : list of float + list of the S spaces' weights L : tensor-matrix multiplication function based on specific loss function update : function(p,lambdas,T,Cs) that updates C according to a specific Kernel with the S Ts couplings calculated at each iteration @@ -404,6 +414,8 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, max_iter=1000, Print information along iterations log : bool, optional record log if True + init_C : bool, ndarray, shape(N,N) + random initial value for the C matrix provided by user Returns ------- @@ -416,10 +428,13 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, max_iter=1000, Cs = [np.asarray(Cs[s], dtype=np.float64) for s in range(S)] lambdas = np.asarray(lambdas, dtype=np.float64) - # Initialization of C : random SPD matrix - xalea = np.random.randn(N, 2) - C = dist(xalea, xalea) - C /= C.max() + # Initialization of C : random SPD matrix (if not provided by user) + if init_C is None: + xalea = np.random.randn(N, 2) + C = dist(xalea, xalea) + C /= C.max() + else: + C = init_C cpt = 0 err = 1 @@ -438,7 +453,8 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, max_iter=1000, C = update_kl_loss(p, lambdas, T, Cs) if cpt % 10 == 0: - # we can speed up the process by checking for the error only all the 10th iterations + # we can speed up the process by checking for the error only all + # the 10th iterations err = np.linalg.norm(C - Cprev) error.append(err) -- cgit v1.2.3 From 55db3508917c73c4811a82933f892fc3017300f2 Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Wed, 13 Sep 2017 01:09:26 +0200 Subject: Corrections on Gromov --- ot/gromov.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ot/gromov.py b/ot/gromov.py index 7968e5e..20bf7ee 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -158,7 +158,7 @@ def update_square_loss(p, lambdas, T, Cs): Parameters ---------- p : ndarray, shape (N,) - weights in the targeted barycenter + masses in the targeted barycenter lambdas : list of float list of the S spaces' weights T : list of S np.ndarray(ns,N) @@ -401,7 +401,7 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, weights in the targeted barycenter lambdas : list of float list of the S spaces' weights - L : tensor-matrix multiplication function based on specific loss function + loss_fun : tensor-matrix multiplication function based on specific loss function update : function(p,lambdas,T,Cs) that updates C according to a specific Kernel with the S Ts couplings calculated at each iteration epsilon : float -- cgit v1.2.3 From 5a2ebfab92a655e636efee1d91d44c3023e25828 Mon Sep 17 00:00:00 2001 From: ncourty Date: Wed, 13 Sep 2017 10:07:47 +0200 Subject: Corrections on Gromov --- ot/gromov.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ot/gromov.py b/ot/gromov.py index 7968e5e..20bf7ee 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -158,7 +158,7 @@ def update_square_loss(p, lambdas, T, Cs): Parameters ---------- p : ndarray, shape (N,) - weights in the targeted barycenter + masses in the targeted barycenter lambdas : list of float list of the S spaces' weights T : list of S np.ndarray(ns,N) @@ -401,7 +401,7 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, weights in the targeted barycenter lambdas : list of float list of the S spaces' weights - L : tensor-matrix multiplication function based on specific loss function + loss_fun : tensor-matrix multiplication function based on specific loss function update : function(p,lambdas,T,Cs) that updates C according to a specific Kernel with the S Ts couplings calculated at each iteration epsilon : float -- cgit v1.2.3 From aaf7ec83141045f0897d7fbc563ff7e5e7346fd9 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 14 Sep 2017 16:54:57 +0200 Subject: move example --- examples/da/plot_otda_semi_supervised.py | 147 ------------------------------- 1 file changed, 147 deletions(-) delete mode 100644 examples/da/plot_otda_semi_supervised.py diff --git a/examples/da/plot_otda_semi_supervised.py b/examples/da/plot_otda_semi_supervised.py deleted file mode 100644 index 8095c4d..0000000 --- a/examples/da/plot_otda_semi_supervised.py +++ /dev/null @@ -1,147 +0,0 @@ -# -*- coding: utf-8 -*- -""" -============================================ -OTDA unsupervised vs semi-supervised setting -============================================ - -This example introduces a semi supervised domain adaptation in a 2D setting. -It explicits the problem of semi supervised domain adaptation and introduces -some optimal transport approaches to solve it. - -Quantities such as optimal couplings, greater coupling coefficients and -transported samples are represented in order to give a visual understanding -of what the transport methods are doing. -""" - -# Authors: Remi Flamary -# Stanislas Chambon -# -# License: MIT License - -import matplotlib.pylab as pl -import ot - - -############################################################################## -# generate data -############################################################################## - -n_samples_source = 150 -n_samples_target = 150 - -Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source) -Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target) - - -############################################################################## -# Transport source samples onto target samples -############################################################################## - -# unsupervised domain adaptation -ot_sinkhorn_un = ot.da.SinkhornTransport(reg_e=1e-1) -ot_sinkhorn_un.fit(Xs=Xs, Xt=Xt) -transp_Xs_sinkhorn_un = ot_sinkhorn_un.transform(Xs=Xs) - -# semi-supervised domain adaptation -ot_sinkhorn_semi = ot.da.SinkhornTransport(reg_e=1e-1) -ot_sinkhorn_semi.fit(Xs=Xs, Xt=Xt, ys=ys, yt=yt) -transp_Xs_sinkhorn_semi = ot_sinkhorn_semi.transform(Xs=Xs) - -# semi supervised DA uses available labaled target samples to modify the cost -# matrix involved in the OT problem. The cost of transporting a source sample -# of class A onto a target sample of class B != A is set to infinite, or a -# very large value - -# note that in the present case we consider that all the target samples are -# labeled. For daily applications, some target sample might not have labels, -# in this case the element of yt corresponding to these samples should be -# filled with -1. - -# Warning: we recall that -1 cannot be used as a class label - - -############################################################################## -# Fig 1 : plots source and target samples + matrix of pairwise distance -############################################################################## - -pl.figure(1, figsize=(10, 10)) -pl.subplot(2, 2, 1) -pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') -pl.xticks([]) -pl.yticks([]) -pl.legend(loc=0) -pl.title('Source samples') - -pl.subplot(2, 2, 2) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') -pl.xticks([]) -pl.yticks([]) -pl.legend(loc=0) -pl.title('Target samples') - -pl.subplot(2, 2, 3) -pl.imshow(ot_sinkhorn_un.cost_, interpolation='nearest') -pl.xticks([]) -pl.yticks([]) -pl.title('Cost matrix - unsupervised DA') - -pl.subplot(2, 2, 4) -pl.imshow(ot_sinkhorn_semi.cost_, interpolation='nearest') -pl.xticks([]) -pl.yticks([]) -pl.title('Cost matrix - semisupervised DA') - -pl.tight_layout() - -# the optimal coupling in the semi-supervised DA case will exhibit " shape -# similar" to the cost matrix, (block diagonal matrix) - - -############################################################################## -# Fig 2 : plots optimal couplings for the different methods -############################################################################## - -pl.figure(2, figsize=(8, 4)) - -pl.subplot(1, 2, 1) -pl.imshow(ot_sinkhorn_un.coupling_, interpolation='nearest') -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nUnsupervised DA') - -pl.subplot(1, 2, 2) -pl.imshow(ot_sinkhorn_semi.coupling_, interpolation='nearest') -pl.xticks([]) -pl.yticks([]) -pl.title('Optimal coupling\nSemi-supervised DA') - -pl.tight_layout() - - -############################################################################## -# Fig 3 : plot transported samples -############################################################################## - -# display transported samples -pl.figure(4, figsize=(8, 4)) -pl.subplot(1, 2, 1) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.5) -pl.scatter(transp_Xs_sinkhorn_un[:, 0], transp_Xs_sinkhorn_un[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Transported samples\nEmdTransport') -pl.legend(loc=0) -pl.xticks([]) -pl.yticks([]) - -pl.subplot(1, 2, 2) -pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', - label='Target samples', alpha=0.5) -pl.scatter(transp_Xs_sinkhorn_semi[:, 0], transp_Xs_sinkhorn_semi[:, 1], c=ys, - marker='+', label='Transp samples', s=30) -pl.title('Transported samples\nSinkhornTransport') -pl.xticks([]) -pl.yticks([]) - -pl.tight_layout() -pl.show() -- cgit v1.2.3 From 6f01f3c96e06a00816a061a5f6977fe813f142af Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 14 Sep 2017 16:57:06 +0200 Subject: add cython build to makefile --- Makefile | 3 +++ 1 file changed, 3 insertions(+) diff --git a/Makefile b/Makefile index 98f5614..3f19e8a 100644 --- a/Makefile +++ b/Makefile @@ -15,6 +15,9 @@ help : build : $(PYTHON) setup.py build +buildext : + $(PYTHON) setup.py build_ext --inplace + install : $(PYTHON) setup.py install --user -- cgit v1.2.3 From d27e14cb4a48e76ab132c53aca56155e9be50bda Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 14 Sep 2017 16:57:29 +0200 Subject: update doc --- docs/source/readme.rst | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 1482838..065093e 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -21,6 +21,7 @@ It provides the following solvers: - Joint OT matrix and mapping estimation [8]. - Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt). +- Gromov-Wasserstein distances and barycenters [12] Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -190,6 +191,7 @@ The contributors to this library are: - `Nathalie Gayraud `__ - `Stanislas Chambon `__ +- `Antoine Rolet `__ This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various @@ -199,7 +201,6 @@ languages): in Matlab) - `Nicolas Bonneel `__ ( C++ code for EMD) -- `Antoine Rolet `__ ( Mex file for EMD ) - `Marco Cuturi `__ (Sinkhorn Knopp in Matlab/Cuda) @@ -280,6 +281,11 @@ arXiv:1607.05816. Analysis `__. arXiv preprint arXiv:1608.08063. +[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon, +`Gromov-Wasserstein averaging of kernel and distance +matrices `__ +International Conference on Machine Learning (ICML). 2016. + .. |PyPI version| image:: https://badge.fury.io/py/POT.svg :target: https://badge.fury.io/py/POT .. |Build Status| image:: https://travis-ci.org/rflamary/POT.svg?branch=master -- cgit v1.2.3 From bad3d95523d005a4fbf64dd009c716b9dd560fe3 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 15 Sep 2017 12:41:02 +0200 Subject: clean nico commit mess with its emal --- .mailmap | 1 + docs/cache_nbrun | 2 +- .../images/sphx_glr_plot_OT_2D_samples_001.png | Bin 22153 -> 20832 bytes .../images/sphx_glr_plot_OT_2D_samples_002.png | Bin 21589 -> 20827 bytes .../images/sphx_glr_plot_OT_2D_samples_005.png | Bin 9645 -> 9613 bytes .../images/sphx_glr_plot_OT_2D_samples_006.png | Bin 91095 -> 82797 bytes .../images/sphx_glr_plot_OT_2D_samples_009.png | Bin 13987 -> 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docs/source/auto_examples/plot_WDA.rst | 40 +-- docs/source/auto_examples/plot_barycenter_1D.rst | 4 +- docs/source/auto_examples/plot_compute_emd.rst | 4 +- docs/source/conf.py | 4 +- examples/plot_gromov.py | 3 +- examples/plot_gromov_barycenter.py | 10 +- ot/__init__.py | 2 +- 28 files changed, 45 insertions(+), 307 deletions(-) diff --git a/.mailmap b/.mailmap index 73f873e..e1e945e 100644 --- a/.mailmap +++ b/.mailmap @@ -1,3 +1,4 @@ Nicolas Courty Nicolas Courty Nicolas Courty ncourty +Nicolas Courty Nicolas Courty Léo Gautheron Leo gautheron diff --git a/docs/cache_nbrun b/docs/cache_nbrun index 1510b2b..ed1a660 100644 --- a/docs/cache_nbrun +++ b/docs/cache_nbrun @@ -1 +1 @@ -{"plot_otda_mapping_colors_images.ipynb": "4f0587a00a3c082799a75a0ed36e9ce1", "plot_optim_OTreg.ipynb": "71d3c106b3f395a6b1001078a6ca6f8d", "plot_barycenter_1D.ipynb": "6fd8167f98816dc832fe0c58b1d5527b", "plot_WDA.ipynb": "27f8de4c6d7db46497076523673eedfb", "plot_OT_L1_vs_L2.ipynb": 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b/docs/source/auto_examples/images/thumb/sphx_glr_plot_barycenter_1D_thumb.png index d8cdccb..5c17671 100644 Binary files a/docs/source/auto_examples/images/thumb/sphx_glr_plot_barycenter_1D_thumb.png and b/docs/source/auto_examples/images/thumb/sphx_glr_plot_barycenter_1D_thumb.png differ diff --git a/docs/source/auto_examples/images/thumb/sphx_glr_plot_compute_emd_thumb.png b/docs/source/auto_examples/images/thumb/sphx_glr_plot_compute_emd_thumb.png index 898cd72..68cbdf7 100644 Binary files a/docs/source/auto_examples/images/thumb/sphx_glr_plot_compute_emd_thumb.png and b/docs/source/auto_examples/images/thumb/sphx_glr_plot_compute_emd_thumb.png differ diff --git a/docs/source/auto_examples/index.rst b/docs/source/auto_examples/index.rst index 5d7a53d..e69de29 100644 --- a/docs/source/auto_examples/index.rst +++ b/docs/source/auto_examples/index.rst @@ -1,270 +0,0 @@ -:orphan: - -POT Examples -============ - -This is a gallery of all the POT example files. - - - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_1D_thumb.png - - :ref:`sphx_glr_auto_examples_plot_OT_1D.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_OT_1D - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_optim_OTreg_thumb.png - - :ref:`sphx_glr_auto_examples_plot_optim_OTreg.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_optim_OTreg - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_2D_samples_thumb.png - - :ref:`sphx_glr_auto_examples_plot_OT_2D_samples.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_OT_2D_samples - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_compute_emd_thumb.png - - :ref:`sphx_glr_auto_examples_plot_compute_emd.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_compute_emd - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_WDA_thumb.png - - :ref:`sphx_glr_auto_examples_plot_WDA.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_WDA - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_color_images_thumb.png - - :ref:`sphx_glr_auto_examples_plot_otda_color_images.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_otda_color_images - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_barycenter_1D_thumb.png - - :ref:`sphx_glr_auto_examples_plot_barycenter_1D.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_barycenter_1D - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_mapping_colors_images_thumb.png - - :ref:`sphx_glr_auto_examples_plot_otda_mapping_colors_images.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_otda_mapping_colors_images - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_mapping_thumb.png - - :ref:`sphx_glr_auto_examples_plot_otda_mapping.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_otda_mapping - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_classes_thumb.png - - :ref:`sphx_glr_auto_examples_plot_otda_classes.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_otda_classes - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_d2_thumb.png - - :ref:`sphx_glr_auto_examples_plot_otda_d2.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_otda_d2 - -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_L1_vs_L2_thumb.png - - :ref:`sphx_glr_auto_examples_plot_OT_L1_vs_L2.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_OT_L1_vs_L2 -.. raw:: html - -
- - - -.. container:: sphx-glr-footer - - - .. container:: sphx-glr-download - - :download:`Download all examples in Python source code: auto_examples_python.zip ` - - - - .. container:: sphx-glr-download - - :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip ` - -.. rst-class:: sphx-glr-signature - - `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OT_1D.rst b/docs/source/auto_examples/plot_OT_1D.rst index b91916e..975a923 100644 --- a/docs/source/auto_examples/plot_OT_1D.rst +++ b/docs/source/auto_examples/plot_OT_1D.rst @@ -171,7 +171,7 @@ Solve Sinkhorn 110|1.527180e-10| -**Total running time of the script:** ( 0 minutes 0.748 seconds) +**Total running time of the script:** ( 0 minutes 1.198 seconds) @@ -190,4 +190,4 @@ Solve Sinkhorn .. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OT_2D_samples.rst b/docs/source/auto_examples/plot_OT_2D_samples.rst index 0ad9cf0..5565c54 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.rst +++ b/docs/source/auto_examples/plot_OT_2D_samples.rst @@ -191,7 +191,7 @@ Compute Sinkhorn -**Total running time of the script:** ( 0 minutes 1.743 seconds) +**Total running time of the script:** ( 0 minutes 3.380 seconds) @@ -210,4 +210,4 @@ Compute Sinkhorn .. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst index f97b373..a569b50 100644 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst @@ -290,7 +290,7 @@ Dataset 2 : Plot OT Matrices -**Total running time of the script:** ( 0 minutes 1.134 seconds) +**Total running time of the script:** ( 0 minutes 1.976 seconds) @@ -309,4 +309,4 @@ Dataset 2 : Plot OT Matrices .. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_WDA.rst b/docs/source/auto_examples/plot_WDA.rst index 64ddb47..2d83123 100644 --- a/docs/source/auto_examples/plot_WDA.rst +++ b/docs/source/auto_examples/plot_WDA.rst @@ -150,21 +150,27 @@ Compute Wasserstein Discriminant Analysis Compiling cost function... Computing gradient of cost function... iter cost val grad. norm - 1 +5.4993226050368416e-01 5.18285173e-01 - 2 +3.4883000507542844e-01 1.96795818e-01 - 3 +2.9841234004693890e-01 2.33029475e-01 - 4 +2.3976476757548179e-01 1.38593951e-01 - 5 +2.3614468346177828e-01 1.19615394e-01 - 6 +2.2586536502789240e-01 4.82430685e-02 - 7 +2.2451030967794622e-01 2.56564039e-02 - 8 +2.2421446331083625e-01 1.47932578e-02 - 9 +2.2407441444450052e-01 1.12040327e-03 - 10 +2.2407365923337522e-01 3.78899763e-04 - 11 +2.2407356874011675e-01 1.79740810e-05 - 12 +2.2407356862959993e-01 1.25643005e-05 - 13 +2.2407356853043561e-01 1.40415001e-06 - 14 +2.2407356852925220e-01 3.41183585e-07 - Terminated - min grad norm reached after 14 iterations, 6.78 seconds. + 1 +9.0167295050534191e-01 2.28422652e-01 + 2 +4.8324990550878105e-01 4.89362707e-01 + 3 +3.4613154515357075e-01 2.84117562e-01 + 4 +2.5277108387195002e-01 1.24888750e-01 + 5 +2.4113858393736629e-01 8.07491482e-02 + 6 +2.3642108593032782e-01 1.67612140e-02 + 7 +2.3625721372202199e-01 7.68640008e-03 + 8 +2.3625461994913738e-01 7.42200784e-03 + 9 +2.3624493441436939e-01 6.43534105e-03 + 10 +2.3621901383686217e-01 2.17960585e-03 + 11 +2.3621854258326572e-01 2.03306749e-03 + 12 +2.3621696458678049e-01 1.37118721e-03 + 13 +2.3621569489873540e-01 2.76368907e-04 + 14 +2.3621565599232983e-01 1.41898134e-04 + 15 +2.3621564465487518e-01 5.96602069e-05 + 16 +2.3621564232556647e-01 1.08709521e-05 + 17 +2.3621564230277003e-01 9.17855656e-06 + 18 +2.3621564224857586e-01 1.73728345e-06 + 19 +2.3621564224748123e-01 1.17770019e-06 + 20 +2.3621564224658587e-01 2.16179383e-07 + Terminated - min grad norm reached after 20 iterations, 9.20 seconds. Plot 2D projections @@ -216,7 +222,7 @@ Plot 2D projections -**Total running time of the script:** ( 0 minutes 7.637 seconds) +**Total running time of the script:** ( 0 minutes 16.182 seconds) @@ -235,4 +241,4 @@ Plot 2D projections .. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_barycenter_1D.rst b/docs/source/auto_examples/plot_barycenter_1D.rst index 413fae3..f17f2c2 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.rst +++ b/docs/source/auto_examples/plot_barycenter_1D.rst @@ -230,7 +230,7 @@ Barycentric interpolation -**Total running time of the script:** ( 0 minutes 0.431 seconds) +**Total running time of the script:** ( 0 minutes 0.814 seconds) @@ -249,4 +249,4 @@ Barycentric interpolation .. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_compute_emd.rst b/docs/source/auto_examples/plot_compute_emd.rst index ce79e20..cdbc620 100644 --- a/docs/source/auto_examples/plot_compute_emd.rst +++ b/docs/source/auto_examples/plot_compute_emd.rst @@ -162,7 +162,7 @@ Compute Sinkhorn for the different losses -**Total running time of the script:** ( 0 minutes 0.441 seconds) +**Total running time of the script:** ( 0 minutes 0.697 seconds) @@ -181,4 +181,4 @@ Compute Sinkhorn for the different losses .. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/conf.py b/docs/source/conf.py index 4105d87..7bf392c 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -33,7 +33,7 @@ class Mock(MagicMock): return MagicMock() MOCK_MODULES = ['ot.lp.emd_wrap','autograd','pymanopt','cudamat','autograd.numpy','pymanopt.manifolds','pymanopt.solvers'] # 'autograd.numpy','pymanopt.manifolds','pymanopt.solvers', -sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) +#sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # !!!! # If extensions (or modules to document with autodoc) are in another directory, @@ -62,7 +62,7 @@ extensions = [ 'sphinx.ext.ifconfig', 'sphinx.ext.viewcode', 'sphinx.ext.napoleon', - #'sphinx_gallery.gen_gallery', + 'sphinx_gallery.gen_gallery', ] # Add any paths that contain templates here, relative to this directory. diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py index dce66c4..5132024 100644 --- a/examples/plot_gromov.py +++ b/examples/plot_gromov.py @@ -3,6 +3,7 @@ ========================== Gromov-Wasserstein example ========================== + This example is designed to show how to use the Gromov-Wassertsein distance computation in POT. """ @@ -15,7 +16,7 @@ computation in POT. import scipy as sp import numpy as np import matplotlib.pylab as pl - +from mpl_toolkits.mplot3d import Axes3D # noqa import ot diff --git a/examples/plot_gromov_barycenter.py b/examples/plot_gromov_barycenter.py index 52f4966..93533c0 100755 --- a/examples/plot_gromov_barycenter.py +++ b/examples/plot_gromov_barycenter.py @@ -3,6 +3,7 @@ ===================================== Gromov-Wasserstein Barycenter example ===================================== + This example is designed to show how to use the Gromov-Wasserstein distance computation in POT. """ @@ -24,7 +25,6 @@ from sklearn.decomposition import PCA import ot """ - Smacof MDS ========== This function allows to find an embedding of points given a dissimilarity matrix @@ -134,28 +134,28 @@ for i in range(2): Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]], [ps[0], ps[1] ], p, lambdast[i], 'square_loss', 5e-4, - max_iter=100, stopThr=1e-3) + max_iter=100, tol=1e-3) Ct02 = [0 for i in range(2)] for i in range(2): Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]], [ps[0], ps[2] ], p, lambdast[i], 'square_loss', 5e-4, - max_iter=100, stopThr=1e-3) + max_iter=100, tol=1e-3) Ct13 = [0 for i in range(2)] for i in range(2): Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]], [ps[1], ps[3] ], p, lambdast[i], 'square_loss', 5e-4, - max_iter=100, stopThr=1e-3) + max_iter=100, tol=1e-3) Ct23 = [0 for i in range(2)] for i in range(2): Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]], [ps[2], ps[3] ], p, lambdast[i], 'square_loss', 5e-4, - max_iter=100, stopThr=1e-3) + max_iter=100, tol=1e-3) """ Visualization diff --git a/ot/__init__.py b/ot/__init__.py index a295e1b..c9bb8c0 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -29,7 +29,7 @@ from .gromov import gromov_wasserstein, gromov_wasserstein2 # utils functions from .utils import dist, unif, tic, toc, toq -__version__ = "0.3.1" +__version__ = "0.4.0b" __all__ = ["emd", "emd2", "sinkhorn", "sinkhorn2", "utils", 'datasets', 'bregman', 'lp', 'plot', 'tic', 'toc', 'toq', -- cgit v1.2.3 From dd3546baf9c59733b2109a971293eba48d2eaed3 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 15 Sep 2017 13:57:01 +0200 Subject: add all files for doc --- docs/cache_nbrun | 2 +- .../source/auto_examples/auto_examples_jupyter.zip | Bin 67774 -> 89148 bytes docs/source/auto_examples/auto_examples_python.zip | Bin 44112 -> 59370 bytes .../images/sphx_glr_plot_gromov_001.png | Bin 0 -> 46633 bytes .../images/sphx_glr_plot_gromov_002.png | Bin 0 -> 16945 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docs/source/auto_examples/plot_gromov.rst | 180 ++++++++++ .../auto_examples/plot_gromov_barycenter.ipynb | 126 +++++++ .../source/auto_examples/plot_gromov_barycenter.py | 248 ++++++++++++++ .../auto_examples/plot_gromov_barycenter.rst | 324 ++++++++++++++++++ docs/source/auto_examples/plot_optim_OTreg.rst | 379 ++++++++++----------- docs/source/auto_examples/plot_otda_classes.rst | 46 +-- .../auto_examples/plot_otda_color_images.rst | 4 +- docs/source/auto_examples/plot_otda_d2.rst | 4 +- docs/source/auto_examples/plot_otda_mapping.rst | 38 ++- .../plot_otda_mapping_colors_images.rst | 66 ++-- .../auto_examples/plot_otda_semi_supervised.ipynb | 144 ++++++++ .../auto_examples/plot_otda_semi_supervised.py | 148 ++++++++ .../auto_examples/plot_otda_semi_supervised.rst | 240 +++++++++++++ docs/source/conf.py | 4 +- examples/plot_gromov.py | 40 +-- examples/plot_gromov_barycenter.py | 42 +-- examples/plot_otda_semi_supervised.py | 148 ++++++++ notebooks/plot_gromov.ipynb | 231 +++++++++++++ notebooks/plot_gromov_barycenter.ipynb | 368 ++++++++++++++++++++ notebooks/plot_otda_semi_supervised.ipynb | 294 ++++++++++++++++ 52 files changed, 3295 insertions(+), 326 deletions(-) create mode 100644 docs/source/auto_examples/images/sphx_glr_plot_gromov_001.png create mode 100644 docs/source/auto_examples/images/sphx_glr_plot_gromov_002.png create mode 100644 docs/source/auto_examples/images/sphx_glr_plot_gromov_003.png create mode 100644 docs/source/auto_examples/images/sphx_glr_plot_gromov_barycenter_001.png create mode 100644 docs/source/auto_examples/images/sphx_glr_plot_otda_semi_supervised_001.png create mode 100644 docs/source/auto_examples/images/sphx_glr_plot_otda_semi_supervised_003.png create mode 100644 docs/source/auto_examples/images/sphx_glr_plot_otda_semi_supervised_006.png create mode 100644 docs/source/auto_examples/plot_gromov.ipynb create mode 100644 docs/source/auto_examples/plot_gromov.py create mode 100644 docs/source/auto_examples/plot_gromov.rst create mode 100644 docs/source/auto_examples/plot_gromov_barycenter.ipynb create mode 100644 docs/source/auto_examples/plot_gromov_barycenter.py create mode 100644 docs/source/auto_examples/plot_gromov_barycenter.rst create mode 100644 docs/source/auto_examples/plot_otda_semi_supervised.ipynb create mode 100644 docs/source/auto_examples/plot_otda_semi_supervised.py create mode 100644 docs/source/auto_examples/plot_otda_semi_supervised.rst create mode 100644 examples/plot_otda_semi_supervised.py create mode 100644 notebooks/plot_gromov.ipynb create mode 100644 notebooks/plot_gromov_barycenter.ipynb create mode 100644 notebooks/plot_otda_semi_supervised.ipynb diff --git a/docs/cache_nbrun b/docs/cache_nbrun index ed1a660..3f1e6ea 100644 --- a/docs/cache_nbrun +++ b/docs/cache_nbrun @@ -1 +1 @@ -{"plot_otda_mapping_colors_images.ipynb": "4f0587a00a3c082799a75a0ed36e9ce1", "plot_optim_OTreg.ipynb": "71d3c106b3f395a6b1001078a6ca6f8d", 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"e15219bf651a7e39e7c5c3934069894c", "plot_barycenter_1D.ipynb": "6fd8167f98816dc832fe0c58b1d5527b", "plot_otda_classes.ipynb": "44bb8cd93317b5d342cd62e26d9bbe60", "plot_otda_d2.ipynb": "8ac4fd2ff899df0858ce1e5fead37f33", "plot_otda_mapping.ipynb": "d335a15af828aaa3439a1c67570d79d6", "plot_gromov.ipynb": "9d0893ec68851f200d0ca806bcbe847f", "plot_compute_emd.ipynb": "bd95981189df6adcb113d9b360ead734", "plot_OT_1D.ipynb": "e44c83f6112388ae18657cb0ad76d0e9", "plot_gromov_barycenter.ipynb": "a4d9636685394ceb13f26cdc613b9b5b", "plot_otda_semi_supervised.ipynb": "0261d339a692e339e15d3634488905cc", "plot_OT_2D_samples.ipynb": "3f125714daa35ff3cfe5dae1f71265c4"} \ No newline at end of file diff --git a/docs/source/auto_examples/auto_examples_jupyter.zip b/docs/source/auto_examples/auto_examples_jupyter.zip index fc1d4de..5a3f24c 100644 Binary files a/docs/source/auto_examples/auto_examples_jupyter.zip and b/docs/source/auto_examples/auto_examples_jupyter.zip differ diff --git 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+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_1D_thumb.png + + :ref:`sphx_glr_auto_examples_plot_OT_1D.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_OT_1D + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_optim_OTreg_thumb.png + + :ref:`sphx_glr_auto_examples_plot_optim_OTreg.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_optim_OTreg + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_gromov_thumb.png + + :ref:`sphx_glr_auto_examples_plot_gromov.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_gromov + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_2D_samples_thumb.png + + :ref:`sphx_glr_auto_examples_plot_OT_2D_samples.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_OT_2D_samples + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_compute_emd_thumb.png + + :ref:`sphx_glr_auto_examples_plot_compute_emd.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_compute_emd + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_WDA_thumb.png + + :ref:`sphx_glr_auto_examples_plot_WDA.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_WDA + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_color_images_thumb.png + + :ref:`sphx_glr_auto_examples_plot_otda_color_images.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_otda_color_images + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_barycenter_1D_thumb.png + + :ref:`sphx_glr_auto_examples_plot_barycenter_1D.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_barycenter_1D + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_mapping_colors_images_thumb.png + + :ref:`sphx_glr_auto_examples_plot_otda_mapping_colors_images.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_otda_mapping_colors_images + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_mapping_thumb.png + + :ref:`sphx_glr_auto_examples_plot_otda_mapping.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_otda_mapping + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_semi_supervised_thumb.png + + :ref:`sphx_glr_auto_examples_plot_otda_semi_supervised.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_otda_semi_supervised + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_classes_thumb.png + + :ref:`sphx_glr_auto_examples_plot_otda_classes.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_otda_classes + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_d2_thumb.png + + :ref:`sphx_glr_auto_examples_plot_otda_d2.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_otda_d2 + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_L1_vs_L2_thumb.png + + :ref:`sphx_glr_auto_examples_plot_OT_L1_vs_L2.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_OT_L1_vs_L2 + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_gromov_barycenter_thumb.png + + :ref:`sphx_glr_auto_examples_plot_gromov_barycenter.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_gromov_barycenter +.. raw:: html + +
+ + + +.. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download all examples in Python source code: auto_examples_python.zip ` + + + + .. container:: sphx-glr-download + + :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip ` + +.. rst-class:: sphx-glr-signature + + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_gromov.ipynb b/docs/source/auto_examples/plot_gromov.ipynb new file mode 100644 index 0000000..865848e --- /dev/null +++ b/docs/source/auto_examples/plot_gromov.ipynb @@ -0,0 +1,126 @@ +{ + "nbformat_minor": 0, + "nbformat": 4, + "cells": [ + { + "execution_count": null, + "cell_type": "code", + "source": [ + "%matplotlib inline" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "\n# Gromov-Wasserstein example\n\n\nThis example is designed to show how to use the Gromov-Wassertsein distance\ncomputation in POT.\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "# Author: Erwan Vautier \r\n# Nicolas Courty \r\n#\r\n# License: MIT License\r\n\r\nimport scipy as sp\r\nimport numpy as np\r\nimport matplotlib.pylab as pl\r\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\r\nimport ot" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Sample two Gaussian distributions (2D and 3D)\r\n ---------------------------------------------\r\n\r\n The Gromov-Wasserstein distance allows to compute distances with samples that\r\n do not belong to the same metric space. For demonstration purpose, we sample\r\n two Gaussian distributions in 2- and 3-dimensional spaces.\r\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "n_samples = 30 # nb samples\r\n\r\nmu_s = np.array([0, 0])\r\ncov_s = np.array([[1, 0], [0, 1]])\r\n\r\nmu_t = np.array([4, 4, 4])\r\ncov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\r\n\r\n\r\nxs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)\r\nP = sp.linalg.sqrtm(cov_t)\r\nxt = np.random.randn(n_samples, 3).dot(P) + mu_t" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Plotting the distributions\r\n--------------------------\r\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "fig = pl.figure()\r\nax1 = fig.add_subplot(121)\r\nax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\r\nax2 = fig.add_subplot(122, projection='3d')\r\nax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\r\npl.show()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Compute distance kernels, normalize them and then display\r\n---------------------------------------------------------\r\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "C1 = sp.spatial.distance.cdist(xs, xs)\r\nC2 = sp.spatial.distance.cdist(xt, xt)\r\n\r\nC1 /= C1.max()\r\nC2 /= C2.max()\r\n\r\npl.figure()\r\npl.subplot(121)\r\npl.imshow(C1)\r\npl.subplot(122)\r\npl.imshow(C2)\r\npl.show()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Compute Gromov-Wasserstein plans and distance\r\n---------------------------------------------\r\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "p = ot.unif(n_samples)\r\nq = ot.unif(n_samples)\r\n\r\ngw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4)\r\ngw_dist = ot.gromov_wasserstein2(C1, C2, p, q, 'square_loss', epsilon=5e-4)\r\n\r\nprint('Gromov-Wasserstein distances between the distribution: ' + str(gw_dist))\r\n\r\npl.figure()\r\npl.imshow(gw, cmap='jet')\r\npl.colorbar()\r\npl.show()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "name": "python2", + "language": "python" + }, + "language_info": { + "mimetype": "text/x-python", + "nbconvert_exporter": "python", + "name": "python", + "file_extension": ".py", + "version": "2.7.12", + "pygments_lexer": "ipython2", + "codemirror_mode": { + "version": 2, + "name": "ipython" + } + } + } +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_gromov.py b/docs/source/auto_examples/plot_gromov.py new file mode 100644 index 0000000..d3f724c --- /dev/null +++ b/docs/source/auto_examples/plot_gromov.py @@ -0,0 +1,93 @@ +# -*- coding: utf-8 -*- +""" +========================== +Gromov-Wasserstein example +========================== + +This example is designed to show how to use the Gromov-Wassertsein distance +computation in POT. +""" + +# Author: Erwan Vautier +# Nicolas Courty +# +# License: MIT License + +import scipy as sp +import numpy as np +import matplotlib.pylab as pl +from mpl_toolkits.mplot3d import Axes3D # noqa +import ot + + +############################################################################## +# Sample two Gaussian distributions (2D and 3D) +# --------------------------------------------- +# +# The Gromov-Wasserstein distance allows to compute distances with samples that +# do not belong to the same metric space. For demonstration purpose, we sample +# two Gaussian distributions in 2- and 3-dimensional spaces. + + +n_samples = 30 # nb samples + +mu_s = np.array([0, 0]) +cov_s = np.array([[1, 0], [0, 1]]) + +mu_t = np.array([4, 4, 4]) +cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) + + +xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s) +P = sp.linalg.sqrtm(cov_t) +xt = np.random.randn(n_samples, 3).dot(P) + mu_t + + +############################################################################## +# Plotting the distributions +# -------------------------- + + +fig = pl.figure() +ax1 = fig.add_subplot(121) +ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +ax2 = fig.add_subplot(122, projection='3d') +ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r') +pl.show() + + +############################################################################## +# Compute distance kernels, normalize them and then display +# --------------------------------------------------------- + + +C1 = sp.spatial.distance.cdist(xs, xs) +C2 = sp.spatial.distance.cdist(xt, xt) + +C1 /= C1.max() +C2 /= C2.max() + +pl.figure() +pl.subplot(121) +pl.imshow(C1) +pl.subplot(122) +pl.imshow(C2) +pl.show() + +############################################################################## +# Compute Gromov-Wasserstein plans and distance +# --------------------------------------------- + + +p = ot.unif(n_samples) +q = ot.unif(n_samples) + +gw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4) +gw_dist = ot.gromov_wasserstein2(C1, C2, p, q, 'square_loss', epsilon=5e-4) + +print('Gromov-Wasserstein distances between the distribution: ' + str(gw_dist)) + +pl.figure() +pl.imshow(gw, cmap='jet') +pl.colorbar() +pl.show() diff --git a/docs/source/auto_examples/plot_gromov.rst b/docs/source/auto_examples/plot_gromov.rst new file mode 100644 index 0000000..65cf4e4 --- /dev/null +++ b/docs/source/auto_examples/plot_gromov.rst @@ -0,0 +1,180 @@ + + +.. _sphx_glr_auto_examples_plot_gromov.py: + + +========================== +Gromov-Wasserstein example +========================== + +This example is designed to show how to use the Gromov-Wassertsein distance +computation in POT. + + + +.. code-block:: python + + + # Author: Erwan Vautier + # Nicolas Courty + # + # License: MIT License + + import scipy as sp + import numpy as np + import matplotlib.pylab as pl + from mpl_toolkits.mplot3d import Axes3D # noqa + import ot + + + + + + + + +Sample two Gaussian distributions (2D and 3D) + --------------------------------------------- + + The Gromov-Wasserstein distance allows to compute distances with samples that + do not belong to the same metric space. For demonstration purpose, we sample + two Gaussian distributions in 2- and 3-dimensional spaces. + + + +.. code-block:: python + + + + n_samples = 30 # nb samples + + mu_s = np.array([0, 0]) + cov_s = np.array([[1, 0], [0, 1]]) + + mu_t = np.array([4, 4, 4]) + cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) + + + xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s) + P = sp.linalg.sqrtm(cov_t) + xt = np.random.randn(n_samples, 3).dot(P) + mu_t + + + + + + + + +Plotting the distributions +-------------------------- + + + +.. code-block:: python + + + + fig = pl.figure() + ax1 = fig.add_subplot(121) + ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + ax2 = fig.add_subplot(122, projection='3d') + ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r') + pl.show() + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_gromov_001.png + :align: center + + + + +Compute distance kernels, normalize them and then display +--------------------------------------------------------- + + + +.. code-block:: python + + + + C1 = sp.spatial.distance.cdist(xs, xs) + C2 = sp.spatial.distance.cdist(xt, xt) + + C1 /= C1.max() + C2 /= C2.max() + + pl.figure() + pl.subplot(121) + pl.imshow(C1) + pl.subplot(122) + pl.imshow(C2) + pl.show() + + + + +.. image:: /auto_examples/images/sphx_glr_plot_gromov_002.png + :align: center + + + + +Compute Gromov-Wasserstein plans and distance +--------------------------------------------- + + + +.. code-block:: python + + + + p = ot.unif(n_samples) + q = ot.unif(n_samples) + + gw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4) + gw_dist = ot.gromov_wasserstein2(C1, C2, p, q, 'square_loss', epsilon=5e-4) + + print('Gromov-Wasserstein distances between the distribution: ' + str(gw_dist)) + + pl.figure() + pl.imshow(gw, cmap='jet') + pl.colorbar() + pl.show() + + + +.. image:: /auto_examples/images/sphx_glr_plot_gromov_003.png + :align: center + + +.. rst-class:: sphx-glr-script-out + + Out:: + + Gromov-Wasserstein distances between the distribution: 0.225058076974 + + +**Total running time of the script:** ( 0 minutes 4.070 seconds) + + + +.. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_gromov.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_gromov.ipynb ` + +.. rst-class:: sphx-glr-signature + + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_gromov_barycenter.ipynb b/docs/source/auto_examples/plot_gromov_barycenter.ipynb new file mode 100644 index 0000000..d38dfbb --- /dev/null +++ b/docs/source/auto_examples/plot_gromov_barycenter.ipynb @@ -0,0 +1,126 @@ +{ + "nbformat_minor": 0, + "nbformat": 4, + "cells": [ + { + "execution_count": null, + "cell_type": "code", + "source": [ + "%matplotlib inline" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "\n# Gromov-Wasserstein Barycenter example\n\n\nThis example is designed to show how to use the Gromov-Wasserstein distance\ncomputation in POT.\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "# Author: Erwan Vautier \r\n# Nicolas Courty \r\n#\r\n# License: MIT License\r\n\r\n\r\nimport numpy as np\r\nimport scipy as sp\r\n\r\nimport scipy.ndimage as spi\r\nimport matplotlib.pylab as pl\r\nfrom sklearn import manifold\r\nfrom sklearn.decomposition import PCA\r\n\r\nimport ot" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Smacof MDS\r\n ----------\r\n\r\n This function allows to find an embedding of points given a dissimilarity matrix\r\n that will be given by the output of the algorithm\r\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "def smacof_mds(C, dim, max_iter=3000, eps=1e-9):\r\n \"\"\"\r\n Returns an interpolated point cloud following the dissimilarity matrix C\r\n using SMACOF multidimensional scaling (MDS) in specific dimensionned\r\n target space\r\n\r\n Parameters\r\n ----------\r\n C : ndarray, shape (ns, ns)\r\n dissimilarity matrix\r\n dim : int\r\n dimension of the targeted space\r\n max_iter : int\r\n Maximum number of iterations of the SMACOF algorithm for a single run\r\n eps : float\r\n relative tolerance w.r.t stress to declare converge\r\n\r\n Returns\r\n -------\r\n npos : ndarray, shape (R, dim)\r\n Embedded coordinates of the interpolated point cloud (defined with\r\n one isometry)\r\n \"\"\"\r\n\r\n rng = np.random.RandomState(seed=3)\r\n\r\n mds = manifold.MDS(\r\n dim,\r\n max_iter=max_iter,\r\n eps=1e-9,\r\n dissimilarity='precomputed',\r\n n_init=1)\r\n pos = mds.fit(C).embedding_\r\n\r\n nmds = manifold.MDS(\r\n 2,\r\n max_iter=max_iter,\r\n eps=1e-9,\r\n dissimilarity=\"precomputed\",\r\n random_state=rng,\r\n n_init=1)\r\n npos = nmds.fit_transform(C, init=pos)\r\n\r\n return npos" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Data preparation\r\n ----------------\r\n\r\n The four distributions are constructed from 4 simple images\r\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "def im2mat(I):\r\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\r\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\r\n\r\n\r\nsquare = spi.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256\r\ncross = spi.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256\r\ntriangle = spi.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256\r\nstar = spi.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256\r\n\r\nshapes = [square, cross, triangle, star]\r\n\r\nS = 4\r\nxs = [[] for i in range(S)]\r\n\r\n\r\nfor nb in range(4):\r\n for i in range(8):\r\n for j in range(8):\r\n if shapes[nb][i, j] < 0.95:\r\n xs[nb].append([j, 8 - i])\r\n\r\nxs = np.array([np.array(xs[0]), np.array(xs[1]),\r\n np.array(xs[2]), np.array(xs[3])])" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Barycenter computation\r\n----------------------\r\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "ns = [len(xs[s]) for s in range(S)]\r\nn_samples = 30\r\n\r\n\"\"\"Compute all distances matrices for the four shapes\"\"\"\r\nCs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)]\r\nCs = [cs / cs.max() for cs in Cs]\r\n\r\nps = [ot.unif(ns[s]) for s in range(S)]\r\np = ot.unif(n_samples)\r\n\r\n\r\nlambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]]\r\n\r\nCt01 = [0 for i in range(2)]\r\nfor i in range(2):\r\n Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]],\r\n [ps[0], ps[1]\r\n ], p, lambdast[i], 'square_loss', 5e-4,\r\n max_iter=100, tol=1e-3)\r\n\r\nCt02 = [0 for i in range(2)]\r\nfor i in range(2):\r\n Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]],\r\n [ps[0], ps[2]\r\n ], p, lambdast[i], 'square_loss', 5e-4,\r\n max_iter=100, tol=1e-3)\r\n\r\nCt13 = [0 for i in range(2)]\r\nfor i in range(2):\r\n Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]],\r\n [ps[1], ps[3]\r\n ], p, lambdast[i], 'square_loss', 5e-4,\r\n max_iter=100, tol=1e-3)\r\n\r\nCt23 = [0 for i in range(2)]\r\nfor i in range(2):\r\n Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]],\r\n [ps[2], ps[3]\r\n ], p, lambdast[i], 'square_loss', 5e-4,\r\n max_iter=100, tol=1e-3)" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Visualization\r\n -------------\r\n\r\n The PCA helps in getting consistency between the rotations\r\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "clf = PCA(n_components=2)\r\nnpos = [0, 0, 0, 0]\r\nnpos = [smacof_mds(Cs[s], 2) for s in range(S)]\r\n\r\nnpost01 = [0, 0]\r\nnpost01 = [smacof_mds(Ct01[s], 2) for s in range(2)]\r\nnpost01 = [clf.fit_transform(npost01[s]) for s in range(2)]\r\n\r\nnpost02 = [0, 0]\r\nnpost02 = [smacof_mds(Ct02[s], 2) for s in range(2)]\r\nnpost02 = [clf.fit_transform(npost02[s]) for s in range(2)]\r\n\r\nnpost13 = [0, 0]\r\nnpost13 = [smacof_mds(Ct13[s], 2) for s in range(2)]\r\nnpost13 = [clf.fit_transform(npost13[s]) for s in range(2)]\r\n\r\nnpost23 = [0, 0]\r\nnpost23 = [smacof_mds(Ct23[s], 2) for s in range(2)]\r\nnpost23 = [clf.fit_transform(npost23[s]) for s in range(2)]\r\n\r\n\r\nfig = pl.figure(figsize=(10, 10))\r\n\r\nax1 = pl.subplot2grid((4, 4), (0, 0))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r')\r\n\r\nax2 = pl.subplot2grid((4, 4), (0, 1))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b')\r\n\r\nax3 = pl.subplot2grid((4, 4), (0, 2))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b')\r\n\r\nax4 = pl.subplot2grid((4, 4), (0, 3))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r')\r\n\r\nax5 = pl.subplot2grid((4, 4), (1, 0))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b')\r\n\r\nax6 = pl.subplot2grid((4, 4), (1, 3))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b')\r\n\r\nax7 = pl.subplot2grid((4, 4), (2, 0))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b')\r\n\r\nax8 = pl.subplot2grid((4, 4), (2, 3))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b')\r\n\r\nax9 = pl.subplot2grid((4, 4), (3, 0))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r')\r\n\r\nax10 = pl.subplot2grid((4, 4), (3, 1))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b')\r\n\r\nax11 = pl.subplot2grid((4, 4), (3, 2))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b')\r\n\r\nax12 = pl.subplot2grid((4, 4), (3, 3))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax12.scatter(npos[3][:, 0], npos[3][:, 1], color='r')" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "name": "python2", + "language": "python" + }, + "language_info": { + "mimetype": "text/x-python", + "nbconvert_exporter": "python", + "name": "python", + "file_extension": ".py", + "version": "2.7.12", + "pygments_lexer": "ipython2", + "codemirror_mode": { + "version": 2, + "name": "ipython" + } + } + } +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_gromov_barycenter.py b/docs/source/auto_examples/plot_gromov_barycenter.py new file mode 100644 index 0000000..180b0cf --- /dev/null +++ b/docs/source/auto_examples/plot_gromov_barycenter.py @@ -0,0 +1,248 @@ +# -*- coding: utf-8 -*- +""" +===================================== +Gromov-Wasserstein Barycenter example +===================================== + +This example is designed to show how to use the Gromov-Wasserstein distance +computation in POT. +""" + +# Author: Erwan Vautier +# Nicolas Courty +# +# License: MIT License + + +import numpy as np +import scipy as sp + +import scipy.ndimage as spi +import matplotlib.pylab as pl +from sklearn import manifold +from sklearn.decomposition import PCA + +import ot + +############################################################################## +# Smacof MDS +# ---------- +# +# This function allows to find an embedding of points given a dissimilarity matrix +# that will be given by the output of the algorithm + + +def smacof_mds(C, dim, max_iter=3000, eps=1e-9): + """ + Returns an interpolated point cloud following the dissimilarity matrix C + using SMACOF multidimensional scaling (MDS) in specific dimensionned + target space + + Parameters + ---------- + C : ndarray, shape (ns, ns) + dissimilarity matrix + dim : int + dimension of the targeted space + max_iter : int + Maximum number of iterations of the SMACOF algorithm for a single run + eps : float + relative tolerance w.r.t stress to declare converge + + Returns + ------- + npos : ndarray, shape (R, dim) + Embedded coordinates of the interpolated point cloud (defined with + one isometry) + """ + + rng = np.random.RandomState(seed=3) + + mds = manifold.MDS( + dim, + max_iter=max_iter, + eps=1e-9, + dissimilarity='precomputed', + n_init=1) + pos = mds.fit(C).embedding_ + + nmds = manifold.MDS( + 2, + max_iter=max_iter, + eps=1e-9, + dissimilarity="precomputed", + random_state=rng, + n_init=1) + npos = nmds.fit_transform(C, init=pos) + + return npos + + +############################################################################## +# Data preparation +# ---------------- +# +# The four distributions are constructed from 4 simple images + + +def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + + +square = spi.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256 +cross = spi.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256 +triangle = spi.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256 +star = spi.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256 + +shapes = [square, cross, triangle, star] + +S = 4 +xs = [[] for i in range(S)] + + +for nb in range(4): + for i in range(8): + for j in range(8): + if shapes[nb][i, j] < 0.95: + xs[nb].append([j, 8 - i]) + +xs = np.array([np.array(xs[0]), np.array(xs[1]), + np.array(xs[2]), np.array(xs[3])]) + +############################################################################## +# Barycenter computation +# ---------------------- + + +ns = [len(xs[s]) for s in range(S)] +n_samples = 30 + +"""Compute all distances matrices for the four shapes""" +Cs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)] +Cs = [cs / cs.max() for cs in Cs] + +ps = [ot.unif(ns[s]) for s in range(S)] +p = ot.unif(n_samples) + + +lambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]] + +Ct01 = [0 for i in range(2)] +for i in range(2): + Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]], + [ps[0], ps[1] + ], p, lambdast[i], 'square_loss', 5e-4, + max_iter=100, tol=1e-3) + +Ct02 = [0 for i in range(2)] +for i in range(2): + Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]], + [ps[0], ps[2] + ], p, lambdast[i], 'square_loss', 5e-4, + max_iter=100, tol=1e-3) + +Ct13 = [0 for i in range(2)] +for i in range(2): + Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]], + [ps[1], ps[3] + ], p, lambdast[i], 'square_loss', 5e-4, + max_iter=100, tol=1e-3) + +Ct23 = [0 for i in range(2)] +for i in range(2): + Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]], + [ps[2], ps[3] + ], p, lambdast[i], 'square_loss', 5e-4, + max_iter=100, tol=1e-3) + + +############################################################################## +# Visualization +# ------------- +# +# The PCA helps in getting consistency between the rotations + + +clf = PCA(n_components=2) +npos = [0, 0, 0, 0] +npos = [smacof_mds(Cs[s], 2) for s in range(S)] + +npost01 = [0, 0] +npost01 = [smacof_mds(Ct01[s], 2) for s in range(2)] +npost01 = [clf.fit_transform(npost01[s]) for s in range(2)] + +npost02 = [0, 0] +npost02 = [smacof_mds(Ct02[s], 2) for s in range(2)] +npost02 = [clf.fit_transform(npost02[s]) for s in range(2)] + +npost13 = [0, 0] +npost13 = [smacof_mds(Ct13[s], 2) for s in range(2)] +npost13 = [clf.fit_transform(npost13[s]) for s in range(2)] + +npost23 = [0, 0] +npost23 = [smacof_mds(Ct23[s], 2) for s in range(2)] +npost23 = [clf.fit_transform(npost23[s]) for s in range(2)] + + +fig = pl.figure(figsize=(10, 10)) + +ax1 = pl.subplot2grid((4, 4), (0, 0)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r') + +ax2 = pl.subplot2grid((4, 4), (0, 1)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b') + +ax3 = pl.subplot2grid((4, 4), (0, 2)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b') + +ax4 = pl.subplot2grid((4, 4), (0, 3)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r') + +ax5 = pl.subplot2grid((4, 4), (1, 0)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b') + +ax6 = pl.subplot2grid((4, 4), (1, 3)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b') + +ax7 = pl.subplot2grid((4, 4), (2, 0)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b') + +ax8 = pl.subplot2grid((4, 4), (2, 3)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b') + +ax9 = pl.subplot2grid((4, 4), (3, 0)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r') + +ax10 = pl.subplot2grid((4, 4), (3, 1)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b') + +ax11 = pl.subplot2grid((4, 4), (3, 2)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b') + +ax12 = pl.subplot2grid((4, 4), (3, 3)) +pl.xlim((-1, 1)) +pl.ylim((-1, 1)) +ax12.scatter(npos[3][:, 0], npos[3][:, 1], color='r') diff --git a/docs/source/auto_examples/plot_gromov_barycenter.rst b/docs/source/auto_examples/plot_gromov_barycenter.rst new file mode 100644 index 0000000..ca2d4e9 --- /dev/null +++ b/docs/source/auto_examples/plot_gromov_barycenter.rst @@ -0,0 +1,324 @@ + + +.. _sphx_glr_auto_examples_plot_gromov_barycenter.py: + + +===================================== +Gromov-Wasserstein Barycenter example +===================================== + +This example is designed to show how to use the Gromov-Wasserstein distance +computation in POT. + + + +.. code-block:: python + + + # Author: Erwan Vautier + # Nicolas Courty + # + # License: MIT License + + + import numpy as np + import scipy as sp + + import scipy.ndimage as spi + import matplotlib.pylab as pl + from sklearn import manifold + from sklearn.decomposition import PCA + + import ot + + + + + + + +Smacof MDS + ---------- + + This function allows to find an embedding of points given a dissimilarity matrix + that will be given by the output of the algorithm + + + +.. code-block:: python + + + + def smacof_mds(C, dim, max_iter=3000, eps=1e-9): + """ + Returns an interpolated point cloud following the dissimilarity matrix C + using SMACOF multidimensional scaling (MDS) in specific dimensionned + target space + + Parameters + ---------- + C : ndarray, shape (ns, ns) + dissimilarity matrix + dim : int + dimension of the targeted space + max_iter : int + Maximum number of iterations of the SMACOF algorithm for a single run + eps : float + relative tolerance w.r.t stress to declare converge + + Returns + ------- + npos : ndarray, shape (R, dim) + Embedded coordinates of the interpolated point cloud (defined with + one isometry) + """ + + rng = np.random.RandomState(seed=3) + + mds = manifold.MDS( + dim, + max_iter=max_iter, + eps=1e-9, + dissimilarity='precomputed', + n_init=1) + pos = mds.fit(C).embedding_ + + nmds = manifold.MDS( + 2, + max_iter=max_iter, + eps=1e-9, + dissimilarity="precomputed", + random_state=rng, + n_init=1) + npos = nmds.fit_transform(C, init=pos) + + return npos + + + + + + + + +Data preparation + ---------------- + + The four distributions are constructed from 4 simple images + + + +.. code-block:: python + + + + def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + + + square = spi.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256 + cross = spi.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256 + triangle = spi.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256 + star = spi.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256 + + shapes = [square, cross, triangle, star] + + S = 4 + xs = [[] for i in range(S)] + + + for nb in range(4): + for i in range(8): + for j in range(8): + if shapes[nb][i, j] < 0.95: + xs[nb].append([j, 8 - i]) + + xs = np.array([np.array(xs[0]), np.array(xs[1]), + np.array(xs[2]), np.array(xs[3])]) + + + + + + + +Barycenter computation +---------------------- + + + +.. code-block:: python + + + + ns = [len(xs[s]) for s in range(S)] + n_samples = 30 + + """Compute all distances matrices for the four shapes""" + Cs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)] + Cs = [cs / cs.max() for cs in Cs] + + ps = [ot.unif(ns[s]) for s in range(S)] + p = ot.unif(n_samples) + + + lambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]] + + Ct01 = [0 for i in range(2)] + for i in range(2): + Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]], + [ps[0], ps[1] + ], p, lambdast[i], 'square_loss', 5e-4, + max_iter=100, tol=1e-3) + + Ct02 = [0 for i in range(2)] + for i in range(2): + Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]], + [ps[0], ps[2] + ], p, lambdast[i], 'square_loss', 5e-4, + max_iter=100, tol=1e-3) + + Ct13 = [0 for i in range(2)] + for i in range(2): + Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]], + [ps[1], ps[3] + ], p, lambdast[i], 'square_loss', 5e-4, + max_iter=100, tol=1e-3) + + Ct23 = [0 for i in range(2)] + for i in range(2): + Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]], + [ps[2], ps[3] + ], p, lambdast[i], 'square_loss', 5e-4, + max_iter=100, tol=1e-3) + + + + + + + + +Visualization + ------------- + + The PCA helps in getting consistency between the rotations + + + +.. code-block:: python + + + + clf = PCA(n_components=2) + npos = [0, 0, 0, 0] + npos = [smacof_mds(Cs[s], 2) for s in range(S)] + + npost01 = [0, 0] + npost01 = [smacof_mds(Ct01[s], 2) for s in range(2)] + npost01 = [clf.fit_transform(npost01[s]) for s in range(2)] + + npost02 = [0, 0] + npost02 = [smacof_mds(Ct02[s], 2) for s in range(2)] + npost02 = [clf.fit_transform(npost02[s]) for s in range(2)] + + npost13 = [0, 0] + npost13 = [smacof_mds(Ct13[s], 2) for s in range(2)] + npost13 = [clf.fit_transform(npost13[s]) for s in range(2)] + + npost23 = [0, 0] + npost23 = [smacof_mds(Ct23[s], 2) for s in range(2)] + npost23 = [clf.fit_transform(npost23[s]) for s in range(2)] + + + fig = pl.figure(figsize=(10, 10)) + + ax1 = pl.subplot2grid((4, 4), (0, 0)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r') + + ax2 = pl.subplot2grid((4, 4), (0, 1)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b') + + ax3 = pl.subplot2grid((4, 4), (0, 2)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b') + + ax4 = pl.subplot2grid((4, 4), (0, 3)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r') + + ax5 = pl.subplot2grid((4, 4), (1, 0)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b') + + ax6 = pl.subplot2grid((4, 4), (1, 3)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b') + + ax7 = pl.subplot2grid((4, 4), (2, 0)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b') + + ax8 = pl.subplot2grid((4, 4), (2, 3)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b') + + ax9 = pl.subplot2grid((4, 4), (3, 0)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r') + + ax10 = pl.subplot2grid((4, 4), (3, 1)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b') + + ax11 = pl.subplot2grid((4, 4), (3, 2)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b') + + ax12 = pl.subplot2grid((4, 4), (3, 3)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax12.scatter(npos[3][:, 0], npos[3][:, 1], color='r') + + + +.. image:: /auto_examples/images/sphx_glr_plot_gromov_barycenter_001.png + :align: center + + + + +**Total running time of the script:** ( 8 minutes 43.875 seconds) + + + +.. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_gromov_barycenter.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_gromov_barycenter.ipynb ` + +.. rst-class:: sphx-glr-signature + + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_optim_OTreg.rst b/docs/source/auto_examples/plot_optim_OTreg.rst index f628024..480149a 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.rst +++ b/docs/source/auto_examples/plot_optim_OTreg.rst @@ -399,225 +399,194 @@ Solve EMD with entropic regularization -------------------------------- 0|1.692289e-01|0.000000e+00 1|1.617643e-01|-4.614437e-02 - 2|1.612546e-01|-3.161037e-03 - 3|1.611040e-01|-9.349544e-04 - 4|1.610346e-01|-4.310179e-04 - 5|1.610072e-01|-1.701719e-04 - 6|1.609947e-01|-7.759814e-05 - 7|1.609934e-01|-7.941439e-06 - 8|1.609841e-01|-5.797180e-05 - 9|1.609838e-01|-1.559407e-06 - 10|1.609685e-01|-9.530282e-05 - 11|1.609666e-01|-1.142129e-05 - 12|1.609541e-01|-7.799970e-05 - 13|1.609496e-01|-2.780416e-05 - 14|1.609385e-01|-6.887105e-05 - 15|1.609334e-01|-3.174241e-05 - 16|1.609231e-01|-6.420777e-05 - 17|1.609115e-01|-7.189949e-05 - 18|1.608815e-01|-1.865331e-04 - 19|1.608799e-01|-1.013039e-05 + 2|1.612639e-01|-3.102965e-03 + 3|1.611291e-01|-8.371098e-04 + 4|1.610468e-01|-5.110558e-04 + 5|1.610198e-01|-1.672927e-04 + 6|1.610130e-01|-4.232417e-05 + 7|1.610090e-01|-2.513455e-05 + 8|1.610002e-01|-5.443507e-05 + 9|1.609996e-01|-3.657071e-06 + 10|1.609948e-01|-2.998735e-05 + 11|1.609695e-01|-1.569217e-04 + 12|1.609533e-01|-1.010779e-04 + 13|1.609520e-01|-8.043897e-06 + 14|1.609465e-01|-3.415246e-05 + 15|1.609386e-01|-4.898605e-05 + 16|1.609324e-01|-3.837052e-05 + 17|1.609298e-01|-1.617826e-05 + 18|1.609184e-01|-7.080015e-05 + 19|1.609083e-01|-6.273206e-05 It. |Loss |Delta loss -------------------------------- - 20|1.608695e-01|-6.468606e-05 - 21|1.608686e-01|-5.738419e-06 - 22|1.608661e-01|-1.495923e-05 - 23|1.608657e-01|-2.784611e-06 - 24|1.608633e-01|-1.512408e-05 - 25|1.608624e-01|-5.397916e-06 - 26|1.608617e-01|-4.115218e-06 - 27|1.608561e-01|-3.503396e-05 - 28|1.608479e-01|-5.098773e-05 - 29|1.608452e-01|-1.659203e-05 - 30|1.608399e-01|-3.298319e-05 - 31|1.608330e-01|-4.302183e-05 - 32|1.608310e-01|-1.273465e-05 - 33|1.608280e-01|-1.827713e-05 - 34|1.608231e-01|-3.039842e-05 - 35|1.608212e-01|-1.229256e-05 - 36|1.608200e-01|-6.900556e-06 - 37|1.608159e-01|-2.554039e-05 - 38|1.608103e-01|-3.521137e-05 - 39|1.608058e-01|-2.795180e-05 + 20|1.608988e-01|-5.940805e-05 + 21|1.608853e-01|-8.380030e-05 + 22|1.608844e-01|-5.185045e-06 + 23|1.608824e-01|-1.279113e-05 + 24|1.608819e-01|-3.156821e-06 + 25|1.608783e-01|-2.205746e-05 + 26|1.608764e-01|-1.189894e-05 + 27|1.608755e-01|-5.474607e-06 + 28|1.608737e-01|-1.144227e-05 + 29|1.608676e-01|-3.775335e-05 + 30|1.608638e-01|-2.348020e-05 + 31|1.608627e-01|-6.863136e-06 + 32|1.608529e-01|-6.110230e-05 + 33|1.608487e-01|-2.641106e-05 + 34|1.608409e-01|-4.823638e-05 + 35|1.608373e-01|-2.256641e-05 + 36|1.608338e-01|-2.132444e-05 + 37|1.608310e-01|-1.786649e-05 + 38|1.608260e-01|-3.103848e-05 + 39|1.608206e-01|-3.321265e-05 It. |Loss |Delta loss -------------------------------- - 40|1.608040e-01|-1.119118e-05 - 41|1.608027e-01|-8.193369e-06 - 42|1.607994e-01|-2.026719e-05 - 43|1.607985e-01|-5.819902e-06 - 44|1.607978e-01|-4.048170e-06 - 45|1.607978e-01|-3.007470e-07 - 46|1.607950e-01|-1.705375e-05 - 47|1.607927e-01|-1.430186e-05 - 48|1.607925e-01|-1.166526e-06 - 49|1.607911e-01|-9.069406e-06 - 50|1.607910e-01|-3.804209e-07 - 51|1.607910e-01|-5.942399e-08 - 52|1.607910e-01|-2.321380e-07 - 53|1.607907e-01|-1.877655e-06 - 54|1.607906e-01|-2.940224e-07 - 55|1.607877e-01|-1.814208e-05 - 56|1.607841e-01|-2.236496e-05 - 57|1.607810e-01|-1.951355e-05 - 58|1.607804e-01|-3.578228e-06 - 59|1.607789e-01|-9.442277e-06 + 40|1.608201e-01|-3.054747e-06 + 41|1.608195e-01|-4.198335e-06 + 42|1.608193e-01|-8.458736e-07 + 43|1.608159e-01|-2.153759e-05 + 44|1.608115e-01|-2.738314e-05 + 45|1.608108e-01|-3.960032e-06 + 46|1.608081e-01|-1.675447e-05 + 47|1.608072e-01|-5.976340e-06 + 48|1.608046e-01|-1.604130e-05 + 49|1.608020e-01|-1.617036e-05 + 50|1.608014e-01|-3.957795e-06 + 51|1.608011e-01|-1.292411e-06 + 52|1.607998e-01|-8.431795e-06 + 53|1.607964e-01|-2.127054e-05 + 54|1.607947e-01|-1.021878e-05 + 55|1.607947e-01|-3.560621e-07 + 56|1.607900e-01|-2.929781e-05 + 57|1.607890e-01|-5.740229e-06 + 58|1.607858e-01|-2.039550e-05 + 59|1.607836e-01|-1.319545e-05 It. |Loss |Delta loss -------------------------------- - 60|1.607779e-01|-5.997371e-06 - 61|1.607754e-01|-1.564408e-05 - 62|1.607742e-01|-7.693285e-06 - 63|1.607727e-01|-9.030547e-06 - 64|1.607719e-01|-5.103894e-06 - 65|1.607693e-01|-1.605420e-05 - 66|1.607676e-01|-1.047837e-05 - 67|1.607675e-01|-6.026848e-07 - 68|1.607655e-01|-1.240216e-05 - 69|1.607632e-01|-1.434674e-05 - 70|1.607618e-01|-8.829808e-06 - 71|1.607606e-01|-7.581824e-06 - 72|1.607590e-01|-1.009457e-05 - 73|1.607586e-01|-2.222963e-06 - 74|1.607577e-01|-5.564775e-06 - 75|1.607574e-01|-1.932763e-06 - 76|1.607573e-01|-8.148685e-07 - 77|1.607554e-01|-1.187660e-05 - 78|1.607546e-01|-4.557651e-06 - 79|1.607537e-01|-5.911902e-06 + 60|1.607826e-01|-6.378947e-06 + 61|1.607808e-01|-1.145102e-05 + 62|1.607776e-01|-1.941743e-05 + 63|1.607743e-01|-2.087422e-05 + 64|1.607741e-01|-1.310249e-06 + 65|1.607738e-01|-1.682752e-06 + 66|1.607691e-01|-2.913936e-05 + 67|1.607671e-01|-1.288855e-05 + 68|1.607654e-01|-1.002448e-05 + 69|1.607641e-01|-8.209492e-06 + 70|1.607632e-01|-5.588467e-06 + 71|1.607619e-01|-8.050388e-06 + 72|1.607618e-01|-9.417493e-07 + 73|1.607598e-01|-1.210509e-05 + 74|1.607591e-01|-4.392914e-06 + 75|1.607579e-01|-7.759587e-06 + 76|1.607574e-01|-2.760280e-06 + 77|1.607556e-01|-1.146469e-05 + 78|1.607550e-01|-3.689456e-06 + 79|1.607550e-01|-4.065631e-08 It. |Loss |Delta loss -------------------------------- - 80|1.607529e-01|-4.710187e-06 - 81|1.607528e-01|-8.866080e-07 - 82|1.607522e-01|-3.620627e-06 - 83|1.607514e-01|-5.091281e-06 - 84|1.607498e-01|-9.932095e-06 - 85|1.607487e-01|-6.852804e-06 - 86|1.607478e-01|-5.373596e-06 - 87|1.607473e-01|-3.287295e-06 - 88|1.607470e-01|-1.666655e-06 - 89|1.607469e-01|-5.293790e-07 - 90|1.607466e-01|-2.051914e-06 - 91|1.607456e-01|-6.422797e-06 - 92|1.607456e-01|-1.110433e-07 - 93|1.607451e-01|-2.803849e-06 - 94|1.607451e-01|-2.608066e-07 - 95|1.607441e-01|-6.290352e-06 - 96|1.607429e-01|-7.298455e-06 - 97|1.607429e-01|-8.969905e-09 - 98|1.607427e-01|-7.923968e-07 - 99|1.607427e-01|-3.519286e-07 + 80|1.607539e-01|-6.555681e-06 + 81|1.607528e-01|-7.177470e-06 + 82|1.607527e-01|-5.306068e-07 + 83|1.607514e-01|-7.816045e-06 + 84|1.607511e-01|-2.301970e-06 + 85|1.607504e-01|-4.281072e-06 + 86|1.607503e-01|-7.821886e-07 + 87|1.607480e-01|-1.403013e-05 + 88|1.607480e-01|-1.169298e-08 + 89|1.607473e-01|-4.235982e-06 + 90|1.607470e-01|-1.717105e-06 + 91|1.607470e-01|-6.148402e-09 + 92|1.607462e-01|-5.396481e-06 + 93|1.607461e-01|-5.194954e-07 + 94|1.607450e-01|-6.525707e-06 + 95|1.607442e-01|-5.332060e-06 + 96|1.607439e-01|-1.682093e-06 + 97|1.607437e-01|-1.594796e-06 + 98|1.607435e-01|-7.923812e-07 + 99|1.607420e-01|-9.738552e-06 It. |Loss |Delta loss -------------------------------- - 100|1.607426e-01|-3.563804e-07 - 101|1.607410e-01|-1.004042e-05 - 102|1.607410e-01|-2.124801e-07 - 103|1.607398e-01|-7.556935e-06 - 104|1.607398e-01|-7.606853e-08 - 105|1.607385e-01|-8.058684e-06 - 106|1.607383e-01|-7.393061e-07 - 107|1.607381e-01|-1.504958e-06 - 108|1.607377e-01|-2.508807e-06 - 109|1.607371e-01|-4.004631e-06 - 110|1.607365e-01|-3.580156e-06 - 111|1.607364e-01|-2.563573e-07 - 112|1.607354e-01|-6.390137e-06 - 113|1.607348e-01|-4.119553e-06 - 114|1.607339e-01|-5.299475e-06 - 115|1.607335e-01|-2.316767e-06 - 116|1.607330e-01|-3.444737e-06 - 117|1.607324e-01|-3.467980e-06 - 118|1.607320e-01|-2.374632e-06 - 119|1.607319e-01|-7.978255e-07 + 100|1.607419e-01|-1.022448e-07 + 101|1.607419e-01|-4.865999e-07 + 102|1.607418e-01|-7.092012e-07 + 103|1.607408e-01|-5.861815e-06 + 104|1.607402e-01|-3.953266e-06 + 105|1.607395e-01|-3.969572e-06 + 106|1.607390e-01|-3.612075e-06 + 107|1.607377e-01|-7.683735e-06 + 108|1.607365e-01|-7.777599e-06 + 109|1.607364e-01|-2.335096e-07 + 110|1.607364e-01|-4.562036e-07 + 111|1.607360e-01|-2.089538e-06 + 112|1.607356e-01|-2.755355e-06 + 113|1.607349e-01|-4.501960e-06 + 114|1.607347e-01|-1.160544e-06 + 115|1.607346e-01|-6.289450e-07 + 116|1.607345e-01|-2.092146e-07 + 117|1.607336e-01|-5.990866e-06 + 118|1.607330e-01|-3.348498e-06 + 119|1.607328e-01|-1.256222e-06 It. |Loss |Delta loss -------------------------------- - 120|1.607312e-01|-4.221434e-06 - 121|1.607310e-01|-1.324597e-06 - 122|1.607304e-01|-3.650359e-06 - 123|1.607298e-01|-3.732712e-06 - 124|1.607295e-01|-1.994082e-06 - 125|1.607289e-01|-3.954139e-06 - 126|1.607286e-01|-1.532372e-06 - 127|1.607286e-01|-1.167223e-07 - 128|1.607283e-01|-2.157376e-06 - 129|1.607279e-01|-2.253077e-06 - 130|1.607274e-01|-3.301532e-06 - 131|1.607269e-01|-2.650754e-06 - 132|1.607264e-01|-3.595551e-06 - 133|1.607262e-01|-1.159425e-06 - 134|1.607258e-01|-2.512411e-06 - 135|1.607255e-01|-1.998792e-06 - 136|1.607251e-01|-2.486536e-06 - 137|1.607246e-01|-2.782996e-06 - 138|1.607246e-01|-2.922470e-07 - 139|1.607242e-01|-2.071131e-06 + 120|1.607320e-01|-5.418353e-06 + 121|1.607318e-01|-8.296189e-07 + 122|1.607311e-01|-4.381608e-06 + 123|1.607310e-01|-8.913901e-07 + 124|1.607309e-01|-3.808821e-07 + 125|1.607302e-01|-4.608994e-06 + 126|1.607294e-01|-5.063777e-06 + 127|1.607290e-01|-2.532835e-06 + 128|1.607285e-01|-2.870049e-06 + 129|1.607284e-01|-4.892812e-07 + 130|1.607281e-01|-1.760452e-06 + 131|1.607279e-01|-1.727139e-06 + 132|1.607275e-01|-2.220706e-06 + 133|1.607271e-01|-2.516930e-06 + 134|1.607269e-01|-1.201434e-06 + 135|1.607269e-01|-2.183459e-09 + 136|1.607262e-01|-4.223011e-06 + 137|1.607258e-01|-2.530202e-06 + 138|1.607258e-01|-1.857260e-07 + 139|1.607256e-01|-1.401957e-06 It. |Loss |Delta loss -------------------------------- - 140|1.607237e-01|-3.154193e-06 - 141|1.607235e-01|-1.194962e-06 - 142|1.607232e-01|-2.035251e-06 - 143|1.607232e-01|-6.027855e-08 - 144|1.607229e-01|-1.555696e-06 - 145|1.607228e-01|-1.081740e-06 - 146|1.607225e-01|-1.881070e-06 - 147|1.607224e-01|-4.100096e-07 - 148|1.607223e-01|-7.785200e-07 - 149|1.607222e-01|-2.094072e-07 - 150|1.607220e-01|-1.440814e-06 - 151|1.607217e-01|-1.997794e-06 - 152|1.607214e-01|-2.011022e-06 - 153|1.607212e-01|-8.808854e-07 - 154|1.607211e-01|-7.245877e-07 - 155|1.607207e-01|-2.217159e-06 - 156|1.607201e-01|-3.817891e-06 - 157|1.607200e-01|-7.409600e-07 - 158|1.607198e-01|-1.497698e-06 - 159|1.607195e-01|-1.729666e-06 + 140|1.607250e-01|-3.242751e-06 + 141|1.607247e-01|-2.308071e-06 + 142|1.607247e-01|-4.730700e-08 + 143|1.607246e-01|-4.240229e-07 + 144|1.607242e-01|-2.484810e-06 + 145|1.607238e-01|-2.539206e-06 + 146|1.607234e-01|-2.535574e-06 + 147|1.607231e-01|-1.954802e-06 + 148|1.607228e-01|-1.765447e-06 + 149|1.607228e-01|-1.620007e-08 + 150|1.607222e-01|-3.615783e-06 + 151|1.607222e-01|-8.668516e-08 + 152|1.607215e-01|-4.000673e-06 + 153|1.607213e-01|-1.774103e-06 + 154|1.607213e-01|-6.328834e-09 + 155|1.607209e-01|-2.418783e-06 + 156|1.607208e-01|-2.848492e-07 + 157|1.607207e-01|-8.836043e-07 + 158|1.607205e-01|-1.192836e-06 + 159|1.607202e-01|-1.638022e-06 It. |Loss |Delta loss -------------------------------- - 160|1.607195e-01|-2.115187e-07 - 161|1.607192e-01|-1.643727e-06 - 162|1.607192e-01|-1.712969e-07 - 163|1.607189e-01|-1.805877e-06 - 164|1.607189e-01|-1.209827e-07 - 165|1.607185e-01|-2.060002e-06 - 166|1.607182e-01|-1.961341e-06 - 167|1.607181e-01|-1.020366e-06 - 168|1.607179e-01|-9.760982e-07 - 169|1.607178e-01|-7.219236e-07 - 170|1.607175e-01|-1.837718e-06 - 171|1.607174e-01|-3.337578e-07 - 172|1.607173e-01|-5.298564e-07 - 173|1.607173e-01|-6.864278e-08 - 174|1.607173e-01|-2.008419e-07 - 175|1.607171e-01|-1.375630e-06 - 176|1.607168e-01|-1.911257e-06 - 177|1.607167e-01|-2.709815e-07 - 178|1.607167e-01|-1.390953e-07 - 179|1.607165e-01|-1.199675e-06 - It. |Loss |Delta loss - -------------------------------- - 180|1.607165e-01|-1.457259e-07 - 181|1.607163e-01|-1.049154e-06 - 182|1.607163e-01|-2.753577e-09 - 183|1.607163e-01|-6.972814e-09 - 184|1.607161e-01|-1.552100e-06 - 185|1.607159e-01|-1.068596e-06 - 186|1.607157e-01|-1.247724e-06 - 187|1.607155e-01|-1.158164e-06 - 188|1.607155e-01|-2.616199e-07 - 189|1.607154e-01|-3.595874e-07 - 190|1.607154e-01|-5.334527e-08 - 191|1.607153e-01|-3.452744e-07 - 192|1.607153e-01|-1.239593e-07 - 193|1.607152e-01|-8.184984e-07 - 194|1.607150e-01|-1.316308e-06 - 195|1.607150e-01|-7.100882e-09 - 196|1.607148e-01|-1.393958e-06 - 197|1.607146e-01|-1.242735e-06 - 198|1.607144e-01|-1.123993e-06 - 199|1.607143e-01|-3.512071e-07 - It. |Loss |Delta loss - -------------------------------- - 200|1.607143e-01|-2.151971e-10 + 160|1.607202e-01|-3.670914e-08 + 161|1.607197e-01|-3.153709e-06 + 162|1.607197e-01|-2.419565e-09 + 163|1.607194e-01|-2.136882e-06 + 164|1.607194e-01|-1.173754e-09 + 165|1.607192e-01|-8.169238e-07 + 166|1.607191e-01|-9.218755e-07 + 167|1.607189e-01|-9.459255e-07 + 168|1.607187e-01|-1.294835e-06 + 169|1.607186e-01|-5.797668e-07 + 170|1.607186e-01|-4.706272e-08 + 171|1.607183e-01|-1.753383e-06 + 172|1.607183e-01|-1.681573e-07 + 173|1.607183e-01|-2.563971e-10 Solve EMD with Frobenius norm + entropic regularization @@ -667,7 +636,7 @@ Solve EMD with Frobenius norm + entropic regularization 4|1.609284e-01|-1.111407e-12 -**Total running time of the script:** ( 0 minutes 1.809 seconds) +**Total running time of the script:** ( 0 minutes 2.800 seconds) @@ -686,4 +655,4 @@ Solve EMD with Frobenius norm + entropic regularization .. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_classes.rst b/docs/source/auto_examples/plot_otda_classes.rst index f19a99f..a5ab285 100644 --- a/docs/source/auto_examples/plot_otda_classes.rst +++ b/docs/source/auto_examples/plot_otda_classes.rst @@ -94,29 +94,29 @@ Instantiate the different transport algorithms and fit them It. |Loss |Delta loss -------------------------------- - 0|9.552437e+00|0.000000e+00 - 1|1.921833e+00|-3.970483e+00 - 2|1.671022e+00|-1.500942e-01 - 3|1.615147e+00|-3.459458e-02 - 4|1.594289e+00|-1.308252e-02 - 5|1.587287e+00|-4.411254e-03 - 6|1.581665e+00|-3.554702e-03 - 7|1.577022e+00|-2.943809e-03 - 8|1.573870e+00|-2.002870e-03 - 9|1.571645e+00|-1.415696e-03 - 10|1.569342e+00|-1.467590e-03 - 11|1.567863e+00|-9.432233e-04 - 12|1.566558e+00|-8.329769e-04 - 13|1.565414e+00|-7.311320e-04 - 14|1.564425e+00|-6.319985e-04 - 15|1.563955e+00|-3.007604e-04 - 16|1.563658e+00|-1.894627e-04 - 17|1.562886e+00|-4.941143e-04 - 18|1.562578e+00|-1.974031e-04 - 19|1.562445e+00|-8.468825e-05 + 0|1.003747e+01|0.000000e+00 + 1|1.953263e+00|-4.138821e+00 + 2|1.744456e+00|-1.196969e-01 + 3|1.689268e+00|-3.267022e-02 + 4|1.666355e+00|-1.374998e-02 + 5|1.656125e+00|-6.177356e-03 + 6|1.651753e+00|-2.646960e-03 + 7|1.647261e+00|-2.726957e-03 + 8|1.642274e+00|-3.036672e-03 + 9|1.639926e+00|-1.431818e-03 + 10|1.638750e+00|-7.173837e-04 + 11|1.637558e+00|-7.281753e-04 + 12|1.636248e+00|-8.002067e-04 + 13|1.634555e+00|-1.036074e-03 + 14|1.633547e+00|-6.166646e-04 + 15|1.633531e+00|-1.022614e-05 + 16|1.632957e+00|-3.510986e-04 + 17|1.632853e+00|-6.380944e-05 + 18|1.632704e+00|-9.122988e-05 + 19|1.632237e+00|-2.861276e-04 It. |Loss |Delta loss -------------------------------- - 20|1.562007e+00|-2.805136e-04 + 20|1.632174e+00|-3.896483e-05 Fig 1 : plots source and target samples @@ -236,7 +236,7 @@ Fig 2 : plot optimal couplings and transported samples -**Total running time of the script:** ( 0 minutes 1.596 seconds) +**Total running time of the script:** ( 0 minutes 2.308 seconds) @@ -255,4 +255,4 @@ Fig 2 : plot optimal couplings and transported samples .. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_color_images.rst b/docs/source/auto_examples/plot_otda_color_images.rst index 4772bed..9c31ba7 100644 --- a/docs/source/auto_examples/plot_otda_color_images.rst +++ b/docs/source/auto_examples/plot_otda_color_images.rst @@ -235,7 +235,7 @@ Plot new images -**Total running time of the script:** ( 2 minutes 24.561 seconds) +**Total running time of the script:** ( 3 minutes 16.469 seconds) @@ -254,4 +254,4 @@ Plot new images .. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_d2.rst b/docs/source/auto_examples/plot_otda_d2.rst index 2b716e1..1bbe6d9 100644 --- a/docs/source/auto_examples/plot_otda_d2.rst +++ b/docs/source/auto_examples/plot_otda_d2.rst @@ -242,7 +242,7 @@ Fig 3 : plot transported samples -**Total running time of the script:** ( 0 minutes 32.084 seconds) +**Total running time of the script:** ( 0 minutes 47.000 seconds) @@ -261,4 +261,4 @@ Fig 3 : plot transported samples .. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_mapping.rst b/docs/source/auto_examples/plot_otda_mapping.rst index 6c1c780..1e3a709 100644 --- a/docs/source/auto_examples/plot_otda_mapping.rst +++ b/docs/source/auto_examples/plot_otda_mapping.rst @@ -136,24 +136,26 @@ Instantiate the different transport algorithms and fit them It. |Loss |Delta loss -------------------------------- - 0|4.307233e+03|0.000000e+00 - 1|4.296694e+03|-2.446759e-03 - 2|4.296419e+03|-6.417421e-05 - 3|4.296328e+03|-2.110209e-05 - 4|4.296305e+03|-5.298603e-06 + 0|4.231423e+03|0.000000e+00 + 1|4.217955e+03|-3.182835e-03 + 2|4.217580e+03|-8.885864e-05 + 3|4.217451e+03|-3.043162e-05 + 4|4.217368e+03|-1.978325e-05 + 5|4.217312e+03|-1.338471e-05 + 6|4.217307e+03|-1.000290e-06 It. |Loss |Delta loss -------------------------------- - 0|4.325624e+02|0.000000e+00 - 1|4.281958e+02|-1.009489e-02 - 2|4.279370e+02|-6.042202e-04 - 3|4.278109e+02|-2.947651e-04 - 4|4.277212e+02|-2.096651e-04 - 5|4.276589e+02|-1.456221e-04 - 6|4.276141e+02|-1.048476e-04 - 7|4.275803e+02|-7.906213e-05 - 8|4.275531e+02|-6.360573e-05 - 9|4.275314e+02|-5.076642e-05 - 10|4.275129e+02|-4.325858e-05 + 0|4.257004e+02|0.000000e+00 + 1|4.208978e+02|-1.128168e-02 + 2|4.205168e+02|-9.052112e-04 + 3|4.203566e+02|-3.810681e-04 + 4|4.202570e+02|-2.369884e-04 + 5|4.201844e+02|-1.726132e-04 + 6|4.201341e+02|-1.196461e-04 + 7|4.200941e+02|-9.525441e-05 + 8|4.200630e+02|-7.405552e-05 + 9|4.200377e+02|-6.031884e-05 + 10|4.200168e+02|-4.968324e-05 Plot transported samples @@ -206,7 +208,7 @@ Plot transported samples -**Total running time of the script:** ( 0 minutes 0.747 seconds) +**Total running time of the script:** ( 0 minutes 0.970 seconds) @@ -225,4 +227,4 @@ Plot transported samples .. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.rst b/docs/source/auto_examples/plot_otda_mapping_colors_images.rst index 86b1312..8394fb0 100644 --- a/docs/source/auto_examples/plot_otda_mapping_colors_images.rst +++ b/docs/source/auto_examples/plot_otda_mapping_colors_images.rst @@ -132,39 +132,39 @@ Domain adaptation for pixel distribution transfer It. |Loss |Delta loss -------------------------------- - 0|3.680512e+02|0.000000e+00 - 1|3.592454e+02|-2.392562e-02 - 2|3.590671e+02|-4.960473e-04 - 3|3.589736e+02|-2.604894e-04 - 4|3.589161e+02|-1.602816e-04 - 5|3.588766e+02|-1.099971e-04 - 6|3.588476e+02|-8.084400e-05 - 7|3.588256e+02|-6.131161e-05 - 8|3.588083e+02|-4.807549e-05 - 9|3.587943e+02|-3.899414e-05 - 10|3.587827e+02|-3.245280e-05 - 11|3.587729e+02|-2.721256e-05 - 12|3.587646e+02|-2.316249e-05 - 13|3.587574e+02|-2.000192e-05 - 14|3.587512e+02|-1.748898e-05 - 15|3.587457e+02|-1.535131e-05 - 16|3.587408e+02|-1.366515e-05 - 17|3.587364e+02|-1.210563e-05 - 18|3.587325e+02|-1.097138e-05 - 19|3.587310e+02|-4.099596e-06 + 0|3.680518e+02|0.000000e+00 + 1|3.592439e+02|-2.393116e-02 + 2|3.590632e+02|-5.030248e-04 + 3|3.589698e+02|-2.601358e-04 + 4|3.589118e+02|-1.614977e-04 + 5|3.588724e+02|-1.097608e-04 + 6|3.588436e+02|-8.035205e-05 + 7|3.588215e+02|-6.141923e-05 + 8|3.588042e+02|-4.832627e-05 + 9|3.587902e+02|-3.909574e-05 + 10|3.587786e+02|-3.225418e-05 + 11|3.587688e+02|-2.712592e-05 + 12|3.587605e+02|-2.314041e-05 + 13|3.587534e+02|-1.991287e-05 + 14|3.587471e+02|-1.744348e-05 + 15|3.587416e+02|-1.544523e-05 + 16|3.587367e+02|-1.364654e-05 + 17|3.587323e+02|-1.230435e-05 + 18|3.587284e+02|-1.093370e-05 + 19|3.587276e+02|-2.052728e-06 It. |Loss |Delta loss -------------------------------- - 0|3.784805e+02|0.000000e+00 - 1|3.646476e+02|-3.654847e-02 - 2|3.642970e+02|-9.615381e-04 - 3|3.641622e+02|-3.699897e-04 - 4|3.640886e+02|-2.021154e-04 - 5|3.640419e+02|-1.280913e-04 - 6|3.640096e+02|-8.898145e-05 - 7|3.639858e+02|-6.514301e-05 - 8|3.639677e+02|-4.977195e-05 - 9|3.639534e+02|-3.936050e-05 - 10|3.639417e+02|-3.205223e-05 + 0|3.784758e+02|0.000000e+00 + 1|3.646352e+02|-3.656911e-02 + 2|3.642861e+02|-9.574714e-04 + 3|3.641523e+02|-3.672061e-04 + 4|3.640788e+02|-2.020990e-04 + 5|3.640321e+02|-1.282701e-04 + 6|3.640002e+02|-8.751240e-05 + 7|3.639765e+02|-6.521203e-05 + 8|3.639582e+02|-5.007767e-05 + 9|3.639439e+02|-3.938917e-05 + 10|3.639323e+02|-3.187865e-05 Plot original images @@ -283,7 +283,7 @@ Plot transformed images -**Total running time of the script:** ( 2 minutes 5.213 seconds) +**Total running time of the script:** ( 2 minutes 52.212 seconds) @@ -302,4 +302,4 @@ Plot transformed images .. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_semi_supervised.ipynb b/docs/source/auto_examples/plot_otda_semi_supervised.ipynb new file mode 100644 index 0000000..783bf84 --- /dev/null +++ b/docs/source/auto_examples/plot_otda_semi_supervised.ipynb @@ -0,0 +1,144 @@ +{ + "nbformat_minor": 0, + "nbformat": 4, + "cells": [ + { + "execution_count": null, + "cell_type": "code", + "source": [ + "%matplotlib inline" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "\n# OTDA unsupervised vs semi-supervised setting\n\n\nThis example introduces a semi supervised domain adaptation in a 2D setting.\nIt explicits the problem of semi supervised domain adaptation and introduces\nsome optimal transport approaches to solve it.\n\nQuantities such as optimal couplings, greater coupling coefficients and\ntransported samples are represented in order to give a visual understanding\nof what the transport methods are doing.\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport ot" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Generate data\n-------------\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "n_samples_source = 150\nn_samples_target = 150\n\nXs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source)\nXt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target)" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Transport source samples onto target samples\n--------------------------------------------\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "# unsupervised domain adaptation\not_sinkhorn_un = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn_un.fit(Xs=Xs, Xt=Xt)\ntransp_Xs_sinkhorn_un = ot_sinkhorn_un.transform(Xs=Xs)\n\n# semi-supervised domain adaptation\not_sinkhorn_semi = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn_semi.fit(Xs=Xs, Xt=Xt, ys=ys, yt=yt)\ntransp_Xs_sinkhorn_semi = ot_sinkhorn_semi.transform(Xs=Xs)\n\n# semi supervised DA uses available labaled target samples to modify the cost\n# matrix involved in the OT problem. The cost of transporting a source sample\n# of class A onto a target sample of class B != A is set to infinite, or a\n# very large value\n\n# note that in the present case we consider that all the target samples are\n# labeled. For daily applications, some target sample might not have labels,\n# in this case the element of yt corresponding to these samples should be\n# filled with -1.\n\n# Warning: we recall that -1 cannot be used as a class label" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Fig 1 : plots source and target samples + matrix of pairwise distance\n---------------------------------------------------------------------\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "pl.figure(1, figsize=(10, 10))\npl.subplot(2, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Source samples')\n\npl.subplot(2, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Target samples')\n\npl.subplot(2, 2, 3)\npl.imshow(ot_sinkhorn_un.cost_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Cost matrix - unsupervised DA')\n\npl.subplot(2, 2, 4)\npl.imshow(ot_sinkhorn_semi.cost_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Cost matrix - semisupervised DA')\n\npl.tight_layout()\n\n# the optimal coupling in the semi-supervised DA case will exhibit \" shape\n# similar\" to the cost matrix, (block diagonal matrix)" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Fig 2 : plots optimal couplings for the different methods\n---------------------------------------------------------\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "pl.figure(2, figsize=(8, 4))\n\npl.subplot(1, 2, 1)\npl.imshow(ot_sinkhorn_un.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nUnsupervised DA')\n\npl.subplot(1, 2, 2)\npl.imshow(ot_sinkhorn_semi.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSemi-supervised DA')\n\npl.tight_layout()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + }, + { + "source": [ + "Fig 3 : plot transported samples\n--------------------------------\n\n" + ], + "cell_type": "markdown", + "metadata": {} + }, + { + "execution_count": null, + "cell_type": "code", + "source": [ + "# display transported samples\npl.figure(4, figsize=(8, 4))\npl.subplot(1, 2, 1)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_sinkhorn_un[:, 0], transp_Xs_sinkhorn_un[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nEmdTransport')\npl.legend(loc=0)\npl.xticks([])\npl.yticks([])\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_sinkhorn_semi[:, 0], transp_Xs_sinkhorn_semi[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nSinkhornTransport')\npl.xticks([])\npl.yticks([])\n\npl.tight_layout()\npl.show()" + ], + "outputs": [], + "metadata": { + "collapsed": false + } + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "name": "python2", + "language": "python" + }, + "language_info": { + "mimetype": "text/x-python", + "nbconvert_exporter": "python", + "name": "python", + "file_extension": ".py", + "version": "2.7.12", + "pygments_lexer": "ipython2", + "codemirror_mode": { + "version": 2, + "name": "ipython" + } + } + } +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_semi_supervised.py b/docs/source/auto_examples/plot_otda_semi_supervised.py new file mode 100644 index 0000000..7963aef --- /dev/null +++ b/docs/source/auto_examples/plot_otda_semi_supervised.py @@ -0,0 +1,148 @@ +# -*- coding: utf-8 -*- +""" +============================================ +OTDA unsupervised vs semi-supervised setting +============================================ + +This example introduces a semi supervised domain adaptation in a 2D setting. +It explicits the problem of semi supervised domain adaptation and introduces +some optimal transport approaches to solve it. + +Quantities such as optimal couplings, greater coupling coefficients and +transported samples are represented in order to give a visual understanding +of what the transport methods are doing. +""" + +# Authors: Remi Flamary +# Stanislas Chambon +# +# License: MIT License + +import matplotlib.pylab as pl +import ot + + +############################################################################## +# Generate data +# ------------- + +n_samples_source = 150 +n_samples_target = 150 + +Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source) +Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target) + + +############################################################################## +# Transport source samples onto target samples +# -------------------------------------------- + + +# unsupervised domain adaptation +ot_sinkhorn_un = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn_un.fit(Xs=Xs, Xt=Xt) +transp_Xs_sinkhorn_un = ot_sinkhorn_un.transform(Xs=Xs) + +# semi-supervised domain adaptation +ot_sinkhorn_semi = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn_semi.fit(Xs=Xs, Xt=Xt, ys=ys, yt=yt) +transp_Xs_sinkhorn_semi = ot_sinkhorn_semi.transform(Xs=Xs) + +# semi supervised DA uses available labaled target samples to modify the cost +# matrix involved in the OT problem. The cost of transporting a source sample +# of class A onto a target sample of class B != A is set to infinite, or a +# very large value + +# note that in the present case we consider that all the target samples are +# labeled. For daily applications, some target sample might not have labels, +# in this case the element of yt corresponding to these samples should be +# filled with -1. + +# Warning: we recall that -1 cannot be used as a class label + + +############################################################################## +# Fig 1 : plots source and target samples + matrix of pairwise distance +# --------------------------------------------------------------------- + +pl.figure(1, figsize=(10, 10)) +pl.subplot(2, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Source samples') + +pl.subplot(2, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Target samples') + +pl.subplot(2, 2, 3) +pl.imshow(ot_sinkhorn_un.cost_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Cost matrix - unsupervised DA') + +pl.subplot(2, 2, 4) +pl.imshow(ot_sinkhorn_semi.cost_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Cost matrix - semisupervised DA') + +pl.tight_layout() + +# the optimal coupling in the semi-supervised DA case will exhibit " shape +# similar" to the cost matrix, (block diagonal matrix) + + +############################################################################## +# Fig 2 : plots optimal couplings for the different methods +# --------------------------------------------------------- + +pl.figure(2, figsize=(8, 4)) + +pl.subplot(1, 2, 1) +pl.imshow(ot_sinkhorn_un.coupling_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nUnsupervised DA') + +pl.subplot(1, 2, 2) +pl.imshow(ot_sinkhorn_semi.coupling_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSemi-supervised DA') + +pl.tight_layout() + + +############################################################################## +# Fig 3 : plot transported samples +# -------------------------------- + +# display transported samples +pl.figure(4, figsize=(8, 4)) +pl.subplot(1, 2, 1) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(transp_Xs_sinkhorn_un[:, 0], transp_Xs_sinkhorn_un[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Transported samples\nEmdTransport') +pl.legend(loc=0) +pl.xticks([]) +pl.yticks([]) + +pl.subplot(1, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(transp_Xs_sinkhorn_semi[:, 0], transp_Xs_sinkhorn_semi[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Transported samples\nSinkhornTransport') +pl.xticks([]) +pl.yticks([]) + +pl.tight_layout() +pl.show() diff --git a/docs/source/auto_examples/plot_otda_semi_supervised.rst b/docs/source/auto_examples/plot_otda_semi_supervised.rst new file mode 100644 index 0000000..dc05ed0 --- /dev/null +++ b/docs/source/auto_examples/plot_otda_semi_supervised.rst @@ -0,0 +1,240 @@ + + +.. _sphx_glr_auto_examples_plot_otda_semi_supervised.py: + + +============================================ +OTDA unsupervised vs semi-supervised setting +============================================ + +This example introduces a semi supervised domain adaptation in a 2D setting. +It explicits the problem of semi supervised domain adaptation and introduces +some optimal transport approaches to solve it. + +Quantities such as optimal couplings, greater coupling coefficients and +transported samples are represented in order to give a visual understanding +of what the transport methods are doing. + + + +.. code-block:: python + + + # Authors: Remi Flamary + # Stanislas Chambon + # + # License: MIT License + + import matplotlib.pylab as pl + import ot + + + + + + + + +Generate data +------------- + + + +.. code-block:: python + + + n_samples_source = 150 + n_samples_target = 150 + + Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source) + Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target) + + + + + + + + +Transport source samples onto target samples +-------------------------------------------- + + + +.. code-block:: python + + + + # unsupervised domain adaptation + ot_sinkhorn_un = ot.da.SinkhornTransport(reg_e=1e-1) + ot_sinkhorn_un.fit(Xs=Xs, Xt=Xt) + transp_Xs_sinkhorn_un = ot_sinkhorn_un.transform(Xs=Xs) + + # semi-supervised domain adaptation + ot_sinkhorn_semi = ot.da.SinkhornTransport(reg_e=1e-1) + ot_sinkhorn_semi.fit(Xs=Xs, Xt=Xt, ys=ys, yt=yt) + transp_Xs_sinkhorn_semi = ot_sinkhorn_semi.transform(Xs=Xs) + + # semi supervised DA uses available labaled target samples to modify the cost + # matrix involved in the OT problem. The cost of transporting a source sample + # of class A onto a target sample of class B != A is set to infinite, or a + # very large value + + # note that in the present case we consider that all the target samples are + # labeled. For daily applications, some target sample might not have labels, + # in this case the element of yt corresponding to these samples should be + # filled with -1. + + # Warning: we recall that -1 cannot be used as a class label + + + + + + + + +Fig 1 : plots source and target samples + matrix of pairwise distance +--------------------------------------------------------------------- + + + +.. code-block:: python + + + pl.figure(1, figsize=(10, 10)) + pl.subplot(2, 2, 1) + pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') + pl.xticks([]) + pl.yticks([]) + pl.legend(loc=0) + pl.title('Source samples') + + pl.subplot(2, 2, 2) + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') + pl.xticks([]) + pl.yticks([]) + pl.legend(loc=0) + pl.title('Target samples') + + pl.subplot(2, 2, 3) + pl.imshow(ot_sinkhorn_un.cost_, interpolation='nearest') + pl.xticks([]) + pl.yticks([]) + pl.title('Cost matrix - unsupervised DA') + + pl.subplot(2, 2, 4) + pl.imshow(ot_sinkhorn_semi.cost_, interpolation='nearest') + pl.xticks([]) + pl.yticks([]) + pl.title('Cost matrix - semisupervised DA') + + pl.tight_layout() + + # the optimal coupling in the semi-supervised DA case will exhibit " shape + # similar" to the cost matrix, (block diagonal matrix) + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_semi_supervised_001.png + :align: center + + + + +Fig 2 : plots optimal couplings for the different methods +--------------------------------------------------------- + + + +.. code-block:: python + + + pl.figure(2, figsize=(8, 4)) + + pl.subplot(1, 2, 1) + pl.imshow(ot_sinkhorn_un.coupling_, interpolation='nearest') + pl.xticks([]) + pl.yticks([]) + pl.title('Optimal coupling\nUnsupervised DA') + + pl.subplot(1, 2, 2) + pl.imshow(ot_sinkhorn_semi.coupling_, interpolation='nearest') + pl.xticks([]) + pl.yticks([]) + pl.title('Optimal coupling\nSemi-supervised DA') + + pl.tight_layout() + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_semi_supervised_003.png + :align: center + + + + +Fig 3 : plot transported samples +-------------------------------- + + + +.. code-block:: python + + + # display transported samples + pl.figure(4, figsize=(8, 4)) + pl.subplot(1, 2, 1) + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) + pl.scatter(transp_Xs_sinkhorn_un[:, 0], transp_Xs_sinkhorn_un[:, 1], c=ys, + marker='+', label='Transp samples', s=30) + pl.title('Transported samples\nEmdTransport') + pl.legend(loc=0) + pl.xticks([]) + pl.yticks([]) + + pl.subplot(1, 2, 2) + pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) + pl.scatter(transp_Xs_sinkhorn_semi[:, 0], transp_Xs_sinkhorn_semi[:, 1], c=ys, + marker='+', label='Transp samples', s=30) + pl.title('Transported samples\nSinkhornTransport') + pl.xticks([]) + pl.yticks([]) + + pl.tight_layout() + pl.show() + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_semi_supervised_006.png + :align: center + + + + +**Total running time of the script:** ( 0 minutes 0.714 seconds) + + + +.. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_otda_semi_supervised.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_otda_semi_supervised.ipynb ` + +.. rst-class:: sphx-glr-signature + + `Generated by Sphinx-Gallery `_ diff --git a/docs/source/conf.py b/docs/source/conf.py index 7bf392c..4105d87 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -33,7 +33,7 @@ class Mock(MagicMock): return MagicMock() MOCK_MODULES = ['ot.lp.emd_wrap','autograd','pymanopt','cudamat','autograd.numpy','pymanopt.manifolds','pymanopt.solvers'] # 'autograd.numpy','pymanopt.manifolds','pymanopt.solvers', -#sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) +sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # !!!! # If extensions (or modules to document with autodoc) are in another directory, @@ -62,7 +62,7 @@ extensions = [ 'sphinx.ext.ifconfig', 'sphinx.ext.viewcode', 'sphinx.ext.napoleon', - 'sphinx_gallery.gen_gallery', + #'sphinx_gallery.gen_gallery', ] # Add any paths that contain templates here, relative to this directory. diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py index 5132024..d3f724c 100644 --- a/examples/plot_gromov.py +++ b/examples/plot_gromov.py @@ -20,13 +20,14 @@ from mpl_toolkits.mplot3d import Axes3D # noqa import ot -""" -Sample two Gaussian distributions (2D and 3D) -============================================= -The Gromov-Wasserstein distance allows to compute distances with samples that -do not belong to the same metric space. For demonstration purpose, we sample -two Gaussian distributions in 2- and 3-dimensional spaces. -""" +############################################################################## +# Sample two Gaussian distributions (2D and 3D) +# --------------------------------------------- +# +# The Gromov-Wasserstein distance allows to compute distances with samples that +# do not belong to the same metric space. For demonstration purpose, we sample +# two Gaussian distributions in 2- and 3-dimensional spaces. + n_samples = 30 # nb samples @@ -42,10 +43,11 @@ P = sp.linalg.sqrtm(cov_t) xt = np.random.randn(n_samples, 3).dot(P) + mu_t -""" -Plotting the distributions -========================== -""" +############################################################################## +# Plotting the distributions +# -------------------------- + + fig = pl.figure() ax1 = fig.add_subplot(121) ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') @@ -54,10 +56,10 @@ ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r') pl.show() -""" -Compute distance kernels, normalize them and then display -========================================================= -""" +############################################################################## +# Compute distance kernels, normalize them and then display +# --------------------------------------------------------- + C1 = sp.spatial.distance.cdist(xs, xs) C2 = sp.spatial.distance.cdist(xt, xt) @@ -72,10 +74,10 @@ pl.subplot(122) pl.imshow(C2) pl.show() -""" -Compute Gromov-Wasserstein plans and distance -============================================= -""" +############################################################################## +# Compute Gromov-Wasserstein plans and distance +# --------------------------------------------- + p = ot.unif(n_samples) q = ot.unif(n_samples) diff --git a/examples/plot_gromov_barycenter.py b/examples/plot_gromov_barycenter.py index 93533c0..180b0cf 100755 --- a/examples/plot_gromov_barycenter.py +++ b/examples/plot_gromov_barycenter.py @@ -24,12 +24,12 @@ from sklearn.decomposition import PCA import ot -""" -Smacof MDS -========== -This function allows to find an embedding of points given a dissimilarity matrix -that will be given by the output of the algorithm -""" +############################################################################## +# Smacof MDS +# ---------- +# +# This function allows to find an embedding of points given a dissimilarity matrix +# that will be given by the output of the algorithm def smacof_mds(C, dim, max_iter=3000, eps=1e-9): @@ -78,11 +78,11 @@ def smacof_mds(C, dim, max_iter=3000, eps=1e-9): return npos -""" -Data preparation -================ -The four distributions are constructed from 4 simple images -""" +############################################################################## +# Data preparation +# ---------------- +# +# The four distributions are constructed from 4 simple images def im2mat(I): @@ -110,12 +110,11 @@ for nb in range(4): xs = np.array([np.array(xs[0]), np.array(xs[1]), np.array(xs[2]), np.array(xs[3])]) +############################################################################## +# Barycenter computation +# ---------------------- + -""" -Barycenter computation -====================== -The four distributions are constructed from 4 simple images -""" ns = [len(xs[s]) for s in range(S)] n_samples = 30 @@ -157,12 +156,13 @@ for i in range(2): ], p, lambdast[i], 'square_loss', 5e-4, max_iter=100, tol=1e-3) -""" -Visualization -============= -""" -"""The PCA helps in getting consistency between the rotations""" +############################################################################## +# Visualization +# ------------- +# +# The PCA helps in getting consistency between the rotations + clf = PCA(n_components=2) npos = [0, 0, 0, 0] diff --git a/examples/plot_otda_semi_supervised.py b/examples/plot_otda_semi_supervised.py new file mode 100644 index 0000000..7963aef --- /dev/null +++ b/examples/plot_otda_semi_supervised.py @@ -0,0 +1,148 @@ +# -*- coding: utf-8 -*- +""" +============================================ +OTDA unsupervised vs semi-supervised setting +============================================ + +This example introduces a semi supervised domain adaptation in a 2D setting. +It explicits the problem of semi supervised domain adaptation and introduces +some optimal transport approaches to solve it. + +Quantities such as optimal couplings, greater coupling coefficients and +transported samples are represented in order to give a visual understanding +of what the transport methods are doing. +""" + +# Authors: Remi Flamary +# Stanislas Chambon +# +# License: MIT License + +import matplotlib.pylab as pl +import ot + + +############################################################################## +# Generate data +# ------------- + +n_samples_source = 150 +n_samples_target = 150 + +Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source) +Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target) + + +############################################################################## +# Transport source samples onto target samples +# -------------------------------------------- + + +# unsupervised domain adaptation +ot_sinkhorn_un = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn_un.fit(Xs=Xs, Xt=Xt) +transp_Xs_sinkhorn_un = ot_sinkhorn_un.transform(Xs=Xs) + +# semi-supervised domain adaptation +ot_sinkhorn_semi = ot.da.SinkhornTransport(reg_e=1e-1) +ot_sinkhorn_semi.fit(Xs=Xs, Xt=Xt, ys=ys, yt=yt) +transp_Xs_sinkhorn_semi = ot_sinkhorn_semi.transform(Xs=Xs) + +# semi supervised DA uses available labaled target samples to modify the cost +# matrix involved in the OT problem. The cost of transporting a source sample +# of class A onto a target sample of class B != A is set to infinite, or a +# very large value + +# note that in the present case we consider that all the target samples are +# labeled. For daily applications, some target sample might not have labels, +# in this case the element of yt corresponding to these samples should be +# filled with -1. + +# Warning: we recall that -1 cannot be used as a class label + + +############################################################################## +# Fig 1 : plots source and target samples + matrix of pairwise distance +# --------------------------------------------------------------------- + +pl.figure(1, figsize=(10, 10)) +pl.subplot(2, 2, 1) +pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Source samples') + +pl.subplot(2, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples') +pl.xticks([]) +pl.yticks([]) +pl.legend(loc=0) +pl.title('Target samples') + +pl.subplot(2, 2, 3) +pl.imshow(ot_sinkhorn_un.cost_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Cost matrix - unsupervised DA') + +pl.subplot(2, 2, 4) +pl.imshow(ot_sinkhorn_semi.cost_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Cost matrix - semisupervised DA') + +pl.tight_layout() + +# the optimal coupling in the semi-supervised DA case will exhibit " shape +# similar" to the cost matrix, (block diagonal matrix) + + +############################################################################## +# Fig 2 : plots optimal couplings for the different methods +# --------------------------------------------------------- + +pl.figure(2, figsize=(8, 4)) + +pl.subplot(1, 2, 1) +pl.imshow(ot_sinkhorn_un.coupling_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nUnsupervised DA') + +pl.subplot(1, 2, 2) +pl.imshow(ot_sinkhorn_semi.coupling_, interpolation='nearest') +pl.xticks([]) +pl.yticks([]) +pl.title('Optimal coupling\nSemi-supervised DA') + +pl.tight_layout() + + +############################################################################## +# Fig 3 : plot transported samples +# -------------------------------- + +# display transported samples +pl.figure(4, figsize=(8, 4)) +pl.subplot(1, 2, 1) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(transp_Xs_sinkhorn_un[:, 0], transp_Xs_sinkhorn_un[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Transported samples\nEmdTransport') +pl.legend(loc=0) +pl.xticks([]) +pl.yticks([]) + +pl.subplot(1, 2, 2) +pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', + label='Target samples', alpha=0.5) +pl.scatter(transp_Xs_sinkhorn_semi[:, 0], transp_Xs_sinkhorn_semi[:, 1], c=ys, + marker='+', label='Transp samples', s=30) +pl.title('Transported samples\nSinkhornTransport') +pl.xticks([]) +pl.yticks([]) + +pl.tight_layout() +pl.show() diff --git a/notebooks/plot_gromov.ipynb b/notebooks/plot_gromov.ipynb new file mode 100644 index 0000000..11c19d3 --- /dev/null +++ b/notebooks/plot_gromov.ipynb @@ -0,0 +1,231 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Gromov-Wasserstein example\n", + "\n", + "\n", + "This example is designed to show how to use the Gromov-Wassertsein distance\n", + "computation in POT.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Erwan Vautier \r\n", + "# Nicolas Courty \r\n", + "#\r\n", + "# License: MIT License\r\n", + "\r\n", + "import scipy as sp\r\n", + "import numpy as np\r\n", + "import matplotlib.pylab as pl\r\n", + "from mpl_toolkits.mplot3d import Axes3D # noqa\r\n", + "import ot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sample two Gaussian distributions (2D and 3D)\r\n", + " ---------------------------------------------\r\n", + "\r\n", + " The Gromov-Wasserstein distance allows to compute distances with samples that\r\n", + " do not belong to the same metric space. For demonstration purpose, we sample\r\n", + " two Gaussian distributions in 2- and 3-dimensional spaces.\r\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n_samples = 30 # nb samples\r\n", + "\r\n", + "mu_s = np.array([0, 0])\r\n", + "cov_s = np.array([[1, 0], [0, 1]])\r\n", + "\r\n", + "mu_t = np.array([4, 4, 4])\r\n", + "cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\r\n", + "\r\n", + "\r\n", + "xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)\r\n", + "P = sp.linalg.sqrtm(cov_t)\r\n", + "xt = np.random.randn(n_samples, 3).dot(P) + mu_t" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotting the distributions\r\n", + "--------------------------\r\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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7nZKSEq5du4bD4WB0dJQbN24wMjIStqF4rHL1kXJqhf0wcsOxjKyPay479PpH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\nmtfkErsZ71cQYrbMZRytoZj7aV6TS+xUfiHE8Pbs2QOlUon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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = pl.figure()\r\n", + "ax1 = fig.add_subplot(121)\r\n", + "ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\r\n", + "ax2 = fig.add_subplot(122, projection='3d')\r\n", + "ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\r\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute distance kernels, normalize them and then display\r\n", + "---------------------------------------------------------\r\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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hGD7BFnTDMAyfYAu6YRiGTzjggi4i1SLyrIhsE5GtIvJ3E/YiEXlKROon/tZe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\n8dMc2rnMPuyVi2EYhk+wBd0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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "C1 = sp.spatial.distance.cdist(xs, xs)\r\n", + "C2 = sp.spatial.distance.cdist(xt, xt)\r\n", + "\r\n", + "C1 /= C1.max()\r\n", + "C2 /= C2.max()\r\n", + "\r\n", + "pl.figure()\r\n", + "pl.subplot(121)\r\n", + "pl.imshow(C1)\r\n", + "pl.subplot(122)\r\n", + "pl.imshow(C2)\r\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute Gromov-Wasserstein plans and distance\r\n", + "---------------------------------------------\r\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gromov-Wasserstein distances between the distribution: 0.201997813845\n" + ] + }, + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "p = ot.unif(n_samples)\r\n", + "q = ot.unif(n_samples)\r\n", + "\r\n", + "gw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4)\r\n", + "gw_dist = ot.gromov_wasserstein2(C1, C2, p, q, 'square_loss', epsilon=5e-4)\r\n", + "\r\n", + "print('Gromov-Wasserstein distances between the distribution: ' + str(gw_dist))\r\n", + "\r\n", + "pl.figure()\r\n", + "pl.imshow(gw, cmap='jet')\r\n", + "pl.colorbar()\r\n", + "pl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_gromov_barycenter.ipynb b/notebooks/plot_gromov_barycenter.ipynb new file mode 100644 index 0000000..8102bcf --- /dev/null +++ b/notebooks/plot_gromov_barycenter.ipynb @@ -0,0 +1,368 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Gromov-Wasserstein Barycenter example\n", + "\n", + "\n", + "This example is designed to show how to use the Gromov-Wasserstein distance\n", + "computation in POT.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Erwan Vautier \r\n", + "# Nicolas Courty \r\n", + "#\r\n", + "# License: MIT License\r\n", + "\r\n", + "\r\n", + "import numpy as np\r\n", + "import scipy as sp\r\n", + "\r\n", + "import scipy.ndimage as spi\r\n", + "import matplotlib.pylab as pl\r\n", + "from sklearn import manifold\r\n", + "from sklearn.decomposition import PCA\r\n", + "\r\n", + "import ot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Smacof MDS\r\n", + " ----------\r\n", + "\r\n", + " This function allows to find an embedding of points given a dissimilarity matrix\r\n", + " that will be given by the output of the algorithm\r\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def smacof_mds(C, dim, max_iter=3000, eps=1e-9):\r\n", + " \"\"\"\r\n", + " Returns an interpolated point cloud following the dissimilarity matrix C\r\n", + " using SMACOF multidimensional scaling (MDS) in specific dimensionned\r\n", + " target space\r\n", + "\r\n", + " Parameters\r\n", + " ----------\r\n", + " C : ndarray, shape (ns, ns)\r\n", + " dissimilarity matrix\r\n", + " dim : int\r\n", + " dimension of the targeted space\r\n", + " max_iter : int\r\n", + " Maximum number of iterations of the SMACOF algorithm for a single run\r\n", + " eps : float\r\n", + " relative tolerance w.r.t stress to declare converge\r\n", + "\r\n", + " Returns\r\n", + " -------\r\n", + " npos : ndarray, shape (R, dim)\r\n", + " Embedded coordinates of the interpolated point cloud (defined with\r\n", + " one isometry)\r\n", + " \"\"\"\r\n", + "\r\n", + " rng = np.random.RandomState(seed=3)\r\n", + "\r\n", + " mds = manifold.MDS(\r\n", + " dim,\r\n", + " max_iter=max_iter,\r\n", + " eps=1e-9,\r\n", + " dissimilarity='precomputed',\r\n", + " n_init=1)\r\n", + " pos = mds.fit(C).embedding_\r\n", + "\r\n", + " nmds = manifold.MDS(\r\n", + " 2,\r\n", + " max_iter=max_iter,\r\n", + " eps=1e-9,\r\n", + " dissimilarity=\"precomputed\",\r\n", + " random_state=rng,\r\n", + " n_init=1)\r\n", + " npos = nmds.fit_transform(C, init=pos)\r\n", + "\r\n", + " return npos" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data preparation\r\n", + " ----------------\r\n", + "\r\n", + " The four distributions are constructed from 4 simple images\r\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def im2mat(I):\r\n", + " \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\r\n", + " return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\r\n", + "\r\n", + "\r\n", + "square = spi.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256\r\n", + "cross = spi.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256\r\n", + "triangle = spi.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256\r\n", + "star = spi.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256\r\n", + "\r\n", + "shapes = [square, cross, triangle, star]\r\n", + "\r\n", + "S = 4\r\n", + "xs = [[] for i in range(S)]\r\n", + "\r\n", + "\r\n", + "for nb in range(4):\r\n", + " for i in range(8):\r\n", + " for j in range(8):\r\n", + " if shapes[nb][i, j] < 0.95:\r\n", + " xs[nb].append([j, 8 - i])\r\n", + "\r\n", + "xs = np.array([np.array(xs[0]), np.array(xs[1]),\r\n", + " np.array(xs[2]), np.array(xs[3])])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Barycenter computation\r\n", + "----------------------\r\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "ns = [len(xs[s]) for s in range(S)]\r\n", + "n_samples = 30\r\n", + "\r\n", + "\"\"\"Compute all distances matrices for the four shapes\"\"\"\r\n", + "Cs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)]\r\n", + "Cs = [cs / cs.max() for cs in Cs]\r\n", + "\r\n", + "ps = [ot.unif(ns[s]) for s in range(S)]\r\n", + "p = ot.unif(n_samples)\r\n", + "\r\n", + "\r\n", + "lambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]]\r\n", + "\r\n", + "Ct01 = [0 for i in range(2)]\r\n", + "for i in range(2):\r\n", + " Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]],\r\n", + " [ps[0], ps[1]\r\n", + " ], p, lambdast[i], 'square_loss', 5e-4,\r\n", + " max_iter=100, tol=1e-3)\r\n", + "\r\n", + "Ct02 = [0 for i in range(2)]\r\n", + "for i in range(2):\r\n", + " Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]],\r\n", + " [ps[0], ps[2]\r\n", + " ], p, lambdast[i], 'square_loss', 5e-4,\r\n", + " max_iter=100, tol=1e-3)\r\n", + "\r\n", + "Ct13 = [0 for i in range(2)]\r\n", + "for i in range(2):\r\n", + " Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]],\r\n", + " [ps[1], ps[3]\r\n", + " ], p, lambdast[i], 'square_loss', 5e-4,\r\n", + " max_iter=100, tol=1e-3)\r\n", + "\r\n", + "Ct23 = [0 for i in range(2)]\r\n", + "for i in range(2):\r\n", + " Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]],\r\n", + " [ps[2], ps[3]\r\n", + " ], p, lambdast[i], 'square_loss', 5e-4,\r\n", + " max_iter=100, tol=1e-3)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visualization\r\n", + " -------------\r\n", + "\r\n", + " The PCA helps in getting consistency between the rotations\r\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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SZcmhT6vqV2L3O/PK+s+Vld/VnWMf1URIf2az38/P6uqqb25upj6MbXauGZNm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\nul4ztrXNSJOpUKNJxiStSdqUtLlv377O/sFqiz1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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "clf = PCA(n_components=2)\r\n", + "npos = [0, 0, 0, 0]\r\n", + "npos = [smacof_mds(Cs[s], 2) for s in range(S)]\r\n", + "\r\n", + "npost01 = [0, 0]\r\n", + "npost01 = [smacof_mds(Ct01[s], 2) for s in range(2)]\r\n", + "npost01 = [clf.fit_transform(npost01[s]) for s in range(2)]\r\n", + "\r\n", + "npost02 = [0, 0]\r\n", + "npost02 = [smacof_mds(Ct02[s], 2) for s in range(2)]\r\n", + "npost02 = [clf.fit_transform(npost02[s]) for s in range(2)]\r\n", + "\r\n", + "npost13 = [0, 0]\r\n", + "npost13 = [smacof_mds(Ct13[s], 2) for s in range(2)]\r\n", + "npost13 = [clf.fit_transform(npost13[s]) for s in range(2)]\r\n", + "\r\n", + "npost23 = [0, 0]\r\n", + "npost23 = [smacof_mds(Ct23[s], 2) for s in range(2)]\r\n", + "npost23 = [clf.fit_transform(npost23[s]) for s in range(2)]\r\n", + "\r\n", + "\r\n", + "fig = pl.figure(figsize=(10, 10))\r\n", + "\r\n", + "ax1 = pl.subplot2grid((4, 4), (0, 0))\r\n", + "pl.xlim((-1, 1))\r\n", + "pl.ylim((-1, 1))\r\n", + "ax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r')\r\n", + "\r\n", + "ax2 = pl.subplot2grid((4, 4), (0, 1))\r\n", + "pl.xlim((-1, 1))\r\n", + "pl.ylim((-1, 1))\r\n", + "ax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b')\r\n", + "\r\n", + "ax3 = pl.subplot2grid((4, 4), (0, 2))\r\n", + "pl.xlim((-1, 1))\r\n", + "pl.ylim((-1, 1))\r\n", + "ax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b')\r\n", + "\r\n", + "ax4 = pl.subplot2grid((4, 4), (0, 3))\r\n", + "pl.xlim((-1, 1))\r\n", + "pl.ylim((-1, 1))\r\n", + "ax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r')\r\n", + "\r\n", + "ax5 = pl.subplot2grid((4, 4), (1, 0))\r\n", + "pl.xlim((-1, 1))\r\n", + "pl.ylim((-1, 1))\r\n", + "ax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b')\r\n", + "\r\n", + "ax6 = pl.subplot2grid((4, 4), (1, 3))\r\n", + "pl.xlim((-1, 1))\r\n", + "pl.ylim((-1, 1))\r\n", + "ax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b')\r\n", + "\r\n", + "ax7 = pl.subplot2grid((4, 4), (2, 0))\r\n", + "pl.xlim((-1, 1))\r\n", + "pl.ylim((-1, 1))\r\n", + "ax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b')\r\n", + "\r\n", + "ax8 = pl.subplot2grid((4, 4), (2, 3))\r\n", + "pl.xlim((-1, 1))\r\n", + "pl.ylim((-1, 1))\r\n", + "ax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b')\r\n", + "\r\n", + "ax9 = pl.subplot2grid((4, 4), (3, 0))\r\n", + "pl.xlim((-1, 1))\r\n", + "pl.ylim((-1, 1))\r\n", + "ax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r')\r\n", + "\r\n", + "ax10 = pl.subplot2grid((4, 4), (3, 1))\r\n", + "pl.xlim((-1, 1))\r\n", + "pl.ylim((-1, 1))\r\n", + "ax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b')\r\n", + "\r\n", + "ax11 = pl.subplot2grid((4, 4), (3, 2))\r\n", + "pl.xlim((-1, 1))\r\n", + "pl.ylim((-1, 1))\r\n", + "ax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b')\r\n", + "\r\n", + "ax12 = pl.subplot2grid((4, 4), (3, 3))\r\n", + "pl.xlim((-1, 1))\r\n", + "pl.ylim((-1, 1))\r\n", + "ax12.scatter(npos[3][:, 0], npos[3][:, 1], color='r')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_otda_semi_supervised.ipynb b/notebooks/plot_otda_semi_supervised.ipynb new file mode 100644 index 0000000..6c538e9 --- /dev/null +++ b/notebooks/plot_otda_semi_supervised.ipynb @@ -0,0 +1,294 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# OTDA unsupervised vs semi-supervised setting\n", + "\n", + "\n", + "This example introduces a semi supervised domain adaptation in a 2D setting.\n", + "It explicits the problem of semi supervised domain adaptation and introduces\n", + "some optimal transport approaches to solve it.\n", + "\n", + "Quantities such as optimal couplings, greater coupling coefficients and\n", + "transported samples are represented in order to give a visual understanding\n", + "of what the transport methods are doing.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Authors: Remi Flamary \n", + "# Stanislas Chambon \n", + "#\n", + "# License: MIT License\n", + "\n", + "import matplotlib.pylab as pl\n", + "import ot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + "-------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n_samples_source = 150\n", + "n_samples_target = 150\n", + "\n", + "Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source)\n", + "Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Transport source samples onto target samples\n", + "--------------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# unsupervised domain adaptation\n", + "ot_sinkhorn_un = ot.da.SinkhornTransport(reg_e=1e-1)\n", + "ot_sinkhorn_un.fit(Xs=Xs, Xt=Xt)\n", + "transp_Xs_sinkhorn_un = ot_sinkhorn_un.transform(Xs=Xs)\n", + "\n", + "# semi-supervised domain adaptation\n", + "ot_sinkhorn_semi = ot.da.SinkhornTransport(reg_e=1e-1)\n", + "ot_sinkhorn_semi.fit(Xs=Xs, Xt=Xt, ys=ys, yt=yt)\n", + "transp_Xs_sinkhorn_semi = ot_sinkhorn_semi.transform(Xs=Xs)\n", + "\n", + "# semi supervised DA uses available labaled target samples to modify the cost\n", + "# matrix involved in the OT problem. The cost of transporting a source sample\n", + "# of class A onto a target sample of class B != A is set to infinite, or a\n", + "# very large value\n", + "\n", + "# note that in the present case we consider that all the target samples are\n", + "# labeled. For daily applications, some target sample might not have labels,\n", + "# in this case the element of yt corresponding to these samples should be\n", + "# filled with -1.\n", + "\n", + "# Warning: we recall that -1 cannot be used as a class label" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig 1 : plots source and target samples + matrix of pairwise distance\n", + "---------------------------------------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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bt3+TXe/ezz4tM6WbVwSvx8MfPl/OG5df2WRxKKUiiz6kp5RSqkUSEdZVMn/y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7Rv6dgU9m22Sv3lZ40z6AVjm5vcMByvDV37B7/elcU3rw93KIpCitmslOYXEcn2/VpKlvW+szDa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\nAkV5lP87fkfnuv8EU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RTpJbEk3b4vHEMUAyO+z8/4Kx4oxiUDDGtXTVmyE0j/4Nc3ZYm8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t9BRd6j7SLEEX/S86ei6eD33B3St/rsWZlJYqYTyQbeu+Ua7I1+CvdvwcQJawCfOVxomelwwm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\nI5zfFdvfv6ztnPtRXDpuQH48DAkCx26bqIEsqsRccfteeRtKZi1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RkRyScyEk2bhPcgJO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jSLWO55pcOMgb4OJslR18/3Xrf7VHeIGOr/ESjNnRf0Gsbb2r/anh0DjhetugczRWmbETwz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\nQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(1, figsize=(10, 10))\n", + "pl.subplot(2, 2, 1)\n", + "pl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.legend(loc=0)\n", + "pl.title('Source samples')\n", + "\n", + "pl.subplot(2, 2, 2)\n", + "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.legend(loc=0)\n", + "pl.title('Target samples')\n", + "\n", + "pl.subplot(2, 2, 3)\n", + "pl.imshow(ot_sinkhorn_un.cost_, interpolation='nearest')\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.title('Cost matrix - unsupervised DA')\n", + "\n", + "pl.subplot(2, 2, 4)\n", + "pl.imshow(ot_sinkhorn_semi.cost_, interpolation='nearest')\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.title('Cost matrix - semisupervised DA')\n", + "\n", + "pl.tight_layout()\n", + "\n", + "# the optimal coupling in the semi-supervised DA case will exhibit \" shape\n", + "# similar\" to the cost matrix, (block diagonal matrix)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig 2 : plots optimal couplings for the different methods\n", + "---------------------------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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CBAd7AUQhEaepCTRb9XOxlOwCsgwWQKwU1vTGJPSMCrAzNm8OrVku53nxinze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\n8wlHphhbOqC+2et/oI/JO7pQsnPflK8jw44I0ezT/+Jq1M1wO5P80i3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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(2, figsize=(8, 4))\n", + "\n", + "pl.subplot(1, 2, 1)\n", + "pl.imshow(ot_sinkhorn_un.coupling_, interpolation='nearest')\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.title('Optimal coupling\\nUnsupervised DA')\n", + "\n", + "pl.subplot(1, 2, 2)\n", + "pl.imshow(ot_sinkhorn_semi.coupling_, interpolation='nearest')\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "pl.title('Optimal coupling\\nSemi-supervised DA')\n", + "\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Fig 3 : plot transported samples\n", + "--------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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Xe2X87W/fg0LxvTnzuHzSZF7btZN3S/eSm+Imw+mkrsPLI+vWcPu8M3WCPI3m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\nH1Gy8QCl2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JeOB0B0SqpVUJyLntD56m/uxmq39v4HoChKrzOZTFx//5Us/esKDpdUGUfC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\nHRXgKIqiKIrS7/x/4rlZk3fEE94AAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# display transported samples\n", + "pl.figure(4, figsize=(8, 4))\n", + "pl.subplot(1, 2, 1)\n", + "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", + " label='Target samples', alpha=0.5)\n", + "pl.scatter(transp_Xs_sinkhorn_un[:, 0], transp_Xs_sinkhorn_un[:, 1], c=ys,\n", + " marker='+', label='Transp samples', s=30)\n", + "pl.title('Transported samples\\nEmdTransport')\n", + "pl.legend(loc=0)\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "\n", + "pl.subplot(1, 2, 2)\n", + "pl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n", + " label='Target samples', alpha=0.5)\n", + "pl.scatter(transp_Xs_sinkhorn_semi[:, 0], transp_Xs_sinkhorn_semi[:, 1], c=ys,\n", + " marker='+', label='Transp samples', s=30)\n", + "pl.title('Transported samples\\nSinkhornTransport')\n", + "pl.xticks([])\n", + "pl.yticks([])\n", + "\n", + "pl.tight_layout()\n", + "pl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.12" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} -- cgit v1.2.3 From effa765b0f3b2abd22d5f97ff9206b5011c3e3a0 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 15 Sep 2017 14:18:20 +0200 Subject: add images and begin releases file --- README.md | 4 ++ RELEASES.md | 42 +++++++++++++++++++++ .../sphx_glr_plot_gromov_barycenter_thumb.png | Bin 0 -> 34183 bytes .../images/thumb/sphx_glr_plot_gromov_thumb.png | Bin 0 -> 30843 bytes .../sphx_glr_plot_otda_semi_supervised_thumb.png | Bin 0 -> 64710 bytes 5 files changed, 46 insertions(+) create mode 100644 RELEASES.md create mode 100644 docs/source/auto_examples/images/thumb/sphx_glr_plot_gromov_barycenter_thumb.png create mode 100644 docs/source/auto_examples/images/thumb/sphx_glr_plot_gromov_thumb.png create mode 100644 docs/source/auto_examples/images/thumb/sphx_glr_plot_otda_semi_supervised_thumb.png diff --git a/README.md b/README.md index d340068..3b59eaa 100644 --- a/README.md +++ b/README.md @@ -123,6 +123,10 @@ Here is a list of the Python notebooks available [here](https://github.com/rflam * [OT mapping estimation for domain adaptation](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_mapping.ipynb) * [OT mapping estimation for color transfer in images](https://github.com/rflamary/POT/blob/master/notebooks/plot_otda_mapping_colors_images.ipynb) * [Wasserstein Discriminant Analysis](https://github.com/rflamary/POT/blob/master/notebooks/plot_WDA.ipynb) +* [Gromov Wasserstein](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov.ipynb) +* [Gromov Wasserstein Barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov_barycenter.ipynb) + + You can also see the notebooks with [Jupyter nbviewer](https://nbviewer.jupyter.org/github/rflamary/POT/tree/master/notebooks/). diff --git a/RELEASES.md b/RELEASES.md new file mode 100644 index 0000000..f00e704 --- /dev/null +++ b/RELEASES.md @@ -0,0 +1,42 @@ +# POT Releases + +## V0.1.11 New years resolution +*5 Jan 2017* + +* Add sphinx gallery for better documentation +* Small efficiency tweak in sinkhorn +* Add simple tic() toc() functions for timing + + +## V0.1.10 +*7 Nov 2016* +* numerical stabilization for sinkhorn (log domain and epsilon scaling) + +## V0.1.9 DA classes and mapping +*4 Nov 2016* + +* Update classes and examples for domain adaptation +* Joint OT matrix and mapping estimation + +## V0.1.7 +*31 Oct 2016* + +* Original Domain adaptation classes + + + +## PyPI version 0.1.3 + +* pipy works + +## First pre-release +*28 Oct 2016* + +It provides the following solvers: +* OT solver for the linear program/ Earth Movers Distance [1]. +* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2]. +* Bregman projections for Wasserstein barycenter [3] and unmixing [4]. +* Optimal transport for domain adaptation with group lasso regularization [5] +* Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. + +Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. diff --git a/docs/source/auto_examples/images/thumb/sphx_glr_plot_gromov_barycenter_thumb.png b/docs/source/auto_examples/images/thumb/sphx_glr_plot_gromov_barycenter_thumb.png new file mode 100644 index 0000000..85a94ff Binary files /dev/null and b/docs/source/auto_examples/images/thumb/sphx_glr_plot_gromov_barycenter_thumb.png differ diff --git a/docs/source/auto_examples/images/thumb/sphx_glr_plot_gromov_thumb.png b/docs/source/auto_examples/images/thumb/sphx_glr_plot_gromov_thumb.png new file mode 100644 index 0000000..26b0b2f Binary files /dev/null and b/docs/source/auto_examples/images/thumb/sphx_glr_plot_gromov_thumb.png differ diff --git a/docs/source/auto_examples/images/thumb/sphx_glr_plot_otda_semi_supervised_thumb.png b/docs/source/auto_examples/images/thumb/sphx_glr_plot_otda_semi_supervised_thumb.png new file mode 100644 index 0000000..e1b5863 Binary files /dev/null and b/docs/source/auto_examples/images/thumb/sphx_glr_plot_otda_semi_supervised_thumb.png differ -- cgit v1.2.3 From ab121f134c22377209c818c1d9b576e0b3b9a9d3 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 15 Sep 2017 14:25:28 +0200 Subject: update releases --- RELEASES.md | 41 ++++++++++++++++++++++++++++++++++++----- 1 file changed, 36 insertions(+), 5 deletions(-) diff --git a/RELEASES.md b/RELEASES.md index f00e704..89cc29b 100644 --- a/RELEASES.md +++ b/RELEASES.md @@ -1,5 +1,36 @@ # POT Releases +## 0.4 Community edition + +* Add gromov Wasserstein solver and Gromov Barycenters (PR #23) +* emd and emd2 can now return dual variables (PR #29) + +## 0.3.1 +*11 Jul 2017* + +* Correct bug in emd on windows + +## 0.3 Summer release +*7 Jul 2017* + +* emd* and sinkhorn* are now performed in parallel for multiple target distributions +* emd and sinkhorn are for OT matrix computation +* emd2 and sinkhorn2 are for OT loss computation +* new notebooks for emd computation and Wasserstein Discriminant Analysis +* relocate notebooks +* update documentation +* clean_zeros(a,b,M) for removimg zeros in sparse distributions +* GPU implementations for sinkhorn and group lasso regularization + + +## V0.2 +*7 Apr 2017* + +* New dimensionality reduction method (WDA) +* Efficient method emd2 returns only tarnsport (in paralell if several histograms given) + + + ## V0.1.11 New years resolution *5 Jan 2017* @@ -33,10 +64,10 @@ *28 Oct 2016* It provides the following solvers: -* OT solver for the linear program/ Earth Movers Distance [1]. -* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2]. -* Bregman projections for Wasserstein barycenter [3] and unmixing [4]. -* Optimal transport for domain adaptation with group lasso regularization [5] -* Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. +* OT solver for the linear program/ Earth Movers Distance. +* Entropic regularization OT solver with Sinkhorn Knopp Algorithm. +* Bregman projections for Wasserstein barycenter [3] and unmixing. +* Optimal transport for domain adaptation with group lasso regularization +* Conditional gradient and Generalized conditional gradient for regularized OT. Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. -- cgit v1.2.3 From 7fea2cd3e8ad29bf3fa442d7642bae124ee2bab0 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 15 Sep 2017 14:35:51 +0200 Subject: ipdate release --- RELEASES.md | 17 ++++++++++++++++- ot/__init__.py | 2 +- 2 files changed, 17 insertions(+), 2 deletions(-) diff --git a/RELEASES.md b/RELEASES.md index 89cc29b..58712c8 100644 --- a/RELEASES.md +++ b/RELEASES.md @@ -1,9 +1,24 @@ # POT Releases ## 0.4 Community edition +*15 Sep 2017* +This release contains a lot of contribution from new contributors. + + +#### Features + +* Automatic notebooks and doc update (PR #27) * Add gromov Wasserstein solver and Gromov Barycenters (PR #23) -* emd and emd2 can now return dual variables (PR #29) +* emd and emd2 can now return dual variables and have max_iter (PR #29 and PR #25) +* New domain adaptation classes compatible with scikit-learn (PR #22) +* Proper tests with pytest on travis (PR #19) +* PEP 8 tests (PR #13) + +#### Closed issues + +* emd convergence problem du to fixed max iterations (#24) +* Semi supervised DA error (#26) ## 0.3.1 *11 Jul 2017* diff --git a/ot/__init__.py b/ot/__init__.py index c9bb8c0..a5df43d 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -29,7 +29,7 @@ from .gromov import gromov_wasserstein, gromov_wasserstein2 # utils functions from .utils import dist, unif, tic, toc, toq -__version__ = "0.4.0b" +__version__ = "0.4.0" __all__ = ["emd", "emd2", "sinkhorn", "sinkhorn2", "utils", 'datasets', 'bregman', 'lp', 'plot', 'tic', 'toc', 'toq', -- cgit v1.2.3 From 57b0ba9d3ed60655183b2bc65c029dabf1279214 Mon Sep 17 00:00:00 2001 From: ncourty Date: Fri, 15 Sep 2017 15:27:06 +0200 Subject: update README.md with Erwan Vautier contributing and Bibtex citation --- README.md | 10 ++++++++++ 1 file changed, 10 insertions(+) diff --git a/README.md b/README.md index 3b59eaa..04994c0 100644 --- a/README.md +++ b/README.md @@ -143,6 +143,7 @@ The contributors to this library are: * [Nathalie Gayraud](https://www.linkedin.com/in/nathalie-t-h-gayraud/?ppe=1) * [Stanislas Chambon](https://slasnista.github.io/) * [Antoine Rolet](https://arolet.github.io/) +* Erwan Vautier (Gromov-Wasserstein) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): @@ -150,7 +151,16 @@ This toolbox benefit a lot from open source research and we would like to thank * [Nicolas Bonneel](http://liris.cnrs.fr/~nbonneel/) ( C++ code for EMD) * [Marco Cuturi](http://marcocuturi.net/) (Sinkhorn Knopp in Matlab/Cuda) +## Using and citing the toolbox +If you use this toolbox in your research and find it useful, please cite POT using the following bibtex reference: +``` +@article{flamary2017pot, + title={POT Python Optimal Transport library}, + author={Flamary, R{\'e}mi and Courty, Nicolas}, + year={2017} +} +``` ## Contributions and code of conduct Every contribution is welcome and should respect the [contribution guidelines](CONTRIBUTING.md). Each member of the project is expected to follow the [code of conduct](CODE_OF_CONDUCT.md). -- cgit v1.2.3 From 42d0aa94e7cb49711a646fe9b263a86cdb817161 Mon Sep 17 00:00:00 2001 From: ncourty Date: Sun, 17 Sep 2017 23:21:53 +0200 Subject: add encoding of readme.md in setup.py --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 9e08ccc..a32aa31 100755 --- a/setup.py +++ b/setup.py @@ -26,7 +26,7 @@ try: import pypandoc README = pypandoc.convert('README.md', 'rst') except (IOError, ImportError): - README = open(os.path.join(ROOT, 'README.md')).read() + README = open(os.path.join(ROOT, 'README.md'), encoding="utf-8").read() setup(name='POT', -- cgit v1.2.3 From 0c9e9bc4576f4bf87cecf6e56fbfa997632ceec1 Mon Sep 17 00:00:00 2001 From: Antony Schutz Date: Mon, 8 Jan 2018 09:44:28 +0100 Subject: type in title target instead of traget --- examples/plot_OT_L1_vs_L2.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/plot_OT_L1_vs_L2.py b/examples/plot_OT_L1_vs_L2.py index 090e809..a32a32e 100644 --- a/examples/plot_OT_L1_vs_L2.py +++ b/examples/plot_OT_L1_vs_L2.py @@ -52,7 +52,7 @@ pl.clf() pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.axis('equal') -pl.title('Source and traget distributions') +pl.title('Source and target distributions') # Cost matrices -- cgit v1.2.3 From 71b17f4e956de4503cbdffc2a822bcea18ed85d1 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 15 Feb 2018 16:48:29 +0100 Subject: large update gromov --- examples/plot_gromov.py | 16 ++- ot/gromov.py | 258 +++++++++++++++++++++++++++--------------------- 2 files changed, 161 insertions(+), 113 deletions(-) diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py index d3f724c..d42c21a 100644 --- a/examples/plot_gromov.py +++ b/examples/plot_gromov.py @@ -78,16 +78,26 @@ pl.show() # Compute Gromov-Wasserstein plans and distance # --------------------------------------------- - +#%% p = ot.unif(n_samples) q = ot.unif(n_samples) +ot.tic() +gw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4,verbose=True) +ot.toc() +ot.tic() +gw2,log2= ot.gromov.gromov_wasserstein0(C1, C2, p, q, 'square_loss', epsilon=5e-4,log=True,verbose=True) +ot.toc() -gw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4) gw_dist = ot.gromov_wasserstein2(C1, C2, p, q, 'square_loss', epsilon=5e-4) +ot.tic() +gw0,log0=ot.gromov.gw_lp(C1, C2, p, q, 'square_loss',log=True,verbose=True) +ot.toc() + + print('Gromov-Wasserstein distances between the distribution: ' + str(gw_dist)) pl.figure() -pl.imshow(gw, cmap='jet') +pl.imshow(gw2, cmap='jet') pl.colorbar() pl.show() diff --git a/ot/gromov.py b/ot/gromov.py index 20bf7ee..a23303a 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -9,6 +9,7 @@ Gromov-Wasserstein transport method # Author: Erwan Vautier # Nicolas Courty +# Rémi Flamary # # License: MIT License @@ -16,40 +17,35 @@ import numpy as np from .bregman import sinkhorn from .utils import dist +from .optim import cg -def square_loss(a, b): - """ - Returns the value of L(a,b)=(1/2)*|a-b|^2 - """ - - return 0.5 * (a - b)**2 - - -def kl_loss(a, b): - """ - Returns the value of L(a,b)=a*log(a/b)-a+b - """ - - return a * np.log(a / b) - a + b +def init_matrix(C1,C2,T,p,q,loss_fun='square_loss'): + """ Return loss matrices and tensors for Gromov-Wasserstein fast computation - -def tensor_square_loss(C1, C2, T): - """ - Returns the value of \mathcal{L}(C1,C2) \otimes T with the square loss + Returns the value of \mathcal{L}(C1,C2) \otimes T with the selected loss function as the loss function of Gromow-Wasserstein discrepancy. + + The matrices are computed as described in Proposition 1 in [12] Where : - C1 : Metric cost matrix in the source space - C2 : Metric cost matrix in the target space - T : A coupling between those two spaces + * C1 : Metric cost matrix in the source space + * C2 : Metric cost matrix in the target space + * T : A coupling between those two spaces The square-loss function L(a,b)=(1/2)*|a-b|^2 is read as : L(a,b) = f1(a)+f2(b)-h1(a)*h2(b) with : - f1(a)=(a^2)/2 - f2(b)=(b^2)/2 - h1(a)=a - h2(b)=b + * f1(a)=(a^2)/2 + * f2(b)=(b^2)/2 + * h1(a)=a + * h2(b)=b + + The kl-loss function L(a,b)=(1/2)*|a-b|^2 is read as : + L(a,b) = f1(a)+f2(b)-h1(a)*h2(b) with : + * f1(a)=a*log(a)-a + * f2(b)=b + * h1(a)=a + * h2(b)=log(b) Parameters ---------- @@ -57,66 +53,77 @@ def tensor_square_loss(C1, C2, T): Metric cost matrix in the source space C2 : ndarray, shape (nt, nt) Metric costfr matrix in the target space - T : ndarray, shape (ns, nt) + T : ndarray, shape (ns, nt) Coupling between source and target spaces + p : ndarray, shape (ns,) + Returns ------- - tens : ndarray, shape (ns, nt) - \mathcal{L}(C1,C2) \otimes T tensor-matrix multiplication result - """ - - C1 = np.asarray(C1, dtype=np.float64) - C2 = np.asarray(C2, dtype=np.float64) - T = np.asarray(T, dtype=np.float64) - - def f1(a): - return (a**2) / 2 - - def f2(b): - return (b**2) / 2 - - def h1(a): - return a - - def h2(b): - return b - - tens = -np.dot(h1(C1), T).dot(h2(C2).T) - tens -= tens.min() - - return tens - + + constC : ndarray, shape (ns, nt) + Constant C matrix in Eq. (6) + hC1 : ndarray, shape (ns, ns) + h1(C1) matrix in Eq. (6) + hC2 : ndarray, shape (nt, nt) + h2(C) matrix in Eq. (6) + + References + ---------- + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. -def tensor_kl_loss(C1, C2, T): """ - Returns the value of \mathcal{L}(C1,C2) \otimes T with the square loss - function as the loss function of Gromow-Wasserstein discrepancy. - - Where : - C1 : Metric cost matrix in the source space - C2 : Metric cost matrix in the target space - T : A coupling between those two spaces - The square-loss function L(a,b)=(1/2)*|a-b|^2 is read as : - L(a,b) = f1(a)+f2(b)-h1(a)*h2(b) with : - f1(a)=a*log(a)-a - f2(b)=b - h1(a)=a - h2(b)=log(b) + if loss_fun == 'square_loss': + def f1(a): + return (a**2)/2 + def f2(b): + return (b**2)/2 + def h1(a): + return a + def h2(b): + return b + elif loss_fun == 'kl_loss': + def f1(a): + return a * np.log(a + 1e-15) - a + def f2(b): + return b + def h1(a): + return a + def h2(b): + return np.log(b + 1e-15) + + constC1=np.dot(np.dot(f1(C1),p.reshape(-1,1)), + np.ones(len(q)).reshape(1,-1)) + constC2=np.dot(np.ones(len(p)).reshape(-1,1), + np.dot(q.reshape(1,-1),f2(C2).T)) + constC=constC1+constC2 + hC1 = h1(C1) + hC2 = h2(C2) + + return constC,hC1,hC2 + +def tensor_product(constC,hC1,hC2,T): + """ Return the tensor for Gromov-Wasserstein fast computation + + The tensor is computed as described in Proposition 1 Eq. (6) in [12]. Parameters ---------- - C1 : ndarray, shape (ns, ns) - Metric cost matrix in the source space - C2 : ndarray, shape (nt, nt) - Metric costfr matrix in the target space - T : ndarray, shape (ns, nt) - Coupling between source and target spaces + constC : ndarray, shape (ns, nt) + Constant C matrix in Eq. (6) + hC1 : ndarray, shape (ns, ns) + h1(C1) matrix in Eq. (6) + hC2 : ndarray, shape (nt, nt) + h2(C) matrix in Eq. (6) + Returns ------- + tens : ndarray, shape (ns, nt) \mathcal{L}(C1,C2) \otimes T tensor-matrix multiplication result @@ -126,28 +133,43 @@ def tensor_kl_loss(C1, C2, T): "Gromov-Wasserstein averaging of kernel and distance matrices." International Conference on Machine Learning (ICML). 2016. - """ + """ + A=-np.dot(hC1, T).dot(hC2.T) + tens = constC+A + #tens -= tens.min() + return tens - C1 = np.asarray(C1, dtype=np.float64) - C2 = np.asarray(C2, dtype=np.float64) - T = np.asarray(T, dtype=np.float64) +def gwloss(constC,hC1,hC2,T): - def f1(a): - return a * np.log(a + 1e-15) - a + tens=tensor_product(constC,hC1,hC2,T) + + return np.sum(tens*T) - def f2(b): - return b +def gwggrad(constC,hC1,hC2,T): + + return 2*tensor_product(constC,hC1,hC2,T) # [12] Prop. 2 misses a 2 factor - def h1(a): - return a +def gromov_wasserstein(C1,C2,p,q,loss_fun,alpha=1,log=False,**kwargs): - def h2(b): - return np.log(b + 1e-15) - tens = -np.dot(h1(C1), T).dot(h2(C2).T) - tens -= tens.min() + T = np.eye(len(p), len(q)) + + constC,hC1,hC2=init_matrix(C1,C2,T,p,q,loss_fun) + + G0=p[:,None]*q[None,:] + + def f(G): + return gwloss(constC,hC1,hC2,G) + def df(G): + return gwggrad(constC,hC1,hC2,G) + + if log: + res,log=cg(p,q,0,alpha,f,df,G0,log=True,**kwargs) + log['gw_dist']=gwloss(constC,hC1,hC2,res) + return res,log + else: + return cg(p,q,0,alpha,f,df,G0,**kwargs) - return tens def update_square_loss(p, lambdas, T, Cs): @@ -205,10 +227,10 @@ def update_kl_loss(p, lambdas, T, Cs): return np.exp(np.divide(tmpsum, ppt)) -def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, +def entropic_gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, tol=1e-9, verbose=False, log=False): """ - Returns the gromov-wasserstein coupling between the two measured similarity matrices + Returns the regularized gromov-wasserstein coupling between the two measured similarity matrices (C1,p) and (C2,q) @@ -259,25 +281,34 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, T : ndarray, shape (ns, nt) coupling between the two spaces that minimizes : \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T)) + + References + ---------- + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. + """ C1 = np.asarray(C1, dtype=np.float64) C2 = np.asarray(C2, dtype=np.float64) T = np.outer(p, q) # Initialization + + constC,hC1,hC2=init_matrix(C1,C2,T,p,q,loss_fun) cpt = 0 err = 1 + + if log: + log={'err':[]} while (err > tol and cpt < max_iter): Tprev = T - if loss_fun == 'square_loss': - tens = tensor_square_loss(C1, C2, T) - - elif loss_fun == 'kl_loss': - tens = tensor_kl_loss(C1, C2, T) + # compute the gradient + tens=gwggrad(constC,hC1,hC2,T) T = sinkhorn(p, q, tens, epsilon) @@ -298,15 +329,15 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, cpt += 1 if log: + log['gw_dist']=gwloss(constC,hC1,hC2,T) return T, log else: return T - -def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, +def entropic_gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, tol=1e-9, verbose=False, log=False): """ - Returns the gromov-wasserstein discrepancy between the two measured similarity matrices + Returns the entropic gromov-wasserstein discrepancy between the two measured similarity matrices (C1,p) and (C2,q) @@ -350,25 +381,25 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, ------- gw_dist : float Gromov-Wasserstein distance + + References + ---------- + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. + """ - if log: - gw, logv = gromov_wasserstein( - C1, C2, p, q, loss_fun, epsilon, max_iter, tol, verbose, log) - else: - gw = gromov_wasserstein(C1, C2, p, q, loss_fun, - epsilon, max_iter, tol, verbose, log) - - if loss_fun == 'square_loss': - gw_dist = np.sum(gw * tensor_square_loss(C1, C2, gw)) - elif loss_fun == 'kl_loss': - gw_dist = np.sum(gw * tensor_kl_loss(C1, C2, gw)) + gw, logv = entropic_gromov_wasserstein( + C1, C2, p, q, loss_fun, epsilon, max_iter, tol, verbose, log=True) + + log['T']=gw if log: - return gw_dist, logv + return logv['gw_dist'], logv else: - return gw_dist + return logv['gw_dist'] def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, @@ -421,6 +452,13 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, ------- C : ndarray, shape (N, N) Similarity matrix in the barycenter space (permutated arbitrarily) + + References + ---------- + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. + """ S = len(Cs) @@ -444,7 +482,7 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, while(err > tol and cpt < max_iter): Cprev = C - T = [gromov_wasserstein(Cs[s], C, ps[s], p, loss_fun, epsilon, + T = [entropic_gromov_wasserstein(Cs[s], C, ps[s], p, loss_fun, epsilon, max_iter, 1e-5, verbose, log) for s in range(S)] if loss_fun == 'square_loss': C = update_square_loss(p, lambdas, T, Cs) -- cgit v1.2.3 From 341a6b55cde2c0826e4c5a558b7507828d01ae08 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 15 Feb 2018 16:54:23 +0100 Subject: add documentation --- README.md | 2 ++ ot/gromov.py | 110 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++-- 2 files changed, 110 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 04994c0..c5e0575 100644 --- a/README.md +++ b/README.md @@ -200,3 +200,5 @@ You can also post bug reports and feature requests in Github issues. Make sure t [11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). [Wasserstein Discriminant Analysis](https://arxiv.org/pdf/1608.08063.pdf). arXiv preprint arXiv:1608.08063. [12] Gabriel Peyré, Marco Cuturi, and Justin Solomon, [Gromov-Wasserstein averaging of kernel and distance matrices](http://proceedings.mlr.press/v48/peyre16.html) International Conference on Machine Learning (ICML). 2016. + +[13] Mémoli, Facundo. [Gromov–Wasserstein distances and the metric approach to object matching](https://media.adelaide.edu.au/acvt/Publications/2011/2011-Gromov%E2%80%93Wasserstein%20Distances%20and%20the%20Metric%20Approach%20to%20Object%20Matching.pdf). Foundations of computational mathematics 11.4 (2011): 417-487. \ No newline at end of file diff --git a/ot/gromov.py b/ot/gromov.py index a23303a..c3ce415 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -140,17 +140,123 @@ def tensor_product(constC,hC1,hC2,T): return tens def gwloss(constC,hC1,hC2,T): + """ Return the Loss for Gromov-Wasserstein + + The loss is computed as described in Proposition 1 Eq. (6) in [12]. + + Parameters + ---------- + constC : ndarray, shape (ns, nt) + Constant C matrix in Eq. (6) + hC1 : ndarray, shape (ns, ns) + h1(C1) matrix in Eq. (6) + hC2 : ndarray, shape (nt, nt) + h2(C) matrix in Eq. (6) + T : ndarray, shape (ns, nt) + Current value of transport matrix T + + Returns + ------- + + loss : float + Gromov Wasserstein loss + + References + ---------- + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. + + """ tens=tensor_product(constC,hC1,hC2,T) return np.sum(tens*T) def gwggrad(constC,hC1,hC2,T): - + """ Return the gradient for Gromov-Wasserstein + + The gradient is computed as described in Proposition 2 in [12]. + + Parameters + ---------- + constC : ndarray, shape (ns, nt) + Constant C matrix in Eq. (6) + hC1 : ndarray, shape (ns, ns) + h1(C1) matrix in Eq. (6) + hC2 : ndarray, shape (nt, nt) + h2(C) matrix in Eq. (6) + T : ndarray, shape (ns, nt) + Current value of transport matrix T + + Returns + ------- + + grad : ndarray, shape (ns, nt) + Gromov Wasserstein gradient + + References + ---------- + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. + + """ return 2*tensor_product(constC,hC1,hC2,T) # [12] Prop. 2 misses a 2 factor -def gromov_wasserstein(C1,C2,p,q,loss_fun,alpha=1,log=False,**kwargs): +def gromov_wasserstein(C1,C2,p,q,loss_fun,log=False,**kwargs): + """ + Returns the gromov-wasserstein discrepancy between the two measured similarity matrices + + (C1,p) and (C2,q) + + The function solves the following optimization problem: + + .. math:: + \GW_Dist = \min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l} + + Where : + C1 : Metric cost matrix in the source space + C2 : Metric cost matrix in the target space + p : distribution in the source space + q : distribution in the target space + L : loss function to account for the misfit between the similarity matrices + H : entropy + Parameters + ---------- + C1 : ndarray, shape (ns, ns) + Metric cost matrix in the source space + C2 : ndarray, shape (nt, nt) + Metric costfr matrix in the target space + p : ndarray, shape (ns,) + distribution in the source space + q : ndarray, shape (nt,) + distribution in the target space + loss_fun : string + loss function used for the solver either 'square_loss' or 'kl_loss' + + max_iter : int, optional + Max number of iterations + tol : float, optional + Stop threshold on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + Returns + ------- + gw_dist : float + Gromov-Wasserstein distance + + References + ---------- + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. + + """ T = np.eye(len(p), len(q)) -- cgit v1.2.3 From ab2fc5486005732d60714b76eecf59d1104346f6 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 15 Feb 2018 17:09:28 +0100 Subject: awesome documentation --- ot/gromov.py | 188 ++++++++++++++++++++++++++++++++++++++++++----------------- 1 file changed, 135 insertions(+), 53 deletions(-) diff --git a/ot/gromov.py b/ot/gromov.py index c3ce415..8d08397 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -204,11 +204,66 @@ def gwggrad(constC,hC1,hC2,T): """ return 2*tensor_product(constC,hC1,hC2,T) # [12] Prop. 2 misses a 2 factor -def gromov_wasserstein(C1,C2,p,q,loss_fun,log=False,**kwargs): + + +def update_square_loss(p, lambdas, T, Cs): """ - Returns the gromov-wasserstein discrepancy between the two measured similarity matrices + Updates C according to the L2 Loss kernel with the S Ts couplings + calculated at each iteration - (C1,p) and (C2,q) + Parameters + ---------- + p : ndarray, shape (N,) + masses in the targeted barycenter + lambdas : list of float + list of the S spaces' weights + T : list of S np.ndarray(ns,N) + the S Ts couplings calculated at each iteration + Cs : list of S ndarray, shape(ns,ns) + Metric cost matrices + + Returns + ---------- + C : ndarray, shape (nt,nt) + updated C matrix + """ + tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) + for s in range(len(T))]) + ppt = np.outer(p, p) + + return np.divide(tmpsum, ppt) + + +def update_kl_loss(p, lambdas, T, Cs): + """ + Updates C according to the KL Loss kernel with the S Ts couplings calculated at each iteration + + + Parameters + ---------- + p : ndarray, shape (N,) + weights in the targeted barycenter + lambdas : list of the S spaces' weights + T : list of S np.ndarray(ns,N) + the S Ts couplings calculated at each iteration + Cs : list of S ndarray, shape(ns,ns) + Metric cost matrices + + Returns + ---------- + C : ndarray, shape (ns,ns) + updated C matrix + """ + tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) + for s in range(len(T))]) + ppt = np.outer(p, p) + + return np.exp(np.divide(tmpsum, ppt)) + + +def gromov_wasserstein(C1,C2,p,q,loss_fun,log=False,**kwargs): + """ + Returns the gromov-wasserstein transport between (C1,p) and (C2,q) The function solves the following optimization problem: @@ -247,14 +302,21 @@ def gromov_wasserstein(C1,C2,p,q,loss_fun,log=False,**kwargs): Returns ------- - gw_dist : float - Gromov-Wasserstein distance + T : ndarray, shape (ns, nt) + coupling between the two spaces that minimizes : + \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l} + log : dict + convergence information and loss References ---------- .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, - "Gromov-Wasserstein averaging of kernel and distance matrices." - International Conference on Machine Learning (ICML). 2016. + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. + + .. [13] Mémoli, Facundo. Gromov–Wasserstein distances and the + metric approach to object matching. Foundations of computational + mathematics 11.4 (2011): 417-487. """ @@ -270,73 +332,93 @@ def gromov_wasserstein(C1,C2,p,q,loss_fun,log=False,**kwargs): return gwggrad(constC,hC1,hC2,G) if log: - res,log=cg(p,q,0,alpha,f,df,G0,log=True,**kwargs) + res,log=cg(p,q,0,1,f,df,G0,log=True,**kwargs) log['gw_dist']=gwloss(constC,hC1,hC2,res) return res,log else: - return cg(p,q,0,alpha,f,df,G0,**kwargs) - - - -def update_square_loss(p, lambdas, T, Cs): - """ - Updates C according to the L2 Loss kernel with the S Ts couplings - calculated at each iteration - - Parameters - ---------- - p : ndarray, shape (N,) - masses in the targeted barycenter - lambdas : list of float - list of the S spaces' weights - T : list of S np.ndarray(ns,N) - the S Ts couplings calculated at each iteration - Cs : list of S ndarray, shape(ns,ns) - Metric cost matrices + return cg(p,q,0,1,f,df,G0,**kwargs) - Returns - ---------- - C : ndarray, shape (nt,nt) - updated C matrix +def gromov_wasserstein2(C1,C2,p,q,loss_fun,log=False,**kwargs): """ - tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) - for s in range(len(T))]) - ppt = np.outer(p, p) - - return np.divide(tmpsum, ppt) + Returns the gromov-wasserstein discrepancy between (C1,p) and (C2,q) + The function solves the following optimization problem: -def update_kl_loss(p, lambdas, T, Cs): - """ - Updates C according to the KL Loss kernel with the S Ts couplings calculated at each iteration + .. math:: + \GW_Dist = \min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l} + Where : + C1 : Metric cost matrix in the source space + C2 : Metric cost matrix in the target space + p : distribution in the source space + q : distribution in the target space + L : loss function to account for the misfit between the similarity matrices + H : entropy Parameters ---------- - p : ndarray, shape (N,) - weights in the targeted barycenter - lambdas : list of the S spaces' weights - T : list of S np.ndarray(ns,N) - the S Ts couplings calculated at each iteration - Cs : list of S ndarray, shape(ns,ns) - Metric cost matrices + C1 : ndarray, shape (ns, ns) + Metric cost matrix in the source space + C2 : ndarray, shape (nt, nt) + Metric costfr matrix in the target space + p : ndarray, shape (ns,) + distribution in the source space + q : ndarray, shape (nt,) + distribution in the target space + loss_fun : string + loss function used for the solver either 'square_loss' or 'kl_loss' + + max_iter : int, optional + Max number of iterations + tol : float, optional + Stop threshold on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True Returns + ------- + gw_dist : float + Gromov-Wasserstein distance + log : dict + convergence information and Coupling marix + + References ---------- - C : ndarray, shape (ns,ns) - updated C matrix + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. + + .. [13] Mémoli, Facundo. Gromov–Wasserstein distances and the + metric approach to object matching. Foundations of computational + mathematics 11.4 (2011): 417-487. + """ - tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) - for s in range(len(T))]) - ppt = np.outer(p, p) - return np.exp(np.divide(tmpsum, ppt)) + T = np.eye(len(p), len(q)) + + constC,hC1,hC2=init_matrix(C1,C2,T,p,q,loss_fun) + + G0=p[:,None]*q[None,:] + + def f(G): + return gwloss(constC,hC1,hC2,G) + def df(G): + return gwggrad(constC,hC1,hC2,G) + res,log=cg(p,q,0,1,f,df,G0,log=True,**kwargs) + log['gw_dist']=gwloss(constC,hC1,hC2,res) + log['T']=res + if log: + return log['gw_dist'],log + else: + return log['gw_dist'] def entropic_gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, max_iter=1000, tol=1e-9, verbose=False, log=False): """ - Returns the regularized gromov-wasserstein coupling between the two measured similarity matrices + Returns the gromov-wasserstein transport between (C1,p) and (C2,q) (C1,p) and (C2,q) -- cgit v1.2.3 From 4a585de94109102c89bcd7ad43e35772e1027cd2 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 16 Feb 2018 11:58:59 +0100 Subject: update examples --- examples/plot_gromov.py | 29 +++++----- examples/plot_gromov_barycenter.py | 8 +-- ot/__init__.py | 3 +- ot/gromov.py | 109 ++++++++++++++++++++++++++++++++++++- 4 files changed, 129 insertions(+), 20 deletions(-) diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py index d42c21a..5f2d826 100644 --- a/examples/plot_gromov.py +++ b/examples/plot_gromov.py @@ -81,23 +81,26 @@ pl.show() #%% p = ot.unif(n_samples) q = ot.unif(n_samples) -ot.tic() -gw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4,verbose=True) -ot.toc() -ot.tic() -gw2,log2= ot.gromov.gromov_wasserstein0(C1, C2, p, q, 'square_loss', epsilon=5e-4,log=True,verbose=True) -ot.toc() -gw_dist = ot.gromov_wasserstein2(C1, C2, p, q, 'square_loss', epsilon=5e-4) +gw0,log0 = ot.gromov.gromov_wasserstein(C1, C2, p, q, 'square_loss', verbose=True,log=True) -ot.tic() -gw0,log0=ot.gromov.gw_lp(C1, C2, p, q, 'square_loss',log=True,verbose=True) -ot.toc() +gw,log= ot.gromov.entropic_gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4,log=True,verbose=True) -print('Gromov-Wasserstein distances between the distribution: ' + str(gw_dist)) +print('Gromov-Wasserstein distances: ' + str(log0['gw_dist'])) +print('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist'])) -pl.figure() -pl.imshow(gw2, cmap='jet') + +pl.figure(1,(10,5)) + +pl.subplot(1,2,1) +pl.imshow(gw0, cmap='jet') pl.colorbar() +pl.title('Gromov Wasserstein') + +pl.subplot(1,2,2) +pl.imshow(gw0, cmap='jet') +pl.colorbar() +pl.title('Entropic Gromov Wasserstein') + pl.show() diff --git a/examples/plot_gromov_barycenter.py b/examples/plot_gromov_barycenter.py index 180b0cf..fde822b 100755 --- a/examples/plot_gromov_barycenter.py +++ b/examples/plot_gromov_barycenter.py @@ -132,28 +132,28 @@ Ct01 = [0 for i in range(2)] for i in range(2): Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]], [ps[0], ps[1] - ], p, lambdast[i], 'square_loss', 5e-4, + ], p, lambdast[i], 'square_loss', #5e-4, max_iter=100, tol=1e-3) Ct02 = [0 for i in range(2)] for i in range(2): Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]], [ps[0], ps[2] - ], p, lambdast[i], 'square_loss', 5e-4, + ], p, lambdast[i], 'square_loss',# 5e-4, max_iter=100, tol=1e-3) Ct13 = [0 for i in range(2)] for i in range(2): Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]], [ps[1], ps[3] - ], p, lambdast[i], 'square_loss', 5e-4, + ], p, lambdast[i], 'square_loss',# 5e-4, max_iter=100, tol=1e-3) Ct23 = [0 for i in range(2)] for i in range(2): Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]], [ps[2], ps[3] - ], p, lambdast[i], 'square_loss', 5e-4, + ], p, lambdast[i], 'square_loss', #5e-4, max_iter=100, tol=1e-3) diff --git a/ot/__init__.py b/ot/__init__.py index a5df43d..cee7379 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -33,5 +33,4 @@ __version__ = "0.4.0" __all__ = ["emd", "emd2", "sinkhorn", "sinkhorn2", "utils", 'datasets', 'bregman', 'lp', 'plot', 'tic', 'toc', 'toq', - 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim', - 'gromov_wasserstein','gromov_wasserstein2'] + 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim'] diff --git a/ot/gromov.py b/ot/gromov.py index 8d08397..e4dd112 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -590,7 +590,7 @@ def entropic_gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, return logv['gw_dist'] -def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, +def entropic_gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, max_iter=1000, tol=1e-9, verbose=False, log=False, init_C=None): """ Returns the gromov-wasserstein barycenters of S measured similarity matrices @@ -696,3 +696,110 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, cpt += 1 return C + + +def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, + max_iter=1000, tol=1e-9, verbose=False, log=False, init_C=None): + """ + Returns the gromov-wasserstein barycenters of S measured similarity matrices + + (Cs)_{s=1}^{s=S} + + The function solves the following optimization problem with block + coordinate descent: + + .. math:: + C = argmin_C\in R^NxN \sum_s \lambda_s GW(C,Cs,p,ps) + + + Where : + + Cs : metric cost matrix + ps : distribution + + Parameters + ---------- + N : Integer + Size of the targeted barycenter + Cs : list of S np.ndarray(ns,ns) + Metric cost matrices + ps : list of S np.ndarray(ns,) + sample weights in the S spaces + p : ndarray, shape(N,) + weights in the targeted barycenter + lambdas : list of float + list of the S spaces' weights + loss_fun : tensor-matrix multiplication function based on specific loss function + update : function(p,lambdas,T,Cs) that updates C according to a specific Kernel + with the S Ts couplings calculated at each iteration + max_iter : int, optional + Max number of iterations + tol : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + init_C : bool, ndarray, shape(N,N) + random initial value for the C matrix provided by user + + Returns + ------- + C : ndarray, shape (N, N) + Similarity matrix in the barycenter space (permutated arbitrarily) + + References + ---------- + .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. + + """ + + S = len(Cs) + + Cs = [np.asarray(Cs[s], dtype=np.float64) for s in range(S)] + lambdas = np.asarray(lambdas, dtype=np.float64) + + # Initialization of C : random SPD matrix (if not provided by user) + if init_C is None: + xalea = np.random.randn(N, 2) + C = dist(xalea, xalea) + C /= C.max() + else: + C = init_C + + cpt = 0 + err = 1 + + error = [] + + while(err > tol and cpt < max_iter): + Cprev = C + + T = [gromov_wasserstein(Cs[s], C, ps[s], p, loss_fun, + numItermax=max_iter, stopThr=1e-5, verbose=verbose, log=log) for s in range(S)] + if loss_fun == 'square_loss': + C = update_square_loss(p, lambdas, T, Cs) + + elif loss_fun == 'kl_loss': + C = update_kl_loss(p, lambdas, T, Cs) + + if cpt % 10 == 0: + # we can speed up the process by checking for the error only all + # the 10th iterations + err = np.linalg.norm(C - Cprev) + error.append(err) + + if log: + log['err'].append(err) + + if verbose: + if cpt % 200 == 0: + print('{:5s}|{:12s}'.format( + 'It.', 'Err') + '\n' + '-' * 19) + print('{:5d}|{:8e}|'.format(cpt, err)) + + cpt += 1 + + return C \ No newline at end of file -- cgit v1.2.3 From f089a3cbc27c30ba9416ea1659c2fdbac1857146 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 16 Feb 2018 13:53:34 +0100 Subject: better pep8 but not solved --- examples/plot_barycenter_1D.py | 8 +- examples/plot_gromov.py | 25 +++-- ot/__init__.py | 4 +- ot/da.py | 6 ++ ot/gpu/__init__.py | 2 +- ot/gpu/da.py | 3 + ot/gromov.py | 235 +++++++++++++++++++++-------------------- ot/utils.py | 2 + 8 files changed, 152 insertions(+), 133 deletions(-) diff --git a/examples/plot_barycenter_1D.py b/examples/plot_barycenter_1D.py index 620936b..ecf640c 100644 --- a/examples/plot_barycenter_1D.py +++ b/examples/plot_barycenter_1D.py @@ -25,7 +25,7 @@ import ot from mpl_toolkits.mplot3d import Axes3D # noqa from matplotlib.collections import PolyCollection -############################################################################## +# # Generate data # ------------- @@ -48,7 +48,7 @@ n_distributions = A.shape[1] M = ot.utils.dist0(n) M /= M.max() -############################################################################## +# # Plot data # --------- @@ -60,7 +60,7 @@ for i in range(n_distributions): pl.title('Distributions') pl.tight_layout() -############################################################################## +# # Barycenter computation # ---------------------- @@ -90,7 +90,7 @@ pl.legend() pl.title('Barycenters') pl.tight_layout() -############################################################################## +# # Barycentric interpolation # ------------------------- diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py index 5f2d826..9188da9 100644 --- a/examples/plot_gromov.py +++ b/examples/plot_gromov.py @@ -20,7 +20,7 @@ from mpl_toolkits.mplot3d import Axes3D # noqa import ot -############################################################################## +# # Sample two Gaussian distributions (2D and 3D) # --------------------------------------------- # @@ -43,7 +43,7 @@ P = sp.linalg.sqrtm(cov_t) xt = np.random.randn(n_samples, 3).dot(P) + mu_t -############################################################################## +# # Plotting the distributions # -------------------------- @@ -56,7 +56,7 @@ ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r') pl.show() -############################################################################## +# # Compute distance kernels, normalize them and then display # --------------------------------------------------------- @@ -74,33 +74,32 @@ pl.subplot(122) pl.imshow(C2) pl.show() -############################################################################## +# # Compute Gromov-Wasserstein plans and distance # --------------------------------------------- -#%% p = ot.unif(n_samples) q = ot.unif(n_samples) -gw0,log0 = ot.gromov.gromov_wasserstein(C1, C2, p, q, 'square_loss', verbose=True,log=True) +gw0, log0 = ot.gromov.gromov_wasserstein( + C1, C2, p, q, 'square_loss', verbose=True, log=True) -gw,log= ot.gromov.entropic_gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4,log=True,verbose=True) +gw, log = ot.gromov.entropic_gromov_wasserstein( + C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True) print('Gromov-Wasserstein distances: ' + str(log0['gw_dist'])) print('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist'])) -pl.figure(1,(10,5)) +pl.figure(1, (10, 5)) -pl.subplot(1,2,1) +pl.subplot(1, 2, 1) pl.imshow(gw0, cmap='jet') -pl.colorbar() pl.title('Gromov Wasserstein') -pl.subplot(1,2,2) -pl.imshow(gw0, cmap='jet') -pl.colorbar() +pl.subplot(1, 2, 2) +pl.imshow(gw, cmap='jet') pl.title('Entropic Gromov Wasserstein') pl.show() diff --git a/ot/__init__.py b/ot/__init__.py index cee7379..1500e59 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -16,7 +16,6 @@ from . import bregman from . import optim from . import utils from . import datasets -from . import plot from . import da from . import gromov @@ -24,7 +23,6 @@ from . import gromov from .lp import emd, emd2 from .bregman import sinkhorn, sinkhorn2, barycenter from .da import sinkhorn_lpl1_mm -from .gromov import gromov_wasserstein, gromov_wasserstein2 # utils functions from .utils import dist, unif, tic, toc, toq @@ -32,5 +30,5 @@ from .utils import dist, unif, tic, toc, toq __version__ = "0.4.0" __all__ = ["emd", "emd2", "sinkhorn", "sinkhorn2", "utils", 'datasets', - 'bregman', 'lp', 'plot', 'tic', 'toc', 'toq', + 'bregman', 'lp', 'tic', 'toc', 'toq', 'gromov', 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim'] diff --git a/ot/da.py b/ot/da.py index 1d3d0ba..ee73ec8 100644 --- a/ot/da.py +++ b/ot/da.py @@ -933,6 +933,7 @@ def distribution_estimation_uniform(X): class BaseTransport(BaseEstimator): + """Base class for OTDA objects Notes @@ -1180,6 +1181,7 @@ class BaseTransport(BaseEstimator): class SinkhornTransport(BaseTransport): + """Domain Adapatation OT method based on Sinkhorn Algorithm Parameters @@ -1289,6 +1291,7 @@ class SinkhornTransport(BaseTransport): class EMDTransport(BaseTransport): + """Domain Adapatation OT method based on Earth Mover's Distance Parameters @@ -1377,6 +1380,7 @@ class EMDTransport(BaseTransport): class SinkhornLpl1Transport(BaseTransport): + """Domain Adapatation OT method based on sinkhorn algorithm + LpL1 class regularization. @@ -1486,6 +1490,7 @@ class SinkhornLpl1Transport(BaseTransport): class SinkhornL1l2Transport(BaseTransport): + """Domain Adapatation OT method based on sinkhorn algorithm + l1l2 class regularization. @@ -1608,6 +1613,7 @@ class SinkhornL1l2Transport(BaseTransport): class MappingTransport(BaseEstimator): + """MappingTransport: DA methods that aims at jointly estimating a optimal transport coupling and the associated mapping diff --git a/ot/gpu/__init__.py b/ot/gpu/__init__.py index c8f9433..a2fdd3d 100644 --- a/ot/gpu/__init__.py +++ b/ot/gpu/__init__.py @@ -5,7 +5,7 @@ from . import da from .bregman import sinkhorn # Author: Remi Flamary -# Leo Gautheron +# Leo Gautheron # # License: MIT License diff --git a/ot/gpu/da.py b/ot/gpu/da.py index 05c580f..71a485a 100644 --- a/ot/gpu/da.py +++ b/ot/gpu/da.py @@ -188,6 +188,7 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10, class OTDA_GPU(OTDA): + def normalizeM(self, norm): if norm == "median": self.M_GPU.divide(float(np.median(self.M_GPU.asarray()))) @@ -204,6 +205,7 @@ class OTDA_GPU(OTDA): class OTDA_sinkhorn(OTDA_GPU): + def fit(self, xs, xt, reg=1, ws=None, wt=None, norm=None, **kwargs): cudamat.init() xs = np.asarray(xs, dtype=np.float64) @@ -228,6 +230,7 @@ class OTDA_sinkhorn(OTDA_GPU): class OTDA_lpl1(OTDA_GPU): + def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None, **kwargs): cudamat.init() diff --git a/ot/gromov.py b/ot/gromov.py index e4dd112..b1e9ee0 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -20,12 +20,12 @@ from .utils import dist from .optim import cg -def init_matrix(C1,C2,T,p,q,loss_fun='square_loss'): +def init_matrix(C1, C2, T, p, q, loss_fun='square_loss'): """ Return loss matrices and tensors for Gromov-Wasserstein fast computation Returns the value of \mathcal{L}(C1,C2) \otimes T with the selected loss function as the loss function of Gromow-Wasserstein discrepancy. - + The matrices are computed as described in Proposition 1 in [12] Where : @@ -56,18 +56,18 @@ def init_matrix(C1,C2,T,p,q,loss_fun='square_loss'): T : ndarray, shape (ns, nt) Coupling between source and target spaces p : ndarray, shape (ns,) - + Returns ------- - + constC : ndarray, shape (ns, nt) Constant C matrix in Eq. (6) hC1 : ndarray, shape (ns, ns) - h1(C1) matrix in Eq. (6) + h1(C1) matrix in Eq. (6) hC2 : ndarray, shape (nt, nt) h2(C) matrix in Eq. (6) - + References ---------- .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, @@ -76,39 +76,45 @@ def init_matrix(C1,C2,T,p,q,loss_fun='square_loss'): """ - if loss_fun == 'square_loss': def f1(a): - return (a**2)/2 + return (a**2) / 2 + def f2(b): - return (b**2)/2 + return (b**2) / 2 + def h1(a): - return a + return a + def h2(b): return b elif loss_fun == 'kl_loss': def f1(a): - return a * np.log(a + 1e-15) - a + return a * np.log(a + 1e-15) - a + def f2(b): - return b + return b + def h1(a): - return a + return a + def h2(b): return np.log(b + 1e-15) - constC1=np.dot(np.dot(f1(C1),p.reshape(-1,1)), - np.ones(len(q)).reshape(1,-1)) - constC2=np.dot(np.ones(len(p)).reshape(-1,1), - np.dot(q.reshape(1,-1),f2(C2).T)) - constC=constC1+constC2 + constC1 = np.dot(np.dot(f1(C1), p.reshape(-1, 1)), + np.ones(len(q)).reshape(1, -1)) + constC2 = np.dot(np.ones(len(p)).reshape(-1, 1), + np.dot(q.reshape(1, -1), f2(C2).T)) + constC = constC1 + constC2 hC1 = h1(C1) hC2 = h2(C2) - return constC,hC1,hC2 + return constC, hC1, hC2 + + +def tensor_product(constC, hC1, hC2, T): + """ Return the tensor for Gromov-Wasserstein fast computation -def tensor_product(constC,hC1,hC2,T): - """ Return the tensor for Gromov-Wasserstein fast computation - The tensor is computed as described in Proposition 1 Eq. (6) in [12]. Parameters @@ -116,14 +122,14 @@ def tensor_product(constC,hC1,hC2,T): constC : ndarray, shape (ns, nt) Constant C matrix in Eq. (6) hC1 : ndarray, shape (ns, ns) - h1(C1) matrix in Eq. (6) + h1(C1) matrix in Eq. (6) hC2 : ndarray, shape (nt, nt) h2(C) matrix in Eq. (6) - + Returns ------- - + tens : ndarray, shape (ns, nt) \mathcal{L}(C1,C2) \otimes T tensor-matrix multiplication result @@ -133,15 +139,16 @@ def tensor_product(constC,hC1,hC2,T): "Gromov-Wasserstein averaging of kernel and distance matrices." International Conference on Machine Learning (ICML). 2016. - """ - A=-np.dot(hC1, T).dot(hC2.T) - tens = constC+A - #tens -= tens.min() + """ + A = -np.dot(hC1, T).dot(hC2.T) + tens = constC + A + # tens -= tens.min() return tens -def gwloss(constC,hC1,hC2,T): + +def gwloss(constC, hC1, hC2, T): """ Return the Loss for Gromov-Wasserstein - + The loss is computed as described in Proposition 1 Eq. (6) in [12]. Parameters @@ -149,15 +156,15 @@ def gwloss(constC,hC1,hC2,T): constC : ndarray, shape (ns, nt) Constant C matrix in Eq. (6) hC1 : ndarray, shape (ns, ns) - h1(C1) matrix in Eq. (6) + h1(C1) matrix in Eq. (6) hC2 : ndarray, shape (nt, nt) h2(C) matrix in Eq. (6) T : ndarray, shape (ns, nt) - Current value of transport matrix T + Current value of transport matrix T Returns ------- - + loss : float Gromov Wasserstein loss @@ -166,16 +173,17 @@ def gwloss(constC,hC1,hC2,T): .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices." International Conference on Machine Learning (ICML). 2016. - + """ - tens=tensor_product(constC,hC1,hC2,T) - - return np.sum(tens*T) + tens = tensor_product(constC, hC1, hC2, T) + + return np.sum(tens * T) + + +def gwggrad(constC, hC1, hC2, T): + """ Return the gradient for Gromov-Wasserstein -def gwggrad(constC,hC1,hC2,T): - """ Return the gradient for Gromov-Wasserstein - The gradient is computed as described in Proposition 2 in [12]. Parameters @@ -183,15 +191,15 @@ def gwggrad(constC,hC1,hC2,T): constC : ndarray, shape (ns, nt) Constant C matrix in Eq. (6) hC1 : ndarray, shape (ns, ns) - h1(C1) matrix in Eq. (6) + h1(C1) matrix in Eq. (6) hC2 : ndarray, shape (nt, nt) h2(C) matrix in Eq. (6) T : ndarray, shape (ns, nt) - Current value of transport matrix T + Current value of transport matrix T Returns ------- - + grad : ndarray, shape (ns, nt) Gromov Wasserstein gradient @@ -200,10 +208,10 @@ def gwggrad(constC,hC1,hC2,T): .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices." International Conference on Machine Learning (ICML). 2016. - - """ - return 2*tensor_product(constC,hC1,hC2,T) # [12] Prop. 2 misses a 2 factor + """ + return 2 * tensor_product(constC, hC1, hC2, + T) # [12] Prop. 2 misses a 2 factor def update_square_loss(p, lambdas, T, Cs): @@ -261,7 +269,7 @@ def update_kl_loss(p, lambdas, T, Cs): return np.exp(np.divide(tmpsum, ppt)) -def gromov_wasserstein(C1,C2,p,q,loss_fun,log=False,**kwargs): +def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, **kwargs): """ Returns the gromov-wasserstein transport between (C1,p) and (C2,q) @@ -307,38 +315,40 @@ def gromov_wasserstein(C1,C2,p,q,loss_fun,log=False,**kwargs): \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l} log : dict convergence information and loss - + References ---------- .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices." - International Conference on Machine Learning (ICML). 2016. - - .. [13] Mémoli, Facundo. Gromov–Wasserstein distances and the - metric approach to object matching. Foundations of computational + International Conference on Machine Learning (ICML). 2016. + + .. [13] Mémoli, Facundo. Gromov–Wasserstein distances and the + metric approach to object matching. Foundations of computational mathematics 11.4 (2011): 417-487. - + """ T = np.eye(len(p), len(q)) - constC,hC1,hC2=init_matrix(C1,C2,T,p,q,loss_fun) - - G0=p[:,None]*q[None,:] - + constC, hC1, hC2 = init_matrix(C1, C2, T, p, q, loss_fun) + + G0 = p[:, None] * q[None, :] + def f(G): - return gwloss(constC,hC1,hC2,G) + return gwloss(constC, hC1, hC2, G) + def df(G): - return gwggrad(constC,hC1,hC2,G) - + return gwggrad(constC, hC1, hC2, G) + if log: - res,log=cg(p,q,0,1,f,df,G0,log=True,**kwargs) - log['gw_dist']=gwloss(constC,hC1,hC2,res) - return res,log + res, log = cg(p, q, 0, 1, f, df, G0, log=True, **kwargs) + log['gw_dist'] = gwloss(constC, hC1, hC2, res) + return res, log else: - return cg(p,q,0,1,f,df,G0,**kwargs) + return cg(p, q, 0, 1, f, df, G0, **kwargs) + -def gromov_wasserstein2(C1,C2,p,q,loss_fun,log=False,**kwargs): +def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False, **kwargs): """ Returns the gromov-wasserstein discrepancy between (C1,p) and (C2,q) @@ -383,40 +393,41 @@ def gromov_wasserstein2(C1,C2,p,q,loss_fun,log=False,**kwargs): Gromov-Wasserstein distance log : dict convergence information and Coupling marix - + References ---------- .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices." - International Conference on Machine Learning (ICML). 2016. - - .. [13] Mémoli, Facundo. Gromov–Wasserstein distances and the - metric approach to object matching. Foundations of computational + International Conference on Machine Learning (ICML). 2016. + + .. [13] Mémoli, Facundo. Gromov–Wasserstein distances and the + metric approach to object matching. Foundations of computational mathematics 11.4 (2011): 417-487. - + """ T = np.eye(len(p), len(q)) - constC,hC1,hC2=init_matrix(C1,C2,T,p,q,loss_fun) - - G0=p[:,None]*q[None,:] - + constC, hC1, hC2 = init_matrix(C1, C2, T, p, q, loss_fun) + + G0 = p[:, None] * q[None, :] + def f(G): - return gwloss(constC,hC1,hC2,G) + return gwloss(constC, hC1, hC2, G) + def df(G): - return gwggrad(constC,hC1,hC2,G) - res,log=cg(p,q,0,1,f,df,G0,log=True,**kwargs) - log['gw_dist']=gwloss(constC,hC1,hC2,res) - log['T']=res + return gwggrad(constC, hC1, hC2, G) + res, log = cg(p, q, 0, 1, f, df, G0, log=True, **kwargs) + log['gw_dist'] = gwloss(constC, hC1, hC2, res) + log['T'] = res if log: - return log['gw_dist'],log + return log['gw_dist'], log else: return log['gw_dist'] def entropic_gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, - max_iter=1000, tol=1e-9, verbose=False, log=False): + max_iter=1000, tol=1e-9, verbose=False, log=False): """ Returns the gromov-wasserstein transport between (C1,p) and (C2,q) @@ -469,34 +480,34 @@ def entropic_gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, T : ndarray, shape (ns, nt) coupling between the two spaces that minimizes : \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T)) - + References ---------- .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices." - International Conference on Machine Learning (ICML). 2016. - + International Conference on Machine Learning (ICML). 2016. + """ C1 = np.asarray(C1, dtype=np.float64) C2 = np.asarray(C2, dtype=np.float64) T = np.outer(p, q) # Initialization - - constC,hC1,hC2=init_matrix(C1,C2,T,p,q,loss_fun) + + constC, hC1, hC2 = init_matrix(C1, C2, T, p, q, loss_fun) cpt = 0 err = 1 - + if log: - log={'err':[]} + log = {'err': []} while (err > tol and cpt < max_iter): Tprev = T # compute the gradient - tens=gwggrad(constC,hC1,hC2,T) + tens = gwggrad(constC, hC1, hC2, T) T = sinkhorn(p, q, tens, epsilon) @@ -517,13 +528,14 @@ def entropic_gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, cpt += 1 if log: - log['gw_dist']=gwloss(constC,hC1,hC2,T) + log['gw_dist'] = gwloss(constC, hC1, hC2, T) return T, log else: return T + def entropic_gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, - max_iter=1000, tol=1e-9, verbose=False, log=False): + max_iter=1000, tol=1e-9, verbose=False, log=False): """ Returns the entropic gromov-wasserstein discrepancy between the two measured similarity matrices @@ -569,20 +581,19 @@ def entropic_gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, ------- gw_dist : float Gromov-Wasserstein distance - + References ---------- .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices." - International Conference on Machine Learning (ICML). 2016. - - """ + International Conference on Machine Learning (ICML). 2016. + """ gw, logv = entropic_gromov_wasserstein( - C1, C2, p, q, loss_fun, epsilon, max_iter, tol, verbose, log=True) - - log['T']=gw + C1, C2, p, q, loss_fun, epsilon, max_iter, tol, verbose, log=True) + + log['T'] = gw if log: return logv['gw_dist'], logv @@ -591,7 +602,7 @@ def entropic_gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, def entropic_gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, - max_iter=1000, tol=1e-9, verbose=False, log=False, init_C=None): + max_iter=1000, tol=1e-9, verbose=False, log=False, init_C=None): """ Returns the gromov-wasserstein barycenters of S measured similarity matrices @@ -640,13 +651,13 @@ def entropic_gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, ------- C : ndarray, shape (N, N) Similarity matrix in the barycenter space (permutated arbitrarily) - + References ---------- .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices." - International Conference on Machine Learning (ICML). 2016. - + International Conference on Machine Learning (ICML). 2016. + """ S = len(Cs) @@ -671,7 +682,7 @@ def entropic_gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, Cprev = C T = [entropic_gromov_wasserstein(Cs[s], C, ps[s], p, loss_fun, epsilon, - max_iter, 1e-5, verbose, log) for s in range(S)] + max_iter, 1e-5, verbose, log) for s in range(S)] if loss_fun == 'square_loss': C = update_square_loss(p, lambdas, T, Cs) @@ -698,14 +709,14 @@ def entropic_gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, return C -def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, +def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, max_iter=1000, tol=1e-9, verbose=False, log=False, init_C=None): """ Returns the gromov-wasserstein barycenters of S measured similarity matrices (Cs)_{s=1}^{s=S} - The function solves the following optimization problem with block + The function solves the following optimization problem with block coordinate descent: .. math:: @@ -747,13 +758,13 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, ------- C : ndarray, shape (N, N) Similarity matrix in the barycenter space (permutated arbitrarily) - + References ---------- .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices." - International Conference on Machine Learning (ICML). 2016. - + International Conference on Machine Learning (ICML). 2016. + """ S = len(Cs) @@ -777,7 +788,7 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, while(err > tol and cpt < max_iter): Cprev = C - T = [gromov_wasserstein(Cs[s], C, ps[s], p, loss_fun, + T = [gromov_wasserstein(Cs[s], C, ps[s], p, loss_fun, numItermax=max_iter, stopThr=1e-5, verbose=verbose, log=log) for s in range(S)] if loss_fun == 'square_loss': C = update_square_loss(p, lambdas, T, Cs) @@ -802,4 +813,4 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, cpt += 1 - return C \ No newline at end of file + return C diff --git a/ot/utils.py b/ot/utils.py index 31a002b..9eab3fc 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -223,6 +223,7 @@ def check_params(**kwargs): class deprecated(object): + """Decorator to mark a function or class as deprecated. deprecated class from scikit-learn package @@ -320,6 +321,7 @@ def _is_deprecated(func): class BaseEstimator(object): + """Base class for most objects in POT adapted from sklearn BaseEstimator class -- cgit v1.2.3 From d41ffdb24de5cb234237482b3f332b06514b10f0 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 16 Feb 2018 14:04:28 +0100 Subject: no more pep8 problem, need cleanup plots in test and examples --- examples/plot_gromov_barycenter.py | 8 ++++---- ot/da.py | 20 ++++++++++---------- 2 files changed, 14 insertions(+), 14 deletions(-) diff --git a/examples/plot_gromov_barycenter.py b/examples/plot_gromov_barycenter.py index fde822b..58fc51a 100755 --- a/examples/plot_gromov_barycenter.py +++ b/examples/plot_gromov_barycenter.py @@ -132,28 +132,28 @@ Ct01 = [0 for i in range(2)] for i in range(2): Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]], [ps[0], ps[1] - ], p, lambdast[i], 'square_loss', #5e-4, + ], p, lambdast[i], 'square_loss', # 5e-4, max_iter=100, tol=1e-3) Ct02 = [0 for i in range(2)] for i in range(2): Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]], [ps[0], ps[2] - ], p, lambdast[i], 'square_loss',# 5e-4, + ], p, lambdast[i], 'square_loss', # 5e-4, max_iter=100, tol=1e-3) Ct13 = [0 for i in range(2)] for i in range(2): Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]], [ps[1], ps[3] - ], p, lambdast[i], 'square_loss',# 5e-4, + ], p, lambdast[i], 'square_loss', # 5e-4, max_iter=100, tol=1e-3) Ct23 = [0 for i in range(2)] for i in range(2): Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]], [ps[2], ps[3] - ], p, lambdast[i], 'square_loss', #5e-4, + ], p, lambdast[i], 'square_loss', # 5e-4, max_iter=100, tol=1e-3) diff --git a/ot/da.py b/ot/da.py index ee73ec8..c688654 100644 --- a/ot/da.py +++ b/ot/da.py @@ -330,17 +330,17 @@ def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False, if bias: xs1 = np.hstack((xs, np.ones((ns, 1)))) xstxs = xs1.T.dot(xs1) - I = np.eye(d + 1) - I[-1] = 0 - I0 = I[:, :-1] + Id = np.eye(d + 1) + Id[-1] = 0 + I0 = Id[:, :-1] def sel(x): return x[:-1, :] else: xs1 = xs xstxs = xs1.T.dot(xs1) - I = np.eye(d) - I0 = I + Id = np.eye(d) + I0 = Id def sel(x): return x @@ -361,7 +361,7 @@ def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False, def solve_L(G): """ solve L problem with fixed G (least square)""" xst = ns * G.dot(xt) - return np.linalg.solve(xstxs + eta * I, xs1.T.dot(xst) + eta * I0) + return np.linalg.solve(xstxs + eta * Id, xs1.T.dot(xst) + eta * I0) def solve_G(L, G0): """Update G with CG algorithm""" @@ -520,8 +520,8 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', K = kernel(xs, xs, method=kerneltype, sigma=sigma) if bias: K1 = np.hstack((K, np.ones((ns, 1)))) - I = np.eye(ns + 1) - I[-1] = 0 + Id = np.eye(ns + 1) + Id[-1] = 0 Kp = np.eye(ns + 1) Kp[:ns, :ns] = K @@ -535,14 +535,14 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', else: K1 = K - I = np.eye(ns) + Id = np.eye(ns) # ls regul # K0 = K1.T.dot(K1)+eta*I # Kreg=I # proper kernel ridge - K0 = K + eta * I + K0 = K + eta * Id Kreg = K if log: -- cgit v1.2.3 From b4665fe3c12780af4228cb1fb7dc8e1159c81f63 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 16 Feb 2018 14:08:55 +0100 Subject: should pass tests now --- ot/gromov.py | 2 ++ test/test_gromov.py | 31 ++++++++++++++++++++++++++++++- test/test_plot.py | 2 ++ 3 files changed, 34 insertions(+), 1 deletion(-) diff --git a/ot/gromov.py b/ot/gromov.py index b1e9ee0..2a23873 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -307,6 +307,8 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, **kwargs): Print information along iterations log : bool, optional record log if True + **kwargs : dict + parameters can be directly pased to the ot.optim.cg solver Returns ------- diff --git a/test/test_gromov.py b/test/test_gromov.py index e808292..625e62a 100644 --- a/test/test_gromov.py +++ b/test/test_gromov.py @@ -28,7 +28,36 @@ def test_gromov(): C1 /= C1.max() C2 /= C2.max() - G = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4) + G = ot.gromov.gromov_wasserstein(C1, C2, p, q, 'square_loss') + + # check constratints + np.testing.assert_allclose( + p, G.sum(1), atol=1e-04) # cf convergence gromov + np.testing.assert_allclose( + q, G.sum(0), atol=1e-04) # cf convergence gromov + + +def test_entropic_gromov(): + n_samples = 50 # nb samples + + mu_s = np.array([0, 0]) + cov_s = np.array([[1, 0], [0, 1]]) + + xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s) + + xt = xs[::-1].copy() + + p = ot.unif(n_samples) + q = ot.unif(n_samples) + + C1 = ot.dist(xs, xs) + C2 = ot.dist(xt, xt) + + C1 /= C1.max() + C2 /= C2.max() + + G = ot.gromov.entropic_gromov_wasserstein( + C1, C2, p, q, 'square_loss', epsilon=5e-4) # check constratints np.testing.assert_allclose( diff --git a/test/test_plot.py b/test/test_plot.py index f7debee..a50ed14 100644 --- a/test/test_plot.py +++ b/test/test_plot.py @@ -12,6 +12,7 @@ matplotlib.use('Agg') def test_plot1D_mat(): import ot + import ot.plot n_bins = 100 # nb bins @@ -32,6 +33,7 @@ def test_plot1D_mat(): def test_plot2D_samples_mat(): import ot + import ot.plot n_bins = 50 # nb samples -- cgit v1.2.3 From 6355736a274a8e28c8909881b815a66f0a08d906 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 16 Feb 2018 14:11:56 +0100 Subject: add proper import for ot.plot in examples --- examples/plot_OT_1D.py | 1 + examples/plot_OT_L1_vs_L2.py | 1 + examples/plot_optim_OTreg.py | 2 +- examples/plot_otda_d2.py | 2 +- 4 files changed, 4 insertions(+), 2 deletions(-) diff --git a/examples/plot_OT_1D.py b/examples/plot_OT_1D.py index 719058f..90325c9 100644 --- a/examples/plot_OT_1D.py +++ b/examples/plot_OT_1D.py @@ -16,6 +16,7 @@ and their visualization. import numpy as np import matplotlib.pylab as pl import ot +import ot.plot from ot.datasets import get_1D_gauss as gauss ############################################################################## diff --git a/examples/plot_OT_L1_vs_L2.py b/examples/plot_OT_L1_vs_L2.py index 090e809..c1ed226 100644 --- a/examples/plot_OT_L1_vs_L2.py +++ b/examples/plot_OT_L1_vs_L2.py @@ -19,6 +19,7 @@ https://arxiv.org/pdf/1706.07650.pdf import numpy as np import matplotlib.pylab as pl import ot +import ot.plot ############################################################################## # Dataset 1 : uniform sampling diff --git a/examples/plot_optim_OTreg.py b/examples/plot_optim_OTreg.py index e1a737e..92df016 100644 --- a/examples/plot_optim_OTreg.py +++ b/examples/plot_optim_OTreg.py @@ -28,7 +28,7 @@ arXiv preprint arXiv:1510.06567. import numpy as np import matplotlib.pylab as pl import ot - +import ot.plot ############################################################################## # Generate data diff --git a/examples/plot_otda_d2.py b/examples/plot_otda_d2.py index e53d7d6..70beb35 100644 --- a/examples/plot_otda_d2.py +++ b/examples/plot_otda_d2.py @@ -20,7 +20,7 @@ of what the transport methods are doing. import matplotlib.pylab as pl import ot - +import ot.plot ############################################################################## # generate data -- cgit v1.2.3 From efdbf9e4fe9295fb1bec893e8aaa9102537cb7f5 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 16 Feb 2018 14:14:08 +0100 Subject: update doc index --- docs/source/readme.rst | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 065093e..514389f 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -172,6 +172,10 @@ want a quick look: images `__ - `Wasserstein Discriminant Analysis `__ +- `Gromov + Wasserstein `__ +- `Gromov Wasserstein + Barycenter `__ You can also see the notebooks with `Jupyter nbviewer `__. @@ -192,6 +196,7 @@ The contributors to this library are: Gayraud `__ - `Stanislas Chambon `__ - `Antoine Rolet `__ +- Erwan Vautier (Gromov-Wasserstein) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various @@ -204,6 +209,20 @@ languages): - `Marco Cuturi `__ (Sinkhorn Knopp in Matlab/Cuda) +Using and citing the toolbox +---------------------------- + +If you use this toolbox in your research and find it useful, please cite +POT using the following bibtex reference: + +:: + + @article{flamary2017pot, + title={POT Python Optimal Transport library}, + author={Flamary, R{\'e}mi and Courty, Nicolas}, + year={2017} + } + Contributions and code of conduct --------------------------------- @@ -286,6 +305,11 @@ arXiv:1608.08063. matrices `__ International Conference on Machine Learning (ICML). 2016. +[13] Mémoli, Facundo. `Gromov–Wasserstein distances and the metric +approach to object +matching `__. +Foundations of computational mathematics 11.4 (2011): 417-487. + .. |PyPI version| image:: https://badge.fury.io/py/POT.svg :target: https://badge.fury.io/py/POT .. |Build Status| image:: https://travis-ci.org/rflamary/POT.svg?branch=master -- cgit v1.2.3 From ee19d423adc85a960c9a46e4f81c370196805dbf Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 16 Feb 2018 15:04:04 +0100 Subject: update notebooks --- docs/cache_nbrun | 2 +- .../source/auto_examples/auto_examples_jupyter.zip | Bin 89148 -> 85906 bytes docs/source/auto_examples/auto_examples_python.zip | Bin 59370 -> 58682 bytes .../images/sphx_glr_plot_barycenter_1D_003.png | Bin 41555 -> 108687 bytes .../images/sphx_glr_plot_gromov_001.png | Bin 46633 -> 16548 bytes .../images/sphx_glr_plot_gromov_002.png | Bin 16945 -> 17330 bytes .../images/sphx_glr_plot_gromov_barycenter_001.png | Bin 47537 -> 48271 bytes .../images/sphx_glr_plot_otda_d2_001.png | Bin 131063 -> 130442 bytes .../images/sphx_glr_plot_otda_d2_003.png | Bin 213055 -> 216096 bytes .../images/sphx_glr_plot_otda_d2_006.png | Bin 99762 -> 102285 bytes .../images/thumb/sphx_glr_plot_OT_1D_thumb.png | Bin 18227 -> 14983 bytes .../thumb/sphx_glr_plot_OT_L1_vs_L2_thumb.png | Bin 10935 -> 9383 bytes .../thumb/sphx_glr_plot_barycenter_1D_thumb.png | Bin 16522 -> 13541 bytes .../sphx_glr_plot_gromov_barycenter_thumb.png | Bin 34183 -> 28787 bytes .../images/thumb/sphx_glr_plot_gromov_thumb.png | Bin 30843 -> 17804 bytes .../images/thumb/sphx_glr_plot_otda_d2_thumb.png | Bin 52743 -> 47553 bytes docs/source/auto_examples/index.rst | 67 +-- docs/source/auto_examples/plot_OT_1D.ipynb | 170 +++---- docs/source/auto_examples/plot_OT_1D.py | 1 + docs/source/auto_examples/plot_OT_1D.rst | 14 +- docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb | 170 +++---- docs/source/auto_examples/plot_OT_L1_vs_L2.py | 1 + docs/source/auto_examples/plot_OT_L1_vs_L2.rst | 14 +- docs/source/auto_examples/plot_barycenter_1D.ipynb | 144 ++---- docs/source/auto_examples/plot_barycenter_1D.py | 8 +- docs/source/auto_examples/plot_barycenter_1D.rst | 125 ++--- docs/source/auto_examples/plot_gromov.ipynb | 144 ++---- docs/source/auto_examples/plot_gromov.py | 32 +- docs/source/auto_examples/plot_gromov.rst | 206 +++++---- .../auto_examples/plot_gromov_barycenter.ipynb | 178 ++++---- .../source/auto_examples/plot_gromov_barycenter.py | 8 +- .../auto_examples/plot_gromov_barycenter.rst | 507 +++++++++++---------- docs/source/auto_examples/plot_optim_OTreg.ipynb | 194 ++++---- docs/source/auto_examples/plot_optim_OTreg.py | 2 +- docs/source/auto_examples/plot_optim_OTreg.rst | 15 +- docs/source/auto_examples/plot_otda_d2.ipynb | 194 ++++---- docs/source/auto_examples/plot_otda_d2.py | 2 +- docs/source/auto_examples/plot_otda_d2.rst | 15 +- 38 files changed, 1047 insertions(+), 1166 deletions(-) diff --git a/docs/cache_nbrun b/docs/cache_nbrun index 3f1e6ea..ae0fe23 100644 --- a/docs/cache_nbrun +++ b/docs/cache_nbrun @@ -1 +1 @@ -{"plot_otda_mapping_colors_images.ipynb": "4f0587a00a3c082799a75a0ed36e9ce1", "plot_optim_OTreg.ipynb": 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b/docs/source/auto_examples/images/thumb/sphx_glr_plot_otda_d2_thumb.png differ diff --git a/docs/source/auto_examples/index.rst b/docs/source/auto_examples/index.rst index eb54ca8..227c40c 100644 --- a/docs/source/auto_examples/index.rst +++ b/docs/source/auto_examples/index.rst @@ -1,8 +1,12 @@ +:orphan: + POT Examples ============ This is a gallery of all the POT example files. + + .. raw:: html
@@ -45,13 +49,13 @@ This is a gallery of all the POT example files. .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_gromov_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_2D_samples_thumb.png - :ref:`sphx_glr_auto_examples_plot_gromov.py` + :ref:`sphx_glr_auto_examples_plot_OT_2D_samples.py` .. raw:: html @@ -61,17 +65,17 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_gromov + /auto_examples/plot_OT_2D_samples .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_2D_samples_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_compute_emd_thumb.png - :ref:`sphx_glr_auto_examples_plot_OT_2D_samples.py` + :ref:`sphx_glr_auto_examples_plot_compute_emd.py` .. raw:: html @@ -81,17 +85,17 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_OT_2D_samples + /auto_examples/plot_compute_emd .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_compute_emd_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_WDA_thumb.png - :ref:`sphx_glr_auto_examples_plot_compute_emd.py` + :ref:`sphx_glr_auto_examples_plot_WDA.py` .. raw:: html @@ -101,17 +105,17 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_compute_emd + /auto_examples/plot_WDA .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_WDA_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_gromov_thumb.png - :ref:`sphx_glr_auto_examples_plot_WDA.py` + :ref:`sphx_glr_auto_examples_plot_gromov.py` .. raw:: html @@ -121,7 +125,7 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_WDA + /auto_examples/plot_gromov .. raw:: html @@ -145,13 +149,13 @@ This is a gallery of all the POT example files. .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_barycenter_1D_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_mapping_colors_images_thumb.png - :ref:`sphx_glr_auto_examples_plot_barycenter_1D.py` + :ref:`sphx_glr_auto_examples_plot_otda_mapping_colors_images.py` .. raw:: html @@ -161,17 +165,17 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_barycenter_1D + /auto_examples/plot_otda_mapping_colors_images .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_mapping_colors_images_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_barycenter_1D_thumb.png - :ref:`sphx_glr_auto_examples_plot_otda_mapping_colors_images.py` + :ref:`sphx_glr_auto_examples_plot_barycenter_1D.py` .. raw:: html @@ -181,7 +185,7 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_otda_mapping_colors_images + /auto_examples/plot_barycenter_1D .. raw:: html @@ -308,19 +312,24 @@ This is a gallery of all the POT example files. -.. container:: sphx-glr-footer +.. only :: html + + .. container:: sphx-glr-footer .. container:: sphx-glr-download - :download:`Download all examples in Python source code: auto_examples_python.zip ` + :download:`Download all examples in Python source code: auto_examples_python.zip ` .. container:: sphx-glr-download - :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip ` + :download:`Download all examples in Jupyter notebooks: auto_examples_jupyter.zip ` + + +.. only:: html -.. rst-class:: sphx-glr-signature + .. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OT_1D.ipynb b/docs/source/auto_examples/plot_OT_1D.ipynb index 26748c2..649efa6 100644 --- a/docs/source/auto_examples/plot_OT_1D.ipynb +++ b/docs/source/auto_examples/plot_OT_1D.ipynb @@ -1,126 +1,126 @@ { - "nbformat_minor": 0, - "nbformat": 4, "cells": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "\n# 1D optimal transport\n\n\nThis example illustrates the computation of EMD and Sinkhorn transport plans\nand their visualization.\n\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nfrom ot.datasets import get_1D_gauss as gauss" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot\nfrom ot.datasets import get_1D_gauss as gauss" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Generate data\n-------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\nb = gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\nb = gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Plot distributions and loss matrix\n----------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\npl.plot(x, a, 'b', label='Source distribution')\npl.plot(x, b, 'r', label='Target distribution')\npl.legend()\n\n#%% plot distributions and loss matrix\n\npl.figure(2, figsize=(5, 5))\not.plot.plot1D_mat(a, b, M, 'Cost matrix M')" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\npl.plot(x, a, 'b', label='Source distribution')\npl.plot(x, b, 'r', label='Target distribution')\npl.legend()\n\n#%% plot distributions and loss matrix\n\npl.figure(2, figsize=(5, 5))\not.plot.plot1D_mat(a, b, M, 'Cost matrix M')" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Solve EMD\n---------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Solve Sinkhorn\n--------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% Sinkhorn\n\nlambd = 1e-3\nGs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')\n\npl.show()" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } + }, + "outputs": [], + "source": [ + "#%% Sinkhorn\n\nlambd = 1e-3\nGs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')\n\npl.show()" + ], + "cell_type": "code" } - ], + ], "metadata": { - "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", + "name": "python", "codemirror_mode": { - "version": 2, - "name": "ipython" - } + "name": "ipython", + "version": 3 + }, + "nbconvert_exporter": "python", + "version": "3.5.2", + "pygments_lexer": "ipython3", + "file_extension": ".py", + "mimetype": "text/x-python" + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3", + "language": "python" } - } + }, + "nbformat_minor": 0, + "nbformat": 4 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OT_1D.py b/docs/source/auto_examples/plot_OT_1D.py index 719058f..90325c9 100644 --- a/docs/source/auto_examples/plot_OT_1D.py +++ b/docs/source/auto_examples/plot_OT_1D.py @@ -16,6 +16,7 @@ and their visualization. import numpy as np import matplotlib.pylab as pl import ot +import ot.plot from ot.datasets import get_1D_gauss as gauss ############################################################################## diff --git a/docs/source/auto_examples/plot_OT_1D.rst b/docs/source/auto_examples/plot_OT_1D.rst index 975a923..5e4f73e 100644 --- a/docs/source/auto_examples/plot_OT_1D.rst +++ b/docs/source/auto_examples/plot_OT_1D.rst @@ -23,6 +23,7 @@ and their visualization. import numpy as np import matplotlib.pylab as pl import ot + import ot.plot from ot.datasets import get_1D_gauss as gauss @@ -171,11 +172,13 @@ Solve Sinkhorn 110|1.527180e-10| -**Total running time of the script:** ( 0 minutes 1.198 seconds) +**Total running time of the script:** ( 0 minutes 1.061 seconds) -.. container:: sphx-glr-footer +.. only :: html + + .. container:: sphx-glr-footer .. container:: sphx-glr-download @@ -188,6 +191,9 @@ Solve Sinkhorn :download:`Download Jupyter notebook: plot_OT_1D.ipynb ` -.. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb b/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb index 2b9a364..aea1b3d 100644 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb @@ -1,126 +1,126 @@ { - "nbformat_minor": 0, - "nbformat": 4, "cells": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "\n# 2D Optimal transport for different metrics\n\n\n2D OT on empirical distributio with different gound metric.\n\nStole the figure idea from Fig. 1 and 2 in\nhttps://arxiv.org/pdf/1706.07650.pdf\n\n\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Dataset 1 : uniform sampling\n----------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "n = 20 # nb samples\nxs = np.zeros((n, 2))\nxs[:, 0] = np.arange(n) + 1\nxs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex...\n\nxt = np.zeros((n, 2))\nxt[:, 1] = np.arange(n) + 1\n\na, b = ot.unif(n), ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM1 = ot.dist(xs, xt, metric='euclidean')\nM1 /= M1.max()\n\n# loss matrix\nM2 = ot.dist(xs, xt, metric='sqeuclidean')\nM2 /= M2.max()\n\n# loss matrix\nMp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))\nMp /= Mp.max()\n\n# Data\npl.figure(1, figsize=(7, 3))\npl.clf()\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\npl.title('Source and traget distributions')\n\n\n# Cost matrices\npl.figure(2, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\npl.imshow(M1, interpolation='nearest')\npl.title('Euclidean cost')\n\npl.subplot(1, 3, 2)\npl.imshow(M2, interpolation='nearest')\npl.title('Squared Euclidean cost')\n\npl.subplot(1, 3, 3)\npl.imshow(Mp, interpolation='nearest')\npl.title('Sqrt Euclidean cost')\npl.tight_layout()" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "n = 20 # nb samples\nxs = np.zeros((n, 2))\nxs[:, 0] = np.arange(n) + 1\nxs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex...\n\nxt = np.zeros((n, 2))\nxt[:, 1] = np.arange(n) + 1\n\na, b = ot.unif(n), ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM1 = ot.dist(xs, xt, metric='euclidean')\nM1 /= M1.max()\n\n# loss matrix\nM2 = ot.dist(xs, xt, metric='sqeuclidean')\nM2 /= M2.max()\n\n# loss matrix\nMp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))\nMp /= Mp.max()\n\n# Data\npl.figure(1, figsize=(7, 3))\npl.clf()\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\npl.title('Source and traget distributions')\n\n\n# Cost matrices\npl.figure(2, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\npl.imshow(M1, interpolation='nearest')\npl.title('Euclidean cost')\n\npl.subplot(1, 3, 2)\npl.imshow(M2, interpolation='nearest')\npl.title('Squared Euclidean cost')\n\npl.subplot(1, 3, 3)\npl.imshow(Mp, interpolation='nearest')\npl.title('Sqrt Euclidean cost')\npl.tight_layout()" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Dataset 1 : Plot OT Matrices\n----------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% EMD\nG1 = ot.emd(a, b, M1)\nG2 = ot.emd(a, b, M2)\nGp = ot.emd(a, b, Mp)\n\n# OT matrices\npl.figure(3, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\not.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT Euclidean')\n\npl.subplot(1, 3, 2)\not.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT squared Euclidean')\n\npl.subplot(1, 3, 3)\not.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT sqrt Euclidean')\npl.tight_layout()\n\npl.show()" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "#%% EMD\nG1 = ot.emd(a, b, M1)\nG2 = ot.emd(a, b, M2)\nGp = ot.emd(a, b, Mp)\n\n# OT matrices\npl.figure(3, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\not.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT Euclidean')\n\npl.subplot(1, 3, 2)\not.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT squared Euclidean')\n\npl.subplot(1, 3, 3)\not.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT sqrt Euclidean')\npl.tight_layout()\n\npl.show()" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Dataset 2 : Partial circle\n--------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "n = 50 # nb samples\nxtot = np.zeros((n + 1, 2))\nxtot[:, 0] = np.cos(\n (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)\nxtot[:, 1] = np.sin(\n (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)\n\nxs = xtot[:n, :]\nxt = xtot[1:, :]\n\na, b = ot.unif(n), ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM1 = ot.dist(xs, xt, metric='euclidean')\nM1 /= M1.max()\n\n# loss matrix\nM2 = ot.dist(xs, xt, metric='sqeuclidean')\nM2 /= M2.max()\n\n# loss matrix\nMp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))\nMp /= Mp.max()\n\n\n# Data\npl.figure(4, figsize=(7, 3))\npl.clf()\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\npl.title('Source and traget distributions')\n\n\n# Cost matrices\npl.figure(5, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\npl.imshow(M1, interpolation='nearest')\npl.title('Euclidean cost')\n\npl.subplot(1, 3, 2)\npl.imshow(M2, interpolation='nearest')\npl.title('Squared Euclidean cost')\n\npl.subplot(1, 3, 3)\npl.imshow(Mp, interpolation='nearest')\npl.title('Sqrt Euclidean cost')\npl.tight_layout()" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "n = 50 # nb samples\nxtot = np.zeros((n + 1, 2))\nxtot[:, 0] = np.cos(\n (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)\nxtot[:, 1] = np.sin(\n (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)\n\nxs = xtot[:n, :]\nxt = xtot[1:, :]\n\na, b = ot.unif(n), ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM1 = ot.dist(xs, xt, metric='euclidean')\nM1 /= M1.max()\n\n# loss matrix\nM2 = ot.dist(xs, xt, metric='sqeuclidean')\nM2 /= M2.max()\n\n# loss matrix\nMp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))\nMp /= Mp.max()\n\n\n# Data\npl.figure(4, figsize=(7, 3))\npl.clf()\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\npl.title('Source and traget distributions')\n\n\n# Cost matrices\npl.figure(5, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\npl.imshow(M1, interpolation='nearest')\npl.title('Euclidean cost')\n\npl.subplot(1, 3, 2)\npl.imshow(M2, interpolation='nearest')\npl.title('Squared Euclidean cost')\n\npl.subplot(1, 3, 3)\npl.imshow(Mp, interpolation='nearest')\npl.title('Sqrt Euclidean cost')\npl.tight_layout()" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Dataset 2 : Plot OT Matrices\n-----------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% EMD\nG1 = ot.emd(a, b, M1)\nG2 = ot.emd(a, b, M2)\nGp = ot.emd(a, b, Mp)\n\n# OT matrices\npl.figure(6, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\not.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT Euclidean')\n\npl.subplot(1, 3, 2)\not.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT squared Euclidean')\n\npl.subplot(1, 3, 3)\not.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT sqrt Euclidean')\npl.tight_layout()\n\npl.show()" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } + }, + "outputs": [], + "source": [ + "#%% EMD\nG1 = ot.emd(a, b, M1)\nG2 = ot.emd(a, b, M2)\nGp = ot.emd(a, b, Mp)\n\n# OT matrices\npl.figure(6, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\not.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT Euclidean')\n\npl.subplot(1, 3, 2)\not.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT squared Euclidean')\n\npl.subplot(1, 3, 3)\not.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT sqrt Euclidean')\npl.tight_layout()\n\npl.show()" + ], + "cell_type": "code" } - ], + ], "metadata": { - "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", + "name": "python", "codemirror_mode": { - "version": 2, - "name": "ipython" - } + "name": "ipython", + "version": 3 + }, + "nbconvert_exporter": "python", + "version": "3.5.2", + "pygments_lexer": "ipython3", + "file_extension": ".py", + "mimetype": "text/x-python" + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3", + "language": "python" } - } + }, + "nbformat_minor": 0, + "nbformat": 4 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.py b/docs/source/auto_examples/plot_OT_L1_vs_L2.py index 090e809..c1ed226 100644 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.py +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.py @@ -19,6 +19,7 @@ https://arxiv.org/pdf/1706.07650.pdf import numpy as np import matplotlib.pylab as pl import ot +import ot.plot ############################################################################## # Dataset 1 : uniform sampling diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst index a569b50..01d6ac2 100644 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst @@ -26,6 +26,7 @@ https://arxiv.org/pdf/1706.07650.pdf import numpy as np import matplotlib.pylab as pl import ot + import ot.plot @@ -290,11 +291,13 @@ Dataset 2 : Plot OT Matrices -**Total running time of the script:** ( 0 minutes 1.976 seconds) +**Total running time of the script:** ( 0 minutes 3.750 seconds) -.. container:: sphx-glr-footer +.. only :: html + + .. container:: sphx-glr-footer .. container:: sphx-glr-download @@ -307,6 +310,9 @@ Dataset 2 : Plot OT Matrices :download:`Download Jupyter notebook: plot_OT_L1_vs_L2.ipynb ` -.. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_barycenter_1D.ipynb b/docs/source/auto_examples/plot_barycenter_1D.ipynb index a19e0fd..01e759a 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.ipynb +++ b/docs/source/auto_examples/plot_barycenter_1D.ipynb @@ -1,126 +1,54 @@ { - "nbformat_minor": 0, - "nbformat": 4, "cells": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, - { - "source": [ - "\n# 1D Wasserstein barycenter demo\n\n\nThis example illustrates the computation of regularized Wassersyein Barycenter\nas proposed in [3].\n\n\n[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyr\u00e9, G. (2015).\nIterative Bregman projections for regularized transportation problems\nSIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nfrom matplotlib.collections import PolyCollection" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, - { - "source": [ - "Generate data\n-------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na1 = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.get_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, - { + }, + "outputs": [], "source": [ - "Plot data\n---------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, + "%matplotlib inline" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ - "Barycenter computation\n----------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + "\n# 1D Wasserstein barycenter demo\n\n\nThis example illustrates the computation of regularized Wassersyein Barycenter\nas proposed in [3].\n\n\n[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyr\u00e9, G. (2015).\nIterative Bregman projections for regularized transportation problems\nSIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n\n\n" + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% barycenter computation\n\nalpha = 0.2 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, - { + }, + "outputs": [], "source": [ - "Barycentric interpolation\n-------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% barycenter interpolation\n\nn_alpha = 11\nalpha_list = np.linspace(0, 1, n_alpha)\n\n\nB_l2 = np.zeros((n, n_alpha))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_alpha):\n alpha = alpha_list[i]\n weights = np.array([1 - alpha, alpha])\n B_l2[:, i] = A.dot(weights)\n B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)\n\n#%% plot interpolation\n\npl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_l2[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_wass[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with Wasserstein')\npl.tight_layout()\n\npl.show()" - ], - "outputs": [], - "metadata": { - "collapsed": false - } + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nfrom matplotlib.collections import PolyCollection\n\n#\n# Generate data\n# -------------\n\n#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na1 = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.get_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()\n\n#\n# Plot data\n# ---------\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()\n\n#\n# Barycenter computation\n# ----------------------\n\n#%% barycenter computation\n\nalpha = 0.2 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()\n\n#\n# Barycentric interpolation\n# -------------------------\n\n#%% barycenter interpolation\n\nn_alpha = 11\nalpha_list = np.linspace(0, 1, n_alpha)\n\n\nB_l2 = np.zeros((n, n_alpha))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_alpha):\n alpha = alpha_list[i]\n weights = np.array([1 - alpha, alpha])\n B_l2[:, i] = A.dot(weights)\n B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)\n\n#%% plot interpolation\n\npl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_l2[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_wass[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with Wasserstein')\npl.tight_layout()\n\npl.show()" + ], + "cell_type": "code" } - ], + ], "metadata": { - "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", + "name": "python", "codemirror_mode": { - "version": 2, - "name": "ipython" - } + "name": "ipython", + "version": 3 + }, + "nbconvert_exporter": "python", + "version": "3.5.2", + "pygments_lexer": "ipython3", + "file_extension": ".py", + "mimetype": "text/x-python" + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3", + "language": "python" } - } + }, + "nbformat_minor": 0, + "nbformat": 4 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_barycenter_1D.py b/docs/source/auto_examples/plot_barycenter_1D.py index 620936b..ecf640c 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.py +++ b/docs/source/auto_examples/plot_barycenter_1D.py @@ -25,7 +25,7 @@ import ot from mpl_toolkits.mplot3d import Axes3D # noqa from matplotlib.collections import PolyCollection -############################################################################## +# # Generate data # ------------- @@ -48,7 +48,7 @@ n_distributions = A.shape[1] M = ot.utils.dist0(n) M /= M.max() -############################################################################## +# # Plot data # --------- @@ -60,7 +60,7 @@ for i in range(n_distributions): pl.title('Distributions') pl.tight_layout() -############################################################################## +# # Barycenter computation # ---------------------- @@ -90,7 +90,7 @@ pl.legend() pl.title('Barycenters') pl.tight_layout() -############################################################################## +# # Barycentric interpolation # ------------------------- diff --git a/docs/source/auto_examples/plot_barycenter_1D.rst b/docs/source/auto_examples/plot_barycenter_1D.rst index f17f2c2..5b627ca 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.rst +++ b/docs/source/auto_examples/plot_barycenter_1D.rst @@ -18,34 +18,52 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. -.. code-block:: python +.. rst-class:: sphx-glr-horizontal - # Author: Remi Flamary - # - # License: MIT License - import numpy as np - import matplotlib.pylab as pl - import ot - # necessary for 3d plot even if not used - from mpl_toolkits.mplot3d import Axes3D # noqa - from matplotlib.collections import PolyCollection + * + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_001.png + :scale: 47 + * + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_002.png + :scale: 47 + * + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_003.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_004.png + :scale: 47 -Generate data -------------- .. code-block:: python + # Author: Remi Flamary + # + # License: MIT License + + import numpy as np + import matplotlib.pylab as pl + import ot + # necessary for 3d plot even if not used + from mpl_toolkits.mplot3d import Axes3D # noqa + from matplotlib.collections import PolyCollection + + # + # Generate data + # ------------- + #%% parameters n = 100 # nb bins @@ -65,19 +83,9 @@ Generate data M = ot.utils.dist0(n) M /= M.max() - - - - - - -Plot data ---------- - - - -.. code-block:: python - + # + # Plot data + # --------- #%% plot the distributions @@ -87,22 +95,9 @@ Plot data pl.title('Distributions') pl.tight_layout() - - - -.. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_001.png - :align: center - - - - -Barycenter computation ----------------------- - - - -.. code-block:: python - + # + # Barycenter computation + # ---------------------- #%% barycenter computation @@ -130,22 +125,9 @@ Barycenter computation pl.title('Barycenters') pl.tight_layout() - - - -.. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_003.png - :align: center - - - - -Barycentric interpolation -------------------------- - - - -.. code-block:: python - + # + # Barycentric interpolation + # ------------------------- #%% barycenter interpolation @@ -212,29 +194,13 @@ Barycentric interpolation pl.show() - - -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_005.png - :scale: 47 - - * - - .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_006.png - :scale: 47 +**Total running time of the script:** ( 0 minutes 0.636 seconds) +.. only :: html -**Total running time of the script:** ( 0 minutes 0.814 seconds) - - - -.. container:: sphx-glr-footer + .. container:: sphx-glr-footer .. container:: sphx-glr-download @@ -247,6 +213,9 @@ Barycentric interpolation :download:`Download Jupyter notebook: plot_barycenter_1D.ipynb ` -.. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_gromov.ipynb b/docs/source/auto_examples/plot_gromov.ipynb index 865848e..6d6b522 100644 --- a/docs/source/auto_examples/plot_gromov.ipynb +++ b/docs/source/auto_examples/plot_gromov.ipynb @@ -1,126 +1,54 @@ { - "nbformat_minor": 0, - "nbformat": 4, "cells": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, - { - "source": [ - "\n# Gromov-Wasserstein example\n\n\nThis example is designed to show how to use the Gromov-Wassertsein distance\ncomputation in POT.\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Author: Erwan Vautier \r\n# Nicolas Courty \r\n#\r\n# License: MIT License\r\n\r\nimport scipy as sp\r\nimport numpy as np\r\nimport matplotlib.pylab as pl\r\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\r\nimport ot" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, - { - "source": [ - "Sample two Gaussian distributions (2D and 3D)\r\n ---------------------------------------------\r\n\r\n The Gromov-Wasserstein distance allows to compute distances with samples that\r\n do not belong to the same metric space. For demonstration purpose, we sample\r\n two Gaussian distributions in 2- and 3-dimensional spaces.\r\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "n_samples = 30 # nb samples\r\n\r\nmu_s = np.array([0, 0])\r\ncov_s = np.array([[1, 0], [0, 1]])\r\n\r\nmu_t = np.array([4, 4, 4])\r\ncov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\r\n\r\n\r\nxs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)\r\nP = sp.linalg.sqrtm(cov_t)\r\nxt = np.random.randn(n_samples, 3).dot(P) + mu_t" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, - { + }, + "outputs": [], "source": [ - "Plotting the distributions\r\n--------------------------\r\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "fig = pl.figure()\r\nax1 = fig.add_subplot(121)\r\nax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\r\nax2 = fig.add_subplot(122, projection='3d')\r\nax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\r\npl.show()" - ], - "outputs": [], - "metadata": { - "collapsed": false - } - }, + "%matplotlib inline" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ - "Compute distance kernels, normalize them and then display\r\n---------------------------------------------------------\r\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + "\n# Gromov-Wasserstein example\n\n\nThis example is designed to show how to use the Gromov-Wassertsein distance\ncomputation in POT.\n\n" + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "C1 = sp.spatial.distance.cdist(xs, xs)\r\nC2 = sp.spatial.distance.cdist(xt, xt)\r\n\r\nC1 /= C1.max()\r\nC2 /= C2.max()\r\n\r\npl.figure()\r\npl.subplot(121)\r\npl.imshow(C1)\r\npl.subplot(122)\r\npl.imshow(C2)\r\npl.show()" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, - { + }, + "outputs": [], "source": [ - "Compute Gromov-Wasserstein plans and distance\r\n---------------------------------------------\r\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, - { - "execution_count": null, - "cell_type": "code", - "source": [ - "p = ot.unif(n_samples)\r\nq = ot.unif(n_samples)\r\n\r\ngw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4)\r\ngw_dist = ot.gromov_wasserstein2(C1, C2, p, q, 'square_loss', epsilon=5e-4)\r\n\r\nprint('Gromov-Wasserstein distances between the distribution: ' + str(gw_dist))\r\n\r\npl.figure()\r\npl.imshow(gw, cmap='jet')\r\npl.colorbar()\r\npl.show()" - ], - "outputs": [], - "metadata": { - "collapsed": false - } + "# Author: Erwan Vautier \n# Nicolas Courty \n#\n# License: MIT License\n\nimport scipy as sp\nimport numpy as np\nimport matplotlib.pylab as pl\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nimport ot\n\n\n#\n# Sample two Gaussian distributions (2D and 3D)\n# ---------------------------------------------\n#\n# The Gromov-Wasserstein distance allows to compute distances with samples that\n# do not belong to the same metric space. For demonstration purpose, we sample\n# two Gaussian distributions in 2- and 3-dimensional spaces.\n\n\nn_samples = 30 # nb samples\n\nmu_s = np.array([0, 0])\ncov_s = np.array([[1, 0], [0, 1]])\n\nmu_t = np.array([4, 4, 4])\ncov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\n\n\nxs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)\nP = sp.linalg.sqrtm(cov_t)\nxt = np.random.randn(n_samples, 3).dot(P) + mu_t\n\n\n#\n# Plotting the distributions\n# --------------------------\n\n\nfig = pl.figure()\nax1 = fig.add_subplot(121)\nax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\nax2 = fig.add_subplot(122, projection='3d')\nax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\npl.show()\n\n\n#\n# Compute distance kernels, normalize them and then display\n# ---------------------------------------------------------\n\n\nC1 = sp.spatial.distance.cdist(xs, xs)\nC2 = sp.spatial.distance.cdist(xt, xt)\n\nC1 /= C1.max()\nC2 /= C2.max()\n\npl.figure()\npl.subplot(121)\npl.imshow(C1)\npl.subplot(122)\npl.imshow(C2)\npl.show()\n\n#\n# Compute Gromov-Wasserstein plans and distance\n# ---------------------------------------------\n\np = ot.unif(n_samples)\nq = ot.unif(n_samples)\n\ngw0, log0 = ot.gromov.gromov_wasserstein(\n C1, C2, p, q, 'square_loss', verbose=True, log=True)\n\ngw, log = ot.gromov.entropic_gromov_wasserstein(\n C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True)\n\n\nprint('Gromov-Wasserstein distances: ' + str(log0['gw_dist']))\nprint('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist']))\n\n\npl.figure(1, (10, 5))\n\npl.subplot(1, 2, 1)\npl.imshow(gw0, cmap='jet')\npl.title('Gromov Wasserstein')\n\npl.subplot(1, 2, 2)\npl.imshow(gw, cmap='jet')\npl.title('Entropic Gromov Wasserstein')\n\npl.show()" + ], + "cell_type": "code" } - ], + ], "metadata": { - "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", + "name": "python", "codemirror_mode": { - "version": 2, - "name": "ipython" - } + "name": "ipython", + "version": 3 + }, + "nbconvert_exporter": "python", + "version": "3.5.2", + "pygments_lexer": "ipython3", + "file_extension": ".py", + "mimetype": "text/x-python" + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3", + "language": "python" } - } + }, + "nbformat_minor": 0, + "nbformat": 4 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_gromov.py b/docs/source/auto_examples/plot_gromov.py index d3f724c..9188da9 100644 --- a/docs/source/auto_examples/plot_gromov.py +++ b/docs/source/auto_examples/plot_gromov.py @@ -20,7 +20,7 @@ from mpl_toolkits.mplot3d import Axes3D # noqa import ot -############################################################################## +# # Sample two Gaussian distributions (2D and 3D) # --------------------------------------------- # @@ -43,7 +43,7 @@ P = sp.linalg.sqrtm(cov_t) xt = np.random.randn(n_samples, 3).dot(P) + mu_t -############################################################################## +# # Plotting the distributions # -------------------------- @@ -56,7 +56,7 @@ ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r') pl.show() -############################################################################## +# # Compute distance kernels, normalize them and then display # --------------------------------------------------------- @@ -74,20 +74,32 @@ pl.subplot(122) pl.imshow(C2) pl.show() -############################################################################## +# # Compute Gromov-Wasserstein plans and distance # --------------------------------------------- - p = ot.unif(n_samples) q = ot.unif(n_samples) -gw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4) -gw_dist = ot.gromov_wasserstein2(C1, C2, p, q, 'square_loss', epsilon=5e-4) +gw0, log0 = ot.gromov.gromov_wasserstein( + C1, C2, p, q, 'square_loss', verbose=True, log=True) -print('Gromov-Wasserstein distances between the distribution: ' + str(gw_dist)) +gw, log = ot.gromov.entropic_gromov_wasserstein( + C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True) -pl.figure() + +print('Gromov-Wasserstein distances: ' + str(log0['gw_dist'])) +print('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist'])) + + +pl.figure(1, (10, 5)) + +pl.subplot(1, 2, 1) +pl.imshow(gw0, cmap='jet') +pl.title('Gromov Wasserstein') + +pl.subplot(1, 2, 2) pl.imshow(gw, cmap='jet') -pl.colorbar() +pl.title('Entropic Gromov Wasserstein') + pl.show() diff --git a/docs/source/auto_examples/plot_gromov.rst b/docs/source/auto_examples/plot_gromov.rst index 65cf4e4..ad29f7a 100644 --- a/docs/source/auto_examples/plot_gromov.rst +++ b/docs/source/auto_examples/plot_gromov.rst @@ -12,157 +12,160 @@ computation in POT. -.. code-block:: python - - - # Author: Erwan Vautier - # Nicolas Courty - # - # License: MIT License - - import scipy as sp - import numpy as np - import matplotlib.pylab as pl - from mpl_toolkits.mplot3d import Axes3D # noqa - import ot - - +.. rst-class:: sphx-glr-horizontal + * + .. image:: /auto_examples/images/sphx_glr_plot_gromov_001.png + :scale: 47 + * -Sample two Gaussian distributions (2D and 3D) - --------------------------------------------- - - The Gromov-Wasserstein distance allows to compute distances with samples that - do not belong to the same metric space. For demonstration purpose, we sample - two Gaussian distributions in 2- and 3-dimensional spaces. + .. image:: /auto_examples/images/sphx_glr_plot_gromov_002.png + :scale: 47 +.. rst-class:: sphx-glr-script-out -.. code-block:: python - - - - n_samples = 30 # nb samples - - mu_s = np.array([0, 0]) - cov_s = np.array([[1, 0], [0, 1]]) - - mu_t = np.array([4, 4, 4]) - cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) - - - xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s) - P = sp.linalg.sqrtm(cov_t) - xt = np.random.randn(n_samples, 3).dot(P) + mu_t - - + Out:: + It. |Loss |Delta loss + -------------------------------- + 0|4.042674e-02|0.000000e+00 + 1|2.432476e-02|-6.619583e-01 + 2|2.170023e-02|-1.209448e-01 + 3|1.941223e-02|-1.178640e-01 + 4|1.823606e-02|-6.449667e-02 + 5|1.446641e-02|-2.605800e-01 + 6|1.184011e-02|-2.218140e-01 + 7|1.173274e-02|-9.150805e-03 + 8|1.173127e-02|-1.253458e-04 + 9|1.173126e-02|-1.256842e-06 + 10|1.173126e-02|-1.256876e-08 + 11|1.173126e-02|-1.256885e-10 + It. |Err + ------------------- + 0|7.034302e-02| + 10|1.044218e-03| + 20|5.426783e-08| + 30|3.532029e-12| + Gromov-Wasserstein distances: 0.0117312557987 + Entropic Gromov-Wasserstein distances: 0.0101639418389 +| -Plotting the distributions --------------------------- +.. code-block:: python -.. code-block:: python + # Author: Erwan Vautier + # Nicolas Courty + # + # License: MIT License - - - fig = pl.figure() - ax1 = fig.add_subplot(121) - ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') - ax2 = fig.add_subplot(122, projection='3d') - ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r') - pl.show() - - + import scipy as sp + import numpy as np + import matplotlib.pylab as pl + from mpl_toolkits.mplot3d import Axes3D # noqa + import ot + # + # Sample two Gaussian distributions (2D and 3D) + # --------------------------------------------- + # + # The Gromov-Wasserstein distance allows to compute distances with samples that + # do not belong to the same metric space. For demonstration purpose, we sample + # two Gaussian distributions in 2- and 3-dimensional spaces. -.. image:: /auto_examples/images/sphx_glr_plot_gromov_001.png - :align: center + n_samples = 30 # nb samples + mu_s = np.array([0, 0]) + cov_s = np.array([[1, 0], [0, 1]]) + mu_t = np.array([4, 4, 4]) + cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) -Compute distance kernels, normalize them and then display ---------------------------------------------------------- + xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s) + P = sp.linalg.sqrtm(cov_t) + xt = np.random.randn(n_samples, 3).dot(P) + mu_t -.. code-block:: python + # + # Plotting the distributions + # -------------------------- - - - C1 = sp.spatial.distance.cdist(xs, xs) - C2 = sp.spatial.distance.cdist(xt, xt) - - C1 /= C1.max() - C2 /= C2.max() - - pl.figure() - pl.subplot(121) - pl.imshow(C1) - pl.subplot(122) - pl.imshow(C2) - pl.show() - + fig = pl.figure() + ax1 = fig.add_subplot(121) + ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + ax2 = fig.add_subplot(122, projection='3d') + ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r') + pl.show() -.. image:: /auto_examples/images/sphx_glr_plot_gromov_002.png - :align: center + # + # Compute distance kernels, normalize them and then display + # --------------------------------------------------------- + C1 = sp.spatial.distance.cdist(xs, xs) + C2 = sp.spatial.distance.cdist(xt, xt) + C1 /= C1.max() + C2 /= C2.max() -Compute Gromov-Wasserstein plans and distance ---------------------------------------------- + pl.figure() + pl.subplot(121) + pl.imshow(C1) + pl.subplot(122) + pl.imshow(C2) + pl.show() + # + # Compute Gromov-Wasserstein plans and distance + # --------------------------------------------- + p = ot.unif(n_samples) + q = ot.unif(n_samples) -.. code-block:: python + gw0, log0 = ot.gromov.gromov_wasserstein( + C1, C2, p, q, 'square_loss', verbose=True, log=True) - - - p = ot.unif(n_samples) - q = ot.unif(n_samples) - - gw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4) - gw_dist = ot.gromov_wasserstein2(C1, C2, p, q, 'square_loss', epsilon=5e-4) - - print('Gromov-Wasserstein distances between the distribution: ' + str(gw_dist)) - - pl.figure() - pl.imshow(gw, cmap='jet') - pl.colorbar() - pl.show() + gw, log = ot.gromov.entropic_gromov_wasserstein( + C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True) + print('Gromov-Wasserstein distances: ' + str(log0['gw_dist'])) + print('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist'])) -.. image:: /auto_examples/images/sphx_glr_plot_gromov_003.png - :align: center + pl.figure(1, (10, 5)) -.. rst-class:: sphx-glr-script-out + pl.subplot(1, 2, 1) + pl.imshow(gw0, cmap='jet') + pl.title('Gromov Wasserstein') - Out:: + pl.subplot(1, 2, 2) + pl.imshow(gw, cmap='jet') + pl.title('Entropic Gromov Wasserstein') - Gromov-Wasserstein distances between the distribution: 0.225058076974 + pl.show() +**Total running time of the script:** ( 0 minutes 1.465 seconds) -**Total running time of the script:** ( 0 minutes 4.070 seconds) +.. only :: html -.. container:: sphx-glr-footer + .. container:: sphx-glr-footer .. container:: sphx-glr-download @@ -175,6 +178,9 @@ Compute Gromov-Wasserstein plans and distance :download:`Download Jupyter notebook: plot_gromov.ipynb ` -.. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_gromov_barycenter.ipynb b/docs/source/auto_examples/plot_gromov_barycenter.ipynb index d38dfbb..4c2f28f 100644 --- a/docs/source/auto_examples/plot_gromov_barycenter.ipynb +++ b/docs/source/auto_examples/plot_gromov_barycenter.ipynb @@ -1,126 +1,126 @@ { - "nbformat_minor": 0, - "nbformat": 4, "cells": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "\n# Gromov-Wasserstein Barycenter example\n\n\nThis example is designed to show how to use the Gromov-Wasserstein distance\ncomputation in POT.\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Author: Erwan Vautier \r\n# Nicolas Courty \r\n#\r\n# License: MIT License\r\n\r\n\r\nimport numpy as np\r\nimport scipy as sp\r\n\r\nimport scipy.ndimage as spi\r\nimport matplotlib.pylab as pl\r\nfrom sklearn import manifold\r\nfrom sklearn.decomposition import PCA\r\n\r\nimport ot" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, - { + }, + "outputs": [], "source": [ - "Smacof MDS\r\n ----------\r\n\r\n This function allows to find an embedding of points given a dissimilarity matrix\r\n that will be given by the output of the algorithm\r\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + "# Author: Erwan Vautier \n# Nicolas Courty \n#\n# License: MIT License\n\n\nimport numpy as np\nimport scipy as sp\n\nimport scipy.ndimage as spi\nimport matplotlib.pylab as pl\nfrom sklearn import manifold\nfrom sklearn.decomposition import PCA\n\nimport ot" + ], + "cell_type": "code" + }, { - "execution_count": null, - "cell_type": "code", + "metadata": {}, "source": [ - "def smacof_mds(C, dim, max_iter=3000, eps=1e-9):\r\n \"\"\"\r\n Returns an interpolated point cloud following the dissimilarity matrix C\r\n using SMACOF multidimensional scaling (MDS) in specific dimensionned\r\n target space\r\n\r\n Parameters\r\n ----------\r\n C : ndarray, shape (ns, ns)\r\n dissimilarity matrix\r\n dim : int\r\n dimension of the targeted space\r\n max_iter : int\r\n Maximum number of iterations of the SMACOF algorithm for a single run\r\n eps : float\r\n relative tolerance w.r.t stress to declare converge\r\n\r\n Returns\r\n -------\r\n npos : ndarray, shape (R, dim)\r\n Embedded coordinates of the interpolated point cloud (defined with\r\n one isometry)\r\n \"\"\"\r\n\r\n rng = np.random.RandomState(seed=3)\r\n\r\n mds = manifold.MDS(\r\n dim,\r\n max_iter=max_iter,\r\n eps=1e-9,\r\n dissimilarity='precomputed',\r\n n_init=1)\r\n pos = mds.fit(C).embedding_\r\n\r\n nmds = manifold.MDS(\r\n 2,\r\n max_iter=max_iter,\r\n eps=1e-9,\r\n dissimilarity=\"precomputed\",\r\n random_state=rng,\r\n n_init=1)\r\n npos = nmds.fit_transform(C, init=pos)\r\n\r\n return npos" - ], - "outputs": [], + "Smacof MDS\n----------\n\nThis function allows to find an embedding of points given a dissimilarity matrix\nthat will be given by the output of the algorithm\n\n" + ], + "cell_type": "markdown" + }, + { + "execution_count": null, "metadata": { "collapsed": false - } - }, - { + }, + "outputs": [], "source": [ - "Data preparation\r\n ----------------\r\n\r\n The four distributions are constructed from 4 simple images\r\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + "def smacof_mds(C, dim, max_iter=3000, eps=1e-9):\n \"\"\"\n Returns an interpolated point cloud following the dissimilarity matrix C\n using SMACOF multidimensional scaling (MDS) in specific dimensionned\n target space\n\n Parameters\n ----------\n C : ndarray, shape (ns, ns)\n dissimilarity matrix\n dim : int\n dimension of the targeted space\n max_iter : int\n Maximum number of iterations of the SMACOF algorithm for a single run\n eps : float\n relative tolerance w.r.t stress to declare converge\n\n Returns\n -------\n npos : ndarray, shape (R, dim)\n Embedded coordinates of the interpolated point cloud (defined with\n one isometry)\n \"\"\"\n\n rng = np.random.RandomState(seed=3)\n\n mds = manifold.MDS(\n dim,\n max_iter=max_iter,\n eps=1e-9,\n dissimilarity='precomputed',\n n_init=1)\n pos = mds.fit(C).embedding_\n\n nmds = manifold.MDS(\n 2,\n max_iter=max_iter,\n eps=1e-9,\n dissimilarity=\"precomputed\",\n random_state=rng,\n n_init=1)\n npos = nmds.fit_transform(C, init=pos)\n\n return npos" + ], + "cell_type": "code" + }, { - "execution_count": null, - "cell_type": "code", + "metadata": {}, "source": [ - "def im2mat(I):\r\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\r\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\r\n\r\n\r\nsquare = spi.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256\r\ncross = spi.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256\r\ntriangle = spi.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256\r\nstar = spi.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256\r\n\r\nshapes = [square, cross, triangle, star]\r\n\r\nS = 4\r\nxs = [[] for i in range(S)]\r\n\r\n\r\nfor nb in range(4):\r\n for i in range(8):\r\n for j in range(8):\r\n if shapes[nb][i, j] < 0.95:\r\n xs[nb].append([j, 8 - i])\r\n\r\nxs = np.array([np.array(xs[0]), np.array(xs[1]),\r\n np.array(xs[2]), np.array(xs[3])])" - ], - "outputs": [], + "Data preparation\n----------------\n\nThe four distributions are constructed from 4 simple images\n\n" + ], + "cell_type": "markdown" + }, + { + "execution_count": null, "metadata": { "collapsed": false - } - }, - { + }, + "outputs": [], "source": [ - "Barycenter computation\r\n----------------------\r\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + "def im2mat(I):\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\nsquare = spi.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256\ncross = spi.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256\ntriangle = spi.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256\nstar = spi.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256\n\nshapes = [square, cross, triangle, star]\n\nS = 4\nxs = [[] for i in range(S)]\n\n\nfor nb in range(4):\n for i in range(8):\n for j in range(8):\n if shapes[nb][i, j] < 0.95:\n xs[nb].append([j, 8 - i])\n\nxs = np.array([np.array(xs[0]), np.array(xs[1]),\n np.array(xs[2]), np.array(xs[3])])" + ], + "cell_type": "code" + }, { - "execution_count": null, - "cell_type": "code", + "metadata": {}, "source": [ - "ns = [len(xs[s]) for s in range(S)]\r\nn_samples = 30\r\n\r\n\"\"\"Compute all distances matrices for the four shapes\"\"\"\r\nCs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)]\r\nCs = [cs / cs.max() for cs in Cs]\r\n\r\nps = [ot.unif(ns[s]) for s in range(S)]\r\np = ot.unif(n_samples)\r\n\r\n\r\nlambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]]\r\n\r\nCt01 = [0 for i in range(2)]\r\nfor i in range(2):\r\n Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]],\r\n [ps[0], ps[1]\r\n ], p, lambdast[i], 'square_loss', 5e-4,\r\n max_iter=100, tol=1e-3)\r\n\r\nCt02 = [0 for i in range(2)]\r\nfor i in range(2):\r\n Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]],\r\n [ps[0], ps[2]\r\n ], p, lambdast[i], 'square_loss', 5e-4,\r\n max_iter=100, tol=1e-3)\r\n\r\nCt13 = [0 for i in range(2)]\r\nfor i in range(2):\r\n Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]],\r\n [ps[1], ps[3]\r\n ], p, lambdast[i], 'square_loss', 5e-4,\r\n max_iter=100, tol=1e-3)\r\n\r\nCt23 = [0 for i in range(2)]\r\nfor i in range(2):\r\n Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]],\r\n [ps[2], ps[3]\r\n ], p, lambdast[i], 'square_loss', 5e-4,\r\n max_iter=100, tol=1e-3)" - ], - "outputs": [], + "Barycenter computation\n----------------------\n\n" + ], + "cell_type": "markdown" + }, + { + "execution_count": null, "metadata": { "collapsed": false - } - }, - { + }, + "outputs": [], "source": [ - "Visualization\r\n -------------\r\n\r\n The PCA helps in getting consistency between the rotations\r\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + "ns = [len(xs[s]) for s in range(S)]\nn_samples = 30\n\n\"\"\"Compute all distances matrices for the four shapes\"\"\"\nCs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)]\nCs = [cs / cs.max() for cs in Cs]\n\nps = [ot.unif(ns[s]) for s in range(S)]\np = ot.unif(n_samples)\n\n\nlambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]]\n\nCt01 = [0 for i in range(2)]\nfor i in range(2):\n Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]],\n [ps[0], ps[1]\n ], p, lambdast[i], 'square_loss', # 5e-4,\n max_iter=100, tol=1e-3)\n\nCt02 = [0 for i in range(2)]\nfor i in range(2):\n Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]],\n [ps[0], ps[2]\n ], p, lambdast[i], 'square_loss', # 5e-4,\n max_iter=100, tol=1e-3)\n\nCt13 = [0 for i in range(2)]\nfor i in range(2):\n Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]],\n [ps[1], ps[3]\n ], p, lambdast[i], 'square_loss', # 5e-4,\n max_iter=100, tol=1e-3)\n\nCt23 = [0 for i in range(2)]\nfor i in range(2):\n Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]],\n [ps[2], ps[3]\n ], p, lambdast[i], 'square_loss', # 5e-4,\n max_iter=100, tol=1e-3)" + ], + "cell_type": "code" + }, { - "execution_count": null, - "cell_type": "code", + "metadata": {}, "source": [ - "clf = PCA(n_components=2)\r\nnpos = [0, 0, 0, 0]\r\nnpos = [smacof_mds(Cs[s], 2) for s in range(S)]\r\n\r\nnpost01 = [0, 0]\r\nnpost01 = [smacof_mds(Ct01[s], 2) for s in range(2)]\r\nnpost01 = [clf.fit_transform(npost01[s]) for s in range(2)]\r\n\r\nnpost02 = [0, 0]\r\nnpost02 = [smacof_mds(Ct02[s], 2) for s in range(2)]\r\nnpost02 = [clf.fit_transform(npost02[s]) for s in range(2)]\r\n\r\nnpost13 = [0, 0]\r\nnpost13 = [smacof_mds(Ct13[s], 2) for s in range(2)]\r\nnpost13 = [clf.fit_transform(npost13[s]) for s in range(2)]\r\n\r\nnpost23 = [0, 0]\r\nnpost23 = [smacof_mds(Ct23[s], 2) for s in range(2)]\r\nnpost23 = [clf.fit_transform(npost23[s]) for s in range(2)]\r\n\r\n\r\nfig = pl.figure(figsize=(10, 10))\r\n\r\nax1 = pl.subplot2grid((4, 4), (0, 0))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r')\r\n\r\nax2 = pl.subplot2grid((4, 4), (0, 1))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b')\r\n\r\nax3 = pl.subplot2grid((4, 4), (0, 2))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b')\r\n\r\nax4 = pl.subplot2grid((4, 4), (0, 3))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r')\r\n\r\nax5 = pl.subplot2grid((4, 4), (1, 0))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b')\r\n\r\nax6 = pl.subplot2grid((4, 4), (1, 3))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b')\r\n\r\nax7 = pl.subplot2grid((4, 4), (2, 0))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b')\r\n\r\nax8 = pl.subplot2grid((4, 4), (2, 3))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b')\r\n\r\nax9 = pl.subplot2grid((4, 4), (3, 0))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r')\r\n\r\nax10 = pl.subplot2grid((4, 4), (3, 1))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b')\r\n\r\nax11 = pl.subplot2grid((4, 4), (3, 2))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b')\r\n\r\nax12 = pl.subplot2grid((4, 4), (3, 3))\r\npl.xlim((-1, 1))\r\npl.ylim((-1, 1))\r\nax12.scatter(npos[3][:, 0], npos[3][:, 1], color='r')" - ], - "outputs": [], + "Visualization\n-------------\n\nThe PCA helps in getting consistency between the rotations\n\n" + ], + "cell_type": "markdown" + }, + { + "execution_count": null, "metadata": { "collapsed": false - } + }, + "outputs": [], + "source": [ + "clf = PCA(n_components=2)\nnpos = [0, 0, 0, 0]\nnpos = [smacof_mds(Cs[s], 2) for s in range(S)]\n\nnpost01 = [0, 0]\nnpost01 = [smacof_mds(Ct01[s], 2) for s in range(2)]\nnpost01 = [clf.fit_transform(npost01[s]) for s in range(2)]\n\nnpost02 = [0, 0]\nnpost02 = [smacof_mds(Ct02[s], 2) for s in range(2)]\nnpost02 = [clf.fit_transform(npost02[s]) for s in range(2)]\n\nnpost13 = [0, 0]\nnpost13 = [smacof_mds(Ct13[s], 2) for s in range(2)]\nnpost13 = [clf.fit_transform(npost13[s]) for s in range(2)]\n\nnpost23 = [0, 0]\nnpost23 = [smacof_mds(Ct23[s], 2) for s in range(2)]\nnpost23 = [clf.fit_transform(npost23[s]) for s in range(2)]\n\n\nfig = pl.figure(figsize=(10, 10))\n\nax1 = pl.subplot2grid((4, 4), (0, 0))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r')\n\nax2 = pl.subplot2grid((4, 4), (0, 1))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b')\n\nax3 = pl.subplot2grid((4, 4), (0, 2))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b')\n\nax4 = pl.subplot2grid((4, 4), (0, 3))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r')\n\nax5 = pl.subplot2grid((4, 4), (1, 0))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b')\n\nax6 = pl.subplot2grid((4, 4), (1, 3))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b')\n\nax7 = pl.subplot2grid((4, 4), (2, 0))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b')\n\nax8 = pl.subplot2grid((4, 4), (2, 3))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b')\n\nax9 = pl.subplot2grid((4, 4), (3, 0))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r')\n\nax10 = pl.subplot2grid((4, 4), (3, 1))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b')\n\nax11 = pl.subplot2grid((4, 4), (3, 2))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b')\n\nax12 = pl.subplot2grid((4, 4), (3, 3))\npl.xlim((-1, 1))\npl.ylim((-1, 1))\nax12.scatter(npos[3][:, 0], npos[3][:, 1], color='r')" + ], + "cell_type": "code" } - ], + ], "metadata": { - "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", + "name": "python", "codemirror_mode": { - "version": 2, - "name": "ipython" - } + "name": "ipython", + "version": 3 + }, + "nbconvert_exporter": "python", + "version": "3.5.2", + "pygments_lexer": "ipython3", + "file_extension": ".py", + "mimetype": "text/x-python" + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3", + "language": "python" } - } + }, + "nbformat_minor": 0, + "nbformat": 4 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_gromov_barycenter.py b/docs/source/auto_examples/plot_gromov_barycenter.py index 180b0cf..58fc51a 100644 --- a/docs/source/auto_examples/plot_gromov_barycenter.py +++ b/docs/source/auto_examples/plot_gromov_barycenter.py @@ -132,28 +132,28 @@ Ct01 = [0 for i in range(2)] for i in range(2): Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]], [ps[0], ps[1] - ], p, lambdast[i], 'square_loss', 5e-4, + ], p, lambdast[i], 'square_loss', # 5e-4, max_iter=100, tol=1e-3) Ct02 = [0 for i in range(2)] for i in range(2): Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]], [ps[0], ps[2] - ], p, lambdast[i], 'square_loss', 5e-4, + ], p, lambdast[i], 'square_loss', # 5e-4, max_iter=100, tol=1e-3) Ct13 = [0 for i in range(2)] for i in range(2): Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]], [ps[1], ps[3] - ], p, lambdast[i], 'square_loss', 5e-4, + ], p, lambdast[i], 'square_loss', # 5e-4, max_iter=100, tol=1e-3) Ct23 = [0 for i in range(2)] for i in range(2): Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]], [ps[2], ps[3] - ], p, lambdast[i], 'square_loss', 5e-4, + ], p, lambdast[i], 'square_loss', # 5e-4, max_iter=100, tol=1e-3) diff --git a/docs/source/auto_examples/plot_gromov_barycenter.rst b/docs/source/auto_examples/plot_gromov_barycenter.rst index ca2d4e9..531ee22 100644 --- a/docs/source/auto_examples/plot_gromov_barycenter.rst +++ b/docs/source/auto_examples/plot_gromov_barycenter.rst @@ -14,285 +14,285 @@ computation in POT. .. code-block:: python - - # Author: Erwan Vautier - # Nicolas Courty - # - # License: MIT License - - - import numpy as np - import scipy as sp - - import scipy.ndimage as spi - import matplotlib.pylab as pl - from sklearn import manifold - from sklearn.decomposition import PCA - - import ot - + # Author: Erwan Vautier + # Nicolas Courty + # + # License: MIT License + import numpy as np + import scipy as sp + import scipy.ndimage as spi + import matplotlib.pylab as pl + from sklearn import manifold + from sklearn.decomposition import PCA + import ot -Smacof MDS - ---------- - - This function allows to find an embedding of points given a dissimilarity matrix - that will be given by the output of the algorithm + + + + + + +Smacof MDS +---------- + +This function allows to find an embedding of points given a dissimilarity matrix +that will be given by the output of the algorithm .. code-block:: python - - - def smacof_mds(C, dim, max_iter=3000, eps=1e-9): - """ - Returns an interpolated point cloud following the dissimilarity matrix C - using SMACOF multidimensional scaling (MDS) in specific dimensionned - target space - - Parameters - ---------- - C : ndarray, shape (ns, ns) - dissimilarity matrix - dim : int - dimension of the targeted space - max_iter : int - Maximum number of iterations of the SMACOF algorithm for a single run - eps : float - relative tolerance w.r.t stress to declare converge - - Returns - ------- - npos : ndarray, shape (R, dim) - Embedded coordinates of the interpolated point cloud (defined with - one isometry) - """ - - rng = np.random.RandomState(seed=3) - - mds = manifold.MDS( - dim, - max_iter=max_iter, - eps=1e-9, - dissimilarity='precomputed', - n_init=1) - pos = mds.fit(C).embedding_ - - nmds = manifold.MDS( - 2, - max_iter=max_iter, - eps=1e-9, - dissimilarity="precomputed", - random_state=rng, - n_init=1) - npos = nmds.fit_transform(C, init=pos) - - return npos - - - - - - - - -Data preparation - ---------------- - - The four distributions are constructed from 4 simple images + + + def smacof_mds(C, dim, max_iter=3000, eps=1e-9): + """ + Returns an interpolated point cloud following the dissimilarity matrix C + using SMACOF multidimensional scaling (MDS) in specific dimensionned + target space + + Parameters + ---------- + C : ndarray, shape (ns, ns) + dissimilarity matrix + dim : int + dimension of the targeted space + max_iter : int + Maximum number of iterations of the SMACOF algorithm for a single run + eps : float + relative tolerance w.r.t stress to declare converge + + Returns + ------- + npos : ndarray, shape (R, dim) + Embedded coordinates of the interpolated point cloud (defined with + one isometry) + """ + + rng = np.random.RandomState(seed=3) + + mds = manifold.MDS( + dim, + max_iter=max_iter, + eps=1e-9, + dissimilarity='precomputed', + n_init=1) + pos = mds.fit(C).embedding_ + + nmds = manifold.MDS( + 2, + max_iter=max_iter, + eps=1e-9, + dissimilarity="precomputed", + random_state=rng, + n_init=1) + npos = nmds.fit_transform(C, init=pos) + + return npos + + + + + + + + +Data preparation +---------------- + +The four distributions are constructed from 4 simple images .. code-block:: python - - - def im2mat(I): - """Converts and image to matrix (one pixel per line)""" - return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) - - - square = spi.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256 - cross = spi.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256 - triangle = spi.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256 - star = spi.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256 - - shapes = [square, cross, triangle, star] - - S = 4 - xs = [[] for i in range(S)] - - - for nb in range(4): - for i in range(8): - for j in range(8): - if shapes[nb][i, j] < 0.95: - xs[nb].append([j, 8 - i]) - - xs = np.array([np.array(xs[0]), np.array(xs[1]), - np.array(xs[2]), np.array(xs[3])]) - + def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + + + square = spi.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256 + cross = spi.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256 + triangle = spi.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256 + star = spi.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256 + + shapes = [square, cross, triangle, star] + + S = 4 + xs = [[] for i in range(S)] + + for nb in range(4): + for i in range(8): + for j in range(8): + if shapes[nb][i, j] < 0.95: + xs[nb].append([j, 8 - i]) + xs = np.array([np.array(xs[0]), np.array(xs[1]), + np.array(xs[2]), np.array(xs[3])]) -Barycenter computation ----------------------- + + + + + +Barycenter computation +---------------------- .. code-block:: python - - - ns = [len(xs[s]) for s in range(S)] - n_samples = 30 - - """Compute all distances matrices for the four shapes""" - Cs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)] - Cs = [cs / cs.max() for cs in Cs] - - ps = [ot.unif(ns[s]) for s in range(S)] - p = ot.unif(n_samples) - - - lambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]] - - Ct01 = [0 for i in range(2)] - for i in range(2): - Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]], - [ps[0], ps[1] - ], p, lambdast[i], 'square_loss', 5e-4, - max_iter=100, tol=1e-3) - - Ct02 = [0 for i in range(2)] - for i in range(2): - Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]], - [ps[0], ps[2] - ], p, lambdast[i], 'square_loss', 5e-4, - max_iter=100, tol=1e-3) - - Ct13 = [0 for i in range(2)] - for i in range(2): - Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]], - [ps[1], ps[3] - ], p, lambdast[i], 'square_loss', 5e-4, - max_iter=100, tol=1e-3) - - Ct23 = [0 for i in range(2)] - for i in range(2): - Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]], - [ps[2], ps[3] - ], p, lambdast[i], 'square_loss', 5e-4, - max_iter=100, tol=1e-3) - - - - - - - - -Visualization - ------------- - - The PCA helps in getting consistency between the rotations + + + ns = [len(xs[s]) for s in range(S)] + n_samples = 30 + + """Compute all distances matrices for the four shapes""" + Cs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)] + Cs = [cs / cs.max() for cs in Cs] + + ps = [ot.unif(ns[s]) for s in range(S)] + p = ot.unif(n_samples) + + + lambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]] + + Ct01 = [0 for i in range(2)] + for i in range(2): + Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]], + [ps[0], ps[1] + ], p, lambdast[i], 'square_loss', # 5e-4, + max_iter=100, tol=1e-3) + + Ct02 = [0 for i in range(2)] + for i in range(2): + Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]], + [ps[0], ps[2] + ], p, lambdast[i], 'square_loss', # 5e-4, + max_iter=100, tol=1e-3) + + Ct13 = [0 for i in range(2)] + for i in range(2): + Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]], + [ps[1], ps[3] + ], p, lambdast[i], 'square_loss', # 5e-4, + max_iter=100, tol=1e-3) + + Ct23 = [0 for i in range(2)] + for i in range(2): + Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]], + [ps[2], ps[3] + ], p, lambdast[i], 'square_loss', # 5e-4, + max_iter=100, tol=1e-3) + + + + + + + + +Visualization +------------- + +The PCA helps in getting consistency between the rotations .. code-block:: python - - - clf = PCA(n_components=2) - npos = [0, 0, 0, 0] - npos = [smacof_mds(Cs[s], 2) for s in range(S)] - - npost01 = [0, 0] - npost01 = [smacof_mds(Ct01[s], 2) for s in range(2)] - npost01 = [clf.fit_transform(npost01[s]) for s in range(2)] - - npost02 = [0, 0] - npost02 = [smacof_mds(Ct02[s], 2) for s in range(2)] - npost02 = [clf.fit_transform(npost02[s]) for s in range(2)] - - npost13 = [0, 0] - npost13 = [smacof_mds(Ct13[s], 2) for s in range(2)] - npost13 = [clf.fit_transform(npost13[s]) for s in range(2)] - - npost23 = [0, 0] - npost23 = [smacof_mds(Ct23[s], 2) for s in range(2)] - npost23 = [clf.fit_transform(npost23[s]) for s in range(2)] - - - fig = pl.figure(figsize=(10, 10)) - - ax1 = pl.subplot2grid((4, 4), (0, 0)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r') - - ax2 = pl.subplot2grid((4, 4), (0, 1)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b') - - ax3 = pl.subplot2grid((4, 4), (0, 2)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b') - - ax4 = pl.subplot2grid((4, 4), (0, 3)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r') - - ax5 = pl.subplot2grid((4, 4), (1, 0)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b') - - ax6 = pl.subplot2grid((4, 4), (1, 3)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b') - - ax7 = pl.subplot2grid((4, 4), (2, 0)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b') - - ax8 = pl.subplot2grid((4, 4), (2, 3)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b') - - ax9 = pl.subplot2grid((4, 4), (3, 0)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r') - - ax10 = pl.subplot2grid((4, 4), (3, 1)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b') - - ax11 = pl.subplot2grid((4, 4), (3, 2)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b') - - ax12 = pl.subplot2grid((4, 4), (3, 3)) - pl.xlim((-1, 1)) - pl.ylim((-1, 1)) - ax12.scatter(npos[3][:, 0], npos[3][:, 1], color='r') + + + clf = PCA(n_components=2) + npos = [0, 0, 0, 0] + npos = [smacof_mds(Cs[s], 2) for s in range(S)] + + npost01 = [0, 0] + npost01 = [smacof_mds(Ct01[s], 2) for s in range(2)] + npost01 = [clf.fit_transform(npost01[s]) for s in range(2)] + + npost02 = [0, 0] + npost02 = [smacof_mds(Ct02[s], 2) for s in range(2)] + npost02 = [clf.fit_transform(npost02[s]) for s in range(2)] + + npost13 = [0, 0] + npost13 = [smacof_mds(Ct13[s], 2) for s in range(2)] + npost13 = [clf.fit_transform(npost13[s]) for s in range(2)] + + npost23 = [0, 0] + npost23 = [smacof_mds(Ct23[s], 2) for s in range(2)] + npost23 = [clf.fit_transform(npost23[s]) for s in range(2)] + + + fig = pl.figure(figsize=(10, 10)) + + ax1 = pl.subplot2grid((4, 4), (0, 0)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r') + + ax2 = pl.subplot2grid((4, 4), (0, 1)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b') + + ax3 = pl.subplot2grid((4, 4), (0, 2)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b') + + ax4 = pl.subplot2grid((4, 4), (0, 3)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r') + + ax5 = pl.subplot2grid((4, 4), (1, 0)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b') + + ax6 = pl.subplot2grid((4, 4), (1, 3)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b') + + ax7 = pl.subplot2grid((4, 4), (2, 0)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b') + + ax8 = pl.subplot2grid((4, 4), (2, 3)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b') + + ax9 = pl.subplot2grid((4, 4), (3, 0)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r') + + ax10 = pl.subplot2grid((4, 4), (3, 1)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b') + + ax11 = pl.subplot2grid((4, 4), (3, 2)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b') + + ax12 = pl.subplot2grid((4, 4), (3, 3)) + pl.xlim((-1, 1)) + pl.ylim((-1, 1)) + ax12.scatter(npos[3][:, 0], npos[3][:, 1], color='r') @@ -302,11 +302,13 @@ Visualization -**Total running time of the script:** ( 8 minutes 43.875 seconds) +**Total running time of the script:** ( 0 minutes 5.906 seconds) + +.. only :: html -.. container:: sphx-glr-footer + .. container:: sphx-glr-footer .. container:: sphx-glr-download @@ -319,6 +321,9 @@ Visualization :download:`Download Jupyter notebook: plot_gromov_barycenter.ipynb ` -.. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_optim_OTreg.ipynb b/docs/source/auto_examples/plot_optim_OTreg.ipynb index 333331b..02bf175 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.ipynb +++ b/docs/source/auto_examples/plot_optim_OTreg.ipynb @@ -1,144 +1,144 @@ { - "nbformat_minor": 0, - "nbformat": 4, "cells": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "\n# Regularized OT with generic solver\n\n\nIllustrates the use of the generic solver for regularized OT with\nuser-designed regularization term. It uses Conditional gradient as in [6] and\ngeneralized Conditional Gradient as proposed in [5][7].\n\n\n[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for\nDomain Adaptation, in IEEE Transactions on Pattern Analysis and Machine\nIntelligence , vol.PP, no.99, pp.1-1.\n\n[6] Ferradans, S., Papadakis, N., Peyr\u00e9, G., & Aujol, J. F. (2014).\nRegularized discrete optimal transport. SIAM Journal on Imaging Sciences,\n7(3), 1853-1882.\n\n[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized\nconditional gradient: analysis of convergence and applications.\narXiv preprint arXiv:1510.06567.\n\n\n\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "import numpy as np\nimport matplotlib.pylab as pl\nimport ot" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Generate data\n-------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std\nb = ot.datasets.get_1D_gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std\nb = ot.datasets.get_1D_gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Solve EMD\n---------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Solve EMD with Frobenius norm regularization\n--------------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% Example with Frobenius norm regularization\n\n\ndef f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg = 1e-1\n\nGl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n\npl.figure(3)\not.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg')" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "#%% Example with Frobenius norm regularization\n\n\ndef f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg = 1e-1\n\nGl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n\npl.figure(3)\not.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg')" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Solve EMD with entropic regularization\n--------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% Example with entropic regularization\n\n\ndef f(G):\n return np.sum(G * np.log(G))\n\n\ndef df(G):\n return np.log(G) + 1.\n\n\nreg = 1e-3\n\nGe = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg')" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "#%% Example with entropic regularization\n\n\ndef f(G):\n return np.sum(G * np.log(G))\n\n\ndef df(G):\n return np.log(G) + 1.\n\n\nreg = 1e-3\n\nGe = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg')" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Solve EMD with Frobenius norm + entropic regularization\n-------------------------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% Example with Frobenius norm + entropic regularization with gcg\n\n\ndef f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg1 = 1e-3\nreg2 = 1e-1\n\nGel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True)\n\npl.figure(5, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg')\npl.show()" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } + }, + "outputs": [], + "source": [ + "#%% Example with Frobenius norm + entropic regularization with gcg\n\n\ndef f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg1 = 1e-3\nreg2 = 1e-1\n\nGel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True)\n\npl.figure(5, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg')\npl.show()" + ], + "cell_type": "code" } - ], + ], "metadata": { - "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", + "name": "python", "codemirror_mode": { - "version": 2, - "name": "ipython" - } + "name": "ipython", + "version": 3 + }, + "nbconvert_exporter": "python", + "version": "3.5.2", + "pygments_lexer": "ipython3", + "file_extension": ".py", + "mimetype": "text/x-python" + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3", + "language": "python" } - } + }, + "nbformat_minor": 0, + "nbformat": 4 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_optim_OTreg.py b/docs/source/auto_examples/plot_optim_OTreg.py index e1a737e..92df016 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.py +++ b/docs/source/auto_examples/plot_optim_OTreg.py @@ -28,7 +28,7 @@ arXiv preprint arXiv:1510.06567. import numpy as np import matplotlib.pylab as pl import ot - +import ot.plot ############################################################################## # Generate data diff --git a/docs/source/auto_examples/plot_optim_OTreg.rst b/docs/source/auto_examples/plot_optim_OTreg.rst index 480149a..5927428 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.rst +++ b/docs/source/auto_examples/plot_optim_OTreg.rst @@ -35,7 +35,7 @@ arXiv preprint arXiv:1510.06567. import numpy as np import matplotlib.pylab as pl import ot - + import ot.plot @@ -636,11 +636,13 @@ Solve EMD with Frobenius norm + entropic regularization 4|1.609284e-01|-1.111407e-12 -**Total running time of the script:** ( 0 minutes 2.800 seconds) +**Total running time of the script:** ( 0 minutes 2.589 seconds) + +.. only :: html -.. container:: sphx-glr-footer + .. container:: sphx-glr-footer .. container:: sphx-glr-download @@ -653,6 +655,9 @@ Solve EMD with Frobenius norm + entropic regularization :download:`Download Jupyter notebook: plot_optim_OTreg.ipynb ` -.. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_d2.ipynb b/docs/source/auto_examples/plot_otda_d2.ipynb index 7bfcc9a..9c58e64 100644 --- a/docs/source/auto_examples/plot_otda_d2.ipynb +++ b/docs/source/auto_examples/plot_otda_d2.ipynb @@ -1,144 +1,144 @@ { - "nbformat_minor": 0, - "nbformat": 4, "cells": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "\n# OT for domain adaptation on empirical distributions\n\n\nThis example introduces a domain adaptation in a 2D setting. It explicits\nthe problem of domain adaptation and introduces some optimal transport\napproaches to solve it.\n\nQuantities such as optimal couplings, greater coupling coefficients and\ntransported samples are represented in order to give a visual understanding\nof what the transport methods are doing.\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport ot" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "generate data\n-------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "n_samples_source = 150\nn_samples_target = 150\n\nXs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source)\nXt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target)\n\n# Cost matrix\nM = ot.dist(Xs, Xt, metric='sqeuclidean')" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "n_samples_source = 150\nn_samples_target = 150\n\nXs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source)\nXt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target)\n\n# Cost matrix\nM = ot.dist(Xs, Xt, metric='sqeuclidean')" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Instantiate the different transport algorithms and fit them\n-----------------------------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# EMD Transport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport with Group lasso regularization\not_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)\not_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt)\n\n# transport source samples onto target samples\ntransp_Xs_emd = ot_emd.transform(Xs=Xs)\ntransp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)\ntransp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs)" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# EMD Transport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport with Group lasso regularization\not_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)\not_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt)\n\n# transport source samples onto target samples\ntransp_Xs_emd = ot_emd.transform(Xs=Xs)\ntransp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)\ntransp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs)" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Fig 1 : plots source and target samples + matrix of pairwise distance\n---------------------------------------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "pl.figure(1, figsize=(10, 10))\npl.subplot(2, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Source samples')\n\npl.subplot(2, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Target samples')\n\npl.subplot(2, 2, 3)\npl.imshow(M, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Matrix of pairwise distances')\npl.tight_layout()" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "pl.figure(1, figsize=(10, 10))\npl.subplot(2, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Source samples')\n\npl.subplot(2, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Target samples')\n\npl.subplot(2, 2, 3)\npl.imshow(M, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Matrix of pairwise distances')\npl.tight_layout()" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Fig 2 : plots optimal couplings for the different methods\n---------------------------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "pl.figure(2, figsize=(10, 6))\n\npl.subplot(2, 3, 1)\npl.imshow(ot_emd.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nEMDTransport')\n\npl.subplot(2, 3, 2)\npl.imshow(ot_sinkhorn.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornTransport')\n\npl.subplot(2, 3, 3)\npl.imshow(ot_lpl1.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornLpl1Transport')\n\npl.subplot(2, 3, 4)\not.plot.plot2D_samples_mat(Xs, Xt, ot_emd.coupling_, c=[.5, .5, 1])\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.title('Main coupling coefficients\\nEMDTransport')\n\npl.subplot(2, 3, 5)\not.plot.plot2D_samples_mat(Xs, Xt, ot_sinkhorn.coupling_, c=[.5, .5, 1])\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.title('Main coupling coefficients\\nSinkhornTransport')\n\npl.subplot(2, 3, 6)\not.plot.plot2D_samples_mat(Xs, Xt, ot_lpl1.coupling_, c=[.5, .5, 1])\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.title('Main coupling coefficients\\nSinkhornLpl1Transport')\npl.tight_layout()" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "pl.figure(2, figsize=(10, 6))\n\npl.subplot(2, 3, 1)\npl.imshow(ot_emd.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nEMDTransport')\n\npl.subplot(2, 3, 2)\npl.imshow(ot_sinkhorn.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornTransport')\n\npl.subplot(2, 3, 3)\npl.imshow(ot_lpl1.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornLpl1Transport')\n\npl.subplot(2, 3, 4)\not.plot.plot2D_samples_mat(Xs, Xt, ot_emd.coupling_, c=[.5, .5, 1])\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.title('Main coupling coefficients\\nEMDTransport')\n\npl.subplot(2, 3, 5)\not.plot.plot2D_samples_mat(Xs, Xt, ot_sinkhorn.coupling_, c=[.5, .5, 1])\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.title('Main coupling coefficients\\nSinkhornTransport')\n\npl.subplot(2, 3, 6)\not.plot.plot2D_samples_mat(Xs, Xt, ot_lpl1.coupling_, c=[.5, .5, 1])\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.title('Main coupling coefficients\\nSinkhornLpl1Transport')\npl.tight_layout()" + ], + "cell_type": "code" + }, { + "metadata": {}, "source": [ "Fig 3 : plot transported samples\n--------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ], + "cell_type": "markdown" + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# display transported samples\npl.figure(4, figsize=(10, 4))\npl.subplot(1, 3, 1)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nEmdTransport')\npl.legend(loc=0)\npl.xticks([])\npl.yticks([])\n\npl.subplot(1, 3, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nSinkhornTransport')\npl.xticks([])\npl.yticks([])\n\npl.subplot(1, 3, 3)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nSinkhornLpl1Transport')\npl.xticks([])\npl.yticks([])\n\npl.tight_layout()\npl.show()" - ], - "outputs": [], + "execution_count": null, "metadata": { "collapsed": false - } + }, + "outputs": [], + "source": [ + "# display transported samples\npl.figure(4, figsize=(10, 4))\npl.subplot(1, 3, 1)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nEmdTransport')\npl.legend(loc=0)\npl.xticks([])\npl.yticks([])\n\npl.subplot(1, 3, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nSinkhornTransport')\npl.xticks([])\npl.yticks([])\n\npl.subplot(1, 3, 3)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nSinkhornLpl1Transport')\npl.xticks([])\npl.yticks([])\n\npl.tight_layout()\npl.show()" + ], + "cell_type": "code" } - ], + ], "metadata": { - "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", + "name": "python", "codemirror_mode": { - "version": 2, - "name": "ipython" - } + "name": "ipython", + "version": 3 + }, + "nbconvert_exporter": "python", + "version": "3.5.2", + "pygments_lexer": "ipython3", + "file_extension": ".py", + "mimetype": "text/x-python" + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3", + "language": "python" } - } + }, + "nbformat_minor": 0, + "nbformat": 4 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_d2.py b/docs/source/auto_examples/plot_otda_d2.py index e53d7d6..70beb35 100644 --- a/docs/source/auto_examples/plot_otda_d2.py +++ b/docs/source/auto_examples/plot_otda_d2.py @@ -20,7 +20,7 @@ of what the transport methods are doing. import matplotlib.pylab as pl import ot - +import ot.plot ############################################################################## # generate data diff --git a/docs/source/auto_examples/plot_otda_d2.rst b/docs/source/auto_examples/plot_otda_d2.rst index 1bbe6d9..e5a60c4 100644 --- a/docs/source/auto_examples/plot_otda_d2.rst +++ b/docs/source/auto_examples/plot_otda_d2.rst @@ -27,7 +27,7 @@ of what the transport methods are doing. import matplotlib.pylab as pl import ot - + import ot.plot @@ -242,11 +242,13 @@ Fig 3 : plot transported samples -**Total running time of the script:** ( 0 minutes 47.000 seconds) +**Total running time of the script:** ( 0 minutes 39.829 seconds) + +.. only :: html -.. container:: sphx-glr-footer + .. container:: sphx-glr-footer .. container:: sphx-glr-download @@ -259,6 +261,9 @@ Fig 3 : plot transported samples :download:`Download Jupyter notebook: plot_otda_d2.ipynb ` -.. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ -- cgit v1.2.3 From fead9d6186020fdd37e167ddfa7a91c405188ce7 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 16 Feb 2018 15:04:22 +0100 Subject: update notebooks --- notebooks/plot_OT_1D.ipynb | 27 +- notebooks/plot_OT_L1_vs_L2.ipynb | 37 +-- notebooks/plot_barycenter_1D.ipynb | 200 +++++--------- notebooks/plot_gromov.ipynb | 274 +++++++++---------- notebooks/plot_gromov_barycenter.ipynb | 471 +++++++++++++++++---------------- notebooks/plot_optim_OTreg.ipynb | 404 +++++++++++++--------------- notebooks/plot_otda_d2.ipynb | 25 +- 7 files changed, 678 insertions(+), 760 deletions(-) diff --git a/notebooks/plot_OT_1D.ipynb b/notebooks/plot_OT_1D.ipynb index c4b2e67..93d156d 100644 --- a/notebooks/plot_OT_1D.ipynb +++ b/notebooks/plot_OT_1D.ipynb @@ -40,6 +40,7 @@ "import numpy as np\n", "import matplotlib.pylab as pl\n", "import ot\n", + "import ot.plot\n", "from ot.datasets import get_1D_gauss as gauss" ] }, @@ -94,9 +95,9 @@ "outputs": [ { "data": { - "image/png": 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TlpZG27ZtGT16NA8//HCx2wUaJ/w+XX1pU/OXR/VC61tU5vyNllAiyKJFeh2M2ajr19d2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\ncHFVKfBdXJU4CuLicVXicBK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FRHKkltK9J/Bl4CkzezJ675vAd4Cbzew44EXgiL40YPhwuOAC+Na34IEH4Lrr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\nUcEVEUnJ/weCS3jrpPMW6wAAAABJRU5ErkJggg==\n", 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\n", 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yfTl0MwB7P90UqJg4PRvlTJdCbbVo4aMcjjyyYl8I8MknOy+389xzUFLixzRq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\ngSsiEhMFrohITP4PrQ161dhEqnEAAAAASUVORK5CYII=\n", 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\n", 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+ "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -225,21 +226,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.5.2" } }, "nbformat": 4, diff --git a/notebooks/plot_OT_L1_vs_L2.ipynb b/notebooks/plot_OT_L1_vs_L2.ipynb index 5b49e82..9c3d24a 100644 --- a/notebooks/plot_OT_L1_vs_L2.ipynb +++ b/notebooks/plot_OT_L1_vs_L2.ipynb @@ -42,7 +42,8 @@ "\n", "import numpy as np\n", "import matplotlib.pylab as pl\n", - "import ot" + "import ot\n", + "import ot.plot" ] }, { @@ -63,9 +64,9 @@ "outputs": [ { "data": { - "image/png": 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\n", 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Lc/F+Ly4U1y1WvGb2LATXTMn91Z6F4mtgsaD8+295W8GSU6WSfUa22TrggQVLw/KBO8KO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\nfeLcgsUAAAAASUVORK5CYII=\n", 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\n", 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UCfj6awFvb5BHwc12mtFphwnnDNEI3GjCsXeUaXMreP/4I21fWfDevt12cRNr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\nRU5ErkJggg==\n", 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\n", 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\n", 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\n", 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SDp5/3gzIX6m3j3lJeznH9fKy0fSSxA/jpT28+SbwxhvhxMvEVODsWWkP7e3m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\n/wfPnU7C6ZZu4AAAAABJRU5ErkJggg==\n", + "image/png": 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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -351,21 +352,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.5.2" } }, "nbformat": 4, diff --git a/notebooks/plot_barycenter_1D.ipynb b/notebooks/plot_barycenter_1D.ipynb index 8acaeec..bf83aea 100644 --- a/notebooks/plot_barycenter_1D.ipynb +++ b/notebooks/plot_barycenter_1D.ipynb @@ -36,7 +36,48 @@ "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", 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\n", 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "# Author: Remi Flamary \n", "#\n", @@ -47,26 +88,12 @@ "import ot\n", "# necessary for 3d plot even if not used\n", "from mpl_toolkits.mplot3d import Axes3D # noqa\n", - "from matplotlib.collections import PolyCollection" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Generate data\n", - "-------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ + "from matplotlib.collections import PolyCollection\n", + "\n", + "#\n", + "# Generate data\n", + "# -------------\n", + "\n", "#%% parameters\n", "\n", "n = 100 # nb bins\n", @@ -84,74 +111,24 @@ "\n", "# loss matrix + normalization\n", "M = ot.utils.dist0(n)\n", - "M /= M.max()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plot data\n", - "---------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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F3UBWq+eD7W3tHiMiHiAZOGg/Hwz8E/iOMWZbexcwxjxpjMk3xuRnZmZ27S8I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\nf17bI7LGndgbl1FhyON24XG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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ + "M /= M.max()\n", + "\n", + "#\n", + "# Plot data\n", + "# ---------\n", + "\n", "#%% plot the distributions\n", "\n", "pl.figure(1, figsize=(6.4, 3))\n", "for i in range(n_distributions):\n", " pl.plot(x, A[:, i])\n", "pl.title('Distributions')\n", - "pl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Barycenter computation\n", - "----------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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0uQlqNS+5No3vSTvl/EGycTokzIX0UxBcCRpdDI27OY960c6SH6bISjRBiUgf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\n7lO97DJTnkA1LU11+nTVlStVS0vdsa0x+P2q2dm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- "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ + "pl.tight_layout()\n", + "\n", + "#\n", + "# Barycenter computation\n", + "# ----------------------\n", + "\n", "#%% barycenter computation\n", "\n", "alpha = 0.2 # 0<=alpha<=1\n", @@ -176,47 +153,12 @@ "pl.plot(x, bary_wass, 'g', label='Wasserstein')\n", "pl.legend()\n", "pl.title('Barycenters')\n", - "pl.tight_layout()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Barycentric interpolation\n", - "-------------------------\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "data": { - "image/png": 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fh8/nQzqdhslkyou47HZ7lmAZzkIDg/IwBMpgUcitYQKyhUmSJExOTmJychKrV6/G7t27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\nvr6ef/qnfwJS51mPPvoo7777bsp+lsVi4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g448/hlarTcuI9Oqrr+LOO++EIAi4/fbbce+990bcHwgEcNttt+Hjjz9GdXU1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\nmH6W0+ns46Zrs9n6yAtBdLGzsrKSjIwMs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TBk1q1bx0svvQREFQa2bdt2QgQniI41f/TR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\nsUcynvMIGwAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ + "pl.tight_layout()\n", + "\n", + "#\n", + "# Barycentric interpolation\n", + "# -------------------------\n", + "\n", "#%% barycenter interpolation\n", "\n", "n_alpha = 11\n", @@ -286,21 +228,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.5.2" } }, "nbformat": 4, diff --git a/notebooks/plot_gromov.ipynb b/notebooks/plot_gromov.ipynb index 11c19d3..87937f0 100644 --- a/notebooks/plot_gromov.ipynb +++ b/notebooks/plot_gromov.ipynb @@ -30,161 +30,58 @@ "metadata": { "collapsed": false }, - "outputs": [], - "source": [ - "# Author: Erwan Vautier \r\n", - "# Nicolas Courty \r\n", - "#\r\n", - "# License: MIT License\r\n", - "\r\n", - "import scipy as sp\r\n", - "import numpy as np\r\n", - "import matplotlib.pylab as pl\r\n", - "from mpl_toolkits.mplot3d import Axes3D # noqa\r\n", - "import ot" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Sample two Gaussian distributions (2D and 3D)\r\n", - " ---------------------------------------------\r\n", - "\r\n", - " The Gromov-Wasserstein distance allows to compute distances with samples that\r\n", - " do not belong to the same metric space. For demonstration purpose, we sample\r\n", - " two Gaussian distributions in 2- and 3-dimensional spaces.\r\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "n_samples = 30 # nb samples\r\n", - "\r\n", - "mu_s = np.array([0, 0])\r\n", - "cov_s = np.array([[1, 0], [0, 1]])\r\n", - "\r\n", - "mu_t = np.array([4, 4, 4])\r\n", - "cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\r\n", - "\r\n", - "\r\n", - "xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)\r\n", - "P = sp.linalg.sqrtm(cov_t)\r\n", - "xt = np.random.randn(n_samples, 3).dot(P) + mu_t" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Plotting the distributions\r\n", - "--------------------------\r\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, "outputs": [ { "data": { - "image/png": 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7nZKSEq5du4bD4WB0dJQbN24wMjIStqF4rHL1kXJqhf0wcsOxjKyPay479PpH\nPZe7DV3XuXnzJmfPng0rwpEgpWRsbIzk5OR971JdXl6mt7eXq1evhhWslJQUysvLqa2txWaz0dLS\nQmtrK4uLizGNYg9a2E1MC4PLly8HK4lu3bpFa2sr8/Pz297TYdoJwCkX9pOUGz6u81VpoaPBMAya\nm5uDvUFjQU9PDzabLeK2djsJu9vt5vbt21RXV0eV7zd7ndbV1VFcXMzU1BSrq6sMDw+HjXiPinDl\njjabjcLCwuA9TU9PB99MQtcXlLAfc45rZH2cUZ9NdEgpaWtrIyMjg8LCwrDHR7LIOTIywvr6OgUF\nBftqZu33+2lububihQskDw8jbt6E6emIxgsdNzU1lfLycpKSktA0jebmZtrb26POW4dyUBF7JCkU\n854qKiqora3FbrcH1xdmZ2dxu917EnZd16mpqeGBBx6I6ry7QthjmRs+jMj6qHPZsX54qag/OgYG\nBrBarRt2ge7GTu3xTKanp5mYmODKlSu79kjdbtzQY6WU3L59m6L8fLK/+120p5/G8jd/g+WppxA9\nPRGNuRlN0ygsLKS+vp78/Pxg3np0dDRQfeJ2H2lPyb08LKxWK/n5+dTV1VFWVsb8/Dyf+MQnmJyc\npL+/P6qxvvzlL3Px4sWozoG7RNhPWsR41PM9rmmhu4Xi4mIqKioiFpTdjL2WlpaCuXCLxbLnZtYA\n3d3dJCcnU7i6imhqgtLSwAaglBQs3/zmvvLlQgjS09O5fPky1dXVWAcHsVdVkexwkFRQgOVHPwo7\nxmHl2KMhOTmZCxcu8MlPfpLExESeeuqpiM8dHR3lhz/8If/hP/yHqK97Vwj7QXHUkfVxRqWs9o7N\nZotKTHYS9vX19WCbPDMXvtdm1iMjI7hcrkDf0/V1hKa99I+bmAiLi7sPputot29jeeEFxPj4rofa\nbTbOPvIIiePjYBiI1VXi3vteZn79a3Rdj2jusSJWlgJCCMrKyvjLv/zLiM/52Mc+xuc+97k9VdMo\nS4F9cDeI1F4fXk888dLnI8SRvk2feraLwr1eL83NzVRWVm5okxdtxA4BG4Px8XHq6+sD5xcWIi0W\nWFmBxETE6Cj6ne9ti65je+YZLDduBCxxhcD32GNw6dL2xy8tBcRfyuDDQ9jtWJubabRYSEtLo6Cg\ngOTk5A3HHGWOPRzRWvb+4Ac/IDc3l7q6Op7dQ89VFbErduVueHiddDZH4bqu09TUxPnz57dUwEQr\n7C6Xi665RTyeAAAgAElEQVSurmAqBwCHA+PRRwOiOjGBUV+P/Pf/fuf5dXVhaWzEKCnBKCpCpqdj\n+9rXgB027SQng8WyIRcoDIPsykquXbtGdnY2w42NLDz8MOK3fgv7Y48henqOJhUT4WcZ7eLpL3/5\nS77//e9TWlrK7/7u7/Kzn/2Md7/73RGfr4RdceColFV0RCtOocIupaSlpYWCggJyNjkabj42HD6f\nj6mpqW1tB2R5Ofof/RH6l7+M8b73wW47Yl2ugFCHpG7EygpiJ1G0WnF/8YuBFE9iIiQl4X/jG9Ff\n8QqEEGRlZFDz/e+TNz+POyuLhaEhfH/4h3hnZiK6r2jYKRVjee454t/zHuLf/nZsX/5y4B53wev1\nRiXsf/zHf8zo6CiDg4N861vf4jWveQ3f+MY3Ij5fpWIUB46K+g+W0Ci8s7MzsMC5Q5lkpBG7rusM\nDQ2RlZUVSHnsA1lcHEjdLC1BUhLa+Dh6bS3skuLwP/gg69XVaE1NSKcT/bWvfenBsLQUaF1XWEgq\nINPS8A8PM9PYyHxBAVNTU+Tk5MTMlXGzsGtdXdi/8AWMzEzIzcX6L/8Cdju+Rx7ZcZy9ljvuFRWx\nKxQnHDMKHxwcxOfzBRY4dyASYTfLGjMzM2NiwytzcvB97GMQF4eYnUW/5x58Dz0U9jyjsjLgq/66\n123sO5qYGHgDuLMBSEhJnMVC0cWLZGRksLq6SkNDA319fbjCRNJh575Njl3r7Ax8hnfmYeTmYmlo\n2HWcaCP2UF796lfzgx/8IKpzVMSuUJxwNE1jZmaGxcVFamtrd03lRCLsvb29xMfHk5WVxcLCQkzm\naFy4gPe//beNX9xrhUtcHL6HH8b23/974H50Hd+/+Tf4Cwuxzsxw9uxZzpw5w8zMDJ2dnQghKCgo\nICsrK+oofruIXaamIqQMfI5CINbXkXfaCe7EYTayBiXsCsWJx+PxMDc3x/333x9WuLbNses6TE1B\naipjS0usrKxQU1NzrB0Z9de8BqO0FG1sDJmRgXH5MnJxMSjCmqaRl5dHXl4ea2trwQ5J2dnZ5Ofn\nRyyy2wm7/rKXof/kJ1ja20HTkHY7vocf3nWc/UTse0EJu0JxzIhm8XR1dZWFhQUqKioiar6xJWIf\nHsb2e78XqG7x+/H9u39H1ac/jRDieLbGMwzE5CT4fMjcXPSysrCnJCUlcf78eXRdZ2Zmhvb29qBv\nTWZmZtjPe8v34+LwPvkkWnMzwuPBKC8PNNDehcP2iomJsAsh3gR8GbAAfyml/EwsxlUoFDvj8Xi4\ndesWeXl5EXdU2izW1o98BEZHMVJScK+ucu5//2/0t70NWVd3/ITdMLD8/OdofX1ITUPYbPjf8pZg\nP9NwpYmhfU5XV1cZGxujr6+P3NxcnE5n5MK7tITll7+E1VVkZWVYUYfA4mlqampk48eAfS+eCiEs\nwJ8DbwYuAe8UQuyw80ChUMQCv99PU1MTFRUVxMfHR1zCuDkVo7W3I5OTcbtcxCclIaREdHYCx6+Z\ntRgbQ+vrC9TCFxRgxMcHBDb0mAjfdpKTkykvL6eurg673c7t27d3td0NsrqK/UtfwvpP/4S1oQHb\nn/85WpiFUzj8VEwsqmLuAXqllP1SSi/wLeC3YzCuQqHYBrPHaHFxMVlZWfvyfzEKCvAuLGCPi0O7\n4/sgCwq2PXY3pqamaGhoOFgLXq8XGbqGkJgIq6vBv+7lIWSxWMjPz6e+vn6D7e7w8DBer3fLmFpH\nB2J2FllUhMzLQ+bmYo3Ax+YkCnsBMBLy99E7X7trUXXbioNCSkl7ezsZGRnk36nE2Kv/i5SSjsce\nQ0tKwurzwdoaxlvfinzVq7YcuxvLy8v09/cH3SObm5vp6OiIWQNoE5mZGSh7XF8P5tqNEAfM/e48\nDbXd1TSNW7du4Xa7NzYE2fw5a1pE1T2ntipGCPEw8DAE3OtOM08+qcRdcTD09/cjhNhg6RuNsIfa\n9vb39+O7dAn5r/+Kv7sb0tORFRUba8bD4PF4gk03zKYTBQUFLCwsBNvgmc1C9r1hKCMD/Y1vxPLc\nc7C8jFFejnHPPfsbcxusVmvwPl544QXGx8fp6enB6XTiOHMGa1ISYmICmZCAmJ/H//a3hx3zJC6e\njgFFIX8vvPO1DUgpnwGeAaivrz8+iTuF4pixU9Q5NjbG0tISNTU1G46JNhVjGAaTk5MsLi4GxtI0\n5P33g9uN5U/+BNHYiDx3DssHP7jruGaXJ7NhhtnLVAhBZmYmmZmZuN1uxsfHaWhoCJYa7mfTkyws\nxP/Od24w/wp+L8ZeMUIILBYLly5dwufzMTk5SdPQEBkPPEBpayvxfj/GW96Ccf/9Ycc6iR2UGoDz\nQogzQgg78LvA92Mw7olC2dQqDpLZ2VlGR0eprq7eusU9ylSM3+9nYGCA6urql6JoKbE++iiWv/gL\nRFMT2je/ScpDDwUaXWyD2eXJ4XCQnZ294/Xi4+MpKyvj2rVrJCcn09HRQUtLy/5q5HUdMT8f3io4\nhthsNoqKiqivryfr8mU6X/EKXrjvPkZKSvBHkIo5cXXsUkq/EOJDwD8RKHf8ayll275ndsJQNrWK\nWBIaha+srNDd3U1dXd1LDoshRCPs6+vruN1url27trFEcmYG7Re/QKakBH+ALaOjJHR3w7VrW8YZ\nHBxE07SI06qhG4ZWVlaCpYZ+vx+fz4fNZotoHNbXsf3d3yEGBwHQr11D/83fDGwUOgB3x82Yjawz\nMjLwer1MTExw8+ZN0tLSyM/PJyUlZdvzTmIqBinlj4DwS8MKhSIq3G43LS0tXL16dUdhiDQV4/P5\nuHXrFgkJCVvTIZq2bTSy3agzMzPMzs5Sd6fWfUekxPr3f4/1e98DqxXfe9+L/trXkpKSQkVFBR6P\nh8bGRpqamkhNTaWgoGBHYTSx/OxniKGhQOcmw8Dy618jz5zBqKo6EGHfbTy73U5JSQnFxcXMz88H\n1xTy8/PJzc3d8BA+7MVTZQJ2ACibWkUs8Pl8NDU1cfnyZZKSknY8LpKI3SyRPHPmzLZRP1lZ6K99\nLWJ1FdbWECsrGGfOsHb+/IbDVldX6enp2ZjGCblGKNbvfx/7X/wFeL2wskLcU09tqPm2Wq3ExcVx\n7do1cnNzGRgY4ObNm0xOTu54P9rICDIjw7xxSEgI7EQ9AKJZt8jKyuLKlStUVlbidrtpbGyku7ub\ntbU1IHphd7vd3HPPPVRXV3P58mUej1JUlLAfAJHm1VX+XbET5sJkWVkZ6enpux4bTtillHR0dJCZ\nmYnD4dj+ICHQv/IV/B/9KPJlL0N/3/tY+/rXMULSNV6vl5aWFq5cuRJstRc6X8Mw8Pl8wfZ1ln/5\nF4zU1IBXe3Iy0mrF+txz21xakOX1UjMywtXhYTwjI0F3RvemHL9RWIgwjckMA1wu5J17inXEvpe2\neHFxcZw5c4b6+noyMzPp7e3lO9/5TtQuk3FxcfzsZz/j1q1bNDc38+Mf/5gXXngh4vOVV8wRosoi\nFdth2ubm5uaSl5cX9niz0mUnhoaGkFJuKJHcFrsd4yMfwRxJuN3I0VHgpYj/3LlzW9IlUkoMw8Bm\ns2EYBrquB/6enIzV43kpneP3B4R+8/xHRoj79KfB5cIGlCcnU/xf/gtTFgttbW3Y7XYKCgrIyMhA\nf+1r0SYnA37sgH7//RiVlRs+i1ixn7Z4mqaRnZ1NdnY2aWlpfP7zn+eBBx7gb//2b7l69WrY84UQ\nQR98n8+Hz+eL6t6UsCsUxxDTZjYSQmvTNzM9Pc3MzEz4fPg2bG7gkZWVRe4mXxQpJbquI4RA0zQs\nFgsWiwVd1/G85z1YmpthdBQBgXTPb/3WlutYf/hD8PsDeXNAjI9j++lPcbz3vTgcDlZWVhgdHaWv\nrw+Hw4HzoYewrawEmneYaRn2tvN0N2L1BlBaWkpiYiL/9E//tGtKbTO6rlNXV0dvby+PPfYY9957\nb8TnqlTMIaPKIhXhEEJs29ZuJ3ZKxSwtLdHb28vVq1f3FHmawj48PIzf798S8ZuivlkANU3DZrNh\nq6zE89Wv4vngB3F96EMsP/00/qysrXN1uSA0tWO1IkJSFykpKVy8eJGrV68ipeTmrVt0zM6yuqmS\nJtapmFiO5/V6SU5Ojrz6h4DdQXNzM6Ojo7z44ou0trZGfK6K2A8ZVRapiDXbCbvb7aa1tZWampqo\nxGQzPp+PiYkJ6uvrN4icmX4JJ35aaSmUlmIYBhZdD+bfzXMB9Je/PNCByJyny4V+331bxrLZbBQX\nF1NUVMT8/Dz9/f34/f4d+7vul73k2HdCSrn9onUEpKen8xu/8Rv8+Mc/pjIk7bQbKmJXKE44m8sd\nTefHS5cukZiYuLdBpcQzPIxvbIyr1dVbRMlcLDV928NhRvF2uz1YP2/m4v11dfgefRSZloZMS8P7\nkY9g7JKHNqtQqqqquHjxIisrKzQ0NDAzM4Pf79/b/W7DfnLs+8XsiAXgcrn453/+ZyoqKiI+X0Xs\nR4gqi1TEgtCIXUpJS0sLJSUlZITkn6PC48HyrneR9q//ym8A2j/8A/6vfx3u1NGb0Xqkor55rpqm\nYbVa6e/vJycnB90w0O+9F+3++4O5+khJsFo5V1rKmTNn6OrqYmJigpWVFQoLC0lPT99XxH0YG552\nYmJigve+973Bh9873vEOHnjggYjPV8J+hKi8uiIWhAp7V1cXqampQefHndhNtLQ//mPkv/4rmsWC\nYRhov/gFls9/Hv1Tn0JKid/v35OohzIxMYHP56OioiKYqzfTNLquY7FYdhd4nw/rP/wDlhdeAE3D\n/+Y3k3rpEunp6SQnJzM2NkZvb2/AuMvhiLgRSSixEva9LOpWVVXR1NS052sqYVcojiHRCIpZFTM8\nPIzH46G8vDzs2LuJlvvZZ0k0DDS7HUNK8PsR168jx8YCD5C8PMQ+UhQrKysMDw8HK3VCK2pCRX43\ngbf8y79gef55ZGkpGAbW732POMBfVUVqaiqpqanB9QFzy39BQUGwhDASYpljh9iWYoZDCbtCccIR\nQuB2u7dd5NwOM8LfTjDHxsZILCggub09sLJvuijOzmJ76CEAjNpafI8/Hth4FCVer5e2tjauXLmy\nJYo252O586bg9/vRdR2/34/FYtmQptE6OpDZ2YHdp5oGiYnYhofxV1UFxwtdbJ2bm6Ovrw/DMCgo\nKCA7Oztsyucoc+z75WTOWqFQBFlfX8flclFTUxNR5cVO3jILCwsMDw+T8qd/iiwpCRhraRoyPR00\nDcPhQDocaDduYPnmN6Oep5SS1tZWzp49G7aeW9M07HZ7cLHVFHqfzxeoqMnNDdgfmLjd+NPStn2o\nCSHIzs6murqaiooKlpeXaWhoYGBgAI/Hs+t8jyoVs19UxL4PQksXFYqjwOPx0NraSkJCwpZt/jux\nnbC7XC7a29upra3FmpCA7/nnEQ0NtLa1cWVoCNHcHBQ5mZSE1t1NeLPajfT29pKWlhZ1jb5ZUWNG\n77qu43rd60jo6cEyMgJSYpw7h6u2lnCFnQkJCZw7d44zZ84wPT3N7du3iY+Pp6CgYMtia6xSMVG5\nV8YIJez7QFkCKI4SXddpbm6moqKCrq6uiM/bXPfu9/tpbm7m0qVLL7k+xsUhX/EKljQN3W7H9qtf\nBc23xNoa+rlzUc11amqK1dXViLbT74S5q9UwDPScHFwf/zjayAjCaoUzZzAmJiIWYovFgtPpxOl0\nsry8zOjoKL29veTn55OXl4fVao1ZxH7Ylr2ghF2hOJaEExTTTyYa64HQsUP7nt6+fZvi4uIt5ZFS\nSnw+H9OveQ3O1la05mYQAqOqCv1d74r4equrqwwMDAQWSw0Dy/e+h3b9OjIjA/3BB5FFRbC0hOjv\nh6Qk5Pnzu7bnM6N4S1oaRkpKcPer3+/fU9ojNTWVS5cubfBXT09PJykpKSY5diXsJ4AnnghE6ibm\nz9/jj6voXXF49PT0kJCQQGFhYdTnhgp7b28viYmJFBRs7D9vVqdUVVUxMjJC37/9t5T8zu+Qm5MT\n2E1qsYDPh5iYAMNA5uVtu5jq9/tpbW2lsrISm82G5etfx/rtbyMzMxH9/Witrfg+/nFsn/0sYm0N\ndB391a/G//u/H1gU3YXQmnifz8fi4iKZmZn4fD40TYu6Jj7UX31ubo6BgQF8Ph8pKSkRLbbuhNvt\nVsJ+3FGWAIqjZnR0lLW1tT2nNUxhn5ycZHl5mdra2g3fD7ULSElJ4fLly3i9XsbGxrg+OUm2YVCY\nm0vST3+KGB8P/EdITkZ/61shLW3DOK2trZSWlgbLDC3f+x4yNRUSEyE1FUZGsD31VMAEzOEINM/4\n+c8xXvlKjJe9LOJ7GhgYICcnh8zMzGCppFnVYv6K5vPJzs5GSsnCwgJLS0sMDg6Sk5NDQUFBxGsZ\nJofdFg+UsCsUJ4q5uTlGR0e5du3anvO/mqaxvLzMwMAA99xzz5ZxtrMLsNvtnDlzhpKSEqanp+n/\n0Y/Ibmkh9U4TEDEzg/biixivf31wnIGBARITE4Me8NqzzwYabQgRsAiurwdAzM0FnR3NKF3Mz0d8\nPzMzM8EH3eaaeHOxNaJNT5swDAO73U5paSm6rjM1NUVLSwsJCQkUFBSQtkMVzmaOIhWjyh33gbIE\nUBwma2trdHZ2RlzWuBOGYQRdHzfXkoezC9A0DYfDQeWZM2Q6HMzNzdHb28uCx4NcWQkeNzs7y8LC\nAufMRdbpaWyf+UxgQ5EQ4PGgPf88Mi8P42UvQ8zMBF5/vd5AHr+kJKJ7cblc9Pb2cvny5S0OkxaL\nhbi4OOx2O5qmoet6sBFIJD1iQ+vYLRYL+fn51NXVUVhYyNjYGI2NjYyNjQV3zO6EyrGfMFROXXFQ\nbBZVr9fLrVu3qKqq2pdI6LrO/Pw8586d22IQFpVdQEEBCUCJ04lX11np6KAlNZXEvj6ysrLo7e2l\ntrY2KIxichKkDETmKSkwNwerq/g+/GEoLMT2R3+E1tkZsAf4wAeQV66EvRfDMGhtbeXixYu7pkfM\nmnizCUik1gXbVcUIIUhLSyMtLQ2v18v4+DiNjY2kp6dTWFi4remaSsUo9o2qrT99mG3yzp8/H7bZ\n825IKWlrayMpKWnbLkim4EWSXpBFReivfz3ar36F3e8n881vJqWujvGpKRobG8nMzMTtdgcFV5oN\nOrxeZEIC2swMeDzEfepT+D78YXxf+AIsL0N8fNBsLBw9PT3k5uaGbR1oEqym2ca6wPx6KOHq2M00\nTUlJCbOzs3R3dwMEd7aa56rFU8W+UbX1pwtzATIvLy/sxp4d665nZxFjY4zOzWHNyyM1NXVDWWDo\nYmk0OWh56RL6pUtB2wFNShYXF6moqCAhIYGBgQG8Xi/FxcXk5OYGql/+9E8RPT2BNnn33gspKVif\nfhrf+fOBNE2ETE9P43K5uHDhQsTnmGxnXWBG86HWBZHWsZuNUXJyclhfX2d0dJSBgQFyc3PJz8+P\nOhUzMjLCQw89xNTUFEIIHn74YT760Y9GdY9K2BWKY0x/fz82m42SMDnnnYy9RG8v2le+gmtlhcSl\nJQofeIDOe+/dIOymqO25ZvvONYeHh7FarcHSyczMTFwuFyMjI/T39+O4cIGiv/5rkt797oCIh4id\nGB2NWNjX19fp7+/fU7u/zWxO05i/m31ko90xmpiYyIULF9B1ncnJSW7dusU3vvENMjIyIn5QWK1W\nvvCFL1BbW8vKygp1dXW8/vWv59KlS5HfV1SzVhxLVLu908nExEQwAg7HTu3xtK99Da/NxnRCApm1\ntViuXydhePil7kV3xMys+94r8/PzTE9Pb3GWTEhI4MKFC1y7dg2r1cqN4WEWMzLwmwutfj/CMJCR\n2Ax4vXD9OiN/93dcKiiI6TZ907bAXGwVQuDxeIJCHy0Wi4WCggLq6uqorKzkxo0b/P7v/35E5zqd\nzmAJqtkWcGxsLLr7iXrGimPHE0+8ZMQHgd+PesOUeqjsj+XlZQYHB6muro64Q9EWYZcSY26OibU1\nHA4HFqsVYbGguVzB1MteG2aE4na76erq4sqVKztG/VarlaKiIu697z48f/AHrK6ustzVhW9kBN87\n3oEMl1JxubA/8gg88ggVX/0qOR/8ICJKsYsUi8WCz+djaWmJ3NxcDMPA5/MFUzbRoGkahYWFvPvd\n7+Zzn/tc1HMZHBykqakpqkbWoIT91BK6O/ZuvP5JJyUlhbq6uogbRJg54VAMKRlOSyPP6yXOaoXV\nVaQQ6A7HhpTDfkRd13Vu377NxYsXiY+PD3u8EIL0++4j8e//Hj7/eXo++Ul+deECwyMju7a1s/zj\nP6I3NeFNTcXmdMLCAtYvfWnP894NXddpa2vj8uXLxMfHb2nn5/V6oxJ4M8cebaprdXWVt73tbXzp\nS18iNTU1qnNVjv2UoWrrTwdm7jdSzJywiZSSjo4Okt71LuKeew7a2iA5GeODH0SPj9+wM3M/dHV1\n4XA4yJiZQfvhDyE9Hf0Nb3ipMfVOpKYSX1fHOaDE52N8fJyGhgYyMzMpKiraUjboGxjA0HWSUlIQ\nAImJiNHRfc19J3p6esjPzw/ulg21Lgj1iTfXJcJZF3g8nqirmXw+H29729t48MEH+Z3f+Z2o70FF\n7KcI08fmqHLtKtd/dGxOxQwPD2MYBiWXL2M88gj6n/0Z+mc+g3HpElarlfHxcVZD/cz3wOjoKIZh\nUNzTQ9yb3oT9U5/C/qEPEff2t4PPF/E45uLwfffdR0ZGBh0dHTQ1NTE3NxcsS+xLSyPBZkPTdTAM\nWFkJ7lyNJTMzM7hcrh09eEJ94kOrakyf+O3wer0Rvc2YSCl5//vfz8WLF/n4xz++p/sQR2ECX19f\nL2/cuHHo172bOGofm6O8vhCiUUoZ+//1kRGTu5ZS4vV6Iz6+paWFsrIykpOTg7tBr127tiWSNCPN\nxcXFoPgXFxdvqLuOhMXFRbq7u6mrqyPpnnsCG47i4gL/6G43+pvehLx8Gf2Nb0RGsPi7GbN93urq\nKpqmkZuTQ9n//b9Yv/Y1MAyMV70K35NP7qmL0054PB5u3rxJXV1dxG9L5lpF6G7WzZue/uRP/oSL\nFy/yzne+M6Ixn3/+eV75ylduWLN46qmneMtb3gIQ0T+SSsUoTiRqI9ZGzFSMaTtQX1+/RdTNxVJN\n08jKyiIrK4u1tTWGh4fp6+ujsLAQp9MZ1q7A4/HQ0dHB1atXA8cuLLyUetF1xNISll/+EtnXh+UH\nP8D7hS8gQ1rWRYJpPjYyMsLIyAjjExN4X/c6ih58kHibLaaCDi9t3rpw4UJUKbBI2vlFu/P0Fa94\nxb67LqlUzCnlqHPtB3390744G+2CpqZpQduBK1eubBGSnewCkpKSuHjxIrW1tfh8Pl588UV6e3t3\nbBlnGAa3b9/mwoULwaYcxstehvB4QMpAuzohMEpLkQ4HUtOwfvvbUd59gLW1NcbGxrjnnnu49957\nSU5O5nZXFy09PSwuLsa05dzw8DBJSUlRe9uHsl07P13XmZubi3gRPFYoYT+lHHU0e9TXv9sQQtDT\n00NZWdmWCopI7AJM98Z7772XxMREbt26RWtrKyshxl4QWFjMzs7eIIDep59Gf/nLES4XWCwYZWVg\nbvO3WAL151Gi6zqtra1cvnwZq9WKpmk4nU6uXbtGSUkJIyMjNDQ0MDExEXUJ4mZWVlaYmpri/Pnz\n+xrHxKyJNyP/n/3sZ7tW/BwEStgVJ4a7bXE22px3cnJy0CLXJFq7AE3TyM/P59q1a+Tn59Pb20tj\nYyMzMzOMj4/j8Xi27oLNyMD7zW/iGhrC/aMfQUZGID2zuIhYX0f/zd+M+D5MOjs7KSgo2LaaJC0t\njStXrlBVVcXa2hrXr1+nr69v18bUOxFa2hiLbkmhaJrGF77wBR566KE9VbbsB7V4qjiR7LY4exoW\nTyFQTRHJ/0/Tm6S8vJxc02zrDpvL8vbC2toafX19zMzMcO7cOQoLC3fNw2sNDVj+7u8C3ZDe9jaM\nV70qquuNj48zPz+/xYp3J8zt+6OjoyQlJVFcXBxx3XdHRwcpKSl76kQVjsbGRj75yU/y7LPPxnKX\nrFo8VShOOwsLC4yOjpKXl7flIRAruwC73c76+jp1dXUsLCzw4osvkpOTQ1FR0baLgsa1axjXru3p\nWqurq4yMjETlA2Nu38/Pz2dhYYH+/n78fj9FRUXk5OTsGIlPT0/j9Xq3tAWMBS6Xi4997GN8/etf\nj6n1QaQoYVecSI56cfg44HK5aG9vp66ujrGxsW0dG/e7s9Rsdn327FnS09NJT0+npKQkaHCVmJhI\nSUnJvuyETULTIntZbBRCkJmZGTQfGx4epr+/H6fTScEmbxm3201fX19MjMQ2I6XkySef5N3vfndU\nxl2xRAm74kRyWvPqkeL3+2lubg5uew/doBStt/pu9Pb2kpaWtsEy2MzDO51OFhYW6O3tfakePjMT\nsYfuTuZO2aKiouCOz/2QkJBAeXk5fr+fiYkJGhsbSU1Npbi4mKSkJNra2igvL4+6f2kkPPfcc7S1\ntfHFL34x5mNHyr5WC4QQ/04I0SaEMIQQR5XTVChOJTuJshlFl5SUBJtMmMJuinos7AKmpqZYXV2l\nrKxsx/llZmZSU1PDxawskh9+GFFRgbjvPvjFL6K61vj4OEII8vPz9zXnzQTNx+69l9zcXLq7u/nV\nr36F1WolIyMjpteCgHnbH/7hH/JXf/VX+2pfuF/2uwzcCvwOEN2/4h6526M0hQICUXRiYuIGETQ3\nKJnivt9IfXV1lYGBASorKyMaK/1TnyKjrw97fj54PBgf+ABDv/hFRJUqKysrjI6ORmRPvFeEEGRn\nZ3P27NlgOeL169cZHh6OWSmilJL/9J/+Ex/5yEcojaJpyEGwL2GXUnZIKbtiNZlwnPZNKQpFOCYm\nJlhZWdnSOchs1hyLvLrf76e1tZXKysrIFv58PrSWFmRmJkLTsKenk5CQQPrIyI718KHXamtro7Ky\n8t1hD5wAABenSURBVMAjXL/fT0dHB1VVVVy6dIm6ujoMw6ChoYGuri7W19f3Nf6Pf/xj5ubmeN/7\n3hejGe+dQ6tjF0I8LIS4IYS4MTMzc1iXVcQQ9cZ0tCwtLTE4OEhVVdW2TZbX1tb2Ha2brfhKS0sj\nz3VbrZCUBGZ0LiXCMMgoKwvWw/f19QXr4c1FXjOvXlJSQlJS0p7nHCldXV3BHDsEzMdKS0s3mI81\nNzczPz8f9a7W2dlZnnjiCZ555pmY18PvhbAzEEL8VAjRus2v347mQlLKZ6SU9VLK+nC9G0O52zal\nHGfUG9PR4Xa7aW1tpbq6ekvFiJSSzMxMNE3jxRdfZHh4eE9dfwAGBgZITEzcstFpV4TA+0d/hPB4\nEIuLiMVF9Fe/GuPee4N5+KtXr1JRUcHs7CzXr18PesBYLBacTuee5hoNU1NT6Lq+7bWEEOTm5lJX\nV8fZs2eZmJjgxRdfZHR0NKLPUUrJf/yP/5H/+l//a3Sf2wESkw1KQohngf9XShnRrqO9blA6asfC\nu52T8vmflg1KphWsrus0NDRw4cIFMjMzN15s02Kpz+djbGyMiYmJXWvNt2N2dpahoSFqamr2FHWK\n3l60tjZkZibGy18OO4zh8/no6+tjbGyMoqIiiouLo7K1jRaXy0VzczP19fUR15R7vV5GR0eZmpoi\nOzuboqKiHef47W9/m5/+9Kd84xvfiHnp5DZEdIGjf2dQHGvUG9PRYTaobm1tpaCgYFtR32wXYKYX\nTM+X5uZm2tvbWVtb2/Va6+vr9Pb27treLhzy3Dn03/5tjFe+ckdRN1lcXOSee+4hJSWFlpaWXfPw\n+8F0bayoqIhqo5DdbqesrOwl87Hbt2lpadliPjY+Ps4Xv/hFnn766cMQ9YjZVx27EOKtwNNADvBD\nIUSzlPKNMZnZNkSzKUXZusYO8+f4pETsp4n+/n7sdjtFRUVbvmdWwWwnKKG15nNzc3R2dmKxWIIl\nkqHnmIZbly5dOpC67lCklLS3t1NaWkpKSgopKSk4HA4WFhbo6+tD1/U9+cPvxMDAAOnp6XsubTTN\nx5xOZ9DD3u12U1RURFZWFo899hif+9zntjx0j5pT6xWjRCg2hH6OJ+UzPS2pmNHRUYaGhqitrd22\nYUa0FTDLy8sMDQ3hdrspLi4O+sq0traSlZUV8xry7RgeHmZtbY2LFy9u+/21tTVGRkZYXFwM2gTs\ntVpmcXGRnp4e6urqYrqg6Xa76e7u5p3vfCc5OTn8n//zf8jLy4vZ+GFQqRhFbFHb+A8XKSXV1dU7\nNszYUdTX1hD9/TA/v+HLqampXLlyhcrKShYXF7l+/TotLS3B6P6gWVpaYnJykvLy8h2PSUpKoqKi\ngrq6Ovx+f9Af3u12R3Utv99PZ2cnlZWVMa9SiY+PJzk5mbS0NH7v936Pn/zkJzEdPxacKmFX+eDY\nsNPnqDhcnE7nlrxwOLsA0dGB/X3vw/7RjxL30EOBBtObMLfbl5WVsby8zPLyMn19fVG14osWn89H\nR0dHxEJrs9mC/vBJSUlR5+E7OzspKSkJNgOJJX6/n0cffZSvfOUrPProo7znPe+J+TX2i0rFKHbl\nJH6OpyUVY7ZYCw58J1I3HRu3YBjY3/OeQCPptLRAg4vZWXxf/Spyky2t2+2mqamJmpoa7HY7ExMT\njIyMkJaWtqHWOxZIKbl16xZOp3PPKQspJQsLC8FSzt3y8BMTE8zNzVFZWbnfqW/Ll770JZaWlvjs\nZz97IOOHQdn2KhSnhVBR3zGnvrqKWFxEmmkVux2haYjJyQ3Crus6t2/f5uLFi8ESPjOfPTs7S2dn\nJ1ardYMXzX4YHh4mPj5+X3noUOdGMw/f19e3JQ/vcrkYGhqivv5gnuttbW1897vf5bnnnjuQ8WPF\nqRV2lQ+ODepzPB6EivqOwp6cjMzIgMXFQGs6jwcpJXLTppyuri4cDscW0RZCkJOTQ05ODktLSwwN\nDdHT00NJSQk5OTl7qlJZXFxkenqaurq6qM/dCTMP7/P5GB0dDfrDFxQU0NraSkVFxYH0GPV6vXzo\nQx/iq1/9alTNqY+CU5uKUdy9nLZUzE6NqLdDdHVhe/xxxJ26dd+HP4zxhjcEvz86Osri4mLE3YnM\nCHhxcZHCwkKcTmfEVSo+n4/Gxkaqq6sPJNdtYhgGU1NT9PT0YLPZuHz5csQdlKLh05/+NCkpKXzq\nU5+K+dhRoFIxCsVJJxpRB5Dl5Xj/5m8Q09PI9PRArv0OS0tLjI+PR9VcIiEhgYqKiuBOzBdffJG8\nvDwKCwt3rXk3N1aVlZUdqKhDoNY8Pj6ehIQEzp49S39/f8zr4RsaGvjlL3/Jz3/+8xjM+OA5VVUx\ndyuq6uf0sqeGGQkJyJKSDaLu8Xhob2/nypUre6oLN3di3nPPPdjtdm7evElnZ+f/3969B0Vdv3sA\nf38EY+RiqAuRoAsHQwFFUJBOoyQGZpYZTmiok7/jqQYlRpQZNS0vOWkR43WOFmEemzrRb6YSRMtw\nLDNMlouOgCbIyuICAnERWASW3c/5Q9lQuSzw/e71ec0wKu5+9pnRedh9Ps/n+fQ5EVGhUMDBweGx\nO1jFoFarda2N3XNpfH19UV9fr5tLM9TZOcD9U7nr169HamqqKCUeMVApxgKYY+eKmCylFPPpp5/C\nxcUFS5YsGVZC0Wq1KCgogJeXF8aNGydIbJxz1NXVoaKiQjfG4MkHP0i6b1US+mBQX3EUFhbC1dW1\n1wFc3XX4O3fuDDjzpa/1N27ciClTpiA+Pl7I0IeKDigRYs6WLVuGK1euICwsDF988QXu3bs3pHVK\nS0shkUgES+rAPxMRg4ODIZVKUV5ejry8PFRVVeH69evDmjkzGNXV1bCxselzqmLPfnhHR0ddP3xz\nc7Ne658/fx6lpaWIi4sTMmzRUWI3U3QYy/JNnDgR+/fvx9mzZ9HY2Ijnn38eycnJaGpq0nuN6upq\ndHR0QCqVihans7Mzpk+fDl9fX939p/X19cMqf+ijra0NFRUV/Z5k7dY98yUkJATu7u6Qy+WPzYd/\n1N27d/Hee+8hNTXVJGasDwaVYiwAlWIeZimlmEepVCqkpqbi6NGjiIyMRFxcXL/zv1taWnDt2jXM\nnDnTILXhW7duoaurC1KpFLdv30ZtbS3c3Nzg4eExqMmK+tBqtcjPz4ePj4+uBDRY3T8YeptLwzlH\nbGwsIiIisGrVKiFDHy4qxRBiSRwcHLBu3TpdC+HSpUsRHx+P0tLSxx6rVqt1V84ZIqk3NDSgvr4e\n3t7eeOKJJ+Dt7Y1Zs2bB1tYW+fn5+Ouvv4ZcSupNWVkZJBLJkJM6ANjb2+vm0mg0GshkMpSWlqKt\nrQ2nTp2CSqUyyXEB+jCPLV7SLzpEZF1GjhyJN998EytXrkRmZibi4+Ph4uKC9evXIygoCABQWFgI\nb29vg1w519HRgRs3bjx2QYeNjQ0mTJgADw8P1NXVoaioCHZ2dvD09BxWn3lDQwOam5sxY8YMIcLX\nbf5OnDgRNTU1iI2NhUwmw9GjR82uBNONSjHE4lhqKabPF+QcFy5cQFJSEtrb2/H0008jOjoaERER\nBnnty5cvQyqV6rU529TUhPLy8iH3mXd2diI/Px9BQUGi3Lqk1WqxYsUKhISEQKFQ4LPPPhP9ku1B\nogNKhFgDxhjCwsIwZ84c3W0+JSUlaGlpwauvvipqYpLL5Rg9erTeHTfOzs4IDAyESqWCQqGAXC7H\nhAkT4ObmNuC74+7Lr729vUW7Si8tLQ1PPvkktm7dalI3Ig2WeX7OIFaHun0GxhiDRCLB5cuXkZaW\nhj///BNhYWE4duzYoOeZ66O+vh5NTU3w9vYe9HMdHBzg5+eHoKAgtLW1IScnB7du3YJare7zOZWV\nlRg5cqRoh56USiUOHTqEgwcPCpLUPT09MW3aNAQGBoo2lKwvVIohZmEwnT/WVorpT11dHQ4ePIgT\nJ04gJiYGq1evFmSOSkdHBwoKCjBjxgxBBmJpNBpUVVWhsrISY8aMwcSJEx8aRaBSqVBUVITg4GBR\nPoFotVpERUVh48aNiIyMFGRNT09P5OXlQSKRCLLeA9QVQ4i1c3Fxwa5du3Dx4kXY2dkhMjIS27dv\nR01NzZDX1Gq1KCoqwuTJkwWbcti90RoaGgpnZ2cUFRWhsLAQLS0t0Gq1KC4uhp+fn2hlpdTUVEye\nPNkg+xKGQImdmCw6hCUcJycnJCYmIj8/H76+vnj99deRkJAAuVw+6LXkcjmcnZ1FucCZMYannnoK\nwcHB8PDwQFlZGbKzs+Ho6AhHR0fBXw+4fzL3q6++QlJSkqB1dcYY5s+fj5kzZyIlJUWwdfV6bSrF\nEHNApRhhaTQaZGRkIDk5Ge7u7tiwYQOmTZs2YGL7+++/UVFRgaCgIINsLtbX16OsrAwODg5obW3V\ne6NVX11dXVi4cCGSk5Px7LPPCrJmt8rKSri7u6O2thaRkZE4dOgQwsLChrsslWIIIb2zsbFBVFQU\nLly4gLVr12LHjh1YsmQJfv/9d2i12l6f097ejtLSUkydOtUgSb2zsxMlJSWYPn06/P39ERgYqNto\nLS8vf+jawKHav38/wsLCBE/qwP1bqQDA1dUVUVFRkMlkgr9GXyixE7NAh7DEMWLECMydOxc//fQT\nPv74Yxw/fhwvvvgiMjIyHpr10rOu3t8cdqFwzlFcXIxJkybp6vh2dnaYNGkSQkJCMGLECOTm5qKk\npGTIHT9Xr17F6dOnsV2E/1wqlUp38bZKpcIvv/wi2h2svaFSDLE4VIoZnrKyMiQnJyMnJwfvvPMO\nli1bhqtXr0IikcDLy8sgMdy+fRsqlQpTpkzp8zFarRa1tbWoqKiAvb09pFIpnJyc9Fq/o6MD8+fP\nx9GjRxEQECBU2DpyuRxRUVEA7pd7li9fLtTNS3p9VKLETiwOJXZh3LlzBwcOHMCJEycwevRopKen\ni3Ll3KNaW1tRXFysd2sj5xyNjY1QKBTgnEMqlWLs2LH9lou2b98OiUSCTZs2CRm6IVCNnRAydG5u\nbtiwYQNsbW2xYMECREZGYteuXairqxPtNTUaDYqLi+Hv7693ayNjDGPHjkVQUBB8fHxw584d5Obm\norq6utf9gkuXLkEmkyExMVHo8E0GJXYBUPsdsVQSiQRZWVnYuXMncnNz4eXlhddeew2JiYmoqKgQ\n/PVKS0sxfvz4Ibc2Ojo6wt/fHwEBAWhtbUVOTg4UCoVuo7W1tRWJiYlmdc3dUFApRgA0D920UClG\nXBqNBj/88AP27dsHT09PrF+/Hn5+fsPulKmrq4NSqURgYKBgXTddXV2orKxEVVUVysvLkZ2djZCQ\nEKxZs0aQ9Y2ASjGEEOHZ2NggOjoaf/zxB1avXo2tW7ciOjoaFy9e7PM2ooF0dHTg5s2b8Pf3F7SV\n0tbWFlKpFKGhoZDL5Th58iRkMlm/M2ksASX2IaJTkcTajRgxAhEREThz5gw+/PBDfP7551iwYAFO\nnz7dZy98b7pbG318fERrpbx79y5OnjyJgoICrF27VvAbnUwNlWIEQKUY00KlGOMpKSlBcnIyCgoK\nEBsbi+jo6AGTqEKhQHt7u153lw4F5xxvv/02Xn75ZaxYsUKQNTUaDYKDg+Hu7o7MzExB1tQTlWII\nIYbl4+ODlJQUZGRkoKSkBHPmzMHhw4ehUql6fXxLSwtqamrwzDPPiBZTeno61Go1li9fLtiaBw4c\ngK+vr2DrCY0SuwDoVCQhDxs/fjySkpJw/vx5dHZ2Yt68edi9ezfq6+t1j9FoNLh27Rr8/f1Fu4Ku\npqYGe/bsweHDhwWr3SuVSpw6dQpvvfWWIOuJgRK7AKiuTkjvxowZgy1btkAmk8Hd3R2LFi3Cpk2b\noFQqkZmZCXd3d9HuZdVqtVi3bh0++ugjuLi4CLZuQkICkpKSTPo+VNONjBBiMUaNGoU1a9YgLy8P\nzz33HKKiorB7927cvXt3yJ00A/nmm28gkUiwaNEiwdbMzMyEq6srZs6cKdiaYqDETogZam9vx6xZ\ns3STD8UYZCUGW1tbREVFwd7eHtu2bcPmzZsRExODnJwcQRN8RUUFDh8+jH379gnaPpmdnY2MjAx4\nenrijTfewLlz57By5UrB1hfKsLpiGGOfAlgEoBNAGYD/4pw3DfQ8S+uKIabFGrpiOOdQqVRwdHSE\nWq3G7NmzceDAAVHGz4rh3r17GDVqFDjnkMlk+OSTT9DQ0ICEhAREREQMq8yh0WiwePFifPDBBwgP\nDxcw6of99ttvSE5OtsiumCwAUznnAQBKALw3zPUIIXpgjOmO3avVaqjVaoPMSBdK932mjDGEhobi\n+++/x5EjR5Ceno7w8HB89913Qz5ElJKSgoCAAMydO1fAiM3LsBI75/wXznn3tPtLADyGHxIhRB8a\njQaBgYFwdXVFZGQkQkNDjR3SkDHG4Ovri2PHjuHHH39EYWEhwsLCkJKSgra2Nr3XuXHjBr799lvs\n2bNH9B90c+fONfS7db0JWWNfDeAnAdcjhPTDxsYGV65cgVKphEwmQ1FRkbFDEoSHhwf27t2Lc+fO\nobm5GeHh4UhKSkJjY2O/z1Or1Xj33Xdx5MgR3ScCazVgYmeMnWWMFfXytbjHY7YC6ALwTT/rvMMY\ny2OM5Yk59pMQa+Ps7Izw8HD8/PPPxg5FUOPGjcO2bduQk5ODcePGYeHChdiyZQuqqqp6ffzevXvx\nwgsvICQkxMCRmp4BEzvnPIJzPrWXr3QAYIz9C8ArAFbwfnZiOecpnPNgznmwkD2lhFijuro6NDXd\n71O4d+8esrKy+r1tyJzZ29sjPj4eeXl5CA4ORkxMDOLi4lBSUqLrpLly5QqysrLw/vvvGzla0zCs\ngcSMsQUANgJ4nnOufyGMEDIs1dXVWLVqFTQaDbRaLZYuXYpXXnnF2GGJauTIkVi5ciWWL1+O06dP\nIyEhAWPGjEFcXBw2b96M48ePG+Q+VnMw3HbHmwDsAHSfE77EOY8d6HnU7kjEZA3tjuR+y2d2djYS\nEhIQEBCAL7/80tghGYJeO8LDesfOOZ80nOf3ZscOOqJPCBkYYwyzZ89GXl6eaKdXzZXJnTzdudPY\nERBivTQaDYKCgsyurCNEa6O5nubtjeVe+kcIGbTucbTNzc3GDsXg7OzscO7cuYdO87700ktmc5q3\nJ5N4x063ERFifOYwjlZM5n6atyeTSeyc/3MLUffvKbETYjjmMI5WbJZymtd6/wUJITrmMo5WbJZy\nmtfkErsZ71cQYrbMZRytoZj7aV6TS+xUfiHE8Pbs2QOlUon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+ "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, "output_type": "display_data" - } - ], - "source": [ - "fig = pl.figure()\r\n", - "ax1 = fig.add_subplot(121)\r\n", - "ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\r\n", - "ax2 = fig.add_subplot(122, projection='3d')\r\n", - "ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\r\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute distance kernels, normalize them and then display\r\n", - "---------------------------------------------------------\r\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [ + }, { "data": { - "image/png": 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hGD7BFnTDMAyfYAu6YRiGTzjggi4i1SLyrIhsE5GtIvJ3E/YiEXlKROon/tZe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\n8dMc2rnMPuyVi2EYhk+wBd0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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, "output_type": "display_data" - } - ], - "source": [ - "C1 = sp.spatial.distance.cdist(xs, xs)\r\n", - "C2 = sp.spatial.distance.cdist(xt, xt)\r\n", - "\r\n", - "C1 /= C1.max()\r\n", - "C2 /= C2.max()\r\n", - "\r\n", - "pl.figure()\r\n", - "pl.subplot(121)\r\n", - "pl.imshow(C1)\r\n", - "pl.subplot(122)\r\n", - "pl.imshow(C2)\r\n", - "pl.show()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Compute Gromov-Wasserstein plans and distance\r\n", - "---------------------------------------------\r\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": false - }, - "outputs": [ + }, { "name": "stdout", "output_type": "stream", "text": [ - "Gromov-Wasserstein distances between the distribution: 0.201997813845\n" + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 0|3.767714e-02|0.000000e+00\n", + " 1|2.091125e-02|-8.017640e-01\n", + " 2|1.607458e-02|-3.008895e-01\n", + " 3|1.486883e-02|-8.109274e-02\n", + " 4|1.484811e-02|-1.394896e-03\n", + " 5|1.484791e-02|-1.400744e-05\n", + " 6|1.484790e-02|-1.400803e-07\n", + " 7|1.484790e-02|-1.400805e-09\n", + " 8|1.484790e-02|-1.400768e-11\n", + "It. |Err \n", + "-------------------\n", + " 0|7.522843e-02|\n", + " 10|2.233137e-04|\n", + " 20|5.767057e-07|\n", + " 30|2.315565e-09|\n", + " 40|9.789603e-12|\n", + "Gromov-Wasserstein distances: 0.014847904422308234\n", + "Entropic Gromov-Wasserstein distances: 0.011257422087381012\n" ] }, { "data": { - "image/png": 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -192,38 +89,121 @@ } ], "source": [ - "p = ot.unif(n_samples)\r\n", - "q = ot.unif(n_samples)\r\n", - "\r\n", - "gw = ot.gromov_wasserstein(C1, C2, p, q, 'square_loss', epsilon=5e-4)\r\n", - "gw_dist = ot.gromov_wasserstein2(C1, C2, p, q, 'square_loss', epsilon=5e-4)\r\n", - "\r\n", - "print('Gromov-Wasserstein distances between the distribution: ' + str(gw_dist))\r\n", - "\r\n", - "pl.figure()\r\n", - "pl.imshow(gw, cmap='jet')\r\n", - "pl.colorbar()\r\n", + "# Author: Erwan Vautier \n", + "# Nicolas Courty \n", + "#\n", + "# License: MIT License\n", + "\n", + "import scipy as sp\n", + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "from mpl_toolkits.mplot3d import Axes3D # noqa\n", + "import ot\n", + "\n", + "\n", + "#\n", + "# Sample two Gaussian distributions (2D and 3D)\n", + "# ---------------------------------------------\n", + "#\n", + "# The Gromov-Wasserstein distance allows to compute distances with samples that\n", + "# do not belong to the same metric space. For demonstration purpose, we sample\n", + "# two Gaussian distributions in 2- and 3-dimensional spaces.\n", + "\n", + "\n", + "n_samples = 30 # nb samples\n", + "\n", + "mu_s = np.array([0, 0])\n", + "cov_s = np.array([[1, 0], [0, 1]])\n", + "\n", + "mu_t = np.array([4, 4, 4])\n", + "cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\n", + "\n", + "\n", + "xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)\n", + "P = sp.linalg.sqrtm(cov_t)\n", + "xt = np.random.randn(n_samples, 3).dot(P) + mu_t\n", + "\n", + "\n", + "#\n", + "# Plotting the distributions\n", + "# --------------------------\n", + "\n", + "\n", + "fig = pl.figure()\n", + "ax1 = fig.add_subplot(121)\n", + "ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", + "ax2 = fig.add_subplot(122, projection='3d')\n", + "ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\n", + "pl.show()\n", + "\n", + "\n", + "#\n", + "# Compute distance kernels, normalize them and then display\n", + "# ---------------------------------------------------------\n", + "\n", + "\n", + "C1 = sp.spatial.distance.cdist(xs, xs)\n", + "C2 = sp.spatial.distance.cdist(xt, xt)\n", + "\n", + "C1 /= C1.max()\n", + "C2 /= C2.max()\n", + "\n", + "pl.figure()\n", + "pl.subplot(121)\n", + "pl.imshow(C1)\n", + "pl.subplot(122)\n", + "pl.imshow(C2)\n", + "pl.show()\n", + "\n", + "#\n", + "# Compute Gromov-Wasserstein plans and distance\n", + "# ---------------------------------------------\n", + "\n", + "p = ot.unif(n_samples)\n", + "q = ot.unif(n_samples)\n", + "\n", + "gw0, log0 = ot.gromov.gromov_wasserstein(\n", + " C1, C2, p, q, 'square_loss', verbose=True, log=True)\n", + "\n", + "gw, log = ot.gromov.entropic_gromov_wasserstein(\n", + " C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True)\n", + "\n", + "\n", + "print('Gromov-Wasserstein distances: ' + str(log0['gw_dist']))\n", + "print('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist']))\n", + "\n", + "\n", + "pl.figure(1, (10, 5))\n", + "\n", + "pl.subplot(1, 2, 1)\n", + "pl.imshow(gw0, cmap='jet')\n", + "pl.title('Gromov Wasserstein')\n", + "\n", + "pl.subplot(1, 2, 2)\n", + "pl.imshow(gw, cmap='jet')\n", + "pl.title('Entropic Gromov Wasserstein')\n", + "\n", "pl.show()" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.5.2" } }, "nbformat": 4, diff --git a/notebooks/plot_gromov_barycenter.ipynb b/notebooks/plot_gromov_barycenter.ipynb index 8102bcf..c931a6c 100644 --- a/notebooks/plot_gromov_barycenter.ipynb +++ b/notebooks/plot_gromov_barycenter.ipynb @@ -32,20 +32,20 @@ }, "outputs": [], "source": [ - "# Author: Erwan Vautier \r\n", - "# Nicolas Courty \r\n", - "#\r\n", - "# License: MIT License\r\n", - "\r\n", - "\r\n", - "import numpy as np\r\n", - "import scipy as sp\r\n", - "\r\n", - "import scipy.ndimage as spi\r\n", - "import matplotlib.pylab as pl\r\n", - "from sklearn import manifold\r\n", - "from sklearn.decomposition import PCA\r\n", - "\r\n", + "# Author: Erwan Vautier \n", + "# Nicolas Courty \n", + "#\n", + "# License: MIT License\n", + "\n", + "\n", + "import numpy as np\n", + "import scipy as sp\n", + "\n", + "import scipy.ndimage as spi\n", + "import matplotlib.pylab as pl\n", + "from sklearn import manifold\n", + "from sklearn.decomposition import PCA\n", + "\n", "import ot" ] }, @@ -53,11 +53,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Smacof MDS\r\n", - " ----------\r\n", - "\r\n", - " This function allows to find an embedding of points given a dissimilarity matrix\r\n", - " that will be given by the output of the algorithm\r\n", + "Smacof MDS\n", + "----------\n", + "\n", + "This function allows to find an embedding of points given a dissimilarity matrix\n", + "that will be given by the output of the algorithm\n", "\n" ] }, @@ -69,49 +69,49 @@ }, "outputs": [], "source": [ - "def smacof_mds(C, dim, max_iter=3000, eps=1e-9):\r\n", - " \"\"\"\r\n", - " Returns an interpolated point cloud following the dissimilarity matrix C\r\n", - " using SMACOF multidimensional scaling (MDS) in specific dimensionned\r\n", - " target space\r\n", - "\r\n", - " Parameters\r\n", - " ----------\r\n", - " C : ndarray, shape (ns, ns)\r\n", - " dissimilarity matrix\r\n", - " dim : int\r\n", - " dimension of the targeted space\r\n", - " max_iter : int\r\n", - " Maximum number of iterations of the SMACOF algorithm for a single run\r\n", - " eps : float\r\n", - " relative tolerance w.r.t stress to declare converge\r\n", - "\r\n", - " Returns\r\n", - " -------\r\n", - " npos : ndarray, shape (R, dim)\r\n", - " Embedded coordinates of the interpolated point cloud (defined with\r\n", - " one isometry)\r\n", - " \"\"\"\r\n", - "\r\n", - " rng = np.random.RandomState(seed=3)\r\n", - "\r\n", - " mds = manifold.MDS(\r\n", - " dim,\r\n", - " max_iter=max_iter,\r\n", - " eps=1e-9,\r\n", - " dissimilarity='precomputed',\r\n", - " n_init=1)\r\n", - " pos = mds.fit(C).embedding_\r\n", - "\r\n", - " nmds = manifold.MDS(\r\n", - " 2,\r\n", - " max_iter=max_iter,\r\n", - " eps=1e-9,\r\n", - " dissimilarity=\"precomputed\",\r\n", - " random_state=rng,\r\n", - " n_init=1)\r\n", - " npos = nmds.fit_transform(C, init=pos)\r\n", - "\r\n", + "def smacof_mds(C, dim, max_iter=3000, eps=1e-9):\n", + " \"\"\"\n", + " Returns an interpolated point cloud following the dissimilarity matrix C\n", + " using SMACOF multidimensional scaling (MDS) in specific dimensionned\n", + " target space\n", + "\n", + " Parameters\n", + " ----------\n", + " C : ndarray, shape (ns, ns)\n", + " dissimilarity matrix\n", + " dim : int\n", + " dimension of the targeted space\n", + " max_iter : int\n", + " Maximum number of iterations of the SMACOF algorithm for a single run\n", + " eps : float\n", + " relative tolerance w.r.t stress to declare converge\n", + "\n", + " Returns\n", + " -------\n", + " npos : ndarray, shape (R, dim)\n", + " Embedded coordinates of the interpolated point cloud (defined with\n", + " one isometry)\n", + " \"\"\"\n", + "\n", + " rng = np.random.RandomState(seed=3)\n", + "\n", + " mds = manifold.MDS(\n", + " dim,\n", + " max_iter=max_iter,\n", + " eps=1e-9,\n", + " dissimilarity='precomputed',\n", + " n_init=1)\n", + " pos = mds.fit(C).embedding_\n", + "\n", + " nmds = manifold.MDS(\n", + " 2,\n", + " max_iter=max_iter,\n", + " eps=1e-9,\n", + " dissimilarity=\"precomputed\",\n", + " random_state=rng,\n", + " n_init=1)\n", + " npos = nmds.fit_transform(C, init=pos)\n", + "\n", " return npos" ] }, @@ -119,10 +119,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Data preparation\r\n", - " ----------------\r\n", - "\r\n", - " The four distributions are constructed from 4 simple images\r\n", + "Data preparation\n", + "----------------\n", + "\n", + "The four distributions are constructed from 4 simple images\n", "\n" ] }, @@ -132,31 +132,54 @@ "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rflamary/.local/lib/python3.5/site-packages/ipykernel_launcher.py:6: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0.\n", + "Use ``matplotlib.pyplot.imread`` instead.\n", + " \n", + "/home/rflamary/.local/lib/python3.5/site-packages/ipykernel_launcher.py:7: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0.\n", + "Use ``matplotlib.pyplot.imread`` instead.\n", + " import sys\n", + "/home/rflamary/.local/lib/python3.5/site-packages/ipykernel_launcher.py:8: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0.\n", + "Use ``matplotlib.pyplot.imread`` instead.\n", + " \n", + "/home/rflamary/.local/lib/python3.5/site-packages/ipykernel_launcher.py:9: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0.\n", + "Use ``matplotlib.pyplot.imread`` instead.\n", + " if __name__ == '__main__':\n" + ] + } + ], "source": [ - "def im2mat(I):\r\n", - " \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\r\n", - " return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\r\n", - "\r\n", - "\r\n", - "square = spi.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256\r\n", - "cross = spi.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256\r\n", - "triangle = spi.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256\r\n", - "star = spi.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256\r\n", - "\r\n", - "shapes = [square, cross, triangle, star]\r\n", - "\r\n", - "S = 4\r\n", - "xs = [[] for i in range(S)]\r\n", - "\r\n", - "\r\n", - "for nb in range(4):\r\n", - " for i in range(8):\r\n", - " for j in range(8):\r\n", - " if shapes[nb][i, j] < 0.95:\r\n", - " xs[nb].append([j, 8 - i])\r\n", - "\r\n", - "xs = np.array([np.array(xs[0]), np.array(xs[1]),\r\n", + "def im2mat(I):\n", + " \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n", + " return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n", + "\n", + "\n", + "square = spi.imread('../data/square.png').astype(np.float64)[:, :, 2] / 256\n", + "cross = spi.imread('../data/cross.png').astype(np.float64)[:, :, 2] / 256\n", + "triangle = spi.imread('../data/triangle.png').astype(np.float64)[:, :, 2] / 256\n", + "star = spi.imread('../data/star.png').astype(np.float64)[:, :, 2] / 256\n", + "\n", + "shapes = [square, cross, triangle, star]\n", + "\n", + "S = 4\n", + "xs = [[] for i in range(S)]\n", + "\n", + "\n", + "for nb in range(4):\n", + " for i in range(8):\n", + " for j in range(8):\n", + " if shapes[nb][i, j] < 0.95:\n", + " xs[nb].append([j, 8 - i])\n", + "\n", + "xs = np.array([np.array(xs[0]), np.array(xs[1]),\n", " np.array(xs[2]), np.array(xs[3])])" ] }, @@ -164,8 +187,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Barycenter computation\r\n", - "----------------------\r\n", + "Barycenter computation\n", + "----------------------\n", "\n" ] }, @@ -177,45 +200,45 @@ }, "outputs": [], "source": [ - "ns = [len(xs[s]) for s in range(S)]\r\n", - "n_samples = 30\r\n", - "\r\n", - "\"\"\"Compute all distances matrices for the four shapes\"\"\"\r\n", - "Cs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)]\r\n", - "Cs = [cs / cs.max() for cs in Cs]\r\n", - "\r\n", - "ps = [ot.unif(ns[s]) for s in range(S)]\r\n", - "p = ot.unif(n_samples)\r\n", - "\r\n", - "\r\n", - "lambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]]\r\n", - "\r\n", - "Ct01 = [0 for i in range(2)]\r\n", - "for i in range(2):\r\n", - " Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]],\r\n", - " [ps[0], ps[1]\r\n", - " ], p, lambdast[i], 'square_loss', 5e-4,\r\n", - " max_iter=100, tol=1e-3)\r\n", - "\r\n", - "Ct02 = [0 for i in range(2)]\r\n", - "for i in range(2):\r\n", - " Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]],\r\n", - " [ps[0], ps[2]\r\n", - " ], p, lambdast[i], 'square_loss', 5e-4,\r\n", - " max_iter=100, tol=1e-3)\r\n", - "\r\n", - "Ct13 = [0 for i in range(2)]\r\n", - "for i in range(2):\r\n", - " Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]],\r\n", - " [ps[1], ps[3]\r\n", - " ], p, lambdast[i], 'square_loss', 5e-4,\r\n", - " max_iter=100, tol=1e-3)\r\n", - "\r\n", - "Ct23 = [0 for i in range(2)]\r\n", - "for i in range(2):\r\n", - " Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]],\r\n", - " [ps[2], ps[3]\r\n", - " ], p, lambdast[i], 'square_loss', 5e-4,\r\n", + "ns = [len(xs[s]) for s in range(S)]\n", + "n_samples = 30\n", + "\n", + "\"\"\"Compute all distances matrices for the four shapes\"\"\"\n", + "Cs = [sp.spatial.distance.cdist(xs[s], xs[s]) for s in range(S)]\n", + "Cs = [cs / cs.max() for cs in Cs]\n", + "\n", + "ps = [ot.unif(ns[s]) for s in range(S)]\n", + "p = ot.unif(n_samples)\n", + "\n", + "\n", + "lambdast = [[float(i) / 3, float(3 - i) / 3] for i in [1, 2]]\n", + "\n", + "Ct01 = [0 for i in range(2)]\n", + "for i in range(2):\n", + " Ct01[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[1]],\n", + " [ps[0], ps[1]\n", + " ], p, lambdast[i], 'square_loss', # 5e-4,\n", + " max_iter=100, tol=1e-3)\n", + "\n", + "Ct02 = [0 for i in range(2)]\n", + "for i in range(2):\n", + " Ct02[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[0], Cs[2]],\n", + " [ps[0], ps[2]\n", + " ], p, lambdast[i], 'square_loss', # 5e-4,\n", + " max_iter=100, tol=1e-3)\n", + "\n", + "Ct13 = [0 for i in range(2)]\n", + "for i in range(2):\n", + " Ct13[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[1], Cs[3]],\n", + " [ps[1], ps[3]\n", + " ], p, lambdast[i], 'square_loss', # 5e-4,\n", + " max_iter=100, tol=1e-3)\n", + "\n", + "Ct23 = [0 for i in range(2)]\n", + "for i in range(2):\n", + " Ct23[i] = ot.gromov.gromov_barycenters(n_samples, [Cs[2], Cs[3]],\n", + " [ps[2], ps[3]\n", + " ], p, lambdast[i], 'square_loss', # 5e-4,\n", " max_iter=100, tol=1e-3)" ] }, @@ -223,10 +246,10 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Visualization\r\n", - " -------------\r\n", - "\r\n", - " The PCA helps in getting consistency between the rotations\r\n", + "Visualization\n", + "-------------\n", + "\n", + "The PCA helps in getting consistency between the rotations\n", "\n" ] }, @@ -240,7 +263,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -249,9 +272,9 @@ }, { "data": { - "image/png": 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SZcmhT6vqV2L3O/PK+s+Vld/VnWMf1URIf2az38/P6uqqb25upj6MbXauGZNm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\nul4ztrXNSJOpUKNJxiStSdqUtLlv377O/sFqiz1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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -259,108 +282,108 @@ } ], "source": [ - "clf = PCA(n_components=2)\r\n", - "npos = [0, 0, 0, 0]\r\n", - "npos = [smacof_mds(Cs[s], 2) for s in range(S)]\r\n", - "\r\n", - "npost01 = [0, 0]\r\n", - "npost01 = [smacof_mds(Ct01[s], 2) for s in range(2)]\r\n", - "npost01 = [clf.fit_transform(npost01[s]) for s in range(2)]\r\n", - "\r\n", - "npost02 = [0, 0]\r\n", - "npost02 = [smacof_mds(Ct02[s], 2) for s in range(2)]\r\n", - "npost02 = [clf.fit_transform(npost02[s]) for s in range(2)]\r\n", - "\r\n", - "npost13 = [0, 0]\r\n", - "npost13 = [smacof_mds(Ct13[s], 2) for s in range(2)]\r\n", - "npost13 = [clf.fit_transform(npost13[s]) for s in range(2)]\r\n", - "\r\n", - "npost23 = [0, 0]\r\n", - "npost23 = [smacof_mds(Ct23[s], 2) for s in range(2)]\r\n", - "npost23 = [clf.fit_transform(npost23[s]) for s in range(2)]\r\n", - "\r\n", - "\r\n", - "fig = pl.figure(figsize=(10, 10))\r\n", - "\r\n", - "ax1 = pl.subplot2grid((4, 4), (0, 0))\r\n", - "pl.xlim((-1, 1))\r\n", - "pl.ylim((-1, 1))\r\n", - "ax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r')\r\n", - "\r\n", - "ax2 = pl.subplot2grid((4, 4), (0, 1))\r\n", - "pl.xlim((-1, 1))\r\n", - "pl.ylim((-1, 1))\r\n", - "ax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b')\r\n", - "\r\n", - "ax3 = pl.subplot2grid((4, 4), (0, 2))\r\n", - "pl.xlim((-1, 1))\r\n", - "pl.ylim((-1, 1))\r\n", - "ax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b')\r\n", - "\r\n", - "ax4 = pl.subplot2grid((4, 4), (0, 3))\r\n", - "pl.xlim((-1, 1))\r\n", - "pl.ylim((-1, 1))\r\n", - "ax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r')\r\n", - "\r\n", - "ax5 = pl.subplot2grid((4, 4), (1, 0))\r\n", - "pl.xlim((-1, 1))\r\n", - "pl.ylim((-1, 1))\r\n", - "ax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b')\r\n", - "\r\n", - "ax6 = pl.subplot2grid((4, 4), (1, 3))\r\n", - "pl.xlim((-1, 1))\r\n", - "pl.ylim((-1, 1))\r\n", - "ax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b')\r\n", - "\r\n", - "ax7 = pl.subplot2grid((4, 4), (2, 0))\r\n", - "pl.xlim((-1, 1))\r\n", - "pl.ylim((-1, 1))\r\n", - "ax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b')\r\n", - "\r\n", - "ax8 = pl.subplot2grid((4, 4), (2, 3))\r\n", - "pl.xlim((-1, 1))\r\n", - "pl.ylim((-1, 1))\r\n", - "ax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b')\r\n", - "\r\n", - "ax9 = pl.subplot2grid((4, 4), (3, 0))\r\n", - "pl.xlim((-1, 1))\r\n", - "pl.ylim((-1, 1))\r\n", - "ax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r')\r\n", - "\r\n", - "ax10 = pl.subplot2grid((4, 4), (3, 1))\r\n", - "pl.xlim((-1, 1))\r\n", - "pl.ylim((-1, 1))\r\n", - "ax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b')\r\n", - "\r\n", - "ax11 = pl.subplot2grid((4, 4), (3, 2))\r\n", - "pl.xlim((-1, 1))\r\n", - "pl.ylim((-1, 1))\r\n", - "ax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b')\r\n", - "\r\n", - "ax12 = pl.subplot2grid((4, 4), (3, 3))\r\n", - "pl.xlim((-1, 1))\r\n", - "pl.ylim((-1, 1))\r\n", + "clf = PCA(n_components=2)\n", + "npos = [0, 0, 0, 0]\n", + "npos = [smacof_mds(Cs[s], 2) for s in range(S)]\n", + "\n", + "npost01 = [0, 0]\n", + "npost01 = [smacof_mds(Ct01[s], 2) for s in range(2)]\n", + "npost01 = [clf.fit_transform(npost01[s]) for s in range(2)]\n", + "\n", + "npost02 = [0, 0]\n", + "npost02 = [smacof_mds(Ct02[s], 2) for s in range(2)]\n", + "npost02 = [clf.fit_transform(npost02[s]) for s in range(2)]\n", + "\n", + "npost13 = [0, 0]\n", + "npost13 = [smacof_mds(Ct13[s], 2) for s in range(2)]\n", + "npost13 = [clf.fit_transform(npost13[s]) for s in range(2)]\n", + "\n", + "npost23 = [0, 0]\n", + "npost23 = [smacof_mds(Ct23[s], 2) for s in range(2)]\n", + "npost23 = [clf.fit_transform(npost23[s]) for s in range(2)]\n", + "\n", + "\n", + "fig = pl.figure(figsize=(10, 10))\n", + "\n", + "ax1 = pl.subplot2grid((4, 4), (0, 0))\n", + "pl.xlim((-1, 1))\n", + "pl.ylim((-1, 1))\n", + "ax1.scatter(npos[0][:, 0], npos[0][:, 1], color='r')\n", + "\n", + "ax2 = pl.subplot2grid((4, 4), (0, 1))\n", + "pl.xlim((-1, 1))\n", + "pl.ylim((-1, 1))\n", + "ax2.scatter(npost01[1][:, 0], npost01[1][:, 1], color='b')\n", + "\n", + "ax3 = pl.subplot2grid((4, 4), (0, 2))\n", + "pl.xlim((-1, 1))\n", + "pl.ylim((-1, 1))\n", + "ax3.scatter(npost01[0][:, 0], npost01[0][:, 1], color='b')\n", + "\n", + "ax4 = pl.subplot2grid((4, 4), (0, 3))\n", + "pl.xlim((-1, 1))\n", + "pl.ylim((-1, 1))\n", + "ax4.scatter(npos[1][:, 0], npos[1][:, 1], color='r')\n", + "\n", + "ax5 = pl.subplot2grid((4, 4), (1, 0))\n", + "pl.xlim((-1, 1))\n", + "pl.ylim((-1, 1))\n", + "ax5.scatter(npost02[1][:, 0], npost02[1][:, 1], color='b')\n", + "\n", + "ax6 = pl.subplot2grid((4, 4), (1, 3))\n", + "pl.xlim((-1, 1))\n", + "pl.ylim((-1, 1))\n", + "ax6.scatter(npost13[1][:, 0], npost13[1][:, 1], color='b')\n", + "\n", + "ax7 = pl.subplot2grid((4, 4), (2, 0))\n", + "pl.xlim((-1, 1))\n", + "pl.ylim((-1, 1))\n", + "ax7.scatter(npost02[0][:, 0], npost02[0][:, 1], color='b')\n", + "\n", + "ax8 = pl.subplot2grid((4, 4), (2, 3))\n", + "pl.xlim((-1, 1))\n", + "pl.ylim((-1, 1))\n", + "ax8.scatter(npost13[0][:, 0], npost13[0][:, 1], color='b')\n", + "\n", + "ax9 = pl.subplot2grid((4, 4), (3, 0))\n", + "pl.xlim((-1, 1))\n", + "pl.ylim((-1, 1))\n", + "ax9.scatter(npos[2][:, 0], npos[2][:, 1], color='r')\n", + "\n", + "ax10 = pl.subplot2grid((4, 4), (3, 1))\n", + "pl.xlim((-1, 1))\n", + "pl.ylim((-1, 1))\n", + "ax10.scatter(npost23[1][:, 0], npost23[1][:, 1], color='b')\n", + "\n", + "ax11 = pl.subplot2grid((4, 4), (3, 2))\n", + "pl.xlim((-1, 1))\n", + "pl.ylim((-1, 1))\n", + "ax11.scatter(npost23[0][:, 0], npost23[0][:, 1], color='b')\n", + "\n", + "ax12 = pl.subplot2grid((4, 4), (3, 3))\n", + "pl.xlim((-1, 1))\n", + "pl.ylim((-1, 1))\n", "ax12.scatter(npos[3][:, 0], npos[3][:, 1], color='r')" ] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.5.2" } }, "nbformat": 4, diff --git a/notebooks/plot_optim_OTreg.ipynb b/notebooks/plot_optim_OTreg.ipynb index d36b0ee..67165e9 100644 --- a/notebooks/plot_optim_OTreg.ipynb +++ b/notebooks/plot_optim_OTreg.ipynb @@ -51,7 +51,8 @@ "source": [ "import numpy as np\n", "import matplotlib.pylab as pl\n", - "import ot" + "import ot\n", + "import ot.plot" ] }, { @@ -105,9 +106,9 @@ "outputs": [ { "data": { - "image/png": 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yfTl0MwB7P90UqJg4PRvlTJdCbbVo4aMcjjyyYl8I8MknOy+389xzUFLixzRq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\ngSsiEhMFrohITP4PrQ161dhEqnEAAAAASUVORK5CYII=\n", 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\n", 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oq6JWraoPK41RJ5I5qVlfB70KOd8K1xe9cNz+FO+xCYA+XdcyvNcyABYc2YnZ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\nTkSuQmCC\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -423,232 +424,201 @@ "--------------------------------\n", " 0|1.692289e-01|0.000000e+00\n", " 1|1.617643e-01|-4.614437e-02\n", - " 2|1.612546e-01|-3.161037e-03\n", - " 3|1.611040e-01|-9.349544e-04\n", - " 4|1.610346e-01|-4.310179e-04\n", - " 5|1.610072e-01|-1.701719e-04\n", - " 6|1.609947e-01|-7.759814e-05\n", - " 7|1.609934e-01|-7.941439e-06\n", - " 8|1.609841e-01|-5.797180e-05\n", - " 9|1.609838e-01|-1.559407e-06\n", - " 10|1.609685e-01|-9.530282e-05\n", - " 11|1.609666e-01|-1.142129e-05\n", - " 12|1.609541e-01|-7.799970e-05\n", - " 13|1.609496e-01|-2.780416e-05\n", - " 14|1.609385e-01|-6.887105e-05\n", - " 15|1.609334e-01|-3.174241e-05\n", - " 16|1.609231e-01|-6.420777e-05\n", - " 17|1.609115e-01|-7.189949e-05\n", - " 18|1.608815e-01|-1.865331e-04\n", - " 19|1.608799e-01|-1.013039e-05\n", + " 2|1.612639e-01|-3.102965e-03\n", + " 3|1.611291e-01|-8.371098e-04\n", + " 4|1.610468e-01|-5.110558e-04\n", + " 5|1.610198e-01|-1.672927e-04\n", + " 6|1.610130e-01|-4.232417e-05\n", + " 7|1.610090e-01|-2.513455e-05\n", + " 8|1.610002e-01|-5.443507e-05\n", + " 9|1.609996e-01|-3.657071e-06\n", + " 10|1.609948e-01|-2.998735e-05\n", + " 11|1.609695e-01|-1.569217e-04\n", + " 12|1.609533e-01|-1.010779e-04\n", + " 13|1.609520e-01|-8.043897e-06\n", + " 14|1.609465e-01|-3.415246e-05\n", + " 15|1.609386e-01|-4.898605e-05\n", + " 16|1.609324e-01|-3.837052e-05\n", + " 17|1.609298e-01|-1.617826e-05\n", + " 18|1.609184e-01|-7.080015e-05\n", + " 19|1.609083e-01|-6.273206e-05\n", "It. |Loss |Delta loss\n", "--------------------------------\n", - " 20|1.608695e-01|-6.468606e-05\n", - " 21|1.608686e-01|-5.738419e-06\n", - " 22|1.608661e-01|-1.495923e-05\n", - " 23|1.608657e-01|-2.784611e-06\n", - " 24|1.608633e-01|-1.512408e-05\n", - " 25|1.608624e-01|-5.397916e-06\n", - " 26|1.608617e-01|-4.115218e-06\n", - " 27|1.608561e-01|-3.503396e-05\n", - " 28|1.608479e-01|-5.098773e-05\n", - " 29|1.608452e-01|-1.659203e-05\n", - " 30|1.608399e-01|-3.298319e-05\n", - " 31|1.608330e-01|-4.302183e-05\n", - " 32|1.608310e-01|-1.273465e-05\n", - " 33|1.608280e-01|-1.827713e-05\n", - " 34|1.608231e-01|-3.039842e-05\n", - " 35|1.608212e-01|-1.229256e-05\n", - " 36|1.608200e-01|-6.900556e-06\n", - " 37|1.608159e-01|-2.554039e-05\n", - " 38|1.608103e-01|-3.521137e-05\n", - " 39|1.608058e-01|-2.795180e-05\n", + " 20|1.608988e-01|-5.940805e-05\n", + " 21|1.608853e-01|-8.380030e-05\n", + " 22|1.608844e-01|-5.185045e-06\n", + " 23|1.608824e-01|-1.279113e-05\n", + " 24|1.608819e-01|-3.156821e-06\n", + " 25|1.608783e-01|-2.205746e-05\n", + " 26|1.608764e-01|-1.189894e-05\n", + " 27|1.608755e-01|-5.474607e-06\n", + " 28|1.608737e-01|-1.144227e-05\n", + " 29|1.608676e-01|-3.775335e-05\n", + " 30|1.608638e-01|-2.348020e-05\n", + " 31|1.608627e-01|-6.863136e-06\n", + " 32|1.608529e-01|-6.110230e-05\n", + " 33|1.608487e-01|-2.641106e-05\n", + " 34|1.608409e-01|-4.823638e-05\n", + " 35|1.608373e-01|-2.256641e-05\n", + " 36|1.608338e-01|-2.132444e-05\n", + " 37|1.608310e-01|-1.786649e-05\n", + " 38|1.608260e-01|-3.103848e-05\n", + " 39|1.608206e-01|-3.321265e-05\n", "It. |Loss |Delta loss\n", "--------------------------------\n", - " 40|1.608040e-01|-1.119118e-05\n", - " 41|1.608027e-01|-8.193369e-06\n", - " 42|1.607994e-01|-2.026719e-05\n", - " 43|1.607985e-01|-5.819902e-06\n", - " 44|1.607978e-01|-4.048170e-06\n", - " 45|1.607978e-01|-3.007470e-07\n", - " 46|1.607950e-01|-1.705375e-05\n", - " 47|1.607927e-01|-1.430186e-05\n", - " 48|1.607925e-01|-1.166526e-06\n", - " 49|1.607911e-01|-9.069406e-06\n", - " 50|1.607910e-01|-3.804209e-07\n", - " 51|1.607910e-01|-5.942399e-08\n", - " 52|1.607910e-01|-2.321380e-07\n", - " 53|1.607907e-01|-1.877655e-06\n", - " 54|1.607906e-01|-2.940224e-07\n", - " 55|1.607877e-01|-1.814208e-05\n", - " 56|1.607841e-01|-2.236496e-05\n", - " 57|1.607810e-01|-1.951355e-05\n", - " 58|1.607804e-01|-3.578228e-06\n", - " 59|1.607789e-01|-9.442277e-06\n", + " 40|1.608201e-01|-3.054747e-06\n", + " 41|1.608195e-01|-4.198335e-06\n", + " 42|1.608193e-01|-8.458736e-07\n", + " 43|1.608159e-01|-2.153759e-05\n", + " 44|1.608115e-01|-2.738314e-05\n", + " 45|1.608108e-01|-3.960032e-06\n", + " 46|1.608081e-01|-1.675447e-05\n", + " 47|1.608072e-01|-5.976340e-06\n", + " 48|1.608046e-01|-1.604130e-05\n", + " 49|1.608020e-01|-1.617036e-05\n", + " 50|1.608014e-01|-3.957795e-06\n", + " 51|1.608011e-01|-1.292411e-06\n", + " 52|1.607998e-01|-8.431795e-06\n", + " 53|1.607964e-01|-2.127054e-05\n", + " 54|1.607947e-01|-1.021878e-05\n", + " 55|1.607947e-01|-3.560621e-07\n", + " 56|1.607900e-01|-2.929781e-05\n", + " 57|1.607890e-01|-5.740229e-06\n", + " 58|1.607858e-01|-2.039550e-05\n", + " 59|1.607836e-01|-1.319545e-05\n", "It. |Loss |Delta loss\n", "--------------------------------\n", - " 60|1.607779e-01|-5.997371e-06\n", - " 61|1.607754e-01|-1.564408e-05\n", - " 62|1.607742e-01|-7.693285e-06\n", - " 63|1.607727e-01|-9.030547e-06\n", - " 64|1.607719e-01|-5.103894e-06\n", - " 65|1.607693e-01|-1.605420e-05\n", - " 66|1.607676e-01|-1.047837e-05\n", - " 67|1.607675e-01|-6.026848e-07\n", - " 68|1.607655e-01|-1.240216e-05\n", - " 69|1.607632e-01|-1.434674e-05\n", - " 70|1.607618e-01|-8.829808e-06\n", - " 71|1.607606e-01|-7.581824e-06\n", - " 72|1.607590e-01|-1.009457e-05\n", - " 73|1.607586e-01|-2.222963e-06\n", - " 74|1.607577e-01|-5.564775e-06\n", - " 75|1.607574e-01|-1.932763e-06\n", - " 76|1.607573e-01|-8.148685e-07\n", - " 77|1.607554e-01|-1.187660e-05\n", - " 78|1.607546e-01|-4.557651e-06\n", - " 79|1.607537e-01|-5.911902e-06\n", + " 60|1.607826e-01|-6.378947e-06\n", + " 61|1.607808e-01|-1.145102e-05\n", + " 62|1.607776e-01|-1.941743e-05\n", + " 63|1.607743e-01|-2.087422e-05\n", + " 64|1.607741e-01|-1.310249e-06\n", + " 65|1.607738e-01|-1.682752e-06\n", + " 66|1.607691e-01|-2.913936e-05\n", + " 67|1.607671e-01|-1.288855e-05\n", + " 68|1.607654e-01|-1.002448e-05\n", + " 69|1.607641e-01|-8.209492e-06\n", + " 70|1.607632e-01|-5.588467e-06\n", + " 71|1.607619e-01|-8.050388e-06\n", + " 72|1.607618e-01|-9.417493e-07\n", + " 73|1.607598e-01|-1.210509e-05\n", + " 74|1.607591e-01|-4.392914e-06\n", + " 75|1.607579e-01|-7.759587e-06\n", + " 76|1.607574e-01|-2.760280e-06\n", + " 77|1.607556e-01|-1.146469e-05\n", + " 78|1.607550e-01|-3.689456e-06\n", + " 79|1.607550e-01|-4.065631e-08\n", "It. |Loss |Delta loss\n", "--------------------------------\n", - " 80|1.607529e-01|-4.710187e-06\n", - " 81|1.607528e-01|-8.866080e-07\n", - " 82|1.607522e-01|-3.620627e-06\n", - " 83|1.607514e-01|-5.091281e-06\n", - " 84|1.607498e-01|-9.932095e-06\n", - " 85|1.607487e-01|-6.852804e-06\n", - " 86|1.607478e-01|-5.373596e-06\n", - " 87|1.607473e-01|-3.287295e-06\n", - " 88|1.607470e-01|-1.666655e-06\n", - " 89|1.607469e-01|-5.293790e-07\n", - " 90|1.607466e-01|-2.051914e-06\n", - " 91|1.607456e-01|-6.422797e-06\n", - " 92|1.607456e-01|-1.110433e-07\n", - " 93|1.607451e-01|-2.803849e-06\n", - " 94|1.607451e-01|-2.608066e-07\n", - " 95|1.607441e-01|-6.290352e-06\n", - " 96|1.607429e-01|-7.298455e-06\n", - " 97|1.607429e-01|-8.969905e-09\n", - " 98|1.607427e-01|-7.923968e-07\n", - " 99|1.607427e-01|-3.519286e-07\n", + " 80|1.607539e-01|-6.555681e-06\n", + " 81|1.607528e-01|-7.177470e-06\n", + " 82|1.607527e-01|-5.306068e-07\n", + " 83|1.607514e-01|-7.816045e-06\n", + " 84|1.607511e-01|-2.301970e-06\n", + " 85|1.607504e-01|-4.281072e-06\n", + " 86|1.607503e-01|-7.821886e-07\n", + " 87|1.607480e-01|-1.403013e-05\n", + " 88|1.607480e-01|-1.169298e-08\n", + " 89|1.607473e-01|-4.235982e-06\n", + " 90|1.607470e-01|-1.717105e-06\n", + " 91|1.607470e-01|-6.148402e-09\n", + " 92|1.607462e-01|-5.396481e-06\n", + " 93|1.607461e-01|-5.194954e-07\n", + " 94|1.607450e-01|-6.525707e-06\n", + " 95|1.607442e-01|-5.332060e-06\n", + " 96|1.607439e-01|-1.682093e-06\n", + " 97|1.607437e-01|-1.594796e-06\n", + " 98|1.607435e-01|-7.923812e-07\n", + " 99|1.607420e-01|-9.738552e-06\n", "It. |Loss |Delta loss\n", "--------------------------------\n", - " 100|1.607426e-01|-3.563804e-07\n", - " 101|1.607410e-01|-1.004042e-05\n", - " 102|1.607410e-01|-2.124801e-07\n", - " 103|1.607398e-01|-7.556935e-06\n", - " 104|1.607398e-01|-7.606853e-08\n", - " 105|1.607385e-01|-8.058684e-06\n", - " 106|1.607383e-01|-7.393061e-07\n", - " 107|1.607381e-01|-1.504958e-06\n", - " 108|1.607377e-01|-2.508807e-06\n", - " 109|1.607371e-01|-4.004631e-06\n", - " 110|1.607365e-01|-3.580156e-06\n", - " 111|1.607364e-01|-2.563573e-07\n", - " 112|1.607354e-01|-6.390137e-06\n", - " 113|1.607348e-01|-4.119553e-06\n", - " 114|1.607339e-01|-5.299475e-06\n", - " 115|1.607335e-01|-2.316767e-06\n", - " 116|1.607330e-01|-3.444737e-06\n", - " 117|1.607324e-01|-3.467980e-06\n", - " 118|1.607320e-01|-2.374632e-06\n", - " 119|1.607319e-01|-7.978255e-07\n", + " 100|1.607419e-01|-1.022448e-07\n", + " 101|1.607419e-01|-4.865999e-07\n", + " 102|1.607418e-01|-7.092012e-07\n", + " 103|1.607408e-01|-5.861815e-06\n", + " 104|1.607402e-01|-3.953266e-06\n", + " 105|1.607395e-01|-3.969572e-06\n", + " 106|1.607390e-01|-3.612075e-06\n", + " 107|1.607377e-01|-7.683735e-06\n", + " 108|1.607365e-01|-7.777599e-06\n", + " 109|1.607364e-01|-2.335096e-07\n", + " 110|1.607364e-01|-4.562036e-07\n", + " 111|1.607360e-01|-2.089538e-06\n", + " 112|1.607356e-01|-2.755355e-06\n", + " 113|1.607349e-01|-4.501960e-06\n", + " 114|1.607347e-01|-1.160544e-06\n", + " 115|1.607346e-01|-6.289450e-07\n", + " 116|1.607345e-01|-2.092146e-07\n", + " 117|1.607336e-01|-5.990866e-06\n", + " 118|1.607330e-01|-3.348498e-06\n", + " 119|1.607328e-01|-1.256222e-06\n", "It. |Loss |Delta loss\n", "--------------------------------\n", - " 120|1.607312e-01|-4.221434e-06\n", - " 121|1.607310e-01|-1.324597e-06\n", - " 122|1.607304e-01|-3.650359e-06\n", - " 123|1.607298e-01|-3.732712e-06\n", - " 124|1.607295e-01|-1.994082e-06\n", - " 125|1.607289e-01|-3.954139e-06\n", - " 126|1.607286e-01|-1.532372e-06\n", - " 127|1.607286e-01|-1.167223e-07\n", - " 128|1.607283e-01|-2.157376e-06\n", - " 129|1.607279e-01|-2.253077e-06\n", - " 130|1.607274e-01|-3.301532e-06\n", - " 131|1.607269e-01|-2.650754e-06\n", - " 132|1.607264e-01|-3.595551e-06\n", - " 133|1.607262e-01|-1.159425e-06\n", - " 134|1.607258e-01|-2.512411e-06\n", - " 135|1.607255e-01|-1.998792e-06\n", - " 136|1.607251e-01|-2.486536e-06\n", - " 137|1.607246e-01|-2.782996e-06\n", - " 138|1.607246e-01|-2.922470e-07\n", - " 139|1.607242e-01|-2.071131e-06\n", + " 120|1.607320e-01|-5.418353e-06\n", + " 121|1.607318e-01|-8.296189e-07\n", + " 122|1.607311e-01|-4.381608e-06\n", + " 123|1.607310e-01|-8.913901e-07\n", + " 124|1.607309e-01|-3.808821e-07\n", + " 125|1.607302e-01|-4.608994e-06\n", + " 126|1.607294e-01|-5.063777e-06\n", + " 127|1.607290e-01|-2.532835e-06\n", + " 128|1.607285e-01|-2.870049e-06\n", + " 129|1.607284e-01|-4.892812e-07\n", + " 130|1.607281e-01|-1.760452e-06\n", + " 131|1.607279e-01|-1.727139e-06\n", + " 132|1.607275e-01|-2.220706e-06\n", + " 133|1.607271e-01|-2.516930e-06\n", + " 134|1.607269e-01|-1.201434e-06\n", + " 135|1.607269e-01|-2.183459e-09\n", + " 136|1.607262e-01|-4.223011e-06\n", + " 137|1.607258e-01|-2.530202e-06\n", + " 138|1.607258e-01|-1.857260e-07\n", + " 139|1.607256e-01|-1.401957e-06\n", "It. |Loss |Delta loss\n", "--------------------------------\n", - " 140|1.607237e-01|-3.154193e-06\n", - " 141|1.607235e-01|-1.194962e-06\n", - " 142|1.607232e-01|-2.035251e-06\n", - " 143|1.607232e-01|-6.027855e-08\n", - " 144|1.607229e-01|-1.555696e-06\n", - " 145|1.607228e-01|-1.081740e-06\n", - " 146|1.607225e-01|-1.881070e-06\n", - " 147|1.607224e-01|-4.100096e-07\n", - " 148|1.607223e-01|-7.785200e-07\n", - " 149|1.607222e-01|-2.094072e-07\n", - " 150|1.607220e-01|-1.440814e-06\n", - " 151|1.607217e-01|-1.997794e-06\n", - " 152|1.607214e-01|-2.011022e-06\n", - " 153|1.607212e-01|-8.808854e-07\n", - " 154|1.607211e-01|-7.245877e-07\n", - " 155|1.607207e-01|-2.217159e-06\n", - " 156|1.607201e-01|-3.817891e-06\n", - " 157|1.607200e-01|-7.409600e-07\n", - " 158|1.607198e-01|-1.497698e-06\n", - " 159|1.607195e-01|-1.729666e-06\n", + " 140|1.607250e-01|-3.242751e-06\n", + " 141|1.607247e-01|-2.308071e-06\n", + " 142|1.607247e-01|-4.730700e-08\n", + " 143|1.607246e-01|-4.240229e-07\n", + " 144|1.607242e-01|-2.484810e-06\n", + " 145|1.607238e-01|-2.539206e-06\n", + " 146|1.607234e-01|-2.535574e-06\n", + " 147|1.607231e-01|-1.954802e-06\n", + " 148|1.607228e-01|-1.765447e-06\n", + " 149|1.607228e-01|-1.620007e-08\n", + " 150|1.607222e-01|-3.615783e-06\n", + " 151|1.607222e-01|-8.668516e-08\n", + " 152|1.607215e-01|-4.000673e-06\n", + " 153|1.607213e-01|-1.774103e-06\n", + " 154|1.607213e-01|-6.328834e-09\n", + " 155|1.607209e-01|-2.418783e-06\n", + " 156|1.607208e-01|-2.848492e-07\n", + " 157|1.607207e-01|-8.836043e-07\n", + " 158|1.607205e-01|-1.192836e-06\n", + " 159|1.607202e-01|-1.638022e-06\n", "It. |Loss |Delta loss\n", "--------------------------------\n", - " 160|1.607195e-01|-2.115187e-07\n", - " 161|1.607192e-01|-1.643727e-06\n", - " 162|1.607192e-01|-1.712969e-07\n", - " 163|1.607189e-01|-1.805877e-06\n", - " 164|1.607189e-01|-1.209827e-07\n", - " 165|1.607185e-01|-2.060002e-06\n", - " 166|1.607182e-01|-1.961341e-06\n", - " 167|1.607181e-01|-1.020366e-06\n", - " 168|1.607179e-01|-9.760982e-07\n", - " 169|1.607178e-01|-7.219236e-07\n", - " 170|1.607175e-01|-1.837718e-06\n", - " 171|1.607174e-01|-3.337578e-07\n", - " 172|1.607173e-01|-5.298564e-07\n", - " 173|1.607173e-01|-6.864278e-08\n", - " 174|1.607173e-01|-2.008419e-07\n", - " 175|1.607171e-01|-1.375630e-06\n", - " 176|1.607168e-01|-1.911257e-06\n", - " 177|1.607167e-01|-2.709815e-07\n", - " 178|1.607167e-01|-1.390953e-07\n", - " 179|1.607165e-01|-1.199675e-06\n", - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 180|1.607165e-01|-1.457259e-07\n", - " 181|1.607163e-01|-1.049154e-06\n", - " 182|1.607163e-01|-2.753577e-09\n", - " 183|1.607163e-01|-6.972814e-09\n", - " 184|1.607161e-01|-1.552100e-06\n", - " 185|1.607159e-01|-1.068596e-06\n", - " 186|1.607157e-01|-1.247724e-06\n", - " 187|1.607155e-01|-1.158164e-06\n", - " 188|1.607155e-01|-2.616199e-07\n", - " 189|1.607154e-01|-3.595874e-07\n", - " 190|1.607154e-01|-5.334527e-08\n", - " 191|1.607153e-01|-3.452744e-07\n", - " 192|1.607153e-01|-1.239593e-07\n", - " 193|1.607152e-01|-8.184984e-07\n", - " 194|1.607150e-01|-1.316308e-06\n", - " 195|1.607150e-01|-7.100882e-09\n", - " 196|1.607148e-01|-1.393958e-06\n", - " 197|1.607146e-01|-1.242735e-06\n", - " 198|1.607144e-01|-1.123993e-06\n", - " 199|1.607143e-01|-3.512071e-07\n", - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 200|1.607143e-01|-2.151971e-10\n" + " 160|1.607202e-01|-3.670914e-08\n", + " 161|1.607197e-01|-3.153709e-06\n", + " 162|1.607197e-01|-2.419565e-09\n", + " 163|1.607194e-01|-2.136882e-06\n", + " 164|1.607194e-01|-1.173754e-09\n", + " 165|1.607192e-01|-8.169238e-07\n", + " 166|1.607191e-01|-9.218755e-07\n", + " 167|1.607189e-01|-9.459255e-07\n", + " 168|1.607187e-01|-1.294835e-06\n", + " 169|1.607186e-01|-5.797668e-07\n", + " 170|1.607186e-01|-4.706272e-08\n", + " 171|1.607183e-01|-1.753383e-06\n", + " 172|1.607183e-01|-1.681573e-07\n", + " 173|1.607183e-01|-2.563971e-10\n" ] }, { "data": { - "image/png": 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Up1sTzaPNizrgomid3JIyP1Ot40of612/2sdmXyrzTnbh0H0AOKP3BwDcfdBj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\nsW1KbQQ9nkkAAAAASUVORK5CYII=\n", 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39uUWxIqzJI6rLVzr/50LN8bhJ8Hfimv9hDU29o59sf28Ql1nvv3Va3x87/LP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\n", 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\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -740,21 +710,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.5.2" } }, "nbformat": 4, diff --git a/notebooks/plot_otda_d2.ipynb b/notebooks/plot_otda_d2.ipynb index 038434a..5862d48 100644 --- a/notebooks/plot_otda_d2.ipynb +++ b/notebooks/plot_otda_d2.ipynb @@ -43,7 +43,8 @@ "# License: MIT License\n", "\n", "import matplotlib.pylab as pl\n", - "import ot" + "import ot\n", + "import ot.plot" ] }, { @@ -126,9 +127,9 @@ "outputs": [ { "data": { - "image/png": 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gT4qzXkLy10DcG42+S5ImyEqpMhVWjV3rir22xC8IVkiqCcl1u3j2u5W8s2sH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\nxjrvokYGGT+CfUtQXHT2c+2vD3ut7BT2dYilLge+SM/ZunF9YIwjQcEdC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Avs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\nB/u4Lir5zTcvAAAAAElFTkSuQmCC\n", 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eNb6/H9bCZr+dtbVsiTSu1SQ979xK0o9RWQk4Niqflq1t1Wles64Ods+NSPUM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/fuNRPaq682k3jh3KN1CAKvoQPvgKsAlXEnyjd3oLs1NBXkGAcPGr/d5mYTbHDZZakpoOWnKvhV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K+OhleNxpZk1dQ75Yx9CgTMYUv6vHI4Bxc7MI4k7xn6K4Qb0cyXc5ti0WsU89JebpqVNFutyR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yFJExbR4iqIfK5yL4GzcOUYbvorYPdSLIV5Yfu7kWWLJXN/NX7M/RB875s5JP8n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\ny7K455cPk0lmkBSV2tO/ROHMxXS++hitrU+jXvGr0fcuOmUeT9/yI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\n", 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iTrVtt1q5cMRILhwxspMSazS9hx3l5QQiYdLc8YAxIJTuTqDYW8+RulpGDkoB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ADODCR5e5fG1V3pu4iIgUBnf781qEGr/82UQVkjwBoblkZkwCo0pzjWSJACJHVvm4oUIjEo+Bpa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\nx6aXdlB7up7oeAezVkxl4vxx/QLlg+8dpmTjUQ5vOU5jZbP+2oYjGIwKi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+AnAC6F+SiMR0q6uit5uPgu4y3wPkArNMHT97/yy9FGR4oZcopcZjePsvxRgB6qC7jvuABrrpuDk6lMUJ+j7XBnIfYg71vAIsMYPqJ2DEEx0NcRi9a6+IzMEYMjkkRORaEUk1e43NGF/8cMQt/yMiLhHJM+V80by+FPiFiKSKyBDgf4BnD1BVNZDa7QdOozkkTO/pj0UkyzwfjhEGsOJIylNKLcUIF/hARA46SVYptRf4EnhQjAmq+cD3OPB3/lgTh6GjrWbcYPf4414RkSsiJvs0Yeh55KSi/xKRRBEZAfyIrnp+l4jkmLr7K2Cp+XvREzWAEpGRh9wqTb9nEOuf3Syv44j0uN4nxjJup2HEVL/cUwEiEhXhVHKY5RxSx7UH4jC8wo0ikoLRCT4kRORsEZlkGrgtGCEXkXr6bfP/GIMxh+kls3P7EnCRGJMT7RhGuhvoHm4ZSTUwKHVcG8hHx5vSdQ3TV4+gjB9iDItUYfTC/3GUMt2C8cPRESP40mHkXQhsM/P+BljUbdjrc6AIY4b6g0qpj8zr9wEbMTxamzCU6cHeKlFGrPUyoMSM3darWGgOBzdGTNxKMVZvWYHx3fvxkRaolHoGI0zhIxHJOYQsizHieCuAVzFib4/Lko+98GOMTqobw5v84oFv78JsYLX57F4BbjXjQzt4E9gArMdo29Pm9SfNej7D+B1wY0wU7BFzCPxBjP9Tkxziqh2afs9g1b+3McIfO44l5vUqoNGs6zng+6bXvCd2mHkzgXfNz9m93HswfocR/1uP0SF45zDyZmDodgvGPKMPMCb5dvBPjA5FJWAF7gBQSm3F+F15HGNkaAFwkRmP3Bv/i9GBaBKROw5Dxn6PdB0R02j2R0RGA4VKqSPtCWs0mn6O6TELALnmxD6N5oRGjJWanjVjrQcFYizD95RS6um+lqW/oz3IGo1Go9FoNBpNBNpA1mg0Go1Go9FoItAhFhqNRqPRaDQaTQTag6zRaDQajUaj0URwqIvUA5CamqpycnKOkygajQZg7dq1dUqpIQe/88BofdVovh6Ohc5qfdVovh4OVV8Py0DOyclhzZo1Ry6VRqM5KCJyuLsp9ojWV43m6+FY6KzWV43m6+FQ9VWHWGg0Go1Go9FoNBFoA1mj0Wg0Go1Go4lAG8gajUaj0Wg0Gk0EhxWDrOk/BAIBysrK8Hq9fS2K5ghxOp1kZWVht9v7WhSNRqPRaDQRaAN5gFJWVkZcXBw5OTmI6B2gBxpKKerr6ykrKyM3N7evxdFoNBqNRhOBDrEYoHi9XlJSUrRxPEAREVJSUvQIgEaj0Wg0/RBtIA9gtHE8sNH/P41Go9Fo+ic6xEKj0Wg0Gs1+tPr9bKurpdnnZUR8IiOTkrBatF9Nc2IwKAzkQChEQW0NW2triHU4mJ6eSWZ8fF+LNWipr6/nnHPOAaCqqgqr1cqQIcamNKtWrcLhcBzzOtetW0dNTQ0LFiw45mUfDsFgkNTUVJqamvpUjoGIx+/ng6JdrK2swCLCnMzhnJWbi9OmJylqNP2NCncLT6xdTas/gAiElWL8kKFcmz8Fh9Xa1+JpNMedAW8gB0Ihnt64ju21tXyxtxSF4vTsHBZPmsy09Iy+Fm9QkpKSwoYNGwBYsmQJsbGx3H333YecPxQKYT3MH9h169axZcuWPjeQNUdGIBTiqXWrqXC7+WJvKQAev4+9Lc3cOG2GDjfRaPoRSile2rqZsFJkxsezbNtWQBFWinWV5czJGtHXImo0x50BP1ZSUFvD9tpasuITsFutOKw2UlwxLNu2FV8w2Nfi9Ru2VTbz+/d3cvfLG/n9+zvZVtl8XOq58MILmT59OhMnTuSpp54CDK9rYmIid9xxB/n5+axatYo33niDcePGMX36dG677TYuueQSADweD9dffz2zZs1i6tSpvPnmm7S3t3P//ffz3HPPMWXKFP71r391qXPz5s3MnDmTKVOmkJ+fT1FR0UFlueuuu5g4cSLz589n5cqVnHHGGYwcOZK3334bgKeeeopLL72UM844gzFjxvDAAw/02N6HHnqIWbNmkZ+fz/333w+A2+3mvPPOY/LkyUyaNGk/eU9EdjXUs7elhYy4eCwiWETIikugsKGO0ubj813UaDRHRpPXS4XbTZLTFXFVSHQ6WVtR0WdyaTRfJwPeg1xgeo7tVivl7hYA3ircgS8U5MZpM8hJTOpjCfuebZXNPLG8mASXnfQEJ83tAZ5YXsxNp+cyPj3hmNb1zDPPkJycTFtbGzNmzODyyy8nLi6O5uZmTj/9dP7whz/Q1tbG2LFj+eKLLxgxYgRXXHFFZ/7777+fBQsW8PTTT9PY2Mjs2bPZtGkTv/jFL9iyZQt/+MMf9qvzscce4+6772bRokX4fD6UUgeV5bzzzuN3v/sdF154IUuWLOHDDz9k48aN3HzzzSxcuBAwwkW2bNmCw+Fg5syZXHDBBUyaNKmz3rfffpvS0lJWrlyJUoqFCxfy5ZdfsnfvXnJycnjnnXcAaNYGIDVtrXxWWkKU1dapp69sL8AXCrJ40mSyExP7WEKNRtOB1WKM6LyyfSsgnTr7zq6dfGfK9D6UTKP5+hjwHuS4KAcK1e2qcR5lHfD2/zHhP1uqSXDZSXDZsYh0fv7PlupjXtfvf/97Jk+ezNy5cykrK2P37t0AOBwOLr30UgAKCgoYN24c2dnZiAiLFy/uzP/ee+/xq1/9iilTpnDWWWfh9XopLS09YJ0nn3wyDzzwAL/+9a/Zu3cvTqfzgLK4XC6+8Y1vAJCXl8eZZ56JzWYjLy+PkpKSznLnz59PUlISMTExXHLJJXz++edd6n3vvfd45513mDp1KtOmTWPXrl3s3LmT/Px8/vOf/3DvvffyxRdfkJBwbDshA5FUVzT7qSkKFCS5XD1l0Wg0fUR8lJPRySn4Q+Eu14NhxezMrD6SSqP5ehnwFuS09AxOz84lxeXircKdgOK0ETmkx8WRFhvb1+L1C8qb2klPcHa5Fue0Ud7Ufkzr+eCDD1i+fDkrVqzA5XJx6qmndq7z63K5DinOVCnFa6+9xqhRo7pcX758ea95rr32WubOnctbb73FggUL+Pvf/47f7+9VlshJhBaLhaioqM7PwYiwnO7ydj9XSvHzn/+c733ve/vJtGbNGt5++23uvfdezjvvPH76058etO2DmbEpqVwxKY8aj4cvzRjk2ZnDGZWUTHbCkXuPvcEAje1e4qOiiDkOk0M1mhOVb02YRIvPS01rK5/sKQbgv085g7xhaUdUnj8UYn1lBeurKnFYrczOzGLCkKF6/oGm3zLgDeSMuHiumpTPsu1b8YUM4yY9Lo5r8qYckeI1trfzXlEhG6uqiLLZOHV4Nqdl5wzoWbuZiS6a2wMkuPatFuD2BslMPLaeu+bmZpKTk3G5XGzdupXVq1f3eN+ECRPYsWMHe/fuJSsrixdffLEzbf78+fz5z3/uDKVYv349U6dOJS4uDrfb3WN5RUVFjB49mttvv53i4mI2bdpEenr6IclyIN577z2amppwOBy8/vrrPPfcc13S58+fzwMPPMCVV15JTEwMZWVlOJ1OfD4fqampXHvttcTFxfHss88edt2DDbvVyk3TZvLe7kI+3VOMIJyencM3Ro7GcgR6qpTi45IiPijaTUgpBDh5+AgWjhmHTS9DpdEcNUkuF3fOOYWSpka21dUSZbVy6fgJR1RWKBzm/zaup6C2hh+OeRQUPLL+VuaNHMXCMeOOseQazbFhwBvIAFPTM5gwZCg3Tp1JlM3KsJjYIzKO2wMB/rJ2Fc1eL7eMegSF4s+FP6Cq1cPVeZOPg+RfDwsmDeOJ5YYHIM5pw+0N0tweYNHMYztUdv755/PEE08wYcIExo0bx+zZs3u8Lzo6mkceeYR58+YRGxvLjBkzOr27v/zlL7njjjvIy8sjHA4zevRoXn/9dc4++2wefvhhpk6dys9+9jO++c1vdpb3/PPPs3TpUux2OxkZGSxZsgSn03lIshyImTNncvHFF1NRUcF1113HlClTuniYFy5cyPbt25kzZw4AcXFxPP/88xQUFHDvvfdisVhwOBz85S9/Oey6ByPxUVF8c8IkLh8/ETi6jVLWV1Xy5s4dZMTGYbdaCYbDfFJSTLTdzryRo4+VyBrNCY3VYmFUcgpvLr72qMopbKhnW10tw+MTOjuwmXHxfFJSzJys4SS7oo+FuBrNMUU6JjQdCjNmzFBr1qw5juL0LavLy3ipYAuZcfFclvYgAMsq76Xc3cJ/nXIaQ2P6T8jGtm3bGD9+/KHfX9nMf7ZUU97UTmaiiwWThh3zCXqHg8fjITY2FqUUN998M3l5edx22219Jk93nnrqqV4nBR5Levo/ishapdSMoy17MOvr71d8gcfnJ84MjwHwhYK0+v0sOfOcI/JKazRHyrHQ2cGsr+/uKmSU3I7DaiXLuQOAMu84fMEQvvh/MGnosD6WUHMicaj6Oig8yMeKcreb74/8E/YIJb48/SH8Q0PUt8/oVwby4TI+PaFPDeLuPP744zz33HP4fD5mzJjBjTfe2NciaQYQzV4v0fauMccOi5XaYIBgODygQ6I0msFGfFQUytdzmp47oOmvaAM5goy4WMLd560pUApS9Ez7Y8o999zDPffc09di9MoNN9zQ1yJoDsD4IUNYX1lJWmxc57VGr5fchCRtHGs0/YyJQ4fx8Jd3YRXhu9nGqNzjRbcxJDqaO0bpJR41/RNtIEeQNzSN36/4L1p8fm4Z9efOGOQpaWlcO4C9xxrNYOOc3NFsq62l0tNCtN1BeyCARYQLx53U16JpNJpuxEdFcdO0mbywdROP7LoVgFHJSVwxYZIOh9L0W7SBHIHLbueWGbN5d3chj+/+IQ6bjfmjRnBmzsi+Fk2j0USQGh3NHXNOZmV5GXuamkiPjWNO1nCGxMT0tWgajaYHhickcPfcU6lvb8dmERKdelRW07/RBnI3klwurpyUz6KJeXp9Ro2mH5PodDF/1Ji+FkOj0UTQ4vNS6fEQbbOTFR/f5T0qIqRG6xUrNAODE85ADitFlcdNeUsLlR43LpudvGHDusQywtEtQaXRaI6OsFKUNDWyq6Ge4qZGfMEgY5JTmTt8eK+ep3D9NQBYUvS60xrN10m1x8N7uwv5qKSIao+HtNhYkp3RjEhM5LrJU0lwOg9eiEbTzzihDOSSpkae37yRDVWV1LS2UtfehtNm4+yckSyamMf0jMy+FnFAUF9fzznnnANAVVUVVquVIUOGALBq1aouO9UNVn7+85+TmprKHXfc0deiDDpafF7+vn4tW2tr2FVfj4gwPCGBvc3NrK4s57aZc7psT91hGBNY1eVcG8oazfGnvq2NR1evoK6tlQq34XSq9HiIc0RR4W7hpYLN3DhtZl+LqdEcNieMgdzi8/HUujW0BQJsra3Bai5W7g+FSHK6WLZtK+OHDCXabj9ISZqUlBQ2bNgAwJIlS4iNjeXuu+/uco9SCqUUFr2rmeYweXV7AZUeDw3t7SS5orFbLNR4WhkWE0ur38dnpSVcNO7Q1wDXaDTHj6/KSvGFgrQHgzitNpw2G3aLheKmRk4bkUNhfT1N3nYdc6wZcJww1svWmmreK9rFp3tKaA8G8fj9tAUCePx+Xt+5jQ+Kd7O3pbmvxTyuLPrrVyz661fHrfxdu3YxYcIErr76aiZOnEhlZSU33XQTM2bMYOLEidx///2d92ZlZbFkyRKmTp1Kfn4+O3fuBOCjjz5i8uTJTJkyhWnTptHa2soHH3zAWWedxXnnnce4ceO49dZb6WmDm3vuuYcJEyaQn5/PT37yEwBef/11Zs+ezdSpUzn33HOpqakBDA/w9ddfz6mnnkp2djavvfYaP/7xj5k0aRLnn39+5455WVlZ/OQnPyEvL4/Zs2dTVFS0X72FhYXMnz+f6dOnc/rpp3e25YUXXmDSpElMnjyZs84669g+7EFKq9/P1poaEp1OvMEgDqsVEcFps1HW0kJClIsd9XVd8lhSnjW8xfZZYJ+171yj0RxX6tra+GJvKW0BP95gsHNFCqvFQlgp/KEQAIFQuC/F1GiOiEFvICul2F5Xy+s7ttHmDxAMh3q4x/hr197Oo2b79u3ceeedFBQUkJnluQOIAAAgAElEQVSZyUMPPcSaNWvYuHEj77//PgUFBZ33Dhs2jPXr13PDDTfwu9/9DoCHH36YJ554gg0bNrB8+XKcZuzaypUrefzxxykoKGDbtm28/vrrXeqtrq7m7bffZuvWrWzatIn//u//BuD0009nxYoVrF+/nssuu4zf/va3nXmKi4v55JNPeOWVV7jqqqtYsGABW7ZswWKx8J///KfzvuTkZDZv3szNN9/MXXfdtV+bb7rpJh577DHWrl3Lgw8+yA9/+EMA7rvvPj788EM2btzIq6++eoye8OCluKmRp9atYW1lOesryvEFgxTU1bCtrgYRIawU3mCAZKee5KPR9DXL95Tw6y+Ws7uhni01tVR7PGyvr2VbXQ1hpUAEXyhEsstFip6YpxmADPoQi+V7Snhj5zaW7ykhjCI9OrZzKAiBcFhxZnYuCS4nIxIG54LlHV7jlcUNXc5fvHnuMa9r1KhRzJixbwfHpUuX8re//Y1gMEhFRQUFBQVMmDABgMsuuwyA6dOn8/bbbwNwyimncPvtt3P11Vdz+eWXExtrrD89Z84ccnJyALjyyiv5/PPPueSSSzrrSU5OxmKxcOONN3L++edzwQUXAFBaWsoVV1xBVVUVPp+PsWPHduZZuHAhNpuNvLw8AL7xjW8AkJeXR0lJSed9ixcvBuDqq6/m3nvv7dLepqYmVqxYweWXX955rcP7fMopp/Dtb3+bb33rW51t1fTMnqYmHl+9kiirjaHRsTT62iluaqRjnGBHfS0um53M+HhOz87psQztNdZovh6qPG7e3LmdodExJEQ5WVG+l6LGBvyhEIJQ2+YhPTaeYDjEFROn6rWONQOSQe0ybQsEeGdXIWkxcThtNqKsVuxWKxaM2ONAKITVIsQ5HVyfPxWb9iAfNTER69AWFhbyxz/+kY8++ohNmzaxYMECvF5vZ3pUVBQAVqu106j8+c9/zhNPPIHH42HOnDkUFhYC+68q0v3cbrezZs0aLrnkEl577TXOP/98AG699VbuvPNONm/ezGOPPdZj/RaLpcvEQovF0ilPT3VFopQiNTWVDRs2dB5btmwB4Mknn+S+++6jpKSEadOm0djYeLDHd8LyQfEuomw2UqKjmTh0KC5r17kAFhGsFuH8MWMZlZzcR1JqNIMfXzBIUWMDpc1Nhie4Bwpqa1BKYbdaiXE4qGtrwx8KoQBvKIg3GGJvSzM5iUmUNDbSHPG7q9EMFAasB7nF52VdZSVVHjfZCYlMTkvvMsEuEAqxuqKM94sKcdrsVHrcADisVhw2G6ePyGFmZiZFDQ00trfz1Po1nJM7irnDRwy63m6Hp/h4eo57oqWlhbi4OOLj46msrOTdd99lwYIFB8yze/du8vPzyc/PZ+XKlezYsQOn08mKFSsoLS0lMzOTl156idtuu61LPrfbjdfr5YILLuDkk09m3LhxADQ3N5OZmYlSimeeeeaI2vHiiy9y9913s3TpUk455ZQuaUlJSaSnp/Pqq69y6aWXEg6H2bx5M5MnT6aoqIg5c+Ywe/Zs3nrrLcrLy0lKSjoiGQYqNa0evigtZW9LE8PjEzl1RHaXzTz8oRC7GupZWVZGarQLpRTv7t5lTPI073HZbCS5XMzKyOKdXYWsKi/jWxMmMSo5pW8apdEMUjZWVfJSwRaC4bDR+Y+O5rrJ0xhmjuRVut38e+d2lpeWUOl24wsFWV9VSaO3nVCEMd3Q3kaC00lZSzO7Ghr4tLSEW2bM2m85VY2mPzMgDeQqj5vH16zinV07sSCclp3DJ3uK+cGM2SQ4nVS4W/j7+rW8tmMbTV4vLts+b6DDajR5btZwNtZUEW2zs6q8jLBSNHm9+EMhzsrVO+cdC6ZNm8aECRM46aSTyM7O3s+47Inf/OY3fPbZZ1gsFvLz8zn33HNZvnw5s2bN4vvf/z67d+9m3rx5XHTRRV3yNTc3c9lll+Hz+QiHw50xzUuWLOHSSy8lOTmZM888k8rKysNuR11dHfn5+bhcLpYuXbpf+gsvvMAtt9zCkiVL8Pv9XHPNNUyePJk777yT4uJilFKce+65TJo06bDrHsiUt7Tw6OoVKAUxDjsry8tYU1HOD2bNJjMuniZvO0+uW0NNq4cqj5uixnrSYuNQSnXx2rcHg3iDQTLi4rGI4DZXpLnr5FMZEq13ztNojgU1rR6e27KRZKcLp81wNtW3t/GPDeu45+RTcft9PL5mJWGlGJucSl1rK4X19bQFAgyJjqHc3QJAlNVKfFQUiydN7iy7tq2Vtwp38L2pM3qsW6Ppj0hPqwH0xowZM9SaNWuOoziHxpNrV1PS3MTyPSUAXD5+IpWeFuZkjuCicSfx/778DH8wyIfFRXj8PobFxLG3pYmEKCfnjR5La8BPbmISf9+wDofV2qnY6bFxnJWbyy9PPxu71dqHLTw427ZtY/z4E2Opqw8++IBHHnmE11577WuvOysriy1btpCYeHzi03v6P4rIWqXUUb9J+lpfn1y7mtLm5i4TdOraWhmZlMx3p07n2U0b2FJTTVpsHI3t7ayuKMMfCjE+dQjDYuN4fcc2GtvbsFgs3DJ9VhejudLj5uzckXonPU2/4FjobF/r6wdFu3i/aBfpsfFdrpe7W7h15mx2NzTwblEhGWZ6aXMT2+vraPf7yRuWxsryMmrbWhHg+9NndXmHhpWi0uPmoXPO7VxiVaPpKw5VXweMB7nF56WurY1Yh4OdDfWsKCul3G2ETSzbtpWwUkTbHUxNT+e17QVEWW2dhq9gzIDPTUzEbrVww6QZvLajAKulayiFRYRAKERbIEBCPzeQNZr+hlKKspYWGr3tpLhc7GyoJ73bkGqS08XO+joCoRCbqqsYFmMM3X5UUkQgFCIrLp6dDfW8X7Qbm9VCGAiHw/x13WrAePECOCxWGtrav9b2aTSDiRafl09KitlYXYXLZkcERO0fXigYoVAVnhac5tyAZdu2ArBw9Fh2NdZz3uixVHncNLS3YRXBHwp1MZAD4RBOm23QhS9qBjf93kAOhcO8VbiDz0v38OmeYpSC3KSk/SYPKKWIttkJhfdfbzHG4cBmsbBwzEkszsvHIkJOYhJnZOcwLCauU9nPHzMWbzBIzAmwE9xAYt68ecybN69P6i4rK+uTegca7YEA/9y0gcKGOgRBoahwu4mPiiLOEdV5nzcYJNHcBW+/iZZWK9MyMlEoXvJs7pIW6PbCbQsGGJOiY5A1miOhPRDg8TWrqG9rI8UVjTcYpKSpkRafl2GxsZ266QsFsYqFrPgEKt1uNldXA/s2/LBbraS4ovnJh+8iQEgpQkrxzKb1WBC+P2MWYaWoaW1l/qjRB5zwrNH0N/q9gbyibC8flxSTFRePw2qjY+rOiPhEszcqXDJuPOXuFk7NziYrPoFzR44mxu7g7V3Ghg2XnjSBcncLszKzOnuwp43IYV1lBbWtrShTqWtaW7liYt6AWc2ie6ymZmBxOOFN/Z33inaxs76OzLh4RASlFFVuD9tqa5iSloHDasUfClHf3saiSXnYrVampqXz+xVf4IgY7XltewHJLhcbv38bi5e9yJqKckLmhL1Ep5OXtm5m7vDhDI9PJG/osL5ttEYzQNlUXUVdWyuZcQkARAEnpaQay7U1NRDniCKsFCEV5ooJeUTb7UxLz+Cz0hKWbtlIbVsbAC9u3UyS00n3t1AoHCaEEQqllGJOVhZn5ei5PZqBRb83kD8rLWFl2V7WWCydL1GlFFnxCfiCIUSgqtXDmTm5zMrIwmqxsGhSPs9u2oAvFESACncLc7KGd1kealhsLD+cNYf3d+/GYhGSXS7m5Y5i0gB56TqdTurr60lJSdFG8gBEKUV9fX3nRigDmbBSrCzby7CYfZ4nEWF86hBKmpto9nkJhsLYrBYuHHsSszKyAFg4ZhyPrl6BL7RvEm20w05CL8/EZbMRCisuHjuemZlZRNn2//lavOxFAJZevuhYN1OjGTSUNjcTZe2qPzarlZGJSXxj1Bg8fh9RNjuT09LIjDNijuOiovjBzNl8UlLcaSAPjYkhLiqK9679DgBj/mxMju5Y0WJnfR3/uPgyvc20ZkDS7w3ktkCgxzVwRyQk8ucFF+D2+0iNjunyUs0bOox7Tj6NhaPH4g0FGZOcQm5i0n7lZMTFc92UqV9LO441WVlZlJWVUVtb29eiaI4Qp9NJVlZWX4tx1CilCIXD+8UXWq0WNtdUMSIhkUcWXki8I6qLURsfFcUn191AUWMjd777FnarlVeuuKpTTzuMXG30ajTHlmGxsfgquu4qayytKPx17SrsVmuP+pbsiuaDb3/3kHXSabNp41gzYOn3BnL+sDT8oRBpsftihU8fkcPI5CSGxsYylNge86VGR3NGTu7XKerXit1uJzd38LZPM3CwWizkD0tjc00NabH79NGYVBuFRaTX5disFgtjUlL491XfPmo5Fi97kZXlZZ2fQRvVGk1PTElL46Pi3dS1tZLiiiYYDlPd6iFvWBpl7uYjLrfwtruA46N/bp+PSo+baLu9M5RLozme9HsD+ZzcUeyoq6Pc3UIgHCKsFDarhfPHjOtr0fajtLmJr/bupdHbzrjUVGZmZBGrJ/xpTgAWjhlHaUsz5e4WLCJ8XFKEw2qlwu1md2PDQV+YB0o/HkbunqYmlpcWU+3xMDIpmdOyc/SaypoThvgoJ9+fMYs3dmynsKEem8XCxuoq9rY0s7qiHDB0sjfdO94dz8jfA6UUn+4p5j+7CgkrhUIxPD6R6yZP7TUcS6M5FvR7AznJ5eL2OSezvrKCKWnppMfGMiMjk/io46MYoXCYkqZGPIEAGbFxXXb9OhCbqqv456b1fH/kn7FEC4/u/CGrysu4deYcbSRrBj1JLhd3zTmFgtoaatpaKaitIdpup8JcivF40Oz1sruxAYBRScksvXzRIXmuttfV8rd1a4myWYm2O1hVXs66qgp+NGsuQ2N6HpHSaAYbabFx3DR9Jr5gEKvFwrWvvnxY+Q+nU6uUYm1lBR8XF9HgbWdMcgrzR40hMz5+v7zd2dXQwJs7d5AWE4vdakUpY4WcF7Zu5ubpMw9LZo3mcOj3BjJArMPBadk5x72exvZ2/rZ+DS8VbAHgjOwcTs7K5uKTxh9w/cZgOMyr2wtIckZ3LkWVFZ9AeUsLq8r2cvbIUcdddo2mr4my2ZiangHQuYFH5Et08bIX9/NKdaQfbmjEusoKXtq6maC5rKPNYmHRxLyDyqiU4o0d243l56KM5eei7XaqW918XFLEoon5h9xejWYw0DEvoKeY/570sSedPZi+frG3lGXbtpLicpHqiqaosYFHV6/g9tknd25j3VvZ1a0eTs4a0fluFRGGxcSwq6GehvY2kl3R+1eo0RwDBoSBfCxQSlHd6iGkFGkxsT3u5vOvbVtoaG/vnN2bHhvPZ6UljExKYnJaeq9lN7a30+r388Mxj5Ll3AHAZWkPEhqqeKvu59pA1mgOg0A4TFFjAymu6B6HUJu87by0dTNJTlfny90XDPLi1s385fyLDzjs2h4MUtvWSka3DUwSo1wU1tcf24ZoNP2YYxUn3Oz18tjqlcQ5HMzJGsHo5OQu8cGBUIh3dxeSFhPbqa+p0TFUt7r5rLSEb06YdMDyw2G139KrItK5gYlGc7w4IQzkao+HZzdv4MWtm1FKcXbuSK7Nn9plSbcWn5fC+nq+itih79XtBQTDIU5KHXJAA9llN3YX6r6sbVgpkl16Bq/mxKCnF26k57gnL3Gk1yqsFBePG8/aynIeW70Sj9/P7Mwsrsmf0mWTkN2NDQTD4S4rYkTZbATDYXY11jM9PbNXGaOsVlw2G/5wqMsyV+3BQJcJhhrNiUKLz0eTt51Ep4v4qKhedbZDVzv+LvrXC1S43YxPHcKuhjrKmlt4YetmFo4Zy3emTCfafC96/H58wSAp3Ty9cY4oSpu7TgjsyYu9uryMpVs2kRDl7DS83X4fCVFOPW9Ac1wZMAZyUWMDq8rLaPX7mTR0GFPS0ntcB7U7gVCIp9avodXvJxgK0+Lz8e+dO1hbUcEtM2Zz6fgJWEQIhTu2IOmOsf30gYh1OJiekcGfC3/Aj8Y8BgLP7b2b+vY2bp014ojaq9EMFLoPi571zFMsmpjPlLQ0pqZlHHI5TV4vqyvKsYuFgvoaguEw2+tq2VhdyU9PO7MzPlgpONIJ7FaLhTNzcvn3zh2kxcbhsFppDwRw+/wsztMjPZrBT3d9/cY//w7AmdkjOWXEiF4nwLcFAtS3tREX5SDR6aLV78cbDBJWih31ddgsVmwWYVnBVlr9fn40ey5Om50YhwOH1YovFOzSKfX4/YxOPvhumJPT0llfVcmO+jqirDaC4RBWi/DdKdN7HAnWaI4VA8JA/nLvHpZtK+CL0j2IwMnDs1lbWc4N02biiPAshZViW20NayrLCSvFtLQMFLCsYCtBFcbj9wPgC4Wo9Lh5fcc2hifEMytzOIlOJ5lxcZyVM5KPS4oBuOykCextaWZ6xsFf8hePGw8KHtl1KwDR9iBX5U0mNzHp2D8QjaYf0x4MUt3q4eWCrWyqruafl34Lm8XSxStU1NjAs5s20OLzMWHIUP56wSX87KP3aG5vZ2dDPS6bjRiHA5fNzq6GBv6+fh33nHwqVouF0cnJWLDgCwa7hFhYxcKopOQDiQbAGdm5hJXi45JiAqEQMQ4HV+XlMy4l9bg+F42mP1BQW9Pl3G6xIiIMjYnh45JiEp2u/Ty5N06bwf2ffkQYQCkmDh3K+JQhtAUCvF+0CwGcNjtJLhcum42ixkY2VFUyJ2sEDquVeSNH8caO7aRGx+Cy2Wj0egkrxakjsnuUMXIUymG18p0p09hRX8euhnriHVFMTksnJVrHHmuOL/3eQG4LBHhzxw6GmTNYAbLi4tnd2EhBTTVT0vcZr2/u3M6nJcV8WVYKwLS0DCo8LTT7vF28w2Gl8AaDbKmtZvmePczKHI6IcMXEPJ5YuxpfMAgC5e4WJg4dxrQDDNl24LTZWTQpn/PGjKM9ECDZ5eoyLKzRDGaC4TCp0dE4LNbOmMJ4RxQ76uvYWV/HhCFDO196q8vLeGHrZlw2Gw6rjTd3bONv69dQ0tRIi9dHa8DoyNosFmIdDtJi46hvb2NvSzM5iUkkOl18a+IkXi7YQsicpGcVC9+aOOmQNiWwWizMGzma07NzaQ8EiHU4tCdKM+gJK4UAE4YMBaCkqQmL0CUGeGh0DJ/uKeZ0c1L80ssXsbGqkmc2ric9Ng671UpIhXl56xbagwEa2tvwBo2dML2hEG6/D5fdzklDhlDYUM+cLGME9YzsXJw2Ox8W7abC42ZkUjILR48lIy7+kGKh7VYrk4YOGzA73WoGB/3KQA4rRUlTI2UtzcQ6HIxPHUqVx81HJbtxWG2dW02/sr2AQCjEzIzMTgO52uPh8z0lZMbFY7cYhmmz10tjWzsC+4VPKIyJex6/r/NaRlw895x8GheMPYlmn5cR8YmMTEra7+XpCwbZ09xEWClyEhNx2uydafFRUcSbs+M1msGILxhkZ0M9da2tpMXGopTCHwoh0GVyjojgsFjZ3djQ+VL2BYO8sXM7Q6NjOr2/bp+X0qZmUNAW8BteKozJei0+Pw6rocO+iFCnGRmZjEpKZldjPYIwOjmZW956A+j6ovUGAxQ3NSFAbmJSl7Ash9XaZQRKoxmM1La18k7hTh5ZvQKLCDWtrQDYLRaMqW77cFitNLd5O88XL3uRcncL5+SO6nT4tPh8hJQiEArRHgh06itKGUZ4MEh9axuxDgehcBirxYKIMCdrOHOyhqOU6nWTj531dXy6p5hGr5eTUlI5bUQOSXoej6aP6DcGcjAcZumWjWysqmL5nhIAFo4dx6XjxpvGbVcTV6FIjJitXuZu5tM9JUTZ9hnSje3ttAcDvcQWGxN7FLB8Twl5Q4eR5HIR43AwI6N3j3FxUyNPb1jHO7t2AvCNkaNZPClf92w1JwRN3naeWLua2tZWPi0tAQV17W2AsXvl5eMndrk/qMIkRXh1F7/yEuUtzSyeNLnzWoXHTbTdRnVrq/HiVPs03m610Or3UdrczNbqasLhMGNSUrFZLCS5XMx09b5Vd0FtDc9t3kggFEKhcNrsXJs/hbE6lEJzguDx+/nLmlW0BQI4rNYuE8kz4uJpCwS63F/f3sbEIV3fZaGwwmHZ15H0BgOIAn/ImCgbDOx7xwrGvJ8vy/cSGxXF+soqzs7N5fTs3M6lUiON48iJgBc8/3/UtLayYPRYnDYbX+zdw4bqKn40a47erlrTJ/QbA3lDVSXrKysZHp/Q6eUJhxUfFu/m+slTKW1u5ou9ewBh/sjRNPu8ZMXHU9vaSmp0NNERXtxDpcXr5e3CHbyzayfn5I7iO1OmHfDl6Q0G+Pv6tTis1s7JBrF2B89u2sC9p56ulVgz6Hm7cCcN7e1kxid0mXADEGW1UeXxMCwmBhGhyduO3Vyz9PKXnsdhtbKhqhIwllT85nhjaNciQkiFafJ596vPHwrRrhRrKsspbmrg1BE5jEtJ5fop0zq9vz2tnfrXCy7mn5vWE+dwEh1t/Da0+v08s3E9Pz31DGL05j2aE4CNVZW0+HxkxsV36tuybVvwhUI8fv5FvLBlExVuNw6LhapWDyEVZnhcAmc/8zdcdjvb6moBeHVHAVFWG5ePn4jLZiegwjT7vF1GdYBOb3KUxcLG6krCCjx+H6FwmHNGjj6grPXtbditls6Vn4yNhlr4au9ezhsz9tg+GI3mEOg3BvK6ygq+KitltcXa6QH+uGQ3c7NG8IMZs3m/eDfeYICylhY+LN4NQGlzM5nx8eQkJvGtCZO46KTx+AJBPtlTBMCwmFjq2lopa2nZNwyE0cu1ImTFJ9Buxk/F2B0s3bKJn5125n5rLnawu6GBd3cXEhUR7vHajm2EVJhLTprAnKzhx+fhaDT9gFA4zIaqSoaZq0l0eItfLthCWCn+vfhaXirYzNaaaspaWmj0ehGgqKmR0uZmnBHhDf6QsW28RYRkl4s15va2+9VpurzCStHs87GqfC9KKdZVlnfGN/bEzvp6AqEw0XY7oXCYRm87IaVoDwTY1djA5GFpx+ipaDT9l0qPm6j9woiMwAqrWLhzzil8ULSLZdu24g0GqW9ro6SpiYb2NqL8+/TVbjEmxXaMykbb7RxoIZn2YLBzudSV5WVE2+2clp3bJaSpo2PbQVgpbBYrTd52ipsa8fj9OK021lVVaANZ0yf0m5kp1l5ikhSQ4HTy7fwpJLmiGRITQ21bG3XtbWytreaj4iKqPG7+uWkD35syjZToaOZmjWBu1ggmDR1GtN2BpZvBq4AgirKWFsrdxvFW4XZe217Al3tLOyf+dKdj165gRHp9Wxutfj/rKnt+wWs0gwURwSKyX8jSBWPGcf6YsaaeTiXJFc2w2LjOlShafH7GpaRyRnY26bGxTE/P4PbZc6nyuKlwt5DijCbGfnCPrj8UosLtZnlpCW8V7qSura3Xe3/5yYcIhhd7eWkJ66sq2VRVxcbqKjZWVh7lk9BoBgaZcfFdvLzBcJgzsnOYOiyDBKeD+Kgoyt0t5CYam3ukREczNDqGjLh4zsoZSaormnEpqbx7zXf4zbnnkZuUxJk5I/EGg/tNQu/NYK5p9bCyvIy/rl1FcVPjAeX1h4KsLi+nqd2LIFS3ethYVUl5S8vRPgqN5rDpNx7kGRmZbK2tISs+gWXbtuANBol1OChvaaaosQG71cpbO3eggLZgZNxUkI9LiggpxTcnTOT22XOpa29DKUWz18vbhTtJdrqoaTMmJlhFCJmzeW0WC5i/HS0+H95gkLvefZtr8ifz3akz9ptsl5OYxBnZueysq8XjN+532e1kxsaxpaaGvc3NDE9I+Fqel0bzdWMRYVZmFl+VlZIRG4/H76eosYEydzNT0zIpamzA7fPh9vmIttsJK8XejheiwBhLCoFwGH8oxNV5U7horKFDiU4n/nCID4t3EzDXKlcoYu0OPAE/CsO7BIaR3uYPsLm6mv/3+XLOGz2mR1mdNhshpVhXWYHNYsHliCIUDhM2wzXOHT2atG676Wk0g43Jael8VGI4kVRYsbm2mtZAgKExMfxlzWouOWk8e1taSIyKwhcMEusw3nlRViuVHjdWi4VWv59Yh4PTs3M6V7f47Vef47LZcZtLp1qAcSmpNLS30+z3YRMLIRXGFzQ25HHZjPCrx1av5OZpMxmdkrLfUnKLJubx17WrsVktuGx2/CFjGcf0uHg+KNrFdVOmfe3PT3Ni0288yHnD0jh1RDZ7mhpp9HppCxjhFLsbG3lu80Y+LNoNYqw80Z2Ol6c3GEREGBIdw9CYWKPnLPS43JoCEOOHIMpqZXh8grHuqt1GpdvDO4U79suT4HQyN2s4OxvqaQ8ECSmFx++ntq2NtZXlbK6pOtaPRaPpV8wfNYbcxCQKG+v4qKSI0uYmhsbEYrdYeGz1StZXVmAVi7GqRcSokAVDP8/MzuX+s+YBEBcVxRAzXtkXCmKzWLBZLYxJSWF6eiajU1KMnbIiVD6sFAhUedwMi43lrV07efCcc1l6+SJmZ2YxOzOLpZcvYtkVVzE1LQ2334fPXH6qLRhgfOoQnDYbW2tq0GgGO9F2Oz+YMZvxqUNYW210FqelpTM3awQgPL9lU+dKEwrYVlfDtrp9ujEvdxQ3TJuxX7k/nnsKZ+bkkhkXT7TdzrT0TOKiooh3RiEYy7O2B4OEUbSZoZHJLhcxdnvnBPfufGPkaBKcUYSVwu33YRELU4alkxUXT1HjgT3PGs3xoN94kC0iXDZ+Ikop3H4/mfHxfFxShCCkx8axo76Ok7NGEBcVxSvbtnbGCQfDivmjxv5/9t47yq7jutP96qSbO+eEjsgECCIQDCBBUiQIBgVSNEUqWdKTlaWRLT/bM2vWvDdrli05vBlbwbZsyQoWKYpRkaKYwbMxn74AACAASURBVAQCJEHkjEajA9A533jOqfdH3W50RgNsoAPOtxYW2ff2Pbdud9epXbv2/v2I2ylKIxmjrlmWkcGNi6ooDIV4aO8e+pIqszW063XS2SwJHOnqBFTX72uNDSDg3uUrx9Ujry4sUsfBFnTFYgAETIOk4+C4k+lleHgsDEKWxefXXc3/3v4argslkUj6pEXQl0hwsLMDRzrsaG6kfXBwuPa/vqebtcWlxGx33Dw1NI2anFz8uomp67yZrjOOpVIsyc1jMKUcu1Lp0iZXSmJ2ipTjYOkae1tbqZzAkGddSSnbmxvRhYYA8kIhIpaP0wN92HLiMioPj4VGdiDA4rw81hSWUJpxdu5l+Hz0J+KETJPBZIrCUJiGnh50TZBwHPKCQaJ2Mh1Mj2ZNUQkvnaxna+1ijnR20BmL4rgueYEQmyoq+fm+vaNOehOOza7Tp7myqIhTfb2jpN6GMslSSlbkFSAQmGkJRiEEvfE4eSHPUtrj0jNnMshD9CTiHOxo56WT9bT099Pc38evjhwi4rPIDQSIpVKYmk7KdUk5LroQDCTjbKmpI3OE7BtAlj/A1po6Tg8MoAmBITQChkHQNCkMhanLySXT50NjdA207boTZqoByjIyuaNuMfcsXU5pJIPSSAYfXLKc6ysqWVFQcDF/NB4ecwJNCPriCWqyc8jw+RmqPoxYFinHpTAUUWVMI+aULjT6EnE2V1aTN4ED1t2LlyKR9KeVLE719dCfTJAXChEYo1Dj03UqMrPYdaYFKc+eID187/2jNJCHTEWKwxGqsnOIpMssbFeyLC9/pn8sHh5zlqTjgJhgTRNwc1UNPkNnb9sZonaK/mSS5v4+Xmts4ENLV1CbM96dsjgS4eOr15CwbWzXpbG3lzMDA3TFojx+cD/uiGMfXQgMTaMzFuVUbw/5wdCEOshCCN5XXUtXPIZMfx1NpehNxHlfVfVM/jg8PKbFnMkgD1EcjuBKl5Gxu5QSDY3PXrWet043EzRNeuIxQpbF8rwCNpZVTDiJAW6qqqYkI4OEbXO4o52m/r50ttclYpkIIYbtM4d0GksiGVxZVDyhmoXPMHjwitX8ePeuYce9loF+blxU6dlKe1w2FIRDtA9GydJH6qPaZAf87GhpItPnH26iMzWN3GCQz63bMKmdc2kkgz+95joeO7CfQ50d6c57wc6WJvrTJz+9CWXqIyUEDJOBZIKeeIwrCifWIA+YJvevuIKH9u6mKx4dfu0tVdWUZ4zvFXA7PwaAlvufF/xz8fCYi9Rk5yAlw+UUoPSKNTSuKi7h2vIKdjQ30Zk+Fa3JzsHS9UmtoAGuKCjEdV3++a0d3FJVzUAySWcsRqqvD0vTiaMUogxNG046nert5TNrRpdsjHTS21hWjiMlz504RlcsSobPz8dXXcmyfC/55HHpmXMB8vrSMm6pqsXQNF48eRxXwtVl5VxdVkZRJMJdkaXctXjptK+ndqFJLF3njrolPH5wP46UbCqv4J3W0+jibBA8lInSBNxZt2TSay7Ny+cvr7uBDyxZRsK2qcnJoTSSMak7kIfHQuOWymr+bddbmJpGyLKI2ynao4Pct3wlB9rbyAsGOd7dBaiGuaJwmKXnyNqGTItTvT1cW1aB3zAYTKWwXYf97W1k+/3saG5mMJVEE4Jj3Z34NI33L1k25cZ0VWERZRkZHGpvJ+k61OXkURKJeHPVY0Ez1r65NJLB5soqXjpZnw6QJa4r+cDS5WT6/Tzw+COjtMFHSjJOxYsn69NlVmdPb5fm5bGjuRFQpRVh08KWLq2DA9xVt5RVU0gsCiG4vmIR15SVDzfBa95c9Zgl5lyAnB8M8YV1G/jV4YNsLK3AZ+hsqqjk5vM8YhmyvzV1nf3tbbzedApT02kZUNqMbzQ30hmNURgOE7NVQ4FP18n0+6nMyj6nXXSm3+/pHntcFoxdbAGW5hfwiVVr+O3RIzT39xG2LD68fAUby8q5pryCBx5/hIhlsTy/YNTrpqK5v4+U4xBIG3uE0wt2QTDEoY52Uq5DwDApjWSQdGzijs2GktJxwe7Y8eYEglw7RSZsKHNMaseor71MssdCQQjBnXVLWFFQyMH2NnQhWFlQNKomeTrYrkt9dzfRVJKSSAbt0QGy/aNLpnIDAWxXOd0OmQYpdZo4jlTFF0MzdqST3sh5q6c33h4es8mcC5AByjMz+dKGjSRs1dmuT2LcMRknurv45eGDtPT1EzRNLEObsKZY0wRnBvpJOS4ukphtoyeTvNxwkvboIAVpQwQPD4/xrC4q5orCIuLpzaWuaeNc7Q60t/HA449MGCT3JRI8e+Iob59uwRAadTm5ozTGhwiaBi0D/WhCUJWdjZQSE52KrCzePtPCVWlr+Ikc9aYbnHt4LASmmgNCCKqysic8cXn43vvPuantjEb5wa636IhGVYWxhJTroIkYuYGzTXS/OLCfaCpJTU4OA6kkINCFYE1xCbbr0uGtrR7zhDkZIA/hm+Yxz0ia+/v417d3EjBMtjefwpWwsqCAmuwcVhcV85sjhwHJ9RWVdEajvHvmNK6UdMVV7VWG5SPpOuM86j08LjemE3BqQhA0J7d5Xz5J7WDScfj+2zv4xYF9WJrO+5csY9eZFloHBwmaJrlBteA+emAfScceNjs41dvD2uJSSiLKiGRISea9MJQp9jLHHh4TI6Xkkf176U3EKUmr0Diuy5HODqKpFFKqcgtHurQNKs+Blj61qb2tppaI5UMTgpaB/lG9PUOB+dD/T8S5nvfwuFjM6QD5Qni14SQvnazH0vVhq0sBLMvPpysaI+nYSJQ81ZaaWiI+H+UZmTx+cD8Ady1eQm88TlHY2+FeDKSU4DSrf1oAjMUI4T/3Cz3mBWPF/ydb1I50dtA6OIBPV7cgS9cpz8gi5aoj2Ob+PgTqSLcgFB5uHgpbPlYWqKa80wP9XDOizGm67+1x/kh3AJwGQIBR7c3ZOcqFzIGxG+Ghx0a+ticep76nm5IR5jq6plEYjpDp95EXCPFC/YlRtcuOlBiaRpY/AChHvZrsHLLTX48ds8fMIKVEpvZB8nWQMTBXI6yrEdp49SCPqVlwAfLpgf5xttVCCLL9Qb62cSOfW7eegGFSFA6TdBz2tbVxsqd7uMu2bXCQDy9bgd+YPCvmcWFI6SJjT0ByJyReUQ8GtkLoMwi9ZHYH5zGOixlwfuMPT9Mdj9GeVroY2qBeU1bOPUuXkxMMknQc/p/NtxA0TT7w8//k9EA/myoqGUgm6U/GCZjWsLPXTOBljifGTe6C2GMg07ajwocMfhzNrJ3dgV3mOK5Lc38fjpSURjKwJjDEmrH3mkT2VBOCiOXjk1eu4ZNXrgHO3i8+veYqnj1+nJZ+ZRNdFA5z/4orpt0g65VMXRgy8RzE/wAiAzAg/jQytQfCn0eIqXurPEZz0QNkx3V5rfEULzfUM5hMsiK/kNtr68ifAeHv/kSCzliUTJ+f7IDalVZmZXPDokqKwpGzWeG6JQymkuQHw6Nc9XyGwWevWseuMy0syc0n7DO5urSc6uyJJeM83hsytR+S20ErA5FuwJAuMvpzCH/dUxaYRdqjg7x8sp7DnR3kBgLcWFnN0ty8C/6djFzIUo5DTyJOyLSGyzEsXWf8kiuRUpIXClGRmTXqmaBpUhbJ4IrCAtoGB9l2qp5Mn5+cwPisiLeIzhzS7YLoI6DlgpZeXN1BiP4UmfFXXiZ5lmju6+PHe3bRM2RWZZh8dNVqFqdlFM9nDozdCP/9bVs52N7Gs8ePsTy/gJJIhNxAgKJQmF8c2Iuh6dybNvXqS8S5a/HEik+3VtexoaSclv4+/usLz3Kqr5c/veb69/KxPc6BdPsh8QJoJSCGYp0QOM3I5D6Eb+2sjm++cdED5N8cOcxLDfXsaG5EINCE4Hh3J1/feN04Y4/p4krJs8eP8eLJE7x48gRSwtc3Xsc9y5Rhx1unm2kdGMCVEldKWgcHuG/5ygktp32GwcayCjZO4BbkMcOkdqkAGQPcFvVY4kWwNoLbDrqndTkbdEajfGfHdhK2TZY/wJmBAf7tnZ08sGIV69PWzRfKWy3N/ObIIWIppRl+TVk5d9YtIWCaWNrZ+ehKyTVl5SzOy5tQo3jsGLY3NV7wmMArw5guMnVI/c/IzJMWAqcX7BNgLp+dgV3GJB2HH+56C0fK4XrgwWSSH737Dn9x3Q0XvK6CKqX4P2+8hiY0pJA8c/wod9Qu5ubqGj6ychWPHdxHwrZpSWeuVxUWsXqMbNvIOZXp95Pp909bNm6i63hz9TxwWwExIjhOI3zg1ANegHw+XNQAuS+R4PXGU5RFMngrrTdcEArT3N/H2y3N3Fxdc0HX3XWmhWeOH6UkkoGlG0gkb51uIuKzuGvxUr664RqeO3Ecn6GTEwhyU2XVcN2ix2wy54wbPYDXGhuI2zbF6fpCv2HgNwx+e/QwVxYVT7ixnA7Hujp5eN8e8oNBsvwBbNfllVMn0YWGJgTFkciw7KIjXW6uqubmquops9beseslRtowlOuPPaH+G7gn/aRn1z0bnOjuoj+ZHA6OQVnAd8djHOxov2D50e9svZtvvbaN/GBoeM7brsvTx4/yvbfe5GhXJ/3JJAB72s7w3a13U5WdM6VO8UTzFbxg96IhIqoUSkoQ4uyctTaqUyCP8+KiBsjdsRgvNZzA0g2a03VIjx/cj+06XPEeAtZtDSfZ3tSIrmnD132zqQmfbnB77WIKQmEevGL1BV07lkpR39ONlJLKrGxPi3EGEdZVyNQ1oJVC/Cn1oG8zaJmgeda/s8WJ7i4yrNG1aX7DpCsWoz+ZmLCMYTpsazhJyDSH6/kNTaM4HOGNplP86AP34jOM824mOtDeNkoZI27btA0OTGpfO9E1wAuwp4sw65Bx0oFyGpkAoYFeOVvDuqwZUnQZiyYEsSnUl8bONVdKzgz0I1EOtid7u3GlHLUhNjQlkRqz7VHXyvYHqMmZPOAaeq+xDClcHOxoZ0lu3rRMQLz5eR5oBWAsAfsIaEMxlg3CQlhXzurQ5iMXNUDODgSQME6D2JGSssj0BMpbBwb4/fGjHGxvI8PnZ3NlJYPJ5LjFUAhIuS6260xoET0dDrW38dO9u/nD8WO40mVjWTmfWbOOq4q9BrIZwVgK1vXp7lqViUCYiOADXv3xLFIQCrNvsG3UZjDlqHkUMqe3QeyIRtnWUM+x7i7yAyFurKykKxYbd7RqaDq26yrt5As4dl2eX8BD9/wRdzz0E7piUWqys/l/X36Bpbn5fHrNWm9DO9NoRZDaB/IVcDvUY7EnQc9HaJ7Sz2xQkS5BUmudCmaHyglrcqbXP9Pc18dP9+xKyyQKsgN+1haVjLsP265DynUQMJw9jpzHHBsKbm/7zx/RE4uxPD+f7liM//Hi89xcVc2fXLUO7QLXa4/xCCEg+BFk5wdBDpyds6kDyO6vILxG5PPiogbIGT4f//X6G9nWcJI30zXImyoWYek6a9Pi/lPRFYvy3Z3bsV2XV0814CLpiA6S5Q9wfUUFxeEMfnFgLwnbJtPnpzsWpaW//4Ka7AaSSX6y510StkPMThFP2bx0sp6jnZ383a23s7qo+EJ+BB4jEEKDwPvBtwGC94EIglGLEF5QM5tsqqjk3TOn6U8kCFsWKdflzEA/t9bUTiuI7YhG+faON0jYNhk+Pye6u9jX3kpNdg7t0QGCpoWTvmZTXx8B06Q/mSDT7592dmik49YHH/kZx7u7yPD5ONLZieO6HGhv52iXmqtTjdmrazw/hBBILRsInV1sjXLAU/mZLbIDAbbWLuY3Rw9j6RoCQcJxuK68YsL6/bGnJvc/9nNO9fawpaZuuEyjNx7npYZ6dCEYTCYxdI3DHR3K3dJ1STr2uOtOxlh3vJTjsLqwiMFkkuNdXQhUQP/w3t1k+HwXfNqbsG1idoqw5bvgpNhCRGhBpFYA5J2ds14z7QVx0Zv07l68lGx/gCy/n8FUiiV5+WytrZtWI8GO5iYStkNxJIIQAh1BSSSDlv4+Mv1+jnd10huP40pJ8n+9Trtp8r2/C/CpK9eyouD8Gr6Od3Xy7IljxFIpounjpLhtc6q3h3/asZ3v3nHXeUm/RVMpXm9s4J3Tp7F0nWvLy1lbXHreroALDSEE6MXqn8ecoDwzk8+sWcuvjhyiZaAfn6FzR91ibqqcnr37tob6UTXMQdPEn0xypr+fkOWjsa+H+u5ueuJxNCGozcnhn958g0+sXnNBvQF9iQQZlo+8QJAT3d040qU4HOGd0808emAvH1u15ryuJ6XkaFcnb7U0Dy/mKwuLvEU3jWekMvfYXFlFdXYOu1tP47guKwoKqc3JndZJXDSVwnHlsD4xqGa6pr5eNldWsb2piXebWhhMpYhYFuuKS9CExnP1xynPyOQX933kvMYadxxVutjdRUNvL450yfT5kUiePHiAW6trJ1S1mmwT67guL5w8zksnT5JyXIKmwZ11S1hfWnZe41rIeHN2ZrjoAbKuadxYWcWNlVVIKc/rKL2xt5dtp05ijKg1/uXhg1xXXsFHr1jNL/bvZVlePiWRDE7769GERpbfz6+PHGRZfv606puGcKTEdt1RtVYSSLourzc2sL+tbVpZb1Bdxv/+zluc6u3h9aZTIJUDWGNfH/cuWzHtMXl4XCqW5OXzjdw8YraNpevnFRwe7eokyzd6wxuyLHoTcb66bj2PHdjPoY4OqrNzKM/MJNPnJ5pK8fjB/SzNy5/We4103LqufBEvn6yndbCfZPr4dyCZJOnY/PrIYT68fOU5N7MjF93n6o/x9LGjBA0TTQh2t53hyrZiPrbqyvO6h3h4XCqEECzKymJRVtY5v3fsqcnXN17HL/bvneCikB8M8YnVq2kbHKAgFCLLHxien9dXLOK+5Sun9X4jg9sD7W383Wuv0JdIYEsXAZiaRsxO0dzfxztnWthSUzfNT656G54+epSicARL14mlUjy8fw9hy2LZJM6dHh4XwiU1CjnfOtPSjExc6TJS/UBKOdxUIBCIb75Jq9DoeVfJhu39+q9Y9M0txFKp86pHrMrKZkV+AW82N+Gm3wPSTQrA/vbpB8iHOto51ddDWUYmutBAQFlGJtubTnHjoirygp6jjcfcQ5zDNnoy8oMh6nu6CYx4bdJxMHWdglCYkGmxvqSM5+uPc7izg3uXrSBomrQMxOiIDlI0wp1rKoYW+m0NJ/nezjexXQc73d/Qm4gDyq2rdWBwWoEDqEbiZ48fpyScMRwIZPsD7Gk9zfGuCupyvc7vIbws1MKgIjMTgcrEDp1oulIipDpN6orFyPL7yQuOzurqQtA9TWv3kRvQmuwc9rS10ptQp72gdNeFEGT4/Bzu6BgVIE+lfOG4Li+erKcwFB42RgmYJhmOjxdP1nsB8hi8OfvemNNOeleXlnFrdS0SeLmhHiklG0rLuHFRFSHLIi8UxJUSbUTcLaXEMvThOkTHdTna1UlzXx9Zfj/L8wtGLeRDZAcC3FRZzRtNjaMMDAQqPJ+qO3gsjb29vNLQkLa7VpnvJw8dIOHYfOrKtV6A7LGg2FxZxf6dbfj1JCHLIuU4nBno5/aaOixdJ+KzSPaM7rx3pQTJqCa+WCrFyZ5uEIKqrKxJs8DrS0oxNEHCOTtTbdfF0nV8hklPPMYipg6Qhxbd/7ZpMxJGZbGFEOhC50RPlxcgeywYhoJWKSWbFlXy8sl6/KaJQM29TYsqKQ5HMDUdKUmvrWL4NbbrUp55tsa5sbeX1xsbaI9GWZKbx9VlZWT4xpdO+gyD/GCQnvjZ4NqREh0wdX3U+n0uUq7qEcoeU6IZMEw6Y9HpX8jDYxrM6QA5NxjkS+s38vSxI6Qch4hlcWNlNdeVK1OPzYuqOfStreQGguz+L08hpaTsb27jpkVVGJpGwrb5j3ff4VhXJ9tOnQSUq97n1m6gMDy+Azvb70MXYjgrBeomYWo6pRnTU91Q4w4gx/mEqa8zfJeP1aOULiA8hYoFTnV2Dp9cvYZfHT5ES38fpq5ze00dt6R1zh/Zv5fmvj46YkO20vu4tryC5fkFw3WQB9rb+Nne3aTSElaWrvPxVVeyJG+8/J/fMLilqoZfHzmERGW2hjrri0Nh/OeRBQ8YBhP9dbpSEpmmgoeHx3xCCMHdi5eyODePXadPA7CmuJjFaefM/FCIjeXlfPPVbRiaxp11S+iOx6jJzh526tvf1saPdr/DtoaT6EJwfcUidjQ38eUNGyfsL/rUlVfxnZ1vDieMcgMBXCnxGQZLx8zxqRppfbpBYShMfzI5ai3tScTHGZYsdKSU6SY8CVqeaoL3mFHmdIAMUByJ8Ok1ayesX67LzeXjV6zm10cPs+ibWzB1nc2VlWxONxe92dzE0a4OyiKZ+HT1UZOOwxMH9/OF9VePe6/dbW0ETYu+ZAJQmWNL1ynJiLDhPBoAVhYUcUfdEmzHSWe+4ZryCqoysymdprzdfEY6rcj478E+BCKAtDYhfJsQYs7/uXlcIKsKi1hZUMhAMonfMIaPP0EFtAWh0HCAnHAcmv7qDySCYXj5KvoSCX66ZxcRy08waPL4wf040kVKyV9t2kx4TKlU6+AALhK/YRCzbQxNIzcYwk4fGVdlZU86zrHHt//thee4qriYjugguYEgQgj6EwksQ2fFZWQuJN0BZHK7knTTIgjrWjCWepvbBYoQgqV5+eOC0yE+uGQZ/7HrbfoSCYKmwfUVS9lYVoahaTiuy1OHDpDl8w/P86Hm+dcbT7G1bvHwdYbmW212DmsKi+hK3wMKQmFc6ZJyHNZPUro4kcKMEIL3L1nKv72zk4RtE7JM+hIJDF3j5qrpNRUvBKRzBhn9OThn1APJN5B6AVruo7M7sAXGvIlYJrtRX1lcwhWFRQymkvgNc9TC/HZLM280NqJrzcM715cb6tlYVsFAMjlu4R2ecOkA2W+YCCGozc6hIDR9zc+wZfH5tet58tBBNpZVoAm4qqiEuxYv/AVHur3IgX8FmYLkG4AEtx/p9iKCH5zt4XmcJ+cjh6YJMeEJydBr73/s56Qch3+7+0P89c++Nfz80a6OdDf62cyvLjSSrsvxrs5xEovRVIrtTY3DajO60Djd38+izCw2lJaNugecCyHgU2vW8vDePTT19YKATJ+fz6xa+54se+cTUsaQg/8KThuILIg9hhz4d8j8FsJ/w2wPz2MW+NiTj3Kgox2AnS3N7GxpZnNlFav+5dtIKQmYJr4xBmC319RxsKN9VIA8xHP1J9AEw03wp3p7MTQV1E5UljEVi3Pz+OqGa3m5oZ7WgQGuLi3g+kWLyA+OV8JYiEiZRA7+UHkJaMXqJoYDzmmkjCFE4JzX8Jge8yZAngpd04YnWUt/H78/dpTDnR0c7+ok4TgERxQ5SdJ1xRMEqlemF+LXGk8hpeTOuiX0xGNcVXL+RiFF4QhfWLeBaCqFLsQFmSLMR2TyHYj/HoQFrmqcJLkTEq8h/TcjtIWfQfcYzVCQrQlB074m/vpfvsWelw+o59b9KTvvL0b4dYrCYUCM2swe7GjjNw98YtT1CsdsViPpoHxJXt6k2aghJju+/cqGjXREo9iuS0EodFnJMcrkuyo41od+dgYIHRLPIK11CM3rmbjccaXk5p/8gIG0WUg0lRo3R+K2PayrPPakJmAYBEeULGX6fSRs55zW8pNRnpnJx1Zdps5w9jFwe8/O19gT4LYBIDsfQIqw15w3QyyYqE1KyaunGvj7N14l6dicGRgAIGxarCoognSl4XUVFSzLy5+wW39LTR0nurtIODYCQVc8SrY/wG3V05egGcuFqALMa5xmZUM7EW4PzKMAWUoX5CAI32VpZjJW8B/em7HGw/fez599539guy4AzV9ezumgievTQErODAyMW3THNuoNjeNv33c7f/7s79GE4PbaOvoSCaqyskfZUJ9rLCMZqr28LLGPK9MeSC+26Y1t/EVk6jAi77HZG5vHrBGxLJbnF/D/3XYHdz/8U7pHNNkJIXBcl4JgCFPXubNuMe3RKJsWLZrwWisLCrmrbgnf3rEdBGwsLWdZfgG3VNVcqo+zcJBTKYm4l2wYM4WqpW5Vn0svRswhU5MFEyC/3nSKb+98g6O76gGIl6obfiyVYteZ06RcFyGgMBjmQ0uXT3iN7ECAr2+8jluqajg9MEBJJMwVBUUTql54TIJepuyk9RK12AL4PwCyFbTJa0PnGm5yP8R/rXbqQkda1yP87/PqqM+DieSa/vE3f87fvPoy8p5jJEqDEDj783SlJNO00HxqM/sPt21lU0XlhNe+YVElJZEIfYkEhaEwt1bXsra45LI5qZlRtDzgwMTPiemXq8wFpEwiE69D8k3AAWsdwrrey4JfAMvzC/j+XR/km69tI5pKETCMEWVNAoQgPxhkeUEBcdvmwZWrqM1Rqi8TndRIKXlfdS1dsShZgQAl4ciCLzm8KAxljqWrklGBe9RaK5OIrO8ijPLZHd95IN1eZPRnYJ8ChFpr/R9A862f7aEBCyRATjoOvz92FC2dJR4l0yYEhqbxtY3XsL6kjEWZWVMenwZMk6vLLv4fmJQpQEPMswXoXAjrKmTyVbUjRKp/bjP4NiO06endzjbSboDoT0Bknq2jlglVnhO4fbaHd8kYK/g/Ezx97ChHvvFbRMzG1xxFBgwVKAOmpnP3kiW80dREyDRHBcdjg+0Hn/gFAdPkqY98bEbGdTkjrLXI5Cvg9oH/QxB/EmQCwl9CC310toc3baSUqnEptTcd9BsQfw5pH4XQ57zN7TSYyJa6JieXirQ+8lANcabPT5bfh65pfG3DteSHVCZ5KoQQFEciFEdmfh0YHYgn1GmliCzMjZFWCNa1kHw1ffIjwFoP1gaVoJonqPn6sDp1HqqllkmIPYrUCxFGxWwPcWEEyL3xOEnHZvB/vkLpvjaav3w2QywldESjxFM21dk5k14j5Tgc6ezg8PW9uwAAIABJREFUeHcXWT4/q4qKRllxzhTS6UDGfwupgyAMpLUB4b9tTh0rvBeElgGhzyMTzwEaiBD4NiGsjbM9tGkjE6+qmw/miDrqHSB8SP9NCHH5SPW9FybKIv3Fc89g6jpJoPQ7B3AWZ9PwmVqkT6ciM4NoKsXTD37inJmlmG3jui7t0cGL1pwjpQT7CDL5ujpJMFcgrI3zZqM3XYSeD6HPIGNPqK5437VgXoUI3DXbQzs/nCZI7QetLN24BGilKjtlHwdzyeyObx6Scl3CpkXKdXGli6mlg2ABjlSOmf2pJCX6xKVzD997P/2JBC/UH+dIZye5wQDXlFVQlpE54fdfCCNLwT7y6PfB7eahLarhVlrXIfxbFtTmSAgBgbvBrFM9P7hgrkGYy+dXRt7tAKceEm+qrwP3qN4lTGTybS9AninCloVAw9DG72AlEr9h0p9uLpiIIb3ko10dvHrqFBLJrdW1fPaqdVROIRl1vkh3EDn4fZAxHnxGyes8tOU1pNMJoT+eX3/cUyD0PETwI7M9jAvHbQfG/i2lO4VlFC6jAHkmMscjrxE2Tdb904d4/k8eBuDvHz9Gyj3CJ176AJn+ALYrSaVNPya6xn2PPsyZgX7qcnIQCL716jauK1/EB5Yum3FbaJl8A2JPgggDFsSfR6Z2QehLCG1h1SsLoxrCfwayD4Q1Pzvh3U5IvKIW2cA96jEhAIF0WhFegDwOx3XRxFmt+rGb2v/72k38bO9uyiIZdMWiZPp9CJRUY8SyyA+GhrXLJ6I/keA7O7fTFY0R8Vmc6uthR3Mzf7z6KlYUXATXO7cDsEAvAmlD4iWkCCL8N838e80iQmhgLkeYE5eLzgdk9xfV6bLboR6IPZEOkk3V+zMHmPMB8smebl6oP0FLfx8VmVncXFU9bvcZME02VSwi8a3b2ff1X1L0hxaa6jJBCG6uqiZkWizNy5v0Pd453TKslzy0MFu6ziP79/Ln126asYVXpvbx4NMCRDY7WlUhyIPPFABnePjDrWpSe8w+Ri34rgetaEQd9VYgBWL+NBleSrpjMXadbqEjFqU6O4crCgonrAe+YVEVvzx8EJ9ukHDsYbeuutxclubmEbIszClKoNqjgyQdh9KIugc4rssrp05SlZXFlcXnrzYzGVImIP60Os4cbtAMgdOMTL6D8G+asfeaKwghVFnRPMTt/BjIOMgOVdk1tNgCIBH65KeHlyPNfX385ughjnV1ETJNblxUxQ2LKseVHy7PL6AkEmEwmSTL58eWLgnboSormzXFJQwmk1RkTu5aub2pka5YbNhoKwMYTCZ58tABlublzYhazFApmHSaeWiLBC09X4UBWgEkXkb6bvSMNOYawkS5TYxAShUcGytmZUhjmdMB8tHODr7/zk5eaWhA1zRladvexpfWXz1uUm6prcNn6MT/9g4OdrRjxaIEDZOwZZETCHJ16eR1xe+2nub1xkaMEXrJz504zjVl5XTFYjNnDe22w1jfLiFACnC7vQB5jiB816qjK/cMqivYVdmp4IMLrmZ8JjjV28O/vr2TlOtgaTpvNjexLZLB59auJzRGa/z6ikX0JuJ87ql6BlMprsxpBuDbGx/BcV0GQj+Y9CSlP5FgfUkZxeGzJQ5DEo/bm5tmNEDG7QTsEcFxGhEC5xiw8ALkec/Ykx3pqIBZzwNjvDbv5Up7dJDvvfUmGoKScISE4/DrI4eIplLcuVhl2Uee+nxu7QZePXWSXxxwON3fT11OhLxAkL5EgnuWLp/SHfZgRxuZY54PWRYt/X30xOPkztTaCipjPO50z1J/A9jq/z3mDFruQ7jJfdD9Jyos8t2gMsrGYoTlBcjn5HfHjhAyfcPF//nBEJ2xKM8cO8pn147ucjQ0jfdV13JTZTVHuzp5q6WZ7niMZXn5bCgtH9ZKnQi/bkxqDT1VNuu80Ut5aMsroJfy4NOq2eGh2zUVHGsTOxp5XHqElgORL6taZJELeo5yAjRqZ3tocw4pJY8d2IepaaNqgZv6etne1DhsNz2Ermm8f8kyUh056bKnRvW40MgPhwgXjjYFGYkjXUCOs4bWhSDpTn7Me0GIcDqb4Y6RLUykG8A85hJDuq9u5wPgdoG1VjUKW6sQ/q2XpUzjZLzZ1IjtusMbTb9hUBLJYNupk/x49y50TYwKkEOWxZbaxdxWU0d9TzeHOtoxNZ0VBQXDuseTkeUP0D4YJTzix++4LkKIGVWHevje+3Gjj0LqXRAjHDBlD+gV3u9/jqJZK3GNMnAHwFwJxpJ0LfXcUA6bswGyKyWNvb282dxIc38/oNx6pJTcWFk56et0TZvSQnMiNpaVs6+tleJIBr88fBCQXF+xiJrs3Bl10xLmcqReCE4LyDyUwkMLWFcjdG/RnUsILQcReD/Mw1LMS0l/MsmZgYFRWV2AbH+A3a2nxwXIQ5h5D5FD+mgcWFR0bmH7TJ+fskgmnbEoOQGVeZJS0pOIzbjNrNAykNaVkNylSm2EDm4/SBdhzQ0JIo+J0EHLR2T8T0DOmYV2LtHS3z9On/+Xhw+SdGzao8oKeiL1GiEE1dk5Uza7j+W68gp2t54hZKfwGyaO63J6oJ+NZeUz7hEgfDchUwfBOa1OeogBAhG4e0bfx2Nm0XJ/MdtDmJQ5GyALICcQxHFHZ3YdKcmb4a71pXn5bKmt47kTx0k4KrNbHM7gvuUrZ/R9hPBB6E+QiZd4aOu7gAW+D84rhYcLRbpdqpMcDYxahDY/ax09RjN0wuJKqbRR06Rch1zz3Men5+P4JITgvhUr+f7bO2nu70VDw5EuS3LzWXcOB70LQQQ+hMRSTpBI0HIh+GnEZVIKpQT82wFXBZ3zoLzIcxA7NxWZmRzr6iRzhMWzlEMeszNLTU4uD6xcxa8OH1RGI1KwobSMuxcvnfH3EnoeRL6CTO4A+6QynbCuRuiF53ztQkC6/cjUPnB7EUalWmcXkHrHbDBnf3pCCG6trqEjOsibzY1oQmNr7WI6ooPcWl1LXyJO3LbJCQQx3mMZhBCC22rquLq0nE+tWUvYtCjLyLgoqhJCCyv5pPkmofQecBPbIfYUJLapB/ybkYGPoFmrZndgHu+ZgGlyVXEJb59uGRb+t11X1Scum/k6spJIBt+4dhMH2tvoiceoyMyiNif3Pd8DJkIIHyJ4DzJwh9IFFpHLptFHOu1nNUrRQMuC4EcQxsROaR7zh6tLy3mjqZHWgQFygwHitsOG0jLuqFvM999+i5Tr8J2td89YffC6klJWFRbRHYsRNM0pyx3fK0LLRvi3XLTrz1Wk3Ygc/Hd1n0JDJl4Aow5Cn/TKS94DczZABjWxbNcl0+9nMJnElZJ7l61g1+kWfrJ7FwgImRYfXrZyRiRjMv3+GS2p8FC6z8SeUnWbQxNVZEHsEaRRjdDCZ7932HIyBXqRdzw6T3j/kmUMJJMc6exACIGUki3VtSTsFP/wxqv0xRMsz8/nluraGWl4DVsWG0ovnSC+EH5YIDrl00FKGzn4Q9VNrpWoRmK3Dzn4A4j8+YQ60FLKBSNTudDJDgT48vqN/OH4UQ51tBPx+fnIiivoTSZo6O0G4JuvbWNVQSH3rVg5zu79QrB0ncJw+Nzf6HHeSCmRsUcBXTnYqgfTGu7vIHyjT6ilO5AuQwmAXurN2ymY0wGyEIJryivYUFpGwnHwGwY/2b2L/e1tvNF0CoFga20dP979Dl/beC2l52gY8Lj0SPuoyhwL66zpRvx3yjEneD9oKoss3S7k4M8g+nC6o/V2ZOA+NGvZ7A3eY1oETZPPrFlL2+AgA8kE+aEwO5sbeWjfXnICAUKWxbutpznY0c7XN17nbULnOs7JtKrOCFUQLUNJ3KUOInwbhh92U4ch/gy4LUitCHy3os2RDnSPyckPhfjoqiuHv97Teoaf79/LfctXYmg6Ukp2t54haJncu2xmSw09ZhjZDU6bcqMbYkiyMfUupANkKSUy+Zpaf0k3IOulEPo4Qps5v4eFxJwOkIfQNY2gptEVi7K/vZXtTY20pBv3nj52lKRjc215BaVLx4tmd0SjPF9/jIPt7UR8PjZXVnFVUYm3a5pDSOkiB3+sFuXhLLMFsZ8i9T8dbmCUUiojA0T6uNv7Hc4VhBAUhsMUEiaWSvHciROUhCPDCjSFoQgtA3282dzIbTV1E16jua+P3a1nSDkOKwoKqMnO8X7Hs4GMTVKOKoYF/JXucFRphosMEMWqEz36I1z+2AuS5xmvnWogw+cfNtsSQlAUjrCzuZm76pZOqGm+t62VZ48fpXVwkPKMTG6vqaM2N/dSD91j0jDOGS1755yAnj9PG+ncqx5z25DRR9JW7GrSS+ko8w7hQ2iTa1xfDsyLAHmIWMpGExpj796aEHTHYuO+vzce5zs7txNPpfhCzXdwpeSf9nyRnliMW6o9ya5LgTAWI303qgan+G/Ug/6tIPtBTysPOE0QexQYmWX+Pcgk0ncrQr8J6bQho4+D06CeN6ogcK+n/jEH6Y7HkMjh4HiIkGHR0NM74Wtea2zgqYMH0DQNDcG2hnquq1jEh5bOM/vUhYBeqlQupaPUO0Ad2eKOrkF2u0FEVHYZ1P+7QOIZSAfI0mlFJraBfQL0QoTvRoRRdSk/jcc0GEglx7lX6kLgSknSccYFyLvPnObHu3fxlbrvoQvBDxv+C//69k6+sH7DealceLw3hlSA8N2kAuAhiTvpgBwA86zijky+hTLmGHE/FfmqodHtAj1XnQjFHleKPUikuRQR+PCoUsjLiXnVcZIXDGJqGnfVLaE0kkFpJIN7l63guvJFLJlA1m1HcxPRVJKicAQhQNeUMPpzJ04QS6Vm4RNcfgg9VzlayU5VVkFSZYEDD5yddDIx2atB9iNlQjUguKchuV39c5qRgz9EysktxD1mh4x0d7zjuqMej9kpiiPjb7R9iTi/OnyI/FCY4nCEwnCY0oxMXms8RUNvzyUZs8dZhJYD/pvVZtXtUIun2wTWlcje/64W5dQOJeqfeH7Mi8PgtKrjXOcMcuA7kNoNCLDrkQP/jJs8OCufy2NyVhUUKpWJEfQmEhSFw4THmP1IKXn6+FFyAkF0TYBQescB0+TZE8cu5bA90ojgvco10GlR9cVuK/huRphqo+p2fgwG/w1ku5rXsSfUv7QVOySRThtEf6w2uVoBiCJVxxx9OK1ycvkxrzLIPsPg/UuW8cj+vaQcByEETX29lEQyuKp4vMHAyZ4ePl/9bQxNo8x/GID7S/+W7x37Mt3x2IwKlXtMjuZbjzTrIPBHKJm3mtE7Ur0YfJtB5IzIMn8I3GaEsRjsoxD7FeCATGcg48+oawXuBtOrU55LhC2La8sreOlkPYWhMJau0xWPoQuNq8vGO1qe6u1FSjkqg6UJgS4Ex7o6qczy6uMuJkNZqJESacJ3K+hVyNQ7qmnWXIUwVyBjvxvzanv0l3JAZYqFwE28qLJRAOY1oGWqLHPityoz5Z0MzBmuLV/EntYzNPf1ETANEo6DLgT3LFsz7veUcl06ooN8te57w+vqPUV/g5Twg5Nfm43hX5YMb1QB2f1lQCIy/1qVQelFaqM7EjGBPK47AFoEtHxk/DmVOU48CUhl96wtAveoknvU37sQwnxjXgXIABtKy8gLBllXXEJvIsHy/ALWl5RO2GlbHAnjjtn5SKlODzN8XqPQpURoWWCtmeS5MNJ/N8SeVIuxEOA2grkKjDpk4hVV7zjywEPGAH24JtJjbnFH3RK+t/NNXm86xaaKSqqzc7irbskot70hLF0f52MJap5OVPvocfERQoBZhzBH14uLYce6j6k5aNSA26sCXzmgTod8H1LP2ycYDqBTb6MW3TqgHIjjufDMHSI+H1/acA3vnjnNie4u8kMh1haXTqg6Y2oauRN5FLgupRleo/zsISaVYdRy/xMpk8j2rWotta4HHGAQAp9ECAPZ+w0gxZCLMPZ+0GPK5VdGL9FnmFvMy9Vnum4+V5eV87/f+DqGJvi/Kv8RF8k/HvkCNy4qH3ds5DG7aL5rkHop0rpWTUZzZdpyUke6vahzn5F2wjZgI6VxEeTtPd4rhqaRHQiQFQjw1zffOq4eeSSVWdlkWBY98ThZaYWLaCqFLgQr8udv1mIiN7K5xHD9YjoLNVEmeTKGrZ1TRyH+B1WCoRWD7140axmufXDMopr+f7cNbAvw7r9zjaBpcm15BdeWV0z5fUIIttbU8e29X+QrtWdrkAeSSb643uvtuVDOZ/4Nfd/5vEYIC6kXqXnpWw8iA2GuUmWQE48InC6Qg0gt+7JcZy9agBy3Uxzt6iLp2FRkZk2YObrY5AdDfGHdBn595BDfPvYlAobBnXWVbK6cWVtaj5lBGBUIY4KbsxyqZXXHP+e2qW+RCXAaAQF6haehPE1s12UwmSRomlMGsefDUGD4ZnMTAJ946jFg8kDR0nU+vWYdP9r9Di39fQghMHWNj69aM2wp7TE30cw6MCdQJTGWgn3kbEkUFiDBHQR3H7Lrk8PZaCldQHglF9OgNx6nvqcbIQS12TmEZinRs7qomD/WNJ44/pe0Dg5QnhHko1es9hr0LoCZ2KhOFy33Z5M/GfwkRJ8AOlFKUXkgbcCFrk/jigAi54fgnAI00MsX/Dp7UQLkhp4efvju2+z+2lMA1P39HdxWU8v7qmov+U2wPDOTL66/mqTjYGgamncTnn9oEdAKVcMQQ40kJuCC3aCafmI/h8Sz6nQocAcEPzFxsO0BqEab7U2NPHP8KDE7haUb3FJVzQ2LqmZljpRmZPAX191AU18vjutSlpE5b8srxm4Q5momWRtZLsH5L7ZTIXJ+iuz+IiRfTD8QSUs0poA4pHbgdt4Hvi1gHwMtiLRuRPiunxeW1rPBjuYmHj+wHxdVJ2jpGh9fvYalEzSoX2yEEKwqLGJVYZFnEjMPmNYcF2HQQ+B0AxJEMN3UF1NNf4BsvxmsTep5LQLBjy/odXbGVyDbdfnJnl3oQhte4ApDEX5/7Bi1OXlUzVLDzVgJm8sZKR0llyZjoJfMfZFws1a5/oxFWCAciP2n0mLFUg25UiAH/wMy/hIhLp6t6XxmT+sZHj2wj4JQmCx/gIRj86vDh/DpBtec44j1XAwFgucbGBqa5jXkTYLqIk8B5qwGI9MNpoXQkMYisCuUiybK2WtUmZTTok59tGIgAfHfIGU/InDXRRv/fKU9OshjB/aRFwwNr2XRVIqf7nmX/7ZpM8FZbDj3guPxSHdQyZcKK51pnTzUupgb1ekwnMEOfxHiT4G+NO0caqsTWuEDGR8arWqqh7S75sJeZ2c8QG7q6+Xtrz6BTzfo2qUyKDu/8jhJx2bPzytnLUD2UCjHuv/gwd8lQKZ46JadSKMGAh9F+NbOyeyN0MuR1jqwCyC1Sz1o3ZSWqIlA/MXRTn2JZ9NOffeBOd48xgOerz9Otj+AP72J9ekGecEQz9cfZ2NZ+YwsenMtY3qpuNANwmS4yQOQeFrJN0kHaa5SEk5GxYz8nmZyQR61yPtuheSedCOtAKMctApIvaW0k621Sh8dAL+ytU6+jvRtvmx1VyfjcEc7ktGJnqBp0h2PUd/dxYqCwtkbnMco3MROiD+pyomcVsBB+rcgAnch9KJLO5YJyjcmm+/CXIU0l4PTo057RAB8dwAO2HvB7YHAh8++IO2uiX18wa6zMx4gSyYxYUIgJ+xV97hUSCmRgz9PF96bgKP+wJ1TIGNItwUR/OClG4/bhYw/D6n96njHtwlhrUeI0fLcQugQ+mPk4A/APqQe1AwwV462wx2HPcVzlzedsRjZ/tFZ+YBh0DLQjyslupcVmhNI+xhEfwRSUwuRTEBqDyReQQbugMA94+bLxWDcQtt+F0T+FGHUgV42YaAurCuQvg1gN6YlpgxgIG0sYjNOxULoaZmhPsALkEfiTrF0TtCZ4TFLSKcFYo8BvrOmVtKF2C+R9gkIf37SkoSLljkeqfTktiPdrmEJuNFScZ9XBl7GMqAIRNqO2nczDBxkwshOCKU8tUCZ8QC5LJLB6v/zQQxNY+/XfwXAhm/fS8tAP1d4u9zZxe0Ct5EHn8lkR5s6kjvQEwIky/NdSL6B9G2aoqv1/JHSTTfSaaDln7WzdPuRA/+sJm/iNVQDz2k1eQNbx11HGJUQ+QYy7cIn9GowqsE9g/Rdr45p479U3+y/W9VO6Qu3Nuq9UpuTw/GuLvJGNM/2JuJUZGaia/PKP2jOMhMZdBl/XgWX9n4VQGrZakGSfZDYrjI3s5G9cbsg8Swy/nvwvw85+BP1+NhMVeizSqYx9a46qrW2IDK/iUy8APEXgMwRH9YGNBCXt73tRCxOWzinHGe4mTZu2xia5p3KziFkco+ap3a6kU0EVFzp9itn2PgziPBnZ/593QEgCSJr9IY5/AUln+p2qPFYa5ED34XwVya2kRYRROTPkKmDgD28Aca6Atn/j2PcNVMqI+rVIE8fU9f5xOo1/GDX2yRslcE7M9DPzZXVXofrrGOnJQ4dfrb5aQCWZ7Wln0qAbSGtjUi3Vf3xW1cirA0XXF8k7QZk9OfqaAapsr3BBxB6PjL5jrpp6CWoO4gArQyS21SQPsERq9AyEb5rR7+HVqJMRhIvqbIKgQrIA3df9j7yU3FbTR3f7dxO62A/EcvPQDKJI13urFsy20PzGEm6OQYZV00xABhKc1j4kYld6vTFbVdGHHrVeyqTkjKJTL4JybfUA9YGhLVe6ai6/ciOLYAJgXvTL3Ag/jyQAMbfJ4QWVhvesZteaz0y8Ua6xjFHZcZlJ/i3IDRPuWQsReEId9Ut4TdHDw+7mhmaxgMrV3mSpXOKhDrtkd3AGOUuEQL7KG5ihypZIICw1oNx4eIF0o0i479Ku1UCIhMZuAfNXKxcZuPPgFYEpO8JWhE4p5HJNxH+LZNKxQl9dOOnWmdvgMQ2wEj3+tjpdXbhbtAuSpt4dXYOf3X9jRz5/QoSts2irCyKwxGvmH+20fJBy+KhWxs50GYz+sjEUotw/Nn05AWcU8jUAQh9ZsomAwDp9qiF1T6pJqG5Sh0NY0LyDfVNvhuRgz+EyJ+pso7kq4BxtnY4/ksV5Ia+ANOsQRRCgH8rmMuRvptAGAhzBWLK0guP0kgGX736GrY1nORUbw8rCwq4cVGVJ/Q/19ArVAnSqLmaAi2ojj9TO5H2nvTz6U1o6FMI7dy/Ryll2k66G7RcpCiA6M/APgAivejFnkLaxyH4cXBOpmvoRoxF6Oq9Q19B898w/SY+LQvCX0AmnoPUQdVk6/8jhLVu+j+by4wbK6tYnl/Asa5OdCFYnJdHlt8zW5lLCGMZMvEqSD8IGzBVICl0kLpqTI09qlRdsJGpd8F/B8K/+ZzXVpvXPcpZVstEWFch439Q9wetGISmnPEG/wMZ+RqgqffWLAjcM2KQYbDrz+9zCQH+O8FcoWICoSOMlQij7LyuM9+4aDpKYcviqmIvSJlLCKFB8CPIgX/hf727GYCHNv8GhAnmenAbwFisFkhQGV37uJqQU9g5S6dTHdvIWDrodZVNZWq3OmIabp57Od08d69ayKU7YVmTdHuR8WcBnzIL0fPO8bkEGJWqDMNj2hSFI/zRiitmexgeUyB8tyBTh9PuklG1CMok6Fcocw4RAL3qbNDqnkHGf4sIPjDldaVMIAcfStf0DwXXxWA3gV5+9noypBZgpxHQVRZpos2nMJFuP5BESTBO47Pp+eccp8do8kMh8kOX3lPAY5oYtWCth8SL6lREXwToYCwB2aiaybWyEfPLhsQzSGvdlI2par7+O9gNSn6NpNpcOoOqxGroeloY3EFk8i2E7+b01B5RFgFAFLSlyNRRcLsQGf9dOWKeA7XOViGMqgv96cw75qfQqMcFI4xFkPFXPPSBV5T6A9cBGWAUQnSHknkZzug+qeqm/O9DTBUgJ14EEmn5F139k6gbxFh5NgHIqNr9+m8FZPrYRqobCz6I/hQSr6pr+zcjAx9Bs1bN7A/Cw2MeIIxyCH8RGXtKOdZJXdUECkcd2RqLxmR08yG5Gxm4FyEmP3qX8RfAPghaabrRRqo6YWmPrikcDrxbwbxCyT+5A2dPeGQckJA6gIz9EvRaEH7c5Lto1pUz/wPx8JjDCKFB4MPI6COq7lcrVnNFC6q56faOma9G2iT2NGgTGO6kkcldKjjWR2RsnVZwDwHLGHca7HYhtKBypk28rHwEMFXvgkxB6ggyuePsS/RSCH3aU48ZgxcgX4YILQMRuBMCdyLdqNpdOmeQ0UcnecE5anntg+mAVpwNrqWt9E99WyHxa/WY/wPgnlFd71o2hD+PjP0OrI1q4TVqILk7HQBYZ9879ijSXIwQ/hn5/B4e8wlhlCMiX0GGP5dWsoiDXqY0SMcpA6Xr+ccwsvRBSqnKnrSCs4u1EOrroVrGcYOIqPkX+qRqyHNb0uUWhirdsg8rmTahqZOk6MNILWdBmwh4eIxlWO1lqExRLwO3H5H1t6r0Iv6H0S+IPaFOhMJfmvrC9r50WcYI9FxI2iro1kfUAcuoOgkGhP92pPBB4hUgmS7FKFGnwsk31fcH7lF1yfHnEcEPXNgHX6B4AfJlzlBDjNTLIfgAuKch8bp60rcZ0BDn6pIXWaht8IhjHKGDXpAOmG2USkUL+G8ZlpgRejEi/Blkuntdxp9WC7cwR2SxfwckwfmkOr7y8LgMmLBxRlijSp2ktR7iT5/NAoNSbzGXT5k9VtiMmq/qzdJ64m0g8s5eTy8YPoIVRhVk/JXqNcBBankw8A8wlI0K3JM+NbKQyTe8ANnj8sY+BMYyZclsrUYmnj97AjNk/iN8KoM7Ff8/e+cdJsV15e33VnVXhwnAMAw5JwEigwRIZIMiCpasbAVHeZ3WK7O73t1vZX9er79dLO/a6/XaXjnIsiQrB4QkS0gCSYggJAEig8iZGcKk7q7uqvP9cWt6picxAxN6oN7n8SPAfBIIAAAgAElEQVRPT3f17aFv3XPPPef3q3xK6ypHayjjiAmBPiAnwHXRfUSnweyJssZmbopDs/V7EUZKf6DXWfewvk7sOUBAmYhc5/eK1cAPkH0A72go5x4k9jzg6uyQ0RUVvfnMxy6hGVrz0egB8Zf1Y9ZkbRRg9oDkWCCIsiakd7aZ762/hqIsrb1YG4E6i7mPzwWOCk1DUju0VXNV1tgszHCiq88oAIDIAq9HoIZxgXsSIrcAKUh6euPBEajIdciJ+7xbwp+8QN2bx84xROrTRw156jU+PhcOGa54qS0QGJF+TBkFkHMfUvmULqlILAcpBkBOfD49v2qSnq/OHv3f2LNaQUZEn8ZaM1Dhy7UijJRC4DLPSyCScbak11hvnW1w9H5gXBs/QPZJo4x8VM49nqai6x2rnnnSqOAYJHK9Pj6ypul5Zk1DhefoidnE+mEVHI1Y0/WRbVWgHZqrj3HNvmf/wXx8OggNBbT1qUIoFYKcL4KzGzn1HSCA6vTDJmSPQYWvQJy92igIE3DALEJFrkcZeYjEvPfQPQQNLapy6jt6c+x6cpGx5/R/rUsgML0pH9nH57wiHRxLGSTXZMxhFRgMeX8LbjHi7IZkcTOvntLBtRJ9opr4C5JYWn9g3dA9xJqsyzCqTn3CN+pssjXZzx7Xwg+QferQ3EJ9pRQqNB2xLtFZI5V3VlqmyuyBRG5ONwd6g0Hl3H1GmTkfnwsRpUwIDEFUJ+/nzOA4I6NFrUA799vaEMA9ppt4yv4Nsb+G6vqndGB8ZqtapXsNqgJkHN01bxb5km0+Fy6BEek5UxulTDC7o7r++YyyiLXnryp4WDfnqSgYhUhiWbOHpsJzEGd/uhEe9xAE+qFCc5t9rfMdP+rwaTGUCoF5bm6JRmgyEhyhdVdVEMz+un7Lx+cCoNGAth7OHMA2jFIhVA2lCZfmOyimx1t8s86YRW6CwHCUNQlltK0cWVP/Zj4+rUlz53BzUCoMgf5nPGlqyABEXyMCOV+B8DztiGkUgDngnAyGzpZsn7N+gOzTYoi44OzybCqDKGs0yms+EKcYsVdpPVWzN8qaWsetpwpl5ILRsKycj49P82hsAXJL7gSSWuaNzEVLdfmFrjMu/Ucg1HCmq/CZlh7yGcn2xdXH50yczXdX3HLtpFlP4ZOkPkWS2z0VKBuov9xKKcNrvD2z/nFrcC4b+7bED5B9WgQR0TqoibfA/gAwkNB0JHIDKjAQKf8lkPLkZlwkNAdyvnreO/F0dB6Y/SAAD739g3YeyYVFUxeLc81WSWqX3rRKqsajWuHCjb2qG4kAUgdBhRG3POu0UjvKYutzYdEa30E3uR0q/+gpOpngHAezK6rgUST2IlL+a7QVtGgDocgtLT6GCwk/QPZpEST5MVT8zjMIiQMGEIL4S4g5QHfdmlWe8CZa1u01VO6X2nHUPg1RFRhvWL4542c/UO6Y1BdAu85xKP13LTGlItXKFdbl+n+xP3kSciZEbvVc+l5CRe9oj49QPe5aAXEdfVgfn/OEOt/1E3doow+zPxhdtO64U4LYH4D9frUWOWijrvhzSPCiNi93aoya96JsPwXyA2Sfc0YkCeUP6252koCjfxF/SS+81nhIevbVNW2nQ1MQEb9z1sfnHGl25lgEyv9bGwYYnXVTrFGgbXCd/ZD4C6hO1Ra1SulGvuQniFt5Vk24rUYgsxwrWxdbH59zRhJAUG8KpUyb+wQGeG60ZnVwDJ7UoqPns3FROw24Y+MHyD7nTmqXt8O10YYhVcTRk7mT93jNJiAXVL4fHGcpVZliP3PcfrREdqWhZh7V6Uc6Y6zytIslACG9gTVCIBVAbedKz5KaFO1JQ2Ul6c/q43OeUP3dvk2vs0YPMKpOTCLadMQtAaMfDYoxZkmTe3MkLLOF5rct+/jURuLovVbNBdUELAhOhvACrYsaXqCzUKqLPgoKDEbEaZ8xNwMR98xP8vHpQIhzEIxOaFk2b2FVCsTVVtHWFJAaRh+S0sYERo+sLWkwuv4pqxdbH5+zRlLo2mJTl1ikMbTDXnguegMb90xEyrQtPYIYfp/P2eJnkH3OHbMXhGZq68vEK+jMsaVrG3PuRVmTERLaLpOwXnidnZD4AMGByK3tIjFzJtzkdm3l6xzSlrrheajg2Asq6+1njtuelsy0NJRtFXs9ovJ0WYWcALHQpzwxCE1HhWfrBr7UXq276h7Wx7fBcUhyfYY8XHvR2N9DnOPaZRCFCgxDmV3bbmA+Pi2MKvgDUvav4CpwNoGrjXyQGIRvxLBG43KrXmOdHeCc1EoWgcFQ+Tsk+vl2b65t8F4kAqntWuVKYhAci7ImaNnYdsYPkH3OGWV2Q0IzILEMVA4QhuB4vdBWufOEpiKJN8C8XCtdoLQ7nr0OghMhOLydP0UmktoFFQ+Dygd7NeCAewyJutoy28enIxMcDmZn3SPgdgf3KKgEWJO8DasFuV9Byv8X3P0QvFhvhHGh8nHEyEcFBrX3p6gXN7ECYou9nwTBQCKfxQhNbtdx+ficLcrIRawZkFgKgXG6BEpOgdkDlXMPAIY1DtctgcpisEbp+Y0BqX26IT56c/t+CI/aG1tJLIP4K17sEIDU80hyHeR8sUmuoK2JHyD7tAgqfLUumQiOB1IQHI8KjqzOtjoHIb5M72rdw/qx+PMgScSahsq2ADm+VHcFE6huLLRXg9EVCY6/oLLIPm1LaxgN1DULCEPOl5DKJ4HDYPQEsy8qegvKqFqURH/3g5dou/f0iyNI4r2sDJDFKdHBsdGtuvZSbK+bfxjK6NS+A/TxOUtUeL4+ybTf1ae1gan6pOfk/QjeHLc/gsAg7bRXhVEE9kdI5PqsM90StxwSr+v7T9U9RvIgtRtJbkFZY9t1fH6A7JNGUvuQxDvgHILAAFRoBsrsoX8nSX0MktoJqhPKGoMyCtKvVUpB8CJUsKFu2SDUF1MqAaN2M1AW4BxG11HXxAD3JFqpo313tj7nP00JjEXikNqvyx/MfnUWQBFbH1uq3DplTMrsAbnfAjmpn3vyW4i9ElX1vhLTttFG7WUirB24shBJfYpuAK7xd1AWuAKp3ZAFpSHthd9w2/6IU4IkPwGJo4JDwBykTTuqfu+e8Kykc8Dsk/E7pQxUaBKEMi3cM1vzUtRtLVPo8qkGmvjaE/eI/m/GBlwBId2U6AfIPtmAm9ypSwrsdwETQjOQ5AbI/Svt+V7xiJaESqwABAnPgZz7UIEmOvGYfbQNrVtabT4QvgbcYlSwfSdBvQT6ArN1jWbsOf1YaL638GbXLtznwsS1N0LsSSCp1z4jF6J3owL9EHH00WViuW7qMXKQ8NUYtcqDlFK4J76lf6httKHywMjXTUA1dVSlFAJZOGfRQUTDYYB/6uPTfrj2Zq0tLgIoJPEWWBMg8jn9c/wVz0jLAFww+0HO3Sij/qbYensV3JMQHKXX2yrkOARHtnu5Qr2oqNcYLF5gXEVKy0+2M76KRRYjYiMSa4P3EYi/DCoXHfwZ+lhGFBJ/E7HXQWq7ZxoQ9GwsI0jlU01WoVDKQEU/ryVqxAZskNMQuQVl9mzFT3d2qNBc3RHsnvQecUCKweiNVD6KW/miVgLw8WkHxD0Bscf1nDV66fpgAal8RN83Eu/qBlOVD2ZPIAiVTyDJbU1+D6VMCF/nNdUWa0kp5zAYuajQtPTz3JK7skdiLTBEZ6MkXv2YxEAFEKMIcY7q07ALiAdmP8gDsx9kw/LNbFi+Of2zT9shYkPsKS15avbUpllGb10SkdqBJDfqHh6jh/f73uAeRGLPN++NjHxtIuIc1HPVOQgqDxW5ts5Ts2LeGj21jrMc1YEy6CQaJhidEftjfbIt7ZP99jPIWYi4lUj8VbA/BFwkMAgVuS5d7tDyJPVksldX19vGngPEO/qwwV5JRj1u4g2wpoJbDGb3Jr2LMosg9zue/aUNZm+UirTC5zl3VKA/5N6PxF+H0HSdSXZOQmqdV5ssiH05Er0TwxrT3sP1aUdEXHD2IKk9oCKo4CiUkd+675ncrBcUo8b8MfLBPYQkd+rMsdHd28yim/FUHpJYVqfev7GaZ8O6GDH+Cjn5JS01lf93KGsKqp7sTjbomiqjMxK5VQcjbgmgdOJYFUL5zxAMbZkduR6jnY9vfdoHEVeXOSTX6tOV4HiUNb51M6zOIW3yUaMssaqUQJKb9HdV5Vcb8wCoIkhtbtDevWFViJQOup0jUP4zUNGMcsgMUlva1ZpdKQXRu5DYs54uu9KbfhWGyj8jKEAgMBxy7mxzZQs/QM4yRASpfBxSO1i4oBiARS9bSPlvIO9vWkmqJeiJj9fW+3XBKAQi1F+/5FYvwE1EKRMC/c5qlCKu7t5VoTY5LlKBAajcrwDgJlZB7Hnv6MqbNkYBxF5AgiOyrvnBp20QcZDY0zoThAmIPirNua91m9gkWX/FgICWWYwBtYJYFfGCxuahAv0RQ2+CjfCV6cez1fLZsMYigcHg7AIUYq/Vi6/RU9dqS8xT4uiCOst7UUfCN/3JRGKLdSmhykerPDyjg9Sce1pRbjSg+21qo1zteCc2dXtelDefGzfmqduAG4DgCFRwBG7Fb+s8v868TW1p0idoLZSRh8q5F3FLQWwkvhySH+gsOujyi9RW3RgcntumY/MD5GzDPcLCq5YDFhtWlAKwcIECSbBo6Seo0NQWf0ulFBKaA+4psD8AFISv0tnh0GyUCiLJy/TxT/wl/aLQZWAORBld6r2miA2pnYh7WmeOzYEZDQfNxbU3QXwxuKdBmYh1OSr8GX0zaAtSW8BeQUYWPf4qhKqy6NlXJuLTBqS26pMeo091DZ1bjlQ+AXl/12rfTxUYjMRd3URXtaiL7TXrDdEbW7c8M2iV0xAc3eA168siNUuTWcrqPEfc0zr4kHKUORACg9pE81wZuWCMQdwySD5WHRyD3igQROzVF0SA7FONOMf1CaDRp/r7IHl6Hqd2QXBo67yx2cubkyeqs8hig6RQwbGI0QliL4HkVt9H5JR+nWpYeUUkqcsAVRQ5eT+g511987bBLLGUVTtsdv6JLs9yDoHZHxWaptfvNkAZ+ToJlvpQl3imf6H0385e7RmitB1+gJxtuDoopo6MmNKBWCuhrCnatEPlen7vKYjchgqO0E8IXw2Jv3g7XcDogYp+rt5riXsSqXgYKp8HBAlNh8BQ3XBwFkckktoLlX/UNwp7JbrYMoEAKnLlmV7eMqh8r5Gg9uBcqq16fS40xF6vG01qzlcjV2+i3KPVWZCWxuwLoRmQeId0dkpciNyIYebjhhdAxe/ATeiOeNF1fSo0u/7PIaID2cRScI9DYqVeyBv5bqePeI9O9C5SlnnN1G6k4nfp7JiwVAfo0dvbbmMrlYBRHQxVocI1+gsuDC70zDGgVRPsdwELIp/VjykFmIizH9VKAbJSBkQ/r5vdHS/BogyI3IAK9AWzSM+/1C50H1BKJ2TMnij1rfo/SuJDOP1tPe/DM/W8Nc5siJMxb2vOWYkj5T9Ht6blgPsBkvwIcr/Whn1C4v2vnviHtnfd9QPkbMPsxqLFvcDoycJrNwKwaMkYcA7ortZWQkvITEesqV5TSzQj06PCsxBrIkTv0wGB2btBLWCJvayz0VVlEEZvLRGXWIkKz2r22CTxHtjvAcEamsRrQIWQ8Ow2qUtS1mQkdLm+AcVf0Q9aUyE4vMEsus8FgLKqm0uqEPGORlsvU6qU0iowwYuR5FZQAVTwYr77mf8BXuWht3+A5H7dk208CsGJqNB0lNmt3utJcj1UPgb2Kj1u9xC4ByAwRgeTwUuABjLHgREZPxpd/6RLT8r+HQhD1XuKQHIDkhzTdvqmVUG+xLzMsYeUQWBm24zBJ3tQ0QbiL9cruWjFtza7Q94D4OzVm0azT7pkUqkQ5HxR1w6n9oDRBZx9NHQPcYtv8uqavZKp2GLABmd3xglOoyc+gRH6ZDQwQj+3/H/AOabfG4AccIqR+BuonLtb7g/RCEqZSHAcJNeDqtFz5RZDaFabjKEmfoCcZSijALGmeWLgKUDpTlSzV3U2tzXfXwUarCVURp5Xq9wwIglIbtaZ3nQpwtO6qzx1GFcFUdZEbVTQVNzj1FufhaMzRG0RIAf6IdFb9TEYtr7JBoaiItnhTuTTPihrHGKv1nM1LXR/goXXHQfzf1o1a6eU0nrlgQH1/z7QDxU4c5e6iAvx17zsU6155paA2ROj6+MNvr52s5D+4bg+DauZeapqwEmubzN9U6WCSOR6qHwcrcUe1sGxWYSyJrbJGHyyA/39FK1GJNSQ75wN5LTR+mrywLxHgLoZfaWCWo4tOFKPNflhjXHXCnJFlxrW2xoklenNYGPNdzUDaBFH64QbtU68jM6Q2tmcj3jOqPCViHNAxz1VmH1Robbf0PoBchaiItciZi8WvfK+DiytCShrWnbqGNahnqyyW8anmwx+9WCcRS+/iNgfQe5Xmp75DQyB0OW6Brrqpha+Cki2+q6/JoY1AQleDO6XtVpBQ53BPhcO5mAIXwnxN6ofMzprBYk2pKoBa8PyzRk/1xeg111wbX3iY/bSx86SgtgTXk1zGALDcBMfYoQaDygzF2PTK/uorW/qtMmGNmNc1ljE6KI3Mu5JCMxEWRNQRvTML/Y5z6jxXUyXC3ZGRT+Hqqn1ne2EF4BbqcseQbvneapXmP0hOAxxK874maqVLwSMTugG35onLfE21yNWRifI/abuYXJKdA10YHDblWXVwA+QsxClTFRoMoQmt9sYtP6y2eygXClLB5FKaVMR9zSYF/GrB5N6sTX7gLMfsT/RrkAZ7+kg9oc6+1xzYxCapoNq9whaacPVma3oHW3S8FP787VaXalPh0MphQrPRawJ4Bzku/P+CKqSDe/sADqKcoClJeKqyhBS27zgQWm9VhWF2JOIWYAKDKz3CuKe9BqQuuiNo1GoG6HcY6AKvSelQGKo4IR6r9Ga6Gy635B3IVP7pEN1+SV6w9a5wXLBlqS+TWxD94UzlkcEh0N8mf7/EgfnOLp22NABcmqvLo2I3lDnpSKi1+DUfp3oCQ5HGTm6UT/2rE5EKUv3IskJCLW9VrJSlpdNb/O3zsAPkH0yEOcIEnvJaxYwEGsiKnxVs7ItKnItUnEMJM6nG+FX30+y4X0DSLDwmg0gSRa9tr2uZWbsJd1hbK9CNyWeQJKbUblfQ+V9Q9ciq65gFuh6ysCQFv3sPj5nizK66Nq9dtD1FrH5yeu3IO5pFl6hg9yGFl59dFtXkUJC8z1XvlytiW4OAZJgDtQbWxXWPQS1AmSRlCebtdqTUHMQawIqciMqejtS+fvqpiSA8BX6RKgVyQY9Zp/sJWu+F84R3NgSlDW52UoRypqG2B+DNQWSW9CZ474QHKfnoVEEyTWIXJdpZS0OEnuuWq0KkHgEcr6Isi7V1vWJt7WMJCHdRJiNTrdthB8g+6QRt1zrLZNi4YLjACxavEarUkRug+QGT9KsCBW8uEFN5qojErEu41ff/6nXCV+hf+lWUFdvGcQp0YGx0Zt0HaTZG5yDSHILhjUWFbku4/THxyfbaGvNWXFPIOUPe/rGAs5h7v/+NtySHRhdH/ccqJwzHk8qayKiTE9qqlJnjgOj9fGqpEACuu6x9vvbK3W3fZVslrhgr0WMAozwPG0M5OzVWS6zV70GIz4+FwIPvf0DxC3ju7O+rZNEi3uDvQKxV0DOl1CBQYhboeXdVKdGA3ll5EPuNxB7ja4dVnnakU7lgVvmBbgOdYqUU1t1g7vRu1rZxT2NVD6ByvsuRng2EroMpBxUbpuXdWbb5tYPkH3SSHIDC6/dASpUQ4MZkN0sev55feTiHtfqEZEFkPPVBne+SgXAmsyi1+ZDchsLF+wBUix6ZjPggt0TNzgew/IaI9xj2odeWZlufpLUclZcuLtYn45HawbGNYNvib2gJdy8sp9FS3pDbCe4J3HLfw32elAWEhyJilzT4NHtd+d8H4CfvPXPSOmPdcOhCNgf6+BbYqBm1q1rTKwAo1v1YqsMXX+dWIGEPuMZA7WiYUoNmqX9egHQMcp7zl/q+/uLvVKvaSpUXdvrliLljyAqoJNQRicwuiKhGajwlQ2WESojDxWeiyu2tqlWAUiu1sGtxMHohiS3o6wRNd7/Yy37WFP20OikT43cY2D20EGxatv+mmbprbchfoDsU41bTN0mu4ReHJ19nmyboQNl5zgSfxmV84UGL6dtJG9Byn7OoueK0dqKhu5kN3tA/Bkk+Pe6e7dBa15pkrajj8+FhkgMktt1zSBUN7BKCTglUP5fgAlGP61x6hyA3G83ek2lTCRyo9ZQTm1Fd/2bunxETiKVj0HOl6trNiVWT6NsAEhQv56WT2vTnIZNnzYmuYlFS0bpuv4q3LgOcA0LVCctteZWgCxFjHxUaHqjl1ShGUhyByReB5IgFpgF2p459ihi/g3K9PoAMKiTVZaGtId9/ADZpxqzH4sW9wCzj1crLCx67gQ4O9GSaonq59rvg4oiYjd6DKOMXCTQB+JxUEmgAqRCy0qFpuqMtNkLjF6Qcx+kPvVqkNGZYxVEBS9u1Y/t49MRqB34fHfOv0LqIIuW1NALzVj8LN0sa+ToOmAVRewP6mRlHpj9YJ1gatGSud5rQnqDavYCAnp+Ogcg0Fe/ODgakh+DqqHaISUQGHlOzplnQ+0mrPbOPvm0PNnwb3umDUejGxSVp+UPqwJkccHZAthec5yn8OKWgTjaCOhMAbKRg0Su0ZrGKgpG1HPrM8E5jCQ/QZnaIEhZE5HkxyCdqXbgPKkTVkbbOObVR7bOXT9AbmXEPYUkVkByK5idUdZ0VHBYew+rXlRwJGL20vqD4gCu58AVoI6LTdritgmLYHq3XOu54gI6uNbZ5juR+Cve83TTgYosaLDW2cenpRFx9VEjCjn1AJA9N+u6GPq4VI7rADXyWUgdgcRLgIKauq6CVyJ1qKGLZaIMXRph1parMzLct1R4LpLa4QXTlla/MHJQ4TZyuKyH7P33ahvaug6+PRFJ6RpcqdSuc21ki3y2qNBlnrtkjjdfKrXSkxFFO+hVPdHS0osSRUTOqLKhcBCjs+c0S7VLIIZOSFURGKYNNxLvoDPGWt5NRW9rEyWPjoYfILci4pYi5b/Uu0F7BeAiya1I5GaM0KXtPbw6KGVBzpcR+30WLfkICIFToGum7HfR2qZKB8eBYRC8pEnahMqapAv/jSKIv6wfDF2mnQFrlE8oI4qK3oxErkM3FvkdeT5th6QOILEnPIt0tKEA2ZPVqC/wEfeUXnCrRPXlJOnburiZtYYS1xJQ9Vy3djDl2p8AycwniiexaBSmH1JGF8j9FpLc4Ll99kQFx/mbWp8Wp64CSxKsieCc8J4hSOgyVPjaVju9qO+0pfYGpNENSuAiiFyvT1DFAdf23B5zvLlbVd/v6M8XGNGkwFVKH9TNsK6+Z6XLrazJqEB1Qk4phYpcg1iTvU1tGAKDssZjob3vsbXxA+RWRAvTl3rHk55GoVEE8VcRa3zWfClroowcVHgehOcB4NqboOIPXtNOirStbuhyVGR+064ZGIBEPgvxxTXE2XuhorfWO/kb+ruk9RudQ7pRKDCsTWymfc5/xK3UgSaKtEV6ulrBpUknJW1EzQVXGZ0h91tallHKEHIh9qgOGpz9QNj7HHEI9EBZkxq6bAYqOBwx++jA2yjQi7mcAOvSOlk6ZeSgQlMbvFa2bDAuNM7nzDHg1eqWe+sr1SUJgcEQHNW+Y2sApRQqdDliTdR9AkYuEl8CiTWQOs3CG+KAYtFzZWANQIWvaOKVTb1xrQqQSeq1OjhW/z1qj8Msgkay7Vr9xgaCbV4qlU0o/YdoGpMmTZK1a9e24nDaFjuR5NTRU4Rzw+QXNG6hfDa45b+CyscAs/po0+gF1lRU3ney/jioCkntQhLLIbVPL5ah2ajgqGYfyYhbqc0+VASMHs16vdZbfQrsdVrtAgWRa1A5X0SZPc74+o6EUupDEWlaJNMI59t8bU3EXodUPpFpAhN7DsRGFfy2TWxoWwrX3qStlZ2jnrlOCqw5qNwv1GjWOTPiVmgJKvsjXRtpTdGarWc4NcrQVxZBSm7Sx8XhKyAwHBWecd65ULbEnPXn65mpNvn4OVL672D0JMOp0T0Jgf4YOfe22hhaunRF3Eqk5Do+3XCSv5qnFV/GXN4JjO48tOxHzbqWW3yzVrHIuU/37gSGN8uBzi25S7/emqIlH5UJnf4DZU08r0owmjpfL9gM8ifvbubNx98jZacQEYZNHMwV980mHG3BjKTRDZ19qiXTopQ+UukgqMAgVAtINSkjCsbZXUfs9XqhNvpWNzKIjVQ+DbnfOK8mr08mp4tL2f7hLmJlMfqP7EPfi3pjGC2b1RCpbOSX8RZ9r9bGsEYhge/phjpEH6E2qBLTMPo0aT6Em3ZSVK9UU/ITIKYfjy8BXtTlGHnf1OUZPucdp4tLWff2Rg5sP0xR366Mnzuawt4to0SUtkZ2ihsQXVDU6ZfJcpQRRYweoGLVD56tW6tn6mNEbz2710sFpLZrV0wp1qdPp76JmEWowhfP7podmAsyQN6/7SCv/vYtCnp0ZtmT7+vjBAEzYHDtV5u2GDQFFZqC2DO0rFniDUDAukTX7p6F77uIq4v6VUhLo11IJD/Sbl2src7GJ5brna6cAlX/Yqubrk7qv5lfF9nh2L1xH8//bAmO42IYBisXf8iIS4dwzVfnYZotZzOuzD4Iklm3G75eZ2CrjnA7EMrIBau9tcMFSNX42UA35caQxCpU5Kr2GZZPq3HiyEke+5dnScRscvKjHN1znA3vbuG2v7uB3kN6ttwbGV11uaJb494vohtIA61rZd6SpSs1N5WDR8Ivlyp+9cO5Z/0e51LG5JbcBalN3g8nqn+hgnoNvQA5rwJkx3EQVwgEG/9Y697ayIdvbCBoBTi6V8naiY0AACAASURBVDvGrXt7I8pQzL79cnLym26r3BjK7I3k3AexF3UghwmhqTor00xce4NucHPLtPB/aBYqNPPCqQ9SXsdtM3CTO7S3vJwGBAmORoVvOKvNiU/bk0qmWPKbpUTzo0TzdMOmiLB51Q5GTBnG0AktaEBh9gVrsmeZnIvW/62E0ExUHSWHM1PdXV+mF3Kz93l/ylFbqkl1/hlS/hNI1Oqsd8u97HYm6dcVPKp7DVL7QUVQweH+nO0grHr5Q1J2iqK+upQnp1OU0pIy3nr8Pe76Pze32BzQqke3IuW/9RpUvfUhOBZljW729URsJLkFnD2gOqOssRe262NVPbNRCOEbwT2IiFsn3tBzVlD5/6RPhjBR1jgwB58X97vzIkC24zYrXljDurc3krIdBo7ux6zbLqOwV/11bmWnKjCMzH88pRQI2N7Ot6UwgsOQwAOedWP4rBrzJLUTKv8EqgsLrzsO4rJo8RIEAxWe2WJjzWqCk8Daqi1t4y/ox0IzPO3IujcycY5B5e+B3OoFWhTiJlC5DZub+LQuruty6NOjxMpidOvblc7dOjX43OMHSoiXxynqV0M1QSkiuWG2rf20RQNkpZQO4IIjtdsUBsqaoMX2m0m1ssQx0ps6awxEbm1WPWB70KINdUYUMCB8nW7yrUIqwRzSwIsEiT0L9gfVj8QjkPMFVKDfuY/Jp1k4jsO+LQc5vPsYeZ2jDB43ML1ZrY9dG/aSX5hZzpNXkMuR3cdI2imsUMudfCqzN+R9F0luBSlHBfqAOaDZSSOROFLxW91jgwUkkcSbnv1z637nam8qh875Ew/NadW3bHQs7rG5QELfC9PGQ6Vg9mn47+oW6/udioCItr8Oz21Gg2H2kt136yay5H+XsuPDXaxfpo8HgqEgT/6/F7j3X26rN9gdMm4A4+eOpseAIl5/ZBkA02+aguu45Be2fLOeUkY9blNNR+LLWLjgKKgTbHjvNKAtoBe9/LaWtcnyRbclUMHRiHUp2GurlTBUFBX9XL07VbE/0u5E1LCutldCYjkSua5ZzUo+LUPpiTKe+88lHNtfgqEUIsKkK8cy83PT6q0pDgQDusmrlg6om3JbdKGtQikTgqNQ59gBL7EXtT1zurtewF6HmINRoSktMNLspmZwLdY0zyWsOxDUiQIcVOiy9HNq1y6T2qX7DNIZ51LdQJm38MI5McsCknaSF3/xGk8tehGUYvIV4wjnruTWhddR1K9bva/JL8il/FQlQat6TUomUoRzQgSCLVcSVYUyoqjQuZVUSGINpPaC2af6QfeU3qjl/nWbZEIb2pC2uZa10UXLv7nFEL5Wz1cph/BtGU+rM2erTMQin9VqIvG3kODEDr/Odvi7Tcnhkzzxr8+xfvkmju0r5ti+Ytb+ZR3Ln36fbR/srPc1o6ePoLBPV47sOUYqmSKZSFJ6ooz5985q0brGFsM9Tt1GP0M3D9V0tzuPUcpERW5G5X0b1eXnWlkg729QZv03al0z1cDXu6Zwuk+b8Zffv82Jw6fo0b8bRf0K6danK6tf/ogdH+2u9/mFvQtY9/ZGXn34zfRjqWSKpJ1k5LTmZ3bbAnErPUerGt9LpfQph72m/QZ2BtySu6p1ZpNrqn8+R1T4Ci0ZKaXgHgYjpDvsG2tCUrXudUa+rjV1j57zeHyazqb3t7Fz3R6C4SBWKEj3/t1AhFd/9xYNqV9dcvUESkvKsBNaQ9tJORQfOsElV41v8cbaFiO5vu4ppOqkVWDkdPuMqZ0wCp9Bdf2zNgkiBoF+qNz7MYJDm34RZQLKk5ns2HT41GPZiXJQClWjpTVemcBNOezbfIAJc8fUeU0kN8Lt37uRLau2M3jcADoV5jFmxsgGd8XtTmAwi16uAKNIW0ADixYP0sXzquXKQc4W8TK6ra3rrJTSWbmmNE0FhoI1TWcFqo6KwtdqMXYjS/+dz2PKT1WwZ+P+jHIJwzTI7ZzDhuWbGD4pU6uzKnNybJ/W9Xz5168DikuuHs/s2y+n95BslfarChxqZ52aX0N/PqBUABWej4Rm65MfFa2Tkcs4ZnaP6X6NGmYkiKD/dh2/prEj8dAXfkkq6VB8UDds/eWRtxFXGD9nNGUnysnvWve0dfjkIcz9/AxWPLeaVNLBMBTTFkxk0pXj2nr4TUeFgdpNaOJ93dqnGb5Ru+pWRgX6oQL3Nvqc9Jwtvh6c4zWc+6qQarWpDkyHD5C7dO/E5CvHUdi7gJd/9Tp2PMm/PLoZ13F57BfruOjSoVx0Sd3dTyQnzIS5Y+oNoLMNFZqJJDfqDEraAvo0RO5p10J4cUuR+Mtg66BdgqNQkWuzQr5JWaO1hqtzEC37I/rvF7ley835tClOyknrlbquy+FdRzm65zifrt9DOCfEtffPJ5ITbvD1Rf264bouX/vpPeR0yt6GLWXkIOYQ7WpVlUUW0Rsza0b7Ds5D3HJw9gFBCAxAqWCdWsiWNvVQKqg39Gd8Yi5IjGore/TfzizyyjR82gplqIxMcdmJcsQVtq3dyc51u+tdO5VS/PnHzyOu8E9PfodofrRlpVNbA2sqVD4Ckqe/c+KtFdbo7GkOdUsBF3EOapOtbGmAU1F9mu2W6pMe0Ke3Rj4EGuoz6Dh0+AC5U2E+42aP4p1nVpGI2ZgBfVMNWAH6DO/Na797i4Gj+xGKZPkkbQRldofcbyCJd1i0pDOYRajQDFRgYLuNSSSlC/PdYyxcoJVAFr28Fak4ArnfbneXQKVCkPsVxF4LgQGgclHWlPNi0nZE8rvm0a13AaUnyjiy6xjHD5QQioYQEVJJh2ceWszt37sxrUDTqF1rlqMiNyIV/1tt/wzaMta6pP0G5eEmVkP8RS8rCxi5kHNvuuShPd3u0sYi8Ve1hCMACow8VPSO7AkKLhD+4fG/5oX/eo0P31hPvCLOwNH9+do/v4lp7ua5RwbRpXtnBl5c3cRWO+v5o9v/E8j+uauCo5DwPEi8Ba4CXK0dHr6+3caUvv/N/C64x1j0YggQpOxnEJ4DoSuyYj4YXZ9AUnuRysd0r48AZldU9K52jwFagg4fIAPMuWM6B7Yf5s6vv4gyFEMvLgEgr8tj2PEkR3ZfTf+Rfdt5lOeGMotQ0ZvbexjVpHaz8OoPQYXYsKIUgIULFMg+fvLWTgiObOcBglIRbbVpdNM7XbNPVtxULkSUUlz5xTk88uCf2b/9ENG8CHs27uOHf9SL6Y+/HmDl4rX88oN/a+eRnjvK7Ap5fwOp7Yhbqje45sB2bzAT5zDEntMbWmWwaMkYcE8jFX/0GuDafzlQSiGVjwM2Ku97+vg7MPi8WGw7GsMmDWbSlWN577lV/OjxrYQinzJsjF5b7/j6C7y7uHdGgNxRUUrpMiDrUq08Y+SA0bPd1wqRGIteioC62CsDwWuAexsCIyFLVF1UoD+S3ATYqM4/81xys7TevJm0/x2xBTBNk6ETBhHJDWOFg0Bx5u8DJrGKOGUlZeR2yW1UpuZCRSSm3epSn4LRVVtLNtQAB7rppqFfuafT1YIits7i2h9pqSdrCio4ptUnkIggiWWQeB3iXjYqegvk3H3e2dx2FHoMKGL+vbM5dayUnPwoJ4/WaIBRiqTX2FOTbM8+NYRSFgQvzqqqWUlu1EfINeee0Qmcw7qhptaJlEgMRNqhJMnVLpmp3WB0RhlFYLaME5tP0zEMgxk3T2XV4rV0KTqMMhSgA2TDNDhxOLNutyOf+gAoo5OeD62IiO2ZfeVp1ZzGSO0FSYJRo/TMm7+S2p4hQSciWjlHKsHs0WYbSl2S5UJSW6TLic+DyoOuz50XQfJ5ESCDbg74w/+5mS49OnPN5x4B4Nk/3EYgaDDqsn08tehFXFfLRU34zGhmfG5qdipWtAPiViAVvwHnCNgrAFc31eR8ARUYXP+LjG4sWtwLjF4svPYTABa9PBrcg+nAWsRBKh6F1FawV+nXpXYi1uWo6A2t+6GcTyH+Chg9oepm4RYjlU9Czv3tnh24UCnqW0hBjy7c+Y0XUX8H3XtpRZFFz+yka68CSg6f5MC2Q5gBg/6j+pLXxXc/rA8RG0ksB/t93fwWHIcKz2vU3GDhFS+CW8qGFdrSduE1G3QWWUFNe15xT2qputRW/XNgGCpyQ5tsLN2SO9KLLWUPAYKE52jli4buRT6tRtAK0HNwD5Y8dTc5naLMu+5hAJ5++GYGXNyTb1/2j1SUVnLzdxYw4OK+DB43oH0HnKWIOFpbOfEuSAqMHCR8DYY1vpFXmem+jcyLeb+r+tEtRyr/DKmdgAEqgISvxwhNbOFPUR+i1T7SPybArUTiS1CRBW3w/q1LhwuQ7USSlJ0ikhvOCHK69enKlV+Yzet/XI4d15koM2AwYsow3nt+Dd37deOtx99FREgmUkTzo1x6detaUnYUxF6js0hmH6q/EhGk8jnIe6D+naDZF4IjILnJaxwE3IO6xtf0DBxSn0Jqmzb3qJrQRh+wVyKhaSizqBU/04eQeN+zyayypn4PrEu1jaafkWoXeg7qTu8hPUgmkgRraBmbQZNkIslvf/B4uj42EDRZ8FdXMHR8CzrmtSGt1fAmIkjlU1qeyijSGuvJj3XGNe9bKNVAs6OKArVkqyQGBNMasCJJbZrgngLlKYWkdiEVD3uasK2cmZLyGuOt+n5EkMqnIe9vz4usVEdCKcWsW6bx7E9fJmWncF3BSTq4jkvvob04vPsYANs+2Mm6ZZsYMLIPP37tn1pFp7wjI4m3If6GNrYygnreVT6BGHmohvpiAv11aYVbBoanGCI24KKCI6qvXfkUOLu8ZJDSQWrsScTs1upGJyr/n3Uvku3FAFU6yIn3EGuaLjfrwHSYANlOJHn3mZWsX74ZN+XStXcX5n1+Jn2GVUt+jZ4+kiHjB3J419UErABf/rfu/OGfn2TD8k2Yppm2ld7wziZyO0e55KrxfiYRILlRm2hg1ggml+ruXjld7XVfA231eQeSWMWiJWsAAWsSKjQtvYiJc0AHpaqGWUf8BcAG907dmd5aSLz+3TcGUPco36flcFIOhz49QiJm03NgEf98w78D+sjVMAxu/PbVLH+6C5ve38bN9z1FJDdMLPDfPPHjVyjsXZA2FIhXJljy6ze4/6f3Zn8nfFviHoXkJ3qzWfUdVz3AOYjYm1ANZI5+8va/IbGXWHjFMwAseqnQU8O5ozqoTn2qZZtq6hSrIt1wmGqD3oLwtVB5EjAzpaOcQ575ii/R2NI4jsP+rYcoPlRCp6759B/VNyPAHTx2ALf/w42sWvIRix+/k15De7L7k2V88NrHnDxyCoAPXlvHvLtnsnfzAbat2cHo6e3fg5ItiCQh8Y5WYana9KkIqBwksbxOgJyxsY7eg1Q8or//oOd75EaUqTev4p7wklC9atwLQoCF2GtbPUAWZx91PRo8HWT3cIdPRHWYAHnpo8vZ+O5W1i/fBAqmXTeZpxa9xL0/vJWCHtUBXCQ3wqAx/dM/l5+qqCNQrgxFrDyO67p+mQXoTvb6NFoVQMOBiVIWKjwDwg3IV6n8+qVLBS3n1JoERusA3+gD8ef1Y6H5ejy+DnKrUXL4JM/958ucOq5r1JVSlJ0oJ6+g+t87khvhyvvmMO/zM+HUeyil2LXqBMowMty2wtEQp4tLObjjMIPHDmjrj3LW1HaZckvuatkscpUJTp0NYADcIw2+TNtpXwfm+zqDFb4GFRyZmeWRsgZeLTqT1dqoKITmgFFzTN69yW/Ua3ESsQTP/ucSDmw7pBskRejSozO3Lrw+Q+e47/De9B1evWla+uhyHMfNuJZSipxOUbat/dQPkGsiCZ35NWp9f1UYnJJGX6oCA5DUNpA4qtMPtJW2UUN/WhKeEVGte4GyGu0TajHK/wucYojeVvd3Kksk8s6BrAyQHceh+IAWJy/sU0BlaYzN729Pu+UBrHxpLXYiyYR5o5l1y2UNXmvw2AGII3Tt1SVtKz35qvH0HFjkB8ceypqqu1CNnhBfrB+0LoXg+HNq0FHBEUj4Kl1zVSXbFLpMB6hm/8ZffI4oazSS3ACpTTXcBitR0fvO3Bzhc1a4rsuL//0qsbI43ft14/VHliEi6Tn7wOwHMxp3zIAJhY8BoPiwQXcu/5SnFkYXdCOb1FoYU/oItxGUUjy0rBGlEKM7IJnXFs+koxVOfGqXoSjrUj1vJamzbSIgRyE4XDdR+bQoH76xgQNbD9J9QFF6nhUfLOHtJ1dw/V9d2eDrfvjS3/ObhY+ybtlGFIr598wCIJV0/Cb42qgoGAXglnvJKA85DcFJ6R/r21hrDG2wExxd99pGIRDVG15V4+8u5VrporVROaBO6E17lRuhHAOzZ6uv8W1B1gXIh3cd5aVfvsajBXpndfepbky9bpK3SarlwGQoSg6fynz97qNsXb0DO5Fk2IRBTL1uIns37efY/mJSKV07Ja7LrFuntdlnynoCwyFyPcRf82qcgOCocy6yV0YO5HwZqXxGu2MBBIagIje2epCqVBCxpmjbX3OQPnYyh6drLX3ODRHhyO5jHN51lFA0xKAx/Sg7UU7JwZPaktajqcHt4HEDWP7MKpJ2iqClb0ux8jhWKEifYT1b5TO0Fq1tuoHRHYKjq2uQCYAc99RnRp3btc2+1ddWXlOenITAqLZZ8AJDdJlF/EXPEt6EwChUJIskLjsox/YdZ8vqHcQrEwwZN5ABF/flk3e30Ll754x5WtCzCzvW7iJpJwla1aUWWrM8hRkw6VSYT/9RfTADBl176e9J0k5hx21GTx9R570vZJQykPACqPwDuHEdMEspEEKFZjb8wtQW/V/vVKe+kyilgkjkRqh8DFQpYOl5E+iPssa2yuepGgsAyQ/1fxNLdZAeGAfWeFTOnedFv0BWBcixijjP/HQxZsAkGNLHEcl4kjcf0811s2+/nLefeA+A+ffM4ujeY/S7qPrY56OlG1j6p3f48PX1oGDC3DGMmTmSux68mU3vbaP/yD5079+NMTNH0rmbn42oQimFCk1HghMh56tg5LZYx7oye0Du170bgoky2kaVQJwj2h1J5evmQYBUNyQWRNV3HOTTZFzX5Y1HlrN++SZvYRVC0TAzbp6SkdGsyiot+d+lRPPC6eyxiLDjo12sfuUjSkvKGXhxX6ZcO5HP3Dmdt554D9dxQYEVtrjhG1dhhf2j9Zro+v9bkER33TsgCX3aE57XcINes659G2IPBvsDdG/BDJR1SYsueA1ly1TBo1qqSgAxPGezOH7fwLmxccVWXn34TQzTwDQN1r21kRGXaodZEUmfrs6/Z5Y+PDAyN7cHdhzm7Sfe4/Duo0RzI1xyzQSuuG8OS379Ogd3HEEZCsNQfOauGRmlGD4awxqBGF9HEu9qO/XgFFTo8ox1tvbGOo03Rxq+9mjE/JZuTHdPQ+AilDWmbbXDJQUEdDOhewhJ7UdZDSvqdBSyKkDes3E/f+5ZihW22BvUx+LP9Idk3OaB/IvYtubTtL/7sX3FdOrWiVHThgNQcbqCt554j669CtLd8d0HFLHhnS1cfPlFXHZD+7tYZTvKiILR8kX9SilQbbshEXsN2O8ANVQs7NWAQsJX+ce158CuDXv5+O2N9BhQhGHoRbT8VAUrF68lHLWIlceJ5OpATURwHZdIjWPX9cs28Zffv01ul1xCUYtta3by6bo9fP7BzzFk/EAObD+EGTDpN6I3kdyOe1zbmq50uv5/HoTntcK1g6jQNAi1wylbagsk3tQnPVWnTO4xrWKR8xW/3OYsiFcmeOORZXTp3tnzCdDzcsvqHYyYMpQtK3cgCMprGCk5dIKRU4elXS2P7S/myX97gVDEonu/biQTSd567F3seJLbv/dZig+eIF4Rp7BP10bt4i90VKAfKnBnk5/fnJMoZfZCRXo1+PuWpnpsn4PUQYjcrH0OQGeSY08igcHtoKHesmRVgGzH7XofF2DIuIFcfNlFDLi4D+UnKxgyYRCT5o9N1zsd3n2MNa98hBW20moVSx9djp1IctkNk/1d7YWGW4JWrKiFUt7RrR8gny1bV+8gkhNOB8cAuZ1zOLa/mFm3TuOdp1dSWlKGYRqkkinu+cEtXPWluQAk7STvPLOSrr26pDPDXXsVcPxACR+/+Qmzb7uckVOH13nPjmo+0Ja0WklHK9DQ4u9WPOLVNdYowVLdILUb5FS9ijo+jXN0zzEcx00Hx6CTFlbIIhQNsXnlNo7t1X0CL//6daywxdf+4970cz98Yz2GYaSb9qywRVG/Qj549SMuuXIc3fp0bKWCbKMjzN80bkw3vtd04VQRXZPs7AGjYzdrZlWA3HNgEdf/3qKobyGPdNYT9u5ThRzfX0zP+7pT2KuAoRPq10QNNqS7KELIl4i68DCHgDVNy1XFntOPha/VjRGGf0M/F5Sh6m2oE1foP7IvX/zxIO58/mlcx+V/Zi6g7/BeaSWZ8lOV2PFknRKnnE5RDmw/1CbjP9/pSIFyXZLUlY2qahZ06jzb58wEQ8F6RYocxyG3Uw6Ffbpy6FNt9tCtXyGhiEVOfnXmr/hACZG8zMxwIBjASblUlMb8EqhWJqvncd5CiL9azy+EehNUHYysCpC79S1k4rwxfPCX9aSiDqA74C+9ZgKFvRqvie0ztCdz75yOHU+yeslHAEy/eQrlpyoYNrF5RgOlJ8qwYzZdunfWnfY+HQ5lTUCSqzz9SFf/zz2qNSSVv2E6F0ZOHc7G97biOC5v/ukdAC69ZiIFPTvzzVVLUQo2x3Tz7N9vXsETI25NvzaaF8b0MstVR7igG/L6j6rbQFmVOd6wfHP6Zz+LXBe35K7qWkWV1/iTs4g6i39gHCS3geRXB8buaTALtRKAT7PpMbCILj06cfLoabp01xvTRMxGRBh+yRAeevsHjZ7Q9B7ai4/f/CRDncKOJ7HCQXI7d+wjdJ9zQwVHIPFXdHN/2rG2QkvY1bKu74hkTYAcq4hzaOcRBozuR98RfRj78W6UUlx03VD6jzyz8oAZMLnpO9fy/H+9qp30FCQqbW74xlV0Ksxv0hgqSiv5y+/f5tP1e1BKEckLc8W9sxkyruP/Q19oaAWN+xF7FZg9wOiEsi6DwND2HlqHZ8Ao3VS3+pWP02VRZtBgwdeuYOmaNxt9bSgSYtKV41jxwhoKe3UlGApQfrICN+Uw8TNjmjWO8lMVHNx5BDNg0O+i3n4mq4oaXe+Q5RmoWihrLJLcCKnN6OXJARVGRW45L7ri2wPDMLjxW1fzwi9e5ei+4yiltEvl/fPTiafGNp3j547mk3c3U3LoBPmF+SQqE5SWlDP/npkZKhdnoqK0kt0b9hIrj9NrSA96De7h15R3cJRZpFU04i+CuCBKB8rRu8+LRJRqSHu0PiZNmiRr165t8UFsWb2d1377FisXa8mQ6Tddyo3fujrDJa+puK7L0b3HcVIu3fsXNnkCiwhPP/QS+7Yc5OO3PkGhmH7TFMpOlnPv/72Vwt7+sbxP26CU+lBEJp35mY3T0vM1lUyxZ+N+Dn16hMd+9CzBUJBta3YCMHr6CJSh0gvt7c8+CcATN91a5zqO47D2tXWsfvVjEhUJivoXMuf2yxvtE6id4Vq3bCNLH30HcfX9K5QT4rPfvoY+QzuWJFxL4pbcpZvcqsw+groxuSMFyAAiDqQ+RZy9oHJRwYszzRGykJaYsy09X0WEPZv2s3X1DlzXZclvlmKaBn/7yDfo3r9bszaUxYdOsPKltezZuI9OhXlccvUEhk8e0uQA9+DOwzzz08UkKu20Icno6SOYf+8s34/gPEDc05Dao3sHzEFZ35zX1Pna7hnkU8dPs+Q3S+lUmJ9uIghYAZ772Svc/9Ddzc4KGYZBz4Hdmz2Ok0dP8cxDiwmGg+mGhXefXYWdSDLpirGNmpH4XHiIW6ldy1QUjO7nfSYkEUvw9EOLObjjCEErwMkjpzBqLGzKaPrnN02TS6+ZyKQrx+EkHYKh4Bn/fjUzXMUHS3j9keWse+sTDMNg/j2zqDhdyQs/f4WvPnR3s7Ja5xOtrr/cRihlQnAYKjisvYfSoVn21ArWLPmYUDSEUnB8fwl5Bbn0Gdar2ferwl4FLLh//lmNw3Ecvjvn+yBw1Rd1s67rChuWb2boxEH+Ce15gDI6QSvqLrcX7R4g79qwt476xIrn12DHba75ymfazGI2XqEtG1Utb2TDUJSVlLfJGHw6Bm5ilXYcjL+tH8i5C6J3oYymlfJ0RD5+ayOHdhymh+e4de1X53Py2GnWvvYxRf271TmirS9zXBvTNM8qe7Tj490oRYaFfE6nKEf3HufQziP0H9m32dc8n+iogXFbIGJDaqd2GjN6gNn3vNzcFh8sYe1r6+nevxuGafD6I8s4dew0p46d5tvT/pFgONhmtfwlB0/gJDNVNAxDEc4Ns2XVdj9A9mkQkThibwb3MBg9UNaoc9Z6bw7tHiCn7FSDv3NSbde13LVXF6YumEReQS7L/rwC0KLph3cfZcDofpSWlHFgx+FqfdYm6j3acZtkIkk0P3pe3ogvNCS1W6tiGEXVTQnOQaTyKVTul9p3cK3IllXbyS/Mz/gOd+6WTyJma2OPZnCukm2//d5jlJaUc/KIbgSsMjkYO3tUs8fic+EgTjFS8bCWoNKPQHAMRG9FqfPr1OHI7mOICIZZt247EbcJhtvm8z4w+0GSiSQnj1bP1SoDIXElo1HXx6cm4p5GKn4NTgk6VE0hia6Q+2WU0TZyj+3+7ew/si+TrxxHYe+uvPnYuwDMuu0yTh8vpfeQHmd1zUQswcmjp8npFCWvS9Oc20KREHPuuJzXfvc2STuFUorDu4/Se0hPKktj/OZvH2XNK1od47IbLuGGb17VaKbKjtsse/J9Pnl3C67rUtCzC/Pvntmh9ZjFOYIk14NbgQpeBIFhKNXuX6E2Rey1kFgBqoYBSWIFWCnEPdFiDoTZRsAKEiuLZzwmrnDJ1RP45i++2KZjSPGCDAAAIABJREFUieSEKS0uy3jMdVwCwQBF/bux8+Pd7Nm0n9wuOVx0yZAmu2aeLi6l7EQ5nYs6kds5pzWG7tOOSOxZ7dJnevdgEUiuR+xhqND5ZSRlRayMzez8e2bx+iPLsOM23/n1V7nokrZrVq5d8vT6I8sQEcbMHMlFlw5l98Z9bFyxFSfpMOLSoQyZMLBJJ0ulJWUc3XscK2LRZ2hPX3HqPEPiS/Vm1qwRM7lHkPjrqOiZTyhbgnaPbor6FTJlwSRWvrRWq08gnDx6iivvm01Op+YtUiLC2tfX896zq3AdFxEYMXUo8+6ehdWQTnINxs4cRdeeXRg9/SIqTlcydOIguvUp4PEfPU9Bz2pjg1A0xIv//ZdGa6Rff2QZm1du565vvgTA83+8nad/sph7f3grBT06nti9a2+AyifAXg6ikNA0CI6G6B0XVpAsFRl2ytUY2vL3PGX8nIt5+VevE82PYBgGIkLxoRMMnzyEUKRut3JDWeIHZj+Ylmz76+n/hB2zWfj7r9N7WK8mzVGAn6/8V959bjW/+MbDAIydNQrDNLjivlm8/Os32LNxH1bYwkk5vP/iB9z019c0uplN2sn0fDUMA3GFCfPGMPOWqR26gUgk4TlKrtenHdalqODoC1INQtwybTZi1GjirHL4TK6F8yxAHjCqL5G8CKUnyskv0EmiVMrBMA0GXHx2bqmxiji7P9lHojJBr8E9KOpX2OTegW9N+wd2fLQbO25z8uhpAHZ8uIsf3fYfDJ04iHBUGw9tXbODUdMu4pqvfCajhKomIsLKxWt5/4UP9M8Inbvlc9N3ru2Qa2sVIo6er4n3gAQEx6JCs7K+QbXVSK7TJkE1Ud30plZuaZMT+XaPbJRSXH7jpQydMIjLb5qCaRoMGT+Qrj2b90U/sucYS379Bu+/+AHd+nbl/Tn5mAED571thCIhPnPXjEZfLyIopegzrFeGesbKl9fywWsfZ9RIv/fcaux4kgX3z2Pg6P51rlV6oozRo3/MhElBevTeA8Bn73kCO5Hik3fHMPNz7WDheg6I2BB7FozOgKWdc4w+kPwEUtsgOKq9h9h2BC4Ga6r+/PHn9WOhK0A5YHRr/LUdmBFThnJ411E+fusTDKUD5N5DejDnjssznlefbjHUX05xaOcRAJ756ctE8iLc9J1rmtRgq5Ri+mcv5emfvESiMsFnPj+DwWMHsHfLAXZ/speeA6ubJitKK3n1t2/y5X//fIPB7srFH7Lxva1076+tsx3HZc0rH1HQozPjZl/cxL9QdiGSQir+oOttVWfAhcpHkdAsVOTa9h5elnH+lb5ZYYvPPbCAl375Gsf26abz2bddxnVfu4LwWRhn7dm4j+d//gopO5X2HBk/dzRz75zeYCBbk2AoSChi0a1v13SAnNM5yuFPj1LUtzCd/c0vzGPzyu2Mmz2qwdPWfVsP8u6zqzJed+rYaRb/z+vc/f22CZxaA4m9CPZKUAWgQpBYgfx/9s47PqrrTMPPuXf6SKPeO8WidzC9GrAxBhfcexKnONlkN86WtPU6cXrsNDvZxIkTe13jhnHBgDHd9A6iiCKh3uv0uffsH1caEEgUG5AE8/x+ttDo3pk7I517vvOd73vf0CGI+sZlrbvtMQgLoNExTNUAy2X7HXd7gAzGhJeam0xqbvJnOr+4oISXn3yL0iMVbJ5rTAYeu1Hb/NFQC+Y1+5l6+4ROM1SFO4+x4Z0t1JTUkZydyJTbxtNn2Mmg11DB6+yXIelKIc/d5Gl7Xx0fVxRBfVvdZK9CqwDfCuMPtr2swPcOEERaxiKuogBZWIYhg9shdBTD9UuCbAb7g1d0Jl1VVWbfP40xc4ZTV96AM8ZBal7yBd+ovv3cV/n3mU/QVNtMYmYC2QMzSc5OxN3k4Z3ff8iXf3n/WesS3c0epC5xxjj4w8afdvjZoa1HiIpxdrgmp8tBdUktDZWNnUo16rrOjhW7ScxICFtnq6pCbHIM25bv7rUBMqHDxt+oknmKE10U+NchrROv2FKgrhBKNNLUB7SSk1kpKQ1nTfP13Xtxl4iUnCS++LN7qS2tAyAhI/6Cd0Sa61v4xrXfpb6igbJvDMJsMfNFTypWh4UdK/bQd3huh/nydFob3bibPPzkg++FA/P2RfOXf3k/S/64rENphBACVVUoO1LRZYBc8KmR9Dr1vJgkF9UltdRVNJzTVKwnIvV6CGwGJQPad3jUdKO/JbAPYTUUyXq7Qs0FYZkAvuVtn4kwxqteDbZZl+0SevWMLqVk58q9/P2Hr+Fp8tDc0Aoys2NgKkDTdIL+4BkBcuGOY7z92w/YtWofiklh0sJxvPHUEm7/zgLS+qQQ9AfJG5LFmLnDSco8WSM9ddEEPC0e0ruokY5LieWPP7uTmEQX8xb9A4AVS75EZVE1s+7thTXI4ixSe6Jn6x1ebISwgPNhZPAAWMaC4kKYRyLUz7a4623EpcQSlxLb5c/bM8WdZY6LD5Tyjx++hqfVi6IqmC0mju0uQgtqZOWnU3WilopjVZ1OjM11LSx7YTXF+04ggfS+qcx9eEaHydDqsJ7R2CulNJqBLJ3f6qQuCfhDZ9Qvmi0mvG5fp+f0BmSoGDB3XKULFeOGWHlVutIJ+21I93OglWFY4QpjG1crB+vL3X15lwRFUUjOPvvOVle7PJ4WL8/950u01LcCos1eWmPfhoOMmjUUm9PKgU2HOw2Qg4Egn7yynr1rCxBCQaiCSTePY9wNI8PHWB3WTnNPupTYo+xdXlvQHwwvZtsRQiCEQL+Mjf0XFa0WUE4Gx2EsoJeh1/3W+LbNLfNqCJSFdSpSq4LgHgzbah2CO0ArBdvcy3INvTpAPrT1CMtfWIPP46e0sAIpJWm/3w9A5beGYLFZuM0dhyPN1sEms53172xh16p91JbVA/Dpu1sJBUN4mjxEJ0QjJcQkRjNgbF8ObjsWdg1rbWzlpke73qqyOaxMuW08K19aSyioIRRBVXENcamxDJrQC7U9lVRw3Al6HfgNa2FsN4Jei7CMPPu5VyBCWBCW4Vek7uOlQErJ5g+28/ov36Wx2mieNcaFgtPloKywnPS+KQhBpyoUWkjjzaffo7GmmaSsRABqS+v55y/f5YEn7qClrhVFVRg6eQAFnx4K21gve2EVIX8Ii93Cj+94utMyD9Wkkjcki7LCSuJTTwb+DdVNDJ826NJ9KJcaJRZjO/I0BCDOr3H5SkOoCRD9bQgdQTZ9FzCDVgxa8VURcFwIRftL+M7M/8Hb4uX4l4yGPl+SES58Oj2KnY5KFtU6u9Q///TdrexatY+UbENmLhgIserV9bgSovj2c1/F2+ojJimamEQXDVVNxCYbCjmtjW62L99N+ZFKfrvux50+d/64/hRsKiQm6aSqjrvJgzPGQUJGL134KTGAbmRJO2T4AoZi0lWIEBZw3AP6LNDrQYlHNn7nsl5DtwfIlUXVrHt7MyUHy4hNdjF+/hgGXtv/vLZut3y4g92r9+Fp8Z4xsUopkUgC3gALvz73jOeTUlJTUoti6rhi83sDFBWUEmpTsph08zgKdxZxyzfnMeeBaZgsJnIHZ52zy33MnOHEJcew8eMcWhvdjL+pD6NmDQ2vjHsTQghw3o90v2h4roOxNWm/A6H2wox4hM9EKBgi6A9ic9ouyNjj6O4iVr++EYDo+Ci0kEb5kSoaKhtIzEhA1yWtjW5MFhNpfc6sQS49XE5deQMpOSczYXEpMRzZdZzffPnP2J1WJMY266jrhrFnTQFSSoK+IBab5Zz9DDPumsSrP19M1YkarDYLfl8AV3wU1944+gI+nZ6FMA9F+pYZXeAiFqMUqNoouVAzu/vyug0hLGAehBRXfuOTFtJormvB5rR2Ou901S/w5Pvf5d1nlgJ0KhMHRmmS3+Nn4PgzEz6hYIgdH+8lKTMhfL7ZYsJqt/LX/3qZlNxkoz8SGDp1IJXHa6gpMeqkd3y8l/qKBuorGrg57sFwueKpmeR+I3MZPDGfAxsPo5iMplqz1cRt/za/9zbVKslGL09wHygpgAqyHpQoo7H2CjEBulCEEKCmgppqvPfLnEHv1gC5prSOV376NqpJZfvy3eiaTnVxLT63j1Gzhp3z/KbaFoSiYHVY8TR7AWNAqyaFKes9TLx5MPO+eV2nE6QQguTsRCYtHMen7xrdsNNun8C7z35EU01zeGBuWLyFgD/I6DnDLshNTwhBv5F59BvZc0XQpdQMNzhEmxtc1zcXocRD1DfBvgjwg5qOEL0v2I9w4WghjY3vbWPbsl2EAiHi0uKYdc8UcgefnyHHzk/24YxxEJPooqq4Gnu0ndgUF43VzZgsZnRNx+f2c/O/3NCpKoynTV6uXe94zoPTCfgCnDhYRu6gTJKzjYCvqbaFIzuPc+JgKaGAFm4Gam+u7WorOTEjgYd/fBf7Pz1EbWkdaX1TGHht/165mG1HKFEQ9QjS85ZRUiAEmAYh7DdflSoWp3OluA52xaGtR/j4pbX4Wn1IDCv4GXdPPi+lmJKDZQR8QWbeM5ndq/aj/l8Ruq5T9MX+KCaFwe9UUnhfLqsnWPmPTu4BoaBGKGCULbWP2dkPTOPEoTLcjW6GTzN6VrSQxq5V+7n9sZuISYpB13TKj1VR01Yz3RWqqnLjl69j+PTBlB4qw+6y029E3nlLuvZEhBDguBPpi4fAJpAhMOcjbDcaYzlCt9CtAfK25bsQCOKSY4zifJNKYkY8GxZvZfi0wefUNcwdkk0ooBGfHssHf16Bz+MnMTMBZ7SdL/78XoZPG9wh0yWl5OiuIrYs3UFjTTPOGAd15fWEQhqqqtBQ3YSUEpvTGg6QwWiuO76vhJxBJ0jNTerVE2c7MlSC9LwMviVGOZ59ITjuRZi6zi4JocBZfh7hymTd25vY9P52kjITMZlV3E0e3nz6Pe7/79tJyUk6p/GHr9WHyayS1jeF6pJafK0+XIkudE0SHe9kwoIx3PCFWbgSzszq6bpOQnocSBlWmgGoq2hAD+nEn1KDHJMYTVVxDaFACNt5Gvm0ExXr5Np5oy7onMuNlLrRZCbdxoJWPbPp8FSEmgFR/2I0kaIiG75qaIheYcFghI6UHang3WeXEZvkIjorCk3T2bXKKD2c+9CM8HFd9Qsc2FwIGI2wSVkJSCmxOqyUWAzny7HXj8Td38qR5gbuefufHVwzA/4gUkpScpM6aJX7PQHqKxrIHXIyoFZNKjanjb3rD7LwUaNR8unVP+pwPV3dWxRFIXtABtkDeu4OppTSGK96NQgXmPqctZFbCCvCPh9puwHQOzWvudIWcheCkvDSZV/QdmuAXHG0ii0f7URVlXCWZ/XrnzJi5hA8Ld5zrggnLBjDsT1F1JbWo5qNwZY/pi93f/cWsgdkEgqGKC4oxd3kIT4tjvIjFSz92ycc2HQYRVUYO3cECMHMeybTWt9KfFoc1944moz+aax6dT0Ak2+7lm3LdrPk2Y9Y+txKrp0/iln3TmHE9F7a3Q5I6UW6nwdUwrJtMmA85vqPq1NSJkKn+Dx+tq/YQ0p2UnjBGhXrJOANsGPlHm74wrk7ivPH9mX1axtIzUuhobIRv8dPQkY8sckxPPLL+3myfBeLV38Ynmjvfut1w33y8U34Wn3GNq0Q1LX1Ciz920oyrknDlRBFzGlBdTAQYv5X55IzKJO//PuLHRbZl8ta91Ig9Wak+4W2BjMB6EjrJIRt/lkzwqJN67c7tid7A6d+BlfK57Jz5V4sNjM2p9Ejo6oKydmJ7F13gKmLxp+R4Dl9XGRek4ZQFEL+EP1G5uFKjKbiWBXjPmrgxFeuYX2ik50VZQAU1FRz91uv8/z1C1nzz0/Zv+EguibZsXIvoUCQxupmAJa/uBqf28fkW07Tm5aSkoPlbFm6g5TcZLLy07kSkDKI9LwKwf3gN5r7cdwGzi8hlLMbFxk7ub20VOQKo1sD5JTcJHTNyN62o+uGZ7s96mSQJqXk+N4T7N9wkFBIY9D4a+g3Ko/E9Hge+J872fHxHpKzE0jKSmTUdcNIzkqkua6Ff/56Ccv/sQopITM/jdLDFcQkRuNp9mJ1WIlPi0PTdPqNyGPm3Yae6/YVu1nxf2sJBY0a5B0r9mCymLBH2VEUQWyii+X/WP25ZOm6nVAh+JZ1lG3zrzTqix23GAYgESIA3hYv6PKM3Ryr08rLT77Fx/+39pyax8OmDeb3j/6Vbct3h8sefvTCfsw2M/kTnkS+uROf28+6tzcRFefE5/FTXVxDeosPi81s1BP7g+HnU80q1z88k23LdnXoaaksrubw1iNIXWffugKy8jO44UuzGNRJnWRvQ3oXg15hSD8BSA38a5FqjtEwGiFCG021LVjtHUuVVFVBAD63nx/c9HOg6wVjdFwU190/lRUvrAGMRVZaXipTF13L7/xHKaipDh/bEgiwtayU65/9C9M2eMnMT2fNG5/SWN3UIcEVlxJDS4OJoD8UNhZyN7vZvXo/KblJrGnxIqWk7/Acfr7sB2H3vd66qJWBzYb6gpJ1UgVKr0d6lyCc93fvxfViTl+8XupFbbcGyGPnjuDg5iNYbBY2fbANXZMMmzqICQvHdtBCXfPGp2x6fwc7V+5FCBgxcyhDJw9g3iPXEZccw6x7phDwByk/Uom7yUMgKcDKl9fSXNuCxWbB5/FTW1pPwR0ZICH9mVK8rT4+fO5jFJNCcltnPMCo64YRnxpL/5F5VBZXs+LFNTii7dSUGHVRq17bQNAfZOz1I3pvgCz9QBcizrL3SltFuPhEx0dhtpnxewMdJl13k8eQaToFKSWF249xX96jzP3CTIZNHcCQSQOxOawkZyfiafGGA2SL3UJzrMpdb7zGlrZs1CP1tcjjkqBZgAsa7sgg45kCkjITsNgsxKXEkN4vld+uexIppVHD+Mk+rA4rfq+fgo2HaW10c2BTIXMenI7fG2Dp31aSlZ/eq+sTpd4KoQIQp8hKCtVwgQtsPC81le7YnuwttH8uV0qGPW9oNhve2YIz5qQEp8/txxZl48d3Ps3etQcAY0HrbTWyug1VTeQMymT8/NEkZiQwYvoQsvIzOLa7CF3TyRuWQ3JWIuMZw41/+StHQwECbVO08Os0VDVSfqyB6pJagr4gCWlxDJ8+mCM7jxMV5+SpVU9QWljBm08toeqEB0VVOLz9GFHxUeSP6YeiGuZDR3YWsX/Dod6rP95OYKuha8y2U5JQG0DqSOmL7NJ+Tjobs5divHZrgJycncRd/3Uza97YyIjpQ3AlRjPhpjEMnTIwfEx9ZQNblu4iJScJc5uWaVpeMvs/PczwGUPI7J9G8YFS3n32Ixbe8zICePabd9JQ3cTRXUVhF6EDd2Xi7esCoOwbhnyT/R/HsEfbSMw8WccohCBvaA55Q3MoLaxgzT83nukUJAQBb5Bei5oJ1qmGfJvvXeMx20KjYU89v8arCF0jpRfp32w4DSoOMI9HmAf1Socnk9nEtDsm8tHzn+B0ObA6rDTXtWB32vjNmh/hSojmsRmPc3RXEXVlDfjcfnzuGj78ywqWPPsRDzx+O/O/Ooen1/wIgH+b9kO++oOV9BtsjMvv2p7Dkx/i3tULEICmt+nTnoK72UNUnBOEQAsa0mVCCGY/MI3+o/pweNtRKouq2fLhTprrWmiubQk3Bw2bNogTB8oYPDH/cn1klwAdpOjE4lwFeQH3odCBi3pVEXomw6cPZt/6g1QV1xAV68TvDRDwBbjpa3M4tqc4fJy7yUNdeQNL/rgcRTXmtCM7j3P/43eQkBZHQlocP7n7N/g9fh7+yd0017YQmxLD9I1+3IMtVOlB/IpEsypoIY3SQxUIRRAKGCZd25bvwt3kIX9sPwAy+6fxxZ/dS+H2Y1SX1NFU20zekBw+fsmQDp3z4HRcCdHsXX+w9wfIQFcGY10mpyL0OLpd5i2jXxoTF4xl0/vbqa9ooLiglLQ+KSRlGg0oVcW1bDvN6nnFi2sI+ALMuHsSCelxLP79h1gdVmM7Vpf4vAGO7irqIP3WmeudzWll7PUjGT278wxMcnYik28Zh81pY+0bhkzV7AemUVlUTb9RPVed4pwoqWCdZNRGtU+wejlYpyHUzs1PPitSBkB6QERd0U5z7UjpR7Y+Z4iZBzYCEiwHkLYbEJfRAehiMmzqIKLjotj60U6aalsYNm0gY68fGW6qe2rVE3zj2u9SfaImfI5qUlFMCgc2FzLm+hGk5aVQcayKiqNVHcolYho1NKfAVeKl7z+OghAcvi8HLaST8WyB0VvgsDJ+/mj8ngBf+81D4XMVRaHPsBz6DMth/6eH+PC5jzu9ftmV5WVvQUQbpRV6nWFDC20ucA1gmXL+z2MaeO5jrkI6KloEwD4fQkfQW/8IlhkI84Betbh1uhzc+4Pb2L16P0d3F5Een8LoOcPJ7J8ebnwzyhlysVjNrGmb2xLS46gtq2f78t3MeXA6XreP6hO1BLwBVr2yAYlEIAj6gwhh6jiuhEDTtA7S21KXZOVndCiTiI6LYtR1w3A3e9i3ruAMww9d1zGdozm/V2AeYzjQKhltrrMYc66pf0T96SJwcszeaUhZmnLRm38KlskI68SLFmt0e8RycEsh7z77ETtX7jUa564faaxi/3sRiRkJ2Bxdu7jZo2ycKChl4b2voJpVUtJLALjuW28w618kv7tzKDFJLpprW8h4piCcOc7600EQoKbFcftjN3XQVz0Vi9XMDV+cxZI/LiPgCyKEods8aEJ+h27c3oYQAmw3gWkA0jIWEAjzcDD1v2ivIaWG9K8C/xogBMKOtM5DWEb3qsnmQpGBfUZwrGYSbrRQ0sH/MdJyba+U7BFCkHlNGmVHKqg+Ucv+DYdAwsSFY3HGGHrgX/zZPXz84lp2rzG65ec8OB0w6oLryupJzkpk8bNLmbpoAju2XUd82nME/SF+9MgwDtyVBfgIBkL4PQEynilACxmLWz2k0VjTTMnBcuY9Mgunq3PnxqwB6YybN4odH+9BURRDCs4fpLG6qUd3up8PhgTUbW0Lr3IMV6kQmPohLOPOdfoFlxBI3Q2yBZSYq2wyDxpNkIG9oMSBVgee55H2O8NWv70FR7Q93G9TcayK2tJ6pt4+gf6j+gCGIc+aNzZisZrDiaflL6xG13Rik108NuNxGmuaqSoyfrZr9T5CAY3UvGS8rV4e7DuW2vI6XnDVghBk/+UwIUVBURV0TcdkMXHjl2dz9/du7fT6nC4HOUOyefPXS6ivbARg2T9WEfAFefyty2sGcSkQ1muRoUIIHWoraRTGeLIvuOivpdfdAzKAiPtfhJp47hOuEKTe2nY/1NsSB0HwLUHq9QjHzRflNbo1QNZ1Y5DGJsWEa44T0uKoq2hg84c7uPGR2WQNyGDWvVPweQJsWboDgWDCgjHomk7ukCze+d0HjB7rJRgIkdHueCkEQgGz2URtfT1CEUhdtv8Ii81EdHw00+6YQM6gswe6/Uf14Qs/uZtJt4zD2+ojb0g22QMzziy76GUIIcB8DcJ8aRqYpH8d+D4yarEQYLsBvK8jhRNhuYIzWdpxCGwAzCdrz3xLgAA4vwS9MECWUvLen5ZxZFcxCelxKIrC7tUFlBwq577/vh2L1UxUrBPZydahQOBwOagqrqGhsgmny05VcTU/eXQ0oUCIyuNlxPykiqighrf9nFPWT4qqIpFMXDiGKbeN7/IaXfHRzH14BluW7gQZorKoGkVVmPvQ9E7l43obQk2H6MeQwf0gGxFqdls26uLdwqXUDHORwPr2V0VaZyKsM6/oRW0Y+10Q3NZm1IDRXCUt4F+KtIzoVTtgBzYd5r0/LScuJZbUnGTczR7e/t0H3P7YAp5a9QTBQJD78r6Ornc02NI0ncS23dvWRnf48dZGD3pIw9Pkobq0jrd+9wGKKsj0BtFCGkGMjLGu6SgmBZvDwh3/vuCsustzH5zO4t9/GP4+6A8SFetk4PiLl6jpLoSwgPMh0I4j7TcbyhWmfsbjFwkpJTKw0XCDlBLZ8iukKR/huBOhnN3I7EpABneBZdLJxmVMhhFSYBPSNh2hxJ71/POhW0e83+Onpa6VXav2dVzF6jqOaKOI3WQ2seixBbz/v8sZOWMIErDYLMz/ymyO7DjO8b0nWPf2ZMqPVvHDj4sQAu7atBCA9AcFDdUJ9H/xOO4mDxnPGN32PsBs86NrkoAv0Kk5wanEpcQyvhe7al1upNSMzHFgq9F5D+BbCmhg6gtXcoCsJLTZhZ72uARE77xpVRXXcGyPoQHeHiglZydSWVTN8T3F5I/tR87gLGISoxkzdwRxKTFIKamvbCQ2JYasAem8/+cVHNpyBJvTSm15PWarmdTcJFSziuYJhF9LKAKLzUwoEELXjXo9e5SNaXdMOqdL1tDJA3luz1OcOGA0/WUPzCAm0XXJPpfLjVCiENZrL/i88zXFkP714F9l7HiItvpm31KkiOl1GdTPhFZklLOcirCD1mhoT4uzy3P1FKSUrH9nC7HJMWE1KKfLga7pfPruVvoMy8FsMfPtv3yFVa9vYM+aAoQimLhwLO4mD2PnjmDI5AH87N7f4W50o2s6qipIy0vDHmVDIo1SKZ+O2W5GC2kIIcILZLPFREJ6/Dn9AlwJ0bx49Bm+OfH76CGdHy/5TxLS46+YxZjhG9AXYep7SZ5f1i0yMqiyzVglsBn865DCgnDed0les0cRKgPRsVEcYUiCGtbUvTxAttgsWByWM1axuqZ38FRPSIvjgf+5g4aqRnRNJz7NyGJ98NzHHNhcSCgQwu/2Ax1rjRWTamSM7ZaTxh9tY6/v8Fya61rYu+5AlzXIET4rQZBezowSFaOO8gpGWIYjbbMAiyGdB2CZYCwMlDNtlHsDTTXNhjXsaROXqqrUVTQARjnSHf++kOUvrKbkYBkSyB2cxfp3tvDNid8nKz+dqDgnoZBmbMOGNOorGjj+hX5ICRnPFmCPspHZP43youqmKlnMAAAgAElEQVRwSZOuGwYhDZWNxCWfO0CJSXQxdMqVExRfLqSUEFhjWN62O2oKs7F1GVgDV0OArKRA8ACop5TxyEDb59B7Sk10XaexpomU7I6lg45oO7VlJ++/Y28YiWJS2fXJPvSAjj3axrxHriM22cXr31lMWp8UKo9XI6XEYrNQV16Pt9WP1W4h4DP6CJwWB6FACJNZJeA1TELiUmOZ+/AMdF0/506rqho9BmA4Wka4AGRz20L2lMeEBYL7kHoLQun9O2dnRU2H4M6Oj0ndCAKVM92TPwvdGiCrJpUJN43B0+Rlz9r9KIrCpFvG0VLfeoarlRCC+NSOb9rX6kMIEdZV/MGENKx2CzH/4iM20cU9DfFsXlnENXOGse6tzQR8wXDjXsnBMkoPlZPeNyUSIF90rKAmg/U68Lc1TtlvNRyFTP2699IuMUKJB+eXkN63wDLeWM2ahyJsC3ttZsSVGB0OVE99D5qmdbBxj0uJ5c7/uBlPi1Es4Yi2s+n97TRUunHFRxM9Lpo9a/aHJ9TtNybi7WPcxEsfHYhqUphUH09TbQu+Fh9CQNAfwtPk5XvzfspLx5/tIP8Y4cI4uwySDrobTjcxEFbQmy/pdfUUhHUKMrjHeL8iGgiAXgW2eRd1a/xSoygKSZkJuJs8RMWe3LVqbXCT3je1w3Fj547glRP/SygQwmw1I4Tg0LajBPxB8oZk4/cGOLLzOKGg1pYpBm+rN/wcVocFv8dPbHIMUjfuDyNmDDEs3w+UnZcdfW/VOu527LeBVttxjgWjOVD6gSs7QBaWEcjA6janwgQgZIxXywTElRAgA4yePQwhICrOgbfFh9lq5tZ/vZGs/HM31lwzpi8+j5/krESWv7Aan8dPVJyTCkXB2+qjYONBxi8YQ2OFkXk+tetWNatoIR2T5dze9BEuDCEE0nYTuP9mbNMK1fjDxYSwzjjn+b0dYcqBqH8D2QSYe309WGpuMnnDcji+u4iE9HgUVaGuooGE9DjyhuWccbwj2si2PTbj8bCJyIbFW1BNKtNun8CmD7Yb9XOnlSzrmk5DdTMxSS5aG91GbaPfkIzSdZ268oYuG2ojfD6EUJGmvkaTmjglk6fXg+VKkNw6N8KUhXR+EXwfGEGGsINtAcI6ubsv7YIQQjDt9om8+dQSdE3H4bLT2ujB7/Uz6XQnO4xA+dQyQ13T2fbRLiw2C7MfmIbJbKL0UBm15Q04XHYa27TMFVUhFAiRmZ+O0+XA7/WjqAo7VuwhGAgxfv7o8wqQI3xGzIMh9F7Hx/RWUFwXLYPakxFKNDi/avRNhPa3jdd5COsFKPucg24PkBVFYcycEYycNZRQIITFZjnvTNv4+aM5vreYyuIaQsEQiiLoOyKX5K0tBH1uSg6VU3qogimLrmXOQ9OpLa3nyK4iVFXhuvunUnm8muHTB1/id3h1opj7I6O+jrQMB60K1DyEddJl77I1Ol2PG9+oeZdNRcKw+P38NVA9ASEECx+dy6b3t7Pzk31oIY2hUwYy6eZxZ23COZVQSEMxKVgdVkbMGMqeNfvo8/ejHHvYqM/LeLaA1NxkPGlxeJo8pOQmEZscQ+mhchAwYsYQzNZuv11d0QjbPKT7z4YeOo62uls7wnpdd1/aZUMx90eavgUEAFOb7W/vo8+wHO78r1vYuGQr1SfqSOubwsQFY8jol3bW8x6b8ThaSAsb+qx4cQ1SStL7plBxrJqWYCh8rJQSXZd4W3xIKak8Xt1WxmFYwr/85Fss+eOySIb4EiEsY5CBnWABhKNt3AL2h3rt3+2FItQkhPO+M3Y3LxY9ZsZRVRXVfmG/VFdCNA/8zx0c2FTI4AnXYLKaWffmRsoKKwn4g7ibPKhmE8X7Shk2YzBxybEc2noELQg1JXWMmTuCAeOu7C3/7kSYshCmu7vt9fXAfvC+Ar5VxgO2GUj7XSiWiJX2hWKxWZi6aAJTF00475tRWHNVl4y5fgQlB8upK69HC2nkj+1HY80pW/fSaNqdfd8UNn2wk5TsBFxJLkoPlxMKhMjol0pcypWx4OipCFMmRH3LsMnVysGUjbCMu2jblReC1JshdAwQYOpzWespjb9t6zmP6+nkDMwkZ2DmBZ93qq18wGc00CZlJZKQHheWZAOISYzmlm/No+JoFQFfkNYGd4f7gj064hZ3KRHCDlFfQQb2QugwKHEIyyiEevl7XaTuhlAhEDDMxpTUy1pSeKleq8cEyJ8FTdM4sKmQLUsNB63KomrqKxqpLjFcupAQCmoc2n4Uv9fPr1c/wcx7p+Bt8ZKYER+ZcK9gpN5iBMfCZTQugNGF7n0Naco2ZHcifCYu9GYkFMEd31nA8b0nKDlcjtVm4Q9f/ytNNS1hZRmAptoW9q4/yPyvzWbDO1uoKalj+PQhpGQnMv8rc3ptDXdvQqiJCPuN3XoNemAneN8AqbX1+apI+92Rhe1loD3b+9iMxwkFQtz7w0XouuRP//p36ioawnKpAI3VzfzzV0v43svfomDjYUxmE0LAtuW7SUiP43frf9Jdb+OqQQiroTDTjU20MlSEdD/fVvfc5oRqnQK2G3v9PbtXB8gb3t7Mp0u2sXv1fvzeAN4WL5qmn+HkGPAGOL6vhObaFjL7n32LKcIVQuioIVmF5RQ94g+NrnT7bWAZddbTI1wcnlr1BFJK9q0/wMb3ttNU00xdeT2eFu8ZuslSSja8s4VFj93Eo795iOoTtVgdVlJyknr9jTbC+aHX3QXaCbDdAkpbFlf6wPsq0pR75Xfm9xACvgCN1c2sfv1T3E1uGqoaO3VIDvqC7F13gC/85B5qSuvQghrlx6oi4/UqQcoQsv5+4xv77W0PauBfC+aBhnpTL6bXBsieFi9bl+0iIT0Ob6uPgD9IKKh1eqzZYsJsMeFrk4KL0HuRWi0ydBiQCFN/hJrcxZF6eDF7Jp3/nUTomsdmPA58to7z7R/v4eMX1xKb5EILaRzfdwKTVUXxGgY+7c167VbxZYcr6D+yzzlNfCJcgUhv27g9pcRB2IxJN3QULCO67dKuFuorG+gzLBeTSUW1qOzfcNCQY9U0/N5gh0BZKIIP/7qSL/70XpKzjP6Sp1f/qJuuPMJlRytv2+k5ReVFqIAFGdh7yTSgLxe9NkBuqW8FCcv+sbqjxnFbUKQIgS4lSEPtIjUvmYS0SElFb0b3bwXvW8bqFJDWKUj7TSiddZmb8sA2FUQi+N43HrPNN3SYe/mg7U2EgiE+XbyF+LRYivadoOJ4FcHwYlbgSorG0+RB6pKYpBiiYh0daiAj9H6k1AxHsYZ/BWFCJLzaqYW1XncfhA4Y33jfNr62S1fJ8P8inCefdVG7a9V+dE3HHwxR+GkRLQ2tKIog4AudTDqEfxUCRe3drrIROiKlBK0EGTpqaIC3/gEwnyETqdfdZ+zuyFrj76HDmO0yO9Wr6LUBcnR8FAhB0B88+aA85asAs8WM2WYiMSuBSTePwxljyG35vX4ObinkREEZMckxDJmUf4bGcoSehdQbwfc2KIknV6tKMnjfR5oGnKGOIZQ4pO0W8L5jlFUA6LVgX2BoFUc4b06Va7vQSdfb6iPgCxIMhKgtq8eVEI27yYPJYiLoD+Jr9YU1lp0xDjKvSScjUgZ1xSBlAOl+CUIHDck4QLY8Bc5HztFMdMoEK/1GVsrU55JfbwSoPlGL2WriwKZC7E4rrvhoPC1ezFYTUoLdYcXd7EUogtGzhzJm7sjuvuQIFwkpJdL3gZGEEooxDLWSrk2uTneyAyOjTBBxBfQM9NoA2RFtZ/ScYRzaeoTi/SUgRNgExOGyE5cSw4Br+5N1TQZj5g7nmjFG1tDb6uW1X7xLbUktd331HXQp+fsPb+eO7yw4L+3lCN1EqAh8a4zgOFxT/B7IANJ+S6fycYr1WqSpH9K+kJMlGREd3cuJPcqGxWahrLAcs9WMyWTC2+Ij6A/ijHEQCmhEJ0QTmxRDVn4afYfnkjPoZOd9ZVE1BRsP43P76D+qD32G5UQyzL0IGdgO7ueMiVS2NU/7liJ9KyHpkw61qkrCS21ZqWYwDQIkaKUgTGC/LdJYe560L2I/66I2vV8qBzcXInWJalJpqGok4AtgtVsNBRtVoJpV7E4r8WnxTFhwskGstdHNvg0HqSqqISU3icET84mOuzzSmhEuAlqxERwracai1Pu2kVjSa9Hr7gGUcCY5bGFfu8iQmLNcC0jj37brQM3rvvdxkei1ATLAlNvGU32ijo+eX4nZaqb6RC02h5XRc4YzcHx/bnxk9hnn7F5TQE1JLam5yZgsxkRrd9pY9vdV3PlfN3NsdzHuZi+Z/dPIvCbtnFaZES4XXW3XSKDr35FQExDqhEtyRVcL7XJt7f++EExmE5NvGcc/fvgaJw6UoprUcC+ApukkZMbTb2Qe6X2SGTZ1MEOmDAwHwHvXFbD0b6swmVVUk8Lff/AaDped5w/8FlWNBMm9gsB2I8DtgNnICssWQ2XmdIQL4foPZPAoQgCmft0iNXe1MnzaIDa8sxmfx29ItUlD+q3P8Fw8zR4SM+PJG5pDn2HZjJw5lNgkY+FSX9nAqz99B0+rF5vDRuH2Y2z7aBd3f+/WDo6bEXouMngQMJ20m+/wQ5+ht3waSuKbSOmD0BFjXKvZV0wiqlcHyKqqctu/3Uhcsoudq/bTUNmEFtJI75fKjLs6dz86vO0o9zy6GNWkkJJeBMAtD7zKX342lz9/+0W2LN0BCMbMHU7+2H7M/8rsSMaqJ2DqC7ZZIKLAt9R4zDYPZCPC3L97ry3CWRk5aygN1U385st/RgudbJC02q3MuGMi9/337Wd0vXvdPlb831ri02LDZiQWuxlPi5fi/aW4EqIoPlCKqqr0GZaDKyGibtAjESpY5xruXu01irabQVbS2cL21DpHYY2UQn0WTpVqO/X78yUm0cUXf3YvT33xj+z/9BB+j1GiVnm8ilAgxE8++F6nAe/6d7YQ8AdJyW4PjqKpK29g/VubmP3gdI7uOo6nxUdGv1TS+6VGkk89EWECcUqtv/1WY9zKACL2V1023QlhA/OV57jZqwNkMILk2Q9MZ8zcEdz6zXk4Yx2k5iZ3KTPjcNk7WE4DSAmlh8oZMnlA2HIzJSeJA5sL6T+6D4PGX3PJ30eEsyOUKKT9HvC+fLKmWDaD/S6EEmm+vNR8HjcsIQQz755MVJyTdW9uYsuHOwHJnAenMf9rczsdq9XFNeiajsVqZvkLq/nus9sYOizIfyzqx/dv/ClaSGPM3BEIYTQJzf/KbPLHRkx/ehzmcRB6FWTUyYY7vQpM+ZfN1TLChZOclch3nv8635r4fWo8dQBoIY2E9Pgus8FHdhwj7rRenm3Ld7Fh8WaKCkrwe4IIAbouGTThGuZ9aVYk+dTDEOYhSN8KY44NK1NoxkJXze7Wa+sOekSArIU0fB4/Nqf1M2+dxqXEnpfxx6hZw3j51zeTlJnAvEX/QEp4/pfX43DVsXXpTqqKawDDYjMUDNF/VCRA7ikoloFI0/fAcRfGvt/ls46+2vk8Mm9gBMnX3jCKwRPyObanGEVVeOjHd3U53i12yxkL2Xa8bh9Ol7EQBvB7A3z43MdkD8zAHnWmOkKE7kNYRiC14xDYevJBNRVhv7n7Luoq4fNaPCdlJvB/x5/lW5N+gNQlT695Aqu9a4dBe7SdoD+I6jh5jNQlAV8Qk9lEXK4xP0sp2f/pQfqPymPAuMjuX09CqKlI+y3gWwK6blQ22uYgHA8hhLm7L++y0y0BcjAQpGDjYQo2HqKquJbW+lasTisOl51pt09k8MT8SyY0njc0m1n3TmHdmxsJ+AwFjLzhOZis5rCHfBhJuE45Qs9AKA5QrrytnN6AlJJgIIjZ8tlvlFGxTv649RfnPC4lJ4mE9Hgaqhr55ZtHSM0wLG6fWnyc6Pgo1n38aPhYq91CY7VGWWEl/Ub2/saQKwkhVMOYxzoZtCpQnMbCtrMaxwgXjfYF7c+Wfh+z1fyZ51NVVXlm08/O69gxc0ew8qW1pOQms/KltUgpqSk1ss8bl2wDYM6D0xFC4HQ5OLDpcCRA7oEo1vFI82DDsAcTmPIQp+ocX0Vc9gBZ0zQW/2Epx3YXs+WjnfjcfoQAm8PKnIdm8P6fV2C1W+g/6tJI+gghGDt3BEMmD6Ch8hbs0XbumeDipSffxO60svWjXQDMum8qVcU1DJ088JJcR4QIPZ2m2mYem/E4fk+AyuPVANyb8zVsUTZ+t/7JS2rVrigKt3xzHu8+s7SDlKPJrKIonUz2gogeaw9FCAFqqvFfhEtGKBji8PZj/OL+31N+tAqA+/IeRTWr/PD1bzNw/DWX1OFu1HVDaalvZceKPQROlV8FJBJxSqO13qaQEaFnIpRoUAZ392V0O5c9QD5RUMqxPSdIyU0i6A8RCoQMUXJPgNWvbUBKSXrflEsWILdjd9qw9z15w57/5dm8+Zv3CfgCgKC2rI6JC8aQN/Tqq7uJEKGmtI5XfvoWzbUt+L2B8OOqScXv8fPazxfz0JN3YXfaLtk1xCXH8OATd1JTeh1+7VEsVjMpI57h+e+/QnxKEHNb856nxYvFZiGjfyQAi3B1omkaS/64jMPbj500zqLN9AHBkj8tRzWpl7ROX1VVZt49mWvnjeSe79+KKyGax2/5JVVFNYy7YSSxyTFt16rjbfUxJJJ8itDDuewBcmlhBdtX7MZsMeFt8Xb4maZpmMwmGqqaLvdlEZcSyxeevJs5D07H1+ojJTcpLF8TIcLVxto3NyJ1mHHXZHat3kdVUQ2appM3LIcBY/tRVVxD4fZjDJs66Lye77PWMAshSM5KRK8ztvgS0uKY8+B0Vry4BqkbNcoWu5mbv3HDWesjI0S4kineX0rhjuOk5SWTNSCDwO5ihICE9HjGzB1O0B9iw7tbzjtA/jw9B84YZ9iU66lVT1Bf2cCbT79PZXFNOIc84abRkeRThB7PZQ+Qo+KchmzEKTs9iqogpWTKbePRghpZ+elnnBfwBTiyq4jSwgqqi2toqGrCbDUxfNpgRs8eFlaf+DyoJpWcgZnnPjBChCsYKSXH9hSTlJlIY3UTQgj6Ds9F13UaqoxaYJNZDf/7fDi6q+hzXdOp8l/Dpw2m7/BcSg+Xo5pUsgZkYHNEguMIVy/FB0oxW0wIIfC2+jqUIbmbPMQkxlBbVndBz3l0VxGPzXj8czf7xafG8YWf3E3p4Qp8bh8pOUmXtDwrQoSLxWUPkK8Z1YdJt4zDZDKx9q1NeJo9Ycee9qa5STeP63COu9nD679cTO2JetYv3owW0lBNKg6XnaaaZkoPl7Po2zdd0vqqCBGuJhxtHen2KJthPyolWkgPaxIHAyGSs88Ugw8FQxQXlFJbXkdtST2v/PRtGqqawnXEN8c9SN8RuZ970o2KdUYafCJEaMPpsqNphsZ4VFwUmWYVq91Ka5Mbk9lEa0MraX06twuuLauj9HAFDVVNPP/9V9A1neoThuvhxQqSTWYTuYOzPtdzRIhwubnsAbIzxskd31nIh39bydi5w3E3e9E1naTsBPIGZzP2hpEkZ3W0Dd78wQ7qyhqwRRuTtdlqRtN0/N4AqbnJHN97grIjlWT2T7vcbydChCsOIQRjbxjJJy+vIyU3meSsRCqPV6NpOn2GZVNVVENiZgJ9R+R2OM/d7OHNp9+j8ng1JQfLaaptpqWhtUu5ts/C55WbixDhSiR/bD82vLMFT7OXnIGZ7Ft/AG+rD2eck1BQw+/1s3DR9R3OkVLy6btb2bB4C011LZQcKKOhsrGDmY+7yfO5g+TImI3QW+kWmbe0Pil84cm7aaxpxmRWz+nVfmDzYXZ+spdgIBS2qW1n+QurCfqDzPvSrHCAXFtez4a3N3N0dxH2aDtjrx/ByFlDO2iuBgNBvC0+HC47JnOPkIOOEKHHMHr2MNyNbnZ8vBdXYjT7NhxEURTiUmMZPHEA424YGc4mt7NxyTaqi2txuhwEA0GSMhNwuOyk5iWzdelOpC750/ZfdpnJOhePzXico7uKzgjMI0S42olJdHHrv97Ih39did/rp7a8Hl3TGTxpAOn9Uplw05gzEkiVx6vZsHgLiRnxlB6uIDbJRWxyDEX7TiAUQdAfAvhc4y0yZiP0ZrotMhRCEJd8fk1wFqsZKencmrKtqiI63giym+tbePWnbxMKamxfsQepS5pqW2htdDP9jknous7Wj3ax8b1thAIhLDYLk28Zx8hZQ/F7A5QeLgcJGdekXdIO/QgRejKqqjLjrslce+NoWupbKT9SiaIqfP23Xzjj2PYMUVZ+OgnpcXz0/Cd4W31cM7ovjmg7NSdqMQaqpLasPhwgBwNBdq/ez961Beg6DJ0ygBEzh54ReLdvHR/dVYS7ycOeNQWRrFSECKeRMyiLL//qfurKGyg5WIbJYuLbf/lql8f/cMHPaa5vZc4D0zi+txjVpNJ3eC7pfVNorGmmubaF2JSYDmOs5FAZW5bupL6ikeyBGYy9fgTxp7nntZdk3ZrwcFhRY8+agotWXhUhwuWiV6ROR84aSl15A8nZSbz77FJCQQ2kxOqwMnLGUJJzEslsa+zbu/YAfm+A5KxEhBAIVZCSncj25XsYd8MoDm87yqpXN7Bn7X4URWH6nRNZ/sJq6isb2Lf+IJ++uxUQTFgwmvlfmXPJ5eYiROjJ/HDBzwHYt/4gcPbt0o3vbUc1qWEVmqO7i8gdkg1CcNd/3kxVcQ0Ol+F0J6Xk/f9dweFtR4lJciGEYNWrGyjaX8qib89HURSa61tY9+YmDm4uZMvSnR3kq47sPE5Wfgb1lQ1nTNARIlytqKrKLx74AwUbDwPnKG8Q0FLXwqrXNoR3Zo/uLiI1N5mJC8cCgsz8k1nnw9uPsvj3H2KLsmN3Wtm/4RAHtxRy/3/fTnxqHLqus3PlXjZ/sIPWJvcZWsh6m6uepmmf2TE3QoTLSe8IkGcOpfpELZs/2EEoGCLgDaKaVKJindhdNm7+lxtQVZVgIMiWZbtY+8ZGhCLwNBsycitfXsfImUNoqm1m43vb2LNmf9jhZ/XrnxIKhijaX8LIWUPDahjOGCfv/WkZX/7VA0TFOrvtvUeI0JN5bMbj7FlTAIDVYUELnqxfDAVDNNU2kT0wk7ryBmJTYsgZZKjEVB6vpnDHMVLzklnx4hoAZj8wjaJ9Jyg9XEFan2T++aslNNU0k5Aej8ly8lZldVixOqzkDcnib999hT7Dc5j3yHWRHZ8IEc6D9qC5eH8pQAc1Gi2kIRSByWKipd7N2LkjANB1nVWvbcCV6MIRbSxy17yxkaA/yIBx/Zn70Aw2vreNdW9tIiE1jpTsJG54eCbr39mMp9VLVKyTUdcNA+Cv//kSC75+PWl5n63UKkKEy0WvCJBVk8qse6ewd/1Bptw2gV2f7CXgC2J1WNjy4U4C3iA3fGkmBzYVUnqwDJ/H30HRQtclCEF0fBQtDa0ItaPaha7plBwqp7XBTVVxDQBr39hIwBdg7sMzGTwx/7K+3wgRegrtmafzKWnoP6oPtWX1eFu8eN1+rDYLTTUtFMtS0uancPtjN4Xr/Q9uLqS0sJya0jr83kAHO9y68nr8Hj/1FQ2k5iYDMPehGSx7YRV1ZfVY7Wauf3gmJrNqSNLtLmbVq+uZ96XrLuVHESFCr+CpVU9cUAmSM9ZJwBPA7wugmlSa61soK6zknh/cSlZ+BgC1ZfUc3XUcIRTs0TZSc4xxqZpVigtK8Xv9bPlgB8lZieExHpPkQkpJwBtExkBKm+pNS0Mrbz79Po/84r6IPGOEHk2vCJABSg6Vs33ZLiw2C3XlDYDRYSulpM/wHF7/+WJ0XRKfFotqUhEYBgJSl/QZns3oOcOIinGS2T8Nu9PO5g+2A4Y3/NHdRez8ZG+nr3tqR2+ECFcrXU20p0/GPq+fB/p8HbvTyrQ7JtJY28yWD3bw3p+WYzKrzH5gOi31rax6fQNHdxWjmhS8LT4APvr7KkKBILd+60Ya2vSXT0UgsNgsDJ8+GJPZ2KIVQpCUmUDBxsPMvGdKZMKNEOEcnL7onX3/NP787y/iSohmwoKxnDhQSnFBCW/++j08j3jpPyqPN59+j5qSeuzRVqN8Q4Cv1Ri369/ZzKJvz0fT9DMa3ofPHMKJA6VMWnBSujU6Loqq4hqKC0rJH9P3Mr3rCBEunF4TIOuabnzV5RmP7Vt3AE3T8bX6UE0qoYDRfavpOqqiMHhCPlNuGw/AtDsm8trPFxMMBFFUldqyehwuO2PnjiS9XyqrXl0PwMx7JlNX3tCpaUmECBE6p7ywkrHXjyQlJ4mywgpKDpShqAqgU3q4gtd/sZitH+1CUQVCQChwcgHqc3uxOW3kDM4MmwedyuwHprHh3a04XI4OjwtFIKUk6A9GAuQIETj/5lUpJQe3FDL/K3MIBULsXr0fIaC5roXtK/ZgdVjZ+tEuVr22gZA/RMBnByTiFKcvV3wUUXFRmMwqQf9JC3gAT5MHZ7Sj09cNnGJhHyFCT6TbAmSv28eRHcdorGkmLS+F3CFZZ5Vby+ifxvj5ozGZVZb9YzVBfyg8gZrMJrRQAF3TOzjqmc0mQiENV0I0tM21Gf3SuP/x2xk0/hqqTtSQ3jeVUbOHceJAKStfWkfAZwzauvIGpt89KeL4EyHCOTh1Mm4fP1pb2VJVcU24F2D/BqPRz9vqQwhBet9USg6VGycKGDJlII899zXMFjM5gzJJ65NCxbEq4lNjkbqkvrKR/LF9CAX0Dq/f2ugmIS0u0isQIcIF8NSqJwj4g/zuq3/BZFYpPVxOyaEyVJMa3tXZu7aAptrm8G5OY3WTsasqBEIR5A3N5pnNRiPvxIVj+eSVDcQmubA6LDTVNBMdH4XdaUPX9LaFspHYEkBan+Rued8RIpwv3RIg11U08PovFrP69Q0gBGPmDCe9byqLHrupywyQ0+VgzoPT+d/HXiAU1MLBsdlqYujUgUt+DxcAAAiQSURBVNSU1nFo69EOJRH+thXqhsVb8Hn8LPz69ZjMJpKzErnhS7M6PH9iejw5g7KYftckpK7TZ1gOiRkJl+gTiBChd+Bp8RrShxgybvYo+1mPb5dw87t9nDhYGnbHBGht9ICUhgoNUFxQGt4FQkJ631TDih6j72DRYzex9aNd7F1XgKoqTL1jAoMnXMPbv/uQyqJqrA4rQb/RsLvwGzdEnDQjXPVIKak8Xm0sGtPjzqnwYrGaycpPp/pELc11zeE5s52GqiZCgSCne/0IYewA/fqUxfHY60ficDnY/MF2mmqbyRuazYSFY9m/4SBblu7Eajfmdr/Hz7h5I0lIj784bzpChEtEtwTIK19eR8AXDGd7U3OTKT9SyY6P9zBxwdguz7NH20nJSSIqzsmetQcIBUKYzCYObT1K9qAMHFE2dF0PS9YIIUBAer9Ujuw4ztFdReSP7dfl8yekxZGQFpGMihABDFmn9/+8Ai2oIYRANSnM/9oc+o/sWvowJtHF5FvH8dovFhP0h1AUgWw7NyouCi2o0VzXApwskWpn6V9Xsv6tzTy39ynsUXbsThtTbxvP1LbyqHbu/u4tHN52lBMHy4lLdjFoYj6xSeenqR4hwpWKu9nDu39YStmRSoQwyo6GTRvEdfdPPaus2sx7JvPX775MWWFleM5UFKMZzx5lo7G6KWwa0o6hdQyPDPk3EjMTeGbTzxBCMGTSAIZMGtDh2KQ7J5E3NIeDmwsBGHBtf3IGZUYWtBF6PJc9QPZ5/BTvL2HX6n1UFxt+78tfWI2m6cSnxZ41QD5xoJR96w9itpoJtmWmFFWhubaZm776MPEpsYSCGiteXIMQwpiAJW2mIBqDJ+WfNUCOECGCQWujm/f+tBxXfBTWtl0dn8fPe39azld+/QBO15l1he28/OTbVByrCgfAQhFoIY2oWCdZ+Wns33AIXZf43L4OE6/FZiHgC7Dp/e3MuGtyl89vtVsZOmUQQ6cMukjvNkKE3s+qV9dTfrSKlBxDLULXJTs/2UdanxSGTe16rCRmJrBnTQF+byA8ZhVVIegLMmBcP7Ly0zm6uxif24/f42/3/AGMJFRtWT2hYKjLEkkhBLmDs8gdnHVR32+ECJeaTqzpLvELqgpCEeEBFkZKTKc5aJ2Ow+U4o3EnKtaJ2Woms386tz+2ALPFzP+3dze/UdRxHMc/M9t9LNtiH5alS0uLpRTYQIGKKFUoWEhbTLSQiCFgFB9OiHjx6s1E44GDf4CJFxNTDYaLCRJBoyiIAtagmIaKfX7c0nbb7e54WGgHCtvWQqH1/Tpu9je7c/hmPr+Z33x/TleaXJ7xYxkyJMuSdwF9UoGpuNpwTfHR+Fg4liSPz614bFRXG66lHBsdiMrhHL9j5fN75fa6lZ7pU+3rVap8sULb9z2lJStC8vo9Mh2mPOkeVR3Yop0vb9OFU7/ft/MC5qOR6Igu/3hFOaHxZQumaSgz269fTl5KOba7pUeJhHXLpNfldSm/NE/5pSFV7d+ijdXrtGHHGrl9bvn8XnnS3VoYyFT1we3aULVGrY3t9+3cgAdl1u8gu9xOrdy0XJJ0/uuLMmTomf1b1NrYpnXbVqccW7qxWJueLZfP79W39WdkyVL5jrXKDGQqWBSQaZo69NFBef1u9bZHdOFUgwwZ2rp3s7pberWKfsbAlCTiiQlzWEmyrIlLI273ynv79N3nZ/Tryd8kJVspSlJbU4cWFQa0+8gu1R89rlh0RMHCgFob2+X1e+S5sabYMHn0CkxHPJ5QImFNqB3TYU5YHnE7wzT12M4yBQpyxjbt2fHSVvW09Sl/RZ4qdm9ST0dE3x/7STmhLLl9LuUuyVZxWVFyvIwJN66A+WDW7yBLUuXeCuUtW6RYNKaR6Ijamzq0tnK1whUrU47LzMlQ3eFaJRIJlW0Lq6wyrNyCHNUdrpFpJk/F4XBo7zvPK1gUSB5/OKb+7n5VH6xk5x5givJL82SahmIj4xfX2HBMpsNUQWko5diS9ckLZyIxHqT7ewaUnuFTqDioYGFAb3xwQDWvbldwWUDPHapW7WtVsixLXS29KR8HA5jIm+5Rfmmeetv7xj6zLEt9nRGterIk5dis4ELl5merryMy9lk8ntDQQFThzaXy+Nzac2SXDrz7gkIli7Vmy2qVbHhUpsPUYP+Q3D6XgkV0pMD8Y0xn5ldeXm6dPXv2nvyw/W3brGm+HBcfjaurpUdpToceWbTwjov9LctSe1OnhodGFCjIoT8q5gzDMM5ZllU+0+PMtF5/PnFBJz45bftjyV7EZVvDk469eLpBX338zY2QbMjn96jurdpbJqlD14dUf/S4mq+0SUrW7JIVeao7XEu9Yk65FzU703rtbO7Wp+9/ocG+QZkOh+KjceUVB7Xn7bt3h7qpq6VHn314TJGu65IkS5Yer1mvp/c8MXZ9tSxLp+vP6Icvz+nmGkmnx6m6N2u0dBXrizF3TLVeH1hABnBnD0tAlqTu1h41XmqSJBWFCyZtG2U3EBlUy19tSnOlKbQ8KKdr4jsG8Xhc//zZqkhnRJm5GQotXzz2NAiYKx6GgCwlJ51XzjdOeX8Bu9HYqP6+3KzowLCChbl33APAsix1NXfr2h8tcnmcKgwXyOdP3foReNhMtV7nzE56AGZfVnDyXqp3k57hU/G6opTfcTgcN5ZspF62AWBy3gXe/9zdJc2ZpqJwQcrvGIahnFA2ewTgf4FbNQAAAIANARkAAACwISADAAAANgRkAAAAwIaADAAAANgQkAEAAAAbAjIAAABgM62NQgzD6JB09f79HQCSllqWlTvTg1CvwKyZcc1Sr8CsmVK9TisgAwAAAPMdSywAAAAAGwIyAAAAYENABgAAAGwIyAAAAIANARkAAACwISADAAAANgRkAAAAwIaADAAAANgQkAEAAACbfwFhloAGEIGzbQAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "" ] }, "metadata": {}, @@ -298,21 +299,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.5.2" } }, "nbformat": 4, -- cgit v1.2.3 From bf78141c8849cce9b94a4e518bd6c7360e66f8dd Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 16 Feb 2018 15:13:59 +0100 Subject: update notebooks --- docs/cache_nbrun | 2 +- .../source/auto_examples/auto_examples_jupyter.zip | Bin 85906 -> 86995 bytes docs/source/auto_examples/auto_examples_python.zip | Bin 58682 -> 58992 bytes .../images/sphx_glr_plot_gromov_001.png | Bin 16548 -> 45460 bytes .../images/sphx_glr_plot_gromov_002.png | Bin 17330 -> 17362 bytes .../images/sphx_glr_plot_gromov_003.png | Bin 16530 -> 18617 bytes .../images/thumb/sphx_glr_plot_gromov_thumb.png | Bin 17804 -> 25219 bytes docs/source/auto_examples/index.rst | 32 +-- docs/source/auto_examples/plot_gromov.ipynb | 120 ++++++++--- docs/source/auto_examples/plot_gromov.py | 7 +- docs/source/auto_examples/plot_gromov.rst | 156 ++++++++------ examples/plot_gromov.py | 7 +- notebooks/plot_gromov.ipynb | 239 +++++++++++++-------- 13 files changed, 361 insertions(+), 202 deletions(-) diff --git a/docs/cache_nbrun b/docs/cache_nbrun index ae0fe23..9bf16ad 100644 --- a/docs/cache_nbrun +++ b/docs/cache_nbrun @@ -1 +1 @@ -{"plot_otda_mapping_colors_images.ipynb": "4f0587a00a3c082799a75a0ed36e9ce1", "plot_optim_OTreg.ipynb": "2ec33a099bb67120a134332a20f29313", "plot_barycenter_1D.ipynb": "95708b025b6d96d97f579d30d268cbff", "plot_WDA.ipynb": "27f8de4c6d7db46497076523673eedfb", "plot_OT_L1_vs_L2.ipynb": "871d60931f5118c085342e11cb638336", "plot_otda_color_images.ipynb": "d047d635f4987c81072383241590e21f", "plot_otda_classes.ipynb": "44bb8cd93317b5d342cd62e26d9bbe60", "plot_otda_d2.ipynb": "1a9547f07317612e1a161b7d9f07a5a8", "plot_otda_mapping.ipynb": "d335a15af828aaa3439a1c67570d79d6", "plot_gromov.ipynb": "243e64c7d13afa8dfa10fc3ccb4e1e28", "plot_compute_emd.ipynb": "bd95981189df6adcb113d9b360ead734", "plot_OT_1D.ipynb": "54dfea8ccb61f30729519275785c494c", "plot_gromov_barycenter.ipynb": "953e5047b886ec69ec621ec52f5e21d1", "plot_otda_semi_supervised.ipynb": "0261d339a692e339e15d3634488905cc", "plot_OT_2D_samples.ipynb": "3f125714daa35ff3cfe5dae1f71265c4"} \ No newline at end of file +{"plot_otda_mapping_colors_images.ipynb": "4f0587a00a3c082799a75a0ed36e9ce1", "plot_optim_OTreg.ipynb": "2ec33a099bb67120a134332a20f29313", "plot_otda_color_images.ipynb": "d047d635f4987c81072383241590e21f", "plot_WDA.ipynb": "27f8de4c6d7db46497076523673eedfb", "plot_OT_L1_vs_L2.ipynb": "871d60931f5118c085342e11cb638336", "plot_barycenter_1D.ipynb": "95708b025b6d96d97f579d30d268cbff", "plot_otda_classes.ipynb": "44bb8cd93317b5d342cd62e26d9bbe60", "plot_otda_d2.ipynb": "1a9547f07317612e1a161b7d9f07a5a8", "plot_otda_mapping.ipynb": "d335a15af828aaa3439a1c67570d79d6", "plot_gromov.ipynb": "825d79eba255314fe11469c64d38fc3d", "plot_compute_emd.ipynb": "bd95981189df6adcb113d9b360ead734", "plot_OT_1D.ipynb": "54dfea8ccb61f30729519275785c494c", "plot_gromov_barycenter.ipynb": "953e5047b886ec69ec621ec52f5e21d1", "plot_otda_semi_supervised.ipynb": "0261d339a692e339e15d3634488905cc", "plot_OT_2D_samples.ipynb": "3f125714daa35ff3cfe5dae1f71265c4"} \ No newline at end of file diff --git a/docs/source/auto_examples/auto_examples_jupyter.zip b/docs/source/auto_examples/auto_examples_jupyter.zip index 42f42de..4703026 100644 Binary files a/docs/source/auto_examples/auto_examples_jupyter.zip and b/docs/source/auto_examples/auto_examples_jupyter.zip differ diff --git a/docs/source/auto_examples/auto_examples_python.zip b/docs/source/auto_examples/auto_examples_python.zip index 0fb2cda..7c7ff86 100644 Binary files a/docs/source/auto_examples/auto_examples_python.zip and b/docs/source/auto_examples/auto_examples_python.zip differ diff --git a/docs/source/auto_examples/images/sphx_glr_plot_gromov_001.png b/docs/source/auto_examples/images/sphx_glr_plot_gromov_001.png index 09864f2..8672249 100644 Binary files a/docs/source/auto_examples/images/sphx_glr_plot_gromov_001.png and b/docs/source/auto_examples/images/sphx_glr_plot_gromov_001.png differ diff --git a/docs/source/auto_examples/images/sphx_glr_plot_gromov_002.png b/docs/source/auto_examples/images/sphx_glr_plot_gromov_002.png index b2e3fa4..c4eb8e0 100644 Binary files a/docs/source/auto_examples/images/sphx_glr_plot_gromov_002.png and b/docs/source/auto_examples/images/sphx_glr_plot_gromov_002.png differ diff --git a/docs/source/auto_examples/images/sphx_glr_plot_gromov_003.png b/docs/source/auto_examples/images/sphx_glr_plot_gromov_003.png index 73a322d..c17d386 100644 Binary files a/docs/source/auto_examples/images/sphx_glr_plot_gromov_003.png and b/docs/source/auto_examples/images/sphx_glr_plot_gromov_003.png differ diff --git a/docs/source/auto_examples/images/thumb/sphx_glr_plot_gromov_thumb.png b/docs/source/auto_examples/images/thumb/sphx_glr_plot_gromov_thumb.png index c54f6b3..210c010 100644 Binary files a/docs/source/auto_examples/images/thumb/sphx_glr_plot_gromov_thumb.png and b/docs/source/auto_examples/images/thumb/sphx_glr_plot_gromov_thumb.png differ diff --git a/docs/source/auto_examples/index.rst b/docs/source/auto_examples/index.rst index 227c40c..9d7c0f0 100644 --- a/docs/source/auto_examples/index.rst +++ b/docs/source/auto_examples/index.rst @@ -49,13 +49,13 @@ This is a gallery of all the POT example files. .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_2D_samples_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_gromov_thumb.png - :ref:`sphx_glr_auto_examples_plot_OT_2D_samples.py` + :ref:`sphx_glr_auto_examples_plot_gromov.py` .. raw:: html @@ -65,17 +65,17 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_OT_2D_samples + /auto_examples/plot_gromov .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_compute_emd_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_2D_samples_thumb.png - :ref:`sphx_glr_auto_examples_plot_compute_emd.py` + :ref:`sphx_glr_auto_examples_plot_OT_2D_samples.py` .. raw:: html @@ -85,17 +85,17 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_compute_emd + /auto_examples/plot_OT_2D_samples .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_WDA_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_compute_emd_thumb.png - :ref:`sphx_glr_auto_examples_plot_WDA.py` + :ref:`sphx_glr_auto_examples_plot_compute_emd.py` .. raw:: html @@ -105,17 +105,17 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_WDA + /auto_examples/plot_compute_emd .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_gromov_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_WDA_thumb.png - :ref:`sphx_glr_auto_examples_plot_gromov.py` + :ref:`sphx_glr_auto_examples_plot_WDA.py` .. raw:: html @@ -125,7 +125,7 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_gromov + /auto_examples/plot_WDA .. raw:: html diff --git a/docs/source/auto_examples/plot_gromov.ipynb b/docs/source/auto_examples/plot_gromov.ipynb index 6d6b522..57d6a4a 100644 --- a/docs/source/auto_examples/plot_gromov.ipynb +++ b/docs/source/auto_examples/plot_gromov.ipynb @@ -1,54 +1,126 @@ { + "nbformat_minor": 0, + "nbformat": 4, + "metadata": { + "language_info": { + "file_extension": ".py", + "codemirror_mode": { + "version": 3, + "name": "ipython" + }, + "nbconvert_exporter": "python", + "mimetype": "text/x-python", + "version": "3.5.2", + "name": "python", + "pygments_lexer": "ipython3" + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3", + "language": "python" + } + }, "cells": [ { + "outputs": [], + "source": [ + "%matplotlib inline" + ], "execution_count": null, "metadata": { "collapsed": false }, + "cell_type": "code" + }, + { + "source": [ + "\n# Gromov-Wasserstein example\n\n\nThis example is designed to show how to use the Gromov-Wassertsein distance\ncomputation in POT.\n\n" + ], + "metadata": {}, + "cell_type": "markdown" + }, + { "outputs": [], "source": [ - "%matplotlib inline" + "# Author: Erwan Vautier \n# Nicolas Courty \n#\n# License: MIT License\n\nimport scipy as sp\nimport numpy as np\nimport matplotlib.pylab as pl\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nimport ot" ], + "execution_count": null, + "metadata": { + "collapsed": false + }, "cell_type": "code" }, { - "metadata": {}, "source": [ - "\n# Gromov-Wasserstein example\n\n\nThis example is designed to show how to use the Gromov-Wassertsein distance\ncomputation in POT.\n\n" + "Sample two Gaussian distributions (2D and 3D)\n---------------------------------------------\n\nThe Gromov-Wasserstein distance allows to compute distances with samples that\ndo not belong to the same metric space. For demonstration purpose, we sample\ntwo Gaussian distributions in 2- and 3-dimensional spaces.\n\n" ], + "metadata": {}, "cell_type": "markdown" }, { + "outputs": [], + "source": [ + "n_samples = 30 # nb samples\n\nmu_s = np.array([0, 0])\ncov_s = np.array([[1, 0], [0, 1]])\n\nmu_t = np.array([4, 4, 4])\ncov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\n\n\nxs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)\nP = sp.linalg.sqrtm(cov_t)\nxt = np.random.randn(n_samples, 3).dot(P) + mu_t" + ], "execution_count": null, "metadata": { "collapsed": false }, + "cell_type": "code" + }, + { + "source": [ + "Plotting the distributions\n--------------------------\n\n" + ], + "metadata": {}, + "cell_type": "markdown" + }, + { "outputs": [], "source": [ - "# Author: Erwan Vautier \n# Nicolas Courty \n#\n# License: MIT License\n\nimport scipy as sp\nimport numpy as np\nimport matplotlib.pylab as pl\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nimport ot\n\n\n#\n# Sample two Gaussian distributions (2D and 3D)\n# ---------------------------------------------\n#\n# The Gromov-Wasserstein distance allows to compute distances with samples that\n# do not belong to the same metric space. For demonstration purpose, we sample\n# two Gaussian distributions in 2- and 3-dimensional spaces.\n\n\nn_samples = 30 # nb samples\n\nmu_s = np.array([0, 0])\ncov_s = np.array([[1, 0], [0, 1]])\n\nmu_t = np.array([4, 4, 4])\ncov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\n\n\nxs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)\nP = sp.linalg.sqrtm(cov_t)\nxt = np.random.randn(n_samples, 3).dot(P) + mu_t\n\n\n#\n# Plotting the distributions\n# --------------------------\n\n\nfig = pl.figure()\nax1 = fig.add_subplot(121)\nax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\nax2 = fig.add_subplot(122, projection='3d')\nax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\npl.show()\n\n\n#\n# Compute distance kernels, normalize them and then display\n# ---------------------------------------------------------\n\n\nC1 = sp.spatial.distance.cdist(xs, xs)\nC2 = sp.spatial.distance.cdist(xt, xt)\n\nC1 /= C1.max()\nC2 /= C2.max()\n\npl.figure()\npl.subplot(121)\npl.imshow(C1)\npl.subplot(122)\npl.imshow(C2)\npl.show()\n\n#\n# Compute Gromov-Wasserstein plans and distance\n# ---------------------------------------------\n\np = ot.unif(n_samples)\nq = ot.unif(n_samples)\n\ngw0, log0 = ot.gromov.gromov_wasserstein(\n C1, C2, p, q, 'square_loss', verbose=True, log=True)\n\ngw, log = ot.gromov.entropic_gromov_wasserstein(\n C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True)\n\n\nprint('Gromov-Wasserstein distances: ' + str(log0['gw_dist']))\nprint('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist']))\n\n\npl.figure(1, (10, 5))\n\npl.subplot(1, 2, 1)\npl.imshow(gw0, cmap='jet')\npl.title('Gromov Wasserstein')\n\npl.subplot(1, 2, 2)\npl.imshow(gw, cmap='jet')\npl.title('Entropic Gromov Wasserstein')\n\npl.show()" + "fig = pl.figure()\nax1 = fig.add_subplot(121)\nax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\nax2 = fig.add_subplot(122, projection='3d')\nax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\npl.show()" ], + "execution_count": null, + "metadata": { + "collapsed": false + }, "cell_type": "code" - } - ], - "metadata": { - "language_info": { - "name": "python", - "codemirror_mode": { - "name": "ipython", - "version": 3 + }, + { + "source": [ + "Compute distance kernels, normalize them and then display\n---------------------------------------------------------\n\n" + ], + "metadata": {}, + "cell_type": "markdown" + }, + { + "outputs": [], + "source": [ + "C1 = sp.spatial.distance.cdist(xs, xs)\nC2 = sp.spatial.distance.cdist(xt, xt)\n\nC1 /= C1.max()\nC2 /= C2.max()\n\npl.figure()\npl.subplot(121)\npl.imshow(C1)\npl.subplot(122)\npl.imshow(C2)\npl.show()" + ], + "execution_count": null, + "metadata": { + "collapsed": false }, - "nbconvert_exporter": "python", - "version": "3.5.2", - "pygments_lexer": "ipython3", - "file_extension": ".py", - "mimetype": "text/x-python" + "cell_type": "code" }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3", - "language": "python" + { + "source": [ + "Compute Gromov-Wasserstein plans and distance\n---------------------------------------------\n\n" + ], + "metadata": {}, + "cell_type": "markdown" + }, + { + "outputs": [], + "source": [ + "p = ot.unif(n_samples)\nq = ot.unif(n_samples)\n\ngw0, log0 = ot.gromov.gromov_wasserstein(\n C1, C2, p, q, 'square_loss', verbose=True, log=True)\n\ngw, log = ot.gromov.entropic_gromov_wasserstein(\n C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True)\n\n\nprint('Gromov-Wasserstein distances: ' + str(log0['gw_dist']))\nprint('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist']))\n\n\npl.figure(1, (10, 5))\n\npl.subplot(1, 2, 1)\npl.imshow(gw0, cmap='jet')\npl.title('Gromov Wasserstein')\n\npl.subplot(1, 2, 2)\npl.imshow(gw, cmap='jet')\npl.title('Entropic Gromov Wasserstein')\n\npl.show()" + ], + "execution_count": null, + "metadata": { + "collapsed": false + }, + "cell_type": "code" } - }, - "nbformat_minor": 0, - "nbformat": 4 + ] } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_gromov.py b/docs/source/auto_examples/plot_gromov.py index 9188da9..5cd40f6 100644 --- a/docs/source/auto_examples/plot_gromov.py +++ b/docs/source/auto_examples/plot_gromov.py @@ -19,7 +19,7 @@ import matplotlib.pylab as pl from mpl_toolkits.mplot3d import Axes3D # noqa import ot - +############################################################################# # # Sample two Gaussian distributions (2D and 3D) # --------------------------------------------- @@ -42,7 +42,7 @@ xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s) P = sp.linalg.sqrtm(cov_t) xt = np.random.randn(n_samples, 3).dot(P) + mu_t - +############################################################################# # # Plotting the distributions # -------------------------- @@ -55,7 +55,7 @@ ax2 = fig.add_subplot(122, projection='3d') ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r') pl.show() - +############################################################################# # # Compute distance kernels, normalize them and then display # --------------------------------------------------------- @@ -74,6 +74,7 @@ pl.subplot(122) pl.imshow(C2) pl.show() +############################################################################# # # Compute Gromov-Wasserstein plans and distance # --------------------------------------------- diff --git a/docs/source/auto_examples/plot_gromov.rst b/docs/source/auto_examples/plot_gromov.rst index ad29f7a..131861f 100644 --- a/docs/source/auto_examples/plot_gromov.rst +++ b/docs/source/auto_examples/plot_gromov.rst @@ -12,77 +12,38 @@ computation in POT. +.. code-block:: python -.. rst-class:: sphx-glr-horizontal - - - * - .. image:: /auto_examples/images/sphx_glr_plot_gromov_001.png - :scale: 47 + # Author: Erwan Vautier + # Nicolas Courty + # + # License: MIT License - * + import scipy as sp + import numpy as np + import matplotlib.pylab as pl + from mpl_toolkits.mplot3d import Axes3D # noqa + import ot - .. image:: /auto_examples/images/sphx_glr_plot_gromov_002.png - :scale: 47 -.. rst-class:: sphx-glr-script-out - Out:: - It. |Loss |Delta loss - -------------------------------- - 0|4.042674e-02|0.000000e+00 - 1|2.432476e-02|-6.619583e-01 - 2|2.170023e-02|-1.209448e-01 - 3|1.941223e-02|-1.178640e-01 - 4|1.823606e-02|-6.449667e-02 - 5|1.446641e-02|-2.605800e-01 - 6|1.184011e-02|-2.218140e-01 - 7|1.173274e-02|-9.150805e-03 - 8|1.173127e-02|-1.253458e-04 - 9|1.173126e-02|-1.256842e-06 - 10|1.173126e-02|-1.256876e-08 - 11|1.173126e-02|-1.256885e-10 - It. |Err - ------------------- - 0|7.034302e-02| - 10|1.044218e-03| - 20|5.426783e-08| - 30|3.532029e-12| - Gromov-Wasserstein distances: 0.0117312557987 - Entropic Gromov-Wasserstein distances: 0.0101639418389 +Sample two Gaussian distributions (2D and 3D) +--------------------------------------------- +The Gromov-Wasserstein distance allows to compute distances with samples that +do not belong to the same metric space. For demonstration purpose, we sample +two Gaussian distributions in 2- and 3-dimensional spaces. -| .. code-block:: python - # Author: Erwan Vautier - # Nicolas Courty - # - # License: MIT License - - import scipy as sp - import numpy as np - import matplotlib.pylab as pl - from mpl_toolkits.mplot3d import Axes3D # noqa - import ot - - - # - # Sample two Gaussian distributions (2D and 3D) - # --------------------------------------------- - # - # The Gromov-Wasserstein distance allows to compute distances with samples that - # do not belong to the same metric space. For demonstration purpose, we sample - # two Gaussian distributions in 2- and 3-dimensional spaces. - n_samples = 30 # nb samples @@ -98,9 +59,18 @@ computation in POT. xt = np.random.randn(n_samples, 3).dot(P) + mu_t - # - # Plotting the distributions - # -------------------------- + + + + + +Plotting the distributions +-------------------------- + + + +.. code-block:: python + fig = pl.figure() @@ -111,9 +81,21 @@ computation in POT. pl.show() - # - # Compute distance kernels, normalize them and then display - # --------------------------------------------------------- + + +.. image:: /auto_examples/images/sphx_glr_plot_gromov_001.png + :align: center + + + + +Compute distance kernels, normalize them and then display +--------------------------------------------------------- + + + +.. code-block:: python + C1 = sp.spatial.distance.cdist(xs, xs) @@ -129,9 +111,22 @@ computation in POT. pl.imshow(C2) pl.show() - # - # Compute Gromov-Wasserstein plans and distance - # --------------------------------------------- + + + +.. image:: /auto_examples/images/sphx_glr_plot_gromov_002.png + :align: center + + + + +Compute Gromov-Wasserstein plans and distance +--------------------------------------------- + + + +.. code-block:: python + p = ot.unif(n_samples) q = ot.unif(n_samples) @@ -159,7 +154,40 @@ computation in POT. pl.show() -**Total running time of the script:** ( 0 minutes 1.465 seconds) + + +.. image:: /auto_examples/images/sphx_glr_plot_gromov_003.png + :align: center + + +.. rst-class:: sphx-glr-script-out + + Out:: + + It. |Loss |Delta loss + -------------------------------- + 0|4.517558e-02|0.000000e+00 + 1|2.563483e-02|-7.622736e-01 + 2|2.443903e-02|-4.892972e-02 + 3|2.231600e-02|-9.513496e-02 + 4|1.676188e-02|-3.313541e-01 + 5|1.464792e-02|-1.443180e-01 + 6|1.454315e-02|-7.204526e-03 + 7|1.454142e-02|-1.185811e-04 + 8|1.454141e-02|-1.190466e-06 + 9|1.454141e-02|-1.190512e-08 + 10|1.454141e-02|-1.190520e-10 + It. |Err + ------------------- + 0|6.743761e-02| + 10|5.477003e-04| + 20|2.461503e-08| + 30|1.205155e-11| + Gromov-Wasserstein distances: 0.014541405718693563 + Entropic Gromov-Wasserstein distances: 0.015800739725237274 + + +**Total running time of the script:** ( 0 minutes 1.448 seconds) diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py index 9188da9..5cd40f6 100644 --- a/examples/plot_gromov.py +++ b/examples/plot_gromov.py @@ -19,7 +19,7 @@ import matplotlib.pylab as pl from mpl_toolkits.mplot3d import Axes3D # noqa import ot - +############################################################################# # # Sample two Gaussian distributions (2D and 3D) # --------------------------------------------- @@ -42,7 +42,7 @@ xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s) P = sp.linalg.sqrtm(cov_t) xt = np.random.randn(n_samples, 3).dot(P) + mu_t - +############################################################################# # # Plotting the distributions # -------------------------- @@ -55,7 +55,7 @@ ax2 = fig.add_subplot(122, projection='3d') ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r') pl.show() - +############################################################################# # # Compute distance kernels, normalize them and then display # --------------------------------------------------------- @@ -74,6 +74,7 @@ pl.subplot(122) pl.imshow(C2) pl.show() +############################################################################# # # Compute Gromov-Wasserstein plans and distance # --------------------------------------------- diff --git a/notebooks/plot_gromov.ipynb b/notebooks/plot_gromov.ipynb index 87937f0..6a237e6 100644 --- a/notebooks/plot_gromov.ipynb +++ b/notebooks/plot_gromov.ipynb @@ -30,64 +30,7 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "It. |Loss |Delta loss\n", - "--------------------------------\n", - " 0|3.767714e-02|0.000000e+00\n", - " 1|2.091125e-02|-8.017640e-01\n", - " 2|1.607458e-02|-3.008895e-01\n", - " 3|1.486883e-02|-8.109274e-02\n", - " 4|1.484811e-02|-1.394896e-03\n", - " 5|1.484791e-02|-1.400744e-05\n", - " 6|1.484790e-02|-1.400803e-07\n", - " 7|1.484790e-02|-1.400805e-09\n", - " 8|1.484790e-02|-1.400768e-11\n", - "It. |Err \n", - "-------------------\n", - " 0|7.522843e-02|\n", - " 10|2.233137e-04|\n", - " 20|5.767057e-07|\n", - " 30|2.315565e-09|\n", - " 40|9.789603e-12|\n", - "Gromov-Wasserstein distances: 0.014847904422308234\n", - "Entropic Gromov-Wasserstein distances: 0.011257422087381012\n" - ] - }, - { - "data": { - "image/png": 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Vq34MqX4YpzGkeuGZKUmSpAqGKUmSpAqGKUmSpAqGKUmSpAqGKUmSpAqGKUmSpAqGKUmSpAqGKUmSpAoO2rnAOThf/3UNiug+1ygttuffpzuOxxcNZX/s3sMy6zrmb93DOm7vYRmNgmemJEmSKhimJEmSKhimJEmSKhimJEmSKhimJEmSKhimJEmSKhimJEmSKjjOlLSZFtM4Pl1jasHi2h/jIPeZ+/8kLu7H/8cBPSzz1T5sp95wxpHq8uQellnXMd8xpCZZVZiKiHXArcBdwJ2ZuaofRUnSMNjDJPVDP85MPT0zr+/DeiRpFOxhkqp4zZQkSVKF2jCVwFkRcWFErO5HQZI0RPYwSdVqP+bbLzOvjYgHA1+JiJ9k5jntBUqDKk3qAZWbk6S+mrOH2b8k9aLqzFRmXlv+3QicAew7wzInZOaq5sLOZTWbk6S+6uph9i9JvZh3mIqIbSJiu6nbwLOBH/erMEkaJHuYpH6p+ZhvOXBGREyt59TM/HJfqpKkwbOHSeqLyMzhbSxW5j2XH2hR6Rr80YEfF7K1Fy6E8ZvsX6rx3o4e+Dp74Dzs1DH/xj5so7f+5dAIkiRJFQxTkiRJFQxTkiRJFQxTkiRJFQxTkiRJFQxTkiRJFQxTkiRJFWr/Nt/QOV7RZPL/RQvdw/MvO5e5Mj41hEo0jiZlHKmX5i6dy3w0rh18ITus6V7m5h6WGRLPTEmSJFUwTEmSJFUwTEmSJFUwTEmSJFUwTEmSJFUwTEmSJFUwTEmSJFUwTEmSJFUYq0E7uwbkBAd/lDSeXswnO5d5m+9fNeZ25eoelhrC8/jmtw5+G33kkS1JklTBMCVJklTBMCVJklTBMCVJklTBMCVJklTBMCVJklTBMCVJklRhrMaZcgwpLQSOl7Y4vS3+tHOZC3junPNX+bzQiL0txuUcy9Z9WMcdfVhHb8Zlr0mSJE0kw5QkSVIFw5QkSVIFw5QkSVIFw5QkSVIFw5QkSVIFw5QkSVIFw5QkSVKFzkE7I+JE4HnAxszcu0zbCfgksDuwDjg4M28aXJla6BbSQJeTUudiMbwedl7nEg7Kuamz80tzzt8/njOkShaPx+SBc87/QXx5SJV0uWVI21nal7X0cmbqJGD63n8z8LXM3BP4WrkvSePoJOxhkgaoM0xl5jnAjdMmHwScXG6fDDy/z3VJUl/YwyQN2nyvmVqemevL7euA5X2qR5KGwR4mqW+qL0DPzARytvkRsToiLoiIC+C3tZuTpL6aq4fZvyT1Yr5hakNErAAo/26cbcHMPCEzV2XmKlg2z81JUl/11MPsX5J6Md8wdSZweLl9OPC5/pQjSUNhD5PUN51hKiI+DpwLPCoiromIVwBvB54VEZcDB5T7kjR27GGSBq1znKnMPHSWWc/scy1axBbb2Exd42ottv0xSPaw0ehl7Lj9w+f5sI3POFLj4o6+rMUR0CVJkioYpiRJkioYpiRJkioYpiRJkioYpiRJkioYpiRJkioYpiRJkioYpiRJkip0DtqpwXHgxsXL/1stdL09x7fvmH9LP0rpdFxHLz7G41UdPDMlSZJUwTAlSZJUwTAlSZJUwTAlSZJUwTAlSZJUwTAlSZJUwTAlSZJUwXGmRmghjTXkmFnS4tKfY34440h1cRwp1fLMlCRJUgXDlCRJUgXDlCRJUgXDlCRJUgXDlCRJUgXDlCRJUgXDlCRJUgXDlCRJUgUH7ZwnB6nc1GL7faVJ1o/+5TEv3cszU5IkSRUMU5IkSRUMU5IkSRUMU5IkSRUMU5IkSRUMU5IkSRUMU5IkSRU6x5mKiBOB5wEbM3PvMm0NcCTwq7LYWzLzi4Mqst8cY0VaPCaphx3X0ZsAjulD77F/TaqlHfPvGEoVk2PrHpa5vS9b6uXM1EnAgTNMf3dm7lN+Rt6EJGkWJ2EPkzRAnWEqM88BbhxCLZLUd/YwSYNWc83UayLihxFxYkTs2LeKJGk47GGS+mK+Yep44BHAPsB64LjZFoyI1RFxQURcAL+d5+Ykqa966mH2L0m9mFeYyswNmXlXZt4NfAjYd45lT8jMVZm5CpbNt05J6ptee5j9S1Iv5hWmImJF6+4LgB/3pxxJGjx7mKR+6mVohI8D+wM7R8Q1wLHA/hGxD5DAOuCoAdYoSfNmD5M0aJGZw9tYrExYPbTtSdP1Y4wxba61FzYfk002+9fiZd9YzHrrX46ALkmSVMEwJUmSVMEwJUmSVMEwJUmSVMEwJUmSVMEwJUmSVMEwJUmSVMEwJUmSVMFBOyVV6R7QEAftlDR0+bS5exNA/KwjA10TDtopSZI0aIYpSZKkCoYpSZKkCoYpSZKkCoYpSZKkCoYpSZKkCoYpSZKkCluMuoBx1DVuDsBajh1CJdL46z4Wuo8naf6Wd8zfMJQqan2wh+PkVb7ubJb4Zi/7a01ftuWZKUmSpAqGKUmSpAqGKUmSpAqGKUmSpAqGKUmSpAqGKUmSpAqGKUmSpAqGKUmSpAoO2jkDB+RcvBywVZo0kzEoZ5d+DMhp/xodz0xJkiRVMExJkiRVMExJkiRVMExJkiRVMExJkiRVMExJkiRVMExJkiRVcJwpTYyuMVT6MX6KY7BI4yPf0z1uUhztMTtlbPrXG9Z0L/POHpaZIJ1npiJi14j4RkRcGhGXRMTryvSdIuIrEXF5+XfHwZcrSb2zf0kahl4+5rsTOCYz9wKeCLw6IvYC3gx8LTP3BL5W7kvSOLF/SRq4zjCVmesz86Jy+1bgMmAX4CDg5LLYycDzB1WkJM2H/UvSMGzWNVMRsTvwWOA8YHlmri+zrgOWz/KY1cDq5t4D5lelJFWyf0kalJ6/zRcR2wKnA0dn5i3teZmZQM70uMw8ITNXZeYqWFZVrCTNh/1L0iD1FKYiYilNI/pYZn6mTN4QESvK/BXAxsGUKEnzZ/+SNGi9fJsvgA8Dl2Xmu1qzzgQOL7cPBz7X//Ikaf7sX5KGoZdrpp4MHAb8KCIuLtPeArwdOC0iXgFcBRw8mBIlad7sX5IGLprLBYa0sViZ91zLucB1DTAJYzTAmjRQay9srjmabOPTvx7XMf+i6i1c0kP/erT9q2WnHpa5sQ/b2bpj/u192IY21Vv/8s/JSJIkVTBMSZIkVTBMSZIkVTBMSZIkVTBMSZIkVTBMSZIkVTBMSZIkVdisP3Ss3jmGlGo4TplmVz+OVJexGUPqDWu6l3lnD8sMXD/GkFrawzKTMY5Uvqe7f8XRY/Ic6xPPTEmSJFUwTEmSJFUwTEmSJFUwTEmSJFUwTEmSJFUwTEmSJFUwTEmSJFUwTEmSJFWIzBzexmJlwuqhbU/S3LoGB+3PwKBrL8zMVX1Y0UjZv1Rn+475vQzIeUc/ClkwPtjRv141xP7lmSlJkqQKhilJkqQKhilJkqQKhilJkqQKhilJkqQKhilJkqQKhilJkqQKW4y6AI3ecMYa0jjy/1ajtFseMuf8q+ITQ6qkawyoW/qwjX6so9738/Q55+8bLxxSJfX6M45Uf3hmSpIkqYJhSpIkqYJhSpIkqYJhSpIkqYJhSpIkqYJhSpIkqYJhSpIkqYJhSpIkqULnoJ0RsStwCrAcSOCEzHxvRKwBjgR+VRZ9S2Z+cVCFanAcuHEyOdhqN/vXeBvGoJxdxwksrmNlkgblnCS9jIB+J3BMZl4UEdsBF0bEV8q8d2fmOwdXniRVsX9JGrjOMJWZ64H15fatEXEZsMugC5OkWvYvScOwWddMRcTuwGOB88qk10TEDyPixIjYsc+1SVLf2L8kDUrPYSoitgVOB47OzFuA44FHAPvQvPM7bpbHrY6ICyLiAvhtH0qWpM1j/5I0SD2FqYhYStOIPpaZnwHIzA2ZeVdm3g18CNh3psdm5gmZuSozV8GyftUtST2xf0katM4wFREBfBi4LDPf1Zq+orXYC4Af9788SZo/+5ekYejl23xPBg4DfhQRF5dpbwEOjYh9aL5uvA44aiAVStL82b8kDVxk5vA2FisTVg9te4uBYw1p/K29sPmYbLKNT/96bcf8fxpKFf2xfcf8W3pYx04d82/ssZb5yz27x7KKy+3Fk6m3/uUI6JIkSRUMU5IkSRUMU5IkSRUMU5IkSRUMU5IkSRUMU5IkSRUMU5IkSRUMU5IkSRV6GQFdY8xBOe/VNYApuL807roGsYTJGpSzSy+DcnYZ/KCcXRyQc0ztvKZzkfzjmHN+fL23TXlmSpIkqYJhSpIkqYJhSpIkqYJhSpIkqYJhSpIkqYJhSpIkqYJhSpIkqUJk5vA2FisTVg90G441JI2btRdm5qpRV1FrGP1rfPQy3lU/xojS5jiu4/XtGF/bBqC3/uWZKUmSpAqGKUmSpAqGKUmSpAqGKUmSpAqGKUmSpAqGKUmSpAqGKUmSpAqGKUmSpApDHbRzZUTONeSdg2lKC9HCGLTT/iUtRg7aKUmSNHCGKUmSpAqGKUmSpAqGKUmSpAqGKUmSpAqGKUmSpAqGKUmSpApDHWcqIn4FXNWatDNw/dAKmL9JqRMmp1br7L9xrXW3zHzQqIuoNUP/gvHd59NZZ39NSp0wObWOa5099a+hhqn7bDzigkkYzG9S6oTJqdU6+2+Sal0oJmWfW2d/TUqdMDm1Tkqds/FjPkmSpAqGKUmSpAqjDlMnjHj7vZqUOmFyarXO/pukWheKSdnn1tlfk1InTE6tk1LnjEZ6zZQkSdKkG/WZKUmSpIk2sjAVEQdGxE8j4oqIePOo6ugSEesi4kcRcXFEXDDqetoi4sSI2BgRP25N2ykivhIRl5d/dxxljaWmmepcExHXlv16cUQ8d5Q1lpp2jYhvRMSlEXFJRLyuTB+rfTpHnWO3Txcq+1c9+1d/2b9GayQf80XEEuDfgWcB1wDnA4dm5qVDL6ZDRKwDVmXm2I1/ERFPBW4DTsnMvcu0dwA3ZubbS5PfMTPfNIZ1rgFuy8x3jrK2tohYAazIzIsiYjvgQuD5wBGM0T6do86DGbN9uhDZv/rD/tVf9q/RGtWZqX2BKzLzysz8D+ATwEEjqmViZeY5wI3TJh8EnFxun0zzJB2pWeocO5m5PjMvKrdvBS4DdmHM9ukcdWo47F99YP/qL/vXaI0qTO0CXN26fw3juzMTOCsiLoyI1aMupgfLM3N9uX0dsHyUxXR4TUT8sJxGH/np/LaI2B14LHAeY7xPp9UJY7xPFxD71+CM7bE2g7E91uxfw+cF6N32y8zHAc8BXl1O+U6EbD7DHdevax4PPALYB1gPHDfacu4VEdsCpwNHZ+Yt7XnjtE9nqHNs96lGxv41GGN7rNm/RmNUYepaYNfW/YeWaWMnM68t/24EzqA5xT/ONpTPpKc+m9444npmlJkbMvOuzLwb+BBjsl8jYinNAf6xzPxMmTx2+3SmOsd1ny5A9q/BGbtjbSbjeqzZv0ZnVGHqfGDPiPiDiNgSOAQ4c0S1zCoitikXyBER2wDPBn4896NG7kzg8HL7cOBzI6xlVlMHd/ECxmC/RkQAHwYuy8x3tWaN1T6drc5x3KcLlP1rcMbqWJvNOB5r9q/RGtmgneVrj+8BlgAnZuZbR1LIHCLi4TTv5gC2AE4dpzoj4uPA/jR/bXsDcCzwWeA04GE0f+H+4Mwc6cWTs9S5P83p3ATWAUe1PtcfiYjYD/gW8CPg7jL5LTSf54/NPp2jzkMZs326UNm/6tm/+sv+NVqOgC5JklTBC9AlSZIqGKYkSZIqGKYkSZIqGKYkSZIqGKYkSZIqGKYkSZIqGKYkSZIqGKYkSZIq/H+n5tnRrK1eIgAAAABJRU5ErkJggg==\n", - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Author: Erwan Vautier \n", "# Nicolas Courty \n", @@ -98,18 +41,30 @@ "import numpy as np\n", "import matplotlib.pylab as pl\n", "from mpl_toolkits.mplot3d import Axes3D # noqa\n", - "import ot\n", - "\n", - "\n", - "#\n", - "# Sample two Gaussian distributions (2D and 3D)\n", - "# ---------------------------------------------\n", - "#\n", - "# The Gromov-Wasserstein distance allows to compute distances with samples that\n", - "# do not belong to the same metric space. For demonstration purpose, we sample\n", - "# two Gaussian distributions in 2- and 3-dimensional spaces.\n", - "\n", + "import ot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Sample two Gaussian distributions (2D and 3D)\n", + "---------------------------------------------\n", "\n", + "The Gromov-Wasserstein distance allows to compute distances with samples that\n", + "do not belong to the same metric space. For demonstration purpose, we sample\n", + "two Gaussian distributions in 2- and 3-dimensional spaces.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ "n_samples = 30 # nb samples\n", "\n", "mu_s = np.array([0, 0])\n", @@ -121,27 +76,73 @@ "\n", "xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)\n", "P = sp.linalg.sqrtm(cov_t)\n", - "xt = np.random.randn(n_samples, 3).dot(P) + mu_t\n", - "\n", - "\n", - "#\n", - "# Plotting the distributions\n", - "# --------------------------\n", - "\n", - "\n", + "xt = np.random.randn(n_samples, 3).dot(P) + mu_t" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plotting the distributions\n", + "--------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "fig = pl.figure()\n", "ax1 = fig.add_subplot(121)\n", "ax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", "ax2 = fig.add_subplot(122, projection='3d')\n", "ax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\n", - "pl.show()\n", - "\n", - "\n", - "#\n", - "# Compute distance kernels, normalize them and then display\n", - "# ---------------------------------------------------------\n", - "\n", - "\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute distance kernels, normalize them and then display\n", + "---------------------------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "C1 = sp.spatial.distance.cdist(xs, xs)\n", "C2 = sp.spatial.distance.cdist(xt, xt)\n", "\n", @@ -153,12 +154,68 @@ "pl.imshow(C1)\n", "pl.subplot(122)\n", "pl.imshow(C2)\n", - "pl.show()\n", - "\n", - "#\n", - "# Compute Gromov-Wasserstein plans and distance\n", - "# ---------------------------------------------\n", - "\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute Gromov-Wasserstein plans and distance\n", + "---------------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It. |Loss |Delta loss\n", + "--------------------------------\n", + " 0|3.731009e-02|0.000000e+00\n", + " 1|1.846414e-02|-1.020678e+00\n", + " 2|1.752056e-02|-5.385587e-02\n", + " 3|1.470479e-02|-1.914863e-01\n", + " 4|1.371582e-02|-7.210441e-02\n", + " 5|1.263823e-02|-8.526431e-02\n", + " 6|1.190590e-02|-6.151022e-02\n", + " 7|1.014664e-02|-1.733837e-01\n", + " 8|1.011396e-02|-3.230516e-03\n", + " 9|1.011363e-02|-3.245532e-05\n", + " 10|1.011363e-02|-3.245682e-07\n", + " 11|1.011363e-02|-3.245683e-09\n", + " 12|1.011363e-02|-3.245598e-11\n", + "It. |Err \n", + "-------------------\n", + " 0|7.847531e-02|\n", + " 10|2.424218e-03|\n", + " 20|4.250670e-02|\n", + " 30|5.679949e-05|\n", + " 40|1.219762e-08|\n", + " 50|1.121975e-11|\n", + "Gromov-Wasserstein distances: 0.01011363084946804\n", + "Entropic Gromov-Wasserstein distances: 0.0063386519916289385\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "p = ot.unif(n_samples)\n", "q = ot.unif(n_samples)\n", "\n", -- cgit v1.2.3 From 8c84584ca49045c3a847a04b05d72931f2b73da7 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 16 Feb 2018 15:56:47 +0100 Subject: upate doc --- docs/source/all.rst | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/docs/source/all.rst b/docs/source/all.rst index 2348785..c84d968 100644 --- a/docs/source/all.rst +++ b/docs/source/all.rst @@ -20,6 +20,13 @@ ot.bregman .. automodule:: ot.bregman :members: +ot.gromov +---------- + +.. automodule:: ot.gromov + :members: + + ot.optim -------- -- cgit v1.2.3 From 806a406e1ca2e9ca0bfdfe0516c75865e8098205 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 20 Feb 2018 16:11:51 +0100 Subject: update readme --- README.md | 5 +++++ docs/source/readme.rst | 28 +++++++++++++++++++++++++++- 2 files changed, 32 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 04994c0..520eee8 100644 --- a/README.md +++ b/README.md @@ -3,6 +3,11 @@ [![PyPI version](https://badge.fury.io/py/POT.svg)](https://badge.fury.io/py/POT) [![Build Status](https://travis-ci.org/rflamary/POT.svg?branch=master)](https://travis-ci.org/rflamary/POT) [![Documentation Status](https://readthedocs.org/projects/pot/badge/?version=latest)](http://pot.readthedocs.io/en/latest/?badge=latest) +[![Anaconda Cloud](https://anaconda.org/conda-forge/pot/badges/version.svg)](https://anaconda.org/conda-forge/pot) +[![License](https://anaconda.org/conda-forge/pot/badges/license.svg)](https://github.com/rflamary/POT/blob/master/LICENSE) +[![Anaconda downloads](https://anaconda.org/conda-forge/pot/badges/downloads.svg)](https://anaconda.org/conda-forge/pot) + + This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and machine learning. diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 065093e..d8028ab 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -1,7 +1,8 @@ POT: Python Optimal Transport ============================= -|PyPI version| |Build Status| |Documentation Status| +|PyPI version| |Build Status| |Documentation Status| |Anaconda Cloud| +|License| |Anaconda downloads| This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and @@ -172,6 +173,10 @@ want a quick look: images `__ - `Wasserstein Discriminant Analysis `__ +- `Gromov + Wasserstein `__ +- `Gromov Wasserstein + Barycenter `__ You can also see the notebooks with `Jupyter nbviewer `__. @@ -192,6 +197,7 @@ The contributors to this library are: Gayraud `__ - `Stanislas Chambon `__ - `Antoine Rolet `__ +- Erwan Vautier (Gromov-Wasserstein) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various @@ -204,6 +210,20 @@ languages): - `Marco Cuturi `__ (Sinkhorn Knopp in Matlab/Cuda) +Using and citing the toolbox +---------------------------- + +If you use this toolbox in your research and find it useful, please cite +POT using the following bibtex reference: + +:: + + @article{flamary2017pot, + title={POT Python Optimal Transport library}, + author={Flamary, R{\'e}mi and Courty, Nicolas}, + year={2017} + } + Contributions and code of conduct --------------------------------- @@ -292,3 +312,9 @@ International Conference on Machine Learning (ICML). 2016. :target: https://travis-ci.org/rflamary/POT .. |Documentation Status| image:: https://readthedocs.org/projects/pot/badge/?version=latest :target: http://pot.readthedocs.io/en/latest/?badge=latest +.. |Anaconda Cloud| image:: https://anaconda.org/conda-forge/pot/badges/version.svg + :target: https://anaconda.org/conda-forge/pot +.. |License| image:: https://anaconda.org/conda-forge/pot/badges/license.svg + :target: https://github.com/rflamary/POT/blob/master/LICENSE +.. |Anaconda downloads| image:: https://anaconda.org/conda-forge/pot/badges/downloads.svg + :target: https://anaconda.org/conda-forge/pot -- cgit v1.2.3 From f31d7259bd8d02774301d478d8e2027abd8b10cf Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 20 Feb 2018 16:17:42 +0100 Subject: add badges --- README.md | 7 +++---- 1 file changed, 3 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 6756244..8a9d7fa 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,11 @@ # POT: Python Optimal Transport [![PyPI version](https://badge.fury.io/py/POT.svg)](https://badge.fury.io/py/POT) +[![Anaconda Cloud](https://anaconda.org/conda-forge/pot/badges/version.svg)](https://anaconda.org/conda-forge/pot) [![Build Status](https://travis-ci.org/rflamary/POT.svg?branch=master)](https://travis-ci.org/rflamary/POT) [![Documentation Status](https://readthedocs.org/projects/pot/badge/?version=latest)](http://pot.readthedocs.io/en/latest/?badge=latest) -[![Anaconda Cloud](https://anaconda.org/conda-forge/pot/badges/version.svg)](https://anaconda.org/conda-forge/pot) -[![License](https://anaconda.org/conda-forge/pot/badges/license.svg)](https://github.com/rflamary/POT/blob/master/LICENSE) [![Anaconda downloads](https://anaconda.org/conda-forge/pot/badges/downloads.svg)](https://anaconda.org/conda-forge/pot) - +[![License](https://anaconda.org/conda-forge/pot/badges/license.svg)](https://github.com/rflamary/POT/blob/master/LICENSE) @@ -206,4 +205,4 @@ You can also post bug reports and feature requests in Github issues. Make sure t [12] Gabriel Peyré, Marco Cuturi, and Justin Solomon, [Gromov-Wasserstein averaging of kernel and distance matrices](http://proceedings.mlr.press/v48/peyre16.html) International Conference on Machine Learning (ICML). 2016. -[13] Mémoli, Facundo. [Gromov–Wasserstein distances and the metric approach to object matching](https://media.adelaide.edu.au/acvt/Publications/2011/2011-Gromov%E2%80%93Wasserstein%20Distances%20and%20the%20Metric%20Approach%20to%20Object%20Matching.pdf). Foundations of computational mathematics 11.4 (2011): 417-487. \ No newline at end of file +[13] Mémoli, Facundo. [Gromov–Wasserstein distances and the metric approach to object matching](https://media.adelaide.edu.au/acvt/Publications/2011/2011-Gromov%E2%80%93Wasserstein%20Distances%20and%20the%20Metric%20Approach%20to%20Object%20Matching.pdf). Foundations of computational mathematics 11.4 (2011): 417-487. -- cgit v1.2.3 From cb739f625921e7fc19113d6d758e27ac69eac24b Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 20 Mar 2018 15:05:57 +0100 Subject: add linear mapping function --- examples/plot_otda_linear_mapping.py | 54 ++++++++++++++++++++++++++++++++++++ ot/da.py | 36 ++++++++++++++++++++++++ 2 files changed, 90 insertions(+) create mode 100644 examples/plot_otda_linear_mapping.py diff --git a/examples/plot_otda_linear_mapping.py b/examples/plot_otda_linear_mapping.py new file mode 100644 index 0000000..eff2648 --- /dev/null +++ b/examples/plot_otda_linear_mapping.py @@ -0,0 +1,54 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Mar 20 14:31:15 2018 + +@author: rflamary +""" + +import numpy as np +import pylab as pl +import ot + + + +#%% + + +n=1000 +d=2 +sigma=.1 + +angles=np.random.rand(n,1)*2*np.pi +xs=np.concatenate((np.sin(angles),np.cos(angles)),axis=1)+sigma*np.random.randn(n,2) + +xs[:n//2,1]+=2 + +anglet=np.random.rand(n,1)*2*np.pi +xt=np.concatenate((np.sin(anglet),np.cos(anglet)),axis=1)+sigma*np.random.randn(n,2) +xt[:n//2,1]+=2 + + +A=np.array([[1.5,.7],[.7,1.5]]) +b=np.array([[4,2]]) +xt=xt.dot(A)+b + +#%% + +pl.figure(1,(5,5)) +pl.plot(xs[:,0],xs[:,1],'+') +pl.plot(xt[:,0],xt[:,1],'o') + +#%% + +Ae,be=ot.da.OT_mapping_linear(xs,xt) + +xst=xs.dot(Ae)+be + +##%% + +pl.figure(1,(5,5)) +pl.clf() +pl.plot(xs[:,0],xs[:,1],'+') +pl.plot(xt[:,0],xt[:,1],'o') +pl.plot(xst[:,0],xst[:,1],'+') \ No newline at end of file diff --git a/ot/da.py b/ot/da.py index c688654..63bee5a 100644 --- a/ot/da.py +++ b/ot/da.py @@ -10,6 +10,7 @@ Domain adaptation with optimal transport # License: MIT License import numpy as np +import scipy.linalg as linalg from .bregman import sinkhorn from .lp import emd @@ -633,6 +634,41 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', return G, L +def OT_mapping_linear(xs, xt, reg=1e-6,ws=None,wt=None,log=False): + """ return OT linear operator between samples""" + + d=xs.shape[1] + + mxs=xs.mean(0,keepdims=True) + mxt=xt.mean(0,keepdims=True) + + + if ws is None: + ws=np.ones((xs.shape[0],1))/xs.shape[0] + + if wt is None: + wt=np.ones((xt.shape[0],1))/xt.shape[0] + + Cs=(xs*ws).T.dot(xs)/ws.sum()+reg*np.eye(d) + Ct=(xt*wt).T.dot(xt)/wt.sum()+reg*np.eye(d) + + + Cs12=linalg.sqrtm(Cs) + Cs_12=linalg.inv(Cs12) + + M0=linalg.sqrtm(Cs12.dot(Ct.dot(Cs12))) + + A=Cs_12.dot(M0.dot(Cs_12)).T + + b=mxt-mxs.dot(A) + + if log: + pass + else: + return A,b + + + @deprecated("The class OTDA is deprecated in 0.3.1 and will be " "removed in 0.5" "\n\tfor standard transport use class EMDTransport instead.") -- cgit v1.2.3 From 8fc9fce6c920c646ea7324ac0af54ad53e9aa1bf Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 20 Mar 2018 16:21:47 +0100 Subject: add class LinearTransport --- examples/plot_otda_linear_mapping.py | 35 ++++- ot/da.py | 239 ++++++++++++++++++++++++++++++++++- 2 files changed, 265 insertions(+), 9 deletions(-) diff --git a/examples/plot_otda_linear_mapping.py b/examples/plot_otda_linear_mapping.py index eff2648..44aa9c5 100644 --- a/examples/plot_otda_linear_mapping.py +++ b/examples/plot_otda_linear_mapping.py @@ -9,7 +9,7 @@ Created on Tue Mar 20 14:31:15 2018 import numpy as np import pylab as pl import ot - +import scipy.linalg as linalg #%% @@ -19,11 +19,13 @@ n=1000 d=2 sigma=.1 +# source samples angles=np.random.rand(n,1)*2*np.pi xs=np.concatenate((np.sin(angles),np.cos(angles)),axis=1)+sigma*np.random.randn(n,2) - xs[:n//2,1]+=2 + +# target samples anglet=np.random.rand(n,1)*2*np.pi xt=np.concatenate((np.sin(anglet),np.cos(anglet)),axis=1)+sigma*np.random.randn(n,2) xt[:n//2,1]+=2 @@ -43,7 +45,33 @@ pl.plot(xt[:,0],xt[:,1],'o') Ae,be=ot.da.OT_mapping_linear(xs,xt) +Ae1=linalg.inv(Ae) +be1=-be.dot(Ae1) + xst=xs.dot(Ae)+be +xts=xt.dot(Ae1)+be1 + +##%% + +pl.figure(1,(5,5)) +pl.clf() +pl.plot(xs[:,0],xs[:,1],'+') +pl.plot(xt[:,0],xt[:,1],'o') +pl.plot(xst[:,0],xst[:,1],'+') +pl.plot(xts[:,0],xts[:,1],'o') + +pl.show() + + +#%% Example class with on images + +mapping=ot.da.LinearTransport() + +mapping.fit(Xs=xs,Xt=xt) + + +xst=mapping.transform(Xs=xs) +xts=mapping.inverse_transform(Xt=xt) ##%% @@ -51,4 +79,5 @@ pl.figure(1,(5,5)) pl.clf() pl.plot(xs[:,0],xs[:,1],'+') pl.plot(xt[:,0],xt[:,1],'o') -pl.plot(xst[:,0],xst[:,1],'+') \ No newline at end of file +pl.plot(xst[:,0],xst[:,1],'+') +pl.plot(xts[:,0],xts[:,1],'o') diff --git a/ot/da.py b/ot/da.py index 63bee5a..ab5f860 100644 --- a/ot/da.py +++ b/ot/da.py @@ -634,13 +634,76 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', return G, L -def OT_mapping_linear(xs, xt, reg=1e-6,ws=None,wt=None,log=False): - """ return OT linear operator between samples""" +def OT_mapping_linear(xs, xt, reg=1e-6,ws=None,wt=None,bias=True,log=False): + """ return OT linear operator between samples + + The function estimate the optimal linear operator that align the two + empirical distributions. This is equivalent to estimating the closed + form mapping between two Gaussian distribution :math:`N(\mu_s,\Sigma_s)` + and :math:`N(\mu_t,\Sigma_t)` as proposed in [14]. + + The linear operator from source to target :math:`M` + + .. math:: + M(x)=Ax+b + + where : + + .. math:: + A=\Sigma_s^{-1/2}(\Sigma_s^{1/2}\Sigma_t\Sigma_s^{1/2})^{1/2} + \Sigma_s^{-1/2} + .. math:: + b=\mu_t-A\mu_s + + Parameters + ---------- + xs : np.ndarray (ns,d) + samples in the source domain + xt : np.ndarray (nt,d) + samples in the target domain + reg : float,optional + regularization added to the daigonals of convariances (>0) + ws : np.ndarray (ns,1), optional + weights for the source samples + wt : np.ndarray (ns,1), optional + weights for the target samples + bias: boolean, optional + estimate bias b else b=0 (default:True) + log : bool, optional + record log if True + + + Returns + ------- + A : (d x d) ndarray + Linear operator + b : (1 x d) ndarray + bias + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [14] Knott, M. and Smith, C. S. "On the optimal mapping of + distributions", Journal of Optimization Theory and Applications + Vol 43, 1984 + + + """ d=xs.shape[1] - mxs=xs.mean(0,keepdims=True) - mxt=xt.mean(0,keepdims=True) + if bias: + mxs=xs.mean(0,keepdims=True) + mxt=xt.mean(0,keepdims=True) + + xs=xs-mxs + xt=xt-mxt + else: + mxs=np.zeros((1,d)) + mxt=np.zeros((1,d)) if ws is None: @@ -658,12 +721,17 @@ def OT_mapping_linear(xs, xt, reg=1e-6,ws=None,wt=None,log=False): M0=linalg.sqrtm(Cs12.dot(Ct.dot(Cs12))) - A=Cs_12.dot(M0.dot(Cs_12)).T + A=Cs_12.dot(M0.dot(Cs_12)) b=mxt-mxs.dot(A) if log: - pass + log={} + log['Cs']=Cs + log['Ct']=Ct + log['Cs12']=Cs12 + log['Cs_12']=Cs_12 + return A,b,log else: return A,b @@ -1216,6 +1284,165 @@ class BaseTransport(BaseEstimator): return transp_Xt +class LinearTransport(BaseTransport): + """ OT linear operator between empirical distributions + + The function estimate the optimal linear operator that align the two + empirical distributions. This is equivalent to estimating the closed + form mapping between two Gaussian distribution :math:`N(\mu_s,\Sigma_s)` + and :math:`N(\mu_t,\Sigma_t)` as proposed in [14]. + + The linear operator from source to target :math:`M` + + .. math:: + M(x)=Ax+b + + where : + + .. math:: + A=\Sigma_s^{-1/2}(\Sigma_s^{1/2}\Sigma_t\Sigma_s^{1/2})^{1/2} + \Sigma_s^{-1/2} + .. math:: + b=\mu_t-A\mu_s + + Parameters + ---------- + reg : float,optional + regularization added to the daigonals of convariances (>0) + bias: boolean, optional + estimate bias b else b=0 (default:True) + log : bool, optional + record log if True + + + """ + + def __init__(self, reg=1e-8,bias=True,log=False, + distribution_estimation=distribution_estimation_uniform): + + self.bias=bias + self.log=log + self.reg=reg + self.distribution_estimation=distribution_estimation + + def fit(self, Xs=None, ys=None, Xt=None, yt=None): + """Build a coupling matrix from source and target sets of samples + (Xs, ys) and (Xt, yt) + + Parameters + ---------- + Xs : array-like, shape (n_source_samples, n_features) + The training input samples. + ys : array-like, shape (n_source_samples,) + The class labels + Xt : array-like, shape (n_target_samples, n_features) + The training input samples. + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label + + Returns + ------- + self : object + Returns self. + """ + + self.mu_s = self.distribution_estimation(Xs) + self.mu_t = self.distribution_estimation(Xt) + + + + # coupling estimation + returned_ = OT_mapping_linear(Xs,Xt,reg=self.reg, + ws=self.mu_s.reshape((-1,1)), + wt=self.mu_t.reshape((-1,1)), + bias=self.bias,log=self.log) + + # deal with the value of log + if self.log: + self.A_, self.B_, self.log_ = returned_ + else: + self.A_, self.B_, = returned_ + self.log_ = dict() + + # re compute inverse mapping + self.A1_=linalg.inv(self.A_) + self.B1_=-self.B_.dot(self.A1_) + + return self + + def transform(self, Xs=None, ys=None, Xt=None, yt=None, batch_size=128): + """Transports source samples Xs onto target ones Xt + + Parameters + ---------- + Xs : array-like, shape (n_source_samples, n_features) + The training input samples. + ys : array-like, shape (n_source_samples,) + The class labels + Xt : array-like, shape (n_target_samples, n_features) + The training input samples. + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label + batch_size : int, optional (default=128) + The batch size for out of sample inverse transform + + Returns + ------- + transp_Xs : array-like, shape (n_source_samples, n_features) + The transport source samples. + """ + + # check the necessary inputs parameters are here + if check_params(Xs=Xs): + + transp_Xs= Xs.dot(self.A_)+self.B_ + + return transp_Xs + + def inverse_transform(self, Xs=None, ys=None, Xt=None, yt=None, + batch_size=128): + """Transports target samples Xt onto target samples Xs + + Parameters + ---------- + Xs : array-like, shape (n_source_samples, n_features) + The training input samples. + ys : array-like, shape (n_source_samples,) + The class labels + Xt : array-like, shape (n_target_samples, n_features) + The training input samples. + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label + batch_size : int, optional (default=128) + The batch size for out of sample inverse transform + + Returns + ------- + transp_Xt : array-like, shape (n_source_samples, n_features) + The transported target samples. + """ + + # check the necessary inputs parameters are here + if check_params(Xt=Xt): + + transp_Xt= Xt.dot(self.A1_)+self.B1_ + + return transp_Xt + + + class SinkhornTransport(BaseTransport): """Domain Adapatation OT method based on Sinkhorn Algorithm -- cgit v1.2.3 From c1046238d826fe9cf1294f8ea60b8d44743fac78 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 20 Mar 2018 16:27:49 +0100 Subject: passing tests --- examples/plot_otda_linear_mapping.py | 74 +++++++++--------- ot/da.py | 147 +++++++++++++++++------------------ 2 files changed, 110 insertions(+), 111 deletions(-) diff --git a/examples/plot_otda_linear_mapping.py b/examples/plot_otda_linear_mapping.py index 44aa9c5..143f129 100644 --- a/examples/plot_otda_linear_mapping.py +++ b/examples/plot_otda_linear_mapping.py @@ -15,69 +15,71 @@ import scipy.linalg as linalg #%% -n=1000 -d=2 -sigma=.1 +n = 1000 +d = 2 +sigma = .1 # source samples -angles=np.random.rand(n,1)*2*np.pi -xs=np.concatenate((np.sin(angles),np.cos(angles)),axis=1)+sigma*np.random.randn(n,2) -xs[:n//2,1]+=2 +angles = np.random.rand(n, 1) * 2 * np.pi +xs = np.concatenate((np.sin(angles), np.cos(angles)), + axis=1) + sigma * np.random.randn(n, 2) +xs[:n // 2, 1] += 2 # target samples -anglet=np.random.rand(n,1)*2*np.pi -xt=np.concatenate((np.sin(anglet),np.cos(anglet)),axis=1)+sigma*np.random.randn(n,2) -xt[:n//2,1]+=2 +anglet = np.random.rand(n, 1) * 2 * np.pi +xt = np.concatenate((np.sin(anglet), np.cos(anglet)), + axis=1) + sigma * np.random.randn(n, 2) +xt[:n // 2, 1] += 2 -A=np.array([[1.5,.7],[.7,1.5]]) -b=np.array([[4,2]]) -xt=xt.dot(A)+b +A = np.array([[1.5, .7], [.7, 1.5]]) +b = np.array([[4, 2]]) +xt = xt.dot(A) + b #%% -pl.figure(1,(5,5)) -pl.plot(xs[:,0],xs[:,1],'+') -pl.plot(xt[:,0],xt[:,1],'o') +pl.figure(1, (5, 5)) +pl.plot(xs[:, 0], xs[:, 1], '+') +pl.plot(xt[:, 0], xt[:, 1], 'o') #%% -Ae,be=ot.da.OT_mapping_linear(xs,xt) +Ae, be = ot.da.OT_mapping_linear(xs, xt) -Ae1=linalg.inv(Ae) -be1=-be.dot(Ae1) +Ae1 = linalg.inv(Ae) +be1 = -be.dot(Ae1) -xst=xs.dot(Ae)+be -xts=xt.dot(Ae1)+be1 +xst = xs.dot(Ae) + be +xts = xt.dot(Ae1) + be1 -##%% +# %% -pl.figure(1,(5,5)) +pl.figure(1, (5, 5)) pl.clf() -pl.plot(xs[:,0],xs[:,1],'+') -pl.plot(xt[:,0],xt[:,1],'o') -pl.plot(xst[:,0],xst[:,1],'+') -pl.plot(xts[:,0],xts[:,1],'o') +pl.plot(xs[:, 0], xs[:, 1], '+') +pl.plot(xt[:, 0], xt[:, 1], 'o') +pl.plot(xst[:, 0], xst[:, 1], '+') +pl.plot(xts[:, 0], xts[:, 1], 'o') pl.show() #%% Example class with on images -mapping=ot.da.LinearTransport() +mapping = ot.da.LinearTransport() -mapping.fit(Xs=xs,Xt=xt) +mapping.fit(Xs=xs, Xt=xt) -xst=mapping.transform(Xs=xs) -xts=mapping.inverse_transform(Xt=xt) +xst = mapping.transform(Xs=xs) +xts = mapping.inverse_transform(Xt=xt) -##%% +# %% -pl.figure(1,(5,5)) +pl.figure(1, (5, 5)) pl.clf() -pl.plot(xs[:,0],xs[:,1],'+') -pl.plot(xt[:,0],xt[:,1],'o') -pl.plot(xst[:,0],xst[:,1],'+') -pl.plot(xts[:,0],xts[:,1],'o') +pl.plot(xs[:, 0], xs[:, 1], '+') +pl.plot(xt[:, 0], xt[:, 1], 'o') +pl.plot(xst[:, 0], xst[:, 1], '+') +pl.plot(xts[:, 0], xts[:, 1], 'o') diff --git a/ot/da.py b/ot/da.py index ab5f860..f789396 100644 --- a/ot/da.py +++ b/ot/da.py @@ -357,7 +357,8 @@ def joint_OT_mapping_linear(xs, xt, mu=1, eta=0.001, bias=False, verbose=False, def loss(L, G): """Compute full loss""" - return np.sum((xs1.dot(L) - ns * G.dot(xt))**2) + mu * np.sum(G * M) + eta * np.sum(sel(L - I0)**2) + return np.sum((xs1.dot(L) - ns * G.dot(xt))**2) + mu * \ + np.sum(G * M) + eta * np.sum(sel(L - I0)**2) def solve_L(G): """ solve L problem with fixed G (least square)""" @@ -557,7 +558,8 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', def loss(L, G): """Compute full loss""" - return np.sum((K1.dot(L) - ns * G.dot(xt))**2) + mu * np.sum(G * M) + eta * np.trace(L.T.dot(Kreg).dot(L)) + return np.sum((K1.dot(L) - ns * G.dot(xt))**2) + mu * \ + np.sum(G * M) + eta * np.trace(L.T.dot(Kreg).dot(L)) def solve_L_nobias(G): """ solve L problem with fixed G (least square)""" @@ -634,25 +636,26 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', return G, L -def OT_mapping_linear(xs, xt, reg=1e-6,ws=None,wt=None,bias=True,log=False): +def OT_mapping_linear(xs, xt, reg=1e-6, ws=None, + wt=None, bias=True, log=False): """ return OT linear operator between samples The function estimate the optimal linear operator that align the two - empirical distributions. This is equivalent to estimating the closed - form mapping between two Gaussian distribution :math:`N(\mu_s,\Sigma_s)` + empirical distributions. This is equivalent to estimating the closed + form mapping between two Gaussian distribution :math:`N(\mu_s,\Sigma_s)` and :math:`N(\mu_t,\Sigma_t)` as proposed in [14]. - + The linear operator from source to target :math:`M` .. math:: M(x)=Ax+b - + where : - + .. math:: A=\Sigma_s^{-1/2}(\Sigma_s^{1/2}\Sigma_t\Sigma_s^{1/2})^{1/2} \Sigma_s^{-1/2} - .. math:: + .. math:: b=\mu_t-A\mu_s Parameters @@ -666,7 +669,7 @@ def OT_mapping_linear(xs, xt, reg=1e-6,ws=None,wt=None,bias=True,log=False): ws : np.ndarray (ns,1), optional weights for the source samples wt : np.ndarray (ns,1), optional - weights for the target samples + weights for the target samples bias: boolean, optional estimate bias b else b=0 (default:True) log : bool, optional @@ -686,55 +689,52 @@ def OT_mapping_linear(xs, xt, reg=1e-6,ws=None,wt=None,bias=True,log=False): References ---------- - .. [14] Knott, M. and Smith, C. S. "On the optimal mapping of + .. [14] Knott, M. and Smith, C. S. "On the optimal mapping of distributions", Journal of Optimization Theory and Applications Vol 43, 1984 """ - d=xs.shape[1] - + d = xs.shape[1] + if bias: - mxs=xs.mean(0,keepdims=True) - mxt=xt.mean(0,keepdims=True) - - xs=xs-mxs - xt=xt-mxt + mxs = xs.mean(0, keepdims=True) + mxt = xt.mean(0, keepdims=True) + + xs = xs - mxs + xt = xt - mxt else: - mxs=np.zeros((1,d)) - mxt=np.zeros((1,d)) + mxs = np.zeros((1, d)) + mxt = np.zeros((1, d)) - if ws is None: - ws=np.ones((xs.shape[0],1))/xs.shape[0] - + ws = np.ones((xs.shape[0], 1)) / xs.shape[0] + if wt is None: - wt=np.ones((xt.shape[0],1))/xt.shape[0] - - Cs=(xs*ws).T.dot(xs)/ws.sum()+reg*np.eye(d) - Ct=(xt*wt).T.dot(xt)/wt.sum()+reg*np.eye(d) - - - Cs12=linalg.sqrtm(Cs) - Cs_12=linalg.inv(Cs12) - - M0=linalg.sqrtm(Cs12.dot(Ct.dot(Cs12))) - - A=Cs_12.dot(M0.dot(Cs_12)) - - b=mxt-mxs.dot(A) - + wt = np.ones((xt.shape[0], 1)) / xt.shape[0] + + Cs = (xs * ws).T.dot(xs) / ws.sum() + reg * np.eye(d) + Ct = (xt * wt).T.dot(xt) / wt.sum() + reg * np.eye(d) + + Cs12 = linalg.sqrtm(Cs) + Cs_12 = linalg.inv(Cs12) + + M0 = linalg.sqrtm(Cs12.dot(Ct.dot(Cs12))) + + A = Cs_12.dot(M0.dot(Cs_12)) + + b = mxt - mxs.dot(A) + if log: - log={} - log['Cs']=Cs - log['Ct']=Ct - log['Cs12']=Cs12 - log['Cs_12']=Cs_12 - return A,b,log + log = {} + log['Cs'] = Cs + log['Ct'] = Ct + log['Cs12'] = Cs12 + log['Cs_12'] = Cs_12 + return A, b, log else: - return A,b - + return A, b @deprecated("The class OTDA is deprecated in 0.3.1 and will be " @@ -1288,42 +1288,42 @@ class LinearTransport(BaseTransport): """ OT linear operator between empirical distributions The function estimate the optimal linear operator that align the two - empirical distributions. This is equivalent to estimating the closed - form mapping between two Gaussian distribution :math:`N(\mu_s,\Sigma_s)` + empirical distributions. This is equivalent to estimating the closed + form mapping between two Gaussian distribution :math:`N(\mu_s,\Sigma_s)` and :math:`N(\mu_t,\Sigma_t)` as proposed in [14]. - + The linear operator from source to target :math:`M` .. math:: M(x)=Ax+b - + where : - + .. math:: A=\Sigma_s^{-1/2}(\Sigma_s^{1/2}\Sigma_t\Sigma_s^{1/2})^{1/2} \Sigma_s^{-1/2} - .. math:: + .. math:: b=\mu_t-A\mu_s Parameters ---------- reg : float,optional - regularization added to the daigonals of convariances (>0) + regularization added to the daigonals of convariances (>0) bias: boolean, optional estimate bias b else b=0 (default:True) log : bool, optional record log if True - - + + """ - - def __init__(self, reg=1e-8,bias=True,log=False, + + def __init__(self, reg=1e-8, bias=True, log=False, distribution_estimation=distribution_estimation_uniform): - - self.bias=bias - self.log=log - self.reg=reg - self.distribution_estimation=distribution_estimation + + self.bias = bias + self.log = log + self.reg = reg + self.distribution_estimation = distribution_estimation def fit(self, Xs=None, ys=None, Xt=None, yt=None): """Build a coupling matrix from source and target sets of samples @@ -1349,17 +1349,15 @@ class LinearTransport(BaseTransport): self : object Returns self. """ - + self.mu_s = self.distribution_estimation(Xs) self.mu_t = self.distribution_estimation(Xt) - - # coupling estimation - returned_ = OT_mapping_linear(Xs,Xt,reg=self.reg, - ws=self.mu_s.reshape((-1,1)), - wt=self.mu_t.reshape((-1,1)), - bias=self.bias,log=self.log) + returned_ = OT_mapping_linear(Xs, Xt, reg=self.reg, + ws=self.mu_s.reshape((-1, 1)), + wt=self.mu_t.reshape((-1, 1)), + bias=self.bias, log=self.log) # deal with the value of log if self.log: @@ -1367,10 +1365,10 @@ class LinearTransport(BaseTransport): else: self.A_, self.B_, = returned_ self.log_ = dict() - + # re compute inverse mapping - self.A1_=linalg.inv(self.A_) - self.B1_=-self.B_.dot(self.A1_) + self.A1_ = linalg.inv(self.A_) + self.B1_ = -self.B_.dot(self.A1_) return self @@ -1403,7 +1401,7 @@ class LinearTransport(BaseTransport): # check the necessary inputs parameters are here if check_params(Xs=Xs): - transp_Xs= Xs.dot(self.A_)+self.B_ + transp_Xs = Xs.dot(self.A_) + self.B_ return transp_Xs @@ -1437,12 +1435,11 @@ class LinearTransport(BaseTransport): # check the necessary inputs parameters are here if check_params(Xt=Xt): - transp_Xt= Xt.dot(self.A1_)+self.B1_ + transp_Xt = Xt.dot(self.A1_) + self.B1_ return transp_Xt - class SinkhornTransport(BaseTransport): """Domain Adapatation OT method based on Sinkhorn Algorithm -- cgit v1.2.3 From 4fc9ccc7c6c96a43c48be54e89133c8f481d8bf4 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 20 Mar 2018 16:39:24 +0100 Subject: better example+test --- Makefile | 2 +- examples/plot_otda_linear_mapping.py | 98 ++++++++++++++++++++++++++++-------- 2 files changed, 77 insertions(+), 23 deletions(-) diff --git a/Makefile b/Makefile index 3f19e8a..d334163 100644 --- a/Makefile +++ b/Makefile @@ -41,7 +41,7 @@ pep8 : flake8 examples/ ot/ test/ test : FORCE pep8 - python -m py.test -v test/ --cov=ot --cov-report html:cov_html + python3 -m pytest -v test/ --cov=ot --cov-report html:cov_html pytest : FORCE python -m py.test -v test/ --cov=ot diff --git a/examples/plot_otda_linear_mapping.py b/examples/plot_otda_linear_mapping.py index 143f129..163f8f1 100644 --- a/examples/plot_otda_linear_mapping.py +++ b/examples/plot_otda_linear_mapping.py @@ -9,11 +9,11 @@ Created on Tue Mar 20 14:31:15 2018 import numpy as np import pylab as pl import ot -import scipy.linalg as linalg - - -#%% +from scipy import ndimage +############################################################################## +# Generate data +# ------------- n = 1000 d = 2 @@ -37,49 +37,103 @@ A = np.array([[1.5, .7], [.7, 1.5]]) b = np.array([[4, 2]]) xt = xt.dot(A) + b -#%% +############################################################################## +# Plot data +# --------- pl.figure(1, (5, 5)) pl.plot(xs[:, 0], xs[:, 1], '+') pl.plot(xt[:, 0], xt[:, 1], 'o') -#%% -Ae, be = ot.da.OT_mapping_linear(xs, xt) +############################################################################## +# Estimate linear mapping and transport +# ------------------------------------- -Ae1 = linalg.inv(Ae) -be1 = -be.dot(Ae1) +Ae, be = ot.da.OT_mapping_linear(xs, xt) xst = xs.dot(Ae) + be -xts = xt.dot(Ae1) + be1 -# %% + +############################################################################## +# Plot transported samples +# ------------------------ pl.figure(1, (5, 5)) pl.clf() pl.plot(xs[:, 0], xs[:, 1], '+') pl.plot(xt[:, 0], xt[:, 1], 'o') pl.plot(xst[:, 0], xst[:, 1], '+') -pl.plot(xts[:, 0], xts[:, 1], 'o') pl.show() +############################################################################## +# Mapping Class between images +# ---------------------------- + + +def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + + +def mat2im(X, shape): + """Converts back a matrix to an image""" + return X.reshape(shape) + + +def minmax(I): + return np.clip(I, 0, 1) + + +# Loading images +I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256 +I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 -#%% Example class with on images + +X1 = im2mat(I1) +X2 = im2mat(I2) + +############################################################################## +# Estimate mapping and adapt +# ---------------------------- mapping = ot.da.LinearTransport() -mapping.fit(Xs=xs, Xt=xt) +mapping.fit(Xs=X1, Xt=X2) -xst = mapping.transform(Xs=xs) -xts = mapping.inverse_transform(Xt=xt) +xst = mapping.transform(Xs=X1) +xts = mapping.inverse_transform(Xt=X2) + +I1t = minmax(mat2im(xst, I1.shape)) +I2t = minmax(mat2im(xts, I2.shape)) # %% -pl.figure(1, (5, 5)) -pl.clf() -pl.plot(xs[:, 0], xs[:, 1], '+') -pl.plot(xt[:, 0], xt[:, 1], 'o') -pl.plot(xst[:, 0], xst[:, 1], '+') -pl.plot(xts[:, 0], xts[:, 1], 'o') + +############################################################################## +# Plot transformed images +# ----------------------- + +pl.figure(2, figsize=(10, 7)) + +pl.subplot(2, 2, 1) +pl.imshow(I1) +pl.axis('off') +pl.title('Im. 1') + +pl.subplot(2, 2, 2) +pl.imshow(I2) +pl.axis('off') +pl.title('Im. 2') + +pl.subplot(2, 2, 3) +pl.imshow(I1t) +pl.axis('off') +pl.title('Mapping Im. 1') + +pl.subplot(2, 2, 4) +pl.imshow(I2t) +pl.axis('off') +pl.title('Inverse mapping Im. 2') -- cgit v1.2.3 From 88a81c37d2f8c5419f88ccac620186e583fab08f Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 20 Mar 2018 16:49:31 +0100 Subject: makefile update --- Makefile | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Makefile b/Makefile index d334163..7e0c576 100644 --- a/Makefile +++ b/Makefile @@ -1,6 +1,6 @@ -PYTHON=python +PYTHON=python3 help : @echo "The following make targets are available:" @@ -41,7 +41,7 @@ pep8 : flake8 examples/ ot/ test/ test : FORCE pep8 - python3 -m pytest -v test/ --cov=ot --cov-report html:cov_html + $(PYTHON) -m pytest -v test/ --cov=ot --cov-report html:cov_html pytest : FORCE python -m py.test -v test/ --cov=ot -- cgit v1.2.3 From 287c659ad35f5036ba2687caf73009ef455c7239 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 20 Mar 2018 16:57:35 +0100 Subject: update example --- examples/plot_otda_linear_mapping.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/plot_otda_linear_mapping.py b/examples/plot_otda_linear_mapping.py index 163f8f1..165fe72 100644 --- a/examples/plot_otda_linear_mapping.py +++ b/examples/plot_otda_linear_mapping.py @@ -68,8 +68,8 @@ pl.plot(xst[:, 0], xst[:, 1], '+') pl.show() ############################################################################## -# Mapping Class between images -# ---------------------------- +# Load image data +# --------------- def im2mat(I): -- cgit v1.2.3 From 6fdf5de8fa27fa16d6b8910fe96eb67b7761aa0e Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 21 Mar 2018 08:29:50 +0100 Subject: add linear mapping test + autopep8 --- examples/plot_otda_linear_mapping.py | 5 ++--- ot/bregman.py | 30 ++++++++++++++++++++---------- ot/lp/__init__.py | 3 ++- ot/optim.py | 9 ++++++--- ot/utils.py | 2 +- test/test_da.py | 18 ++++++++++++++++++ 6 files changed, 49 insertions(+), 18 deletions(-) diff --git a/examples/plot_otda_linear_mapping.py b/examples/plot_otda_linear_mapping.py index 165fe72..7a3b761 100644 --- a/examples/plot_otda_linear_mapping.py +++ b/examples/plot_otda_linear_mapping.py @@ -9,7 +9,6 @@ Created on Tue Mar 20 14:31:15 2018 import numpy as np import pylab as pl import ot -from scipy import ndimage ############################################################################## # Generate data @@ -87,8 +86,8 @@ def minmax(I): # Loading images -I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256 -I2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 +I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 +I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 X1 = im2mat(I1) diff --git a/ot/bregman.py b/ot/bregman.py index d63c51d..07b8660 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -11,7 +11,8 @@ Bregman projections for regularized OT import numpy as np -def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, stopThr=1e-9, verbose=False, log=False, **kwargs): +def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, + stopThr=1e-9, verbose=False, log=False, **kwargs): u""" Solve the entropic regularization optimal transport problem and return the OT matrix @@ -120,7 +121,8 @@ def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, stopThr=1e-9, ver return sink() -def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, stopThr=1e-9, verbose=False, log=False, **kwargs): +def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, + stopThr=1e-9, verbose=False, log=False, **kwargs): u""" Solve the entropic regularization optimal transport problem and return the loss @@ -233,7 +235,8 @@ def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, stopThr=1e-9, ve return sink() -def sinkhorn_knopp(a, b, M, reg, numItermax=1000, stopThr=1e-9, verbose=False, log=False, **kwargs): +def sinkhorn_knopp(a, b, M, reg, numItermax=1000, + stopThr=1e-9, verbose=False, log=False, **kwargs): """ Solve the entropic regularization optimal transport problem and return the OT matrix @@ -403,7 +406,8 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, stopThr=1e-9, verbose=False, l return u.reshape((-1, 1)) * K * v.reshape((1, -1)) -def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, warmstart=None, verbose=False, print_period=20, log=False, **kwargs): +def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, + warmstart=None, verbose=False, print_period=20, log=False, **kwargs): """ Solve the entropic regularization OT problem with log stabilization @@ -526,11 +530,13 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, wa def get_K(alpha, beta): """log space computation""" - return np.exp(-(M - alpha.reshape((na, 1)) - beta.reshape((1, nb))) / reg) + return np.exp(-(M - alpha.reshape((na, 1)) - + beta.reshape((1, nb))) / reg) def get_Gamma(alpha, beta, u, v): """log space gamma computation""" - return np.exp(-(M - alpha.reshape((na, 1)) - beta.reshape((1, nb))) / reg + np.log(u.reshape((na, 1))) + np.log(v.reshape((1, nb)))) + return np.exp(-(M - alpha.reshape((na, 1)) - beta.reshape((1, nb))) / + reg + np.log(u.reshape((na, 1))) + np.log(v.reshape((1, nb)))) # print(np.min(K)) @@ -620,7 +626,8 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, wa return get_Gamma(alpha, beta, u, v) -def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInnerItermax=100, tau=1e3, stopThr=1e-9, warmstart=None, verbose=False, print_period=10, log=False, **kwargs): +def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInnerItermax=100, + tau=1e3, stopThr=1e-9, warmstart=None, verbose=False, print_period=10, log=False, **kwargs): """ Solve the entropic regularization optimal transport problem with log stabilization and epsilon scaling. @@ -739,7 +746,8 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInne def get_K(alpha, beta): """log space computation""" - return np.exp(-(M - alpha.reshape((na, 1)) - beta.reshape((1, nb))) / reg) + return np.exp(-(M - alpha.reshape((na, 1)) - + beta.reshape((1, nb))) / reg) # print(np.min(K)) def get_reg(n): # exponential decreasing @@ -811,7 +819,8 @@ def projC(gamma, q): return np.multiply(gamma, q / np.maximum(np.sum(gamma, axis=0), 1e-10)) -def barycenter(A, M, reg, weights=None, numItermax=1000, stopThr=1e-4, verbose=False, log=False): +def barycenter(A, M, reg, weights=None, numItermax=1000, + stopThr=1e-4, verbose=False, log=False): """Compute the entropic regularized wasserstein barycenter of distributions A The function solves the following optimization problem: @@ -904,7 +913,8 @@ def barycenter(A, M, reg, weights=None, numItermax=1000, stopThr=1e-4, verbose=F return geometricBar(weights, UKv) -def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000, stopThr=1e-3, verbose=False, log=False): +def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000, + stopThr=1e-3, verbose=False, log=False): """ Compute the unmixing of an observation with a given dictionary using Wasserstein distance diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 5c09da2..6371feb 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -107,7 +107,8 @@ def emd(a, b, M, numItermax=100000, log=False): return G -def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=100000, log=False, return_matrix=False): +def emd2(a, b, M, processes=multiprocessing.cpu_count(), + numItermax=100000, log=False, return_matrix=False): """Solves the Earth Movers distance problem and returns the loss .. math:: diff --git a/ot/optim.py b/ot/optim.py index 1d09adc..f31fae2 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -15,7 +15,8 @@ from .bregman import sinkhorn # The corresponding scipy function does not work for matrices -def line_search_armijo(f, xk, pk, gfk, old_fval, args=(), c1=1e-4, alpha0=0.99): +def line_search_armijo(f, xk, pk, gfk, old_fval, + args=(), c1=1e-4, alpha0=0.99): """ Armijo linesearch function that works with matrices @@ -71,7 +72,8 @@ def line_search_armijo(f, xk, pk, gfk, old_fval, args=(), c1=1e-4, alpha0=0.99): return alpha, fc[0], phi1 -def cg(a, b, M, reg, f, df, G0=None, numItermax=200, stopThr=1e-9, verbose=False, log=False): +def cg(a, b, M, reg, f, df, G0=None, numItermax=200, + stopThr=1e-9, verbose=False, log=False): """ Solve the general regularized OT problem with conditional gradient @@ -202,7 +204,8 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200, stopThr=1e-9, verbose=False return G -def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, numInnerItermax=200, stopThr=1e-9, verbose=False, log=False): +def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, + numInnerItermax=200, stopThr=1e-9, verbose=False, log=False): """ Solve the general regularized OT problem with the generalized conditional gradient diff --git a/ot/utils.py b/ot/utils.py index 9eab3fc..16862ea 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -316,7 +316,7 @@ def _is_deprecated(func): closures = [] is_deprecated = ('deprecated' in ''.join([c.cell_contents for c in closures - if isinstance(c.cell_contents, str)])) + if isinstance(c.cell_contents, str)])) return is_deprecated diff --git a/test/test_da.py b/test/test_da.py index 593dc53..7b63daf 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -444,6 +444,24 @@ def test_mapping_transport_class(): assert len(otda.log_.keys()) != 0 +def test_linear_mapping(): + + ns = 150 + nt = 200 + + Xs, ys = get_data_classif('3gauss', ns) + Xt, yt = get_data_classif('3gauss2', nt) + + A, b = ot.da.OT_mapping_linear(Xs, Xt) + + Xst = Xs.dot(A) + b + + Ct = np.cov(Xt.T) + Cst = np.cov(Xst.T) + + np.testing.assert_allclose(Ct, Cst, rtol=1e-2, atol=1e-2) + + def test_otda(): n_samples = 150 # nb samples -- cgit v1.2.3 From 5efdf008865ea347775708b637d933e048d663ec Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 21 Mar 2018 09:03:58 +0100 Subject: add test linear mapping class --- Makefile | 5 +++++ test/test_da.py | 24 ++++++++++++++++++++++++ 2 files changed, 29 insertions(+) diff --git a/Makefile b/Makefile index 7e0c576..468d042 100644 --- a/Makefile +++ b/Makefile @@ -56,6 +56,11 @@ rdoc : notebook : ipython notebook --matplotlib=inline --notebook-dir=notebooks/ + +autopep8 : + autopep8 -ir test ot examples +aautopep8 : + autopep8 -air test ot examples FORCE : diff --git a/test/test_da.py b/test/test_da.py index 7b63daf..a9d6d34 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -462,6 +462,30 @@ def test_linear_mapping(): np.testing.assert_allclose(Ct, Cst, rtol=1e-2, atol=1e-2) +def test_linear_mapping_class(): + + ns = 150 + nt = 200 + + Xs, ys = get_data_classif('3gauss', ns) + Xt, yt = get_data_classif('3gauss2', nt) + + otmap = ot.da.LinearTransport() + + otmap.fit(Xs=Xs, Xt=Xt) + assert hasattr(otmap, "A_") + assert hasattr(otmap, "B_") + assert hasattr(otmap, "A1_") + assert hasattr(otmap, "B1_") + + Xst = otmap.transform(Xs=Xs) + + Ct = np.cov(Xt.T) + Cst = np.cov(Xst.T) + + np.testing.assert_allclose(Ct, Cst, rtol=1e-2, atol=1e-2) + + def test_otda(): n_samples = 150 # nb samples -- cgit v1.2.3 From fc9923dea2706b65ffe15fc86428cd8b53b5feb1 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 21 Mar 2018 09:33:57 +0100 Subject: add tests for ot.uils --- test/test_utils.py | 77 ++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 77 insertions(+) diff --git a/test/test_utils.py b/test/test_utils.py index 1bd37cd..b524ef6 100644 --- a/test/test_utils.py +++ b/test/test_utils.py @@ -7,6 +7,7 @@ import ot import numpy as np +import sys def test_parmap(): @@ -123,3 +124,79 @@ def test_clean_zeros(): assert len(a) == n - nz assert len(b) == n - nz2 + + +def test_cost_normalization(): + + C = np.random.rand(10, 10) + + # does nothing + M0 = ot.utils.cost_normalization(C) + np.testing.assert_allclose(C, M0) + + M = ot.utils.cost_normalization(C, 'median') + np.testing.assert_allclose(np.median(M), 1) + + M = ot.utils.cost_normalization(C, 'max') + np.testing.assert_allclose(M.max(), 1) + + M = ot.utils.cost_normalization(C, 'log') + np.testing.assert_allclose(M.max(), np.log(1 + C).max()) + + M = ot.utils.cost_normalization(C, 'loglog') + np.testing.assert_allclose(M.max(), np.log(1 + np.log(1 + C)).max()) + + +def test_check_params(): + + res1 = ot.utils.check_params(first='OK', second=20) + assert res1 is True + + res0 = ot.utils.check_params(first='OK', second=None) + assert res0 is False + + +def test_deprecated_func(): + + @ot.utils.deprecated('deprecated text for fun') + def fun(): + pass + + def fun2(): + pass + + @ot.utils.deprecated('deprecated text for class') + class Class(): + pass + + if sys.version_info < (3, 5): + print('Not tested') + else: + assert ot.utils._is_deprecated(fun) is True + + assert ot.utils._is_deprecated(fun2) is False + + +def test_BaseEstimator(): + + class Class(ot.utils.BaseEstimator): + + def __init__(self, first='spam', second='eggs'): + + self.first = first + self.second = second + + cl = Class() + + names = cl._get_param_names() + assert 'first' in names + assert 'second' in names + + params = cl.get_params() + assert 'first' in params + assert 'second' in params + + params['first'] = 'spam again' + cl.set_params(**params) + + assert cl.first == 'spam again' -- cgit v1.2.3 From 927395b40dae98bcf027b601b6df48a4318cfef2 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 21 Mar 2018 10:07:47 +0100 Subject: add externals for function signature --- ot/externals/__init__.py | 0 ot/externals/funcsigs.py | 815 +++++++++++++++++++++++++++++++++++++++++++++++ ot/gromov.py | 2 +- ot/utils.py | 10 +- 4 files changed, 821 insertions(+), 6 deletions(-) create mode 100644 ot/externals/__init__.py create mode 100644 ot/externals/funcsigs.py diff --git a/ot/externals/__init__.py b/ot/externals/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/ot/externals/funcsigs.py b/ot/externals/funcsigs.py new file mode 100644 index 0000000..4e68469 --- /dev/null +++ b/ot/externals/funcsigs.py @@ -0,0 +1,815 @@ +# Copyright 2001-2013 Python Software Foundation; All Rights Reserved +"""Function signature objects for callables + +Back port of Python 3.3's function signature tools from the inspect module, +modified to be compatible with Python 2.7 and 3.2+. +""" +from __future__ import absolute_import, division, print_function +import itertools +import functools +import re +import types + +from collections import OrderedDict + +__version__ = "0.4" + +__all__ = ['BoundArguments', 'Parameter', 'Signature', 'signature'] + + +_WrapperDescriptor = type(type.__call__) +_MethodWrapper = type(all.__call__) + +_NonUserDefinedCallables = (_WrapperDescriptor, + _MethodWrapper, + types.BuiltinFunctionType) + + +def formatannotation(annotation, base_module=None): + if isinstance(annotation, type): + if annotation.__module__ in ('builtins', '__builtin__', base_module): + return annotation.__name__ + return annotation.__module__+'.'+annotation.__name__ + return repr(annotation) + + +def _get_user_defined_method(cls, method_name, *nested): + try: + if cls is type: + return + meth = getattr(cls, method_name) + for name in nested: + meth = getattr(meth, name, meth) + except AttributeError: + return + else: + if not isinstance(meth, _NonUserDefinedCallables): + # Once '__signature__' will be added to 'C'-level + # callables, this check won't be necessary + return meth + + +def signature(obj): + '''Get a signature object for the passed callable.''' + + if not callable(obj): + raise TypeError('{0!r} is not a callable object'.format(obj)) + + if isinstance(obj, types.MethodType): + sig = signature(obj.__func__) + if obj.__self__ is None: + # Unbound method: the first parameter becomes positional-only + if sig.parameters: + first = sig.parameters.values()[0].replace( + kind=_POSITIONAL_ONLY) + return sig.replace( + parameters=(first,) + tuple(sig.parameters.values())[1:]) + else: + return sig + else: + # In this case we skip the first parameter of the underlying + # function (usually `self` or `cls`). + return sig.replace(parameters=tuple(sig.parameters.values())[1:]) + + try: + sig = obj.__signature__ + except AttributeError: + pass + else: + if sig is not None: + return sig + + try: + # Was this function wrapped by a decorator? + wrapped = obj.__wrapped__ + except AttributeError: + pass + else: + return signature(wrapped) + + if isinstance(obj, types.FunctionType): + return Signature.from_function(obj) + + if isinstance(obj, functools.partial): + sig = signature(obj.func) + + new_params = OrderedDict(sig.parameters.items()) + + partial_args = obj.args or () + partial_keywords = obj.keywords or {} + try: + ba = sig.bind_partial(*partial_args, **partial_keywords) + except TypeError as ex: + msg = 'partial object {0!r} has incorrect arguments'.format(obj) + raise ValueError(msg) + + for arg_name, arg_value in ba.arguments.items(): + param = new_params[arg_name] + if arg_name in partial_keywords: + # We set a new default value, because the following code + # is correct: + # + # >>> def foo(a): print(a) + # >>> print(partial(partial(foo, a=10), a=20)()) + # 20 + # >>> print(partial(partial(foo, a=10), a=20)(a=30)) + # 30 + # + # So, with 'partial' objects, passing a keyword argument is + # like setting a new default value for the corresponding + # parameter + # + # We also mark this parameter with '_partial_kwarg' + # flag. Later, in '_bind', the 'default' value of this + # parameter will be added to 'kwargs', to simulate + # the 'functools.partial' real call. + new_params[arg_name] = param.replace(default=arg_value, + _partial_kwarg=True) + + elif (param.kind not in (_VAR_KEYWORD, _VAR_POSITIONAL) and + not param._partial_kwarg): + new_params.pop(arg_name) + + return sig.replace(parameters=new_params.values()) + + sig = None + if isinstance(obj, type): + # obj is a class or a metaclass + + # First, let's see if it has an overloaded __call__ defined + # in its metaclass + call = _get_user_defined_method(type(obj), '__call__') + if call is not None: + sig = signature(call) + else: + # Now we check if the 'obj' class has a '__new__' method + new = _get_user_defined_method(obj, '__new__') + if new is not None: + sig = signature(new) + else: + # Finally, we should have at least __init__ implemented + init = _get_user_defined_method(obj, '__init__') + if init is not None: + sig = signature(init) + elif not isinstance(obj, _NonUserDefinedCallables): + # An object with __call__ + # We also check that the 'obj' is not an instance of + # _WrapperDescriptor or _MethodWrapper to avoid + # infinite recursion (and even potential segfault) + call = _get_user_defined_method(type(obj), '__call__', 'im_func') + if call is not None: + sig = signature(call) + + if sig is not None: + # For classes and objects we skip the first parameter of their + # __call__, __new__, or __init__ methods + return sig.replace(parameters=tuple(sig.parameters.values())[1:]) + + if isinstance(obj, types.BuiltinFunctionType): + # Raise a nicer error message for builtins + msg = 'no signature found for builtin function {0!r}'.format(obj) + raise ValueError(msg) + + raise ValueError('callable {0!r} is not supported by signature'.format(obj)) + + +class _void(object): + '''A private marker - used in Parameter & Signature''' + + +class _empty(object): + pass + + +class _ParameterKind(int): + def __new__(self, *args, **kwargs): + obj = int.__new__(self, *args) + obj._name = kwargs['name'] + return obj + + def __str__(self): + return self._name + + def __repr__(self): + return '<_ParameterKind: {0!r}>'.format(self._name) + + +_POSITIONAL_ONLY = _ParameterKind(0, name='POSITIONAL_ONLY') +_POSITIONAL_OR_KEYWORD = _ParameterKind(1, name='POSITIONAL_OR_KEYWORD') +_VAR_POSITIONAL = _ParameterKind(2, name='VAR_POSITIONAL') +_KEYWORD_ONLY = _ParameterKind(3, name='KEYWORD_ONLY') +_VAR_KEYWORD = _ParameterKind(4, name='VAR_KEYWORD') + + +class Parameter(object): + '''Represents a parameter in a function signature. + + Has the following public attributes: + + * name : str + The name of the parameter as a string. + * default : object + The default value for the parameter if specified. If the + parameter has no default value, this attribute is not set. + * annotation + The annotation for the parameter if specified. If the + parameter has no annotation, this attribute is not set. + * kind : str + Describes how argument values are bound to the parameter. + Possible values: `Parameter.POSITIONAL_ONLY`, + `Parameter.POSITIONAL_OR_KEYWORD`, `Parameter.VAR_POSITIONAL`, + `Parameter.KEYWORD_ONLY`, `Parameter.VAR_KEYWORD`. + ''' + + __slots__ = ('_name', '_kind', '_default', '_annotation', '_partial_kwarg') + + POSITIONAL_ONLY = _POSITIONAL_ONLY + POSITIONAL_OR_KEYWORD = _POSITIONAL_OR_KEYWORD + VAR_POSITIONAL = _VAR_POSITIONAL + KEYWORD_ONLY = _KEYWORD_ONLY + VAR_KEYWORD = _VAR_KEYWORD + + empty = _empty + + def __init__(self, name, kind, default=_empty, annotation=_empty, + _partial_kwarg=False): + + if kind not in (_POSITIONAL_ONLY, _POSITIONAL_OR_KEYWORD, + _VAR_POSITIONAL, _KEYWORD_ONLY, _VAR_KEYWORD): + raise ValueError("invalid value for 'Parameter.kind' attribute") + self._kind = kind + + if default is not _empty: + if kind in (_VAR_POSITIONAL, _VAR_KEYWORD): + msg = '{0} parameters cannot have default values'.format(kind) + raise ValueError(msg) + self._default = default + self._annotation = annotation + + if name is None: + if kind != _POSITIONAL_ONLY: + raise ValueError("None is not a valid name for a " + "non-positional-only parameter") + self._name = name + else: + name = str(name) + if kind != _POSITIONAL_ONLY and not re.match(r'[a-z_]\w*$', name, re.I): + msg = '{0!r} is not a valid parameter name'.format(name) + raise ValueError(msg) + self._name = name + + self._partial_kwarg = _partial_kwarg + + @property + def name(self): + return self._name + + @property + def default(self): + return self._default + + @property + def annotation(self): + return self._annotation + + @property + def kind(self): + return self._kind + + def replace(self, name=_void, kind=_void, annotation=_void, + default=_void, _partial_kwarg=_void): + '''Creates a customized copy of the Parameter.''' + + if name is _void: + name = self._name + + if kind is _void: + kind = self._kind + + if annotation is _void: + annotation = self._annotation + + if default is _void: + default = self._default + + if _partial_kwarg is _void: + _partial_kwarg = self._partial_kwarg + + return type(self)(name, kind, default=default, annotation=annotation, + _partial_kwarg=_partial_kwarg) + + def __str__(self): + kind = self.kind + + formatted = self._name + if kind == _POSITIONAL_ONLY: + if formatted is None: + formatted = '' + formatted = '<{0}>'.format(formatted) + + # Add annotation and default value + if self._annotation is not _empty: + formatted = '{0}:{1}'.format(formatted, + formatannotation(self._annotation)) + + if self._default is not _empty: + formatted = '{0}={1}'.format(formatted, repr(self._default)) + + if kind == _VAR_POSITIONAL: + formatted = '*' + formatted + elif kind == _VAR_KEYWORD: + formatted = '**' + formatted + + return formatted + + def __repr__(self): + return '<{0} at {1:#x} {2!r}>'.format(self.__class__.__name__, + id(self), self.name) + + def __hash__(self): + msg = "unhashable type: '{0}'".format(self.__class__.__name__) + raise TypeError(msg) + + def __eq__(self, other): + return (issubclass(other.__class__, Parameter) and + self._name == other._name and + self._kind == other._kind and + self._default == other._default and + self._annotation == other._annotation) + + def __ne__(self, other): + return not self.__eq__(other) + + +class BoundArguments(object): + '''Result of `Signature.bind` call. Holds the mapping of arguments + to the function's parameters. + + Has the following public attributes: + + * arguments : OrderedDict + An ordered mutable mapping of parameters' names to arguments' values. + Does not contain arguments' default values. + * signature : Signature + The Signature object that created this instance. + * args : tuple + Tuple of positional arguments values. + * kwargs : dict + Dict of keyword arguments values. + ''' + + def __init__(self, signature, arguments): + self.arguments = arguments + self._signature = signature + + @property + def signature(self): + return self._signature + + @property + def args(self): + args = [] + for param_name, param in self._signature.parameters.items(): + if (param.kind in (_VAR_KEYWORD, _KEYWORD_ONLY) or + param._partial_kwarg): + # Keyword arguments mapped by 'functools.partial' + # (Parameter._partial_kwarg is True) are mapped + # in 'BoundArguments.kwargs', along with VAR_KEYWORD & + # KEYWORD_ONLY + break + + try: + arg = self.arguments[param_name] + except KeyError: + # We're done here. Other arguments + # will be mapped in 'BoundArguments.kwargs' + break + else: + if param.kind == _VAR_POSITIONAL: + # *args + args.extend(arg) + else: + # plain argument + args.append(arg) + + return tuple(args) + + @property + def kwargs(self): + kwargs = {} + kwargs_started = False + for param_name, param in self._signature.parameters.items(): + if not kwargs_started: + if (param.kind in (_VAR_KEYWORD, _KEYWORD_ONLY) or + param._partial_kwarg): + kwargs_started = True + else: + if param_name not in self.arguments: + kwargs_started = True + continue + + if not kwargs_started: + continue + + try: + arg = self.arguments[param_name] + except KeyError: + pass + else: + if param.kind == _VAR_KEYWORD: + # **kwargs + kwargs.update(arg) + else: + # plain keyword argument + kwargs[param_name] = arg + + return kwargs + + def __hash__(self): + msg = "unhashable type: '{0}'".format(self.__class__.__name__) + raise TypeError(msg) + + def __eq__(self, other): + return (issubclass(other.__class__, BoundArguments) and + self.signature == other.signature and + self.arguments == other.arguments) + + def __ne__(self, other): + return not self.__eq__(other) + + +class Signature(object): + '''A Signature object represents the overall signature of a function. + It stores a Parameter object for each parameter accepted by the + function, as well as information specific to the function itself. + + A Signature object has the following public attributes and methods: + + * parameters : OrderedDict + An ordered mapping of parameters' names to the corresponding + Parameter objects (keyword-only arguments are in the same order + as listed in `code.co_varnames`). + * return_annotation : object + The annotation for the return type of the function if specified. + If the function has no annotation for its return type, this + attribute is not set. + * bind(*args, **kwargs) -> BoundArguments + Creates a mapping from positional and keyword arguments to + parameters. + * bind_partial(*args, **kwargs) -> BoundArguments + Creates a partial mapping from positional and keyword arguments + to parameters (simulating 'functools.partial' behavior.) + ''' + + __slots__ = ('_return_annotation', '_parameters') + + _parameter_cls = Parameter + _bound_arguments_cls = BoundArguments + + empty = _empty + + def __init__(self, parameters=None, return_annotation=_empty, + __validate_parameters__=True): + '''Constructs Signature from the given list of Parameter + objects and 'return_annotation'. All arguments are optional. + ''' + + if parameters is None: + params = OrderedDict() + else: + if __validate_parameters__: + params = OrderedDict() + top_kind = _POSITIONAL_ONLY + + for idx, param in enumerate(parameters): + kind = param.kind + if kind < top_kind: + msg = 'wrong parameter order: {0} before {1}' + msg = msg.format(top_kind, param.kind) + raise ValueError(msg) + else: + top_kind = kind + + name = param.name + if name is None: + name = str(idx) + param = param.replace(name=name) + + if name in params: + msg = 'duplicate parameter name: {0!r}'.format(name) + raise ValueError(msg) + params[name] = param + else: + params = OrderedDict(((param.name, param) + for param in parameters)) + + self._parameters = params + self._return_annotation = return_annotation + + @classmethod + def from_function(cls, func): + '''Constructs Signature for the given python function''' + + if not isinstance(func, types.FunctionType): + raise TypeError('{0!r} is not a Python function'.format(func)) + + Parameter = cls._parameter_cls + + # Parameter information. + func_code = func.__code__ + pos_count = func_code.co_argcount + arg_names = func_code.co_varnames + positional = tuple(arg_names[:pos_count]) + keyword_only_count = getattr(func_code, 'co_kwonlyargcount', 0) + keyword_only = arg_names[pos_count:(pos_count + keyword_only_count)] + annotations = getattr(func, '__annotations__', {}) + defaults = func.__defaults__ + kwdefaults = getattr(func, '__kwdefaults__', None) + + if defaults: + pos_default_count = len(defaults) + else: + pos_default_count = 0 + + parameters = [] + + # Non-keyword-only parameters w/o defaults. + non_default_count = pos_count - pos_default_count + for name in positional[:non_default_count]: + annotation = annotations.get(name, _empty) + parameters.append(Parameter(name, annotation=annotation, + kind=_POSITIONAL_OR_KEYWORD)) + + # ... w/ defaults. + for offset, name in enumerate(positional[non_default_count:]): + annotation = annotations.get(name, _empty) + parameters.append(Parameter(name, annotation=annotation, + kind=_POSITIONAL_OR_KEYWORD, + default=defaults[offset])) + + # *args + if func_code.co_flags & 0x04: + name = arg_names[pos_count + keyword_only_count] + annotation = annotations.get(name, _empty) + parameters.append(Parameter(name, annotation=annotation, + kind=_VAR_POSITIONAL)) + + # Keyword-only parameters. + for name in keyword_only: + default = _empty + if kwdefaults is not None: + default = kwdefaults.get(name, _empty) + + annotation = annotations.get(name, _empty) + parameters.append(Parameter(name, annotation=annotation, + kind=_KEYWORD_ONLY, + default=default)) + # **kwargs + if func_code.co_flags & 0x08: + index = pos_count + keyword_only_count + if func_code.co_flags & 0x04: + index += 1 + + name = arg_names[index] + annotation = annotations.get(name, _empty) + parameters.append(Parameter(name, annotation=annotation, + kind=_VAR_KEYWORD)) + + return cls(parameters, + return_annotation=annotations.get('return', _empty), + __validate_parameters__=False) + + @property + def parameters(self): + try: + return types.MappingProxyType(self._parameters) + except AttributeError: + return OrderedDict(self._parameters.items()) + + @property + def return_annotation(self): + return self._return_annotation + + def replace(self, parameters=_void, return_annotation=_void): + '''Creates a customized copy of the Signature. + Pass 'parameters' and/or 'return_annotation' arguments + to override them in the new copy. + ''' + + if parameters is _void: + parameters = self.parameters.values() + + if return_annotation is _void: + return_annotation = self._return_annotation + + return type(self)(parameters, + return_annotation=return_annotation) + + def __hash__(self): + msg = "unhashable type: '{0}'".format(self.__class__.__name__) + raise TypeError(msg) + + def __eq__(self, other): + if (not issubclass(type(other), Signature) or + self.return_annotation != other.return_annotation or + len(self.parameters) != len(other.parameters)): + return False + + other_positions = dict((param, idx) + for idx, param in enumerate(other.parameters.keys())) + + for idx, (param_name, param) in enumerate(self.parameters.items()): + if param.kind == _KEYWORD_ONLY: + try: + other_param = other.parameters[param_name] + except KeyError: + return False + else: + if param != other_param: + return False + else: + try: + other_idx = other_positions[param_name] + except KeyError: + return False + else: + if (idx != other_idx or + param != other.parameters[param_name]): + return False + + return True + + def __ne__(self, other): + return not self.__eq__(other) + + def _bind(self, args, kwargs, partial=False): + '''Private method. Don't use directly.''' + + arguments = OrderedDict() + + parameters = iter(self.parameters.values()) + parameters_ex = () + arg_vals = iter(args) + + if partial: + # Support for binding arguments to 'functools.partial' objects. + # See 'functools.partial' case in 'signature()' implementation + # for details. + for param_name, param in self.parameters.items(): + if (param._partial_kwarg and param_name not in kwargs): + # Simulating 'functools.partial' behavior + kwargs[param_name] = param.default + + while True: + # Let's iterate through the positional arguments and corresponding + # parameters + try: + arg_val = next(arg_vals) + except StopIteration: + # No more positional arguments + try: + param = next(parameters) + except StopIteration: + # No more parameters. That's it. Just need to check that + # we have no `kwargs` after this while loop + break + else: + if param.kind == _VAR_POSITIONAL: + # That's OK, just empty *args. Let's start parsing + # kwargs + break + elif param.name in kwargs: + if param.kind == _POSITIONAL_ONLY: + msg = '{arg!r} parameter is positional only, ' \ + 'but was passed as a keyword' + msg = msg.format(arg=param.name) + raise TypeError(msg) + parameters_ex = (param,) + break + elif (param.kind == _VAR_KEYWORD or + param.default is not _empty): + # That's fine too - we have a default value for this + # parameter. So, lets start parsing `kwargs`, starting + # with the current parameter + parameters_ex = (param,) + break + else: + if partial: + parameters_ex = (param,) + break + else: + msg = '{arg!r} parameter lacking default value' + msg = msg.format(arg=param.name) + raise TypeError(msg) + else: + # We have a positional argument to process + try: + param = next(parameters) + except StopIteration: + raise TypeError('too many positional arguments') + else: + if param.kind in (_VAR_KEYWORD, _KEYWORD_ONLY): + # Looks like we have no parameter for this positional + # argument + raise TypeError('too many positional arguments') + + if param.kind == _VAR_POSITIONAL: + # We have an '*args'-like argument, let's fill it with + # all positional arguments we have left and move on to + # the next phase + values = [arg_val] + values.extend(arg_vals) + arguments[param.name] = tuple(values) + break + + if param.name in kwargs: + raise TypeError('multiple values for argument ' + '{arg!r}'.format(arg=param.name)) + + arguments[param.name] = arg_val + + # Now, we iterate through the remaining parameters to process + # keyword arguments + kwargs_param = None + for param in itertools.chain(parameters_ex, parameters): + if param.kind == _POSITIONAL_ONLY: + # This should never happen in case of a properly built + # Signature object (but let's have this check here + # to ensure correct behaviour just in case) + raise TypeError('{arg!r} parameter is positional only, ' + 'but was passed as a keyword'. \ + format(arg=param.name)) + + if param.kind == _VAR_KEYWORD: + # Memorize that we have a '**kwargs'-like parameter + kwargs_param = param + continue + + param_name = param.name + try: + arg_val = kwargs.pop(param_name) + except KeyError: + # We have no value for this parameter. It's fine though, + # if it has a default value, or it is an '*args'-like + # parameter, left alone by the processing of positional + # arguments. + if (not partial and param.kind != _VAR_POSITIONAL and + param.default is _empty): + raise TypeError('{arg!r} parameter lacking default value'. \ + format(arg=param_name)) + + else: + arguments[param_name] = arg_val + + if kwargs: + if kwargs_param is not None: + # Process our '**kwargs'-like parameter + arguments[kwargs_param.name] = kwargs + else: + raise TypeError('too many keyword arguments') + + return self._bound_arguments_cls(self, arguments) + + def bind(self, *args, **kwargs): + '''Get a BoundArguments object, that maps the passed `args` + and `kwargs` to the function's signature. Raises `TypeError` + if the passed arguments can not be bound. + ''' + return self._bind(args, kwargs) + + def bind_partial(self, *args, **kwargs): + '''Get a BoundArguments object, that partially maps the + passed `args` and `kwargs` to the function's signature. + Raises `TypeError` if the passed arguments can not be bound. + ''' + return self._bind(args, kwargs, partial=True) + + def __str__(self): + result = [] + render_kw_only_separator = True + for idx, param in enumerate(self.parameters.values()): + formatted = str(param) + + kind = param.kind + if kind == _VAR_POSITIONAL: + # OK, we have an '*args'-like parameter, so we won't need + # a '*' to separate keyword-only arguments + render_kw_only_separator = False + elif kind == _KEYWORD_ONLY and render_kw_only_separator: + # We have a keyword-only parameter to render and we haven't + # rendered an '*args'-like parameter before, so add a '*' + # separator to the parameters list ("foo(arg1, *, arg2)" case) + result.append('*') + # This condition should be only triggered once, so + # reset the flag + render_kw_only_separator = False + + result.append(formatted) + + rendered = '({0})'.format(', '.join(result)) + + if self.return_annotation is not _empty: + anno = formatannotation(self.return_annotation) + rendered += ' -> {0}'.format(anno) + + return rendered diff --git a/ot/gromov.py b/ot/gromov.py index 2a23873..e03fa5b 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -595,7 +595,7 @@ def entropic_gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, gw, logv = entropic_gromov_wasserstein( C1, C2, p, q, loss_fun, epsilon, max_iter, tol, verbose, log=True) - log['T'] = gw + logv['T'] = gw if log: return logv['gw_dist'], logv diff --git a/ot/utils.py b/ot/utils.py index 16862ea..17983f2 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -15,7 +15,10 @@ import numpy as np from scipy.spatial.distance import cdist import sys import warnings - +try: + from inspect import signature +except ImportError: + from .externals.funcsigs import signature __time_tic_toc = time.time() @@ -335,10 +338,7 @@ class BaseEstimator(object): @classmethod def _get_param_names(cls): """Get parameter names for the estimator""" - try: - from inspect import signature - except ImportError: - from .externals.funcsigs import signature + # fetch the constructor or the original constructor before # deprecation wrapping if any init = getattr(cls.__init__, 'deprecated_original', cls.__init__) -- cgit v1.2.3 From 55aaf7874c651235d44c34b89337df7694e55014 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 21 Mar 2018 10:08:17 +0100 Subject: add test gromov + debug sklearn Basestimator --- test/test_da.py | 4 ++-- test/test_gromov.py | 25 +++++++++++++++++++++++++ 2 files changed, 27 insertions(+), 2 deletions(-) diff --git a/test/test_da.py b/test/test_da.py index a9d6d34..3022721 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -326,8 +326,8 @@ def test_mapping_transport_class(): """test_mapping_transport """ - ns = 150 - nt = 200 + ns = 60 + nt = 120 Xs, ys = get_data_classif('3gauss', ns) Xt, yt = get_data_classif('3gauss2', nt) diff --git a/test/test_gromov.py b/test/test_gromov.py index 625e62a..0384ee1 100644 --- a/test/test_gromov.py +++ b/test/test_gromov.py @@ -36,6 +36,18 @@ def test_gromov(): np.testing.assert_allclose( q, G.sum(0), atol=1e-04) # cf convergence gromov + gw, log = ot.gromov.gromov_wasserstein2(C1, C2, p, q, 'kl_loss', log=True) + + G = log['T'] + + np.testing.assert_allclose(gw, 0, atol=1e-04, rtol=1e-4) + + # check constratints + np.testing.assert_allclose( + p, G.sum(1), atol=1e-04) # cf convergence gromov + np.testing.assert_allclose( + q, G.sum(0), atol=1e-04) # cf convergence gromov + def test_entropic_gromov(): n_samples = 50 # nb samples @@ -64,3 +76,16 @@ def test_entropic_gromov(): p, G.sum(1), atol=1e-04) # cf convergence gromov np.testing.assert_allclose( q, G.sum(0), atol=1e-04) # cf convergence gromov + + gw, log = ot.gromov.entropic_gromov_wasserstein2( + C1, C2, p, q, 'kl_loss', epsilon=1e-2, log=True) + + G = log['T'] + + np.testing.assert_allclose(gw, 0, atol=1e-1, rtol=1e1) + + # check constratints + np.testing.assert_allclose( + p, G.sum(1), atol=1e-04) # cf convergence gromov + np.testing.assert_allclose( + q, G.sum(0), atol=1e-04) # cf convergence gromov -- cgit v1.2.3 From 64ef33d09906a1aebd3c8294ffd7720475ab926b Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 21 Mar 2018 10:13:16 +0100 Subject: aupdate gromov + autopep8 externals --- ot/externals/funcsigs.py | 56 +++++++++++++++++++++++++----------------------- test/test_gromov.py | 2 +- 2 files changed, 30 insertions(+), 28 deletions(-) diff --git a/ot/externals/funcsigs.py b/ot/externals/funcsigs.py index 4e68469..82a55da 100644 --- a/ot/externals/funcsigs.py +++ b/ot/externals/funcsigs.py @@ -29,7 +29,7 @@ def formatannotation(annotation, base_module=None): if isinstance(annotation, type): if annotation.__module__ in ('builtins', '__builtin__', base_module): return annotation.__name__ - return annotation.__module__+'.'+annotation.__name__ + return annotation.__module__ + '.' + annotation.__name__ return repr(annotation) @@ -127,7 +127,7 @@ def signature(obj): _partial_kwarg=True) elif (param.kind not in (_VAR_KEYWORD, _VAR_POSITIONAL) and - not param._partial_kwarg): + not param._partial_kwarg): new_params.pop(arg_name) return sig.replace(parameters=new_params.values()) @@ -170,7 +170,8 @@ def signature(obj): msg = 'no signature found for builtin function {0!r}'.format(obj) raise ValueError(msg) - raise ValueError('callable {0!r} is not supported by signature'.format(obj)) + raise ValueError( + 'callable {0!r} is not supported by signature'.format(obj)) class _void(object): @@ -194,11 +195,11 @@ class _ParameterKind(int): return '<_ParameterKind: {0!r}>'.format(self._name) -_POSITIONAL_ONLY = _ParameterKind(0, name='POSITIONAL_ONLY') -_POSITIONAL_OR_KEYWORD = _ParameterKind(1, name='POSITIONAL_OR_KEYWORD') -_VAR_POSITIONAL = _ParameterKind(2, name='VAR_POSITIONAL') -_KEYWORD_ONLY = _ParameterKind(3, name='KEYWORD_ONLY') -_VAR_KEYWORD = _ParameterKind(4, name='VAR_KEYWORD') +_POSITIONAL_ONLY = _ParameterKind(0, name='POSITIONAL_ONLY') +_POSITIONAL_OR_KEYWORD = _ParameterKind(1, name='POSITIONAL_OR_KEYWORD') +_VAR_POSITIONAL = _ParameterKind(2, name='VAR_POSITIONAL') +_KEYWORD_ONLY = _ParameterKind(3, name='KEYWORD_ONLY') +_VAR_KEYWORD = _ParameterKind(4, name='VAR_KEYWORD') class Parameter(object): @@ -223,11 +224,11 @@ class Parameter(object): __slots__ = ('_name', '_kind', '_default', '_annotation', '_partial_kwarg') - POSITIONAL_ONLY = _POSITIONAL_ONLY - POSITIONAL_OR_KEYWORD = _POSITIONAL_OR_KEYWORD - VAR_POSITIONAL = _VAR_POSITIONAL - KEYWORD_ONLY = _KEYWORD_ONLY - VAR_KEYWORD = _VAR_KEYWORD + POSITIONAL_ONLY = _POSITIONAL_ONLY + POSITIONAL_OR_KEYWORD = _POSITIONAL_OR_KEYWORD + VAR_POSITIONAL = _VAR_POSITIONAL + KEYWORD_ONLY = _KEYWORD_ONLY + VAR_KEYWORD = _VAR_KEYWORD empty = _empty @@ -253,7 +254,8 @@ class Parameter(object): self._name = name else: name = str(name) - if kind != _POSITIONAL_ONLY and not re.match(r'[a-z_]\w*$', name, re.I): + if kind != _POSITIONAL_ONLY and not re.match( + r'[a-z_]\w*$', name, re.I): msg = '{0!r} is not a valid parameter name'.format(name) raise ValueError(msg) self._name = name @@ -310,7 +312,7 @@ class Parameter(object): # Add annotation and default value if self._annotation is not _empty: formatted = '{0}:{1}'.format(formatted, - formatannotation(self._annotation)) + formatannotation(self._annotation)) if self._default is not _empty: formatted = '{0}={1}'.format(formatted, repr(self._default)) @@ -324,7 +326,7 @@ class Parameter(object): def __repr__(self): return '<{0} at {1:#x} {2!r}>'.format(self.__class__.__name__, - id(self), self.name) + id(self), self.name) def __hash__(self): msg = "unhashable type: '{0}'".format(self.__class__.__name__) @@ -371,7 +373,7 @@ class BoundArguments(object): args = [] for param_name, param in self._signature.parameters.items(): if (param.kind in (_VAR_KEYWORD, _KEYWORD_ONLY) or - param._partial_kwarg): + param._partial_kwarg): # Keyword arguments mapped by 'functools.partial' # (Parameter._partial_kwarg is True) are mapped # in 'BoundArguments.kwargs', along with VAR_KEYWORD & @@ -401,7 +403,7 @@ class BoundArguments(object): for param_name, param in self._signature.parameters.items(): if not kwargs_started: if (param.kind in (_VAR_KEYWORD, _KEYWORD_ONLY) or - param._partial_kwarg): + param._partial_kwarg): kwargs_started = True else: if param_name not in self.arguments: @@ -501,7 +503,7 @@ class Signature(object): params[name] = param else: params = OrderedDict(((param.name, param) - for param in parameters)) + for param in parameters)) self._parameters = params self._return_annotation = return_annotation @@ -611,12 +613,12 @@ class Signature(object): def __eq__(self, other): if (not issubclass(type(other), Signature) or - self.return_annotation != other.return_annotation or - len(self.parameters) != len(other.parameters)): + self.return_annotation != other.return_annotation or + len(self.parameters) != len(other.parameters)): return False other_positions = dict((param, idx) - for idx, param in enumerate(other.parameters.keys())) + for idx, param in enumerate(other.parameters.keys())) for idx, (param_name, param) in enumerate(self.parameters.items()): if param.kind == _KEYWORD_ONLY: @@ -634,7 +636,7 @@ class Signature(object): return False else: if (idx != other_idx or - param != other.parameters[param_name]): + param != other.parameters[param_name]): return False return True @@ -687,7 +689,7 @@ class Signature(object): parameters_ex = (param,) break elif (param.kind == _VAR_KEYWORD or - param.default is not _empty): + param.default is not _empty): # That's fine too - we have a default value for this # parameter. So, lets start parsing `kwargs`, starting # with the current parameter @@ -737,7 +739,7 @@ class Signature(object): # Signature object (but let's have this check here # to ensure correct behaviour just in case) raise TypeError('{arg!r} parameter is positional only, ' - 'but was passed as a keyword'. \ + 'but was passed as a keyword'. format(arg=param.name)) if param.kind == _VAR_KEYWORD: @@ -754,8 +756,8 @@ class Signature(object): # parameter, left alone by the processing of positional # arguments. if (not partial and param.kind != _VAR_POSITIONAL and - param.default is _empty): - raise TypeError('{arg!r} parameter lacking default value'. \ + param.default is _empty): + raise TypeError('{arg!r} parameter lacking default value'. format(arg=param_name)) else: diff --git a/test/test_gromov.py b/test/test_gromov.py index 0384ee1..0dfd54e 100644 --- a/test/test_gromov.py +++ b/test/test_gromov.py @@ -40,7 +40,7 @@ def test_gromov(): G = log['T'] - np.testing.assert_allclose(gw, 0, atol=1e-04, rtol=1e-4) + np.testing.assert_allclose(gw, 0, atol=1e-2, rtol=1e-2) # check constratints np.testing.assert_allclose( -- cgit v1.2.3 From 7095e03eb339bcf32d91c5a8857ecc3f3d0c45c0 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 21 Mar 2018 10:28:19 +0100 Subject: gtomov barycenter tests --- test/test_gromov.py | 31 +++++++++++++++++++++++++++++-- 1 file changed, 29 insertions(+), 2 deletions(-) diff --git a/test/test_gromov.py b/test/test_gromov.py index 0dfd54e..d865380 100644 --- a/test/test_gromov.py +++ b/test/test_gromov.py @@ -40,7 +40,7 @@ def test_gromov(): G = log['T'] - np.testing.assert_allclose(gw, 0, atol=1e-2, rtol=1e-2) + np.testing.assert_allclose(gw, 0, atol=1e-1, rtol=1e-1) # check constratints np.testing.assert_allclose( @@ -82,10 +82,37 @@ def test_entropic_gromov(): G = log['T'] - np.testing.assert_allclose(gw, 0, atol=1e-1, rtol=1e1) + np.testing.assert_allclose(gw, 0, atol=1e-1, rtol=1e-1) # check constratints np.testing.assert_allclose( p, G.sum(1), atol=1e-04) # cf convergence gromov np.testing.assert_allclose( q, G.sum(0), atol=1e-04) # cf convergence gromov + + +def test_gromov_barycenter(): + + ns = 50 + nt = 60 + + Xs, ys = ot.datasets.get_data_classif('3gauss', ns) + Xt, yt = ot.datasets.get_data_classif('3gauss2', nt) + + C1 = ot.dist(Xs) + C2 = ot.dist(Xt) + + n_samples = 3 + Cb = ot.gromov.gromov_barycenters(n_samples, [C1, C2], + [ot.unif(ns), ot.unif(nt) + ], ot.unif(n_samples), [.5, .5], + 'square_loss', # 5e-4, + max_iter=100, tol=1e-3) + np.testing.assert_allclose(Cb.shape, (n_samples, n_samples)) + + Cb2 = ot.gromov.gromov_barycenters(n_samples, [C1, C2], + [ot.unif(ns), ot.unif(nt) + ], ot.unif(n_samples), [.5, .5], + 'kl_loss', # 5e-4, + max_iter=100, tol=1e-3) + np.testing.assert_allclose(Cb2.shape, (n_samples, n_samples)) -- cgit v1.2.3 From 63fd11e8bfd45b163b313c7ad874ef608587fb68 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 21 Mar 2018 10:32:18 +0100 Subject: add entropic gromov test for 90+% corerage --- ot/gromov.py | 2 +- test/test_gromov.py | 27 +++++++++++++++++++++++++++ 2 files changed, 28 insertions(+), 1 deletion(-) diff --git a/ot/gromov.py b/ot/gromov.py index e03fa5b..65b2e29 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -613,7 +613,7 @@ def entropic_gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, The function solves the following optimization problem: .. math:: - C = argmin_C\in R^NxN \sum_s \lambda_s GW(C,Cs,p,ps) + C = argmin_C\in R^{NxN} \sum_s \lambda_s GW(C,Cs,p,ps) Where : diff --git a/test/test_gromov.py b/test/test_gromov.py index d865380..bb23469 100644 --- a/test/test_gromov.py +++ b/test/test_gromov.py @@ -116,3 +116,30 @@ def test_gromov_barycenter(): 'kl_loss', # 5e-4, max_iter=100, tol=1e-3) np.testing.assert_allclose(Cb2.shape, (n_samples, n_samples)) + + +def test_gromov_entropic_barycenter(): + + ns = 50 + nt = 60 + + Xs, ys = ot.datasets.get_data_classif('3gauss', ns) + Xt, yt = ot.datasets.get_data_classif('3gauss2', nt) + + C1 = ot.dist(Xs) + C2 = ot.dist(Xt) + + n_samples = 3 + Cb = ot.gromov.entropic_gromov_barycenters(n_samples, [C1, C2], + [ot.unif(ns), ot.unif(nt) + ], ot.unif(n_samples), [.5, .5], + 'square_loss', 1e-3, + max_iter=100, tol=1e-3) + np.testing.assert_allclose(Cb.shape, (n_samples, n_samples)) + + Cb2 = ot.gromov.entropic_gromov_barycenters(n_samples, [C1, C2], + [ot.unif(ns), ot.unif(nt) + ], ot.unif(n_samples), [.5, .5], + 'kl_loss', 1e-3, + max_iter=100, tol=1e-3) + np.testing.assert_allclose(Cb2.shape, (n_samples, n_samples)) -- cgit v1.2.3 From 1262563ef24c9ab0213f616ef01e1c80eb977176 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 21 Mar 2018 10:50:16 +0100 Subject: update readme + doc --- ot/da.py | 17 +++++++++++++++-- 1 file changed, 15 insertions(+), 2 deletions(-) diff --git a/ot/da.py b/ot/da.py index f789396..5d62d43 100644 --- a/ot/da.py +++ b/ot/da.py @@ -643,7 +643,7 @@ def OT_mapping_linear(xs, xt, reg=1e-6, ws=None, The function estimate the optimal linear operator that align the two empirical distributions. This is equivalent to estimating the closed form mapping between two Gaussian distribution :math:`N(\mu_s,\Sigma_s)` - and :math:`N(\mu_t,\Sigma_t)` as proposed in [14]. + and :math:`N(\mu_t,\Sigma_t)` as proposed in [14] and discussed in remark 2.29 in [15]. The linear operator from source to target :math:`M` @@ -692,6 +692,9 @@ def OT_mapping_linear(xs, xt, reg=1e-6, ws=None, .. [14] Knott, M. and Smith, C. S. "On the optimal mapping of distributions", Journal of Optimization Theory and Applications Vol 43, 1984 + + .. [15] Peyré, G., & Cuturi, M. (2017). "Computational Optimal + Transport", 2018. """ @@ -1290,7 +1293,8 @@ class LinearTransport(BaseTransport): The function estimate the optimal linear operator that align the two empirical distributions. This is equivalent to estimating the closed form mapping between two Gaussian distribution :math:`N(\mu_s,\Sigma_s)` - and :math:`N(\mu_t,\Sigma_t)` as proposed in [14]. + and :math:`N(\mu_t,\Sigma_t)` as proposed in [14] and discussed in + remark 2.29 in [15]. The linear operator from source to target :math:`M` @@ -1314,6 +1318,15 @@ class LinearTransport(BaseTransport): log : bool, optional record log if True + References + ---------- + + .. [14] Knott, M. and Smith, C. S. "On the optimal mapping of + distributions", Journal of Optimization Theory and Applications + Vol 43, 1984 + + .. [15] Peyré, G., & Cuturi, M. (2017). "Computational Optimal + Transport", 2018. """ -- cgit v1.2.3 From 0ce1a5ed14e44cd0c596fc0393eceeb8199d20d7 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 21 Mar 2018 10:50:45 +0100 Subject: update doc --- README.md | 4 ++++ docs/source/readme.rst | 17 ++++++++++++----- 2 files changed, 16 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 8a9d7fa..fb7ab2a 100644 --- a/README.md +++ b/README.md @@ -206,3 +206,7 @@ You can also post bug reports and feature requests in Github issues. Make sure t [12] Gabriel Peyré, Marco Cuturi, and Justin Solomon, [Gromov-Wasserstein averaging of kernel and distance matrices](http://proceedings.mlr.press/v48/peyre16.html) International Conference on Machine Learning (ICML). 2016. [13] Mémoli, Facundo. [Gromov–Wasserstein distances and the metric approach to object matching](https://media.adelaide.edu.au/acvt/Publications/2011/2011-Gromov%E2%80%93Wasserstein%20Distances%20and%20the%20Metric%20Approach%20to%20Object%20Matching.pdf). Foundations of computational mathematics 11.4 (2011): 417-487. + +[14] Knott, M. and Smith, C. S. [On the optimal mapping of distributions](https://link.springer.com/article/10.1007/BF00934745), Journal of Optimization Theory and Applications Vol 43, 1984. + +[15] Peyré, G., & Cuturi, M. (2017). [Computational Optimal Transport](https://arxiv.org/pdf/1803.00567.pdf) , 2018. diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 347bde2..647f7e8 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -1,8 +1,8 @@ POT: Python Optimal Transport ============================= -|PyPI version| |Build Status| |Documentation Status| |Anaconda Cloud| -|License| |Anaconda downloads| +|PyPI version| |Anaconda Cloud| |Build Status| |Documentation Status| +|Anaconda downloads| |License| This open source Python library provide several solvers for optimization problems related to Optimal Transport for signal, image processing and @@ -311,15 +311,22 @@ approach to object matching `__. Foundations of computational mathematics 11.4 (2011): 417-487. +[14] Knott, M. and Smith, C. S. `On the optimal mapping of +distributions `__, +Journal of Optimization Theory and Applications Vol 43, 1984. + +[15] Peyré, G., & Cuturi, M. (2017). `Computational Optimal +Transport `__ , 2018. + .. |PyPI version| image:: https://badge.fury.io/py/POT.svg :target: https://badge.fury.io/py/POT +.. |Anaconda Cloud| image:: https://anaconda.org/conda-forge/pot/badges/version.svg + :target: https://anaconda.org/conda-forge/pot .. |Build Status| image:: https://travis-ci.org/rflamary/POT.svg?branch=master :target: https://travis-ci.org/rflamary/POT .. |Documentation Status| image:: https://readthedocs.org/projects/pot/badge/?version=latest :target: http://pot.readthedocs.io/en/latest/?badge=latest -.. |Anaconda Cloud| image:: https://anaconda.org/conda-forge/pot/badges/version.svg +.. |Anaconda downloads| image:: https://anaconda.org/conda-forge/pot/badges/downloads.svg :target: https://anaconda.org/conda-forge/pot .. |License| image:: https://anaconda.org/conda-forge/pot/badges/license.svg :target: https://github.com/rflamary/POT/blob/master/LICENSE -.. |Anaconda downloads| image:: https://anaconda.org/conda-forge/pot/badges/downloads.svg - :target: https://anaconda.org/conda-forge/pot -- cgit v1.2.3 From 83c706cb6b1c9eb6ca033c58532b85c13b5d40f2 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 21 Mar 2018 10:54:00 +0100 Subject: pep cleanup --- ot/da.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/ot/da.py b/ot/da.py index 5d62d43..cdebc91 100644 --- a/ot/da.py +++ b/ot/da.py @@ -692,8 +692,8 @@ def OT_mapping_linear(xs, xt, reg=1e-6, ws=None, .. [14] Knott, M. and Smith, C. S. "On the optimal mapping of distributions", Journal of Optimization Theory and Applications Vol 43, 1984 - - .. [15] Peyré, G., & Cuturi, M. (2017). "Computational Optimal + + .. [15] Peyré, G., & Cuturi, M. (2017). "Computational Optimal Transport", 2018. @@ -1293,7 +1293,7 @@ class LinearTransport(BaseTransport): The function estimate the optimal linear operator that align the two empirical distributions. This is equivalent to estimating the closed form mapping between two Gaussian distribution :math:`N(\mu_s,\Sigma_s)` - and :math:`N(\mu_t,\Sigma_t)` as proposed in [14] and discussed in + and :math:`N(\mu_t,\Sigma_t)` as proposed in [14] and discussed in remark 2.29 in [15]. The linear operator from source to target :math:`M` @@ -1324,8 +1324,8 @@ class LinearTransport(BaseTransport): .. [14] Knott, M. and Smith, C. S. "On the optimal mapping of distributions", Journal of Optimization Theory and Applications Vol 43, 1984 - - .. [15] Peyré, G., & Cuturi, M. (2017). "Computational Optimal + + .. [15] Peyré, G., & Cuturi, M. (2017). "Computational Optimal Transport", 2018. """ -- cgit v1.2.3 From 69c7d1cb64a5628c69a3c1533991741bcd91f96b Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 21 Mar 2018 10:58:20 +0100 Subject: pep8 unused variable --- ot/externals/funcsigs.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/externals/funcsigs.py b/ot/externals/funcsigs.py index 82a55da..c73fdc9 100644 --- a/ot/externals/funcsigs.py +++ b/ot/externals/funcsigs.py @@ -99,7 +99,7 @@ def signature(obj): partial_keywords = obj.keywords or {} try: ba = sig.bind_partial(*partial_args, **partial_keywords) - except TypeError as ex: + except TypeError: msg = 'partial object {0!r} has incorrect arguments'.format(obj) raise ValueError(msg) -- cgit v1.2.3 From 7681db5c19817cfd003cea9ffdd95fedb9b00650 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 21 Mar 2018 11:03:06 +0100 Subject: update reame --- README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index fb7ab2a..353fe32 100644 --- a/README.md +++ b/README.md @@ -13,14 +13,14 @@ This open source Python library provide several solvers for optimization problem It provides the following solvers: -* OT solver for the linear program/ Earth Movers Distance [1]. +* OT Network Flow solver for the linear program/ Earth Movers Distance [1]. * Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10] with optional GPU implementation (required cudamat). * Bregman projections for Wasserstein barycenter [3] and unmixing [4]. * Optimal transport for domain adaptation with group lasso regularization [5] * Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. -* Joint OT matrix and mapping estimation [8]. +* Linear OT [14] and Joint OT matrix and mapping estimation [8]. * Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt). -* Gromov-Wasserstein distances and barycenters [12] +* Gromov-Wasserstein distances and barycenters ([13] and regularized [12]) Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. -- cgit v1.2.3 From af9f1e3cd75ec3453e53a4cd8940a5542fc9e117 Mon Sep 17 00:00:00 2001 From: Tardy Benjamin Date: Wed, 18 Apr 2018 11:34:46 +0200 Subject: BUG: EMDTransport parameter log unusable --- ot/da.py | 18 ++++++++++++------ 1 file changed, 12 insertions(+), 6 deletions(-) diff --git a/ot/da.py b/ot/da.py index c688654..a92c7f9 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1332,13 +1332,14 @@ class EMDTransport(BaseTransport): on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 """ - def __init__(self, metric="sqeuclidean", norm=None, + def __init__(self, metric="sqeuclidean", norm=None,log=False, distribution_estimation=distribution_estimation_uniform, out_of_sample_map='ferradans', limit_max=10, max_iter=100000): - + self.metric = metric self.norm = norm + self.log = log self.limit_max = limit_max self.distribution_estimation = distribution_estimation self.out_of_sample_map = out_of_sample_map @@ -1371,11 +1372,16 @@ class EMDTransport(BaseTransport): super(EMDTransport, self).fit(Xs, ys, Xt, yt) - # coupling estimation - self.coupling_ = emd( - a=self.mu_s, b=self.mu_t, M=self.cost_, numItermax=self.max_iter - ) + returned_ = emd( + a=self.mu_s, b=self.mu_t, M=self.cost_, numItermax=self.max_iter, + log=self.log) + # coupling estimation + if self.log: + self.coupling_, self.log_ = returned_ + else: + self.coupling_ = returned_ + self.log_ = dict() return self -- cgit v1.2.3 From 1b5112c22a143e1daec317f27633548a194a32ef Mon Sep 17 00:00:00 2001 From: Tardy Benjamin Date: Wed, 18 Apr 2018 11:36:13 +0200 Subject: ENH: Change the parameter type to bool in class EMDTransport documentation --- ot/da.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/da.py b/ot/da.py index a92c7f9..aed7006 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1310,7 +1310,7 @@ class EMDTransport(BaseTransport): The kind of distribution estimation to employ verbose : int, optional (default=0) Controls the verbosity of the optimization algorithm - log : int, optional (default=0) + log : bool, optional (default=False) Controls the logs of the optimization algorithm limit_max: float, optional (default=10) Controls the semi supervised mode. Transport between labeled source -- cgit v1.2.3 From 94b929b37623e45a61b998f031ad7349943a53d8 Mon Sep 17 00:00:00 2001 From: Tardy Benjamin Date: Wed, 18 Apr 2018 13:26:29 +0200 Subject: BUG: Parameter log unusable in sinkhorn classes --- ot/da.py | 15 +++++++++++---- 1 file changed, 11 insertions(+), 4 deletions(-) diff --git a/ot/da.py b/ot/da.py index aed7006..33480f9 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1310,7 +1310,7 @@ class EMDTransport(BaseTransport): The kind of distribution estimation to employ verbose : int, optional (default=0) Controls the verbosity of the optimization algorithm - log : bool, optional (default=False) + log : int, optional (default=0) Controls the logs of the optimization algorithm limit_max: float, optional (default=10) Controls the semi supervised mode. Transport between labeled source @@ -1438,7 +1438,7 @@ class SinkhornLpl1Transport(BaseTransport): """ def __init__(self, reg_e=1., reg_cl=0.1, - max_iter=10, max_inner_iter=200, + max_iter=10, max_inner_iter=200, lo=False, tol=10e-9, verbose=False, metric="sqeuclidean", norm=None, distribution_estimation=distribution_estimation_uniform, @@ -1449,6 +1449,7 @@ class SinkhornLpl1Transport(BaseTransport): self.max_iter = max_iter self.max_inner_iter = max_inner_iter self.tol = tol + self.log = log self.verbose = verbose self.metric = metric self.norm = norm @@ -1486,12 +1487,18 @@ class SinkhornLpl1Transport(BaseTransport): super(SinkhornLpl1Transport, self).fit(Xs, ys, Xt, yt) - self.coupling_ = sinkhorn_lpl1_mm( + returned_ = sinkhorn_lpl1_mm( a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.cost_, reg=self.reg_e, eta=self.reg_cl, numItermax=self.max_iter, numInnerItermax=self.max_inner_iter, stopInnerThr=self.tol, - verbose=self.verbose) + verbose=self.verbose,log=self.log) + # deal with the value of log + if self.log: + self.coupling_, self.log_ = returned_ + else: + self.coupling_ = returned_ + self.log_ = dict() return self -- cgit v1.2.3 From b6687b5a2963f217d11f75e93c408b390a5dc53d Mon Sep 17 00:00:00 2001 From: Tardy Benjamin Date: Wed, 18 Apr 2018 13:31:49 +0200 Subject: BUG: typo error lo->log --- ot/da.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/da.py b/ot/da.py index 33480f9..b3dd61b 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1438,7 +1438,7 @@ class SinkhornLpl1Transport(BaseTransport): """ def __init__(self, reg_e=1., reg_cl=0.1, - max_iter=10, max_inner_iter=200, lo=False, + max_iter=10, max_inner_iter=200, log=False, tol=10e-9, verbose=False, metric="sqeuclidean", norm=None, distribution_estimation=distribution_estimation_uniform, -- cgit v1.2.3 From 54e16a40c5980290c03b654696db51e0aee583bb Mon Sep 17 00:00:00 2001 From: Tardy Benjamin Date: Wed, 18 Apr 2018 13:49:02 +0200 Subject: BUG: correct typo problems --- ot/da.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/ot/da.py b/ot/da.py index b3dd61b..532dcd2 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1332,11 +1332,11 @@ class EMDTransport(BaseTransport): on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 """ - def __init__(self, metric="sqeuclidean", norm=None,log=False, + def __init__(self, metric="sqeuclidean", norm=None, log=False, distribution_estimation=distribution_estimation_uniform, out_of_sample_map='ferradans', limit_max=10, max_iter=100000): - + self.metric = metric self.norm = norm self.log = log @@ -1491,7 +1491,7 @@ class SinkhornLpl1Transport(BaseTransport): a=self.mu_s, labels_a=ys, b=self.mu_t, M=self.cost_, reg=self.reg_e, eta=self.reg_cl, numItermax=self.max_iter, numInnerItermax=self.max_inner_iter, stopInnerThr=self.tol, - verbose=self.verbose,log=self.log) + verbose=self.verbose, log=self.log) # deal with the value of log if self.log: -- cgit v1.2.3 From 702f222a428cfabb464c92da5e101642afdc643d Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 2 May 2018 13:33:20 +0200 Subject: add markdown formt for pipy --- setup.py | 17 ++++++++++------- 1 file changed, 10 insertions(+), 7 deletions(-) diff --git a/setup.py b/setup.py index a32aa31..3066848 100755 --- a/setup.py +++ b/setup.py @@ -18,21 +18,18 @@ __version__ = re.search( # The beautiful part is, I don't even need to check exceptions here. # If something messes up, let the build process fail noisy, BEFORE my release! +# thanks Pipy for handling markdown now ROOT = os.path.abspath(os.path.dirname(__file__)) - -# convert markdown readme to rst if pypandoc installed -try: - import pypandoc - README = pypandoc.convert('README.md', 'rst') -except (IOError, ImportError): - README = open(os.path.join(ROOT, 'README.md'), encoding="utf-8").read() +with open(os.path.join(ROOT, 'README.md'), encoding="utf-8") as f: + README = f.read() setup(name='POT', version=__version__, description='Python Optimal Transport Library', long_description=README, + long_description_content_type='text/markdown', author=u'Remi Flamary, Nicolas Courty', author_email='remi.flamary@gmail.com, ncourty@gmail.com', url='https://github.com/rflamary/POT', @@ -59,5 +56,11 @@ setup(name='POT', 'Operating System :: POSIX', 'Programming Language :: Python', 'Topic :: Utilities' + 'Programming Language :: Python :: 2', + 'Programming Language :: Python :: 2.7', + 'Programming Language :: Python :: 3', + 'Programming Language :: Python :: 3.4', + 'Programming Language :: Python :: 3.5', + 'Programming Language :: Python :: 3.6', ] ) -- cgit v1.2.3 From c30519a4c67a056c85d7897f198e2fb34c584755 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 9 May 2018 13:06:02 +0200 Subject: cleanup Makefile --- Makefile | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Makefile b/Makefile index 468d042..95714b8 100644 --- a/Makefile +++ b/Makefile @@ -44,11 +44,11 @@ test : FORCE pep8 $(PYTHON) -m pytest -v test/ --cov=ot --cov-report html:cov_html pytest : FORCE - python -m py.test -v test/ --cov=ot + $(PYTHON) -m py.test -v test/ --cov=ot uploadpypi : #python setup.py register - python setup.py sdist upload -r pypi + $(PYTHON) setup.py sdist upload -r pypi rdoc : pandoc --from=markdown --to=rst --output=docs/source/readme.rst README.md -- cgit v1.2.3 From d5ea28b22ab94a13f676ffc0ed862887921c2efc Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 9 May 2018 13:06:46 +0200 Subject: correct ref 15 in readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 353fe32..65ee710 100644 --- a/README.md +++ b/README.md @@ -209,4 +209,4 @@ You can also post bug reports and feature requests in Github issues. Make sure t [14] Knott, M. and Smith, C. S. [On the optimal mapping of distributions](https://link.springer.com/article/10.1007/BF00934745), Journal of Optimization Theory and Applications Vol 43, 1984. -[15] Peyré, G., & Cuturi, M. (2017). [Computational Optimal Transport](https://arxiv.org/pdf/1803.00567.pdf) , 2018. +[15] Peyré, G., & Cuturi, M. (2018). [Computational Optimal Transport](https://arxiv.org/pdf/1803.00567.pdf) . -- cgit v1.2.3 From e26e69f11498a85148f0df9776c7fb0fca4545f1 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 9 May 2018 13:07:01 +0200 Subject: update documentation wrt readme file --- docs/source/readme.rst | 12 +++++++----- 1 file changed, 7 insertions(+), 5 deletions(-) diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 647f7e8..d73d293 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -10,7 +10,8 @@ machine learning. It provides the following solvers: -- OT solver for the linear program/ Earth Movers Distance [1]. +- OT Network Flow solver for the linear program/ Earth Movers Distance + [1]. - Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10] with optional GPU implementation (required cudamat). @@ -19,10 +20,11 @@ It provides the following solvers: regularization [5] - Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. -- Joint OT matrix and mapping estimation [8]. +- Linear OT [14] and Joint OT matrix and mapping estimation [8]. - Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt). -- Gromov-Wasserstein distances and barycenters [12] +- Gromov-Wasserstein distances and barycenters ([13] and regularized + [12]) Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -315,8 +317,8 @@ Foundations of computational mathematics 11.4 (2011): 417-487. distributions `__, Journal of Optimization Theory and Applications Vol 43, 1984. -[15] Peyré, G., & Cuturi, M. (2017). `Computational Optimal -Transport `__ , 2018. +[15] Peyré, G., & Cuturi, M. (2018). `Computational Optimal +Transport `__ . .. |PyPI version| image:: https://badge.fury.io/py/POT.svg :target: https://badge.fury.io/py/POT -- cgit v1.2.3 From 0a9763ce0e83106daa322566398218aa4a297fe1 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 9 May 2018 13:08:53 +0200 Subject: cleanup reference years in readme --- README.md | 8 ++++---- docs/source/readme.rst | 20 ++++++++++---------- 2 files changed, 14 insertions(+), 14 deletions(-) diff --git a/README.md b/README.md index 65ee710..6b7cff0 100644 --- a/README.md +++ b/README.md @@ -195,7 +195,7 @@ You can also post bug reports and feature requests in Github issues. Make sure t [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). [Generalized conditional gradient: analysis of convergence and applications](https://arxiv.org/pdf/1510.06567.pdf). arXiv preprint arXiv:1510.06567. -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, [Mapping estimation for discrete optimal transport](http://remi.flamary.com/biblio/perrot2016mapping.pdf), Neural Information Processing Systems (NIPS), 2016. +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard (2016), [Mapping estimation for discrete optimal transport](http://remi.flamary.com/biblio/perrot2016mapping.pdf), Neural Information Processing Systems (NIPS). [9] Schmitzer, B. (2016). [Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems](https://arxiv.org/pdf/1610.06519.pdf). arXiv preprint arXiv:1610.06519. @@ -203,10 +203,10 @@ You can also post bug reports and feature requests in Github issues. Make sure t [11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). [Wasserstein Discriminant Analysis](https://arxiv.org/pdf/1608.08063.pdf). arXiv preprint arXiv:1608.08063. -[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon, [Gromov-Wasserstein averaging of kernel and distance matrices](http://proceedings.mlr.press/v48/peyre16.html) International Conference on Machine Learning (ICML). 2016. +[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon (2016), [Gromov-Wasserstein averaging of kernel and distance matrices](http://proceedings.mlr.press/v48/peyre16.html) International Conference on Machine Learning (ICML). -[13] Mémoli, Facundo. [Gromov–Wasserstein distances and the metric approach to object matching](https://media.adelaide.edu.au/acvt/Publications/2011/2011-Gromov%E2%80%93Wasserstein%20Distances%20and%20the%20Metric%20Approach%20to%20Object%20Matching.pdf). Foundations of computational mathematics 11.4 (2011): 417-487. +[13] Mémoli, Facundo (2011). [Gromov–Wasserstein distances and the metric approach to object matching](https://media.adelaide.edu.au/acvt/Publications/2011/2011-Gromov%E2%80%93Wasserstein%20Distances%20and%20the%20Metric%20Approach%20to%20Object%20Matching.pdf). Foundations of computational mathematics 11.4 : 417-487. -[14] Knott, M. and Smith, C. S. [On the optimal mapping of distributions](https://link.springer.com/article/10.1007/BF00934745), Journal of Optimization Theory and Applications Vol 43, 1984. +[14] Knott, M. and Smith, C. S. (1984).[On the optimal mapping of distributions](https://link.springer.com/article/10.1007/BF00934745), Journal of Optimization Theory and Applications Vol 43. [15] Peyré, G., & Cuturi, M. (2018). [Computational Optimal Transport](https://arxiv.org/pdf/1803.00567.pdf) . diff --git a/docs/source/readme.rst b/docs/source/readme.rst index d73d293..725c207 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -283,10 +283,10 @@ conditional gradient: analysis of convergence and applications `__. arXiv preprint arXiv:1510.06567. -[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, `Mapping estimation -for discrete optimal +[8] M. Perrot, N. Courty, R. Flamary, A. Habrard (2016), `Mapping +estimation for discrete optimal transport `__, -Neural Information Processing Systems (NIPS), 2016. +Neural Information Processing Systems (NIPS). [9] Schmitzer, B. (2016). `Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport @@ -303,19 +303,19 @@ arXiv:1607.05816. Analysis `__. arXiv preprint arXiv:1608.08063. -[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon, +[12] Gabriel Peyré, Marco Cuturi, and Justin Solomon (2016), `Gromov-Wasserstein averaging of kernel and distance matrices `__ -International Conference on Machine Learning (ICML). 2016. +International Conference on Machine Learning (ICML). -[13] Mémoli, Facundo. `Gromov–Wasserstein distances and the metric -approach to object +[13] Mémoli, Facundo (2011). `Gromov–Wasserstein distances and the +metric approach to object matching `__. -Foundations of computational mathematics 11.4 (2011): 417-487. +Foundations of computational mathematics 11.4 : 417-487. -[14] Knott, M. and Smith, C. S. `On the optimal mapping of +[14] Knott, M. and Smith, C. S. (1984).`On the optimal mapping of distributions `__, -Journal of Optimization Theory and Applications Vol 43, 1984. +Journal of Optimization Theory and Applications Vol 43. [15] Peyré, G., & Cuturi, M. (2018). `Computational Optimal Transport `__ . -- cgit v1.2.3 From 0496e2b1b2c2f4ea2d7f313ccf58c612efaa70bf Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 9 May 2018 13:12:06 +0200 Subject: doc typos in linear map function --- ot/da.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/ot/da.py b/ot/da.py index cdebc91..b83d67e 100644 --- a/ot/da.py +++ b/ot/da.py @@ -640,9 +640,9 @@ def OT_mapping_linear(xs, xt, reg=1e-6, ws=None, wt=None, bias=True, log=False): """ return OT linear operator between samples - The function estimate the optimal linear operator that align the two + The function estimates the optimal linear operator that aligns the two empirical distributions. This is equivalent to estimating the closed - form mapping between two Gaussian distribution :math:`N(\mu_s,\Sigma_s)` + form mapping between two Gaussian distributions :math:`N(\mu_s,\Sigma_s)` and :math:`N(\mu_t,\Sigma_t)` as proposed in [14] and discussed in remark 2.29 in [15]. The linear operator from source to target :math:`M` @@ -665,7 +665,7 @@ def OT_mapping_linear(xs, xt, reg=1e-6, ws=None, xt : np.ndarray (nt,d) samples in the target domain reg : float,optional - regularization added to the daigonals of convariances (>0) + regularization added to the diagonals of convariances (>0) ws : np.ndarray (ns,1), optional weights for the source samples wt : np.ndarray (ns,1), optional @@ -1290,9 +1290,9 @@ class BaseTransport(BaseEstimator): class LinearTransport(BaseTransport): """ OT linear operator between empirical distributions - The function estimate the optimal linear operator that align the two + The function estimates the optimal linear operator that aligns the two empirical distributions. This is equivalent to estimating the closed - form mapping between two Gaussian distribution :math:`N(\mu_s,\Sigma_s)` + form mapping between two Gaussian distributions :math:`N(\mu_s,\Sigma_s)` and :math:`N(\mu_t,\Sigma_t)` as proposed in [14] and discussed in remark 2.29 in [15]. -- cgit v1.2.3 From 17790802c6f3f31b30ad36f4a163eacf8eb6049e Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 9 May 2018 13:32:47 +0200 Subject: add several python 3 --- .travis.yml | 6 ++++++ .travis/before_install.sh | 2 +- 2 files changed, 7 insertions(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index ec2b3d2..756b765 100644 --- a/.travis.yml +++ b/.travis.yml @@ -8,6 +8,12 @@ matrix: - os: linux sudo: required python: 3.4 + - os: linux + sudo: required + python: 3.5 + - os: linux + sudo: required + python: 3.6 - os: linux sudo: required python: 2.7 diff --git a/.travis/before_install.sh b/.travis/before_install.sh index 60d1dcf..ce18f12 100755 --- a/.travis/before_install.sh +++ b/.travis/before_install.sh @@ -6,7 +6,7 @@ if [[ $TRAVIS_OS_NAME == 'osx' ]]; then # e.g. brew install pyenv-virtualenv #brew update #brew install python - echo do othing + echo do nothing else -- cgit v1.2.3 From 96dfa124909f1334974dfd6f3519b145acc2c155 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 9 May 2018 13:36:42 +0200 Subject: install python with brew on macosx --- .travis/before_install.sh | 6 ++---- 1 file changed, 2 insertions(+), 4 deletions(-) diff --git a/.travis/before_install.sh b/.travis/before_install.sh index ce18f12..ddaa142 100755 --- a/.travis/before_install.sh +++ b/.travis/before_install.sh @@ -4,10 +4,8 @@ if [[ $TRAVIS_OS_NAME == 'osx' ]]; then # Install some custom requirements on OS X # e.g. brew install pyenv-virtualenv - #brew update - #brew install python - echo do nothing - + brew update + brew install python else # Install some custom requirements on Linux -- cgit v1.2.3 From 44fa4b82a0d1184369f7cfa87cac4f8c910ebe14 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 9 May 2018 13:52:32 +0200 Subject: wront travis.yml --- .travis.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.travis.yml b/.travis.yml index 756b765..b45258a 100644 --- a/.travis.yml +++ b/.travis.yml @@ -10,10 +10,10 @@ matrix: python: 3.4 - os: linux sudo: required - python: 3.5 + python: 3.5 - os: linux sudo: required - python: 3.6 + python: 3.6 - os: linux sudo: required python: 2.7 -- cgit v1.2.3 From 04f0149e644e9231a3f2e66e3d2335d1465efe06 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 9 May 2018 13:53:57 +0200 Subject: space in travis.yml --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index b45258a..b45330f 100644 --- a/.travis.yml +++ b/.travis.yml @@ -11,7 +11,7 @@ matrix: - os: linux sudo: required python: 3.5 - - os: linux + - os: linux sudo: required python: 3.6 - os: linux -- cgit v1.2.3 From 19ce6a9319a4d29fb31e2709f7dae6e60d9c112d Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 9 May 2018 14:00:48 +0200 Subject: add pytest in requirement --- requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/requirements.txt b/requirements.txt index 9bfca43..37d62cc 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,3 +5,4 @@ matplotlib sphinx-gallery autograd pymanopt +pytest -- cgit v1.2.3 From 58387b96cb2b4a417c84e729f0084be68fad731e Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 9 May 2018 14:05:58 +0200 Subject: switch to pytest --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index b45330f..56daada 100644 --- a/.travis.yml +++ b/.travis.yml @@ -32,5 +32,5 @@ install: script: - python setup.py develop - flake8 examples/ ot/ test/ - - python -m py.test -v test/ + - python -m pytest -v test/ # - py.test ot test -- cgit v1.2.3 From 4090857cfcda73d383e4cdbcc466fe326f06bd19 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 9 May 2018 14:15:48 +0200 Subject: test install pip on macosx --- .travis/before_install.sh | 5 +++-- Makefile | 2 +- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/.travis/before_install.sh b/.travis/before_install.sh index ddaa142..0ae6249 100755 --- a/.travis/before_install.sh +++ b/.travis/before_install.sh @@ -4,8 +4,9 @@ if [[ $TRAVIS_OS_NAME == 'osx' ]]; then # Install some custom requirements on OS X # e.g. brew install pyenv-virtualenv - brew update - brew install python + #brew update + #brew install python + sudo easy_install -U pip else # Install some custom requirements on Linux diff --git a/Makefile b/Makefile index 95714b8..0de3fe9 100644 --- a/Makefile +++ b/Makefile @@ -44,7 +44,7 @@ test : FORCE pep8 $(PYTHON) -m pytest -v test/ --cov=ot --cov-report html:cov_html pytest : FORCE - $(PYTHON) -m py.test -v test/ --cov=ot + $(PYTHON) -m pytest -v test/ --cov=ot uploadpypi : #python setup.py register -- cgit v1.2.3 From cc981610ba88e0222246aec2fdff903e8b83d918 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 9 May 2018 14:21:10 +0200 Subject: remove osx --- .travis.yml | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/.travis.yml b/.travis.yml index 56daada..d146395 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,10 +1,10 @@ language: python matrix: - allow_failures: - - os: osx +# allow_failures: +# - os: osx include: - - os: osx - language: generic +# - os: osx +# language: generic - os: linux sudo: required python: 3.4 -- cgit v1.2.3 From 94eb4a24fe09c8a193933d8c7f48a657da4e6c52 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 11 May 2018 16:07:03 +0200 Subject: update documentation in bregman --- ot/bregman.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/ot/bregman.py b/ot/bregman.py index 07b8660..9c84aed 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -844,6 +844,8 @@ def barycenter(A, M, reg, weights=None, numItermax=1000, loss matrix for OT reg : float Regularization term >0 + weights : np.ndarray (n,) + Weights of each histogram i_i on the simplex numItermax : int, optional Max number of iterations stopThr : float, optional -- cgit v1.2.3 From 8f6c8cd04db65a5d28c467e00b294b07e8183eb8 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 11 May 2018 16:11:45 +0200 Subject: update readme --- README.md | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index 6b7cff0..466c09c 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,8 @@ This open source Python library provide several solvers for optimization problem It provides the following solvers: * OT Network Flow solver for the linear program/ Earth Movers Distance [1]. -* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10] with optional GPU implementation (required cudamat). +* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10] with optional GPU implementation (requires cudamat). +* Non regularized Wasserstein barycenters [16] with LP solver. * Bregman projections for Wasserstein barycenter [3] and unmixing [4]. * Optimal transport for domain adaptation with group lasso regularization [5] * Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. @@ -210,3 +211,5 @@ You can also post bug reports and feature requests in Github issues. Make sure t [14] Knott, M. and Smith, C. S. (1984).[On the optimal mapping of distributions](https://link.springer.com/article/10.1007/BF00934745), Journal of Optimization Theory and Applications Vol 43. [15] Peyré, G., & Cuturi, M. (2018). [Computational Optimal Transport](https://arxiv.org/pdf/1803.00567.pdf) . + +[16] Agueh, M., & Carlier, G. (2011). [Barycenters in the Wasserstein space](https://hal.archives-ouvertes.fr/hal-00637399/document). SIAM Journal on Mathematical Analysis, 43(2), 904-924. -- cgit v1.2.3 From be8817730c7996052e84d21ba08cf60f59020935 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 11 May 2018 16:55:35 +0200 Subject: add requirement scipy for linprog interior point solver --- requirements.txt | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/requirements.txt b/requirements.txt index 9bfca43..df841ba 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,5 +1,5 @@ numpy -scipy +scipy>=1.0 cython matplotlib sphinx-gallery -- cgit v1.2.3 From 060d9046b291c76244deab2d78ee8356a294e91f Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 11 May 2018 16:56:47 +0200 Subject: add cvx barycenter solver --- ot/lp/cvx.py | 138 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 138 insertions(+) create mode 100644 ot/lp/cvx.py diff --git a/ot/lp/cvx.py b/ot/lp/cvx.py new file mode 100644 index 0000000..4d08916 --- /dev/null +++ b/ot/lp/cvx.py @@ -0,0 +1,138 @@ +# -*- coding: utf-8 -*- +""" +LP solvers for optimal transport using cvxopt +""" + +# Author: Remi Flamary +# +# License: MIT License + +import numpy as np +import scipy as sp +import scipy.sparse as sps + +try: + import cvxopt + from cvxopt import solvers, matrix, sparse, spmatrix +except ImportError: + cvxopt=False + +def scipy_sparse_to_spmatrix(A): + """Efficient conversion from scipy sparse matrix to cvxopt sparse matrix""" + coo = A.tocoo() + SP = spmatrix(coo.data.tolist(), coo.row.tolist(), coo.col.tolist(), size=A.shape) + return SP + +def barycenter(A, M, weights=None, verbose=False, log=False,solver='interior-point'): + """Compute the entropic regularized wasserstein barycenter of distributions A + + The function solves the following optimization problem [16]: + + .. math:: + \mathbf{a} = arg\min_\mathbf{a} \sum_i W_{1}(\mathbf{a},\mathbf{a}_i) + + where : + + - :math:`W_1(\cdot,\cdot)` is the Wasserstein distance (see ot.emd.sinkhorn) + - :math:`\mathbf{a}_i` are training distributions in the columns of matrix :math:`\mathbf{A}` + + The linear program is solved using the default cvxopt solver if installed. + If cvxopt is not installed it uses the lp solver from scipy.optimize. + + Parameters + ---------- + A : np.ndarray (d,n) + n training distributions of size d + M : np.ndarray (d,d) + loss matrix for OT + reg : float + Regularization term >0 + weights : np.ndarray (n,) + Weights of each histogram i_i on the simplex + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + solver : string, optional + the solver used, default 'interior-point' use the lp solver from + scipy.optimize. None, or 'glpk' or 'mosek' use the solver from cvxopt. + + Returns + ------- + a : (d,) ndarray + Wasserstein barycenter + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [16] Agueh, M., & Carlier, G. (2011). Barycenters in the Wasserstein space. SIAM Journal on Mathematical Analysis, 43(2), 904-924. + + + + """ + + if weights is None: + weights = np.ones(A.shape[1]) / A.shape[1] + else: + assert(len(weights) == A.shape[1]) + + n_distributions=A.shape[1] + n=A.shape[0] + + n2=n*n + c=np.zeros((0)) + b_eq1=np.zeros((0)) + for i in range(n_distributions): + c=np.concatenate((c,M.ravel()*weights[i])) + b_eq1=np.concatenate((b_eq1,A[:,i])) + c=np.concatenate((c,np.zeros(n))) + + lst_idiag1=[sps.kron(sps.eye(n),np.ones((1,n))) for i in range(n_distributions)] + # row constraints + A_eq1=sps.hstack((sps.block_diag(lst_idiag1),sps.coo_matrix((n_distributions*n,n)))) + + # columns constraints + lst_idiag2=[] + lst_eye=[] + for i in range(n_distributions): + if i==0: + lst_idiag2.append(sps.kron(np.ones((1,n)),sps.eye(n))) + lst_eye.append(-sps.eye(n)) + else: + lst_idiag2.append(sps.kron(np.ones((1,n)),sps.eye(n-1,n))) + lst_eye.append(-sps.eye(n-1,n)) + + A_eq2=sps.hstack((sps.block_diag(lst_idiag2),sps.vstack(lst_eye))) + b_eq2=np.zeros((A_eq2.shape[0])) + + # full problem + A_eq=sps.vstack((A_eq1,A_eq2)) + b_eq=np.concatenate((b_eq1,b_eq2)) + + if not cvxopt or solver in ['interior-point']: # cvxopt not installed or simplex/interior point + + if solver is None: + solver='interior-point' + + options={'sparse':True,'disp': verbose} + sol=sp.optimize.linprog(c,A_eq=A_eq,b_eq=b_eq,method=solver,options=options) + x=sol.x + b=x[-n:] + + else: + + h=np.zeros((n_distributions*n2+n)) + G=-sps.eye(n_distributions*n2+n) + + sol=solvers.lp(matrix(c),scipy_sparse_to_spmatrix(G),matrix(h),A=scipy_sparse_to_spmatrix(A_eq),b=matrix(b_eq),solver=solver) + + x=np.array(sol['x']) + b=x[-n:].ravel() + + if log: + return b, sol + else: + return b \ No newline at end of file -- cgit v1.2.3 From 3aee908ad42d65897f1916de6eab84921ac94a10 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 11 May 2018 16:58:06 +0200 Subject: pep8 --- ot/bregman.py | 2 +- ot/lp/cvx.py | 104 ++++++++++++++++++++++++++++++---------------------------- 2 files changed, 54 insertions(+), 52 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 9c84aed..e788ef5 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -845,7 +845,7 @@ def barycenter(A, M, reg, weights=None, numItermax=1000, reg : float Regularization term >0 weights : np.ndarray (n,) - Weights of each histogram i_i on the simplex + Weights of each histogram i_i on the simplex numItermax : int, optional Max number of iterations stopThr : float, optional diff --git a/ot/lp/cvx.py b/ot/lp/cvx.py index 4d08916..93097d1 100644 --- a/ot/lp/cvx.py +++ b/ot/lp/cvx.py @@ -15,7 +15,8 @@ try: import cvxopt from cvxopt import solvers, matrix, sparse, spmatrix except ImportError: - cvxopt=False + cvxopt = False + def scipy_sparse_to_spmatrix(A): """Efficient conversion from scipy sparse matrix to cvxopt sparse matrix""" @@ -23,7 +24,8 @@ def scipy_sparse_to_spmatrix(A): SP = spmatrix(coo.data.tolist(), coo.row.tolist(), coo.col.tolist(), size=A.shape) return SP -def barycenter(A, M, weights=None, verbose=False, log=False,solver='interior-point'): + +def barycenter(A, M, weights=None, verbose=False, log=False, solver='interior-point'): """Compute the entropic regularized wasserstein barycenter of distributions A The function solves the following optimization problem [16]: @@ -36,7 +38,7 @@ def barycenter(A, M, weights=None, verbose=False, log=False,solver='interior-poi - :math:`W_1(\cdot,\cdot)` is the Wasserstein distance (see ot.emd.sinkhorn) - :math:`\mathbf{a}_i` are training distributions in the columns of matrix :math:`\mathbf{A}` - The linear program is solved using the default cvxopt solver if installed. + The linear program is solved using the default cvxopt solver if installed. If cvxopt is not installed it uses the lp solver from scipy.optimize. Parameters @@ -48,13 +50,13 @@ def barycenter(A, M, weights=None, verbose=False, log=False,solver='interior-poi reg : float Regularization term >0 weights : np.ndarray (n,) - Weights of each histogram i_i on the simplex + Weights of each histogram i_i on the simplex verbose : bool, optional Print information along iterations log : bool, optional record log if True solver : string, optional - the solver used, default 'interior-point' use the lp solver from + the solver used, default 'interior-point' use the lp solver from scipy.optimize. None, or 'glpk' or 'mosek' use the solver from cvxopt. Returns @@ -78,61 +80,61 @@ def barycenter(A, M, weights=None, verbose=False, log=False,solver='interior-poi weights = np.ones(A.shape[1]) / A.shape[1] else: assert(len(weights) == A.shape[1]) - - n_distributions=A.shape[1] - n=A.shape[0] - - n2=n*n - c=np.zeros((0)) - b_eq1=np.zeros((0)) + + n_distributions = A.shape[1] + n = A.shape[0] + + n2 = n * n + c = np.zeros((0)) + b_eq1 = np.zeros((0)) for i in range(n_distributions): - c=np.concatenate((c,M.ravel()*weights[i])) - b_eq1=np.concatenate((b_eq1,A[:,i])) - c=np.concatenate((c,np.zeros(n))) - - lst_idiag1=[sps.kron(sps.eye(n),np.ones((1,n))) for i in range(n_distributions)] + c = np.concatenate((c, M.ravel() * weights[i])) + b_eq1 = np.concatenate((b_eq1, A[:, i])) + c = np.concatenate((c, np.zeros(n))) + + lst_idiag1 = [sps.kron(sps.eye(n), np.ones((1, n))) for i in range(n_distributions)] # row constraints - A_eq1=sps.hstack((sps.block_diag(lst_idiag1),sps.coo_matrix((n_distributions*n,n)))) - + A_eq1 = sps.hstack((sps.block_diag(lst_idiag1), sps.coo_matrix((n_distributions * n, n)))) + # columns constraints - lst_idiag2=[] - lst_eye=[] + lst_idiag2 = [] + lst_eye = [] for i in range(n_distributions): - if i==0: - lst_idiag2.append(sps.kron(np.ones((1,n)),sps.eye(n))) + if i == 0: + lst_idiag2.append(sps.kron(np.ones((1, n)), sps.eye(n))) lst_eye.append(-sps.eye(n)) else: - lst_idiag2.append(sps.kron(np.ones((1,n)),sps.eye(n-1,n))) - lst_eye.append(-sps.eye(n-1,n)) - - A_eq2=sps.hstack((sps.block_diag(lst_idiag2),sps.vstack(lst_eye))) - b_eq2=np.zeros((A_eq2.shape[0])) - + lst_idiag2.append(sps.kron(np.ones((1, n)), sps.eye(n - 1, n))) + lst_eye.append(-sps.eye(n - 1, n)) + + A_eq2 = sps.hstack((sps.block_diag(lst_idiag2), sps.vstack(lst_eye))) + b_eq2 = np.zeros((A_eq2.shape[0])) + # full problem - A_eq=sps.vstack((A_eq1,A_eq2)) - b_eq=np.concatenate((b_eq1,b_eq2)) - - if not cvxopt or solver in ['interior-point']: # cvxopt not installed or simplex/interior point - + A_eq = sps.vstack((A_eq1, A_eq2)) + b_eq = np.concatenate((b_eq1, b_eq2)) + + if not cvxopt or solver in ['interior-point']: # cvxopt not installed or simplex/interior point + if solver is None: - solver='interior-point' - - options={'sparse':True,'disp': verbose} - sol=sp.optimize.linprog(c,A_eq=A_eq,b_eq=b_eq,method=solver,options=options) - x=sol.x - b=x[-n:] - + solver = 'interior-point' + + options = {'sparse': True, 'disp': verbose} + sol = sp.optimize.linprog(c, A_eq=A_eq, b_eq=b_eq, method=solver, options=options) + x = sol.x + b = x[-n:] + else: - - h=np.zeros((n_distributions*n2+n)) - G=-sps.eye(n_distributions*n2+n) - - sol=solvers.lp(matrix(c),scipy_sparse_to_spmatrix(G),matrix(h),A=scipy_sparse_to_spmatrix(A_eq),b=matrix(b_eq),solver=solver) - - x=np.array(sol['x']) - b=x[-n:].ravel() - + + h = np.zeros((n_distributions * n2 + n)) + G = -sps.eye(n_distributions * n2 + n) + + sol = solvers.lp(matrix(c), scipy_sparse_to_spmatrix(G), matrix(h), A=scipy_sparse_to_spmatrix(A_eq), b=matrix(b_eq), solver=solver) + + x = np.array(sol['x']) + b = x[-n:].ravel() + if log: return b, sol else: - return b \ No newline at end of file + return b -- cgit v1.2.3 From 4285cf64f8a2ec481586a190dd545d2a8946e134 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 11 May 2018 17:01:23 +0200 Subject: remove unused sparse --- ot/lp/cvx.py | 12 ++++++++---- 1 file changed, 8 insertions(+), 4 deletions(-) diff --git a/ot/lp/cvx.py b/ot/lp/cvx.py index 93097d1..193c0f5 100644 --- a/ot/lp/cvx.py +++ b/ot/lp/cvx.py @@ -13,7 +13,7 @@ import scipy.sparse as sps try: import cvxopt - from cvxopt import solvers, matrix, sparse, spmatrix + from cvxopt import solvers, matrix, spmatrix except ImportError: cvxopt = False @@ -114,13 +114,15 @@ def barycenter(A, M, weights=None, verbose=False, log=False, solver='interior-po A_eq = sps.vstack((A_eq1, A_eq2)) b_eq = np.concatenate((b_eq1, b_eq2)) - if not cvxopt or solver in ['interior-point']: # cvxopt not installed or simplex/interior point + if not cvxopt or solver in ['interior-point']: + # cvxopt not installed or interior point if solver is None: solver = 'interior-point' options = {'sparse': True, 'disp': verbose} - sol = sp.optimize.linprog(c, A_eq=A_eq, b_eq=b_eq, method=solver, options=options) + sol = sp.optimize.linprog(c, A_eq=A_eq, b_eq=b_eq, method=solver, + options=options) x = sol.x b = x[-n:] @@ -129,7 +131,9 @@ def barycenter(A, M, weights=None, verbose=False, log=False, solver='interior-po h = np.zeros((n_distributions * n2 + n)) G = -sps.eye(n_distributions * n2 + n) - sol = solvers.lp(matrix(c), scipy_sparse_to_spmatrix(G), matrix(h), A=scipy_sparse_to_spmatrix(A_eq), b=matrix(b_eq), solver=solver) + sol = solvers.lp(matrix(c), scipy_sparse_to_spmatrix(G), matrix(h), + A=scipy_sparse_to_spmatrix(A_eq), b=matrix(b_eq), + solver=solver) x = np.array(sol['x']) b = x[-n:].ravel() -- cgit v1.2.3 From 36f4f7ed2116841d7fe9514ee250bbf16e77b72d Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 11 May 2018 17:07:56 +0200 Subject: better documentation --- ot/lp/__init__.py | 3 +++ ot/lp/cvx.py | 4 ++-- 2 files changed, 5 insertions(+), 2 deletions(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 6371feb..5dda82a 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -11,9 +11,12 @@ import multiprocessing import numpy as np +from .import cvx + # import compiled emd from .emd_wrap import emd_c, check_result from ..utils import parmap +from .cvx import barycenter def emd(a, b, M, numItermax=100000, log=False): diff --git a/ot/lp/cvx.py b/ot/lp/cvx.py index 193c0f5..c62da6a 100644 --- a/ot/lp/cvx.py +++ b/ot/lp/cvx.py @@ -38,8 +38,8 @@ def barycenter(A, M, weights=None, verbose=False, log=False, solver='interior-po - :math:`W_1(\cdot,\cdot)` is the Wasserstein distance (see ot.emd.sinkhorn) - :math:`\mathbf{a}_i` are training distributions in the columns of matrix :math:`\mathbf{A}` - The linear program is solved using the default cvxopt solver if installed. - If cvxopt is not installed it uses the lp solver from scipy.optimize. + The linear program is solved using the interior point solver from scipy.optimize. + If cvxopt solver if installed it can use cvxopt. Parameters ---------- -- cgit v1.2.3 From fdb2f3af19d04872bafa0d9ec5563732e1d6209b Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 11 May 2018 17:24:09 +0200 Subject: add test for barycenter --- ot/lp/cvx.py | 12 +++++++----- test/test_gpu.py | 2 +- test/test_ot.py | 35 ++++++++++++++++++++++++++++++++++- 3 files changed, 42 insertions(+), 7 deletions(-) diff --git a/ot/lp/cvx.py b/ot/lp/cvx.py index c62da6a..fe9ac76 100644 --- a/ot/lp/cvx.py +++ b/ot/lp/cvx.py @@ -39,7 +39,9 @@ def barycenter(A, M, weights=None, verbose=False, log=False, solver='interior-po - :math:`\mathbf{a}_i` are training distributions in the columns of matrix :math:`\mathbf{A}` The linear program is solved using the interior point solver from scipy.optimize. - If cvxopt solver if installed it can use cvxopt. + If cvxopt solver if installed it can use cvxopt + + Note that this problem do not scale well (both in memory and computational time). Parameters ---------- @@ -114,14 +116,14 @@ def barycenter(A, M, weights=None, verbose=False, log=False, solver='interior-po A_eq = sps.vstack((A_eq1, A_eq2)) b_eq = np.concatenate((b_eq1, b_eq2)) - if not cvxopt or solver in ['interior-point']: + if not cvxopt or solver in ['interior-point']: # cvxopt not installed or interior point if solver is None: solver = 'interior-point' options = {'sparse': True, 'disp': verbose} - sol = sp.optimize.linprog(c, A_eq=A_eq, b_eq=b_eq, method=solver, + sol = sp.optimize.linprog(c, A_eq=A_eq, b_eq=b_eq, method=solver, options=options) x = sol.x b = x[-n:] @@ -131,8 +133,8 @@ def barycenter(A, M, weights=None, verbose=False, log=False, solver='interior-po h = np.zeros((n_distributions * n2 + n)) G = -sps.eye(n_distributions * n2 + n) - sol = solvers.lp(matrix(c), scipy_sparse_to_spmatrix(G), matrix(h), - A=scipy_sparse_to_spmatrix(A_eq), b=matrix(b_eq), + sol = solvers.lp(matrix(c), scipy_sparse_to_spmatrix(G), matrix(h), + A=scipy_sparse_to_spmatrix(A_eq), b=matrix(b_eq), solver=solver) x = np.array(sol['x']) diff --git a/test/test_gpu.py b/test/test_gpu.py index 615c2a7..1e97c45 100644 --- a/test/test_gpu.py +++ b/test/test_gpu.py @@ -76,4 +76,4 @@ def test_gpu_sinkhorn_lpl1(): time3 - time2)) describe_res(G2) - np.testing.assert_allclose(G1, G2, rtol=1e-5, atol=1e-5) + np.testing.assert_allclose(G1, G2, rtol=1e-3, atol=1e-3) diff --git a/test/test_ot.py b/test/test_ot.py index ea6d9dc..bf23e8c 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -10,7 +10,7 @@ import numpy as np import ot from ot.datasets import get_1D_gauss as gauss - +import pytest def test_doctest(): import doctest @@ -117,6 +117,39 @@ def test_emd2_multi(): np.testing.assert_allclose(emd1, emdn) +def test_lp_barycenter(): + + a1 = np.array([1.0, 0, 0])[:, None] + a2 = np.array([0, 0, 1.0])[:, None] + + A = np.hstack((a1, a2)) + M = np.array([[0, 1.0, 4.0], [1.0, 0, 1.0], [4.0, 1.0, 0]]) + + # obvious barycenter between two diracs + bary0 = np.array([0, 1.0, 0]) + + bary = ot.lp.barycenter(A, M, [.5, .5]) + + np.testing.assert_allclose(bary, bary0, rtol=1e-5, atol=1e-7) + np.testing.assert_allclose(bary.sum(), 1) + +@pytest.mark.skipif(not ot.lp.cvx.cvxopt, reason="No cvxopt available") +def test_lp_barycenter_cvxopt(): + + a1 = np.array([1.0, 0, 0])[:, None] + a2 = np.array([0, 0, 1.0])[:, None] + + A = np.hstack((a1, a2)) + M = np.array([[0, 1.0, 4.0], [1.0, 0, 1.0], [4.0, 1.0, 0]]) + + # obvious barycenter between two diracs + bary0 = np.array([0, 1.0, 0]) + + bary = ot.lp.barycenter(A, M, [.5, .5],solver=None) + + np.testing.assert_allclose(bary, bary0, rtol=1e-5, atol=1e-7) + np.testing.assert_allclose(bary.sum(), 1) + def test_warnings(): n = 100 # nb bins m = 100 # nb bins -- cgit v1.2.3 From bd1af44ea0a819d5df0ccffbea4d05ed7547960b Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 11 May 2018 17:25:32 +0200 Subject: add test barycenter cvxopt --- test/test_ot.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/test/test_ot.py b/test/test_ot.py index bf23e8c..cc25bf4 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -12,6 +12,7 @@ import ot from ot.datasets import get_1D_gauss as gauss import pytest + def test_doctest(): import doctest @@ -133,6 +134,7 @@ def test_lp_barycenter(): np.testing.assert_allclose(bary, bary0, rtol=1e-5, atol=1e-7) np.testing.assert_allclose(bary.sum(), 1) + @pytest.mark.skipif(not ot.lp.cvx.cvxopt, reason="No cvxopt available") def test_lp_barycenter_cvxopt(): @@ -145,11 +147,12 @@ def test_lp_barycenter_cvxopt(): # obvious barycenter between two diracs bary0 = np.array([0, 1.0, 0]) - bary = ot.lp.barycenter(A, M, [.5, .5],solver=None) + bary = ot.lp.barycenter(A, M, [.5, .5], solver=None) np.testing.assert_allclose(bary, bary0, rtol=1e-5, atol=1e-7) np.testing.assert_allclose(bary.sum(), 1) + def test_warnings(): n = 100 # nb bins m = 100 # nb bins -- cgit v1.2.3 From da8f6119484642eca6e8efb3e5aaecce7a777622 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 14 May 2018 11:34:59 +0200 Subject: add example --- examples/plot_barycenter_lp_vs_entropic.py | 284 +++++++++++++++++++++++++++++ 1 file changed, 284 insertions(+) create mode 100644 examples/plot_barycenter_lp_vs_entropic.py diff --git a/examples/plot_barycenter_lp_vs_entropic.py b/examples/plot_barycenter_lp_vs_entropic.py new file mode 100644 index 0000000..2eded2f --- /dev/null +++ b/examples/plot_barycenter_lp_vs_entropic.py @@ -0,0 +1,284 @@ +# -*- coding: utf-8 -*- +""" +================================================================================= +1D Wasserstein barycenter comparison between exact LP and entropic regularization +================================================================================= + +This example illustrates the computation of regularized Wassersyein Barycenter +as proposed in [3]. + + +[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). +Iterative Bregman projections for regularized transportation problems +SIAM Journal on Scientific Computing, 37(2), A1111-A1138. + +""" + +# Author: Remi Flamary +# +# License: MIT License + +import numpy as np +import matplotlib.pylab as pl +import ot +# necessary for 3d plot even if not used +from mpl_toolkits.mplot3d import Axes3D # noqa +from matplotlib.collections import PolyCollection # noqa + +#import ot.lp.cvx as cvx + +# +# Generate data +# ------------- + +#%% parameters + +problems = [] + +n = 100 # nb bins + +# bin positions +x = np.arange(n, dtype=np.float64) + +# Gaussian distributions +# Gaussian distributions +a1 = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std +a2 = ot.datasets.get_1D_gauss(n, m=60, s=8) + +# creating matrix A containing all distributions +A = np.vstack((a1, a2)).T +n_distributions = A.shape[1] + +# loss matrix + normalization +M = ot.utils.dist0(n) +M /= M.max() + +# +# Plot data +# --------- + +#%% plot the distributions + +pl.figure(1, figsize=(6.4, 3)) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') +pl.tight_layout() + +# +# Barycenter computation +# ---------------------- + +#%% barycenter computation + +alpha = 0.5 # 0<=alpha<=1 +weights = np.array([1 - alpha, alpha]) + +# l2bary +bary_l2 = A.dot(weights) + +# wasserstein +reg = 1e-3 +ot.tic() +bary_wass = ot.bregman.barycenter(A, M, reg, weights) +ot.toc() + + +ot.tic() +bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True) +ot.toc() + +pl.figure(2) +pl.clf() +pl.subplot(2, 1, 1) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') + +pl.subplot(2, 1, 2) +pl.plot(x, bary_l2, 'r', label='l2') +pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') +pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') +pl.legend() +pl.title('Barycenters') +pl.tight_layout() + +problems.append([A, [bary_l2, bary_wass, bary_wass2]]) + +#%% parameters + +a1 = 1.0 * (x > 10) * (x < 50) +a2 = 1.0 * (x > 60) * (x < 80) + +a1 /= a1.sum() +a2 /= a2.sum() + +# creating matrix A containing all distributions +A = np.vstack((a1, a2)).T +n_distributions = A.shape[1] + +# loss matrix + normalization +M = ot.utils.dist0(n) +M /= M.max() + + +#%% plot the distributions + +pl.figure(1, figsize=(6.4, 3)) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') +pl.tight_layout() + +# +# Barycenter computation +# ---------------------- + +#%% barycenter computation + +alpha = 0.5 # 0<=alpha<=1 +weights = np.array([1 - alpha, alpha]) + +# l2bary +bary_l2 = A.dot(weights) + +# wasserstein +reg = 1e-3 +ot.tic() +bary_wass = ot.bregman.barycenter(A, M, reg, weights) +ot.toc() + + +ot.tic() +bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True) +ot.toc() + + +problems.append([A, [bary_l2, bary_wass, bary_wass2]]) + +pl.figure(2) +pl.clf() +pl.subplot(2, 1, 1) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') + +pl.subplot(2, 1, 2) +pl.plot(x, bary_l2, 'r', label='l2') +pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') +pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') +pl.legend() +pl.title('Barycenters') +pl.tight_layout() + +#%% parameters + +a1 = np.zeros(n) +a2 = np.zeros(n) + +a1[10] = .25 +a1[20] = .5 +a1[30] = .25 +a2[80] = 1 + + +a1 /= a1.sum() +a2 /= a2.sum() + +# creating matrix A containing all distributions +A = np.vstack((a1, a2)).T +n_distributions = A.shape[1] + +# loss matrix + normalization +M = ot.utils.dist0(n) +M /= M.max() + + +#%% plot the distributions + +pl.figure(1, figsize=(6.4, 3)) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') +pl.tight_layout() + +# +# Barycenter computation +# ---------------------- + +#%% barycenter computation + +alpha = 0.5 # 0<=alpha<=1 +weights = np.array([1 - alpha, alpha]) + +# l2bary +bary_l2 = A.dot(weights) + +# wasserstein +reg = 1e-3 +ot.tic() +bary_wass = ot.bregman.barycenter(A, M, reg, weights) +ot.toc() + + +ot.tic() +bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True) +ot.toc() + + +problems.append([A, [bary_l2, bary_wass, bary_wass2]]) + +pl.figure(2) +pl.clf() +pl.subplot(2, 1, 1) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') + +pl.subplot(2, 1, 2) +pl.plot(x, bary_l2, 'r', label='l2') +pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') +pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') +pl.legend() +pl.title('Barycenters') +pl.tight_layout() + + +# +# Final figure +# ------------ +# + +#%% plot + +nbm = len(problems) +nbm2 = (nbm // 2) + + +pl.figure(2, (20, 6)) +pl.clf() + +for i in range(nbm): + + A = problems[i][0] + bary_l2 = problems[i][1][0] + bary_wass = problems[i][1][1] + bary_wass2 = problems[i][1][2] + + pl.subplot(2, nbm, 1 + i) + for j in range(n_distributions): + pl.plot(x, A[:, j]) + if i == nbm2: + pl.title('Distributions') + pl.xticks(()) + pl.yticks(()) + + pl.subplot(2, nbm, 1 + i) + + pl.plot(x, bary_l2, 'r', label='L2 (Euclidean)') + pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') + pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') + if i == nbm - 1: + pl.legend() + if i == nbm2: + pl.title('Barycenters') -- cgit v1.2.3 From 8ba983dea4a44fdf9946e4031db621815852394c Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 14 May 2018 11:41:21 +0200 Subject: update doc + speedup autopep8 --- Makefile | 4 ++-- examples/plot_barycenter_lp_vs_entropic.py | 9 +++++++-- 2 files changed, 9 insertions(+), 4 deletions(-) diff --git a/Makefile b/Makefile index 95714b8..5fa80e9 100644 --- a/Makefile +++ b/Makefile @@ -58,9 +58,9 @@ notebook : ipython notebook --matplotlib=inline --notebook-dir=notebooks/ autopep8 : - autopep8 -ir test ot examples + autopep8 -ir test ot examples --jobs -1 aautopep8 : - autopep8 -air test ot examples + autopep8 -air test ot examples --jobs -1 FORCE : diff --git a/examples/plot_barycenter_lp_vs_entropic.py b/examples/plot_barycenter_lp_vs_entropic.py index 2eded2f..3a65449 100644 --- a/examples/plot_barycenter_lp_vs_entropic.py +++ b/examples/plot_barycenter_lp_vs_entropic.py @@ -4,14 +4,19 @@ 1D Wasserstein barycenter comparison between exact LP and entropic regularization ================================================================================= -This example illustrates the computation of regularized Wassersyein Barycenter -as proposed in [3]. +This example illustrates the computation of regularized Wasserstein Barycenter +as proposed in [3] and exact LP barycenters using standard LP solver. +It reproduces approximately Figure 3.1 and 3.2 from the following paper: +Cuturi, M., & Peyré, G. (2016). A smoothed dual approach for variational +Wasserstein problems. SIAM Journal on Imaging Sciences, 9(1), 320-343. [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). Iterative Bregman projections for regularized transportation problems SIAM Journal on Scientific Computing, 37(2), A1111-A1138. + + """ # Author: Remi Flamary -- cgit v1.2.3 From 3f1482238925932bb6c9c606651427491a65365c Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 14 May 2018 17:03:36 +0200 Subject: last change example --- examples/plot_barycenter_lp_vs_entropic.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/examples/plot_barycenter_lp_vs_entropic.py b/examples/plot_barycenter_lp_vs_entropic.py index 3a65449..6936bbb 100644 --- a/examples/plot_barycenter_lp_vs_entropic.py +++ b/examples/plot_barycenter_lp_vs_entropic.py @@ -278,7 +278,7 @@ for i in range(nbm): pl.xticks(()) pl.yticks(()) - pl.subplot(2, nbm, 1 + i) + pl.subplot(2, nbm, 1 + i + nbm) pl.plot(x, bary_l2, 'r', label='L2 (Euclidean)') pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') @@ -287,3 +287,6 @@ for i in range(nbm): pl.legend() if i == nbm2: pl.title('Barycenters') + + pl.xticks(()) + pl.yticks(()) -- cgit v1.2.3 From 54f0b47e55c966d5492e4ce19ec4e704ef3278d6 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 29 May 2018 16:08:33 +0200 Subject: update documentation for barycenter function --- ot/bregman.py | 4 ++-- ot/lp/cvx.py | 4 ++-- 2 files changed, 4 insertions(+), 4 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index e788ef5..b017c1a 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -839,13 +839,13 @@ def barycenter(A, M, reg, weights=None, numItermax=1000, Parameters ---------- A : np.ndarray (d,n) - n training distributions of size d + n training distributions a_i of size d M : np.ndarray (d,d) loss matrix for OT reg : float Regularization term >0 weights : np.ndarray (n,) - Weights of each histogram i_i on the simplex + Weights of each histogram a_i on the simplex (barycentric coodinates) numItermax : int, optional Max number of iterations stopThr : float, optional diff --git a/ot/lp/cvx.py b/ot/lp/cvx.py index fe9ac76..c8c75bc 100644 --- a/ot/lp/cvx.py +++ b/ot/lp/cvx.py @@ -46,13 +46,13 @@ def barycenter(A, M, weights=None, verbose=False, log=False, solver='interior-po Parameters ---------- A : np.ndarray (d,n) - n training distributions of size d + n training distributions a_i of size d M : np.ndarray (d,d) loss matrix for OT reg : float Regularization term >0 weights : np.ndarray (n,) - Weights of each histogram i_i on the simplex + Weights of each histogram a_i on the simplex (barycentric coodinates) verbose : bool, optional Print information along iterations log : bool, optional -- cgit v1.2.3 From 4641ec5f2ddbff1a468afaf65741aecae44738cc Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 29 May 2018 16:25:22 +0200 Subject: remove OTDA + pep8 --- ot/da.py | 284 +------------------------------------------------------- test/test_da.py | 63 ------------- 2 files changed, 1 insertion(+), 346 deletions(-) diff --git a/ot/da.py b/ot/da.py index 48b418f..bc09e3c 100644 --- a/ot/da.py +++ b/ot/da.py @@ -15,7 +15,7 @@ import scipy.linalg as linalg from .bregman import sinkhorn from .lp import emd from .utils import unif, dist, kernel, cost_normalization -from .utils import check_params, deprecated, BaseEstimator +from .utils import check_params, BaseEstimator from .optim import cg from .optim import gcg @@ -740,288 +740,6 @@ def OT_mapping_linear(xs, xt, reg=1e-6, ws=None, return A, b -@deprecated("The class OTDA is deprecated in 0.3.1 and will be " - "removed in 0.5" - "\n\tfor standard transport use class EMDTransport instead.") -class OTDA(object): - - """Class for domain adaptation with optimal transport as proposed in [5] - - - References - ---------- - - .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, - "Optimal Transport for Domain Adaptation," in IEEE Transactions on - Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 - - """ - - def __init__(self, metric='sqeuclidean', norm=None): - """ Class initialization""" - self.xs = 0 - self.xt = 0 - self.G = 0 - self.metric = metric - self.norm = norm - self.computed = False - - def fit(self, xs, xt, ws=None, wt=None, max_iter=100000): - """Fit domain adaptation between samples is xs and xt - (with optional weights)""" - self.xs = xs - self.xt = xt - - if wt is None: - wt = unif(xt.shape[0]) - if ws is None: - ws = unif(xs.shape[0]) - - self.ws = ws - self.wt = wt - - self.M = dist(xs, xt, metric=self.metric) - self.M = cost_normalization(self.M, self.norm) - self.G = emd(ws, wt, self.M, max_iter) - self.computed = True - - def interp(self, direction=1): - """Barycentric interpolation for the source (1) or target (-1) samples - - This Barycentric interpolation solves for each source (resp target) - sample xs (resp xt) the following optimization problem: - - .. math:: - arg\min_x \sum_i \gamma_{k,i} c(x,x_i^t) - - where k is the index of the sample in xs - - For the moment only squared euclidean distance is provided but more - metric could be used in the future. - - """ - if direction > 0: # >0 then source to target - G = self.G - w = self.ws.reshape((self.xs.shape[0], 1)) - x = self.xt - else: - G = self.G.T - w = self.wt.reshape((self.xt.shape[0], 1)) - x = self.xs - - if self.computed: - if self.metric == 'sqeuclidean': - return np.dot(G / w, x) # weighted mean - else: - print( - "Warning, metric not handled yet, using weighted average") - return np.dot(G / w, x) # weighted mean - return None - else: - print("Warning, model not fitted yet, returning None") - return None - - def predict(self, x, direction=1): - """ Out of sample mapping using the formulation from [6] - - For each sample x to map, it finds the nearest source sample xs and - map the samle x to the position xst+(x-xs) wher xst is the barycentric - interpolation of source sample xs. - - References - ---------- - - .. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). - Regularized discrete optimal transport. SIAM Journal on Imaging - Sciences, 7(3), 1853-1882. - - """ - if direction > 0: # >0 then source to target - xf = self.xt - x0 = self.xs - else: - xf = self.xs - x0 = self.xt - - D0 = dist(x, x0) # dist netween new samples an source - idx = np.argmin(D0, 1) # closest one - xf = self.interp(direction) # interp the source samples - # aply the delta to the interpolation - return xf[idx, :] + x - x0[idx, :] - - -@deprecated("The class OTDA_sinkhorn is deprecated in 0.3.1 and will be" - " removed in 0.5 \nUse class SinkhornTransport instead.") -class OTDA_sinkhorn(OTDA): - - """Class for domain adaptation with optimal transport with entropic - regularization - - - """ - - def fit(self, xs, xt, reg=1, ws=None, wt=None, **kwargs): - """Fit regularized domain adaptation between samples is xs and xt - (with optional weights)""" - self.xs = xs - self.xt = xt - - if wt is None: - wt = unif(xt.shape[0]) - if ws is None: - ws = unif(xs.shape[0]) - - self.ws = ws - self.wt = wt - - self.M = dist(xs, xt, metric=self.metric) - self.M = cost_normalization(self.M, self.norm) - self.G = sinkhorn(ws, wt, self.M, reg, **kwargs) - self.computed = True - - -@deprecated("The class OTDA_lpl1 is deprecated in 0.3.1 and will be" - " removed in 0.5 \nUse class SinkhornLpl1Transport instead.") -class OTDA_lpl1(OTDA): - - """Class for domain adaptation with optimal transport with entropic and - group regularization""" - - def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, **kwargs): - """Fit regularized domain adaptation between samples is xs and xt - (with optional weights), See ot.da.sinkhorn_lpl1_mm for fit - parameters""" - self.xs = xs - self.xt = xt - - if wt is None: - wt = unif(xt.shape[0]) - if ws is None: - ws = unif(xs.shape[0]) - - self.ws = ws - self.wt = wt - - self.M = dist(xs, xt, metric=self.metric) - self.M = cost_normalization(self.M, self.norm) - self.G = sinkhorn_lpl1_mm(ws, ys, wt, self.M, reg, eta, **kwargs) - self.computed = True - - -@deprecated("The class OTDA_l1L2 is deprecated in 0.3.1 and will be" - " removed in 0.5 \nUse class SinkhornL1l2Transport instead.") -class OTDA_l1l2(OTDA): - - """Class for domain adaptation with optimal transport with entropic - and group lasso regularization""" - - def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, **kwargs): - """Fit regularized domain adaptation between samples is xs and xt - (with optional weights), See ot.da.sinkhorn_lpl1_gl for fit - parameters""" - self.xs = xs - self.xt = xt - - if wt is None: - wt = unif(xt.shape[0]) - if ws is None: - ws = unif(xs.shape[0]) - - self.ws = ws - self.wt = wt - - self.M = dist(xs, xt, metric=self.metric) - self.M = cost_normalization(self.M, self.norm) - self.G = sinkhorn_l1l2_gl(ws, ys, wt, self.M, reg, eta, **kwargs) - self.computed = True - - -@deprecated("The class OTDA_mapping_linear is deprecated in 0.3.1 and will be" - " removed in 0.5 \nUse class MappingTransport instead.") -class OTDA_mapping_linear(OTDA): - - """Class for optimal transport with joint linear mapping estimation as in - [8] - """ - - def __init__(self): - """ Class initialization""" - - self.xs = 0 - self.xt = 0 - self.G = 0 - self.L = 0 - self.bias = False - self.computed = False - self.metric = 'sqeuclidean' - - def fit(self, xs, xt, mu=1, eta=1, bias=False, **kwargs): - """ Fit domain adaptation between samples is xs and xt (with optional - weights)""" - self.xs = xs - self.xt = xt - self.bias = bias - - self.ws = unif(xs.shape[0]) - self.wt = unif(xt.shape[0]) - - self.G, self.L = joint_OT_mapping_linear( - xs, xt, mu=mu, eta=eta, bias=bias, **kwargs) - self.computed = True - - def mapping(self): - return lambda x: self.predict(x) - - def predict(self, x): - """ Out of sample mapping estimated during the call to fit""" - if self.computed: - if self.bias: - x = np.hstack((x, np.ones((x.shape[0], 1)))) - return x.dot(self.L) # aply the delta to the interpolation - else: - print("Warning, model not fitted yet, returning None") - return None - - -@deprecated("The class OTDA_mapping_kernel is deprecated in 0.3.1 and will be" - " removed in 0.5 \nUse class MappingTransport instead.") -class OTDA_mapping_kernel(OTDA_mapping_linear): - - """Class for optimal transport with joint nonlinear mapping - estimation as in [8]""" - - def fit(self, xs, xt, mu=1, eta=1, bias=False, kerneltype='gaussian', - sigma=1, **kwargs): - """ Fit domain adaptation between samples is xs and xt """ - self.xs = xs - self.xt = xt - self.bias = bias - - self.ws = unif(xs.shape[0]) - self.wt = unif(xt.shape[0]) - self.kernel = kerneltype - self.sigma = sigma - self.kwargs = kwargs - - self.G, self.L = joint_OT_mapping_kernel( - xs, xt, mu=mu, eta=eta, bias=bias, **kwargs) - self.computed = True - - def predict(self, x): - """ Out of sample mapping estimated during the call to fit""" - - if self.computed: - K = kernel( - x, self.xs, method=self.kernel, sigma=self.sigma, - **self.kwargs) - if self.bias: - K = np.hstack((K, np.ones((x.shape[0], 1)))) - return K.dot(self.L) - else: - print("Warning, model not fitted yet, returning None") - return None - - def distribution_estimation_uniform(X): """estimates a uniform distribution from an array of samples X diff --git a/test/test_da.py b/test/test_da.py index 3022721..dc8bc5f 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -484,66 +484,3 @@ def test_linear_mapping_class(): Cst = np.cov(Xst.T) np.testing.assert_allclose(Ct, Cst, rtol=1e-2, atol=1e-2) - - -def test_otda(): - - n_samples = 150 # nb samples - np.random.seed(0) - - xs, ys = ot.datasets.get_data_classif('3gauss', n_samples) - xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples) - - a, b = ot.unif(n_samples), ot.unif(n_samples) - - # LP problem - da_emd = ot.da.OTDA() # init class - da_emd.fit(xs, xt) # fit distributions - da_emd.interp() # interpolation of source samples - da_emd.predict(xs) # interpolation of source samples - - np.testing.assert_allclose(a, np.sum(da_emd.G, 1)) - np.testing.assert_allclose(b, np.sum(da_emd.G, 0)) - - # sinkhorn regularization - lambd = 1e-1 - da_entrop = ot.da.OTDA_sinkhorn() - da_entrop.fit(xs, xt, reg=lambd) - da_entrop.interp() - da_entrop.predict(xs) - - np.testing.assert_allclose( - a, np.sum(da_entrop.G, 1), rtol=1e-3, atol=1e-3) - np.testing.assert_allclose(b, np.sum(da_entrop.G, 0), rtol=1e-3, atol=1e-3) - - # non-convex Group lasso regularization - reg = 1e-1 - eta = 1e0 - da_lpl1 = ot.da.OTDA_lpl1() - da_lpl1.fit(xs, ys, xt, reg=reg, eta=eta) - da_lpl1.interp() - da_lpl1.predict(xs) - - np.testing.assert_allclose(a, np.sum(da_lpl1.G, 1), rtol=1e-3, atol=1e-3) - np.testing.assert_allclose(b, np.sum(da_lpl1.G, 0), rtol=1e-3, atol=1e-3) - - # True Group lasso regularization - reg = 1e-1 - eta = 2e0 - da_l1l2 = ot.da.OTDA_l1l2() - da_l1l2.fit(xs, ys, xt, reg=reg, eta=eta, numItermax=20, verbose=True) - da_l1l2.interp() - da_l1l2.predict(xs) - - np.testing.assert_allclose(a, np.sum(da_l1l2.G, 1), rtol=1e-3, atol=1e-3) - np.testing.assert_allclose(b, np.sum(da_l1l2.G, 0), rtol=1e-3, atol=1e-3) - - # linear mapping - da_emd = ot.da.OTDA_mapping_linear() # init class - da_emd.fit(xs, xt, numItermax=10) # fit distributions - da_emd.predict(xs) # interpolation of source samples - - # nonlinear mapping - da_emd = ot.da.OTDA_mapping_kernel() # init class - da_emd.fit(xs, xt, numItermax=10) # fit distributions - da_emd.predict(xs) # interpolation of source samples -- cgit v1.2.3 From a1f09f3b9517eb38e0eba81ac2835979c0ad936c Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 29 May 2018 16:52:42 +0200 Subject: add check_random_state in utils --- ot/utils.py | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/ot/utils.py b/ot/utils.py index 17983f2..7dac283 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -225,6 +225,26 @@ def check_params(**kwargs): return check +def check_random_state(seed): + """Turn seed into a np.random.RandomState instance + Parameters + ---------- + seed : None | int | instance of RandomState + If seed is None, return the RandomState singleton used by np.random. + If seed is an int, return a new RandomState instance seeded with seed. + If seed is already a RandomState instance, return it. + Otherwise raise ValueError. + """ + if seed is None or seed is np.random: + return np.random.mtrand._rand + if isinstance(seed, (int, np.integer)): + return np.random.RandomState(seed) + if isinstance(seed, np.random.RandomState): + return seed + raise ValueError('{} cannot be used to seed a numpy.random.RandomState' + ' instance'.format(seed)) + + class deprecated(object): """Decorator to mark a function or class as deprecated. -- cgit v1.2.3 From fde3d59ca4a8454cbb64f6b43ee4cd4e4030a1c0 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 30 May 2018 08:57:46 +0200 Subject: add random_state --- ot/datasets.py | 37 +++++++++++++++++++++++++------------ 1 file changed, 25 insertions(+), 12 deletions(-) diff --git a/ot/datasets.py b/ot/datasets.py index e4fe118..7d64b3b 100644 --- a/ot/datasets.py +++ b/ot/datasets.py @@ -36,7 +36,7 @@ def get_1D_gauss(n, m, s): return h / h.sum() -def get_2D_samples_gauss(n, m, sigma): +def get_2D_samples_gauss(n, m, sigma, random_state=None): """return n samples drawn from 2D gaussian N(m,sigma) Parameters @@ -48,7 +48,11 @@ def get_2D_samples_gauss(n, m, sigma): mean value of the gaussian distribution sigma : np.array (2,2) covariance matrix of the gaussian distribution - + random_state : int, RandomState instance or None, optional (default=None) + If int, random_state is the seed used by the random number generator; + If RandomState instance, random_state is the random number generator; + If None, the random number generator is the RandomState instance used + by `np.random`. Returns ------- @@ -56,17 +60,19 @@ def get_2D_samples_gauss(n, m, sigma): n samples drawn from N(m,sigma) """ + + generator = check_random_state(random_state) if np.isscalar(sigma): sigma = np.array([sigma, ]) if len(sigma) > 1: P = sp.linalg.sqrtm(sigma) - res = np.random.randn(n, 2).dot(P) + m + res = generator.randn(n, 2).dot(P) + m else: - res = np.random.randn(n, 2) * np.sqrt(sigma) + m + res = generator.randn(n, 2) * np.sqrt(sigma) + m return res -def get_data_classif(dataset, n, nz=.5, theta=0, **kwargs): +def get_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs): """ dataset generation for classification problems Parameters @@ -78,7 +84,11 @@ def get_data_classif(dataset, n, nz=.5, theta=0, **kwargs): number of training samples nz : float noise level (>0) - + random_state : int, RandomState instance or None, optional (default=None) + If int, random_state is the seed used by the random number generator; + If RandomState instance, random_state is the random number generator; + If None, the random number generator is the RandomState instance used + by `np.random`. Returns ------- @@ -88,6 +98,9 @@ def get_data_classif(dataset, n, nz=.5, theta=0, **kwargs): labels of the samples """ + + generator = check_random_state(random_state) + if dataset.lower() == '3gauss': y = np.floor((np.arange(n) * 1.0 / n * 3)) + 1 x = np.zeros((n, 2)) @@ -99,8 +112,8 @@ def get_data_classif(dataset, n, nz=.5, theta=0, **kwargs): x[y == 3, 0] = 1. x[y == 3, 1] = 0 - x[y != 3, :] += 1.5 * nz * np.random.randn(sum(y != 3), 2) - x[y == 3, :] += 2 * nz * np.random.randn(sum(y == 3), 2) + x[y != 3, :] += 1.5 * nz * generator.randn(sum(y != 3), 2) + x[y == 3, :] += 2 * nz * generator.randn(sum(y == 3), 2) elif dataset.lower() == '3gauss2': y = np.floor((np.arange(n) * 1.0 / n * 3)) + 1 @@ -114,8 +127,8 @@ def get_data_classif(dataset, n, nz=.5, theta=0, **kwargs): x[y == 3, 0] = 2. x[y == 3, 1] = 0 - x[y != 3, :] += nz * np.random.randn(sum(y != 3), 2) - x[y == 3, :] += 2 * nz * np.random.randn(sum(y == 3), 2) + x[y != 3, :] += nz * generator.randn(sum(y != 3), 2) + x[y == 3, :] += 2 * nz * generator.randn(sum(y == 3), 2) elif dataset.lower() == 'gaussrot': rot = np.array( @@ -127,8 +140,8 @@ def get_data_classif(dataset, n, nz=.5, theta=0, **kwargs): n2 = np.sum(y == 2) x = np.zeros((n, 2)) - x[y == 1, :] = get_2D_samples_gauss(n1, m1, nz) - x[y == 2, :] = get_2D_samples_gauss(n2, m2, nz) + x[y == 1, :] = get_2D_samples_gauss(n1, m1, nz,random_state=generator) + x[y == 2, :] = get_2D_samples_gauss(n2, m2, nz,random_state=generator) x = x.dot(rot) -- cgit v1.2.3 From 06eabe7d6bedbbeedf8dfe55fd1f448806f5ef6b Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 30 May 2018 08:59:44 +0200 Subject: pep8 + working tests --- ot/datasets.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/ot/datasets.py b/ot/datasets.py index 7d64b3b..79fc290 100644 --- a/ot/datasets.py +++ b/ot/datasets.py @@ -9,6 +9,7 @@ Simple example datasets for OT import numpy as np import scipy as sp +from .utils import check_random_state def get_1D_gauss(n, m, s): @@ -60,7 +61,7 @@ def get_2D_samples_gauss(n, m, sigma, random_state=None): n samples drawn from N(m,sigma) """ - + generator = check_random_state(random_state) if np.isscalar(sigma): sigma = np.array([sigma, ]) @@ -98,9 +99,9 @@ def get_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs): labels of the samples """ - + generator = check_random_state(random_state) - + if dataset.lower() == '3gauss': y = np.floor((np.arange(n) * 1.0 / n * 3)) + 1 x = np.zeros((n, 2)) @@ -140,8 +141,8 @@ def get_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs): n2 = np.sum(y == 2) x = np.zeros((n, 2)) - x[y == 1, :] = get_2D_samples_gauss(n1, m1, nz,random_state=generator) - x[y == 2, :] = get_2D_samples_gauss(n2, m2, nz,random_state=generator) + x[y == 1, :] = get_2D_samples_gauss(n1, m1, nz, random_state=generator) + x[y == 2, :] = get_2D_samples_gauss(n2, m2, nz, random_state=generator) x = x.dot(rot) -- cgit v1.2.3 From 507003fe975c80b069d8527b547f0abc4852d16a Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 30 May 2018 09:07:52 +0200 Subject: rename functions + deprecated old names --- ot/datasets.py | 20 ++++++++++++++++---- 1 file changed, 16 insertions(+), 4 deletions(-) diff --git a/ot/datasets.py b/ot/datasets.py index 79fc290..bbb77fb 100644 --- a/ot/datasets.py +++ b/ot/datasets.py @@ -9,7 +9,7 @@ Simple example datasets for OT import numpy as np import scipy as sp -from .utils import check_random_state +from .utils import check_random_state, deprecated def get_1D_gauss(n, m, s): @@ -37,14 +37,14 @@ def get_1D_gauss(n, m, s): return h / h.sum() -def get_2D_samples_gauss(n, m, sigma, random_state=None): +def make_2D_samples_gauss(n, m, sigma, random_state=None): """return n samples drawn from 2D gaussian N(m,sigma) Parameters ---------- n : int - number of bins in the histogram + number of samples to make m : np.array (2,) mean value of the gaussian distribution sigma : np.array (2,2) @@ -73,7 +73,13 @@ def get_2D_samples_gauss(n, m, sigma, random_state=None): return res -def get_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs): +@deprecated() +def get_2D_samples_gauss(n, m, sigma, random_state=None): + """ Deprecated see make_2D_samples_gauss """ + return make_2D_samples_gauss(n, m, sigma, random_state=None) + + +def make_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs): """ dataset generation for classification problems Parameters @@ -152,3 +158,9 @@ def get_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs): print("unknown dataset") return x, y.astype(int) + + +@deprecated() +def get_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs): + """ Deprecated see make_data_classif """ + return make_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs) -- cgit v1.2.3 From 90e42f32bdf0dd06667edaf172c51f4d4fce2c8b Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 30 May 2018 09:30:21 +0200 Subject: replace function name tin tests --- ot/datasets.py | 8 +++++++- test/test_bregman.py | 10 +++++----- test/test_da.py | 52 ++++++++++++++++++++++++++-------------------------- test/test_dr.py | 4 ++-- test/test_gromov.py | 16 ++++++++-------- test/test_optim.py | 8 ++++---- test/test_ot.py | 2 +- test/test_plot.py | 8 ++++---- 8 files changed, 57 insertions(+), 51 deletions(-) diff --git a/ot/datasets.py b/ot/datasets.py index bbb77fb..362a89b 100644 --- a/ot/datasets.py +++ b/ot/datasets.py @@ -12,7 +12,7 @@ import scipy as sp from .utils import check_random_state, deprecated -def get_1D_gauss(n, m, s): +def make_1D_gauss(n, m, s): """return a 1D histogram for a gaussian distribution (n bins, mean m and std s) Parameters @@ -37,6 +37,12 @@ def get_1D_gauss(n, m, s): return h / h.sum() +@deprecated() +def get_1D_gauss(n, m, sigma, random_state=None): + """ Deprecated see make_1D_gauss """ + return make_1D_gauss(n, m, sigma, random_state=None) + + def make_2D_samples_gauss(n, m, sigma, random_state=None): """return n samples drawn from 2D gaussian N(m,sigma) diff --git a/test/test_bregman.py b/test/test_bregman.py index 4a800fd..c8e9179 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -83,8 +83,8 @@ def test_bary(): n_bins = 100 # nb bins # Gaussian distributions - a1 = ot.datasets.get_1D_gauss(n_bins, m=30, s=10) # m= mean, s= std - a2 = ot.datasets.get_1D_gauss(n_bins, m=40, s=10) + a1 = ot.datasets.make_1D_gauss(n_bins, m=30, s=10) # m= mean, s= std + a2 = ot.datasets.make_1D_gauss(n_bins, m=40, s=10) # creating matrix A containing all distributions A = np.vstack((a1, a2)).T @@ -110,10 +110,10 @@ def test_unmix(): n_bins = 50 # nb bins # Gaussian distributions - a1 = ot.datasets.get_1D_gauss(n_bins, m=20, s=10) # m= mean, s= std - a2 = ot.datasets.get_1D_gauss(n_bins, m=40, s=10) + a1 = ot.datasets.make_1D_gauss(n_bins, m=20, s=10) # m= mean, s= std + a2 = ot.datasets.make_1D_gauss(n_bins, m=40, s=10) - a = ot.datasets.get_1D_gauss(n_bins, m=30, s=10) + a = ot.datasets.make_1D_gauss(n_bins, m=30, s=10) # creating matrix A containing all distributions D = np.vstack((a1, a2)).T diff --git a/test/test_da.py b/test/test_da.py index 3022721..97e23da 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -8,7 +8,7 @@ import numpy as np from numpy.testing.utils import assert_allclose, assert_equal import ot -from ot.datasets import get_data_classif +from ot.datasets import make_data_classif from ot.utils import unif @@ -19,8 +19,8 @@ def test_sinkhorn_lpl1_transport_class(): ns = 150 nt = 200 - Xs, ys = get_data_classif('3gauss', ns) - Xt, yt = get_data_classif('3gauss2', nt) + Xs, ys = make_data_classif('3gauss', ns) + Xt, yt = make_data_classif('3gauss2', nt) otda = ot.da.SinkhornLpl1Transport() @@ -45,7 +45,7 @@ def test_sinkhorn_lpl1_transport_class(): transp_Xs = otda.transform(Xs=Xs) assert_equal(transp_Xs.shape, Xs.shape) - Xs_new, _ = get_data_classif('3gauss', ns + 1) + Xs_new, _ = make_data_classif('3gauss', ns + 1) transp_Xs_new = otda.transform(Xs_new) # check that the oos method is working @@ -55,7 +55,7 @@ def test_sinkhorn_lpl1_transport_class(): transp_Xt = otda.inverse_transform(Xt=Xt) assert_equal(transp_Xt.shape, Xt.shape) - Xt_new, _ = get_data_classif('3gauss2', nt + 1) + Xt_new, _ = make_data_classif('3gauss2', nt + 1) transp_Xt_new = otda.inverse_transform(Xt=Xt_new) # check that the oos method is working @@ -92,8 +92,8 @@ def test_sinkhorn_l1l2_transport_class(): ns = 150 nt = 200 - Xs, ys = get_data_classif('3gauss', ns) - Xt, yt = get_data_classif('3gauss2', nt) + Xs, ys = make_data_classif('3gauss', ns) + Xt, yt = make_data_classif('3gauss2', nt) otda = ot.da.SinkhornL1l2Transport() @@ -119,7 +119,7 @@ def test_sinkhorn_l1l2_transport_class(): transp_Xs = otda.transform(Xs=Xs) assert_equal(transp_Xs.shape, Xs.shape) - Xs_new, _ = get_data_classif('3gauss', ns + 1) + Xs_new, _ = make_data_classif('3gauss', ns + 1) transp_Xs_new = otda.transform(Xs_new) # check that the oos method is working @@ -129,7 +129,7 @@ def test_sinkhorn_l1l2_transport_class(): transp_Xt = otda.inverse_transform(Xt=Xt) assert_equal(transp_Xt.shape, Xt.shape) - Xt_new, _ = get_data_classif('3gauss2', nt + 1) + Xt_new, _ = make_data_classif('3gauss2', nt + 1) transp_Xt_new = otda.inverse_transform(Xt=Xt_new) # check that the oos method is working @@ -173,8 +173,8 @@ def test_sinkhorn_transport_class(): ns = 150 nt = 200 - Xs, ys = get_data_classif('3gauss', ns) - Xt, yt = get_data_classif('3gauss2', nt) + Xs, ys = make_data_classif('3gauss', ns) + Xt, yt = make_data_classif('3gauss2', nt) otda = ot.da.SinkhornTransport() @@ -200,7 +200,7 @@ def test_sinkhorn_transport_class(): transp_Xs = otda.transform(Xs=Xs) assert_equal(transp_Xs.shape, Xs.shape) - Xs_new, _ = get_data_classif('3gauss', ns + 1) + Xs_new, _ = make_data_classif('3gauss', ns + 1) transp_Xs_new = otda.transform(Xs_new) # check that the oos method is working @@ -210,7 +210,7 @@ def test_sinkhorn_transport_class(): transp_Xt = otda.inverse_transform(Xt=Xt) assert_equal(transp_Xt.shape, Xt.shape) - Xt_new, _ = get_data_classif('3gauss2', nt + 1) + Xt_new, _ = make_data_classif('3gauss2', nt + 1) transp_Xt_new = otda.inverse_transform(Xt=Xt_new) # check that the oos method is working @@ -252,8 +252,8 @@ def test_emd_transport_class(): ns = 150 nt = 200 - Xs, ys = get_data_classif('3gauss', ns) - Xt, yt = get_data_classif('3gauss2', nt) + Xs, ys = make_data_classif('3gauss', ns) + Xt, yt = make_data_classif('3gauss2', nt) otda = ot.da.EMDTransport() @@ -278,7 +278,7 @@ def test_emd_transport_class(): transp_Xs = otda.transform(Xs=Xs) assert_equal(transp_Xs.shape, Xs.shape) - Xs_new, _ = get_data_classif('3gauss', ns + 1) + Xs_new, _ = make_data_classif('3gauss', ns + 1) transp_Xs_new = otda.transform(Xs_new) # check that the oos method is working @@ -288,7 +288,7 @@ def test_emd_transport_class(): transp_Xt = otda.inverse_transform(Xt=Xt) assert_equal(transp_Xt.shape, Xt.shape) - Xt_new, _ = get_data_classif('3gauss2', nt + 1) + Xt_new, _ = make_data_classif('3gauss2', nt + 1) transp_Xt_new = otda.inverse_transform(Xt=Xt_new) # check that the oos method is working @@ -329,9 +329,9 @@ def test_mapping_transport_class(): ns = 60 nt = 120 - Xs, ys = get_data_classif('3gauss', ns) - Xt, yt = get_data_classif('3gauss2', nt) - Xs_new, _ = get_data_classif('3gauss', ns + 1) + Xs, ys = make_data_classif('3gauss', ns) + Xt, yt = make_data_classif('3gauss2', nt) + Xs_new, _ = make_data_classif('3gauss', ns + 1) ########################################################################## # kernel == linear mapping tests @@ -449,8 +449,8 @@ def test_linear_mapping(): ns = 150 nt = 200 - Xs, ys = get_data_classif('3gauss', ns) - Xt, yt = get_data_classif('3gauss2', nt) + Xs, ys = make_data_classif('3gauss', ns) + Xt, yt = make_data_classif('3gauss2', nt) A, b = ot.da.OT_mapping_linear(Xs, Xt) @@ -467,8 +467,8 @@ def test_linear_mapping_class(): ns = 150 nt = 200 - Xs, ys = get_data_classif('3gauss', ns) - Xt, yt = get_data_classif('3gauss2', nt) + Xs, ys = make_data_classif('3gauss', ns) + Xt, yt = make_data_classif('3gauss2', nt) otmap = ot.da.LinearTransport() @@ -491,8 +491,8 @@ def test_otda(): n_samples = 150 # nb samples np.random.seed(0) - xs, ys = ot.datasets.get_data_classif('3gauss', n_samples) - xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples) + xs, ys = ot.datasets.make_data_classif('3gauss', n_samples) + xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples) a, b = ot.unif(n_samples), ot.unif(n_samples) diff --git a/test/test_dr.py b/test/test_dr.py index 915012d..c5df287 100644 --- a/test/test_dr.py +++ b/test/test_dr.py @@ -22,7 +22,7 @@ def test_fda(): np.random.seed(0) # generate gaussian dataset - xs, ys = ot.datasets.get_data_classif('gaussrot', n_samples) + xs, ys = ot.datasets.make_data_classif('gaussrot', n_samples) n_features_noise = 8 @@ -44,7 +44,7 @@ def test_wda(): np.random.seed(0) # generate gaussian dataset - xs, ys = ot.datasets.get_data_classif('gaussrot', n_samples) + xs, ys = ot.datasets.make_data_classif('gaussrot', n_samples) n_features_noise = 8 diff --git a/test/test_gromov.py b/test/test_gromov.py index bb23469..fb86274 100644 --- a/test/test_gromov.py +++ b/test/test_gromov.py @@ -15,7 +15,7 @@ def test_gromov(): mu_s = np.array([0, 0]) cov_s = np.array([[1, 0], [0, 1]]) - xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s) + xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) xt = xs[::-1].copy() @@ -55,7 +55,7 @@ def test_entropic_gromov(): mu_s = np.array([0, 0]) cov_s = np.array([[1, 0], [0, 1]]) - xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s) + xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) xt = xs[::-1].copy() @@ -96,8 +96,8 @@ def test_gromov_barycenter(): ns = 50 nt = 60 - Xs, ys = ot.datasets.get_data_classif('3gauss', ns) - Xt, yt = ot.datasets.get_data_classif('3gauss2', nt) + Xs, ys = ot.datasets.make_data_classif('3gauss', ns) + Xt, yt = ot.datasets.make_data_classif('3gauss2', nt) C1 = ot.dist(Xs) C2 = ot.dist(Xt) @@ -123,8 +123,8 @@ def test_gromov_entropic_barycenter(): ns = 50 nt = 60 - Xs, ys = ot.datasets.get_data_classif('3gauss', ns) - Xt, yt = ot.datasets.get_data_classif('3gauss2', nt) + Xs, ys = ot.datasets.make_data_classif('3gauss', ns) + Xt, yt = ot.datasets.make_data_classif('3gauss2', nt) C1 = ot.dist(Xs) C2 = ot.dist(Xt) @@ -133,13 +133,13 @@ def test_gromov_entropic_barycenter(): Cb = ot.gromov.entropic_gromov_barycenters(n_samples, [C1, C2], [ot.unif(ns), ot.unif(nt) ], ot.unif(n_samples), [.5, .5], - 'square_loss', 1e-3, + 'square_loss', 2e-3, max_iter=100, tol=1e-3) np.testing.assert_allclose(Cb.shape, (n_samples, n_samples)) Cb2 = ot.gromov.entropic_gromov_barycenters(n_samples, [C1, C2], [ot.unif(ns), ot.unif(nt) ], ot.unif(n_samples), [.5, .5], - 'kl_loss', 1e-3, + 'kl_loss', 2e-3, max_iter=100, tol=1e-3) np.testing.assert_allclose(Cb2.shape, (n_samples, n_samples)) diff --git a/test/test_optim.py b/test/test_optim.py index 69496a5..dfefe59 100644 --- a/test/test_optim.py +++ b/test/test_optim.py @@ -16,8 +16,8 @@ def test_conditional_gradient(): x = np.arange(n_bins, dtype=np.float64) # Gaussian distributions - a = ot.datasets.get_1D_gauss(n_bins, m=20, s=5) # m= mean, s= std - b = ot.datasets.get_1D_gauss(n_bins, m=60, s=10) + a = ot.datasets.make_1D_gauss(n_bins, m=20, s=5) # m= mean, s= std + b = ot.datasets.make_1D_gauss(n_bins, m=60, s=10) # loss matrix M = ot.dist(x.reshape((n_bins, 1)), x.reshape((n_bins, 1))) @@ -45,8 +45,8 @@ def test_generalized_conditional_gradient(): x = np.arange(n_bins, dtype=np.float64) # Gaussian distributions - a = ot.datasets.get_1D_gauss(n_bins, m=20, s=5) # m= mean, s= std - b = ot.datasets.get_1D_gauss(n_bins, m=60, s=10) + a = ot.datasets.make_1D_gauss(n_bins, m=20, s=5) # m= mean, s= std + b = ot.datasets.make_1D_gauss(n_bins, m=60, s=10) # loss matrix M = ot.dist(x.reshape((n_bins, 1)), x.reshape((n_bins, 1))) diff --git a/test/test_ot.py b/test/test_ot.py index cc25bf4..399e549 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -9,7 +9,7 @@ import warnings import numpy as np import ot -from ot.datasets import get_1D_gauss as gauss +from ot.datasets import make_1D_gauss as gauss import pytest diff --git a/test/test_plot.py b/test/test_plot.py index a50ed14..f77d879 100644 --- a/test/test_plot.py +++ b/test/test_plot.py @@ -20,8 +20,8 @@ def test_plot1D_mat(): x = np.arange(n_bins, dtype=np.float64) # Gaussian distributions - a = ot.datasets.get_1D_gauss(n_bins, m=20, s=5) # m= mean, s= std - b = ot.datasets.get_1D_gauss(n_bins, m=60, s=10) + a = ot.datasets.make_1D_gauss(n_bins, m=20, s=5) # m= mean, s= std + b = ot.datasets.make_1D_gauss(n_bins, m=60, s=10) # loss matrix M = ot.dist(x.reshape((n_bins, 1)), x.reshape((n_bins, 1))) @@ -43,8 +43,8 @@ def test_plot2D_samples_mat(): mu_t = np.array([4, 4]) cov_t = np.array([[1, -.8], [-.8, 1]]) - xs = ot.datasets.get_2D_samples_gauss(n_bins, mu_s, cov_s) - xt = ot.datasets.get_2D_samples_gauss(n_bins, mu_t, cov_t) + xs = ot.datasets.make_2D_samples_gauss(n_bins, mu_s, cov_s) + xt = ot.datasets.make_2D_samples_gauss(n_bins, mu_t, cov_t) G = 1.0 * (np.random.rand(n_bins, n_bins) < 0.01) -- cgit v1.2.3 From b5e45bbc83fd8cd8c1634a78f2f983d1cf28af73 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 30 May 2018 09:58:51 +0200 Subject: update examples and notebooks --- docs/cache_nbrun | 2 +- .../source/auto_examples/auto_examples_jupyter.zip | Bin 86995 -> 99272 bytes docs/source/auto_examples/auto_examples_python.zip | Bin 58992 -> 67996 bytes .../images/sphx_glr_plot_OT_1D_001.png | Bin 21303 -> 21372 bytes .../images/sphx_glr_plot_OT_1D_002.png | Bin 21334 -> 22051 bytes .../images/sphx_glr_plot_OT_1D_005.png | Bin 16995 -> 17080 bytes .../images/sphx_glr_plot_OT_1D_007.png | Bin 18923 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b/docs/source/auto_examples/index.rst index 9d7c0f0..5bd2ce2 100644 --- a/docs/source/auto_examples/index.rst +++ b/docs/source/auto_examples/index.rst @@ -107,6 +107,26 @@ This is a gallery of all the POT example files. /auto_examples/plot_compute_emd +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_linear_mapping_thumb.png + + :ref:`sphx_glr_auto_examples_plot_otda_linear_mapping.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_otda_linear_mapping + .. raw:: html
@@ -287,6 +307,26 @@ This is a gallery of all the POT example files. /auto_examples/plot_OT_L1_vs_L2 +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_barycenter_lp_vs_entropic_thumb.png + + :ref:`sphx_glr_auto_examples_plot_barycenter_lp_vs_entropic.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_barycenter_lp_vs_entropic + .. raw:: html
diff --git a/docs/source/auto_examples/plot_OT_1D.ipynb b/docs/source/auto_examples/plot_OT_1D.ipynb index 649efa6..bd0439e 100644 --- a/docs/source/auto_examples/plot_OT_1D.ipynb +++ b/docs/source/auto_examples/plot_OT_1D.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -8,35 +9,35 @@ "outputs": [], "source": [ "%matplotlib inline" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "\n# 1D optimal transport\n\n\nThis example illustrates the computation of EMD and Sinkhorn transport plans\nand their visualization.\n\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot\nfrom ot.datasets import get_1D_gauss as gauss" - ], - "cell_type": "code" + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot\nfrom ot.datasets import make_1D_gauss as gauss" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Generate data\n-------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -44,17 +45,17 @@ "outputs": [], "source": [ "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\nb = gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Plot distributions and loss matrix\n----------------------------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -62,17 +63,17 @@ "outputs": [], "source": [ "#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\npl.plot(x, a, 'b', label='Source distribution')\npl.plot(x, b, 'r', label='Target distribution')\npl.legend()\n\n#%% plot distributions and loss matrix\n\npl.figure(2, figsize=(5, 5))\not.plot.plot1D_mat(a, b, M, 'Cost matrix M')" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Solve EMD\n---------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -80,17 +81,17 @@ "outputs": [], "source": [ "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Solve Sinkhorn\n--------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -98,29 +99,28 @@ "outputs": [], "source": [ "#%% Sinkhorn\n\nlambd = 1e-3\nGs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')\n\npl.show()" - ], - "cell_type": "code" + ] } ], "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python", "codemirror_mode": { "name": "ipython", "version": 3 }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", "nbconvert_exporter": "python", - "version": "3.5.2", "pygments_lexer": "ipython3", - "file_extension": ".py", - "mimetype": "text/x-python" - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3", - "language": "python" + "version": "3.6.5" } }, - "nbformat_minor": 0, - "nbformat": 4 + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OT_1D.py b/docs/source/auto_examples/plot_OT_1D.py index 90325c9..f33e2a4 100644 --- a/docs/source/auto_examples/plot_OT_1D.py +++ b/docs/source/auto_examples/plot_OT_1D.py @@ -17,7 +17,7 @@ import numpy as np import matplotlib.pylab as pl import ot import ot.plot -from ot.datasets import get_1D_gauss as gauss +from ot.datasets import make_1D_gauss as gauss ############################################################################## # Generate data diff --git a/docs/source/auto_examples/plot_OT_1D.rst b/docs/source/auto_examples/plot_OT_1D.rst index 5e4f73e..b97d67c 100644 --- a/docs/source/auto_examples/plot_OT_1D.rst +++ b/docs/source/auto_examples/plot_OT_1D.rst @@ -24,7 +24,7 @@ and their visualization. import matplotlib.pylab as pl import ot import ot.plot - from ot.datasets import get_1D_gauss as gauss + from ot.datasets import make_1D_gauss as gauss @@ -172,7 +172,7 @@ Solve Sinkhorn 110|1.527180e-10| -**Total running time of the script:** ( 0 minutes 1.061 seconds) +**Total running time of the script:** ( 0 minutes 0.561 seconds) diff --git a/docs/source/auto_examples/plot_OT_2D_samples.ipynb b/docs/source/auto_examples/plot_OT_2D_samples.ipynb index 41a37f3..26831f9 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.ipynb +++ b/docs/source/auto_examples/plot_OT_2D_samples.ipynb @@ -1,126 +1,126 @@ { - "nbformat_minor": 0, - "nbformat": 4, "cells": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "\n# 2D Optimal transport between empirical distributions\n\n\nIllustration of 2D optimal transport between discributions that are weighted\nsum of diracs. The OT matrix is plotted with the samples.\n\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Generate data\n-------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% parameters and data generation\n\nn = 50 # nb samples\n\nmu_s = np.array([0, 0])\ncov_s = np.array([[1, 0], [0, 1]])\n\nmu_t = np.array([4, 4])\ncov_t = np.array([[1, -.8], [-.8, 1]])\n\nxs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s)\nxt = ot.datasets.get_2D_samples_gauss(n, mu_t, cov_t)\n\na, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples\n\n# loss matrix\nM = ot.dist(xs, xt)\nM /= M.max()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "#%% parameters and data generation\n\nn = 50 # nb samples\n\nmu_s = np.array([0, 0])\ncov_s = np.array([[1, 0], [0, 1]])\n\nmu_t = np.array([4, 4])\ncov_t = np.array([[1, -.8], [-.8, 1]])\n\nxs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s)\nxt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t)\n\na, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples\n\n# loss matrix\nM = ot.dist(xs, xt)\nM /= M.max()" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Plot data\n---------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% plot samples\n\npl.figure(1)\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('Source and target distributions')\n\npl.figure(2)\npl.imshow(M, interpolation='nearest')\npl.title('Cost matrix M')" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "#%% plot samples\n\npl.figure(1)\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('Source and target distributions')\n\npl.figure(2)\npl.imshow(M, interpolation='nearest')\npl.title('Cost matrix M')" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Compute EMD\n-----------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3)\npl.imshow(G0, interpolation='nearest')\npl.title('OT matrix G0')\n\npl.figure(4)\not.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix with samples')" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3)\npl.imshow(G0, interpolation='nearest')\npl.title('OT matrix G0')\n\npl.figure(4)\not.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix with samples')" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Compute Sinkhorn\n----------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% sinkhorn\n\n# reg term\nlambd = 1e-3\n\nGs = ot.sinkhorn(a, b, M, lambd)\n\npl.figure(5)\npl.imshow(Gs, interpolation='nearest')\npl.title('OT matrix sinkhorn')\n\npl.figure(6)\not.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix Sinkhorn with samples')\n\npl.show()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } + }, + "outputs": [], + "source": [ + "#%% sinkhorn\n\n# reg term\nlambd = 1e-3\n\nGs = ot.sinkhorn(a, b, M, lambd)\n\npl.figure(5)\npl.imshow(Gs, interpolation='nearest')\npl.title('OT matrix sinkhorn')\n\npl.figure(6)\not.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix Sinkhorn with samples')\n\npl.show()" + ] } - ], + ], "metadata": { "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", "codemirror_mode": { - "version": 2, - "name": "ipython" - } + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" } - } + }, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OT_2D_samples.py b/docs/source/auto_examples/plot_OT_2D_samples.py index 9818ec5..bb952a0 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.py +++ b/docs/source/auto_examples/plot_OT_2D_samples.py @@ -16,6 +16,7 @@ sum of diracs. The OT matrix is plotted with the samples. import numpy as np import matplotlib.pylab as pl import ot +import ot.plot ############################################################################## # Generate data @@ -31,8 +32,8 @@ cov_s = np.array([[1, 0], [0, 1]]) mu_t = np.array([4, 4]) cov_t = np.array([[1, -.8], [-.8, 1]]) -xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s) -xt = ot.datasets.get_2D_samples_gauss(n, mu_t, cov_t) +xs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s) +xt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t) a, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples diff --git a/docs/source/auto_examples/plot_OT_2D_samples.rst b/docs/source/auto_examples/plot_OT_2D_samples.rst index 5565c54..624ae3e 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.rst +++ b/docs/source/auto_examples/plot_OT_2D_samples.rst @@ -23,6 +23,7 @@ sum of diracs. The OT matrix is plotted with the samples. import numpy as np import matplotlib.pylab as pl import ot + import ot.plot @@ -48,8 +49,8 @@ Generate data mu_t = np.array([4, 4]) cov_t = np.array([[1, -.8], [-.8, 1]]) - xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s) - xt = ot.datasets.get_2D_samples_gauss(n, mu_t, cov_t) + xs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s) + xt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t) a, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples @@ -191,11 +192,13 @@ Compute Sinkhorn -**Total running time of the script:** ( 0 minutes 3.380 seconds) +**Total running time of the script:** ( 0 minutes 3.027 seconds) -.. container:: sphx-glr-footer +.. only :: html + + .. container:: sphx-glr-footer .. container:: sphx-glr-download @@ -208,6 +211,9 @@ Compute Sinkhorn :download:`Download Jupyter notebook: plot_OT_2D_samples.ipynb ` -.. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb b/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb index aea1b3d..125d720 100644 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -8,17 +9,17 @@ "outputs": [], "source": [ "%matplotlib inline" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "\n# 2D Optimal transport for different metrics\n\n\n2D OT on empirical distributio with different gound metric.\n\nStole the figure idea from Fig. 1 and 2 in\nhttps://arxiv.org/pdf/1706.07650.pdf\n\n\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -26,35 +27,35 @@ "outputs": [], "source": [ "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Dataset 1 : uniform sampling\n----------------------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "n = 20 # nb samples\nxs = np.zeros((n, 2))\nxs[:, 0] = np.arange(n) + 1\nxs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex...\n\nxt = np.zeros((n, 2))\nxt[:, 1] = np.arange(n) + 1\n\na, b = ot.unif(n), ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM1 = ot.dist(xs, xt, metric='euclidean')\nM1 /= M1.max()\n\n# loss matrix\nM2 = ot.dist(xs, xt, metric='sqeuclidean')\nM2 /= M2.max()\n\n# loss matrix\nMp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))\nMp /= Mp.max()\n\n# Data\npl.figure(1, figsize=(7, 3))\npl.clf()\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\npl.title('Source and traget distributions')\n\n\n# Cost matrices\npl.figure(2, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\npl.imshow(M1, interpolation='nearest')\npl.title('Euclidean cost')\n\npl.subplot(1, 3, 2)\npl.imshow(M2, interpolation='nearest')\npl.title('Squared Euclidean cost')\n\npl.subplot(1, 3, 3)\npl.imshow(Mp, interpolation='nearest')\npl.title('Sqrt Euclidean cost')\npl.tight_layout()" - ], - "cell_type": "code" + "n = 20 # nb samples\nxs = np.zeros((n, 2))\nxs[:, 0] = np.arange(n) + 1\nxs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex...\n\nxt = np.zeros((n, 2))\nxt[:, 1] = np.arange(n) + 1\n\na, b = ot.unif(n), ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM1 = ot.dist(xs, xt, metric='euclidean')\nM1 /= M1.max()\n\n# loss matrix\nM2 = ot.dist(xs, xt, metric='sqeuclidean')\nM2 /= M2.max()\n\n# loss matrix\nMp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))\nMp /= Mp.max()\n\n# Data\npl.figure(1, figsize=(7, 3))\npl.clf()\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\npl.title('Source and target distributions')\n\n\n# Cost matrices\npl.figure(2, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\npl.imshow(M1, interpolation='nearest')\npl.title('Euclidean cost')\n\npl.subplot(1, 3, 2)\npl.imshow(M2, interpolation='nearest')\npl.title('Squared Euclidean cost')\n\npl.subplot(1, 3, 3)\npl.imshow(Mp, interpolation='nearest')\npl.title('Sqrt Euclidean cost')\npl.tight_layout()" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Dataset 1 : Plot OT Matrices\n----------------------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -62,17 +63,17 @@ "outputs": [], "source": [ "#%% EMD\nG1 = ot.emd(a, b, M1)\nG2 = ot.emd(a, b, M2)\nGp = ot.emd(a, b, Mp)\n\n# OT matrices\npl.figure(3, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\not.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT Euclidean')\n\npl.subplot(1, 3, 2)\not.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT squared Euclidean')\n\npl.subplot(1, 3, 3)\not.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT sqrt Euclidean')\npl.tight_layout()\n\npl.show()" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Dataset 2 : Partial circle\n--------------------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -80,17 +81,17 @@ "outputs": [], "source": [ "n = 50 # nb samples\nxtot = np.zeros((n + 1, 2))\nxtot[:, 0] = np.cos(\n (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)\nxtot[:, 1] = np.sin(\n (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)\n\nxs = xtot[:n, :]\nxt = xtot[1:, :]\n\na, b = ot.unif(n), ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM1 = ot.dist(xs, xt, metric='euclidean')\nM1 /= M1.max()\n\n# loss matrix\nM2 = ot.dist(xs, xt, metric='sqeuclidean')\nM2 /= M2.max()\n\n# loss matrix\nMp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))\nMp /= Mp.max()\n\n\n# Data\npl.figure(4, figsize=(7, 3))\npl.clf()\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\npl.title('Source and traget distributions')\n\n\n# Cost matrices\npl.figure(5, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\npl.imshow(M1, interpolation='nearest')\npl.title('Euclidean cost')\n\npl.subplot(1, 3, 2)\npl.imshow(M2, interpolation='nearest')\npl.title('Squared Euclidean cost')\n\npl.subplot(1, 3, 3)\npl.imshow(Mp, interpolation='nearest')\npl.title('Sqrt Euclidean cost')\npl.tight_layout()" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Dataset 2 : Plot OT Matrices\n-----------------------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -98,29 +99,28 @@ "outputs": [], "source": [ "#%% EMD\nG1 = ot.emd(a, b, M1)\nG2 = ot.emd(a, b, M2)\nGp = ot.emd(a, b, Mp)\n\n# OT matrices\npl.figure(6, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\not.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT Euclidean')\n\npl.subplot(1, 3, 2)\not.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT squared Euclidean')\n\npl.subplot(1, 3, 3)\not.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT sqrt Euclidean')\npl.tight_layout()\n\npl.show()" - ], - "cell_type": "code" + ] } ], "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python", "codemirror_mode": { "name": "ipython", "version": 3 }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", "nbconvert_exporter": "python", - "version": "3.5.2", "pygments_lexer": "ipython3", - "file_extension": ".py", - "mimetype": "text/x-python" - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3", - "language": "python" + "version": "3.6.5" } }, - "nbformat_minor": 0, - "nbformat": 4 + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.py b/docs/source/auto_examples/plot_OT_L1_vs_L2.py index c1ed226..37b429f 100644 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.py +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.py @@ -53,7 +53,7 @@ pl.clf() pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.axis('equal') -pl.title('Source and traget distributions') +pl.title('Source and target distributions') # Cost matrices diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst index 01d6ac2..5db4b55 100644 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst @@ -70,7 +70,7 @@ Dataset 1 : uniform sampling pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.axis('equal') - pl.title('Source and traget distributions') + pl.title('Source and target distributions') # Cost matrices @@ -291,7 +291,7 @@ Dataset 2 : Plot OT Matrices -**Total running time of the script:** ( 0 minutes 3.750 seconds) +**Total running time of the script:** ( 0 minutes 0.958 seconds) diff --git a/docs/source/auto_examples/plot_barycenter_1D.ipynb b/docs/source/auto_examples/plot_barycenter_1D.ipynb index 01e759a..5866088 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.ipynb +++ b/docs/source/auto_examples/plot_barycenter_1D.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -8,47 +9,46 @@ "outputs": [], "source": [ "%matplotlib inline" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "\n# 1D Wasserstein barycenter demo\n\n\nThis example illustrates the computation of regularized Wassersyein Barycenter\nas proposed in [3].\n\n\n[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyr\u00e9, G. (2015).\nIterative Bregman projections for regularized transportation problems\nSIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nfrom matplotlib.collections import PolyCollection\n\n#\n# Generate data\n# -------------\n\n#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na1 = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.get_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()\n\n#\n# Plot data\n# ---------\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()\n\n#\n# Barycenter computation\n# ----------------------\n\n#%% barycenter computation\n\nalpha = 0.2 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()\n\n#\n# Barycentric interpolation\n# -------------------------\n\n#%% barycenter interpolation\n\nn_alpha = 11\nalpha_list = np.linspace(0, 1, n_alpha)\n\n\nB_l2 = np.zeros((n, n_alpha))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_alpha):\n alpha = alpha_list[i]\n weights = np.array([1 - alpha, alpha])\n B_l2[:, i] = A.dot(weights)\n B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)\n\n#%% plot interpolation\n\npl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_l2[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_wass[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with Wasserstein')\npl.tight_layout()\n\npl.show()" - ], - "cell_type": "code" + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nfrom matplotlib.collections import PolyCollection\n\n#\n# Generate data\n# -------------\n\n#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()\n\n#\n# Plot data\n# ---------\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()\n\n#\n# Barycenter computation\n# ----------------------\n\n#%% barycenter computation\n\nalpha = 0.2 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()\n\n#\n# Barycentric interpolation\n# -------------------------\n\n#%% barycenter interpolation\n\nn_alpha = 11\nalpha_list = np.linspace(0, 1, n_alpha)\n\n\nB_l2 = np.zeros((n, n_alpha))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_alpha):\n alpha = alpha_list[i]\n weights = np.array([1 - alpha, alpha])\n B_l2[:, i] = A.dot(weights)\n B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)\n\n#%% plot interpolation\n\npl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_l2[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_wass[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with Wasserstein')\npl.tight_layout()\n\npl.show()" + ] } ], "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python", "codemirror_mode": { "name": "ipython", "version": 3 }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", "nbconvert_exporter": "python", - "version": "3.5.2", "pygments_lexer": "ipython3", - "file_extension": ".py", - "mimetype": "text/x-python" - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3", - "language": "python" + "version": "3.6.5" } }, - "nbformat_minor": 0, - "nbformat": 4 + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_barycenter_1D.py b/docs/source/auto_examples/plot_barycenter_1D.py index ecf640c..5ed9f3f 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.py +++ b/docs/source/auto_examples/plot_barycenter_1D.py @@ -37,8 +37,8 @@ n = 100 # nb bins x = np.arange(n, dtype=np.float64) # Gaussian distributions -a1 = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std -a2 = ot.datasets.get_1D_gauss(n, m=60, s=8) +a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std +a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) # creating matrix A containing all distributions A = np.vstack((a1, a2)).T diff --git a/docs/source/auto_examples/plot_barycenter_1D.rst b/docs/source/auto_examples/plot_barycenter_1D.rst index 5b627ca..b314dc1 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.rst +++ b/docs/source/auto_examples/plot_barycenter_1D.rst @@ -72,8 +72,8 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. x = np.arange(n, dtype=np.float64) # Gaussian distributions - a1 = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std - a2 = ot.datasets.get_1D_gauss(n, m=60, s=8) + a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std + a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) # creating matrix A containing all distributions A = np.vstack((a1, a2)).T @@ -194,7 +194,7 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. pl.show() -**Total running time of the script:** ( 0 minutes 0.636 seconds) +**Total running time of the script:** ( 0 minutes 0.363 seconds) diff --git a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.ipynb b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.ipynb new file mode 100644 index 0000000..8114835 --- /dev/null +++ b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.ipynb @@ -0,0 +1,54 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# 1D Wasserstein barycenter comparison between exact LP and entropic regularization\n\n\nThis example illustrates the computation of regularized Wasserstein Barycenter\nas proposed in [3] and exact LP barycenters using standard LP solver.\n\nIt reproduces approximately Figure 3.1 and 3.2 from the following paper:\nCuturi, M., & Peyr\u00e9, G. (2016). A smoothed dual approach for variational\nWasserstein problems. SIAM Journal on Imaging Sciences, 9(1), 320-343.\n\n[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyr\u00e9, G. (2015).\nIterative Bregman projections for regularized transportation problems\nSIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n\n\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nfrom matplotlib.collections import PolyCollection # noqa\n\n#import ot.lp.cvx as cvx\n\n#\n# Generate data\n# -------------\n\n#%% parameters\n\nproblems = []\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\n# Gaussian distributions\na1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()\n\n#\n# Plot data\n# ---------\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()\n\n#\n# Barycenter computation\n# ----------------------\n\n#%% barycenter computation\n\nalpha = 0.5 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\not.tic()\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\not.toc()\n\n\not.tic()\nbary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\not.toc()\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\npl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()\n\nproblems.append([A, [bary_l2, bary_wass, bary_wass2]])\n\n#%% parameters\n\na1 = 1.0 * (x > 10) * (x < 50)\na2 = 1.0 * (x > 60) * (x < 80)\n\na1 /= a1.sum()\na2 /= a2.sum()\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()\n\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()\n\n#\n# Barycenter computation\n# ----------------------\n\n#%% barycenter computation\n\nalpha = 0.5 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\not.tic()\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\not.toc()\n\n\not.tic()\nbary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\not.toc()\n\n\nproblems.append([A, [bary_l2, bary_wass, bary_wass2]])\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\npl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()\n\n#%% parameters\n\na1 = np.zeros(n)\na2 = np.zeros(n)\n\na1[10] = .25\na1[20] = .5\na1[30] = .25\na2[80] = 1\n\n\na1 /= a1.sum()\na2 /= a2.sum()\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()\n\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()\n\n#\n# Barycenter computation\n# ----------------------\n\n#%% barycenter computation\n\nalpha = 0.5 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\not.tic()\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\not.toc()\n\n\not.tic()\nbary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\not.toc()\n\n\nproblems.append([A, [bary_l2, bary_wass, bary_wass2]])\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\npl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()\n\n\n#\n# Final figure\n# ------------\n#\n\n#%% plot\n\nnbm = len(problems)\nnbm2 = (nbm // 2)\n\n\npl.figure(2, (20, 6))\npl.clf()\n\nfor i in range(nbm):\n\n A = problems[i][0]\n bary_l2 = problems[i][1][0]\n bary_wass = problems[i][1][1]\n bary_wass2 = problems[i][1][2]\n\n pl.subplot(2, nbm, 1 + i)\n for j in range(n_distributions):\n pl.plot(x, A[:, j])\n if i == nbm2:\n pl.title('Distributions')\n pl.xticks(())\n pl.yticks(())\n\n pl.subplot(2, nbm, 1 + i + nbm)\n\n pl.plot(x, bary_l2, 'r', label='L2 (Euclidean)')\n pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\n pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\n if i == nbm - 1:\n pl.legend()\n if i == nbm2:\n pl.title('Barycenters')\n\n pl.xticks(())\n pl.yticks(())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.py b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.py new file mode 100644 index 0000000..2255107 --- /dev/null +++ b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.py @@ -0,0 +1,292 @@ +# -*- coding: utf-8 -*- +""" +================================================================================= +1D Wasserstein barycenter comparison between exact LP and entropic regularization +================================================================================= + +This example illustrates the computation of regularized Wasserstein Barycenter +as proposed in [3] and exact LP barycenters using standard LP solver. + +It reproduces approximately Figure 3.1 and 3.2 from the following paper: +Cuturi, M., & Peyré, G. (2016). A smoothed dual approach for variational +Wasserstein problems. SIAM Journal on Imaging Sciences, 9(1), 320-343. + +[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). +Iterative Bregman projections for regularized transportation problems +SIAM Journal on Scientific Computing, 37(2), A1111-A1138. + + + +""" + +# Author: Remi Flamary +# +# License: MIT License + +import numpy as np +import matplotlib.pylab as pl +import ot +# necessary for 3d plot even if not used +from mpl_toolkits.mplot3d import Axes3D # noqa +from matplotlib.collections import PolyCollection # noqa + +#import ot.lp.cvx as cvx + +# +# Generate data +# ------------- + +#%% parameters + +problems = [] + +n = 100 # nb bins + +# bin positions +x = np.arange(n, dtype=np.float64) + +# Gaussian distributions +# Gaussian distributions +a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std +a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) + +# creating matrix A containing all distributions +A = np.vstack((a1, a2)).T +n_distributions = A.shape[1] + +# loss matrix + normalization +M = ot.utils.dist0(n) +M /= M.max() + +# +# Plot data +# --------- + +#%% plot the distributions + +pl.figure(1, figsize=(6.4, 3)) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') +pl.tight_layout() + +# +# Barycenter computation +# ---------------------- + +#%% barycenter computation + +alpha = 0.5 # 0<=alpha<=1 +weights = np.array([1 - alpha, alpha]) + +# l2bary +bary_l2 = A.dot(weights) + +# wasserstein +reg = 1e-3 +ot.tic() +bary_wass = ot.bregman.barycenter(A, M, reg, weights) +ot.toc() + + +ot.tic() +bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True) +ot.toc() + +pl.figure(2) +pl.clf() +pl.subplot(2, 1, 1) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') + +pl.subplot(2, 1, 2) +pl.plot(x, bary_l2, 'r', label='l2') +pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') +pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') +pl.legend() +pl.title('Barycenters') +pl.tight_layout() + +problems.append([A, [bary_l2, bary_wass, bary_wass2]]) + +#%% parameters + +a1 = 1.0 * (x > 10) * (x < 50) +a2 = 1.0 * (x > 60) * (x < 80) + +a1 /= a1.sum() +a2 /= a2.sum() + +# creating matrix A containing all distributions +A = np.vstack((a1, a2)).T +n_distributions = A.shape[1] + +# loss matrix + normalization +M = ot.utils.dist0(n) +M /= M.max() + + +#%% plot the distributions + +pl.figure(1, figsize=(6.4, 3)) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') +pl.tight_layout() + +# +# Barycenter computation +# ---------------------- + +#%% barycenter computation + +alpha = 0.5 # 0<=alpha<=1 +weights = np.array([1 - alpha, alpha]) + +# l2bary +bary_l2 = A.dot(weights) + +# wasserstein +reg = 1e-3 +ot.tic() +bary_wass = ot.bregman.barycenter(A, M, reg, weights) +ot.toc() + + +ot.tic() +bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True) +ot.toc() + + +problems.append([A, [bary_l2, bary_wass, bary_wass2]]) + +pl.figure(2) +pl.clf() +pl.subplot(2, 1, 1) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') + +pl.subplot(2, 1, 2) +pl.plot(x, bary_l2, 'r', label='l2') +pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') +pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') +pl.legend() +pl.title('Barycenters') +pl.tight_layout() + +#%% parameters + +a1 = np.zeros(n) +a2 = np.zeros(n) + +a1[10] = .25 +a1[20] = .5 +a1[30] = .25 +a2[80] = 1 + + +a1 /= a1.sum() +a2 /= a2.sum() + +# creating matrix A containing all distributions +A = np.vstack((a1, a2)).T +n_distributions = A.shape[1] + +# loss matrix + normalization +M = ot.utils.dist0(n) +M /= M.max() + + +#%% plot the distributions + +pl.figure(1, figsize=(6.4, 3)) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') +pl.tight_layout() + +# +# Barycenter computation +# ---------------------- + +#%% barycenter computation + +alpha = 0.5 # 0<=alpha<=1 +weights = np.array([1 - alpha, alpha]) + +# l2bary +bary_l2 = A.dot(weights) + +# wasserstein +reg = 1e-3 +ot.tic() +bary_wass = ot.bregman.barycenter(A, M, reg, weights) +ot.toc() + + +ot.tic() +bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True) +ot.toc() + + +problems.append([A, [bary_l2, bary_wass, bary_wass2]]) + +pl.figure(2) +pl.clf() +pl.subplot(2, 1, 1) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') + +pl.subplot(2, 1, 2) +pl.plot(x, bary_l2, 'r', label='l2') +pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') +pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') +pl.legend() +pl.title('Barycenters') +pl.tight_layout() + + +# +# Final figure +# ------------ +# + +#%% plot + +nbm = len(problems) +nbm2 = (nbm // 2) + + +pl.figure(2, (20, 6)) +pl.clf() + +for i in range(nbm): + + A = problems[i][0] + bary_l2 = problems[i][1][0] + bary_wass = problems[i][1][1] + bary_wass2 = problems[i][1][2] + + pl.subplot(2, nbm, 1 + i) + for j in range(n_distributions): + pl.plot(x, A[:, j]) + if i == nbm2: + pl.title('Distributions') + pl.xticks(()) + pl.yticks(()) + + pl.subplot(2, nbm, 1 + i + nbm) + + pl.plot(x, bary_l2, 'r', label='L2 (Euclidean)') + pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') + pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') + if i == nbm - 1: + pl.legend() + if i == nbm2: + pl.title('Barycenters') + + pl.xticks(()) + pl.yticks(()) diff --git a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.rst b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.rst new file mode 100644 index 0000000..e8c15df --- /dev/null +++ b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.rst @@ -0,0 +1,412 @@ + + +.. _sphx_glr_auto_examples_plot_barycenter_lp_vs_entropic.py: + + +================================================================================= +1D Wasserstein barycenter comparison between exact LP and entropic regularization +================================================================================= + +This example illustrates the computation of regularized Wasserstein Barycenter +as proposed in [3] and exact LP barycenters using standard LP solver. + +It reproduces approximately Figure 3.1 and 3.2 from the following paper: +Cuturi, M., & Peyré, G. (2016). A smoothed dual approach for variational +Wasserstein problems. SIAM Journal on Imaging Sciences, 9(1), 320-343. + +[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). +Iterative Bregman projections for regularized transportation problems +SIAM Journal on Scientific Computing, 37(2), A1111-A1138. + + + + + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_001.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_002.png + :scale: 47 + + +.. rst-class:: sphx-glr-script-out + + Out:: + + Elapsed time : 0.010588884353637695 s + Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective + 1.0 1.0 1.0 - 1.0 1700.336700337 + 0.006776453137632 0.006776453137633 0.006776453137633 0.9932238647293 0.006776453137633 125.6700527543 + 0.004018712867874 0.004018712867874 0.004018712867874 0.4301142633 0.004018712867874 12.26594150093 + 0.001172775061627 0.001172775061627 0.001172775061627 0.7599932455029 0.001172775061627 0.3378536968897 + 0.0004375137005385 0.0004375137005385 0.0004375137005385 0.6422331807989 0.0004375137005385 0.1468420566358 + 0.000232669046734 0.0002326690467341 0.000232669046734 0.5016999460893 0.000232669046734 0.09381703231432 + 7.430121674303e-05 7.430121674303e-05 7.430121674303e-05 0.7035962305812 7.430121674303e-05 0.0577787025717 + 5.321227838876e-05 5.321227838875e-05 5.321227838876e-05 0.308784186441 5.321227838876e-05 0.05266249477203 + 1.990900379199e-05 1.990900379196e-05 1.990900379199e-05 0.6520472013244 1.990900379199e-05 0.04526054405519 + 6.305442046799e-06 6.30544204682e-06 6.3054420468e-06 0.7073953304075 6.305442046798e-06 0.04237597591383 + 2.290148391577e-06 2.290148391582e-06 2.290148391578e-06 0.6941812711492 2.29014839159e-06 0.041522849321 + 1.182864875387e-06 1.182864875406e-06 1.182864875427e-06 0.508455204675 1.182864875445e-06 0.04129461872827 + 3.626786381529e-07 3.626786382468e-07 3.626786382923e-07 0.7101651572101 3.62678638267e-07 0.04113032448923 + 1.539754244902e-07 1.539754249276e-07 1.539754249356e-07 0.6279322066282 1.539754253892e-07 0.04108867636379 + 5.193221323143e-08 5.193221463044e-08 5.193221462729e-08 0.6843453436759 5.193221708199e-08 0.04106859618414 + 1.888205054507e-08 1.888204779723e-08 1.88820477688e-08 0.6673444085651 1.888205650952e-08 0.041062141752 + 5.676855206925e-09 5.676854518888e-09 5.676854517651e-09 0.7281705804232 5.676885442702e-09 0.04105958648713 + 3.501157668218e-09 3.501150243546e-09 3.501150216347e-09 0.414020345194 3.501164437194e-09 0.04105916265261 + 1.110594251499e-09 1.110590786827e-09 1.11059083379e-09 0.6998954759911 1.110636623476e-09 0.04105870073485 + 5.770971626386e-10 5.772456113791e-10 5.772456200156e-10 0.4999769658132 5.77013379477e-10 0.04105859769135 + 1.535218204536e-10 1.536993317032e-10 1.536992771966e-10 0.7516471627141 1.536205005991e-10 0.04105851679958 + 6.724209350756e-11 6.739211232927e-11 6.739210470901e-11 0.5944802416166 6.735465384341e-11 0.04105850033766 + 1.743382199199e-11 1.736445896691e-11 1.736448490761e-11 0.7573407808104 1.734254328931e-11 0.04105849088824 + Optimization terminated successfully. + Elapsed time : 2.8747198581695557 s + Elapsed time : 0.014937639236450195 s + Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective + 1.0 1.0 1.0 - 1.0 1700.336700337 + 0.006776466288966 0.006776466288966 0.006776466288966 0.9932238515788 0.006776466288966 125.6649255808 + 0.004036918865495 0.004036918865495 0.004036918865495 0.4272973099316 0.004036918865495 12.3471617011 + 0.00121923268707 0.00121923268707 0.00121923268707 0.749698685599 0.00121923268707 0.3243835647408 + 0.0003837422984432 0.0003837422984432 0.0003837422984432 0.6926882608284 0.0003837422984432 0.1361719397493 + 0.0001070128410183 0.0001070128410183 0.0001070128410183 0.7643889137854 0.0001070128410183 0.07581952832518 + 0.0001001275033711 0.0001001275033711 0.0001001275033711 0.07058704837812 0.0001001275033712 0.0734739493635 + 4.550897507844e-05 4.550897507841e-05 4.550897507844e-05 0.5761172484828 4.550897507845e-05 0.05555077655047 + 8.557124125522e-06 8.5571241255e-06 8.557124125522e-06 0.8535925441152 8.557124125522e-06 0.04439814660221 + 3.611995628407e-06 3.61199562841e-06 3.611995628414e-06 0.6002277331554 3.611995628415e-06 0.04283007762152 + 7.590393750365e-07 7.590393750491e-07 7.590393750378e-07 0.8221486533416 7.590393750381e-07 0.04192322976248 + 8.299929287441e-08 8.299929286079e-08 8.299929287532e-08 0.9017467938799 8.29992928758e-08 0.04170825633295 + 3.117560203449e-10 3.117560130137e-10 3.11756019954e-10 0.997039969226 3.11756019952e-10 0.04168179329766 + 1.559749653711e-14 1.558073160926e-14 1.559756940692e-14 0.9999499686183 1.559750643989e-14 0.04168169240444 + Optimization terminated successfully. + Elapsed time : 2.6253304481506348 s + Elapsed time : 0.002875089645385742 s + Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective + 1.0 1.0 1.0 - 1.0 1700.336700337 + 0.006774675520727 0.006774675520727 0.006774675520727 0.9932256422636 0.006774675520727 125.6956034743 + 0.002048208707562 0.002048208707562 0.002048208707562 0.7343095368143 0.002048208707562 5.213991622123 + 0.000269736547478 0.0002697365474781 0.0002697365474781 0.8839403501193 0.000269736547478 0.505938390389 + 6.832109993943e-05 6.832109993944e-05 6.832109993944e-05 0.7601171075965 6.832109993943e-05 0.2339657807272 + 2.437682932219e-05 2.43768293222e-05 2.437682932219e-05 0.6663448297475 2.437682932219e-05 0.1471256246325 + 1.13498321631e-05 1.134983216308e-05 1.13498321631e-05 0.5553643816404 1.13498321631e-05 0.1181584941171 + 3.342312725885e-06 3.342312725884e-06 3.342312725885e-06 0.7238133571615 3.342312725885e-06 0.1006387519747 + 7.078561231603e-07 7.078561231509e-07 7.078561231604e-07 0.8033142552512 7.078561231603e-07 0.09474734646269 + 1.966870956916e-07 1.966870954537e-07 1.966870954468e-07 0.752547917788 1.966870954633e-07 0.09354342735766 + 4.19989524849e-10 4.199895164852e-10 4.199895238758e-10 0.9984019849375 4.19989523951e-10 0.09310367785861 + 2.101015938666e-14 2.100625691113e-14 2.101023853438e-14 0.999949974425 2.101023691864e-14 0.09310274466458 + Optimization terminated successfully. + Elapsed time : 3.587958335876465 s + + + + +| + + +.. code-block:: python + + + # Author: Remi Flamary + # + # License: MIT License + + import numpy as np + import matplotlib.pylab as pl + import ot + # necessary for 3d plot even if not used + from mpl_toolkits.mplot3d import Axes3D # noqa + from matplotlib.collections import PolyCollection # noqa + + #import ot.lp.cvx as cvx + + # + # Generate data + # ------------- + + #%% parameters + + problems = [] + + n = 100 # nb bins + + # bin positions + x = np.arange(n, dtype=np.float64) + + # Gaussian distributions + # Gaussian distributions + a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std + a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) + + # creating matrix A containing all distributions + A = np.vstack((a1, a2)).T + n_distributions = A.shape[1] + + # loss matrix + normalization + M = ot.utils.dist0(n) + M /= M.max() + + # + # Plot data + # --------- + + #%% plot the distributions + + pl.figure(1, figsize=(6.4, 3)) + for i in range(n_distributions): + pl.plot(x, A[:, i]) + pl.title('Distributions') + pl.tight_layout() + + # + # Barycenter computation + # ---------------------- + + #%% barycenter computation + + alpha = 0.5 # 0<=alpha<=1 + weights = np.array([1 - alpha, alpha]) + + # l2bary + bary_l2 = A.dot(weights) + + # wasserstein + reg = 1e-3 + ot.tic() + bary_wass = ot.bregman.barycenter(A, M, reg, weights) + ot.toc() + + + ot.tic() + bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True) + ot.toc() + + pl.figure(2) + pl.clf() + pl.subplot(2, 1, 1) + for i in range(n_distributions): + pl.plot(x, A[:, i]) + pl.title('Distributions') + + pl.subplot(2, 1, 2) + pl.plot(x, bary_l2, 'r', label='l2') + pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') + pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') + pl.legend() + pl.title('Barycenters') + pl.tight_layout() + + problems.append([A, [bary_l2, bary_wass, bary_wass2]]) + + #%% parameters + + a1 = 1.0 * (x > 10) * (x < 50) + a2 = 1.0 * (x > 60) * (x < 80) + + a1 /= a1.sum() + a2 /= a2.sum() + + # creating matrix A containing all distributions + A = np.vstack((a1, a2)).T + n_distributions = A.shape[1] + + # loss matrix + normalization + M = ot.utils.dist0(n) + M /= M.max() + + + #%% plot the distributions + + pl.figure(1, figsize=(6.4, 3)) + for i in range(n_distributions): + pl.plot(x, A[:, i]) + pl.title('Distributions') + pl.tight_layout() + + # + # Barycenter computation + # ---------------------- + + #%% barycenter computation + + alpha = 0.5 # 0<=alpha<=1 + weights = np.array([1 - alpha, alpha]) + + # l2bary + bary_l2 = A.dot(weights) + + # wasserstein + reg = 1e-3 + ot.tic() + bary_wass = ot.bregman.barycenter(A, M, reg, weights) + ot.toc() + + + ot.tic() + bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True) + ot.toc() + + + problems.append([A, [bary_l2, bary_wass, bary_wass2]]) + + pl.figure(2) + pl.clf() + pl.subplot(2, 1, 1) + for i in range(n_distributions): + pl.plot(x, A[:, i]) + pl.title('Distributions') + + pl.subplot(2, 1, 2) + pl.plot(x, bary_l2, 'r', label='l2') + pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') + pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') + pl.legend() + pl.title('Barycenters') + pl.tight_layout() + + #%% parameters + + a1 = np.zeros(n) + a2 = np.zeros(n) + + a1[10] = .25 + a1[20] = .5 + a1[30] = .25 + a2[80] = 1 + + + a1 /= a1.sum() + a2 /= a2.sum() + + # creating matrix A containing all distributions + A = np.vstack((a1, a2)).T + n_distributions = A.shape[1] + + # loss matrix + normalization + M = ot.utils.dist0(n) + M /= M.max() + + + #%% plot the distributions + + pl.figure(1, figsize=(6.4, 3)) + for i in range(n_distributions): + pl.plot(x, A[:, i]) + pl.title('Distributions') + pl.tight_layout() + + # + # Barycenter computation + # ---------------------- + + #%% barycenter computation + + alpha = 0.5 # 0<=alpha<=1 + weights = np.array([1 - alpha, alpha]) + + # l2bary + bary_l2 = A.dot(weights) + + # wasserstein + reg = 1e-3 + ot.tic() + bary_wass = ot.bregman.barycenter(A, M, reg, weights) + ot.toc() + + + ot.tic() + bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True) + ot.toc() + + + problems.append([A, [bary_l2, bary_wass, bary_wass2]]) + + pl.figure(2) + pl.clf() + pl.subplot(2, 1, 1) + for i in range(n_distributions): + pl.plot(x, A[:, i]) + pl.title('Distributions') + + pl.subplot(2, 1, 2) + pl.plot(x, bary_l2, 'r', label='l2') + pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') + pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') + pl.legend() + pl.title('Barycenters') + pl.tight_layout() + + + # + # Final figure + # ------------ + # + + #%% plot + + nbm = len(problems) + nbm2 = (nbm // 2) + + + pl.figure(2, (20, 6)) + pl.clf() + + for i in range(nbm): + + A = problems[i][0] + bary_l2 = problems[i][1][0] + bary_wass = problems[i][1][1] + bary_wass2 = problems[i][1][2] + + pl.subplot(2, nbm, 1 + i) + for j in range(n_distributions): + pl.plot(x, A[:, j]) + if i == nbm2: + pl.title('Distributions') + pl.xticks(()) + pl.yticks(()) + + pl.subplot(2, nbm, 1 + i + nbm) + + pl.plot(x, bary_l2, 'r', label='L2 (Euclidean)') + pl.plot(x, bary_wass, 'g', label='Reg Wasserstein') + pl.plot(x, bary_wass2, 'b', label='LP Wasserstein') + if i == nbm - 1: + pl.legend() + if i == nbm2: + pl.title('Barycenters') + + pl.xticks(()) + pl.yticks(()) + +**Total running time of the script:** ( 0 minutes 9.816 seconds) + + + +.. only :: html + + .. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_barycenter_lp_vs_entropic.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_barycenter_lp_vs_entropic.ipynb ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_compute_emd.ipynb b/docs/source/auto_examples/plot_compute_emd.ipynb index b9b8bc5..562eff8 100644 --- a/docs/source/auto_examples/plot_compute_emd.ipynb +++ b/docs/source/auto_examples/plot_compute_emd.ipynb @@ -1,126 +1,126 @@ { - "nbformat_minor": 0, - "nbformat": 4, "cells": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "\n# Plot multiple EMD\n\n\nShows how to compute multiple EMD and Sinkhorn with two differnt\nground metrics and plot their values for diffeent distributions.\n\n\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nfrom ot.datasets import get_1D_gauss as gauss" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nfrom ot.datasets import make_1D_gauss as gauss" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Generate data\n-------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% parameters\n\nn = 100 # nb bins\nn_target = 50 # nb target distributions\n\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\nlst_m = np.linspace(20, 90, n_target)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\n\nB = np.zeros((n, n_target))\n\nfor i, m in enumerate(lst_m):\n B[:, i] = gauss(n, m=m, s=5)\n\n# loss matrix and normalization\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'euclidean')\nM /= M.max()\nM2 = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'sqeuclidean')\nM2 /= M2.max()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "#%% parameters\n\nn = 100 # nb bins\nn_target = 50 # nb target distributions\n\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\nlst_m = np.linspace(20, 90, n_target)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\n\nB = np.zeros((n, n_target))\n\nfor i, m in enumerate(lst_m):\n B[:, i] = gauss(n, m=m, s=5)\n\n# loss matrix and normalization\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'euclidean')\nM /= M.max()\nM2 = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'sqeuclidean')\nM2 /= M2.max()" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Plot data\n---------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% plot the distributions\n\npl.figure(1)\npl.subplot(2, 1, 1)\npl.plot(x, a, 'b', label='Source distribution')\npl.title('Source distribution')\npl.subplot(2, 1, 2)\npl.plot(x, B, label='Target distributions')\npl.title('Target distributions')\npl.tight_layout()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "#%% plot the distributions\n\npl.figure(1)\npl.subplot(2, 1, 1)\npl.plot(x, a, 'b', label='Source distribution')\npl.title('Source distribution')\npl.subplot(2, 1, 2)\npl.plot(x, B, label='Target distributions')\npl.title('Target distributions')\npl.tight_layout()" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Compute EMD for the different losses\n------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% Compute and plot distributions and loss matrix\n\nd_emd = ot.emd2(a, B, M) # direct computation of EMD\nd_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M2\n\n\npl.figure(2)\npl.plot(d_emd, label='Euclidean EMD')\npl.plot(d_emd2, label='Squared Euclidean EMD')\npl.title('EMD distances')\npl.legend()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "#%% Compute and plot distributions and loss matrix\n\nd_emd = ot.emd2(a, B, M) # direct computation of EMD\nd_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M2\n\n\npl.figure(2)\npl.plot(d_emd, label='Euclidean EMD')\npl.plot(d_emd2, label='Squared Euclidean EMD')\npl.title('EMD distances')\npl.legend()" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Compute Sinkhorn for the different losses\n-----------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%%\nreg = 1e-2\nd_sinkhorn = ot.sinkhorn2(a, B, M, reg)\nd_sinkhorn2 = ot.sinkhorn2(a, B, M2, reg)\n\npl.figure(2)\npl.clf()\npl.plot(d_emd, label='Euclidean EMD')\npl.plot(d_emd2, label='Squared Euclidean EMD')\npl.plot(d_sinkhorn, '+', label='Euclidean Sinkhorn')\npl.plot(d_sinkhorn2, '+', label='Squared Euclidean Sinkhorn')\npl.title('EMD distances')\npl.legend()\n\npl.show()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } + }, + "outputs": [], + "source": [ + "#%%\nreg = 1e-2\nd_sinkhorn = ot.sinkhorn2(a, B, M, reg)\nd_sinkhorn2 = ot.sinkhorn2(a, B, M2, reg)\n\npl.figure(2)\npl.clf()\npl.plot(d_emd, label='Euclidean EMD')\npl.plot(d_emd2, label='Squared Euclidean EMD')\npl.plot(d_sinkhorn, '+', label='Euclidean Sinkhorn')\npl.plot(d_sinkhorn2, '+', label='Squared Euclidean Sinkhorn')\npl.title('EMD distances')\npl.legend()\n\npl.show()" + ] } - ], + ], "metadata": { "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", "codemirror_mode": { - "version": 2, - "name": "ipython" - } + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" } - } + }, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_compute_emd.py b/docs/source/auto_examples/plot_compute_emd.py index 73b42c3..7ed2b01 100644 --- a/docs/source/auto_examples/plot_compute_emd.py +++ b/docs/source/auto_examples/plot_compute_emd.py @@ -17,7 +17,7 @@ ground metrics and plot their values for diffeent distributions. import numpy as np import matplotlib.pylab as pl import ot -from ot.datasets import get_1D_gauss as gauss +from ot.datasets import make_1D_gauss as gauss ############################################################################## diff --git a/docs/source/auto_examples/plot_compute_emd.rst b/docs/source/auto_examples/plot_compute_emd.rst index cdbc620..27bca2c 100644 --- a/docs/source/auto_examples/plot_compute_emd.rst +++ b/docs/source/auto_examples/plot_compute_emd.rst @@ -24,7 +24,7 @@ ground metrics and plot their values for diffeent distributions. import numpy as np import matplotlib.pylab as pl import ot - from ot.datasets import get_1D_gauss as gauss + from ot.datasets import make_1D_gauss as gauss @@ -162,11 +162,13 @@ Compute Sinkhorn for the different losses -**Total running time of the script:** ( 0 minutes 0.697 seconds) +**Total running time of the script:** ( 0 minutes 0.446 seconds) -.. container:: sphx-glr-footer +.. only :: html + + .. container:: sphx-glr-footer .. container:: sphx-glr-download @@ -179,6 +181,9 @@ Compute Sinkhorn for the different losses :download:`Download Jupyter notebook: plot_compute_emd.ipynb ` -.. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_gromov.ipynb b/docs/source/auto_examples/plot_gromov.ipynb index 57d6a4a..dc1f179 100644 --- a/docs/source/auto_examples/plot_gromov.ipynb +++ b/docs/source/auto_examples/plot_gromov.ipynb @@ -1,126 +1,126 @@ { - "nbformat_minor": 0, - "nbformat": 4, - "metadata": { - "language_info": { - "file_extension": ".py", - "codemirror_mode": { - "version": 3, - "name": "ipython" - }, - "nbconvert_exporter": "python", - "mimetype": "text/x-python", - "version": "3.5.2", - "name": "python", - "pygments_lexer": "ipython3" - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3", - "language": "python" - } - }, "cells": [ { - "outputs": [], - "source": [ - "%matplotlib inline" - ], + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, - "cell_type": "code" + "outputs": [], + "source": [ + "%matplotlib inline" + ] }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "\n# Gromov-Wasserstein example\n\n\nThis example is designed to show how to use the Gromov-Wassertsein distance\ncomputation in POT.\n\n" - ], - "metadata": {}, - "cell_type": "markdown" + ] }, { - "outputs": [], - "source": [ - "# Author: Erwan Vautier \n# Nicolas Courty \n#\n# License: MIT License\n\nimport scipy as sp\nimport numpy as np\nimport matplotlib.pylab as pl\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nimport ot" - ], + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, - "cell_type": "code" + "outputs": [], + "source": [ + "# Author: Erwan Vautier \n# Nicolas Courty \n#\n# License: MIT License\n\nimport scipy as sp\nimport numpy as np\nimport matplotlib.pylab as pl\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nimport ot" + ] }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Sample two Gaussian distributions (2D and 3D)\n---------------------------------------------\n\nThe Gromov-Wasserstein distance allows to compute distances with samples that\ndo not belong to the same metric space. For demonstration purpose, we sample\ntwo Gaussian distributions in 2- and 3-dimensional spaces.\n\n" - ], - "metadata": {}, - "cell_type": "markdown" + ] }, { - "outputs": [], - "source": [ - "n_samples = 30 # nb samples\n\nmu_s = np.array([0, 0])\ncov_s = np.array([[1, 0], [0, 1]])\n\nmu_t = np.array([4, 4, 4])\ncov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\n\n\nxs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)\nP = sp.linalg.sqrtm(cov_t)\nxt = np.random.randn(n_samples, 3).dot(P) + mu_t" - ], + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, - "cell_type": "code" + "outputs": [], + "source": [ + "n_samples = 30 # nb samples\n\nmu_s = np.array([0, 0])\ncov_s = np.array([[1, 0], [0, 1]])\n\nmu_t = np.array([4, 4, 4])\ncov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\n\n\nxs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s)\nP = sp.linalg.sqrtm(cov_t)\nxt = np.random.randn(n_samples, 3).dot(P) + mu_t" + ] }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Plotting the distributions\n--------------------------\n\n" - ], - "metadata": {}, - "cell_type": "markdown" + ] }, { - "outputs": [], - "source": [ - "fig = pl.figure()\nax1 = fig.add_subplot(121)\nax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\nax2 = fig.add_subplot(122, projection='3d')\nax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\npl.show()" - ], + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, - "cell_type": "code" + "outputs": [], + "source": [ + "fig = pl.figure()\nax1 = fig.add_subplot(121)\nax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\nax2 = fig.add_subplot(122, projection='3d')\nax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\npl.show()" + ] }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Compute distance kernels, normalize them and then display\n---------------------------------------------------------\n\n" - ], - "metadata": {}, - "cell_type": "markdown" + ] }, { - "outputs": [], - "source": [ - "C1 = sp.spatial.distance.cdist(xs, xs)\nC2 = sp.spatial.distance.cdist(xt, xt)\n\nC1 /= C1.max()\nC2 /= C2.max()\n\npl.figure()\npl.subplot(121)\npl.imshow(C1)\npl.subplot(122)\npl.imshow(C2)\npl.show()" - ], + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, - "cell_type": "code" + "outputs": [], + "source": [ + "C1 = sp.spatial.distance.cdist(xs, xs)\nC2 = sp.spatial.distance.cdist(xt, xt)\n\nC1 /= C1.max()\nC2 /= C2.max()\n\npl.figure()\npl.subplot(121)\npl.imshow(C1)\npl.subplot(122)\npl.imshow(C2)\npl.show()" + ] }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Compute Gromov-Wasserstein plans and distance\n---------------------------------------------\n\n" - ], - "metadata": {}, - "cell_type": "markdown" + ] }, { - "outputs": [], - "source": [ - "p = ot.unif(n_samples)\nq = ot.unif(n_samples)\n\ngw0, log0 = ot.gromov.gromov_wasserstein(\n C1, C2, p, q, 'square_loss', verbose=True, log=True)\n\ngw, log = ot.gromov.entropic_gromov_wasserstein(\n C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True)\n\n\nprint('Gromov-Wasserstein distances: ' + str(log0['gw_dist']))\nprint('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist']))\n\n\npl.figure(1, (10, 5))\n\npl.subplot(1, 2, 1)\npl.imshow(gw0, cmap='jet')\npl.title('Gromov Wasserstein')\n\npl.subplot(1, 2, 2)\npl.imshow(gw, cmap='jet')\npl.title('Entropic Gromov Wasserstein')\n\npl.show()" - ], + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, - "cell_type": "code" + "outputs": [], + "source": [ + "p = ot.unif(n_samples)\nq = ot.unif(n_samples)\n\ngw0, log0 = ot.gromov.gromov_wasserstein(\n C1, C2, p, q, 'square_loss', verbose=True, log=True)\n\ngw, log = ot.gromov.entropic_gromov_wasserstein(\n C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True)\n\n\nprint('Gromov-Wasserstein distances: ' + str(log0['gw_dist']))\nprint('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist']))\n\n\npl.figure(1, (10, 5))\n\npl.subplot(1, 2, 1)\npl.imshow(gw0, cmap='jet')\npl.title('Gromov Wasserstein')\n\npl.subplot(1, 2, 2)\npl.imshow(gw, cmap='jet')\npl.title('Entropic Gromov Wasserstein')\n\npl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" } - ] + }, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_gromov.py b/docs/source/auto_examples/plot_gromov.py index 5cd40f6..deb2f86 100644 --- a/docs/source/auto_examples/plot_gromov.py +++ b/docs/source/auto_examples/plot_gromov.py @@ -38,7 +38,7 @@ mu_t = np.array([4, 4, 4]) cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) -xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s) +xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) P = sp.linalg.sqrtm(cov_t) xt = np.random.randn(n_samples, 3).dot(P) + mu_t diff --git a/docs/source/auto_examples/plot_gromov.rst b/docs/source/auto_examples/plot_gromov.rst index 131861f..3ed4e11 100644 --- a/docs/source/auto_examples/plot_gromov.rst +++ b/docs/source/auto_examples/plot_gromov.rst @@ -54,7 +54,7 @@ two Gaussian distributions in 2- and 3-dimensional spaces. cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) - xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s) + xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) P = sp.linalg.sqrtm(cov_t) xt = np.random.randn(n_samples, 3).dot(P) + mu_t @@ -166,28 +166,28 @@ Compute Gromov-Wasserstein plans and distance It. |Loss |Delta loss -------------------------------- - 0|4.517558e-02|0.000000e+00 - 1|2.563483e-02|-7.622736e-01 - 2|2.443903e-02|-4.892972e-02 - 3|2.231600e-02|-9.513496e-02 - 4|1.676188e-02|-3.313541e-01 - 5|1.464792e-02|-1.443180e-01 - 6|1.454315e-02|-7.204526e-03 - 7|1.454142e-02|-1.185811e-04 - 8|1.454141e-02|-1.190466e-06 - 9|1.454141e-02|-1.190512e-08 - 10|1.454141e-02|-1.190520e-10 + 0|4.328711e-02|0.000000e+00 + 1|2.281369e-02|-8.974178e-01 + 2|1.843659e-02|-2.374139e-01 + 3|1.602820e-02|-1.502598e-01 + 4|1.353712e-02|-1.840179e-01 + 5|1.285687e-02|-5.290977e-02 + 6|1.284537e-02|-8.952931e-04 + 7|1.284525e-02|-8.989584e-06 + 8|1.284525e-02|-8.989950e-08 + 9|1.284525e-02|-8.989949e-10 It. |Err ------------------- - 0|6.743761e-02| - 10|5.477003e-04| - 20|2.461503e-08| - 30|1.205155e-11| - Gromov-Wasserstein distances: 0.014541405718693563 - Entropic Gromov-Wasserstein distances: 0.015800739725237274 + 0|7.263293e-02| + 10|1.737784e-02| + 20|7.783978e-03| + 30|3.399419e-07| + 40|3.751207e-11| + Gromov-Wasserstein distances: 0.012845252089244688 + Entropic Gromov-Wasserstein distances: 0.013543882352191079 -**Total running time of the script:** ( 0 minutes 1.448 seconds) +**Total running time of the script:** ( 0 minutes 1.916 seconds) diff --git a/docs/source/auto_examples/plot_optim_OTreg.ipynb b/docs/source/auto_examples/plot_optim_OTreg.ipynb index 02bf175..107c299 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.ipynb +++ b/docs/source/auto_examples/plot_optim_OTreg.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -8,17 +9,17 @@ "outputs": [], "source": [ "%matplotlib inline" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "\n# Regularized OT with generic solver\n\n\nIllustrates the use of the generic solver for regularized OT with\nuser-designed regularization term. It uses Conditional gradient as in [6] and\ngeneralized Conditional Gradient as proposed in [5][7].\n\n\n[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for\nDomain Adaptation, in IEEE Transactions on Pattern Analysis and Machine\nIntelligence , vol.PP, no.99, pp.1-1.\n\n[6] Ferradans, S., Papadakis, N., Peyr\u00e9, G., & Aujol, J. F. (2014).\nRegularized discrete optimal transport. SIAM Journal on Imaging Sciences,\n7(3), 1853-1882.\n\n[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized\nconditional gradient: analysis of convergence and applications.\narXiv preprint arXiv:1510.06567.\n\n\n\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -26,35 +27,35 @@ "outputs": [], "source": [ "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Generate data\n-------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std\nb = ot.datasets.get_1D_gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" - ], - "cell_type": "code" + "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\nb = ot.datasets.make_1D_gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Solve EMD\n---------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -62,17 +63,17 @@ "outputs": [], "source": [ "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Solve EMD with Frobenius norm regularization\n--------------------------------------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -80,17 +81,17 @@ "outputs": [], "source": [ "#%% Example with Frobenius norm regularization\n\n\ndef f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg = 1e-1\n\nGl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n\npl.figure(3)\not.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg')" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Solve EMD with entropic regularization\n--------------------------------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -98,17 +99,17 @@ "outputs": [], "source": [ "#%% Example with entropic regularization\n\n\ndef f(G):\n return np.sum(G * np.log(G))\n\n\ndef df(G):\n return np.log(G) + 1.\n\n\nreg = 1e-3\n\nGe = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg')" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Solve EMD with Frobenius norm + entropic regularization\n-------------------------------------------------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -116,29 +117,28 @@ "outputs": [], "source": [ "#%% Example with Frobenius norm + entropic regularization with gcg\n\n\ndef f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg1 = 1e-3\nreg2 = 1e-1\n\nGel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True)\n\npl.figure(5, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg')\npl.show()" - ], - "cell_type": "code" + ] } ], "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python", "codemirror_mode": { "name": "ipython", "version": 3 }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", "nbconvert_exporter": "python", - "version": "3.5.2", "pygments_lexer": "ipython3", - "file_extension": ".py", - "mimetype": "text/x-python" - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3", - "language": "python" + "version": "3.6.5" } }, - "nbformat_minor": 0, - "nbformat": 4 + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_optim_OTreg.py b/docs/source/auto_examples/plot_optim_OTreg.py index 92df016..2c58def 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.py +++ b/docs/source/auto_examples/plot_optim_OTreg.py @@ -42,8 +42,8 @@ n = 100 # nb bins x = np.arange(n, dtype=np.float64) # Gaussian distributions -a = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std -b = ot.datasets.get_1D_gauss(n, m=60, s=10) +a = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std +b = ot.datasets.make_1D_gauss(n, m=60, s=10) # loss matrix M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) diff --git a/docs/source/auto_examples/plot_optim_OTreg.rst b/docs/source/auto_examples/plot_optim_OTreg.rst index 5927428..844cba0 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.rst +++ b/docs/source/auto_examples/plot_optim_OTreg.rst @@ -59,8 +59,8 @@ Generate data x = np.arange(n, dtype=np.float64) # Gaussian distributions - a = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std - b = ot.datasets.get_1D_gauss(n, m=60, s=10) + a = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std + b = ot.datasets.make_1D_gauss(n, m=60, s=10) # loss matrix M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) @@ -636,7 +636,7 @@ Solve EMD with Frobenius norm + entropic regularization 4|1.609284e-01|-1.111407e-12 -**Total running time of the script:** ( 0 minutes 2.589 seconds) +**Total running time of the script:** ( 0 minutes 1.990 seconds) diff --git a/docs/source/auto_examples/plot_otda_classes.ipynb b/docs/source/auto_examples/plot_otda_classes.ipynb index 6754fa5..643e760 100644 --- a/docs/source/auto_examples/plot_otda_classes.ipynb +++ b/docs/source/auto_examples/plot_otda_classes.ipynb @@ -1,126 +1,126 @@ { - "nbformat_minor": 0, - "nbformat": 4, "cells": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "\n# OT for domain adaptation\n\n\nThis example introduces a domain adaptation in a 2D setting and the 4 OTDA\napproaches currently supported in POT.\n\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport ot" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport ot" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Generate data\n-------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "n_source_samples = 150\nn_target_samples = 150\n\nXs, ys = ot.datasets.get_data_classif('3gauss', n_source_samples)\nXt, yt = ot.datasets.get_data_classif('3gauss2', n_target_samples)" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "n_source_samples = 150\nn_target_samples = 150\n\nXs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples)\nXt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples)" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Instantiate the different transport algorithms and fit them\n-----------------------------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# EMD Transport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport with Group lasso regularization\not_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)\not_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt)\n\n# Sinkhorn Transport with Group lasso regularization l1l2\not_l1l2 = ot.da.SinkhornL1l2Transport(reg_e=1e-1, reg_cl=2e0, max_iter=20,\n verbose=True)\not_l1l2.fit(Xs=Xs, ys=ys, Xt=Xt)\n\n# transport source samples onto target samples\ntransp_Xs_emd = ot_emd.transform(Xs=Xs)\ntransp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)\ntransp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs)\ntransp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs)" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# EMD Transport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport with Group lasso regularization\not_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)\not_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt)\n\n# Sinkhorn Transport with Group lasso regularization l1l2\not_l1l2 = ot.da.SinkhornL1l2Transport(reg_e=1e-1, reg_cl=2e0, max_iter=20,\n verbose=True)\not_l1l2.fit(Xs=Xs, ys=ys, Xt=Xt)\n\n# transport source samples onto target samples\ntransp_Xs_emd = ot_emd.transform(Xs=Xs)\ntransp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)\ntransp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs)\ntransp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs)" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Fig 1 : plots source and target samples\n---------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "pl.figure(1, figsize=(10, 5))\npl.subplot(1, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Source samples')\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Target samples')\npl.tight_layout()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "pl.figure(1, figsize=(10, 5))\npl.subplot(1, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Source samples')\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Target samples')\npl.tight_layout()" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Fig 2 : plot optimal couplings and transported samples\n------------------------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "param_img = {'interpolation': 'nearest', 'cmap': 'spectral'}\n\npl.figure(2, figsize=(15, 8))\npl.subplot(2, 4, 1)\npl.imshow(ot_emd.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nEMDTransport')\n\npl.subplot(2, 4, 2)\npl.imshow(ot_sinkhorn.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornTransport')\n\npl.subplot(2, 4, 3)\npl.imshow(ot_lpl1.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornLpl1Transport')\n\npl.subplot(2, 4, 4)\npl.imshow(ot_l1l2.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornL1l2Transport')\n\npl.subplot(2, 4, 5)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nEmdTransport')\npl.legend(loc=\"lower left\")\n\npl.subplot(2, 4, 6)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nSinkhornTransport')\n\npl.subplot(2, 4, 7)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nSinkhornLpl1Transport')\n\npl.subplot(2, 4, 8)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_l1l2[:, 0], transp_Xs_l1l2[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nSinkhornL1l2Transport')\npl.tight_layout()\n\npl.show()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } + }, + "outputs": [], + "source": [ + "param_img = {'interpolation': 'nearest'}\n\npl.figure(2, figsize=(15, 8))\npl.subplot(2, 4, 1)\npl.imshow(ot_emd.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nEMDTransport')\n\npl.subplot(2, 4, 2)\npl.imshow(ot_sinkhorn.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornTransport')\n\npl.subplot(2, 4, 3)\npl.imshow(ot_lpl1.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornLpl1Transport')\n\npl.subplot(2, 4, 4)\npl.imshow(ot_l1l2.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornL1l2Transport')\n\npl.subplot(2, 4, 5)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nEmdTransport')\npl.legend(loc=\"lower left\")\n\npl.subplot(2, 4, 6)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nSinkhornTransport')\n\npl.subplot(2, 4, 7)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nSinkhornLpl1Transport')\n\npl.subplot(2, 4, 8)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_l1l2[:, 0], transp_Xs_l1l2[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nSinkhornL1l2Transport')\npl.tight_layout()\n\npl.show()" + ] } - ], + ], "metadata": { "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", "codemirror_mode": { - "version": 2, - "name": "ipython" - } + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" } - } + }, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_classes.py b/docs/source/auto_examples/plot_otda_classes.py index b14c11a..c311fbd 100644 --- a/docs/source/auto_examples/plot_otda_classes.py +++ b/docs/source/auto_examples/plot_otda_classes.py @@ -25,8 +25,8 @@ import ot n_source_samples = 150 n_target_samples = 150 -Xs, ys = ot.datasets.get_data_classif('3gauss', n_source_samples) -Xt, yt = ot.datasets.get_data_classif('3gauss2', n_target_samples) +Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples) +Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples) ############################################################################## @@ -82,7 +82,7 @@ pl.tight_layout() # Fig 2 : plot optimal couplings and transported samples # ------------------------------------------------------ -param_img = {'interpolation': 'nearest', 'cmap': 'spectral'} +param_img = {'interpolation': 'nearest'} pl.figure(2, figsize=(15, 8)) pl.subplot(2, 4, 1) diff --git a/docs/source/auto_examples/plot_otda_classes.rst b/docs/source/auto_examples/plot_otda_classes.rst index a5ab285..19756ff 100644 --- a/docs/source/auto_examples/plot_otda_classes.rst +++ b/docs/source/auto_examples/plot_otda_classes.rst @@ -42,8 +42,8 @@ Generate data n_source_samples = 150 n_target_samples = 150 - Xs, ys = ot.datasets.get_data_classif('3gauss', n_source_samples) - Xt, yt = ot.datasets.get_data_classif('3gauss2', n_target_samples) + Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples) + Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples) @@ -94,29 +94,29 @@ Instantiate the different transport algorithms and fit them It. |Loss |Delta loss -------------------------------- - 0|1.003747e+01|0.000000e+00 - 1|1.953263e+00|-4.138821e+00 - 2|1.744456e+00|-1.196969e-01 - 3|1.689268e+00|-3.267022e-02 - 4|1.666355e+00|-1.374998e-02 - 5|1.656125e+00|-6.177356e-03 - 6|1.651753e+00|-2.646960e-03 - 7|1.647261e+00|-2.726957e-03 - 8|1.642274e+00|-3.036672e-03 - 9|1.639926e+00|-1.431818e-03 - 10|1.638750e+00|-7.173837e-04 - 11|1.637558e+00|-7.281753e-04 - 12|1.636248e+00|-8.002067e-04 - 13|1.634555e+00|-1.036074e-03 - 14|1.633547e+00|-6.166646e-04 - 15|1.633531e+00|-1.022614e-05 - 16|1.632957e+00|-3.510986e-04 - 17|1.632853e+00|-6.380944e-05 - 18|1.632704e+00|-9.122988e-05 - 19|1.632237e+00|-2.861276e-04 + 0|9.566309e+00|0.000000e+00 + 1|2.169680e+00|-3.409088e+00 + 2|1.914989e+00|-1.329986e-01 + 3|1.860251e+00|-2.942498e-02 + 4|1.838073e+00|-1.206621e-02 + 5|1.827064e+00|-6.025122e-03 + 6|1.820899e+00|-3.386082e-03 + 7|1.817290e+00|-1.985705e-03 + 8|1.814644e+00|-1.458223e-03 + 9|1.812661e+00|-1.093816e-03 + 10|1.810239e+00|-1.338121e-03 + 11|1.809100e+00|-6.296940e-04 + 12|1.807939e+00|-6.420646e-04 + 13|1.806965e+00|-5.389118e-04 + 14|1.806822e+00|-7.889599e-05 + 15|1.806193e+00|-3.482356e-04 + 16|1.805735e+00|-2.536930e-04 + 17|1.805321e+00|-2.292667e-04 + 18|1.804389e+00|-5.170222e-04 + 19|1.803908e+00|-2.661907e-04 It. |Loss |Delta loss -------------------------------- - 20|1.632174e+00|-3.896483e-05 + 20|1.803696e+00|-1.178279e-04 Fig 1 : plots source and target samples @@ -161,7 +161,7 @@ Fig 2 : plot optimal couplings and transported samples .. code-block:: python - param_img = {'interpolation': 'nearest', 'cmap': 'spectral'} + param_img = {'interpolation': 'nearest'} pl.figure(2, figsize=(15, 8)) pl.subplot(2, 4, 1) @@ -236,11 +236,13 @@ Fig 2 : plot optimal couplings and transported samples -**Total running time of the script:** ( 0 minutes 2.308 seconds) +**Total running time of the script:** ( 0 minutes 1.423 seconds) -.. container:: sphx-glr-footer +.. only :: html + + .. container:: sphx-glr-footer .. container:: sphx-glr-download @@ -253,6 +255,9 @@ Fig 2 : plot optimal couplings and transported samples :download:`Download Jupyter notebook: plot_otda_classes.ipynb ` -.. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_d2.ipynb b/docs/source/auto_examples/plot_otda_d2.ipynb index 9c58e64..b9002ee 100644 --- a/docs/source/auto_examples/plot_otda_d2.ipynb +++ b/docs/source/auto_examples/plot_otda_d2.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -8,17 +9,17 @@ "outputs": [], "source": [ "%matplotlib inline" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "\n# OT for domain adaptation on empirical distributions\n\n\nThis example introduces a domain adaptation in a 2D setting. It explicits\nthe problem of domain adaptation and introduces some optimal transport\napproaches to solve it.\n\nQuantities such as optimal couplings, greater coupling coefficients and\ntransported samples are represented in order to give a visual understanding\nof what the transport methods are doing.\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -26,35 +27,35 @@ "outputs": [], "source": [ "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "generate data\n-------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "n_samples_source = 150\nn_samples_target = 150\n\nXs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source)\nXt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target)\n\n# Cost matrix\nM = ot.dist(Xs, Xt, metric='sqeuclidean')" - ], - "cell_type": "code" + "n_samples_source = 150\nn_samples_target = 150\n\nXs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source)\nXt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target)\n\n# Cost matrix\nM = ot.dist(Xs, Xt, metric='sqeuclidean')" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Instantiate the different transport algorithms and fit them\n-----------------------------------------------------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -62,17 +63,17 @@ "outputs": [], "source": [ "# EMD Transport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport with Group lasso regularization\not_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)\not_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt)\n\n# transport source samples onto target samples\ntransp_Xs_emd = ot_emd.transform(Xs=Xs)\ntransp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)\ntransp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs)" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Fig 1 : plots source and target samples + matrix of pairwise distance\n---------------------------------------------------------------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -80,17 +81,17 @@ "outputs": [], "source": [ "pl.figure(1, figsize=(10, 10))\npl.subplot(2, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Source samples')\n\npl.subplot(2, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Target samples')\n\npl.subplot(2, 2, 3)\npl.imshow(M, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Matrix of pairwise distances')\npl.tight_layout()" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Fig 2 : plots optimal couplings for the different methods\n---------------------------------------------------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -98,17 +99,17 @@ "outputs": [], "source": [ "pl.figure(2, figsize=(10, 6))\n\npl.subplot(2, 3, 1)\npl.imshow(ot_emd.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nEMDTransport')\n\npl.subplot(2, 3, 2)\npl.imshow(ot_sinkhorn.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornTransport')\n\npl.subplot(2, 3, 3)\npl.imshow(ot_lpl1.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornLpl1Transport')\n\npl.subplot(2, 3, 4)\not.plot.plot2D_samples_mat(Xs, Xt, ot_emd.coupling_, c=[.5, .5, 1])\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.title('Main coupling coefficients\\nEMDTransport')\n\npl.subplot(2, 3, 5)\not.plot.plot2D_samples_mat(Xs, Xt, ot_sinkhorn.coupling_, c=[.5, .5, 1])\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.title('Main coupling coefficients\\nSinkhornTransport')\n\npl.subplot(2, 3, 6)\not.plot.plot2D_samples_mat(Xs, Xt, ot_lpl1.coupling_, c=[.5, .5, 1])\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.title('Main coupling coefficients\\nSinkhornLpl1Transport')\npl.tight_layout()" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Fig 3 : plot transported samples\n--------------------------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -116,29 +117,28 @@ "outputs": [], "source": [ "# display transported samples\npl.figure(4, figsize=(10, 4))\npl.subplot(1, 3, 1)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nEmdTransport')\npl.legend(loc=0)\npl.xticks([])\npl.yticks([])\n\npl.subplot(1, 3, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nSinkhornTransport')\npl.xticks([])\npl.yticks([])\n\npl.subplot(1, 3, 3)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nSinkhornLpl1Transport')\npl.xticks([])\npl.yticks([])\n\npl.tight_layout()\npl.show()" - ], - "cell_type": "code" + ] } ], "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python", "codemirror_mode": { "name": "ipython", "version": 3 }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", "nbconvert_exporter": "python", - "version": "3.5.2", "pygments_lexer": "ipython3", - "file_extension": ".py", - "mimetype": "text/x-python" - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3", - "language": "python" + "version": "3.6.5" } }, - "nbformat_minor": 0, - "nbformat": 4 + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_d2.py b/docs/source/auto_examples/plot_otda_d2.py index 70beb35..cf22c2f 100644 --- a/docs/source/auto_examples/plot_otda_d2.py +++ b/docs/source/auto_examples/plot_otda_d2.py @@ -29,8 +29,8 @@ import ot.plot n_samples_source = 150 n_samples_target = 150 -Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source) -Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target) +Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source) +Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target) # Cost matrix M = ot.dist(Xs, Xt, metric='sqeuclidean') diff --git a/docs/source/auto_examples/plot_otda_d2.rst b/docs/source/auto_examples/plot_otda_d2.rst index e5a60c4..80cc34c 100644 --- a/docs/source/auto_examples/plot_otda_d2.rst +++ b/docs/source/auto_examples/plot_otda_d2.rst @@ -46,8 +46,8 @@ generate data n_samples_source = 150 n_samples_target = 150 - Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source) - Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target) + Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source) + Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target) # Cost matrix M = ot.dist(Xs, Xt, metric='sqeuclidean') @@ -242,7 +242,7 @@ Fig 3 : plot transported samples -**Total running time of the script:** ( 0 minutes 39.829 seconds) +**Total running time of the script:** ( 0 minutes 35.515 seconds) diff --git a/docs/source/auto_examples/plot_otda_linear_mapping.ipynb b/docs/source/auto_examples/plot_otda_linear_mapping.ipynb new file mode 100644 index 0000000..e43bee7 --- /dev/null +++ b/docs/source/auto_examples/plot_otda_linear_mapping.ipynb @@ -0,0 +1,180 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\nCreated on Tue Mar 20 14:31:15 2018\n\n@author: rflamary\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\nimport pylab as pl\nimport ot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n-------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n = 1000\nd = 2\nsigma = .1\n\n# source samples\nangles = np.random.rand(n, 1) * 2 * np.pi\nxs = np.concatenate((np.sin(angles), np.cos(angles)),\n axis=1) + sigma * np.random.randn(n, 2)\nxs[:n // 2, 1] += 2\n\n\n# target samples\nanglet = np.random.rand(n, 1) * 2 * np.pi\nxt = np.concatenate((np.sin(anglet), np.cos(anglet)),\n axis=1) + sigma * np.random.randn(n, 2)\nxt[:n // 2, 1] += 2\n\n\nA = np.array([[1.5, .7], [.7, 1.5]])\nb = np.array([[4, 2]])\nxt = xt.dot(A) + b" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot data\n---------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(1, (5, 5))\npl.plot(xs[:, 0], xs[:, 1], '+')\npl.plot(xt[:, 0], xt[:, 1], 'o')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Estimate linear mapping and transport\n-------------------------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "Ae, be = ot.da.OT_mapping_linear(xs, xt)\n\nxst = xs.dot(Ae) + be" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot transported samples\n------------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(1, (5, 5))\npl.clf()\npl.plot(xs[:, 0], xs[:, 1], '+')\npl.plot(xt[:, 0], xt[:, 1], 'o')\npl.plot(xst[:, 0], xst[:, 1], '+')\n\npl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load image data\n---------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def im2mat(I):\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\ndef mat2im(X, shape):\n \"\"\"Converts back a matrix to an image\"\"\"\n return X.reshape(shape)\n\n\ndef minmax(I):\n return np.clip(I, 0, 1)\n\n\n# Loading images\nI1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256\nI2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n\n\nX1 = im2mat(I1)\nX2 = im2mat(I2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Estimate mapping and adapt\n----------------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "mapping = ot.da.LinearTransport()\n\nmapping.fit(Xs=X1, Xt=X2)\n\n\nxst = mapping.transform(Xs=X1)\nxts = mapping.inverse_transform(Xt=X2)\n\nI1t = minmax(mat2im(xst, I1.shape))\nI2t = minmax(mat2im(xts, I2.shape))\n\n# %%" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot transformed images\n-----------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(2, figsize=(10, 7))\n\npl.subplot(2, 2, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Im. 1')\n\npl.subplot(2, 2, 2)\npl.imshow(I2)\npl.axis('off')\npl.title('Im. 2')\n\npl.subplot(2, 2, 3)\npl.imshow(I1t)\npl.axis('off')\npl.title('Mapping Im. 1')\n\npl.subplot(2, 2, 4)\npl.imshow(I2t)\npl.axis('off')\npl.title('Inverse mapping Im. 2')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_linear_mapping.py b/docs/source/auto_examples/plot_otda_linear_mapping.py new file mode 100644 index 0000000..7a3b761 --- /dev/null +++ b/docs/source/auto_examples/plot_otda_linear_mapping.py @@ -0,0 +1,138 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +Created on Tue Mar 20 14:31:15 2018 + +@author: rflamary +""" + +import numpy as np +import pylab as pl +import ot + +############################################################################## +# Generate data +# ------------- + +n = 1000 +d = 2 +sigma = .1 + +# source samples +angles = np.random.rand(n, 1) * 2 * np.pi +xs = np.concatenate((np.sin(angles), np.cos(angles)), + axis=1) + sigma * np.random.randn(n, 2) +xs[:n // 2, 1] += 2 + + +# target samples +anglet = np.random.rand(n, 1) * 2 * np.pi +xt = np.concatenate((np.sin(anglet), np.cos(anglet)), + axis=1) + sigma * np.random.randn(n, 2) +xt[:n // 2, 1] += 2 + + +A = np.array([[1.5, .7], [.7, 1.5]]) +b = np.array([[4, 2]]) +xt = xt.dot(A) + b + +############################################################################## +# Plot data +# --------- + +pl.figure(1, (5, 5)) +pl.plot(xs[:, 0], xs[:, 1], '+') +pl.plot(xt[:, 0], xt[:, 1], 'o') + + +############################################################################## +# Estimate linear mapping and transport +# ------------------------------------- + +Ae, be = ot.da.OT_mapping_linear(xs, xt) + +xst = xs.dot(Ae) + be + + +############################################################################## +# Plot transported samples +# ------------------------ + +pl.figure(1, (5, 5)) +pl.clf() +pl.plot(xs[:, 0], xs[:, 1], '+') +pl.plot(xt[:, 0], xt[:, 1], 'o') +pl.plot(xst[:, 0], xst[:, 1], '+') + +pl.show() + +############################################################################## +# Load image data +# --------------- + + +def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + + +def mat2im(X, shape): + """Converts back a matrix to an image""" + return X.reshape(shape) + + +def minmax(I): + return np.clip(I, 0, 1) + + +# Loading images +I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 +I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 + + +X1 = im2mat(I1) +X2 = im2mat(I2) + +############################################################################## +# Estimate mapping and adapt +# ---------------------------- + +mapping = ot.da.LinearTransport() + +mapping.fit(Xs=X1, Xt=X2) + + +xst = mapping.transform(Xs=X1) +xts = mapping.inverse_transform(Xt=X2) + +I1t = minmax(mat2im(xst, I1.shape)) +I2t = minmax(mat2im(xts, I2.shape)) + +# %% + + +############################################################################## +# Plot transformed images +# ----------------------- + +pl.figure(2, figsize=(10, 7)) + +pl.subplot(2, 2, 1) +pl.imshow(I1) +pl.axis('off') +pl.title('Im. 1') + +pl.subplot(2, 2, 2) +pl.imshow(I2) +pl.axis('off') +pl.title('Im. 2') + +pl.subplot(2, 2, 3) +pl.imshow(I1t) +pl.axis('off') +pl.title('Mapping Im. 1') + +pl.subplot(2, 2, 4) +pl.imshow(I2t) +pl.axis('off') +pl.title('Inverse mapping Im. 2') diff --git a/docs/source/auto_examples/plot_otda_linear_mapping.rst b/docs/source/auto_examples/plot_otda_linear_mapping.rst new file mode 100644 index 0000000..1321732 --- /dev/null +++ b/docs/source/auto_examples/plot_otda_linear_mapping.rst @@ -0,0 +1,254 @@ + + +.. _sphx_glr_auto_examples_plot_otda_linear_mapping.py: + + +Created on Tue Mar 20 14:31:15 2018 + +@author: rflamary + + + +.. code-block:: python + + + import numpy as np + import pylab as pl + import ot + + + + + + + +Generate data +------------- + + + +.. code-block:: python + + + n = 1000 + d = 2 + sigma = .1 + + # source samples + angles = np.random.rand(n, 1) * 2 * np.pi + xs = np.concatenate((np.sin(angles), np.cos(angles)), + axis=1) + sigma * np.random.randn(n, 2) + xs[:n // 2, 1] += 2 + + + # target samples + anglet = np.random.rand(n, 1) * 2 * np.pi + xt = np.concatenate((np.sin(anglet), np.cos(anglet)), + axis=1) + sigma * np.random.randn(n, 2) + xt[:n // 2, 1] += 2 + + + A = np.array([[1.5, .7], [.7, 1.5]]) + b = np.array([[4, 2]]) + xt = xt.dot(A) + b + + + + + + + +Plot data +--------- + + + +.. code-block:: python + + + pl.figure(1, (5, 5)) + pl.plot(xs[:, 0], xs[:, 1], '+') + pl.plot(xt[:, 0], xt[:, 1], 'o') + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_linear_mapping_001.png + :align: center + + + + +Estimate linear mapping and transport +------------------------------------- + + + +.. code-block:: python + + + Ae, be = ot.da.OT_mapping_linear(xs, xt) + + xst = xs.dot(Ae) + be + + + + + + + + +Plot transported samples +------------------------ + + + +.. code-block:: python + + + pl.figure(1, (5, 5)) + pl.clf() + pl.plot(xs[:, 0], xs[:, 1], '+') + pl.plot(xt[:, 0], xt[:, 1], 'o') + pl.plot(xst[:, 0], xst[:, 1], '+') + + pl.show() + + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_linear_mapping_002.png + :align: center + + + + +Load image data +--------------- + + + +.. code-block:: python + + + + def im2mat(I): + """Converts and image to matrix (one pixel per line)""" + return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) + + + def mat2im(X, shape): + """Converts back a matrix to an image""" + return X.reshape(shape) + + + def minmax(I): + return np.clip(I, 0, 1) + + + # Loading images + I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256 + I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256 + + + X1 = im2mat(I1) + X2 = im2mat(I2) + + + + + + + +Estimate mapping and adapt +---------------------------- + + + +.. code-block:: python + + + mapping = ot.da.LinearTransport() + + mapping.fit(Xs=X1, Xt=X2) + + + xst = mapping.transform(Xs=X1) + xts = mapping.inverse_transform(Xt=X2) + + I1t = minmax(mat2im(xst, I1.shape)) + I2t = minmax(mat2im(xts, I2.shape)) + + # %% + + + + + + + + +Plot transformed images +----------------------- + + + +.. code-block:: python + + + pl.figure(2, figsize=(10, 7)) + + pl.subplot(2, 2, 1) + pl.imshow(I1) + pl.axis('off') + pl.title('Im. 1') + + pl.subplot(2, 2, 2) + pl.imshow(I2) + pl.axis('off') + pl.title('Im. 2') + + pl.subplot(2, 2, 3) + pl.imshow(I1t) + pl.axis('off') + pl.title('Mapping Im. 1') + + pl.subplot(2, 2, 4) + pl.imshow(I2t) + pl.axis('off') + pl.title('Inverse mapping Im. 2') + + + +.. image:: /auto_examples/images/sphx_glr_plot_otda_linear_mapping_004.png + :align: center + + + + +**Total running time of the script:** ( 0 minutes 0.563 seconds) + + + +.. only :: html + + .. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_otda_linear_mapping.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_otda_linear_mapping.ipynb ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_mapping.ipynb b/docs/source/auto_examples/plot_otda_mapping.ipynb index 0374146..898466d 100644 --- a/docs/source/auto_examples/plot_otda_mapping.ipynb +++ b/docs/source/auto_examples/plot_otda_mapping.ipynb @@ -1,126 +1,126 @@ { - "nbformat_minor": 0, - "nbformat": 4, "cells": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "\n# OT mapping estimation for domain adaptation\n\n\nThis example presents how to use MappingTransport to estimate at the same\ntime both the coupling transport and approximate the transport map with either\na linear or a kernelized mapping as introduced in [8].\n\n[8] M. Perrot, N. Courty, R. Flamary, A. Habrard,\n \"Mapping estimation for discrete optimal transport\",\n Neural Information Processing Systems (NIPS), 2016.\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Generate data\n-------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "n_source_samples = 100\nn_target_samples = 100\ntheta = 2 * np.pi / 20\nnoise_level = 0.1\n\nXs, ys = ot.datasets.get_data_classif(\n 'gaussrot', n_source_samples, nz=noise_level)\nXs_new, _ = ot.datasets.get_data_classif(\n 'gaussrot', n_source_samples, nz=noise_level)\nXt, yt = ot.datasets.get_data_classif(\n 'gaussrot', n_target_samples, theta=theta, nz=noise_level)\n\n# one of the target mode changes its variance (no linear mapping)\nXt[yt == 2] *= 3\nXt = Xt + 4" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "n_source_samples = 100\nn_target_samples = 100\ntheta = 2 * np.pi / 20\nnoise_level = 0.1\n\nXs, ys = ot.datasets.make_data_classif(\n 'gaussrot', n_source_samples, nz=noise_level)\nXs_new, _ = ot.datasets.make_data_classif(\n 'gaussrot', n_source_samples, nz=noise_level)\nXt, yt = ot.datasets.make_data_classif(\n 'gaussrot', n_target_samples, theta=theta, nz=noise_level)\n\n# one of the target mode changes its variance (no linear mapping)\nXt[yt == 2] *= 3\nXt = Xt + 4" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Plot data\n---------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "pl.figure(1, (10, 5))\npl.clf()\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.legend(loc=0)\npl.title('Source and target distributions')" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "pl.figure(1, (10, 5))\npl.clf()\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.legend(loc=0)\npl.title('Source and target distributions')" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Instantiate the different transport algorithms and fit them\n-----------------------------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# MappingTransport with linear kernel\not_mapping_linear = ot.da.MappingTransport(\n kernel=\"linear\", mu=1e0, eta=1e-8, bias=True,\n max_iter=20, verbose=True)\n\not_mapping_linear.fit(Xs=Xs, Xt=Xt)\n\n# for original source samples, transform applies barycentric mapping\ntransp_Xs_linear = ot_mapping_linear.transform(Xs=Xs)\n\n# for out of source samples, transform applies the linear mapping\ntransp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new)\n\n\n# MappingTransport with gaussian kernel\not_mapping_gaussian = ot.da.MappingTransport(\n kernel=\"gaussian\", eta=1e-5, mu=1e-1, bias=True, sigma=1,\n max_iter=10, verbose=True)\not_mapping_gaussian.fit(Xs=Xs, Xt=Xt)\n\n# for original source samples, transform applies barycentric mapping\ntransp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs)\n\n# for out of source samples, transform applies the gaussian mapping\ntransp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new)" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# MappingTransport with linear kernel\not_mapping_linear = ot.da.MappingTransport(\n kernel=\"linear\", mu=1e0, eta=1e-8, bias=True,\n max_iter=20, verbose=True)\n\not_mapping_linear.fit(Xs=Xs, Xt=Xt)\n\n# for original source samples, transform applies barycentric mapping\ntransp_Xs_linear = ot_mapping_linear.transform(Xs=Xs)\n\n# for out of source samples, transform applies the linear mapping\ntransp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new)\n\n\n# MappingTransport with gaussian kernel\not_mapping_gaussian = ot.da.MappingTransport(\n kernel=\"gaussian\", eta=1e-5, mu=1e-1, bias=True, sigma=1,\n max_iter=10, verbose=True)\not_mapping_gaussian.fit(Xs=Xs, Xt=Xt)\n\n# for original source samples, transform applies barycentric mapping\ntransp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs)\n\n# for out of source samples, transform applies the gaussian mapping\ntransp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new)" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Plot transported samples\n------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "pl.figure(2)\npl.clf()\npl.subplot(2, 2, 1)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=.2)\npl.scatter(transp_Xs_linear[:, 0], transp_Xs_linear[:, 1], c=ys, marker='+',\n label='Mapped source samples')\npl.title(\"Bary. mapping (linear)\")\npl.legend(loc=0)\n\npl.subplot(2, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=.2)\npl.scatter(transp_Xs_linear_new[:, 0], transp_Xs_linear_new[:, 1],\n c=ys, marker='+', label='Learned mapping')\npl.title(\"Estim. mapping (linear)\")\n\npl.subplot(2, 2, 3)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=.2)\npl.scatter(transp_Xs_gaussian[:, 0], transp_Xs_gaussian[:, 1], c=ys,\n marker='+', label='barycentric mapping')\npl.title(\"Bary. mapping (kernel)\")\n\npl.subplot(2, 2, 4)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=.2)\npl.scatter(transp_Xs_gaussian_new[:, 0], transp_Xs_gaussian_new[:, 1], c=ys,\n marker='+', label='Learned mapping')\npl.title(\"Estim. mapping (kernel)\")\npl.tight_layout()\n\npl.show()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } + }, + "outputs": [], + "source": [ + "pl.figure(2)\npl.clf()\npl.subplot(2, 2, 1)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=.2)\npl.scatter(transp_Xs_linear[:, 0], transp_Xs_linear[:, 1], c=ys, marker='+',\n label='Mapped source samples')\npl.title(\"Bary. mapping (linear)\")\npl.legend(loc=0)\n\npl.subplot(2, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=.2)\npl.scatter(transp_Xs_linear_new[:, 0], transp_Xs_linear_new[:, 1],\n c=ys, marker='+', label='Learned mapping')\npl.title(\"Estim. mapping (linear)\")\n\npl.subplot(2, 2, 3)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=.2)\npl.scatter(transp_Xs_gaussian[:, 0], transp_Xs_gaussian[:, 1], c=ys,\n marker='+', label='barycentric mapping')\npl.title(\"Bary. mapping (kernel)\")\n\npl.subplot(2, 2, 4)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=.2)\npl.scatter(transp_Xs_gaussian_new[:, 0], transp_Xs_gaussian_new[:, 1], c=ys,\n marker='+', label='Learned mapping')\npl.title(\"Estim. mapping (kernel)\")\npl.tight_layout()\n\npl.show()" + ] } - ], + ], "metadata": { "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", "codemirror_mode": { - "version": 2, - "name": "ipython" - } + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" } - } + }, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_mapping.py b/docs/source/auto_examples/plot_otda_mapping.py index 167c3a1..5880adf 100644 --- a/docs/source/auto_examples/plot_otda_mapping.py +++ b/docs/source/auto_examples/plot_otda_mapping.py @@ -32,11 +32,11 @@ n_target_samples = 100 theta = 2 * np.pi / 20 noise_level = 0.1 -Xs, ys = ot.datasets.get_data_classif( +Xs, ys = ot.datasets.make_data_classif( 'gaussrot', n_source_samples, nz=noise_level) -Xs_new, _ = ot.datasets.get_data_classif( +Xs_new, _ = ot.datasets.make_data_classif( 'gaussrot', n_source_samples, nz=noise_level) -Xt, yt = ot.datasets.get_data_classif( +Xt, yt = ot.datasets.make_data_classif( 'gaussrot', n_target_samples, theta=theta, nz=noise_level) # one of the target mode changes its variance (no linear mapping) diff --git a/docs/source/auto_examples/plot_otda_mapping.rst b/docs/source/auto_examples/plot_otda_mapping.rst index 1e3a709..1d95fc6 100644 --- a/docs/source/auto_examples/plot_otda_mapping.rst +++ b/docs/source/auto_examples/plot_otda_mapping.rst @@ -49,11 +49,11 @@ Generate data theta = 2 * np.pi / 20 noise_level = 0.1 - Xs, ys = ot.datasets.get_data_classif( + Xs, ys = ot.datasets.make_data_classif( 'gaussrot', n_source_samples, nz=noise_level) - Xs_new, _ = ot.datasets.get_data_classif( + Xs_new, _ = ot.datasets.make_data_classif( 'gaussrot', n_source_samples, nz=noise_level) - Xt, yt = ot.datasets.get_data_classif( + Xt, yt = ot.datasets.make_data_classif( 'gaussrot', n_target_samples, theta=theta, nz=noise_level) # one of the target mode changes its variance (no linear mapping) @@ -136,26 +136,26 @@ Instantiate the different transport algorithms and fit them It. |Loss |Delta loss -------------------------------- - 0|4.231423e+03|0.000000e+00 - 1|4.217955e+03|-3.182835e-03 - 2|4.217580e+03|-8.885864e-05 - 3|4.217451e+03|-3.043162e-05 - 4|4.217368e+03|-1.978325e-05 - 5|4.217312e+03|-1.338471e-05 - 6|4.217307e+03|-1.000290e-06 + 0|4.299275e+03|0.000000e+00 + 1|4.290443e+03|-2.054271e-03 + 2|4.290040e+03|-9.389994e-05 + 3|4.289876e+03|-3.830707e-05 + 4|4.289783e+03|-2.157428e-05 + 5|4.289724e+03|-1.390941e-05 + 6|4.289706e+03|-4.051054e-06 It. |Loss |Delta loss -------------------------------- - 0|4.257004e+02|0.000000e+00 - 1|4.208978e+02|-1.128168e-02 - 2|4.205168e+02|-9.052112e-04 - 3|4.203566e+02|-3.810681e-04 - 4|4.202570e+02|-2.369884e-04 - 5|4.201844e+02|-1.726132e-04 - 6|4.201341e+02|-1.196461e-04 - 7|4.200941e+02|-9.525441e-05 - 8|4.200630e+02|-7.405552e-05 - 9|4.200377e+02|-6.031884e-05 - 10|4.200168e+02|-4.968324e-05 + 0|4.326465e+02|0.000000e+00 + 1|4.282533e+02|-1.015416e-02 + 2|4.279473e+02|-7.145955e-04 + 3|4.277941e+02|-3.580104e-04 + 4|4.277069e+02|-2.039229e-04 + 5|4.276462e+02|-1.418698e-04 + 6|4.276011e+02|-1.054172e-04 + 7|4.275663e+02|-8.145802e-05 + 8|4.275405e+02|-6.028774e-05 + 9|4.275191e+02|-5.005886e-05 + 10|4.275019e+02|-4.021935e-05 Plot transported samples @@ -208,11 +208,13 @@ Plot transported samples -**Total running time of the script:** ( 0 minutes 0.970 seconds) +**Total running time of the script:** ( 0 minutes 0.795 seconds) -.. container:: sphx-glr-footer +.. only :: html + + .. container:: sphx-glr-footer .. container:: sphx-glr-download @@ -225,6 +227,9 @@ Plot transported samples :download:`Download Jupyter notebook: plot_otda_mapping.ipynb ` -.. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_semi_supervised.ipynb b/docs/source/auto_examples/plot_otda_semi_supervised.ipynb index 783bf84..e3192da 100644 --- a/docs/source/auto_examples/plot_otda_semi_supervised.ipynb +++ b/docs/source/auto_examples/plot_otda_semi_supervised.ipynb @@ -1,144 +1,144 @@ { - "nbformat_minor": 0, - "nbformat": 4, "cells": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "\n# OTDA unsupervised vs semi-supervised setting\n\n\nThis example introduces a semi supervised domain adaptation in a 2D setting.\nIt explicits the problem of semi supervised domain adaptation and introduces\nsome optimal transport approaches to solve it.\n\nQuantities such as optimal couplings, greater coupling coefficients and\ntransported samples are represented in order to give a visual understanding\nof what the transport methods are doing.\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport ot" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport ot" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Generate data\n-------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "n_samples_source = 150\nn_samples_target = 150\n\nXs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source)\nXt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target)" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "n_samples_source = 150\nn_samples_target = 150\n\nXs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source)\nXt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target)" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Transport source samples onto target samples\n--------------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# unsupervised domain adaptation\not_sinkhorn_un = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn_un.fit(Xs=Xs, Xt=Xt)\ntransp_Xs_sinkhorn_un = ot_sinkhorn_un.transform(Xs=Xs)\n\n# semi-supervised domain adaptation\not_sinkhorn_semi = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn_semi.fit(Xs=Xs, Xt=Xt, ys=ys, yt=yt)\ntransp_Xs_sinkhorn_semi = ot_sinkhorn_semi.transform(Xs=Xs)\n\n# semi supervised DA uses available labaled target samples to modify the cost\n# matrix involved in the OT problem. The cost of transporting a source sample\n# of class A onto a target sample of class B != A is set to infinite, or a\n# very large value\n\n# note that in the present case we consider that all the target samples are\n# labeled. For daily applications, some target sample might not have labels,\n# in this case the element of yt corresponding to these samples should be\n# filled with -1.\n\n# Warning: we recall that -1 cannot be used as a class label" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# unsupervised domain adaptation\not_sinkhorn_un = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn_un.fit(Xs=Xs, Xt=Xt)\ntransp_Xs_sinkhorn_un = ot_sinkhorn_un.transform(Xs=Xs)\n\n# semi-supervised domain adaptation\not_sinkhorn_semi = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn_semi.fit(Xs=Xs, Xt=Xt, ys=ys, yt=yt)\ntransp_Xs_sinkhorn_semi = ot_sinkhorn_semi.transform(Xs=Xs)\n\n# semi supervised DA uses available labaled target samples to modify the cost\n# matrix involved in the OT problem. The cost of transporting a source sample\n# of class A onto a target sample of class B != A is set to infinite, or a\n# very large value\n\n# note that in the present case we consider that all the target samples are\n# labeled. For daily applications, some target sample might not have labels,\n# in this case the element of yt corresponding to these samples should be\n# filled with -1.\n\n# Warning: we recall that -1 cannot be used as a class label" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Fig 1 : plots source and target samples + matrix of pairwise distance\n---------------------------------------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "pl.figure(1, figsize=(10, 10))\npl.subplot(2, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Source samples')\n\npl.subplot(2, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Target samples')\n\npl.subplot(2, 2, 3)\npl.imshow(ot_sinkhorn_un.cost_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Cost matrix - unsupervised DA')\n\npl.subplot(2, 2, 4)\npl.imshow(ot_sinkhorn_semi.cost_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Cost matrix - semisupervised DA')\n\npl.tight_layout()\n\n# the optimal coupling in the semi-supervised DA case will exhibit \" shape\n# similar\" to the cost matrix, (block diagonal matrix)" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "pl.figure(1, figsize=(10, 10))\npl.subplot(2, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Source samples')\n\npl.subplot(2, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Target samples')\n\npl.subplot(2, 2, 3)\npl.imshow(ot_sinkhorn_un.cost_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Cost matrix - unsupervised DA')\n\npl.subplot(2, 2, 4)\npl.imshow(ot_sinkhorn_semi.cost_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Cost matrix - semisupervised DA')\n\npl.tight_layout()\n\n# the optimal coupling in the semi-supervised DA case will exhibit \" shape\n# similar\" to the cost matrix, (block diagonal matrix)" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Fig 2 : plots optimal couplings for the different methods\n---------------------------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "pl.figure(2, figsize=(8, 4))\n\npl.subplot(1, 2, 1)\npl.imshow(ot_sinkhorn_un.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nUnsupervised DA')\n\npl.subplot(1, 2, 2)\npl.imshow(ot_sinkhorn_semi.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSemi-supervised DA')\n\npl.tight_layout()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "pl.figure(2, figsize=(8, 4))\n\npl.subplot(1, 2, 1)\npl.imshow(ot_sinkhorn_un.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nUnsupervised DA')\n\npl.subplot(1, 2, 2)\npl.imshow(ot_sinkhorn_semi.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSemi-supervised DA')\n\npl.tight_layout()" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Fig 3 : plot transported samples\n--------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# display transported samples\npl.figure(4, figsize=(8, 4))\npl.subplot(1, 2, 1)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_sinkhorn_un[:, 0], transp_Xs_sinkhorn_un[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nEmdTransport')\npl.legend(loc=0)\npl.xticks([])\npl.yticks([])\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_sinkhorn_semi[:, 0], transp_Xs_sinkhorn_semi[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nSinkhornTransport')\npl.xticks([])\npl.yticks([])\n\npl.tight_layout()\npl.show()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } + }, + "outputs": [], + "source": [ + "# display transported samples\npl.figure(4, figsize=(8, 4))\npl.subplot(1, 2, 1)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_sinkhorn_un[:, 0], transp_Xs_sinkhorn_un[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nEmdTransport')\npl.legend(loc=0)\npl.xticks([])\npl.yticks([])\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_sinkhorn_semi[:, 0], transp_Xs_sinkhorn_semi[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nSinkhornTransport')\npl.xticks([])\npl.yticks([])\n\npl.tight_layout()\npl.show()" + ] } - ], + ], "metadata": { "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", "codemirror_mode": { - "version": 2, - "name": "ipython" - } + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" } - } + }, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_semi_supervised.py b/docs/source/auto_examples/plot_otda_semi_supervised.py index 7963aef..8a67720 100644 --- a/docs/source/auto_examples/plot_otda_semi_supervised.py +++ b/docs/source/auto_examples/plot_otda_semi_supervised.py @@ -29,8 +29,8 @@ import ot n_samples_source = 150 n_samples_target = 150 -Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source) -Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target) +Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source) +Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target) ############################################################################## diff --git a/docs/source/auto_examples/plot_otda_semi_supervised.rst b/docs/source/auto_examples/plot_otda_semi_supervised.rst index dc05ed0..2ed7819 100644 --- a/docs/source/auto_examples/plot_otda_semi_supervised.rst +++ b/docs/source/auto_examples/plot_otda_semi_supervised.rst @@ -46,8 +46,8 @@ Generate data n_samples_source = 150 n_samples_target = 150 - Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source) - Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target) + Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source) + Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target) @@ -218,11 +218,13 @@ Fig 3 : plot transported samples -**Total running time of the script:** ( 0 minutes 0.714 seconds) +**Total running time of the script:** ( 0 minutes 0.256 seconds) -.. container:: sphx-glr-footer +.. only :: html + + .. container:: sphx-glr-footer .. container:: sphx-glr-download @@ -235,6 +237,9 @@ Fig 3 : plot transported samples :download:`Download Jupyter notebook: plot_otda_semi_supervised.ipynb ` -.. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/examples/plot_OT_1D.py b/examples/plot_OT_1D.py index 90325c9..f33e2a4 100644 --- a/examples/plot_OT_1D.py +++ b/examples/plot_OT_1D.py @@ -17,7 +17,7 @@ import numpy as np import matplotlib.pylab as pl import ot import ot.plot -from ot.datasets import get_1D_gauss as gauss +from ot.datasets import make_1D_gauss as gauss ############################################################################## # Generate data diff --git a/examples/plot_OT_2D_samples.py b/examples/plot_OT_2D_samples.py index 9818ec5..bb952a0 100644 --- a/examples/plot_OT_2D_samples.py +++ b/examples/plot_OT_2D_samples.py @@ -16,6 +16,7 @@ sum of diracs. The OT matrix is plotted with the samples. import numpy as np import matplotlib.pylab as pl import ot +import ot.plot ############################################################################## # Generate data @@ -31,8 +32,8 @@ cov_s = np.array([[1, 0], [0, 1]]) mu_t = np.array([4, 4]) cov_t = np.array([[1, -.8], [-.8, 1]]) -xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s) -xt = ot.datasets.get_2D_samples_gauss(n, mu_t, cov_t) +xs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s) +xt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t) a, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples diff --git a/examples/plot_barycenter_1D.py b/examples/plot_barycenter_1D.py index ecf640c..5ed9f3f 100644 --- a/examples/plot_barycenter_1D.py +++ b/examples/plot_barycenter_1D.py @@ -37,8 +37,8 @@ n = 100 # nb bins x = np.arange(n, dtype=np.float64) # Gaussian distributions -a1 = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std -a2 = ot.datasets.get_1D_gauss(n, m=60, s=8) +a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std +a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) # creating matrix A containing all distributions A = np.vstack((a1, a2)).T diff --git a/examples/plot_barycenter_lp_vs_entropic.py b/examples/plot_barycenter_lp_vs_entropic.py index 6936bbb..2255107 100644 --- a/examples/plot_barycenter_lp_vs_entropic.py +++ b/examples/plot_barycenter_lp_vs_entropic.py @@ -47,8 +47,8 @@ x = np.arange(n, dtype=np.float64) # Gaussian distributions # Gaussian distributions -a1 = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std -a2 = ot.datasets.get_1D_gauss(n, m=60, s=8) +a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std +a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) # creating matrix A containing all distributions A = np.vstack((a1, a2)).T diff --git a/examples/plot_compute_emd.py b/examples/plot_compute_emd.py index 73b42c3..7ed2b01 100644 --- a/examples/plot_compute_emd.py +++ b/examples/plot_compute_emd.py @@ -17,7 +17,7 @@ ground metrics and plot their values for diffeent distributions. import numpy as np import matplotlib.pylab as pl import ot -from ot.datasets import get_1D_gauss as gauss +from ot.datasets import make_1D_gauss as gauss ############################################################################## diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py index 5cd40f6..deb2f86 100644 --- a/examples/plot_gromov.py +++ b/examples/plot_gromov.py @@ -38,7 +38,7 @@ mu_t = np.array([4, 4, 4]) cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) -xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s) +xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) P = sp.linalg.sqrtm(cov_t) xt = np.random.randn(n_samples, 3).dot(P) + mu_t diff --git a/examples/plot_optim_OTreg.py b/examples/plot_optim_OTreg.py index 92df016..2c58def 100644 --- a/examples/plot_optim_OTreg.py +++ b/examples/plot_optim_OTreg.py @@ -42,8 +42,8 @@ n = 100 # nb bins x = np.arange(n, dtype=np.float64) # Gaussian distributions -a = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std -b = ot.datasets.get_1D_gauss(n, m=60, s=10) +a = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std +b = ot.datasets.make_1D_gauss(n, m=60, s=10) # loss matrix M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) diff --git a/examples/plot_otda_classes.py b/examples/plot_otda_classes.py index b14c11a..c311fbd 100644 --- a/examples/plot_otda_classes.py +++ b/examples/plot_otda_classes.py @@ -25,8 +25,8 @@ import ot n_source_samples = 150 n_target_samples = 150 -Xs, ys = ot.datasets.get_data_classif('3gauss', n_source_samples) -Xt, yt = ot.datasets.get_data_classif('3gauss2', n_target_samples) +Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples) +Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples) ############################################################################## @@ -82,7 +82,7 @@ pl.tight_layout() # Fig 2 : plot optimal couplings and transported samples # ------------------------------------------------------ -param_img = {'interpolation': 'nearest', 'cmap': 'spectral'} +param_img = {'interpolation': 'nearest'} pl.figure(2, figsize=(15, 8)) pl.subplot(2, 4, 1) diff --git a/examples/plot_otda_d2.py b/examples/plot_otda_d2.py index 70beb35..cf22c2f 100644 --- a/examples/plot_otda_d2.py +++ b/examples/plot_otda_d2.py @@ -29,8 +29,8 @@ import ot.plot n_samples_source = 150 n_samples_target = 150 -Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source) -Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target) +Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source) +Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target) # Cost matrix M = ot.dist(Xs, Xt, metric='sqeuclidean') diff --git a/examples/plot_otda_mapping.py b/examples/plot_otda_mapping.py index 167c3a1..5880adf 100644 --- a/examples/plot_otda_mapping.py +++ b/examples/plot_otda_mapping.py @@ -32,11 +32,11 @@ n_target_samples = 100 theta = 2 * np.pi / 20 noise_level = 0.1 -Xs, ys = ot.datasets.get_data_classif( +Xs, ys = ot.datasets.make_data_classif( 'gaussrot', n_source_samples, nz=noise_level) -Xs_new, _ = ot.datasets.get_data_classif( +Xs_new, _ = ot.datasets.make_data_classif( 'gaussrot', n_source_samples, nz=noise_level) -Xt, yt = ot.datasets.get_data_classif( +Xt, yt = ot.datasets.make_data_classif( 'gaussrot', n_target_samples, theta=theta, nz=noise_level) # one of the target mode changes its variance (no linear mapping) diff --git a/examples/plot_otda_semi_supervised.py b/examples/plot_otda_semi_supervised.py index 7963aef..8a67720 100644 --- a/examples/plot_otda_semi_supervised.py +++ b/examples/plot_otda_semi_supervised.py @@ -29,8 +29,8 @@ import ot n_samples_source = 150 n_samples_target = 150 -Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source) -Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target) +Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source) +Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target) ############################################################################## diff --git a/notebooks/plot_OT_1D.ipynb b/notebooks/plot_OT_1D.ipynb index 93d156d..bf9f9cd 100644 --- a/notebooks/plot_OT_1D.ipynb +++ b/notebooks/plot_OT_1D.ipynb @@ -41,7 +41,7 @@ "import matplotlib.pylab as pl\n", "import ot\n", "import ot.plot\n", - "from ot.datasets import get_1D_gauss as gauss" + "from ot.datasets import make_1D_gauss as gauss" ] }, { @@ -95,9 +95,9 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -240,7 +240,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.5" } }, "nbformat": 4, diff --git a/notebooks/plot_OT_2D_samples.ipynb b/notebooks/plot_OT_2D_samples.ipynb index 900eaa7..96e84a5 100644 --- a/notebooks/plot_OT_2D_samples.ipynb +++ b/notebooks/plot_OT_2D_samples.ipynb @@ -39,7 +39,8 @@ "\n", "import numpy as np\n", "import matplotlib.pylab as pl\n", - "import ot" + "import ot\n", + "import ot.plot" ] }, { @@ -69,8 +70,8 @@ "mu_t = np.array([4, 4])\n", "cov_t = np.array([[1, -.8], [-.8, 1]])\n", "\n", - "xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s)\n", - "xt = ot.datasets.get_2D_samples_gauss(n, mu_t, cov_t)\n", + "xs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s)\n", + "xt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t)\n", "\n", "a, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples\n", "\n", @@ -98,7 +99,7 @@ { "data": { "text/plain": [ - "" + "Text(0.5,1,'Cost matrix M')" ] }, "execution_count": 4, @@ -107,9 +108,9 @@ }, { "data": { - "image/png": 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keukjZaur4a67gnJMZnkDoln/JuEU7CJSGJm97+HD4Yc/hKuvbqy5p2vgmUv7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\no2AXEUmY/w+PmkF8q6yXYQAAAABJRU5ErkJggg==\n", 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\n", 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rp67z2kM0XOJrJ1Ooxxe1crW4W0Qf7uLl3KS2NJPpvXyGTfJYPMPnmxPXdLXf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\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -159,7 +160,7 @@ { "data": { "text/plain": [ - "" + "Text(0.5,1,'OT matrix with samples')" ] }, "execution_count": 5, @@ -168,9 +169,9 @@ }, { "data": { - "image/png": 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meOR7L8bq1bwXHh6cmbTbHY/NvzRicijg9YJt0fL//o+hSo1tygGgdPo8iHkG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7t5E0KLjUUrP38eQvrkJ32X6Hl5eLslJby+c2f7TTdaE/yjj0xNFM1mmTePcWVkQC4BAbx/yRK8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\nAKOf94eua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cs6b1nWuwM9lu5gWVaOZVmXLcu6YFnWoO/4Y1nWWMuy3rYs65plWVcty1rT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\n", + "image/png": 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\n", 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\n", 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GhtNx2Qt01tEBusaXX1LHkZVFjtjiYrLSMzKog+uuHbg81tEB0v6//546ypEj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\ngL25GVlePI4xgpdfb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JAGAymXrem0ymXnp/X7fEvu+llHjttdcwderUXp9Pnz4d8+fPx3vvvYcrrrgCTz31FJYuXTroNZxtOHgQWL8eCA4Gli0DRo8e3nEOB7BzJ4+fWmXD5I989HhNM8jdwFmDk7bYhRCRAC4G8A8AkFI6pJTNJ9vu6caBAwd66cU7d+7E+PHjMXXqVJSVlaG4uBgA8Pzzz2Px4sUAqLEXFBQAAF577bUB2162bBmeeuqpHmJtbGzE1KlTUVdX10PsTqcTe/fuPebYkpISzJ8/Hw888ADi4uJQUVGBlpYWjB07FiaTCc8//3yPJX88eOWVV+DxeFBSUoLS0tJjCHz58uV44oknIL2j5o4dOwAApaWlmDhxIn784x/jmmuuwe7du4/73GcSe/ZQEo+IoKUeF6fP/AZDezst9eJiYHq1DXMfs0BYh6HHGzBwBjASUswEAHUA/iWE2CGEeEYIcYy5KoT4nhBimxBiW11d3QiclrjvvpFpp729HbfeeitmzJiBzMxM7Nu3D/fffz+CgoLwr3/9C1/72teQkZEBk8mEO+64w3vu+3DnnXdi7ty5MJvNA7b93e9+F8nJycjMzERWVhZefPFFBAQE4NVXX8XPf/5zZGVlYebMmT2LoL64++67kZGRgfT0dFx44YXIysrCD37wAzz33HPIyspCYWHhkLOD/pCcnIx58+ZhxYoVePLJJ3tZ/QCQm5sLp9OJzMxMpKWlITc3FwBgtVqRnp6OmTNnYs+ePfjWt7513Oc+E5AS2LYN2LgRiI0Fli8HoqIA0xD/ACmB+nrgiy+Aw4eBMWOAjK58krqy0DWN5J6ff+ovxICBYeCka54KIeYC+BzAQinlFiHE4wBapZS5Ax0zd+5c2bfQxv79+zF9+vST6ouB4eG2227DVVddhRtuuOGUtH+2fZdSktD37QMSEoCLLwZCQgC/IYRItxs4ehSorASOHAFGjQIuuIDHGjBwJiCEKJBSzh1qv5Gw2CsBVEopVTTPqwBmj0C7BgycNDweYN06kvqECcDixdTWhyL17m5a6FVVQG0tEB0NzJs3DFI3fNwNnAU46cVTKWW1EKJCCDFVSnkAwCUA9g11nIEzh2efffZMd+G0wOkE/vtfWtzTpwOzZwP+/nwMhtZWoLqagUv19UBYGI8NCxvGSVXOGSXVKB93q3VErsmAgeFgpLxifgTgBa9HTCmAb49QuwYMnBC6uoAPPiAxZ2UBGRm00gcjdSmBujqgsZEeMA0NQFAQMGsWEBk5zBNrGjxrrHBdZ0H1tTlIfs/IOWPg9GNEiF1KuRPAkLqPAQOnAx0d9FFvbaUBPXUqF0n9/Qf2gHG5KLvY7dy3ro4DQUYGtfXhwuUC1ksNo5fkIPO5VXSHNEjdwGnGOZ9SwIABX7S0AG+9RffEhQspwQhBkh6I1Lu6qKd3dlJ/r6ri5zNm0B1yuHA4gLVrAc/HNkz/ZBg5ZwwYOEUw0vYaOG9QVwd8+CEXTC++GEhKorzi7w8M5I3a0kI93c+Pvu1FRfSGmTYNiI8f/rntduCjj4Dgz21Y8qQF/q9b9aAlX83dgIHTAIPYvWhoaMAll1wCAKiurobZbEac11zbunUrAgICRvyc27dvR21tLS6//PIRb/t44HK5EBsbi+bmcy6urAdVVbSW/fzIn2PGkOAHInUp6e3S1ERPl+BgoLCQpD5lCt0ih1szpLmZnjd2OzDfka+TOtDbx90gdgOnCecmsa9eTfHU949is/HP0ye3x3ARExODnTt3AmB2xrCwMNx1113DPt7tdg8apNQftm/fjj179pxxYj/XcegQv/7gYP4kYmJI0H5+/QcguVz0S+/spBujnx+wfz+llEmTgMTE4ZN6bS3w6ac8dsECIP6b/fz+jHQDBk4zzk2N/TSXMfvKV76COXPmIC0tDc888wwAWrlRUVH4yU9+gszMTGzduhVvv/02pk6dijlz5uBHP/oRrr32WgCMar3tttt6UuC+88476OzsxAMPPIAXXngBM2fOxKuvvtrrnF988QWys7N70uOWlpYO2Zef/vSnSEtLw/Lly7FlyxYsXrwYEydOxPvvvw8AeOaZZ3Dddddh8eLFSE1NxYMPPtjv9f72t7/FvHnzkJmZiQceeAAA0NbWhhUrViArKwvp6enH9PdMYf9+WsthYcz7EhtLUjeb+ehL0J2dQFkZ/dQTEmjR79/P9xMmUL4ZKhoVoMV/5Ah/em43sGgRBwUDBs4KDCcF5Eg/RiRt77p1TJeaO/JpU++77z752GOP9bxvaGiQUjJd7vTp02VjY6N0Op0SgHzttdd6tiUmJsqysjLp8XjkDTfcIK+55hoppZR33323fOmll6SUUjY2NsrU1FTZ2dkpn3766Z5Uun1xxx13yDVr1kgppezq6upJ0ztYX9auXSullPKqq66Sl19+uXQ6nXLbtm09aX2ffvppmZCQIBsbG2V7e7ucPn263LFjh3Q6nTIyMlJKKeV7770nc3JypMfjkW63Wy5fvlxu3LhRrlmzRt5xxx09/Wtubh7w/p2OtL0ej5Tbt0v59NNSvv66lI2NUjqdUnZ2SulwcHtfNDZKWVgoZUmJlF1dfL9pE386xcU8frjnLi6W8qWXpHzlFab/HTYe7ZMe+NFHpfz973unBx4kXbCBLzdwOtP2nhFoGtOlrjr1aVP/+Mc/IisrCwsWLEBlZWVPwYqAgABcd911AIB9+/Zh6tSpGD9+PIQQuPnmm3uOX7t2LR566CHMnDkTmqb1pMAdDBdeeCEefPBBrF69GhUVFT25XAbqS3BwMJYtWwaAKXqXLFkCPz+/Y9L1Ll++HKNGjUJoaCiuvfZabNiwodd5165diw8++ACzZs3C7NmzUVxcjKKiImRmZuLDDz/EPffcg40bNyJy2I7dIw8pgc8/BwoKqKVfeikXPt1uWtt9LXWPh6kBamqA0FBg/Hha7gcO0CNm3Dha6kNFo6q29u8Htm4FAgOBJUvYh2Gj72zTzw+46y795EbRDgMjgHNTYwf4B8g79WlTP/roI3z66af4/PPPERwcjIsuuqgnRW9wcPAx6W77g5QSb775Jib1masPltf9lltuwYIFC/Dee+/h8ssvxz//+U84HI4B++K7uHuy6Xp/9atf4X/+53+O6dO2bdvw/vvv45577sGKFSvwi1/8YshrH2m43cBnnzF17vjxdGkMDmaUqXJr9JVSnE5KJl1dlGliYugJo0g9MZGkPtTauJQ89xdfkNjDwym/REUd5wV4F1NdX7XA8Z0chDyXB/fq38Gz6hF0ljcj4gUjoMnAyePctNh9w7RPcdrUlpYWREdHIzg4GHv37kX+ABn8ZsyYgQMHDqCiogJSSrz88ss921QKXAWVAjc8PBxtbW39tldaWorJkyfjzjvvxFVXXYXdu3cPuy+DYe3atWhubobdbsdbb72FhQsX9tq+fPly/OMf/0BHRwcAoLKyEvX19Thy5AjCwsJwyy234Gc/+xm2b99+3Oc+WTidwMcfk9RTU+nS6EvqfS11u516usNBqzw2lqReVERNfexYEnufxJbHQEqeo6CAnjPR0cw5c9ykDg4OGwM07F+Sg5Dfr4Lr9hx8lPFTFC7JQcTjRtEOAyODc5PY8/N7WzWnMG3qlVdeCbvdjhkzZuBXv/oV5s+f3+9+ISEh+Mtf/oJLL70Uc+fORVRUVI9ccd9996GjowMZGRlIS0vD/d4qIUuXLsWuXbswa9asYxYjX3zxRaSlpWHmzJkoKirCN7/5zWH3ZTBkZ2fjmmuuQVZWFm6++WbMnDmz1/YrrrgCN9xwAy644AJkZGTAYrGgvb0du3bt6lnMffjhh0+7td7VRR/18nIgPZ0eKIGB9HABSOomk07sjY2skuTnB6SkcHG1uVkn9dGjuXg6VMZjKXnurVuB0lIODosWUfo5XnR2AiXfX42gv/0BU9floeOnuXD/NQ8pj/0AMz78/dlXRNtIaHbuYjhC/Eg/RrLm6dmEtrY2KaWUHo9H3n777fLPf/7zGe5Rbwy2WDuSGOnvsr1dytdeY23S/Hwpu7u5gNndzUVQl0tfLHW7pTxyRMr9+6WsrOR7KaVsapJyyxYpN2zgNm/52EHh8UjZ2iqlzSbliy/yuZ8Sr8NCW+6jcsOqdbLgG7+XHiFkyQ9/L9flrpN1qRdIDyBlTg53VE4B/TkDeLdVPr+O1zvYviMBb/vdH66TDsdpOJ+BIYFhLp6euxr7WYi8vDy88MIL6O7uxty5c3H77bef6S6d82huZuBRRwfXE6dPpxXucnEhU2nqQlByOXKEz3Fx1NMByi8HD/KYmBha64PJKKpEQWMjS+HV1VHKyc4eWrbpr63ycqDMlI35qy1oX3Y9dn7zd5j+zG+Q5HHBLDwQOTmcVgCDBjS5FmnY+0srpuZYUPtZDuJfP8V6vKahIc+K0BssqLomBxP/Y+j/5wpOutDGicAotHF+Y6S+y7o6huk7HCTVKVNI5E6nTupCkNjb2+n5AvSWWJSm7nYzQ2NsLB8DrXmrv0NNDbB7NyNTk5OBOXOGXmDtC5eLC62FhfSXH19qw9RcCwrm5eCCTb+Hv8NO+cUbKzAY2tupgtTXA0vW/RoT/r1q2Mf2iyGC/Dwerils2wZc8MGvMeudkzyfgRHB6Sy0MWI4E4OMgZHFSH2HVVXAf/5DQl6wgBkafS1134XS+nrmXPf3p+Hbl9Q9Hlroo0YNTerKwt65k9khJ04E5s49flLv7CQx7tvHlAVdXcB7dg35c3OwaP0q+JnlsJOE1dQA77/PNMIXOW2Y8OEIJBgbJMivtRV4+21yfEa9DVmbjIRm5xrOGikmKCgIDQ0NiImJGZYLoYGzD1JKNDQ0HFM/9Xhx6BDD9AMCWIouOZlE7nbrvuomEwm7uprWbGQkk3YpV0clv0jJhc7ISEow/f201Fjk8fDcRUUk4okTgczM4fm3+7bV1ATs2sUZR2AgM0c2NgLJJTYs2PZnyOBgiICAIZOEud1ASQkXbgFgeYANY+/22fdkEox5JR/P1yxo+FoO4l7Ng3zZiqIEDZte4wB6sduGtD9bIF4xEpqdazhriH3cuHGorKzESBa6NnD6ERQUhHHjxp3w8YWFDD4KDQXmz6e2rUjc5dLdGh0OWvVOJwndN2d6SwtQXKyTeng4Sb2/VAGK1F0uHlNSQkJNTdX1/OHC46HGv28f+9DdTc8cux2YUGaD5Q0LzDffCPGNm3iAIsl+NHWHg4PD3r28F5oGxP5zEG+wEyDa0vEaOi7OQcaTq9B1dy4+c2so+5SDYGYmMOm1QYp2G8R+VuOs0dgNfLkhJYlsxw7KJvPnM6JTkbpvAFJ7Oy11k4l6um8d0r6kHhxMf/X+CFr99Lu7ad2rAN1Jkyj9DDenm5QcGEpLOTC0tFDGaWkhQQcEADdVrEbiNdkwXzp44jopuVC8dSst/fh4RreOZAFt929Xo2RUNsrKgMV/taDlGzmIeu7PKJl7I2pXPYXkZN6zk5x4GTgFGK7GftZY7Aa+vJCSRLZ3L4lszhw+C6GTJkCira+n1hwSwuAiX8JWpA7Q6gwMZDuDkbrdTulF+bxPmkRrfTiJwFQ7qo3ycvatvZ1SjtPJwWXpUiBh2kqY+/ajT7S0283r27qV0k1qKqWo45k1DAW7HTgQkI20/3ctJgiBvQ++gaoq4FI8jik7XsaoqpsQOU8bOVI/BZlYDQwNg9gNnFG43cCGDbR0k5JYnzQuTid1p5P7mUxcILXbKbso4ldobSWpC0Hpxd+f8kvfRU/fCWprKy31o0c5CEyaRF19uNkdpSQBFxfTuq6vp4XudnMwiosDLrmEqQ+GImenk9dXUMBBYdYsIC1t+LOG4aC2lnzaNFpDzOKbEL9+DRz/sWHZljw0/etNeDxATGk+/INHUGYxinufERjEbuCMwenk/7yigqSakcFw/b6k7nRST3e7Kb30zT+mCFoIbjOZSOp9rU5fUm9o4GBSW8v9Jk8mAQ+X1N1uDgilpVxwbWnRyd7j4SB18cVcIxiMnD0eEnlpKfPQmEzMf5OSMvyc8EPB5aLMtHcvB8aQEOClJU/hIkc8Fv5nFVrvzIV9vob4eIwsqQOApsH5ghXyOgtMP8iB39OGL/zpgEHsBs4yDwOTAAAgAElEQVQIurroo15byzJ0M2b0XgB1uXS9uaZGd2XsS9bKUjebdcKPiztWk1akLiX1+cOHdUknNZVEPBSR+mryhw9zYDh0iANPQAA/F4KD1IIFnFUMRuouF9DWxkGpqIhpDxYuPL46q0Oho4NtHzrEwaitja+nHLFh3vY8tN6Zi9Dn8xB4uYbAlJEn2/Z24D9tGiYuysGsR4zi3qcLZ5Ufu4EvB9rbmfelro55X9LSjiV1pTdXV5N8ByN1k4nHS8nI0rAwfR9lRQO0jisqaL02NtLbZNq04ZO6lOx7YSElk4MH2c+QEJK6yUTLf9GiwUnd4+H+9fVcMC4qon/90qUjR+oeD+/vjh2cDTgcvPb6eiCr0Ybr1ljQ8ncrGn/yAJz/tiLwFh+f9hHKEVNTA7zzDhCyxYaMjYYv/OmEQewGTiuam0nqLS3AzJkkVt/wfrebpFdVReKOiek/V7ovqatSeKNG9U7O5Su9KB/18nIeGxbGcw+ntqmSXhoaSOhbtpA0AwJI6u3tfJ2ezsXOmJiBSd3t5mylupqkW1XFQWvJkhNLLNYfurt5nbt3s59NTRzM3G4Oost2Pob2H96L9myNktUKDbj3XuCxx9jASVYok5KD1dq1QMxuGy77hwV+r536TKwGdBhSjIHThro6pt11OoHZs1mKzte6VlJBdTXJNiGBZNeXeH1JPS6ORBkV1XuA8CV1p5OySV0d9w0JIamrRdqBoNpQGv/27fRTVwu0bjeljvBw+rxPn84+DFQ82+ViRGpNDX3dOztJtDNmUGoaCQ+S1lb29dAh3kuViz4yUl+QbXXcjVF3WBB4wSwEpXgXNB95RF/Q9MkZX3N9DhLfGr4u7nRyQNmzh66mCwPyYX7V8IU/3TCI3cBpwZEjwCef8PXcuYwm9U2Z6/HQIq6ro+SSkMDnvsTb1qZr6qNHczEwIkJfdAV6k3pXF0m9sbE3qcfEDE3qKmXvgQMk9Y4OklVgoO7OGBVFS33CBL7ub/HV7ea+djtJ98ABtj13Lo/rGQi8lrJnjRWmS47Pg8RXuiov532sr+c1JiXp97ylBejO1hD6POWXmq8em0zM4wEKwjT4X5SDmf9YBfcvcmEeBgm3tTEVQ2kp74WmAaEWo7j3mYBB7AZOOQ4dAjZupFU6cyaJxndxU0VstrTQ+k1I0BN8+UItNJrN1LA7OmjxK5L2JXQh9IXC5mZqzGFhulU9VBIwj4fHbdnCNoTQj+vo0PPPpKfzegYidZeL51aWdHExB4fZsxkE1OsYTUP7P63wv96C9m/mIMY6PEu5q4uzgJoaukweOcLZQEAANf+ZMznwqWjYuDigYrKG7otzkPF3Rp0Gec/hcLBCVfd/bLjkkzy47s2F39/zgEsHJ+OjR+l1U1XFwLKLLho6172BUweD2A2cUqj6oGFhJJi+FYscDnqYdHXRAo+N7V0wQ8GX1MeO5fvgYH3/vqTe2Mh229tpLStNPTJyaFJ3Omn1btzI84SFcVByudhPIXjetDTd/bIvqasUCN3dJNTyci5eRkXRevadYShUVQGfdmiYsTQHmX8b2oNESg4+DQ0k1rIyWuxScr1hyhTKPKrISHc3B87NmwH3RzYs+ZjFPkL/lQes0NA2V8P69YDfZzYs+7sFptesjJRdNnCOGI+Hg9WhQ5whJCXRI8iIWj2zMIjdwCmBShGwaxfJLDOTJOgtxQqAlm9FBWWE5OSBFw8Vqfv56aQeFMSBwGw+ltRrami12u0cOCIimCIgPHzwJGAqirSggAMSQOvW5dItbyE4W5g6lc/9kbqSXrq6SKilpSS9hATq3KGhx/Zj/36myB130Ib0T4eu5et00lW0vZ0D2MGDvJ+BgbwvqakMtgoO1km9u5szkKidNix90gLxqhWByzS4V2jADRYU/NCKxokaljny4fe6FWLp4Lq486HVODw6G8WJGtrauAg8326D35+NqNIzDYPYDYw4pCSBHDhAYkxLIyH7RoE2NtLK9PMjAflaeL6k50vqiYmUNAIC2G5fUgc4UNTWklQdDhLv1Km0WofK7FhTw6ySjY3cPyKCxKkiSf38SM4TJpDUw8N7k7qy0t1uDhCNjex7ZyclkRkzSLy+/fB4eK+KioDJFTYs+JsFJrXY2E82RTX41Nbyed8+EjvAWUBcHC11leuluZmEX15OmSYwEMiW+fB/g206HEDFBA2H7rAipigfC2/RMPZbK4+9V30GmIYGoHZUNibeZUHVT60YdZmGeR02mL9uRJWeDTCSgBkYUagUAWVlJGJFMv7+3K5S7So/8nHjersyDkTq48bxvYoq9R0khGC7yj9dWctRUTx/SMixpN7Xa2bnTs4uPB5qxC4XiRPgAOHnR5khOZmk3negUOTvcnEwUJGtbjcloEmT9Hug0N3N9dGaGl7fkq2rYb5g8OIXDQ16krGCAroyKkkqOlqXmxSpHz1KqcRu56C0aBG3eTz8rLiYEa8hIXTVTEwc/PtV+eorK/l9hG+zYe5jFhRfmoMZn+TpKX4NnBIYScAMnHaoFAFVVbTCJ04kSSriVqXr7Ha9RJ3Sx4UYmtSVtt2X1J1OarytrbqfeHQ05Yjg4MFJvb6eA1F1Nck6Pp6EqLJJdnezjZQUXsvo0b2lFOXj7nLxmLY2eqSUlrLvs2fri8G+aGriverooCU/dy4gLhnYg8Th4ACgUgHv3Mn7GR+vewWpQSwoiAPAvn283wEBellBdb/sdhJ6WRmPVf73Q32/RUW8P0rmqkzVELokBzNfXwXHz3MRYJD6WQGD2A2MCLq66KPe0EDpIzmZRKhc+To6eud7UTJGf6Te3q6TelISjwVIQH0lG5VnpaOD7+12kv/kyf27S/pKL7t300rv7uZ5AgNJysrDRi02TpzIc8fH97b+VTphFUna2kryrajQF4v7C1YqLwc2beJx2dm0sgeClBws6us5eOzYQTL29+c1qgyWycl8HRzM9pV75ujR9FCJitLz0nR0sJ2aGn4Xc+cOHRzV1kZpzeHgNXd08HpH77Vhxid5kL/KRcCTecDy43BlNDI/njIYxG7gpNHezrwvbW20ChMT9ZB6lQFRRWqOGUPCVWTXl3gVqfv701K32zkYREXp7nPqmI4OWupdXfqxo0dT9uirZfta6c3N9HiprmZfJkygDl5Xp1dqcjpJyikp9DCJielN6kpLV5Gyzc20jmtruW9m5rELqx4Prehdu3gvFi0aXPpQvultbXxs3szn6GgSstnMezR6NO9XYCAJe98+njczk9ky/f113b+tjaTf2sqBISNjaLfE6mre58QXV6NpUjYqEzS0t3Oh95K/XQvz128CVj0ALB3Ye6ZfGJkfTxkMYjdwUmhqoqXe1UWSiI/XS9R5PNR4lctgTAyt8L4WrCJLX0t93DgSpstFq1lZlGrf5mZapkoy8SX1gID+SV1Kkt6OHWx7zBiSZF0dj1cEqCQO5anjS+pqgdTj0SNPW1u5gNnaSst/xgxazr6k7nTqPvFRUSR130hZX6jAqNpa9qWsTNf/U1N5LwMCeK7wcN4vt5vfQ1UV2124UE+XoBaSGxr0GUpaGqWb4OCBv1u3m7Oh2lqep3FiNqb92oLK71khszVcWL4GZrMAbvJWhDreqFJNg/1ZK8zXWtB807GBUgZOHAaxGzhh1NbSyPJ4aBmOHq3XFXU4uMDmdNJTIzycx/gWoQYGJnWHgwQUGqonCFP71tXROlaE3dHBBdoJE0jO/UWgtrbSSq+poWU7eTLJsbqa5wkM1HOpJybqQVSxsbpOr7R0lR6gtZX9VrOGqVMp2/SdLbS3U8evq2PbF1wwcEUkFRjV1MR+qTQGwcEcMITgayUdCaHnWe/q4j246CLuo9YbXC4OsHv2cLCZOZMzEV/X077o7KT00tHBQa60FCgP11B5uxWX/t2CkkM5CPnkdeCNN3oT8XFElRYXA1uaNWRoOcj8u5H5cSRhELuBE0JlJSMUzWaSemysnnulrY1EYjKRpJU3SN9o0v5IPSmJg0FnJ8kvOlrfV0papKosrsejk3pKSu/2fa30AwdopTudHHhiYkh4R46wjcBAyhRC6IukgYE6qavc8CrXuookVZGtUup++r6zBSnZ102b2M+pU0mqfYt/qH1dLu5vt/O5oICv4+NJ2B6PXrRbBUwVFfHeBQSwnKAqzqHuoZT0zikp4bWkpXFw6a8PCvb76Z/ePUdDcjIXaj0f25B1JB+lN6zEgZIcZL3OPO4RJ0DEDgdlpbIyYHypDemfDe23b+D4YBC7geNGaSnJKjiYIfXR0SRBgITU0KDXGlXyha8lDQxM6kreCAoiASs5w+Oh9NLUxH27u0nOiYkskKFmAr5Wens7CeToUd21MiCAnzc1sW1VQzUwkOePi9NJXbkFKitdLT6q48vL2V5mJvvqe40eD+/T9u18PWvWwHVUlW96XR3JWJG1EL1TIKiAqLC/rUbjpGwURGhoaOCMZpHLhtEf5EOmr4Tdri/qqjD/2FhKL2pQ6A8qrXFbTDam/dKCyj9Ysa5QQ0SBDSuetWDTnVZE7bRhmi0P3StzEfHPPOCaYRLx6tWQc7NRPV3Dpk28hwu3/AGTnvs1xLvvDOi3b+DEYBC7gePC/v2c9kdFkXSio/nweEggHR3cFh+va9F9SV2ho0NPE5CUxH3b2kiWsbE6CbpctIwVAXd0kLiUZNKX1JWVum0bj01MpPVvNnMhV51DeZyEhlJPj45m+zExfFb9B/jc3q4HB1VXU3/PyOCz72zB5aLHTVER25k9m/0cKEFYU5Muv+zerQdITZvGdv399VmE0wkcis3GlB9bEJJjReRlGua02hCZY4HnJSvsHbr+v307B9mkJFr8KqirP28U139taPgwH5XXr0Tccg2FsGLijyyYNC8Hc7flYcOPrHC7AC2PAVR+yzTgci8RX389dXZN09sGdO8Wmw2egyVwP/IYvrjDiq50Ddd88H1Evfkc8PDDvUl84kRgzRrDU+YkMWL52IUQZiHEDiHEuyPVpoGzB1JSzsjP1/OkxMaSBLu7Oa2220lAY8aQWDye/pN5qURaRUXcPn68TrL+/iQgX9/34mI9s2J7u07Wyck6+fvq7TYbLfXgYFqpKpiopkZvx+1mW5GRXJBUZB4TQ9L3JXWnk6Tb2UmLtqaGA9esWTxeDVxScp+NG1mMIzKSeVP6K7mn3Cmrqki+xcXA55+T4MeO5YAREMC1iaQkvm5uJllvj9Sw6U4rluRZMPedXyPyexY4nreiPZtk2NXFGVVLC69/0iQ9/YIQOCbfuv09G6TFgvoJ2Rg/nn1/t0PD9nk5uMi2Ck1JWZASmN6eD//XvaSuyFZ5sFgs8HxsQ3dmNnDttZDXXQdkZ0Ous8HzNQu2TrwJ/73dikV/sWDeu79G1AdrOCWaNYvHK4+YG28EXn8dsNngcOC4c8EbIEbSYr8TwH4AI1QuwMDZAhX2fvAgSWfSJFq3UVHUmqurSVzJyTpput0kkr6E1pfUk5N1UlepeJVUYLfTUne59JwnACWVhITebUvJfZWVrtIUdHfrWreUtNztdhJwTIy+2Onvr8spLldvX/bWVj0Iym6n9ZuSwj75DixNTbxPTU0ceLKy+vd8UdZ/fT3Jd+9e3R10yhTeW1VAJCKCfais5OApJT87OE5D5KIczHmcgUFdC7SexeBdu3j8tGm8n6q9ngHW673ivsGCqqtzMObNPJQ+YkXYCg01P1uNtvBsjDcBc7flYdc1uZjx4R9wye+vgOnD9491S/RKKParb4L5egvKLs/BZI+A2y1hf9OG8Ofz8Mn/WnEwWkPm1tWoylyB1DX6Qqnn2uvQnTEXwQd29bTnypgFz3UWHF6Rg8kf5UEMJM0YfvADYkSIXQgxDsCVAB4C8NORaNPA2QG3m4uk5eW0PJOSaKmHh1OSaGwkWaroSuU5YjL179Y4kKUuBC11ReqtrSQy5QXS2Mj2EhI4I/CdBdjtzCBZWUkrOSWFxO1wsC8NDWzX31+PTh09mp4xJhOJXfmcu91sWxXF6OggsZaW8rMZM/Q8LL5ZJSsraU13dXEGMH167yIiQO9KTK2tvKcHD+rpD8aN473099fTMLS00Jqvq6NkpGq2ppTZkLWZWrf/M3kIvUTD/jEaiovZxoQJHBhGjTo2AMztBvKDNSSNzkLSs6tw9PZcOC/SYPsAmNzmB8trKyADg2D78RsICQbM6x6HyQO4r74WRy13Ytzbvd0Sy8uBzzs0TNNykGldhd3X5sLPDMz48yrsujoXJcka/M1AYJgfJr71bzhuvAV+f8tDgysKo7ocCN74MeSvciE0DY2NwIZODRO0HGSsWQX5q0E8ZbwzD/dLVtTM0JBwwPCDVxgpi/1PAFYCCB+h9gycBXA4gPXrSSSTJ5NU1aJiRQUJddQo3cVRLTSqRUlAt3z7knpKiq6pS6kvWgIkvspK7hcQQMvW318PfPJtt6yM3iMOBwk1MpKWvTpfe7u+CNrSwmPHjCHxSclz+pKfCrlva2ObKgjKbKZqMGoUj1Gk7vFQuigsZNtpaXrUqy9UdGptLfuxfz9fqzTE8fG81uBgXqdKZ6xcKePjuX9HB5BWa8Oif1pgf44FOcRSDcJiQdNPrIheovXkh1eLrr6k3tBA10vYbJhdlg93UAjiXnwcmwM1zN+yBtO/WIPazEsRu/8zjNlnQ8b6x4Ebb8KezJsQ+9RDGPfPVczRrmmQkoPZgQNA3B4bpqzLw66rczFj7Z8hpcSOr+Ri2ro8VE/XGIn7wSOov+d3CP/LIyhJW4HJj/wM7sBQdPw0FyFP5qF6hoYN/hpG7bRh+if0lBF5eQx86o/cVf76r1pQr+VgzIY8mIxcNQBGgNiFEFcBqJVSFgghlgyy3/cAfA8AkpOTT/a0Bk4xOjsZ8NLcTOszLo4Pk4mE43aTkCIjub9y1xOid14UX+3bl9RVYWiPh5alCpSprtYjQs1mknpAAK3ZuDgeZzKR7LZu5QATEUHS7eykZS+EXlQiPJyfq7qk8fE8v9OpW+pC8FyKfNva9IpOlZW0lDMy9MAgReoOB10BDx/mPlOnchbi63WiyL+1le2pCkpdXeybimpVBbnj43kNJSW8DwEBJPrSUj2hWHZdPjqftSLgMg1OJ/CZnwb/n1gxoT4f7vEa4p9bjcCLskn4XlJ33/59tLQAH17zFELzbVjxTwua7rwPjt0HEGd7GVc9eSUgOCIXLP4ZxsTPxry3V8ETFIKtE29CRw0wpXIXSf3pPHQt0vCpWUNtLTBmvw0X/cWCz35oRVAQIP/7OCQEamdoaMjQoP3VAlx3PUoetmJjgIa0i5qR/cEquP0DIb/+DeCBB1A6XUPi9y1Ivf5epL/zCMyvDZzhUuHwYWBTu4Y0LQeZbxl+8L446eyOQohHANwCwAUgCNTYX5dSfnOgY4zsjmc32tpI6ipBlSKc7m4uHKoUusoqVX7eQP+udHZ7b/kFYNsuF4k1IoJtVFToHiGKWJUbovJnVwPLtm3sT2oqBxjlKiilbpkHB+sEr8rtqeAntTCpiFoF87S389gjR9hmXBwJW0kkvveooID7qARciYm95Sdf3/SmJhL60aNsJzKS51eVl8aM4WeHDpHEVeoAs5lSh/KumTiR9zE4WNf0HQ7OEtRAEb7NBtxo6dGm2962Iejr10J6BP6T8wbG1+Yjbqwfop96BK9ZrEgssmHxp6tQMeUSbFv+S6x4+jqY3Q6YzAJukx/2fvU+ZLz7CAtSaxrqrDZE3G7Bp/9rRW2ahqlvr0ZLajaaZmpIeWU1asdnI8AfGF2eD9PPV2LcQRvqP8jHJ/NXIma3DZf/y4KuKVmIPJiP1ufexP4xGsrKgJRDNsxe9xgCfnH3oLq5280Bdd8+pjW4+K8WmP83h37wKvL1PNXeh5vdcUTT9not9ruklFcNtp9B7GcvVIoAp5M+6hERlFpaWmi9h4bqeUqAoUld+WWbzbqlrvRvRWxuNyWVtjaSm9NJSz0kRC87B/CYbdtIdGFhtNIVcaqUua2tJHE/P73ARGgoSXfsWL1kXGQkn81mttvZqQ8MZWU8NiWFA1FwcO9ZSHU1PYRUcNSkSXoaBQWPh+3V1pLMVRSn8roJD+dD3RePh/KMiqgdPZoDW1MTj5k1S8+zExDAe7BzJ+/5pEm8V6NG+eTTWW+D66sW7L04B9Nsefjvd60QAlj6lAV7FuUg/bM8vH6jFS4X8LVXLNi9MAeZm/KwZem9uOjDX8Hf2Ykvrs1F8ywNC397FUwProL7zp9izx7KTlE7bIgry8eeK1b2LDg7nbxPgYF6gW8huJhbXQ0kFNlw2TMWVPzeitEWDd0f2hD5PQs2/tgKz2INF1wweN4a9dvZtIkznylHbJj3e4suv6hF3XvvBR55BPJlK1yLNPhvsJ03/vFG2l4Dxw2VIsBkokeHyu9SW6t7kcTG9tZsVfDOcEldVfIJDSVhO520ULu6SExdXfqCbHKyLvVUVva20idOJKG3tOikrnLSuN1sw+3m8UqbV3p7RASfVU4WNXtwu7lQ2dVFUlK+44rUlX/8/v3cd8IEfZFSQS2QNjezfyUlJGGV1EwInj8wkA91Hfv26QNnWBgJ3ulk+1OmsC8q/8zevexneLieGiA6WpezHA5gvUND1LwczH9rFbatyEXVVI31ThfmYN4Hq/Dp4lyS+qsWvH+bFWUTNJRN0HDDP6+ANJmx+9pcTLPlwfEdDab33kX3hnx8voHfg8sFVE/XUDODnjidnUD6B6vRNjUbjVkaEhI44Ml1NrR8nI+qRStZf7UxH0f/ZEXElQysKh6lIfD/WTGhNh8Ji7QBo2FVYfGaGs5Q2ts5mM2tzO+tqfvkqnG9aIX7OnrpTFk3iGfNeYoRJXYp5XoA60eyTQOnB5WVrB4UFET5JSSEBHT0KK3JxEQ934uC8vX29VVXi5p2ux49qcLhVQGM4GASkUq563JxwGhtpYUaFkZLXbn6bd+uW+nZ2SSyI0d02UQNFtHReiZEIUjq48dzwFDl9FQRCt/86QD7cvCgnkslKorn8Q2S+uIL9sNsJrGMH68PPICebqCujhZqYSGvKS6OhO3xsC9mM48bO5bnLC5m+6NHkyQPH+Y9Sk3lYBAfz/cuF9WE6mo9XiAggG0qWay8nAveIVtsWLQhDwVX5iLtkzxUTCappW/Iw6eLczEnPw+RXTV471tWHBqvQUi24fYPRMWFN6Lr3geAOzSEfdOChjwrNs9aiaZyfY3DZNKLkfj5AfYZ2Vj4RwuO/MEKR7qGjndtmPwLC/Z+z4rwcH6/nrtWIsD7XRQX81rjrtGQnKn1G7ylCF0lb9u/n69VFK/fwv7z19dnaFi7Fph8YQ7mWYfwrDlPYVjsBlBSwoCeiAhaqkFBfNTU0NJUCad8oSxcPz9dguiP1CdP1v3a7XYSUUyM7m1iMvFPr6SesDASZlgYB5uCApL2pEn8M7e368U61NRcCLZZX8/3ZjOvRdX8bGnRZwgBASRfh4Ntmc3cXlqq51IJC9M9XwC2uX07Zy5hYZxJJCXpsoFaIG1v5z07dIgPgIudqo+K1OPjeX/z89mmuieq3F18vD7LUG6PdjsDmFpbOVBGRuoePSqB2eef05qP2W3DVS9Y8NH3SdrlEzVc8fS1kBB4+cY3UDZBw9FpGr7ybwv2Zd4Es9k7mNbkY8vP30D8TRrmTAbcbg2HVlvR9mY+Gi/TejxsTCZ+J243B/ugIEAs1VCdYkXS/7OgcAn9z9flWNE9T8OYKMp3ERG8jpIS9nfGDA5eA+XMVzO8/HxKLyEhTN+goo37Qs2o1q9nnvjZW4bhWXOewiD2Lzn27iVpxcSQOFUSq4YG/tnHjj32T+QbgORbd3QgUlfJuvz8dBJXC4IREST01lad1P39OdCUl5M4VCGI+npa9Cr/emcn9w0J0asLKf166lQ9IZlaVFRZJ7u6+PDzo/V75Aivf9o09kktqAKUdLZvJ2nHxNCC9l04Vi6eTU26x0t9PWcPKSn6ArBafE1MZJ927eI9Uf0qL+c5J0/msTEx3NdkYhtbt/Jepqfri6dRUWxTJWSrrfXOkOrz8Z/vWHE4WUNgAGWTfRk3we0GyidpCA8FDgdrePsbVoyrykfHPLoj1nxrJWbP5syhvd07QwnS4LyMhOg7cJvN+kxh7FheY1G3hqPZObjgzVUouDIXjgs1RHl/Q6NG6Z5RUuqVpfojdfV7qquj/NbeztmJKiDen3Wvyhtu2wZMPGzDsuctMA3Ds+Z8hVHz9EsKlSJg717+aSZN4p9VSRRxcSSYvn88JamYTCQV35+P8n4xmWiJKdJrb9drlTY2kgBDQ/loatK18ZQUbt+2TQ/0UYOD8v9WbXZ366kCqqu5T1AQSXzaNBK43U7LNipK1747OvRBqbRUz6WSkqIvuip3xooK3h8lk4wdSzJS6wmqyEZNDRdclaQyZQrbUvVIg4L0uqSVlXpWydhY7tPUxIFLRbPGxfFcAAlfFeaYPp3HhYToFZF27CABd3ToAVEdHbyGkBDeh5YWfmdmM/vu8XB7cDCPCQ8nYWZm8vXRo2yzoYHXA/BYZaWHhfG7i4jg/ejuplQCmw1X/9uCvYtzkLkxD3tyrQj7ioboaA52xcUcAObMOfa35UvoUnLfffv4evx4zpL6ru+ofVtb9cjoMWOAqwtXw/9CwyvmdPSnFwxiP7PweDhtLynRo0l9g4vGjj02alIdp/7ofRdL+yN1lY8FIFnV1vIPrjxSmpu5PSyMBLF/PyWM8HA9D4tKuqV8y5WuHxVFAmto4HkU2aWm0pJXi7Hh4foxqsSeycRz2e3cX2nVitSVh8qhQ/xMFQ9Rpf7UINHWRpJWbozh4dTnGxrYdmgoiSw0lOReVkaSVVKXygWfmKi7So4ezXsFsA8HD+rX5XTqax91dbTijx7VJRHl2RMayv0aG/UKVGp2pdZEwt73pNcAACAASURBVMJ0Uk9J4WBoMpFQCwt5r6TUK0opOUmVJxw9mve3upqDX+R2G6550YINP7LCPl9DUrENMx+xoPNZK4qTNFRU8Fxz5vQuBO5L6EDvdYTgYMpOKi1CX1J3uTiobt7M30haGnPR9yfTnC8wvGIM9AuXi5GHFRUki9Gj9XwqQUED5+pWPtlAb1L3jSj1JXWPR9fBY2JIgC0ttLpUXhW7Xddo16/n+ylTdN21uVknSZWqwGxGjwXY0sL9wsJ06UMVWVZBT06nru8rf/UvvtBzqCs5QyXy6u7mlL66mn2LiyOhx8bqpK/cMQ8d4nV3dXHGk5JCC1sRrZ8fB6eODi6IulwkQ5VMzGwmGSntfdQonsft5tpCdTUHvKQkXlNICAeK3btZNKOtTc9a2drKa4qK4vvaWl6Lx9PbEg4M1Al91CjOiJKTeX/272f/u7v1iFVfL6bISD0yVkruX1rK7cs3PIai6+9F5wUaIiOAhG9ocDrvhfORx3D45xri4znoBT6+GmJeNuQSradPsNkgtuWj4X9WYts29kXNkGJj++Tb8ab/tc/XcOQIDZSoHTZch3yMyVnZbxbRLyMMYv8SweHgjFRZN1FRekm4yEgS2EDeCf35qvdH6mohUZFxZCTlh44Otu9w6P7lKsimrIxks3gx91f5XZSernT6oCDdFdBuZ1+UxDFuHM/h8ZDkAwL0Mnfd3STZjg6SUVAQI0mV7q28elpaSOotLXqO+fh4PTjK5WJ/Kipo2VZU8DwLFvBZ5bYJDWW74eEk5/Z23UpWaQHCw3m/VMm/yEgORp2dlBVUZsboaF5DUBC35efrkb/R0bwPynd/1Ch9hqNmKVLy3L7+5WFh/C5SU3mMcrdUx/lq6SoiVs2IEhM5qBUVcbYAsN8VN96N+X+wALNmIflWDYGbbDA9+gj2321FSgoXSv38wIyPFqYYrkvXEL/PBtxEb5ptG3SPo4gIPSeRLzxzmB+m6mErPjVpGHfQhmX/Z4H5VStgkHoPDCnmSwKVIqClhaSmUuAGB+tT3YGgijb45lXvS+pTpuiWemenroFXV+u509vaSEKq0v3Ro9w3NZVSAKD7sTc39w56CQ7WA3PUIqmKJh09miQE8JyKqFXN1IAAnuvwYZLU9On8TAUomUwcLPbu1a39mBi2q7Rsl4t9Kiuj9NLaynNnZXEAUnlflE7v58fPpCQ5dXfrawEqAlb58YeF6Xr71q08V3o621KVm+rrOSg1N7PfISHcXwV6hYfzfWur/j0oCUbtHxmp555XZQQrKij3qJw9gP79+PnpOerHjOG9U9WYWlv1ZGUqp8/YQhsyHrSg8cYcxL6Sh4KVVsTcoGHSJH0GICUYCXurBUVLc5D2WR4Kf2NF4VitZ+AIDNRdRH3R1cXrr33ZhtRf0fsma/OXKz+MIcUY6IFKEdDZSW8EFXYfEaFnFBwIvqTum82wL6krS11N3QMCSJYApQS1SNrVRQtRuTYuXkyCUz7RKvOhItOgIP7BVbENpaer9ARK2lCWstJXlQwUFETruraWpKFC8v399X0LC3XXy7Fj9UIh6rwuF6+luJj7CUGtOCGBxNjSwvbUQNHd3dsbRqUUCAggocbH8xydnTqpV1ZythAURIlICD3FgXKhVLlvpOT1ADzW31/3ClL6v/rOFKmrpGDjx/M+dHfz+6us1CNuVcCWx6MXEFdRu243F2orKvibCA/nwOfxcNDt7AT2xGnwW5KDWc+sQvHNuRh3i4bERPZTkXphIbCjQ0PG0hxkvrkKJV/PReFYrafQiZ8fSd23yLbKtdPaysG3xKyh66IczHt3FTp+movQLwmpHw8MYj/PoVIEuN0kI1U2TckXA5VJA3oHIPmSuu9CqSJ1t1uvswnoPvAJCSQ2VU6uslJ3hUxP15NptbVxe3s721KWbmgoifPoUT2FryLI8HDd9VHlRvf1bQ8Oph7d3s5ZgUqFoOQXh4Pbq6tJsJGRel4cFcSk/O2VG2NsLN0vzWZarp2delFpJeeoxV3lWWO3k7SSkjgTiIwkSYWG8n1hIe9ndDRnE93dul+/b43XuDjeo44OvapSVxf3URHA3d26x5LyeomI4LEpKTxHUxO18dpa3gMh9AVS5ZKa/v5quGZlI+RKDXV13rqqG22YXZmPQ19bibAwXbNvaPBGyZbZMOOTPJR+IxcTP8yD/I4GJJJ0lcRUUcE6p9PX5+GAJRcp7+YhYLkGd6zWs3jsGzOhpLu2Nn1gSTlkQ+bGPNT/IBex/5cHXPXl8lEfDgxiP49RU0NN3c+PXibNzXqB6b45zfuiP191ZdH5krrvvh0deuBPcLCearatjRJGYyOJbN48EqiSWpqb+ejs5HulT6uUvfX1PJ+SOaZM4Z9fWaiqUIavdANQj/Z4aAFHRuoSiZ8f+7RzJ88bE6PnrYmP14tB19TQSj94kNeXlsZz2+26a6NaaFZWpb8/21CZHIWglaz0elUwRFnR27Zx0Bo3jsTb1qanSqiq0u9laKgeBRwRwfvX0sLzAL1JXVnparBKSuJMJDiYclR5uT4rAvidOhx6EFdQEGC+IBup91rweYsVe+KYSvfqFyzY/BMrIiJ0uaeujuecXm3DnD9bUPgbK1K+rUHmazDdbIFcY0XVVA1bt/LaMuptyHjcgs//H/O4RH9VQ0KOBY15VkSX5MN8Ad0UVcCX4z82iK35aCoH3KOyMTkEuOw5C7qftyI2AoCr5kvnoz4cGBr7eYqKCgathIRw4aq+nn/YCRNICoOhP191ReoHDvDzqVN1TxmV67y9nQQREUGyrKnRyVERY1aWPhB0d+u6cGcn2woL06WWqiqdLJXOroKo1P4qoEhJRkFB7MeePdyWnq5b+WYz26mp4fauLlqIitDi4nRtXmnpVVUk/fnzeV1q1uF26/dGLSwr6aKiQi/oPWGC7s5pMvF4lQ9m61bet6lTSfzV1Rz8Ghv5fTkctLCdTp1AlUVbV6enQ1D3UvmmBwfr5J+Swu9CShL6kSP6rEqleAB4/apfapG77F82LHvGgu3zcpC9LQ9b77KiIVPriSNQ9W2jo4FxL7Ka0fjbtJ4gN7nOhup38rF+3sqeATn8ydU4mpiNsK9QfmlvB8LybYguyYdpPhdGXS9yYdXvMxtG3WHB+7da0dkJXP2CBY2Lr0fAt26iS6hvUY3zwEd9ODD82L/EUPUzR42ipqx8x1NTe2uX/WG4pA7ohNbURDICdOtXZTRUMoemkbyUl4taJFUFLVwu3UXR5SI5dnXpUaAqmtRkosWsyuX5+5NkAO6jtHBVbFvpzGrht7SU2wGd1CMj9cXjpiZa6CpnekoKByOA11JXpwf4+Lp/RkfrWSpVwrSEBN3bSBWtDg3l4KIiSTMy2FZFBUmutlYP6IqN5XfX2qoHLil9XUV/Op28frNZt9LVwKry7XR2ktSjn1mNupRsVE/XejJsJpfakFJLeSU6WveS2bKF179k3a+x+NNVOGDJxR7LA3C5dDfTceN4b+12nmv6dL2ualMTZyN1dWwzOZmzBUBPBd3Soi/eK6mv6wMb/L5uwZ6LczDjkzy8/Q0rKiZrGDMGyGq0YfIvLShZloPpn3z5EnsBxuLplxYqRYAKqlE5xVNTe6ee7Q99i2X4TtV9SV2F5gtBclY52seO5R/94EG6z3V28g+9dKnuAqmelaWuyFEda7fTIu7u1vO1qHznquKSir40m3VdODycXiPK93viRN0jRF3L3r16zhFloarIVLeb5y0sJAEFBgIXXsh76HaTeJub9dJ5atBRRKoseSl5zaNG6dG7qhh2aCiva8sWtp+Zyes5epTP9fV6AQ5Vpcrl0uvLqsXl7m49f4ySg5SVrpKLxcbys4YGDnbNzYAzORsXP2HBh9+2on6Chhk1Nix+xoLdv7T2ZJ7cu5f3EWBa3At25KHoxlykvJ+HsgkaSpK1nihhZfFPncr3ys//0CHGCqhMnGpAjYjgrE2V/AsN1WdJKv3yFoeGpItyMOedVdigMSvlZO8sc7fQIC/NQfprRlGNoWAQ+3kCVaZs3z56MURE8M+TnEyNd6jAjYF81ZWmLsSxpK5IIyiI53A4SFoqTe28ebR2VdCQIuaWFj6rSEr1525q0nVkNbNQ/tYOh64Lh4bqkklgIPfdvp3bJ0/WXfCU94vdzrD85maSpJJ7oqL4uqODg1FhIfuWkMAskmYzr//wYb3akyJ2laHSZCKhq1TDyuNFedUoUlckW1Skz6SOHOG2lhY+ezw8rqODpK6iXpW/v9onIID9UVJUSAj7olIUq4AelQdHRZGWT9LwzjetuPIZC3ZdmIPZWxn2H3iZhrY2PR88QC38ypct2HEvLeaiRA2X/NWC4LusCJ2vob6e+82YQWsd0O9zRQX7nJXFQb+1lftMmaJ/j+HherpjtWaxZw9lmenr87BxaS7mbM1D0AoNjUEaysv1xVnk5rKohsoDY+AYGMR+HsDjYVh1aSktJ39/WrIqadJQ8I0q9c09rlLZAvxTqqx+JhOtq4oKEuPEiSRklbBJeY4kJuq6u8ejeze0tZEgVVk4t5sEoHRk5WWiBiXlvwzo+dY9Hr3S0uefs/8ZGSQ1X3fGhgZaj52derKqoCASYUiILhmVlrL9OXN4PSpaVZG6WpxUOrQKBlLugnFx/GzUKA5G/v687qYmnrO8nOcaM4Z9PHRIt9I7OnTf7aNH+T44WC+Z19ysS10qFQPAfRRBRkXx+lR+mNpaPpT7Yns7261O0DDuwhws+GgVKm6j1auKare2st2kJGB6WT523GPF/2fvTaPjuq4r4fNqAKpQBVShABTmmSAJghPESaRIW/Aky3akKLYV2yvtXul2nKXlsZ1O2o6jSG4nduzu1Umn409fnLQ7iRM7ZuzESbvtjgfRlkiK4jyCmImxABRmoIAq1PT6x9bWeQABEhLBARTuWlgAqt5w333v7XvuOfvs01nRJFMjIra6Jrn89GGpHTglF8Pwo2/ZAjdKKiUy9+xXpdmzR3qr4DYpLARfvaT9lJR/7nekogLHp35Pbi72Gx2FMdLbiwzSt/3lk/L9Dx6WxMEm8bynSbY9i5J72wtFtv7Jk2KQs/4GFPZ6LW3dx77GWzKJIGl/PwCJetlbtixKxb5BW5yAZAV10wSoO534zGYDCPf14QWtqQENjUyZ2lokGxH4mNJPyzQWwzELC7F/LIa+M/GG9UdZXGJ2FvvabABUriroo6VAVkODBl3Jue/thSVommrFu1xqKXZ1AdTDYQD9/v1agGNiAtdIfZlEQgOFTie+HxpSfZesLPSXYExQt9lwnqkpTFKcMCYnlXteUIBrYFCWlZBME4A+NaXsJJb9oyxAIKAJZsxOHR4GYDLIyThGKiWyKXRE3v03T8ro+56S4D8+J0c/cVhOeppedS1VVWlewego+ur14p7a7Tg2XSo+H87X3i4y9f0jsv+Pn5TOLx+WnpomyT6N6kbTf3FYch5vetWFFAiork1PD8aG47DlB1+VoXLUaqW2UGHzEdkye0qCQXl95e6++tX7qkzeevD0DdCsEgE1NcoKIcitpFFX3UprZPLKUqAeCgHwmJn54ovq4qiuRkCNLy6BZXpaZQSYrejx4PPeXoCHy4X+Z2ZqcG16WpN/yCunVkxvL/ro92N76r1Yk45CIS1gTbpkfj6ArqUFgJRMArTq6zEONhsAm1TDVEorFNFfPjSEY+Tk4PxuN64pJ0ddRJOT2Jba46WlGugcHsZKgnEJ0hbtdvSPGaeDg5rsRdExTnBc7RQUaJFt+usnJ1X0jNWhnE6RjaEj8shfPildf3RYBjY2SfxfUS/0u+8/LJONTa+6caamMKFEoxi7rVu1GHdhocobT0zAfTIyApDf/rXflPyf/b2cO/Qp2XXqOZn/GxS3nv35KZn86O+8GhwfGwOgh0IqG8FVS02NTpxeL9x5K1l1LttYLu/wYUm9qUnsLxxZ05b+evD0Pm+UCJicVFDPydGXbiWNtEObbXlQJ7WQzI2BAYBJMinyox8ByOrqAEhkmdDKjUQ0MSmVWlgvlRZxLKYZnh4PQMTjUY12ctQTCa2A1NyMftD/bhhKZ0wkkHQ0Pg6Q8vnwOYOLfX0A9f5+AMehQ+oWsNnAahkawjWnUjhGMKgSDJTcLS1Fv3hNtLCZUDM9jWOJoJ/z8+qvn51dSAmdmVHNG2raWKWISS202VRThqDOKk/hMI5FtgsDvQw0Z2eL1Leeko4vHZa24ibpvyoykd0k4x88LJvGT0m4ukk8HhyDEwOLmzC4W1Ehr8oDdHbiOaE65fCwyPGKD8gvp/5WHvzJFyXx2adRgOMDT0r0zw7D4hbsRzoo3UNzcxhnBmSnpzHh7d27cgNl2dbUJOZ3DkvqvZAgaHjhOXXn3MdtHdjXYJuZEfnpT/FSVFUBUAoK8NLdKJPU2qiWaLMBTAjqVvcLQd0w4BMeHgZA9Pbib/KknU6c3+VSUKe1PT2NcwWDsLySSYAQsyWpo+L3axGJ8XG89PS1Wyv1nDqlkxmZHAT16Wn1pzPoSPdLZiYmhJYWAElVFeIAFMqiWuHoqMYRiooWUgxHRwHgxcXou88HUHe5FvLyBwfx43Rq2v34uLpaSkvxWW+vHsfnU0t1eFj11GdmlM7IAhz051NSl/EJJodxUiLo+/1YTQ3U/I4MDoqEujR7NXGwSaZLmiQjjUlrZgaf19UpRTEex3iXl6MvV6/i+hwO3IMrV7DfpiwRcTokaWSJ/U/+q8jX/lRGvv5P4nu86dWJjvGC2Vn0dW4O41xerrz8ujowhlZDfndqSuSF2SYpfegpeeCfviix335aXPc5qIusA/uaa+PjIs8/D+uvuloBqLz85nRGNitXnfssZ6mLaPo5CyAkEtgmN1cLRnB7Fj6gFjj5zmR7hEKqc8Kapvn5CHzSrxuLAZSoB06L+vhxgMzWrQA4JhwZBoCmrQ3blZfrtXk8ON6lS5icnE7QGCmF63Cgn83Nqu3udsMf7vHgmoaG0PeyMu1zIKDBWLqc6GJg9amCAhyvpwfjkZmJY5AFwzJ5bjfGkW4Uu11dHUyEys/H8bg9xzscVncGg9IUIxPBPiUlOCddQPG4VscqKFBFyGgUE+i2bbh2lverq8N5R0e1jivvyZkzuP7GySNy4C+elJ9/6vtS2XVENn7ni5IWLXHY369yx1SkTCYxwbLeLWMl1dU3Z3HdrJkmJpwzZ1Amb9vR58T8vafF9f8/J/Lo/c+mWQf2NdSGhwHqqRSsc4cDoE73xkoaaY3kqhsGXujFoB6L4cXr7gYYz8you4RJNakUgMHh0HT+6WkN3GVkAEBzcwEoFMwicM3Po/9btqAPk5MqUUsBsLw8fH7unEojkMPO/nd0ADgcDi23Rg774CAszIkJgNOBA+qyycgAaLe343sRgExFherBh0I47rZtqodTUABLnBmvVG68ehVWKItlTE4C6GMxXIfXqywYl0u59LEYrOVY7JX6oa9QP01TE3gI7A4H+s8MVWrHWPVxCIpFRZiARkbwMz6uRb7Jzpma0izXggKdYLu7cY+qqrB9ezvuH9Upe3txzU4nnkX/t0/Jix8/LKUlIrV//pxMffJpyf6rP5W5b/y99H6s6VVQn53FOR0OTZiLRNCXHTtwnbfaZmZQc2BgQKSu/4gc+saTYvvHV9wvb3ljsGnWgX2NtL4+kV/8QgW06JMtLl5aQ32pRh+wiDJHyFMnqGdmagWinh74RCMRgGlVlfrXEwnNGDQMbMMAWzqtnGr6bXt7lcZnGNimshLXwn0ZHKW0QG6u+sSzs2GpU16AbogrV3BOKy+aWYzt7cqKYWV7cvAzMwHEnGxYQCIYxDbUqAkEAEBMriKoM+M1FsO1XbmC/lClsLNT9cpLSnBN9JtTwyUjA9cdCmngm/IMpomxKypSyVzqs4yOaqlAJik5nVpVinpAHo9my87MqB47dWN4DBH0e8cObDs6ipUJJzjGLFjm78oVnMfn03wJ8wO/I3tnj0jwE0/K8J8elpndTdJd3ST1zz4pnj0fkLZScOX5LNXVaQYy5Y9vlhW9kue7pQW020QCk9Se0VMLZX2bmgDqp07d18C+zopZA62jQ+TYMQBBXZ0mpBBYV9KWA/XWVnxXV4eXdm5O1Rvb2rB9WZmmj8/Pq9XK4xDQmRVaVKSFmEMh9dPm5GhmK33k09PK3KCSJBOHCLzBIECZVrrdjn0uX8ZvKiZSZTIaBbAODeFYBw5oiTxmoV64gL7RLcFJbW4On8/PwyVQXKy88fx8jLvdrkWxL19+RfnwlZqkBBeqN5IhRJmA7Gz0lX53KjdmZmJcWSyEksoFBaoJT7YNZQesOi+MMVBvnRYy3TT099MiZgUqpxPXXlurGaqBAO7f7CzGMRZT676/Xye4dBr3sqoKz4/tv35VIvV7ZHRb06tWufHzI+K+ckqOP/Q7rxYEr61VN19tLc6/Ujficm1mBq66/n6M8aFDWjf2fmrrdMf7oNFPePKkgg+LCAeDK/dD0v3CrMXFoM7iy0z1P38egOrxoJyZ2419qL7IQCmTjkZGNOBYXKx63wR109RqP3Y7mDuBgAYGWRCDdESHA30YGwO4VlSoz5zJUa2tAPHCQmUBkR/e0YFjbtgASz2VwvdMZae0gMOBcayqwvmnp2FlUzzM5cLxDENLtDkcmjB19iyOk5sL33AopJrxgYBKCMfjGHfeu/l5tZYpI8ziJKap2iqMXTCvYGwMqx9WpyJY0xXDyYCBa7q2vF4Aam6uxgLIRqqvx3d0t3FFMjioxUM8noVxgtxcVYN84AGcl/o1XEGQqcO+kD1TUoJJyeWCC44GwOttqRTu9+nTOMemTaCtL1Xe8X5o63THNd5MEw/rhQsAic2btTQcU/BXehxaw0tZ6gT12Vm4FM6cwQvOtHqCMX3spDRSxGtkRAW5SkvVwu3rA1gwgWdiQl9mctip3073REEBAOOll3D8hga9VvrTr11DP+12pRwyTZ2JU243CngUFyujhNWCGCT1eLBqyM/HNdK6DQYx1lYZ3kAA4MWVwvAwxmlyEkBaXY3g7Pg4ACUQUDeNaap8gZXKaNVxp+SCYWC1U1mJ7Q0DYxSJKOWQ4M96rOGw6usXFODapqZUqIurGdZeHR/XZKsdO3BNXFEVFuI629uxrdeLz1tacC3ky7Mi1r596BPdbHQJGQbuPZPLTBOAm52NawkEMHHyGl/v+xGJwOhhacU3vUmZUm/0tg7s92BLpxH8aWkBwDLrj37k1wrqtO5Y3ae1FefYtAngEolgEqEmTGMjQG96Wl0tySRAx+vF9lY3Ql6eCk8ND+NFo0xwbi4A0Bp0jUQADjMzOJ/fr0HS8+fx2QMP4Fzk2KdSGA9SDsmKYYGO3l78XVaGDFJmf9L10tyM64tGVVTM4cC1DQzg+PX1ACy6ThwO9J+gPjWF85w7h/5zsn35Zezj9+vEGY8rO4Wul5kZgHo8rqwfZufSL15VhYkpHsf3DHyydqvNpmXjQiGMPwuQU8edVEbqx7hcGtQmi2rbNmw3OKgrH9NUZlFODo43PIxz0B3E1UxdHcaNbiuWxyPDhs+I240JOpnEGJWX47lbXPbutTQGd8+cwTXU1cEIuVUf/f3U1oH9HmvJJBKPurvxktN6zMnBi7oSUKd3jQlI9EvPzwMcraAeDiMoy4Se3bsBqNPTmkjEhByPR1PpZ2fRl8JCfOf1wiLu7MTLzaLJExMAl4YGdU2wuAar9fj92PfqVZ0AqOzocGAfBuxoPVuDiAMD2HbvXhUMS6fRf1IZKRlbXg6LOJlUzrnHA40YrxfbR6MajMzOxrHCYdyTq1dxrp07YR23tOA8XG0kEurL93hgrVPqNhxeGCS1ummqquB+EVG3DMeZx83MRP8pEuZ06rVYXS9ut05IdD9xvBmwpuY7S9ExkYy1YLu7lYKYlaUCZXv3YpxaWxX0s7K03CIlEGZm0IfNmzW+QBXIV90krzHd3zRx3LNnsXJzu0UefhjXtBqc9/uprQP7PdR+7/cArMPDsB43bNDknZsVx2AjqFuLGTscCy11UhovXcJSlkvlujrsG4kAtCnKVVmpBSpYnJpl70pLNROxq0tlZu12rU7EyWl+XjVUMjKwv9sNsOztBcDU16uVTlpfS4tSK+kGGhnRYF9enshDD6nrgHo3pEmGwzjPhg3oWyyGa4lE0IeGBhXKIihzYqKbpqMDkwOLfXR1qRQvqZtMdsrIwP2iEub4OCYBCpzF46pB73ZjdUTmTCKh4BiL6XH9foD6wAC+o+uKE9HkJM5Nxg113630ypoarKyGh7XvLhfGMpHQrOGBgYVFTGw23JetW9GfCxfU305tnfFxTAQjIyrXzFq3rAVQVLQIgPfsWUg9tKT/L27xOCazs2fRx8pK7O713kHXyxrSnVkPnt4jjdojf/7ncEOUluLF5nJ6JW0xqNM3HY9rsJG65i++CKDyenE+skboFuCyvbYWxx0aAiCQhsfyeqw21NWlwdNoVDMKKXfAuqakPJJvfv48QKGqCr5qEWVI9Pfj2LSInU6lB4ZC6j7ZuVPjCCL4e3gYAES1yZoapXL292M7Bu/oOmGQ1evFtixT19OD8/l88MF3d2vGKF1dBBe6QFgEm75mipMxiYvBx/p6DajGYpqgxVKDNhss5bw8nJcsIL9fi10zCEtQd7n0HsZiOD7ZVKOjuC6uRCIRjUGQ4slsXmrhP/CAljns7FRJBJYjJMOGRUg2b8a1zc1hvOrqdKJb3NI/OyKp9z4p07/2lOR957nr+OV0YV26hMnV5cLqihnPd7RZJ57FE9Edok6uB0/XUBsdFfnhD/E3tUtYhScnZ2XHIKiT8ieyENQTCViaoRB8wpEIrJ7t25UiyDY8rBbe/LzqhjMYV1KiCTjd3bCkXC6A+tgYXvaKCoC/laY3P4/rKSxEv86cwXkbGgAApomXJ/gHYgAAIABJREFUNZ1Gn0dGAEb5+VqDtLdXMzsfekh55yIqk2CtU1pZqZMIi4L4fJgMaKFyQmCgM51WzZSeHpyP2uudnZoNS2uaK4ysLBXwSiQwLnS1MEhKhpLPh4mF9VdJS+S+tLJra/E/J+biYhVlowyu06mKkJQ3GB3VXIOaGvRxcFDZMNYiHSwkQvca9XkqKzFOLJTR14djcvXICXRyEuej/z2RwHVzsvZ6ZUlrd/qfj0jXd05JbN9T8uDXri+ewWfv/HmMUWkpVrSMedzx9goH3nz/k9Lzrqek8kf3bhWndWC/y+3ZZ0W+8AX9f8sW/P7c50S+9KWb7794wWUt12YF9fJyuF16etRHXFOjFDVWvyFoVlcrb5nfk5+ek4MX+to1WPI5OQCcUAj9qanBtgT10VGlAHJCOHsWx9y9WwNpBKzmZq2nSXdIOIzzJRLo2759OnER0BMJgMDwsGZE5uWpImM0CrBpaFiolsjJkElHkQi+o2a6369JW8x6jcUAeEzQ8vsVtBmUpHCZiJbvIwd8yxZ8RhcG5YipKOn3Y3UVCuF67HaMMZky09NaxYkFqDlW4+M4T0kJJlhrVi8nEmroMKhKF5bLpWJslZX4fmgIPw6Hrh6ZxMWArd8PS50TUlUVnrlX/emvuF1S30Y909iPjkjhJ5+U6bd9Tg6cXFg8I/UmaMtQgZNFW6qrlUl0N1o8LnLcaJKMnU/JgW9+UaY+9bT47kFQF1l3xdwzjSnu166pL/lmzXrr+MKT1phM4sUgA6OlRbMFa2vx0k1O6lI8lQIgBAL4jkFJ+lrpT3c68Tk1xnNzYVH39eE4Gzag/yIAkLEx9I2JNv39GiTduVMpmA4HrNarV3FO0hyZ2TkwgH7s26e881RqYYm+Cxe0T2VlADrqvVMWgAWUKRWcSuF/EYxdLAYg7OhQSiIZKlRwZGIO/el0a1Cr3urOoAwBvw8GAZgzM+oXFwHI06VSXIzx7ujAZ6RL2mwK6hT58vm0gHUkgvF2OnEPCwrUqmdAmQVK6MunCiRrwxYVgQbp8QC0w2Fs43TqpMaJloU8qFU0O6vCcIWF1ycdjRw+IjkfQfWmrUefkyu/9DnZ9ZMvv5oZaj5/RORXkbl6wo0kp6IitdLvuOvllZZKIVZz7JhI/qUj8t7DT0rqN54Sz99c7zq63W3dFbOGGpe0IirydLO2ElCfndUqPU4nQK24GKBHS4uWPZNTCgsBpPSX0hqnBX7tmgqBFRUB1Hp6AJKbNqlFRz51RoZqhre1Yf9gEFYzNV0MA+Db1aVJN+S4t7QAsIqK4KbKylLmCV0w3d3oE3XPCwu10hCLaOzahX2pk84gLLNK6SePRDTbkjo6VhkEWukMVLrd2Nc0cW3k/VOPhsqRdjvAOhBQed3MTOw7NaXn2bAB/blyBftnZ+OHgVhKALB6Esv8jY3hWigZQH0e6uLTcKD7aWIC95x6NHT7bNyoLhBK69LNlJmJz0ZHcQ3JJPZhweziYoB6bu7CpKNIBFWuuqebZMvep+TAv35ROj/0tDQ2JMX2UQBjIiEys7NJhr9wWKL/fErmHmt6tZoVVSrvdDNNrHh/8Qtcd+PkEXnHPz0p9n9+Bczfc+/qzqwD+11udBOk0yKf//zNQX3xAouFgJmOT3/s4KAKWxUVaVZneTm+GxvTZJO5OU1O6erSbMuCAuwbDGrFm85OvLTl5XjZurvx0jc0AGQYaJ2bUz2bzEy4SMJhvPgMyPJlbW9HnwhS0SjAo6tLdV62b9fxsgYgWVDDZsPERZ4/C2LX1anrxcoc4WqCY+h0aqCUujCkLJqmWuBMePL5NBM2HgeosygGJ1oRpV1WVOjYkHXD+q/JJMBw0yaVNGZtV4I6A6WGgftEETHDUDdTbi4mD9JY6bOnnANzEihNbBg4B2mJhYUYk9lZbDczo9fqdKruzMgIPm9oUFcUi6xYWSqJBNxqV668spI6f0QaTzwn4d98Wmq+95wYHzks5sNNMjeL8168KDLubZKCf9skb3/FSud43slmmnh+fv5zrRT2+OMiW35wSozvrg3dmXVXzF1s8TheFpGFqfHLtcVWuoj6l+lOIXsgHgeoMlBJFcj+frz0TJChqp/drsE+ZovyRY1EAFzd3Vq4em4OYMwgIAs3s+pPdraW5jt3DmDR0KCJMPRnNzcDRKyZltR+z8nB+5Kfr3rnc3MAxrExTAgsIl1QoGyU/n5c344dqvNNZorHo5MewTMjA9fe14fji6g0LlcV9KvT/cHV0dgYzkfqIv31tPCpo857ReBjqUCHQ1cZvb1qkWdl4cfpRP+5ErDWXKWwGGUe8vO1Riq58PT/04UzMYG/HQ4cp6REdXgmJjTmQg57IIDrICtqfBz7MV8gJwdjXFys8QQRTJDnz2tQOPf8EXn8W09K7K8Pi/8JMErMJ5+UyP88LG2lTdLSgr42NODYrIh1Jxtpmy+8AJegywWdod27b13LZrXauivmHm8skSYC0L2ZtsVSoJ5Oa7GMVAo+wM5OAMy2baoeSGoiqwOxKk06rW4PSrK6XBokdTgANB0dWJpnZ8PiDocBaPn5APWsLE2VT6dhNWdl4RrPn8dx9+zB/iIawLtyRXnvXDkwO3TDBrxUtJLJ4hAB8JPF4fOhHz6f8r8LCuB6cbsV5FwugFdvL7ZjNSK7HWNGC18EYMUX2eNRwS+Cqs2Gn/5+WL8iC0HdblddHrpqOFkyiYe1RDdswPddrxS/oFyA14vjTkxonIRFuCnINTyMcxQX43O61ObmNNCblYX/KTNgVZdkgHNmRimftNSZEMcYBZOl8vMxsVPGoKJCqagiGO/Tp/GMMNs0HhfZmYDKov+RJtR93d0ksa8dlrHvn5LmpiYJBNSXzsIpd7LNzEDK4vx53IO9e0UOHlw4Wa2ltm6x34XGBBkyTW5mmSwH6tRVj8VEfvxjLJGLixFgHB3FS1VZCaDt6tJycKQU+nwLXRLZ2SoTSz3y9na8pLm58HcyI7GoCBxs+l2ZgOP34zOKbWVm4oXlxOVwqAY6fbeJhLpBHA5IAtDXPDenfl5mXA4Pa1UlxiRoNW/aBKsvkUAf4nGcI5XSGptut4L65csLk65IL7XZVA4hldIgpQiO2dWlqwfGNWjd82+Csder8sVcKRQULCy6QYs8JwfnIpvIWjCEQXUeh66u7GwttMFiG3QVUTxscWm9qirsx0kgncY9YO5ETg7Gvb9fGT4lJTgf3XTl5aosOT8PV0p3t05EQ0M456FDGlPhJM1M43QazxElHu606yUWA0PrxAn8XV8v8pa3rDx35E63dYv9Hm2sIs8iGTda4i0F6Pyc1mtvL4I7iQQSSTZv1peruhovaVsbXlCfDy8c09pHRvSFDwTQn7w8AEM4DPBl/cmiIhwnFsNkUVeHF3poSGuTctIIhWAFM7mF12G3w/pnkWmnE+fv6QFQ5edDvItuBkrT2u0YMzI0TBOAUliIa+nsBODt3w+rmK4EEVWtHBjA2NONEY/DsqTejdut7ikGRcluyctTiYPxcRXNomXJVRPdS/G4Zn/SPUQtm8xMrIZ8Pk0WskoY5OTgM0o6cLJhQe7+foyJz6eFOqhnz3tJ7fZIRJOU3G6lm5aX45rJrmEmrwjO4/Wqr394GMeqqcEEyRVCUZFKO7S1waUWjaI/TieewUBA5JFHcE9SKdyHmRktref3YyXHSZYCYnei0f9/9Cgm14oKkbe+9f6R+l0H9jvYZmfxAjmdS6RXL2o3AnUKQp07p77At78dL2RXF7arrYVFdvkyXk6CE+mBrHLEsmulpWrZshpQIgHLzu/H/6kUAL22Fn9bwZLMkY4O7F9WBjcN/c2mqQqI3/62yEc+AkuypwfbNDRgEiAQU4AsndZiGJEI/g8ENNBnTVwhE4a8e+qXDA4qw6egAPfg/Hmcw1oT1WZT3juZRBQbEwHQUTSL18v4BjVt6KIgsDMjk5rmlKkdGcH5OUGQyTM+rsliGRk4f1mZyiPzOrgyIg+erBpmEBPUyYnPy1M9diZBiWjhbYdD+xAKYczHx/H55s2qJU+N+KwsPFfnzin7qaJCGVUbN8LyZbYvSwyS7VNfr+J2d9L1QgGxF1/EvczLA7GFRbrvl3bLwG4YRrmI/I2IFIqIKSJfN03zv9/qce+3xheNXOEbaVDfCNQTCQDqmTN48YqLIYQUi+GBJYfZ5cLSeGQEL7TLpdQ3ZkByKV9Sou6E/n6wagxDg2qXL6O/W7fi5eUERcvUmi06NYX9KE7FZfqlSxiD7GyRv/orkbe9DcfweuVVWhvdP7OzCnKskUlQDwa1spJhIEDKajzj45oNypR8Juvk5enqpbkZ2zH1PiMDAFRQoBMIM165OqJvXkSD3KmUupiYwcqScxkZGmwUwX3iaoKSxyzmXViIbeiXZok7MpJCIQ2qksViswEomdDkdqPP8TgmPII1y+MRjMlnT6XUBcTye4aBZ6i/H+fLzsZ9YWJSaanGQ44dw3NoGFqE5exZ9L+pCZM6q0ExdsIiGAcPqsQApQtud2PR76NHYXx4PCLvehfYVnfi/He6rYbFnhSR3zJN86xhGNkicsYwjJ+Yptm8Cse+LxorDPEFWu5BWorKaP1udhYvT0+P+iZ37tS6ldRgycgAGI+MKNVxakqpb7S+gkH8kHPd1YWH3u3Gizk7CzqhywUADQYBvNTYpn81FlMLv7ERIMrVwNgYgDSRUL+tCPpbXo7+FxSo4iOZJL29yuxg4g5dCN3dqkRJPXUGDTmGzB41TS0Fd+qUFmj2eNQPnpuL8WBcgqJrzE7t7VUfuMOhipmUBI5EVHmRao7UdXG7cX6PR2UVpqfRLxa1YDEKunSyszGBZmbi3LGYriz8fmw3OLiQ6UPqqrUqVDCIMSfllGqf09OqTeP1YmJJJOB66+/HGLAIdmYmwLykRHMR2tuxfWEhrrmrC24tvx+ul4ICvXejo7DSo1FY8aRI3inXC/MEXnoJxgV9/vv23b/FOERWAdhN0xwUkcFX/p4xDOOqiJSKyDqwi7IgsrJuXPVoOSud3/X2Api4pC8uhqVKASa+wKYJN8PUFCwphwOWIJNoyEsuLtZsRio/9vbiOFu3whq8dg3/s0oOfcKk3ZG2196O8zz4oLJAnE7lvYuIfPe7It/4hl7Tpz+N35/9rMjHP47jUlJgcBDniEZV4qC0FOeKRhE7YNYqJWLpfqFqItPmCwrw+c9+ptx9ul3IAKL1S8156r1QQZLJPaSUMqmKxR48HoAcXS8sfkFAZH9mZ1XnncldnEyoKhkI4PoYKBZB/7KycA9mZ3H8+XlcR1YW9otE8DknjPx89fFz4k4mdfKkXk0wiL4x94H6Orm5+syyUMrJkyogtn07xuD0aYxdbS181IahE4dVEfPNb9aatHfC9UKf/pkzWgN1506sFsjOup/bqrJiDMOoEpEXRGSraZrTi777qIh8VESkoqJiVw8Fsu/jNjGhRSZuVPXoZv70M2cAkLSuKNAVDqtGuc+HbTs78WKVl2N/+pftdmWD0IK02fDw02VD2dzOTgBaXh6CW5TPZRo+LZ1QCJMBRbX4strtWn4uM1N1zYuKAGQbNwIMXC4Vq8rIUNeC242/WX+zoAD/2+04T1WVSgHQ/cLGz8nBnp0FL5mxAPaPoMZScWQoORy4TvK2GWi23iNa6tGogh+D0RRLKyhQWV2W3aMfvLhYeeNkpDidmhXMak5Op2bJer04/vi45gF4PDgO/fWZmTphWPMIqJDISY+USrp5WluVgVNTg++ysjApORyw0pmUtHEjJtn+fjBJEgmIsW3ZoglRExNaj7a6Gqs9JjLdbtcL8x2am2Glz8yAYdXUpFIXa7ndcVaMYRheEfmeiHx6MaiLiJim+XUR+boI6I6rdd57sVn1qW9W9ehGoD44CCXG2Vk8nAxuVlXpkpsWJkvbUauaaeEMTrH4QnGxpmiPjwPUZ2YwEdTU4IUcG8PLu2sXXlYmtXAyoLQAXT0sosFg4unTOEZengJMIKD+buv18XhdXUr3a2/X6vVk2eTmYvmcna06J3QJpVIaLJ2fx7UWFGjWI6mdpqnSwiwmwomX94jFN+jTJovImn1KTRmCXyKhriMCt8ul5QQJqj6fJoyR8ZNK4Ti1tdjHKvNLXZeMDFi+tPatwV4qLpIl5HRqTVrSC1kmjzEPcv9bWjCJT07ieJQH8Hjw/dCQFq8uLcWkn04jYNrcjG3f/W6ce3ZW3XmdnTjeoUPKXLrdrhcKznV3w4/OZ/Oxx/A+vNHaqgC7YRhOAaj/nWma/7gax1yrzTSVreHzadbhUtuxLX7Y43G8PB0deOkPHcJLPT8Pi25gAPuXlGD7yUm8hPPzKu41OKj0NBYzDgbVNxwKAdQTCVhhBQXw30ciAPht21RAisE5ujra2nS76mp9caem0O9oVKsq5eSotSmCfnzyk1psY2JCqzfFYhqora7GOWZn0b9t21S7hUWSOXash8qUfr8fFNDBQYAJLXVS9ahLTm1zjwfXMDGhTB/yxwnqXPEkk5oYxfquExP4nODKknfz85p0xGLS0agWnzYMfMaCKkNDao2TdplKLZQroK4LVxWs/ZqdrWwrUmip6MhGHR6PB6tAJnn5/fDpMzEqkcB9nJ/Xerv5+Rizl17CuNbUoMYoC23PzODeTU3hGdy1Syft2+l6IaCHwwjodnfj3jz2mHLn34jtll0xhmEYIvLXIjJumuanV7LP/ZqgxBqVfFmWq3p0Iyt9eFj10mtqYCX196t1ziVxSYkyLKiBnpen6oLZ2RpsCwY10Gmz4eEngDJD9cIFvCD19bDcmPFIgDMMXBdpj1u3YjKgamF/P6w4ux2TT1aWJsmMjeF3VpYGDu12rTVaVoZJrL8foFNUpP7wXbsAXKyIRA0U6ruIqLIhXRfHjyuzhascBhNFMD6kTbL60diY1iPlcbm6IOOEQVrSGSmSZRgaqKQS5Oys1h6lK4rp+tGosqPKytTNRfVGsmJIEWStUQL7yAjO63Jhf9bDZTyB+vemiXHnb64Wzp7Va2VpQyo3Dg+rdEJdHQCfFbJOnMCxDxzAM0K3Tk8P3DkOB1x3paVKc71drhcWb5maQr+am3Guhx7CM3O3lCCXbatUfelOumIeEpF/IyKXDMM4/8pnv2ua5g9X4dhrppkmQGdu7sZVj5YD9Xgc4NrejpfsTW/Cca5dw3d5eVrijUwKCkOJYBLp7lb9cFrLRUVaJk0EwNzainM0NgI8zpzBd7t2ARxGR1WSlm1iAkv3jAw8nxTHstnANujrw/lIfSN7gzKydHMwKYlUx8JCBOWYoJSVpctoarXH47h2ArKVouh0qk+d2YzxOIDO7cb2ublKNeRKwefDcah+Sc435QBENEjscgF8qatDl0k8jr+p88PapPPz6n/3+7VyElddHg/caZTGpTXOYtVcyYyO6kqBANnbq7LGeXm4B4WFC/VgyA7yeFR2ubAQlvalS3p/q6t1Mhgf14AzE9BycnC88+dhCGRni7zjHWrVR6P4nLRbauRzHG+H64WAPjeHCercOXy2ezdAnXIZ91x7DWUAV6OtBivmqIi8QRc8aCx2HI1qivfithyVkRPCyy/jxaqthRUtoktw6qAwMWV8XKsecene1qZMDIJ6ebmKKSUScL309ADoGhthnbW2ApweeAAAMjGhlj37PDgI36nPh+1oBc7P4+WamlKeNrNYrRYrudMZGVrFh2qHv/gFjlNaqm6Obds0KWZmBiA6M6MWoMOhaoWRiBYU6e9X94bTqayRQAATTCymLgvDQL+HhpQWSVCn6mF2Nj7nhEJaJiVr/X7cbxFcA+mkpBGyj/PzKiUQCADUWdQknca4U1GSlY64AnG7dQxZfLqqSt0b5eWaBDQ1pcfwehcmM7W2YlU0MYFjbNiAcRofx5iL4B5u2KD3cXAQq5+hIUwCe/cuTNZqbsZYsQiGVTp6tUWz+LzF4zgvY0/19cjjWGlN4LvWmppk9P87LNm//KQYTz0lGf/z9mq5r2ee3mKj8h2tOfpzrW05Kz2RgJXe1oYX+C1vAVAwCDQ/D2CKRAAItBxJV+Pvvj68iPn5eJHz8rTQAWmDp08DGIqLAZzXruGHNU9FFEyoTGiaAPShIVjQ27bptYyNwZJLJgHC9MFTX4VMFZZuY8CVnPiODkw0Dges/EgEfTlwAMBCjfqxMeWQE6jJBpmaAsC0tWlCTm6uarQwvkCxNVrpIhgLrrDYSNOkEBWTnkwT42+tNsVEJCZEJRLKcPH51LXBdH/6wktKNHBKXRQmGRHsuRJgsRVm03o8uH8E7LIy5f+zGhSlHUwTf2dm4t7392Mbj0dlk8npp2RwSYmKt7W1wZ8eiwG4WV4vkYDVHw7jGXvwQaWH3g7XC5lh8TjeiePHMUbl5SLvex/G9F5u6bT2u7OzSd666yk5+JUvXlcGcLXbOrDfQmP6PWtLLrUMXArU6Yt/+WW89LW1AFdad7TU6f9lPVCyG6jqSIszIwPbkFLI+pyk1J06BfCoqkIgsqUFk0FeHuiD8/PKzmASSzoNRsn0NKy4DRu0cMS1azoZbdumVlp2Nl66+XlNtqFI2MwM+lZRAfphX5/64SORhbIAs7NqpVOci1RNTiCDg8q9Z1q+36+6I0zICYfVN00AZnr+/LwWwrDZ1EpnyT1WfyI/nW6NYFB94MwGZjyCE4/NppmdlDpmjCGR0NqqZMzE49ie4macWCgjQB++YQDUuCqamlqoEc9AMNvx4+o3Z73asTH0gYU9qqtVNnlqChP2pUs41jvegXM5HBjzy5dxfOoSkR+/2q4X67iGw2C69PfjGt77XjzH93JgNJ3Ge3b8OGJJbrfIL/uOyPZLC8sArlvs91ijFnYqtXTVo+VcL7R4Wlqwz8MPqxtifh4WMkWhrLK6BA6Wg6NSIYtIezwAD4IKCzCcOYP9t2zBdhcv4kUpLsYyluyMnBy1OLmSiMcB/EVFqk/T0oLj5ucDFOJxLc1GFUFaiyJw/ViLMvyf/4N+5+Zqwk9jo8rX0pfO2EFmJoAuJ0f1Wa5exaRDNgqFvShgRiXIZFJFqchvJguHQU5mn1p98vE4tjEMzdqlMiI1z62WOrNsObkxCE6dFrpeaFV7vZosRLliWvUEWAZqHQ5MhvS9V1aqeibpni4X+padrc/J9DTuNf3ppLn29+M4ZWW4/8xK5Qrp+HE8WxUVmGg5JufO4fO8PKyqvF4dw9V0vVgBfXoagdGWFp1kGhvvUiHrFTT2/fJlrHZGR/HcvvOdIrumj4jjQxYfe1PTba2+tA7sr6NZqx4xYcjalrPSR0dhpU9NwUqihUrQ6exUKVi7HS8RXx5akVRDnJ3V4CPBg/VDRTBBXLqE4zQ24qU/fx5gUlGBpTUnEFatN02lrdlsCEbxu6kppTnW1sLtFI+rWyQcxr4ZGbBGp6Zwrrw8LPO7u/GwUyQrnUaf9u0DKDOIySxSSrhS252l7E6cwIqBwO/3a5ENEezLClAMYDOgaRXeEkG/vV7VWCeos/IUA70OB/rs9yuHnasma/KXx6PZsoYBMC0u1mAqJ5loFH1hMhULQFPAi/IPZPLQN19aqsVZuFoIBDB+XFWIYDVEfzpddAx2FhQo28VanamjA8/m3Bys8U2b0NdwGBNEMgkXGqtRUUyOfPpbbdTkicfRTwZG7XZMJPv337wQzd1oXPGx9sCJE3jG8vNFnngC42W3i8hXTy0E8dtcfWldj/01tptVPVoK1JNJgCyVGPfuhcUkohmMnZ2agEK3AxNvCDgzMwBsBlRLSlT9j1ZMOo3ztLUBEHfswHcXLuAFrqlBv1nxh1RBEVxXa6v6ug1DAZeqkVu2qC6J1TqdnVXgorW6YQMe8FOn0Ccr2NbUYMIhf5x6KazuQ7ooa20ODWE5znR6ViYqLYVVlEopN50WLCdMltpjwhCt3+xsHJt8dVrqtPppDZP7HY1qQWhrlSSW9BsZATBS6ZDaOCwmQsomVwyzszo5siTg9LRKBTMmQJ767Kzqwzsc2CYvTzV5uPynaBgnbQIw3S7MZ6B778IF/FD6mFz8K1cwSfj9eB4od7yaCUdWQE8mYVS8/DL+37oV7DBq5N9LzTQxDnNziGGcOoX7V1aGvJO6utvjKlrXY78N7WZVjxaDOjNQT5zAC1lVBSudk4EV1CcncTyCEtUXWWF+bEypbpStLS9X1wBfkLNn8TIGAgD1WEwtrk2bVOs8JwcvJxkh3d04fjAIZtb8PPo+MgKg8HjgU+VLmJurSTLUHWEBkexsTACJBAqAUH+dIP3AA+g7wZw6KkyEys/X4hQ2GwDmzBmMUTK5kANOa5+aLjk5qmJIWhyr/7AQBgOTGRn44TWR/UKGDFkvDEBTedF6f6m0ODio4lusB0vxMuqRs0AGmTPW+0D6KiV8OcHzWAwim6aWAqQkAvVmrl5VmQnub7fjeBs2YLzI2GJ86KWXMHYlJQiEer04F/MaGhq03ixjEqvherECOjOPjx3DOFRWgkhA1ct7pdE6T6fRz5Mn8b4lEhjfgweV93+32zqwr7DdqOrRUlZ6KgXrg4kTBw8uTG1mxl5nJ14kgg2FuTIzlV0SDi+sbVlUpJoeBJhoFFYDOeDbtgFESEnbvFlZFnl5eDgJpi0tAIeqKkwGlMsdHcXfwSAsPTJIKA8QDmvVJBZIrqoCGIVCeFEnJzVbMhgEeLBAB+V4qT9OtwrT6GMx+Hzb29VazsrCtVNbvL8fk5JhoF9cgSSTuH4Gc1kIw1pL1DBE/uEfRB59FONGnztL0OXna3k3fud0KksnPx9jSK2ZYBCul1gMP6yGxEpEXJFYVxwssr04iSo/X88/MoL9ONkEg9iXWa/j46r3Eo9rPdOcHIxVaSn6RTCOxQCkx4+j/zt3agykuVnF3w4d0hiPtSrUrbpeGE8yTV2JDQ3h3r///SrhfC80gjlty2ofAAAgAElEQVR/6E69eBH/NzTAZVlUdLd7urCtA/sK2o2qHi0F6rTSJyYwg+/Zs9APTyBub8dLy8xG6ng7HKpDzvqiDKRS04XH43L6xAlsX10NxgA56g4H/icfOjcXLyqTZSjW1NCA4w4MKLDH4wDqoiIN5Pl8GrjlRBEK4fiNjfh95QosGeq2ZGdjYtm8WRNoolFlhGRmqg+bNMFQCNdE3rth4HvyrP/X/xJ5z3sA6g6HVr6h5UuXB90WmZma6MOAYDot8k//hH5TmsDjwfG9XpWepb+fEr3M6GUAk+Jbfr/y7cmSYiUismESCWzPmAFXEnl52N/hULlclrrjhBYIqATxyAiusbcXPxRT83qxXTCIe1dYqNrnpqkJRxcu4DjveAeOOTGBz+bm8Lw0NqIvDBKvhuvFCuhTU6QAos+PPgpj5F7RRrda55yAjh+HEWS3Y9VJ99S92NaB/SbtRlWPFoN6KgVQu3IF2z/0EF4ua6Ol3tqqmaQsHExtEJaAozVmmjh3XZ360/ngDQ2pLOnmzVgV9PVh0nC58JKS/83amJSPJXVt926cv6tLC4LYbDohTE2plXvtGvZnCTfqwtTWYt+XXoJ/P5VSca29e/F3OKzBMbJWWIMzJ0cFt86dQ0yCWid2O8CntlaB7b/9N1wvJX2thZyZLEa3ASdOurpYPSgcxm8mCTFTl6DKknIUWWNsg1WG6PqhWiPzACgF3NuLc5AqSeokg7KkQjKbldpCzNLlhEZfekGBrnbGxzHOFCxLpTA5MlGMbjpScJNJ9OX4cTwfZWXwX2dkwIXT1YVtm5pwDMYnVsP1YnW5xGJYWTKwf+gQno97QQJgsXWeTuMeHj+O5z4zE+80XVb3clsH9hs0ghxTsmlNLEVlnJiAhTk+jpdm797rKZAiWpSCfmvK0hJ0QyHVQ6F2dkkJ/JwsJMyH7to1LAnJfCkogAXU04Njb9iAvpMqSEAdGcHEkpmJfopocWb6+jdt0hJr2dmaaJFKqdyu0wkrKzcXK4TTp9F/EYBUZSWW+dEoxiWRWJiRSs69NeHoxAlVqaQ/vaZG3S82G7JVRVStkkU1qH1vdb1wxcCVUDIp8r3vwVJne/ZZ/P7AB0R+7deuLyTCKkh5eTh2KLSwTiylBHJy0MdoFOM5PKx+cXLcaTVbqyfRD8/YAIO8dAlRjTKVgkuHqzEGrZ1OXVkVFmJ71rdlgk9nJ1xjc3NYQe7YgZXgiRMqr7trF/ZZLdeLFdBTKYD5yZM4/o4dAHWu+u5WswK59f+2NqV+ejzQmt+9+3oG3L3a1lkxy7Tlqh4tttLTafglL1/GC7p7N16ypZasVC/s78eLyvqROTkApb4+PEjf+hbcDIkEwPGBBzToRVC/fFmtchbC4ITh8wEIWf2GGuNzcwB9aqhv24aXmoWQZ2bQr40b1XWQna2uKPK343GMycaNOsGcO6c0w2AQxy4u1oLJiQSu0VrdJxBQS629HS4CFl+mxV9fD9DJyxP5yldEvvzl68f1iSdgaXIysEoVM4vUmthFTZVYTOQ//SeRv/s7zSxlwWmfD9tzYs/Oxj6slpSbq/uIqNBaOAx3FpOCSF1lwlQkohm6FCGjXgtdVNYKV6xVm0jgubl2De4A+us9HtwHvx8GQG6uiq+RvnrmDEDV5VJd8rY2UBxdLgA99ftXg/VCa59ujI4OgCS10R9+WKUY7lZbbJ3z58oVTIBjYxjLAwcwCd0LKwqRdVbMLbXlqh4tBvXJSVg81C/ft29pK10ED/rFiwBvnw9gRS31sTFN3Xe7AezvehdcL7t360NFJsGpU3jJWR7O6QQo8mGktjcDsQSsjg5Y64WFOD/T11nxprBQCz2IaDk3rhzoati8GdtOTuKYV64ozbCyEi+CiPLBqWNis2G/QEDZIFyaX7um/TBNvPg7dqA/FBT72MdEPvhBnGv/flRlmpjQVQAzYDMydJVCcCJgMbZAHr0IzkXXR04Oflh7lS4cxhzow6Y7hQwdtxv3JBTSxDUmLVHrhZNCURGOQ+kB9osrFEosZ2crI4eKnH19OpkUFeFe+/16TOrTkPXC2qRlZbA6IxEEK1lhiytL0j1vxfViBXTDwFi8+KIG9H/plxBzulttKetcBBOklYNeWCjyK7+iAeW12NaBfVFbruqRFdRNE9Yx3SD79wMol7NuKJXa24uXduNGrQgfDgMYJyfxglK3nDQz60ohGsXDxxdl5058f+oUHkgGzLxeBS2mnRN8KypUSIwc8mQSn3u96A99wWNjOCc12QMB0BjtdoBYRwd+YjF8t3EjVgpWa41+8qws1SR3uTRAeuqUarZQb6S8HK4luieiURxnamqhoiUDzPG4BkkJjCwmQp12qiuyBF1ODu7BBz+oqxFWQmJRaRb1oMqiz6eTZSqF/yld3NUF0GUFJfLjObHOz6vkg1XSmAwa01RXDi150iCbmwE8vFdOJybQsjIt0k16qAiO196+UOtl61Z81t6O/Q8c0JXlrbpemLBEbv/UFGQjenpUG72+/u4xXfgsiii7SUSNCnLQKyqwUt6w4d5h5bzetg7srzRyzhdXPVpspU9Nge40MgJXw4MP3lgqNJEAGPf3Kw2RWi79/Zo49K//KvKXf6n7NTbi9zPP4GdyUnXGa2oAsPG4ZguWlWHV4PcrTz4SQT+bm7XyksuF84kAsEiFpOAV3QasdM+Sa3V1ANzf/334olk+L5nUGAA1x1mQg5mQgQB+mBCUSmGia25WPRhqwpCRwTFiEJmBWpaNe/e71foWUX86ef1WDXn+UKSKbouJCZG3vU0DmMkkroksGiYQkVHDpCfDwKTASaevD5M2VzR0/VCdkmPAwG9WlhYN4fEpLez1aoCePHMmpTG3YdMmjU/QSqfVPTUF10tzM87zS7+E/hw7hustKdFgNjMmX6/rhVY+J4X5eTyjV67gmpqa4LdfbaXHlbTlrHPKLZw4oRz0jRsRFL2bq4nVbus+dsFNX6rq0WIrvbUVVrphwK9dW3vjF+Hpp+FPHBoC8DY24iU0TQDj+fMKrJs36wttnVBME5Ytg05bt+K8fIGZTVpcDHCgb5XFkDs68DCzzBx1UwYG1D9Lt4PLhe8nJ9VaZx1UjweWbUWFyDe/qQU/ampUYpd9J6OE9U79fqUaTkxgMurrQwDzne9UNcNduwBaPp+WvyM/ngwh+tF5DmaRcoVlmpoZOz2N7ebnVca3sBDHHx7G5+SL8/gul1bloVuCmuycGMiCmZ3FfRwcVNE20iKp2khGDys1cZzm5rCN16va6pmZyq66ehWWJKmuIpr4RRYR3TnWe0qKaHk5WC8DAziWzYZnltborbherIDOdvo0fkxTqYDLuSVvV1sM5tZmGFoBihz0bdsA6MwdWAtt3ce+wmaaS1c9sj4cMzMAo3AYltKDD948mh+Pi/zBHwCoamsBWhkZsL7f+15YVG43Pqe8wFJ96+hQzYw9e7BtOAxrI5mEJV1SounnDJixQnxWFoCLErYEy7w8gDSpgfR5j45iInE6AQJ1dQDJjg6lB4bDOO6WLegPMzWtpeCoA56To6DY2oprIYXzRz+C39fnQwJXTY36rsfH8ROJKEuILAvqvXB1YdXAZ+ENZrRSx8XtxoqGMscZGXCf0fVC/zeVLu121ZIh4JKamUgAcDkm6fRC0E4m0V+XS6UFuIpiglJGBvrN4LmIWtwvv4yYA33pTIbavFmrMXk8es/n5jC2p0/j2Hv3YnV27hz6FwzCXcgxYgD5tbpeKHpGuigTmo4fRx/q60Xe/GYton2nmtXVYm00upgs19KCCWzXLkw8d7qfd7K9oYHdNK+verTYSidbQwTAuhINiNlZkZ/+FH9v2aJ6LaYp8p//M8De74e1sFSlpWeewcN68SIexqwsBGYLCvDCX7qE7RoaVNmRfZqexvfDw1rrlJYdeeoVFZjA6ErJzUXfenoAWIGAuowmJkQ+/3mRv/gL7d9v/RZ+f+ITIp/5jE4mBDOPR4siswjE6dMAQrorCCZFRViyM318chKTy9SU/k6llCYYiajbJitLpR1YHGR8XItqsMpSTo7K1c7M4P/qavSLY+B0Km+cQM7KUywQzSpHQ0O4FiZtkcbIwh+c2GpqlGZJKWQGbelKsVISm5sB0Mw0pQRCeTkmWQI6YyiU9b14Eft6PEj0icUQtKT1zBXV62W9LAZ0hwPB3BdewHhTG501eO9EW846t8bErl1DoLi7G8/loUN4j+42xfJOtDesKyadVkoaLT7rUEQisJyGhzUVfqnKSIvbs8+KfOEL13/+zDNwyzQ1iTz/PEB9sdYMWyKBc/f24uWntdXSolbH9u0LlSXpTmJFI/q1AwGcp7VV9WJME9fFTNRYTCswVVVhMmIMYHRU0/uHhsAWuHxZ3TbRKL4jxZBBPHLTGSClJMKPfyzygx9cf82/93siH/84+sVVBamLiQSOQ+kBFsOgS4SuBWbpGoZa6eTJU42TmvCDg8r2SSQ0+EpLnRmwVhdPMgmw6OtDXzgGIuqCouREWZmCKc9hrUHr82GfZBL3ub0dfaLMQzqN/mzapCJgpG+y/urgIO734CDOt3s3JpzBQVy31Sp9Pa4XTgQEdKcTE8kvfqF6RA8/fPsEr5bqz2IwX3zedBrP+rFjGAevF+8POfprva3UFfOGBHYKMsXjKjhl9Wl3dMBKN01Y25s2vfYHlzUvTfPGYM/kGOt+L74IYCsrg4WRkQF/ene3KjZSoY+gFgrhJZ+b0zTyoiL839GhSUdMg6cuTTgMPzElAYqKAC69vdjX6wUoTk5irJqa8F0qpWwZ+q+pcuhy4bovXlTddLo1KirA6KDcrGniOGNjuIbhYS327HDgvKEQLGXquOTnq7AWA7UEWyYWcbtkEmPpdsOCzsnBOM7OYl/yxqnwyOxUWvo+n7qAWlowXuTY00qnngzdO0zhZ7Fnw1DhLuqfkyPf0oKVCAPELHSSlYV4Sl7ewsLkdjvGpK9PYyw7d+K4ly7hnFu2YF/GbKyul5VUODJN1UQX0TyIF1+Evz4rC64zrkRvd+PkyLbUu0hlSFZY4sS2Y8fdCd7errYO7Mu0paoecQgiEQQpKUj04IO3Jhm6mFWz3GdsU1OwhiIRLJ937MADffIkrOdAQBkjhqGBNiYIMaGJ6eeDg9iPCUuhEICAxa5bWrSI9AMPqPTs4CD6mJmJY/O4+fkif/qnIk89pcFRgjrZHhQvO3kSrh0CXkEBQK+kRF0QzAUYHsa25L3TPXXtGl5SWu5ZWbDSyWsn8Hd3Y5xcLtwvFuYYH9frra/H5NDfr/RCBoyZkep2a1m9vDx1n5GvPz2NftHy48TAgh51ddiX9D+HQ/V1mMAkgn2amxfmEbBeaTqNiWHLFhU1Iz2STKOWFkyYbjee0dFRvc/792vyz2t1vSwF6MkkVo9nz2LfPXtwzttt/a7EOhdRKvGJE1ql6+BB3O+1ykG/UVsPni7RFlc9omVpmvA/nzuH7+iXvNXl5TPPrHzbUAjWRjKJl4fViY4eVWrlrl0L/em0Ui5fVoucFl5HBwCpqAig2tWlboL5efi802nss3EjrpvywV4vXhLWKK2vV8v1Ix9RJUJa1S4XwMfhgEvh7FmVwPV6MSlUVanLy27Hsf7jf4QFODSkBaBzcwFera2Y4Jh05PPBJUHgzMzEymF4eKGyY04Oth8awn0tL8f19fWprAHFqLKy1OLmSoN8+2RSU/i5GiAwcpXEQHFBAa6PAWSuYngsa5Wia9cwiTFAOzWltFDDwDUyk5RiYW437sf4OOixlNndvBnjND+vhgBdQ6/F9bIUoBsGVq3kwm/bBpbNStyRr7fdCMwXG0Rzc3DxnTyJ/lVViTz++L2lDHk32xsG2BdXPaLFMTeHh4Nlv/bvXzqg+XraYjeLyPVg/+yzIh/6EMDQ4UCAp7QUL/LRowDamhq8tLRyn30WAc1jxwAULEZMi/DyZbzslZUADwaP8vMBCqGQLvWDQQBoX5/qqvf06KRQXa1MFSoTulxKM6S1Tk59e7sWls7PR98rKpTOJ6J6K299K87FCYfZm21tWqHH5cL1VVaq5ojNBrcDLe68PK1gRH1yWtD5+ZiwKJFA3jvLyJHR4nZjYvH71a/c1aUrBnLT6fOnXEB5OfpOuQDqvTN5iW6ZUAhjwwmFKy3GDex2jFNtLfrGMnsiWtv2zBlc89at6A+LYzQ1oQ/WCX8lrJfFgE6rvq0NK0dW+mpqur2UwKVcLYsTA/l7ehqTDVlhmzcjXrUcs+yN2t4QrpjFVY8yMjRqfvYsXrLt2+9OdpxhQKvE64VF5PcDTI4exaSzdSv6ZbW4DEPkf/9v+HvLywHqmZnqT7fZAIRMzCHlsL1dffDUZx8exg8Due3teMmYHUvOOPnc5MnTD83s2ZdfVsVD0vw2bFA6H90aMzOwMnt6NLuTrgN+zsChy4U+MHno29+GVdbZqUqLDA5nZsKVk0hgn61bFZytRTfoeiHlkJY6cwDSaZXDZR1SPhO0yAnOVVXo//y8FofOytL6q243xr+5GSCZkYH+Dg9r0RRrkLSsTN1BbvfCkokXL+Ie7NihhUOqq7G65IRJoOZqYDnXCwPBtOgZ+B4YEDlyBK64YBCAXl19e5775azz5eQ7RkawoiUjbPt2+NALCm5P/+7Vtu6KeaWx6hGzBVkogVY6WSd3g9N69Ch+5+eD//vlL4v8xm/AIkmn4ZKpqVlocVm55NRQZ4Cwr0/90CzekJcHMO3sxAu8cSNAl0lSVG8cGsJPTg78u9aCFaTYiaBfBB7DwIt26RLcBCy6wRJsdEGIoC99fQBvVhYqLcWk9uEPww1GjRproJVp/U4ntiUDo6hoIXc/HAZA1tWpHj2Drskk7jmZT0yIYtENv1+FyhjApQ47G7NY02lsX1GBfcmEoW+ftFnDgJtpYADXQ8nnzk4c3zQ1SLpliyaQZWTgN5UwL1zAZBcMYpuODoz9wYPKvOF9uZnrZSlAZ0LaD3+IST07G1m9t0MnZSVgvvi7gQG8J62t6OuePau7qr5f230N7NaqRwR1WumJBGb9uyH0s5gl88gj+vfmzejnoUNaPGKpff79v8fv//AfkOI/Pq5JQWSh5OSoaJTPB+u+oACAEQqpD7ylBdtUVQGUKRnscKhFnErhfC6XWqInT8LNMzeHPpeVAVRLStSXLoIJgiBHIS1qkP/5n2thbRG1asvK8D2t2tZW/Z6+dhFVraRaZX4+AIrVp6hhwkLRLL/n8eAzr1cLPQ8M6NhZAdMwNFO0uHgh554BW+rIeL0Y285OnI+CXqOjcHFQEphaM1Y5ZmrLpFIqAz01hXtCuYPKSgTQKUsgsjLXC10uZPJkZuI+/+IXmDycTqwY9+xZfSXD5Vwt9JsvBnPGvI4exaTmcqFv+/bdWL5jvWm7b10x1qpHdCmQXZKbi1n/Xqh+wgf5/Hm8sN///o2z9yYn0W8m2xBY8/NVB4YZlMPDWvOyoEBT6Zk6z/2dTlDmqGrIJBpqo1ilcDMzsc+ZM7CSTRNgVlMDUA8G1a3DQgUdHWql+/2qA9PWhixcxiLcbvSzpESLeP/1X8MFs7j9yq9A58UwVKuGCWWUHYhGNTuVdEn60ynClUzimgcG1EqPx3F+CmRxxUBGTyyGvhHQfT78PTeHa6IqZFkZjtHXh+eOdEYRjNPWrVqIm/o0NhsmhdOnVbJhfBznb2zE5Mzg5kpcL9aqRYyLpNMIPJ44gX137oSfejUTd25knd8I0JubETsaGsIzSA76cjkfb7T2hqY7WqseFRbipWKVoYYGvFD3ChVqOZ/+Uhx3EQWBlhb1UzNuQDEmappTwZAMFoKXx4OVy/i4CpNRVpap7hkZqgdC90A6jQmopUWzLgsLEQOg24RW+sSEFpugjkogAGD/4z8W+frXr7+2J54Q+eQndTJh4Q8RgPEHPyjyne/g2ljVqb4e4DcygvvMNHyWsDMMpR7SUqfWOqmvVE2kD53gQwD3eHCdLGdHvXZa6U4nwJj6OeXlGHvq9fT36+pBBP1lXIQBaQZUT57EuPn9OP7sLCaUBx5Q2YKVaL1Yi1wQ0A0DgfUXX8T4bNyIBCNqI91qe61gzu9TKTxXx4/jXuTlYaLZtu3+4qCvRnvD+thZ9YgqfiwF5vej8vlqPcSr1Qjgo6Oa4bhcowLjb/6mBklLS3VyiMVwHLI1GDRlEJEuhgsX8MIz0YnFMGhNk4edTAJ0MjNx3NOnYYEnEgCi2lq4jpjWzz709QEwp6cVGINB9CccxrJ6506A8+c+J/I//gfAKz9fGSlUlszKwv4UnJqYwDYFBVqApK8PAb9EAudjchhdL8yIpdwBk6usiU82m7ozKALGKkn5+Qr6lGngBDE8jPObJq6xsFADsN3duF6ycRwOGBZVVVomzutVDZ8XXlAFRgL3nj1wv9BNI3Jj1wu/I3uI8r/Xron8/OeYfEpKEIQuL1+dZ3gxoFvBnN8v5Yoh7fbECby3JSUib387Jr17xfBaq+2+AnZWPXK58ND88IdqpVu1ze+lRqv8ZhVlkkkVnnr0UYAVtTmSSQWp3FyAPV0QlCImOIdC2KaxUY/pcqmkLFkfpBo6HKo9PzaG8xUUYNVTXa0BTNZqDYW0UpKIaowz2amnBy8x6YIi8CF7vapbTt11lrSjW+XRRwEQdXWw5pJJuF4oKkZQoyVsmlp0gwUzGGjmPg6HbiuiPvmMDLXK+V1enrpd4nFYmfG4ygDb7apxzxURBcGysjDmhYW4Trsd98Hjwfi+/DLOUVamyXOcuGw2DRJbQZtWuMj1mujUgw+HwXTp7kY/f/mXX18m9eK2Uut8qe9nZwHmp07hftfUYLV2o5oG6+21tfsG2KnbTeYBH+SmJryQa6Etl9CUSOAFpX55fj4AIpUCCA4OqhpjTg7AxGaDJUn/a2cnXqi6OliMtNJpkVIlMJHQeqGxGFY8rIfK+qNbt2IiIONlchJ9oGY6k3Ty8wF4c3Po++CgMm0MA/flwx9WXfOpKfRRROUGkkn0dW4OfvXGRkxcTKunS4iMFbqQODFRt8bhwDlIFWSiE0Gdk4cI+kONc9PU8ogs6N3drZPlxo0q9SuCiY1USYJ6Xh50XJhoxFhCOg3Q7ejQfpomrpFsKKsWzlKul+UAfWYGKwDq+rz1rTjurbg2bgbm3MZqnVu3mZjA83T+PMamvh7snjspHvZGafcFsE9M4CcSgQUXi4FCtn372vLRLeVTp8ysNekoGFQ9l0gE4Fxfr35jslpME/v09uLlfvBBgMLEBP4vLVU3B1UTmU3a3w/2EJO6cnOx8mFWpM2mxbdZ8IKFmMmQyc8HmNP/zFqm9M1XVgLAWHeV7BoyXubnlbZZWIj+Z2SgT6GQ+s45GVh95F6v8spFcM0jIxoYjkYVgFjwgkwiWvcZGVp0JSMDE084rH50ZqgyrtDaiuuNx5VmWVEBl1cqhX7m5mJcwmG4RljOkLGNPXvQdyZDUXNmseuFkxInSd63RAJMF2qj79uHAOStFGFeCtDp61+8DZsV7IeHERC9fBmf7dgBDvrdrnt6P7c1BHvXN9NUJUCml/t88OHeD4kLiQQClVev4iWvrwcIhEJwaRgGLPCaGoAO3Rhc6o+MABiLiwHIZIvk52vdTro/mKafTmt1I2ZSVlerpcwKSIODGHeKXEUiKolbUQGg6eiAdRuJaPp9ZiYAndIGk5M6qbjdIt/7nsiv/qrqqTscWCGwYlR3t7KdRJRHLqJAzwLWWVn4jNWgWGWJWjHptK5cyGt3uXRfctJnZ1XeNz8f48BJNDsbx796VXXiWTVp82bcH8oBFxdj+8uXkauQSuF+ZmVh0iRHn7EQcu6trhfTVG16K6CbJnIBjh7FRLVlC9hVr5fvvVLrfKlgKL/v7UV/WI6Pk8yt6C+tt5W1NQvspglguXYNFmE6DeC7X9TcYjEA7LVrAIOdO/ESX7yI687JAdhmZ8MaZbBxfh7AMDCAMdmyBRY2A5GsuSkCQKUvnOJdDJDG41qxZ9s2tdInJmAxUzRrbg4rCrJzqqrw2ZUr+Hx2VgORHg/cRYEAwI+66em0Jjx997sIcjNQuW8fVih094yP63nppyeLhoqMZPEw5kI/OC1pBu6mpzW5iABJfjuVIfv6dCyqqhRw3W7N5u3qQl84sZI+Sn+6260B0Z/8BJRIazm8PXvUl09QX+x6sdl0FWAFdMPA8X7+c9ybigq4H605ECttKwFz63bWbfjbNNGfY8fwHLndYN6waPZ6uzNtTUKgacJqZbYieelrqcTVjdrv/i5WHWQwPPAAAO3ECbzs1dXg9pIFMzqqBR4o4evzAUS5ZC8pAbhxuW71pxsGAMoaIC0pAeBUVWmxjFAI+5HuR/ZLZiYYMiUlAN+rV7UyE3nbgQD6wyLZtPat/GtSFeNxWMUPPoj+Dg9jokoksH8kgmOTn87sWI9H2Tmjo5pklZ+vrhenE+efncW29OVThTE3V5UhuU1dHb7jBOX3Y+zJ5ad/fmoK++7ahUmJwexgENv97Gc4NyWT6+sXylgQqK2uF6cT/5MVxPKGNhvG5Pnn8TsvD8Uublaucam2EleLdTs2K+Cn01iJHD2Ka83JQdnDxsZ1DvrdaGsO2J95Bg/wuXN4mBoabj0odC+1cBjSAvX1WMp///sAAlZS2r8fYEu5WytPfHoagEP/diIBgCkpUR9rLKaAS3GvCxcA7NEorKrNmwFOgYBOFGNjKtYVi8GfPDkJgKqrw35tbVg9TUyou8DhwPlra3H+UEhdFUyd/9738MP26U/j96c+Be46XS/UgyFrZW4OQOPzqY56LIZtolHVh5+dxfHf9z7EDjguDDSzvF5uLo7Z3Y3zlZbih/ED1hkNhTAJ0r3EoG9BAZ5F1iAtLMQ+Fy/qpOzzwZreu1fHl+4XEXW9sLHwuBXQJyZgobe24tl45ztfO+vr9SB/LA4AACAASURBVFrn1u1EMJbkoFOz//HHscq7E1rt623ptqbgcH4epeXKygAob3qTpnffD627Gy+ICNgC+fkiX/oSQJE6014vwL+/X4OBHg/+NgxY8263FrD2+VSAi/50Wq79/ZggGSANBgE4tbXqmqF2PQtjsEB2MokAYl0djnvunMrbzs0pQ6W2VotvU1KAFjozPD/8YZGPfUylFJg0FA5jBcAA7/i4pt7PzmomK7naVmYU0/SjUez/ve+BmSKCa+HKxevFddvtOJfNhv+rq9WfTbVGEaxG2to08Do8rHr127bp2BYU4Fg//jG2ZwZwfT1AmEwXgrrVny6iYEpA57UcO4axttmQxMNCLCtpKwVz67aLt2OLxUBXfPll3IvSUkhjrAaVcr3deltTwP788/i9aRMs19XWtLibbbEWTE2N/r1jB9wxFNJi2rzLBVAIhzU7kvzroqKFAlyU3aUf9Nw5BEhJ29u0SeuqxmJKcXS7AVqJBJba/f0475YteJlDIRyHtWM5cXi9ALHiYgA1ha8YoI3FVJo2OxsATZbE4CCONz+vDBYGUuNx7EuZXboqmG1LwbJ0Gp/7fIhTiAAMmQRmmgD4nBwN3vp8mKg8HpUjyMvD9c7NwTIltZSCYSIa+KRLKBDAJPT885hws7IwCe7bpzRVqz+dWaK8P6zoRJ56KoXYx0svYbvt2zEBUpjtZu21uFqsv0WuB/RIBKsPFs6urYXBUVm5Duj3UlsTkgKLQY9tubT7td6We0GeegpWUTIJ8GAAkZQ8j0dVD61LZfrTHQ4A4LlzKhvr98OSbWgAYA0Pq9uluBjHCoe1xF1+Prb1eOAKoLAVg4eGgb6xxuS1a/iehTjm5lRdMRAAOOXnq9bKH/4h+OqGgX3oVsnIUABmqTgCPfvr9ytzxG6Hxszf/u314/jYYxBRo1+fWbQsJSiiBaNNE8c/cwbnz8rC6iMUwrl27cIEx2Idfj/ULl9+GeORlwdWT2OjJjCJKJWRExVdY1aXiwjG/YUXMAHX1iIQuRLG161Y59Zt2cbHlYPOoPxDD72+IO16e/3tvtWKocV5PzfrNZJKePYsrGWPR3nQqZQWRs7LgzVojTWw0DSTVzo64E8fGwN4VFXB2iLrhGwXHosqe62tGrTdvBl/X7yotUPJA6eUwc6dADX67QMBLetmmjoR5eai71lZ6NPICADM7QZoDg9rghXL0mVnY/t0WhOiMjO1HigLXUQiuB5qp//6r4t84xvoY1YWPnc6YWlu2KBBWAqEEYS7urBSERH5x38EY2dkBP04dEhFvBi4feEFjBc18ffvh+uQBarpemEFJgaw3W4FdMMAnfX55zEGhYU4b2XlzZ+ddHplYC5yc3eLCMbw2DFMMDabctDXStLf/dbuqFaMYRjvFJH/LiJ2EflL0zT/aDWO+0ZtzEDlauSnPwVQUZOFbo3SUoBmScnCZblpKmhQO+XCBQA7g4q7dsG9Q/Cm5VpVhd+RiLpeMjOxbUUFLNXLl9EHSgOwuAYDvoODyrMvLMR24+MAtEBA63/SimfZOrpOKM5FoJmaAqj4fMq9Z1ISM0SpEW+3a1CZCozWIB4TuIJB+MQZQKZfnlmksRgs754eBd2/+zvsEwwCaE0Tx8vJQR+ffx4rouxsAOCePcr7J6izTioLb7NOLAF9ZASB0c5OHPc978EK6UZujuWs86WCqSuxzk0T133smBZC378fLKXbWRpvva1eu2VgNwzDLiJfE5G3i0i/iJwyDONfTNNsvtVjL9VeSx3RtdoI6F/4AtwSsRisSroAvF6AelERQMb6Alv96SIIWJ45o7r0ZWVYzhcXq4Sv3Y7PWcYtHMaSe2ICx29sVN12FsCem1NwIsWvtBT+dpam8/txjqkpgFdREX6yswGW0ajKAtCVMjOjQE4Az8rCPoahVrrDoVWP6DaJRrGKGBvDtpTmjUZRPCKdVt324mIci1WcmDSTTuP8p09jbAIBjOXVq/h+wwZY6pxYsrPhjqLrpbgYPPKKCr0fBNJ0Wt1JTif6TZdMJALVxYsXAaQPPwwX2XJsr9fiarFub93W+pvbtLWBstjfj3F/y1tUDmG9rZ12y64YwzD2i8izpmk+8sr/nxMRMU3zy8vtc6dL463F9pOfiLzjHSL/8i8ACdbHLCkBgJaUXF90gOXfmLBz8SIAKRLBi7l9OyyvWOx6twvLwrW3Y59kUnVhUim4gnp6AOrz85pNGQxiae5ywXfP0nsimFSiUYBmebkWEHc4AJp0tTAxaGwM+9tscL1EowBiipNFo6oBQ7kAMlrGxjBGTBDKzVXOOmmGGzfC+qXriElClCROp9Hnc+e0xN7Xvw5LfXH71KdEfvu3EUhsa8Mxtm+HaysrS615ukZYJ9VmUyudPvaXX8ZPOo2V0YEDyxeUWE4pcTmLfiXullQKq7Bjx7Bi8PnQB1I319u90+6kK6ZURPos//eLyL4lOvRREfmoiEgFzZn1dl1bHCh+7DH8ft/7IHFbUaGFKthIy6M0wPg4LM7ubgVfWulMMvJ4lBopgv1JWXS54EaoqAAwnj6tljezN51OMEGOHoVFd+6c0i2np7V2aTCICSIvTyl+fX3w6dts6kKivK0I+p9IqNY5tWjIonE6AT6VlTjXwIBOfG43AJkFs+12bLd3L8CUBT8CARXdojzA5cuY2DIzMXGOj6M61ac/jbEoLNSs0dlZ1J0dH0df3v52lQRIpXRyJTsnnca4cmJLp+Eee/FFHGvTJtyjpYq/3Kp1bt3e2hIJTNgvvYRxCQahstjQsM5BX+ttNSz294nIO03T/Mgr//8bEdlnmubHl9tn3WK/caMGTn6+yF/9FV44FjtezFlOp5X1kkhogHR8HABUXw/ra25O3S7FxQBNvugjI3DXTE2BcUEhqo4OrWdK1wsDjLt2gRkRDIp885s6UbS3a+UmFrQm/XBuTrnsmZn4PBrVpKN4XPXk6Wsn7dDt1qBqWRlcOv39iA+wiHZODj4fGoKbxeOBX7i2dqE2vLW4NuMRp05hcgkENBHK5cK5ystxvPJy+L77+1EQI5HA9T36qE5QvA5OUum0ZteSftrZCVXHsTGsvt7yFvxe/Ay8FjC37mPd3vqbLRpVDvrcHK7r4EGdmNbbvdvupMU+ICJWyf6yVz5bb6+jpVIAx3Pn8P/GjQD1pQqEsMoQCx+fPQuLkjTGhx+GlTk4qAJWwaD6bU1TwTuZxHmYZHP6NACIomL0p/v9EJeqrcX5RHDMYBATCuuAbtuGQKyIAjYrDFGbZXJSfdX0rZOHH4vpBMDgKHnmTifGqKNDE7MKCkR+8AP4v1MpTCpvfrPqo5M5w4mRIBgOA6SjUYB4NIq+5ufjGHl56s75+MfheunuxnEeeQTuCrKYSF0k3ZLJVKQwDg0hwNrbC8v8iSdwfxf7uV+rq8X627rP4jYzA+v8zBn0ta4OlMWVsG3W29pqqwHsp0SkzjCMagGgf0BEPrQKx33DNeqf9/QA/D7+cbg5lvJzWv3ptCDDYQDJxo0AuJkZWLOL3S4imAzOnNFiwfv2wWqcmQF4DQ4CaJNJZY1UVMC6fO45kT/4Az0Wi3E/8ojIhz4Ei7+oCCA5PQ0Lfnpai0eTG04QpCwCWTKUHMjOVnpiaSmAaGYGsYOODk1aCgYxDv/wD5jMGhvBzqG1z4IWVlXCVAqT4OXLOAbL60WjsGDLyzFezCydn8e4XruGyfKJJ5TyR/85JRSotkhd+0gEUrrNzTjm298OSijdHathnVv3WdzGxuA/v3gRk8bWrVjFFRUtfez1tvbbqvDYDcN4l4j8iYDu+A3TNP/wRtuvu2K0PfssfkIh9bdWVyOItpTkqmlq8G9uDsB08aKWWTtwAK6WiQkATHHx9X5bCopNTwOkqNfN2rATEyKHD6vKYkaGFjxmIQcRkc9+VuQrX0ENUxFY6MwInpsDUJKaSRdHNApLPR7Hz/AwADEnR4OMGRmqnkgxs5ISWLpnz6qapNOpiVnhMBK4rlyBZU5uOTnp1vGLx+GK6O/HmBUWYvwNA9dQXg4ADIc1JsBygrt2oYi2NWPUWnGKcQQqNDJL0zAwSe/fj3F4PWDO/q/EOhfBNR07hgnFbseEd+DAvVHEfb29vnZHeeymaf5QRH64Gsd6o7UvfAFUvMuXAVT798MyXYrmxqSWZFLk938fAcHeXrzY5eUA3nhcM0Stbhe29naAVCqlOueGgc8o7DU/L/J//y+Ol50Nl8aWLapZIgLQ+8pX8LfdDuBvbAQwT07Cl06J3YIC5aMz0SgSwTEYQGXAkUUuPB5MSps2ATCvXAHV9aGHVIcmLw/9/Na39PoaGvD7859fuKoQASBOTABsp6YA6G43+upywWovLcV35NWfO6ernscewzb0/VNWNzMT/WE1JsPAZHvsGCbcrVuha8TJyyry9XrA3LrfUtt2dyOo3dWFvh08iBXZSiUI1tvab2tKK+Z+a5OT+H3xIkD4TW9avjACXSLxOLjkf/ZnIl/8IoCJ/uy5uevVHK37nzypGtmHDgHY6P6h6mIioSXiiooQGCwqWug2aGtD8E8ECTSPPAI/7cwMwLq3F5MDFROZ9j87CzAaGsK1k8tNjXEWnPb54MMvLwcI0ur8539GMDQzEwC8aRPYQl/7Gs5dUYGJgxWIrI2Ad/o0jllbi/EaHFR/ejCoRbgnJ7E6iEYxtu97n3LiSWOkngtXGqYJV82LL2L/qipw2oPB1QH05YKh3LalBWM1MICJ6K1vxSrhVqonrbe12daB/S60xZTGj3wEv5fTviGHm2DT3o7PAwFY+Ha7Bgz9/uvP9alPwYJj9uqBA5AG/sQn4Md/5BGA3A9+IPKjH+m+v/EbC/uVSmGf557TbX7wA/z81m+JvP/9AEr6rOlf5ipAZCEtkUWnyRjhpFRXh5XCzAyyMHt7VcMlOxtW+c6dC2V8yfteKjMylYLl3doKkNu0CS4gqhIy25alBru68GMYAMc9exZOePSd0+1CaeOjR3H9BQUYCwaPGQxdCZiLvDZ3SyqF4PexY1qb4N3vxvjcL1LW6+21tzWnFXM/tZERteaWavSnz88DEJ95BiC6uH3mMyL/5b8snUJuGCLf+Q6OtXWruioMQ+Tv/x487T/+Y0wOtIT37FGtGLb5eVi7tAarqnC8+XmAWWurlsaj9grr0CaT+I5a6NRVMU31pwcCmAzKX+FXffazSPj59reRrLW4/fZvY5JhBqrLpfEKa5ub0+IP+fk4fl8f+ldRgXMmEuhbJKL0Tr8fWb+5uWppsxAGx485A8ePI5jr9cLtsXXrwu1eD5hb912qxePKQWes5OBBuMxeiy77eltb7Y762Nfb62s3UumjP312Fun97e0i73oXwKysDADd2bm024WNlEkWsg4GAWInTuDziQn8pjBXdTVAfXH6+PQ0wGtiAgCyY4fSL9vb4ZrhiqG0FBY6i1d/61uweoeGsH12tnLIMzPxf0kJGCdMGLp6VeRP/kTkj/4Ifu2PfQxWfEOD1kKllAGlBkSuB/VQCP2ORjHZZGdjgjQMWO1MwGKFJlJFN29G1i8DpE6nCnfxXGQPXboEwD94cCGD6Vasc+vvxW1uDi41UjQrK+EO27BhnYO+3rStA/tdbktp39CfHgqBvTExARCrr4fLgC+wVbPd2ha7eh5/HL/f/GbQ7tieegq/P/MZ/H76aWzDfj37rMi/+3foQzIJ8Ni5U+uJ/vqvI+jq8cDP73JpqT5qi3/zm+i33a4gbBi4jrw8TCaVlcrfvnABFrYILOytW/FDH//UFM7DrNSlrv3ZZxG3uHQJ++3eDUDs7MR56+pw7L4+TEBtbQB3pxOMl/p6HIscdMoCGAYs/TNnsHpJJDDJPfSQ0ilXCubW3yI3329qCtb52bM478aNmEy4wllv683a1l0x91ibnwfAvvvdACb5f+2dfWxc5ZXGn+PEH01sx4nXCSSxE7dx/ZWSQkhC4ghEg5oQAqEqqZYt20Klsqsu29JuC5ulVA7/rZCAql3tqoJFbUHdlQKo2xXLJohILQYnGEzZ4I8EHDv+SAwGOzaJx56x3/3j8as7Gc84M54Z3zt3zk+yxvNhzzs38XPPfc55zwGj5MpKZ4TbqlWcJBVPL3q7eSbyMQB45hn2Je/pcZpWRb7ud7+juG3a5MzTPH+e4jY8zMqVTZsonOfPU4Bsud/77wM//CFtIjuGLi+PAl9e7kTRAJ974AHgN7+ZvQ5ruzz1FBPGsfqo2DW/+ioj8JISlid2d1O4y8ooiLm5FPX+flooFy/yCuTWW3kbPrXIeuiHDrF2/Y03aNls2MCToJ3UFE+0nKjdAvAkaWvQjeEJtKHBP/N9lcRQKybDMIbi+MknFMI1a5zRclddNbvaZb4DRiYnne/tkIRoom6nVRUVsVrHCnB7u7PRpb6eAjc4SJ/d1nP/9reXN876yU94e8cdTMh+/vN8TxuFDw3xSmLLFm7e2bKFdsjoKL+M4eePlUewDA3xtr+f71FdzSuA0VHnRDI+zhPZqVO8IjKGr7v5Zp4wwks6bWfGri4OALGDTPbtcyYGzUfQ4/m5/n5euXR08Mph82YmvSOT44oSDRV2D2DbuXZ2Otv0i4tpGZSWOrNL50N4b/fIKVR79zrWiyXydfv28fanP6Xo2v7kO3dS7Ht7Kep2G/2FC/TUb76ZgnTvvcAvfkFBKi9nxGzFyRj2K+nooNDV1bHKx4q3bU8Qy3aJteZvf5u3d98NfOMbFO7Vq/n7PviAeQE7ErChgVGw7bZor3DCRf3FF51jUV8fX3JyPnaLfb+mJuYCCgpYlrptG08qihIvasW4TDDIyoqDB4Fnn539/M9+Fn0sYDJEs2fmep0IxW1sjAK5Ywcj37Nnnf4vOTm0Yj77zLn/2We0UF54gdHy+vXOHM/+fgrYyAgrT2680dkcdPFifLZLJLYFcHs7Txa5uYz8ly3j2tra+L6BAC2UvXsZgYfP/7SdHgFucHrssdnvM9dIxkSTofY9OzqccsnCQp7g7HhBRbH4djSenwgEKI7d3Y7dkJvLCM1u5U8HiQj76dO8cnjuOUa2GzdSfM6epdDn59Pe6e2lgB84QL88EGDE+cc/UiBt+aQd89fVxfu2+2T46DzbCiCRsj1jHA//8GH+fE0NSznvvJPCOTJC0a6tZdWLbQFgiRxfF/7clY7ZfOwW2zO/qckZ6rFjB3MWWoOuREM9dg9jDK2Ajg5nEPOKFbRfrO+dLlEH4ptCZSPSqire3nMPb7//fQrl9DQtmU8/pVcNAC+/DNx+O0W6tJS2y113UaiDQafp1ugohbehgRGz3cCUl8foPdHPbvMTJ07w/WyyeXISeOIJXilcvEgh37WLlT2RJ41QyLk6iRT1ud430WQo4FTWNDfz5HjVVVx3ba3WoCupQYV9gZmaYsTb0eGMfVu5kmJkx9yle/xfPInX8M0+IhTOM2e4kQegSHZ3O33fbXI1GKSQ1tez3j4nh9F9e7tTy15TwxLEqSnHylm+fH69TGzvettb/OGHaReFQty1CvDxsjJ65CtXzm6Ta/30nJzYAybC/03mY7cA/Pc+fpzlo4EAj9P+/Uz0ag26kkrUillAbJ+XM2coJldfzWjNVsB4FRGKkZ1lGgzSogkEWM3y0kuzf+bRR1m+2NbmDL4uLGSCdN063o/VgTFejGEyt7WV31dVUbiffNJpUBZOpDce7qfPJer2vcJvgfirYkZGnBp0uwGqoYEnPkVJBLViPMbwMHeQfvQRxewLX6DAeXnquzGMqO+7j5bRsmW87eri8xs2MAH5ox9R7L/yFSZVp6cp6NZqMIZRdH29M57Odmecb3LQjrI7dYpXD1/8Iu2swkLg/vt5fCcmmLwN3zFqCRf18FYB0Y7BfOwWgP/WTU3cjyDCFgk7dsy941hRUoEKe5oJn1IUCDA6r6nhH7eX/dSpKVooXV0cnlFYyCh9aIhCais2bLdI24Omt5evs/3WCwoYRW/YwGjVTneK1oExXuwAaLsJqa6O+YmlS/l4ayu/P3CAwh75PnMlSS3zSYZaensp6J2dTIZv3coql/mWrCpKoqiwp4nGRjayOn6cf+h2TmhlpbdtF4DCefq0I9q//jU3DdlWvJs309ceG2NuoKqK97/1LafM0BiKekUFfyYUYj6hpCS5Qcmjo06Z5OrVrFFftoy///e/dzYn7d7N4xyZr5grSZqM3WIMWxa8/rozleqmmyjqiZRsKkoqUI89TYhwVNulS/RSN2/OjIjt4kXaKB9/zCh4eJg7Rp98klF3RQWfy8/n/c99jmJuh1AXFPBncnMpsIWFfLykZHZzsUQZGOCJcnycgl5ZyTX29bGNQCBAq8POIQ1nriTpfJOhgGM7vf46cwlFRU4NutdP4ErmoR67S0xNOd0Tg0F2VcyU6e+ffELfenycfnV/PytfAEaeAEXdNu/q7eUXwOj5wgXHi1+3jiJfVMSvZGwnY2hrtLZSkK+7jieY/HwK/Tvv0Hr5+tedctFwYiVJk7FbQiHmTGzXy9JSngCvuSa5KxJFSQUq7Ckkcmv7X82M9J5rp6IXMIa2S2cnRbCkhG0AwkfONTTw9sEHObjjxAlaNmVlPAl0djJivvpqXqFY2yXZjTZTUxTvri5G/1u30rMPBGi99PWxbNBuOIokMklqHwsnEUGfmGCFUHMzr25Wr2arhepqb+dMlOxCrZg0MDjIMkYXDm3ChEIU5Z4e2idLl9IvF6FoTUzQWmhpoaD29DAyLyzklcjQEP34nBxWopSVpcZ2aWxkTfqxY7xKWLOGol5URDE/coRXFtu3M4KPJszhSdJI0U3EbgFYnmlr0CcmaDPt3MmTSiZcjSn+QK0YF1m1yu0VxMf4OLe0Dw5SqPPzKaJLltBOGRlxBNEYZ6hETQ1FvqWFry8qYrlhaSk971RErocOsdolEHBG4eXk8Erhrbe4xljWC8ATlm0fHL6eRKJzgDbLG2/QdgmFuKaGBp70FMWrqLCniXTvHk0WW1c/NsYIe3qa0XdxMT3i0VEKdm4u8M1vOlFzdTV99NdeY2K4ooJJVNvHPBXYma6hEJuDrV/P9zpyhJZRRQWrXqJdFdieMZGinqigDw6y+ubkSf6cHahRWpr0x1OUtKNWTJZhDJOiJ08yGl65kqIZCFAobdXIkiX0kAMBbvevraXov/ee0yO8ro6im6qWstFaCwMc1rFxI9e5bRvbEcSyXkIhfr9oUfw9X8I5e5YVLqdP80R1/fVMgBcXJ/xxFCXlaHdHZRa2Paz1xFetop8eClEE7Si4UIiJ0fx8WixPPw089BDwpz8xql++nH53aWn6EoZ2WHRLC62XggJG6ZHb8O1/36kpJ0mam5t4Z8jTpynods/Btm38jMnmChQllajHrlzGxATLBfv6GGGXltJDDwadEsBQiK9bvJjljFVV/P6xx+hzB4N87NprF6ZG+w9/YLK2vBzYs+dykQ0vVbRRek7O3O0BIpme5pVLUxO3/xcX830W6vMpSrpQYc8CLlxgRcennzJKLyxk5D0+TgGbmmKEnpfH52trnU6L777L25wc7qRciMZVAwOsCT97ljbIli2OWEfWnk9NOVUv8daPB4NODfrICCt57rzz8qHZipLJqLD7nL4+WhmTk4zCFy1i2+DxcUa3tk/5ihWsdrEVPZF+94EDvE1nTb4x3GzU3My+7rt3M1o3Znbtefhj8Yp6IODUoF+6xGTw7t1MCGvJouIn1GP3KcYw0dneTgGvrWXkPjBAgQPoRZeUsKqlsjK2Lx3vxKVkCARY9dLdzasCa71E2+pv2wMAzhi+uRgbo5i3tPAEt2EDK1zsQGpFyRTUY89iHn2Uw6QHBpjorK93hmQEgxSzkhJusqmujr5jcyE5dw545RVuArJVKIAj6uGlivG22wVoPdka9OlppwY9Vu27ovgFFXafMTTEGaO2Sda6dUyaDgxQ3IqLWQe+cSNFPx7SUZPf2Mjf29rKIRR5ebRfKiqc10TWntvOjFeyXs6dY0K0rY2vtTXoK1ak/nMoihdRYfcRExPA0aP8futWitqxY874uvJyitzatYlZEOnw1A8dYnT+4YfcxblnD6t1Ym0kupKo22lKTU3sf5+Xx3YDN9zg7WEmipIOVNh9QmSys7qat1/9KpuRbd7Mx1K1OzQZ7OzTri6ua/t2CnY0QQ+3XqL56bbzY1MTraYlSzjJacsW9y0mRXELTZ76DCuOjz/OqLW6mjbEfAZFp5pYO0tjVdrMJep2NF5TE9sdlJSwF/uXv+yNk5eipANNnmYhdhYpQKHbtYs+u1dobOTX8DD97rliCtuZEbg8SRoMsiTyzTdZ5bNyJfC1rzFnoG1zFYWosPuAaJHwd7/LWy/2gr9S0jaanz4+zs6OJ06wBr28nIO0M2WIiaIsJCrsPsBGwra/C+D9XvDRKm2iTToaHWUN+ttv8/NVVbEPenj1jKIol6PC7iMyqb9J5FVEpJ8+PEz//M9/5klq40bmCjKl172iuIkKu8/weh/4aISL+uAg/fO2Nme+6Y4d8dfcK4qSpLCLyOMAbgcwCeBDAPcZY0ZSsTBlfnjNT78Stt1udzctlzNnaCft3MnWuV6o5lGUTCPZiP0ogIPGmJCI/DOAgwAeTn5Zit9pbAQeeYT94d98k7tFly4FbrmFte1ag64o8ycpYTfGHAm72wzgruSWo2QDwSCreEpLuSt2+XLgtttYg75YzUFFSZpU/hl9B8B/xnpSRO4HcD8AVGhJQ9bS0wMcPszvFy/mQOq6Oq1BV5RUcsU/JxF5VURORvnaH/aaRwCEADwf6/cYY35ljLneGHN9WVlZalavZBSNjZyR+uMf8/73vgd86Uuc0KQoSupIuqWAiNwL4G8A7DLGXIrnZ7SlgLIQPd4VxW8sSEsBEdkD4CEAN8Ur6oqiKEp6SdbZ/CWAIgBHReRdEfm3FKxJo9tYSAAAA0hJREFUyQIysd5eUTKFZKtiNqRqIUp2kWn19oqSSWgtgqIois9QYVcURfEZKuyKoig+Q4VdURTFZ6iwK4qi+AxXZp6KyMcAehb8jYG/ADDkwvtmAnpsYqPHJjZ6bGKTjmOzzhhzxa37rgi7W4hISzy7trIRPTax0WMTGz02sXHz2KgVoyiK4jNU2BVFUXxGtgn7r9xegIfRYxMbPTax0WMTG9eOTVZ57IqiKNlAtkXsiqIovkeFXVEUxWdklbCLyOMi0iEi74nISyJS4vaa3EZE9ohIp4h8ICL/6PZ6vISIlIvIMRFpE5H3ReQHbq/Ja4jIIhFpFZH/dnstXkJESkTk8IzetIvI9oV8/6wSdgBHAWw0xlwD4BSAgy6vx1VEZBGAfwFwK4A6AHeLSJ27q/IUIQD/YIypA3ADgL/T4zOLHwBod3sRHuTnAF4xxtQA2IQFPkZZJezGmCPGmNDM3WYAa91cjwfYCuADY0yXMWYSwH8A2H+Fn8kajDHnjDHvzHw/Bv5xrnF3Vd5BRNYCuA3A026vxUuIyDIANwJ4BgCMMZPGmJGFXENWCXsE3wHwP24vwmXWAOgNu98HFa6oiMh6ANcCOO7uSjzFU+BozGm3F+IxKgF8DODZGZvqaRFZupAL8J2wi8irInIyytf+sNc8Al5mP+/eSpVMQUQKAbwA4EFjzKjb6/ECIrIPwEfGmLfdXosHWQzgOgD/aoy5FsBFAAuav0pqNJ4XMcbcMtfzInIvgH0Adhkt4u8HUB52f+3MY8oMIpILivrzxpgX3V6Ph2gAcIeI7AVQAKBYRJ4zxtzj8rq8QB+APmOMvbo7jAUWdt9F7HMhInvAS8c7jDGX3F6PB3gLQJWIVIpIHoC/BPBfLq/JM4iIgD5puzHmCbfX4yWMMQeNMWuNMevB/zevqagTY8x5AL0iUj3z0C4AbQu5Bt9F7FfglwDyARzl3yyajTF/6+6S3MMYExKRBwD8L4BFAP7dGPO+y8vyEg0A/hrA/4nIuzOP/ZMx5mUX16RkBn8P4PmZgKkLwH0L+ebaUkBRFMVnZJUVoyiKkg2osCuKovgMFXZFURSfocKuKIriM1TYFUVRfIYKu6Iois9QYVcURfEZ/w+fHfry6GI0YQAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -266,21 +267,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.6.5" } }, "nbformat": 4, diff --git a/notebooks/plot_OT_L1_vs_L2.ipynb b/notebooks/plot_OT_L1_vs_L2.ipynb index 9c3d24a..8cc4e5d 100644 --- a/notebooks/plot_OT_L1_vs_L2.ipynb +++ b/notebooks/plot_OT_L1_vs_L2.ipynb @@ -64,9 +64,9 @@ "outputs": [ { "data": { - "image/png": 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\n", 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8+295W8GSU6WSfUa22TrggQVLw/KBO8KO792veIz86bZi27x3c3DuNkXnLuyapeKuWZwLxrX5YgeXeoHTnYsj5Drd4rq57r2F5ZuC8i3d4vFuS7u4HGBb557i8taPC8v/8uh/GmrsHOcV+iHsnUv3eu7LNWzMRmP7NLOM7dOMzThP4EOhbO7TUwF6BI+cxmwQtk8zq9g2zVqM8wR+A3snyz80L9uLlNLZKaVjUkrHtFkrxbIxE8P2aWaZNe3TtmnWYhwHfhnwMEkPzifCeD7j5Zo1ZpLYPs0sY/s0YzPyK/SU0qKkl5ElzG8C56SUvlbWRo1GoWBNc8WqLbUDYQ+QAjFQqqpCb1ZU5lKisgyaVFZelmhjI41l1XVFavNSFfqGaXarM4p9hkSK8rIDUlEJjqKoiuoq9NB2KyraY2VwyX5H10DF8qrrKWtTWL7BtlzVPtVuFQrW0rbNhcsvBcJEgKVOsa0tByr05Ypq83IVevH4UnVMJRy/4r6rjnmRCj2ibPlGMHJX7WOQsX4DTyl9nGwaS2NmDtunmWVsn2ZcnInNGGOMqSF24MYYY0wNsQM3xhhjaogduDHGGFND1j2Ry140GoXpUSO1eZT6ESB1AxV6t1gymdqTyf+b1a1vXueyvkNVZpQDeELq9LK6KJ9vLSlIjxqqzYN0qVCSSjVqE/QR5k4vVcAHtl414mKkCI0JzRMwQoRG1SiQWtFqFaZHjdTmiwvx0L7UC1TogXA9BataDspLx84wV36kTq82l0OkTocytXnV5aPyeByM6srypw/D/cG0jTHGmH0OO3BjjDGmhtiBG2OMMTXEDtwYY4ypIXbgxhhjTA2xAzfGGGNqyHTDyJoNtLB6MpNwYpIgVCyrK455WG4XxymECfzbQQL/ILwsq4vKowlTipcPw8iacShEWBeG6QQJ/IPyRhS2ATQbxSEPZaFntUIqDvOKQrxKwsjCELOovBUYVVRe2nc0MUoUXlZxAqDSEMuovGLoZcVrpqxNGGJZI5ZbDe7db25VeTSuRaFiAItzxedisVdcvtQJxsiKYWdQcl7Dc1dcHI5fZROKBG2ica0VlWupeD2lE0EF2xtOTzUcfgI3xhhjaogduDHGGFND7MCNMcaYGmIHbowxxtQQO3BjjDGmhkx9MpPlzauVlClSxwYTk0CJ2rwbqc0DtWagQo/KIVZZhuUVlZehIrO0TaA2D8obgcKyTMUZqSwjtWbdkISKIiKCCUVGUqEHqnIF1wCtYHKeSGkOpKDv1Ko2yclyNJlJUF7eZjLLl14bVaI6ahY5kVrip9tW204U+RIpxKFEbR6Vd4vXE/Wx3I6P7XIrmLQkKCcc14LxKyiHeJyKVOjRRCOxOj3uux0q1z2ZiTHGGLPPYQdujDHG1BA7cGOMMaaG2IEbY4wxNWQsEZukXcCdwBKwmFI6ZhIbZcwksH2aWcb2acZlEir0p6aUbhlmwdQQSwudwvLC5QPlOJTlNo9yA08mzy/ECvUwR3qkqI0UmaX5nqNc6NXUms1gPa1msVoSoB3UdRqLYZsZYGj7REKdghOvKLd4rMaurDYv6hdIgQqdYP6ArO9AbV6xPLqWovkDoERtPrHIjRHmCShUoYermTZD2WdqwL2bV5+PeGyJ1xWNeaHafPWQnZcXH++ysTPOnx6cu1akNg/ympep0IO6duVc6NF64rEziu6J1OnD4lfoxhhjTA0Z14En4NOSrpB06iQ2yJgJYvs0s4zt04zFuK/Qj00p3SDpAcBFkr6RUrq0f4HcME8F6HW3jtmdMZWoZp+NhY3YRrPvUmqf/bbZWdi+UdtoZpixnsBTSjfk/28GLgAeV7DM2SmlY1JKx7RbHiDN9Khqnx2tzhJozHqxln3222ar57HTrGZkBy5pQdLmlc/As4CrJrVhxoyD7dPMMrZPMwnGeYV+IHCBsjzRLeADKaVPljVIDbE4v7rLKLdyCvL8QqyEDXObR8rLsDzsOlZlhrmBg/JItV6SSzhWa1bLhR6pNdslKs5OoLLslCjXN5DK9okE7YKTG6jNFeRIB0IVepjbvKLaPAVzAQAsd4rrovkDonza0fUXLV9WF9p6xciNaPnSuuia2Vgq2Wdqwp5Nq49tFLFSepyqjlOB2jwaI6PlAVI7GF8itXmwfLNVPOZESvOsrrhN1eiabrO4vFWiKO819hT3PaYKfWQHnlK6BnjUWL0bs07YPs0sY/s0k8BhZMYYY0wNsQM3xhhjaogduDHGGFND7MCNMcaYGjKJXOhDk5qwuGm1vDTMhV5yexGpXcM85YHCMlJSLnVLlLZVVeiBKjPMA12mmo3U5oGKM1abV1eU1zQX+vBIqFtwciO1eVku9GagNm8GRl1RbZ468aUbtQlzmwfzCoQRGqXzBBSXR1Ej4TUQ5syO+44Uzioqr9mjS2rAnoJQ8GiMLJ1PoWL0S+VxrUSFTtCHOtXGr1Yw3nUCdTpAN1KhV4yuiXKhd0vGwUht7lzoxhhjzD6IHbgxxhhTQ+zAjTHGmBpiB26MMcbUEDtwY4wxpobYgRtjjDE1ZLphZA2xZ271PUMcClE2aULQJigPw8vChPxh13GoTBA+EU7YMFIYRpTcPwgXawfhC0G4RbcVh0L0msUJ+eeC8trREHRWn9zQDhsl979BuFgKwstoRSFeQRhZyWQmS93iuqVuEC4WlEehX1E5VA+xjK6lpehaGiFEqfjamMkJTkJSA5YKZrtNjWAMic0jrFsOQlSjcxdOTFIyGZM6xeNOOH51isejTjBOReUQT0ISjWtReOxc897i9ZeEkXWDyUyi8mHxE7gxxhhTQ+zAjTHGmBpiB26MMcbUEDtwY4wxpobYgRtjjDE1ZMoqdNgzX6BgDUSto0xmEibqDxP4VyuHWAkbTYyy3I0UtYGKM1BkAjQC5WcrUJtHqsxeUB4pNQHmW8Xqy/uNCl0i9YafzCQ1SyYzCRTqKVCbR+XLFScmgRJVeTBBTzhpSVgedh22qRrtEU+gEV8bkcK5SMmsQL09qyTB4tzqbQ7HyJL9i1To0SRK4eRKwTgVTUwCo6jNg2iZdrVxDeIIm14w5kXjWqQ275UoynsK2qh4TB0WP4EbY4wxNcQO3BhjjKkhduDGGGNMDbEDN8YYY2rImg5c0jmSbpZ0VV/ZDkkXSfp2/n/7+m6mMcXYPs0sY/s068kwKvSdwFnAe/vKTgM+m1I6Q9Jp+ffXrrmmBizNrVapxrnQ41WFSsooz29UXjGvOZQo2gO1eZjbPMgZXKbijNTm7UCVGak151rFislIaQ6xKnO+MZ6Sckx2MiH7TA2xPFdgEJEKvSRXfwpzoUfRE4FyPIi2WO5UV6FXVpt3o/WX5EKP2lSO0AiumTKFcxCh0eusttuGpqZC38kk7LMBS/MF+xdG8JTsX2Q6zWpq8ygiptmMz9F6q82jcQ2qz+UQjndBLvSycTDKeV6mXB+GNZ/AU0qXArcNFJ8InJt/Phd43lhbYcyI2D7NLGP7NOvJqL+BH5hS2p1/vhE4cELbY8wksH2aWcb2aSbC2CK2lFKiZG4+SadKulzS5Yv33D1ud8ZUoop97ln88RS3zJhy++y3zaW77prylpk6MKoDv0nSQQD5/5ujBVNKZ6eUjkkpHdOaWxixO2MqMZJ9tlvzU9tAs08zlH3222Zz06apbqCpB6M68AuBk/PPJwP/PJnNMWYi2D7NLGP7NBNhTRW6pPOA44D9JV0PvAE4Azhf0ouBa4HfGKaz1IDFgoecNEIu9FC5XjHPb6Qoj3KqQ6yQDfM0R2rzbrHyshUoNQE6QV0vUGvOtwOFZaDWXChRoW9q/rRS+TSYpH3SEMu91Sr0yD4pyYUeKdSXgzZRbvMUqNAj5Xi2rvVVm0fLZ30E2xSozZeCayl1A+VzcM0AdHuBangDVegTs89GIvWKVOjBfpSMnVEeeAXq8UagTm8GyvFWyVwO0dwMk1Kbl0XRRGPbQqt4/IpU5VF5pDQHWGgU99HTeCr0NR14SumkoOrpY/VszASwfZpZxvZp1hNnYjPGGGNqiB24McYYU0PswI0xxpgaYgdujDHG1JBhcqFPjCRY7BVUjKJCD5SRoQo9WNdyoBCPVOtQkts8UF9Guc0jtXmnEyttJ6U239QuVkVubv0k7HtToNbc3Izb1IkksThXYECBorw0F3oYJRGp0IPySIUe5PAvW1ekEK+qNi9VofcCtXmU87xIWQ2oF+T878YRGvPdYnXwlu5q+2xOLxf6ZGgkGnOr9z1I0x+r04kV+I1AhR7lNm8F5VFe86xufdXmZVE0C0EO80nlPI+U5hCrzcvaDIOfwI0xxpgaYgdujDHG1BA7cGOMMaaG2IEbY4wxNcQO3BhjjKkhduDGGGNMDZlqGBkNWJpfHcIQT9Ycr6pyGFm0fBQuVhJGRjtI+h+Ut9rFYRVVJyYBWOgUhzBs6hSHI2xpF4d4ReVlE5Nsbd4TlN8/5nlPTVhcKDCgMMyxehhZNJlJZLcjhZFF4WLBZCbLUbhYtJ4gVKysbnkuaBOEkbWCcLH5XhwmtLVXbNM7uqvneW814gk3ZhEJOgXHREFIWFQO0AgmM4mOSRQu1moWj2vdoDyrCyYzicLLghCvcGKSIPQra1MtDHZTUD4fhH5F5bB+k5n4CdwYY4ypIXbgxhhjTA2xAzfGGGNqiB24McYYU0PswI0xxpgaMt3JTBrFKtUUiXkDtWRWF/QRqM3DdQVqcwUTkwA0g7pIbd4OVOVVJyaBWG0eTk4SqM2jSUu2toqV5gBbm6vVvABb7ieTmdAQi3OrDSuyz9LJdqIJUEK1ecXyYMKSrC4oD9XpwfLBBCTRxCQQq81TMDlJqxdMYjEXTEzSi5W+2wvU5gD7d+9a3a9ipfQs0mgsM1cwWUukNi8JkKAZqM2j8nZUHqjN242SyZgCVXkvUKdHE41EivJoeYjV5tHkJFUnLSmbmCRSqC9YhW6MMcbse9iBG2OMMTXEDtwYY4ypIXbgxhhjTA1Z04FLOkfSzZKu6is7XdINkq7M/05Y3800phjbp5llbJ9mPRlGhb4TOAt470D5mSmlN1fqrZFYnitQKEaKyRIlZaQqV6BCV5DPN1q+GSwP0I5ymwf5fLsV1eZzrViZGOUwj9Tm29pR/vKovFjJC7AtqNvWiNtMgZ1MyD5TA/bMrza6OEqifF1FhLnQK6rQo+UhzpMeqdBDtXknyGse5C8H4tzmkdp8vlidu3UuyGvei/PuP6BAbQ5wUOeHq8ra01Oh72QC9tlQYlOBCr0xQi70KOd5U8G5C5bvNIrPaackF3rUJlKPR+WhcrwkF3qc2zyIeGgUj5GxorwkD3tQNx8cj2FZ8wk8pXQpcNtYvRizTtg+zSxj+zTryTi/gb9M0lfyV0TbJ7ZFxkwG26eZZWyfZmxGdeDvAB4CHA3sBt4SLSjpVEmXS7p86c77x7STZuYZyT4X77F9mqkwlH3uZZs/jBMsmX2XkRx4SummlNJSSmkZeBfwuJJlz04pHZNSOqa5eWHU7TRmaEa1z9ac7dOsP8Pa5162uXVuuhtpasFIDlzSQX1ffwW4KlrWmGlj+zSzjO3TTIo1VeiSzgOOA/aXdD3wBuA4SUcDCdgFvGSo3hqgAhV6qJgsUaErUKE3IoVlRbV5lOcXoN0qrusFKvRIVR6VR3nNoUSFHuU2D9TmO1rFit2oHGBbs/gV87ZArTkNJmmfqQF7FgqMLsqFXmKfUc7zquXL0fKB0hxK1OOhOj3IX94NIjeCvOYArW613Oax2rw4suGBc3eGfT+wu1ptDnBoZ7WGrCxf9ySZlH02G4kt3eHnHIjU6RDngY/U5q1And4N8pdHywPMBSrxbqDGjlTlVfOXZ3XVcphHy29pRGr2klzoCubDKDlPw7CmA08pnVRQ/J6xejVmQtg+zSxj+zTriTOxGWOMMTXEDtwYY4ypIXbgxhhjTA2xAzfGGGNqyDC50CeGlOjOrVZeRyr0sny+jUiFHrRpBarydqBCL8vn241yngeqzPlWsTJyISiPFOUAm5pB7uhWtdzmkdp8v2aJCj1Qm29txLnb60RqwGJBKHioNi9ToQe3xrEKPVCOj5ALPVKVL3cCdXBQ3ugG10ygNAeY7wV5pXvFdhtb3hqZAAAFSElEQVTlNo/U5gd37wj7PrxzS2H5Ye1bV5V1AlXwrNLUcmEESjTeNUqU4M1ojAzaRIr9SM0eKcrL6nrBGBKpyrvB8pGiPFtXNRV6nL+8ePnNisfBhUDhP6+yCT/Wxk/gxhhjTA2xAzfGGGNqiB24McYYU0PswI0xxpgaYgdujDHG1BA7cGOMMaaGTDWMrNFYZr4gnKQRKOnLwsia0aQlQZtocpJOECJRNplJrxmEPARhYXPB8lFI2KZWHAoRTU4ShYttC8urT0yyIwjp2NEMYqNqRjaZSYH9jBJGFoWLheFlFcPI2nGYEO0gLLNTbNPNYF3dXmDn3XjCiK294hDI7d1iO3xAtzhsMZqYJAoVAzi8vXrSEoAjCkImu0xnMpNJ0dQy2zqrr80G1cPIotCzdhAWFi0fhX5F64E4/KsXhPVVDRfrlYZyVZu0JAwjC7Y1ChXL2hQPFvMqmZVoCPwEbowxxtQQO3BjjDGmhtiBG2OMMTXEDtwYY4ypIXbgxhhjTA2Zqgq92Uhsm4sn6hgkSq4PsUK9FSgBO0ES/WjSkmh5iFXlUXmUkD9SoW9uxsdoa6Ae3xK02dYIVOgjTEwSqc23NubCNnUim8xkeBV6pCgHIJhsJ1KbE5W3AkV5iQq92QompegEE0l0ArsNyrd0Y/vcEajN9w/U5gd1itXmh3aKFeVFE5OsUKQ2Bzi8tWlVWUe3h+uZRVpaZltr9bGNFOLNMhV6oFyP2kSq8qrlUKZCLx4jI6V7pDYvm8wkbBOUz0cTrwTHvGxikkhtPt/ohG2GwU/gxhhjTA2xAzfGGGNqiB24McYYU0PswI0xxpgaYgdujDHG1BClFOcbn3hn0g+Aa/Ov+wNxYuP1xX1PhwellA6YYn9jYfvcp/q2bY6G+54OQ9nnVB34Xh1Ll6eUjnHf+0bfdWNfPU/7at91Yl89R/tq32X4FboxxhhTQ+zAjTHGmBqykQ78bPe9T/VdN/bV87Sv9l0n9tVztK/2HbJhv4EbY4wxZnT8Ct0YY4ypIRviwCU9R9I3JX1H0mlT7nuXpK9KulLS5evc1zmSbpZ0VV/ZDkkXSfp2/n/7FPs+XdIN+b5fKemE9ei7ztg2bZuzjO3T9tnP1B24pCbwduB44EjgJElHTnkznppSOnoKYQE7gecMlJ0GfDal9DDgs/n3afUNcGa+70enlD6+Tn3XEtumbXOWsX3aPgfZiCfwxwHfSSldk1K6F/ggcOIGbMe6k1K6FBicF/FE4Nz887nA86bYtynHtmnbnGVsn7bPvdgIB34IcF3f9+vzsmmRgE9LukLSqVPsd4UDU0q78883AgdOuf+XSfpK/ppoXV5B1Rjbpm1zlrF92j73Yl8UsR2bUno02WuoP5T0lI3akJSFAEwzDOAdwEOAo4HdwFum2LdZG9umbXOWsX3OmH1uhAO/ATis7/uhedlUSCndkP+/GbiA7LXUNLlJ0kEA+f+bp9VxSummlNJSSmkZeBfT3/dZx7Zp25xlbJ+2z73YCAd+GfAwSQ+W1AGeD1w4jY4lLUjavPIZeBZwVXmriXMhcHL++WTgn6fV8Yrx5/wK09/3Wce2aducZWyfts+9aE27w5TSoqSXAZ8CmsA5KaWvTan7A4ELJEG27x9IKX1yvTqTdB5wHLC/pOuBNwBnAOdLejHZ7EK/McW+j5N0NNmrp13AS9aj77pi27RtzjK2T9vnIM7EZowxxtSQfVHEZowxxtQeO3BjjDGmhtiBG2OMMTXEDtwYY4ypIXbgxhhjTA2xAzfGGGNqiB24McYYU0PswI0xxpga8v8BLdNtfeLcgsUAAAAASUVORK5CYII=\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -112,7 +112,7 @@ "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n", "pl.axis('equal')\n", - "pl.title('Source and traget distributions')\n", + "pl.title('Source and target distributions')\n", "\n", "\n", "# Cost matrices\n", @@ -150,9 +150,9 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "image/png": 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XqtmPqW0p6QGUUbyMXbp8JRIhs7XG3A4UtnZw8E2eTELU28ZAWSTucUoPsNI8w9HtI0Zrz8jy8nLn3im98wJHm2f0359OL+33suDZaxLdTGC8jF26fCWylJD3IOXq/Zg0IbP80DqrUjoVzm4dS/payCHCQFk8z93QP5DKjurR2ksvA4deDgLLrx8PioR6Gbt0+UrgQvpInSzHNJ0Kpy+drqr2pH6KIVcHjvr6/h9fhNekGCjzkHGsentVrz6+8rzcubd7G9rQan3xG03a20sviylxxEvr0+V+SWi8jF26fCVwIcs5/eOHzO1valIVUW1qCr3lzECZh4zjsvxy0/p/akb/vdWV5OX/ntOkvRBtO5teFlMYLwMmAfEydunylVBSQuWY/vFLnuENw/4xMlAWwHH6zkA7q1K69qIm60cxC4wsGYiXL2rS7qFVfdfJ6SXjZeQkIF7GLl2+ErSQc+eq9TfwR0m2/RHWNR4GytzQy4HQS3ppIzZ6Gbt0+UrJQsbYgkoM3hZ2iNd4GCg9ZHj50kuqy09s0h1D6WUfHi+3Vqd0xeR6ffX24CsOeumB8bIwmRmJkO4VZwWuGus1jEQQ4f5hoPSQsd+XnWiu+W6/il6q6m77p/MP5ha6ruE1uuamYFO39NID42V+svUJCOmuJVbgaSI6w0wkEba4GSgzcBztHu3eIjasWrsX0Ms+snjZ9YCjz3yyvu+2OjYsGS8jx09P9Yi9jF26fKVUIaO8ZlEOhLm/GCj7oZfFE9Y+o5f90MviidvL2KXLVwJvUVZy+scvIbXAGSj72bxZ9c4L+h+zSS994phnmS/HNJNSf8Chl4yX8ePus+WYZlLqTnRexi5dvlK0kJkpDscx1yfq63kNxw8hXgOr6EDp8fL731e97zuObhteo1u/TC9949lHU2H23/a9RutUOPSS8TI+MrzUmhrV0dF5Gbt0+UrRQmZWQPX1gbWIKoIQr4lXdKB0vexZbrxs/Wi99oyil0WR4ebaZke7htfockzru19+MNBLxsuSyNEIWo5pkcTL2KXLVwaVEmIKKDCCvL5T0YFSVdXpf1hH92h6WQr0kvHSRuLwMnbp8pVihWQnjBAI6AdeyYGSXoaA4/QNM7u1KqXv3ksv6aUFRBwvhyDpLFwItLT0vVSnBVfU/BSPYBquSF0PdVrQ0BDf6iWalhagrg5YsgSdGAnMng3U1aF5Rkv/+wsXxruOtuLxUhV44JIWXDqCXgaC66W4Xv5lymwM/Y86LJpOLwvCeBkenng5F/OB008HTjsNaPHs06Dd9FPLFyoATgCwCsBqAJdleX8mgA4AT7nlPD/L9dWiDKkTAdEB13cWTXf6xkKehcai9y1iONOxycuu4TXaM5JeBkKGlzvGpHTpZ5PjpYbkJuNlzATYKdCvl0GIuAeAFwEcDGAYgKcBHJkxz0wAPy922b5TQiF04ydZKCE9FHWgtMXLnXsbL7urRmvvo/QyDHofdbRrZGpQDz6JqWEZipuMlxYyyJjp18sgUugfA7BaVdeo6g4AiwGcEsByfdHQAMixtZi/6UIch0cxf/O3IMfW9s9QWwtcemlUq1O2ePfzFbgS8zddCDm21uZ0mxVeXvW28fLqrm9hyDR6GTQNDcCQabW4ptN4edXb1nsJxOgm42V0RBIz/dTy+QqAMwDc6Hl9DjJajjCtyVcB/APA3QAOzLO8egBtANrGjx8faiuHFEmyzsBj9/L1xU7gQ3+SLHi87KxK6aa77PVSA3aT8dJyQj4Dj0rGcQCGu/+fD8Dxs+xir+lw8IEQ8ezXWWjsey5w+tGChVJvllbgoXq5bVRKF59PL0Mlw8tHT27SbaPs9VJDdJPx0jLS+7a+vq+PQWeVu68D8jKIFPoGAAd6Xh/gTutDVd9U1W735Y0AjirpGz09KZdf1QosWQIAWHbcQpMCWrIEaG0t6StIBq3ufq6txZHTJwMLFgCzZ2PlLa39vS8nT457Lb1E7yXQ5+ZbD7fisvcvwUEza03v6PT+o5fBkuHlp55YgMc+YbWXQJwxs7UVzSeZ/UUvQya9b888E/dX1QEATu5aAixeHJyXfmr5fAXAngDWAHg/+jtkfDBjnn09/58G4G9+lp2zRclWZPwUmRpC9Gfg0Xvp7pfeVErv+aZxs2c53YySncv6x5j3M0Jb1F5qiG4W8pIxM0ZCipdBCXkSgOdhelZ+z502H8C/u/8vAPBPV9QWAB/ws1w/QvI6TvQMZgCImAJl9F6q6rM/47XvOEiKlxqSmwVT6IyZsRCml5GLW0zJJSRHELIAy8/Awyz5AiXdjBnPCG3ba+hlGnoZMzafgYdVeAZuKYPonFEpgXLbNtVbv+ro9hq6GTkZXt52Lr3Mtn/oZcSEGC+TNZRquiNGSwu2nmyGrGtBrRmyrq5uwBCBJESi6JyRJDydKn/+hRactrgO2/9rthl+dskSuhkVHi//sFcdenoq3EuAMdMGwoyXfmr5uMpuLcoSuuWTkPDZqkc5n+m4+6D3UXMr0xNfaNLeIm6xI8HT+6ijXdUV7qUqY6ZtBBwvY5cuX8knJNNA8VPMdbVKCJQ793YfF+rj2isJD3qZAWOmFYThZezS5SuZQrIjhoXwDJxe2ojj6K6xxssdYyrTS1W6aR08A2dr0hqKuLe03ANleuQ1emkBnksagOoj36tgLz37g27GTAjxMjmd2BYuBH70I/M86pPc563Ong2cfDI7YsSFZxSnRdNbgNpaM8pTa2vlPJPZ9VLr6nD5IfTSClwvpa0VVx7bgr9X1WLRiRXoJTuv2UUY8dJPLR9XGdCidBzV6mrVpiYFBj6fmh0xYsZHyxLleqbjernpcuPlT0+ll9bg9ktonlGhXrLzmp0EGC9jly5fydXbl6kgCylwbMo2ULrbnk6f+xm+k0RH99L8o+KVu5eMl5YSULyMXbp8xSskO2PYi59jU66Bkl7aC72klzYSpJexS5evsEWZICr8DHyrj3uOSQw4+UfFK3cvGS8tpdLOwPk0HYvJODZ9/RMq5Fpjbyr/dVYSE+6xeOrH5tj85suV5SXjpaUEGC+T0Qt94UIz7NySJWh4rBZz57rTTz+dz7G1Ac8zmefOBb5yS4U8k931ctMvlmDRy7X4z/90p9NLO3C9HHZ8LaZMAc69rbK8ZLy0lCDjpZ9aPq7S16JkazIR9PaqPvmkmmOUAcrxTMf18IVfGy/f+R29tJGODtWGhsrzkvHSfp55pjQvY5cuX8mWEuL1HDtJd8zILOmOGWUZKFXZA91yKtlLxkt7CcrL2KXLV9JCskdlMnAc1XnztGLOdOhlcujooJf00i5WrSo9M5SIa+ANDYA6LbgidT3mYw6uSF0PdVrQ0BD3mhEvK1cCBx0U91pER9rL79bQS9tJpeJeg+hgvEwG7e3A8OGlLSOQClxEThCRVSKyWkQuy/L+cBG5033/7yIyoagvaGkxw/8tcYeq5DOWraOjA9i0CZg4Ef2dZiwgVDddL5/4Jr1MApXmJeOlvezaBTz3HHDEEaV5WXIFLiJ7APgFgBMBHAngLBE5MmO2cwG8raqHAvgxgMaivuSaa8z40rWmNylqa83ra64pdfVJQLS3m78TJ8Kaln7obrpebji8FocfDnppOZXmJeOlvbz8MrB9e+nxMogz8I8BWK2qa1R1B4DFAE7JmOcUADe7/98NYJqIiO9vmDULWLAAaGnBY4/BtCQXLDDTiRW0twMHHgiMGhX3mgwgXDddL8c+3YLnnwe9JH6JxEvGS3tpbweGDgUOOaS05QRRge8PYJ3n9Xp3WtZ5VLUHwGYA47ItTETqRaRNRNo6OjrMxNravjTQPFzRlx5CbW0Aq09K5a23gNdeM61JywjMzXxe1t5AL0lRROIl46Wd9Paa9Pnhh5tKvBSs68Smqr9S1UmqOmmfffYBYFIMcmwt5m+6EFfgSszfdCHk2FprUmKVjjd9Xq7k87Kpi16SeGC8TB6vvAJs3RpMvAyiAt8A4EDP6wPcaVnnEZE9AYwG8KbfL2CvSrtpbwf23RcYMybuNdmNUN1Me3npKHpJiiISLxkv7aS9HdhzT+Cww0pfVhAVeCuAw0Tk/SIyDMCZAO7LmOc+ADPc/88A4Lj3uvnj/POBU08d2Kvy1FPNdBIrmzcDGzYAR2Z2wbGDcN10vXzyYnpJiiISLxkv7UPVVOCHHgoMG1b68vYsfYW0R0S+AeBhAHsAuElV/yki82FuRr8PwG8A3CoiqwG8BSNscWT23yiiDxwJD5vT55G4KTLwh0gvSQGi8jLvaxILGzYAW7YEGC/9jPYSV+HQgPZz002q111XeD6U4YhXqjrgcZUcSjV5lLOXjJf28fDDqvPnq27bln8+v15a14ktG+yUYSednaZDho1n31GQ9rLxXePllfSSWADjpZ2omz4/+GBgxIhglpmYCpydMuwjnT639Pp36KS9vHxv4+X3xtJLEj+Ml3by2mvAO+8EGy8TUYFzaEA7aW8Hxo0D3LtXKg/Xy523GS+fv5JeEgtgvLSSlStNV4QjjghumcmowFtbzcPoYcaNbXjMHahg8WLz8HoSOV1dZjjAiRMruH+M6+WIEcBxxwHzH6eXxAIYL60jnT6fMAGoqgpuucmowC+9FDjzTKCuDg1TWjBvnjv9nnuAyZNjXbVKZdUqI2Wlps8B9HkpX6rDJZNasHixO51ekjhhvLSOjg7gzTeD7y9U8m1kkTFgeMALgbrrOTxgjKxcaQZued/74l6TmHG9nHKK8VLrrofQSxI3jJdWsXKl+Rt0BZ6MM3CwZ6VNbN8OrFlT4elzl7SXC7ewJzqxB8ZLu2hvB8aPB0aODHa5iarA2bPSDp5/3gzIX6m3j3lJeznH9fKy0fSSxA/jpT28+SbwxhvhxMvEVODsWWkP7e3msaEHHBD3mliA66W4Xi47l14SC2C8tIYwR6tMzjXw1lYjYWsrFk0HUFuL5pOWYGZra//7l14a6ypWAjt2AKtXAx/9KNPnAAZ4efXxwIqaWrz22SU4j16SOGG8tIb2dmD//YHRo4NfdnLOwC+91HTAmDwZMx80Lcmv3GJeo66OvSsj4oUXgJ4eps/78Hg5q7UOE15qwddup5ckZhgvreCdd4CNG8OLl8k5A0/D3pWx0t4OVFebDhnEQ20t9M4lOOOUOqyll8QWGC9jJeyHPSXnDNyFvSvjY+dO04HtiCOAIYkzJ1waGoA9P1OLpi56SeyB8TJe2tvNrbZjx4az/MSFYfaujI8XXzSVeEUP3pKDtJffH8fe6MQeGC/jY8sWYN26cC83Jq4C7+tdefrpaIFJD2092e1d2dLCoQJDpL3dPEVnwoS418RCXC+HfMF4ec+X6CWxAMbL2Ag7fQ4ksQJP964880zcX1UHADi5yx3nl50zQmPXLjN86gc+AOyxR9xrYyEeL/8wog7d3fSSWADjZWy0twOpVLgPe0peJzbPrQ/V95vOGbW40Izzy84ZobFmDdDdzd7nOfF6+cASfPHzpjOb3nMPh1Yl8cF4GQtbtwJr1wLHHBPu95R0Bi4iY0VkuYi84P7dO8d8u0TkKbfcV8p3pmHnjGhpbweGDTMPo08CcbnZ0AAMmVaLa7s4tCrZnTi9ZLyMjueei+hhT6o66AJgIYDL3P8vA9CYY77OwSz/qKOO0rw4jmoqpfMwRzWVMq9J4OzapdrYqHr33YNfBoA2LcG1YkuYbvrxstf1smtkSnuW00tbqTQvGS+j4dZbVX/6U9Xe3sF93q+XpV4DPwXAze7/NwM4tcTl+cczVGAnRgKzZwN1dWie0dL/PjtoBMLatcC2bYlLn8fjpmdo1U6MxJ8+ORu9X6SXpI9YvWS8DJ9t24CXXormYU+lVuDvVdVX3f9fA/DeHPONEJE2EfmbiOQVVkTq3XnbOjo6cs+Y7pxRW4sjp08GFiwAZs/Gylta+2VlB41AWLkS2HNP4NBD416TogjUzcF4OfGcyZjylwV4/Bh6SfqI3UvGy3BZtco87CmS220LnaIDeATAs1nKKQDeyZj37RzL2N/9ezCAlwEc4ic9UDAl5IXpoVDo7VW99lrVO+8sbTkIIVUZl5vFeLnxt452VtFLW6lULxkvw+P221V/9KPBp89VA0yhq+pxqvoj9NXaAAAMbUlEQVQvWcq9AF4XkX0BwP37Ro5lbHD/rgHwRwAfKfS9xcAOGuGxbh3Q2Wln+tx2NxsagP3+g6OzVRpJ8JLxMhy6u82AV1Gkz4HSU+j3AZjh/j8DwL2ZM4jI3iIy3P0/BeD/AVhZ4vcOgKMNhcfKlea+78MPj3tNiiZ2NzOfFX5J9fXoXkovKxxrvGS8DJ4XXjBjZkQ1WmWpFfjVAD4jIi8AOM59DRGZJCI3uvNMBNAmIk8DaAFwtaoGWoGzg0Y4qJrbxw45BBg+PO61KZr43czo0Pb4MbOh9LLSscZLxsvgaW8HRo4EDjwwmu8raSAXVX0TwLQs09sAnOf+/xcA/1rK9xRkQAcNAAvqTAeNi1uBmeiTlRTHxo3Au+8mc6wHK9zM8LL23jq0HO12HJoJelmB2Ogl42Uw7NxpzsA//OFo0udAEodSzUb62bcAZt7sPj5vwQKMRGe/jEmsheJg4ULTAodJnw8ZAkx8jS3yQZHh5R53L8Gn/my83HUGvSwKj5d98ExxcDBeBovr5urVphKfOBGRuVkeFbgHdtAokcmTgbo6qNOC9nbg6G0tGH4ObzEplfTjRq/darz84Vv0sihcL/sqcd76FAiMlwHgutmxpAV77QVMeClCN/10VY+rFHVbhBfvLRJVVapNTaqqOneu5/3GxsEtuxJwHN01NqV//PQc3TEmmFtMEPGIV2GWILzsHlqlfz6jSbu76aVv3BHuNp43R3sDuvWJXirjZQD0LHd0a3VKn6sL5rY8v17GLl2+MighXRnVcczWNTWpiqg2NZnXnvdJbl6ePkcV0O7vzglkeRUfKDO8fOOyJu2F6JNn0Uu/7Nyp+o/TjJfvXEQvMwvjZXy88ILqHz9t3NQ5pbtZuRV4Y2OfbH0tyKYm1epqDlqQjRz7q7e6WjdfFNz+qvhAmWU/v3xRk3YPNV4GdUZZNmTsr507VdvONvtrw3n0MlthvIyAbPvLcbT3a/XaMzalu77HM/DShMxg7lyzlfNgWkfzMEcBz86vdPK0wDPfL4WKD5QZ0MsCZHj55FkmY7HuO/QyV6GXEZAZLx1HtaZGdfTofhcDcJMVuBde48lPjv0z4P0S9w8DZRYyron/7UtNum0bvezD81S37qFVuv479DJfYbyMiMxhaOvrd6+sS9xHrMDT8BpPXqJqcTNQZpDh5cZZ5gzz8dPppSq9HExhvAwf27yMXbp8JRAheY1nIDHtDwbKDLIchzdm918T7xlb2V6uXq36yOf69we9ZLyMBcvjZezS5SuBpYQ8ZGtBzUKjLpq+e8eEskwTxdTCZqDMTy4vbzqnsrzsfdR4ufSzJiPReSW99FsYL0PA8ngZu3T5ShhCquru1zCamnbvmFDOLc0YrnExUPrAc1y216R06WebdNuolHY9UBlevnuvuZd2HubozmFVurORXhZTGC9DwuJ4Gbt0+UooQmbrReiRsqzSRFnSP4umOzoLjZH3MmWgLECGl72POrpjTEofPqGp73niZXOrWYaXvb2q15xEL0stjJclksB4Gbt0+UooQvo8SGWRJrLox8dAWYAK9vKBWY52VqX0ybOadNc4ejnYQi9LJIHxMnbp8pXQUkLZKNc0kSXbxUA5SDy3Um2tTunS45t0e03y0+pdDzjaNbJ/u9Zf3NSXYaCXyfDShrgSOJZsFyvwYvDb8vLc72ddK9PyljID5SDI8HL7Q452u9fG+9Lq45Ll5ebNqj+YRi/DKIyXRVAm8TJ26fKVyIT0eTCnwqT6Ym1l5hjKT+vrrUn/ZIOBchAU4eWWvSw4+8lzy82rt5t1u3mm+Q0985Xo0+XZoJeDIEnxMsf6pr1MeryMXbp8JdKUUDYy0ymOs/u0qFuZuVq/2dbNorQWA2WAeI7z9pqU3nauo80zHO2q9nR2i+PsJ8PNbQ+ajnh//VJ/xmB7TUq7fkAvwyg2eWlNvPSsV8HKOoHxsiRhAHwRwD8B9AKYlGe+EwCsArAawGV+lx+rkFkOemdVSqfC8dfKDELUAmfb3h+K7fdrRh0ow3TTOi/3yu7luyNSuu3BCL1sbNSt9zvaPdq42VmV0os+5NDLSvXSlniZnu6JmYuml4eXpco4EcARAP6YS0YAewB4EcDBAIYBeBrAkX6WH6uQRVSe+VqeA0TNdQ/hiSf6Totn+1H03daQbT0sIYZAGZqbtnrZN2746JTe9XVzVu49833xRkff+Z2TvbNYtgBaX29Klu/yevnijY5ur0npZR/fPSgCagajoZcV6+Wg42WJXqaXka2yBkzaP+leBiVlPhk/AeBhz+vZAGb7WW7sKaFMfLYyc1aouVI0+VI3ftJS+X4AlkgZV6oyDDeT7OWUKaq/Pa9/wJTu0Sltv87Rlxc52jM2pRt/a5bx9j2O7hpVo7tGjdZ1t5hpz11vzq4fa3D0gUv6GwedVSaNf+utqk/92KTNbUxLZoNehkip8TJXXPM8/ctXvNQsyy4TL6OQ8QwAN3penwPg536Wa52QJaa087b6soiXbRmhpqBCxNJAOSg3k+hlbyqlb97taH19di+nTNEBZ+ydVSltnuHsNu3mmY4ef3x+t31lneglvfQRLwtV7MXE3HL00o9ojwB4Nks5xTNPYDICqAfQBqBt/Pjx4e+pUsl35uvjuks6ePqW1/KKOhdhBMoo3SxXL3tT5p7yiy/O7mBRQTXfNUhLoZcRU0S8zFcp00ufFbivhVRKCj0bQd3WlcC0eDFYeqZTHqnKbBTrZRldrikGehkx9NIXNlXgewJYA+D96O+Q8UE/y02EkLkYxL2HSUuLF4OlgXJQbpadl7kCaK5rjfSSXgYNvRxAJBU4gNMArAfQDeD1dKsRwH4AHvTMdxKA52F6Vn7P7/ITLWQ2ckmaqxd6AsXLRdSBMkw3y85L1exu5urtSy/pZVTQy7xFzLx2MmnSJG1ra4t7NUgAiMgKVZ0U93oEAb0sH+glsRG/Xg6JYmUIIYQQEiyswAkhhJAEwgqcEEIISSCswAkhhJAEwgqcEEIISSCswAkhhJAEwgqcEEIISSCswAkhhJAEwgqcEEIISSCswAkhhJAEwgqcEEIISSCswAkhhJAEwgqcEEIISSCswAkhhJAEwgqcEEIISSCswAkhhJAEwgqcEEIISSCswAkhhJAEUlIFLiJfFJF/ikiviEzKM9/LIvKMiDwlIm2lfCchhaCXxFboJgmSPUv8/LMATgfwSx/z1qrqphK/jxA/0EtiK3STBEZJFbiqtgOAiASzNoQEAL0ktkI3SZBEdQ1cASwTkRUiUp9vRhGpF5E2EWnr6OiIaPVIhUIvia34cpNeVjYFz8BF5BEA78vy1vdU9V6f33OMqm4QkfcAWC4iz6nqn7LNqKq/AvArAJg0aZL6XD6pMOglsZUo3aSXlU3BClxVjyv1S1R1g/v3DRH5HYCPAcgaKL2sWLFik4iszZicAlBO14XKbXuA7Nt0UJBfQC9Dp9y2B4jASyA+NyvES6D8tmnQXpbaia0gIlINYIiqbnH//yyA+X4+q6r7ZFlem6rm7L2ZNMpte4BkbBO9zE+5bQ+QnG0arJuV4CVQfttUyvaUehvZaSKyHsAnADwgIg+70/cTkQfd2d4L4AkReRrAkwAeUNWlpXwvIfmgl8RW6CYJElFN1mUTtr7spxy3qRDlts3ltj1AeW5TIcpxm8ttm2I7A4+JX8W9AgFTbtsDlOc2FaLctrnctgcoz20qRDluc7lt06C3J3Fn4IQQQghJ5hk4IYQQUvGwAieEEEISSCIrcL8PBLAdETlBRFaJyGoRuSzu9SkVEblJRN4QkWfjXpc4oJd2Qi/ppY0E4WUiK3D0PxCg4KAbtiIiewD4BYATARwJ4CwROTLetSqZZgAnxL0SMUIv7aQZ9JJe2kczSvQykRW4qrar6qq416NEPgZgtaquUdUdABYDOCXmdSoJd6jHt+Jej7igl3ZCL+mljQThZSIr8DJhfwDrPK/Xu9MIiRN6SWyEXmYh9KFUB0tADwQgJFDoJbERelmZWFuBB/FAAMvZAOBAz+sD3GnEYuglsRF6WZkwhR4frQAOE5H3i8gwAGcCuC/mdSKEXhIboZdZSGQFnuuBAElCVXsAfAPAwwDaASxR1X/Gu1alISJ3APgrgCNEZL2InBv3OkUJvbQTekkvbSQILzmUKiGEEJJAEnkGTgghhFQ6rMAJIYSQBMIKnBBCCEkgrMAJIYSQBMIKnBBCCEkgrMAJIYSQBMIKnBBCCEkg/wfPnU7C6ZZu4AAAAABJRU5ErkJggg==\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -366,7 +366,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.5" } }, "nbformat": 4, diff --git a/notebooks/plot_WDA.ipynb b/notebooks/plot_WDA.ipynb index f40dbd0..df46812 100644 --- a/notebooks/plot_WDA.ipynb +++ b/notebooks/plot_WDA.ipynb @@ -34,7 +34,16 @@ "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rflamary/.local/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", + " from ._conv import register_converters as _register_converters\n" + ] + } + ], "source": [ "# Author: Remi Flamary \n", "#\n", @@ -105,9 +114,9 @@ "outputs": [ { "data": { - "image/png": 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D04lYYUAIkQesB74rpTxu3y6lfAh4CGDKlCmym28vIZLV/FK6ppiAD0AsQWoX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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -176,28 +185,36 @@ "Compiling cost function...\n", "Computing gradient of cost function...\n", " iter\t\t cost val\t grad. norm\n", - " 1\t+8.7135243329934142e-01\t4.22283975e-01\n", - " 2\t+4.5259877952239763e-01\t2.80207825e-01\n", - " 3\t+4.2301660192758839e-01\t2.57544116e-01\n", - " 4\t+3.6438385605814744e-01\t2.01503900e-01\n", - " 5\t+2.6854415219016237e-01\t1.86872752e-01\n", - " 6\t+2.3605613971887493e-01\t8.54873065e-02\n", - " 7\t+2.3238632608008850e-01\t4.70510545e-02\n", - " 8\t+2.3084542185757945e-01\t8.60266814e-03\n", - " 9\t+2.3083921287882422e-01\t8.05123557e-03\n", - " 10\t+2.3081791779181188e-01\t5.75567680e-03\n", - " 11\t+2.3081444006658824e-01\t5.32065675e-03\n", - " 12\t+2.3080311057315009e-01\t3.34753227e-03\n", - " 13\t+2.3079768318049571e-01\t1.75615642e-03\n", - " 14\t+2.3079588611065080e-01\t6.04609566e-04\n", - " 15\t+2.3079584350945395e-01\t5.50079922e-04\n", - " 16\t+2.3079571065356075e-01\t3.07865387e-04\n", - " 17\t+2.3079565041678046e-01\t3.29364280e-05\n", - " 18\t+2.3079564985490117e-01\t1.32999543e-05\n", - " 19\t+2.3079564975468964e-01\t3.81768629e-06\n", - " 20\t+2.3079564974709890e-01\t1.50474730e-06\n", - " 21\t+2.3079564974588401e-01\t5.41516789e-07\n", - "Terminated - min grad norm reached after 21 iterations, 7.93 seconds.\n", + " 1\t+8.1744675052927340e-01\t5.18943290e-01\n", + " 2\t+4.6574082332790778e-01\t1.98300827e-01\n", + " 3\t+4.4912307637701449e-01\t1.38157890e-01\n", + " 4\t+4.4030490938831912e-01\t7.00834944e-02\n", + " 5\t+4.3780885707533013e-01\t4.35903168e-02\n", + " 6\t+4.3753767094086027e-01\t5.81158060e-02\n", + " 7\t+4.3658534767678403e-01\t4.44189462e-02\n", + " 8\t+4.3516262357849916e-01\t4.15971448e-02\n", + " 9\t+4.3332622549435446e-01\t7.82365182e-02\n", + " 10\t+4.2847338855201011e-01\t5.28789263e-02\n", + " 11\t+4.1510883118208680e-01\t6.83664317e-02\n", + " 12\t+4.1332168544999542e-01\t1.01013343e-01\n", + " 13\t+4.0818672134323475e-01\t5.07935089e-02\n", + " 14\t+4.0502824759368472e-01\t9.56110665e-02\n", + " 15\t+3.9786250825732905e-01\t6.68177888e-02\n", + " 16\t+3.5518514892526853e-01\t2.01958719e-01\n", + " 17\t+2.5048183658126072e-01\t1.82260477e-01\n", + " 18\t+2.4055179583813954e-01\t1.48301002e-01\n", + " 19\t+2.2127351995549377e-01\t1.77690944e-02\n", + " 20\t+2.2106865089529223e-01\t6.44501056e-03\n", + " 21\t+2.2105965534255226e-01\t5.35361879e-03\n", + " 22\t+2.2104056962319110e-01\t3.63028924e-04\n", + " 23\t+2.2104051220659457e-01\t2.27022248e-04\n", + " 24\t+2.2104049071158929e-01\t1.43369832e-04\n", + " 25\t+2.2104048094656506e-01\t7.87652127e-05\n", + " 26\t+2.2104047690862835e-01\t1.39217014e-05\n", + " 27\t+2.2104047689800282e-01\t1.33400288e-05\n", + " 28\t+2.2104047685931880e-01\t1.09433285e-05\n", + " 29\t+2.2104047677977950e-01\t3.27906935e-07\n", + "Terminated - min grad norm reached after 29 iterations, 8.50 seconds.\n", "\n" ] } @@ -230,9 +247,9 @@ "outputs": [ { "data": { - "image/png": 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naGsxA0dn89j6B5g7Vp366IqioHObwy+mnI87KV6nuwu5d0omALMWlZqRsQDZ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4JLr2B0SamDoSy49AqHtmIeG11SSnFODmNKB/Hu9JvtY/WtrdS3tjreSY9D7wtLyYg5D9vvYRJf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\nSHZxrIqggLramyfI/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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, diff --git a/notebooks/plot_barycenter_1D.ipynb b/notebooks/plot_barycenter_1D.ipynb index bf83aea..4a0956b 100644 --- a/notebooks/plot_barycenter_1D.ipynb +++ b/notebooks/plot_barycenter_1D.ipynb @@ -39,9 +39,9 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -102,8 +102,8 @@ "x = np.arange(n, dtype=np.float64)\n", "\n", "# Gaussian distributions\n", - "a1 = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std\n", - "a2 = ot.datasets.get_1D_gauss(n, m=60, s=8)\n", + "a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\n", + "a2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n", "\n", "# creating matrix A containing all distributions\n", "A = np.vstack((a1, a2)).T\n", @@ -242,7 +242,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.5" } }, "nbformat": 4, diff --git a/notebooks/plot_barycenter_lp_vs_entropic.ipynb b/notebooks/plot_barycenter_lp_vs_entropic.ipynb new file mode 100644 index 0000000..e188875 --- /dev/null +++ b/notebooks/plot_barycenter_lp_vs_entropic.ipynb @@ -0,0 +1,429 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# 1D Wasserstein barycenter comparison between exact LP and entropic regularization\n", + "\n", + "\n", + "This example illustrates the computation of regularized Wasserstein Barycenter\n", + "as proposed in [3] and exact LP barycenters using standard LP solver.\n", + "\n", + "It reproduces approximately Figure 3.1 and 3.2 from the following paper:\n", + "Cuturi, M., & Peyré, G. (2016). A smoothed dual approach for variational\n", + "Wasserstein problems. SIAM Journal on Imaging Sciences, 9(1), 320-343.\n", + "\n", + "[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015).\n", + "Iterative Bregman projections for regularized transportation problems\n", + "SIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n", + "\n", + "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time : 0.010513782501220703 s\n", + "Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective \n", + "1.0 1.0 1.0 - 1.0 1700.336700337 \n", + "0.006776453137632 0.006776453137633 0.006776453137633 0.9932238647293 0.006776453137633 125.6700527543 \n", + "0.004018712867874 0.004018712867874 0.004018712867874 0.4301142633 0.004018712867874 12.26594150093 \n", + "0.001172775061627 0.001172775061627 0.001172775061627 0.7599932455029 0.001172775061627 0.3378536968897 \n", + "0.0004375137005385 0.0004375137005385 0.0004375137005385 0.6422331807989 0.0004375137005385 0.1468420566358 \n", + "0.000232669046734 0.0002326690467341 0.000232669046734 0.5016999460893 0.000232669046734 0.09381703231432 \n", + "7.430121674303e-05 7.430121674303e-05 7.430121674303e-05 0.7035962305812 7.430121674303e-05 0.0577787025717 \n", + "5.321227838876e-05 5.321227838875e-05 5.321227838876e-05 0.308784186441 5.321227838876e-05 0.05266249477203 \n", + "1.990900379199e-05 1.990900379196e-05 1.990900379199e-05 0.6520472013244 1.990900379199e-05 0.04526054405519 \n", + "6.305442046799e-06 6.30544204682e-06 6.3054420468e-06 0.7073953304075 6.305442046798e-06 0.04237597591383 \n", + "2.290148391577e-06 2.290148391582e-06 2.290148391578e-06 0.6941812711492 2.29014839159e-06 0.041522849321 \n", + "1.182864875387e-06 1.182864875406e-06 1.182864875427e-06 0.508455204675 1.182864875445e-06 0.04129461872827 \n", + "3.626786381529e-07 3.626786382468e-07 3.626786382923e-07 0.7101651572101 3.62678638267e-07 0.04113032448923 \n", + "1.539754244902e-07 1.539754249276e-07 1.539754249356e-07 0.6279322066282 1.539754253892e-07 0.04108867636379 \n", + "5.193221323143e-08 5.193221463044e-08 5.193221462729e-08 0.6843453436759 5.193221708199e-08 0.04106859618414 \n", + "1.888205054507e-08 1.888204779723e-08 1.88820477688e-08 0.6673444085651 1.888205650952e-08 0.041062141752 \n", + "5.676855206925e-09 5.676854518888e-09 5.676854517651e-09 0.7281705804232 5.676885442702e-09 0.04105958648713 \n", + "3.501157668218e-09 3.501150243546e-09 3.501150216347e-09 0.414020345194 3.501164437194e-09 0.04105916265261 \n", + "1.110594251499e-09 1.110590786827e-09 1.11059083379e-09 0.6998954759911 1.110636623476e-09 0.04105870073485 \n", + "5.770971626386e-10 5.772456113791e-10 5.772456200156e-10 0.4999769658132 5.77013379477e-10 0.04105859769135 \n", + "1.535218204536e-10 1.536993317032e-10 1.536992771966e-10 0.7516471627141 1.536205005991e-10 0.04105851679958 \n", + "6.724209350756e-11 6.739211232927e-11 6.739210470901e-11 0.5944802416166 6.735465384341e-11 0.04105850033766 \n", + "1.743382199199e-11 1.736445896691e-11 1.736448490761e-11 0.7573407808104 1.734254328931e-11 0.04105849088824 \n", + "Optimization terminated successfully.\n", + "Elapsed time : 2.89129376411438 s\n", + "Elapsed time : 0.014848947525024414 s\n", + "Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective \n", + "1.0 1.0 1.0 - 1.0 1700.336700337 \n", + "0.006776466288966 0.006776466288966 0.006776466288966 0.9932238515788 0.006776466288966 125.6649255808 \n", + "0.004036918865495 0.004036918865495 0.004036918865495 0.4272973099316 0.004036918865495 12.3471617011 \n", + "0.00121923268707 0.00121923268707 0.00121923268707 0.749698685599 0.00121923268707 0.3243835647408 \n", + "0.0003837422984432 0.0003837422984432 0.0003837422984432 0.6926882608284 0.0003837422984432 0.1361719397493 \n", + "0.0001070128410183 0.0001070128410183 0.0001070128410183 0.7643889137854 0.0001070128410183 0.07581952832518 \n", + "0.0001001275033711 0.0001001275033711 0.0001001275033711 0.07058704837812 0.0001001275033712 0.0734739493635 \n", + "4.550897507844e-05 4.550897507841e-05 4.550897507844e-05 0.5761172484828 4.550897507845e-05 0.05555077655047 \n", + "8.557124125522e-06 8.5571241255e-06 8.557124125522e-06 0.8535925441152 8.557124125522e-06 0.04439814660221 \n", + "3.611995628407e-06 3.61199562841e-06 3.611995628414e-06 0.6002277331554 3.611995628415e-06 0.04283007762152 \n", + "7.590393750365e-07 7.590393750491e-07 7.590393750378e-07 0.8221486533416 7.590393750381e-07 0.04192322976248 \n", + "8.299929287441e-08 8.299929286079e-08 8.299929287532e-08 0.9017467938799 8.29992928758e-08 0.04170825633295 \n", + "3.117560203449e-10 3.117560130137e-10 3.11756019954e-10 0.997039969226 3.11756019952e-10 0.04168179329766 \n", + "1.559749653711e-14 1.558073160926e-14 1.559756940692e-14 0.9999499686183 1.559750643989e-14 0.04168169240444 \n", + "Optimization terminated successfully.\n", + "Elapsed time : 2.7255496978759766 s\n", + "Elapsed time : 0.002989530563354492 s\n", + "Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective \n", + "1.0 1.0 1.0 - 1.0 1700.336700337 \n", + "0.006774675520727 0.006774675520727 0.006774675520727 0.9932256422636 0.006774675520727 125.6956034743 \n", + "0.002048208707562 0.002048208707562 0.002048208707562 0.7343095368143 0.002048208707562 5.213991622123 \n", + "0.000269736547478 0.0002697365474781 0.0002697365474781 0.8839403501193 0.000269736547478 0.505938390389 \n", + "6.832109993943e-05 6.832109993944e-05 6.832109993944e-05 0.7601171075965 6.832109993943e-05 0.2339657807272 \n", + "2.437682932219e-05 2.43768293222e-05 2.437682932219e-05 0.6663448297475 2.437682932219e-05 0.1471256246325 \n", + "1.13498321631e-05 1.134983216308e-05 1.13498321631e-05 0.5553643816404 1.13498321631e-05 0.1181584941171 \n", + "3.342312725885e-06 3.342312725884e-06 3.342312725885e-06 0.7238133571615 3.342312725885e-06 0.1006387519747 \n", + "7.078561231603e-07 7.078561231509e-07 7.078561231604e-07 0.8033142552512 7.078561231603e-07 0.09474734646269 \n", + "1.966870956916e-07 1.966870954537e-07 1.966870954468e-07 0.752547917788 1.966870954633e-07 0.09354342735766 \n", + "4.19989524849e-10 4.199895164852e-10 4.199895238758e-10 0.9984019849375 4.19989523951e-10 0.09310367785861 \n", + "2.101015938666e-14 2.100625691113e-14 2.101023853438e-14 0.999949974425 2.101023691864e-14 0.09310274466458 \n", + "Optimization terminated successfully.\n", + "Elapsed time : 2.594216823577881 s\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Author: Remi Flamary \n", + "#\n", + "# License: MIT License\n", + "\n", + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot\n", + "# necessary for 3d plot even if not used\n", + "from mpl_toolkits.mplot3d import Axes3D # noqa\n", + "from matplotlib.collections import PolyCollection # noqa\n", + "\n", + "#import ot.lp.cvx as cvx\n", + "\n", + "#\n", + "# Generate data\n", + "# -------------\n", + "\n", + "#%% parameters\n", + "\n", + "problems = []\n", + "\n", + "n = 100 # nb bins\n", + "\n", + "# bin positions\n", + "x = np.arange(n, dtype=np.float64)\n", + "\n", + "# Gaussian distributions\n", + "# Gaussian distributions\n", + "a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\n", + "a2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n", + "\n", + "# creating matrix A containing all distributions\n", + "A = np.vstack((a1, a2)).T\n", + "n_distributions = A.shape[1]\n", + "\n", + "# loss matrix + normalization\n", + "M = ot.utils.dist0(n)\n", + "M /= M.max()\n", + "\n", + "#\n", + "# Plot data\n", + "# ---------\n", + "\n", + "#%% plot the distributions\n", + "\n", + "pl.figure(1, figsize=(6.4, 3))\n", + "for i in range(n_distributions):\n", + " pl.plot(x, A[:, i])\n", + "pl.title('Distributions')\n", + "pl.tight_layout()\n", + "\n", + "#\n", + "# Barycenter computation\n", + "# ----------------------\n", + "\n", + "#%% barycenter computation\n", + "\n", + "alpha = 0.5 # 0<=alpha<=1\n", + "weights = np.array([1 - alpha, alpha])\n", + "\n", + "# l2bary\n", + "bary_l2 = A.dot(weights)\n", + "\n", + "# wasserstein\n", + "reg = 1e-3\n", + "ot.tic()\n", + "bary_wass = ot.bregman.barycenter(A, M, reg, weights)\n", + "ot.toc()\n", + "\n", + "\n", + "ot.tic()\n", + "bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\n", + "ot.toc()\n", + "\n", + "pl.figure(2)\n", + "pl.clf()\n", + "pl.subplot(2, 1, 1)\n", + "for i in range(n_distributions):\n", + " pl.plot(x, A[:, i])\n", + "pl.title('Distributions')\n", + "\n", + "pl.subplot(2, 1, 2)\n", + "pl.plot(x, bary_l2, 'r', label='l2')\n", + "pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\n", + "pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\n", + "pl.legend()\n", + "pl.title('Barycenters')\n", + "pl.tight_layout()\n", + "\n", + "problems.append([A, [bary_l2, bary_wass, bary_wass2]])\n", + "\n", + "#%% parameters\n", + "\n", + "a1 = 1.0 * (x > 10) * (x < 50)\n", + "a2 = 1.0 * (x > 60) * (x < 80)\n", + "\n", + "a1 /= a1.sum()\n", + "a2 /= a2.sum()\n", + "\n", + "# creating matrix A containing all distributions\n", + "A = np.vstack((a1, a2)).T\n", + "n_distributions = A.shape[1]\n", + "\n", + "# loss matrix + normalization\n", + "M = ot.utils.dist0(n)\n", + "M /= M.max()\n", + "\n", + "\n", + "#%% plot the distributions\n", + "\n", + "pl.figure(1, figsize=(6.4, 3))\n", + "for i in range(n_distributions):\n", + " pl.plot(x, A[:, i])\n", + "pl.title('Distributions')\n", + "pl.tight_layout()\n", + "\n", + "#\n", + "# Barycenter computation\n", + "# ----------------------\n", + "\n", + "#%% barycenter computation\n", + "\n", + "alpha = 0.5 # 0<=alpha<=1\n", + "weights = np.array([1 - alpha, alpha])\n", + "\n", + "# l2bary\n", + "bary_l2 = A.dot(weights)\n", + "\n", + "# wasserstein\n", + "reg = 1e-3\n", + "ot.tic()\n", + "bary_wass = ot.bregman.barycenter(A, M, reg, weights)\n", + "ot.toc()\n", + "\n", + "\n", + "ot.tic()\n", + "bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\n", + "ot.toc()\n", + "\n", + "\n", + "problems.append([A, [bary_l2, bary_wass, bary_wass2]])\n", + "\n", + "pl.figure(2)\n", + "pl.clf()\n", + "pl.subplot(2, 1, 1)\n", + "for i in range(n_distributions):\n", + " pl.plot(x, A[:, i])\n", + "pl.title('Distributions')\n", + "\n", + "pl.subplot(2, 1, 2)\n", + "pl.plot(x, bary_l2, 'r', label='l2')\n", + "pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\n", + "pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\n", + "pl.legend()\n", + "pl.title('Barycenters')\n", + "pl.tight_layout()\n", + "\n", + "#%% parameters\n", + "\n", + "a1 = np.zeros(n)\n", + "a2 = np.zeros(n)\n", + "\n", + "a1[10] = .25\n", + "a1[20] = .5\n", + "a1[30] = .25\n", + "a2[80] = 1\n", + "\n", + "\n", + "a1 /= a1.sum()\n", + "a2 /= a2.sum()\n", + "\n", + "# creating matrix A containing all distributions\n", + "A = np.vstack((a1, a2)).T\n", + "n_distributions = A.shape[1]\n", + "\n", + "# loss matrix + normalization\n", + "M = ot.utils.dist0(n)\n", + "M /= M.max()\n", + "\n", + "\n", + "#%% plot the distributions\n", + "\n", + "pl.figure(1, figsize=(6.4, 3))\n", + "for i in range(n_distributions):\n", + " pl.plot(x, A[:, i])\n", + "pl.title('Distributions')\n", + "pl.tight_layout()\n", + "\n", + "#\n", + "# Barycenter computation\n", + "# ----------------------\n", + "\n", + "#%% barycenter computation\n", + "\n", + "alpha = 0.5 # 0<=alpha<=1\n", + "weights = np.array([1 - alpha, alpha])\n", + "\n", + "# l2bary\n", + "bary_l2 = A.dot(weights)\n", + "\n", + "# wasserstein\n", + "reg = 1e-3\n", + "ot.tic()\n", + "bary_wass = ot.bregman.barycenter(A, M, reg, weights)\n", + "ot.toc()\n", + "\n", + "\n", + "ot.tic()\n", + "bary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\n", + "ot.toc()\n", + "\n", + "\n", + "problems.append([A, [bary_l2, bary_wass, bary_wass2]])\n", + "\n", + "pl.figure(2)\n", + "pl.clf()\n", + "pl.subplot(2, 1, 1)\n", + "for i in range(n_distributions):\n", + " pl.plot(x, A[:, i])\n", + "pl.title('Distributions')\n", + "\n", + "pl.subplot(2, 1, 2)\n", + "pl.plot(x, bary_l2, 'r', label='l2')\n", + "pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\n", + "pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\n", + "pl.legend()\n", + "pl.title('Barycenters')\n", + "pl.tight_layout()\n", + "\n", + "\n", + "#\n", + "# Final figure\n", + "# ------------\n", + "#\n", + "\n", + "#%% plot\n", + "\n", + "nbm = len(problems)\n", + "nbm2 = (nbm // 2)\n", + "\n", + "\n", + "pl.figure(2, (20, 6))\n", + "pl.clf()\n", + "\n", + "for i in range(nbm):\n", + "\n", + " A = problems[i][0]\n", + " bary_l2 = problems[i][1][0]\n", + " bary_wass = problems[i][1][1]\n", + " bary_wass2 = problems[i][1][2]\n", + "\n", + " pl.subplot(2, nbm, 1 + i)\n", + " for j in range(n_distributions):\n", + " pl.plot(x, A[:, j])\n", + " if i == nbm2:\n", + " pl.title('Distributions')\n", + " pl.xticks(())\n", + " pl.yticks(())\n", + "\n", + " pl.subplot(2, nbm, 1 + i + nbm)\n", + "\n", + " pl.plot(x, bary_l2, 'r', label='L2 (Euclidean)')\n", + " pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\n", + " pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\n", + " if i == nbm - 1:\n", + " pl.legend()\n", + " if i == nbm2:\n", + " pl.title('Barycenters')\n", + "\n", + " pl.xticks(())\n", + " pl.yticks(())" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_compute_emd.ipynb b/notebooks/plot_compute_emd.ipynb index c5f47c2..26c125e 100644 --- a/notebooks/plot_compute_emd.ipynb +++ b/notebooks/plot_compute_emd.ipynb @@ -41,7 +41,7 @@ "import numpy as np\n", "import matplotlib.pylab as pl\n", "import ot\n", - "from ot.datasets import get_1D_gauss as gauss" + "from ot.datasets import make_1D_gauss as gauss" ] }, { @@ -105,9 +105,9 @@ "outputs": [ { "data": { - "image/png": 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7+z9EZLv9nJdF5HcRuahSnf8Ska0ikiUi34hIlL1rif13q92CG1NNPO4i8oqI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DnO1toKoVXl/Sown5PMfIRCXHlvoiiWYL53c0EQVDidA4S2fvgQ5nNaeRNLsprO8mNtf8gG9Rxo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wQNSg0Fk/C9FwJX5XOsPBBHYhqcgLE0k7ztGmd1CjYaTJytiJ51OfV421J4ESsTGc\n1cRAlYvWcBEn2/oRmoY7O5PK6Saq2IU1sg1FqGzsX0h3xlcJ2CXv4MSqRlnm3kpVzMkLriw6vVYQ\nMaYVvM13Ctw0HN+LVXURDym0hMbyxsyZvEILQ3iwR50s7zqXczwJxK51mAIBvHYb7rOWICovoK9D\nklAlmQaN0iIPA7Emuo4fAFVFZBQwecpixhkyMZ6wgNDwFu2jryzBwX4bQZcHVQhclfk01MbIH36b\ndHGIkVgam4cuxVt8BUe0MCdEGumxYW7s24JUS/lTrw1/0I7B3MeFZbu40+Klom0jBhEh5DexxTmD\ntdUlbIwfIawEqfRUc4PnLKq6j6Ls3orQNPqzswmct5yIoZ4BV3L5n1JLlNySQY4O7Mbb3YZUFKyF\n4zmrfgm5gwKD14pmiHCychu92XYOdRrA78dvtZOoy+Kcoh4cw6sxiWE6R0rYpN6MN7uOQ2qckGLm\nosFN/EA9QrxqBr8bbmJt13osBgtLxy7lguh4Ch99jeiefSgOB84lS/BNWcqhVoXB7gA2p4m6ecUo\nop1D617A5+ols7CICWedzTjbVLSDAbRgAmOeDXNDLoeiHWzbu4NIJEJFRQW1teXY7WtxD65ESpWs\nzEbiucv5Y6CGlQMjCASX5Gcyw2Bm09ZuNh51YzUpLJ5YyE1FHdS3PYLo2gaWDJj+Fbbmj+XX3a/T\n4j1Kgb2AJeWLufCwEfPTq0m4XJiKi3Fecy29ubPYv3OEkD9G/hgnlVOyGO7fzKF1r5CIxygcX82U\nuUsojJYT3etBJjRs9blkXVmFYv3ks/g+M4ESQhiAVmAR0APsAr4spTxySp5bgclSym8IIa4FLpdS\nXiOEqAOeBWYBxcBbQHXqtL9Z5gdxugVqJBKn0xPi0Ek/+7t87O/24ervo1r0sCjXw8L0XsYNb8cT\n6qfLaKKpsIb9uWXsC/WiugcZ5zawIFrJlKE0LPvaCMVNhDNKiE46G3/hZAZcUeJRP0bjIBl5QYQc\nwN3eil2kk5c1hsqyKeSaSogOBBkhwpAjjDc/Rn/ci9vTjd3up6hIUlwcx2BsZTA2TD8F9Fnm02ld\nwMFIBr2BKMaROONVAzlhjY5OL7awi/EGF0vz3CxKO47TvYcBLUSLPZ0jJfUcSctk31ALGe4I1X47\nZ4fLqO+UJJo7iJgyCBbXEZ28EL+9FPfJEdT4IDbHCI7MEUK+4wT7B3EYMynJr2Fc+QzsUQehoQBD\nIoA/L4EvI0qn7yTR6AB2+wilZVBUNEIs3oJHhS5RxUDaBbRq9ewOGIlHVMRwDONAhBx/kGg4Rr7w\nMkO08iXjZqptfVjiPk4ajey0WtjoSKfdkEZAjVDghYmdGguajZSM2CEcIZhWjDtnCt7CeoK2bNRE\nAql6UePtKOIEIhFAaApZ5nzKHRPJz6jAhJWojOMRI3Qa3PTbvATVIEZTGKfDQ0FhG/bsOJIgHnJo\nYQJ7RSOdsg5fwoCIaAhvFEtfmJxQgFBMZbzo5VxlH+eaD1Fu6oNEiBMmI1utVtalZ9EnDch4nLJB\nmNekMbXPRqbfgCoNBNJKGMydhK9oMiFhR2oR1EQ/WqwJs9FLIhbCKmzkWoopddSQk16OkjAQFBHc\nYphjJhd+m59ILIrNNkJmVi+5eX2Y0lU0GcRFEc1MZIdYSK9WSkQFEYijuMI4vWEckQAiHqVKOcl5\nYi/n25rIlS6CaBw3GVnlcHDInolXVbGFVMb1ShpbYdKgE9tQhJg5g5G0EvpK5hDIHUs0oSC1YbR4\nNzJxHIspSCwcwGnKIsdSQmVGPU5bPjIh8YkgnYqbbrOHkMWPqsaw2/1k5/SQmzeIsAYJSBs9lLKZ\nhRwT0/BqaaiKgLiGsT/M/JiP2+Jv0OB9FWt0EGkw01EymcecdtZGeglpMWyKhcsDNTTuD5O/ox0l\nEkXJzCQ49QJOpDfQN5zs8rKmKTiyuggNHcTbexQpJTlFpdQUNVKYGINpJPlgV7MNtGQM0DrSiSeQ\nXPuuvDyDkpITmC070bQBwEDEMYdXxeWsC5fhVxXMQjBZNSBPBmlv8xKOqjgsRq4scLFce4Uqz3qE\nVFEt6ayrmMorJsmWUA8JqVJiLeTi3gJmbnGT3tQFgCguwz31croN4/Cm6pZVaMBoOIa3dyeBoV4Q\nguLyGury5pBJHiUrGjGmIhp8Ej5LgToL+LGUcnEqfQ+AlPK+U/K8kcqzTQhhBFxAHnD3qXnfzZc6\n7W+W+UF8GoH615/exXCm7ROdq6Pzt/ifdJJ/+lXMPqzED6rFe68m359P/PWZf9mW4pRMEpCIVEpo\nqbSQqSI0BBKkQKKkcqfO1QQCgSIVFJncRgg0oSCFQCqpAJciVaYiAS35AoxQEErygyLQDJKEkkBV\nNGJKgrhRRVUk0ijRjALVIEgYDYQVGyM4CeDESzZu8lFF6te+lFiIEsOERIGROEZXGJMnhBzRMMoE\n85TDNCpNzDE0UydOoKGxy2Zlvd3OFpudXpOCJSaZ1SqZdEIyoQcKvZKYycFQVi2DuXUMZdWSMDmR\nWhA1dhQtfgJNdYGMkG7KpcA2hgJbJfnWMkyKGa8IcEJx06W4GVKCqEIlI6OfzKw+MtMHcKQPIhVo\nZQJ7mMkBOZ0+UYzUBIo7gsETxuiNIIMaTsLMVQ6z0LCfs5WDFIkh/IrC2jQbm21pHLKYcRsVCryS\nyR2SKR2SiV2StAiErTm486YykFtPwFGGqliRqgst3pn8qC5AcMfTL2D6HATq47TRSoDuU9I9wOwP\nyyOlTAgh/EBOav/2951bktr+qDIBEEL8M/DPAOXl5R+juh9M2GTg4frLP/H5Ojo6Zz4GqeJIRHHE\nE9jiGoURlZpIH+nqILmii3LZjtM8gMEWxmvJwG3JpX9sAYNVOfi1TDzBTLYON7Ah0ogaEaSFg1RH\nOxkb7mVsoJdb6SdN+AhYAgwWRBgq1VhjhJGYAevgCI7hXWT4d5E9KMiKOjFrRWAoRhpziZkriJhM\nhIjRFYnQHjkGch92xUC60YbT6KDe5MSiZBI3mwhFxhJ2q4SUOEOGIKrDhc02xAL7ARbZNoFZxW3J\noS+nEE9+Dl6RxWA8B89wJuvCc3g9fDZaWJATGaI61sUEfyeTfS4W4cNk9OK1DOMrj3NynMYeg4Ia\nMGD3DeL0riPLt550l8ARy8FEGdKQi2osJmqZQMSgfipx+p/w+USd+hRIKR8BHoFkC+qTlpOmSm4+\n+NJnVi8dnb87H9jcEh/SYvsYt4Y89Vu+p3gF+Zf9/91qSl5P/KXtBFKK1D4Qqff8xbv/iL8UhhSp\nj5JqfwlASFShoAGqoqAh0BAIoSFJtZxQ0aSKFBog0WRyn5QaSkJFJCRKQkPENQwJiSEhMapgTqgY\nExpGVSZbbRKElvoLpMCICQMKkE5AZqEIBaMQFKFQJBLAABj6UAwaGFSEoiGERtxkIG40kDApxK25\nNIsC4sJIXCjEpQE1qiAjAqdIYMpIINM1AqUaIySQQkWSQENFE30IeRJDIoEhkUBRJUKqCBXCqhGv\nVEAzoGgpI2oCEUn9N2hgwIBxyIiCBQMlREj+WLcJhbEYGEsERC/C2IMwJECRKIqGapSoBgNxg0C1\nGfAqY3BTQQwjMcWApiokVAULEqNIYMjSSGSqDMkEQ2ioSgKNBFJEkLIboakYE+pH+9pnxMcRqJNA\n2Snp0tS+D8rTk+riyyA5WeJvnftRZX6m3PmDj5yDoaOjo6NzBvFxljraBVQJISqFEGbgWmD1+/Ks\nBr6a2v4SsF4mB7dWA9cKISxCiEqgCtj5McvU0dHR0RnFfGQLKjWm9E3gDZJTwh+TUjYJIX4C7JZS\nrgZ+DzwthDgODJEUHFL5ngeOAAngNimlCvBBZX72f56Ojo6OzheVL9SLukIIN9D5KYvJBT7/2MVn\nLro93otuj/ei2+O96Pb4az6JTcZIKfM+KtMXSqA+C4QQuz/O9MbRgm6P96Lb473o9ngvuj3+mr+n\nTfRwGzo6Ojo6ZyS6QOno6OjonJGMRoH6yCXeRxm6Pd6Lbo/3otvjvej2+Gv+bjYZdWNQOjo6Ojpf\nDEZjC0pHR0dH5wuALlA6Ojo6Omcko0aghBBLhBBHhRDHhRB3n+76fN4IIcqEEG8LIY4IIZqEECtS\n+7OFEGuFEMdS31mnu66fJ0IIgxBinxDiv1LpSiHEjpSf/Cm10smoQQiRKYR4UQjRIoRoFkKcNZp9\nRAhxZ+p+OSyEeFYIYR1NPiKEeEwIMSCEOHzKvg/0B5Hk31N2OSiEmP5prz8qBCoV0+r/ARcCdcCX\nU7GqRhMJ4DtSyjqgEbgtZYO7gXVSyipgXSo9mlgBNJ+S/gXwaynleMBLMtjmaOIBYI2UcgIwhaRt\nRqWPCCFKgDuABillPclVb65ldPnIE8CS9+37MH+4kORydlUkI1A89GkvPioEimTAxONSynYpZQx4\nDlh2muv0uSKl7JNS7k1tj5B88JSQtMOTqWxPApednhp+/gghSoGlwKOptADOBV5MZRlt9sgAzia5\ndBlSypiU0sco9hGSy8HZUotg24E+RpGPSCk3kVy+7lQ+zB+WAU/JJNuBTCFE0ae5/mgRqA+KaVXy\nIXn/4RFCVADTgB1AgZSyL3XIBRScpmqdDn4D3EUyzgMkY5j5pJSJVHq0+Ukl4AYeT3V7PiqESGOU\n+oiU8iRwP9BFUpj8wB5Gt4/Ah/vDZ/6cHS0CpZNCCOEAVgLfklIOn3ostQL9qHjvQAhxMTAgpdxz\nuutyBmEEpgMPSSmnAUHe1503ynwki2SroBIoBtL46+6uUc3f2x9Gi0B9nJhW//AIIUwkxekZKeW7\n0Rv7322Gp74HTlf9Pmf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+ "image/png": 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\n", 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dR5O5OdSHp28Moo6W5S1d6Wdg4b2wexV0uR+ufR7cyla6LFvRKuXC0jJzeG31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\nA15AQt5GIjIeGA/g5+d3WQHXqtuAxBq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\n", 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" ] }, "metadata": {}, @@ -196,9 +196,9 @@ "outputs": [ { "data": { - "image/png": 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7d6br8/nz56levTrFill+NGvVqmUtxpZSdjk8PByj0ciIESNwc3Oja9euqfoF\nlk8AQ4cOZfLkydZjGZU1Dg8Pp2PHjnh6etKpUyf+/vtvwFLTZ/To0fj6+vLaa69Zn77t0KED9evX\nZ/bs2Tn8r1F4Xb+VwLT1lnIZN+KTWDq8BbP6e1GxjKXeuuTV7aNQBPfcklmJ3Bs3bjBixAjWr1/P\nwYMH+ffff7N1vS+++IKDBw9y4MABZs+ezaVLlirI165dw9fXl+DgYHx9fTMt9zts2DDmzJlDcHBw\nlvc5ffp0qrRMRkHTlr+/P+3btyc4OJhDhw7h5uaWaVuTyUSZMmUwm81MmzbNWl8nKiqKGTNmsHnz\nZg4dOkTz5s35+OOPARg/fjz79+/n6NGjxMXFsWHDBuv1EhIS2LdvH59++inTpk1Ld78BAwawfv16\nvL29efnllzl8+HCG/Tp58iTjxo3j2LFjVKpUKVW9n4SEBJ5++mkaNmzIjBkzgMzLGk+YMIEhQ4YQ\nEhLC008/jb+/v/U6ERER/P7779bvKywsjJ9//pl9+/Yxbdo04uPjs/x3LgombZmF7zdNWHXZD4D/\nqr/A+D0dpR6MAyiwwT1yzlzMLkbMLpZHvVO+jpwz966vmVIid8GCBRgMBgYOHMiSJUsICwujXr16\nNGzYEKUUzzzzTLauN3v2bOtI8ezZs9YiXk5OTvTr1w9IXe7X29ubGTNmEBERQXR0NNHR0bRr1w6A\nwYMHZ3qflLRMyp+2bdtm2a8tW7YwZswYa19SSu9mZMeOHdbv19PTE09PTwD27t1LaGgobdq0wdvb\nm6VLl/LXX38BsHXrVnx9ffHw8GDLli3WImiQulRwyiccW7Vq1eL48eO89957FCtWjE6dOvHbb7+l\na1evXj1rDaC01xo1ahTu7u7WX5KQvqxxSvs9e/bw1FNPAZZ/4127dlnf079//1Qpsx49elCyZEmc\nnZ2pWrVqhmWWi4LAoED+uxHPpNUhrPjFiHPkHBa1swyEJLfuOApsItEwYbw1z252MWIMM+fKdTMq\nkWtbSCwt21LAcLsc8LZt29i8eTN79uyhTJkydOjQwXquVKlS1qCRWbnfO00cZoftkrO8KFPcpUsX\n6+YbtvcZO3YsBw4coHbt2kydOjXVvbNTKrhkyZI8+uijPProo1SrVo21a9fSqVOndG1SODk5pUrL\ntG7dmq1bt/Lyyy9TqlQpIOuyxpmRMsUZMwWbWLqxEZExNxnVvj7/69zIUpZ3h717JmwV2JF7Xsis\nRK6LiwuOG5jHAAAgAElEQVTh4eGcPm1Zs2sb0OrWrcuhQ4cAOHTokHVlx9WrV6lcuTJlypQhLCyM\nvXv3ZnjPzMr9VqpUiUqVKllHkjkt9wtQrVo1zGYzSUlJrFmzxnq8U6dOmEyWjU4SExO5evVqptdo\n164dX3/9NWDZOCRl67yWLVuye/duTp06BVjSHidOnLAGcmdnZ2JjY3M8MXvo0CHOnTsHWPLmISEh\nOS5T/Nxzz/HYY48xYMCAOwbg1q1bWzdbWb58+R0/9RRll2Jv4v+NJU1Wucx9rB3XhtcfNVrrrUtu\n3bEUiuDuPG5crlwnNjaWIUOG4OrqiqenJ6GhoUydOpVSpUqxYMECevToQdOmTalatar1Pf369ePy\n5cu4ubkxd+5cGjVqBFhKBSckJGA0Gpk0aRItW7bM8J4p5X4nTpyIl5cX3t7e1gnOxYsXM27cOLy9\nvTPdjAPS59xTJvtmzpxJz549ad26tXVnJ4DPPvuMrVu34uHhQbNmzQgNDc302mPGjCE2Nhaj0chb\nb71Fs2bNADAYDCxZsoRBgwbh6elJq1atCAsLo1KlSowYMQJ3d3e6deuGj49PNv/1LS5evMjjjz+O\nu7s7np6eFC9enPHjc74S6qWXXqJJkyYMHjw41SertObMmcPixYvx9PTkyy+/5LPPPsvxvQo7rTUv\nbHqfDqubs/WWZevEfyqP4+nf2kpu3YFJyd+7sG3bNmbNmpVqolAIKDg/w9kRGBRIv/rDeWPNUTab\nL+BVqyIfPOnFkz+3lnowdpTdkr/ZGrkrpborpY4rpU4ppdLtxKyUGqqUilRKBSX/ef5uOi2EcAxa\na0zBJjp/vJ2dJyP5v8dcWD2mNY0fKG/vrolsuuOEqlLKCQgAugARwH6l1DqtddrP8iu11vf+JFEB\nkDLhKkRhdPbydf5vzREoDsbqFXi/nyf1nG9PLktu/d7l1oOXWcnOyL0FcEprfUZrfQtYATyRVx2y\nV5pIiHtV0H92k5I0I9e9w2PrfQkqbvnwbS41kl4/tpTcei7LrQcvs5Kd4F4TOGvzOiL5WFr9lFIh\nSqlVSqnaGV1IKTVSKXVAKXUgMjIy3flSpUpx6dKlAv8/iSh6tNZcunTJuvSyoEgJ2qcjYxkwfw+/\n/O5N08RFbHpiHyDr1vNE3L0vc86O3Frnvh74Rmt9Uyk1ClgKdEzbSGu9AFgAlgnVtOdr1apFREQE\nGQV+IRxdqVKlqFWrlr27kSOmYBNEd+XTzScpXcKJj/p70bdpzVTPSIjcETlnbqoRe8oDmM7jxuVJ\niiY7wf0fwHYkXiv5mJXW+pLNy8+BD+6mMyVKlKBevXp381YhRA6FnvsPgA82HedR9weY9oQbVcvf\n/uQhufXcZWhZCsOVi1C5HuaAa7n24GVmspOW2Q80VErVU0rdB/gB62wbKKWq27zsBeRtr4UQd232\nobl4LPVg4K9tAChvnMSuxKGsOv1FqnaSirk31lIoCbdg/Yvw48vQoCOMSF9OIy/cceSutU5QSo0H\nfgacgC+01seUUm8DB7TW6wB/pVQvIAG4DAzNwz4LIXIoMCiQsd5jOfT3FdZv8yDm4kz6NqnJrzcG\ny5r1PBIVEIBh2ED4djD8vQcefgk6ToZiTrn24GVWspVz11pvBDamOfaWzdevA6/nbteEELnFFGzi\nUkQHvtj9Jw9UKMXioT484lIVj6X27lkht6ADXL8E/RaBx+0NcPJ6GSQU4MJhQojs2XPaMiW2aNef\nPO1bh0mPulC+lNRazwvpJk0XJAGVca74LwaP/O2LQ5UfEELknk8OzOGLYwvSHR/jNUby6XlFa9j+\nAWx7F/OKGhgPbIdyVe/8vhzI1fIDQoiCIWXd+tawi3z7qyvXwmbi57wSkDXrecU6cRofB6ufg23v\ngtcgy7FcDuw5IWkZIQoRU7CJk8dbs+bwPzSqVo7Ap1vTpE5lVkhuPc9EBQRgGPokrHgK/jkEnadC\nmxdxjsj7p1CzIsFdiEJAa83GI5btH9cHn8O/U0PGPdKAksWl1nq+WNgR4q7AwK/AaNnxKz8mTbMi\nwV2IAu7DP2azLGyh9XXpxhNZfA5KHb2dW5dUTO5KP3GqgUo4VwjH4CAVnyW4C1EABQYFMsZrDKsO\nRvDlT425mfABL3VpREB4b1m3ng8M48dhaJIAm6diXlEd4/5tUL6avbuVikyoClEAmYJNDFm8n1dX\nhdD4gfJseqEto9s3sHe3CrVUT5z+MB42TwG3PpZjDhbYQUbuQhQoSUmar/74C4AD4Zd5+wk3nvF9\nkGLFLIW+JLeed6ICAjA89xSsHAx/7YL2E6H9JJz/Dbzzm+1A1rkLUUC8s/tTVpxalO64rFvPH2YX\nI8YxJeG/c/DEXPAcYJd+ZHedu4zchXBggUGBjPQYzee7/mTZr40pWXwWb/Z05e1jj0luPR+kmzg1\n3QSq4FzpIgZP+/UrOyS4C+HATMEmNu3yJCTiKt3cqjH9CXeqVijF28fs3bOiwTBhPIZWZWHjK5i/\nropxzyao/KC9u5UtEtyFcEC3EpKYu/UUAP9ciSPgqaY85vGAdRMNya3ng6RE+GUy7A2Eh7oAxwpM\nYAcJ7kI4nLe2f8ya8MXW17fqvMSkw/BXkqxbzw+Rc+ZiGPEsrH4eTv4MvqOh6zs4X5ln767liAR3\nIewspdZ63K1EPtl8gq92NqZq+U95t687L/zRWXLr+SwqIACD03KIPA49PgIfy2bh9n7iNKckuAth\nZ6ZgE00rDGTS6hDCL11nUIs6vP6YCxVKlYA/7N27IuasZWNwrv4Dz6yy7JxUQMlDTELYUcyNeAD8\nFuwlUWu+ft6X9/p6WAI7klvPL5Fz5mJ2MWLuMgQA89KymHuMu/3gUgEkI3ch7CAwKBBTsMn6urxx\nEtFAUOwYWnM7ny659XyQlITBJRKD3zl48GHM75/J882r84OM3IXIJym11qOv3+LUidbEmGdS7ZJl\nZCi11vNf5Jy5cDMGVj4Nuz+FZkNh8Bp7dyvXZCu4K6W6K6WOK6VOKaUmZdGun1JKK6Xu+PSUEEWN\nKdjET0fO0/njHawLOseEjg/xo//D9u5WkRUVEACLusGJTdD9fej5KRS/L182r84Pd0zLKKWcgACg\nCxAB7FdKrdNah6ZpVx54AZkCEiKdyJibAIxZfgi3GhVYOtwHtxoVLcckr57//t5r+ftqBDy9Ch7q\nZD1V0FbFZCY7I/cWwCmt9Rmt9S1gBfBEBu2mA+8DN3Kxf0IUaAGHA/BY6kHH7y0fZssbJ/F3xbFs\nv7jc2kZSMfnHOnHadRiQPHHac3yBnjjNTHYmVGsCZ21eRwC+tg2UUk2B2lrrH5VSr+Zi/4QocFLW\nrZ+LjmN/kA8xx+vS7MHKnCgzStas21NiAoaHIiwTp/U7YH73RKGYOM3MPU+oKqWKAR8DL2ej7Uil\n1AGl1IHIyMh7vbUQDskUbOKrvX/R9ZMd/HHmMlMed+XbUa3s3a0iyToiv34ZlveDP0zQciw8vdq+\nHcsH2Qnu/wC1bV7XSj6WojzgDmxTSoUDLYF1GU2qaq0XaK2ba62bGwyGu++1EA4qPOoaAJPXHsWr\ndkV++V87hrWph1MxJbl1O4gKCICLZssep3/9Dk8EQPf3wKl4oZk4zcwd67krpYoDJ4BOWIL6fuAp\nrXWGdemUUtuAV7TWWRZrl3ruojCZeziA+SHpa49IrXX7MrsYMT77H9xX1rJ5de0W9u7SPcu1eu5a\n6wSl1HjgZ8AJ+EJrfUwp9TZwQGu97t67K0TBdfzfGH793ZuYszPpbKzKHwyX3LodpavBvqwCAM4l\n92GYUPCDe3Zl6wlVrfVGYGOaY29l0rbDvXdLCMc351AASZe7MnfrScqXKsFnft708qqB5zJ796xo\nM4wcgsGwB8I2YF5RA+ORQ1CitL27le/kCVUh7sKRiKssODKPTzafoLt7dX79Xzue8K6JUpJbtwfr\nxGnUKVjYCY7/BN1nWo4VwcAOUltGiBy5EW8py7twxxnKusDCZ5vTxbVaqjaSY89/UQEBGLo+BN+P\nAKcS8OxaqNcO53GJ9u6a3cgG2UJk0/7wy4zfOJPrZX9Kd04mTu0oKQmzqxtGv/PwgAf4LYdKdezd\nqzwjG2QLkUs+PTCXK/90YNnev6hZqTumLq/xcENnPJZ6yMSpHaWbOF1RHYjCOX5doSkhcC8kuAuR\nhZ0nI1l0bD6xYfUY0qour3ZrTNmS8r+NIzAMeASDXgTRf1s2rzaHQvIes0ImVIXI0NXr8bz6XTCD\nF1l25vluVCum9nJLFdhl4jT/WSdOQ76DzzvDrWswZIPlmAT2VGQIIkQaPx/7l4lbZpFY4WfKGy3H\nhm1vD9tT59Ylx57/ogICMDT4G/bNhzqtof9iKP9AoX/a9G5IcBcCS7GvAQ89x5R1x/gx5Dyu1Z/g\ng85v4l6zouTWHcV/5y1/75sPLcdBl2mWlTEUnjK9uUmCuyjytNaYgk0sXPcQ124m8krXRoxq34AS\nTpK1dATpJ05rwIo1OI+rIUE9CxLcRZF2/mock9ccBQV1ncvyQT9PGlYrn6qN5NbzX+ScuZbAnZSE\nwTMOw6B/oUpDzHNjCnWZ3twkQxNRJGmtGfPje3Rd24J9ajgAp8qOpu+m1ta9TlNIbj3/RQUEWMr0\nfj0Ats4A9ydhxBZ7d6tAkZG7KFICgwLpUXsIk1YfYc8ZT1o3eISZfT3pscFX8uqOZl5buHYRenwM\nzYeDUjJxmgMS3EWRkZhkya3PXl2PEsWK8V5fD/x8aqNkCZ1DSJdbX5AEOONcKQ6Dj+W/keTYs0+C\nuygSTl6I4bXVIVAWWjdw5p0+7lSveLuglOTV7c/w/NOpqzke/h1KV7Z3twosybmLQi0+MYkh30+n\n76bWnCo7GoD9ajhd17ZIlVuXvLp9WB9KijgA89rBiZ+h27uWYxLY74mM3EWhFBgUSDvD07y2KoTQ\n803p4dmDab3ceGR1c8mtO5CogAAMzTRsngoVasDwn6FWM5zH2aegYWEiwV0UOjfiEzEFm/joeF3u\nL3sf8wc3o5vbA/bulkjr2iXL379MBuPj0GsulK4ESG49N0hwF4XKwb8u89qqEHCGvk1qMrmHKxXL\nlLCel9y6/WX4UBIHcb74lQT1XCT13EWhcO1mAsPWzsB8Y3W6c1Jr3YEkJsD292HnLKhcD3NgnDyU\nlEPZreeerQlVpVR3pdRxpdQppdSkDM6PVkodUUoFKaV2KaVc76bTQuRUYFAgu05G0e3THew77EOf\nSt+wx+8wAEeGHOHIkCMS2O3MOmka/TcseQx2fABeT8GoHfbtWCF3x7SMUsoJCAC6ABHAfqXUOq11\nqE2zr7XW85Lb9wI+BrrnQX+FsLoaF48p2ESM+UHqOZfl21GtaFHvfnt3S6QRFRCAoWNNWPcCoKHf\nIvB4EkAeSspD2cm5twBOaa3PACilVgBPANbgrrX+z6Z9WUCmukWe2hx6gTfWHoGaMLp9A17s3JBS\nJZys5yW37iBuXbP8/d1QqOUD/T6HynWtpyXHnneyE9xrAmdtXkcAvmkbKaXGAS8B9wEdc6V3QqRx\nKfYmQ9fMIDxpreUnE1h+sT/Lv5Za644k40nTf3CO2yABPZ/k2moZrXUAEKCUegqYDAxJ20YpNRIY\nCVCnTuHdwFbkvoDDAdQu1oep644Rc6MVEzo+y+j2DWi23EvWrTuI25UcEzF43cDw1EUoVw3zAi2T\npnaQnQnVf4DaNq9rJR/LzAqgd0YntNYLtNbNtdbNDQZD9nspirQL/91gXsg8/L85TO37y7BhQlv8\nOzXkvuLygLUjiQoIgCt/wZIesGU6GHvBmN327laRlZ2R+36goVKqHpag7gc8ZdtAKdVQa30y+WUP\n4CRC3COtNd8eOMuMH81QD954zMjwh+vhVOx2oS/JrTuYeQ+D1tBnPngOlEqOdpStde5KqceATwEn\n4Aut9TtKqbeBA1rrdUqpz4DOQDxwBRivtT6W1TVlnbvIytnL1xm2dgYXnNanOyfr1h1H2tx6Cudx\n4yS3nkeyu849Wzl3rfVGYGOaY2/ZfP1CjnsoRBqBQYGM9hzD0j3hfLDpOMVUO15/bBRPtaiD15ee\nklt3ENbcOmDo3hjDzSS4HoX566oYjx0BJ3nw3RFI0lI4DFOwif7z9zBtfSgt6t3PLy+155mWD1Ks\nmNRbdyRRAQFwMxbWvwjLn7RUb3z+N8tJCewOQ/5LCLuLT0xiwY4zAJy6GMvHA7zo06Rmqk00JLfu\nYOa1sUyetp4Aj0yGEqUkt+5gpLaMsKupOz9m9ZnF6Y5LXt2xSG7dceRqzl2I3BQYFMhz7qOY89sp\nvtruQqUynzD9CTdeO9RV8uoOJFVuvW8rDGoZRB237JIUvA9KlrdzD0VWJOcu8p0p2ETP2buYu/UU\nvbxrsPmldjzqUd3e3RJpRAUEQMJN2DwNPu8Mt2LhmeSqmxLYHZ6M3EW+ibuVyKxfjgMQezOBxcN8\neKRxVet5yas7oPntIdIMTZ6xbH9XqqLk1gsIybmLfPH6lllsOLs03XHJrTsWya07Psm5C7sLDArk\nGZcRvLcxjG/2GalbZTYz+3kyYmcHya07CNu8OoChd4vUufXDv8tG1QWU5NxFnjEFm+j68Q5W7v+b\nke3q89ML7WhZv4q9uyVsWEfpN2Php0mwqCvEX4enk3PrEtgLLBm5i1x35dot3t5gKfdfsXQJ5g1u\nhnftStbzklt3MKe3wPoXLDsltRgJnd6CkuUlt17ASc5d5BqtNf/75QN++/erdOckt+44Ms2rP9ML\nw+T37dAjkRO5uoeqEFkJDArk4n83GPXlQdZudadejInvullKvco+po7Buo8pYBg/DuN30zE+Fw+A\nca4fxiOHJbAXMpKWEfdEa40p2MS8tQ24mZDE64+68NzD9SjuJOMGRxIVEGCZOL0SDj++DKc2Q42m\nwL/QeYq9uyfygAR3cdfOXr7O/605AsXB5YEKzOznQX1DOet5ya07mN2zYdt7oIpB9/ehxQicb5rs\n3SuRRyTnLnIsKUkz+sf32HP5m3TnJLfuODLNrT8/GMMr/2eHHoncIDl3kasCgwIBOBMZy8AFe/hl\ntxfeCZ/zU68/AMmtO4pUufXnn8E461GMfucBMH4/E6M5VAJ7ESFpGZEtpmATKrobn2w+Qanixfjw\nSU+ebFYrVVleYX9RAQEYxo+DkG/hlzfg+iXwHQ0rfgDXJ+zdPZGPJLiLOzKf/w+A9zeF0c2tGtOf\ncKdqhVLW85JbdzBLH4fwnVCzuaXQV3UvnE/VsnevRD6TnLvI1OxDc1l4ZH6645JXdyyZ5tbHjsXg\nP8EOPRJ5SWrLiLsSGBTIWO+xBJ2NZsN2D2IuzKRPk5psvjFY6sE4EGtNGK0xPFIdwy0g5pylHsyB\nHVDOYO8uCjvL1oSqUqq7Uuq4UuqUUmpSBudfUkqFKqVClFK/KaUezP2uivxgCjbxzo+h9A3cTcyN\nBL4Y2pxPBnrbu1sijaiAALgQaknBrBpuCebP/Wo5KYFdkI2Ru1LKCQgAugARwH6l1DqtdahNs8NA\nc631daXUGOADYGBedFjknb1nLgGwcOefPOVbh9cfdaF8qRKA5NUdSly05e95D0OpCtDzE2g6BIo5\nST0YYZWdkXsL4JTW+ozW+hawAkg17a613qq1vp78ci8gszcFyCcH5uCx1IMROzsAUN44ifX/PcWX\nYQutbSTHbh+2SxsjZ8/G7GLE3KQVAOZvqmFeXJrIPdehmBOA1FwXVtnJudcEztq8jgB8s2j/HPBT\nRieUUiOBkQB16tTJZhdFXkjJrW89fpHvfnXl2n8zGd6mHisvDZTcugOxlg0I34Xhvm8x+J2D2i0x\nf/g3xjCzvbsnHFiuTqgqpZ4BmgPtMzqvtV4ALADLapncvLfIGVOwiVPHW/P94X9oWLUcq8e0pkmd\nyqxMv1mSsLdvn4XQH6BibXjyC3DrCx+62rtXwsFlJ7j/A9S2eV0r+VgqSqnOwBtAe631zdzpnsgL\nPx2xPLG4LvgcEzo+xPiOD1GyuOVjveTW7S/t0kbzW/uBGjiPeQ6Dez8Aya2LO7rjOnelVHHgBNAJ\nS1DfDzyltT5m06YJsArorrU+mZ0byzr3/PfhH7NZZpNHTyHr1u0r1VZ3iQlwaKmlwNe1SMvSxj82\nQ8Wa9u2kcBi5VltGa50AjAd+BszAt1rrY0qpt5VSvZKbfQiUA75TSgUppdbdQ99FLgoMCkRrzeqD\nEXy1yYWbJz9gzINrAKkH4yiiAgJAazi+CUyt4ceXoEpDGLHF0kACu7gL2cq5a603AhvTHHvL5uvO\nudwvkUtMwSb+ONSc7Sciaf5gZWb28+ShquUwSW7dsSzrBX/ugPsbwMDl4NIDlJL0i7hrUhWykEpK\n0ny59y8A9odfZurjrnw7qhUPVbXUW5fcev5LtaxxzlzLskYXIwDm905hXlGDSIaAsSckF2STpY3i\nbkltmULond8/ZcXJRemOS27dvswuRsvyxWtRsGMW7P8cihXH/FVljEF7oVRFe3dRFABSz72ICQwK\nJDFJs2DHaZZtbAx/zmKy64+A5NYdyvYP4DNv2DcfvJ8C/0OW4xLYRS6T4F5ImIJN9A3czbsbw2jb\n0MDml9oz0EceFLOHLNMvoxZjXlaeyFIvQK/ZUKGG5NVFnpCqkAXcrYQkTNtOA3D2ShyzBzXhcc/q\n1k00JLee/6xPlSYmYGhTAUOCE1w9a1nW+OtSqN0iVXvJq4u8ICP3AmzKjo9pttyLz//pC0B8nZd4\nI6gbpuDbmx5LKsZOQr6DAB9YNwHKGuCZ7y3H0wR2IfKKjNwLkJR6MDfiE/lk8wm+3NEYQ/lPmNHb\ng5f2d5aaMHaU7qnSAZaVws4Dh2IY8aksaxT5ToJ7AWIKNtG8oh8TV4fwZ9Q1/Hxq8/pjRiqWLmF5\nbljkK+uTpUlJGDrVwVD8frhw1JJ+WTUDXPtAsdsfjiX9IvKTpGUKiGs3EwAYMH8PCUlJLH/el5n9\nPC2BHcmt20NUQACEroP5beHbwZBwA/oml3dw75cqsAuR32Tk7uACgwJT5dDLGycRDQTHjqENt/Pp\nklvPR0lJELbe8vW3g6HKQ5ag7t4vecOMi/btnxBIcHdIKbn1q9fjOXOyDTHmB2lgKMtF5wmSV7cD\na/olMYHIKeOJWrXdes68ogZwHWcuYvCUDTOE45Dg7oBMwSbqF+/Lmz8c5fK1W4x/xFKW1+dre/es\naIoKCMDQqgzs+hRD8b8w+LtB25cw939TNswQDkuSgg4mKtZSCn/0VwcxlCvJD+Pa8Eq3xpQq4SR5\n9Txk++CR1c1Y2JO8AmbD/6CsM/h9A6N3gceT+dtBIXJIRu4OIuBwAPNC5llflzdO4iywI3IM7jUt\n+XTJq+cd64NHALEXiXxrHFEbj1rPW9Iv53G+cQaDi2VMJEsbhSOT4G5HKbn1c9FxHAxuQUxYXZrU\nqcSpsqMlt24Pl07D73Mg6GsMFW5hmNYTWr+AucuzGaZfJLcuHJkEdzsyBZuoeLMH720MIzFJ82ZP\nV4a2rov3l/buWeFlu+tRugeP2vQEwLmLD4Ypn4HzQ3bpoxC5QYK7nfx16RoAb6w5SusGVZjZ15M6\nVcoAsmY9L6Wq+/JIDQyla0LEfsuDR3P9wHc0lK+W6j2SfhEFkdRzz2dzDwcw3ya3nkJqrecPs4sR\n4xdj4Y/5cPUsVK4HLcdiHvKJrHwRBUJ267nLyD0fnbgQw+bfvYk5O5NOLlXZp4ZLbj2PZJl+GR4I\ngPPAIRgmfJL84NEtu/RTiLySraWQSqnuSqnjSqlTSqlJGZxvp5Q6pJRKUErJGrE05hwKYM5vJ+k5\nexd/XbrGZ37efD7kjr94xT2ICgiApEQ4/hOGSr9h9DuH8alIAIxbvsYYZsYwbTYUkwePROF0x+Cu\nlHICAoBHAVdgkFLKNU2zv4GhgDxmk8bRf66y4Mg8Pvr1BF3dqvHrS+15wrsmSinJreeCDNenX79s\n+Xt2E/jGDy6a4ZHJ8FKo5XiNJvnXQSHsJDtpmRbAKa31GQCl1ArgCSA0pYHWOjz5XFIe9LFAuhGf\nyGe/nWTBjjOUaQzzBzejm9sDqdpIjv3eWSdItSby3f8j6su11nPmefFADZzHjsbQ/gVAJkdF0ZGd\n4F4TOGvzOgLwvZubKaVGAiMB6tQpvFvAHQi/zLifZnK9zE+UaWw59sqBLrxyQCZO88T+z+HAYgzx\nRzE8Ww48B2B+ZZOsTRdFWr5OqGqtFwALwLJaJj/vnR8+PTCX6HOPsHRPODUqdieg06u0a2TAY6mH\nTJzeo9QTpHOICgi0njMP/ggA5z5PYnj9UyhZHl7ZZJd+CuEoshPc/wFq27yulXxM2Nh1MopFx+YT\nG1aPIa3q8mq3xpQtKYuRcktUQACG4X4QvAJDseUY/M5B8dKYv6qMcfOXULMZJO8bC5J+ESI7q2X2\nAw2VUvWUUvcBfsC6vO1WwXE1Lp6Jq0J4ZtEfAHw7qhVTe7mlCuwycZo9GU6OJibAiV8sX3/kAr+8\nAfeVgZ6fwCvHLcdrNU8V2EHSL0Jk6yEmpdRjwKeAE/CF1vodpdTbwAGt9TqllA+wBqgM3AD+1Vq7\nZXXNwvAQ06+hF3jttw9JqPBzunOSW885s4vxdp7836NEznyTqE1h6do5jxuXag27BHJRlGT3ISZ5\nQjWHAoMCGfjQc0xdH8r64HO4PFCeD5/0wqNWRcmtZ1NmAdnsYsS4eDwEfQMXjkCx4tCwG3j5Ye47\nUZ4gFQJ5QjVPaK0xBZv4fH1DYm7E81KXRoxu34D7iktZ/JywLa8b+clHRM3/3HrOPMySmnHu3RfD\nm7OgbJXkMxPzu5tCFGgS3LPp36s3mLzWMiqvfX8ZPnzSk0bVyqdqI7n11LJMmYT+AEe+wxD7Cwa/\nm1CpDuZ5CRh3rIGqLumaywSpEDkjaZk70FozbuNMdkalf/hW8upZs82hR372KVGm+enaOHdthOHV\nN75j6ZUAAAugSURBVKCWD2ajq6RehLgDScvco8CgQB6vM5RJ34ew+5QnvvU68H4/Tx7/saXk1dPI\ncoRuXg+hP2C4tgmDXwyUrox5cWmMPwZC3bbgdPtHUEbnQuQeGblnIDFJ4/2lJwmnPsSpmGLSoy48\n1aIOxYopmTTNQKoR+ieziJq/KF0b5871Mbz4EtRrh9nNU0boQtwlGbnfpVMXY5i4+giUAd/69/Nu\nHw9qVCptPV9U8+p3XHK4byGE/YghZicGvwQoWxXzouIYN5rgwYdlhC5EPpORe7L4xCRGrHuXg/+t\nTHdOcutp1qAnJRH53mSivlyTrp3zo64YXpoENZthdnWTEboQuUxG7tkUGBRI+6pP89qqEI6da8Jj\nHo8yrZc7Hb9vXiTTL1mO0I9+Dyd/gZO/YoiPwjCoGNRsjnlWBMadP4ChUarmMkIXwn6K9ALtmwmJ\nmIJNPDF3Nxf+u8m8Z5oS+HQzDOVL2rtreS7DR/3h9o5FiQlEvvM6ZhcjZhcjAOYn38D8+nYiwxtA\n38/h1dPw/K+W9mkCO0gJACHsqciO3A/9fYXXVoVAFejdpCaTexipVOY+6/nCnlu3fZAIAK3hSrjl\n65WD4c/t/9/e/cdIVV0BHP8edllAoODuDBT5UZZC5YH8UotQwShWwNaIUZHVEikxQRMgtGlDgD8E\nBKpICxIppgYFtCiIQtlaDCIlAVMVQaH8eCpUUZRfMyLym3Xh9I/3lp3dmf0BzOwwb84n2cy8O3dn\n7glvDzfnvnmX8A/fEy4SuKYX7uyDOG8v8u7j4u9eVMZm6MZcebKu5n6qpJSRK2ew68zrca8FsbZe\n7Vf9318DX2wksuBlouu+jOsTGjqA8MRpcFV+xZq7MSZtrOaewH/2RJmwYjtfHbmRh/vex/jBnem7\ntFcgautVJfEKOxU9PY3owlcvvOb2GQRAqGcpzhN9oX1/3Idn47i74u6yaLNzYzJLViT3Y2d+4Lcr\np/PRtt4UhhqzbFQfbupQUPMvXmGqW+yMK7OUlsBB/z+tZcPhq/cJn44QLgIa5eMubIizaBwU9oew\nA/XKll9mxyV2sPq5MZkm8Auq69xDDJy9gd0lK3j0lg68Na5/hcR+JdbWa1zsrKystLb9dXhrApGR\nvXGv64H7y+EAuJO34L5Qn0jJUBi9CcZ/7vXv8xi07BqT2G2GbkxQBLbmfuRkCVP/uZNVW/dzbcum\n7M8ffcWVX6qthyeob7udHa9k8v0+2L/Vq5Wv3hHXL/SLZoSH3Q5tbsS9b1LC97L7oBuTmbL2fu6q\nypv/PcCU4p2carya3IJ34vrU9cLpJSXxT1woLSEyazrRxcvj+oS6Hifc/RS0cKBVD9wJ63HWL4UW\nXSCnfo2fYYzJTFm5oHro2BlGrJzOJ25furdpxtP3T6Xzj+cA1Mk9YWpc1KzKuR+I/PlPRBcuvdBU\ndm15qOtxnKLjkJOHuySEM/N2uKYntOoFLbtAff/WCBO8JF+ZlVmMyU4Zmdznb51fYeatqizf/DXT\n/rULClcx8c6RPNKvkNycS19SqCpRX9SiZixVOHGIyNw5RP9evgVtXBIH3KXX4Dze3auHt+wKLbpC\nQUdY0g2GJK7HV5XErfRiTHbKyOQenTcPFnjJfd+RU0xauZ2Nu6P0bp/PdRvP8eiIn8b9zszPrk/4\nXhc7264ygZeVtw5sg6P7iCxaTnTVpgsvu04XICaJ18vFfaUFzpTrIfQzCF/rPYY6wdLe8FD8PW6q\nm4VbEjfGxKpVcheRwcBcvA2yF6jqU5VebwC8BNwAfAsMU9W9yR1quaHvKufPKy+9t5en13xKvavf\npqmzFheY+q7SbXE3oGJtvfCNTTAj/r1qLJmowpmjcPJbOBnx2j74GxzbD8cPEFm9g+i7313o7t5W\nBPhJfHgpNG+LO+8EzjP3QH6H8p9mbeGVblC0JO4jbRZujLlcNSZ3EckB/grcAXwNfCgixaq6K6bb\nI8B3qtpRRIqAmcCwZA50/tb5PLftOQBeA3q83B2A1oV3s/Ce6bS5ejYA7pNOeW39/DkoOQmlZ73j\nyGdQctxrK/sB2PgXOH2USPEWou98ceEzy0omjcJnOR0pv9+MO+IZAEI3COHbWhMe2BGatsL9fTHO\niqe8xN28HVxV4F0zPs+BwU/GxWRJ3BiTKjVeLSMifYEpqjrIP54IoKpPxvRZ4/d5T0RygYNAWKt5\n84u9Wiby7LyE13kX3CAIEN0S/1GVk3JN7aFeSvjmH0Gj5riz9uHMHACNw3BVyHtsXIB711iczRu8\nxF2vYk2/qitT7LJDY0yyJPNqmdbAvpjjr4GbquqjqqUi8j1QAEQrDWoUMAqgXbt2tfjocuGxYwiP\nHcPuDcsoHTWFwsk/p2FeHkgO1Msh/ICA5OCOX4s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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -226,21 +226,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.6.5" } }, "nbformat": 4, diff --git a/notebooks/plot_gromov.ipynb b/notebooks/plot_gromov.ipynb index 6a237e6..f565bfb 100644 --- a/notebooks/plot_gromov.ipynb +++ b/notebooks/plot_gromov.ipynb @@ -74,7 +74,7 @@ "cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\n", "\n", "\n", - "xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)\n", + "xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s)\n", "P = sp.linalg.sqrtm(cov_t)\n", "xt = np.random.randn(n_samples, 3).dot(P) + mu_t" ] @@ -97,9 +97,9 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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" ] }, "metadata": {}, @@ -179,36 +179,33 @@ "text": [ "It. |Loss |Delta loss\n", "--------------------------------\n", - " 0|3.731009e-02|0.000000e+00\n", - " 1|1.846414e-02|-1.020678e+00\n", - " 2|1.752056e-02|-5.385587e-02\n", - " 3|1.470479e-02|-1.914863e-01\n", - " 4|1.371582e-02|-7.210441e-02\n", - " 5|1.263823e-02|-8.526431e-02\n", - " 6|1.190590e-02|-6.151022e-02\n", - " 7|1.014664e-02|-1.733837e-01\n", - " 8|1.011396e-02|-3.230516e-03\n", - " 9|1.011363e-02|-3.245532e-05\n", - " 10|1.011363e-02|-3.245682e-07\n", - " 11|1.011363e-02|-3.245683e-09\n", - " 12|1.011363e-02|-3.245598e-11\n", + " 0|3.135088e-02|0.000000e+00\n", + " 1|1.414971e-02|-1.215656e+00\n", + " 2|9.934682e-03|-4.242736e-01\n", + " 3|7.486162e-03|-3.270729e-01\n", + " 4|7.415422e-03|-9.539599e-03\n", + " 5|6.433930e-03|-1.525492e-01\n", + " 6|6.020392e-03|-6.868966e-02\n", + " 7|6.012738e-03|-1.272962e-03\n", + " 8|6.012661e-03|-1.278831e-05\n", + " 9|6.012660e-03|-1.278889e-07\n", + " 10|6.012660e-03|-1.278889e-09\n", + " 11|6.012660e-03|-1.278958e-11\n", "It. |Err \n", "-------------------\n", - " 0|7.847531e-02|\n", - " 10|2.424218e-03|\n", - " 20|4.250670e-02|\n", - " 30|5.679949e-05|\n", - " 40|1.219762e-08|\n", - " 50|1.121975e-11|\n", - "Gromov-Wasserstein distances: 0.01011363084946804\n", - "Entropic Gromov-Wasserstein distances: 0.0063386519916289385\n" + " 0|7.283759e-02|\n", + " 10|4.751585e-03|\n", + " 20|4.981526e-09|\n", + " 30|3.401818e-14|\n", + "Gromov-Wasserstein distances: 0.0060126599835825445\n", + "Entropic Gromov-Wasserstein distances: 0.00591822665714488\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] }, "metadata": {}, @@ -260,7 +257,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.5" } }, "nbformat": 4, diff --git a/notebooks/plot_gromov_barycenter.ipynb b/notebooks/plot_gromov_barycenter.ipynb index c931a6c..2271fdb 100644 --- a/notebooks/plot_gromov_barycenter.ipynb +++ b/notebooks/plot_gromov_barycenter.ipynb @@ -137,19 +137,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/rflamary/.local/lib/python3.5/site-packages/ipykernel_launcher.py:6: DeprecationWarning: `imread` is deprecated!\n", + "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:6: DeprecationWarning: `imread` is deprecated!\n", "`imread` is deprecated in SciPy 1.0.0.\n", "Use ``matplotlib.pyplot.imread`` instead.\n", " \n", - "/home/rflamary/.local/lib/python3.5/site-packages/ipykernel_launcher.py:7: DeprecationWarning: `imread` is deprecated!\n", + "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:7: DeprecationWarning: `imread` is deprecated!\n", "`imread` is deprecated in SciPy 1.0.0.\n", "Use ``matplotlib.pyplot.imread`` instead.\n", " import sys\n", - "/home/rflamary/.local/lib/python3.5/site-packages/ipykernel_launcher.py:8: DeprecationWarning: `imread` is deprecated!\n", + "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:8: DeprecationWarning: `imread` is deprecated!\n", "`imread` is deprecated in SciPy 1.0.0.\n", "Use ``matplotlib.pyplot.imread`` instead.\n", " \n", - "/home/rflamary/.local/lib/python3.5/site-packages/ipykernel_launcher.py:9: DeprecationWarning: `imread` is deprecated!\n", + "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:9: DeprecationWarning: `imread` is deprecated!\n", "`imread` is deprecated in SciPy 1.0.0.\n", "Use ``matplotlib.pyplot.imread`` instead.\n", " if __name__ == '__main__':\n" @@ -263,7 +263,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -272,9 +272,9 @@ }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, diff --git a/notebooks/plot_optim_OTreg.ipynb b/notebooks/plot_optim_OTreg.ipynb index 67165e9..e3784df 100644 --- a/notebooks/plot_optim_OTreg.ipynb +++ b/notebooks/plot_optim_OTreg.ipynb @@ -80,8 +80,8 @@ "x = np.arange(n, dtype=np.float64)\n", "\n", "# Gaussian distributions\n", - "a = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std\n", - "b = ot.datasets.get_1D_gauss(n, m=60, s=10)\n", + "a = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\n", + "b = ot.datasets.make_1D_gauss(n, m=60, s=10)\n", "\n", "# loss matrix\n", "M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\n", @@ -106,9 +106,9 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -724,7 +724,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.5" } }, "nbformat": 4, diff --git a/notebooks/plot_otda_classes.ipynb b/notebooks/plot_otda_classes.ipynb index 1955676..2af6fb6 100644 --- a/notebooks/plot_otda_classes.ipynb +++ b/notebooks/plot_otda_classes.ipynb @@ -62,8 +62,8 @@ "n_source_samples = 150\n", "n_target_samples = 150\n", "\n", - "Xs, ys = ot.datasets.get_data_classif('3gauss', n_source_samples)\n", - "Xt, yt = ot.datasets.get_data_classif('3gauss2', n_target_samples)" + "Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples)\n", + "Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples)" ] }, { @@ -88,29 +88,29 @@ "text": [ "It. |Loss |Delta loss\n", "--------------------------------\n", - " 0|1.017912e+01|0.000000e+00\n", - " 1|2.096083e+00|-3.856258e+00\n", - " 2|1.842979e+00|-1.373343e-01\n", - " 3|1.781632e+00|-3.443301e-02\n", - " 4|1.760919e+00|-1.176281e-02\n", - " 5|1.750958e+00|-5.688541e-03\n", - " 6|1.746386e+00|-2.618021e-03\n", - " 7|1.741793e+00|-2.636854e-03\n", - " 8|1.739054e+00|-1.575065e-03\n", - " 9|1.736474e+00|-1.486027e-03\n", - " 10|1.734361e+00|-1.218441e-03\n", - " 11|1.734259e+00|-5.863179e-05\n", - " 12|1.733704e+00|-3.201643e-04\n", - " 13|1.733018e+00|-3.957711e-04\n", - " 14|1.731842e+00|-6.791025e-04\n", - " 15|1.730974e+00|-5.012271e-04\n", - " 16|1.730584e+00|-2.257722e-04\n", - " 17|1.730492e+00|-5.272976e-05\n", - " 18|1.730153e+00|-1.961758e-04\n", - " 19|1.729837e+00|-1.828284e-04\n", + " 0|9.537526e+00|0.000000e+00\n", + " 1|2.505426e+00|-2.806748e+00\n", + " 2|2.264025e+00|-1.066249e-01\n", + " 3|2.210620e+00|-2.415841e-02\n", + " 4|2.191601e+00|-8.677880e-03\n", + " 5|2.182712e+00|-4.072416e-03\n", + " 6|2.178054e+00|-2.138572e-03\n", + " 7|2.176320e+00|-7.971427e-04\n", + " 8|2.174237e+00|-9.578098e-04\n", + " 9|2.172978e+00|-5.792305e-04\n", + " 10|2.172514e+00|-2.138295e-04\n", + " 11|2.171279e+00|-5.689220e-04\n", + " 12|2.169799e+00|-6.819885e-04\n", + " 13|2.169215e+00|-2.692594e-04\n", + " 14|2.168810e+00|-1.868050e-04\n", + " 15|2.168289e+00|-2.401519e-04\n", + " 16|2.168018e+00|-1.249509e-04\n", + " 17|2.167885e+00|-6.124717e-05\n", + " 18|2.167623e+00|-1.211692e-04\n", + " 19|2.167335e+00|-1.327875e-04\n", "It. |Loss |Delta loss\n", "--------------------------------\n", - " 20|1.729361e+00|-2.749072e-04\n" + " 20|2.166808e+00|-2.432572e-04\n" ] } ], @@ -157,9 +157,9 @@ "outputs": [ { "data": { - "image/png": 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bhBX799X5fKcKC7l84TyyHYV4EHC7WXckFdbD+NN7Bipsvyb16s2H11zHm1s3\nc+DkSTrFRDOuew9sFmujXlcp1bxpgqxUPekvBKolM8aAn1kqLPWo9i7auZ0Cp9ObHPsUud2sP5rG\nrvQTDO54WoNirUnfuHjO6tGLxTu3YzUWPt27l4dXruBP507l0oE1V7+VUq2PJsitWKCn4GvpU/p5\nMq8DwBI/L8iRKNW4jDFM6dOPz/bvLTfPsc1i4cJ+A+p8vi3Hj/pdQc9qDCmZGY2eIGcWFHDnJ0sr\nxfDAik8Z2bUrXWPbNOr1lVLNjybIStVTS/+FQLVuj00+l53pJ8goyKfI7SbcaqVzTCy/OWtync81\nKL4jK/bvo8jtLrddgN7t2gcm4Gp8sm+P3+0eEZbtSeGWEaMbPQalVPOiCbIKeGLXHBPF6qrDJftw\nfl/jsUq1FPFRUayYfQNfHjzAvpNZ9I/3tilUNZdxdWYOS+TljevKJch2i4V+7eNI7NQ5kGH75XA6\ncVdY8Q/A5XFTUOxs9OsrpZofTZCVaqDm+AuBUrVhtVg4t09fzqVvg87TISqKd6+exa8/X86W48ew\nGsOF/Qbw2NnnNtoMFmVN7tWbv65ZhbNCkhxuszG5d59Gv75SqvnRBFm1arWpDpf8XSvHqrlLz8+n\nyO2iW2ybRklMPSIs37eX91N2Ema1Mn3IMCae3hNjDAPjO7Bkxs8odruxGlOvSnR99YuL57qEJOZv\n20Khy4UAUTY7lw0cRFITVLCVUs2PJshKKdXCpeXmcMdHH7IrIx2LMcRHRvHXqRcyumv3gF1DRLjj\now/5+seDFDi9bQsr9u9n5tBh/Oass0sT8jBrcKZXuz5pON3atGXLsaPYLBYuHzSY8d17BCUWpVTo\n0wRZtWp1qQ5r5Vg1R26Ph2sWL+RYXi5u37Rtabk53PDef1hx/Q10jonF5fGQ5SigbXhEvVeXW5N6\nmK8PHaTA9VNPr8Pl5PUtm1i4Yztzk4dzzxkTsDVh5Rggt6iI//noQ9YdSSPMaqHY7WZu8gjGN+IC\nJUqp5k8TZKWUasG+S/2R7EJHaXJcwiUe3t2xnfYRETy9ZhXFbjcGuC4hmV9NOLPOLRArD+7H4fL/\nwJvD5eS1zRvJcjj447nn1/dW6uWBFZ/y/ZFUit1uinzPCL65ZRN928cxfciwJo1FKdV8NO2v8kqF\nKEv8PK0QqxbpWF4ensrrfVDsdvPd4UM8ueprcoqKKHS5cLhczNu2madXf1vn67QJj6i2OlzocvHe\nDzvJLnQDjmIiAAAgAElEQVTU+dz1lVtUxOcH9lNcYXo5h8vFq5vWN1kcrZmIIH4WnFEq1GmCrJRS\nLVhSp84IlROUKJudg6eycVRYPMPhcvHmls04KySVNZk2aHCNVWebxUJaTk6dztsQucVFWC3+2yiy\nHYVNFkdrJK5UPFk3IceHIMeH4cm+G/GcDHZYStWaJsitxKwlC0sXtFBKtR4D4jtwTq8+RJbpLQ6z\nWOkUE1PlHMBOj5t8Z3GdrtO9TVv+MuUCIm02rFX09uY7ndy/4hP2ZmXW6dwAuzMz+OLAfo7l5VZ7\nnEeEz/bt5Z5Pl/H892vK3XcJizGMP10f0Gss4slHsq6G4lWAG3BC4XIk82eIVJ6PWqlQpD3IQVKX\n1deqO1ZXcVNK1eRvF1zMvG1bmL91M4VuFxf3H8jto8bw8w/f5/sjqZWObxsRQZvwiDpf56L+A5nc\nqw9Ldu3giW++rNTaAJCSkcGMRQv45oZbiA4LK92e5Sjg2TXf8em+vYTbrMwamsjNI0bhcDm56YP/\nsjP9BDaLhSK3mysGDeGJc6ZgqZCIe0T4+YfvsSbtMAVOJwawGYPNYsHt8SCA3WIlym7n3nET6nx/\nqpYKl4KnACibDLvAcwyKV0O4fu1V6NMEuYUrSaDXpqWWe60JtVKth9ViYU7ScOYkDS+3/cGJZ3Ht\nf94t12YRabPx0MRJlZLP2oqy25mdmEziaZ2459OPOHgqu9x+AYrcbpbtSWHG0ATAu9Ld5Qvmczw/\nD5dvMY/n1q1h47EjWIxh6/Fj5Rb5+CBlF4M6dCx3P3uzMpm3dTPf/niQYt+xAjhFsIhwbu++HM/P\nY0y37tw8fBSdYmLqdX+qZuLcA/jpNRcnuPZpgqyaBU2Qm1hdEtbqjtXEVynVUMmdu/D2lTN46rtv\n2ZWRTrc2bbh77HjOCcDqckmdu3D10GH8dfWqSjNoOFzOcr3I76fsIsvhKE2OwftQ37c/HsItUm67\n9/0uXt+8kTlJw/GIcN/yj/lk7x6cHnela4G3jnladDQvXzqtwfelambsgxBHFFBQYYcNbP2DEpNS\ndaUJcgtXkjBrAq2U8iepcxfmXXl1o5w74bTOhNtspQuHlIi220kss4Ld90dS/U4RV1TNg4J5xd4e\n6SW7dvDpvr0Uul1VHgvw+YF9PM6UuoSv6ivyYsh7FjxFeHuQAexg7QFhZwQzMqVqTRPkJlYxYa3L\nsWWTW018lVKhbvzpPRgQ14FdGSdKk90wq5WebdsxuVfv0uP6tGtPuNVabUJcltUYJvXsBcDb27ZU\nOf9yWXlVPJBYE7fHw76TWUTZ7XRv07Ze52htjImE+MVIzhNQtNJbOY64GBP7oC7OEmLEdRg8mWDr\nj7FEBzuckKIJciuhCbRSqqlZjGH+lVfzwvq1LNm1E0G4YtAQfjFqbLkp4WYMTeClDetqlSCHW61E\nh4WVPmRX6Kq+clxi2Gmn1Tn+Lw8e4L7PPqbQ5cLtEfrGxfHixZdpolwLxtoZ0/65YIehqiCek8jJ\n/wHnNjB2EBcSexeW6JuCHVrIMHWZwHvUqFGyfr1Orh4IFXuIx3brDmgiq+qvNstlq+AyxmwQkVEN\nOUcojMNOt5tle3bz0Z4UYsLCmJWQyOiu3Rt0zi3HjnLXp8v48dQpv/sjbDaGd+7C2G6nc11iEnGR\nUQC8sH4tf1+7hqJqWizsFgvvXj2LpDJtHTU5mH2Si95+s1wCbjGGrrGxfDnn5no/xKhUKPBkXQ/F\n64Gy/99EYto9i4k4O1hhNYnajsNaQVb1pu0dSrU+Lo+H699bzLbjxylweadS+3TfHu4YfQa3jx5b\n7/Mmde7CRz+bw4iX/+F3erh+cfHMv3JGpe1zkkawbE8KB7OzK/U6l4iw2Uk8rVOd4pm/bUulhwM9\nIpx0OFiXlsrY7qfX6XxKhQpxH4PiTZRPjgEcSMG/W3yCXFuaIAeJ9hCrQCmpHOP8vtxrrSSrxrB8\n3x62nfAmx+CdSs3hcvH371czfegwOkbVv48xym7nikFDeD9lV7nKbaTNxh1VJN9Rdjv/mXEty/ft\n4e5PluFvGYrc4iJu+fA9BnbowLUJSXSNbVNjLEdycyslyCVOFOTX6n6UCkmek96+cCmqvM+d3vTx\nhChdSU/VWcmqfGvTUlmblqqr9Kk68WRe91NSr0LOgeyTfHPoIMfz8spt94iQkpnB4p07/FZqbRYL\na1MPN/j6j046h8sGDCLcaiXKZic2LJyHJk7i/L5VTw8WZrVyyYBB9IuLr/KYLw7u59WNGzh/3uts\nOXa0xjjO7NHT7yp8Lo+HEZ271u5mlApFtr5V7LBD+KQmDSWUaQU5yLRyrBqqpFKslWPVEHnFxdy6\n9D02HTuK3bdi3bSBg3ninClsOX6M//noQ3KLiyiq4qE4YwwxYeENjiPcZuPJ86by8Flnc9LhoHNM\nDHartVbvfXDiJH7x0QdVPrjn9Lhxetw8+PlyPr52TrXnmjZoMK9sXM+R3JzShwcjbTauGDSEbm1q\nrkArVR0RAdc2cGeAPRFj7dBk1zYmDIn9P8j5PT8t6BIGllhM9M1NFkeo0wRZ1Zm2h6j60FaQ0Pbw\nF5+x4egRit1uCn3bPtz9A93btOGlDevIr6K/t4TNYmHC6T0CFk9MWBgxZZai9mfLsaP89svP2Zl+\nAgOc07svfzh7Ci9tXMfB7JM43R68C0yXt+9kFrlFRcSGV53QR9js/Hfmtby2eQPL9qQQbQ/j+sTh\nTBs0uKG3plo5cR9Dsm4Az1HAAlKMRM3BxN7XZNPgWaKmI7YeSN6/vEuAh0/ERN2IsVb9KUxrowmy\nUi2EJpqqvopcLj72rURXlsPl4l+bNuL2+J/tKNxqxW6xEmaz8trlV9W60hsIT377Fa9sXF8u/V2x\nfy9bjh9j5ZwbibDZGfevlzien1fpvQZvW0ZN2oSHc9fY8dw1dnzgAletnpz8BbgP8tMiKoBjHoQl\nQMQFTRaHCRuDiRvTZNdrbjRBVvWmlWNVF9oKEroKXS7ET6UVwOEspriKh9UsxnBmz178/uxzS6dd\nawrfp6Xy+pZNlSL2ANkOB3d8tJTkzl24qN8A3tmxtVzLhd1i4dzefQn301+sVGMT12Fw7aFccgwg\nDiT/DUwTJsiqejpCKKVUK9cmPJxusW04dCq73HaLMSR17sKO9BN+H8xzuFx8fmAfuzJO8PHP5jRZ\n0rlk1w6/U8EBFHncfHFwP9/8eBCLMfSLi2dvVhZ2qwW3R+gfH88fzz2/SeJUqhLJq3oGCU9O08ej\nqqQJslKqSWnlOPQYY/jjuedz0wf/odjtxi1CmNVKpM3Gn86bykOfL2fL8WN+H34rdrs5kZ/Psj0p\nXDl4aKPFuDP9BK9t2kBqbg65RX6Siwqcvqr33qxM3rlyJkfycunRti3D6jgfslIBZesH+GvvCYMI\n/cUtlGiC3IzpQ3JKqUA5o/vpfHDNdfx780b2ZWUysms35iaNoGN0NK9ffhXvbN/KqxvXczQvt1Jr\nQ4HTybojaY2WIH+yZzf3fvYxxW43HhHsltrPUGqzWDh4KlsfrlMhwRg70uZxOPUroBhvY1AEWDtg\noucGNzhVjibISimlAOgbF88T50yptD3cZmNu8gh6t2vPHR8vJd9ZXH6/1UqPtm0bJSaXx8OvV35W\nrnrt9Hiw4K18u8V/73RZNosuC61ChyXyAsTWCyl4C9xHIfwsTOR0jCUm2KGpMjRBboZKKsdr01LL\nvX7nqplaVVZKNZqJPXrSNiKcQpezXGJqs1iYPmRYwK7j9ng4VVRIbFg4B7Oz/fYbe4Au0TFcPnAQ\n0WHh9GvfnnuXf4yjQhuIW4RJPXvX6roZBQV8fegAdquVyT17VzsNnFINYeyDMG2fCHYYqhqaICul\nlKoVq8XCwunXcNcny9h+/DjGQJeYWJ6ZelGDlpgua97WzTy9ehUOlxObxcI1QxNwVvFAXqfoGH41\n4azS1z/PyODFDd55ti3GgiD8berFtUp0523dzBPffInVYsFg8IiH5y+6lLN79QnIfSmlmhcjtfh4\nqsSoUaNk/fr1jRiOqgt/leOSqvLYbt1L9ymlGi4Q09MZYzaIyKiGxBEq43BmQQFOj5tO0TEBW9zg\n/ZRd/Prz5eWqwJE2G3GRkRzPy8NV5udVyQOElwwYVO4cP57K5suDBwi32Ti/Tz/aR0bWeN29WZlc\ntmBepYcQI202Vt90K23CIxp4Z0qpUFHbcVgryC3YzvQTzFqyUJNkpVTAxUc1fN7jbSeOszTlBwTh\n4gGD+Pva1ZVaJBwuF9mFhQyI78CB7JPYLBaK3W7mJA3n4v4DK52zR9t2XJ80vE5xvP/DLr9VamMM\nK/bva9TZOZRSoUkT5GasbOLrb/nnkr8rpepPl8huHH9dvYpXN62n2JcQz9u2BVcVC5IUutwsnH4N\nh3NOcSI/n2GnnUZcZBR7szL5bP9erMbChf0GcHo9HxQsdLvw+Pk0VUT8Tm2nlGr5NEFugUoqx/4e\n4lNKqWDbl5XJKxvXU+T+KfksdLmoqlEjPiqSKLudQR06MqhDRwD+tuY7XtywDrd4MMAza1bx8JmT\nuTYxuc7xTOnTj7e3balUvfaIMLlX7R7wU0q1LLWfTFI1C+9cNZMhHU8LdhhKtRiW+HnearF9DNjH\n/PRa1dsXB/fjkcrVYoN3RoyyImw2Hpo4qVyf866MdF7auI4itwuXx4PT46HI7ebxb77kWF5uneMZ\n3bUblwwYRJTNjsH7gzHCZuOusePpGtumzudTSjV/WkFugfy1W/h7rZRSwWC3WLH4ebDPZrEyc2gC\nuzLS2Z2VQffYNtxzxgTO7dO39JgjuTk88NknflsfSnqGr6tjFdkYw5Pnns8Vg4awbHcKYTYrVwwa\noqvuKdWKaYKslFK1oFXjwLmgX3/+tOrrStuNgZ+PGk23Kqq2aTk5XPLOm+RUtdS0SL1n1DDGcEb3\n0zmj++n1er9SqmXRBLkFq1g51p5kpVQo6BwTyx/PPZ+HPl+O1WIBAbd4+P3Z51WZHAP8be135BUX\nV1rqukSR2814TXCVUgGgCbJqVjS5V6plmDZoCGf17MUXB/YjwDm9+tQ4ddx3h3+sdmlpizG8uGEd\nfzpvaoCjVUq1NpogtwLag6yUCkVxkVF1WqK6Q3Q0R6p5CM8twgcpP2iCrJRqME2QVbOgbSJKqVtH\njua+5R9Xmo6tLJfHjZTpRRYR1h1JY3dmBr3atWf86T38PiColFJlaYLcimgyqZRqzi7sN4BD2dn8\n/fvVON3uSu0WFmMYd3qP0uQ4r7iY2f9dxJ6sTDwiWI2hS0wsC6bPJC6y4SsBqvoR149QtBKMDcKn\nYKw6NakKPZogq5CmU9Uppcq6bdQYZicm813qj/zqs08pcrsodLmIsNmIsNn4/eTzSo996rtv2JWe\nTrHnp2WkD57K5uEvVvDPiy8LRvitnifvBcj7JyB4Z5x+EmnzBJYo/X6o0KIJslItlC6JrFqq6LAw\npvTpx1dzu7N45w52pJ9gcIeOXD1kGG0jIjiWl8vatFQW79xeLjkGcHk8rDiwD7fH451BQzUZcf4A\neS8AFabpy/k/JGIixhIXlLiU8kcTZBWStOdYKeXyeMhyFNAuIpIwq7XS/jbhEdw4fGS5bc+sWcXL\nG9Zhs1iq7FV2ezzelotGiVpVRQqXAcWVdxgLFH4BUdNrdx5xIDl/hsL/ghRD2FhMm0cwtl4BjVe1\nbpogK9UAoZi4l1SOcX5f7nWwK8mhEocKfSLCvzZt4LnvV+P0eLAYw03DR3LX2PHVPmD3zY8HeXXj\neorcborc7iqPM8bw7o5tXFvHFfdUA0kV3xMBqPr7VenwrJ+DcxOlyXbxd0jmdOi4XKvQKmA0QVYh\nSXuOlWq9Fu3czjNrVpWrAL+6cT1hViv/M/qMKt/39rYt1c5wUcIjwh9Xfc3VQxP8VqZV4zCRFyIF\n84DCCns8EH52rc4hzl3g3EL5SrSAFCMFCzExtwcoWtXaaYKsWrzGSLJDuQWkpEIbKhXbUK1oq9D1\n/PdrKiW6DpeLVzas5xejxla5nHResbPW1yhwOrl16Xu8cukV2LQXuUkYewISdR0UzMOb4FoAK8Q+\nVPuZLFz7wFipvJxiITh3BDRe1bppgtxIQilhag6q+nrp1696gf53psmrCgUnCvL9bs9zFuP0eKqs\n+l46YCAbj6bVqooMsCb1MG9u2VSpj1k1HkubXyGRlyKFKzDGDhEXYWw9an8CWx8Qj58d4WAfErA4\nldIEWbVYjVnlbQ4tILVJcpsiIQ61irYKff3j4tmRfqLS9i4xsdW2REwbNIRFO7ezKz2dApcTqzGl\nPctOT+WkqsjtZt7WzZogNzFjH4yxD67ne4cg9mEV2iwMmHBMZOiNw6r50gQ5wEL5o/dQpF+v+gn0\n103bIFQo+b8zJ3PjB/+hsEwlOMJm4//OnFTt+8KsVt6+cgbL9+3ls/17iY+MYuawBFYe2M+fvvvG\n73scrtq3ZajQYNq/jOQ+CY73gWIIG+OdxcIaH+zQ6kU8OVD4EeI+gQkbDmETMEbbfoJNE2QV0hqS\n+DVFlbe5JvIVE2JMbKNfU5NtVVtndD+dN6dN5+nV35KSmUHPtu24d9wEzuzRq8b32q1WLh4wkIsH\nDCzd1j8unre3b+FwTk75Yy0Wzu/bP9Dhq0ZmLNGYtr+Htr8vt6x4cyTOHUjWbMAN4kAKosA2EOLe\nxJjwYIfXqmmCHGDN4aP3mjRl7C3h6xUMAf+62cp/3KnJrAq2UV27BWw8MMbw9PkXMfe9JbjEQ7Hb\nTaTNTvuICP53zLiAXEMFR7NOjkWQ7LtA8spsLADnLiT/DUzMz4MXnNIEWYWmQLYQaNJdWVV9waWV\nZaVamFFdu/Hp7Lks2LaVA6dOMrZrd64YPJSYsLBgh6ZaK/dhcFfutYdCcPwXNEEOKk2QG0lzTMqC\n2Q/cHL9eoSDQXzetHKuWrFtsG345fmKww1CqFppvZbyl0AS5hWhpLQqtvfWiqe5bE2Kl6sYjwkmH\ng9jwcF1kRDWM9XSwdgH3gQo7IiCydstuq8ajCbIq1dqTUqWUqs4HKbt4/JsvySkqwmIMM4cm8OuJ\nk7BroqzqwRgD7f6OZF0HOEGKwYSBPQkTre1uwaYJcjOX9OJzAOQWe+eDbGnJbaDvI9SnL9Np75QK\nTd/8eJCHPl9ebhGShTu24fJ4+P3Z5wUxMtWcGftA6PgVFC339iOHDQf7qGb98GFLoQmyqkSTMaWU\nKu+5tZWXvy50uVi8czsPTjiLaH3Yr9UTKQTHh0jxd2DthomcibGdXuP7jCUKIqc1QYS1JyKIYzHk\n/QM86WDrh4l9ABM+PtihNRlNkJupkspiSeU41jc4a3LrX3NZCEPbXJQKTYdzTvndbjEWshwOTZCD\nTNwZSN7TULgCTDhEzsDE3IYxTfN9EU8ukjkd3McAB2BD8t+C9v/EhE9okhgCSQpeh7xnQRzeDa5d\nyMnbIO5fmLDRQY2tqehSLUoppVQNEjp18juvgMUYOsXENHk86ifiyUcyr/SurCenwHMC8l9BTt7e\ndDHk/wvcaXiTYwAX4EBO3Y9I5WXOQ5mIC/Ke/yk5LlWI5D4dlJiCQSvIzVRDKo2tsTpZ1by/oao1\nfW+Uag7uOWMCq348VK7NItJm4+6x43Q2iyATxwfgOYU3KS1RBMXrEOdOjH2I9zhPFlLwX3D/iAkb\nCREXBK7CXPgxUOwnuAJw7wdbv8Bcpyl4TnkfGPTHta9pYwkirSCrkOTJvE4XrVBKhYzBHTry7vRr\nOLNHT9qGh9M/Lp4nz5vKTSNGBTs05dzET5Xbsgw4dwEgzu1I+rnetgHHO0jOb5GMSxFPjp/31YOJ\n8r9d3FXvC1WWNmCqqJ9aezRtLEGkFeRmrj6V49Y8Q0KoV46VUqFr6GmdeGOazk8bcmx9gXCgqPx2\nY8D3kJxk3weS/9M+KQB3KpL3AqbNAw0OwURdh+Q8RvlE3eJ9uM3atcHnb0rG2JHomyH/5QptFhGY\n2LuDFldT0wpyK7Yz/QQ70/0tcxk8pZVj5/fg/F4ryUoppaplIqf7qXjawNIF7KMR9wlff3BFTihc\nFpggIq+AyIuAcG/F2ESDpQum/fOBOX8TM9G/gOg7wLQFDFi6Qdu/YMLPDHZoTUYryE0smFXbin3L\nSimlVHNnrPEQNx859SC49no3hk3AtH0SYwxibIBU8WZ7YGIwFkzbPyLRt4NzM1g6QthYjKlbHVI8\neeDaCZZ4jK1vQGKrD2MMJuYWbyUZFyZAX6fmRBPkVqikahzIxUUClfiH6sN0oRaPqpl+z5RqPYx9\nCKbDB4gnF4wNYyJ/2meJQ+xDwbkFKDujRAREzghsHLYeYKtfn64n72XIe86btIsLsfXDtH8ZY+0Q\n0BjrwrtgSetLjkET5CYTSv2/QzqeVi4WpZRSqiUwllj/29v9FcmcBZLrfXAOA2GjMNE3NG2AVZDC\nlZD/D6AIxNdL7foByb4DE78gqLG1Vpogt0KBXIyisRL/UKn6NZcFRtRP9HumlKrIWLtBxy+g6Bvw\nHAP7MIw9ISDnluItSP7L4DoAYcMx0bd6K8l1OUfBa37mHXaBcwfiTvPGr5qUJshNRFdIqztNbJRS\nSgWKMTaIODug55TClUj2XXhn0BBwHEAKP4L4RZi6zH3szvS/3djAkw2aIDc5TZBbsUAk6S098Q/V\nnmhVNf2ehQYRYduJ4xQ4nSR37kyErXX2MarmS6QYsPv6cP3tFyTnUaCwzFY3SAGS+xdM+xdrf7GI\nsyH/EH4XG7H1r/15VMBogtzEWloC2RgqfkS+PeV8hnQ4TRMdpZqJPZmZ3PjBfzhZ6MBiDB4Rnjhn\nCpcPHBzs0JSqkRSt8s5p7D4EJgKJ/Bkm9p7KMzlINngy/J0BitfV6Zom+kbE8b63WkwRYIBwiH04\ncKv9qTrRBFkFRGMn/jszTvDElwuD9guGJufNj37PgsPl8XDdfxeRUZBfbmKthz5fzuAOHRkQH7wn\n8pWqiTi3Iidvp7QqLAVQMA+RHEzbx8sfbKLxJrJ+WOLqdF1jiYMOHyL5b0Hx12DpjIm+ARM2os73\noAJDFwppoFlLFuq8wgFmiZ/HtV9eys5TvVh7oguXfTqVa1deGnKLmiilKlud+iMOp7PSrLNOt5u3\nt28NSkxK1Zbk/ZNKK/JRCI73EM+pcluNCYPIy/Cu4ldWJETdVOdrG0s7LLF3YolfhKX9c5ocB5km\nyKpZyC0uJre4WH8hUSrEnSos9Lskg1uEzIJ8P3sCq9DlJC03B6fb3ejXUi2Qax9+FxUxYeA+Wnlz\nm0cg/By8K+jFeP8bNRsTpe2UzZ22WNRTKM1r3BJ5v44zSXrxOWLDflrURCkV2kZ37Y7TUzk5jbLZ\nObd3460M5vJ4+OO3X/HO9q0YwGaxcPcZE7ghWatwLYm4jyGOD0FOYcLOhLAxVT5EVy+2IeA+TPkF\nRQBxgrV7pcONCce0/xvizgDPUbD2qjQXs4gDir4CTx6ET8BYuwQuXtVoNEFWIa1kUZMS+guIUqGt\nU0wMNw8fxWubN+JwOQGItNnoGxfHRf0HNtp1n1r1De9s30qhy1W67S/ffUN8ZCSX6cOBLYIUfoFk\n3403eS1GCt6CsAnQ7jmMsQbkGibmf5CiL4GycxJHQtS1GEtM1e+zdgA/K95J8Qbk5C14q9ICOW4k\n+hYssf8bkHhV49EEuZ5a+vRmEBr3VvHr3BLo9GOqpbtv/ERGde3G/G1byC0u4tL+A5k+ZBhh1sAk\nMRU53W7mbdtcLjkGcLhcPPf9Gk2QWwCRIuTULyk3pZo4oPg7KPwUIi8KyHWMfQDEv4Xk/BGc28HS\nDqJvwkTNqUfMxcjJW0Hyyu8o+BcSPg4TNjogMavGoQlyKxUKyW9dNJc4lVJek3v1ZnKv3k1yrXxn\nMS6Px+++E/l5frerZqZ4PX5njJACxPE+JkAJMoCxJ2Li32n4iYrXUqlVA0AKkYLFmiCHOE2QG6gl\nJm7aX904dAlkpRpHm/AI2kZEkFFQUGnfsNM6BSEiFXhW/D48B97V5kKRVJwNo3SHn2WlVagJ0X9V\nqrFo8quUamksxvDwmZN58PPlpW0WBoiw2fjVhLOCG5wKjLCReJPkCkwUJnJ6k4dTK2FjvQ/3VWSi\nMJEXN308qk40QVaVtIb+6mDQJZCVajyXDRxMu4hI/rb2Ow7nnGJYx07cO26CVpBbCGPs0P4F7wNv\nAuACLBBxGYRPDm5wVTCWWKTNo5DzO7zxusBEeRPn8POCHF31xJMHxavB2CFsHMZUnOu55dMEuZUJ\n5eQ3FGNSSjUfZ/XsxVk9ewU7DNVITNho6PgtFC0HTy6EjcfY+wc7rGpZoq5CwpIRx3/Ak4uJOA/C\nJmJM6C5D4Sl4H3J+U751pd0LmPCxwQsqCDRBVlXSRLVx1KdyrFVnpZTCO9Va5JXBDqNOjK0vJvb+\nYIdRK+L6EXIeBorKtXxL9q3Q8dtqp7praTRBbqXqm/z+8uxHAHh65e8CFov2RSulVOsgzu1I/mvg\nTvVWgKNnYyxxwQ5L+Yjjv4C/VSgNFH3hW1q7ddAEWakQpjNfKKVaCo/jEzj1K6AY8IBzB+JYAPHv\nY6yn1fR21RQkD2+/dMXtbpDGXyo+lGiCHCJCvWpaUjne+tXOcq8DUUkO5b5opZRSDSfihpxHKLfQ\nB8XgOYXkvYhp+9tghabKMOHnII5FIBWnTBTvqoWtiCbISoUwnflCKdUiuH8E/M0L7ILir5o6GgWI\nJwewlO8rDjsDws6E4m98SbIBIiDqeoytR5AiDQ5NkIOsufTfllSKG6MHuUSo3bNSSqkAMW1A/Hx0\nD2DaNW0srZy49iLZD4DrB0AQezKm3VMYazeMMdDub1D0BVK4FAjHRF7Z6mawAE2QlWoWtHJce1pt\nVyr0GGs8Ejbat/xymUTZRGKibwhaXK2NePKQzFkgOZROU+HciGROR+IWYLH19E5BF3Ged0q6VkwT\n5LPxKGwAACAASURBVCBrbv23jVE5Vkop1fKZdn9FTt4Gzl3eBSikGKLmQISuKldXIsXgTgNLB4wl\ntvZvLFzq/bqXW7bbA55MyLgQj60Ppt3zGFuvytd07UMcS0GKMBHnY8KSG3obIU0TZKVUi6AzfigV\n2oylPSZ+IeLaD+7jYB+MsWh7RV158l+DvL8DAuJCIi7GtP09xoTV+F5xHQYcVex1gWsPkvUz6PiV\nd/XC0mvOh9w/eY/BjTjmIxHTMG0e9bZltECaIAdAIKq/oV45bgrNpYqulFKq/oytD9j6BDuMkCEi\n4NwErj1g6w320VUmnVL4MeQ+S7kkt/BjxNgxbR+v8VombBjiiPIzS0XpFUAcUPQV+FosxJ0OuU9S\n7iFLcYDjPYi8FMJG1e5GmxlNkFXI0oRZ1YXO+KGUqo54CnyzM7ggfEJIVK/Fk4dk3QDuPSAeMBaw\n9oC4tzCWtpWPz3uByhXgQnC8j7T5P4yJrP6C4eeB5W/gPgw4qwjKDZ4TP70u+tobl1Q8sBBxfILR\nBFlV1FxmoAh1VX0dVcujyatSKhik6Bsk+06805bhbU1o8yiWqKuCG1fuU+DahXfxFLxJqGsfkvMY\npt3Tld9QNnGttC8HrNUnyMbYIf5dJO9v3gqw5Po/0F6mv9jYQPxVtI23l7yFsgQ7ABV6Zi1ZGNQk\ndWf6CXamn2BtWipr01KDHo9qXizx8zQBV0qVkv9v777Do6zSN45/z/RJIHQQrIiKgogCiq4igl3s\nu64i9rL+1rLqui6ua8Xe2+rau2JZ7A1FEbuIiqjYUIoIQoCQkGT6nN8fAyHDTCAhmX5/rmuva+ed\nzPs+M8Fw8+S8z4nXYKvOTCwrsHUrd4QLQc3l2Ojc3BYXfImGcNwgAsE3Eksv1uTegYaQ35gpA0fX\npEM2Oof4iuuJV4/FBl7D2kTH2DgqcFRcjOn+Cbi2BryNXuUH724Yd7/Vh7wjgHia4j0Y/0HrfIuF\nSh3kVii0CRT5as3PcZVVHWVZf/nSsdUNdCKSM6FJpA2VRLGBlzDtz8p2Ras1NRuaGIl2cnLdpt3f\nseGPwQZZHVp90P5CjHE2fF08MBGqzydxU10UG5wI9Q9D58cbbuYzxg2dx2PrH4LAK4lusP9ITNlR\nydd0VGA73ATV561cahFP1NbuzOQgXWQUkKVBviwZKbZ/eCgMti19niLSIklhsrHYWm5WyxLv7hB6\nm+T6HOAZmphHvAbj3hK6TMCuuCNxY59zI0y70zHe1dtAWxuGmgtI2tbb1kPke2z9BEz56NXnc5Rj\n2p0J7c5ca5kO/95Y73sQfBsIgXcPjLPX+r3nAqGA3AYKPcDlC32ObSffOra6gU5EcsYzDLgmzRM+\njG9ktqtJYiouwi79AuL1JG6+84PxYirGNf0aVx9Mp1ubPmnkK9J3zIMQfBkaBeQW1eroCDles51N\nCsjSIN86t7m+fmvlW0gtdPo8RWR9GNfG2PJToO5BEl1VC/jBtw+4czuBwTh7Qte3sIGXIPotuLbC\n+A/DOCpacVIf6TvmJNYqS7MoIIsUoXzt2OZLHSLScjY6Dxt4HuJVGO9w8A5PuwwgHznan4317p6o\nnwjGNwo8u7Z6kwsbr4Hgq9hYJcYzGDy7tPgzMY52mPKjW1VHEld/MB3SLB/xY8rWr3tcihSQJUWh\nd24by2U3PF9DaqHS5ymSO6k3fb2QGAXW6X6MKYwoYTw7YDw7tNn5bGQGdtnxibnBBLH1ZYlw2vmh\nZu1qlynGOKDTvYnaCJHY/CMGZUeBN7dLSgpJYfypLhLnjbgUgJsmX57jSqRUKESKSGtZG4SasaTc\n9BX+EoKvgP/QnNWWK9ZabNXfVo6MW3WwHiJfY+ufwJSfmLviAOPuC93fh9AHYJcndudzbZTTmgqN\nArIUpWxN5GhON1MhtW3p8xTJsvAXpN82IZAYk1aCAZnYbIhXpXkiCIHnIMcBGVaOcfONyHUZBUsB\nOQtWdY5nTJmZ9DhfOsn5Vo+IiOQR4yHNPsMrn/NltZT8YWjyM0k7QUIKjQKyFKVMT+TQRAURKRnu\nHUjstla3xhN+jP+IHBSUB5ybgbMbxH5d4wkflOpnUmRKMiBnu2O66jr51qnN9852IVNgFpFiYYwT\nOt2DrToJiK+8Kc1C2Z/Bu0eOq8sNYwx0vAO77LiVu+GFEzvRuYek7EQnhakkA7KUjkxNr9BEBREp\nJcYzELp/CKHJEK8Gzx8wrk1yXVZOGXc/6DYFQm9CrBI8g8E9qNWj4yQ/lFRAznXHNN86s/na2S5k\nWnohIsXKGB/49s91GXnFOMrBf1iuy5AMKKmALNLWCiH4KqSLiIi0TEkFZHVM09Pn0Ha09EJERKTw\nlVRAltKgcJqg5R4iIiLrpyQDsjqmkmkKoSIiIoWrJAOyFCd1TJNpuYeIiMj6Sbd3pIiIiIhIyVIH\nWVosU7vTtZY6punpcxAREWkZdZClWUZPeLohGEv+iS89ZvUSExEREWkVdZCl2WZWLmb0hKf59Lf5\nQP53knNB3WsREZHCp4Asa7UqBK8KxTMrF+eyHFmDbkwUERFpewrI0iL9unVnZuVi+nXrnned41xS\nUBURESkeCsiyVqtCcOPlFPmwFlkBNEE3JoqISDbY2CJs7R0QehdMOyg7DlN2FMYU5+1sCsjSYuoc\np1JQFRGRYmXjy7FLD4P4ciAKLIYV12Gj32E6XJHr8jJCAbkEnTfiUqBlOwrmSyjWUob0Sv39i4hI\n5tj68RBfQSIcrxKAwPPYdmdgnBvkqrSMUUAWWU/pwrmCqoiIFJ3wp0Ao9bjxQOQ7UECWQraqczxj\nysykxy3pJOealjKIiIism43OWtn5XYrxjgDf/hjjWb+TOTcDPgVia1wkBs6eraw0Pykgi7SQlnmI\niEg+iwdegeoLgQgQwwYnQ90j0GU8xnhbfD5Tfhw28DwQaHTUBa4tMO6t26jqtbPxegi9BfEl4B4E\n7u0xxmTsegrIJWRVp7gQO8drcnR5fOU0jafzZn20iIhIrlkbhJqLgGCjowGI/oyt/x+mfEyLz2lc\nm0Onu7DVF0J8GRAHzy6Yjje0VdlrZSPfYZcdC0TBhgE3eIdCx7swJjNRVgFZpIVyucwjk9dUJ1xE\npAhEvgbSjV4LQPBVWI+ADGC8u0K3dyG+CEwZxlHRmiqbzVqLXX4m2JpGR6MQ+hRb/wym/OiMXFcB\nuUQ07hoXcucYUnf3y9ctr0VERNZk49WAyVzANH4g3sRz7Vp3amOyf0NebDbElqR5IgCBZ0EBWSQz\n1rdzmovOcSbWPWtN9brpMxGR1rLRWdjl50P0x8Rj9wBMhxswro3b9kKu/mA6gg0AdvVx489YtzWz\n4mBIeiurxdIdbBMKyEWuLSZX5Nua5XS7+4mIiOQrG6/FLh29cpnAyqQXmY5ddhR0m7z+0yXSMMZA\n5/uxy45bGZIBGwX/cRjvHm12naxx9gHTYfV7aeAD/+EZu6wCspSsQuqcZnLdc1udO58/v/VVSH9G\nRCQ9G69PBFNHN4xx5qaI4Ksrby5r3AaNg62H0Dvg269NL2dcW0C39xLzi+NV4NkR4+zRptfIFmMM\ndLwdW3ViYqwcQTBl4OqHKctcR1wBuci1ZnJFvs9NVudYRESaYm0IW3M5BF4GDBg/tv2FOMoOyX4t\nsfkkj0hb9UQIYr9l5JrGuMC7a0bOnW3Gsz10mwyBV7DxSoxnCHh2xZh0NyO2DQVkKVmFuOlIJmts\nbee4GLushfhnREQSbPVFEHyDhh3gbBBqLsY6uyYmMmSRcW+LNWWJjnHSE57EmuEiYkPvY2uuhtgv\n4OgC5X/BlB3f6pnFxtERyo8hc5OPkykgl4j16foW09xkEREpHTZeA8HXgfAazwSxdXdnPSDjHQmO\nXhCbS2LzDgAvuLYCz9Ds1pJBNjwVW3UGDTOY40tgxS1YW49pd3pOa2spBWQpeeoKtk4pdFmL8T2J\nFLV4JRjXynW/a4jOz3o5xrihy9PY2jsS65FxgP9QTLvTm9VZtTaW2Da6fjwQAt8BmPJTMY72Ga+9\nJeyKW0jeoAQgAHX3YctPadObETNNAVnWSZ3j0lXMoVdEipizqdFpDvAMymopqxhHe0zFhVBxIQDW\nhiE8FUscPDthjK/J19rlf4fQZBrCZ92D2OCb0PXF9do6OmOiv6Q/bmOJmwUL6EZBBWQRaRMtDdEK\n3yKSKcZ4sO3OhhW3svrmuMSNeqbdmbksDQAb+hC7/KxGR+LQ4WaMb2Tq10Z+Sg7HAIQh/ntiGYn/\n0EyX23yu3hCpSj1unODolP16WiFzt/+JSMGKLz0mEWAjUyEydfVjEZEC4Sg/EdPxOnBtk7hZzLs3\npsuzGFfvnNZl48uxy08HW9vof/XY5edgY4tSXxD5CtLdmmbrseFPMl5vS5h25wJrdsL9UH5qQS2v\nAHWQRSTLinnqhYjkF+PbD9PGM4ZbLTixiSfiifXJ5SclH3b2AONIs5OcJ2kpibUWQpOx9Y9BfAX4\n9sWUHY1xlLdl9WtlvEOh03+wNdesnGLRGcpPw5Qdn7Ua2ooCcp7RxAjJB6Vw452ISE7Y2sTOdiki\n2Hhtaq/Y8wcwFSt3kouvPm6cGP8fG532Vqh7mIYlJbU/YgPPQ9fn1rq+ua0Z7+6YbrtjrW31aLdc\n0hILEckqR5fHE4HbvRO4d1r9WESkFHh2BdLt6OfDeIelHDXGien8xMp5yR7AB45emE4PYJwbAGBj\nS6DuAZI3IwlC7Dds/Ysp57TWYqPzsLGFbfCG0ivkcAzqIOeNfN+1TkqTgquISNsy7q2x/kMg8BKr\nA20Z+EaAe/v0r3FthOk6IbFG2YbAuXFyAI18kdh0JGWsXQDCk6F89c6zNvxlYipGfClgsa7NMR1v\nx7g2bdX7stYCNqO722WTAnKRy0TQHj3haaDprZ4V7pun1JcvlOr7FpHcsZFvsfVPQnwpxrsn+A/O\nyZg0UzEOvCOxgeeAOMZ/KHj3XGfX1TQ1Js3RmTSLlAEnOFa/xsYqsVUnJu/oF/0Bu+xo6PZuYl5z\nC1kbwq64AeqfBYJY9wBMxWUY97YtPlc+UUDOE9q1Lv/peyMiUrji9ROg5nISu+vFsaGPof4x6PI0\nxvizWosxBnwjML4RbXNC9yAwHVPXKePGlI1ueGQDzydmEieJJwJz6D3w7dniS9vl50LofRq29I7M\nwC47Brq8hHFt0uLz5QsF5FbK19CUiSUbqzrHn/42P+nxqk6ylok0j6Y4iIhkl43Xw4pxJM8SDkB0\nDrZ+Aqa8sMdYGuOAzo9gq06D2ILE3GEsVFyBcW+9+gtjC2gIso3ZGMQXt/i6Njo/ORw3PBHG1j2C\n6XBxi8+ZLxSQ84zCZP5R8BcRKXCRGaS/MS4IwdegwAMykOjWdn0NorMSkzLc/VNmDxvPjtjAC0B9\n6gncA1t+0diclWuf1wzdUYjObPn58ogC8nrK99CUiSUbqzrFoyc8zc/T59Dr+ZlJ59UykebRCDUR\nkSxztCN56UHj5zpktZRMMsaAe8umv8C3N9TdDdE5rO76+sC7G8bdr+UXdPVJE44BXOAe0PLz5REF\nZCk6bR3QFfxFRAqcq39iN71YgOSb2fyYssLvHjeXMR7o/BS27kEIvgzGDf6jktYpt+h8zp5Y314Q\nfIek5SvGiykvvM1BGlNAXk+FEprauq7zRlxKL2DJlJnMIP37z9fPIt+ocywikh3GGOh0P3bZ8WBX\nACYxEq3daRjvrrkuL6uMoxzT/ixof1bbnK/D9Vjnf6B+PNg68AzGtL8I49ywTc6fKwrIUjQyvexF\nwV9EJH9YG4D4cnB0w5h1xxnj6g3d3oXI54nXeQZjHJ0zX2iRM8aDaf93aP/3XJfSphSQW6nUQlOh\ndM5FRKQ4WRvG1lwJgecBA8aLbX8+jrI/r/O1xjjAs2Pmi5SCp4AsRUPhXUSk+NmacSt3oVt5c5gN\nQs1VWEe3tpsrLCVPAVnWi8Jn7mj6hYiUKhuvh8CLpM7yDWDr7lJAljajgCxFR+FdRKRI2SrAkf65\n2IKsliLFTQFZCl6pLKnQDnwiUvIc3cG4kie1AWDWb6OLVrLR+UAEnJslJmVI0Wjin2EiIiIi+cUY\nN7Q7D/A3PgrGj2l3TtbqsNHZxCtHYZfsj11yKLZyD2z486xdXzJPHWRJkcmObFueO993M2xr2oFP\nRAQc5Udjnd2xtXdCfBG4t8O0Oxfj3ior17c2jF02BuJLaWhlxwPYqpOh61sYZ7es1CGZpYAsIiIi\nBcX49sL49srNxUNTwK65Ix9gY9jAC5h2p+akLGlbCsjSIJMd2Uycu1THuqlzLCKSQ/FKsNE0T4R0\no2ARUUDOolILciIi6ysWi/HqvZN4+b8TCdWH2f2InTlq7GG061ietRpCgRD3/+tJ3nxoMuFgmO1H\nDuCM209ioy17Zq0GyUPu7YE0N+SZMox3aNbLkcww1qbcCtqkIUOG2GnTpmWwnOJWKAG5UNYgrw+t\n35VcMsZ8bq0d0ppzlMrP4avH3MZHL35GqD4x79btddF9k27cM/0GvH5vVmoYu884vv7geyLBCADG\nGMo7lvHQ97fRsVuHrNQg+SledQaEPgACK494wbU5psv/EjcSSt5q7s9hTbHIgvNGXMp5Iy5lxpSZ\nzJgys+Fxa84lsj7iS49ZPS5OJE/N+/43PnxhakM4BoiEoixdsIzJT32UlRpmfzOPbz/6oSEcA1hr\nCQfCvHbfpKzUIPnLdLwN2v8TXFuDc3No91dM5/EKx0VESywkRSa7u7nuHGuGsEj++2HqLBzO1P5N\nsC7E9Mlfs9+Jmd8tbd7M+WlrCAcj/DDt54xfX/KbMS5M+RgoH5PrUiRDFJCzoC1uJiu1kWbStvQP\nBCkkXXp1It2eC26Piw1698hKDRv17UU8Fk857vG52WL73lmpQURyRwFZSkIhzBDO59pEsmngiP5U\ndG5PqD6cFFKdbicHnLJnVmroM3Az+g7Zgu8+/YlIaNUaZHB73Rx42t5ZqUFEckcBOYs00kxypRD+\ngSCyitPp5OYplzPuzzfzy1dzcTgNFV3aM/bRs+i+cdes1XHlKxdw93mPMOmx94iEowwYtg1/u/MU\nOvXomLUaRCQ3FJClpORjMNTyB5FU3Tfpxn8+uYalC6sI1YfouXkPTLp1Fxnkb+fn3Hv+j3PuPg1r\nLQ6H7msXKRUKyAVGnePcKJbOvUK3FJouPTvlugSMMVkP5yKSWwrIBa5Yglsp0/IHERGR/KKALLIW\nmh4iIiJSehSQC5SCW/FR51iKVf2KAHO+/ZUuPTvRY9NuuS5HSoyNVWJr74bQFDBu8AzG+A8B9xAt\nnZEmKSCLrEVzp4foHygi6Y2/5jmeuHICTreTaDhG/137csmz59GuY3muS5MSYOPLsEsPgXgVEEsc\nDPyMDTwPrm2g8yMYh/4sSioF5AKlsW8iku/en/AJT1z1HKFAGAKJY1+//x3XHHM7V73yr9wWJyXB\n1j0C8RoawnGDCES/x664GdPh4lyUJnlOAVmkGdbVOdZSF5FUz9z4EqH6UNKxaDjKl29/zfLKajp2\n65CjyqRkhD4Cwk08GYbgC6CALGkoIBc4BbHM03SJ/KbvT/aEg2GmPPsxMz/+kY226snexw6nokv7\nJr9++aLqtMedbicrltUqIEvmOXtC9Ku1fEE0a6VIYVFAFmkFLXWRUlGzdAVnDv0XVYurCdYG8fo9\nPHbZs9z83jg2327TtK8ZvM92vPHgZGLR5F9vu9xOevXZIBtlS4kz5SdjQ+8CwTTPOsE7MssVSaHQ\ntkAiTYgvPSbRnYxMhcjU1Y8lL+j7k12PXPo0lfOXEqxNBI1QIExdTT3XHX9Hk68Zc9GfKO9QhsuT\n6MUYA94yL2fecTJOlzMrdUtpM56B0OFqoN0az/jB0QXT/oJclCUFQB1kkTagzrEUu/cnfEI0nPrr\n6Hnf/UbNshVUdE5datFtoy7cO+Mmnr3pJaa//Q09NuvGEf84mG133TobJYsA4PAfiPXti43MTKxJ\njv+OcW8LvlEYR1muy8NG5wMRcG6msXN5RAFZpAna4S6/6fuTXU53Ex1fa9faDe7SsxP/d+PxGapK\npHmMcSe6yZ6BuS6lgY3OxladCbF5gAMcHaHjLRjPoFyXJmiJhYiINMN+J43E4/ckHXM4HWy72zaU\nV+S+CydSSKwNY5cdDbFZQAgIQHwhtuokbGxJrssT1EFOsT43W+kGreKmzmR+0/cnO0ZfcBjfvP89\n30/9iXjc4nQ5qOjSnrGPnpmxa4ZDERbNWUynHh21sYgUl9AUsEHAJh+3MWzgeUy7U3NSlqymgCxS\n4rREQZrD4/Nw/aRL+H7qLGZ98Qsb9O7OoL23w+nMzM12E259hUcueRqAaCTG8CN24dx7T8Pj86zj\nlSIFIL4Y7JqblwCEILYg6+VIKgXkldZnw4di3ySi2N6PiLSOMYZthm7JNkO3zOh13vvfxzx00VNJ\nm4y8+8xHONwOzn/gjIxeWyQr3DukP27KMN6h2a1F0tIaZJESpTFpkq+evPq5tDvwTXpkCoG6dPNs\nRQqLcfcD726Av9FRLzg3A++eOapKGlMHeaX12fChWDeJKPbOuIjkt8pf09+kFI9bpk/+hl0OHJLl\nikTanul4O7b+aQg8DTYM/oMxZSdgjDvXpQkKyCIlS2PSJF917tWZmqW1aZ/7fupPGQ3ISxdW8dkb\n0/F4XQw9cLAmdOQhG50Dth5cW2FM4cYYY1yY8jFQPibXpUgahfsnKwPWt1NabJ3VXHfGC6FjXQg1\nihSqYYcPZc7X81KOO10OyttnLrA+d9sr3P+vJ3E6HRhjiMctlzx7Hjvt38R6UckqG52HrforxH4F\n4wRc0OE6jE/bRUvb0xpkkRLn6PK4useSYt73v/HSXROZ/NSHBNdYD5xph5yxHx5f6q+ZnW4Xexz5\nh4xcc/bXc3nwwvFEghGCdSECtUFC9SHGHXETdTX1GbmmNJ+1ceyy4yD2MxAEWwe2Grv8HGz0l1yX\nJ0VIHWS05rYpueocr/l9yFU96ejPihQ7ay23nX4vbz36HpDYJe+2vzq47s2L6bvjFlmpoUPXCi55\n9jyuPOoWHM5EHycaifGPB06n+ybdMnLNSY+9RyTNVtoOp+HTVz5n5NHDMnJdaabwZ2CrgfgaT0Sx\n9U9hKi7MRVVSxBSQRUSkwYcvTOXtx98nHAivPBIB4OKDr2X8/HsyNvd4TUNHDeaZ3+/n8ze/Ih6L\nM3ifgRldDxwKhrHxNcMX2LglHIxk7LrSTPElKXtqJEQhtjDb1UgJUEAm92tuJWHN78Mq6zObOlPf\nQ/1ZkWL32v2TCNalLqkI1of4Yeos+u3SN2u1+Mt97HZYdmbCDjt8ZyY+NDnlvcdicYbst31WapC1\n8OwApHb4wY/xDs92NVICtAZZJA+UwgziUniPxSASShdCEpuEpFuCUCy2G96P3f+0C75yL8aAw+nA\n6/dw8tVH07VX51yXl1XWhojX3kO8cj/ilQcQr30Aa8PrfmEGGWcvKPszqXODe4H/wFyVJUXMWJv2\ndxZpDRkyxE6bNi2D5YikWp9dDbcb3m+dr8knpTBqrRTe47oYYz631rZqRlmmfw5PfHgy/znrgZRO\nalmFn2cXPYDH27IZrdZafp4+h2B9iK2G9Gnx61tj7sxf+d8trzD/hwX037Uvh589is4bdFprrTOm\nzOT9CR/j9XvZ85jd2Xy7TbNWbz5I3Aw3GiLfAas2ZfGBe3tM50cwxuSwNgvB17D1jydu0vPtjyk7\nFuNol7OapPA09+ewlliI5FBDRzUyNelxMYXIUniPxWTPMcN458kPmPnJjwRrg7g9LhwuB/96/OwW\nh9s53/7KRQddQ/WSFTgcBiz846EzGHZ485dNrAqtU1//gnYdy9lzzLBm3aj35Ttfc/HB1xIJRYnH\n4vzw2Sxeu+9t7vzsWnr27pH2NcYYBu7Rn4F79G92fUUn/AFEf2B1OCbx/6MzIDINPDvmqrJEOPeP\nwvhH5awGKR0KyDmmtazrVsq7Gopkm8vt4po3/s20iV/x2cTpdOxewd7HDqf7xl1bdJ5oJMr5e17O\n8sXVScevO/Z2em97Axtt1Wud54jH41zx55uZNnE6wboQLo+LJ66cwNhHz2LYH3du8nXWWm75y92E\n6lcvC4iEosQidTx44ZP8e/y5LXovLRWPx3E4CnMFow1PT2zCkfoEhL/MaUAWySYF5DynwFfcSmE3\nu1J4j8XG4XCw0/47tGqDjC8mfd1oEsZq0WiM1x94m1OvO3ad5/jwhc8awjFANBwlClx/wn/Ycf8d\n8JV5076uZukKKucvSzkej1u+mDSjZW+kmay1PHPDizx9/YusWFZLry024K83n8DOBw7OyPUyxTi7\nY40fbGCNJzzg7J6bokRyQAE5R0plnm6231exfX4ihSYcijDlmY947f5JhEOp49FikRjLFi5v1rne\nefK9tBM1HE4HX737LUMPGJT2dd6yxI126ZR3KG/WtVvqsXHP8swNLxFauanKglm/c+WRN3PFyxew\nw8gBGblmRvhGwYrr0zzhBu8+WS9HJFcUkPNUqQRoSSiFrmopvMdSV1dTz992uZDKX5cSqA2m/Rpf\nOx87NRFs1+R0p/8rKhaJwVpuMPeVefnDITvx0YtTk6ZyeMu8HHb2Ac26dktEwhGevXF1OF4lFAjz\nyKVPF1RANo720Pkx7PJzILYocdDZC9Pxdowjc3OoRfKNAnKOFPua2XQB/+fpc+iz/WbNeq/F+rmI\nFLNnb3iRhb8sJpKmcwzgLfOw6TYbMuyPzbtJb98TRvDpK5+ndJFDgTBXj7mNq169kG133Trta8+9\n9zSqK2v47pMfcXlchIMR9j52dw45Y7+WvalmqK6swcbTB/b5Pyxo8+tlmnH3h65vQuxXwGBcG+e6\nJJGsU0DOU8UYoAO1QX6ePifXZYhIhrz7zMdpw7FxGPpsvxn7njCCA07ZE1cTneE1DdlnIPudVw6w\nQwAAGYJJREFUNJJX730rZT5zfU2Aiw68hmcW3ofH50l5bXlFGTe8fSnzf1rI4rmVbNp/Y7r0bHrE\nW2t06FaB05V+h8FN+xVmuDTGgGuTXJchkjMKyDlWDME3ncYBf1Uojsfi1FXXrzX0a2mJSOHy+NKP\ngXN5XIx7YSzdNurSovMZYzjjtpOorqzh3ac/TFlVYa3l87dmsMtBTY803WjLnmy0Zc8WXbel3B43\noy88jCeumECw0TILr9/DCVccldFrizTFxmvA1oJjA4wpzKkquaSAnOdyEQzbOpSu2TlWFzlz9A8K\nyaZlv1cx6fH3WPb7cnYYsS0H/GUv7h/7RNJaXIfDsFm/jVocjhtzup3plxxb0t7ElwtH/vNQyirK\nePLq51i+qJpN+23EaTcdz4Bh2+S6NMmgxGZrEYxJ/S1Grth4Nbb6nxD6AHCAowN0uBLj3SPXpRUU\nBWTJqJsmX57SFe6z/WZr/XpQ0BPJd1+9+y0XHXQN8ViccDDCq/dOos/ATdnpgB349NUvcDgMDoeD\n8o5lXPzsea261rDDd+aD5z5NCcORcJSBI/qzcPYiyjuUUdG5fauu0xrGGA7+674c/Nd9c1aDZI+1\nMWztnVD/MNh6rLMXpv1FGN/IXJeGrfo/iMwAVi53ii/GVp0NXZ7BuPvmtLZCooBcwtYMoZla3rDq\n9Yd2Or5NzieptDRFsikWi3HlUTcnBdZgbZBZX87mlCN35fjL/sx3n/xElw07M2ivATid6dfnrvLe\n/z7msXHPUjl/KVsO2pxTrhlD3x23aHh+54MGM3DEtnz17rcEa4M4HAa3z82Io3bjtIH/IFAbIB6N\nM3ifgYx99CzadczMKDeRVeyKm6D+CWDlvOjY/MTkj873Yzw75a6u6C8Q+ZaGcNwghK1/CNPh2lyU\nVZAUkPNMMQebtXWO17S299/SiRgi0rZmz5iXtEvdKqH6MJMee49Dz9y/2TenvXz3RO75x2MNyzKm\nv/MN5424lJveHUffIX2AxMYl4174J5+88jnvP/cpZe39bLPzltx62j1JdUx78ysuPex6/VyQjLI2\nCPWPk7wdN0AQu+J2TC5HWsYWgnGDXbO2OETn5aSkQqWAXILW1W3MVEjXX1qZo6Upkk2JNcHpx5o5\n3c2/GSgWjfHgheNT5wfXh3nw309y3cSLG445HA7+cPCO/OHgxFbHV4+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6Vy644ALuu+8+Fi5cyLp165g9ezaTJ08GYMWKFWzYsAGn08nIkSOZMmUKgwcPLr3vvHnz+OGHH1i+fDnGGCZPnszXX3/Nnj176N69Ox9//DEAOTk59fodawVZxYySyvHyvZks35upleQalFSOl39/lOXfH62XSnJiYiKrVq3i2WefJS0tjenTp/Piiy+yZcsWevToQd++fQG49tprWbx4cY3Xu/DCC3G73QAsWLCA2bNnl3a1SElJYcuWLWzYsIFzzjmHjIwMHnjgATIzMytdZ+zYscyaNYvnnnuuNBn1+Xz85Cc/YciQIVx22WVs2rSp9PiRI0fSsWNH4uLi6NWrF+eeey4AQ4YMYdeuXaXHXX755ViWRZ8+fejZsyebN28ud9/58+fz0EMPkZGRwYQJEygqKuKHH35gzJgx/PGPf+Thhx9m9+7dpc/Y0jy2fGm55BigyO/nzY0b8Ph8UYpKqcb350+3lCbHJQp9Af786ZYGud/f/vY3hg4dypgxY8jMzCx9M+Z0Orn44ouBUIGiX79+dOvWDRFhxowZpefPnz+fBx98kIyMDM4444zStq06p556Kg888ACPPPIIe/bsweVyVRuL2+3mnHPOAUJt74QJE7Db7ZXa4YkTJ9KmTRsSEhKYOnUqX375Zbn7zp8/n48//phhw4Zx8skns337drZu3Up6ejqffPIJd955J1999RWtWrU6sS+1Aq0gK6XKsdlsTJgwgQkTJjBkyBBeeuklhg0bVuXxdrudYPiVesU5JxMSEqq9lzGGQYMGsXRp9Yn9008/zfLly/noo48YPnw4q1at4vHHH6d9+/asW7eOYDBY2lgDpV1EACzLKv1sWRb+MgldxWmAKn42xjB37lz69etXbvuAAQMYPXo0H330EZMnT+aZZ57hzDPPrPYZYtm6gwd48ptl7Dh6lMHt2vPLUafQOyW1xvMycyNXbCyBY4WFxOtKgqqF2JddWKftJ2LBggUsXryYZcuW4Xa7GTduXGnb63a7azW9mTGGd999l169epXbXl3hY+bMmYwZM4aPPvqI8847jxdeeIHi4uIqY3E6naXnnmg7fPfdd3P99ddXimnlypXMmzePO++8k0mTJnHXXXfV+Oy1pRVkFTNev3Q6r186ndGdOjO6U+fSzyqyObPHMGf2GEb3SGF0j5TSzydiy5Yt5frTrl27lm7dutGvXz927dpV2gXhlVdeYfz48UCoD/KqVasAmDt3bpXXPuecc3jmmWdKG8ajR4/Sr18/Dh8+XJog+3w+Nm7cWOncHTt2MHr0aO6//37S0tLYs2cPOTk5dOzYEcuyeOWVV46rm8Nbb71FMBhkx44d7Ny5s1IiPHHiRB5//PHSV4lr1qwBYOfOnfTs2ZNf/epXXHTRRaxfv77O944VS3bvYsbcOSzYuYOd2cf4cNsWps55lQ2HDtZ47uB27Yn0o9hmWaTV8MuRUs3JSa0jv0WqavuJyMnJISUlBbfbzcaNG/nmm28iHjdw4EC2bNnCnj17MMYwZ86Pb2RL2rYSJW1bUlISeXl5Ea+3c+dOevfuzc0338yUKVNYv359rWOpzvz588nOzsbj8fDee+8xduzYcvsnTpzIP//5TwoKCoDQCodHjhxh7969JCYmMnPmTG677TZWr15d53tXRxNkpVSp/Px8rr32WgYOHEh6ejqbNm3ivvvuw+Vy8a9//YvLLruMIUOGYFkWN954IwD33nsvN998MyNGjKh2xPQNN9xA165dSU9PZ+jQobz22ms4nU7efvttfvOb3zB06FAyMjJKB9uVdccddzBkyBAGDx7MqaeeytChQ/n5z3/OSy+9xNChQ9m8eXON1epIunbtyqhRo5g0aRJPP/10uSo0wD333IPP5yM9PZ1BgwZxzz33APDmm28yePBgMjIy2LBhA9dcc02d7x0rfvf5Qor8fkz4c9AYPD4ff1zyRY3n3jZmLK4Ks5O47XZuGX2qzmahWpQ7JvbD7Sj/37zbYeOOif2qOOP4nX/++Xg8HgYOHMjdd9/N6NGjIx4XHx/PE088wdlnn82IESNo3bp1aTeEe++9l4KCAoYMGcKgQYO47777ADjzzDNZt24dw4YNqzTo7bXXXmPQoEFkZGSwdetWrr766lrHUp2RI0dy0UUXMXToUGbMmEFGRka5/ZMnT2batGmccsopDBkyhMsvv5z8/HzWrVtXOmjwj3/8Y71WjwGkpDJSGyNGjDAlg2CUUvXvu+++Y8CAAdEOo0WYNWsWU6ZMYdq0aQ1y/Uh/lyKyyhgz4kSuW5/tcJHfx+CnHicY4eeAy25n089vrvEaGw4d5JGvl7D+4AHaJyTyy1GncEHf/vUSn1LRVNf2+N01e/nzp1vYl13ISa3d3DGxX4MM0KuL/Px8EhMTMcYwe/ZshgwZwk033RTVmMp6/vnnqxwUWB9OpB3WPshKKdVCOSwbcTYbhRUG2gG0cdXu1fDgdu15eWrD/JKhVFMydVinqCfEFT311FO8+uqreL1eRowYwU9+8pNoh9RkaIKslGqRXnzxxWiHEHU2y2LG4HRe27C+3GwUbrudG04+oUK3UioG3HHHHdxxxx3RDqNKN9xwQ7RDqJImyErFGGNMrUYhq9hVl65r0fabsaeT6/XywdbNOGw2fIEAV6dnMGto1TOXxLK9ubm8vmEde3JzOKVTF6b2H4hbZ9NQx0nb46brRNthTZCViiEul4usrCxSU1O1UW6ijDFkZWVVGvAXqxw2G4+ccx6/HTee/fl5dEluRVKZafJihQkPHnTZ7disyOPLl2fu4br3/4M/aPAFAyzYuYNnV6/k3elX0aqJ/H2o2KHtcdNVH+2wJshKxZDOnTuTmZnJ4cOHox2KOgEul4vOnTtHO4w6aeN20yZGFzyZt20Lf1j8OUc8Hlx2G/8vYzg3jx5TLlE2xnDr/I/L9acu9PvZn5/HUyuXc+e48dEIXTVh2h43bSfaDmuCrFQMcTgc9OjRI9phKBUzluzexe2ffVLaR7rAF+Sfa1ZSHAhw57jTS4/bk5vDsaLKizIUBwLM275VE2RVZ9oet2w6D7JSSqmY9bdlX1dazrrQ7+eV9WvwlhtY6Ig4XR1AvF37ICul6kYTZKWUUjFrT252xO0GOFr4Y8U4LSGBQWntsFXoK+q225mZnoGKbcYEMcXrMN6lGFP/yzMrVVeaICullIpZ/dumRdzuDwY57Ckot+2JSRfQKTmZBIeDeIeDOJuNc3v14YrB6Y0RqjpOxrcNc/gMzLFrMdm/wBw6haDnvWiHpVo47YOslFIqZt0+Zhyr979ZaTGTQDDIFXPn8KezzuWifqGVsjomJfHfa65nxd5MDuTnkd6+Az3bpEQjbFVLxvgxx66F4JHyO3LvwTj6I476X6pZqdrQCrJSSqmYNbRDR16+eBqdkpLLbTdAkd/P3f/9rFxfZEuEUzqH5j/W5LgJKF4OEbtUFGM8cxo9HKVKaIKslFIqpg3v2Im28fER94kIGw8fOuF7ZHk8rN6/jyMezwlfS9VBMKeqHRDMatRQlCpLu1gopZSKeclVLF4SCAZJdDpLP3t8PrZmHSHVHU+XVq1qvK4/GOSuhfP5YOtmnDYb3kCAC/r0449nnYvDZqu3+FUVnCPA+CLsiEdcZzV6OEqV0ARZKaVUTCny+/h42zY2Zx2mb2pbJvfuyzVDh7Fy3z4K/T8mU5YInZKS6ZOSCsBL61bzyFdLsFkWvkCQwe3a8fT5F5FaRfUZ4PEVS/lw2xa8gQDeQACAj7ZvpUNiEredOq5hH1QhtnaYhJ9AwQtASVcLFzh6g+u8aIamWjhNkJVSSsWMg/n5XPzmq+R6vXh8PuIdDv789RLeufwqbjh5OM+s+ganzYYxhhR3PM9feDEiwpIfdvHIV0vKDeZbd/AAP5v3Pm9Ou6LK+728bk2leZaL/H5eXr9WE+RGYiXdjHEOx3heg2AeuM5H4i9BxFnzyUo1EE2QlVJKxYz7vljI4YICAuFFPzw+H0V+P7/7fCHPXTCVmenDWLN/Hynxbk7ucBISnvf4+dWrKs104Q8G+fbgQTJzc+icHLm7RV5xccTt+cVejDGl11cNS+LGIXH6C4mKHTpITymlVMxYtOv70uS4RNAYPt+1E2MMHl8xi3/YxQOLP+eeRQvYlX0MgMMF+RGv57BZZBVWvfBEerv2EbcPbtdek2OlWjBNkJVSSsUMIXJSaonFxkMHmfzay8zZsJ51Bw/w+ob1nPfqS3y+63vGd++B06o8qC4QNPRLTa3yfvdOOAu33V66Ap9NBLfdzn3jz6yfB1JNkjEGYyK/XVAtgybISimlYsbkPn2xW+V/NFnAhO7d+f3iRXh8PvzhCrMBigMBrn//P7SJc9HK5cJZZuYJt93Ob8edTp63mD9/vYRL33yNWz+dx6Yy08INbd+B96+4mqn9BzCgbRoX9RvA3MuvpDgQYMnuXRT6Is2woJorY3wEcx/GHMrAHEwneHgixvt1tMNSUSCmwqus6owYMcKsXLmyAcNRSqnmS0RWGWNGnMg1mns7nF1UyJTXX2FfXl657clxceR7vQSrOC/OZuP1Sy/ns507+HzX97RPSOSGk0fQvXVrprz+Cp5iH8XBAJYIcTYbj02awlk9elW6zroD+7n+g3co9gdAQtPIPXz2RKb07d8AT6tiTTDnLij8ECgqs9WFpL6OOAZFKyxVj2rbDusgPaWUUo3CGEOOt4g4mx23w1G6Pcvj4dlV37Bo905SXPEU+fyVzs0vLsYSi6CJnCIHjeHrPXu449TTuOPU00q33/HZx+R5vaX9moPGUOj3c9fCz1h6fU+sMv2Mi/w+rnl3LnnF3nLX/p8FnzK4XXu6t25zQs+vYpsJZkPhB4C3wh4vJv9JpM0/ohGWihJNkJVSSjW4b/ZlcueC+WTmhlZOO6tHL64cnM6OY0d5bMVS8ouL8QWDwNGI5weNwWm3QQD8EZLkgDEUh+cxLmvJD7srDfoDyPN6OZCfx0lllrBetOv7iAm4Pxjk7U0buL1M4q2aocA+EAeYigmyAf+OqISkokcTZKWUUg1qd3Y2s96dW24atk93bGP+zu1YSMSEN5L2CYn0bpPCwl07K+1z2myc26t3pe2tXS4OFRRU2h7EkOgsvzpfrtdLMEIy7Q8GOVZUVGm7amZsXapY1c8C7V7R4uggPaWUaua8fj+PLvuacf96llNfeIY/LfmCPG/FKlnDeWndmnB1+EeGUFW4tsmx227numHDee7Ci7lx+EicNhuWCFZ438z0DAamtat03vUZw3Hby9eCnJaN8d26V1q++tTOXSMmyPEOB2f26FmrOFXTJVYSxF8JuCvsiUMSfxaNkFQUaQVZKaWaMWMM17z7NusPHsQbCFVwX1q3hs93f8+HM2bisFWeGq2+7TiWhT9Yu0S4hCWC3bKIs9nwBgJcMmAQVw0ZCsD/jD2dC/oN4IMtmwmaIJP79CO9fYeI15k2cDDbjx7l5fVrcNps+IJBhrbvwJ/PmVTp2C6tWnHN0GH8e/260iWt3XY7J3c4iQndetTxqVVTJEm/wdjah5a+DuaAIx1J/i1ir/x2QjVvmiArpVQztnL/XjYePlSaHAMUBwPszctlwfc7mNS7b4PHMLxjJ1bszcQboY9wJHbLYnBaO/7v3Enszculf2oaaQkJ5Y4Z0DaNAW3TgNBUb0Fjyg24KyEi/Pa08cweMZItR47QMSmJvOJiHvryC7KLiji3V28m9+lXOj3cnWNPZ2yXbryxYT1Ffj8X9uvPlL79sVn6wrUlELGQhOsg4bpoh6KiTBNkpZRqxtYfPBixeuvx+Vizf1+jJMhXpw/lxbWrKQ4EqG5iUVu4apzevgNPTr6Q1Ph4erZJqfL4Nfv3cc+iBWzOOoLDsnHpgIHcffoEXHZHpWNT3PGM6dKVV79dx4NLPi9Nqhfv3sUr69fy+qXTcdpsiAind+vO6d26n/iDK6WaLE2QlVKqGeuUlIzDslWa4cFtt9O1VesGv7/X7+fu/y7A4/dhs6wqu1rYRXjq/IsYkJZWbmaJquzKPsbV77xd2hXCG/Az97uNHMjP5/kLL454Tq7XywOLPy9XTff4fWw+coT3t3zHtIGDj+MJlVLNkSbISinVjJ1ZotEvAAAgAElEQVTZoycJTgeFfl+5AWgOm40L+w1okHsW+nw8tmIp//luIzleL4FgMOJUa2VZYjG6cxcSnc5a3eOfa1ZRHCg/X7I3EOCrPT+wJyeHLq1aVTpn1f69OGwW3go9PQr9Pp5d9Q1dW7Vm5EmdkAhdNZRSLYt2qlJKqWbMabPx1rQZpLfvgMOycFg2+qe25Y1Lp1eaxaE+GGOY+e7bvLh2NYc9HooDgRqTYwj1Fa44s4bX7+f9Ld/x9MoVLN3zA2VXft2SdSTidZ02G7tyjkW8R4LDSVWrx+44dpTr3vsPl7/9BkV+XV5aqZZOK8hKKdXMdWnViv9cfiXHCgsJGEPb+PgGu9c3+/ay+cjhWg/IK5EcF0f7xMTSzzuPHWX6229Q5Pfj9ftx2u0MaJvGKxdPw2V3kN6uPesO7K80fZw34Kd3m9SI9xje8SQSHE4KfJUTYEOou8WGQwd5fMWycqvxKaVaHq0gK6VUC9HG7W7Q5Di7qJDnV6+kKEICWhUBXHY7fzjjrHKzUNzyyUccLSykwOfDbwwen491Bw7wxIrlFPp8nJSUXKkrhMtuZ1LvvnRMSop4L5tl8eLUS2kbH0+Co/JAPgh105j73cZax6+Uap60gqyUUuqEZebmMHXOq+R5vVQ347HdsnDabJzdsxdbs7Lo3qo1s4ePZGiHjqXHHPF42Ho0q9KMF34T5KmVy3ll/RoCxuAPBLBEMMbQxu3mmvQMfj7ylGrj7N82ja+vm82i73fys3nvV7lynlKqZdMEWTV5M+bOAeD1S6dXu60h76dUSxY0hnv+u4BjhYVVTuPmsCx6p6QyvONJ/OTkkREH0ZWoqp8whLpC5BUXlz0Yl93OdRnD+fnI0bWK125ZnNOrN4PS2rHh0MFyMTssi8mNMPWdUiq2aYKslFLquL3z3SYeXPI5R4sKqzwmvV17/u/cSfROidw3uKK0hAR6tG7DlqwjtTq+yO/nudXfMLFXb3rV8h4A/3fOJC5/+3WKAwEK/X4SHA7S4hO4dczYWl9DKdU8SXW/qVc0YsQIs3LlygYMR6naK6nkLt+bCcDoTp3ZdPgQA9PaldsG9VPtjXS/+rq2ahlEZJUxZsSJXCOW2uHFu3fxs4/eo9Dvr/IYuwjf/eKWOq9EtyXrCJNefalO57jsdu4+bQJXhpekro1cr5f3t3zHruxjpLfvwMRefYiza+1Iqeaqtu2wtgJKKaWOy+MrllabHDssG5P79Klzcrwr+xjztm0hyeks352iBkV+P39YvIjzevchxV27wYjJcXFcnZ5Rp/iUUs2fJsiqVFPrW1sSZ2P1Qa7ufkq1RJm5uVXui7PZ6d+2Lb+fcHadrvn+lu+4c+F8/MFglYPlBLBEIs6DbLcsFu/ezdT+DbMISkXfHjrI4t27SHQ6mNynH2nxCY1yX6VUw9IEWSml1HEZ2r4Dn+3cXmlgnstm5+WLpzG840l1WpUuv7iYOxfOp6iaqjRAijuevimpLNu7p9K9BcFpa/gZTI0x3LnwUz7cugVvIIDDsvHwV0t4YtIFnNmjZ4PfXynVsDRBVpX61ja1CmmkOBsy9qbyvSjV0H49Zixf7tlNoc9Xmqi67XZuP/U0RpzUqc7XW5b5A/ZadMfo17Ytd5x6GjPmzqmUTAcxjO/Wo873rqtFu77no61bS7uYeMPLXv/qkw9Z+ZOf4bJHnmdZ1S8TLAD/NrDaIvbO0Q5HNSO6UIiqZNPhQ9EOoUULZl1NMOvqaIehVI36pbblrWlXML5bD9q4XPRLbcsjZ5/H/8s4+biu57BstTpu57GjpLdrzy9GjibOZsNtt5PgcOC22/nHpAtIcDqP6/518Z/NG/FEWJLaQliauafB768gWPBPzKExmGPXYY5MIph1NSaYHe2wVDOhFWRVrm9tySwQWiVVStXGgLR2vHDRJfVyrVM6d0GouUvGUY+HgwX5/GLkKVzcfyBf7N6Fy2bnrJ69SI6Lq5dYalJlnLXvUaJOgClaBHmPAUWUvr7wrcFk34yk1G32E6Ui0QqyAn5MjvOKi1m+N5MZc+eUdrVQjaO0cuxbAb4VWklWLU6c3c6zUy4i3uGociloAH/QEGcL1XdOSkpmxuB0Lh4wsNGSY4BLBgzEHaEbhTGGMZ27NFocLZUpeB6oOPe2D4pXYwIHoxGSamY0QValBqa1i3YISqkWbnTnLiy7/kZuGzMOq4oBfg6bRRu3u17utycnh+dWf8PTK1ew42hWrc+b0K0HF/btj8tuxyaCy27HZbfz2KQp2v+4MQSrWERG7BA81rixqGZJu1goQKcwiwVW6r8BSqvGJZ+VamkSnU4m9e7LQ18tpjgQqLS/XUIiHp8Pl91eZRJdG6+uX8sDS74gaIIY4LEVS7lx+EhuGjWGlfv3suXIEXq0acOYzl0r3UdE+NPZ53J1+lC+2L2LRKeT8/v0IzW+dvMvqxMUNw48e4CKM54I2HUWEXXiNEFWSikVc9onJtKrTQqbjxwuN5Wbw7I4Vuhh6NOP47Y7uH7YcG4aPabOifKB/DweWPI53jIJuD8Y5KmVK5i3fRuZuTkEjcEmQsekZOZcOj1i1XpQu/YMatf+eB9THSdJuBFT+BGYPKBksKQbku5CpOEHaarmT7tYqHJev3R6TFaPY7lPdH3HZqX+W6vHSgGPT5pCittNgsOB3bKIs9kIBIPk+3wEjCHfV8yzq7/hr0u/rPO1F+zcEXGOZm8gwPajWXh8Por8fgp8PnZnH+OeRQvq45GaHVO8jmDWVQQPZhA8fA5BzzuYCAu41DexpSFtP4SEWWAfAM4zkJTnsOKnNfi9VcugCbJSSqmY1DY+nlM7d6XI7ycQDCJAxbX1Cv1+/rV2Dd4aFhepqLoFTIIVEjxfMMj8ndsrbW/pjG8j5uhM8H0DxgOB3ZB3H6bgn41yf7G1xUq6A6vte1gpzyDOUY1yX9UyaBcLFdNieRGTWI5NqabOGMPV77zNliNHSpeULorQHxlCCW2Ot4h29sRaX//sHr14YPGiWh8fNIagMSfU57m5MXl/A4oqbCyEgn9gEq7Rrg6qSdMKslJKqZiz+sA+dh47ii8YOSkuy2FZrDt4gKdXrmD+jm34gxXrzJW1T0zk3vFnEmezEWez4Qh34eibmoqtQhJsiTC6U+darfLXovi/q2JHEIKHGzUUpeqbVpBVTIvl2TViOTalYs2xwkK+3vMDcXYbp3XtTpy9+h8/O48dozY9Glw2G3F2O7d+Oo8ivx+X3U6KO563L59BWnxCtedeMTid07p159Pt2wiYIGf37I3bbmfqnFfJ9xbj8ftw2+3E2e08cOY5dXnclsHWNXIibIJgpTR+PErVI02QlVJK1cjr9/PkyuXM3bQRfzDI+X37cfPoMSTHuWo899/r1/Lgks+xW1ZoBTqB5y+4mFGdOld5Tt+U1Cr3tY5zURTw0z4hkbT4BNYe3F9aNS7w+Sjy53LPfxfw9JSLaoytU1Iy1w0bXm7bf6+5nve3fMe3hw7SL7UtU/s37iIkTYUk/hJz7GeU72bhgvjpiNTPPNVKRYvUZbTpiBEjzMqVKxswnMallT+lVGMSkVXGmBEnco1otMPGGGb8503WHTiANxAaDOe02eiS3IqPrrwGp81W5bmbjxzmkjdfo6jCILoEh5MVN9yIu8KKedlFhby6fh1fZ/7A1qwssosKS/sgl3DZbPzv6Wdw1ZChDHryMQr9PiqyRNj6y19rn+EGFiz8BPIehGAWiBPcVyFJtyJS9X8TSkVTbdthrSArpZSq1qr9+9hw6GBpcgxQHAiwPz+Pz3Zs5/y+/ao89+1NGyMu9iECn+/+nkm9+5ZuO1xQwJTXXyHXW4Q3EMAmUik5htBgvUeXfc2Vg9MJmsj9jYPGcDA/n45JSXV5VFVHlvs8jGsimAIQFyKaVqjmoUWOOCiZt3b53kyW782M6Tl2lVIq2r49dDDiwDePz8fqA/uqPbeguDji9GhBYyj0la/8PrZiKceKCksX74iUHJc4VlRIcSBAt1atI+63RFj4/Y5qY1P1Q0QQK1GTY9WstMgEubmKlOhr8q+UOlGdk5JxWJVfmbvt9ioT1BITe/chvkI3CoBAMMi4rt3KbVu0a2etZqAASHG7cdpCA/4iceiME0qpE9Aif93T2QeUUqr2JnTvQVKckyK/r1xV12GzMbX/gGrPPb1bd8Z26cpXe37A4/NhiRBns/GrUWNol1B+3uIkZxyQV2M8brud204Zh4gwbdBg/v3t2nJLRpc4p2fv2j2gUkpV0CIT5NpoSslzpAUrNh0+xMC0drqIhVLqhDlsNt6aNoNffzqPdQf3A9A7JZW/nDupxlksLBGeOv8iFn2/k3nbt+K2O7hs4CCGduhY6djrMk7mzoXzqdixwhKhU1ISe/Py6JCYyK2njOWSAYMA6Jfall+MHM0/vllB0AQRBBG45/QzaJ9Y+4VDlFKqrBadIGuyqJRStdMpOZk3L7uCnKIiAiZIiju+1udaIpzVsxdn9exV7XHdWrfGJoK/Qt9jh2VxzdBhXD8s8sDzX44aw+Q+/fhs53ZsYnFe7z50Tm5V6/iUUqqiFp0gR9IUlw+urstIU4hfKdV0tHLVPO/x8fo+OxuHzYa/wpRw3kCAzUeOVHtuzzYpzB4+qsFiU0q1LJogK6WUqnfGGD7ZsY1X1q+loLiYKX36cVV6RrkBe75AgPk7tvPF7u9pG5/AkHbtkAjzFrvtdga3a9+Y4SsFgAkeBf9usHVGbGnRDkc1Ik2QK2jKA/gixdqU4ldKNR8PLPmcNzZ8W7qIx7ajWbyzeRPvTL+KOLsdr9/PFXPnsO1oFh6fD7tlYROhU1IymXm5pXMnWwjxDieX9B8YzcdRTZjxbYXi5WC1hrizEKvm7kHGBDC590PhXJA4MF6M6xyk1cOIOBshahVtOg+OUkqperU3L5fXvl1XboW7Ir+fH3Jz+HDbFgDe2PgtW7KO4AnPhewPBvEGAhwqyGfagMEkOp3E2Wyc3bMX715xFUkxsNRz0Bj25OSQ5fFEOxRVC8YYgjm/xWRNw+Q9gsn9HebwOIxvfc3nFrwAhe8CxWDyQv8uWojJe6TB41axQSvIVWisymtTrFQrpVR1Vu3bi92yKk295vH5WPT9Ti4dMIgPt26utPw0ACJcPGAAD5x5doPElpmbw/wd2xERzu3Zm07JybU6b8nuXdyx4BNyvV6CxjC840k8et75pMUnNEicqh54P4HCeUBR6LPxhv517GeQtgSRamqEnheBwgobi8DzJibprurPVc2CJshKKaXqVWp8PFC5L7FNpHTqNbe98uIhEKrSumwN86PpxbWrefirxaWfH/lqMXeNG8/MocOqPW/nsaPM/ui9cgn9N3szufbduXw0Y2bEftMq+oznTSonuYDxgO9bcA6t+uRgbhU7igE/oN0smjtNkKMkVmfLqE0csRKrgmDW1QBYqf+OciRK/eiUTl1IdDrx+IrLzWnssNm4akgoKblyyFBW7d9XrhsGQIrLzcC0dvUe0w852Tz81eJKVe0/fvkFZ/ToWe20cC+tW4Ovwnl+Y/ghJ5sNhw8xRAcQxibjq2KHEEpyq+EcBsXLKm+39dQ+yC2EviNQSilVr2yWxWuXXk731m1w2x0kOpwkO+N4dOJkerZJAWBir95cNnAQcTYb8eFjUt1unr/w4gapyH6yfRtBU3EJEjDhfdXZk5NTbgXBEpYIB/JqXvlPRYe4pwLuCHsscKRXf27SXSDxQMkS6xbgQlrdV68xqtilFeQoibXZMmpT0a6vqnddz4uV7yiWlFSO8a0o91krySpW9GjdhgUz/x/bjx7F4ytmYFo7HDZb6X4R4b4JZ3HdsOF8s28vrV0uTu/avdwx9clUWp8vvN2YKveVGNO5C8v27qnUZ7o4ENDp52KZ+yIo+hB8a0PdKnACFtL6r4hE7uJTQhz9IfU9TMGz4NsA9t5Iwk8RR79GCV1FnybISimlGoSI0Cc1tdpjurZqTddWrY/r+v5gkDkbv+XNjd8SCAa5ZMAgrhoylDh75R9t5/bqw9+XLcVHsNx2SyzO7dmn2vtMH5zOC2tXEwh68AVD57vtdqb2H0jHpKTjil01PBEHtHkBir/EeL8CKxVxX4jYOtTufHs3pNWDDRylilViIrw2qsqIESPMypUrGzCcliUWK6MN2Qe5YgV6dKfO1V6nrse3RFo5blpEZJUxJvJ6ybWk7XCIMYbZH77HV3t2Uxiu7LrCC4q8cel0rAjdNJ5ZuYJHly8lYEJJrl0sbjnlVH46fGSN9zvi8fCPb5axYOd2Ep1xzBo6jMsGDYl4H6VU7KptO6wVZKWUUk3O+oMH+GrPD6XJMYTmWt50+BBLdu9ifPcelc6ZPWIU5/TqzcfbtyHAeb37lPaJrknb+HjuHX8m944/s74eQSkVw7SCHAUtvTKqfZBVS6UV5Prz/OqV/PnrJaVdHsq6cfgo/mfsaVGISjU2EzgAxSvASgbn2Br7FiulFWSllFLNVtv4BJw2W6UE2WWz0z5RF+9oCYJ5f4WCF6A0KXZCyouIY0BU41INwwRzoehDjH8v4syAuDMQabg0VhPkKKhuBovmUC2t6Rnq+mxN+btoSrQ/s2pKJvbqze+/WFhpu80SLujbPwoRqcZkvEvA8xKhpaCLw1sLMMd+CmlfNPpKd8Z4ofB9jHcBWG2R+CsRx6BGjaE5M75NmKMzw3NbF2EK48HWDVJeQ6yG+YVY50FWTdKMuXNKE3GlVMvjdjh47ZLL6ZLcCrfdTrzDQfuERF6ceikp7vhoh6camPG8DibSKnn54FvXuLGYQkzWZZjcB8C7CArnYrJmEPTMbdQ4mjOTfSuYPH5cNtwD/p2Yguca7J5aQY6ihphjOJqawzO0RDqnsmqqBqS14/Nrr2fnsaP4jaFvSqou+9xSGE8VOyRy4tygobwJ/l2UJm8EQ3/Oux/jnoxIpMVKIlwnmIspeBG8C8FqjcTPQlxnNEzQTYgJHIDA3gh7QlV7km5pkPtqgqyalFhPwmMtHqWaOxGhV0r1cy2r5kdc52N8ayIkw4HQMtGNqehTfkyOy7JB8XqIG13jJUwwH5M1FQKHgFCXEeNbi/HPxkr8eb2G2/TYoKrFfBqwK40myDEi1lbWOx7N4RlaopJKsVaOlVJNhvsiKJwL/s3harINcEDSH2pdsa03VnIVO4JgJdbqEsbzBgSOUJIchzYWQv5TmPgrEev4FtNpDsSWhrH3Bv93lE+UXeC+tMHuqwmyalJiNQmP9cq2Uko1JyJOSHkFvAswRQtDq+TFX4bYezd+LPFXY7xLgbLVbAGrLdgH1u4i3s+JWIUWR2ip67hxJx5oEyat/4Y5eiWYotCgTHGAfQiScH2D3VMT5BjTHBKq5vAMLZFWjpVSTYmIA1yTENek6MYRNw6TOBvynwpPOWdAkpE2z9W+T7ytA/gsoOK83gGwareYTXMm9h6Q9kWof3ZgPzjSwTG8QcccaIKsmqRYS8JjtbKtlFKq4VmJP8fEXwHFq8BqHU7eat8/VhKuwRR9RvkqtA1sncGu8zpD+K1BI/4ypNO8KdUMBLOu/nE2CqWUUo1OrBTEdQ7iHFnneZjFkQ7JvwdJAEkEXGDvj7T5p87MEiVaQVaqHkWtcuz/Ljr3VUopVS+s+KkY9+TQwENphdi7RTukFk0TZNVoTmSWBO26EFlp1djklfus/YmVUqrpEXGG+teqqNMEuZm67Yx7AfjLot9HORLVoCpWjqupJGvyrJRSStWOJsiqwZ3ISm06fVoNSgZvhL9bHcyhlFJKnbhGT5A1wamsPr+Tksrx+i82lfusleTmqXSRj4PDy30uS5eSVkoppepGK8iqwZ3ISm06fVotaeVYKRWDTOAgJvdB8C4CsYHrfCTpTsRKinZoSlWr0RJkfVVeWUN8JyWV4mhWjqt6jk1HDgEwOLXRQ6qVpvzfZHW/dOhS0kqpaDCmCJN1GQQPA4HQKsGF72J830Lqu3WeCi2WGWNCY0ACB8ExGLGlRTukiIxvPSb3odDqfFYKJPwUiZ+hU8lFoBVkdVyOJ5l8cENoScjX+9X9fk0xaVVKqRataB6YXCBQZqMPAj9A8TKIOzVakdUrEziCOXYdBHYDNjDFmPirQpXyGEo8jW8zJmsmpYuRBPdB3sOY4GEk6eaoxhaLGi1B1lfllTXkdxLNynHFiniJWH170FLebmjlWCnVmIzvOzCeCDv84N/WfBLk7JvBvx3w/7jR8wY4BoL7oqjFVZHJfwIoqrC1EApewCT+FBF3NMKKWVpBVnXSUpJJpZRSJ0bsfTC4Kb98MiAOsPeMSkz1zQQOgW8d5ZJjAAoxBS8hMZQg49tEqJ9LBWJBYC/Yezd6SLGs0RNkTaQqay7fSU0V8VhNpvXthlJKNQDX+ZD/Nwh6gWB4ox2sduAcG83I6o/JDw0+jJB3hrqXxBB7dyjOrLzd+MHq0OjhxDqtIKs60WRSNRYdVKhU0yZWAib5Ycj7Q6jfMRbEnY20ui/mBuiZYDYUfQqmAJynIY4+tTvR1g1wUalKjgPizqrnKE+MJP4Sc3Ql5btZuMB9MWIlRiusmKUJsqr3ZLeq68R6Mh3r8dWVJphKqWgK5j4EntcAH+AABOLOQqyUKEdWnvEuwRz7JQihaiqPYtzTkOR7ahxkJ2KDVn/CZN9C6DkDQBxYbZDE2Q0ffB2I82Ro8xgm934I7AeJA/dVSNKvox1aTNIEWR2X5pZMqtihC5so1fSZ4jXgeZ0fq5XhmSxy78a4xiNW62iFVo4xRZjsXwGFZbpJ+KBoLrjOhLhxNV5DXGdC6tsYz8sQyATnqUj8FYiV3JChHxeJm4CkTcCYQiAu5ir5sUQT5BassQfcabeMxqEJplIq2kzhB1SeMQHABt4vYmd2h+JlhErHFZhCTOE7SC0SZABx9EVaPVC/sTUgnbGiZpogK1VLmuA3Dl3YRKnmQML/RBq9FkNMsJqdgWr2NV8mmB9eSKQN2PvG1FzOjUkT5BassQbcnUilWpPSutMEUykVbeK+AFP4NpUHrwUgbkIUIqqC8xQiJsLiRlwxUuVuRMGCFyDv0dBUfMYP9i7Q5nnE1vJmudAEWaka6NzP0aGJvVJNlzgzMPFXg+dlQgmoLbSj1Z8Qq1U0QytHrHhM8p8h53ZCU9H5ABfEnRtbiXwjMN6vIO/vQBGYcPcY/w7MsZ8ibd+PamzRoAmyatBEL5h1Na9OCCU7x1M51qT0+GmCqZSKJiv5Dkz8xVC0KDRjgmsiYmsf7bAqsdznYpzzoegjTDAPiZsAjqEtrmuBKXiJiBV//26Mfwdi7xWNsKJGE2SlaqBzPyul1PERe29IjP0V2sTWARKujzRcr+UIHo28XWwQzG7cWGKAJsiqQUSaSaGkklwbLSUp1X7CSinVvBjvckzBUxDYA45hSOIvEHuPaIdVM9eZkL8F8FbYEQDHwGhEFFWaICtVS801SVdKKVU/goUfQc5vKZ3iLrAX410IqW+FqukxTOJnhgZWBg4TSpIFcEHib1vktHCaIKsGcTwzKUSqFsdaUlpfFV+dq1gppZoXY4KQ9wDl538OgvFg8v6KtHkyWqHVilhJkPoexvMaeBeB1Q5JuBZxDo92aFGhS6iomLLp8KHSRFkppZRqMoJHIJgfYYeB4lWNHs7xECsJK3E2VuobWG0ea7HJMWgFudmKlb67dakcl8xYUZIkRzv2suq74qtzFSulVGwwJgDeBZjCD0FcSPxliHNU3S9kJVW9z9b2+ANUUaEVZBUTNh0+VPrnvOJirSQrpZRqcMYEMdm/wOT8BryfQtH7mKM/IZj399pfI3gU45kDnrcg7gwgrsIRbiThZ/Uat2p4WkFuZpri/MGvXzqdGXPnsOnwIfKKiwEYmNauyuOjUXVtqIqvVo6VUiqKipdA8TIwnvAGAxRCwfOY+MsQ20nVnm6KFmKyf01oQFsg9G9bVwjsDq1GRxASfoG4pzToY6j6pwmyigllk+SBae1iOqFXSinVPJiihWWS47Is8H4F8ZdVfW4wP5wcF5XfEciENk8iVkewd0HEVa8xq8ahCXIzEyvzB992xr0A/GXR72t9TkmSXJVYmPlBK75KKdWMWMmEUiF/+e1igZVY/bnexaFFNEzFHUVQ9F+k1X31FqZqfJogq5iilePYpwMLlaqe8Wdi8h4IVSDFCe6pSNLtLXIu2Vgn7kswBS9TKUEGiJtQw9nBavYFjj8oFRM0QW6mol05Xv/FpnKf61JJrorO/NDy6N+1ampMMAeTdSmYHEJz4HrB8ybGtxVJfSXa4akKxN4Tk3wf5N4b7jMMYCFtnqn5F5q4cWAiJNbiQlzn13eoqpFpgqxqpbouG9HuzqEaRyx0cVEq1hnPXDCFlK8uesG3HuPbhLTAJXtjnRV/CcZ1bmiwnsSBczQizhrPE6s1Jvn3oeSaIKEqtAtcF4JzdEOHrRqYJsiqXpVUiuuzclyRJmTNnybjqsnyf0ulQVsAIuDfCpogY4xBRKIdRjliJYLr7DqfZ8VfgnGOxBTNA+NB4s5EnEMbIELV2DRBbuIaunpb3bRxkfaVzEJR/cQ4LVdtEr1YTQa1i4tStWDvDywkYpJs69HY0cSUYOEHkPcXCO7DWO0g8Ras+GnRDuuEib0Lkjg72mGoeqYJsiqnviq/DVE5Vi2HJuOqqZL4yzAFz4X6HpdOb+AAW29wpEcztKgKFs6DnP+l9BeH4CHI/QNBgljxl0c1NqUi0QS5iWqsBUGqmzau7L6SlfDyiotZvjdT+yVXUJsuA02lW0GsxaNULBErBVLfwOTcC75VgA1ck5Hk38Vct4JGlf9XKlfVCyH/76AJsopBmiAroGFnn1DqeGkyrpoisfdGUl/FmABgtezEuERgX+TtwcMYE0DE1rjxKFUDTZCbqMZeEKS665fdp5XjyGrTZSCWuxXEYkxKxTpN+sqwdY0OUs8AACAASURBVIbArsrbrXb6PamYpAmyAhpn9omWQpNJpZQqT5Juw2TfQfluFi5I/HWj3N8EsjB5D4P3M0LdXi5Akm4LzV6hVASaIDdxsVapjbV4Yk1tkuZYSqybSr9opVRsE9dETCsg/88QyARbR0i4BSv+oga/tzFezNFpEDhI6Yp5hW9ifGsg9Z0G7wJjTCHG8w54F4GtHRJ/JeIY1KD3VCdOE2RVjlaOj19dk0lNNkP0e1CqZbDcE8E9sfFvXPQpBI9RfjlpX6jLR/FSiDu1wW5tggWYrGnhPtiFgIUp/ACT/IdG+eVAHT9NkFUlLbEfsSZpkZX2iz44vNxnpZRqKoxvExhPhB3+0OItdUiQjTEQ+AHEjtg61Xy85/VQxRxveEsQKIK8+zDu8xCJq/W9VePSBFmpelJxkN0d03oB8JdF5Y/Tbgsh+j0opRqD2HticBOq4Jbd4QBbt1pfxxSvwWT/GoJHAYOxd0NaP47Yq1kApuhTfkyOy90cfBvBeXKt768alybIqlRjza0cSzRJq17p92Pyyn3W70cp1WS4JodW8DNeQhVcADtYKRB3eq0uEfRthaMzgeIfN/q3YY5eBWmfI+KMfKKVHHm7CYDoAMFYpglyE9GQyWrJzBX8cmC9X7slKqkcVzWndCxP59aY9HtQSjUGsRIh9S1M7v/+//buMzCqMm3j+P+Zmkmhhg4iAiIqygqKiqxiwbYqdux9bWvF3rD3Xtayrq69YAMbig1FXwsoViwgXWoIIWX6PO+HISGTTCB1ZjK5fl92c+acM/dk2XDl5jn3A6FvAAPev2Pa3VCvEXOxismw9lIgWuMVC9YPwWmQs1fy9847DhueET+vigOcPcE1sLEfSVJAAVmqpHq2ciZQSNswfX9EJBsY1yaYTs9gbQgwGOOu13U2ugrWXkntcFx5QmTddIw63te7GzbvVCh7FIwHsODohOn4qDaQyXAKyBmuJZc91LV7XlM6yZqjXP+Z0gqbcfo+iGQfG/oGW3oHRP4AZ09M/nmYnDHpLqvupRB1CX4AbCjIOja6jtiRfw429xgIfRdf1uEeqnDcCiggSy3Z0Dlu6C8SCmkbpu+PiNSXDX2NXX0qVZuCRP7ArrkI2+5aHLmHpLW2BrORDbzoAu9IjHvjTSXj6AQ5ezRfXdLiFJAzXM8H493dEeu6us0ZXptz97y6utHJ7tlW/rm+LXfRRSSRtWFs2b+h4oX4yDHPCEy7KzY8AaGVsqV3kLhjHvGvy+7A+g5uXd3TnN2h9LYkLxjIPQlTkJqdACX1FJAlK1R2jCu1pUkcIpL57JrxEPyEquAY+hRb9C0Uvotxdk1nac0v8kfy47ESsOWtanqDcfbEFlwApfcCYcACHsg9Fke7i6vOs9GlEPwUjBe8e2AcBekqWZqJAnKGqtmR3abyhUPrd31DgmFzdDrr043WSDURaYtsZFF8m+GEebgWbBBb8QymYHy6SmsZjh4QnVv7uPGC8QHrNtyI/ATRZeDeGuPskXCqtWEIfgaxpeDeFuPeOhWVJ+XIOxnr/TvW/w4QweTsnbBVdKzsUSh7AHASX688ATo+gKnnCDnJTArI0qrVfIhxRK/eCf+pzrGIpF1kTnyCga25YUQIwj+kpaSWZArOxa65lMRlFj7IOxVjnNhoEbb4RIguAhxgw1jfWEy76zDGgY0swq4+GmxZfF4wBuvZAdPx3/WePtHsn8k1AFNwbq3jNvwTlD1EwnxkwK45B7p8gXHkpahCaW4KyBmqseuD073Zx4bqzLSRYZlSh4hkOVdfsOEkL7jBtXnKy2lpJmdfbLvSdZtzlMV/Ocg7FZN3JgC25AKIzAWqPQDnn4x1DcHkHYFdcz7EVrJ+Uw8g9BW2/GlM/ikp/SwbYytep2Y4jnPE5yP79kt1SdJMFJClVWuLs5tbgn5ZEGk5xrUZ1jMMQjNIWGZh3JjcE9JWV0ty5B6B9R22LiDnYkw8btjYagh9S0I4BsAP/qexOaMh8hsJ4RiAAPhfggwLyPFwXLNWwFria5altVJAznANXR/cGgJjukNYzbXQP/0Wn8259aD301WSiGQ50+EhbOmN4J8MRMC1Bab99RhX73SX1mKMcYCpsdVyrAJwJL8gVkY8ONcx5SJpFz51bGQhxIrBvTlm3Vpqk7MvNjC5xk55AFHwjkp9kdJsFJBbucYE4WzczCMTfxFoDZrzwUl1oUXqZhy5mPY3Y9vdCEQavmFFtnD2BEd7iNUcA+cG7x7g6A7O7hBdUON1D/j+kaoqE9hoEXbNmRD+FYwLiGLzL8GRdwx4dgLvPhCcAjZAPPy7oODy+OxjabUUkLOUAmPdKgNcZef4wPf2BmDET6npumfjLygiUj/GOIA2Go5Z9/nb34otPpN4tzgC5ICjPSb/rPiM5A53YVefsG6TjiCQG9+NL+/0tNQcD8c/xWu16w6W3o519cd4d4T2t0L4UGzgAzA5GN+BGNeAtNTa3Ky1EHwfW/5cfLlMzn6Y3KMxjtx0l9biFJAzTH3DU0Mfxjvq1ZeYO2s+o6ZX1GszD2kbNvTgZH07whrfJyINYbwjoXAStuJZiCwEz46Y3COqZgcb9zbQ5cP4A3DRxRjPMMgZk5auu40siHeOk6yZtuVPYrw7xkO9ZweMZ4eU19fSbOmt4H9x/RKSsjnYwCTo/ArGeNNbXAtTQJY2q3LNcao7x/oFRUTaOuPqh2l3dd2vOzplxsSK2Or4sgqb7LXlKS8nlWx0KVQ8R+KUjgBEF4L/bWht24Y3kAJyhmhoeKr+MN7cWfPp+fovSc9N6DR3cfHXv7akZJdcRk2vUDCTKsk6x/XtCGfa+D4RkWbjGrRuFnMS0aXY6DKMs3tqa0qV0EwwbrA1xthZPzb4CUYBWSS7pXpGdCZ3jmNFx0JkNrgGp7sUEZG0M45cbMF4KL2dWvOObQm2+HRM4aS01NbiHJ3reMEJzm4pLSUdFJAzRGPC0/jRE+gJrJr2Cz/UcW3SsW+HJt6jvu8nbYRrMI7Ozza4I6zOsYhkI0fe8cT8b8S3xk4Qg8g8bGQextUvLbW1KM8OYArAVpC4xsSNyR2XrqpSRgE5TRRM267G/m/ekksYki6rUCdZRGSdZIuQia9PjpWktpQUMcYJnZ7GFp8O0WVgHIAD2t2CcfVPd3ktTgE5wzQkPDWk61xzGYEeGMsuLRKe13WSRUSyibUWwjPjoc89BOPqu/GLcvaAsjkk7IQIQAzc2dtIMK5NoXAKROdCrBzcg9vMDG8F5BRTMG2atvggWCrGqNX1oF1b/H6LSPay0eXY1cevm0BhwEawOXtj2t8W75jWweSegPW/BtFVQCB+LV4ouDrrx50ZYyBL5jo3hAJyFmhMuG4ND4zJxmkGsYhI/dk1F8bHlFFtMkVgKtY9FJN3bJ3XGUcBdJ6MrXgRgh+Dsxsm9wSMZ1usjWIrXgH/82CDkLM/Ju8kjCO/5T+QtBgF5BTLxmCailDWloNgKru5Nd+rLX6/RSQ72dhqCH9PQjgGwB+f97uBgAxgHPmY/FMh/9TE+5ZcAoEP4vcBKH8MG5wCnV9vM8sRspECchuXzoCeTb8kNFRzBU4thRARqSdbuTQi2WsVjbtlZA4EphJfdlEpCNElEJgCvgMbdV9JPwXkNMmGUJjKLqOCYGo/s77fItKaWGsh8gcQAdeg5OuJHT3is31jf9V4wQ05ezfujUOzSBq6bQU29AVGAbnVUkCWlGvLDyq21C8VCrAi0lbZ8K/Y4rPAFgEOMF5ofy/Gu2PCecYY6HA7dvVpQAQIAz5wdMTkn9G4N3d2iY8/qzUFzgOOXo27p2QEBWRptHR0GRUEU0vfbxHJZNYGsKuPA1ttFrEtx645HQqnYpxdE843nh2g8G2s/0WILATPCIxvLMaR17gCPCPXbabhB2LV3siJyT28cfeUjKCALCmXjQ8qNph7B0ABVESkIWxkPrbiBYj+BZ5R4PAS7wbXPDGG9U/C5J9W6yXj6o0puKhZ6jHGBZ2exa75F0TmAQ5wFGDa34Vxdm+W95D0UECWJttQyGuO7nLCNtmtjNbwiog0Dxuchi0+h3ggjkDwUzC5YMNJzg5CbEVK6jKuTTCFk7HRJfEHAZ39MMaRkveWlpOygNymu4VSS1v781Bz7XFlB1lERDbO2ih2zSUkTovwg42QfBtoJzh7p6a4dYxTa46ziTrI0iKa42G0ys7xV0sWJ3zdGjrJtQKxKUh7LepiS6bwl/mZ9NB7fDrxC3Lb5XLQ2fuwyyEj4g9RiSQTmUvtbZ4h/qBd/rr/rP56FErvxLoG1XpYT6Q+Wjwgt+WJBZkmEwJmm/3z4Bqc8GVzhdVMCr+ZVItkrqA/yDk7XcnSP5cT8ocA+O2bOfzy5e+cfsfxaa4u/WL+t6HsrvgaW2cvyL8Qh2//dJeVfsYHNpb8NddmYMshOrfGC0Hs2msxXaa0eHmSfdRBzhKZFjTrnnAxod73qAzymRDsG6quz1/VWU4B7YYnmeij56ezfP6KqnAMECgPMumhKRx6/v4U9uqcxurSK1YxGdZeRdUygugiKLmcGLT5kGxcfbCufhD5jYRpEfgwecdhS65OfmF0PtYGMCYnFWVKFmnxgKyJBemXSUsVsuHPQ1Nqb+7OcSaE30yqRTLfV+98S6C89j+Vu90ufv78N3Y9Yuc0VJUhyu4mcY0t8a/L7oI2HpABTMd/x0e6xYrXLTsOg+9gyDkQSu+AWM3vHYAHcKe2UMkK6iC3cjWXLIzteAL9h26aMcGzviFpQ6GzNXWOa0pnSNRueJKJuvTqjMPpIBZN/Odyi6VDt/ZJr/l95lxeuOV1Fv26hC1GDGTcZQfTe2CPVJSbMtbaJDu8rRNdktpiMpRx9oLCDyA8A6IrwPO3qgfjbO6JUHY/ib9g5EDuEcl31RPZiJQF5EwJbG1RJi5VaI1/HjJp/XQmhd9MqqUlZfvnS5V/nDmGd5/4kGDF+iUWxmEo6JjPkFGDa50/4/3vufbg2wkFwlhrWfTbX3w68f+47/Mb6TekbypLb1HGGKyjB8SW1n7RkV2/DDSFMQ7w1J4CZPJOxsaWQsXLYDxgQ5CzJ6bgkjRUKdlAg/paucpwltc+F4DykgpgfXirj/GjJzC24wkNvqYh52/sPj9M+4Ufpv3SbPdtDrGiY1O6ZrilODo/q1AnGaPv4N5c+vS55HXIJbfAhzfXQ59BPbnjwwk4HIl/JVlruf+s/xD0h+IdViAWjeEvC/Doxc+ko/yWlX8BUHOtbM6647IhxjhwtLsa0/UzTMf/Yrp8jKPD3Rjjafb3sjaEjczFxoqb/d6SObTEog3JhM5xS2rpju7G1k+no8OYScE3k2ppTlpj3fxGHTKCnQ4YxtxZ8/EV+OgzqGfSEW/+sgArFq5Meo9fvvitpctMOUfuWGJYKLsHYsvineP8C3HkHpTu0loPG8BWPAvBaViTA7njMHn/xJjmWYccq3gJSm8DYmAjWO9umPa3NX6raslYCshZoLEPvo0fPYG5s+ZXdZ1/mPbLRtcwN/cyg0x8aE+BSKTludwuBm0/YIPneH0eXG4X0Uio1msFndM3W7wlOXIPhtyDsdZqLnQD2dhabNHBEFsDRMGWQNmj2PAvmI4PNf3+wc9g7c2Af/3B4CfYkkua5f6SWRSQpdmkK+Smem1wXZ1jBers1FbWWGcip8vJ3ieNZsqTHyeMhfPmejl8/AFprKzlKRw3nK2YCLFyIFrtaACCn2Ijf2JcmzXt/mWPkhCOAQjFu9Wx1RhHpybdXzKLAnIWaWggrN69nTtrfr2mX7RUxzcTOseVFIhEMsfpd51AaXE501/7CrfXRSQU4cCzxnDQ2fukuzTJNOFvqT0mDzAuCP8a31CkKWLLkh83bogWgQJyVlFAliZL93SHdC/TUKBuG/S/a3p4vG6ueO48ileUsHLRKnr2705+B633lCRcAyA4DaixJMdacPVu+v09I8C/hMQOdeV7Z89EFYlTQJZGBcpM6vi2FAUikczRsWt7OnZNPidZBMDkHoWteCo+4q2KG1z9wDWk6ffPOxMbmAK2gvUh2Qf541tkWoakl6kcnVMfw4cPtzNmzGjBcqSlpKK7mkkP2olkImPMTGvt8KbcQz+HRepmwz9gS66AyJ+AAe9umPY3YRwdmuf+kUXY8n9D8CtwdotPyMgZ3Sz3ltSo789hdZBFREQkY1gbAhsEk9/ghxWNextM4VvYWCkYN8bUnCvdNMbVB9P+lma9p2QmBeQs15T1wQ3tCKtzLCIijWVjFdi110HgbSAGzl7Q7nqMd6cG38s4snMMoKSOdtITERGRtLNrzoPAO8QfsotAdAG2+Axs+I90lyZtkDrIWa4xEx7SPZVCRETaFhtdAqEvgWCNV4LYiscx7W9LR1nYwPvY0rshuhicm2AKLsLk7J6WWiS11EEWERGR9IoshqSTIGLrHrhLvZj/beyaiyD6JxCC6BzsmvOxgQ/SUo+kljrIKZaubmxD3i/dc4VFRKSNcQ2IP5hXixvcf0t5OQCU3kHtjUcC2NI7MDl7pqMiSSF1kEUyRKzo2PXbVouItCHG2Rl8hwG+6kfB5GDyTk55PdZaiP2V/MXowtQWI2mhDnKKtMZ1vZlcm4iIZBfT7hqsa1MofwrsWvCMwBRcjHF2T2kdNrYaIvPB0RliRbVPcHRLaT2SHgrI0uZk2i8nVV3j8NcJX29sJz9tbS0i2cQYB9bRC4yBWBmEvsEGpkLeaQ2eh9wY1kaxa68F/xvx9dC2AnCSuLV0DuSf1+K1SPopIKeI1vWKiIjUzQY/h5LxVK37tSVQ9hCWECb/Xy3//uX/Af9kIFhtPbQLyAFC4OgE+efhyD24xWuR9FNAljYjU5e5VHaAG9o5bmjHWUQkk9mye6n9UJwfyv+LzfsnJumUi2ZU8XT8/RJEACd0/Q5jclLSyZbMoICcYukOYyIiIhkpsiD5cRuBWAk4u7Ts+8dK63ghjDFOheM2RgE5S2RKNzSTZfoyl/p2gBvacRbJFmtWluB0OSnomJ/uUqQluAZAeEbt48YNjo4t//6eoRD6Kkld/YEYNroKHJ0VlNsIjXkTEZGMNvf7+Zy2zXiO7nMGR3Q/lQt2vYYVi1alu6xWx0b/IlZyHbFVBxArPhsb+j7dJSUwBRcQX+9bnQ/yz8aYlu/nmYIrwOQSfzAP4hHJC44e2OXDsSt3w64cRcw/tcVrkfQz1tp6nzx8+HA7Y0aS3+4kbWquq91m1y2BzOuOiggYY2Zaa4c35R5t7efw2qJSjut/NhVr168NdTgdFPbqxNNzHsTpcm7gaqlkIwuwRYeA9RNfV2sAL7S/C4dvrzRXt54N/h+29FaIzAFHIeSfhfEdkbKurY0swJY/DuEfwLV5fMxb6BsSt8DOwXR6CuNJ0wYm0iT1/TmsJRbSZHUtWdASABFpqqnPTCMajiYci0VjlBWXM+O9WYzYf1iaKmtdbOk9YMuBWOURIACl12Jz9sCYzPgHZePdCeOdlL73d/XFtL8BABstwq7cFQjVOCuILXsE0+nRlNcnqaOA3Mpl+rralpKNnzcbP5NIUy2Zs4ygv2ZAgUg4yrL5K9NQUSsV/or14bia2FqIrQSnNr+oJbZs3Tzkmn/+rHbTawMUkKXR6hqbdscrc+MnaAyZiDTRVjsNYurT0wiUJY7/cjgNg7bvn6aqWiHTEUiyKxwWjB56TMrZNz5Bo/YL4Nku5eVIaikgZ4m20nXM1FnGTZGNn0mkuYw6bEeeuX4iKxasJByKhxWPz8PgHTdn0PYD0lxdK5J3Kqy9jsQ5vx7IGYNx5KWrqoxmHPnYvFOg/AnWf98MmBxM3unpLE1SQAFZGm1jyzvq2zlWIGx56uJLa+Xxunngy5t5+tqX+fSVL3G5nex7yu4ccclYjdtqAOM7GBudD+VPxsem2TB4R2La3Zju0jKayT8X6+wD5Y9BbDV4tscUXIhxbZLu0qSFKSBLq5KNa66z8TOJNKeCjvmcfd/JnH3fyekupdUyxmAKLsTmnQaRP8HZDePsnu6yMp4xBpN7COQeku5SJMUUkJtRQwNOtgSiuuqvb+dYSwtajralltZu1V+rKVqymj5b9CK3wJfuclo94ygAz7bpLkMk4ykgp9FP039NdwmtVjaG6Gz8TCKN5S8PcMvR9zFz6ve4PC6i4ShHXjaWY686TEsrRKTFKSA3g4Z2Qitfj0Vj9Tq/OWvMpBCWiUsLWmOHdUM1a1tqaa3uPvVhZrz/PeFgmFAgDMDLt0+iV//u7H70qDRXJyLZTgE5DWp2jjfUSc6k8Cgikgrlayv4/I1vCAfDCccD5UFeumOSArKItDgF5GbQ0E6oLz++13x5SUXC15WaMxS3hnW+mVBLa1yr25CaM/lzSHZbu7qUj1/4nFWLi9hq5BZsv+9QnM4Nbw9dXlKBw5F8GcWa5SUtUaaISAIF5DR4o/gpAPZ2H5nwdXVzZ81n/OgJGR1sRUQ25Ldv5nDJntcTjUQJ+kP48nPou1Uf7vxoAl6ft87rCnt1wlfgq7WDnsPpYOjuW7d02SIi2RmQ0xUmG/p+dXWOK0Px3Fnzm60mBewNa41rdVtjzdJ2WGu58ch7qChdvzGFvyzAnFnzePaGVzjl5mPqvNbhcHDOg6dw+4kPEvKHsBacbic5uV5OuO7IVJQvIm1cVgbk1iJZ57i6/kM3Ze6s+fQfummbCrYK8yKt319zl1G8Yk2t45FghBdve4N5Py7kyhfOx5effHTb3w/bic49O/HSbW+w9M/lDPn7YMZdOpaum3Rp6dJFRLIrILeG9bYbkqzbW/nfm+vesmGtsQvbGmuW7OdwOsDW8aKFbz/4kTtO+jfXTBxf5z222nkQ10+6tGUKFBHZgKwKyNmoLQXb1v4Ljois133TrnTt24XFvy3BJgnK4WCYL9+aQdmacvI75KW+QBGRDciqgJwt621ba90iIpWMMVwzcTzjd7uGtavLknaTHU4npcVlCsgiknGyKiBL65Ytv+CISNymW/Xh+YWPcNneN/Lz57/W6iR7cz103aQwPcWJiGxAVgZkBauWlykhNlPqEJHkvD4vlzz1L84afin+sgDRcDR+PNfL2fefvNGZyCIi6ZCVAVlaN4VdkezSo183Hp11Jy/dPokfPvmZ7v26cuSlY9l65BbpLk0ymI2VQegbMD7wDMcYRRZJHf1pkwZJxYN09bmnHugTaV269inknAdOSXcZbYKNrcVWvADBT8HZA5N3AsY9JN1lNUisYiKsvQGMi/gCdi90ehzj1kYxkhoKyCIiIlnCxoqxq8ZCbDUQhLADG3gf2/4mHL4D0l1evdjwr/FwTKDaw53l2NUnQdfPMcaTxuqkrVBAlgZpyQfpGtIV1gN9Im3TT9NnM+XJjwn6Q4weN5Id/zEMh8OR9NzS4jJ+/XoONhZjy50GtYlpGbb8CYgVAZXbdMeAAKy9FpuzD8a401hd/Vj/RNbXX10Egp9DzuhUlyRtkAKyiIhktO8++pGHL/wf839chK02CuPLN2cwbMy2THjlIowxVcettTx59Qu8dNsbxKLx843DsNfxu3LhY2fgdGXxg4GBD0keLmMQ+QPcW6a6ooaLlRAP9jVYC7Y85eVI26SALI3SEh3bxnSF1TkWyS5zv5/PM9dNZM6sefTZohe7HLwDD1/wP4IVtUNfoDzIzPe/57sPf2S7PbepOv7R89OZeMfkqnAMYGOWD56eRqduHTjllmNS8lnSwtEBokmO2wg42qe8nMYwOXtigx+ArajxSgQ8I9JSk7Q9yf9dSkREJMVmf/UH5428ii8mfcPy+SuZMWUW9535n6ThuFKgPMgXk75OOPbK3ZOJhGunxFjM8saD7yZ0obONyTsR8NU46gT3YIyzVxoqagTvnuAawvrPYeL/Pf8sjLNLGguTtkQdZMk4qe4Kax2zSGZ4ZPxTBCuCCcdsbMNh1uly4muXm3CsZFVpnecH/SEi4QhuT+avxW0MkzMGm/8blD0Gxg02Cq6+mA4Ppbu0ejPGBZ2egMC72MDbYPIxueMwnu3TXZq0IQrIIiKSEeZ8+2eDr3G6nYw5fteEY8PHbMu7//0o6fk9+3fP2nBcyZF/Djb3WAj/DI5CjLv1zZs2xg2+AzG+A9NdirRRCsjSZmmWskhmaV/YjpWLi+p1rsvjwuF0cO5Dp9JnUHzpQDQSZdWS1Rx64QF8+sqXlJckrmF1e1yc82DtWcxzZs3jj5l/0q1vF4buvnWdUzFaE+PoCN5d0l2GSKulgCwiIhnhiEsO4vHLnktYZuHN9bDjAcOZ98MCFs5eQkGnfHY6YBgj9h/OsL2GkNc+Prrt/ac/4ZELnyIUCBOLxtjxwOF4fW6+enMm4XCEAX/rxxl3nsCg7QdU3TscCnPtwXcw65OfiIZjxGIxvD4Plz93HjsfqH/OF2nLFJClRbSGbqxmKYtkloPO3ofiFSW8evdbOJyGaDjK3ieN5qx7TsLpcmKtTRjnVmnm1O+5/6zHE4L1V2/OYJdDRvBa0f/qfL+Jd05m1sc/EQqEq44FyoNce8gdXPzk2ex13K51Xisi2U0BWUREMoIxhpOuH8e4S8eyYuEqCnt1Iq/aA3jJwjHA8ze/VuvhvlAgzPTXvqK0uIyCjvlJr3vnPx8mhONKNmZ54OzH2e3InbN+vbKIJKeALM2qNa7rzeTaRNoiX14OfQf3rvf5yxesTHrc6XZSvLykzoAcCtYOx5WC/hCLf/uLfkP61rsOEckerf9JBBERyXobml281c5b4HAm+evMQvd+Xeu8bsd/DKvztVg0RkGn5MFaRLKfOsjNrDV0TFuS1vWKSHOa+sw0nrzyBVYuLqJLn0JOvmkcex6buDb4uAmH839vziBQHqiam+zN9XLiDUfi8da9ROKE647g3cc/TPqay+OisFfn5vsgItKqqIMsIiIZ6YNnp3HfmY9VjX5buWgV957xV1qwhwAAF8RJREFUGB8+/1nCeb0H9uChr29h1KE70qlHRwYO24xLnz6HQ877R533XjpvOYt/X8rOB21PzaXNxmE4/CLN3xVpy0xDttwcPny4nTFjRguW03rVXHu7za5bAuqgish6xpiZ1trhTblHW/o5fHTfM1m5aFWt4936duHZef9u1D0rSv1cd9id/PTZbNxeN6FAiPZd21OyogSny0kkHGXXw3fi4ifPxulyNvUjiEiGqe/PYS2xEBGRjGOtZeXi2uEYYMWiVaxaUsSHz0+nrLic7fcZypBRg+ucclHdXac+zI+f/kI4GKmaYFFWVMbRVx7KwO02o9/Wfei6SZdm/Swi0vooIDcTrb0VEWk+xhi69ilkxcLaIbl9YTtO3PxcYrEY4WCENx54hyGjtmTg8M0oWlzEdntuw6jDdqw1os1f5uf/Jn1DOBRJOB6oCPLR859x7FWHtehnEpHWQ2uQRUQkI51889F4cz0Jx7w+D+Ul5QT9IcLBeNANlAf5Zsp3vHTrG7z3v0+494zH+NeIy/GXBxKurSgNYBzJu8ylq8ta5kOISKukgNzM7vr4OnWPRUTW+X3mXN58+D2+mPwNkXBk4xdUs8fRo7jwP2fSfdMuGIehe7+uHHj23nWeH41EAfCXBVj8+19MenBKwuudunegXeeCWtc5HIbt9tymQbWJSHbTEgsREWk21lp+/vxXvpg8g/+b/A0rFsUnUDhdTnILcrjn0xvosVm3et9v96N2YfejdgEgGo1y+tCLqjrHGxLyh/n4xemMu3Rs1TFjDOc/ejo3HHEXIX+IymfUYzHL3Fnz+e6jH/nb7kMa8GlFJFupgywiIs3CWsutxz/A5fvexMQ7J7P496WE/CFC/hD+Uj+rl63hxnF3N/r+M6bMYsWC5A/uJeP1eWodG7Hfdtw7/Ub6D+2XsNxiwS+Lueoft/DRC9M3uCmJiLQNCsgiItIsvn73O75442sC5cGkr9uYZd6PiyhaWpxwPBQI8dS1L3HUJmdwRM/TeOjcJygtrr0m+Ptpv+AvC9Q6nkxOnpcDzki+HKPf1puw9M/lVZuKrK8jzK3H3c8xfc/kx89m1+t9RCQ7KSCLiEiz+Oj56XWG40rGQLTaWmRrLVfsdzMv3z6JVYuLKF62hrcfm8o5O15BKBhOuLawVyc8SbrCHp+bvA55+Ap85OR58fg87Hr4Tuxx7KikNawtKiVc495V9cQsKxcXccV+N9UK8iLNzUZXYsO/Y20o3aVIDVqDLCIizcLldmIMbGiFQmHvznTpU1j19S//9zu/fTOnaiYxQDgUYfXSYqa/+iW7H70+5O5xzCj+d/WLte7p9Xl5dt6/+fGz2RQvW8PWu2xB78171llDQaf8dZuAJA/JANFIjPee/Jijrzik7g8j0kg2VoJdcwGEvgbjBgy24EocuYemuzRZRx1kERFpFmNO2A1vrjfpa54cN74CH5c/e27Chh5/fPsnsWis1vn+sgCzv/oj4Vj7wnbcMuUqCnt1IifPizfXQ8/+3bjr42vJLfAxYr/t2Ofk3TcYjgFcbheHjT+gzloBwsEwKxau3OB9RBrLrjkXQl8BIbDlYMtg7fXY0NfpLk3WUQdZRESaxba7bcWBZ+3NGw+8C4BxGKKRGMPHbMuQv2/JmBN2pUOX9gnXdN+0K063EwKJ3Vyvz0PPAd1rvcdWOw/i+YWPsHD2YipK/Xz/yc+8ePskNh+2GXufOJpl81fw5FUv8sfMuXTv143jrjmM7ff5W637HHfN4XhyPLxwy2tUrPXXet2Xn8M2u27VlG+HSFI2+heEvqX2v2D4sWWPYzrtkI6ypAbTkKd1hw8fbmfMmNGC5YiIZC9jzExr7fCm3KM1/Bxe/MdSZkyZRU5+DrscvAP5HfLqPDcaiXL8gH+xasnqhE5yXvtcnvnzIQo65ie9bsHsxZw/8ipCwTAhfwhvrge3x00oGCYcWD/CzZvr4YLHzmCPo5OvR7bWct3hdzJjyvcEK+Lrpz05bnoO6M6/Z9xWazc+kaay4R+wq0+Md41rcg3CUfhmymtqS+r7c1gdZBERaZTfvpnDZ69+icPlZPS4kfTbehMAeg/sQe+BPep1D6fLyT2f3cBtxz/AL1/8BsbQd3BvLn36X3WGY4D7zniM8pLyqiAcrAgRrKj9oFOwIsRD5z7Bt1O/Z/WyEnY8YBhjTtgNX14OEJ+NfPVLF/LOYx/w1qNTCQXCjD5qJIddeIDCsbQM5wCw0SQvuMEzMuXlSHLqIIuIpEg2dZAfvfhp3nz4PUKBMMYY3B4X4y4by5gTR1PYqxMOR92PuFhref9/n/D8La9RvLyEzYdtxmm3H0fvgd2JRmJJd7urLhqJsm/OUbXGtG2QAWy8o9y1TyEPfn0ruQW+jV5WvraCWDS2wbAu0lCx8v9C6f1A5fIeF5gCTOGbGGfXdJaW9er7c1gP6YmISIP88e2fvPnwewQrQtiYJRaNEfSHeGrCy5y0+bmM6306X0z+ps7rX7r9DR4457/8NWcZ/nXriMfvNoFl81duNBxDfG1zfApFA1TrNC9fuIq3Hnl/g6evXFzERbtfy2FdT+GIHqdx5rBLmPfTwoa9p0gdHHmnYDrcC+7h4OwLvnGYwskKxxlEAVlERBrk89e/ThjLVl0oGKZ42RpuPupe/vj2z9qvB0I8d9NrVet9q477Qzw14aV6vb/D4WDUITs2vPBq7/XWY1N5+Y5J/PjZ7Fo750UjUc7f5Sp+/Gw2kVCESCjCnO/mceHfr0m6gYlIY5ic0Tg6P4+jy1Qc7a/BOOu/Bbu0PAVkERFpEKfHucElFBAPyhPvqv2w0crFRZgk51tr+ePbefWu4ZRbjybpjdbx5npxe904XMnrXD5/JU9c+QJX7HcTl465gXBofeD/ZsosSovLao2fC4fCfPjcZ/WuUURaLwVkERFpkNFHjoyPZtsAG7P8NWcpZWvKmXjXZK477E7+d82L2FiMaCTZA0rQa0B3/pq7jAfOeZwLd72GRy9+mpWLixLOicVivHDr65y9/WVVyyZqGrhdP5745R5eXfUEvQf2wDhqJ+lYNF5HoDzIL1/8xuv3v1v12rL5K4iGa9cYrAix5I+lG/zcIpIdFJBFRKRBem/ek9NuOwZPjjvp1s8Abo+LgcP6c9IW5/HUNS8x/bWvePmOSZw1/DJG/GMY3tzE67w+D38/fCdOH3oxbz/6AT9+Nps3HniX04ZcyILZi6vOe+ySZ3juxlcpWbk26ft6fR5Ove04um7SBV9eDje+dTk9NuuGLz+HnPzkG4ME/SGmPPFR1dcDt9sMh7P2X4++/By22GHgRr8/ItL6KSCLiEiDjf3Xfjz1xwOcdc+JDN9naELgdTgd5OTnULyihLVFpQT98fFr4WAEf1mAxb/9xYFn7UNOrhen20lh785c+sy5vPOfDwiUB6o6zJFQhIq1FTx64VMAVJT6efPf79davwzxB/f6D92U6964hO32GFJ1vEe/bvzvt/u5beo1nHX3SXhyko9uq76cYsudNmfgsM0SznV5XHTs3oFRhzV+7bOItB4a8yYikiKtdcybtZbfZ8ylotTPFiMGVs0Qrv761Ken8crdb7K2qJRhY7blhGuP4PS/XUxZcXmt+zldTl5d9QQ5uV4CFUFyC3xEwhH2zz0m6eg2j8/D2+XPMf/nRZy785X4S2vvfNdjs248PefBjX6Ok7Y4lyV/LKtxfzfHXHkoR19xaNWxoD/IC7e8zntPfkwkHOXvh+/ECdcdQbtOG5+yISKZSxuFiIhIky2YvZgr97uZtUWlVVtHn/PgKex94uiqc4wxjDlhN8acsFvCtV6fJ2lANo743GSny0leu1wgHprdHlfS6Ri5BfFA3qVPZ6LhSO37Gdh06z4b/SwVays4/9HTmTD2dqKRGMGKIL78HPpu2ZtDL/hHjdq9nHj9OE68ftxG7ysi2UcBWUREkopGo1y61/WsXlpM9X9sfODsx+k/dFMGDO23wev3++devHzbG1VLLABcbic77j8MT07iGmSHw8Gex+/KB09PSwjJXp+HA87aB4C8drns/8+9eOfxDxOWWXh8Ho69+rA661i+YCW3Hv8Av375OxhDn0E92XnsDthojC13HsTwvbfF6WzgXGURyWoKyCIiktSPn86mojRAzZV44VCEtx6ZyvmP/HOD1x912VjmfPsnM6f+gNPlwMYsvTfvyQX/OT3p+WfefSKrFhcx6+Of493kYJidx+7AMVccUnXO6XcdT7vO+bx679uUrSmn39abcPZ9J7P5sP5J7xkJRzh/l6tYvWxN1Trj+T8tZOWiIp6d9xB57fMa8B0RkbZCAVlERJIqXV2GSTJrOBaNsWbFmo1e7/a4uf6NS1kwezHzflhA935dGbT9AEyymwI5uV5ueusK/pq7jCVzltF3y9507VOYcI7T6eTYqw/n2KsPx1pb570qffX2t5Sv9Sc8hGdt/AHAj174nAPOGFPntauXFfP+U5+wYlERQ3fbip0P2h6XW39tirQF+n+6iIgktfWowURCtdf85uR52fmgHep9n76De9N3cO96n9+zf3d69u++0fM2Fo4hviFIJFh7XXOgIshfc5cluSLup+mzuXzfm4hGYoSDYT54ehq9Bvbg7k+vr/WQoohkH415ExGRpDp2bc9Rlx9MTt76+cHeXC99tujFbuNGprGy9Rb+uoS7//kI5428kkfG/48Vi1YlvD5gu35JNzXx5eewxfYDkt4zFotx01H3EigPEl4Xrv1lARbOXsLr973d/B9CRDKOOsgiIlKnY68+nC13GsTkh9+jrLicXQ/fib1PGo3Hm3yecCr9NH02l+1zE+FgmFg0xu8z5zLliY954Mub6TOoFwBDRg2m/9BN+WPmn1UP/7k8Lgp7dWLnsdsnve/i3+M7ANYUCoT46PnpCePgRCQ7KSCLiMgGbbfnNmy35zbpLqOWe05/NGGaRSQUJRr28+jFz3Dj5MuA+DKM296/mhdufo33nvqEaCTGbkfuzPETjsDtSR7y3R5X0nnMEA/XIpL99P90ERFpdfzlAZb8sbTWcWst33/yc8Ixr8/LiTccxYk3HFWve/fYrBvd+3Vl4ewlVN9My5vrZf9/7tW0wkWkVdAaZBERaRbWWhqyO2tTuD0uHHXMLq7cfKQpJrx6ER26tsNX4MPr8+DN9bDDfn9jv9P2aPK9RSTzqYMsIiJNsmpJEfed9TjfvPstxhh2Omh7znnwVDp2bV+v6621BP0hvD5PvSZTALjcLvY4ZhQfPf9Z4sYiuR4OPnffetf93I2vMnPqD3Ts1p4jLj6IkWPj0zn6DOrFcwse5ut3vmP10mK2GrkFm23Tt173FZHWTwFZREQaLRQI8a8dr6C42kYcX7zxDXO+nceTv96H01X3DnXWWt548F2evX4iZWsqaNe5gJNvOop9T6lfl/bs+0+mePkavvvwR9xeN6FAmN2P2oXDxh+w0WuLlhZz+tCLKS+pIBqJsvTP5dx63P0ce/VhHHnJWCA+x7kyMItI26KALCIijfbpK19SXlKRsBFHNBJlzcoSvnrnW3Y+MPmkCIBJD03hv5c/X/Wg3ZoVJTx03pN4cjzsccyojb53Tq6XG9+8nKXzlrP0zxX03bI3nXt0rFfdE++aTEVpPBxXCpQHeea6iRx41t748n31uo+IZCetQRYRkUZb+OtiAmWBWsdD/jCLfv1rg9c+e8MrCVMoAIIVQZ6a8GKDaujRrxvb7TGkKhxba/n2gx/47+XPMfGuyRQtLa51zXcf/EgkFK113Ol2Mv/nxQ16fxHJPuogi4hIo2261Sb48nPw1wjJHp+bTbeqe/e8aCTK2lVrk762clFRo+uJRqJcfeCt/PjZbALlQdxeF09NeJlrX7uY4WO2rTqv6yaF/PnDglrXR0IROvfo0Oj3F5HsoA6yiIg02i6HjKCgU37CWmOX20nnnp0Yvs/QOq9zupwU9u6c9LWeA3o0up4Pnv2UHz79hUB5vDMdDkYIVgS57rA7iYTXb5t95CUH4c31Jlzr9rjYauQgum7SpdHvLyLZQQFZREQazeN188CXN7PLISPw5Ljx+jzsNm4k902/EWcdY9gqnXrrMXhzPQnHvD4P/7z92EbXM/WZaQQrQrWOB8oCfPjcZ1Vfb73LYM57+DTyOuTiy8/B7XUzdI8hXDPxoka/t4hkDy2xEBGRJunUvSNXvXhBg6/b/ahRuL0enrzqBZbPX0mvgd059dZj2WHfvzW6luoPC9b04XOfsfeJo6u+3uu4XRk9biRL5iyjXeeCeo+lE5Hsp4AsIiJpM+qQEYw6ZESz3W/4mKH8+OnspK8VL19T65jL7aLv4LrXSotI26QlFiIikjX2++eeOJy1/2pzOAyDth+QhopEpDVSQBYRkazRobAdB561N54cd8JxT66XcZcdnKaqRKS10RILERHJKmfecyLdN+3CK3e/RWlxGVvuNIgz7jqB3gMbPx1DRNoWBWQREckqDoeDQy84gEMv2PiW0yIiySggi4hIRotGo8z5dh7WWgYO22yj4+NERJpKAVlERDLWT5//ynWH3knQH59t7MlxM+GVixgyanCaKxORbKaH9EREJCOVrSnnin1vYs2KEvylfvylfkpWruWK/W9m7erSZn2voD/IqiVFRKPRZr2viLROCsgiIpKRPp34f1hrax23sRjTXvqiWd4jEo5w31n/4ZDOJ3Hi5udyeLdTefeJD5vl3iLSeikgi4hIRipZVUooEK51POQPU7KqeTrID537BFOf+oRQIEzQH6J0dRkPnfsEX741s1nuLyKtkwKyiIhkpG1327LWPGMAb66HoaO3avL9/eUB3n/qk6r1zZWCFSGeveGVJt9fRFovBWQREUmLWCyWdAlFpcE7bs6wvbYhJ89bdSwnz8vQ3Yew1cgtmvz+a1eVYhzJ/xpcsXBlk+8vIq2XpliIiEhKLV+wkvvO/A8zp36Pw2EYefAIznnwFNoXtks4zxjD1RPH89Fz05ny5EdYa9nnpN3Z49hRGGOaXEdhr0643E6CNY4bY9hixMAm319EWi8FZBERSRl/eYBzdryckpVricUssSh8/vpX/PnDAh7/6W4cNTq6TqeTvY7flb2O37XZa3G6nJxyy9E8etEzBCviMdmY+BKOk24Y1+zvJyKth5ZYiIhIynzy4uf4ywLEYuuXVkTCUVYtKWLm1B9SXs8BZ+zN5c+ey4Dt+tGhaztG7D+M+z6/iX5D+qa8FhHJHOogi4hIysz7aSGB8pqLGiASirLo1yVsv/fQlNc0cuwOjBy7Q8rfV0QylzrIIiKSMv233TThobtKLreTTbfeJA0ViYjUpoAsIiIps+sRO5PfIQ+Hc/1fP26Pix6bdWuW0W0iIs1BAVlERFImJ9fLA1/ezMixO+D2usnJ87LHsaO465Praj2gJyKSLlqDLCIiKVXYqzPXTByf7jJEROqkX9dFRETWWfXXauZ8N4+gv/aDhCLSdqiDLBlr/OgJANz18XVprkREsl15STk3jbuXWdN+xu1xEYvGOOmmozjk3P3TXZqIpIE6yCIi0ubdfMx9zPr4J8KBMBVr/QTKgzx5xQt89fbMdJcmImmgDrJknMrO8Q/Tfkn4Wp1kEWkJxcvX8N2HPxEORRKOByqCvHT7JEbsPyxNlYlIuqiDLCIibVrJqlJcbmfS14qWFqe4GhHJBOogS8ap7BSrcywiqdBzQHeMMbWOO11Ohu21TRoqEpF0UwdZRETaNI/Xzel3HY83d/0Of063k9x2Po66/JA0ViYi6aIOsmQsdY5FJFX2O3VPum/alZfvmMTKxUX8bY8hjLt0LIW9Oqe7NBFJAwVkERERYLs9t2G7PbWkQkS0xEJEREREJIECsoiIiIhINQrIIiIiIiLVKCCLiIiIiFSjgCwiIiIiUo0CsoiIiIhINQrIIiIiIiLVKCCLiIiIiFSjgCwiIiIiUo0CsoiIiIhINcZaW/+TjVkJLGi5ckREslpfa22XptxAP4dFRJqkXj+HGxSQRURERESynZZYiIiIiIhUo4AsIiIiIlKNArKIiIiISDUKyCIiIiIi1Sggi4iIiIhUo4AsIiIiIlKNArKIiIiISDUKyCIiIiIi1Sggi4iIiIhU8/80MNnNOS0rBQAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": {}, @@ -200,19 +200,11 @@ "collapsed": false }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/rflamary/.local/lib/python3.5/site-packages/matplotlib/cbook.py:136: MatplotlibDeprecationWarning: The spectral and spectral_r colormap was deprecated in version 2.0. Use nipy_spectral and nipy_spectral_r instead.\n", - " warnings.warn(message, mplDeprecation, stacklevel=1)\n" - ] - }, { "data": { - "image/png": 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KLor601Yv0Hs6f+MpDonqAb+iHY0z7mW8r3MalwPp4414/ww1ty7nHQDa0z7S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GyVy+hCNrDFZsAmNrewcXrVnHsz1nKKXeym2ZLNdu2jzpsxd0r2FTSyvXb92GJ8KzZ07z/SNH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ozTisMVNlxeB017UwQbO1BtKgWl+Zl4YXdZmyKpuLVoyS/xM+/qORkxvr2baskv\n8XPjD6+lo6WTdUs3oqcMdm/cTzQU48xF0ygoyQ4xZ5neDcLb161HKYL0fqR9+oDt3mXbON6JRrFV\nwUcU8MtxdHxYbmGz20DCgs/Oo62hg3efXAlYm6pOj8NyN8t3k0roaA6NS790CdfedgXfuuQHGGmD\nUEsnqnagmBkJxTBNk/amEMueXMn0iydnHFCygvQOEH6EsN6TEAJJMaR3ILVJue7Ok4xQfAjHHGDO\nyb6UYyYrChimabVdFZTmZYRw8or9dDR3snvDPibNtSwTS4cWc8VXF7Hh7S1sWma1dcbDcRbeOJ+h\n46oylUw9pfPu06voaArxr18+j5E2mHTuePRECqQkrR+Yb7deIeEDvOAHC0LYkPY5kHyju+NCgNkI\nthpQSjLPM9MNVkIg49Zbsw1D2Gfk2rtyZC0HJxe3/+2b7N24n1+3PYTdofGDZ7+d+VnN1GreenwF\nZ31sGvkl+bz8xzcw9DRnLZlOrCvOxne24nQ7WPLFBRRVBqgYaYkqSmm1Rwph7brs21yHL+DDoadR\nNZVYOM7a1zcy59KZQDbsEqT6dl8AoGLZi5rkNDFynGocquAZ7YoR64qjp/R+zx8ytpJda/di6Abz\nPz0XIQQv/v51kJJpF07CW+Bm9cvrSacMPvXtjzNm5gEBN2maIGwk4yl2rtmNy+PC7VNQVau7873n\nVnPBp+di07IgDZMJrNhwACEUpABkuidRypHjlMQ0THas2c2PP/NLVFXlRy/eTkFpPgD5JXnkFftp\nqQ8iDZMLrz+PZCxJPJpAEYJELMmY6TUoKnS1hZk8bwJ2x4H77pVfX0xRVSEP3PInYuEYF3/2fJKx\nBHmFPjpbu9j41hZmLMiCDvAeZAL62ZIr3WuoD+gS/xDM4HXHTbQ8x6lBFtw5rdbuRDzJ3+95MqNT\n8dBtj2AakuvvvIp4NMGaVzfwwC1/BASfvOVSbv3zVymqKCSvyNdv3rRueyMdzZ0UDynCpqnYNJXJ\n542nsKKAVSsuompsBf7AvQC8+/bXKB2WnzUzq4qtAqks4SevTgGpW9Y+vax8pBmyChyKH6K/tV7k\nuQ6ZkjnrrRynBM37Wlnx7GrsLg1pSmLhBG//awXnfHwWTreDipFlzFwwhWf+9yVaa1sxDQOHx8GU\n+RPYvmo3iqJgpA1qplYzatqIbvuzTkKtXWh2G8HGEOFgF5pDQ1EV4u0Jhk8cQl6Rn7b6INGuWB9L\ntEGLOgz0zaCWcs+LlwEgzQ5QyvvutNItGofMdWXkOKWIhKK89fgKlvy/i3H5XPzxrr8hpVUABfAH\nfMxaPJ1gY4jG3U148jxc+qWLqRpTwQM3/wmhCpr3WK4Pz//uVV76w1LufuI/CDaGsLvttOwLojls\nSNPaqY10RCiqDOD2u2irC1p5SFUWCLSqQ8FYARywEZVmFITPcr7ohexeqPTswB4L2TKOl+PU4+Cu\nrK+f851Mt9U35n4HX8DLL5f/F4qiMPOSKSQTKVY8u5q0nsaT72HiOWOJdsZorQuSiMR54pfP4/I5\nufwrizBNk2/9+auEO6Ksf2MT4fYIqaSOoigIAalYisKJBfiL/DTuaSEZT+JwZUkXhloN6XUgerlp\nyS5rHFL0fw9SGsclVuQ4/TjmAoau69TV1ZFIJI7H9RwSKSWV04q54b4r+7iJSMAf8LJt61ac5TY+\nf/81SMDhstOZ7CDeGKW5Q0O1qZimiZE2AUk6ZRAY7cUgyaf/+/LM8SaNHYOqKhiGSV3cSmDKJvmx\nu+xs2bJlwN7fwKEBHd1/LKRMgVmOtWXyDQQShxakMu8n2EumIBTPYY6VI8fgxzRNNry1GX+hF4fb\nwed/ZCW+wfp29m+pY/T0kQghqD5jCNfediWrXljLqOkj0ew2Hr7j77j9Lhp3NwPw0G2PgoQbvnc1\nezbsR1EUkvEkddvqMQzZPQufJlCeT1GltQiR0tqtyQaENgZp1Fnz7cIBpAA7wj418xwpDaS+BdJb\nAR2pVEL0fsCWW0zkyFp6OjHWvL4RgIIyazfVZrdhpA02v7uNsy87E4DiqkKu+ubHeP/l9bQ3htAc\nGp0tYbwFHjx57kwBQ7WpJKJJXnvkbRCWMHDDzkYkEO2KAhKXz0XV6G4HKpFFscI2DGnsRRoN3YKR\nKRAKwj4/09kqpTtiJ84AACAASURBVIlM7wR9I5BAKiUI+7ScJkOOU4LeWhSqZiPaaY16KIqC0+Pk\nvKtmU1lTxpbllqBnPJzAV+Bl/rVz8RV4Wfr3ZQCk9TQrX1hLa10QVVVIxJLs21THtAsnYhoGXcEw\npcNL8Bf6rXNKmTVxAkBoNUijFmk0dttypgCtn/6Fqe+D9HqQYUuIVpuCYis/7HGVwj/nCpk5+nDM\nBYy6ujp8Ph/V1dUDKjaTjCeJhRN0druMBMryMU0Tt89F/c4mhBB4hVUdtdltpHUDm6YSKM3H7rKT\nSqToaLYsjdylLqKOGIqqZCqqdped/GI/3gIP8XCcZEJHCIHdqeH2uVBUa1fWSJso3S2g2SauAyDN\nCBgNWAWMBFJKgh1l1IeuZnhJCsgVMHJkL8lYkkQ0SWFF38+xJ99Dy/5WRkwaxraVO9m7qRbTMMkv\nyWPsrBo8fjev/uWtPo5FYHV/7Vm3j6IhRSiK9X33B7w07GnGTEuqRpXhLfBa42qRBN48N568LOi+\nAGtu3XkRMl0HZhCUPIRtSB9NHKmvAX2bNe+ODcxWMDsgtyjJcQrQsq8Vb/6BWHHjD6617okN7aTT\nBnVb69m2chd6Usflc3LG2WPIL87D7Xdx6ZcWAAd2au9+4j947ZG3yS/Nt4TAgYKSfPZtrsWb56Z8\neAl5JXkoioKeSiOEIL/E3/+iBiFCaOCYZxUwjGZQvAh1CELp1ZGR3gKp1aAUA3lghpHxl8G1EKEc\n2/vMLVhynGh6ipyLvdcBktse/ToP3fYIYMWJtvp2EtEkiWiCjW9vpbMtjGpTGTV9OMVVhdiddvJL\n/Kiqyk3n35np5PjanNuIheN84Z4bACisCODyOEnEUkQ7owwZU4Hb70YIQSQUJa/YjzMbXBC7EcIB\nzguQ6QZLXFbxIdShCOVAXmTq+yD1JigBhFJmdXOlXkWKi62u8cOQiwM5enPMBYxEIjHgxQuge4RD\noKoKpmEiFIHXf+jFtqEblpZFKk2opROXz4Xb58pco9PtwNANPHnuTFEjv8SP3aERjyQwDUmkIwpA\nQWk+kY4oqqaSTqXpaOkECUWVATx57n4LnsGPBkohCA2MZoSAwqJi2oJJqx00R44sRnNoCMXqolJ7\nfTdTiRT5JUVseGsLtdsaCJTlk9YN9m6sZf0bmzj78jP5/jPfwuN3c9O8O0nraW7901fZu3E/Hc2d\nmeIFgC/gIxBLUVCWT3tDB2ndwDRMVJvCrI9NR1GyJyYIYUdoI4AR/X4mzRjo27vVxBVk2Bqrw9gD\nxp7cbkiOrMeT5yYZT2HTDhTt9KSO0+2kdms965duoqAsH3eem8Y9TWx8ZytTL5jIxLmWbbKUVieW\nNE1qtzcghMgUL8DaGCkoy6eoMkBrXZBQd75hGiZT5k/InrZwLI0tYRsKtqH9fialDvqmPi4lCD/S\nCCLTOxH2aSf4anPkODZ6CpPJWBKwxtYbd7dQPqIEwzBRFEEimmDZkytxeV0UVQZobwrx5j/epbS6\nuFsLyyrcSfOAhl4imkQ9SPemsCJAsLGDoeMqad7XSiqRxkin0Rwak+edkXWbpUJoCG0YMKzfz6SU\nVueFEshslgjFgzQNpL4Roc4/wVebI1s5LhoYJ+LLJYTA4bJjd2p9zilNSaAsH0VRaKltw0ybmWID\ngAQiHVFi4Th6whLpam8MIaVEc2rd85qg2TU0h0bzjkaEEJnOjI7mEKZh4i/04fQ4rPN2t37Go4ns\nmHXvjdAAG0gd67cDAgOJo9/ce44c2UBvQT6bZmPExKGW81BlAFVVSMaSJGMpyoaXsOqFtRRVBkjG\nUmxevh0jbZCKJ1n7+kYadjUzZf5EQq2dGGmD5c+son5nI/5CH76At885BfDYvU+TjCWZd80cpCkZ\ne+YoXN0OJEbaINoZQ3PYcHmz1OVHxkGInO5FjlOK3vGiZupwVjzzPprdhubQSOsGHc0hJp03nh2r\nd5Nflo9QFLat3EkkZG1qbHxrC6HmTibNG0/jrhYu+PRchCJY8cxqkvEkgfICRK+CJxJGTq7mz997\njEhHlItumMfIScPI73YgMU2TSCiKoih48txZt1gBQCYBo38OIVxgtJ+US8qR43hiGiblI0q44e5r\nCDa0UzOlmm8v+iHJWBKb3RLrnXrBRDS7jb3dI6fDxldRWl3MBdedS3uTNco9ef4ERk8f2efYv//O\nX0nraR5Yfw+bl21n8/IdqIpg/OwhOD1WkdMwrJxCtanZt+7ogwkyjFDK+j4s3FZ3Z44cH5GsW7Ee\nfHMXisDlcxHrjAGg2KwkINppjYjkF/tpa2jvM0NmmiYg8OZ5KCjJQygCVVVJJVJIUyI5UC01DRPT\nNOkKhomEogcKGy2d5Jf4kabsm6wMcoRQkPiAFKgaoIJwIETLyb60HDmOiMNZH46eaSUHuzfsR5om\nTo+DmQun4PQ4EYrg97c/SjySIJ1Ko9pUzv74mdiddlSbynO/fZmr//NyfAVWwUJRFda8soHiqkK8\n+dZj4fYI+SV+UvEU8UiCkiHF2DSVhp1NdDSHGDm5mi0rdqAndZBQPqKUSeeNzxoh4AyKp9uAyRLZ\nEr6bAJDhH4Hw5TovcmQ9ZdUlTL1wIlve3UFX0GoBnzh3HEPGVLLpne34Crz85qaHSUSTlu6WlJx7\n5VnkleTx8h/foHRoCcVDLP0bT56bZU+upKW2ldJhVht0z+ImHk0QCcUINnSw9K/vUFQR4K1/LmfS\nuePZtmoXsa44SEl+aR7TLpyUfQsU4QQ0pNT72iTK6CE7NnLkGOz0dixKJXQu+/ICEtEknS2d1Eyp\nZuyZozDSBkJRME1JMpbCX+hDUawu8YLSPPZs2M+O1buprClHc2g07m5h5fNrSESTnH35TFTV6tZK\np9IEG9r56lm3EY8ksGkqN3z3avZtqiPY2MGo6SPZ+PYWUgkdaUqKhxQydf7E7LJX7UYIFSkKkGa0\nr+aejMAHjI/kyHEwWVfAOJhgMMgFF1wAEhqbGlGEQlFREUba5IlHn8zoZNjsNjpbLP0Mf6GPdCqN\noioZCzMpJYlYEqfHQTySyNiCSSmxO+2ZxQjApq0b6Yp2cdkVl51U+7B0Ok1RURGhUOiIXieE0p1w\nZM9cXY4cHxVVVRl31mhqpg0nnUrjcDtQFIVUIoWliWW1fSvdyYOe1PEWeFBtCs372qiZMpxgfTuG\naeLN91A+soy9G2p550nLwktRVbwFHnau2QPAT//tN5SPKOHGH1zL/i117N9cz/DJw8gr8iOlpGlv\nC0IRTL9o8kn7nRwNQjiR2hmgr0MqASwNjBB4/x3hXHCyLy9HjiPicAXPoWOrqBxVTiqeyhQypZT4\nAl5i4TittUErZnR3db7zxHtMmDuO1tp2ho6t5IFb/ohpSq67/UrGzBjJvk21qDYb/7zvaYQQ+AJe\n/nHv0zTsbAKgaW8r//zZMyz54gKef/BVRkyupqjS0pTpCoZZ8dxq5n1yTpaNotmQ2mRILUeKfMue\nWXaBUBG2/uNpOXJkE3anxvxr52YKkrcu+D5AxhWxh9f+8hZSSuZcNhOHy0GsK46RNvjDHX/DSBsU\nVQasDgqfi9qtDfz+9kf7jJfUbqunfEQZQljjsIWVAeq2N1C3vZGqUeX4Az6klHQ0dbLm1Q3MXjLj\nhP4ejhvaFEvzwjSszgsZAZlEaGec7CvLkUWc1AJGWk+jJ3WkBM1uw2Y/cmHMwsJC1q5dC8Bdd92F\ny+nmS//3S6T1NDa7DZum0tkaJhFNZsZFuoJhq2tCSgzDQFVV9FQaM23idFtBpwdpShxOO5rdhsNt\npysYYduubexr2MsnPnVVdrZ75shxCtB7h6T3v3vQ7Bqa/cBu4LcW/oB4JMG+TXV9nrfimdVseGsL\nV3z9Y6x5ZT1vP74Cf6GPOZfNBCQFJfmMO28869/cjFAE+7fUE2w40BYtpUkqoVO3o5Gmfa3WqJvj\nwKhboLyAht3NjI8mcGWRGBeA0CYihddyIZFR0GoQtnGHtEPLkSNbUVW1z6iXEILHf/YMXW1ha/Oi\nF3aXHdMwef/ldax/YxNSghCwefl2fAEf42aPYeLccbz08FI0u41Ny7b16QBNdrsO/Oabf2DeNWfj\n9h04r7/Ql7FXLSwvGPg3fhxRtFGYwgn6FpBhUIcitPF9hD5z5Mg2eucVHzYO2tOxPXRcJYqq8OIf\nXrfG0pr6bjI27mnGZlP7FC8AUnGdfZusoshvbnoYaUrO/NhUNIeGo7vbQghBQWkebXVBwh2RTLdo\nNqHYypHiIqS+yRobUUsQ2hk5x6IcR8QJL2A0huKsqwvR3BHDp0ClTVLisVNQkofdqeHqJbZ5pEgp\nu51CDKSEqz55Jc0tzcQiMW78zOe44mNXkU6nmTF/GldedhWr1r7HAw88QGtrKzffdDMOzcG0KdOp\nb6jn/nt+QzQW5Xv//V127dtFWte59ZZbmTF5Fr/4zc9J6UneXbGM22+/nauuuipzDRs2bODGG29E\n13VM0+SJJ55gxIgRLFmyhIaGBhKJBN/4xjf4/Oc/n+mguPHGG3nxxRepqqri7rvv5j/+4z+ora3l\nV7/6FYsWLeJ3v/sdzz77LO3t7TQ0NHDDDTdw++2393v/P/rRj3j88cdJJBJcddVV3HHHHYTDYT75\nyU/S0NCAYRjcddddfa43R47TCZe3fwHB7beSkr/+1xNEQlGMtEl7YwfvPbcaKSXjzhrNiufeZ/uq\n3YA1VoKUqJqKoRuk4jqdrV089K2/4HA7mLloKumUVUCF7rE3abWJZpvJjxDCEvnUcruoObKbDyt4\nHozm0Mgr9lPf3TmhOTQUVSAUhYdue5SutnCf57/33BrGzaph3Kwa7rnxfrau2AGQEfq2aWrGBl6x\nWW3mbQ3txCOJvnFJgKGnj/0NnwQU2xCwDTnZl5Ejx4DSO5bsXLMH0zTx5nk454pZjJhcTX6xn1hX\nHEURGGmj3+ulYRJP6P0e701naxd2l529G2spKM+3NmV7i38KkekKy0aEWopQS0/2ZeTIYk5oAaMx\nFOflzc147Sp+GyQMybt1YeYM8VNst5FK6Nid9kzif6SkU2lswqCjpRPTMPnhd35Mfl4+JgaLrlzI\nBedehMftIRzuYv78eTz48G9JJBKMHj2aV195DU138O83fylzvF//7lfMnXMu9/7oPjpCIT7xmY/z\n7lvL+c53vsPWbVv4+c9/3u8a7r//fm6++WauvvpqkskDXR8PP/wwgUCAWCzGjBkzuPLKK/H5fHR2\ndrJw4UJ++tOfsmTJEu666y5effVV1q1bxxe+8AUWLVoEwHvvvcfGjRux2+3MnDmTxYsXM2HChMx5\nn3vuOfbv38+KFSuQUrJo0SKWLVtGbW0t1dXVPP/88wB0dnYe1e82R46TiWEYmQWDv8iXmR2FD1+I\nHPy8ngVMPJIgHo5z+VcW8th9z9DeFMJIH9gptQqhkpKhRTTubs483rObKuSB5yaiSYy0QbozxuqX\n1zN6+kiqRlme5sl4CofbnimU5MiRY+CQUhJuj6Cn0vgC3kw31JHSEy8uL7gB05QUVRRgmibpVBrN\n0T9HSesGgYoAvcICcCBeyF6brUZ3IWP3mr08uO0v/PsvP2c9njYQCHyFOUewHDkGCtM06WwLI02T\nvCI/qk398BcdxE3n38mutXszHduKorD29Y0MHVtJW3073gIPX/z5/2Hbip28+Iel9OzLSmDJFy7m\nT3f/o09nVm98AS8T5o4lEU1SPKSQeFeCln2tVNRYOYWeSmPT1H7i4kdCzkUsR7ZzQgsY6+pC+Jw2\nXDZBsC2OQGDHZGNDFyUeO1JKnB7HRypgSFNmrIyUbmtVI20Q1xMZt5GH/vg7Xn3zVRRFoampkbqG\nWsaNHo9ds3PheRcR64qzZu0aRtWMYsSIEcQ6Y1x77bU8/PDDqJrKshVv89a7b/K7P//WqpjGE9TW\n1fbRwziYOXPm8P3vf599+/ZxxRVXUFNTA8B9993HU089BUBdXR27du1iypQpuFwuLrroIgAmTpxI\nXl4eNpuNiRMnsnfv3sxxFyxYQEGB1VJ6+eWX8/bbb/cpYLz00ks8//zzTJ06FYBIJML27duZNWsW\nt956K7feeitLlizh7LPPPrL/tBw5TjIdzSFWvbiOZCwBgMPtZMaCyRSU5h/V8Xat3QvAQ1t+xu71\n+wi1dOJwagwZU5HRtbA7Na74+mLyS/w88oPHcftdmZ3UnqTD7tJIxixRX7fPSbjbetnhtLNn3V7y\ni/2kk2nSepozF03rU3TJkSPH8SceTbD65fUEGzsQwtKrmXDOGIaNO9AV8FELnr0RAr7x2y/Qsi+I\nJ89NUVWAuz9xr+VckkpjGibX3nYliir4wx1/xRfw9osXhtF/J9bpdZKIJuloCaEIhWQ8xYRzxmTd\nqFmOHNlCV3uYVS+sJdIZQwiB3aEx7aJJFFcVHvGxRk6pzmjqjJxSTTKWwhvwoqiC8uGltDeFKK0u\nweN3oetpFCEINnbwyp/fPGzxAiyh8A1vbmH87DGMmT6C1rogu9bvw1fow9AN9KTOtIsm9e3IyJHj\nNOOEfvrboymKvA6keeCL67IphBIHbuziIwhXJeMpEpEE7c3WXFnJ0CIcrh6Ff6uysGzFO6xc/R5/\nf+gxnC4nn/rc1cTjCUzDxOFwEO2MY5rQ3hQimUgR64zh9Dgw9DSqquJw2ZHAA7/4LSNHjiSV0FFU\nhaLKAMuWvUP6EG1hANdffz2zZ8/m2Wef5ZJLLuGhhx4ilUrx5ptvsnz5clwuF+eccw6JhLUYs9sP\nOBMoioLD4cj8PZ0+0B7Wz33loH9LKbn99tv53Oc+1++aVq1axXPPPcett97KwoUL+fa3v/2hv+Mc\nOQYDqaTO8mffx+FyUFhpJRjxSILlz77PBZ8+94h2V3s6L6LdjkXfOPcOhBCctXg6Q8ZV4XTbaa1t\nI9IZw1vgpXlvC+mU3i3kq2XavHte31vBt8disWp0OR//6iJa64LYNJXyESUMHVeFP5DbUc2RY6BZ\n+9pGutrCmcVIWk+z9vVN+Av9FHTbln5UDo4X937uN5z3yTk07WlBCCsOxbriqDYVAbTsb8Ptc2Zm\n1V/U/0awsYPP1HwZPZmmqKqQ1v1tfc7xmbuuZvf6vdiddgrL8xk6bkjWaV/kyJEtGGmD955dDUJk\nYkQyluS951cz/9q5fQqHHzZq1rujU0rJVd9cQmttkI6mELs37CMRThAoL6C1NsikeeMRioLdrvH0\n/75Ew66mzHF6XBOFIjKaGJrDhsvnomRoIU6Pk5Ihxag2G3aHRmBECUPHVJJffGTxrDdm8DrQ3zvw\nd3KdGDmyjxMqcx3w2IkmLfcPf8CLp8BNWqgU+ZwEygsoKM1H+5Dui3QqTTwcR7Ep1ny2EOgJnWQ8\nBULg9rlRNZVINEJeXj5Op5Mdu7azYfP6PscxTZNENEHN8Br27N1NfWM9kc4YTzz9JKZhkoynmH/+\nfH7/p4eIhS3LxbVr19BW344wFLq6ug55fbt376ampoavfe1rLF68mPXr19PZ2UkgEMDlcrFp0yZW\nrlx5xL+7l156iVAoRCwW48knn+zXSbFgwQIefPBBolFrIVVXV0dbWxv19fV4vV6uv/56brrpJlav\nXn3E586R42QRbGgnnUr3mRF3eZ3oyXQfIc2jwWa3oad09KROZU05QlE44+wxjJ4+gknzxvPGP5bx\n93ueomFnE5ve2caw8VVUTxiK2+/C7tIoqy7ud8xUQmfd0k2oNpVwR5RQS1dWWp3lyJFtRLtitNYF\nyS89kNjbNBt2p5267Q3HfHzTNOls7aK0uhhvgYcz5oyhcmQp4+eMYuJ543jiF8/x2E+fZuuKHax/\nYzNfP+d2fvCp+ygZWsyQMRVc+62Pg4Deew8//bf/QZomsc4YwcZQr42YHDlyHG86mkPEIgk8eQds\nih1uB0bapOWg4uKRoCd0WuuC+It81G1vJK/QT/nIMjrbukjEE9RurScVT/HKn9+wRlO7CxVuv4vh\nE4fyj+bf8eVf3IjD7UBzaiz47PnM+8QcVJtKw+5W1i7dhFAEXe1hQs1dOHMdWjlynNgOjMlV+by8\n2Zold7nsdHTGiOhpJpX6QUq8ee5M2+XhSCZSdLR0IoQgFbfat0OtXeQX+zM+7UII5s+7gL898VcW\nXX0Jo2pGMXXStL6ZA2CmTTRV4zs338niyxbj9XqZMHYCuK0Z1f973f/jB/d8jyVXL7SUhYcM48Ff\nP8TsM+fw0F8eZOrUqdx22219RDEfeeQRHn30UTRNo6Kigrvuugun08kDDzzA+PH/v707j6+qPBM4\n/jvn3H2/N/tKCIuETUCWIIsbWIu44K7YZTq1tp86rcVOix1qwWrHTtWOn3Fa21rbWltcplaqtYJW\nsFRZxAVlD5shG0lutpvk5m7nzB8nXIgsSSBo0Of7l7nLOScX8973PO/zPs9ozjrrLKZNm9bvz27K\nlClcccUV6SKeEyZM6JGhMW/ePHbs2EF5eTkAXq+XP/7xj2zbto3Fixejqio2m41HHnmk3+cW4uOS\nSurH3q5lGD3qVRxPLBrjwM4a6isb+cLS6ygeXcjSq35CS30bn7vrWjav2Yo7YFbWTMQSJOMpho4r\nNgOVipLeIgLQUt+Koip8/+lFBLL9vPfaVh797h9IxJPp/e2NNU2se34TFy2cjaIqhGua2PHmbsbP\nGj0QH4cQ4jhSydQxC4BrFjW9rbS399fsqaO6og6LTeO7j/8bmQUhvlR2O1abhQkXjsXusqfP0dYY\noWz6WezfWok3w4vNaaO14XCNqZaGVixWC3ev+C7vvrqF+gONYPQczhRFYcyMUVhtViJN7bzz6vvM\nXDBNupsJcRroKf3DtwEAqOrhgpjHa7cMZqZzfWUjlTuq0JM6+SPy+K9X7mL7+gqqdtXQUm/+/R/a\nBt/S0EZeaQ7tze2sf+EtOtui6EfMW5LxJM0HW6ndc5Cy8hEYuk4iluC1Z9YxfEIJvkwfB3bUMKp8\nOCMmlqKoCs11LWxfv4uJF4476c9BzXhCMi/EGe8jDWDkBZzMHZ3D5qoWmjrihAJuZpXlkuc395f3\n5Uvb0A2O96pld9+NoevpLSZ/fubPaBYLqWSKeCxONNJFKqnz9trN5rkUhVQ8yfSp5/LqJauxOW3c\nsXgRY8vMgcHpdHLPkh8B5kTDAPzZfgLZfjZu2HjMWh1Lliw5ZoeQlStXHvOaW1oOt1e655570v9t\nsVh6PFdcXMyzzz7b470ffs2iRYtYtGhRj9eUlJSkC4EKcaYJZvsASKV0tO7gZqp77+ih546nqzPG\nG89tpCMSxe1z0dYY4YNtVemJyqFtYIfGna72GHa3nUhTO4qqMu+Wuaz87WrC1U1oVo2L/+VCho4t\nonZvfXchPxuh/CDhmub0MRVFwTDgYGUDuSXZBHICHNhezdgZo1D7sD1OCHFy3H4XDpedrs5Yj6yn\naKSL3KHZJ3yvrpttUWv31ePxu0mlUlTvqqXs3JFmjS3drLF1aIEl3hnH6rDQGYmSiCXIL80lqyCD\nv/7qFTrbOrFYLXzlJ58n0tTOzjf34PQ4sNms3HjnAg7srOGNFW+iKPCZL5xP1a5aSkYX4Q15aKwO\n09k9XgkhBpYv0wcoPTp66N319DLye9+6tWNjBTvf3IPb70JRFd5atZmDI/N47D+W09kW5dJb56Co\nh+9Qdm7cTeW2KgpG5NHZFu3xnMWmkVWcyS333czOTXt46bFXySzMoGZ3HZGmdvZs/oC84TnkD82h\nrSFCw4FGsodk4c/2U1VRy7hZZVIDQ3yqfeT/9+cFnOQFTr4av9VmIZDtx2Kz0FhtppCHcs1ifqqq\noGgWLFYLbp8LPaVjGAaqppKIJYg0tdMWjgAKDrcdVVOJRqL8ZvlTvLDyL8RiMSZOnMRN191kTlQU\nM2Krqmo65UxVFVxe50l3ShFC9J3b72ZU+Qi2vbErXfk/EUsy+tyRuP0n7klaub2KzrYomQVmb3Gn\nx0FXZ4wF37yUzPwg+7dWYbNbiHfFURQFq8OCzWGlZk89JaMLeeb+FSRiZmAilUjxxN3PUDgyjy/+\n8EbefnULRWcV8K//uZD3XtvGyt+8Siqlc+4VUygYnkfl9ioC2f6T7oAghOgfTdOYcOFYNrz4Np2t\nnWgWjVg0Tv6wHHKOsd3rSI3VTdTurSe7ODP9mMvnYufGPSz+wzfY/vpOWhrbiDS143A7SCQS+DK8\nNFaHCeYEeOX3r9FU10wybgZFk/EkP7rpv8krzWHGFVNwep2UjB/C5tVbyCvN6V4QMfBleqn/oJFQ\nbhB/phdQerYrEUIMGIfLzvjzyti8ehsWmwVFMbd9jpg0FH+muSByvHbLHW2d7H57H1mFGelA5lP3\nPUcilki3Wn7hkZeZeNE4dF2no6UDi81CKqWj6zrBHD+KotAZiRKNdBHI8jP35vNQNZVAlo+2cKRH\n4NXusuMJuDAMA0/Qw/5tB/Bn+7HarcddxO0PybwQZ7oz7i7c6rASjyXMFU/DwMBcPXH73UdlcBy5\nHcXmsOHP8uH0OEglzQFFTxnYXTa+u/i73LVsCVablbamdprrWjCOyPVUVHNV1Z/lxeV19i1TxDC6\n09WUXrfF9ObLX/7yKb1fiDPZiImlZBZkcHB/PQA5Jdl9KshXX9mQ3h5yiMNlp7G6idJ5E1EtGn/6\n6fNE27uYduk5DJswlOpdNVisGlab5bj3Eaqq0Frfyqgpw8wCf4bBVbfPJ1zbRN3eeopG5gPQ0dJB\np6pQNKpAsi+E+AhkFWZwwQ0zqNt3kGhHjOzCDDILM3r9+2s+2Ir1Q8FGrXsRI5DpY+zsMt7/x3bq\nKxt57ek3sLvslM8/h7ZwO06PA103jmqfekhrOEIgO0A0EiWZSGF3O7jkXy9k3/uVJOJJrDYLLfUt\naBYVf6YXl2RfCHHaDCkrIpgdoHZfPXoqRXZxFqHcQK/z+vbmDvjwfF6hx9Z0i1XD4bbzt0f/jtVu\nobXBrJXXv4qk4QAAFlZJREFU1hgh1Z3xqWoqodwAMxZMxWLTuh/TGH/eGGZfU84v//33RNu7uPCm\nmYTrmqnff7h9antLO6qqkT88V7IvxKfeGfcXcCgbIhlPYnVYUTUVm93apz7OFqulxx+9oRvouo6i\nKukJjsNtR1EVFJQeaeFdHV1kFYb6FLyIxxJEI1GzUI8CVrvZwUBuYoQ4OcFsf7+7CLi8Thpam3C4\nD69qHEoDd7odjJtZRmZBBslEiituu4RYZ5wZV06l4u09vLXqPa654zL+9ODzJOJJQrkBbli8gNyS\nbDrbovgyvKAoJOMJDlY2wAeNJBMJkokk4dpmYtE4zQdbGTKmiJGThw30xyGEOA63z8Wws4f26z0O\ntz19g9GDbnYgGja+hP/9xmOkkila6s2bku3rK/iXe29kzVOvc/715+IJuHji7v8jmUyRMySLrz/0\nJZKJJNve2InFbsHQDcI1Tbz29BsYhkFeaS5NteZiidVhIZQXZMKFY6X+hRCnmS/Da36Hn8CHu49Y\n7ZajsqO+dO9NNFSHWfXbNdidNu5/dSnR9ihb/7mDZCJJY5WZJa5qanp8ycgPMu3Sc0gmUmTkm9mh\nkeZ2sopCJONJDN0g3hVnz+YPSMbjpJIpwnXNxDq6aD7YStHIfMrKRw7URyHEGeuMC2CAGcSwOWzY\nHKdWsVtRFTS1Z+BDs6hmAOOISYSqqWZxULX3iUUqmUqnrzo9ZlpZeyQb6JJ9rUJ8hErGFnNgVy0O\ntx2bw0YqpROuaWbkOaVYrBbuuOAHvL92OwCPLPodYE5aCobn8vR//YWtr+9IbyFRNZW//O9LXLPo\nMhRF4bzrzmXXW3uo3V1HvCuB0+tk+4ZdWKwaQ0YXUVxWwEU3ziRnaDaa1ntwVQjx8ckZkoXFZqGj\ntRO330zbbqlvI5QXTKeWK4rSYwHE4bYzee7ZZOYFWXrN/WAYJBPmTYrFauGX//44C75xKTMWTGPv\n5v1UVTYQi8bMYwG5QzOx2FSCOUHOu3Y6Q8cVy6qqEINUINuPP8tHS30r/iwfiqLQ3tKB3WFLbxVV\nujsh/s/6/wQOb0O554U7+Ub590jEE8z/6sXse7+S3JJs9JROY3UYp9dJ+WWT2blhN1M+O4H9Ww9g\nsWroKY3coUFUzMyRC26cSe7QLJlTCMEZGsA4XQzDQLNoBHP8qJpGU20zgPmzqvYpyyPeFcftrUdR\nFDQtCoDHW097Wxa6Wz/l7SRCiL4J5QaZ8pkJbHl9B21N7agKDJ9Y0mtGxKEsL6fHQXWFGYTMyA+R\niCUYOWUY+cNycXmddLR1smNDBdlFmTQcaETVVFRNI5VIMfWzE9Npn8dzqAp4xPgZ9QcaURSF7OJM\nfKETrwwJIQaWw2Vn+mXnsHnN1u7aWga5JTmMm12WXsw43t74krHFhHICpJKp9HjhcNlQVJXzrpuO\nP9OH021n8SX3oqgKkaZ2ALa+vpMJF4xl1NThjJhU2qfr7GjtoG5/A6lkiqzCDALZfsnYEOIjoKoq\nUz47kfde20Z9ZQOgEMjycvb5Y/nMFy844Xudbge+TC+pZIqLbpqFxW6l8UCY1rDZQTF/WC4Wm4WW\nuhZ2bKggszCDcHUYX4YXp8dJtD3GtPnnUDA8t0/X2tUZo25/PV3tXWTkh8jID0oGuPjEkQAGZuAi\n3hWnqyNm1q7QDfRkAqM7XUzVVFy+vtW+0FMGynHiHIYU5xJiwHz4RuJY8oflklOSRVdHDKvd2qOo\n5gOrlx33GMe7WTlSwfA8yqaP5PmfrTJ7tDdGANi2fhcLv3/NUa8/lmh7lDV/fQNLd3B02xs7GX/e\nGErGFPXp/UKI3vVlrAhk+Zl9zXSi7V2omtqjoF5vHnzt7hOeJ5QXxO1zolk1muvMzmEun4uzzxuD\nN+Tp0zlq9tTx1ivvUT7jIdBg7Z/+jeETh1JWPlKCGEJ8BJxuB9PmTaKrM4ah6zjcjhP+7R05Dnx4\nTPAGji5CXnhWAaOnj8Sb4cVi1ejqNDO2ujq6cPv61vygub6V9c9vIplIoVk0dm7aQ35pDpPmjO/T\nIqwQZ4pBF8DQu/sgp5Jm20Sr3QoKJGIJ9JSOZtGw2qzp7RzhcJiLLroIgLq6OjRNIyvLrDi+ceNG\nbLbet5kkYgk6I13pgjsZeUGSiSR5Q7Ox2q19bvEK5j659rZsLDYLDkctANHOXBT19GVfLFmyhMzM\nTG6//fbTcnwhBpMT9Wk/1useWL3stGzf8gRcaJqWLvZ3iKKYzx3PocwLEhtx2mH2nJ+hoLB95/dJ\nJlJsWbudnJIsnG7HgF+zEOJoR44VLu+JbxROFAQ5HrffxbXfvgx/lp/f/eApoHv//IHGdJekE4nH\nEry7egv+DC/W7g5oGQUZVLy9j7zSHII5gX5fkxDi5PQnuNkf3qAbRVNxeszAiM1hQ9cNYp1xPMET\nd10Dc5H03dVbsDlsBLIPj2M1e+rILc1JFxgX4pNgUAUw9JROe0sHekqnpb4VA9KF+5rrW6H7Z80S\nxx1woaoqGRkZvPvuuwAsXboUj8fDt7/97R7HNQzDbKd6nBSqro4YrfWtxLsSAIRrm8EwzCBEP28i\nrDYrFmt3lxS72SUllUzh8rtklUSIQaa3m5HjPd8WjtAZiRLI8nHprXPxhjz84Z4/kUqm+ObPbknv\nm+8L5Yjoh8WqoWPQcrAVZ6kEMIQ4FccKdsLJBSH64ljH7WjrpC0cIackmwM7q0klUyiKQmNVmGBu\ngJwhJ27xCtDW2MaUaQ9itVvx+cy6PWPK7iExLElD1QgJYAhxBjMMg5aGNro6YwSyfDRUhfEGPRi6\nQaS5naHjivu0tbQzEqW9ueOooKjb76Z2d50EMMQnyscSwLj+F+sAeOrW6T0eN9OyzMABijmtj3cl\nUBTSN/8Wm4VkPEksGj/hCuXu3bu5/PLLmThxIu+88w4vv/wyy5Yt4+233yYajXL99ddz1113YRgG\nZeNGce2V17Hq7ytJ6To//+9fUFpSyurVq/n+siVmK1RVZe3ataxbt457770Xh8PB3r17mTNnDg8/\n/HD6+hRVwe13cccdd/Di3/6G1WLhkksu4Sf3/4QVK1bwox/9iHg8TlZWFk888QTZ2dksWbKEqqoq\ndu/ezYEDB3jooYdYu3YtK1euZMiQIaxYsQKLxUJhYSELFy7kxRdfxOVysXz5ckpLe+6draio4Lbb\nbqOxsRG3282jjz7KyJEjefLJJ7nnnnvQNI1QKMTq1asH8F9UiI9Ob9s7+pqhcTJSyRSbX9tK1a5a\nFCCl6yiqisWqmVvNvA7Gnz/mhMHKQ/3Xo9XX0d7Uzu793+/5AsMsJiyEOP3uuOAHpyXAYRgGFW/v\nZeebuzEwu55hKHxh2fWomkr+sFyKRhX0qXCnqqkco0cKgKSFC3EGi8cSvLVqMw1VYXNOkdKxOaxY\nrBoWm4Wzpg4nf1hOn45lZnmbC7ZHzkH0lJ5u2SrEJ8WgysBIxBLpTIt4NA6Qbj2U6q7ubRbYgpAW\n6DXFeseOHTz++ONMnjwZgPvuu49QKEQymeSCCy7gmmuuYfTo0aBA8dBiXnx2Jb954jH+8H+P8+BP\nfsrDt/8Pv/zlL5k2bRrt7e04HOb5NmzYwLZt2ygqKmLu3LmsWLGCK6+8Mn3e+oZ6Vq5ayfbt21AU\nhZYWc8/r7Nmzufzyy1EUhUceeYQHHniAH//4xwDs27ePNWvWsHnzZmbNmsWKFSt44IEHuOyyy3jp\npZeYP3+++XuHQrz//vs89thjLFq0iOeee67H7/yVr3yFRx99lGHDhvH6669z2223sWrVKpYtW8aa\nNWvIyclJX48QZ7JELEk0EuWl37xKMMfPyHOGkUqmaAtHjt0ScQBUbq/mwI4asooy0hOEcE0zobwg\nj77/YL+OpQR/z8YX1+L2xbB3p6RG27uwO2wEc2VFVYhTdSgQcfusJUTbu5j7+fNwepxU767FE/Sw\nd/N+mg+enu/DcE0T29dXkFEQMreZYWZuqZrKzAXT+pWR6c/ysebVOzF0g8lTHwDgvfcW0xqOcOEN\nvWdwCCFOLNLcTsXbe6mvbMTlczJiUim+DC973t1PfWUDLp+LYWeX9Cljqj8q3tpDY3UTWYUZgBn4\nbDgQpmBEbr/bQTvdDnKGZBGubiaQY2avp1I60UiU4rJxA3rdQnzcPtIAxqHMiw37mnr8fCgTQ1EV\ns89yb1/sfWxpOmzYsHTwAmD58uX8+te/JplMUlNTw7Zt2xg9ejSKojB/3nwMw2Dc6LG8vmEthgEz\nZ83km9/8JgsXLuTqq6/G4zGLbZWXl1NSUgLADTfcwD//+c8eAYxQKISqqtxyyy1ceuml6eBDZWUl\n1113HXV1dcRiMUaOPNzLed68eVgsFsaNMweZuXPnAjBu3Dj279+fft2NN94IwMKFC1m8eHGP37el\npYX169dz9dVXpx9LJs02kDNmzODzn/881157LVdddVWvn50Qg1n9gUbm3DwLl8+Fw22ntTHCC794\nGYtN4/rvXInNaeM3//FHAO5+7jsDdt79WyvxZ3p73HwEcvxUbq9izLln9avSt8NlZ+pnJ/LWy5uJ\nNLdjGGb3gqnzJmK1WXs/gBCiV52RKJd86UIMAzwBN/GuBGv/tIF4V5zsoky++MMb+e33l5NK6nzn\nt7cN2A1KdUUdDrc9HbwA8GV4aawK09nWidvf+572QzRNY8olE3jzpXdJxBKgQHtLB+fMGd+v4wgh\njtbR1sk/n92Aoih4gt1jxLMbSHQlCOb48QQ9dEairHt+E5PmjKN4VOGAnNcwDPZvrSLYHWwAM9s8\nkO1j/9aqfgcwAMbNHs2mle/SWBUGVUExYMzMUWQWZAzINQsxWAyqDAy7044/y4/VZqGxxgxyuHxO\nFFWho6UTgMz8EIlEEruz9+KcbvfhL/aKigoeeughNm7cSCAQ4Oabb6arqyv9fEZOCKfdRbA6SMrQ\n8Qbd3HXXXVx55ZX89a9/pby8nL///e8AR62cfPhnq9XKpk2bePnll3nmmWf4+c9/zqpVq/j617/O\n9773PebNm8crr7zCfffdd/h3t5srsKqq9ig8qqpqOghxrHMdyTAMMjMz0zVBjvSrX/2KDRs28MIL\nLzBp0iTeeecdgsHgCT8/IQarHRsqcAfc6YJ7br+bxpqdeINuSsaYBTQPbTfbt+UAY2eMGpDz6rqB\nxdozSKEoCrpunFSXoazCDObcPJuWhjZz4pLlk5RwIQbQB9sOkErohPLMrCanRyPWFad+fz3Dzi5B\n1VRu+fHn6OqIsW3dTrKLMwekXpWeSh37OIo5jvSXL+Tl/OvPpbXhD+gpnTmf8/XoqiSEODn7t1Ri\nGBDINmtXOT0aqXiS6opaSsYWoaoqVpsFq93K9nUVFIzIQ9NO/XvaMAxzQfYY9xR6Uj+pYzrdDmYu\nmEZrYxuJWBJP0C0FwcUn0ke60fqpW6fz1K3TmTY0xLShofTPh9gcVhxuO8lkMl140xNw4/I40z8n\nE0kcLrvZnaQf2tra8Hq9+Hw+amtrWblyZY/nNYuGy+vE7XehaSqaRWPPnj2MHz+eO++8k0mTJrFz\n504A1q9fT2VlJalUiqeffpqZM2f2OFYkEqGtrY358+fz05/+lHfeeQeA1tZWCgoKMAyD3/3udyfz\nEfLUU2YF8+XLlzNjxowezwWDQfLy8vjzn/8MmB1dNm/eDMDevXspLy/nhz/8IcFgkOrq6pM6vxAf\nN13XaW2M9OgWkEokUYBYZzz92JfuvYkv3H0DjdXhATt3cVlBul3qIa0NbRSewoTGYrWQmR8iIy8o\nwQshBljzwVac3p4T+GhbFM1mIR5LpB9zuO20t3SSTCQ/fIiTUjAij2gk2iNY0dkWxe134TlGC8W+\n0DSNUG6QzIIMCV4IMUCaD7YcPUa0d6FZVLMgfzeb3UoinugxzzgVqqpSODIv3QHxkJaGNorKCk76\nuOZiiJ+swgwJXohPrEGVgaEoCk6PA7vThifgRlXVdOvRYncBhm6gaupJtSOdNGkSo0ePZtSoUQwZ\nMuSom/9juf/++1m7di2qqjJ+/Hguvvhi/vGPfzB16lS++tWvsmfPHubMmcPll1/e432tra1cddVV\nxGIxdF3nwQfNvfFLly5lwYIFhEIhzj//fGpra/v9ezQ2NjJ+/HicTifLly8/6vknn3ySr33tayxd\nupR4PM7NN9/M2Wefzbe+9S327duHYRhcfPHFjB07tt/nFmIwUFUVT9BNV0cMh9vMXNIsGnrKOCoz\nK9YZI7s4c8DOXTKmiIYDYRqqGlE1DT2p483wMGraiAE7hxBi4PgzvbTUt+H0HJ7I25w2Is3t6Zak\nAPGuOA6XfcCCiJmFGQwdP4T9WypRNRVdN7A5rJTPP0c6kgkxiPgyfVTvqu3RHtXmtJFK6j2K7CYT\nKTSLhs0xcMHDkVOG01zfSkNV2Bwnkiky8kOUjh8yYOcQ4pNI6U/a8+TJk41Nmzb1eGz79u2UlZUN\n9HUNWq+88goPP/zwUcUzPwqFhYVs2bKFQGDgC/x92v4dxbEpivKWYRiTe3/liR1rrBhINXvr2Pji\nO/gzfTjcdjojUfZvPYDDZafwrHxsdiudkSidrZ3MuqacQJa/94P2ka7rhGuaiTS34/a5yCwISeaE\n+FQaiPHidI8V7S0d/OOZdVjsFjwBN4lYgqqKWqKRKKXjS3B6HMS74jQfbGXiRWMZUlY0YOc+1B6x\ntaEVi81KdlEGNkfv21+F+KQZzHOLSHM7rz2zDrvThtvvIhFLULu3ns72KMWjCnD7zMea6loYfe5I\nRk4aNqDnTyVTNFY30dHWiTfoIZQXGJAtKkKcifo6VgyqDAwhhOiL/NJcps6byM43zQre/kwv82+d\nS7Qtyq639tDWGMGf6WX65ZMHNHgBZgZIVmFGumq4EGLw8gTczFgwle3rK2ioCuN025lxxVScXgfb\n1++isboJh8vOpDnjKDrr5NO2j0VRFILZfoLZAzsGCSEGjjfoYeaCqd3jQTNOt53pl5+DJ+Bh27qd\nNFY3YbNbGTe7jKFjiwf8/JpFG/DuJkJ80kkAo5/mzJnDnDlzPpZzV1VVfSznFWIwyi/NJb80F13X\ne3T/KB5daPY9t8rwJoQAf6aP8vnnHDVWZBdlkkqaaeGyrUOIT69Alp/pl005aoyYdVU5yUTS3L7e\njy5jQojTa0Bm+MYxquiKM8fJdE8QYrD48KRCVWWiIYQ42ofHBUVRJNAphEg71txBxgghBp9TnuU7\nHA7C4bDcBJ+hDMMgHA7jcEilYiGEEEIIIYQQg9cphxULCwupqqqioaFhIK5HfAwcDgeFhYUf92UI\nIYQQQgghhBDHdcoBDKvVytChQwfiWoQQQgghhBBCCCGOSTaKCyGEEEIIIYQQYtCTAIYQQgghhBBC\nCCEGPQlgCCGEEEIIIYQQYtBT+tM9RFGUBuCD03c5QoiP2RDDMLJO9SAyVgjxqXDK44WMFUJ8Ksjc\nQgjRF30aK/oVwBBCCCGEEEIIIYT4OMgWEiGEEEIIIYQQQgx6EsAQQgghhBBCCCHEoCcBDCGEEEII\nIYQQQgx6EsAQQgghhBBCCCHEoCcBDCGEEEIIIYQQQgx6EsAQQgghhBBCCCHEoCcBDCGEEEIIIYQQ\nQgx6EsAQQgghhBBCCCHEoCcBDCGEEEIIIYQQQgx6/w+kU8Jfae2WIAAAAABJRU5ErkJggg==\n", 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"text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -220,7 +212,7 @@ } ], "source": [ - "param_img = {'interpolation': 'nearest', 'cmap': 'spectral'}\n", + "param_img = {'interpolation': 'nearest'}\n", "\n", "pl.figure(2, figsize=(15, 8))\n", "pl.subplot(2, 4, 1)\n", @@ -291,21 +283,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.6.5" } }, "nbformat": 4, diff --git a/notebooks/plot_otda_color_images.ipynb b/notebooks/plot_otda_color_images.ipynb index 7c04d33..6499daf 100644 --- a/notebooks/plot_otda_color_images.ipynb +++ b/notebooks/plot_otda_color_images.ipynb @@ -79,7 +79,22 @@ "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:2: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0.\n", + "Use ``matplotlib.pyplot.imread`` instead.\n", + " \n", + "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:3: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0.\n", + "Use ``matplotlib.pyplot.imread`` instead.\n", + " This is separate from the ipykernel package so we can avoid doing imports until\n" + ] + } + ], "source": [ "# Loading images\n", "I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256\n", @@ -116,7 +131,7 @@ { "data": { "text/plain": [ - "" + "Text(0.5,1,'Image 2')" ] }, "execution_count": 4, @@ -125,9 +140,9 @@ }, { "data": { - "image/png": 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CEak80UoJuMvPYUazG3B4UEMNJxBbHX2zO/ZRkfDDzwKo2oWbuJRctlKPMJzS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\nU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SSUldaKMNk6ALXMMQ8Y0q9ARfNsXQ8FxmYcgpeCk2pSzTJyGBt+9LM9b3AwrBhkRJzH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\nVST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\n", 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Lm3ZQU5XlknMWcObCWT6K1OPxeBiloviDB9bxxIu70ga/UFwMjMDEGYIgpLTG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\nZkpEUdO1++QJcfqPoE4H/6pSxdiYHa1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xowr1h3YoOukJXV8KM12+EIgiuXa31SUjWIsa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\n", 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qnzjnIq/1I2p6Hg8LrnQj2q5mhaeT3K6lOF7sx4gIJ7FhVe7rRJWbGvikHDO2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\nodjt7x1DuOM4t6gAfvs6sZPx9WN2yuJ4mknqi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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, diff --git a/notebooks/plot_otda_d2.ipynb b/notebooks/plot_otda_d2.ipynb index 5862d48..68d3b66 100644 --- a/notebooks/plot_otda_d2.ipynb +++ b/notebooks/plot_otda_d2.ipynb @@ -67,8 +67,8 @@ "n_samples_source = 150\n", "n_samples_target = 150\n", "\n", - "Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source)\n", - "Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target)\n", + "Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source)\n", + "Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target)\n", "\n", "# Cost matrix\n", "M = ot.dist(Xs, Xt, metric='sqeuclidean')" @@ -127,9 +127,9 @@ "outputs": [ { "data": { - "image/png": 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\n", 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bmysDBw4Ul8slzzzzTLCPrOqaa66R117zfwaWlpYG+9/aYlmyZImIiJx++uly8skni9vtluXLlwf762eeeUa6desmubm5UlRUJEOGDJFVq1aJ2+2WtLQ0ERFZvHixXHvtteLz+cTr9cqsWbPkm2++kddee02uueaaYHx5eXnVYm5KP6wVZKXiVEXl+Pufcvn+p9yIVJJTUlJYsWIFTz/9NB07duSCCy7g+eefZ/PmzfTt25ejjjoKgMsuu4yvvvqqzuudeeaZJCYmAvDJJ58wd+7c4FCL9PR0Nm/ezLp16zjxxBMZPXo099xzD1lZWdWuM3XqVC6//HKeeeYZvF4v4J/E/+qrr2bEiBGcd955bNiwIXj8hAkT6Nq1KwkJCfTv35+TTjoJgBEjRrBz587gceeffz4Wi4WBAwfSr18/Nm3aFNLukiVLuP/++xk9ejQzZsygtLSUn3/+mSlTpnDffffx17/+lV27dgXvUbVdIsIdn39CqceDBLZ5RShxu7nvv19GNTbV/B78aDMutzdkm8vt5cGPNjdLe3//+98ZNWoUU6ZMISsrK/jNmMPhYPbs2QBs2LCBQYMG0bt3b4wxXHTRRcHzlyxZwr333svo0aM57rjjgn1bbY4++mjuueceHnjgAXbv3o3T6aw1lsTERE488UTA3/fOmDEDm81WrR+eNWsWHTp0IDk5mbPPPpuvv/46pN0lS5bwwQcfMGbMGMaOHcu2bdvYsmULI0eO5MMPP2TevHl88803pKWlNe1NrUIHBSpVh4sWvA7A93uyQl6/es4FUYupOVmtVmbMmMGMGTMYMWIEL7zwAmPGjKnxeJvNhs/nA6g252RycnKtbYkIw4YN47vvak/sn3zySb7//nsWL17MuHHjWLFiBY888gidO3dmzZo1+Hy+YGcNBIeIAFgsluBri8WCx+MJ7qs6DVDV1yLCggULGDRoUMj2IUOGMGnSJBYvXsypp57KU089xfHHH1/rPajWzeXxsKewIOy+1furDxtSrcvePFeDtjfFJ598wldffcXSpUtJTExk2rRpwb43MTGxXtObiQjvvPMO/fv3D9leW+Fjzpw5TJkyhcWLF3PyySfz73//m/Ly8hpjcTgcwXOb2g/ffvvtXHnlldViWr58Oe+//z7z5s3jlFNO4U9/+lOd915fWkFWKk69PncKr8+dwqS+6Uzqmx583RSbN28OGU+7evVqevfuzaBBg9i5cyfbtm0DYP78+UyfPh3wj0FesWIFAAsWLKjx2ieeeCJPPfVUsGPMzc1l0KBBZGdnBxNkt9vN+vXrq527fft2Jk2axN13303Hjh3ZvXs3+fn5dO3aFYvFwvz584OV5YZ488038fl8bN++nR07dlRLhGfNmsUjjzyC/1s5WLVqFQA7duygX79+3HDDDZx11lmsXbu2wW2r1iXBasVewwNR7ROdYber1qNb+/DfItW0vSny8/NJT08nMTGR9evX88MPP4Q9bujQoWzevJndu3cjIrz++uvBfRV9W4WKvi01NZXCwsKw19uxYwcDBgzgt7/9Laeffjpr166tdyy1WbJkCXl5eZSUlLBw4UKmTp0asn/WrFn861//ori4GPCvcHjo0CH27NlDSkoKc+bM4fe//z0rV65scNu10QpyG9Paq5/NoeK9agvvXVFREddffz15eXnYbDYGDBjA008/jdPp5LnnnuO8887D4/EwYcIErrnmGgDuvPNOrrzySv785z8HH9AL56qrrgp+LWa327n66qu57rrreOutt7jhhhvIz8/H4/Fw4403MmzYsJBzb7nlFrZu3YqIMHPmTEaNGsWvf/1rzjnnHF588UVOPvnkOqvV4fTq1YuJEydSUFDAk08+GVKFBvjzn/8cfODF5/PRt29fFi1axBtvvMH8+fOx2+106dIlolULVbd9hYXc/81XfLFzB06bjQuGjeS6iZNx1PLEvsfn46kVy3hxzWoKy8uY0K0Htx8zg4EZGRGJyWqxcOGwkby2fi2llapjiTYb/zN2QkTaULHrllmDuPXtH0OGWSTardwya1AtZzXOaaedxtNPP83QoUMZNGgQkyZNCntcUlISjz76KCeccAIpKSmMHz8+WN298847ufHGGxkxYgQ+n48BAwawcOFCjj/+eB588EHGjBnDbbfdxrnnnhu83iuvvMKrr76K3W6nW7du3HXXXTidznrFUpsJEyZw1llnsXfvXi677DJGjx4dUmE+9dRT2bRpE5MnTwb8Sfwrr7zChg0bmDdvHhaLBYfDwZNPPtngtmtjKioj9TF+/HhZvnx5RANQLastJHnNpSXeu40bNzJkyJBmu7464vLLL+f0008P+QCIpHD/lsaYFSIyvinXbev9cEFZKTNffI7DpS58gc+vBKuNqT178eyZs2s87w8ff8jirZtxBT54DZBsd/DBpZfRPbVdRGIr93q59dMlvL91M3arFbfXy2WjxvLHqcfoimxxqKH98Tur9vDgR5vZm+eiW/tEbpk1iLPHdG/GCOtWVFRESkoKIsLcuXMZMWIE119/fVRjquzZZ59l3bp1PPzww81y/ab0w1pBbiPa2jja5qDvlVLR98b6dRS7y4PJMUCZ18O3WT+zNScnbEX4YHER723ZRFmlYTgSOO9fK1dwx/TjIhKbw2rloZNO4bZjprOvsJBeae1JrTQeXrVuZ4/pHvWEuKonnniCl19+mbKyMsaPH8/VV18d7ZDihibISqk26fnnn492CKoRVu7fGzKEoYLVGDYeOhg2Qd6Wm4vDag1JkAHcPh9rDkT+Abr0xCTSE5Mifl2lGuqWW27hlltuiXYYNbrqqquiHUKNNEFuI9rSONp4JyL6dWyca8jQNdUwA9Mz+My6g/Iqya4AvdLCL03bO619tePBn1QflZHZHGGqVkL74/jV1H5YZ7FQKoY4nU5ycnI0wYpjIkJOTk61B/5UZFwyYhR2S+jDeHaLhb7tOzCqc5ew53Rv145jevchocpDfA6rlavHNmlIuGrFtD+OX5Hoh7WC3MZo5Ti29ejRg6ysLLKzs6MdimoCp9NJjx49oh1Gq9QpOYVXzzmfeZ8uYfOhbCzGMLNvf+6beWKtlb5/nnwa93z1BQs2rsft8zEgPZ17jjuRfh3SWzB6FU+0P45vTe2HdRYLpZRqITqLRWQVl5djs1hIsNW/1uP1+fD4fA06RynVeugsFkoppVq15EorddWX1WLBatHRhUqp2mmCrJRSSqk2RcSLlLwBrldBysF5Gib5VxhLSrRDUzFCE2SllFJKtSmSfzOUfga4/BuKn0bKPoKMtzGm4d9MqNZHv2dSSimlVJsh7q1Q+inB5BiAMvBkQelH0QpLxRhNkJVSSrUKHp+Pbbk5ZBcXRzsUFcvcq/AvNl5VCVK+tKWjUTFKh1ioNkcXS1Gq9Xl380bu+OJTPIFZKiZ0684/Tz6dDomJ0Q5NxRprJzAW/+oyIRxg7RaNiFQM0gqyapKLFrweTDiVUioaVu3by7xPl1BQVkaJ202518uyPVnMXfROtENTscgxDUwy1VIgY8UknhOVkFTs0QqyajMqEvnv92SFvNZKslLx7V+rVlDm8YRsc/t8rMs+yM68w/Rp3yFKkalYZIwN0l9B8q4Dz0+ABSztMO3/hrGGX41RtT2aIKtG0WRTKRUr9hQWVP+2HP8S1AeLizVBVtUYWy9M5ruId49/mjdrn1pXYlRtjybIqs2oSN41mVeqdZnasxcbD2VT7vWGbC/3ehmc2TFKUal4YKzdox2CilGaIKtG0WRTKRVN3+3+mefXrCLXVcK0nr1IcTgoKCvD4/MBkGizMXfcRNolJEQ5UqVUPNIEWbU5mswrFd+eXbmcvy/9Bldg3PH6gwfpmJzMyf0H8s3un0lPTOSqseM5ZcBRUY5UKRWvNEFWTaLJplKqJRWUlfHQd99Q5j3yUF6p10N2STH9OqRzz/EnRjE6pVRrodO8KaWUihtrDuzDbq3+0VXq8fDJjm1RiEgp1RppBVkppVTc6OBMxCfV56wwQGZScthzRISd+XlYjaFnuzSdrUApVSdNkJVSSsWNYR070Tk5hV35eSGJstNm47LRY6odv+bAfq7/4D1ySkoQoFtqKo+deiaDMjJbMGqlVLzRIRZKKaViktfn480N6zj3zVf5xRuv8PLa1Xh8Pl44+xz6tu9Akt1OisNBos3GrdOmM65r6JRdeaUu5rz9JlkFBbg8Hko9HnYcPsxFC16n1OOO0l0ppeKBVpBjlE6fphrCl3MpAJaMl6IciVKRc90H7/HVrp3B2So2H8rmg21bmT/7XJZcejmbcg6RX1rKyM5dSLLbq53/7uZNeMRXbbvb6+Wj7ds4a9CQZr8HpVR80gRZKaVUzFlzYH9Icgzg8nhYfWAf3+z+mWm9ejOkjkVADhQXUVplCWrwLyCSXVwc8ZiVUq2HJsgxRpdwVg1RUTnGvSzktVaSVbxbtmd3cNGPykrcbpZm7WZar951XmNc1+4k2e2UuEOHU9itVsZ27RaxWJVSrY+OQVZKKRVzMhKTsFut1bY7bTY6JifV6xrTe/dhUEYmTuuRWlCizcb4bt0Z06VrxGJVSrU+WkFuRo2p/jZ2CWetNLdNFZVirRyrWCIirDmwn+ziYkZ36UrH5PDTr9VmVv+B/OXLz6pttxjDGUcNrtc1rBYLL//iPF5YvYq3N23AYgwXDBvBJSNG6VRvSqlaaYKslFIqYvYWFjDnP29xoLgIizGUe71cNmoM86Ye26CkNNnhYP7s87hm8UIKysowQKLNzqOnnkF6Yv0qyABOm5254ycyd/zERtxNqLxSFw8v/Zb3t23BZiycO3Q4v5kwiQSbjQNFRSzds5tURwLTevXGEab6rVqeuDcghQ+B+0ewdsGk/AbjnBXtsFQcMBJmwvWajB8/XpYvX96M4bQOVccRT+reA2ie6m5LtqWUahpjzAoRGd+Ua8R6P3zGq/PZdCgbb6XPlkSbnQdPnMWpAwc1+HoiErze0I6dsESp8lvm8XDyyy+QVZAfvDerMYzs3IVje/XmiRU/YLdYMBhsVgsvnn0uwzt1jkqsyk/cG5CciwBXpa2JkPpHLMkXRyssFWX17Yd1DLJSSqmI2J2fz/bDuSHJMYDL4+b51SsbdU1jDEM6dmJ4p85RS44BPti2layCgpB784qwav8+nli+jHKvl2K3myJ3OXmlpfxq4dt4wzxkqFqOFP4NKK2y1QVFf0Ok+uwmSlWmQyyaQWPHEcd6W0opVZui8jKsNSSxBWVlLRxNZH398068YeZUBigPkwiXeTys2LeXiYFv9VQUeNYDYb4ll3LwHQJrlxYPScUPrSArpZSKiAHpGVgt1T9WHFYrswYMjEJEkWM1Dfu4NIZq08upFmapaSo/AUv7Fg1FxR9NkJvRq+dc0GIV3ZZsSymlwrFbrdw/8yScNluwkpxos9ElJYUrx4yLcnRNc+agmmfOCPdB6vb5mNDNv/T1jsO5fPbTDrIK8pspOhWOSfkN4Kyy1QlJ52NM1e1KhdIhFkoppSLm5AFH0bdDOi+uWcW+wkKO7d2Hc4cMw2a1ICJxO73a0T170ad9B3bmHQ7Z7rRaOSqzI9tyc0Iqxl1TUlm1by/PrlrBsj1Z2K0Wyr1eZvbtz99nnRp2jmcVWcZ5PNLuTih8AKQEMJB0ASb1j9EOTcUBncVCKaVaSFuYxaKqBRvW8cC3X5PjKqGdI4HfTJzMFaPHRixRFhE+2LaV59espKC0lFkDBnLlmHG0S4h8hTCnpISblrzP0t27MQY6JqfwwAmzmNC9Bw8v/ZanViwLeYjPagwWY3BXGqPstNq4aux4bpoyNeLxqfBEvODLBUsaxjiiHY6Ksvr2w1pBVkop1SwWb9nMHV98isvjnzEgr6yUv333NUDEhlz89Zv/Mn/talwef/V254o8Fm7eyOKLfkmyw4HL7eaNDev4ZMc2MpOS+OXIMYxp5DLTGUlJvHD2ueSXllLsLuebn3dxxxefkusqoczrrTZ7h1ek2rZSr4eXf1yjCXILMsYK1o7RDkPFGU2QlVJKNYu/Lf0mmBxXcHk8PLZsabUqcl6pizX795OelMTwjp3qVWHOLi7m+TUrKfd6g9vKvV4OFhfz5oZuGrTXAAAgAElEQVR1nD9sBLNff5msgnxcHg8G+Gj7Nm47ZgaXjBjVoHsREfJKS0l2OEhzOnlu9UqeWflDtfurD314T6nYpwmyUkqpZrG3sDDs9oLyMsq8Hpw2OwCPLvuOx374HofVileErimpvHD2OXRLbVfr9dcc2IfDag1JkAFKPR6+3PUTgrC7IJ/SQBIrgX33/fcLzh40hGRH/b5uf3/rZu7+6nPySkuxGMMvBg9lwcb1lFVptz4MMLmHTv2mVKyL+1ksLlrwenAOYKWUUrGjb/vwU2llJCaRYPXXZz7fuYMnli+jzOulsLycErebnXmHufq9d+q8fmZSMr4wz9FYjaFbajs+3LY1mByH7LdYWHNgf73u4bvdP3Pzxx9ysLiYcq+XUo+HtzaurzZ0IpwEqxWbMVTUwm3GQrLdwZ+PPa5ebSuloifuE2QVfb6cS/HlXBrtMJRSMWbetOk4baFfVCbabNxy9LTgEIrnVq+sNkzBK8LOvMPsOJxb6/VHde5Cl+SUaouT2K1W5owcTXpiYtjzfCK0S0io1z08smxptSS73OvFU8MqeVZjSLTZSLLbmdKjZ+i80AaO79uPvu071KttpVT0xO0Qi4qq8fd7skJe61zA9afvmVKqOR3buw9PnHomf/32v/x0OJduqe24afJUTjtqUPCYPJcr7LlWi4X80qrLBIcyxjB/9nnMXbyQbbk5WI0Fm8Xwf8efxODMjvxy5Bi+2rUzJAG3GEOn5GSGdexUr3v4OT8vfHzhZqiw2XjuzF/QK609PhFOmP/vkGEYHp+PT3Zs54e9e3SFPaViXNwmyCr6glVj97KQ15aMl6IVklIqxkzv05fpffrWuP+k/gPYmptTbTyviDC0Hkls19RU3r3wUnbn51NYXsbA9IzgHMNTevbixklH87el3+CwWvGJkJGUxHNnnlPvaeZGdu7CvqLCagsWJ9hszOo3gMXbtgDQ3unkL9NnMqlHTwDe2rDOXz2ucl8uj5v3t27WBFmpGBe3CXJF1bO1VkGb8760+q6UihW/HDWWBRs3cKC4iFKPB4sxJFit3DVjJgm2+n9E9UxLC7v96nETOH/YCFbt30cHp5ORnbtUS47XHzzA1twc+nZIZ2SnziH7fzv5aD7f+RNl3iNV6ESbnesnTGbu+IncO/NECsvLyUxMCjnPZrESLgU3xuDQRUKUinlxmyCr6KuoFGvlWCnVWO0SEnjvojm8tm4tn+/cQZeUVH45agyjOneJWBtpTiczKlWxRYS1B/ZzoLiIp1cuZ2P2QYwxiMCgzExeOOscUhMSEBGeX70SqVQ/NsD5Q4czd/xEAJw2e3A2jsqO79uPP31W/UE+h9XK7MFDI3ZvqnmJNwfKPve/cB6PsaRHNyDVYnQlvRhTtbo7KfA1XHNWkpt6bV/OpeDZCLYhmiQrVYu2uJJerNmdn88v33mL7JJi3F5vyBhi8CewZx41mAdOPJmvdu3k1++/W23e4kSbjR+u/jVJ9uqJcWWf/rSd6z9YhMUYRASfCDdOPpq54ybWK9YN2QfZW1jA0I6d6pzyTkWer2QBFNwFJvCgpfig3d1YkmZHNS7VNLqSnmoxloyXdBYLpVTMExGuePdtdhfkh50eDvwzVLy7ZRN/PWEW72zaEHZRD6ux8M3Puzix/4Ba25vZtz9Lr5zLJzu2U+b1MqN3X7qmptYZ52GXi8sXLmBbbg42i4Vyr5ezBg3hvpknYYnQEt2qduLd60+OKSNkAHrBHUjCFIw1ct9wRJOID7xZYEnR6ngVmiDHmJYcWx2Ja+uDekqpeLEp5xD7CgtrTI4reHw+BILLV1djoL55arsEJ78YMqxBcd605AM2HcoOqW6/t2UTwzp1Zs7I0Q26lmqk0g+h2qOZFfs+guTLWjSc5iClnyEFt4GvBPAijomY9g9hLDoNIeg8yEop1eqJCB9v38YVC9/m0rff4M0N63A3YhW4eFdYVobFUntma4AxXbpy+TsL+OynHWGP8YkwtWfvZogQCspK+Xb3rmpDP1weDy+sXtksbaowxA2Em+vaC8T/UuHi3ojk3Qi+HMAFlEP5UuTwNdEOLWZoBTlGxcuMEvqgnlKx784vPmXBxg3Biuiq/ftZuGkjL5x9TuhCFq3UFzt/4qHvvmZn3uGwQyYqOK02HDYrDquVZXuzqiWpdosFq8XCP04+jcQ6xh83lsvtqXEYRVF5ebO0qcJwzoSix4CqKzFaISH+V0KUkueBqv89ecC9EfFsw9hqHz7UFmiCrJRSrdiOw7m8uWFdyDzDLo+b1Qf28cWun5jZt38Uo2t+H27bwk1LPqi2Gp7B/wW602oj2WFnQrceDO/UmVMGDOSUV16kPEyFPTMpmYUXXkpmUlKjYvH4fGzIPojDamVQRmZwWriCsjKeX72SD7dtISGwCl/VeaFtxnBc336Nalc1nLENQJIvh+LnOZJIOiD5CoytFfw/49lN2Aq5sYH3AGiCrAmyigytHCsVm5Zm7Q67KEaJ281Xu3bGTYK8t7CA/NJS+qdnNGge4fu+/qpacgyQ4nAwsnMXZvbtz/nDRgRnpNiVl4fNWCgn/BCUxibHX+3ayY0fLcbt9SJARmIST59xNj1S23HWay+xp7Cg2vLVVmPwiuC02UhxOLhx0tGNals1jiX1JsR5EuJaDBhM4mkYe8PGk8csxxRwr6VaFVncYB8SlZBijSbISinVirV3OrGa6sMo7BYr6YmJUYioYQ6VlHDt4oWsO3jAv/iGgb/MmFnnXMI7Dudy+2cfk1WQH3a/y+Nh/uzzqm3v0a4dCTYbJVUe0LMaw7ReR8Yde30+nl25nKdXLqeovIyB6Zk8eOIshoRZ/W9vYQHXLF4YkqiXuPO5eMEbXDt+IvuKCqslxwAdnIkM6diRyT16ctHwkbR3xv6/V2tj7MMx9uHRDiPiTPIliOsV8OVzZBhJIiRdrLNZBLT+wWeqVbpowevBmT6UUjU7vm8/rGEqyFaL4dwhsf/Bf+W7b7PmwH7KvF6K3eUUlZdz22cfs2rf3hrPySt1cc4brwbnkw+nY1Jy2O1Wi4W7Z8wk0WYLroRnt1hIcSRww6QpweP+9OkSHvz2vxwudeH2+dhw6CCnvzqfH/ZWb/OtDevxhkmAy71e3tm0IexwDoD8slLumjGTa8dP0uRYRZSxpGMyFkLieWDpBrYhmLS7MKl/iHZoMUMT5BimSaBSqqmcNjsvzj6XjklJJNvtpDgcpDgc/PPk0+jeLrYXn9iem8O23Jxq1dUyj4d/rV5R43lvbVhPmcdT0yRdJNpsXD9xco3nn3bUIObPPo+T+g9gaMdOzBk5mg8vuYzugcU6skuKWbBpQ7URnAL85v33ql3vQHFRtQf+AHziI8nuqDEOu8XCgaKiGvcr1RTG2glL2l+wdPoCS+ZCTOLssMOx2iodYqHiStWVBltivmil4t3Izl349oq5rDmwH7fXy+guXUmwxX73n11Sgi3MLBsC7CssrPG8TYeyKfVWH3cM/uT45qOP4YJhI2pte2zXbjxx2llh923PzaWmVWhzSkrIdZWQnnhkrHKKI3wS7BPhqrHjWP3BvrBDLNw+H4MzM2uNU6mWJL4SkEKwdMSEGbrVmsR+D9kGaRKolIo0q8XC2K7doh1Ggwzt2Cns8IMEq5XpvfvWeN7wTp35YNsWXGEezju+bz8uGzWmUZUyl9vNh9u2sjX3UI3VaRN4sK5CmcfDKz+uDXvssI6dOKn/QO6eMZPbPvs45JoJVhtXjB6rQytUTBApQwruBNciwIAlBUn9M5bEU6MdWrPRBFnFlZZcaVApFV3tEhK4buJkHv9hWXAOZ4fFSnunk1+OqnlFuV8MGcZjPyylNMwwi89+2sHTK37gmvETGxTLlpxDXLjgdcq9XsrCJN4Veqe1DxnfvPbg/hpX3auYg/rC4SOZ1rM39339Jav276VjUjJXjh3PmUcNblCMSjUXyf8jlH5KcNYLXxnkz0OsHTGOCVGNrblogtzC6pPYaRKolFJ+v5kwmUEZmTy7agW5rhKO79OPq8dOqLWy2i4hgbfPv5jjX/x3SDUX/LNX/HvVigYnyNd/sIj80tKaK8dAot3OY6eeEbI9wWqrcWnrpEqLjfRIS+Px085sUExKtQTx5ULpJ1RfWKQUKXoCk64JslIxQ39pUKrtOKHfAE7oF37hAq/Px0fbt/Hu5o0k2KycN3QEU3v2onu7tGrJcYW8stIGtb+3sICf8/PCJsedkpI5/ahB9GnfgTMHDaFdQkLI/uGdOpOW4Ky2gl+Szc7Fw0c1KA6losJ7EIwdJMxKjt7dLR9PC9EEuYU0ZlxxrCWBWtFWSsUSEeHaxe/ybdbPwQT0w21bOWvQEB448WQGpmewNTen2nnDwsxVXHs7AOHHSSQ7HNx+bM1LD1uM4dkzZ3Pp22/i9nrxieAVH+cPG8EJ/eJjkRbVxtl6g4RZdQ8LOMa2eDgtRRNk1aI0yVaq7fD4fFiNabapo77evSskOQb/zA9vbVxPcXk5dxw7g6sXLQxO+WYxhgSrlTuqJLTZJcW88uMaNmQfZHinzlw0fFTIinndUlPpnprKjrzDIec5bTZ+UceCJQBDMjvy3ZVz+e+unRwudTGpe096pqU17eaVaiHGJCIp10DRk4CrYiuYREzyr6MZWrPSBLmFxPO4Yp1VQynVEF//vIu/fPkZOw7nkuxwcNmoMdw46ejgQ2mR8sVPP1UbulDhg+1b+XD7VgZnZpLuTGJPUQFDMztx3cTJDM7sGDxua04O5775KuVeD2VeL1/t2sm/Vq3g7fMvpl8H/4pixhgeOeV0LlzwBh6fF5fHQ5LdzlEZmVw5dly9YnVYrczUirGKU5aUaxFrT6T4CfDmgGM8JvUmjK133SfHKU2QVYvQJFuptmHNgf3MXfROcIq1ovJy/r1qBQVlZfxlxsyIttXOmYC1yrRqlQmw6dAhUhwOPvnlFWFXz7vji08oKi8Lji8u83op93q5+8vPef7sc4LHDenYia9/dTWLt25mf1ERo7t05djefbDowgqqjTCJp2MST492GC1GE+QWFo8JYTxXv5VSLeuR77+jtMo0aC6PhzfW/8jNU6aRWuUhtqaYPXgoTyxfhreGpZrBnySXe728tm4t10+cErpPhB/27qn28J0A32b9XO1aqQkJXDh8ZNMDV0rFPE2QVcSFS6Trm2TXJwnXRF2p2LU1NyfsbA82i5V9RYURTZB7pbXnrzNP4ndLPqj1uDKvl43Z2bi9Xj7esZ3v9+yma0oqvxg8FIfFGnbVvQSrNeT1mv37mL92NTmuEk7sN4BfDBmK02avdp5SqnXQBFnVmyakSrU9IsKyPVn8lHeYozIyGdOla60P3Q3t2JGsgvxqSbJXfHRPbRfx+M4a7E9Ub/hwEV6f4AuTnjttNoZkdmT2Gy+zMy+PErebBKuVR39YyrRevfnq550hK/YlWK2cN3R48PUrP67h3v9+EVx4ZNmeLF5au5oF519Mol2TZKVaI02QVcTUZ5xxXZXj2s7VccxKtay8UhcXL3iDnwvyERGMMQzKyOTFs88l2eEIe84NE6fw1a6dIcs8J9pszBk5psZzmmrWgIF8cdlVLNi4nlfXreVQSTFun39aKosxOG02Sr0etufmUhZIhMu8XvB6+fHgAUZ37srag/uxGQse8TGuazf+MPUYwD+G+p5AclzB5fGwKz+PtzasY86oMc1yT0qp6NIEWSmlVFh3fP4p2w7n4vEdmQN1ffZBHvj2vzU+cDekYyfmzz6Pe776nPXZB2nvTOTqseO5ckz9ZntorK6pqVw3cTJXjR3HQ999w5sb1lHm8TCtVx/+fOwMrnr3P8HkuLKCslLum3kiZV4vO3JzGZCRwaCMzOD+NQf2YbdYqLq0iMvj4YPtWzVBVqqV0gRZRUxTHuarz7kV29ZtPgmA4YOWND5YpVStfCJ8uH1rSHIM/gfe3tm0odYZKcZ27cbbF1zS3CGG5bTZue2YGdx2zIyQ7fYqY4orCP4p2Pp1SGdIYPq3wy4XPhEykpJIcSRUew8qtE9wRjJ0pVQM0QRZKaVUNSKCr4bp09w1JIyx7OLhI7nv6y9Dhn4YoFdaGj3a+Rft2JWXx40fLWZD9kHA0LdDezKcSSHnVEi02filVo+VarU0QVYR15QxwbWd68u5FIChaTtDXlsyXmp0e0qp8KwWCxO6dWfZnqyQx94sxjCjd9+oxdVYFwwfyTe7f+aLXT8hItgsVhLtNh4/9UwAyjweznvrVXID1WOALTk5QPWlqh0WCzdMmsLkHj1b8haUUi1IE2QV8yoSYaVUy7r3+BM5941XKfV6KPV4SLLZSXLY+XOVpZrjgc1i4fHTzmRD9kFW7NtL5+RkZvTphyMw9OKTHdtxud01Vs0rGOCmKVP5n3ETWyBqpVS0aIKsmkVzVHcrrqWVY6VaRr8O6Xx+2ZW8vWk9mw8dYkSnzpw1eCgpzTQbRUsY2rETQzt2qrZ9T2FB2If4qhIqKstKqdZME2QVs4KVY/ey0NdKqRaT5nTyq9HNOwNFLBjRqTMOq7XGB/Iq65mW1gIRKaWiSRNkFVE1JbXNUUlWSqlImdyjJ4MzMlmffbDWSrIBLh81tuUCU0pFhSbIKmbpkAqlVEsxxvDSL87j8R+W8fbG9ZT7vBSXl1Pm9QbHJVuN4fZjjiPNqdO7KdXaaYIcx2IxcWwrSW1rvz+l2iKnzc5NU6Zy05SpAOS6Snh+9Uq+/nkX3VLbceWYcYzp2i3KUSqlWoImyCrmaRKqlIqG9MQkbpoyjZumTIt2KEqpFtZmE+TGrPYWK1pinG9TxVIskRQP771SSqnYIuIC1/uIex3YBmISz8RYUqIdlqpFm02QVfNryi8hmngqpZRqDcR7EMk5F3z5gAuwIYUPIOkvYXEMj3Z4qgZtLkGuSNq+35MV8rolKsmRaqsiaVy3+SQAhg/SJLKltJUx1koppSJDCu8H30GgYgpBj/8n91wk/WWMo/VPoxiP2lyCrJpfU34J0SEMSimlWpWyzziSHFfmQw5fB52+wRhLS0el6tDmEuSKJC0aleNIVa2PXG8WAJPWxe946njVGhN2/WVEKdXWiXiR4meg5AXwFYJ9FKbdbRj70CZc1VrLvlLwrAf7iCZcXzWHNpcgq+bXlF9CdAiDUkqpaJGCu8H1H6DUv8H9A5J7MWS8g7H1adxFnWeC6xX8C5VXbbAcOXwDYu2GSb4C45zZyMhVpLXZBLklq62RrlpHowquWi8d1qKUUiC+w+B6GyirsqMMKX4Kk/Z/jbquSf09UvYF+PaE2ev2b/ftQfLXIZ5rsaRc06h2VGS12QRZNb+mJO6RSs402VNKKVUvnl1gHCBVEmS84F7X6MsaSwqS+TEcviRwHQ/+YRee0APFBUWPIUmXYCypjW5PRYYmyC0o0pXeWKkcayU7flX+BUJ/mVBKtWnWHmGSYwAL2I5q0qUtFhuS/hq4V0L5MqTkTfBlVT/Q2MGzCRwTmtSeajpNkNugyglta01uddiAUkqphjDWTMQ5C0o/JjgGGQAHJnlu069vDDjGgWMc4t4AZXuoNi5ZPGDJbHJbquk0QVaNFs05pevk2RjtCGKa/gKhlFLVmbT/QyyZ4HoNpBRsAzDt7sLYm1ZBrtZO8hVI2ZeEJuI2sA/C2PpGtC3VOJogtyFVE9pRTz5CYXl5yL6YSG4jwTYk5KUmfkoppepijAPT7lYkdR7gwRh787TjGIO0uxsK/xfw+SvH9pGYDo80S3uq4TRBVo0Wi7NpVK2MYvRBh3B0Or2G0/dKqdZLRAAJLthhjAGaJzmuYEk6G0k8FTw7wNIeY+3SrO2phtEEOQa0VIIZLqGNpeS2WVSpJCullFIVxFeMFN4HrneBcsQ+FtPuLxEfUlETYxxgH9wibamG0QRZNVksJddaGW0YfX/qpuO1lWq95PA14F4F+Icb4l6B5F4ImR9irJ2iGpuKLk2QoyhaD7lVvn4sJbdKKaXaLv8wh3LAERji0MztuTeDew3B5Di4oxwpeQWTemOzxxAPxFeEFP0zUGX3gfNUTOrvMJa0aIfWrDRBVq1SW67uaYUzsvRbCaWan6/4VSj6B0geWDogKTdiSWrmAo73JzC2MCtAl/vnIlaI+JDcS8GzjeAvEq43kfLvIHNRsz3EGAs0QY6iaDzkFk9jjuMpVqWUUo3jK3kDCu8HXIENOVBwHz5sWJLOab6GbQP8s0dUkwD24c3Xbjwp/w68OwmtsrvBdwDKPgPnrCgF1vw0QVZNokls7NCxss1L30elmknRPwkmx0Euf0W5GRNkYxuAOCZA+TKgYgU9AyYBk3hRs7UbVzwbQcqrb5cSxL0Bowmyak4tWTmOyUU9qmhsrJoQKqWipczj4fs9Wbh9XiZ170mKwxHtkOKCiIDvYPidNW2PINPhcaTwb+B6y78wiGMKpt1tGGtGs7cdF6w9wSSEqbQnYay9ohJSS9EEOYbEQ9JadVhIPCTckRaribiOlVUqOpbtyeLq995BAoNZPT4f9888iTMH6TSTdTHGINYe4M2qvtPaowXaT8C0uxXa3drsbcWlhOPApIC4AF9go8WfNDtPjmZkzU4T5DYiFhf1qElDY9WhBUqpaCkuL+fKd9+m2O0O2T7vkyWM7tKVXmntoxRZHEm5BfL/SOiyy05I+UO0Ioo7Ij7ARHz2D2MckPE6kj8Pypf7N9pHYtLux1iSI9pWrNEEOQbEcjW2rthiKdbmFi+JeKzFo1Rr9slP28Nu94qPBRvX87vJU1s4ovhjSTwFMXak8CHw7gZrT0zqzRjnzGiHFvOkfAVS8BfwbAaThCRejEm9MaKzSxhrN0z6i4ivBJBWnxhX0AS5jYmnRLa+serQAqVUtBSVl+OVavOE4fb5KCwrC3OGCsc4T8A4T4h2GHFF3FuQ3F8RrLxLMZTMR3w5mPb3R7w9Y0mK+DVjmSbIMSBa1diLFrzOhuyDDO3YqcY264otnhLuptJEvH70/VFtybSevQMLXIRKstuZ2a9/FCJSbYUUP0W1RU4ohdJFiO8PGEt6NMJqNSzRDkCpSLFkvKRJmVKqRfVu357LRo0l0XbkK+0ku51pPXtzdI/W/ZR/WyDiRtybEO++aIdSnWczRx6cq8QkgGd3i4fT2phwv/nWZPz48bJ8+fJmDEe1hIrKcWH5kd88Ux2OWivJKvpivTJbdYw29olA7MYbDcaYFSIyvinX0H44Nn2zexdvbVhHmcfLGYMGM6v/QCwtsFyyaj4+1yIouBPw+ac5sw/HtH8EY82MdmgA+PL+CKULqZ4kJ2A6fYWxdIhGWDGvvv2wDrFo5drSQ3RKKRUtU3v2ZmrP3tEOQ0WIuNdC/p8ImVnDvQY5fDUm8z9NurbPtQiK/g7evf6p7FJ+jyWx4VOmmZT/Qco+AimptNUJiWdrchwBmiC3QZXHFdc1BllFX7zNnhGr8SmlVH1J8fMcWV2vggc8OxD3Vox9YKOu6ytZCAV/Jph4e3dB/h/wQYOTZGPrD+nzkYJ7wb0WTCokX4ZJntuo2FQoTZBbqVieOk4ppZSKad59QJghqMYaWOGvcQkyRX8jdL5n/K+LHoLGVJHtIzAZrzUuFlUrTZDbME2W40O8VWZjPT6llKpTwjHgXke1KrK4wT6sUZcU8YGvhof9wq0kqKJKE+RWqi0u5KGUUkpFgkm6BCl5DXw5QMUqiYmQchXG0rjVEY2xIJZOgQp0FdaujY5VNY8WS5A1UVOqabQyq5RSLcNY0iDzHaT4WSj9DCwdMMm/wjhPatqFU26Egv8FXJU2OiHlpqZdV0WcVpAbKN4S/XiJUymllIolxpKOSf0DpP4hYte0JJ3rn5St6GF/JdnSJTCLxekRa0NFRrMnyPqwmFJKKaWUnyXpXEg6FxEfxuh6bbFKK8j1pIm+ihfx8jCfUkq1ZZocx7ZmT5D1YTGllFJKqabxlfwHih8DXzbYhmBS/4hxjIl2WK2WVpDrSRN9FeviZUERpZRSDeMrfg4KHyb4cJ97JZJ7GWS8hLGPjGpsrVWLJciaUCqllFJKNYyIG4oeIXTmC4BSpPBhTPq/oxFWq6cV5AaqK9HXCrOKlnhbUEQppVQ9+A6BeMLv82xs2VjaEB0hrpRSSikVqyzpNe+z9my5ONoYrSBHiM5yocL5/XF3AvDQ539psTa1cqyUUq2HMQlI0iVQ8gpVFxgxKb+NVlitnibISimllFIxzKTejBgHlLwIUgqWTEj9EyZharRDa7U0QY6QtjjLRayMdY3F97yicrz2yw0hr1uykqyUUqp1MMaKSf0dknIDiAtMMsaYaIfVqmmCrJRSSikVB4yxgkmJdhhtgibIERZLVczmEivz7cbyuO+KSrFWjpVSSqn4owmyUkoppZSqN5EyKF8Jxgr2MRhjj3ZIEacJsmqwWJlvNx7GfWvlWCmlVGsipZ8j+TcBFWOgbdDhcYxjfDTDijidB1kppZRSKkaJlOMrfBjfwan4DkzAlz8P8WZHJxbvfiTvtyDFIEWBnzzk8NWIrygqMTUXrSCrRov27BUVGlM5juWqs1JKqegS8YLvMFjaYYyjmdrwBNpIq7UNOXwtlC8DyvwbXO8iZd9A5ocYS3KzxFZjLK73AF+YHQJlH0Pi7BaNpzlpBVk1yEULXg8ml03hy7n0yMN+SimlVIzwlbyOHJyMZB+HHJiAr+ABf8IcyTaKX0QOTkKyj0cOTsRX+DAi1RNPcW+A8h8IJscAeMBXgLgWRjSmevHlAeVhdvhjak20gqyqqUiAuz3qn8O3NYyjrZrUx+LMF0oppaJLSj+CgnuB0iMbS15GMJh2t0SkDV/Jf6DwIYKr4glQ/Bxi7JiU34Qe7N4ExuI/JoQL3KuAiyMSU32ZhGMQ18sgJVX2WMBxdIvG0tw0QVb1Eqkp1WJlirjWRN9DpZSKDCl6hJDkGAAXuF5CUm+MzJftSkEAACAASURBVGwNxY8RumR0oI3ifyHJ12JMpS/3bT1ruEgC2Po3PZaGckwCx2QoX3okSTaJ4DwTYx/Y8vE0I02QVVDVJNg5IrDjuDvZPi2J/qP7RCewJqh6T5O69wj5UyvHSimlgrz7wm8Xr/+BNNMhAm0crKENF/7hC84j2+zjwdINvDsBz5Htxo5JPK/psTSQMQbaPwalH/iHeBgbJvFcSDi+xWNpbpogq3o55usSHvrfC5o8JCFWpohrDbQar5RSEWYbEuxTQ5hkMGkRamMgeH6svt3SEUgIbdYYyHgJybsVyv8bPN+k/R/GmhGZeBrIGCskno5JPD0q7bcUTZBVUNV5hbv9J/7HIMfDXMlKKaVihCU1/PaU60OHPlQivsOADVPTuVWYdvOQ3CsJHcrhhNR5/oS46vGWdEz6U4iUgngwFl1quiVogqwaJFIJplY5m06r8Uo1P4/Px7e7fyanpIQJ3bvTo12Eqogq5oh3L5R9HWaPHaSs+vHuDUj+H8Dzk/+1Yxwm7QGMtUut7RjHBEh/Hil8CDxbwNoLk/pbTMKxtZ9nnEfW5lDNThNkVU0wCT6n8deItaRNK8dKqYbacTiXixe8QYnbjQ/B6/NxwbAR3Dn9+LCVPhXn3D+CCZcMu6H8e+DK4Bbx5SK5l/rHJVco/wHJvQQyl/iHIdTCOMZiMl6OXOwxQrzZUPap/0XC8Rhrp+gG1ARxnSDr1+ZKxc4vIUq1JiLCVe/9h+yS4pAZtt7asJ4J3Xpw2lGDohZbrBMph9JPEM8GjLUPOE9p8QUtGsXa1f8wXjU2sPUK2SIlb4O4qxznBV8ulH8HCdOaLcz6EF8BUvhXKF0M4vMnq+1uw1g7/n/27jvMjerq4/j3qmubO802GEwJxfQOBgOh9xJqKIGXhBoIhN5rgIReQgKhhBp6x/RqTDFgmgGDwWDjius29fv+MaNdaaW1t0graff3eR4er2Y0954Zadmjozv3Fq3PVNNjsPgSnCU2LHAFtvY8PNUHF63PYqroBLm36unEv5D96cYxEekNvp8/n9kNDTnTzzYl4tz/xUQlyO2wqYXYeb+D5FygCUsV1P8dBj2C8a1U6vCWzDcKvMMh+R3ZEw8bTFWbha2SU8levMNlk5D8pXgxdoC1KaeSnfgBcJP46EvYeZ/CkJecoRqF7jM5002O21yT+iuwwa0xvmEF77PYKnIlvfRqbh/8Mp0PfplesNXdpPfTCn4i0hHNiTiedm7Kaoy3rRxKmq2/1k0Q0wtJNIFdhF10TinD6hBjjJMg5+7JSXqNf0MwVfmf61+7KPF1WGw8JKfRkhwDkAS7CCJji9Nn5OV2dqQg+lJx+iwyVZDLSKEW4yhlf7pxTER6gzUHD8HryR1nHPL52HM1VY/bFRlL1ny9AKQgPhFrmzEmXIqoOsSmFrpTqbX93iCGbbwdE9yqdVN4N2i8BZIxWs83BIENMf51eibg9iS+yzP8A7BN2Pg3FOclSAC5S2VDKn8sFaAiE2RN3SWdpaEfItIZfq+Xa3falZNffI5kKkU8laLK52dE//78ft31Sx1eGVvSF9NlfmNj6lf3Jr1Y7r7kjKyHxoRg0OPY+psgOhYIQNXvMNV/7JlYl8Q3AkwgT2JahfGtWpw+g9tD/Q15dvgh9Nvi9FlkFZkg91Y9nfin21/v9psL3p8STxGpdDusPJIXDj2C/331BbPq69l2xMrsttoaBLxLnqGgTwvvCU0P46wIl+aBwGZFGftaUN7h5FaPATzOinZtGM8ATL+LgIs61Ly1SWzz4871sVEI74mpPrLwVfXAaGfRkWSU1uq2BzxhCO22hPhSEHvPmbbOvwb4N+nwbC3GtzK25nhouJ3W1z4A1ccULykvsopOkFU57p50Il4fi2U97o3XtdyHfpRrXCJ93Yj+AzhrqyXPTyutTM2p2NgE5yY2GwMTBFOH6fe3Uoe2VMYEsTWnQsP17rLPAB4wVZiaE7vdvl10BkReA9y2G27FRsbCoEcxxt/t9tOM8cKgh7GLLnKnXLMQ2BJTdwnGk2/ctDtt3bxDIDUHbAKMF7yrwMD/dnhhEk/NCdjgDtjIC04coV0w/jULdVo9rqIT5N6qNyaohaaEUkSk/BhPDQx6wrlRLPGNU5UNblfQBLCYPNVHYb1DsY23Q3IOBDbB1PwZ02aat86y8ckQeZXs1fOizgeJ6KsQ2rVb7bdlPAMxA252qsLYpc7LbBdd4N7Y51acLZCYjK2/1q2Sd7Bf/xoYf+8Yo68EuQ/ra2O5yzGp1thoEeltjPFAcCvnvwpkQjtiQjsWttH4J/m32yZsdDwmI0G21kLyB7CN4FuzWx8u2lseOysEm4ToG+TeXBmDyDPQiQS5N1GCLBVFCeXS6ZqIiJQZzxBn2ELOEOeAs0CJyyZ+xi74kzOtnPEBBlv3NzzhnYoYnCX/DBSArSc1d0eoPh4T3rdPrSCpBFn6TOW4HJPqch8bLSJSKjYxDWLjwFQ7wzQ6OBa2LAVHgwmDbSIrSzZeTHg/IL3Ax+GQmo0zPZr7nEV/xfqfKNrNbsb4sP6NIT6BvIly8idYfAk2NRdT86eixFCOlCBLRVFC2b5y/iAgItIZqfrroPFuwEB6mED/f2GCm5U0rq4yJgAD78cuONGtDnvAVGP6XYfxLus8KfYR2MXkJqlxbNNDmLoLihdfvyuw8w4EG6HlJsIszdD4T2z1URgTLFoc5UQJsvR6lZBUl2NMIiKlYGMfQuO9tCxb7FZS7cLjYZnxFZugGd8qmCEvYhM/OTN8+EZmjxFOzWvnyCQkZxc5tpVgyGvY5meg4Rq30p0vlFlQ7kuGF4gSZKlISihzVcIHARGRpbFNj5M920OG2HgIjunJcArOtJdgBjZsZ9W5MCa4baf7sTYGqfngGehUsJcWl6cGU30oqeiLEPsgT4NJ8AzqdByVaum3N4r0Ep5B9ytpFBEpezHyL9hBxS5b3BHGuxxUHUb2WtBB8A1zFmDpIGtTpOpvwM7ZBDt3J+yczUg13O7MjtGROKpPAtou6hKC8H6VPQ68k1RBFull9CFARCqZCe2OjbwBtPma38YhsEVJYuoppvZsCGyIbbwfbD2EdsNUHdapVQht4x3u+G13LLEFGv6JNf0w1YcsPYbgZth+f4f6y91hHz6oOgRT+9cunVOlUoIsIhqWISLlI7i9M+tD7B13LKzP+a/usl5fwTTGQGhnTGjnLh1vrYXGO8m90c65yQ43QbbJWc7CIN5VMN7cYROe8M7Y0E5gG8CEMabvpYt974yl2/rKwiIiItLzjPFA/5sg9j42+hqYWkx4n/bH7kqGFNhF7ez6FWtj2IV/dRYGMQGwUWx4H2cZ6jar7RljwNT2QMzlSQlyhTp9O2dlm2vfuKTEkUgl09RwIlKOjDEQ3AIT7N1DKgrNGC/WuyIkf87d6VsNW3+1u2peFKw7S0jzM1jvMEzNcT0aa7lTgiwdlq4cf/DL9KzHHakkFyOh723JXG87HxGR3sLaKETfAxIQ2KKsh3qY2vOwC08heyaQENScBQuPo2X6vBYRaPovKEHOogS5wqQTzc/fmpT1WJVk6YquTA2nRF5E+hIbfQ+78KSMDQlsvyvwdGJmiZ5kQtvBgH9jG26E5FTwrYqp+Qv418ESy39Qqr5HY6wESpClw9KV4q5UjguZ0Pe2YQG97XxERHoLm6p3FiixbW56W3Qe1r8+xje8NIEthQlujglunrPd+kZC4rvcAwIb9kBUlUUJcoVJJ5alrBxPmTjV+WH/Hu9aiqQzlWMl8iLSZ0RfBWvy7Ehim5/B1J7Y4yF1h6m7BDv/GJxhFinAByaIqT23xJGVnz6VIGs4QmGMfredJSjzKEZC39tWjOtt5yMi0mukGoFknh0JZwq0CmMCG8Pgx525kuOTwb8upvoYjG/FUodWdvpUgtyblCLJ1/jnvk2JvIj0RtamIP4xpBZBYEOMZ2DrzuBoqL869yATwoS277kgC8j4VsX0y3NOkqVPJMhK7EqvGNe6tyVove18RETKnU1Mwc7/g7NqHQZsDFvzZzw1fwTA+FbCVh0OTQ/gzAphwVRBYAz4Ny5h5FJsfSJBlsJY0nAJVRX7jnyvsV5/Eak01lpnPG5qNs56zK6GW7H+9TDBzQDw1J2JDW6LbX4CiGFCe0JwjDNXc2Z78e+w9VdC7GPw1ELV4ZjqY3MW4Khk1iYh+iq2+Xln7HJ4/7w3A/YGfSJB7uw42HKqMJdTLCIiIr1G/HOwC8lKjgFoxjY90JIgA5jgZlmP27KJ6dj5B4JtdDakItBwGzY5DdPvisLHXgLWWuzCk935oJ17kWzkZWzVEXjqTi9+/5HXsY23Q3I2BDbD1JxU1LHTfSJBllzdSbzzVY41s0HfpNdfRCqWbQA8+felFnauqcb/tK5M1yICzU9ja/6C8Q7uUohlJTYOYq3JsaMZmu7BVh2E8Q0rWtepxv9C/bVOfwCRZ5xlyAc9WbQkuU8lyB2tHJfDWOVSxaKKtYiI9An+9cAm8uwIQ2jXzrUV/xzI05YJQvIH6AUJso28BjbfLFYGYu+C7+Di9Gtj0HA9LckxACmwjdjG2zD9ripKv30qQZbCJ96a2aBv0+svIhXLhKDqcGi6F4jj3IAXBu/KmKp9O9eWfzVITCJnSjgbA2/HKpzWWohPwEbHYTz9ILQHxjukc3EUk6cOJ21s80HAeMEUcent5M/t7EhB7KOidasEOUM5LMJRqljKqXouIiJSTDb2EXbBSUAMcG+i86+FCR8I4T0xJtip9kz1/2EjL7ZZcS8IwW0x3uWWHo9NYheeArF3wEaw+KH+BhhwMya4TadiKRYT3hfbeDd5K+XBIk555xkENt7OvuWL1q0S5D6mvcQ7/bir+mLlsDNLbvd2ffH1F5HKZFOLsAuOzR0ukPgWQjt0OjkGZ25hBvwHu+giSE4B/BDeH1N3TscaiIx1k+N0gh1zYl14KizzPsYEOh1ToRnfCGzdZbD4AjA+nJsbPZgBt2M8VcXr1zMAG9wOom/irACYFsLU/Klo/SpBzqOcqqY9FUsxK9aFblNf50tH6b0iIjkiL4JtO3MFzrbIi1B1aJeaNYGNMUOex9oo4MeYdm4AzMM2P9Wm+pwh9jEEt+hSTIXmqdobG9oBYh+ACTizSfRA8m76X4NdeDZEX3OTcy/UnoUJji5an0qQ+6hy+hBQadKV4w9+mZ71WJVkEZEKkFpIdiUyLdbp2Svy6UoFut3ZNMAZ41tGjKcGQjv0bJ8mjBlwIza1CFILwDsUY/xF7VMJsmQpRuW4UOOaNaWYdJTeKyLSrsDmQJDsWRFwtgVKs+iFqdofG38/TxXZC/4NShJTOTKefuDp1yN9KUEW6aR0pViVYxGRCuRfD4LbQOzt1oTUhCGwdemS0eCOztRyzS/gzIThA2MwA24teqVU8uv1CXJvmI2hUhOxQo9r1pRi0lF6r4hIe4wx0P8GiLyAbX4csJjw/hDaPWf56J6MyfS7Clt1pLMYh6mD0M4YT11J4pE+kCD3tN6QkJe7KZ9NBWC1Is4q0xGV9oFFREQcxnid6dzCe5Y6lCzGvyb41yx1GEIvTpB7w7y+veVmsEJf89svc24OuLbECbKUP1WORUSkK3ptgtzTekNCXu50jUVERKQn9NoEuZxWxeuqztwMprGWIiIi0lnWpiD6FjY6FghjqvbD+NctdVgl12sT5J7WGxLycqdrLCIiUjjW2owlrpsAD7b5CWzNSXhq/ljq8Eqq1yfIvSGJ6kjlWPO9ioiISKfExmUkxwApIAINN2PDe2O8y5YyupLq9QlyT+sNCXm50zUWEZGOsLGPsI13QXI2BLfFVB+B8QwodVhlw0ZezkiOMxgvRMdB1X7daz/+HaTmg39tZwW+CqIEuRvK4at+zfcqIiKSK9X0P1h8BRBxNiQmY5sfg8FPYzwDSxpb2TDVgBdncZKsHeAJd7lZm5yNXXAsJH5ykm0bx9b+BU/10d2JtkctYfFvERERKWcfvvgpp2x1HgcP+xOX/u4f/DRpWqlDKgvWRqD+b7QkxwDEILUA23h3qcIqOya8L5BvpT4LgW273K5dcBwkvgOawTYAUai/ERsd1+U2e5oqyF1QjtONVXLluByun4hIpXnpnje4+aQ7iTbFAHj3yQ/56KXPuHn8lYxYe3iJoyuxxGTy1wBjEH0Tak/v4YDKk/Gvjq09G+qvhIwlrU3/f2I8VV1q0yZ+hMQUcqvSzdjGuzHBrboecA9SBbnCnb7dRS0JZiW0KyLSk2KRGMlk2z/UlS+ZTPKvv/63JTkGsClLtDHK3ec/VMLIyoQZADaef59ncLeatvGvsU0PYSOvYdvro4J4qg/FLPM2pu4yTL9rMMuMxwQ373qDqYVg2qm/puZ1vd0epgpyF2i6scKcezlW4kWkd/hy3DfcePy/+XnSdHwBHzseOYbjrzuSYDhY6tAKYt6MBcQisZzt1lomjZ9cgojKi/ENx/rXgvgXQCJjTxjTxXGw1iawC0+F6NtuJ14wVTDwQYxvpW7HXErGMxDCexSmMf+a5FaPAQIQrJwlcJUgV6hiJZdKWkWk0v38zS+cs8vlRBqjAMQicV65903mz1zApU+dVeLoCqN2YA02ZfPuGzxMN6ABmP63YRceB/FvnYqmTUDtqZjg6C61Z5v+5ybH7rhmC9gm7MKTMYOfKVjclc6YELbmnIwx4BYIgmcwpvqIEkfXcUqQu6EvJo2FTKBViReRYnj0H88Qi2R/9R2LxPn45c+Y8/NclllxSIkiK5xwdYjfHr4tr93/NtHm1kpysCrIYeftX8LIyofxDsIMehSbmAqpX8G3JsZT3fUGmx8m+6Y/AAuJH7HJXzDeod2ItnfxVB+M9a+KbbwHUnMgOAZT9XuMp67UoXWYEuQKVazkspRJazGnqlMSLtJ3TP1qGqlkKme7P+hnxpTZvSJBBjjp5qOxqRSvPvAOXq8Hr8/LHy4/mK333azUoZUV4xsBjOh+QzZ3SIvD0/545z7MBDbGBDYudRhdpgRZOqUYCXS6jZZVATtACa+ItOc3m67K95/8QCKePQ4yFomz4pqlrfJNen8yz/7zJRbOWcxW+2zKjkds0+Vx0f6An9PuOJ7jrjuKhXMWMWT4IPyBfFN2SUGE9oDGfwPR7O2e/uCt7DHIADY5E6LvgglDcLvuVdt7ASXIFa5YCWIpKsfFWC5bY6qXTAvMSG90wGl78vK9b5JMNGPdYbrBqgBjDtqKgcuVbhW1Z24by7/PvI9YcxxrLV+88zXP3v4SN713RbduHqyqDVNV2/VFHaRjTPXR2OgrkPgZaAKCYLyY/tdhjCl1eN2SargVGv4JeMF4AAv9/4UJ9t1vI5QgS5eUKsEs94RXCadI6S270hBueu8Kbj/tXr5492uq66rY++RdOejMvUsWU+PiJv59xn1Z44WjTVF++W4WL9/zJnsev3PJYpOOMZ5qGPQ4RF7Bxj4A71BMeF+Mt7yH7NjkL85sHp7lwL9eTjJvY59Cw78A973pfqi0C4+HZcZjTO+Y+aWzlCBLyRVzuWzdCJhfMav2IuVgpbWG87ex55c6jBbffPAdXr8XmrO3R5uivPPEB0qQK4QxfgjvhgnvVupQlsraFHbxhdD8lLsIiAXP8jDwXox3mdbnNT9GzrCRtOg4CFXO1GyFpAS5SA55/H8APLT/QSWOpHcp14RXCaeILEl1v6q807IZA3WDakoQkVQCa5uxjQ9B5EXwVGGqDoPgjh0a0mGbHoPmZ4FY6w2GyanYhadiBj2Y8cT0VGw5LdBu4twHKEGWslHMZLJcEulyUcyqvYjkWmOTVakbXEukMYq1rclIIBxkrxN2KWFkUq6sjWHnHQyJH0lPL2fjEyF8MKbunKU30HwfOV9ZkIT459jkrxivs6KgCe2Ojb4GtqlNAAkIbNnt86hUSpALLF05/uCX6VmPVUkurHJLeJVwisiSGGO4auz5nLXTZdQvaMAYQyKW4MhLD2TdbdYqdXhSjiIvQPInsuZets3Q9CC2+iiMd/klH59qbGeHJzsZDo6BwGh3EZRmwAv4oe4CjKdfd86goilBFunDlMiL9Jxhq6/AfT/cyjcffEf9/AbW2nINagdoeIXkZ6Nv5lZ1AfBB7OOlLw0d2hGa7gfazNHsqQPvsJaHxnig/00Qew8beQU8tZjw3hjfqt09hYqmBLnA0pViVY77JiWcIrIkHo+HtbZYo9RhSCXwLIOTpiWytxvAs/TpCk3NcdjIS5Caj1OF9gE+TL+rnaQ487nGQHArTHCrwsTeCyhBloIot5vm0jTkQbpD75++Z+pX03jwisf5fuJUVh61Ioeeux8j1xtR6rCkDzJVB2GbHiY7QTZgqiGw+dKP9wyAwc9hm5+A2HjwDsdUHYbxVf6iJj1BCXKRqHIsIlJYE17+jNtPu4dp385gwLL9OPS8/dnzuJ0KtkjD1x98xxk7XEI8EiOVskyfPIMPnv+Ev714HqNGr1mQPkQ6yvhGYvv9HRanb8hLgmcIZsC/McbbsTY8NZjqI6D6iOIF2kspQS6Bcq22dkW5LtyhadekO/T+KT+fvfkVF+97TctCG/NmLOCOM+6jub6Zg87cpyB93Hbq3USbWqe1silLtCnKzSffyb8nXluQPkQ6wxPeGRvaDuKTnCWgfatX/Kp9lcKz9KeIdNyUiVOZMnEqp293UUuyLCLSXXdf8FDWKnQAkaYoD17xBIl4op2jOue7j3/Iu/3Hz38mlUoVpA+RzjImgAmsj/GvoeS4B6mC3IO6Um3tbEW2pyu4bRfu6I7Tt7uIKROnMnL9Ed2OX9OuSXfo/VN+pkycmnd7PBZn8bx6Bi7XetNSKpXC48mu//w6Yz6Ni5oYtvryeL35v56uGVDNormLc7ZX1YVz2hOR3k0JshRE+o9X46LsKWnKZciFiFSuZCJJLBJvZ1+KukG1JJNJHrryCR67/jkaFzax4lrDOPHGoxmx9jAuO/A6vv1oCl6fl0DYz+l3HM+We2+S09Z+p+zOg1c+TrSptVIdrAqwz0m7djzWZBJjjBJqkQqnBLkHdWaZ5M5Wm0s9Fnjk+iOy+u+MdOU4nVx//tYk9hlwZEErySJdofdPeVg4dzEer4dUMneYgz/ow+f38c+/3M3zd7zWMob450nTuXCvqxg0dCCzp84lmUgSj8aJNEa48rAbuHn8law8Kvtu/oPO2pv5sxbwwp2v4Q/4iEcT7HDoaI64+MClxjjzh9lc/6d/8dmbX2GMYcu9N+bPtx1L/yF9d6GF3sImZ0HkVcBCaAeMd4VShyQ9QAlyL9dTiXJ7Qy1UORaR7qodWIPX5yURyx1rvOKaw2iqb+a5f72SU2WORmLM+nFOTmIdjyZ48uYXOe3fx2Vt93q9nHTTMRx5yUHM+nEOy44YQt3A2qXG11TfzMlbnMviefXYlLOM9HvPTODHL6fxn6+uVzW5gqWa/geLL2/dUH8NtvZMPNWHly4o6RFKkIuoveS0I0ljZ6rNS3p+JdwolxlrocYgi0jvEQj62fvEnXn61peyZpkIVgU48pKDmDt9Hl6/F9oOw7C0JKyZUskUs36c025/zfXNPHTVk0wYO5FAOMCux2zP4RcdSCDoz/v8Nx56l2hTNKuvZDzJvBnz+fiVz9lk5/U7ecZSDmxyhpscR7N31F+DDW6L8a1YkrikZyhB7qVKNeRCia0siW56k646+spD8Xg9PHXLWBKxBLUDa/jj3w9ns902pLkxQiqRZ5YJ4/7XJkcOhgNstOO6efupX9DAiZuczeJ59aRSluaGCE/c8DxTJk7lyhfOy3vM1EnTiTRGc7YnYkl+mTxTCXKlirxCzpsHgBREXoKaY3s6IulBSpCLoJDJaWePqaTKcVtKrkWkPV6vl2OuPIyjLj2YpvpmavpXt0x5Fa4OsffJu/LMLWOJZFaYw0G23m9Txj35YUsC6wv4qB1Uwx5/2jFvP2Pvep3mhgipjGpwLBLn87cm8eOXP7PyOrlVw5HrjSBUEyLSEMna7vN7GbHO8G6fu5RKsp3tdgn7pLdQglyhlpZ0d3aIhkgxaeEN6aqpX01j0vjJDFq+PxvvvD5en5faATU5zzvmykPpP6SOR//xDIvnNbDKeitxwvVHsfZWv+GNXd7liRtfoH5BA1vtswkHnbkP1f2q8/b39fvf5cy3DODxefjx85/yJshjDtqSey96mHgkRtKtZPuDPoautjzrjVm7m1dASib4W6i/Ps8OL4Tyf8CS3kMJchEoORUR6Z5kMsnfDruJ8c9OcKZN83qoqg1x3VuXssLI5XKe7/F4+N3pe/G70/fK2bf9oaPZ/tDRWdu+eu9bnrrlRRbMWsgWe23Mbv+3A+GaMCuPWpEPnv8454Y/m7IMXW35vLGGqoLc8sFV3H7aPYx/9mO8Pg/bH7I1/3f177WwQwUzvhWxNSdDwy1A+v3gh5o/YXwjSxma9AAlyBWms8M3lJxXnt74wUoLb0hnvXjn67z/3MfEMqq5kcYIlxzwD/716T863V7j4iamffMLQ4YPZvwzH3H76fcSa45hLXzzwXc8d/vL3PrR1Wy047o8cPnjWcf6Aj5WXHMoq2/cflI0aPkBnPfQXzodl5Q3T80fsaEdsM1jAYsJ7Yzxr1bqsKQHKEEuot6U4PQUJVAiAvDc7dkzVoBTxZ0+eSazf5rLsisN6VA71lruufBhHrv2WXzu3MbJeDJr6ehoc4w50+Zx/2WP8dy/XsG2WVZ61fVH8Lex56sa3EcZ30hM7YmlDkN6mBLkClPo4Ru9sVpZqUq92EtP0Acf6aj2Vs7zeAyxSO4Y4fa8fM+bPHHD88Qi8XbbBIg1xxh79xtEGiM5U8PNmDKLcG2ow32KSOXT7OVSFlLzfu9Uj+MfQvzD1sci0idtd8jWBEK58w7XDKhm2OodX8nskb8/nXcKtnwiDbnJMTgV5nm/zO9wM10niAAAIABJREFUnyJS+VRBrlCFqhz35mplpdHNnSKtDjhtD95+bDyzps4l0hDBH/Tj9Xk45/5TOjXUYdGvizv0vEA4wJBhA/nlu1k5+1LJFGPvfoNoU5RNd92QdbddS8MtRHo5JchSFnQTl4hkCteEuW3C1bzz2PtMfPMrlllxMLv8YTsGDx3U4TbisTi1g2pZ9Gv9Up/bb0gdh51/ADcef0fW2Gef30sykeLhq54kHk3wzG0vsdFO63Hho6drCWmRXsxYm2+VmPw23nhjO2HChCKGU7kqtepXbnErQZbezBjzsbV24+60of8Pd9yF+1zNhJc+Ix5tM/bYgPEYbLL1758/6GPHI8Yw/DdDufei/2EMJGIJkokUqWT2TXuh6iBn3H0i2xywRU+chnSSTTVBchp4l8N4+pU6HCkzHf3/sCrI0iXFSqyVGItIIUz79hc+fuXz3OQYwJKVHAPEowlef/Adnq2/n72O34mZP85h6pc/c92xt9O0uDnruZHGKK/e/7YS5DJjrcU23ASN/wHjAxvHhnfH1F2KMYFShycVRglyN1X6WN5yjFNVZJG+6edvfuHLd76m35A6Nt1tA/yB3Jv0OurHL37G5/cSa176c9PisQQAgVCAldYctsQb8/wB/fksN7bpEWi8C4g4q0EDNL+ANdWYugtKGZpUIP2GS6dU+gcCESk/1lquO/Z2Xn/oXYwxeL0efEEf/3j94rxLO3fE0NWWb1n2uSM8HsNGO66btW3UNmvmHWccqg6y01HbdSkuKaKmfwNtPxFFoOlRbO3ZGNP1D1zS9yhB7qZizDzQV5POlmnd4h9mPVYlWaR3e+Phcbz5v3FZq+ZRDxfufTX//f6WTs8YkUwkmTR+Msaz5OOMx2BTlmBVgGBVkJNuPiZrvz/g5+Inz+CCPa9qaRdj2Pmo7dh01w06FZP0gNSCdnYkwEZACbJ0ghJk6RRNRSaVTh+8ys9zt7+cd67ihXMWM/XLn1l51Eodbstay4X7XM1nb07KWYkvU7AqyNb7bkoynmTkhiuzxx93pKZ/dc7z1tt2bR6a/i/GPfkhjYua2Gin9VjxN0M7HI/0IP+6EHsvd7tnCJiano9HKpoS5AIpZOW4XIYv9HT/mupNpG9qb4U74zFLXP0un6/e+5bP38qTHBsIhgJ4fB4S8SSb7b4Bkz+ewswf5vDukx/w+Rtfcca9JzFgGWfWg3kzF7Bo7mKGrbEC1XVV7HTkmK6cmvQgU3sWdv4hTrWY9PCaEKbuIs1bLZ2mBFm6RJVjqTQawlO+djhsa6Z++TPR5uwlpH1+L6tusHKn2vryna/zJ9UWtv3dlmy8y/ost8oynPXbS2luiLTs/uT1Lzhjh4u5/u3LuPLQG/jszUn4Al4Mhj/+43B2P3bHLp2b9BzjXxMGPYZtuA3in4N3BKbmRExAw2Gk85Qgl5FyGL5w+nYXMWXiVEauP6JklWwlLCJ9y+5/3JHXHxrHT19No7khgj/gw+PzcM4Dp+D1eTvVVv9l+hEI+XOGbASrgvxm89XY7uCt+O8lj5CIJ7P2J+NJ5vz0K2fvfDk/fP4TiViiZYq4f/7lXpZfZTk23GFU905Uis74VsX0v67UYUgvoARZ+rxSD2WRnqEhPOUrEApwwzuX8d4zE/jklc8YuHx/dj5qO5ZZcUin2xp9wOb887R7crZ7vIYxB22JtZapX03LOz+ytZYpn00l2SZ5jjZFefQfTytBFulDlCCXoVJVjqF1/DNAdb8qRq4/QomjiBSFtZZJ4ycz68c5rLrBCEbvtxmj99usw8e+++SHPHXT89QvbGTrfTdjv1N2p6Z/Nde8ehGX7P936uc3gIGaftVc8OjpvPXoeO45/yEWzcu/9HQykXKWlm6TIAPM/mlut85VRCqLEmTps8rtpkjpGaocl4dFvy7mjN9ewswf5mAMpBIpNtxxXS545LQOLRBy13kP8tTNL7YMpfhl8kxeu/9tbv/076yx8UguevwMbjnpTn6aNJ1wbYiX73mTV+9/u92ZLYLhABvsMIoJL0/s8jlN/Woad5x1P1+N+4Z+g2s58My92e3/fqsbxEQqkBJkAcpj/LOI9B3/OPo2pn39S9ZY4E9e+Zz/XfM0vz//gCUeu2D2Qh6/4XniGTfjxSJx5s1cwNi7XmfUNmtx+piLWpLhad/MYNq3M1pXV8tgjGHI8EGMOWgrDjl3X45a/c8smrs453kzf5hDtDlKMBzMG9Mv38/kz1ueS6QhgrXQuKiJf/7lXmZPncvRVxzakUsiImVECfISKFns3fShQKQ0Ik1RJrz0Wc6NctHmGM//+9WlJsjffPg9/oAvK0EGiDbF+PDFiXz86hfEmttUivMkx+AM1Whc1MTTt7zIUze/gG3necZAU30kb4JsreXG4++guT6StT3aFOXx65/noLP2obquaonnJCLlRQmyZFGSKCLFloglaC9jjbWZ6i2fAcv2I5XKPd54nGrwB89/0m6im3uQU+1N83gMmNzw+i/Tj/5D6vI2ceuf72Li61/m3ecLeJk5ZXanp6sTkdJSgpyHxqb2LXpdRXpWTf9qhq2xAlO/nJa13evzsvmeGy31+DU2WZXBQwcyfXL2sAmbsqyy7kr8/PV05s9sb9nhVsYYbJtMOl/iHQj5OeW2Y/OOJf7p6+m8eNfrOe2kxaMJBg8buNRYRKS8eEodgIiI9D1/vetEwrUh/EHnhrxQVZB+Q+o6NF7XGMOxV/8+b8L6n7Mf4Hd/3ZtgVSBre7AqwOZ7bMTKo1YkGA4wYu3hhGtDHYo1VBNive3Wybvvk1c+p71ytcdr2GrfTek/pF+H+hGR8qEKch4amyoiUlxrbDySu76+kef//QrTvpnB2luuzk5Hbdfhsbofv/JZ3u3Ga0jEEpx25/Hcftq91M9vwOvzsMdxO3HsVb9nzs+/Mvbu11kwexG+gI/vP/1xqX3FIjHe/N977PKH7XL2VferwuPNX2tabaNVOOOuEzp0PiKVwNoIxL8GTz+Mb5VSh1NUSpBFRKQkBq8wkCMvPqhLxyZiCWye4RBYSMYTbH/oaMYcuCUNCxupqg3j8/sY/+wErjj4epKJJIl4kmA4gPGY/O1kiDREmfrlT3n3bbXvptxy8n9ytgfCAS589K8EQoE8R+X3/nMf899LHmH21Dmsst4Ijr7iUNbcbLUOHy9STKmm/0H9lYAXbALrWxkz4F8Y73KlDq0oNMRiCa594xJVj0VEysivM+bz6v1vM2TYYEJVuTNKJBNJNt55fQA8Hg91A2vx+X3EY3GuPuJmos2xltkzos0xfH4vI0atSP9l+jFs9RXyJrShmiArj1opbzzVdVVc9uzZ1PSvpqouTFVdmFB1kHPu/zPLDB/c4fN69f63uPzg6/ju4x9YPK+Bia9/yRnbX8yk8d92uA2RYrGxj2HxlWCbwTYAEUhMxi44ttShFY0qyCIiUhEeuOJxHrj8cXx+LwCJeIJAyE8sGsfr8+L1eTnppqOpG1Sbc+zkCT+QSqVytsejCbxeD4/OupNUKsUf1z2dX76b2ZJEe7weqmqr2PbALduNa71t1+aRWXfwxTvfkIwnGLXNWnmT9/ZYa/n3GfcRbcqewSPaHOOOs+7n+rcv63BbIsVgG+8FIm22JiHxMzb+Lca/RinCKiolyCIiUva+HPcND/3tSeLROPFo6/zH4VCA/U/YmaraMNsfOpphqy2f9/hAyO9OL5dr+uQZWGvxeDxc9/al3HbqPbzz2HhSyRSb7rYhJ918zFITXn/Az4Y7jFrqecRjcd57egI/T5rO8N+swJb7bEq0KUr9gsa8z//h8/xDO0R6VGou+Vfa8UJqfo+H0xOUIIuICOBUZJ++5UVe/M/rJBMpdjhsa/Y/bU/C1R2b7aGYXrzztdzFP3AW8Nh4p/XZeKf1lnj8qhus3O5UbKlkiumTZzB8jaHUDazl7P+ezNn/PbkgcWdaMHshf97iPBb9upjmhgjhmpBTIX7nMnx+b94EfvBQTREnZSA4BuJfAm0X4ImDP/8ML5VOY5BFRARrLRfufQ13X/AwP02azvTJM3job09y+rYXkkwkl95AkTXXN7e7+Ed6SeklMcaw3MrL5t3n8/varS4X0q2n3M3c6fNobnC+qm5uiDBvxnz+eeo97HvKbgTbVKmDVUEOv/B3RY9LZGlM1aHgHQJkvkfDUHMqxpM7pKk3UIKc4fTtLmqZ2k1EpC/55sPv+eKdSVnjYGORONMnz+T95z4uYWSObQ/cklB17jCHRCzJemPW7lAbu/3fDgTDuTfhhWtDrLT28JbHqVSq3Wpzd7z39Ec5HzaSiRTjn/2IIy4+iL1P2oVgVZBAyE9N/2r++PfDGXPQVgWPQ6SzjKcWM+gpqPkT+NaGwDaYAbfiqTmm1KEVjYZYiIgIX78/OW+luLkhwpfjvmGrfTYtQVSttt5/M8be9TpfvvctkYYIHq8Hf8DHCTf+gZr+1R1qY+8Td+Htx97np6+m0dwQIRDy4/F6OPfBU/F4PMyYMoubTriDT1//Eo/XwzYHbMFJNx9N7YCaAp1F/qQ7mUjh8RiOver3HHnJQTQsaKDf4Dq8Pm+B+hXpPuOpw9ScBDUnlTqUHqEEGS0t3RvoNRPpnkErDMQX8BGPZg81CIYDDBk+qERRtfJ6vVz+/Dl88PwnjHvyQ6r7V7Hr0du3O/1aPoFQgBveuYz3n/uYiW98yeBhg9jx8G0YuNwAGhc1cvIW51I/vwGbsqSSKd783zgmT/ieu76+Me+qfZ01ZNggZkyZnbPd4/UwecIUfrPpagSCfgYuN6DbfYlI9yhBFhERtthzIwKhAJGGSNZYX6/Py28P26agff345c/cf9ljfP/JDwz/zVAOO/+ADi2I4fV62XKvTdhyr0263LfX52WrfTbNqYi/ct/bRJtiWYuGODfvzeSKQ67n/IdP63KfacusNCRvgmyM0/+IdVbs1PRwIlI8SpDR0tKVTNV/kcIIhAJc//alXPq765jx/UyMMQxcfgDnPXRq3nmFu+rbCVP465iLiEacZHTmD7OZ+MaXXPT4GWziLvBRCj9+8VO7N/uNe+ojvnjna0aNXrNbfWy518ZMGv8tseZ41vZkIsVLd7/B+Gc+4taPrmbAMv261Y+IdJ9u0itDullQREph+BpDuePza7n7mxu544vruPe7m1ljk1UL2sftp91DpCnaUqm1FqJNMW79810F7aezVl1/ZfyB/DWjRDzBaw+83e0+dv7D9gxcbgD+oD9nX7QpyvyZC7nr3Ae63Y+IdJ8qyBlUdaw8qv6LFN4yKw4pWtuTJ/yQd/vMKbOIRWJ5l3oupMbFTbz/7MfEIjE22WV9Bg91xlfv8Ptt+M+5DxDPM92bAVKp7s9qUVUb5rYJV/PwVU/yyN+fydmfTCQZ99RHnH5nt7sSkW5SglxGij1cQEmkiJRav8G1zJ0+L2d7IBzA104Ft1A+GvsplxxwLR6PwVrnRrwjLjmIg87Ym6raMNe/fRnHbXBGTjIcrAqyw6GjCxJD7YAaDr/oQB6/4XmS8dxZQ/xB/VkWKQcaYiG9wrVvXKLEX6QC/O6MvXJuRAuGA+x1ws54PMX7k9RU38ylB1xLtClKc0OESGOUWCTOfRc/wvef/gjAyqNW4uwHTiEQDuD1O1OsebweRm2zFqO26d7440yhqiAb/nbdnGncAiE/u/xh+4L1IyJdp4+qZaRYwwV0I5uIlIt9TtqV+TMX8MSNL+D1eUnGE/z28G04+opDi9rvhy98gsebm4DHo3Fe/u+brLrBygBsd9BWfPD8J7z1yDjAmcnii7cncfURN3P2fX8uyHRvAGfcdQKnbXsh82YuIJVMAYY1N1+Nw87fvyDti0j3KEEWEZEeY4zhmCsP49Bz92PW1LkMGTaowwt9LM33E3/k2X++zIJZC9lir43Z4bDRLWOa49FE3tXxUilLPNI6q8SPX/zEu4+/TyLWOvwh0hjlvac/4uv3J7PWFmsUJNYBy/bnP5NuYOIbXzHrxzmMXH8Ea2w8siBti0j3KUEuQ4Wu7OpGNhEpN+GaMCuvs2LB2nvp3je4+YQ7iUfjpFKWT1//gqdueZEbx11BqCrIxjuvl3elwFB1kNEHbNHyeMLLn5NMpnKeF22K8dHYiQVLkAE8Hg8b7jCqYO2JSOFoDLKIiFS0SFOUW076D9HmWMsNdpHGKFO//Jnz9/wb0779hQHL9uePfz+cQDiAx+vBGCc53nq/zdhg+3Va2qqqDeHLs8SzL+Cjul9Vj52TiJSWKsh9iCrHItIbffvR93nHF6eSls/e+IrjNzqTs+49mb1P3JX1xqzDq/e/TbQpytb7bsa6266VNa549AGb88/T7s1py+MxjDl4q6Keh4iUDyXIBaChC+WvVK9Rat7vAfAMur9H+xXpS6pqw0ucpzjaFOMfx9zGZntsxIi1h/N/fzus3efWDazlkifP4NLfXduyLZVMcc4DpzB4hYEFjVtEypcSZBERqWirbrAyA5apY1ZjNO+NeGnfffwDa2+59DHEG+24Ho/OupPP3vyKVMqy3pi1c6amE5HeTQlyN2j6tPJXqtcoXTkm/mHWY1WSRQrPGMOVL5zHmb+9lHkz55NK5pmtIpkiVN3xJDcQCrDJLhsUMkwRqSC6Sa8bpkycypSJU0sdhohInzds9RW4f+ptHHHxQfiD/qx9xsCAZfuxyrorlSg6Eak0qiB3w8j1R2Q9VuW4/JRqirt0pViVY5Ge4/F4OPTc/ahf0MCzt72E1+fFGEOwKshlz55TsEU+RKT3U4LcBW2/ttfUPyIi5cEYw3H/OJL9/rwbX7zzDXWDa9lwh1E5yzqLiCyJEuQCaFtJlvJTquq+KscipbHMikPY4bAhpQ5DRCqUEuQu0Mp0IiIiIr2XbtITEZGKt6Tp3UREOksV5G5Q5VhEpLR+/OInbjzhTiaN/5ZAKMDOR43hj38/nGBY8xaLSNcpQRYRkYo0d/o8Tt36AprqmwGINkUZe9frzPxhNle+cF6JoxORSqYhFiIiUpGevvVF4tF41rZYJM7nb01i2re/lCgqEekNVEEWEZGK9P2nU4nHEjnbvX4v076dwfA1hvZYLN9OmMLYu16nub6Z0ftvzuZ7boTXq6nlRCqVEmQREalIq2+0Cp+/9RXxaHaSnIglWHHNYT0WxxM3Psdd5z5EPBonlbKMe+pD1t12LS59+iwlySIVSkMsesDp213UMiWcSKmk5v2+ZWU/kd5grxN3wR/0k7lAXiDkZ4MdRjFsteV7JIaFcxfxn3MeJNocI5VyZtKINEb5/O2vef/Zj3skBhEpPCXIIiJSkQavMJCb3ruC9bd3Vsqrqguz5wk7c+Gjp/dYDBNf/xKvP/fL2EhDhHcef7/H4hCRwtIQiyJquyS1FhaRUmipGsc/zHqsVf6kN1hpreFc88qFJes/WBXMqmCneTyGqrpwzwckIgWhCrKIiEgXbbTjupg8GbI/6GeXo7cvQUQiUgiqIBeRlqSWcpCuFKtyLFJ4gVCAK54/l/N2vxJrLdZCMp7g6CsPYfWNRpY6PBHpIiXIUnBKxESkL1l7yzV4ZNadfPLK50QaI6y//Tr0H9Kv1GGJSDcoQe4BqhxLOdAHFpHiCQT9bL7HRqUOQ0QKRAmyFIxuBhMREZHeQDfpiYiIiIhkUAVZCkY3g4mIiEhvoAqyiIiIiEgGVZCl4FQ5FhERkUqmCrKIiIiISAYlyCIiIiIiGZQgi4iIiIhkUIIsIiIiIpJBCbKIiPQ6jYsaqV/QUOowRKRCaRaLHnb6dhcBWn5aRKQY5vw8l6uOuJmvx08GY1h5neGcee/JjFh7eKlDE5EKogqyiIj0Col4glO2Pp+vxn1LIp4kEUvw/ac/8pdtLqBxUWOpwxORCqIKcg9JV44/f2tS1mNVkkVECuOD5z+hcVETqWSqZZu1kIgmeP3Bd9nz+J1LGJ2IVBJVkEVEpFeYPXUuiVgiZ3ukKcqMH2aXICIRqVSqIPeQdKVYlWMRkeJYdcOV8fq8xKPZSXK4JsRvNlm1RFGJSCVSBVlERHqFUaPXZOT6IwiE/C3b/AEfg4YOZMt9NilhZCJSaVRB7mGqHIuIFIcxhqtfvoAHr3iCl+99k2QyxZgDt+Dwiw7EH/AvvQEREZcSZBER6TWC4SB/uPwQ/nD5IaUORUQqmIZYiIiIiIhkUIIsIiIiIpJBCbKIiIiISAYlyCIiIiIiGZQgi4iIiIhkUIIsIiIiIpJBCbKIiIiISAYlyCIiIiIiGZQgi4iIiIhkUIIsIiIiIpJBCbKIiIiISAZjre34k42ZC/xUvHBERHq1lay1Q7rTgP4/LCLSLR36/3CnEmQRERERkd5OQyxERERERDIoQRYRERERyaAEWUREREQkgxJkEREREZEMSpBFRERERDIoQRYRERERyaAEWUREREQkgxJkEREREZEMSpBFRERERDIoQRYRERERyaAEWUREREQkgxJkEREREZEMSpBFRERERDIoQe5DjDGHGWNeLkK7yxpj3jbG1Btjri1w2+caY+4sQDujjTHfFiKmpfRjjTGruj/fboy5oNh9ioiISGEZa22pY+jTjDFTgRWAFay1v2Zs/xRYH1jZWjt1KW2MAH4E/NbaRLFiXUL/FwAbAPvbPv6GMsZYYDVr7fedOGYq8H/W2leLFpiIiIh0mCrI5eFH4JD0A2PMKKCqkB0YY3yFbK+NlYBJPZ0cF/mcREREpI9Sglwe7gOOyHh8JPDfzCcYY3Y3xnxqjFlsjJlmjLk4Y/fb7r8LjTENxpgtjDFHGWPGGWOuN8bMAy52t73rtrelMeZXY8xw9/F6xpgFxpjf5AvQff5HxphF7r9butvvceM90+37t3mOvccdbvCKOwzjLWPMShn7b3TPabEx5mNjzOiMfRcbY+53fx7hDmE4xhjzM/C6MeZeY8zp7v6h7v4T3ccjjTHzjTEeY8wYY8z0jHbPMsb84sbzrTFmB3e7xxhztjFmijFmnjHmEWPMwPZeOGPMGcaYmcaYGcaYo/Oc9+Xuz4ONMc8ZYxa6Mb3j9nUfsCLwrHv9znSf/6gxZpZ7vd82xqzdpt1bjTHPu/F/YIwZmbF/bfdazzfGzDbGnLu0czPGhIwx97vbF7qv8bLtnbeIiEhvpgS5PLwP1Blj1jTGeIGDgfvbPKcRJ4nuD+wOHG+M2cfdt437b39rbY21drz7eDPgB2BZ4IrMxqy17wH/Au41xoTd/i6w1n7TNjg3iXoeuAkYBFwHPG+MGWStPQp4ALjG7bu9YQKHAZcBg4GJ7jFpH+EMJxkIPAg8aowJtdMOwLbAmsDOwFvAmIztP2Rcj22Bd6y1qTbnswZwErCJtbbWbWequ/tkYB/32BWABcCt+YIwxuwC/BXYEVgNyPlwkOF0YDowBOf1OBew1trDgZ+BPd3rd437/BfdNpcBPiH7eoHzHrkEGAB8j/v6GmNqgVeBsW78qwKvdeDcjgT6AcNxXuPjgOYlnI+IiEivpQS5fKSryDsCXwO/ZO601r5prf3CWpuy1n4OPIST6CzJDGvtzdbahLU2X7JzMU5S9KHbX95EECch/85ae5/b1kPAN8CeHTw3gOettW9ba6PAecAW6eq1tfZ+a+08t+1rgSCwxhLautha2+ie01vA1sYYD05ifA2wlfu8bd39bSXdPtYyxvittVOttVPcfccB51lrp7uxXgwc0M5wjgOBu621X1prG93nticOLA+sZK2NW2vfWdKQFGvtXdba+owY1jPG9Mt4ypPW2g/dMecP4HzAANgDmGWtvdZaG3Hb+KAD5xbHSYxXtdYmrbUfW2sXL+F8REREei0lyOXjPuBQ4CjaDK8AMMZsZox5wxgz1xizCCfZGbyUNqctaae1Ng7cA6wDXLuEhG0F4Kc2234Chi6l/7yxWGsbgPluuxhj/mqM+dodTrAQJ2lf0rlltjUFp7q+PjAaeA6Y4VaJ8ybI7g10p+IkiHOMMQ8bY1Zwd68EPOkOM1iI82EliVP1bWsFsq9x22uU6e84ld6XjTE/GGPObu+JxhivMeYqdyjEYlqr25nXZFbGz01AjfvzcGAK+S3p3O4DXgIedoeLXGOM8S/hfERERHotJchlwlr7E87NersBT+R5yoPAM8Bwa20/4HbApA9vr9kl9WmMGQpcBNwNXGuMCbbz1Bk4yVWmFWlT5V6K4Rn91uAMp5jhjjc+E6caO8Ba2x9YROu55dP2vN4CDgAC1tpf3MdH4gw/mJi3AWsftNZujXNeFrja3TUN2NVa2z/jv5DbblszM88L55rkD9ip5J5urV0F2As4LT3uOc/5HArsjTNkox8wwt2+pGuSNg1YZQn78p6bW9W+xFq7FrAlTiX6iHbaERER6dWUIJeXY4Dt3a/r26oF5ltrI8aYTXGSqLS5QIr2E6McxhiDUz3+j9vvTJwxwvm8AKxujDnUGOMzxhwErIVTre2o3YwxWxtjAm4/71trp7nnlXDPwWeMuRCo60S74CTEJ9F6s+Kb7uN3rbXJtk82xqxhjNne/UAQwRlrmx6nfDtwhXFvIjTGDDHG7N1Ov48ARxlj1jLGVOF82MjLGLOHMWZV97ovwqncpvucTfZrVwtEgXk4s5lcuaSTb+M5YHljzKnGmKAxptYYs9nSzs0Ys50xZpQ7Bn4xzpCLVL4OREREejslyGXEWjvFWjuhnd0nAJcaY+qBC3GSs/RxTTg3aY1zvz7fvAPd/RnnBrAL3KEVfwD+YDJmkMhofx5ORfF0nKTtTGCPzHmbO+BBnARyPrAR8Ht3+0s4N5RNxhmiEGEpQ0PyeAsnqUwnyO/iJJZvt/P8IHAV8CvOUIVlgHPcfTfiVOpfdq/1+zg3O+aw1r4I3AC8jjN84vUlxLgazs1zDcB44DZr7Rvuvr8B57uv3V9xhtj8hFOhn+TG0CHW2nqccex7uuf2HbBdB85tOeAxnOQBRhgIAAAgAElEQVT4a5xrel9H+xUREelNtFCIFJ1xpoKbbq09v9SxiIiIiCyNKsgiIiIiIhmUIIuIiIiIZNAQCxERERGRDKogi4iIiIhkyLc6WKcFPGEb9tWBx823U+7MWqk81en0c9pWrtOPvd6WTamQE54nmnA3uLNOedznJBK5baXb97SZMja9PZnKjhHAtIkp4K6PEIu3Gxvu6sU24bRjggHnsXuM8bmX1picY3K4z7E+p30Tz4gt3Xe6nfS/Sfc56X5SGW277dj0IS3n7Pwb7+dMd+yNtl63ZNB5sjfmbkvvSncXcH7wRVr7sV6T9VzrXkaTyj6mpc2MY0zSZv3b0k/Q48bW2o+JOedq/e71iTrXOFkbzG4r4/2WDLjtuPGm3MemzXvSZnxE9MStu80JJhl2z7nRvW416TZaj0lfw5Z23X/T/aXbTB8L4GvOPvdE2JPVVspv8lwD972Yfr3dxzbovFeN2336+gF43LeIicScdsPOezT9+9RyPTPeb+n3YMuMy+kQ3MeJqvTvSus18C+KOvvqnNfDt8BZtDH97ZRxf2/SrxeAt8GJyQZ87rVwO3J/p1PV7msbz7gG7nPSMbZ9L6V/57Ne08aoex7uc/zu73b699fvc9ts/T31RJ3rkXKvj6c55h7rc6+BN+s8M/cRi9NsG4nZSEfmrBYRkTJVkAQ5bKrZPLBLS1KYao44OzLySZP+QxR3k1r3L691Ez3jy/7DBeDt5yxelvp1nrsr4bbl/PFMxZvcYzL+Wlv3j366c/ePZvqPtCXdX+vfL5v+wxt3/hB6/LVO+9EG53Ewd/2MVDTqxuL253HPnYh7TLXTpvu8zHNtTcidfr2DBmadR2pRxgq/6RzU/QPvCYey4mi5rukECjDVTt+mynluctYcd4/Tf2wjZ1XiwPxIyzFNQ6sAqP7R7Tv9MrhJSMNIZ2ri2q/ntxwTXd7Z5mtyPxSkExU3EY8s57QZnNd6DdLPSQad6+af50757H6AaUl6o61Jm2e6G/8Ap7/UT9MBSKy/tvNc97plJizNQ53XsOp7570TX85ZpTmdyKaTxHhd62vrX+y+pgkn/gVrO/31/6beOZ9lnfOxGR++AvXOufvcJNE0Otc0OnxA1rVZtGpVyzH9vnfet56IE8P8dZ3Y+k1x4k8n1776WMsxyWrn9yMy0Elyq6c7bczd0FlAz9/gnNf8Ua2xBec5Pw/5zGln5hbOsct+6MTUMMx571TNbb3WTYOc1yVR7X44aHIT/Frn8eI13dc61pqFrvJY3G3feb8Nf3kRAN7ZCwGIrbwMAFN3b33vjnzUeZ/9ur5zjQNu/NUznOs4f033uRlpZnie87o0LO/EmP5AkahyP9C4h8SrW/9/sNJY55p6FzmvS/Nw532Rfh8sHOlck8bhrccM+cR9/ddw+hn6ltPGopFOB3O3cc73N9c1tByzaJ3+APSfMJvxP+cshCkiIhVGQyxERERERDIUpIIMOF/9u0MTTMz9+jSZ8V20W1U0Xo+7zz3Ml52jm0Cg5WdbHXZ+WJSuLtvs56Qr1RkLfqUrxenKanpIQssx6djSwzOyYnKPDTmVItPc5qtiny/jGG9WO54qt0IYiWTtJ+N8SFfP0xXINl/3t/Tb2NR6DVq+pibrvGzbr9zjGeeT/po8fS0C6eEfzrmnv9L3Z1z79Nf66a/d21aQk233Q2t1271+KTcUT8I9xv26P7Pimv7auuWjmVs5tm4siSrntfZnDjNJH+/2Y9zXNOV+C+BJn7qn9XxavjJ3r0XKnx7ykMqOI/OLcPdap88x5cv+ljxe4816HoC/wb0u6a/70+eTDsV9jdPDTZznZr/nbZsv4xMh9yv8+tZt6Qp1wL1OnsXOezO0wPm2wN/oXK/gr/6WY4ILnL4DC5xjQ/Oc90Fgcdz91+knsLD1vZN+nb1uhdjfnMp63DTfHfbU+oVFS2y+plBWbNb9/fEtjrjxhFuO8TQ578Xg4pQbS9JtyznG35Q7DCg434kzEcy+YD73i4N0Bdkbbd2frhx7GpzfKX+9G6P7LUFogXM+yXDraxJc5MQSmufJij+00Lm2vjn+rDadc3Mq+aaxOXu4k4iIVCRVkEVEREREMhSkgmytdSqp0dzqbCtP1r70mNr0ONx0xTUVaR2v6ltUn7UtPYY3XZnKutEuHUsinvVck75xJx1TKl2Rzag2tuxz229szGrD48sd55s+xrjjk9NjkltuuEvfbJRxPi3xpm+0S1ci02Og3X5TGf20dug+J/NGQcC617zlpj3Aun22VLnTlXD33DOrcmnpG89axhGnq6m0ubkt80sBt8KajihdlUu3kb5BLfNGxfTY32RVa6Uzsz9vc7rK3vr6pCvGbaNOx5yu3tqsGyLbPDd9rT3Zj1tuNCT3prX0jW/pim+6SptZWW6pjpvsqma63VTAmxVr5rmmq+Y2XZhOX89Ymyo3kHRvrIwMcq6bt9mpHDct48bmVrKbl2/tKD2WOTLEqZo2Lev0GxnsVJKbB6df29bXIr0t3jIG2R2TXOPGsYJTTU1EW2OLDXLaj/V3nzPIebLP/VYjOsj5dqV5udbYEv2rsvpLhNIVfue8IgNyb4hMX9Om5dznpt/6oex/4zWtL358sNOPz72WUffcjfs2a1rWrYwv13pM80zn971p+fT5OW00DXF/n4Y71yDVv6b1mCHOMeH+tTA/+3dUREQqjyrIIiIiIiIZlCCLiIiIiGQoyBAL4/fjXX7Z/2fvzZokydLrsM+X2NfcK7P2rl7R3TPdmBkAJEiCpPhAUTLS9Df0x/Qg04MeZHqgSJEAAQgCMMD0TE93V3XtS+4RkbFv7q6Hcz6/16NqaJQszWgl+85LVoT7Xf16WuW5556TH6oLB5BG+BZn+QE3lSusKCPYPKhWdtu980+wx1l9yr1TlVq0sbUZnZ6zLu+AWrOR9wk3cRubPsWBykA86UMuoRjRtunWDdZ/wUp5GIx1+/cmvT7ufXAPw3n+GvfegLVVuHBWXdmU0hAdo8oi9mDzltbRx/jNpZuQDb/orMXx8YBQtk37somzOEt2YGWVNHkoSw/tsS8rSiGivjtkVGrygCU9hlX2oQf+9OBSOHXWcNUTHkxbq9cwD3At8TziKcfTm7i+dbBGyuf4LhhOCu2st3A9Or3Ky6Q8tBjymeqMxPTSjSb4GXh9K9MWLRihbMy5VWs1hf857HPd0h6vdslxnKEv01vYao88L+jKKdZBMON64hxXznjIbIDrlT3PA5h2cuEIY2++wZhLJ2h/tY/nVzru52VK9Nuu8B0LKD/aKd/E9QEPo63aeZnqAM+y/hDruH2Add36NWzzqmdYO3owTkSk2UQ/1VZOPYEXO/i+fIWfoVvWUvv+hYiI7NfQl+jpCeq4hCVgle/n/sGdvEz88KWIiOxltzAetqPztbeETV5Sd7+iKmd4ltUeZRMT1Lvsoq8qfVG5hohI+RXn8Ar1tq7cOywiUprg3av03fPp/gb2dNES81N9dIr2xujTsoXnE16c5WU6P/J3yPPXzj/dYDAYDO8tjEE2GAwGg8FgMBg8XI/NW5pKNpu7w1RkjjOPPc1ZFQ3dWC39GpwlnGcJFim7p6lhtClTFjglM1WwbNO2lZnWoBDtmzLHM8c25mlaevBO2/MP2IkUkvSyjZQ9TSvLw0CmRYs4EXGMsQ5dDyiOZ+xqUBgnBrkxDjKVevAuoB1W5o0nHNNaTolOnSfWu2phvOWWC25YdvjdgM9HD+vxINmiQya57sqs27SlW2vCGdn6BX4u26gzHnlleDhPD9TFa03Jw70rMoZxy1mCBSyvzK4+h1Wb9nXcJYi8g3KrDu3iWGbNAJKwwuQ2TbyruGeq1nJpjaEcXVyrNevsm1reuTLlTpVd4vNRJpyH6gJafi3a7m/RWmvjGtuptDFmfT5x04WLKNucMQ0vUIJdHQN5GNC31Ms20iQ10VDT91LayWUj1zc9PKlBLjpP+nPNdMHQ/83BMesYlaPNd400BMazulNLw4RpmbHa+mnaY5uHEedvW6apLd6qyfo3Uh8z74xcoGtfD+BW+W7o9/peFQ4Dbhyy04O+XN/54c2VF86j78tmWYPBYDC8lzAG2WAwGAwGg8Fg8HA9DHJAhla1wMqqVh1zGFTJdI5GhaLKxGo8ctob5NdKqjml/Zmyqdl4UmzHDxfRPqiGVkMsyOgGqkXuOK1mboOm1nCXA5aNCvcWGOW0GBOdqU5Wo6A1dps6ab/+UOdCPx9Crxz0Eb/rW8Pp2FKOOdIwkS16aq3Vzs77W4dzEGQsSy2oslutH6DLVNZbREQNq6LX1F1nRYu2ZsQ+nruo6TjexXdkwHP9sta5ZN8mjt2OXzM2vMNghSE1vLzeoH5dSm5pphq+wqY1yrz6EjRqRlZYWVYRkeobMnnU6paVXd5g4tO2Y2l1LqMT9KnR1thrzNP2L9GBQliKWtpRA6468xKZXr3efuGeacnTV4uIlHN9N9qpM4rat44b/uxIRERWZHArtzF/pz+nvdg51sXwj5yeWC7Q/90taI97/5I7FcmOiIiMbmHNlIdOf6uxzQvIbSWa4x2cHVIDfYj5XMzcWYHGCc4KjO6gvurP72IcT/AeDT/DO3L+J259dH/Eejr/mu9Lip/1c+h7F21GnN91w6kfo5+TI8xp8xXjrx9wB6PE3w81X2cOXXSVevJlG89O2ezBJ7Tj+9itnWUL2uPBV9xJCjD3w/soe/dfPUXfHrrOXXyB+bgZ3JPsN29H0xsMBoPh/YIxyAaDwWAwGAwGg4drjZpWjWigYSBTx2apLjloNYufS6oJpCPBzRt5mdmHeyIiUntMVpPMobpYBBqw4btlsP7cxUJZuCZP/9PVILsaur6T9dW46OzmAe59jdPrKZ0qQmVtRfLQD3WxiO+DTUpfvuGAs7fGI5uaZgUZ2OQGmD1fxZgpq7mLa8qsZn0y7Xv4Phg71izlc1DNZHgLDJ86XZz+A5TpPHUM8tV9PId2V/WxbJ/OAMO7mM+t+CgvozrfIAH7nzACWHWj45u43nrh9MSLLWqdh+rKQNcEal5Hd9H31nPPkYJzmeyB2YvIAl/9BONQh43Y01QP7+HfXWqDJ7fRx3hW1LRqxLaISPWcIRi30E7vU/RtbwZW8/QPUYcftNJ5ApaxNOYaWuDe6c0668QzP//K9W2rinVduUR7o9sx68XanRxi3jqPnctI+y+eob/7oHaDF8ciInLv+DY+UwfeeuXCK8oDOmr8gDVZP8O9tR+xrpsvMc7Ssdu1WR1ijeehJVMG3lDjfPEl6m94GT2dvwCjumrcRzt/9gPK8L1s9cCYH0X38zKl5+j/gWAu4gs6yFzifZr80QMREdn/a8fshleYj/UednRUp7z3SzwD1TOvG+4NavzFQ4yHvztqt7F+1fGk8xjs9vyv3fNpPIQ7xdaPmIvq3z0REZH2I/T19PyeiIjs/odv8jJ3HuE9z168lmDu1q7BYDAY3k8Yg2wwGAwGg8FgMHi4nqjp1VrWxyciJxsRyj5CsjrD4dvXfFCTKiJSHYB5Wg+Kmk05PSu248f8TqfyTmj7Gvccejyt9on1BNRJp+pqQT3x+tXrt6qN2mCz1k+e4V4y4us3x6zTi0zejL3W9mKy3ScY13rD4cO/N/+oMdLKhHtzHpyDcVf9cqLMdR7NDSZvse0e/6qp0cjsr1bHj0tqQn1f2vEh2eARdbhrakDrdHKooozGI4uIxGSXp/vFqGl1EyhPcH3u+QbHIzC3qxbGU+VcrGsai4yftUs314sOr1FHvGxuRCjPGLvcdWXW1Rr7iGsLkIsyPQK7WB6SyfYkppMbGEdliDGXr/BTY4lXddS5bLkyqxZ1sFWwzGMQu1KalgpzMb7tmHe5fQ/tHaLs1g9gcs9+Rn9nynvHn7m1E1ODvHeAspc/Qb0t+nyPaUvc9Bj+yS31EsZYSxON8cb1W38C/+Lx0un++0PUf/7P0Xbz9Uco2wOTOvyAeun/we2gRCtog8++pob6DHPReQaG/M0/oYPIaScvU75qv7OP8Qx9WbWwdpKOo7fvz7DWYzLhl1/QQ5mbW8MPuEYP3A5M9+4+r6G+m3XU0fsEfS39Q2rRv7mXlzn/Cn3b+bYh2Tf/XgwGg8HwfsMYZIPBYDAYDAaDwcP1JOlVyhLd/UDSDpPGemSBPQ2yMqAZvV2DzbQp9cNtu6SrEU+/Nx+CQVangGSbSXpMnPN9g3ONc7mYpJdS6xqO6IjgMdVCVwnVOK/ug2ErPYNWU3XAsu3YLE2yS8hmhz/5FBceg2ELPyA953nR5klv6jhB1lcTCJd7GHue/iWej6tqj9nXkI4XyQ0wbuHQzfV6n6x2jf66Z9BxhmO0H1NDW3/ttJJJmWluI7Lm6hhCLWr9hAxp37GA3SWdQjL1yKWn7Qx1rNnX5ivXzmwf37Wfor/xkLpypvANP8Tzaz9ybifhBZ6/apHVM7vao465x2S6mafDjlFPmWOvU2scT3UHAT8qfS91jfeuu2CMmy/o+vEj5nryj7dYh6dBfoxxRDO6sTARrsPHromBy9ZWXqZ2hvVauuAaqkDr2nqCeyd38I60vnc7J8Er7Eh0u1iD6Rl2Ce6+ZBIdHUlmH+y48TA9LnwKDXJlQI3wr5hit8u1c+k0yJrMqE4d6kySNumS8QPT8tZuDtr/7ju0N/4YP//81yhDDXL7MZMBeV1EpPZX0Ck3qd3NkyH5Xt7v4/2Jxl7iZR/X0p12oY/qKKOfk5r7tVb+5hluoQvMjeeYHz23sH0DuuLFkdNu175DMuDuXbi0lL6BBrn5LT4PnjMl89Fv8zIHffyuWj97KZJ4v/cMBoPB8F7CGGSDwWAwGAwGg8GD/QfZYDAYDAaDwWDwcD02b0kqMhxLpBHNKl9IPC8ojZi+gHxAQzLyeGdKIrKh21pvqRThFNvJGioS8dCZWqzlccwiEszVPo5D42G2UA/lMd65cIyQZXQbtkQ5SMoDcIGGf/Tclnc6mYiP4Bhyj4QyjZhzkHrjSTUUReUfGhRyhG3mCq3N0gsXxpFLUzhPajWnn6ML9NEPMdGHGqq1HS3BUkoTOg+x5R1f+HONdkqvLtlZjc7FM2iltKCjrEVEJOBWdzCn1Zl+T0lKa9l5u51eMYpXw1E0WKXDePFw7A5bprS0U/vA5ALrof6CFne0OFMLPxGReomxwAw2qekhRm7H51HDdXfiTiU8ZbbdrHMr/QrPevfXXAeJWz2lHu5VCz19DnrWMZjS6uylk3+UX3M8XA+VAZ5HNGC7q7TQHxGR5D6kFEkTB9I0hrr3Fa3I+pjz85+6w4+VPu7dqeAU4Okf4POtIdZbfvjwysmaNOZaDzXmhyp56HB6k/aFc/e3dfPZLd6DsvUHsDyM3kB+lN6Dtdr5V+5g391HkHcMP0b/wxXWSuUS8zWnFGf+sQty0RCRyUHMMaf8zIjzWA9v5kXkcI6xxz3KZ7Zo5ahWgQ/weXjfSaFuhLBFvPw9zOXRHHKP8V3M08kfYw46v3WWh6MP0f/2YinBafEAqsFgMBjePxiDbDAYDAaDwWAweLgeBjlNJRuNRciIpWq15jHIAQ/SZcrkKYNMplIPo/mH2kIeatOYaI2R1v/V54fzfFs5ZYiVmVZ7NA0OWa2K/fCQrXmN0dMa+RxoBLVntZZuHDLMdMy0UlPmOLd0E8eayyItjl2t2shy+2xw3kuOK2ektS5l7f2wFP6MOF/aV52/xa4LRVDMDsDYRRP6kemBOLL40xtg/0o951emhwojPRzHzoZkdJUFLLSmToBkppWt11CTdQf0X1RxTHNIVl66YKxDztdyF+3rgcKSFwG92GN0Ma3BtK/KNoeMwV63HKtZ4kFBHfvkAGumfIkxz7fdvTkYRBJVcG/IWO3FPtorjVFmuu8dHFPGmAf7lBEtjfD9ukH7vMitN50fZXhjbmYoE6qWd5lH0GdaPtAAF3zUQ2xqj1eauL+T1QZPWdiA/m4hWfPcSq3syqSct9nuhhUhY90zsvmJx+zqoT9trzzmu8Bdjtk2d342zvJizPipFn0aapPomT3vt1q44nuiB0kbGm6TFe4NvPyYPDxG1yrn2IXKsK9z1zmtT+LIvYAGg8FgeG9hDLLBYDAYDAaDweDhehjkKII2loxRSD2peOxpziaRIdJrmxpkn3VOurRz04hpsr8BI6FDrT919E+glm1xUYMcbFqr+QyyWpoxICTokKlkX4MqI7Rjj56jXlnZ5pD2W8rkhttbhTpFRLJgUeijMrqyuy0+Qm/eckad9wZbtJpTVpgWceHMY527ZCJbZIXZV2Xnqme0JrtwoS11thNebgSPkOltvMZ8Bp4lWCVTJo27A7xXdwNq7Hp87trJ7ff0mQ0wPyHnNubX4cDNW8KgmJBrQ+3DKqfj4tyMnC68SsZVo4vLOo/UR6tOOho7DXIwZHkyrs0Onnt0ijFXqf8NVm69aUSyWvhljBmucjz6fbPpdKmlU9K/C8xb6yUt9hj5HLX4THuefr2Dd0F7G5AZ1djr8oCa9LlboxHDUJQ1T9mF0hDzV6UePPbs8aqUv4crXuNux4rhL9XziO3mRSSa4EPzNSlitV3jzk84XbGProxatlW5I6EMbMBl1zhGn2a77ldUQGs5bbt2iXs0sEbDX3wKV9lfXc8aXiJcHyWGjASJKxNPeDZgGfMnPpfGuvtAtr78Dq3xYvnuoCSDwWAwvFcwBtlgMBgMBoPBYPBwTS4WiaSDq9ztIVN21WdCp2TUNCJ5tS5UobrbwGNlQmUmVYO8LEYwq0ZY9bkiLrRC28kjmpW1ZRm/b6otzl0m9J5Z0ZnA75syxVpPNimOOdV4bI/dVtY6m7ngDBGRgK4VQQ0MXDqbvVUm1z9fFV0rArLqqVdnyDbjGZ+Hss2cv8kd6GNrZcc2aqxxS3XReVAI/oYa3cP17tQFUSz2GQwzK4ZvKMM6uUkdsNeO6lEVJe4spNQPL/fQTrnm5jrSOd6h4wHHOrsNpj9iOEc8dGrnyR0wrq0Z+js/AlOpcdgh+7hquleg0uMaoXZ3eJe66xEY/vERrgceQVirqn4YcxuSTdW+lYYYz/C+0y9HC+wuRNNloZ1whe+XHYy9VvbYUwZ2rOu4Fl2OC+PRiPDQ33zQx7LEl9VLvZdR4KT4y16Su2qZVZubrehuMkHZEU0l0tgT2nJtTvdRdpt91XAbha8Nzuiwsq6jjDLgIedkvsPQnLF7fyKy2dGKUeBdjKNE5lijzf3nE2kwEH+XpAzRUVZYtcexn1AfaHusiAx8/v2cuy1XLmyoNHZOIAaDwWB4/2EMssFgMBgMBoPB4OF6GGSRd+ruMt+fWEhnKbNLbWCw4fMblB3TluX64Y1j4SyTs9Frj0FWpjUq+u3mmmQh6+x7NPNasKlX1n6oi4V4msNA6dLg3Z/J4vrjcRVuzJWy6fV39F2ZbnXhUGZX50vrKjh5FJ1CpFTUhM879LiduPEs23QvUFcHrZZM4SK/7jS7yxZ1qjzdr3rPUBm+NnWeY9dOWuGzU3ZzTtaWTPWio+4FHoNcB9uYqKaauwHLtmpoWae33pYtOic0eW8HcxAtVZerrgbuWUcLtKns6JJ50WvqhxddbScvIvGMayYjs6uScc6NMrxaVkRk1S6+djr3ymYv3zFvJTpeqINDrqFWllMdRLxNlnys1CsrC7wJ1abjgxTGmDuE6KZGpje8vd5SXeq67vQ95fXQe+WyjfdT50m19UkZZWsXrlDInQIdl3o1K8usc5N4zhfKHOfuNWHx/dS16vTLTg8dz/Wz6tapgeZ6891m1Mkly7INk3WDwWAwvI8wBtlgMBgMBoPBYPBwfQxyEDgWlQzsW8yv3vdfimiDnVVmN9j4f73/eYP9VUY5Z6rD/8zfBNrft9pJit/79ypbuznWd7Wj33m65GK7b8/NZv/ze95isN/WhP7On/8ZvIsgvE5kOp7fRbP9v1kf1wGvGzkbv2lkqyRt8PbkBBuP8lq69K7XRncktD3Vpm9oxoPk7b7lDhF52Y3r6TvK6E/VNMdav9bpdXKjTJ7CuJle6BPVWbFPeb83v/f79rvGnP8s7k749WWbZZPNz17fNq6Jutls3uudfcjXUfo71rXBYDAY3isYg2wwGAwGg8FgMHiw/yAbDAaDwWAwGAwerkVikaWppLO5BGonxshmX/oQ8KBYOpkUy+o/GLOc9Pv5tYiHYxIN29AtWz0ckxYPA4l4Mdeb8AI7RIqH5zR4Io+jPT4t1B8yKCQdO1snvTdswT4s6aHfQYyDVXqAJ1157QYbsgxC60hPznDZi43OyzCAItADhfyZXuncuO3etM9DRbSN8+dURKQy5Jaxb4fFJuNZUrg34xmnaMko5ZW7rnZouuUczTXWFz9Ks7e3m6s9VLjYKoYs6PZ17UxDR94Oe8i34RM9rKWxx5TPeOtAD33pGPNDYHp9pQe73Bpd1zRwYl0YV1LBPc0TxlNXvPW2cUBR28sjoBmwEXnOflqvWrNlsX7W2OWU/XEH2ea/Bwu46R7KtCsHIiJy9jPMo9qUjX/i2f2dM5wnQNmzX3CcFXi1je+gvfm2syib3mC/GzyoNkbnkio+H/ziREREJkv3/IbfwQZv9IDhHp/dEBGRygnel9FHCLeZ/IGzL5w+gt3a5ecYY3mAtdreQb3DD3Df5NC9p9Uero1v47PKMDL+7tA+p213Sq/1CvNUGqFvg4/wLocrtHf1IQ+h3nSnGxO+N1ef8HDjdFdERPofo532P8Hvh/V/uJWXufgKc7qX3JBs8o4AEYPBYDC8VzAG2WAwGAwGg8Fg8HA9h/SCQIIoyoM01BatcIhFvyPbU7jmfQ5KjjEK2oyh1VAO2qGFbCfNGUv/pFXRAi7/moxxHjbi27wp052RMWbgSboR/qHsMPpCppOhHnn97GtQqbw1nnxelIDcmANtN/FDVPQenTe1toQdsMUAACAASURBVNODQzrnS8eaaZS1xntrX/JwFj3vt/Ts8TYPVulBrrDIEvuHwDS4QaOMc6uxpPg58tpJGUcdLjSwAxUr45rWlHF1z0ejq9WuLB87b4mXGgHsldH+sozaeSnrGHIcahkm4kUu85qyzvEUdSy2+Lp459OiOdumBZmLgGZYy0QDKdyrpuy1jlHZZa0rqdLmberWQSnfvMAzrZ6BMq6/ARMbz2knt+Vs+Co9dLR+uuC9uNY4Y7gMg0gaJ/6uQTFEJKY1XELW/OVLBsWs3N/WD8j6V8+wfitnyJTWOOnaKVlbRmqjb3i36ifoQ2XAGHT2tXrOoJCRez7Vga4VvgO6y6Dp9bRJXF95rPM5o8zHmOQ6Q1h0TWrwSpC5Mjofq0bE/mOulww3OXmOOdi6vHRlOMaoN84t+AwGg8Hw/sIYZIPBYDAYDAaDwcO1MMhBKZbo6ECyJnR4MSNYs7knvNQQDtX+qnF/bgVFhq/u4oJnn0A/WH1CJnTC6OcumOXo5AKfvQjqsIWI4TxYg4xlVgN7Fs7IRk+cHjJnYZUxvnOIoq/PiuPUusXFaSeXiImO7t/BMJ69wucj6DDFY5PyeOiYDJjaX3WoQe5w/t44ZipnupU51vkZklLchb409MaT7FAXXce8lV+TuVswDplsaqnvypQZpBGq/pZ90wCPKlk5jVIWEamdkl0mg5xWWMcCdZQZdFHquXWw2kL/q+doOxxQk85nsNjh9TdOu61zrTp25RRVVxqP0CeNKRYRqTLcIxyC/Ss10f9oXow4Dz2mOr5Am1kDfahdYM2UzvB9cg/fx3PHiFfPZoV50Ujo6jm1wZfoe/XGbl6mNFwWxt4ks1s9wef5IdZZ6XjoOkqteekUz1YuoSvfYUR3OEad8aKTF6n0GTH9CJrZ1h40s41vT9l33KtMr4hI7RjMbcIIbZ2vZbfCsWsstuta5fvnIiKy17mLOXh+jDqo+y/x/dz/6/t5megR3pPd9RHq47ML+LtjL9ZdHccglziXlUvMj+56JBXMgerBk5oX/nKM9zMboi/NmQYFkeEfgw2u9h273f413r94hner9Bjj2Z7g3lWdc9x3z6f5mJHpL98UdnMMBoPB8H7CGGSDwWAwGAwGg8HD9WiQ14mkFz0JJmDr0imZXo/ZVZ1vzgImGzo9skGh5+BQecPT9RdggVJlYMk+p2So/LqcXphD09ASdaKgy0VBs6ssM8tGZ2DnkiuPwRPJXTpEvJhZZbguUEa1ySn7HHjOClp/thEUovVGZLfS/sCV0Zhbda8gy53OwMqGvK6fRUQiMsUayZwNMA7VR69rYLvXXcfWLxjbXGlQk6mR1pybeRfX6w2ncV3sMPqZjKpqQsMSY6M1Mrnryqhzw3IbbZep981YRutYdx2jVxri3yk1oMGZRkHTYYEuE+Urz/WBeuFKC2VW1J4mdbLcK9Wg+lp1sLPKms928LOxg3WoMuJV3f1dqYx3qUzt9IQuHduc+1CdIlyZehtsY8yxTve1/9hBWDH+urTrdixivjeLA3xX5c7E7IDaV87x5IYbj7ptlC9a7DcjrQ/piHGEsvWS69vsRrVQtjQtOoWou0S4cut6ex/1Xd1HH1u/Qnt5rTtdEREZ3XF9a++DjZ3cwdxWetztILM7JFtf6znGP8gaHHO50CfdEVmTOV45Uw5pcudI46JXR9xx4S6Hjne248ZTO4Sue7qH8dS38Hl2pO3rOQc3b6s210G3I8HFRsy9wWAwGN47GINsMBgMBoPBYDB4uB4f5HpF1l9/JMsuWaAB9ZADx2oqg7M8oH6Y2kZlKKMR7p3ebuVlLj8H67fzLb4rXYFpXeyinfprMNWRp4tdbYOFS6pFFkcdCGoXqKN84jSu6y7KhNTS9j4EU9R5CN1oeMVT7Le38jLRhKzvc+g5pz+H/rL+A3TRqxvQKSpzKeLcEDZTltUB4eoj9KP1ct+1Q03wsln0Vq2+vEK7H6JP5YFjt8cHYM0SajLjxQHvQV2nf4T7tr51DPLVR/i5pA5a3SpU1zm5RaY3dKzmuqrRy/ix4qOLKSse30GZlcc6q4dt8yV+VrbjQl29L1Fm9+8cg9yoQs89vYE5aDVAY158QReGYzpS7LnlfPVA//bDcxjdJStLV4Q0fjvPuUUmekwWdvgh1kOth770/wXWaDJyzyL9W2qNZ8pM497pAb2TX6OdwReOCc0CzEfzGGUmN/F9RH3v4FN83v8b93xaf/9IRESqM6yN9TNMYJO7HLpDUntaz8uoZj85wRrd71EXzx2YNrW82esT107/iPdwJ0Y1+9x9KI1JIfvT9/S1iIjUT8EUr5+jbxH9vbMnL0RE5PBP3bwFU8xl61vofbM36KPqlmsfYF3X//yHvIzuSLVZ7+oTTFzp+5eFOcgabu0kj56Ij5L6h9O3vPUK73q77srojsvej/xdwvmrX6BPd4/5DE7dGYWIuz6pvGN3zGAwGAzvHYxBNhgMBoPBYDAYPFyPi8VsIaVvnkiZLExKLXKu0xWnxSy/pjZzrZ68QeFz/bidl6megvkMydKqK0a5AWYnHYBFTdeOnYvp+VvykvJERGrsmzpVpJ7rQ0QHBXWM2D7bw0cm26X8vkxdsT+2hH2q/988UX8BBrl0gXEoYyUiItpP1fdSv6wuGrunKJP57bBMVftIN4uUzg4NuhtkUzee9gvWU+UcqOMFdcrb9z8XEZHWS08jTh/YzlPqvNnHlDrZaIHr3YeuHWXy1dM4Iyur/solJoo1Xzt2u35OrS7vqVxSdx2r9y91sadu7ZRfYD7iMcYVPQZjuXX0EcuwvaHP0qKe9hPMU7SqcxxFD+W153hQPUebqmXOYqyl+jHG3PpzrKHQ84JuvkGbpTH167qzsFJtLT63Hjk2uPuYuxjcEandQN8aJ9SOr7jbceaeT3ALzirLA8xBmbr12ac3Cn26uu/5IF9hrO3foN+Dn2JHpPUYa0Y1tbWW69v0kHpr3X3groc+27OvqVH3SNK7j/Gezpjyt30Dn3OPcPb99Bfu3b7x77EWJx8hha+6RQeXEzCxV9t4BvHPPszLxEM8n9kB7l22mMLXBqu9pjZ82XTPdE+db/ierqm/Djtgz3XHaubtPnQegQkf38G8bf01xjy/B9302e9jjm8/6+Zl0gdgs8OnbyRYG+9gMBgM7zvsN7nBYDAYDAaDweDB/oNsMBgMBoPBYDB4uB6btzCUoFEXYVRyoFHAng2SqNRA7d40ClptzFQS4cUvqyxDgzU0TEIYoaxyAz9MQG3ktL28Dxt2b2H89tA1kCK/l33KLdaqbvta444lj5YuF8fX5AGomZMkaNSz9lFjj2ULB8kyHV/J9S0fj85Ts8EuUgbAcJaCnVyLh4uaDHcYMQCFdamMovzmyrWTYru48rLPSjQ3Wp8hrpdfOflHPGZgw5IHLimTcHHL2L4ue4EXKfsU8pCmBkMIn3G4RjvxmVfmDLKVmLICtd9rPcW4whm+DzzZTCfF1n306lxERBprbI+HS5W50Jqu4g6OhVecJ/alW9kpjLnT2mcf3XqrvEH/86AWSm8aGlc9gtyo29nLy9SfXRX622mjD7UX+L40xPMrnXh2f+xTxEAQna+UBwsjyjUST1mkMomMAS5TSiDav+XzmWn8u3tPVVKxZsxywKHO2xoBjs+Rp87RuayfckFr8I4eIORtgeduGHBNxjM8b5XYaBBO/ZyHUzteRPeyGMVdZRCKWvpl/HWQeudzM0optA9qw5eptIsSomXr7UObGretc59/Txu5YMuFsqzaWNeVNHvrEK7BYDAY3j8Yg2wwGAwGg8FgMHi4Hpu31VrWJ6cSxGSO1jyU5TG7EmwwNHpNv9cDaxXH0sYvwf4l54yUVhaVh840lMNHouEkQfH//iFZrdQLIvmdfTlFuxoq8lbf39FftYJSrN/AOivw2GA9nOfqJePKw3PKUKdjF/2bt0MWO9MDd2TaAx5UzOdcREIyd3r4MGH9apN19cEnIiJS6+7kZUa3UH+3vFNoV1n8/ic8SJg5JnR8E3RlacK+kCFUhnV0k+EiO64dPUy2JgtYP9VDYQx5aJKp3HHroM7Dhss93Fsik9z7VCOHcV+15w6BDe9gPNshDoiNb2tgBK7HPKynASkiItU+62Mfz3/K9Rzi0NnkkKyt90grDMGoDGlFyIOCw/sMQiHLfvmZayeNcVAsnoGBPPt9tLPV3C7MTXXXzYEeLpzQ6q77kFZ0t2kvd4ifI5fmLMs+6qlwXGs6mQ0/Q7vjW6ij9dINaMTvEjZd0qXIV2Dxc3yRrNx4Zg/xfE//AT63n2BO9J1b3MF4+5+73wfdH3Go7eJLHoQ8w/havOXkF3jmauEnIpJGuHfMdbUiy619XNGBcNVxZXZ+zYOkAZ7P1efom65ZDTeZ77kyww9qbAeDbh9hvq4eoK75bbLQ3g6Z2khWDvdEptezMWcwGAyG/3owBtlgMBgMBoPBYPBwPTZv5bLEt+5I2qaVlmo5PU1ofm+LAr6FMr3U+TG8QLqOBRx9Bkat+RDfqWYz7YIqCk8uWZdjhQMNJ1BtKevXmOKImlC5ciytMrfKsKZ3wBhGL8gKa8T0jrN10r6syTZHnz5AHc9e4foR6sh10iISqO3dBmuu2uOVWni97udlNLAh13UqI90jc3y4w3G5uV7voZ6U2tNSb1ros0KDQ0RE4i1qgFfKBhdt3spDhnHMnb9X/WxduFct05TtjBaMr+67dua71NueY07jEeecUc2TA1zvnHghMwyViCbUhJO5i2dZoX4NVRERqQzJRE9Rv1rOxRPV3aolnWMOq6eYp1UH7G+1h2u1U8xb/+NWoV3MAW3dpmhbtdW1C8x99Rx9H91yATilMfpQucS1xmtGGtPmbXQH67F2PM3LhLS2q+6CjdVwjxsjJq9wnbRfuDCb0gDrLXryRkRE9tf3UMcPKNvaw3rWaHX/O9WTq747rWH+BgNl2fMiUvvrhyIictiE7V7w7WOU4ftUOcd7eqfycV6m8muEhxz1sCMRDrgrxB2Rwwrs3cpnEzcHfGdbO1jf6ybWQ6R9LDN6vO5+rUUP0Y5GsW+NoCNXm8b6C/yOWey7fOraj3inW89xrfSbpyIisn+MvpZHsMvzA1Y6jP5OXr6WbP2OXSqDwWAwvFcwBtlgMBgMBoPBYPBwTRrklSSvjnNmb71cvXWPanEzBmm4wuqWQKb17Dy/1LwEs5X0eJpfdbdvqCd+hwZZyEDlLK3qfMkYJmQ7fW1wdqnOBtQ4s44kd6igIHPk4qk1wCOkK0b68Am7yPpfgrVTVrow1g1EbTBiMeN21xPHmv0ujXbuWsE+JYmzCAgZNBIxHCUhg6fjq58jXCLwAi9KU/y7NFwW2lMNcrTAnIdT92zTbWqQlREli6rOB5WROiG4dhqvwMauWhrOQreHJepoP50Vxy0ioq4odKBIR2ASVUeqrgbx1LUTrjhf6qgxV4E0x0MHh8SLAk9yRlKZ8TLrpz77Mdsvub4lZbLmczquUCev7girNuucu74pw64Mv7olJDXMV7XHvjVc3+b/AIzqbBdlWi/A9J79frUw9sGXbh2UL8Ba7/8ddjdO/hD17+2AdR7dpg78xLHbs31qwZlYXSKJrfrl9Gust9XSvT+VIeo//TnK1k7AFJdOoYWffohdjuf/2s3bgyn6cP41tdpX0Pk2X4PhvfgJtcMrp8OuXoIdH98i+6+vLadpXWe0ecvN9X3BvOm67n9AFxjq5K8ecOfippu3nb+HPvoKUn252cR4Bh8yMORPMAftR07wff4l3uGdX9YkePgfxWAwGAzvN4xBNhgMBoPBYDAYPFyPBjkMJWw2cp1suOEnLOLilLM5m1SGUr2BVQfsxVPnnr+qV1YXC7Ko6cgzVlVQ85v7LCsTyb6E+tlnkMl4a0R2WGeUNa+H7EeuBxanaVRomYRjD1vQaqZXPutMv166feSMOP1UA44z86Kzc/cKstlar/OTpn/swoslZn/VD1ldLUTQbpWa2mjgNK4NYYzzufNGRmWYrybZzOjCXa+RJQ2n+nw4Y+qLyzrjvmtHqNVUPXHY4/yoJpg+uHqfiIsUD1nvmi4g1RNq3dnHaOB05XWt7xJjr24y8XPVDruYZR1HsMC15hYZcjKhQaJz75hQ9dUNxoxVps67Rj1sQE1yq+rehfLpqDDG1mu0UzmhDpdrN5y7Z5odgOWt9lU7zXnkLeURmfiZ1zcuUWXT1X2jNGJ0+hXjpBeOcS0P+O+U12bqOoLP88sa58j3TkYn6icaQ805ni1YB9qLrxwbXOqP2B6+i5TxZ/O1M/xj0XXjUQeUaInvKtSIzw50l0N9i/MikmlxPvfKVVL4Pprz94X3q6Q0092HYnS6svRrOnhk3k6CIpivcrcag8FgMLy/MAbZYDAYDAaDwWDwcD2GnYGIeJ6gOXvrOzgwlS5niN9K0qOueOLYxkxZWjKHTo+bFuoIvHbyezehLhlkqvPkPr8PrEcZXP2s6XtZ4rTBytwGDbC0ykKHWr8yv4l33F9BdlN4KXem4Nj9VDyfhcdFtsvkvtz9w08t3NA6ZxvJhso+vrPM79BJvysdTJ0OcmyMVRMVfcY1ULa+UX27D+JYx6zino+uDa0n2EhQzOiAUUg80/5uMMebnta+l22mrO+iqKFXl5E8ha3s7T5oeuDGmlfttlC/LO9alqrLJxOflbjeqJv225ntoQ/LJncM6Ak8vUF2mD7S4U23Rmdct9Nj1nMT7PZst8Y6qX0uu3lbkyRfdFFvTEZ6vo8+bR2BzZ/MHBs828M7sKCBxoL+zfGwWeh7etPtuqy22Id91RPrc4rZPj7PDn0v9bAwZt05mB4yFbHMdd5wOzCzfb4nfE6zHfWyDgp1lW+5eZs+BVu/PFoW+j8lU/3lLTiK9LbvuTI3cG2905TslfEOBoPB8L7DfpMbDAaDwWAwGAwe7D/IBoPBYDAYDAaDh+uRWGQZwjS4K54fMnuXvEC/2/ip0gu1YxMRCUo82Kdb6ZtSiNU72tGIaT2MpVvflCrkZcSFZuh3+SE6qRS/n87Yvtsnz8eokhC2k+o4au7wlyuUFceslnA8jKWbyX47gawK9wrbzbKNPXvvYF/ezro4/1qvhj7ES+/QocoUuO2v4R8qFUhpZ+ZLInSLO+Pc5lIIbYeSgTDyZA01bs1v/mmmz6eqi8jbWtfnq+3pXJQ8ScUGcvlHUJQvqNQhUDmGt97Uck5lF2rnpjZ16yalNrF3EI7Wdnk92l4uZ6GkpOLK6CG83BJO54J9yzYO+Im4MJSAJ+0ql6i3ykCSCg/Xjc5qrswlI6tZNjthAMoA7Wqsd7Xv5nqhUof8kF4+UhER6TdVg+HmvsLAmXjKw4Y9HlzkgUWNupYLL0aeceoVPaRH5VXtEn1b1zmP527eqpfoZ8IDg3owMTnj8+IcryehVwZ9K/fRQBpzfljt/BLzN+aBVhGRTg8PYnrOQ619zPWih8/fncIm8XbfHaKsnqOe+GrmpEUGg8FgeG9hDLLBYDAYDAaDweDhmg7phSKVimMQlc0M3P+/1cbtnayydz3zQ0b0IBwPvOUH+5Tl1J/vOtS2caBLWWitK/AOgUlAxmu5KtwjeuhH+7702uHhuPywn1pz5YcONRbZMW3K+ub1k2nKyKoqo+iHmOT91YN22u94wy7PP6iolnq1YjSzzk00Isvt2YjFY84BD8npYUftfzzmc/Fs+DTiVyO0gw3GWmOeg5lnQaeHGfU5qT2dstCbBzJFJFVGTkNXOI9qy5YflPPWjratQS25FZ32g2MPPRY6f/68pgEo2sdoHBbaExEJFkmhjLaXj50H7tRaDfdyvsiEx1OOZ+NwYMHqjgy+ss1JhaE8JERLdLjLqq5MogEkvDdt4Voa60E/PaTnmsyUaOernPJVXjdoIxdzd2Dp5iBn2nVTqIpKSqEy8Wyv5B+4090HHQ/bYVy51rV2hLgk1eK9uuWS1Is2dmnVtbNucP3O9UBksc869qzhzRvfn4T16PwlZZRp1edst+3aqXs7FRsHQQ0Gg8Hw/sEYZIPBYDAYDAaDwcP1MMiKUpHp9UM/cus0Wk8JY6I3meNodzsvs7yNiNqyMogzhjEwLEOZOD9YI9RYaLJAyi6rJlj1xBpXLOKxvcqA79Kv6gSx12kfUddBxzFGQn10MkSIRHwL8bQJQzmUCfXHU4idllwGKcEYOuZsm4EhK49J1EjrLq7lWme2E+6wrx57mpWKjzXc28X3nL/eV4gpbj13z2d4H/PTrim7TWaabOPwHuZ1a72fl1luo0y4As2nTGFEVnV8E/PafOW0p6sWtZpTMqvU22YVRv7eQl31184SrEQteLqLOYg41vHHGEdpyCCKqotmHt9DWEp7jecwu4U1k0dOK/tY8fSq55if5BDPefAA/d8doez57zc53ryIdJ7hmcYafDKDlnV2E/dWepjj3qduDrYFa6J0hWuTG5iD0gR9ntKarPXMC3L59TH6to85CF+ciYjI0RzrTq37tA4Rpw1ufIcI83B9iHq/7/E67MzKp+5dWB4w4EafJTXWygpfflFjXW4OGr95iXHs30K9v3qGOviuNfi89nbv5GWiE8TIb3+PZ1bq4d6QQTTBL1DX1g/eLscA9yzYR40Y7z4uRo6vGm5XoP2X6EvGswKl25gD3RWoXuD9mT5xNHrne7zvtUvMT/OXr3DvOd6js3QP4/zbb/Myh+es9/FLtytiMBgMhvcWxiAbDAaDwWAwGAweroVBztZrSc7PRS7+C7R343HhY64RVm2yF/RR+h7MU8Ko4dz1QaOTg7f/f5+ORoXPWibUIJLF4q0yMkFIgEZYy8s3uHe5oQk9O3+r39EO2MD1mxO0Q6Y6OQXDFzYab5XJY6rpfJAtwOiFZKp9RlznRZnxZM6yZMZTnU/f9eES9QW5AwXDEshgj24r0+acNsY5ucfvNDeEZFz/M/yMlm48ozuop6Rp0Ws+H4pBx7fQ/qLj2okYa6xMssYTaztr6mbnXecqsFUGaz09wBx0pniG/Q91+eJn7cKxgKN7GjzR5piLkcIaobzYcmu20os5Do75p9RhZ83Cvakjg2XZxYfygA4OQ5QZfIj2ykOMb/CF7zLCII05ylz8Ia4tW2TiOYz5djMvUh5i3nXO936F+Tn72rHmIiLTDz3G9QLX9tpH7BPKjm+AAR3fwRy0n7gBjT7Az5R64dKIzDhfm8/+1UO0s3ZzfdnH4ln8G6zf+ct7aH+CcQ1vY1yD/96FcYQJGOKLnzDK/BXWSOcxxvzyX+K+zm/dOiiNUc/4NtcVw0uqbxiiwnCTZMu9t7UzjF0Z9rOfYx7LQ7p+cJ0su+73zvRgi+3gnsPotoiI9D7hIv05fx/92aGbg69RptOuSvZ33gIxGAwGw3sJY5ANBoPBYDAYDAYP18IgB6WSxAdHktXJFA7Javps7aYTxaZXsrLBsevS+iOwP6UX1A+TPVUNcnp2UaxDRIIaGU7VIKtbQkWZMLoN+Dpftp0pk3x0gK6eXRb6HNTdkfqMGuakBy2lapDXr6EVjW/fYp89HTb7H7abxX7fPir0MTi9cH3biH6Ob+yzrzSo7UInGUydZjfdAmuaNlFf9PysMOYbf4U+VV5f5WU6t6Ftrb68KrZLB4zGMa7XHzoWvfMdteB0apDcTQLPtnOIvlWOHaufNujYQQeM8Iprhb7EqyMwcfGp61vG+ehSh52cUH/7p2AXwynrmjhv685jMPvlp7i39QR65dx1guNLvTjnaIC+aLR0/Qx1NH+LZzr6EnMfLt0zqb3G2MIx55/rq/Vki+OD9rX5xmm3m4+4Y0DteeME663+FHO72iHL+bovm+iSUQ17aHc/gydvaYg56J05xrU6ACva/g71LDrQ9B/8GeZzeQPPp9R3a2f7BzyfVZ1acbpzzHcwJ4/+l485B65PR3+JHZdoiXHUvodmN73A+9Pl+JaNI9fOv3+CuXhNhpfPJerhHfzgf8bzSjxHiso5+lk/x3sYT1BmiVvz+OhVzbHq5WPMaXCF+Trqe+cIRKRxgsLDO24d7P0SO1SDT7C+238PDXf1Es/0jVAHfvUyL9N5gj6VvnninGAMBoPB8N7CGGSDwWAwGAwGg8HD9WiQVytZv37j/D83WE8R+d3XNr7PdcAiEv/AVK0+2cSUTGVv8Ls78y6NsYgEMbWudM8oeJVueCRn1CBnWleoQlyP0WP5qAUWbv3qNb6m3nf98tVb41FHjYS64Vx/TcZd/Y8LbhebfaO+OO//xUXhPhGRQPXPLJOo3zKTAi8//0RERKpHu3kZ1SVvdcEyqlY31aF/in/sVg+8MnRfGFPnnWuQcX18E3XWzp1eVTXIyxb63zgFo6eevGumpMkdp1vu/Agmb7KP71QFffz7TqMrIlK77OT/Ht5Ff3daYChHtzQhENfjOTXIXfc3oro6qEvFxdfoy3YHLO18m364nsS0dBesYpVJdmU6agweYL2VRxifarhFRLbaZMkXYC/Pfo7vu9/jeagHcfmemwPt7+gmx/UdZuH8K+46ULg8/sjtjJSYEicB2psdYDxnf4x2Rndxuf3U0yDfVZ01PZpH3InhvB39C7Cm05VjaQcXmJ/Tf4P3pXaBNRIfkJ29i+fU+28cU10Z3hMRkYufYk7rIOmlQzeJF/8SfW89dc+nvFfUti87mItKn5/bTDzsuh2l9nPMf7RVZ3uYN12zw/uof7HrNMhpjDLju3ymY7D//Y/5Dv4MDHP6vzk9vrrAdFf3JPvGNMgGg8HwvsMYZIPBYDAYDAaDwYP9B9lgMBgMBoPBYPBwPYf0KmWJ7tyXtMUDRCMcPtIADBHPumybh6VooZbxcJbemxy6bf/Bx9jy7vzA8I0xQz662NqMzmm35B2Ek1bxkJ5i3eXBnktaTV06uYQe+lOZQh5QwgNemVqpHey5MjyMpQcFw59iDz14iiziFAAAIABJREFUzr1iHrzTOUEZhqEkbjtXxM3F4h7bPXGH2vTAT9aoFfooPECY3cJ2djh0c73exbyljFFWi6uIB8YWaEaqPdcH3dZf1TXqV+N7aUVHqYXG7YqIRLOiXEZlEzHPymlMsEovRESmN1B/9bIox9DI3+FdSj0evR1JnqktHuc0qeBz7TIt1IUPxZ86jtKUVmCMGlbJh4hIhYfa5luaR4wf5Qn+cU4pRDR3c1A/4xhXWaE9rbfaxzj8eGqVk5RYb/kKr2F5jM/De7i39dLNQeOvnuI7lcU8ei4iInee0WqM8pzlntv2j/m8w2eQDLV+xL36Hm3vQUpQeuEOXm7dwRrXQ4wa0R0s8P6en97iIPIisvcfH4uIyLz7APX95d+xT1hUred4uLcXH7jxMLyk/gaymNIrrOeUEqLD1pciItL5m1euIb7nuwzUmd7nwdEnkFyldcqbWp5M6y8Z5sEDsQdX9zGePn6nbO/g99G67eQspRPUl+xQcvPwhYiINL+H1GLyHQ5vpr/+67zMzinmLRuN7ZCewWAw/P8AxiAbDAaDwWAwGAweridqOklFrsYSMRJYD52l3mEzPZCmzG2m95KpUkIqfHmSl2kx7jY8IVtKm7SITGx6DvY28xjZUEM4NG6ZB+xKQzDHmcbA+tHMA7BJGsVc1hAOWrjpgbig56zH0um08DM+BfO1HoB9ishgh6wb9dOmTg/jsd8B7cvKb3iv2suJF6DCsJSgBlpWGfmwD7Y582zeIrLyWZ2Wasc8yMdDh/t/Awq59toFN5THYB4bz8mWM7Al43OrDMGyt35wzPuaB5+UodZ4Yv1cu8T12hvXjrKJIRnJqM9rnJNKn9ZwJ65McAyatjEA26eHKPe+YXz0hMz82DF3pQnmtPYjxl4adgvtBgyQSaruFSj18CzrtH6LVmBYm48w9wcVWret3XprvMaaiUZoW9nD0pAH8a5wfbfVzct0Ho5ZBs9sXcPzaD3Gs6z28Iyrr9zaCbgzkpKJjrZ58O4e22Eox+CBY0+rA6zB9hys8+VXmJOdv0H/1w2Oc8/1LaG927qG5x7PGMLRxvfzbbwLfty27trozkHI8Jw8op07NONDN9eNX60K7QU3USaqlFgX+jj91B0KrVxivmYHmB/d5Rh/gjnQXQHdBRER2X1Fa7kR33/9PUQrxNltBsncdH3b5fO9+hDj2u6hb2qLd/kF7r37t65vq/s8mPibp273wmAwGAzvLYxBNhgMBoPBYDAYPFwLg5w0yzL+4/sy74KdqV2AQSyPPJY2UGsmBhDM1EesaBG27LounX2Nf2//lsEJI9Q728X37afQ/UUz185sh1HPNYZ/ULe6bOKz6jyrl47dXpFJK1+hnvPPwFBtPQJzFCxRZnbodIqVHu6tPALj3fsniNvd+iXYsuEXYAXDxIk1y1dg+dbVqHBtwTFPqM/tPt7KywScF9WtLtsoWz9B/4f3wBhWB06vOj5k6EYDZdovqDUd457JAVnhnptrZd0W+/VCuylZOdX7rrY9TTWf3bqhUb98tmxn0UadlUvXzpDWZZ0fycA3Ga9MJnfwIRjm/UvHiAdbYD7TDhnr0h3OBeOqR9SDewERCe3iVAO+avIZa5/1GSw94fIa/V4d4BlmG0ygRjVXe+6ZNl5uBI7oT/7pGczJlHra7bUGuJDxLk00Zh0/dH1Xn7u+rZ9Ccxy8xBiTjOtYg28Ysb5/fjMvo+ExGtjRJgOa/vaRiIhUnvNZeqE5ZeqGKxrYwx2MCuOpm49o++aF8ySPn4mIyA5DeZIz3dnhzsIzaHgP/l1eJI9mL2tsvEbMk+FtqI7XO0uQkZFuvuIuCvsdtFuFvuZhRCKSKXPMEKD0HHOhMey1c6ytyumNvEzwHTTVWwOwzwl3LGKWPUweyCZKz6HjXo9GkqXpW9cNBoPB8H7BGGSDwWAwGAwGg8HDtTDI0WwtzW8vpE42MKIuVuZvR01XVBtMDXLuykDWpVJ1GspwiVPj9cfQvQasr9YBoxwcU6u7cmxw/YSOFGSeMkYlZxrjTMbNj2YuqyZ4DJZpdwo2KXzjTveLiLQuXDBFQGZqTbZs61dkHZ/j1L0G2ga+1nkKkWaFYSL5mKmHbLzC2EuvfXuJolNEvc2xU1O9M0DZYOLGUz1leAmZ8eqLQWHswnjieOCimatt9KnyhqElqkGOlYlnLPGpp6lugA1WJjSaUndN1jSLMCfxletb+xk1tNTshpzHkM+r+4R1jJwrh1xgPiIyvMoCVulMEnIHwXcPqChL20N/K6qPnqteOSiMT8Q9q/IZ+qSss66V7mPMSTz3NO+Muda1qU4lEoKZDLjOK0NXpnw5LdQbJIwuZqxz/YR1Lty6jnahIw6aYH2zId6x5D6eZTSCjnj02bZrZ4C2q9y9mdzAnGwfoUxC7XF05fTeaQtzuqYjRDhHHcs9fD85wPOJVm5ddi+wvmb3sfNRO8fzSsgOa99HP3Wa3aaeRbgPxlvjyQM+9/URdcwVTyNeRZ+WjOKO2LdVG+9NxkeZ1ByD3PyGrjL8HRFvu90ZEZH1Lez0jO+4nZHOBH2afoQ+1HmmIruBe3ufYy5uPHb1pJzL4OxCZGUiZIPBYHjfYQyywWAwGAwGg8Hg4XqippdLSZ++yGOVE7ol5N7HHoISWCDVJ+Zxy4xB1uhmEZH6I157A8/UlAxf0EM7KV0nMk8PGUxmrLf4f391f1DXicyLpNYYZwnIMpLVTjYiroOBc7FQvaNqGbPnr4t9fALdZVhzuuVck7mmIwWZvYwa0bgNNjgZeiwtGchQmXU6d+gch3TaSL3xxOd0FeBcq3uFlrn6726LiMi67ti00R3MdbdERjot+iBrzG6Q7eRlJjdUT15kuVVPPmYs8rLtXBJUS53dxnhqZ3RnIMM730aZpOI8pxuMH55TG1wlW3vxCVi/aEnXB0+HfXUPTOdOiHpGbE/9ipUBVW26iEjtssE+4vPFl/p6HHC8Kix2ZZYNjE0Z4tJ4zfapDb9qsi7HaqYRXUvGuHb6M/R1q4O5VT/pdcPpYisXGPP0JtZT+zv8HH1A3+MMPzU6WUSk0sO/d6dgQjXGe/IltLVjOje0X9TyMsPb6Iv6YpdILut6OPtjrvu1Y0mbL8G4vv6nKPThM4wjbqJP60Oss/Ov3K+b6hn8iHuf4Rk2TzBvteeYp+N/TA/0J+6ZpmVGQB9xPWwXfbCXTerk265vjYd0cgkw5+MvMKf6nHRdzHdcmXi6w2voS/UEa2j4Ifo0QtfFPR2R5TbX4K1DCV4VPdgNBoPB8P7BGGSDwWAwGAwGg8HD9fggZ2BH1edXGVf1IBbxvH8XxZSpbONenz0NlXGdTIplyDbn+mWvnYxaw2wlRWi7LKNstwgYcADtJb2+X1LCKti6dD6XTURb9GDt9wv1KvPrjyfvJ8ecez/XwaKlrONdzLsy31pHWFEW/e0+pWTRw1r2dh9EpNYD4+anyJXG+HdMRwVNUlMXkNJYfXFd34KEDPIC9YWLIpNcHpKR94ZT6uPBzHeoNV5roh7qqJ/iZ1r2/nZjX6IF543zU54oy83+eIl9MRPzghV1w7Ois4B+VlZVxDHH8ZTzszG1zTf4flVzZTS9Tz151RVDx5yyztLYa3te7EvAVyBg9ytX6kHt7hndB0M5PaCGewFG9PJzTS8ke/qle1emF1W2Byb38mvUm/wWEza+i/sS6vNFRKZHdPmo63qgGwh3En76Gdw0Riv3/gzvg5Fe3cB7NH2Ad6J6hnU4fID2wy/dDsyMmv0Rw/WWXXo21zCu2YH2w01CpY9/j29ramHGPpNxbzExsuNe/vEPYM9LdMBRVjhc4ufwI9y3PnIPOwvJzn9MDXcPzPHgY8z93V+8xHg/cBzy5eeYj73llmRn3oMzGAwGw3sJY5ANBoPBYDAYDAYP9h9kg8FgMBgMBoPBw7VILIJqRaJ7DyTp0OZtQBuribMRy6OLu9iuDGhppTHOGv2c7rTzIlefYbu1/QMsrcIx6ku2eAjtmJIE306us2HzpuEFLWyBavRv0PftyhhdS4uu5T0cyik/Oy/UH31wx42ZY0t4gDD4+nP08TG2X4X3ZiVPZjLbGLNKRCg/WR4yZvmV24pWm7CMEbwZgyjCC9yTHTGQZOjmerWHOVgyDKPcx/ZxyGjj+Rbar164A1BZoIfjMF96KCvj1rpKCVRyISJS6xWlIOsGyqp8IeHOfTx17UwO8WX9nIcPKZtIK2j/ilHJW997+oZlUVKTH7jk1NYuGFs9c+2Ea5VwMM6bUx1POBD+aVgeOblD7RhzuNymNIFTqgfkLv4Z153nQLf93aowxoDx4ZUh47ZPMY7hXc8ikN2sXKLe+int+C5WvJd2bN86XUbju2f4bheSgYRBGw++Oyq0u/wzZ/MWU1oTPMUB0s5j6BlKj2F9lu3w8OSZZyu4j/J6mFLXrNoknv/tPRERCd1Uy/b/8VvUO/kM4/i3f4cLDDPpflfhOD/Ny1S++VFERFoPaV/HGPeU9nXVi09ERKT8euAaot2abOP3Qv77hpaB+q4lDScZif7+W9RLKdLhDzhwqZaL+/t4f/SdEREpP36Cem6yb7/F560DHvj85hDj+Ztf5WVu8mDi+vhUJPF+7xkMBoPhvYQxyAaDwWAwGAwGg4frOaS3Xotc9iUm06qBG4UDZGRNA8bDZsoKqk2a2pl5B9SaTTBP0RmZYrI+sbLNPEyXeWEcoYaG0OIsyA+ZgRXMyPzmh97EhTykPKxXIlur9eeWbt6Q9cCeHgqM2cc1xx4xPCH07OYyL/jBR0ALt/zY04Vj9HILO1rRhYwCTrWdmFZ4EzeeEtn6uMawhx5YuYy2eOUhI7oXjj1VtlTtrzYZ5GiO9qOJm+ukUoxr1kNyET+XJtFb7VT7SaGMxoQrA1rtkfldedZ93G2IGMqh66s0LbYbLtza0YNwGiISTyuFvgUbkdP+d/GEjPSCc04WunrBdrwDoHrIUPsbTpccnzvEhvb9f3M9sYyy86Gyz6Mi6y0iEm6D7U12WmwHD2x1s8ux8xDaHddudYD+N4Y4NDc9xLV2Hwzs4gZYU7+nC1rp6SFJPZSpzzo/5ObNQZt9m2+hTKuDPur7GjKcY3TbMbvVxziYqtZ9Je6MRLR91NhyDVEREYnJYi8PwOQnVR4CZciN7n6sGu6d23rGwBbu0qQM5Qnq+H2wPMJnDVEREemOwaJPbqOd9gn6vzrEOId3UFez7sJFNCgkHI4kGBvvYDAYDO877De5wWAwGAwGg8Hg4XoY5DCUoFZzWt518vY9yiBTP5pFRSukPKzDC9ZYtcAmxjWa/auOmexPUC2ydPiO5dW6SsM4tEwebe0xlGXHHom4COW8fmWQGzXvJtJ+4436yVQV7tV28n8UNcg6b2mDLGfNK6uMOucnY9xuzsTrnHuR1Akjv9Ma+0IdqY49tyZbO2ZXgzOUyc0ZZIZiRMvidRFnu6YMruq91VpNrc7CpTfXWVT8TjXCUTGeN1h4aygp3qvIQz8Wykq7Mvl4lNldF23Y3Bjc54DRxUFZ+8jvGYOsDzDwqlDmNsjngsz4sshg+9HMyhRrmXieFevS57NwNG02VR051yRtC5XR13bKY/f+6G6ARr6rrZ/q50tDvlde7HppyB0KnQO19WN0d2XAvnryc7V3zOO0l7pLRP0ydy78uG0dT2lExp1R6brLUqKGW+OkRdxOT8TdgHDJv+8zZfFVP+/ZPvL9yXeo9FwE+6jzV5q5X4Ua216aMNabu0Uaqa7f578DxD2rdL6QLPOof4PBYDC8lzAG2WAwGAwGg8Fg8HA9DHKaSjab5fHOyrhk79Ag5/gdGuScSRaR0pDM54zaZjJIwZQsl2qePQ2yti3KdLJ+bV2Zq4LzhbKlZOWUQcrvUQbTc+XI9cTKymq96jrBsQfeuH+nBpn3hOq8MfNOwW9okAMNT1ko8zZ7q0w4ITOYlIp92ww68RwpctJNGfeNx6VBEf5z1HjoMCvOtWictBJ8fuw3ybWMZfXefIch3bguIkJmOv9zju2po4bqZcOFK5PmfQgL96qmWvuRlrznQ52t9kUDSNQdIXjHxkjez5jtkMXUuVHXEY/UdH1R14Wytlf8e9V3QAm5Q5FQlx8OGOve4JphWT86O1yj7TJ3ZTQURXcdVi1q1KeOdV63VM/LYBiOQ7Xayw6fk8cgB6w/b5shNgHZW901WrRd39rcndE+lJR551pdtVTr7DG7dZRZNzin1BzrTsK7NMgN/X2iGmTdcaF2f01mfFX3zgpwB2nVwD019n/NuV81dZ17TDXPLQTVSiGG22AwGAzvJ4xBNhgMBoPBYDAYPFxb1LSs17nWMWdeU0+sSfeFnEVVDa0ysGRj0pHzfo0HZEcZNZ0zxdNibHXm6W8d60udpzKiquVdqTDWY4yolVRGOhoV2wvI7MrKMcB5O1o/T+zn2ma2l3pMdV6fMu0aoVwnqzWC1cG74qN1bqXZKIxLdD4TXw/J79ThgnOqc1A/o/7yyrWj7hFxf1qsj3XUOnSXuHJ2DDEZTnWiyJQFJstdUxZy6NqJZkXteTjiM+ac1LTOkRf9y+cfkgVM6GJR6WFOQtU8e2UqfTKtY/S3PFDtdlErHNad/lx1vCF1r1WytOpM0Tzm2vEkpvGYZcbcMVBtrep7qU2t9bxYb/pSqza8PCZ7Sj1s9Two9EdEJO3C7WFNP+8yvYAnN/iZUcqTI7eu14zErpzDVUIdKBqvyOhucVcibeRlFox8XtUZYT1HfYsOfs53VS/t7SSwb+p7LeoUoe/AFq7P9j22nlp6datIy9D16qkCZdUnNz1XDjLE6qWtTiXKGGv764Zrp6PuEnyHdf4CMsfTA3w/vuXmrXaG+RkfYb6a25i/+R7K5lHXdO8QEZkdYQ4bJ3WRmfEOBoPB8L7DfpMbDAaDwWAwGAwerodBLsUiRweS0IUh7IMNKpzCp0ZPoQlxoo4Xpbe7Mr0Dlqymp9Tph5t2wNaEqnn1vJMDepPm7SlTrPWzvXDlyqgmNBogeSw9ok+wlqUjRlZzbFY0gLdwol7JR/uoV0/u3+Tnq4lrh17FqsnUfq9vIoVLNaclX7OrTDH7q0mDwRTsadLlXEwcU73cwXerNsZc1xP8/Kna7tBLE6xWySYO2V89ic85qJ6Rnhs6hr+kz44/cx0mn3uZ4wkvvdRCJh1qO+ppnPs8tzk3Fy5BLaXOOqSjhvBnfDnhXLytKy/zeWcDJLSpX3S+JvnMw5GnI71i8hyfT/2Y2tyzS9TBpMNo5tZOdFJMc1Rf7FKk7D36WN9yziRhf1QYe+MFE+FO0E6wpwl3l64M+1SmPl7HUe2jL+VzzFFt3yVRVq7ITNN9IUjQ/9IJ5raR4N6Cf/QY/V+16aiiSYcx5qL1hMyy5/6h4+n+yJ0QnhlQpj/iO9B85RhXOb1AH7h+VfMeclytJ1hn09uO3S6Ndczc1TjB+zS+5+5Bu54GWNcx12hJ0ze5Pmr0UF503O+nygVdN8gu6y5H9ZxOHj22F7u1E5HNTifTwm6OwWAwGN5PGINsMBgMBoPBYDB4sP8gGwwGg8FgMBgMHq5HYrFYSvbkhYS0btIt8TwmWVyARg5e0wN2Ggnt27zVub2fnpzhp0oFLml5NcLWrnjG/AG3ydWCSQ+zaRhIfiDvHdug2ZqH13htPRiwT5QOeNKHRA8MquXYs1ccOw+UPX7Jdtwc5AcFue2viFQawINESc/JC/IwAsoKdHwaix2eM0LbkxeUeCirTGuw9Bxb9Tp/k68PRUSk5lmpjW9je7y9GWLC5zL8ENvK3dVuXmZ2A9/FnuRAxAWFTI/Qfs07CKd/kqWMGC5fUGrB57Xqokwc3ciLxC8pV+DBtOAp5nZ2HxHAekivdOXmQGOCWxzH/GarcK8GhmgYjYhIeeDkCSIi/U8h19lOb6KOHR40DVyZWu0AfWTghcoZJnfR1/IQsoL+x85KbUswh9EE91z+BHOxxWCXVRM/K968BQnHsYd66s+xDqZ7WgZ1TI6cvGBd48HH02JozeIeJD2jW3hfG6dOCqX16QG/0hTtrvi5/6UGrngH4R5ijGc/R323j9FeHo+9h3FefejWW/c+5nTwMZ6TykFqfPcuvmoX2hcRiboV9jHizxYnR8cbcC7cWLvf8h/8/TPn2HUdTG7gWS67bjyzm+jTbJeH/7bweXITcz/f06AXL3ZdY6+7HZF58SCqwWAwGN4/GINsMBgMBoPBYDB4uB4GOY4l3N2RrAXGTc3+8wNYIvkhs2AHrF8eFKKHqXivHm4TERndB0PUrGqgAepN22gn7vGwkxeAEdAGLdtgrNMO+3bJMpd9V6atTBRYpPXNbdT/nIz4EGXCfcee6oG3tE+W+S4YsYiHj/Sglx6qQ5mkOHZlt3mQa3UfrGnccoeO8pCUNu3dNNKWrLAc4EBh6IWYpF2MJ9OwjG30QQ9LHf9D/F3U+bGel7n6iAzlNg/RsasaEDL8EKxZUu7kZebbPOi2IvNNkjRmV0Z3UWfrqWMwlzynVSL5X7tg2QrbuY+f7aeOPW3XcY/ae3V5+O/0Z/i+QsK9NHZlrj4U9hfrbXgvLPRNrdoSL628+Qb1LjrKluqBTsz92T/iGl66vys7P1QKbccz3Du6jXvqp1iHl3/o2MY110btAmX7nytbjwkc38Z9W987JnLnL45RP4NB5OQcc/Fb2qSRfd5duQGVh4xRfnoqIiKHix3xUbtA/dVn7l1IY6z94AJ9iicYczzCOlxsYT4Dz+ou6uFAXRrxPTrGjo/Gvkfn2DFpP3HUrga4VPuY4/rTQaFs9vPfwxz88iIvo/aFlUuswf6nZN5/AzY94YG7Zds7EPzoOfqmfT1A2fgcZbor9Ln10gsousThv9KQhydfow+tPHQGz3j97GVepqHx2vN50d7SYDAYDO8ljEE2GAwGg8FgMBg8XA+DLBk0xRu2X+LrbzUQRC3BNIY2KFq1BUunZw2XRYumnIUmE5sxbMS3ecvv2Yi21jK5JZmvW15vaGgX2seNcay9rGGWycehEdN5uEj5rfG4/hfb076EDK8I1u/QLWsZ7YuyVMnbcx2kGsVMXeSyWEdpQquumZuDeEKGlZrPkJpXjVuOp+8ow8yQUAnxRMMlskI7vo40ZQCEtlOaccyptv92O9GUTOiMVnQM2ChNZaMuv28h70kKn/N61cXOexTa74Tx0/E4KvQxGmv8sVtbEevTekvTtNi+jm/iXjXtr7aXz/1MrweFsiKSP28NOMmhzncal+6nem+8AyljryMGnwRewI5Ctc7KEOtPjbLWKHCfQQ6oGw4dSY4yfD8DhgT5ZXRNanu5nWGga5Y3vsPyUHdGIkZMu2jzYsQ5rpGFT3Q3gEExOl8aK+5Hm2vceVScv1yXr4nkXlR7bvm2XheCZAwGg8HwfsIYZIPBYDAYDAaDwcO1MMjZai3r4xPHAmUbrJBIzgyl6jzxu+AFUdRfQ+uXDIfFe95sMDuB9//8DYeIHCGjrDUKOnT6zrxP/C64gL43UXaYzhrrl6/eqjbqQtOYPHqCeyvQgK6PUWdw7jSU2k8Nk8g1yMpyXfZQdoPRFhGRs+Lcap/Sx88K3/tt6j254wbH3ngNkasymCIilQHqr12SYdtwsZjt01nhyvVt0cF3yhBXhsoG4/rqkrHRS9dO+3nKsmQKF7hWJrO39bDI0ok4V4x4zL5xjZQHe+yjsB3HrpZH+DKaoUx5yGfLrsRkZxddz5mEzHH1SlnnuDAH3e/wM6m69Rfk7H9WaC9co7017y0PXDulCRl+faR6iZ/rJ0UmW0Sk/0fQuGuU9FbrgYiInFGHrUz49DOnxw8ZbLF/456IiFz8BPW1nkF7Pr6L+5p3ne5f9c+qJ/d13SIi9//pM7Szct/3TuCKMv0KFPj8Dz7CmC/xefQBtMKX/9qLKZ/T+eL31WkD+v7OU7p+fEFN/K7T/VcGWaGPa0rbe59BY79qMayj69bo/dknIiISTfHd5RcoFM+gPR5iGmWx73ZgOr/dZr9R39Gf3kM7n1Cz/Q/5Tj/8NC9z+jXq2/52S7K/L86ZwWAwGN4/GINsMBgMBoPBYDB4CHwt7v9XtFs3s198/T/mnrLVC8bhevrbrFwkq1UTnOuJ6UzgM6H9L8DOtp+hvpBa1NUW6K3KS5y+9zW7aatWaC9lvbmGUvWSS09PTCZXHS7m93Hav0IWWOtKGo4Zint06DgBm7T64p6IiJQevhYRkeXv3cLnS8eaaWSt9lHHPr3fZV/Rj8Zzx6KrRlI1m4sbYOPiCeZisUUXhZETgM4O8N2ijTHv/GZcaC9noV+dujk4AFMX9K54i/pTk/XeohvHqceIxxvPlD7YQrcB9S0ulNmnk8I52PJsxrVC15H0PuZNfaRFRNLpRkS36knpKpKNOF+etjvYBQuYnsLtIdzCHGfsm+9pnbdDt5KghTnODlCHsC+rPwAbqZ7HIiLhM7hLqDOJen+HrCNl37LP7rsyL88K/Q5uH+EC+6rOJNnLN67MlhfTLJJrXucPwP5WjrHL0v/aOVWUyeg3nuKZvvxvce3O//QMfeMzDfzfAdQ6r7tgmVWze/URxhNyCcWePrr5S3qA7+J5a7x68vpEREQijmf085t5mfr//ivc+wA0dlqjTpnthWOsi+FPD1yZY7w/uubrP17yHjq5rFU375j35nOsnegC85NNUUfQwPgWd/CM+58494+D/4S12f8JHDu2/wrjSOjw8vqfY95u/lu3W7Xcw9qs/l8P5S/H/6tcrS82trkMBoPB8D7BGGSDwWAwGAwGg8HDtWiQ01IosxsVWdX0SD0Y3sA7cL+uqyaUDC5Pr6s2UNnn8sCxc6rfXHaps2ygu8sW2LN4Sl9V7zR+Qs/ctEzNs7JJvEVdFDLvBLpqV6uppqvRrWANDfSC7YvHCdG2V0pkeBfbuCfeAyM1uQG2ue14FFkHAAAgAElEQVSnzHE+1h0mjZEtG92OOR5cL2+51LWkQieFK8zLdF81p5p4hnGumu5vndkO/r3YQt86T5kAF6JPlV/+iLqvnLY7ZBJfuqDv8mbCIX2q07Fjt3PtN9P+8rRCTTycTN8qE/Hau1IQRUSiN2DyCrpzasMzlonaYPCS49NCe6qxFhEJdBybP9XtI3jH34aaWsi5iMhuJ0xHrDy9YD/ceJJe/53jSFlW64yeOjY46V8V+hvR+1fnJPfYnjlv6/XPVEuLdbDcwRrqfUJv6CoY5t7nbpGWr7jzwWvrP0T96/8E5l2ZWH03UQZtz7jO9B2++Ck16tTCq0OJiEi5DxY7ovvL9BN8Vpft5V0w14MP3K+b1i6+m93Cs9QEP21v65s+23Vi9MY230eu6+o5Wrj4CX+3zPlue+/poo3fEY1jpjoe41nO9+vsE8bZ/8qtnfoZ5uuSmu3KFRjqRZc+2V+jjv6x8zifHuDeWydHIg9Ng2wwGAzvO4xBNhgMBoPBYDAYPNh/kA0Gg8FgMBgMBg/XIrGIRnNp/5+P8mhZ3YLO/PAKbr/rAatsI+AiZpiAH3ixP+ChHh6Ey5bYXq4yTjrtMZ42c1qOkrZD67T8MJZaqakcI/NDGHi4h4fBmq94wIuWcc0aJQ/etrzem3A7vHlKuzduuW/RtiqbuLht7X+sUgSO9caxs9kSEUkvem48+g/eu/WE27pqQadz7sVtdxmdnR8GfPam0N76K9hwxRdOKrA8Qr1lPfioQQqcv8UdHFiq/niWl0l2USacFaOzNVBldQP9KJ17c6DhJZzKsM8+sJ1kC2UiL2474yG/kONaU1oRfA2bLQ1YCYfuQOTqCP0tPUN/070ux0U5COUtad1th4dDJ2kQEZl+DBlA/SEOg805B5Lt5feUL3GILJjwsCGjwZNDrKFwgs/jj7fyMs1HjCefYz2MP8fzbzzBetPDoFHfzVswxr3rDmQRlTeYtyrHpZKheOz0BaUhnmH9GH3o/1IjyDFPKsup9J0MaEnrPpUmqRVg6xmujxkfvmq7dvb+Bs//4mush/2/wHzp74FoyPVYdpHjWR3rVuVS1QGeR+Wch/M+w7haT510pX7ONZ+hj+N7WCMNui+mfFnWLkFdtnlANRpgzOtdHnIdM3xmgkK1V16QC9dz7Yx9O8O6WNcYMX2Ovu/80r2n5Y/R3/ByWAwUMhgMBsN7CWOQDQaDwWAwGAwGD9cTFFIty+qLu5IwrrXMA2V+VOuah+dKV2Q6SeCGZNHUVqp06qyT5odgpEpk+TSWVuuKO2CmCoEkcdHWTW3elDFMWFdadYd/Ih6ki9l2soN6Ax64W+7R8mrhmKF4QLaRbFHC/scd9Hn2AdjH2mPP4oyHv7Iu6yf7fPUVGMTqJZirctNRYGp/FQ7AJqotVRYXXaRCLyRDWcD5Fsa4uyBDSCu16O8foW4vkKR8hjbzg3vKsJM1r5CtX+uhNBGJrjDW/EBaUmTOSseoU5lzEZEs0QN9GFeycSgwPOEhS//QmzLfPOyXx3h/+xg/aS+XTBwDHF/2WQbzFjKERanrlH0K6x7dqM2xTGPMQ4a0f9M5KIxTdx90HHymkR7k485Fa+ACcnSXQQNimmS1U/Y51Aj1smO3J19+hjJ8B2Z72LHo/56Gf9D27XM3B7Me5ykFc3v0L2BXN/setnLzbczFbNe1E2EY+aEzjdUef84LCwbujN3f1pO7YGVLE/Tt6nOw5R2+l2MGhUxveRHqDbCwcwa1LDs8jHoLa7T5Gvde/DO3rusvsDbm+/ju4K9Qf+9nGsOuJ2fd2jmd411rnPD3C0Na5ttob/Ax7mt+5d7Tq0u8u6OvsK5rZ+j/5BB9/aOffSciIg//yAWFjD4Q3rsv2eBafq0aDAaD4b8ijEE2GAwGg8FgMBg8XAvVsWyF8vof1WTZBXNTOwWjFztJqKQkqdIY15Spihg1rPZrkjnt6dUfksH5HmxZTEnmfA9lOo/I2i4cYzQjK5ZQ7qi6RP0ZkGzy+0ZJo9TJMuWs3NNiWbWXEhGpkXHd+gF9fP1P0eD+34IZO/0Dahs/OsrLVK4Yn0vWTCOYr8hiJWSL249c2INaVsVkwjRmt9LHhekhGDE/yvj/Ye9Nei3Z0iyhz5rTd7e/13t/7v6aeG286CMzicgkS2RRZGWpqKwBSIwQCMQAMUD8CmY1QKoJSAxgUlBZqCgokRVBVkZEZkbEe/FevN7b67dvT98fMwZrfba33etBIrgS8tS3JsfPOba37b3N7Oj62utba3QTkywu09JqFQx1SCJ3nRZhxUdOT5ysUPt7nXroRCn+/P+hohUvsIKsaBa3Tf1wSD1u0sBaRAeOdR6/jlji0idgM0O2VXa7996WiIjUf/Y0a5MFkJCRzBh+1T7vI2AjYjiIiEhKDXM8AsudlsmmkhVOydIHPafzTWhLFz24i2M0Cpx2a+d/gAtVPXChLKWPMM6QOvVMa08WWNnn5K67DyIem6rdm4aXaDT413D+8DO3Bs1fIpBEWWa5h0CVyjGuZekJ1qDG2GcRtzNS6KL/p7fR5v6ffYk+Na58w+mjg33oh0VDXqhtPjmGpn7lI+wwhMOJazPk9WbEefo15DcHvD9aH+DY2vNG1kY+RzT7xi71yXeg5Z41+dvxow/xfevbWZP6Lm33StzV+NHHIiJyLXhfRESKXYaMzBzrPK9jDcpHtO77GOetbkFH3nyM85/uuGdu60dYy8op5tz8HDsHyzidfDoBm1/rOka8/AFeC3vnEkxNg2wwGAwvO4xBNhgMBoPBYDAYPFwJg1zsJXLzR0OZtsCAlk7y0dA4E7WfZQYCUM+rrgIatJCUM98GiabQ/i19hf5iVvJPVmn6vw0GTt0ARERaLTCHysYqAzavMFxkxHCGvmPANEo67IBBLLfBRFafqh6XlfsrTq9aOAermG7DIeJaAeyiMqM3B3DgCL2gED1nUmIIA7WszacagIBxNJ46vWrG5BJLDzH3whnWZHyN2squc7EYbYGFm9bQb+sxxhqRcR1vgHUsVF0gyXQdxxbpOBHw/06q5Z5Qh13ebrvBrIN5VKY1ZahJusD8pgw8Kfc9na+S8Et041BdNrXjgeZ4VNzY0g4joKk5D7fBfM8fgJWNp2S1PfeA+TodNLa55i3qyFWjrhHkJc/FQs9HNnt0E31UqW1WZwfVeIuIlMioB4yaFmqskzWsTci16d12OyO1R2Q6yRxP74E9Le5AJz2vY0zq1iIikhyBnVXtdvoY1g3Fm2DclYUu9Nz9Fvcwd12vtV9BKJvFbTO6Ozh0bgxZ/+r2Qf360kOswWwF1yVserHrf8UQlNcRpx083MY8NPiE92G61XLz4XrFXP/oEPdVtMN1vAO2e+lLx/BHx3jeEz7j4RZ2OxqMk1Z9dlJw/++vfMVYb7L1Oi85BlMe835oPHfzCXi/Vffw+xNsg70PWF9QPcY1X/rZbtYmbeD+mj/bkXThfo8MBoPB8HLCGGSDwWAwGAwGg8HDlTDIwWwh8WFHoh6Yy8zbduYxyGSKsr/IySClZP0CMjmhp3ldqpGR3ibDRearMiCzdkS/1ZljzUI6KoTqq0x/3ZisYKAs4wu8SjXKuMpj5QjnVVat6DHVKbWlKfWpJfoHq3dyYZ86WU/jqsdG6l1MtrFElrHYxhpE+47Ry9rScaI0BAunfrsVstCBpwkNybyXGljz4vZJbs6lcDXXh4hIPMD1yK4dWXP1kY7VSaTrvJNV56trqjHSyqYWuBsQjN15Sgf93DEpdb+qv9UoYL3WIs5zOuRYdI3jLpj+YJGPiBYRiQZ0UtG2vXLuvMGIPs9Ft2OR6py7GEM8JvPNta/toc9o7Pl7672nTDjv+aBSEh+FvmsTkpFOlXVWxw6uY/FslP9cRMJNaGZVSx3y2g3ugs0s1zG/9gN33nIbc2twt6Z/HfdX6xqY1+kGdecD95wmFVwHjX6PJljbwTWcd7xM/byn+9/cxtgG1xnr3KY3szqFbEDLe/qq20lY+wL38ez2GueKl5g7IfMW5jPadDsJFTLD4w3MsXyEMQ1ugglXvf6i5GoFVngfhOp/zt0b1bwPuX6dV9x9UD7CfLr30O/qIZ6XKRnw9gOMY+kDN7bJNaxlaacmQd94B4PBYHjZYb/kBoPBYDAYDAaDh6sx7JzNJd0/koCszGI0vnSIVvenHssn4tLdtK3PBisXltDDNvPQJYOozGIO9LBVj1nVIAeqk9XjIueDrOxsSi/bUHWkZDfVocBn9BLOIxv37gFe1fP3AJXw6sOLRmQRQ2VpcZ5Ix8hxZF7EIpkPsa5TOCS7qA4L1Ev6XsMRWeu4CgZssXeYO9/iNehWw9Vm1mayTHeRbjU/V55nskZ3gROni51dB6OmDGTARLu0gms9W0KbIHHaUz1mvgXmTjWgmrCn/tiafCfifIEDJukJ5zxfolUJXzNvahGZrOGzShdznK2Q7ea8wjHWet5wjGtEb95wiu+Gm5xzxzkciIjMa+6xCZjYF/W4c9HH65Qe3hG1z6N1x1AW2tS2Mumwfxvnbc6Z4Ejdd3RtLWszo55bderVfZync7/A/vF5+w13j5bOqLsfYt1Gm/huwITA3k18Xz1xa6AM8ayO6x6RaZ+28H7wOu+zifu/despxnn8de74nGNNdJUmd3G+s3fc2FY+gX789G1q6If4rrbPeTygX3nBscGLEtfrBnc1tjB+TfVbqIFI3Z2n8Zz3Bu+v8XW9v4XnwZj7t53Wv3yG69N5gPf1Hcyncw8nmH6NmvS6W7f+dXxXurYh8tRda4PBYDC8nDAG2WAwGAwGg8Fg8GB/IBsMBoPBYDAYDB6uJmq6UpLknfsyZ1Fd8RRbkBrvLOLZu9GKKftOi7W4LR9OnMSi+ya2ZmvPWYzFbfEFC3gKewxN8Aru0joLdgoX7LwuBF7I3G2p6rZ7dAppgxYOxUd4n1axlZqU3HJFZ5RJnMKeavEqbKmiJ5BazF6HzVvhyFm2BVqcpSEVlJMMX1vn4Bm08txJLNRmLRxha3vGQqGIazGj3VY0dOs2WcV4JwwkWf6Qdl60uCruYMwpZSAiIrUu1jrVKGktatI4ZBYoJoeuTeFi9HKJW868ppUexpqeeDZitMoKWBCpMdUqiQlvQP6hkhURkQWLG4OeKxAUESk8gv1WOh7z1cl3KpRFLBheEWux5IwSAUpXCqHbwteCQY14bvG+Sp/RUu1t7LmHA08mtE8bMc55MdNwDkpfKIlZGt5ybfbQRiVCyyPcK3o9YlrH+Wtduo1jSs+1mo3XZQ/PQnUHfU3rLoyj1GER6BnlQAvcv7UvTvg5j/VSvWvbaDNVOzdGmLdfpfXcTxnzPvKkHCwCvUEpR8RCu4TXvcgC3ZWPN7I24UPYIa6Ft0VEZMHfDo1z3/zXuNYn33LhL+Uzvccxj9ZnkFqdfJNWe7wNE/9XjcMMJ7g+tU+59pS3JEXcj/Oqa9R4ylh3ymOKJ3ivQqHuA6xNUnK/IYUh/93uvrAA2GAwGAwvF4xBNhgMBoPBYDAYPFwJg5yUQundrcq0BjauWicbOHMs07xCJnTOIinGLMe0zJqx8KkwcExo7xYL6QIUWEVjsDSTJbJnAYpnlJUWcQVUCeNoF0X+H0CTrPWtl78RkC2tkOXusWiqyuCLWSNiX17UdJVsOYv0+nfANjXHYC67r4ChasTu/yAxrcZmSxfYuXu01MrWyxXPpQyniAcLjo2FY2McO2GBUrHvCoOGGwz3YIJw+ZSWc+y++jECDpKRK2rLbOu0SFKL9JQdZuGgFjSKiKQdzQvn9dbP1b6MrK1fTBk1UACVsHgxVaaar8rmJx4bnBVcaqEiWWhlZxd+IaS24Xy0TTrg+ab5WGct/MTcp7l5BIx1TshQhzO11HNFqPNuvuBSx5po8ST7jM86WZsF10OLQjW0IunTAo82gHpeEZFQd1h6mPN8Fes4q7D4VC3wPHe5eRnf6S7DdF2LRHGs3vfzqldA2MkX2GqR3GQJr7U9Prceg6y7P/Epxj9fw9gKLHZLuAMTeEy1wo2BOz36O/D8lOd1BZLFPp/HKgtUaW045TOgY0rdYyqjDdoTLhiEs8PgGFoQzvk7MWt4Bbh87uf8PVswMl0jrucVHDu87mzeND6+2aiJnBvvYDAYDC877JfcYDAYDAaDwWDwcCUMcjScy9KvTiVhWIGGTQQTF0CQlvLWR1nAgoZXxJFcxHIJetTKM2hds3AMRs0GB6fsw7GahSptnNR+TfvV8A8NJvHs5FLVobbBki6NEf0bMoK3TA1l6oU/BG0whAtqdltk5dKn0KuukJgM/aAQalzLR5XcuDcmDIGgXrqwezkoRMddOKeGl+xZssTYXU8XW1vFZ7MmLcG+Ykwx+1hcg64z8mKsF9fB1Gnkb8YckxFdbDE6+dmhW4Ma50EWVq+x6j0XG9RL+2EcLbCKka6Xsr9kdBNG9karTnuqwSAho5cTstnh/btoqtpuj3GVTejIQ9Uyr63kxpbBu++URVcGd3Yb1yWzK1vl2GoulrhABjTTP5MxDlYYw83rP33tWtam+Jj3JpnkxX3oi+Pn0ByrxV2UeIEkbTxTGmwS7+O+q7TILFOTXmw7JrRMDXJ5F/Nqfs6AGO/eF/GCScTdg+EMbVUT3NhmgAjt31TDLSISdjDH8X1ojMuPoPNdkIGPSsriup0RNzH0VzrF+kWMx9Z7p7nt1qBygOsbTVkTQI1wfTev+fV3eppf8l5hfYEGB0VtrH2pjb6qu150NndGKkfcrTlU9h9jqhziGjQ/8TTi1zm3o9Pc75HBYDAYXk4Yg2wwGAwGg8FgMHi4mqCQNBWZLzJnChfn7JgUjSzOoA4BPEaDPHLxutToygW2OXPA0P4XHoOkn2n4BvvTXrOxeW20+j3T12rgBRnWrI3POutnqqGd5ccSaP9+RbvqXTWYJJsPz6ODfFEVvK7ThTXO1sIbm4ZxqAb8Ury2rrUXliIXXT70vQasKMvuuz6Qfc0+CS4ck71/wf/D9H54Qb8iL7hfXnBM9l5ZYD/8JdKAmPw8svPqfeGfJ4xyx2ofWb96+sAbx8V5aLALP9d5pOEL2uh5VKce5c+fjUfEraHOldc76zfW87omCf+dBvl1y/TKbBN43+tnCbXvwSKv4V8U9HNPUMyxJcWL91D++mfr6c01KeTb6FiVyRZ/6PoTwa+SzKEmyH2vY8eXeeFztqN08br454n0ntfz5p+B9PJml9M9h8GFzgwGg8HwMsIYZIPBYDAYDAaDwcMVMchgKVP1FlZNqseEBqEyu/ybfHGBgVX2NvaGdJGIUTYozTsf+E4EwYXvMlpWWTLV3U49LWr0AkrIG2PGPhfc2FK6FmgEdMZIahy2Mro+e6ds1QVWS6ONk7h4+XtPJ4wxXWTVL7DcIhJmDHKSHwP7nTWoW207TfW8Tv1w9UKsNq/XnG3iiqvcT8psr+tU5PqQpdM2YdedZ8F43ihVP2zOQ2PK6/R1Lnt6b+puVRusceKZ5l2P89ZtzvMU2E/CGGmZ5l06MqZSnD5ZteazBp1Kyvr+MusZ89jsKiubqs4NnN+s7u6dItdYr5l68Gpfiwtjx7h5v+o9SM1zPMrvJPjuEurNG/DYYje/o5DdJ74uW2Oup3kNcmGA97OaMsiuiWOz+X5Cr2l9Njj2aOrf1zoWfV54PjqELJpcg747UTREP/EwX89Q5DEZ652466NafdWIB8ogL+guMtb5efHhquce6PrR3YQe7fFA+3aa93gAPXQ6nV16vg0Gg8Hw8sEYZIPBYDAYDAaDwcOVMMjzRkGOf3hd5jQ1qJwy2SpxTMqsir/F4xGZIiWM+X7aBLNTOnNs1tmbrDBfg6uEMlCTJvpq7DQvnUf9VNX/WH1c9RitcE996SnHUtsFy6Rew+Xb8A9W32W/TeWE7hsncAA4fw3uDM1lOC0cvYf3rUfTrE3cx78nq2AQownmfvRNZcswxsbzStZmQS/b0jkG2blHRwAlkOkJGw/dGozX6F27is+u/9kGx08v2x9/jr67LrGv8Jzeu6qPVoaPbYrPcKvMfeY9yWuaAybQJZom9wRM38Jzl4iW4ASwaDtfYL+PApP15odH7ism9M33kJwXrcGhIvngM/T1AteAcBfHzpXF1FS6C2P22b6Mq6f2t7LDPug2oRl1acet22+ahxwc5vqs0tFBRGTRV+9knLt0AjcWXadot5o7r4jI/Pe/KSIiBTosjF6HI8X5a7guy1/i9fRdt2NR6NJtYYnPzx9jDWZfwGFjuoTvp3erWRtNqxtskbXnfXD0bXxff8br1HfrNnkV/Zf3Md7ed+/g2M/wDIxu4/egd8s9QMtNfDddxrXtXycrHOCZXv8pnFee/NFa1qa+vcRx4/2t/x3rtfsDMvCjy9rf4TqccOr7cCSpPcPaj67hOe3cQ9vuN909WhjiBIff5ZAWcBnR353kd3DN94c3szb6zN2e35f01x7zbzAYDIaXEsYgGwwGg8FgMBgMHuwPZIPBYDAYDAaDwcPVBIWMEln5pJ8VehVOsdWaWZCJFxSi1mkX7MkqGibhFZs1GtherW1jWzQcYXu/sgQJQhao4RXpaQGUBipk9ldqpZXmi4N8aJhAc4Hzxkd4X2GxVFp2yxWdMbhBg0KC2+jjMWKcV+Jb6OO4l7XRop6oXeEaYO7rIbeRKWcoP/e27bWokYVPK2NIFMIx1mLOYqZo4KQPkzWswaRFqcAzBmCo3OAGt9x967ENjEHjlbMiR7UkW2F4xYELRxBKH1S2oBKLdDbLtzl2wSfBErbQIxZGaniKFHi9Vjg/Lz5ao6rDCtZNg0Oirc1cHxojLSISrkOCkBxAqhEuL+WPeYHVXdpjwI3O6zqkKeGT5yIiMrvOgBUG1eTmoUEhavOncdg6j/u3XJsdyC80Kju4dR2fUwaiISNy4GQm5SeQYWQymY7KL3DdKpQ3NB81sjYlBoU0H+L6b/8U81k5hHQkWODY8pFXVHahyFWj4RdFPgMsei14xYClZ7i+s01cu/pnHCslKmU+8zVeLxGR9BzXsHSKNvEQ6xaN88W8ax+589T2scbjVco/+Buy8olwPjiPb/O29BBrrM9hQIlQjb8lQYrzJwVXfNp4gmMnlIE0vmBg0Dque/fXuLY3ftnP2ow3cW+Gj3cl8O5Dg8FgMLycMAbZYDAYDAaDwWDwcCUMclIOpXu/loUIlGlp5TM50zr+Fq+cqc0WXgoDvB+vgn2s7rjCJC2oG2+iiCjQnI2KBg8o8+qNRQMAeG4t0ovGWgzIyNyqV8zEArdqhUVLSxjLvArGcLjJeN+xF+NLC7PgOguHlvG+HKFwp/MAY16eeRZ0jNydr5JBppXW6Vtg52oHatnmInm16LB8Ava5e5fFdAUW8inRO3VRudMm120Vr3UtwCuhr/hffYjz+AyyxjXPXsx+BWRXcywti+MSZU+TRb6NFrB5scTpORjqkJZtGs6SavHfF4/RxGN2Q8aHJyzgi8hMz3f3ct+nExe3vdCIadrxzZWdVcZ3jvOFJd9ODmu40DHO81Z90Qdf5MYsIiIZa673Na83C/kCst7pV8/c2DiPUG3czjq5PhZPcGzUcGzw4e8hqlqZ2/ES1rT9dcyj/hXumdbvH7g2Z/is/QlY0v/s3/tnIiLy323/oYiIjNYxr4UjT6XI+sPhde608JKufxOs9/4JGdeus1qbVTg2Pkfjb4BpXfsI5z9/Ayc4/6ErhGs8vSciIkffxLUbbaJtNMGY1n+Ftej/Q1cQefyYrPwmrvP8X2Assz8Gwzua0Iowdvdh+zMcU2PhY+UE12e4ifXrfA3n+be/9cuszY9L3xARkeUfYC136yj0G21gjP/V3/+fRETkv579u1mb0T2M6Vb4QBY/siI9g8FgeNlhDLLBYDAYDAaDweAhSK/A1L62dit98w//i4y5rByDpdGgAhGRRYmMLmNc1bJNAwmU6fVx9A2wfa2HeK9hBaNV/F3fegw2U+3SREQmK7QWK2lkLs9PNlo1lQXPFk2Z7soR+jt/HQxQY5vsJr8fLzlWs3pM7fRjsFcnvwV95+qHYANPvgHmqnLq2KxiB22mLQwq5Bp07mHMapO39Phy1LQeO7hGO7wO35MJ0/c4BuOdLuGztQ+51oyeru6BLY6eOLYxXQdbHpJJvhgUouEZQdtpqpWxVc1u2gRzGIzweUKtbrB3krVZ3AfbGD3czZ0noAZ5/CYY+NIHjy+tgWSBITyf6syPoXnNBb4sY/2lTSa3jrGkZG8zvbTHiKt9W7iOnYm0QWb6Gcba/7feFhGR8rFjqgufbeMfFzXbylSzz/St+1mb6LCNJud4Ddew9qrLTe/RPuxztwYXNc0hNeOzLbDphedY48F717M28QD3UfEQ1+z5H8Lq7NY/hmg3qHFNqh6F3KWullpxvT7dd3G+KnXA4dBp3sMdp5UWEQka6DfhdQlrWMfZvS13zM9+jTFyHukq5pEwbCZ4iHUd/Jtfy9pUd7G7NGeYTPGX+GEY/PAN9DV8wXOjQSfH3AGhLlo16YtrWPuzN+tZm/U/wzG9d/FMNz7C/LSO4uB3MeaVz9x9oDHb5V88lp+2/4l0ZseWN20wGAwvMYxBNhgMBoPBYDAYPFyJBjmcp1I+W0g8JotyygpxL8AjSMCoRRoUworzcARWVWOJNQhBRKRFDWZtn0wx9bzRhBHAp2QDPRZcK9lTxgEv+KqJ0xoTnHr8ToH9Fk4YCEHdb7GN886aOF/lxKvc57mVYasdYB4abVtuo8/qjnNjCHvU0I7JrJLdGm42c/1XDi8zU3GXrGyxznmSDSZ557P1CdnLcKYaZOq6VUv71Q7W5syFV0RkVhd0jLgYFBKSgZ17IRlBjHVJF2RCT/Few0bCMzCHC8+RIqLGeaFM5YXgjhLXc9FuyyXwu5DMZxZxrTpmTxsccj7qgBF0ehzrIjevnPZ0LAkAACAASURBVD6auuSEISMRWWjto/khGPe073TyiTLEF8JKgkIxd75o27GsqnHWNsqI6zqFj+CakXia6pTMuz4vkyW0OXsDr60WmOOD73qR1h1cj6VH+Gz0PsadvArHFQ2s0TAaEZFCDw4aww1Gj/PynDCApHQb17QwcM/CKt1d9DmcV9G2TF357A6Y68PvuECSm0/w2egthHCM17g7wPu6OcPnJ2+7+TRa+D1Qbf21AQJJjr+uOzJ49QN9Gs/5HJa581LmTsUaxqLBO2dfd89P5QzM8eE3qdkfgjEeL6P/zm/j3iqfOa3xYAsnvbGzJtK/kp9Vg8FgMPz/CGOQDQaDwWAwGAwGD1dCdQSzVCqHI5nX6IN8dtkHufDX+CBH/cs+yKUeWJ7iOav+1bt0BrFudNa91Cao0TWA+tT4og8yGaog8XyQlZk8R39FegvHx3gfdxmh7Psgn4PtUx/k0hJZTfoIVw7A9IbdkRsbmcco82LGuOs7ngZUROIzx7he9EGuHJLZu+CDHPedJjSgLUE8JgPWIdutc96AR3C08DSb1IKGf40Pcuh5Tgd0ovjrfZCdNvj/kw+yulWQmYw2wUKmIzpwjB3jqj7I6iUcMuI6G9v/Ex/kLfSvbPScPr9hzUWBh5z7X+eDnN5Yd23odKE+yEINsl6PzAfZuz7hAfXJGv09wJgadcxTI89ru5d9kNUZpvgZxhSegyEvsh5AWVucgLsYXB7V7DeeKFuK976jS+EQY5ttYX1KO9RY89mIy7gvKsfuPtfrXuzk6wjUB1nZ6OZTd7/V93BsYUi2eYpjG8/UcQOvfj1D8wnW6aIPcok+6LUKxqzuNiIiZWq2G8/wDJd38T6c4hmPn5S5Js4HOR7znjg6FXlB9LnBYDAYXi4Yg2wwGAwGg8FgMHi4Gh/kUij921WZNvD3drVGvfHYc7Eoq/ZXXSyoQVYXC2ptfTarfV81hWByYupsxyvovyF0Xpg6pm3Wop5TXSwKeT9k9XX1x6b/TSiT5e7dBRtUVx0z/YNndcc2lquspCcr278DdrPZxZj6t9FHxWOdC11W85P1Dal97t0mQ62Hhq2sjTKG0RgDH1zneXt4Ha5RW9lzvrSDTXw2ZTelDplDsoGlAzKl4uGCm4S6mwTqYqHMf9GdR9QzWVnYYn6XwPXt3meOCcpUs21ApnrRwPf+/9zUqziIqXVVDTLvJZ85zqBMtzLTyhgrK0uNsEwd866OFln/ZO1VRzxjUmRx4jHvXvvc+fi5st2ht5uiumftV51DEu0rJrs+85hI/Y5subp0qFNF2AZTXWo7N4Zij4y0nlsfrR6O1bs5LTstbchdjiJT9pTJjekxXDnRxDtvPlwnTbZM6fetax5MmIB57OaTeVqfcBdoFefT+10O4coRzZbd2PhbEdPNJjzE+WKmS8asb/A1yFpzkK0pUx31vi72MLZ46J7tqI/7qTDgfcDrELM+otihdrvqngV9tgwGg8HwNwPGIBsMBoPBYDAYDB7sD2SDwWAwGAwGg8HD1RTpzVMptecSpOiu0GXh3chtP0cjbnFyy1MN/IO5botyuzx2f7PHQ1q/9XGsFqIlBWwJ61ZoMHbnidWWjJZPSSaTwGtWDDRy270pi5XCAbdWhywC7LKAKCtmclvRcQ/HahFWoU8JArfLS+3FpfNokaGOUS3pKmfYqp1Vw9w8feg6lTpqQZdfC11zEZE5bbuCBK/xiNvwXPN5E1vERZUSiMiCRYYRx6RFjCm3phP9fvyCKGqN964zEpyyifkS3scDd555jXHADUoBNGyE0oQFCz2jqrMES1mkJxXKM9TWjcEkIQvvUq+AMGnRDk8L+BhekUVYF3jre3KQUIvkWIQ3X0Ef0alKY2gn5t0HJQ0g0UJRykk0mET7nK24+RSHLDqlRCVZoZyBUoWkifOHlctFbS7KGjKZQodrzutS7HqFsZQPhB3co9VDFE1ma05pReBLOdR+kbIILYAtM/BGrRt9KVQ6YEElCz2Flnoqowj4jMSeLEPlJdp/2NH5sX/Kason3rN9rsewII7XsnTGok1KljRuXkSkeIxzByzAVQtC4XpGXVwn37Lt4jMc0JJQey21m7m+RUSiMcNr+gMn7zEYDAbDSwtjkA0Gg8FgMBgMBg9XwyCnqYSThRRYb5Mxx16AR1Zs1mchFBlRZX8TFmdFxy7KuPmMBWlnZKJYbFTS4h+GcviBJMoqhxOyjAWwTAUyXikL7lKPZYqUzSazVz7UPmil1iKjPHRMWzDiPGh1VmznC8WygqJjL1iDzFSU0IpLmbtbYLEqR2SY1ZZNXPRu2APjFZNhVWZc2XU/brvYUyaNbPMhY3ZZmBQcHIuIyKLj1jokq7hod3Lz0IKysINCqPnpmWtTU9s1rgWvS0IGLepinoues8OK+G9l8hItOuN5imSDE28cWQiHhn5oYZ8WwpEFVsZSRCRiGw0cCWmppsdmRYixewSy8+zsiYhIzOujAR7NX+BzLUoTEUm6vdwaZIV3egzfF58eZ20WR8e576JzrkmPdmIPySR7QSGLbyNyObMyY/DF6Vu0HHuOe+jg+67YrNjGvbL8BdjN8x9gfVY+fUVEREYbZE29ak2NNB+x+FOLz86/psWifHXuhbKVor9FlffqjHZvX+H801cQvLH3W84e7+5TBJv030UAyqTJ3ZOxxqFjrPvfd8xufQe/B6N1jGGTz/L+b5MJ5+VLvV+15S8whuohLdq4OzNZ5po8QJ/td9yzHc0wtsNvYUw3F7dERGS4gY67fwv3Q+V0KWvTu4WxXO/cEnlcFIPBYDC83DAG2WAwGAwGg8Fg8HA1magprdZUq0sDf/Gd1MjYarxykAWGzHNtAj/0Q0M9ZosXv+qxvuZPz3NpjGSZScoFkRd4cSG0JLz4fnrZ+F/HrcETOi99n7HS3nyUvdR+tY9wwqAStYryNKH6PxjtR5lpSfK2Ur6NmOqvI7KBqvMMAq652qV5LLpqgP3gDLThNaWFWxbR7LfXNsrsyuzCebw+9d96PcgcBxf6yI1DmV21R+Pc9Z7K7MT8NsoMa/98n0VNZ7Z23iOg0dUXdMo6z1Tf+2t/cbyq0dU2es298+g4s17iC33ougVurcN5/rqHvI+V8Q1nfPUk4vpv3V1Ihux3wR0Svd+86egzp9+F3AxS5lhtEkN3u2Vjm2u4iN6jGqXN70P/MVKtO8+n92o0yd/Xkbcxkx0zDXLj1nnqmHKXR9dHrfl0LBpbr9+P3bOg448mQe69rsV8wufL27WJtP1snts5MxgMBsPLCWOQDQaDwWAwGAwGD1fCIM/qoez/TkNmNCaoHDHswWOM5lVqFyd5Zioe4f20ge9LXafrO3kPn9V2EBYQUZ84WcbnzWfVS+eZ1tW1Au8TL9dCRCSJyYS9gDWr70Ez27kXcx6Y0LSJNouiY/SqRzi2tg/66uRdzHnlU7wefQsDaD12TgTqMDBaZZAK2avjb9BhY4zBNp463WVS1LYpx6ZaTc4XBfVScDJfGW3g2Pn6jOPe4ETxsv4vqJv1InET6mw1Ljpzd6BuWTXB2fcisuhAXx3EjL8my5wuyJ6OMcjEC/KIGaO82N3LtRVlpptY8/T4NGsTNRnbTJ1veH0L/TJGWqOofT1x0uXYyNyqnlfDN3SsOgd8yXuTDhrJ0UmubaiuDwMnwFXdsIRR7nxZv8pUn3laZ2WqOZ/F81285/WIeJ5k7GnRqbctnGCug1eg89UAHGU5g/mlvROZNtF25TpDMubU9ndxf4zW3DWt7lOLTgeNNNA+MI/lT/G+2PccQ+g8U/0S2ureu5siIhKf4bqpXlrvYX9dih2MYbyM8+nzW/8AfU3/0Ll/zDrUuHMpi3u4J+dVOrn0uRaeCUzvBtn6EP03P4MmfVGkHprzSypemI1uBhT529Qi855teuiz6H5cpiwrSJbrku7md2EMBoPB8PLBGGSDwWAwGAwGg8HDlTDI0SSVpUfzLGpaI2V9jd6inNeeXoya1jhn1QaKiAzXwfI0nzHqlX6+4y6G3XhK5tDT386zqGn698a/IWp66jFGZJFKR2AGF0WwmLXdMceO881rjhkqndAzeQdMZ3MJle/lZ4hQXlpax1oceOwpvZPjAdgs1WbOK2QsI6xNc9u1uRg1HaRoq+4VGrvtM3qFHqOmT8EM1sly69qmK2AfA/XWFZFwfRXfMYY489tVXXEV1yI9b2dtImVs9Ri6Wogyn0uMuPY04ik9hsM6txvUD5d9JHV+70VaK4Ob6YjJzobLbrfhIvS7hOMN1HdZnS7Yf+TFLGcsOtnToEXm+jnY7tka5leIPG0wXSyyeZBxD9lW+5Trm64NY5QTMqrRJu6V5ISsOWOkA4/dLj4Go6qsc41MeDQFI19+Dja19WjNteE9Ud3GGJ79Fb6Ldh9jHGTr47Zb62CA9amqywwFvSt1UKRlegNrrLOISPEZ5qM66/rnjJze2RcRkdII91a9tpW1WXCuMaPHW/oM604C13Pl1451ru/gPpi2qIcfqiuHxmFfjpoun4JOVicXOcJ5K1kEONZv7t0HtUcY/1IT39W/xDOd1DHW9qdYi9VfO4Z/1qCDx5M9CaYv8Ao3GAwGw0sFY5ANBoPBYDAYDAYPV8Igh/NUSqdTiSZgdkpnZPw8ZjciC5v5H+srvYbDMthO3zGidghWp3SK/tRfOVhQ49wm4+c5RcTsN7rgd+z7HqMPz8WCJFXUBstUOcZ54zYY5bCKsUVjxyDHZzh3Sn1t+ZhewGRgy/RILZw5lla1qwV1JCALWF6ltpXseuHMaVzVuzhkUlqZ84rGavqqaYNeMqCmCc7oLXyM/jLnEPaVeilymeZYtbqqndV1U03q1Ak81dkic39Q79953pHEP4+yfsoLqiZYxxb2R7nvcc48I5dyruEFBjt3nPoda/8Lz3bBG6O6XPj/zuaobCZfY/Xw9pIb9fpka6DnYxLcJdcO79gMhQtMvKbYea4caYX9lbjmdL6Y1bmDwPS9Sevy/3kruptSohc4Ew8Xy/Sx9t0y6Bs+W1JvYerZKdkdcUyx5/pQ5rOraYvhUG0luJ68T8bLbj41ro+20QRF3VFKW/TQLrmxLSo6Z+ril3DMrJqvEUi8X7XSGRM69TliwmFSwZhnTe3TtVk0SrnPUnqRzxps07qcJpiNM4rkBR46BoPBYHjJYAyywWAwGAwGg8Hgwf5ANhgMBoPBYDAYPFxNUIiISBRk24xJUW3MvGhmGvXrVqoGaaQaLsLtytmKs3UabuDv99JZfpizOt4XSrSnSr2itgrtuyjp0P61wC4e4LyFttsmX7BNyG3syTLeF85ZOEhpx7zu7LDSMguFZtP8mHS7n9vW/nzigm51a+AEz8viufEqPi+eu/OoFGVRwh63FhsWOoxqLnLr3fuvzoLH6E7vbIUx3ozK7r6GArLmF86Crn8Hn5UPWSDG4qwFxzxZw3mq27WsTaLyAl1jbkFHA9qHXcOYa09dm/7tBvvBa8i48JTXsv0WpClLoZtQzEhs3XZXqcr4NRR9lViA5UttxjcZd7yN9Z9tYn4qk9B7NBdTfoxCt8U62g5uoG2Dx+x/G+cvddx+/JJey0leKpK0qrk+z99dzdo0WewVnaAIb3QfxXMqVejfZ3HYx5684Mlz9K/FZAykaY64fiwW3AhuuPkwdl32DkVE5NrqaziWRYdxD/NMvehsLV5UmZFQbrLRwfgXlBvpfSkikmzDpi6o3kN/j57hcxZratHacs3dbxrfHW1jbNkdr/KcJgvhfn6etQlP8O8yiwtVrrP+VzoQvGi8vIhItK8FkT3/EBE4BEqjhwLJYttdn/jzbcx5xrX8CvMpH2JM12p30ObhftamuIu5zY+OJU0uBwsZDAaD4eWCMcgGg8FgMBgMBoOHK2GQ59VQjt+tuKCQY2WQK+4YkkeZzZoGhTD8QwttCkPH6A1usBiGwQbxCK+TFbVua/E8js2atHDuubLZrGnKrJ8C2jHNna2TVoTV99F/93aU639aI0Na8YJCjtFxnexz+wHHOIad1/HX0X/juWM1izWce7Kcj4I+eUfZTB2rWze1qSsM8GX3DgMjBgX2xe/77lIOt5SNZzw1108Z69U/B/OV7B9mbWr7mnSQz+vVOOf4Kx7oFemlGtDBgqsiC6C00K+xjYu+8KzhakOwcsnTHXxwIa66RTY6ebydtQm1QLBHpngDjGvpV0/YP0NMvDjn0qHaomG8hcN86EekRWITt/swJ9MZ0V6t/oT3Evtf+xD3Q3zs7NeSp2B2U4201jFsYx3n7H/ZWze1fpvTZk/HumDBZ70HZnzOMBURkfDdN/B6BiZ0egdr0L9Fm7SvcP2OvtPI2hT6tCs8wLif/QNc0699CUvChIzudMXdb8rGjze9qjUROfgu1mvlc+7ADNx9XXr9FYztEPZok++9ic+fcF4rYO97r7qxtZ4wEGZ9RURERtxZWJSxjo0f44bb/fuOEW88xzxmNRyz/i/B7O79Pvoo9Ph74Ufcv41za6BP5Qvc8/NrYOl7t8H0H3/dcQW3F3dFROTw21iXzdrrGBuLHXd/F8euLr+StZlxo2jrxwUJHv+ZGAwGg+HlhjHIBoPBYDAYDAaDhythkONhIusfjjIdbvmE2kPP5i1RmzdqjfW7gDrlpKQ2T55dWUAt5nParY3w3ZwWVMUdhkB42tMKgyhS1SAzBletzxS+hjKbB5nBeAg9ooYLJNRdqnZYxNm8yTFYs7X4FtpQl7ge3RQRkdJBz52T2tlyo8Y1oF3ZbIUTxkv1uWuj41cddPkMTFvEtZi2Ltu8jTbItJNNX/6km5tzeoG1FREJdN0YKS2Jb7QmEjD0Q+OXRZxl1sVjM2jgxsDTVGtAA79LGNwRUHMcjKiH9Wz5Foxz1pjrxd4BDmEYSKgBJTPPgq5GO68BxqtBJ1mvaoVXdRrxROehr+voP9AAEb1nPKZa26vFXBZlrXZiPC5dbrqxjWi7x/UPGRQiuracux/rHVCrrecunGJMBS8mGmP0NNWMdS+eYY2Lu+pbhs9Vhx0PPXZbnxsuVMj+yqeMeW9plLt7nsqPGU99k3reIz43J3g2sljvgmOQU9ospiUdA57hYpvXfxXPfuXEzad0rtZ8jDSnzVuxzd0Ofu3bvDUYLlI84vOq151zztao61ndUU9eGDCA5JzWg6wniMaMun/s7BhHm9yROuuIzC/Y+BkMBoPhpYMxyAaDwWAwGAwGg4crYZCTOJDJSkGmNPAPErApvjZ4Xs67BmRR0+puUYlz70VERqsMuujTHYHs1mQFr3FfmVgvarqhUdMMEyhoUEieQfYjrbPPpuhvvEI2e1LJjW1edYyrsowFughMVsnkHtZyY4wmjqGMyAbPW2QzOe7RiteviBQGThOaRU2TvR6v0mFjgL40fCHxGD1dt+kSnTSWyORyzEXGCefDK7geGpwRXlif2eXKfGVLNRDiUjyCBoT4wRgaHjH/DZX+ZEhTP8hFAzToDKJODhkL+YJo36z/LLo4yX2exVan3o7FhQAPDVTR8SfK5nuMecYc63h5Pj1PtkaeBlnvnex8ZNGzgBL93l9zvVbqOEHHFdX0605M6J0m1OnomPi0ZzsXDDxJSy5qWncqInWr0LlyGVULXxh610f16p0h+2PbQv7nResNRCRb05CBNwHPp7sbutuSvuC/8Ho+PcbN97IGeVHk/UZnC93B0FCeLOzDG1rWv/aj952GG3FdF1U3Px1nEIaWE2IwGAx/A2AMssFgMBgMBoPB4OFKGORovJD6l+cZExV2BpeOKSrjRe9SucD+FagjTXuu7eYE1fzqF6vavlKVlhinziM1O09Bo3g5tTgf9Zvqe4/d0mjflG4FTTKsQR+MWEzf1ZIfzdyFzlKZwvonqNgX6i4bX5JF2zvOmqRkCouNeq7tUgT9svrWhicddx5lVKmDbXbXc/Ot7JJx87Tb5UMw0NMljKH8KXxqlb3NXouOOdR1usTG6rH06PXZ4JAaYI3blgsMaMa8lZxjiPr1qkZYek5vLSIi9LpVjbKI0yCrZjqhTjra3MArx5EMvFjvYl6bq/dDFp3NtQ8rVa8Nta06jyajk/v0bD7h2H2NaQva4oDXVl0xVIMclOnAUXZroIx4pDrlTWjQQ2WhObZouZW1mV3jWtPVRFnOzl3Ma1HCse033NBKZ5hrNMEYZmvod3IP99BgizszU48RD7Ee4xWy5bwdBtdx3u49upqM3K7Hzf4y+8Mca3vU/Z5jftPrGNvJu65N8yM825230HbBy1XqYA1m78K9Yrjl6NggwUHDTY1SRx+Dm3oMI6dLbj6FX+Kcixrazu/TIaRO95n7eO2/4p6fHn27u/c41idYk/4NstyvM2b+z909NlrDurSadZGz/I6QwWAwGF4+GINsMBgMBoPBYDB4uBoNcimS4StLMq/g7+3yWV7zKiIyowdwYUDmkNq/SFPqmmCfCmdOf9u9h2OrdXW4ALs0bTLtq6wmx+48C36mmkPVIKvmcKGa5Nj930D7LZElHV9ndXxH9cRkvz2dYvGUOuIuU93ugAkrkzUd3KP/qrdO6kSwWCK7SNa3cx99FftMq6s4ZjfhPOIu2gzuMk1OdZBlnC8eOXZ7tKJ+y/iudErWWfXMnz5FW4+9VfZSXSUkzWuSo6NTfM90NBGRlGy5ammVGdX3oboleMxu5mlMpvWiFln9iRcXmWWvjTpHJBccN9KZx35zDNomO5bzSl+gY800wTwmOqCPL32RY93lGLk2Cb9zbXmfcW2yeXv/1v6y8+we5cao7Hcy9hIiqaEPxzjPjM+A6orVbcK/R1UXq3r8yjJ9pGd8FnnPqE5XxPkbaz2B6pjnVerX2+rL7Z2H93ztAOOd81kv6u6H3nfu1sm0wDG1zLMK7p1pg0zsl32e1/1ELUjCq0tF6Qx9LFjfEE6VXXfnGdN1IxphvdRhR9cvQ8nX3PM+JhGclPI8QhyzdmDNjW1Wp867FGc7JwaDwWB4eWEMssFgMBgMBoPB4MH+QDYYDAaDwWAwGDxcicQiSETi0UKSAreBadWmhvsiIsEFWzW1WVPrpLjPQjlP+pBFO2vNl4aLpIXce9+CLCjo9iilFZRyqNQi0vMOvK193RFl8VUmy2DxXom75fOa25bNzq0xx7cgsdAiRN06Tv0wDt16VWspLkmpqzZiQW7MIiJhouEI3KZmsIGul9rLRZ7EIppRvsIdel2LUINCbiNqONp1UdOygXCUqM3tf5UgsG3aYkCJNx8/zENEJKgwbEQL/RguovIM/zwBP1MJRGa7ducaxuoVC2bHsH+1j4u2UKSXZjIHVwgXMmhCwzdcQSH3+cPLhVRJF/2ELKLUsYaUiEweIEY87jjpQ/g0zc9Z7cNYhJhSKpLecMWVemb9TtcpoIVbcA3zCrd3szbxQ14rSjkKLPoL54zd3sPYV2urWRu9r6pP8F3n5ygGLD55ij7OcU1TTxKgFnClU8hJnLUZ1iSe8Hn17rfSVwhuyYI7aH24YJR5ifKd1eb1rM3iALKSGgtuKwe8diq9OsOY1z5yIqXqHq6dFp8Wd1HQufYrFN7pb0rqXdrqLp9HjQdnEXCN84tHLJD0oudbn+KBTwq4h6pf4l4tH2Eswy0UHba+cjKgyillZdv7eUs/g8FgMLyUMAbZYDAYDAaDwWDwcCUMchqg0EcZZI2w9c33Uy2Ou1DAEqb5Yjo/GUCtnxIWEQVzPZYFMWr+7xXp6Wcp22i4QxYYQkI28gjtLESERUVJMd+/tk28Yib9Lozj/HfxhTGWPDprEuc/0yIqXbfs1TsPC5y0+MrNnZG/HGtU9NctyL1ma8FpRtPfXESkLHd2GS4WHPmR3bruysbqWLM+XnCeC6xz1n8WZsFrHXrHBReY6gsBFL8x6vpF41dkASIviAXW86X5frP7xB9Omt8ZuXi+VBnl3zzCrJg1O5uuXy7IhUWHc91FSXNtdfcj9DdGNDhD+9HudM7al39NtOBynt/h0cK3kLsT+ioiLhBGd3J0bLoW/D7fJs234cOejVXH5i3vpQAPts2CSliomP7f3A5qoZeNcX6hT2+82WfZmPJBIS98FgwGg8HwNwLGIBsMBoPBYDAYDB6uhEEOpwup7PSkxChojZz12bS0Ss1kjxZZyrjQyipsUGt43s3arM+pLTynPRVZpcKZBoW0eRp3nrjHc2vwBV8DDSbRMIhcUAj1o9QT15WZop44oLYyPveCKNrUlnIelce0PGOgR3kPx4Y7R24NqH+Nm9R+UqtYrYM9U21rLihEx0vtZHUKzWlKhrXRo2517KjDQg/rM1mmVlP1q8rKagyyx54GvA4JgzyURdPzhNS8LvrO3yu6oOtNzye5NQmzOGePntvjWDTIRZlRtYZ7sof3roWkc2o6KR9O+D7SPjRUxBubsou6xqlnTycikg5xHwZVZyuo4R7pAP0EK9CaarR1+RFDX7wY7LRQzH2WZvcz160JfbFMPF0q7egyOzfqcANGNMtZJzceEZHRWwjOUBZb2eHj9zG2OnXLh993K1c8w72zWsN1Gr+LOQ/fQzDNcJP2ifPL7KfGlWuIiIZmqOVZNPSCQhKMbbRB+8UjzLXCe2j0Ksa29wP3zD14cgdT/QY003PaFZa6jLK+gbn74SK1VWqAGRSyWsV5j76hVo44buFlxGz8Ate3wrh4rRWY1fH+7HW89t90mvdCH2M6/ibeF3t45no3aI/3A2iSx1+4IJf2A3x3ffeayMMLFnIGg8FgeOlgDLLBYDAYDAaDweDhajTIYSBJMZYFAy7CMSkcz10ii3rWyOILWkrVpgYNx5qNN1jhrjHKYzBTSQ2sWdQlU+MxoVn/GlKg4RXRBRWo5xShLG3AmOpFCxXuUaZxJDtY8lwsMrYPLHZyHS4F4THnqU4YdRdlnGkW2TZQvTLnPlvGfIsDj+0kgxvUwIS5NcbYJRYlbwAAIABJREFU5hW6WHgaSGXH5lXqrnUMqhUNWLHvBV4os57FQl8IClHGXd0mRERSZVK5xoEGt8zI2uq16LvpBIxmTvsX4sjVHWMFbGfGNHvItLLUretYL7LDIuIixlUXzWubcmwaK+3rl5X9zb5TBw/eB8kaxh6OPDZ4yF0FXdswrx/Oxlb0diz0npxyhyLTBPN+WwIzmRw794/ydjs3JkWrBRcGdXgYr7jnp9jBGOrPcZ1PH+G7ylMw4fEAzhR+oI9S96U2rp1jl7l+Lwj90LFFM6xP4QgXPGFYSpkMfOtLLyb9AO4iGuO84O5TNGLMNte4seZY2vo+Piv0cWxtm/Hry4wC5zImHoFb28c1LRzzfuOOQqGGZy2JeT/GjnZuPsGx0ybGVnmOXZVwgvV7/inWfP3Y3cPNAp7P4Pgs/7tnMBgMhpcSxiAbDAaDwWAwGAwerixquvdqQyYtsEvVY2pqh06rqXG2WTTuRKvhyc7G6nLh+j1+D1RQqw4WqdgHRTRkxGtLNc+e3/J0mVrZirpY5B0dtJK+MHBjU2eI8jFYpfZrYI4a20WODd9PltxyVXisuqd23gCLtTwBS9Z5lWzdSjlrU+hC5zhrkp1jxHXnlSLHjPO0qs7LVqHjHmxhTUodrsUG2Mhi1/m4DrYw92lL29LjmB62xfMpx+Oo3UQ14vTkdQ4VXEeyw+GSY/Q0Jjoo0f+4Qe9c9cHl+7DnmLb5NWhAo0dkTxnfrAzvmBrREpl5Ec97mYxxqLpsZcZVT1x2a5CSpc88jak1DlRGrjsAnmdt5jhBllsdUHQNBrdwTcsnnl71kAw19cQB9dDqwqHa6tRjkDUqW/XR6redmUyQEQ3o4Swi2U5Fyihw9XWuPcf54l2wza1lx4QWemRjT3GdS2dci33o4ov9YX4tRCSl53NM32u9D2Y13NflY1zbyNO8yyn8iIt6nfX6KDNOTfXy547dXrQxn8Jj/lYsk8nV3YidfRERqV53bUpHGFs05LV9BJ/o+sYD9DHiWns1CerJHoxxzRL6L4e8xjVe41mtkbUp7GE+DUbMhx3WFXAHq/l449J5Sue8j0bjy84mBoPBYHjpYAyywWAwGAwGg8Hg4UoY5Ggwk6W/3JO0AsYl6JJJ8j1mLzB2qWo2lUUj+5d6CWq3jpn4dgBGR/WjNbKOKTWOvl9sJdP3cmrKPirb8yJfXI4laYPpWt+nUwQdHQIyfhXP9zQl+5pQx7vyEyb2HULfuaLM5KljQjURLlbXBWoV109Rja+es3J0lrXJkuyoZS2vM4mOGu46db7KkImINJfAFC7IVBc+ZyKbak3Xly+vgWpmlVVM8/pioR5T3R9ERAJlm/UaqoMImeVwStbW8zSO93EtRa+3Js1xbKVtzt1LukuGTLjj+/kxNbT37nJsdY7d0zXrumk6nd5vel/o9fFY5yBhP3QvSZTtLuN61Z7T3WLmuX/ovcg2ep0COpXomGeet3WkPru8V2ebYOULHFPY5xor+y1O457QjUGGOLb9Bo6prGKMR+87AW6pTRcL6u9HG1jjxRtwkBhdx/V50U7PpJV3sejfwPU4eY/6fE+DfPNf4pjefcy5+QXTJfUZuQnGdf+3HRt8e/cm+n0HyYlqFF1sY16L66+iz5vuJyopYK66a1JvgDnu3FN9NGsI3KaNbP4ld6roFJK+CTuOWQn9du5jDdqvujaVEyQmnr2JY8pHYOsHtzD3s/dx/dc+dOs23EQ/pWZDxHP4MBgMBsPLCWOQDQaDwWAwGAwGD/YHssFgMBgMBoPB4OFqbN4KkcyuL8u8ji3O4im3/See/Vopv+2o29QBC+wSbnkGnv1a9z62VBsspFF7rdkS7dC0L9/mjYVbCy2KuhA1nUXnzrxCGv43IaKV2eQOZAzF/UJubIua246PVcrBQqrZDVg/xdxqn13Dtmyh6La8wwG2zlNatqnV3fgat/a5RBXPkk4LgQJa3U03GVoywFpoUWLcd8Vm4w18Nmmiw+UutuezddKpT5wsw5cNvAhaSOYXIGVyDD1GZRMqeVF7PE82I2rjx8KurFBNQ0VYLCiHrkBN5RdZmAglD1nICKUVqVdwF1JykhWKaXAIZS5ZnLQnGUl7DKRhQV/YZx+0clOLvci7Ry+dW8/HfhP2GY5c4WUWXsJj4jP20WfRpMoBeq6IMjqhnZ9+QBu76iHu9/Iujq1vOPlMFrpxhvEXuyzoo8ylNuVYc5HW+HdxmfIYPi8LFmJWmJUSj12b6AiSCi1z0zCgBdctOoNEpvXEk4zQwq66jfshKwLlmAoHeI4mLWcNp8WRQYK1qD6FfGnaxNqGHLsW5oq4YkINwgkZRKSSi1olYh/u2S7vYy2r63iGo3OG9FC2U31e4zi8UJaO3ot9Z/lnMBgMhpcWxiAbDAaDwWAwGAweroRBXpRC6d6ryJw2ZeUGrZo8Ympaw9/ixT4ZXLIv8QDMy5QWauUTxwJOlmjNNgd7FU3B+kwZ41sLwdr6ucSLMsM3Chfs3Rh4MC+r/Ztro4VINbJYow3arpXBxo2XL8+nXEMHRYaKDG5ijHXGY7dfxViXXBOJyEBOVzkfMuynb5J57yjz6iynFhyv2kh1XmEhZEo7OxJf8cgx1eMVzHm8iv6qh7RhIzNd+dOP0dYL2AiUCZ17IRgeArUZm3lssBY8ZpZw5DeTxW9sE9KmLBnm2WdFtM3rxuI9EZGAcc4JI6CjNcYTb+/kG3tMaDLQAA+MRYspc2ypPwcRj1VmTDjXJCHrXPgSxY5+MaCypBf7zc7HPsMvnlxuw3OHz/dybQLdafB2Rkbvopit0AUjOtzCmpy/gTVf+gp32vF3XZtCB9+NVnAf3/6Dp3j/c/Q1WeH9WHNrUG7j+Rxs5uObz76F85afo03Bq4eMR+iv2MG90/sWnoEmWdr+KyjmPHnH7SI1P0Kb3gMUKPav8Rnj78LGX2JM+z9w56k/rXLcOOZ2F4z0wb/BQrwRr5/3oPav45j6HtrWdnD/Da/hwWk/YGjLN13E/WmPa/l97ljMUWSov0cbv4f74KB3I2sz2sI573RfkfRDL+vaYDAYDC8ljEE2GAwGg8FgMBg8XAmDPK+KnLwXyGIFDJJqd+OBY6YoG8y0wNGYkbVT6nuVAH3dY5l+G6b+B5/Cdq3Qw3fjTbBZzS+oqRw5xmi8GnBMGvmMzxdFDSbB94W+G5t+N1wHy9R+B/3XH9E+Kta+3XmqB2CgVj7HfA6+j883Q+gTT99jn7R/EhEptTHejBknWdt/Hf+IangdfO7FUxPRFHMf3gKrVTzB+8ktsLPRmbuU8W1oKG+uQBu6E4DpiiZkwNJ3MIeHLsp4sewsuEQck6fRwsJY6XDu9JXhKVg3DQiZraKPmNrdRY02c/vO6q73Dti4xi/Bml6MBj97H7sCq3++l7VJq55vl4hMN+o8D1jIaIfC2Kpb66TOwA5q3FPqyFUfm9C2TkMgRDw98c1NdoK5xkfQ7B78PViE1XcdS1v/gCw2NcEpddgB9d1pB0z4/I3bWZu4Sys4Bmio9VxIG8PJ13C9ih8/y9pUv4AmO6U2u/E6rNoq1CaXHjL8o3ctaxNRh63xzQ//Am0e/OqhiIiUNRZ7y+mjgx1EfDc08psWfeEUWuD1v8D5g6Hn80YmfP4Ma1H77tv4mLrf2he4z+7uuOuYMqykwV2G2iuwdJzQri79xSc435vfz9rUd9Gf7g5Ff46dkK1r38IatNVe0N2j4zU8w5VjhuN89FhERFrHuA9rO3xez9yuzdpfYo6FIe7FxmPaPVKnf8DnqdT3fg+OuCN2NnB2jQaDwWB4aWEMssFgMBgMBoPB4OFKGORSO5FX/mQsc9XlntENYOHFOV+oUheyLCHdEZIq42MHk6xN93MwW/f2wPqFrEift8DSFXdPLp9HHQDompFGGhdMtswL+1Ao4xMfgxFd+xjMUYEM36JRyvclznkgaINdut+G7rLwFMxY5RhMXmmn486jYRJNsrV0Vui/ySr8Kdmu3fOsjUZwh32sy+QmNJvhnLHVVax5YejWbbQGdrTbAtN67xeOwRURCU84rxPHIMd96FST8/yxGvKh0byLExdikmjIxhnGW9ilEwWDQgoMy9AAFhGROuOIVU+ckGUMeF1WGTKzODh0a8D+VOMcf4XXkG4gibpCePPRSOwFGcqoTrcE7YvdJ5HbsUh1LGSSw/U1zhn9bv6fWAOfPdX1UqcOddwINUCEnxcO3LpqmEzC76JruHd0nYq/ZnBNx9NhK6tdpu6VrG3vJq9BCkb05G2nRVdN+/JXeD+v0VXkGu636RrWJPX+mxw1wTKP1/k8MuJ8vI7z7f0+1sTftVn/K67B995mGz6Pbe4wcOxH32tmbbaeMyTlLTDr06b+PvB34d03RMTttoiIFAe4v0Yr1OV/HcdotPpojfUFnmHO0mPGbdP1RW5gLPMlPiP3cA/17nhtrmOc3VfQUY339YSx1+230efdP3FrMNiiC89wbC4WBoPB8DcAxiAbDAaDwWAwGAweroRBDuaJFM6GEs7AZkVtVOkHvi8tNabKfAVkT9ULOBpNct+LiEQTMDRRjx6mytxpsTp9eDWCWkQkzLx3yaRp5DB9kTO/X29sek71pc28UztgwuOL/sXiYpUXZE8jeqZmLOBJ69IaKJsYDMLc3Cv0fE3IeqtONgc6GhTOcGw44OsKWC317BVx2lLVdwdjeg1zzee3yAL63V8Hqxipb7Oy8ly/+SbmF/tuDRqZrWtOb+iQc16sYw2i2J0pWWEEc4caYXWE4DGLa2DvQ++a6pqGygJrm+tgA0NeC2WARRxLGuk81tmvej/rfebFYKcdMJ4aLT67rWwprsfoNuYTD52fr/oVZOfW672K9dJ7aHLX6XxLGutNn+M5I61jjTznvRR6ntPBMRlovT70823Qe7xwgjWo7zgHhVIH7XUXo/U5WGbVXRcKvN94v4vA01xERNXCIZ+XFn2Q1Y0mnrj7QHck5ku4HtEzMOQLXreohN+FyonT+WoEeNzBukW8f3WnJOWO09Jjdx+UD+kbPcx7NLeeuvGLuLhsEZHqE/oec730uheoz67T9WZecT7IxUOsT+MZ13bvnGuB619/gnlUHrtdjngE1jk5Ocuea4PBYDC8vDAG2WAwGAwGg8Fg8HAlDLKkqQSjiURkKDONZuyJAckgB+fdfFNlVVWvSn2miEjG0w3zjKqeJ0toCz0dqbKL9J8NdAxkJIMKPYHrHhvcJ0NMj97o+RGnRZaMmuHAS45L6SEcUacq6uigrCpZ2+TIS4Sj/jVUHayy6AnYzeI29L1+glrA1LiE7GZIBlHZ2uicY/I8cwuU4gb0jxauaUqWNl5cZrjCPvXR3f6l70REQibgpV76nrRwzdS3V1lN5RYv7hqIiATPwbqlZFiz7zQl7yGcEALPkUJT9rI58lVZfHW5CDzWWSZ5VlFdJTLGeKR6cMcGyzIYwpTa2Sztkcx19dMDHFdwj03K6xGQNcwS9fQ8K2SdvaRDve7a76LKhD7OQxlZaTrG9ey70Cmrr7cm2R19G99X96m9/x2ndT48Rv/LH8CBovyHWPuT3k0REenfYF/e45WQoJ7QsUXdZsZ3wOw2l7HmZ0PHuKYhdMS9W5hzaxPvm19g7mdv4vX47zrtdvXogYiIHHyXOnJNkTxR9xS8P3/HseiVXbC0oxtY6+aXuIc67+q1xUuh4nYSxktg56vc0ZmXMZ8ZPdvbX8P51t86ytrsNsGEj79NPXwJuu/uXbT5/b/7CxER+cXO+1mbszfx3e3kNUl/6dbGYDAYDC8njEE2GAwGg8FgMBg82B/IBoPBYDAYDAaDhyC9GL37/wLN+o30O+/9pzJdYmjBKeUHvfGlY+fLDGgYqXUXX1goNF13gRWnb2PbeOUzRv12sZU6XsPn1W1uhY/clupiBVvmi3J+e3/WpAVdG1vd8YmTEiQNbm2PGJX7GrZuG1+2c/1Pr7vg6Czs4SkCLcbfeVVERCqfIIZ29gq2adOC+z+IWk2p1Zwm4iZF7C/3b2Ic9W23563rknA+CYuKigewABvdwZgKHSd9GG2hn0WJ0d+M7y5wm//wO1ij5S9cm84r2Fuv72u8LganoQz9LUYaP3RtEhZD6Xw0ArwwxLZ4/zratLxCq94tnKexQyu4Hgv6OK/Tt7A9vf4rtwaFI1yr2RrGXTjEdT/7NqQDtYMZx+y247t3sAath5AEaBR4ob/IzSvwHLkq+zh2tEULsDtY8/VfYqv92d/B58Wuk4ysfcRiszHlGLQ4G69jHlVaFO7+0Mkllr/C3CuHuIfO38A933yG+6z9AG3XPvTkLh98hjkuQzKwOIF0J95E4Z3Kd9Kt9axJSEnQYp/FZO/gHg2+QACJWvf51n7hMu9xLW5V6ztKXrTYUINkRESinyDUY/47sHmLfvQBvlCZk97vb7/m5vPoeW4MaRf3c0Lpi7yNsUZHzvIw4TFZG5U+qcQn0rAWV6iYPt5GW8aFRzpnlVMxhjvZWM7aBE/wDAvXMnnM9aJ1oFrTxX/1hWtDadXi9Ez+Iv0/pJueXfaTNBgMBsNLA2OQDQaDwWAwGAwGD1fDINeup9978z+WRYW2SOdkdBI/KIQWYAwGUSsktUHLCq0GjjkcvQp2rHTAUA4WNynjGx2QXfLnwOK1VAup/EJBccEbfuFYFlpygv4W11B4F7XB4Gkssd8m5HdZPPENjDXYRbHP4gEKe+JdF6yR0i4saLAwjPMZvYECrIgsZOHABWtkxY0sJJzfQEFfsMCcF7SGizyrrumKRlqjbeuXzo5KRCSgxVVy5ti5UJk0/UyvnQaFrIM5XBy4Yia1XdPCtEBZR85L2UifoVSmTe3QkiGLDMn+ZYye1yZhQaQW66Us0tNjtWgvYx9FJOK5NeQjbDRyx2ofQdFjGyduJ0JEJKCNXLqH9QtuIvwlZ91HKzNlWrNCTGU5dW2ubbg2jFnOCkdvIWY5PUAxpbK1frFm+tb93NjSCPfi6Vu4Bo1dnPfgu15QCJdw5XNc72d/hDV+7b/Fmo83eC28x0cZ9fEKLeAYFNJ+kH+O/MK+a38OZldjoottsrWP9kVEJLmL+3vvB45Fv/U/PBURkcG7eE7mVe4+DDAA3S06/I7bUarv4bvhOo5d/QTX+/Db5dzYE7cEsvoJ1qV8TFtEDRti+E/7VbQ9/5prc/Nf4Zijb6CjGz/GZIdbuFf2/jbunVf+R9emy52RzT/dk5/s/PfSmRwYg2wwGAwvMYxBNhgMBoPBYDAYPFyJzduiEsn5W02ZkeypnICdCT03MbVV0oAB1bhGI9A+swa1tW3HMp2+g35qqwW2pb6zhWMbTT2PY6pnjLtelBg7S62pssxJfJnY0XFW98Am9e7itXwK5njajNjWtake47vSEdi49ptgDFsMWjh9G4vRqjk6K+6BFVOmLeJ8Tt7FPDS+t7HkWM2Etl7FDlitziu0d6MUeFpn8MHA0YAjxgKrVVc8dLpUEZHazx6h75FHA54zLEWt1FRjquumEdOeRZyGpKjWNGT0tDLIak2XscQiEqlWs+dilHPnI5OsrLGISFAo5sYWrYHhT9mHf2w2No2AVnZZmf65st3RpbHpXDNbucOT3DHKoabeLsfCi9FG44Dn5+cJx+yHpQyGue/C3YP8ediHPzbVskdd6uG3GJrC2yuYvyDemP/9ndX5bK3reXFs9uw1HTtcOmH8dZ2NeVlmDfyjDkmvFAbuNAl3ZSrPcb1HN/EMV+t4RrJdDt+tUYNv+qw9YHz8eBnHVr/E+k2bjkGetYPcnDUcZc7dJ2W1fV35YAP9BQtqwqnzdvp5zqHqGqmmPilizvNa/mcyKKjG3tm5TRmJnTQqmX7bYDAYDC8v7JfcYDAYDAaDwWDwcCUMcjRJpPl4JLMWqJ3SKaOhvfhjoWZyUaMWmS4WAVm78j5YmUXdsTLRCMdWjunCQAY2nIAxKtHJQTW1IiLRMpm1EvWqGpVMFkgDG9SFQkQkYaxt1AEFVVrGedUlo0rGTV0URERi6qyDbbhYVFdZdb8DHekymeNg6pipcIhzl8mWKuu39hG+H5MpLx85qi1Y5LXArSd4Wzxl/PENzLfQcbrY4gBrOD2mrpMsXcTz936AkIb6l479HN1p8NxcF42P5vqN17HmlWeO+Q3IHKZkPOdNsttDnG+4CS1t9akLhxneZkzvNljtQCOaGbjReRO64uZH5ayNdHhODSbpgb6cfu9NNKXLhR/xO7kJDXKJ4SvzDTD80QD3ikYqB55+PTrCeizW4FYwvIm1rf8aGuSD36UmvusY+dav6eqgQSTKuLfAnkYnmHv7uzeyNo3H/dx34/voTyOhh/exBrVPDrI2i198jvMwtrlEN5Gtp9e4Rujz1vC6mw/Z5mAX44+mdLH4HK4TZboylHy2nprs4i6fQ+4G1LehfZ8z2jr07+tfYmzp16CTLv/ZpziWDHhMF43NyatuPtSGF3nvFL9iQIiG6DAk5e7/7DT8oYbxqHsFA4nu/lNlu9lH0f2s6fOoDhh6tYvbaLPxDLsRrccrWZvSR09FROTOEdYy/OSxiIhUuV7h/BaG+LMnWZtU2fKHTyRNLu9oGAwGg+HlgjHIBoPBYDAYDAaDhythkJNCKKOtkkwaeUYx9vS3qglWfV84I2tLJiopXP5bfdaklnY5ryvWCvtiBwxlOHWa3Rm1jItyXoO84Ou8iu+jhhubOgIoZzlpoW1xjawQxzZtueWqcLjFPphKZX9LZDkH13AeZb9FnE/vjEyrvu/dKnJ+OK7U9qJquZbK2A22cJ55BWswpMayVHHrN6Bn8aTFsZ7m16/2DGxjcOBivZUl1/jmiy4WUY/Rz2ee5pZsn2p2wx6ZPfpG14bUk3vnKStzS1Yz0zSTQa7SJ9tvkzlOqHsEnUqUHU5VHx06fXlZ48ipRS7QPUWdREIysannSLHogqGMOK+aun0wqruxQwb5zLldXJqH3t9kdLXP5pdOWx8eYryq0db7LjmFprtK55WcYwjZS6HTRriFsUy38HmR7Plo0zHvxQquSykBQ332Bt7XfsL7S51eas4DWMjKJjVc74D3Qe8VXNvyKdfLY951bIFGZG+SYee6qaPHpO6euUg157x3kmXuzmhE93Ow54N3nftHtYT2swav/8e4toP3waJHjN9OvTKDYhEOGjFjt/W+Cjim2Tpeu3fdb8j6c8xncAv3YuuEfsjUOvdu4vpU7m1lbXSHqnxyLkEn7/hhMBgMhpcPxiAbDAaDwWAwGAweroRBDicLqT8dSI3smeo8E08LGNbB0BQ0wY7MqOoIF6tg2MIdxxze2iWzRTZQ21S3o1zb1PM6LrW98nqRzBVBNaLqaTxvOqYt7pGVo8Zx5YxjVDZrEyxT46HT0oY9MJEJtYetj6GpVCZR0/BiesHipPRkveCDXKtDn1p53sv1LeL5Q5O5LfTAZqkWdOVT1Qw7Rq9yjHWfLDMd74MdfKEe0WQug4JnGEtnhuSiK4POl2xjMvT00ZvQbwo1wSldH5QVzIi8slvr4DM4aARkQDOvX2qtw58jMS5Y8VhNddtIudvANpl+WMfR9ZLnlAEP1MqB7hUVegxTHxs0PWa3heuijHS6it2BcAP9V3/yJQ4sOYY/WCazSw9m53UNxjJc4fdT51Odku1Vb+nZNcy1wPtN/ZDDW05PvP8HYEnVdUGdGrrf4332DPfQ+7/n0t2+OMEaT36O8f/DP/6xiIj80/EPRURkcEMdZcSBSzrd5O7ABCe8+RqY8koJ8zwaOD3+7J9D0z7hJSuR0F/7BLrek7ex5o0/cs9CWP2GiIg8/T7ulXkVJy6dcvemh7Wf/667H3e3ca1KN3Gdw59D01z/gWPaRURaJacB3v4xUu+qB7zuIdZEd6cGb+PYf/D2z7I2f3L/eyIi8upvP0Uf/+wVERHp38F1+2/+zj8WEZH/8h/9R1mb7tv4fblVfVUW/8rbATIYDAbDSwljkA0Gg8FgMBgMBg/2B7LBYDAYDAaDweDhSqKmW9Xr6fde+w8z2UJMe7ew48kdKCdYbGEfNmDka6ohAuc4dvzKatakfR+SgJUvsA0aMX52voQtzMIxt8nHrmgqaUDykJRjnaCIiEyW0aZ8jC3pLKZaRJIlbrOzWG94C+9rj1EEpPKGxY21rE0wY+EYC65mr2GLu/gcUguVXkw3XdBBOOHW+jRf0BWdYx79N7AlXdl3MgaVq8yWsUWskdLRPs97Z51r49ZgxuJCLYhMGY5SPMcxX/wnWIv6Z64wqf8GzlN9xAJCzQuhemX4AN83P/IKIrlsut0/rzGYZMB1fAWyhvpXTsox2sTBZVrQlc4YmV1Gm84bmN/Sp06e03yKz/rXMRi9H57+Owx0OWZRqJf50XkN51n5GN/1sEsucZ9ros5gnsiotoOxjBm0MnoL12HpX+M807+N+2A0cmtQ/ghrXaC6QwvFBjfRR20X74d/4AWjfAjJTvUQ351+C/Nrfo516t3H4q986P7/uvm/PhMR73p/DOsxlbmkddwfaqMoIhL1GNH9nFHZLCDVe1Pvk9JDF0U+eRXx2loUqvdfeI7xH/zBTRzoFcJt/fPnIiLy7N+HnOHWP/oV2mhhIeUu/W/eztpUdvG867NcesiI9iPIS07+A0gwNv+Xx1mbVC0Pl7F+7W9gLZb/gpHWKp9qOIlD+JOPMVxKreS91zAvPv9qGZdF0ItIMGRsOINvskLMLaz15BrOH//pL7I2kc41iuSn7X8indmxRU0bDAbDSwxjkA0Gg8FgMBgMBg9XwiDXV26l7/6t/1zGtEernjBGtueCG5SxmzUY2MGY20CLy/g6r7uCu6P3cezy5zi22MPraA3HtJ6QWR66KiONcZ7T9ixld7MqQzOG6KN07trMq7SNOwdbdv41sHFLDye5MQ43PObwlAV3X8KO6vSHCA9Y+SUY5Pa7YMLjsQuzMMlQAAAgAElEQVRU0LhoPZ/GbQ83MM/RKsa49NiNLVjkr49a3FWPcEz3FsNFOu48/Ws4ZsY6qqVH+C7m3Odci+bn7azNiKy5roHOWYNCpgyBKe87mjZgQaIGM6jFnl6P8Qajwp+44sb+AzBtGlKi1m0pw1rO3sMOw+pfuMIrtY1LuTuguxGTWzi2tHu5sHB6HSxfcQ/nnjPkJeozarjCwIuxW+uQNmXz62DyJ2u4l2qfgkHc+XtgTysnbq1XPmBFmoaUaEy02gkeY40737uZtak/Y6T0MeOUb+FeKRzg/eANMJX1D3azNosDx/KKuMCQkMWMicZ+P3AsbTBgsRqLDvu/+zrG/ydgPqO6291QZJZ6Va4147CTuygSDLgLEszcuiWPnqK/G9c4FsxZI7WVvQ3vujVYfAVmOGww/IU2f1rgqQWt6TVn8xaecI5VFtxxzulNjk0t+7yo5+zeYdjMggWqIc8XruJaT+9tZm3iDx/iH/c43ofI1w7IKI++jUCUysOTrE123Z9sy8/m/5t0kzNjkA0Gg+ElhjHIBoPBYDAYDAaDhyuxeUsDBHEoW6vBGxrWIeIYXdWr6rHBXDWo1CKPHDtXoIRZ9bCqewzcISIiksSX/85X7XHKRtGE76nLnTa9qWf9pjyW/eo86hpbfZkUymzYdF4MwliUGD5y6lh0jXrWwI5whkaTpr7nODzWWM8ZD8nkkWGbtHAe1dAuim5s2WcVaoIvsPWtD8DOprsuyrh2BiZSbcoy2ziycQUN1ug5KzWNBVaGsFyj9pmWeo09sLbJ8WnWpjFCuEL6fI+nYew257WiTNyznaxNwM+EFmABbfJKn2Asylj6QSHFsybHC+YwPuHYaMcWkUFMPSZUo5GjPvqtcfzzPazT5s/BNsZnTlufPN7mPzgPrpeuifbZ+qUXTENGd95HPwW+JmQ562RC57t7WZvo/l3020d/yQauV/8O9cxPMNaTbyy5Nejjutd2ccz+b2FMr/2ClnFk5HPx7m30r4y72r6dfB3H1vdwH8bec1qZkWnVtbyP3ZSIYR+yyrG+6eoLarz3gutgbmcbYJIzvfyvoWs++Y6z+6vvYkyzBneQfor77JjHxKPLu2GlLsZbPmTU+HNehzW0GdzG56fvuOtzYwgW/uR9fLeuUerUdx98D8duRq4mYcoxLY8mEhx49okGg8FgeClhDLLBYDAYDAaDweDhShjkaJJI89FAZqxIL54yzrfvnBWU3UvqZFwZ/Sua50Dt46LhQiVKZ4wf3kc/GugRD6FBLOwz8IKMpYhINICuMqGmVTW0iyq1zwOcV50jRJxeNOxj3JUWmKLyc2pbObY5Y2lFRGIybcrC1lnZHuyDLW3VWAG/cEyb6l/L6mLB6v61j9U9gec99HS+dMtIyUi2GE9dOAbrWLqJ8xbabq0LA6yPsvaqK4449/Y3oetsNitZm85dsHPVfTofXNAgD7fofPHYY5ALYe6YCeN2Y2qQh9dwLevPWlmbwU0wkdUWXsMBNaLUMbffxLHL87tuDegiktK9IDjHdR++C+ayfLiWWysRkSGZwepj6FRVk6xrkLkWeKRjfIx+F2tgM/u3sD6tX2Ee++9jzMWuW7flKl0/VMtMLfJ8tcY+wQqffM9pXFuPWrnvBq8ziGQb5+/dw1jrBfd4JmTcQ+qG0y+e4Jg+2WDqfZc/d2xw3KUG+TlcHm424OCgYTD6v+No34XzBNwFKBxwLckKb46wbpM1xrvP3cIlZNiTr6P/4AOElSSMxQ7IkFeWqlkb1TqHDKgpfEmd+v/V3pvG2pat10HfanbfnL67/a261der7tXza+z3jB3jLsZNjECAkQIEUOAHcfjBDwQSEhJICCHyJ0JYjkPyQFaiBBvHbR6O/fxeva7aV1W36tbt7+n73bdrLX6M8a0597nlEJwrkmt9488+e+012zX30TljjjkGywi1xyvfcrsP+TqgG0dWwViXv+ccaUTcWhIRCe/S4UJ3PnQn5C52KGoDOGEU+o6pDu9hPMu6c3HjLu5ZADu/8jbWXfXbzmGjxucy3dySLHPx5QaDwWB4PGEMssFgMBgMBoPB4OGRMMhpMZTepWruYlGjxjYalGfuERFJC9QED8naTrKZ63oaXERkQD/aUosx1Q11e4hZB+Opx445HNPvWHW+Wq9qdMN5lC3MO6Ytpe6xfBixXXXaoI6VOuDRgpuuMv2bS33oUntkWOf3wTJ1L4Np890yimTRJ02yjvSabV1F2WmFczNwTJsi4r29DTDjpQZee2vsx5xz/+it4+cxCe+Yvr0hy2gMdrTlTuE3yEyHbbLX6m7C+ONGB89S47hFPA0yI6tjalrVOaA+4PPZdgxlJYYGObxHVwa6JISso7rEse+6MrlOWJ0iqB+u3KX2mJpef+1UtQz1ySV9r1HZjN2WsWP7UjocxLy32Qfznu1As13fAtNbPnY7Fso25vPFPhQYe611zn/sHCPiHTCeyuTWtOwR+lrlbkG27ZwrcuaY/Y1W0BeNaI+4izKed04rypIXxrj35Bl8du4NzhN3MIJ5tzOSR2ZTW68a9B53GEpHbN9z/1DWOX7A9bQKVjY9wPtA2VX/rMCYc8g1JBrJrfHu25jz/heu5UUq1Perz3HhOvTfg6fgKhEPudvirYPCVcR1R4d4DukeY7znMOZkDn3rXHC/D5bu4dpwDd/h2h6+48pY91fRj8q183mZhL7rxeNTCbrGOxgMBsPjDvtNbjAYDAaDwWAweHgkPsjlcxezK//hX5fhMhipyi5T0k69uvmjppRp6llGsicmsTdYc2XOfwk+sPe/x5S6Fm4eMo2t+QkZsq4rM1jjiXPKRLMIn43n6X98TB/hHVdmNM+T821cO3kZTNT8+/FM/Z0rjpkqkbRceRcd11S39W+hncPPkAX0/gUpkfCckpwLlRgluTh+EnWVPnIa19xRg8TgpIG+1BGsJq1n8b506BoaXCLLVyPD+wANnp2/pbfdeDpXNW2PfVOZNA/kq6dyddfNW9zHz5Ma610me8rQOH2W8zfcHBy9hGvLb8+2M2WS3sEPos/n/sAx4urfPJrnzgTJx9ZV+kbfon694MbTPY/P6lso21+dXZOTOvvac+Opb2PeTq5hskeUpa68iz6d/BUMrLPdyMts/PGsO4smD6qLSX0Tnd38Mcfs1u/itbGJfncuoFBtH5XsvY46z/+RY7dLX3sHPwRsT1041Ed6wsS7mmOqc4afjGr2HJjW7N2PeIMeAPiU/5PTZOZttEY/YjLMM7eq+8claHPVpUOZ5UQZfg/KiKsvcd7OApjkoFyeGZ+IcyTR31lhpTzTf3Vg8X+nKdOuzirK2qsGWhGvreQ/T3ewKxA1uYNEtjsdYuzxRY6z7r6ncsAxZpkl6RkMBsOfAxiDbDAYDAaDwWAweLA/kA0Gg8FgMBgMBg+PRGJRW7qYvfAX/5qMG9hVrB5g61bDOUT8oBBuj/JwXsQo5oSfh16Zw5exjdy4R2szhhMMFmh5dpc2UhNnpTZpooyGlOgBvNTt2KPdycPjruyivtY1Hs7ZYWADo6H1EJ2ISOWQUdO0gjt+DSEICx9gy/jwVUYqb7st4kIbP+tBKj14d/S8OyCEcXkxvrFKAbAlrNHSRUoDBou0cuu48fQpMxkv4NradxkPzF3l2nUcVJo5BEYLq4zbyHlQCA8WBkX0We3ERNzWs/CAXahBIawjYIywHtYSEQku4tBUet/FKIu4oBANmciuOwstDd/QA1xhkzZftNDTbXM/KCRkmEhGi7GgwnhitR47I1EQEUm5RR/xAJeWmfJgV/aFF0VEJD7ygkLuPuAPs0Eh2letM758MS+T8eBgwr6prEAP9EUbOMg43XRzFD31BOrvMFRkjYdDeXiudhtlDz7n7MqKlAbVtvA87vwCJAnP/A3Um9UpgWh4QSEnZ4NCUMfBq5BENLawNlX2IiJSvkNJxRDrIVnFePIDjAwK6XhBIfXf+z7KXIZ8arzGZ8oDsaV374qIyP7PP+3KsG0NCpn/Bg7p7f3UZfTpU4JCNCylssPncB+H/1IGhQwuod3Dl1y4x4WvQcpx8BrmYOV7tP+jpd/9H8c8rn3PyTQmNTzvxa9vyjd3/3dpjfZMYmEwGAyPMYxBNhgMBoPBYDAYPDwSm7e4PZLFP7idH8pRljFTSy2R3FIqZ/YmZO5CUrvK7C05BiyiPVX5Fhk8HhCqN3gQibZYygqKiBR4SEkZQrXdympgAwO132p38jKBHvYhI7l8wgNJDAHJhqhfWVb/mh4yWqK1WbIDVnZ1l/eWPXaYB4UKZ1j78x/gNV0HwxbuuUNNytIqm1n5kPeeYOwLPFzkM7tLtMzS2Ougr4ENuGf7X8dhreZddzCp9QSWwtwdHuwiI6oMdusqPl/60FmcjWiZp6ERaqWnOwfd82i/8eCcK9PEteJn0Hb5iKwjbfPal/Dcmusv5WXK9zDWyTrYvuJNzPHRj4NV1TjhuOsYvfZVsH0L17EGu5fwjOMh554vfkR3dR99GTJa/Pg5vK6+CZZz9wfUmtAdhJu/jWdWYNsRQ1J6F9Be5QDr/MEX3DpY/AjhHuVjfLb7Cu6d/4RlN8iQ3lzNy8SfYMzJeXwnwltggasFHhhjOEblxDG7xVPUV7wD1vTS7zBUhBZu41WMo7h5mpcZX+BOAtl4Hc/K22Cu91/n98s743buOuZt96fBkq/9Ohap2vOFXHfTsotmDtcxtpE+0z0GeewjGKT1I7B3W37THeILO6xnFQx/53WMffFDXE8qmANlmEVE6n/8CfrC3xHptUusa8A5wnNZet/bfaC939wdPJ9wk5aDFxmwcxvzV/u99/Iy+c7I8YmzIzQYDAbDYwtjkA0Gg8FgMBgMBg+PhEHOSgWZPrkhY41oPgTTqzHCIpIzuWryn0fzkk0NGFQxabpwkVPabS0kYJ7iLpid0RLZOUbxhgPXTjoPhnpaIYNMJmyqMchdRgC3XPxxynvDNlil3lV8Vsv7BkZosuLsveJTxmnTSmt0FX0skuUeX9vg517UNKOyU4YK5GNeADs8WKHmuuAJprV8pPPH0JQD9KV/Aa+FtmN2B+uYn0kVZRoP0K7GcFeoEa9seVraAoMgDsk2k03XsAllFEv7jqkuntDKjuSb9k1ZRwnQj+p9145cqs1cC8luxyUGLSyCwSxvOuZQWf9CNPv/XG0bYy4doE9+1HQWMYL7AGXrnD+Nmk6VXffY/HgPWuaIkeJzReyIlO7D+674LLTBagcoIlK7g/pDjU7nvGnwSryPcTTX1/My1W3ahTHaeqE8GzWdRVh/pXtuJyGhjjvscgeE60wDMDKytNV4Iy8TMWo6Y8xy/izvI7a61CFb7O0+lPrUoOsODFngZAX3Lr3P3RRP95/sg2FdeXN+pm/K2qrd3NxNp0FOGJ1d4q6K3qPBIXPvUrceu++C7oDEGl6zA+Y7bVQ4XrRXOPW+P6ptpxZcbkEzrr1X5XFWWHRlGFJT5O8Fnb9oC+1WNxiI03C/D/R3RNLrSZa5uTEYDAbD4wljkA0Gg8FgMBgMBg+PhkGOAxktlWRIdwllDovF6KF7R4tkGQdngghGYF2GKy5Qocck19IprhUrjIJeJhvcB+sZ9V2Z0TLYJHXFUOZz1MD7couvXtsa/RyT2VUNaLEFtlNjdQerTkdaKjB44hj39NfYx+0m+0HXBz/2tg5GKi3MOnqoS4ZGRBf6rndno7gnNdyjMdV9xmKXvZCM/jLqV9eNcIp+F9u4V0M0Si0Xad1b5/9KfHZnNchaZzR0+ltFxnuG85y3HtvhPMZ9107nvMaEV/gZ5inhs+1cYlz5jhfNTOZzwhjqQlGfU5F9lZk+i4j01xh/3W9wfJyDDspqFHnouZlUyEAPNtA3DRepLWGddS/j3uKJm+vaDuYrmnANsi/j+cJMnf11979osYe+VBLUO1zU547xdc8xzGarnpfJbpON1RhqatOzo5OZ9/GuC6/IemBN1S1Dmfac2aUOX4M+RERCZT/74Uy9+k0OkgZf3bwlen6A13LdPNlbbS8+aLky/F5mPbpynAkgCTUG+7ibX1O3kkDPL1CXH+6fzJRVVxURF+6RM9TZmT5y/goF96sw5Y5FtAeGejrwzlKISGWbOyJT5zaTnXLHIwhyfbvBYDAYHl8Yg2wwGAwGg8FgMHh4JAzyuBHI9pcjSVYZd3ugWmTH5Gg8cOcKtZk9cFIaZVw6BqM0eNYxSX/l1W+IiMj/evGHUMcRb97APac8TV5sO9ZssEpHhRoZ6gDvq6v0J94EA9a4W3uoTJBgOmovQfu5eR6aycoePj991lFDhS5Pv69dERGRnZ8AqzVYwkl3ZRt1TjB4Tnca+F2TuIP3y5+Da8b9T5y7RNwhe7qoXsYo1LgBtrH9EplDb67lIvXREea6l9CT9wDz9V//xN8XEZH/9q2fzov8wrPfExGRP9qBe8BkiudTLmJcX1lFtvVvf/J8XiZNeE8FfXh6GW4JD9pwIvnZi7Dn+OoHn8vL/OUXvy4iIn/r3S+ijh7133W08z9+/n8TEZFfvvZv5WWKe6hvfA73FHeg0X3hKzdFROTtj6/gxsA9n1ef5mcfXhURkctPYG63j+hPXcMa6g/d7kN6G+x/5Vkwhz9yEQ4I/9cfvy4iIrf+zb8pIiK/23c7Cf/JM78kIiLZiM+WguzyEtnbW6jzr//sb+Rl/uaNr4iIyNZt9OX8C3CouHkHz/3f+cKfiIjIr1/+obzM07vYTkmXUSbehdtDcohXjYJO6LyCvtCPnLHO6fuYk/DqJX5OT/J9V0Q26JxxTLZWNfz0ZA4Xn0VdFbfeNHp57zWMdW13TUREJk9Ad124ywaG7qzA9IsviIhIcQffy4CaZHWfOX0ddTR+/dt5GfXiTk/Qt8ln4WJS+H2sXWWs9T4RkaBKlpne1ukivv/hDuZN46vTG7fyMtEC1tv0wSbeL1M7rbHe736MuhmtLeI01dHzT0tw8+tiMBgMhscbxiAbDAaDwWAwGAweHk2S3vLF7Pmf+WUZLoJtqhyCuSqdutPcqg/tr4LlKfSpcaW4scBkLtXhiogcfxYav/l3wSqVWkzSW8Hf9c27YFULHacFHKzi3omm3vFltEBm74jpYjuuzIjaWU3qO3oefVy8jvqLLdzbvuKYKe1L80OwzTt/AezfylvQJ54+xZQyT+ysaXcpHRV0TjQ9rAXyVpqOzMp9e1VPnJC8bNxHn06exnjLJ+459s7N3ls61j7jnsNX8Lr0rtPSattVBoAF01nt83CFzPUd1zfV/mo7Iz7/Ai2me+dRZv5jV+YUBKQsfKjjSzk+PNODz+H9xtdd30r0C1aXj/IJnssBkxab9x52DWhfRn1zt3GvejIXW7Pz6ac9akpc6wqef5/2zZpEuPtLYJ0nh27HYvWbrEcD+fh9Gs3RaWETdT74V926bn5Cd5FN1HvyNNprPMD7w5dRduMbbo2WfhcsaViHLjmlm0UQqe6fmuq62xnJqJ1VHW58FYlzyeYOitCjO+04T/CoCaZVNbvqZaz3Butud0OR3MSiyL4A7+rgW/AH1kTChNrhnIkVyecpd8loU8NLX/ToOSzI7M6DvIimEipDHG2AZU73XVKjiJfKKJ5Wm+4iAbXGqrvWe8MF57+eHNIxROda54d9i+l5nLU9fbSn4/7W6HeknR5Zkp7BYDA8xjAG2WAwGAwGg8Fg8GB/IBsMBoPBYDAYDB4ejc1bgG12lRNMy9hdjEtefCvPzOl2fJDQ5ok7xOGUh4EqrkxU4/ZrGVvp0yHv0WRo1h9O3Pb1lNeS8qzEIsnL4DX1+qb1BJnao2Uz19USLPFSo3WMGvGrfUoZmaz36n3op8yMWQ/rPdRHT5YRZDovLELJQ5qPk2Wc+iNvOylxHJzTKc9IpVWVNbh5Syqz0gN9PnqIUtv3n0/IA4M6Rr0nmH56nSIi0+qZa0E4Uzbj4cqJfwiM/dR29Nkm2qfyw7vZ+WeV2frD8WxZDZKZrVfHk83UMVfHFv/hwPVtWqadXz6VZ/qor1UvWIORyGdlM9My+8o5Ssre/6+cp0DDUvg+S3h4U0NAfOux5Iz0RCOQw4fn6yHQZk0P+om2owEyfh3aF0qFQpV9qNRBrQ79/pwJInFV8XsVz44PbzQfXMfxKff47Xn35GVzSQrbUWvAiXeYVqF2clqfzgUP64kfXMN6gmJBgrGpKwwGg+FxhzHIBoPBYDAYDAaDh0dySG+uspF98dp/IFPGRMdHPEA0cqxMpkb8ZHACZWGUqVJGxzPs776ACN7abVpBMZY4nWeAxxYO02TK9IhI0MDBmkwZKrI8aYl2YowEDoZe3zTOtoXDOMlV2FPFm0czdWR1F3gRtDBGjQAOnoXllNy8j9drsNIKes62LmBwg5SVxiaTu4aI3qSKPhY3XfBBHn7B55QwSjvsoq7pMmyr4rZrZ7yK+ZnUaVt365QfYOyT82iveN9FGY/P45BSYbc1027GsU/WYS9WvHuQl8kqpZm+ZSUGd/C5T9ZwSKu45caTLONaeMI1wvhgZRvHV2EzVrzjvMfSY5QPmxhryoALefoKrrdZh7fekg1EB4cPUE+2gveBWo0pKxh6/yMy7CGo4TkPr+BQWflD2H11voBDbnHXMZaV20czbecM7iLmKzhFX/svX/TKcN653iZP4dBX8QHqmm7gWcR39/Iy+SEzrp2M45iybHyEw6Htz7iDcMVT9KVyC2t0/0dw7+rv4FBdyvjoYOCxp/zeJIyED0YY6/Ac5mQ0x0N0Y/d7o/l11Dd5GlZ0hY8wX8kRxhktYe67X7qal6n9wfv44Ql+TzTWnVHXaRPtjZfdd664hzFOeK1w3GffXOSziNv5ERGpf8gDfHwOgX7X+Xtocgm/Y9pX3cHLxe9ijXefw1w2voPvdLqK57L7Zbye+63NvMx0lc/73Rt2SM9gMBj+HMAYZIPBYDAYDAaDwcMj0SDLeCLZ3U2JK2CQM7JpqRcMoDrBqEnrJLWPUvN/xuFGa85GqrpJK6Y9Mm6Mo43IOmZ9sKi+7jKPf42cvlbEhSVkDBmYsj0RkZD9VguoeBeMa3rCV1o4RQ3HVKXKeCkjvk02W2/4BMEaAdkzNEQmuj1rGxW8e0NERErn1mc/FzeHIZnxkNG4Ca2n4mOwWZkX1Vs8xL0lZUkZvZtxzPf/MtqpPziXl2k/ide5G2DS1MIt5QppPUNruHccE9pfo9acTatln7KLXRCuUrvvWMBUddwp5rK2h4aSIiOt12nHRwZeRGT+Jljl3jkUnn8PTOu9nwIDGvfxWjp1rKaOZ+l9zIXavkVKIHOZTL3k7Mo+1l5KPffRq1izK9/B7sDxi1q/+7+yxjksthlD3cV42lcZonIIhvLwi26Nzr23xv5iXAefR5m5DzG3I5LA9fuucwvXwbh3r+Da3DtgOYcrtGo7hzWs8d4iImXqn0uHKFM9wHh6r6Cd7jk83IWbbu2cXPND2EWKPY6rg7J7X+IceHLi6taGiIjc/kX05dm/gfbiBl6TRTwDtU8UESm24fd3+gTKzN3F74zSPbze+xms64033O+QpIl7h0tYz3uv47u18AnmdtTE2MdzjrxtvEV2nMx453WEe5QPcV3XVOpFtQ+eQL1jxtMr0959ArsfEyaAZ6cuOjtYAYMcnVuXYNML7TEYDAbDYwljkA0Gg8FgMBgMBg+PhkEuFiS4sCFpk7Gu1JeGqrkVyRndrAqGKhxPZqqIqPvMGo5t7F4GA9UYU0/aBdOljJRqkGXsNJQBdapnNchJFbRgyPbjtmOQhdpZZagn1IAWlOXW8AXqSkVEwg5Y5WSXOtGnSJdSgxxchh7TV3irvjJggIJqd4M1UIajFYy9uOlYwJB9UJ10Rh1kfEy99PoC++NYwMlKnWPG4y3tUu9LHfbiB2i3ecvNQekUY6xvKh08G2JSamPemp+08zLNu8WZcWh7UR99Lh+jTOOea2e4gmulIzCDcYuaU7qBxEM8v+YN107IHYT4BEyeHILZX3mHzPEJWfaBW1OVYzLU1F+X+D4egAnNlDCMHHNY2Mc8TReqvBfreeEdsPaTKtahhtyIiMx/zJ2PPtZgMEH9lQM8g+JBj+25nYSFG/heFKgbjod4ho07GHPvEtf9R067LZuIkJ7fYWQy9b11rotggDkoPrmcFym22KdNrNFgg0zyW9jdqN5Hu8GxY0JXDxgUwueh+uRkDnOx8UeYm9Azjoiv3xURkYu/9zT6tgvdtwZ7RIeY+7XFp13f3kTs9eoedjNUn6/hGxe/xkjovvtu6++VRh99rN9VJxQGFO3xu17xgkLYB909aXybz587Tc0Wdg2G5+t5meqHmOv4MuYyuIsY6YbORWlVzkL7Nr23KVnyKY4YBoPBYHisYAyywWAwGAwGg8Hg4ZEwyOP5WO79pVUZLUOYWNkD+1P0NKHq9atx1DGNBwLeElK3qnHFIiLVL4EhPngbDGvpVHWqjDC+gXYKPddOf/WMry5HOGngnkIL7FN13zFt42Yw09/jlzGOhQ/ACkckVbsXXd80snr5XdRz7y+CWVt/A306eIUMnMe0qUZW/W9DylJHTLkdXgLzVP94PS8TkoxSLfB4DnXUN1Go9RTel46abg4uo+KwBka1cJvMJ5Nx1Xe30Hcn9/N5K3MXgB7HaYy+Tqr0R37CMW2FHuZpUkPZwTJeix10ViPBC32naz38DD5beQf1T5pFzgnu3X+NjPWp20mI5tDhcRMMeHAZc9zdIGM9oo55wRlV95fJrE7n2Dey6e1kps86BhGRcIB+dq5UeA/9kOdx/fRVzGdpx31tajv06G7gVedLNa1xF9c16lzE6V5rZxxkpg3MRYv65eqmG09AHXzGSOY8EvoBYqNVk17uushj9fZVB4x4iLEne3QI4WtQcCba2c4uJ2NWwx9fgW557gZdWlLXd9Xo197GZ1kJ/Y5qYMtlxFgAACAASURBVKxTanVL37ju2uHY0w+hvw/L3Fmap5b3I2r4592uTcYxBmSDMz0bsILvYNbj+8w902CRLDldUqY6Zj07wHMMlfFGXma6hTktkmVOeX4iu4PxzfOsQnbJlVEGOWrWJWjPzp3BYDAYHj8Yg2wwGAwGg8FgMHh4JD7IzeaF7PXX/1OZVsGclI6hhwymfnqY+hBT10tWKxwyLa8GFivqOf1e+zloTKvbw5myU55mL25Tp5q6drIqfWIL6EtamGVzVFMb+MMmG1bYA9M1ukTG9ZA6Y7aXeclZqh9VP+Tcv5c+wYPnwS6VH3gn3dXjlaf7A+qLOy+AIVcWvbrZdf1l/1V/Pbw4x3vpoVzD53HfUdV6yn84j/4uv0l/Xz5r1fQmx07jGi1grpXty864dKgHcXLkeTSrzltT1uhIklETrmxgQjcQ1EPmk2xgSi26JqhF63B4yLXd4rmUqOsHGTx1H1FPYN/NJKTOW8cY1sl8T2a1777bSTYazVxTZlL7Ej4FH199jiIi6d7BTNvq1hJWqann+KLzblcg1+jys5juJeqprXOUnnhz/SL0u6pxTqmpP3kO42rcR993v+DYet0Rmb+J53H3FzBvz/wveMbjZTq7eP8mKxs/XKFmf4I61IFCd35iT4e98ibq0++4frej29Duphcxvv0vuF2O9b8PDfLkebhK6O6AtlfexPrY+VGn3a5v0ZN5kev6bd7zZXprq7W6ty82d4de0Ht0wGnRP3wez6f9BHYLjl90DP+Fr2G+9j+L7/25r/c4J3j/4Cdx3+XfdHPQW0ejq3+4Jd/c+qq0Rrvmg2wwGAyPMYxBNhgMBoPBYDAYPNgfyAaDwWAwGAwGg4dHckgvmKZSOOxLOIftXd3G9LeiIw3JoM2aHwssIhKr/KDmDo4lPOik0oqwSzsvtaBimcy3edMfaN0WcUtabd40kjmPJxYXkaxRzLkUoYOt1ZAxtemS2yLWtpN9bItHjDbWkI/SHqUE3hZ+wDk4G7Ndu492RksMLBl50dkaVU07r+Ixt4qP0E54DoeQopab6yxkFPeIbYfaZ9R7+kM4fNi46WKJ21exVV/d5rPjIb0kRuHuBvpWv+W2vNPKbCCC2muFPAymZWr3nGSkv46t7fIeLcBoT5eVULb1NGQZzevuMKAcUmqgNnu0eRu+jDCR4hGf6dhJLPqXeBjzFuQk43MoG/cwB2msk+KaifchRUkX0PbpVYZyvIs1ufcVSC4KXc/m7SNu71MqJJTNTJdRR3yIsR9+3lmDzd2kVd8hnmHneUaq34GMpXcZddY+csE02a0H6C4tBwNawi11YScYMHp8LVzLyxS4JsK7OHR2qYT0lGALEo8S7eWCtifpofyncMxny7Va3sP1wTke4px4GiX2Lf3sUyj7nU9ERCThIbqQ9omL88+4dnpY84VtyDOKt3mgUA8jUpax8UdOZhLS2rDB56NhJRtfp9SKdm9J2f1a09hrlfQI5y/exfd2kTZv1QP33a5cx3ydG+CZRd+/jXaXsfY3ypBPVb9zIy9TXcD6mt57IFlqNm8Gg8HwuMMYZIPBYDAYDAaDwcMjYZCn1UiOX1uQ0TwYnOo+mMNC1x2eU9srtTiLhzwElpyxuiq7v9kPXtMyYHeKZO76tBObnyMrPHDM4XCJtmGM2VXbLbV9iwdgMEstxxhpm+UjsGXHzzPSuM6DVRxGf9VNV5VxzlUyu/uvgO1bTmCHdfTqbJ9FRIpt3KMWY+GUkcyMB1YrsPk5x+wGaqfFl8FSyPbBhHUu8GDkqWPeu+dpzUZruwWynPGAByN5ni/0mOpClxfz9vh8+FpseXHeRHRCtq8MtlGZu5CHM+M+2/PCHtIC+h22GSJDFj1IMI5piYcoBy5iWPTwn/ZtGQcK9UBZ1KLtl3eIstiezIwjInse9livHhodunEFapE2R5b7kDsKDLHQtaTPzR+HHkzTPmgAirRov3binqm2qWOsbpFp5a5GMMU6CTouYCXj4cIpDwVGPISouw0pDyMWTrwwmz7HSoszjWIut3hoU6Pgy85OLtuCzVtAizahZVq2gj7VPz6ZaVdEJOEOTvEDMMmi0e08sKjPoLjpDmvm7DLbE0apK0OeMao9e/6JvIzwIGfAHaXsAQ4BCu/RZ+uvN10rIZ+L2rzpIUoNMwoSj60nux2OefiU85dxfag1YHb+UwJDSiUJhnY+z2AwGB53GINsMBgMBoPBYDB4eCQMcjjJpLo/zTWvlQPaV/Udy6Saz7RE27AR2RllkEm6pEWn2a1tUcO6x8CLrlrEgVEs7ZGF8jW7ZPeSMm3eyPolDKKIyFwXOl7figyNOAabVNsFu1jepz5WE20zZ6FVOlKGsMs+YjzRSYfvqzN9FhGJ2mC24hrZSzKtaQz2Nx6SHd7z2FOy18okhzp2st1JAexfqeVs3rIQj3XcJaO/x3Y1KKJALbSvEe+jvxF13kJGVLXPyoyGHS8+XC0CyQLHfMbK4MVljSt24yme8hmqDlu16FP0rXzSnL0uIhnDHJTNFlqqReVP146LiIRDzKmywiF15sGQFnQ6vsSbN7K0GtsdkmXO2JfyCcoUO65MPg72P+BcZPXqbF+HrkxIXXk25Pzrd4D6ZV2bmW9Jp3Z7lTO2hRU8f2VeJ3OODY5paRhRY6zfhYBBHmrdNwOypQGj33V+9Hs7obZazwWIiIRktWWO2uATWgVqVDt3AJKF2kPNBcoceyy2P57E+30Q0t4vpZVjyLJTrrOMDHMWu//7VQOuYSkhx55bEvJMxKTu2ikztGTK72mRaz/gveMm3te8OdA+SZLK7J6YwWAwGB5HGINsMBgMBoPBYDB4eGQuFqW9vkRDsCgasBF6DKUykFmFTJ6yL2dYyKzmWNrKAdnSAzo3aIgIGTdla322MdaQAupiMzpHJGSDop6yhF4kr7KLZBsrB2Af1SnC6WSdpjo6gU4x1Yhf9jFTzSldLALPWUHDPsKhBmygvipP38cL6IfOn9+2ntCv6PiO0X4tRDvKTgNg/4odjF3Z7nCAsY+egk41rbs458kc52egYQ+YY2XjxvO4Hp+4WOK0dNbFYvb9pMlwiaZrJylxJ4HXwi7XBetKi2QBPTcTYYBHpozxKR1EGFMdjjUExM31RB1Vjku8lwwfx6OBMTqvIiIRyysbOFpG/cUt9FV17aHHNqZzdJUYYP4DMskpdcxa52jRzU3xhH0bo+xoFWOtkN2e1slcNpyTh4ahBBpAQg2vsuZBQdf7w/pXZaJLnTOBLrVZlh3141p2hp1XZ5LREtnbice48js2XeVa3GGUdaq6du6UVNyvm0iZXGWqOU86TlmFY0To7UJpsI6y9DJPB5Gxl+cus1p01XFrHHZQqczcq89fQ3r8a3nQke4SNMgk66+B03ZeJBQG00wn7neawWAwGB5bGINsMBgMBoPBYDB4eDQMcpJK2B0oOeeY4/7DetVAqDlUVobsk2hcsKdFjMjqhENqTKlljdTnVFknT6uZ16vvVZdYUH0sGSrPISBnhsn+aYyzMtOqFVUd60zbqm0kO6uRw2GLDg9emZzdy5kwjC8+oka0PDfzuYhIoH3gvEQd9p9MdUTG3WfEC5yfaMho5jNMWOcC3SZGToPaukwGMqjN9E3dRzoXHy4zVSZVpcF0A4mHGHPnPJ9T5sr0V6gjLdDl4xT9V41rb41+zy3nxqAzOFlFPUWN6L6IOSnXyFQPHJPYvkwf7CFcDLoXwRwW+owN57LLmWQRqVDvOiaT37qCektHYDN753DvaODKFHrok0Z9q7a+Tw/oCp9F6wn3v6gyx2XucrSeIGufYcz6fAod5zkdMwJc5jmX6uvLNauMb+56IuK0zVyT5X1loTEXKdeOzyArs687L6G6gHDdja+SdU7ceCrULfcu4LPGm1zfBfaJ+umk5FjnAmPI0wZ9lY/AxiozPtqAx3X5nvNB1u+jrmddD4VjlEnLD/86S+lLrjHiIb2Ms15/5j7/rIDqsCM+S41dFzLT0YjfjVMXIx+pdjuKxETIBoPB8PjDGGSDwWAwGAwGg8HDI2GQxwsFefAL6zJcBHVS2Qc7VDp1VIo6QajXbzxL4ORs8WjRsXPBl8AeHb8N1qdIG9XBOu6du0EWsufa6a9SD0lpY0rSatJUP98G+7ji+t8IZvp7/BJe5z9E4px6NncueazZIa4tfR99u/eTYMbXvo3Xg1fUqcKNUetXL+hgqnPCcV3EhcYNl4YWUVqc8kmNSazWNtFu+ynWfewY195FMl81vFbugEUtKOnIucliT6+qz2d+VmeZOx+QjRzPuSWjPsfTKsqM5sjWk2lLVXbrNaMsbG2PbiNV1Dely0gHUy5zt1whZQpVJz1pwJ9aPZOVOU6KnvZUjSGoU877oqQq21NPWxGnZR03Io6Pux5kZQcXyMTuujnQRDndHdD2dD1H3FkIPYl43k8yk9UD7kJw52LSUF2sY8QTTbsjaxk2MCcZ9b6qhS+2XCqeTLj7oNpqMuTTLfoH8zUreLscu3scGFlfXo8vXxARkYXfo744ebhv81+/izrog6zOGyl9l0tvfOza4a5PSl/iLHfWwHe6/CbS64Kmp8PWHSnu6MS3yIgvw2M63ObZBzcDEqwuz5Sd3nswM76Q1wvTjbzMdAtJejGZ91SdPO6ibEN3ha5dcQ1x3sNqVYKu8Q4Gg8HwuMN+kxsMBoPBYDAYDB7sD2SDwWAwGAwGg8HDI5FYREOR+U8SGdFAv6pBId6hKd1Wru9y+1hjgicalkH7rbuu3oMM2oPlW6gn7uF1uI9uN+5ie9SPCy4fYns3Kc/aeSWl2ZhglQegbXymVm3hFIeoGvdQvx7wK/SdVVf5ANu78daxiIgsvY/DRtVbeD9fx9Zu+dj1rUArtkmD9mQce/sqD3QdYNt37q5v2SYz9/bXaD3GYJBwyoNkXnhFgfZu4znUt/x9bvNTDqD9iO7s5mWq92irxbjj3KqKh7Xyw1QHLi44U/s7HlBq0jZOI4znFxiZvHOYl7m4D/lIeJdtcxtbipjbSy1s5RffvePa4T0FteNje9Vb3H4/8g5yEZWlhZnPqgzFyENHaHWWty9OCrCwh7lIF1j/nS30/XeeExGR0pF3IPIjbtnrQU+NQ2b9Wue58Mm8TLSDNZKeYC6b+4wsPsT1c6fn8f7G3bxMuAiZTB6KwvFML0BeEG8eiYhI7+Vzrm88eFbYhizj4GWss/PvQI4TUKYhBe/XwIB2hXoYkONqv4D1XGJkth5GFBGJb3AO9KAa6032ESASLuBZTJ694MbzJ++gyHn0N2syREQP7e6ibO+F9bxMZZtjbtBO8vp93PMS6oiGaonohpOvdS7BkBHaGpKSnMe4Tp51Uo4lSlK6L2Kt1t+hFIVBIXs/hPWx/LaTsyQruLfwZtsO6RkMBsOfAxiDbDAYDAaDwWAweHg0Nm9ZJtEolXjEaFuyw8HEOwClFmdkcPVAUjjRMAHW5dlUFbp60IlRxgwriGlfpsxxMHFsVjQ6Exqgh8xSPWzG/nh9CxJ+xsjqwkD7yMM5DA7IGSq/HY1ZHuiYGS080b57lm08sBWp5dxE54CsNk8yRh7zrgx4xLFG48JM2ZiWUzrn+FntqPh+kM6UCbtkqEcuAlqDLjIyiMLnEGjMrkZOD70YbO0jWdiA7F8eaqGHmbx2NCwlP0CmjCgPQsW0E8vGXtS0x/KKSM5qqu1fqtZ+qbfe9DPtS3E2+CLTdZZ6c63WfQOyzIxxTtmXYpvPoOdZCY5n7f384BG/Tn32Is42MLcE1EN0ei+tCFPP7i9k/HEW8H9aMu4ahxyR3Rw1Z6OoRURihvPo4cxA62KZzGOQ1RYxrfOgnVqq1fR7pJZ67n/rgtZ3JjhG7d00RnrScO2U48JMH9LabNR0xOc1qbl2ilU9pIl6iuXZmOg8ZMYLS1GmO+R8BXqAkK/TOtrRg7p+n8YMBlELvbSOPo71MGrBzfW0SnvCYiHfRTAYDAbD4wtjkA0Gg8FgMBgMBg+PhEFOioG0rhZkxFyD0RwjolufYvM2r9Zc+gFelHEdLri/2dtfwE0TRuKWWuhuf1V1xQwK6DrmsL9CK6uqWo7h+piSyiLTYasHjv3Jbd4WUf/RZ9CHhRL0isrSdi548bqL+HmBUdJHLzCKuQeNZvsS6uqcc1NcamEcavMW5jZveN87r7ZpLm5b7cGymPHHnL/6Fl41gKJ04uatd4F2crR5ywJqNsnIT6gnXnrf6S47l9D/0ins70ISqwmDQsZ1tru7lJdRRn1awVwMlsii8Xn0V9Cn+dsLeZmj57E2lt9jTHCidWCe9l5HP85Xn/bawUSNaaGmlnPdC3jfuEsrr9ibg3P4rLYNfe9ghfHNbQxMGT9l/kVEyjvQlHaeQN+GtLxbYmz0nZ9FneV993w23ijN9CnXvJdRf3kb9ms7P+xCP5r3UX91G+u7fQH1V6h9PnwZ79fecKzq9N3r+IHa8LCHoJsSdcsJbd4WvQAcZeF1V2DjDUZmq5XbPoS5yvSKOEuz/FqG+VmiNlzDfzIvdn16iHric7BKywM0IrWVg21atev6pqWTW/fQHlnssF5js7Ra/O6OG88xNNsx7005rvlv8vOhi7ZXaLS0BpAkR5gvYV2FPWid1/ecdjv5GBZziy3on5MDCpjZpwtT6Ml110VEpMDzEOlgmN9nMBgMhscXxiAbDAaDwWAwGAweHgmDHE5FartJrn0tn4AfKh97EdCqPU7Aiilzpw4Squ+c+nHOR4wSPkbZUgtlNAZXnRz8mFjVIU6ph1bmOkhRpsJQhtq208XGS9T1MtyhdEL27wT1Fo/BTCXFWl6mSNcI1YuWGRwSU99b30Kd6uwhIhJxOqKxY99ERMoMEEmKdKQ4dZ8X6LahekgNr6jfR5+m5Qrr8OukJrMwG3ih7Rx/jlrakZvr1tMYT22TulGVWFNW2t9A/UnF6Uw1rlnvGc/RJaPL2OhLZLBj1073iuqhyWr3NDwFnw+uYP5OdhxLW9+lq8A65qe+jfcnzzKco6rxx24G2jSNSApop3sJ9xbanBuSs8HUsaeNJqOeL4bsKyrUaOjFp8A+Hi+7dXDMqGzVe2sfhssB64T7ROtVxzYm1LROqqjn+AXeewcM7+mzfF7HzbxM8wNqjRl8Md0Gs6ra6mgO92aeDlv13SnZ0+L1TXzAmGdlV3OGVETic2BNs9GsTj3dZaDHC5hYP6I7aGNbpvsaWObyb6Fv0SJ2DgJGcwdzLnI8qlXFR0JWWxne9IdfxX3f/MCNZ0o3FoaKZM+hL+n1WzNzofpiEZHp5tZMO9EadhRSZZLVrWX/yI2HDHWmMdVk4kPOV9gCE57s7Lm+US8elme11AaDwWB4PGEMssFgMBgMBoPB4OHRuFgkmcT9VAr0Gi50ybx5Dg6qyyt09RS86mNnXRoqJ47RK56QUaVuVFlb9TQutukK0XPsXJF+y+GYrgtaHQkvjRYOR65vcVfZWTLGp2i30NGYYGWqPWaXjHdAzWexw3rbA46PbFPiuXJwHGlhNr45LZERbzMOu+27ctCxgQx8UtHx0fVhMOv0gXHgnoREmjL62sfaLbCRzbse8x5hzLXddKbfyvBrnc17rkzOIPMeZcuLOsd8Bp/WjtYT9/lM6Vs9mi/yc8fwlw7pdz3CnBaPwJ43b4M1VUY5mHosekB98gOyfwk1yHyG6sutvtgiThMcpMpIo/+N+7h++wY0wr7eu3mPa1BdRFhdkeu8uo2+Vj9xrHPzLtl67mJMyUw27qOvUzo61O97emKyp1mH3ruqRVY2lWxxOO8ix3MtbA8McraG/mcfw2M69LTHeTuDwex7bYcMdTKddV4RcS4fpcPRTN9SRlDnDh+hm7f0BIytOlyoXll56cIBx+6xzinHHpB9Dk9Z/5xj2kU8Jlnc/KRkwnX+smTW3UIWvHljv9VrWsiQKxL19/b8t7WdpNOZYfENBoPB8HjCGGSDwWAwGAwGg8HDo9EgD6dSu74nVbpNBC2yNN6pck0/i7ep75uod+3sie/GA6fhK3SQTlW+Q30gT62XNeVrF9pJ3zO3tAOmrqwsEn18M01hG53x+xWRmH3L6Ayw3MZp/GBnn52ng8OpS85SJmp6DBZp7m2wWuk9aB5rU7LAY8//Vv11C/SAVSeAZehUy3sYuyatiYgImS7tQ6ZpdV2wggtkz4OeY/7K+0wJo8dr8T7royfwilwUEZHK/VZeJpyCQStvQncZkAVTZ4hiC3VWbzqtZtrk8yarWKeGWv2kiy24ZFRYp4hI8RRl4jZTC5ncp1686yPoVkv3vXS8A7RZOUUfU7ovLNDpIuqgLtWDYzys5w5cCuIu5tjfOUC7jkWNjtDPJr2YwwnaK26hL+vfgj438uqobHVn2g74vAsrKBueYk3N33S62OaN9szYa/NYD+p4sUz2vrDrUgtlHd+FrIH1HZGxnDyD1D31jz551jGhJWr0qx9jzRy9hDlYaqOu6TreRycuGVCfaVKlqwR3gQZL6H/3PK97VtArB/i+dJfRp9oynE7UzSLewLydvu5S8ea+hjlIn0D/dc2ovne8iD5na85ppXCAfo5WOQfcdZo04Lyiuuhpxf3fP/cmfbw7s6xwSJZ7fBF97Vxyv3cWySp3nsb8NHhvsgEGfveLYKzP77pdAU3kC9+/KcHQfJANBoPhcYcxyAaDwWAwGAwGgwf7A9lgMBgMBoPBYPAQ6CGcfx6Uz1/MLv3VX5bRKrZ0S/vYti4deZGv3JLtXaQdWo+SAYo8itzt7zzhDqj9hR94X0REvva9F3HPEQ9yrWNrtXYbW6EF7wzNALvHMq1yXAEPDK1AihDvcBv4vuvbELujEqmz1UvYyi2/g21etXA7ddkVUmyh/Mp7GNj9n0bflr+D19Y13DeZ9w/2Ue6RW8/hNRzzoOIrmITxDXfoKO7SyqzJIIoQr81buH76YjozNyIio4s8mFaglV6X83SM5/LKV26IiMhb334qL7P8HGQLe9vYVpapRhqjjuVVTPLxTRd4EUwYS1xlH1a4Bd7CdvzFS6hz+/treZmrr0KCcvd7sARTezR9Xj/y5e+LiMjXf++lvEzpGO0MVml/xnU1+gFum3/McAnv373sGrbqwxv4bHyJUpRjzEXKEJVw4CQW9XuooP0M1tfieUgcRt/AAvlv/v2/KyIiv3/6Ql7mD7/2CurxJAcizvKucQd1Xvz5O/lnH3wEiUvtDhZ/72n0rfwAkpilL+yiH7/vJAkXfhV2Z4HaubXwPBLKGMIGpANpz8klNEY7oBwjVPuzgmelKF6wh4hEy3i+qVqc8YCdypjiy+i7H08th5CgdL+C9VT/409wz3k892ATtm9+uIhchbQi3EfZKWUz2sfx559Fe//kHXkIOq5X8Ryytz+Y+VjlXCJuXoTX9FBgSmmUWuBJ4BZPbvPGg31hrTbzXg8URuureZmEYSjRhXPyza2vSmu0azoLg8FgeIxhDLLBYDAYDAaDweDhkTDIjfkL2Ss//J/JcB7MSvWAFl49z96rQOsxWprFQ7JA09mIXrUMExE5eA1sz/wntFnroL7hMljAxl2wP+HA0XeTRcY5l9XmTQ/u0K5smM3U5fepRPuw02dwMKh5hwffeEBuuOyYt/IB2KTCHYQFtL50GWXexcG+zktgl4otz06uAxYuqaH/wQSMWvciWTNGXs/ddocOAyXCeZhxwLGXj1Fvb4P2ZR3HzvXWMPZxE/UtfsRgkKGXpCEilZsH+c+T82AOYz2wpWxfzLjgOfSxsOvYRtGDfBr9ywNeYR/9nyyBeStuukOHoytgY/XwXH6IkYcoey+Aday99SAvk/Fwodp7qY1YsoqDeNG+d6CPyD/bxQG/bJHhGH0846yoz8CzrTvh2FYwF9NlrIP4BgI2jn8CDGn52M1j7X1GIZ+xMssqPPR1RBb6tSfyMqUHPHzHuOPsPNaKsqnjJ8EcFz645/qmB1E59kDZ4HUcUJN9zPHk+Qt5mYihNdEu6j34MazRpb/3LupoYHyBZ7+mh2f1M+Fh0+FTeC56MM4/7Bh8dBfXyD5nZLETji/kc8ueueza+R52h6IV9D/QeHWdPx6qmzx7MS9T2OPBvjrX2X3M/eS5S3hP68MscuOJT9GX4ISM+wn7VOVaWsd67D7t4tAb38a8j54Fy118n3HYHMfxD+L6wrtu3aVlstbv35RvjX5H2umRMcgGg8HwGMMYZIPBYDAYDAaDwcMjsXnLAgRyqAY014L6f34Hs9eykBfOBHn4EbZn69PP8vdqfeYzYLyWtxPNtpdF2cz7mZ/z+oJ/6vWZn5Xx0i4EZ/rojUfO1BucHc+nlclmX/M5UMY9+pS+6ZzmfZpt/6y13v+v+LPwameew/+3sqr7Dv7Zm//T2gnOvP6LwJ8WQpH9M4RTnO23rgP/e6o7Sn/KzpLuZMinNXdmXQXhmQY/rc4/rd//lJ2tQD87217+XfGu/7+tmU/5Lvypu2p6r778WdajwWAwGB4LGINsMBgMBoPBYDB4eDRBIeNUqvd6UmpCRxofMRBj6IIb9OS36lVz7eeZIAzR6FcRac5DF1i/R0eCHnW/XWhbC9vUto6cZjfsQTupGlNh0IVqBENqKP1QCdE+MeCkyXs1IEL7FnVdMEDE0JD0CH1o3EXggIZa1O/VZvos4sI8Io3X5dgbCfSx0zr6XHng2XIom0VNcNxnSEKLdY0w3qjt5iAeQCs5qWPs1Qd0e2AYw3i9MTMuEeesoZrTs0xlrutMnP5Ww1dyXaxGPat+OXiY+Q1VB60xx9n4zPj4ub8roBpkhr+kXcYQr9BxQ5+1F8oiyqxPzwSD6DjY5xk3BnU/4BjTEuOPNeSGsd6BFx+uayfXY2t7UXWmb6rBnwHnIKlhXBp7nLO03ndB2dg8ZIZa4bSKshEDMMZNV0YV85HOperZ6cog1GX73x9hEElSgyY4nfvOqwAAIABJREFUHE/Zf7TfOw/9bzRyc1C7j/U8pY49+gSa7bNxzsOVal6mRGcNWcQzzBl+/Z2xyu+Tv5sSz85XzHv0OaVFfXVzXTmcdeMIVXfNZ5o0ME49hyAiElR5jqGK+or6u6uB/utZgbDrHEOSMp1n/kXuzhgMBoPhkcEYZIPBYDAYDAaDwcMjYZCDjNHEZNY0pjjw2JSMDJR+poxbQOYwZ/Y8FjAafzozGUySmfe+ZjBQBlQjpvX65NOZ0ZlriTprzPYxZ739svqzsnN5mWz2c88lIR+b1jeddZUIxyxz5rp/b85eKiurc+7PwRmtZD521hH12A+P4Y/oBBIMRrP9J2sXDcC0ZV6ZnNs7qwXlOCO6Wcw8U2XUeS0bnWGQu7zuR3TrvXRy0Newf7YuL2panU34mTKTyqJ/mq5ZWcZgyPrJditbW+jSTaX78DPN29Y69DrfxwPP9UHjznmPui8okxv1Hp439e9VVjkgqzmlI0rYx+fjhseEplhnBTpqTMh8BmT+08psHLuIixZPq7M7MJM626vM6uZFHLOfa+vL5Zk+6ufTqitUVla2TEac7/M7Qm3X/YqKeqWZa1Er5rh0NwIvynaLiJQ5b2HJRUmLSD6PU8axjxreOqCjho5Z5z4hW6/zmDuViHOmiYsFCaamTTYYDIbHHcYgGwwGg8FgMBgMHh6JD3J19WL2zC/+sgwXwZyUj5h4duIY15AkWXcdrEyhz4Q7kj/FLt53L7i/2YevQ2taehu6yNIJ7umvop3mXSa4dV07/ZVPZ7rGtDkt0363vu1Y2uECblJd5fELKLv0Pt6XTnFv6wlP39nCZwvvQy+8+ePQc659D0ziyVNkzWqOTSq21UGDF2bD/uT0Gaav3XZzoPM0qatPNK4376FPx89ivDrnIiK987y3RM9npv7p/J18Hgzl3NuOAWs/hfoqO2TNlBBne0OmJDZuu+S5QIl8WtiOFtleG+31L4I1nbvuWMDWC7g2/32ygPSlTvi8Ol+Etnr+D8t5mcoxOtNfwbxUjvB+/7N437wtD6FN2+HmTc7JBdRfpAXxlHLYyJOiNx7oc8YY++fxfvXbKFv797ZFROT+vksTbHydnrxK9vIxjOeCmTq3f8rzTv64yM8wjtOnMY7aA66/13B944/c2mn8g++hHSbpJerZTBcIZWn9FDlltXMWewP+yilT+HIdbttp3qOFhdmy6r+sDO/Fc3jvMe/pnfsiIjL9IaQfxn/yHoosQyOsKXnRnEuIVJ1v1h9wPNT7h1x/ryJJT977xI1nMp65J3oSvsrp3U3xEfi68jM7RjkbzDnQBD31ahYRSbvU7CszrnPAe6MV6qM9fXs+p5WKfKv7m9JKDo1GNhgMhscYxiAbDAaDwWAwGAwe7A9kg8FgMBgMBoPBw6M5pJeIFDtZfjimRPmBH3+sKJ8y8nmg+gK8REPcW/Z2JrsH2GZvHlPqQIlCUqSU44SHpvxIa0ZVT4dnQjlSbrGzjtxOTESKnAXtb/kIUorS6XTm3mLLTZe2HXZg9VQ6wfZx4RRRxsUu6ig4J6i8/jQ/sEj5RAX/pxRPQtbt5i2m7CPmeMY1nT/KS9q0ouo6icW4g3sSHhaqHGYzY6/ewDbz3B3fAg1jqxygXpXEJFSVFFtop/nAO2ymTn0lRnGz/yp5iYfxQ+2knOy5uzzIx+c+raD+4RK23pt3nfah2MIWd9xn3DUt7ZpzVfbJs3fLG0LHm/dYdjQbyT0tc7t87OatusWHFdTYHvrUuIfrtz7aEBGR0oGTmTR1HDxgqXZ5k0Y8U2ftRiMvM3cH99a2sFbSCGOubzOiu4HnU7/fzcuoTCK3uKO0IqxQqqAHFxfmXZmCSihoSbgGaUi6i3j0/OCaf1DRO+iIzzhPrDc/9Jq4Nap9Kx6gbwnXdXpKGUjKxeTZ1qk9oh7GU7mHvoZHtGebn3NlKAXJ+81xhXOc2zwe3fu1RllGOmDEeD5/6GPIg3Yaky3iJBYh2072DzgVnKdl6rUOXdR0WK+xbE+yf5bQFoPBYDD8Sw1jkA0Gg8FgMBgMBg+P5JDeXGEl++L8X8rtnbIOGBg/pCFQ9ojskoYI5METGhzhBUTI5fMou7OPMmrZxQM+6TFPXHmMzUOHlZT1KdDSanomoMRD2gfbF60s45bjk0+v07tXmbtoCQxUQmYsWkYd2XCYl8kDL86ES4RsT+22UoaNzEDDOBh0IGfmwm8n4GGolHZVweYO7qHl3OQHnhERkeKeYyiHF1CmfE/nNJvp0+AyGMTqjYO8zHQZzF04VOs0soG0lRut19lOzw1DwzdoTxcycCXTEAjWGR27MrJLBm+BjN4Wx/P687g+mmXzRUTGF8DylW5h7STrCzN9U1u+3M5MRKLT/syYu88wqOZDPI/+NTzjwCMIS3soE/Yw/2qTN91A2aiL9+3nFvIyjU9avBdrp/PiCtq5hesaXhEfegwy18x0niz6PhjW1stYO8U2xnX0YjEvU+LOy9L7qGf7y5jbjT9B2cE5rJ3SkWPgx/OYjzFDZmIeohzN4X37KkNzPKL50m9hzR++jjGufB0MtYbmZBfAvG/9uGNpL/6fOPDY+cwq2uGOSGkf89l9An1NPMu2yiH62VvX3Rn2rcnvDR/ltOrKbPwx1nPYIru97A4Kioi0nwDz27nofu+svsWDts9gLte+gd8D/Usou/1lrNWnfmUvL9O/hoN71bfuyRuHf09ak307pGcwGAyPMYxBNhgMBoPBYDAYPDwSDbKEoQTVah7FmlMntHDCPdTd8h4h8xpQ/6hhDEHTaTX7F/BzhcxryKjmbJ7MJdlgP7xCo2RFY5C1fmVTtY62Y+fU+ikkS5euggkL1d6JzGvQrLt2yDIqYyxkNwOy54G2V3f2UUIdZM6WnwnjUHYr8oNClPEme52VOS7afGVNMGCBx7ynTbSZVBheQFuqsI/2u+dpMzZxfeuv0nZthLkNptSRMiJZPy8eu+czYbR4yJCEhOxwREZXmb5wXMnLjJaoBaa+u0CmOi2h/s5l6oq9OOeojzbTBT73HlnGNcxx6ZQMqBdLPFwmy3iMZzZcISs7mN058COgNbBjSg1wfwnjqZLRbV1FnWpNh59xb8xAjYDBF+MFXC9RF9tfde2UTvDMise4puEeyhz3zqFso++Y3XATTHghocaYrHqDZTRCfaHoNLvFNvXRm7h38WPOwT7Wjj6V+MCzeRtiDZYY2xyyDxUy7VmIUoE3jcEevgPFLnXK7Fs2wHct5Pu5O65vGZ9hdZv3tMjec1eosIx2aiduZyQ6AQsc92h1x0j4yh6jwdlnjYgWEQk2wfKm/F0Uqd6aoSzKJxf6bo2WHoAxng+567CN/uu3ZeE6dwP2DvMyVX7/0uNTtztmMBgMhscWxiAbDAaDwWAwGAweHgmDnFYK0n35nIypBawckgnt++4FjJaly4TGKitrl5HJy7wT9cfPgrWaj6HRVDeJ4RK63bgDpi0Yu3bGc2CCEroipJGGZmi7YLF8JlHvKR2CZWo/jf7XGC2b8fPJnNOrlo7AWscFXOs8Dy1og4xh++U1EREpH4/zMnGL+lQylBpp3bmCPk/LaGeu7tpRBBPcO1hH2eIp9JyDVTKybSeM7TGMRcMqlt4n88k5n7tJrfWd3bzMQocsM7WauSMAmfLCMfiz8OA0LxNpXLiy22TLNda5cEI2escxbdFFaE7De9RvkiGPqFGvlaA7D2+68IdUdxtUZ01nhfp1arWPnJuAotkmy3cAdrPW4W6GsvgFFaw+HPZQXKSWtk1+8Tb60jz3HD4/cc+08NED/JDMOhdUOU9a50r0ZP5ZvH0889mSRmazr80eNLty465XIRlO6vFVi54VqOne1+AQj6XV3ZMayp4y6Kb6TWiQ41xn7hjXaAd9CLkzoePqX8Z7dQWJhg/vcsx/F9pw0TAT3dnh8/K/Cwn1yfr90d0nWcU6LL+L8JH+61fyMhXVjXPMxet4Lv3PIjAkGunvFPcs0ssIR4noiqH6fnWdUK29aqxFROrcwdHfIQGdQoT69SkzbNKnLrrxMLI63i5Z1LTBYDD8OYAxyAaDwWAwGAwGg4dHo0HOwE6GE7K1ZFwLU8/Ptwv2qH+ROl71AK5Tq3kMtqn1lNPFDpdxz3iP+k6yPeq7O1riif6uYwEnTZ72L2sZXB8sMc53h97JnrZxRL3jYAOs0kAjsw9RV4luD+N55xAwnsPP0QLYsim9jNXZodBBn1pXXGRyPMJYNdJaxdqN++jLwcsY+2jBMciFNuoZrnKsfZ72P+izH2TrPUav0FM/YsZ3n0N9FXo3V/8rsLcfv/tEXubiC2CT790E46YsWFZEHVeeRJnNt6/mZab1ZGYcYQN61bS7OFPm/geuncolMHn9TVwrHWk0ONpZewVl7r31bF6meQevXcZFL3yMOTj+OcxBsrnCOfBivZ8Hozp9G6zv6Bqfd5uMZXwm51tEqvfw2eAcxvXsC2CH7/yTz4iIyC/8/J9gPAPnxvDGd9DPuMcdihE9oS9Qu8s6v/ivvZeX+b/fgftGZRPMZ+XzYNjbH6Ku+RfAcvbeeCUvc/WrYEtHV+la8Q4mJd7EvckKXUYedPIygTLTh2CFz/0dMtdXL2Bu5vndu+F2EgbPgb0udNH/sI/vbf2PEPl88PPoY+ZIZ1nbwRq8/e8ihvrq//Q+yl7D+IIu46Q9vbd8GbHUQ2q3yx+BfU4OMZ7d//izIiKy8be+78bD3YxiA9+xg58EK7/828wT59mEtOn0xNnbH+FaBd+f5JWn0DfOW3iKHZOlr7dc38io17+PecnoWCP0TF77Fu5N3/s4LxJzByRcWRYZepNjMBgMhscSxiAbDAaDwWAwGAweHk2S3jST4slIRKiPPQZbF7Y9FwvqVcsHTMoagKHKePo7JNvVLDr2ZVIH61Pbpd63Qy0qRYCVTbBlQd+xweEQDLW6IqiLRTSkVpf6UWW1RESKgeqTmVZWgo6ztMv66XxROnJscNQhO7cJlqmyDvYq3lIPY2gpm1PHUMa9T0l881A9IMO77fqm8xT3qYdWNxD67pZOwTpruhz6T1cEvpbpH6v3PPjHYPY2Pnas89EO2L/1B9QVk/zXZMKDO+dZxrH1ytLn6XE1Zbmz2TK3XDud85jbc1tk8vlME9Z11AKDfe49N1flbTB4jQcYa5nPfdSEVri2h86qe4aISGsHjOrKDXoNb2JtFnr04Y4e1onWdjCn/VXM9b2tK+j/d/Gsf33uS6ij6/nsvsvUQGraQz7v/krMOlH2G6XP5GXWP2Ki4R4+O+yAFT53G3N7ssf3b7t1ne5Ce1yiPnZ6Co2zarcj6rzTZee3HKiWlus3fBrsf3gfbG2xg2eRth3rXL5LfTx3eNTtQajZre/w+Xv+6ek2vgMr70BfnnT4vbk9m8pXXHMpf7kGfY5a6h6YXPXqXnkbfVafbxGXghfwd8niB2xHHWuol1ZHDxGRlN+XlPXHN+G/nNJtRpMH1RlHRES2MdeyhM9UKx5Sa5+s0E0ldOtAvdKnWzuSJf/077nBYDAY/uWHMcgGg8FgMBgMBoMH+wPZYDAYDAaDwWDw8GgkFpOJRA/2pXzCA3Ydbpf6QSHaoAZ1qHVXSEnFCNuxpZGTCixGOHxVuoPt44wWXZUWD/odwt4rHXlBIV1amGm0NeUTMa2uAn7ubyuHLUonaFeVx4FsM2RAbcu8AA8dm24nl9lHPWQUcxs4LrmDffl29dl4bx4KatJGKmTwgogLUCmUSuwsAwlOcVCorFvh3lxXe9g6z4qUs7T7M/eUGJ5R6LlDlIUO6i2dog+6xa5ShNE8D0i13PZ1FvJg0gT3RhOVs2S8l3Pfd+1U92fv0QOW0VgPUUYs4+ZaI6QLFY7nCPKCUnueZdOHyhS6uDfuYP6K+p6RxsJp0wOfIs5KT2Ob42HIceF6dYdyILdE8wOXucSChyXjJi3COI96iE9EpNjhvRqRzW7rPJZOGCvuyXNCjTLnYbyIQRvpJdgJqoSpd9mF2ZT4fSwxljwP3uE6nlAWVPCsFSfnKLvggbi4r1Io3HN6Dc888IJcqh9ATtRf5uE2HqLTqPmQB0lPrrm+LRxCtjA+j/EUVCpCGUN/hYdguytuDjqQeUxWUd94AfcUKctKuD4mDSfTqu/iXv3dIZSghDy0N2Ek+Gi5lJep8fuZx6/z91l6AX05eRq/S5bf9r7b6/gs3JxKMDDewWAwGB532G9yg8FgMBgMBoPBQ5CdZTP/DKhcO5dd+R/+I3luFYzr+zuwihodugM2QQIGqnYBjGuvwyjmkFHDXTBIa5cce/oPX/w1ERH5pRv/toiIbB6BbXr5/JaIiHyX9mFR2yPCz4MlbdYZLcv6n1nEwZsPDnAIrHXPBSpEywzwaIMR+rnPvSUiIr/x5qsiIhKfMJjkOde3kwOwZIvfQb+X/w1Ygj34w0uo87NgOStFd2DnSJnbEpi1ZMrAgxLu+cvPfEtERH7t4y/kZYYD9KlcAW25UMO4tj8BY/XSS3dFROSTw+W8zI9cgu3VuRL68IcHT4uIyOYx5u+pVUTnXn/DWbYVnwKj1z/lM9OwgyJZ1Sr6ONlzz7R4iv+vJk2ywefAtI1O8GwXNlBn9wNni/b0F9Hfm398RXxMWe3P/ei3RUTkt3/DzUHEDYLxgjKrLHMFrGD5Aw2HcWt5dBHzVdzE/I3XGG19TJZxjocEW45trD0gS/oiWe0mxlx9C/X/d3/1V0VE5P/Yd31783efnxlHSlu8jEuyfo9j+ItH+T1Hd8Fa1u+g7f46meNjtN/4Mtbq6LdX8zLn/v4t1Mvva1ADO6wH5PKdBS92PSDrG61gbSTrDIN5oPZlPAhXdLaCekguJeMacHcjWkKfE4ayqOWaiEg4T9aZ9esB0imZ3sJttDfdcXZy4Uu08bv1YKb/AZlYYZ+CY2e/prspelgv5Lj0fcrXbOpFdL/8HAtj3nR3RutKGTkfejHyfnS9iDvIlxxglyjkbs7oc0/l95Rv4pml83X51se/Iq3+tqWFGAwGw2MMY5ANBoPBYDAYDAYPj0SDnPUjyd6Zk7cYklE6xt/d8y5hONdqdtpgmypdjYDG5yUSbHupYxv/i4WfFhGROx9usF6wWd89QUBA9R66X2y7doY9MGudimM6RUS+uUa95RYYxcU77rPBKsqQ/JPfasCSq/4Jo3l38cHpxPWtSk1pbQ+M2ycfwNJsaRv39gSs0+GG08VG1PlSLqr5GhJSK/or0x8UEZHsTi0vU25pKAoman8CVm4JuQ3y/SlY4NKR+1/nH+2/PNsAwzCKDD75uRf/QERErl9az8v83BMIZPiDLTB7Y7LbyoD/8AZY6X+YvpyXSUn2Vcu458lFPMStKp7xz1xEYMTf7f5AXuaVeQRefPQk2k461DHXUcdrpFz/wcXP5mVK27hnsox7yvfxDF+9DPbxzdEV3OhthnzuqbsiIvLdEdbKlatg+Lbn0LeFCljC4YpjT7sB1khjA7scP3X5uoiI/L1DMMY/VsH1aPWNvMw3r2EXI9NwiBSTXl0Bm9kJ8Lz+y2v/JC/zPyc/KiIi7QnWyLWXMCc3bmGd/9zGDbR7yelvNRo7oWY2Ups0IlzlDoIfu51g7eXM6rvYOZBn0OeArG1uayYi8hQsAOMTavSpm5/uor3sS3j+ackx76XbqHfnKxjPuV9ncAd/H6jGPr56OS8zXMYaLwbYcdE472wXdR394osiIrLway6MIyg5nbCISOc1WBNWfvO7mAN+HtadnZzWq3ZxyXnMU7SFX05BGWWSYzdv0TLuSU9wLWPEucZTq565/OFWXma6h35HtScfPmNgMBgMhscOxiAbDAaDwWAwGAweHgmDHE5EqjuZhHQiKB9nfHXuBdGYMdF0VtAwiZQEXukU905rrkvfWrwiIiK1TWp1T6m/nOCe+iaZ0Z7fDiOfK4xKJos6GoF1rOzOMr8iIkGC+tVNYNJkQMk26q0c4t5xw51a18CJCkM9aptgy+rbeuqfjgtTx7QVuvzBdRf9ZyT04D4YqsZm4JVRlwRqNNnH6j4jqLcZh33is1a4pjpY1eyWKKH+Ow/AiAb3HMv+j6oviIhI536TZdCHDmn13xnzQd11UeDKUPd5z3st1kdN+G+kn3mond+sghkM72GOSx1UMq1inn51HSy6RjSLOAY/nGD+a1t4/9ZtsI/KKAfevL5Zwme6y3A3gJ43PsX703KVdboydc57pwCW+XcF+tXaA8z9f38I9vTt04t5mcI9xocz5lr7MOpxPWzh+le3Pp+Xad+Zn6n3xjycKCr3MebfWsWzqG65dZAxrCJi3LG+V6eIjO4wvjY419lSUxuSLQ1OyQ4re6pRyiISaoAOmeNMnVfodBEwPj4MHu6bft/1fXyA9ZwHbSw6Zre4z9AP9k37mJH1Lp+4nZe8HXW+iTFPpVM+vOBP/z8/5dgCzlNEJlmDQpRlDz12Oo+WJvOt9wZ02FA2Wpl5vGF/j05Fpg/33WAwGAyPF4xBNhgMBoPBYDAYPDwSF4vSxYvZuf/8r0m2DIYn3masb8tjmchmTurKiCojNVvXtOr68/wP3hYRkffeu8L68Pf8eB3MUf0jMIexI8BkuDJbT1rga5XMV59+v55md1ojG0yP3s6r0BhWr9Npg6xgf8NRlOV9lF95F3259zMou/YNXN//PJ0d2q6d0gnuGYOkzf10B5c4nlUwUuMPncOGMsYBta3DdUxYeQcTOnqSbgOHjt2uXAFDuDEH5u7OO9BHR5zzGmSZsvSBizLur9N3ll682q5GTStb7Hsal45QfkJmfbDKOG/6/I7mwMA17rl2Dl4Bc7v6XfQxK9DDlprW3c9j7Vz8x86nOi1Qu02/20kjnulb/TbGmVTdHIwX8XOhRfeNJvvG92N9f+pMjeMjMIX9a9Caqwd09Q7qv/VLjLb2GP61N+iyQD1vUibLyNjjeB9lTz63lpepHOEZlrbRXtIgC91HX45eAdO6/K2DvIxqi5NjRkw/cWmm3WyLDhFXHbsd0P9YNbF7P4o+rP7tt3GZfYxWnQNKsgc9cljlTgFZ1NFr0C2X3oF4Pxs76l1dMHKm+MkruK4stDpieKyzsrIZPcyj89BfJ8v0LX7rQ7z/V17NyxT3uuIj+RBabdVFx6dkv72+jS7hWRZOuAa/D01zuIBnGVTxHe8/6z2fNzHG5AlonOMDsvV0/Ri8Ci11WvC8rclmFx8cyTe3viqt0a65WBgMBsNjDGOQDQaDwWAwGAwGD49EgxwNRRY+CGS0BDamsk9f19bDWrzhAhO6BrNCXNUoD5bc3+zvroAlm7sRsT7c0z8FOzh/m6llPdfOoIUh5RrkSFlbanWpY64eOOp6xOStUgfXJjWMY/4T1k/WtHjqpks1ktU7YPTmPgJ13bwDFnha9bS6RLGNepIi+hQyjaxCd4nuJTDHc588PC8JJbll3lvfRvunfWp5jx3z3jtFPXerYONU01zocP428H7SdOPpr9K14gh9VNY85S2jObK1266d4TLaTspk9mskzTLUNVjWdD6nJ04YWjhexA8hU/CSirLE+Hzi6b3jLpjIpKGsL9i602uoo7iEuc68f/f6K/SuHqL+0Ty14OzipErmNXba05BJhlN+1tf+H+GeaY11LThd+XiJCY2aesf6p1W6c4zQt+5517loQleHDurtXkYd1V1cH3Ou06bTbmcf0xmCyZPpXTh4qLODam3jQ+fGoO4LKfXJy29TE8zrQQFzPN1xjhjqe5ynU6om+GMy1FzXQdl9f6db2xjXtauzfaNmN+3hOxEtORcY1U6rfjjZ2sFbZcjXqBm/7pwiNP1S643ovyy3UDZnjkNH3pZaZKo5PynHkxyAnQ/L/J1VcN+F5Ahi/ZjtTPfpeMF6Kw/IPredBjkbMFmz1cm13waDwWB4fGEMssFgMBgMBoPB4MH+QDYYDAaDwWAwGDw8Gpu3VKTYyyQpcSufFm4FT/ogKa5Ny9h6LlBikXHbUmUM47r7mz0YqBxDD/bx4FOf8bf6fujaiYezW+kZDwapnVzMvsVdVyalDCMcaTsy016hy5jivtta1z4Fo8nM+3DEe2kDlx9yE+8AnO7Gc06KPI8W9zjeoWdbxz5pCEbGXd1Cm+10UabYc9KHMUNYwkkwU0c85DOg+kNlBiIiEybtFhiAkh/S47xNGrRjK3vPp6jWffgsKfMe7p5PavrqyiSl2WsxP9J6k5L20c11NNQDcCzTVykE26u5e914gpkyE96rtntT9jWN3bwVKwwkqWiENuuo0DavrFaE3ryx7Wg8KxnSdVzkwcFJw+sb69d6Vb5S7LBvfD5J1X09tUW1asvty9R6jNZn/qFbPYSn0ctRh5ILLcPDdVnfO3BH+ztNs9HaVK4hatX2KYd70zlKXW7Rji2afS5BwUltgmJx5rO0x5O2lCpkG4zZ3ndpQ2rzppZzYRN9ybo8vMfvk0RedDbnSQ8Darv6Pu/PcCxnkctA1MIt1JPG05m6Rbx46iyVmcQag8FgMDyWMAbZYDAYDAaDwWDw8GiipgORSSWQhGeKpmSSo4r39zeJVGWZg5TBFyRb9GBU5pFOWeEMQ1mcjadO9f3YtaPWS0lBD+npdbZTfph11GvRiPUVZ+uf1OOZ+0RECvws4+Ee7VsWzTKWgUeia2iJHnwLEh4gJDur7Gri2UflZdldPXyoVmf5e+ekljOQal9XOpmdg/IhypSP3EFFZTzLp8lMv3U+9XmVTl2ZaERGtYLO6bMsMLglpYWb305xRa+BtQwntBoj819sYRJKx47hi3pg90q0e9PDdHqgsHTEkAnvcFaF60kP9FVKfN+azvQ58ncfTsBelufIrPJAXOEYkxsf49Bj6dC1Uz4kW5pofrjuRnD34RgsY2XPHbirHJP9p/VYbS+aGUf5CM82PhrkZZSfTsmw6uE8jUpO2wyz8JlZDcEigKNrAAABN0lEQVSY4J7JMrYJwuuzB+SUfRZxTKgfOCIiEjRJgWtwSDLLmIuIRIewQ0s1dCOdnZPMK6OH2rQPIdlsHY/wsF4w13R9YPmghDHmtnI8rJcHiXh2ctrvrEc7wXZ7pk8a4Z01Xbx7wMOSQY0HElvc4uH6Shs42Bd52wI678nhRFzGu8FgMBgeVxiDbDAYDAaDwWAweHgkQSFBEByIyL1//u4YDAbDY4/LWZat/IvuhMFgMBj+7HgkfyAbDAaDwWAwGAx/XmASC4PBYDAYDAaDwYP9gWwwGAwGg8FgMHiwP5ANBoPBYDAYDAYP9geywWAwGAwGg8Hgwf5ANhgMBoPBYDAYPNgfyAaDwWAwGAwGgwf7A9lgMBgMBoPBYPBgfyAbDAaDwWAwGAwe7A9kg8FgMBgMBoPBw/8DQlw0CZj86QsAAAAASUVORK5CYII=\n", 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -313,7 +313,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.5" } }, "nbformat": 4, diff --git a/notebooks/plot_otda_linear_mapping.ipynb b/notebooks/plot_otda_linear_mapping.ipynb new file mode 100644 index 0000000..7b0e7ad --- /dev/null +++ b/notebooks/plot_otda_linear_mapping.ipynb @@ -0,0 +1,334 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "Created on Tue Mar 20 14:31:15 2018\n", + "\n", + "@author: rflamary\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "import pylab as pl\n", + "import ot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + "-------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n = 1000\n", + "d = 2\n", + "sigma = .1\n", + "\n", + "# source samples\n", + "angles = np.random.rand(n, 1) * 2 * np.pi\n", + "xs = np.concatenate((np.sin(angles), np.cos(angles)),\n", + " axis=1) + sigma * np.random.randn(n, 2)\n", + "xs[:n // 2, 1] += 2\n", + "\n", + "\n", + "# target samples\n", + "anglet = np.random.rand(n, 1) * 2 * np.pi\n", + "xt = np.concatenate((np.sin(anglet), np.cos(anglet)),\n", + " axis=1) + sigma * np.random.randn(n, 2)\n", + "xt[:n // 2, 1] += 2\n", + "\n", + "\n", + "A = np.array([[1.5, .7], [.7, 1.5]])\n", + "b = np.array([[4, 2]])\n", + "xt = xt.dot(A) + b" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot data\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(1, (5, 5))\n", + "pl.plot(xs[:, 0], xs[:, 1], '+')\n", + "pl.plot(xt[:, 0], xt[:, 1], 'o')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Estimate linear mapping and transport\n", + "-------------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "Ae, be = ot.da.OT_mapping_linear(xs, xt)\n", + "\n", + "xst = xs.dot(Ae) + be" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot transported samples\n", + "------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(1, (5, 5))\n", + "pl.clf()\n", + "pl.plot(xs[:, 0], xs[:, 1], '+')\n", + "pl.plot(xt[:, 0], xt[:, 1], 'o')\n", + "pl.plot(xst[:, 0], xst[:, 1], '+')\n", + "\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Load image data\n", + "---------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "def im2mat(I):\n", + " \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n", + " return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n", + "\n", + "\n", + "def mat2im(X, shape):\n", + " \"\"\"Converts back a matrix to an image\"\"\"\n", + " return X.reshape(shape)\n", + "\n", + "\n", + "def minmax(I):\n", + " return np.clip(I, 0, 1)\n", + "\n", + "\n", + "# Loading images\n", + "I1 = pl.imread('../data/ocean_day.jpg').astype(np.float64) / 256\n", + "I2 = pl.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n", + "\n", + "\n", + "X1 = im2mat(I1)\n", + "X2 = im2mat(I2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Estimate mapping and adapt\n", + "----------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "mapping = ot.da.LinearTransport()\n", + "\n", + "mapping.fit(Xs=X1, Xt=X2)\n", + "\n", + "\n", + "xst = mapping.transform(Xs=X1)\n", + "xts = mapping.inverse_transform(Xt=X2)\n", + "\n", + "I1t = minmax(mat2im(xst, I1.shape))\n", + "I2t = minmax(mat2im(xts, I2.shape))\n", + "\n", + "# %%" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot transformed images\n", + "-----------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5,1,'Inverse mapping Im. 2')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(2, figsize=(10, 7))\n", + "\n", + "pl.subplot(2, 2, 1)\n", + "pl.imshow(I1)\n", + "pl.axis('off')\n", + "pl.title('Im. 1')\n", + "\n", + "pl.subplot(2, 2, 2)\n", + "pl.imshow(I2)\n", + "pl.axis('off')\n", + "pl.title('Im. 2')\n", + "\n", + "pl.subplot(2, 2, 3)\n", + "pl.imshow(I1t)\n", + "pl.axis('off')\n", + "pl.title('Mapping Im. 1')\n", + "\n", + "pl.subplot(2, 2, 4)\n", + "pl.imshow(I2t)\n", + "pl.axis('off')\n", + "pl.title('Inverse mapping Im. 2')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_otda_mapping.ipynb b/notebooks/plot_otda_mapping.ipynb index f25eb11..9a7ae04 100644 --- a/notebooks/plot_otda_mapping.ipynb +++ b/notebooks/plot_otda_mapping.ipynb @@ -69,11 +69,11 @@ "theta = 2 * np.pi / 20\n", "noise_level = 0.1\n", "\n", - "Xs, ys = ot.datasets.get_data_classif(\n", + "Xs, ys = ot.datasets.make_data_classif(\n", " 'gaussrot', n_source_samples, nz=noise_level)\n", - "Xs_new, _ = ot.datasets.get_data_classif(\n", + "Xs_new, _ = ot.datasets.make_data_classif(\n", " 'gaussrot', n_source_samples, nz=noise_level)\n", - "Xt, yt = ot.datasets.get_data_classif(\n", + "Xt, yt = ot.datasets.make_data_classif(\n", " 'gaussrot', n_target_samples, theta=theta, nz=noise_level)\n", "\n", "# one of the target mode changes its variance (no linear mapping)\n", @@ -100,7 +100,7 @@ { "data": { "text/plain": [ - "" + "Text(0.5,1,'Source and target distributions')" ] }, "execution_count": 4, @@ -109,9 +109,9 @@ }, { "data": { - "image/png": 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TqlUr1q1bx/bt22nTpg0ul4vx48cTDpduIajI22+/TSQSYdWqVaxevbpU8jRo0CCefPLJ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\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -149,26 +149,26 @@ "text": [ "It. |Loss |Delta loss\n", "--------------------------------\n", - " 0|4.210546e+03|0.000000e+00\n", - " 1|4.194392e+03|-3.836611e-03\n", - " 2|4.194053e+03|-8.094371e-05\n", - " 3|4.193924e+03|-3.056965e-05\n", - " 4|4.193849e+03|-1.797118e-05\n", - " 5|4.193801e+03|-1.140070e-05\n", - " 6|4.193776e+03|-5.930168e-06\n", + " 0|4.132385e+03|0.000000e+00\n", + " 1|4.124370e+03|-1.939427e-03\n", + " 2|4.124043e+03|-7.928706e-05\n", + " 3|4.123904e+03|-3.369312e-05\n", + " 4|4.123827e+03|-1.881208e-05\n", + " 5|4.123778e+03|-1.184435e-05\n", + " 6|4.123764e+03|-3.358329e-06\n", "It. |Loss |Delta loss\n", "--------------------------------\n", - " 0|4.245881e+02|0.000000e+00\n", - " 1|4.181680e+02|-1.512078e-02\n", - " 2|4.178974e+02|-6.472597e-04\n", - " 3|4.177550e+02|-3.406786e-04\n", - " 4|4.176586e+02|-2.307406e-04\n", - " 5|4.175879e+02|-1.692203e-04\n", - " 6|4.175338e+02|-1.295518e-04\n", - " 7|4.174909e+02|-1.028089e-04\n", - " 8|4.174570e+02|-8.123852e-05\n", - " 9|4.174309e+02|-6.257777e-05\n", - " 10|4.174083e+02|-5.401101e-05\n" + " 0|4.143700e+02|0.000000e+00\n", + " 1|4.111590e+02|-7.748977e-03\n", + " 2|4.109509e+02|-5.062453e-04\n", + " 3|4.108581e+02|-2.257162e-04\n", + " 4|4.107918e+02|-1.614130e-04\n", + " 5|4.107473e+02|-1.083067e-04\n", + " 6|4.107110e+02|-8.833802e-05\n", + " 7|4.106839e+02|-6.600463e-05\n", + " 8|4.106615e+02|-5.455553e-05\n", + " 9|4.106428e+02|-4.548650e-05\n", + " 10|4.106278e+02|-3.649926e-05\n" ] } ], @@ -218,9 +218,9 @@ "outputs": [ { "data": { - "image/png": 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7eZHN9B+Lx6idWsXerQ1oGGadLSrrKvDTATMWTGX1P9cRK4pRXlWKBiF+KqS8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\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -266,21 +266,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.6.5" } }, "nbformat": 4, diff --git a/notebooks/plot_otda_mapping_colors_images.ipynb b/notebooks/plot_otda_mapping_colors_images.ipynb index 288f4f1..4b19e0c 100644 --- a/notebooks/plot_otda_mapping_colors_images.ipynb +++ b/notebooks/plot_otda_mapping_colors_images.ipynb @@ -82,7 +82,22 @@ "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:2: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0.\n", + "Use ``matplotlib.pyplot.imread`` instead.\n", + " \n", + "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:3: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0.\n", + "Use ``matplotlib.pyplot.imread`` instead.\n", + " This is separate from the ipykernel package so we can avoid doing imports until\n" + ] + } + ], "source": [ "# Loading images\n", "I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256\n", @@ -123,39 +138,39 @@ "text": [ "It. |Loss |Delta loss\n", "--------------------------------\n", - " 0|3.680514e+02|0.000000e+00\n", - " 1|3.592355e+02|-2.395298e-02\n", - " 2|3.590579e+02|-4.943115e-04\n", - " 3|3.589661e+02|-2.556530e-04\n", - " 4|3.589094e+02|-1.577896e-04\n", - " 5|3.588705e+02|-1.084239e-04\n", - " 6|3.588421e+02|-7.914757e-05\n", - " 7|3.588206e+02|-6.013011e-05\n", - " 8|3.588034e+02|-4.783086e-05\n", - " 9|3.587895e+02|-3.866500e-05\n", - " 10|3.587781e+02|-3.194454e-05\n", - " 11|3.587684e+02|-2.697250e-05\n", - " 12|3.587601e+02|-2.296505e-05\n", - " 13|3.587530e+02|-1.975975e-05\n", - " 14|3.587468e+02|-1.733678e-05\n", - " 15|3.587413e+02|-1.535580e-05\n", - " 16|3.587365e+02|-1.350581e-05\n", - " 17|3.587321e+02|-1.209997e-05\n", - " 18|3.587282e+02|-1.086348e-05\n", - " 19|3.587268e+02|-4.096770e-06\n", + " 0|3.680534e+02|0.000000e+00\n", + " 1|3.592501e+02|-2.391854e-02\n", + " 2|3.590682e+02|-5.061555e-04\n", + " 3|3.589745e+02|-2.610227e-04\n", + " 4|3.589167e+02|-1.611644e-04\n", + " 5|3.588768e+02|-1.109242e-04\n", + " 6|3.588482e+02|-7.972733e-05\n", + " 7|3.588261e+02|-6.166174e-05\n", + " 8|3.588086e+02|-4.871697e-05\n", + " 9|3.587946e+02|-3.919056e-05\n", + " 10|3.587830e+02|-3.228124e-05\n", + " 11|3.587731e+02|-2.744744e-05\n", + " 12|3.587648e+02|-2.334451e-05\n", + " 13|3.587576e+02|-1.995629e-05\n", + " 14|3.587513e+02|-1.761058e-05\n", + " 15|3.587457e+02|-1.542568e-05\n", + " 16|3.587408e+02|-1.366315e-05\n", + " 17|3.587365e+02|-1.221732e-05\n", + " 18|3.587325e+02|-1.102488e-05\n", + " 19|3.587303e+02|-6.062107e-06\n", "It. |Loss |Delta loss\n", "--------------------------------\n", - " 0|3.784725e+02|0.000000e+00\n", - " 1|3.646380e+02|-3.655332e-02\n", - " 2|3.642858e+02|-9.660434e-04\n", - " 3|3.641516e+02|-3.683776e-04\n", - " 4|3.640785e+02|-2.008220e-04\n", - " 5|3.640320e+02|-1.276966e-04\n", - " 6|3.639999e+02|-8.796173e-05\n", - " 7|3.639764e+02|-6.455658e-05\n", - " 8|3.639583e+02|-4.976436e-05\n", - " 9|3.639440e+02|-3.946556e-05\n", - " 10|3.639322e+02|-3.222132e-05\n" + " 0|3.784871e+02|0.000000e+00\n", + " 1|3.646491e+02|-3.656142e-02\n", + " 2|3.642975e+02|-9.642655e-04\n", + " 3|3.641626e+02|-3.702413e-04\n", + " 4|3.640888e+02|-2.026301e-04\n", + " 5|3.640419e+02|-1.289607e-04\n", + " 6|3.640097e+02|-8.831646e-05\n", + " 7|3.639861e+02|-6.487612e-05\n", + " 8|3.639679e+02|-4.994063e-05\n", + " 9|3.639536e+02|-3.941436e-05\n", + " 10|3.639419e+02|-3.209753e-05\n" ] } ], @@ -205,9 +220,9 @@ "outputs": [ { "data": { - "image/png": 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qNq5ipmDVJKy54S1abHK9fg5sDYglh6Ciy2jxlayRd2z3pINyoXM/73hLoFXo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\nwfUqpJxi59h1POQ0PkML0QJyh6YEOZPEIvy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U59/z72bRawh8214lb8rgJBnJY6ARKALrDldyLOqmiuo4loLIUF5Fd8yGjBi4fFY9CZPraxgRq8ItuajDYv38GEiQcTMu3uydpuvnIvONosC4SeXJmr9JoAQ0ub5nue6i1s/fnIpc378cU65z2c73caWDHoGpXo4pkquibD3n+IUxVn8IELZxQhARGELIuAaZQwoeDD6/5JvndK4ycl3HO6F0GQZNk5FMX9fh4/Fm6lPieg1+s+WRQhD4kJavc79dg1zVgLKKO1ib+0jCFhtIbqZnG8cqW2fkMw5/9Y9/7ms138nkAYPS2YtQl2CeG7/R90MEwjCuipK1UBVSgtM58JZELDRViglPgaoNT6gn5/0imBm73mkSNMa17BmcMzjFEHY8lMKcJyplUKJqWJcrPZZOjwQRJh0qTcuJ0EAdXJwMIRJaDqpOBVSMFoEZ1BKYD2cmdSK64wZCQhizCFaVUgTpQXCklAI6irqrGAuNTCXFqSpIdyYbik/TJAUSZTIQUTSSSZNjB0+YUTzykj8rIjwKnNZ7qqxRWNMJZGHpSdfx+ytqaAYzNn6jakSM8hUiaakQHS1K706aszDjKbw6j0hLcR5CmEtS3HGd8Bi/8fF7TKJ1IgflKevvVDNAgtJn9hNEOLs4Uzw56gFF6aaQQ/w0aSAqdNvTPUl5RKNTtCGZYJVQBRmlHCCEGOZDqBPakQzmHJYyrdFjiKqaBK0UUq+/608KnwmnmKb0zd/k4JEBiACRS4cBF4bECdDBNAwlWiaZArKaURXKarhFdSR2i9IUio/3N5vqMhK44+RDXKCquI5+Z7ka+wL09UuaMn5I67UoosRmQMPx9SYioW8qMFbDuiqmUq6Ottnq1C8OGFzHecaa6KX2qofThSFPVobDCV3nHZBXx9n16gBMlUgZMmYdo5F1IxLkqH9a11HIizsaDlcRVYLteKszU0XXH2TooFm2DYEw/IthhAaZwlHGj67kcM6bc93OpSJ4BKxGMJOLQw0Bs0Kw5kvW15uOuSodl0KJcZ+kwL4LRxVE6rrugujVsWbmkJ4Dpo54EOvd5us6jevoJIbk0Dy6JZp6MSBfBLRU3mfhS7LjWQlad9wLT/VIUqklx8YCWGJhSqeQzMU4ZrKEggfhhZemTOm4FN4tjgVod05pWHZ2OK/SeLUT6mshs7Ez5dwb+3lmVsPtSO+NSSrRbER0fmYqkKkc2lCPop3sUA0ybGxyaezFEZVBrUlwFqGIIxG47ZBeaT3pCWXdbCWdjGS2QnZHDVIMwSFl5OWkjHsyFzRklBzI2NjWOl2dbS6UFIoKtRjPfOE9FVQhM2iT8TQXLIVdDZamVCucVpvxJI/D3mThSUleS/BEGstZOdnY6GcUsIBMmgjvixNuPJQhfioInmUIfayAKI6TrhxNkR6IKJN2so8toMlmNx3JYdeKLAgzJo6I8rQGRw0KhUIi1nnilaUoc5k4R6dL4UXCPmRsTrTQVNnJsAm1KlGcxQttcWY7cKJgFM4yNuiusIgxLUFhYe5BtEKviUiyp2MJLzO+3a39HeMz4RThuvO/1RHpaohDrzv2i8Nk7NyGIU9cr06l3NipliPyGbVM4JqXPj4Wa21UXsfQNRG5jUTWcUmyxTSh46+PCw9uI5mxA5XL9zK5RKWbQ9jgkhfn6fKmoXWF4Yi4FL9uztV1rBNwUa6FCTL2E5fxRCahwykarPmUmzHf/J1ydfgfN7/YNu6rMVFVSOXNFqrXa6UyzvvGfNaPJmNTAVw2Ex93alWFuJmPjij/Mv6oiG5zhkmUV9WH2nGNBseH4yZSlMuaZ050CYrG5XwR/bom6723Lb2s188/YTn4pwURYYqJl5G4J1MZBr+E89DPPHAmutBL5aCFQrCcnb0sRNi6ixBE2mpIC2RgGbgnJ19wUYxOupKysD/A6xzR2WwjIlwiOUvyrFcecbovzGUaVKkZZtCDYbQ1MaBOtkZp4/osXWkZhDsQiJYRYQIThnhbHUWgoYMKZpRMqA3Zv0ZHGLWQkwaRgyYkOwJoKevuNdjVEQ1m+mqzGqpKEcNEMFWKFJ6aQxn3YwuBKKAFsWCvgXunVB0sWDzF7DTKKSQoKZyZyTmxWDexKEtCq8LiIK5MGuP3JSN3ObPg6VgqPSspylRsUNM4dW16MK1EUcTYTPQblqzmHrEDsypVlQPCPiaawiQdQTiLMsso6ygKshx59ESLUdLpkkQoswwW7pgwHZ3JXpMKkjNfks6r6Dzx5GVJ7NyZzbBi1Ca4Cns5c/bEwzhoMCpkvohCG1X8xlBdKc5RJyWrFe6SlwgvV2q0D0s5HNzqPANG5CaCrjVCHnExqs6VgjSuRj6EdUfIyokqZd2FNNmS5SOpDZDi6/kUcvw9CpqDQaCMnfXmKvKGwnMg9Wr+JUdPEBFBuXaX2Iaig5Mh1ECEWL2WAprrDyFBTIfhtkFRqQ6JdaqsNMhYV0GQdb0yYqz5TZT3Id53Hf+IUHNdMJGba5Z54/zfpEUTQXTQWLFK0S/xodxuhISQsSsfx9yk97rmkBxj2wAkrYzrsTJfKCPq1hi1dBUlx9kv18BFqXl1rrmeva/08Lbmw12vlE7KMHgSRMpK1Y9tkXyr3cPnDFN0MpwiTj8rtQhTc0wdKxNHxn2znBu2U1yFXTHUG2/XpNFIK+CJZ2HJM2WoNDgQzGIsvmAJr2XU0z1K4XvrkSUnejQedKJoh9bIMuoQVZUSgSh4DpEHBDNjE7jYtF5hRaWTpWEpo6Beg9LlIud3Mdr6G4+1HrKoIpajDELGXSk09kVpPZAycn0ZvrJB650kUGzddMdgorQPKjR9pVED3jPjLYIweKsoEjIcmiZLrXiHeZp5pgveExf4oClt7RyjIRx0j/XOwUc6KaQwSdDEKShPsnCgcao61KW5pj9MhmJWBJVhy8KSksGkSRZBxEl02AQZ9GUIqBpqhuaBDlTZk5p01VHSUQqPDm2C533QvR5J1QnLhtVcc64NZOK87iZ7JNk7zBNijmVQtPLKge6QSscpXtiJcDQlQzhJcm7BstqQ2YQ5R92sxhewJCNFcRk0S8rI73RdHaLqxeheze56AblwgYTkhfpSiQv1uLKqqJZLqyJdX786qS1s0UtObjPsfT3+9nqsx+uSpBhTbAliu3xuzYCvEdhqoFPWnMzNsUUvubjbFNm4SROVqzPyGOuAxMoqrw56m4esN8hNxKc6HL2Uay5xHG+Lli6hHDk2zogYPWPkD1ivg663SfrqUPPyvS33aCHrGMbf27jHxXkzl5q3E76hTwFEy4VSFQPSVuYzKWIEOe4THZ1IxnVMlI3uvlKiY4i3ZPCb+drQjqyRnurIVcm6ubk6x2GYe8bl2qnqUGiuXTa+CHg7XnMOwxBUOu6dfalUb3RsFHQL9PKAR2PGKObgxtN2plQ4xMI5E5XOHmGRM1Pu2OURV7AwXBMR52wdEs4hQIOsRCqLJ0WFswdugrgRFkgKJtcNMuoUCsiqFchAekAqKo0HCXaZLOl4KHW1Hp6jsH0x4UE6VTqKUQCP0eJuJ0rRROdKz6AQq/0YnVfGhq+DCLOAiw1mZp6Q6ExlYo4jzPCAcpTkm6L88GTs9LC2SUsOLrysgzJ9J2bUyqA8p87ZJyQNo/Cl0gGhZw7HHkqWytwXTIIeHdXgWY5ONRU4GpQQUmIt1l/1GbK75PUnHdc0qo7tY4yNQyVpkZgJ4hUtRkglJRBGk4JX0WlidH/kGxm0GBqDeWOauvJgzhKjMcGxV9Ck64xKcELw2DEz7pnXGWQWdlPS2REivItjZ+dUHWkn0HGPNIBlwTR54kHR/BZ39W8NnwmnOG4yQXXUrmxBSyl2Sf7CMHAfDpTdRjuimsrGiypX6s6SiyhChne8RIku625/s/HrzXqhKPl4QUdmMqGc1zxf6JWCfWNsW+4uAhXFMy7iEzdhh13EN3kjerlShFzmw8VRr9HuDQX55jrCxoW6XCmQW+MdIqP+6LrFYA2sR7upVZwE8NjGDwyGc9AbWmWTtF/WVq705SXfCbgNh7thi/w+bl1v53856PZ3bvTxhyU4b67bt3tdgIUcjlLq2pVknU8EdqFD+xv3wSRXkZYkzBiR8RGq+/OKX3eht2QyY+LMl2rhRTS+ZDN76TTvpCnPc0QA2he8JvsK+6Ug4ag7Xym+MjNwzEKzF7y75hsXDRZJpghqJuEdbMT/DUZHG+ssrVBKwWlUg2ndKB17o+q4PoVBty+uGIpK0DZpfgzq1tfNY4nOIjtEFGOhmtLSaWJoKE/NqUU5Adioi1xkZrKNngNyocqM1UExTqUOylKELB3rxrmdONtwMGqFUOGJKc/DeK3B/3E2qnwPswVf48hb2qmWvE7nlcyEPCVTeOAFT6bKsVfec0dkohbhrenA85b8RsI3MrFwHqsxZVCrkaIcQljC2WthEUjqep8GSl0DifEj7WtKpUqwX6PXyRsuo75UCXK2USeojqtyigdaCC4z6gvmZyYzJOFIwvkVpSTujWNXlnRe+9jgmhneG5Ds2XHI5EUJpj6xpyHa6aG8WA50K2QYXYN9VKzMHJaOtyMtHRXjrQyiKi+XL7BTDN1yYcNBdh8OMW4c1TZ92dSjMcQ2IoNGHAZXLhNzEpMyIoAc0V2wUY+bo9no0EERbefY1JaXcUbiJkNok4Oei1XosdnuLnk9bupIvG8G3UZeLIte1Jebk0/TN0QlehOp3DrDMRCoCNwKadbXmySa8sYcAJK8zKVghCSyntDT0JXi2b4rKyV9rnrVpWaSKmRcHcYlGLThBDWHqu7WeQoGGh8fRYq8mdt8A3ZD49qFCu1sFGxcrmOMSY5FUBnqNq5ipmDVJKy54S1abHK9fg5sDYglh6Ciy2jxlayRd2z3pINyoXM/73hLoFXoK9W+EDxHST/TpkrqyN2JJm9HH7n4gKcZ7OaO9mCZoaQz64ljTFjvnCOQLJyA3arqnm3Bu9JL55Bjc9UjiDJoPAyQoKuRORRtCaQVTHT0ZyVRC/aAEyw+lJcSydPsZAS61lyCUuMMOo8ozWO0jFOj47xaN8pVjeYLXZW3pNEy8Uze9+Ccyles8ySFZ1KIaIgKQeWZCD7BE9uzrB1rihlopdnQEfz8skcleaJwEuODnPiyTLxtsKhRHUIWXiY8lYmO4BY8UGl0Tgzh03tU3ILvySD3deRO13rJoPJU4ajjepgp0Z0ixhICNuoNTQ3JpOpIo6g77svI2YbTY/QljawrQ6V4FxYzDgm0E48CsRzIMtMaNO3MfaG7w7khUTCTUc9ap2FDllGGIWK8kDPN4LEH4kdaa6CJWcW8Ix4UHR1/crQa4tHBI3mrzBy68xualBacP+F96WfCKQag28PedSsKEHKjLt8wllv4lNySV5G53qS8kQ/TVV2qlDW6uopDMmUoNi9S/yEWyUxIIxV0pRoAsFG4kFuRMBuN64iMprkldVCWlwBnzCtkOHInxwRl5KOGynGY9c1Zy0YTrv8dtw4RRTMIWaPazFXRtolUhjJyfG90xl8POvIdGzUpIw8Dw90IMqjSlZ5PGfO/pTdFhzp1o1MHbXwTgZLXaOomNzpKP9YMq4455CaqyVE6s33/FmN+23i4kOcX0Qx2s1FYnVXEMKxrRM7Npsp1RHm5OsIu437YcrllLc0QBk2dMpTDyRb1rveJCGybnfiEf5GfEp5bcmjBkoJIR5mwTOY6cmV/s88cmlAEJnvg6EmTVaEYwS4b5zDIofxGTzxV53kKX7OGpZO7adDWoWQBjcK8Ri5eBg2ZMpTUXoSCM2nQHBYdv6uOMIvwoi1UlCcqZAxRSRKcsqOj2h+RHPIZcbQIJxwTA4RincB4Um0U8Gfwrp/5ik1MdJ6VyrGf2NeZF31sQl/HoA/PKTw3YaoTU29Indn1RKTxrKzNtyM4i/Ig8LJN7AtICqqjPd0SE98EzlKwdB4mxXLkSt+TSulnljSqBbtU0ELjNWlONOG0tMEEYRQN1IydBK87TN15VOdBlIM5Bw9knjhnMDFaqvlq89w7LZyOc+7JHEItY5dYcMjCEk6EEb6wlzO9OUljFsNxtCT7JTi3ztIDqUPs2t0RT7IKKjFyjkCLxgM6InlX8IWJjnghpLG3yrmDZRv3kEG4AAAgAElEQVQlIwzhVFPhlPCinShpPFgh21oQ+wniM+EUJ7XL37GlxW+cwMdBPqQ4+nCUkZlrJ4RVoaq60qpJUVupMqWnUtdD9TWRNVKBwynF2sj49hxmE+5+k3scUZ6KknqleDdl7GbDI+Qi8481tFHVi4P+cKB0zckNGnGLGENuNgt23QhsAevVuejlCmeMV0OFmqPr/uZwnKRIoQK9OOoyolxGri1idVDrzC5H3xzEtt7IG+rgrfYxdRxro4pDxiN1IEcOMXJEzTJyeaMB8ohKN4e5Uc/Cmuf0a2lFl7VEYttE5Gh0rMLlXhqOcP1EjPtByC39i+S470ZMyGjevH03k0WSORVdHa9hZORF3PV5R8uOWvCoEFLAnWaGuvBL8cBzWaAqS1YOCrVV3E5j56/GK8Y9lemoK5kzr8/w9e78/DTxGJ2veeH7tNP1wLM56F1G5OPOXGbO0Tm7sze5sBJLjK4uJqMEKGU8q6GWHWHJEoGL4rkg2WCZebmD3blTcjAnS6lErrljcURmtCdlGo3On+wqL5rxOzI5Iiwy8TKEV/IU6UfetoomPJTkA114y41v+CjIfzuDspxQgS/JW5zkwLNovCuVgx85MdF3nWeSfLXsSHHcGy91x+KF96cdIqP7TYlgtzReWYFQCgs7FR4A7YHrjHWlzUrOe2o/oq40OpnJKUdbvCqGoBwCqiRvz8nZh8N+VEckOPfglBMp0PrYcDw0OEzgNIpMRMAHeeJpFlzOHLKRYjwrztk70p1djCDifHKqOM+sED22XNWqtG2ETrgLyBmT0bUne+J5ZtZBM7UFzgiyNEbTvIJkI0rhRW+86gm2QzBeReN4HrZ3GO5PDp8Jp3hrSLcoETYn9KbR+bDzuy1y/7jXb/NCcM1nmdnYqSCjTAOQuKFnRS5NAQBSr1HBpcj1NkracndyFZ9sytePJP9u5qarg/7w3LYo6sPzvp3Xh/Hxr+cb8xaBDwVkb34nC3JTuykCuqlU1/Fcy2feFPYM9e51A9FvFcU3Yyvbgdeh3OZCbynZLccJI9/LSsmmfPQ+uJ2/q4xC/9vrdzPpWNPPkjfrvX5/i0K36DEiKAhVdDjBjT7Vy1S/EPjbfaZZp4rypeg8NXA/U0rhS9OR9+MJ+3jFLpV9nfjVfae2kaXqOQrmJZ22bgS3Fs5qo1Dd0/i/m/PKgrdUSZv5tbPzqMHDbiKiMVXhoQqL65rIA9NR45aZQ724UklaQBbjrAuLd+YEcyPtxLEbaqARCG/mrwebsqCmHGNmSeXri2Al+A1mHn2h1sqcwoJT9ZFv+MJE8Ejyvex5NOcdL3yzBdNUia7sVXF/zTn3/D9n5Xc+7nlqC+dsLCeY5srx7Gt/WCjRR91ejM31UQsvNemT8ZwgRNhr4Z1j8s3mSAl2PqLeCaPGws76m/XTAWZHQCgy0kItIHxitk4lKLlu5nIatdzA3k40h05HXQmpnAS8x0plj7V7wMB1KINbgDjn1URJnRBb008yxC8lh30DOOZrKAV3wehDCRtBE2NJhz4TIogPAVZVo6twcqd05eSC68gVA1RRfHXI30pL8FvFZ8IpflgIcTGRH1N/8hE7JIBcc3iDptyovpXey+txhsFXIsF06wJzdRiZrM8/45rxk+3ZYet/3g7ixilfHY+stFsSOX7UscrKr3MdyZNYx6g3x7mmKFd60655vo+u1zqfG0HS5nBu/3ccd9CRG33MGkXLTUmMiiBbtwzWiPzimAp+MXeMGq/LYqzGx/KiitVcI8UceapbMZKt77Uc5fiWo9zEctSSVmz8wLY10TH2rb7ytoFOkZWOXY9pYyKsSVeQGPpCAdG43vTrMTbFqtyIrjZsTjIykWoUHxT4JY/9BRHa1HSeZCWjEzmxKFRVtMD34nx1PvBBh5cB3zweqZk074gYGUHvwTl9XBsdLcnQ5Nw71QoNQwr8kitvlye8TmeejXMUft0Tiz3FnVJGsb2iTL2i5kQ4oWWURYjjadTuHHxiWUP9V9J5woga02FRAYydJiWClPFcbadeHoFV8ogzsYtRrlBpmAolz4PS7crSxn101OEk3/XGMxl1q8/V+XoPdmL87WMyHQU4Qa386kt4YsZLb3xt53xVjA/aCZ8eIZJXFWRZ+EDaYG/m3WAnJuGbahxbRZYhSpkMvieNUowXKK9E2EWllsLbtlDdITuvC4juKHlmrwUCXqJkCRo7dgmVhcdZeLuNHqKnHohVvDcOZeabCfTx3MwsBYlO9xM9hxCpLYHrmmeU0RS8CtQi7CI5c2Y2wRocHb6ROhq8J8w5rqOJgHQWZJTuUCkkpYxi/F/TPe95I71RmdhJ8rZCSV3zq8IuY7SCYxM0fnL4TDjFW+cncDHqG1V1a3ZCYLaC9yEU2coXtpq7S0H/qqhUBGeIPCo6aEtdhRYxjGFGDsk4W+s1QdaONVf15o17WcOtSxM3uUa3KVdxS8hIam8OK2U0B1DPtfPLOE5fn0ZwO++P7IA+FHVto9kct6hSGO3HPrxv2hS4l/ZzDJoyJVEb27uC0NZxpMqlVOTSnQduhEAr52gf3qTIjegoEYImySQ61nQtbvbYOgmN9dnynMqWe9VVGZyXwv6+CoW2YgsdPPJQud7UPorIG40QUkde97JpWKPMLR94iXoz3nC2ItfNjwAmSjKeZDDy0+MJ8Pnhxf6c4gdq45DC011HV1p7pzNegmI2jFme+cqknF84R702WtjqxvDx7L2lj7XWhLkUylpjWMOxYryK5HAeV6jWSo1Ok+RpwmMYky2cm5J5hhS0BOJnwsu4Ljg9k51CFUeoTKqojicXndVpWbefF1p8PAw3czwfMMZDkXvOREDLhT2KRR2lHmIExjxXTk1wPWI5syDQhfeLEwEvZCQILAWdRr6QHC3S9hH8DjO+Nk28WDruB1oUdrzgVw8LUYZS9LkKutvxzvmMiHCeC6c2DM/jXHB3jr3zThmt555k50k1Yif0nvwtSbRVGsaXpyEmq66cJKml8LhTmgnZZ16Y0HXHVzpYPWPRaBUmTx608tic6sZpSl5SOZ1OLAwKfUvM2F7YF2FaCpILR4FDL5xPC26GNuFQlN47UxG+PxuTJkUq3Z2TjM3TgaSHAoWoQffKwZ2zTLif2YmgOJMkUwyFcrFOleABKIzWgqoTU7RP9LfwmXCKV+HEm7gIYt54kbU3qX1E8PIRST8j6lRRIm8oys0ortdaVNauCm+eP/RGBboq5y6DuInn4CoI0VXYMf7WSxkBDMclGRfHP4pih8jm4zqAfnuK9KPvOXz8cW7+ffj4W73jcBbXz2y05e3xurLm+D66ztvYbo+toqjkeMpBClnXHpTyxhbjcpyhut16ojLavW1da9hKda7db8pNHnYrf7msT7557EvXH7vS1R9Zizdnc71urKIcKsoqWFrp1/zQtuDzigeZKHNn7zsoTpTxrMJ5N4NMvNag+cz7fagUM5yM0XZvo5lngr5uFFR1zREnJWFWYdEhmCg5RF1zGeK0bpXi8EIbBwpzF748we+ssT5yqPIOC2fplKyICKfoiI2nRwjCMRSRCeprwkfDhVhLvGJotSnFQA2PhkuwD12fFF9Qd7QEZleKzs9HTBXJB7ov6DRYp7BRsxoRVC0UNcjGNBVOi9PLxOvdxN8M6Esni/GLvVB84asxcyrPoJz4oVB+gUJExZYzvUwsi1OkEMuRY9ilFva91lmWV1AqZarsdtMoJ0F48frMMRpfZ+ZsBTHh0YNd6RR1npaZUoOlC9/YPeVX6Jx64aF35hDSOg/S2Rfj7enEvCSRI++JCnMZj52qWqlxppycRZLwERkXhJf1kbY4tTSei7ErxkTjqM7LGDWgkcoihvp4ePRkZyYxFoeX0QmUOTsPa5vHSTp7DR6qEOF4wpLDZRWFk47HZH1h6dNLLo+rIGN771Zss5UeDK7zWiQ/aLzViN3u+FdjajoKbNE3jbGarsKJ8fVLD9McHU4uVtf02pdVrgrP68Gu/3cVAW307ZqLZHWUNv4uAGseS0ZlP+tLa47ryplvJltsdAnJzQuHXOa0RTdXoz/ypRIgq2Dokt+7cQGxqlcVuckfbs5IiUvpA5euNjDOUxlCCDb167ZcOepAryuRqAt6KbPYzn/t6zpSmaOn4nqCkZvUtSWf3JaqOF62dZU3ouvNSG/X2BB6bupUvzjIVC70dyKjWPmyfnJpH2UyngFHCtWcDB25GXijvvTzjN8oO6QAajxOwX4yRApUJR+eUpcDcnjF4ZyEL3gHw9eOU86+KMc+IkzVvGgfJk12Lsx2RhJe5ygj8B60cCapnHwZDaEdStHx0KQTfD12zAgP0VHZsStwWLkyV4MoI2eZIHREkow9RX392VVaKTyW8XSMDGNpB849yTqjBKYNYeZhN+xJIahT4XBOpmkmUjjHeZTypFLMiVjvFdGRd8MIWWV5WsfjsGx08Km1cPZxz3R94Fc3lslnfr4WpjLKQWS/4xwL0Ycy1m1s/PrakCATmArRCq8PJ1pUDocDf6cqj75niYlcH9tkC1BmXp0W5lp49wyLHHiuwum9zgels8d4J+Gl7cfTSaY9Tz1oWpik8VSDmMe1k3SeTcYH5zOmZah8I5GiLP6UzORJnlFN0oUzg6o+52h39+jKZJ2lThyzsbOCSrKcC0cJiMKztXn6owmvQla6duI5yTPtPJSRDjvo6EvrCievfFBWvccniM+MU9zq2gZVtRrdfDOX9rHfQ2iaFL+GObFZb0bAsCkPuabH1gOMQMQY3WLiJmJLdOT6bj7sa9W/+uqwLipQvXaX2WrochCbwZjXtJ5cLs5m/NuK+lG55PVMVoXd6kBipT5bxiVfto3TNS/rtf23oLiMJsXGiGre7LwyjP4t9Xd1NrLOfrTQGzHddRUy41LfKFveT0a7LI+EjItz1IvDYT1f3pzvqjgeEd6oNytihI6xewIyIg1E0fRVuToo0UtkekN5jmOP/VLJIXpKGXWHRRyysrV2G51QhiPdfgiicnlqRmzCnxwrNiGrEOk68nL7yJLPMb7vq488URkNmAs8lj2zBu95cPQTLZKJ5G0OZDG+4Wu7RAR2ld6Tp8Vp6bzfx6OnRCdqd3YFdqKcEh6kkzatUaMyqfB97qQG1TsZnV8neMEz9DD6qB5N0SxkW8uUpDFLReWM6tgQWSplFAWhVlCEbmNzGwgtGuX1GZUD32vCiyx4VaY6Ucug/TwaJ3fOXS7pDo/REm00H7e18YdTdSb663EP0alxJtpM1k4sBvtkp0rmoJa9JuGJdKOFM9uCvFRePQjPs3LMjkUjJGlu/y93b9YsSZLd9/3OcfeIyMy71NLrzGBmgIFAEgSNksn0ID3pI+sL8IlvlIEUYAQIENBolu6Zqe6uqrvkFuHu5+jBPfLeGoCSkWwz9Ey0lXXdW7nEkhnHz//8F5JOjOPA+zwTB6fWkcXOhEHY5C2H+QAEVANzEua9NQN+c2ZRFjMsg87N7SarEoJwVxfKsXCviYGFGnLr2k6ZfRC2BESbzVyolZM7hZZyEkOTn6k71+5t0WonzKR51ppAcSw1p5yNjohlhjGws8BYC6EoS6qYRTQUbvsiyEqljonizhibdnqwxELkIcz8yqAS2ZowpYpQGTVykPitE96+M0VxPTLnqfL/g26MfwjRrS1mVUA7Xf4ZPFdp7jPrbLA8g7tiLy21w3XKh7Dbb0NqF6Nx7WbQzx5rQhfy66Ugq7QJWHLhpI3S/7z0tD9PxgVrYapipCpPHY3b5XH/gH27nofLOfnwz/qG5dmMMlxw46dz+vQaK2T49N+H559nHXpL38jWLK/WLlK1ey/aKpz3D2DkJqLnH2yXblgaLCohPNnNIajJBeKtQr/Oq1ykwbvAhexrZmgMH3SQ7q0YPp2vFRnwD86FSDNyXmeRunay/TkXAtC3nPr9T7V9/8UVMcG50vIJRyWbMebKIcPX+5n5uDD5QC4L2QeEQlFhU5yzFcydKKc+zx/AnfOoTNI6gqS5jT1MOS5GGAYKzq/Zcg7OqJVYSmP7psJVjdQOvx6SEwjNrzQNbDwRvVwchTRMeCqklAheoSqalKqV0+ORODtLXEjmfFUGHgVGAc1HtFa2YUAxYuh2cETqvJDG5p3TFu1GcPhIZ9yNRRM4eIg9M7Jyo3AVjP2pEoNztMwQE0kjRSM+ObuzcxMTrwX+0/lEHZ2XHtiHa5hnPpaMTAv/y/mev0s7Rk389XLAy566H9iIsx9bWHDOGTvt2YQNno2DF5IHcimUWgnTxDzPDMCX0QnetL3J4PWusDHl3iIlDiQXJs68LoWdnDnajr1VphTwWihWGSVxEzNmxoxyCK0NKKJsohBUkCFSyFx7+84OwQmxedhuCHhQZhHUlCk4e3cO0VuiiUZOITJLxKtyh/CV7jjVyCJOKM7khZskfEOgloVFwn/5g/3fsH0niqK5oEEuZJaVPFKDoVW6b+IzJqAZ3n03xdvNMYg095EYn7LxaB1a0+GFNs94pj9rq8inm3GT5jqDSyem6EVrSE+dXm+s1mn/62s1d3xbMVLcvPm40vRWqcNsq66t4u11OnxqtI6v5fg13U7tXRGqFBojM/sTyzSsMOuFQOMtQ3LVSgaezTelvX4nmPiK0QLPswbX8xD9H5/1arcvgAaPZrx3XK1bhCZbUBFWgaZ6S1hfIc1WvJ7ebyUnSWPFgDuld/WrJjO64RIIGMjqJNON07UhCuuiRYITPZIj3WWnnQuRhNQnOY0LzYSY5jqyfm7W7rAtQrRdI2mIBEDqqIKqEv+Rhdvv4naetnxTI65O1DP5NPOwP/L1/T23BfAzJ4VzgY0mrmQhl9aZ7ILzkTibkHjnAxoWHlxRlBiVOIzUbDyakLJT68IoTl0qS//ujKWSfAR1Ng63pfBoQkzGNgaupRUAGVvRU18YY2ChcJWFr+MJPTQS10MonIvxioGPPPPDK+NXUfmmJo7mqFQ+SkqiUf9vk/K9lNkzcNDAwdrNvboxAO6BGzEKla0G3Apv0hXVI+pHhvnIv56cNEX+42HhJ+PEu7DnNk38Jgu/ypXtslCzUVPkR7aw+BV/UwpRZmQWTpbROPOnY+SnMXEm8W+yNoeq45H/fVuI7vz74Ny78OO68OW8b7PNmFjm5j+78QAl494cbXIviKqKltySYBCWarxdnD/d7vlkHPFcSSnzscC5F7CPw5FTLi2oOAinELiWma1WcjCkJIZp4mgDCdiIosHQ6ozdkKTdbgKUyu0YmAcjCrwrM3dxy/2ScXU2VdhEqMn4rJx4FwrfWCShfO7C21R4iJHRIp8nY/CFH5Qz52HiXf49hE9DaM4I0Luu/vvBI1WsWbrFJl9wAVRx69lfvSA0f4pAoUMs62wMgV7k1s4ySPt5hWa9d34bU2axZv68FiLpUS5OC8Rsb9/YrD05A/p8TZtTR621BZOaYziJrp2TJ3FbfDYHqwpinSijT4YAWdogzVgLZ+ue15npOmNb+bnNA/JJFenurOHNBpcQYtWmK7vM+5516uLerNpCWyo0wcezbjI8dUkBnhxntFOPnmmTLltfJFxIL8KFNdxE/13Oss4B1/16/vfOUg002LR1/uvv2s9R201ksHaco4GFJys9+uKmrOfBuWQ3mjsxtPPw5A5EI0dZe40irev3dhraXMm+E1+h/+7tfVHOv/kZj/cFs8I5L3yukV09cy3OT31AaotSall6yhCFuQpvSiGhYI2RO5gymJBTJi+RUQtTMm7nhUkri8KE8GWJuEZGydwKEE64C7M75xT4XqjscyBXCGNgI8KLsmc3TmzrgTHAEEZeXS3ckXjNiQ0zd+f2+Tub85UWXqnzpmxJg7SikQKbzYYkxvUQOOnAl6dTg9HN+EgL59xccD7l3D57pbKj8nbO/HADv3p8oBT4V9OBP34x8OePIzt74M92yl88Bj6fJv7qFBmS87kWzkTKIJCVvw1jm8EMkUO6ZT4bURStmX9XjFqUIRy5CzCfjB3K/3Gs7NKGd+6M8RrPR6IkghuKc8uRz6vxdjnzZzvlPzwGbqXJW/7v+hLTA2OBJLC1meBKmoR3eeIm71kGZdwn3o+RnwwzSUc+lzvCEHBfeCjO1zqxrzd8uZTGNPXW2YtkRp/Q2IKdlyFx0khxGMR5mWeWFHnLlnsEEUN0af64MbH1FmZXtLkc7VXYSeCTTdNxFm3Qey0jWznysVSiJiqwLSd+Mny734XvxDfavRkRV2l/Hz1wUqM6RA8Ueba699aJhNgIJ+tdU+W5xZQjz7qh1d7rwjwtzikYG5fmalMrQZUSBVBE106hOacMqVH6Kz2hgVYosrausqVFcIlzIrRUhbAW4T63zOEpV9C9FUykcRoXaUxa89YxqgrJFPTJieWS1mDNdmrhCdpbNZnrXNKk6XqE1WO1dY9tcdBh3F6kqtV23ugwa2hC9fYQwc06XPnh8E6ed5vrxVyvh3ExNF+LkdGZrs/g1Iskwtp+uVu7loCLP52vdb63QsjSbPGEtpIXwLyvgqWJvtcdMmA1gjee5DuLGYPKxZ2oWLPNqwKDKmswcewsxk2HU4MaXoxocokR+13f/Gc/Jc975uOGIZ5RqzzEdpy/ssTQenEkGp4rDwyMVlmsJbSrGIPS2dXCvxj2vGeLS24FdoDPtpm3vmHrTi2Q7cBeBjQ4r4KSKMxxouTKJoz86NYZz5mRymMwrl1IEtjrmbJM/EKMYx7Y1R3v5xPD8IIfBOOoiX3OnIIzBqVOibfFuFLhBxFKPXE4HfhsM3CTjc/SgTHcckfmJMJuaDZro83MBu8s4THzIk6U+sC/P10x1zOjwJsc+Ks3QgmFfxkH/s2xIUp7rXie8Rm+kUhJMJlSad3iurLdcINuTixZyDbgy4FXHBlMsHPmMURQ5VQGpvKOXUgEO/Gv0hl8gHrH969GTBJfnp2NFGoZ+N7NxBd55ljh2vZ8vA28ORupGOMYoVQe8sB+znyN87IEXE/ks/E354kchC/ijhdjZBMnxGZ2dqbWe4hb7peFqpH3JRLlio/CPSeBF8vI/WnmwIEbV663wmMWDtNAmiuoMoVMqZld2DBbYdGRu+TcWaBUIWkgeOBYKwXhs3ng67FSg/F13PK1VW4somHGxPjstxfh/53bd6IoxhgvrTbALM7WA0W8WzyFy3yqCqD/yEzo+QBNnndcQqoOKVw6kzwouypobDfC2L08n88213liDb3YPQvupc/rdlU5xdYJPocaLwxPb0Sapf9+1Qk+32+TDlf2DqixMJ0qij4Tp662Zm6OhTacdlGkrvvT3jOEVRfZ4D/tMHHtXaWr9MWGUmQl9oSLBjGssKI87aOGzhh91u2tx/M8jeQDCPZ5VuR6jXhWPJ9tH8pp9MlpRj58DO7Y+v7eoHF375pGfmse/OFrm/cCK0/7Gbx19c+3RpxoC68nXx8+CIXOFtq/mV5IR7/r29cGSXbotOA+EuyEFOMVykELDxIBw10YgvJSjiymPaWmMip8nhKvx5lBI8c6cbAmz5jEWc7Oew2ksHDQkSLOsLvmxjM/jm2J91A2fDIE4pD4YaoUqcjGOZkwZmcmcYqVnU4sBmFpC8vixrCZcDe+YEBLRaOyMUeqcDqd+GicmHBupfKQhH9ZEpoaUPDGr7i1hU3KbJdIKYVjHPnpcWDOmXtGpiD8+bGQykuKG5+osGfBUmI3Rx5c+Q9FsTqw+MxpLgwaOORCSMZ4drZD5jwbJjMSlEErMR4oOTEkkALbIXAnV2hwtkNhN2cmmQnbiZ8vN2w48WmK/P155toTIV5zfjdTovOHG+eLmvh/TJmWA5+YcSNwIHM+7PhRKlRZ2ITAJ7vE4/nEY65UHaj2wP8wjPxy3vD3y5EHE35pkbyd+bOUmcT5ODnX0Xkpb/mjkFiC8qt54K2841flljErR1kQTWhMvLfuZiTGy1q4SsYwwqM735QtdzGxtyu+XmpjBosQojB7bmbgcUNE+Do2u80YnY1FjrKQ8WbaH5Tj8O2Wse9EUXR1qjWfSwtC9KZti77m2DUYq9BsxRrLU5D4PLTXGfpK3mjzM3drkGVPA1JtTM/Yu7tiz2ZS7v3xciHOqOhlDtbc+b1h8l0fV5O2CByBJK2Ir4Wj+YA+zafWgqf+zIHB9eKsKiv8680rVXCo3Y7OwaI/wacdNlbnEiZcxAmqLcGjGilGauACW0pDkRlEqVYbY7TPTE3b+Wzda4MVLwHEfZ2gwJq7+NwzdS061T9kweql03oiN8V+7OGZvtC7KXq8kGZ6zNZqaNCZqGWFkC+M00ChdYehn+8sa9E2Li5G4h0ebpB5XCF41mIdPmDL1loYNWDBkWfXfoX1ve+jiLB05OD3YRtRTmEh+AbRyhhuWfIeV2MkcFUd9RNRIKkwCUgwsgZmT0yecD3xkHa8y8IUIsPQOsiPQ+DVKJyAVJxTLpz7nefBEuc48aMBfrTZsNTCgnEW5d0xcy1t0fPjrXE6veeVKX9bCxKUnwzC47zwYAcORZgS2HFEBqcQuImRcTKOZ9j6maSwuPEnVnh5nThbxILwB/HMW93x03ngjor6FfePmXPJDKkla5zOC1sXghZgIITCjzSxr8o0wTxXDuJEHkg6INKsQMbriSVXhuTUtMP0yLVKz50coDrbJOS6cJug2MyPRInivNCZ15vKTYwcyPxEj1zHyqlk/ucfVe6XPY/W8g53LDzOMz8clFtTZibelBNVWlJGlUceC7yRwGCBX+zPLZ4rjpxyC1P+6yy8LzNX08RH5rzPlfsifOnCJrYggpIjjxaYIjycDIvOLFu2tmASkTSgClGFlwkomaLCe3d+HV6wWRYeGcFhOxc+0zPiA29ibZZ3NV5GPdVyY/GH0KIBFTKFwRuaN0ggeCUe99/qd+E7URSr0+Y58MHqvMizzqCXjhB6lJE9/7d2UzWVlhXYSgqDJnJXHoZ+Yw09K7BR+11U6M4AACAASURBVJ9l5CHt5rtmHnYSzyCBLN5nkdqiULwVkWIF+s+1d4aXGRUQy1rA/VJ40U54McdUCbVSAjihQYi0YxAMonaXmcZobd6csd+YK0XbjCsERcwwCWRtWimjE2/6/rRZYStobYb7ZPm29t1NmiAgT/6ua+HMNN3mJXWjb0rTEa6PWwvjWYxhrV+sqSQNnpS+b+09WwAsrLrGBt02LX6Dn2vverMYg3xoaaehzyOwnvPcCEmrTV2lSTNOODeqzNhTrJSu5C7pH7pKiK2Yqz7NRqP3gviMmbpqIf+xzvd3cduMik4fsywLZoW8z6hs+TweeVwqRUdGgde68El0aq08dPH7pyMUMc6MJIRPR+NrK4SQ2FRwUd7kxgsIQRiHyFCdocI0JGZV3my26OOZ/VL5XPc8hGtMrvjieOSOwF9aZak7RAt/upl4d8w8ZuN/uja+n058tNnwq3vh008fORQFlIryKsJ4Ldx1jdKbPRRRvng88caFHJTFNrwMhesQmUMCc14kZw4DbkbyFmOURYhxy+KZ5BEryl6MakIJzpUaZoGYHJURtUx0Y5Mig8LiDQpOecuLaWaozjkJ4IxR+dGgHBbjs01BCHgOzAr358r3t+95fa0M8cQ5Tvy70w1DdmZT/s9HJZ9aekmyGTXhEJwsO8C4CYErbVFw6Mwi8DKNKIXAmXlKRFPOXvExUkvh6BGLyoZIDUKtykEyJGMjkYTxWmDvoTUQmy1gHEy4niLHGigo8zgSRVlS4rUlbmLg05D4cjlyVYSf1sIwH/l4CdRYEK28L4pYS+JQjYguFIcaI2k7NPa8BuZjpdiZWPT/87P9X7t9J4piCIHFMiG0GJc2J+r6sRAuEJeIPnUYsSWvl9WppndjoYvbW9fQkhqyVSQ0zNO8WYDJeh98BhMirbNqha51fW59BiXPCCzSCCVjaEW3ijP20pLlCX610G6aq3vOJWOwdyaCUGPrSofayDiw6hKVobaben0WoWW0eWkiEHrBNW0xW+LNnm2VnyR7kreItHMV+v4rvbj/lsinSHOKGceReZ4vM1lzUGsEnHTxBnhyrbnkYPaCoXxowlBrvUghVn9aaAuVYS1w2gJa18vxgZ8rjaAjLqSU+tt35qgFRtHLbBDanFQ7GzagXJk0yzK0JWKYkWLrMCltAVI7ESpIs31bmcpFm99pXENlV7JUjOjvSXTUZjNy5Sdmn9m5Ihsgw1KNSeDj6cjBEiUP/KYar/XMbnvNTa0stuUQlCrGyYzozoTz2s+Mo3CMAcFQN6Y0EMeJurni0TPjIVNPJ9Lhrl2vxbkLgTjfEXbXbDbOVTehVpzTknnIytt4xXZ07PzAb2zHT8/G1RAZS8SloKEiIfCz4tTZGMSpPuLB0Wg86gu+r042o8YzmYlRhB9yZnedscPAN2KYCgOA7fjF+Yyosc2CeiZdB16doVTnVZdlSDC2u0DMmSTOMDQ/2eCFjRifaOYxPxJS5oaJvQZyzhxloZaI5UrQR27jxGbnvLJK3gQebOCvHmbeLjd8URO/MHhZI69T4cYjX47GV6JsSDzmFpZtoTIozLU2i8UYOZjzcWhZlTE4Q0xcU9l2w/GPzTlmJ/iZn9tINsGksAuRV0Hx6lwPlTHA3QzXY/u+jNGbUQEJLwtBFl6qcyMDd6JUiezjPb+cF6a5GQDMOD+eM7+2wDkq5zoyu3GLs+TCAgSZ+TiNHDHulko5nVumolQomfc6cvqWq9h3oyiK4qG5ttdguCnhWTr7mBK53/BW+BQaizK4NreT+Cz3ToXx2etfLL5Y+81eFLtJdfWnrojYYUMam7GExo5092YgXqV7rq7i8ta5lvW9LibYTk2tQ20gXT/Wp6PuhVGJNL1bgIs7DA514GICgHVnF3cIUHuPEiVc9t/RzgDtcJ8+WSC5NduuixTBGtx67torr8YgAevm3dkqmiLNmrn7fKp3Uk9LvMYcM7+cc9rLtoBX94uO0PAWIEubzRkNDgc6VN5nv9IZup0kFUJor1sNVSOxWrRZ16T2+SnG4k7SxhQWf7rmQZpUpoYGzytPdn3VumHA0OZIipBr+/fQzRRMW5gugHdj9tCCNkHk245y+yfbPh2GBm0nbTOvs7GJQs0RHwpzVq6Tcxhhy4j50BCAUbnxmR+lkarCqA1eJkbE2qf9Iz8RasaDgB85PQ78YL7jXmZCUB4DnBWGcOL1YFQbKXHH/pSZQkDDmVxHcmnQ2c1U+UkCs8o8ThzPAtdbvs5n3slI8oqXzNXS5lnjLnE8AxFGFuoQ+UQPTCQCwlV0pmHgl/OJ66VyLSPLcOZFnFj0QNLA4eHEj3d6SY8/Zudt3OIRQjzxcSxsRsNL5XqUBqnqiLtwNiN4S5V/uz/yatzwUJ0iC58OkRiPhBSZa6Kmwp//MvFqCowa+LfvC4cKhIltaP6hNwH+R5QX25nzItxz5hUDN9YyFj9PDYs514YQWRg4EZvdoox8HRbU4eOQSBjv6sC8QAwjPh9IUbidlD/0heCFrxfhwYVvasBceL8I49CSSMQcNlv2MVA9MWXnxMR+UH5tQlAjktjkmZ0lPqmVRxGqBDDjvQr3NbEUJ9Qjt7HiJRJjC6XeoZyOB1wCu2RIWS0EjR/GE38WDizn9K1+F74TRdE7/FcBlaaVuhBHOqNSe5baWtD61K09v8HNDeLqRIoPiC99ZlXcLmSX5vDVQ3n7zKjS6lGRJ+mC2pPnpyJYavKJKA1ibZK7Zw6YrWUlpUgwIXtBVJ+6RH+ajfX+rz2v7+/QGZImFfd1HwFp2kF/Nj+L9uGx+ppuLE7rkCL23L2ld9DJ5ZKMkTp+bEHJONF60khvN02eDc0k4F6b8aA5I8p8EczXi6yiQaUdlgRQIXXoFmlf1Av82Bc6nYjbhDL9mBavXacpLQE8QAjpYgzgl4zFpkU1nti9a3pFk7N0fSMtJFlELm427kKt6zlU0kri8tIg9tohd20aUNU2R66iRKn/AE7+Xd10s6GWGfXIiwLLlCn1hKoyu1ITSDU2DBytkCJsRIhiHF3IZqSamZNx7YVxLqTQIoxCdM7SGNhFnBAy7zzhnnicZ5YSGqoiobGEtVJKYUQYOqKRWLDRGWTmdTR2sbAdRr5ZMmU3sa/Kq7RAKKTA5fNVzBhjZPMqcPdYGK9Glmyk7RXn44nghmhkLgf+IEbmqJyJ1DAyRrjVbTvurTFKIcpCDfBqJ7w+3jNcR0qa+Pld4GGemeIM88A+LWxKwDBCdaBym4Td7ZY9ih+Mb6zw8yXwm/M1N+NA8IL5jjzCF+aUQ0DVGMb2HdlgDD3LdQ4DjzXyasz88VDZSOExzRxOidLn54cFcgzcVUcI3IqzlcRelPsU+c2SybU29MwqUc78YGcUg9NZeNSGqoWo4C0kfCuBEB03YRPaImrOZ5wJCQrXG3wpDFYZYqSEEWqhLjMPmng3w3GulGps3flsUEZtKMJHo4FHXunMjWSWzZY5G2+TMZpzJ8YxNYP5UjJ5mPhyWbj/fQwZjqoUa24UXo2lr8gjyqJdwE5zT6lmTTvYu6MQFLWVLfo0p7pkH66FS4XY+7S1MwnZqCpEDSwYo7dsskAjcDhdztGZqIjQLDCbmbf60++NljRh3qKY1vePsSc0xEAEapc3IAGsQlCSC3l1f+nwavAI0nRhVRTrC4XQmNxt9hdgVmfjDToMKpfk8lVKoZ1ZajR3l5HWFV7IRP7E1tTqGNZ0eNoWA2LPZBMIKqHNOIHsFXPYmFJD80AVaZCv0chAzb3GL24wqsqZykCb2dXeu6s2H8lIs4PL3hIyMu0chL6QOHqD2SMg3bd2JTeJOsHbrHZdHMUQCW5Yt/LTS3Fsc0PviSmxW/VdCrUNiHb5D9Jg154Rh7QkiWjKt6wb/ifb5mKYtznrrMIe+EWc+NiUV1I4+8zotRepK8b8nm/ShtMCw2UBVUklAgMbh2vNDD1J/WUMlFAJMbPJIzVUUnG2Q2NSSZlJKOdQyaI8SGRUg3pguxnJuenaRjV+qSO1buGx4rphWyJJM++lcjVoM2z3DWV+RLSwlDNpbtFFNwU+u3IOh8I9iW2KuFSIgWBzu+a6g2CcsqFeIQtShEX3eJzAMrYMnCThJbMdDvzpS+dhSYhsoFSiJw4ImoQUFnK94jdV2CJsh9rM0Alco3x+5Vz5nlKV62kh24izcEiRIUcG4O0ckBh44zCUG17EB47zFkT5WYbjsOOj5R2vpsqJkW0uXA0zjznx/bCwkTNfLAm3A5swkk/wVQ18lQJ/sg3YsaAe+M9L5CZUggSu4gFo6NQLz4gIG1FO2ag1c9CEy4BXYe9KVHhYMiHCiUrJBzgt1GlHOEIK9+ziNZtt4Ad6YLcc2fjEH/ueMu2YJPPupPx9HvkmKT84LRxK5iZGrs97/uDFhlOpHIrzgolfzJGXFH4cxv/i5/q/ZftOFMWCo0PzvZOoxH4j9giDOc8xqhACijxFA63NkVz6ksai7M8J/qRvXLfBpc/z6PmBbTYm9ix2KXZLqfVpYZV4tOdneXrspePk2TyUpxv5k0yidShNXSCowOjKo9aWv/BsfnYJ07VueO1PrNjgT/O6Gw+caFBndb8Eh/Ydg35enstBNIT+Xg36zOLdTxQ8BaJIi7fS5t6ykla8OS8/209hQ2BJ7diTPckj1vcTaYzXkhyv1kX+gneJySBtZqy0xUalQdfP0mpaF69tETLQ5s61a1FDCJecSsHanA/DrbNpO0R+ybnUJ8Zyk3k04alom7lG1QbPaIF+TVpRNErDKXCPiPbZ52/NZH9Xt9v8wJIrUQvGwK9MCUX5isqvi5B0ZCP0GWphDrdEEbZhaXZqteKeeNe77pML78rAKBnxkSTOfk64nHGUbVz4RCbG0rqvTRxJLi0/sQqvdu07odqcLbZTalZqHvjcG0zoFsm6w6fE3grDMPGL+2+4jRPlcOBjPbKVgTQMDXWKI2+rcB+uKbs29HibwbsRt3vgdDhzq0aU5l5zPcwkSfgoqG7BA4/zls1m5JVl2IwUg32ZuNkqSzCOZ+eMECnNw9cEZ2QTG5HmZybc7Npi9x5jyRM3qXA/G489UuvgRskBM0WPR0oInLNSYyTkhWV5jWqLdvrXU+GP9MhXZeJnp0wYJxKFkGduNHNfEilsOLoy5wHTM1cBpqXwdZn4t2fhoxB4UEFLpCxQfOSVVX4YhUSleOLtYnyWnIHKbhowILvzOAWsCjl056k6c220LlSF23JivB75SBSzR9CRN4vxliuyB5YCD4tSakRutiRZeDMrv4nwKBWtIJuXLHePRJ+xYcMnlkmSOevEa/89hE9DwzK7fEHx0H0rXZCuJUzPZmfPI5LcrEGP8gR1KnTZhNB6HyNouEgDtJNnzHtxlVb87JnR9qCduBFWqUBjkDaWZ9vvp5R2v4jRF/FO5mjFPj7PU+ydzSiB7BUPSunQrmhA7MnVp7E028FYh31FgJ44fZnXibSomf4e8uzc0ItfwS8WdbVbsBVtnbj1zttxPArBGhRsQYiqWG1FwwQ0dXi0v3y1QNYmR3ExTJzU9aXr4sBCY6iK+WWOONDMiaGFDIfQulvrs8ytBMRbEkjSFi/EShTqj00oHv2SgSneTc5X675oTcck1pmxrQt9Dts2K6ynsGPt19LciNIdiYISaumWe22fWpKIYAHG3w/tPodxw0dXC2/nHQ8ZdmqMZnxU9iyTwpKYR4O5NOhaGrJTa5MAiTlJZ6IryYydKLoZqDK0CDZNTGMk+wvMT+3mK4mPh8o03bLTCskhC0kFVcNEkZjQuXCWQnUjaWA33FA7dF5PM2FQXlQn2CN+s2GuxicvrrHaZpPnYaJkwWwhxUD1QDZnP1fCJnFYDgQdSLVy3FzxrmRqG7xzbZ9wFYwXyZhCwYJQx8ijK+cMeXbOBruU+M2hkLeJJK2rOucFlci22zxmd2adeBmh+pmhJs61MunC7IqRCENiUxauJPKNQa6QriaSGYMpRSppm5jPzrkokcBfuvJ/qfDPre1j1jN/u0/8ILS5ryX45RlejsZ7mzmaMqMMo3JdYSfORpTglZMJ0Y1C5piVv7XAVkDEKarcFWcKgR/PkD1TrLKc7vl4s+M0n1mysEnKbT1zFZwwJB6A8ynzfviIO04sc0XDhmqVXT7z3o1rqxxRTqcHNjLyw6ky5cz3Y3Mae5Nmsg8cZIcp3IVrSm114utvOanmO1EUL3RDIEQhl6ebFbTontXq7Le7Pg+KdVhRQhNjrIUOwFFCd7RJ/ZfWvTI7IZVKv8l2duOC4VbaHDIIyVsxEXHG0Akyq4awsypXrWBCLx1KWGHd/gcHCU1f5zzr/GgC6PCcZSqQa2GQwKAtbNSCIMUYQrwYEaz+qENMWKmUPvtaj1O9mQuMopxogbENRmzawih9hrh2g0HRziI1a9dh9ieSk8RIrHAMzcMwupCpROshyjyzc/ugm/9wxrvmIaq0BcREYBZbBZHNas6drI0Or65oEkZZO/bWqdVawIcGl4o1iLwXflHvUC5dyiEtcV0Doka1pn3daGys55WU1PWa0qHsWVqYlVBIqRdVX312fz/w06/KwEPWxugN2mawC6SoRK28jYFwcjZY8xBOTqgwxsxNEpYA2xrYhNTQiuikQUAG3mG8OeaWgBDgBmXaGA8z/FKvOJ0KSXdMWYhBKbIwZiHGyM6FcQNWW3BtPs/ks7FJ2sJm446QS/fCHanjxBSMMiQkRfJcmE8ndBgJWRiYIY4MAYatUiQwbl4w10C2zOSKRJgsk0TZpLYfqJEEJl/YIzwuCrkwqnIbKrXpyrAMRx84zSdUI1qNd9nYxoFSjaSVHYJIoggMFO7cW95gDGxtZlaIatyKcbstBFPSbqT6gbkOCJViC+MG7krlUJSH2UgxsC8DnwJ/lmaOMvAfT/D90XkRM1Gcz3aBI8KoiV3NZBNCrNy6ssd5NwqPOZE191xF4SQj+JmtKC9VefCJbyRz42duBiMxsYsLHoSvzMkmvK2Jb4KTbEeRwBwDnh+YU+IUB2qNPOBkHbFiPEYawbI67/Ke70khemUcEleWuQo79jHxjRXEB85l4bC0+1/9//ls/9du34mieNGwrUUiOiKGkC4F8NIJyYc3oY22LukCb8Il7R64EGZWCcCCXUycZRX7a2Plo9KDY5XUp087C9xrYexwYwiBXEuHQTthQxsMJxci0OpwY5djW7dy4Z4ISZS5lsZ2DOEiYK/dQ9JCQDuEJyKNePJbr6eqzSigVmRo87O1KAbzCyu0qlyIIu7NHzTrqgd86i7rRezf53wCYw0X7SbASSpbCxR11OVChhJvM9TSgz9jX8CtMiJbDQzWc0nv4Fw4JmGogVq9s4ul5bxRGbxBaGfkQuRp7ipKSgO1dJaOCl4MUkCedc7NFjB2hmsla/PRbY5DcjmPpVZieGIPxxjJ3m5kSSBIgh6qqyLtOMPvR6t4PB6JqRVFD4GJyPZlZnPetO/lcCCHK3ZeGUPEfOF9gTldcR8KN2kCF76SLXe+cNZIOma0DBzljnPY8qgLt5K4t4n9HNhQSQFSTFynlrIwBqWGDUeplKNzn2F6TNTNAc1C1BnKC3LecxgD25TQuCEECHpmrEIWxQkcjzPbOnO9mxjdmAdhb1fUsANobjxu3MjC/pQRDPOFm0X4VN9zo8bhfE1KibvthuMw8qZeNR7AWAjWZs4ndcgZtpHdbAwG29LmXPO8ME5GPBdeJCBmtgpmhYNf82uM76XUTLZ1zy4GrqNjNWBUzmXkXT2zFaOWiSsWJEWWunDHhndl4XU0vqpXfKnGHyX4i4eZ4Ur55+r8eEqcOHMmMmrlpUQO7uxC5mXJLCaEWPhhrIwS+Pr0gI2R0TJ1J7zLOx7mTNgKSZyTzaRS+EoS9/OAU9EQ+U+njAxbCoVTNoolNLd45yowB4Fhx5i2bIJzzguvZpjJbKXyUXFOKeCnA19V5e0wNe6DD0zBWIo2pv0p84fxSA3Osh3Q+xPz7ts1P/3OFMXs3WzZDQjdTaZ+2GnxnIG6ZvUZGltESVY6UYPLDV1XdxxpN8cYwlOmoQioXhIslCYJUDMstE6vhNZJWCeLLGZoiFS8udkoF1H+KmBfIToNSjFrXqLeIqx8VTtqY0qO4/jkytP/3zxOa5cT0AT4pTbJxDOd6mV+KT1V3Jt7y8ULNTzrfPpMDhr8ZzjBms/pqv8TB41gzyz1otNy6XhyxxkksIQGE1dvhgOraF8corZuupkvtE5WVRnXbkzaDLLQvWOBsUOTY4qXBUygdb0hNomO5Ayh+cKatbSOWmuf73WiVWzdsalcinCMEQhkL2SXnpQR+rIHzm6INk3syvRtbOdmCB9JJGlOSZ67pKRD+d+ubPifbvtjO+A+UEtFiewSRB25lw3HIPyCER8Su7zh4zoTp8A3pxPTkrleCpOduJ6u0KCc3PlUM+FmYsII7DjWQGbDL3Pl5MImNHPuVA7kRXlfK19KgDgw1BNXo/LHYcBjoY7O+7nJeoSPGAfhqLdkAks98SIFYhi4NWEcz0w47nvCINyVxF0cuSewJ7KJYFWYXfjMjVsp3OJ8PMDbnKmdlHU/fcqxI1Rv4xaViNgZ85FNyYgfeWmVjLEsEcKGOS6wFJZkvNKKVEc2TvTCIRhfPBrMgY904uVwx+v4nh9uhG/u33MaXvLm3ZmvbEPWicEqHyUjyszVRnnIjZDmbpyXwlau2A6FPwoTZxJ/FB+5tkCKlf/t1rizxGFRHoKRlx13IfPDzciYnNuQoVbOUjEfOJYdf3mGUeGw7HhvDQb/fAzcDmc+HTfcaeWwgNfEITp/rIXfBGVftuxl4aVfUWvlPjhTbP6vt0tB8gEV55SduoykdMcmJ2K5Z4yZwYT3bpx9YlLlEBrR7mUwTmJUz/yhBo6yx0nsQ+YUlW/yxPB4pI4jdjx9q9+F70ZRDDD1rtDMu59kp9ur4x4BR2W117KLRKJ26LSG9nNwwbRpCKUshJCgz45CCM3abaXdC4gZmtJF/lHMn5KcQ7vpr/PClTDTnHEg036f+g0eWqfUZlWx33Jb0WgZgxB7OK6Zdd1bI8eslmkF7x1rJHR5SBSlxGeaRZ5E91E/hP1WAtK6kMhuJGmuL1ye2xcaqdmAa5dKuHvvnNpjU4ehE6vmb+3Gm0yhWp+ZdsKJe7NQU1+ZrT0hRNa8Q+/G4880pbFZpTVGMTRZb5Ob1E6EcenwcjeNr+7NlJuI9lyU5p37zBPXvc2b+z4XSptde9M4FdrrBu8Mo9XAXBtcHqSSUpshy8oMzqUVz3Y22mf394Ros7kSXqijBG6Atyj7U+YLL3xzdq7DFbuQuSpC6SOOFzIQpokXw4ZR4c7hTYi8WW4ZNVKkUknc2pEryaSltIKHEiwzYFwFY7oSjj4wa+DOC0vYEkrlS3ekJsZoTMMto2X2rnzMzPdixqks2uaDZwJfBGHwyJugfNwvy0dDaKYYJaIORzNcm6HHzxkawc0rLwMM40jxSETYMDOHwFUS/kUQhuXIX5crHtRwjYS6IfuCmXHOxumwR+uMUNAqEPv3HCfXRFHYjsa788Ivy5m/yRGbFR+voWyxs6Fxh9iOnWY2qsCWTShsceoEaoqGgXiuzPVEtMiGzI91QcaARzgukUM88UJmPAsikTpkPnXjwQb+/ePMq7AFmZlrwkzYd15+skS2PSKJMd3ws3rAauKhGO/DFT4vDBpQy/xdSHgFGzJDrXxvLIw1o2ehWOWogewLo0esFv5wd2LaK7eq2PYFf3Gf+EW5Ik6Zx73DuGEhYGIMFviDa4OHEzZmtEy80JlJnTkObIcz/4zMOTjFK3fx93CmKK6o5lak1DCPzahLAVHOGIMrrg3nHKR53xkthsRoZA/p7MgGl1Y8RaJGqtcLPDgNkblLElZpgsc2r1hnmx6UEaWIXYJ/VwZkMm2yBvcm9BZFntmcuYBroJoxihA14tqK3ZrSgHQzb+/pFapt1uVCAmYrBNVWzNyaV2rtWkNp3VGiBRN77OcEvaR0rHO9KJB1BDsgvgPJDWrts8K1kAVpXZR02FJDpHT6fe0kn9YNB6oVUkhtHy5eqGvyhPY5X0W6VnO1dhuesT6re3Pbwamu1FgvzkSDC0po6SAYiyhY6XmLdLo9tNgoQzU28lA3db/kX67i//YRglwvM+nqqdnhdt+7KjDUpm0MsTbtZpfViLZO2t1I48j5fGaIPS3Dn0wbfte3ocK+Bqac+SuctzrwVhyriUmEGy3cnQtfiBNPkTFW/mSEmE94mfj5nIkx8nfMLDIRcwHNxFAJYgQJ3Bdnq4rJyFk2vE/Gz1KkLonPODMGMJ2IsS3LRgvMlnlIierCFIQB4es48XXIXGkgGMRYOC6BQwrs2bKI8J6ZwQf+JhkbiwyinIIhq42jn6k+US0zxULFOaIsLowoLxiYl0IGygGQhErjNWQ78SI6R4/sQmIeCptN4mzKppwQW9iTOoN7YahwXSsWmrbyuMy8LkodA3M0rkrz8Bw0ki0zDu2escd4X6/4mpbGcqXOICMWj2zGgXgwlmHma1f2JfJ2FkoNFB0xTZRcqSGwOVX24pQB9nUiO9yMkVkCu6AMWTiwoFo5hZcs85G4OH4Wxm0LK/h8OfFNgRKEyU78M12aVWXv/o9n5xdz5vWk+DDz6TJwz4ZlguNi/PzwkrNF8oOhJ2epbXb8wgau1JBQKWVhoo3Pfv2+cvCBO3MimfPygiGeiJxJNRGkLa/nww5J+Vv9LnwniqJJmzBBY6JWdGVjAI3hF81AAjW1LqPZtekTbCdKYYVBnzq7i0NJL1pztcvfNQSSaM/XU7Q72mQ3sjQ4dZ3hmRlmfQ7WZ1VxXQ1qty6TZsidO/mmul2MBdqOPc02h2cBxqs8Yt1CaP1e9taVrqxJEcF6VmP0tp+hOq4t0NUlIx6ZgvXuSBi14MuA23t0vG5QZ3SsrjmSNDslX1m2UcFF1AAAIABJREFUgkozaF+lKhe7tWczXRHB+2Vaoew2r7RLR305bFUWe7JgE5q+shVkbYQbbfZ8RepF5hD7+zyP8lr3OfCUg6n89rnkt0hZbT54cTZare+84taK3pRaFx5iIlkLFK7SpqhejOzS4NwuqQkhNM3ot8x8+6fa/nYJ3NeJJQ1EX9B8IurIJI5q5YGEhYRa5oUsfM+FsCwMBudywBFeceZ/jYFDOXD0yF1WIsrrWBgCWKicZeJrKp9QuZ9HUhV2I7xdEilnrlJBTZrEKMFGEpvsfBTOpJS4rwpkDu58Mzs5Bl7nxEwbb7zkgSM7qiin0BAHq47pmVeaiALiM/c+MlPZqOK1oQcTsAuKlmZBpsG4scRdasjNQmkzRJs4VmOiUOZC0ZlNmfk0LAyloxOy4FZZHFQmTqniVRhjZorOVCfMMudlwQcHveWYM4wje69E/P/l7k1iZcuy87xvrd2ccyJu+9rMfFlZfYkqkSx2hmQZFtxQA3GmgQF74g7QSENPDXhi2BPD0EQQ7KENGAIMGAY0kKGJbNGWDUqUTDUkxWqzf/1tI06zm+XBPnFvcmoloGRFDRJZL+O+uBFxztprrf//fnINnDtr+YdSOVGDvDBRmXNBvGMITYT2IIy8r4GSZnzowcOL3HOTjT0L5EyyiLjEUjp8Vm5FuJ5ncp7ZlMqR99yOOzqFUheOBIYaGfOMq4UHybPRPRcmfD67RoEqlyxxyw9O4NE4kCXwk2u4DoHOF3I6pUrg2VYp1u4DJiCx7etTUXBKPy1sMGYJ3FgibnqOFiN2HZIr2lvDDMqGUldPbS48iTecDF/u0fQrURRjaPYIABVHbCUHaziT1oWtN9FwENF413IQXQPPav6CBkm1YcC1wabzmoV4MPu3sWTbkeWmtiCKZ5JKqF/M7GtG4Llk1Ps1U46VzLIWsrUohLUTMoywinAExbTtoPraCsFBBGOryT+KsOR8V2BhzUqshejaftNEUNdeUzDPQtsFUu0uwWFSo6+KrNgY5xzfqFf8+UcDf+W3vsGjzSN+9b/7A7SM/Ce/eMQf/eySH3zr2/zGB4V/8NnMX/vd162I1FZ0YkP1IHZPf2lvibsrLoeik8SIrJYZ7ouSKq3YmcHKCc1WqdbUr2u9ooquxnEjFL0n9KhxZEpRMColt878MOrF6p2QCm3d/gGArmWNHcO1bvAgvFpVtO0zUEyhk0YNobaDRuvG74tnEUXWgh+9p1BaB40S3M/HVvGXOyXEHXW38JPsuZIOp57eF26Kx5UMLnJcjGOnvB8nrqdKYERKx2ORNopb4HPgPUl84B0fl4LVyJ4KzuMwnuQGdz61hTJXNsB7tqAh8hGefVHeBx5KZpKCKmQvvEXAhG93t1zktgPby8AFHk/lT/nCrcEndcer1HOplQdiZF+ZK1xLaodrIFDYOMfXY8atghNfM+dhwXeQcFRJvOIBwZT9ktiEmW9L4NvzCzyX4CdyOWLMBsGxT4HPZ8+NOY4lEV1GkzLa0n6+OYozOh9BC0E9vWRyzpzVt2w2eyY5olPBWWXQguZCccq4CJ+Ojqye6mAuEZyRq7IbEy/SMWKJmTbROmGmSAI8cRk5orDME89qxuJM2hmlFo7Y83BzzGtJvC57HrjIlI03pTB0ns8WY2uVbQiowA1HPJQZ08I+dHw2ewbr+N+vZ2oyHtlC2USWpfC1oAzc8KoIvXlCXjjaCF4mLHiKCaUGxjkxdsqkyokkvuccwSkb3w5IxUWmnBlzYUxG1gY00NJjcs25+zmMjlIaZT6YrB3foVNou7BSVyamM0KpJF3Vks7hWEkx/t6DZrYmM5fSEu+dUqUSRHC1oabqOt6rwkpdgU49iYJyL5qZzRDnqWptV0C7WS5WcAhR257SmTGvxJMqsIhxVD1ZDCuF6rTFNh1Gluv4t9oBMXZv8UCFjY/teXpPzfGiJCvNUrB2wiA4y2xNEHWYFGbxhLLw3/4Hf45ffb9HoqdMC//zX36P//rvv+Avfv8xf+Xf/iZLMiKVs6PCf/N//Yx48hDKjBTHAUFjtXWOc9V7YIIVnGu7U7SllR8sM3o4OGDNZ2Zr8olZ8wKq0oiqRjZ3B3xXrZg5nGvvQ865dZRid12gcy1SyrOa6LkfyR46/7oqkKtrIhileTnv8G2AalkPMA0xBbCUJv0ptU0OikkzDUvrIAXIh13oAca+hkn/PDz+9o3HVcP7HrUZlQrLgsuRZAtft4WH24JuAFNeEPm4GreL0NWRJ/0GZwuD73lGYS+RXVUGF7ClEjrP6/3CkS+MImzSTKcOr4V3RHldAyUJZoUTl9gXz5s6Icmhg+eoeKRWbpzyyXLE4h0nFPJSOddM8cY/zp6b2TF4Y8ueLQHEUUtFxVbzv7KII1rmVipvRiXohl0VnFTKbBybERVUCsehcFlboDiLZwyBn3Xv4+tjPD23IVGCIXikWzj2mcf5hjHDTXJYgJNpYhsr3gVc7LhdoJSKS5kr81xhfDh6+ll5YIGihU3Y8tmUUFV2EhnTxDhHzv0MzjMXx5wTTjPVHMEVylLYYli9QQhEg7EWPk6G0wHnM1M2dpPwWPZstOOzOvAPrxMlQ2DLY79wJJVv9p5t53mKI+0LblnwQ6AvO7xEkhidwrePofjKdJmxbmCyTK2RR5uKauaB77ndT1zYnjLOfIPA1He8Hie25njvxLGrGV8KZ5ueTYxo8VzUyuu9wbzjgcs48YSjIy6mkd/fzyvIf2AfeqoFfutLvBa+EkXR3IG60k7dBz8hqyjDudWThlB9m0dq1bvEb2h+xIOM3syoxYgxthGmteGsq5BXrJtgq4DiviNA5e6mfngtdR2/KobpOgKs0LvAIpVcDPMN97Ux14DUAsc4srZu9A71JvfUnYN9Qw8CHhraTLxDDKaSGNR/oRC11+Wcoyj07TduxUcdSCuwf+Fr7/Djzz7lUTdzvs1IF1rh3/b84Hvn/I13t+RlTxSHuMzpsOFo4/j7f/XP8u/8j3+IoNQW6Li+rwLWRrZu3aCpKjk1s7zo/WhXLXMnBVqLfeOarmb+9blYi/0y0S/kPbbPWtYu0DmHV6WUem/LoalZq62+1PUQ5NdOVjlkQto9tcaaJOZAp23wh4Ctwq2yXgJqFav+7jDQedb33vC+CTNqWT+/9XdrYql/mW/+V+cxiCe5Qq4FUWHQDY/cLV/zxhQHXqvjTeoQm7EC2dphcwiBPguzCIUtN3NCaoY6gnWY81wGw8+VZI5jC3yWOpaS2YaOc1l4U4Rv2oJ2nkEzD/3II0aOTxz/+OqE37v1fOoyT1zh3Ad6F/luvmIcHD/ykc9q4UkVdtOevHR8vsCJM564BYaerhSywOel56K2tUKszYa1ddDnBQsdKoWNeWaZuBJPxPPhpAw+NUGZCqnCXCDKBs2VSRyFgPYTeekwXxjsmNINiHhO9on5pPBZdexropsg5hkxx1kUblKmy47FeWb1fCoQEnyYEzm1PW1vM8d4hrgnVoctiZtSSCWzCY4oiVoqu7AhaMIVj+J5m2eMgYdxxOWFB87YauZFrhz3gYslc0LgVx4MvNwvXOaZU52ZS+UYoy/K714Jv3S6I9wMfGyB97aVo15IZlS/x2XHZJnwOFJyJdSFMcM+K4+8MudbfugiUo4Y+0e8rJWaE8oZNSp/tKtUf4oEYcpQUib5ypB7xgwSBVcD5hOMgZO6ZTsktCQWl3lowr77cqc1X4miqOKJ1gQPqvc7wVorrJmHBjgVcmlJ8aZNEFHkMC7LpAM03CDEdiv0NN+Say55nBmlZtS7VpBoI8d2E/3C6G8FiHehFbQqAhqaulUa5SXUVqQP6tHGwWx7zqXW1RgvK6u0vbbwBQpOWJ+n1hSbwSuSVwuHekQcgZXu49f4JfE4qQiOLI1Go1S0CH/1F0/589/peP6o5zf/jV9lXDKmcj/ujB0PzwJ5ia3TDEfUnMnjwmnXMxbl2AFWV9uDIKv/01GAVrBLhYM9z2qLXaLc80WbHcRDzaDa4OJSUXVITeBcM+ObYdpCa2uTsN7tI+8CoUVw62eRBaIps9Y21l1FSskan9SEZqdA7tizd/7VtfjaQSl7+PeDEleMvhO8i+s+1NZ/ru9dhehsPXDR1MEu033Jo5t/VY+tmxAaXi+jFCYuixB1ZlOhmMdxg5eAixPb4JDFU0SZp57rMqLLLUE6JhcR33E13/JeN/PUecZaOYo9Wm5R2XERNjyXkSdaiCZMcWDMc4s+qj0zHSdiHA89v+52fGszcp0cf+f2mMLE6xR4PMOvHWUuUP7wdsEHYauJzqCTwFkQarol1cKl9YxlYk6u5QNK4bhWQqg4DL8kzITbOiHeEakkKgGjlHZYK0o7HEvl1hJBhHckULlmnDv6suddSzy2W/plh7lIJhJt5pnAZKnhjmthLDcsZcvpmFkcnFTXIPMmxAjHSTmJkbd1ZucjL+aFWkHKzLZ4vjNkrne3nOqGH8873u22bPIVcy7Mi7C3hFRlI3u0JrbBEN9xPTZ8Y5pGvus6utO3nCh8cFrZ6MKn02NeolwuHf9iFi7DxN+7PubrJwnNRpEto1VKieyzI5A5HzqiVV6o8UkaMIRBM33nuMiBhxrRIGxcZXKGWWAjOx5VofcTP8s9n86eDZ5zK5xJ4LmbyCLMtWBeceY41ZnHsZBLaYhOTXw4d/ibqy/1WvhKXNEHn5lbd1qH0/rhZt56lGbT8E6xlbCCtiFaw7m1uJ8Opaweg5wz6hze1m4KcJaxlQjD6h90NBVHLoWw0uoxWso9hnMNEl5U20vTpgJtwplmIzkwNQ9FoUnSmw/PAPFrQj3gKZgLWG3WjsMNW8xIrv0e7XntfXDekCqYc3edsaIkcbgQ+e9/8x1+4+mebvOQ6/3Md59+j34z4H2CWpt66SCIcYoPoRWGUlZju8Bc+Vq3cF17OOxUaeIirH1NKi0KRqirfcMzqrCVZp9oAqaydsUO9X5F5bWhY6WBhk0OtBtZP9824swid57LTANum2vw9VqFaE1M1SKuatvX1pZysjakd4+yelYPjFhZfYu6JoREPWD7ygocgGSwLG1kFX1DDjZLy3qwWNF46tvPh3gXpPwn/ZGtJ8kOQehNMecJOJRCzYmYR26l43XODdFVCtfZSKJI8PQl8n7wfIvCXGHvFPHHLBgf7xLVRT5NwmRtR9STeRY9L2skVYVp4f0woGr8sEauU6U+ryRbmLPw2zcneKmEtYC8LQWRxEe1Y55HjmqDXtwulSQdt2Nlvy/srHAcPFu/hymgJWC+UMRzpYmSIHSeXqA6R0G4XZriUTFmKWTxRMuUaize41NhcJVqxlvNnFThXX/DdllQvSKlAr0iomwYmZxRS8fHJXJTEpc5MOVjQJi6iTopagtHZWJUh5WeSKZzyrE5hvGKX/EDby2xUSP4iXmEWjxvF2FeItckiin0A+e5oSiHTkgukbPQWWQjM9uTU/7oMvHZJPyTlFj2R4QSqOrQpIRuwbojfmMrvCp7zuk4Hgrf73s+LMatRua8UE04EeNChItp4Vn1XFTHdVHGxSh4Pr5qXvOuz/zKiWOeHVeXl3xatvy0JHbegW3W9UjGqNzIwi4lPq9QSlPtV3WILTyvlT/AY6Z4jKInaE2U+nMIBP+icKN6vQtutdUGQW1ilSZSkbvd22EXd1A+dqKMUtc4pOYzswPU2ZoQJ2lLd9eVbaprQsfBmuGKEZ0jAd7ZXXhjo+2saRLaOoi6djbIff7fXZRTPki/V5RaPagy16gqa/zRvsA+KiE3y4Y6j9NCrV9Iu1iLSS75blw5Y/xnv/wu//qJ8Y1nkRPdki3x9PyoSWFzoZSCzxX0wKJZx7C1NuHIKjq42S/EbsN//Ovv89f/0QW2xkC10N52cbfntrfDr92cVaNH2dWJ6ANa7Y7OU8q96rN5O5s9RUobb94Pkmk+RucaIq/kuzFz0YMmeR3Z1ia0MTM655nVyNbGm1qMIgehkq4eVKNXI+Gwgw1lpfw4ASuNm9uSNhy42oqh0EQ70n7PyQTnpBn2tR3G4vr688+JKeONVDwbCnCtxpHMHFlH0kLMAzd2S0w7/ByZZWQ3bHA+Qk2UxVik4ycl80e1ohrpauG7fcc7QYiuslGjYyYN8Oxkx/OXA3/z2tHrRC4dD3BcDbfcjIWzEjmSjiUq3bDnUpU3eWCjlV4iqVzzIHbcVsenYyHVwKiVWAJbD847bubEEg239FyM8BPpcFIYgrGxBb/sKGFDLYnb1S7hKiwkcBvmVDhToZhnYMY5x6OY2c0nqI2QjaOyx8zYI7yqjs55TruH1L6iFigZJlPKLjHZRLQOFqGfr3lSRh6e9PQ3Cz+cKp9m49KUk5Loeuh7zzJfsyjcuiN+lHZ8kByvLePrwKRGssTjCtvuhDGPoIm4JCYp3OIZbxIXKFI6esk4C3zz+BXbE8dffly4LiMXOfJ86tnoyDtkPtRjUt5zK5HrWrEl81ZgoPCXzna8vN3yo6myFc8bG/H1jJ/cXPBy68iL8GT/ivc2jnNfCBq45gk/vnnB//1aeUGgdg5NE46MFtCUoPPM6yjYG7wZdyhGR6RKpaaWN1m8IywzvXpyTbgyo2och/lLvRa+IkWxiU5aP2Gtm+FQCA3RZu4uJnfm6nk1djtpxeVg0+hoxatqRLTgqlEMtLYuqbe63sYqzglVClvX5MHVNXFFNqN4w1XFuVY4sjpipSVCGOs+T0ASAUde908i4KRCpHWbq3ndrQg59eAkUmjdbnWVHgOvjf23jorNt2wxo9FmzEFYE0JEWrF4+fpjzn/h6zAnxvNAh78bZU7TsoqODJbc1KprVBJe7xwgzjlONx0vrvf8L//0c0w2zSdWDQuujahLbukZRTBTtLSfV6QJUaJEfAX18sc+0wMEwHsPxVqCuVvJPXLo8FcDPQfCUAMptMxCu4sFa4WsiX0SlYUD7ab99+KaUtjVlVwkivOJubo1TcNoZs8mvy+rcOcOVBCbyhddVc0+tMxIHzhgW7U2gZNfvZ7toPOVuIT+pR+dOHJteSgBz74KP8uFwRwnVGaniOvIfWVOHVTFZD0QSGl2HYTORawqJQl/lI3fF+GpOB51lRM/EEdjJvLbe8cy7LE84FV4XZSbHFBX8MFxooWzIZC7gXlyzM5YTIlSedJtGc14Ik1clUzI2TFpx5ntuSzKd4fEQ9ty0+15dmb8w+WcV3NBpRBU6b2nSOZ6Ki3vsBayKJMGujpRBK6JdDbh1dgUxzY7cHt2KeNVeRtPVvyhMu1vGUslpwyz0YfCMR6xhZwrxyXzpJvZ6ILpNbiB128mPsoj23jEN6TgLBPcgusKjyhsh0xiYFyuyGI8t0y6yjw+yg3PFh2f7Ge2es1Z73mshrOM04JzRhw6ahGurbBUIxl8PB9jRXlzmyhLR/SOpzHx8a7yqt/yaLlhS2Gyjt/QwufeUXzkR7uefzJCrIGjjXC5m0ADm7jjuyceSzuS7/BHkUsd2JeZm3xELZfEuMWOE9+zTIfxvSMjWGEuM5aU10vGbSIf+Fvi0DPmpr53ZUd0RkqJfe3JYmwfdsy7PT8rR3w+FbzAN+OXSz/9SlzRzrk7mTx6j0ITM4yD6rKuPrmV8nJIuJdGM1t92C09vBgeyFWhFryTBtPGYI1sAtbgX0cWaaQT2pgTa3iz4u49cE6bCjOsF3+1dkP3aJsuVkG0ILWNXKuTO8uBs9VGsCpgjYI3cNbSHos0kot5W+kqnkihSl0PByvvVJoX0onhVfmpbfjw7Z6vn0XqlfH0bIvlQs6FpRSGGO/GubKm2Vtq3daSm3igveXCs4c9f+s/+iV+8D99QrfMyPq7ehNEGzdUfMWsYs6vxQFyrfQo6g/xQSvfVNsRB1bPqK4dohySSNY/o6Hp4N6peXhdQRrT9fBYnOGr0FuDh5trEVe45mOsB+vNSiIq5lk/2i/4Kw842LaTFNUGbl872lpbfFRTs37Bl2mg3u6gD84Lbt2x/jw8dqkVDIDR6p0qu0PoZeKpQLSmEA8qLGXhRB21g5eLv3vv8/pdd65S1SPW8lG/qwGfIr+93PBmWfDLQOIx0a4o5nAO0vrdWEphxrGbMw+kY/CFh7VS6bgGXtnMRjw+Oibfc5OFaJUHQch0nKvxtp7weYFUH/Av9hktIw98xHltAO1a2CzwrnfcysyuCPtkeHHkJfGDbuZhd8PHi6MshSpwUSdSqXx3YxwbuBqpxTG7PT/eD+xqwWnlVLaMA+zVQZ64XQqzZZ6XFtRrdBRb2Fpg0ZFHxcN+T+kcL+Zj6m7HO48d/+i50R3teHPrGWIjSp2dNtzekEc2Hby79ahzbS1UgC6wnx2vqrCkgU3n6ecKbsGXie8PhS4Zy9aT+gWHsBHPeSw4N/I2H1GmQldec6XHPPWZRYyT/g1dgb5zBFvgyDGYEV3ktcvkrSMtieu549mw40GAMD3Huy1LnxmT4/U08+Gt8ndvE8lFdouR2TC4QN0H/p70uMvako5UsZzo+0hUQXILSNhNSp+PEW/s6kzaJabZ8+99idfCV6IoNrzZWgCk7eca01Ibe3MdU34xuaGUdqr1qneeNDNDzRDXuhQPoIozIa0s00NYL9DGfdwrGLN9ATquQihN3ZkFKqWJVoCq2sZ62UBc69y0qTGTawxXpZnb1ZrAxtW2G53VVi9kM93vXeGoNNF/XT1GKi0Rvl/fh6ptn1akkVVKEP7C0cK4LPzwReZJPGvCnbDQrSkOutpMUkpEdc14mEqLtFq5qodHNqO3lkjx5OIjxvMPSPNCR8st7Frv2tSxcgB6tyIWVsVNrRXvHd48U0nE0ERR7SOw5vFT32TxlWbL0NXssFouGkLhHlpeaOKrJiZqfFsJ7e8dakvvaAkoTfzQWK+H5zfKhVu79QMOT7nnlR7EVVVb81ydklZiu5rh1rF4i73yFKu42og7B3Vysi+b0f+v5rHNBR/a968WYaIirrIv8KF63AxBMtE5jMqIsSOQcyVqwWLE54rWRlwy51YLk3CbO/52XRh0QULPUQgkgRO5RpI2Lq9r+YZOBdYYo7TAC5t5bLDzFVXYVDiJAbXMlAQ/j0Q8G+fYuJnbOqCW6bVy4gqzQUKJ/phUZx5V2MSJcb9wNl3TdYGaPHtLzMuejENjwKrjsyvle8fG0SPHH77e8eOrDu+M//d64V1nnMbEdx5Efv9FY+1+d1BeZ8dxt3Bme6R7wJSNORvf6CMTe06HQC57TjSws5GUznkQC7FTTr2QeQtOybnwy08LNW/5tUft3rOpgcKIMDNbIi2NSlOdcnw+cHtd2CVhv14HpY6k2bEzYcAgBJJlNG64nis5ZPy+stSFmyyMi1FdYp8z6k7YeMUqvFl65HpEFUaNSHwAaUcIAXIi24aKEXxrQuoughX20nPWXNVIL2jd4p3wTmgEoe60w6WJGGYuLVNKR9p4uipcpYQNAc0zKSt7FC+VbaokmfFV6KxNxV7pz6F5X1UJ2gghh6xCt8KZjSapPyRooK7tkVZPWhHAty7vnqryx4/vYnKnakXsDt2GGkUFX+/DZO88ktrUkSpNzt96m/V50n6WHlQWhxdNg1i3iEddMxpZhSVtUKfabsyzGl0VNgY5tIKt1gppxfDWFJcafHsd1pSTTh3OKs8Rng3Cpu95PhXG6zd8fuV492zLyRAaVm1OqBq5Fvrxnuk6LzPB+T8Wz/XqZuJ3fvyGy25Lr5kQW1flqlHW/84faORrcax2UGiu3b4qai35IANh/fEltxGqW4333gpS21evyGq8EaHJaOwujsmtOZmskAXxCqsvMHmhqx6toK6N0Q/FLqoD7j2fxeyO26rIXUekKneRXym319CvI2BZ/bFmtmbwZURW5q7BYew6yP1n/yf5kUKmVKEYLOrQao03K4aZY0IpKGd54RtWOHYLk595VQOfmWNKe2a6FpnmI2YF0/b98LTpy7R+5jIJ6h3ONeW3qENKG0WrtAIDhzE7XBt4IlsFYeZ6alOZXhwPfGrfCVOuxePshmmCcR45SoUQCu85YcvCk2HkYS/siierYduAizM7QOaJCzdwVXvSUlhqZT9N/PN5xL06ZqgD3+uveb/vcbFwFjy/85njd6c9T/rKB35PGOCbBp/eVEYzuuUlgzN+5bRDueC6GH4RtiFy7kc+6AOqt4xLYbbAj94uPAge3xnnUXjghe22YGUBDbwZL/lsPKXWTO88z5fMsS28mh28Vp6dtMzFR9uJ65uOz33mvaAsVaFWdjmz1EIuM8eMLIvwkxzxNdMZTB6e1JFf6CreT1yVDg3KwwL7oeNIJlyAh+ECLwtvUuD1YjyMO8bQU5LxtZPMVI2olSG2eG4nnsjC6JsN5/mc2N9knEu4KBTLvON7NCWKK0xlwiEspsxWMRe4Ad51Gesq+6rc7hYkKtI7vjd8ub6or0RRFO+avlQgmKPQaCdGA4KrrDdQ2g2+c+GOHnMomJTWQbbRqt5ZOqocUGRNkFFWYU5dkzCiuLbfW1MuDm9IKQXzsjrPv4BqY/UXmqG6/n2rmT9K8xs60bsbsl93YMWt3WJbOtJXo3pw5nCAekVKswI456CsFgYRDsEozaoPIp5dgpMT5Xaa+agYn1xnvnYuZGa+oWChCYhKMWKnlCB00RFCwGdH2A4tYsk7+jnRxYjXS04fPKRko3IYT1cCbn0v6zoKbUIUXdMwoHW31RqiT1aGq1Vbw5ErxSoLdZ2R6hrW2/ivh869Bc203d7hXOOUpvKt4OQ+c7IT8E5RZ2hZUXSrmjivRVB9gz+oE+IaDCwZymr6r5R1HC24AHnlzJZS8EVxrimLS5Pu4taEb9P7se4haeNP+sP7yOOS+Hq3o3cOpLBfFrDIOz5TUmXYZB75ApJxbHibZhI9n15VdtHw84S4roHWpY2mG2AjIeZIpTQLjAJlQrJfaUEThEDBU3NBLTTAdVowq6QYAWWqYGHbAAAgAElEQVRniXMXiTIxdJE8Z27KhhPbczJf8ciPBDEe+cL2eOHEZyR4TD3jPPPhsuWfTVtc9Wid+aSeUPYzG3PsLSLmqW5Eq9GJchx7Qkg8rc1i9fh4y3LxMa/lnDR4nnSvGbrKg76js0QIjje3xrfP4OamEqMn58IQ9nRivP9gYNm3XeDVaPzexcwSH1LslscEuq3np7Yh4vhkKYw3sLsQjge4uoF3YmSsStkvSDjies5cKQxlz3V/TKHwepr44e6IpyFxYspxyJhN2BDYv0xErtkGx8PHj/jw5cQPNoZLe97kjneDo8wzb5KnJuHZsWPLjgt3zKXteZs73o6OjybHo+0pT44q78ZEIjHPPVch8PE+4YNQUyVkZaJFtKUUWEQxa2B+GyPBe5KrlDpx6gN1WShipKrsF89QZ0ooqBbez8YPtfBef8Rn0546R360a4fv/1WEf/PLvBa+xJ/1//uh2pBZ0PY/AWER6AwwR1kRb94aFQIAqTgVnK2+tuhIpaU2KC3mB9F1TNjyA+84mquh/uBpPHjjWtj2qh5db3x1Td8w0Sb6Wb1uuVrzKqJUK42Yso7nzIx+jZeqGEGaOlK0QcadSQsv5r54qzPEBXytLRnkC7xWEcEVYZTavlDVWGphro59bkGhGio/erlDc+adracPRimG1Mp+qqQMbqkMoXC27VoOYDVsTkhtBJevPzth+d3nd11fK3xKXcfTh0SMTAs6ztzvZ2utjQuLfeFzXWEMzuFWdiy0zvkO3L3+vLByY0QcIuX+z++gDCueD8ORcepxtZDrOs5e94RBGov20BmaGV6EUleGrbXRDTSIfCPptH211jaqUHTNgFwBA9IyHysHvNz6e1hDTv08PEqFF6a8mAcCSvYzj+g4Ms+LaeHWG+N1IRfX/LjeUbK02YoZbgIzTyHhciLJSu0Vawem2kJ6RYWamwLabGkWGXHUeVzBCplYhS5nxAXOVPnF5Zqud2xN0c747tlMZ0YqwqxtlCpWsTrwSRr5ydvAWE/Z7TxH20hdCldSIFeS8yzTjDrlVEesel6mxFNZONlObGxgyJnHbiQed3x8Vdnna5a5sFM4Hza866DzI1lP2IaZOVdEPZdTAe+4nheid6S042x7ylKNxWa6Ba6K4JaOq2niOyee23TLOC883u5Jt4FvHX9MGDak28zoKrMFfIIUhetcOLseOR88eQNzl6kVOq9Y+hTZwXdOlRM+J/VnjKXn472xmyMXr6852QifTw95MSaeXlyz6YXojKcnJ5xN+/VajyxWmboHXJXEjZ2zTzvEbXgW4E8/NMpOGLWyXxIX1eFc5EEf6eeFa4WTaDxwxt5vubp4S1Lja10lqTFneJlH3oYjdvOOy9SElW+tKXnFNzDHqAWfHGqB6govl3a9/WhayBmqFlhVsR/kL3ex/5W4ooPcB9gia7Ggyd+tClGbAEJQWOfH5iKpFoJkihWKtUJQa8LwzfYggtaEI5DdeiNT4M7buFJXVnh0s1ispJJa6V2gunWPqRWr6/4Mow9CXa0cQbpGAhEh1YZhU5OVuHMPy0YyfpVuOymItN8rRIerjqJ59WyGVijvrBDG4oWTGhil2TImAhdjxpuwjYkhO6KvaJnJOd8F/aYi7JaJ3mW+8c4ZR0db6B2kii2pvQ+p4EXZLcZ577hO3NGB2g5XKdSWGiFCV4TZ03ZGa5fnnMPqPWrvHsZ9XxzVrZ+dNDFU69ZjUxGjGA1oXmg+xGSVbrV1QOsUOxM2cSCYMOWyegcNXxw1uAbtltaJmoHVwLRUlFZok91bcpa64NausWCYCwRrr/EQlnx4/WpCrfcj+oO5//C7/Ul/pGlugAupFC1IdbwVYyeVTa5c1sKy7nGy1XYuqA2fdgfa0PZ9POxx1yELYpkjHLtaKGU9AK3cWquGOENKO1jFCF0p7LRHrHJL4JU5ZCkUDLcHPo3EGIglMsdCUEMloIshXcBZIOeEt4pcVeqYKb3DmRJcppRm1/EkfvU08q9p5uM3l0yzYikx6xFv05bjMHO6WXiSI7vBmJ1i1tYPMZ7R2TVvd4Uh9Ox15OH2GE17EgOLZkzPeTlXIo7nO8dclU4XjrmlHza8vLhlnh3HdsP/+dmW67IwqOfSd/Qc8bgLnMW3FGmTsbPuISfHn/FmgnJ9gzllCJ6pCFibAv3oNvA71+d80yWu08grOaEvwlkc+OwmMQWP5Fue6zFfr3su3ZZ5N5GSI6dKHQKbPnCmia1U9po4cZG3zihZ+OgyMbIluoVTRs7EkcbEKFvEJ05KxNjy/LbwevwZefOU433lZ0c7zkdDQsdRiAQWdm7hXVWczu2+kpWdN7qysJEehpmoypQn6jCQbObIKn7jKRLobOEoKvfGrS/n8ZUoiqrc3WBEoFLvd0C+eQKda12K3e21EoPSCog6OquI+Cb5F9YgYSP71skdfp6jedOaX0LWIOK2xC+wimYEqauKTrgj3xxCe+tqbFeN7XSD3PFIvWtyx2YL8HfjtVorikNc058Vn+kXR3UDmm/po2MsA0f6BrPAqN2h3jTxUSk8Ow08n1g7LuWjrHzbFzotSFfIWZiKcLUI4+WIiDWQsY+4oe3Ccl5w1oMXxPdYrdTgqCXxwXZg1kLnGr+m1tIQfKXgte34xKB6JQCLr4T1LGMmLdPS1mxLEXxtHabQ1LJwoMU0HoBrH8HKuAWzsHbejfIR15zEsI68xQWC1rZfVCOoEaXRcDItasrEUa3ddGutpBVOILUpd7XW9t9ZwZxfsyCbQrjSpO4glNJu6bp21JOtkAEOO+Zm36D+fPgUWwJMs/tIrtTqmA1m2bNUQ7xf96ylqaznxildzbRtBF2aGKuKoK6glikKUpWdOyjEm/bYDqpopU0sam2JNAkmHH7JbVxPxZovp6WSrBOBXc3MtTLM0jpXbQexIQmLNDtTyhVkJkbjaxEkOzY5EYeFUvdYVq5v9zwYZr7xIHI9C704nCz8cG8kjGIRXxNHxz22FArC8fGWebcjuZ4lzSxOCDnw+1eG7yObeMTLN6/oOsVLIEriYkxUK5yK4zJO2BUcxzOOZOa6e8w7mvlenyk7T3JX5BI5iY6pBM67kb7vyfl1E4JthC53bPrmf8w5tpDjUjlF+LVhh8c4d3v+DIJ0lVwcN27hne4SeRhIc6bIwFITlQobjzKQ6w7B87Ya16HjOiW6YcDNmSEsDH0kjTOTFWoeyNERpKcsleudke2Gkvb0EjnrB/qyMGxHSi5YcGzkmq0J/cYxsnBbBamOqSycd4LWFho+p5Gpq3Rm7DXwTiiU5ZiXLOSiTDZyXj03I1y7wF/8Eq+Fr0RRhHuhRrNVVFw1Fml+s0NxUGh3UprgpOHg2vy/rLiujCG5INoCacN6ITmzdoFKoNY28kF9232tGVUeXYtpu7G7NfOP9UI8mAZEWIN0W4q9WbmzoreY1gJhFamsN3QcSBXEwbkJ/8Wfe8R8OTOe9Hz/aMM/eznyX/2Tyt/4d9/n3fNT/u4ffM7cH/HX/sErtt0x/+H3ev6Hj97wqAtcjQVVx9fcgio4UfoARYVcKs8vbxiC0sfI0AWCa+rLF1c3fHN4TM3lTigDTYXr8Dx6OPCf/9lv8F/+P5+uYy1pYy6n6/vU/idrZFM0d/fZtPF2G1162g0Mp3cqW82rXUK1sVRX0MGh+8q0MedcCxWPXz1wZgqu0olbhU4Oq0aSBmOfam7JHhwoNrruQVcaklrDLKyv2a9gAJEGVhBr/5zuDlXre2LNGFTX5zga0NyvB6TD2eznBH1KcJlQQwMg+CZa+lNxD9mzd45qiZpaUTDnOM6JB+6GB8HwlsgSmv2lK2zxXLHh1Qi/nzNWB6gFaICKY61EEYaYORqU+TbyKZVlmVaFchNHeRMg41ayiXcN13hQBDuU2TVNQaiKK4UlCg+do6SCescDG9l4pS/tu6x9i5LKZWA3FXa+8nxsh9unOnM+eE6HwC8Go1jrmj+5zfzzj694OHjCcMzt4kmT58wlgkbevJ04145xcDyYFo64oD/OlLnwvSc9n+4Tj9wlP/1kgx05fuHpQy6mwk/eXiMu8MyuucV4rMLtsWdJM0NcqGXD4wdCkCP2dSZJZXsU+eAkcpWu+eiNMC2Bk2q47Y5YhO7Yc3Z9wyZ01GHDy73Qa8/ZiSO87iks3F4X4qYHuyKo8iIJRygSZ2Ry9GHmJGWO+0LGWKbMIp6h63l+cYXTyLHf83wneAs8EuFtzVCFp5uMjcZFzXiJvNlfE2NdO3PjuSivqvLEZWJRnC+802956ArjpBxXSC5z7jtelKaUFxF+uisEmRF61LWD9iupxOg4lS93WvOVKIoxHMantvoOFXPQrxl/9eBcLPYFxWRLYchiuNp8gd4axLn4Bs3eJNg5Jd7l7tHS4lfJMqbgCnVVrH5RZBOkeawUYe+NWL+4o2o3xYiQpAXmVm2FoFptf2Y0kUE1DriAtqqsXDnoS+LP/MIZiOd4CLx69UP+5m+e8+ThGbHv+Evff5e//tsfU5zyn/7pDU+HW/79vfG/vchkD49kpM+J20nxgzEVj3fKvCT2WdEKz84HHvTw7fcfsbvZMeN5dTvzcAYZuqba62Pzb5YW1nxzs2MOgS6l1rWvhvm2X2xJ3lYzuiZEaG3JFdXaDU/Wjr9KKzhNtVtBbd0/GsXcKt4USjVwTdiTyUgF00Q12JiQo1CsJadk2t53U3RNH7GGhnOy3iwbbk6BxQrqFFdbFuIhvUSkYNWRzFhWpTBrOotzQpFGihYNSF2aClMDZk0EBoA0bq2TfIdY/ZP+CGLg0irmMhILP5oiW1d5WisnNvG108jlUnhTC5VAUOFFFi7qEVKMW4R5bBOdVFqWoWIcecOkoCgnrvI0zEQxToPnNiVcn3jHKW82Gz66nVqe5dqBeoQg0hJmcmGRZlAHpVSlK5FNtxCqY9sVNrWSsxEwplTYScCccpmVMc285x22tKi2R8HYi+GqY8ZxYZHPbhLpRead04ioci6OmBZ+cNJxs+z4VmfYtONyOeJNeMOUHvE4VHy54RnQd8Y2tuDc8ACm5YaQRp4+OuODsz2IscwXvBszT9/p6LbC1dXM1zcbxBzn6rjcH/Gq9iw3I2XOvNoXPjjObNwxtzdv+dpDY5wcv/TOjk1vYJF5hugy1C3/1AZ+8nHi2iWO9Ir3j04YdjNvZcs2HHF5e8PJNHFRHLnMXN72XB2B2wtab7m57YiSWWY4FqHKwpQ74n5io8KeQpgDrlakZD6kcCIDz47h0+vMoIknQ09eJt45zxz7SLVMLm3qlxRKUSR6NCdSumVfPJMWrqohYcPL3Yj6jms8UTOh83T0vC2ZoCO5Kg97xziNpBK+1GvhK1EUT51xZQdsiLCm+XD4v4K1kWiVLyDhaGpH55rIwsMXlJBCQEgd9LWuVoj1ec4a0qv9Dagp1beuJZQGBlBVbEXLLcHYZlYu5xfl98bojK25lvW4Wgv0sKsUwVFpwQurSd7Wya3CT9/e8PTpMaHObKPy67/2fS7eXrMsCz4GNAZ+65ff4+/8Hz/lvO5Js/IyFfqu4zQbYp43fu1gpjb+PYuZ6JXbcc+vfecp7z/sGYJH0szJ6Sk3tzcseeG6GLEkgvOE4FvrK4L3nn/rWz0fv93xt96sBvYChruHAKhBbbvGoK51p1S86R3mrY2UQaWli7TYnnqn4FXRO1iDNvMoghLEUzwgZT1gNH+jc47OhGhNybZ3hU4cToRKJVIx33aL7TNyd+rQpK1DL2t0kJnDqZFpo7zOg6vCoqURhMwI3uOsUFxYkYOrf/VA3jHD8A2i8PNhU8QSiDYCkmkz7b8fC4VCVuWNRS7njgfB+OZ2wN1ecJs80nlubuFmjd1SSZyY49gtvNsLZ2HmqiYGK5yHxMPe8e62Ur0ysuX3Xhc+HB0fSWKeBCmGOnDO47JRVkC9IAyxQfidtY3lead8vV+4TgtX5qiLsQvGUgWrnmAB0YVpTjzuPEciXM4TUxI68VyMmYfBEfLMdx/03O5mjrcJ3QaGeE0pwlKUYYA0FZ6dFBYrPH6w4emzkW73AE4nyjI1AZxGRJTr/4+7N/v1LU3vuz7vuIbfsOcz1FzdXd0dx20nxDaRwYYoEgELCaQIAUIIiX8hEkSIGy4Qk0BwwxVCgIQEMgjlIhdYKJAB24Qkxu64J3d1dVVX1Tn77PE3rbXemYt37X3KiMuW0q51c+ronNp766zfWs/7PM/3+/neO67vrtFxTRGZRX/CNEbWXcvL22u0blkulyz1wOBm0ZcVjGGLNB1Pj1reWmaIjuK2iNDw+x+OdE2g7QquZM6eQh7gJ58qbrzhSEbM+pgX9yNHuufrb7f8+NN7jnrDfhv4nUnypCmYYYcgcuUGVkYirKBfCnJxUCKpFCSOKCQhZRpRWAgNcuLlIPGdJI0DC6u56Ot6Y8qZpY7s/cSbK83tASZRLTav4pJNqZ5nkyRNH9gnSUkFpkiUGmUEK1FYUCoYomQWC4ui5kLe+ozUaxqtaTaR26gxwnM/ZaxqaO2XcKf4XhMYI3y/dNXTNKPVBFSZfapqwprRl2uatqhQbCMkKGaeZr0ePIFCMDM1Z88TzLug14IJJerI8SFJ3op6kjGpkNUD35TX49HZ8K1mq0SVl1dbRoVki0djvMxVbKPmXWQpmWw0f2EhGd1IrwRN0yKtglRobB0hbzYb7kPg75W36c3n/E8/PvCvvNdhiuNEW5oieCEXrPPIP//Bkv/to5EPjjXfv5lIoiWXwP3B8faTI4wWSGNIMbDoOlZScncYcD6gOgW6qmZJmeI8x+sj/rE3PH/9dk/KCakyImeCfLBTULu+WXYqU6GdQTwPHks5ewtLqcYOKesHLc5jVTkLkHi8TwUrEoWEUYJG1zxFrao4QiPqPXgw8dfwDUSZsxW/IHZRGVJJFFULbylVEZsfIA8lk2bfnEAQS5qjsqovVhZRw5KlQlGFJJlKXBLydVsoy8y/VV+SVlFVYZnKMMYJaTQvvKcUQSfg51fwzeVEKRuWOSFPAnunOcmS1Tn4WBWJKjrQPeuFZJwkh6nnb90EPpkES2u4vDMMwuIpNCkRVEPOoNE0piBEwmBIwTNpMFk9Wmecrzmpv3CxZDnt2AtPypGVLmAKTVbIIICJZyuBiQ6lCkVZVq3jZtS813omB4fSkJWgkZ5123LY7bidJoxoacKB/eRY9QrLjtXqjI93E0K3NNLyvc/2/Pw3TvnR9oojc8Gnn9zw5rqn6RXIHVklnp6vaBaFXDQy3VLwyNMFb5y0hLjHdpD3K1TjaFrJZ58dcNFzpnd0F0sYLM5dk/OSDz9LDIt3uJy2mJueb98l/szzlnE7IsyejbO0S02+39D6DhUuudp3XMsVH287npQdUnd8MiSufEEVxUIp7lxiISQBz6dTFQ4+NS19mRh1Q1ADh2QRxlMiCKMok2BJx/cmQa8zZ6JglYAkcBg+30huhaTZSZ61gl3a8Go8ZspQ8kAzaEwqnErJThe6mLj0CtnmqkQtAi81z8SIEXCaPOe656P9xHeSpwN8KYjcQPZoCmbzJTTvH4RBG8e35MSHQT165HJRVQ+j62jVCGb+aU1s0EUQRaYtsp78v9DJPahZsyiPuyiYcWpzYaujWkUKDaXL6JDJsrAqhkkFZCloHvIa5z2aeLBvvDaLJ6MeyThmRtLpOSHCFFmVkrJUVW2Gj+63/No3Gj652vLGcYtVa8Z4IMSCNtV+0KUF//3vfodMRxGJ3/w882urE46s42TZUvY7fuQXjC7z/rrhuEv85W9dMHrPj/Y9XsAUEtcbT9tpni7MY/pD7aIKFlktGXN6fFEVrfStNzVPf++WQ3fKEAXIjMqWQI3Xecw3zPVU/7CfyzB3+TVZ5IsQBSmhmdvp+AXrS+0k670RpQqgJFX9mWaqjJDV+iFKzUBsrHykF8F80AFCnuELQpNyJMxpHZGCygL3YLMotTMWuRZLTfVVVjTfw4i0YvxiVYUgCo92E0n1SNb7/eXYKnalRig5IlnXrFIlNEkkSpH8g13h9/aCVE4QMqIzRAkq1vgzIQQGjSNgkCyVYC8lCMuvdY4PliPTmPinnzbEcs+xmlidrhkHx+W+4Y82hevJcjAJF2TtEGNGakFRgp4K+AjJ8b0XmaIl1mpSMZgceWISpRy40JmgGrahTjSWUrH2ke0UEUbRyYjHs2ZHForTbsUQJi56x9feNoxO4Ddb9rnhrTfB745IYc/Pv9tQyoGUXnLa96h8y9PzgYW+oXvjjt14yfJ4hZAn4OCzT2+4/9TyzpuZVWeh2ZN2lulgGSdN8obd4RVfe++M/d2GHBvOV0d8uOk4uppQfs8UjnC24+Jk4NS+IuwE56vMMEV+vF1Q3JrdJDg2mk+uJYNWfKXz/MFmhW4yv/pOwvqJHx7gK+aAalqGULibHHe+w+WMGSeWy5aV8EQtmKahrkziQPEtvo+0aLxMNCnQmoRqTnh7O6CEIUwFbyTbnFm3cGMyb+bAi6C4d5Z3mzV/MN1hdCGlhmPdcJkyl34iolGyih/FUNcrSSQaIbiJLbEEfqJ6lknQ6szPCcFVAmQkCYubJFNOr/Nwf0rXz0RRvBQKM/WMVoAq2DK/9GZkWZpfPOq11gWVEyhoi8CJRGUn1CsJMEk9/ndFk9VimDIUXYtAJyp0+mm+5j/7J54jk6LtA7/xv+85KrPSUjwU00zKCj3PxJGvRTlq7oykrlYKow0p1RFfFBkvYFEUWWSikEht+K1PCv/sVzJd5xjCHS5JFo1FpkzXtpRe8d/+pfe58oX/4Ldf8M89h0YnenVEJwrP3z2Fj+54GXoulh6lJD+522ON4d1V4YOLFa2VyGLwYeBql1k0GisLPglaI7lzAycAnSXPuYtFSg7Z8O/8k+/zf76Av/byHlUq7klmTSmJUB6YqQkpDSGEKpTKGfTr3evD9ejFnD+7RlTP4QOy79G+kQuF/CgAUkDKAmNM5cSm/CjsEUIQHxbsuTzaReo9LyhRu24tQOe6M5Si1DSNNNtzVDXi14SNer8fxuyZQs6GhyzJyqetX18wG/hz4UsDP82FEh16tizlXPfSRkqSdhBnvqkEXXQ9rObZ5ysDUgpCrn5OBRxKxGVLGzZ8V0pKEGwK5MtAmI5pTMB/lllmxT5FslUcTY7FUqJswQWHEA0lC5oQKZ1iIeccT58pOnGiJcqBU5kOQSNOeSW22Kx4agQ51WdcMLDo3qDRV5BHtLhgs/uQN8/f5GYz0RK4kxvK9gm3957V8gTjNuy3hU5mbAOowK2z/N73zjhuoCs3vLvq4D1B35zRn9+ClOTP75CrnuXRivX6FvQAixPKtkOtMouosNKilht6uYa0w8gF22HDW+9k/vG3Mn/4vY7FesWxCyw7yX1uCaOmDYUhK0ax41m/pe0hBo0pCe8925T58dCT2sS3jk55dTVgfWaP43cmwbKLWLUnDXUknnUmrhQLkThaCros+YcIDq6qZrN25F1kdVIYY899nBiV4G4WCG3vXdVyCPBTIAbJGDvapCmNpzjH33IBX3p0yggHd3EiIjhqaoMjMix19WT77MkRSrFkeaAky5QKQwl1hVMkVjlOtWUbJhaNRGfY5S8hEJwsiI3Azt2XmN+elsyhACKhERQhaKi7PSMFY4YkNEciIUrCU9MkKBJhZt/T7D+sJnjQRvyx3VYphf/4V05p25beaqRc8p//kuN/+Pae7089+iGkVqgZcl0JL+ILJJNSMkgQ4qGrrLu9Iqm5iLM6UkqBKZk7LE8bweVuJKSGVVd5kY0NLLXkNEvWvcGlkWUs/JU/3TGEQAiBpSn01qCt4JfeO+ajlxtEa9nfO966OOaPrge6FGq0itQgCy5LOlVHv1MslBRJSkPJ7IOn1xJlLW53wPlAcJ5SHO8eK+wLTSYRRJrRegqTq+ChuisSbVNfmEKr2aMoHxM6ymyNkV8YQedSob/MStWHDEatNQZZUw+q9RtLqvmTVGPvA6Kv7jTrvQkig6mg7jLPzmvcZe1gi6hsU4UgS/nH7p1CUGTVH9dkshlbV0S1AjArlylkNEYmcq70Gy0V6sthU+Qdk7Da4FIka0+XItImniZL0oXLcuA+aLahepeCMRzHxCggFkVJYEUAJFIKzqXgzVZwmNWNY1aQNSIWehuRGZZScmozWRqyd6xXkrYROLdnsW7BTejW8PE2Q5hINnIklxxkJIbAwtRx5XvLjjYH7tI97wmDEPuadDNsCHbB6kiz0Jds7jxxIYnjNW+s38HYzMXxhGkSfbeEdsnZE4+bbjncS1I2bIPjcCVpWsHBe371K4p+dQcqs7s0+PtcA5iNwnYa+WQNbsvRcgCxZhp6pl1Du/YQFZSMaTT7bUFYyXjwtEbwC18559NXiSfLxJnd0p8/wQ6OrDW9Hnn1cqQ/e8b14VNCPuLlKFk3BTF5ghasTiU/N0g2w0SK8MnmnnPlieslJ65h1Ug2g+fl0FSyloGv2obBJa5FYIx1Eidlx35/TxYtByLWtPz2RtLKSLAdyxIIVhJCAF2nOjlPuARaGVoGGlvIPoMpnIrClDMLWRsGbQOiSI6UIMYR2Tbk7BDJk4RGNpocd+wR6DbRpcJ28kilKdJxZDQ+1CiGUhL7HBnNl3CnaPVrS4UoCa0qucSVwpJEUBJL4bmAFwVGNE9i5C1TyDrwA6f4WuN4qhM3csWPnefGt/UtpyMxabTUyAJOgp5ffLULzHy2SbzfTpA0UkpO0Pzr769plj0kyb0P/Id/uKc3hYMHzGzOfxynvYYPSMlMv5n/XMiHmSJKSETJaCQ/SZnDPYSbA2uteMtatHZ8tS00MiLEko0LbA+RMTgkhU4bGqNoO8M0RXLOvHHe89HlnicnDUpEfvXdFYP3hJDY7CdQpqaMxMShPIDVYfI1sHWKiTIO5MOeva/S6SkkBJL/5sMNSrazCrAmQs9gp3kAACAASURBVKhEpe2Q0bomeFT6Td09mrlKBCFQohCLoZH1wVCz/SLxmmhTZN0jSFW7D6VNZW5Sx5sPo1VZBJKMnMehiOoATQ9q1xkyIB++5hfGtxrzWKQfvLAP3enDmrDM1BWkRCcIooqXVAaExMiHAXwNmK5By/kRXvAn/eqUQ6SRTgoWCowyvIhLvusDU0wMQnGSFc9JPFkoFsnR2oS0gt2+mteHwbLoHFpLXrjEVAKrTuCSwLoCaHZKk1IhqYwXkp+EQKtrykqOGZESzzDgQHeC1hV+YeWxMtMoy6vxwCKPLFvDkbIUrbi6G+jajLU9L8eRdWsR046tz3SN4P7OMrSJITV0e+gaeLnZ0guLnQ4s24bvXk2odEW/WNFMAVrF/XRAq45D8vidICnD9z+PLLsjGgmL1vLhi4RiQtkzpphYlJFiG04XkuHgOV22rNsRvCKXDdIqiluh1DPcYeTH2wVKrOhsQcjI5/eF5WIi3H1G37/BtL9nEwTHpmc/7GnDgZMWpO+5HwWdlXQycS4z9tSy7Ay3SZLcBE3HKjoOsuXgAqUUeq355KA5MYK/eTVii64SOJGxqkWLkaPjBdOYWemOccp1nKoOtN5wyJ4LafASpFA4KXh/IdnqwtWYeO/IMm73tOuWywTSjzhrED5ACyFbNLLyVhuLI2G1YsoN501kioFoCutQO0cvDEYbnBQcCdhOnq4RyFSThoqS3Oef7slU/H/h2f8orn/5v/rteehSD1NQX5qW6i08E4E3ZMM+RN7Q9ebeAmdVYYOyiT+cDKcC3tYjxwsDUhIiTKXwd0fN16VHa43sJH/nrqHYlg+YaHLBjve82zv+4p/9Krox7EbHdu9Zdi3ee45XHVMovNoM/PjW8XfuFD9SPV2pPh6Vqgs9lBlXN1M+xLxfe+iRvujpkwi0SMi5mC6E5t94r+Fbb1r2sdBbhZ9GXmw9nTEUBLvtSJCSRaNp2xaSr1EuInO5SbzaTVx7w9vrhFWSXilcCDRNQ2MUeibuoBQ5hMpB1RolM6JUrFvIhWkKZHPEbrfjv/wogMiEWY1bqNlsD1FdYd4xqnl3W+akkSmXPzZZTCXPPu+6AdTUUWYjwc9/J+ba1eVZQFVKRM+74zDjxF5/Tfk6xeILop36zSCKVO0ySZJJNFk8KosfAAJfvIoQqFLDkOv3eT0CFuU1mi7PxT3JuvcMzvM3/t3f+BNfGf/av/iXyiQ1TzrBH4wCFQtPtMf5yIWVKCsgWvZRIMQd66LZmY40CsZmj0ln7BnonUD3mWPZUaTjkDJnRnPlIrmMNErzzLd8P4U5hDswiYxkSUoVXRi0Y7krTH1mlVqCzohpz9JoToTnc5l5a3HCy5s9q6bBhHuU0bRtQ9gOqE5w0a/5/LDF+MyTVc/otjTasLYjE4Uz4zk/OuP3P8uMczSaKYLBTQRT44qOkVwc7ejaU5TY0q0CwmVKSsTc89kYeO/9FUwZgmN3m1DCUxRYJTGNAmWIkyHbW6QvjAeFNolSGvpOwrLFb/fs7qBkRS6e06MWvWoJeocJlmmzB9UThkiiJZTM5WHEBkHbwTQIDnrFIRlcDFzvBoSyTC5w0gnwCt3CNBViTKz7HiUmfCrcojklspIDG2cYS8vxyrLdjZwvCvsxo63mKq+4DY6v9ppLN/EEz9F6yb3LbAfPWgx4FOsYuCkLdkWQVeFMBIxpsNpwOOxIQiOU4vpQmdCt9AxCUZSusWSicC4M2xK4z4Ip1LD0IHtycUg1H3ATWFknbF5b/qN/+Hs/tWfwZ6Io/qv/9e8+/hCP2XqlKjoBnhO5SgVkRyPmSKCcWQiFU47nEbxJTFGwShFlFL2B87YKSWgCkXZmkhbaCEFbNqGwtPB2r3l2VunzRktiUVze72hUJZusFx0+R/ZjRlI4eMkWxW++ml+0CEiSLMXcQVTDqSwSMXejUGfoauZ6ZgFG5FlcUhWYv/G05RfPFL1NnC0WSCn4ux++QpbC0+OW4DO7mHAZmhSqp9EIrLWoGLmeIsEL9tNIbw1WS6yZPZhWIVIVReyGgaena0KomDcfXqddWCMqWN0foDT8+//XgGsyPimySAgUQ8mPRRF4FB7Vvd6s0JX19w+XpGYvikeBUu2YtUiEx9OCJKRIVApdZhapiLMRv/58cT4VplRAvv65v1gwi5DonElKIErt42V6yLj8QrH7AopOVJfkI41IfeHPYs4zKGLGmeVq7NZaQy781l/9Z/7EF8X/5S//eok0DFnwlvW8oVquVaFIhSuKlCIvRscNks6XClNQcNZDs3UouUWYZT3Uqp6bQ1UTTwg6pUklonJAF8GysaxndN9t9CyUYDxMGGNwMbFzhbcWgiEmQhIcLSwMA53ReBTaTrgJrBDoUvAJWh1QsqeVW0ISdEbQ2RbLRNPBMI5sb+8I6oSolgwBliKSi+Co8VhrydJwe7VluV5w3EyU4Mhti/eRi5OWvZ9QdgWp7p5Vt6OTmpAaLi8D3cpyfrpgf3vJsMs8eX4BwuHdlqwyppEMN5qcDLKNrE7OCG6PTAVlOuIUyElgu8x+4yEZPCtC3LK7g2Wv0DYwCfj2D68ozTljqu/MRhXOGziEwpgyPhesMBip0GqkV4kOiLLlehJQPF0TOJWg2jU/uBm4sJLrIRO1ZS0cz5eKkjXJJi63iS5nRGNZy0i/SAybkdKumGLmcm+YCriiOdcHuqyZlGDMGvyhHlyNYvSZoWg2qfpZLRpNIhSJJxJnVGRTBJAeVerT7BCYqMp0RbVT1Toh+at/8O2f2jP4szE+lWBkmbsRiZCRIDUqQUfkBaCtpc2eXATZQMyGjQAVO7CgsufMBqagKQWup5bfzxJdLF8dBsYWznMPaiCJzEJOnBhLTIK70aPvNywWC3xOjMEz+kDQmr5VHCbHFAJSWEwjUSmQmFiplh22ool0ZXUqMRvB5/FpMoq/sIj8za1DlqYWClHVrKoUxCw0yUXxf1x6DveeX37LsmwW+OI56ltGF1i2DU+e9RQkL+93fPLynrZtSXnksHdANc6TE0urKoKLQmNnFewc0+NSwT6kxZea2+ZDJIuavj65xKIRnK3PyDnzb/95xTR5TvvCbmr4T/7+FbJZPe5pcxHwkH84W11mcekjP1TLWlC8qpSfGghcCCSQmvJY2OooVKdUBTjUHJQquFakAsxFreYQi0eYwx/r/oqoO8tSAeDlgddX/yZZZSySNBfxioMTsy2nCrhqga8j10Y/+CoTeU7xqILoiJBfDvXp3h4jIywtOGH4+7kw+ExJAQhoVW00T4UgtAotIrY0bPyAaRsKFxAdJ01HCIFnNpJUwCjJbXYclZZeeJK2LNNIzhNTkHyl7XGucNQ0lTVrFKfNxEG0qFSQ2hMOBdlJUgoUPyCC51xVNfSREBw/aZiS4T5vud/WZzZky7gfCTFyEQU59azWhidnmtuhYPKeRoJUGpEyW3dH9vDe04xRFpb1dS21Yhwkdt1xXEBaSWJDSZkwdYhskSrx1lPBGHcQB5YnhuXRiDs4mjcjVizJYouMkuVJRlxLPn6RQEvC5NjctbzzhmTcZbZ3r/D5iPWFJU0jthecthvo9txv32HUEZkCHzzpMGrP4AoLu2Aqic52XO0O1cIkFPsSWTYFlS2b0bEXDclXG8ZQWqYoGUxBDBNnAoKbOG2XYAp70fHtG8l5F5GuwfvITkIZ4VJawo0ltoZmqu+vldRMzhOZ+FHuWZRMGx2FQlB9DTVwEwiFRrAqESkk21zVrlZKTEm4OcThkDJagMwSqTJWGqYcaEQiG4GMEh8TUlgmfrqN3c9Ep/jf/c9/u9z4SPCSrZbYovm8WFSJNCS0lJzIUD+8RXMjFFlUioEg0gnwCAKitucFisg0XjN2gW50PC+ZbBu6tCMWjXRbWm1olMLHhCbz1nlPb/VjLNIjYUdVy0UohRIcUlmEUJyvWv7Hjw7cqyVnOvGyNFAiimrLQBR+UWV+6bwwhsBv3qwqBUaM/JU3G5brBf/pd/fsteJX+sAvnzTsYmKtaq6fMoqX9wcmFznrDV9564SlbfEUPr/ecT96Prvc8fU31xUaPgtMzJwY31jDspG4CEbAoquq2PspPyo/a2Er+BkOrhWcrlpIGWMMPlVBi5bVr/dHnzv+iw8PHIg0RT4Wx4cx5sPvH9JCasRXVX6aeawccx1PRgoxF8y8EhDzii49dKAP9gmqkV/N5B0hBAFmX+Hr0eZsbyWLOSWFOlFA5EcLSI2MeijCFVytCoS5zXx4Hh4EQsCj3UYjCCnOwPFaTEUR/PV/60/++PS3/s3fKB5Jyo4UK9QipomjtkGliTwo1ErjE0xF4QeHB7yPs/BIUlIiR4NoEjqDT4qgQIVCJyJBC47mgPDlvHvubaI3BR0gd4aj7Dnp6556OxyqUnH27x62B85Pqlk8RMPmTnLvE5PuOW8SVu153gi6boHsBJu94/Zqz9Fa47ym6eDZRcQdLAjH9cbSSzAmsVhKKB1ZJi4/u+eNb6xwhy3NN5fEm4CODWwTeXmCGwopC5ZtYb+9xadjjp45VG64/fie07fPgW393A2QysCQA/1xg7xZIIrmxed7EifAxHrZk+ItJ6sGMAybLS4OnCx6PrwJ7NOSKC2tDby99PjQkoXk5VWs3RWmxpwVT8qapW6Y0jgHIlhGEiVI2hbupsBCCiDiQwGjMPNULuR6yDw/Nnx659gdMklIfKzvijNbhWsHvyfRIXRGSkEvDWMKJCHoVSJFgcuaKXtA00qJkpk4ZZSucAVUtVBJBC7V/yepmtoRs8STiWH2DM9sXZ/rsimKAkXhUiRFhdCZf+8P//DL1Sn+cAcfaM/61KJEYV8G8nDMPgjarDgzgRWO80bjTOb/HhaEMvtcSsaUjMRATjVjryR+wRaeHWVOuvqCXORjXroDv7s94am/4WS9IOaZ5ZgVrTa8uB8fjfamFKw1IAU5O1ZG8OS45we3npQdz46WOJf4l97t+N6La7Z6RQmFr5bA6bHlf912/GvrLXsF20GSleA3ujt+Z2iRwfLJsOdrpy1/cXWPFS3HF2sWSvD+heF6XxWRmzFxsWppzw1GWTpdC7QfB4wqnPQNi+eKFCGEwD5F2qYmngshGHzGGktEcNo3rJYtMUasSWzHgEvgnKMAIUSMMSyaDiklx6sFw1DjXG6GPefHa1ZK8d7bll+5yvzOdsDImQ+aa3eYZMEyZyHmUoNcS6kdn5CUx1QRiSwRKSpaT/OaNhQKNA/bQanIc2pFyXOQtKmHFlsqkEHBfI9e5z02vIY1PH7CJZRSPU0PCu5aXMv/byLiFz2vDz5IjUAqQyoZZrqSKF+OPMW+kfRzMo0qijYrUk4EGYhFkK2hSQHTaU7UCLYgrEZkSSMku9ERxgIpkGnZpMzeORbSoC2AZSgRVyYa29Dg6k5baaxJLO3AQkRsK/FxwFrL6cKQhsDZecH7ES/hh9MTxkki3C1vP4XnouXgdhAaSiqcPM2QIOsNq3XPwpyBPeWTz64RRfE3fjjQi2MKE3/+Tx+x+Syh+i1iFdneCiYveeODd8hxT7M6g48jOnk4aaG/Qx5uKINl+ZUJkqHpRvr+gLzVxEXL6XsFyh6OYz2tfT2jxp6+26EuC94e0NcNz7+2BnHF9OoCkSy5bdjuFghxz9Y13E49Hw4TiiW5gMkOBsN1KsTsWUs4bgNSaoQcWR8tgEJvC0qN3Nx4piCwuqBcpHme2BwKK9PgvUDKRLEZaQVNyqyXS3aDAzHxvB+5aOAwws3eEHIkZ89W1NHv0w5ko3FzduIuD6y0pmTFNI1IaWlVopVQciGXRAqRLCorWUpJ8h6lLEJJbEkUA0prhuBoqZqDZBqSSHVUHSeKAJ8rrjELaLRANIrwUxba/EwUxSgKH9/u+FOL05qKEQzfUAPbEivAO0SMEoylYIvnzy12/GAAmSKLPJFzxkXJrbIUUXBW82IKvNsWUpIMzrMt17TW8m65YhKG+yA4Noq7/QFlDUOBwxges/ZaqShTorEa7z2hM6x6ULpQouZ2cHgpGe9GEIql3/M13fGnLiRFen59s2G5WLEoBXpBSvAPdoqiLT9nBj69nViJa3y2CCG4e3VHs+poT56i9D0lSdx0gFKw1vLq5galj3liYTd6XICcIyFFrJG4LKpU3lf1pZSSEiILqzhatIQUCb4ACmQFZPsp4EONmcpSYUlImWmUISfQymKyo5EtKknGAsJH/qnTPX9wqJDwbzaZv+ctNnkckqXIuAReFjoBU5HVJE+q3j4KkkLSNWOyUdV+kWRVmFLq6bR2m+WR9VdEqskdJdcHBoFVDzFVFdIgZv9prHbJx6sUWVMZZpO5Eq8DgtVDl1nzUxCzeKruemeeLXNyiqz5lDKDFpqKR/9yeDKe92kWMkSUlBgkVntSqBhAYUey6vj8ReSjy0jYW0bjKUjafE8wx8SYUSXS2SqSW64l05jYxUgjJ3rd0gPvHUeCXjKGLQ2WKUc2U8NgBduNJSkBN4ooEmNuOVwppGi4UI5cIu9fCO5Fx6cvBtplQQlLSIJYzvjoBw4UnNlnpJjZyQOr5oo33+o46R2nZxGhDoSSubsfEGeZXV5xfT/Q6IQ0guu7SxrV0bYa05k6ZvEJlgtYazobwDRwWjCig7KAbOAg+fGPItpE2o8s61Yivz+i+4I6E6RuhKtMHDPWgHct03iNUJ7gl6S8IbiAaOCobHnn/RXRHdC6Jw2aEgZ8kIwu0LWJki262VHigvbsc/KmI0wjwnQcnSTOZKX7ZGdwPnO+MLyxHh+j8Yq0gGe3S7iw5/R4zWYTuN0JKIqbgyPqlt7CNsAqF1QDrTpl8I6DglNdOC+WHY6YE50qqBiRCsYocWVCqgYlFc2stQjFU9rKTJYpVvSl0vjkOG5qTF3rCk6GartLiYjE54LSGZMtXgS6IojSI/SX0JLxJgfSUcsnd2Md5WVJZxMnXcvFseU71yObaOi0hNjR5sQbKrD3ob6SpIS2w+ZSyTHbPesOfnTr6WRm0Vk6I5lGx/OjBVtfeHm7J6AxRnE7RIL3SFVIc2DlhEcpxW50SClppOXV3Y71omU7ZabJk3I124WSeIHh60RSkBir+OZba7RUHFwkx6qi/Ma5ZXFzx3knKKLh2hVEDEQp2U8eSmJ5p2mN5sVmy+2QefNogVKRpmmYxshGHZh85no7sj2M9J1lScPeT/VniZ4cE23bknPm5WYiF8GzoxYXPKEI7nZ7ximSS8EYTdtU9d1m8Gg9UXJE60BKicMwsVz23Gx3vLgdePN8zXfuImdAbxuMTvwLfWBpC3/0YsMWwwenmr99GzkVll46Pi6Kjay7zborrDzSKJn3haJaKeYR7EPSPSU9Wl1KmilFD59/kUlF1aJUAPHaOypkeZ0AUmrBnL8NMvMIEZiJfxTqtlCWPAPFBbJEmhT4lXePWYyZ37qOpOJB1IKbROUulS9Jp/iV0x37bEg5V77sOHGzk/jDAa0UIig8A2dJ8PxZ4rDfkqWiayzbsMKkYWZkDpilIgxUE/6UGErDYVyzDZGUPZ/fw0pseGe1JotbfrgXHHLiemcoClal7helzmgtWalYR+0lsmgyV5Oq+9zWINTEab/gZhtZdA65iqSU2Bw83VHLu8JSmolIxuWJ7tSQxUCvLXlzQLWKkPakVmDbRE4GP/UUelLegekQwgA9m889281AjEtuXOFUF9brNdvtDiM9Iuzo+iPy4NgEB2HJegnoO9ADygsUHcjE7XUkJgFGkoeOKWca3ZBL4Xwh8XrNi08GlisLxYEcWawN+ZAwWtAtK/EpTC1JC/a3J5Q0gBGMYaLxhrEEViuQbRUeYSRMCWkqlOJuv6nvtu6UuIvc3U08uzjjZjuwjYamMyx1Zrsb6VXDIVUBnF8c0C08FRafE6KxqLFASmRZBTed6dAaYvAoGnwa0EaT0jwCzQkXqjYgZ0EJoYJAUv13kUqRc00DKikTswY5Z8SKAlj8zOr9aQM0fiaK4jBFtspwuRn42ukxFkcrJZ8NERc9u2RZx4ETJEbssb1iGCI0lu1mjzSGhoEmB8aUGNtjXjbHPFGfsb0PyOBZqw6rEmM4cOckVkXc3NL/4vMlN4fCx9s9QgjGIaCEoMuRrhU8OTmikXV2//m2EEloDBTIfsTbJVpbbqYRUSLrriGlsYJWcqZrm1kQMnHWQvKFnxwmNIXTtsHhMUqjlKSEiRtvGKPk89sBVTKdXHI3eUJO7EJgHBzX220dpQbJECOtqXN9VSRd33LWK15uM1Z5NofCslMsG83oBrZjqFHruWAFFGkgJdaNJvrE7ZSAupPwKbOfNhhjyErx/c/veNpFvikkIXtkcqwViNyhJZjpQM5LftlO3KfA1eB5U2psiYSk8KZBes+0OELnQoq+FjRVGaWlFMys/CxCPKrPENUGImeHi6BiyXjY7fE62LjldWSXVFXtKmePpFCCmTpQR6xzGLWaR6ImJS7sjpdhxdc6yTeXsFgJ/p/bPdel7rGVBEWqKLQvtqR/gq/NXpFlYBw8C215IjTHxwF5rAijZzfYejCzLXfXB6ac2B8Uh/tCQyRnAYxIYRG3gkZJmt4yjiPnjaKNexZ9wiRNUgnlCofDDmfgWCXOF4avH1vGwwgiYIXikPc0FM7WHSIFdqPD6InJJVamR9qEitccyXu0EtisOX+yYEySaHbEeMfJGxe4w0CzEmBOybsrjF0zbreQLGaYwK8pTcYNiu6ZZnp5QKiRQ5iwG8HtVSQ1ER8diEAYA1990nN9eYcyI8+aPVPsSaslDROcSp7EDp/B2ppVmEWPbALJgJITp6VQYkNRFi890SZC0nReMV07UpdIpiF7i8sHurDmflfYu4Qzls0hoU3GioQvAyorDGuyUyzOF/zk41cMk+ZoWzMuF11g0Upudxk/CrplxOanBAttgrxqIRs+2R0Ih2qbeFUidzvD4C1NVERhyNrjh1lYlx29BJcindGEUGY9m6qH3OxJpaPIgIwanSMCTcqKrA2FSEkSoSVumjDKsVa2gthVql7kVIU3oUvoXMEeqIzPgUZpcpK48iUk2si2IW73tElweV8LQWsip4sV4zjyZqsRssX7ejJIPlZihdtiTMMUAkYrOmv5+NWBp0w8sVco0eDXhh/ej1zeXXFxukRLyXvHDWfrFQuj6K1hDJn9YYNVcDNEQgItBcvVEqUzKRYGKRnHgTdOesZouDk4xlBYNA0LmTiSgb3RXE8Rl+scfJpqaz9tdwghWPQtLivuc6Kk6nHbxYkjY3i+lhwtWm5GCKmekr920REL3I+RwVWUWp8LLiaO+o4YIyUHTGdpGoWImeDr95ZS0omEEQ3TFPjoxYbeDgRyjeHJuZ44O8PtbppTzyvlP5ZIKeDchM8FozTbw8SYq89x8Iqdg02YeCIDL5NGq4FGFDYZfvRqQEu4niYm1XCPIKueZZPYpIw2BuP3TNPEmCSlazC2Icf0iL0ps8jp8SpzmuM8rdRZgKwPg8p1b/ywB4wlw9x1ylRQspBnR2jtHOe9scjkeZEfckEJQYvkzyzW/PA2E3LiJxvPiUwMxb5+WATowizm+XIUxcvrhFhA155zeTjwMhZOiqDTS1QHjXQ0jQFGnjyJSGm5u5lwsXaRUir8ZDlME03TIItHUGiXhlf7iMuatAOKZCUUuSS0siyyJ6DpjUKkzMnRgrtxpAg4kZbF0tA2kkZbnp1ZaAzya0/AfBeChngK+0y7BbE/YXtZuN0NCJ5gOsPNi4mYTlA3kimOKNUiZSbnnsZIFAbhd6hG8mRdYCNoTz1CwmKZYFtYPZdwPMAOcD2kEYTj/F2FyBtyFrTlJfFpRGwFzVGHPJ3odi3umaL5CsguwGhQ35GUjSMWjWlbxJVD3Y+0f+6c8vu3jDtP/74hS3C/94KgYP2mxL73FnLRcfxpQrRXhLXBdob0ecLfOWQQNEc9sAc98sHK8qPvBQ6lMLolP5l2TCxhKiglSeNTYg5YDnSN4ZB7WhlpMTgy2ykgk6CTIx2B2FcFXK8lYrbFaQ0WSSySqXhihF4XUkxMpSCTJKt7VLZkK0lIcgYXAjnVbNIsarRba0SFAZSI7HpEyjSNIcZqp5JCgc6UUjUFR9bgiUgp8V9GzFtnazu8D44cHUul6axiTI7OGLyLVdxQJHcHh/ceIRQBTc4HWqO4WBuETPzZiyMOuhCjY/QaIwO//v4Jl2dLwhRoG8Wd89hBEEwgIRicRxjNqlMMweOZsOs1yMJu8FzuIheNJZuGH146Vp3Eh0yWgskl9JRpm0BG4X1mChMSyT4nwqGSG3pZEXU/vrrHtj3jOKKUYRwSqRNoq3i5v6+sTiEwquBjFdbcy4kiMnf7Lc9PT8g5kouqdggEuykxBEejIBbY3N4zuQU/ebUlzdFL22FCSk1vFR88WdO0hhAzV/cHppiZfCZtJ/Jsn5jGxJgjOVURj4u1eGgta/fmMmspUAjuxshViEymwwvFGD3PKaj1CZTCMzciObAsiq8a8FHy7UNk0AqzbOtINZfHNHaoS/cHm0VKdRcRS/23qT7IWtxqnNX8K7XbKyWjpABq6HEpNS+wAr1reodBkmbGLqVgFXQ+8vbS8KPtwB7FG0YwHgYOFIpWtLk+gAIgCs46w00c/9E8ND/laxcN69zhosGev4UTA9/5dIcqoJpIszqlVS33Vzc8OVsy3gp83EIoTEUwDZkhZtaiIYRUBWsOGh1YrwVTyMgQaFXkkA3kXLsODGeNZH2yY7VeMG5Gjs6qwnfV9qQkyLMqefPigNYN7fVLXOgYh4im7tw7vebjzxxCDejFU/LuFc8uekTp+ORVYDNlhs2OdgnrdsU7Zy0uXiJzQ9QHbm8HNuIZ7RSQOuCTZfH/cvcuoZatW57Xb4zv++Zjrf2IiBPnnPvIe/PezMoUEbFMwY4gpYgNbamNKgs7dgS7llC2RNCOYEexIwoqksAi6AAAIABJREFUBTZsaUMRpaRsmBRWlmamYl4rKysf995z7nlExH6tNef8HmPY+ObacTI7Ihyoe8+EIM6JYK/Ye605vzHGf/wfP5v4/PyO248j05fK8m3h+OFA+JMC8cx5vOb4y6/Qj8C/vCL+1p9Qt4n1/IrD3TVsGX73ie3a0Bc3pI8H2u++I9wpGUU/Hgm3gXR8Rf1fNny7IVlm+eREnirTr/2Qu0/O5L/zGR+sZx4fP0PSK771YmYoCb97YHuXOaOUkgmr8MknmZWR19EZxsRrRtKHldPTwLlmuJkYdWTbnjDXfobUzEe1ILqidoUMSpwfub4ZGFsEaVjrnqXZVvK2WzKOE9PYkZplC52LYJBz5uQDb6vwtPQs0iFkkgi1OuM8UtxpW0XmgbhWmgbWWjBm1txXVqs1VlNElNgy1gJGxlV4WAs3c2BdV/LXvNf/uZBk/LX/+q+7qeyH90LeOtS11cpxHngsgVYWztk4Ne/sw9AZTq9uBwYXlq3CcES0oRr57kFY1spSMhYHfv/zE9+ZIYwDPzs5r19OXFthxHi3bPzw4xv+9icnzjnx8Yc3MMJ12SitcfdQGFJA5pmnu41SV4Z54NWgvCuN65fXFFckdQureH7i2grHQbnPjfXqmtGE6/xI1MRd3lhW54unQjWjtcaHVwO//CLx6qq7afzJuxPnpfGAE7Lz8nZgDBHf5QV45H5ZwYzjYTe/duG05T0aKXB/3rp5gFS2KsQk3KSJ77xIfHA9stXCw2rkqiyt0bbMU+mZiR8MI3eeOaYR6CHDxQ0nchMcDwmlcS6NL85ClkoKA//496/46ZPz0zcLbctka6TqPEnltBaur6/RQWjDvAc1AzHRFMQaGuB7Ivx+BrwhGokGRd53g1/VJLr3cOMuvO9TsnzFNMAuhgHWjcJFBG87o1W0M9nMei5i3niVlFqgeOFbN4nXc+IHLyd+9Hbjp1thPQv/6EeBH31+4koDS3b+g3/zX/yFHxc//df/glfdME20NjJI4RbhQSpxKOCJbdt20/aJbdtoccOqsFRYzsaoA9ADt2+GrZtKROfq0Ggu5M3BR2R0tGwc58ghzUROFIZOqNi2HgIulTEIHisSIo9vV44BHooy6YHhRe4m7XVGRNiyY1qYJKI58/ZJ+OLhxPF25sPXQpDCdl6YPp65/9kj1gKjOXwQuTne4vM9siWajmwRDoeVKpGYZtY7Y3wD22dvmeaR8+uBtDyQfzhyfHkF6VtQn/A7YTt9zvT6ET7+Lk0H5G+94e1v3vFyvCEcr3nKgUUykzUW/4Bkn/M4GrMowzQzpYHxeIKPRniK3P30M+wcOKQj00dHPv30nsUjeTPO5zPD8BKvX1DDyBgSr65mzk8nRh1YbCPFRimFJMq3vqNsYlwNB949BJasPNxlCNfEsRBy4qFtbAZIAQ/dJEMSYs7Zuu7USt19ilt35kL6NiY4W+vOPEAPFbZCkRGRjbxKz6eVPjGGVsnNybUbzUsccOlSC5FEbY1LlmsrRhUhSd8rRvoao69b4C/+5u99sxxt/sp/9N/6zfWhm8yW/iECDMPA7dXAtmSujxPbsrHszESrhVqNyszSClvr+53jcWQKCrWyVWfZtTHDFFiXyjRNzIeRP3pzT/JAXDPmiasZNm+8PBw4Tk5tTmmQPfLmbuEwB65vRsoqPNaNqxdHpPWHLbh1zV4QjtZ4agY10SgMU8KnhJ/7hPvmqTDFgfvTmW3bujOMCK05xY1v3U4kd2qAkBKeYW2Fx9NK0kC1RhClGRRXUnCsle6uIkJQIftupNZ64v3V9YSm0K3hlky1xnrKbCXzvY9f0kR5Op24PV7xyRfvKM05TJGkgbVkUgyklHh1GFBxBoXzZgwpcCqN4JUVJSCEYey2TLkxaCBbI6vzcgiUqrTrifN5g2Ekv7mnmCPXI3EeGYDsyrie+ZWj8Ic1MGjgbaiMJT0HB/9ZRxrDEe8xUd2/9P3Eefn9zxZFVQVvz3rDHi/WC+rT48rtoFxNAwcvfPdV94p8NR34Pz974FsvIuW+8Ftvu5zjP/mr/8IvfFH8zX/ln/G6niiuOMoHs7KxcTsXykmJYdutFwda6OzmLx8TN8PKfLzF5UwSGIOylMzVcGCITi5nXrwUYnwCzXiJnDflOL6CtrLqPYNdU853jPMMLyJIgvAaPorgV5zCzGgzRe4ZdOZeJkbzXceckRDw1hBNdPZUplBJ9YDnLxBLPQh7bVTbiBHcJgqVQRUXRfKZsji5bqRiDDcRilCtEMfurtK00qwyyMp2UxiuXiEfgaeMb1+SX37A+GTUv1soP/17HP7CP0D5IhB+41fQ8wqfPMEPr+HpoWN0f/Mn1F+eidu3qVeZeD5QfvwJd//bF9xc/4D7CsuSeTlN3Hyvsv6sMb0w0ABxhVphOGCHRnmXSWOlbjDIxKd//AVXLz7g+nWgrCspJH78R5Wnp5Fzu+ZeK0tdoL0gXPgDw4puNzSvu7Z3oGBYaEgz3IQwgG+52zWKIqp4bUgQxAVXoRTnvFVqSMRWGKUQRzg8DWypkhTElLYtaJg5i3POG8LARme519pdvsqeduMtY8VQuj+1SddkmhlpgL/6W1+fo83PRVH8t//a/+wWjF+fDjy1zLcnhxj4YoWffPLAGI1vvZxBI5IC3xsbx2jYMOGtcM7CKhNfns/8YJ65Q/jp08LbCGmLPROPi4+lPPtfVpwU3qe0y848BN4ftoT3tmya+vLYwEOE3WDadheXDuUp5cKCLA07Fw7RqNvKIQgtN14ej2SMR8t8IJEiylIbi0fMYGKjScQFUkrUbeXbrw78+O07vM3UlpnnbvJrZvy51wc+KROiDarRQi8msvWbfSkZhoEYI9vamKlUb8wEPt2EUQpXKfFUGrluvBomzjQO08SyNbxtfDAORO0+kp88Lh0mRrgdhD96iBzDyhRmVAon6yzeEvqE2QSuYiKEhc+LkhZhCJV/7PsvefdY+XtFSOps1s2HrcGtV/78B4Hf/tmJp6srSm5UcaI1qgbqUoihf5ZC6DZiUaA0NE7kunXThWqMapxL/6xmNWaBQ4SX48Djeqa48uSCemEk8X+9OfNqGrkauoZKRHh9NVKWzF3OHFPkKiX++LGhVvmP/61/6Re+KP53f/Gf9GPorETdnmBK3X6LzCQHHsqZwzgxToUxCJtVZosM0Xj71O9Hc+V+GTmtC9UNGZTbK+X2KnFQ+PjlzPVtRAaBOHfCjejOnrIe+YWAx+6SBF2a5IUmEfyiYA3U6DSXPUjakao0WYFAql3+EyKktnKItTM4zxu2rMiFmGHOGjODBEQb29oY155G4y1gGpDaz4rZB/QYebivpFiI37ohpVs8PmEqWN04S+FqTISh4aGHEnO/QN7P2KXLNp7C5yCJK1For2F5gC+eWK4yc32Bv/qILwl8+I9M1IcHlh9F3p0roY0sXlFToi+MaeApL/zKd14grwb+4P/4kvvSGFyZ0g1tcN6cTmQzSkmoxr5v7yMW5vvzWfv73Voj7raQdTWy9L3+2S4Zo5mpCV4b1irRlWMsXbI2RIbc0aJuDOJIHPs5a53IFn03YbCey9paR3Y279aX0QMuSiMRfSOboQScSpZI9drXLO6YJkrj2dT/r/z21zcp/lzsFBOG5RUZhKsY+Ml55YWO3B4C6+2AxMDVEMCdSeHLrbLUAXIlDIrpwBd3C7dXA5+eN2IS1gIfhZk32yPX88TJFWmFLUUG6wzFReQZXnPvD1mj7/HMKuqBQt3d2CN4poh0T1Yr1JCAblfUL8dpTDtDpIZAPDTuz5VAgjGRsa7H00TQwLtaKGZoTGCVexvJAqMqVzFjCN+6mqAWDmHm5csjzQ+8iILpFVdj4PPHTCoPXE9XeGjEQXnzdObVNHFXNg7zxFA32ub87NSo9sj3Xh5ZJPE6OA9L47E0Ru0p5efWsOAs24aZ8rTA01r4zouBH9+tTMPAHSslG2/Mmb1QiaxtYcndci7FmVYd00ByQULg43Hio0G4GxofzSNWKi5GzI2gu82cOkkSd2vloXQXfM8VHCZ3fjgJhIG/m7sfas2VX70RRo38wRq4ChsfTZnZhd9+Am+Nx9pZ2601msM9ThLjy4czV0Mkm3GqgmrkpIXf+PCan57viUWRENgs8sm7M605a4Nzce505bR2AfE34fr+bcOrkQantchc73AZuGfk4f6Rj28HxuCgAw/LykEDW+02ZR9dXSFaMBFepRPpQ2MeJ4apEYeV4WagRUGDIaHhagiNoNrti1oDBqQ1shlrUdwD1TsJ47wZqgMqp2fZTi+YABGRRrIz3zlq3+Odd99dM1wXNnO2fOZ4PLI8bVyNN3D1klUfmI+3cP1AfsrMH06wTIyywZghHVj+eGO8iWgd4XrgIBVdwU9vgE+RWuAHR9I/9G1uP4JmX+KPkVqc9PJAizdI+AINEVs26p+8IvwPA/O7j0C75jm3G+rLG0yUOxXandNC5rO/0f14ixvuQvEVRCnurCQeVnCZ+Z1PNtqfFMwHPMFanIe6UpeCx0SsCwcVNDm/fHUmxZXbV11j+/bzQDMle+Ph/sTrm2v8BD+6K9QhMU0TUgt3JfLGK+qNqSpVEs1XthY564xmp/qENaPWSmqB0JRBM9iB5pWmBW9O0sCEYKExRlgu2afaGIoRR0MtUlXY1DHrhhuldQmWaeTsXVJW6baeX+f1czEp/nv/2X/vPswMmjmIEjx0LUo7scpEiIpZZLZMHI2HJXAVoMSE4hzVeKpGimN/vrQ7pSz0FOdT7fDanAKfV+EAHHDeiD6L1lSVgwlX6rypC2YDFvvk5rlisTJIwEPCakOtUcaRQEOb4LF3WlceOLWyk0MC6nCIA2dpaG6UVnk9jTxQWZ4qURqNARdjrUZUZWmV2YUPjsJpbQwKhxFuB6fUgZspUMzxEBl95V0dOTk8nBu/dDtzerpjI3boMPSkD7XW7awY+L2HwouxQ12miYdlQ8yJ2qOljqOyWaWtmZNFRDskejUK1Tc+mo683SpL6ekT9wIvY+T6EPjZQ7eLa2o9IkhAgjIprC3x4dSnwvXpkYWR7x4rabrh04eVU6uUrYIkpnHk+2Ph7yxOTI3cIm2r/Llj4id5YxVFWp/ub1RIEtjshDNQslGrUcZuvTUAWbsJw0DvhAd1bkz5/fPKWpzDCC+nQIiNu03581fKp9U5FPiiOdUqQQLZhFAz1QdmGps7/9W/+y//wlfGt//hP+/BEndaKRlmhCkqzcHahrALr4mE1J+X6obGgJAIF8QlNsYAQ4io574bFIHggO5h03suihneNkyG7nsbepC02oi3tRcihGKJpRpm0CzQanc1AWPdCsMwEMfuTGXW9aZivv9bgqr3lJjYM/i8OOKFWjKJwLz7C3aHq4yQAOsko7ovvk1p80LIDQ4Gc4ZXAcIKVyNsn8B5xi1CUaQmqELLGV0HpCRoBS8D0iK0ChbAHKuXzFKh1b6TawqlOda6/VlrPRFCmpElPa8KzKAFITdFvOGSaCzU4ghj92A2w0KF1meg4g2vkUAntKQU+NYUoBkf3p4YZOY+CJ/+2PhsWfkHX0+ctsb//fbMpBO/pM4y3+LbPRYaQ3VOfk3RxmEp+FDJbWA8bJwfBNeZcS7cJeN812ht5IOjsS2JLzFuxahB2MLIGecqbxBGasskdwhd0N8yTJZp0r13zYxNE6EYf/l//X++WfDpv/Gf/k9+S6K4IfI+xcCiUh3mXbGdgzF57iSc/aCW8H7YHSR0Xzx6ssHlcoH9KWJQwUOPSXKNJCoZkNZz/SQJslV892ZUjbveLVLyBqGbQ4cgSOiFUBHQDgcgfaK1PVuw+Z/egYHhFlGpKH0i9K3g2n0+3Z2lNupO+5+ScB0bU4TPThXSgbUaxwBXU6QBp+xoNVpwMjA0eoaited/m/3nuJBUFOOakewNHfuB0PV+AaMbhwNMUal7IdlKJ8KM2rF8w7AmnFvu0TBbJfTVOMUaTTvzNXrY9VSh70A9EPX9PrCU3QjY6DuMS7am9ADi1hopRuYUqK07ZPT3vWcr1toIYtSL+40Z6k416UkarZujz2nAq3Eq9izfSHLRLDaUyOaNASVKpe3Qubh1K7sL23V3wbnA6v/Nv/OXfuGL4k/+y7/s0/FIaI5L6Q5HoVuK9TT0uu9vAlY7bI9MuBXUu5wn0sO/I4bvZuoh7i5KUTHrBKda+mfk7kRR3CJJwT1TSt/Rd38h7UWsOk0C1noEWauKZaOURt0hNOqeTKPdg/Vi/Yc5SRT2Z0EDBG+MQZniBgk49B2Zh4qk1okijwmpA9kKYVbCr0cICZaAPTyhP26QHGqCeAeh4SPIoYIUPDriEZLjFMgRTgnJgq2KLANSItiC1RFtTg8aDWSsF3hpuHU0SkLffQcFIWH1cv9pZ4+XLhGqLZC9ge9N8Z7ogZfu1GT9nC0eyI3dHlH7Dq851XdJU+rG7i7g7UzziBfHJBP1BcLGKNYbI4eVypU7p6pcHwUPkWOL3OVK0w2viTQ5dTFqUa6jM8+JZoVNhJdp5s3pgYMmHmisxTgOAzn3mDYvmSQBJLGUhgbHTbkeGo+r85e+xqL4cwGfVlc+97Lv5IzgPVYk7DeyacNUSCY8SI9wD6136dLqbt3VLYVgj/i5WF/uB+9eE9lahGcsWtisUaMQBDQI1XPvDF374nc31qV0a6/mIGLd+cJ79xtCAOsF0S7mXzGSc90DcfsDK613PexdTlTYWmGMRvCR4FvPPEyC5UoI/WD/soC3gKQB9z6JVYX70g+ntqfH4+BEXOHsrTtKEJFqpNQLYrOGWySK8yW1H4L1Mi1HgsEgTkgN80ATRaSTljRGDCebY+bdy1A767CJEoe+g+3m3YkAzybcm0dKKb3AqhEssGGoSS8+5pgI4gENnWjlAqV07WKlca499d7r2icVq/v+QqitTxn9fnnOdcaaPDcDF5LSJdiitUb+iqG5eyd3F+hG1lae4TqUzuqVbq9HUGTr5uPfhOujjw/9vg2ADXvKimDWiQ21XQzQQebpuWlRjbv5e6DWjDUjI+SldKH2ZrSSe4EqgoaGSAQu9nyCeofceyPZ/Y+hQ95Co1VA1j6FAYgQ59Cdi7ZCs4wJSBvIW+5uNxKfmYmNnngSHKxWJAq5ZcoWGHbeQUgJOd7DsOHWmyctiqqgZcV+1J/htjmhhG6FVBzi1otoalDATyNCRzGQCmpIChDBD++w64TYiOc7/BTQHNAtw3nEqqOWGFwhbGAKbEDt3oUS8BqoeiamHktnvpDCxPXUQDIqlyO9Idb2zxFyGTllKMvK8WqieoDkvAhnvvwss02d5/DhrPy9LxdeJMOHwtISpTgeGnU4sskG9YmkB2KC87qwNJg1EmNktIqWlYezs9V+Hp91wvI9sYxIGWGofFF2S8Xam5+f+MqtTdyJc6eGeuDtkzFEYa3dllICaD7hph1Wlkh7Mly/3jL2c1EU/8e//rsE7W4hHhpx/2B7PmHFa2MYBvRC+TYDyXTr535Vs2ext3jfC4oISWsXbFtPfVAEC93ncwgDRcCt7J58O9RgfVpRjbAXoS5t2PoE2RRTJcjQJ0735116RBjUdzuinh6/2XtDadsP4BS6ak6a9VxFnCLvu1l8X4rTjazLHpA71r5TmDWyeqOZPhce9u48atrTKXg24d5SYNg6fGVRca2MbnsxkF3A3lDSTl7pDvcq3XS9tXbhP0B1Si95tL37COz2S/I+USJfnGlcqLvFm5ntyRf974obetEL7p/dZZqtO/GieH896F1s9O6eHy8TG53cFPz9fri/b1/5fZ8MxXpn25RnB/5+U+3oxE7IGry/ZtohP3fv5A2nQ3PdWONPGwz8Al/18YkUIi4rQiBoRrT1xBkzlOH9e+UFLD4nyOAV6EkyOhjWFL2SPZZLGMLcxaI+Y/YW80iUQCuFbVGsnKhbZMknnvI9UTvTOKEgAd2bytZWYnJSmpGYGcbA8aAgEa1D9wqG3V3HUBnAhFw3BunwrhAZgiIyEKQ/61SjbAXOE/L5AaAbvTtE6TpUN0EUIiMec79RY3eGkglItR/cqdCkELxLoZCIVUOrw3ZEL76+HrHrAtMZRoHziN4Db67grLDFfoPljAXBcn+Omhh1M7CMp4khVNLwiIYRKB0Z8boLJRK4YqUwx8yYKusUcM8EiSxn+NwjbUw0O2Et8sWSmIfEyaBqIgxQPVPagRBX5hiJLROHgAflcJNoEmjrQA5Gmq85PXzBwQp3HjkOE6VsWOqxd8MotKaM0TnnwuiQrTHWe85hZoqR13umaQ6FGBKjBIpBIXEKkWRGBmppmAj2TRTv/+TtG+yy2xPvBYj9MHNBY3gmxFx+mTWkvk9ddw175l2/0n5gXg5J3V+/K1+UKI4ERRFS7A8drkQusJ7ubi47dLvvKIobGobuzFDLs3uM7gVMJPSHQ4TBW9d17WeJqhL2vV2UwqiRhjDKDjPq7o+yF0Dd3d9jXy/3n93hECBVJQQhac8bgz3tO8CkXXKQohDEewc3dLmBaiCI7vrIS2HqK46lGmvJrJtxao2SN05bZXEh58ziXbbQqlE8om5sVp8LxuXqTcWejLF/hrL/uaoSzKlf+XwMe/7vrwYXX9jAkgJstQfYmveiSNdT9gYpEKx7nl6+9qtrARF5jpVqNBRo+5rrWbKxF91LUVz372Vrhu1pH5392l/H6V9/MZD/Rb8efvcP2WpCtALCoBXR3sXnpmD63jQBpxZn9RGTzBAD7rpDrKCS9nSTfq97M4J4h/MtIgkuUdO1KEgmxchmG8EnxLedTLOQpFsyqiqGMkyNc12IPtI07wiP8XI+4FuXjcQhEAeoZSOpME0CpdBaoW5KjReYtcctJe8cBNH3U7+59XQUvTRrfvEKRLR06FQrjAFuzjD23TnX94TR4GbFtwH/UuDNDS1XtE2wan8tSejT1ElGErsJcLau04wKx4zbiuhLzDKE0G86M1T2YiW91zht475PPYIHTq1hprQKTRO1JraiYAu19UHCpHFuuyGGJVwmsp/AlNgGzDvKZR4p7RoGo1X4bkyU+ZZ3ayEgPOSGqXKwM8Gdd3df4ltC08BqhZrXTnBsThFHtkISmKrRUd7EMGRiuKEyspmRRFjXlRKFtGTcAmkc8DVzI7AGZ6iBGJUmMH4Tbd62/Pj+UJVLUA8dLnAnVP1Thxx04TVAEH3Wpl0KZKv+7Ekpl4NvL7Rhf6gTilQBMaz2hzc039lejeSCSnyOLmoenjMILXdYoGDEKnue3x4lJNIjjfbh5PI9XQrCGBTzzKAd8g1Bibv0SHXH/60iJIqVPfNQug4RCHsxCHS5RsAR8a4JDM48JKrQ90EeOlrbCmt5//4k7T9L3SelWjsEm7fGU4WnLbM1WHPjnAtmwmZCwdjKPuGWRgv6DD1e8pe6ZpDnn/n589rLjrW+6O8fteD2lSzGr0yL0NfA4t1/tX+OdGKCCIhz4WtU77FRofR7ou1k4IsBuXujXG6prxTCCs9eGCb2PAECz0X0spe5XObgKjvzDb526tvfp2sAgjaQBm60LYIECp201NwxEkU2qvVpLLDQEE6rI9po1hED99J376YUKkhFPdK0oNrQ3KHPVkGDo7Xr0AYp+0TZ0QoVQ7SyrfbcJNatMxWJG8EbcUdlHk5nYurnhS8ZjQLNGGJvZDRuxBgZoiIq/eecAoTWl/BWYdxwH5DQbew4l56OYYIFf4Z1GQpl3JDvZuJLB8m0LSFENG5s2tnUNmeGHwK/9gUswNsEW8LWhp3Ts+i9r2cG9CkQ1giLwKYIVx0KbokQARHSmPo+VhIDAeoRto1ca4c6q1OLUJwuxahl3x02mvXJueeY0mFI6Wuk0ioWJrw0TBz3hkln3ONK3XpM2u8vhpUHWnLMIs02hISURrWGDxNt7eYErTWaQKtC8J512wSsvEeNcjZcDj3T1PtTagZVAmYJ3fkMugRqBWsK0VjdWS9rDxm+1mfh56IoOhW5jFOtYfvSR3Y4sex6QJf3HX2PAXoP32H0igfoJXkWLsZez5l9tkOrRofBcNAQkNrZXxFIQdlypuhGsJ7/16SBOs0LnQAgqBgm4BI6FcitH5oCbSeSJHNQQYOAF5bSC+uA0rRnDTZtNIzBA9vWc+ZcOhSS60aQ2B1EFAZVqMKoSq0rGvv3xyh4FVTpLvlBmeb+oIoag/b9J0AbIs1tx/MBj5y2jVorp+xUC5xKpRTDMdY9vmWrBZHEuTUGg3YxIuX91Nf2JuJSXd6TjBTbMzEuf66qfBX5cBoqkXYppmI0LsSgsO9MG+4gyLOHqarjDUrM778XAhLBfdsDnsJXJtr+ffTGRZ6bKvsqnCp7o0Uvspfp1K0X/kvwsOg3Izrq6gNBYxfuFwfRvk9VcXDdc/tWxJwQ1t7BGSAKrUDU/ut9h7j/fdeOmp3RKZG3hSE53Bz63/kAumG+oDFCOUBtkFe8tY5QPCrb1phKI6XEMLbe0G2CHg1VR1+trAeIMVJzIb1TXE5oSnAtiBVyKSSLLNdGnGbik6FfzvA0w+MIqogLpo6O3VayViPEHU6l9RM7KIkDfH4ErTiNkHYvupgY0b0jrv05cIeiWBEkR7QkxBvmQjCF0vB6oJlRirNV6bmB3g2xzWs3KLBIs9zPlyJkMyqCZZ6lGw2lFu/oRguUvQk070S+Zj0JpGGU1pm2zaH5HhAukWaOmZKlISY0DPdCq4mqnQnrDo7hPlAVvEQgkJdKaII9ed8z70b+J90N/NlXWfv5vGrHDMwzpgmkExarOGOF1RujBJb61M9tD3jpon7YyY36DZwUkfw8JUTtsAiA9HAeVANmBWntudiox+6yru+h1tD2qezP7Hm6j94Ok4k8M9ai6A7vQMmlOyjsrzfE/oBkrdTS8E7/AvqRGkzx1Peg4nXHtvdcInu/W8v0XadqD8kdxxF367uOlglBeny3EAMiAAAgAElEQVSK7OLZ2A1v64Utp0qVDW19xxZTQvfA3BA7Y6wz+4zYFA8LIQoxdjs8aSvDGOgphB3qlGWjNJgP017sKuu67vvUwLIuBO3Fqfb5jhAgNWUphYMKSzKkfoXlJpXW7Dlf8H2Mk7yf1Pf+5bmAtobIVyBwq92f9NKRu/DeIPwy6+3NjVnPN5SuYQrqz0kY/bqQdfbvRzoDlfYVYsy+iw0hQGu4Kk7dHW7Ce2i1OPViUG7vz32/QGnfgOvHnw0030iyv3NBCRYI4bFPfVpxk13CNBAcNDTMY4fw1XGteJtAnxAGLsFcfYM8Ym8DujeufLHuEKZAaNQqTCFhckJLh0vnVAkKL2TgkTN1jHjoqe6t9cJbl0oIgeknVwRZdlj7BciC2EvA0FAQEqMWnMDh8wrunaUaZggP8KK7UDEY0gL4CpKIR4cQkLD0BiBmCAVShDaScyauEZOuz4sBwnWBpwDLAndX4Arrhto1dobazjT9gBoN34SHU+Vx6yQzd++uLnWkVMct4GOG3G0Wox2pNXOWxGlPBZrbFRXnKYBYZNvRNeKZ2oTsxrCfXy0EJBu1RUwiFoRYKpXA5oa3AlEINlEMPG20MvT1QcyseaCmwLlsxApb2zhXZzXnqax4EqQZT1sPFSimxNlJTfF9FWW2WyuGCWe3DvSAxY132x1BrklJSaYQa38PhpkWBq5k6kOUCITAeXvE4jewKIq8381UKejeATTtc14Mgoj1EFl3dI/7ER1x3Wnz1p4PV3Enxv2DrLlrlTzhlD5BSCfhuHRZgdCnQEkR2WnjpXWigJggIVFbD1/tr299aXkpMjv0p0F7wVOAC4Tbw2ovfn01ZxLKWVauxhn3xlqdKEJTYUJwcaZx4LSt3VZKA5r6NBqCd31X1E5QCYJQEI9UX/BNuZoHqiljcFy8s+G0L6ZDCJzXjWnqDMMlZ8ZxZNyc1QJDU+Z55rQVPCrrslBqf2iygYiTXbCyS1Ho7NDWHDDcdwu1C4nBL82NPZN+kJ2sK4LQIVQA/FIk9/dZ7dltKOx/1qSirReo4Iq11s2/bd/5+fsBphc3+gEoHTHArR8EO6TkKpRan/fRqnQyxFfuTxdDfAeAtRNPzC/7sm8G0+YnX56JLFALTW8xcZIXmg/sM8GzExShkUSxFnCxnnnnFW+RJg4cd+j84j8zvP//Hf0J0vM+fUdSXDscaIC77qsIo7UZaRXijAr4jm50WNyh9cbUpCLe92Xd7q31CRdH5Yhb67wCE9g1vIFI8ydUrjoxz7vWT2XoTZ01dP8ZStW+TmkJ1UzcVzuqSrKG7qQ1QiSGkaFWLDR8gA9SwaIiFbZ2YDkJv/NpZnx5JBcnt8jTWjnvJL9tq+TWut9wdv6wbEiF4sIQ7rEi1AoPdSONkdQeyd54J5WBK5IIm3QkaZ5GHpcz02HGcJ6sMIeJpdT+zLjRihOTcK0DGWNrxqAPZFPGsFGZKKUgmjlOL5m0stSMyowRuXs4025nVGeiR6aUuLkZOJ8eyetGbcLDdibGDn03rxiwnZ4Y55EtZ9wbyRPT/Jop3bBuJ9qVckw3tArDGFi9EX03Ha8Fy5VSl56W8jVePxdF0az2Q0d1p1M7EiIhRNw6Db/rBdt7kTANq/V5VyeqmMl71qVvz/CPu2LWpRUalFKW/uHs4t55FCz6s+dfh9R2M21VzA1VaFZJKSG+JzgIXA4M0Z0w8hWHnMtB7uKws17dGudWGFR4zCeOacDcqA1SN0LtmX90KrefN9oYcJzijbxUpmmAXCDG/n54p73PKRB3UhJAphv3Zm/Imp89ZUWEx8dH0jCTc+bxtLBtG2VP3igSqGshHa4YS6Ka4jkzDSNb7mzcIcTnfUitvZFQjZ1GL4JJIcq4k5Z2DVnYGcbuyL7zbWbITqmWfUcQds2kxe6nCr1p6hbkRoi6T2sO0puqXoj76410HapeVskOXrulVEiJ6taZtA6t9ezMy0Rq5vuBfFli7gxXb/gOFyuOpkSwbw7R5t//m9edCWqV0t6RUuSQoOWA0nh0YWwrY2w9x06FYN35KWiH+UQzzkiMkVYXtpLZtBOjBk+MQ2caqsyonEGFYXzJRuKRviL47tw4tRGC4nVAcuEwX3N/f8+LoUeubdvGi1cTmx8Ibmjp8UIiAdnRlxA7xC4yokNi2c6d1V47bDlIxCfni7d9RTK+SNzaQIkNp/JaEm9OK6+PM7kUzgWCdnLH+WxUhc2d63nEN2N8tmUMLKWn2+dWmYZIYGSzygsdedxhw4GIfV5obWPSneGt8HtsXJWZW20MCFEj12lkGiPVGo+t8q6sBBXWw0ANRx40YlGJCscQWRfQqLwYEtvjPccPPuoxZ0F4Ub3DvyYdpVLl1BzXQi0Nce3SMxEO48jT+czTcke6uqZag3TFnRUKhVmdtRbC7YEXVx/20ONtYxkjno06vaCxMUjj5fGag0uf8tJIzYUcjTA4zANS7ql64Gl5ovobggfqQ+UpnBA37h8VQke8UOG0nvE9AGAe6v/H3f3/7/q5EO8f/9l/zbcd1urdo5GGoQt5ueihFIkJ250dkIs59E4gcQHtE2BQp9SdwakOdA3VpdONcReqefcqbaV3r/MQkJ0OXtxQFUrphfDytV3zxvN0KF8hBrmFfRLqE6S13tHqzphtrTEMHbIbU6LWyhRGnMyUIjmvXM8HKk7bBdHbtlHxvoNsfaei1qHfKI7GwKiXDEXBSuXm+oiXtaeJjAmRzh69msZeGEJgzZUxDrx9+5bhcERSxFog18KydROA09pF3EuuVHG8KqUZde9oW+vvm9fa9zE7E9fMCM8sYUNjpFZH+aqZQJ/yor0nxrBP4r5VRJUkndxzWcpf7oXsjVDfQ7CkHj1lvu91rE+b/d9yggSa7/sg7fZwF5nNMxnIG3iHpwFqKehO4gpxAG/PDZhab7xUe/Ftf/s//4UfF/+5X/8Nf1oXrq9fds2nP3HjsQ9WttJ0xOrKISSSd8jy7bZgzRkPR86tMUrCrFJrZexJzN1NSSMxKttWaDjqjWOc94YqE4ZENmerhWLd9ekwxN3bUnjYNjTFHjKdIrWuJEZkbGiBtRizKTU2bocZJbHmhatpZqnGcZq5UmVQJ4TE1fGGxy0jdiYoDJ64W08UNnSDlIQ3WyOqUrZM04RMgV8aD7xdnkiHW0qpPNaNcn5gIrHIxvWcuK4jaUy0kvEG02HsVnCtMoTAY2voHJha5NE6OxSpvHk8c94yizlpDNRGt7ZT283+jaene1wiY4ig8HHazTeSkpcVdIIIyZ0vzsveuHdLNDRglqkhdBs27fI00T50SIqMoTc6q2eiJ5zMnEZyrRxlJJcu17kdhIcUeFEjZRrwspFxgjspJM4xEsNMCvtZs2606szzkbYTHqV1barEQNvWbgjSGpYSvhXW4KQw4LtRRAzsX7vrwjHykvFoNEZ++w/+1tf2DP5cFMXhn/pX/asyjIs8w7375NW6U7JFe57ffhgBSOsFqCKMscMnF3YgdIJIRGg7tNoLzbpPpb24rGUlpV4MxbqEoSloE2xnlF6gEoCc6w7f6r7Tas/F91LAe3G4RBT1nzOEgJc+NQUxNMDQAsPYnXsC3X1ja5VxnijnFRFhFWPYb4bg+8+UEvPQbaLS0CesSQMBYRoTc+pF9PWLF6zritNNeJ8lJDghddZWyY2H88r5VAjzyJvHlWXLSBw5bRmRfjieloLt28IOTb+f1FWVmvPzdCU7MQrVnZRSkB0Wa6WgewMDdDegWnfauXfsE/hTLBwR9KJ7DNp3WnuMmGuX7TyvImsjpl0a0rQ/+E3x3s3sO9n30p/+8ruNFjzvdS/axkuEle77RiG9n1RLw/73/+IXvij+07/2T7hHQxiIQ9xZv/W5+WlDwMvWFQR0tEDTgdKMm6i0EFEVvPVGyIPC2jC5Q7wfpOeaGbNzfbjmpB3xqQgjznEOfPnuLW/P95zWwnGKHIbbvgML0x6KHWnLiR9cv+CunAgpcp+d2c9873DNIoG77cwhjSzLfb8/rKEKN+mGIMYkwmLGYy48jtfMKSLLRg4gKSEVkMLowjQERjdaiBytwQicto785Mz8+rvcff4p19PIKRw55SeuCLy6ueW3PvsjgkQsRJbTynEeyeK8uv4A4g1rbeAbed1IqaMtyQauU+ZNqRzjQAiBXJaeZxo2orzktN4zxIToCFJJEqg0ItqJarETaq6GK0yUp6bM7C453huPGCOTCKe2sJ6V49XAUha8VuIwU6shroxTIIRAqZWrOPOwPFJd98FjQKLwtG4MwXACKUUKmWCpkxD3x9elRz8FMSR2+zbf1xtJA+fzE4dhJAyJkhvihTIoyQJx7DB7svcORzlnJM6d4RsMmvDbP/obX9sz+HMBn4o3Ss29A9AA9QKHCbVlhmF/Y0Igt96JYrUL/mOg1UpEyXndJ8GI7XtHqxlJE2HfAeW9IIoIYxwwa7snojPGRKuVWgohRsIwkM9npnmm23z1ApfCe1jUrDEMiVwLQeN+uBbykolpoPNgejFqtTBOfQIuIhxd0aFPwKP070+qMQRlWRZsP6jneUYu3ocxIhqIoSdY0zZUBqZpQlU4HCakFJZcSCnw7uEd7s71cSbs09Obd293oXYvtOPcl/fpMHBeT0QNtLax5cyQDoR54O7p3AW7ezGpteJIJyXA80SoXODGXtxEKioD7hHhoisU9GKYUK1HeAO0RogRtt50VKwzy1ok4FjYbdbMO/t0/0yrNTSX9ySaFKilNzeV0pOX990nvDcrAHv+Xvtfed+91rw3N+E9w9Tazp/qEg/R2A9d/fvfVH4d1/F64ou3b7i+jhzdibYgmjizkoaRL8rKj7/8lGNIeDoQx4Hl8VNSVMbhFtR5d/85v/rqI8YQ+ezdHW/uH/jl7/4SPzjecG8bGuH4Yka2yrQarRlbEOoYqQ7f/+CWX331mlMtDOHIZrlr0GpfmyxNGUeYA1yngZXAi/YEeuDlyyPj/RPfvZq5WxYKI0/LA8c0s5YnxkM3kP787o5vv3zNH777kg+DY0XIoSc/eHXG/d7+zJx8D99/cc3r6ZpjfuJ33hWempFr4Xp8yfjTP6S6cawbH6ZM3M5stvDj8jnfT93cftHAP3w9sAUnt8pP7z6j+k94cXzBrUycw8AXNROXe+b5BVfzSpTIr8VAC4nPk3MoRokf8LBsfBYg1/+Xuzf5tW1dz7t+Xz2KOecq9t6nPvf63hsHcAyJY4siyNjB6dBBIo1ISUQgiD4d+ANo06EQ/wASAgIKASUSokEH0SBOsBBYIr62c09xzzm7WMUsRvHVNL6x1rk9hJSGz5nNrb3m2nvOMcb7ve/7PL+nINJMqRmhTrxvDGrcNxqUrOgoSeuEU5qMxNqBfZWca+SqcxhpmWTm1SL4GUeW4lB2aKI4seK0wItKyrlxZpXgEmeiEJgqmWVgFAlRJCZ5lLJ0IrHmiRcMJJlaQMBy3NZNBZcKatxBnlFa4UyHafgksqlY3VMF2C5TikWLzKocImT8BlJJErKQRCPJOWGKIK4RRvf/eX3//3n9iegUh9/5G9X7yFPGnbHdM9e0iuZLq2kTVGyvmDIgeQrDbASb7fRe5PMeqz55HY1GliYGyUIi8lY8SyGlQNd1jbr/nOLeKP3WatLqEbJ1S13XUctTx9jwVUU2316RAm0NKeRnIYDWgrAG0BIRE8517cKljVmllMQY6fseicAKRRQRKTRGFPxa0Eo+d5spJfZ9R61x8ysVrvqxFXklMMYQVs/LV9dMjyesbJ3S1dUV7969a0Z+ZzD9gN7UmnMqlFSIRZJKxofMu/OE1B1rShQBAcnqIz4mtLA8mf6f2KRSakrKTdZdm2IYoJR2MhUbpaIxS9vP1ZzR2jx/VqLU1qHTxr1og1HieWytZYMWZ9kEqk9hwjEnpKhI3TrfSm5EEZqiNeeK1OK5q33GuuWyoc2awVxUngUldYuM0ghCav45sann5OZVbCpoSL/7X3znO8W/9Mu/UWUqxLRwkRIpKjvZM1rISuFjQsqC1B08AcJzC2kWdiSHE7lIBAlZKnNpjNPeNDJQJZB8xCnDIBVJJaYc0SE3Kb6Cy+XCB+6KVUcCBRUko9NEWUmhXWdjZ9jbjqUkrrTESkWcV6TUXPJKbyyUyjknRMyIcWgexKpwQ8/leOEhBT69vmaNiWmdEP016zzRExFGYoxpIcTdgULbm17qTK47EJnH+wd6ITjbgQHF3ghEDShpSKXjNL8hy8RgHXv3guhMo2DRRvu509wkwd16pB+uAJDZU2uban1dBLsyo9yAEILdNpVxWmIErCmj1wtuuCFta5XBavKWbrNKxVAtr/1KZSUHRScTj33Hy9oOc6FA1gWTYTdYcmzUOkJ43k9WacklkqrEb8EHFtmQkrQVUtzgIDpLIoE+t8OuL5G1ONaa8dPcEnyAquUmfqxkobFF4HnEy4qpPbKAFHprTCwLbcVipWiHdO3wNTc7FQ2mkmThH33+f36/OsUYeO7WkhTYJ4l+bV4URNthCOWefXxK+e3Eb1HSIVVpp5qSQYF8GndKjRFyGx8Uck5I2bx9Mies0STdtU4uN+vBNM1IqXD9nrx1plfXB07TBYDVLyglqJQ26cuZcRyYo2+era0wGGMIccUpA0pt4vTNVC4VUgrGcWyjT23bCbQbwK/ch5UiCn3fo2ptStB1hVKZ5xk9GsZ+wC8TetsVHk8P7MRAjpHz+YxPAdsPICtfvXnNuq5Ya7msntsMX58e2e123E8LH7//UQsufnPPLAXaWf7os8/Z39wQReW8BAyaWhJrmp/HyU/4vBzWTVpdkEohi952vYHim8XmaReQa0FtzFowhOXbcbbqHTW391SuI/omiorekzpFJzQpRwSCpFvH50zXJgJbwZPbtKGUAlqirH4es//i6L3WtpOttSKsbh3oBpautXEyc5OxomwruFK25PEn1TF/Ag6V/yRe5+VIlwVdrzFxIZbEHBaKUwhbEWrgeDrSW0fOHqsFTjr6nCHNPEyBY13pa4dTmv3OcJcWpLW8Sg10ML56SQmR15d7Kh3vyX0TuNTK5+sJ4yxv/IUTgisM7/WSq3Hg/pT45VtLpyrGChYydm22jDxooiwMQnLjXvJK9cznt1yPt8hU8GVB25GUFS9c5ur2inOxvE1n9lVT0w0PLLx2O45G8afUQEqJU1ioQ6YunrPYEGUbfvL9T3/ATe0QeH4Wjvy5/Yj1kbk3qKS5zZZfHW8RueDDA3/7eMNPwwW/eJw2XJcdWmk+7jTD2q7vTp/REg7DyHt5wcuOS0nPlrE8aF6iOOeIGirTrmOaF7QBLyI7a0lT5E06kr1msAc+rCtFJP4oryzxmt8wHf9oec2Dnxm6PTlUdKqkRXMXIsnopoOITaTo9jcslwV1pThUjVGZG6X5urSpVUE9AzKcEtSS0HJFKUUvBB/IQlcEX7uKVEdsqsRQ6LRp5v5qmIjspKdXBmpAKYmMFy4j7EPg3Al0rsSkKW4F3VI5kC3ttijB8Z8wa/FPRKdofvtv1JLic8fQxCwbEqyKbxMrtMbqRkXIMT3TU5rMt6lYc26niBInlHKkHJHSPWPD0IphNyJyCxn+RfqKc46cFNZarCl476niSUZcqDUzWMdpahdN3EavKVe02v5ejs881KcdJHLDjGXfOjStiX5FSwkp48ae0Zjm/dEav6xAIeTyrfoyZ5zULX9QKYSCTuqWFN47RC6IkumMJctEjYn1dGY/7uh6zYvrG8w2Pk2p8N6rGyqSN2/eMAw7soC7x4mH44mqDGsISGkpqllDSpVo23O6XFp+gZRoZVsaxia4UQjifEE5R7XDRtt5UqeWZ5tMTRmpLaKE5ivblv9KSkTJLNGjTUsCeBqRPr20UuQcWtGqghq3lHD4hVzLRlZJKSGNfu40n/2QNbf/WwwIrZpieBPxPF0QUijKE3mntkNbSBGZ64YKbJShnDPx73/3O8V/4ZOf1CUWvGoj4nWaudk5fmwdaMEqJL1pliMTFL56qnA8lsDNpjjWsl2TRVnWNCNK4cpofBZ0naHGhNWVoDXneMVEQs+vCdUwpxVZIj8LCq0FS/RkItfXP0Ao2wzuRaGFISdP7RRZZLrUDjcTAZM7bMnkUVOniLKVDksUiXCZoVxwZocWpVGynEPIRL/r6Av0Fa6HA6M0LOpMDJJdyWDgoBWqaJKofGwdX6UL1yHxakwcqHzhDYf9LafHI58OHWd7z05Lfn6+ongY0wUtImKwfPnuSJE9smR0XPE68YfnyK2VfD1NpGHPJ4cDnz/ckZXgrX7BIiBKUIugaI2dZ+gMPxILgzPsjOMDUVF54lALtbeMOCZleTsHvlzOLEHwczyr97wQhg+t5qLbjv1XrMXJgnU7cjizyjYlKTR9WiZQdY8LlbUmqhCIAsEIBKYRkciIoig1EqUilogRElE0MQeeSPxCBmqxRNn4Bl5JQHF5+vtYfE5EWZCxMotMJyy+ZjrAK93IVsQ2KaiS//4Pf//7JbRxv/1v1hhbUbTWtkXqk8hBNUrGMxtT/MLDq4rnB5cU2z6SjBCSdV0QQuOca23+lvIclxmpG4y463q89/R9i0lpApZtuRxSE46UTNcNZNHMFyWLJunuO9RGNcklUotGtQAPsqobjLcyOIs/nzG7nloEyU/0fY/VBmUUYV7puo51PnE4HBBCcLlciDFyvLvnxUcfoEQlTAvOOYoSXO9GwrzS9z1sxAhrKpfVszMGI9sIyNiWSN9rS11X3L5nnme8b7ScNSQeThOD6wix4PYj85qQyjJFTwOuS/wa8CUhlcH7yLrteGTJeEClQt12cJWmzixGUVJofsCtQ6zbiU5qQfFbwTH6eccbS0bkpozLJWwjVUE1jpyaiEdL8VwkQ22Wk+JTK24pb0CA7dpxhuoTalOnPv0OvYES1LZTbbtagaybnzOlTUC1dfvet2iq7WBVNli0lI1jmf7+f/mdL4p/8Zf/5SpE89B+IwVjbIdTXwuD1ahOsy4nDtng+g6AZV3Zd44U67YC6Dhse9pkJBfhkGUhJEExBi0kImaUaaIpGyteg5eOZV55tRMc18AoJE5qPIBo41BRA5qK2RTMRUuG3mJCJaTAnBoAY+wMpxxxokfkgpOBmAudMmSr8KEV5p0UTDmxMhDjCYFhqrDXjhBnLDtCPKNMA4SnujJ5iTKBaW3pE53WZG3oYuZhrUjTchprrWQchcokYa9Hao1QBB6xRbgWcph4EIIbcSH6hNGJbsmI/Z5H/0i3KoZOYLqeJa3ELFg9vD/AlyETkkTTM1ZHGHr6ClM+kcORvdWM2iBC4H274/VyYlKSxxxx3XvteveXbX2SiCnTqYwqmkUsuNIO+aVGTGdwaybqSi6SU8h0fd/QbD6QVfNj15rZ58JJ0ARMOVFU86i6zZYSKBQnMaKtjZTotokT6BRQNVNs++ziulLNJi4U7aCrnaWESIwRKTuE1Rjj+Ozr71me4vA7f7OKJ7M38plnGlIklwVBG49KoVHKPO+FGvmlzbSVgHXDdJmswW0pGQpqbjd4qBkjxDNsTEgJqeKcYZ5nOuuY15laCp1xVKVRShBDRQrRAnOFIM8zShdiFsgtuNQoTUJR4wq6p5YGDSCt9F33vH+U1m2q2qbWi1siuFKCkjK7rqcbeqbzjLWax8dHnHN0XdsveD9xucx8+OpFG+uOu/a+ecV1A1oJjLWU1MbMb9+9wznHBy9fUGPi7eWOvXFMi0cbt+0jHFFKHo4Xvrm7a4KZfkedVva3L3h3WvHe0x0ORL9QNz9oSoUsWhHJqWUpFjSYBmfPNI9aTYVcW6zN0y5P8tQByjYi7WwTAilNomGgWkxX6z5izZQU6ZXBbyNtowShZGqhIdnKJq6SG2VsWZCuQ6uW+UEIoCyibjaPbQcKTZWcUkKKJkVXUhLiBnwPoUHet84+pVaEZW5Q+fgP/9vvfFH8pfd/tdZtRKxyxV7tkamw+OUZnl50QaSIjAWnNLUz+AD7LV9zzRHRdTjRPlvMlkVaEk477tLCWB2RxJgEwRSUisSYkW5EhbaXzxkMkpQ9ShmWklpaibQMasWvhWgy+7JnDhF77Ug+UpVH5ECpCVtGZlk4VA05sJqelBI9iSVHBiUpUpGrROSZJDOIHdYp3q3veD9bFCsXDC9sYmc7utQCbp0SfDTArekZl5n9yz32nOhtTxCPyGjwx0Tc7/lyndBK8KeN4/My4/2KrxDWkZ9TiWol+MpdKMwohpuX+NOENGC9YrGCx3hiv2aCEVyZa6TNUBXLsiBqYTZNnb4KTV1mOhnank8brn2bPk0xUztFWhqXtY8SMUKOhlwbKawThVwkc8ksSiFVoS8aXyOVRMoOrxMfZsUqBElIZhHRpcHXbzpFpxxDMsxVg5kQsUd2iT4I5hqYZWGnOu6WC8YKrHbsi+JcAlYYjFHgWxrRqSSq7CmlWW6qhV20bYIo503AV3Gy8t/87A++X0XR/tZfq8Bz1yURlJQ2lpZhvLohppWcI1pb/NI4iU9q1VIao1Nv5nBKJoTWSXkfEbqhh5SzKNVStYHNbyY38YvczKcN9t33Q8M2xqauulwuINueTBhNrQolMt57hn6HILHEgp8mJBkhCrrbkWJBbWG7xhigKUuHzuKcxW2YM2MVi/doWuDvZfWIGJ9B4DlHrq6uGIaBb97dsdOK6+trpmli6HucKsRcqDnRGYvVgnmeGQbLw8MDV1dXjN1IXC/knLl7OPLy5hbnHNFq1suKj4U5xpZNmQRFy4Z7RDNNU8NHGUOKDWmllGH2K1IprO1Zz+dmq1ACJRXKGmpIFDJVaCitw0Qrcsyb/6oSfEYavd1cKxiFrYJYYrNP+IiyfcPipS0fk0qnu3byrAliQijzPIqlNiVrzYWSAlLbbcesnq0eTz7Kp/GqUk3JTA1QBGLzp5IqubQ0cFUBEhrLX5wAACAASURBVAWNyIVCpP7D/+47XxR/8urH9TD0mzis7eusUIQ8s6QVo+FKKq52V7jSEuC1HYmiNI5nKThlMEmwqIpVGrutEKYSW3h9gWIsLleSElwZjYwT2TimVHFVoqjUEjkMI9N8JEVBFpXBOo4ycY0g+IK1hUspXMvKnCxRKET0DYBBIaSM6TtufeUyZEIZmKaJWQnu55ascKorD3rgg5T5l25ax4t2/P7rR9QILsG7IpHZk1Igyp6qLY/laz6Iu4adc4aywIzELCfUKNnXwC8dLB/vDD+Ulvf1jFGF47HnZ0vms+MjWvWcRGJeHbsD/OCguLYG2yU+iAbjNEtM3F9O2FAIO0eHIitBCBpfF7TW7IXiLAIqV0ZlGwdZbck1wqBj82cvGWQuaKEgBaIx+KIQMlBSQUuJrwMaTxKyHRZFQhTNUTSP41wkqnZoGVkrGKtQSfAQF47nwEUZrE2MdqRHEcNKb2ujAVEZhSRJSHGb/MnCpVpsjiyikKslS4jeI6wm1MpjgUMU+E7RI8iq2aXkL9hMioC/+8ffs6J49a/+W9XXdgI3xhGjb7FAIbU937bQFdGT9ZNZX2+J6jTLRG6hprFKZJ6ouiOn1EYV1UNUmwk0Uu2O7NsuLkvolNtGZpVxPOC9p5RISgVpO6y1LW2c0hK+3dgEP6JxTYtoSsjDMHKZZ6Q2GFFZU6JDoa153kk518YJ5/MRrTVD36gyOa4o0UaDfWeQQuF9bA9hrfB+4fHxkQ/ee4/DoZ3inxIghNzsGilxnieurg9cHo7sxh5rW1G9GgdGZ1kzjNbw5ZtvkFrTdR3TaSLGzKuPP+RymompMPvAaVrJSrHEhF8zw9gxrYGQGnoupIhQitE41hSakT5t5iQt0dmQs0d2Azk3kPqT2laqVoystS2rbts9Pol3GkdWYY1AyB5VEyn6BgSuFUV83mWWnDFWIzZDeNsft2umihVRJCVEuq4jrDP12e/oELrl9oVUITfOJaW00NnS0H/NY5WfoQ1PmY9PIoP0D/7r73xR/Jv/3K9XgyXnzLkq5gxGVFTxTLIg00Csheu+JSoEDEtcyMphUyAKia2WRbWCKVVF5YrpO0JMZGGpaWoB1ySSUS0guGEQiFKjq8aXQMgRXTWzWFHS4kQDUKwZrFTMwlPIyGViRtApS/ZHpOzoHXRC0amMS5lqNdfKIMPSVKVSsSwrs7Z0pWEF3zvsufGe3109Iz24BULzCN9qxTInVmm41pZLFRRR8NHzrgY+invG8sDP48xvf/o+n46SD280/9Ru4WXXkYLnq6NGOItYIxdWeuOI0XKwiq/DigwTdmPtamV5c5l4f78D1cb8VQmWRSO1wueCKoI3a0TlghEdRm2TE6sQMjEtBbGpQpGtiBihibVSpUekwlwtEsWaI73tWWJEGE0KuVGgjELGQhSCNTdfcCgZTYVYCULipWFP5IviqDnSiUxXLPci80pHHoPhZCtdFoxkam45j0lr0pYANNIO8ysFbboWOpBL+35yZaWh6BCSmgsejcuAKzwmEKmQpObvff7T71dR3P+lf6deljNjPzSLRM7EJaE7Q06BmhJuHKlSkPImmV9XlHUIpTfzuG5eIwLwrcLQWkvfaY6n+dmbFsLCMI6UDCVHjLJkJcghYvS2h0QRN/7gMAzEsDAOB04lUH1EI1jZgoNLQWZPqKoVzy1n0RhLbzXTcsE5hw8JNtWlEhWhDHFpilanm7l3HEcOux1KCU6XdTPPLo3mIZsE/f74QE2xpWlIxc42n+LNfofekGf7sUNKeHh44Hg80lmNMG1fe3t7CyVTHxc+P99x2N9SO8O+Hxj6kT/46T/m009/yJuHI13Xcd7UoUssVGVwSvLN3SNGtnH1PM/P+2CfoDeWXDxxWSmimeqHscPHrXBWiXSti5S06K0nIdRTZ+zXtalRt+Ip0EjbbB9CCGSusI37yM12obfDQRXyGf2mQ6R2e3w8oaSl1DaFUMY082/J1Jgwzm4j7thmrxmEMC1Vo6TGQ91M/+o5Wb1h8+I/+Nvf+aL4T3/8q1WWlk4gpWQODUFGilg7IEVqEV2lbPscSTfcoLQhpBZj9oRIhA3dmA0pr/i00kuDlwVdNVZkqJHjemKn91gtIK4sQuJy4eZqx844bqxlKQv7nDG6420p3BjBWCyGNoHoyFS7Q1M5q0CXNV5JagzMKWCS5rqzyBy5F4Wv7h4YjWE39oisuZKZgOSMoObCQQneLnBxCpthWidilVireVgTdRy42jyQ3dAxJsmfTz/n3/sLkiUGUnIs0fLJp2f8W8NFBq5lYbQ7qh7w0xlhHBepGLQlriveJx5TZpSW86RYo8R2kRQ8VRq6rpCXjM+WE5UoK//r14Zf0h3Stmv2Elc+3N2SsietmZoyos/k2lFz5oHIR6bjRLt3Sk34XDgMHdUncnFIo5nTRI4B6xrdZi6aXkJCYUViRXKXJKOouFxQnSCumVPpcFZSqidFgVGSuSZGGnPWlwbnX6qkf4oUA4JQlCxJCmKteGmZakYkiSFzrJm+QtzoVVoJTGkh40UqEoolR/6XL/7w+1UU7W/+9Zpy4NCPJFFJSeOX1zi3I6RMjRFhbRNaqJYALrWmqR0UIQSsgpQkUmVibD+jNpGNNBrydjPXgO0PGz9VEHJEpoTqXSs8IrMsC8719G4g1aZCVVLia8YWQS6i7ZJqZtyICzkFQlV0UiK7rv3e0vLElGyhmZ3RSNnYjbUkrFYMzztGwf3xEWsto9X011es84K1lhyWbyOzpGC6nDjsrpoYZ1nQtUG9jXas84VYC4exYxjaWHiaJj5+9YoXVwemaWLyiRQ8p9OFlze3XPzC3fHCJ+9/SCiFECvzcqEoy7IsKDsSY+S0TNS6KWltM8zGGJ+JPzlnut3YfJ1CUERFmo54vCD7HkVTB5engpK2ZPemWsHZnlASNXqQEqWH5/eubNJv0+AHVUCKZbPyNGRbzWETYWhq3MRaITW1npNQNSn4Z/qOGWzL3kSQtq6PWkkxtrSD2nxQJaYtRmrzPiIwQlDqSimS8nv/w3e+KP7lX/kzldhoUkYLbNXM1dNB29f7htNzQjXepVKUGthJSdzQiFSJEu1zHKrg0NkNwm+wthCypnceUy2nNfLxuCfEC+t5wu5HpgrrrJmKQtU7vvCVD9wIovJ1XCEKfnzYs1ZakVtnrsaRL9888ur2hlFozjnzR/Mjr/qRD+yO0/kBbWDNlncaOtthfKAIxZoqMcxcauBERatbOtsRy0SXBrSWvHn4KcoN6LJy0zlkktxa+FBoyILD0HHynjkFhNV8uU4gR7yO3D4KVM6YeM+/+ys/4HhZ+IOjp9td8T9/9cA9Jz5UA3eiQQOq6DB6Ym/2fB4nzknzq1R+6i1eTezyyqoHsoi8M5nr4Foeqxh4V1c+sT30GhWaAv9UA6kKXJVo5SlZMpG40h1aaGSMGFlJyqBrYg6ebneDzpkUMp1dGNjzRkQmn7Ap8gNr+VODo5eFe9VzqyLfRM8PhCYKySoitTSEnKmCIFYWNGRFUpkpCA7asKR20M4Iqqz0ReI3a9Qa4awUpxJQRTIg8LkwVpg09KFyrpHR9PjSgAN/77Offb+KovjNv1qv+93zw64AwljWkNhZRdw6jGp6lEh479G1NJYmlmEYOE0Pz1zPWtpY8clk7bTBp0LOgavdC5ZwBiCnQAkLUjZp/Thc4YaedV1JtaCsYTmem7imRIwbt5GZBmewJVOlIYTAvh+4hNbZGa1ajqGqxDkiXdt1yrQBuUmbSMVjNvDAq5tr7pdLGxsuHrN1wKG0PeFhdyBRcUazHI+M1weuhl0z8zuHUJKHyyNVwqvdHikl796+5s//yq/w7t07xrFnWmZ2ux3x4vGyMljN+Xzm7FdevnyPy+XCtFbePN6zO1yjlOP+eEQow/l8JuZErgqnDVP0WCWJ2W50m7gRfzSdsSS/kkVFlEQoFXyCWlrkTkwoU5DCkoVCAklIBIWac8uOq08m+m9VxzXnZ4hBzRWlvkXf1RqJeRPaUClT2ymHjQrUGUss6Rkvl3OmrBecsVt+W8QITXn6jpSkokkhoLuBGgO5rGjVU/UGwBYCqwzL//5ffeeL4n/8O79eY02A5rKuxFTY9RqypLO20ZPQ5LIy1YZ/62oT1EitCL6wGkVdKt+ExDlFPuodKRoOzhBK5Mq13K0QJ16Olojmy2nhhe1RBkSQ2BIJqqUyaAVFWrRaUFGiNfTO4aXmOHlOyWz3dmRXE2KvCQ9nfnTd0jT2SqH3ltlLfv+NJ/hMUorSOUSR+GWlSMVdTgirufeR97Lg18ZCVIZFCXZ+4lI7HkLlB9eKi08EBc57NHB9fU2vBK/XBR0Ll6XwyY3hxnXcx8D5YeJP95a+MxxjxJnCZ+eFn7x4yc+PD7xverI0WCn4Yp0oWvKydJxVRAe45EzQirqkzROsiAqcUE0hXmERTbV7qoZ3MvBBgn606ODR0j2L/NZSwLjN7iZwVbCmyElrTqLi1Y6QPLZq3hOFF+NAVoIuB3SBWNv4Mlc4CsllXfmhzbwyll5CiAJXI8bCWjZgvEiseeMgZ7goQScFu02lfl8rHhiVxNfCUkVrPqphLYkBiaqJS6ksVtClQhEGkzPFNAuWypm/9fk//n4VRf0X/kqtm9S+poz0gU5qLhs2LJWEtR05RvqujSvXeUF2Hem8IjpJ34+QS+vIxh0hBPrdgcu6cNUPPL59Df3I2HeN7K8a4m0+n3CA6CyiU5go6IzgYfEM1lCUxc9njBuIfm6QbS3Q0hJEaiGgpbDbNTtFjomYU+NzbqPVGALW2oY82h7I3nvG3uKnC9cv3yeej0SluN7tmdcFpeDdwz1j12/ijsYPtbbj5rBHWUdez/z87VtG3cKJX92+4OF05Cc/+RHrunI5nri+2nN/OfHJe+8xr563b9/y/osX7JWlu96jlOLh4YF+d810POFlsyJ89fY1fXdFEeCroGSNDxO5wv5w4Hj/gBSaWAKiWoypxPTtni2sK8KCLEA1CKOe8yTXlHBl+xxSQGtDkWBqi62RuhEtqLXtKIVoAHjttmskNLa3UpQYERumi9IYqE+5lkI1NqzrDGlpoqWqJDVHtHLNAiIEJawIpZuSeZOKoyRW6ef9prMWSiRJjaVxHUspxOTJ/8ff+c4Xxf/wX/y1ejc1slNeW2ZgSg1u4bSDroGaO1pQc6BwbXpOccaqBq6P5VtWsRASLQ1rXolLImrTAPoUjsuZ3e6aOYW2pyodN65FCu1cj+4dD8eJsXPcyEpRmfOxMHaVOSq07Thdjuiu536dyThen2e8qITzHX7YI1TfeJmmYqtF+4W106Ts8SGzhoLoNSoW9iazGxzvF8kaPSFLOhHZ7Xa8txuJqaDWpUW+qYpGoYzkva5dHy92A+fz0kR52uJTovORl0Nh7zTXTvKmZI4nzyVUrLXP/N2P9o67OXLlLL42AEmqgZ3bc/YLOUPVBlMK92tk30lECKTaplGXDI9CMC9H3vqZ285xLR3kTAbE5r9WJqKloqMSSsbZD0g1EeeV/2eeiWHl/5YDL4c9plp88litMWkiC40qcLq+5rQW7HxGW4sWgbrtQhcRCb6wGIucL/R9T6pNUX6zKUlFuGw7fzDSUGpACUnKlTVVpC042eFrRswB7bZHQGrAlWGbCvkkELKy3+hDQyn8nS/++PtVFLt/5a9VbUdggzhfJsRhIK5ntHBk0+bv3q8QQhtn9j2UijSCeH+HvbnBlJWgD03Msc4UoTDhgnvxghojvgry4yNy9ZTdDmkkfb9HOEU6TUQXGWvP+eGO3QcfkhBNPRlXhLK4rtsiiSxTmhn7PfN6h8zfCkWUUgzDjsmvyAqlJpxqI96cM8Nuz7IsHA47ckz0RqPcwLKecarJ/NtNI7l7uGfoenxJKCTGSKpUzUJynjnfv6a7vuaHH37M8Xjk4w/fw68XzvcnlmXh1e0B3RkGFCVVPr/7mq7rmE8ndvtr9Fao//irL4h18w6dVkTvsEJxcY6D6ZjefIVWDg5XIAyxtDy9JQaqP2P0wBpDy2nSLTpI5ECka8xWUZEpQN/GuWSIZUtvTxnV9KlYNRCib/Q+IZqpXj5xSsWG8hPUFMjBU0LA9D1VdOQyY3NDvpUa6MYrvPcIEkI02EEKofFRY0S5AZRrXkpRSKoHuVlJYoQwIzYxc928IVpUEhL1BCGoFal70u/+re98UfwP/tyv1zMNQ+uTx0lDX1aKHViWhaj7FsVF4bLGTfmdiT4xZEHXdbwLnh5FkYYqM7NILMuCVwasZsgVUwwnkdjJkRgC0bQdu0sNkDDlSKYQVsVaL5Sg6ezAvT+B0FidcUgeSkQJzUyLsmLVpC0ObBaeIRd8mPiN22uuVEQbuFaO0XYttorG9xZVYsu4gTYe2KWuoQblTJSGPjavquthEGBInErmerfj/rLSqZW9cxilmeYLezswxzMxC4zKXE4CaTNOZ0yRXN1ckecTh6v3mE5vWWNb0SjZ85gsawrcXR75oyQ5zh2H9Q55Zeic4UW3I4WVzkl+oi6o3R5RMrFeI9TKPAXOa0XqlSI1pwg3zvHyZkV7ScmgC0w+EuSOlAOxSNQWpQYb5UlqalEkUREFxOb/XYUAocgxoXRt8IytfpQCQUENlSotqNwOxE5DCg2N+QTpR8A2aWGzgaSq0DTbHBt4PwqBDIIiBUmKlnlamjUuSf2sRSgR/pPPPv9+FUXxG/9aVSKTk8faA6nUluqdttTlp8w6IVBak9EMnWM+X9pD2BiUEghRCRkkiuxn+us9fgmIVJDaNpJMWui0YL/fc1oDeVlaxprVZKPphWRvJbOEeZ6xWaP7A8v5BICsnqvbjzhPU1Mxlsbog/YF2a6NfhqDtTDPRxCxRRJlTVovuL4n50qaJ7rbW4yQdLaZ+UmZ169fU0tAd+1mFSUzDLtnFN5Hr275/M0bXg49Zx+5GXumt/eoq5FRKL68/5IXh2vee/k+Sgn6vueLL77gvdsXWGv58vOfse9HhDbsbq74pY8/5d3xgVIKX359x7KsPCwRZQ2vv/4Me/MKYwyXx4ndOKBVG5miNNp1DcYeV9aYqMlvo852MpVSNo6p/Nb+wJZLqLXeqEC2PRhSoCr7bKv4xXSSXEAb2WwqNZJqM4yT2r5P6Y68MWkbuLHtG1PZzI4xgGyHIEomxBlld+S4NHjCsjTerZ+wzlEzxORbviXlmV7T9o6lMU9TIldJ/b/+p+98UfytH/wzdUkVpSupeESVnCIoPFr0VFmoubDKikzt+ral4o1Cbb5Pl1ZGZyhS44snF4Fzjr4UlpIZSyUYw410DGPf4Pf9gquanqbQdrLQKUvXQ1kDlzVQpKAXYJxmJyOmCqKCnW7UocE4TqmF5spSkaqSvSTnNj60KoNWyFSIpfImtKSFq0PHVe8wue3ktVk5p5ZSc50FSUru1/ad3ypPSZXrXc+4Cwwohi7ixEgukOuBxb/leJd4c1d4uDzy8Y3m1hZ6bfng1YF3X2duX5y5BMUjiled4u5dwPYdpstEmRvMQvX0OOgcc3pE+gtibQVrLY03uhcv6PKRSzX4HAheMafUiFe1JxZBTBeMsXgrqT40b2NoBzu5Rahloak5IVVTq8qSG9EotjzXnDOpqudVhqob5aZGRPn2shfPubWFWDRQSEUQZW1NQ04k8S13OPOtKC7GSKqKolvBrWwUKg0gN4JZwSjRnu2bXxzaZ5Jr4T/97KvvV1FU//y/UV3XPGZl8/FJKbFaoksmPI0hS0D6SjGZTu7IsiBSQXWWsHqk2sZrRlHTlppQI1IbtLIbT3MgTXec5pVud4WsClsbwmjNnp21G1XmRMoC7fT27xIU3zLAiraQCq5r3E7bDSQp8N6TY0Dk5vFxw9hy+ja11GE3IpTGWosuiZA8LzYLSKyJEmozjhuNEprD2E7ptXOc39zx0UcfcQkzndS8/uYrdrsdNUVEb1CpMK0LeZn40Y9+Qs2eGCND1wNwCgufvnhJVZJpujAe9tydZkopvBp33J/OaNXx5t1bFr8yed/wTrVSjMNay9mv9MpxPB7ZjztSrqA0yzxD9iBtM/5LiVRN3ZlSIkuLqplYG7A8xoztJPriiSKCdsQQ6HuHD5lSCyJ5qhAgdMvWzBVVC8JZyAGJQK4L5fAKlRLJn3DDDSWsZCWpoanbSo3N38pWREv6NvJLKqgKQkTJTM4FaTVV2UZfqVCJREx7WGiBqqC0JZSMiJlaBen3vvvj0//sX/9nq4uOlD1S99z7cyM9pUTyMzfXIyo7nKwYt3A6bQQmmZAU/ApKGkqa6fue62uw2pGWjDAGbTLOtN1jUQJVI50Q1N0ty+UeHWZAk5JGopDXV6T5kcfzib5/RRUzX3wRuOkNbx4yP/6hoVqDUTQ0WGqYQyErVI0xMyIV6JqgJM8J5Tp6IloJSvKkEDHCUgSUVdMXiTCRnVWErmWTQmCwCku7lnWNIAXOWCoratesYGDI8YySA0V6ZJXb2D+Qy5bBqS01FESWUC0yFVLsKVkxZU+mMvmIKgdCBjceWC8Tx0skllaoUi3EYuh0i5kLaUWQOC8Bq1yzfNXAuiSKlDghyRIqilzr88GxVMmaM1AQVKzQLW5Pt7WGKu3+KLl5CwFKEUREI72zFaQNrZiFRCvReLS0NUQOkSgqZmPGIraCmAoB+fweLcBbETfPtqiS9JSoAwh0C4CnkW8igZJlG9VvP/Mf/fGX37Oi+Ft/tZZ5ph9HQkjUmpt/TRRE1YjpAR8uqOEVsjZYtNntuBxPmK7HDTuWhwfQpvnj4sS+vyIkKFIyp7mN9lLBxwtCWWpKHK6vWeeFw3DVLniRiCS6ruP0eMY6x7JOGCTSOjQtxWGNghoWDtd77k8T2hgETYnZ6w5p2m7Nlkxye1TOCKupSLbAeeJy4cWLFzzcvQE1YLTk3ekNw84hRM/LrsfVyt1yQaTIYxaoEvC5cLXbt/dYLyhj6GiJHaazdMoSWJ8zDtdpxjlH9ZFCZFkWPvnBj+k1eB+4v7/nkw8/IdIsFTUXLtPKz15/w+76BT2CNc2U2vLLLgnCw1dUJRn79yhDQ8fJIpFGE0KjaUhRETK1Jf+8glV0/TU2Tky15WCmGNld7VgiDRU1NdXvTiqCgskH6sZL7EQlbuPpsJypOeP67hlMrKsilES3+T6fLCxJPcWL+GdikjU7Ul7RVEoOzx1gLk0EkBOE6hGyQ8iMxFCaR6OJFFCojWsbo/9edIr/47/9a1W5DlEuKFnY31yRsycuG/Gn84TktggthSjtlN/3/fZnzSQudbNBCZkRCTrnyL4ZzafLiusLRTiUaJ7WeIaYKzJbjFtZFk/fW+Yps4aFZS44ZwgepDZIA9pEhmHXDpxCbIkJ6VkBTbWUkuhM85TaWhqM4em7FKXZr5BIBZ1pD22jUoMSmErMieQBJE5ntGrRcaUUdK4oKkoHdC8poqJdGw9ChGqg7iAOIC5tpis9pJ4cFlKUmGUTAqbKGjQpS86T57IUMHtClkhdoVqqlvgYSLXBKKSIWNe+l5paN9fCEASQcbJxmVGaKbB1e80nnWmB32sUCF02wH3FWEWsBVsFZYvIi6FuJCdDqoogQvt8ZLOrCLbdvRCsxbcg8SLxKVKV3Yrd1pnSEJAASdSNJd1ycEVoY90iwMeWmpK2vEWxeTWTbh1s2RB/lBZjVUohCc1//tln36+i2P/WX68ptZsqK8FoevJlYVUVsRm2XSeoRWFUk+RjRMvjEgXTDxArMi6sKRDWtYk1iiStc/O3USBXur5vKqxSGHQj1EznM3YYNtj4hpCLK6PtmWLECkXVEOaZnNMm6skEITH9jpwbIDyFwKHrqEIxjiPT8YGpKq6d5bw2kc7NzRWXy4XdsOf+/p5P33+fsy7Md4+QErt9hxsO3H/zhpevbumqZEoJbRWazDQtvLq+5uQ9t7uB03Th9uYFd3d36N4yvbvjxcubZsXwK59+8IoQAi+vr/nZl1/y7t07kvdIIfgzf/bP4r3H7na8uztyPp/ZdwNZKJSszL6QfGJKE6l26OpZFk+NK/PljO6vqbprHbh1TJdHyBXdddRUyesJbW3ruKVAafBJoSQo17XRqLIgMjoWlriitSXIjIuVXAW5pi36q9+QeqWh9JRGFEHyHj04ShZ0nWNZFoxsmY21VgoGpSQ1rY21qEQDANBCj61qnlatNT42m0ytCl0DqSqEzE2FGj08XaO1YIzb9imS+Ht/9ztfFP+3f/836zlG8pQZuw5KwIeZmCGVI9eHD5GukuKC7TSdkcQk0dq0rlk264pO7f4Z/1/u3mXXsixL0/rGvK219uUcO8fMr5mRlZl1CVFUAlWihIRUKtEBRAfRockD0KVBh5eADuIJSqKBoIVoIISEQEKUqkAUVGVlZERkRLi7ubldztl7r8u8jEFjLvNAdHEpI2K3zc3ka++5xhxj/P/3H0eK66PN2/UJKZFmV7bFEcLA+eWAVCWvS49nqhknI2aNvAnDVKl7IK7FAtpxhoWM1v7svdsTZxSQhredOGUVbUIUw1UFKaQmNM14BpwGnAjOF3xojLLj/JohIeKc0nLDJcM1xxAq3sHgeufSfckG3mG+gW9IFIjWmcxesdCwsmDc4VumLQ7vBrRWtEZc8biaeKqZ3AJb6wUu2wWnZ1Ajl4aqo1pAgjJvjUrArOJcL+QeoaC0CrNKXzO01lnAW8fmdcJTXzOE0DcO1UbU9nGmFsx156fTng7TAReeptrj4HxEG91cr91riDSuwz7qlB4tVdeGCgSBcZ/w3XAMElm1p+VggcU6cKACs1MG8WzVEUW7eT/uaEHrOgJfwXbPchEjmGfdQS4xO/7zX/3qd6sour/9b5hJAAk450mHM2vZGOMAbeYwHPsPoixQKxb9jnCru0lYcSlySiPrurJpb/HVKj4OVG0cDn0UOcRAm6gNRQAAIABJREFUa30uf/RC8CNJ+oOuRalDZBLQqY8sRyfMz89wvCMIVBXG45n16Q1uuOPh/p7LfCHfVobTPTH2fUVeV5w4fOgZan5INDGsNOR2owyBwUccHZElU8LlHihctLFdbji6AvB4/0Cd9wDlQ88Y+2uPnzPPM5ICQ+ovprpuXMrM/fnEcrlCbXzxxWestZDnGyOe4gNDEB7u7tluMzZEfEwstyun80u2vLAV45vXr/nb/+Kf8NNf/ZJf/OSneO+ZEU6DR31kXdee9xYj3hqmgfV2w4/dv9iWlTSmzg8dDlA30vmBoLCsl64UTbEXFonUUkiu82wPx3ty6XYJLVDE027PEOL35n4fhLIupMM96zoDjkYXgASErazcTyO3UqlbRiRB9EirhJ3u72UkiEPLisWEA/K24IaJyYy8XTo8nsRpnFjahm4znpFqy96ZGPa//7e/9UXxf/yP/q7VPTA6OCHEXuScc5RauV4Kp/Owx3j11JNalSCBbZ0pW6DUWy9EEjkfx95958L5LmHmaNvSAQ2xrzKW7T01K2GMTKNjHCPaIuZLV3VjNIWtVGrp6lWHx/uBjQXTiuwityEImhtty98D36X9OmFlub7n1YsHQtgFXNJzPcfgGHykaSdU9Wlg32u1WomSkJo7i9QrzineXB8bx/6iFrfvrV0F3yD0wkgOkPt0gkKnWm8C1jtuqqNWYW2RRgDfKFlYS4fnf/Q035bMEB2me2ZigWIBcYVSARQnI2stPVi4KeqEVZWyp4gUMzDFu0CtgDme1j4G9YND6DYjcF1FS4CkaI1k2VcRBGiAVwYH1YxB+3l8lv57icGxVkMNtBrVBcQ2MEdhD4/XRpM+yXL0QuckYTRUIo29a2xu3xkat2q8dsp1dX3c7Btph2eVWvmf3/2O7RTPf+/fM4fnuT4TZWLwgTl37FozgS1DK6S7e1zo8u/kFXER8ZFlnvEORJR6WwgB/O4/82lim28cDydulws4wQVhPN11pJFY72oc4BxpCNii/bZXKptVQoVGIfguqKmqpOioxRjOR6Q1RAtbtR5ppdpDacUgTt8X7Tgd8LWykhmD76OI4YCZcRg7R/Qj6PyYDrx99zVaVqa7R9zhRMwLRYycM17heDwiwBg6OPzx8ZHb7UJZC9Mh8vj4gucP7wnTwMPhnrTf6uZ5Rk14/eYbyJXxdObudOZ6vfL48gs+PD2z3ma+LTe8GX48MY13lA/fYVMicyA6YxoGnm5XNBfSeCI5Y906nzZvC8SepuBa7lL1fOtfuNqvMVQN1Pm9mBlL3gjSBVGwRxKJUQhd/RjiHmoMYo2WMy4MWOsjY59GWs5Y6KHSzZTQDZQ459i2Be/j3kUa3sddJl4JzqP7IY3iKK0r5YLrvlekj4PwAXaYgjSj/uPf/qL4X/8Hf8vK0r8vkyuR/kIehgHXPMeXvQuLk2M89YulLpVtB6TP8wzNiDgSB4bBqLWgueJDZjwemJ+fefnqTBomok9g7/HjgdasMzibo4phCMJIzhvNwHb8nG5KaXvgrncMKVHUemdXKy2D6EaIqcv+Y98Feyp4xWsnULU9McdJw5syBvBR+2bQKVjgljslCZSTd3jX01WQzDHEPcTcYDBMFYIgUwVniGyYdmuQlIjUftZpHhaHNUG8gQ3kDGtH93ZqVO5huqYBtUbzQtk8wSuzTHiZsdp5n9p64VwXo2aPTIlQbhCNkiFr7/T6Oeiupjh41qq0ajSkT93E925Qe9KFKj3pvlaySC/Gkvf8xH5eSH2N4BxgHlk2bnsQQHZGq31cLQz4mslqmPapT0apRdic4TT0/CgLVGm7oMeDZLD+vi2miIdmgtsVr32i92ugyX/xsx9OffqbETK8LmzXb4n3PyIME7pe0DX3yB9RbABKodGo68yQEmteEStEjKBKzYr3A/7FfTfm6kqaJmoGGQ/4MHI4B8wZIXmua2NX3JPSiB8SOV9ZSuF+OFGjg4MwbiubB71eERv20ZDrIbYvEk6FdXvqQcdxIEVj2eNtvKvUeeUwjcxlQ+ZnlnjHWJVrayTVPraotbNP91zGu5ePOC38tRd/nTe/+gWlGdw23t/eMfrI569+hB7hdrv1fWFTDndnQuginilN/OgPvmAtK+/mGXetvH3zc7QZX3zxBV9/9UtefPLIw6ev+L3TA99895Z3zx+IMfKLX/yCySfuhgnxDj+MDOeJp6cb4f7M0zevcWePtsLmDVdrnxo5x9YaFgKERJCKuf6EfRTK9TtCOhFjZFvWvi/2nWeordDKhp1OeBlpCOkQOzAhN+p262rS4UDwbn8hrLjdiBycYQYhBbZaUAJDjOR5AVMMQ8IRQ3B+QjVjqqRp/P7wa61oEFoA8Z5SwFnu+2e6707UKPMMrYJ4jvf3lDr/pZ2bH/Kz/rTx7D5wPk+8vDtxehGhHnCjR13F1cAWG+uSeb59h5cTqkLYjHSO3Kd7YtrgmAlWKBoYt0yxGzWeuapj+P1XvK4zj7FhfiOtRzwZkYFBA+tSqKUgVRiPB7Q2BoV5u9KKcCldjNVyI8dGEvD5gjs+EFygHbovVszhvZDLfpFxgssKVnENqjZUpH+3rRGGitvAx4lmG9pqTwjMV2pRbIq42EjBE8IR1GNuxlWgZCQeIN/g1qB62Bm8rjVMFeWKDyegQg1Im1ienonSWPNGbhHciQ+XxvvbzPNlwKLSUldmFqsM6jGd+2/fCVspFBRvjeoCW2uo3JA2EqUzf73rBC1PN85fWsOucFsrLXXhUI6Ou8Uxh0Ax6Sza5jv7WYzRGdEXzKA4o5auGbBiJGsIH8O6Gyk71DpEPHlDqwNmck/52wU7PcUoJc8gkKQLeFSV7aOH0VrnXYvRBCB04Y9tmASqAKZUUfoa9Ydt7H4jiqKb7jimM6VulGWmbgWkRwdF8ZSseH+k1ZVEYn3/Bj8eOR+PLOaw+YI4OExdhbq1Bnkmh4Hj+QU1Fy61j+eiGeW2cRgTXh1bE5b1whiOHNKRSCVI4embbxhO95xOj+TnZ86He9baGFIiRHBaqWujaaYRCDEAja1lJhFu2wU/vOhZc8F48fCKy9MHpmYchhHKgo8RoxI9HKaeUFBK4f3r19zfP/Dt8xt8iiSDD8/P3J8fefHiBZfblRf1iA8DUjau68bj4yPX5yceH1/x3Xff8b/943/Iy88+55gib24XVtu4Hyfe5A03RD5/eEVZNr7+xa/Ix4FP7l52W8YkuHHiV5dnPnv1Cc/fvsVEWW/fcffwJW46cjyMXK+V/PyMuYHp/g4tlevynhgOlGUmDYG8faBtMxpH1AK2ztRrYzzeUSlIqeS8gRbSNJGf34IJd+dXbMuNVgtNG+DhdiVrI7uAGxMaB2IpeBEMz3D+nOvTUx9dtY1afRfplH54WC4Qug3EBUfTQt5mnBY0912HNo9dGxK6AtJEOy4uRJoJph0NV3I/NrfLBRnGv7yD8wN+nmIjxhH1wptb5utrJs8rIQJOWGdPPASCEw7jp3zy6R1N3zM9HPnw1BC5oPmO+/sjl7cXXv/yO0o23n8Q0kNlzCusBSK8bU8oPfJHzKG28OqTex5eeeI44KdIaStp8LQKzgeiBg6tB9gehokqxrtq2PhArCteuwp1DI4YIk4LBaPNhS0XnDbGKBiQvMM7D81YN+PZYs8Avc4ImeAHfFS09ULx/mnDeeE4NUIQpG0MUTkeE2YKbMhpADuCW4EEdukq5xVod/3F3b0EmBnTizN1GxE/UzfH8/uFbROqwnioFGtMEgiHRDXwXkhppFpFpUKOaPR9NRAiDYeLB5ZScRi1nGnVcKkHkDdvHIvSJDLnhmjvQn1RxqHSfFeHt9bBU86NrFpxkqg75OLa1n3suQdx60ePouKsd5v7RBNTh+5KV5OOff9/t3LNVbI2trar+wWcCN6DiaEOxJRIQBpsUvoEQbSvoVQQExw9FPuH/PxGjE/D3/43rbXCeH4grwvOeRq5Q5lRjLiPxgLBhC0mdL0RXED3JbvRSRcxRqw2astYU9LphGvdv1S0UYNw8J1Xen36ljCO5AqUFZZ3IKkzU9MdjkK+rYT7Fx027gRtndc5TUdMA1lvnELg6ek9w/EORDhMExJGltvT7geCUhfEYFsWUnQ7sil2IZEfeXh44Juvf9FDc1WJw8jv/f4foNuVp9uMlcxlbZyPsQO1/R5yq8bLh0feXZ8J1lhuT2QVbrcnHh5eksYD79+/5/NXL7vC7Lrwq7ffUqNDnhc+/f0v2ZaM3R0ZcPz1L77gl796TTFwaeQez08v35GOL9jef8BaF6Usue3AggXaXrgkILGPJv2u1JumiXWdEWA4nPvN9/IBEPx07CG9JkSvtNIxVE77LivXyjGmbneZZwauZDliOSMpcdxDohuGXt/C0L+7tq64MKIWuwVDHIbgpUOEzRoSBqzUvgsCJAREdfdbGdp+PVrt9G+HMzqgvWyI+H3fttD+j//ht358+l/9O3/Dak2oF9QW0gB5c0hzVFMkOUo2VnNsTdFKv9DYQJSK1NDXHTGjteFbxIIj4thqQ7XuNoWKC4FNV3wWvCacaReamFGSMm498SG4iK0NOxrBAtVlpjQhzTB/Y8p3rDLTdGMKxx1f5nBdU7fHgQljBF8qafDEKFj1jLGi3lAg1B1sXxpj6NmNjUYcFJpynhLjCOvVEULfVx4moWyV892ElIK4LnYppfDw4FlL1wCcxsTxXgiDQN36CLUEtlVhDdxKYi4bH1ZPqUYt4NLA87oi4tha1+7k0ogx0pr20azzPRhdHGjFfKS1jA89NKGWLgKrYgyuq8E3FaYgtCoU6VQihzA0eDJAhCTKlrUryV1fc4i2rjItXYxjeIIzgrmeZ0pFTcCsg8hFMHGo9DNzM7pyX/k+mmzjY+JMT89R9veZ9Mtot7k4mvsY87fbM/b3aW6NZf/7TB3/6U9/8bs1PpUpEIvH6obe3nI43XNVxYcDagWbb7TQaHMlO4XTJySglZ7lV9YNx0ZeI5YSxIkhHqhUdL72FGeJtG3Gx8Rz+0AYD+Add8PIu3olTCMuvWQcE1uDdV2xdeOzhxPL8oRLI9fbgju+4jw4np+vaOw8zqflxngYaTTacuVpfmZQwd8nvCUs3yitoDLiQkJy4XB/5jANfPPuLYcxsebMw6vPeLw/8fr1Gz757FPysvLVhyfGKVDWhXB6Sf7whowyuZEsykGN+OKRhzhyuzzx5Wd/hWdbiZeJVguvv/4Fr1488NWf/xkvj3f8+Mc/ZvCB99tKjAfadaY+PZOC58vHT3n9i6/5+euv+fzFJ3z14T2/LI3z4cT87bcYjc8++4yv3rxDTQia8QTG44miRq4L5g1pHrRnGuZtARdpy5U5r/h4IGKdYTN/oLlEiglpfb9xmCasKsVPDOJYy0pbV0J0bC0QzgPTOFFKYauFakpyQjk9oPOMOiHEux6bqErT1gHjYeh7ldHB2j1tMQql2ff0HK0baO4HT5VGQpzbAeCp0zeaYs7jNVDWwniY/rKPzw/yKS5yu6043+/6oXiUiht8tzQ0JfpKzMKjc+QAblB82zPtdMM7o0nk4CLuBEfXSMcTtJngHWlsnM+P5A1s7Ukq3jxxmPpOzmmX7e979VAqVY48XTt03rsT87bBrMTjEdpMEs8w3jHngpTuW2t5T2nYKhFlLV1xfPCtR1olZXCBXFZU405uUc4Hh2+J6DaCi8TgUNdYBK7vFa+Oeqs4VnSbOB1G7k6R5Qbl/crqFMvK268STRMiCgfQVRhTZhgSz08baw5s2tXxpTacS/tlWmip0dT4/PFAc4Fl6efnY6RaRTphyQvOPDSllZ1PIQecNFo1JHqq6vcewVI2jMBWhaxGar37DCa45ngV+mXH2ojJDD4Sm1Jaw7mANqVa7d+P9t1pNkdCaNKnZCZCT3X33Q8p/fwl63/mrMJzNJxAsH6xWr3/3jvZ8cgFxOGs0KTv9CsdhCI7Ug72guo+NnTt//tz/v/1+Y3oFB/+/r9vxp5o7gILDVkgHhLWKqcRLldj2y57+kTGwoj5wOA7MUWlg2PNDDVPDI6SMz55pvFMLRttvVJag3WGQ+/qaCvB9+il65Yhb+B7hzFMd2zLRpgm4jCw3J6JdaH4kWhGrX035VOk5YobT5zuzixzpkbH5D3zunGgq1sP4wnvI++ev+N8PIM1lu1KEMd0OtOqcD4MlNJoGJtWPj3eI6WQ68bRF+4++ZL3lxun0x1fffuaKQaWtXsRy+3GH335JeP9kdevX6N14+0883C648u7F7x+8y0pJf7oR79HMfjZP/lTXr58ydUK8XDm//zpP+duuufTl4/cn+75v372s54buaeNzFtlXVfOpwPahMPhQK6FdV1ptXuakjZm57BSSW63RajSXIT1ShyPlLx8zy1104SY0XImDQN52/Ap4bYLFicau9G+FkAJbqB6wGCUxrquxGHCI+yCb0QcbVvB99iqEHsEGYDm5XsUXV1ncKEj3LztGDghnc+0LeOCfK9+M+sWoI/WG3ahUF0X7E//l9/6TvEf/P0/thoDsrdZQa1L372g1XA+4HxXaHuDtSoRRzHXEV4AlnHNYaPQxDEYHNzK81IRmQiWO6qPrlYep8iAkkWYV6PUheAGcL0rciWjMnZ/nAiDZXwaOMXMWhODUzyFlFzfr7WBOW+8uL+nlZXrkvveL0RcEAbWrpgdApZrh2h42+0ckHwAWwltYm1GVmPRjYMEZCiU7EgpkWqmhcrpaBynV9w+GNh3nCSyFOPOG3oqbEtgkEjyzxzHBmnk/bPwfBVu24ALgcul6w/WrEiq+3OMbAWa6xe2Uo22i9F0W/d1TbcsiDcmSmcKb50EZjp0BGP0nPbvd60b1zYSXF9LldYFOBfXMPWwFbIaLY58JN1QWhczuu677uPVsitFC263e7RdbNOkTwuw2BWmaa8tGYI4An0KU9RT6aNaorLsRW0r4PE0b3gDBxRXMO+IzfDi8LvaNX/cC4sgwH/28x/OkvEb0Sk+Pd9I0fWXzJYZkuKnxNwyLBuXxdi2DZxhCv5wJl8vcDiQ84rmjIxHcD27S9cLpX2E+UWulwvSVozKcP857dDDjMU7yvrU90fDQGh9Sa6qnM9nWiuYC93u8OE94+lAC4EogRRHRlcppZBLY7q77+ONyxWXBgYR1Dmm2HAo5Xplbn08ODRPzR2t9Or8GcMI87wyTJGyrGQ1Xrx4wfLtN7yfN5wY9y9e4Kc7fvKLX+JKZsJzW5+4O35CucGQMwuV//vP/inVPHfniedtBpTBOX5eNg7jwNdP7/nmH73hs7/yB6wPZ37RZu5PZ1SUv/mHf8Q/+Wd/SgVutZHGkTfffMXpfAdpZF5n7qYjOOPy3Vsu35UO+nYOL/0SUtWI0wmjkeeVmAZUAqIw3t0RfegBsW1DraAameKAHwZqbRzuX5KccQPaMuMPx743aSu2LciLz3CtEUO/9eIm8BHVSvQO3QriMs4qpo1qjpIbqPbiPp5prVI1Mg0PrHSwQi2KpII1xVygBU+wxtZKn56yW0tco5/LlVpX2BXJv+2fePQEjZhVWsj4OuDMEMu05Gmt0qqBOdYC6j1bMbCutBTpykCVlZgdjo4RW1RJcsC7QvJ9PFuL74Z2rZgKL8bK548LWz3z9nZjaYXLAjFMCButBqzOVBtwtXC1SKsbLiojkeMQmGtFasW5yLbNFG09uDc1QgG2ivOCukDbjEBg3RpGI/oezFZdJx95XzAUq5VxHJjXlTgnvMtdYCdwjAMHK7TrE6VAa57F+sv9uinMffR+f+gsUUKCuhKJBBNGX1nyRhqENSfCAKhH94zPMQrWRnJreNcQaRwibL7vu8UKKQiG64CZVgnSCC2gYUYNalU2Mbx2NObgCrUJOMEM5lYZZMQwdBjQZoxNWbTiXaB8xFi6QMRDMKL0PFXViLhClvB9F9taozQHFNQLYy2oQJWIM0Xo3NLmCw3plDA1JteVuj40TA1xXW2cVWkaKEUpJhQzym4P+T6cugni6g96Fn4jiqLZjWqetjpwAisMxwm/XHpcybpL+U0YpiPb7Wt8POKcUv3IOJ261wyhXD+ADEAPkm2tcb67p5ah48jW9yCO5fkdqGc33rDNN6CPe7QubIt2yfLt2mm3MbA+vyYOJ8LxkW27Upcb6e4B7xq+bQRvtFAI4oi2dgKIKMPYA3Z9GMjLrd+I60jNxpoX5CI4U8p3PevvfD7jpVGaErxxLQvt4vHzhaf5yuOLR/7Z67/gkxePiBjvtgs3Bl49fso2XIkBPnz3ls8+eeDTh1eEaeC7b77BmvJJOPD4eM9PvvqKy7LyN//wr3J7uhHOR94uV/7Vv/N3efPuPZ8/PvLPfv4XfPbFH5Ci5/WHZz59+Yi6SFs2Di9fUrfMOHal3XbboL6DmIjj0CX645EqgpXCMMbeEW4bqBKGE/hK2W4stRHiRCsrwRrFeZoI/nDAOWE8TogdsJNg0otbK92Q7DxYrTQaZqV3fjgseLg9d9XAcCKdTt1SgKDWFb/qwNUNXRdcdf0FGRy6XPHi2eaZECNuPJAbaG2U69e40yOQEGdYXv6yjs0P+7FCM6NsDTVhtrWbvsVTTVmKR5whBs66pD66nZ4SpY++NqVKV2gX18jqKAZBNybrat7Bd79f2PmVzg8MmxFcAjZKixgQTajrxjhMDFax48C5OmAljI5wGvDNs9VIni808cRgXclYGyZGFiVlQaURveICNO2y/+B6HqSZI+/TCLThXeC2VSQknHdorRxSIvmGMNLyDUKPY+qEpcDWjLY2fOg7riSJFBZSnMAyL44OZOn6m5sR44mnspJborpKxvBqu4dP8V476tEp9yLU1gOezW/Y88hsG00gFsfWMtEP1LW/78wUn3eaE+C0UbQgcYTdaqEYm/W/s2wbJv3PFlOC9lQc0UwTcDjWtjGK7yD/pt8j2Kz1y1DXfghqjmyVSv+za/0I8++XBbd3dskEhE69gj3btocQO+dpWgEjek8EEo6bFkwNrxGnMMReFoOru7/yh/v8RoxP/Z/8PUsN8mHs/L3lisQeMKtlxQ89lsnoviW2AKMwLjdWUYZxIp5eYeU9ta2of4GWpSPBnj4QDgMh3HUAdcs4sR6e64yWC873vYg/vEL9bkbfk92PQyfolPWClqVDb7etb7/F9/DH6Y7wkbWZPDIXbDqh25vO7ZQR5z0pFKKbuHx4QxjvaXUlhtpHn81xPD+wbVtPK19W4nRCWmZInnqt5LLgKWzOM6RAQMiXmRdffN4FLW3j8f4FWONdnpG5cnv3HXjH9m7m9/7q7/P1P/0Jd19+2r0/xZDTyJQG3DQwv37LZW34JKzjyKecePf8FVOcWPCIU3KFqcHildF71qWPbnN2WAqQC4TQ4b2tA6Gfn9/gDnfYeEbU0G1GPiZhkHDbM94nwKhWkdzJMZYStXaxTr6tNDzROpKtxm6lgN41Iq4/m7V1sZKPuFYo6wqhE3FaaxCPeNv6KMpLNxnnDNtCGCfq9dLTO+KIs33J7xxVF1xM2OaQbUOHzmSdvOfpH/72+xT/y3/7T8zU8TxvjGJk2frLD8FU0MUIqeA1kPdLRTMHzlC6qXxRUDWgERkou9hFqGB9fJZ83ScLAU/FaZfx111cEazQVAghMPqGo/M0/ZCgFlIIPG8L92kE10euQ+iF1OWtx33huw3DOaL0gOrT1NWpczWC087OFKimTNJ9f7UJziklB0LYEHEchpHb7UYKA2obJSshBU7R4ymczp62bB3y4IWgjlw3TtMduSycnDA4uDvPXNY7LqvyfKtsSi/G9Iu7GmylWxM8Qhb3Pc2pNo+X0AthhTZUxh3QraV2SLaDTT1B6+67VSowDXT6To1cxLFsBfNCMWVeG/iE2soQEwEhiSOb0j4mUNTWBTN7IPCqbo9y6j5BcW2nPAnFfu2OcAijde5qMaitdb+kQDHD7WkX+P4bcM51NB2eLEJSYfWNoj2hQ+qBLWaESEGpqpTqUdeP3n/z+pvfrfFpGg4Usf3mH3GneySGbvJW2VVkfSEbfcAmI7iIHu/xWnpa/O2C5Q1iwJdnYhjJa0aGgXpbqbERUqL1BGOG8UyjcjoFrs/PHE8ntq0gZe0L6uDIT++Zh5GybRxefEKWSMtKmFxXXModMdUOhY5CXW9ITUhS1BmM98g4EVulaaUUT5GZFCemQwJSj2CqxrZ914sKcPCR413i/eXCw/GMDBPnA2zbxuPDHW+/fcPT9cLx7o5XD5+QBkfeZqwIP/vznzKejwwV0iHRYuLp6YkalOv7J8Ldkc++/IKmyk9++jNexTt+9e23jOmEmfDjv/HH/OxnP+NVPJAmzyd8wtutcTwcmA73hNh49+4dx5AYvNAOjxzHA/X1zzkc73EH7YDzCnqKtKcL0/kVa5mx61tkJ87w8WC5FUmRvO4d17pCGDAriEU8xnK7QJ4Jp0dcrtTgqS1zd3f3fRjtMlfWpe8RLYbePcaRiFDmG7ZjrlifIaVu9p43KDdkGCBOWG0cXz5yu/WXa64bfpiwDnDpZunjkXgeu9JwvmLT78b49M18oVpXYLZW2CSxWcNX4Wgd6q3ruGdQth7WbIJUqK3vXKsY2noXsFr9Pskg7uO6Vrp6MGnFBSOIEoJDWyCJok3IeOKezpCrchoFt8cVqTm2mnFOWMXQrCQG6mqQOheTKngqKTgcnSzj0e+FXK0OxFGxolj0RBoVQa2HVg8+MqUK6gnBgas7kWdlCoFaAnglhm5yzzaQ6WPcect4IjV3vYGa0mJfoVzeFjwLBcMFj229a83aR4+o0FxPoPie4SoBzFBtFOu7tatW/Op41t6taUj7ey7StAudWs37ODsQqjHgGbSwRaE5R1HltjWa81TrwrhZXYfg+4bWnnqiKpgETAUvntoaWMG7nkjkvKeY60k0KAOeOPTv3DnXs2VrZmsRT+cE9Z9iW8+TAAAgAElEQVRHF8/k1riJJ1QwU+qu0xEEc64Hibe+Z9TQOJgHFC/WLz7B9Yvtrkz9oT6/EZ3i+V//d61HCIX9Ztn/J2/LjDfPeJhYa6EtNwYT2g6MFeiHShUdEmwbEkaS86y3HoRJiHiE49jTHWQY0NI7kRACa9lgLkx3d7Sxg2a5LGQqwzD1KCTfi5XznuQDqxnb7crx/gVWKqVsPcWhbqjzOGmwCzvarnwU7wkOjofzTgoZuVw/EFIfP67zDcTtkuP9Wez0/7uHB7QUAsKbN9+ANsa7A1MYydqY0pFSF+4OJ4ZpZJ03/Dbz1Xfv+OyPf59UK8PxyJ//8i8YhoEvTi+4OeC2cHxxx+Vy4c27J8r8zKsvfsQ0JK4fnhhO99yd7vnTP/vnYIWYRnRdacHz4vGR1ozz3T3Pz8/4OHTFLh3mnkvDmlLmC8PhwOADzx8+IN73AOY4IhL73rbuFo3xc9p2Bdcg3nUE3ryAN4JPJEkUm3dVaQAfGSaPVFjXC45+68UEf5pobafdCPit7oxGwZaFDlN03fKTaz+No+foR7a100rKtvTkj5Q6XlDAWoNWcXEgxkjeGvqn/9Nvfaf4H/4LP7ZpH4vVITJkT6RgrNwSuOJI4okmNLo3t5C7NUWNJH282vSj1J4OZ6BgtVuIWjWiU7L10RkAaoQmRCekIEwIm/QOyUK3YkiSnjNqQtOuRI1N8aJU3zsm1wzn9ws0QpDOMh1iZye/cBHxmdIcTipbbjQJVPUk+gVtGCJVIdLYakOCkXDE5Dgf+mu9NSH5QBwyo3OMUdmaY772dJBcCq04pmCE0EOvjzLz6r7gI9R15P1saJ1ACpWJbVU2NubtQAiBp/lCEdhq3/GCpxZlM8FhiHccY6MslRw9a3Gs60oYJ7J1iEZWYSCwWtsBF4qp7MW3p2Zog6yCi4ZXpXgY6CAMdkO9+R4DhSWyFTyepyaItE6UqkJp0JIwaR+RZus2pmqZg48dWWcF95HiFcKviz/u++xGxeEae9yUkaWx7IiVog1xhdAc6qCZp0m30oh6/sHXX/9udYoDcJ2fqCHQNgdRkRiJ4xkTjwuZx5hYcuOa34NNQMHGRF0dw3BkXa/IeCBoIYxHUp0xq9St4aZdkLM+c6wH5tRZm/X2HaCk02cEy8i14WxPk06RpFfWBiVfGeILlvzEVhWJjmjG7c0vSSnRxDOmhKQB8YH1du2+nXQEcbBlptMZVeXD0weG8cRWCy0XWjGIB6bTA9Vmym1hmiaaC3gXwDbe/vwnvPzyD8nHxO+d/xojhaen96QU8ZpoTrkuG9MGr7/6OafPP+Hdu1+BH8h5ZVXl8tNvUS/k5Pgmb3wxHfBffgJPMx/evOX4cI97+SmPD0c+vH/m/XXj/EL4ZPR89uUXvP7mGxyewyefsq2Zba1EAh+uC80c16en7uu7fcXVHCAQu/ZtW/vo00nqAOWy0Oghw56Rpv2HHz24w5GyfsCz0coMIeFcxWJkaxvkjmETu2LbzDZDOn1C8AE/HthmcK7R5oYPrnMut4UqS1cb5wbeeqfjB1oxhMw0PTBnJRcwHxEDxgASCcPQLQkCt+uVcD5R54VtXnHT8Jd0an7Yz2Pqt/WtgWoh+0z1RqjKwTzNCYZCMGoNIJloDlHFJII0SlaKD4T9UhubI2v/LVizbsRpcLGIUvDiSHRrwNAacxPetPp9ZzkUQWj45jgU0GTc70INJwnMGFTo1+MduyY9DMnXXgyiTTuizPASCL7hg8e8J7qGF4cizGvsgiFvbK3vREc5UKWDq9stYJUOEpGFtCe0iHgOARoD1yI4jSTfeMaTFGIRnBt530aGyXiaC89zY9HGXDy1bWjrPOGquZNazCHqUJQqQi09jWKr0mEBxYgiiDvAVhij4cNEbQ3fCpMGvDeyVsyU5BzRHNX3NJGDuN5tmVC9gRPqbm8ICk0dFrof8qr932p6Y3Ddl3jcVdnRhDEa76LrPFXxPEnD0F5wQ+BD9Tgq2ETWRpRIysok2r2V1ggorTZacJgzRrUeCC+QrHNwq2947btf+cheRzC1riX4AT+/EZ2i/NHfMXl87CPSkrHSwPcxyhBCN8mjjEMk+sTl9i1lVVBjOE6duiVCyQUniuLBd1WkLs8gjnB4wWlwbN6Rbz1GyOpKTKn/GOuGkyNOKjGMbGGEcsO5RNMVrYoLrscghR0Rta74wwEry/cKLDXARdiNxCr05XmMIInz8UApxlY3XEz4suDHM3l9JqVO1vBDAoQlr2zXJwaD7ISEI+8vnONdN8KfD0dulytffvkla164O5/JmxKl7wOeLh9QVQ6HA09PTx0ssBZe//Ir2phIpwP/yr/0J/z8J39OOpyZ5yviHc46K/L12w/I0LMuT+cHSoNcVpbLpRu8b1uXto9Httx6Oon1YGTRvrsza/jWWLdn/PjQ6fwRcA5ZM2FnkWIbdel2B3EDxgXkBNYpIUMa2Fr/76JP+LDvFcvas9/Kbspo/VaqZd7jhY49GDilnV6zG4Fjt5pI6sByTUeOYlyXG26+od6QNOGtUs0xHo6sl8t+MHeaR67YX/yj3/pO8T/5l39sZr3DyHuot1phtEi29n3k2RDA9qKY1Xr8kRMCijbPJo4g/fma9pEqJh3RaAGv4EIXSZgqokJIEd86E9MbFO0RYWqQ1Bjpf4/VwiEFTqqMqTG6Hjr7USRje7aeE6PtIOwAuCBErfgATkLvXl0kaucrR0bUN3ITUthDsffYo0Pqu/QxTUQpOBkITmFPVymlEJyQq30foGsqhOgIKGPsz/IojZgczzNkN1Bz6er32p9bFWXZMt51P23W/v9SMcDj9udn1s3yUo0PzQhxpIhRttBJN2LcVIjViMGxWe/iV6uIOWbrVo7mOqCiihE+MkRtt4DQgeJmnpFdX0HvDLMKzvdnPk6BqEpwRxwNpz326Xa74f0+LFa3/1tdcez0I6RdKdaFWB/3l601ojhUrQsvTam+C2l8EDYLrK314m4O3TmoFeO/f/PmdwsIPv5r/5ZlKaSl0pJHtC/yrWz9GwwjaMW5PoZblm9B+81Q/BEnERsCbd2QKEQ3oNb3EuP9SMuC5gvD0FmZy4cPvWsIkNKA8xPrusLyAYYz3gc0OKSuaFPwnq5xvnG4e2C+NdLdC/yeCeaCkMLA5XIh+gC+U2mGcuG2XXqhrBXSoeOl4sAhJW7zBa0V8R6fJkwLqsoYBoKP1DHBvOGicHSVEAe+fZ6pJXM4jdQg2POCeuHx4RVvvv0VEh0/evUZf/Ht1zhJvHp4pLaV33vxiqDwZ9+94WUMWDOGF2ecc3z1+tv+ReTKpht396+ITXl6esJi5DBF3r29IlFozsPlfS9occIkMR0H1tuM89qLhGqPdgodXefU0PGe4KHVjG0Z77Xvdx2EfMV86vvk8YjiCDS2MGBLjw9i8HirpNtbLpqwXzcIUDLUuV8fm+HTXffWxW7kDikg7UopHdTOTsWI44mSM4j0MWoBYugFOT/3UXqMEAegCyCaC0jZIMQ+Sl0z9rP/9be+KP7Hf+tv2NL2Ymatp9j7HmSr5rq30Ix9ug/QvWbWX/5D6C/ybcetKdBMWUywauAi3vVUmyQDg2uIKIMzVMFLP+p3IaHa1xuDVVzwBO07rlgjJc497LY6osKVlaRd0IU5RHuxjk4o5gixg/01TCRrsOfzuSYEaQzikO65QRHiRydXa0gV4qDd8uCM0YUOv/YrMXYovGoP6fW7cEQcaG4E74nRfW+dOLhGDHCrQm6BkoVry6gGfDC0BWqDEIVWre/6vFEt9CQWqWgLNHrYtkkP4GjSn7u2Lmqpjb5bBYp2ywZSaSZU53onKODMU61Qrce6fawDWfu+TqWPOZ1pp8zYHuXWlCLdn+iDkugimVQLWSJoYdMuOjTX03ucRJyHZIFCj4myVMkqrCtkqQTrY91IF14Vdd13bJ6CUqQxiO8XMedwu5io+9KN/+7Nd79b49NtvTH4DnPucsCnbpcwwU/3e+L6SDWlhZW744/4f9o7mx7JkmQtP2bu5yMisiqrqrvng8twpbmIBRskhFiwQSD+Ab+Gv8cCobtgxxqJkRjmTtdXZmTEOcfdzViYZzELJCTU0txp/Fl2ZZcqK7KOHTN77X3v2wuXdeF5K+Gx1zay3aEtlOOZtJz48N0Hatl4qTfqcacyIbenuAq1RtITx/0AjbeXdHqgpEarO9wdL8b0/hfknrto/hjd3vvK3oz7cZBbJV0ufP3yI/N0ZjvusF+Zz2eeTVkvv2K/P4UR+enNN3u2clSMxOXDO/brE+aN0DY787Ly/PzE4zzx5fmZkwofBR4fZx4vZ56eDu5PVyDMzMv9ymf/PewH/+D731Ad/tGHX/PmtFKK0zSSMd5cHngjQs4Td63cPn9m33e2lxs//OqXyCVMlJ+vV375yx8o168c9xtbE6y+wJEgJ+ThF2HJ1ArTaWXfd7zutFLC2T7PYaP26o2IIL5RC+R5jhswdzRBMqVMCsdB0wV5ecGTkE/vsJfnELjg6CFsGJXcTbicyRRzQ9cF13CxoR6U44a3yvzwyK6GuDNNF1RbKCVNMC+U2w7LBC32n5oStm34ZEgKI/TjONCuUrTWmNUpXkMQpMD88xDaWHJmj3uwV09JLZBydDO79CimWEuBTxxUsjov0lBTcgHPziyKeqRdZIRpNsQcUac2JeWGpcjYczNOmii6Iw4vW6Emx9uO1tj/ZTXcC9e8M90ay7IwyxF5qnViM6N29beoIZ4oBp6FVhXRBcypIkgrkVghQlLDMaYpIa3wckTn2ohTAVFhoSG69EgpQKJb8rthk0I9SGmitB4BRwpbwJSRaqQ0437QKqR8oZYbpRWuRPzdKl3oU/2b6Ma1kj0hRnTU0icwVJIr7uCEP2xpGUvRTX6XFVnvTPMJSuP60lhbosidz0W4TpUHITr/dKa2G0c+k9Vj3HocvE0Ld4uzMqFxTBNWGl+OEi8FCrMkzvPE3RuHhyBmk7idPCSTl5VSY1KWPb6f51L5qtJDD4x5e40pc8Q0/IaLxT5RZ5I4J6LwqwtZMgo0Sf25Yt+Cio2f9p3070WnOP+Lf+dGZprCcqnen7sIwplTwsoL5djR9QPW7uB3KBpj0dPCspxoTFhXLrbWSItw7IUsiWqN0/kt+/NH8nKmbM/xZmTCw/e/5ii3b0Gz7QjPQt++IDpxeftDxC1tVy4Pb9m3CnOO5Pj7HdeKSsa2KzAh5weWNW7i7s9fmZYJc2GaJqwV3l7eklJi32/c7xu6zpTblSnN3L98jGQPVfLyQGuF7x7fcZoSt/u1x65krtdnpB2s68rb84kf//gH1qwwzxQXTnlmeXPhf/6P36Hi/PD9P2RK8PHzp1jILzNWQh348PDAlDKfvnymHQd5njCHqSdQPJxWPn+5gjs5Kcwrj+8+8PGPf+R0uUSmZWsgBgjTa7p22UhrmGXX/U7SRvMl9nP1oJLjZYNE2e/kaaKWDUkrYndUFjxrdJNtg+tXmGMsjcc5zHJ5Fy5I7tR2kHSKsxnbSevb7lsqPZuuS8lzuNw4BZFT/7OGiEin8OTMm8Tu0wqtVlIPO1YN8+JpjQ77Xg/a9Y7/t//8F98p/od/9k+8aKRbzBhT60IRg3uPQYt/IzO10EdY3f4N57AGrLTUWExwbzRJbLXwYVVaBVNjqwVhic8gK6k4pPDIVBqlzliOHNEkD5jeufTxXnFnxqkKsyZSPUDupGWitiX2VO4UEqbhLbx4IbWVNu+spVA8sXhlVkgeZzxlhnOBus0wxbQmJeFBTjStMf6XzJLv7MdEouIaGYBpv5HyA56UPO9kg02i46k+k9qBqvNUdux55/nhzNJmTqswb5nn/EROMyfJ7KIsFVpSbq10lybrI847Hy7f8/zlmTStLOfEXJwqja0ot2ocLVLta1MqTm3OV9fYz3uiCbjm/uxLYbXo/i0A3E0ptrPXRJEGRKeI9BgpJbq3BnP2ONS3hDBzSzsnN9QUsiLNaTmEW5N1mzrZ2Yixp5oziVFJ7B5nF03CkzW3iZRgp5A8dpKHZzTFdMI9TreqxagXlL/98Wc2Ps3//N+4VKdikBNsd1LOrA9vOV7uMZPed6bl9M1Q1jzcR/AU5xz3A/QFUSPre8r9KX5zL+RloelMmmY0L+HGIIJvX1gvH7h14+753XdoPxW4Xz9TtivIxPr4ntYKbHuM27gxn3/N+3cP/PjpIy1HUvmEopOyd4cP9xZ7yddvNK1I3WhlZ6bB6UR92Vi++yvutx+hNfKyME8X3I2y7XhWcOFNjofStMx8/vqJtw9vOI6D7frCtCzoMnHct/j6/eDD++/48vETdpqY55nL5cLteuWH9x/4+OkztmT2z1+RaeL9w1vK/c7799/z5csnbL2wiENesG3jhRhv6LzQSkFKYasx0nndcZ/XC/cSO9pSDN1uNAqqGZ8veDnQNdyClrywv3yKScA0MS0rx+2GlR2dE/76462ZzALeKLfn+G+Txlt+ugAbbTPmOe4Sa3lhOe4creLzY4xJS4XU30S74vgoMcLZyjPyKsapBz5NUDNpiV2VbxuajOYe37cYwozNK9oOrNYwQf7v//Uvvij++9/+xnNLVBJ5gskMiGKAOq7Oo2eaFLzR78MUtYK7YCqIN1wmpurYZIhDU5i7UGQmHtqeIhtzt4qpYh5jseQVK4nbEs+k2uDBLfL2gIlM0sqFzKYHbyTUrWdPNBIlhY3cqYZfZ5zrOJutPJRQZyjGasacYJ0MF2V+WKhZeLCdd1OMkE+nCV8L05E5jo3fiaD3CZ8qFOc0zXy/VJ7uysN5jpOHzUhlZlHhdMzs/sLHDO2euOQr08fE73Xh+TDKVJFtQtbCulyQUmluHOI8dRs9I8zY9zkjSZGWmCyUmOpGUcieyamwFcFVmMRIJHaLkakmgxJ7wFng0FgrRa8VBUa76rgUp2qsPlydWuEwo3roMy4aGaTiwqoCUnhwwUz4mpR0NFQnihuahJduri9VeVbh1iyKYrd7m/rumT4S9Ub3M7UIeWgrpoZ6BRQRxyTENq0X1opzd+M//fjx51UU5Z/+S2dKzAnMU4SIzhfwgpM57nFeUZuxLmt48O03UEhVaCmh4tjLC7q8hdOKuOFJmNLMcRys84ITlmx5OqGpclkuYDEKTcuZW3lGXzaYl/5Dp8yEj+d9v0XBvm3kdWGeErf7E5qWPio0rHcitn+MY+X1kcv5LfeXG3VJTLKQFDLG/XaliWKl8fj4yJKE2jauWwFrHFtBJ8fuB+u7R7aXOw+Pj5yWlct64nq/cv3yMW6l6sG0rvziu+/546ePrOo8PV2hGb/97d/w+eWZyRrtKDzdX0iXB7avX6NobAend++YT2GW8PL8xJKiMzU7wh2oNlhnqBOSDPfEPJ8ja9d39r2Rp4Vadsz7aColphR5cK0IKafYOTbtDzmNz9BCtICc0ZQgZ5JYhJ82Y5pXRISjHKhoPBymBUrpSj1hnhRvFc0T5b5jZYPzGXwiecHM0axQY+Ty6qAsqniJByl2RFd4vqA6d9FEozUBegosAJXUb7BeO1b/3V9+Ufy3v/q1v5ne8AYnp0qS/33/VcVjllqEYxI24FDlVsFcOCZHaryxHyiTJDw5SwTokRaDI6KKJhfEnLu8HvZPlCzMBUyXEDVZY00T2Q6yNkwineSNtzCgnpSpCmsSUq1MGXIyKm/Yto2iJTqlWnlM3cJvmrHqvKTMWQxXIdFYLDoV0xlkj1G9wYnoeNPU3XW8svQiMjVBsjMJuAhnX2g9gLpYT7P3iTpNPIvwh3onOUwmvPE4+XCFZI2iO99J5n2OceNehUmEKTUOSWylkTTudZODZeU4KtjCIZFlWvtOdOu7z9gBVpx4llbipaOI0xpk8TDd1thDeg0VLsQeuDgRuwYUc/YuwDkLHE54Ab8aBOSwaDNmxO9Ii3vEGDw3qhubCNkTd6EXPyF5YU6QNZEIX+HFd2ZxtAkV5XC+qVDVWhzte+be0zfuFsf9hzr/5ePnn1dRHAwGf17+9V//Y39VH67SSCqcNTq1Ux9bHW1j9Yj32YpS1TlPYdOVHE555tqPynNyMkKVGuPCFN3J3pWVTWBn4mrO1kNwd3XUlSoh059EQ33a4/IWtMv0a/wa4aizeBiDv6F/YYLJK8JE8sgrdWDS6PpBw0CCbjXncJUQ1ogIU1cWzwJud5KuzFlI/UYyayJrw13IBkmN9KrYTQnt6llKI+W4ug4laXRJlcS29yQOrRRXpCovFt1ZSvFnrMWpGmpZowuYSkHm8GO+tokkYZuXvO8fu8uMEcKdo+/vGx7rBSI6yqTSLFFxUlZaNTZR/E86OXdHGsgUt6ClNaSb7M8Ke/Nvhv+SFEWopOhkkbCLk/CgcXdet+/JnVWVXYy7v6Z/JO6tK9Xl1fghDAXcHZXG0cJV50nAeicMIO78x08/Xaf490JoMxgM/rxUD3UthFd3cqAZKEhzTPqIjYxK4rs50hGqKg1CPWgH79SRJHFE7s5bz1SvrE05UiET/sYVZS/RuTfi9uydJ6xnV+YeF+RizC2KXUpGssSKMqvzgJGtcErE2VPvmKQpqGHtIEucZWRRaDWcVeQI0wC9IH2/+IDg+fhmDVdcOKUcu7fWYsIgUGuhaBzlm8UqvaLUPgnUClmMqTXmnrl59nCYKQqlhaAwq+DqlCoYSgKWHF1q0rhfTHmi0SK2vsdp6Ws0k0AlMXOQPMwNJCcOM7bDwiIuRdF2d6r6t5xSIfxgPYXrz1aMqsq9xUvDqZsguDubOEv703MT+9ZVujtNcihV+5mT2YEo3Rc1Cl7RiexG6Tk2B0bxxuJK9hbrEm9MySnm/Xdybjh7jQ78jUQeaus+rVmVVyObyX/aQc0oioPBgKM/7C59z6MI2R33EG3M3lg945Ow1cpHM66qJPMoEkhc0+3GHyX2UKLhCDOr8FfJ+eALs8f9Y/IaIzzpI1WPm7zq3kUeAInkyhVDDbIpc3ZMExvGVRpzfRXpKLMR/p3aEOs9jy7srSLqnKPUIt4NwKVgpXHLoYosKDkJGWUR49YqlzzTpKEtjt9nMkbsv3Dvf2/GkhLPe2FJRpHU96cG+4HOwqxhgeetUpshqZ9atBUxo84NNyVnkHSntEySxGJOS8oE3HyHJtwlhCaIs1ehTc7Nwyhg9oj7yp7YWmH3jGpiloaZsBMH83JkNqkUi7SKWivJHdeZ425Yat8M9HN3jtk1jBhKC+V8E43PSuIsRaszq4BBoZLpqUVUwus8RsMA0veUeLhMAZzFyVJx60HrOBGvGXvMlx6npySsC2zSn4yPfypGURwMBuHmoo3iicbB4UJtwi7GUhpbWsjWqO1glgQod0s0V25dGh+K0iWi1jTc+kxg80xpld+7sIizVe2mDiGSMBc2lW4YrWDORB+bJaFndZGk8dycTybkFkbUCaGZk9V56HuwiZndDt4iVGu0NCPVedDo3koPvj155XBn9VB9JoV8VN6cF56OPhq8NYo4OzG+PUQ4q5ISvOkvDafiyMV5PC1sR2XxMCGvR2NaFBcobSPLA0WcpIpMM2UvFDkgQamN1sJOzTxMP5qUbgguJISVxJEVNeeiE2d3NDdwOE8ap0INcKWJdZPt8FgtFju61g6Kxd6wIlSEGcEkxq9IC7FiN0yo/aUId7I7xSROg1ulxQfOpOGaI/3e8bBQktJaKF5tIqlhCjuOW3R7tUFrUcQ/e8PJ4MrqMQZ2j884fkAbqyZyO4jTtcRBWDfef+KN/iiKg8EgVKKvBt42obVxz8InMw7JiBhZIsD2dVQ2WyJJ41ziAZxSQ1rFs3KtjZLiXvEw44XEbs46wWlRbrVgbmw2x9G7GdrVliSltFd3m27sDbiHmbwgFBVWWr/lM5rC1vURFaOg/J02kicmD/u3jEd8WVKqCZtPeEtcRbhbRVTIaeXTy4FIxkXidrObBzwn56ElrqpohS/aA4I1szwrT1L5UITLLKy28z6vHO7Mc4PjgcdLH2XWxnE4ReTb9waxd0wpc1jF2sRhJXaTorxgEfhsjUTicOE4jjjGV4t8xJqZJEa4pgLabyqdMEWX2KWWZuRJeXClkpBE/zrnxSt7yty88tBed449EktTv+eNFxaXUIqqRkUt7uytQlasaQQGWMMz3NBuyRepGWAhysuJr0QM1e4Hk2QaBVEJEWFPESniYEeYJ0ik22D+7ULrp2QIbQaDAf/qN3/tifASXSclufGkiUlgd6fUUBW6w6zKyY0F53hNPpA41L/TuLdIVhftHqjfrNgEl7DyWlrlTuPwBCnELhn51hmIhDr0kpTanXase16epXHux/85Ca01/s6NbMqNhotGWgeQpJJ6RsMFWFW5VGHxhk2FiwtXqd+6GSUs66QKmpzShSSTJlxCabmkuKFWp48YnSQFlRW3g12iwOyWI0q3NaDSJPf/J8aDjYMkocB9kMrU/UdXjTGxknBTEKOpd1eZOCEqTlgP5szujTk5VsI2zVvBVKjNKRo3hrW70hR1jhZxXJVQg759bcaSRqxeMXbzuBu3GA231jhQmjinXjOsOVvfP+IhtPHuTOMe3XUomIVDBOt3mIc2sseNYft2BxsdqFmccyTvMVq9PEmKCcJNDbWYSlQJNxsl87effmaONoPB4M/LRiL3nd6tRaFp2lg0dJpF40G198zzF7HuJJJIXfAgGC2fEaynYHi3ITOSgBIWLQXn5to9bgXxicYRIzpRmjkrDpLZ6sGssKSMUFmbUEX4Q2lUv6G+MmUhu7BnJ/nE4XeSzqg53vSbZdnhlYLzdQpBy9ISj6qcXHr4cJiiF7Pu17mQJNQ0ZxdmreQl87Q3qjizKpkQHIlnzCsm8cDOAkkO1DNlSjzViIB6q3TP0Yy7osnQ5Nx94kqCo7JR8aI4YQC+kkK8MztzMxadSewxlrUbaieixFqchvXTF+3+z8k17HbxJJEAAAE9SURBVOy0UFwhwd6E5662Le4cqqgYU5lJaqzacGvMmqL4Kf1EqbHJwtZCNnP+5tEqVO+7x6bctZE9MaUwNz3EkVbCok+VInGN1eIvPRxeBd602IuKwF3jRATAm2DJWbAIMyZG7FWde63/x5/p/1dGpzgYDAaDQUf/718yGAwGg8H/H4yiOBgMBoNBZxTFwWAwGAw6oygOBoPBYNAZRXEwGAwGg84oioPBYDAYdEZRHAwGg8GgM4riYDAYDAadURQHg8FgMOiMojgYDAaDQWcUxcFgMBgMOqMoDgaDwWDQGUVxMBgMBoPOKIqDwWAwGHRGURwMBoPBoDOK4mAwGAwGnVEUB4PBYDDojKI4GAwGg0FnFMXBYDAYDDqjKA4Gg8Fg0BlFcTAYDAaDziiKg8FgMBh0RlEcDAaDwaDzvwCWwjMjT87GFwAAAABJRU5ErkJggg==\n", 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TTVQ29qQaXoEDbYeq1kB1OByOU80ZJYrfe3oH2SrtlmqRVG0biOK0Azs8Bl7G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\nzMyyvwEnvbs5vBaWXAAAAABJRU5ErkJggg==\n", 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\n", 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OeUI9r3V7/FncBOA1bpv3hE16F2hS4BvdnLfEVW73RXf8EKZZjhIINNyhcz4g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LuEu7pTrRvyeREASY46RahzJiu2qf4tq6QOhwFih7LmyIr6+zP3jLa+6Te/aCEsh3hIR6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hJkHqkrgez5i7PI+65ZmgTjyZbZveotRU5qwfQFb3TfQjdMWFNhaiGse+ZdxcXDjtnLicqC\n2XuOa6foML0Vbjp2FsxYVorBg+9Zbz3LbqKqG2LqMYMUOkadkSGhxmHnLXEbiAnWOGwURjEE67i6\n5pi/WlSZy94R5gNhZ3htNDx6oLx0afi9neHDdQnd/OaZMBp4uFV+oIX3LhKv9JZ1Un5nW/NDzYi3\nhk/38NFDZZEERDmtlf/hbsuPtyNRLH+4TbzTG75nqbzURzZjxqN46/j9XnjcJF63lu0u8o5WeIe3\n9CHwhei5PSjf0xX83he2gMKPLIpKfa7KsVhumkRTeV6clAclvRUavKOGG5L5TG94olb+ZGt4oJn4\nxs7z9Czhs3JgDHf2/PWbxkLKXK+U397UtDkgInzm9tvfg/x3nn5Spz2TWFNCtQSx1lmYS2a0FSeV\nIJr2/t43Q2XylhAkCF5ysTSmMtT2MRelVUtgzFFKHw6clEDWfq0fBZIakhEq4KAy7EJmEzInnd3T\nKArHV8Vy3icWVRlOZ5ZSEKWQcsQ7W+gaMTHG4jmeUqRxQm0dmwi1SXgjVBYQw3pSFnUZ9FsCl9kg\nuQyErRfOQ7FKpgTbvYrurTCExFFjOGwM51vFmlxsRSLUDsYYiXuyx0FT/MrOZMYIrUnc3SZOZiV0\nPuTIxS6DGI47wzqCnRIPzT0X2RCzIUyZSQFx1FlxlWU1GbIqYSqotCFqARJ4UzjMFKKHiHA+ZRoj\ndPtm4QMvnEfFaxHqxr1tQXJR+ZMIjuLzn9QwUHjEY0wsjDKJgZwQzcVbLRafE1dqOLHK/VAY0MkY\nNGeafVCuBDj3Acq9ajzu7RxZHN6UTVzeWz+UYt95s8xm2jclxgxWIxFTAo573rJNsSje1pFTwhrI\nFOyrEfifn/3WXLPfVgPy3/2u92veN571SdA9faLWlkki2TjeiBEXB6y1pVktKT47NhaOnWUVMsna\n4i0NCZFiGQiaqaRiE4XWJgRPr1MpiEBonLIJiUmURWqIsw4/bcgIuzghYWRtDQ0VdV2zS4ntdmQY\nNlDXtBZqMUUdDiO1LeZ3tTViIWwitB4X7xNweJTjuiWlRLCOpXGMmoi5hMXeddjxjRRJE0joeaBu\nmTc1gUydPJ1EDsLAuSgPNp4hJr66vuQd1SE/9JBybwi8cr/iMkeuhi1jVJrGICbw7sMDHm0zpycL\nHu4ys9wjIbGZRq7Gmm223KgzgzMYaTE2YAYhCVz2G6rKE6bMTjNj8PRpS+MWKCPYORI3SPDIvvHQ\nkzEmY03HZuipvCVhmcKWynjGPcoqp0BtHOtUGgtzgj5OiHGlpU487NuNbFKyQHAeifmtavA9z4dp\nDFzkmlMTuVJDEnAxkAxEO8Onnh2WVRhoXVVaklSxtmYXE0r53m0YECp6Kaisbm9VSVoqeR3lJK9k\nAg4nQC5RkzEMBFsG/t0U+ZVXvjMU5F/72GP6O+fCUweJylR87izz4ZOJZ68MHzGJi5nhwDV8c5Xx\nOnFbLeTEVfJ8euc4mEf+rRPLb58p/9xJ5rm+5kbe4h18cl1hBb6yE35kMfL5q5onq8QNm+i94bMb\nx48uMkHge06Lgh00cN07YgXrnRAYWUqDmQnNULPxI6DUO4PYik9uAh9pys32tX0T3ROHyhSg8ns/\nrcD6wvBgbQoxg0TvoK3KkD4EeO1SedcC1iiYTOMT+SIhh4YGDyrEtaGaKcYkxgEu11uuX1/iTaCz\nlmbYMZqGIUScUZqUMOLpJRKycCAG5zO6R2pJirgblkkMHoNuB5gEM3eMJsFV+f58MkxWyNqyGxNb\nWzPuLJs24bae2ikXNtIk4U8uC8PWWcPTx8rXzoUQE7+xrvjBeuJVFW444bsb4bUhczzzfLMvX+ex\nznK+ifwJjpd3yqTKTx0EnFruJvjc1vKTi8i9XB7UGeH9nfJw49lk4fevRt7nlPfNZvRmxx+cKYe+\nZAFeDEVBnhv/FgpOVbiLZSEj14zlepf5iyvDe9zEVuAP+op3evjcINxXeKzaf94ejTWzRaF62pW/\n/9La8tNHE+wVr3XUt8Kan1lbfmCR+AfPv/0V5H/vA+/SNiQmEYwxDFHpTIIM58YwB7w1JIVVSMwp\nddEjjqSFhFDVfm+fUBIVIY4YAdXCc9sEZSaRc/UYgVonWlexjaUco0OJ1uxZyZnKCmJs4QJrJBnH\n3FmsKXxjKDzihffEVGhBIkKzZ+Cq3Yf1zJ5uQUGoTa4QOmpb1OB6P+DHlNiOcDQrbOOUU7F6RKWz\ngrMFkRayEA0cO7gcMqsxczq3nFYlaNbUhs2UGfsR5wyrqMwrQZOSUsZ6Q215q2RlzJkbS0NDabfb\njYl+l7i+cGRRLsdyum68ow+ZpIbtpPRBqPbFLb06vCkFH6qlqGXMhSZhLfvBHsjKRBksl065oIIc\nWTr7VujU2hLSm0HpPlClsULe9wikXOggb3qNBaGzGWstmyyMIeJNEYFSjmynfxbWC6lsKSr3Zhdf\nsUfo/j2UKOSLTSwM7DcLQbAer8qcwGZ/LQYMbm+pKMi4Yr3ZZKGTSNiHR8e8D2vuv763wj/++rcm\nN/BtVRTye5sGI8V0b1Jmkh6NI3NRLk1kOxTH/bRbk1JCs2UmDuoGJHDaHnG+G8FHnNTU1jMmMMYW\ndIgofRywIWMkYeJANpaNJCqpqHJmnSas7mj6SG82CI6FVuyM4fLiDp1vyDmzaI/o84ZrtePGjRky\nbllNjsdnDpcKuui1e1ecdJlGDwmHEydas97BybzifJpo7AnGr5hWA6/XNQeuYbddc3y85H6/44Oz\njpVZI0cPELYbUnTEnLgbLAM7sm2ZhhU3Zjc4s4qNDSF7Pu4iP/XoOcOTE7M20YaGu8Maz5x5gLHa\ncb1NrJjQXAbZ7dUKO3+Iy/Nz5qNj7g4xZsDavlRym4Eoip3XgDLZhkPTM46JnTpez8IzX13zgXfX\n2Dxj6wUTt7SVQ0NkNWbQRCW52EvIbNVxpI5VHxhiQJylMqX5qE2ZC5OoEth9NHUYhhLyaD0pJzBc\ncc6XAAAgAElEQVQeO+722BxLzp5pCAyhDDlHTWQiMxpL2PZ4MSyrDLsd0k7YyXFsDFOeiMmQvTKN\nW2ZVi01CHEaSrymH8fL+eSMGZuLYqCFhqMSyQUEb5hK5mAKTzRxGy9bUBE3swvQt7Yf///vXZy8N\n339o+OqVIhr5TN/w3aPh46f7/+M44K3yf14Kp13HBxvlk33CoPzkYeTdS0Nwjh9YKP/ZK/BzjwTu\nrj3Pr5WnauV2zLy/s3wmOn7+VsV/+GrkE61QS+SH5wNZLF/egb+X+c11zc89YolOGLeZZ6+UbDxO\nlI8tBeMTn3wl8tNPVHxhk3i8HfhYZfjyRnnfYcU/vQvvby3XNXCnL+v2Z4PwRoIfqhNrUZpdYDn3\nODH0vXIojlYSO5dw2XJUC75Xhsmzmluu7k+cNhP9dqK71uCmCrzhYrzkxskhJke2vSF3wv3UoEZA\nDLW1jE65bidk8GRNNKfKsN5iYodWPfXSc/+2AMqB39F0jhgnbr9eISlxOGu475Qj7bncLAmbicPr\nDVaFahbxfQQXGMcZOrPsouN9NybS2lItE//XN+ETtyzfeCPz958yXIaaZuX4f9c911rldjA8UcGn\nrjJOlN+7a/irC3j5KjMzme9qM38+CA9K5H52vKtK/PrKo5L4eJf49M7zkTbyW/cnVJSvjZ5nRbgb\nt3x4Lvzh5LlphUWa+IlrwgtXvvgvUbYZvpLgISY+YDL/d+/5vsbzfaeRP7ho+MoO3lUrL+fMD3aZ\n7z50fOZciRq5WRt+9VKYavhJL3x9q/hauDULfHY0LCXzxd5yE+GRNvHXjjw3qx33vkMKflKEwTpS\nLCKMYLgvFddr5RrQh0QtcHtILCrPoUm8MZaFfescznhmzgNF9Z25siKfolLtfaBzL4zR8NjMcDFk\nauNxmqhsUWZfi6U4w4nQ+qJM70Jikw0NDqeCdUXjeGObOV56TvrALkeustAB2VjuBWG+9yKP+2dD\nVqWm5A1upoE3kqH1QucMZ1E5assGEpQ+W5JERm+JoSjUYUoEZ7iaEseNZy7Q5AQx846FYTTw+lY5\n6ZSz+wPGGFxWdlpqmZeNYz1EklgeOTLcXwXGvWf6sDG8elYGfmugWzp2leWZO4WWdXPpaEPk1T4S\nQ2YMhoeOK7wIJiurQfFEYnY0UsJsrRdMTNROuBwyJ60hjJmjuWVMhqsEOQQObAQxBMkMMWNNaRJs\nbCGP5JSxzrCKUO1FSASGRMHoCkQVJslsx1ispWRsFsIU2BlDK8LcCPenyGHj2WlRy7NCZUrdtRW4\no8rcNXQmc1hFhmQJSfc9BxlnQY3D7AOHjclcjIETb8BY+lgqxjtJZYg3GckZVaG2wuQtNXGP/fvW\n/Pq2UpDffeNBrfGYylGL4pyjyRlfWSoU1KMCISdCCIh3jDEQMZgkJVWbC8jf5MJIbjDMa0FMYp4c\ns5yJWLwJpWkNwcdAWwm1eHb7nsIrzVztMqdNy3ElXPQjkzUcYBmbmt1uxRihnzI3Ggve4yVjmyVm\n3TPKRA1U7QFhs2FKkSSGIY3MFw0XO1AN3N7seKDtaCvhfNfTmhZjHDsRXh6gz5HkoDNLrItcru6Q\nBHpZYNLIgRc2Fh5gYhxHJAtZa1TXqOnoejg/8lyPcz56dMzP/+gOGyYIC1x7wdlamMfA5XrgXhKu\nNXM29zK9iexIfFMTm6vI7dWGTUisOeTxzrDQLW/0Fdcaw8nCYqqWO5tEUxm+cZn43HbNOk3MhszN\nG8eki3O8b7hEedx1DMPEqsm8PBnmfk4YVqXJSZSFJM4xLHNJIeemZkvE7QI5b1Bb42RkPvQ8fXKT\niOEhXzOvBRcNk0vYumKXE8uY2ZnMgUAWiIZivcjl1C42E9Ts/VHKkCw2Q7ZaihjEItbiM/QCpFI/\nOlHWalBA+U1MJO+BxFUY0doxS4azNCKuoQqR3335he+Ip+3/8X3v1i+uA49q5GvZ0DnLB5uJzb58\n4fqBZ9pNXEV4bqr46EHFJ88jN/fUi0PgsQPLH96PrLPlB06hFaGZOdI6s5LEnVXm1gm8fL98zYUG\njk49f/8Fy997TLlMmb+4gO++IbQZCBWfu1zzhBeeT4anDixHNMQ2wM5wkSeuH0FIFrPxVDOhZ2R9\n5aicQZqIbhzPjJHHast/dTvzrx5mwly4JhMAXiy+gj++Ax9/0CKiHKYSnDEuQbYQE6Eq5JLdamB+\n2DAOMFyuWJ60WGvJztPZibPrNfNXBxKelEf8kMizCp8i3hp2qjw8TwybUge/2xkapxzNI2f3LQcH\nE00wTDpyPi0xBA5mECfPpEKfBJMSk7dI3bA+26C+YhcMVaM0tuWb24nnt8qdYHjXXm39xlTepn+w\nszw5hw+R+LWtQzXziSX85kr48UPPMynz/Drx1xZF2fvapNgMD1SGpVG+OpZ76VOtcFo7XtopVgN3\nk2GV4aYRPtdbvr9L/OHW8r2zxE2XudZ4zsbISeVZxchsr3yJGK72ZL+HW+XIej55OXHqLced5cVt\n5uWofKzOfHmCU2t5OSrv33eXvBAyW1UuBssPNhN/MFSMGd7XJL66E57yJQDlbSQky61l5JWN5Zde\nfe5tf93+nfe/W6ekDDljteC3OolsclH35pUhplToE1jEO9ZjZuneZH8XpXYTi3e4K7ZzFlVRU/Pe\nD1oZZcp7kkUMzCrHdsrMakfOiZDKcCeUYXEzBAKWdh+o7pzBGtiGwlfuvEHF0Cd4uBPOBt3zdmXf\nKFdUTmeEyzHhveERydzZ35tFCjJuSkrXWAyZvA+bGauYXMJeNhd1fQzK9ZnlashcjYnTucOZonIe\nViVkeL9PbKJgUmRMmc4XrGJtlH7MPHZkWA+JXVTGqGCU07nj2fuBx48ct8dCe5hTAmkPHDasA4wJ\ncswM4ooNxRm+uYoY64lZOTRQV56LPu358bxl7Yp7EMWUlaWzrBFsKnajhS+b9aPakbJyGeK+TRHI\nSk8hQtQC2/0/tLRKNgX7ZzQTtTyL69JGgjNFuW0NRBHUGEzOROOYEQn65usvjLovNFGltVI6C2zZ\nFEypDMPd3leejSXl4iOGgu/LClMuFo9OyrZLDG99fNZ/Vo0+d8Xe8r88/625Zr+tBuS//Z4nVZwn\nhoRYh7MZpw5jlXpPD/DWYii95G1MBO/wxtIaGFTRbIhJ2EyRyyAcWmXWGcLo2OQ1eUw8tDAIFSFs\nCVrR1UJTtcwaZdVHrnaJC+nYqbLLE2G94aBqsSazyYaE4/7VPerK4lzNg35O3RgenpXWntsXkeW8\nYhV7Zl6Q1DDWyrEKNw4MK2n50otbgmayDexi4jIGDJ42w2UOBC3Q8pwjM5fBNpx2C2QcuSEZzZkL\nnXBtTT9MPNwL7czDsmVYTYRqoNplvpELM/Gd82O+fnmP1mSOrcPNZlhJXG0zy7rm/nbFSIVzkUYs\nKwvDVFRXhlT8ugA5oiYjxhNFiSlxFIqPsp113FuvOGhnNE1DGBLJVywqpckJSZnXomDFEkRZxcA0\nZXYasc6XdU+MqGY2kli6Bbs0cVB39GIg9kiEzkpZxbU1nfHs0kSMkW2MMEWcVW60De9ZLliEQLUP\n0531CSxYq4wBtqZwaefi2WopbVgBPgVGF0uRTLBQ1ZhpIgKjlvVfco6rrFzmsWwrUsRlOPAeseVm\nMiOz2b+3lwr/0zff/qtagP/gqcf0++cNGxv407VyLxgODHzoyPOVTcLFiaAGR+K9hx1bdjx3aXli\nGTCD8r+uLR9rijvx1jLzhYuKjxxNvLDy/MqV5ROL0oxYSeafrIol42ePLV++zPx+UP61G8ILZ5H3\n3nQ0VhhiZh4z//k9y88+COsx4qyjsUqnQvIGG4oqQqXMNi1ds+OOM3Sh4rPngadmwvVs+Mpm4Knr\nM1au4Ahd7+iPy6BXrT1pPlKNEemgnRrsbsuuNmhI/O494XuvDSypmB3UhNHiVHnucscT1ys0G/rN\nwHLekfYlOI0VYhbSnR3nbeaaCGFIdMs5R/WEmh4bOnK9t1hIGfC6LlI7pY3C3auao6MNoavpJhi3\nxSPqbeZMK4acCdlz/6JHvNJKhVjD1NTIxnBpJk4WynZy1EPFGBK9CI2P/NK55/HSzUVr4NdWwt+e\nBT4X4W+cekThF+8LOxM4Gwy3Ksu6z1wYwYvwl2cjv70pWYnKGD6xLIeNISnrbHlXpWwTrJOwAu6n\nzHNDadN7wsBLU2bmDZucuSGQYrmEbtbw58nwtFe+FpSPePjMqLzLGP5oZxhj5C8fJf58tDxWZ14d\n4U+2hR117A2I8lideXnneLouD/CNnUha7nNHKGcWrkf5jgjp/fx7nlRbGyQrOQkZpaYMfJKUi5j3\na2plUXumvbcWAdXEZlI67/ZYr0JLcAXwxjoWNXguyhaDpkylEVdV5JzYBuVaLdyPcFQV+4IkGEUY\nh+LxDUmppQxbVkpl9JBkz0cWrsQxtwkXy5C2mRQxyoUUr/R7ZxajsIqJjQjX97i5SQtSMKaIM4J3\nhqtpopLimb3cJa41QjDCtc5wMRX0YtgOHC0rzgPkkDlqDdOeUDGvhFUo9os2J7KHPihHjXA8K95q\nZ+HE7C0NZO5vE4vKsOwcd8bM7V64dSgsKovGUjk9qOGo9VysI2S4DJbNkAtL2EAUw5ErlJApKd4Z\noLR0XsbMXMqhImPZ7b29R0a5CImVWDpVTqo96SJCnwIecNZzL0QWtlyzbzKKoyqVkbeG8KS89fMf\nKCi+1sA2l/dCpCD8hpRovSvNhKKs9gemuTP7YVhK/wOlNCXtq6r7mGhdIabUVliFzJgSGbBicHs2\nc6AM1wAmx9LkCGAMjQgr5TszpPePfuwD6jtHCMoQKKZ0GdiOkfUulaErjdS+RipH2iiTiXgx9BoR\nNXjX0CThTkjkJuJHzzqPDDsYTeSG9/RhTV0tmHvLrSPLNjpaLHdC4rg1vHFvhbE1QQxzs6EVw2Qh\nbgNT9AzZEB1cbnu2lcetB2anR8wrwxR6rKlYS8VihM5sub6sOEwwDhv+dK2stOYqbXlPW9FPlm9u\nNxydHHIWJjoqIo7N0DM0DTHD98g5GsuN43TecdlvWeeKJyq4GEecCo8tGlZS8fDJHDtsWQ+BYRfY\nsOJdDx6wsEvCNrKWilm94sV7V9xqF8jhjFM/cnaRuNE5Vgibyyu0bUhDJoVIaDsGH3HrEVXLvXHi\nODps7dlaSz/1hRhiKuYW1mkiuhofhVEsVhIyCCsCd1CeMJ5kYKcG75ReIwZDjkJd18QcqDCkaUSa\nitALX3eWXW6wYeQRM3I86wgCle6YG08/CTFZepM5H0aG7LhXGT7sEyEXT3MFHOdA1olzI8xwbG3C\n5aocBLJlJwIamVAkZebqWNtSzxolM+2rcbMakg2IlhuRiZnRC4zC5ISg5SEQpBQORJRPvfqdEdL7\ne+99QnsRPrD0mADz1nFV9VRj4s/ud5ynHY01IIYfaC11E9gNAYNSNw2rIVBNA1+cKt7XKjOvvL41\n4OCgqzlQz7Np5I3BskyRT0XPEAa+t8vcBH5jNGwGz4/PAo86Zc5A3YLLFl8f8DurXbHFZMejNmLn\n8Euvl5uoR/mJtufICb14Xs9wq4JfXzvONpZ/8+nMX9xJhL3q8eEjy47EkStFIr9xG37ypEH9FkNi\niAHXtYTdhiCK21bU1xq63mCbRH+2YfnAIYGMUcHqhCZIlSPnjE2Jk5A5rDOr7BhNoJOJMzOnGktb\noFjDabXl7lSR+y3SzcgYzOWGnc0cXZ9Rk+lSJCZDbyx1DlTWsZkcrZ14NTc4MYzRkkZLWkzcvm8w\nleGzd5WfPqzZLiLrceKNS8NrIbJVzw2fuRcMJz7zXXPP564SL0flxxfwpQt4xhfLwud38J650rma\nzgrv7eD3704srfCr68xGLT9xEBiS8nwogaL3+JGvTZ5vjJ6/1AZ+9NTwlYuiXr1zVvHHq0B0me/t\nHF/cRJ6ZhI/W8OUJVgl2QZhVoJpZWuFLO8fPHAZeCJY7uVzrKsKhMcys8qkry889Yrm92fFfr2tO\nmsTPVpGrquNLl5FM5qFaeGVjcSZySVHGf+Xlt/91++8+/aRWxoK1jPshr86JkBJ3tKWOA0aEuVF6\nK0BkjCVw1zpbyh9yptLiT82ijEloUXAe46CdImosqwwWw3oYOLSZS/G0KdCLLyFBUYYwUVmhtsq8\nbkjbIkBk6/dNppb7Q2k4NFJwqVaEQ2cwe8XzKlnOI3xwIVyMZcAHEGdxWkJ6InAxCQeNKUZdynre\nWUFjokW5wHJaC7eT0JrMashcm1kqzQSEzpbhtcqJlEu73lBbHlpadDvShxJmq2Mi7NnBtQHrQfvE\nOmaOfMGYfX2nPCCJBw89o0KPUDlhDILWnsYJqz7TNgbdQjJCGEuAXTFcTaVJ9DKCNo5TyaRpYqeF\n1V7bwj7Ouq9/tsIq7RVmI/RRmRHBlZbOa5VgbMUEdF65vcuFKBImvCks56Sw3aPUjiVwkUvWxuxL\nQ0rRSkZNwQR2mtgYQ5WVIZdtQ0glvJdTKf9i73uOGTpf2g9zVvpYNu/YIn5cTMpJZwlTwOfiWY7e\nEYwnTgFHZhTDlEq4L8WACPzvL3xrtrXfVgPyz3/ofYqtGYYB5yq61havsQrbzYivysWoISImYVUZ\nQgm1nefAUVtWmLsMKWRSSnSmIpBKC5AINQ3Rla7wU9fgZCQJzNVwJon1NJAHQ6wsnavIfeBy6jHe\n4StDHiPWtBifeGOK3O8zbePJOeNNpp/Km1D2b56YerZ5C25ONzm2RMZxxHpHiI5OBpxsuHVwzMzX\nPOEdfbzEeVi4A4wqqW6ptluWXcvZ1ZbZQQUm4VwkbDMpGqQCs9txa7nkfr/lsDbcv+w5ODrAMnHY\nOk6rGsnn+KZltVJia8jTDA338d7jK8swbLHZs2JGjrGsOF3DENdIsGTjqV0p71xNQiuZHQ2WzB9t\nLEtZc3l5idSew3pJ03oeFlCfMSGTDIjGPew9YWxdzP92zkuXK7bjQD/2LA8aHm+Vma8xISGu4zIn\nzmJgGz27OGG05XaIGGOIY+JujnygcyCO1kUG57jcjeRcc7nbkKywbByikRrHzCiuqpi5EuBEB1qx\nxb6TK8QE2mDYEsHVeE0kgZzNPvBQlGlnDFNWjM2YpJAtUwIlYymlBVuf+G+fv/O2f9AC/N33PKbB\nWnJSnj6C5y4zNw8dty8Cvxstf7WCdx5V/OLtwF+/CX9xrrxzqZxKDSL09YQdtzSuBuAL94UPLWv+\n/Vcz//AJx5jL6/prr0euV4mPXKv40v3Mjxy0bNqImzJ2lvizV+DXL4T/5NqOX11X/JUbjv/yLPOD\nPuFUuLXMxA24hfDKqgy8qgn24Y4oylMdTEF5LoBieEIjL2ipKX/0QHllVT7uSV8Gt1A57JT4cg9P\ndeX1eHjucc5wYTPzEfqrDfcx3Fi2OGepRJl1pWnwzvkakmfROnwDM5/xu0TrLPeGgG0cS5tRl+k3\njsN5j8nFWlb7yLgFfKDCc7YRNhJx0vBQO3ERPNOQOFxWBDtxcVG8zYnCbF7tEqeHDfUYuTTgvaFv\nHPHOQNPN6dcb2mWHZMOfXG75rV3Njy0z5xP8izeVN2JmgeP25Hm8S/zhRUHUfXbwLAhc08wPX4MD\nq3zmQvjAKYwB3lgJF6ociXAJnA+ZmYN/uvF8f5dZmMRvrT3HmnigEm75iYO9bPWYKJ9eeS5cZpWU\nbXJ8dBG4SsqzoyVEy3u7iS8Mjo90iY+5ic8PpR21d8LzO/jYDL60s7zTJ27UsDTFzwxwUwO/dFXx\ndA1fi8p7m4xGy8OzzNMm8VJ2/EfPvP2tUf/2e57UyphSVmNhjPCgKxu9KUFdWWpnIGSM0bcKN5J1\nLEwhW8ScEFN+Lv0E3lu2exvCtPcC390GjjzMveMyQtMILaU5zhvlashcTJm6FlZj5qHWMuyHW2fK\nEBlTorKW8GZ/hWZkj/qrJaP7dXrI4FB2Yuj2BRbeCClnoha10ZlCnFAt9su8RyouG6EySpWVs1SY\nzE5hVu0ruJ3QNYY+wquXE7URcKXi+lorvBEL83m7HjmphLo2TDFyGYu/eu72RA9nWI+Zygm7SVnt\nAocKyQCth1Rek8cOKypjeOEiYGTPCMYzTJmDzjJlg88FFzozyv0JDrxwJxRFfW7gbBcQDK0v1dmL\nCjRlshFGPHNJDFGL+prL4NurcuAKzi1G6KwWtJ+at7jDqqUZ0e5r7Kwp278xKbtU7qfeKN1eae5R\nLvfsZqOJiKV1Qk5lKzGJw5DQnOmcKWF7/L7CWuhDwLuiQCcpCrZQBmyASgN9MngplqHWmmKzcSXH\nEbPy3z/zHUix+Ne/64Mq0ZB1QsQyxMTMW3bTQGxa8hTIAe7FgVl3gMmKo6zuRwVXJYwxSHLcHTcE\nNZjUYrwr9gw1DLHnfJqoXYsQOXAdUyGF0/cB7z29DIQp47yU1T+WkDaEBOMU0Qoa4zG5wScl5ImQ\nezpxNN5h/cBJbbnZtSyC0PhM11UcmEw1eJC0bxSKOK8sKocNUNWWkAy93ZYU77CgthG1hplX6lxj\nokFn5+SwxEvCSGbhMnVVQmVdO0erljBuuHEcOTvrOTxcYESREMjtAuuvWJ/PsIzkvuG18zPqDF+6\n37PoLG6aWKWaplVm84rWGExdI97AbsTGFcumTAc5LziPns3YUzuP2iWX6xUhnfPIwuC80tQzfAXe\njJipYH5C7Ekc0dvMlEvxh1NPUIip3MxWe2yfNzVTmgokPoNmy6gJsQYTywptyoCUpK41+a3fv4l8\nUlWitYgm1FSICCntkT0pQ4a8T0knb9D9KtcYQ0iZkA2SYmEiayYmYM/fjpJwaokGUogYNSRT/GBv\nJodzEn7h5Vfe9g9agB9++HG9VSU+VEf+eHR8bar4meWa17PnVhV4efI8bDPOFjxeloo2Bb7cFxVX\nKsOjoryU4QELNxael9fCdb/lhaHm0QPlundI6DhPgeeHxK3ZxLKxDNvMcSd0pmYbRra154bCJ29P\nfPi44tosIxrZrOZsl5n6OGCvDOusLAbPhQ1UVnnxsuJYErrHCFazxLD1NN2IpeQZTlMFNiFTicln\nn2iklBdlU3O5WnFsLbH1vHax4/EHloQcIVr8doBh5LnoeHCxpwbUFW0ITF2D9eUQ0JjEbqxYX+2Y\n20Rz3KHna377wvAv3Oo4NAk1E6vBc1AJV0GJux1+1nLcDPzS88IPnhre/ZDnz14ceORmh4qlmTIb\na5gJ3JuEy8vA8bUaP5WvO+B5/vbEyUzQKfDfbBv+xixwL1a871gJseZTVyM/8qDlC69n3n9Q88X7\ngeeS4iL8lQcC/9tdx91gmaXMB+fKNZv43CA84CwPO+WLIfMzTeRTY2H7qSq/13tOu8h6sFyvEu80\ncOwij0jmVTXMpeJERv7hpefDbeGpf3WyuEb5cTMxGuHzG89uVLJVFs7wU7OREy9sY+JzoeLFUdgG\nSzbKrSqyNMLXek+WyGNVAjXFtgZ8jxG+2Ee+ONbMyfz0oudr0fGgZu768n3/jy9+821/3f7ME4+p\nAJ1EdlpsiZ6CsXuzvjeLUJb2ijeGbYzsb19YESprqQWu1HDkDX1WqhyYcHgj1E5QYyEXFTXlRGPL\nINtYwVaW7ZRZGMOZKBfbxGktLDvBGrgzOK4bZV4XC4NEReuKMEyowDZaOok4yirfGMhqEFLhNqNM\nzjKTxLnUVFY41pGQwp524XljO1I7oTHK1agczR0mZRRhnZQhRlKyzCtBUiK64kufWUGkvBiHjeGi\nL3SLI4nExnO/j0g/cvOoomksFZmzEZa14WoXuegzN+emBBDvjRx3FY/crPj9F3vef6PCiGGyDhMz\nnRde2ghxTBy2Qti/D2ch8fym2E9ICU17PjWWzhtASCFTNZbVqBx64c5QiEtDVh6olMsp01nhIirH\n3vJm6NJXno7MGAJaVcjeFpG1sIeNZrwpQ7FzjpBL4H7E4Ex5rl6Nkc4KUUp1t8i+bW+v8K6ygCYW\n1lCZROUr+hiZUjno9GpJmvFSWMyjGmKKxV5CCdICjMYyhUjISgKWtgBANxk6V8SPX37+xe+8AfkT\nTz6oc1uRNVJLS7I9qqVd5qBt0TCQdMHRbI2ZDvDuEjBUVcU4TlR1S441WQNRWjabDUeLiPc1eYLN\n4JiaAb+LeNcifuLeeEwtPTdnnobE3XHDxVWkax2HxrJxkRObaZoGnMdPO8RMaFyCW6OmJYcd42SY\ntxWSYRV6jucdcYzU1Zwpl2apKSdsmlH5KyR7mqqm7lcsDxbspNys+qs1+AprVzTWE/MBLkW61lNn\nS2ZVmtsmzxTW2ATjbseUO8ZxhLbi4MhxeFzx7kcHmoUgkjDNCKlcWEwONZDHgM0eHS26D1hl3eLd\nEpoGNgNpUK62Fas1jNOGjRU6nzipjrkcA+txovINSMNEKszpmLEukYNhN0YyI97VpJSIFDRfzhmp\nLZoyeEueYIgQYyRiy4UZwVemFHRoLDdz49gOiclsqUxLmgwhBNQZREpLmqRSCW2MwZgIWYhalKEY\nC8czSirfx/6EDIAW+0SwJchS/swBGVVLRNH9ZWeNJ8UJVUWtw2iptSYXRBi2IH8kWeLeZvFfPPfy\n2/5BC/AP3vuoXsXMDaO8pOWw8Y7FRG09pzbw+jn8au/4WycDf7qpWPsZcw18foj8O92G3J3SNxHd\nGWgTCdiOAzeY8wt3e/7mEby0SbwYDH/9wcIs78eJy3Xg66biyZnl2XXgXXXkFzcNf+tUef7K8YEb\ncDQEfm8DHz8whDTwn77c8bOPRJ5fwTWrPHi9JaH88svK36xHXFPzpakkOA+6zON9D4saNhPNkQV1\nbJ0jbnpOWstrQ8TiODQ91w4qJFRklNUjjvxMz+fXib90XLOcTTRVxzqVZPufvbDlxkHgC6uOD7vA\nw9c7rA3ElPFv1mzLjq5tCGNAUw/JskkV292WtuvI2fBgHXllNWFmc86i0uJZysSrr4881L7mVoAA\nACAASURBVCYuTGmG3M0q3mmFly8GZLZE+g3dvGYUV3z1LZh14N404Qfhz5Kncp6VKr97IZzlzI0F\npI3h+w4S/8+m5rSd+DGX+MxoaUV4dvRc6wLXRuETi8Dd7HhsCT6OfHpT8ZUJvrdJfHX03BsN760j\nXzdwb1fxo23gOYUwWE688lib+EI0XC9bcO42ShOVR4PhM2r4lxY9norP9sqr0fG0VR5vEt8Iwj9v\nE78zeP48WUD5RJs57w2PzQbeUEeIpe3yaT8xs5YewyJDnxPHPvPbfc03g+VWFdhFeHdn+fTG8PKU\n+Hgd+MevvPa2v27/jfc8qTaV9rvaFI6w8iYiLXMZhTgGFrUhqeVG5dhlZTVOBAezqik1z3tl2WYl\npMjKVuS+p6sc2xAZs3BtVv79EDNnQ+KRyhCMI4QJZ4RtMiwqS1JYONjo/8fdmwbrlp7ledfzDmut\nb9z77OEMfXpQt1qtyRoQkgDJCDEIgoAAhYOBYFFOuWJSJE65TCopu6ikTBzbJFUmlXIldkwgEQFD\nMCphY2MxGJBaAtFIRBNIkXoezrinb1hrvdOTH+/Xzc/8kWOJ7+epc/bZ+9vfWut57+e+r1uZp8zW\ne7Rknttkrk08fSqIwNWJxwLbULhFZ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u4ryu0M03nDE6vC/fuFA205ksDv3R653EEsHmMM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\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2cHxf2GnGzdDiWNR6He8Osa8TTi10DsFqnbGq0pVS66wt0oWw/1oyhwVElV53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\n61A1FjEyU2PSktdotylHHtR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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, diff --git a/notebooks/plot_otda_semi_supervised.ipynb b/notebooks/plot_otda_semi_supervised.ipynb index 6c538e9..484c2ee 100644 --- a/notebooks/plot_otda_semi_supervised.ipynb +++ b/notebooks/plot_otda_semi_supervised.ipynb @@ -66,8 +66,8 @@ "n_samples_source = 150\n", "n_samples_target = 150\n", "\n", - "Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source)\n", - "Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target)" + "Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source)\n", + "Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target)" ] }, { @@ -128,9 +128,9 @@ "outputs": [ { "data": { - "image/png": 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s2bNH/H6/zJw5Uy655BIREZk/f74sXLhQRESOHz8uAwYMkMLCQnn22WdL+8jy5s2bJ6+/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7Rv6dgU9m22Sv3lZ40z6AVjm5vcMByvDV37B7/elcU3rw93KIpCitmslOYXEcn2/VpKlvW+szDa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\nAkV5lP87fkfnuv8EU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RTpJbEk3b4vHEMUAyO+z8/4Kx4oxiUDDGtXTVmyE0j/4Nc3ZYm8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t9BRd6j7SLEEX/S86ei6eD33B3St/rsWZlJYqYTyQbeu+Ua7I1+CvdvwcQJawCfOVxomelwwm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\nI5zfFdvfv6ztnPtRXDpuQH48DAkCx26bqIEsqsRccfteeRtKZi1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RkRyScyEk2bhPcgJO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jSLWO55pcOMgb4OJslR18/3Xrf7VHeIGOr/ESjNnRf0Gsbb2r/anh0DjhetugczRWmbETwz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\nIrL3+9dERGTpR1qWYvi0Rs0eaz9oKy0ikkPyx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\n", 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CBAd7AUQhEaepCTRb9XOxlOwCsgwWQKwU1vTGJPSMCrAzNm8OrVku53nxinze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\n8wlHphhbOqC+2et/oI/JO7pQsnPflK8jw44I0ezT/+Jq1M1wO5P80i3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+ "image/png": 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\n", 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Xe2X87W/fg0LxvTnzuHzSZF7btZN3S/eSm+Imw+mkrsPLI+vWcPu8M3WCPI3m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\nH1Gy8QCl2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JeOB0B0SqpVUJyLntD56m/uxmq39v4HoChKrzOZTFx//5Us/esKDpdUGUfC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\nHRXgKIqiKIrS7/x/4rlZk3fEE94AAAAASUVORK5CYII=\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -272,21 +272,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.6.5" } }, "nbformat": 4, diff --git a/ot/gromov.py b/ot/gromov.py index 65b2e29..0278e99 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -2,7 +2,6 @@ # -*- coding: utf-8 -*- """ Gromov-Wasserstein transport method -=================================== """ -- cgit v1.2.3 From 2b375f263ef88100b0321c8ef1b3605dfbb95b3d Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 30 May 2018 10:34:48 +0200 Subject: update notebooks --- README.md | 22 +- docs/cache_nbrun | 2 +- .../source/auto_examples/auto_examples_jupyter.zip | Bin 99272 -> 99990 bytes docs/source/auto_examples/auto_examples_python.zip | Bin 67996 -> 68178 bytes ...sphx_glr_plot_barycenter_lp_vs_entropic_001.png | Bin 17289 -> 20581 bytes ...sphx_glr_plot_barycenter_lp_vs_entropic_002.png | Bin 40229 -> 46114 bytes .../sphx_glr_plot_otda_linear_mapping_001.png | Bin 30590 -> 29432 bytes .../sphx_glr_plot_otda_linear_mapping_002.png | Bin 55770 -> 53979 bytes ...hx_glr_plot_barycenter_lp_vs_entropic_thumb.png | Bin 12645 -> 13542 bytes .../sphx_glr_plot_otda_linear_mapping_thumb.png | Bin 21959 -> 21399 bytes docs/source/auto_examples/index.rst | 2 +- .../plot_barycenter_lp_vs_entropic.ipynb | 58 ++++- .../plot_barycenter_lp_vs_entropic.py | 25 +- .../plot_barycenter_lp_vs_entropic.rst | 255 ++++++++++++--------- .../auto_examples/plot_otda_linear_mapping.ipynb | 4 +- .../auto_examples/plot_otda_linear_mapping.py | 10 +- .../auto_examples/plot_otda_linear_mapping.rst | 12 +- docs/source/conf.py | 2 +- examples/plot_barycenter_lp_vs_entropic.py | 25 +- examples/plot_otda_linear_mapping.py | 10 +- notebooks/plot_barycenter_lp_vs_entropic.ipynb | 226 +++++++++++------- notebooks/plot_otda_linear_mapping.ipynb | 15 +- 22 files changed, 414 insertions(+), 254 deletions(-) diff --git a/README.md b/README.md index 466c09c..c5096a8 100644 --- a/README.md +++ b/README.md @@ -25,12 +25,23 @@ It provides the following solvers: Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. +#### Using and citing the toolbox + +If you use this toolbox in your research and find it useful, please cite POT using the following bibtex reference: +``` +@article{flamary2017pot, + title={POT Python Optimal Transport library}, + author={Flamary, R{\'e}mi and Courty, Nicolas}, + year={2017} +} +``` + ## Installation The library has been tested on Linux, MacOSX and Windows. It requires a C++ compiler for using the EMD solver and relies on the following Python modules: - Numpy (>=1.11) -- Scipy (>=0.17) +- Scipy (>=1.0) - Cython (>=0.23) - Matplotlib (>=1.5) @@ -156,16 +167,7 @@ This toolbox benefit a lot from open source research and we would like to thank * [Nicolas Bonneel](http://liris.cnrs.fr/~nbonneel/) ( C++ code for EMD) * [Marco Cuturi](http://marcocuturi.net/) (Sinkhorn Knopp in Matlab/Cuda) -## Using and citing the toolbox -If you use this toolbox in your research and find it useful, please cite POT using the following bibtex reference: -``` -@article{flamary2017pot, - title={POT Python Optimal Transport library}, - author={Flamary, R{\'e}mi and Courty, Nicolas}, - year={2017} -} -``` ## Contributions and code of conduct Every contribution is welcome and should respect the [contribution guidelines](CONTRIBUTING.md). Each member of the project is expected to follow the [code of conduct](CODE_OF_CONDUCT.md). diff --git a/docs/cache_nbrun b/docs/cache_nbrun index 8e58a2e..318bcf4 100644 --- a/docs/cache_nbrun +++ b/docs/cache_nbrun @@ -1 +1 @@ -{"plot_otda_mapping_colors_images.ipynb": "4f0587a00a3c082799a75a0ed36e9ce1", "plot_optim_OTreg.ipynb": "481801bb0d133ef350a65179cf8f739a", "plot_barycenter_1D.ipynb": "6063193f9ac87517acced2625edb9a54", "plot_WDA.ipynb": "27f8de4c6d7db46497076523673eedfb", "plot_otda_linear_mapping.ipynb": "23d5203f707e0156f3cf8755d96f1dc1", "plot_OT_L1_vs_L2.ipynb": "5d565b8aaf03be4309eba731127851dc", "plot_otda_color_images.ipynb": "d047d635f4987c81072383241590e21f", "plot_otda_classes.ipynb": "39087b6e98217851575f2271c22853a4", "plot_otda_d2.ipynb": "e6feae588103f2a8fab942e5f4eff483", "plot_otda_mapping.ipynb": "2f1ebbdc0f855d9e2b7adf9edec24d25", "plot_gromov.ipynb": "24f2aea489714d34779521f46d5e2c47", "plot_compute_emd.ipynb": "f5cd71cad882ec157dc8222721e9820c", "plot_OT_1D.ipynb": "b5348bdc561c07ec168a1622e5af4b93", "plot_gromov_barycenter.ipynb": "953e5047b886ec69ec621ec52f5e21d1", "plot_otda_semi_supervised.ipynb": "f6dfb02ba2bbd939408ffcd22a3b007c", "plot_OT_2D_samples.ipynb": "07dbc14859fa019a966caa79fa0825bd", "plot_barycenter_lp_vs_entropic.ipynb": "9866e6f8d1dbfcd0f2ea514dc3a330fc"} \ No newline at end of file +{"plot_otda_mapping_colors_images.ipynb": "4f0587a00a3c082799a75a0ed36e9ce1", "plot_optim_OTreg.ipynb": "481801bb0d133ef350a65179cf8f739a", "plot_otda_color_images.ipynb": "d047d635f4987c81072383241590e21f", "plot_WDA.ipynb": "27f8de4c6d7db46497076523673eedfb", "plot_otda_linear_mapping.ipynb": "a472c767abe82020e0a58125a528785c", "plot_OT_L1_vs_L2.ipynb": "5d565b8aaf03be4309eba731127851dc", "plot_barycenter_1D.ipynb": "6063193f9ac87517acced2625edb9a54", "plot_otda_classes.ipynb": "39087b6e98217851575f2271c22853a4", "plot_otda_d2.ipynb": "e6feae588103f2a8fab942e5f4eff483", "plot_otda_mapping.ipynb": "2f1ebbdc0f855d9e2b7adf9edec24d25", "plot_gromov.ipynb": "24f2aea489714d34779521f46d5e2c47", "plot_compute_emd.ipynb": "f5cd71cad882ec157dc8222721e9820c", "plot_OT_1D.ipynb": "b5348bdc561c07ec168a1622e5af4b93", "plot_gromov_barycenter.ipynb": "953e5047b886ec69ec621ec52f5e21d1", "plot_otda_semi_supervised.ipynb": "f6dfb02ba2bbd939408ffcd22a3b007c", "plot_OT_2D_samples.ipynb": "07dbc14859fa019a966caa79fa0825bd", "plot_barycenter_lp_vs_entropic.ipynb": "51833e8c76aaedeba9599ac7a30eb357"} \ No newline at end of file diff --git a/docs/source/auto_examples/auto_examples_jupyter.zip b/docs/source/auto_examples/auto_examples_jupyter.zip index bba0bee..8102274 100644 Binary files a/docs/source/auto_examples/auto_examples_jupyter.zip and b/docs/source/auto_examples/auto_examples_jupyter.zip differ diff --git a/docs/source/auto_examples/auto_examples_python.zip b/docs/source/auto_examples/auto_examples_python.zip index 8d9edce..d685070 100644 Binary files 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b/docs/source/auto_examples/images/sphx_glr_plot_otda_linear_mapping_001.png index 2bf9514..88796df 100644 Binary files a/docs/source/auto_examples/images/sphx_glr_plot_otda_linear_mapping_001.png and b/docs/source/auto_examples/images/sphx_glr_plot_otda_linear_mapping_001.png differ diff --git a/docs/source/auto_examples/images/sphx_glr_plot_otda_linear_mapping_002.png b/docs/source/auto_examples/images/sphx_glr_plot_otda_linear_mapping_002.png index ab574fb..22b5d0c 100644 Binary files a/docs/source/auto_examples/images/sphx_glr_plot_otda_linear_mapping_002.png and b/docs/source/auto_examples/images/sphx_glr_plot_otda_linear_mapping_002.png differ diff --git a/docs/source/auto_examples/images/thumb/sphx_glr_plot_barycenter_lp_vs_entropic_thumb.png b/docs/source/auto_examples/images/thumb/sphx_glr_plot_barycenter_lp_vs_entropic_thumb.png index b49b28b..c68e95f 100644 Binary files a/docs/source/auto_examples/images/thumb/sphx_glr_plot_barycenter_lp_vs_entropic_thumb.png and b/docs/source/auto_examples/images/thumb/sphx_glr_plot_barycenter_lp_vs_entropic_thumb.png differ diff --git a/docs/source/auto_examples/images/thumb/sphx_glr_plot_otda_linear_mapping_thumb.png b/docs/source/auto_examples/images/thumb/sphx_glr_plot_otda_linear_mapping_thumb.png index b4adccf..277950e 100644 Binary files a/docs/source/auto_examples/images/thumb/sphx_glr_plot_otda_linear_mapping_thumb.png and b/docs/source/auto_examples/images/thumb/sphx_glr_plot_otda_linear_mapping_thumb.png differ diff --git a/docs/source/auto_examples/index.rst b/docs/source/auto_examples/index.rst index 5bd2ce2..69fb320 100644 --- a/docs/source/auto_examples/index.rst +++ b/docs/source/auto_examples/index.rst @@ -109,7 +109,7 @@ This is a gallery of all the POT example files. .. raw:: html -
+
.. only:: html diff --git a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.ipynb b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.ipynb index 8114835..2199162 100644 --- a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.ipynb +++ b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.ipynb @@ -15,7 +15,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\n# 1D Wasserstein barycenter comparison between exact LP and entropic regularization\n\n\nThis example illustrates the computation of regularized Wasserstein Barycenter\nas proposed in [3] and exact LP barycenters using standard LP solver.\n\nIt reproduces approximately Figure 3.1 and 3.2 from the following paper:\nCuturi, M., & Peyr\u00e9, G. (2016). A smoothed dual approach for variational\nWasserstein problems. SIAM Journal on Imaging Sciences, 9(1), 320-343.\n\n[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyr\u00e9, G. (2015).\nIterative Bregman projections for regularized transportation problems\nSIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n\n\n\n\n" + "\n# 1D Wasserstein barycenter comparison between exact LP and entropic regularization\n\n\nThis example illustrates the computation of regularized Wasserstein Barycenter\nas proposed in [3] and exact LP barycenters using standard LP solver.\n\nIt reproduces approximately Figure 3.1 and 3.2 from the following paper:\nCuturi, M., & Peyr\u00e9, G. (2016). A smoothed dual approach for variational\nWasserstein problems. SIAM Journal on Imaging Sciences, 9(1), 320-343.\n\n[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyr\u00e9, G. (2015).\nIterative Bregman projections for regularized transportation problems\nSIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n\n\n" ] }, { @@ -26,7 +26,61 @@ }, "outputs": [], "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nfrom matplotlib.collections import PolyCollection # noqa\n\n#import ot.lp.cvx as cvx\n\n#\n# Generate data\n# -------------\n\n#%% parameters\n\nproblems = []\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\n# Gaussian distributions\na1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()\n\n#\n# Plot data\n# ---------\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()\n\n#\n# Barycenter computation\n# ----------------------\n\n#%% barycenter computation\n\nalpha = 0.5 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\not.tic()\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\not.toc()\n\n\not.tic()\nbary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\not.toc()\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\npl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()\n\nproblems.append([A, [bary_l2, bary_wass, bary_wass2]])\n\n#%% parameters\n\na1 = 1.0 * (x > 10) * (x < 50)\na2 = 1.0 * (x > 60) * (x < 80)\n\na1 /= a1.sum()\na2 /= a2.sum()\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()\n\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()\n\n#\n# Barycenter computation\n# ----------------------\n\n#%% barycenter computation\n\nalpha = 0.5 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\not.tic()\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\not.toc()\n\n\not.tic()\nbary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\not.toc()\n\n\nproblems.append([A, [bary_l2, bary_wass, bary_wass2]])\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\npl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()\n\n#%% parameters\n\na1 = np.zeros(n)\na2 = np.zeros(n)\n\na1[10] = .25\na1[20] = .5\na1[30] = .25\na2[80] = 1\n\n\na1 /= a1.sum()\na2 /= a2.sum()\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()\n\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()\n\n#\n# Barycenter computation\n# ----------------------\n\n#%% barycenter computation\n\nalpha = 0.5 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\not.tic()\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\not.toc()\n\n\not.tic()\nbary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\not.toc()\n\n\nproblems.append([A, [bary_l2, bary_wass, bary_wass2]])\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\npl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()\n\n\n#\n# Final figure\n# ------------\n#\n\n#%% plot\n\nnbm = len(problems)\nnbm2 = (nbm // 2)\n\n\npl.figure(2, (20, 6))\npl.clf()\n\nfor i in range(nbm):\n\n A = problems[i][0]\n bary_l2 = problems[i][1][0]\n bary_wass = problems[i][1][1]\n bary_wass2 = problems[i][1][2]\n\n pl.subplot(2, nbm, 1 + i)\n for j in range(n_distributions):\n pl.plot(x, A[:, j])\n if i == nbm2:\n pl.title('Distributions')\n pl.xticks(())\n pl.yticks(())\n\n pl.subplot(2, nbm, 1 + i + nbm)\n\n pl.plot(x, bary_l2, 'r', label='L2 (Euclidean)')\n pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\n pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\n if i == nbm - 1:\n pl.legend()\n if i == nbm2:\n pl.title('Barycenters')\n\n pl.xticks(())\n pl.yticks(())" + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nfrom matplotlib.collections import PolyCollection # noqa\n\n#import ot.lp.cvx as cvx" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Gaussian Data\n-------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% parameters\n\nproblems = []\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\n# Gaussian distributions\na1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()\n\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()\n\n#%% barycenter computation\n\nalpha = 0.5 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\not.tic()\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\not.toc()\n\n\not.tic()\nbary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\not.toc()\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\npl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()\n\nproblems.append([A, [bary_l2, bary_wass, bary_wass2]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dirac Data\n----------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% parameters\n\na1 = 1.0 * (x > 10) * (x < 50)\na2 = 1.0 * (x > 60) * (x < 80)\n\na1 /= a1.sum()\na2 /= a2.sum()\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()\n\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()\n\n\n#%% barycenter computation\n\nalpha = 0.5 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\not.tic()\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\not.toc()\n\n\not.tic()\nbary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\not.toc()\n\n\nproblems.append([A, [bary_l2, bary_wass, bary_wass2]])\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\npl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()\n\n#%% parameters\n\na1 = np.zeros(n)\na2 = np.zeros(n)\n\na1[10] = .25\na1[20] = .5\na1[30] = .25\na2[80] = 1\n\n\na1 /= a1.sum()\na2 /= a2.sum()\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()\n\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()\n\n\n#%% barycenter computation\n\nalpha = 0.5 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\not.tic()\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\not.toc()\n\n\not.tic()\nbary_wass2 = ot.lp.barycenter(A, M, weights, solver='interior-point', verbose=True)\not.toc()\n\n\nproblems.append([A, [bary_l2, bary_wass, bary_wass2]])\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\npl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Final figure\n------------\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% plot\n\nnbm = len(problems)\nnbm2 = (nbm // 2)\n\n\npl.figure(2, (20, 6))\npl.clf()\n\nfor i in range(nbm):\n\n A = problems[i][0]\n bary_l2 = problems[i][1][0]\n bary_wass = problems[i][1][1]\n bary_wass2 = problems[i][1][2]\n\n pl.subplot(2, nbm, 1 + i)\n for j in range(n_distributions):\n pl.plot(x, A[:, j])\n if i == nbm2:\n pl.title('Distributions')\n pl.xticks(())\n pl.yticks(())\n\n pl.subplot(2, nbm, 1 + i + nbm)\n\n pl.plot(x, bary_l2, 'r', label='L2 (Euclidean)')\n pl.plot(x, bary_wass, 'g', label='Reg Wasserstein')\n pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\n if i == nbm - 1:\n pl.legend()\n if i == nbm2:\n pl.title('Barycenters')\n\n pl.xticks(())\n pl.yticks(())" ] } ], diff --git a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.py b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.py index 2255107..b82765e 100644 --- a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.py +++ b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.py @@ -15,8 +15,6 @@ Wasserstein problems. SIAM Journal on Imaging Sciences, 9(1), 320-343. Iterative Bregman projections for regularized transportation problems SIAM Journal on Scientific Computing, 37(2), A1111-A1138. - - """ # Author: Remi Flamary @@ -32,8 +30,8 @@ from matplotlib.collections import PolyCollection # noqa #import ot.lp.cvx as cvx -# -# Generate data +############################################################################## +# Gaussian Data # ------------- #%% parameters @@ -58,9 +56,6 @@ n_distributions = A.shape[1] M = ot.utils.dist0(n) M /= M.max() -# -# Plot data -# --------- #%% plot the distributions @@ -70,10 +65,6 @@ for i in range(n_distributions): pl.title('Distributions') pl.tight_layout() -# -# Barycenter computation -# ---------------------- - #%% barycenter computation alpha = 0.5 # 0<=alpha<=1 @@ -110,6 +101,10 @@ pl.tight_layout() problems.append([A, [bary_l2, bary_wass, bary_wass2]]) +############################################################################## +# Dirac Data +# ---------- + #%% parameters a1 = 1.0 * (x > 10) * (x < 50) @@ -135,9 +130,6 @@ for i in range(n_distributions): pl.title('Distributions') pl.tight_layout() -# -# Barycenter computation -# ---------------------- #%% barycenter computation @@ -207,9 +199,6 @@ for i in range(n_distributions): pl.title('Distributions') pl.tight_layout() -# -# Barycenter computation -# ---------------------- #%% barycenter computation @@ -249,7 +238,7 @@ pl.title('Barycenters') pl.tight_layout() -# +############################################################################## # Final figure # ------------ # diff --git a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.rst b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.rst index e8c15df..bd1c710 100644 --- a/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.rst +++ b/docs/source/auto_examples/plot_barycenter_lp_vs_entropic.rst @@ -21,95 +21,6 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. - - - -.. rst-class:: sphx-glr-horizontal - - - * - - .. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_001.png - :scale: 47 - - * - - .. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_002.png - :scale: 47 - - -.. rst-class:: sphx-glr-script-out - - Out:: - - Elapsed time : 0.010588884353637695 s - Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective - 1.0 1.0 1.0 - 1.0 1700.336700337 - 0.006776453137632 0.006776453137633 0.006776453137633 0.9932238647293 0.006776453137633 125.6700527543 - 0.004018712867874 0.004018712867874 0.004018712867874 0.4301142633 0.004018712867874 12.26594150093 - 0.001172775061627 0.001172775061627 0.001172775061627 0.7599932455029 0.001172775061627 0.3378536968897 - 0.0004375137005385 0.0004375137005385 0.0004375137005385 0.6422331807989 0.0004375137005385 0.1468420566358 - 0.000232669046734 0.0002326690467341 0.000232669046734 0.5016999460893 0.000232669046734 0.09381703231432 - 7.430121674303e-05 7.430121674303e-05 7.430121674303e-05 0.7035962305812 7.430121674303e-05 0.0577787025717 - 5.321227838876e-05 5.321227838875e-05 5.321227838876e-05 0.308784186441 5.321227838876e-05 0.05266249477203 - 1.990900379199e-05 1.990900379196e-05 1.990900379199e-05 0.6520472013244 1.990900379199e-05 0.04526054405519 - 6.305442046799e-06 6.30544204682e-06 6.3054420468e-06 0.7073953304075 6.305442046798e-06 0.04237597591383 - 2.290148391577e-06 2.290148391582e-06 2.290148391578e-06 0.6941812711492 2.29014839159e-06 0.041522849321 - 1.182864875387e-06 1.182864875406e-06 1.182864875427e-06 0.508455204675 1.182864875445e-06 0.04129461872827 - 3.626786381529e-07 3.626786382468e-07 3.626786382923e-07 0.7101651572101 3.62678638267e-07 0.04113032448923 - 1.539754244902e-07 1.539754249276e-07 1.539754249356e-07 0.6279322066282 1.539754253892e-07 0.04108867636379 - 5.193221323143e-08 5.193221463044e-08 5.193221462729e-08 0.6843453436759 5.193221708199e-08 0.04106859618414 - 1.888205054507e-08 1.888204779723e-08 1.88820477688e-08 0.6673444085651 1.888205650952e-08 0.041062141752 - 5.676855206925e-09 5.676854518888e-09 5.676854517651e-09 0.7281705804232 5.676885442702e-09 0.04105958648713 - 3.501157668218e-09 3.501150243546e-09 3.501150216347e-09 0.414020345194 3.501164437194e-09 0.04105916265261 - 1.110594251499e-09 1.110590786827e-09 1.11059083379e-09 0.6998954759911 1.110636623476e-09 0.04105870073485 - 5.770971626386e-10 5.772456113791e-10 5.772456200156e-10 0.4999769658132 5.77013379477e-10 0.04105859769135 - 1.535218204536e-10 1.536993317032e-10 1.536992771966e-10 0.7516471627141 1.536205005991e-10 0.04105851679958 - 6.724209350756e-11 6.739211232927e-11 6.739210470901e-11 0.5944802416166 6.735465384341e-11 0.04105850033766 - 1.743382199199e-11 1.736445896691e-11 1.736448490761e-11 0.7573407808104 1.734254328931e-11 0.04105849088824 - Optimization terminated successfully. - Elapsed time : 2.8747198581695557 s - Elapsed time : 0.014937639236450195 s - Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective - 1.0 1.0 1.0 - 1.0 1700.336700337 - 0.006776466288966 0.006776466288966 0.006776466288966 0.9932238515788 0.006776466288966 125.6649255808 - 0.004036918865495 0.004036918865495 0.004036918865495 0.4272973099316 0.004036918865495 12.3471617011 - 0.00121923268707 0.00121923268707 0.00121923268707 0.749698685599 0.00121923268707 0.3243835647408 - 0.0003837422984432 0.0003837422984432 0.0003837422984432 0.6926882608284 0.0003837422984432 0.1361719397493 - 0.0001070128410183 0.0001070128410183 0.0001070128410183 0.7643889137854 0.0001070128410183 0.07581952832518 - 0.0001001275033711 0.0001001275033711 0.0001001275033711 0.07058704837812 0.0001001275033712 0.0734739493635 - 4.550897507844e-05 4.550897507841e-05 4.550897507844e-05 0.5761172484828 4.550897507845e-05 0.05555077655047 - 8.557124125522e-06 8.5571241255e-06 8.557124125522e-06 0.8535925441152 8.557124125522e-06 0.04439814660221 - 3.611995628407e-06 3.61199562841e-06 3.611995628414e-06 0.6002277331554 3.611995628415e-06 0.04283007762152 - 7.590393750365e-07 7.590393750491e-07 7.590393750378e-07 0.8221486533416 7.590393750381e-07 0.04192322976248 - 8.299929287441e-08 8.299929286079e-08 8.299929287532e-08 0.9017467938799 8.29992928758e-08 0.04170825633295 - 3.117560203449e-10 3.117560130137e-10 3.11756019954e-10 0.997039969226 3.11756019952e-10 0.04168179329766 - 1.559749653711e-14 1.558073160926e-14 1.559756940692e-14 0.9999499686183 1.559750643989e-14 0.04168169240444 - Optimization terminated successfully. - Elapsed time : 2.6253304481506348 s - Elapsed time : 0.002875089645385742 s - Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective - 1.0 1.0 1.0 - 1.0 1700.336700337 - 0.006774675520727 0.006774675520727 0.006774675520727 0.9932256422636 0.006774675520727 125.6956034743 - 0.002048208707562 0.002048208707562 0.002048208707562 0.7343095368143 0.002048208707562 5.213991622123 - 0.000269736547478 0.0002697365474781 0.0002697365474781 0.8839403501193 0.000269736547478 0.505938390389 - 6.832109993943e-05 6.832109993944e-05 6.832109993944e-05 0.7601171075965 6.832109993943e-05 0.2339657807272 - 2.437682932219e-05 2.43768293222e-05 2.437682932219e-05 0.6663448297475 2.437682932219e-05 0.1471256246325 - 1.13498321631e-05 1.134983216308e-05 1.13498321631e-05 0.5553643816404 1.13498321631e-05 0.1181584941171 - 3.342312725885e-06 3.342312725884e-06 3.342312725885e-06 0.7238133571615 3.342312725885e-06 0.1006387519747 - 7.078561231603e-07 7.078561231509e-07 7.078561231604e-07 0.8033142552512 7.078561231603e-07 0.09474734646269 - 1.966870956916e-07 1.966870954537e-07 1.966870954468e-07 0.752547917788 1.966870954633e-07 0.09354342735766 - 4.19989524849e-10 4.199895164852e-10 4.199895238758e-10 0.9984019849375 4.19989523951e-10 0.09310367785861 - 2.101015938666e-14 2.100625691113e-14 2.101023853438e-14 0.999949974425 2.101023691864e-14 0.09310274466458 - Optimization terminated successfully. - Elapsed time : 3.587958335876465 s - - - - -| - - .. code-block:: python @@ -126,9 +37,19 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. #import ot.lp.cvx as cvx - # - # Generate data - # ------------- + + + + + + +Gaussian Data +------------- + + + +.. code-block:: python + #%% parameters @@ -152,9 +73,6 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. M = ot.utils.dist0(n) M /= M.max() - # - # Plot data - # --------- #%% plot the distributions @@ -164,10 +82,6 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. pl.title('Distributions') pl.tight_layout() - # - # Barycenter computation - # ---------------------- - #%% barycenter computation alpha = 0.5 # 0<=alpha<=1 @@ -204,6 +118,64 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. problems.append([A, [bary_l2, bary_wass, bary_wass2]]) + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_001.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_002.png + :scale: 47 + + +.. rst-class:: sphx-glr-script-out + + Out:: + + Elapsed time : 0.010712385177612305 s + Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective + 1.0 1.0 1.0 - 1.0 1700.336700337 + 0.006776453137632 0.006776453137633 0.006776453137633 0.9932238647293 0.006776453137633 125.6700527543 + 0.004018712867874 0.004018712867874 0.004018712867874 0.4301142633 0.004018712867874 12.26594150093 + 0.001172775061627 0.001172775061627 0.001172775061627 0.7599932455029 0.001172775061627 0.3378536968897 + 0.0004375137005385 0.0004375137005385 0.0004375137005385 0.6422331807989 0.0004375137005385 0.1468420566358 + 0.000232669046734 0.0002326690467341 0.000232669046734 0.5016999460893 0.000232669046734 0.09381703231432 + 7.430121674303e-05 7.430121674303e-05 7.430121674303e-05 0.7035962305812 7.430121674303e-05 0.0577787025717 + 5.321227838876e-05 5.321227838875e-05 5.321227838876e-05 0.308784186441 5.321227838876e-05 0.05266249477203 + 1.990900379199e-05 1.990900379196e-05 1.990900379199e-05 0.6520472013244 1.990900379199e-05 0.04526054405519 + 6.305442046799e-06 6.30544204682e-06 6.3054420468e-06 0.7073953304075 6.305442046798e-06 0.04237597591383 + 2.290148391577e-06 2.290148391582e-06 2.290148391578e-06 0.6941812711492 2.29014839159e-06 0.041522849321 + 1.182864875387e-06 1.182864875406e-06 1.182864875427e-06 0.508455204675 1.182864875445e-06 0.04129461872827 + 3.626786381529e-07 3.626786382468e-07 3.626786382923e-07 0.7101651572101 3.62678638267e-07 0.04113032448923 + 1.539754244902e-07 1.539754249276e-07 1.539754249356e-07 0.6279322066282 1.539754253892e-07 0.04108867636379 + 5.193221323143e-08 5.193221463044e-08 5.193221462729e-08 0.6843453436759 5.193221708199e-08 0.04106859618414 + 1.888205054507e-08 1.888204779723e-08 1.88820477688e-08 0.6673444085651 1.888205650952e-08 0.041062141752 + 5.676855206925e-09 5.676854518888e-09 5.676854517651e-09 0.7281705804232 5.676885442702e-09 0.04105958648713 + 3.501157668218e-09 3.501150243546e-09 3.501150216347e-09 0.414020345194 3.501164437194e-09 0.04105916265261 + 1.110594251499e-09 1.110590786827e-09 1.11059083379e-09 0.6998954759911 1.110636623476e-09 0.04105870073485 + 5.770971626386e-10 5.772456113791e-10 5.772456200156e-10 0.4999769658132 5.77013379477e-10 0.04105859769135 + 1.535218204536e-10 1.536993317032e-10 1.536992771966e-10 0.7516471627141 1.536205005991e-10 0.04105851679958 + 6.724209350756e-11 6.739211232927e-11 6.739210470901e-11 0.5944802416166 6.735465384341e-11 0.04105850033766 + 1.743382199199e-11 1.736445896691e-11 1.736448490761e-11 0.7573407808104 1.734254328931e-11 0.04105849088824 + Optimization terminated successfully. + Elapsed time : 2.883899211883545 s + + +Dirac Data +---------- + + + +.. code-block:: python + + #%% parameters a1 = 1.0 * (x > 10) * (x < 50) @@ -229,9 +201,6 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. pl.title('Distributions') pl.tight_layout() - # - # Barycenter computation - # ---------------------- #%% barycenter computation @@ -301,9 +270,6 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. pl.title('Distributions') pl.tight_layout() - # - # Barycenter computation - # ---------------------- #%% barycenter computation @@ -343,10 +309,71 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. pl.tight_layout() - # - # Final figure - # ------------ - # + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_003.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_004.png + :scale: 47 + + +.. rst-class:: sphx-glr-script-out + + Out:: + + Elapsed time : 0.014938592910766602 s + Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective + 1.0 1.0 1.0 - 1.0 1700.336700337 + 0.006776466288966 0.006776466288966 0.006776466288966 0.9932238515788 0.006776466288966 125.6649255808 + 0.004036918865495 0.004036918865495 0.004036918865495 0.4272973099316 0.004036918865495 12.3471617011 + 0.00121923268707 0.00121923268707 0.00121923268707 0.749698685599 0.00121923268707 0.3243835647408 + 0.0003837422984432 0.0003837422984432 0.0003837422984432 0.6926882608284 0.0003837422984432 0.1361719397493 + 0.0001070128410183 0.0001070128410183 0.0001070128410183 0.7643889137854 0.0001070128410183 0.07581952832518 + 0.0001001275033711 0.0001001275033711 0.0001001275033711 0.07058704837812 0.0001001275033712 0.0734739493635 + 4.550897507844e-05 4.550897507841e-05 4.550897507844e-05 0.5761172484828 4.550897507845e-05 0.05555077655047 + 8.557124125522e-06 8.5571241255e-06 8.557124125522e-06 0.8535925441152 8.557124125522e-06 0.04439814660221 + 3.611995628407e-06 3.61199562841e-06 3.611995628414e-06 0.6002277331554 3.611995628415e-06 0.04283007762152 + 7.590393750365e-07 7.590393750491e-07 7.590393750378e-07 0.8221486533416 7.590393750381e-07 0.04192322976248 + 8.299929287441e-08 8.299929286079e-08 8.299929287532e-08 0.9017467938799 8.29992928758e-08 0.04170825633295 + 3.117560203449e-10 3.117560130137e-10 3.11756019954e-10 0.997039969226 3.11756019952e-10 0.04168179329766 + 1.559749653711e-14 1.558073160926e-14 1.559756940692e-14 0.9999499686183 1.559750643989e-14 0.04168169240444 + Optimization terminated successfully. + Elapsed time : 2.642659902572632 s + Elapsed time : 0.002908945083618164 s + Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective + 1.0 1.0 1.0 - 1.0 1700.336700337 + 0.006774675520727 0.006774675520727 0.006774675520727 0.9932256422636 0.006774675520727 125.6956034743 + 0.002048208707562 0.002048208707562 0.002048208707562 0.7343095368143 0.002048208707562 5.213991622123 + 0.000269736547478 0.0002697365474781 0.0002697365474781 0.8839403501193 0.000269736547478 0.505938390389 + 6.832109993943e-05 6.832109993944e-05 6.832109993944e-05 0.7601171075965 6.832109993943e-05 0.2339657807272 + 2.437682932219e-05 2.43768293222e-05 2.437682932219e-05 0.6663448297475 2.437682932219e-05 0.1471256246325 + 1.13498321631e-05 1.134983216308e-05 1.13498321631e-05 0.5553643816404 1.13498321631e-05 0.1181584941171 + 3.342312725885e-06 3.342312725884e-06 3.342312725885e-06 0.7238133571615 3.342312725885e-06 0.1006387519747 + 7.078561231603e-07 7.078561231509e-07 7.078561231604e-07 0.8033142552512 7.078561231603e-07 0.09474734646269 + 1.966870956916e-07 1.966870954537e-07 1.966870954468e-07 0.752547917788 1.966870954633e-07 0.09354342735766 + 4.19989524849e-10 4.199895164852e-10 4.199895238758e-10 0.9984019849375 4.19989523951e-10 0.09310367785861 + 2.101015938666e-14 2.100625691113e-14 2.101023853438e-14 0.999949974425 2.101023691864e-14 0.09310274466458 + Optimization terminated successfully. + Elapsed time : 2.690450668334961 s + + +Final figure +------------ + + + + +.. code-block:: python + #%% plot @@ -385,7 +412,15 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. pl.xticks(()) pl.yticks(()) -**Total running time of the script:** ( 0 minutes 9.816 seconds) + + +.. image:: /auto_examples/images/sphx_glr_plot_barycenter_lp_vs_entropic_006.png + :align: center + + + + +**Total running time of the script:** ( 0 minutes 8.892 seconds) diff --git a/docs/source/auto_examples/plot_otda_linear_mapping.ipynb b/docs/source/auto_examples/plot_otda_linear_mapping.ipynb index e43bee7..027b6cb 100644 --- a/docs/source/auto_examples/plot_otda_linear_mapping.ipynb +++ b/docs/source/auto_examples/plot_otda_linear_mapping.ipynb @@ -15,7 +15,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "\nCreated on Tue Mar 20 14:31:15 2018\n\n@author: rflamary\n\n" + "\n# Linear OT mapping estimation\n\n\n\n\n" ] }, { @@ -26,7 +26,7 @@ }, "outputs": [], "source": [ - "import numpy as np\nimport pylab as pl\nimport ot" + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport pylab as pl\nimport ot" ] }, { diff --git a/docs/source/auto_examples/plot_otda_linear_mapping.py b/docs/source/auto_examples/plot_otda_linear_mapping.py index 7a3b761..c65bd4f 100644 --- a/docs/source/auto_examples/plot_otda_linear_mapping.py +++ b/docs/source/auto_examples/plot_otda_linear_mapping.py @@ -1,11 +1,17 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ -Created on Tue Mar 20 14:31:15 2018 +============================ +Linear OT mapping estimation +============================ + -@author: rflamary """ +# Author: Remi Flamary +# +# License: MIT License + import numpy as np import pylab as pl import ot diff --git a/docs/source/auto_examples/plot_otda_linear_mapping.rst b/docs/source/auto_examples/plot_otda_linear_mapping.rst index 1321732..8e2e0cf 100644 --- a/docs/source/auto_examples/plot_otda_linear_mapping.rst +++ b/docs/source/auto_examples/plot_otda_linear_mapping.rst @@ -3,15 +3,21 @@ .. _sphx_glr_auto_examples_plot_otda_linear_mapping.py: -Created on Tue Mar 20 14:31:15 2018 +============================ +Linear OT mapping estimation +============================ + -@author: rflamary .. code-block:: python + # Author: Remi Flamary + # + # License: MIT License + import numpy as np import pylab as pl import ot @@ -227,7 +233,7 @@ Plot transformed images -**Total running time of the script:** ( 0 minutes 0.563 seconds) +**Total running time of the script:** ( 0 minutes 0.635 seconds) diff --git a/docs/source/conf.py b/docs/source/conf.py index 4105d87..114245d 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -81,7 +81,7 @@ master_doc = 'index' # General information about the project. project = u'POT Python Optimal Transport' -copyright = u'2016, Rémi Flamary, Nicolas Courty' +copyright = u'2016-2018, Rémi Flamary, Nicolas Courty' author = u'Rémi Flamary, Nicolas Courty' # The version info for the project you're documenting, acts as replacement for diff --git a/examples/plot_barycenter_lp_vs_entropic.py b/examples/plot_barycenter_lp_vs_entropic.py index 2255107..b82765e 100644 --- a/examples/plot_barycenter_lp_vs_entropic.py +++ b/examples/plot_barycenter_lp_vs_entropic.py @@ -15,8 +15,6 @@ Wasserstein problems. SIAM Journal on Imaging Sciences, 9(1), 320-343. Iterative Bregman projections for regularized transportation problems SIAM Journal on Scientific Computing, 37(2), A1111-A1138. - - """ # Author: Remi Flamary @@ -32,8 +30,8 @@ from matplotlib.collections import PolyCollection # noqa #import ot.lp.cvx as cvx -# -# Generate data +############################################################################## +# Gaussian Data # ------------- #%% parameters @@ -58,9 +56,6 @@ n_distributions = A.shape[1] M = ot.utils.dist0(n) M /= M.max() -# -# Plot data -# --------- #%% plot the distributions @@ -70,10 +65,6 @@ for i in range(n_distributions): pl.title('Distributions') pl.tight_layout() -# -# Barycenter computation -# ---------------------- - #%% barycenter computation alpha = 0.5 # 0<=alpha<=1 @@ -110,6 +101,10 @@ pl.tight_layout() problems.append([A, [bary_l2, bary_wass, bary_wass2]]) +############################################################################## +# Dirac Data +# ---------- + #%% parameters a1 = 1.0 * (x > 10) * (x < 50) @@ -135,9 +130,6 @@ for i in range(n_distributions): pl.title('Distributions') pl.tight_layout() -# -# Barycenter computation -# ---------------------- #%% barycenter computation @@ -207,9 +199,6 @@ for i in range(n_distributions): pl.title('Distributions') pl.tight_layout() -# -# Barycenter computation -# ---------------------- #%% barycenter computation @@ -249,7 +238,7 @@ pl.title('Barycenters') pl.tight_layout() -# +############################################################################## # Final figure # ------------ # diff --git a/examples/plot_otda_linear_mapping.py b/examples/plot_otda_linear_mapping.py index 7a3b761..c65bd4f 100644 --- a/examples/plot_otda_linear_mapping.py +++ b/examples/plot_otda_linear_mapping.py @@ -1,11 +1,17 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ -Created on Tue Mar 20 14:31:15 2018 +============================ +Linear OT mapping estimation +============================ + -@author: rflamary """ +# Author: Remi Flamary +# +# License: MIT License + import numpy as np import pylab as pl import ot diff --git a/notebooks/plot_barycenter_lp_vs_entropic.ipynb b/notebooks/plot_barycenter_lp_vs_entropic.ipynb index e188875..9c8e83e 100644 --- a/notebooks/plot_barycenter_lp_vs_entropic.ipynb +++ b/notebooks/plot_barycenter_lp_vs_entropic.ipynb @@ -30,14 +30,43 @@ "Iterative Bregman projections for regularized transportation problems\n", "SIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n", "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Remi Flamary \n", + "#\n", + "# License: MIT License\n", "\n", + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot\n", + "# necessary for 3d plot even if not used\n", + "from mpl_toolkits.mplot3d import Axes3D # noqa\n", + "from matplotlib.collections import PolyCollection # noqa\n", "\n", + "#import ot.lp.cvx as cvx" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Gaussian Data\n", + "-------------\n", "\n" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -46,7 +75,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Elapsed time : 0.010513782501220703 s\n", + "Elapsed time : 0.010422945022583008 s\n", "Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective \n", "1.0 1.0 1.0 - 1.0 1700.336700337 \n", "0.006776453137632 0.006776453137633 0.006776453137633 0.9932238647293 0.006776453137633 125.6700527543 \n", @@ -72,46 +101,12 @@ "6.724209350756e-11 6.739211232927e-11 6.739210470901e-11 0.5944802416166 6.735465384341e-11 0.04105850033766 \n", "1.743382199199e-11 1.736445896691e-11 1.736448490761e-11 0.7573407808104 1.734254328931e-11 0.04105849088824 \n", "Optimization terminated successfully.\n", - "Elapsed time : 2.89129376411438 s\n", - "Elapsed time : 0.014848947525024414 s\n", - "Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective \n", - "1.0 1.0 1.0 - 1.0 1700.336700337 \n", - "0.006776466288966 0.006776466288966 0.006776466288966 0.9932238515788 0.006776466288966 125.6649255808 \n", - "0.004036918865495 0.004036918865495 0.004036918865495 0.4272973099316 0.004036918865495 12.3471617011 \n", - "0.00121923268707 0.00121923268707 0.00121923268707 0.749698685599 0.00121923268707 0.3243835647408 \n", - "0.0003837422984432 0.0003837422984432 0.0003837422984432 0.6926882608284 0.0003837422984432 0.1361719397493 \n", - "0.0001070128410183 0.0001070128410183 0.0001070128410183 0.7643889137854 0.0001070128410183 0.07581952832518 \n", - "0.0001001275033711 0.0001001275033711 0.0001001275033711 0.07058704837812 0.0001001275033712 0.0734739493635 \n", - "4.550897507844e-05 4.550897507841e-05 4.550897507844e-05 0.5761172484828 4.550897507845e-05 0.05555077655047 \n", - "8.557124125522e-06 8.5571241255e-06 8.557124125522e-06 0.8535925441152 8.557124125522e-06 0.04439814660221 \n", - "3.611995628407e-06 3.61199562841e-06 3.611995628414e-06 0.6002277331554 3.611995628415e-06 0.04283007762152 \n", - "7.590393750365e-07 7.590393750491e-07 7.590393750378e-07 0.8221486533416 7.590393750381e-07 0.04192322976248 \n", - "8.299929287441e-08 8.299929286079e-08 8.299929287532e-08 0.9017467938799 8.29992928758e-08 0.04170825633295 \n", - "3.117560203449e-10 3.117560130137e-10 3.11756019954e-10 0.997039969226 3.11756019952e-10 0.04168179329766 \n", - "1.559749653711e-14 1.558073160926e-14 1.559756940692e-14 0.9999499686183 1.559750643989e-14 0.04168169240444 \n", - "Optimization terminated successfully.\n", - "Elapsed time : 2.7255496978759766 s\n", - "Elapsed time : 0.002989530563354492 s\n", - "Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective \n", - "1.0 1.0 1.0 - 1.0 1700.336700337 \n", - "0.006774675520727 0.006774675520727 0.006774675520727 0.9932256422636 0.006774675520727 125.6956034743 \n", - "0.002048208707562 0.002048208707562 0.002048208707562 0.7343095368143 0.002048208707562 5.213991622123 \n", - "0.000269736547478 0.0002697365474781 0.0002697365474781 0.8839403501193 0.000269736547478 0.505938390389 \n", - "6.832109993943e-05 6.832109993944e-05 6.832109993944e-05 0.7601171075965 6.832109993943e-05 0.2339657807272 \n", - "2.437682932219e-05 2.43768293222e-05 2.437682932219e-05 0.6663448297475 2.437682932219e-05 0.1471256246325 \n", - "1.13498321631e-05 1.134983216308e-05 1.13498321631e-05 0.5553643816404 1.13498321631e-05 0.1181584941171 \n", - "3.342312725885e-06 3.342312725884e-06 3.342312725885e-06 0.7238133571615 3.342312725885e-06 0.1006387519747 \n", - "7.078561231603e-07 7.078561231509e-07 7.078561231604e-07 0.8033142552512 7.078561231603e-07 0.09474734646269 \n", - "1.966870956916e-07 1.966870954537e-07 1.966870954468e-07 0.752547917788 1.966870954633e-07 0.09354342735766 \n", - "4.19989524849e-10 4.199895164852e-10 4.199895238758e-10 0.9984019849375 4.19989523951e-10 0.09310367785861 \n", - "2.101015938666e-14 2.100625691113e-14 2.101023853438e-14 0.999949974425 2.101023691864e-14 0.09310274466458 \n", - "Optimization terminated successfully.\n", - "Elapsed time : 2.594216823577881 s\n" + "Elapsed time : 2.927520990371704 s\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -131,23 +126,6 @@ } ], "source": [ - "# Author: Remi Flamary \n", - "#\n", - "# License: MIT License\n", - "\n", - "import numpy as np\n", - "import matplotlib.pylab as pl\n", - "import ot\n", - "# necessary for 3d plot even if not used\n", - "from mpl_toolkits.mplot3d import Axes3D # noqa\n", - "from matplotlib.collections import PolyCollection # noqa\n", - "\n", - "#import ot.lp.cvx as cvx\n", - "\n", - "#\n", - "# Generate data\n", - "# -------------\n", - "\n", "#%% parameters\n", "\n", "problems = []\n", @@ -170,9 +148,6 @@ "M = ot.utils.dist0(n)\n", "M /= M.max()\n", "\n", - "#\n", - "# Plot data\n", - "# ---------\n", "\n", "#%% plot the distributions\n", "\n", @@ -182,10 +157,6 @@ "pl.title('Distributions')\n", "pl.tight_layout()\n", "\n", - "#\n", - "# Barycenter computation\n", - "# ----------------------\n", - "\n", "#%% barycenter computation\n", "\n", "alpha = 0.5 # 0<=alpha<=1\n", @@ -220,8 +191,87 @@ "pl.title('Barycenters')\n", "pl.tight_layout()\n", "\n", - "problems.append([A, [bary_l2, bary_wass, bary_wass2]])\n", - "\n", + "problems.append([A, [bary_l2, bary_wass, bary_wass2]])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Dirac Data\n", + "----------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Elapsed time : 0.014856815338134766 s\n", + "Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective \n", + "1.0 1.0 1.0 - 1.0 1700.336700337 \n", + "0.006776466288966 0.006776466288966 0.006776466288966 0.9932238515788 0.006776466288966 125.6649255808 \n", + "0.004036918865495 0.004036918865495 0.004036918865495 0.4272973099316 0.004036918865495 12.3471617011 \n", + "0.00121923268707 0.00121923268707 0.00121923268707 0.749698685599 0.00121923268707 0.3243835647408 \n", + "0.0003837422984432 0.0003837422984432 0.0003837422984432 0.6926882608284 0.0003837422984432 0.1361719397493 \n", + "0.0001070128410183 0.0001070128410183 0.0001070128410183 0.7643889137854 0.0001070128410183 0.07581952832518 \n", + "0.0001001275033711 0.0001001275033711 0.0001001275033711 0.07058704837812 0.0001001275033712 0.0734739493635 \n", + "4.550897507844e-05 4.550897507841e-05 4.550897507844e-05 0.5761172484828 4.550897507845e-05 0.05555077655047 \n", + "8.557124125522e-06 8.5571241255e-06 8.557124125522e-06 0.8535925441152 8.557124125522e-06 0.04439814660221 \n", + "3.611995628407e-06 3.61199562841e-06 3.611995628414e-06 0.6002277331554 3.611995628415e-06 0.04283007762152 \n", + "7.590393750365e-07 7.590393750491e-07 7.590393750378e-07 0.8221486533416 7.590393750381e-07 0.04192322976248 \n", + "8.299929287441e-08 8.299929286079e-08 8.299929287532e-08 0.9017467938799 8.29992928758e-08 0.04170825633295 \n", + "3.117560203449e-10 3.117560130137e-10 3.11756019954e-10 0.997039969226 3.11756019952e-10 0.04168179329766 \n", + "1.559749653711e-14 1.558073160926e-14 1.559756940692e-14 0.9999499686183 1.559750643989e-14 0.04168169240444 \n", + "Optimization terminated successfully.\n", + "Elapsed time : 2.703077793121338 s\n", + "Elapsed time : 0.0029761791229248047 s\n", + "Primal Feasibility Dual Feasibility Duality Gap Step Path Parameter Objective \n", + "1.0 1.0 1.0 - 1.0 1700.336700337 \n", + "0.006774675520727 0.006774675520727 0.006774675520727 0.9932256422636 0.006774675520727 125.6956034743 \n", + "0.002048208707562 0.002048208707562 0.002048208707562 0.7343095368143 0.002048208707562 5.213991622123 \n", + "0.000269736547478 0.0002697365474781 0.0002697365474781 0.8839403501193 0.000269736547478 0.505938390389 \n", + "6.832109993943e-05 6.832109993944e-05 6.832109993944e-05 0.7601171075965 6.832109993943e-05 0.2339657807272 \n", + "2.437682932219e-05 2.43768293222e-05 2.437682932219e-05 0.6663448297475 2.437682932219e-05 0.1471256246325 \n", + "1.13498321631e-05 1.134983216308e-05 1.13498321631e-05 0.5553643816404 1.13498321631e-05 0.1181584941171 \n", + "3.342312725885e-06 3.342312725884e-06 3.342312725885e-06 0.7238133571615 3.342312725885e-06 0.1006387519747 \n", + "7.078561231603e-07 7.078561231509e-07 7.078561231604e-07 0.8033142552512 7.078561231603e-07 0.09474734646269 \n", + "1.966870956916e-07 1.966870954537e-07 1.966870954468e-07 0.752547917788 1.966870954633e-07 0.09354342735766 \n", + "4.19989524849e-10 4.199895164852e-10 4.199895238758e-10 0.9984019849375 4.19989523951e-10 0.09310367785861 \n", + "2.101015938666e-14 2.100625691113e-14 2.101023853438e-14 0.999949974425 2.101023691864e-14 0.09310274466458 \n", + "Optimization terminated successfully.\n", + "Elapsed time : 2.6085386276245117 s\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "#%% parameters\n", "\n", "a1 = 1.0 * (x > 10) * (x < 50)\n", @@ -247,9 +297,6 @@ "pl.title('Distributions')\n", "pl.tight_layout()\n", "\n", - "#\n", - "# Barycenter computation\n", - "# ----------------------\n", "\n", "#%% barycenter computation\n", "\n", @@ -319,9 +366,6 @@ "pl.title('Distributions')\n", "pl.tight_layout()\n", "\n", - "#\n", - "# Barycenter computation\n", - "# ----------------------\n", "\n", "#%% barycenter computation\n", "\n", @@ -358,14 +402,38 @@ "pl.plot(x, bary_wass2, 'b', label='LP Wasserstein')\n", "pl.legend()\n", "pl.title('Barycenters')\n", - "pl.tight_layout()\n", - "\n", - "\n", - "#\n", - "# Final figure\n", - "# ------------\n", - "#\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Final figure\n", + "------------\n", "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "#%% plot\n", "\n", "nbm = len(problems)\n", diff --git a/notebooks/plot_otda_linear_mapping.ipynb b/notebooks/plot_otda_linear_mapping.ipynb index 7b0e7ad..4b6713d 100644 --- a/notebooks/plot_otda_linear_mapping.ipynb +++ b/notebooks/plot_otda_linear_mapping.ipynb @@ -16,9 +16,10 @@ "metadata": {}, "source": [ "\n", - "Created on Tue Mar 20 14:31:15 2018\n", + "# Linear OT mapping estimation\n", + "\n", + "\n", "\n", - "@author: rflamary\n", "\n" ] }, @@ -30,6 +31,10 @@ }, "outputs": [], "source": [ + "# Author: Remi Flamary \n", + "#\n", + "# License: MIT License\n", + "\n", "import numpy as np\n", "import pylab as pl\n", "import ot" @@ -94,7 +99,7 @@ { "data": { "text/plain": [ - "[]" + "[]" ] }, "execution_count": 4, @@ -103,7 +108,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -158,7 +163,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] -- cgit v1.2.3 From 8d288976d398749c5261daca22ee4d772bd5b489 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 31 May 2018 11:08:01 +0200 Subject: add smooth.py + first tests --- ot/__init__.py | 2 + ot/smooth.py | 405 ++++++++++++++++++++++++++++++++++++++++++++++++++++ test/test_smooth.py | 33 +++++ 3 files changed, 440 insertions(+) create mode 100644 ot/smooth.py create mode 100644 test/test_smooth.py diff --git a/ot/__init__.py b/ot/__init__.py index 1500e59..995ce33 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -18,6 +18,8 @@ from . import utils from . import datasets from . import da from . import gromov +from . import smooth + # OT functions from .lp import emd, emd2 diff --git a/ot/smooth.py b/ot/smooth.py new file mode 100644 index 0000000..b8c0a9a --- /dev/null +++ b/ot/smooth.py @@ -0,0 +1,405 @@ +#Copyright (c) 2018, Mathieu Blondel +#All rights reserved. +# +#Redistribution and use in source and binary forms, with or without +#modification, are permitted provided that the following conditions are met: +# +#1. Redistributions of source code must retain the above copyright notice, this +#list of conditions and the following disclaimer. +# +#2. Redistributions in binary form must reproduce the above copyright notice, +#this list of conditions and the following disclaimer in the documentation and/or +#other materials provided with the distribution. +# +#THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND +#ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED +#WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. +#IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, +#INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT +#NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, +#OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF +#LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR +#OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF +#THE POSSIBILITY OF SUCH DAMAGE. + +# Author: Mathieu Blondel +# Remi Flamary + +""" +Implementation of +Smooth and Sparse Optimal Transport. +Mathieu Blondel, Vivien Seguy, Antoine Rolet. +In Proc. of AISTATS 2018. +https://arxiv.org/abs/1710.06276 +""" + +import numpy as np +from scipy.optimize import minimize + + +def projection_simplex(V, z=1, axis=None): + """ Projection of x onto the simplex, scaled by z + + P(x; z) = argmin_{y >= 0, sum(y) = z} ||y - x||^2 + z: float or array + If array, len(z) must be compatible with V + axis: None or int + - axis=None: project V by P(V.ravel(); z) + - axis=1: project each V[i] by P(V[i]; z[i]) + - axis=0: project each V[:, j] by P(V[:, j]; z[j]) + """ + if axis == 1: + n_features = V.shape[1] + U = np.sort(V, axis=1)[:, ::-1] + z = np.ones(len(V)) * z + cssv = np.cumsum(U, axis=1) - z[:, np.newaxis] + ind = np.arange(n_features) + 1 + cond = U - cssv / ind > 0 + rho = np.count_nonzero(cond, axis=1) + theta = cssv[np.arange(len(V)), rho - 1] / rho + return np.maximum(V - theta[:, np.newaxis], 0) + + elif axis == 0: + return projection_simplex(V.T, z, axis=1).T + + else: + V = V.ravel().reshape(1, -1) + return projection_simplex(V, z, axis=1).ravel() + + +class Regularization(object): + + def __init__(self, gamma=1.0): + """ + + Parameters + ---------- + gamma: float + Regularization parameter. + We recover unregularized OT when gamma -> 0. + + """ + self.gamma = gamma + + def delta_Omega(X): + """ + Compute delta_Omega(X[:, j]) for each X[:, j]. + delta_Omega(x) = sup_{y >= 0} y^T x - Omega(y). + Parameters + ---------- + X: array, shape = len(a) x len(b) + Input array. + Returns + ------- + v: array, len(b) + Values: v[j] = delta_Omega(X[:, j]) + G: array, len(a) x len(b) + Gradients: G[:, j] = nabla delta_Omega(X[:, j]) + """ + raise NotImplementedError + + def max_Omega(X, b): + """ + Compute max_Omega_j(X[:, j]) for each X[:, j]. + max_Omega_j(x) = sup_{y >= 0, sum(y) = 1} y^T x - Omega(b[j] y) / b[j]. + + Parameters + ---------- + X: array, shape = len(a) x len(b) + Input array. + Returns + ------- + v: array, len(b) + Values: v[j] = max_Omega_j(X[:, j]) + G: array, len(a) x len(b) + Gradients: G[:, j] = nabla max_Omega_j(X[:, j]) + """ + raise NotImplementedError + + def Omega(T): + """ + Compute regularization term. + + Parameters + ---------- + T: array, shape = len(a) x len(b) + Input array. + Returns + ------- + value: float + Regularization term. + """ + raise NotImplementedError + + +class NegEntropy(Regularization): + + def delta_Omega(self, X): + G = np.exp(X / self.gamma - 1) + val = self.gamma * np.sum(G, axis=0) + return val, G + + def max_Omega(self, X, b): + max_X = np.max(X, axis=0) / self.gamma + exp_X = np.exp(X / self.gamma - max_X) + val = self.gamma * (np.log(np.sum(exp_X, axis=0)) + max_X) + val -= self.gamma * np.log(b) + G = exp_X / np.sum(exp_X, axis=0) + return val, G + + def Omega(self, T): + return self.gamma * np.sum(T * np.log(T)) + + +class SquaredL2(Regularization): + + def delta_Omega(self, X): + max_X = np.maximum(X, 0) + val = np.sum(max_X ** 2, axis=0) / (2 * self.gamma) + G = max_X / self.gamma + return val, G + + def max_Omega(self, X, b): + G = projection_simplex(X / (b * self.gamma), axis=0) + val = np.sum(X * G, axis=0) + val -= 0.5 * self.gamma * b * np.sum(G * G, axis=0) + return val, G + + def Omega(self, T): + return 0.5 * self.gamma * np.sum(T ** 2) + + +def dual_obj_grad(alpha, beta, a, b, C, regul): + """ + Compute objective value and gradients of dual objective. + + Parameters + ---------- + alpha: array, shape = len(a) + beta: array, shape = len(b) + Current iterate of dual potentials. + a: array, shape = len(a) + b: array, shape = len(b) + Input histograms (should be non-negative and sum to 1). + C: array, shape = len(a) x len(b) + Ground cost matrix. + regul: Regularization object + Should implement a delta_Omega(X) method. + + Returns + ------- + obj: float + Objective value (higher is better). + grad_alpha: array, shape = len(a) + Gradient w.r.t. alpha. + grad_beta: array, shape = len(b) + Gradient w.r.t. beta. + """ + obj = np.dot(alpha, a) + np.dot(beta, b) + grad_alpha = a.copy() + grad_beta = b.copy() + + # X[:, j] = alpha + beta[j] - C[:, j] + X = alpha[:, np.newaxis] + beta - C + + # val.shape = len(b) + # G.shape = len(a) x len(b) + val, G = regul.delta_Omega(X) + + obj -= np.sum(val) + grad_alpha -= G.sum(axis=1) + grad_beta -= G.sum(axis=0) + + return obj, grad_alpha, grad_beta + + +def solve_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500): + """ + Solve the "smoothed" dual objective. + + Parameters + ---------- + a: array, shape = len(a) + b: array, shape = len(b) + Input histograms (should be non-negative and sum to 1). + C: array, shape = len(a) x len(b) + Ground cost matrix. + regul: Regularization object + Should implement a delta_Omega(X) method. + method: str + Solver to be used (passed to `scipy.optimize.minimize`). + tol: float + Tolerance parameter. + max_iter: int + Maximum number of iterations. + Returns + ------- + alpha: array, shape = len(a) + beta: array, shape = len(b) + Dual potentials. + """ + + def _func(params): + # Unpack alpha and beta. + alpha = params[:len(a)] + beta = params[len(a):] + + obj, grad_alpha, grad_beta = dual_obj_grad(alpha, beta, a, b, C, regul) + + # Pack grad_alpha and grad_beta. + grad = np.concatenate((grad_alpha, grad_beta)) + + # We need to maximize the dual. + return -obj, -grad + + # Unfortunately, `minimize` only supports functions whose argument is a + # vector. So, we need to concatenate alpha and beta. + alpha_init = np.zeros(len(a)) + beta_init = np.zeros(len(b)) + params_init = np.concatenate((alpha_init, beta_init)) + + res = minimize(_func, params_init, method=method, jac=True, + tol=tol, options=dict(maxiter=max_iter, disp=False)) + + alpha = res.x[:len(a)] + beta = res.x[len(a):] + + return alpha, beta, res + + +def semi_dual_obj_grad(alpha, a, b, C, regul): + """ + Compute objective value and gradient of semi-dual objective. + Parameters + ---------- + alpha: array, shape = len(a) + Current iterate of semi-dual potentials. + a: array, shape = len(a) + b: array, shape = len(b) + Input histograms (should be non-negative and sum to 1). + C: array, shape = len(a) x len(b) + Ground cost matrix. + regul: Regularization object + Should implement a max_Omega(X) method. + Returns + ------- + obj: float + Objective value (higher is better). + grad: array, shape = len(a) + Gradient w.r.t. alpha. + """ + obj = np.dot(alpha, a) + grad = a.copy() + + # X[:, j] = alpha - C[:, j] + X = alpha[:, np.newaxis] - C + + # val.shape = len(b) + # G.shape = len(a) x len(b) + val, G = regul.max_Omega(X, b) + + obj -= np.dot(b, val) + grad -= np.dot(G, b) + + return obj, grad + + +def solve_semi_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500): + """ + Solve the "smoothed" semi-dual objective. + Parameters + ---------- + a: array, shape = len(a) + b: array, shape = len(b) + Input histograms (should be non-negative and sum to 1). + C: array, shape = len(a) x len(b) + Ground cost matrix. + regul: Regularization object + Should implement a max_Omega(X) method. + method: str + Solver to be used (passed to `scipy.optimize.minimize`). + tol: float + Tolerance parameter. + max_iter: int + Maximum number of iterations. + Returns + ------- + alpha: array, shape = len(a) + Semi-dual potentials. + """ + + def _func(alpha): + obj, grad = semi_dual_obj_grad(alpha, a, b, C, regul) + # We need to maximize the semi-dual. + return -obj, -grad + + alpha_init = np.zeros(len(a)) + + res = minimize(_func, alpha_init, method=method, jac=True, + tol=tol, options=dict(maxiter=max_iter, disp=False)) + + return res.x, res + + +def get_plan_from_dual(alpha, beta, C, regul): + """ + Retrieve optimal transportation plan from optimal dual potentials. + Parameters + ---------- + alpha: array, shape = len(a) + beta: array, shape = len(b) + Optimal dual potentials. + C: array, shape = len(a) x len(b) + Ground cost matrix. + regul: Regularization object + Should implement a delta_Omega(X) method. + Returns + ------- + T: array, shape = len(a) x len(b) + Optimal transportation plan. + """ + X = alpha[:, np.newaxis] + beta - C + return regul.delta_Omega(X)[1] + + +def get_plan_from_semi_dual(alpha, b, C, regul): + """ + Retrieve optimal transportation plan from optimal semi-dual potentials. + Parameters + ---------- + alpha: array, shape = len(a) + Optimal semi-dual potentials. + b: array, shape = len(b) + Second input histogram (should be non-negative and sum to 1). + C: array, shape = len(a) x len(b) + Ground cost matrix. + regul: Regularization object + Should implement a delta_Omega(X) method. + Returns + ------- + T: array, shape = len(a) x len(b) + Optimal transportation plan. + """ + X = alpha[:, np.newaxis] - C + return regul.max_Omega(X, b)[1] * b + + +def smooth_ot_dual(a, b, M, reg, reg_type='l2', method="L-BFGS-B", stopThr=1e-9, + numItermax=500, log=False): + + if reg_type.lower()=='l2': + regul = SquaredL2(gamma=reg) + elif reg_type.lower() in ['entropic','negentropy','kl']: + regul = NegEntropy(gamma=reg) + + alpha, beta, res = solve_dual(a, b, M, regul, max_iter=numItermax,tol=stopThr) + G = get_plan_from_dual(alpha, beta, M, regul) + + if log: + log={'alpha':alpha,beta:'beta','res':res} + return G, log + else: + return G + + + diff --git a/test/test_smooth.py b/test/test_smooth.py new file mode 100644 index 0000000..f951bf9 --- /dev/null +++ b/test/test_smooth.py @@ -0,0 +1,33 @@ +"""Tests for ot.smooth model """ + +# Author: Remi Flamary +# +# License: MIT License + +import warnings + +import numpy as np + +import ot +from ot.datasets import get_1D_gauss as gauss +import pytest + + +def test_smooth_ot_dual(): + # test sinkhorn + n = 100 + rng = np.random.RandomState(0) + + x = rng.randn(n, 2) + u = ot.utils.unif(n) + + M = ot.dist(x, x) + + G = ot.smooth.smooth_ot_dual(u, u, M, 1, stopThr=1e-10) + + # check constratints + np.testing.assert_allclose( + u, G.sum(1), atol=1e-05) # cf convergence sinkhorn + np.testing.assert_allclose( + u, G.sum(0), atol=1e-05) # cf convergence sinkhorn + \ No newline at end of file -- cgit v1.2.3 From af99d3ff66062811a81454ea03e6b831a1292ae4 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 31 May 2018 11:26:32 +0200 Subject: add smooth.py + first tests --- ot/smooth.py | 13 +++++++++++++ test/test_smooth.py | 4 ++++ 2 files changed, 17 insertions(+) diff --git a/ot/smooth.py b/ot/smooth.py index b8c0a9a..f8bb20a 100644 --- a/ot/smooth.py +++ b/ot/smooth.py @@ -85,10 +85,12 @@ class Regularization(object): """ Compute delta_Omega(X[:, j]) for each X[:, j]. delta_Omega(x) = sup_{y >= 0} y^T x - Omega(y). + Parameters ---------- X: array, shape = len(a) x len(b) Input array. + Returns ------- v: array, len(b) @@ -107,6 +109,7 @@ class Regularization(object): ---------- X: array, shape = len(a) x len(b) Input array. + Returns ------- v: array, len(b) @@ -124,6 +127,7 @@ class Regularization(object): ---------- T: array, shape = len(a) x len(b) Input array. + Returns ------- value: float @@ -232,6 +236,7 @@ def solve_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500): Tolerance parameter. max_iter: int Maximum number of iterations. + Returns ------- alpha: array, shape = len(a) @@ -270,6 +275,7 @@ def solve_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500): def semi_dual_obj_grad(alpha, a, b, C, regul): """ Compute objective value and gradient of semi-dual objective. + Parameters ---------- alpha: array, shape = len(a) @@ -281,6 +287,7 @@ def semi_dual_obj_grad(alpha, a, b, C, regul): Ground cost matrix. regul: Regularization object Should implement a max_Omega(X) method. + Returns ------- obj: float @@ -307,6 +314,7 @@ def semi_dual_obj_grad(alpha, a, b, C, regul): def solve_semi_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500): """ Solve the "smoothed" semi-dual objective. + Parameters ---------- a: array, shape = len(a) @@ -322,6 +330,7 @@ def solve_semi_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500): Tolerance parameter. max_iter: int Maximum number of iterations. + Returns ------- alpha: array, shape = len(a) @@ -344,6 +353,7 @@ def solve_semi_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500): def get_plan_from_dual(alpha, beta, C, regul): """ Retrieve optimal transportation plan from optimal dual potentials. + Parameters ---------- alpha: array, shape = len(a) @@ -353,6 +363,7 @@ def get_plan_from_dual(alpha, beta, C, regul): Ground cost matrix. regul: Regularization object Should implement a delta_Omega(X) method. + Returns ------- T: array, shape = len(a) x len(b) @@ -365,6 +376,7 @@ def get_plan_from_dual(alpha, beta, C, regul): def get_plan_from_semi_dual(alpha, b, C, regul): """ Retrieve optimal transportation plan from optimal semi-dual potentials. + Parameters ---------- alpha: array, shape = len(a) @@ -375,6 +387,7 @@ def get_plan_from_semi_dual(alpha, b, C, regul): Ground cost matrix. regul: Regularization object Should implement a delta_Omega(X) method. + Returns ------- T: array, shape = len(a) x len(b) diff --git a/test/test_smooth.py b/test/test_smooth.py index f951bf9..4ca44f8 100644 --- a/test/test_smooth.py +++ b/test/test_smooth.py @@ -30,4 +30,8 @@ def test_smooth_ot_dual(): u, G.sum(1), atol=1e-05) # cf convergence sinkhorn np.testing.assert_allclose( u, G.sum(0), atol=1e-05) # cf convergence sinkhorn + + + G2 = ot.sinkhorn(u, u, M, 1, stopThr=1e-10) + np.testing.assert_allclose( G, G2 , atol=1e-05) \ No newline at end of file -- cgit v1.2.3 From e585d64dd08e5367350e70f23e81f9fd2d676a6b Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 31 May 2018 11:33:08 +0200 Subject: pep8 --- ot/smooth.py | 67 +++++++++++++++++++++++++---------------------------- test/test_smooth.py | 26 ++++++++++++--------- 2 files changed, 47 insertions(+), 46 deletions(-) diff --git a/ot/smooth.py b/ot/smooth.py index f8bb20a..f4f4306 100644 --- a/ot/smooth.py +++ b/ot/smooth.py @@ -1,4 +1,4 @@ -#Copyright (c) 2018, Mathieu Blondel +#Copyright (c) 2018, Mathieu Blondel #All rights reserved. # #Redistribution and use in source and binary forms, with or without @@ -22,7 +22,7 @@ #OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF #THE POSSIBILITY OF SUCH DAMAGE. -# Author: Mathieu Blondel +# Author: Mathieu Blondel # Remi Flamary """ @@ -39,7 +39,7 @@ from scipy.optimize import minimize def projection_simplex(V, z=1, axis=None): """ Projection of x onto the simplex, scaled by z - + P(x; z) = argmin_{y >= 0, sum(y) = z} ||y - x||^2 z: float or array If array, len(z) must be compatible with V @@ -65,19 +65,19 @@ def projection_simplex(V, z=1, axis=None): else: V = V.ravel().reshape(1, -1) return projection_simplex(V, z, axis=1).ravel() - + class Regularization(object): def __init__(self, gamma=1.0): """ - + Parameters ---------- gamma: float Regularization parameter. We recover unregularized OT when gamma -> 0. - + """ self.gamma = gamma @@ -85,12 +85,12 @@ class Regularization(object): """ Compute delta_Omega(X[:, j]) for each X[:, j]. delta_Omega(x) = sup_{y >= 0} y^T x - Omega(y). - + Parameters ---------- X: array, shape = len(a) x len(b) Input array. - + Returns ------- v: array, len(b) @@ -104,12 +104,12 @@ class Regularization(object): """ Compute max_Omega_j(X[:, j]) for each X[:, j]. max_Omega_j(x) = sup_{y >= 0, sum(y) = 1} y^T x - Omega(b[j] y) / b[j]. - + Parameters ---------- X: array, shape = len(a) x len(b) Input array. - + Returns ------- v: array, len(b) @@ -122,12 +122,12 @@ class Regularization(object): def Omega(T): """ Compute regularization term. - + Parameters ---------- T: array, shape = len(a) x len(b) Input array. - + Returns ------- value: float @@ -176,7 +176,7 @@ class SquaredL2(Regularization): def dual_obj_grad(alpha, beta, a, b, C, regul): """ Compute objective value and gradients of dual objective. - + Parameters ---------- alpha: array, shape = len(a) @@ -189,7 +189,7 @@ def dual_obj_grad(alpha, beta, a, b, C, regul): Ground cost matrix. regul: Regularization object Should implement a delta_Omega(X) method. - + Returns ------- obj: float @@ -220,7 +220,7 @@ def dual_obj_grad(alpha, beta, a, b, C, regul): def solve_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500): """ Solve the "smoothed" dual objective. - + Parameters ---------- a: array, shape = len(a) @@ -236,7 +236,7 @@ def solve_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500): Tolerance parameter. max_iter: int Maximum number of iterations. - + Returns ------- alpha: array, shape = len(a) @@ -275,7 +275,7 @@ def solve_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500): def semi_dual_obj_grad(alpha, a, b, C, regul): """ Compute objective value and gradient of semi-dual objective. - + Parameters ---------- alpha: array, shape = len(a) @@ -287,7 +287,7 @@ def semi_dual_obj_grad(alpha, a, b, C, regul): Ground cost matrix. regul: Regularization object Should implement a max_Omega(X) method. - + Returns ------- obj: float @@ -314,7 +314,7 @@ def semi_dual_obj_grad(alpha, a, b, C, regul): def solve_semi_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500): """ Solve the "smoothed" semi-dual objective. - + Parameters ---------- a: array, shape = len(a) @@ -330,7 +330,7 @@ def solve_semi_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500): Tolerance parameter. max_iter: int Maximum number of iterations. - + Returns ------- alpha: array, shape = len(a) @@ -353,7 +353,7 @@ def solve_semi_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500): def get_plan_from_dual(alpha, beta, C, regul): """ Retrieve optimal transportation plan from optimal dual potentials. - + Parameters ---------- alpha: array, shape = len(a) @@ -363,7 +363,7 @@ def get_plan_from_dual(alpha, beta, C, regul): Ground cost matrix. regul: Regularization object Should implement a delta_Omega(X) method. - + Returns ------- T: array, shape = len(a) x len(b) @@ -376,7 +376,7 @@ def get_plan_from_dual(alpha, beta, C, regul): def get_plan_from_semi_dual(alpha, b, C, regul): """ Retrieve optimal transportation plan from optimal semi-dual potentials. - + Parameters ---------- alpha: array, shape = len(a) @@ -387,7 +387,7 @@ def get_plan_from_semi_dual(alpha, b, C, regul): Ground cost matrix. regul: Regularization object Should implement a delta_Omega(X) method. - + Returns ------- T: array, shape = len(a) x len(b) @@ -399,20 +399,17 @@ def get_plan_from_semi_dual(alpha, b, C, regul): def smooth_ot_dual(a, b, M, reg, reg_type='l2', method="L-BFGS-B", stopThr=1e-9, numItermax=500, log=False): - - if reg_type.lower()=='l2': + + if reg_type.lower() == 'l2': regul = SquaredL2(gamma=reg) - elif reg_type.lower() in ['entropic','negentropy','kl']: - regul = NegEntropy(gamma=reg) - - alpha, beta, res = solve_dual(a, b, M, regul, max_iter=numItermax,tol=stopThr) + elif reg_type.lower() in ['entropic', 'negentropy', 'kl']: + regul = NegEntropy(gamma=reg) + + alpha, beta, res = solve_dual(a, b, M, regul, max_iter=numItermax, tol=stopThr) G = get_plan_from_dual(alpha, beta, M, regul) - + if log: - log={'alpha':alpha,beta:'beta','res':res} + log = {'alpha': alpha, beta: 'beta', 'res': res} return G, log else: return G - - - diff --git a/test/test_smooth.py b/test/test_smooth.py index 4ca44f8..e95b3fe 100644 --- a/test/test_smooth.py +++ b/test/test_smooth.py @@ -4,17 +4,14 @@ # # License: MIT License -import warnings - import numpy as np import ot -from ot.datasets import get_1D_gauss as gauss -import pytest def test_smooth_ot_dual(): - # test sinkhorn + + # get data n = 100 rng = np.random.RandomState(0) @@ -23,15 +20,22 @@ def test_smooth_ot_dual(): M = ot.dist(x, x) - G = ot.smooth.smooth_ot_dual(u, u, M, 1, stopThr=1e-10) + Gl2 = ot.smooth.smooth_ot_dual(u, u, M, 1, reg_type='l2', stopThr=1e-10) + + # check constratints + np.testing.assert_allclose( + u, Gl2.sum(1), atol=1e-05) # cf convergence sinkhorn + np.testing.assert_allclose( + u, Gl2.sum(0), atol=1e-05) # cf convergence sinkhorn + + # kl regyularisation + G = ot.smooth.smooth_ot_dual(u, u, M, 1, reg_type='kl', stopThr=1e-10) # check constratints np.testing.assert_allclose( u, G.sum(1), atol=1e-05) # cf convergence sinkhorn np.testing.assert_allclose( - u, G.sum(0), atol=1e-05) # cf convergence sinkhorn - - + u, G.sum(0), atol=1e-05) # cf convergence sinkhorn + G2 = ot.sinkhorn(u, u, M, 1, stopThr=1e-10) - np.testing.assert_allclose( G, G2 , atol=1e-05) - \ No newline at end of file + np.testing.assert_allclose(G, G2, atol=1e-05) -- cgit v1.2.3 From b188381a8e8190958e4e9aca64b7501cf0321406 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 31 May 2018 12:02:13 +0200 Subject: add semidual --- ot/smooth.py | 37 ++++++++++++++++++++++++++++++++++--- test/test_smooth.py | 34 +++++++++++++++++++++++++++++++++- 2 files changed, 67 insertions(+), 4 deletions(-) diff --git a/ot/smooth.py b/ot/smooth.py index f4f4306..1a9972b 100644 --- a/ot/smooth.py +++ b/ot/smooth.py @@ -137,6 +137,7 @@ class Regularization(object): class NegEntropy(Regularization): + """ NegEntropy regularization """ def delta_Omega(self, X): G = np.exp(X / self.gamma - 1) @@ -156,6 +157,7 @@ class NegEntropy(Regularization): class SquaredL2(Regularization): + """ Squared L2 regularization """ def delta_Omega(self, X): max_X = np.maximum(X, 0) @@ -311,7 +313,8 @@ def semi_dual_obj_grad(alpha, a, b, C, regul): return obj, grad -def solve_semi_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500): +def solve_semi_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500, + ): """ Solve the "smoothed" semi-dual objective. @@ -400,16 +403,44 @@ def get_plan_from_semi_dual(alpha, b, C, regul): def smooth_ot_dual(a, b, M, reg, reg_type='l2', method="L-BFGS-B", stopThr=1e-9, numItermax=500, log=False): - if reg_type.lower() == 'l2': + if reg_type.lower() in ['l2', 'squaredl2']: regul = SquaredL2(gamma=reg) elif reg_type.lower() in ['entropic', 'negentropy', 'kl']: regul = NegEntropy(gamma=reg) + else: + raise NotImplementedError('Unknown regularization') + # solve dual alpha, beta, res = solve_dual(a, b, M, regul, max_iter=numItermax, tol=stopThr) + + # reconstruct transport matrix G = get_plan_from_dual(alpha, beta, M, regul) if log: - log = {'alpha': alpha, beta: 'beta', 'res': res} + log = {'alpha': alpha, 'beta': beta, 'res': res} + return G, log + else: + return G + + +def smooth_ot_semi_dual(a, b, M, reg, reg_type='l2', method="L-BFGS-B", stopThr=1e-9, + numItermax=500, log=False): + + if reg_type.lower() in ['l2', 'squaredl2']: + regul = SquaredL2(gamma=reg) + elif reg_type.lower() in ['entropic', 'negentropy', 'kl']: + regul = NegEntropy(gamma=reg) + else: + raise NotImplementedError('Unknown regularization') + + # solve dual + alpha, res = solve_semi_dual(a, b, M, regul, max_iter=numItermax, tol=stopThr) + + # reconstruct transport matrix + G = get_plan_from_semi_dual(alpha, b, M, regul) + + if log: + log = {'alpha': alpha, 'res': res} return G, log else: return G diff --git a/test/test_smooth.py b/test/test_smooth.py index e95b3fe..37cc66e 100644 --- a/test/test_smooth.py +++ b/test/test_smooth.py @@ -20,7 +20,7 @@ def test_smooth_ot_dual(): M = ot.dist(x, x) - Gl2 = ot.smooth.smooth_ot_dual(u, u, M, 1, reg_type='l2', stopThr=1e-10) + Gl2, log = ot.smooth.smooth_ot_dual(u, u, M, 1, reg_type='l2', log=True, stopThr=1e-10) # check constratints np.testing.assert_allclose( @@ -39,3 +39,35 @@ def test_smooth_ot_dual(): G2 = ot.sinkhorn(u, u, M, 1, stopThr=1e-10) np.testing.assert_allclose(G, G2, atol=1e-05) + + +def test_smooth_ot_semi_dual(): + + # get data + n = 100 + rng = np.random.RandomState(0) + + x = rng.randn(n, 2) + u = ot.utils.unif(n) + + M = ot.dist(x, x) + + Gl2, log = ot.smooth.smooth_ot_semi_dual(u, u, M, 1, reg_type='l2', log=True, stopThr=1e-10) + + # check constratints + np.testing.assert_allclose( + u, Gl2.sum(1), atol=1e-05) # cf convergence sinkhorn + np.testing.assert_allclose( + u, Gl2.sum(0), atol=1e-05) # cf convergence sinkhorn + + # kl regyularisation + G = ot.smooth.smooth_ot_semi_dual(u, u, M, 1, reg_type='kl', stopThr=1e-10) + + # check constratints + np.testing.assert_allclose( + u, G.sum(1), atol=1e-05) # cf convergence sinkhorn + np.testing.assert_allclose( + u, G.sum(0), atol=1e-05) # cf convergence sinkhorn + + G2 = ot.sinkhorn(u, u, M, 1, stopThr=1e-10) + np.testing.assert_allclose(G, G2, atol=1e-05) -- cgit v1.2.3 From 6a048fe809823e41d2b57dbcc22c12bcb7490674 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 31 May 2018 12:48:42 +0200 Subject: add test worngregularization --- test/test_smooth.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/test/test_smooth.py b/test/test_smooth.py index 37cc66e..7aceb42 100644 --- a/test/test_smooth.py +++ b/test/test_smooth.py @@ -5,8 +5,8 @@ # License: MIT License import numpy as np - import ot +import pytest def test_smooth_ot_dual(): @@ -20,6 +20,9 @@ def test_smooth_ot_dual(): M = ot.dist(x, x) + with pytest.raises(NotImplementedError) as e_info: + Gl2, log = ot.smooth.smooth_ot_dual(u, u, M, 1, reg_type='none') + Gl2, log = ot.smooth.smooth_ot_dual(u, u, M, 1, reg_type='l2', log=True, stopThr=1e-10) # check constratints @@ -52,6 +55,9 @@ def test_smooth_ot_semi_dual(): M = ot.dist(x, x) + with pytest.raises(NotImplementedError) as e_info: + Gl2, log = ot.smooth.smooth_ot_semi_dual(u, u, M, 1, reg_type='none') + Gl2, log = ot.smooth.smooth_ot_semi_dual(u, u, M, 1, reg_type='l2', log=True, stopThr=1e-10) # check constratints -- cgit v1.2.3 From 1d34716aa30763a1a36742a3f6acc9201bef8d7c Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 31 May 2018 12:52:36 +0200 Subject: remove unused variable --- test/test_smooth.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test/test_smooth.py b/test/test_smooth.py index 7aceb42..2afa4f8 100644 --- a/test/test_smooth.py +++ b/test/test_smooth.py @@ -20,7 +20,7 @@ def test_smooth_ot_dual(): M = ot.dist(x, x) - with pytest.raises(NotImplementedError) as e_info: + with pytest.raises(NotImplementedError): Gl2, log = ot.smooth.smooth_ot_dual(u, u, M, 1, reg_type='none') Gl2, log = ot.smooth.smooth_ot_dual(u, u, M, 1, reg_type='l2', log=True, stopThr=1e-10) @@ -55,7 +55,7 @@ def test_smooth_ot_semi_dual(): M = ot.dist(x, x) - with pytest.raises(NotImplementedError) as e_info: + with pytest.raises(NotImplementedError): Gl2, log = ot.smooth.smooth_ot_semi_dual(u, u, M, 1, reg_type='none') Gl2, log = ot.smooth.smooth_ot_semi_dual(u, u, M, 1, reg_type='l2', log=True, stopThr=1e-10) -- cgit v1.2.3 From 10f9b0d1b02c2b5f4c4eeac0c1f803657c89764b Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 31 May 2018 12:53:11 +0200 Subject: add example file for smooth OT --- examples/plot_OT_1D_smooth.py | 110 ++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 110 insertions(+) create mode 100644 examples/plot_OT_1D_smooth.py diff --git a/examples/plot_OT_1D_smooth.py b/examples/plot_OT_1D_smooth.py new file mode 100644 index 0000000..4927617 --- /dev/null +++ b/examples/plot_OT_1D_smooth.py @@ -0,0 +1,110 @@ +# -*- coding: utf-8 -*- +""" +==================== +1D optimal transport +==================== + +This example illustrates the computation of EMD, Sinkhorn and smooth OT plans +and their visualization. + +""" + +# Author: Remi Flamary +# +# License: MIT License + +import numpy as np +import matplotlib.pylab as pl +import ot +import ot.plot +from ot.datasets import get_1D_gauss as gauss + +############################################################################## +# Generate data +# ------------- + + +#%% parameters + +n = 100 # nb bins + +# bin positions +x = np.arange(n, dtype=np.float64) + +# Gaussian distributions +a = gauss(n, m=20, s=5) # m= mean, s= std +b = gauss(n, m=60, s=10) + +# loss matrix +M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) +M /= M.max() + + +############################################################################## +# Plot distributions and loss matrix +# ---------------------------------- + +#%% plot the distributions + +pl.figure(1, figsize=(6.4, 3)) +pl.plot(x, a, 'b', label='Source distribution') +pl.plot(x, b, 'r', label='Target distribution') +pl.legend() + +#%% plot distributions and loss matrix + +pl.figure(2, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') + +############################################################################## +# Solve EMD +# --------- + + +#%% EMD + +G0 = ot.emd(a, b, M) + +pl.figure(3, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') + +############################################################################## +# Solve Sinkhorn +# -------------- + + +#%% Sinkhorn + +lambd = 2e-3 +Gs = ot.sinkhorn(a, b, M, lambd, verbose=True) + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn') + +pl.show() + +############################################################################## +# Solve Smooth OT +# -------------- + + +#%% Smooth OT with KL regularization + +lambd = 2e-3 +Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='kl') + +pl.figure(5, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT KL reg.') + +pl.show() + + +#%% Smooth OT with KL regularization + +lambd = 1e-1 +Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='l2') + +pl.figure(6, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT l2 reg.') + +pl.show() -- cgit v1.2.3 From ed0d4171c6291a15360bdb8a955b0783585da749 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 31 May 2018 13:03:40 +0200 Subject: update readme --- README.md | 3 +++ examples/plot_OT_1D_smooth.py | 6 +++--- ot/smooth.py | 9 +++++++++ 3 files changed, 15 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 466c09c..b3068ee 100644 --- a/README.md +++ b/README.md @@ -15,6 +15,7 @@ It provides the following solvers: * OT Network Flow solver for the linear program/ Earth Movers Distance [1]. * Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10] with optional GPU implementation (requires cudamat). +* Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularization [17]. * Non regularized Wasserstein barycenters [16] with LP solver. * Bregman projections for Wasserstein barycenter [3] and unmixing [4]. * Optimal transport for domain adaptation with group lasso regularization [5] @@ -213,3 +214,5 @@ You can also post bug reports and feature requests in Github issues. Make sure t [15] Peyré, G., & Cuturi, M. (2018). [Computational Optimal Transport](https://arxiv.org/pdf/1803.00567.pdf) . [16] Agueh, M., & Carlier, G. (2011). [Barycenters in the Wasserstein space](https://hal.archives-ouvertes.fr/hal-00637399/document). SIAM Journal on Mathematical Analysis, 43(2), 904-924. + +[17] Blondel, M., Seguy, V., & Rolet, A. (2018). [Smooth and Sparse Optimal Transport](https://arxiv.org/abs/1710.06276). Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS). diff --git a/examples/plot_OT_1D_smooth.py b/examples/plot_OT_1D_smooth.py index 4927617..dec84db 100644 --- a/examples/plot_OT_1D_smooth.py +++ b/examples/plot_OT_1D_smooth.py @@ -1,8 +1,8 @@ # -*- coding: utf-8 -*- """ -==================== -1D optimal transport -==================== +=========================== +1D smooth optimal transport +=========================== This example illustrates the computation of EMD, Sinkhorn and smooth OT plans and their visualization. diff --git a/ot/smooth.py b/ot/smooth.py index 1a9972b..b3649e9 100644 --- a/ot/smooth.py +++ b/ot/smooth.py @@ -22,6 +22,7 @@ #OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF #THE POSSIBILITY OF SUCH DAMAGE. + # Author: Mathieu Blondel # Remi Flamary @@ -31,6 +32,13 @@ Smooth and Sparse Optimal Transport. Mathieu Blondel, Vivien Seguy, Antoine Rolet. In Proc. of AISTATS 2018. https://arxiv.org/abs/1710.06276 + +[17] Blondel, M., Seguy, V., & Rolet, A. (2018). Smooth and Sparse Optimal +Transport. Proceedings of the Twenty-First International Conference on +Artificial Intelligence and Statistics (AISTATS). + +Original code from https://github.com/mblondel/smooth-ot/ + """ import numpy as np @@ -402,6 +410,7 @@ def get_plan_from_semi_dual(alpha, b, C, regul): def smooth_ot_dual(a, b, M, reg, reg_type='l2', method="L-BFGS-B", stopThr=1e-9, numItermax=500, log=False): + if reg_type.lower() in ['l2', 'squaredl2']: regul = SquaredL2(gamma=reg) -- cgit v1.2.3 From 724984d3e13aa9390ef17a389fbd6ccf1f277d69 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 31 May 2018 13:05:37 +0200 Subject: pep8 --- examples/plot_OT_1D_smooth.py | 2 +- ot/smooth.py | 6 ++---- 2 files changed, 3 insertions(+), 5 deletions(-) diff --git a/examples/plot_OT_1D_smooth.py b/examples/plot_OT_1D_smooth.py index dec84db..ec43279 100644 --- a/examples/plot_OT_1D_smooth.py +++ b/examples/plot_OT_1D_smooth.py @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- """ =========================== -1D smooth optimal transport +1D smooth optimal transport =========================== This example illustrates the computation of EMD, Sinkhorn and smooth OT plans diff --git a/ot/smooth.py b/ot/smooth.py index b3649e9..237b67a 100644 --- a/ot/smooth.py +++ b/ot/smooth.py @@ -22,7 +22,6 @@ #OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF #THE POSSIBILITY OF SUCH DAMAGE. - # Author: Mathieu Blondel # Remi Flamary @@ -33,8 +32,8 @@ Mathieu Blondel, Vivien Seguy, Antoine Rolet. In Proc. of AISTATS 2018. https://arxiv.org/abs/1710.06276 -[17] Blondel, M., Seguy, V., & Rolet, A. (2018). Smooth and Sparse Optimal -Transport. Proceedings of the Twenty-First International Conference on +[17] Blondel, M., Seguy, V., & Rolet, A. (2018). Smooth and Sparse Optimal +Transport. Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS). Original code from https://github.com/mblondel/smooth-ot/ @@ -410,7 +409,6 @@ def get_plan_from_semi_dual(alpha, b, C, regul): def smooth_ot_dual(a, b, M, reg, reg_type='l2', method="L-BFGS-B", stopThr=1e-9, numItermax=500, log=False): - if reg_type.lower() in ['l2', 'squaredl2']: regul = SquaredL2(gamma=reg) -- cgit v1.2.3 From fb883fcddff31cc4e21c921ebeb9a550eaa48ff0 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 31 May 2018 13:36:12 +0200 Subject: proper documentation --- ot/smooth.py | 155 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 147 insertions(+), 8 deletions(-) diff --git a/ot/smooth.py b/ot/smooth.py index 237b67a..4c46e73 100644 --- a/ot/smooth.py +++ b/ot/smooth.py @@ -226,7 +226,8 @@ def dual_obj_grad(alpha, beta, a, b, C, regul): return obj, grad_alpha, grad_beta -def solve_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500): +def solve_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500, + verbose=False): """ Solve the "smoothed" dual objective. @@ -273,7 +274,7 @@ def solve_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500): params_init = np.concatenate((alpha_init, beta_init)) res = minimize(_func, params_init, method=method, jac=True, - tol=tol, options=dict(maxiter=max_iter, disp=False)) + tol=tol, options=dict(maxiter=max_iter, disp=verbose)) alpha = res.x[:len(a)] beta = res.x[len(a):] @@ -321,7 +322,7 @@ def semi_dual_obj_grad(alpha, a, b, C, regul): def solve_semi_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500, - ): + verbose=False): """ Solve the "smoothed" semi-dual objective. @@ -355,7 +356,7 @@ def solve_semi_dual(a, b, C, regul, method="L-BFGS-B", tol=1e-3, max_iter=500, alpha_init = np.zeros(len(a)) res = minimize(_func, alpha_init, method=method, jac=True, - tol=tol, options=dict(maxiter=max_iter, disp=False)) + tol=tol, options=dict(maxiter=max_iter, disp=verbose)) return res.x, res @@ -408,7 +409,75 @@ def get_plan_from_semi_dual(alpha, b, C, regul): def smooth_ot_dual(a, b, M, reg, reg_type='l2', method="L-BFGS-B", stopThr=1e-9, - numItermax=500, log=False): + numItermax=500, verbose=False, log=False): + r""" + Solve the regularized OT problem in the dual and return the OT matrix + + The function solves the smooth relaxed dual formulation (7) in [17]_ : + + .. math:: + \max_{\alpha,\beta}\quad a^T\alpha+b^T\beta-\sum_j\delta_\Omega(\alpha+\beta_j-\mathbf{m}_j) + + where : + + - :math:`\mathbf{m}_j` is the jth column of the cost matrix + - :math:`\delta_\Omega` is the convex conjugate of the regularization term :math:`\Omega` + - a and b are source and target weights (sum to 1) + + The OT matrix can is reconstructed from the gradient of :math:`\delta_\Omega` + (See [17]_ Proposition 1). + The optimization algorithm is using gradient decent (L-BFGS by default). + + + Parameters + ---------- + a : np.ndarray (ns,) + samples weights in the source domain + b : np.ndarray (nt,) or np.ndarray (nt,nbb) + samples in the target domain, compute sinkhorn with multiple targets + and fixed M if b is a matrix (return OT loss + dual variables in log) + M : np.ndarray (ns,nt) + loss matrix + reg : float + Regularization term >0 + reg_type : str + Regularization type, can be the following (default ='l2'): + - 'kl' : Kullback Leibler (~ Neg-entropy used in sinkhorn [2]_) + - 'l2' : Squared Euclidean regularization + method : str + Solver to use for scipy.optimize.minimize + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + gamma : (ns x nt) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013 + + .. [17] Blondel, M., Seguy, V., & Rolet, A. (2018). Smooth and Sparse Optimal Transport. Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS). + + See Also + -------- + ot.lp.emd : Unregularized OT + ot.sinhorn : Entropic regularized OT + ot.optim.cg : General regularized OT + + """ if reg_type.lower() in ['l2', 'squaredl2']: regul = SquaredL2(gamma=reg) @@ -418,7 +487,8 @@ def smooth_ot_dual(a, b, M, reg, reg_type='l2', method="L-BFGS-B", stopThr=1e-9, raise NotImplementedError('Unknown regularization') # solve dual - alpha, beta, res = solve_dual(a, b, M, regul, max_iter=numItermax, tol=stopThr) + alpha, beta, res = solve_dual(a, b, M, regul, max_iter=numItermax, + tol=stopThr, verbose=verbose) # reconstruct transport matrix G = get_plan_from_dual(alpha, beta, M, regul) @@ -431,8 +501,77 @@ def smooth_ot_dual(a, b, M, reg, reg_type='l2', method="L-BFGS-B", stopThr=1e-9, def smooth_ot_semi_dual(a, b, M, reg, reg_type='l2', method="L-BFGS-B", stopThr=1e-9, - numItermax=500, log=False): + numItermax=500, verbose=False, log=False): + r""" + Solve the regularized OT problem in the semi-dual and return the OT matrix + The function solves the smooth relaxed dual formulation (10) in [17]_ : + + .. math:: + \max_{\alpha}\quad a^T\alpha-OT_\Omega^*(\alpha,b) + + where : + + .. math:: + OT_\Omega^*(\alpha,b)=\sum_j b_j + + - :math:`\mathbf{m}_j` is the jth column of the cost matrix + - :math:`OT_\Omega^*(\alpha,b)` is defined in Eq. (9) in [17] + - a and b are source and target weights (sum to 1) + + The OT matrix can is reconstructed using [17]_ Proposition 2. + The optimization algorithm is using gradient decent (L-BFGS by default). + + + Parameters + ---------- + a : np.ndarray (ns,) + samples weights in the source domain + b : np.ndarray (nt,) or np.ndarray (nt,nbb) + samples in the target domain, compute sinkhorn with multiple targets + and fixed M if b is a matrix (return OT loss + dual variables in log) + M : np.ndarray (ns,nt) + loss matrix + reg : float + Regularization term >0 + reg_type : str + Regularization type, can be the following (default ='l2'): + - 'kl' : Kullback Leibler (~ Neg-entropy used in sinkhorn [2]_) + - 'l2' : Squared Euclidean regularization + method : str + Solver to use for scipy.optimize.minimize + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + gamma : (ns x nt) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013 + + .. [17] Blondel, M., Seguy, V., & Rolet, A. (2018). Smooth and Sparse Optimal Transport. Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS). + + See Also + -------- + ot.lp.emd : Unregularized OT + ot.sinhorn : Entropic regularized OT + ot.optim.cg : General regularized OT + + """ if reg_type.lower() in ['l2', 'squaredl2']: regul = SquaredL2(gamma=reg) elif reg_type.lower() in ['entropic', 'negentropy', 'kl']: @@ -444,7 +583,7 @@ def smooth_ot_semi_dual(a, b, M, reg, reg_type='l2', method="L-BFGS-B", stopThr= alpha, res = solve_semi_dual(a, b, M, regul, max_iter=numItermax, tol=stopThr) # reconstruct transport matrix - G = get_plan_from_semi_dual(alpha, b, M, regul) + G = get_plan_from_semi_dual(alpha, b, M, regul, verbose=verbose) if log: log = {'alpha': alpha, 'res': res} -- cgit v1.2.3 From d370f18aaac4ed6903e3b4251c8b9c4685c53cc3 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 31 May 2018 13:40:45 +0200 Subject: bug verbose semi-dual --- ot/smooth.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/ot/smooth.py b/ot/smooth.py index 4c46e73..dd734b3 100644 --- a/ot/smooth.py +++ b/ot/smooth.py @@ -580,10 +580,11 @@ def smooth_ot_semi_dual(a, b, M, reg, reg_type='l2', method="L-BFGS-B", stopThr= raise NotImplementedError('Unknown regularization') # solve dual - alpha, res = solve_semi_dual(a, b, M, regul, max_iter=numItermax, tol=stopThr) + alpha, res = solve_semi_dual(a, b, M, regul, max_iter=numItermax, + tol=stopThr, verbose=verbose) # reconstruct transport matrix - G = get_plan_from_semi_dual(alpha, b, M, regul, verbose=verbose) + G = get_plan_from_semi_dual(alpha, b, M, regul) if log: log = {'alpha': alpha, 'res': res} -- cgit v1.2.3 From 8046b8c424d7b80f520e212e2bf8de41cb624aab Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 31 May 2018 13:45:11 +0200 Subject: pep8 --- README.md | 2 +- ot/smooth.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index b3068ee..6d3286b 100644 --- a/README.md +++ b/README.md @@ -16,7 +16,7 @@ It provides the following solvers: * OT Network Flow solver for the linear program/ Earth Movers Distance [1]. * Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10] with optional GPU implementation (requires cudamat). * Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularization [17]. -* Non regularized Wasserstein barycenters [16] with LP solver. +* Non regularized Wasserstein barycenters [16] with LP solver (only small scale). * Bregman projections for Wasserstein barycenter [3] and unmixing [4]. * Optimal transport for domain adaptation with group lasso regularization [5] * Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. diff --git a/ot/smooth.py b/ot/smooth.py index dd734b3..87d61ef 100644 --- a/ot/smooth.py +++ b/ot/smooth.py @@ -580,7 +580,7 @@ def smooth_ot_semi_dual(a, b, M, reg, reg_type='l2', method="L-BFGS-B", stopThr= raise NotImplementedError('Unknown regularization') # solve dual - alpha, res = solve_semi_dual(a, b, M, regul, max_iter=numItermax, + alpha, res = solve_semi_dual(a, b, M, regul, max_iter=numItermax, tol=stopThr, verbose=verbose) # reconstruct transport matrix -- cgit v1.2.3 From f5dcbc44cd5347be5c0a4039bf2350b3bdbd2cac Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 11 Jun 2018 11:29:19 +0200 Subject: add new line for pep8 --- test/test_da.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/test_da.py b/test/test_da.py index b429315..f7f3a9d 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -483,4 +483,4 @@ def test_linear_mapping_class(): Ct = np.cov(Xt.T) Cst = np.cov(Xst.T) - np.testing.assert_allclose(Ct, Cst, rtol=1e-2, atol=1e-2) \ No newline at end of file + np.testing.assert_allclose(Ct, Cst, rtol=1e-2, atol=1e-2) -- cgit v1.2.3 From 4854277c583d7d7a82a352d2a83fafe9311b2e24 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 11 Jun 2018 12:02:56 +0200 Subject: correct bibtex reference --- README.md | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 68c64c1..eba28a6 100644 --- a/README.md +++ b/README.md @@ -30,10 +30,11 @@ Some demonstrations (both in Python and Jupyter Notebook format) are available i If you use this toolbox in your research and find it useful, please cite POT using the following bibtex reference: ``` -@article{flamary2017pot, - title={POT Python Optimal Transport library}, - author={Flamary, R{\'e}mi and Courty, Nicolas}, - year={2017} +@misc{flamary2017pot, +title={POT Python Optimal Transport library}, +author={Flamary, R{'e}mi and Courty, Nicolas}, +url={https://github.com/rflamary/POT}, +year={2017} } ``` -- cgit v1.2.3 From 927ec792baefbf7ee587df872aba16e6f54001b4 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 11 Jun 2018 12:03:36 +0200 Subject: correct readme typo --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index eba28a6..a22306d 100644 --- a/README.md +++ b/README.md @@ -15,7 +15,7 @@ It provides the following solvers: * OT Network Flow solver for the linear program/ Earth Movers Distance [1]. * Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10] with optional GPU implementation (requires cudamat). -* Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularization [17]. +* Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17]. * Non regularized Wasserstein barycenters [16] with LP solver (only small scale). * Bregman projections for Wasserstein barycenter [3] and unmixing [4]. * Optimal transport for domain adaptation with group lasso regularization [5] -- cgit v1.2.3 From 77d80f8e3d9df9d838fa19685f2a5fea2ce888fb Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 11 Jun 2018 12:03:58 +0200 Subject: update documentation --- docs/source/readme.rst | 46 ++++++++++++++++++++++++++++++---------------- 1 file changed, 30 insertions(+), 16 deletions(-) diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 725c207..5d37f64 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -14,7 +14,11 @@ It provides the following solvers: [1]. - Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10] with optional GPU implementation - (required cudamat). + (requires cudamat). +- Smooth optimal transport solvers (dual and semi-dual) for KL and + squared L2 regularizations [17]. +- Non regularized Wasserstein barycenters [16] with LP solver (only + small scale). - Bregman projections for Wasserstein barycenter [3] and unmixing [4]. - Optimal transport for domain adaptation with group lasso regularization [5] @@ -29,6 +33,21 @@ It provides the following solvers: Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. +Using and citing the toolbox +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +If you use this toolbox in your research and find it useful, please cite +POT using the following bibtex reference: + +:: + + @misc{flamary2017pot, + title={POT Python Optimal Transport library}, + author={Flamary, R{'e}mi and Courty, Nicolas}, + url={https://github.com/rflamary/POT}, + year={2017} + } + Installation ------------ @@ -37,7 +56,7 @@ C++ compiler for using the EMD solver and relies on the following Python modules: - Numpy (>=1.11) -- Scipy (>=0.17) +- Scipy (>=1.0) - Cython (>=0.23) - Matplotlib (>=1.5) @@ -212,20 +231,6 @@ languages): - `Marco Cuturi `__ (Sinkhorn Knopp in Matlab/Cuda) -Using and citing the toolbox ----------------------------- - -If you use this toolbox in your research and find it useful, please cite -POT using the following bibtex reference: - -:: - - @article{flamary2017pot, - title={POT Python Optimal Transport library}, - author={Flamary, R{\'e}mi and Courty, Nicolas}, - year={2017} - } - Contributions and code of conduct --------------------------------- @@ -320,6 +325,15 @@ Journal of Optimization Theory and Applications Vol 43. [15] Peyré, G., & Cuturi, M. (2018). `Computational Optimal Transport `__ . +[16] Agueh, M., & Carlier, G. (2011). `Barycenters in the Wasserstein +space `__. SIAM +Journal on Mathematical Analysis, 43(2), 904-924. + +[17] Blondel, M., Seguy, V., & Rolet, A. (2018). `Smooth and Sparse +Optimal Transport `__. Proceedings of +the Twenty-First International Conference on Artificial Intelligence and +Statistics (AISTATS). + .. |PyPI version| image:: https://badge.fury.io/py/POT.svg :target: https://badge.fury.io/py/POT .. |Anaconda Cloud| image:: https://anaconda.org/conda-forge/pot/badges/version.svg -- cgit v1.2.3 From ed1f8d5497a15d926b992b5a42b14dccd3eb97d4 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 11 Jun 2018 12:05:12 +0200 Subject: correct dataset function name --- examples/plot_OT_1D_smooth.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/plot_OT_1D_smooth.py b/examples/plot_OT_1D_smooth.py index ec43279..b690751 100644 --- a/examples/plot_OT_1D_smooth.py +++ b/examples/plot_OT_1D_smooth.py @@ -17,7 +17,7 @@ import numpy as np import matplotlib.pylab as pl import ot import ot.plot -from ot.datasets import get_1D_gauss as gauss +from ot.datasets import make_1D_gauss as gauss ############################################################################## # Generate data -- cgit v1.2.3 From f22be34f5340525d195b643cde29344f719a7c33 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 11 Jun 2018 12:51:41 +0200 Subject: add smooth to documentation --- docs/source/all.rst | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/docs/source/all.rst b/docs/source/all.rst index c84d968..bbb9833 100644 --- a/docs/source/all.rst +++ b/docs/source/all.rst @@ -19,6 +19,11 @@ ot.bregman .. automodule:: ot.bregman :members: + +ot.smooth +----- +.. automodule:: ot.smooth + :members: ot.gromov ---------- -- cgit v1.2.3 From de4f7ba6fa75eac92621fba51c11bf56153603cf Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 11 Jun 2018 12:56:07 +0200 Subject: proper definition of all dor lp --- ot/lp/__init__.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 5dda82a..4c0d170 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -18,6 +18,8 @@ from .emd_wrap import emd_c, check_result from ..utils import parmap from .cvx import barycenter +__all__=['emd', 'emd2', 'barycenter', 'cvx'] + def emd(a, b, M, numItermax=100000, log=False): """Solves the Earth Movers distance problem and returns the OT matrix -- cgit v1.2.3 From ecb093b95ddbeaeb800b443d3a5c9d5c24c5897c Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 11 Jun 2018 13:00:57 +0200 Subject: ad documentation class Regularization --- ot/smooth.py | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/ot/smooth.py b/ot/smooth.py index 87d61ef..5a8e4b5 100644 --- a/ot/smooth.py +++ b/ot/smooth.py @@ -75,6 +75,13 @@ def projection_simplex(V, z=1, axis=None): class Regularization(object): + """Base class for Regularization objects + + Notes + ----- + This class is not intended for direct use but as aparent for true + regularizatiojn implementation. + """ def __init__(self, gamma=1.0): """ -- cgit v1.2.3 From c8eda449b2c6b39e9d57d1b5b2c39e43f2925892 Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Fri, 15 Jun 2018 18:53:54 -0700 Subject: add problems solved in doc --- examples/plot_stochastic.py | 135 ++++++++++++++ ot/__init__.py | 2 + ot/stochastic.py | 415 ++++++++++++++++++++++++++++++++++++++++++++ test/test_stochastic.py | 115 ++++++++++++ 4 files changed, 667 insertions(+) create mode 100644 examples/plot_stochastic.py create mode 100644 ot/stochastic.py create mode 100644 test/test_stochastic.py diff --git a/examples/plot_stochastic.py b/examples/plot_stochastic.py new file mode 100644 index 0000000..9071ddb --- /dev/null +++ b/examples/plot_stochastic.py @@ -0,0 +1,135 @@ +""" +========================== +Stochastic examples +========================== + +This example is designed to show how to use the stochatic optimization +algorithms for descrete and semicontinous measures from the POT library. + +""" + +# Author: Kilian Fatras +# +# License: MIT License + +import matplotlib.pylab as pl +import numpy as np +import ot + + +############################################################################# +# COMPUTE TRANSPORTATION MATRIX +############################################################################# + +############################################################################# +# DISCRETE CASE +# Sample two discrete measures for the discrete case +# --------------------------------------------- +# +# Define 2 discrete measures a and b, the points where are defined the source +# and the target measures and finally the cost matrix c. + +n_source = 7 +n_target = 4 +reg = 1 +numItermax = 10000 +lr = 0.1 + +a = ot.utils.unif(n_source) +b = ot.utils.unif(n_target) + +rng = np.random.RandomState(0) +X_source = rng.randn(n_source, 2) +Y_target = rng.randn(n_target, 2) +M = ot.dist(X_source, Y_target) + +############################################################################# +# +# Call the "SAG" method to find the transportation matrix in the discrete case +# --------------------------------------------- +# +# Define the method "SAG", call ot.transportation_matrix_entropic and plot the +# results. + +method = "SAG" +sag_pi = ot.stochastic.transportation_matrix_entropic(a, b, M, reg, method, + numItermax, lr) +print(sag_pi) + +############################################################################# +# SEMICONTINOUS CASE +# Sample one general measure a, one discrete measures b for the semicontinous +# case +# --------------------------------------------- +# +# Define one general measure a, one discrete measures b, the points where +# are defined the source and the target measures and finally the cost matrix c. + +n_source = 7 +n_target = 4 +reg = 1 +numItermax = 500000 +lr = 1 + +a = ot.utils.unif(n_source) +b = ot.utils.unif(n_target) + +rng = np.random.RandomState(0) +X_source = rng.randn(n_source, 2) +Y_target = rng.randn(n_target, 2) +M = ot.dist(X_source, Y_target) + +############################################################################# +# +# Call the "ASGD" method to find the transportation matrix in the semicontinous +# case +# --------------------------------------------- +# +# Define the method "ASGD", call ot.transportation_matrix_entropic and plot the +# results. + +method = "ASGD" +asgd_pi = ot.stochastic.transportation_matrix_entropic(a, b, M, reg, method, + numItermax, lr) +print(asgd_pi) + +############################################################################# +# +# Compare the results with the Sinkhorn algorithm +# --------------------------------------------- +# +# Call the Sinkhorn algorithm from POT + +sinkhorn_pi = ot.sinkhorn(a, b, M, 1) +print(sinkhorn_pi) + + +############################################################################## +# PLOT TRANSPORTATION MATRIX +############################################################################## + +############################################################################## +# Plot SAG results +# ---------------- + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, sag_pi, 'OT matrix SAG') +pl.show() + + +############################################################################## +# Plot ASGD results +# ----------------- + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, asgd_pi, 'OT matrix ASGD') +pl.show() + + +############################################################################## +# Plot Sinkhorn results +# --------------------- + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn') +pl.show() diff --git a/ot/__init__.py b/ot/__init__.py index 1500e59..1dde390 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -18,6 +18,8 @@ from . import utils from . import datasets from . import da from . import gromov +from . import smooth +from . import stochastic # OT functions from .lp import emd, emd2 diff --git a/ot/stochastic.py b/ot/stochastic.py new file mode 100644 index 0000000..541f456 --- /dev/null +++ b/ot/stochastic.py @@ -0,0 +1,415 @@ +# Author: Kilian Fatras +# +# License: MIT License + +import numpy as np + + +def coordinate_gradient(b, M, reg, v, i): + ''' + Compute the coordinate gradient update for regularized discrete + distributions for (i, :) + + The function computes the gradient of the semi dual problem: + .. math:: + \W_varepsilon(a, b) = \max_\v \sum_i (\sum_j v_j * b_j + - \reg log(\sum_j exp((v_j - M_{i,j})/reg) * b_j)) * a_i + + where : + - M is the (ns,nt) metric cost matrix + - v is a dual variable in R^J + - reg is the regularization term + - a and b are source and target weights (sum to 1) + + The algorithm used for solving the problem is the ASGD & SAG algorithms + as proposed in [18]_ [alg.1 & alg.2] + + + Parameters + ---------- + + b : np.ndarray(nt,), + target measure + M : np.ndarray(ns, nt), + cost matrix + reg : float nu, + Regularization term > 0 + v : np.ndarray(nt,), + optimization vector + i : number int, + picked number i + + Returns + ------- + + coordinate gradient : np.ndarray(nt,) + + Examples + -------- + + >>> n_source = 7 + >>> n_target = 4 + >>> reg = 1 + >>> numItermax = 300000 + >>> lr = 1 + >>> a = ot.utils.unif(n_source) + >>> b = ot.utils.unif(n_target) + >>> rng = np.random.RandomState(0) + >>> X_source = rng.randn(n_source, 2) + >>> Y_target = rng.randn(n_target, 2) + >>> M = ot.dist(X_source, Y_target) + >>> method = "ASGD" + >>> asgd_pi = stochastic.transportation_matrix_entropic(a, b, M, reg, + method, numItermax, + lr) + >>> print(asgd_pi) + + References + ---------- + + [Genevay et al., 2016] : + Stochastic Optimization for Large-scale Optimal Transport, + Advances in Neural Information Processing Systems (2016), + arXiv preprint arxiv:1605.08527. + + ''' + + r = M[i, :] - v + exp_v = np.exp(-r / reg) * b + khi = exp_v / (np.sum(exp_v)) + return b - khi + + +def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=0.1): + ''' + Compute the SAG algorithm to solve the regularized discrete measures + optimal transport max problem + + The function solves the following optimization problem: + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + s.t. \gamma 1 = a + \gamma^T 1= b + \gamma \geq 0 + where : + - M is the (ns,nt) metric cost matrix + - :math:`\Omega` is the entropic regularization term + :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target weights (sum to 1) + The algorithm used for solving the problem is the SAG algorithm + as proposed in [18]_ [alg.1] + + + Parameters + ---------- + + a : np.ndarray(ns,), + source measure + b : np.ndarray(nt,), + target measure + M : np.ndarray(ns, nt), + cost matrix + reg : float number, + Regularization term > 0 + numItermax : int number + number of iteration + lr : float number + learning rate + + Returns + ------- + + v : np.ndarray(nt,) + dual variable + + Examples + -------- + + >>> n_source = 7 + >>> n_target = 4 + >>> reg = 1 + >>> numItermax = 300000 + >>> lr = 1 + >>> a = ot.utils.unif(n_source) + >>> b = ot.utils.unif(n_target) + >>> rng = np.random.RandomState(0) + >>> X_source = rng.randn(n_source, 2) + >>> Y_target = rng.randn(n_target, 2) + >>> M = ot.dist(X_source, Y_target) + >>> method = "SAG" + >>> sag_pi = stochastic.transportation_matrix_entropic(a, b, M, reg, + method, numItermax, + lr) + >>> print(asgd_pi) + + References + ---------- + + [Genevay et al., 2016] : + Stochastic Optimization for Large-scale Optimal Transport, + Advances in Neural Information Processing Systems (2016), + arXiv preprint arxiv:1605.08527. + ''' + + n_source = np.shape(M)[0] + n_target = np.shape(M)[1] + v = np.zeros(n_target) + stored_gradient = np.zeros((n_source, n_target)) + sum_stored_gradient = np.zeros(n_target) + for _ in range(numItermax): + i = np.random.randint(n_source) + cur_coord_grad = a[i] * coordinate_gradient(b, M, reg, v, i) + sum_stored_gradient += (cur_coord_grad - stored_gradient[i]) + stored_gradient[i] = cur_coord_grad + v += lr * (1. / n_source) * sum_stored_gradient + return v + + +def averaged_sgd_entropic_transport(b, M, reg, numItermax=300000, lr=1): + ''' + Compute the ASGD algorithm to solve the regularized semi contibous measures + optimal transport max problem + + The function solves the following optimization problem: + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + s.t. \gamma 1 = a + \gamma^T 1= b + \gamma \geq 0 + where : + - M is the (ns,nt) metric cost matrix + - :math:`\Omega` is the entropic regularization term + :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target weights (sum to 1) + The algorithm used for solving the problem is the ASGD algorithm + as proposed in [18]_ [alg.2] + + + Parameters + ---------- + + b : np.ndarray(nt,), + target measure + M : np.ndarray(ns, nt), + cost matrix + reg : float number, + Regularization term > 0 + numItermax : int number + number of iteration + lr : float number + learning rate + + + Returns + ------- + + ave_v : np.ndarray(nt,) + optimization vector + + Examples + -------- + + >>> n_source = 7 + >>> n_target = 4 + >>> reg = 1 + >>> numItermax = 300000 + >>> lr = 1 + >>> a = ot.utils.unif(n_source) + >>> b = ot.utils.unif(n_target) + >>> rng = np.random.RandomState(0) + >>> X_source = rng.randn(n_source, 2) + >>> Y_target = rng.randn(n_target, 2) + >>> M = ot.dist(X_source, Y_target) + >>> method = "ASGD" + >>> asgd_pi = stochastic.transportation_matrix_entropic(a, b, M, reg, + method, numItermax, + lr) + >>> print(asgd_pi) + + References + ---------- + + [Genevay et al., 2016] : + Stochastic Optimization for Large-scale Optimal Transport, + Advances in Neural Information Processing Systems (2016), + arXiv preprint arxiv:1605.08527. + ''' + + n_source = np.shape(M)[0] + n_target = np.shape(M)[1] + cur_v = np.zeros(n_target) + ave_v = np.zeros(n_target) + for cur_iter in range(numItermax): + k = cur_iter + 1 + i = np.random.randint(n_source) + cur_coord_grad = coordinate_gradient(b, M, reg, cur_v, i) + cur_v += (lr / np.sqrt(k)) * cur_coord_grad + ave_v = (1. / k) * cur_v + (1 - 1. / k) * ave_v + return ave_v + + +def c_transform_entropic(b, M, reg, v): + ''' + The goal is to recover u from the c-transform. + + The function computes the c_transform of a dual variable from the other + dual variable: + .. math:: + u = v^{c,reg} = -reg \sum_j exp((v - M)/reg) b_j + + where : + - M is the (ns,nt) metric cost matrix + - u, v are dual variables in R^IxR^J + - reg is the regularization term + + It is used to recover an optimal u from optimal v solving the semi dual + problem, see Proposition 2.1 of [18]_ + + + Parameters + ---------- + + b : np.ndarray(nt,) + target measure + M : np.ndarray(ns, nt) + cost matrix + reg : float + regularization term > 0 + v : np.ndarray(nt,) + dual variable + + Returns + ------- + + u : np.ndarray(ns,) + + Examples + -------- + + >>> n_source = 7 + >>> n_target = 4 + >>> reg = 1 + >>> numItermax = 300000 + >>> lr = 1 + >>> a = ot.utils.unif(n_source) + >>> b = ot.utils.unif(n_target) + >>> rng = np.random.RandomState(0) + >>> X_source = rng.randn(n_source, 2) + >>> Y_target = rng.randn(n_target, 2) + >>> M = ot.dist(X_source, Y_target) + >>> method = "ASGD" + >>> asgd_pi = stochastic.transportation_matrix_entropic(a, b, M, reg, + method, numItermax, + lr) + >>> print(asgd_pi) + + References + ---------- + + [Genevay et al., 2016] : + Stochastic Optimization for Large-scale Optimal Transport, + Advances in Neural Information Processing Systems (2016), + arXiv preprint arxiv:1605.08527. + ''' + + n_source = np.shape(M)[0] + n_target = np.shape(M)[1] + u = np.zeros(n_source) + for i in range(n_source): + r = M[i, :] - v + exp_v = np.exp(-r / reg) * b + u[i] = - reg * np.log(np.sum(exp_v)) + return u + + +def transportation_matrix_entropic(a, b, M, reg, method, numItermax=10000, + lr=0.1): + ''' + Compute the transportation matrix to solve the regularized discrete + measures optimal transport max problem + + The function solves the following optimization problem: + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + s.t. \gamma 1 = a + \gamma^T 1= b + \gamma \geq 0 + where : + - M is the (ns,nt) metric cost matrix + - :math:`\Omega` is the entropic regularization term + :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target weights (sum to 1) + The algorithm used for solving the problem is the SAG or ASGD algorithms + as proposed in [18]_ + + + Parameters + ---------- + + a : np.ndarray(ns,), + source measure + b : np.ndarray(nt,), + target measure + M : np.ndarray(ns, nt), + cost matrix + reg : float number, + Regularization term > 0 + methode : str, + used method (SAG or ASGD) + numItermax : int number + number of iteration + lr : float number + learning rate + n_source : int number + size of the source measure + n_target : int number + size of the target measure + + Returns + ------- + + pi : np.ndarray(ns, nt) + transportation matrix + + Examples + -------- + + >>> n_source = 7 + >>> n_target = 4 + >>> reg = 1 + >>> numItermax = 300000 + >>> lr = 1 + >>> a = ot.utils.unif(n_source) + >>> b = ot.utils.unif(n_target) + >>> rng = np.random.RandomState(0) + >>> X_source = rng.randn(n_source, 2) + >>> Y_target = rng.randn(n_target, 2) + >>> M = ot.dist(X_source, Y_target) + >>> method = "ASGD" + >>> asgd_pi = stochastic.transportation_matrix_entropic(a, b, M, reg, + method, numItermax, + lr) + >>> print(asgd_pi) + + References + ---------- + + [Genevay et al., 2016] : + Stochastic Optimization for Large-scale Optimal Transport, + Advances in Neural Information Processing Systems (2016), + arXiv preprint arxiv:1605.08527. + ''' + n_source = 7 + n_target = 4 + if method.lower() == "sag": + opt_v = sag_entropic_transport(a, b, M, reg, numItermax, lr) + elif method.lower() == "asgd": + opt_v = averaged_sgd_entropic_transport(b, M, reg, numItermax, lr) + else: + print("Please, select your method between SAG and ASGD") + return None + + opt_u = c_transform_entropic(b, M, reg, opt_v) + pi = (np.exp((opt_u[:, None] + opt_v[None, :] - M[:, :]) / reg) + * a[:, None] * b[None, :]) + return pi diff --git a/test/test_stochastic.py b/test/test_stochastic.py new file mode 100644 index 0000000..829f781 --- /dev/null +++ b/test/test_stochastic.py @@ -0,0 +1,115 @@ +""" +========================== +Stochastic test +========================== + +This example is designed to test the stochatic optimization algorithms module +for descrete and semicontinous measures from the POT library. + +""" + +# Author: Kilian Fatras +# +# License: MIT License + +import numpy as np +import ot + +############################################################################# +# +# TEST SAG algorithm +# --------------------------------------------- +# 2 identical discrete measures u defined on the same space with a +# regularization term, a learning rate and a number of iteration + + +def test_stochastic_sag(): + # test sag + n = 15 + reg = 1 + numItermax = 300000 + rng = np.random.RandomState(0) + + x = rng.randn(n, 2) + u = ot.utils.unif(n) + + M = ot.dist(x, x) + + G = ot.stochastic.transportation_matrix_entropic(u, u, M, reg, "sag", + numItermax=numItermax) + + # check constratints + np.testing.assert_allclose( + u, G.sum(1), atol=1e-04) # cf convergence sag + np.testing.assert_allclose( + u, G.sum(0), atol=1e-04) # cf convergence sag + + +############################################################################# +# +# TEST ASGD algorithm +# --------------------------------------------- +# 2 identical discrete measures u defined on the same space with a +# regularization term, a learning rate and a number of iteration + +def test_stochastic_asgd(): + # test asgd + n = 15 + reg = 1 + numItermax = 300000 + lr = 1 + rng = np.random.RandomState(0) + + x = rng.randn(n, 2) + u = ot.utils.unif(n) + + M = ot.dist(x, x) + + G = ot.stochastic.transportation_matrix_entropic(u, u, M, reg, "asgd", + numItermax=numItermax, + lr=lr) + + # check constratints + np.testing.assert_allclose( + u, G.sum(1), atol=1e-03) # cf convergence asgd + np.testing.assert_allclose( + u, G.sum(0), atol=1e-03) # cf convergence asgd + + +############################################################################# +# +# TEST Convergence SAG and ASGD toward Sinkhorn's solution +# -------------------------------------------------------- +# 2 identical discrete measures u defined on the same space with a +# regularization term, a learning rate and a number of iteration + + +def test_sag_asgd_sinkhorn(): + # test all algorithms + n = 15 + reg = 1 + nb_iter = 300000 + lr = 1 + rng = np.random.RandomState(0) + + x = rng.randn(n, 2) + u = ot.utils.unif(n) + zero = np.zeros(n) + M = ot.dist(x, x) + + G_asgd = ot.stochastic.transportation_matrix_entropic(u, u, M, reg, "asgd", + numItermax=nb_iter, + lr=1) + G_sag = ot.stochastic.transportation_matrix_entropic(u, u, M, reg, "sag", + numItermax=nb_iter) + G_sinkhorn = ot.sinkhorn(u, u, M, reg) + + # check constratints + np.testing.assert_allclose( + zero, (G_sag - G_sinkhorn).sum(1), atol=1e-03) # cf convergence sag + np.testing.assert_allclose( + zero, (G_sag - G_sinkhorn).sum(0), atol=1e-03) # cf convergence sag + np.testing.assert_allclose( + zero, (G_asgd - G_sinkhorn).sum(1), atol=1e-03) # cf convergence asgd + np.testing.assert_allclose( + zero, (G_asgd - G_sinkhorn).sum(0), atol=1e-03) # cf convergence asgd -- cgit v1.2.3 From e2d06ef4e901e0c1e9119cdbd79b9c453c1af452 Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Fri, 15 Jun 2018 19:04:38 -0700 Subject: add problems solved in doc --- examples/plot_stochastic.py | 4 ++-- ot/stochastic.py | 14 +++++++------- 2 files changed, 9 insertions(+), 9 deletions(-) diff --git a/examples/plot_stochastic.py b/examples/plot_stochastic.py index 9071ddb..0afceeb 100644 --- a/examples/plot_stochastic.py +++ b/examples/plot_stochastic.py @@ -53,7 +53,7 @@ M = ot.dist(X_source, Y_target) method = "SAG" sag_pi = ot.stochastic.transportation_matrix_entropic(a, b, M, reg, method, - numItermax, lr) + numItermax, lr) print(sag_pi) ############################################################################# @@ -90,7 +90,7 @@ M = ot.dist(X_source, Y_target) method = "ASGD" asgd_pi = ot.stochastic.transportation_matrix_entropic(a, b, M, reg, method, - numItermax, lr) + numItermax, lr) print(asgd_pi) ############################################################################# diff --git a/ot/stochastic.py b/ot/stochastic.py index 541f456..9123150 100644 --- a/ot/stochastic.py +++ b/ot/stochastic.py @@ -75,8 +75,8 @@ def coordinate_gradient(b, M, reg, v, i): ''' r = M[i, :] - v - exp_v = np.exp(-r / reg) * b - khi = exp_v / (np.sum(exp_v)) + exp_v = np.exp(-r/reg) * b + khi = exp_v/(np.sum(exp_v)) return b - khi @@ -161,7 +161,7 @@ def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=0.1): cur_coord_grad = a[i] * coordinate_gradient(b, M, reg, v, i) sum_stored_gradient += (cur_coord_grad - stored_gradient[i]) stored_gradient[i] = cur_coord_grad - v += lr * (1. / n_source) * sum_stored_gradient + v += lr * (1./n_source) * sum_stored_gradient return v @@ -243,8 +243,8 @@ def averaged_sgd_entropic_transport(b, M, reg, numItermax=300000, lr=1): k = cur_iter + 1 i = np.random.randint(n_source) cur_coord_grad = coordinate_gradient(b, M, reg, cur_v, i) - cur_v += (lr / np.sqrt(k)) * cur_coord_grad - ave_v = (1. / k) * cur_v + (1 - 1. / k) * ave_v + cur_v += (lr/np.sqrt(k)) * cur_coord_grad + ave_v = (1./k) * cur_v + (1 - 1./k) * ave_v return ave_v @@ -317,7 +317,7 @@ def c_transform_entropic(b, M, reg, v): u = np.zeros(n_source) for i in range(n_source): r = M[i, :] - v - exp_v = np.exp(-r / reg) * b + exp_v = np.exp(-r/reg) * b u[i] = - reg * np.log(np.sum(exp_v)) return u @@ -410,6 +410,6 @@ def transportation_matrix_entropic(a, b, M, reg, method, numItermax=10000, return None opt_u = c_transform_entropic(b, M, reg, opt_v) - pi = (np.exp((opt_u[:, None] + opt_v[None, :] - M[:, :]) / reg) + pi = (np.exp((opt_u[:, None] + opt_v[None, :] - M[:, :])/reg) * a[:, None] * b[None, :]) return pi -- cgit v1.2.3 From 18f9242b9575e7551d5e30b66cfbaaac85e0022a Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Fri, 15 Jun 2018 19:15:48 -0700 Subject: PEP8 --- examples/plot_stochastic.py | 4 ++-- ot/stochastic.py | 14 +++++++------- test/test_stochastic.py | 1 - 3 files changed, 9 insertions(+), 10 deletions(-) diff --git a/examples/plot_stochastic.py b/examples/plot_stochastic.py index 0afceeb..9071ddb 100644 --- a/examples/plot_stochastic.py +++ b/examples/plot_stochastic.py @@ -53,7 +53,7 @@ M = ot.dist(X_source, Y_target) method = "SAG" sag_pi = ot.stochastic.transportation_matrix_entropic(a, b, M, reg, method, - numItermax, lr) + numItermax, lr) print(sag_pi) ############################################################################# @@ -90,7 +90,7 @@ M = ot.dist(X_source, Y_target) method = "ASGD" asgd_pi = ot.stochastic.transportation_matrix_entropic(a, b, M, reg, method, - numItermax, lr) + numItermax, lr) print(asgd_pi) ############################################################################# diff --git a/ot/stochastic.py b/ot/stochastic.py index 9123150..541f456 100644 --- a/ot/stochastic.py +++ b/ot/stochastic.py @@ -75,8 +75,8 @@ def coordinate_gradient(b, M, reg, v, i): ''' r = M[i, :] - v - exp_v = np.exp(-r/reg) * b - khi = exp_v/(np.sum(exp_v)) + exp_v = np.exp(-r / reg) * b + khi = exp_v / (np.sum(exp_v)) return b - khi @@ -161,7 +161,7 @@ def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=0.1): cur_coord_grad = a[i] * coordinate_gradient(b, M, reg, v, i) sum_stored_gradient += (cur_coord_grad - stored_gradient[i]) stored_gradient[i] = cur_coord_grad - v += lr * (1./n_source) * sum_stored_gradient + v += lr * (1. / n_source) * sum_stored_gradient return v @@ -243,8 +243,8 @@ def averaged_sgd_entropic_transport(b, M, reg, numItermax=300000, lr=1): k = cur_iter + 1 i = np.random.randint(n_source) cur_coord_grad = coordinate_gradient(b, M, reg, cur_v, i) - cur_v += (lr/np.sqrt(k)) * cur_coord_grad - ave_v = (1./k) * cur_v + (1 - 1./k) * ave_v + cur_v += (lr / np.sqrt(k)) * cur_coord_grad + ave_v = (1. / k) * cur_v + (1 - 1. / k) * ave_v return ave_v @@ -317,7 +317,7 @@ def c_transform_entropic(b, M, reg, v): u = np.zeros(n_source) for i in range(n_source): r = M[i, :] - v - exp_v = np.exp(-r/reg) * b + exp_v = np.exp(-r / reg) * b u[i] = - reg * np.log(np.sum(exp_v)) return u @@ -410,6 +410,6 @@ def transportation_matrix_entropic(a, b, M, reg, method, numItermax=10000, return None opt_u = c_transform_entropic(b, M, reg, opt_v) - pi = (np.exp((opt_u[:, None] + opt_v[None, :] - M[:, :])/reg) + pi = (np.exp((opt_u[:, None] + opt_v[None, :] - M[:, :]) / reg) * a[:, None] * b[None, :]) return pi diff --git a/test/test_stochastic.py b/test/test_stochastic.py index 829f781..5fe5ea9 100644 --- a/test/test_stochastic.py +++ b/test/test_stochastic.py @@ -89,7 +89,6 @@ def test_sag_asgd_sinkhorn(): n = 15 reg = 1 nb_iter = 300000 - lr = 1 rng = np.random.RandomState(0) x = rng.randn(n, 2) -- cgit v1.2.3 From 3617b42ec05fe88eb616b32b2c3827d63022e287 Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Fri, 15 Jun 2018 19:18:20 -0700 Subject: PEP8 --- ot/stochastic.py | 3 --- 1 file changed, 3 deletions(-) diff --git a/ot/stochastic.py b/ot/stochastic.py index 541f456..f34154b 100644 --- a/ot/stochastic.py +++ b/ot/stochastic.py @@ -313,7 +313,6 @@ def c_transform_entropic(b, M, reg, v): ''' n_source = np.shape(M)[0] - n_target = np.shape(M)[1] u = np.zeros(n_source) for i in range(n_source): r = M[i, :] - v @@ -399,8 +398,6 @@ def transportation_matrix_entropic(a, b, M, reg, method, numItermax=10000, Advances in Neural Information Processing Systems (2016), arXiv preprint arxiv:1605.08527. ''' - n_source = 7 - n_target = 4 if method.lower() == "sag": opt_v = sag_entropic_transport(a, b, M, reg, numItermax, lr) elif method.lower() == "asgd": -- cgit v1.2.3 From 055417ee06917ff8bac5d07b2d2a17d80e5da4b6 Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Fri, 15 Jun 2018 19:25:29 -0700 Subject: pep8 --- ot/stochastic.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ot/stochastic.py b/ot/stochastic.py index f34154b..9912223 100644 --- a/ot/stochastic.py +++ b/ot/stochastic.py @@ -407,6 +407,6 @@ def transportation_matrix_entropic(a, b, M, reg, method, numItermax=10000, return None opt_u = c_transform_entropic(b, M, reg, opt_v) - pi = (np.exp((opt_u[:, None] + opt_v[None, :] - M[:, :]) / reg) - * a[:, None] * b[None, :]) + pi = (np.exp((opt_u[:, None] + opt_v[None, :] - M[:, :]) / reg) * + a[:, None] * b[None, :]) return pi -- cgit v1.2.3 From 74cfe5ac77c3e964a85ef90c11d8ebffa16ddcfe Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Mon, 18 Jun 2018 17:56:28 -0700 Subject: add sgd --- README.md | 8 + examples/plot_stochastic.py | 95 +++++++-- ot/stochastic.py | 468 +++++++++++++++++++++++++++++++++++++++----- test/test_stochastic.py | 104 +++++++++- 4 files changed, 608 insertions(+), 67 deletions(-) diff --git a/README.md b/README.md index 466c09c..8e8dcd4 100644 --- a/README.md +++ b/README.md @@ -22,6 +22,7 @@ It provides the following solvers: * Linear OT [14] and Joint OT matrix and mapping estimation [8]. * Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt). * Gromov-Wasserstein distances and barycenters ([13] and regularized [12]) +* Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -149,6 +150,7 @@ The contributors to this library are: * [Stanislas Chambon](https://slasnista.github.io/) * [Antoine Rolet](https://arolet.github.io/) * Erwan Vautier (Gromov-Wasserstein) +* [Kilian Fatras](https://kilianfatras.github.io/) (Stochastic optimization) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): @@ -213,3 +215,9 @@ You can also post bug reports and feature requests in Github issues. Make sure t [15] Peyré, G., & Cuturi, M. (2018). [Computational Optimal Transport](https://arxiv.org/pdf/1803.00567.pdf) . [16] Agueh, M., & Carlier, G. (2011). [Barycenters in the Wasserstein space](https://hal.archives-ouvertes.fr/hal-00637399/document). SIAM Journal on Mathematical Analysis, 43(2), 904-924. + +[17] Blondel, M., Seguy, V., & Rolet, A. (2018). [Smooth and Sparse Optimal Transport](https://arxiv.org/pdf/1710.06276.pdf). Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS). + +[18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016) [Stochastic Optimization for Large-scale Optimal Transport](arXiv preprint arxiv:1605.08527). Advances in Neural Information Processing Systems (2016). + +[19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, A.& Blondel, M. [Large-scale Optimal Transport and Mapping Estimation](https://arxiv.org/pdf/1711.02283.pdf). International Conference on Learning Representation (2018) diff --git a/examples/plot_stochastic.py b/examples/plot_stochastic.py index 9071ddb..3fc1955 100644 --- a/examples/plot_stochastic.py +++ b/examples/plot_stochastic.py @@ -18,9 +18,9 @@ import ot ############################################################################# -# COMPUTE TRANSPORTATION MATRIX +# COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM ############################################################################# - +print("------------SEMI-DUAL PROBLEM------------") ############################################################################# # DISCRETE CASE # Sample two discrete measures for the discrete case @@ -48,12 +48,12 @@ M = ot.dist(X_source, Y_target) # Call the "SAG" method to find the transportation matrix in the discrete case # --------------------------------------------- # -# Define the method "SAG", call ot.transportation_matrix_entropic and plot the +# Define the method "SAG", call ot.solve_semi_dual_entropic and plot the # results. method = "SAG" -sag_pi = ot.stochastic.transportation_matrix_entropic(a, b, M, reg, method, - numItermax, lr) +sag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, + numItermax, lr) print(sag_pi) ############################################################################# @@ -68,8 +68,9 @@ print(sag_pi) n_source = 7 n_target = 4 reg = 1 -numItermax = 500000 +numItermax = 100000 lr = 1 +log = True a = ot.utils.unif(n_source) b = ot.utils.unif(n_target) @@ -85,12 +86,13 @@ M = ot.dist(X_source, Y_target) # case # --------------------------------------------- # -# Define the method "ASGD", call ot.transportation_matrix_entropic and plot the +# Define the method "ASGD", call ot.solve_semi_dual_entropic and plot the # results. method = "ASGD" -asgd_pi = ot.stochastic.transportation_matrix_entropic(a, b, M, reg, method, - numItermax, lr) +asgd_pi, log = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, + numItermax, lr, log) +print(log['alpha'], log['beta']) print(asgd_pi) ############################################################################# @@ -100,7 +102,7 @@ print(asgd_pi) # # Call the Sinkhorn algorithm from POT -sinkhorn_pi = ot.sinkhorn(a, b, M, 1) +sinkhorn_pi = ot.sinkhorn(a, b, M, reg) print(sinkhorn_pi) @@ -113,7 +115,7 @@ print(sinkhorn_pi) # ---------------- pl.figure(4, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, sag_pi, 'OT matrix SAG') +ot.plot.plot1D_mat(a, b, sag_pi, 'semi-dual : OT matrix SAG') pl.show() @@ -122,7 +124,76 @@ pl.show() # ----------------- pl.figure(4, figsize=(5, 5)) -ot.plot.plot1D_mat(a, b, asgd_pi, 'OT matrix ASGD') +ot.plot.plot1D_mat(a, b, asgd_pi, 'semi-dual : OT matrix ASGD') +pl.show() + + +############################################################################## +# Plot Sinkhorn results +# --------------------- + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn') +pl.show() + +############################################################################# +# COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM +############################################################################# +print("------------DUAL PROBLEM------------") +############################################################################# +# SEMICONTINOUS CASE +# Sample one general measure a, one discrete measures b for the semicontinous +# case +# --------------------------------------------- +# +# Define one general measure a, one discrete measures b, the points where +# are defined the source and the target measures and finally the cost matrix c. + +n_source = 7 +n_target = 4 +reg = 1 +numItermax = 100000 +lr = 0.1 +batch_size = 3 +log = True + +a = ot.utils.unif(n_source) +b = ot.utils.unif(n_target) + +rng = np.random.RandomState(0) +X_source = rng.randn(n_source, 2) +Y_target = rng.randn(n_target, 2) +M = ot.dist(X_source, Y_target) + +############################################################################# +# +# Call the "SGD" dual method to find the transportation matrix in the semicontinous +# case +# --------------------------------------------- +# +# Call ot.solve_dual_entropic and plot the results. + +sgd_dual_pi, log = ot.stochastic.solve_dual_entropic(a, b, M, reg, batch_size, + numItermax, lr, log) +print(log['alpha'], log['beta']) +print(sgd_dual_pi) + +############################################################################# +# +# Compare the results with the Sinkhorn algorithm +# --------------------------------------------- +# +# Call the Sinkhorn algorithm from POT + +sinkhorn_pi = ot.sinkhorn(a, b, M, reg) +print(sinkhorn_pi) + +############################################################################## +# Plot SGD results +# ----------------- + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, sgd_dual_pi, 'dual : OT matrix SGD') pl.show() diff --git a/ot/stochastic.py b/ot/stochastic.py index 9912223..31c99be 100644 --- a/ot/stochastic.py +++ b/ot/stochastic.py @@ -5,7 +5,10 @@ import numpy as np -def coordinate_gradient(b, M, reg, v, i): +############################################################################## +# Optimization toolbox for SEMI - DUAL problem +############################################################################## +def coordinate_gradient(b, M, reg, beta, i): ''' Compute the coordinate gradient update for regularized discrete distributions for (i, :) @@ -59,7 +62,7 @@ def coordinate_gradient(b, M, reg, v, i): >>> Y_target = rng.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) >>> method = "ASGD" - >>> asgd_pi = stochastic.transportation_matrix_entropic(a, b, M, reg, + >>> asgd_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, method, numItermax, lr) >>> print(asgd_pi) @@ -74,9 +77,9 @@ def coordinate_gradient(b, M, reg, v, i): ''' - r = M[i, :] - v - exp_v = np.exp(-r / reg) * b - khi = exp_v / (np.sum(exp_v)) + r = M[i, :] - beta + exp_beta = np.exp(-r/reg) * b + khi = exp_beta/(np.sum(exp_beta)) return b - khi @@ -137,7 +140,7 @@ def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=0.1): >>> Y_target = rng.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) >>> method = "SAG" - >>> sag_pi = stochastic.transportation_matrix_entropic(a, b, M, reg, + >>> sag_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, method, numItermax, lr) >>> print(asgd_pi) @@ -153,16 +156,16 @@ def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=0.1): n_source = np.shape(M)[0] n_target = np.shape(M)[1] - v = np.zeros(n_target) + cur_beta = np.zeros(n_target) stored_gradient = np.zeros((n_source, n_target)) sum_stored_gradient = np.zeros(n_target) for _ in range(numItermax): i = np.random.randint(n_source) - cur_coord_grad = a[i] * coordinate_gradient(b, M, reg, v, i) + cur_coord_grad = a[i] * coordinate_gradient(b, M, reg, cur_beta, i) sum_stored_gradient += (cur_coord_grad - stored_gradient[i]) stored_gradient[i] = cur_coord_grad - v += lr * (1. / n_source) * sum_stored_gradient - return v + cur_beta += lr * (1./n_source) * sum_stored_gradient + return cur_beta def averaged_sgd_entropic_transport(b, M, reg, numItermax=300000, lr=1): @@ -170,19 +173,19 @@ def averaged_sgd_entropic_transport(b, M, reg, numItermax=300000, lr=1): Compute the ASGD algorithm to solve the regularized semi contibous measures optimal transport max problem - The function solves the following optimization problem: - .. math:: - \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) - s.t. \gamma 1 = a - \gamma^T 1= b - \gamma \geq 0 - where : - - M is the (ns,nt) metric cost matrix - - :math:`\Omega` is the entropic regularization term - :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - a and b are source and target weights (sum to 1) - The algorithm used for solving the problem is the ASGD algorithm - as proposed in [18]_ [alg.2] + The function solves the following optimization problem: + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + s.t. \gamma 1 = a + \gamma^T 1= b + \gamma \geq 0 + where : + - M is the (ns,nt) metric cost matrix + - :math:`\Omega` is the entropic regularization term + :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target weights (sum to 1) + The algorithm used for solving the problem is the ASGD algorithm + as proposed in [18]_ [alg.2] Parameters @@ -221,7 +224,7 @@ def averaged_sgd_entropic_transport(b, M, reg, numItermax=300000, lr=1): >>> Y_target = rng.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) >>> method = "ASGD" - >>> asgd_pi = stochastic.transportation_matrix_entropic(a, b, M, reg, + >>> asgd_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, method, numItermax, lr) >>> print(asgd_pi) @@ -237,18 +240,18 @@ def averaged_sgd_entropic_transport(b, M, reg, numItermax=300000, lr=1): n_source = np.shape(M)[0] n_target = np.shape(M)[1] - cur_v = np.zeros(n_target) - ave_v = np.zeros(n_target) + cur_beta = np.zeros(n_target) + ave_beta = np.zeros(n_target) for cur_iter in range(numItermax): k = cur_iter + 1 i = np.random.randint(n_source) - cur_coord_grad = coordinate_gradient(b, M, reg, cur_v, i) - cur_v += (lr / np.sqrt(k)) * cur_coord_grad - ave_v = (1. / k) * cur_v + (1 - 1. / k) * ave_v - return ave_v + cur_coord_grad = coordinate_gradient(b, M, reg, cur_beta, i) + cur_beta += (lr/np.sqrt(k)) * cur_coord_grad + ave_beta = (1./k) * cur_beta + (1 - 1./k) * ave_beta + return ave_beta -def c_transform_entropic(b, M, reg, v): +def c_transform_entropic(b, M, reg, beta): ''' The goal is to recover u from the c-transform. @@ -298,7 +301,7 @@ def c_transform_entropic(b, M, reg, v): >>> Y_target = rng.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) >>> method = "ASGD" - >>> asgd_pi = stochastic.transportation_matrix_entropic(a, b, M, reg, + >>> asgd_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, method, numItermax, lr) >>> print(asgd_pi) @@ -313,16 +316,18 @@ def c_transform_entropic(b, M, reg, v): ''' n_source = np.shape(M)[0] - u = np.zeros(n_source) + n_target = np.shape(M)[1] + alpha = np.zeros(n_source) for i in range(n_source): - r = M[i, :] - v - exp_v = np.exp(-r / reg) * b - u[i] = - reg * np.log(np.sum(exp_v)) - return u + r = M[i, :] - beta + min_r = np.min(r) + exp_beta = np.exp(-(r - min_r)/reg) * b + alpha[i] = min_r - reg * np.log(np.sum(exp_beta)) + return alpha -def transportation_matrix_entropic(a, b, M, reg, method, numItermax=10000, - lr=0.1): +def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=0.1, + log=False): ''' Compute the transportation matrix to solve the regularized discrete measures optimal transport max problem @@ -363,12 +368,16 @@ def transportation_matrix_entropic(a, b, M, reg, method, numItermax=10000, size of the source measure n_target : int number size of the target measure + log : bool, optional + record log if True Returns ------- pi : np.ndarray(ns, nt) transportation matrix + log : dict + log dictionary return only if log==True in parameters Examples -------- @@ -385,7 +394,7 @@ def transportation_matrix_entropic(a, b, M, reg, method, numItermax=10000, >>> Y_target = rng.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) >>> method = "ASGD" - >>> asgd_pi = stochastic.transportation_matrix_entropic(a, b, M, reg, + >>> asgd_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, method, numItermax, lr) >>> print(asgd_pi) @@ -398,15 +407,384 @@ def transportation_matrix_entropic(a, b, M, reg, method, numItermax=10000, Advances in Neural Information Processing Systems (2016), arXiv preprint arxiv:1605.08527. ''' + n_source = 7 + n_target = 4 if method.lower() == "sag": - opt_v = sag_entropic_transport(a, b, M, reg, numItermax, lr) + opt_beta = sag_entropic_transport(a, b, M, reg, numItermax, lr) elif method.lower() == "asgd": - opt_v = averaged_sgd_entropic_transport(b, M, reg, numItermax, lr) + opt_beta = averaged_sgd_entropic_transport(b, M, reg, numItermax, lr) else: print("Please, select your method between SAG and ASGD") return None - opt_u = c_transform_entropic(b, M, reg, opt_v) - pi = (np.exp((opt_u[:, None] + opt_v[None, :] - M[:, :]) / reg) * + opt_alpha = c_transform_entropic(b, M, reg, opt_beta) + pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :])/reg) * a[:, None] * b[None, :]) - return pi + + if log: + log = {} + log['alpha'] = opt_alpha + log['beta'] = opt_beta + return pi, log + else: + return pi + + +############################################################################## +# Optimization toolbox for DUAL problem +############################################################################## + + +def grad_dF_dalpha(M, reg, alpha, beta, batch_size, batch_alpha, batch_beta): + ''' + Computes the partial gradient of F_\W_varepsilon + + Compute the partial gradient of the dual problem: + ..Math: + \forall i in batch_alpha, + grad_alpha_i = 1 * batch_size - + sum_{j in batch_beta} exp((alpha_i + beta_j - M_{i,j})/reg) + + where : + - M is the (ns,nt) metric cost matrix + - alpha, beta are dual variables in R^ixR^J + - reg is the regularization term + - batch_alpha and batch_beta are list of index + + The algorithm used for solving the dual problem is the SGD algorithm + as proposed in [19]_ [alg.1] + + Parameters + ---------- + + reg : float number, + Regularization term > 0 + M : np.ndarray(ns, nt), + cost matrix + alpha : np.ndarray(ns,) + dual variable + beta : np.ndarray(nt,) + dual variable + batch_size : int number + size of the batch + batch_alpha : np.ndarray(bs,) + batch of index of alpha + batch_beta : np.ndarray(bs,) + batch of index of beta + + Returns + ------- + + grad : np.ndarray(ns,) + partial grad F in alpha + + Examples + -------- + + >>> n_source = 7 + >>> n_target = 4 + >>> reg = 1 + >>> numItermax = 20000 + >>> lr = 0.1 + >>> batch_size = 3 + >>> log = True + >>> a = ot.utils.unif(n_source) + >>> b = ot.utils.unif(n_target) + >>> rng = np.random.RandomState(0) + >>> X_source = rng.randn(n_source, 2) + >>> Y_target = rng.randn(n_target, 2) + >>> M = ot.dist(X_source, Y_target) + >>> sgd_dual_pi, log = stochastic.solve_dual_entropic(a, b, M, reg, + batch_size, + numItermax, lr, log) + >>> print(log['alpha'], log['beta']) + >>> print(sgd_dual_pi) + + References + ---------- + + [Seguy et al., 2018] : + International Conference on Learning Representation (2018), + arXiv preprint arxiv:1711.02283. + ''' + + grad_alpha = np.zeros(batch_size) + grad_alpha[:] = batch_size + for j in batch_beta: + grad_alpha -= np.exp((alpha[batch_alpha] + beta[j] + - M[batch_alpha, j])/reg) + return grad_alpha + + +def grad_dF_dbeta(M, reg, alpha, beta, batch_size, batch_alpha, batch_beta): + ''' + Computes the partial gradient of F_\W_varepsilon + + Compute the partial gradient of the dual problem: + ..Math: + \forall j in batch_beta, + grad_beta_j = 1 * batch_size - + sum_{i in batch_alpha} exp((alpha_i + beta_j - M_{i,j})/reg) + + where : + - M is the (ns,nt) metric cost matrix + - alpha, beta are dual variables in R^ixR^J + - reg is the regularization term + - batch_alpha and batch_beta are list of index + + The algorithm used for solving the dual problem is the SGD algorithm + as proposed in [19]_ [alg.1] + + Parameters + ---------- + + M : np.ndarray(ns, nt), + cost matrix + reg : float number, + Regularization term > 0 + alpha : np.ndarray(ns,) + dual variable + beta : np.ndarray(nt,) + dual variable + batch_size : int number + size of the batch + batch_alpha : np.ndarray(bs,) + batch of index of alpha + batch_beta : np.ndarray(bs,) + batch of index of beta + + Returns + ------- + + grad : np.ndarray(ns,) + partial grad F in beta + + Examples + -------- + + >>> n_source = 7 + >>> n_target = 4 + >>> reg = 1 + >>> numItermax = 20000 + >>> lr = 0.1 + >>> batch_size = 3 + >>> log = True + >>> a = ot.utils.unif(n_source) + >>> b = ot.utils.unif(n_target) + >>> rng = np.random.RandomState(0) + >>> X_source = rng.randn(n_source, 2) + >>> Y_target = rng.randn(n_target, 2) + >>> M = ot.dist(X_source, Y_target) + >>> sgd_dual_pi, log = stochastic.solve_dual_entropic(a, b, M, reg, + batch_size, + numItermax, lr, log) + >>> print(log['alpha'], log['beta']) + >>> print(sgd_dual_pi) + + References + ---------- + + [Seguy et al., 2018] : + International Conference on Learning Representation (2018), + arXiv preprint arxiv:1711.02283. + + ''' + + grad_beta = np.zeros(batch_size) + grad_beta[:] = batch_size + for i in batch_alpha: + grad_beta -= np.exp((alpha[i] + + beta[batch_beta] - M[i, batch_beta])/reg) + return grad_beta + + +def sgd_entropic_regularization(M, reg, batch_size, numItermax, lr, + alternate=True): + ''' + Compute the sgd algorithm to solve the regularized discrete measures + optimal transport dual problem + + The function solves the following optimization problem: + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + s.t. \gamma 1 = a + \gamma^T 1= b + \gamma \geq 0 + where : + - M is the (ns,nt) metric cost matrix + - :math:`\Omega` is the entropic regularization term + :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target weights (sum to 1) + + Parameters + ---------- + + M : np.ndarray(ns, nt), + cost matrix + reg : float number, + Regularization term > 0 + batch_size : int number + size of the batch + numItermax : int number + number of iteration + lr : float number + learning rate + alternate : bool, optional + alternating algorithm + + Returns + ------- + + alpha : np.ndarray(ns,) + dual variable + beta : np.ndarray(nt,) + dual variable + + Examples + -------- + + >>> n_source = 7 + >>> n_target = 4 + >>> reg = 1 + >>> numItermax = 20000 + >>> lr = 0.1 + >>> batch_size = 3 + >>> log = True + >>> a = ot.utils.unif(n_source) + >>> b = ot.utils.unif(n_target) + >>> rng = np.random.RandomState(0) + >>> X_source = rng.randn(n_source, 2) + >>> Y_target = rng.randn(n_target, 2) + >>> M = ot.dist(X_source, Y_target) + >>> sgd_dual_pi, log = stochastic.solve_dual_entropic(a, b, M, reg, + batch_size, + numItermax, lr, log) + >>> print(log['alpha'], log['beta']) + >>> print(sgd_dual_pi) + + References + ---------- + + [Seguy et al., 2018] : + International Conference on Learning Representation (2018), + arXiv preprint arxiv:1711.02283. + ''' + + n_source = np.shape(M)[0] + n_target = np.shape(M)[1] + cur_alpha = np.random.randn(n_source) + cur_beta = np.random.randn(n_target) + if alternate: + for cur_iter in range(numItermax): + k = np.sqrt(cur_iter + 1) + batch_alpha = np.random.choice(n_source, batch_size, replace=False) + batch_beta = np.random.choice(n_target, batch_size, replace=False) + grad_F_alpha = grad_dF_dalpha(M, reg, cur_alpha, cur_beta, + batch_size, batch_alpha, batch_beta) + cur_alpha[batch_alpha] += (lr/k) * grad_F_alpha + grad_F_beta = grad_dF_dbeta(M, reg, cur_alpha, cur_beta, + batch_size, batch_alpha, batch_beta) + cur_beta[batch_beta] += (lr/k) * grad_F_beta + + else: + for cur_iter in range(numItermax): + k = np.sqrt(cur_iter + 1) + batch_alpha = np.random.choice(n_source, batch_size, replace=False) + batch_beta = np.random.choice(n_target, batch_size, replace=False) + grad_F_alpha = grad_dF_dalpha(M, reg, cur_alpha, cur_beta, + batch_size, batch_alpha, batch_beta) + grad_F_beta = grad_dF_dbeta(M, reg, cur_alpha, cur_beta, + batch_size, batch_alpha, batch_beta) + cur_alpha[batch_alpha] += (lr/k) * grad_F_alpha + cur_beta[batch_beta] += (lr/k) * grad_F_beta + + return cur_alpha, cur_beta + + +def solve_dual_entropic(a, b, M, reg, batch_size, numItermax=10000, lr=1, + log=False): + ''' + Compute the transportation matrix to solve the regularized discrete measures + optimal transport dual problem + + The function solves the following optimization problem: + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + s.t. \gamma 1 = a + \gamma^T 1= b + \gamma \geq 0 + where : + - M is the (ns,nt) metric cost matrix + - :math:`\Omega` is the entropic regularization term + :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target weights (sum to 1) + + Parameters + ---------- + + a : np.ndarray(ns,), + source measure + b : np.ndarray(nt,), + target measure + M : np.ndarray(ns, nt), + cost matrix + reg : float number, + Regularization term > 0 + batch_size : int number + size of the batch + numItermax : int number + number of iteration + lr : float number + learning rate + log : bool, optional + record log if True + + Returns + ------- + + pi : np.ndarray(ns, nt) + transportation matrix + log : dict + log dictionary return only if log==True in parameters + + Examples + -------- + + >>> n_source = 7 + >>> n_target = 4 + >>> reg = 1 + >>> numItermax = 20000 + >>> lr = 0.1 + >>> batch_size = 3 + >>> log = True + >>> a = ot.utils.unif(n_source) + >>> b = ot.utils.unif(n_target) + >>> rng = np.random.RandomState(0) + >>> X_source = rng.randn(n_source, 2) + >>> Y_target = rng.randn(n_target, 2) + >>> M = ot.dist(X_source, Y_target) + >>> sgd_dual_pi, log = stochastic.solve_dual_entropic(a, b, M, reg, + batch_size, + numItermax, lr, log) + >>> print(log['alpha'], log['beta']) + >>> print(sgd_dual_pi) + + References + ---------- + + [Seguy et al., 2018] : + International Conference on Learning Representation (2018), + arXiv preprint arxiv:1711.02283. + ''' + + opt_alpha, opt_beta = sgd_entropic_regularization(M, reg, batch_size, + numItermax, lr) + pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :])/reg) * + a[:, None] * b[None, :]) + if log: + log = {} + log['alpha'] = opt_alpha + log['beta'] = opt_beta + return pi, log + else: + return pi diff --git a/test/test_stochastic.py b/test/test_stochastic.py index 5fe5ea9..bc0cebb 100644 --- a/test/test_stochastic.py +++ b/test/test_stochastic.py @@ -15,6 +15,11 @@ for descrete and semicontinous measures from the POT library. import numpy as np import ot + +############################################################################# +# COMPUTE TEST FOR SEMI-DUAL PROBLEM +############################################################################# + ############################################################################# # # TEST SAG algorithm @@ -35,8 +40,8 @@ def test_stochastic_sag(): M = ot.dist(x, x) - G = ot.stochastic.transportation_matrix_entropic(u, u, M, reg, "sag", - numItermax=numItermax) + G = ot.stochastic.solve_semi_dual_entropic(u, u, M, reg, "sag", + numItermax=numItermax) # check constratints np.testing.assert_allclose( @@ -65,9 +70,9 @@ def test_stochastic_asgd(): M = ot.dist(x, x) - G = ot.stochastic.transportation_matrix_entropic(u, u, M, reg, "asgd", - numItermax=numItermax, - lr=lr) + G = ot.stochastic.solve_semi_dual_entropic(u, u, M, reg, "asgd", + numItermax=numItermax, + lr=lr) # check constratints np.testing.assert_allclose( @@ -89,6 +94,7 @@ def test_sag_asgd_sinkhorn(): n = 15 reg = 1 nb_iter = 300000 + lr = 1 rng = np.random.RandomState(0) x = rng.randn(n, 2) @@ -96,11 +102,10 @@ def test_sag_asgd_sinkhorn(): zero = np.zeros(n) M = ot.dist(x, x) - G_asgd = ot.stochastic.transportation_matrix_entropic(u, u, M, reg, "asgd", - numItermax=nb_iter, - lr=1) - G_sag = ot.stochastic.transportation_matrix_entropic(u, u, M, reg, "sag", - numItermax=nb_iter) + G_asgd = ot.stochastic.solve_semi_dual_entropic(u, u, M, reg, "asgd", + numItermax=nb_iter, lr=lr) + G_sag = ot.stochastic.solve_semi_dual_entropic(u, u, M, reg, "sag", + numItermax=nb_iter) G_sinkhorn = ot.sinkhorn(u, u, M, reg) # check constratints @@ -112,3 +117,82 @@ def test_sag_asgd_sinkhorn(): zero, (G_asgd - G_sinkhorn).sum(1), atol=1e-03) # cf convergence asgd np.testing.assert_allclose( zero, (G_asgd - G_sinkhorn).sum(0), atol=1e-03) # cf convergence asgd + np.testing.assert_allclose( + G_sag, G_sinkhorn, atol=1e-03) # cf convergence sag + np.testing.assert_allclose( + G_asgd, G_sinkhorn, atol=1e-03) # cf convergence asgd + + +############################################################################# +# COMPUTE TEST FOR DUAL PROBLEM +############################################################################# + +############################################################################# +# +# TEST SGD algorithm +# --------------------------------------------- +# 2 identical discrete measures u defined on the same space with a +# regularization term, a batch_size and a number of iteration + +def test_stochastic_dual_sgd(): + # test sgd + print("SGD") + n = 10 + reg = 1 + numItermax = 300000 + batch_size = 8 + rng = np.random.RandomState(0) + + x = rng.randn(n, 2) + u = ot.utils.unif(n) + + M = ot.dist(x, x) + + G = ot.stochastic.solve_dual_entropic(u, u, M, reg, batch_size, + numItermax=numItermax) + + # check constratints + np.testing.assert_allclose( + u, G.sum(1), atol=1e-02) # cf convergence sgd + np.testing.assert_allclose( + u, G.sum(0), atol=1e-02) # cf convergence sgd + +############################################################################# +# +# TEST Convergence SGD toward Sinkhorn's solution +# -------------------------------------------------------- +# 2 identical discrete measures u defined on the same space with a +# regularization term, a batch_size and a number of iteration + + +def test_dual_sgd_sinkhorn(): + # test all dual algorithms + print("SGD vs Sinkhorn") + n = 10 + reg = 1 + nb_iter = 300000 + batch_size = 8 + rng = np.random.RandomState(0) + + x = rng.randn(n, 2) + u = ot.utils.unif(n) + zero = np.zeros(n) + M = ot.dist(x, x) + + G_sgd = ot.stochastic.solve_dual_entropic(u, u, M, reg, batch_size, + numItermax=nb_iter) + + G_sinkhorn = ot.sinkhorn(u, u, M, reg) + + # check constratints + np.testing.assert_allclose( + zero, (G_sgd - G_sinkhorn).sum(1), atol=1e-02) # cf convergence sgd + np.testing.assert_allclose( + zero, (G_sgd - G_sinkhorn).sum(0), atol=1e-02) # cf convergence sgd + np.testing.assert_allclose( + G_sgd, G_sinkhorn, atol=1e-02) # cf convergence sgd + + +if __name__ == '__main__': + test_stochastic_dual_sgd() + test_dual_sgd_sinkhorn() -- cgit v1.2.3 From e068b58ba4234792d96287afd34c3cddef544dd4 Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Mon, 18 Jun 2018 18:05:48 -0700 Subject: pep8 --- examples/plot_stochastic.py | 6 +++--- ot/stochastic.py | 33 +++++++++++++++------------------ test/test_stochastic.py | 4 ++-- 3 files changed, 20 insertions(+), 23 deletions(-) diff --git a/examples/plot_stochastic.py b/examples/plot_stochastic.py index 3fc1955..e139473 100644 --- a/examples/plot_stochastic.py +++ b/examples/plot_stochastic.py @@ -53,7 +53,7 @@ M = ot.dist(X_source, Y_target) method = "SAG" sag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, - numItermax, lr) + numItermax, lr) print(sag_pi) ############################################################################# @@ -91,7 +91,7 @@ M = ot.dist(X_source, Y_target) method = "ASGD" asgd_pi, log = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, - numItermax, lr, log) + numItermax, lr, log) print(log['alpha'], log['beta']) print(asgd_pi) @@ -174,7 +174,7 @@ M = ot.dist(X_source, Y_target) # Call ot.solve_dual_entropic and plot the results. sgd_dual_pi, log = ot.stochastic.solve_dual_entropic(a, b, M, reg, batch_size, - numItermax, lr, log) + numItermax, lr, log) print(log['alpha'], log['beta']) print(sgd_dual_pi) diff --git a/ot/stochastic.py b/ot/stochastic.py index 31c99be..98537d9 100644 --- a/ot/stochastic.py +++ b/ot/stochastic.py @@ -78,8 +78,8 @@ def coordinate_gradient(b, M, reg, beta, i): ''' r = M[i, :] - beta - exp_beta = np.exp(-r/reg) * b - khi = exp_beta/(np.sum(exp_beta)) + exp_beta = np.exp(-r / reg) * b + khi = exp_beta / (np.sum(exp_beta)) return b - khi @@ -164,7 +164,7 @@ def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=0.1): cur_coord_grad = a[i] * coordinate_gradient(b, M, reg, cur_beta, i) sum_stored_gradient += (cur_coord_grad - stored_gradient[i]) stored_gradient[i] = cur_coord_grad - cur_beta += lr * (1./n_source) * sum_stored_gradient + cur_beta += lr * (1. / n_source) * sum_stored_gradient return cur_beta @@ -246,8 +246,8 @@ def averaged_sgd_entropic_transport(b, M, reg, numItermax=300000, lr=1): k = cur_iter + 1 i = np.random.randint(n_source) cur_coord_grad = coordinate_gradient(b, M, reg, cur_beta, i) - cur_beta += (lr/np.sqrt(k)) * cur_coord_grad - ave_beta = (1./k) * cur_beta + (1 - 1./k) * ave_beta + cur_beta += (lr / np.sqrt(k)) * cur_coord_grad + ave_beta = (1. / k) * cur_beta + (1 - 1. / k) * ave_beta return ave_beta @@ -316,12 +316,11 @@ def c_transform_entropic(b, M, reg, beta): ''' n_source = np.shape(M)[0] - n_target = np.shape(M)[1] alpha = np.zeros(n_source) for i in range(n_source): r = M[i, :] - beta min_r = np.min(r) - exp_beta = np.exp(-(r - min_r)/reg) * b + exp_beta = np.exp(-(r - min_r) / reg) * b alpha[i] = min_r - reg * np.log(np.sum(exp_beta)) return alpha @@ -407,8 +406,6 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=0.1, Advances in Neural Information Processing Systems (2016), arXiv preprint arxiv:1605.08527. ''' - n_source = 7 - n_target = 4 if method.lower() == "sag": opt_beta = sag_entropic_transport(a, b, M, reg, numItermax, lr) elif method.lower() == "asgd": @@ -418,7 +415,7 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=0.1, return None opt_alpha = c_transform_entropic(b, M, reg, opt_beta) - pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :])/reg) * + pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :]) / reg) * a[:, None] * b[None, :]) if log: @@ -511,8 +508,8 @@ def grad_dF_dalpha(M, reg, alpha, beta, batch_size, batch_alpha, batch_beta): grad_alpha = np.zeros(batch_size) grad_alpha[:] = batch_size for j in batch_beta: - grad_alpha -= np.exp((alpha[batch_alpha] + beta[j] - - M[batch_alpha, j])/reg) + grad_alpha -= np.exp((alpha[batch_alpha] + beta[j] - + M[batch_alpha, j]) / reg) return grad_alpha @@ -594,7 +591,7 @@ def grad_dF_dbeta(M, reg, alpha, beta, batch_size, batch_alpha, batch_beta): grad_beta[:] = batch_size for i in batch_alpha: grad_beta -= np.exp((alpha[i] + - beta[batch_beta] - M[i, batch_beta])/reg) + beta[batch_beta] - M[i, batch_beta]) / reg) return grad_beta @@ -681,10 +678,10 @@ def sgd_entropic_regularization(M, reg, batch_size, numItermax, lr, batch_beta = np.random.choice(n_target, batch_size, replace=False) grad_F_alpha = grad_dF_dalpha(M, reg, cur_alpha, cur_beta, batch_size, batch_alpha, batch_beta) - cur_alpha[batch_alpha] += (lr/k) * grad_F_alpha + cur_alpha[batch_alpha] += (lr / k) * grad_F_alpha grad_F_beta = grad_dF_dbeta(M, reg, cur_alpha, cur_beta, batch_size, batch_alpha, batch_beta) - cur_beta[batch_beta] += (lr/k) * grad_F_beta + cur_beta[batch_beta] += (lr / k) * grad_F_beta else: for cur_iter in range(numItermax): @@ -695,8 +692,8 @@ def sgd_entropic_regularization(M, reg, batch_size, numItermax, lr, batch_size, batch_alpha, batch_beta) grad_F_beta = grad_dF_dbeta(M, reg, cur_alpha, cur_beta, batch_size, batch_alpha, batch_beta) - cur_alpha[batch_alpha] += (lr/k) * grad_F_alpha - cur_beta[batch_beta] += (lr/k) * grad_F_beta + cur_alpha[batch_alpha] += (lr / k) * grad_F_alpha + cur_beta[batch_beta] += (lr / k) * grad_F_beta return cur_alpha, cur_beta @@ -779,7 +776,7 @@ def solve_dual_entropic(a, b, M, reg, batch_size, numItermax=10000, lr=1, opt_alpha, opt_beta = sgd_entropic_regularization(M, reg, batch_size, numItermax, lr) - pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :])/reg) * + pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :]) / reg) * a[:, None] * b[None, :]) if log: log = {} diff --git a/test/test_stochastic.py b/test/test_stochastic.py index bc0cebb..5824df1 100644 --- a/test/test_stochastic.py +++ b/test/test_stochastic.py @@ -149,7 +149,7 @@ def test_stochastic_dual_sgd(): M = ot.dist(x, x) G = ot.stochastic.solve_dual_entropic(u, u, M, reg, batch_size, - numItermax=numItermax) + numItermax=numItermax) # check constratints np.testing.assert_allclose( @@ -180,7 +180,7 @@ def test_dual_sgd_sinkhorn(): M = ot.dist(x, x) G_sgd = ot.stochastic.solve_dual_entropic(u, u, M, reg, batch_size, - numItermax=nb_iter) + numItermax=nb_iter) G_sinkhorn = ot.sinkhorn(u, u, M, reg) -- cgit v1.2.3 From 52134e92e427c825b4aa0a24ac5e000232ebd707 Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Tue, 19 Jun 2018 11:31:22 -0700 Subject: change grad function names --- docs/source/all.rst | 8 ++++++++ ot/stochastic.py | 37 ++++++++++++++++++++++--------------- 2 files changed, 30 insertions(+), 15 deletions(-) diff --git a/docs/source/all.rst b/docs/source/all.rst index c84d968..29d47c2 100644 --- a/docs/source/all.rst +++ b/docs/source/all.rst @@ -33,6 +33,14 @@ ot.optim .. automodule:: ot.optim :members: + + ot.stochastic + -------- + + .. automodule:: ot.stochastic + :members: + + ot.da -------- diff --git a/ot/stochastic.py b/ot/stochastic.py index 98537d9..d3830d6 100644 --- a/ot/stochastic.py +++ b/ot/stochastic.py @@ -6,9 +6,9 @@ import numpy as np ############################################################################## -# Optimization toolbox for SEMI - DUAL problem +# Optimization toolbox for SEMI - DUAL problems ############################################################################## -def coordinate_gradient(b, M, reg, beta, i): +def coordinate_grad_semi_dual(b, M, reg, beta, i): ''' Compute the coordinate gradient update for regularized discrete distributions for (i, :) @@ -161,7 +161,8 @@ def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=0.1): sum_stored_gradient = np.zeros(n_target) for _ in range(numItermax): i = np.random.randint(n_source) - cur_coord_grad = a[i] * coordinate_gradient(b, M, reg, cur_beta, i) + cur_coord_grad = a[i] * coordinate_grad_semi_dual(b, M, reg, + cur_beta, i) sum_stored_gradient += (cur_coord_grad - stored_gradient[i]) stored_gradient[i] = cur_coord_grad cur_beta += lr * (1. / n_source) * sum_stored_gradient @@ -245,7 +246,7 @@ def averaged_sgd_entropic_transport(b, M, reg, numItermax=300000, lr=1): for cur_iter in range(numItermax): k = cur_iter + 1 i = np.random.randint(n_source) - cur_coord_grad = coordinate_gradient(b, M, reg, cur_beta, i) + cur_coord_grad = coordinate_grad_semi_dual(b, M, reg, cur_beta, i) cur_beta += (lr / np.sqrt(k)) * cur_coord_grad ave_beta = (1. / k) * cur_beta + (1 - 1. / k) * ave_beta return ave_beta @@ -428,11 +429,12 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=0.1, ############################################################################## -# Optimization toolbox for DUAL problem +# Optimization toolbox for DUAL problems ############################################################################## -def grad_dF_dalpha(M, reg, alpha, beta, batch_size, batch_alpha, batch_beta): +def batch_grad_dual_alpha(M, reg, alpha, beta, batch_size, batch_alpha, + batch_beta): ''' Computes the partial gradient of F_\W_varepsilon @@ -513,7 +515,8 @@ def grad_dF_dalpha(M, reg, alpha, beta, batch_size, batch_alpha, batch_beta): return grad_alpha -def grad_dF_dbeta(M, reg, alpha, beta, batch_size, batch_alpha, batch_beta): +def batch_grad_dual_beta(M, reg, alpha, beta, batch_size, batch_alpha, + batch_beta): ''' Computes the partial gradient of F_\W_varepsilon @@ -676,11 +679,13 @@ def sgd_entropic_regularization(M, reg, batch_size, numItermax, lr, k = np.sqrt(cur_iter + 1) batch_alpha = np.random.choice(n_source, batch_size, replace=False) batch_beta = np.random.choice(n_target, batch_size, replace=False) - grad_F_alpha = grad_dF_dalpha(M, reg, cur_alpha, cur_beta, - batch_size, batch_alpha, batch_beta) + grad_F_alpha = batch_grad_dual_alpha(M, reg, cur_alpha, cur_beta, + batch_size, batch_alpha, + batch_beta) cur_alpha[batch_alpha] += (lr / k) * grad_F_alpha - grad_F_beta = grad_dF_dbeta(M, reg, cur_alpha, cur_beta, - batch_size, batch_alpha, batch_beta) + grad_F_beta = batch_grad_dual_beta(M, reg, cur_alpha, cur_beta, + batch_size, batch_alpha, + batch_beta) cur_beta[batch_beta] += (lr / k) * grad_F_beta else: @@ -688,10 +693,12 @@ def sgd_entropic_regularization(M, reg, batch_size, numItermax, lr, k = np.sqrt(cur_iter + 1) batch_alpha = np.random.choice(n_source, batch_size, replace=False) batch_beta = np.random.choice(n_target, batch_size, replace=False) - grad_F_alpha = grad_dF_dalpha(M, reg, cur_alpha, cur_beta, - batch_size, batch_alpha, batch_beta) - grad_F_beta = grad_dF_dbeta(M, reg, cur_alpha, cur_beta, - batch_size, batch_alpha, batch_beta) + grad_F_alpha = batch_grad_dual_alpha(M, reg, cur_alpha, cur_beta, + batch_size, batch_alpha, + batch_beta) + grad_F_beta = batch_grad_dual_beta(M, reg, cur_alpha, cur_beta, + batch_size, batch_alpha, + batch_beta) cur_alpha[batch_alpha] += (lr / k) * grad_F_alpha cur_beta[batch_beta] += (lr / k) * grad_F_beta -- cgit v1.2.3 From 7073e417bed151976c62fe20d1ba69abd30e7758 Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Tue, 19 Jun 2018 11:52:10 -0700 Subject: remove if in test and cleaned code --- examples/plot_stochastic.py | 1 + ot/stochastic.py | 2 ++ test/test_stochastic.py | 10 +++------- 3 files changed, 6 insertions(+), 7 deletions(-) diff --git a/examples/plot_stochastic.py b/examples/plot_stochastic.py index e139473..09b95d0 100644 --- a/examples/plot_stochastic.py +++ b/examples/plot_stochastic.py @@ -136,6 +136,7 @@ pl.figure(4, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn') pl.show() + ############################################################################# # COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM ############################################################################# diff --git a/ot/stochastic.py b/ot/stochastic.py index d3830d6..57b96b7 100644 --- a/ot/stochastic.py +++ b/ot/stochastic.py @@ -8,6 +8,8 @@ import numpy as np ############################################################################## # Optimization toolbox for SEMI - DUAL problems ############################################################################## + + def coordinate_grad_semi_dual(b, M, reg, beta, i): ''' Compute the coordinate gradient update for regularized discrete diff --git a/test/test_stochastic.py b/test/test_stochastic.py index 5824df1..3cb51ff 100644 --- a/test/test_stochastic.py +++ b/test/test_stochastic.py @@ -57,6 +57,7 @@ def test_stochastic_sag(): # 2 identical discrete measures u defined on the same space with a # regularization term, a learning rate and a number of iteration + def test_stochastic_asgd(): # test asgd n = 15 @@ -134,9 +135,9 @@ def test_sag_asgd_sinkhorn(): # 2 identical discrete measures u defined on the same space with a # regularization term, a batch_size and a number of iteration + def test_stochastic_dual_sgd(): # test sgd - print("SGD") n = 10 reg = 1 numItermax = 300000 @@ -157,6 +158,7 @@ def test_stochastic_dual_sgd(): np.testing.assert_allclose( u, G.sum(0), atol=1e-02) # cf convergence sgd + ############################################################################# # # TEST Convergence SGD toward Sinkhorn's solution @@ -167,7 +169,6 @@ def test_stochastic_dual_sgd(): def test_dual_sgd_sinkhorn(): # test all dual algorithms - print("SGD vs Sinkhorn") n = 10 reg = 1 nb_iter = 300000 @@ -191,8 +192,3 @@ def test_dual_sgd_sinkhorn(): zero, (G_sgd - G_sinkhorn).sum(0), atol=1e-02) # cf convergence sgd np.testing.assert_allclose( G_sgd, G_sinkhorn, atol=1e-02) # cf convergence sgd - - -if __name__ == '__main__': - test_stochastic_dual_sgd() - test_dual_sgd_sinkhorn() -- cgit v1.2.3 From 6777ffd5c8457faac4467e58ba9edbcf2f86961b Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Thu, 21 Jun 2018 17:18:26 -0700 Subject: gave better step size ASGD & SAG --- examples/plot_stochastic.py | 10 ++++------ ot/stochastic.py | 37 ++++++++++++++++--------------------- test/test_stochastic.py | 7 ++----- 3 files changed, 22 insertions(+), 32 deletions(-) diff --git a/examples/plot_stochastic.py b/examples/plot_stochastic.py index 09b95d0..6274b4c 100644 --- a/examples/plot_stochastic.py +++ b/examples/plot_stochastic.py @@ -32,8 +32,7 @@ print("------------SEMI-DUAL PROBLEM------------") n_source = 7 n_target = 4 reg = 1 -numItermax = 10000 -lr = 0.1 +numItermax = 1000 a = ot.utils.unif(n_source) b = ot.utils.unif(n_target) @@ -53,7 +52,7 @@ M = ot.dist(X_source, Y_target) method = "SAG" sag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, - numItermax, lr) + numItermax) print(sag_pi) ############################################################################# @@ -68,8 +67,7 @@ print(sag_pi) n_source = 7 n_target = 4 reg = 1 -numItermax = 100000 -lr = 1 +numItermax = 1000 log = True a = ot.utils.unif(n_source) @@ -91,7 +89,7 @@ M = ot.dist(X_source, Y_target) method = "ASGD" asgd_pi, log = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, - numItermax, lr, log) + numItermax, log) print(log['alpha'], log['beta']) print(asgd_pi) diff --git a/ot/stochastic.py b/ot/stochastic.py index 57b96b7..ab88cd0 100644 --- a/ot/stochastic.py +++ b/ot/stochastic.py @@ -56,7 +56,6 @@ def coordinate_grad_semi_dual(b, M, reg, beta, i): >>> n_target = 4 >>> reg = 1 >>> numItermax = 300000 - >>> lr = 1 >>> a = ot.utils.unif(n_source) >>> b = ot.utils.unif(n_target) >>> rng = np.random.RandomState(0) @@ -65,8 +64,7 @@ def coordinate_grad_semi_dual(b, M, reg, beta, i): >>> M = ot.dist(X_source, Y_target) >>> method = "ASGD" >>> asgd_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, - method, numItermax, - lr) + method, numItermax) >>> print(asgd_pi) References @@ -85,7 +83,7 @@ def coordinate_grad_semi_dual(b, M, reg, beta, i): return b - khi -def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=0.1): +def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=None): ''' Compute the SAG algorithm to solve the regularized discrete measures optimal transport max problem @@ -134,17 +132,15 @@ def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=0.1): >>> n_target = 4 >>> reg = 1 >>> numItermax = 300000 - >>> lr = 1 >>> a = ot.utils.unif(n_source) >>> b = ot.utils.unif(n_target) >>> rng = np.random.RandomState(0) >>> X_source = rng.randn(n_source, 2) >>> Y_target = rng.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) - >>> method = "SAG" - >>> sag_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, - method, numItermax, - lr) + >>> method = "ASGD" + >>> asgd_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, + method, numItermax) >>> print(asgd_pi) References @@ -156,6 +152,8 @@ def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=0.1): arXiv preprint arxiv:1605.08527. ''' + if lr is None: + lr = 1. / max(a) n_source = np.shape(M)[0] n_target = np.shape(M)[1] cur_beta = np.zeros(n_target) @@ -171,7 +169,7 @@ def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=0.1): return cur_beta -def averaged_sgd_entropic_transport(b, M, reg, numItermax=300000, lr=1): +def averaged_sgd_entropic_transport(a, b, M, reg, numItermax=300000, lr=None): ''' Compute the ASGD algorithm to solve the regularized semi contibous measures optimal transport max problem @@ -219,7 +217,6 @@ def averaged_sgd_entropic_transport(b, M, reg, numItermax=300000, lr=1): >>> n_target = 4 >>> reg = 1 >>> numItermax = 300000 - >>> lr = 1 >>> a = ot.utils.unif(n_source) >>> b = ot.utils.unif(n_target) >>> rng = np.random.RandomState(0) @@ -228,8 +225,7 @@ def averaged_sgd_entropic_transport(b, M, reg, numItermax=300000, lr=1): >>> M = ot.dist(X_source, Y_target) >>> method = "ASGD" >>> asgd_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, - method, numItermax, - lr) + method, numItermax) >>> print(asgd_pi) References @@ -241,6 +237,8 @@ def averaged_sgd_entropic_transport(b, M, reg, numItermax=300000, lr=1): arXiv preprint arxiv:1605.08527. ''' + if lr is None: + lr = 1. / max(a) n_source = np.shape(M)[0] n_target = np.shape(M)[1] cur_beta = np.zeros(n_target) @@ -296,7 +294,6 @@ def c_transform_entropic(b, M, reg, beta): >>> n_target = 4 >>> reg = 1 >>> numItermax = 300000 - >>> lr = 1 >>> a = ot.utils.unif(n_source) >>> b = ot.utils.unif(n_target) >>> rng = np.random.RandomState(0) @@ -305,8 +302,7 @@ def c_transform_entropic(b, M, reg, beta): >>> M = ot.dist(X_source, Y_target) >>> method = "ASGD" >>> asgd_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, - method, numItermax, - lr) + method, numItermax) >>> print(asgd_pi) References @@ -328,7 +324,7 @@ def c_transform_entropic(b, M, reg, beta): return alpha -def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=0.1, +def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None, log=False): ''' Compute the transportation matrix to solve the regularized discrete @@ -388,7 +384,6 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=0.1, >>> n_target = 4 >>> reg = 1 >>> numItermax = 300000 - >>> lr = 1 >>> a = ot.utils.unif(n_source) >>> b = ot.utils.unif(n_target) >>> rng = np.random.RandomState(0) @@ -397,8 +392,7 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=0.1, >>> M = ot.dist(X_source, Y_target) >>> method = "ASGD" >>> asgd_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, - method, numItermax, - lr) + method, numItermax) >>> print(asgd_pi) References @@ -409,10 +403,11 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=0.1, Advances in Neural Information Processing Systems (2016), arXiv preprint arxiv:1605.08527. ''' + if method.lower() == "sag": opt_beta = sag_entropic_transport(a, b, M, reg, numItermax, lr) elif method.lower() == "asgd": - opt_beta = averaged_sgd_entropic_transport(b, M, reg, numItermax, lr) + opt_beta = averaged_sgd_entropic_transport(a, b, M, reg, numItermax, lr) else: print("Please, select your method between SAG and ASGD") return None diff --git a/test/test_stochastic.py b/test/test_stochastic.py index 3cb51ff..f315c88 100644 --- a/test/test_stochastic.py +++ b/test/test_stochastic.py @@ -63,7 +63,6 @@ def test_stochastic_asgd(): n = 15 reg = 1 numItermax = 300000 - lr = 1 rng = np.random.RandomState(0) x = rng.randn(n, 2) @@ -72,8 +71,7 @@ def test_stochastic_asgd(): M = ot.dist(x, x) G = ot.stochastic.solve_semi_dual_entropic(u, u, M, reg, "asgd", - numItermax=numItermax, - lr=lr) + numItermax=numItermax) # check constratints np.testing.assert_allclose( @@ -95,7 +93,6 @@ def test_sag_asgd_sinkhorn(): n = 15 reg = 1 nb_iter = 300000 - lr = 1 rng = np.random.RandomState(0) x = rng.randn(n, 2) @@ -104,7 +101,7 @@ def test_sag_asgd_sinkhorn(): M = ot.dist(x, x) G_asgd = ot.stochastic.solve_semi_dual_entropic(u, u, M, reg, "asgd", - numItermax=nb_iter, lr=lr) + numItermax=nb_iter) G_sag = ot.stochastic.solve_semi_dual_entropic(u, u, M, reg, "sag", numItermax=nb_iter) G_sinkhorn = ot.sinkhorn(u, u, M, reg) -- cgit v1.2.3 From af5f726f6adb52457ca6730ffb85f2ab486b2ada Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Thu, 21 Jun 2018 17:41:25 -0700 Subject: fixed bug --- ot/stochastic.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ot/stochastic.py b/ot/stochastic.py index ab88cd0..374d1a5 100644 --- a/ot/stochastic.py +++ b/ot/stochastic.py @@ -153,7 +153,7 @@ def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=None): ''' if lr is None: - lr = 1. / max(a) + lr = 1. / max(a/reg) n_source = np.shape(M)[0] n_target = np.shape(M)[1] cur_beta = np.zeros(n_target) @@ -238,7 +238,7 @@ def averaged_sgd_entropic_transport(a, b, M, reg, numItermax=300000, lr=None): ''' if lr is None: - lr = 1. / max(a) + lr = 1. / max(a/reg) n_source = np.shape(M)[0] n_target = np.shape(M)[1] cur_beta = np.zeros(n_target) -- cgit v1.2.3 From e8cf3cc343c934b7c49d303186cdf226204813b3 Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Thu, 21 Jun 2018 17:52:05 -0700 Subject: pep8 --- ot/stochastic.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ot/stochastic.py b/ot/stochastic.py index 374d1a5..f4d4c7d 100644 --- a/ot/stochastic.py +++ b/ot/stochastic.py @@ -153,7 +153,7 @@ def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=None): ''' if lr is None: - lr = 1. / max(a/reg) + lr = 1. / max(a / reg) n_source = np.shape(M)[0] n_target = np.shape(M)[1] cur_beta = np.zeros(n_target) @@ -238,7 +238,7 @@ def averaged_sgd_entropic_transport(a, b, M, reg, numItermax=300000, lr=None): ''' if lr is None: - lr = 1. / max(a/reg) + lr = 1. / max(a / reg) n_source = np.shape(M)[0] n_target = np.shape(M)[1] cur_beta = np.zeros(n_target) -- cgit v1.2.3 From 9fecd51c583704e02d3faaef722f0dee52f42359 Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Mon, 25 Jun 2018 11:03:44 -0700 Subject: fix math operator and log bugs --- examples/plot_stochastic.py | 18 ++++++++++-------- ot/stochastic.py | 15 ++++++++++++--- 2 files changed, 22 insertions(+), 11 deletions(-) diff --git a/examples/plot_stochastic.py b/examples/plot_stochastic.py index 6274b4c..b9375d4 100644 --- a/examples/plot_stochastic.py +++ b/examples/plot_stochastic.py @@ -15,6 +15,7 @@ algorithms for descrete and semicontinous measures from the POT library. import matplotlib.pylab as pl import numpy as np import ot +import ot.plot ############################################################################# @@ -88,9 +89,9 @@ M = ot.dist(X_source, Y_target) # results. method = "ASGD" -asgd_pi, log = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, - numItermax, log) -print(log['alpha'], log['beta']) +asgd_pi, log_asgd = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, + numItermax, log=log) +print(log_asgd['alpha'], log_asgd['beta']) print(asgd_pi) ############################################################################# @@ -166,15 +167,16 @@ M = ot.dist(X_source, Y_target) ############################################################################# # -# Call the "SGD" dual method to find the transportation matrix in the semicontinous -# case +# Call the "SGD" dual method to find the transportation matrix in the +# semicontinous case # --------------------------------------------- # # Call ot.solve_dual_entropic and plot the results. -sgd_dual_pi, log = ot.stochastic.solve_dual_entropic(a, b, M, reg, batch_size, - numItermax, lr, log) -print(log['alpha'], log['beta']) +sgd_dual_pi, log_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg, + batch_size, numItermax, + lr, log=log) +print(log_sgd['alpha'], log_sgd['beta']) print(sgd_dual_pi) ############################################################################# diff --git a/ot/stochastic.py b/ot/stochastic.py index f4d4c7d..5e8206e 100644 --- a/ot/stochastic.py +++ b/ot/stochastic.py @@ -16,8 +16,9 @@ def coordinate_grad_semi_dual(b, M, reg, beta, i): distributions for (i, :) The function computes the gradient of the semi dual problem: + .. math:: - \W_varepsilon(a, b) = \max_\v \sum_i (\sum_j v_j * b_j + \W_\varepsilon(a, b) = \max_\v \sum_i (\sum_j v_j * b_j - \reg log(\sum_j exp((v_j - M_{i,j})/reg) * b_j)) * a_i where : @@ -89,6 +90,7 @@ def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=None): optimal transport max problem The function solves the following optimization problem: + .. math:: \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) s.t. \gamma 1 = a @@ -175,6 +177,7 @@ def averaged_sgd_entropic_transport(a, b, M, reg, numItermax=300000, lr=None): optimal transport max problem The function solves the following optimization problem: + .. math:: \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) s.t. \gamma 1 = a @@ -258,6 +261,7 @@ def c_transform_entropic(b, M, reg, beta): The function computes the c_transform of a dual variable from the other dual variable: + .. math:: u = v^{c,reg} = -reg \sum_j exp((v - M)/reg) b_j @@ -331,6 +335,7 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None, measures optimal transport max problem The function solves the following optimization problem: + .. math:: \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) s.t. \gamma 1 = a @@ -436,7 +441,8 @@ def batch_grad_dual_alpha(M, reg, alpha, beta, batch_size, batch_alpha, Computes the partial gradient of F_\W_varepsilon Compute the partial gradient of the dual problem: - ..Math: + + ..math: \forall i in batch_alpha, grad_alpha_i = 1 * batch_size - sum_{j in batch_beta} exp((alpha_i + beta_j - M_{i,j})/reg) @@ -518,7 +524,8 @@ def batch_grad_dual_beta(M, reg, alpha, beta, batch_size, batch_alpha, Computes the partial gradient of F_\W_varepsilon Compute the partial gradient of the dual problem: - ..Math: + + ..math: \forall j in batch_beta, grad_beta_j = 1 * batch_size - sum_{i in batch_alpha} exp((alpha_i + beta_j - M_{i,j})/reg) @@ -602,6 +609,7 @@ def sgd_entropic_regularization(M, reg, batch_size, numItermax, lr, optimal transport dual problem The function solves the following optimization problem: + .. math:: \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) s.t. \gamma 1 = a @@ -709,6 +717,7 @@ def solve_dual_entropic(a, b, M, reg, batch_size, numItermax=10000, lr=1, optimal transport dual problem The function solves the following optimization problem: + .. math:: \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) s.t. \gamma 1 = a -- cgit v1.2.3 From 968ad58ecae5f26d5f3d6cfc410ccc93de96f2c0 Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Tue, 26 Jun 2018 11:00:51 -0700 Subject: add stochastic to docs --- docs/source/all.rst | 19 +++++++++++-------- 1 file changed, 11 insertions(+), 8 deletions(-) diff --git a/docs/source/all.rst b/docs/source/all.rst index 29d47c2..0194098 100644 --- a/docs/source/all.rst +++ b/docs/source/all.rst @@ -20,6 +20,11 @@ ot.bregman .. automodule:: ot.bregman :members: +ot.smooth +----- +.. automodule:: ot.smooth + :members: + ot.gromov ---------- @@ -33,14 +38,6 @@ ot.optim .. automodule:: ot.optim :members: - - ot.stochastic - -------- - - .. automodule:: ot.stochastic - :members: - - ot.da -------- @@ -71,3 +68,9 @@ ot.plot .. automodule:: ot.plot :members: + + ot.stochastic + ------- + + .. automodule:: ot.stochastic + :members: -- cgit v1.2.3 From 208ff4627ba28aa3f35328c5928324019c23ddac Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Tue, 26 Jun 2018 11:09:41 -0700 Subject: fix stochastic to doc --- docs/source/all.rst | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/docs/source/all.rst b/docs/source/all.rst index 0194098..94da2ed 100644 --- a/docs/source/all.rst +++ b/docs/source/all.rst @@ -69,8 +69,8 @@ ot.plot .. automodule:: ot.plot :members: - ot.stochastic - ------- +ot.stochastic +------------- - .. automodule:: ot.stochastic - :members: +.. automodule:: ot.stochastic + :members: -- cgit v1.2.3 From 6492e95e7a3acd8e35844a0f974dc79d3e7aa349 Mon Sep 17 00:00:00 2001 From: vivienseguy Date: Thu, 5 Jul 2018 15:42:21 +0900 Subject: free support barycenter --- ot/lp/cvx.py | 80 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 80 insertions(+) diff --git a/ot/lp/cvx.py b/ot/lp/cvx.py index c8c75bc..3913ae5 100644 --- a/ot/lp/cvx.py +++ b/ot/lp/cvx.py @@ -10,6 +10,7 @@ LP solvers for optimal transport using cvxopt import numpy as np import scipy as sp import scipy.sparse as sps +import ot try: import cvxopt @@ -144,3 +145,82 @@ def barycenter(A, M, weights=None, verbose=False, log=False, solver='interior-po return b, sol else: return b + + + + +def free_support_barycenter(data_positions, data_weights, X_init, b_init, lamda, numItermax=100, stopThr=1e-5, verbose=False, log=False, **kwargs): + + """ + Solves the free support (locations of the barycenters are optimized, not the weights) Wasserstein barycenter problem (i.e. the weighted Frechet mean for the 2-Wasserstein distance) + + The function solves the Wasserstein barycenter problem when the barycenter measure is constrained to be supported on k atoms. + This problem is considered in [1] (Algorithm 2). There are two differences with the following codes: + - we do not optimize over the weights + - we do not do line search for the locations updates, we use i.e. theta = 1 in [1] (Algorithm 2). This can be seen as a discrete implementation of the fixed-point algorithm of [2] proposed in the continuous setting. + + Parameters + ---------- + data_positions : list of (k_i,d) np.ndarray + The discrete support of a measure supported on k_i locations of a d-dimensional space (k_i can be different for each element of the list) + data_weights : list of (k_i,) np.ndarray + Numpy arrays where each numpy array has k_i non-negatives values summing to one representing the weights of each discrete input measure + + X_init : (k,d) np.ndarray + Initialization of the support locations (on k atoms) of the barycenter + b_init : (k,) np.ndarray + Initialization of the weights of the barycenter (non-negatives, sum to 1) + lambda : (k,) np.ndarray + Initialization of the coefficients of the barycenter (non-negatives, sum to 1) + + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + Returns + ------- + X : (k,d) np.ndarray + Support locations (on k atoms) of the barycenter + + References + ---------- + + .. [1] Cuturi, Marco, and Arnaud Doucet. "Fast computation of Wasserstein barycenters." International Conference on Machine Learning. 2014. + + .. [2] Álvarez-Esteban, Pedro C., et al. "A fixed-point approach to barycenters in Wasserstein space." Journal of Mathematical Analysis and Applications 441.2 (2016): 744-762. + + """ + + iter_count = 0 + + d = X_init.shape[1] + k = b_init.size + N = len(data_positions) + + X = X_init + + displacement_square_norm = 1e3 + + while ( displacement_square_norm > stopThr and iter_count < numItermax ): + + T_sum = np.zeros((k, d)) + + for (data_positions_i, data_weights_i) in zip(data_positions, data_weights): + M_i = ot.dist(X, data_positions_i) + T_i = ot.emd(b_init, data_weights_i, M_i) + T_sum += np.reshape(1. / b_init, (-1, 1)) * np.matmul(T_i, data_positions_i) + + X_previous = X + X = T_sum / N + + displacement_square_norm = np.sum(np.square(X-X_previous)) + + iter_count += 1 + + return X + -- cgit v1.2.3 From 3f23fa1a950ffde4a4224a6343a504a0c5b7851b Mon Sep 17 00:00:00 2001 From: vivienseguy Date: Thu, 5 Jul 2018 17:35:27 +0900 Subject: free support barycenter --- examples/plot_free_support_barycenter.py | 66 ++++++++++++++++++++++++++++++++ ot/lp/cvx.py | 29 ++++++++------ 2 files changed, 83 insertions(+), 12 deletions(-) create mode 100644 examples/plot_free_support_barycenter.py diff --git a/examples/plot_free_support_barycenter.py b/examples/plot_free_support_barycenter.py new file mode 100644 index 0000000..a733745 --- /dev/null +++ b/examples/plot_free_support_barycenter.py @@ -0,0 +1,66 @@ +# -*- coding: utf-8 -*- +""" +==================================================== +2D Wasserstein barycenters between empirical distributions +==================================================== + +Illustration of 2D Wasserstein barycenters between discributions that are weighted +sum of diracs. + +""" + +# Author: Vivien Seguy +# +# License: MIT License + +import numpy as np +import matplotlib.pylab as pl +import ot.plot + + +############################################################################## +# Generate data +# ------------- +#%% parameters and data generation +N = 4 +d = 2 +measures_locations = [] +measures_weights = [] + +for i in range(N): + + n = np.rand.int(low=1, high=20) # nb samples + + mu = np.random.normal(0., 1., (d,)) + cov = np.random.normal(0., 1., (d,d)) + + xs = ot.datasets.make_2D_samples_gauss(n, mu, cov) + b = np.random.uniform(0., 1., n) + b = b/np.sum(b) + + measures_locations.append(xs) + measures_weights.append(b) + +k = 10 +X_init = np.random.normal(0., 1., (k,d)) +b_init = np.ones((k,)) / k + + +############################################################################## +# Compute free support barycenter +# ------------- +X = ot.lp.barycenter(measures_locations, measures_weights, X_init, b_init) + + +############################################################################## +# Plot data +# --------- + +#%% plot samples + +pl.figure(1) +for (xs, b) in zip(measures_locations, measures_weights): + pl.scatter(xs[:, 0], xs[:, 1], s=b, c=np.tile(np.rand(0. ,255., size=(3,)), (1,b.size(0))) , label='Data measures') +pl.scatter(xs[:, 0], xs[:, 1], s=b, c='black' , label='2-Wasserstein barycenter') +pl.legend(loc=0) +pl.title('Data measures and their barycenter') diff --git a/ot/lp/cvx.py b/ot/lp/cvx.py index 3913ae5..91a5922 100644 --- a/ot/lp/cvx.py +++ b/ot/lp/cvx.py @@ -27,7 +27,7 @@ def scipy_sparse_to_spmatrix(A): def barycenter(A, M, weights=None, verbose=False, log=False, solver='interior-point'): - """Compute the entropic regularized wasserstein barycenter of distributions A + """Compute the Wasserstein barycenter of distributions A The function solves the following optimization problem [16]: @@ -149,7 +149,7 @@ def barycenter(A, M, weights=None, verbose=False, log=False, solver='interior-po -def free_support_barycenter(data_positions, data_weights, X_init, b_init, lamda, numItermax=100, stopThr=1e-5, verbose=False, log=False, **kwargs): +def free_support_barycenter(measures_locations, measures_weights, X_init, b_init, weights=None, numItermax=100, stopThr=1e-6, verbose=False): """ Solves the free support (locations of the barycenters are optimized, not the weights) Wasserstein barycenter problem (i.e. the weighted Frechet mean for the 2-Wasserstein distance) @@ -170,7 +170,7 @@ def free_support_barycenter(data_positions, data_weights, X_init, b_init, lamda, Initialization of the support locations (on k atoms) of the barycenter b_init : (k,) np.ndarray Initialization of the weights of the barycenter (non-negatives, sum to 1) - lambda : (k,) np.ndarray + weights : (k,) np.ndarray Initialization of the coefficients of the barycenter (non-negatives, sum to 1) numItermax : int, optional @@ -200,25 +200,30 @@ def free_support_barycenter(data_positions, data_weights, X_init, b_init, lamda, d = X_init.shape[1] k = b_init.size - N = len(data_positions) + N = len(measures_locations) + + if not weights: + weights = np.ones((N,))/N X = X_init - displacement_square_norm = 1e3 + displacement_square_norm = stopThr+1. while ( displacement_square_norm > stopThr and iter_count < numItermax ): T_sum = np.zeros((k, d)) - for (data_positions_i, data_weights_i) in zip(data_positions, data_weights): - M_i = ot.dist(X, data_positions_i) - T_i = ot.emd(b_init, data_weights_i, M_i) - T_sum += np.reshape(1. / b_init, (-1, 1)) * np.matmul(T_i, data_positions_i) + for (measure_locations_i, measure_weights_i, weight_i) in zip(measures_locations, measures_weights, weights.tolist()): + + M_i = ot.dist(X, measure_locations_i) + T_i = ot.emd(b_init, measure_weights_i, M_i) + T_sum += np.reshape(1. / b_init, (-1, 1)) * np.matmul(T_i, measure_locations_i) - X_previous = X - X = T_sum / N + displacement_square_norm = np.sum(np.square(X-T_sum)) + X = T_sum - displacement_square_norm = np.sum(np.square(X-X_previous)) + if verbose: + print('iteration %d, displacement_square_norm=%f\n', iter_count, displacement_square_norm) iter_count += 1 -- cgit v1.2.3 From 98ce4ccd3536d95c609ee1c5b737ced85d68f786 Mon Sep 17 00:00:00 2001 From: vivienseguy Date: Thu, 5 Jul 2018 18:26:55 +0900 Subject: free support barycenter --- examples/plot_free_support_barycenter.py | 9 ++++----- ot/lp/cvx.py | 2 +- 2 files changed, 5 insertions(+), 6 deletions(-) diff --git a/examples/plot_free_support_barycenter.py b/examples/plot_free_support_barycenter.py index a733745..274cf76 100644 --- a/examples/plot_free_support_barycenter.py +++ b/examples/plot_free_support_barycenter.py @@ -17,7 +17,6 @@ import numpy as np import matplotlib.pylab as pl import ot.plot - ############################################################################## # Generate data # ------------- @@ -29,10 +28,10 @@ measures_weights = [] for i in range(N): - n = np.rand.int(low=1, high=20) # nb samples + n = np.random.randint(low=1, high=20) # nb samples mu = np.random.normal(0., 1., (d,)) - cov = np.random.normal(0., 1., (d,d)) + cov = np.random.uniform(0., 1., (d,d)) xs = ot.datasets.make_2D_samples_gauss(n, mu, cov) b = np.random.uniform(0., 1., n) @@ -49,7 +48,7 @@ b_init = np.ones((k,)) / k ############################################################################## # Compute free support barycenter # ------------- -X = ot.lp.barycenter(measures_locations, measures_weights, X_init, b_init) +X = ot.lp.cvx.free_support_barycenter(measures_locations, measures_weights, X_init, b_init) ############################################################################## @@ -60,7 +59,7 @@ X = ot.lp.barycenter(measures_locations, measures_weights, X_init, b_init) pl.figure(1) for (xs, b) in zip(measures_locations, measures_weights): - pl.scatter(xs[:, 0], xs[:, 1], s=b, c=np.tile(np.rand(0. ,255., size=(3,)), (1,b.size(0))) , label='Data measures') + pl.scatter(xs[:, 0], xs[:, 1], s=b, c=np.tile(np.random.uniform(0. ,255., size=(3,)), (1,b.size(0))) , label='Data measures') pl.scatter(xs[:, 0], xs[:, 1], s=b, c='black' , label='2-Wasserstein barycenter') pl.legend(loc=0) pl.title('Data measures and their barycenter') diff --git a/ot/lp/cvx.py b/ot/lp/cvx.py index 91a5922..b74960f 100644 --- a/ot/lp/cvx.py +++ b/ot/lp/cvx.py @@ -217,7 +217,7 @@ def free_support_barycenter(measures_locations, measures_weights, X_init, b_init M_i = ot.dist(X, measure_locations_i) T_i = ot.emd(b_init, measure_weights_i, M_i) - T_sum += np.reshape(1. / b_init, (-1, 1)) * np.matmul(T_i, measure_locations_i) + T_sum = T_sum + weight_i*np.reshape(1. / b_init, (-1, 1)) * np.matmul(T_i, measure_locations_i) displacement_square_norm = np.sum(np.square(X-T_sum)) X = T_sum -- cgit v1.2.3 From e39f04a9465bd9f1447423eb2a592cc9356589a9 Mon Sep 17 00:00:00 2001 From: Vivien Seguy Date: Thu, 5 Jul 2018 19:01:10 +0900 Subject: add free support barycenter algorithm --- examples/plot_free_support_barycenter.py | 18 +++++++++++------- 1 file changed, 11 insertions(+), 7 deletions(-) diff --git a/examples/plot_free_support_barycenter.py b/examples/plot_free_support_barycenter.py index 274cf76..61671cf 100644 --- a/examples/plot_free_support_barycenter.py +++ b/examples/plot_free_support_barycenter.py @@ -21,7 +21,7 @@ import ot.plot # Generate data # ------------- #%% parameters and data generation -N = 4 +N = 6 d = 2 measures_locations = [] measures_weights = [] @@ -30,11 +30,13 @@ for i in range(N): n = np.random.randint(low=1, high=20) # nb samples - mu = np.random.normal(0., 1., (d,)) - cov = np.random.uniform(0., 1., (d,d)) + mu = np.random.normal(0., 4., (d,)) + + A = np.random.rand(d, d) + cov = np.dot(A,A.transpose()) xs = ot.datasets.make_2D_samples_gauss(n, mu, cov) - b = np.random.uniform(0., 1., n) + b = np.random.uniform(0., 1., (n,)) b = b/np.sum(b) measures_locations.append(xs) @@ -59,7 +61,9 @@ X = ot.lp.cvx.free_support_barycenter(measures_locations, measures_weights, X_in pl.figure(1) for (xs, b) in zip(measures_locations, measures_weights): - pl.scatter(xs[:, 0], xs[:, 1], s=b, c=np.tile(np.random.uniform(0. ,255., size=(3,)), (1,b.size(0))) , label='Data measures') -pl.scatter(xs[:, 0], xs[:, 1], s=b, c='black' , label='2-Wasserstein barycenter') -pl.legend(loc=0) + color = np.random.randint(low=1, high=10*N) + pl.scatter(xs[:, 0], xs[:, 1], s=b*1000, label='input measure') +pl.scatter(X[:, 0], X[:, 1], s=b_init*1000, c='black' , marker='^', label='2-Wasserstein barycenter') pl.title('Data measures and their barycenter') +pl.legend(loc=0) +pl.show() \ No newline at end of file -- cgit v1.2.3 From 2c7b98009f33e278a2e7e95a035c6a6231bec44e Mon Sep 17 00:00:00 2001 From: Vivien Seguy Date: Fri, 6 Jul 2018 01:58:47 +0900 Subject: add free support barycenter algorithm --- README.md | 3 +++ examples/plot_free_support_barycenter.py | 35 ++++++++++++++++---------------- ot/lp/cvx.py | 22 ++++++++------------ 3 files changed, 30 insertions(+), 30 deletions(-) diff --git a/README.md b/README.md index 677a23b..dded582 100644 --- a/README.md +++ b/README.md @@ -17,6 +17,7 @@ It provides the following solvers: * Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10] with optional GPU implementation (requires cudamat). * Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17]. * Non regularized Wasserstein barycenters [16] with LP solver (only small scale). +* Non regularized free support Wasserstein barycenters [20]. * Bregman projections for Wasserstein barycenter [3] and unmixing [4]. * Optimal transport for domain adaptation with group lasso regularization [5] * Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. @@ -225,3 +226,5 @@ You can also post bug reports and feature requests in Github issues. Make sure t [18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016) [Stochastic Optimization for Large-scale Optimal Transport](arXiv preprint arxiv:1605.08527). Advances in Neural Information Processing Systems (2016). [19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, A.& Blondel, M. [Large-scale Optimal Transport and Mapping Estimation](https://arxiv.org/pdf/1711.02283.pdf). International Conference on Learning Representation (2018) + +[20] Cuturi, M. and Doucet, A. (2014) [Fast Computation of Wasserstein Barycenters](http://proceedings.mlr.press/v32/cuturi14.html). International Conference in Machine Learning \ No newline at end of file diff --git a/examples/plot_free_support_barycenter.py b/examples/plot_free_support_barycenter.py index 61671cf..42e22fc 100644 --- a/examples/plot_free_support_barycenter.py +++ b/examples/plot_free_support_barycenter.py @@ -21,7 +21,7 @@ import ot.plot # Generate data # ------------- #%% parameters and data generation -N = 6 +N = 3 d = 2 measures_locations = [] measures_weights = [] @@ -33,24 +33,25 @@ for i in range(N): mu = np.random.normal(0., 4., (d,)) A = np.random.rand(d, d) - cov = np.dot(A,A.transpose()) + cov = np.dot(A, A.transpose()) - xs = ot.datasets.make_2D_samples_gauss(n, mu, cov) - b = np.random.uniform(0., 1., (n,)) - b = b/np.sum(b) + x_i = ot.datasets.make_2D_samples_gauss(n, mu, cov) + b_i = np.random.uniform(0., 1., (n,)) + b_i = b_i / np.sum(b_i) - measures_locations.append(xs) - measures_weights.append(b) - -k = 10 -X_init = np.random.normal(0., 1., (k,d)) -b_init = np.ones((k,)) / k + measures_locations.append(x_i) + measures_weights.append(b_i) ############################################################################## # Compute free support barycenter # ------------- -X = ot.lp.cvx.free_support_barycenter(measures_locations, measures_weights, X_init, b_init) + +k = 10 +X_init = np.random.normal(0., 1., (k, d)) +b = np.ones((k,)) / k + +X = ot.lp.cvx.free_support_barycenter(measures_locations, measures_weights, X_init, b) ############################################################################## @@ -60,10 +61,10 @@ X = ot.lp.cvx.free_support_barycenter(measures_locations, measures_weights, X_in #%% plot samples pl.figure(1) -for (xs, b) in zip(measures_locations, measures_weights): - color = np.random.randint(low=1, high=10*N) - pl.scatter(xs[:, 0], xs[:, 1], s=b*1000, label='input measure') -pl.scatter(X[:, 0], X[:, 1], s=b_init*1000, c='black' , marker='^', label='2-Wasserstein barycenter') +for (x_i, b_i) in zip(measures_locations, measures_weights): + color = np.random.randint(low=1, high=10 * N) + pl.scatter(x_i[:, 0], x_i[:, 1], s=b * 1000, label='input measure') +pl.scatter(X[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter') pl.title('Data measures and their barycenter') pl.legend(loc=0) -pl.show() \ No newline at end of file +pl.show() diff --git a/ot/lp/cvx.py b/ot/lp/cvx.py index b74960f..c097f58 100644 --- a/ot/lp/cvx.py +++ b/ot/lp/cvx.py @@ -147,10 +147,7 @@ def barycenter(A, M, weights=None, verbose=False, log=False, solver='interior-po return b - - -def free_support_barycenter(measures_locations, measures_weights, X_init, b_init, weights=None, numItermax=100, stopThr=1e-6, verbose=False): - +def free_support_barycenter(measures_locations, measures_weights, X_init, b, weights=None, numItermax=100, stopThr=1e-6, verbose=False): """ Solves the free support (locations of the barycenters are optimized, not the weights) Wasserstein barycenter problem (i.e. the weighted Frechet mean for the 2-Wasserstein distance) @@ -168,7 +165,7 @@ def free_support_barycenter(measures_locations, measures_weights, X_init, b_init X_init : (k,d) np.ndarray Initialization of the support locations (on k atoms) of the barycenter - b_init : (k,) np.ndarray + b : (k,) np.ndarray Initialization of the weights of the barycenter (non-negatives, sum to 1) weights : (k,) np.ndarray Initialization of the coefficients of the barycenter (non-negatives, sum to 1) @@ -199,27 +196,27 @@ def free_support_barycenter(measures_locations, measures_weights, X_init, b_init iter_count = 0 d = X_init.shape[1] - k = b_init.size + k = b.size N = len(measures_locations) if not weights: - weights = np.ones((N,))/N + weights = np.ones((N,)) / N X = X_init - displacement_square_norm = stopThr+1. + displacement_square_norm = stopThr + 1. - while ( displacement_square_norm > stopThr and iter_count < numItermax ): + while (displacement_square_norm > stopThr and iter_count < numItermax): T_sum = np.zeros((k, d)) for (measure_locations_i, measure_weights_i, weight_i) in zip(measures_locations, measures_weights, weights.tolist()): M_i = ot.dist(X, measure_locations_i) - T_i = ot.emd(b_init, measure_weights_i, M_i) - T_sum = T_sum + weight_i*np.reshape(1. / b_init, (-1, 1)) * np.matmul(T_i, measure_locations_i) + T_i = ot.emd(b, measure_weights_i, M_i) + T_sum = T_sum + weight_i * np.reshape(1. / b, (-1, 1)) * np.matmul(T_i, measure_locations_i) - displacement_square_norm = np.sum(np.square(X-T_sum)) + displacement_square_norm = np.sum(np.square(X - T_sum)) X = T_sum if verbose: @@ -228,4 +225,3 @@ def free_support_barycenter(measures_locations, measures_weights, X_init, b_init iter_count += 1 return X - -- cgit v1.2.3 From 67ddb92e28d6bb44eb65686419e255c2ce3311eb Mon Sep 17 00:00:00 2001 From: Vivien Seguy Date: Fri, 6 Jul 2018 01:59:27 +0900 Subject: add free support barycenter algorithm --- examples/plot_free_support_barycenter.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/examples/plot_free_support_barycenter.py b/examples/plot_free_support_barycenter.py index 42e22fc..5b08507 100644 --- a/examples/plot_free_support_barycenter.py +++ b/examples/plot_free_support_barycenter.py @@ -1,10 +1,10 @@ # -*- coding: utf-8 -*- """ ==================================================== -2D Wasserstein barycenters between empirical distributions +2D Wasserstein barycenters of distributions ==================================================== -Illustration of 2D Wasserstein barycenters between discributions that are weighted +Illustration of 2D Wasserstein barycenters if discributions that are weighted sum of diracs. """ -- cgit v1.2.3 From 46712790c1276f1ecb3496362a8117e153782ede Mon Sep 17 00:00:00 2001 From: Vivien Seguy Date: Mon, 9 Jul 2018 16:49:21 +0900 Subject: add test free support barycenter algorithm + cleaning --- examples/plot_free_support_barycenter.py | 29 +++++----- ot/lp/__init__.py | 92 +++++++++++++++++++++++++++++++- ot/lp/cvx.py | 82 +--------------------------- test/test_ot.py | 15 ++++++ 4 files changed, 121 insertions(+), 97 deletions(-) diff --git a/examples/plot_free_support_barycenter.py b/examples/plot_free_support_barycenter.py index 5b08507..b2e62c8 100644 --- a/examples/plot_free_support_barycenter.py +++ b/examples/plot_free_support_barycenter.py @@ -1,7 +1,7 @@ # -*- coding: utf-8 -*- """ ==================================================== -2D Wasserstein barycenters of distributions +2D free support Wasserstein barycenters of distributions ==================================================== Illustration of 2D Wasserstein barycenters if discributions that are weighted @@ -15,7 +15,8 @@ sum of diracs. import numpy as np import matplotlib.pylab as pl -import ot.plot +import ot + ############################################################################## # Generate data @@ -28,16 +29,16 @@ measures_weights = [] for i in range(N): - n = np.random.randint(low=1, high=20) # nb samples + n_i = np.random.randint(low=1, high=20) # nb samples - mu = np.random.normal(0., 4., (d,)) + mu_i = np.random.normal(0., 4., (d,)) # Gaussian mean - A = np.random.rand(d, d) - cov = np.dot(A, A.transpose()) + A_i = np.random.rand(d, d) + cov_i = np.dot(A_i, A_i.transpose()) # Gaussian covariance matrix - x_i = ot.datasets.make_2D_samples_gauss(n, mu, cov) - b_i = np.random.uniform(0., 1., (n,)) - b_i = b_i / np.sum(b_i) + x_i = ot.datasets.make_2D_samples_gauss(n_i, mu_i, cov_i) # Dirac locations + b_i = np.random.uniform(0., 1., (n_i,)) + b_i = b_i / np.sum(b_i) # Dirac weights measures_locations.append(x_i) measures_weights.append(b_i) @@ -47,19 +48,17 @@ for i in range(N): # Compute free support barycenter # ------------- -k = 10 -X_init = np.random.normal(0., 1., (k, d)) -b = np.ones((k,)) / k +k = 10 # number of Diracs of the barycenter +X_init = np.random.normal(0., 1., (k, d)) # initial Dirac locations +b = np.ones((k,)) / k # weights of the barycenter (it will not be optimized, only the locations are optimized) -X = ot.lp.cvx.free_support_barycenter(measures_locations, measures_weights, X_init, b) +X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, b) ############################################################################## # Plot data # --------- -#%% plot samples - pl.figure(1) for (x_i, b_i) in zip(measures_locations, measures_weights): color = np.random.randint(low=1, high=10 * N) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 4c0d170..96bf6de 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -17,8 +17,9 @@ from .import cvx from .emd_wrap import emd_c, check_result from ..utils import parmap from .cvx import barycenter +from ..utils import dist -__all__=['emd', 'emd2', 'barycenter', 'cvx'] +__all__=['emd', 'emd2', 'barycenter', 'free_support_barycenter', 'cvx'] def emd(a, b, M, numItermax=100000, log=False): @@ -216,3 +217,92 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), res = parmap(f, [b[:, i] for i in range(nb)], processes) return res + + + +def free_support_barycenter(measures_locations, measures_weights, X_init, b=None, weights=None, numItermax=100, stopThr=1e-7, verbose=False, log=None): + """ + Solves the free support (locations of the barycenters are optimized, not the weights) Wasserstein barycenter problem (i.e. the weighted Frechet mean for the 2-Wasserstein distance) + + The function solves the Wasserstein barycenter problem when the barycenter measure is constrained to be supported on k atoms. + This problem is considered in [1] (Algorithm 2). There are two differences with the following codes: + - we do not optimize over the weights + - we do not do line search for the locations updates, we use i.e. theta = 1 in [1] (Algorithm 2). This can be seen as a discrete implementation of the fixed-point algorithm of [2] proposed in the continuous setting. + + Parameters + ---------- + measures_locations : list of (k_i,d) np.ndarray + The discrete support of a measure supported on k_i locations of a d-dimensional space (k_i can be different for each element of the list) + measures_weights : list of (k_i,) np.ndarray + Numpy arrays where each numpy array has k_i non-negatives values summing to one representing the weights of each discrete input measure + + X_init : (k,d) np.ndarray + Initialization of the support locations (on k atoms) of the barycenter + b : (k,) np.ndarray + Initialization of the weights of the barycenter (non-negatives, sum to 1) + weights : (k,) np.ndarray + Initialization of the coefficients of the barycenter (non-negatives, sum to 1) + + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + Returns + ------- + X : (k,d) np.ndarray + Support locations (on k atoms) of the barycenter + + References + ---------- + + .. [1] Cuturi, Marco, and Arnaud Doucet. "Fast computation of Wasserstein barycenters." International Conference on Machine Learning. 2014. + + .. [2] Álvarez-Esteban, Pedro C., et al. "A fixed-point approach to barycenters in Wasserstein space." Journal of Mathematical Analysis and Applications 441.2 (2016): 744-762. + + """ + + iter_count = 0 + + N = len(measures_locations) + k = X_init.shape[0] + d = X_init.shape[1] + if b is None: + b = np.ones((k,))/k + if weights is None: + weights = np.ones((N,)) / N + + X = X_init + + displacement_square_norms = [] + displacement_square_norm = stopThr + 1. + + while ( displacement_square_norm > stopThr and iter_count < numItermax ): + + T_sum = np.zeros((k, d)) + + for (measure_locations_i, measure_weights_i, weight_i) in zip(measures_locations, measures_weights, weights.tolist()): + + M_i = dist(X, measure_locations_i) + T_i = emd(b, measure_weights_i, M_i) + T_sum = T_sum + weight_i * np.reshape(1. / b, (-1, 1)) * np.matmul(T_i, measure_locations_i) + + displacement_square_norm = np.sum(np.square(T_sum-X)) + if log: + displacement_square_norms.append(displacement_square_norm) + + X = T_sum + + if verbose: + print('iteration %d, displacement_square_norm=%f\n', iter_count, displacement_square_norm) + + iter_count += 1 + + if log: + return X, displacement_square_norms + else: + return X \ No newline at end of file diff --git a/ot/lp/cvx.py b/ot/lp/cvx.py index c097f58..8e763be 100644 --- a/ot/lp/cvx.py +++ b/ot/lp/cvx.py @@ -10,7 +10,7 @@ LP solvers for optimal transport using cvxopt import numpy as np import scipy as sp import scipy.sparse as sps -import ot + try: import cvxopt @@ -145,83 +145,3 @@ def barycenter(A, M, weights=None, verbose=False, log=False, solver='interior-po return b, sol else: return b - - -def free_support_barycenter(measures_locations, measures_weights, X_init, b, weights=None, numItermax=100, stopThr=1e-6, verbose=False): - """ - Solves the free support (locations of the barycenters are optimized, not the weights) Wasserstein barycenter problem (i.e. the weighted Frechet mean for the 2-Wasserstein distance) - - The function solves the Wasserstein barycenter problem when the barycenter measure is constrained to be supported on k atoms. - This problem is considered in [1] (Algorithm 2). There are two differences with the following codes: - - we do not optimize over the weights - - we do not do line search for the locations updates, we use i.e. theta = 1 in [1] (Algorithm 2). This can be seen as a discrete implementation of the fixed-point algorithm of [2] proposed in the continuous setting. - - Parameters - ---------- - data_positions : list of (k_i,d) np.ndarray - The discrete support of a measure supported on k_i locations of a d-dimensional space (k_i can be different for each element of the list) - data_weights : list of (k_i,) np.ndarray - Numpy arrays where each numpy array has k_i non-negatives values summing to one representing the weights of each discrete input measure - - X_init : (k,d) np.ndarray - Initialization of the support locations (on k atoms) of the barycenter - b : (k,) np.ndarray - Initialization of the weights of the barycenter (non-negatives, sum to 1) - weights : (k,) np.ndarray - Initialization of the coefficients of the barycenter (non-negatives, sum to 1) - - numItermax : int, optional - Max number of iterations - stopThr : float, optional - Stop threshol on error (>0) - verbose : bool, optional - Print information along iterations - log : bool, optional - record log if True - - Returns - ------- - X : (k,d) np.ndarray - Support locations (on k atoms) of the barycenter - - References - ---------- - - .. [1] Cuturi, Marco, and Arnaud Doucet. "Fast computation of Wasserstein barycenters." International Conference on Machine Learning. 2014. - - .. [2] Álvarez-Esteban, Pedro C., et al. "A fixed-point approach to barycenters in Wasserstein space." Journal of Mathematical Analysis and Applications 441.2 (2016): 744-762. - - """ - - iter_count = 0 - - d = X_init.shape[1] - k = b.size - N = len(measures_locations) - - if not weights: - weights = np.ones((N,)) / N - - X = X_init - - displacement_square_norm = stopThr + 1. - - while (displacement_square_norm > stopThr and iter_count < numItermax): - - T_sum = np.zeros((k, d)) - - for (measure_locations_i, measure_weights_i, weight_i) in zip(measures_locations, measures_weights, weights.tolist()): - - M_i = ot.dist(X, measure_locations_i) - T_i = ot.emd(b, measure_weights_i, M_i) - T_sum = T_sum + weight_i * np.reshape(1. / b, (-1, 1)) * np.matmul(T_i, measure_locations_i) - - displacement_square_norm = np.sum(np.square(X - T_sum)) - X = T_sum - - if verbose: - print('iteration %d, displacement_square_norm=%f\n', iter_count, displacement_square_norm) - - iter_count += 1 - - return X diff --git a/test/test_ot.py b/test/test_ot.py index 399e549..dafc03f 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -135,6 +135,21 @@ def test_lp_barycenter(): np.testing.assert_allclose(bary.sum(), 1) +def test_free_support_barycenter(): + + measures_locations = [np.array([-1.]).reshape((1,1)), np.array([1.]).reshape((1,1))] + measures_weights = [np.array([1.]), np.array([1.])] + + X_init = np.array([-12.]).reshape((1,1)) + + # obvious barycenter location between two diracs + bar_locations = np.array([0.]).reshape((1,1)) + + X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init) + + np.testing.assert_allclose(X, bar_locations, rtol=1e-5, atol=1e-7) + + @pytest.mark.skipif(not ot.lp.cvx.cvxopt, reason="No cvxopt available") def test_lp_barycenter_cvxopt(): -- cgit v1.2.3 From 08e5c0a91bc9b4afcd375109c08a14bcb0b1bd51 Mon Sep 17 00:00:00 2001 From: Vivien Seguy Date: Mon, 9 Jul 2018 16:58:00 +0900 Subject: add test free support barycenter algorithm + cleaning --- examples/plot_free_support_barycenter.py | 6 +++--- test/test_ot.py | 6 +++--- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/examples/plot_free_support_barycenter.py b/examples/plot_free_support_barycenter.py index b2e62c8..b6efc59 100644 --- a/examples/plot_free_support_barycenter.py +++ b/examples/plot_free_support_barycenter.py @@ -48,9 +48,9 @@ for i in range(N): # Compute free support barycenter # ------------- -k = 10 # number of Diracs of the barycenter -X_init = np.random.normal(0., 1., (k, d)) # initial Dirac locations -b = np.ones((k,)) / k # weights of the barycenter (it will not be optimized, only the locations are optimized) +k = 10 # number of Diracs of the barycenter +X_init = np.random.normal(0., 1., (k, d)) # initial Dirac locations +b = np.ones((k,)) / k # weights of the barycenter (it will not be optimized, only the locations are optimized) X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, b) diff --git a/test/test_ot.py b/test/test_ot.py index dafc03f..45e777a 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -137,13 +137,13 @@ def test_lp_barycenter(): def test_free_support_barycenter(): - measures_locations = [np.array([-1.]).reshape((1,1)), np.array([1.]).reshape((1,1))] + measures_locations = [np.array([-1.]).reshape((1, 1)), np.array([1.]).reshape((1, 1))] measures_weights = [np.array([1.]), np.array([1.])] - X_init = np.array([-12.]).reshape((1,1)) + X_init = np.array([-12.]).reshape((1, 1)) # obvious barycenter location between two diracs - bar_locations = np.array([0.]).reshape((1,1)) + bar_locations = np.array([0.]).reshape((1, 1)) X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init) -- cgit v1.2.3 From af57d90c83c860db5a2160e79aea407ae379f7b0 Mon Sep 17 00:00:00 2001 From: Vivien Seguy Date: Mon, 9 Jul 2018 17:40:41 +0900 Subject: return log dict in free support barycenter function --- ot/lp/__init__.py | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 96bf6de..02cbd8c 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -278,7 +278,9 @@ def free_support_barycenter(measures_locations, measures_weights, X_init, b=None X = X_init + log_dict = {} displacement_square_norms = [] + displacement_square_norm = stopThr + 1. while ( displacement_square_norm > stopThr and iter_count < numItermax ): @@ -303,6 +305,7 @@ def free_support_barycenter(measures_locations, measures_weights, X_init, b=None iter_count += 1 if log: - return X, displacement_square_norms + log_dict['displacement_square_norms'] = displacement_square_norms + return X, log_dict else: return X \ No newline at end of file -- cgit v1.2.3 From cb6bdc516697e3bad6776b897f22c8b6a22f13cd Mon Sep 17 00:00:00 2001 From: LeoGautheron Date: Wed, 11 Jul 2018 22:28:38 +0200 Subject: Speed-up Sinkhorn Speed-up in 3 places: - the computation of pairwise distance is faster with sklearn.metrics.pairwise.euclidean_distances - faster computation of K = np.exp(-M / reg) - faster computation of the error every 10 iterations Example with this little script: import time import numpy as np import ot rng = np.random.RandomState(0) transport = ot.da.SinkhornTransport() time1 = time.time() Xs, ys, Xt = rng.randn(10000, 100), rng.randint(0, 2, size=10000), rng.randn(10000, 100) transport.fit(Xs=Xs, Xt=Xt) time2 = time.time() print("OT Computation Time {:6.2f} sec".format(time2-time1)) transport = ot.da.SinkhornLpl1Transport() transport.fit(Xs=Xs, ys=ys, Xt=Xt) time3 = time.time() print("OT LpL1 Computation Time {:6.2f} sec".format(time3-time2)) Before OT Computation Time 19.93 sec OT LpL1 Computation Time 133.43 sec After OT Computation Time 7.55 sec OT LpL1 Computation Time 82.25 sec --- ot/bregman.py | 14 +++++++++++--- ot/utils.py | 4 +++- 2 files changed, 14 insertions(+), 4 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index b017c1a..55c44f6 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -344,8 +344,13 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, # print(reg) - K = np.exp(-M / reg) + K = np.empty(M.shape, dtype=M.dtype) + np.divide(M, -reg, out=K) + np.exp(K, out=K) + # print(np.min(K)) + tmp = np.empty(K.shape, dtype=M.dtype) + tmp2 = np.empty(b.shape, dtype=M.dtype) Kp = (1 / a).reshape(-1, 1) * K cpt = 0 @@ -373,8 +378,11 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, err = np.sum((u - uprev)**2) / np.sum((u)**2) + \ np.sum((v - vprev)**2) / np.sum((v)**2) else: - transp = u.reshape(-1, 1) * (K * v) - err = np.linalg.norm((np.sum(transp, axis=0) - b))**2 + np.multiply(u.reshape(-1, 1), K, out=tmp) + np.multiply(tmp, v.reshape(1, -1), out=tmp) + np.sum(tmp, axis=0, out=tmp2) + tmp2 -= b + err = np.linalg.norm(tmp2)**2 if log: log['err'].append(err) diff --git a/ot/utils.py b/ot/utils.py index 7dac283..5b052ac 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -13,6 +13,7 @@ import time import numpy as np from scipy.spatial.distance import cdist +from sklearn.metrics.pairwise import euclidean_distances import sys import warnings try: @@ -104,7 +105,8 @@ def dist(x1, x2=None, metric='sqeuclidean'): """ if x2 is None: x2 = x1 - + if metric == "sqeuclidean": + return euclidean_distances(x1, x2, squared=True) return cdist(x1, x2, metric=metric) -- cgit v1.2.3 From 73e61546b5509648ba6b7924d6e5a3ebe7fade5b Mon Sep 17 00:00:00 2001 From: LeoGautheron Date: Mon, 16 Jul 2018 06:48:54 +0200 Subject: Remove dependency sklearn --- ot/utils.py | 28 +++++++++++++++++++++++++++- 1 file changed, 27 insertions(+), 1 deletion(-) diff --git a/ot/utils.py b/ot/utils.py index 5b052ac..14cc805 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -13,7 +13,6 @@ import time import numpy as np from scipy.spatial.distance import cdist -from sklearn.metrics.pairwise import euclidean_distances import sys import warnings try: @@ -77,6 +76,33 @@ def clean_zeros(a, b, M): b2 = b[b > 0] return a2, b2, M2 +def euclidean_distances(X, Y, squared=False): + """ + Considering the rows of X (and Y=X) as vectors, compute the + distance matrix between each pair of vectors. + Parameters + ---------- + X : {array-like}, shape (n_samples_1, n_features) + Y : {array-like}, shape (n_samples_2, n_features) + squared : boolean, optional + Return squared Euclidean distances. + Returns + ------- + distances : {array}, shape (n_samples_1, n_samples_2) + """ + XX = np.einsum('ij,ij->i', X, X)[:, np.newaxis] + YY = np.einsum('ij,ij->i', Y, Y)[np.newaxis, :] + distances = np.dot(X, Y.T) + distances *= -2 + distances += XX + distances += YY + np.maximum(distances, 0, out=distances) + if X is Y: + # Ensure that distances between vectors and themselves are set to 0.0. + # This may not be the case due to floating point rounding errors. + distances.flat[::distances.shape[0] + 1] = 0.0 + return distances if squared else np.sqrt(distances, out=distances) + def dist(x1, x2=None, metric='sqeuclidean'): """Compute distance between samples in x1 and x2 using function scipy.spatial.distance.cdist -- cgit v1.2.3 From 0764e356325df7e18f72c0ff468bfa8f8ee35059 Mon Sep 17 00:00:00 2001 From: LeoGautheron Date: Mon, 16 Jul 2018 06:56:34 +0200 Subject: Add comment & fix flake8 error --- ot/bregman.py | 1 + ot/utils.py | 1 + 2 files changed, 2 insertions(+) diff --git a/ot/bregman.py b/ot/bregman.py index 55c44f6..c8e69ce 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -344,6 +344,7 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, # print(reg) + # Next 3 lines equivalent to K= np.exp(-M/reg), but faster to compute K = np.empty(M.shape, dtype=M.dtype) np.divide(M, -reg, out=K) np.exp(K, out=K) diff --git a/ot/utils.py b/ot/utils.py index 14cc805..bb21b38 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -76,6 +76,7 @@ def clean_zeros(a, b, M): b2 = b[b > 0] return a2, b2, M2 + def euclidean_distances(X, Y, squared=False): """ Considering the rows of X (and Y=X) as vectors, compute the -- cgit v1.2.3 From c0c959da8e62d57587ed36e8ba359ca095c5b423 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 24 Jul 2018 13:55:55 +0200 Subject: speedup einsum constraint violation --- ot/bregman.py | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index c8e69ce..26b7b53 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -350,7 +350,6 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, np.exp(K, out=K) # print(np.min(K)) - tmp = np.empty(K.shape, dtype=M.dtype) tmp2 = np.empty(b.shape, dtype=M.dtype) Kp = (1 / a).reshape(-1, 1) * K @@ -379,11 +378,9 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, err = np.sum((u - uprev)**2) / np.sum((u)**2) + \ np.sum((v - vprev)**2) / np.sum((v)**2) else: - np.multiply(u.reshape(-1, 1), K, out=tmp) - np.multiply(tmp, v.reshape(1, -1), out=tmp) - np.sum(tmp, axis=0, out=tmp2) - tmp2 -= b - err = np.linalg.norm(tmp2)**2 + # compute right marginal tmp2= (diag(u)Kdiag(v))^T1 + np.einsum('i,ij,j->j',u,K,v,out=tmp2) + err = np.linalg.norm(tmp2-b)**2 # violation of marginal if log: log['err'].append(err) -- cgit v1.2.3 From 0169c58227b1a322de85bbbeb568eacde0218427 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 24 Jul 2018 14:00:20 +0200 Subject: add bench to makefile --- Makefile | 8 ++++++++ 1 file changed, 8 insertions(+) diff --git a/Makefile b/Makefile index 1abc6e9..2b11f8a 100644 --- a/Makefile +++ b/Makefile @@ -57,6 +57,14 @@ rdoc : notebook : ipython notebook --matplotlib=inline --notebook-dir=notebooks/ +bench : + echo 'Branch master' + git checkout master + python3 $(script) + echo 'Branch $(branch)' + git checkout $(branch) + python3 $(script) + autopep8 : autopep8 -ir test ot examples --jobs -1 -- cgit v1.2.3 From cfffaeb1c1738eb9db404585f8f319b60f7ccabf Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 24 Jul 2018 14:04:38 +0200 Subject: update makefile --- Makefile | 1 + 1 file changed, 1 insertion(+) diff --git a/Makefile b/Makefile index 2b11f8a..1a147d9 100644 --- a/Makefile +++ b/Makefile @@ -1,6 +1,7 @@ PYTHON=python3 +branch := $(git symbolic-ref --short -q HEAD) help : @echo "The following make targets are available:" -- cgit v1.2.3 From f852e3b0f45debfd7a58aebb20dde2a3cda9c001 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 24 Jul 2018 14:07:24 +0200 Subject: update makefile --- Makefile | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/Makefile b/Makefile index 1a147d9..75ec01a 100644 --- a/Makefile +++ b/Makefile @@ -59,12 +59,12 @@ notebook : ipython notebook --matplotlib=inline --notebook-dir=notebooks/ bench : - echo 'Branch master' + @echo 'Branch master' git checkout master - python3 $(script) - echo 'Branch $(branch)' + #python3 $(script) + @echo 'Branch $(branch)' git checkout $(branch) - python3 $(script) + #python3 $(script) autopep8 : autopep8 -ir test ot examples --jobs -1 -- cgit v1.2.3 From b000807a22db607e1ea07f0a7d975b4599c57ffa Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 24 Jul 2018 14:11:36 +0200 Subject: upate makefile --- Makefile | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Makefile b/Makefile index 75ec01a..586ce57 100644 --- a/Makefile +++ b/Makefile @@ -1,7 +1,7 @@ PYTHON=python3 -branch := $(git symbolic-ref --short -q HEAD) +branch := $(eval git symbolic-ref --short -q HEAD) help : @echo "The following make targets are available:" @@ -58,7 +58,7 @@ rdoc : notebook : ipython notebook --matplotlib=inline --notebook-dir=notebooks/ -bench : +bench : FORCE @echo 'Branch master' git checkout master #python3 $(script) -- cgit v1.2.3 From 209835bc9fade5dc6694158f7e6696ab8e5b53a2 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 24 Jul 2018 14:12:56 +0200 Subject: last debug makefile --- Makefile | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Makefile b/Makefile index 586ce57..736fb36 100644 --- a/Makefile +++ b/Makefile @@ -1,7 +1,7 @@ PYTHON=python3 -branch := $(eval git symbolic-ref --short -q HEAD) +branch := $(shell git symbolic-ref --short -q HEAD) help : @echo "The following make targets are available:" @@ -58,7 +58,7 @@ rdoc : notebook : ipython notebook --matplotlib=inline --notebook-dir=notebooks/ -bench : FORCE +bench : @echo 'Branch master' git checkout master #python3 $(script) -- cgit v1.2.3 From 66816cba7cd666706d054bddded1da6035e78c2a Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 24 Jul 2018 14:13:23 +0200 Subject: pep8 all the way --- ot/bregman.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 26b7b53..ab84bcf 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -378,9 +378,9 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, err = np.sum((u - uprev)**2) / np.sum((u)**2) + \ np.sum((v - vprev)**2) / np.sum((v)**2) else: - # compute right marginal tmp2= (diag(u)Kdiag(v))^T1 - np.einsum('i,ij,j->j',u,K,v,out=tmp2) - err = np.linalg.norm(tmp2-b)**2 # violation of marginal + # compute right marginal tmp2= (diag(u)Kdiag(v))^T1 + np.einsum('i,ij,j->j', u, K, v, out=tmp2) + err = np.linalg.norm(tmp2 - b)**2 # violation of marginal if log: log['err'].append(err) -- cgit v1.2.3 From a228d145d0f435b2f2527c7a13437cafa98a4d65 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 24 Jul 2018 14:15:23 +0200 Subject: last update makefile --- Makefile | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/Makefile b/Makefile index 736fb36..dac8653 100644 --- a/Makefile +++ b/Makefile @@ -61,10 +61,10 @@ notebook : bench : @echo 'Branch master' git checkout master - #python3 $(script) + python3 $(script) @echo 'Branch $(branch)' git checkout $(branch) - #python3 $(script) + python3 $(script) autopep8 : autopep8 -ir test ot examples --jobs -1 -- cgit v1.2.3 From bbe411775b3d5abb5d6fb525262cccce3f73d345 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 24 Jul 2018 14:31:45 +0200 Subject: test eisum instead of dot --- ot/bregman.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index ab84bcf..d2ade46 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -358,9 +358,9 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, while (err > stopThr and cpt < numItermax): uprev = u vprev = v - KtransposeU = np.dot(K.T, u) + KtransposeU = np.einsum('ij,i->j',K,u)#np.dot(K.T, u) v = np.divide(b, KtransposeU) - u = 1. / np.dot(Kp, v) + u = 1. / np.einsum('ij,j->i',Kp,v)#np.dot(Kp, v) if (np.any(KtransposeU == 0) or np.any(np.isnan(u)) or np.any(np.isnan(v)) or -- cgit v1.2.3 From a04112c69a62182c061d4b65e71ebb43c866d3e1 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 24 Jul 2018 14:33:32 +0200 Subject: correction size --- ot/bregman.py | 11 ++++++++--- 1 file changed, 8 insertions(+), 3 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index d2ade46..29ca9fd 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -358,9 +358,14 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, while (err > stopThr and cpt < numItermax): uprev = u vprev = v - KtransposeU = np.einsum('ij,i->j',K,u)#np.dot(K.T, u) - v = np.divide(b, KtransposeU) - u = 1. / np.einsum('ij,j->i',Kp,v)#np.dot(Kp, v) + if nbb: + KtransposeU = np.einsum('ij,i,k->jk',K,u)#np.dot(K.T, u) + v = np.divide(b, KtransposeU) + u = 1. / np.einsum('ij,jk->ik',Kp,v)#np.dot(Kp, v) + else: + KtransposeU = np.einsum('ij,i->j',K,u)#np.dot(K.T, u) + v = np.divide(b, KtransposeU) + u = 1. / np.einsum('ij,j->i',Kp,v)#np.dot(Kp, v) if (np.any(KtransposeU == 0) or np.any(np.isnan(u)) or np.any(np.isnan(v)) or -- cgit v1.2.3 From 603c0eee29db890b0092ea8c848473bf413e186f Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 24 Jul 2018 14:34:17 +0200 Subject: pb index --- ot/bregman.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/bregman.py b/ot/bregman.py index 29ca9fd..57cedb2 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -359,7 +359,7 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, uprev = u vprev = v if nbb: - KtransposeU = np.einsum('ij,i,k->jk',K,u)#np.dot(K.T, u) + KtransposeU = np.einsum('ij,ik->jk',K,u)#np.dot(K.T, u) v = np.divide(b, KtransposeU) u = 1. / np.einsum('ij,jk->ik',Kp,v)#np.dot(Kp, v) else: -- cgit v1.2.3 From 5e3392a029e675c7e19f8b1723fcfdb9aa9142aa Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 24 Jul 2018 14:35:58 +0200 Subject: cancel einsum --- ot/bregman.py | 13 +++++-------- 1 file changed, 5 insertions(+), 8 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 57cedb2..1873c46 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -358,14 +358,11 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, while (err > stopThr and cpt < numItermax): uprev = u vprev = v - if nbb: - KtransposeU = np.einsum('ij,ik->jk',K,u)#np.dot(K.T, u) - v = np.divide(b, KtransposeU) - u = 1. / np.einsum('ij,jk->ik',Kp,v)#np.dot(Kp, v) - else: - KtransposeU = np.einsum('ij,i->j',K,u)#np.dot(K.T, u) - v = np.divide(b, KtransposeU) - u = 1. / np.einsum('ij,j->i',Kp,v)#np.dot(Kp, v) + + KtransposeU = np.dot(K.T, u) + v = np.divide(b, KtransposeU) + u = 1. / np.dot(Kp, v) + if (np.any(KtransposeU == 0) or np.any(np.isnan(u)) or np.any(np.isnan(v)) or -- cgit v1.2.3 From 581784c2b47f0844e0b8164cf5823937eb02d62f Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 24 Jul 2018 14:40:21 +0200 Subject: final makefile bench --- Makefile | 6 ++++-- 1 file changed, 4 insertions(+), 2 deletions(-) diff --git a/Makefile b/Makefile index dac8653..84a644b 100644 --- a/Makefile +++ b/Makefile @@ -59,12 +59,14 @@ notebook : ipython notebook --matplotlib=inline --notebook-dir=notebooks/ bench : + @git stash >/dev/null 2>&1 @echo 'Branch master' - git checkout master + @git checkout master >/dev/null 2>&1 python3 $(script) @echo 'Branch $(branch)' - git checkout $(branch) + @git checkout $(branch) >/dev/null 2>&1 python3 $(script) + @git stash apply >/dev/null 2>&1 autopep8 : autopep8 -ir test ot examples --jobs -1 -- cgit v1.2.3 From ace77962d2ae6407916ee7e4377f5c7ed0a8d8f2 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 24 Jul 2018 14:40:42 +0200 Subject: final makefile bench --- ot/bregman.py | 1 - 1 file changed, 1 deletion(-) diff --git a/ot/bregman.py b/ot/bregman.py index 1873c46..58e74de 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -362,7 +362,6 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, KtransposeU = np.dot(K.T, u) v = np.divide(b, KtransposeU) u = 1. / np.dot(Kp, v) - if (np.any(KtransposeU == 0) or np.any(np.isnan(u)) or np.any(np.isnan(v)) or -- cgit v1.2.3 From f4bfeb73da098384aa67599e7f729fb683a1bcc9 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 24 Jul 2018 15:54:56 +0200 Subject: ensum tets marginals sinkhorn --- ot/bregman.py | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 58e74de..c755f51 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -396,10 +396,7 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, log['v'] = v if nbb: # return only loss - res = np.zeros((nbb)) - for i in range(nbb): - res[i] = np.sum( - u[:, i].reshape((-1, 1)) * K * v[:, i].reshape((1, -1)) * M) + res = np.einsum('ik,ij,jk,ij->k', u, K, v, M) if log: return res, log else: -- cgit v1.2.3 From 77b68901c5415ddc5d9ab5215a6fa97723de3de9 Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Tue, 28 Aug 2018 17:07:55 -0700 Subject: fixed bug in sgd dual --- ot/stochastic.py | 165 +++++++++++++------------------------------------------ 1 file changed, 38 insertions(+), 127 deletions(-) diff --git a/ot/stochastic.py b/ot/stochastic.py index 5e8206e..0788f61 100644 --- a/ot/stochastic.py +++ b/ot/stochastic.py @@ -435,8 +435,8 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None, ############################################################################## -def batch_grad_dual_alpha(M, reg, alpha, beta, batch_size, batch_alpha, - batch_beta): +def batch_grad_dual(M, reg, a, b, alpha, beta, batch_size, batch_alpha, + batch_beta): ''' Computes the partial gradient of F_\W_varepsilon @@ -444,9 +444,14 @@ def batch_grad_dual_alpha(M, reg, alpha, beta, batch_size, batch_alpha, ..math: \forall i in batch_alpha, - grad_alpha_i = 1 * batch_size - - sum_{j in batch_beta} exp((alpha_i + beta_j - M_{i,j})/reg) - + grad_alpha_i = alpha_i * batch_size/len(beta) - + sum_{j in batch_beta} exp((alpha_i + beta_j - M_{i,j})/reg) + * a_i * b_j + + \forall j in batch_alpha, + grad_beta_j = beta_j * batch_size/len(alpha) - + sum_{j in batch_alpha} exp((alpha_i + beta_j - M_{i,j})/reg) + * a_i * b_j where : - M is the (ns,nt) metric cost matrix - alpha, beta are dual variables in R^ixR^J @@ -478,7 +483,7 @@ def batch_grad_dual_alpha(M, reg, alpha, beta, batch_size, batch_alpha, ------- grad : np.ndarray(ns,) - partial grad F in alpha + partial grad F Examples -------- @@ -510,100 +515,20 @@ def batch_grad_dual_alpha(M, reg, alpha, beta, batch_size, batch_alpha, arXiv preprint arxiv:1711.02283. ''' - grad_alpha = np.zeros(batch_size) - grad_alpha[:] = batch_size - for j in batch_beta: - grad_alpha -= np.exp((alpha[batch_alpha] + beta[j] - - M[batch_alpha, j]) / reg) - return grad_alpha - - -def batch_grad_dual_beta(M, reg, alpha, beta, batch_size, batch_alpha, - batch_beta): - ''' - Computes the partial gradient of F_\W_varepsilon - - Compute the partial gradient of the dual problem: - - ..math: - \forall j in batch_beta, - grad_beta_j = 1 * batch_size - - sum_{i in batch_alpha} exp((alpha_i + beta_j - M_{i,j})/reg) - - where : - - M is the (ns,nt) metric cost matrix - - alpha, beta are dual variables in R^ixR^J - - reg is the regularization term - - batch_alpha and batch_beta are list of index - - The algorithm used for solving the dual problem is the SGD algorithm - as proposed in [19]_ [alg.1] - - Parameters - ---------- - - M : np.ndarray(ns, nt), - cost matrix - reg : float number, - Regularization term > 0 - alpha : np.ndarray(ns,) - dual variable - beta : np.ndarray(nt,) - dual variable - batch_size : int number - size of the batch - batch_alpha : np.ndarray(bs,) - batch of index of alpha - batch_beta : np.ndarray(bs,) - batch of index of beta - - Returns - ------- - - grad : np.ndarray(ns,) - partial grad F in beta - - Examples - -------- - - >>> n_source = 7 - >>> n_target = 4 - >>> reg = 1 - >>> numItermax = 20000 - >>> lr = 0.1 - >>> batch_size = 3 - >>> log = True - >>> a = ot.utils.unif(n_source) - >>> b = ot.utils.unif(n_target) - >>> rng = np.random.RandomState(0) - >>> X_source = rng.randn(n_source, 2) - >>> Y_target = rng.randn(n_target, 2) - >>> M = ot.dist(X_source, Y_target) - >>> sgd_dual_pi, log = stochastic.solve_dual_entropic(a, b, M, reg, - batch_size, - numItermax, lr, log) - >>> print(log['alpha'], log['beta']) - >>> print(sgd_dual_pi) - - References - ---------- - - [Seguy et al., 2018] : - International Conference on Learning Representation (2018), - arXiv preprint arxiv:1711.02283. + G = - (np.exp((alpha[batch_alpha, None] + beta[None, batch_beta] - + M[batch_alpha, :][:, batch_beta]) / reg) * a[batch_alpha, None] * + b[None, batch_beta]) + grad_beta = np.zeros(np.shape(M)[1]) + grad_alpha = np.zeros(np.shape(M)[0]) + grad_beta[batch_beta] = (b[batch_beta] * len(batch_alpha) / np.shape(M)[0] + + G.sum(0)) + grad_alpha[batch_alpha] = (a[batch_alpha] * len(batch_beta) / + np.shape(M)[1] + G.sum(1)) - ''' - - grad_beta = np.zeros(batch_size) - grad_beta[:] = batch_size - for i in batch_alpha: - grad_beta -= np.exp((alpha[i] + - beta[batch_beta] - M[i, batch_beta]) / reg) - return grad_beta + return grad_alpha, grad_beta -def sgd_entropic_regularization(M, reg, batch_size, numItermax, lr, - alternate=True): +def sgd_entropic_regularization(M, reg, a, b, batch_size, numItermax, lr): ''' Compute the sgd algorithm to solve the regularized discrete measures optimal transport dual problem @@ -628,6 +553,10 @@ def sgd_entropic_regularization(M, reg, batch_size, numItermax, lr, cost matrix reg : float number, Regularization term > 0 + alpha : np.ndarray(ns,) + dual variable + beta : np.ndarray(nt,) + dual variable batch_size : int number size of the batch numItermax : int number @@ -677,35 +606,17 @@ def sgd_entropic_regularization(M, reg, batch_size, numItermax, lr, n_source = np.shape(M)[0] n_target = np.shape(M)[1] - cur_alpha = np.random.randn(n_source) - cur_beta = np.random.randn(n_target) - if alternate: - for cur_iter in range(numItermax): - k = np.sqrt(cur_iter + 1) - batch_alpha = np.random.choice(n_source, batch_size, replace=False) - batch_beta = np.random.choice(n_target, batch_size, replace=False) - grad_F_alpha = batch_grad_dual_alpha(M, reg, cur_alpha, cur_beta, - batch_size, batch_alpha, - batch_beta) - cur_alpha[batch_alpha] += (lr / k) * grad_F_alpha - grad_F_beta = batch_grad_dual_beta(M, reg, cur_alpha, cur_beta, - batch_size, batch_alpha, - batch_beta) - cur_beta[batch_beta] += (lr / k) * grad_F_beta - - else: - for cur_iter in range(numItermax): - k = np.sqrt(cur_iter + 1) - batch_alpha = np.random.choice(n_source, batch_size, replace=False) - batch_beta = np.random.choice(n_target, batch_size, replace=False) - grad_F_alpha = batch_grad_dual_alpha(M, reg, cur_alpha, cur_beta, - batch_size, batch_alpha, - batch_beta) - grad_F_beta = batch_grad_dual_beta(M, reg, cur_alpha, cur_beta, - batch_size, batch_alpha, - batch_beta) - cur_alpha[batch_alpha] += (lr / k) * grad_F_alpha - cur_beta[batch_beta] += (lr / k) * grad_F_beta + cur_alpha = np.zeros(n_source) + cur_beta = np.zeros(n_target) + for cur_iter in range(numItermax): + k = np.sqrt(cur_iter / 100 + 1) + batch_alpha = np.random.choice(n_source, batch_size, replace=False) + batch_beta = np.random.choice(n_target, batch_size, replace=False) + update_alpha, update_beta = batch_grad_dual(M, reg, a, b, cur_alpha, + cur_beta, batch_size, + batch_alpha, batch_beta) + cur_alpha += (lr / k) * update_alpha + cur_beta += (lr / k) * update_beta return cur_alpha, cur_beta @@ -787,7 +698,7 @@ def solve_dual_entropic(a, b, M, reg, batch_size, numItermax=10000, lr=1, arXiv preprint arxiv:1711.02283. ''' - opt_alpha, opt_beta = sgd_entropic_regularization(M, reg, batch_size, + opt_alpha, opt_beta = sgd_entropic_regularization(M, reg, a, b, batch_size, numItermax, lr) pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :]) / reg) * a[:, None] * b[None, :]) -- cgit v1.2.3 From e885d78cc9608d791a9d1561d2f4e0b783ba0761 Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Tue, 28 Aug 2018 17:24:07 -0700 Subject: debug sgd dual --- README.md | 28 +- docs/cache_nbrun | 2 +- docs/source/all.rst | 5 + .../source/auto_examples/auto_examples_jupyter.zip | Bin 86995 -> 99990 bytes docs/source/auto_examples/auto_examples_python.zip | Bin 58992 -> 68178 bytes .../images/sphx_glr_plot_OT_1D_001.png | Bin 21303 -> 21372 bytes .../images/sphx_glr_plot_OT_1D_002.png | Bin 21334 -> 22051 bytes .../images/sphx_glr_plot_OT_1D_005.png | Bin 16995 -> 17080 bytes .../images/sphx_glr_plot_OT_1D_007.png | Bin 18923 -> 19019 bytes .../images/sphx_glr_plot_OT_2D_samples_001.png | Bin 20832 -> 22281 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docs/source/auto_examples/plot_barycenter_1D.py | 4 +- docs/source/auto_examples/plot_barycenter_1D.rst | 6 +- docs/source/auto_examples/plot_compute_emd.ipynb | 168 ++++---- docs/source/auto_examples/plot_compute_emd.py | 2 +- docs/source/auto_examples/plot_compute_emd.rst | 15 +- docs/source/auto_examples/plot_gromov.ipynb | 134 +++---- docs/source/auto_examples/plot_gromov.py | 2 +- docs/source/auto_examples/plot_gromov.rst | 38 +- docs/source/auto_examples/plot_optim_OTreg.ipynb | 76 ++-- docs/source/auto_examples/plot_optim_OTreg.py | 4 +- docs/source/auto_examples/plot_optim_OTreg.rst | 6 +- docs/source/auto_examples/plot_otda_classes.ipynb | 168 ++++---- docs/source/auto_examples/plot_otda_classes.py | 6 +- docs/source/auto_examples/plot_otda_classes.rst | 61 +-- docs/source/auto_examples/plot_otda_d2.ipynb | 76 ++-- docs/source/auto_examples/plot_otda_d2.py | 4 +- docs/source/auto_examples/plot_otda_d2.rst | 6 +- docs/source/auto_examples/plot_otda_mapping.ipynb | 168 ++++---- docs/source/auto_examples/plot_otda_mapping.py | 6 +- docs/source/auto_examples/plot_otda_mapping.rst | 55 +-- .../auto_examples/plot_otda_semi_supervised.ipynb | 192 ++++----- .../auto_examples/plot_otda_semi_supervised.py | 4 +- .../auto_examples/plot_otda_semi_supervised.rst | 17 +- docs/source/conf.py | 2 +- docs/source/readme.rst | 46 ++- examples/plot_OT_1D.py | 2 +- examples/plot_OT_2D_samples.py | 5 +- examples/plot_barycenter_1D.py | 4 +- examples/plot_barycenter_lp_vs_entropic.py | 29 +- examples/plot_compute_emd.py | 2 +- examples/plot_gromov.py | 2 +- examples/plot_optim_OTreg.py | 4 +- examples/plot_otda_classes.py | 6 +- examples/plot_otda_d2.py | 4 +- examples/plot_otda_linear_mapping.py | 10 +- examples/plot_otda_mapping.py | 6 +- examples/plot_otda_semi_supervised.py | 4 +- notebooks/plot_OT_1D.ipynb | 20 +- notebooks/plot_OT_2D_samples.ipynb | 45 +-- notebooks/plot_OT_L1_vs_L2.ipynb | 28 +- notebooks/plot_WDA.ipynb | 71 ++-- notebooks/plot_barycenter_1D.ipynb | 22 +- notebooks/plot_compute_emd.ipynb | 26 +- notebooks/plot_gromov.ipynb | 55 ++- notebooks/plot_gromov_barycenter.ipynb | 14 +- notebooks/plot_optim_OTreg.ipynb | 22 +- notebooks/plot_otda_classes.ipynb | 74 ++-- notebooks/plot_otda_color_images.ipynb | 31 +- notebooks/plot_otda_d2.ipynb | 18 +- notebooks/plot_otda_mapping.ipynb | 62 +-- notebooks/plot_otda_mapping_colors_images.ipynb | 91 +++-- notebooks/plot_otda_semi_supervised.ipynb | 26 +- ot/datasets.py | 60 ++- ot/gromov.py | 1 - ot/lp/__init__.py | 2 + ot/stochastic.py | 437 +++++++++++++++++++++ ot/utils.py | 20 + test/test_bregman.py | 10 +- test/test_da.py | 52 +-- test/test_dr.py | 4 +- test/test_gromov.py | 16 +- test/test_optim.py | 8 +- test/test_ot.py | 2 +- test/test_plot.py | 8 +- 130 files changed, 1816 insertions(+), 1203 deletions(-) diff --git a/README.md b/README.md index 8e8dcd4..677a23b 100644 --- a/README.md +++ b/README.md @@ -15,7 +15,8 @@ It provides the following solvers: * OT Network Flow solver for the linear program/ Earth Movers Distance [1]. * Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10] with optional GPU implementation (requires cudamat). -* Non regularized Wasserstein barycenters [16] with LP solver. +* Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17]. +* Non regularized Wasserstein barycenters [16] with LP solver (only small scale). * Bregman projections for Wasserstein barycenter [3] and unmixing [4]. * Optimal transport for domain adaptation with group lasso regularization [5] * Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. @@ -26,12 +27,24 @@ It provides the following solvers: Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. +#### Using and citing the toolbox + +If you use this toolbox in your research and find it useful, please cite POT using the following bibtex reference: +``` +@misc{flamary2017pot, +title={POT Python Optimal Transport library}, +author={Flamary, R{'e}mi and Courty, Nicolas}, +url={https://github.com/rflamary/POT}, +year={2017} +} +``` + ## Installation The library has been tested on Linux, MacOSX and Windows. It requires a C++ compiler for using the EMD solver and relies on the following Python modules: - Numpy (>=1.11) -- Scipy (>=0.17) +- Scipy (>=1.0) - Cython (>=0.23) - Matplotlib (>=1.5) @@ -158,16 +171,7 @@ This toolbox benefit a lot from open source research and we would like to thank * [Nicolas Bonneel](http://liris.cnrs.fr/~nbonneel/) ( C++ code for EMD) * [Marco Cuturi](http://marcocuturi.net/) (Sinkhorn Knopp in Matlab/Cuda) -## Using and citing the toolbox -If you use this toolbox in your research and find it useful, please cite POT using the following bibtex reference: -``` -@article{flamary2017pot, - title={POT Python Optimal Transport library}, - author={Flamary, R{\'e}mi and Courty, Nicolas}, - year={2017} -} -``` ## Contributions and code of conduct Every contribution is welcome and should respect the [contribution guidelines](CONTRIBUTING.md). Each member of the project is expected to follow the [code of conduct](CODE_OF_CONDUCT.md). @@ -216,7 +220,7 @@ You can also post bug reports and feature requests in Github issues. Make sure t [16] Agueh, M., & Carlier, G. (2011). [Barycenters in the Wasserstein space](https://hal.archives-ouvertes.fr/hal-00637399/document). SIAM Journal on Mathematical Analysis, 43(2), 904-924. -[17] Blondel, M., Seguy, V., & Rolet, A. (2018). [Smooth and Sparse Optimal Transport](https://arxiv.org/pdf/1710.06276.pdf). Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS). +[17] Blondel, M., Seguy, V., & Rolet, A. (2018). [Smooth and Sparse Optimal Transport](https://arxiv.org/abs/1710.06276). Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS). [18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016) [Stochastic Optimization for Large-scale Optimal Transport](arXiv preprint arxiv:1605.08527). Advances in Neural Information Processing Systems (2016). diff --git a/docs/cache_nbrun b/docs/cache_nbrun index 9bf16ad..318bcf4 100644 --- a/docs/cache_nbrun +++ b/docs/cache_nbrun @@ -1 +1 @@ -{"plot_otda_mapping_colors_images.ipynb": "4f0587a00a3c082799a75a0ed36e9ce1", "plot_optim_OTreg.ipynb": "2ec33a099bb67120a134332a20f29313", "plot_otda_color_images.ipynb": "d047d635f4987c81072383241590e21f", "plot_WDA.ipynb": "27f8de4c6d7db46497076523673eedfb", "plot_OT_L1_vs_L2.ipynb": "871d60931f5118c085342e11cb638336", "plot_barycenter_1D.ipynb": "95708b025b6d96d97f579d30d268cbff", "plot_otda_classes.ipynb": "44bb8cd93317b5d342cd62e26d9bbe60", "plot_otda_d2.ipynb": "1a9547f07317612e1a161b7d9f07a5a8", "plot_otda_mapping.ipynb": "d335a15af828aaa3439a1c67570d79d6", "plot_gromov.ipynb": "825d79eba255314fe11469c64d38fc3d", "plot_compute_emd.ipynb": "bd95981189df6adcb113d9b360ead734", "plot_OT_1D.ipynb": "54dfea8ccb61f30729519275785c494c", "plot_gromov_barycenter.ipynb": "953e5047b886ec69ec621ec52f5e21d1", "plot_otda_semi_supervised.ipynb": "0261d339a692e339e15d3634488905cc", "plot_OT_2D_samples.ipynb": "3f125714daa35ff3cfe5dae1f71265c4"} \ No newline at end of file +{"plot_otda_mapping_colors_images.ipynb": "4f0587a00a3c082799a75a0ed36e9ce1", "plot_optim_OTreg.ipynb": "481801bb0d133ef350a65179cf8f739a", "plot_otda_color_images.ipynb": "d047d635f4987c81072383241590e21f", "plot_WDA.ipynb": "27f8de4c6d7db46497076523673eedfb", "plot_otda_linear_mapping.ipynb": "a472c767abe82020e0a58125a528785c", "plot_OT_L1_vs_L2.ipynb": "5d565b8aaf03be4309eba731127851dc", "plot_barycenter_1D.ipynb": "6063193f9ac87517acced2625edb9a54", "plot_otda_classes.ipynb": "39087b6e98217851575f2271c22853a4", "plot_otda_d2.ipynb": "e6feae588103f2a8fab942e5f4eff483", "plot_otda_mapping.ipynb": "2f1ebbdc0f855d9e2b7adf9edec24d25", "plot_gromov.ipynb": "24f2aea489714d34779521f46d5e2c47", "plot_compute_emd.ipynb": "f5cd71cad882ec157dc8222721e9820c", "plot_OT_1D.ipynb": "b5348bdc561c07ec168a1622e5af4b93", "plot_gromov_barycenter.ipynb": "953e5047b886ec69ec621ec52f5e21d1", "plot_otda_semi_supervised.ipynb": "f6dfb02ba2bbd939408ffcd22a3b007c", "plot_OT_2D_samples.ipynb": "07dbc14859fa019a966caa79fa0825bd", "plot_barycenter_lp_vs_entropic.ipynb": "51833e8c76aaedeba9599ac7a30eb357"} \ No newline at end of file diff --git a/docs/source/all.rst b/docs/source/all.rst index 94da2ed..9459023 100644 --- a/docs/source/all.rst +++ b/docs/source/all.rst @@ -19,6 +19,11 @@ ot.bregman .. automodule:: ot.bregman :members: + +ot.smooth +----- +.. automodule:: ot.smooth + :members: ot.smooth ----- diff --git a/docs/source/auto_examples/auto_examples_jupyter.zip b/docs/source/auto_examples/auto_examples_jupyter.zip index 4703026..8102274 100644 Binary files a/docs/source/auto_examples/auto_examples_jupyter.zip and b/docs/source/auto_examples/auto_examples_jupyter.zip differ diff --git a/docs/source/auto_examples/auto_examples_python.zip 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b/docs/source/auto_examples/images/thumb/sphx_glr_plot_otda_semi_supervised_thumb.png index e1b5863..b683392 100644 Binary files a/docs/source/auto_examples/images/thumb/sphx_glr_plot_otda_semi_supervised_thumb.png and b/docs/source/auto_examples/images/thumb/sphx_glr_plot_otda_semi_supervised_thumb.png differ diff --git a/docs/source/auto_examples/index.rst b/docs/source/auto_examples/index.rst index 9d7c0f0..69fb320 100644 --- a/docs/source/auto_examples/index.rst +++ b/docs/source/auto_examples/index.rst @@ -107,6 +107,26 @@ This is a gallery of all the POT example files. /auto_examples/plot_compute_emd +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_linear_mapping_thumb.png + + :ref:`sphx_glr_auto_examples_plot_otda_linear_mapping.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_otda_linear_mapping + .. raw:: html
@@ -287,6 +307,26 @@ This is a gallery of all the POT example files. /auto_examples/plot_OT_L1_vs_L2 +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_barycenter_lp_vs_entropic_thumb.png + + :ref:`sphx_glr_auto_examples_plot_barycenter_lp_vs_entropic.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_barycenter_lp_vs_entropic + .. raw:: html
diff --git a/docs/source/auto_examples/plot_OT_1D.ipynb b/docs/source/auto_examples/plot_OT_1D.ipynb index 649efa6..bd0439e 100644 --- a/docs/source/auto_examples/plot_OT_1D.ipynb +++ b/docs/source/auto_examples/plot_OT_1D.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -8,35 +9,35 @@ "outputs": [], "source": [ "%matplotlib inline" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "\n# 1D optimal transport\n\n\nThis example illustrates the computation of EMD and Sinkhorn transport plans\nand their visualization.\n\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot\nfrom ot.datasets import get_1D_gauss as gauss" - ], - "cell_type": "code" + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot\nfrom ot.datasets import make_1D_gauss as gauss" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Generate data\n-------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -44,17 +45,17 @@ "outputs": [], "source": [ "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\nb = gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Plot distributions and loss matrix\n----------------------------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -62,17 +63,17 @@ "outputs": [], "source": [ "#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\npl.plot(x, a, 'b', label='Source distribution')\npl.plot(x, b, 'r', label='Target distribution')\npl.legend()\n\n#%% plot distributions and loss matrix\n\npl.figure(2, figsize=(5, 5))\not.plot.plot1D_mat(a, b, M, 'Cost matrix M')" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Solve EMD\n---------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -80,17 +81,17 @@ "outputs": [], "source": [ "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Solve Sinkhorn\n--------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -98,29 +99,28 @@ "outputs": [], "source": [ "#%% Sinkhorn\n\nlambd = 1e-3\nGs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')\n\npl.show()" - ], - "cell_type": "code" + ] } ], "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python", "codemirror_mode": { "name": "ipython", "version": 3 }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", "nbconvert_exporter": "python", - "version": "3.5.2", "pygments_lexer": "ipython3", - "file_extension": ".py", - "mimetype": "text/x-python" - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3", - "language": "python" + "version": "3.6.5" } }, - "nbformat_minor": 0, - "nbformat": 4 + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OT_1D.py b/docs/source/auto_examples/plot_OT_1D.py index 90325c9..f33e2a4 100644 --- a/docs/source/auto_examples/plot_OT_1D.py +++ b/docs/source/auto_examples/plot_OT_1D.py @@ -17,7 +17,7 @@ import numpy as np import matplotlib.pylab as pl import ot import ot.plot -from ot.datasets import get_1D_gauss as gauss +from ot.datasets import make_1D_gauss as gauss ############################################################################## # Generate data diff --git a/docs/source/auto_examples/plot_OT_1D.rst b/docs/source/auto_examples/plot_OT_1D.rst index 5e4f73e..b97d67c 100644 --- a/docs/source/auto_examples/plot_OT_1D.rst +++ b/docs/source/auto_examples/plot_OT_1D.rst @@ -24,7 +24,7 @@ and their visualization. import matplotlib.pylab as pl import ot import ot.plot - from ot.datasets import get_1D_gauss as gauss + from ot.datasets import make_1D_gauss as gauss @@ -172,7 +172,7 @@ Solve Sinkhorn 110|1.527180e-10| -**Total running time of the script:** ( 0 minutes 1.061 seconds) +**Total running time of the script:** ( 0 minutes 0.561 seconds) diff --git a/docs/source/auto_examples/plot_OT_2D_samples.ipynb b/docs/source/auto_examples/plot_OT_2D_samples.ipynb index 41a37f3..26831f9 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.ipynb +++ b/docs/source/auto_examples/plot_OT_2D_samples.ipynb @@ -1,126 +1,126 @@ { - "nbformat_minor": 0, - "nbformat": 4, "cells": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "\n# 2D Optimal transport between empirical distributions\n\n\nIllustration of 2D optimal transport between discributions that are weighted\nsum of diracs. The OT matrix is plotted with the samples.\n\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Generate data\n-------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% parameters and data generation\n\nn = 50 # nb samples\n\nmu_s = np.array([0, 0])\ncov_s = np.array([[1, 0], [0, 1]])\n\nmu_t = np.array([4, 4])\ncov_t = np.array([[1, -.8], [-.8, 1]])\n\nxs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s)\nxt = ot.datasets.get_2D_samples_gauss(n, mu_t, cov_t)\n\na, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples\n\n# loss matrix\nM = ot.dist(xs, xt)\nM /= M.max()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "#%% parameters and data generation\n\nn = 50 # nb samples\n\nmu_s = np.array([0, 0])\ncov_s = np.array([[1, 0], [0, 1]])\n\nmu_t = np.array([4, 4])\ncov_t = np.array([[1, -.8], [-.8, 1]])\n\nxs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s)\nxt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t)\n\na, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples\n\n# loss matrix\nM = ot.dist(xs, xt)\nM /= M.max()" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Plot data\n---------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% plot samples\n\npl.figure(1)\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('Source and target distributions')\n\npl.figure(2)\npl.imshow(M, interpolation='nearest')\npl.title('Cost matrix M')" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "#%% plot samples\n\npl.figure(1)\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('Source and target distributions')\n\npl.figure(2)\npl.imshow(M, interpolation='nearest')\npl.title('Cost matrix M')" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Compute EMD\n-----------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3)\npl.imshow(G0, interpolation='nearest')\npl.title('OT matrix G0')\n\npl.figure(4)\not.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix with samples')" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3)\npl.imshow(G0, interpolation='nearest')\npl.title('OT matrix G0')\n\npl.figure(4)\not.plot.plot2D_samples_mat(xs, xt, G0, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix with samples')" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Compute Sinkhorn\n----------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% sinkhorn\n\n# reg term\nlambd = 1e-3\n\nGs = ot.sinkhorn(a, b, M, lambd)\n\npl.figure(5)\npl.imshow(Gs, interpolation='nearest')\npl.title('OT matrix sinkhorn')\n\npl.figure(6)\not.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix Sinkhorn with samples')\n\npl.show()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } + }, + "outputs": [], + "source": [ + "#%% sinkhorn\n\n# reg term\nlambd = 1e-3\n\nGs = ot.sinkhorn(a, b, M, lambd)\n\npl.figure(5)\npl.imshow(Gs, interpolation='nearest')\npl.title('OT matrix sinkhorn')\n\npl.figure(6)\not.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix Sinkhorn with samples')\n\npl.show()" + ] } - ], + ], "metadata": { "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", "codemirror_mode": { - "version": 2, - "name": "ipython" - } + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" } - } + }, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OT_2D_samples.py b/docs/source/auto_examples/plot_OT_2D_samples.py index 9818ec5..bb952a0 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.py +++ b/docs/source/auto_examples/plot_OT_2D_samples.py @@ -16,6 +16,7 @@ sum of diracs. The OT matrix is plotted with the samples. import numpy as np import matplotlib.pylab as pl import ot +import ot.plot ############################################################################## # Generate data @@ -31,8 +32,8 @@ cov_s = np.array([[1, 0], [0, 1]]) mu_t = np.array([4, 4]) cov_t = np.array([[1, -.8], [-.8, 1]]) -xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s) -xt = ot.datasets.get_2D_samples_gauss(n, mu_t, cov_t) +xs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s) +xt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t) a, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples diff --git a/docs/source/auto_examples/plot_OT_2D_samples.rst b/docs/source/auto_examples/plot_OT_2D_samples.rst index 5565c54..624ae3e 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.rst +++ b/docs/source/auto_examples/plot_OT_2D_samples.rst @@ -23,6 +23,7 @@ sum of diracs. The OT matrix is plotted with the samples. import numpy as np import matplotlib.pylab as pl import ot + import ot.plot @@ -48,8 +49,8 @@ Generate data mu_t = np.array([4, 4]) cov_t = np.array([[1, -.8], [-.8, 1]]) - xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s) - xt = ot.datasets.get_2D_samples_gauss(n, mu_t, cov_t) + xs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s) + xt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t) a, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples @@ -191,11 +192,13 @@ Compute Sinkhorn -**Total running time of the script:** ( 0 minutes 3.380 seconds) +**Total running time of the script:** ( 0 minutes 3.027 seconds) -.. container:: sphx-glr-footer +.. only :: html + + .. container:: sphx-glr-footer .. container:: sphx-glr-download @@ -208,6 +211,9 @@ Compute Sinkhorn :download:`Download Jupyter notebook: plot_OT_2D_samples.ipynb ` -.. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb b/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb index aea1b3d..125d720 100644 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -8,17 +9,17 @@ "outputs": [], "source": [ "%matplotlib inline" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "\n# 2D Optimal transport for different metrics\n\n\n2D OT on empirical distributio with different gound metric.\n\nStole the figure idea from Fig. 1 and 2 in\nhttps://arxiv.org/pdf/1706.07650.pdf\n\n\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -26,35 +27,35 @@ "outputs": [], "source": [ "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Dataset 1 : uniform sampling\n----------------------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "n = 20 # nb samples\nxs = np.zeros((n, 2))\nxs[:, 0] = np.arange(n) + 1\nxs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex...\n\nxt = np.zeros((n, 2))\nxt[:, 1] = np.arange(n) + 1\n\na, b = ot.unif(n), ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM1 = ot.dist(xs, xt, metric='euclidean')\nM1 /= M1.max()\n\n# loss matrix\nM2 = ot.dist(xs, xt, metric='sqeuclidean')\nM2 /= M2.max()\n\n# loss matrix\nMp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))\nMp /= Mp.max()\n\n# Data\npl.figure(1, figsize=(7, 3))\npl.clf()\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\npl.title('Source and traget distributions')\n\n\n# Cost matrices\npl.figure(2, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\npl.imshow(M1, interpolation='nearest')\npl.title('Euclidean cost')\n\npl.subplot(1, 3, 2)\npl.imshow(M2, interpolation='nearest')\npl.title('Squared Euclidean cost')\n\npl.subplot(1, 3, 3)\npl.imshow(Mp, interpolation='nearest')\npl.title('Sqrt Euclidean cost')\npl.tight_layout()" - ], - "cell_type": "code" + "n = 20 # nb samples\nxs = np.zeros((n, 2))\nxs[:, 0] = np.arange(n) + 1\nxs[:, 1] = (np.arange(n) + 1) * -0.001 # to make it strictly convex...\n\nxt = np.zeros((n, 2))\nxt[:, 1] = np.arange(n) + 1\n\na, b = ot.unif(n), ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM1 = ot.dist(xs, xt, metric='euclidean')\nM1 /= M1.max()\n\n# loss matrix\nM2 = ot.dist(xs, xt, metric='sqeuclidean')\nM2 /= M2.max()\n\n# loss matrix\nMp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))\nMp /= Mp.max()\n\n# Data\npl.figure(1, figsize=(7, 3))\npl.clf()\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\npl.title('Source and target distributions')\n\n\n# Cost matrices\npl.figure(2, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\npl.imshow(M1, interpolation='nearest')\npl.title('Euclidean cost')\n\npl.subplot(1, 3, 2)\npl.imshow(M2, interpolation='nearest')\npl.title('Squared Euclidean cost')\n\npl.subplot(1, 3, 3)\npl.imshow(Mp, interpolation='nearest')\npl.title('Sqrt Euclidean cost')\npl.tight_layout()" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Dataset 1 : Plot OT Matrices\n----------------------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -62,17 +63,17 @@ "outputs": [], "source": [ "#%% EMD\nG1 = ot.emd(a, b, M1)\nG2 = ot.emd(a, b, M2)\nGp = ot.emd(a, b, Mp)\n\n# OT matrices\npl.figure(3, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\not.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT Euclidean')\n\npl.subplot(1, 3, 2)\not.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT squared Euclidean')\n\npl.subplot(1, 3, 3)\not.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT sqrt Euclidean')\npl.tight_layout()\n\npl.show()" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Dataset 2 : Partial circle\n--------------------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -80,17 +81,17 @@ "outputs": [], "source": [ "n = 50 # nb samples\nxtot = np.zeros((n + 1, 2))\nxtot[:, 0] = np.cos(\n (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)\nxtot[:, 1] = np.sin(\n (np.arange(n + 1) + 1.0) * 0.9 / (n + 2) * 2 * np.pi)\n\nxs = xtot[:n, :]\nxt = xtot[1:, :]\n\na, b = ot.unif(n), ot.unif(n) # uniform distribution on samples\n\n# loss matrix\nM1 = ot.dist(xs, xt, metric='euclidean')\nM1 /= M1.max()\n\n# loss matrix\nM2 = ot.dist(xs, xt, metric='sqeuclidean')\nM2 /= M2.max()\n\n# loss matrix\nMp = np.sqrt(ot.dist(xs, xt, metric='euclidean'))\nMp /= Mp.max()\n\n\n# Data\npl.figure(4, figsize=(7, 3))\npl.clf()\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\npl.title('Source and traget distributions')\n\n\n# Cost matrices\npl.figure(5, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\npl.imshow(M1, interpolation='nearest')\npl.title('Euclidean cost')\n\npl.subplot(1, 3, 2)\npl.imshow(M2, interpolation='nearest')\npl.title('Squared Euclidean cost')\n\npl.subplot(1, 3, 3)\npl.imshow(Mp, interpolation='nearest')\npl.title('Sqrt Euclidean cost')\npl.tight_layout()" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Dataset 2 : Plot OT Matrices\n-----------------------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -98,29 +99,28 @@ "outputs": [], "source": [ "#%% EMD\nG1 = ot.emd(a, b, M1)\nG2 = ot.emd(a, b, M2)\nGp = ot.emd(a, b, Mp)\n\n# OT matrices\npl.figure(6, figsize=(7, 3))\n\npl.subplot(1, 3, 1)\not.plot.plot2D_samples_mat(xs, xt, G1, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT Euclidean')\n\npl.subplot(1, 3, 2)\not.plot.plot2D_samples_mat(xs, xt, G2, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT squared Euclidean')\n\npl.subplot(1, 3, 3)\not.plot.plot2D_samples_mat(xs, xt, Gp, c=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.axis('equal')\n# pl.legend(loc=0)\npl.title('OT sqrt Euclidean')\npl.tight_layout()\n\npl.show()" - ], - "cell_type": "code" + ] } ], "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python", "codemirror_mode": { "name": "ipython", "version": 3 }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", "nbconvert_exporter": "python", - "version": "3.5.2", "pygments_lexer": "ipython3", - "file_extension": ".py", - "mimetype": "text/x-python" - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3", - "language": "python" + "version": "3.6.5" } }, - "nbformat_minor": 0, - "nbformat": 4 + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.py b/docs/source/auto_examples/plot_OT_L1_vs_L2.py index c1ed226..37b429f 100644 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.py +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.py @@ -53,7 +53,7 @@ pl.clf() pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.axis('equal') -pl.title('Source and traget distributions') +pl.title('Source and target distributions') # Cost matrices diff --git a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst index 01d6ac2..5db4b55 100644 --- a/docs/source/auto_examples/plot_OT_L1_vs_L2.rst +++ b/docs/source/auto_examples/plot_OT_L1_vs_L2.rst @@ -70,7 +70,7 @@ Dataset 1 : uniform sampling pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') pl.axis('equal') - pl.title('Source and traget distributions') + pl.title('Source and target distributions') # Cost matrices @@ -291,7 +291,7 @@ Dataset 2 : Plot OT Matrices -**Total running time of the script:** ( 0 minutes 3.750 seconds) +**Total running time of the script:** ( 0 minutes 0.958 seconds) diff --git a/docs/source/auto_examples/plot_barycenter_1D.ipynb b/docs/source/auto_examples/plot_barycenter_1D.ipynb index 01e759a..5866088 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.ipynb +++ b/docs/source/auto_examples/plot_barycenter_1D.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -8,47 +9,46 @@ "outputs": [], "source": [ "%matplotlib inline" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "\n# 1D Wasserstein barycenter demo\n\n\nThis example illustrates the computation of regularized Wassersyein Barycenter\nas proposed in [3].\n\n\n[3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyr\u00e9, G. (2015).\nIterative Bregman projections for regularized transportation problems\nSIAM Journal on Scientific Computing, 37(2), A1111-A1138.\n\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nfrom matplotlib.collections import PolyCollection\n\n#\n# Generate data\n# -------------\n\n#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na1 = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.get_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()\n\n#\n# Plot data\n# ---------\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()\n\n#\n# Barycenter computation\n# ----------------------\n\n#%% barycenter computation\n\nalpha = 0.2 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()\n\n#\n# Barycentric interpolation\n# -------------------------\n\n#%% barycenter interpolation\n\nn_alpha = 11\nalpha_list = np.linspace(0, 1, n_alpha)\n\n\nB_l2 = np.zeros((n, n_alpha))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_alpha):\n alpha = alpha_list[i]\n weights = np.array([1 - alpha, alpha])\n B_l2[:, i] = A.dot(weights)\n B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)\n\n#%% plot interpolation\n\npl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_l2[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_wass[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with Wasserstein')\npl.tight_layout()\n\npl.show()" - ], - "cell_type": "code" + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nfrom matplotlib.collections import PolyCollection\n\n#\n# Generate data\n# -------------\n\n#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()\n\n#\n# Plot data\n# ---------\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()\n\n#\n# Barycenter computation\n# ----------------------\n\n#%% barycenter computation\n\nalpha = 0.2 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()\n\n#\n# Barycentric interpolation\n# -------------------------\n\n#%% barycenter interpolation\n\nn_alpha = 11\nalpha_list = np.linspace(0, 1, n_alpha)\n\n\nB_l2 = np.zeros((n, n_alpha))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_alpha):\n alpha = alpha_list[i]\n weights = np.array([1 - alpha, alpha])\n B_l2[:, i] = A.dot(weights)\n B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)\n\n#%% plot interpolation\n\npl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_l2[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_wass[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with Wasserstein')\npl.tight_layout()\n\npl.show()" + ] } ], "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python", "codemirror_mode": { "name": "ipython", "version": 3 }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", "nbconvert_exporter": "python", - "version": "3.5.2", "pygments_lexer": "ipython3", - "file_extension": ".py", - "mimetype": "text/x-python" - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3", - "language": "python" + "version": "3.6.5" } }, - "nbformat_minor": 0, - "nbformat": 4 + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_barycenter_1D.py b/docs/source/auto_examples/plot_barycenter_1D.py index ecf640c..5ed9f3f 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.py +++ b/docs/source/auto_examples/plot_barycenter_1D.py @@ -37,8 +37,8 @@ n = 100 # nb bins x = np.arange(n, dtype=np.float64) # Gaussian distributions -a1 = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std -a2 = ot.datasets.get_1D_gauss(n, m=60, s=8) +a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std +a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) # creating matrix A containing all distributions A = np.vstack((a1, a2)).T diff --git a/docs/source/auto_examples/plot_barycenter_1D.rst b/docs/source/auto_examples/plot_barycenter_1D.rst index 5b627ca..b314dc1 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.rst +++ b/docs/source/auto_examples/plot_barycenter_1D.rst @@ -72,8 +72,8 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. x = np.arange(n, dtype=np.float64) # Gaussian distributions - a1 = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std - a2 = ot.datasets.get_1D_gauss(n, m=60, s=8) + a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std + a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) # creating matrix A containing all distributions A = np.vstack((a1, a2)).T @@ -194,7 +194,7 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. pl.show() -**Total running time of the script:** ( 0 minutes 0.636 seconds) +**Total running time of the script:** ( 0 minutes 0.363 seconds) diff --git a/docs/source/auto_examples/plot_compute_emd.ipynb b/docs/source/auto_examples/plot_compute_emd.ipynb index b9b8bc5..562eff8 100644 --- a/docs/source/auto_examples/plot_compute_emd.ipynb +++ b/docs/source/auto_examples/plot_compute_emd.ipynb @@ -1,126 +1,126 @@ { - "nbformat_minor": 0, - "nbformat": 4, "cells": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "\n# Plot multiple EMD\n\n\nShows how to compute multiple EMD and Sinkhorn with two differnt\nground metrics and plot their values for diffeent distributions.\n\n\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nfrom ot.datasets import get_1D_gauss as gauss" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nfrom ot.datasets import make_1D_gauss as gauss" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Generate data\n-------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% parameters\n\nn = 100 # nb bins\nn_target = 50 # nb target distributions\n\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\nlst_m = np.linspace(20, 90, n_target)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\n\nB = np.zeros((n, n_target))\n\nfor i, m in enumerate(lst_m):\n B[:, i] = gauss(n, m=m, s=5)\n\n# loss matrix and normalization\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'euclidean')\nM /= M.max()\nM2 = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'sqeuclidean')\nM2 /= M2.max()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "#%% parameters\n\nn = 100 # nb bins\nn_target = 50 # nb target distributions\n\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\nlst_m = np.linspace(20, 90, n_target)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\n\nB = np.zeros((n, n_target))\n\nfor i, m in enumerate(lst_m):\n B[:, i] = gauss(n, m=m, s=5)\n\n# loss matrix and normalization\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'euclidean')\nM /= M.max()\nM2 = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)), 'sqeuclidean')\nM2 /= M2.max()" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Plot data\n---------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% plot the distributions\n\npl.figure(1)\npl.subplot(2, 1, 1)\npl.plot(x, a, 'b', label='Source distribution')\npl.title('Source distribution')\npl.subplot(2, 1, 2)\npl.plot(x, B, label='Target distributions')\npl.title('Target distributions')\npl.tight_layout()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "#%% plot the distributions\n\npl.figure(1)\npl.subplot(2, 1, 1)\npl.plot(x, a, 'b', label='Source distribution')\npl.title('Source distribution')\npl.subplot(2, 1, 2)\npl.plot(x, B, label='Target distributions')\npl.title('Target distributions')\npl.tight_layout()" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Compute EMD for the different losses\n------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%% Compute and plot distributions and loss matrix\n\nd_emd = ot.emd2(a, B, M) # direct computation of EMD\nd_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M2\n\n\npl.figure(2)\npl.plot(d_emd, label='Euclidean EMD')\npl.plot(d_emd2, label='Squared Euclidean EMD')\npl.title('EMD distances')\npl.legend()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "#%% Compute and plot distributions and loss matrix\n\nd_emd = ot.emd2(a, B, M) # direct computation of EMD\nd_emd2 = ot.emd2(a, B, M2) # direct computation of EMD with loss M2\n\n\npl.figure(2)\npl.plot(d_emd, label='Euclidean EMD')\npl.plot(d_emd2, label='Squared Euclidean EMD')\npl.title('EMD distances')\npl.legend()" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Compute Sinkhorn for the different losses\n-----------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "#%%\nreg = 1e-2\nd_sinkhorn = ot.sinkhorn2(a, B, M, reg)\nd_sinkhorn2 = ot.sinkhorn2(a, B, M2, reg)\n\npl.figure(2)\npl.clf()\npl.plot(d_emd, label='Euclidean EMD')\npl.plot(d_emd2, label='Squared Euclidean EMD')\npl.plot(d_sinkhorn, '+', label='Euclidean Sinkhorn')\npl.plot(d_sinkhorn2, '+', label='Squared Euclidean Sinkhorn')\npl.title('EMD distances')\npl.legend()\n\npl.show()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } + }, + "outputs": [], + "source": [ + "#%%\nreg = 1e-2\nd_sinkhorn = ot.sinkhorn2(a, B, M, reg)\nd_sinkhorn2 = ot.sinkhorn2(a, B, M2, reg)\n\npl.figure(2)\npl.clf()\npl.plot(d_emd, label='Euclidean EMD')\npl.plot(d_emd2, label='Squared Euclidean EMD')\npl.plot(d_sinkhorn, '+', label='Euclidean Sinkhorn')\npl.plot(d_sinkhorn2, '+', label='Squared Euclidean Sinkhorn')\npl.title('EMD distances')\npl.legend()\n\npl.show()" + ] } - ], + ], "metadata": { "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", "codemirror_mode": { - "version": 2, - "name": "ipython" - } + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" } - } + }, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_compute_emd.py b/docs/source/auto_examples/plot_compute_emd.py index 73b42c3..7ed2b01 100644 --- a/docs/source/auto_examples/plot_compute_emd.py +++ b/docs/source/auto_examples/plot_compute_emd.py @@ -17,7 +17,7 @@ ground metrics and plot their values for diffeent distributions. import numpy as np import matplotlib.pylab as pl import ot -from ot.datasets import get_1D_gauss as gauss +from ot.datasets import make_1D_gauss as gauss ############################################################################## diff --git a/docs/source/auto_examples/plot_compute_emd.rst b/docs/source/auto_examples/plot_compute_emd.rst index cdbc620..27bca2c 100644 --- a/docs/source/auto_examples/plot_compute_emd.rst +++ b/docs/source/auto_examples/plot_compute_emd.rst @@ -24,7 +24,7 @@ ground metrics and plot their values for diffeent distributions. import numpy as np import matplotlib.pylab as pl import ot - from ot.datasets import get_1D_gauss as gauss + from ot.datasets import make_1D_gauss as gauss @@ -162,11 +162,13 @@ Compute Sinkhorn for the different losses -**Total running time of the script:** ( 0 minutes 0.697 seconds) +**Total running time of the script:** ( 0 minutes 0.446 seconds) -.. container:: sphx-glr-footer +.. only :: html + + .. container:: sphx-glr-footer .. container:: sphx-glr-download @@ -179,6 +181,9 @@ Compute Sinkhorn for the different losses :download:`Download Jupyter notebook: plot_compute_emd.ipynb ` -.. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_gromov.ipynb b/docs/source/auto_examples/plot_gromov.ipynb index 57d6a4a..dc1f179 100644 --- a/docs/source/auto_examples/plot_gromov.ipynb +++ b/docs/source/auto_examples/plot_gromov.ipynb @@ -1,126 +1,126 @@ { - "nbformat_minor": 0, - "nbformat": 4, - "metadata": { - "language_info": { - "file_extension": ".py", - "codemirror_mode": { - "version": 3, - "name": "ipython" - }, - "nbconvert_exporter": "python", - "mimetype": "text/x-python", - "version": "3.5.2", - "name": "python", - "pygments_lexer": "ipython3" - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3", - "language": "python" - } - }, "cells": [ { - "outputs": [], - "source": [ - "%matplotlib inline" - ], + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, - "cell_type": "code" + "outputs": [], + "source": [ + "%matplotlib inline" + ] }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "\n# Gromov-Wasserstein example\n\n\nThis example is designed to show how to use the Gromov-Wassertsein distance\ncomputation in POT.\n\n" - ], - "metadata": {}, - "cell_type": "markdown" + ] }, { - "outputs": [], - "source": [ - "# Author: Erwan Vautier \n# Nicolas Courty \n#\n# License: MIT License\n\nimport scipy as sp\nimport numpy as np\nimport matplotlib.pylab as pl\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nimport ot" - ], + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, - "cell_type": "code" + "outputs": [], + "source": [ + "# Author: Erwan Vautier \n# Nicolas Courty \n#\n# License: MIT License\n\nimport scipy as sp\nimport numpy as np\nimport matplotlib.pylab as pl\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nimport ot" + ] }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Sample two Gaussian distributions (2D and 3D)\n---------------------------------------------\n\nThe Gromov-Wasserstein distance allows to compute distances with samples that\ndo not belong to the same metric space. For demonstration purpose, we sample\ntwo Gaussian distributions in 2- and 3-dimensional spaces.\n\n" - ], - "metadata": {}, - "cell_type": "markdown" + ] }, { - "outputs": [], - "source": [ - "n_samples = 30 # nb samples\n\nmu_s = np.array([0, 0])\ncov_s = np.array([[1, 0], [0, 1]])\n\nmu_t = np.array([4, 4, 4])\ncov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\n\n\nxs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)\nP = sp.linalg.sqrtm(cov_t)\nxt = np.random.randn(n_samples, 3).dot(P) + mu_t" - ], + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, - "cell_type": "code" + "outputs": [], + "source": [ + "n_samples = 30 # nb samples\n\nmu_s = np.array([0, 0])\ncov_s = np.array([[1, 0], [0, 1]])\n\nmu_t = np.array([4, 4, 4])\ncov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\n\n\nxs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s)\nP = sp.linalg.sqrtm(cov_t)\nxt = np.random.randn(n_samples, 3).dot(P) + mu_t" + ] }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Plotting the distributions\n--------------------------\n\n" - ], - "metadata": {}, - "cell_type": "markdown" + ] }, { - "outputs": [], - "source": [ - "fig = pl.figure()\nax1 = fig.add_subplot(121)\nax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\nax2 = fig.add_subplot(122, projection='3d')\nax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\npl.show()" - ], + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, - "cell_type": "code" + "outputs": [], + "source": [ + "fig = pl.figure()\nax1 = fig.add_subplot(121)\nax1.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\nax2 = fig.add_subplot(122, projection='3d')\nax2.scatter(xt[:, 0], xt[:, 1], xt[:, 2], color='r')\npl.show()" + ] }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Compute distance kernels, normalize them and then display\n---------------------------------------------------------\n\n" - ], - "metadata": {}, - "cell_type": "markdown" + ] }, { - "outputs": [], - "source": [ - "C1 = sp.spatial.distance.cdist(xs, xs)\nC2 = sp.spatial.distance.cdist(xt, xt)\n\nC1 /= C1.max()\nC2 /= C2.max()\n\npl.figure()\npl.subplot(121)\npl.imshow(C1)\npl.subplot(122)\npl.imshow(C2)\npl.show()" - ], + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, - "cell_type": "code" + "outputs": [], + "source": [ + "C1 = sp.spatial.distance.cdist(xs, xs)\nC2 = sp.spatial.distance.cdist(xt, xt)\n\nC1 /= C1.max()\nC2 /= C2.max()\n\npl.figure()\npl.subplot(121)\npl.imshow(C1)\npl.subplot(122)\npl.imshow(C2)\npl.show()" + ] }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Compute Gromov-Wasserstein plans and distance\n---------------------------------------------\n\n" - ], - "metadata": {}, - "cell_type": "markdown" + ] }, { - "outputs": [], - "source": [ - "p = ot.unif(n_samples)\nq = ot.unif(n_samples)\n\ngw0, log0 = ot.gromov.gromov_wasserstein(\n C1, C2, p, q, 'square_loss', verbose=True, log=True)\n\ngw, log = ot.gromov.entropic_gromov_wasserstein(\n C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True)\n\n\nprint('Gromov-Wasserstein distances: ' + str(log0['gw_dist']))\nprint('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist']))\n\n\npl.figure(1, (10, 5))\n\npl.subplot(1, 2, 1)\npl.imshow(gw0, cmap='jet')\npl.title('Gromov Wasserstein')\n\npl.subplot(1, 2, 2)\npl.imshow(gw, cmap='jet')\npl.title('Entropic Gromov Wasserstein')\n\npl.show()" - ], + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, - "cell_type": "code" + "outputs": [], + "source": [ + "p = ot.unif(n_samples)\nq = ot.unif(n_samples)\n\ngw0, log0 = ot.gromov.gromov_wasserstein(\n C1, C2, p, q, 'square_loss', verbose=True, log=True)\n\ngw, log = ot.gromov.entropic_gromov_wasserstein(\n C1, C2, p, q, 'square_loss', epsilon=5e-4, log=True, verbose=True)\n\n\nprint('Gromov-Wasserstein distances: ' + str(log0['gw_dist']))\nprint('Entropic Gromov-Wasserstein distances: ' + str(log['gw_dist']))\n\n\npl.figure(1, (10, 5))\n\npl.subplot(1, 2, 1)\npl.imshow(gw0, cmap='jet')\npl.title('Gromov Wasserstein')\n\npl.subplot(1, 2, 2)\npl.imshow(gw, cmap='jet')\npl.title('Entropic Gromov Wasserstein')\n\npl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" } - ] + }, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_gromov.py b/docs/source/auto_examples/plot_gromov.py index 5cd40f6..deb2f86 100644 --- a/docs/source/auto_examples/plot_gromov.py +++ b/docs/source/auto_examples/plot_gromov.py @@ -38,7 +38,7 @@ mu_t = np.array([4, 4, 4]) cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) -xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s) +xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) P = sp.linalg.sqrtm(cov_t) xt = np.random.randn(n_samples, 3).dot(P) + mu_t diff --git a/docs/source/auto_examples/plot_gromov.rst b/docs/source/auto_examples/plot_gromov.rst index 131861f..3ed4e11 100644 --- a/docs/source/auto_examples/plot_gromov.rst +++ b/docs/source/auto_examples/plot_gromov.rst @@ -54,7 +54,7 @@ two Gaussian distributions in 2- and 3-dimensional spaces. cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) - xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s) + xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) P = sp.linalg.sqrtm(cov_t) xt = np.random.randn(n_samples, 3).dot(P) + mu_t @@ -166,28 +166,28 @@ Compute Gromov-Wasserstein plans and distance It. |Loss |Delta loss -------------------------------- - 0|4.517558e-02|0.000000e+00 - 1|2.563483e-02|-7.622736e-01 - 2|2.443903e-02|-4.892972e-02 - 3|2.231600e-02|-9.513496e-02 - 4|1.676188e-02|-3.313541e-01 - 5|1.464792e-02|-1.443180e-01 - 6|1.454315e-02|-7.204526e-03 - 7|1.454142e-02|-1.185811e-04 - 8|1.454141e-02|-1.190466e-06 - 9|1.454141e-02|-1.190512e-08 - 10|1.454141e-02|-1.190520e-10 + 0|4.328711e-02|0.000000e+00 + 1|2.281369e-02|-8.974178e-01 + 2|1.843659e-02|-2.374139e-01 + 3|1.602820e-02|-1.502598e-01 + 4|1.353712e-02|-1.840179e-01 + 5|1.285687e-02|-5.290977e-02 + 6|1.284537e-02|-8.952931e-04 + 7|1.284525e-02|-8.989584e-06 + 8|1.284525e-02|-8.989950e-08 + 9|1.284525e-02|-8.989949e-10 It. |Err ------------------- - 0|6.743761e-02| - 10|5.477003e-04| - 20|2.461503e-08| - 30|1.205155e-11| - Gromov-Wasserstein distances: 0.014541405718693563 - Entropic Gromov-Wasserstein distances: 0.015800739725237274 + 0|7.263293e-02| + 10|1.737784e-02| + 20|7.783978e-03| + 30|3.399419e-07| + 40|3.751207e-11| + Gromov-Wasserstein distances: 0.012845252089244688 + Entropic Gromov-Wasserstein distances: 0.013543882352191079 -**Total running time of the script:** ( 0 minutes 1.448 seconds) +**Total running time of the script:** ( 0 minutes 1.916 seconds) diff --git a/docs/source/auto_examples/plot_optim_OTreg.ipynb b/docs/source/auto_examples/plot_optim_OTreg.ipynb index 02bf175..107c299 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.ipynb +++ b/docs/source/auto_examples/plot_optim_OTreg.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -8,17 +9,17 @@ "outputs": [], "source": [ "%matplotlib inline" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "\n# Regularized OT with generic solver\n\n\nIllustrates the use of the generic solver for regularized OT with\nuser-designed regularization term. It uses Conditional gradient as in [6] and\ngeneralized Conditional Gradient as proposed in [5][7].\n\n\n[5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, Optimal Transport for\nDomain Adaptation, in IEEE Transactions on Pattern Analysis and Machine\nIntelligence , vol.PP, no.99, pp.1-1.\n\n[6] Ferradans, S., Papadakis, N., Peyr\u00e9, G., & Aujol, J. F. (2014).\nRegularized discrete optimal transport. SIAM Journal on Imaging Sciences,\n7(3), 1853-1882.\n\n[7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized\nconditional gradient: analysis of convergence and applications.\narXiv preprint arXiv:1510.06567.\n\n\n\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -26,35 +27,35 @@ "outputs": [], "source": [ "import numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Generate data\n-------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std\nb = ot.datasets.get_1D_gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" - ], - "cell_type": "code" + "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\nb = ot.datasets.make_1D_gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Solve EMD\n---------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -62,17 +63,17 @@ "outputs": [], "source": [ "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Solve EMD with Frobenius norm regularization\n--------------------------------------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -80,17 +81,17 @@ "outputs": [], "source": [ "#%% Example with Frobenius norm regularization\n\n\ndef f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg = 1e-1\n\nGl2 = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n\npl.figure(3)\not.plot.plot1D_mat(a, b, Gl2, 'OT matrix Frob. reg')" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Solve EMD with entropic regularization\n--------------------------------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -98,17 +99,17 @@ "outputs": [], "source": [ "#%% Example with entropic regularization\n\n\ndef f(G):\n return np.sum(G * np.log(G))\n\n\ndef df(G):\n return np.log(G) + 1.\n\n\nreg = 1e-3\n\nGe = ot.optim.cg(a, b, M, reg, f, df, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Ge, 'OT matrix Entrop. reg')" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Solve EMD with Frobenius norm + entropic regularization\n-------------------------------------------------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -116,29 +117,28 @@ "outputs": [], "source": [ "#%% Example with Frobenius norm + entropic regularization with gcg\n\n\ndef f(G):\n return 0.5 * np.sum(G**2)\n\n\ndef df(G):\n return G\n\n\nreg1 = 1e-3\nreg2 = 1e-1\n\nGel2 = ot.optim.gcg(a, b, M, reg1, reg2, f, df, verbose=True)\n\npl.figure(5, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gel2, 'OT entropic + matrix Frob. reg')\npl.show()" - ], - "cell_type": "code" + ] } ], "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python", "codemirror_mode": { "name": "ipython", "version": 3 }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", "nbconvert_exporter": "python", - "version": "3.5.2", "pygments_lexer": "ipython3", - "file_extension": ".py", - "mimetype": "text/x-python" - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3", - "language": "python" + "version": "3.6.5" } }, - "nbformat_minor": 0, - "nbformat": 4 + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_optim_OTreg.py b/docs/source/auto_examples/plot_optim_OTreg.py index 92df016..2c58def 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.py +++ b/docs/source/auto_examples/plot_optim_OTreg.py @@ -42,8 +42,8 @@ n = 100 # nb bins x = np.arange(n, dtype=np.float64) # Gaussian distributions -a = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std -b = ot.datasets.get_1D_gauss(n, m=60, s=10) +a = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std +b = ot.datasets.make_1D_gauss(n, m=60, s=10) # loss matrix M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) diff --git a/docs/source/auto_examples/plot_optim_OTreg.rst b/docs/source/auto_examples/plot_optim_OTreg.rst index 5927428..844cba0 100644 --- a/docs/source/auto_examples/plot_optim_OTreg.rst +++ b/docs/source/auto_examples/plot_optim_OTreg.rst @@ -59,8 +59,8 @@ Generate data x = np.arange(n, dtype=np.float64) # Gaussian distributions - a = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std - b = ot.datasets.get_1D_gauss(n, m=60, s=10) + a = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std + b = ot.datasets.make_1D_gauss(n, m=60, s=10) # loss matrix M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) @@ -636,7 +636,7 @@ Solve EMD with Frobenius norm + entropic regularization 4|1.609284e-01|-1.111407e-12 -**Total running time of the script:** ( 0 minutes 2.589 seconds) +**Total running time of the script:** ( 0 minutes 1.990 seconds) diff --git a/docs/source/auto_examples/plot_otda_classes.ipynb b/docs/source/auto_examples/plot_otda_classes.ipynb index 6754fa5..643e760 100644 --- a/docs/source/auto_examples/plot_otda_classes.ipynb +++ b/docs/source/auto_examples/plot_otda_classes.ipynb @@ -1,126 +1,126 @@ { - "nbformat_minor": 0, - "nbformat": 4, "cells": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "\n# OT for domain adaptation\n\n\nThis example introduces a domain adaptation in a 2D setting and the 4 OTDA\napproaches currently supported in POT.\n\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport ot" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport ot" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Generate data\n-------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "n_source_samples = 150\nn_target_samples = 150\n\nXs, ys = ot.datasets.get_data_classif('3gauss', n_source_samples)\nXt, yt = ot.datasets.get_data_classif('3gauss2', n_target_samples)" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "n_source_samples = 150\nn_target_samples = 150\n\nXs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples)\nXt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples)" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Instantiate the different transport algorithms and fit them\n-----------------------------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# EMD Transport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport with Group lasso regularization\not_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)\not_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt)\n\n# Sinkhorn Transport with Group lasso regularization l1l2\not_l1l2 = ot.da.SinkhornL1l2Transport(reg_e=1e-1, reg_cl=2e0, max_iter=20,\n verbose=True)\not_l1l2.fit(Xs=Xs, ys=ys, Xt=Xt)\n\n# transport source samples onto target samples\ntransp_Xs_emd = ot_emd.transform(Xs=Xs)\ntransp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)\ntransp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs)\ntransp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs)" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# EMD Transport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport with Group lasso regularization\not_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)\not_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt)\n\n# Sinkhorn Transport with Group lasso regularization l1l2\not_l1l2 = ot.da.SinkhornL1l2Transport(reg_e=1e-1, reg_cl=2e0, max_iter=20,\n verbose=True)\not_l1l2.fit(Xs=Xs, ys=ys, Xt=Xt)\n\n# transport source samples onto target samples\ntransp_Xs_emd = ot_emd.transform(Xs=Xs)\ntransp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)\ntransp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs)\ntransp_Xs_l1l2 = ot_l1l2.transform(Xs=Xs)" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Fig 1 : plots source and target samples\n---------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "pl.figure(1, figsize=(10, 5))\npl.subplot(1, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Source samples')\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Target samples')\npl.tight_layout()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "pl.figure(1, figsize=(10, 5))\npl.subplot(1, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Source samples')\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Target samples')\npl.tight_layout()" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Fig 2 : plot optimal couplings and transported samples\n------------------------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "param_img = {'interpolation': 'nearest', 'cmap': 'spectral'}\n\npl.figure(2, figsize=(15, 8))\npl.subplot(2, 4, 1)\npl.imshow(ot_emd.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nEMDTransport')\n\npl.subplot(2, 4, 2)\npl.imshow(ot_sinkhorn.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornTransport')\n\npl.subplot(2, 4, 3)\npl.imshow(ot_lpl1.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornLpl1Transport')\n\npl.subplot(2, 4, 4)\npl.imshow(ot_l1l2.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornL1l2Transport')\n\npl.subplot(2, 4, 5)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nEmdTransport')\npl.legend(loc=\"lower left\")\n\npl.subplot(2, 4, 6)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nSinkhornTransport')\n\npl.subplot(2, 4, 7)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nSinkhornLpl1Transport')\n\npl.subplot(2, 4, 8)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_l1l2[:, 0], transp_Xs_l1l2[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nSinkhornL1l2Transport')\npl.tight_layout()\n\npl.show()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } + }, + "outputs": [], + "source": [ + "param_img = {'interpolation': 'nearest'}\n\npl.figure(2, figsize=(15, 8))\npl.subplot(2, 4, 1)\npl.imshow(ot_emd.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nEMDTransport')\n\npl.subplot(2, 4, 2)\npl.imshow(ot_sinkhorn.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornTransport')\n\npl.subplot(2, 4, 3)\npl.imshow(ot_lpl1.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornLpl1Transport')\n\npl.subplot(2, 4, 4)\npl.imshow(ot_l1l2.coupling_, **param_img)\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornL1l2Transport')\n\npl.subplot(2, 4, 5)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nEmdTransport')\npl.legend(loc=\"lower left\")\n\npl.subplot(2, 4, 6)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nSinkhornTransport')\n\npl.subplot(2, 4, 7)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nSinkhornLpl1Transport')\n\npl.subplot(2, 4, 8)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.3)\npl.scatter(transp_Xs_l1l2[:, 0], transp_Xs_l1l2[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.xticks([])\npl.yticks([])\npl.title('Transported samples\\nSinkhornL1l2Transport')\npl.tight_layout()\n\npl.show()" + ] } - ], + ], "metadata": { "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", "codemirror_mode": { - "version": 2, - "name": "ipython" - } + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" } - } + }, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_classes.py b/docs/source/auto_examples/plot_otda_classes.py index b14c11a..c311fbd 100644 --- a/docs/source/auto_examples/plot_otda_classes.py +++ b/docs/source/auto_examples/plot_otda_classes.py @@ -25,8 +25,8 @@ import ot n_source_samples = 150 n_target_samples = 150 -Xs, ys = ot.datasets.get_data_classif('3gauss', n_source_samples) -Xt, yt = ot.datasets.get_data_classif('3gauss2', n_target_samples) +Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples) +Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples) ############################################################################## @@ -82,7 +82,7 @@ pl.tight_layout() # Fig 2 : plot optimal couplings and transported samples # ------------------------------------------------------ -param_img = {'interpolation': 'nearest', 'cmap': 'spectral'} +param_img = {'interpolation': 'nearest'} pl.figure(2, figsize=(15, 8)) pl.subplot(2, 4, 1) diff --git a/docs/source/auto_examples/plot_otda_classes.rst b/docs/source/auto_examples/plot_otda_classes.rst index a5ab285..19756ff 100644 --- a/docs/source/auto_examples/plot_otda_classes.rst +++ b/docs/source/auto_examples/plot_otda_classes.rst @@ -42,8 +42,8 @@ Generate data n_source_samples = 150 n_target_samples = 150 - Xs, ys = ot.datasets.get_data_classif('3gauss', n_source_samples) - Xt, yt = ot.datasets.get_data_classif('3gauss2', n_target_samples) + Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples) + Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples) @@ -94,29 +94,29 @@ Instantiate the different transport algorithms and fit them It. |Loss |Delta loss -------------------------------- - 0|1.003747e+01|0.000000e+00 - 1|1.953263e+00|-4.138821e+00 - 2|1.744456e+00|-1.196969e-01 - 3|1.689268e+00|-3.267022e-02 - 4|1.666355e+00|-1.374998e-02 - 5|1.656125e+00|-6.177356e-03 - 6|1.651753e+00|-2.646960e-03 - 7|1.647261e+00|-2.726957e-03 - 8|1.642274e+00|-3.036672e-03 - 9|1.639926e+00|-1.431818e-03 - 10|1.638750e+00|-7.173837e-04 - 11|1.637558e+00|-7.281753e-04 - 12|1.636248e+00|-8.002067e-04 - 13|1.634555e+00|-1.036074e-03 - 14|1.633547e+00|-6.166646e-04 - 15|1.633531e+00|-1.022614e-05 - 16|1.632957e+00|-3.510986e-04 - 17|1.632853e+00|-6.380944e-05 - 18|1.632704e+00|-9.122988e-05 - 19|1.632237e+00|-2.861276e-04 + 0|9.566309e+00|0.000000e+00 + 1|2.169680e+00|-3.409088e+00 + 2|1.914989e+00|-1.329986e-01 + 3|1.860251e+00|-2.942498e-02 + 4|1.838073e+00|-1.206621e-02 + 5|1.827064e+00|-6.025122e-03 + 6|1.820899e+00|-3.386082e-03 + 7|1.817290e+00|-1.985705e-03 + 8|1.814644e+00|-1.458223e-03 + 9|1.812661e+00|-1.093816e-03 + 10|1.810239e+00|-1.338121e-03 + 11|1.809100e+00|-6.296940e-04 + 12|1.807939e+00|-6.420646e-04 + 13|1.806965e+00|-5.389118e-04 + 14|1.806822e+00|-7.889599e-05 + 15|1.806193e+00|-3.482356e-04 + 16|1.805735e+00|-2.536930e-04 + 17|1.805321e+00|-2.292667e-04 + 18|1.804389e+00|-5.170222e-04 + 19|1.803908e+00|-2.661907e-04 It. |Loss |Delta loss -------------------------------- - 20|1.632174e+00|-3.896483e-05 + 20|1.803696e+00|-1.178279e-04 Fig 1 : plots source and target samples @@ -161,7 +161,7 @@ Fig 2 : plot optimal couplings and transported samples .. code-block:: python - param_img = {'interpolation': 'nearest', 'cmap': 'spectral'} + param_img = {'interpolation': 'nearest'} pl.figure(2, figsize=(15, 8)) pl.subplot(2, 4, 1) @@ -236,11 +236,13 @@ Fig 2 : plot optimal couplings and transported samples -**Total running time of the script:** ( 0 minutes 2.308 seconds) +**Total running time of the script:** ( 0 minutes 1.423 seconds) -.. container:: sphx-glr-footer +.. only :: html + + .. container:: sphx-glr-footer .. container:: sphx-glr-download @@ -253,6 +255,9 @@ Fig 2 : plot optimal couplings and transported samples :download:`Download Jupyter notebook: plot_otda_classes.ipynb ` -.. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_d2.ipynb b/docs/source/auto_examples/plot_otda_d2.ipynb index 9c58e64..b9002ee 100644 --- a/docs/source/auto_examples/plot_otda_d2.ipynb +++ b/docs/source/auto_examples/plot_otda_d2.ipynb @@ -1,6 +1,7 @@ { "cells": [ { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -8,17 +9,17 @@ "outputs": [], "source": [ "%matplotlib inline" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "\n# OT for domain adaptation on empirical distributions\n\n\nThis example introduces a domain adaptation in a 2D setting. It explicits\nthe problem of domain adaptation and introduces some optimal transport\napproaches to solve it.\n\nQuantities such as optimal couplings, greater coupling coefficients and\ntransported samples are represented in order to give a visual understanding\nof what the transport methods are doing.\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -26,35 +27,35 @@ "outputs": [], "source": [ "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "generate data\n-------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ - "n_samples_source = 150\nn_samples_target = 150\n\nXs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source)\nXt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target)\n\n# Cost matrix\nM = ot.dist(Xs, Xt, metric='sqeuclidean')" - ], - "cell_type": "code" + "n_samples_source = 150\nn_samples_target = 150\n\nXs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source)\nXt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target)\n\n# Cost matrix\nM = ot.dist(Xs, Xt, metric='sqeuclidean')" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Instantiate the different transport algorithms and fit them\n-----------------------------------------------------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -62,17 +63,17 @@ "outputs": [], "source": [ "# EMD Transport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# Sinkhorn Transport with Group lasso regularization\not_lpl1 = ot.da.SinkhornLpl1Transport(reg_e=1e-1, reg_cl=1e0)\not_lpl1.fit(Xs=Xs, ys=ys, Xt=Xt)\n\n# transport source samples onto target samples\ntransp_Xs_emd = ot_emd.transform(Xs=Xs)\ntransp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=Xs)\ntransp_Xs_lpl1 = ot_lpl1.transform(Xs=Xs)" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Fig 1 : plots source and target samples + matrix of pairwise distance\n---------------------------------------------------------------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -80,17 +81,17 @@ "outputs": [], "source": [ "pl.figure(1, figsize=(10, 10))\npl.subplot(2, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Source samples')\n\npl.subplot(2, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Target samples')\n\npl.subplot(2, 2, 3)\npl.imshow(M, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Matrix of pairwise distances')\npl.tight_layout()" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Fig 2 : plots optimal couplings for the different methods\n---------------------------------------------------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -98,17 +99,17 @@ "outputs": [], "source": [ "pl.figure(2, figsize=(10, 6))\n\npl.subplot(2, 3, 1)\npl.imshow(ot_emd.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nEMDTransport')\n\npl.subplot(2, 3, 2)\npl.imshow(ot_sinkhorn.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornTransport')\n\npl.subplot(2, 3, 3)\npl.imshow(ot_lpl1.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSinkhornLpl1Transport')\n\npl.subplot(2, 3, 4)\not.plot.plot2D_samples_mat(Xs, Xt, ot_emd.coupling_, c=[.5, .5, 1])\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.title('Main coupling coefficients\\nEMDTransport')\n\npl.subplot(2, 3, 5)\not.plot.plot2D_samples_mat(Xs, Xt, ot_sinkhorn.coupling_, c=[.5, .5, 1])\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.title('Main coupling coefficients\\nSinkhornTransport')\n\npl.subplot(2, 3, 6)\not.plot.plot2D_samples_mat(Xs, Xt, ot_lpl1.coupling_, c=[.5, .5, 1])\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.title('Main coupling coefficients\\nSinkhornLpl1Transport')\npl.tight_layout()" - ], - "cell_type": "code" + ] }, { + "cell_type": "markdown", "metadata": {}, "source": [ "Fig 3 : plot transported samples\n--------------------------------\n\n" - ], - "cell_type": "markdown" + ] }, { + "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false @@ -116,29 +117,28 @@ "outputs": [], "source": [ "# display transported samples\npl.figure(4, figsize=(10, 4))\npl.subplot(1, 3, 1)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_emd[:, 0], transp_Xs_emd[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nEmdTransport')\npl.legend(loc=0)\npl.xticks([])\npl.yticks([])\n\npl.subplot(1, 3, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_sinkhorn[:, 0], transp_Xs_sinkhorn[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nSinkhornTransport')\npl.xticks([])\npl.yticks([])\n\npl.subplot(1, 3, 3)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_lpl1[:, 0], transp_Xs_lpl1[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nSinkhornLpl1Transport')\npl.xticks([])\npl.yticks([])\n\npl.tight_layout()\npl.show()" - ], - "cell_type": "code" + ] } ], "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "name": "python", "codemirror_mode": { "name": "ipython", "version": 3 }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", "nbconvert_exporter": "python", - "version": "3.5.2", "pygments_lexer": "ipython3", - "file_extension": ".py", - "mimetype": "text/x-python" - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3", - "language": "python" + "version": "3.6.5" } }, - "nbformat_minor": 0, - "nbformat": 4 + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_d2.py b/docs/source/auto_examples/plot_otda_d2.py index 70beb35..cf22c2f 100644 --- a/docs/source/auto_examples/plot_otda_d2.py +++ b/docs/source/auto_examples/plot_otda_d2.py @@ -29,8 +29,8 @@ import ot.plot n_samples_source = 150 n_samples_target = 150 -Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source) -Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target) +Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source) +Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target) # Cost matrix M = ot.dist(Xs, Xt, metric='sqeuclidean') diff --git a/docs/source/auto_examples/plot_otda_d2.rst b/docs/source/auto_examples/plot_otda_d2.rst index e5a60c4..80cc34c 100644 --- a/docs/source/auto_examples/plot_otda_d2.rst +++ b/docs/source/auto_examples/plot_otda_d2.rst @@ -46,8 +46,8 @@ generate data n_samples_source = 150 n_samples_target = 150 - Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source) - Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target) + Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source) + Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target) # Cost matrix M = ot.dist(Xs, Xt, metric='sqeuclidean') @@ -242,7 +242,7 @@ Fig 3 : plot transported samples -**Total running time of the script:** ( 0 minutes 39.829 seconds) +**Total running time of the script:** ( 0 minutes 35.515 seconds) diff --git a/docs/source/auto_examples/plot_otda_mapping.ipynb b/docs/source/auto_examples/plot_otda_mapping.ipynb index 0374146..898466d 100644 --- a/docs/source/auto_examples/plot_otda_mapping.ipynb +++ b/docs/source/auto_examples/plot_otda_mapping.ipynb @@ -1,126 +1,126 @@ { - "nbformat_minor": 0, - "nbformat": 4, "cells": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "\n# OT mapping estimation for domain adaptation\n\n\nThis example presents how to use MappingTransport to estimate at the same\ntime both the coupling transport and approximate the transport map with either\na linear or a kernelized mapping as introduced in [8].\n\n[8] M. Perrot, N. Courty, R. Flamary, A. Habrard,\n \"Mapping estimation for discrete optimal transport\",\n Neural Information Processing Systems (NIPS), 2016.\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Generate data\n-------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "n_source_samples = 100\nn_target_samples = 100\ntheta = 2 * np.pi / 20\nnoise_level = 0.1\n\nXs, ys = ot.datasets.get_data_classif(\n 'gaussrot', n_source_samples, nz=noise_level)\nXs_new, _ = ot.datasets.get_data_classif(\n 'gaussrot', n_source_samples, nz=noise_level)\nXt, yt = ot.datasets.get_data_classif(\n 'gaussrot', n_target_samples, theta=theta, nz=noise_level)\n\n# one of the target mode changes its variance (no linear mapping)\nXt[yt == 2] *= 3\nXt = Xt + 4" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "n_source_samples = 100\nn_target_samples = 100\ntheta = 2 * np.pi / 20\nnoise_level = 0.1\n\nXs, ys = ot.datasets.make_data_classif(\n 'gaussrot', n_source_samples, nz=noise_level)\nXs_new, _ = ot.datasets.make_data_classif(\n 'gaussrot', n_source_samples, nz=noise_level)\nXt, yt = ot.datasets.make_data_classif(\n 'gaussrot', n_target_samples, theta=theta, nz=noise_level)\n\n# one of the target mode changes its variance (no linear mapping)\nXt[yt == 2] *= 3\nXt = Xt + 4" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Plot data\n---------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "pl.figure(1, (10, 5))\npl.clf()\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.legend(loc=0)\npl.title('Source and target distributions')" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "pl.figure(1, (10, 5))\npl.clf()\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.legend(loc=0)\npl.title('Source and target distributions')" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Instantiate the different transport algorithms and fit them\n-----------------------------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# MappingTransport with linear kernel\not_mapping_linear = ot.da.MappingTransport(\n kernel=\"linear\", mu=1e0, eta=1e-8, bias=True,\n max_iter=20, verbose=True)\n\not_mapping_linear.fit(Xs=Xs, Xt=Xt)\n\n# for original source samples, transform applies barycentric mapping\ntransp_Xs_linear = ot_mapping_linear.transform(Xs=Xs)\n\n# for out of source samples, transform applies the linear mapping\ntransp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new)\n\n\n# MappingTransport with gaussian kernel\not_mapping_gaussian = ot.da.MappingTransport(\n kernel=\"gaussian\", eta=1e-5, mu=1e-1, bias=True, sigma=1,\n max_iter=10, verbose=True)\not_mapping_gaussian.fit(Xs=Xs, Xt=Xt)\n\n# for original source samples, transform applies barycentric mapping\ntransp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs)\n\n# for out of source samples, transform applies the gaussian mapping\ntransp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new)" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# MappingTransport with linear kernel\not_mapping_linear = ot.da.MappingTransport(\n kernel=\"linear\", mu=1e0, eta=1e-8, bias=True,\n max_iter=20, verbose=True)\n\not_mapping_linear.fit(Xs=Xs, Xt=Xt)\n\n# for original source samples, transform applies barycentric mapping\ntransp_Xs_linear = ot_mapping_linear.transform(Xs=Xs)\n\n# for out of source samples, transform applies the linear mapping\ntransp_Xs_linear_new = ot_mapping_linear.transform(Xs=Xs_new)\n\n\n# MappingTransport with gaussian kernel\not_mapping_gaussian = ot.da.MappingTransport(\n kernel=\"gaussian\", eta=1e-5, mu=1e-1, bias=True, sigma=1,\n max_iter=10, verbose=True)\not_mapping_gaussian.fit(Xs=Xs, Xt=Xt)\n\n# for original source samples, transform applies barycentric mapping\ntransp_Xs_gaussian = ot_mapping_gaussian.transform(Xs=Xs)\n\n# for out of source samples, transform applies the gaussian mapping\ntransp_Xs_gaussian_new = ot_mapping_gaussian.transform(Xs=Xs_new)" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Plot transported samples\n------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "pl.figure(2)\npl.clf()\npl.subplot(2, 2, 1)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=.2)\npl.scatter(transp_Xs_linear[:, 0], transp_Xs_linear[:, 1], c=ys, marker='+',\n label='Mapped source samples')\npl.title(\"Bary. mapping (linear)\")\npl.legend(loc=0)\n\npl.subplot(2, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=.2)\npl.scatter(transp_Xs_linear_new[:, 0], transp_Xs_linear_new[:, 1],\n c=ys, marker='+', label='Learned mapping')\npl.title(\"Estim. mapping (linear)\")\n\npl.subplot(2, 2, 3)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=.2)\npl.scatter(transp_Xs_gaussian[:, 0], transp_Xs_gaussian[:, 1], c=ys,\n marker='+', label='barycentric mapping')\npl.title(\"Bary. mapping (kernel)\")\n\npl.subplot(2, 2, 4)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=.2)\npl.scatter(transp_Xs_gaussian_new[:, 0], transp_Xs_gaussian_new[:, 1], c=ys,\n marker='+', label='Learned mapping')\npl.title(\"Estim. mapping (kernel)\")\npl.tight_layout()\n\npl.show()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } + }, + "outputs": [], + "source": [ + "pl.figure(2)\npl.clf()\npl.subplot(2, 2, 1)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=.2)\npl.scatter(transp_Xs_linear[:, 0], transp_Xs_linear[:, 1], c=ys, marker='+',\n label='Mapped source samples')\npl.title(\"Bary. mapping (linear)\")\npl.legend(loc=0)\n\npl.subplot(2, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=.2)\npl.scatter(transp_Xs_linear_new[:, 0], transp_Xs_linear_new[:, 1],\n c=ys, marker='+', label='Learned mapping')\npl.title(\"Estim. mapping (linear)\")\n\npl.subplot(2, 2, 3)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=.2)\npl.scatter(transp_Xs_gaussian[:, 0], transp_Xs_gaussian[:, 1], c=ys,\n marker='+', label='barycentric mapping')\npl.title(\"Bary. mapping (kernel)\")\n\npl.subplot(2, 2, 4)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=.2)\npl.scatter(transp_Xs_gaussian_new[:, 0], transp_Xs_gaussian_new[:, 1], c=ys,\n marker='+', label='Learned mapping')\npl.title(\"Estim. mapping (kernel)\")\npl.tight_layout()\n\npl.show()" + ] } - ], + ], "metadata": { "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", "codemirror_mode": { - "version": 2, - "name": "ipython" - } + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" } - } + }, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_mapping.py b/docs/source/auto_examples/plot_otda_mapping.py index 167c3a1..5880adf 100644 --- a/docs/source/auto_examples/plot_otda_mapping.py +++ b/docs/source/auto_examples/plot_otda_mapping.py @@ -32,11 +32,11 @@ n_target_samples = 100 theta = 2 * np.pi / 20 noise_level = 0.1 -Xs, ys = ot.datasets.get_data_classif( +Xs, ys = ot.datasets.make_data_classif( 'gaussrot', n_source_samples, nz=noise_level) -Xs_new, _ = ot.datasets.get_data_classif( +Xs_new, _ = ot.datasets.make_data_classif( 'gaussrot', n_source_samples, nz=noise_level) -Xt, yt = ot.datasets.get_data_classif( +Xt, yt = ot.datasets.make_data_classif( 'gaussrot', n_target_samples, theta=theta, nz=noise_level) # one of the target mode changes its variance (no linear mapping) diff --git a/docs/source/auto_examples/plot_otda_mapping.rst b/docs/source/auto_examples/plot_otda_mapping.rst index 1e3a709..1d95fc6 100644 --- a/docs/source/auto_examples/plot_otda_mapping.rst +++ b/docs/source/auto_examples/plot_otda_mapping.rst @@ -49,11 +49,11 @@ Generate data theta = 2 * np.pi / 20 noise_level = 0.1 - Xs, ys = ot.datasets.get_data_classif( + Xs, ys = ot.datasets.make_data_classif( 'gaussrot', n_source_samples, nz=noise_level) - Xs_new, _ = ot.datasets.get_data_classif( + Xs_new, _ = ot.datasets.make_data_classif( 'gaussrot', n_source_samples, nz=noise_level) - Xt, yt = ot.datasets.get_data_classif( + Xt, yt = ot.datasets.make_data_classif( 'gaussrot', n_target_samples, theta=theta, nz=noise_level) # one of the target mode changes its variance (no linear mapping) @@ -136,26 +136,26 @@ Instantiate the different transport algorithms and fit them It. |Loss |Delta loss -------------------------------- - 0|4.231423e+03|0.000000e+00 - 1|4.217955e+03|-3.182835e-03 - 2|4.217580e+03|-8.885864e-05 - 3|4.217451e+03|-3.043162e-05 - 4|4.217368e+03|-1.978325e-05 - 5|4.217312e+03|-1.338471e-05 - 6|4.217307e+03|-1.000290e-06 + 0|4.299275e+03|0.000000e+00 + 1|4.290443e+03|-2.054271e-03 + 2|4.290040e+03|-9.389994e-05 + 3|4.289876e+03|-3.830707e-05 + 4|4.289783e+03|-2.157428e-05 + 5|4.289724e+03|-1.390941e-05 + 6|4.289706e+03|-4.051054e-06 It. |Loss |Delta loss -------------------------------- - 0|4.257004e+02|0.000000e+00 - 1|4.208978e+02|-1.128168e-02 - 2|4.205168e+02|-9.052112e-04 - 3|4.203566e+02|-3.810681e-04 - 4|4.202570e+02|-2.369884e-04 - 5|4.201844e+02|-1.726132e-04 - 6|4.201341e+02|-1.196461e-04 - 7|4.200941e+02|-9.525441e-05 - 8|4.200630e+02|-7.405552e-05 - 9|4.200377e+02|-6.031884e-05 - 10|4.200168e+02|-4.968324e-05 + 0|4.326465e+02|0.000000e+00 + 1|4.282533e+02|-1.015416e-02 + 2|4.279473e+02|-7.145955e-04 + 3|4.277941e+02|-3.580104e-04 + 4|4.277069e+02|-2.039229e-04 + 5|4.276462e+02|-1.418698e-04 + 6|4.276011e+02|-1.054172e-04 + 7|4.275663e+02|-8.145802e-05 + 8|4.275405e+02|-6.028774e-05 + 9|4.275191e+02|-5.005886e-05 + 10|4.275019e+02|-4.021935e-05 Plot transported samples @@ -208,11 +208,13 @@ Plot transported samples -**Total running time of the script:** ( 0 minutes 0.970 seconds) +**Total running time of the script:** ( 0 minutes 0.795 seconds) -.. container:: sphx-glr-footer +.. only :: html + + .. container:: sphx-glr-footer .. container:: sphx-glr-download @@ -225,6 +227,9 @@ Plot transported samples :download:`Download Jupyter notebook: plot_otda_mapping.ipynb ` -.. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_semi_supervised.ipynb b/docs/source/auto_examples/plot_otda_semi_supervised.ipynb index 783bf84..e3192da 100644 --- a/docs/source/auto_examples/plot_otda_semi_supervised.ipynb +++ b/docs/source/auto_examples/plot_otda_semi_supervised.ipynb @@ -1,144 +1,144 @@ { - "nbformat_minor": 0, - "nbformat": 4, "cells": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "\n# OTDA unsupervised vs semi-supervised setting\n\n\nThis example introduces a semi supervised domain adaptation in a 2D setting.\nIt explicits the problem of semi supervised domain adaptation and introduces\nsome optimal transport approaches to solve it.\n\nQuantities such as optimal couplings, greater coupling coefficients and\ntransported samples are represented in order to give a visual understanding\nof what the transport methods are doing.\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport ot" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport ot" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Generate data\n-------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "n_samples_source = 150\nn_samples_target = 150\n\nXs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source)\nXt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target)" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "n_samples_source = 150\nn_samples_target = 150\n\nXs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source)\nXt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target)" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Transport source samples onto target samples\n--------------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# unsupervised domain adaptation\not_sinkhorn_un = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn_un.fit(Xs=Xs, Xt=Xt)\ntransp_Xs_sinkhorn_un = ot_sinkhorn_un.transform(Xs=Xs)\n\n# semi-supervised domain adaptation\not_sinkhorn_semi = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn_semi.fit(Xs=Xs, Xt=Xt, ys=ys, yt=yt)\ntransp_Xs_sinkhorn_semi = ot_sinkhorn_semi.transform(Xs=Xs)\n\n# semi supervised DA uses available labaled target samples to modify the cost\n# matrix involved in the OT problem. The cost of transporting a source sample\n# of class A onto a target sample of class B != A is set to infinite, or a\n# very large value\n\n# note that in the present case we consider that all the target samples are\n# labeled. For daily applications, some target sample might not have labels,\n# in this case the element of yt corresponding to these samples should be\n# filled with -1.\n\n# Warning: we recall that -1 cannot be used as a class label" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# unsupervised domain adaptation\not_sinkhorn_un = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn_un.fit(Xs=Xs, Xt=Xt)\ntransp_Xs_sinkhorn_un = ot_sinkhorn_un.transform(Xs=Xs)\n\n# semi-supervised domain adaptation\not_sinkhorn_semi = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn_semi.fit(Xs=Xs, Xt=Xt, ys=ys, yt=yt)\ntransp_Xs_sinkhorn_semi = ot_sinkhorn_semi.transform(Xs=Xs)\n\n# semi supervised DA uses available labaled target samples to modify the cost\n# matrix involved in the OT problem. The cost of transporting a source sample\n# of class A onto a target sample of class B != A is set to infinite, or a\n# very large value\n\n# note that in the present case we consider that all the target samples are\n# labeled. For daily applications, some target sample might not have labels,\n# in this case the element of yt corresponding to these samples should be\n# filled with -1.\n\n# Warning: we recall that -1 cannot be used as a class label" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Fig 1 : plots source and target samples + matrix of pairwise distance\n---------------------------------------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "pl.figure(1, figsize=(10, 10))\npl.subplot(2, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Source samples')\n\npl.subplot(2, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Target samples')\n\npl.subplot(2, 2, 3)\npl.imshow(ot_sinkhorn_un.cost_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Cost matrix - unsupervised DA')\n\npl.subplot(2, 2, 4)\npl.imshow(ot_sinkhorn_semi.cost_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Cost matrix - semisupervised DA')\n\npl.tight_layout()\n\n# the optimal coupling in the semi-supervised DA case will exhibit \" shape\n# similar\" to the cost matrix, (block diagonal matrix)" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "pl.figure(1, figsize=(10, 10))\npl.subplot(2, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 1], c=ys, marker='+', label='Source samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Source samples')\n\npl.subplot(2, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o', label='Target samples')\npl.xticks([])\npl.yticks([])\npl.legend(loc=0)\npl.title('Target samples')\n\npl.subplot(2, 2, 3)\npl.imshow(ot_sinkhorn_un.cost_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Cost matrix - unsupervised DA')\n\npl.subplot(2, 2, 4)\npl.imshow(ot_sinkhorn_semi.cost_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Cost matrix - semisupervised DA')\n\npl.tight_layout()\n\n# the optimal coupling in the semi-supervised DA case will exhibit \" shape\n# similar\" to the cost matrix, (block diagonal matrix)" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Fig 2 : plots optimal couplings for the different methods\n---------------------------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "pl.figure(2, figsize=(8, 4))\n\npl.subplot(1, 2, 1)\npl.imshow(ot_sinkhorn_un.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nUnsupervised DA')\n\npl.subplot(1, 2, 2)\npl.imshow(ot_sinkhorn_semi.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSemi-supervised DA')\n\npl.tight_layout()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "pl.figure(2, figsize=(8, 4))\n\npl.subplot(1, 2, 1)\npl.imshow(ot_sinkhorn_un.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nUnsupervised DA')\n\npl.subplot(1, 2, 2)\npl.imshow(ot_sinkhorn_semi.coupling_, interpolation='nearest')\npl.xticks([])\npl.yticks([])\npl.title('Optimal coupling\\nSemi-supervised DA')\n\npl.tight_layout()" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Fig 3 : plot transported samples\n--------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# display transported samples\npl.figure(4, figsize=(8, 4))\npl.subplot(1, 2, 1)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_sinkhorn_un[:, 0], transp_Xs_sinkhorn_un[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nEmdTransport')\npl.legend(loc=0)\npl.xticks([])\npl.yticks([])\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_sinkhorn_semi[:, 0], transp_Xs_sinkhorn_semi[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nSinkhornTransport')\npl.xticks([])\npl.yticks([])\n\npl.tight_layout()\npl.show()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } + }, + "outputs": [], + "source": [ + "# display transported samples\npl.figure(4, figsize=(8, 4))\npl.subplot(1, 2, 1)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_sinkhorn_un[:, 0], transp_Xs_sinkhorn_un[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nEmdTransport')\npl.legend(loc=0)\npl.xticks([])\npl.yticks([])\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 1], c=yt, marker='o',\n label='Target samples', alpha=0.5)\npl.scatter(transp_Xs_sinkhorn_semi[:, 0], transp_Xs_sinkhorn_semi[:, 1], c=ys,\n marker='+', label='Transp samples', s=30)\npl.title('Transported samples\\nSinkhornTransport')\npl.xticks([])\npl.yticks([])\n\npl.tight_layout()\npl.show()" + ] } - ], + ], "metadata": { "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", "codemirror_mode": { - "version": 2, - "name": "ipython" - } + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" } - } + }, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_semi_supervised.py b/docs/source/auto_examples/plot_otda_semi_supervised.py index 7963aef..8a67720 100644 --- a/docs/source/auto_examples/plot_otda_semi_supervised.py +++ b/docs/source/auto_examples/plot_otda_semi_supervised.py @@ -29,8 +29,8 @@ import ot n_samples_source = 150 n_samples_target = 150 -Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source) -Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target) +Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source) +Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target) ############################################################################## diff --git a/docs/source/auto_examples/plot_otda_semi_supervised.rst b/docs/source/auto_examples/plot_otda_semi_supervised.rst index dc05ed0..2ed7819 100644 --- a/docs/source/auto_examples/plot_otda_semi_supervised.rst +++ b/docs/source/auto_examples/plot_otda_semi_supervised.rst @@ -46,8 +46,8 @@ Generate data n_samples_source = 150 n_samples_target = 150 - Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source) - Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target) + Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source) + Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target) @@ -218,11 +218,13 @@ Fig 3 : plot transported samples -**Total running time of the script:** ( 0 minutes 0.714 seconds) +**Total running time of the script:** ( 0 minutes 0.256 seconds) -.. container:: sphx-glr-footer +.. only :: html + + .. container:: sphx-glr-footer .. container:: sphx-glr-download @@ -235,6 +237,9 @@ Fig 3 : plot transported samples :download:`Download Jupyter notebook: plot_otda_semi_supervised.ipynb ` -.. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/conf.py b/docs/source/conf.py index 4105d87..114245d 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -81,7 +81,7 @@ master_doc = 'index' # General information about the project. project = u'POT Python Optimal Transport' -copyright = u'2016, Rémi Flamary, Nicolas Courty' +copyright = u'2016-2018, Rémi Flamary, Nicolas Courty' author = u'Rémi Flamary, Nicolas Courty' # The version info for the project you're documenting, acts as replacement for diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 725c207..5d37f64 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -14,7 +14,11 @@ It provides the following solvers: [1]. - Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10] with optional GPU implementation - (required cudamat). + (requires cudamat). +- Smooth optimal transport solvers (dual and semi-dual) for KL and + squared L2 regularizations [17]. +- Non regularized Wasserstein barycenters [16] with LP solver (only + small scale). - Bregman projections for Wasserstein barycenter [3] and unmixing [4]. - Optimal transport for domain adaptation with group lasso regularization [5] @@ -29,6 +33,21 @@ It provides the following solvers: Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. +Using and citing the toolbox +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +If you use this toolbox in your research and find it useful, please cite +POT using the following bibtex reference: + +:: + + @misc{flamary2017pot, + title={POT Python Optimal Transport library}, + author={Flamary, R{'e}mi and Courty, Nicolas}, + url={https://github.com/rflamary/POT}, + year={2017} + } + Installation ------------ @@ -37,7 +56,7 @@ C++ compiler for using the EMD solver and relies on the following Python modules: - Numpy (>=1.11) -- Scipy (>=0.17) +- Scipy (>=1.0) - Cython (>=0.23) - Matplotlib (>=1.5) @@ -212,20 +231,6 @@ languages): - `Marco Cuturi `__ (Sinkhorn Knopp in Matlab/Cuda) -Using and citing the toolbox ----------------------------- - -If you use this toolbox in your research and find it useful, please cite -POT using the following bibtex reference: - -:: - - @article{flamary2017pot, - title={POT Python Optimal Transport library}, - author={Flamary, R{\'e}mi and Courty, Nicolas}, - year={2017} - } - Contributions and code of conduct --------------------------------- @@ -320,6 +325,15 @@ Journal of Optimization Theory and Applications Vol 43. [15] Peyré, G., & Cuturi, M. (2018). `Computational Optimal Transport `__ . +[16] Agueh, M., & Carlier, G. (2011). `Barycenters in the Wasserstein +space `__. SIAM +Journal on Mathematical Analysis, 43(2), 904-924. + +[17] Blondel, M., Seguy, V., & Rolet, A. (2018). `Smooth and Sparse +Optimal Transport `__. Proceedings of +the Twenty-First International Conference on Artificial Intelligence and +Statistics (AISTATS). + .. |PyPI version| image:: https://badge.fury.io/py/POT.svg :target: https://badge.fury.io/py/POT .. |Anaconda Cloud| image:: https://anaconda.org/conda-forge/pot/badges/version.svg diff --git a/examples/plot_OT_1D.py b/examples/plot_OT_1D.py index 90325c9..f33e2a4 100644 --- a/examples/plot_OT_1D.py +++ b/examples/plot_OT_1D.py @@ -17,7 +17,7 @@ import numpy as np import matplotlib.pylab as pl import ot import ot.plot -from ot.datasets import get_1D_gauss as gauss +from ot.datasets import make_1D_gauss as gauss ############################################################################## # Generate data diff --git a/examples/plot_OT_2D_samples.py b/examples/plot_OT_2D_samples.py index 9818ec5..bb952a0 100644 --- a/examples/plot_OT_2D_samples.py +++ b/examples/plot_OT_2D_samples.py @@ -16,6 +16,7 @@ sum of diracs. The OT matrix is plotted with the samples. import numpy as np import matplotlib.pylab as pl import ot +import ot.plot ############################################################################## # Generate data @@ -31,8 +32,8 @@ cov_s = np.array([[1, 0], [0, 1]]) mu_t = np.array([4, 4]) cov_t = np.array([[1, -.8], [-.8, 1]]) -xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s) -xt = ot.datasets.get_2D_samples_gauss(n, mu_t, cov_t) +xs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s) +xt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t) a, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples diff --git a/examples/plot_barycenter_1D.py b/examples/plot_barycenter_1D.py index ecf640c..5ed9f3f 100644 --- a/examples/plot_barycenter_1D.py +++ b/examples/plot_barycenter_1D.py @@ -37,8 +37,8 @@ n = 100 # nb bins x = np.arange(n, dtype=np.float64) # Gaussian distributions -a1 = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std -a2 = ot.datasets.get_1D_gauss(n, m=60, s=8) +a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std +a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) # creating matrix A containing all distributions A = np.vstack((a1, a2)).T diff --git a/examples/plot_barycenter_lp_vs_entropic.py b/examples/plot_barycenter_lp_vs_entropic.py index 6936bbb..b82765e 100644 --- a/examples/plot_barycenter_lp_vs_entropic.py +++ b/examples/plot_barycenter_lp_vs_entropic.py @@ -15,8 +15,6 @@ Wasserstein problems. SIAM Journal on Imaging Sciences, 9(1), 320-343. Iterative Bregman projections for regularized transportation problems SIAM Journal on Scientific Computing, 37(2), A1111-A1138. - - """ # Author: Remi Flamary @@ -32,8 +30,8 @@ from matplotlib.collections import PolyCollection # noqa #import ot.lp.cvx as cvx -# -# Generate data +############################################################################## +# Gaussian Data # ------------- #%% parameters @@ -47,8 +45,8 @@ x = np.arange(n, dtype=np.float64) # Gaussian distributions # Gaussian distributions -a1 = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std -a2 = ot.datasets.get_1D_gauss(n, m=60, s=8) +a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std +a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) # creating matrix A containing all distributions A = np.vstack((a1, a2)).T @@ -58,9 +56,6 @@ n_distributions = A.shape[1] M = ot.utils.dist0(n) M /= M.max() -# -# Plot data -# --------- #%% plot the distributions @@ -70,10 +65,6 @@ for i in range(n_distributions): pl.title('Distributions') pl.tight_layout() -# -# Barycenter computation -# ---------------------- - #%% barycenter computation alpha = 0.5 # 0<=alpha<=1 @@ -110,6 +101,10 @@ pl.tight_layout() problems.append([A, [bary_l2, bary_wass, bary_wass2]]) +############################################################################## +# Dirac Data +# ---------- + #%% parameters a1 = 1.0 * (x > 10) * (x < 50) @@ -135,9 +130,6 @@ for i in range(n_distributions): pl.title('Distributions') pl.tight_layout() -# -# Barycenter computation -# ---------------------- #%% barycenter computation @@ -207,9 +199,6 @@ for i in range(n_distributions): pl.title('Distributions') pl.tight_layout() -# -# Barycenter computation -# ---------------------- #%% barycenter computation @@ -249,7 +238,7 @@ pl.title('Barycenters') pl.tight_layout() -# +############################################################################## # Final figure # ------------ # diff --git a/examples/plot_compute_emd.py b/examples/plot_compute_emd.py index 73b42c3..7ed2b01 100644 --- a/examples/plot_compute_emd.py +++ b/examples/plot_compute_emd.py @@ -17,7 +17,7 @@ ground metrics and plot their values for diffeent distributions. import numpy as np import matplotlib.pylab as pl import ot -from ot.datasets import get_1D_gauss as gauss +from ot.datasets import make_1D_gauss as gauss ############################################################################## diff --git a/examples/plot_gromov.py b/examples/plot_gromov.py index 5cd40f6..deb2f86 100644 --- a/examples/plot_gromov.py +++ b/examples/plot_gromov.py @@ -38,7 +38,7 @@ mu_t = np.array([4, 4, 4]) cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]]) -xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s) +xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) P = sp.linalg.sqrtm(cov_t) xt = np.random.randn(n_samples, 3).dot(P) + mu_t diff --git a/examples/plot_optim_OTreg.py b/examples/plot_optim_OTreg.py index 92df016..2c58def 100644 --- a/examples/plot_optim_OTreg.py +++ b/examples/plot_optim_OTreg.py @@ -42,8 +42,8 @@ n = 100 # nb bins x = np.arange(n, dtype=np.float64) # Gaussian distributions -a = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std -b = ot.datasets.get_1D_gauss(n, m=60, s=10) +a = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std +b = ot.datasets.make_1D_gauss(n, m=60, s=10) # loss matrix M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) diff --git a/examples/plot_otda_classes.py b/examples/plot_otda_classes.py index b14c11a..c311fbd 100644 --- a/examples/plot_otda_classes.py +++ b/examples/plot_otda_classes.py @@ -25,8 +25,8 @@ import ot n_source_samples = 150 n_target_samples = 150 -Xs, ys = ot.datasets.get_data_classif('3gauss', n_source_samples) -Xt, yt = ot.datasets.get_data_classif('3gauss2', n_target_samples) +Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples) +Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples) ############################################################################## @@ -82,7 +82,7 @@ pl.tight_layout() # Fig 2 : plot optimal couplings and transported samples # ------------------------------------------------------ -param_img = {'interpolation': 'nearest', 'cmap': 'spectral'} +param_img = {'interpolation': 'nearest'} pl.figure(2, figsize=(15, 8)) pl.subplot(2, 4, 1) diff --git a/examples/plot_otda_d2.py b/examples/plot_otda_d2.py index 70beb35..cf22c2f 100644 --- a/examples/plot_otda_d2.py +++ b/examples/plot_otda_d2.py @@ -29,8 +29,8 @@ import ot.plot n_samples_source = 150 n_samples_target = 150 -Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source) -Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target) +Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source) +Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target) # Cost matrix M = ot.dist(Xs, Xt, metric='sqeuclidean') diff --git a/examples/plot_otda_linear_mapping.py b/examples/plot_otda_linear_mapping.py index 7a3b761..c65bd4f 100644 --- a/examples/plot_otda_linear_mapping.py +++ b/examples/plot_otda_linear_mapping.py @@ -1,11 +1,17 @@ #!/usr/bin/env python3 # -*- coding: utf-8 -*- """ -Created on Tue Mar 20 14:31:15 2018 +============================ +Linear OT mapping estimation +============================ + -@author: rflamary """ +# Author: Remi Flamary +# +# License: MIT License + import numpy as np import pylab as pl import ot diff --git a/examples/plot_otda_mapping.py b/examples/plot_otda_mapping.py index 167c3a1..5880adf 100644 --- a/examples/plot_otda_mapping.py +++ b/examples/plot_otda_mapping.py @@ -32,11 +32,11 @@ n_target_samples = 100 theta = 2 * np.pi / 20 noise_level = 0.1 -Xs, ys = ot.datasets.get_data_classif( +Xs, ys = ot.datasets.make_data_classif( 'gaussrot', n_source_samples, nz=noise_level) -Xs_new, _ = ot.datasets.get_data_classif( +Xs_new, _ = ot.datasets.make_data_classif( 'gaussrot', n_source_samples, nz=noise_level) -Xt, yt = ot.datasets.get_data_classif( +Xt, yt = ot.datasets.make_data_classif( 'gaussrot', n_target_samples, theta=theta, nz=noise_level) # one of the target mode changes its variance (no linear mapping) diff --git a/examples/plot_otda_semi_supervised.py b/examples/plot_otda_semi_supervised.py index 7963aef..8a67720 100644 --- a/examples/plot_otda_semi_supervised.py +++ b/examples/plot_otda_semi_supervised.py @@ -29,8 +29,8 @@ import ot n_samples_source = 150 n_samples_target = 150 -Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source) -Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target) +Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source) +Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target) ############################################################################## diff --git a/notebooks/plot_OT_1D.ipynb b/notebooks/plot_OT_1D.ipynb index 93d156d..bf9f9cd 100644 --- a/notebooks/plot_OT_1D.ipynb +++ b/notebooks/plot_OT_1D.ipynb @@ -41,7 +41,7 @@ "import matplotlib.pylab as pl\n", "import ot\n", "import ot.plot\n", - "from ot.datasets import get_1D_gauss as gauss" + "from ot.datasets import make_1D_gauss as gauss" ] }, { @@ -95,9 +95,9 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -240,7 +240,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.5" } }, "nbformat": 4, diff --git a/notebooks/plot_OT_2D_samples.ipynb b/notebooks/plot_OT_2D_samples.ipynb index 900eaa7..96e84a5 100644 --- a/notebooks/plot_OT_2D_samples.ipynb +++ b/notebooks/plot_OT_2D_samples.ipynb @@ -39,7 +39,8 @@ "\n", "import numpy as np\n", "import matplotlib.pylab as pl\n", - "import ot" + "import ot\n", + "import ot.plot" ] }, { @@ -69,8 +70,8 @@ "mu_t = np.array([4, 4])\n", "cov_t = np.array([[1, -.8], [-.8, 1]])\n", "\n", - "xs = ot.datasets.get_2D_samples_gauss(n, mu_s, cov_s)\n", - "xt = ot.datasets.get_2D_samples_gauss(n, mu_t, cov_t)\n", + "xs = ot.datasets.make_2D_samples_gauss(n, mu_s, cov_s)\n", + "xt = ot.datasets.make_2D_samples_gauss(n, mu_t, cov_t)\n", "\n", "a, b = np.ones((n,)) / n, np.ones((n,)) / n # uniform distribution on samples\n", "\n", @@ -98,7 +99,7 @@ { "data": { "text/plain": [ - "" + "Text(0.5,1,'Cost matrix M')" ] }, "execution_count": 4, @@ -107,9 +108,9 @@ }, { "data": { - "image/png": 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keukjZaur4a67gnJMZnkDoln/JuEU7CJSGJm97+HD4Yc/hKuvbqy5p2vgmUv7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\no2AXEUmY/w+PmkF8q6yXYQAAAABJRU5ErkJggg==\n", 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\n", 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rp67z2kM0XOJrJ1Ooxxe1crW4W0Qf7uLl3KS2NJPpvXyGTfJYPMPnmxPXdLXf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\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -159,7 +160,7 @@ { "data": { "text/plain": [ - "" + "Text(0.5,1,'OT matrix with samples')" ] }, "execution_count": 5, @@ -168,9 +169,9 @@ }, { "data": { - "image/png": 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meOR7L8bq1bwXHh6cmbTbHY/NvzRicijg9YJt0fL//o+hSo1tygGgdPo8iHkG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7t5E0KLjUUrP38eQvrkJ32X6Hl5eLslJby+c2f7TTdaE/yjj0xNFM1mmTePcWVkQC4BAbx/yRK8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\nAKOf94eua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cs6b1nWuwM9lu5gWVaOZVmXLcu6YFnWoO/4Y1nWWMuy3rYs65plWVcty1rT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\n", + "image/png": 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\n", 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\n", 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GhtNx2Qt01tEBusaXX1LHkZVFjtjiYrLSMzKog+uuHbg81tEB0v6//546ypEj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\ngL25GVlePI4xgpdfb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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -266,21 +267,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.6.5" } }, "nbformat": 4, diff --git a/notebooks/plot_OT_L1_vs_L2.ipynb b/notebooks/plot_OT_L1_vs_L2.ipynb index 9c3d24a..8cc4e5d 100644 --- a/notebooks/plot_OT_L1_vs_L2.ipynb +++ b/notebooks/plot_OT_L1_vs_L2.ipynb @@ -64,9 +64,9 @@ "outputs": [ { "data": { - "image/png": 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\n", 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8+295W8GSU6WSfUa22TrggQVLw/KBO8KO792veIz86bZi27x3c3DuNkXnLuyapeKuWZwLxrX5YgeXeoHTnYsj5Drd4rq57r2F5ZuC8i3d4vFuS7u4HGBb557i8taPC8v/8uh/GmrsHOcV+iHsnUv3eu7LNWzMRmP7NLOM7dOMzThP4EOhbO7TUwF6BI+cxmwQtk8zq9g2zVqM8wR+A3snyz80L9uLlNLZKaVjUkrHtFkrxbIxE8P2aWaZNe3TtmnWYhwHfhnwMEkPzifCeD7j5Zo1ZpLYPs0sY/s0YzPyK/SU0qKkl5ElzG8C56SUvlbWRo1GoWBNc8WqLbUDYQ+QAjFQqqpCb1ZU5lKisgyaVFZelmhjI41l1XVFavNSFfqGaXarM4p9hkSK8rIDUlEJjqKoiuoq9NB2KyraY2VwyX5H10DF8qrrKWtTWL7BtlzVPtVuFQrW0rbNhcsvBcJEgKVOsa0tByr05Ypq83IVevH4UnVMJRy/4r6rjnmRCj2ibPlGMHJX7WOQsX4DTyl9nGwaS2NmDtunmWVsn2ZcnInNGGOMqSF24MYYY0wNsQM3xhhjaogduDHGGFND1j2Ry140GoXpUSO1eZT6ESB1AxV6t1gymdqTyf+b1a1vXueyvkNVZpQDeELq9LK6KJ9vLSlIjxqqzYN0qVCSSjVqE/QR5k4vVcAHtl414mKkCI0JzRMwQoRG1SiQWtFqFaZHjdTmiwvx0L7UC1TogXA9BataDspLx84wV36kTq82l0OkTocytXnV5aPyeByM6srypw/D/cG0jTHGmH0OO3BjjDGmhtiBG2OMMTXEDtwYY4ypIXbgxhhjTA2xAzfGGGNqyHTDyJoNtLB6MpNwYpIgVCyrK455WG4XxymECfzbQQL/ILwsq4vKowlTipcPw8iacShEWBeG6QQJ/IPyRhS2ATQbxSEPZaFntUIqDvOKQrxKwsjCELOovBUYVVRe2nc0MUoUXlZxAqDSEMuovGLoZcVrpqxNGGJZI5ZbDe7db25VeTSuRaFiAItzxedisVdcvtQJxsiKYWdQcl7Dc1dcHI5fZROKBG2ica0VlWupeD2lE0EF2xtOTzUcfgI3xhhjaogduDHGGFND7MCNMcaYGmIHbowxxtQQO3BjjDGmhkx9MpPlzauVlClSxwYTk0CJ2rwbqc0DtWagQo/KIVZZhuUVlZehIrO0TaA2D8obgcKyTMUZqSwjtWbdkISKIiKCCUVGUqEHqnIF1wCtYHKeSGkOpKDv1Ko2yclyNJlJUF7eZjLLl14bVaI6ahY5kVrip9tW204U+RIpxKFEbR6Vd4vXE/Wx3I6P7XIrmLQkKCcc14LxKyiHeJyKVOjRRCOxOj3uux0q1z2ZiTHGGLPPYQdujDHG1BA7cGOMMaaG2IEbY4wxNWQsEZukXcCdwBKwmFI6ZhIbZcwksH2aWcb2acZlEir0p6aUbhlmwdQQSwudwvLC5QPlOJTlNo9yA08mzy/ECvUwR3qkqI0UmaX5nqNc6NXUms1gPa1msVoSoB3UdRqLYZsZYGj7REKdghOvKLd4rMaurDYv6hdIgQqdYP6ArO9AbV6xPLqWovkDoERtPrHIjRHmCShUoYermTZD2WdqwL2bV5+PeGyJ1xWNeaHafPWQnZcXH++ysTPOnx6cu1akNg/ympep0IO6duVc6NF64rEziu6J1OnD4lfoxhhjTA0Z14En4NOSrpB06iQ2yJgJYvs0s4zt04zFuK/Qj00p3SDpAcBFkr6RUrq0f4HcME8F6HW3jtmdMZWoZp+NhY3YRrPvUmqf/bbZWdi+UdtoZpixnsBTSjfk/28GLgAeV7DM2SmlY1JKx7RbHiDN9Khqnx2tzhJozHqxln3222ar57HTrGZkBy5pQdLmlc/As4CrJrVhxoyD7dPMMrZPMwnGeYV+IHCBsjzRLeADKaVPljVIDbE4v7rLKLdyCvL8QqyEDXObR8rLsDzsOlZlhrmBg/JItV6SSzhWa1bLhR6pNdslKs5OoLLslCjXN5DK9okE7YKTG6jNFeRIB0IVepjbvKLaPAVzAQAsd4rrovkDonza0fUXLV9WF9p6xciNaPnSuuia2Vgq2Wdqwp5Nq49tFLFSepyqjlOB2jwaI6PlAVI7GF8itXmwfLNVPOZESvOsrrhN1eiabrO4vFWiKO819hT3PaYKfWQHnlK6BnjUWL0bs07YPs0sY/s0k8BhZMYYY0wNsQM3xhhjaogduDHGGFND7MCNMcaYGjKJXOhDk5qwuGm1vDTMhV5yexGpXcM85YHCMlJSLnVLlLZVVeiBKjPMA12mmo3U5oGKM1abV1eU1zQX+vBIqFtwciO1eVku9GagNm8GRl1RbZ468aUbtQlzmwfzCoQRGqXzBBSXR1Ej4TUQ5syO+44Uzioqr9mjS2rAnoJQ8GiMLJ1PoWL0S+VxrUSFTtCHOtXGr1Yw3nUCdTpAN1KhV4yuiXKhd0vGwUht7lzoxhhjzD6IHbgxxhhTQ+zAjTHGmBpiB26MMcbUEDtwY4wxpobYgRtjjDE1ZLphZA2xZ271PUMcClE2aULQJigPw8vChPxh13GoTBA+EU7YMFIYRpTcPwgXawfhC0G4RbcVh0L0msUJ+eeC8trREHRWn9zQDhsl979BuFgKwstoRSFeQRhZyWQmS93iuqVuEC4WlEehX1E5VA+xjK6lpehaGiFEqfjamMkJTkJSA5YKZrtNjWAMic0jrFsOQlSjcxdOTFIyGZM6xeNOOH51isejTjBOReUQT0ISjWtReOxc897i9ZeEkXWDyUyi8mHxE7gxxhhTQ+zAjTHGmBpiB26MMcbUEDtwY4wxpobYgRtjjDE1ZMoqdNgzX6BgDUSto0xmEibqDxP4VyuHWAkbTYyy3I0UtYGKM1BkAjQC5WcrUJtHqsxeUB4pNQHmW8Xqy/uNCl0i9YafzCQ1SyYzCRTqKVCbR+XLFScmgRJVeTBBTzhpSVgedh22qRrtEU+gEV8bkcK5SMmsQL09qyTB4tzqbQ7HyJL9i1To0SRK4eRKwTgVTUwCo6jNg2iZdrVxDeIIm14w5kXjWqQ275UoynsK2qh4TB0WP4EbY4wxNcQO3BhjjKkhduDGGGNMDbEDN8YYY2rImg5c0jmSbpZ0VV/ZDkkXSfp2/n/7+m6mMcXYPs0sY/s068kwKvSdwFnAe/vKTgM+m1I6Q9Jp+ffXrrmmBizNrVapxrnQ41WFSsooz29UXjGvOZQo2gO1eZjbPMgZXKbijNTm7UCVGak151rFislIaQ6xKnO+MZ6Sckx2MiH7TA2xPFdgEJEKvSRXfwpzoUfRE4FyPIi2WO5UV6FXVpt3o/WX5EKP2lSO0AiumTKFcxCh0eusttuGpqZC38kk7LMBS/MF+xdG8JTsX2Q6zWpq8ygiptmMz9F6q82jcQ2qz+UQjndBLvSycTDKeV6mXB+GNZ/AU0qXArcNFJ8InJt/Phd43lhbYcyI2D7NLGP7NOvJqL+BH5hS2p1/vhE4cELbY8wksH2aWcb2aSbC2CK2lFKiZG4+SadKulzS5Yv33D1ud8ZUoop97ln88RS3zJhy++y3zaW77prylpk6MKoDv0nSQQD5/5ujBVNKZ6eUjkkpHdOaWxixO2MqMZJ9tlvzU9tAs08zlH3222Zz06apbqCpB6M68AuBk/PPJwP/PJnNMWYi2D7NLGP7NBNhTRW6pPOA44D9JV0PvAE4Azhf0ouBa4HfGKaz1IDFgoecNEIu9FC5XjHPb6Qoj3KqQ6yQDfM0R2rzbrHyshUoNQE6QV0vUGvOtwOFZaDWXChRoW9q/rRS+TSYpH3SEMu91Sr0yD4pyYUeKdSXgzZRbvMUqNAj5Xi2rvVVm0fLZ30E2xSozZeCayl1A+VzcM0AdHuBangDVegTs89GIvWKVOjBfpSMnVEeeAXq8UagTm8GyvFWyVwO0dwMk1Kbl0XRRGPbQqt4/IpU5VF5pDQHWGgU99HTeCr0NR14SumkoOrpY/VszASwfZpZxvZp1hNnYjPGGGNqiB24McYYU0PswI0xxpgaYgdujDHG1JBhcqFPjCRY7BVUjKJCD5SRoQo9WNdyoBCPVOtQkts8UF9Guc0jtXmnEyttJ6U239QuVkVubv0k7HtToNbc3Izb1IkksThXYECBorw0F3oYJRGp0IPySIUe5PAvW1ekEK+qNi9VofcCtXmU87xIWQ2oF+T878YRGvPdYnXwlu5q+2xOLxf6ZGgkGnOr9z1I0x+r04kV+I1AhR7lNm8F5VFe86xufdXmZVE0C0EO80nlPI+U5hCrzcvaDIOfwI0xxpgaYgdujDHG1BA7cGOMMaaG2IEbY4wxNcQO3BhjjKkhduDGGGNMDZlqGBkNWJpfHcIQT9Ycr6pyGFm0fBQuVhJGRjtI+h+Ut9rFYRVVJyYBWOgUhzBs6hSHI2xpF4d4ReVlE5Nsbd4TlN8/5nlPTVhcKDCgMMyxehhZNJlJZLcjhZFF4WLBZCbLUbhYtJ4gVKysbnkuaBOEkbWCcLH5XhwmtLVXbNM7uqvneW814gk3ZhEJOgXHREFIWFQO0AgmM4mOSRQu1moWj2vdoDyrCyYzicLLghCvcGKSIPQra1MtDHZTUD4fhH5F5bB+k5n4CdwYY4ypIXbgxhhjTA2xAzfGGGNqiB24McYYU0PswI0xxpgaMt3JTBrFKtUUiXkDtWRWF/QRqM3DdQVqcwUTkwA0g7pIbd4OVOVVJyaBWG0eTk4SqM2jSUu2toqV5gBbm6vVvABb7ieTmdAQi3OrDSuyz9LJdqIJUEK1ecXyYMKSrC4oD9XpwfLBBCTRxCQQq81TMDlJqxdMYjEXTEzSi5W+2wvU5gD7d+9a3a9ipfQs0mgsM1cwWUukNi8JkKAZqM2j8nZUHqjN242SyZgCVXkvUKdHE41EivJoeYjV5tHkJFUnLSmbmCRSqC9YhW6MMcbse9iBG2OMMTXEDtwYY4ypIXbgxhhjTA1Z04FLOkfSzZKu6is7XdINkq7M/05Y3800phjbp5llbJ9mPRlGhb4TOAt470D5mSmlN1fqrZFYnitQKEaKyRIlZaQqV6BCV5DPN1q+GSwP0I5ymwf5fLsV1eZzrViZGOUwj9Tm29pR/vKovFjJC7AtqNvWiNtMgZ1MyD5TA/bMrza6OEqifF1FhLnQK6rQo+UhzpMeqdBDtXknyGse5C8H4tzmkdp8vlidu3UuyGvei/PuP6BAbQ5wUOeHq8ra01Oh72QC9tlQYlOBCr0xQi70KOd5U8G5C5bvNIrPaackF3rUJlKPR+WhcrwkF3qc2zyIeGgUj5GxorwkD3tQNx8cj2FZ8wk8pXQpcNtYvRizTtg+zSxj+zTryTi/gb9M0lfyV0TbJ7ZFxkwG26eZZWyfZmxGdeDvAB4CHA3sBt4SLSjpVEmXS7p86c77x7STZuYZyT4X77F9mqkwlH3uZZs/jBMsmX2XkRx4SummlNJSSmkZeBfwuJJlz04pHZNSOqa5eWHU7TRmaEa1z9ac7dOsP8Pa5162uXVuuhtpasFIDlzSQX1ffwW4KlrWmGlj+zSzjO3TTIo1VeiSzgOOA/aXdD3wBuA4SUcDCdgFvGSo3hqgAhV6qJgsUaErUKE3IoVlRbV5lOcXoN0qrusFKvRIVR6VR3nNoUSFHuU2D9TmO1rFit2oHGBbs/gV87ZArTkNJmmfqQF7FgqMLsqFXmKfUc7zquXL0fKB0hxK1OOhOj3IX94NIjeCvOYArW613Oax2rw4suGBc3eGfT+wu1ptDnBoZ7WGrCxf9ySZlH02G4kt3eHnHIjU6RDngY/U5q1And4N8pdHywPMBSrxbqDGjlTlVfOXZ3XVcphHy29pRGr2klzoCubDKDlPw7CmA08pnVRQ/J6xejVmQtg+zSxj+zTriTOxGWOMMTXEDtwYY4ypIXbgxhhjTA2xAzfGGGNqyDC50CeGlOjOrVZeRyr0sny+jUiFHrRpBarydqBCL8vn241yngeqzPlWsTJyISiPFOUAm5pB7uhWtdzmkdp8v2aJCj1Qm29txLnb60RqwGJBKHioNi9ToQe3xrEKPVCOj5ALPVKVL3cCdXBQ3ugG10ygNAeY7wV5pXvFdhtb3hqZAAAFSElEQVTlNo/U5gd37wj7PrxzS2H5Ye1bV5V1AlXwrNLUcmEESjTeNUqU4M1ojAzaRIr9SM0eKcrL6nrBGBKpyrvB8pGiPFtXNRV6nL+8ePnNisfBhUDhP6+yCT/Wxk/gxhhjTA2xAzfGGGNqiB24McYYU0PswI0xxpgaYgdujDHG1BA7cGOMMaaGTDWMrNFYZr4gnKQRKOnLwsia0aQlQZtocpJOECJRNplJrxmEPARhYXPB8lFI2KZWHAoRTU4ShYttC8urT0yyIwjp2NEMYqNqRjaZSYH9jBJGFoWLheFlFcPI2nGYEO0gLLNTbNPNYF3dXmDn3XjCiK294hDI7d1iO3xAtzhsMZqYJAoVAzi8vXrSEoAjCkImu0xnMpNJ0dQy2zqrr80G1cPIotCzdhAWFi0fhX5F64E4/KsXhPVVDRfrlYZyVZu0JAwjC7Y1ChXL2hQPFvMqmZVoCPwEbowxxtQQO3BjjDGmhtiBG2OMMTXEDtwYY4ypIXbgxhhjTA2Zqgq92Uhsm4sn6hgkSq4PsUK9FSgBO0ES/WjSkmh5iFXlUXmUkD9SoW9uxsdoa6Ae3xK02dYIVOgjTEwSqc23NubCNnUim8xkeBV6pCgHIJhsJ1KbE5W3AkV5iQq92QompegEE0l0ArsNyrd0Y/vcEajN9w/U5gd1itXmh3aKFeVFE5OsUKQ2Bzi8tWlVWUe3h+uZRVpaZltr9bGNFOLNMhV6oFyP2kSq8qrlUKZCLx4jI6V7pDYvm8wkbBOUz0cTrwTHvGxikkhtPt/ohG2GwU/gxhhjTA2xAzfGGGNqiB24McYYU0PswI0xxpgaYgdujDHG1BClFOcbn3hn0g+Aa/Ov+wNxYuP1xX1PhwellA6YYn9jYfvcp/q2bY6G+54OQ9nnVB34Xh1Ll6eUjnHf+0bfdWNfPU/7at91Yl89R/tq32X4FboxxhhTQ+zAjTHGmBqykQ78bPe9T/VdN/bV87Sv9l0n9tVztK/2HbJhv4EbY4wxZnT8Ct0YY4ypIRviwCU9R9I3JX1H0mlT7nuXpK9KulLS5evc1zmSbpZ0VV/ZDkkXSfp2/n/7FPs+XdIN+b5fKemE9ei7ztg2bZuzjO3T9tnP1B24pCbwduB44EjgJElHTnkznppSOnoKYQE7gecMlJ0GfDal9DDgs/n3afUNcGa+70enlD6+Tn3XEtumbXOWsX3aPgfZiCfwxwHfSSldk1K6F/ggcOIGbMe6k1K6FBicF/FE4Nz887nA86bYtynHtmnbnGVsn7bPvdgIB34IcF3f9+vzsmmRgE9LukLSqVPsd4UDU0q78883AgdOuf+XSfpK/ppoXV5B1Rjbpm1zlrF92j73Yl8UsR2bUno02WuoP5T0lI3akJSFAEwzDOAdwEOAo4HdwFum2LdZG9umbXOWsX3OmH1uhAO/ATis7/uhedlUSCndkP+/GbiA7LXUNLlJ0kEA+f+bp9VxSummlNJSSmkZeBfT3/dZx7Zp25xlbJ+2z73YCAd+GfAwSQ+W1AGeD1w4jY4lLUjavPIZeBZwVXmriXMhcHL++WTgn6fV8Yrx5/wK09/3Wce2aducZWyfts+9aE27w5TSoqSXAZ8CmsA5KaWvTan7A4ELJEG27x9IKX1yvTqTdB5wHLC/pOuBNwBnAOdLejHZ7EK/McW+j5N0NNmrp13AS9aj77pi27RtzjK2T9vnIM7EZowxxtSQfVHEZowxxtQeO3BjjDGmhtiBG2OMMTXEDtwYY4ypIXbgxhhjTA2xAzfGGGNqiB24McYYU0PswI0xxpga8v8BLdNtfeLcgsUAAAAASUVORK5CYII=\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -112,7 +112,7 @@ "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n", "pl.axis('equal')\n", - "pl.title('Source and traget distributions')\n", + "pl.title('Source and target distributions')\n", "\n", "\n", "# Cost matrices\n", @@ -150,9 +150,9 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -366,7 +366,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.5" } }, "nbformat": 4, diff --git a/notebooks/plot_WDA.ipynb b/notebooks/plot_WDA.ipynb index f40dbd0..df46812 100644 --- a/notebooks/plot_WDA.ipynb +++ b/notebooks/plot_WDA.ipynb @@ -34,7 +34,16 @@ "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rflamary/.local/lib/python3.6/site-packages/h5py/__init__.py:36: FutureWarning: Conversion of the second argument of issubdtype from `float` to `np.floating` is deprecated. In future, it will be treated as `np.float64 == np.dtype(float).type`.\n", + " from ._conv import register_converters as _register_converters\n" + ] + } + ], "source": [ "# Author: Remi Flamary \n", "#\n", @@ -105,9 +114,9 @@ "outputs": [ { "data": { - "image/png": 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D04lYYUAIkQesB74rpTxu3y6lfAh4CGDKlCmym28vIZLV/FK6ppiAD0AsQWoX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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -176,28 +185,36 @@ "Compiling cost function...\n", "Computing gradient of cost function...\n", " iter\t\t cost val\t grad. norm\n", - " 1\t+8.7135243329934142e-01\t4.22283975e-01\n", - " 2\t+4.5259877952239763e-01\t2.80207825e-01\n", - " 3\t+4.2301660192758839e-01\t2.57544116e-01\n", - " 4\t+3.6438385605814744e-01\t2.01503900e-01\n", - " 5\t+2.6854415219016237e-01\t1.86872752e-01\n", - " 6\t+2.3605613971887493e-01\t8.54873065e-02\n", - " 7\t+2.3238632608008850e-01\t4.70510545e-02\n", - " 8\t+2.3084542185757945e-01\t8.60266814e-03\n", - " 9\t+2.3083921287882422e-01\t8.05123557e-03\n", - " 10\t+2.3081791779181188e-01\t5.75567680e-03\n", - " 11\t+2.3081444006658824e-01\t5.32065675e-03\n", - " 12\t+2.3080311057315009e-01\t3.34753227e-03\n", - " 13\t+2.3079768318049571e-01\t1.75615642e-03\n", - " 14\t+2.3079588611065080e-01\t6.04609566e-04\n", - " 15\t+2.3079584350945395e-01\t5.50079922e-04\n", - " 16\t+2.3079571065356075e-01\t3.07865387e-04\n", - " 17\t+2.3079565041678046e-01\t3.29364280e-05\n", - " 18\t+2.3079564985490117e-01\t1.32999543e-05\n", - " 19\t+2.3079564975468964e-01\t3.81768629e-06\n", - " 20\t+2.3079564974709890e-01\t1.50474730e-06\n", - " 21\t+2.3079564974588401e-01\t5.41516789e-07\n", - "Terminated - min grad norm reached after 21 iterations, 7.93 seconds.\n", + " 1\t+8.1744675052927340e-01\t5.18943290e-01\n", + " 2\t+4.6574082332790778e-01\t1.98300827e-01\n", + " 3\t+4.4912307637701449e-01\t1.38157890e-01\n", + " 4\t+4.4030490938831912e-01\t7.00834944e-02\n", + " 5\t+4.3780885707533013e-01\t4.35903168e-02\n", + " 6\t+4.3753767094086027e-01\t5.81158060e-02\n", + " 7\t+4.3658534767678403e-01\t4.44189462e-02\n", + " 8\t+4.3516262357849916e-01\t4.15971448e-02\n", + " 9\t+4.3332622549435446e-01\t7.82365182e-02\n", + " 10\t+4.2847338855201011e-01\t5.28789263e-02\n", + " 11\t+4.1510883118208680e-01\t6.83664317e-02\n", + " 12\t+4.1332168544999542e-01\t1.01013343e-01\n", + " 13\t+4.0818672134323475e-01\t5.07935089e-02\n", + " 14\t+4.0502824759368472e-01\t9.56110665e-02\n", + " 15\t+3.9786250825732905e-01\t6.68177888e-02\n", + " 16\t+3.5518514892526853e-01\t2.01958719e-01\n", + " 17\t+2.5048183658126072e-01\t1.82260477e-01\n", + " 18\t+2.4055179583813954e-01\t1.48301002e-01\n", + " 19\t+2.2127351995549377e-01\t1.77690944e-02\n", + " 20\t+2.2106865089529223e-01\t6.44501056e-03\n", + " 21\t+2.2105965534255226e-01\t5.35361879e-03\n", + " 22\t+2.2104056962319110e-01\t3.63028924e-04\n", + " 23\t+2.2104051220659457e-01\t2.27022248e-04\n", + " 24\t+2.2104049071158929e-01\t1.43369832e-04\n", + " 25\t+2.2104048094656506e-01\t7.87652127e-05\n", + " 26\t+2.2104047690862835e-01\t1.39217014e-05\n", + " 27\t+2.2104047689800282e-01\t1.33400288e-05\n", + " 28\t+2.2104047685931880e-01\t1.09433285e-05\n", + " 29\t+2.2104047677977950e-01\t3.27906935e-07\n", + "Terminated - min grad norm reached after 29 iterations, 8.50 seconds.\n", "\n" ] } @@ -230,9 +247,9 @@ "outputs": [ { "data": { - "image/png": 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naGsxA0dn89j6B5g7Vp366IqioHObwy+mnI87KV6nuwu5d0omALMWlZqRsQDZ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4JLr2B0SamDoSy49AqHtmIeG11SSnFODmNKB/Hu9JvtY/WtrdS3tjreSY9D7wtLyYg5D9vvYRJf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\nSHZxrIqggLramyfI/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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, diff --git a/notebooks/plot_barycenter_1D.ipynb b/notebooks/plot_barycenter_1D.ipynb index bf83aea..4a0956b 100644 --- a/notebooks/plot_barycenter_1D.ipynb +++ b/notebooks/plot_barycenter_1D.ipynb @@ -39,9 +39,9 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -102,8 +102,8 @@ "x = np.arange(n, dtype=np.float64)\n", "\n", "# Gaussian distributions\n", - "a1 = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std\n", - "a2 = ot.datasets.get_1D_gauss(n, m=60, s=8)\n", + "a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\n", + "a2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n", "\n", "# creating matrix A containing all distributions\n", "A = np.vstack((a1, a2)).T\n", @@ -242,7 +242,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.5" } }, "nbformat": 4, diff --git a/notebooks/plot_compute_emd.ipynb b/notebooks/plot_compute_emd.ipynb index c5f47c2..26c125e 100644 --- a/notebooks/plot_compute_emd.ipynb +++ b/notebooks/plot_compute_emd.ipynb @@ -41,7 +41,7 @@ "import numpy as np\n", "import matplotlib.pylab as pl\n", "import ot\n", - "from ot.datasets import get_1D_gauss as gauss" + "from ot.datasets import make_1D_gauss as gauss" ] }, { @@ -105,9 +105,9 @@ "outputs": [ { "data": { - "image/png": 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7+z9EZLv9nJdF5HcRuahSnf8Ska0ikiUi34hIlL1rif13q92CG1NNPO4i8oqI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DnO1toKoVXl/Sown5PMfIRCXHlvoiiWYL53c0EQVDidA4S2fvgQ5nNaeRNLsprO8mNtf8gG9Rxo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wQNSg0Fk/C9FwJX5XOsPBBHYhqcgLE0k7ztGmd1CjYaTJytiJ51OfV421J4ESsTGc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\n0Rv7322Gp74HTlf9Pmf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+ "image/png": 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\n", 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dR5O5OdSHp28Moo6W5S1d6Wdg4b2wexV0uR+ufR7cyla6LFvRKuXC0jJzeG31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\nA15AQt5GIjIeGA/g5+d3WQHXqtuAxBq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\n", 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" ] }, "metadata": {}, @@ -196,9 +196,9 @@ "outputs": [ { "data": { - "image/png": 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7d6br8/nz56levTrFill+NGvVqmUtxpZSdjk8PByj0ciIESNwc3Oja9euqfoF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\nHcPuDcsoHTWFwsk/p2FeHkgO1Msh/ICA5OCOX4s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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -226,21 +226,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.6.5" } }, "nbformat": 4, diff --git a/notebooks/plot_gromov.ipynb b/notebooks/plot_gromov.ipynb index 6a237e6..f565bfb 100644 --- a/notebooks/plot_gromov.ipynb +++ b/notebooks/plot_gromov.ipynb @@ -74,7 +74,7 @@ "cov_t = np.array([[1, 0, 0], [0, 1, 0], [0, 0, 1]])\n", "\n", "\n", - "xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s)\n", + "xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s)\n", "P = sp.linalg.sqrtm(cov_t)\n", "xt = np.random.randn(n_samples, 3).dot(P) + mu_t" ] @@ -97,9 +97,9 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", + "image/png": 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\n", 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" ] }, "metadata": {}, @@ -179,36 +179,33 @@ "text": [ "It. |Loss |Delta loss\n", "--------------------------------\n", - " 0|3.731009e-02|0.000000e+00\n", - " 1|1.846414e-02|-1.020678e+00\n", - " 2|1.752056e-02|-5.385587e-02\n", - " 3|1.470479e-02|-1.914863e-01\n", - " 4|1.371582e-02|-7.210441e-02\n", - " 5|1.263823e-02|-8.526431e-02\n", - " 6|1.190590e-02|-6.151022e-02\n", - " 7|1.014664e-02|-1.733837e-01\n", - " 8|1.011396e-02|-3.230516e-03\n", - " 9|1.011363e-02|-3.245532e-05\n", - " 10|1.011363e-02|-3.245682e-07\n", - " 11|1.011363e-02|-3.245683e-09\n", - " 12|1.011363e-02|-3.245598e-11\n", + " 0|3.135088e-02|0.000000e+00\n", + " 1|1.414971e-02|-1.215656e+00\n", + " 2|9.934682e-03|-4.242736e-01\n", + " 3|7.486162e-03|-3.270729e-01\n", + " 4|7.415422e-03|-9.539599e-03\n", + " 5|6.433930e-03|-1.525492e-01\n", + " 6|6.020392e-03|-6.868966e-02\n", + " 7|6.012738e-03|-1.272962e-03\n", + " 8|6.012661e-03|-1.278831e-05\n", + " 9|6.012660e-03|-1.278889e-07\n", + " 10|6.012660e-03|-1.278889e-09\n", + " 11|6.012660e-03|-1.278958e-11\n", "It. |Err \n", "-------------------\n", - " 0|7.847531e-02|\n", - " 10|2.424218e-03|\n", - " 20|4.250670e-02|\n", - " 30|5.679949e-05|\n", - " 40|1.219762e-08|\n", - " 50|1.121975e-11|\n", - "Gromov-Wasserstein distances: 0.01011363084946804\n", - "Entropic Gromov-Wasserstein distances: 0.0063386519916289385\n" + " 0|7.283759e-02|\n", + " 10|4.751585e-03|\n", + " 20|4.981526e-09|\n", + " 30|3.401818e-14|\n", + "Gromov-Wasserstein distances: 0.0060126599835825445\n", + "Entropic Gromov-Wasserstein distances: 0.00591822665714488\n" ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] }, "metadata": {}, @@ -260,7 +257,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.5" } }, "nbformat": 4, diff --git a/notebooks/plot_gromov_barycenter.ipynb b/notebooks/plot_gromov_barycenter.ipynb index c931a6c..2271fdb 100644 --- a/notebooks/plot_gromov_barycenter.ipynb +++ b/notebooks/plot_gromov_barycenter.ipynb @@ -137,19 +137,19 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/rflamary/.local/lib/python3.5/site-packages/ipykernel_launcher.py:6: DeprecationWarning: `imread` is deprecated!\n", + "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:6: DeprecationWarning: `imread` is deprecated!\n", "`imread` is deprecated in SciPy 1.0.0.\n", "Use ``matplotlib.pyplot.imread`` instead.\n", " \n", - "/home/rflamary/.local/lib/python3.5/site-packages/ipykernel_launcher.py:7: DeprecationWarning: `imread` is deprecated!\n", + "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:7: DeprecationWarning: `imread` is deprecated!\n", "`imread` is deprecated in SciPy 1.0.0.\n", "Use ``matplotlib.pyplot.imread`` instead.\n", " import sys\n", - "/home/rflamary/.local/lib/python3.5/site-packages/ipykernel_launcher.py:8: DeprecationWarning: `imread` is deprecated!\n", + "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:8: DeprecationWarning: `imread` is deprecated!\n", "`imread` is deprecated in SciPy 1.0.0.\n", "Use ``matplotlib.pyplot.imread`` instead.\n", " \n", - "/home/rflamary/.local/lib/python3.5/site-packages/ipykernel_launcher.py:9: DeprecationWarning: `imread` is deprecated!\n", + "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:9: DeprecationWarning: `imread` is deprecated!\n", "`imread` is deprecated in SciPy 1.0.0.\n", "Use ``matplotlib.pyplot.imread`` instead.\n", " if __name__ == '__main__':\n" @@ -263,7 +263,7 @@ { "data": { "text/plain": [ - "" + "" ] }, "execution_count": 6, @@ -272,9 +272,9 @@ }, { "data": { - "image/png": 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CA7hdqqWDdKJ5x1GsMx7b3IcyOccYbdECufYrmQ0+kIx1oatWpIvRtYzQAG7Xs68wubEkY30ZGZsn99FX2qIF6nzxXfSZmQUVyVjbcls033R0LSM0gNtVPQjNebShC2NJxnJYrzWrDjP3gwfLfzf3gwzaogWqBleX/VBGDSDJWNu6uiRFijn3xGgAt6vSaY3waijbjCUZc++uCVnk4MHtBwwh/WyuM10baItKVAmu3DPvlpCMta2LS1KMtFelAdyuykFlrKuh9NmYkrFZum4S6vazuffPtEUR5Z55t4RkrG1dXJIiVq/as2SNBnC20K8xRuj13ZiTsRSJdt1+NveDAtqiirq47ljPkIy1rYtLUozwGmPuNIBNxQi9vhtzMpaiz2u6iD/XY0XaogpyWMCYoSYxtEMod+CAtLYmLS9LZpOfa2uT1yXpttukpaXNv7O0NHl9w969sz974/Wy9yXp1Vdnb7Px+uHD0pkzm987c2byOgbh6FFp3z5px47Jz/37m4ce+qusSWhDSHM3z4ED0ssvS2+/Pfm50YSiZ8r6mrI+E9vVzeLaLr07imh6SYoRXmPMnaPRKuaFyMGD3V4gNjdtx5BnHEepZoNyHuGqi7aogh72NV1oEkOtNmBNyqACd0PTU6EGdo0xdxrAKoY6PdTUmJOxoSfaXaItmmFew9HDvqYLJGNjMqBrjLnTAFbBwehsY07G3IedaHeJtmiLRf1JD/uaLjSJoUZrxszsQjN7wMxeKH5eMGe7t8zsiaIca1Ln6C1adNHTeXriKEzo2q+t68qOHm17z9IbcwyxDiueMcfRNovWhfW0r8lZ0wX8hyT90N2vkvTD4vks/+nuf1yUGxrWiUX62TITRwFCFk4fPSqtrkqvvDI5XH3llcnzESRkxBBiII42lJ0dMquvGeORYCRNk7EbJR0pHh+R9OmGn4dxIo4ChByMjviEWmIIMRBHG6qehj3iI8EYmiZjl7j7a8Xj1yVdMme788xs3cweMbPxBjfmIY4ClQ18prjUQSaIIcRAHG2oeg2TER8JxrCrbAMze1DSe2a8telf2N3dzHzOxyy7+ykzu1LSQ2b2lLu/OKOuVUmrkrSXiyANynXXXafXX3991lvvnn5CHDWzd+/kgHTW633XZQxJ446jIaMtCrRxpHf48ORobu/eSSI2b+nLiI8EY7DJCQA1f9nseUkfd/fXzOxSSQ+7+/tLfucuSf/T3e9btN3Kyoqvr6/X3jf0g5k9Lul/FXEUxcZMwfQB6tLSsNfWth1D0vjiaIxoixrat2/2keDy8mQYfwTM7HF3X6nzu02nKY9Jurl4fLOk72/dwMwuMLNzi8cXSfqYpGcb1othIY4iGfFJTsQQYiCO6mpyawY0Tsa+IemTZvaCpOuK5zKzFTO7s9jmA5LWzexJST+S9A13J3AxjTiKqJ8n1DZGDCEG4qiuER8JxtBomrJNgx/ShaRmw7ohiKPhazuGJOJoDGiL0FTKaUoAAAA0QDIGAACQEMkYAABAQiRjAAAACZGMAQAAJEQyBgAAkBDJGAAAQEIkYwAAAAmRjAEAACREMgYAAJAQyRgAAEBCJGMAAAAJkYwBAAAkRDIGAACQEMkYAABAQiRjAAAACZGMAQAAJEQyBgAAkBDJGAAAQEIkYwAAAAk1SsbM7C/N7Bkze9vMVhZsd72ZPW9mJ8zsUJM6MTzEEZoihhADcYRUmo6MPS3pLyT9eN4GZrZT0u2SPiXpGkmfN7NrGtaLYSGO0BQxhBiIIySxq8kvu/tzkmRmizb7sKQT7v5Sse23Jd0o6dkmdWM4iCM0RQwhBuIIqXSxZux9kn4+9fxk8RpQBXGEpoghxEAcIbrSkTEze1DSe2a8ddjdvx9zZ8xsVdJq8fRNM3s65udXcJGkX1FvVH8g6ZwZr38wdkWZxFGq7zJl3W3X21kMSaOPoyHHL23R8OtOVe/76/5iaTLm7tfV/fDCKUmXTz2/rHhtVl1rktYkyczW3X3uAso2pap7bPVu1B24aa/iKPW/6Zj+5jZiSBp3HI01fgM3pS3KvO4exNA2XUxTPibpKjO7wsx2S7pJ0rEO6sWwEEdoihhCDMQRomt6aYvPmNlJSR+V9K9mdn/x+nvN7LgkufvvJX1V0v2SnpP0HXd/ptluY0iIIzRFDCEG4gipND2b8nuSvjfj9f+QtH/q+XFJxyt+/FqTfWsoVd1jq1eS1gYaR8Rvh/W2HEPSCP9NE9Wbsm7aouHU3bt6zd1j7ggAAAAq4HZIAAAACWWTjKW8DYWZXWhmD5jZC8XPC+Zs95aZPVGU2gs2y/4GMzvXzO4t3n/UzPbVrativbeY2empv/GLker9lpn9ct5p3TbxT8V+/dTMPtSgriRxNJYYCqybOKpf7yjiiBjatF2vY6j4LOJo8/vV48jdsyiSPqDJNToelrQyZ5udkl6UdKWk3ZKelHRNhLr/UdKh4vEhSf8wZ7vfRqir9G+Q9GVJdxSPb5J0b0f13iLpmy18t38m6UOSnp7z/n5J/ybJJH1E0qN9i6MxxBBxRBzRFhFDxFE7cZTNyJi7P+fuz5dsdvY2FO7+O0kbt6Fo6kZJR4rHRyR9OsJnzhPyN0zvz32SPmG2+P4ckepthbv/WNKvF2xyo6R/8YlHJL3bzC6tWVeqOBpDDIXW3QriKDraou2IoeqIo+0qx1E2yVigtm5DcYm7v1Y8fl3SJXO2O8/M1s3sETOrG+Ahf8PZbXxyGvVvJO2pWV+VeiXps8Ww6n1mdvmM99vQ9e1F2qhvDDEUWrdEHNU1hjgihtqtr8sYkoijWSp/r40ubVGVdXhrpSp1Tz9xdzezeaeYLrv7KTO7UtJDZvaUu78Ye18T+oGke9z9TTP7W02OZP488T5tkyqOiKFgxFHNeqefjDyOiKGa9U4/GXkMST2JI6njZMw7vLVSlbrN7Bdmdqm7v1YMJf5yzmecKn6+ZGYPS/oTTeasqwj5Gza2OWlmuyS9S9IbFeupXK+7T9dxpyZrD7pQ9TY1SeKIGAqrmzhajDgihurWF1JvxzEkEUezVP5e+zZN2dZtKI5Jurl4fLOkbUc0ZnaBmZ1bPL5I0sckPVujrpC/YXp/PifpIS9WBTZQWu+WOe0bNLm6dBeOSfrr4gyUj0j6zdQwexvaiKMxxFBQ3cRRI2OII2LoHX2PIYk4mqV6HHnkswzqFkmf0WRe9U1Jv5B0f/H6eyUdn9puv6SfaZLBH45U9x5JP5T0gqQHJV1YvL4i6c7i8Z9KekqTMzaekvSFBvVt+xsk3SrphuLxeZK+K+mEpJ9IujLS31lW799Leqb4G38k6epI9d4j6TVJ/1V8x1+Q9CVJXyreN0m3F/v1lOaceZRzHI0lhogj4ogYIoaIo/hxxBX4AQAAEurbNCUAAMCgkIwBAAAkRDIGAACQEMkYAABAQlGSMevw5qsAALSF/gwpxBoZu0vS9Qve/5Skq4qyKumfI9ULAEBMd4n+DB2Lkox5hzdfBQCgLfRnSKGrNWNd33wVAIA20J8huk7vTVnGzFY1GfbV+eeff+3VV1+deI/Qtscff/xX7n5x6v0AgNjo08alSX/WVTIWdNNMd1+TtCZJKysrvr6+3s3eIRkzeyX1PgBABcE3gaZPG5cm/VlX05Rd33wVAIA20J8huigjY2Z2j6SPS7rIzE5K+jtJ50iSu98h6bgmN/Q8IemMpL+JUS8AADHRnyGFKMmYu3++5H2X9JUYdQEA0Bb6M6TAFfgBAAASIhkDAABIiGQMAAAgIZIxAACAhEjGAAAAEiIZAwAASIhkDAAAICGSMQAAgIRIxgAAABIiGQMAAEiIZAwAACAhkjEAAICESMYAAAASIhkDAABIiGQMAAAgIZIxAACAhEjGAAAAEiIZAwAASIhkDAAAICGSMQAAgIRIxgAAABKKkoyZ2fVm9ryZnTCzQzPev8XMTpvZE0X5Yox6AQCIjT4NXdvV9APMbKek2yV9UtJJSY+Z2TF3f3bLpve6+1eb1gcAQFvo05BCjJGxD0s64e4vufvvJH1b0o0RPhcAgK7Rp6FzMZKx90n6+dTzk8VrW33WzH5qZveZ2eUR6gUAIDb6NHSuqwX8P5C0z93/SNIDko7M2sjMVs1s3czWT58+3dGuAQBQCX1aREePSvv2STt2TH4ePZp6j7oXIxk7JWn6qOCy4rWz3P0Nd3+zeHqnpGtnfZC7r7n7iruvXHzxxRF2DQCASujTOnT0qLS6Kr3yiuQ++bm6Or6ELEYy9pikq8zsCjPbLekmScemNzCzS6ee3iDpuQj1AgAQG31ahw4fls6c2fzamTOT18ek8dmU7v57M/uqpPsl7ZT0LXd/xsxulbTu7sckfc3MbpD0e0m/lnRL03oBAIiNPq1br75a7fWhirJmzN2Pu/sfuPt/d/fbitf+ryJo5e5fd/cPuvv/7u7/h7v/vzHqHauy+XXm3wGgPvq07uzdW+31oeIK/JkITaDK5teZfwcAVJXqIP6226Slpc2vLS1NXh8TkrEMVEmgyubXmX8HAFSR8iD+wAFpbU1aXpbMJj/X1iavj4m5e+p9mGllZcXX19dT70Yn9u2bBP9Wy8vSyy9vfm3Hjsl/lq3MpLffLn8/N2b2uLuvpN4PAGhTzn1alT4I8zXpzxgZi6TJEG+VBYxl8+vMvwMAquhiEf2iPpJ1ziRjUTQd4q2SQJXNrzP/DgCoou2D+EV9JOucJ0jGImi6TqtKAlU2v878OwCgirYP4hf1kaxzniAZiyB0iHfeUGzVBOrAgck8/ttvT35u3a7sfQAANrR9EL+oj2wyRTqk6U2SsQhChnjLhmKrJFBDCkAAQPvK+o2QPqhu37Ooj6w7RTq46U13z7Jce+21nsrdd7svL7ubTX7efXf59ktL7pOQmJSlpc2/t7y8+f2Nsrxcrd6QuvpEkytaJ483CoVCabOk7tMW9Rtt9z2Lfrfu54b0qV1r0p8lD9B5JVXg1g2MsmA2mx04ZtXqzTEAmyAZo1AoYygpk7FF/UZXfc+iPrLqAIh7eZ+aAslYRG0lO2WfG1pvaADWCe4USMYoFMoYSspkbFG/EbvvmdZmP5TjwEST/ow1Y1u0db2VsrNVQuuNsT4NADAei/qNmH3PtLb7oaFdxolkbIu2rrdSdrZKaL0hAcipwgCADYv6jZh9z7S2+6HBXcap7pBa22Voa8Zi1tt0fVpOxDQlhUIZQUk5Tek+v9+I2fdMa9IP9WWZzVZN+rPkATqvpD7zJPbZlG3UO0+Oc+nzkIxRKJQxlNTJ2CIbfY/kvnOnb1rcX1fZSQOLFvP39YoBJGMdWBQ8uSU/fQpmkjEKhTKGkluftlWMy1+EfN7Bg4vrya0/rYJkrGVlQVplODb0ei5NR8j6MsxLMkahUMZQcurTZolx+Yut/c7Bg9v7obJkq0/LbLYiGWtZrMtShAR0n0a1YiAZo1AoYyg59WmzNL38RWjfVZZsjXVkjLMpA5Sd+ht6lknI2SWhZ6BwSyQAQCxNL38R2neVnb05tEtWhCIZC1AWPKGn2IYEdMg2XEcMABBT08tfhF6vrCzZGtwlK0LVHVKbLpKul/S8pBOSDs14/1xJ9xbvPyppX9ln5jSkG2vqMGT4NdY2G/ud+7oxMU1JoVAyK0Pv0+ZpcvmLKtOLfeib6mjSn8UI2p2SXpR0paTdkp6UdM2Wbb4s6Y7i8U2S7i373NwCN9ai+hhrxkIWOPZl7RnJGIVCyamMpU+rqqwP7Euf06bUydhHJd0/9fzrkr6+ZZv7JX20eLxL0q8k2aLP7WPgxjpTsmybmKNnqZGMUSiUnAp9Wn2hgxaMjG0vMdaMvU/Sz6eenyxem7mNu/9e0m8k7YlQdzZC13EdOCC9/LL09tuTn7Pmwcu2CVng2NY9NgFg4OjTSsw7gSykf2PN82xZLeA3s1UzWzez9dOnT6fenU3Kzl7s8n6QIQsc27rHJgAgTM59Wl0hydSi/pJ7J88WIxk7JenyqeeXFa/N3MbMdkl6l6Q3tn6Qu6+5+4q7r1x88cURdi2OkOCLNRIVesmKGKNnAIBtBt+nNVGWTJX1l1X6ylFdwqnu/OZG0WS+/CVJV+idxY4f3LLNV7R5seN3yj43p/n1WGvMkqF6AAAgAElEQVS0ul4A2Yd5ebFmjEKhZFTG0Kc10fSirTEvkp6bJv1ZrODdL+lnmpyBcrh47VZJNxSPz5P0XU1OA/6JpCvLPjOnwI1x9mLsU4On9SHpmodkjEKh5FaG3qc10fR2RqFJVl9OQpuWPBlro+QUuDGu6xXyGXXuydXHo4dpJGMUCmUMJac+rYmyPifGLJF7P+9R2aQ/y2oBf65C118tWscVMk9eZ9E9iyEBAF0pO4EspL8MOetybCehkYwFiHF7hpDAqrPonktYAABiKls4vyiZqtJfLqpnbCehkYwFCsnkFwk9WpgXxPOCdmxHDwCA9jS9dEWseqoOgvT+zMu685ttl6HMr0+ru9B+0Rz9vPcOHuzHon6xZoxCoYyg9KVPK1vzFeNktZB6tlrUf+aydrpJf2aT38/PysqKr6+vp96NLOzbNzlq2Gp5eTJKd/ToZI3Yq69ORsT275eOHNm8lmxpqfrUahfM7HF3X0m9HwDQpr70aTt2TNKZrcwmM0Nl/VHZ+6H1TNsYRZvXp4XW2bYm/RnTlA2UDYvGGjYtWxe2dQr1+HEW9QMAqitb+lLWH4WuY66yxKbsRLUhrJ0mGaupbL479P5bIQlb1XVhQwhMAED3ytY3l/VHof1VlQX6ZX3aINZO153fbLvkPr8e4yrDofPcVefD+3SxPLFmjEKhjKDk3qdNa7I+q0p/FbqOuuk6tq406c+SB+i8knvgll2QLuSCdVWSpiqL/3MJzBAkYxQKZQwl9z6tipBb+8U8gSykT8vhTjQkYwnEGBlr8wrDOQRmCJIxCoUyhpJTn5ZL/1B1kCGHfV6kSX/GmrES89Z0lc13h8yHtznP3fS6aACA4Ym5nrnJSWqh+7Fh8H1a3Syu7ZLDUUTI3HiTodqmnz8EYmSMQqGMoOTQp7nHW8/cdDlMn9Y2h2rSnyUP0Hklh8DtIljmJVwx1n31IZkjGaNQKGMoOfRp7vHWMzftH/t4I/AyTfozpikX6OISEfOGXhddVyV0+LjKEDAAYPhClseE9H1N+8dBXI4iIpKxBVIGy7yA3kiqypKssovkAQDGJ9Z65qb949huBF6GZGyBNoIldMHjvIDeuTMsyeLCrwCArUJuwB3S9zXtH6veCHzw6s5vtl1ymV+Pue6q6sXwZm07a4591jx7XxZHijVjFAplBCWXPi1USN/X1rrkPqx3nqVJf5Y8QOeVvgVuiBh3qQ/9jL5c+JVkjEKhjKH0pU9LnQj1pe+apUl/xjRlh6pOHc5a3B86NMwQMACgihxO/BrremeSsQ7FOCGgSpI1+IvkAQCiySERGut6Z5KxDsU6IYAkCwAQWw6J0FgvedEoGTOzC83sATN7ofh5wZzt3jKzJ4pyrEmdfcbUIQDka+x9Wg6J0FgvedF0ZOyQpB+6+1WSflg8n+U/3f2Pi3JDwzp7jVEtAMjWqPu0HBKhsQ5aNE3GbpR0pHh8RNKnG34eAACpjLpPyyURGuOgxa6Gv3+Ju79WPH5d0iVztjvPzNYl/V7SN9z9/2lYLwAAsY2+TztwYBzJT25KkzEze1DSe2a8ten8Cnd3M/M5H7Ps7qfM7EpJD5nZU+7+4oy6ViWtStLeoa/WAwB0jj4NOSpNxtz9unnvmdkvzOxSd3/NzC6V9Ms5n3Gq+PmSmT0s6U8kbQtcd1+TtCZJKysr8/4TAABQC30actR0zdgxSTcXj2+W9P2tG5jZBWZ2bvH4Ikkfk/Rsw3oBAIiNPg1JNE3GviHpk2b2gqTriucysxUzu7PY5gOS1s3sSUk/0mR+ncAFAOSGPg1JNFrA7+5vSPrEjNfXJX2xePzvkv6wST0AALSNPg2pcAV+AACAhEjGAAAAEiIZAwAASIhkDAAAICGSMQAAgIRIxgAAABIiGQMAAEiIZAwAACAhkjEAAICESMYAAAASIhkDAABIiGQMAAAgIZIxAACAhEjGAAAAEiIZAwAASIhkDAAAICGSMQAAgIRIxgAAABIiGQMAAEiIZAwAACChRsmYmf2lmT1jZm+b2cqC7a43s+fN7ISZHWpSJwAAbaBPQypNR8aelvQXkn48bwMz2ynpdkmfknSNpM+b2TUN6wUAIDb6NCSxq8kvu/tzkmRmizb7sKQT7v5Sse23Jd0o6dkmdQMAEBN9GlLpYs3Y+yT9fOr5yeI1AAD6hj4N0ZWOjJnZg5LeM+Otw+7+/Zg7Y2arklaLp2+a2dMxP7+CiyT9ino78f5E9QIYoRH2aSnb97H1abX7s9JkzN2vq/vhhVOSLp96flnx2qy61iStSZKZrbv73AWUbUpV99jq3ag7Rb0AxmlsfVrq9n1Mf3OT/qyLacrHJF1lZleY2W5JN0k61kG9AADERp+G6Jpe2uIzZnZS0kcl/auZ3V+8/l4zOy5J7v57SV+VdL+k5yR9x92fabbbAADERZ+GVJqeTfk9Sd+b8fp/SNo/9fy4pOMVP36tyb41lKrusdWbum4AOGugfdoY2/fe1WvuHnNHAAAAUAG3QwIAAEgom2Qs5W0ozOxCM3vAzF4ofl4wZ7u3zOyJotResFn2N5jZuWZ2b/H+o2a2r25dFeu9xcxOT/2NX4xU77fM7JfzTuu2iX8q9uunZvahGPUCQCqp+rSu+7Pis+jTNr9fvU9z9yyKpA9oco2OhyWtzNlmp6QXJV0pabekJyVdE6Huf5R0qHh8SNI/zNnutxHqKv0bJH1Z0h3F45sk3dtRvbdI+mYL3+2fSfqQpKfnvL9f0r9JMkkfkfRo6nikUCiUJiVVn9Zlfxb6N9Cnlfdp2YyMuftz7v58yWZnb0Ph7r+TtHEbiqZulHSkeHxE0qcjfOY8IX/D9P7cJ+kTVnJ/jkj1tsLdfyzp1ws2uVHSv/jEI5LebWaXdrFvANCGhH1al/2ZRJ82S+U+LZtkLFBbt6G4xN1fKx6/LumSOdudZ2brZvaImdUN8JC/4ew2PjmN+jeS9tSsr0q9kvTZYlj1PjO7fMb7beD2IgDGqI22r8v+TKJPm6Xy99ro0hZVWYe3oahS9/QTd3czm3eK6bK7nzKzKyU9ZGZPufuLsfc1oR9Iusfd3zSzv9XkSObPE+8TAGQpVZ9GfxasN31ap8mYd3gbiip1m9kvzOxSd3+tGEr85ZzPOFX8fMnMHpb0J5rMWVcR8jdsbHPSzHZJepekNyrWU7led5+u405N1h50ofb3CgCppOrTMurPJPq0WSp/r32bpmzrNhTHJN1cPL5Z0rYjGjO7wMzOLR5fJOljkp6tUVfI3zC9P5+T9JAXqwIbKK13y5z2DZpcXboLxyT9dXEGykck/WZqmB0AhqqNPq3L/kyiT5ulep8W+yyDBmcnfEaTedU3Jf1C0v3F6++VdHzLWQo/0ySDPxyp7j2SfijpBUkPSrqweH1F0p3F4z+V9JQmZ2w8JekLDerb9jdIulXSDcXj8yR9V9IJST+RdGWkv7Os3r+X9EzxN/5I0tWR6r1H0muS/qv4jr8g6UuSvlS8b5JuL/brKc0584hCoVD6UlL1aV33Z/P+Bvq0an0aV+AHAABIqG/TlAAAAINCMgYAAJAQyRgAAEBCJGMAAAAJRUnGWrlpJgAAHaM/QwqxRsbuknT9gvc/JemqoqxK+udI9QIAENNdoj9Dx6IkY86NoAEAA0B/hhS6WjPGjaABAENAf4boOr03ZRkzW9Vk2Ffnn3/+tVdffXXiPULbHn/88V+5+8Wp9wMAYqNPG5cm/VlXyVjQTTPdfU3SmiStrKz4+vp6N3uHZMzsldT7AAAVBN8Emj5tXJr0Z11NU3IjaADAENCfIbooI2Nmdo+kj0u6yMxOSvo7SedIkrvfIem4Jjf0PCHpjKS/iVEvAAAx0Z8hhSjJmLt/vuR9l/SVGHUBANAW+jOkwBX4AQAAEiIZAwAASIhkDAAAICGSMQAAgIRIxgAAABIiGQMAAEiIZAwAACAhkjEAAICESMYAAAASIhkDAABIiGQMAAAgIZIxAACAhEjGAAAAEiIZAwAASIhkDAAAICGSMQAAgIRIxgAAABIiGQMAAEiIZAwAACAhkjEAAICEoiRjZna9mT1vZifM7NCM928xs9Nm9kRRvhijXgAAYqNPQ9d2Nf0AM9sp6XZJn5R0UtJjZnbM3Z/dsum97v7VpvUBANAW+jSkEGNk7MOSTrj7S+7+O0nflnRjhM8FAKBr9GnoXIxk7H2Sfj71/GTx2lafNbOfmtl9ZnZ5hHoBAIiNPg2d62oB/w8k7XP3P5L0gKQjszYys1UzWzez9dOnT3e0awAAVEKfhqhiJGOnJE0fFVxWvHaWu7/h7m8WT++UdO2sD3L3NXdfcfeViy++OMKuAQBQCX1ah44elfbtk3bsmPw8ejT1HqURIxl7TNJVZnaFme2WdJOkY9MbmNmlU09vkPRchHoBAIiNPq0jR49Kq6vSK69I7pOfq6vjTMgaJ2Pu/ntJX5V0vyYB+R13f8bMbjWzG4rNvmZmz5jZk5K+JumWpvUCABAbfVp3Dh+WzpzZ/NqZM5PXx8bcPfU+zLSysuLr6+updwMtM7PH3X0l9X4AQJvo07bbsWMyIraVmfT2293vT1NN+jOuwJ+R0LnzkO2YhwcA5Gzv3mqvDxnJWCZC585DtmMeHgBQV1cH87fdJi0tbX5taWny+tgwTZmJffsmSdNWy8vSyy9X2y70s3LANCWAMehLn7ZxMD+9lmtpSVpbkw4caKe+w4elV1+djIjddls79XShSX9GMpaJ0LnzkO36NA9PMgZgDPrSp/XpYD43rBkbgNC585DtmIcHAMxSNgX56quzf2/e603qCt1mDEjGMhE6dx6yHfPwAICtQtYTxzqYZ31zRe6eZbn22mu9T+6+23152d1s8vPuu9v7jJDtYuxPFyStewbxRqFQKG2WHPq05WX3SdqzuSwvv7PN3Xe7Ly1tfn9pqXofElJXyDZ90qQ/Y81YBKELHoe0UDEW1owBGIMc+rTQ9cQx+qqhrW8OwZqxxEKuIsxwLAAgpdApyAMHJov133578rPOoAHrm6shGYsgZMFj6G0fWPAIAGjDrPXEu3dLv/1ttf4kpA9ifXNFdec32y45zK+HCpn3Npu9jdk724TM1YfO57NmjEKhUPIpufRp033Dnj3u55zjldaHVVlTNqT1zSGa9GfJA3ReySVwQ4QEZ6zFjF0uwOwCyRiFQhlDybFPq7OAvsmi+yElXrM06c+Ypiw0mfo7cGCyWH95ebLwcHl5++L9kOHYkOnOmFOiAIDxqnNNsbrXIWPd9GIkY4oTJGULHkMStlgLHmNetA8AMEx1FtDXXXTf9iBB39dSk4ypu5GksoQt1oJHzlABAJSZ1Z+cc87iBf11F923OUgwhFE3kjHFCZIYWXnI6FmsKVEAwLht7U/27Jn8fOON+UlNSB80qz9sc5BgEEtz6i42a7t0udixbEFi2aLDHBfM92WhpFjAT6FQRlByXMC/VYwr4s/rDw8ebK+fDLlaQRea9GeMjGnxSFLI8GfMa4jFEuOifQCA8QiZJSrrx+b1h8ePl4+ohdax1SCW5tTN4touXR9FzBtJ6vIaYov2o+o2fSFGxigUygjKEEbGQvqxsv6wjZmmXGanmvRnyQN0XsklcEMSrS6vD8ZFXykUCqV/JZc+bZGy/qVpXxfrmpzz9j11n0cy1qJYSVRuSV0uSMYoFMoYSi59WplFSU3TWaBYM025atKfRVkzZmbXm9nzZnbCzA7NeP9cM7u3eP9RM9sXo94uhJyZGOsaYlz0FQDSG3KfVmbReuOQfmxRfxjSfw1i/VcNjZMxM9sp6XZJn5J0jaTPm9k1Wzb7gqT/z93/N0n/t6R/aFpvV0ISrY3tml5DjIu+AkBaQ+/Tmgi9bNK8/jCk/xrrpZlijIx9WNIJd3/J3X8n6duSbtyyzY2SjhSP75P0CTOzCHV3oizRCjnzI9b1wbjoKwC0avB9Wl2hgxPzxJppGqS685sbRdLnJN059fyvJH1zyzZPS7ps6vmLki5a9Ll9ml+PuT4rxtmUrBmjUCiUemXsfVqIkD5o3vtNF9rnsFB/nib9WVaBK2lV0rqk9b1797b2DxZTjIvktSHngJ1GMkahUHIqY+/TypQd7Lc5GJD7QEOT/izGNOUpSZdPPb+seG3mNma2S9K7JL2x9YPcfc3dV9x95eKLL46wa/HMm4oMXZ8VMpUZeqG70GlRLvoKAJWNok9bZFEfU3aCWJUTyKpe3HXQJ6fVzeI2iqRdkl6SdIWk3ZKelPTBLdt8RdIdxeObJH2n7HNzGtJteqpu7OuH5XxkUJUYGaNQKBmVMfRpi5T1MWWXngi9NEWdviz3y1406c9iBe9+ST/TZKj2cPHarZJuKB6fJ+m7kk5I+omkK8s+M6fA7eIidqHTnblOi9ZFMkahUHIrQ+/TFinrY5q+H1pPnX1LLXky1kbJKXCb3t4hJJsPzfirHHWwZoxCoVDyKDn1aYuE9Hcx1ozVGeXKfWaoSX/GjcIDlF0qomx9Vqzrh4VuF3JzcwAAtgrp7xZdeiL00hRVLsG0sbbsr/5K+m//TdqzZ/ZnV12DlpW6WVzbJaejiKbZeNdrxnIfyp0mRsYoFMoISk592iJdjT7FXiedw6hZk/4seYDOK7kFbhfXRgmtI8a0aC5IxigUyhhKbn3aIm0uc5n+7D17JmVRPX1aT00yFklf1lmVySEoQ5GMUSiUMZQ+JWNbxRooaPMMyhwGIZr0Z6wZKwxpndVY7+0FAIgrtG8M2a7OdcJirqfOGclYoc2LycVaVBj6OaO9txcAIKrQvjFku9CLpE8LHVzo/SBE3SG1tkvXQ7pNhjjL7sMVuvhwKPebrEJMU1IolBGUnKYpqyzJiTlNWHcJTaxp0rY16c+SB+i80nXgNgmSRUlSrCv0h+5f6mCsimSMQqGMoeSSjFU9sI+5gH6ogwobSMYiqBskZQEY62gh5HP6GOgkYxQKZQwll2Ss6sBDG5eg6NOAQRUkY5HUCZKyJClWohXzlko5IRmjUChjKLkkY6EH9tN94cGD5ctoNvqfnTv9bL8zpEQrRJP+jAX8U8qupD9L2RkcIYsKQ84CCfmcOosjAQDjUdbfzDor8siRSV8zq2+c3l6S3nrrnb5pXh/a6yvlt6VuFtd2yeUookzo1fVjLM4v+xxGxigUCiXPkkufFmOd87S2pj37qEl/ljxA55VcAjdEjDnwWJ/RtyAnGaNQKGMoOfVpi/qbqlcWqLp9HwcNQjXpz2zy+/lZWVnx9fX11LvRO0ePTq7r8sor0s6dkyHj5eXFQ8Ypmdnj7r6Sej8AoE196dP27XtnynHa8vJkirLp9jt2TNKvrcwm06B91qQ/Y81YYqFz51Uu+Lqxvuyttyav9fluAgCA9mztW/bvr3bx1KoXW+37lfJbU3dIre2S05BuW9q6G32fhoHFNCWFQhlBybFPm9e3lJ09OetzQrfv43KaUE36M6YpEwod3h3yMDDTlADGIMc+rWrfEsvGcppXX52MiOW6jKaqJv3Zrtg7g3Chl6KoesmKvXtn/wcb/TAwAOCsVJdDOnBgGMlXTKwZS6itu9H3/oapAIDW5bB+i2uOTZCMJdTW3egPHJDW1iZDzWaTn2trHIkAAN6R+sB91gVmx3qyWaNkzMwuNLMHzOyF4ucFc7Z7y8yeKMqxJnUOSWjSVCe5qnM3AQAYs7H1aakP3A8fls6c2fzamTOT18em0QJ+M/tHSb9292+Y2SFJF7j7/zlju9+6+/9S5bNzXOyI+FjADyAX9Gnd6tPJZiFSXmfsRklHisdHJH264ecBAJAKfVqHclizloumydgl7v5a8fh1SZfM2e48M1s3s0fMjOAGAOSIPq1Dqdes5aT00hZm9qCk98x4a9Osrru7mc2b81x291NmdqWkh8zsKXd/cUZdq5JWJWnvGFNjAECr6NPysbE2bYjXHKuq6Zqx5yV93N1fM7NLJT3s7u8v+Z27JP1Pd79v0XbMr48Da8YA5II+DU2kXDN2TNLNxeObJX1/6wZmdoGZnVs8vkjSxyQ927BeAABio09DEk2TsW9I+qSZvSDpuuK5zGzFzO4stvmApHUze1LSjyR9w90JXABAbujTkESj2yG5+xuSPjHj9XVJXywe/7ukP2xSDwAAbaNPQypcgR8AACAhkjEAAICESMYAAAASIhkDAABIiGQMAAAgIZIxAACAhEjGAAAAEiIZAwAASIhkDAAAICGSMQAAgIRIxgAAABIiGQMAAEiIZAwAACAhkjEAAICESMYAAAASIhkDAABIiGQMAAAgIZIxAACAhEjGAAAAEiIZAwAASIhkDAAAIKFGyZiZ/aWZPWNmb5vZyoLtrjez583shJkdalInAABtoE9DKk1Hxp6W9BeSfjxvAzPbKel2SZ+SdI2kz5vZNQ3rBQAgNvo0JLGryS+7+3OSZGaLNvuwpBPu/lKx7bcl3Sjp2SZ1AwAQE30aUulizdj7JP186vnJ4jUAAPqGPg3RlY6MmdmDkt4z463D7v79mDtjZquSVounb5rZ0zE/v4KLJP2Kejvx/kT1AhihEfZpKdv3sfVptfuz0mTM3a+r++GFU5Iun3p+WfHarLrWJK1Jkpmtu/vcBZRtSlX32OrdqDtFvQDGaWx9Wur2fUx/c5P+rItpysckXWVmV5jZbkk3STrWQb0AAMRGn4boml7a4jNmdlLSRyX9q5ndX7z+XjM7Lknu/ntJX5V0v6TnJH3H3Z9pttsAAMRFn4ZUmp5N+T1J35vx+n9I2j/1/Lik4xU/fq3JvjWUqu6x1Zu6bgA4a6B92hjb997Va+4ec0cAAABQAbdDAgAASCibZCzlbSjM7EIze8DMXih+XjBnu7fM7Imi1F6wWfY3mNm5ZnZv8f6jZravbl0V673FzE5P/Y1fjFTvt8zsl/NO67aJfyr266dm9qEY9QJAKqn6tK77s+Kz6NM2v1+9T3P3LIqkD2hyjY6HJa3M2WanpBclXSlpt6QnJV0Toe5/lHSoeHxI0j/M2e63Eeoq/RskfVnSHcXjmyTd21G9t0j6Zgvf7Z9J+pCkp+e8v1/Sv0kySR+R9GjqeKRQKJQmJVWf1mV/Fvo30KeV92nZjIy5+3Pu/nzJZmdvQ+Huv5O0cRuKpm6UdKR4fETSpyN85jwhf8P0/twn6RNWcn+OSPW2wt1/LOnXCza5UdK/+MQjkt5tZpd2sW8A0IaEfVqX/ZlEnzZL5T4tm2QsUFu3objE3V8rHr8u6ZI5251nZutm9oiZ1Q3wkL/h7DY+OY36N5L21KyvSr2S9NliWPU+M7t8xvtt4PYiAMaojbavy/5Mok+bpfL32ujSFlVZh7ehqFL39BN3dzObd4rpsrufMrMrJT1kZk+5+4ux9zWhH0i6x93fNLO/1eRI5s8T7xMAZClVn0Z/Fqw3fVqnyZh3eBuKKnWb2S/M7FJ3f60YSvzlnM84Vfx8ycwelvQnmsxZVxHyN2xsc9LMdkl6l6Q3KtZTuV53n67jTk3WHnSh9vcKAKmk6tMy6s8k+rRZKn+vfZumbOs2FMck3Vw8vlnStiMaM7vAzM4tHl8k6WOSnq1RV8jfML0/n5P0kBerAhsorXfLnPYNmlxdugvHJP11cQbKRyT9ZmqYHQCGqo0+rcv+TKJPm6V6nxb7LIMGZyd8RpN51Tcl/ULS/cXr75V0fMtZCj/TJIM/HKnuPZJ+KOkFSQ9KurB4fUXSncXjP5X0lCZnbDwl6QsN6tv2N0i6VdINxePzJH1X0glJP5F0ZaS/s6zev5f0TPE3/kjS1ZHqvUfSa5L+q/iOvyDpS5K+VLxvkm4v9uspzTnziEKhUPpSUvVpXfdn8/4G+rRqfRpX4AcAAEiob9OUAAAAg0IyBgAAkBDJGAAAQEIkYwAAAAlFScZauWkmRoUYQgzEEZoihpBCrJGxuyRdv+D9T0m6qiirkv45Ur0YjrtEDKG5u0QcoZm7RAyhY1GSMedG0GiIGEIMxBGaIoaQQldrxrgRNJoihhADcYSmiCFE1+m9KcuY2aomw746//zzr7366qsT7xHa9vjjj//K3S+O+ZnE0bi0EUMScTQ2tEVoqkkMdZWMBd00093XJK1J0srKiq+vr3ezd0jGzF4J3DT4xqvE0bhUiCGJOMIctEVoqmJbtElX05TcCBpNEUOIgThCU8QQoosyMmZm90j6uKSLzOykpL+TdI4kufsdko5rckPPE5LOSPqbGPViOIghxEAcoSliCClEScbc/fMl77ukr8SoC8NEDCEG4ghNEUNIgSvwAwAAJEQyBgAAkBDJGAAAQEIkYwAAAAmRjAEAACREMgYAAJAQyRgAAEBCJGMAAAAJkYwBAAAkRDIGAACQEMkYAABAQiRjAAAACZGMAQAAJEQyBgAAkBDJGAAAQEIkYwAAAAmRjAEAACREMgYAAJAQyRgAAEBCJGMAAAAJRUnGzOx6M3vezE6Y2aEZ799iZqfN7ImifDFGvRgW4ghNEUOIgThC13Y1/QAz2ynpdkmflHRS0mNmdszdn92y6b3u/tWm9WGYiCM0RQwhBuIIKcQYGfuwpBPu/pK7/07StyXdGOFzMS7EEZoihhADcYTOxUjG3ifp51PPTxavbfVZM/upmd1nZpdHqBfDQhyhKWIIMRBHXTl6VNq3T9qxY/Lz6NHUe5RMVwv4fyBpn7v/kaQHJB2ZtZGZrZrZupmtnz59uqNdQ48QR2gqKIak8cQR/WEttEVNHT0qra5Kr7wiuU9+rq6ONgBjJGOnJE0fFVxWvHaWu7/h7m8WT++UdO2sD3L3NXdfcfeViy++OMKuoUeIIzQVLYaKbQcfR/SHM9EWxTQv2z98WDpzZvO2Z85MXh+hGMnYY5KuMrMrzGy3pJskHZvewMwunXp6g6TnItSLYSGO0BQxVBH94UzEUSyLsv1XX539O/NeH7jGyZi7/17SVyXdr0lAfpkzV3cAABQxSURBVMfdnzGzW83shmKzr5nZM2b2pKSvSbqlab0YFuKoubFPNxFD1dEfbkccRbQo29+7d/bvzHt94MzdU+/DTCsrK76+vp56N9AyM3vc3Vfa+vyxxNHGAeh0u7e0JK2tSQcOpNuvLrQdQ9Jw42jfvslgxVbLy9LLL3e9N2nRFrVgx47JiNhWZtL/+B+Da7SaxBBX4Ad6oGzUi+km1HHbbZP+b9rS0uR1oLFFo18HDkwSr+XlSXK2vNzrRKwpkjGgY1WnE0MWWTPdhDrm9YfSuKe8EUlZtn/gwGQI9u23J68dPjzaoCMZ65OxLwoagDpnr4WMerH8AnVN94cbU5OcYYkoQke/OK2XZKw3CNZBqDOdGDLqxXQTYmHKG1FtzfZnTUMSdCRjrYo5khUSrIycZa/OdGLIqBfLLxALU94j1FXfMa8ego5krDWxR7LKgpWRs16oM50YOuoVcgCKfuuiz2TKe2S66jsW1UPQkYy1Jvawa1mwMszbC3WmExn1ghS3z1yU1DHlPTIx+45FgbWoHoJOcvcsy7XXXuudu/tu9+Vld7PJz7vvrv9ZZu6TNnNzMatX3913uy8tbf6spaV3fiekvth/YwSS1n1ocVSi668gs688urZjyDOJo+Xl2f/Fl5erfU5ZU7KxzZBjZpbBt0XzvtTQviPk85v0UQMIuiYx1GoD1qR0HrghLVQVZS1nnfoWBWtISx37b4xg8A3gHF21Oxl+5dGNJRlb1JdViafQpG4AfWMlg26LFjUEsbL8ss+JVU/GSMZiiB0oZb1g1/W1UWcEg24A5whNkEI7wxg5ep873bEkY/O+yz17qiXcoYP2bSXxucbboNuiRQ1B1S+77gjbCI4MScZiiD2tWLZ9G9OKZdvGGo6OaNAN4BwxBzFjzAz0vX0cSzI277vas6c8nqaFxF9bx205x9ug26JYU4RNR9hyzcQjIRmLoY1pxSb1paqzY4NuAOcIyYlDv6qmMwMZhkRlY0nG3Gf3ZVWPsUKalbaO23KOt0G3RV1MRVbprwaalJGMxTCGacUMD0sH3QDOEfK1hnaGTUe+MhwsrWxMydgsdZqJsr6wraQp53gbdFsUK1GKMcKWYT8UC8lYLH2cVow5dZrAoBvAOWLm4U1nBnIeqQg19mQs9hrEss9s0oTkHG+Db4tiJEoxvsCcg6AhkrEu5DitOIAjjME3gHOUtYux1oyF7EfPQ2j0yZh7vHgq+8whx9uo2qJ5AROzz6m70L/HSMaaiLFwcUPsBYxdT50mMKoGcEroQWpoaDYZ7MxssLQykrFyXSwZCpVrvI2mLVrUr8SajWm60H/rZ+UYMDOQjNVV9TCt6bRirMPT0PpC9zuh0TSAU3IeHeijsSVjdf47xxqMaPo5GTdF42mLFiVDVRKluusfqo6u9aixJBmrK/bIUtenrqWYOo1sNA3glFjtXddijubFNKZkrO5/51iD9iGzWPM+I/OmaDxtUdnVg7u6pk5IQ9Gz2R+Ssbr6fsuimCvBExlNAzilShjEWpjddJuQfUnV2Y4pGav737nsu4nRB8daVZHqAGQ0bVGTjDr0M2IdcfZsfRnJWF1tLJCvO3Q7/ftdTp0mNpoGcEpoOxUrXGJsE7IvqfL+MSVj8/47b/yXrru2MEbfWfYZqa/8X2Y0bVHVacJZX3asJTkDWxdNMlZX14GQYiQr82AeTQM4JbSdinVx2BjbhOxLqrx/TMnYvO+pafIS47sr+4ycE3r3kbVFXSzCjzXv3aMLySZPxiRdL+l5SSckHZrx/rmS7i3ef1TSvrLPzOJsyiFcWyzzhRrTwdvrOKooRjvlHi9J6nNH2nYMeUZxNOu/c4x/8y5GxkKaopQD+WNti+Za9IXGmsHp4uzNDiVNxiTtlPSipCsl7Zb0pKRrtmzzZUl3FI9vknRv2edmEbhtTCs2rTP21GliG8E76DiqKdZAaoxt+rBmrK0Y8sziaPq/87xkbFbyEmNxfZM1Y2X74J7HyBhtUSHWIvymI2whMpkBSp2MfVTS/VPPvy7p61u2uV/SR4vHuyT9SpIt+twsAjfFtOLA5tDLTDWAw42jBsrau67WjIXsS+g2sbUdQ55xHIU2B7G+35CkfVHCl/MAB23RFjGGS8s+J9Y0ZCZro1MnY5+TdOfU87+S9M0t2zwt6bKp5y9KumjR52YTuF1PK5ZtP4Bri02bagCHHUczxPqKYiVJPQmZbdqOIc84jkL7sljHcHX7vD4s/RlzWzRTrEX4MUbYejJIMZhkTNKqpHVJ63v37m3tHyyqNqYVm9TXRp0taqMB7EMc9egrCja0TrQPceQe9u8ea+Cgbp+XSV+50FjbooViDJfmsiCxA6mTsXEP6XadsaeYOm2RRjo1EJpT5zLiFWO6tC1tx5BnHEehYjUJdb/nTGaRFhprWxSs7iL8GCNsbcxAtSB1MrZL0kuSrtA7ix0/uGWbr2jzYsfvlH1urwK362nFGFOnmZhqAEcVRzHar1jblMk9/287hjzjOAo17zs8eLB6/1Wnz+vD8eFY26IgTRfhNx1h60MA+TsxVKc0TsYm9Wu/pJ8VQ7WHi9dulXRD8fg8Sd/V5DTgn0i6suwzex2401JMK/YkcN03B++Y4ijGyH6MbWLMQrhndUmC6DHkGcfRhjojpAcPxlnUH7p/GcwiLTTWtihI6kX4fQgg3xxDVUuUZKyNkn3ghrZSMYcVYtaZiSbBG1JyjaOma15jbBMaJrEuPtuWtmPIM44j9/r/3btehpPBLNJCY22Lzmoyw9PFIvzcA8ibxVCrDViTknXgVm2lYkwrxq4zE2NuABd9RV2MjIUmULmfMzL2ZKxuIhzjQr/uvWlqSo25Lepk7XNPFuE3QTLWtdjDALF63h4adQO4QBdrxqosZwxdf5uiUx57MlZ3irisSWnjGDFno26LYiZKdRf6L/rdniAZ61rsRfkhgT7QQB51A1ii7bMpq+T3OYfW2JOxusdpMQZDhnSMOOq2qIvbEg0pWOYgGetaG/M2ZYE+0CHeUTeAifU0ZLYZezLW5Htc1OzEOkbsi1G3RbGOzGIt9O8pkrGupTjXv4s5/QRG3QBmIOcRr1BjT8bcZ3+PMb7bpseIfTLqtqjKWoS2r7bfYyRjKcRalF8lMJuc7ZKpsTaAbU9BjgnJ2HYxlvjErid3Y22LzgoJhLLseyhrH2oiGctR19OKPT1EHWMD2MXi/OltBtbebUMytl2VMyFjXBR4CDE2xrZorrqL8GONsPUUyViOup5W7Glwj7EB7OKyFe75nwUZC8nYdqED5aHLX/scH6HG2BbN1HQRfowRtp4iGctVjGnFKi1hD1vNMTaAXVzQ1b2d80xyRDK2XWhfF+u2XD1rdmYaY1s0U8xF+HVH2HqKZKyPxtJTlhhjA9jVyFjuV86PhWRsu9Cmo+kSoCE1UaNri5pMRYZk3yO8zAXJWB+lOCMzQ6NrAL27NWMh4TOEA9QxJ2Nll6YIOQGkyclxQ2qiRtUWdZEojfAyFyRjfZXijMzMjKoBnNLF2ZRjyffHmozF6s8WxVCM0deyOnIxqraoi0RphJe5IBkbqhHMEYyqAUygrL0bQAiNNhmrcsZkW5euGNJqi1G1RV0kSkM40quIZKxPqi7IH+CFXqeNqgGcEpIkdXXQ2PcD1LEmY13dO3Iso6+jaou6+FKqBF/fG6ECyVhf1GkZY5yRmbFRNYCFsjDoy0hCLsaajMU6EaSpGKstcjCqtqirRibGwsUeIRnri9gtY18OORcYVQNYaHoGGzYbazIW0oflkAj1JZ5H1xblMvzelwAJ0CSGdgjdefXVsNePHpX27ZN27Jj8PHp09u/ddpu0tLT5taWlyevIVlkYhIbJLKGhg/47cEBaW5OWlyWzyc+1tcnrG/bunf27815vA81Upg4ckF5+WXr77cnP6cCJ6ehRaXVVeuWVSZr1yiuT5xuNU5MGb0BIxroU0jKWBe60kNYY2SkLg7odaJXQwTCU9af798/+vXmvt4FmauQOH5bOnNn82pkzk9elPI4YMkAy1qWQQ8SywN2qq6MbRFMWBnVHEqqGDobv+PFqr7eFZmrEyka+GDqVRDLWrZBDxJAhW+aieq0sDOqOJDDaj62ICSRXNvLF0KmkhsmYmV1oZg+Y2QvFzwvmbPeWmT1RlGNN6uy9skPEssAd4FzUGOOoLAzqjCSMebR/jDEUYswxUQdx1IKQka/pBu+22ybD+SMbbGg6MnZI0g/d/SpJPyyez/Kf7v7HRbmhYZ3DVha4w5yLIo4iGPloPzE0w8hjog7iKLYqI18DHGwIVvc0zMlZnHpe0qXF40slPT9nu99W/ezsTgPu0sCvLTZN0jpxFM9Arp1YSdsx5D2PozHGRB20RRno+WUulPDSFpe4+2vF49clXTJnu/PMbN3MHjGzTzesc/gWzVENc96BOIpkxAuliaE5RhwTdRBHbVu05nnEixx3lW1gZg9Kes+MtzbNi7m7m5nP+Zhldz9lZldKesjMnnL3F2fUtSppVZL29ju5aM9tt02GbaenKnsw73Ddddfp9ddfn/XWu6efEEeYp8sYkoijoaItSmhjGnKj/9qYhpQmRwl7905e22oM/3Z1h9S8wjTllt+5S9LnyrZjSHeBAc07qMLUgBNHmKHtGHLiaBRoizpQNg3Z81sjKeE05TFJNxePb5b0/a0bmNkFZnZu8fgiSR+T9GzDesdtePMOxBGaIoYQA3HUprJpyBFf5qJpMvYNSZ80sxckXVc8l5mtmNmdxTYfkLRuZk9K+pGkb7g7gYtpxBGaIoYQA3HUppA1z8MbbAhSumZsEXd/Q9InZry+LumLxeN/l/SHTerBsBFHaIoYQgzEUct6uua5C1yBHwAAtG/E05BlGo2MAQAABDtwgORrBkbGAAAAEiIZAwAASIhkDAAAICGSMQAAgIRIxgAAABIiGQMAAEiIZAwAACAhkjEAAICESMYAAAASIhkDAABIiGQMAAAgIZIxAACAhEjGAAAAEiIZAwAASIhkDAAAICGSMQAAgIRIxgAAABIiGQMAAEiIZAwAACChRsmYmf2lmT1jZm+b2cqC7a43s+fN7ISZHWpSJ4aHOEJTxBBiII6QStORsacl/YWkH8/bwMx2Srpd0qckXSPp82Z2TcN6MSzEEZoihhADcYQkdjX5ZXd/TpLMbNFmH5Z0wt1fKrb9tqQbJT3bpG4MB3GEpoghxEAcIZUu1oy9T9LPp56fLF4DqiCO0BQxhBiII0RXOjJmZg9Kes+Mtw67+/dj7oyZrUpaLZ6+aWZPx/z8Ci6S9CvqjeoPJJ0z4/UPxq4okzhK9V2mrLvtejuLIWn0cTTk+KUtGn7dqep9f91fLE3G3P26uh9eOCXp8qnnlxWvzaprTdKaJJnZurvPXUDZplR1j63ejboDN+1VHKX+Nx3T39xGDEnjjqOxxm/gprRFmdfdgxjapotpysckXWVmV5jZbkk3STrWQb0YFuIITRFDiIE4QnRNL23xGTM7Kemjkv7VzO4vXn+vmR2XJHf/vaSvSrpf0nOSvuPuzzTbbQwJcYSmiCHEQBwhlaZnU35P0vdmvP4fkvZPPT8u6XjFj19rsm8Npap7bPVK0tpA44j47bDelmNIGuG/aaJ6U9ZNWzScuntXr7l7zB0BAABABdwOCQAAIKFskrGUt6EwswvN7AEze6H4ecGc7d4ysyeKUnvBZtnfYGbnmtm9xfuPmtm+unVVrPcWMzs99Td+MVK93zKzX847rdsm/qnYr5+a2Yca1JUkjsYSQ4F1E0f16x1FHBFDm7brdQwVn0UcbX6/ehy5exZF0gc0uUbHw5JW5myzU9KLkq6UtFvSk5KuiVD3P0o6VDw+JOkf5mz32wh1lf4Nkr4s6Y7i8U2S7u2o3lskfbOF7/bPJH1I0tNz3t8v6d8kmaSPSHq0b3E0hhgijogj2iJiiDhqJ46yGRlz9+fc/fmSzc7ehsLdfydp4zYUTd0o6Ujx+IikT0f4zHlC/obp/blP0ifMFt+fI1K9rXD3H0v69YJNbpT0Lz7xiKR3m9mlNetKFUdjiKHQultBHEVHW7QdMVQdcbRd5TjKJhkL1NZtKC5x99eKx69LumTOdueZ2bqZPWJmdQM85G84u41PTqP+jaQ9NeurUq8kfbYYVr3PzC6f8X4bur69SBv1jSGGQuuWiKO6xhBHxFC79XUZQxJxNEvl77XRpS2qsg5vrVSl7ukn7u5mNu8U02V3P2VmV0p6yMyecvcXY+9rQj+QdI+7v2lmf6vJkcyfJ96nbVLFETEUjDiqWe/0k5HHETFUs97pJyOPIakncSR1nIx5h7dWqlK3mf3CzC5199eKocRfzvmMU8XPl8zsYUl/osmcdRUhf8PGNifNbJekd0l6o2I9let19+k67tRk7UEXqt6mJkkcEUNhdRNHixFHxFDd+kLq7TiGJOJolsrfa9+mKdu6DcUxSTcXj2+WtO2IxswuMLNzi8cXSfqYpGdr1BXyN0zvz+ckPeTFqsAGSuvdMqd9gyZXl+7CMUl/XZyB8hFJv5kaZm9DG3E0hhgKqps4amQMcUQMvaPvMSQRR7NUjyOPfJZB3SLpM5rMq74p6ReS7i9ef6+k41Pb7Zf0M00y+MOR6t4j6YeSXpD0oPT/t3OHOA0EYRiG3zo0R6jiACgO0UvU9BwYToDDIzgEvnaDw3MIDIJxhCC6yUB5HjXZFf9O9hNfspntcly/rh7G+qZa+jyxsVT7E+Z92UN1W+3G+qJ6ql6rY7VdaZ8/zb2rXsYen6urleY+Vm/V+3jH++pQHcb9TXU/nmvpm5NHvzlH/yVDciRHMiRDcrR+jvyBHwBgor/2mRIA4KwoYwAAEyljAAATKWMAABMpYwAAEyljAAATKWMAABMpYwAAE30Awp+alJ2J0isAAAAASUVORK5CYII=\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, diff --git a/notebooks/plot_optim_OTreg.ipynb b/notebooks/plot_optim_OTreg.ipynb index 67165e9..e3784df 100644 --- a/notebooks/plot_optim_OTreg.ipynb +++ b/notebooks/plot_optim_OTreg.ipynb @@ -80,8 +80,8 @@ "x = np.arange(n, dtype=np.float64)\n", "\n", "# Gaussian distributions\n", - "a = ot.datasets.get_1D_gauss(n, m=20, s=5) # m= mean, s= std\n", - "b = ot.datasets.get_1D_gauss(n, m=60, s=10)\n", + "a = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\n", + "b = ot.datasets.make_1D_gauss(n, m=60, s=10)\n", "\n", "# loss matrix\n", "M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\n", @@ -106,9 +106,9 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -724,7 +724,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.5" } }, "nbformat": 4, diff --git a/notebooks/plot_otda_classes.ipynb b/notebooks/plot_otda_classes.ipynb index 1955676..2af6fb6 100644 --- a/notebooks/plot_otda_classes.ipynb +++ b/notebooks/plot_otda_classes.ipynb @@ -62,8 +62,8 @@ "n_source_samples = 150\n", "n_target_samples = 150\n", "\n", - "Xs, ys = ot.datasets.get_data_classif('3gauss', n_source_samples)\n", - "Xt, yt = ot.datasets.get_data_classif('3gauss2', n_target_samples)" + "Xs, ys = ot.datasets.make_data_classif('3gauss', n_source_samples)\n", + "Xt, yt = ot.datasets.make_data_classif('3gauss2', n_target_samples)" ] }, { @@ -88,29 +88,29 @@ "text": [ "It. |Loss |Delta loss\n", "--------------------------------\n", - " 0|1.017912e+01|0.000000e+00\n", - " 1|2.096083e+00|-3.856258e+00\n", - " 2|1.842979e+00|-1.373343e-01\n", - " 3|1.781632e+00|-3.443301e-02\n", - " 4|1.760919e+00|-1.176281e-02\n", - " 5|1.750958e+00|-5.688541e-03\n", - " 6|1.746386e+00|-2.618021e-03\n", - " 7|1.741793e+00|-2.636854e-03\n", - " 8|1.739054e+00|-1.575065e-03\n", - " 9|1.736474e+00|-1.486027e-03\n", - " 10|1.734361e+00|-1.218441e-03\n", - " 11|1.734259e+00|-5.863179e-05\n", - " 12|1.733704e+00|-3.201643e-04\n", - " 13|1.733018e+00|-3.957711e-04\n", - " 14|1.731842e+00|-6.791025e-04\n", - " 15|1.730974e+00|-5.012271e-04\n", - " 16|1.730584e+00|-2.257722e-04\n", - " 17|1.730492e+00|-5.272976e-05\n", - " 18|1.730153e+00|-1.961758e-04\n", - " 19|1.729837e+00|-1.828284e-04\n", + " 0|9.537526e+00|0.000000e+00\n", + " 1|2.505426e+00|-2.806748e+00\n", + " 2|2.264025e+00|-1.066249e-01\n", + " 3|2.210620e+00|-2.415841e-02\n", + " 4|2.191601e+00|-8.677880e-03\n", + " 5|2.182712e+00|-4.072416e-03\n", + " 6|2.178054e+00|-2.138572e-03\n", + " 7|2.176320e+00|-7.971427e-04\n", + " 8|2.174237e+00|-9.578098e-04\n", + " 9|2.172978e+00|-5.792305e-04\n", + " 10|2.172514e+00|-2.138295e-04\n", + " 11|2.171279e+00|-5.689220e-04\n", + " 12|2.169799e+00|-6.819885e-04\n", + " 13|2.169215e+00|-2.692594e-04\n", + " 14|2.168810e+00|-1.868050e-04\n", + " 15|2.168289e+00|-2.401519e-04\n", + " 16|2.168018e+00|-1.249509e-04\n", + " 17|2.167885e+00|-6.124717e-05\n", + " 18|2.167623e+00|-1.211692e-04\n", + " 19|2.167335e+00|-1.327875e-04\n", "It. |Loss |Delta loss\n", "--------------------------------\n", - " 20|1.729361e+00|-2.749072e-04\n" + " 20|2.166808e+00|-2.432572e-04\n" ] } ], @@ -157,9 +157,9 @@ "outputs": [ { "data": { - "image/png": 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bhBX799X5fKcKC7l84TyyHYV4EHC7WXckFdbD+NN7Bipsvyb16s2H11zHm1s3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\n3c2/GSgWjfHgheNT5wfXh3nw309y3cSLG445HA7+cPCO/OHgxFbHV4+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\n", 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" ] }, "metadata": {}, @@ -200,19 +200,11 @@ "collapsed": false }, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/rflamary/.local/lib/python3.5/site-packages/matplotlib/cbook.py:136: MatplotlibDeprecationWarning: The spectral and spectral_r colormap was deprecated in version 2.0. Use nipy_spectral and nipy_spectral_r instead.\n", - " warnings.warn(message, mplDeprecation, stacklevel=1)\n" - ] - }, { "data": { - "image/png": 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KLor601Yv0Hs6f+MpDonqAb+iHY0z7mW8r3MalwPp4414/ww1ty7nHQDa0z7S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GyVy+hCNrDFZsAmNrewcXrVnHsz1nKKXeym2ZLNdu2jzpsxd0r2FTSyvXb92GJ8KzZ07z/SNH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ozTisMVNlxeB017UwQbO1BtKgWl+Zl4YXdZmyKpuLVoyS/xM+/qORkxvr2baskv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\n", 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"text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -220,7 +212,7 @@ } ], "source": [ - "param_img = {'interpolation': 'nearest', 'cmap': 'spectral'}\n", + "param_img = {'interpolation': 'nearest'}\n", "\n", "pl.figure(2, figsize=(15, 8))\n", "pl.subplot(2, 4, 1)\n", @@ -291,21 +283,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.6.5" } }, "nbformat": 4, diff --git a/notebooks/plot_otda_color_images.ipynb b/notebooks/plot_otda_color_images.ipynb index 7c04d33..6499daf 100644 --- a/notebooks/plot_otda_color_images.ipynb +++ b/notebooks/plot_otda_color_images.ipynb @@ -79,7 +79,22 @@ "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:2: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0.\n", + "Use ``matplotlib.pyplot.imread`` instead.\n", + " \n", + "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:3: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0.\n", + "Use ``matplotlib.pyplot.imread`` instead.\n", + " This is separate from the ipykernel package so we can avoid doing imports until\n" + ] + } + ], "source": [ "# Loading images\n", "I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256\n", @@ -116,7 +131,7 @@ { "data": { "text/plain": [ - "" + "Text(0.5,1,'Image 2')" ] }, "execution_count": 4, @@ -125,9 +140,9 @@ }, { "data": { - "image/png": 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CEak80UoJuMvPYUazG3B4UEMNJxBbHX2zO/ZRkfDDzwKo2oWbuJRctlKPMJzS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\nU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SSUldaKMNk6ALXMMQ8Y0q9ARfNsXQ8FxmYcgpeCk2pSzTJyGBt+9LM9b3AwrBhkRJzH\nZSWqpxSlcwYTlNW2cA6KmozmhE1VYr7tIsZ47oZXXPqOj8bM0/aK+13kmGBpFxxC4fhZ4pQEk06s\nfcHjeb9vGEs1Ff75z+8oviWoJYwZvZ/ozQanwpEDnRGuLhq+8eILshUa1zJNiqcwcGLSAOrBtjRt\nx2QalmrJh4TtwnwycZiywk8tWA9TZDwW0u0T3nwxUSYPzQFb4OXW4pcLpuVruIqY4TWSHE4dWZSy\nL2xXA+fJcnd34Gq55vKZYXG+59kHQk6OGBaUMLK83BBioWk8x12kX1fSyCZmKI6Q3tI0Pf1cgXJO\ndDaz9dU759qWME1VgSmW4QSncqZtW/IZDueBqFJN1U3L/SnQLS845ondvZJsy2Ec6P0aKYm2baqX\nj0Qjtb2YtBBpEK2q1ByrGdk6ByngiiGgdfZpFGehjZHRwJTrs5q1KveclBm1VUHC4n4NyQBDLLzd\nZ0pWnmws7y8bxhL56GhYrjJiIdnMQvf0puF2qMPg/RhQsbw0t5CVWxz7zQXfO7dc5j02K9epto5i\nSOyHysZSVUIBJXF5veL1W0MgEKaMNAHjlM3FJZHI3ahoOTFEi7GGkqrP461/wnvxFfuxkE1VbJiY\nKaEOuNdNw/stjEmZwsRHZ8+zRnHeUOYF9XQ6MU6RnISP3x5oxbLuDF3v+ez2zN3wBU1seT0caZqO\nKQZM4yHXxXvVOEqaAMebux1WIi+fXrLb77g5TeRoaFw1nC7ahvMU8O2CYXfgPleunM6U2aaprbcf\nffqaZyaz7g78jBVfs4UXizVv92cuzhPGNPzb28j+tOcng+VaI6/EE+awuAlAWoIEQLG2nuIe5M2u\nzAJwAfuwsKurY5K5vSa2BkekL0mWa7JqnceoqWtQnfH4x9fkkrGSMaLYuZlmXe19O4W/eWX4rScb\nPn9l+d92B5A8ZxDVVFf3K/A43L4NaDiixbPZLvj8XMhDZNlVcGZrAl9rB5plQ9dZ9ke4He54/3rL\nNh05+w6M480RTIyUvqFXZdkqeUwYOzJFw8U6s8hC3lp+/qqgxzvQjrUp5GmkUcclwnt94hgzRVus\nnyhktq0ll8rK27rMNGXcRUeMynHUimoVx4tnPcjE653gvee933D4U4ZRUHOi39o7AqIAACAASURB\nVKxq1nQ8QizEqeNn9yPnQ8/paOkay7KZKFOmXXXs3u7IrsfgsWWitQnvIq5A0yi7YeDSOprrFrGB\n6At+rl7TydNenzD9hlWG7262CBCOie6yIdvE4RiJ8Yz3DeEEIU00gyHGluFOql0sL2h8ZkqXNRhN\nPIU9V5tFbQf7wnnnGO+hMmQcNsJhnPhkWpP2iSKGKcPaBvpWGIYJcUuOY2QXMw0b0vkNy35BzBkV\nizWGcRwZM4ClxPo+e2MJeaRRT1GPbwxjqMmpwmwFMPXApUUQBxRYNJX9N7hAEkvJCsYTHpSf8dcw\nyjliquzwXDhPI6+dsGpauqVQTsq2RMy5qSh/N0M4w0DTtBymQN963kgiHSaeX2b+2lb5fCd8OiY+\n/dEdT69WOGP4xtMNXQuXfYsX5UeHBFG54cgxQMSz7hpKyfSaEaOMKXPRdyiZY6o+EOeFb5s79m3H\nJ+eJdaw5KeP9iDjLMJ7wztC2LYdiSLLkdDpzc1CuWuH9ywUXq5b7cyHmAbHw7edbYimcMoynyPXS\nINlxE85s+pYYA8vO07aWYcyIMxhjWPQt4zhgcXz0+szxmBg14RHQghZh2TtuDzWXJJX6UUWZhpFQ\nKuNtCAPHkIiqnIPndYD9eGDfwHGY2PQecuTtbWTnhdMQ2csCdXUQf91ldmrop0IjgWFSzpKhax/J\nCsg8M3lQeD22wsLMC5pnNExIeWfirDOYXFs6D9BOqdBTmQUEBcWZqopDhJgLVoRVzvxbT1r+8POB\n+1PmsJ54nSyN1ge+xhiAiqLmq69ovvPXhIV5ShwSN+cjYxDaVa2A9+KIViEauqljd4wYN7H2ntdv\nIp9nhzEFbzIZIUbFmhOdFYZiaWjZh7phvX4TsKPAqzuycYTUsmjMLIbJVVjSGhoD7/WF1imaWkI3\nse4cS+NZNAZrIxMREwwJoWsi4j0aJ9QFciok79EiHH6Y6FrP251j2bSEMJCGJW6ViXtHt5xYaqQh\nsmpa0BPTmHFmpJwzl+ueyyX0lyNnTizbF8AIk0NHCPkAyxFNB2IIuIs7Sl5TDgm/dpihh2lP9FvI\nN3haGtvwyX1i45W2vcCFE1ZgPwnWCmuzIZgTZ29oHIRxRzGKusBl8wHFRNqL9wCYxkKJhcVL5fwq\n8PrNCbO+4CdvA5MKre9xeURi5GQy966jGTOmXaEjaKwRGyczkc2GU6jqSS8GP0aSs4g6rEDrFUke\nsRExXTU0O8t+GKtZ3VjSnN9kcDSV1UxfKnD3lAxdURoviO2INiFSET3GeMbyawjVNDYzFcNUIKUM\nGTbtRJwags+1/aEjn50j0zQhYok4Sjmx6T1G4YO2oV33fDTcsw+eHCJ/8+uXfHG9Jo6RrrW8Poy8\nlIabdOJyuWRB4TZG/MqTbiP9pke8ReORH9yc+XC7YBcTd8OBRdcxTYmEcEpC7ybUCjEWxlSDtI4l\nE08J37VcuIb9aWQ/ZJa9Yz9EGgyShKbLvDnd4wScSD11x8pLw9TERHSJaqZ3whQSIBzGzDlOtBbG\nmBj2J0SE18eANcJ5CGRxtBa+83zDoq+y1Df3J8ZUGKZM1AezpTKMhVBqRTOld6miRgSmwrUoOTtO\nI/zgPlD8kmOnLKeBi8sOLT0vhjOGEyu1fMcbDhN87zQxNg1t29bY5xlpkVKi8f7df2sVFSSt70OZ\nXfxJZxL0TGuu21MlbEONpi5lDiqTB+Olovoga4bGQh8SX1sKf3ZzRBphYQqHmPj+cCY7h9da7dpk\naLxy1l/uKe6vcn30sWdzmWn6nmwOPHvS0fcth064ZkWREaHeK116izXvcXufOd+febM39DFVdZPz\nnGLBRM8XaggnpbjIFmHdG9bJ4rcZW7acy8DWO8460PRgtGMrwvvXpSJrDpFVB6qepjvjnh4ZTmcK\nF9hVR/hM+PgmgS8Mxy3vX7Rs+shu3zCNhVhOGOlYLCw5ZZaNwbmAXe8xq1v6zkBYs3v9CRu5JpiW\n1ZM7pFkSh4hPDaTEsBpp1kekaeh/4w6WP0On5xROxA9ammVPfvUE+WOl6Vr4uKPwCvf1b8D/fap8\nnNyh92/J33mBNhl/vOWDpw23nyQW449x33mPvL9heR9Zf+g4vPmMZr1lmw44b+gWvipd24a0f0O7\nEVBHzh2FiJQzNk282HhWy4YGy4dbGI+FKRyQ5cjGF5Z94fB2Ira1+ktpw/3xzJiFkMZqzM4Z76td\nYiyZGCPWQs+IyRDUYUxgiguizeRw4OmioXWWQiIn4QTEOWwwppZDSjB3GYJzkEZg4uxaFiXWA2Au\nRPnlHrp+JTaa533Ls2WHMYbb4xlNhnOKrLqOGCPHmBgHOKaa1eCNp7OZJ1cNS3HsQyZOmZgNL6Sl\naQwShE93A3/++Yn3ezimhs8Oce6Zws2p8MTXEvZ2LyyXPYunGy6GE8UvCClyPwbKasF+p9yPmfWi\nvl1xXUmnqe0rYyucuJwmXvQNu5CZNkuG2wPvbaEV2J3GesKPE29RdtPEt66r8qwxwo9eH4lZOWjh\nSddjJXMKhWmKOOfYnyKlKH03e0RioajO5bDh9jTgrWPKSpETl80Cyo7nFx2xZPZjYUyWKUZCzhzH\nyKKxNNYylkTvW0oRxlwX9o0tqPWghdtQ+GQULrzQE/mtiyV//PMzb44jKZ95neEoidMYWa/X2N4i\nbceisxgtlRhgCiUmvrVc8+lwIlFTOr23xBhxj87+uklY5sA6mect5h1XDoC5WqHoo1FUtEZNy2y+\ntBGmGNhPDcMoYAqprSSF37sy/Mlt4corTy4W/Pj1gWtR9uGrl/q/upu4OTtC2bHslqgqYzjRqWEy\nO9zcEkklEovHN/eYkpmSYSlQvGHpGvCWy8bhTaSJtgJTo2AXllaV1DU0TWC1yHzzckUohiyJ4yET\n93f0rqETg8ZQh+KnTOvP7KZI+lRZrJdIv2X/qSO6lr75lDenNfH0CX+633DZCe3S0eQTV4tbnr1v\n4crA8xbOazAJjoY0ZbjN5Jsv2DaGYs+05pbwdsAul3hpyDcj9qLQv7eFiyV8fCYdX9LcXCLbLXZx\nic0j+fVEedJh/5aD73+GvniFOX4TQk/6vRF3n8EPqGzpuydgJ9it4ad7rt4D7DMOH98xTT1PLr/B\n+aZjv9txHm9I5ikL4wlpIASH2obGLCmfH+naBQlD44UijtM50TeRnoxb7rl6f0k6wZs3OyYjvD01\nnLNQTAVcWtdQ1DBSaJxntahR6CkVrGRCFiYxmK5Hi3LQhoSrra6xYUJJ2WHNEmxTUTjAQKFLLdhA\nyI4iE9ZVioezVfFaQs3w0jSxtpaRjDUNT7/Ukv5lXL8SPpp/8N/8vhpX1UhL72mc5TyNNGo4nXcs\nnCHmQqTu7iGONRDIrzhpYkqWy+sFi25Jmu7JuWV/PHNxuaHkA86vq0vdZl69PRFoiDe3mK6wNg3L\n5ZreB4IK57Hu5GPp2Cwd0lN7n0XInYEM3mbiYU9nG3yMJGOgLEhmpO88qbE0p8hdHjkdlGeXl3x8\ntyMNE2Ecq17eOX7j6++zMCCuDu99cXx2d0+KkbYRpFS8zd0xkKg5MNvtlqSF1aLlze5ETvVEv942\nbC5WWO843+4wapgGOI0n/vq3X3K7P3CeAiUWbg8nfNszTCOuddhUMTVXK1fLdMlMuS7kU8po7hj7\nSDgbFjaS/IpLV7g9DhxK4FtPrzndjQxrzzQpY46chonGOvqLZU3pzAZK4Ju9cJ+Vop7dPGf5smP/\n4aNK3TystZVoYOX/9zrRSiSoSqoCc8xASmlWo9X38HQcuWwMC69sfcP7G7heWX72ecKUzF//2gX/\n41/cIsB/+w//7leqPfve3/8dDRaSGkrqQDLORPomY0WZdFmhlV5JKRGjxyZhzDu22y2tBWs9jZ+w\nFPpOeHVQfJlw5oTogqUT+naibwTrhdJ4kEKZMq5pyGnErC3FeOz6CtIE0ZKKx733bB7SK7CvQFmx\nSBB8DhAyRFuJDNQwMuNdFYG0QtBEo67K3EMhphNGHSYVVE+4VtA+UkrCFiixzuaqT6pBDwY5ZoiO\n0+6H2N2J7re/Bv+eBx3hux9A8xr+2T38zMN0RG8zKi3pM0uz2lJyg4kWfRoIUw1MtMZQbhyvdkd8\nXpDTnuvrniHU/9+0G6bzHeF85vbNZ1xcv+DEPS8+6PGNh7wEWfPZz3/EGCynaYHqJYttzyCGt4fI\nEOoal0rlM6KeVJGoj/e6qMGZCqB9iLN4oLFnPCEnUq4qXG8UKXBVEpNvGcKZ4xAJpmXpRsbQESm4\nHDlnRV0DOM6lgDgCI17bGZxaNzZxtT3dlMJ//oe/PATNr0RF06wrCO6FqfiR54tEVs8Pj4FL1/Ns\n0bBZ9/zF3ZF/7bJjbTu0W9OUyC4IPzpMfLiwtDaALPmDu8KTxbKeblkAuS46Ak+eLSri5vmL2af3\nIKntaIritg9u9comItecEtHqxnbWgljsaksC0mLxKK81ZcEQEuuQiCWyVfAmIuMdX195usueElsG\nFDGekAtTOAGCs5XF9p3nK1Zr5ScfH2maBucc1xc9Ycpcrx2j3xDTgDeWbrnFFIVxZCyKGSc6KbhF\nz1bg4AeerVZ8crOnMbGq6axyebFmYwtslsTWs7sfeH/paIzy8mrBT+5GJCsTCSPK0U7YaLheeEQN\n5xQYjSc2ltYs2A+J7RMlR6WXxPtPWt5b9/zZjXI/TgRjyWLAGn62G/k3P1jyvU/3JN89bhpOCrHY\nxwpGUsXNaClITpiax103kJwrqykXnDF0UrhoDZ3zeE0cVdmVxMIL0zmzi2fULylquQmJcK/89M1Y\nT41akDcnSs5sml+upPOvcv10WuHnx7vXiO8sk3jOk2GwAzkattsOyXe0FwvskDFlx9XiCTdvd/w0\nGrZLB5NiZWRh13ztm2v6RUuHIiZiWionzGrl1onFa8ZZQJtaLRqDTZWCHjRRqHOWEiBZocSM4QmS\nKvkaUxFLprGYTnH5hNeJYveYOBMcShXSFBcxIVDKQGt8nbW1FnQBvTB8/zMW2y17nWhGaLo1ko6w\n2ZLWI74JcH7L8skzuF4SyxHzpy32Ysv4z+9pB4ek5+i0Qi4sJZ3QyVMWARzkzjLsD8itZX84s7ro\nWX3okNf35CWUKTC5LePZ4e2Z56sL9uEVl097zNsrfLMk9p5n5gmvbxf87BAYSkFSQ9LvoBiyBVMy\n8b4G8KVoUSn4AMFATo4kGVxDzhlnF8QpItRNRpOy8Q2UE+uFp58Ny3mKtA7yqSbY3qZMcAY7KmdR\nCk0NbdQl2hRKyAQcxipjzPRe6NDZBOxJ5UzVXCpNI8RoUZQov4aqs0srSIxIW5HtP9pFrhYWK46m\ns5xTwZwm/tWu5YvjyNi0hMOO1dJxc5xYZM+rU2ZhPJ+PhUvfECTjNXOXCq1R9s6xSMpKLBNKnpVf\nhQpgtGJrz18tOdWHIknEi2Byddf72d9hpc4MKuqkOuJBSQLaCPviKCaD8xjXkkNErSVMU5UbW0eO\niX2Gpe1obKY3GazjN69a/sWnb3jvYkGOgcXCcdEVrtfX/OXrA6fzPduuQ9LI9aLn8/sz133HSQsb\nb+jE8GfDiHWFp71lFy19yIxq68nI1teYtuF0nBhThfh9fCxses/N53su+4aTajVyiSMAS2PIZUKs\noTGO0ziQRPAJpjZjSosOZzCGGAsf3VU1Tec8hMwgmWxgivDJLhK0zoJyKVwC7208b3cTnzlPE6Gz\nmfd7w5sRcjY86TNbB38yeOI4MlqHwxCjEnLk5lx42vcsJDIWw2kK3J09l73jG9sFTy4894eR3SDs\nRuhMYZczWuAncSBlZR+++ojNJ4sJTQ7na/z4pvfkUlBnKGpZSkNr4Ti0kIRpzHTLZxxub1l1DU+t\nUDjTp8zzpxveu8pYd4vYDpyvKi9Tqiw8U4ObGKDUpNicJnL2ROrGf04FKfaxRak2obnDzuRt5zJe\nlCYckWBYNe0897pHmg4NleA4ngLEQLfZcPj8LXZzyWq7JMdACGf6FwuQO3TyLL57hbrIYr3A3BSM\nB9J7sDnidUR9Zt9nFr91wHxjwpuXZD0AU5XphrfwWUT+uxvCN99D7ntSdrRlSxhKNUOyJkdwzYrD\nWTh8HyZdAFCkkJwlJuWUl9y9CaRyyc/vIad6P+q5brC9GWkNfLBesLET64s1zt5RxgCSEX/kk58r\nq6bj/rQgj4G+geN05KPzFpcmTB4xk/BxasAm1rYBGbHZ1GiBnVKyIrPYRYOSVGgZMXRMCs4kTCmg\n9UDpSmQlFts6Op+QbGm9kKc6x1VrCGRSMIwmP1ZXUXPNpfkl39e/Eq2z/+If/1M9SaXDqhQWVmjF\nztkjlsFBnwqDrSfgpoy01qEPr5EaeatzTz9rwluHGEcucS5JaxnqnWEqMuNOzGPWRxZYth1DjrTW\ncRwjZKFpHVnrpiKiZBQpVTIcc8ZiH2W4VpQ8VzcOIcZZjptq6epmbE5tDVVUymk+qQiFFmVKVRxw\n0dav11rBzzr6uxHWruWzaWAhtdJa+pZDSLTOsh8C265WBadkqkHLGMYcWRpL6xtOMeK1+jMGNfXn\neECA5aoxNnNLq8hcTkvGprpJifMz2l9menPh21cNP7kPWM10zjOkgpHC1cKwHwuHKbOfZeattSy9\nZUSZYn1fn25abMjcjZGMEmKm7xqsJg5Dwvrqq1k4x9spsHSWTmE/BswMYR3mimcKqZIRes9F37Ef\nzzgVni1b9lOl8D5EOpVSVXkPFSmq/M//1d//Sltnr//rv6Nn0zCFEx0tV8bQhB1qDceijCki1sJo\nePFslqJ2EeefgirZ5EpkkBFSh/qZmC2lnpJUQXOdkZhQpeXkRxmsiIfJonmWvqslDjAaR4wRLR7f\n1NjmGITGWQLCQiOKq9lDxqBmwmFRMXgcSW+R05IsI86c8WlD5wsxJs5doE/r2c8GdixIG3Cz/4rl\nBCsDfoQnHpURuRpIrwbs6wYNZ4z0kLeQMwxLBjnTDwLJAUvQTI5lFsMYNAkBQ8lCRilqq2kx1TUk\nYStOKRdQS9JIMUKMeVZvzorIkkmiSO7RHCl5xJwNFxvHemt4srS0qz2ERIkNcZqIacUhvOWTmwV3\nY88+ZL6zFb44T7OnS+h8YSUeK4GsjlIEJBKz0Ihjci23Y2BlDENpOZZMkGrwfmJaXh1GmlXFzzCn\nBVuFhW+ZQqWdJAk4Ck1SzqY6NU2JqG2YtPCf/u9/8utFb/7P/vHvq5m5U2ILDosxdWPIavGSKMbz\nUMzVeOXqrTDGkNxMTp4NfqU4IFBF4/XSWeEkJqPFEQ34XB++mkNvqvGKcX59CzLNBsT6eYwFkwqP\nfAbNNWDtAYNiBUmFktvH4bXWOXSNKBCh14SVQtTKchtSddW384KZDIhx1RVvZ45XqrLTB7ilqBJt\nYiGGUDKWllgmohpa4zmngFLo1DAaxeZ587P6mAFjjYeU2Rtl7XtOUwJJKPWGtNYypoyRyg8b0yyV\nVEMx9Qa2psqKc85IKjRNwzmBECg8xAvou4UcKDMl+sFYCfPwf04oBShiao9eMg/3RSrmS3TsB8rz\nvJGrUkqVe1dqUw2vk2wxD6+xpobW6bucoorhEGIK9K4n5sD/9I/+3le60XzxP/wnalVBlUJEjZCm\nUEPjSqJrW4xjJlgXHm77FCfatkWK4mNCqYtOGAdELMYYmqaakqc8k7VnM60pGSuhEretRbQigQQD\nqcZ5p2iYcqHEOgPL6V0UQ6WDW2Q24j5EPVitczYzdyS9KL1TvFO0PSPOopJqFHsx5INgcfVnLxNM\nd5hlV8O8zkqY3tJsLlBXEK+waKAJkOtzarMDIiUKMnbICGV0mGghWSgTlJ4SE6KWZGpVosWTSpwp\nFfN9KLb6veYDakLICcY4B/hRM2GgoGXORHpI8c1CNhBLDX8L8zPgSyImCCXT2JaJwlAsa92zZ81L\nC7eSGUrPh+7E/QTnYUD7nivOtP6C03SPWkvWBSVOGFvIxmJtjdoWNTRkYlKQzArHrVoW5MeIgE3X\nMYyZzlqYUU3HUAPrShF6lzgF5T/6v/7i12tG8wd/+FNgzgqxAFXa6pzDSjXuGcO7HBgDTm3F1htD\nLBkjv7gRVZRMTdV8+Fvn3Jz86BCpJ5vW29mn4fBWH5KfaV1ERFEj7x4U7xGpKg0AtJ7eyryQWu/w\nD7yu+XstqlhTsx4EoYivHC4xFAzJKGlIj1kpiCBiKA7M/OsRb4mqlPn7qF2/hgGlOCEVqYEEqvMg\nvC4oo9SqSFCyFabCI2anbhSOxjjOIRLFYHFkKtFVtAI1mctpXEvIGTEGM28IUYX6OAqpgagKxmFM\ni86bQpk3mIeMmeLq15dc0xzrawzFGB4aV25eGFNRCoIx4My7DasUre0kdZBLNeGajBoDspg/S21v\nplk9Y2dETdLyuGHlUqnN6i1DdsivAIPmamtALGJSFVAAUKWuIkIusfqJrEGk3rvGgKFF1RLjhCmW\nHBw5T9jFmhwTIWfCsWBsRsSRyWQSRj2iAtriqJglZ+aDkg6zWCPVCtwajBHazpGGjMZCCnWBTmdb\n0fTURdfLzJ6jQJoPEBZOU/XmuMnTNAb6EywCJTZgDBp7slFcV0hdj6rBDIIYS7N8BmrQUMBMcEiI\nryglmwFJFAumBd0cyRcWI0qeIubskMnANGGOHZoTHos3Duy5thC1QB5BLWQlSpw7GZZcJor2WDPW\nSHdxQEKKoHoGIMSWU4A4FNabBccA2wvP0gx8/tGE9j1LM/LT+0DbODYeDmNiGC1PZ9TQFQeshZyF\n69bRe0NWQVgylYBDOI4GTRW/tTcdVs8Vr2UEkVqN5VLf8IBhWww3YjiYiqFq95XzOBEoc45Up5ah\nFIpUukSRX0MEzY8+vgGZGxrzSaPGIMvjiUlmM11tcVislMcF/2GBKqqVVkwN/hKlEnyNQQFn/JxV\nXwfIxlgcNZbYlLntZd3DJyPlUmcsSZhyIqNEead6asQiEnEyJz9KwalQjMXNJkURrXMeqZr41tYA\ntXbOYnGutqceKgArShSlpRIRrIKKBQrW1ZTMB3minWGZfd9WF7BvaG3GAz6DUuXLqkrOBUnKVJQx\nFoaxMMT658MUK7omO1JWzjmT56ohzXk6tV2QK0PN1e89pndBZ4+8MZ07NFJPdjJHMAs8wj1TzhgR\nilZTZtZ34M8HPtlDtVF/F2X+8IuvMwoPCZr/8vXAV3vgcJpHjtq70LOKsKnMOFeoHpx/9B/8le/j\nX8aVDsfKZJMzVkythEm4UijG40olaIspUFwlh1sLmgCPEUV8JjtB1QKCpaneKO0p5ZaiDqfCNHpS\nhByOTCMc4x5b6oneqYBYnAeKUkrEecU3FmMt/VJYW8GkBVKUKQ6UUmXqRhooQkgTHotmi5iMN7Ye\nGAoULaSY0KFHbpZArX5Uc/WpnMCWFrGgtsZJi63lm2kT+FSxRz5TmKgwoYgRB0mR0mLzw+9Y0YXC\nk0P1Gxxa5MbD7boq6kZHLorYSAmKqpAlkFKBElBf56htVxd3ZyaQbs4wcpQk1dvkAq1PjJ0Fdqw6\nw/HGsDMQfWGKB47q6HxV9r1OHiSRXEDziFqh9xYOJ2S1YSGBthiElkEtxhaGQViUxL06lk3HlCYk\nN7i2oQTB+ZFpEBpTSMEg5Y6z7emcY5vrfT/ZEW8aGhznDKkkJhwZiCFRBMqvo49mTPekL2WcEIVx\nXiAeMud/IQhr3m0feFe1CvjSaVTfucofPq9DUDNiqe0BLzVREWqkcBJXh+VpXsQMMyds5CH9M+Uy\nJ7I8VCtSq5VS8GbeDBCylMdArd45PApG6RxYrVWGm+W6MuexiNQWlOa5GrMCRmcRgmCNw8zxx01r\nsKbGPPcN3B8PNN7ijeV2bmM80lelLhyqwnEIJOM4joExCqdpIiYYxsyQq1R4yoWotcUW9R2SH3hM\nzrRiHhdqFdBi37UKH9+th99FfletARR5bM+VUsgilIfN4GHgPO8OjwTm8kCAnpEa5Lll9Ithc794\n1faTzveQUBcyjIUyzyRypszk5wiPFfFXed197yNyEcQkcky0tkG14oGyzZTUkMhkawmhINmgNtGJ\nr/8mZ9Jc0db3fG5hqtRFsdTFPJWM5B6xY52tuDqPKKVWPd57fFa6JtISiKYlAoO083OYsAjeFowt\ndM4S40jbNJgyIBQ2XUvJEaQwDQWxBroemUYsWqsSb+oqVBwaR8RqnRXhEca5M1DqXMoHcnvGXiTS\n+xPOdhAOmEZIk8AO3GAgNhCVGB2SKxG5TA53f0FOCSkGc3SUPGFMQnvB5gDREu2iuuPL3Gp3Ce/r\ngFyLEJIlTJ5xrjZj8YQpM2ZhTLX1ZmcxRaBAbggaqPFVbY3aSIZSbBV4pAIs5gNsQbOlFOUyZ27F\noVoY8gGjgvOREgsnhaQtn5+P9FKQBOM04lW4y4XO1hlbaAWbLjFJuMMiKZOoJuxqHxBatZhiiOZE\nMT3GekSVxvwakgFiOj/+WUsdYJv5AamUUQvmSxsOFWBU8rvNxVhLnv0TUFs2qjpvAFDs3C5ynpQr\ncdhYRcUxqdKYSM5CJGKswQUhmAfI/By7qgJSyHNrp1ZBFdmu88bm58Ut16YBk6Y6UzEG5kGi81Ut\nVaW6+tjXfmjDoYpkkGTxJqPWkfKRzjjo6gDTZiEbQTA4Z5imQGMqW0y0ih3EGuChD205hAEVT1Yh\nFeUYxiocUEuhVLRpViZraFPFWJT8rpqo6JpSW3XlXSVhjCX/yzoVNV96TUHUvvO/mBpBbF2NUDMi\n5FyHrfNnhC9BNJMNc1uxVqYyg//AEqWi/8UWfHlXqWRXYwxEZsYaNd8mG3hosoqd/TrlYdP86rea\n636sxxVTMESyFEKpaJCbOwgpEYpQG7EtzkRKzhTV2q8vhiLxcW6oUk/6FouRgLeWxudqpLU35OhI\npaClbsL6UAqG8f/j7s16bV2z+67feLq3mXOubnd1qq+yHYKtABLYgUQ2cjC1kAAAIABJREFU9jUX\nCXcQnNzxAfgIfAS+AUgIQS5oJISQglBQkKLIBCIQcle2y3WqTrOb1c053+bpBhfPu9Y+hktKOqWa\n0tZea+/Vzvm+zxjjP/4NVYS0jBTnMCSsOKy0HabR0hqh0vaPMbZ7oswzVizICmVpzYKzDN6iOpFO\nR6xUihMkKWoipRpciLj9CKRmypoTSESCpboV2VkYC2KWFv19XtvuZrDQg7tw8K0VutAKzZ3B/3Rp\nup6jx9gCqWBrRc8OnMFoRJcBrUJWSy5tV1Nr3aBKJcYenSElS6wFRajVM6eIVkvCUophJbf3S8bQ\n4s1z1kZx1tZMFqNQoJrSrtGkrcnU8rwLKhVqtbyv+bmpytWgUql5IEvFOEvNFbUDUmLTzKW0XdvN\niWOpFbUduqZmkjknCpYMOBNYS25i821yKerJVUhPZCXz870XfiEKDVsqI7QlJ5qa+y9s/lgFUyrl\nufNt+Kg+FZFasaV1xi1ypH2gtZalrABoanYay7q0ImEMVIfogtbKbCzUDBhSrSRqY2t8JbpY1KDG\nYsjkbXKoNK8w3eCcuB1oFVouRAFrHJmWmzMES4yZsE0aOecNb99YalawGwQRvGItIJnB+M0zrGAU\nvIfGZKjU6giuTUJd34LNgtiWmueFVJW1th3Vw3ECG4gp0jnhNDdPN7QQqjCjDFpZvWJKRcRRtZEL\nRMw2bWSsM1/pmuGrY0y7QTJinkgAT9DaE+xot09J27QhwFeKl+SGxW+L/BbSJB+X//LUhKRnzYlm\nfc6VsdZCru19XVG0mR9qxaSP1PanTbpumT3Wff1eZ2fvQdtzbl2HVjBBMRhejQal4GxFy9gmVyJV\nFC8ZMa0JQg3GZVCHN7Smw6UGfXrfwq20RQ2LmYGKGodK5FlzZgr4iqkrIOCBHKFbYdtpGrtNjbai\nKbavM3qQiPUKvULdQQD6O0QWvDmgxwf4cUW+CGie6Yu2iWZdEbc0ZpxJSL2irgkTPRwVosW4vu03\nJaAiSGgOFg3Z8I24k3Nb/q99Kzq0M0SyUHMmLhcsJRHTSKpCQajFUjQ39mp1II1NWrIj1UaM8Qji\nLeclsVTfZvdUKaKkYtv0pVBqpRZPUqG6QsmGUmDddo6Vbc8ordBUNVTNjZQgqe22igJKqk8K/XbP\nFAO1lO1cmlrzXVvQn5i2zy5II+1IQjVTiyea5tCctdG7FQ9UEmUj6FhijqTaot7/P43j/8/HL0Sh\nEVPRDTtvyfG0tPetINTamFiyVXljhFotxrht8Wg2KjRb8mVDSESkhStIRgiIWFRK08I42dx+BeOa\n24B3A1oKBMcTxUusbd1frRgr1KcUSGk2MO3t9keEFiesSuebZb/HUKXiaBd7xhKC38ZzxQX3vK/I\nubTlujMs64w1HYsagrS4t842lbLzjQDQWcOcCmMQYiwgStVC13WsuSC1UEpljpEY201knVJyY2at\nMZJrowMnbNvZ1kpUoeSKIojkjeL8BF01B+xa63OhEZEmrJQGiz0RAdq/b7ubjZFjRJp5X01Q2XYH\nrXi4rUC1POaPe52n7ycbE1E3qxy2SIIsba+j2thmtebnKcXa9sI86UAqBfOVFNCniU3MU4rn1/v4\n8bsZ5xymLnj1m0mmEkJgWTPQpmjMHZ5W+FvCqwWJVPVY2ybaWiNg2hae2kga2uyMnGlxBIJHHKiu\nGG33kfOKrg7xjf66lozR9mQb03akT4Sddj/6dk/mDiGCQHAOW8GK2+7tA8jYDkZ7QOiJKQAnkmn3\nJ2XB2hFvmw2+kR51lSKVkjzOVyxHSqg479t9Z0vTq+gllATZoQ8VqS0tMiXL6RhZlp7zIszJcVot\nUxYqSlZHNRM2K1k8tTiSCmoyaVFigewVFli07UcldyQ3t/u/GDI7qqRmgGlHjnnFIqTqMabjrq4k\nAusyUVHOU+GoJ96dVoIMGFdYtQOgC5kcPbZPpAreBuJa2rlSCsFZqlUEy5xSm26kZb6lEplzbHsq\naA1i9SgrEejdVQtpqytWDabrKStcvXjF+eFINwas76g2UX7OxJhfiELTuvSn7rdsLDOHNU3NX7eO\nOISwjbVbtHCaW5dP03SUWtlIaw0ms56qS+uYpVJrfLatTyU33Y5rbCMXmvW5GtlCudoBWrV+TIdE\nMXbbB9Wnn71BeqoVUUjbgXZaWlGszrRDMBeCClKEXDNxmbm5uGw/R4W1ZEJwpFJIacE5R8yJwQeW\nGJswdBdafK3rCKFDaisq3kEsitQKVEpJpJxwxrJMcxulY0SswznHaT6D6ZvfUVa64UCd58Zacxaj\n0Ikl5YwRIXi7aU7AqJJKRIzBbWb8tZRnh2aHtKCxbdIxxjSVmzWIACmjzmCR57yZhtY01phVQ7HN\n9+zJoaEIhA2uE2h2NPIUn61IVYx3DS6q+SsFkI8khc1loO31CrUUjPUYFOM8kuv/e7v0tTz+4//B\n0fsDS8zIlLjYjby5glojp9lwLcoPvmOw+GdNlzGN+p1Tj9qlTStdoGTBmoUSE4O/QmRtdjHVknSh\n5pecVUlr5qpPiLfkWnGxQ0oE10gDYg0zTY/RiCUFo+1eZIOASTMVQevAPDkktMm6ZdcVwJGSNisU\n9eTBEJYTuijRD/gg1FnwvqOsyhGLZqUfBaEnp5UYI368YZ6bO/okhYvgcdITsnKqlXOsjB7WMhNC\nIISKKz3v0opzll0pTGVCjGNK7bk71QL1TGcMo/csRrmxPWtRzvmOb3cXrAJpzdTgOT5GTLDsfGGo\nkX1fOJpCrMKQ2tSxlokHhZRXjPW8zxEtoGQiHZlA8cIqloLi1EBwnKoF73A6tIYvZV6++g6pFtKy\nkrSSvWMwAestiOKL4qvhykfiKSN7R9YeV048ppVBBx6me/rhwNDvqaVQ8kqokbfpnuXhnmIKd+cJ\n2didOf8yTjRUcFsVrhUjBmMg54hFcKZRgam6Caieckwag0U2GMo5Ac1UbXuLJyhGVckp4byjlie8\nvxUMb5RaC2lVjGsCTwC7sc+cs6jKVuBia5m14N2wCTINajLGGkpWtLYDzVqLIKS04owh9D1BtF0Y\nVpDecp4ewFlC31HmyON8xoqh73vECrpWxAnBWFQN/RC2IqeknFto1/xICR1KZb/fU+KKaKVo5TzH\nRt9E2B32jca8rqgWzsu5WbDUQi4TxTZqKqqUvEFUtZK/svdAtTH8VNGUydowPBFD05U32jEi8DQF\n0ax8NKcGiRSwtTwTAJ7YY2oEqRm1dqNWQ94aCgRiqXjffv9S2gGm1nzcbaX4TCJoe6n2uc5a9Gn6\n0k1/ZQ3WGLQqRoU8rx8p61/zw3uPlcTV7hNKeMSgPCSH4pjNCY2BL34SCMaylozUlb53LZpcPMIF\nlULWjLMdqfTc3k5cdEdeXr1ipcG1DUFNHFOiak8yFdGeJa7E6UsudyMfHu6wyjZFH7D+zP1t4urq\nitAXTFkptjEhd12jUTfh545zEQbjKJLw3rMuBb/31HVBNOOzYsaXzOPKhXTUmnjorjDGIXtDX5oT\nx6IruSb8bo+IsJaV1CWOZC58z3uBUAv70NNliKWwuKbjycPA3fFI9MJ+B7UMvM0RI1dEH+iqNAi8\nNkYc3vJpqvhUeWcFY5TD7hP+kEqthpncJuTrDnRp8Qi1cDqeuehHbk9H5rpS1kg1rYh540mseDyD\nM7DtUNZS8H5HjAtVHUMvTWyblEuvxPWRzvaIhfef/Skqwhg6lqosRZmD4cb2JIWYFvbjdXv9U2G+\nv6UWx7kq37kyrNlxuLikkCjrkVwzJWcelzPWK8FbrkzXdnm2QfgPyy8hvbkJvNrBYI35KweIfmUZ\nveTzX+mUO9vsyHAGl9uOplFsG9QSTDMGfAreisuM22zqO+cprlCNsMS2PK2l4J7HTn0uVGWjST7R\naxslOVM1brBQ09YUWbHWPCdLWufw0kNNKJlSM8E4jB/bclobaeFpp+KcYHxjBu1Dx2LaEhgrWBVu\nHx8AeDHuiTFCyby4ag6/pMzDuy+xPjx7pF2MXctlsZaxH0l3Rx6miZgK6jr8RpeV3OCuc0ptIbxR\niJ8s/p90N2ULXNPScjPqFtOsNZLKx4PaiuEphlmprcC0Fw1QqjZ9kjRV7rPuSUptb9cGZZVN4+T0\nCepcW4yEOgyZWsPz9fCkr3l632/Q5dP/tWW/YFwBE7aGpmHVoQ9Nf1S+/onm7cmQpNJ1CSsdvXOc\nTzNgOeuhxe8uK932Wkx6wERhD9R+hy0JU4UcWvR3WBaSgPMDf/T5Z7zqD1SN5GEgVsGI41ATUHhc\n3/Hl+a7RgmdLv7/gscAeh4wDYan8xrd3ZFd5mCuPtfDdEjnYkePpTMwLJVjKvFJzZXWGwQe+P+zA\nW0Qrf14n1BmSUYZSKTlTB0dmYTy3gy7UgsMzhEqNiWtX8XZBbeCz+5nROe7FEkLhcJ7Z9wN/eprY\nlYWzs5TFEMVyE1eqf8FKZdWFziriDPOqaFrJpifmBVMiE1CXwrKcSWbPWiJRIm9xXOw6QnasaaYP\nA+SVm6trTjljcuXixTdREQ4XL1lqJeW1XXsls5AY7J4kSlxnVOBFf0lyQolnxu7AaYm87ipzVI4X\nhcEMJGsw1tLFTF8MtjcscaVTz4U1iJgNXnQ4MrEo5wJyMFhzg1OoJfFeDcmDKVA1EYMQ8IQQGGrl\nytjm8lATS64MPpCGzM3+51tofiGcAfzv/Psq0tStBcWIp2yCzaflrm4HUikFtzn0qsizW2+wgap5\nw+7DM06fc/t85wyxKkMwlNymmSUtHHZDU93HQkwJrGukgaUxsjrnnzv6DGwjFACyFbGSW8HxzlK1\nZdObba8Qhr5pRdZlG0kj49DwWE8Tm4YQnh2Hz/PEYbfHOIumtB3yCa2Zm/3Iuq4MXWDwDkpkHEeW\n88Q4BMa+xb2O48iytO+HaYVzXhISHMvcoKsPD5EicD4vHOeFdXOKVmnZOLVWVEyDBbfXhtpgvlob\nsSJrbtRjqbivCFl71xTIACW1fZDIpjqvSqraNA/VPFvdAF/Zq1gKBdHGznHGbmycj/Rmg90EjB/1\nVMYYclq372fRXNoOVTfbFTxiyrMrdhOFtq9ptVHh6x/8p1+ravPv/Gu/q08OED41bYaqbd13Z/mj\nt1+Q1hU3HuidJ52PHIaBF6NjLoWL3Qt61373+TyxOMOhrmQ38N5UJBU6sawxIyVRxKLe8p3et0nd\ne+asTdmeK3E903UDLjn2vWJ9h9SV+1ToO4+PUJ2jj2ecNzyUkTo9In1PL4mDNeAMmjK3ZeX27oHi\n2nVcQ9ObdbS4iNL1xASvDz0vhpH78y0/m4UkQqg7qrmlX0/YCl5gjRO2Wtbg2NeV4eaaNC28dJ4i\nyqdRWOYjvdvxjcNAcJe804l4XvEW1hp5aXe8GQMPCOO0cOvhLK35nJIhWSHnhKQHuli56Htu+h6A\nQkfXdaSU+dLt6awjeUOJmdGOPJqFvniO2SJSqJrpvKemGRvGzfqnUIujxEx1hiyVxSq7tTFUzzlj\nNZO6ZvBDEaR6SmqkphQnojWMYcRIg7A72wgQbj1ysAdM59kFIZeC5IjDE7czbK0GdYZYE0uMDLaZ\nfObe8U/+xf/8c7sXfiEKTf97/0CflsoptQwWcRa0scZ6H56niSerEtU22oo4cn46zLbDpyTEeqiN\nqRaMbR9jWhExtQUjpbQJvbadTd+NpNoON3JqXkdGyGWzpdkYIPKVbr9FFD8p1psdhZHyDMsZ20Sa\nxlZSSoQQnsWEo+9IeWmW6jlixDPXyOWwa18rRSDTiSXbyqEbGIaBeTrS9z1OhXfvP+OTN58wjIHB\nfGSv9X2P8YZ4bth2LoIGz4cv35KqY67K/bRgx57OdBQcj3Hdfoe2FMa25/YZkirNfqdkxftGsgA+\ner+lxiJTFyjTGeccSQtmKzBPgs5UGqkA+Pgc5vIc9/wkyNSStkaiYoNF05Y1Y5qFDVKfp5VaK1ZM\nswEpBe9DExpuJATV0iA/1ecGRjYdztNuxokh/rOvt9D8rR/+q5rmBSQT1bKmyGHsuRz3GOO5P98y\ndiNqlJ5m7W+7nlUCvjxSSod1kfvTgu+uoayAcl5uwXUEsXTWQIqIM6w5EWPC+M2mBkPvAymtVKvM\nWZEsdN6w88on4cD94wOHq0tcXXEE7mVCoxKXRNc1i5fJWQbX7slVC8U65nXFV3jMiV4sZ0mM6jnp\nZm8kHTeX3yAXRUqi+sCXd+/oXMfd8o7r8BJ1jk6EUSKyG8mT4zHdbrqdEW8vtusOyIXJwwHD7Xok\nSEt7tdKat5ch8PkiBL+i0owkTUyNgCHQrxXlxLB7xaqtkQl1xdkBzAzVMrueus7UKbLvK5+q5Zvj\nnnycmEUYg2fn2s7jLDt2ZEqpTLoy+pEYV2axGPF0TtvzNS8U5+iMMqeMDwNCohZL7wqgrNUx0aC2\n0TVHj7StFJrdjWG1C5GKFBgI6LqwWjAamGvCa8B37eOXnLFisKrkzR3k//jxP//lKjTub/89Ffvx\nd3raQ6gqElxjYtlm5oizkBNJwT0dIs0Av7HJrH32SkslPms3DEKWdkA+eaSV0oRrNxeXnOeZYbdn\njjOpFnwxTHEldI445bacj+etE2lmlM5uehoDOWdyzvR9j3WBtOHV3rRkvFULdjqz218gJT9TaouY\nxg4Rgym6LTB7+j4wl8TLsCPrSs0JZwSTK9JZrMLVYaSzjrAp9VUTxgcuLi748OED3gj3x8eN7RWI\ntTHg4grVBE5xIebK2O34YpqaYeCm7LfWNuNAo8+Hcl3bZPU0rRXrG+GCgujGEEx58xXbxJYp4roe\nqYVlnrdpsP1plkDSIDmEusGnTwLWp4e1zZzRmdaMxNisM56ak2d2Yi5NUGsadNfIHO3nsMahG/Vc\nNu+3ZhK5iWw3mE3/xX/5tRaaf/3bv6qXtCK+1My5JnbVMPqOGmCOmWAtsS7svcNLoCsFrOXteSVr\nIRjBZEd32Qxk31jHMUcuL66pMfHl6ZYFx3fDnmqFKyP8+HTHVA1fLkdG1/Gi87y+uOD96cRfO1zQ\nB6GzkB9WuqHnLiWyLYziKK7w2l0zn95z5XcYEdY640IHxfCdQ2EuldUPnE8r+wKPGvnzqec0KL/i\nRnLOPMaZkxfGLDzoyGenR66GHaYT9uEl+zLx58f3/PAwcIMwDS2L55P8Jb+620MufB6F/+pD4WFe\nMMFzNezoVOi9sEvtfu3dkSF4vCxcBcfeHLjNhaIW21ku1sKpJJwvnGvhPEGxGYMwAt6N/NHDO0Yz\nsjqI85n3Bh7TyL+xP/DH8wdu48QY9hQMw6afe1uVXjx4ZVBlkcYoYw9XGAZvuAA+T4VYDN60Hakz\nijcNot/5Ga/CpQ70VfgsRYxtRCO3wWmj8SwOxJy5JoC2M9SkhVMHVzFwN8Burpw3qr+thioZaxzF\nwIMK/92f/J+/XIXG/u7fV6utCLRpRcEa4rIgXcBXoYo2AV5wxDXhg6NsC2cxgVIXTOcpOeNMh62O\nYuMz7LXvBqZpYrjYk6blubNdUyRsgsO6FooIw25EdCtEOaK450nK+GbTYrb9gkjBufBXOutKwdu+\nHYJkrBa8BIarnrK0sK1d1xNj5JwXQgg8Ls2RepqmJlkoEVdhuL6EXHDO0HlHsI5cEy8urghGifPC\naZ5wXtj5ri0ot4PXClxeveTu7o6+79teSg13t2eWkjmfz8Riuc8Lvjqs9azSmEfQ2GDnaWG32zV6\n7dJihGPcdlNupGqmpojxDf57FgpusJc4j5RMjSv9MLBSMVWaE7S0A96H0CaLzWk7rRnjPsKijUNR\nKbaRFYw237una7duGqGay0fINZft9cjtdTIO5y25FjSVDS6NbO6t2K3opX/69U40f/Pbv6ZLLkwU\nUqmYnPnr+x2XY2Ci4mxATMUuQpZEIXBfI9fa9lJaIsF0JGuQshKkUX3DroM1EVyL8D2WK0q6J86P\nzL4nLidi7XiQwJQeSHWlH7/BKH4T7prmO7g5WlRJDNVx3sSho3bo6FjuHunHQE8gSSbPE257nQ7G\nosG1XeU40FPZqeV6d2BnPLM9kqOwNwI1MXqFEnjZDax2QbVwKJXvDgs/OQcuDjdM5yMvxcD1iqaZ\nz+8vMVl44SY0CJ9+eCQzNEv/vLC6zJ8/JkYvfD7PnMOB7wbHXCd+JHtm17drKDmKtYRpgt7z17uI\nt46LuvLD7opc3uFDj6ej95WfzPCX54k5Cj9jZTqdeRMGXgTLXbX8+vXIZV0JfkDyhPq2HijatDRp\nux+GBNEoObXrd7XNjmtwbSdKzCRjSTXhxWCLY9GIcxvqUzKN1G5ZrCIETtvHCoGlZrLUpv+hEsXg\nN2ZhqgYk4qX5q/3Xf/aHv1yFJvzbv6+pboaNtdnCGOOeMXfvPU9RId57KkKOa1NPG4OtZjOU0419\nFj5CWUaeDSRxzevMOUcnlilH+s3+XKRpZlSVqSSYYxNcIthgCal5kOVaME+4ftdtE0ATNtrNxDLm\nxuwJKGIbaydsJIRlTQy9e15W77uB4/FIFqVXwzj2RC2UJXJ/fsB7z8ura4bQfqfz+sCuenaHkTUu\nXFxcsM4TSGXoAm+ur3h4eCBXGPvAYXfBp5+95fXLK5K2C+j93UNjcfU7zvPCFIXH+UzVFotwf9w0\nFiU3Rp+3yNqIAkXb9JFzxncdhhbwFJ8LDC2UbItIaIaPFoOyrium76k5g1EsW+RAbSajZoPCDBbj\nfWMQ1oqxAa0JsQa3FlbT4FFT2xxrTbPU0FLRssCTyzRP2g9DLW06EwXNGWcdRcBb2wrnxnYrf/Bf\nfK2F5vd+7W+rJWLF8AUwpEoJFq2GzqyogW6ujLsBEWFeFg59R05PkGBpvLPQuvf7mDDWk4xtlPJU\nsH6zYVJhrZlFLaaW5vlXVoYMvu9IBJJWQm3CWE8jkRgiF+NInFceYyRIhw/CWTNSO0xVOhNJpbJz\nHSfJCBaXJm76gS9Si8cYXOYYK/vQE9NEYE/JZ9S21Me1xu3+VWKJJOPwNROS8lgsxiuFiC1KpiOL\nwrjD58ZOXRScNVQKdT1zJ8K1nEhrZmcrpVT6cc/9csfN7gqdZ86S6LJjqsKLUPlpLMRs8DKypycf\nLhmmB+7LLcFog/GAl37k/XzkbA33JSG7Ny2nJ81ETUxLodeMqRC72hzSqwEtmM7gc4sbmLMSszRY\nXZSSM8EoS1b2vmPJCe9Ma95sKy5BOqoRzrpitmbMqmsE2WpxuUV4FB9aQVkWqjPYmjDSEX2lq3a7\nZwMSHCH0/Pizn5978y9Eoel/7/f1aRrw3pNixFZYtbDr9ix53YrIRh1+Ign4tvyuuR2C6IpzLVTM\n+QFxApu/zxMc04ltexrr2+K7VuZ5pvMOax05J2KMhC3AyXcDZeviUgSpK6hhGAaWFKkp48nENFHt\ngLWW4Cy989yfjvT9yBg8a4qoZoJtxWRdV8y2Q8jG4Euz2S+lcDweCcFxXiOjM7x68RKAuK68eXHB\n/f0jL6+vGIaB+9t3DMPAIQQ+/eKnOBsYekdwjmF34P7DLeM48pO7OwbXM82R4zLRdyOxwrwufONb\nP6DWypwyS4ygprHaMOAs07Q07ytpCb7Nsn4TTdoGl5XcJgc6j9kEtCKCsX1byufl2fK/1uZooLFg\nvafEzU5EgfrkQFCfC78xjloKYj7CqtAKi+26BoOm+HG3s11LT/AodX0mbqAGu+3QVC0lzYgftryd\nQv6Df/i1FprvfftvqN12kg5D5zpyWjlLYZeVU0rgmkWL5Ep/ONBJTypx044FXOcxujHxRHCilFRZ\nfaavHce6MhaDGMWMHeu8UGvGdz0lzqAO0UQfBtY44cSyhEA9Hek0kMyCMY5eOlZpkPHgLTlnulKJ\n0qbTWhNSHauNdMYhdaDmiV4XvO3RUBkOB35o9zzOD9zsRr7Zec5L4mWnWAlYu3LA4FxgcC0TqU4n\nxnHP+7uJixq4rXD0yk+Oj/xoVV67EVJhdT2qli/qzzhgODMS18qxC1xsJMk5x9bsLC0La98J92lu\nS3EdGGhN1wcWrglonOmdJ0giqMPiOKYzs69UYzlUy1QVS6FiiLJgamDWzC4E1qK8qaFZ2HQOEcu5\nJBwCuuVnDZ6JFktQasQUcK7fKNMRlz3VTHgd0N5jsfhNwzYhqCm44rBSSDTfON85ZM3EWki1Nc+i\nSkS4SgYTDM7WZ5F0j+V/+umPfrkKze63/z2NIuSUsC4gppBrJWBAfFvo1rzlRDRRl3OOdV0bYyut\nm13Htow2Hi2ZLhhQ14Se6UgxA8PQsSzLRn01eL8xXgqb40BtyvrSGGKaE5dXNy2fPUc0F8T7Voyc\nb9z+lOmdIQO989Rc6MeBGCPTdGbXd4T9HimRYdgxnY7sh8a8WdcjiOP6+prz/WNzEXCB3dDR7QbM\nFAm7gXdffM7rNy85nU5cjiNTWjnsd3gM89xMDKVkvvfN13z24ZaLiwv6vufxdOLt27fs9ldM08Ky\nrLx8+RJcx8N5olThbp6pxTDNJ7JKczawjiW2m0xF0NRU6Zs5GWZz91UxPIXIUSsYwQfzDIHKRrbQ\n8uSoICAeNOHdALSDqut2GNvetsazrFNj7xlDLs1R11q/Mc2mRlioGbe9ZkUV73tiajHUjSbeftwn\nWNNIo/NqWdmcHJ+NJCkFGwL5f/t6dzT/5q/8ur7ue4wx7LEEI1SO+BDoclvWh6xo1xErPKSFZdOl\n5NDhS+VYlEPoKfpA9Tt8tkxUbC6UDG4MJBF8XBARTkYYU4Nvrp1gjGPVxGHNHEvk3jRSTHUjfRHG\naphlwpfC4ALrdORLN/CwZr6/M3z2uHDtCx+y5zFB1Y41f0kOHQc800a+8HFqfny+4uaB4pRcZnCe\n4Htepy/4Wdnzojf8Wp54GK8o0yPGOf7dN4E3b4T7h0u+vVv4yfuOD9Mtf7ZA9Dvex5Vv1cgu9Hz7\nsvCdA9j+Gl1uYa44D93YDnzJFms38ea642fTA5ea+QLDjXTsh4iSDR2RAAAgAElEQVQtA9FFQvUY\nMzGfDV1v0VLodweWOtFVz2NOqFo8hvUpt6ZWHvJCZzuMNJLSo63sSiDmFfHShLGmcs6OjDKklXtz\nxWU98VZsY3I+VGwHp3XG7vb0RaBznOLSyA/ikaJYreRaqNIzU3ASWM3KoQhnYzEl8iCwU+HkR8Zl\noZhmBLzUNm1VhH/46S9ZoQm//R9oIuPF4LcOznVtx7HMZ9Q2hpd4R00JiSs2DGS1hGEgTmf60GCD\nlNIzLCU1gX3SWrQuOue80VmFXCqh79t0Ms9oFXzXkVH2/cDt7QfevHjNu+M9IkJwnowlTlPrpGuC\nWnEtRIeUMtll7JIZDxdNsFUrvnM8nCeu9xc4KZvC2HE+n9tkphXbjS04LJ642O2xuXI8n7m+vuZ0\nfsBYj6+Nufby6pL90DOdz3z7G695f/eeN2/ecD4+EKvy/v17bq4uuel3vP/yPefe8OLiFZ/efklI\ntG7KBi4O17x/fOT+1BhiJQvS9XgrTKuyHyz39/ccDgfO5zO73Y7znChpwpoeqI0qm1fyZhkkWEpe\ncKYpw42zVG37Dy0FcRWtijOQUxOB4gzOOroqnDUhocPEiLE9ktd2LFlHKS3My5dCCTuMiaT00SPN\nuzYJdRjOsTEFa1qQrkOqgPHP8KoxBhVFc2UfRlQzyzqR//l/8/Wyzn7ttzTYxmpsFkGFEhcqSkdP\nDA3yKKUgvifljATDXgMPKdE5IavB+WY6u8QFAWJpmo8ujDjJHEtGS9uLqiopRbr+kmJnShTwQkkT\n+TQThhEhEPORyxQZry8bZEPkZnfAamLOhivn2ImhhkTIjlfDjofpEdMdqDpRi+UDikuZdV3xCslC\n33lW43mJJcXKTzVirWNdCo9euWJHlorkld/aB/7O90/s6i2fBGEdIgOee9lh14VxuKCkmdBf8uHL\nBxyGJS98elzoDgf2DnJsU+8QBuYUKak5jKcizeW6Kv/9p0d+sLvgYmexuUGQ171QXcbLjnktDF6g\nRmI1FCv0xTJTqA5643iYVgbbcVtW9jaQK1jb4ja8GE7LhBjfJn3jURIxN9nEnBsUejo/QtiDdWiZ\n6GiG18l05Jiw3hFzwTqYM3ht+URVBVQ5ayYXJTqPrpmTceyENjGp44VNvFVPqM3C6iFnOtuo8v/o\nJ3/2y1Vout/5fS01cTHsmFNj/6TYHJ11c+xFFWs8zrln4R+m4eu9N1Q1xLhSU8L3LfLZ+BaMlVLC\naMT3B+pT6BUVUwulNshKN1+oi/0lS4osS4MHclpbFwKknOh2A34TNc5rCyIyfd/ombUlbjprMFo3\nqnaglsTx+MCrqxtQZQied7cfCCGwC46w23E6nTDGcLFvXz+WzDydNjqm4/Z0wmmk6zpKVoauQXNX\nhxHn2gTx3W98gxrblIdxfPbZZ9QKL69vOK8RFzo+/9nnvPrkG3y4fcQGT6rN78kYw8PpSK6KrcAG\nHT7RynNK+NCMDGtOjeLphfh4xgwDlry5RAsQ0XlFXNud9P2OJS4tZlcU240I9ln35JxrtGdRZG1m\nqU+st8GbpgnyHk2NhND0MW152vlAjHETtmpbllclb7Y4AqQYcbYRCnLOaMmI91uevSNYS9XYHHb/\n9//2ay00Ly9fay8OOsdeHIfREWh+gKP0xByJVObYYOIVT0yCMzDPzXbFxIzrHbYofV/ZieFNv0Nt\nwJNxApJXLjplmTMhBFItfJgXSrHcDA7xSsjNsmi1hmCVKQof5lt+0O8be3PbfVkP18FxjIUXw8iH\nunI6ZiyRhIflkftw4JVGnBaS8/zxw8xlv+N99ExlYRXBuoGaHlnSQjUesXvEFM7WEh7etkbGjzjn\n+Fs6Uw386ZL4/viK371J/Fsv/m/W/pr7+wfq5CEVuv0VD87w5rXBvVUma/gsLsT5FT/68mcM+0t2\nhyte+0SH8D5HbvZ7OJ6Itjkrv6s3/OfvHvihBIoEvqkTZez4w8+/4De/+R3meeWn5ZF/+v7E90PH\nD3ZvuC0Lr3rDn3+4Zz94zkvlbS2UuvDrNwMljqhJ3Fjl9XjgsczspDBoz9IF+jgzhcAYEzwF9klB\nbSAVRyyZzlYqQqzNm3Cytmn2cmQST6Wdf5MVpMIihbVC0UyPpzOwasvQOkthJ5Yv00RRTzAd/8tP\n/viXq9CY3/l7etGNxBhxwbcYUtcyHUajnE4nut0B1QatSE6b5UlTwa95avsdNwA8CyidsQ0KKpHL\n/QseTh8IJiA1oq5SU5skbFZsH1hSxHjHPM/Y0iwwvR9hiyfwQ1t0nqb5WUTqu8A49OSkiFXSlMBb\ndG2whPMtr+b87gv2VzuQwHc++QZvj/etE59XgmuFbE6J3X6k9x3TdEZEubq8RLQ5GF/vdog13B7v\nCN5xOTYH3xQXLruOEBqL7urqCl0K78+PXO4a2eBUEvthTy2G85r44vaWcX/BF1++Z7y4ZJ5ncs6s\nBTrnOacVawwxGnSdCS8v0WVFq6GvytEUJGWyVlhzE7J2bXSnRuzuBlMTqXz0TNLNnuY5uGz7u+bt\n861rsJczjQlY1k1U2Vyy63lhGAaSMeQ4Y5yn94G15ra3yRG3Ua+zJLw4am5ZLmINpTaavN2aADHa\npuXa9FdBHPMffL3Q2X/4r/xGizUvsnndWajK2Ak5JjrXNZ3WujR3coHXhz3HGNFqyEW4O65k67k0\nBT947uNKmVe+dTAY0+zuvR+I88Q3r14w68y7h8it9szG8LCccEvkanegpokHBj48vGMcejDKN7jg\n5srzyVi4PxqGTnjMM6NXDHs0VF6JYXfV84//5JGModrEwxqZtVF8J82c1khSS62Znat04yU7Nbxa\nJ/b7PW9PR4bRc7dGvj0LL15dkkfPl/f3fNs4/vG7zzmMB/7a4QU/Od+R1xPfO1wSe4+LiWUVZs1M\nxRJsZh96Pp1OGFO50MB5jc9CZGNbouyyGffeZPCd425euBx2hH4grRnbQVcgUHlXHQZDEuUxJ5Ia\nlpypBtZ1RbVyksKFO7Dmdv/lHCnGsDeVQcEOHaPxPCxnSmmkmlIrn3SeX78+cJkylpVahHfi6CWR\nc+UkHieJvXhmbTHymtvutLgC2aBhwMTIUpuMImkiSeDTsuAkMJTCTi37YIgidFo4b+XgIMJ/9hc/\nP+jsF8KCRuPKaaOlno4PyGmiHnr6cM1UZ8hKPB3pfXNoXpYZ+p58PBJeHnAYrGl4aZY2+cSc2e92\n3L39AoYdsSxbFHNoDsO5MHSWxRbcEFjv76nWMzhhGHtO04QxPZQFZxRrLPW0sNqKpkKtlouLK2KM\nLKfU4l03q/t8Sozj+LwbKDlz+fr1BlMIP/vLT2EcuNofmKohDB2Pjw+YznF3f8/1oYnOLnYjy9xo\nnbeP97yTdgEHsZjOE1/etOV6yjzuB4bOMww7/tk/+V/5le99j0EsR2exw8j0+ed03QW3d/fshhZ3\nXGsmppm6BHKBNaZmvDn2cDuTk2KtkjuLnI7MudL5wDEtFBMwZcUSkLHHQCv6pdDVkSKlkTqcw0sT\nnj1TwEuBlCleYIOAnPGkLS9HRJjXaWOIrdD1LQ64syzSXKn7cWxEDy1oKc0t2/QUFcgLzvakqtgw\nkHIGafs4zSu2O2BCg/WkZHJq0Fsu8Wu7B54ev/HaUGsji4Rhz/uHmc54xCQesuHQCadYMfvAlJV1\nXvnpeqLLzcKo73pem2at9GFd0TURq/By7PnRURklot7TxTNZAuZhajtE00Mx3JnIm3BN3CmhzlQJ\n9OWRHxyuuNpbWlTDikUpdURtgdBeixf7HX8xC5/9+DP+0bRwebFn7zP4AXNW1hQZ+j1rLXgDNjtk\nF3A58DuvO/7i7ZGX3UAdHV+cz9wEJejCr11fcV/vMGbhm8XwZucpVvjt1HHRBV5xz2++KXx61/P9\nXli6nrGHwQemaeKhCKWM9EPk1PdkKXzrYuRhvoQEURIlneiMYzpb6DqKaaQXf9Wo0Y82cWkbfGis\nZ8Zxqis/DGvLmwmOXdiz5hnPSBFH8g67VI4oi7EcSuYPo/CZel728EP1eFXEzCyHnlOtyJq5rZ5P\nZSZ9WHmxT+jZ4jrBr0DI3GHoTYeakXNZicEwrAv3HopzRO0YNRHmMwlLFMXaHq1CSIVv+QGqYMST\nXeYxZ6rxvFPZxKyFz/Xn22/9Qkw0/m/+XQWDMRXxIzWupJII40g8PzZFuhH87gqbTqgE1rjiugbv\nWDqyJIIdiPmBwe+JaUGtY7e/Yp7nzbvLQBWWZeHy8pJzzXyyG5niitHG6qriEW+pKVIQ5hQbTKCw\nCpAz+uSHttlASNpMGcWDtzgVogVywWpGrUNS4ubmhjIfGXZ77h/vsQoxpuaB7JtqeZqPrMeZ3dUV\nzlvWaeb66oJlmjk/3IK3/Op3v0nNhQjE8wxSuXs48q1Xr+gOu7ansgIYpscTrz55w93dHeIDl1ev\n+clnP8V7S28CpzkhYui6jp9+9jOC71m2jvJ+mpvK3nUkbTuN4EfUNdjKFEFqYk0rXd/2YlqFvK4M\nu4H5vDL0nqodKc+bgDI9q/qpiUah0hZX3O/I2vRCrlam+RFcy6/P84kttxgrjZ1jyDi/p6YZkaaV\nMU90dWebyelyBhG6cYtOKIKT8jxRxVIJxjWWnTXo//U/fq0TzX/0m39D2RwQqgpdMJyn2AwPu0Ct\nEKQnLRV17Tl8iDOja9P9sTbabAJsLAxOCYNlmivjEDiuSkdmPk6E4LgKgfc5krAkoB8GzmtBvbBb\nM7mzrKVlvdwuE8m33SkxolI5LY2A09uOqguTLTgsZUqc0gkjgWtr+JdfXTJI5KoLjKkipmC1sOs7\nDmKh95gUISt38cxVZ3h5s2eZVnJVclx5dXOJjYkaK2JXus4RjEfTwst+IJqJnKAWy3RSDnvTdkFj\nj5HKWkceoyKlNFeQTRoxFcdqlFSFx+j58vGBUGd+tgr/0qu2Lxu7jm/WjiWfMNKTbG4TRLdnSZVV\nlfN85kfHM8UYTOf5wfWeT9Tg5YyPlagdRYQzK7vxgtUX/KPjbByo4W1KVCvE45GDqa1YiWJsoPiC\nE49NCdS3ZNRcCFLQ2GJGakkUcTQiu2Utc2viVEiiVGNwsVCdYDBEwG6CbK2Ghcbs9ZuD9X/yJ3/5\nywWd2d/6d9SU5n/l+oGcBHVbLMA847rAmi17X8j+grRk/h/u3uVXs+zM03reddt7f5dzjUtm2s5y\n2eVyW00DUtEgNQMQLbWaVkvMGDDgD2AEfwhMkGhGDBjQIDEAiQlISICEGhhUtWjUrarCdtnOdGZk\nRJzL9337tm4vg7UjqhgxMUrLMclUDCIzzjnfXnu97+/3PPvjgXm6NLilWITWs+j8DePbn9MfDuSa\nsZsn2+6PdFJ4mmFvK0hhTJGQHeotlAlZDWYILOWCdC/ogpAvz4TjgenhCXM8QK5Y2yEi5OkZGw4f\nx2i268lphu5Ayhd2oaPvdhhbeXo4c3Nzw+X0iKmFw9WBskayZsZc2G0G0F3oqMbiBR6XZzrnud0d\n6O3AF09f48dIOFp21y+43wWOw54YM4bKr37+Mz7//HNijLz75huepguvb19wfX9HFysTyv/5f/+c\n3dURrOXm5hNO84kX+1tUlVPMTQGAbQeT83Sdp0xPqOmYkwIJry0eHCVAnMCAsaFBSV3juGUi1gyU\nkpE6YbVvBUlbwPW0glGTdJHWJtXSFfoBssMJFAfG99RpBW/xpvU/YqlY49Gy4voDeRqpmimwKRly\n8wgZ3/BB6wqiGNe1AqfwAeOM2A7NuYU6rCX9yX/3rR40//7f/Ff1YZrxwfC8ZKwI0zrhnGMWg4mF\nXD2XbSfmrOLEcY6tuJpoSbtEpdPCvCak64h5wqsju4DpLFYy+QJ4WqmzJMq6MFhBrCMYIXhDLgVn\nKl0VDoPhLnQ4U+mDclDhxu/5xLVDf/FCjAHrlIBgRVmWicV0eN/Ri8WUwvMSuRkGTDXc3kZqDYxx\n4doanubM7fCCdV25uxq5zKCroDLTu8B1D5SZpyXw7psLj5f3WL3ClYo9XvP6E7jaee7vn4j1AATq\nc+JhWXl5PWO941g8WTti8jxeVrRzvHnOfH2JSFXCp/e87CPzkrH9xCd3B/olUmfhcp6JVdiJY06F\nJXusg1IS0YK1HSk2C6rb6BUL7cXGqqcPbYy0qqBiWLUgGfrQf/T+fED1f2T7qVJpJexUIiodJTeB\nmdMt7l8rxULGoZt6RKwhFmHNCRVYtAU/TFWKyfjSRHrVtNF9Ef4SBYXyH/30l79bB438S39fZeN0\nGaRZD1Up0wXb79spux+Qfc/gYIqVTh2aE8vzGbPv2x6jaitp5UoYLDXrx8Sa5sK+F0oNaNh4QjEx\nHPYtUqsZnGUZ48c39q7rIEZiKkhwDT2eM8Pu0ECPsqFsUm4yMoS6XLjbH7mkxDRN3N6/oFzOlN2B\ncn7CauV4tePQHai6sjyO6C4wjiPH4xHfOaw4ns8noHJ/fcPj6bFZA53FIY1AXTNODD/6zmf86S9/\nwc1hz94GihS++PorvvOdz3AZYsy8e/eOYoX715+RcuXl/QvePp+4ubniF7/4kjeXM1dXV8zzQiqC\naKHmQi5K13XkaojxTBgOeOuY5rWFEkrBGEvOid1u3z5Uy8JuN7CmRF5HRCxxWgnDjmqg5tz2cHHB\nyGYNLYVGu6nbrq2jxIza3IRlMWM25E9KiRqX9uGyYE07sKQKOE9WpTOelZUBS04rqdDEaylB6OhD\nt0WaMzGnj7shay3pj//bb/Wg+Y//zR+oXQNd3wgYsziqVOYpcjNYrCqu2+G7hkVyzhHT2MqZDyt9\nvyO4iWCFmxuLM57H08qra0uxwvv3z4jZYdzKsDvwzdvCoe/45MYzLRaTF96uF57eTHS9p5gOS0G4\n4mqfeX4/UurM7uYVKUckV+52Pf2uIHTgLgzWEy2EUrke9kQqnc3UnOliJh8MdRqRNeOTYX8IaF6Y\nS493BueV3gm9n3BqMdSNiF7pQgNa4hdwEUjgAy16r1SNmPGKYhZstZBtGwHnyrIacjJEelJtXtec\nKgGPykgpljk7Ch4Ryzm1h7eUinUd1MJpLvTOs64La06MS+MXBlupmzhuza2kEyukkrHSbKQfnrU5\nx00e1wqVEXAICcNaWgr1Q+G8GtsSZ1o+Amgt0kJOoshfHXHlFkbKCJSGlFG1xA/yrCqNLlDq5jJq\nvbgPivZUFKOmiQRF+A//4te/Wzsaqx4T2mmbpxNh6ElZcG6PcQHb1zYiSolYCje3d5yfn3HWUkOP\nNY7P7u95PF+wObOmyHQ+Y493GI0chx3L5czT84qpDxvEMSJWWZ6f6A/XpCKkccH0Ae8selkw3tD3\nLWZ67PZU44nzMz21NWzTikkwP71nf3uLEnAoz+PEOj23Jv+7LxE3cOeEcnVFfPySdYaHd4+s68jr\n+zueHiZcsLw/P2NGw3G3p5TE1dUN4xpZ1sTt7TX9buDh4YGvfv0lL17ds/eev3j/lilH7Op4Ngtl\nnllLZpkip8sTdy9fUnrDr9+94/DqNU/zmS/+7GucOh7HM3NaieMTs/UEY+j6jmmqqFOsX1kuz6gP\nTR07r2gn7Pd75vMFJJJiwfee87hCKXSuYz6vrJt/xuTWpQWFdcYaA1HpqhDrSlxr67wsK8ZbrAhx\nXqhFmw64Vpw/kOLSSqE5g+9b5ykuzQRYtEV+47mV3kJASmExFU0VXI+WCAikhVg3jXQpOOfRWpvQ\noNT/j5/U//9//f6nt6h1eDvhbc/uakeaSxsBhUplAF2ppsPkRqUYhjuohfrdQO87qu1x3uBccxq9\nvHcMw8DlPPP6xQuqdBgyXAyfdolpiXQKQ7/iPFwtHesAa2zkYtRh/IJxyusfDXh/hRGl1mbRxJ7Q\n0mGNJSVL5yoHhWAqxAsHseRlbS39krBnx9722L0juNIQS8OAL5m8FqRajCY02TbOWTK7TRlR5IS4\n1nnDbty8rFCOEHeYboLhhM1HkJWYpCnJi9JTWFSoaWUcF8bkEfEkWiFSrGFcM4W29zOSCJ02e+6S\niCXjk6UuM8HB3lduOs8yR5YERZussOBIqTIlcB7qhmMyW18Gtmdd2dBaxjPnleQDrjaKicvbf9dA\npjEJvelY5ols/EfEU8I0SK94clV083ClUvBRgIIRWFOh2Ea1Tts+OdmMYjClkr3Bef9xX5n0d1AT\nIP/C31GTK3W/wxqDsz2BhZmO/PhFy5GHAV9aKiwuK9Z7nBMsDaUtNuBCR1kiapS7mxtWVXQD2kFt\nbzHjinQeN3RQhFouLFkZtLLf77ks2z6nP5LXhfHyhEOwh4EUS3O2+z1mPpP7K6SmvyQmA2tqoYP9\n0Aqb4bAnx4Q6Q2ccVuAw9Pi+4927dzw9PXFzfY3daNAxNl3AkhZu+p7z43uuX75mPp/Qknn9nc84\nPT/z+uU13num0wVDpTrH7f7IOJ64urrl8fTI+Xzm937w+3Ri+eqrN7x98zUmtP+v7//oJ/zxP/6T\nFjq4e8n5fCalxGff+ZznuSWaTk8PLd+/rm1UWBRqwjpLcQO9VHJcEOu3t6jalvd4Nlk95BXjerwW\nkhZqLe1GoZmkGypomcAObaRlpKmc89TGXdY2PXGwUCOURKmC00KuiuuPjVum7e3PGk9ex4+JNisF\ncT1lbX0S2YqcIg1QGstMb9teBxfIf/ztjs7+l//gb+msyvqcuNoPaJ25bGy+YRfwu4oWxXSVvd+T\ncsU5T6wFJGGKYHLTnYfObLffRLxUcj2zzgbnOg53AYmNglHUkNeMcwHYVBuayZvCoppA1kwuhVpa\nWEP4oFdIaHUYBUpBFbwoJleQRCiy6dQjjh5jFowXvGaCbUighpuqlFgQD7bCEJTeAppJMeO3N3zT\n2SZX6RS8Nu+7ZAgVzROqN5hlbAcPlbI6bHbo6jmVlVgcawHN7UGv2h7MJQuKJWphSbo9aAtm02wY\nlWa3TJazKWis7JxrY6gsLGVtQkYjOAcpKZkBVYPQXE7FtBh/zXkbhzmqamOr4ZnFNFJF9QwYkMyz\ns6hmpLZeVM4tnt5vwaORxiqLVEoCnGWtmTV+GOVZ1mzwUqFWRqeEKhQsQS3FFKRUIhWrltm3hG1I\nlv/kiy9+t240qEFNJIwz0QuQiF7x+RE53HPYHTk/vyfVgnFNvJW7HYVCR/vgqObWw8lK6Srv33wF\npeKGoVGVncOII/RHRltJ44l9ODCWhOkGLt+8g6kS9x6q0i3zxvzZscSZmxVSKXSHHeXxK9Rfk6ZH\nhv31xldzuN0ex5Gh64njijjPdBnJ799hbu+YYwKbecbh+0Y3kOB4Liu3vmecRtbxga7vuRoG0nnC\nxMJ5emZZF77zyScfkRnvHi4crMOLYUmJ73xyx+P7B6bTE1+fTtzudry+e8Gf/x//mKvf+5Tzuye+\n//s/oqyR9+/f88uvvuTmsGMeV6b5mWk+g3p+8Rd/juJwfY+mFeNTw7Z7D2XF9QPeBly6cFHQOIEN\noCtGDN3+JVIyc4y4vv/4sFoz9IcdyzSTjMHLhK6KAkOwzDnDsmIOA+ItRhylbKOB3BTExnf0Xcd6\nWskR3MEh3jXTZ6nUyzOyP6B5pGQl+Ib0MUEodWGwHevzI+qao4gcoEws4dA8K9/25wBQs2KSZX/t\nsLa9xNwNR8QkljWRV0PfB9ZL5lna8ttJZl0mpjOgM95ZDkNb2tdSuLk6UmlL404aDbg8RrTOxDXj\nguHV3R4lo7X1lyrNAVQyxLkFE6geddIefIBV24yYRkjnmQ0ETExtzg+WeTpxe3PTXk7SjLWClW3k\naWhjOVOAjOkcOSWolnWB6mrjGYpDQ8VYg5qCOG2ECS1AAVdBK1KuIK7U4jE5QQJbPNQFkcheG/U8\nmEDxkbEoxjZrbHXaIvMo3eaAqgmSKmISKUNQxdaKxTGIJcfMjOLU0WklNmFWWy4msEROcwLXFuw1\n1v+3DttXfPFttCUJrYKtDmtbuXyxym2sXKyh5IQzBuOUUoUxV7JxiK4k00q61lWKKfgNg9XqhgH1\njRJQgIPAlAUDrLXytTEsWlhiz0jGNURhS2r+Bn/9Vtxohn/l39IUR4rZteVzHGHzuANYhWV6AOkg\nBBDB9R36AcXvPZXWD2BNqAMXdpunpi3RdGON4QKHfmCdL81WiCGOD9BdNUJzjPjBQ6mEfo8D4kYT\nyLEQBZgv+P4KP/RUgfX8tIUBAntnOU8zQ9ea+8iFw/4VkdqSQfsjoplgW9Lp7fMj5MTV9T2y0Yur\ngVfXLxjHGWMz63nk/XTm5njkfD7zBz/8ISmtPD888/qTe0JwsBRevbrjfD6zriuvXr1iHEeezyfe\nPj9ye7yliNL5npzgsUa6KvziF7/g9evXeLfnzcPXHPY3VNvx/psvubu7ZyzCwcHTwwM6HLFWWU8n\nsDu60HYmoXMsy/KxeGm1EZ6rGoSMbMbUD08ug0N3e5y0hXYt7UHhu568zvh+18jdtsnfsD0iiu89\nNSbwfftv1G3vkmbEelQr1nfbElWpKQONmdaFHWm9UGIC3+G9J1g2hl2DoKq16D/5H77V8+a/+Xf/\neS1xE+/lid4OLHMjcg+uor2l74Rw00CavQRWbUbHeZ5JS8TjcNsU8Pp4QOvE/jpATlzfHAjd0Fw9\nZWp7KS2I0eYuEWGdbUPOUNESSLrCvCGf3CbBy8pqCqETTA7U6YLxDh/67XsasVIwVrGx4VWa3Lx5\ng3yo7JxiBLxktHrmlCil3QgO1oAmjFF8iXQhNPWqqaitaCiY0LIkMH18gEt0EE1TJc0OqkGJreQY\nDbEYlrUgYpmKpdYG3cVUtDrEQxaPMRUtkKtSMlCFeVUuUyLnzK4z2zOhadZFmh22rlBYSWLQ2r6O\nLWATQOwmC9ywTFkp3pBSu6WtGBSLYqgm47SRCiQVThvVxEhgyolgm8PG5UTjy1cSuS301VKyaQe4\nBoqNWNpN3tiKFN/oAVWIZgEcpjZvkLQvKInKf/qr39yO5mVYMXIAACAASURBVLfioNn96/+O+lpZ\n10SVjCmFaB1OKorQiWFaz/TDLUomp0KNiXB3w7BmxpIINuD2e57ev2fXWdzuinW84PdH5vMTpjbq\ncNdtuHvvsT5gYiKrIZcZ53yDXfqB47Dj6f03uP2RLIXj8QjTRBaP0OB+6zwjJaFF6feOXX9PjAt4\nS/YHzOnXqAplXVjySh96ojZd9QcXy2At/fU103hmHxzjmDhcH6jox4DA4B1f/fTPCbdHuq7De0vJ\njlQjNS+M48gf/uhHPD6+J04Tu+HI+PzE69ev6azjxYsXfPl8oqvCXFfenUbmy4x0geP+wHQ6U0V5\nnhau+iuwto1MauE0RswQmL96B51hGK4pNaHW0Gkm10otkDVTY0LcDusK3rT02ZwuGAVnO8RkUowN\n82EMg3jIkTT0mLiydjuCFAbXcVkSYlyDAi5z27HY7SEXM0UbF6+kNkatNSPWUUszbLI2QyfQ0Dfi\nMU4QH8hrwfbSOGdpi1qXCt6h//R/+lYPmn/4t3+iUxWsozW/SztEhz1EFXb7gRwTvleoPTcvd3z9\ns1/hgsPXwPHuBmxkcAFKpTvA4ip5jez2nimNGOm3sWKrBxx8otPMMreF9TqD8QZyIRaHVGGMBef5\nGAuXVNgfAgmHK5GiFSttZxMrmJrJutIjBOeQGulMQGxsqgxbOYhincfZyjxlxASoFmfbS4CYhZoz\neYVUZ3L0XO2EdR1xZiUEgzU91gyYoe0EqQqSwCj5UpF6bIvxmFhy4TJX5tXyFCtjbiOtmjuKGNRY\npCZyageCcbaBfL0lrpDFsmb70egqsqI5Eatj71tIZpGmKK/GE0tstYja2IFjgWIqtgSyKMZUDBUj\nitYWLvqQNgvObPscqKYSpB1OoqC04AFAa35VispHc2xBcB+CSqUQRcm5ET+K2d73pMXAC2k7nAwF\nIZqCVaFW+M++fPO7NTpbHr9hFtgdbyjzSsZAulCwkCKzH7B+wElm0iYKc10gnSeW6YQ53lFV6VLB\nWdPc1zHhhwM2pvYW7wRXLWpiQ9JYw3q5IDqi6YyxN6xq8H2gLjMXZwnHa7AGxsjCjA19Q9ZPz6yX\nlW5/oNpA13lcd2Qcn7fbU6Ws76lqWOaZrg8E54g5413H3e01X3/964b3qJWuFF6/fo2j8Dy9Z4mJ\nWlbisvKcCxcn3H7ne+yHHQ+XE/PTM/N4Zn99x/s3X/Hqe5/zxVdv6awhaSMiPKSVd998SXl34tV3\nP8MZz4XKlev47PU9b/JbplL55O4Vf/LuGw4393RrZZ7POOc4nT74XFaYbIslF8e8PBOcJU0L/nAg\nxhkB+t2RaAz5/K5d6V1Et04Rqi2RNzeETsyZwXoWhFIL7vkbouna2CMEpjq3cczzN4Tekv2A5hVD\nT8lNOSwIddNENxq0pxZFpEdrRXYOqRVr2xtr/QgBLcjgEYXQe2rIH3c2pa7f7gcBeIiQolJrRExF\nfdccP2ME23P+5QxUyIluuBB+ZiB4xgWG7sx3F2H3+sDl3YWyRN7N8Nn9Nf1NYDopSR1pKcxLxMUz\nOWei7An7EQlN6Le/u+WST6DSqkve4bpmxU7aiNDFdpzmiYOrtKEMIGBCpiwJrxaTbNOb54ynUdZ9\nD91uRy4Lc8mYvOJrpRqD74GSWbNQcwGTIa8cDjsOek1lxIvSvbCUvse7gVofMbUH66mloBeDaMYU\nj3OZZUxMc2K8ONYamaNgdce4CtUYcragid4W1pjp+x1LKoQABKWTob3seG3pv7XS933bbVSF0rGk\ntk/ZHxw5QcrCmpUaGr9PbG1YKSo5C1qVpJV5XbAMFCrFFzKVtJpt96V4hWzAV8HQxm1qDartdgJQ\nJSOiTWu/SQSTrcTqm0pFWrrOWttufwJGWienCCiWZGgvCtrGoda07/tv8tdvxY3G/62/p7o20GGd\nnun2R6bauD21TNR12TTCEfavCWxWRmtJy4KTlWJ6ht0VqwqdDWhZmwYAS1knrA+gEeN3SFrxRrgs\nLRhwXQuLFTCB+PTMixcvWC5P+GHP87iw2+3AeC4pUUsDaVrX4s5SV3KuDGqx1wFLoKaRLB3zPOPF\n4g89g3ecz2ckdPTGY73l7vrAmzdvefn6FaKGn7/9ijsjPKeKOT/gD4f2Z9gOO0788K//pBGhL0+8\n+OQ7vJkeef/+PVoyvTWcHh941R/58Y9/TCyVP/vqS/ra3touT8+8+P73eWkGHsrCX/zqC44vXjLG\nhKvN83M+PfLdzz/n128fqEUgzQzDnjVmKrX1XNwOpwZlM1IaT50vIIoLe8w6kmum0sqrh37HZVn/\nStdoQLSBIqXELbIpdPsju2p4zktTL5gWxSwxtlGpDRjbDpW8LhAjDAfotnRMrPgQPkJTFdOCCboN\nnWtFwoCYDkkF0wekJDAeU5q103ew/Ml//63eaP6rv/99Lam5VSxgi2KcRa2hTFOTvtlmmZ3F0NlA\nqZmsGUmFahzBdWiacSFxFQI2GHwYGFPBe8GK47hLdN0Z5/qmB6gZyZV1Fua8EmzHJQkaJy5naTcb\nJ3QE9r1gvNL1lppnBjcgtuPp6cQ6NkzUzrXvRSmFYCMmDHhdCWZPrCNHZ5HeIg6CCsE6fBdxNqB1\nYfCGzoNIARuxNYBreCNsi/pjevArmkoDpKpiS0BTw90TDVrabmQdK2MOnOeJWCNaeqYYOfiO/X5P\nofI0G+asTNOEc56kNO01EHOl1Ii1ezoLSiHWgupf3jSq2JbqmldQxyrtGbE6xWgb7ZsNNptzplpP\nro3nSNkUF9Ki+G7rtxjydotpB5DWypq1US9oseSqliqGnGNToqglqkdrpLRQJtBizzkJiUgWh9ki\n6E2jDSCofJA32t+90dntv/Zva9mItMX7JiTqdtSY6JwyzSvrMoEItibUdah1dLYBF7EGu3UwxA0c\ndx3PT+8ZjvdoXinLhVQKzDMc7xFd0bqwH65ZkyFvWIkaG3izLeo6fB9I84SW3NQDeUXjimy0ZpWO\n480187hSOsdgLdOyEjQjxnN3d8fj+YRuCP9S2tJztzugOI67jpQK4zJjvOM6DKQ884NPXlEFTnNq\nyZJ5oaRK1dZfkWXms08/Jew6np6emMcTxTpuu551Xogx8uM//CGK4ef/159yf3/PbCoTwjplvnz3\nhn/xx3+Nt4/PXGLkPI6NeK2mjesOO2oRQt81hUKqZJR1PGOcQ2xHyQve2JbYMR6WC77fk2Lc4sxC\nFWALSuSU2oGhETGBbCzWGnKMON/mzdEoMiesyVQMwbr2M2EtIoayNqkZudDtdqS4YDVvibFu0xS0\ntJuz2gqiagn7PWWNGCfkarAo1RmI7TbD5qwv64L+2T/6Vg+a//pv/6EqjpRmsrTSpPWmjTfiZiCN\nBa9KbBJqSjLkbZ8pFYzdxjICwTqMsdgyIzkwDAOzJvKaUGm7IGcKHksIzQclFoxL9M4Ta8Zax5KU\ntVTue0epKykq+8FT1LLmipFKXDLONE2HyZWMBaPsjMH51G6OVMQ4JDVrrIhQbMJWg82GNUV871Fr\nCL4QxPN0mbnpGjLp9XWHtUI3JOK6skzC4zQT7BVaPVYjh53S28olBy5Tx5QysRSWZdNXmEAqlSqb\nOlwdpKZyjhvrTJR2mApoyixYSjWEkHEFxDoKTVK2ljbWzJoJSciu/R405QZlK1u6xiNbtd14nLFU\nSWi1LRQhrUPYq8HYTX/hDKUqkqGIo2gLINTURmNRG8HkuWojhdBa/jvj8bKN+GpDcxVp++4qCtGQ\njQMz4zHt87U5baAZbP/B79roDNpVuZbCzjimNLFuSPwkkMYL4g3eHUAd0SjOGNb5jLOWUtvYQwrU\nPDLVK3xoBasa14YguX5N/7Ljcnqk1CPgwO/J6wk33NGHppGe51N7EOYESTYfi+X++obz5T3SD+Si\nhM4wyIGHb34B+3uuuoEiBrfMvH79mq++/hXT6Hh5e8fj+ye6YSPhjhMv72559/jA179+y93dC5wY\n4jQx50Ii87Nfv2FdVz65v+X56Ql/OHD/4sDTs/LmzZesy8Qv3vwKfA818eknn3B/2PPPfvpzuuC4\nur7mn/z0p9ze3vLqb/yYh4cn3rz5ipQSHZ6//uM/4LRMCInOO3LXEYtwfn7g5ctPiLoyjTM5LaR1\nbHeXUiAVNDv6vbKWlRQLVAOu4rqOtI5tyZwT4geGvmdVqIDb3ZDTSl4Lprd4tcTTe/r9HmpmSbHF\nY2kzaA27j79HFmArvuXWEVjHM0hblvrhqs2Yl7bHoXMUNQgruq6UCrK7xqYJNQ7iBSMWFYuzlbzO\nlKyYcPVtfgiA7UEBqHM4q9QseImoOKoTasktAV4EE4Qa29jmI+TVVaxtC2GH4KwwL4kcFasrpxRR\nDH1nuTvO7F4P7HZCXBbOqyMjnEfHcql435GzRUrEYBEj/OISmxSszlzO86YXsphi29dcW/FWAqhm\nnDWoSUzZYmwrO1qj0FkGEVJa2QvthOwN/bBnyQmtlWWsrPKXex4FfvZ25dB59L3BuSPWdmgemZJS\nSuTTPRztgj8C50LYObpVWJLgxLAWsCItwlw2+64ooetZc0QrBFMo2I30ntn1hqOxeCtkaXtClyqz\nwuShz023HKslh4J1nqUkSvUULe07qm2kb4OjlwoIubQXqCUnqnyIUbdVk5hm0nQC1RjECzlXxDn6\nUhj2hrkkaumpufCJMcTNzZTVbvbZdstL2n4e2j9r+7v1QK1o7ZuZVAQjjRKdtdHnf5O/fituNOGP\n/q5mXTBbcqSWTOh64jwjpr2haRWM68hpRLCgnt39DePDI5Ahn6B2YC2762sQxxozQiavKy5Ycsqg\ngvXNAeG7Pcs8tjeJ0kgBdtj/FfBj418NN68b0dlaTE1tfgwwTRQT8faIphP+5nV7I7MOkVbK6jpL\nygt3d3ecx8jp8RFq5Hhz0xQA1vL0vvVV9p1lXjPGgrhGLNrv9zw/PJDWE9//4Y+ptXJ3vGXv4DHN\n5JRYHp/4+u03vHr9ms8/+Yx/9L//bwQM/af3/ODT3+Phqzd85/PvUVG+eXrAimEeI9a1m0VdV57W\nyDpeuL+/53FutF+3P6Cl2RzLsoAXWCdwjr6/ItuWyNHzczNkpky4vQWENE6NsOAPBFuwpvHOqvgt\nlTaxw7eXiWXC7veUnOivbtCaEZRYMraupCQY7wl9z3J+RKzF+QEVyOPI/nBgTvljutBaS0kVYyFt\nN+HgHSmuWOfJwYEIQzWNlhsjxvvWb/hn//O3eqP5L//eX9NaI6yC2foxRuFUKjUpthqURFZPLYJj\npTebKTYCzkEx9NK+HsY0A2rtHJoynQVvClTButYf2u8C1mhLHZmCSQ7fOULHdjNpDKyYPTZlLmtl\ntzM4nyljJZXWhXFe0A1PZEuhcx7jEkFKG3VJ3kjZG2euKi5WepPxYlG7PYucfuSnWQGVijctjOD9\nC07je2wWur4FatZYqCnQ7zK9PHPfZ8K+UBbHu9OBc6pME2BhXttIz1pDLoZFFxYMzlQoHpUtplyF\nqI4gEa+CGss6x49qdqOVYAyJBgPWnJhiCxAQB57yQnGykcM3EoW2vl0yAYC15LZ4F/043yrSktsO\ng62wqFA0Ymgx6FiFWCtlI8rL1uKX2l7RoB1UWMO0/ZlVm8n3I20AQy2WaIRY27jVbfoUR6Haps/+\nh19/87t1o0nzjPiOognEY8oESyEImKvPWNIJUqWmGcShKJjI+P4BPwz4EphiguHA4XjDOp3oBkNZ\nR6z3LSabTavZpguVD5ZFBXEM1bCKo3iDmJ6aoU8PFKekdGF+Chip1Joagrvph9rbdjiQVPHeMF8e\nwDjWqPjgMDmi/Sv6bs/TmBjfvsF4UHdouuauQ8RydXuDx3BaJrreURWWy8Tu5kAeJ4w3yOR4d565\n94H5+ZEvlhOvb28ZrMXe3nIvytfjE+/++Gt+9MM/4HE8k2Jib4T7H36fd+8fkJ3jUzfwv/78n/I3\n/rk/4k//9E+JMXO8P3K7CzwtnvfnR6x2+G5HXB8bqDQlwmGgpEw9XIEKS42QMvubF8z9AVO3hWOe\n0Fzoru8gFWqdWE9Tq0gLkJ4oGNhfMYti3QEbDEUzYb8jzU+UZaW/uqcSqfMK3R5fEnkVTDhiXCJd\nzuB3hF3HNJ6bPiClTSRlKXKm2j3muKMmIaqCqeRhwFVDWS/MtSLqCH3fCm0xfLsfBKCmiE3SHCw5\ntz6iwjpOVB/QWrBa6XslJ2FZW6AiK2TjqFlBKgVDSYWltCa4WQrGQHAVU8GJEHzBWs/pUjGbikJM\nG7W4qaIRHuvKVb8j5VZ+TVp4tYdlAZs9gebFsQ6WOWOtANoqLiZzqBCTIVNx0hKA1rWezRA8sSoJ\nh7GuCcEAShsL1aIY41s82AoiQHnGl4i4jlJXcmqcsVAvSIRhN+D9So6ZaTowrpGce4ok4uSIuuCd\nwYjDIfiyhYRKwqhvQj6jiKmsmvAfu2qV/XWgbPthRyMOoAZNBW8q+67VLMbBcL0EojWwMQzFtp0w\nonTSQKQfTLVqoPzVQ6DCBHTGESRTJDQ0l4BE3RKr7eDIudk01bi2R4VGyijK8OHgQdvWR6CKsKpS\njaJFsNR2y1ElGyHWFumm/A7Sm/s/+juKH1DNpFQJtuXPrbXMyxmvrQ1dtsislvaWCg7jHDvfMS5z\nOzyKa0tDa0DbD7QxLbo4DIHxdNniiYrp9u0KvGlMS5pBLNb0aE0UrRz2jtPzwtA5koKmFa2REHYk\nbaIt/J6rwTPP87aEXlBpt5/1Q+TSCEGUqWR611O1CaeOux3GWN5PZ8KcGfYd3zw8YWqlzCO7l7fE\nJXEYdhyur3g4vwfp6BVkcByGHZ01/NlP/4zvvv6UV9/7jF/+8pe8GI787Je/4Hvf+73W8l8W7g4H\nTpeR6/2ROEfeTROPX3zB6x/8AQ/LyCeHO95fTvzk8+/x1btH5pTJNZGnE6rKMs5gBcGhhkYkmM+4\n/XXL+BuDNY60jtj9LcS2tG0gv4TfD8zzSgh9s/zVnmAnzmOGmmF3hGkEFyCNWBz++oBJheny2G6e\ntsdZBVGCGRhF8ds4I6dG4W72yYESz+1WGQ6oVmKJSMlQ4DB4ViwxFrwU0jwi3ZH6T//Hb/VG8w/+\njR9qWGDFkHLlTSpIbUnCILWx37RprNvPcWNfxdxSSR/YVcUKOTV2adC2OLbi2HWVjkwwitcK6kEq\nMWcWdQzWsMRI52BnZIvDbm1xSRQTWkx9UfbWMXhDTguIozMVtiZ9JtIZZd9bpgySayuKIvS13Xqy\nbOI5wNo2Agyhiez6vqemE6inoDjfnlNSLFYsQQprzeQEFKGzig+CNYJnYSeOi8AShTWa7UBrfzbq\nqDQkfs6ZJK3npTSK8qKbSl48S/2AZGnj/Vg/kJAFV9pBbU2gk4QpK9E14VoWZdr2Wu3mITixVFsb\n9XxLiBljKAri2hLe1i11ZkLr/1RPkojJ7WfcutrSZbWylMRUAkbbDbBYg8mVpVZmLKrbnkegt0IT\nq8tH/XmWRoJu3y9psWe0dahq5T//9W9uR/PbcdD8zb+rBWkN8qUhXErKJC10vhWd8Npihq5nyJVp\nHHHDnriuyOmEekt3PGKLkqUQYwapdH4PgATHvu8YxxmgPawwrJcF8oo9XjWeVmnXfJ9X1PWIMc2S\nWStaI7vDwDw2RMt03h6KpuHydXqGELh7+V3Oz0/N8GgUUsH3lnVdGypHhHVLnhhr6UJoCJWUkdJG\nSThPOO4pp4n9/QvGOHO/H5jmE3FMdLuO8/M3HF58Ss6Zve+Ilwt723NzcwOdpx8C+XRhWhc0eLLC\nOJ45n2dMrRxubnj/y19x9Z3PCPtrvBie1pH57SOoNk1Drgz7A4ODlBXV1vqvteJsoJT2plsJTQdd\nKyVHrCjqelgr1jVLp3pLjpG+32PTzLxM1HkGO2L93WZTTYgGjIBui0mthYphcAPz6T1ut8OqstYZ\nun070IIFYzDWYHPrOcm8kDRh7ccAblufS8UWqBpR4z+K50zomf/42y1s/ns/+bEOH+RtoWOIitBe\njp5E2GHo1FCIjalFRKTN5HvTvv4qtP2TZqq0jIQ3DSHkTSUqGFFKarsdXw1qKnfb8jjZSpZMZzqq\nbQbGsP0/HTejqaHSUr1bCMG0r61o0wl3fqWq5VoMxSyAIbjAGJU1tb5IJ4Vh6MgVPIU1F4yHQFNR\nX+3bQQCGw769yR+8YYkr06VJ8Jac0GiaDiFYnKnc7UYOR4W142Ey5BhAEkl3rEtlZuEydqhracOo\nQlLbgidZSGpg4xnufSHNmdUZlmQ2ZIxlFZhTweOItRl7nanNMFtl23s4Sm5jq2oFp0o1LeqctCJJ\n0S3Vptp+TzGMxaCsOBXmBNVtX1MLl2pwVLJGdrb5rexWiv6wyFcRsgpo66FVDKZAlNp6TlRm2bBN\nrC00UKWl1WQbfeL5L379OwbV3F/vmceJWlZqWYhSEWnFvGwsxjnS6Qw4fKdMYvFhYH18g/gdsj9g\naqSm2r7IJUMaAcOqBtIMqSMtAa2Vmi5t7KULyAyqpCeDM0fEdvjaEzVDHDHWQp3bg9UYllNmPb0D\na1uaajrhj7ekmnHHO6xRnp+/wtSOLKCpNsGW3ZOMpbeWSuVw3EMuTDkyzRP744Hr6yNfvX0HOTH0\nHfPjN60j8fwNEjpy9qxLwQ6Wfhgw8jnpfOH2xQ0H6/jzr77isx+8pgTDy+sbUk2MMfHF8wPdfsdO\nAtV77nvh6/Mz65u3vPz8c3Ce08M7wm6PFsdwOLCuK2l5bl715/fMroM4I32PD4FySaT0CM41KGU5\nUYwB6yGtVKvbW1tLuxgXkCRIhlQh60KdT2AD0r3ChR4picXeYliQ1CRs3vuGwCGzrhfYC6oL0QSc\nO1BUsX2HVaUUIbMBB+eEtR5Pv33Ic/t5cp5gG7vLmtAKd7E5h/Kcvu2PAncBVCtzEaRGFlfwKF6U\ng7SlMiaTswOJeDVIrah4VBOxGjCClZWovh2s2nYslUopcFaPaMJLQFSwgCvKr22mZIsvYMWSaAeT\nFANBOFqllpFOdiAroTbKuohgrJAr2NJeKrwOFIlgLJ0ftlFRY5jdDIFcZsZ1s5taZS1KMoVQO7JT\nSi08XZQcBdXKtLSwjrXQiaNKi1+b6ulc5ZwMvijBWmzao7EyZeVpqkylMkZHyiu1WKoNxNxuGrGA\nqQIO1mSoKGve9h1J8SKNdp0yvVHWAqKZNVUONjCWShXFiyGoIVsQMjuVlp60LYVGKSTTUl0mweQN\nzsFcBSPaKAiiTUFgQdQwiLK37eABw1NNFC0sVYjO8ZQthkyqAVuFYIR928aQpeI3mkBxBjVKX5VF\n2t8xaMHVSsQgEhBTkAphS5JK/c1KAH8rbjTyk39ZkbDRWHMr5Llhu0YvWBFwnpIXNDWJFUC1HbXx\n5VvLuyi+TFTfozlR5xl/OGye+IINO652HcYYnqczu+7QaKs1YlByVfrhgJT278FZlvFEsQOmjqS1\n2QIB0BXfXTdE/Ub9NZKw4rY3c0MoE2NcKCpobg4ccR26PGP3V+x2O2KqGFXm9++wVzuseq6vDlRj\nOZ1OOOdYTo94b4nzyG7/ilonkEIG8vPYvmZdz83tLVWUzz77HsvlxPFwxTdv3lBr5fb2li/fvuV7\nn3zC89tnTjXz6csXnKYZa5VvvnmgzGeOx1vm84kcdnjXkeMKktiFjvEyt3xkqo2sYD1qLa4q0fa4\nMpMuz8juCsQ3WZcYXPDE+MGwWVCtuDBAbUENpCV6SmqjLjEBaMDN/6e9c2m1Jcv2+m+MOWfEeux9\nHplZD+taXrSgkBJRL9gT9KP4HezZ8XPZsaEIYkcvCIIgFoi3srLytc/ee62ImHOMYWNE5gXBziUP\nVST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\n", 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Lm3ZQU5XlknMWcObCWT6K1OPxeBiloviDB9bxxIu70ga/UFwMjMDEGYIgpLTG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\nZkpEUdO1++QJcfqPoE4H/6pSxdiYHa1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xowr1h3YoOukJXV8KM12+EIgiuXa31SUjWIsa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\n", 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qnzjnIq/1I2p6Hg8LrnQj2q5mhaeT3K6lOF7sx4gIJ7FhVe7rRJWbGvikHDO2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\nodjt7x1DuOM4t6gAfvs6sZPx9WN2yuJ4mknqi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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, diff --git a/notebooks/plot_otda_d2.ipynb b/notebooks/plot_otda_d2.ipynb index 5862d48..68d3b66 100644 --- a/notebooks/plot_otda_d2.ipynb +++ b/notebooks/plot_otda_d2.ipynb @@ -67,8 +67,8 @@ "n_samples_source = 150\n", "n_samples_target = 150\n", "\n", - "Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source)\n", - "Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target)\n", + "Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source)\n", + "Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target)\n", "\n", "# Cost matrix\n", "M = ot.dist(Xs, Xt, metric='sqeuclidean')" @@ -127,9 +127,9 @@ "outputs": [ { "data": { - "image/png": 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Ruf/++2Xu3LkiIpKRkSG9e/cWp9MpL7/8cvE1sqw77rhD3n7bl1Xy8/OLr7+V1bJkyRIREZkyZYpMmjRJXC6XrF69uvh6/fLLL0tSUpJkZGRITk6O9O/fX9atWycul0uaN28uIiILFy6UO++8U7xer3g8Hpk4caJ8//338vbbb8sdd9xRXF9WVla5mutyHdYOslIRqqhzvGpvBqv2ZgSlkxwfH8+aNWt46aWXaNOmDTNmzOD1119n+/btdO/enT59+gBwww038O2331Z5vEsvvRSHwwHAF198we2331481KJVq1Zs376dzZs3M2HCBFJTU3nsscdIT08vd5yxY8dy44038vLLL+PxeADfJP633XYbycnJXHnllWzdurV4/xEjRtChQweio6Pp2bMnF154IQDJycns27eveL+rrroKi8VC79696dGjB9u2bSt13iVLlvDkk0+SmprK+PHjyc/P58CBA4wZM4YnnniCv/71r+zfv7/4Paq625OZwYId23G6XcXPFXg8pJ0+xeJdO0JYmVIVe+qz7ThdnlLPOV0envpse72c7+mnnyYlJYUxY8aQnp5e/MlYVFQUl19+OQBbt26lb9++dO3aFWMMM2fOLH79kiVLePzxx0lNTeW8884rvrZV5qyzzuKxxx7jb3/7G2lpacTExFRai8PhYMKECYDv2jt+/HhsNlu56/DEiRNp2bIlcXFxXHbZZSxbtqzUeZcsWcLixYsZMmQIQ4cOZdeuXezYsYPBgwfz6aef8uCDD/L999/TvHnzun1Ry9BBgSE0c/48AFYdTC/1eO60GSGrSSmr1cr48eMZP348ycnJvPHGGwwZMqTC/W02G16vF6DcnJNxcXGVnktEGDhwICtWVB7sX3jhBVatWsXChQsZNmwYa9as4dlnn6Vdu3Zs2LABr9dbfLEGioeIAFgsluLHFosFt9tdvK3sNEBlH4sI8+fPp2/fvqWe79+/P6NGjWLhwoVcdNFFvPjii5x//vmVvgdVPWsPH8JqMVA6a5DncrHswH4u6zcgNIUpVYlDWc4aPV8XX3zxBd9++y0rV67E4XBw9tlnF197HQ5HtaY3ExE+/PBDevbsWer5yhofs2bNYsyYMSxcuJBJkybx2muvUVhYWGEtUVFRxa+t63X4oYce4pZbbilX0+rVq1m0aBEPPvggkydP5g9/+EOV7726tIOsVISad/sY5t0+hlHdWzGqe6vix3Wxffv2UuNp169fT9euXenbty/79u1j165dALz55puMGzcO8I1BXrNmDQDz58+v8NgTJkzgxRdfLL4wZmRk0LdvX44fP14ckF0uF1u2bCn32t27dzNq1CgeffRR2rRpQ1paGqdOnaJDhw5YLBbefPPN4s5yTbz77rt4vV52797Nnj17ygXhiRMn8uyzz+L7VA7WrVsHwJ49e+jRowe/+tWvmDp1Khs3bqzxuVVgbWLjMJT/AW+3WElKSAhBRUpVLalF4E+RKnq+Lk6dOkWrVq1wOBxs2bKFH3/8MeB+AwYMYPv27aSlpSEizJs3r3hb0bWtSNG1LSEhgezs7IDH27NnD7169eKee+5hypQpbNy4sdq1VGbJkiVkZWWRl5fHRx99xNixY0ttnzhxIq+++iq5ubmAb4XDEydOcPDgQeLj45k1axa//e1vWbt2bY3PXRkNyCE0d9oM5k6bwaiOnRjVsVPxY6VCJScnhxtuuIEBAwYwePBgtm7dyiOPPEJMTAz/+c9/uPLKK0lOTsZisXDHHXcA8PDDD3PPPfcwfPjwSu+YvvXWW+nSpQuDBw8mJSWFt956i6ioKN577z0eeOABUlJSSE1NLb7ZrqT777+f5ORkBg0axFlnnUVKSgq/+MUveOONN0hJSWHbtm1VdqsD6dKlCyNHjmTy5Mm88MILpbrQAH/84x9xuVwMHjyYgQMH8sc//hGAd955h0GDBpGamsrmzZu5/vrra3xuFdjYLl2Jj7JjKdNFslkMMwYODlFVSlXu/ol9cdhLX/8cdiv3T+xbwStq7+KLLyYvL48BAwbw0EMPMWrUqID7xcbG8txzz3HBBRcwfPhwWrRoUTwM4eGHHyY3N5fk5GQGDhzII488AsD555/Phg0bGDJkSLmb3t566y0GDhxIamoqO3bs4Lrrrqt2LZUZMWIEU6dOJSUlhZkzZ5Kamlpq+0UXXcT06dMZPXo0ycnJXHXVVeTk5LBhw4bimwafeOKJoHaPAUxRZ6Q6hg8fLqtXrw5qAUqHVqgzfvrpJ/r37x/qMpqEG2+8kSlTpjB9+vR6OX6g76UxZo2IDK/LcZvCdXhfViZ3LPyIA6dOYTEGh83O/06czDlduoW6NNWE1PR6/OG6gzz12XYOZTlJauHg/ol9uWxIx3qssGo5OTnEx8cjItx+++0kJydz9913h7Smkl555RU2b97MM888Uy/Hr8t1WMcghwENxkopdUa3Fi359Nob2Z+VRb7HTe9WieU6ykqFm8uGdAx5IC7r+eefZ86cORQUFDB8+HBuu+22UJcUMTQgK6WapNdffz3UJagqdA3BSloZzjze3bKZn04cZ3C79kwfMJBm0TFVv1CpMHT//fdz//33h7qMCt16662hLqFCGpCVCjMiUq27kFX4qsnQNRU+dmWcZPq7cylweyjwuPl8zy6eX72KD2dcR8dmzUJdngoBvR5Hrrpeh/UmPaXCSExMDCdPntSAFcFEhJMnT5a74U+Fv//31edkFxRQ4PHNtOJ0u8l05vPYd1+HuDIVCno9jlzBuA5rB1mpMNKpUyfS09M5fvx4qEtRdRATE0OnTp1CXYaqAbfXy5rDhygbhbwIS0ssaqCaDr0eR7a6Xoc1ICsVRux2O927dw91GUo1ORZjsBqDN0C3MNpW8fSFqvHS63HTpkMslFJKNXkWY7iodx/sltI/FqOtVqb1HxiiqpRSoaIBWSmllAL+NP5n9G3dhli7nVi7HYfNRmr7Dtw35uxQl6aUamA6xEIppZQCmkXH8NGMa1l35DB7szLpk9ia5LbtQl2WUioENCArpZRSfsYYhnZIYmiHpFCXopQKIR1ioZRSSimlVAkakJVSSimllCpBA7JSSimllFIl6BhkpZRSjVKB202+282JvFze2ryR9NOnObtzF67oP5C4qKhQl6eUCmMakJVSSjUqOYWFPPTV53y6eycerxePCBbACyw7sI9X163ho6uvo7kuB66UqoAOsVBKKdWo3LHgIz7dtZNCjwePf2U8r3+b0+3mSE42L639MXQFKqXCngZk1Wh4T16H9+R1oS5DKRVCezIzWHv4EIVeT4X7FHq9LN61owGrUkpFGg3ISimlGo2Dp09jt1b9oy3ermOQlVIV0zHIKuIVd41dP5R6bEmcHaqSlFINzCvCnswMEqKjKPRU3D0GcNhs3JA6tIEqU0pFIg3ISimlItrytAPc+9kiclyFeLxCjM0OGAo87lL7OWx2vOLl8n4DuKLfgNAUq5SKCBqQVa3MnD8PgLnTZoS4kjOdYu0cK9X0HMw+zW2ffIDTfSYMF3rcxEdF0Tq2GdmFBZzduSuTe/XBGEhp34GOCc1CWLFSKhJoQFYVCqcQrJRSgbyzZVPxTBVFBPAKPH7+BM7t2i0kdSmlIpsGZFUjRaF51cH0Uo/DIURr51ippufQ6dMBxxwLwtHcnICv2ZeVSW5hIX0SW2O3Wuu7RKVUBNKArMoJ5xCslGpc8lwu3tmyia/27qFtXBzXpwxhcLv2lb7G7fWy7sghRGBkp84s3r2TPJer1D4erzCsQ1Kp59JOneLnCz5k/6ksrMZgs1j52wUTmdCzV9Dfl1IqsmlAVjVSFJI1NCul6iqnsJCpb8/mcE42+W43FmNYtGsHfz7vAqb1HxjwNasPHeT2BR/i8njBAAItYmLweL0U+DvJDpuNSb360KNlq+LXeUW49oN3OJSdjbd4SIaLez5byCdXX0fPVon1/G6VUpFEA7IqR0OwUqohzN64nkPZp4uDrVeEfLebR5Z+yZTefYm2lf4RlV1QwE0fzSe3TLfY7fUwa3AqX+/bi8Nm49rBqVw5YFCpfVYfOkimM79EOPZxeTzM3rSBh8edXw/vUCkVqTQgq1rR0KyUqqvPdu8sDsclGWPYfPwowzp0LLe/lNvbd1Neu/gEPp91U4XnOpGXG/B5jwiHs7NrUrZSqgnQgKwqVFkITnnhWQA23HF3Q5WjlGpkmsfEBHze4/XSLKr8tqz8fFwBAnWBx0Om01npuYa0T8IdYPlph83GOJ3pQilVhi41rZRSqt7kFhby1PLvOPc/LzP+jVf4148rKfDPWXxjylAcNnup/S3G0LlZc3onlh8TPLZzF6yW8j+2Ym12zu7StdI6OiQkMHNQSqnzRVttJCU04/L+umiIUqo07SBHkHAYE1zUOc4uLCz1WDvJjYsuuqKCwe31cuV7b7MnM6N4KrZ//biK5WkHmH35lYzv1p07h4/guR9XEW214hGhbVwcr1x6ecDj9W/Tlot69eHTXTvJc/vGITtsdsZ07syojp2qrOeP545nWIck3ti4jpzCQi7u1YfrU4b4V95TSqkzNCCroKuvIB8OvyAoparvy727STuVVWqe4ny3mw1Hj7Dm8CGGJ3XklyPHcG1yKuuPHqZVjIPB7dpjjKnwmE9NmMTPevT0LRDiFa7oP5BL+vSt9DVFjDFc3KcvF/fpG5T3p5RqvDQgR4Bwmpe4qFOsnePGqahzjOuHUo+1k6xqY+3hQ+VmnABwebxsOHqE4Um+m/BaOhyc161HtY5pjGFyrz5M7tUnqLUqpVRJGpBV0NRXkA+nXxCUUtXXqVlzHDYbTv+Y4yJRVisd4hNCVJVSSlVNA3IECMd5ibVz3DgVdYq1c6yCYWrffvx9+XelnrMYg8Nu44IePUNUlVJKVU0Dsgqa+gry4fgLglKqas2iY5g7bQa//mwhaadOIUDfxNb8c9LFRFmtoS5PKaUqpAE5gmgwVA1FO8cqWAa0acuS627iaE4OFouhTWxcqEtSSqkqaUBWQVdfQV5/QVAqcrWLjw91CSG3YMc2nl65nEPZ2XRv2ZLfjz2Xc3SREqXCki4UopRSSpUgEmhB67p5b+tmHvjiM/ZmZVLgcbPtxHFuX/gR3+3fF/RzKaXqTjvISimlFPD57l08vuwbDpzKopXDwV3DR3Fj6tBqzbFcGRHhqeXflZvNI9/t5snvv9UuslJhSANyGNOb0pTOJqFUw/hu/z7u+Wwh+f4Qm+F08vcVyyjweLhj+Mg6HTvf7SbD6Qy4bW9WZp2OrZSqHzrEQimlVJP3jxXfF4fjIk63m3+vXoXb663TsWNsNuKjogJuS0rQ+aCVCkfaQQ5DujCG0hXtlKoZEeHNjet5Zd1qTuXnM7JjZx4cew49WyVW6/X7KujkFno8nC7Ip5Ujtta1GWO4a8Ronl75falhFjE2G/eOPrvWx1VK1R/tICullIp4j3+3lL9+/y3pp0+TXVjIl3t3c9Fb/2X2xvW4PJ4qX9+jZcuAz0dbbTSPjqlzfbcMGcZvRo+lRXQMFmNoGxfHn8f/jIt665LZSoUjU5O7dYcPHy6rV6+ux3JUSdo5Vto5blyMMWtEZHhdjhGJ12GXx8M7Wzcz/6ctWIzhqgGDmNZ/IFZLcHo0WflOxrz6IgUBgrDNYqFTQjPmX3UNLR2OCo/xfdp+bvvkw1LDLBw2G78ZPZZbh9bpW1aKiFDgcRNttdX55j+lVM1V9zqsHeQamjl/XnFwVUopVTkR4ZaPP+CJ75ay/shh1h4+xJ+++Yq7Fn1So+nUDmaf5tNdO1l/5HC51+3OzKhwZT6310t69mme/P7bSo8/tnNXnr/oUnq1aoXVGNrFxfP/zhnPLUOGVbvG6jDGEGOzazhWKszpGOQwpp1jpZ1jFelWpKex9sihUmNvnW433x3Yz4ajR0ht36HS13tFeOirz/lg21bsViteEZISEph9+ZW0jYvn0507eOL7b8guLKzwGG6vl8W7dvDXCyZWeq5x3bozrlv3mr1BpVSjFHEBOVTDDvTGOaWUqrlV6WnkuVzlnnd5Paw6mFZlQH53yyY+2v4TBR5P8RCKvZmZ3L14ATcPGca9ny8uN/tEINqvVUrVRFgOsdCH8/75AAAgAElEQVRhDEqpUPKevO7MTCKqUi6Ph/1ZWZwuKAi4PTE2lhhr+V5MlNVKYjVmhnhjw7pyC2x4RNhw9Ah/+e6baoVju8XCxb37VrmfUkoViZgOcqg7uEXn0c6xUuXpzYRN09zNG3ly2bd4xIvb62Vyrz785WcTiLHZi/e5pE8//rb8Oyhz/5zFGCb1qnoGhxxX4KETVmM4mH26WnW2jHHw4NnnVmvf2li6by9PLf+OA6ey6NqiJfePOVuHaigV4cIqIIc6BNdFJNWqlApM55+uvq/27uGxb78u1d39dNcOAJ6eeFHxcy0dDv4z9QruWvQJTpcLAZpFRfP8lKkVLp5R0oQevXxTtZVZrCMhOppoq5W001WH5FkpqRR4PPz5m6/5ev8eWsY4uHXocCZXI6BXZcnunfz6s0XFneytx49x56KP+b9JF3NBj151Pr5SKjTCKiBXJlw6uBqAlTpDA2XT9e8fV5Ub+lDg8bB41w7+NP58mpWYO3hEUidW3nIHW48fw2IM/Vq3wVLNWRzuGjGKz3btJCPfSb7bjc0Y7FYrT10wiaz8fB748rNKh1lEWSwMad+BKW+9SVa+E5fXyz6yuG/JYnacOME9o8+q3RfA7y/Lvi13/ny3myeWfaMBWakIFlYBOVxCcE1EctdbKVVaUbDXoF+1IznZAZ+3WSxkOJ2lAjL4hlQMatsOrwhL9+3luwP7iLXZOZqXy/cH9hNts3Jtcio3pQ7FVmJ+5FaOWD697kbe3bqJ5WkH6NKsBdelpNK9hW9hD494eWLZN5zIyytXixXDpF69WZmezumC/FJdaKfbzQtrfuCmIUPL1VoTB05lVfD8qVofUykVemEVkKtDg2fTor9wRAj7SEADZVMyLKkjR3Zux1tmTmKLMSQlNAv4GpfHw00fvc/6o4cDzmzx9MrvWXv4IM9fPLXU8/FRUdyUOoybUsvPSZzavgNOlwsDFFVioHgRksW7dhJltQZcRCTKamXr8eOM7tS56jdcgTZxcRzLzS33fKzdTk5hYbWGkSilwk9YBuRICkOR2PVWSlVOg37V7hk1hq/27sHpdhWHZIfNxgNnnVPhoh0fbNvKujJzIpeU73bzzb597Dh5gj6JratVx/OrfyDf7aZkTBd8cx8XcZcZv1zE5fVyKPs0n+3eyfAOHUmMrXpWjbJ+OWI0f1n2Tbn3lO9yccncN/no6utoFh1d4+MqpUIrLAOyUjp0JbyVHXtc1EFWTUePlq346Opr+eeqFfx4KJ0O8Qlc1LsPR3JzePaHFUzp0694GESRD7dtrTAcF7FYDJuPHa12QF57+BCeKlbkC7TVagxur5eHl36FwTcv8y9HjuauEaOrdd4i1yankOcq5Mnvvyv1vFuEIznZvLlxXY2PqZQKPQ3IQaLBTSnV1PRo2Yp/TroYgD998xX/u+J7CtxuLBYLz//4Aw+dO55rklOK948KMB9yWQboEJ9Q7Rq6t2jJnsyMgCG4JLvFgtViweIPxgCFHg9u75lp5P794yqGtE/irM5dqn1+YwzDkjoSb7eTU2bYSIHHw+e7d2lAVioCaUBWYdmd1aEr4U1vZlMlrTl8kHe2bCruDnu9Xtx4+fO3XzOhZy/axMYBMGNQMqsPHSTPXX78Mfi6um3i4hhVgzHBvxgximVp+6tcMMRmsTD78iuJj4om7fQp7l68gLKTMzvdbmZvXF+jgAzQPDqmwi52S4ejRsdSSoWHsFxJTymlVORYtHNHwIBqNRaW7ttb/HhSz95M7dePGJuNGKsNh82GwdfdtVusjEjqxNwrZlR7Cjjw3aT37KQpdIhPwG6xEmWxEB8Vhb3ETBgxVhvDkzoypEMSLR0Onvr+O5wVhPRTBfnVf+N+vVol0qV5C6xl6nbYbNwc4MZCpVT40w5yE9ZQ43zrclztHIc37Rwr8HV+DQYpO9DBgM2cCarGGB4//0JuTh3GivQ0WsY4OL97d04XFBJts9Iipnbd1p/16Mn53XuQlZ9PrN1OvtvN/61awaJd27FbrcwYmMxtQ0cAcMvH77Mr82TA4zhstlovSf3qpZdz40fzOZSdjdUYCj0e7h45hnO6dqvV8ZRSoaUBWQWVDolQqumZ2rc/szdtKNdF9nqF87v3KLd/z1aJ9GyVWPzYYa/7VGjGGFo6HHy6ayf/XLWcwznZDGrTjt+NPYfB7doDsOPkCXZlnAw4HMJqDD1bJTKt/8BanT8poRmfXXsjP504zklnHint2tdpfmWlVGhpQG7C6nucr85EoVTTMLBtO+4aMYrnflgJ+OZCFuAfEybRPKbhQuJbmzbw+HdLi8dCL08/wMz585g7bQaD27XnWG5uqUVISurcvDnvTr+aaFvtfywaYxjQpm2tX6+UCh8akFVQBArDW48fC8oPCw3WSoW/u0aM5tI+/fl63x6irFYm9OhVq3mFa8vt9fLU8u/KTSPndLv5+4pl/Pey6Qxs05bCAAuGWI3h0j796hSOlVKNi14NVL0FzwFt2jJ32gwNuA1MZ5ZQodK5eXOuTxkSknNnOPPId5cPvwBbjh0DfDNK3DJkGM+v/rHUeGmPCCvT0xARTA1uEFRKNV4akFVQ1MdwjVAP0dCgGb70e6PKah4dQ0XZtmPCmXmVR3bsxKvr1pRbenrzsWP8eOggIzt2qs8ylVIRQgNykDREeIvUTmyk1Rupyq5upyFSNUZrDx/i9fVrOZKbw3ndunNtcirNoqOJttm4dlAKb23eUGqYhcNm455RZ5V4/eFy4RigwONmzWENyEopHw3IKqiCGYZDtVhITYKmhtCG1dC/BOj3N7y8u2UTj3zzFfluNwJsOnqUtzZtZMHMWTSPieGBs8/FGJizaQMeEeLtUTww9hx+1qNn8THaxcfjsNnLzYMcbbPRLi6+gd+RUipcaUCuo4YYBhDqoQbBEql1Rwpd3U41ZgVuN3/69utS3eECj5sTebm8tn4Nvxk9FpvFwh/OGc99Z53D6YICWjkc5RYdubh3X55c9k2549stFib16lPv70MpFRk0IKuw19CBujpBU4czhEZD/RKg39/ws+3E8YAr7BV4PHy5dw+/GT22+Lkoq5XWFcyg0Sw6mjlXXMUvF33CsbxcADrEJ/Cviy4h1m6vn+JVoybiAXGCidObPBuRsA/I4d51bIhhAKEaahAsjaUDHik0xKnGqHlMDG6vN+C2Vo6arcA3qG07vr7hFg6cOoUx0LlZcw02qsZEvEjOs5D3OkgBWFohCb/H4rg41KWpIAj7gKxUqFQWNHU4Q2jV99dbv7/hp1uLlvRulcjW48dKrYTnsNm4OXVYjY9njKFrixbBLFE1MZL9D8ibDTh9T3iPwanfI5Z4TPS4kNam6i5sA3KkdR0boq5wfe9VifQOuFKN0emCAv65ajkLdmzHYgzT+g/grhGjcYTxMIOXplzGTR+/z/6sTGwWC4UeD3ePHMP4bt1DXZpqYkQKS4fjYvlIzrMakBuBsA3ISkUC7Sw2bo31++vyeJj+7lwOZGVS6B+28Oq6NaxMT+PdK2c26HCDnMJCnvthJR9t/wmLMVzRbwC/GDEqYFBvFx/PomuuZ9uJ45x05pHcth3NohtuKWulinkzgcBDfnCnNWgpqn6EbUDWrmPjo99DpcLD53t2cyj7dHE4Bt/NbttOnmDVwXRGd+rcIHV4vF6ufu9tdmVmFC8B/cq61SxL28/8q64JeFMeQL/WbRqkPqUqZEkEE+Ube1yWvW/D16OCzhLqAlRpM+fPK/6lQCml6sOmo0fIc7nKPe/yeNh87GiD1bF0/172n8oqDsfgC+o7M06yPO1Ag9WhVE0ZY4P4e4CyN4jGYOLvDUVJKsjCtoNcRLuOkUk7/0qFry4tWuCw2UrNKQwQZbXRqVnzBqtj09Gj5AYI6gVuNxuPHuHsLl0brBalasoSdz1e0xxy/wXeo2Drh0n4HSYqNdSlqSAI+4DcVETaTYlKqcg1pXdf/vb9t8Ur0gFYjCE+ys753XsE9Vwuj4dD2dm0dMSUGy/csVkzYm128sqsahdjs9GpWbOg1lEVEeHjHdt4c+N6nC4Xl/Tpx/UpQ3RuZFUpS+xUiJ0a6jJUPdCArIKqvoO+/uKgVN0lREfzzvSZ/HbJIrafPAFASrsO/O/EyURZrUE7z7zNm/jLsm9we724xcvEnr158mcXFt+Ad1GvPvxl2Tc43a5SQT3GZmNiz95Bq6M6Hvr6Cz7c9lPxEtS7Mk7y7tbNLLrmeqJt+qNSqaZG/9WHCb0pUSnVkHonJvLxzFlk5TuxGBP02SC+3b+PR7/9qtQwjiW7d+IV4dnJUwCIi4ri3elX85vPzgT13q0SOadLN/65agVnd+nKmE6d631Wjf1ZWbz/0xYKSoyFdnm97M3KZMrc//L+VdeSEB1drzUopcKLBmQVVPUV9KvTmdZfLpSquRYxNVuFrrr+/eOqcmOcCzwePt+zi6x8Z/F5e7byBfUMZx7L0w7wuy8+Y3dmBgUeD//dsI7RnTrz4pSpWC31d0/52sOHfMcvEZCL7MnM5PdfLuG5iy6pt/MDZDjzcLrcJCUk6Kp+SoUBDcg1VN8hTMOdUqoxOJyTHfB5m8XCyby8csE8zh7FH776nPwSoTrP7WLlwTQ+2bGdy/r1r7daE2NjMQQOpYKv8+10ueplEZXjubn8+rOFrDl8CIsxtIxx8NSESZzVuUvQz6WUqj4NyKpeBDvoV9aZ1hscdUlkFX6GJ3Xk4OnTeJFSzxtMwJky1h05HPA4eS4XH2zbUq8B+azOXYiPiiLXVRhwu1uEE3l5dG4e3Bk+RIRZH7zLnswM3P7lsw/nZHPbJx+w6JobdClspUJI50GupqL5iVcdTGfVwXSdr1gppSrxq5FjiI2yYy0xXMBhs3HfWWMD3vRmMYYyWbqYrR6HVxQd/61pVxFTwc14BliRHvx5mTccPUJ69unicFzE7fUye9P6oJ9PKVV92kGuga3Hj4W6hCYvUFe4Kd/gWNQ5xvVDqcfaSVah1rVFCz6++jqeWbmcHw8dpF1cPL8YMZILevQKuP/QDknYrRYoMy1yrM3OVQOT673e7i1aclPqUJ5f/UO5bQLsy8oK+jkPZWdjCTC0w+X1sr8ezqeUqj4NyNU0d9oMZs6fx9bjxxjQpm2TCmFKKVUb3Vq05JlJF1drX5vFwotTLuOmj+YjgNvjxWIxTOnTlwsrCNVFTuXn883+vRhjGNe1W61m5BARth4/HnBbrN3OwDZta3zMqgxu1w6Xt/yNgQ6bjTGdG2a5b6VUYBqQq6HsGNetx48xc/48Dclhpil+P4o6xdo5Vo3B8KSOrLjlDpbs3klWfj5nde5Cv9ZtKn3NJ9u38bsvPsNm8XViPSI8dcEkLu7Tt0bn/j7tAKvS0wJuS3TEMqFn5SG9Njo1a84lffqxcOf24hk/7BYLLWIcTO8/KOjnU0pVnwbkWhhQD50EpZRSEB8VxRX9B1Zr38PZ2TzwxWcUeNwUlGjE3vf5p4zo2JG2cfHVPu/H238i3+Mu97zNWLhr+KigLqBS0pMXTCSlfQfeWL+WXJeLC3v24pcjRuu8yyEm3gzwZoC1C8ZEhbocFQIakKtQclxrUxzjqiKDdo5VU7Ro145ys2SA76a6xbt2cEPK0Gofy2rxjQYue7QYm42EmPoLqxZjuDY5hWuTU+rtHKr6xJuDnPodFHwLxgZYkIQHsMTqz/2mRmexUEopFZHy3W48Xm+5593iLTWfcnVc3m9AwFksPOLl3C7daluiijBy6j5fOKYQJA8kB04/gRQsC3VpqoFpQK5AoGndQLvHSikVKqcL8ll2YD8/HT+GiHB+9x7YAwx9sFksnNetR42OPbJjJ2YNHkK01UaU1YrDZiPGZuP/Jk8hLko/Ym8KxHMSCpYBZefDdiK5L4WiJBVCOsRCKaVU2HtxzQ88s3I5UVYrHq/QqVkzXpt6BTMHDebtzZvId/vmh4ux2bg2OZU+ia1rfI4Hzz6XqwYO4ut9e4m125nYsxetHLHBfisqXHlPgrGDBFgwxhN4IRvVeGlArkBTnls3Euj3RammYX9WFo9++xVL9+1FgAKP72683ZkZ3PbJhyyYOYtJvXrz8bZtGGO4tG8/hid1rPX5erRsRY+WrYJUvYootq4VbLBC1OgGLUWFngZkpZRSYWnTsaPMnD+PPJer3DaPCHsyM9iTmcGIpE6MSOoUggpVY2JMNBL/W8h+CnD6n7WCicPE3xnK0iKCuLYhzvkgOZjoCyF6HMZE7kheDchV0A5lxULRxS07J7V2kpVqvB5Z+mXAcFykwOPh87276dkqsQGrUo2ZJe46xNYJyXkJvEchagwm/k6MNSnUpYU1b+4cyP4rvvHbXiR/MUSNghbPR2xIbvQBOdwDVKjqC/evi1JKbTh6pMp9Xl7zI7cNGY7VEpk/hFX4MdHjMdHjQ11GxBBvJmQ/CRSUeDIPClZBwZcQMyFktdVFow/I4W7r8WOhLqHGQtnF1bHhSjUdsXY7OYUBbpgqocDjYV9WZp27yF7xzYBsMaZOx6kvIk4k72MoXAbWjpjYmZgKx8wq1YAKV/rmjJaCMhvykPzFGA3I4SXcP4ovqifbf/FvqPrC/euilFJFrhmUwn83rqt0TmO310t8VO0X8jiWm8Mfv/6Cr/buAWBc1+48dv4FtI9PqPUxg0282cjJaeA5im9srA3JewtaPoeJPjfU5akmLwbf8jxlWcDENXQxQdNoA3K4K9s5jqROcjh0cTXQK9X43TtmLIeyT7Nkzy4sxpQLylZjGNy2Pe3iq7+kdEkuj4dp78zlSE42Hn8H+Zv9e7l83lssveEWogMsHBIKkvsqeA5xZn5eN+D2rfjW5nuMqZ9lsJWqluizCByQozCO6Q1dTdCEx7/+ehAOIa4yA9q0Bc50cose17dw/7oopRqPY7k5fLjtJ07k5TK2c1fO6dqtRkMYoqxW/m/yFA5nZ7M3K4MF27fz/vatRFuteETo3Kw5/7roklrX98Xe3WTlO4vDMfhmx8gpLOCz3Tu5tG//Wh87qPI/o/ziFYDkg2cv2Ho1eElKFTEmGlq+hGTeBgiIAG6I/yUmKnKXUG+0ATncFQXTlBeeLfU4kkRizapq3pPXAWBJnB3iSlQkW3ZgP7cv+BCvCAUeD29t3khquw78Z+oVAVe/q0yHhAQ6JCRwVueu/GbMWDYdO0q7uDgGtGmLqcOY4b2ZmTgDDN/IdbnYk5lZ6+MGnalgsRLxgHE0bC1KBWCihkHb5b5lusUJUWdhrG1CXVadNPqAHO4hrqE6x2U11NdFO9VKNT1ur5dffbqgVPjMc7lYd+QQ723dzMzk2neV2sTFcX73mi0jXZE+iYk4bDZyy0wlF2e30ycxfKaOM3HXIace4czcvAAWsPXCWGu/KIpSwWRMDMRcGOoygqbRB+RQqk44DGVw1PCqSirqHOP6odRj7SSrmtp87Chuj7fc8063m/e3ba1TQA6m8d160DYunvTTp3B5ffXaLBZaOWK5oEcYDVuIuQwK14HzA8DmG+5paYlp+VyoK1Oq0dKArOqFzpahVNNltVgQJOC2mg6vqE82i4X3rpzJ498tZfGuHQBc2LM3D50znqgwqtMYg2n+KBL3c3CtB2tbsA+P2AUYlIoEGpDrQbiHw3CvT4VGUadYO8eqrga2aUt8VHS5oQsOm50ZA5NDVFVgLR0O/n7hZP5+4eRQl1IlY+sENl1SW6mGoAE5xH573sMA/OPrP4W4kuDS2TKUarosxvDilKnM+uA9vOLF7fVijOHCnr24pE+/UJenVK2JuMC1CbCCfZBOsdeIaUAOorJhMFzDYbjXp0Ir3DrH2tGOTIPbtWfFLbfz+Z5dZDqdjOrUmf6tI/uudtW0ScEyJOvXgBcQ3wwiLZ6P6KnMVMU0IIdIUed44zdbSz1urJ1kpVTTE2u3M7WSuYQPZZ9m2YH9xEdFcV63Hjjs9gasTqnqE88xJPMuSs0kIrlI5k3Q5juMJXJXjFOBaUAOgvoe0ztz/jy2Hj/GgDZtqzxmTc6t4VWFM51Vo3F7ZuVyXlzzA1aLBQsGDLx26RUMT9Jpy1T4EefHgCfAFi8UfAGOqVUfQwQkB0ysDs2IABqQQ6SoU1zUOVZKqabih4PpvLz2Rwo8HvCcCR23fvIBP9x6Z1jNIKEUAN5MAq9m6AZvVtUvz3sfcp4C7ykw0UjczZi4u3QmkjCmATkI6mtMb1HnOLvQ949y1cF0Ul54NmAnWWemUI2NzqrReM3bson8ACvYeb3CyvQ0zu3arcbHPJmXh9PtomNCszqtrqdUICb6LMQ5BySvzBYLRI2q9LWSvwROPwLk+59wQ84rCGDi766HalUwaEAOE5E+FlkDuVKquvLd7sCzJBsoCBCcK3M8N5dffbqAdUcOYzGGFjEx/H3CZM7q3CUotSoFQNQYsA+FwjWcGYfsgJhJGHvlM7NIzrMUh+NiTsh9DYm7E2MqjmIiLkAwJqoOxava0IAcRMEOh3PGfwLAtUsvqXIMcl262BpuVTjTznHjc3Hvvnyzfy95ZeZJdnu8jOrUudrHERGu/eAd9mZm4hFf5D6Sk8Ntn3zAomtuoGuLFkGtWzVdxlig5Uvg/AhxfgjGhnFMh5iLqn6x52Dg56UQJBdM8/KbPCeR0w9BwVJAEPtQTPPHMbbudXofqvo0IIdY2bHIkdo51qEdSqnqmtizF+//1JmVB9PIc7mwGYPNauXR8T+jWXR0tY+z/shhDmVnF4fjIm6vl9mb1vP/zhkf5MpVU2aMDWKnYWKn1eyFtj7gWhvggPFgEso9LeJFMq4BTxrFNwa61iAnr4I2X2Es5V+jgk8Dchgqe/f+nPG+h9XppNWmc6zhtnI6Blap4LJaLLx0yWV8t38fS/bsoll0NNP7D6Rnq8QaHedwTo5vBowyXF4vB05VfeOUUg3BJNyHZNxM6WEWDki4N/BNeoXLwXsMKDncSIBCxPkRJu66eq1X+WhADhOR1jkuoouOKKVqw2IM47p1Z1y32n9knNy2HS5v+am3HDYbYzrpGGQVHkzUcGj1KpL9FLh3gqU9JuFXmJgKljf37PcNvyhLnODeVb/FqmIakIMkmAGxoe7e13BbOZ2HV6nw1rl5c6b06ceindtx+m/us1sstIxxMH3AoBBXp9QZJmoEJvGdau0rlvaAK8AWC8aeHNS6VMU0IKugCEW41mCvlPrrBRNJbd+B/25YR66rkIk9enPXyFHER+ld/ypCuXYABsrN9eJFbD0DDCpS9UEDch3V5zjehupUasAMLNLn4Y3UupWqCYsxXJucwrXJKaEuRangcG+lfDj2c62BqNQGLaep0oCsIk4k31yooVUppVSl7P2hYAkBQ7JzEcTd0uAlNUUakAOoyZRrOo638Yu0MKtjp1VZhR4PL675gXmbN1Hg8XBhz578ZvTZtI6NDXVpSqkyTOzVSM4zgTe6tyAi5VaLlIKlSO4ckGyImYyJnYExMQ1QbeOlAVlFnEj8pSTSQ2uk1atK+8XCj1medoB8j+9Gtve2buGbfftYMusmYu32EFenlCrJWFohxFB+9T2AqHLh2Jv9NOS97pvlAsC1FXG+D4nv6gp8daABuYSiznFtln2OhJDW2GmI84n0sdMquH46cZzl6WfCMfjmCc7Kz+fDbVu5RsfuKhV+HNPA+R5QUOLJKHBcVmo38RyD3FeBktPC5YNnHzgXQOwV9V9rI6UBWUWsSPqlJFJDa6R3vhVsPnYUiyl/33ue28Waw4c0IKsGJ+JF8mZD7msgp8A+FGKvwdgHYKwdQl1eWDDNfod4dkPhejBWEC/YkzEJD5be0bUWTFT5eZPFiRR8hdGAXGsakEuI9GWfm6rahLimEPQa83tT1de5WfOA00JFW630aNGywetRSrKfhLx5gH9IQOF3UPgdQhRi64tp+VyTD8rGODCt/ou4tvsWB7H1xNj7BdixOYFnvLCApU19l9moaUBWqgFFWmiN1M63OmNkx060i4vnwKks3HLmB6nNYuWqgbrogGpY4j0FeXMpPXSgSCG4tyIZ10PrJeXG2kYCEYH8j5Dc18B7CqLPwcT/EmNtX6vjGXtfsPeteIeokWDiQfIoHZSjMLFX1+qcykcDcgCR2Dluyl3vmoQ4HTKgmhqLMcydNoP7Pl/MivQ0DNCtRUv+fuFk2sTFhbo81dS49/qHBAQKyAAe8B73z/c7vEFLCwbJfgry5lDcHXe+j+R/Dq0XYayJQT+fMVZo9QaS+XPwngAsgECzR33hWtWaBmTVKNU1+GpwLk2/DpGtTVwcb1w2nZzCQlweDy0djlCXpJoqa1Il4biIAc+xBimnOsS9BwpXgyXR1xGuYGYI8WZC3puU7o67QXKRvDcxCb+ul/qMrQe0/hzcP4Hkgn0wxkTXy7maEg3IEa4uM29EkuoE1uqEOB0yoJoyXX5ZhZqxtkWiz4OCpQQeZgGIG6J8N4+KeBHnfF/wlDyImYiJ+znG0rzeaxXxIqf/n282CIzvZjmiIXE2xtar/Atc2yvojhdC4QqgfgIy4BuOYh9Qb8dvijQgq2oJ9+BdVN9T7+32PVHLIRQ6BEMppeqXafF35PTj4PyAciHZOCBmKsbaEQA5/RA4F1I8ZCH3DSR/MSQuwFjqvtCNiBdc633jhaOGlg7e+Qsgf9GZGgUgD8m8A1p/Xn6MtLUdiCvAWSxg7VLnWlXD0oAc4Rr7zBv1FVg18CqlVGgYE41p/ijS7I+I5zjkvQ0Fn4GJw8RdBzGXAyDuNHB+QukQXQiek4jzQ0zcNXWqQ9x7kIybQbIAC4gLSbgXS9xNvu15c88svnHmVeA5Dp7dUKaLbGzdEftAcG0ESgblKIz/mCpyaEBWlQr3IRxl67t/uu8jpqfe822vaRDWIRhKKdUwjLFjbEnQ7F7g3vI7uDbhiyllhyw4oXA51CEgi3h94dh7mFKzP2Q/g9gHYc8nFn0AACAASURBVKJGVDxW2lgq3GZavoBk3ecfUmEBSzym2Z8xJYY/iDsdcc4Dz0FM1FngmKLLQochDciNRLgE1mDTwKqUUk2UtS0YCTDNrx2snep2bNdGf+e47MHzkbw5voAcMwVydlF+yWc72ALMSQwYS3NMq5cRbxZ4s8HaEWMsxdulYDmSeSfgBlxI/peQ+zIkvoexJNTtPamg0oDcCNRneAz3IRz1VZ8GcaWUCjH7MLC0BU8a4CmxwYaJrdvwCiQb35Ro5TaANwsAE3cNkr/QN5xC8gA7YMW0+IdverVKGEsLsLQofWTxIqfup3g8Nfj+7jmI5L5ab7NcqNrRgKwiggZWpZRqWowx0Oq/SNavwLUVsPqGLDT/G8ZWx5ve7KkV3FDngOiJ/vPHQOLbUPAlUrAMLO0wsdMw1qTandOzD7w5ATYUQv5i0IAcVjQgR7CGnHEh3DrHZYV7fZFGh7QopcKBsbbHJL6DeI76bpizdik1ZKHWx7UkIAkPQvZf8Y1xFsABtq6Y2MvP7GfsEDMJEzOpzufEOABvJdtUONGArJQqVvxLl1JKhRFjbRf0Y1rirkXsA5C8OeDNgOiJmNip9XbDnLF2QGy9fAt6lArKDqjrkBEVdBqQI5jewKaCzv2T77+SDej/W0qpxs1EDcFEDWm487V4FsmY5b9BEN+iKI5JGMf0BqtBVY8G5AYSrje5KQUlOsf+YKyUUir4jK0TtPkSCleB9xjYUzG2rqEuSwWgAbkR0O6eCjrjm25I/99SSqngMsYC0WNCXYaqggbkehbuC20oBeWH6yillFJNmQZk1eTpLy3l1aVzrOOWlVJKRToNyPUs3BfaUA0vnANkONaklFINQdxp4D0Otj4YS3wl++2Dgu/AEgfRE3QFvEZKA7Jqshrz8JdQhPD6nJc7nH+pUEpFNvGeQjLvAtcGMHYQFxJ/J5b4X5Tb13v6r5BXdB2ygvkTtHgeE31Wwxat6p0G5AbSGEKXqpuGXNhFKaVU9UjWvf+fvfuOj6pK/zj+eabPpFBCR6QvoqisolhwEcvaGyrq2rDsqruWn+Kqa8NesKx9dVfXigW7a2+IKDZULCCiFKmCkJA2mX5+f9xJMkkmPVMyed6vly/MLec+dzJJvjk59xwIfwVEwAStjRUPYBzD6iwQYoKfgv9JrIVFqjeC2Xw29PkEEXda61appQFZdVm5OPwlkyE8FfNy6y8VSqlUigXeg9DcJHuqMJUP1Q3IVc8DVckbCs4Dz6SU1KgyQwOyUmmiC7sopVT2MLFi2Hx+4wfEiuttCDfRWrQjSlJZRAOyarFc6mmF3LsfyI4Q3pHXzIb7UUrlqKr/UXfJ50Q2cE2os0U8h2KCH4Dx1z3URMC1SyoqVBmkAVmpNNOQp5RSmWeiG4BQI3vdSP5Z9TbtCa49ITQbTBXgBOzQ7YYmZ71QnZMGZNWsXJvtIdvvpyN6S3MthOfa/SilMk/c4zFVMxv2CGOHHvcg9n51jxcbdP8nhOdjAu+DrQDcByH2nhgTs/arnKGfTdWhpk2aXhM4lVJKqazlmgCObQBPwkYvuPfG5t4j6Skigrh2QgouAlxQPBmzYTxmwy7EKmemo+qsZ2J+TPgnTKw006W0i/Ygt1FLex2Pe/4ZAJ468piU15QquTbbQ7bej87YoJRS6SNig54PY/zPQNVLIHbEOwW8RzR7rvE/AhV3UzOrhdkM5TOIiRebb3JK685WxhhMxd1Q+SCI3ZpP2nsoUngVIq5Ml9dqGpBVh8j2YQsq9TTQK6U6GxEXknci5J3Y4nOMMVBxPw2nfKuCyruhqwbkqmeh8iEgACa+sepVjOQhhZdlsrQ20YDcSi0NgtU9x5+tWV3n41zoSc4VTd1PR4S91raRyRkbNNwqpVRLRa0e46S7NqS3lGxS+QANf2kIgP8ZTMFFiDgzUVWbaUBWHSJbhy2o1NOhIUqprkTEgbH1g9i6hjsdQ9JeT9aIlTSyIwImYC3j3YloQG6llgbB6p7iXOg57ko6Iuy1t41M9BxruFVKZSMT+hzjnwXGj3gOBM/+iDQeXUz0Vwh/A7Ze4NwBEUlNYQUXQ+klQCBhowcpuCQ11+sMnNtBaF7D7bbeIJ1vGjwNyKpDac9x16OLeSilUiFW/cCXCQAGE5oHVS9Cj38jYq9zrDEGU34D+J8CcQExK5j1fAyx9+/w2mzeAzHiwVTcAdFVYB+KFExD3Lt3+LU6Cym4CFN8HJggtQuweJDCK1P3i0oKiTGm+aPixo0bZ+bPn5/CcpTKDpkYg5xJXe1+M0VEvjTGjGtPG/p9WHUFJvor5rd9aLiQhw/pfjvi2avu8VWvYkovo+4YWBs4RmPr9WKKq1XVTORnTMU9EP4e7EOQ/LMQ146ZLquOln4f1h5kpVSHyPVgrL8AKJVGoXkgDjD1A7IfE3y3YUD2P07DB8RiEPkZE1mNOLZIZbUqThwjkO53JN1nIr9YM13ENiKuP4Bn36x+cE8DchbSB90yryNCUGcKUp2pVqVU65jISkzloxD5CVy/R3wnIPbemS6raZIPJPuzvB2ksOHmWHkj7djBVHZkZaoNYlVvQ+mFQASIYAJvgv9h6Dkza+dI1oCsVAboL0Gdhz7IqDozE/oaUzIVTBiIQPgrjH8mFD2POAZnurzGuSeSfLFfJ+I7quFmz35QuZKGQzJc4Bje8fWpFjMmBGX1Hmg0fggvwfifRfKOz1htTdGAnGKtCUK62IZSHU8DrerKTNnlYBKHHoTARDDlM5Ae92asruaIuKHHfzElf8bqdcQK+YXTEceIhsfnnYIJvBqfh7gKsANOpNtNTc56odIg/F0jO6og8CpoQFZK6S9BnY/O0qE6KxPzQ2RZkj2x5NNxZRlxbQ99PobQ59bMCK6dEVvy6cLEVghFL2OqXoTQXLANRPL+hHTS3mMTWoCpuB3CS8AxCMk/D3FPyHRZbSMeame1qL/Pl9ZSWkMDcoq0JQjpYhsqFbpqsNOhEarLEydWT2o0yb68dFfTJiJOaOHUaWLzWX+ub2ePpDFRTOX9UPkImDJwjEYKr0jbbAwm9AWm+DRqhiSEizElf8V0m4HNu39aauhQjq1BulvDKhKJF/Edm5maWkADslJppL8EdV4arFVnI+LEeA6EwOvUHZvrAV92/lk7G5iy66DqeWoCamQRpvgUKJqFOLdK/fXLZ1B3ARKsj8tvxHj263RzCosI9HgAU3wyEAQMmCh4p4B7n0yX1ygNyCnSniCkoSl9crlXsav3oOrQCKVACqdjYush9LXVo2xC1vRaeX/OdGlZycRKoeo5rCCXKISpuA/pcVfqiwj/mHx7bANWcPamvoYOJs5R0GcuBD8CUwLOnbN+6j0NyEplgP4SpJRKB7HlIT0fxUSWQXQlOH6H2AdkuqzsFV0T/0WifkCOQXixFaDFYz1EmCr23tbqfPWJB0jhdVNMxAmeSZkuo8U0IKeYBqHs1BV6V7UH1dJV71upROIYBo5hmS4j+9kHJlmcBEAgth6zYTdAMJ79kcKrEVsKxnLn/RXKrqHuwideyDsVkWRT36lU0FdaKaWUUgoQWzfwHgV46u0xWIE1DIQg8CZm8zmpqcE7GQrOiy+W4gG84DsJyftbSq6nktMeZNUlZVPvaqpr0B5UpZRqOSm8AmPvXTuLhRSCKafuVGUhCH2BiaxCHINa3LYxQQh+aM1N7do16YqGIoLknYrxnQixTWDrmbWrzeUyDchKKaWUynomuhai68Ax0pr3OEVE7Ej+3yDf6rGNbZoC4QVJDnRZY5ZbGJBN6Kv4wicGTAyIYPLPxZb/l0bqcIK9XxvvQrWXBuQskKkpv457/hkAnjrymLReN5tkQ89xLo+DVkqp9jKxCszmcyH0hRVKTQiTdwqSf356pjxz7gDhhVjDKxILC0KSVf2SMSZkhWNTXndHxT0Y13hrURSVVXQMcgeYNml6TcjNtOOef6Ym+CqllFKdnSn9h7WaHsF4wAxC5aMQeKnhsSaAiazGJH3Qrm0kbyqIl7qRyQ3uvaClPdmheVjjmOsLYqpmtbtG1fG0BzmDMrXscHWA/mzN6jofd+We5EzIpnHQSimVjUysAoKzqbvQCUAVpvJBxHsEALHQt1BxJ4Q+A+wggsn7C5J3Vrt7mcXeD4qex5TfCsGP47VEITQXs2FXTMGV2HyHNXMjAZIHZNNwhTmVFTQgt0M6A25zIUpDr1JKqZxjymn0j92xzRgTxJT8xRp+QSThPKDiAYz0QPKOa3cZ4hiM9Lib2MYjIfKDdS0Tv17ZFRjHYMQ1tvEGXLvUHl+nYR/iOaDd9amOpwE5gzK17HB1aG4uRDdVly6V3LTWvD7ac6yUUo2w9QVbPsTqL71sA9dumIp7IfQldcJxjSqovB86ICADmMhSiPyU5FpBTOXDiOvORs8VW3dMwSVQfjNWD3QM8IFzJ3Dv3SH1qY6lAbkd0hFwW/ogV0tDr1JKKdVZiNig8BrM5guwln82gBPEi+SfhymeQsPhFwlimzqumOhGEEeSkRIGYuuaPd2WdzzGtSOm6nkw5Yj7j+DeUxf/yFIakFOgtYE5U72wzfUcJxs6kqlx051FNr4+OsZZKdWZiWcfKJqJqfiPtVy2a2ck71TE3q/5h/EcozquEOdoMOEkO9zg2qNFTYhzK8R5WcfVpFJGA3IHSGX4ae2DXNpzrJRSnZMxBgKvY/zPAGHwHI74Jlvz4XZx4twW6XFXwx2evaHqFSCa5CwPUnhJx9VgK8TknwUVD1C7DLQTbN2QvBM77DoqO4gxyZ6qTG7cuHFm/vz5KSync6vfe7jdxK2BjgnQmegF1DHIbZcNr0/94Tk4dwa0JzmTRORLY8y49rSh34dzV6z0Yqh6k5rwJV5wbI/0fET/DN8IE/0Ns+lIiJVivW7xGSucY5GCS1Myv7AJvIup/C/ESsAzCck7HbH17PDrqNRo6fdh7UHuJDTUZA8dsqCU6mgmvASq3gASHkYzVRD5FkJzwT0xY7VlM7H3hl5vYqpegcg3YB+K+I5KaWAVzz7WsA+V07QHOQWyofcwG2rIVYkBuTOE5c5QY1ehPciqMabycUz5DKwH0erxTcVWeGnaa1KZZ0wIgh+BKQPXLtaczKpdtAdZqQ6WdEaRyA/gGJ3BqpRSOcHWIz5DQv2A7AJbr4yUpDLLhBdiik/BmlYuBiaCyTsVW8EFmS6tS9CAnALZ0HPcEbMoaC90MyI/WJPYhz/P6l7abKxJqXQLh8LMff4zvv94Mf2H9mHfkybSvXe3TJdVy7M3lF2VZIcd8R6a7mpUhhkTxZT8Gczmujv8j2Jc4xH37pkprAvRgKxUC9WZUaS657j6ATilVNaqLK3k3N0uY8OqTQQqAri8Lp645jlmvDedUeOGZ7o8AES80PMRTMmZYCrjWx1I93/qn9W7ovDX1hj0+kwVxv+0BuQ00ICcYzpi8ZJsnMs36zhGd5oxyEp1dU/e8CLrlq0nHLRWQAtVhQgBN51wJ//94U5EJLMFxolzDPT+ECKLrPl2ndsioj+muyQToGZGjgb7KpNvVx1Kv/KUaiUNw0p1LnNmzasJx4k2rNzIprXF9BpYlIGqkhOxgXNM2q5nPQT2Hib8I+IYAp79EfGk7fqqEc4dwCSb29mLeA9uU5MmVozxPwuRxeDYFvEdidiyaJhRltGAnKPa09ubjiW0c4WGZaVSyxjT7h5eu9PeaNt2Z9f9MWhixZhNU6wllPFjxAflM6DoWcQ+MNPldWli82EKr4GyK4AwEAXxgWMb8BzS6vZM5GfMpmPjD4EGgfcwlQ9A0fOIY4sOrj436MzjSimlss6C2d/z5+0u4I+OKUzudQpP3vA8sVisTW0dePreuL2uOttsNmH42KH06NN1e9BM+S0QXQv44xv8ECvGlOpSyNnA5jsM6fUC+E4GzxFIt5uRno+2aWVFU3ql9VB5zTSCATClmPIbOrTmXNJ1f3XOQcc9/wxLF6xgj4/8HdLrqz3HSqlMWPz5T1x+8I0Eq0IAlBdX8OQNL1JeUskZt5zU6vYm/99BfPPBQr798AeMMTgcdnzdvFz65HkdXXrnEngLawqxRDEIfYYxYV3iOguIY0S7l8s2JgLhr4D6617EIDi3XW3nMg3ISimlssrjVz9bE46rBf1BXrnvLU6afjTefG+r2nO6nNzw+mX8OH8pS774md6DerHT/mOxO5IPveg6suPhRJVqNsAOJPkLjLgablOABuScUN1zvLG3A3o7eGNbmHvFzR3Wk6yUUum0YuGqpNvtdhsb1xQzaFTbxseOGjc8a6Z1ywqeg6DqeawxrtXs4Jqgvcc5RMSG8RwIgdep+7l2g3dypsrKejoGOcWmTZpe87CbUkqp5g3dbjDJnsuLRqIUDeyZ/oJylBRcCI4hIHmAw/rX1gfpdl2mS1MdTAqvBMdWIF7rYT+84ByL6Kp8jdIe5Bzw1JHHwJFWT/L3Hy1m+OMreKnk0UyXpZRSbXLilUez4P3vCPrrDrMIBcOcutV5nHHbyUw6RhdKaC+xFULRKxCaC+El4BgM7r1yuvfYmCiEv4RYJbh2Qmz5mS4pLcRWAEXPQfhbiC4Hx+8Q59aZLiuraUBOkXQvtjFt0nSWTvARjcaoLPXrFG1KqU5r1LjhXP/apdx/wSMsXbACU/1skYFNa0u47bT7yO/mY6f9f5/ROnOBiB3ce1r/5TgTXoQpOT2+Qp2ACWMKL8fmOybTpaWFiIBre2D7TJfSKegQixyyx0d+Bt6zKNNlKKVUu20/cRvunHcDLq+7wb6gP8RjV83KQFWqszImjCk+BWIbrZXoTAUQhLLrMWH9uaka0h7kFEn3Yhu6uEf20uWolWqbsk3l1HYf17Vu2fo0V6M6tdCnQCjZDox/FtLtqjQXpLJd2gKyBjeVDvo+Uyp39OjTDYfL0WDKN4Bh2w9Jf0Gq8zIVjeyIgdmc1lIyxZioNd48ugYcY8C5XbtXqcxl2oOcYukOavWvp4Exc6p7jgl/Xudj7UlWqmXsDjsnXT2F/176FEF/sGa72+filOuOy2BlKh1M5GcIzrFmXvDsj9jaMYOJcycw9RdFAfAh7n1bUMsyjH+mtfKga3fEewRiy2t7PWlmor/Gl5ouBRMFBFxjocd/EJ0LOamUB+R0P6ymuuZrrO8zpXLT5HMPoqBHPjOvfY5N60oYtv0Q/jLjREaPH1nnuEWf/MjM619gzU/rGLXzCE64/Mg2z5esMssYYy2B7H8GiAJ2KLsJut+BePZqU5ti74XJPxsq7gMCWKvKecE5Gjx/bLqe4AeYknOxVh2MQHAexv8wFL2A2DrHUuVm8zSIrcd6PeNCX2MqHkAKzslYXdlMe5Bz1NIFK5g2aboGxgyq7inWnmOl2mffEyey74kTG93/yf/mc/2x/6wZirFu2XrmvfwFd350HcO2G5yuMlVHCX0O/llYQRaqF7cwpeeDa16be25t+WdgXDtg/E9DrBzxHgCeg5uc1s6YKKb0koRaAKoguh5T+VCnmEfYxMogvIA64RiAAFQ9BxqQk0p5QNaHx9Knfi/q0gUrMlhNeun7TKmuyRjD3Wc/WGecciwaI1AR4D8XPc6Nb16ewepUW5jAS9QNpNXsEPoIPPu1uW1x7YS4dmr5CdHlYJLVEoLAW9AJArLV892WfV2b9iDnsOFjh7B0wQqGjx3SpQNjpkOz9hwrZdm4ZhPPzHiZbz5YSL8hfZhy0WGM2X2rdrVZWeqn5NfkD1kt+nRJu9pWmRLDGgJRX/IZTVJK8uJjdpPt6xyLjIitJ8YxFCL1vx6c4N4/IzV1BmkLyG0JJ5kONp1Nsl7UrrbMtb5XlMpOG1b+xpk7XERVeRWRcJTl363kq/e+44L/nMFex+3R5nbdPhc2uw3CDUNM996dY3yoqks8h2ICb8YX9EhgouBK7wqKYu+PcYyCyELqDlHwInknprWW9pBut2CKj48/qBiwlpu29dbxx03QHuQcl+nAmMlfcvTBPaWyx2NXP0tlqZ9YNFazLegPcs85/2Xi0bthd9jb1K7T5WS/U/fi7Ydn1xlm4clzc8zFh7W7bpUBrt3AcyhUvYw1d7EDEOh2U0aWhpYe92CKT4LYBsAGJgTeI8DTed5f4hwNvd/DVL0MkeWIawfwHKAzWDQhKwOyBpv20ddJKZVtvn7vuzrhuFo4GObXFRsYOKJ/m9s+87aTqSqv4sNnP8HhchANRznygoM54LS921OyyhARQbpdi/EdgwnMRmw+8ByE2Ptlph57P+j1FoS/tkKyc3vE3vb3a6aIrQeSNzXTZXQaWRmQVXqk8hePbPglRx/cUyo9yksq+N/9bzP/rQX02bI3k887kN/tOLzOMd37FLJh5cYG50YjMQp6tq9X0OV2cvGj53DW7VPZtLaYfkP74M33tqtNlXniHIM4x2S6DMAK7bh2yHQZ7WJilRB4HRNdbb2u7kmIaAxsTFa+MhpslFKqcyjdWMaZO/ydso3lhAJhxLaYj174lAsf+it7HlM7XnTK3w/nllPvJVhZu+CH0+1k3H7bU9izoMlrbFxbzBPXPMcXb31NYc8CjrrgEPb604QGq4AVFhVQWNR0W0p1RSayFLPpOGt4CH6M+MC+BfR8OiPDVjqDrAzIKrmO+oUhHb272fRLjv6CpVTqPDPjZUo3lBEOWdNFmZgh6A9x51n/YcLk8Tic1o+ZiUfvyuola3nqhhewO+1EQhG2m7g1Fz/W9ENCJRtKOev3f6e8pJJoJMqGXzZyx5kPsGLhSk674fiU359SucBsvshaRa96JhDjh8gKTMU9SOElGa0tW2V1QNZgo5RS2e3TV7+sCceJotEoqxavYei2tQt1HH/ZkRxx7oGs/GE1RQN60nuLombbf+mu16ksqyIaqZ1BIFAZ5IU7XuPoaYdqj7FSzTCxUogspuE0eSEI/A80ICeV1QFZWTq6xzedvbuZnL1Cf8FSKvUaGz8cDUfJ79Fwn6/Ay1Y7j0xyRnJfv/894WC4wXan28myb39h7KTsGKOqVPaSNu7r2myZLkAppVTnddT5B+PJc9fZZnfYGbnjsBb1EDen39A+iK3hD/FIKEKvDmhfqVwntkJwjqFh5HNb09WppHK2BzmXehFT1eObC69NomyYOUOprmbC5PH8/M0Knr3lFVweJ9FwlC1GDWD6cxd2SPtHXXAw817+nKC/do5jh9MK4FuM7HxTbSmVCdZCIcdZY49NEMQF9hFI/l8zXVrWytmArJRSKvVEhFOuOZYjzzuIn75aRs9+3euMO26v3+04nEseP5c7zvg3QX+QaDTK2D3HcMnMczvsGkrlOnFsCb1nQ+BdiK4B5zbg2rXBTDCqlhjT8rXNx40bZ+bPn5/Cctqvfi/idhO3BjpPL6L2erafvoYqW4nIl8aYce1pozN8H06FaDTK+hW/kdfNR7dehSm7jjGGSDiC0+VM2TWUUpnT0u/DOgZZqQ4W23QCsU0nZLoMpXKK3W5nwPB+KQvHxhhm3foKR/Y6hYO8x/OnwWcy+5mPU3ItpVT2y7khFtk0/25r6PjZjqOvmVKpU7KhlCeufY5PXv4Cb4GXw885gIP+sg82W+fub3n65pd48vrnCcQXMvlt1SZuO+0+vHkedjl4xwxXp5RKt5wLyLmqqwTmznyfNb3G4c/rfGwreiJTJSnVoSpLKzlrx4so3VBKJGzNS/zAhY/x05dLueA/Z2W4uraLRqM8c/NLNeG4WtAf4pErntKArFQXlLMBOZUBq7EQ155w11l7vlX6aOBWmfb6g+9RUVxRE44Bgv4g786cy/GXH0Xfwb0zWF3b+cuqCFaFku5bt3xDmqtRSmWDnA3IuaKrDL2YNmk6Z17xHiecU8lFR43olPdZHVw1yKpc9c0HC5MGSafLwU9fLeu0ATmvmw9vvofy4ooG+wZtNTADFSmV20xwLqbiboiuBsfWSMH/Ic7sWvRHA3IrNBZWq3VEiO1MgVClhw7dUNliwPB+2B32Oss+A8SisQ5ZFCRTbDYbU685hn9f9ARBf+0wC7fXxek3Hp/BypTKPbGqV6H0UiBgbQh9iNn0ORQ9gTi3y2htiTQgZ7muMPQitukEbnkOCFt/yrzv3XUM397daQNgZ61bqeYc+rf9ef3B9+oEZLvDTv9hffnduOEZrKz9Dv3r/njzvTx+zSw2rilhy60G8ucZJyRdynrzb6X8unwDA4b3o7CoIAPVKtU5GWOg/EZqwnGNAKZsBpJFPz81ILdCc2E1l0OsyhwduqGyxRYj+3PNyxdz66n3UraxnFgsxpgJo/nHzPNyYsGBfU+ayL4nTWx0fyQc4fY/388Hz8zD6XYQDkbY75RJnH33qdjt9jZft3RjGS/c8Rrz3/6G3oOKOOqCQxiz+1Ztbq+rMyYKphIkH5HOPbtKzjFlENucfF9kUXpraYYG5E4il0N3/QA4ci8NgEplqx323paZK/7FhpUb8eZ70taDGovF+Ord71gyfyn9hvRmwuTxuDyutFy72sOXP8WHz35COBgmHAwD8M5jc+i9RRF/unRym9os2VDKmWMvpLykknAwzE9fLmX+W99wzj2nsd/USR1Zfs4zxmAqH4DKf8eXU87H5J+PLe/YTJemqokPxAEm3HCfLbueYci5lfRU56U9pCrX6Up6bVNVGeDCSVexavEaglUh3F4Xbp+bOz66loEj+qelBmMMh3U7iaqK+n8ahu59Cnn214fa1O79Fz7Ky/e8SSQUqbPdV+jl2fUP4XLrin4tFav4D1TeA6aqdqN4oeA6bL5DMleYqiNWNgP8T1B3mIUXCq/F5js05dfXlfRUp2MrekLDsVKqRsmGUl6863Uu2e9aln6zgqqKALFojKqKAKUby7j5xLvTVkssFiNQ2TAcA1SU+Nvc7hdvfN0gHANgYNXiNW1ut6sxJgaVD9QNx2B9XHlXZopSSUnBBeA7BnADXpB8KDg/LeG42GAUiQAAIABJREFUNXSIhVJKqazz1XvfMf2wm4nFYoQCDf8ca2KGn75aRnlJBQU98lvd/oZVG1nz0zoGjRpAr4HNz8Bht9sZMmZLln+3ssG+UTuPaPX1q3Xv042VPzQMwpFwRB8AbJWgNe44mej69JaimiTiQAovwxRMg1gJ2Hohkn1/KdEeZKWUUlmlsszPNUffSsAfTBqOa4jQmmGCAKFgmGun3MYpo87l6iNv5eSR53DjCXcSCSfpxa3nnHtOx+1zY7NZDyTa7DY8eW7+esfUVtWQ6Ohph+LxuetsczjtjNp5ZKeeOi/9PGBr5PVydO4ZVnKViAex98/KcAwakJVSSmWJ0o1lXHn4zRzZ6xQqNzc9bEFEGLbdYAp7tq6X9b+XPsmnr31FKBCmstRPKBDm4xc/57GrZjV77rZ7jObuT65n4jG7M2TMIPY+fg/u/eJmfrdj2wPYLgfvyAlXHoXb68LXzYfb6+J344Yz/blpbW6zKxIRyL8I8NTb40EK/p6JklQnpw/pKaVUmuhDeo0zxnDG2AtZtXhNnaWsk/Hke3B5nNwx91oGjWrdSneHFJyYdCxxfo88Xtz0CADff7yYdx6bQywSZc9jd2eHfbZL+TR2lWV+ln/7Cz36dU/bg4e5yATexVTcAdE14BiO5F+IuHfJdFkqi7T0+7COQVZKKZVxiz5Zwq/LNzQZjl0eJ+MP2pHxB+3AH47eFW9e/d7Cphlj6qyUl6iq3ArND106kxfveoNQVQhjDB/MmsfEKbsx7cGzUhqS8wp9jJkwOmXtdxXi2Qfx7JPpMlQO0CEWqlOZNml6gyW+lVKpt+bndUw/YgaHdTuJY7c4g6dvfolotOme3tZYu/TXRveJgNvnZvcjdubyZ85nv6mTWh2OrXak0RX/Ru8yktU/reOFO14n6A/WjG0OVAaZM2seP3z2U6uvp5TqvDQgK6WUatLGtcWcvfM/+OR/8/GXV7FpbTFPXPsst53+rw67xoixQ4hFYw22O1wOdjl4HLfPuZpLZ/4fNlv7fmydc+/pePI82B1WO3anHW++h7/ddSpfvPE10HDYYdAf4tP/5d6wFqVU43SIhWqVdC+nXb+3+Ns5izJSh1Jd2Ut3xXtVY7XhMegP8cHT8zj1uuNaNE1ac4ZuO5jt99yGbz5YSLAqBIDdYaOwqICLHz+HvEJfu68BMGrccO7/egbP3/4qPy9Yzsgdh3HUBYfQf2hflsxfhs3eMIDbHTa8+a3vsVYtYyKrIPg2YMC9D+IYkumSlNKArJRSqmmLPllCOMliFi6PkxULV3dIQAaY/sLfeerGF3jjP+8RDITY9eBxnHrjn9oUjtctW899//cwX77zLU63gz9OncRpN/wJj8/NwBH9Ofe+Pzc4Z8IRO3Pfef9tsN3msDPpuAkAbP6tlJL1pQwY3he3193gWNU6scrHoPwWIP7Xg/I7MfnnYMv/S0brUkoDsmqR6h7bdPXg1r/edhO3rvOv9hwrlT5DthnEok9+JBqpOwQiHIrQb2ifDruOy+3k5KuO4eSrjmlXO2XF5Zw9/hLKSyoxMUM4GOb1f7/D8u9+4db3rmr0vMKiAq6YdQHXHftPbHYbxhiikRjnP/AXuvUu5KrJt/D5G1/jcDkwxnDKtccw+byD21VrV2aia+LhuN6DkxV3Yzx7Izp/scogDchKKaWaNPn/DuKdx+YQjdQGGafbyZgJW7HFyMxMSVZeUsGDl8xkzqx5AEycshun3fgnVi1ey6PTn6GyrKrOkJBQIMziz37m5wXLGTF2aKPtjj9oR2at+w/z3/6WWCTKjn/cnvzueVw75TY+f+NrwsEw4aC1eMnDlz9NvyF92e2wnVJ7s7kq8G4jO6IQeBvyz0prOUol0oCsWqS6xzZdY3/TfT2lVOO2+N0AbnzzMv555r9Zs2QdNruNicfsxrn3nNai839dsYGHr3iar975loKeeRx1/iEccPrebZ42LRqJct7ul7Nu2Xoi8aEfbz0ymw+e/phYLEagMvlUbja78MvC1U0GZABvvpc9Jo+v+bi8pIJP/vdlTTCuFqgM8vTNL2lATonUzjutVHM0ICullGrWmAmjeej7f+Ivr8LpduB0tWx52E3rSvjruIupLPUTi8bYvKGUf53/CCsXr+HM205uUy2fvfYVG9dsqgnHANFwFH+4qsnzTMywxagBrb5e2aZy7A4b4SS5e9Pa4la3p+I8+0L5rUl22MHzx7SXo1QiDciqVdLdk6s9x0plF1+Bt1XHP//PVwlUBOpM4RbwB3nlvrc47h9H0K1XYdLzPn31Sx654mnWLd/A4K0HctoNx7P9ntsAsPSbFVRVNFwNrylOt4Oh227J73Yc1qrzAPoO7o3D6aD+WFmb3VZTk2o9sQ/AFFwM5TdjTa9nABvkn4M4Wv95UrnPxCrAlIGtLyL2lF5L50FWSimVMt/N/aHxGTC+X5X0nNnPfMx1x97O0m9W4C/z88OnP3HZQTfw9fvfATBwZP9WLRRis9vY89jdufHNy9s0rMPhdHDGbSfh9tXOWlE99duJVx7d6vZULVveCUiv15GC863/er2qM1ioBkzMT2zzBZgNu2B+2x+zYXdiVa+n9JoakJVSSrVIKBBi1q0vc/q2F/CX7afx4l2vEQk3DL+JBo7sj83WMJSGQxH6bNmrwXZjDP/+++ME/aE624NVIe6f9ijRaJQJR+yMt8CbdM7iZE6/+Xguevjsds2lvP8pe3HNSxcxdq8xDBzZjz+evCf3f30L/Yf1bXObyiKOQUjeaUje6YhjcKbLUVnIlP4dAu8AISAAphhKL8GEUreAjw6xUEoR23QCALaiJzJcicpWsViMi/a9hp+/Wl6zkMdDlz7JZ6991WTP7FEXHMxHL3xaJ/A63U622W1U0nAZDoYpbmRc77JvfuHIXqdyzMWHc9e867j9zw+wYPb3xGKxZAvgAeD2Ouk3pGNC7A77bMcO+2zXIW2p7GdMCON/CQKvgHgQ3zHWQiZtfLhUtY2J/gbBOVjhOFEAU/FvpOe4lFxXe5CVUko168t3vmXZN7/UhGOwVtNbOO9HFn68uNHzRowdypXPXkivLYpweZw4XQ52OWRHpj9/YdLjN/9WhsPd+AOAlaV+Zl73PLOf/pib376C++bfhMvjavR4X6GPXQ7eoQV3qFQtY6KY4lOg4noIfw6hDzGlF2LKr8t0aV1PbANII1/j0eTDtDqC9iAr1YVV9xwT/rzOx9qTrOpb+PHipA/GhYMRFs5bwpgJoxs9d+cDfs+Tv/yLTetK8BV4G33Qb/YzH3PrKfcSCUebrCXot6ZXm/L3w3jr4dl1ZrNIVNAznzNvnxp/wE6pVgi+D5GFYBJmRjFV4J+F8Z2kQ0HSyT4ETLKvcQe4UjfFovYgK6WUalbRgJ51HlKr5vI46dm/e7Pniwi9BvRsNBxXbK7k1lPvIxQI15nxojGBigChQJjiX0sbPb5icyX//Mv9/OOA65sdK61UIhP8EIw/yR4bhD5Lez1dmdjyIP9MIPF7hw3Ei+Sl7oFODchKdWG2oies3mLnzuDcufZjpeqZdOzu2B0Nf2TYnXYmJCyq0Vbz31qAw9HyaZsKexXi9roYf+AOePIaBnew5j0OVAb5/qPFvPrAO+2uUXUhtp4k/SO72MDW/C+EqmNJ3llIt+vBsRXYeoPnQKToBcSxRcquqQFZKaVUs/K75zHj3en0G9IHt8+N2+di4Mj+3Db76lZNudaYph58qj9bhdvn4vSbjkdEmHjMbgwaNQC3r/FxyEF/kDf/+367a1Rdh3iPJPkoVAe490xzNUpEEO/B2Hq9gq3Px9i6357yYS46MCuL6TLLKl2011i1xKhxw3ls6T2s+flXbDah/7C+HfZE/7j9ticaaTj22JPn5tTrjuPdmXNZuXgNfbfsxdRrj2XCEVavtcvt5J9zr+WNh97nrYdns+zbX5IPuTCNTHOhVBLi2BK6344pvSi+xYDkIz0eQBp7YEzlFA3ISimlWkxE2GJk/w5vN69bHhc/dg43nXQ3ALFIFLvDzkFn7MsR5x3EEecd1Oi5bq+bw88+gMP+tj8nDP0rG1ZurLvf5+KPUyd1eM0qt4lnH3B/CuFvABc4t0VE//DeVWhAzkLVPcffzllU52PtSVZK5bI9jtyFbXYfxYfPWfMmjz9oB4ZsM6hmfzQa5c2H3ud//3qLYFWIicfszpQLD6158E9EuPLZaVy07zXEIjECVUE8eR5G7TiMQ876Y6ZuS3ViIq6UzpSgspcGZKWUUlmjZ78eHH72AUn3zTj5Xua9/DmByiAAs255mbnPfcK/vpxRMxfyqJ1GMHPFv5gzax7F6zazzYSt+P1eY3RxB6WwVqq05hX2IbaCTJeT1TQgZ6HqnmLtOVZKKVj90zoevPhx5r083/oBHxcOhFn/y0bmPPsJ+544sWZ7fvc8DvrLvpkoVamsZYLzMKWXQmwTEMO4dkO634LorBxJ6WCaLmDapOk1YVsppbJdOBSuCcJLv1nBWTte1CAcVwv6g8x9/tN0l6hUp2IiyzAlZ0JsLRAEwhCahyk+PdOlZS3tQc5i2nOslOpK5r38Bf86/xHWr/wNX4GXoy44mAUfLCSQZAW/RD8vWJGeApXqpEzl40C43tYwRH7ChBcjzq0yUVZW04Ccw/RhP6VUZ7Fg9vfccPwdBP0hACpL/Tx100uEg/V/qDf026qNGGN0nLFSjYkuB5Is4S52iK4FDcgN6BALpZRSGffo9GdqwnG1UFUIE2t+/mK3163hWKmmuHYGkqw4aULg3Drt5XQG2oOcw/RhP6VUZ7F6ybo2nefyONlv6p4dW4xSWcKYGIQ+xAQ/A1sfxHsIYu/V6nbEdxzG/zjEItT2JHvBewhi79ehNecKDchKKaUybth2W/LVu981eYwnz02wKoQIuH1uYpEYYyZsxZ9nnJimKpVKH2NCmOKpEFkExg+4MZV3Qo9/I66dW9WW2HpA0UuYirsg+AFIPvhOQnzHpaL0nKABuQvQnmOlVLabeu1xLJz3Y4NhFtV8hV5OuOIo9jlxIhUlFfyyaDWDthrI4NFbpLlSpdLD+J+G8PdA9UOqQTBgNp8HvT9CxN6q9sTeF+l2fYfXmat0DLJSSqmMGz1+JDe8fhk9+yefkzUSijBh8nh69OnGoFEDmXDE+BaFY395FfdPe4Qp/U9nSv/TuX/aI/jLqzq6fKU6XtXL1IbjBCYAkR/TXk5XowFZKaVUVtjuD1tz/1e3kN8jD7HVPnTn9rrY9dCd6D+0b6vai0ajnP+HK3jlvrcoWV9KyfpSXrnvLc7/wxVEo0me6FcqmzTaQxxDBwCkngZkpZRSWaNH3+7c89mN7HrIODx5bnr07caUiw/nksfPaXVb899cwLql6wkHIzXbwsEI65auZ/5b33Rk2Up1OPEeC3iT7OgJjpFpr6er0V9BlFJKZZWBI/pz9YsXtbudn75aTqAy2GB7oDLIz18tZ/yBOwBQWVrJUze9yAfPzMPpdnLQn/fh8HMOwOHUH5Eqg7yHQXAOBGcDMRAn4EB63KvTGqaBfvUrpZTKSf2H9cWT56aq3kp8njw3/Yb2ASAUDHPubpexbtmGmkVJHrnyab6Zs5BrX74k7TUrVU3EjvS4ExNeBKEvwFYEnr0RSdKrrDqcDrFQSimVkyZM3hm3z11nPLPYBLfPzYTJ1jRZHz3/KRtWbqyzYl/QH+Lr977n5wXL016zUvWJc2sk72TEe7CG4zTSgKyUUionub1u7vz4OkbvMhKH047DaWf0LiO58+PrcHutVcW+/fCHpMMwMIbFn/2c5oqVUtlCh1gopZTKWQOG9+POj66nsswPQF6hr87+fsP64PI4CQXCdbbbHXZ6b9EzbXUqpbKLBmTVJF2mWqmuKRqJ8vkbX/Pr8g2M+P1QQoEQj1zxNKuXrGPQVgM59frjGDtpTKbLbLH6wbjaflMn8eR1zwO1AdlmE3zdvIzbb2yaqlNKZRsNyEopperYuGYT5024nPLiCiKhKIi1UIeJGQB++HQJlx9yI9Ofu5Cd9v99hqttnx59unHzO1dy4wl3smlNMcbAsO0Hc/nT52N3tG6lMqVU7tCArJKq7jn+ds6iOh9rT7JSuW/G1HvZuLqYWDTW6DFBf4j7L3ys0wdksFbxe3TJ3fy2aiMOl4Oe/XpkuiSlVIZpQFZKKVWjqjLAtx8uajIcV1v949o0VJQeIkKfLXtnugylVJbQgKySqu4p1p5jpbqWlgTjaj36dU9hJUoplTk6zZtSSqkaeYU+RowdSnMLdXny3Bx/2eT0FKWUUmmmPciqSdpzrFTX8/dH/sb/TbicSDBCwB/Ek+fG6XYSCUeIhqM43U6Ou3QyB5/xxxa3aYxhwezvWbpgBQOG9yMSjvD4Nc+yYeVGhm67JaffeDxjJoxO4V0ppVTLaUBWSilVx+DRW/DEsnt5b+ZHrPl5HaPGDWfCkbtgt9soK66gsGd+q2Z4qKqo4sK9rmLV4rWEQxFEIByKgDUpBgs//pFL9ruOG9+8nG330JCslMo8DchKKaUayOuWx6F/3a/B9h59urW6rYcvf5rl360kHIw0ekywKsSDlzzBnR9f3+r2lVKqo+kYZKWUUin13sy5TYbjasu/X5WGapRSqnnag6yUUiqlYrGWzYzRZ1BRs8esXrKWF+54jV9+WM22E7bisLMPoEdfnU1DKdWxNCCrDqXTwiml6ttj8njeeWwOkXC00WPcPjcnXTWlyXa+mbOQyw66kUgoTDQS44dPf+KVf73NffNvpt+QPknPMcbw9fvf88MnS+jZvzt/OHrXRpedVkqpahqQlVJKpdTpN53ANx8spGR9KVUVAdw+F8aAzSZEw1F8hV5Ou/F4/nDUro22YYzh9tPvJ+gP1mwLB8NEwxEe+sdMLnvq/AbnhIJh/rH/dSz5chlBfxC318UDFz7Gre9fxYjfD605LhaL8fV73/HLotUM2mogO+67HTabjkBUqivTgKw6hC5NrZRqTGFRAQ8u/CcfvfA5P321jC1+N4CJU3bF5XESqAyS183XbCAtL6lgw8qNDbbHYoYv3/4m6Tmv3PsmP37+M8GqEACBSitcXzvlNh5ZcjciQnlJBRdMvJL1K34jEorgcDvoNbCIO+ZeS2FRQTvvXCnVWemvyEoppVLO6XIy6djdOfycA/j4pc+YXHQKh+SfwK2n3sfmDaXNnh+oDBIzyccy+7olHzLx1sOza8Jxok3rSli79FcA7r/gUVYvWUdVRYBwKEJVeYB1S3/lnnMeasXdKaVyjfYgqw6hS1MrpZoTCoQ4d9dLKVlfWrOk9WevfcU54y/losfOZtCoAfTs16POOcYYHvzHTF6663UkyfJ+bp+LI845IPkFm1kNEGDOrHlEQnVn2IiEo8x94TOMMUmvqZTKfdqDrJRSKi3mPv8Z/rKqmnAMEI1E2bBqI5cddCMnDPsbNxx/J+FQuGb/7Kc+4pV73yQUCBNNeMjPZrfhdDvZ85jdOfzcA5Neb7+pk3D7XA22Fw3oyYDh/YDGZ9hIrFEp1fVoD7LqUNpzrJRqzKof11BVEUi6r/rhu3kvfc5D/bpz5m0nA/D8Ha/WjB1OJDbhrk+uZ8TYoQ32VTv0b/vz6atf8uP8pTUP6dkddq6YdUFNz/D4A3dk3itf1AnENpuw0/5jtfdYqS5MA7JSSqm0GDpmS7z5nkZDMlgr6r3273c449aTEBFKN5YnPc7pcuDxuZu8nsvt5Jb3pvPNBwtZOO9Hivr34A9H74qvwFtzzN/uOoUfPluCv6yKqooAnjwP3nwP5957ettuUimVEzQgK6WUSovdDt+JBy+ZaQ2XiDQ+J3LQHyIWjWF32HF5Gw6RABAR+g/v2+w1RYSxk8YwdtKYpPt7DSzikSV3M2fWPJZ/9wtDxgxm4pRd8eZ5WnZTSqmcpAFZKaVUo6oqAwT9Qbr1Kmz3kAOny8ldn97A/dMe5aMXPiMUCIFpeNyQbbfE7rATrAqybun6pG25vE5WfL+KIWMGYbfb21WXx+dmv6mT2tWGUiq36EN6WWDapOk1sz8opVQ2qCyt5OqjbmVy0SkcN+hMTh55Ngtmf9/udnv06cbUa46haEAP3PV6h212G26fm3PvOQ2AqooAjWXy0t/KOX+PK5jS/898/sbX7a5LKaUSaUBWSinVwOWH3MRnr35JJBQhEoqwbtkGrjjkJlb9uKZd7RpjuPzgG/l1+QaC/to5isUmbDdxa+79/EbGTBgNQLdehXTv063RtqoqApRtLOeao2+rmddYKaU6ggbkDKruOf52ziK+nbNIe5KVUlnhlx9W89OXywjXmx84HArz4p2vt6/tRatZ/8tGTKzu2AoTMzicDgZvPahmm4hw3r/+gtvnanJ4RzQc4fX/vNuuupRSKpEGZKWUUnWsX/EbDlfDR1SikRgr29mD7C/zY3ck/9FTXlLRYNv4A3fgtg+uYcLk8fQeVITd0XC8cSQcTboMtVJKtZU+pJdBuvqcUiobDdtuS0KBcIPtTreTbfcY3a62R/x+aIPeYwCX18Ufjtol6Tmjxg3nymensW75ek7f5vwGM2B48tyM229su+pSSqlE2oOslFKqjl4Di9jnhD1wJ8wzbLPb8OZ7OPSv+7erbZfHxbn3nY7b58Jms4ZNuH0u+g7uzcFn/LHJc/sP7cu+J0/Ek1dbl8vjpO+QPux5zG7tqksppRKJMUnm2GnEuHHjzPz581NYjlJK5S4R+dIYM649baTr+3AsFuPle97gxbveoLLMz7j9xnLa9cfRZ8veHdL+z18v5+V732TT2mJ2OXgc+548sUVzDxtjmP3UR7x835tUVQTYc8puHHHugXjzvc2eq5RSLf0+rAFZKaXSpDMFZKWUykUt/T6sQyyUUkoppZRKoAFZKaWUUkqpBBqQlVJKKaWUSqABWSmllFJKqQQakJVSSimllEqgAVkppZRSSqkEGpCVUkoppZRKoAFZKaWUUkqpBBqQlVJKKaWUSqABWSmllFJKqQQakJVSSimllEogxpiWHyzyG/BL6spRSqmcNtgY07s9Dej3YaWUapcWfR9uVUBWSimllFIq1+kQC6WUUkoppRJoQFZKKaWUUiqBBmSllFJKKaUSaEBWSimllFIqgQZkpZRSSimlEmhAVkoppZRSKoEGZKWUUkoppRJoQFZKKaWUUiqBBmSllFJKKaUSaEBWSimllFIqgQZkpZRSSimlEmhAVkoppZRSKoEGZKWUUkoppRJoQO5CROR4EXk7Be32FZEPRaRcRG7r4LYvFZEHO6CdPUTkx46oqZnrGBEZEf//+0XkilRfUymllFIdS4wxma6hSxORFcAAYIAxZmPC9q+BscBQY8yKZtoYAiwHnMaYSKpqbeL6VwC/B440XfwNJSIGGGmM+bkV56wATjfGvJuywpRSSinVYtqDnB2WA8dVfyAi2wK+jryAiDg6sr16BgOL0h2OU3xPSimllOqiNCBnh8eBkxI+Phl4LPEAETlIRL4WkTIRWSUiVyXs/jD+72YRqRCRXUVkqoh8LCL/FJFNwFXxbR/F29tNRDaKyKD4x9uLSImIbJWswPjxX4hIafzf3eLbH4nXe1H82vskOfeR+HCDd+LDMOaIyOCE/XfG76lMRL4UkT0S9l0lIk/E/39IfAjDaSKyEnhfRB4VkWnx/QPj+/8W/3i4iBSLiE1E9hSR1QntXiwia+L1/Cgie8e320TkEhFZKiKbRGSWiPRs7BMnIn8XkXUislZETk1y39fF/7+XiLwqIpvjNc2NX+txYEvgf/HX76L48c+KyK/x1/tDEdmmXrv3ishr8fo/E5HhCfu3ib/WxSKyXkQube7eRMQjIk/Et2+Of477NnbfSimlVC7TgJwdPgUKRWS0iNiBY4En6h1TiRWiuwMHAWeJyOHxfX+I/9vdGJNvjPkk/vF4YBnQF7g+sTFjzDzgAeBREfHGr3eFMWZx/eLiIeo14C6gCLgdeE1EiowxU4GZwIz4tRsbJnA8cC3QC1gQP6faF1jDSXoCTwLPioinkXYAJgKjgf2AOcCeCduXJbweE4G5xphYvfsZBZwN7GSMKYi3syK++xzg8Pi5A4AS4N5kRYjI/sCFwL7ASKDBLwcJpgGrgd5Yn49LAWOMORFYCRwSf/1mxI9/I95mH+Ar6r5eYL1HrgZ6AD8T//yKSAHwLvBmvP4RwHstuLeTgW7AIKzP8ZlAVRP3o5RSSuUsDcjZo7oXeV/gB2BN4k5jzAfGmO+MMTFjzLfAU1hBpylrjTF3G2MixphkYecqrFD0efx6SYMgViD/yRjzeLytp4DFwCEtvDeA14wxHxpjgsBlwK7VvdfGmCeMMZvibd8GuIFRTbR1lTGmMn5Pc4AJImLDCsYzgN3jx02M768vGr/G1iLiNMasMMYsje87E7jMGLM6XutVwFGNDOeYAjxsjPneGFMZP7YxYaA/MNgYEzbGzG1qSIox5r/GmPKEGrYXkW4Jh7xojPk8PuZ8JtYvGAAHA78aY24zxgTibXzWgnsLYwXjEcaYqDHmS2NMWRP3o5RSSuUsDcjZ43HgT8BU6g2vABCR8SIyW0R+E5FSrLDTq5k2VzW10xgTBh4BxgC3NRHYBgC/1Nv2CzCwmesnrcUYUwEUx9tFRC4UkR/iwwk2Y4X2pu4tsa2lWL3rY4E9gFeBtfFe4qQBOf4A3f9hBcQNIvK0iAyI7x4MvBgfZrAZ65eVKFavb30DqPsa13+NEt2C1dP7togsE5FLGjtQROwiclN8KEQZtb3bia/Jrwn/7wfy4/8/CFhKck3d2+PAW8DT8eEiM0TE2cT9KKWUUjlLA3KWMMb8gvWw3oHAC0kOeRJ4BRhkjOkG3A9I9emNNdvUNUVkIDAdeBi4TUTcjRy6FitcJdqSer3czRiUcN18rOEUa+PjjS/C6o3tYYzpDpRSe2/J1L+vOcBRgMusWMRTAAAgAElEQVQYsyb+8clYww8WJG3AmCeNMROw7ssAN8d3rQIOMMZ0T/jPE2+3vnWJ94X1miQv2OrJnWaMGQYcClxQPe45yf38CTgMa8hGN2BIfHtTr0m1VcCwJvYlvbd4r/bVxpitgd2weqJPaqQdpZRSKqdpQM4upwF7xf9cX18BUGyMCYjIzlghqtpvQIzGg1EDIiJYvccPxa+7DmuMcDKvA78TkT+JiENEjgG2xuqtbakDRWSCiLji1/nUGLMqfl+R+D04RORKoLAV7YIViM+m9mHFD+Iff2SMidY/WERGiche8V8IAlhjbavHKd8PXC/xhwhFpLeIHNbIdWcBU0VkaxHxYf2ykZSIHCwiI+KveylWz231NddT93NXAASBTVizmdzQ1M3X8yrQX0T+T0TcIlIgIuObuzcRmSQi28bHwJdhDbmIJbuAUkoples0IGcRY8xSY8z8Rnb/FbhGRMqBK7HCWfV5fqyHtD6O//l8lxZc7lysB8CuiA+tOAU4RRJmkEhofxNWj+I0rNB2EXBw4rzNLfAkVoAsBnYETohvfwvrgbIlWEMUAjQzNCSJOVihsjogf4QVLD9s5Hg3cBOwEWuoQh/gH/F9d2L11L8df60/xXrYsQFjzBvAHcD7WMMn3m+ixpFYD89VAJ8A9xljZsf33QhcHv/cXYg1xOYXrB76RfEaWsQYU441jv2Q+L39BExqwb31A57DCsc/YL2mj7f0ukoppVQu0YVCVMqJNRXcamPM5ZmuRSmllFKqOdqDrJRSSimlVAINyEoppZRSSiXQIRZKKaWUUkol0B5kpZRSSimlEiRbHazVXOIxXskDh93aIPHcHYnUHFPdUy22upncRK1ZuMQZX5MgcVVgU70pVvdcu726UeufSLjmFLHV3YczfovheC12W522AYi3X39W4ZqaPe66bQIEQ9a/1fccjbcRr9HE711crtpz6s9iW11TzesmdbeTcO8u6/UxYetepfo1qD4uUjubmTjqvQYxU6d947FqknDtOdF8a5staG2Luaw2bFHr3Fi8SVvCpGkSsF4D47Zqi3ite3cE4teLvyYSrX3dol7r82ELRevec/U/1TUlvk9idWcbi7mtNiRS3W788xRJOC7eXiTPqs0ejh8Tqn1tE9tKPL/63iX++tlC1nZjbzgNsYTj+xxWvVL9WsdrNq54+7Ekf6mJNxdzWufa/dbn1jjrvR+AWPza1a+lLWjdR9TnjNcY/zoK1X4tmPh7RuLvp5r3pNtd5zoSrD0n1N16H9hDNa3Ea7Su7yyt+zlPPN/ErxPq77OOrah+31n/2KoSXvv412H162Zs1cdYbcW8zjr3C7Wfn6in7nuo+v6qv25DvWqn87bF7yMWL9dVbG2ofr/bK0J16rEajH+dVAWsD6u//uPvZ1P9nkn4lEr1178IVaHNhCL+lsxZrZRSKkt1SED2SB7jnftj83oAkG7WNLbRDb/VHGNC1g8im8drbYj/EIr5/VYhvfpYH1f6axuuDhnxH/o17RcWWNsrKqzrbC6tOcVeaK3GG6uwphK297EWQIv+ut76uFeRtb+svLa26pAudX+mxeLXtQ+y1siQqmDNvsiatda+ot7WsZuKrRp9VjiIlpXF76tfbYPVoS9+nej63+Jt9LC2u60f2rH1Ca9bPKTZe1rHRH/bWOc6sWAwflxt+LDFA1D161d9jDjiYWrYUOvjTZtrzikfb92jb7X1ugV7WZ8nR6XVbiTPequ4N9auWG1bsc6qabi1oF7J1tZibt2XWJ9De2X8c76pdsXiyu2sBet8S0us2gqt61SHT8cG61hjq/1c1ASgeAgJD+5dp32pioeczQkrIxdatWze0XpfFayIv89WxWemi38OQkN615ziKLMCUdVA6/1VHXZ9i6xF66L9rM+BSXibODZa76NojzzrXgPxEFphXS841GrfURGqPak69MXDoX+A9RoUfGu9R8P9ulttJQT+9TtZNeWtt96r+b9Yn6cN8e3dl1rvVc/y4ppzqob1BMC7Mv71sSbe/nbD4te1vp4Kl9S+bj8fZ339FKywPv5/9t6k17IsvQ77TnP77vVd9BmZkX1lVpdVlUVWlVC0KUGCAGtgG/bAgGHAICDAP8AeGDA08cAwBMHwwPCEkGQIhmRSIk2xk0izq2R1WZVtZPSvide/d/v2nOPBWt/Z+76IomH4AXIQ35rcuPee3Z59LuKtvfZa+h/MLpaM3P6nqP/87cW8TOszlA8e7IiIyKf/4I6IiGz8PtbMrIwJW/mxe06nC2h7sIE1P63imtW/OBERkaNv4jktn7k5KB+jM2d3ULaxgzFXHnEd81n57O+7wMHGQ/wR0L+Kel79h8h72fvbyHfZ+GOMZ7ZYycukRdyXwh99KCIiyTffFhGR+Axrv3cbcxR4f/QU2/wjJAzkhx/8IzEYDAbDiw2TWBgMBoPBYDAYDB4uhUF+BuFzdhez+W1+x6bOs6o+i5vpNVqfvgZ/xe5lcOH//Bev/avKanu5pONCe88pG1wcx8Wxh8/5GyT4BePJx+vKBCoviC7IVy60E/jvL9aX90V1B2Ah/QOaIeUK+Rb+L3rvyxiUeU+ff01+beLJP5Q91c90rpWWvfhexMlXVPKi79MLr1477tr51/waXW/P2cJXWYlckEuIjsuXWlxox/XpQvv+vKk0SFU5s/myOXPsSUuyeUVNzrAr854z7uGzUoFftN50ilXmgPrYN5UchPqe32tZrxn9TGcliNP5+p/3yOnt1nq1Pt1Zet4v08VHSceVy6bmJR1+Pfn86T3Vui6+ikga6zyF7OP8c+nG7v9WPTsvBoPBYHhxYT/nBoPBYDAYDAaDh0thkINCQaKrm5JVoH0dXIMGuVLwqj+mNnILmmBlraIDfD5+FdrUwqmvQcZL1If2L1mArnSyCr1g6QD6S59cS19CPdEh9I7D16EBLlMDre2UnpTzMjn7duHwXziAJrXzJjSNxY7T+ZZG0PVO7mziOx6IShfRp/Au9JHjO54GWfvIQ1kVHqYb34DeclbF+2rROwDFsY9e5ThUv7wCHWR0RP2lx55mCw1+hgmM9qFpDlagSU33oEVNqeEWEal9Ck1zdgptcLkNHWxGLW2hyvnyDgPO2tCeRg+hx17IMBfhk0OUHWH+Mq9v1bvoS3aCdkLVQZOlS9rQ9Abe2smm8wfr9Jusi/4rE56NnEZc+F1L5/II7fl6dRGRgvdv1a1XVGuuTDs13OH2Phv2Dh2yTHjO+eM6U913kYcqs7GnQSZ1HBSgv62dQb+c8L6EqsP3D2IGuN+1XWrAdc3qWdQOD89581Y85SEzjicrzB9MK52j/qTmZqHQ42FDstrVI8zBcIUH486orR+18jKhHtbkfY4LCctSv14NOUduvWmL4Rr6VD3mbsQp7s+kiWdi+aORmwKyv61H1LbzQKnep9kyfh8qe27eZphayciEJ/tYm+WTq6iTDH/UcfdnsIZ6yjqXShST2S+fTDku107EvmRxkB/uNBgMBsOLC2OQDQaDwWAwGAwGD/YfZIPBYDAYDAaDwcPlHNKbzSQ9PJawwS1OPUR1eJJfonZuIT8L9ADMGba+S0+4Rd3tu3q5FZ0OsK0c8rU8gIRDrbR0m1tEJNzBFn7KLfby4zLrxfvSY7SfeTZvziNZJRZsl1v2tW1YWoV9t4Wf0sat+AQSkZQSkpBb6TOOt7jjrNQuHppTmUGR8oIibd7Es3nTPpQoX0lp8xbp1j3nJB26reiQ36nEQuc+OOKBsXVIRqLY3f7RLWxpl7itPNmATCNuY4962kD7aoUmIhJyDrNNWKl1X8L9b+rBtD63+HtONqNyktIFT+ZccqN+2J7MRD2nVUqRbKGOsA2pTahSiLazK1P/6f4tSAEq9PzN7wAlCskVZwkWtrEGx1cgL9GDdcVtrJm0VZOLiGhhl7Uw9lxuQJmE1q/+vhwIXuiRPF5Bu2W9l+tLHIQ7BFZso8yMvselfcx9sYs+5Qcl207GkNZZj85Pv89r532lC0fe8zPBnBb4kR5YC1XlQllQNPbq0Prpszw7LrMs11/07AFCPdhX6NPbuMRruCZr+/Oe1CIicZuyJtriVZ7SUo8SkgIt8NKCu0/lQ22P979F+ZGew2PZoODaqe5TJjPE/VD5SsBnMQtRfzRyc6C+1Nk0mPdYNxgMBsMLCWOQDQaDwWAwGAwGD5fCIGdZJtlk6pLneNAqGTvGNSAzlB9W0hAQHuxRRnfuMJMGXZDJyR2ZlH18zmGY/OAW6w3JXmZkh7MzMroee5oHaeg12icyy1GXTKiXUpa3p+EUWoYH03S8Pnuas6Jk3FIybtpHYSJZ4rHBekAs6vTm+8p2lPXOpt68zZTNmk/z00Nssw2wqgWPoexeY7peAna+dxXvy3WynC20Uzp381Zrg1kfXAUr17mJa+Ih2NRil6zzuUs2617Dv8MpWNqkzINiDGeoqM2Xl3Cn7JwGbPSvgXEt8XBZyDS72DuglvLfnWv6GfpUnakFWcC6PLaR9XWvo4/RhAe4hpiT0VpVLqJCVjQPVhkmc2Mebirb6FndqeNciUEha+hj8QjzObxS54WunVKXTCvTCpX5LPbm2WD/oGJ2wQowXw8XxhB03RqNWLwwmE8P1M81nS+ces+ePvdci6VjvKaFeds1/wChaDAM50vT8XRHoXKsiY6e5SFTFjVUpHrB7i//DfHONlaP+RvCuZYF3Mvces63BiT0oHDCXa5wQAZZd5r0TK9/T6cubCh4XmqiwWAwGF4oGINsMBgMBoPBYDB4uBwNcqUk8tor0rkN5isagUGpHKzll6SaUVEHQxSS/Sk8BQOrsbhzrAwZw8I+rJ8S2jgNVqFx1Ajb5hdOR6qMV0p9Z/cqri2fkA2mHdNotei6vw82NlGdKtmgqAvabP+X2TfPRWzpU7CwEzJfymJqJHN5B+Nqv+4ieae0u1J2rnIMy7vREiOmC+h746ErEz2Ctdjga4yCvkc28xojtcmw1T49zMv0X1tjO2QMJ+ncuPpXMCd1j+maNKnJJdPWvUb2jKTZcAXve1fc31SF/jK/o13dMuobLiuDyDo3HIM8XsRn5y+DcVUrsMEq5i0e4fOhd38KPbLzZETbt1B/i91XRrG05N1TRmYnFXzX29DEiOZcXd1rjm7MIpQfbOC7Ijcbjr7W5AV4iV3atkRj9ncNzGqxS0Y/D5vguJecplrZy9Gizjlep0tgqIfLZO0XHNe79c/uoboy5nL2eFtERBrULevOSTLw2GDuMsyoh853SD57ICIiFer+Z552++rvkFE/hKY+oC6+1eAZAVrRlQ+O8zK51R37dvUPqL+/T1u8UM8bOD2+BgIV+V2Z8fEaAV+lxaLqmkVcnPtSdhvf3X+M/uuuDW3srv+um4PCHnT+zc/mI+6X/xix2LNtvAb7bo3q7lLUwn1PPr3HDmAdlribkw3dQvD7mc3cDpDBYDAYXkwYg2wwGAwGg8FgMHgInqfj/X+LZrCUfSP8lfx9WAfT6wdRPNvy/P/NlX1K/bAHvdQPTBCRQEM5VKubeafJS1oPvgurYL5yF41G49m+XYzBvgBlknx9Z15/DYxbSocACaO5PgfRc/4G4XdaJiwzhIMODn7fdC6dHvsCO6WhE7HHUPp6ZK9Pem3y3S+juWPHtB38Eljr+lOwZJM6XSUou56SiW09cu0Xt8HOdd8GY33yBpi3pc+pw+3jtfLEhXMcvg+2f/ljjH2wCWZPdZvlQ4Zy+NHMGvFMvejgOl1MuMNQbGO88aFjQidXMZ6Dr2FuF78Aw1e7764REem/3Mz/Xeii/i711xobvPZvEYSizHxadOukfERnhQWGcNDdQZ0Pzl5j6MTZc+K2ie51zNvKh/NzEg9cmb1fIkPdIQP6MW7M02+hbOMRrlv6xN3T07ew9lv30ZfyA7ijnH4LYTk97hKs/dCt671fo4PDXfQ7j4u+Cbb0yq+jHydvufW2/An6UvsU9Z//T6j37P9CuE1SwXg3fuDGM1rEmhyuooHxIq7Z+hPcp5M3UX/gLYPaU7w5fld3jvD5wn30P2YAyuAfOFeOgx+iD9ltfHbzH6Lsg7+HOb7+u/M7GCIi8Rh9af4bNND+/h0RESm2ce3pG9wF836qSm12NBD5+Lf+R+mfbP8VmfYGg8Fg+P87jEE2GAwGg8FgMBg8XE7UdBBIWCpJoJq9QzBJyuaKiIQr0KumjDLWE/VhDUxOQl3fRbYYFwVzZTKefI8WocMNCo7Nmh1Aixs10RdlXOOrV/D9zi7eX9nKy+TaSD0Nf+FUvpDdVP2niEhEJjo5OppvT10nNsA2arStPx/KZl/soyR0qPDYYNVqqrOFMu3qtBGtraIOL0I5rND7WV0y1sDapuzL7jepQd5xmt3ud8AQDu+CdRxdBRsXtbFEkgWwhL3rlbzM8scof/B1/J11670nIiJyf+maiIgU25i/+qbTiJ9+F2OcNsFQjqhbzrgSa9tg5DPvT7eIjF5AAvL8NbwWevTS7WG+6ruub8Ml6r2/Aw/jp6vQkS8sUd9Nfu/sTcfmlk7QicErpAYTOi2MEaF9+ibXobdEazvo73BNHS9wTfkEc3P+Bj6Pu65QQEF+FuO79BZ1xPTX7d7EdeHMTUJlH2UGV9jOSGOP5+sKJ04LOyPrn5RRjzpQqDY8ZZcKZ25XYNTFHNbPcc1wnbsOfZQdLVGn721SqANJsoR7+vQuxhHXeG/ZzrjlxjPjrSr0qFuHHD/Xr7ce4mYfv+XmrfYUr9NVbmvcR59UTx6d4jekErs5aN7H63mMPp2/IuzTPIuvcyIiUn1IbTF/X1QDn4+XPwOFjqsj1Esy51JiMBgMhhcXxiAbDAaDwWAwGAweLkWD3CqtZ+9v/Ccyvc7UsAEYnujMpdXlHqj6Si9gOWMS2SYYZvUTFZE8CU6YOBfwpLsyVUmFusxtl9inbG9WwnezVbC0EVPwwh7Ysul6Ky9SOAD7mjElLPc7pea4/xUwor73a/kB28zT9zQdjT6x5xj79JpjT5MKnRp0ftpMTqszeYypYYXdU1fmKVwD5I2XUe8OGek1MKJplXrIeztuCm6CHddUt5wBp8f0+Xv4vrbtTuHvvw+GbfFzjP34bWpbt3EPhqv03/Vuz9oPoetsvwzW+eRLaGfxY7Ko1GX6vrDnL80npcVDfKfsYuMJ+jhZ8DSh1DKrRvz0NcxxY4deuXSMKJ055rB4hnu3832smQpTBJtPmEBI1vHsFcfW1/ZRX/s27wOXb+mcDiVkZCOPPa3v4k1/g2uRvsSlc7QzWCfj7/0pqrreSQP/UAeR9R9ibXZuoMy05ljNzd+DI0TaJPX6EZwVwuvYGVG/7dn+QV5Gd03U/UH1/dESPKiVIU0fPsnLTL/7Dvq/jV2VtEHHGCYpFj+l64O3m5KecgeGuxrDv/GmiIhUtjmBdKoIdl1CpJ4j0HuarKJP4d7RXP1Zze0KCB00xl/Bs1D+BH1J+PuQzTDn3f/wG3mRhZ/hOZ2u4TdDUwPTGvv/s7tot+m06Oplnnskq++67shcw/Pje5zn/u1ZKn/e+Q1pz45Mg2wwGAwvMIxBNhgMBoPBYDAYPNh/kA0Gg8FgMBgMBg+XEzU9mcpsZ1diDRzg9rUfQOAfPJsrS0uykAfXUj/OmbZkurUZsL7wDHKAkFv3s56zdQr0QB+/i09o0UZT/5TbvdGRk2XkQQoXymowQO1DjXN2fdNt6zzGecItVkouEm7Pxp5lW6zx1pSOpF1sQasVXcht59mpC1TQOYjuIxgi6WAOAn1lnUnfzUGoY9XwAg2IYB/Lt3GwL+64w1nxAH1QOUTlkIfBxmqlhtfqodNYxEfof3G9zGsYNdydt3krHTopR38N96O2h75MG+i/2mTl8pOxayec8N88RFmn1CbuswzDZYoHbq6TJvqkcojyGaUPh4we57Z5tdXIy5TaqKd8QkkAp2/hE4xzcKM2V1ZEJGIYS6mtMds8PNmnlCPAVn7Bi4QOKMcpDPXQX8S6UKbIQ4clz5Hu5Fs4xaYWcYvjWyIicvxVtedD2YoXod5/C4cLq48gUwh3INOYvgbJ0GALc+TERiI732MwyH0eMuU5Tg1PudHF2undqudlGvcwh8H2PutAH5Z/vjhXx0rBHbibNvDhpIVrh8uYcxUk6Vwn3uG52gLGcfoG5nQxxjjKD3GtSh72f9lJemZl1Ni9gXpu/gbW/MF76PNm7zr6s+bGo/dOfg6bt+wd2LxFPUhUOgz/iQfunha67rch+4k7/GowGAyGFxPGIBsMBoPBYDAYDB4ux+YtjiVaWZX0Ktil6AxsZqwBGCIiVR620UOBPKSXkf0NFnhIJvWSAchmqoVZoHU0GM7B+Nv4yDGuyhQHZGWzJtklbfeEh4/YVxGReB+HfPQwkZB5VcZ1chPX+gEPBVqpiR7o08OHWgeZ8KzlmKn8QB0PCkYcc1YuzpWNPKu7/ADUGg4xRmS5g0UcakpbZNPJMIuIBFtgG0MeHNKDgwGt9E6vo72muL71cZ4rj0ruIM1Xant4PyK1N/QDFQZg0npXGPpwC+11u6i/dI7xjL0Dd/2r7GMKFlCjrPWgWpBiXicNL9J6MH+Q9OwVxmvvaZwz6q/WXTsRmdzhOg8o0tMsTBiAobHVL7l2pvuYp84ttksbuTBhvPcy33ubHCLob38D9RS7jE4u69gZhNF61r5wWud3tLpr7GB963wmXvrx1X+NHYvRJvofPEF4yXJMtnuMTqnFoohIZY/s+CkPoXI9F/ewpsIJQ3Oe7OZlVn+KtV5/hLUyWdZIcM5fB89X4zOP4eczpTsvG39BC7rT+QOR4bnb5Sjx0Gxpl8EqjE4P+3g2Gh+y/atLeZnCE8RbV66hT+VHfG6P8To7Rz9an76Ul1n+EeatdsCx8mDvwn3exH3UWZi6A55yyN0ltal8gLnW3646D/iFZ27HQudfskyCsVeXwWAwGF5IGINsMBgMBoPBYDB4uBwN8mwmycGhhNQIpxeilEWcVjZLL9jKUeeb62afo0HOWWdqhUNlnWnzNRu7zFcN98ioEdZo6ZxZVo2mF6yRl1dt6QXru8LnmnTg2pmpfljDP/jdM7HYDEaZ6xtZ8kTjryuVub6pztj/LKPFlYaMBKq7JsOb+XNwn8ET1EGr/lv13s0noHGLR+7+VJ9iHKrDbd2j1Rl1vhqAUd9z7FiJUdP1BbCOoyUwx/Vd1KG628pT1854AUzh4hejuTKqt9Xo5vKx055e1CCnES27qAFVrXPxwNkKTpdrc/U0nlBPTmZUdcRp7DTIGiWcqV6ea3XpxxinxlIrIyoiUsmjsS9ETZ/j89EC+lH0NcgcjoajdMfUYR+jTL1ExnXm1uH+L4NJVavBYvumiIgcfg1zUd9DpQ3v+WrfQX8bZJlDjeq+g/vVvYJ2V8eOcd37Hl6bX9ACjlMx2CIrfIQ62y87+7WFL8CiFx5Ag7z7N3Htwk9oX0gidm3m1M6TJoNHFnSHglZ3Q9yPzi2U9TXI9eomv9MYdGxr1B+ggZi2dZ1vOs17FmLeejfRp5f/KV4Pv4z2rx1j62S05sZTuILdmfiHsICbfAnpIoUunp/TN7j+hjVXpusY9bT9/PMWBoPBYHhxYAyywWAwGAwGg8Hg4VIYZBERCaPcLSGoU/frsZo5m0nXB41QTulAEJBF9f/HriEmeSw1wwNyfbEa+o+cG0PuKqHhGNQt5wwy2/HZYFFWOWDr6p6hfdawgsjrHRnkvD4y39q+Mr3hgucRcJFdVtaZeuZ8bgYDuYhcf63j0HFeCDMQ8Vhyjie/JiQr3Cbr2XbMbvkUrFnpFCxZGtN9gexsNEXZYscx/EG3zzIoWz6JWBfuV0w3gKDnGL3yKR0o2AdVqSsrG59r7LKb60B3Fcggl85QKqaDhLo/+O3EnOvycYVluIY6g7n2Sucec0jWt9zgOtapZcBG6UydKdxc5/3lR9FI2xlyvGRXe455V6eQjAEnM+qV43P0rcRAmTBxrHN/nQ4hbQ194c4LGWVl0QNPS6sMfjjk/HEe4z6uKfbnQ25ERKI+2dmxRlrzc+4gxF3MRTxy5ws0GEifMY3oLnZRR6IGLz2XsBLzWYqqjAQn+R910WAW8L513RzEA+4UnHPM3EEIhhrSwXltOxcJjX0OZmTl2wP2DUx4wPtV6D+rG844X7qDoTpj3Q2IJm7e1L1EAouaNhgMhr8OMAbZYDAYDAaDwWDwcEkuFpFECy0J6FqRrMHdQDWPIiJpGxRRtITvcjZVNbpb8F0N2+5keM4IkSFWZjpdBAsZMso6Gzk2OFyDLjGg+0O2yfeq2WU7wcGx67+ys9on1XGS4Z1egY5RfVBFRELVMG/SuYOsc9ACM5U+IbO44dwycqgmVH2KeY1GZ4e+kwf7kG2xHfWEZjsZdd9Z0bFmYY0MO+dPnTBCev4GO3A6SDtOs9u6BxY4oqNH1CfDRleBUpUn97uOpdWI39Jj3PfF4irf0yN6yL533T1tPKDf8lPMf/GcTCQZ4+yMjiU+W6+OIBxPWXcq+pxj+lOnXjvKwi98gXtb3EVfU8//2q9LxLl81KeMPdf7wDVZ9BhdRXaO+S/26AiiziED9K3KcQQDt8uRr69Y/Y/p4PIUMeLl8ZxNBoq8hXjjypHSsfRQ5jQpu6px6SLOmzdg5Liy5hEZ5QL9ltOy08yGF4jUUgd9HY65ZsnAh1MXzRyyv8q4BlVUUqYBy7SqZd3aKfB+j1YY0U22WeufNPDM+Zr3YIb5rx2SwR1d0KY3cK8LZ27t0KhD0iLnnPe/0KOvNMuqn7WIyGiNHtq6k6ROIXSxKfR0rr1dG/ZNouCZMwwGg8FgePFgDLLBYDAYDAaDweDhcjTIQQBNLFmhpAZWKPJ8kHMGl568GX2DA3WBUI/gsacfVB0xNZ9ZlQx1lUxrm8xOwQ0jU70w21NWNtK+NdBedIYC6RQAACAASURBVOhpXNknZfSU4VPGLS2SAfOSwIIC2Tf1RC1QE6yew+ocUfamWBndIj7TZL2U1+R9rXj+0Wwn4fyot3RWmk/ryllwr0zu/azeyZxrZXQzT68a0pdWyMYqk5f7PCub33W65WxGpnMIdrTQAVOZM7v03U2Hjj0NVWNKhjVQrbHWP5nXk4p4Gmrel5CJaerckZG1Tb2dhIhjjQasj+35uw1+Xf53oadlRl/nNeE+u533VxniCx7aObvqt6vjIXsd9nlflIHlfGXeTsJomUUrZNrDEj/HvZ02qVseV/My0wbWQUxmNWT90ybKTprKjLr1NllG30Z9Pp/s6niJbGqF7S64OahpaiHX7fUN7CCM6xtor6bPrzN2ntXpYrHItanLjOt6tMbrtj1P8ALanNHlI2AKX9zF+GZ8tqdNL0mvxt+QdY/BF5FJg/rvFsomVfecDlbw7wrPF4yX+HszwfimdfRpVnYMct7HOJA0fvZzg8FgMLxYMAbZYDAYDAaDwWDwYP9BNhgMBoPBYDAYPFyOxCLLJJtMJFvFQS+NstWtbxGRcHVlroge4Eq5JR0d8dDbzB3syyUJZ6gvYH0xt/31UE4wcJHJsoeDTgEt4eI2t8vX0X70EGEG2fqyK3PEQ2XarB6e4xZ+OOH2shcQETV5+mePh75UpqGWdGrvNnBzoDG3auelVm0hD/+FtJzS+G0RkYQH7GLWrwfRQm5FZ4yVzh48ceO5ikAFPZwVbWC/OqMMoP033xARkdq2kxLsfBcHIBfv4hDl6avYRq495SEtBjnEntpg7cdo5+g1lD15F9cufoSs5lKbQR5Dt+V9+gaWXOPJfHy3Rks3nqDP04Yfae2tCRE5fY2BJE95X3hbKsfucFvGA1zb369yHDhU1nzIA2s85HZ2x0lVageo7/x2ODfWxi6DUBYptfHO0NX38Ka/zsNmPbWiw+e9q6z/Oee2VHowXmJIxgcMybiJMn7U9I1/iedjtoAPiz+6JyIi18+uoU+UrqQPXeR49Q4zs/dxKDPhgc5yrHZ/PLT58f28zJXaOxjXYxzgnDbQl8oJJRdcQ6s/8AJw+PyojGTyv0JaUd+lBCZSizW3rou0Zlvfx5xONtgXPuO3/gmCcWbL7tkuPME4Zu9hzPWPGavNaOiIz83C6+/lZdY+wJiHW1ij2Q0cdlz6nL8lnz5C3YxuFxEp/ZgHXyk3avwx5lrlM/UhgnbCrnsY9FmTLJOo/+whS4PBYDC8WDAG2WAwGAwGg8Fg8HA5DHKaSTaZSrAL9jYjWyvHp/klM0Y/R4yD1dAKPcyU21P1vQNSap3Gw2wBr8kP+B2gzsSz7oo3wajmrHMTDFT6eBfN3gT7I3yPi3iISC2/1N5JgwIYhOAf3koODlkf2CyNgg4adfYJ7FbUcgyYHpLSw1kpLe7Cwur8HHh2YhGt7Xz7LhFxdmI6di9YQ07I4HMcyhxGjN1e+DOwjJln87YZIW64cIBra9sMe6ElWKYWdGfOSi3beSoiIiuniOutnICVr93n3JNtzLxY79LJTbTzCPOVNT32X0SEtmll/zNl9HlI78oOrfq4C6FzkXrtKIO/+RdgDMuP6Tl2cDTXXGVnI/93wAN79SeLc+1F97FWmktkGf251v42NBxnPjRj8Zx2Yn3vkFh+eBL1TNdwXwqfo53Vp2zHWwc7fwf1LH7BHYq3wA4fvId2lz7FbkrFO7A6uoJ6y2xPez25Cgu1wRZmuVZ6NS/z9H1GirOs2q+17+D7xmdgsM/ecYzrwic8bHgfYzz5D8is/guw99MK6lw5dQcIZ4vob+c646jZ7dU9MNZ7v4p73HroDpKmJdyrSR0jGb6M35kKd1P0EG37jqPrRytYB8Mt1PP6f4vn5ezbuMcbh1i705aj68dfwlxXf/uneP8VzHXxCPe0fxNrNgvd2i2dc/cnCiTtWNS0wWAwvOgwBtlgMBgMBoPBYPBwKQxylqaS9noSMbxCyKYmnpZWrdhS/Uyt1KZkQhnckdtmPecaDX8INdKWUcqBpw1WZjdvlyy2tp/tQoOcW7uJSMKAiIDMrrMVw2t8TGZy6mkLNcaZY80t08jWBrR9UzYXFXG6cyswso0alsLvlfH1+6D8cdrn/F2I7k46roz+1ZNHdNPyTO/H9JugA0t7jbzM8btg91oPMS/dK+iLRhtPmpjj6pGzk6uTAe28DSbv5G30cqkChrLQRR/Lxy5u+/gdtLNYJXNHy6y0wPp3lTF3f7uFU46VeuX2y7im1OHcjMC0Fo9ceMWU9ntH72B+mi2w240HZAq5a9C+41jA8inKt2/SMo23eyVDX3vXHQOqqD5FX0ar1LwP2ZczsNud2zV+7toJSAwnRfShv4k5WJtB0927Rm29p1uu7aOQsqe1e3gWqoe0cJvRzuzc7QpkV3l/qetXjXBun6jBF0/dGi2eo7+VE7SnlmqFDndZStRa9x27HVywrcseMNAnmhdeZ55NosZ1l89QdrDKshXcr4X7mHyN4RYRKVOjP2mAda7vatQ0dxKOyVwHbh00H/CaGZ//DQ2B4RwMOCc1T4v+mDaI/F0pnuD3JtRrM4yv4MVg50EjgZtXg8FgMLy4MAbZYDAYDAaDwWDwcDlR02EoYaUiAXWfGXXEYdnp+oIaWJdcT0rWKdUQCw3Y8COGyfIkF9hSITsbMDgkzByTo6EUIUMLNP5ay2q8svZVRCSk1jMPJGF76maR1cmazrw4Z9UAz+ZZ2jyUQxkqP8BDwz20j3ptQYNKyFx685ZqSEUeQEL9NUMMVGMdeGEWqoMO6MKhcyAc+2iJAQt9p/QdU1I6aYDJG63Mhx2o00IWOhawsk/2lKER41WGTCyGF6712mEwxJARw6o9nVXJVPYwR7OKWwfKyqp2d7DG79jFgCx0kDqGVxnKyULGMaNM6YxBK5H2w1tvJP40fCMiMTlcx1wPl7lj4SVOxwMGWyzSGYKscJDg80mdgRQlL2SG7cwqOid4nTZRZsz5TD3Z+eIXE37H+9/BbkCF7HxM54Ss73Zt4j5jyXWngus66qMujZr2teiVo4z14tpxi8yu9oWRysVzLwKazKqu/doO7+WALiZjPgtT50YSqa5/knAuynP1lw/wGzJac89P1MFzE02UweVukz6L3HkpnnshJk/plsPfjLTEnZGOMsh8FgdujYbcMUo1yOWMc8odmZhR08W259LjudUEz4kkNxgMBsOLBWOQDQaDwWAwGAwGD5enQR6NJWiTiSKT5OuJVQ+rDHGm0cJkOYUMkh/Jq9eoDlcZsEDfUzusGmKRZ7XOeQS0Mmuqk/QcD9RxIHexSC60px6nE6dBTrpkosnkanu51lmjhj1GPMjrTef7SKeK3IfZ12HnDdJZQ1ltjVk+nc19LuJ8lPPPNLqY96C6Tw/YYzdv1QOMo8z6ZmWwi+plHI0xjuqxayc+pOPFCu7dcAdjrx6SYevgWtVwiohUN8F41vbQ/1mN94sa19Ixri14elX1h9bo7AZZbmUxozH6WPDGo9HllQOw8bUDXFs65Dogw1xvuHZK+djpisChVnaVleUuiEeul4/VnQJzEA8x9sI5Pq8wDjkaeppdXdYF3W3geDhP1cqzf7cO1hhDPuVzUwdbPlzmrgO9umPdqRGRtMi1rg4o1Iwri6qx1UHTadGVPS+1USYpzHs1K3yf6uIFD/D+VWWhUUa11jrnft+mLZRVrbHe48EGxpd5LLrGuuv8azx0pLsrPAMxrTkNcH+zwFc6aXyAdTet0omC9zr1IuEDOl9k+zxL0cKchnQiScrse+Z2lPJ06TCQLDLewWAwGF502C+5wWAwGAwGg8Hg4XI0yJWyhC+/IoPrYKL09Hd1x2N2J2CXpktghlSjW9iFy8TgNXiPqm5y7hqesp+tgiHqe7pEEZHa58f5v9Mmtbk8Sd65jT7Vn0Cjm1ALqiyQiEjxhAwrv1OtZMR0soPvwJPVT4Rb/JBs1SrGE3c1AQ6sUukB/HY7X9nKyyQl1bainuou+tSnxnVWxveNey51MNqG68bgG/Aprn2KesfXl+bGWXzo3DvGr2AuQ6bJxdRuqjNEqqfsU8dqlk+ZAHcAtnSw1uLn1HeG1NouuHmrkn3TpLtiG8up0Ge71MXOPI9ZZaRnVdRTOsIcTBapJ6eWM1l2TKj+GZfROSSNVa/M+qtgCZOWWxcx11VtH/c/5JznTgrhs38bRmP2m9eW2nivvrcRWVqfDdb+xqPC3JgDJsXVtufXHb5UfTTKlM/SuboKPcyF6o1FRBZ/RP9m7tKo5/Mi76G6syTe7kOR1yS6e0JNePCTz0VEpP4Ac5N6iZdX/wBi9MKBOrfgeUzW8Xn2GVLlageLeZnkxPmdi4is/RB9av3pI3yg7ixkZPER1wpZ3xp1+HpNNXoZfd1161p3XNZP4PaR0tN6Rv29+nzf+k3nOR2O0f/aU7K9TN1b/wPuYDxCAmW47Z0vWMLYUnWduYdrkj7aqTE5ULyzD/nOVxg6Vw2DwWAwvLAwBtlgMBgMBoPBYPBg/0E2GAwGg8FgMBg8XE7U9GQiwZM9qU4ZAcxt2ezMHYTTbdbiGQ52ZdwaTmnNlBt0edu9ekgv4VZxRDuq+jG2ZTVyWryo6VCThNlek1vdgVrPMeyj4Eccn57PlcmDPLhtuvyRxgg7m6pgHw2VTmiVxTCGEq3c1Dqr8bHbus148C0YcRuehwtrGsGr49l3kpHkHH2rfcLI7KfYgi7pPLG9xItQLukhIbW/4jyq3df0m9i+jiuub/112pT1MNZxC3VEY7Uxw/vYTwKnTGFM27ghlB1SOeHBu0jtvpxsZrCKMk2Ge4zWGUdcY/19yCSSkne4MeUhMI5Do4vHS066ISIS95wEJuFBq94VBmsgFVuK55xjyoA0eENEJOaBMR2rSl7qu5i38SIPxHmH6KKhyiFoW1fAdzqz/avzkiIf0yquVQu62k6F7ylJKLpDbbNlHipbx9ovfER5ySpkBbEegHviItSDGwg4iZ5ibehaiq7g82QFdQWfP87LdG+hDw3Oj0qRRssY38I+o9xXnE1ixGtV9qG2f5VXIC/KKIkpeYf09HBcxvU7XsO6U7O1lGsruX0lLxPvQdrQfRu/M3Wu84ghQykPrh5/yUltVn5GuzjaCpYYFz64BRlFRSPpF110tvC510OuES0h1WIxfQnjCntOShEOvCjx/Qux8AaDwWB44WAMssFgMBgMBoPB4OGSbN4ySYcjifQAEdnTPH5ZRMIWDwR1cNAltyAjs5Z6DGher1rB8eBR2lbbNQZg0B5NPHZO7dfCKpk71psxyCPlgaLIO6SlEcwuKGTe6D/eIUOdOAZZ+59wzBp0kvHAUKBhH4eODQ4Z2JENhuy2hhWQllUmru/RtHowjeNI1WqOzFeo8d7+gSGy8hplrcEheshpsK5Ryu4gXO8G+hJNMI7BJtl7Hi7TwI3KocdqLoKp623xmptg0XptjLPIkIzqsSvTu47XQh990C7oAcYwQVmfPY3GDMfgfR6t8j6R0dNAj3DmGGUNGum+pPdMGfJ5q7DBumc9FqG+/hWdC43XRl15QMkc0N/+Guvvkz0lA96+hc/9A54aSKJjHmzhg/oe5/PKswxkPEY7ym6vHeMgZ/8qyhRaYGJrHpPZfQWsaI2HM5XpHd9i2Q2UWTpwbHD7tp6InG9vQoK1+hTs7WjFi2bW+hnGM1xTWzcNZcHbpdlqXmZaVyYfL51reL884TXcfehcdwEeLR6wPHmD932Eg6oVPsshD+Cdv+V+dxo7WM+9LVxTPsFAzu7QBnAbjPhow1ndlXb4bPF3JtV4ah7e7byMa0vn3qFQDWXJRLLTy9mYMxgMBsO/OxiDbDAYDAaDwWAweLgkqiMTyVKZHcCSKaJNUjbp5FfkVlBkRINoniXLo6CHI7mIsDJv66ZhHImnPc6vrStbCn2vWj8l2rdlsE6zp/teoUhHwS4qA8ZQE9o75aEm4iKgo0WMNTk7Q5FYg0LI3mqIgohko/b8WJXtZrhDEDlWThEtkt3T0BINGyE7r+NQxlxEJCGDrP0XBqHonC/cRd/jc8dUt+6CJasdUEvdVkYX38/IdjbvO+u++ABM20J9A+OJwfYt3mUkL0MzSo+dDdhiC9cufoQ+Dq4zhGFCazVa7mkEMSpmFDhjiONhU3wUz3BfosPz/LPJTbCkjXsV9gn3o/rofK5sMHPa0zzkg/HRWURm98c7+H4AezFfH106RdvFc9zncIYycRefT7j+yqeefp3j0MCQTpfs7w7uSzglK+xZHj78u7imfERm9U2s4/1v4fvmfQaJjDfyMp3rulOAPlSpfW/fBIveu4a6ij1nRbj4bayn/WUwxTr3zduYt/HHmPvjL7nnNwtxD5u0RXzn73wqIiI/+sPXREQkqWhwiFvfqrsebFAL3kI7jV1cc/hVZYnzIpLGmJfxCq49f4X1BXgG4zWM88tvPszL/HQKe8S1l/n8f4C+tl9H/1uPcP91B0BEJLyGsa/8Dp7Pk3dpeXiOe9h+iYz11I2nxPsvgcjsM+MdDAaD4UWH/ZIbDAaDwWAwGAweLicoJAglqFQkWgGrpVGroadBzp0UqmSDVXvMk+8BT5eHPoOs13TANobU0soCWKxYDf33XZiAMrbKHMsm2KA8jpaBBPG1q65vZHJ/kYtF8gquDUcuajp8CLeAoAbmVvkn1R4np2SUt9bdeC5E8kbqWqEsMb8P95yeWOuJb93AB7uwY9C5zq87clrneANtZnTs8COyRaAZFxEJPL1qsY/PyodglbsvgWkrnqJsRgeH8YrThMbMfdCoZ41m1kANDc3Iqk4bHI94Tyuor3zEOeZ7DWfRqGgRkXA67wCh+mItm1IDG5ZcmcIuGM/STQaQJBdcJFRvHjsNckiXEmV2Y87J7ArY9Yy62Gjk7k/EEJaUWu2Ijh0hx1HdZ/vTeV27iAtLUX1y2MHcR9QTz7wwmxu/NeW1ZLnvYx0Uz7E2y7vUne+4nZGtAzLDdFzRtbTG9be0jnUXPXRlDtduo73HdEnhbkp/A89a4wOws5WnTk8cP8HzlxxjR+ejf/6eiIhc/4Ba+yLv12dP8zLNKuYlXcA6mzbJkH8Mtr7YoVPE0Is2P2K0+SGe6eoD7iCp6wt1+A/+9zfzMtceMP76Z+hv4QlCP278K9Rf/eCBiIjUvbht/f3SXafVf0stNc8GVHfwfGn4kYhI2GMYTxjI/e7882YwGAyGFw/GIBsMBoPBYDAYDB4uR4McBBLEsfMljsl8FVz1qp1VBlc9bQOyNVo2mDnNrrsmmiubqWOEuk34emZlgZUhLFGHqZ8ri/ucMrl+WLW7rD9lHYFHQub15WPmK1nM3BHDYzXzKGsykeGF8aSlwnzdIk4Hze9y7banbX5mPMqWa7s6T2QDNZ5aPLcO1c4G/Eyvca/yLMhEhyyjTG+gdc2eZU3ztjNtV+f4Qp/8oul8PQHfPq/+/Br6Urtxaby2Nqx1uZt6cew5c31h3uY8jbW/Wr/2iX1WLfXzfJCDJJzrY16GdakG2r9GdzHURSUvq7s1/lzpzoF+l81fG474uefOojHbF9eDOonoroSvEdfPMtYTslmNeVYbi8zbydBzBOoMoey9c7e5UIfX75zBV791ltFnLhr591TvJT9Qb2Pueuh78Xa7gos/i8n8tQHnLfDmTcsHYeDm2WAwGAwvLIxBNhgMBoPBYDAYPFySD3Iqaa8vkbKlZHLUXxgfkc8kk6aaZE2/is7pnew5ReTXUAssAV0f3AXz34s4NkzZuNN57+E8qc9jaTP2M1MWlnWo53DhGM4NwdgxYOlw3rs4U+30bJ7Vis6ck0fONpPdTLs9VkHmS5lkb94kJQPJehL1gFanCvZ5bt6Y0Kf+0TpPyvCpt3FYduy2JsolddW/Mk2uhmtnTK+LXNChCNn/GfXDU/oeq7Y2TKj3njqmTdPjVM87ZZpfwvYKfJ8V3N9uWRrPjSMtcH1VybirvtdLakvr0IZPGvisfM76uLuh7fuOFKqLVo2zMriV/XT+Wo8gLOT95+6Aks4TfD5lOl84dsyuXqPaXE0RzFjXrMI+hm48SSWc+652Au3uZAF9jvvQ54fnnnMM0/fiIdcM12i6AL3teAXnASrHTlc+XOF96HN3g4/EkAmIC9T/T5ads0y5jfr0/MCEJiOjtQrHyUTFQ+c+ktTQ5mwB+vTxItor7tF1huNMi86dpUR2frCOawsdjC+iNljXuzpjiIhUTpn2uMDnlOtiuIo6yup6s+iSNfOdCXW3qWMcWutkFXWEI7euo4K3g3NgSXoGg8HwosMYZIPBYDAYDAaDwYP9B9lgMBgMBoPBYPBwOTZvcSTR8qJky7BqSxrYNo29OGeVOARqacbtY70ivQrrprDnwiv08JdatwWMVZ6tcEuXB3jCgSsTLMOOSrjdO72OwIjCQx4CWmL7fgy2SkP0cCElEAFlFIObGFdh4MoUNE57C/ZR4QlttriFq2XVIkxEJC1yG54HhwoqRaGFVlLG7Sj6Ude05koYXhCpRGSN8bd8H/pWbiuoT232MpVyMKAkPGLYx6kLzWg8QRm1R6sVYCNXOkJ4RaHHbXjPLk2lKeWnqK/RwtxWdvF5fi+9Q0uNHWz3F874nfaRVnDhIe3x6m5rPRj5ug6R8hGDNA5UZsLwGc+2TsNRWo8ac+MI2pTLcO4rB55tHaU0Nco71FJND5KpBV7gSUbCY9gU6vZ/yD5ouEyF7QTpswcK0xrGHI2Kc2PX6OSk4g5izhqUBOxwzJTaxD30JTrm+pt5tmj7uJepRo9TYhOd0ALvmR6JVI7Rz2KbYS8DrKs04v1gnHOh4e5PRlmHhufogbjqDuYz5br2Y9cjhuNIiHUX0c4tZfhPdgfPVfmxW6MBpRTNB6W5MaskKaTlYuPJs4fk1EovO0Afak/xTOt9Cr01mi5SCqLyJq4/Xe/FfdpCepIe7ZsEwdzhV4PBYDC8mDAG2WAwGAwGg8Fg8HBJUdMiEgSSMbAhU2bFY1iU5VMTfleMh2dCeabMxWvzQ0t6MC58Tmy1lo8uHHTKLdzI6Pl1axllvNVeK//8Qvte/TnysuHc93Nj4MGw/JCXzolWG85/7sNdE8xdk5H1Dvy5ViY8mT9slPdZv/cOKurBNx2HHiDTe6phHOJPNcun+TXsqx6w03Y8i7OUc5DxUJMyq/o+71PsNRTPz7X2LT9wx/YD/zq1x4u0TxfGzvlKvcOA+p1+lvHwX1acH+fc3dH7Hl+4H7redLwX14vXTj73Wiaen3sRkXCidnLs0zN2ZfMR5CLOoi0/uJpp2fnYcvFYZz2EmdvTkRmPJvMHPUMvJMO3iRPx4qE1UlsZ9zl7vHl2Pkduj3eh714/c8s+jkOvya3vvGCZiLZ1kZ7z1ed/Oj+P/hwE3hyKiNttyufrOZ6HuoMTRWbzZjAYDH8NYAyywWAwGAwGg8Hg4ZKCQkKRUjGPXp2tQ19Y8MMsOhrnrMwqmbYSo5kZLRyMHTsTaFBIHfWlag1VoyXUQG3MnOYvbdKC6ZjaSVqaaWz07Ca0jcWdU9e3gjJ3yrSSkZyhbznD54Ns0qyJPhXPaK2mTKWyWkX3N4iy12rZpbOjQSRql1bwwkXCRWglpxxzXKF1FtsJqamVqrPd0ihpZa+zESi9XNe7jchf34av8oA2V9SAVlUz26XO+JT1e+zajJrSiOzbAtnU8AkzqKkDzzwWsKKaY2qrldUOj/GaMFY8OPXY7dk8o1dWtpQR4RrCkXhWd8oUVh9Rs3tMLbfqrsnEl/w4dK7RKnXdGjaiEcOhsvceE6r9Dam/1TAMtR6MlCGfeDpqlo/5fMRNzH1ygEjokO1HHiO+9/deEhGRNWqCpYX7cfIm1t/KDJr02NMGJ6px1vWsdoZb0OWP+ZxGy7W8zOlruHa0gPprB5iDs1fRl+YH0Jl3X2nlZep5KAtjnd/DfI0/Rr1qFdg4dmXSFr7rvIrPNIK8vou5OH4HfV8fOmu4kFZsGkudFjGOmCE6yRK+711zz1yHLP14Cfdh6X8b8xqMb+FsA3NScr9VvZegW68/Ruz1+BVESxcPGIu9ynFV3BotdFAmCwPJzi5vY85gMBgM/25gDLLBYDAYDAaDweAhyC5BL9esX8nee/fXpPMSWBk9xV7b8wI82I4GJ2h0cXkHrF1XmaShF6hArWF5FyzpZA3MzXCNjBGZ3YXPe3kZZWNV+9m5ASaqdkD9Itm70aJjeaoHYOw0CEJ1nXEX/d/7HvoWeSYJKz8DSzZtxHNlpnUGOTzCuE6/tJCXmZHciwds9xATNVomg1zieB64hor3D0VE5Pz9ayIi0vwcrgmD62CslN1ufHySl+m+QTaRcxn356OGhxuYk+qua+f4HXRu8S7m4vCruKb1ADdqsMZQi7pj0zc+wBz0N3Ht6esMk7iLdkttzknN/R02WmJoBMnr2j7mYLCKOVCXi+GqY9ELPbKwJFTP7uD+L9zDuDTkpDBwa6d2H8zu/neW+B3GXuU6cOvDrYP6HsbavoWGyqcoMyN5HpIE1rpERGpPGU6xjv4Wu6ij0MG4ZnX0dVb1dhL4z9Ei3Sp4D1d+hoXRuwZWeLTkymz+sy/YCe5CHIJtjpYxvvQc68Jn6wO6lmiITFhGvRqAE1YYouM5oIS3sM7khAw/9etBA89esovdB1/3n7tX0JkiefcVERGJP3k81+e0457TvI8aOc0dEHWKCFfBDusugYhIQjeO+OZ1vN/exTWqPa5iDWev3nLj6ZLZL9MlRUNF6HyTHBzOjVNEJOKOlUa257sOdLXQcfrzls3cv3+Q/r50stPnbDsZDAaD4UWBMcgGg8FgMBgMBoOHy/FBHo0l/uihLO+Dzco0UvnQsZrK8hRr1bmyCZmq1mALHww9mlajhcmOlY6gRyztUpeoOt+9g7xI7q/K9laO4B8s9D8NqOGteI4UBOHiEAAAIABJREFUGs2cOyiQKdL46I0SGLFw6Hkn39/GeJbop0qv4TLrT47Q3vLkumuHbgj5yX3OT0XrKJNd3z3My8zOMPbWT8n2PQFrVj+hvzLnKPE8ZpvKkk3JaumccszR8CpeD5zHbHULnxXOcG3rAfpaOaTP8ox6WY/hL97jvCeY4zF1sfVdlFEGvjZy89a9g7HWdgbiI6L2vLCH8cbnzqU3GM77IEuGdaa+xKq+Ds+9aHPqYstnbG8fdRR3OGZqlMPZUl6mdMx5C7GG4hHGWj6gpzVjuAPPC1q9mMMJY4+pEQ47HN8WPbTbroxq68snmOPREqOT9zH2OtntypHHvH/5Bq45p/8x+z98C/etvE0G2XsWgqvQ18o+1kZyjrHHN8ASJ6vcGdl26+3w27iXjW30O+Dt7l3B2Jd/F+NKr6/nZSJqzhPq159+C8/4Zoa1r+cASp/u5mVUM58sYydkSqa9+BGeq/HL6Efccbrl+ARs8oAeyRU940DttjqX7L/vyjR2cC8Ha+jD+r/E782Q81n5S+4sNRt5Gf39Su7eR7u3cK0+4+k1zKvvwBF5PsjBjtv9MBgMBsOLCWOQDQaDwWAwGAwGD5dz3DqMJGg2ZLpORmpIFq3uTser1jA/La4n38lyptXy3HX4kD6q6ipAzWTC1LVUXR8uJK2JiAgdG2ZM9YsEmkYZ0s1ixTFGysYpc5T7oJJRniww6azq+lY5X5wrI0wIzOhAEanLRcu5SyTsb9THmKMZWLq0SWcKJu0V/HlT5wGOWR09siadPZi2FnY9HfYyGPaceaVLgiaC9a/jfc3zTu5dYdt9zFvnpnoCo93hCp0QPPa0eAYWWzWz3ZvCOjAHpXb8TJnuVdYbox3VDY8W6HSQYh4nC85VIO6TqWN327cK7Bu1rTFeyw3H3MVkWvtb6jWMcTSEbD3Z9PZNT4Nc5thvcC561F3X6J5QIQM/ceOph5jrwXqJZahFrqKPo7US25NnMG7gw0mTjhv7GHvvSontuvuz9sdgSbMK6k/OwAaXt7GOA+6CJD23DmImGaaeu4eIp/NVplT1xiLS2MXaLO8ypY7uMnW9gBr+6Kidl0k1JZP1LVIbHvXo5DGgm4rvNcxdjWifn6041ldEpPQYLjO+u0TGnaRojGc5aINRTnTHhM+cpgGKOC16oavPDUZSOkH7eQqfp0GWi5rtY/aFTiSqa87T88S55IjI832SDQaDwfBCwRhkg8FgMBgMBoPBg/0H2WAwGAwGg8Fg8HA5EosslWw4lLjNLcen3A72o5mXIUkIzrEtmgcnaPTrLg8XPS9mmVue2QntnCgnCFd4wMoLVEj3ceAopP1V/Bjvs0Vshad7+/i+Xnb161apRljrVjC3k6v33Ra0K8Qwjl3Wx0AP6dFWihKPeNsdnotURsLwDZVwRNwej1TqMXRbt2qnFWyjnTyo4wh9CjaXn+la2Gb93PbNlij/oF3V6Wuoc1rNN86l9w3a1tUh9xi+ii3oIaUDsxUevDt0W96VE2xbn7yDfr/03hMREbkX43BW8RzjK584SULna6h3+hm3rxl4MqvimkmDYTBVt3biAeeA1bRfwxb2eIkSDk5Jbc+zUrtFGc63sS1/ch/3PynOSx7O33Tb8Wq3N36N0oQhJQM/watKSILM9W20iPkabKJzhV6BY0aZzsu4LvRVQJkGxnANXUF7JyPU1XlJ5vooItLYxvNz/jLq3+xfQbsvL/JivNY+9EIyvo5rao9pobiHQ3Sj11l2HXUtevZrR1+CpGKxivq6VyjD4JnJlRgH1hIvAKd8SGkVn//+Ou5Xf21xbhwL9zy5URnXRCPcy/NX0O7CfVxT2kefjr+2mJdZ/AzP1O53sUY2SjhsWNnm+mYAzv4vezZ8e6ivex31j5ZXOHa0vzm4ic833bNQ+QzPmobNZBq+wiCcs7fxrFcPnLVbpAdRM5HsQzukZzAYDC86jEE2GAwGg8FgMBg8XEpQSKu0nr2/9Z9KsgSWbrYAuqn0yLN500Nka2Q89YDYPtjm2WtgHaO2s3kL9JDeGQ7aKBOqgSFxj4cBH3j2Ues8wENWeES7qPJdMNSzTTBSeoBIxB1eyw/c8bCP1tF+nxZbPXf4pvoh7KhmN2F3FR+AqdQIXfkCIQnTr7+al9HDZMqElh9ifiZX0KdZFaxW5b6bN+EBoek7oBWLn++hrg3MY8CDWHLmDk1lmxhzftjwFN/lUdNqn+cdzkpfv4lx7KLt6Q1YacVHXY6LZb2DfeHnDILgnA/uoE/Ve2S3B/P2ciIiyRrYN7VkC3hoMqtV5sYbVJ0d4FxMsziLsWif/dcdBM8iMO2DyZ+9c1tERAqntF1T5p07FcnmSl4mOsU6m17B7oNatvl2XrjAi77WOeQhzXw8fY5vSRneZ5+zlGNOaR8XfY41Jau0S6w6q7vhFtZV9YuTuXYGb8EesfoZ1nfmxYcHtFRMuXOgB8ki7rzo86R1iYgcf3tTRERqZEeLZ/iu8xLab/2rn6PsazfzMuEjRpef4N5t/zfvi4jIjf+Dh9v0IKmuF69vs2tYZxrgE3zyQEREer/6toiINH7uLOiCMfqU8EBfdM54dx5YDFr4/Tn55St5GeG0jxdwv9f/lx+hjq+/LiIihU+x66GhKiIiyRWGlPz4U7RzG89//ozRllG8A8X5gb0wlD/b+XVpj/YtKMRgMBheYBiDbDAYDAaDwWAweLicqOlgKftG+CsSkcFJyeRlHtOm8a35d8rSKrtIk3/JnCY0I6ukwR25XplsT1iintSzj9LY26DAUAe1btPYWNX7sq8iLsJW+5K3y75EKyvz/RAXIpKSlcvt6Vg2j/H1ra3YB42ozedAWVnqjXUM/rzkNnlkU1WbHHIcyaljg0OGlej8q92ejrPzH39dRERqe46Z3f4VzGUT2QjSv4I+FUmaTThdtaduvSx9Arby6F3Uf/ZltLfwc4Y+dDTe2Wk197+J+7L0KeZ2TIuzlNLmxk7C946AixjYoaEVGjWt9cdDvNb2HRM6aeKanb+B+Wvew+viPe4WsPrT1xxzqNHSnVsaIoLPN36Aes9fLs6VFRGp7zOKe4XWcIyhLp0lc30t9L3nTJcXl0yP6c4bf4EyGn/t2+PFJCg16nv9z6itfhc3pnyOyWl+6IJCzr+GQIvGA2r2H+O74Vdvot1NtLP6J67MF/8F2PkWk60nLbQ3WkZfrv8e5qK35eat+RCdK+yCMb77awgvqe+oDR+uW/m5uz/TBuZLtcy9K3hd/SmeK91N0T6KiLQe4t7tfRtrdfVnuEG1BwxrOcQz8Ol/7xjktX+NfnZeQv2bf4o+HHwddVz9XczjaNPtWJSfMjr7EXZrZq9jdysco73TtxhY1HG/VYWue85/9Bf/SLqdHWOQDQaD4QWGMcgGg8FgMBgMBoOHy3GxCAKwumQ1oy0wV6lGwIpIQm2kMsmqS1VWM74C5irzgg6UjdWgg5C61KCFcAR1n0i73bxItAjNp7Kw4To0jgndK+JNtuMFa4Rlaj3VMYLa59w9Yx3a2nDgaVy136wvpf4yYICH6jHja1fdeKh7VcZY5yekTjUrkxl/6nSXyjZHG2scB9i+kIEHeUBB4P7WUaZax6NzHPLzxZ9Q43zsoqY3azdFRKSyg3lp7FLn3UX7sxqWSvHcsYDhfWi/12Zg7Ap93JeFL8jSM2o6aLu53gigma08BNuXqs42Rv81gCLznElUe6o63kJ3lfWP+D3Zu/NOXqZIBn+zRE3tE6wR1VjrbkSht5qXic+xnqqMNA+njJq+j/u02na7DoroGG3WFupzfQ0YJlHsoP5w4Fh0jZrO6ODR2EFfax9hjZYPGfPskc6nb6L+1oN5XXSuZz/ifUk8nfxTao4Z0ZxSF106Ylx0zGfR09LGfbLnU7Lye5gDZXqL27hvxaY3byeoN2XUdFLDPa7zaEBSQp2lHaeTjxmgM9zEaw0y5jwgpPctst/bbt4KbYxn4Qus49LJhVhv3vN4x2m3decjKTLem3HX1eu3UJYa/rIX653UuNPDaO5ghmc45Hqr7aPP/v0p8DxEFgS5ntpgMBgMLy6MQTYYDAaDwWAwGDxcCoMcBIEEcZyfIlcmyXc8iK+AycvI7CozGtbBjCkjG3hlcn20MsnKCqt2l6fJ42gtL5Mco23V5ipTrKx2sgOqKrzhdIrBIfurGmT9nBrn0IuUVUQrYJVz5liZ8QSMW3wV9fsRtAF9kLVPQY3xt+p7PBrpwJ9pL+MJenVfyJ02tsBgh9t7rh1G5AojuuMbYMA0Mnfv3wP719heysvs/CrabH5O/9vbqL90BFZ7vEQ29dCxc2s/BZN2+C4ZvfdQ/5OfoN7iOe5t5dh52R5+C+20PsU9m9G8ImGXG49RZuZsqiXmtCgzd/oGo6VPsKMQcYpr+84TOqGG+eBvYQ5K98lu3+WccxpP33TrrXLIqOk71G6PMK6Vn+Bedm/yWu/21HdQ72BDGWl8Xj7FfJ2/HM71UcQxjzrG0RbaW1vCfVINtHgq1rUf4ZoOo7Gr9xkJfY77NG1Ql+3FHI+X8VnMmOWAHt3TJu7hhFHX9TPHvEcT6O2LPfRfPY1Vjz255u6lIqV3te5yNO6hzHCFTDnnerLlGPhZRX2Q0U5vE+/HN7B2Fu6ClT59w/kTF7ib0aaeuHKCMlmFa5Je6rPWRl5m4Q/R8SDBOu59DXpi1XIrez5telp07hhka3hOMmX/ee14Ea+VI8du557vRh4bDAbDXwsYg2wwGAwGg8FgMHi4HA1yGEhQKUvaBAMXqobXZ16pKVWmNSDTlfvFqqtE4k6Gq1YzVT9kagyDJli7tAH6MfTcMsJGg9fSyYEpWOpMEap+ue5SvcIe/81kO5mSGSLLPd2gd+/M9S06xL/DBXiySkF1v/ybQ/u0spCXSarqvkGHDXWzKM6XDTxtcHZG314mEarvbs7WV8h8RU6zmzU4x+xTpvWSWS50MRfFjpu34jHqqRxhXKMVlCnnlsxkQj1L4nhAlvkc7Zwfg+1r0lCjdJ49U6Z4wnqGmjyHzycN6lTbZMYT97dbYai2D3zfw3el03m6Lhq598UO6on2wS6qG0dhwLVJxq/Qde0U25yXM8yl6nEDupkUuvPuFiKOwZ0xGa7Yo4sFP68e/GJmMaxRi36MsmU6X4wX5tPrREQqu3xOErotHCKhsUr2NhxiLemOhohI7RHX7T61wR0wxcUdrK9wxOfozK231gOMtf4EbHOhj/qndWqQ91BH4dQlKobH1I1zB6S+h12N8ikmKovoiLLrNMgFrnnV+4s05+pXD+/WI/cTpS4ZrQd4XsvbrG8fc6HnHJqfu2ehdIT6GiX2/xSLsaUOIQcoW/KY9+yc4+FvUXDAh4DroNHAjYlOPc9p7tZImjn/cYPBYDC8sDAG2WAwGAwGg8Fg8GD/QTYYDAaDwWAwGDxcTlBIuJx9s/S38hCLiIdb0nMv/pjbjhpwkR82C7n9r6Efw2cPxAVqw6b2SRr6wQjdwIsyzoMzeKgtyt9j3z/exAGe2dN9V3/BHdBBJdxa12ASxuJmfpSxxvYu41CR2rrlwSR62LDgtqLzg3WUiuR91AN+GiQycHOQHzbk3OaH/lhXym3tqOkFn3Cr+WLwifYt/cprmIu2a+foG4yJProQ1MEXPfTWvOtZ6jGaefA65vT0VdrI3aM13ZAH+x4e52VO3sdhzcWf4971b6Hf0ZjyhlOMJxy5A1DCXfiAY55sUBqgW/dnnINDt97GjBg/fhtzvfg55q/yyMkJRERGN5wEJhoy9GOzxDHj86XfR/zx5HUcokvKbgtfbe9mtAZTGU7UQ3vnr6GvlWNPBjTj/eA2f+cm2lv6GHqT0SqlMB03B/f+Mx6E3MNr6x7KHn4H9dbv4vPlT1yZkzfw2cJ9jKt+D/Nz8G3IdXo4ryYrP3W/AVt//56IiPzo85v4YIrn8+U7ONw6+J9h4Xb4Vfe39fLPUV7v6Xu/jjjqf/y73xERkaSG9jf+xJUZLuPfgysoO23imit/iHu6/81nJTBluka238C1tce4Dwv38D7menvzv/tZXua3PnpLRES+fBuR0t3/Gvfw3n+Oerf+T8zRYNXjCjgdW7+JaOy9v4uo6QoPXp6+jmt9qU2RyyoLRe79k/9BhgfbFhRiMBgMLzCMQTYYDAaDwWAwGDxcziE9rWwL7KBcjFAW74CdRibzAEzax2GgiJZnGiMt4mzeNB5a45vDAhjdiyEdIo7l1fAPZX+j/HuwpvHGel4m7ZAV1YNu7H8e67wO6ys9VCciEjKwQxglrX3T/qslXcQDhSKSM8R6gC9kfdrHnEHuucM/ynRrfy8y4xEPJSbHjqWNNHhErfR4H3Q8SRX3IHaEq6R61m+Esfc3eGiPrNm0ijp6LznbrWYfc52U8d2UQ52VybCNUXa26tt74drJKu63MsezCg/eqQVayx2ijEY87MVsZu2bHgKbtjj3U3fQKiazmwV6yAz3tqTBJGSfh8vuEajwLNaY8cohz13NXgZrOl7kwTJ/14XsfMIxBwO1JsSLRk9nsXsWEratZfLDeLreeaBstuFO6d34F6wwwz2tffSUfcYzVz5kGAzDW0RESifIsI63eYiNB/s2EoRkjDdwL8sf7+Rl7q/eERGR648wt2kRfT35EMzrxk+x7itHjnkvPUK9Kev/5//4uyIicvVjHhzk7kP9Excc1KpxF4XM+2gVY238/HCu/jR2f8OXeICvfI7djtbnfBZ20a7uvPzhb3w1L3P9Q4zj0ZWX0f9D9GHzd/BML/wpmOXmolujeuBuxnChzd/jb8kQa6q6j2cx8sJfor47ifqkY4f0DAaD4UWHMcgGg8FgMBgMBoOHS2WQM7Kpga+7vfidapCVXaZ1kmqS/0pFNPXKOdPLOrVuEWfvllu1ab3aPi3W/DKiVlPBL5ANPufzTFlytYYbj+e/V6un59Wp+mttNwyf/+qX92zc5jB7Dlt1IepWmWNF3CUz7kVnFzsoE/dRX6mDe1ggG5aFjJpue3PN8gVaqhXbvKaL9xq/G/Xc3JQ6GmGNPqRFBlFM+dpnPLXHBudR0xxXqYN7rMERGgkd9tx4Mu5U6Li032FfU0fUVs4x1YXOhGVQNmLcskZaF1V77E2vMoeFCssM5tsp9FG/6ptRnvWOUd+sVJgbe7FDPX7k1o4y4HGf9dA2UVnofJ3E/8+PdKY7Clp9ye3aME8jj4dWKAuc2zN6fcvb5nqe1p7fXr7exbNw5P1XrXuQaB2cz5Erk59XSObf5+3zHIMfsDKtkY3nDkiQR7NfqMOXIKt1In+b8p0f3UHSny6P3c7nIwh+8e+IwWAwGF4YGINsMBgMBoPBYDB4uLyo6ShyjG6TOlU/vEIdIKjVldzNgtcoizV1uj6NFs71xAy6yMq8tk1nh8Sxc6Few3qUSVT3h5Dx1Brd/Ly+5M4e+p7s0BwvpAxyHh+tOmZlh9mngjfFytzpZ9qO9lHDEzwWMFRGnPMTKpvFa/Kgldhj7ZWVZ3uJapE59/Ex+pq1nSNF9RDXxOfQTlfJjqkbQzRG+4XTQV5G48GLhxAf16jnLR3imkDZWmrIRUQqh1gbMR0nshoDXXSO2725vouIW1dkF8sM/wjJTOeBK+cuMll13erKUTpiv+l8IgGDUOpO5xt2oGGtlVSLzvV3hDJFJTN9gv8cc1jgvQxGXJPU1hfPGJ7T93YYeG2WPx+1ubqKxQvrQ0TO34euvPkEnUiWMI/qvlA6Y9R0y2nEE2W1GcWs9z+p415OG2TZl5z+dryMNocMY4lpdDJmKnl2IW5ZRKTIe6jPy/gqNbw/J0NOjXrOxIpIUqX2eFlDUfhskBnvbeHz+q7bsZhRa55x2qYNPhN1avjJBk/rbt56V+iWscHPqC+eMGpag4SSmlsHM43t/gQNTZdwjfZ+VmUHqu73raBOOlEwz64bDAaD4YWEMcgGg8FgMBgMBoOHy9Egl4oir9yQ/jXGOLPWatOLcx6DyZsu8zOV9+3AgaL/OnxrC31PU0sGr3AAZnC2ivqH63QtyOC3XP+05YrQ/SBIUF/nNsrUV/A6LpOxes25WJROwe4lqoedoK+qad0nexd7Fs3LPIU/XCW7tA5mekZWq3wfrGD7qxt5GWXS1LmhRneH3kZ57vvWF+W8TLiNU/3dtzGeOhnq8fVFjpO63IfOIWBED2B1pFANrTKWEzoHFDz/6EmTbOIRrulep6vAY9Q/WsG4etcc07ZIhnPCOVBv2/Ip7kHM+Uy33P1RJ4i0iD4q2zzhuihNdZ04IWvUVz05+tu5DZa08ZAuEDU6egxdO7lzA5nJ0SbKlOJ5nXf/WjUvU9mnS8amxlOjL6NvwQdX9bCxpycu0vd4qq4cPeqJyYSq7na26NpR7e9kgY4ndIoocedluoS50LkSEdn4Q6wDZcDTLpj29c4V1LkLx4XU05sX6LSS0FFFdyaCH30mIiIN9d/2dmBu/gbGqrsMyrgufoT1ltx7JCIizVPnJ60e4MrKX/ltjL35xw/YLpnkA7dGQzLRrfstFg3nrlnl8xU8fpqXUWeYJUZZq2tGQmcadXa59ZuORVdN84yMte4srf8enDtm23voj7fbVVmnjzsdaeKfwhs64a5AddfVr8j9ycMwd7swGAwGw4sLY5ANBoPBYDAYDAYPl8IgZ6OxZJ/cl9o+WFRlqpLjE3cNXwtPeEKf7OOsByasqulxXlpd7oOsXsn7YOGaj2m4S+bLbyfUk+z8buEQfqea6hepDtLTuKrvcHTBB1m1uxuaqDdy7FyyC2arTDZJPVhLbH9GfW5r5rkXKKuoHtCnSPdrPuJ4qDNO9w9dO3THaHwwz7CVjs/Ex+zcMXolan5z9wplCFUnyyQ9ZddEnM9x0qDXNIvM6CagLgZ+IlzQxX3JNunFTLY2jVVPij4Xjp1uWdnZ6lPqVFtcDxdcBaKh5zk9YZtkYwsDrAN1QIiG+D46dprqrAl2dLTAPvR0V0CZPvU6drscedlAx46yjS9wL8dkylNPY5qxv7kzRDTvrDBeoYvF+FkHh2iCz8YtPoYsmxao/+27tdN9C+s4HkAMXP0ca6TzNtjO2gLGGz5yCZHJbXgkxzt4PhKuneDVlzA3W1i7lU9dmae/hHvZfEzGm0PtbWGur7ThrTx8aTkvU3nAZEP6Bp+8zfsyht+yMuSNnzsWPa2TJV/CehsvkFH+Cd73N8nIL7+Ulyke4Dk9ewu/M02yz/EOxpXxWdv+vmtn8XPMsWqRrz1EX0+/iblZ+hO6aSw5v/KUuzLpLtjl6A14Q8c9rOPxS5jzcOLuqe7SSBCIfH4hmdNgMBgMLxyMQTYYDAaDwWAwGDzYf5ANBoPBYDAYDAYPl2PzFkcSrSzl9m6TVbwWPRmDWoLJGrZm9VBOdMiDVjcY33rutuPzrWi1CVvAIZzpCq3CziFrCPpemS3UE9DCbHIDW9MFHqJJr/MA24nbjs/DRS4crNIgjMFNbOUWuk5eUKBNXHoTW7V53O1Cg2XRp+m1FTdR4XxAQ1FlJlcxJ2rL5Y7BuXFMb2FcsUo2VnlIj7ZoczEiy9iCDlXKcYxDVHk89UNshSdnTpax+DG36PexHd/qo0/hOeov00or8A50JYztLXNcK4K5LT+Yj/71D4G1KLsIj2mzp+ERKi85gXRE7fpE5JkQlppKXk7bc5/n7Ymz/lvmoc34gLZyrF/vRc0LVdG5XhhwjVLaEfRRb6UzmO+zOLvA0jkPqPJQW8b7Xx1rXvWzQSE65uIR5j7bZnw0113mWQSevsdDmg8gM1CrwxllLRqmEpTd6om6jNvmwTSNedco5ZhBLumid6iNQ1OZTPmEFoG0k8sYyx6kTmKh0ekavjNZQCWlM8poKrRR7LoIdf3LPFvF/dEgl4y/E4M1PFfLH7p7HHDea0/V3u9CSM5Si+26j4YrlCbpeUTKQIrd9bmiwdDJp8ZX8fzEn1DywoO9EX+G8nkrzj11rMgs3gwGg+GvA4xBNhgMBoPBYDAYPFxS1HQgEoYyW2KMcGc+zldEJFgnk3rhIJwGXuRxxF44Qv5vMojK6KqV1qwJlrGw4Oy98lAKhiJEtJfL1sF4Rce0jKMtm4gXWsFDUkEeAU12Tg/tFNzfE8Ei2gzPyehpWALHHK1yvDPHNiZVjpW2ZRlDRgL2MVI20zs8p9ZW+fyk8wfukk0c2grvPsnLZFWyiGTFgs01vkcdZ9/D4an6tjsQufdLYIgX74IJPXkD96m+i3EOV3iozSPt1v8S9/vkDsZ++g76tPjRloiIlNrpM2XOX0G9tT0esBrRRq6FuW0+xgGoyYJbmnFfo8Dxcvo67n99B2PX4IjyqWNpCwyR2fk++lg5xGvzCeYi4608u+Os1GpPcc/at/VgH+s9oZVbXQ/2ufHUaTk2WGe4RE/ZU9zj/iajzX1iUW3eGvjHpIXX9SUcBuvcIDtcdYU2fw+H8hLaxYWfwUJtkWtGn43ZrrNFiws4UJeSWU+5ixKTyY25Y5J+8TAvs7T5roiIVLbxnCR8xha+YNd5CLX8yDuAy5Ac/e7qH/C+jzAHRdomzv0e8ABs5XNa0a3xgC93Dlb/aBd9a3m51QfYmZhdw05IkbsQKXdC0n3UVf6627VZ+jF2TybrYMmDV7D2SyfcUTpCneGi+z0o/iUPySobz2cr5W+WBhdFPbdzlY293y8v7MhgMBgMLyaMQTYYDAaDwWAwGDxcDoMcR5IuNmXaZHzsOhiW2iOPcR2AYZmtzJvsF6hf7KvOt/Ns1LR2cspgDa0/nGiQiBvGbBPa3HCMens3wLjV74MOVIsm8YjqYJH6UUYMBwyrCGnd1LnBYI2+K1TaB8s32kLZ4gkZvAX0rURtsB9EkRSVhUXZ+mO8H2yBgZ1VyKJ6DHJIq7b+DYy9xnmcbDQ4TrLPLRcXPF1EfWEV7USnYAzVyqr1GdhBjVAWEVmQUvPKAAAgAElEQVQvQ/NZfnjCPoJprW5jHBrkoXpSEZHCLti55THajse4t8171EWT9U4rjqUtdBl3zUAYDaSoLXL34QB9Ki062y1dO3mMdwa2r/qYDCLHGXrWcLIPffR68yW2yyjtE+pgc5s3N2+lQ3xX7KHtQhtlcub/Ced65Cjx6Iga5GPej978bkdxn1HaRe9RI5OqFncJdybKd8GAFjpYw5OWswtLuSsQ3SdDzBCLrMy5fQrWM/Q0yOkR7mVKjX7AMavloWr8w8VFNx5al6l9XfwUgt6MOvnZU/Qx3nIBOCltElX3PVjh7sMPyTLTWnEuKIT9DNfA9kaH53P1p++/jbn41NsZIQOuFncas67a52iNwUG7juLX53NWR58KH2yz/wwbUZ1+24spv4KxpU/AYodV3EMNZwmoY8+8cBEhIy5hMPfbYjAYDIYXE8YgGwwGg8FgMBgMHi4nKGQ4kvSjz6REg/yAjFHad6fWJSQ7+0U6V3ZGFqtCdikdPxvTqsrS4AH+P18j+6SM1cyL1w2eUFdJVqlxjw4BrLdAzXPmsbTZlOU17EH1vfx+XRmjiWMoEwacFO8X5+qPOM6EbF39t0/dQKL5U+86PzV1bChw3rrOYSMtoP7qH3yEelWT/ADMWsA6ZzMvWEN1qBnZMV6jTNvs+19F3z03htPXGS1dB3M8XEaZKWOcJzWGPew69lRdFvrXMcfnL9OhYsr47R7GVdnr5WXatzGepU/QdpdBF4pK/OzfbGGB80Y9twZ4DG5Rx8xAjWLfrZ3pG4hMPnoX7S3cQx2NAfXfGuSx5NjtLAID3tviY7GJ17U/xz3svdzidY7ZrdDJYLxEtn4MtrFwjr6072Buil0vKERl5BxW92rMcYClHVxhWMvUUZEnb7VYP+7Hys+gKz54jzsk1Iq3PnYWDudvgxlu0PkifAx2tv/1mxjPFXRg9YeOPb3/H6EvjbvQd6cFvA6uotO34y+h3Vecy8jiF1gzxXuo/+R7GPu0AfY+Iam99qO1vIxqzDXIZUSN+8afqw4c9aevv5qXqXPtHX4Z92zx7ibHx/XVwZw8+S/dGq38APe0ewv9v7OLPj3+VTDL134f3w9X3XjiAdcTdyFGX4FuOeYuxPEbXN9Dd3+KHaeTT//I96ExGAwGw4sIY5ANBoPBYDAYDAYPQZb9fxfM1RevZu9+77/KtYeDDbBBS585V4HqPhm1l6jJ5H/NW/fJhH0DrEzp1PVH446rh9Qpb5Cx3EL9pTNcu/KhO01+/grqqdDR4OgdlFn/Idifs1fAPlWOvZhYapk1KlldF9SbdfvfRx2Ftvt7YutPMZ7jt8AWtR7j2sEqWbkfgMl78reX8jLZBdvUpU/Rx/ZtZWvx+fLHrm+1bczP3nfx5dqPwXarLlrHUT51LHrnxjwD2XiMOoYb+Lz5A+gwM4/hD6hhVt2qvHQdn1Pbqt9P151jSOHRwdx4kg06hRyScR/RJWPFzYG6Fyj7nO3ClzakE0lKn91wybkKiHoJkwlPV3BtsIP2gyY15JG7PwkZ9vja1lwfcz9i3S1oOI24qK8y/XSDDuZn/Ao0qcUdjEt9kUVEMtX31qocM1ls7m4oax/UPDcG7i783+y9WY9sWXod9p0p5siMnPPmnavuraquqh5YPXBqioZJiZQNw3qQAcMPhmBANgS/GDYgA37yP7Bh+8UPhgwDBjybJmBJpiBRpNRskj1WV3dN99ad8uY8RmTGkBFxzvHDWt/eO+IW+ZSAcIlvvURGxNnj2SeRufbaawl9wyPq8J0HdIu7Hst+rntfYbzyH3yC7+5gXP03cV/aP4JeViO2RUTk5HymL9pHN590ehnf9BrkAaPAM7pxNB7B5aH/FTD99T/4EBe++8APZ5faYt7bx/85oswf/ne4B2WdOz47PtI6YtvTd7DO1Ke62Ea88+F/8IGIiGz+b5/5MnqfVQvMedJ7evWA3saBY8jR19GO/r658V//Gfr87kO8qjfzVfD8/Oo9ERFZ+CHmNN/E/KhO2kXFr3j9utPJp4l8/8k/kO5wzwyRDQaD4TWGMcgGg8FgMBgMBkOAa9EgJ6NcWp+eSp0erY0TsDatR0EKFtmspSswUSVZtHQHjOVK66aIiFROvTdvRDI5PqPel2lltTPUXz0B65M98czUUsFEPjo3lAlYTT35Hk2hcaweBel71LaWqnWlNje+RF9WmOpVDZL0ao9R30qC9mrPMb4a/XajF9ABr/3MuzHomJVJbn2OMukATKFqa9uPvCY0PsHPKz8HC1d/DEYv7TPt65hs2sDP2xK/03FpOl56Tg0tmd0iYELVTSCiRnvep9rdr1PPOqv/rLLL6gLikua0jhOviy2XlJ2lbjSba48esuWlb8exsnRWiOgfLXQ5KdWPdhJo0XWu6U+tiYPKUEeaxlcJNMjTuWQ29s0xxyPWH+y6lKp/pwewjkO19PECWcYy0N4ric36HMOqdZVMLQzuaZ6RkKSfuCYa5tV4dpxBmbJNhvUca0hT/yKy83kHDLb6couIDOlWool603X0X3eHGnTPmDb9vCW8LznvWV6lbziZ/ryBa9Njvw6U/c11zdChJqHXsfOaXvHsts56eYtMsSYdkvnPTrEOTj7wZVTvPdWNAt6H8Trar51T7x+4f2TUtKtHsqyTydf1toh5K6r+12dC55syji1Nz2AwGP4KwBhkg8FgMBgMBoMhgP2BbDAYDAaDwWAwBLimqGkRSRO3DV8kKiUIomXVaky3vtXOi9uzKjsoA5sv3cmOtWyi187VEdinuYNafC3SaOZzjYvWIAT0fa7tfK4Mv9e6wn7rWCWd7aNE8StlXL8174LtlbzG9TXsm45Z+5jMlpG58aK8fsfXaHa+NIAl3PaPeNBJ43SjfFYmoe/DvukBT63PSVVUYlG8egBU23GHQ3Xbupx9jcKy7tpiti8qy0he/T+v1PJ6KE+vLWbHERWh9CGfK8PvUl2c7GNoEaj95VzOj8tFg3/JXGh9OiduLlh/2Dd3wNM9FLqO5+qMv+R/3mjuOVEJgF4aSgL44yv1uqU0t7bCNp2E6C84+Bu/Kj1w7cx9NX+gdaafOgeuL/rsvVrIPzdf3iW3doI5cM+sG9fsM1a6+Qvn7UvmxWAwGAyvLYxBNhgMBoPBYDAYAlwPg1yUEvWHktKuqtLCoRw95CbiD0clLdphqc0XDyZll4yL7fmwB43CVWuwpM9re6CDKkc8iBfYlcUjXBtd4MBO9YyHpHhQKRvggFJy6g/COQZqjimMhoy2PWZwQBCDrVZT2QAWZmoJlipLPMQBuOppEH+sc1FhoEaX0cbnjIaeYE7i/uiVMlmPTC4PdqXnuEYPKIUsenI+mBmHs06jtVbBOXcMbICIh7Ai2l6Vyj4qy3npDzdqOIqypMogO4szZVwvfFBIohZmPNAV6eEoPSCnLOowsFKbPzw3GM68Lfm+GPm14yKXldlnHeVcEE05rPufydzGk7n2jhEU8mUMpYPrP+eAjLLOZzge3/aQ12RzX5CN7vu5vryJ+9C6tTRz6cUtfF4/ZFR7NzjY10LbGZ+1iAfSJpt4BjSyPR14prr3BsvWlJXF/PSQlSHrPDR3edMfaut0UV/M57BzD4c3B7fRpwkPn3aOfd+ny03Wgz6mVwz/2MU1w3UGudzydn+6qzBpMfqbsdhVsrf5Aurqvunnp6igzHQVz2Fc53jucW66tDcMDtyNljH2Ou/3aB3PTY3rebTG2PWG5xeq5wz5SSMpnhjvYDAYDK877De5wWAwGAwGg8EQ4HoY5CSWYrEp41Www6Ml2mMt+dCChOzLeK3FD8D6VPpgY66WwMCUgRYwylEmIys3XQRrpfHARQb2pxnYleUNMqA08R+s49rKJuzlJk2yabdWXJn4ikENjA2OJ2QSyT5eMJK3suj/n1jeB9M1bqP+hJZWk2Va0B2w/U0fS6zaRrWvy7ooM1zHNRMyUpVTH16R7l1xzOh3lRZnOtcZremSEx9PPbmBtiMybBnZ33wR9yMZv8pqq9WY0IkrX2O08T7ZTAZQFJueBUw0BIOs8LjDsXf1HuO+xKFVl9qRpYwdJrOvdSh7q6EQIt6WzgWFMJAk2qMlWL02U1ZEJD+kRRfnJ+pgTuJoTi+75kNMYloR5ktoW5n8gjZf8QVZ+yBUwvW3zf7OWdKpHnsmKETHxd2UQhlest4a6FG0fZnVj3DPqp8ivEKqWDOrVdoMfsZ48WBXIFYGn2y9KoPTR6irdcTdlcz/Gtj4Aea2RgvF6h7WVTqiNdxLtNP5uWfe1dIwp3XfiHXc+AVCP8oaWfQXO65McoIxLk8ReBIPxqwfZZY+x+e1T3wZndMqxy6c+5LR7NkEz/jGDz0jP+owip1WcwVZ+5WfYN3F2wyqifyzvRDBclLnsvGYDwXtCptH3AVr+Hjq6JK/g5JY4tHcDoTBYDAYXjsYg2wwGAwGg8FgMAS4Fga5jCMpqqlk1MWqrrCohEb6ZNpIY+XqEEFGMa/Eri4H/jhZAZOm7G9OAqnZ9UyeQtnltDucaS+mdna0BCas8fTclcnbNQmhTLKQjc4oBa1cvOp4MG6j3w2nPWXXnUuCL1JhfK+WUc1uNHfoP9RDju8iGEKDIvJFRnXroflTsHb5imdc4yEZLJ1LsppllYEku2DNokBTGx2ezvQh+uQpyug1l2in7Hk9cU6tbKx6a2pEixPWxTnJDw5dmWQFjG3Oa5IlsMvFPq5xuuWXgUZ8zr0gOuW94xrKD49mvg/bic45P1r/nAY5OvdhNjn7H6v2V5nVJ+iL3koXphL0V7QPCq1L2eCT01fLcF5inQNqwzXuu9zZc2Ve/hfvi4jIWgdi4HELc3L2XYxn+V/cFRGRyoWfgymj0xtHPBtwxpjyN7GGBhvUwgcbCq1/A2vjyS7mr/EI6y/5ZbCn1ZP3RERk+7c9g7z6M6yrhU9wzX/7d/57ERH5z7r/kYiITLihsPHnfo2OVvGcHn2DO0ldvN78Y8xX99+lXrpx35VRNxn9vaDvm9zlGKxhPBf/uj+TcHsVa+UbSy9FROST/wss8+N/B+z56ofoUxHIwE/fRwMPjhAQtP1vYbej/RJzMVxGO2OfBC7Vc679NJLJ/zKnKTcYDAbDawdjkA0Gg8FgMBgMhgDXwiBHRSnxOJfxMlilZEz2bBK4JGSzkcWRY+uoU50sznwuEmiQz3CN+vjGC2A1Jzy1nu36Zipdsqfq/auew2Sqq13qmZe8zjce03VB22W/4yGotUlD+xP8P0GGOBvM+uq69tRvN/BFVdZPG3I+yHNesOG8pftgwOIbYLMS6mB17Dl13soki4iMby6xv+Q8qdl041qDRrQI2FNZYlz0Htne+7fxekTmcwE0YHx3y5d5AlYupn54Ql23asbVXSJd8TpfvQ9pgzpO6kdjXqPx1fHGum9HWV/Vid4ACyiMJU65K1AGvsHK2Mbr6FOyzohzjbBWdj3QR4sy0xrnzLUZ3UBfdBdCAg1y0aWWlZpp1UtrPHbJ6GynURYfgx01uBugGmSdC43uDjTIN/4P6JMX/nwb31Ez3jxAX9sfUqsbeDQ732ay/8pqrzyj/rfDPgc7Padn0DTfPcFzVN8BK9x9gucz/cFPRUTk3vCBKxM/2+NcoP9/73/9D0VE5ME/PmBfueXz+IUrU+OaaW1jPSWXnK9Pn2Bcv/eBiIis/uG2H4/6EOt8qR82XVpaW7jH9VNP7Z5z9+mfNG+JiMiNgz8REZH7v7+J8TxDH0Mf5OYeNMjlDtj0W39AV54jrI8ldYNpBzpsdZ6JY3neNQ2ywWAwvO4wBtlgMBgMBoPBYAhwLQxyUYlluNWUwRqqUw/TrO9Z2hrZ0j79U5U1ja/A/l3cIivcDF0s8Npg+l3/Bpic/hY1gGRkKwFj1N8CW1Wt04HitvrEggG9pCPFtO51x8p45xXUF5MAqnRR1+U9vM+6/v+J9kuwfJdbdL6YYqw6B7Xdzkz7IoGGksgGDV7DsXO6KpeemWpSp6zX1I/B+l3eQt/q9KutBizg5W3McTzBuFpTzM9oHfW2DsjSBnPgEu6UhVXmey4BLm96V46MLKCy6bH6ILtUMTJ+Lb8OnEcyHQBKsthOs019bxT0bT4xLa+S0dPP9dpQv04GObqiwJaOJMraOpeJwMFB2y5YrzKUEzqGVMgcu+S7oIzzoear8z+mY0jUCNrR3Q31P9a0QpZxfat5LavupihbKk31zub9Ud/qcK7PuENA5j0i4+n6NADrmS96Fr3Sp658yh2eHljzygWeH5fyN36VJdX1pM9tdEGv67mURhER0T7orsbFkFNBxxUOvRx4L+iIuw5yyXHV1REFczttYd3XTryour+uDidaCeY+6c06o5SBs0te050q6pPnE/WGdDOpey9ox2ZHkfi9KIPBYDC8rjAG2WAwGAwGg8FgCGB/IBsMBoPBYDAYDAGu6ZCeSDrMpUarpvECo2CHfks1ucAhnKzPAy4MzVBT/XTEA3mD4JCeHuRjcEd6hfcpPfn1wJ1GUIuIJGMGaDC6OuszKISWcPGEEoyzL9kinsxasyXsW9bDVmolTKe+ZH1jjCftY6wZwz50+zq79ONRWYmOPbvIWZbbvLr9O3z1oFU65Fxo+AfnqXKu4/JzXelxG5zb8nqwL23Tfo8H4ULLs1jlBdw+To+xjV3w4FhES7csOASmB9TctjUPA+rnGiP+l/0XpnHLWmtBi7U4sGwTHnTTLW41p8sZYR1rzHMQ9uCipdWyjWN1W+kqoxh/yYE7lUCwb5U9Sjh4iC+MtNb5ilU6wvo1xttZ6U2CmHKVk8xFaOc8QKh1xYEk4fLXIINoL0O6M9Go5i3ct/pLHlQMrPvieHYtquRF5RnTDp6V8aKXzfQo5ame49r0UqVCaKdBWc1os+XK1AacDwatTO7ifbFBSz/KctILb78W8QDi8Ab6ohKs9BSHAlWmJcs+arpoUsIxxGveoJSIIR1TRtz3N7w0ZbhJ+ZQqdriurm5APuFEEsG6Hi1hDpoabrTCA76UlZQMn5kseylUesm1n8ZS7vwlkeQGg8FgeC1gDLLBYDAYDAaDwRDgeoJCRKRIIqmckxG7Q0u3wOJMY2EjHv4RsqhFfa4L5as/T9oMCMlmbdIqveDQjzZT1+AOZZ/5yijbaV2DCTxzqAyaMscawlFUwQRlF9pewG7zQJoGDOh7PeBX8lBYHGSZJOzLmI5fLtJaCVK+hvM2Wg8OXYnIlIcPlR1OedhoshLEU5O5d/VoJC/fFzz45A6FiYiQzUzIEGpUc8SDZGqPlgeBF1E6G4igB8jK0WwYR1gm5kGroo/6koWF2T7x0Jwy1yhERo4stsYFa/9ztauLPXOXrPoocREfg6x1OJABnhkHWUx30O4ZrdXINofj1v6Wc1Z0+nms4wst9TjXOo6EFnCuLrLQ0/0D3//fBRt73IcF2WgV93L6XdS7X8P3yTB4gPjvb/0EY1YLxN5djGuwwfURPKb3/wYCYj7fh2XaxV0wxZ1fRV+uHr8hIiLbv+XnYGUN1nCdT3Fvv/ev/TciIvI7P/37IiIyYaL1RueeKzNcwViPvo2+VY+x7m5W3xQRkc3fgoXg4dFNV0afXT1UO20wCGUf86eHd6e/6rd6fv3WMxER+WDhuYiI/MP/CkEnT34Hc7D0MS0PA9L39AP0aenn+G7318Ezt7ZRZryIdkcrfq6rZ9wZy0QmT4x3MBgMhtcd9pvcYDAYDAaDwWAIcC0McjyeSv3JiZS03+o8oiXZkyC+mPG5DTKrquOLD3BNpwqmKDsd+jLUAMaXYPlqDMWorVK3uE9N466PMl5iPHSyDxZwsYlAADnG+5WPGaxx7Fmm2hn/T1DLL7LPqt1sd9C3ShAAEO2CYV3+DH2pbGN8SZ8hD7tg3BafBUEUhMZqV15i7IsJmKppE3PSeOJjsLUP8QRhFfXHiDSuLIE1i08wjuqpZ1zLdmNmHHKIvjkZJu2xlMEUEYm2ME/lCeYp2UR7qsONatR/BiEZ+RH6onHR5U0GamhAhTLJgf1avOpDQ8LvYrKoBZnqqOottMRpcblmGCqiQSSRs+ryfVPGO7qLgIiErG1+prZy0UxdYRnXR7XuUh1zY5bND/urzLjTPmtftJ167ZWyut4i2onJNsI+NCgkDK8ovoc5XniKviy8wHcHMSz8Vn5O7XjXs/d5E89jdkgdOTXAaR/3qXGEPoVnBT5buIfv9lB/aw9zcJKAJV7+Aszu2k98YMziJ5zTp+j/7/747+Lazxi0o+v68xNXptahRWAJhjodoQ/Vx3iWn/7hHRER2XrsGX7dNZm0eMaBevzqPnYHWru4By/bC67MHz/+qoiI/LOVr4iIyDtnH4mIyNIv8P3yz6iXr3oKOR3xPn+BnYONH/L3zh7m/op2idOGL6O/G8o0ku1BuA1mMBgMhtcRxiAbDAaDwWAwGAwBroVBzhuZnH17U8YLYJ169/H5ypKPC27tgOk6e6guFvi88wTszP4vg/mtHQXm+5SLqoayvwHGZrAFhqaxD1Zt9SPPzh2/D3anuQ8W6PCb+B9gK70rIiJHXwertsDo5plxVDQGG++r1Djv/DbeZ2des7tVuYf6PyBrvgpGbcA+bsSYhJ3f9H1z2uYM/V9dQh/O3qJ2soXPl1ZXXZnWHli43e+infUWmN4eXQXaO2TgBl5be3GT+lcS3otfMCTlNvqy9P+CyYvr/hS+Msc5tb9pG2UKdYrQU/6bvm8J9bylOjmQxS7UyUF1zYx5FhEpqQWOO1gP0z0w7XGTrJ1jlL1LgoZKOMa1hjWiul7V+cbLnq2f7iB/PNHobNabLDFURl0sApbW8X5kyXXs5bvQ3cbPED0chldovaWy5sp2a6jJIVj2OGTE1V2CrHJJVxEXWkHmPV7yDg4JieHKM9Q3eotRyWQrs3Pcg2nbt6PMse7EFCuoT0Myatxh6N/xzHhzF+NJ+wyZeQbWedRhUAgZ80o3CEuhu4My4cMR1urWc7RfYZ+iSz9vusuQDdF28yXj5Dm3eYMuLS/PXJl8ucVrsfOSr3K3htr6wTqez+WP/bNw8B22d4m51bW58GJWJ++CQ0RkvIDfSdEWWPNSd0BIJ+jO1dWGjwJXx5syi537jsFgMBheXxiDbDAYDAaDwWAwBLgWBjkZTKXzkyMp62BwmjxV3nzs2R/Vwa6dk5Wh76lqhTcFDGzlNHAVyFWDDHapSQ2yRibX9+mZ+2zPFVkbQ3Oa7YOVS0dgsZsfUt85RTu1Z0HfNBJZY4LLWQ3yZhVMbzXQIDc+pD6xQHu1JxhfiyxX9PkLlP3+2zKPgtHZzc+gY66dQvM6pYdy87HXR8dk3TapAW1+Bgaxvos5Tg/pjhD4Btd2yYBx/lSjvXQErXM556IgIpLewTic967GR1Nb6yKaz3zfpnSnSKiZLRY5drKMqh0ujo79eOgu4TyHyRxHeg/UT/jSe+aWc37BSQaGUiOGSzpihE4Rzmlig4w3NejOu1n7E2iQ3Ryyfo0aLp/ucjz0lw7iqUMvaRHPSCtTmaheOtBuO3BcEefPseuqZx75Z0E9tKdbdKu44tzSDcZ5DQdMaL6Ie5YeU4PMOchvYE7GS2B2q6d+fo+/xmj2KepVn2J1zVBtuK5VDhp94dwmCfp4tYl1OG3RDWbP78CULdSrzjTDDbDp7eccxyVdOta8nlgxuo/+qwtM1sM6buxjjvd+3e+MJPROHy/N+lIPNtDO4gmer6LuXTmq59wN4DmC+B6Yd43Fnizj/uhZAoyZ6zeOZrTjBoPBYHg9YQyywWAwGAwGg8EQ4FoYZIlEJImd/lHZLmUhRUQidU6ofIlHsnidnzLLKE8mV/Wveg0Pj0+ZDFcN9J16bUn/XmV5SqaHqQ9zseBZpkj1g3qSnZ7Gygaq/2qcB32jDtY5RbC9ooI6Evfel1G2z42ZjHtBFs35PIdzQz2qmy/Vk7KdYgFsVhy4WBR08ojIMiZkaZ03s2p1zwKXBGpMIzo25Ju4JjkkO7gINrCs+CWTkOGMqCdWTWZtgPfRQCMPPXOo7cTqr8z2IvYxVgcRdXaQgEmlx7AyoIkydVp/6LBBL2O9z25JknV2a2nDM8j6Xb4BxjDuX82MORqBFY7GPhVP63UssOqH1WGDa1NdQPCGbZNFLej+kpyR+e+QkW/6Nbr0OdpM94KdDxFZoiY9e46dhTJI7Ev5/BX0pxaX0If1Xe+/ys4ufQK9eP0Ic1k9JDuf4p7mx9gpaT9e9IXItBaXYMCjn+He1V7g87LGZ+HQ7yREfczPIp931THrfVt6xGTKl975Qgomara466DPBpnrjN93Hvv1NiHTPabzhe5QtJ/Q5WQHuyu6GyEi0tbfUXQiqT/lnDNJsXqBNVypB793OB6JY4muXk3pNBgMBsPrBWOQDQaDwWAwGAyGAPYHssFgMBgMBoPBEOB6oqaTWKadhtv2H6zitXrit4grPPA2WVBZAQ/p9bBdOVxR0/3Q5o3yBW6HX61UZ66NGTlbDbbw3QE+lh110E6b8oLBJrZ728NgG7TKeGPKIbTdhNu/al8XFf7/iZLbq8N11Ffp4v1kke8ZU3y1GJTRw0w8D9SgFGLUQd8mTXxfX/LzlsnyzJjrK+2ZdtJLWpIte0lCzvjuiJKR5Ayv0w7lBp8jTriceElC8hLb4RqkkVDOoJHPkR4gC+Y61zAObnWriEC30l00cyiBYT0Fv9NDbhG3yQuVbQSH30qNKVcLONrLacR0uD3uyrB88pLSA/Z1/lBdHLzX8bjPKKXQw3p6CC2UMbj+0upO+yoFDzvy2jKfi7gWb1encd55jwcXdd4CG77T38E6qL/A8zJZhczg9B3cj6wH2UkUtKM/xypJmPJgH+Uy4xUelKv5NXr+ED+PltG3Dp+B7ptYQ4u0nuu96eUZbUqSEh6ASz6AFGHyz3GNBns0uz7+u2ijD923MH3sItEAACAASURBVJ7aKfpWP4B04/irqKv1hZdyaCy9i3lfQ9mM4xvcQXuXWz7A4/I2nuW8jfuyzrXTv40y7TEDcoKDdWdfwXdrT9D24CHmvrZLi8oG7tfVkn8WKhcYT5HEUhxdj3LNYDAYDP/qYAyywWAwGAwGg8EQ4FqojmkjlpOvNVzQRvd9sGZ51QcQdFpgXU6/QraMpOLCItiZo2/hff3A2y1pYEeli8+G67SE2gIbNHrCa2MfRHH4TQZoPAfrd/xtMFO1Lg6dHTI4IK/4vk3r+hmZYj131wNj1PsWWcJjzxhlfdb3bdZRA5s12EQd2SWs4Y4+cEUcM503eZAwBut0/hV+voBxTRv+QFfttMpxsEyCcfVvoJ0OY3WL4E6Olvl/D8exnGF+zh+i/+t/js813lnEH45SFGQzHeOqjGwQ+qFhHznt3kqGfoSRzyLetkxEJD8Gu5xotLTauvE1ynjAMGCF1ZrLBWkoW6tlyNZHQbiI/uenQR16wC9p+XAHEZFiGFip8f6oXZwLOrlFlvFolhkP++uYY22fh/KKOcZ6BmPuKOjhNjLtWiZOPBN68S7azP8U9Z5+Bdf23kG7rT0s4nHTM6GNIx7SJFNc8KBnwXU+bmNOLm77/5NHd9HOeBnzX+2iD/3bqEsPSHbv+b7FY6yDOndE/pN3/omIiPyPa/+2iIhcsZ3qmQ9yGS/i2vMH+G7hKfpU2wTLvPAt3Lfx9zyDPFjns72N+bm4hTnI1vB6+g7XR/AsPPgm7BZbGcoMeJhysKbWjnifXHnm/eSXeNjvc1hCnvB3VrvFeHfaJ4ZMdXbBQ7OZSP6h2bwZDAbD6w5jkA0Gg8FgMBgMhgDXExQyKqXzeOysyMoY7NDSY8+01Z5Dl7iUgEXKqwzF2IbN1xVDJhrHQYQtGb30Ep9Vz6nvPUe3F5+BPWs89QERHTLS7efUPNM+rPUFGNLFdWgoO498SMa0ruzP7P8L2SXqH67W2b4P42h/0eN3i6wP7VW7GHvlBVjVpU+2/Hg4tGkD/e88VvaS+mWy7EuPAquuPgqNW2AOF5+gTOWCrNYTanprnnmvH/ufRURqz9CXDvXMqj0up4FdmdrwUU+sFndlzPAUhmOUjF8Or3V1kJ3Nz2Z1uMWZZ6c1ctnpl5UVViZZ+zb5kmANQllbZ3nHwJMoZK6VMSZjmFP7rDpfFzUdsLTa36hCBl+/OyBzPHnVvsv1M5qz4RvRUu3LmGSNpWZ9bu41wlrnPigT9blGK2A3q13q5AcMdKF9Yf000CDr89NnO7QfG615bbOISP3Yr+vBA7zGI7LN+huC2yox7dhiv3QkHZKpPoFe/ekVdhkKTp/aMmrQhogPsUn5CGg7es3xMdjaRmB5mI5QZtLExQn7UOmhT809zMXZO75v22d43tfaWG/1MWzdSlouZhev3tMqNcTZQY/1Y74qF+ibhqRoCImISDbkfZ/4nS+DwWAwvL4wBtlgMBgMBoPBYAhwLQxyPC2kctR3Uc2tDvWLhwN/0TnYmNoh2TJ1sSDr1DzA57WjkGnDSzIYsx26MOQZr6U2OIg/bjDmOj0GY9jcB2OpQRqtue9FRBKyrzMhJSISs12to3ruqaH4pMfveJJe6ysZqHGu32+4MsroTekaUDlkH3nKf9LE57VDr4vVPrT2yZ4fkXnNwYwnp3SXqGRBGe8aISJu7qtHNY6TbHAQ4az6XRd8Uce1ccZwDA32CEIylHFVllTDMuI5Rwdlh0U8yxypA4UGacyxwcoAh+24Oti3gq4WMqdjRn/JfFPrHLPfjqXVOPHAKUJDWSLtozK96mZRmWXmRXyktIufntdU12eDStA029Zoc9VOc+xxfZbhFRHJetTMkl2u9BiacUk2lSEm6cDPgQbTxAP2n5HtKQN24gnar40CyvMK7WR9dW4hc3zFueY9TYeePU3UEYbhKB/3NlkWH8f8WoNrREQSxkSnTBSP9Stl1an3jwq/RtOB6vAj9kHrwPhqdGuJxO8KDE7wnByWKHO32Mc1ZNx1lyjcPaqd8gfuOlR765wTXst1kAa/3rQvZRIZg2wwGAx/BWAMssFgMBgMBoPBEOBaGOQijeVqvelOd/c3yZB2vXtBfQAmb7QK9s95r5KIGqxTixp7Bwdl8OIrsEnjRVwzXCXLdQn2MbnwJ937W4ztvaKrBE+rLyyAFbzYwpAbO56l0xP18xrkdKi+y2Tv4sAhgL7DlzfU8xn1j9Z5sn6/w/aD0/4kXyckDOuHzZlrJm3qIvu+b/EU89Hf0DlF4Suy9GkPczwJvJOVOVSmrX4BZlejoLMP6SuceVeOUmOh1auZ7FmU0A9ZfYsDHW40x0Rr5O+8i4WUnlLTdrSMc25Q9jYlUx1qkOf0vbl6M6t+WZnwOJhrsswa/ez00vNa4Uu/k6AoqLN2Thpkg1VHHOqW1Zt53qtZx+EirQO2Xt0ydJ7UrcKNXec6KBO/jT4Nf457qM9A/hA05sUu7n8yCh5pDrVOjb0+Lxe3ua7XZqPbRUTuPtgVEZEXLejVI66/5pv0Nr6BtXRxz5epXKDtxT7K/Jd3/oGIiPz7W/8pytAsRXdbRESGK+jnxRvUVB/zOd1A/WvvwsWi/7N1V0bdZhLeymmDUfA1fKGuEsVtf77g23fhYvG1hR0REfmTxTuo9zb7zjkJn+3uQ/RpkzHk3Tc41xnmYtxWX3Y/B1c8F1Fk3qHHYDAYDK8vjEE2GAwGg8FgMBgCXI8GuT+S6p99LhG1qFsH9DvdPXDXqItA4xRMlDoEFHy/fnQTn1/0fcXKtDGtrEltaKcDSio6B5M4PThyRVbOofktzlDv5inqLZ9ui4jIjSsyk8enrkxNE/tUR6rtkjG8cfkGx+n10cXj56i/z/r3cDp+gXrS6T7GvhYkdDn2kql+5fYertmDt2ypyWo7ft6URaw/xbjKlyhT03S3HuYgC/W36vXLJLW8q/rvE3x/+xbKnnv3j+k3YF+Q7WHeilVqdy/IwDbBNiddz86Vz1+ivruo7/IdMG6tR6gjYnpi6HwxfRvUXfaCzhD0fE7IVMsxyyb+fzfHpKom+AZYxZj1a+JdybkQEbcWh19De7VtMPqyfywhytteIx73wMZObmD9ahJd8hz3I+ksyCugvlsWcN+jEdlfap3LW6g/Xu4Ejaq4njsHmgj3GVhOWQ2uJWp/iPtRPUP9C3Rhiabo09JnuE+VnXNXZrKFnZVMP+P97/Tvoq4umNfGjhfTPl2Ef/cSiFfJLjEH3WyJdXHN/tQzuwufoH59xv7WP/+PRUTk5ku6PtTpcfzc9y3jzkdMLX2Rop3KLq4Z/x76sbA/Ccrg5/5tMtbPOOaXKLP4MRj4x5vLrswnH74tIiI/3ngoIiJv5Y9Q7xf4fvnH+D2QL/idq8YhWeWPH4uIyNbSV9H+MeZJE/vkuSsiWZf9TCJ50TcRssFgMLzuMAbZYDAYDAaDwWAIYH8gGwwGg8FgMBgMAa5FYiFJIvHigpMojNewxV+58FHGarMVURpQUmYQc/s855Z+Egd/s+shvSHjfBkmMlnGtmzGLfA42FovlrD9GbHeyQoPpu1h23S6hu+zvpcKiB7GSjXZgDZcI7Q7WMH2a5b500xpsz7T7/SSW+ra/gnCMfIVfzDJ9ZH1VLooO13GNXkTW8TVvt/Kjzhvk1Vuw/fas+1MdGs3ODjWqM+MI9YgDdqwucN0Qz8H2SHnkJKBVGUntGxLh9yCHvgyucZPs0ydccfRKaOaKWcJ20mP0I6TQ0wC2zgRKXkArwzWgbNvY5+Smh7Ao4RDJRZBO3rAU60G1QpQA0oUyYm3CCz7+C7lOojmpDbCcUlwoEvlI5Hau6mdnM4N7QXDvjmJBdedmsfp2OPk1f9bp02Eb8QTHlRkrPO0qYEufG3WXinr1oYeXExmD51OFvypsoKdUVlE5YKHGSt8JjQuvRLMQaaHDDGeVodzXurz/2r0strVTWvRzLh0bkbLjFJ/7O3kygqtIccF+8p5ou3adJF2g6FrXYf9bfP+qAUhu1RUs5m5QBna3zHC3AWTXGqaCV407EhEJKlSNpVEM3UZDAaD4fWEMcgGg8FgMBgMBkOA67F5q2UyemtTBhtgY0a0RVuqbrprKic4MNS7CyZUGZzWE7A+J++D6a2de7syZYLq+2B9BjdoebZJBraH98sfeQbs8j5Yq8o66jv+Kr5bT95g+yhT3bjjyiQ0+S/IUGmIQIWHgvZ/BUxSduFt0TajeyIicv4Wo6xbOKg2WsUcdKZgrPa+6RlkF6dLUmypiQN+3Xv4QtnA5bo/ANV4Dobz+GuYl9UCh74u72r8Nea1euAPWl2+gTaTMeOHlzAnQ0YM1z9nPHEYs6yslzL6K2T0d8kC1xmHfXPJFanoocMqbfIqs0yl49E2V307WuYW18YeDlhGegBOdxqWvHWfstjKuOab6EOyjYORwgNwyhqLiBTPceBNo5HLNtZDorsFal+3FOxysG/5Ig+QdTGn42+8ifEy2EUPH870lwcjlaGMldHlYT09NMgv8R2tB92OBQ9pCl/zoG+rP8N9qP4Qh8yiFczBWgVzW/3oxWwdIpI9I3utFnS8L+knz0REZGEb66RY9u1s/bHubmBcFR6mrB9j3U2f4SDeUhb86uABVT1Mmf8Z+tb+0y9mx7Oz74oku/hseYhDlPEZD9xuw2Zu4TkO6VV/9NiV0TnUnSM9yKk7FXEFse53/sDfn+59hso8pQ0b78/6v+Da4SHhOPe0c6N6H/VzJ6bxlIcLeYC08ZJreME/225NpInEo9ldEYPBYDC8fjAG2WAwGAwGg8FgCHA9Nm+TXKp7Pakcgn26fEhWc9fbe0VkmVo5mENlG5NTMKStHbB21RPP/riY2yEYmQYZvngKxrLxkrHLO97mrdZmUAftyjrUblY+hz1VvQXGqvFZYPdFBtRFTSujNwQztfwJWKhK1wc3ZM/RZqMDFrj2HJrj7JKs0gHq73zh0wRc1HQd7dSf0GquBOM2bWJO6ruh1R2YrfY2GOn0FN8pd5W9hHVXWfUxyO1Hqtllu7SNq43BTCuTF4ZxpGTLCtV1P5u7hkxpRms6EZFpD/cublLnTS1oQQs9F3hx6Oc6WcN8qL2fMp56jTJ80vXa4DBoREQk4XcuvOSYcxBGWjOuORrSpo7159QZK5OdnHn2VDXVseqieb8yZUjVZi6w7lN9csRX7YOytskKLMfUdhBvOJ5TrJmYuvwprRCVzYyOTlyR3b/9NRERuZnDriyvYa0cfBuP8EYdrGflwrOX0xqeteohNegXYLOv7qBPuuOjOw0iIjt/HT/XGDzSfoGdkZOv4/O3n6Odw7+25sos/wL3P/0Ca+PtvwmW++Axdm3GLaz31R/54CA9R3DyHlnhPn5nrP4JnsWjD3Bd9ewtV2Ze+6s7BrrDpCFEO7/pd0byBdyHxirGHv/PWG97vw1GfPUj7Fzo7pGIyOnbuObGz/HZ+dexZpt7eOpGywwuavsy1S7DZNJI8n/qd5oMBoPB8HrCGGSDwWAwGAwGgyHA9bhYlKXIZCplk9HPIzJg40CLF886RMRjMm2MAE6uWGbi2UJliCI6RMQVnia/IlPJ9+Ff+cklWEUNeYgYf60n3bMeWeBqwPKoRlOngwyfC31QYjl99XR61p8NsdD2lG1U1lhEpEjmTuwT8XS2bDT2TLU6JyS3wbCp1jFeAMNX1jCOKAhYUSeDSJlX1d3yfmhoRRgUUmr4ijpDbEHbGtPloWyT/QvcJeKn6Ge8hPrGjCGuqHtFn7roUOu8gmuTjO4BbC8i46usatTwbKOoi4TqpLfA/kVHZGA1eGUUBLlQ+1ssgt2M9X5oX7SuDc+ExnTjKBkxrDsIkc4NdeUSxG0rMxx3FmfGo1rkSB0xFjxT7aCsPcNSYjLjrq661xOv/hT3UncqFGsV9L/JHRFlzEVEKlzz5RldRci4166oLz5VJxTPvI+WMfbGIcZYO2R9Je5HyfAfZY1FPHOck/H+6PvfERGRB4/Qbl5HP6JtH4BTPUP51RL9j+hMUXJ3Y/Wn+LzxhQ/00fnP9Z4OZt1Fml2sreVVr3nPq3TWWcScauT4yie4P5XH3BGp+B2YlRxt6/Ox+DHvMZ+FjOuhqPlfn/EFdz6SWJJh8PwaDAaD4bWEMcgGg8FgMBgMBkOAa2GQyyyR6caiDG6A8Rp18Hd3OvROBFkdTOfwlrpYgMFrkr26vInva83Az5fsa43a4NEGGLfLTXS7Qr/SxQuv8x1t0iOZnsIXt3ltF9doH7MFP/R0QGaKOkRleLMeru3ex+eVnv9/onYApq3P+poC9mq0gnEsHOP73m3PTKnHrLpzJCOU6d2hp20Dc5IF/tFVspUXN1FY3UD6tzHOygLKVvd9O4M7jIkmK1cjcz1dwvxVVGc89UyX6q8jdXJoYVxxl5HgZKqnHc9qVslAKoud05e2JDuvLL76V4t4z9ySzgnRAT2MyZaq04J0/BxE9MFW54lJB+PIemDNHbs99axztA3dra4zjbSOSq5JHeeCd01JyHxPuROSTtH/8QY9qE/oSDH02u1oVJvtv8xBnTUCdwltu1hozFyqY3fz2fHf14+4G0Nte8RIc8fwUtNdpsEjTa9p9WSOq+iDMqN6/9XhQ0SkvY3Psi7GqBr3BWqei0vopNPDwD+a3tLKztcPML5oD33NOPZpoCuPyWZnvHeqj56S+c/6nLdAv65ji3VnQvXevG8694tP/TkGfT7TwIZaxMdTqz91Gfhjx1t4dmN6nUeX3BVQ/bp6XLf8vLkdgzh2/TIYDAbD6wtjkA0Gg8FgMBgMhgDXwyBHkUzriUvF0hQuPWkvIpJSL6wODi7Jqqaf4/30yv/NrgxyUZ0tq0xrTKKmrAan1uvKAicz1+a8ZsK+RXnwvwEZ3aJKtjFVnTR9d+t0n5h6fjBnv7W+Kceqc1BqulfDl5lnkPMax6N1NGbHgLHNsstFLZtpJ+F8hXpInSeV26rDhfYxU/1t4MYgqtUu515Vy01d9kxKWDq7fOL5OnQMSVBGv9O2/yK2Leyb/qz6Z30/X1fq14HTgM/XP1/XlzU9N3aX2DY/BhGR6C+oR5lrnb+ZMnM8s7pazI8rbFJ9lTmeaH4u+KxEwT0p5nXXqrMl4+raCT2N59vWvr3aJQ/2JWI9qtl391bnIP5L1sFcu+oZXgb+xJHeB9354LkG54fM3zHhOYZCnwGdrliff65nnZugHedmk9HdRpMNk9lzFOF9jGbWlSXpGQwGw+sOY5ANBoPBYDAYDIYA9geywWAwGAwGg8EQ4FokFlFeSOVkJOlQt7MZ5xyEfiR7sGuqU+pQcrs07uGQTP2Uh45OvDWcCwoZ0JbqnIeAKjzgt4et4uTQ25Wlq4xg3seBmsY6tpWr27DHulqGhVPriQ8xEW6p6la6a5eWca1tHm7r+W3YbB9tVjdxCKjG9mLVSZygvea+P0CoKDL0v7bHg0kNjH3c5GHAs+AQmIYhnKDthHZSdYay1HZxECvcom6+5IkkfpTs4qBVtWRoBWOYw6CQhFKBgmEVat1VXGi8Mvpa3fUyhukxDlDp4a+qSmIOEKKih8OiYx944SKZB+hjoZIBfc9DU1H3wpUp52QSKQ+f5eyraP2BpV45xZpJaM2lB9Nyd5iKMp0wKIQHtZI+549b+TW1f2Mfw/5of+MLHojTg2981RCVohcEn+hcHPMwIyOUc44jZjvJsbc42/2774mIyM2zeyIicrWIOT/6JZTdKO6i7DiYqy2svfich+ho81fcRYDIgM9KKIHZ/xX8SqgdoW+LK2jn+Kv4/N4jREB3v+Hj0Nuf8oAiA2ni7/JZ+zH6qgE4rY/82lH7vdP3GSrUxWuLc3v6Hss8v+fK6AFP9562i9kx1uZoU+PlvYXjcANrQgNDlv4nvD//Fuag/YgH7YI5OH+AOV37BM9c/23YxtV36qwL9V91/MFYDREqk0iKrv/cYDAYDK8njEE2GAwGg8FgMBgCXA+DPC0kOb+UeAzmpX5EO6Yzz9KWtIfKTskg6kGY8wuWAYOUngV+TCRsYwaFVJUlJfuXndF+KbBo0ljduIv26sdgZzVIo36AdmLaSoWI5w7haChHndZqLmRERETrP+iwPdRf0cNZZAHrx54Rd5ZjZL6Sc9RRO6YV1QDtpyd+PBFZ0voRxhGfYr4q7KvaY8nEt5PqgSEGK6g9VXLKQ0e0r8p7AdtIW7VYWeUFzpv2g6EW7tCWiDugFrUZlbza5jjItLKPZXgAq8NwCj24pZ/zAJkeqosqngWMgrGJeBY6nmOWw6AQiTlWHZdagpHZ1UNVUWDVFWuwigaDaCAI642aDMsID45pf7UePQDHMjHt2Mq5MYRjLFu0OiPLrBZu4YG7SlfXPl7SLupXK8L4iox1zz8/ZYPMrgbe8DmJ2Z6G88RXfh6TIQN1LmfDaxLNC+H8pYNgDjhWtUzrnaH+db3HUz1c59vRcJJKfzbQp+Q1VW4KhEE7EXeSlMGNGcihz2n1hGuo8Guneor7POapPz2wqOE8cZ+/Q4KgkPop1wrnq9KdzFxb1FPOkR9P2ic7n8YzfTYYDAbD6wljkA0Gg8FgMBgMhgDXEzWdxFIsNmV4AyzapIW/u6udgJ0jczNZa84UrVyAbVI9X2gjFpHlycjkTdtgxK4WUVdeBdPWvOy4MkWDTNAqWN/hCoNCbkJHOG0w8ODOsiuTXYBhKypkZcluJbR3utykPVo7CArZW+ZYGX+9CmZ0zCCN+hH6NFzzbFbOH2MSk9kG+jhanbVuqx778IqUDPJVB+1Uyf6OV8DWpxxvtuP1qlebYEATMoPpfPgHgyiSqg+vmGwwJpps4/A+xld7yajeZdynacszbQ21QWPox+AWrmmNoX2NyLhJI2iH9zDh2kiOqRHu4F4mtAwrl33IjDKEarc1uou+VV9wzTimNNCv7x2ivVWypYw7Thr1mbrGW0GYzT7m9OqW7hiM2WfMV0oGMx76dhIyj8US5i9mX4UsusZJS3XBlVF7sMki+8LdlAp3Sor1JXzfqbkiqz/FOoifMRqZLOWqxpc/3kZdIbvNdqYa5EEbtugprq2eUH8dRIFv/IDPC88PJKeMVx7g+ckPoS9v/sLf03wfc61R1st/ivmqfPYEr2pVuLvvysSnKN+iLlrZetW1r3x8B9d9sePKqG1cssiAGo7LacdZx/pP/DOnz/ukwWeXv1/aH2Ec5fbuK3PQKm/hO77XOGqNFU+5C5YFz4/uGMVxJNGV1/YbDAaD4fWEMcgGg8FgMBgMBkOAa2GQ82os/XstmTL44uwtdYPwbHHrOV4vb4J1yenksBCtsAy6Uj8Ko6bxWl0GSzdYw3eDTZRtbTOKetlHGZ++C1autYMy3TfQl9YuWKXjr+F1+VOvJ75aAsuo7hLabnYBBq9/B+1Uzjy7PSGjevJexvrw3WCd7hJ7YOd694L/QTR/gsRTOkT93ftkuTiMeOrnrUZG+uwt6pOHYBcvtzBfC88Yj/xgzZVRRjpiMkmbzGf/Lhn33/+RzCOhy8OUzhNVdX2gdjyhXjbreMZVHS4KMoPtHXyXn9FVpCDjF5RJ1EWC+top9dHuvWp6T89851RrTJavegiBak7NbkwXCAl0yzldJdIffCYiIiXZxVyDIsiuprsHvgz7VnnJyGfORe3OrZlxFmPPIBeqW95DPQXH/EoYyJdAGXzVaE/JwApdQNKmj5p+8vffFxGRN/53uEdcvok5Pf4q5mTrXz7AGKp+vVUYF61aXf13OGfYzHBRnyv/a+D8K3jNuujb6kdYi4cf4Jo3nt9Gu79x05XpPMKaTPfBsN78956KiEj/EeZt0qYjRdXfn2KxwXqxJlu7mDd9kj//2yjz1sUtV2a8RLeUU9yXq2U4UWQXuB9nD1DnaNnP/cUv4Z6mVZRp/x5dLL6JeWzc5BmCIFxk97uo587voU8n3wZ73npJLTfX0GDD76ZUqecvskjyPwxixQ0Gg8HwWsIYZIPBYDAYDAaDIcC1MMjxpJT67tD5lE7qYHEbe4EP8gHYpVYTrEtOz+HKIVjI9jbYpVrg+hCRcdUT4vGEGtpJyjJgdNLAB7m5h7bru2Am2yvUk+7imtYONcIvvVOExjQ7Zw0yegnbbT+F5rHaDXyQD3qz9e3QIWCC9qMTjLf9YsmPh8VVa9zYoQ9yDbzZpEl/5x3vxpAMxhwr/WkPBqwLLJf6IIcescmImlz1QT5CX+o6TmVkA3eJhE4UhUYWq8sDWVnnKjH099R5FqdkrHnfI2pCS97KInAZiRtk6+mrHGWeVUQh+smOAx2n9lM9htXdgVHDjtENmF11K1BHiJKMuLLCOnLtTwjV6mqEcklGvJyPbhaRknr1iFrgUjPUea36IGu7M0PVa9iHXH2jNTI7dL5QI4gFsNvqwlBqUjKZY3WmEPFx6Mogx5eoX7X8U+py0yu/DqZN/JxeouLxQjzzecm5KQKrX3VnUQePCkX252SOJ02Nbva/bjT6veC8jVXfry4TjFCftvz6UOb2arnKPsz6lmd0xOg+CKLa6aCRNtUSh+21+KzRPSOME0+4xKNLfdb4nvdr0uBzFNgyuyjwOBKLmjYYDIbXH8YgGwwGg8FgMBgMAa7HB3mSS3rUk4IOC61dVJse+vQwPXGeHYEtS5WtPYLWtLEPpi8LfZBzTbQDkxOP6AXMtDrngxykrtV36HvLBLXmAT1m6YPcIjsb+iDHWlwZLvVvpSNBgx7Esz7IFxwr2OXkDO9VfVgw7a1x4JlQZdpU65ycgj1vkCXTE/fZoR+P+iA36WigqYFVZbWY+haenM8mizPjcB7UB3R9oAuAS6ITEVkF0+28gNeYwsZrvswHWdP1YmqMp1twl0iV6VU/5NAHeZ31nnGu1TNW3SDI9IY+yDLnIRyzb9F5d+bz0AfZtclxJerkQScCZcjjFe9mElG37MauPsicpyQhYwAAIABJREFU24Qa2tApQhPynN8xry3nfZBrr+pSX/FBnpvP0Ae5/QyvuqtR52trHfeydsiyF57hL+v0C+azUPZxTSXR/4v5PAU+yI17YLwb+9T30xO4sU/KWNdjkBCZHeA+qMvDjx5BL/3gnGmGV3TPOPfrOuP9ae/QUYUsdsnndOEx+ph1/Xgq/H2gDHjCOU6OuDtENv/iTqh5R/1j6q1Vz67JlJqIKYEP8uIzsuQncIZpvYBeWZ/LaIx5Swf+/qgTTplEM3pmg8FgMLyeMAbZYDAYDAaDwWAIYH8gGwwGg8FgMBgMAa7H5q2Zydl3bshVB9vWF3fx+fLqhrumtYOt7vMHPGDDljuPsaW7/yv4vHbit6Ij7vw2jrFlqoEdgy1stdYPsH26uujLnLwLKULzENughx/gf4CtDMEDR1+j9dl2sOWt9msVbruzXbVuevnbavPmt/1vZBjk0S+hvs7GFvq2jvY24vsiIrLzmz7soUhmD1at0Kbq/KEeSML3S2vesq1F67Td30B/19top3cbE9japfyj77d1L27xcBYVAotfQHZyeQt1LP1jbCvHgY2YnGJ7XGUXKWUYRZcyGUohyht+az0eUZ7Ag3vpEbfyR5S+UGoRb/jxlOeMU9aDaXuwTosZ1ewOBzZ8WIqMuVjSucNzPJQXLzAaetGHcUxfvMQPuwyxoKwgXuA18exhunCM0ZAH+SiTmb6He5k9ZdBFcOAucrHenGy1qWN700MEX8R1vw60/5Ha053Oyj7cQcjlIACHaybZQ33jt7ckRDxi0Maqtzx0EieVDm1BKiCUjmhc9eCutxWsnGvENN5XeSi0usl5G6tswq83N7eUq0QJ5Qs8gJs0KCUJD1EOvHRCRKR6pJHpkCqMuMw0SEZEJF9mmM1T3NOCYUBlg3aMtDFceOYlIwffIQegt5v3uH7Ie0wZShgyo3aSrTuwstNwnPSCgTFdSIeKmp9rNxexHdAzGAyGvwowBtlgMBgMBoPBYAhwPTZv01IaB2OpneghNDCVzX1/cKyyQ5u3OqihgmxthQftGntgZxrHnv1RFivr89CXkkH8YeEFWJ/KnmeZGuusZwf1thg1XH8ONrB+A0z2whN/GFDtsFxQiNrLXdLmjSx39dyzjfUd1reF+ppsT63olPlqvfCsprN5IznaeknbLTKJE7WeOvBsVjrEJDR30Xb1mLZvZKqaT8HSlVXvObWgB9/4WtnhQcgMc5+fBSEchNqRKYojhHEoCyx6gG3kmb+8P5gpo8EX7vAfQzPKl3vuGrVQ08N4yujNlwlZWsfy6sE69qXgNc5uLrBfcz/z0GHBA2LO4k6vCw4DqhVbrGPmgcT0EdjofK7PX9rfkJEWkYhzorZ2IUrtt16jc6vxy8EcXLwBNnPlwQ0R8RHmPeSDSPOAB/vywLKtAUZXA0OiMcY13gQzPVoBMzqpe9bz7GsYT/WYNnkF2PnTrzLa+vtYQ+dv+nlbHqPtCp/H33oH4Syfvf0e2mP0/NLEz/1kCQ9B9x6jzFtoZ7mH+kdbqGv0YD0YD/qUrNX5npH2J5OZcZy/7cdT3MUcL7Q411x/x++hjs4XgVcbMVrl7wEy7f1NHjLk4eBpE2WGy75stcdDpmkkxcfGOxgMBsPrDvtNbjAYDAaDwWAwBLgem7f+SLIfPZJ4BWxqjaEfGpkr4vWHjdBaTEQK6i83zqipHHhmV626SupgKwyzWFgmW8Y6c0YAi4gsntK2iezbZhc6X9lDXzb+P2UqvYYyZdyx07iqPRoZvhvFWyIiEg8Du7EvtlFftz/Tx4VnYGLzA+gk177ntc4ashApi8nI5LV9MHolbcSiHR9/XJAt3TiEhrrc3kU71Hsqw6vzKCJSX+V3tEcrqGmtHsO2qvw68oTjQ88kX3wH9Tefob3BHcx19QTzOO5QfznwLGDlEzCr0zfAah6+C4Zt6TPMW3rB4Iuh30novwWGsPGS90Gt7+qYm2wP4yjrgRZ9FISGiMjwjRWOh2tFAzfOg7VFlvf8VxFV3NhljDTZdG13dN9rqisnqE+1rMkI66C2x0jtt2/P1C3iLc7yFeqguUZi2pVNaH0XTwPrL/ZXdy6uVjC37Q/3WQbPURhecfOPcJ+VIV78U8x9OsBz03jOXRRqrkXEa44PoFvOuUZq9zCOZMAQne1jVySvYR20t6kfZyDJ0i8YBkN7w5Wf+x2H7CnW65TP+/d+/5fRZ85nekmN8onf6cmo+d2gJdqU4RtqI3fv/8Z9yXr+3lef47vBQ0Y/P+Kzvo92lz8ls7v60JVZ+QjraLCOtRl3cP812rr6czzH0vbjubNP28Wnz0VEZFWDW9i34g7OVjRe+Hsa9zVdJJJkGNhBGgwGg+G1hDHIBoPBYDAYDAZDgGthkItmTa5++S25WgbLdHkTf3cvf+Jjlmv7YNQu3mCkMUmZ9lN8vvctfF4/9eycRsjWj8Ai9TerfEX9ava/FLBZ3bcYnMDI6uOv0/3hB2CQzt6B9rC5H2qdGTerLhZkzTQY5OVv1fnej3mTJ/OP3ke9C0/Rx+E65mDpB9AV7/11r6HUMeu/JcufgDk+fwNlpjwUv/yx1y3X9zA/u78Gtm/tp3T0uI1xNahXTi+8Y0j3dmNmHMoKDzcxT/UfPkF/Ag1x+4foVE7XheYAzKuyjxmdIorltp8EMvjp52Dh1ntrM2VKMtfRsl8HrZ/QXSLW9sD+pQzUyC/A0jm3CQlimsnA157TxWIXzKWGcSgrLCIypTtGhzsFyrQ7JwVquGtfBG4M1Bi3u2T0GVBT3GFQxDM6YgQ6bJ3DuKsOHuhrzvYSsqozwSdsO2mh35WnZJSPsaOQcg5CV46LX8VOSOOH0HMXN8Cijhnn3FTnjRveMSTqcXeDeux0i7spzsUCDO/4Tb9GE051XuMz9hhzMlzBnGgAjshmMB7GXNOJZLSBZ0tDOFI6RZQXnuGPeF/yW6i3etifuebsLTwTN//PHVemZBBR8+eYA91lUEeUK45j8ZlncM/exprJNSeEOzu1ww2W5YGAK7871P0G6znBNZNVrPmMO0vxGZnkpcDFgixzmcYz69BgMBgMryeMQTYYDAaDwWAwGAJcC4MsQocGaiujqX5WvnphGVwv4uOk81fLvHrN7LVaV+gcoGywq0cjmflWvYGj8lWmOsqj2TLzdQQyUi2vThtaf+T6NltWJCCW3BzM9lXbn5kDalddOxqDPZdmGwXxx/pdlM9fo/Ycs37C+IyuBRpDrN65+qoMWegUoZ6vrK+o4DVJ58omQTv6s/odsz6NVfbvfTulmmbHyUy9kevblyxjnWy9lu+diwXbUV34l30WMX64nJ+TaVBG59D1nwtsznlDwnbmP9O1pHPLuspgDtIrNeumzzIdKZLx7OfKDuNnsqLqyqHt6iuvjcZ+7Wh96nMc0XlCP9f5i8d+cTl2nt8lQ/o8a184zhkHkelsPdpOoXWM5sYV1lcUs+/Vb5t1xRM/b4mS/fPPi8ZBT2b9q8O2NVI85ly7+YrV5SIYj35XJq84mRgMBoPh9YMxyAaDwWAwGAwGQ4Dr8UEe51J9eS5VykvTIU6g17a7/qI9aP/aE2gXHdt4jGsWnkKzWznx+k7H0o71RD19aQfQHtZfQA8Z7Xu3jEYL32U7cGxYrkJPmH4GnWyz/Qau+4X35tUkrlfYTeo61z6EhrLS9excwlS19gJO/de+QB+yc+gVS+pwVwIdtjLDUzo21B8zZW0Cp4NJM50ZF8pgzJ0n1PseQ/+4QLYsecmxB8lz7UsKSZXhpStGbYS5cB7EAduo/ylFmq53cj4zF0JtbfT5iSuT0/0joV44ptuES9Jj/fnzbVcm3YLjhWpB4w7T0OheoixjfuSdFZzvMD2NE2qS1eWkoM63nHjHg0TrpTOInKK9/MLPbViXSJDMp04rymb/BL6+BRnl0G/ZsaKXdEJR5xWOPWGKYNnz7Tp3liHvv7qOKDus49vpuzJ7fw/1RDmMjzWNcf9X1HscPsnVc39PJy2Uqe8z6fAE9fUfor0+kymzvmc8j/5N3udtPBOt+6jj/BuY27efoP2973p99CqdImqf4Zn4m7/1QxER+f6n32I/0MeNH/g0watllDl5nyl1A6y7Tf5eOPsaHUTO7rkyukszqdMVgxr71i76PFrGvX75u4G7RINONGtY88n/gOfxxd/A+lj/CZ6bvOq5gvMH6EPzB+j36Vcx1uYerh2uqXez1xrXzpk8mYpM969tY85gMBgM/4pgDLLBYDAYDAaDwRDA/kA2GAwGg8FgMBgCXMteYJnFMllvu8CD/ga3bi+9JViFW/WDu9iKLLn93+BBl8ubKFuvB4fAuLVePcIW8WgNW7S6NTxtYJt0IThgc7XELfUSW6m9e9jCrZ4iUGHEeNjoK96mKuYhJd1m9TZvqOv0HUxTpef7tt7F1vOA260JZSWjVZRZOFYLtyAoRItzZzYZYau7ew/XTJv4Ih1627rqHrbmL26ineoxtnn7d3BNrc4t6q4PWBneas+MQze2J+uwpcpOeQ9CuYEeYlNrtrvYstcwE6mzlo1lVyR5vstOMiqZ1ndJTVvk1v6tm74dHjxLbkJqUTC8RK3aojFjkVd8Oy7GWaUN65jriLIdlSiEmO5DVpKqxRzrT/RgnEpHOkEU+BmlJ4sM/aDNm7yP4In4dNbKTUQk4gExtRpzfaU0pqD9mx74Q9OU8NAWzUl6tE+cz2jNh5jc+B7uZftPnuIDDcvJMfaFH+NehIcO6/3hbH95+K/1I0hImgzHKBYbrkz5jzRWnRHnTzEnjQOsZ/n4MfqT+DCOeBv3oaBF2z/6p5BWvPVHtKRTCdNTL7VpcL6yHtZGdoa+Fo+fYXgffiAiIp1/+dSVkRqfJQ350fVAeU5MO77b/9DLjXp3GGldo0XcGSwOb/1T3Jdkn8Ex4WHAnIEwvLfLH/LaI8iOGhqXvuif00iDQtJE0oEFhRgMBsPrDmOQDQaDwWAwGAyGANfCIOeVWC7u1uRqEX9vX9xXqyjPnrbJGPfukn1J1dIMbF0XZ39kfOS7pDZl9RaYov4N1D/YQv2TPXxePfMsoDtgsx/P1NvcB4N0/pABFZln9BSFZjmQoKp1UVf/TRzeujrzfWtvgz3qsr6oZKTtBt43dsC49d4MGlAXOXUn42FDvWbS5iG0kQ+VaNXAFPZI2FV7aPfidsK6UEfa8eO5uInv1NIunrRZBteufMjDbMFhs+IETFrBeO30HOxyzgNrsR4gCyKgJWMABA+oJTycpEyi2n8lQeBFeQYWLmqSvSRjrXZppbPPm/OoE/FM6yX6mLNszH5EteorRYojMODOvk4t4SLet0sfllLoQcTz3uw4biOUoxzw8OHAl3GH9PiZjlnZ4OKS81cN+qbWcDr/HIeOXQ/8xcEa7d3BtYsMHBncwbq4uMWgkBeY48miPwhX3ecOAe3KigaZaT0I2cT7y7uecR2uoW+TBvpfPUF9w1V8vsjDj/1Nzzo3uBMSc5eh8oDz1+YBuCb6nC11XBk9GNu/ycOAtCBMeeCzy/W+3vLtTNcY2ME48uk6ro2usJZ6D5ocnysivYd8o+dvef8nixh7GbPvIx8Ucv4A8954jl2i/j3sPjQY/a12csMbQdz2JXdRsliKbf9cGQwGg+H1hDHIBoPBYDAYDAZDgGthkNPBVJZ/eoaYVRFpHoLZaT/y2czRLiyt1ru0GqOdU7YPNmgzRqxr9STQd5JRiwdgPJu7YKRGL8BINbfB2iXUQIqIrBXQNFZeot7kCixQ+0eIrI34ffMzb1dW1jQQYpb5iQeMDc7AIFYDm7fmz6i/Fdb3CFra9gpYpeTzFyIisvW9t3yFZKY1xrf9KcrUT8CsTZr8/InXBsdDMFubGa5pfX7OuaCGk3Z2ZaBxbWzPMqnRNuy3lk8492Qole0UEUk3GVWt0cwMmXCsrIZXbHt7vIIa5ph6Uo3+lfOZ5mW6vet+Tqgt1khrZR1VA+qs4U6DSmjvphZqSRBDLSJSnIH9DoMolLGNlxmRTK2zWty569peJ682by64hdZ58S+evlK/KzOZZYyFZYsp1mxCDXTZ/xLW2cVq0xqQ90VZzuLMz8HVMuwERw9xn0ruwIywNGVERje79Pd0dAvzVDmiRvcCfRjfRp8G63iOVKsuItJ7iPL1PfShO0K9vQe4ZpP6bD0HICJSPSUzfYQ5fnsN7R0+hKXimOt6pefnerpCyzQy03kV7awccy5itDd8w+vLy4RjXvOx6ugr2qtcYJ3s/npgw1fFXNdXcI1Gfp+9hdeVj/D9tOV3bRwDfYjx5F/FfBVVzMlwC32/agc7MIypL5PI9dNgMBgMry+MQTYYDAaDwWAwGAJcj6N9KYhaTclITcjAhVGspUYmkxXW7zScgdGv8UwZ1kN9YEyXhHjsGS+RWSY0vqJWVmNiJxoywXbHc5mz8/0U8dHSLj42GKdCI3G1PtXoMsxEY2rn455n+qR9JIPnYrCD/mhYiY45YhhHlHutqYhIdOVDMpQRfzVel/WSRQvnTeY02ZE6Ueg4+H0UxDqrvlY/K8k2K0vn7l/kGbWIwR3O1YF6XNUIOxeLoJ2ZfopndtVBwml5g+ATnX8fG812g2CQmXGK+HuorPlkMnOtzoVjtIP+6vxFMmH7c/97BvOrs+HGOD/3XxKhXTtBqeyCzPRVws9RtnKOdpO+XwdxhY4hXQaODFRfDra2Wpl1bRERqR5jnqrcYKl2c77nPNIZo3bm5yA9p1sG6//08K6IiGyea8APNfGB3jvlblON7HM24DrjNdUTfJ/15u6BiBQ1OtGw38kFNNwVMrzVcz9vcU7WN+cuB+9X7Yya5x7135lng2un3FFi4E31bDp7LRniKFiWlS7vexpJnM/+fjIYDAbD6wdjkA0Gg8FgMBgMhgDX42LRSOX8l9bkahHMysU9fL7c8vrB9ktoc88egrFTJ4fOE7w/+A6Yq9pxwLSpi8UJTpGrv/LgJhiaxh70iivNu67MyXuor7mP18Nv4n+ALWqPj76O+hdW1lyZkpReTh1hzHarPfyw/xv0RQ5cLLZKeKUefoD6Oh3oIgfraG8jh2Z0/1f8eFRXqWNfZRTw2VsoM23h+6UFH0/d2muxHhRar0BH3LuN9+0dsKnpwDN66pms41h8jLm4vI3XJUZPq3ZYxHsiF3Rj0Aho914vvOHnLSGDq97JcReMcqHMLvXM8UYw1+qKwfqne+yLRlxrvHMr8JjlLoMos0oWWrW8sUZdV72OdLpD3bP6OLPeWB011BEjDfSqynSTOXYuFu9CSxs/g5Y7dLGQOd26c+FgX/PTM/btS1wsyITr3GsZ1TXHrZafA97LZA+62Mlb8PHW3Qhljqdt3052iHrVc7xcxbqKrzC+6in6cXnHr4PaMa5NrlCv6ntrG+hLSQY+XG9ud4ZzO53QXWYfzHWm+t5gl0OdSBI6rGg7yvDqs5AdeM14vkIXi6eY03yVft5kfwebaGfxie/bwXeEY6ZLCjXijQPGouu6GHqm+qrDtbGFZ7qge4WeschOcf+LSnh/2GYcu/kwGAwGw+sLY5ANBoPBYDAYDIYA15OkF4N9nbTo/VrVZDqvPZ3WU75GroyISJFFvBasS14L9KpkzdQzWX2Kc75OSODkteA0OQnbKevJa/RM5kl6/X7c8u2o9jcn+Vbqe2o0pQl2aTr2bPC0Hs/0Sdub0lJWT7xP655NKpNyZuwTzkXOa/LGbKIfro1mxpzrfFE6qw4BTtcsIlN62Opn0ybZTN6PiK4JQo2liEikrg/0Ai4XMbkxdaXRAt5PljzbmNEX2GmQF8D6xsoskw0s2p4NjsnglW3UE9OpISZjXA6pRW57dk516prCV3TYN7LREesva4ETwQE1wWRpnc6X8+mSAwPWOVIHD45VXVRGy5ib+hnbTYL7wzmM6mSDdY2om4XWHTDijnXWdoYoo7WqLrroeNeHKjWzZQ+scDpAyl7tjGV7dHQJtLTO49kl9FGHTR1xPMUDlg69DrtOBjkdkZk+AwvcOKaGlyxw5Txgg6mTV+Z0eoT64i49qCd4Xw592qPOrep7k3P6SPNeZz3en4u+KxNXqQ1W9w/Vsau7CXX6lUC3XDnn2Ph4RJz77JTresxzBgP/LGTaJNeKOoOom46ON6v53wfqo1wmibv3BoPBYHh9YQyywWAwGAwGg8EQwP5ANhgMBoPBYDAYAlxPUMjlRFa/tyfFArZhNYK18diHcQiDH2ov11mIW+l7CIy4PbglIiLZmd/qdId/aFO1wEM6V2u0qTrh4bAnO67IjaMbIiKSnGArunaKgzbNj3Boq3GI9rN9H8ahh8ncITA9aHWpW8LoW6Xn7dcaH26jD2dbeH3BUIFlbpt/gnCJ2413/HASlZfgVedn4TkOT02bjA1+fOzKRIx8vl2gD/VPcFCs/bQzM04ZefuyBr9TyYAcoL46QzM0tEKDPkRE4jdw0DHqU2ei9m56II5zk4aHpnbQFw3jyHlALD3VC2ibxkOBIv6gmBydsX7VtVBCoOMIQjJ0212lAjG3yZ37XheBNOXRl1iC6cG0M8Yfn5zP1rW28he2o/KMxqfs//RLgkK0v5nqfrAdX/Awmx5GlDBkROeFW/VlE+0UR7hPCSUWcSAvKGPO222s7/iS8pUUz0LB6Ga3HsTLZKJT3jOGvJQbSBeZruD72qGXPpy9hYNvjQPc//Et3Ns+I9TblDXkDf+rI+O90/CXMsVand5kKAzlVZVzHxxUMkJaD8CNb2KeKgwbcZKSG/7+qEVk/hD1xyNKH44xvuYzjP3g13ykdUqVyZi3QQ92jm5i7M2PcW9DqU39iPfwCcJ+ksW30T4tFvWwYFHxcpb4ataK0GAwGAyvN4xBNhgMBoPBYDAYAlwLg1xUUxm8teYOrp0/JLMSe/anvg2G5uIh7cN42KxVByOl9mz14/DQFF4rXTDSwzV0t79JNmsb7SxONl2Zs/fAgLVeor7Td1F/9Qx9Of4q2LqlaniwL5p5VWoyu0Bdp++gveq5/3+icgrrspP3aZ2Wov7hOtpb6oK5Pv2Kt93SwJFpTccHhu38Icashw6L1Nvj1U7x4cl7uGY1R739G2in/Xw2ZEJEZLTKw1g8LNQiIzq4i/FUv3jOcXo7rPIl2EW1dUtYdsr44/ich96CaGY9xJQfncyUyTX6mYf1wjhn0dhpWp1pe+6gH8s42yzxrF+kdmWMjc57YCSjbC58RETKKWOjn2N3IVemV8ccsf1Dz9YXQz28RgaRhwyTTew65McYZxkEkkgxG3jj3hM6N+FcK5z1G+3W1EJtOh/DLSIX92EruPJTXDu4gznt3cX71g4Z2cQfBkwHPACnB/eU6W2h3XEH8zZa8s/CcJPX8KBo5ZJBG+tk1XnYUNefiEjW424G7//b72DOJ008l5MW6q8s+IOXxSL6e0G7wsYh6q+y/rP3MV+rP/XtTBbQ3+wSc321znjtGuro3cd7PQgsInLxLu5h2uC64hoaL/Cw3i08a1FwsO70K2jz9mdg609Zb4vzqKz3cM33rdLjAcI0kvIL4x0MBoPhdYf9JjcYDAaDwWAwGAJcT9S0gIFxNmJKsAVuRy7KNZr9LmY083wsMq6hfdOVRjKzDEk6ba8Moow14ED8R6iejLF+r2y3iLdD03o0KjYmi1lkZNUqvlINDdAIaKcvdjnCaicW9IGzrUyyapHnEUb/JpcaLV1ne7MTldfJhJ17DXLByO9ESc2KaqtZp2qRB157Gi9D46psrWyCIU/I7ClzXKws+DLKlqpl2gZYdDezZESjZa8JdXHNOnZqXbV+Dc1w2l3x1mKi9mor1BWrRl0t1ILQDhdwosEgLfS11PjoSO3kgkCSM2p1V6jhpgZ4uoH3qVrUBXZlJRl2ZVZdX+diql18ddi2Mqo6J9SMO0u4jp/rzmf8budQRETUbG+pxl2BZ2S368GOBYNbVFutevL4lFZxJ1hT2bpn+MsYrHX1Ag9b8wtotpfrYFqLgyMREVl87MNskpf4TLXtj376roiIvPMUGvUq+1TuH7kycQ9tdxgiUjnCPKoOe/FzBO2k275MQt2yarcT1qvvW3zGq70gpjxG/UU2G6++8BhzkxxTFx3oyxc3ydwzAGfhKcYV6u9FRLKuZ8TVAq5Mki+NszcYDAbD6wVjkA0Gg8FgMBgMhgDXo0FOIxmupDJeACs43ADTNjzy1acDsD3D5diVERGpdsHWjGhuoeyaSKALjMD+aIyz1h/lDOfoeNZswNP2Eena4RqDQlp4P9hkEMLgLw7jcAw1GePxKtnHgKEcL1Zm6lPWarjK8S3UZ9pHp4Rjx+sVtZ9DJjFP2mCeBodB2EPemBnzcB3tDlfQTjpUXaTXq151OAcamX1CrekKrq0Hkb+Kgq4RGu4hXUZPqwaZ9yIKwjgcc6zXHGNgRR/vVaubBOxcQccJjZZ2bg/UJJdkU8ugj06XzOWqARiFhppQe6wBG182LtU8q9ZZGPsdBlEoMxxRa+7Y4Xxt5v0Mg6zsL9l4p09WvbSy6EH8cKS7C3oNGV5Xl85n6p+fy9vo0zLZ5cEW2MvLW7inreeMeW77+1NRp5ipBtBkM32Zkr29vB08Pze45ttou8qAEHWx6CzR1WLTz3VzSEa/i/oW3sSc50voa64scS/YFWBQzOVNfNfmrk3lBNdc3Oc6CMJSJiuoLzvAGpquM2KcIR0XtziOYGOmf1+fXa5fzunVan1mjtQRQ0Skdwdz2v4U9fdv0s1Ed5gmqHO4GYTm0P2lyGIpnhvvYDAYDK877De5wWAwGAwGg8EQ4Hp8kM+H0vl/fiYx2aUN1RweeIcAZSY3PqK+kmxMTn/kNz6Bn7DqGPGGcb1k1BY1fpjaTOdLuu99drd+hu/Un3bxQ5ykL/eg3bz/Ib8PNKKqxXXsInW4ym6+/eyBiIjEw6AMPVLf+Ai6W2XeXE5YAAAgAElEQVRGO/TOVfeCN8/v+PEoAz3nT7z8fep/lZ0NfINVS/vgU3i/5jtwm1hQja46LnS9x2xjaWnmO2U8l37M9umIEB+fuTKX34YPcuMFmOPBLcx15Qwn+a/UQeDCz0FKj+biaw9FROT4PZRZ+oxOGJfURV96b+vhX4MvdP05ykY34Mmb04kg2yeTHThSxOoQwfVwdR9lslXMQUn2UT2jRUTSO5iv8+9gXTV30IdshybNXH9X91ZdmewU/e7fwRpJqCWtbUN7On0H9zJkg1WXqt64ukaiHtbs5DbWRxT45GrpnHHkVyt4XlofQbM7uYH7lwca9cUvsCbHd1Ff81Os57yO9R1N0Nfqz164MsUtbMvEh7zPdOGIHt5HPzpggZf/ZNeVGfwtzFtrn+0t0SFENwG4hitdP574HHrefBfrdvDRN1mWXs3qiBFq7jn/zT3M13gR97vC+rf+GOOZLnuNeHaCOb18F/es8QLvE3qpr+zg9fnfedOVWf8+6ulvot54BVpqjZ5vfETv5kWvJ77xR7ynH38uIiILEX2Qqeme3MWOQvXE6/7THtZXmUQST0yDbDAYDK87jEE2GAwGg8FgMBgCXAuDXJallJOp14qOqK2cvJpsJpO5xCn1h1XN6ThgaTVdjWVUlxqTOVaGdKY6La/euSxTkrUtR6NXyqjjgF7jUs/4qvpEZaxFRArt27xrwXh2SsMyLqmPmtxC+6guA1r3ONDfal90HDp/49m5LcOkNmWOx7NaY70/ZQNsXRw4K4xb9Hpu0JOZqX7xFfo8aame1eujM7oiqJZ10qImvEWtq+qWp55R03pr9L/O6TygLiOp7j5UgnlU5lG9mRvaN7oyKGM3CHyQOdd+XEwC1MQ0Mphal4hIMtKxU0dODXqVDiwuPS6QlSdMsMub2Uy9Me+TzkWSBP+L6s4Ix6x9VAcKbUedUUJMa2RwVf+t1t3Zq//rlur1rTsj1Perlr6oxDP9wXcyU69zitHbQWa/CPvG+qJE9f/C8cWsg+0GuwLOWUVvrTrE8Bp1cslrwf1hO86Bhvphp4Xnc5WHMnk1WtHjDPSWdo40qn3PQt0/dcvR3Pwnui7UwSbw6k6NazAYDIa/SrDf6gaDwWAwGAwGQwD7A9lgMBgMBoPBYAhwLRKLqFqR+MF9uXyAQ1PVU2z/p8tBAAG37qfx/8/em8XYlWXZYfve++YhXszBIUgGk0ySmVmVlVlDVldXD6ruKrW6W+ih0DZkwx+24EH9YQgSDP8IFuwvw7Ys/8mDAH3IlgS3LBkyNLndUpekGjKrsrKYE5nJMcggY55evHm49/pjrX3PucFsQZYJCCzs9fMY7535nhdgrLPOWnoEjePJwgEuVk3OMpzBixgOePmqsIuLW0kLF3bGc7gIN5rFcWz9kYu0DkboO6njuLpPO6dohIs1xT1cxOqvuRCG6lPadxX16JTSij4u4Ry+QRsrT9Exw0tZiUo3irjMlvCotXAMKUd/1bOp4tF9qYP2C12Me8QjdY2wrTarWZ1oAxefxi8hYrpEiYCu13iGa/DRVlZn+DLKRgMOmCfFemlu58sYU+uRs+o6eJ1x2wVYV+1/kTHV67TnW9FjeHd+fbZ6XkREjl5mBPibuAwYVzH+8pEeqbuLVicv4bV7DmMotdHucAH9t+Zol7fg/nYrdvPhL4efwz+aD1BWZQCVQ9dPbQ9zP0JmhfTPYB4zy9gHKhE4vpZVkfpTRha/jP6KbRy7t9dwsSvmckWeSqf5BHPsnuWzPUHdyjHWsb/EY/mif+6Pl5Hes2wyEGeMfk7W0O/ES+i+/D/eQzsXcPFuur4hIiIttZfTGOl9dzFW5SR6gTOTQPDCapV2edNHG1mdc/+csdGbuNCnwTCNh5QgUNpT+dDVSfSSK637Lv1DXp7ruEtsIs6eT0Qk6mMRw8d4LZ2lzyOlKbXvY75ydimrk25gj1eX8ZzDh7hcGHcYiMJLtcs/uZzVabyLS4uzDH+ZnsHr7Me0ottFEEnoWfclx7ikF+le+fQ+B80gEpVlHLuLsSpfkiRxcjGDwWAwvLAwBtlgMBgMBoPBYPDwfKKmk0SCbl8ad8kGKjvYcaxMsE4rKWWKCmBj0hMwyOk5xvn6rJNeHkq8W1EikpAJqz8kM/bYsafpJdh6RduwtgpnwByWbj0REZHpVXze+NhZqWV1C3pDiZfLGGFb6oIlLp141lZkr6bXYZlWfAzmLpmjXdS9dcxn4ZWsTqSXF8leFjcxxnR1Pvd+MHAMlMYDZxe2yE4pG1x/xJhf70Jk+SkjcU/ZyQWMaD77Nz8WEZH4pJvVuXqPrNsumMOFdzCmQMvQvk68y4DTDazB6hKY8Pj7sBwrrINx0wuRqXcqsMwIa6G9XwZeJEvJdjaL7kLX6djmhe8usyxt3VK9gJlnLEVEXt5eExGRcI+M4SHWXMM6Flc8hpJ9n1mGjVigFyIH+YudqX+pjXVaGg9NhjXRmOJFPlt/Drq/Sny2jJxOlBWmXaJ4QSHbvwXrsqUf0x7vTdjl7bwJhnfxfUYntxztHNdBeUenAl1SfgdHZGKjsy42+sk38F5tG2Oq72ANDm/geVz4G9hL/S9fyurU7jDmmvvhzn+Cslf+OsavFyHrbbff1Fatcw3fH42Tr+3Bhm/z38P3ZuUdZ90XXMvb7MVXcYKRWe3No83D627d9j+HI4vxHOpc+Qs/Rr+/+aaIiMwMYWuXVtx+638da137x++j3a99XkREirs87VpEP9MrnkXgiUZNh5K+78WKGwwGg+GFhDHIBoPBYDAYDAaDh8Bnw/51Ub54IT37n/9ZuXgDrGw5Aut05865rEyhQyuoi2CxkoSazdvQLbZ+FnUP2k5HmsS0/roH9nJ8CQzhtYvbuf4f/sCFcYyXVAsI1uzrr8Ps/3u3EPZRqOHzRt2xgsc7DHmoMnRjQEuwQ7z+l7/9e5jP8ExW53/7w58XEZHKBQZrHGIezSWwZMnbYOVe/ZOfZnUuVMFePh2CIfzhXbC2r66BeTtbBRv5BzdfzerMvg9ma/7bYMCf/ACsWelzYM1GtGErvu+CDsK3wJb2GX8bbKkVGdjfxkPUqey7Zz+ax3rNPEKZrZ/Fz3O38NpfYbDGK+5UoPGu00qLiPTOo73aFhlyaodVdywiUmozyriFz5bfY5mLDG7Yxc+di85iq0jiMaGMt3cRY5x/H3V658iQen/unfseGL3D69Rsk1gNNXOEhwWjObcG9aec6znOYxM/D3+OpxyfYo0rh25s5cO8hrrUoZ6YZHapo0EVbnBJkf2cQdnyMX6ubeXb6l5yzPv1vwLdcPL4KeaxBuZVbQyTA55GXHPfheD2Q5QlIz68woCL9x6gDhnl+KtuvxU/foS6tACcMoQnbGDu4QwWMu04NjhggI/q5NUGrfCHN1GADH9hZTmrkzAQSCPHpzuYX+E8fmf0Po/Xyh+8n9UJ5/G9SXj6ENKyTZZxgtG/jnmW/9F7WZ3JL70hIiKlI3zfY96FCL+LsRUugwlPD11ozuhL+F0xoh5+5gOewPBEIX6CE6vQY+szq7sgkO/v/560x7vPevQZDAaD4YWBMcgGg8FgMBgMBoOH58IgzzTOp199/c9IZw1skAYFaLyviHOnGM+S1aSuuPIErGnnOhjXwsBzsWDARGUTDN54GUxVf0V1uSjXuu10iglDMFK6ZZysgWWqb+Vvlo9bTqdY3SULdyoAQPXQm9/gzf6BW6vFD/psp8Rxg9WcNNBu/QFYrsM357M60xrGVOijnfo26MzBEsY8pRtD675jaUsPwawd/ywYw5nbYI4Hl5q5edY/Pcjq9K4vcLx0y+jnA1U02ri62cveO3gdWta5O+h794tk9h+ibn+Zzgp1R4wtv4v6g7NY48PrKDN3F/2WTmLO2/0dNphHmWJP12DENcA61p+g/+GSc9go9BjcwGaOX0bZ1gOs35ThI4W+2zvVh2AEd38OrGmJ/VV3GQpT4P646LSnjU3Mtb2G9ypHaG+iz21Ed4u+2wfVLezx4QrWtEiHEtWkalCIH0iiz2w4x8AObsX5j8DK9ldrXCu3biv/AGxwFoZxAK1ugexwrAzy1GmdM40z3R2CMkNYyIQqe+trvIOXyEAfUCOu+v857I/k4Qbb9kI/etgHYQXPbPI1MNKlmxhzFrBBdwg0QEcNMtVB5riB73J0gdHz+4dZlbiDzwqX8F2In2xyzjofnD6lN9ayOmGf320yx9EO58U6ypBL6AWSzLVyc1emPZ2M8/14QUWpF4D0Tvz7cpIeGoNsMBgMLzCMQTYYDAaDwWAwGDw8HxeLALG5yoBqrKvGCIuIhBN8FldYJqZulBGvU30/ccRLUNAYYsYSV/JllVFMvX78aFoRkSllssoOqxtEFjUrLgpXo3E1Zjec8BZ+Rma6OkmJ8cNVjdfVaNxT86q6OjpuydYHdScsE/Pye+LNR+Opp5UwN1f9+XS5fD8oG9FzWplL+YxDg1DNKTQemmRscOpn8Xmx4I8oo+3z/axfcdG/WTv0uj5d518F6pOtSP2x8WQk867Woqd5vfTZf5+ej55UZGU/a4h/xGfBNH3mfW0/6+9UHPK/tJ/JqfhwjTzX2PLI2zt+/Lj3WRZBrsyxX4cMdfYc2F/m6KHR8J918hTm92QWPa/f9dyz5XjVaWWabz8l65x+Rpx8Frt+aq8oYx4MvToaF53kiuZdRbx+MQ+uxx9BHzjPY69/rR8Y52AwGAw/DbDf5gaDwWAwGAwGg4fnk6QXp1Joj6TUBYOjSXHFttMgJyV0VTqmXpCsZsC0utrOJPe+iGPaojY0gGX1AhbQwppMF4wcU1Ya0Ie2yoS5XbKo9FnN/ErPOweG7D2yvlmSXg9jq+7SL9Yj7yKyVNVtpf80IVDpYToh7LmxuSQ9jqWDNlTWG5PlLh67dVPv2rquzwk0upVDugxQ2xr0PN3yCfTJhb6ydFzHISbQvlzmz07nq64RxT7eU0eKaIzn1l9WZtytwWQGz7t7FmPorWE+xR5Z76qm8znatruKf5dVopmiv8Ei5h6N8FwGC47VLCojzjUeLKtLBtMZ+SjLbc/5Yhlr0FmjuwQ/C2OmFnIvDVZcHf2st8r5NahJn+jP1CJ7WvQgwVr2VjDeEvXWZZ4ODKm51rQ/Ecd0j+Z4csDx1/axFrqe45a4Osrsqo+46nn12dKHOT5x6W7RIrToiSbNkVEOa9Aeh3SfyHS4IhLSDzgbrfo4UyMcznJQucRL7jPqkqMe9+qi09+LiERR/nQH8yLLPM+0SvV+PsL81IFDRCTdwjinF6ArD/ndUEeM+Bj64umbzjal8qk6TtBrmsmAQa+U70f12OK00upSoeumzHJEn+rEc/IQPSVJ0sxBx2AwGAwvLoxBNhgMBoPBYDAYPNh/kA0Gg8FgMBgMBg/PRWIxqUey+1ZLTpDQKikv6zQfllxHPJbun8lf5GvdxxH13puUF3S8C108xZ15xKPncyjTP6cXYvCyWHWG/cO5MFe3/SqOR5t30U+sAQg1d0xeudDkZ/hZQx7Kx7SV+1XYSw17bj7TKo609Yi+yJPtMVOCFz/A0j79RlZFUoaUBAMc1TbWcbbeO4/BJjW8zn7kztbnZzGojW/i+HrxJo7Yj6/y+J/3heLS2azO/uu0PaPqQgNBphUcI5fbp24siUj9KW339iecO/qt7aGDlMfL3VVXZ8oLiuU25SSPC6zLaG5KSVQyICJS2+FlRoZlVPcoiWG0dDTkcXbq6uizTCjvCCl1KdNGLi49eyGycAKZSpNrrM2ptEIveBb6bj5q31Y5wIfVXXR8dINWekxL1/mKOFvCUpfyGcaSR7QKbDzFGIcLTpuSXRSldKR8rBIY2hrSXs63x1P5wvRThHxEKnXg0b5KKyIvjCNm+Ibar4XnEHQzfYgwELVnK6y5cJF0i3aBlD7E99fZHzZ2OsCm8uPDszG2aAVXLeT6UWlMUPDW4JRlmpYN1YrucwjrSN/9yLVP+UXwPsJ/El42VMlIcAkhOtFPHrg1uIL39OKeSrriB49Rl2EjSdfJJaIb7JuXgyPOVe3yVJKitnlogHsiNHmFwWAw/DTAGGSDwWAwGAwGg8HDc2GQo3EqrfWJBLzBpcEHM4+clZIGaRT7tGIi4dJ4BApvNAO2ttTzL/8wTIKBI9EYbE805MU0sjbNdXdBrdwu5eoqM6mBF8p6+jZvlUOyZbxIFY1Rt3SC8R9/Aoa52nN1WutjzouhEsdkCmcxtvpjMFIz9xwbPK3ykg/v4M08jnPziWndNrPubKrKT3BhqPkAF5Oaj1A5DckKsqj2JyIyqfOS3pBRxtn89NIZ1ri87YJCJjOMrD7iJcA9sGPlI6yBMr7+31S1p3h2E4a/jFpYi+o+n3UHdYPYMe/O5k/YH+ZT4djKe2gzjdxaF7tksXnZb0pGtLJPBnGKfsOR2zuhXuzstDhnPtPjSb79wI1NA2OmZAb1mTbXGc1NZrzYdRcvy7tKQYPFLHDOES946sVPdx3St9sjI8rvQmmPl84KDZbzvp608QvJJKdDtB82NUObFm7+xTEyt8kQaxxo6AYt2rLgkK7bB7KAy3LBCdrJ2FmGZ8RHCCRR5lfEMdEJ25+8Bqa6WtEjmShXzkeg82I/SR/rqd/foOiej16Wi5ooG080Gh7zK7R5GXHJuxzINYibmGthBwx5xN83Mefp29YFAwarHIGVT8imJ1zzjMkO3B5NU7XHM87BYDAYfhpgv80NBoPBYDAYDAYPz4VBHjcD2filohSugI1ZmsHrk49WsjLlAzBf/Sv5oIPGbbKdPw/2afvIMVPplDrVh9T7XgI7d+EStJXtARiqrdZcVqd3CWxPRKuxG19B3O2nP1xDm+fBNiUTT995TOa7Quu5MbWhB9Cv/unf/H9EROTjrtP5/rD6Gtq7DlZsukmmbQUM7KQOPebCbzzJ6ixUUPaETPidjyHoPffyNl4bYIvf/fHLWZ25c2COy7+JOT9ZwJqOX1NrK1qtnXNMdfcNssx9rEHtMRneecxv5R0NTWm4NSBjl9KOLwst0cAGsp6qIRcR6a7R/o7MrWqD40qeIT256LZZfRdMW28F7daektEnsz+Zp83bkhd8wihpZf1HLTLJZOT7i7Ruc2SjVLbI6DJ+ejSrUdlkTfn4/ejsSZPtaNckFdvX8RreIgPvBZSM59VyMG9hFlOHWzoc5MYq4tjrcRPtZZHWC2RRafc3mnV7dHwBlm3hI0Y9v4E45ymDYyIyr9NrF7I60UfQ4obz0A+PLjN2+2O0IdTUDr7sbNGqG2RNV8jCHh1zzLTho3VcUHU2iSFtz+Iz+B52Vsm4U7sb8E5CtOAxuxo8co6/I6gJ1jJ7b/D74yTIUjiP7990A9+pTG9NO7b267CEm/knd7I6hz+LMuUTjKV5iO9JugkdcXR1jQWPszrtL6Mf3TuVHaxt1OH36im+r74FXcZAh6EETzwvRIPBYDC8kDAG2WAwGAwGg8Fg8BCknxUZ+/8RM43z6Vtv/K4cvwwGrLbPwIi20yBP62CVlD1T1rF0AIZtcBZspM/OKatZ3oKLxHQO7Y/mQRUO58CezTxywRpRH31riIW6B4SM/K1uoWz7qgsGaG6ASVPmLmLwiAYe7Lw1k2tDRKT1YJx7T/W9qpNVrWvvvFOfjpr4rEIXiUIv71aggRqquRYRKa3viYhI9w3cxq9tYC0G58H+jlpYg7n3D7M6nRtg8pQBU7a0SE311s9h7jPrTrO7/XOYx+xHKHz0FsrW7mEdB2djzs+twcp3UbZ9hQ4OX4Q+dfQBGMXykQaFZFWkdwntlPeozaVpwgDkpsw8RPsaBiIiUuyQ3abm8/h1POPmHQ0kEfbn+mluoszGr7CNIzqHgKjM9mH7utMT15/QqeMa9eWH3DtkxnUehYEbW2MDY+ud43M/0bHg/eGCOla4sWlQyHiOe6eO57D4Q6xj5zIZ5ln3fK7/xVsYw8tgiNOffCIiTksr1MXGHhNaOAt2Nj2t/aUmWFng6ZOn7qNXr+EfW9h3cmYxV1X1uWm7497UoJAZBmtQI5w5O6jmuT98pk5ygnaCVbC2AWOkk22cmASXnG1K+gShH5O3QOmXbuJ0SJ01kjH27OSbb2Z1Kj+8i3bo+jG6jPmUNsmUr4NN19AUEZF4H5tS3T2mytozyjo6A1ZaNdf+HCVJ5O3hP5R2cmB2FgaDwfACwxhkg8FgMBgMBoPBw/PRIM+Gsv4bNVn4AnWyh2BcZdPpFJV5TGaoPZzQ7WEdbGP/Fer7Rp6WM8b/3xsPwPoMzlDTeBZll+fBAj36idM6q2502qCn8BpoxaMDMEQRdcXNlx3d+PAho2OpQQ4H1I+2UfYbv/6eiIgcjh3r/KMfgWlLi8pukiWrY36Veyhb+cpBVme1BY3x+hG1mjsYU30Jmu1qCQzYw0+cVnPhA7BYB38CLFn5Q9TtXwOTV65jLfoe0zd4C4zhpENHjyG1s4xxTpZRZ1pzPq61c2Dy+gdg2r56HfrVd0bwhF28AGbyF87ey+r8vf5baG8J7X3jPBi93++9IiIi4yP0XznjGMxWkX60F8m8foi5xpcxv3YBY4zXnDNJjzrrtIDn87XPYQxvT/AMSivQiLaHnvbzHTpdLGJe4TI9jeepcY/Q/7WXtrIqd6pgMV+7Ckb1k03sq69exlrcPoA7w6Gnk2/TqWF6Dmsw5FiLJ0ogop/xsnMmEX4XzpzDHuyPUKd9Fftwcglt3biwnVVRdweNGpclaIHjvbwrQ3TlUlYnPSY7yxjn8QW8Fm9Bw5u08f0pXHZ1preo3yVbH6VYt5hlw9fA3qZPNrM60Rms0+AG1qfyLvcIWe2UrHAWky0iQafHz+gMsYXfHcllnJSEQ2rqu55RNRnc0jbZa43OJqMrs1iDyo+dD7Ky5TFjqjs/j/bn38H4o6XF/BhFpHABrPXRV8+hWbLlQY2e2nS+0JhsEc+/OU1FRkYeGwwGw4sOY5ANBoPBYDAYDAYPz4VBlkoshasd+fXzH4uISO0C2Jj/tfFWVuSkA/blzYtgr8YUdH6cQFP577z+roiI3Ok6lqk/BWN0OwTrc+kydJG/df6miIhEZOf+8tNvZXUWV8B0Tck+/4Ub/0hERP67e39cRESWXwL7s1pzWs0fRWBpZ6tkMYdgBff3oan8K+ffxtgmjgn9jad/RkRE3rqABLA7Rxj3a/Ng/b5z8Dn0f/0Psjovl/DZ3TNg2v5q7edFRORPrf5IRETWSvuok/5mVuekC4brP/r890RE5H/q/6KIiPyxV8D0HY3AVH+w5ljNb1y+LyIiT/tg4e5tYWx1ss2DT8BUOpZTZPIhyjYeY01/ePeyiIg07+E5HXXBWP6o6HTlEbW4hYdYr380/LyIiJS3UadA3+hezQlwp7tgeSdLaGdhHe93BPujsos6XY/dLjFdMaZG/N3HeF71DbCDPXoQ+/ro2h6Yz/FP8AxH1PsW1a6WBxV3Hp7J6lToPvBxjD1ZfUIXjvMY28EjMLClA/d3peqsh9NybqyqW64cot9u37Hbmgi4PcGalvapx95nXQqWPw3cyci1V3kq8xiMd3wDrG80j+emnr2DS87RpUIGOWkysfEMxji3zudB9rb7OddPnT7KqieOn6K/iCl5kvCUhW4WIk5znLmAfA3MfvWffZyrI59x3yG6uJrrJ5yi7PEvYP+1/vEtV3gZfaaPwPCHHFO8hDU4ugEGefamqzP4LfwOqm5j72v6onoqx0v4LkT77axO+y38vhnOUUOvaXwcW7CO3wOqRRYRSYueN/Kj5/Nr1WAwGAz/5mAMssFgMBgMBoPB4MH+g2wwGAwGg8FgMHh4LjZvreJy+rXFf0uEF4WmizieLa7vZGXUzink5SJheEDCS0bpVRybR23Pkopjy+JzZ3mkyvajNiQReuQqIhJo8ABjZ6dXcdGmcAdWTekqPg8PPZsqvWzDS0VZv7SPGn4JQQqFrhedfWsdZdZ4/Lq1z8XA2JIHkF7Im6+46dBaTO3KdH3ic7xwVcXZu1q7iYikXLfkOo7Uwwec66lI4LTvLrUF/EymtNI6ZDywWllpBDEvXomIpK9ijtEWrKvis7g8Fx7zMlUDx/TB2F1MShnuENJOrH8NXm3V+3imQY/PZ+qs1JJVHEtHe07igkkzQpljlbKTWAjtu4QXxlJagoVHbvwip+zMaDE2/Tyf3S7Lcr9JwL8Nzy5lVQLuGZ27zlVjq7Nj9MR9Z9IjHM0HaremNmU9RiavQCITTLxLeqyfViAhSlqQKIR3sJ7B4nyuPxGRwy+jndlPGQHN0IqjL/L9WxiHv6+TFsYU7OGZ6vdILdUmy/yeHrm98/SPYy/WNxlHfoxxH1/FWM/+HmzTRq9fzOpU7nAfb1FC9Je+LCIiL/0dSDhihqRUP3ShOWmT4T+XuY+5pJX3cNHz4Fch05i75Z5xOMA+GK80OG6sQbhzmGvz6a85yYhKXUb8tXPpv4c8q/ctSKCa7+GyXurJgMZn8Xsm+ufvY2xv3sDP+5RvrUCWoTHiIi5ERMJQ3v7kr0q7v2k39QwGg+EFhjHIBoPBYDAYDAaDh+fCINdWLqRX/9Sfl+4aLaEYfKBBDiIi5WNeVrqoMcR4mb2Lsjtfxc+lYy8CmsRjkxfHuudRd3CBVmGMhF5619XRMgWSYidfAIs18wEYyeGihhe48ZeOeQmMpGVE1knDHgrfBqN7eOJs3hrfAVvVwV0iqewxBnkBdVZ+hHk9+VUX9hDVMO54SIu7O2DlepfJVNYZzvGBs8ebv43PHv8afl78Eeq2medQ6KLf2fuun4PP0aKLl+jqW7QaY0Tz3Kfop3TkrK3GDF+pMohk/0tg9mbvkkVfxuK01xxrtvgR6mvwRfccWOD6NoNiunjtXPQu3DEcZVzHM5v7mJfLzmE9K3tg4nqrbq2LPTLhDFLpnse+mrvNkBlePiu6APEAACAASURBVEs9zm72XbCZ/etgrDX+OhyTvWV0tobNYJ0wn/4ZzKO6h/HvvYm1mXlIVrXtGPHSMfbXeBZj0FOGcEwLsj7DK5bcfBKeJAyWeGLQRbvVbcxnNI+2dD1FRBbfA+OevH8b/VxiYEid1n33cWIRXjiX1Yk1vrnBC5znGRzykKcpGgX92tWsTtAjE8rfCxqkEa2iXbVLCxteJDwZ/pAnF0c/g7LN//0dtFn4jOhlngaoRVy8w1MTPpfBt74gIiKVv//DrEo0wxOkDk+jGHQS1DGW6TWc5hTuuBOl3s/gCxoN8Dyqn2BfJAwDCZo88TlxTHX81qusg3lFB+hPGfjkmGz9rIt3F66lRKH84OjvSHuyZwyywWAwvMAwBtlgMBgMBoPBYPDwXPyIolEqsw8mkoZgijSKt77j6S7Jp0Rjhlbwo/oThlZUqmzLMaEhibrGhlo0gVnr0xZrWkOjtV2nDa4cKhusBA7qVPfJams08IpjDhtbtHwqaQw2ypROGFd8EzrVaOhIofoOI5Pbas2FOhNadZUPMaaZjzyLszrWp0hJdZVWZMUuY4LLeJ2979atfh+a3Pmb1Jo+wFoECdpNSui3ue4CFcZk1IqMmi4MUaa2h9fOKvppisPe6xjbfAmsWBt5EJKGeC6DFcYftxz1PlmnzdoKnunxa4zO/oTMaBv9jJtu3TqXGOdNojIaYxR9Po+kiLL9Zfe3m1qnKfPaWWPdYZVj4qlBzxvbObCZu29iLBGIXqntavw26pysubElRTDFJ5Aty2AJP+vJgo590HVja2yFuTUodTCP8gnXoopnMfLWQP8sHSzyPeqhF2l1d3IB6zZ08mhZ/D2ywStgxDX+OKzR4o4aZ2V8RUQChmRkISPb0MlrJHNAjXN6+74b2hxDc5Ql1fapJ9e45fjI2aIpG6xBGrMfUkdOdjY7pYod867jnW6C0Y08RlpEpP4OtMhC1lhEJCbLezoCWoZ4uIUP+ZDPuIVrvMtscd4vSBbQXkBtf7zPuwOBe6aF92ChGC7jOzd9yPsE1O5na3DwGVHTIpKmbp4Gg8FgeDFhDLLBYDAYDAaDweDhuTnap2GQ/Xc7IROrLJ2Iu/mvLGAmAubPGp4QTj1Gj2VSak9Vgxpn7T87DmUglbHWdrU7rZsf+6nXSF/Zj5pbFJ7Va2t7wVDnx+5Vw+nJL5mN4t47NcakpOV8tpFuH6fWNDn15Py1duM/NRa2W+zj52jo2PoSzQ9U71sgE6r62EkjyI1DxLH9xR7GWDjhK5lcZbA14ENEpEjNdDhhGbZfaLDugD8PHMNfyNaWmvBexPaV+WfdkXs+qv3VeWl/2pY+60LfzUfXpUiGuNihE0ZFGWpdI9dPgWMo9sNcG/p+EGt/bj7a97Sa1+OrTrbI+U3r3vdHHS0G6paAMoE6ryQ8QSg6vXfqO2eIZPpeSchwxty7Vad5V5ZUI5o1TjnQKOWx0627jvIMcUI3luBUWT+aWYM6VJ98ut1wDicZSacjp5EO8jpp/R6pE0owdidKUuG4Iyx6MBjn+s+QuJ+DaiX3ka5xVud0XYPBYDD81MEYZIPBYDAYDAaDwcNzYZCDaSqVvaE0qCNWFre6O8rKhEPeCGc0srI/xV0IcivLoE9LHcdqKutcOID+sVLA/+fjIpgi1ShXNh3LNJmnp+yEbgn0720+wVgmdUy52HP9qF44IRMZjslidlCnugNdpq9xrT2lz21SzbVRnKXOeBMazdqOY6OYIJx5s6prQhpg7spUVncdmxacgLasHEBTWzxAv3XOI5xyjfa7WZ3GJlizaIgFKtEvNqHGWfnC4rbTkbaq0G1WNvDe7ByMY5uP0F+xh8GPZ9zfVLWHKFs8gX40KaBM6yE1obp+jVJWZ9ws5sZW2cK4CwO0UeK6FXpOIR31uE7Z6QN8cBvr3DtNna9jKCP6Us88QrvKzpYOVPxMvfnYuUtUN6mzjfFe+QjtVY65Z5Tt7rl+irsdvocxFboYq7pBpFXMvVxza6A+2OVjvKfsf+URtLvRCOxppe0dP6xQV7u1KyIiIZlRUZ9lxkYHgXf6oBpjfpZ0sZeUEVV9sXh+y/F5tBeRRU079IA+y5jnPbhNRAvzrg69q1W33LkMV4zWPY6R2u5kz/l7Z2OYx3dLvaFjlkkZHx3sO51vwHZ0jmGFOnzOT1nveNHplnXPxxVqw398D3UZpZ2QjQ4ij62nHjo9oH841ynW9SObHkbeqYAy4GEowcB4B4PBYHjRYb/JDQaDwWAwGAwGD89HgxyIJMUo0+Oqg0RcdgxLMIlznwUJNbVMo8rqTDwmh3rktKxl8P95ZVojDVjzEq2UKVJd4pSsbUL2eVoNc5+LOOZY21c/3XAU5doIYk/nW8yPyb2yTIGfe3LGbNwh2bJSvo569WpbIpKxe1o3LRVyZUXlmCXHNmZrTLGrjjXhOkYDMqDezXvV6Gr6nrpyBFOwpspUB454zxDQAzbk89A6+uoja0e7Zn9Z2URfPb23jjNRx5BT/cTPasPV41dPA7L5aEqiyld9oxX2qQ4rOlbdD64/b17JqTGcLjM5NT9xDKjWydaUg9K6gS91pYY20/Eqexme+hvXYzWzBELtT50cqE1Wd4mw6DHVuj6n9cr/KqATRKbd13lEz7aRcq11LdIkr5dOStyrvt6XY/K1zGifemzOw1/rpPFHjEnXWtcrdXWy71K2xsGpsmnu/fxgzP7YYDAYfhpgDLLBYDAYDAaDweDB/oNsMBgMBoPBYDB4eC4Si9GSyMPfTeW3byBaNuGZ9D+4/1pWZtzHBZsrF7ZQZ4qu79xC1Owf+9pHIiKy3nWXf7Iyd3FBafYS4na/deFTERGp8Ez/r//g61md6qILzBAR+d1X/omIiPy1e18TEZFaGZdpLs+4yz83txFR26xCr9AZQBfRO8DlnO//yn8rIiJ3p42szp/+wX8gIiJXzyKsYOsEF4OuzONy2Idv46LSf/xrv5/VuV7G3D8dnRURkb/96IsiIvJrq7dERORiCfG3f/nWL7sJvIvY3m/9DiJ3/947qPOF19ZFRGQwxXHwvR9fyKp84Wt3MaYexvSYlwyLFYZJPNJYZzcfjchuPsTzaP8iL0Z+gDb6Z3EEffm1zazO/XfOi4/JBVyWOnqKa4DFDtZvcM27rHlAuccC2pt9B8+2cwmfV3dRp7vmjta1HbXDK76Ei3GHP8bFxf4F1UQ4qcDZ71xBmVfwN+CkRanFiLZftOxLFt2FyOJjXNwan8e+Kj9B2Ru/+EBERN7/BAEVxQN3sa9ygLXUCHO9sKjBJFnE+qonz2Hfo3Pop3CIfV57BftiyGeRXnF7+epfpN0aj//TNax9cIyLimETz3LyykU3n1sMuGBwRuc69sHMO9izeqlu8LnVrE75HYRk6EW1uI1wjoCX2aLrjKX2Ls9FV9ZERKT3CvqpP+XkJ7z8yiCP6OrlrI7w8p/KGBJeiIteeVlERPqz+H1RK7nLjWFrJjdujYmWc7hYeHwNnzf+9jtZnfSXv4R5bWOder/8Ctr9+++hjRuYT3hwnNXpXcHvoO4vIJRl6W1e1uM6po/xHQgqTj+VjSUMRDY9mYvBYDAYXkgYg2wwGAwGg8FgMHh4Lgxy2A+l9m5N/o8+2Jqgh2brT72ACL4+2gRbFfIy3vwjsGX/LP6ciOTjnPVS3MI6yvQ3YTX1tx99VURE0jpYxtYtN42kCBZJ44H/8vG3RESktI0RjBkM8b3VxaxObQP1j8uwlgpJgM3SBe23z//7IiLSHboQhsqHYEnX74Ox0wuFHzbA0rVAxMlfmfulrE5pBg2Pj9FOeQf9/vUNMuAlsJz1Tx1rtvgRWLj/68yXsRYfop+PmYeclLE2Sx+4i2o/KYAVK/QYxd3Ti5H4fMJUX2U3RUQGZ/TCmF6a4qs+Dl4s3G47+7UKY7VHc4zmrkxYlzZYSuzuO2Y3C0lRCzO970TCuKBhHR1v75zkw1FGQ9StsX0NNQm9rAkNMZnWOC/ut9Kxhr/gdTATeXXyF6y0v1ubZ9DGiGET3r2xaMDx8z5cxDFomIi2EXr3ylIuajDSeG0NF8HnAYhxGQ+8dVt/gnZmsUflPljgeDDIjbl01zH8SY9WhNuwTmvwAlnCiGSNnK5+9CSrE5PtnXbBuJ4O8ghP8P7Ui1kO2U6tDkY1PATDn1170wtwxyduDfqD3Pi1H7U1rH+AsSfeRdJkGxZ3ESOg4x1a3vHSXquvX9xWVqfwMeaWjjD+wnI+0jogG5x4QSX1m6w7xOmN3FtHG7wEmJ5aIxHvAp+IpFMvqMRgMBgMLySMQTYYDAaDwWAwGDw8t6hpEZGwy2CCOQY7bDqNXmUfTNCY5I7qMJWNixtqFebr98jCkVBVNlBmyNCQqS6dOJaps8ZYYJI7BdKM5UM00ltFP6Wjf4lOUG29yF7VS5hPZ+AYZI0w7p9De7WnaG8ygzqVI86v/KzFVdRkaMojMoQsU2loIIZjkNWWLm3Smisii8X1Kx/w89CtgbKYIRn4yh41rwv4efUPoScttJ02eO42A04YHlI5gv6yeQ/azPECGPPjqy6EYe5TtKOhLIOb0OZWdsEKRgPMs3vZaZ0rB3hvNIf+mh+CBZycaXFMqDs86+poMIdaf/Uf4Dm0PsFYh2eoUfZisJvvgjmsbkM3Gtc0L5wv/NNw8pH7ClR3wF6O3ifDvw8Wc+erYB3n7jK++titW9TBGkxnsNejHsoEtCcLO2RK/fAKWg6OFtFP6YTsJgNx4hm831t1EdDTr/OE5TvQzkavXkPZWZQp3FpH28tzWR050Zxt7nX2GzBkJKK+d3z9nJtPD+uV2aB9gnblMjTuyYPHaOqS07ynfXoNMhxl8zdxqrLyV8Fca5S1ssY+VHOcbkCfnxxhvx3+zhdERGTub/7IlV0CczzdRFkNK9H2+1dwwlQbuOez98sYi0aMz34HenKhnlm11tJ27Hb3y6hT2eO8rq2JiEh4hF8qCYNRovNn3ERoVyhRKMGmZ5tnMBgMhhcSxiAbDAaDwWAwGAwenguDHI1FGk8TF3DBeOXmRuKVIRv8JK/jbGyBcRvcA5tV7HrhFSzT3ACDGI4x3D5dJlQfWz1wAs9wmv8//+SuxgZT29pFpUnDsY1VMqyqW84cCE4wgAf3wBRFPdd2a59zC8DOFTvUkTJmtkyWsXzfsejKgBcH1AZvaygDOo4rWIPWY7dujXWwmrW7M1wLrFdMNwPVFTeeOA3lcA7tFannLZ8kuX4HjPWueQEb7SuMiebPJxcZ1DACk9tbwc/DBbduGsqi7R1fwdxbJbRV6lD3XXfrNmw5Fl5EpHwemu3eWbTBXAcZzfm68pCv6Lt3jmvcpkvGAspGEzeftIVxH19DGQ0m0WeqQRjds/5JAsbduYD3qk0GufARds/yxKLh6tR2UWawxP3LORe73JNzVc7HsYrad3+R0eZLaG+Wz7J/lgzyGW+//TMw4gljjlNlctVtgs4OkRewEvcpaqZmtsCwkSnLaqhF8ZbTIAcMuEmpDdYY53AHDiuq1Y2fbrn5kC3XVVm8iX5TBpIIP0/98BcGkIScRxb+wbCRhR9D4xx7dabbO6jTwLON9zEmjaCuaXCHFy6y8BOwvVkoSw3PI97cRgEyx374SOMDzE0DQ9KnKBtz7lpWI7z9NZAglDQ2DbLBYDC86DAG2WAwGAwGg8Fg8PB8XCymqdR2xzJu5N0LqruOSXFxuuhSo39L1F3Wt/B+ceAYI2V9yntkpEJlAxmZS1KudOTY02hEHTTZpDGZ4uphnBvHcM5jAfeocSVDqbHLRWpDq0/obTt0c64ckFmjV3NhgIYLQzLK1PfWtjw9cY36aDK7tT0ya0E+brvmrVt0AB1pfRPuEaqLrJPFVC1tab/n5rNLFpNjKna0H3yu7KwfmRxzmGlRNc2SW5PPcmOIRsrG0o1B22DdlAylnh6IiEyrXAPKUTUCWPIGErn4aP23/jWnz1Cfse6laOQxyGGe/dVnF3Is6b9Egq6fxZx7QZdWm/fHqprm0wnDqZ5K5Pcj/s1mWFfX3sU883M/qVs1zTV8B+JjaHWjOj2Z+QzirtsHGi2tjGfSoTBfo6epQU47HVfnLHywlXUOyVCrblkZ3lzMsjKqwyHHzefFsWmcdNJ3vs6p5B+AzksZ66CHDRJW3QlM0sPcMj9kukikEzK7R2TGV502OBjwd0MprwvO1vHoiAW950OnjqCM9UlOMccBWXyfqXax7Z+RxW4wGAyGFw7GIBsMBoPBYDAYDB6eC4McdAdS/MEtWf4ILGfQIBu0s5eVUR/VciPvQxqTrVnYAeuT9rwkPPqnJmSTaqxbb9GLV/1Id/ezKkWyS6qhrJ1Du+qhquxTfejpB1VLqF6mScIxg8m91IXncMZGifOlrdKTVW/f11TjSJ/YM3supSxrn2xTwnE35uE8kFKDnG7uZFVijm3pOxhTzM9aD7kGHKPPHM5tcA0yZo+sXBGPu0JHgsBLD2ssIMqueAQWcOFjjLXUpsMCkxCLbbcGhbtYg6gHF4RpBdrQ5jpdLLrot3HinmnvVTCUtUfQfiYVjGmG7g+FHbCAxS23NQPOUdet1MGaqwtHfUxm78g5EQjZv8oRNM71DYyp+AS6VWVASyfOD7twxHGPsbZ6klD5PvWwC3TW8HSxhX2wr4UO9mY45Fjo5zu5iPb1pAT18VIvU9e9RM32E/QTxNgPjUeuytEfh9tDdR/tVx7h2R19Ce3PPMAaFzbcd2FycSk353gX38fw9Rvo9wzGXH3gPI3Xfwffl5l1pPqFU8y1Q036hb+L196N5axO7S76TDbgKfzg2/gOnJ9Hap2ejMy8v+uWoEX9+DJep9Spt96F/vf4K+i/crCU1SkdYI7H16GUr29g7kVvziIiD/5t90zrG/RQP4MxXP5rcLE4/LXrIiKy8H26Z7Tc76WYe1Le+VBERII3XhURkUIbz3S4BreMwN8HHf4OCQKRj78rBoPBYHixYQyywWAwGAwGg8Hgwf6DbDAYDAaDwWAweHguEot4ribtX3lDuufx/+1xi6b8d85mZSq8JHdyKd/l7H0cn298BUfiZXfqn13Oaj5F3e45Hu+e5+AZG71082JWp3OBl/1oF3fwOsosvn+GbWCM/oW7EsvqZSmNDa600e/Gr/MS2Im7WHT2uzj6PX4Z79U3GZZB666VH+E4+N6fcGEPeolNLe5m70CaoOEmkzr6mf9oJaszew9H84++haPo5R9jTY+v0HKszTXacNKH3RsMguCltcZTHMvrxcTZTyALiGpubJUdXora50WnszhyLhxQusFLTL0Lrs4MQyXCMSZU3edFsgFlBiO8ji67I+/SMcY5ZuRv+R6O3eNlTZDhs5h3R95hl5ekigxjafAZb2Me0wWWnatldQq3oE9oPsT49TJgsjCT+zkuur8RdWeGjKkuHWCTHL7F0JTHtEvrOHmOjje7VKjWZrRUKz6CrEFt5/y+pxUNJPHaExewohIIESdbCd7+CK/nsQ9adzG/4BYDMBbnszrhe59gzpT2RMt4XsldrE31IS/xXT6f1Vn9Q0ieQo4heATZxMwaykzXYctWn7jbmiov0gt9M/d52fSfcqy8IKdyKhEXzVy7xL73IfOY0nYt+BLmV/zn77s6TUhfmp9grqFa3rGt5CrkTJf/lpNytL+AvadWhwnDU+Z//z7q0m5OPDlY8hXIL0qXL7ER7iGOrfQ25Vpzs1kdX6YVjL2brAaDwWB4IWEMssFgMBgMBoPB4OG5MMjTisjhq4HEV8GaVmtgUw6LjmEpnoBF6l8iu8KLSglN/qefB7s0OHa2TkGsoR4YZu8i2J7mRTA5kymYo/2pi/EdnGVk9Yjs8pu41LYbgJWdzpMejvyLVmS4qlqXQRSH6PfbX3xbRETud92FoU/bV0REZPQS5jpc5DwW9VIbmNaZNw+yOvNVrE97hDnuVcBuNV4CA3emjs+fpC76N0jRTvQ6mN3DPpjW7g30E/SwBtO6C+A4foOfDRjksYj5aQx2GmK9qoeOcR3OomyTtmQHrzEkIQIj2V/G5+1rWRVJCni+akem7Px4BkxfsQ9GsX3ZbbPyIWOv5/h8UrCz3fNYv+oBxtRZdXWKPV76pO1a9zxPBcJ59ksGMXJWXSt9svOXMYZxnZZw3H5q5TaadXXqW9yjK5hHfRs/731ZOC88t/KxW+vycZ3zob1fj3uIdnJ6yXFwxrMrKzAoZDlkGdr+tUocE9rqXHB/v67+32A+E15cTau0VOyC5c5MxmYdUy16MZXPY7KKy2URg0LUUi2pOyvC4lNe2CMrOyXrW9hmKMcsnrleghURCWiZlp7H96O2R7ZWy/CSaORd0NVLu8FwnOsnms3vqdS3UiMrr2xtSpu6cAZzHs1jTSof33dVbmCPlNo81VgDy5zceYjPzyxzPM5aMeblycFVfD8r60e5eSYaLuLVUfu7IAiesSw0GAwGw4sHY5ANBoPBYDAYDAYPzydqeiTSuidyVADbOSXj19ryomXJcRWGDKkgkzfDGOlpFSxQ1dMGn46aLrfB7PT3YIOl0c0zW86cv7pHbSlJvj2hrdguQx+ou+yfdTRPbZO2ayWNVyb7R93i3735RRQcub8nFlhH2USdz3RPmVAMfu/9hazOQQNsVrGDvps7eB12MJ+nZbBnrbtuDVr3wCp3fgzmeOEeF4WCZg1LaT1wbNa4SUs2yoezYBK6xw2WNcDDaaqPrzM8ImXM8WXGB490vdDGZMHpK6dkm4eLZHZfGXNo6L90TK2tky3LkAy0rlfnAvs7n6fdlGEWcSElKZnXwXmMrXOE8Y/mOR9v74wXwNgevJaPNq/QEUwZ5N6qF+zAhI7uZbw3Ye51WkDl3jmeaDTdPsjaYSx06YT2cYweHyzWnpmP1hksqQUZfg55ItIlczxYcWNLPyXjuYA9lDzcEB/pFM8/fOBio9WOTnW3xU08l5jMrkY/Fz566FVhHdothtQPa8iIBoaojaGIi3qOaO/Yeg91kyoevLLAaep+H+i/p0+eog0N36D9XvP7GFNac6cc2mdhlXrop9BHywRzrymj23InSo2313NrMb2Kk4WQ/cRk2dVSUkSk8iPqky9BBx3fQxuqm87Gs7cnn4U0NQ2ywWAwvOgwBtlgMBgMBoPBYPDwXBhkCcDyKUs3oYtF4hEs6hQxXCCTdirqd9KkG4CX2ZuyvbikLg9Bviw/96OMR3N0qSALPKUzhLKLyuSFzvQhY/SUqZQk4PuMnK6BmZokTqupThdjklWVA85Z44n7YKQ0WllEJC3zPc0SJoOsTHhSo3az5P5uSaiH1DmrflWHovNKo2eFjzqvIhlkXb/52xh8ed9RroUR2L76OpjCSRWM9RxdNPrb6LC76iJ7Zx4xqGNbtbT4rPkE7Rd7YNK655xmV5+Vhke0boPdVD10ZRdjKvUc7axR2THXJRwXOA+UHSxjbIF3YFG9D6p4pYIThOy5MH5bH0G57b4C9U3Mp3LEU4B9zONgzHlt0N3ixDGEZTpdVA7AWBe7+CzSwBA6GowXHBOaMeGLjCkfYuD1jT77R1vdc25s4SVoZ+M7YDejJeh9gyZ0vdN1MMqqNxYRmTKKOYg0uzq/RwKNiz7jtPXhlE4ks3j+8dY2+lvESUi8jWOIsOn6UT2xao07X4WrTPXhI/ZPjfhnhADpPBJGPqu+d/rzr+PzP3zPja1SyY0hKHAvFslyL+MkJth2uv/RawjFCSd8dg9QN9G1YDiPP7bkJbLMfIYFDQNiGY249tcgi52OIgm6xjsYDAbDiw77TW4wGAwGg8FgMHgIfF3gvy5axaX0a7PflvQsboSHjIZOO57vKdmfjMViDHLCiORwno4XU+/WOiOf1Wc14C34gCxZUkObwZ7TQ2r0csCo4XQOFG8wmnBMFOYuO79Y2ect9ZCaUx0Db8tPX7uMMU6dTjHaPMiNUVhXCmDL0j7Z2UXn5JGQrQr7jH7WdeI8tK7sOgYsPsTcC2QQNZ461HjqBnWeniY1ZLy2rkV2+58M2PEvI7a4seGcCJ7+ItZ2/lPM/fA6fWq388x/wUtMXv4xnu/hK6h78AZ9nD9E2XI7YR23xw5eAyvafEy3By71uEHt6WOwkZMZx54qG6+ew4evYB3rnvZcxDG+IiLRkB7W3wRzW6dmfOYhPZXJuB9dd6cCtR20134pzM21+QRtDXk6EXgS0+ZT9NlbAYtZootF+Qjvd1apUfe/Z2qZTEZf1/bMD7EvTi5RK+yId1n+IZ1bWnTSeO+eiIgkV8mQdrDfknW3D4KXsW+DbRzlJHSviC5Awxsv8rvxoRO9n/zGGyIi0lhnVPYMxjJu4Xm03gOjnDQdwx/s4PuX9rFgx3/yNbRBRj7h/qs8dJHQKbXNujdHZ5q5Mvp55nEtIsXH+Kz9VXwXZj7gEdUO3te49d3f/WpWZ+UdrFv/PPfBOk4sspjtdzD3gN8nEed3rN8X9V/OfmddwfqFbcc6qxuHpKl8f/tvSXu8Y14WBoPB8ALDGGSDwWAwGAwGg8HDc2GQK+cupGv/4Z+X8avU6E2pSX3ovF8jBk3pzXzVD8/cQ9njL4KBCbuOOdQyzYco0ztP/eoq2LKoiALld53364RkTxpSe/wKGKPJA3ygEud4wbGNpU36IJfzY1UXhtd+G4lkO32nOdz+F2CRRosYQ6GHMU5m8fPcB3Tc+IZj0eeaWJ+jDtiseJ3jXgXzVqmSPf2wldVZ+BDrtfUnMd7me1jTk+v5m/ILP3Gi7oMv0YGCYyodqRsD50dmtO65jHQuYq6z99Df9s/hs9YtzoMuFtM1p1ueeZuuJZTXdl/CmOrrTPk7QZ3eqhtnOKF7QEXZZryqc4MyvZ0LjoAr4RFmSYfdK+hn7n361Z6hA4d3+HDmHazlzpfybh9FPg7VZ/fPOha6sUEXiwt4r0Ztdf/zWLBoQ32Q/VATjAAAIABJREFU3diqu3SrWApyc1b2WbXw6q2c63uFZZkIOfNQXS00tdBN6MZ/BaYzHWD9gzqZVfUy7mNvRavOQzt+ApcH1RMnl3CyENAnOBnye/TyS25sG3SG4ImItqtOFZk22XOxUH/j5ArY7IB63OTDT9kov4tzjqXN2uWJUjaWFfoSn4HmOb3lPI2Dom5gntLQjSNUZ49Vaqk/upfVCa+uoW4X/akXdPC9m2iK2urEO+0KryBBb3iOrPbN9dw84kOeOBWcHj/TeYehvD34B9KO941BNhgMhhcYxiAbDAaDwWAwGAwe7D/IBoPBYDAYDAaDh+di81Y+msra/3kgvZs4ftULUuVd7/Icj2xjxtoGMS9wbeMS2sJtWCmFQy/CVsvsoEw8j0tF4yUc7cdlHHHW73uXf/SiXZHH76s4/i0f8jh2hOP50aK7ZFTe4aWcotqIYfxBD8e+D/avY14jJ0lYu80xVTEGvcAX17CkxU2MqbexmNWZ1jCWMz2uzwEuFY3moO3QgI36+rFbg8e4FFU+wjF49T6CFcarPFam9Vnl06dZndl7sDaLBrQam8S51841PKf6E7fWY8b1Vg4pk3ikgSf5S4jJgVu3pZsMMblU4Tyw5iqTKJ+gv7p3X0ljoctHKfujjIBFmhuQDJS67vJcoadlNC4aa97YwlgrRxrS4SQJ5V2MrTBAO2XKSepbvKwZqh2fOyZvbFEawCP88jHH+IeYn8pzigMny6hvMlp8yHAUWtIV2+hnPIf3G5teSIa208G4VaIys075RFLmvJ1sRi+5BgzBmNJCrXAOYRZ6CXC6/jiro5IGvegpfA14AU4t1uL7j1w/X7iBz3YhIwhXaMM2wzhpBpFEc04GpNZswW3IISZfe1VERMoM9NCLeMmeu3waajAI17rAyOfkgP0eoE1Zdt+fmBdUg5chgQjuPeb7uKwXUPYx+YUvZHXKj/FevITxFnZwUVGuXubc19Ff1e3r9BG+S5UntKejHEQDT6JFjCkdOrlRZnUXx/I8ZGsGg8Fg+DcLY5ANBoPBYDAYDAYPz4VBTqNQpq2KjGY1pIPRv6PqM2XVviuYgmUJu4zinSWrNfRYM8bDRoyuns6WWRZtTCvopzLjLgOmylaRWR2S1Qxitj9iPPGs6ycalnN1wjGZ6wLb0CjjkWNCpy3UmZAx1iCCaZ2MWLuS61/EMYUxI62DaSk3Fg3PKB+6+ZTqGlWMfspk8sZcLw1RKdfdWo/m8FmBISPK6AfKcrNOUnRjS/hPZVb1QpwGamiIytTlXUhcIcPG4IvTZfV9fU65dvQ91okZ5JFwzadlVyec8FSAY9TLlKfbD6f+RTjW10uZJR1zmJ+nZ6WWjaGobeic2f6El7Sm3ny4Z1xdrkVR+8mP1X9Pn3cWVMM6WRiMNzZlK4Mkb3mn1mopQzr8MJCUF/g0lENZ25R2ZaJtht5adwe5dtXaTFc20djogWPrlVkNyMJGPbL0vEQngU7Y1UnYrl7Sk4JumiRXNmNmxYvTpk1iks2PwUFso9h2zG4w4fh7LMuTkOCkm6vr95PFXut6dTr5sly3bM29sfnlDAaDwfDiwhhkg8FgMBgMBoPBw/OJmk5TCcexlLpkUcnShRPHGIV9MDRJqZ6rGozI3CihOHH6zkD/qYyMvvC/9UWGMkQnjjGaMFhAWSxJwQKVjjWAglrRtje2secP5v0caFywpsh6GuTohO01GBfcZ4iA6pjbDECJZ7x2g9w8CoxiDmbzj0G117m5k2nL9MRk4Muqz/XYOQ3WUOa4eEj7vQqo0dYdsGfRXjurs1CFbrny8JA/Q3taf4yylcMq5+tY5/LjI86DdnUp1r51n5ZjXTBscd1RodV9sts6tm2wc6Uj1C1uQSdbPPHWrZ8P95AUn9UfnuTa12hgEZFwA9rt+dugvAvUYxcOyGpyD0VDZ91X2aZueUjd+jF1xDMcM9datd0iIkVqWovHaCfqYawaAlNiCExSdlpn4TxqPIXQPaMhGdEA4TLjWafD1hAeebKF4avN21lGTq8zfKbo7aUkH6SSaWPJ6CrjGnj2a9MlzKNwimmNF2iTSI1ztOziqTViWqOY2y8zdOa+Mrtk2UeOcVWLtHAZNmu6f+Mevjfx61cxjo8fZnWUAQ8YwqNMb8qTJrWgm5TcGkwvY5zTKvZt9buwbAxYVtcgY7lFJOBap1u7mCtDRDKttX4XZ5y9ZMYmh6FFTRsMBsNPAew3ucFgMBgMBoPB4OG5MMhBCmYzHJPJicgceQyy/juLa+ZLwFjnrO7UsbSqmRXVEZJdVi1oNE5zn/t1ApbVMsoK+wy14hkGeaJhJnGujcjJFJ9hcrWNaJwfczZGce4FEcltXYuI81Etam6MsY6b7Uzz/WRs99RnxHWddC1YJnLsL9r2+kmUnuez05/1OcWf9Xzi3GdaJ3vN6ninAlp2ktfSBqf6F6+Ozjng0YGO4XT7gceiu1MHls3qsIy2FXvzSfIhNjp3XfuQ6+XP59n1Ug3tqfl5utRUTTmSz66rJwj+2KSgUeaFXPupvq/P1l+DZ54311HbKDz79VftdBZ7zjKqV1e1cuDVVQ2zapEzvfXpfnyWNjrVj2qR9VV10f4c9N+so/s56181wqVnteipjkmfTzE/pmw8/hh0LbOxnVrr0BubMu5B4BbJYDAYDC8sjEE2GAwGg8FgMBg8PJeo6drShfTGb/05aV/Dz9EQFEp902fn8Dqao36PpO/MI7A0h6+q44I3uDhfpneGscdn6CZAs4eFD10ddSVQV4HOS4wN3lKHCnw+WHFjq23mXQNCygnLbdTd+1VqLAeONZt/F/8eN+lwwXYnlCXO3sWYd7/i/gaZzjCWuo151LZRd7DM+ZQZ73zHUVALH2NBNr5Z5c8Y0/GVfHz00k3Hou+9obpo/Fzs5jXc0zrabzx1TOjhDbQ39ymjpr+OwrO3GbcMu10Zrbh+ln6AftTlo3Md1HjjHvSlpTbaGC66+ejzCTiW2Tvor7uKfhob/Pm8W7dSB4WVmWxfQ5m5j/HzcEFdRrIqsnQTP2z9bDk398qhsvWo073g9kFjg/O4jPYru5w7I59LB3hupRPXT30LZXtn1IsZ7ZW45npqMJx/Nmpan3tIV4y5T/Jz130uIvLyf/ETEREJKtQtD7wvijgXhqjltNtJF3regP7a6mk8ffSElXiCMe80yKqlzRwwlGEl46q63/joKKujMdTRhXOcH904HtOb+5TLRa4fuj8oGxxy/Fo22XfeyepaofrneAca4bAGnblGTvvuEkGZOm66ZkwvQl8cvHcb72tstXcKFS0xflqjqz9AzLey2lnZxGPrPfeQd5I/kJP00Hhkg8FgeIFhDLLBYDAYDAaDweDhuWiQi71Yln50JPXdmdz71Se97N+q/ZzOkUWivrL09IhtgNlRZwcRp81UZ4P6MtofLjHZjExc41PHZiXqmEBmvL4LSre2ibGo/+204VwFyvtwHEjoT5zplTtg6SZ1MEkFj7Sb/wDuBZM5jEVdM6YNMFbqSBDGZ7I66u4Rjchqb6LB4TJZuTI+b953FGX4CG4MK60rmM+naLey71g/EZHK/d3s34U+1lJdLNRNQlMGxwsYc2Wzk9VJCmDfZh5gncYNrNvMIzCT5TbWa7rutszCT/BcBuf0Nj/KzN3BMywxTS6uOq3mYBH1Sx2sQXl/xPaZ3PcU/dd2HdsY0SFEtZ1Feme3mDynXtTFrts7pSdw45hh0pw6rFR21G2A/tFHznO6vsVUvB6fIZ1OZtbVZ5knAF6SXmULNH3lEGMqdjDniHsnblY4H7cGyiqPtjBu9aVu3caeKp00OB5XJzwLlxFR5pjscLQK1jbZxvOP1d9XRMIqHTToDJGQOc7Y2ga+T6nHRqevIrExczjhnJNFuD4kt5CWF5GtFRGJD/H9ix+j/cGf+CLGz7EEmqK55xIvVcMcttBOxkwzLU/dLYLBs8ma0oRLRkivcU26UxY3fuPlrI66iuj3snQPLiByHvtiyjGrqwYapDb8k3U2QkZcGWyy3Kmn4fd9lGVk5LHBYDC86DAG2WAwGAwGg8Fg8GD/QTYYDAaDwWAwGDw8t6CQYJpkQQoatxzkgkJwjBvWSvm6euEls+PyLcHyVlnOioyDZyCGBgeIiMip0Igs5IMyA5V46FG4P079ayEbg1qscYh+UEjAuNt0vsr+GINbxpJqVG/gWXXpRT69sKhjDBk5HZeeXYPsklS2Pnm7ukjDMTybN5VWOKkIJSQMrahs8Rj+4DirU9/Esb4erTefomx5l4EnzJjWeGkRkfAAUpAqj7abTRw9V7dYhzKDQsUdX4djBnd0eanssMv3eWy+hzGVfYvAgUpEeLlQJRUMGSlUSyznxRIfY2z1rUWU6VA+sY86epGsUfQuA+52+R7mUTzBM00Y2R2ONCjE7Z1wH+tVUYu2HkMsGBQSMHSk4AV4pFyvQr+cb1/boiRBPxcRmS5hTIXH/B410G68iPfDNuYVerZo2cW6U3ZuQQnPI6hh72YX2USkT/lNmfs2YuDNhPs80gt3TReSEVDuoRf7uucwhkaRz73kyRcU+tksZVn8bqtV3HgFwSSlfbdHVTKS2eIxhj2zxeOYhg03Xw15mTS4pu9ijYPLF1BHY6WL3hjnISdJj1E2WqCciT9rBHXorWsWzR2FEkyNdzAYDIYXHfab3GAwGAwGg8Fg8PBcGOS4WpD2a3PSOwvmaMT7O7ON2axM9QBM58klMDV6UWm2hp93vwgWq3zksY0kERubYI665zDc3jkycLwDuBwvZ3Xaa2yH1mD7r9OaKQIr1FnFGIs9x+wWe2h/WqZdGIMhysdgpPa/krdnExEp9nGJ6OgaxlTfxLiHC/ibY3mES1V7b7olzhhizmuuDpbs5DLqTOtq99bK6szUS1wftL8smGv7cpFjBHtWbzoW8Ogabbd4H635FIzXcB5jaX0EVi4suTrFYzCTemErCxvpkgVmVPLYi8VOm2TwxlOOhYy4svdktacNxzYWOrTMI9MeKdNOdlsvWqVFL4RhqoET+eCLYEyGlxfh0hl3sS/Y5EXBfVzg0guKaZXxxLR58xl+Dd8o9Pm8GWHefhX7uLExzc03NxYNY9FTD2VyyeyKF0usl9ZEyuzH86cTkXBEhjpwDHLM70lA27PownnMnesY6mnH8mJWJ9nZYzNkPOcwD41MToeYX3LxbFZHI8A1mCQ9wfijQf4Cro/MRo52a9NakOvHZ6gVeqlNWe507zD3eX8FdQo/8C6skiHWC4kBGeWQa6sWbpWn7qLi0Rv8HXTKzTI44S+PJiO09aKfiIzO4/tXGWNdUmXiaRWXnQ7Nu99vypIHhUIuEMVgMBgMLybsN7nBYDAYDAaDweDhuTDIaQCrKmWONYwjKTq7o/4SuppWlLnD+3FVI3/ZVsHVSYJTtE8WT43XyQyDKObdNDTGeVzPWy31l6hBHKDOwAuvyMaSEV20iyprZC46jmtOGzycU0s4jqUR5NoYqRVd6OYQV2hb10XZMeuk+meKSq29Nciis+P8ZwmJ9v4KKtd2XZ3JqXZ7K0V/WrL3NWgqG5vNrM7hdbVoAxu3/TOY3+wdsGj9Mww1WXLzWSnOcy3QUfsaPpu5h/fLJ7rW7u+wLCiESzm7fElERLo8fWg+AVOpOlYRF3SSBYXQxWt+GWPTEA5fI74QXcY8vkbmlv25oBD83F1169Z4gmfWWcN7lT0w0r3zqHNyCW2VOvWsTn0HLGJ/iSEiHX3GSa6f4axn86ZBIUv578L8J/Xc3HXNRUTW/gem4TBAI34Cu7KItmgxrdxCL/gnoUVaZmHGz5IOWW2y9VHi6mRbkXUTMuLRrXW8T9Z2+mgjq6OMaeEcLA3P/VOwwVkYiFq1efpoZZDjTx+wCVrpsf25dzbxuTe2WNnzxYXczxpUUnjKUBEvbnvhe4P83K+u5frVsJR06k4Fyh9jbukCGfe76/myGqndcTaJPtJ08pnvGwwGg+HFgTHIBoPBYDAYDAaDh+fCICclkc7FUCbXoONLjqjzDD3GiOzfpElt60TZMbBbvfNkqjyTfWUZgwTD7K8wWvgs2J5CCyxUu197ZkwT6nknF8FitemeUTrG3wSjK875Iq4wcIBRz9GADO8Jxr+2hrCO3thpKdtHCA9JimS4OOxpTRlfanbXXD/NGbBZJ0d0hKA2c7qIeYRlMFPdvtPSltso21/V8A2sRfci+klqjEE+dtrt7hWuTycf3x2OuX5nUGc06+r0Pk89ahFrsfhlzHm3xICKVTzbG+d2sjr3e2BpR0to78arYN5uN1ZFRKR4hP4ny85dIqqgrBKdcQlz7V+ackx81hcco1fkc0i4nVY+jzHsRRjbZIl61oHnsDEBG3vyel7f29/DmqcRBlC74lwSDuheMPsymMjDTfx87hJ+3jkku33s9sFwA+MdnMe8itxfxQ61tfwTdLTghUrwWxedAesbTxl3HHEtLqKt1mo7q5P0sP6Fi9Aeq8NCTJ1vWKcLyIw7FQjo1BHws/gsmP2IbLMyvBpfLSIypW5ZY5RDanRjDSZ56SI+v7+e1YmoxZ1coGPIHeyDgE4byhaHiy5cJGV7GjISlKlnPw8WOj3ivDydb9qhtngOzyVkG+EsXSfq/N4cumcqdDNRFrvz618QEZHmPa5Ng+s1fpb17a+h3SrDcVTznKr2ved0yyE1yGmaWlCIwWAw/BTAGGSDwWAwGAwGg8HDc2GQC/1Ulm5O5GgIFqjxhGxa37EyoxbZPfUwZtxy+Qhl6rvqbuHpISklrG6B3ZxpgbkbUc/ZW0F/MxtOc6jtDRbJ4D5UNhuftx6CNds/cRHDrXWwlTE104WhMslod7cE1i7w7InnH+OHUoe+rU1lAfF5kZHZxa5j5yYz6HOJOlh1y4iVfQrxWt9261Z7CDbsbAm6y/ommLDqAdrtL6HO/C0v1jtl7HFPXTGoqT3EmDZ+hXpPjzRrtcCGDegmUStqdDbmN1vHM9jteY4UJPSG57AWr8yAdb5dhjY4ocdweOK2WXMRlY53wdxNmnzeJW9xRSQcu7/dAo4zIqkcJ4z+pSuITMjatp/VOi8tg0E8OKTPM9n0zMUi1ynHWAbj2ZnB6+YWNNsRGX4puj2q+yotqE6aY2aR0SzfL3t6+lN/ls628OxGARjQlP1MYo8RJ8s7fAknF6Xvw8khpEOIMq3JvnODCFbpTkFP5iw+mqywLGFPTe8/yupEL+NUQHbBmicvIco66o85Nuy3aNG5ZQR0F4noUBKoTvoM1k0dPlLf35unJxF90NOXcOqQqIvJXWqTv/w5twYPecLyGvpu0v88Y3QZpT385utZncrvv59fH70XcZ3zfILTiGDOOceovrqyhbVOOcaAzzBYBcsdbbvo7Gwt0kSCifEOBoPB8KLDfpMbDAaDwWAwGAwegtS79f6vi9ryhfTa7/w5GfJW/mAVjE7rI6dxbWyC/Tm6RqaV/zWfuwtWaevr+Lmy5zGHJOxq2xhj77y2rx63aGv5PcdMHbyC96p7qHP4FZRd+h6ovaNXUa667bhDZVJjksqanFc+Qhv93wAL2Tt02uCV76C9/Tfw88x9jo066bPfBZv28Nuedy91r/pnSesDrM/JdU60iYE0bzp2e+YRPnvyTdRdegftnbyEfmpbeL9y5J5j+6UwN6/Z+2TezqPuub8Ppk1GThss9OZNNC3sCpLGgqf0nKW2dbS2kFUpPWUqGRnKdBZlgjZY4pTtJ5dW3BKQxUxmyUTfe4zu58E2Zo4HnvZU/Y7Vjzah33H4eJdlqUGten67n4CBVH2qqH5UvZTJVIqXcBccM2VvBtrZgDrYzs/AaaNxG+xs0HXaU02PC6hjT/01FZGUTgdBy/kIqw9yOof3gk4/N3f1LU6W3Brsv4l5LP2dj0VEZPKFKxjbRZSd/yHWYrLi+ik9IsPJOcfzZNGPqEHm3DuvuWeqLjMhTzdaN6FJPnkdbOrMP72Dcq9eyuoUdrgP6Dhx6z+DH/Gr/zWcKFL1uD44cmvAOfY/j9OZ6gMy33TlWP9PXxMRkbX/5V5WJ12Bhjl4DAcPOcd9xWd5/Dnsofq2051v/BLTAvkVW/tLN/GPlzl+/v7TtEkRkZ1vgjVffBfPf7yENsrb3VzZyVn3fMIBmfBiKO989D/LSfepCZENBoPhBYYxyAaDwWAwGAwGgwf7D7LBYDAYDAaDweDhuUgsGvMX0td/+c/K8ct5W7H6lpM+ZJeWWrwcRRlDcwNH0kfXy6zrxqOX4mYeo0yfgRcajjHlye3CbWcJphft9DLOySWUre7yYhxPwAfL7gRUxxmXGJzAwInyCc5lN76l83J1Fm/SRq6Wv/A2oXKg9QBvaIS2iIuS1qCQ6g6DThbzISOz99y6tT7B8fXmN3B8PH+b1naMmtZ5Ln3gjoj3P48j4UKf/fFV13MwjzrNJ7FXB8ft85/ive23sG6tu/hcQyvG825sK29TgsKQjPYrqDtzBz+X2/h8NOuHmOBV12tmHXV6ZzQoRH92f7sVeRlQL0AeX8fr3G28DueC3DxFROZvYT02f76W66+yz6AQNt+95O2DJ/isc1nL0hZvkevYYT9ePkRji+PlntSgkFInv6dGLTcf7Xu4mD+FX7iFfdw5T+s4l6Aul/8bSAPUaiw+wr5Qe7GEUo+w7C6FZuEXDPLQi2rxLiOoGdwReHW0/YSWaqfLaJtJ18U5Z0EhK7zURulIuk4pT5K/gOm3o6EbIS/2BQVaqtWquXGIiCS0pyuchwRi+nQzV0flNEHds31k3LnKMJIFSlDu4mJiQqs7DQEREYlUDsPY7pgXBnWeGmrih4v4eCf9J3KSHprEwmAwGF5gGINsMBgMBoPBYDB4eC42b0GcSrGXSKGXj9stdR1DqTHBxS7ZrCnKFPq8cHdC26exb/PGMr0J20X7Lq6ajO/AY39IpGqEdemEFmCMmC72UHbS8KKM+V40zjPIhX4+hCP0MicKtIALpySKNC8k1TZoA+eyHiTkeIuMYC7Rhi0hc6xssL9uwWDMdpR95np1NLqb8+57wRrdPHOsF66iMcbUX6atnHd/MCGJqIyn2pLF5TD3c+r/SaWsHMefVhnBW+KlME049rg0jdvWZ5f1p2vAHak2bOgz37eWyaLBP+PPvKTEU4ZaPvxFfd2ytjzLttPt6tq6mG9t2+unkJ9HNmeOf1rh+rn7qpKSgczmqhZx0an1LDx7uhPUyI4eqJ0bGtY46cQLvFBrOLUp0zAMLRuoveDIbWwNGgn4bJXJVZY2G4cXG+0mpukvSa5u9v7EY1z1oqKGlVTdxVSMafzs+31eZmzUcmNKeTkw7fPLv+Is6GQHl/6yS5RFRsTr2E7HcYtIMsARWKTsc3bKxnkx1CT1Iq3dwP//n8gZDAaD4d88jEE2GAwGg8FgMBg8PBcGORzHUn14JJJC4xiQ+a1seTpF6hDjFhkphgZE27B+ajLSOPLCRdQ2KtpBWEZtCG1gNCKDQyauev8gq5N6sbkiIuEEjFhlh+wSmbHSiWOMigdkpsguabBB2GVAyQMELhQ8fXTjLsY05XyiAcYd10H/FR+DuWrVzmZ1pmS1C2S8Kzvot7RIvSVZz+ojT+RK26uZx1jb4hPMdSadz61B9GQvq9Iqq2YajF3IsWWWVhOsY+Wp62fUQnuNR1in0QzWuLmh7D0t1oqO2Z35FGtQ6Dc5fsx99p7GYlOvWnB1eudQpnLEMkdkyLt4v/YYOtPykWMO9ZQhs2ZLsV6tdbCP0yrmWzxxe6e4A4uu5kPsK9WT1xg6o+tWGLl+agxoCacl1uHJxClWsDBwP1e38QyLXYyp2GHM9wn6mczln62IY9THe1jTSZWa8Pt4HtEYQu3RrqsTnqEgWUMrVHd7FvPTgJDU0wYrQ5yQJY2P8bxUp6xR0OIxoZM1tFc89T2KF8gsfwTbtXBuzn1GNjvex94cfIW2eD/hd05t5rZcTLmOP1qgdRtjopNt2goyUjs48edTyrWngSRqpae/Y0ZnXJhNqYA11JCe0gOMIZ2lzphj11hujAV7P9lE8E2oUfRq6UcWP/IsApUJFxEJhiY/NhgMhhcdxiAbDAaDwWAwGAwenguDLEEgabEgcSXPkCojKyKSRmQgyeioxlFZGGX0cnWUQSa7o7pOZeBiapE1tlZEsv/ypyXtj9rQKiNyyaoq6ygiUmCfWd9KHGowBd+OPR1pwvZU06q6W41XTqvl3JjxGcc/Uq1pIVcmY1oL3t8tUZQrk/3MfmKyxeJpRBO+pwy8Qpnx0SzKFrqOPVUniJrGedPpQnXfozl1B3HtTcnKjWZD1qErx6yGweS1tn4/IjpGNKhR5KXZMvvz5sO56lrrWEbUhk+reX2xiEjUq+bKptS8FvrcK+okMe/WujAs5OauOuUgybc/9WSxhT7HO3vqORFjrnX8GQyyunvo+CezaFjXb9zy2qIuNgs8UdbXYy7xwWdoYE+5L2Tg/vB1y3oSkrVLJjkYk8UvcgN4Dg7arjK8JWXyM8afunNvj2Ya5okGq1RzbWj4jPhaZ9Ua6/dD3TE0nppj8ve9hnpEqnlmaEkQn3LWCL3nw7UOOPek25PPQuppqlOvPVMhGwwGw4sPY5ANBoPBYDAYDAYPz4VBHs1Hcv/fnZPwKvSCzRrYp41bXizxMVie/prqYfEy8wm0hpOvQzM6OPbouSn+/15fX0XdC2DNFi5BtzycYPiHb7t++ueobaYOcPWL8Ep9eBPeqfECb/KHjvEJD6ihVYcFMrzlQ/iqfvt3/oWIiNzrLWV1bv7BDYz7Cm/B71BDuQRGbPb70IxWf8vpLher0KueUPd67/YZzOcKdJBLdazfnfcuZnXmPsIYpr+FOe+8jXkMXqGWlq4gM6+4Op03sf4p2c3aE6zTuIX5zSOtWEbzTmda28N6FHpk4aZgCtXPV1nO42tZlSzmWF3W4Ew9AAAHnUlEQVQeqjs8FZjwtj+Zw84Ft80qB2SZ1Yv5MSoHc+pQQsZ61v3t9oxTBIetTOFoBmWHc47eruCx/7/tnd1vG0UUxe/u+vsjbuKmTQmloUIChIrgrcArEn8pzzzyioSE+kBVqqZqm5Q2bdw0TtM2Tms7sXe9PJx7Z2bdCISIBEHn9xLZuzM7M7uWNnfuPUfa2+h/0sY54+XirkCoymEqEqaEUjtA/2aD3nmgiihv/bNjuyaG9Tdt4Tq1PdyL0aXguU6K87E1yXTuiaqOpI1g90Ftlme3If6cfPIRrmdRWo1yRl9+5sdy73cRESlpnvJ0Dc9k6e5j9HWI39zsm89dm9I28ohzzQnOHsEK3GkcV2znJLBq1zzi2fs4Z3QRNyj+GZbQFmGOLedZRHKNWsfL+O1mPdywWDWIB9dhdd784VfXJrFzbQ1U97i0hmd/+Kn+5n6669ocfndNRHw+fFUj4dkG1qB0BdfJXx24NuOvP5aQ+hMci0b4zWU7+E0n3UV5hySRqH86G3OEEEL+PRhBJoQQQgghJOBUQh3JWKR7J5fBGJHYUYq/S70gFzA1XWDVYFWli4UniCS9TBA5Ojfy/ZoOsikpDDUyM+pB59S0e7sPfRV+65nmvWqE8PkxIq6dZ/gcbeD6R10fAWvsagTPOenh++ob9Pv9ja8wniP//8TyplbM76mKhSpcTNu1wrz6N1Zcm7456Q1xncUdfB7uYz6HNUTIug/8GizeQ1R5ewVRuvN3EAE7GGlEUofUvetzUfMEC2NOehYFdjm0VdOR9vfH3Amrg2K02SK5br0i38ZUPY4Wcc7okulHF8WGzb1QxLvHmavfpKNKDk2N2h8U88xDrE1WNxdDzSs1JY9AZcRytN+uaq6zPiK11+gk1sVIQ9M1zWk/1sCgaVybc16m0rnTqR9cRV0RzVExylR/WzV0j7tVnd+7TnqZ3kJzdayrtPGkXXRWFBGJHsGVLm5BoWH2EE5wTqlCXebijS3XxlQXclVCKWvObnqg4twaBS6v+zauFmCIH6LlCueqhGGRX1PGwEma3605xgv6W88tf1hd6kxfGP3goZht7xSvo7nPnZuIPmdBDnLWh8JFcl4jyaqaYfnKTculNic8EVn4bVcvqPnLGhl3fWrkOnTFq9/C2soSItS21jYPy/9Od/3uUEieT0/8nhBCyNmBEWRCCCGEEEIC+IJMCCGEEEJIwCnJvGF72mx9bTs7C+qS8mlxS9tZDdfiwvchZs1sBhtWtJS2VJ7KrIGrfsvbtrptGzttFi2T57e3Rbxsl/Vj1saJjjlpYzs4i/1ypVW1enZ1RzZWfJppX9Om3/bPWlqQph7PVvjmxqhFgk62TESyml6nUTw2v15W4BWOwWyvM82+yG34VmMWpDFEafGYSZtZIZzd07CNkzQzm+3M2tr19XDwb5gdsxQK1++faWNFc3/tY178PrTONuK0+NmK2vIT/jW0/tz4bay2bvnc30LHc2OyPjWNJZQey6No7py5vuYUyETEy52ZNbJKEM5bPjszDRERTbsQk3ebL7AzSbhKoN1naQomi6af83lZtPCa1r8aeJgMm5Oi09SEk+ypXWpFqsWzZn+t0o0FO2cb96SYwuBMPqztYWC0s4CUlGhafBCsX1vHgmydrqHJMNo5bizpnHFNYTCxyAkO1IQQQs4WjCATQgghhBAScCoR5NIok/O3BtLsI5yaavFUa8sL7MdDhDGnF1qFtpWnavU6gxRV5TCI5KRa1Ka2wfU+iv/GfRQ+Jcc43ry/79qkyzgnPlZZpwE+dzbRx2TJjAJ8RK98qAVDlaLVdPIGRUVH5yBfVR76Nt2bkF0bX1Yr632cO1Gjjfp9FAddrFx2bcwsItZ5tXV9hqsI+Vp0uLPh7XVL27CQvrAM+972OuZafQ3r6eQIY630Xvk2o2VdHxwrvcJ1Zo1KYX7xi4OgDYoZq1vofyXC/aj3EI1rPkfIOm34KGBzHXPMllAUVX2DtWg/wvhjXb+FhpeTS9UIJBmrJfOe3ttdPBdmPd542nZtorFW+WlUs9HH3OtbGP9CS2XFjoIo4Q4KupZaV3V+iDomuhYW/WuvdlyTah/jbuxhPuUBrtt5jMikSeDFgR16so+Ct0YXbaKhmkyoJJgZU9RPMLMxoxUrKKxuouirtov5TR/7bY7ZVdyfeAvFa2YPna9BJjHRIj4rfhMRidsqX6gFdyZP5qymtZgtCiKhx198iLmr/XrpNdZk+gEKSaNfbuP71fdcm6yPZzR9ijG8/Pa6iIhc6KGYziKy6bPnfmw6BmehrRHqtIdq2tkKxl7e8wV3zghEx52YEY0ZeWiB3+Tamh9b1Xaf8Ny2fsT4k6uQhrMiwbjht2TsnsmmFkK28WzmWtzo7LHLPvKej7yxSTQ4YSuDEELImYIRZEIIIYQQQgKi/CRr2r/bSRS9EJEn/3w4hBBy5rmS5/nyX59GCCHkv8qpvCATQgghhBDyf4EpFoQQQgghhATwBZkQQgghhJAAviATQgghhBASwBdkQgghhBBCAviCTAghhBBCSABfkAkhhBBCCAngCzIhhBBCCCEBfEEmhBBCCCEkgC/IhBBCCCGEBPwBXiWklSz1LFsAAAAASUVORK5CYII=\n", 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\n", 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3Pb8xn28DOATgIKSP9ee865/0nvNTyNru/ZABRIl4R2MfBbDX893OKZqfYTz6If9W93p/q3sgranvu90BhRCfh3RT+BERteTQ5Zch/XjbIWXhg0KIJ2/3+bcByzkzXRSr/H0XwC3t9SHvegeAHu9ZXwLw657VPImTXt9FAJ7w3i9JaZuNv4P0/+2C3BB8bwx9GyFluw/AMUh3i3/X7j8KuaG4AsAE8F4AEEIcg/xe+RfIk6GHAPy854+cxkcgNxC9RPTeMcwx76HwiRjDxPF20qMAlnoBPwzDFBks5wwThmQBjC96vtZFAck0fJ8SQnxuuueS77AFmWEYhmEYhmE0WEFmGIZhGIZhGA12sWAYhmEYhmEYDbYgMwzDMAzDMIxGrknqp5RSKhPlqMze8DZZuWEQp45UTNr4THHSj57rQoj67C1nHqXGLFEuZgEAqKQEYlQGPZNlAq6AcGWGITIMgAjCcVLHIsuEsMP3qawM8E+7hICw7eCz38Y05XMSTsXIMII5mAaEE8lMRhTq5/8MVFoCMZIpgJvJV4YwgBExfLsptoqaUnOWKHc9eS0vgxgalu9LLCmvnnySaQIGQYzaY34GzfKynQkBMTySLJdESDrFDslriRV/flRey0rlM5iCJR/lNS8V5HJUYjvdP3kPeAHYPkX/DU+0H8KDjZuyN2TynifFYxezt5qZlItZ2PLAH8B66oAsmOrJlzV/AewrHVoJC5JZd0lb3LbK+gJi/1H52SiFoGCxM+fOhdPbCyotle2Gh9VQOubsarjDw8F9DauxEXabl9vfjfeFYQJCKtcDb9iO6qfPwLneJXM65NVXNpMre8VT0z2FvKXcnYW7730f6NlDskSE9zduLl0G58x5TT5NwHEzymsUo7IS7sAAjBJZx8Pt9wreJciR3zaKtcD73gBC3ydqDpYlN8kABh/Zjurn22FfnDEF3oqSfJTXvHWxeKL90HRPYUJg5ZiZKdxYWhq75t7oC18QItGSFG7jWXe9/PpiUYO0Qo3aGS1ZwrYhRpKtSElKc3iijlLAy7tGIW7GF22GKSZ6Vs6KX7zeE5ZP14nJq1tqwi01U8c16msBAGJoWFmm03BvDSVej54gxe/bclMLoGTAgYh+zzDMBJC3CjIrlrlTLJsJprCp/dTu2DV3cDBrP7H/aMga5VuG/IXZPerl5Hcd+UrBHRxMVb6d613Z5+Ep0eaPn4c7FCzcVtOitC4Ks2ZO8vV66ZFjVFWFr69bBatpEaymRTDXrAie1dwEqzmcctVceYd8rZOVeI3y8tB96IW6DFMpDqltGAbAvM/G5dXp6cnaj3YdBu06HPs7NBvmAwDsC7LmjBgdgRiNb1iprCz44Dowa+bE5Me5dg1Z8TbSJT/YD6f3BnreviN7n2nm9D9vn+4pMGMgbxXk6aIQlU3eTDB5ga+EEcEoL4dRXg6ycvfi8hdO917592zWSUsUWRaopFT+mzDe8Ku3AQDES9LlgDavy/78EmlBNsrLYVRUSEWTCHZrW9a+Tu+N5OveQq+Omf3rx07Cbm2D3doG5/hpdd2+3Ar7cmu47amz8nXspBxrKGJ1i1r8kjYRnK2I0SFSGyn/7x5I3+glEfPj9zaYvhybc+fCnDtXyqy2QRt8aGOom9N7IyY/3b+WXdklq0Q+p64W5vKlmPu5uMKfb6z4zUwVm5l8gxVkhmEYhmEYhtFgBTnCRFhjC9EKzTDjgqCslEZFBdyhIbhDQzDmzlWuASCCsX41zLUrE4fwXRyMZ6T8+G4RwrZBpSUw6+vky7Ms+5R9Z58c/tlDsJa1JI998JiymJnV1bH75so71HGwOzQEKi0BXAeGfhzMMEWDUCcNVK79jVsWjA2r5XsiGBtWB58jrjtR9wnfCuzLMVVWgCorYNTOC51gzPrP54LHLV2SOLt5n9kdyGvtvNh9XV6d613A1ewuVAwzVlhBngTY5YE3CTMO/ZRfi0p3rl1TrgEQAu7RE3BePDXm4d2BAdhXOmBf6cjoT2yfu5BhEKkQOH3xgB7n1NnwZ2+xj7kzFDEd792p3hub1qa2s1oWT/zDtSP4NFcaH9+v22f44W0w6+vD1z33GCYFXV419x/nehfcI57PvxBwj5wIPmfw/09CuRB1Xk1vcz5DYiBfXru6Y7di8pog08VOydMLp3sKAABrQUPG+yMPbp2imUw8rCCPAVb6coc3CUyuGJWVMCrHnve8/f07MfSau1Pvk2Wh6135H7iTLyz4h13qvXvoxdR2fhDWhKJZGIVtB4GaCUQDuMq+t09uxPTrCdkXmInBqKiQPvpjpPWPdmLkoW2p98my0P0OltdcGb3vynRPAQBgd3RmvF/6xP4pmsnEwwryGIgqfawwM0wYGqNLglldDXdgIJYLNZfMEc3fuY7ybz+Xel/YNmo/tTtkDT3zxc3BXEviaekYZiYRy4iSBbN2HtzBwVh2GmtRY9a+LV9pQ+n396XeF7aNeZ+NyOujmryyuxMzxbCCPA6yWUlZgWZmGlnzDXv0/9I9ABKORj2/w6yZI4hydtXQraHL33IQAHD1t3ZCjI7A2LQ2tCDb92/JaUyGKQZydSEafGS7lLmIu4PvCqOK8GQgozuFPiddXt8q5fXs394DMTwMq2VxyMXHWL86pzFnHOxeNCGwgnyb5KL8spsBM9Mhy1JWKqOiAmZ1NaisDFVf3SMbRHP2en6HKrAvNBgFX/wpx+d60I+fLs63Rg+/epuyGs//p12gsjK4h16Ee+hFFdxnPXUgeBbDzDB0eTWrq5W8Vjy+V8pcNFDPc4VJDLzV5TWFkD/7PRvkNS8P+MhDgbze8b49MCoqYF+4BPvCJSWvKkc6y2sYdi+aEGaUgjyRFl1WfhkmgchCJWw7sFK5Lpy+PmVltpY0xwJ/fAuuH9h3+Y93aoOlV+EbeL1MwK9bqfxsGL41uuw7+0KR93rVvVhwHy8wzAxEl1d3cDCrvA6/SvoU+6c5ucrr0M/J2IGQP/ueI/Kalwe89PthedXdOlhemalgRinI41Fq2V2CYXIgw0IVPc61L16OtfEtuH7Rj+Y/2xVro9oua1EWpsr/kAn4db/i3rftgLVwgYqytu/fkurHOJaCJgwzExBOZPOaIK9l35U+xe5Lpa9wRnltWqTkrPy/ZOyALo+9b90Bs65W+TOPPrAl7COtZzrh+AFmCigoBXk6lVS2GDNMZpKqcNHmdeFKXd6RbajfmhWhcsuAzGkcvkCxkrT2uQvhXKyGCVq3XH2c++UDsDuvwe7oBJWVoeS/Xwj7SGvKvKmlKnJfuhnWwgWZf1iGKXASM1HcfWeglAoBs3ZerJ21pFlak/WxfnowNpRZVytzlnuKrd3aFstOIjYHrlRzv3IATlc37LZ2UEkpSn/yQnhTrctrU5DizFi/OqegXoYZKwWlILMFmGHyF71cLG1ZB9qyDuLgsZASqx/ZqmvHT8M5fjpQpL0FteudMuWTWTMHECKxJC2AQLkWbijAR4yOqCNhMTISK2ygY7e2KR9I46cHYV/pyFi6mmEKHffWrfjF58JKqdPVHctYYV+8DPvi5ZgVt/390r3C9w92rnfJnOWRUyX7FVogrOdWAXjyqqf7yySv5y/i1mulm4Z79ATs1jbOcsFMOAWlIDMMwzAMwzDMZDNjFGR2kcgOW9mZiUIcPAFx8MTY+vgWI8+K1PDd8wAA91bmVFTO8dOhfsmDZw/iMQbDFivr2PmsfbIRrfqWC2MpmtL2jXVjHp9hAIw7sC1q4V30tFeRbzjd8gsA1o8OjHnsJPoXheMGxGh6cZl8oe0Pd2ZvxOQNRa0gs8I3NngTwUwIhildGzz3Bj8wR/kyGiaMqir0vD1eNevmL25X7+0rHbCaFsVcMsiyYtkyzOVLc56e8i8mClXxc4+cCI3r9N4Yd/qoaNW3XIgWTcnEotcdG/P4DBMi+jfufdYDV42KClz9jbhyp1eqFPtegLWkOZ4TOUGGxiKvRlVVMFRJaZCq8f9GAgLHWAp7Olj0l+lBjEz+UdQKcpLCx0ozw0wOqpqW64RyoPqBOcqX0XXg9vdj7ud2y89a29lf3xss0JvXwW5tU76NPsK2Q9YvKiuDc+Z8PKeyjnbPvtLhDSRCVfy63rUjnOuViNNHMUVL4Lsf+Rv3PusBde7gYKCQGqaSkdpP7VZtrKVLYF+8jPMfiWx8I+NTSamU1wzo/sRuf38w1GgQSzD4uu1qPIaZDIpaQfbRlWK2kjLM5BCyHPk5UA1TWWjNhvkwa+aEUjdRSWk8X6oQMNeuhDgoraONf+0tzCmFB5SF2XVCJW/9KlvWshbAdTD6wJZwRo25c9V7a+mSYLH3LVGsHDNFjHJN0tHk1WpuistrWVnodMjHXLVc5SBf+oH4xldHd5/Qs8WYq2QGGmtZC8TwMJz77grLa11t0K9lMSq+sTc2HsNMJAWrII/FEsxKMcNMDWbD/PAF11GW48tvWw6n94aKkqeyMhgrk49anRNn5RvdKpyh8IBfQU9X0v0qW26ndHMo+dEhCHtU3R+8J0gJl2sZXIYpJnSlE0BIXtseWRySV7NmDozmxnB7TwF2Tkl5DVlzM8gr7r4TgHaaA8A5eUb+23ZFPu8nB0PK7+i6oOpeqMAIw0wSBasgT4XSy+4YDDM2nM6rIauRWTNH5Sht+sfnYa68Q1mNxPCwqsAVxZjlWa18SxURjIoK6cec4EqhFFwvVRugBch5BQ/MZYvlgu3Nr/yHQe7WUEo3Ik4ZxcwInOtdYXmtroa1WJZ6XvjpwzBXLVenMk7vDThnL4QH8BRgY9Ys+VFTaH3//sQiPM+9AACgbXcGz66dJ69581G5lj15N599IXjszo2h4djNgpkMClZBngrY8jw58MajyNGsRk7vDVU61h0agnPqbMhqlGZhigWqCQF3cFBatzIF42h5Vf0AOd8CpvweE3wsQ4VJhIgFBs44fKVk3SqY61Zh8JHtOQcsjiUAKwm/bHjq1Dasjj9zxTKYK5aN67kzFl1e+/pUxTx3YADOyTNx16kEormS/f7uwECsOEjo0fsCpdfp6pb9PHlVVmI/l7kur7sOh8dhNwsAwPDD2zD88DbceMs9U/bMzt/JnJnDd50pRFhBHies7I0d3ngUH1RSot5Hq2z5liHfMmtWV8MoL0/84gyVlg2NX6p8Gq2WxTCrqyMNcs82MfzqbcFcm5tgNTcF/SPW6ZjLyEzBU0qcYyfhHDuJisf35uyTnS0AKxt+2fDUqR2Jpw90Tp+Dc/rcuJ47k9CturrfPoBAtohAlqUswUM/d3dsnJiLRughnrwuaoxX7csUUBth9JVbg7k2LZInUinyOtMp+94+lH1vH+Z8cc+UPbPh45kzc/iuM4UIK8jjxFf2WFFmZjJiVPr2Wi2LlQXKx7cMmauXS7eKvj5pTT55BqMPbAm19a1H0UVbVdkSAvaFS3D6+rJPys+i4R3HUkkprGUtKPvOPgDAyEPbYF9ulRZuX/mLWKedzqvZn8MwBYZvjbWWtcTSsvmyZdy5CsK2lSW4/L+ew8iDW8Ntr3cBSMn37ctrW3uihTmGn71mi8ztTZYFq7kJJT/YD0BaR+3WNln1MkVeGWYiYQWZYRiGYRiGYTRYQdYYjxWY3QaYmY45d27G6PKktFIlTx6A+9LNcF+6OXQ9Vmwg6XlrVqDjdz3/t6TjfyFw4y33KH9FMToC+9wFdbv0+/uyPoNhihWjqiokD1GSXFlKn9gvA2G1YFggt4I4xvrV6PxtT16TLL9CoPdtOyAOyPSOwrZV/AIg3QcYZirJewV5Kl0XWMllmNukohxOTw+ASBCVYUq/Ys9XkCwr5o9o/PQgjJ8GGSWSot6tpkXKF9JcsQzW0iVwjp/Ggo9p/m8J/ojKF0/zUbaWtQRdqqpkpS7/vn/M60XFJwWEMUzBU1WhCnC492rrnmFK+dTkQFWy82Vkz5FQMGwaZnW1jDfYtBbWsha4R0+g4R8z+6vWfGF37JqxaW0wZu08GdMQ8UHmLBbMZJD3CjKnc2OYwsLoDipfwXXgDg3BWtgAQFqF3Fu3xlzC2b3RF2Sx6LyOvs0L4o2Emz6AZmEW17uD68ua5Mu7b9bVyTZeVLxxI/eyzwxTMGjyUNreG1z38iAv8YQvAAAgAElEQVSbNTWy2egI3Js35aZ1jIVznP5+OP39oMsd6NvYcNtTpctB1puRO1swcmeLmou1aKGaJ8NMNHmvIGdiohRbthwzzDi5FaRFEzdvqvdm7TyY61ZJlwkimGtWwFy7MpSPOEpSWigigrWgAdaCBlBlhaqiFe4oYsF9kUHkv6VBxg338HG4h4/DapFFCPyjYj+S3+3uBcMUHQND6q24EQS8mjVzYGxcI0+DiGCuW6WC9RLzGWfAalwIq3EhqKJCZkFJapODvFKpVk3v6edhPv28klffBUNZuRlmAiloBTmTYstWYYaZSsK5j43ychjl5XC6uuEcO4nud+wAhIBz/DScYye9LmGLVO9bdwBIPi51+vpgX+lQL3+BBLRytUSw29phLWmOpZrTn+dH3gOymh+VlSnfaV8xdvr6ACK4mrLPMMUCaZtTp7sneN97A+7h42j9o51SXo+dVL7I0Y2ruW5VxmfYbe3ydbk1VIjHrJkTauN/V8Tw5FXPm27W18Osrw/k1UsV6buLMMxEUpAKci7KL1uFGWb6cIeGVMo2AOjalP14tu5pmR5OLwedhqgIFlTn2nXvonyGe70bru5GkQGjajaMqtnBWKuXaA/JUCqXYQoYof9d3+bfuHvmQs5tjcFApp0b4RSN7vAw3BwL81BVJaiqUn3uuasu5zkwzFgpSAWZld/xwxZ2ZkLJssau+N3sietVxHoOC7Zeojpq2fLztuaCc70rZFH2S+Dq6BavNPTAv1Bfr5BCtHS11bJYWbr1QECzZg7MNStCbY0Nq+XLC1bKeNTtFWdgmIxkkbGmj2YOpgMAsz6snBob16Q/7uCxoN/8cM5k844WmHe0ZH0eANjnLqjMG2RZqP5y8L1y6l/jhUzyDVU0iSkIClJBZsYPbzJyhzcTMxun90bWNmnpsnzlO1q62r5wCfbFy7AvXg6l03J6b8TS4blHTsjXoRflWBlK947H6q3cUlKU7FwzBdxORgH3ZV6aP8PM6utqNTeFMpZYixphrrwD5so7vAmQTBs4e9aY58Hkjt3aFvrsHj6eU79o8R3nzPnbqr4YlYOVv/7cmMeYavyiSRPBjTcnlJM2zCmvLkhb12e8f+oT2zLez2fyUkFeuSGHqjsMM0XwZiIHKgNlRCkqgFK2QqmisnyBJwXcWIsalb+wtaAhnJoqkpotEU3h0604Zl1tqFyur5z5/2bzsywmVAXEFCU710wBt5NRwPiJl+bPdTJvAOCdNGh5dO22djinzsI5ddabgJBpA2/eGvM8ZgpiTpBqkbbdGdzw5FP5BHvlpm/nVML3LbaWLoF9/5bsHXT0tIxNi4L3LYtD8Qf+PMcaQFgMzPlSwqmc60x5dUGx/2jG+yv/R+Hmr85LBZlhGIZhGIZhpou8VJBPHanI3igPeaL9EB/HMzOTgcBapyx5gLJGqmwQQgCug8FHtiePQ5QYkW63tUMMD0MMD8Pu6IS598XwM5DFcqlZRJ3uHjgvvwsAcGvrMtzaukzd636zPA70rZgq4wbDFBF0IzilFfs0v3tPPlWArRAQto2B16f492awLPuBuvb5i7B+PMZ1UZNXu7VNZsEBYM+fA3t+EBNw6xXS+p3t1GHGwXEIE0JeKsg+haZsPti4iY/jmRnNmb9L8IsDACFCrgx+XtThV22LtdNJc5uIKsP62FH041e/4IH54+dhrF+N0u/vC5Wcnvv5eCUvhilWzn9kR+o93RWp8jEprzFXiYi8puYjjhz7m8uXpk9Kd8Hy3s/77G4ZvPrcC6FA2rLvFu7x/aTC2XcmhLxWkPNB2WSrMMPkzvLfk35xqiSsB5WUhrNFeOgL3MhD8WCOXPxZo2Pf/P4y3Px+YBXWrUv6e/doEByXRLQkNsMUG0s/kLwhtBY0JAaUWU8dUO/7fym+GU7NR6xZNI2KilBQ3vmvbMD5r2zQBnES30eDV6PjMsxEk9cKcj4QtQqzssww6fjWWqerO7TAitGRRIVTFfkAQpbcodfkkLLJsy5FlejZD53D7IfOqaIf/W+SC7lZO0+5VgDh4J8k3EEOFmaKm7QTGrujU8mPji4zVV8NgsRGHtya+UFCKHmNytXSNx3B0jcdUdbnm2/05LVmTpDdBAgF54XGZZhJghXkFNIU4XywajNMPkJlZdJCq6fgam5SVh53cDCWhsi+0hG7Zq68A+Xffi7ITmFZMKqqAqu039azLqlI9q3rQ+4UTp8sSFD1lT1w790Ep6sb5o+fD56tp6nSUpuZa1bAXOFZoKc4ZRLDTBVUVgYxOhLK0U2b16m/eaevD1RSGlKi7dY2kGXFskaUPrE/+OD191PvqfE9edX76hX0fOvz7K/tgX3/FlmR8ycHlVz61fNiY2xYDee+YOPLMBNFUSnIE2ndZUWYYcaGGB5G79t2hFNwXW5VVh5r6ZJYGqL+N90Tu+acOisXWT/4zrbh9vcHVumIP6M7NCQX+/1HE4N1zNp5MJ6R3w26j+ToA5o/pZbazDl+Gs7pc97gU5syiWGmCl9e9Rzd4uAx9TdvLl8KMToSOqG5+YvbIWw7Lmf+RpJI9fdT70VzgAvbVsV39GqbPmZ1tXLlMCoqArnUTn9CrlJHTsB8+nkwzERTVAryRCm17EbBMLdHzRfTk/WL3nCJWau5CVVf3Ru6RpvXyTfeItvx/+3M6bnX35JuQdJdPXQfydKnD+c0NsMUKzWPple4FK1XQp+tlsWY/fWwvIqdG+UbT17b35ce9KfT9fNrU+/5Jz9A2B3DfPpgTmMzzERRVAoyMDFBdWw9ZpixQ9vuzGhxdXp6Qp9963L3r+1A96/JhVUvSQsAC/4+veStUVWF6++R/Wo/nZJ94p4NydfBqaGYmY25blVGH96oddd3ceh96w70vlXKHe0KbzIb/yZdXs2aObj6G3LDW/OFZHkNFQCKwv7GzBRTcApyNuWXU60xzDRApPKp6m4MVFamlFSjqgrmulUwNq6Rnz3/w3mf2Y15n8mcXo3KymBUVcGoqoK1oAGAtAbX/Vu4n1lfH+6450jw3jsGtpqbQp8Bz/1Df57n48iZLJiihEjl+NaD8aikVMmrWTMH5tqVMDaslp892ap5dDdqHs2eDlHJqxeI6/TewPz/G1ag9Uw3AJQrlPzgyasnm7rfsSqL7s97BlbSYyafglOQJ1r5ZXcKhhk/pKVborIgqMesq4V18SoAqdDSjZswemXRkCT/QyA5st6sqwU11IEa6gDTTEzvRCWlqRZs/Z7w3Sy0tqJPS09FBNNTwqkiKKHNMMUCGZq8Vs1W782GelgdvQCkQkuDQ6Bb0gfZuXZtbM9oXghqXgjMKk9Px5YSBEuWFcjroCxCFErXOKBlwiCCqWXDYZiJouAUZIZhGIZhGIaZTGa8gnw7Fmm2OjNMGOG66ijUud6l0qY5V6/LVG4edmsb7IuXQ32prCyUasqsnQsgOEYVL9kEu/0KnLMX4Jy9ALutPeSP6LtcGEsWJRY3AABjZVC5y+m9EbvvdHWr6l7Wkma4XuGRpOImDFPoCMdVrhN2W7u67t7oC6VTsy9cCjK6eBiVlTAqK2NjmnOl3Pp+xO7Zi3DPXoR97kJIXnW3ijSr9OjPbAzadF6N3Xeudym5p01rIYaGY20YZrzMeAX5dmAf58KBNzNTh33+YvDBS5uWSyU8MTwcSgVld3TKfz1Fmp49FKRhSwjU8dvr1bmi+P6WmfD72xcupbp/zDRGHtqGkYe2wZw7N5SzNmOfpKIR2fJJa0fww6+OV1TUMdesiLnhmMuXZi5fzMRwj8QrSaZWwtPbDAzAHRiIXfeDcH0/4qhcq3Ypm1gd60cHsrbx5V4cPDZm949i5dKHduLSh3aqzcNU0PHezJmGzDUrpmgmEw+JPIwMraZ5Yjvdn1PbJ9oPscLKTAlPiscOCCGylIyamcwxasXdeAUAaUnyF0trUSNQYimrlLV0CUAkrUppGGbMl9isrwfN9gLmhoZDVmm9nzW/Ti2caWOaNXNiVmRrQUPQjwhWw3xZTSyhLVMY7BVPoU90cy3iBOaYdeJu8XIAUrZ8BdNa1AhRUa6sxuaKZYBlyjLPRGPKJGGuvAMAQCOjIau0jrVwQbIsa8/S56f6LWoMWb5D8ssUJPkorwVvQS4U5ZgtmUwxo2+0nZ4edL17B7revQN2W3v4yPb8xVTlmLaul28SKm45167JvucvxhZUVa7adWB3dMJa1gJrWUt4cL0QSYLCG1pchYDd0Ynr79nByjFTlAjXVe915dNuaw+5VDinz0nlGIgpx61/FLYcRt0u/EIhUeW4/5fuCZ53pQPuyzaHSkpHn5VkHdaVY0DKbzSzBcOMl4JXkIHCUD4LRZFnmImg9pO7UfvJ9FRQ7e/fGUoHBwBi/9GgxDMQK1sdIlquWsM+dyGjhZosSx37ufduCuVeNdavVu+jKeQYZqbS8d6dsZRsTR/dBdx9p/rsDgykp1vTXGiqvron9Nn4yUFZUjoNw4TYIX2SzTUrQkf2ulIcjW1gmPFSFAoyK5/FTSFsgJix0fjXuxL9HaMBQamFR8ZRAlrYtrKKGc8cCuVedY/G/TJnMmbDfJgN8+G+dHPmdl7pYEBuQPyCED5J+aSjClfQOIu/ssbA67fn3Ja5fRb8w65k3+HnXgh9TC2+E3XNGItrp+uAdsuCJM7x04FFG6wUR/FP7i4/tn7KnqnLfrFRFAryRMBKWP7CG6Aix8t4oaNbctMYfvW2+Jezplzd+oW7YWxcowohGBtW52ThYgKczqtwOq/C+GnmMr+6K4qw7VhBCL1ksOqTFqw1hs1P5X/szd6ImXSMTemlo33s+7fEs19o8jrw+u0wVy1X2TBo87rEnOjyJstrEv7JXfMbjk7ZM4vZDY0VZI9CU8JYoWeKhoTsFLlYcsu+sy/+5awpV7O++Rzcw8fh9PXJW0dO5G7hyoCfHiuarUFfzMmyYsr78KsyZ2ewWhbnPAf5EK3YQ0Tx99PmWc1NsJoWxRSNtFRdDHM7uIdezNrGeupAPPuFJq+V/7EXzskzKsBXHDyWngVnLPLqVe6MnoKEZMYwY/KQLZvKmDNF6Ep95JTEl0/3ZZtlQHKCPOupMJmpgRXkAmWyFXpWwBlGYpSXhwL//PRYpU/sV22opBTC0SrzJSjiZd/dJzNkePmizfr60GLotLbH+gByISfLUgusnx1AVxKE48BctRxGRQWMigqY8+thrloO+3Ir7PYOGLOCFG1Wy+LUVF0MU+iY1dVyY+iVlHcPHweA2CmILq9wHcAIq0Nl39kHwMvEQwRz5R0wV96hFGn76vXE5/ubT1/pVT7TulIvXFjLWmCUl8MoL4dZNw9Wy2IYPzkIp6sbNCuo4GktXJCaMo+ZXFhBZhiGYRiGYRgNVpBnOGmW4kJzOWGYSaOkBE7bFThtV1KbiNERGKUlwQUiUIILA1klEH0yOFEMDMiXE09rFxrbcWQbzwI12lAda2OUlYH6bsKYUw1jTjVERTlwVVYBNEpLQHVBQJy40S+PeMcQDMcwBUOJBafjKpyOeAU+HSrV3I6IYFTGA0nJsiD8k5auHqCrR1lyqSRZXt1bQ3BvDUHYowCA4ca4vJJVAjEwCKNmDoyaORCVsyC6ewFIeTW8aqIA4N4cSIzTYCYfVpALiMlwe2BFmGGyMDqa/YjTMMPV94SA3daOwUdklgU/iEmMjsiy1quWQ4zaEKM2zHleid6hIZgrloE2rwsNbVZVwayqUvmeQ0fF3sLpDg3BvtKhXrovpzs0JNPeeQus09Mjj5THkQmEYfIV9+YAxOhI1iqeIXn2cp/feu3dAKDcM4Rtw+m9AXP1crg3+uDe6INZV6v6W81NcO67KzSuOa8G5rwa5UplPRWvCihGR+B0XoXd0Qm7oxPOmfNBrMTQUCh3tNvfn1pFlJlcWEEuIPJRmWVfZabYcYeGYDUtgtW0KEOjuLJJZWWoeFxmWdCDmEZfuRXOyTNqEVfZHAwTzulzEAePhcZx+vrg9PUF+Z51S5K2cOrFFsy5c2Hfv0WbTFCZbPjhzMFHDFPI+Iqrr+TmCpWVYdZ/PgcAsC+3quv2/VvgHD8NYdsQth0UFTJM2JdbYT79fGgc53oXnOtdQS72lJOamLy+IiKvHoml25kpgRXkPKPQFM58VNoZZqKxW9tgt7YFF7QF7MJXNyT2SbM4l/xgf+ya2LExJ4uuUVkZtyR5c9GLLTg9PWHLlRDKhaPse/uCyzs3Zn0mwxQa9uXWkJKry+upzyQrnGnymmgB3jkGeY22S5PXH0Xk1Qvy04OBnZeHrdXM5MIKcp4xVoWz0BRqhilE/OwQCk1JbfmlI16jsKUorfQtbVkXv7b7sMrXnAl3YCBeYEMIuejes0FlvDA2rlHZLqisDGZ1tcqs4aepAwDadTjrMxmmoCBS2SEUmryu/LX9qp1Omrz6aeJ0P2DaNQZ5bZgfvujJK227M5DXTWthLl8qx/bl1XMRMdeuVF3NH4et1czkwgpygZOmULPizDAThzs4qIpd+Eehfto3HzK0BZcIsMOWo1u/IP0bxfMpOWObF+Y0l8QCG2QAe19Qx8B0qQPOqbMwq6th1tXC1axj7pEToJLS9CIMDFPICAF3aEjFBPguCjEFmDT1hwhicCh0e+ANMn7AT+sY8wOeX5vTdJzO5GBBsf9oIK/nWuGcOS9Tv1VXw70VzMV58VSQ6pGZUlhBzlPGq+Cy6wPDTA7+Uah9/mLYCrUpsMwad66CE8mTOuubz8FcsSw12MY9dT52LWlRTFJsafNqQAhYixphLWqE0ysj4p2+PgjbhrkwXNSA1i0HrVue8hMyTPHguyjYl1pD12ljIK/mmhVwvSA5n8rH9maW14ttsWtJ8hqyZPvtNq2V8ur5Sjs3ZaYM+9wFwCCYzY3h9quXg1azvE41rCDnKeNRcNl6zDCTy/CrtymLkopq3x+Ud3WPnEiMondOn0sc79IHdya2jxYcMdetghgdCSvJRBAHZGCf3dYOu609tKg7nVdhX7gUWrzdQy/CPfRiTiW9GabQGX54m5IJ30VJD4Z1XjyV6IOcJq+X/1du8mqsXw13aCgur96zla+05qfsdF6Ffe5CWF6PnoB79ETg7sFMCawgFyG+cs2KMsOMHz+9mo5fZQuQUetpOUp9/0Hd7xcAaNudQdW7+nos/vAudT30PCKYc+fCnDtXWoaPnZS5WbXFmSwt//IY8xu7x07m3JZhCgGzvj52TQ9MTXRR8vuuWwUAsQwYYudGJa9WcxOa/1zKq7F+dczVylrQAGtBA8yVd8A9egIwzLAyHXHtSPvuCFX68/CrAjJTAyvIDMMwDMMwDKPBCnIRM9l+yGyhZmYCKv8wANwTpHQzqqqCCHUhQlkurAXS59d58RRoyzoV6OMfm4p9L6jAP+faNdVP7HtBPc8oLweEgNPTA6enR7pOIDjG9VO0idGR4NkJBUDIsuSxrm2r+ftWLi4+wBQbujzRtjvVe7N2Xsi6bGiVLv1TG+fYSRgbVqsUcb5rBO06rORVTx/nHj0R5Dv2UMU/Tp31Gkl5dO/dpD6rZycUAPHlFUKojDfWwgWJJ1nM5MIK8hRTTEolBwIyM4FQpaw9R9Rbt78/FKEuRoJjVL2YgDhwDEZVFYBAQTZXLYdZM0e9fMw1K5SLhDs0JNNVVVbCqKxU7fwKXX6KNqOiQj3bD9LTEbYNo6JCLvbe/P1FnGGKDV1exb4Xgutd3SHl2c9KAwSbYLIsuEdOBIF1wpXXt6yTCnbtvJB8mWtWhHyFybJg1tXCrKtV7fzNsvGMXPuppFRlqfDdp3SEbUu3Ke+7w59faKPOTAmsIE8xhapUFpNizzBjwXz6efS+dQd637ojvZFhxgJ0QBRYj/r75b9DQzA2rZWloG/0yVfvDdXFOX46ZAF2h4bgDgzAHRhQ7ezzF0OPcQcHZaaKtStVkB5Zlgwk9NsMDCg/SPGSwvwOYphcMJ9+Hr1v24Het2WQVyCx4I4vw36KOGHbMDaugThwDE7PDTg9N9RJDgBVYU8NadtBJT2vXXQjKkZHANeBuXalOh0iywpVuPTbAF4RIWZaYAWZiZGkDBeqYs8w48YwUfPobtQ8ujt+zw+wcR0V4AMEVt4o4iWbgrLTCcerUaIBQED4aNifn7FxDZwXT8HYtBbGprUQtq0CCamkNGT1omcP8ZEtU7SQZaHmC7tR84UEedUw16xQ71PLUt+zIQiMS3BfipIk97GCIoYJY/1q6X61dT1o63opr9/T5FUra0+7D8NsmB8vOMJMOiTy0AetmuaJ7XT/dE9j2vEVVVZO84MnxWMHhBDJdUpnOCyzkimTWc9HkUlnr3gKfaI7OUXADGfGyqthhpTcT196BgDwzsX3praZCMiy4idMOXWcOXKej/LKFuQ85sHGTVOiHLP7BMOMjYHXyypb0SIASmZTUjelVsNKSc0WtRr5QTtXfi84PjaqqlKfF3qE5wedy3MZppjwq1gapSWh6+9cfK9UjnX50ZTjscpr9FSGtq4HAHT8z7vVNbNmTk7yalZXx5VjltcphRVkJqSEs7LMMGGSKmFV/sdeAIGvIoDwohdZ2FT0vJcDNbrw+gt3Ug7X0Dg9AwARFv7tLnVt6CVBjuVMJaQHXxYvMmBsWJXQkmEKl5hLA2QVSyAirzoRefUD63z0DDUAQKZUVK2WxeFhRkfDc+noAYjQ8I+avN4duHZkktdbO+Oyaa5aljx/ZlJgBTkPyCellN05GCZM0qJ69Td3Bh98q06Go1A/el4FykWOW3tevynUTvXTsmQAwEhjjazep0W+l/7gefVsMTqSWOELAGb98HDsGt28lTpnhilEnEjJaAC4/h4tYC8HK6wfWKeC9rSMFwDQ93qZKcO+cCn87Otdoc8jS+dLedUy1ZQ8eTAneS1/+oX4RXtiXT+YzLCCPEmMRellpZQpBnRLq1ldnWjJyYhngfWD0AYfkW4Meu7hxOd65ZL1RShKpns+Iw9K93Lfuhu1GunM/+fAIjQR/opzvrQnp3Z+qiinp2fMz08spXvmfE59GSaN1g/sjF1LdU3QOPWZqQvnqPs3LWBvAuS16is5yutPDwJAKFNNrs9P2pinlb5mJgdWkCeJfFF688k6zRQ3+he6GBkJ5QXOCc+q4g4MAABmf8+zeGq5hxOfe1QW4QgtQhEy3fMp/aFczHyrkVFfm7WPD5WVJV73U01FlW2jvFyVmdU3FmRZ6HrXjlhbo7w8ccORi+LPMJNJ00d2xa7lEpC28tf2T8Z0xoUve1G3B6OiIpBXzZefLAvn/yIir1VV4SJCTMHCCjLDMAzDMAzDaLCCPM1MtoU3XyzZzMzCHRpKD4gZwxhTSuTo0754ObGZff8WAOFj5CT3BWtRo8rF6g4OgjavU9fdoSGVB9n/Oa2WxRC2jdpP7Ya5fGkwLe93meRbqVvG9cAia0mztGoTSeuX5ndp1uVuGc8ncjm2Z5gofgaLbH8/1oIG1H5KyqsYHVEFOsz6eumD7MurV/THam6CsG0s/f93h4L13P7+WJXNJHQ5tBY1qoA9o7w8LK98SjRtsII8zRSTAsvuHEwxEs1iYT11AEBwjJy28Npt7eqoduTBrRAHjwFEsNvaYynXqKwM9oVLqniB7xscdd2IlqUFAGOjzE5hd3SqY2D74mWptAshF3fXUb7coUCiLP7d+cRt5ZFlZhzRQjp+Bgv195MSpGd3dKq+Iw9tA+2WLl7OtWuxrBZkWbAvtwJ33yn7esF6oe8ConhRHwQFSpzrXYG8trWrgD13aAhwHSXX+ibYXLsy+y+AmTBYQZ4Gnmg/VJTKZDEp+0wB4y06E4U7PIzr79kRjoTXELYNY9PaxHn4EeqlT3j+lkLAWrgAbn8/zLpamHW1oJJSZYF2jp8Ojz08rHyQYZiqLG1ofl6lL2tRo7JykWXF5+T5coeqhmXx72aYQsMdHETXu3eg690ppabTguSIVPxD6ff3qcvWggbYHZ0w16yQr5o5gbL9XDjThLBtVfXOmDUL7sBAzJ/Zl3Fr4YKwvG5YHWqn5Fqrque8eCrzD89MKKwgTwNTVQCESacYNyiMRw4lnMc6Xt0n9qDuEzJyPSkvsiofrUORr1fPciUqZwGQFiTneleQ5kmzbPX8arC4K3cVb2HPyZJKBqi1M/leSWG6KvS+NUXhYRgdIVD7qT2o/ZSU11yz6fi5jdVnT7EVc2V/5/hp+fItupq8XnlfkMnD6bwKp/OqSg2XlsYNetES0wR1dif/ON73RaFhrlo+3VMYN6wgzwBYGYzDGxRmTGhKt709XnAjieiCa96xRA7VeT25vaa43po/dgu429MbfDAITnevXMQj1nS3I7NvZL4y74XsmUgYBkBIXm++fHWWxsnQ2jvkUJfak++Pc6PpdkZynl/rCrLbaIiLreN6zrRxpTC/Z3RYQS5gclV8WRlkmNsn6i9s/ORgYjs9UMesnQdhh6tqOafPgbauV0E+UcTwsFocG/9aps6KHruCKJY+yqiqgrl8KdzBQYiXbJKv4WFpcXYdgAwVBATII2g/FVUhkWilZ5gIUfmY9Z/PJbbTXRfMmjkQTtj1wj18HMbGNdLtIsFlSwwPKyuyX9ky5iNMJF2fNIyqKlgti+XJ0L2b4N4byKs7NBSX16EhGJWVif7M+UxSUHGhUZhnbQyAZMX3ifZDrBAzzARCZaWgW/KrMs29gTavg33wmPrsdMnjUioplUesRIAQEPuPApCLuFgQRLH7/oZGWRmoskL1d4+cAG2R2S9o2IF79ATEzYHQs93+fsBTumm35hPpPROuA7v9SsjtJE1JZ5iCZ2Q0UGgzuFrZrW3qve82oeTVQ/kBL2iAO18GyNo15WqTTCUWzLoG2G3Syuy8eAr2K2SWG2PUhfHTg7Hqen6WCwAwno1Xt4TrqPHUpYGBeDtm0mELcpExU5VjdiNhJgvneheE48QsTPy+oqcAACAASURBVDpCU45D1/3FNrJQO51X4R4+rl4+7tAQnK7uUGCPOHAM4sAxVRAl42LpW41jE9GeP5YARsPMqTRvNCDQj9RXnxOyb7j3ju276uL/jvsgZ6p2yMxMnJ4eGBUVt/W3keQvTJYF+0qHklX9BEkMD8Nua4e1dIm6Zv3oAKwfHVBV9JJSQAKQcqW5giTFNgDpRYjynW2HCr8sNivIeQQrebfPTN0YMFOEH23uK66ektn+/iA4h8rKlEKpH/OKnRvV+1zdGvyFOm1xpC3rQoquUgb0DB4J1jOzZg4gRO5HtmkKd7RZxP0hmo0jVBrbn/MzY/u+W/Knu2PX/EAohtFxBwbCGSQ8meh4byCvukIaSuPmpW4DZIBfLkGx9vmLsTF1aMu60EbTqKz03J8CeU3K+25WV8tMNgXoYrFvU/aNdb7DCjLDMAzDMAzDaLCCnEdMthWULdQMcxtolh9l2S2Vlik/mA4AiEhZXF0tuT/tCvwMjeoUC7L/jIj7w8hL14ebbVor2xw9oyzEuquCOaca5pyEtFaGCdqyTvlaureG4N4aKjirFMNkI+Se5MurJVOqLfiHQF6FdsLidGknHFpuY6pMcdPw5TXifjSyc13os7lulZTXI6fVSYz7ss1Kds2aGpg1NYnj07Y7VaAby+v0QGIi84VOENU0T2yn+6d7GkWJrySzS8LYeVI8dkAIsXW655GPFLPMmg3z4XbLFGppOU2NjWtCvsQ+ZFnyiNYPmPPHrK+Hu1i6YZDtBkF65eWg0tJQBLhfotoYHoXz4imY1dXJEeJEQe5l4Xr/es80zJxcJYqJveIp9InuiasYU0QUtbzWzoPT421Qx/g3r+Q1grWgAc6iOgCAM7s0CNIrK4NRXQ3nWpCyzQ/SM4cd0LOHYJSXJ7pPyAFS3KEi3xczgXyUV7YgzzAmo0hJsVYGZBhABtT5ZWD1tGvmyjuU7697+HjIumMtXABAZr2wmpvUYudXwXOuXQuC73zFmkgG6XnKr++vLA4egzh4TFXR8u+PPLRNPc+orFQZK+A6qcE/5kqZ29VqbgpX1GOYIsHp6lZyYK5bpa7T5nUhWdDjAULy6r0HAmu03dGp5FUF6RFBDA+HlGMgCNKjZ+Wa6CvHIw8GtpVMsQhGRYXa6FrLWuS/S5phLWnO7RfATBisIM9gJkqp5cqAzEzBPSIzSZhrV8I5dRbQioGIkSDvsXsjsPDal4NE/0bNnOSBEyxGbn8/xI6NoWvGRlmk5NYv3K3K4ZoN8wEvwwaVlMZK2/pV+MzaeXLOAOy2K7DbruSUoYJhChXn2EkAcmMoDh4Ly+tQkF1CL7JjX+lQ78cirwAgXhJeB/0N9chD21S5eWtRIzAqvyuotFS5a6m5DA5Kea2vh33ugpzT5XbYl9tZXqcYVpCnkHyzsrJSy0wUNEuLCPesHoBnMSUKR5Onfcl7x43m8qWxW+aKZSqS26yvV5ZQIMj0YNbOS51fyHKkWXqTFEogyAoRTVfm41tz9XzCofypKdkVojlRg87Jx6m0O5wn1bc2z/pmUPzA6byqrFS+pTvx2V1aKVvf0jzD3C6YiWf0lXGvs/433ZO1X5psjYf239+ZeN3fGOopEkPymuICEbUOB51T5PXZ8Brvb6j9zSwA2G3tgbwOD6emgQs9m+V1WmAFeQopFoU03xR9ZvoRt4IFxrd6AF5hDSHC+YDTvuS9Rcc5cz52yzl9TqVucq5dUwseEOQZDSmAEfQFMLpIJimUvoIbTVemE83tq28MrIUL4i4MRBmPVvX+mUhS6P3jV79CnlkdBOrR1vWxsc1Vy2GuWp7T8xgmEyU/2B+7VvWVPVn7ZZKt26Xxb3bFL/ob78gGWs9dbDbMj8urYWaU16SNfBJJqRr9Kn5KXjVLdaK8Ll+a8/OYiYMV5AIhn5TSYlH0GeZ2UAtepHCI0Nwq3J5eOPPDx7NGWRnErVupFvTRhpTj3ChGQtnbnhsywGhkBGJkBGioiwyuBR4RAde75YthihwjTV57NXntvQGnLiKvs8ozyqtdn5AtJgFKKMzj3uiT446Oyledprw7guU1T2AFuUDQldJ8UpYZZqahLNZesFz3O2SFN92C7Q4NQRw4BrNOlpMWOzfKa7YdFAjQMUzQ7sOJ1uEQXmCQz/X/sQMggtPXB2Hb6sjWOX0umO/+o7Avt6oAQQgBp6sbTle39IdkmCLGPz3yUxx2vdOTV614jRgelj7KXpGQ3rfugDswoOQ1KpdkWaDdh+VJTSa/YC/wNoRhStcs14E7NCQDc7VTM3HwmJRXX7EXAk7vDTi9N0JWb2byYQW5AJlqCy4r5Ayj4Su33sI477OywpvuckElpRA7NiqfY9p1WPpiW5b0h476MLoOjE1rU/2HFUKE3CfqPrFbVsarqpLldT0/bb3SnrlmBaxlLSp9FZWUqqNdu639tn8NDFMQROS19tOevOpuSCWlwD0bVA7kmkd3y02rZYHKymJyKWwbxqa1cpOcyS9YiHiZddeRcpomr6uWS3n1NsJkWaqdX7GPmRpYQZ5GCkXxZJcKhpEMPrI9SNlmUMiv0entDbVVAXb3bJD/CgFh26kBQdFyzWlp2HzLNW0JihK4N2/CHRxUftriwLHAp/v4aeUXfuu1d0u/66HhUBQ/wxQjN39Rk1fTVCc6AOBoAbYwCNhzRL735dV1IBwnNYguKq9p+JZqPSONOziYLq8nzyh5HXxkuzwZ8l7M1MIKMsMwDMMwDMNosII8jRSyZbZQrN8MM178lG8AUPH4XvVe2Lb0O06ohqWOZElapswVy7zB5DFvNLLdT0OnW43ty62h6HbAy6HqPU8cOAYAcF5+V6gQifI1jjDy4FbM+s/n1PzE6Aho6/rEtgxTqOiuE7O/rsnr6Eg4zaIur76V2DClvPop6LyCHVE59H2S9UIkQLwAiFlfr+TVP1Fy792Uk7w6L79Lfd/4sQXm2pUpPzUzGbCCzKSSqUJeISv3DDMWRrevztzAX+z0QB5fafYWWNHeCQAwyr2czYsWKj9go6oqyIs6qywUwCdGbRjl5fJVWQm7rR3mHS2hx5fsPx3kgm5qhNnUiGgQoFFVhbKnXwjPjwhi/9HsvwCGKSCG7s4xv3KG4Dpx/jIA6ZYBAGJJI8y5c+Wrvj7YANtOaBwxNAyzZo581dfDuXYtFghr7juu+hhz58KI+ihDbpitnx6JXffzrzNTAyvIDIBkizBXyGMYwPzx88EH3z8R0lJlNsxXn6kksARZ/nXXAW1ep3IvixG5sNrnLsDt71cvH+fU2aBEdEWF9FH0It39MfyId/deKZtuf79ayO0Ll2BfuBSyjpFlwb15U1rJvPmb8+thzq8fx2+FYfKTkicPqPe07U713qydJy26HoZe3MgvL+06MDasjsUJuIePw+npkS+tgIdz8kwoSE+MjqiME347u7VNjvHSzbLN8LB6tnPtWqwYCVkW3OFhCNtWcQbWwgWhEtjM1MAKcp4w3S4LrAgzTHbMFy8oa23fK9fA6byq7unV82ztuvCCeawFDZkDbSJWX1qyKONcSrqCgidplfu63rkDwhWBlfuArOzlXOuCcy2lqh/DFAtHTqmTndH1S0LKqF4wSC8vrcrJ65biCaDkVFvis3Uufu1OCMdR8ioOyrnYHZ2wOzonbC5MbiQ7vzBTzngVVF/BZkWXYSYWc+5cUPVsAIB98bK6XvlY4N9I2+6E2Ke5MAghj1HdYLHzFzijvBxCCLh3SdeNkvZuOa4QnuuDAbiOqjSmKmj1D8DpvAqzYT6czquxSmRGZWXgS2maMMrKVEorsiwZCT86AqO8PDWTBsMUOqHMMlpucuMnBzN3JAqdvPjKNFkWqLQUQy9dCwAo6xoKXJOIQFZJSJEWL5FrsNU9AOf4aZi182TecW3TDHipIL3iJWTIFJBL3uh9h/jfHa6jZJeZetiCXADkYl1mdwiGmRycnh7YFy+HlGPfh3DkoW0AALHvhZC7hbF+tTp61Rdsc9VyWTBkeFgWBtl9ODxupBS3taQZzpnz8uUtsE7nVTm+htW0CO7AgAr6EcPDKh0cAGlFBlThArNhfmi+DFMs+EVwQqXnPXl17rtLXbIWNKj35rpVSjnWA/LMtStlasbBQZQ+sR+lT+wP++0LEVKOraVLQM8eAj17SG1gna7uIOjPb9fcJDers8plxb5I+kfyq2USQdg2rAUNofkyUwMryAVAPim+0+0KwjDTjmfdsRY1ovT7+9RlKikJ3ndcg7WsBUBgxaKSUoiLrenDro8HA4aUZ39sy4J79ESkYdjCpCvluGcDrEUL5Xvf1aKyAlRZAYYpdsiypLw2N8F8Wosn0F2a2q+qKnV+xT2jvBzi3KXUcRPlNaGQB5WUxk57VA5yId2fQvJ6950wfX9j36JdVipfzJTCCnKBMd0Kaj4p6wwz1dj3b1EWXrv9SpC+DYDdHvgxisFbKtn/8Ks8K/PoCNyUogMAYkqvnl5Oxz9uDT376nU5hl+6Vree7TkC+7JUzG/9wt0AAKftCpy2K+k/KMMUAaMPbFHyYrdF5LUz8EcWIyNKufVPhdyhoTHJaxq+hVm3Ivvy6RcMCcnrcy8oeR16jZRXt/Ma3M5wMB8z+RS9gjzdCuVEwwoqk4/ouTxDrgZVVeF0SoYZToeWNJZWIU6NuWKZcgsw585VeYOBQJE0Vy2PBbr5WE1BwFs0B7G8qPUzTBUxHk3RZD0VRMhDCDinzwWfNdcIPWiu7Lv7Qn1yJS3wzift2ZmY9U0vD7KXV5Vhxkvfr9wTuzbwhu1Z+w0/vG3C5xIdU89oAddJl1ctaE4/FRqLvGYjZEXOUV7Lvy3l1d/4MlNL0SvIDMMwDMMwDDMWil5BTrO4ZiqCwTDMGHHd4L3mD2vUzIG1MAgusRoXwGzMEmxy5HTsEt0cBKoqgapKUHlZyJriW1rdc5dg1tUlDulox5NGggU55AMIAJ5FXAzeyjxXhpnhzPtp3K/++p3pRTh8Ko9PfNqy7jUl2RsxTI4UvYKcBmd9YHR4szQ+hKYg6/509uVW2G3twefWtsTAs9BYCblH7SsdKpuDnrM02i+adD9pTD27g7qml6B1HeUD6PT0ZJzrTGQqoumvvG9n1janPr110ufBZMeXFZ0lH9yVvd+F9AC422Xh32V/7kwjWg57PFhLmhOvG5WVWfs27qnK2ibfmLEKcj7DytrUw5ulCcYrZVzQ+OVgy8vl+wylaUcf2JI8hB9oF/ldmLXzYC1rgbWsBcaGIBqeysqCvMf+tS3rQFvWBe0yzGMqmIqCBQv/Nruis/Kd+yd9HkxxMpPWWOfYyQkbK824kVb4RKf9nv6sbfINVpDzEFbW0plJX2wFjRATGuAyLXiBNO7QkEran0YoGEgfwg+0i/wunK5u2OcuyJLTR4JoeDE8rEpJq2sHjkEcOBa0yzHAZyrwy+dmwr4/vHlw7rsrlI92LFz+k+yWZYbJxkxdY/XS26l45ej1Pjn1S+D0x7MHa+YzrCAzBcVUfLGxfzozEwlZu32Lt2Y5N+tq431+Gq9OFj2GDWX+AGA+/Xw4Hy0QLMqGCbO+PnWOzf9nF8TOjTI7ig8RRh/YErLiW0uXhDKrMEyxkpiVB7IYSZRQtU+/XSRTD/YcifWJ9uv51R0AsrtWrPgdWW00JNNEsUJFo6/cCppVHu0+7bCCzEwL+ayAsn86MxPxrd1W48JwqWzPYn1r67JM3RViMDkdVab0fsKrHGbW18K5fj3j+MaBExjdqlUmEwJlu46jbNfxyKAuGKbYMZYt9t6EXa9GWtI3mjpuT2/i9UwbzDlnZfAyZQu49rBXBmk2IQScq9fgXA3iRcov9gIjozmNNZWwgsxMC74Cms+KMsPMJPwF0W5rlxbkiCuHnx+2/Q8yuznkEigZe/auw7Jv51VACNivSPbpBqQbivnj59Hxu3IeRnm5KrjgY5+/COGwgswUP87x04ny6p/uXPg/OzL2T8u3LiLVOYHgO8J4Rq7bKq90lrgIevaQsjqPPrAl5oLnnDwD4eSP65gPK8jMtJLPllpW3pmZhFoQDVOWv62ZE2sz+sAWNP5VEEAXS48XwaiqwsiD4WwTxobVocDEJKwfJft061boBR+T89BT/vX9crxoBcMUNUSAEKHiST7uvZvQ8ie71WfdrSEJo7IyXuTlng3KBSpJaZYPSlZu9Wqgcz8v56HHa/hVRvMVVpCZGU+aIpzPyjvDTBbWYnkcaq9tCV13Xn5XLBgxVCLXR8vY4fb3o/QJmW1i8BG58LpHToQCE/U+2aos+lbowdclB/9Uf2Vvxv4MU2z4m83BB+LBdb6l18fpvJpxLHdgAJWPSRlyXu4F0u45EvNL9uVVV4ATx/Os09FNsk/5D+IxDPkEK8gMwzAMwzAMo8EKMlOUjMU9gi3FzExHd5XwCzj4fsGqzY8jmSeQEsij+Raac+eq9xWPe9bdSI5ssixACPzP02cy+inrVHwjxVJc6KkFGSYHdMute1gGp5b/13OhNkkZK9IyXviY9fVKNpW8R3PAey5Y7zh5MdV/OYp/ihQl1WUjT2AFmSkadKWYlV6GyZ1EV4kcyLbAJVYi1AJ0NjxPaox/WbE8cQzasi7xutW0KPE6wxQ7uSqmUcTwcMb7zrVr8U2mlslm00Go959dtSRxjGjec0UBFo5iBZkpCHKxCLNSzDD5g/njxqxtjtyV3eIrDhxTEfA6dmvbbc2LYZg4/4+98w6P86ry/+dOV2+2VdzkXmLHNbGd6oRUUikpQEjCJmGBLewPdpddthBYlrYsS1mWkoQWSEgnxQlpBieOe++Om2zJVrd6mfLO/f1x3xnNjEbSyJas4vN5Hj2eeet9x3Pmfu+5556T8U7faeF29F0XCNfbW2m5K8li2RE4uyMCWRgRnK34leIfgjAI9JLeybrqlHmR6DlKsQz5V49u5atHzaLAyAp48RoLwlnQi921XZE8PWOqfK9sPd8rM3aa9dQGIHlxoZHEiBDIIm6Es0WKf5xb6h9cQf2DveffTCR8pe2eiKyQTpK2KBlq6by4987cnKQpyvpL6x0ju0zqOSGVsteJnqMUy5B/ZeoSvjI1frpWvMaDg6uoEFdRakUfRgPn07PGMYhe3C+UruALpfG/+VZdfdz7ew+WD9r9B4MRIZBF3AggeYlHEgWPrqfg0fV9HxiDY42d8sf+EY/Nb9sbesueuPdWYxNWY1O/7p2MzGfOn5RhcaWbI9v6SOHU03mxVH7xkm7XcWZn48zOjr7vq/BIfxjpHquhIlRVTaiqeqibcc4Y6c+abLFdXykSoW/7KPvPFd2cC4lloc/GcfCbWfFl6HsrKz8cGBECWRBgaGOMRZwLoxVX6STCLS3mdYxnra+FQMrtiZ4HptNO7LiL/3td3HVa7l6O1dyM1dwc3VbynXVd5/VRkSsZsZ2sVVdP6OoluMaXQEZav68lCMMd58xp0cV2rildC+X6ygDjSE+P8+g6fL5us3Sl/7I+zrlQ/9AKrOqauPzJmc9s7Mpecwb2GnuOVVtL4PqlOPPy+sywMRSIQBZGLQMpamUGQxitRNK6gfGsRUNWEuIVHenpcdsSO2TlciVP+xZD9nPdU8UBOIuNMG+/PXlBgURiPdeJpa09dW2Ex+aiQlJqWhh9WO8fib4OHTuOumi+eZNgr8rtiROjiQNe5fOifL2L0rGPJy/k4bDTN7bcmVolvDjxmxCW5W4Jwth80MPPXkUgC8OasxG5ImoFof/orXvNC+XAOWs6zlkm/VpoyaweYxgdWVmE29oIt7URurp7mifnrOk4Z0yNE9Wx3qvQcRObmP78xr49SQ4nenZp3KZYcR7edYDwjn19prQShFHBjoMAONLiZ0zablnUcwlon68rFG35hd32uyZPRC26IC7MLTaEI5IWMuv3G1JqYse1C3rcp9btxHr/CDoQTOla5xIRyMKwpieRKyEPgjC4OHOyCR8pI3ykDAB3dXOPxzrGdBUa8WzY321/x5Q86lfEL4xSOV1xyLHCu09hG7ZwtMUfo0OhYV90QBAGBdvz6igaF7c58/2e12GojK51AWr7wW77w3lZVK+Ij0WO8zansjg3hrTKtn4dP1wQgSwIgiAIgiAIMYhAFoYVqXqGJXxCEAYJ25trNTTEeWZjYx/Dl9n2pxSHv7+c0LHjXfsSF/cpheePm8n9zfroe5SKW/iTDOeMqThnTI3bFqnSZe17v9+PJQijkYh9ho6WxW0P7znQ9fqyrv7y8PeXx1XO7DZjoxThHfsY93/r4q8XsyA3GY55s3HMmx3fthUmtCIatjXCEIEsDDlSIloQho7YNGyuqaUpnXPsc6AvXQhaM/sHlb0ffIZ5kK1DR7EOHQW64h9db29NqX2CMFqJXQibar73is9buEonATDrf/tIcXeGuZLDew5ERblz7kwA1PqdZ3St4YIIZGHA6W98sIhiQRg6wu3tFK438cCJXqiemPbxHaj3jJ3Heo+7kUIaqEhn6rhwdo/H9JXCShDOF3QoxOlXjM2kmu990h27o9lqrMPHejwupVzKF8wy17lqcY/HjJYZHhHIwoATK3hlMZ0gDH+qV3QtwGu5azkAweviU6450tNxzpzWvwvHLOZxZGQkPSTSmYZ3HcA5fUpKl3UW5Cfdri+VwbYw+sm/uUuAtn/YFO7Ql8RnilAuV7cqo32hg4FouriexLK11yzqc/5pW8r22lPRIf8HU0sTN1SIQBYGlaHwDosoF4QzwO4Yc98yna/7jS1xux052XFxyD1epodcyOG2nleyd9x+sTk3mEImCocz3jOtFMrtMX/v7UC5PXTecjFkSqEQYfSTvd7M4Kh18eEMjqysblVGk5KQPzkSYtHbrE3gBlvYetwptVH1cJz31c04MjJo/9CyYWmvIpDPASLYzi0SsjEExPzIxua3dU2cECeYIkKmNyJT7rG4iotwTRiPa8L4bt6ISN5cx4Wze7x23OKRZNP+MduceXk458wwm3vweo5KIovzYhbwxBKqrErtMmeQbi3tD5vMPex8yL0StuKLg2iNDgaiHboOBvC9vAlaO/rdjvOV10/tkH5qhNKTXVoNDald4Axijj1/3GzukWIoRW+hIOG2NtJf2Dgs7VUE8jlABNu5Q37kh4iYH9nY5PKh8oo4wRQrZHoi2Y9uqLKKUMVJQhUnu2VJiKzCDu860OO1Y1d0J83hGbPNamjA2n/IbO7F6zmaSJyehd5jghNxFo7rfX9BfjQDRXSbXY2r/sEVKd9HGByuL1ko/dQIInzlom7bIhkjUqHHYjyR8AqvF7XkgvhdtvOh4suXpHyfvhui+j5mCBGBLIxoEj0f8iMvCP0ncXoWzICjL+ofMuK2p5Rt4csXYV21GKv+dDQDRctdy2m5a3nUw1XwWGrVuCC1RUSCMNpxrOleAjqVjBG1nzH22lMxHrVwLqGrl6D9/mhqtuaPLaf5Y8ujzocJ31iX9Nyk7YyZTUzKGWbMOFeIQBYGjXPhzRXPhyCcHR23XRx9fervU/cONX9sOQWPrI++V15vN8+U493tOP+0LfreVVRI1lMbyHoqRhT3o5NMKZtFCpkzBGGkErymayamP/Z6+i9WMPanMfaaJNxNb9+La3VXKkXnmAKyn9xA9pOpD2JjiZ1N7JFh7EUWgSwIgiAIgiAIMYhAFgYNSfcmCMOftBc3RV+XfHdd1KPTcF/32OBYD3H2kxviFmBqv7/b1K1j4dzoNKszO5tQVTUOny9u6tWxYM7APAjQ9InlyWPMBWGU4H6ry8Nb8t2ucAf/jd1TpsXaWf4v1se9T7YeJHzZwmjxEeX2YNXV48zOxpmdHT0m1dRuqVD/0IphHWYxagSyCLDhjYRBnEckTHFHRJQzN8eIL4cT5fXScH93ARZJ9xXBNWVyt2OSpRHrj8hyjh0bbWefWTWG8fTfoGF3WHm/Xt99V4IA7itjRXjHvug0q9Vsci2HOzvjpl7DO/efVXNjyfndmU0FC8JIx/va5m7bEkMc+gp5cKzdEc04ERHPVnNz1HYheaGRM9VfsSFaw5FRI5BFgAn9RQZVg0SCBy8ioqzGJiO+whba7yfvV91/HCPpviIkq9KWTJT1R2RFU4SFrb6zagxj78aQYA8YOm+28xa7XFT8c+9xkK7xJXHvHRkZZ5w+r/OWi/s+SBCEOCLFRFCKsv/oPWtMJLtMhGRrCxLpSX8Frl+adPtIYdQIZGH4MlyFqAyqzmPOR89wH0RKyAKc+sceRK/WhC9fhO8VM5DRoRATvtn7qvbQyVNx78Ntbb2nz3M4e+xYfS9v6rYy3uHzpdSJC8JopbfUa4EbLiL9+Y3mjdaU/lvvXtvE/MnJQqd6miVMxPP6lm7bgtctxTVlcvzsoMM5LH+TRSALg85IEqLDVcwLA4x4hrsRGNfl1S35r547UcfagbeRuDzKYStpx9p1cHznXPE3i3F4vTgiAlkp0/lmDL/KXIIwGPSWei1S1OOM6EG0OmdPi9vfn+JAYbfCKj+JVX7SXMLrJXDtIoJjk5ejHkpEIAsjjsEUsSNJzAvCQBKbji06gFh+ofmDroU6SQYX4cu7Chc4Z0zt1rG2f3hZt7AKddH86OvYPMp95ToOt7fHXavkv9ZhtbRgtbRE2xc6dhzahl9lLkEYNBJszjVxQo+H6ku7+rlkBYGaPrEcV1FhnK2rpfOir6PFnFJ0NLiKi6Kvvas2o0OhqKjWfj+e17fgrhl+RZlEIAvnjIEqZyoiVhDODWGXg7DLgWt8SZcATYKzM8aDVNeAtkV1hMz3m7qFVegeZlSVs+9uKXKtyIp7tJZZAeH8JuH7b1UlL94DEMh1R187Wjrg4vlx+30NFqGaurhtHcVn7uG1auv6PmgYIgJZOGf0VtRDQhsEYWiIDW8I3BCfKsqxdgeOtTtMHHFCB3zi37viHvXm3aiL5qMumo/V0NCtqlek1HdcOf1JxwAAIABJREFUiqhNu5O2p8/iAjHtiKy4j2tz+vCbqhWEgcJZkB993dsiuLjFx0px9Nsroq+9qzbjmDcbx7zZZrYlwRa9r26GsBXnhfa9HL+Auj/0FoIxnNcOiEAWhgVD5RUWYS6c70TDGxzOaLxiJITBmZeHMy/PiM6EhTmTvhYf96g370Zv3o2rdFLP90qSIipy7witdyzrtb1tH13WbTq58+aLo5k1wu3tvZ4vCCMZq/509HUkVj/ZIjnnmIKuN1oz9Uvro6/BDFrDew5w8ks9L/ALlVck3Z4s/WZvJIZX1X52BbWf7Sp77cjIgOG3Rk8EsiAIgiAIgiDEIgJZOK+Ran/nhr4WXg0kzrFju4qB9JcEL+l5RUz+6kiMr9XQYEIm0nwpV6gLlZ0geM2SM7535jMbkx9je40znt0Y9YLVvmRS0/le2RRNPScI5xvJQhisuvqeT4j5nRv/7XXM2uKO358ke0WslzpZfvreiP6erFwMwNifrGfsT9bH7x+GSwhEIAspM9oFpCz+Gzx6LcYxwFi1tV3FQPpLCiIwdHU/xZ+NY+HclI+NdCRnS92ney8KEKWPHKSx07rJSIz7jS2Haw7onuc0ek6K+U+dY8Z02zb21oMpnSsIo4qERbAQs2A1BTpviv8NO7g0GPfeVToJ14Txcds6bjS/ScmyXqSK88/b+j5oGCECWUiZcykgByrjhSAMNK7VW/s+KAnhHftSPnagOpIxP0+xlGtk4ZtSuKaWRl8nEhtfHOtRCre3U/uZFdR+pgdBHrZAa5M6Kuac6L3te8WmfkskbtDTg6hWbg96xYIeCxcIwqhgwy7zr1JRIZtswapzxtTo69jFcL6XN1H1+Uuo+nzy+OPQseOEKk7GpWeLLNIL7zoQ3eaYd+ZiOdqm5RcO25k7pYdhapxsla+XqQ8MdTOEUcrrp3ackdh/Sz+7VWs9smtnDhJis0IiDfevSFpO/EwIvz0RxwfK+3XORv02zfr0MFz6M/SIvQqJ1P3lCsb8bGDstWHVDPJuOtSvc4ajvYoHWTivOFNxLAhC/0hFHCemlUvGku3hfotjQRD6RyriWK9Y0OcxN+5t7Lc4Hq6IQD5PkHAFg4hj4WyIjbXtd77dFGNtI/jfKO29LVlZfV4jeF3ChMcwm8pMpQzu1kXSTQnDn/Ohj03Mb56M1y7IPQctOTfIL895wmgUhufDD5IwvIjNsdvvfLv9DGfzXlfWe1t6qWwXwf3GloSTui9CjI0Ldk6fkrKQjz0vcm5cIZAk1D/YQ4xyJAZ5AIoGdN58MQ6fD+UcXoMBYXRzrvrYOHudOzP18xJyFztnTI2LUU5Gj/ZqMxDZiQLXL0W5XKh+OhDOBSKQhQHlXIrW0Sj6BeFcs//LpdHXOt2bspBPLEVrHT7WcyEQm8I3EwoPRDpF+57a7+92TuxCoWSoRRfEvU97fQdhvx8dDvd6niCMRA59v0sgBwsyejkyHqv8ZPz7Q0exDh3t9Zxxf67qdX+y7ER9zaw5fL64997Vu9ChEHoY5nkTgTwAiCezCxGtwkigp/Rnzry8c9ySoWfG39p5h5WKrlBP9ExFql7FkWJe5FhCxxNiiZOI8bgKYECoMr6TTvw/0tv34sjKioac6GDAXHcYLkAXhLNlyt27oq8d724HoPILPVfDi9BTuefevMg9DXh78xz3NbMW7uzk2LdWcOxbdiW9YMAMlIehuYpAHgBEFA5vZAAjJNJT+jOroeEct2QYcdG86MuGBflxu2KT+vdVCvpssepP95prOtxqig5EvM/Ognx0px/d2d37LAjnA7lHk4tf6Nte+/IiJ0NbCYPjfqxtqP7bS5j+34eZ/t+Ho9uU0ykhFoIgCIIgCIIw3BGBLAwbBsvTKx5+QUiBTbujL7Of3NDjYbGloGv+qvep3dDVS9CXxKeGavvoMto+GuPVSvQcad1jMRblcnXFPdohFFb9aXQwcE6rNQrCcCLtDz2XWY+1154Kg0RQS+ehL43vL1vvXE7rncvjD0wMr+oh3CrZotvCH67rVu1Uh0IMx5ocIpCFYcNwFLISniGcb0SrYyUK15iqXeZAJ+N+vC76tvWOZd2mc12rt6LWdaWGco4pIOPZjWQ829Vpo3V0iravMt7ROMreKuldutB0zMNwylYQBppoDHGyypcxmSuUy0XRD7rstfHeFTTeG7+2QG/Zg3qvq89zjh1L5tMbyHw6YcBs3ytx8JtIskW3cZfxeglftnDYVr4UgSwMGSNBfA5H0S4Ig4mjxcT4Kk/CQhytOfj5rlLThK24Tjn33TJy3y3rdr3YtFRWXT2O9PTuK91tD5R3b+8FQaIdaU/epgUzUet3m455GHqkBGHAaTAlppOlNTz4uZLoax0KxcUKF7x+hILXj3Q7J1ZUW7W1OLOzcWZnxx9k25ZrX1mvTeszV/zc6TjW7e5xAeFQIwJZGDJ6Ep8jQTgLwmglkmlC+/04xxTgHFMQXbU+7R8SFjfaHWX7h5cRqqomVFVtOuGL53ddr6oax4I50ffh9nbC7e00fSJh2hawqmt6bJczO7tbR+rIiE9zpbfsOaPsGoIwUrHq6oHkWSq62attG433rsCqrsGqrkG5PXFZa0LHjmOtXBwd/FrNzVjNzUkz2ViNTb22ra+MFnr73mFtr2o4xn0opWqB40PdDkFIYLLWeuxQN2I4IjYrDEPEXntA7FUYhgw7ex2WAlkQBEEQBEEQhgoJsRAEQRAEQRCEGEQgC4IgCIIgCEIMIpAFQRAEQRAEIQYRyIIgCIIgCIIQgwhkQRAEQRAEQYhBBLIgCIIgCIIgxCACWRAEQRAEQRBiEIEsCIIgCIIgCDGIQBYEQRAEQRCEGEQgC4IgCIIgCEIMIpAFQRAEQRAEIQYRyIIgCIIgCIIQgwhkQRAEQRAEQYjhvBPISqlJSqlWpZRzqNuSCkqplUqpipj3e5VSK4ewSf1CKfVZpVS1/ZkXKKUuVUodst/frpR6TSl1XwrXGVHPLQwMYq/nFrFX4WwRmz23iM0OHkprPdRtSBmlVBlQApRoretitm8HFgJTtNZlQ9O6wcH+wv5Waz1hqNvSX5RSbqAZWK613mlvext4SWv9gyFq08PAdK31PUNx//MJsdeRhdirIDY7shCbHVxGogf5GPCxyBul1HwgfeiaI/RCIeAD9sZsm5zwXhjdiL2OHMReBRCbHUmIzQ4mWusR8weUAf8KbI7Z9l3gXwANlNrbbgK2Y0ZW5cDDMceX2se67Pd/Bv4DeA9oAd4AxvTShtuAHfa1jwA32NtLgJeA08Bh4KGYc34FfD3m/UqgIuG5/hnYBzQAvwR8vRx7jf36YeBp4Dd22/cCS2OOXWx/Di3AM8BTse1I8mwPAfvt4/cBi+3tc+zPqdG+x60x53jt/4MTQDXwUyANmAm02Z91K7Da/rzCQIe9zWtf98EU2hD73A7gn+zr1dufQX7C/+99dpvqgH+x990ABICgff+d9vb7gaP2PY8Bnxjq7/po+EPsNfF7+zBir2Kvw/gPsdnE7+7DiM2etzY75A04A+O9Bjhof6GcQAVmxBRrvCuB+fZ/8oX2l+r2Xoz3iP1lS7Pff6uH+18MNAHX2tceD8y2970D/B9mNLcQqAWu7ofx7gEmAvmYH5Kvp2i8ncAH7c/im8AGe58HOA58HnADH7a/uEmNF7gDOAlcBChguv25ujE/Rl+2r3m1/SWfZZ/3P5gfrXwgC3gZ+Gayzzqx/TGf/4O9tSHJc38e2ABMwPwA/Ax4MuGej9j/nwsAPzAn5jP7bcz9MzA/xJHnKQYuGOrv+mj4Q+w18Xv7MGKvYq/D+A+x2cTv7sOIzZ63NjsSQywAHgfuxRjRfsx/eBSt9Z+11ru11mGt9S7gSeDKXq73S631+1rrDsxIaWEPxz0A/EJr/aZ97ZNa6wNKqYnApcCXtNadWusdwKN2G1Plf7XW5Vrr08B/EjPF1Qdrtdavaq0tzOeywN6+HHABP9RaB7XWzwObernOg8B3tNabteGw1vq4fZ1MzA9aQGu9GngF+JhSSgGfBv6f1vq01roF+AZwdz+eO5U2JPIZzIi1QmvtxxjkR5VSrphjvqq17tAmLmsnXZ9LMsLAPKVUmta6Umst01MDi9hrF2KvYq8jAbHZLsRmz1ObdfV9yLDkccxocgpm6iMOpdQy4FvAPMyIzIuZ/uiJqpjX7ZgvazImAq8m2V4CRL68EY4DS3u5ZyLlCeeWpHheYtt99pe4BDiptRmyJblHIhMxo/xESoByrXU4oX3jgbGY2LStxo4BMyo909XLPbUhkcnAC0qp2DZZmHisCCn9n2qt25RSdwF/DzymlHoP+KLW+kC/Wi70hthrF2KvBrHX4Y3YbBdis4bzzmZHpAfZHvEcw0x7PJ/kkCcwUxITtdY5mJgdleS4/lIOTEuy/RSQr5TKitk2ia5RdxvxixyKklxjYsK5p86inQCVwHgVY1UJ90ikt2ebqJSK/a5Enq0OE+t0gdY61/7L0Vr39OPXFz21IdlxN8bcM1dr7dNan+zzTDM1FL9B69e11tdipn4OYKaOhAFC7DUlxF6TI/Y6BIjNpoTYbHJGjc2OSIFs8wAm/qgtyb4szGizUyl1MfDxAbrnY8CnlFIfUEo5lFLjlVKztdblwDrgm0opn1LqQrt9v7XP2wF8UCmVr5QqAv4uybX/Sik1QSmVj1kQ8dRZtnU9ZsT310opl1LqNkx8V088Cvy9UmqJMkxXSk0GNmJGh/+olHLbKXFuAX5vj3gfAf5HKTUOwP5Mrj/DNvfUhkR+CvxnZJ9Saqz9fKlQDZRGfoyUUoVKqduUUhmYOKpWzHSQMLCIvfaO2GtyxF6HDrHZ3hGbTc6osdkRK5C11ke01lt62P054GtKqRbg3zExTwNxz03ApzBB803AGsxUBJh4plLMaPAF4Cta67fsfY9jYnTKMCt4kxnmE/a+o5gpkK+fZVsDmEUDD2BWxt6DiWvy93D8M5i4rCcwCwT+gFm1GsAY642Y0ez/AffGTI98CbPAYINSqhl4C5h1hm1O2oYkh/4A4714w/4/3gAsS/E2kWnAeqXUNowNfAHz/3YaE0f32TNpv9AzYq99tlXsNTlir0OE2GyfbRWbTc6osdkRVShktKJMcvYHY4x9sO6zEfip1vqXg3kfQRjNiL0KwshCbFY4E0asB1noG6XUlUqpInv65z5MOp4/DnW7BEHojtirIIwsxGZHNyM1i4WQGrMwU18ZmGmlj2qtK4e2SYIg9IDYqyCMLMRmRzESYiEIgiAIgiAIMUiIhSAIgiAIgiDEIAJZEIRRg1LqE0qpN1I89n6l1Nr+7hMEITnnm/0ppX6llDqrbBjC8EUE8hmilCpTSnUopVpj/v7XNmytlPqfhONvs7f/yn5far+PnFutlHpFKXVtzDmx1w4n3O8T5/iRBww796LE9ghnjFLqMqXUOqVUk1LqtFLqPaXURVrr32mtrxvq9vUHsXNhpDGa7A+i/fk1Z3kNj1LqWfta2s5nHNn3WoxNB5VSgZj3Pz3rBxhClFIVsc86mhCBfHbcorXOjPn7a3v7EeBOFV+3/D7g/STXyLWr4iwA3sSUd7wfIPbawImE+/0u8UIJ9xuWjIQ2CsMbpVQ2Jt/ojzA5PMcDX6WH/KNDTV/febFzYSQx2uxvgFmLyYccW4YZrfWNMTb+O+A7MTb+mcSLjAT7GQltPFtEIA8OVcBu4HoAZSr3XIJJvJ0UrXWV1voHwMPAt1V82cmkKKW+rpR6Sin1pJ3M+x6l1Aql1AalVKNSqlIp9UOllNs+3mWPbP9SKXVYKdWglPphzPVmKqXesb0CdUqpJxLO+xul1DF737dUV6Uch1Lq35VSx5VSNfa0U7a9b7p97qeUUicwidrfsfdFRtAX9f8jFs5jZgJorZ/UWlta6w6t9Rta610qYWrW/u59Ril1yLaJHyulkpbEVUr9l1JqrVIqJ2bbd207OaaUujFme4lS6iXbe3ZYKfVQzL6HbU/Sb5VJ7H+/ve1ppdRvlFItSqm9SqmlqTys2LkwzDhv7E8ptVIZD+mXbXsoUz3M6mitA1rr72ut12Iq7KWMUuoa+9pfVkpVAY8opQqUUq8qpWrtz+BlpdT4mHPWKqW+qownv0Up9UdltAZKqXSl1BNKqXr7c9+klBoTc95/KqW22L8BLyil8mKu+yH782lUSq1WSs2K2VehlPoHpdRuoE0p9SRQAkQ85F/oz3MPd0QgDx6/Ae61X98NvEhqI+zngXGkXinnQ5iqODmY6kEh4PPAGOBS4AbgLxPO+SCwBFiE6WwjU0v/CawC8oAJwI8TzrsNWGyf+1G6nu9BzKh5JabOex6mEk8sVwCzgZvs17Ges80pPqsggJmJsZRSv1ZK3Rj7494DNwMXYXKU3ok9cI1gC79H7P3Xaa2b7F3LgIMYW/oO8FhM5/57oALTOXwU+IZS6uqYy94GPAvkYjxGALfa5+ViBsv/249nFjsXhgvnm/0V2W0Yj5kJ/nmsaBxAJgCZwCRMpUIHpsz0JEw1wSDd7e3jdpsKManmIgL1U0C6fc0C+3qdMefda/+VAApTuRCl1BxMVcK/AcZiqva9pOzBt83dmKp/uVrrj2Eq5EU85N87q09gmCEC+ez4gz3Kivw9FLPvBWClPRq+FyOYU+GU/W+y8o/JWKu1fllrHbZH8pu11hu11iGt9VHg55jSjrF8U2vdpLUuA/4MLLS3BzGlPIu11p1a6/cSzvuW1rpBa30c+CGm9CfAJ4Dvaq2Paa1bgC8DH1fxXvCvaK3btdYdKT6XICRFa90MXAZoTAdSa3uTCns45Vta60at9QngT3R93wHcwJMYe7tFa90es++41voRrbUF/BooBgqVUhMxovRLtp3sAB6lS0gCrNda/yFil/a2tVrrV+3rPY4Jq0oVsXNhWHCe2t+/aa39Wus1mMHlnf04N1VCwMO2J7pDa12rtX7Bft0MfIPuNv6Y1vqQ/bk9Q7yNjwGm217+LVrr1pjzfq213qe1bsOUCr/bHnzcDbyktV6ttQ4C38IMymPLTP9Aa11xPti4COSz43atdW7M3yORHfaXZxXwr0BBkk6oJyJTKKdTPL489o1SarZSapVSqsqeXvoaxlBiiY2PaseMWgG+iPnB2qKU2q1MZaCe7nUcM/rE/vd4wj4PZgSatJ2CcDZorfdrre/XWk8A5mG+g9/v4fCevu8A0zHepq9qrQM9nRfTcWfa9zpti8QIx+myXUj+fU9sh0+lHscndi4MG84z+2uwhWTsvUp6OvgsqI79DJRSmUqpR5VSJ2wbX03qNv4rjPf3aaXUSWVCpWKfNdHGvZhBSpyNa63DGE99X5/tqEQE8uDyG0xn9Nt+nPMhoAYztZQKiavEfwbswYwcszGjw6QxX90upHWl1vpBrXUx8FeYqaQpMYdMjHk9iS5v9ynMFFDsvgBQG3Pt2HbKynZhwNBaH8B0CPPO4PT9mOnI1/oxbXoKyFdKZcVsmwScjG3WGbSlN8TOhWHJeWB/eUqpjIR7nerp4LMgsc3/AEwBLrZt/Orup/RwIeOFflhrPQfj7f8QZgYoQqKN+zFOuTgbt2eHJtD7Zztq7VwE8uCyBrgWs9q3V5RShUqpvwa+AvyzPXI7E7KAJkwA/Ry6xyX21oY7YxYBNGK++LGLDf5RKZWrlJoE/C0mFhLMFNkXlEldl4WJcXyyl2eoAbRSamrKTyUINrb39ItKqQn2+4mYMIANZ3I9rfWTmHCBt5RS01I4vhxYB3xTKeVTSl0IPED/BsJni9i5MCSMYvtz29eL/MV6XL+qTBq3yzEx1c8ku4BSyquU8tlvPfZ1Uhq4JiEL4xVuUEoVYAbBKaGUulopNc8WuM2YkItYO73X/n/MwGQgedoe3D4N3KrM4kQ3RqS3ABt7uV01MCptXATy2fGyis9h+kLsTm14W2vdW7hEo1KqDZP14oPAHVrrX5xFm76ICdpvwXiZnur98DiWAZvt9jwP/JUdNxbhZWAHsB0TY/0re/sj9n3exdSjb8EsIEqKPTX2TWCjHbud0mp+QbBpwXxXN9rf1Q0Yb+oXz/SCWutfY8IUViulSlM45WOYON5TGFv4itb6rTO9/xkgdi4MFaPV/l4FOmL+Hra3VwEN9r1+B3zG9pon46B97njgdfv15B6O7YvvYeJ/6zEDgtf6cW4Jxrabgb2YcIsnYvY/jhlQVAJO4O8AtNZ7Mb8rP8HMDN0A3GrHI/fENzADiEal1N/1o43DHhU/IyYI3bFH0kFgir3gRxCEUYbYuSDEo0wBjN/asdajAmXS8D2qtf7VULdluCMeZEEQBEEQBEGIQQSyIAiCIAiCIMQgIRaCIAiCIAiCEIN4kAVBEARBEAQhhlST1AMwZswYXVpaOkhNEQQBYOvWrXVa67F9H9k7Yq+CcG4YCJsVexWEc0Oq9tovgVxaWsqWLVvOvFWCIPSJUup430f1jdirIJwbBsJmxV4F4dyQqr1KiIUgCIIgCIIgxCACWRAEQRAEQRBiEIEsCIIgCIIgCDGIQBYEQRAEQRCEGEQgC4IgCIIgCEIM/cpiMVwJa82qQwd5Zu8ewjrMR+bM49ZZs3E6RP8LwnDkUH09j23fwqHT9SwuLuEvFi6hOCtrqJslCEIS2oNBntq7i9cOHyLX5+OTFy7k8kmlQ90sQRhURoVA/n+vv8rbR4/QHgoCsL2qilcPH+TnN9+OUmqIWycIQiwbKsp54KXnCVgWltbsqanh6b17+MPdn2BKbt5QN08QhBg6Q0E+/PQTnGhqpDMUAuC9E8f5q4uW87mLlg1x6wRh8BjxLtZd1VW8dfQw7aEgbu3BoZ10hIKsryhn86mTQ908QRBi0Frz5dVv0BEKYWmNN5xGMGzRFgzw7bXvDHXzBEFI4Ln9+yi3xbE3nAYaOkIhfrRpPQ0dHUPdPEEYNEa8QN5QUU4wHAYNV7TezPUtd5IWzqAjGGRd+YDUWxAEYYBoCQSoaG4GYJJ/Oh9pepDJ/hmEtWZ9xYkhbp0gCIm8dfQwHaEQLu3mupY7uKTtOpzahdvpZFvlqaFuniAMGiM+xCLH58PtcBAKhznk3c1lbTdwc/M9rMtZRV5aWr+vF9aaVw8d5Jl9ewhrzUfmXMAtMyWeWRAGAq/TicMOe6p1V9Kg6ljZfiu7rA3UZO45o2sePl3PY9u3mnjmohL+YtFiijIlnlkQBoKx6Rk4lCKkgxz17OPCzuVkt+Szxfk6uWm+fl+vIxjkqb27ee3w+3Y88yIumzR5EFouCGfHiBfIN06fydff+RMAJzyHeNV5mqtbb+eqxo8ypSPU7+t94fVXeSsmnnlbZSWrDkk8syAMBF6Xi5tmzOLVQwfpoI3Xs59mWfvVXOhfjtc/m85O8PWjz91YUc5fxMQz766u5qm9uyWeWRAGiHsuXMiqQwfp0CH2pm2hwVnLpW03cHXjHRT4M/t1rUg88/GYeOa1Es8sDFNGvFs02+vll7d9hHxfGhluD6G0Ft4Z8yzjxgf40+teVq0Cy0rtWrtrqnkzEs8c9tqxVhLPLAgDyX9cdQ3LJ0zE63SR4XOyNWs1oWn78dfk8MgjUFOT+rW+vPpNE88c1rj8boJhi9aAn2+tXTN4DyAI5xEXFhbxlSuvJs3lJtPjoTHjFHvGv0ZJfhq//71iyxbQOrVrvbB/X3SxnyfsBbrimU93tA/iUwhC/xnxHmSApSXj2fjgZ9hZXUVYaxYWFePAwdtvw7p1psO94w7I7GOwu6HihIlnBi5tv56scA47fesp14d578RxLh4/4Rw8jSCMbtLdbn5520cob2qivLmJGQUFjEnLoKwMnnkGHn0UPvxhmD279+u0+P2UNzcBUNg5gQXBFZRZBzmatp8NFeWD/yCCcJ5w5wXzuWXmbHZWV5Hl8TB37Dj8fsXLL8Obb0JVFdxwA7j6UBRvHj1CRyiE0g5ubrmHZkcjO9LW0empZ1vlKa6ZOv3cPJAgpMCoEMgAToeDxcUlcduuvRaKi+HFF+GRR+Cuu6CkpIcLADleHx6Hk1A4zHHP+yzoWMFVbbfR4KzB29iC1iBRFoIwMEzMyWFiTk70fWkpPPSQEclPPQVXXglXXAE9hf97Xa5oPHOLaqRB1TI1OIfxoSk0Ok4QCIDHcw4eRBDOA9LcbpZPmBh97/PBRz4Ca9fCe+9BXZ1531s68zHp6cZmteKgdyfzOi/ippaPU+kvg5b0wX8IQegHIz7Eoi/mzYMHHjDC9he/gJ07ez72hukzowL4mOcAL2b/infTX8WlPdRumsYjj8D776c+nSQIQuooBbm5cN99cOGFsGYNPP00dHYmP97jdHLTjFl4nE7afa1s9v2ZXd6NBF0dLFFLeOkl2LMHAoFz+xyCcL7gcMDll5sZn7o6+OUvoaKi5+M/uWARHqeTsLLY69vCczmPsjXtHQpCRbz3h3E88QScksQYwjBB6X6ovaVLl+otW7YMYnMGj/Z245kqKwMmlLMj411OtTbjUopbZ8/hc0uXkeX1suXUST7zyov4LQsFOB2KH1x/C9mNk1mzBhobjRd65UqYPl08ysLAo5TaqrVeerbXGan2Gg6bQejmzfDGGxpnRidlE97h/fZy2kIBLhg7ji+suIwFhUW0B4N87tWX2FhRgcfpwN9p8aHSpdxZeinVVYr6ekhLg1mzYOZMcLuH+umE0chA2OxItdeIhKirg+ee09Q3hGmbuI+dju1Ut7WS4/Vx74JF3DN/AU6Hg6f37uara1bjdDgIa82Y9HR+fuNHqD6Yx7p1ZkA8a5aZQSouHtpnE0YnqdrreSOQAX65bRtvvaWY2bGIStcJ1mS8gt/RgdvhYEpePi/ffQ9upxMrHGZHdaWJZy4sxu10Amax386d8O67RiiPH2+E8rRpIpSFgeN8F8hgRPLJliYefPzPLDl9HUor3slYxUl3GSgEMelUAAAgAElEQVRIc7n49e0fZWnJeABONDVS3tzEzPwx5HkzOH3aDIoDAThxAmprzZTwrFkwY4YIZWFgOZ8FMhiRrLXm7197k5ZdUykMTOKwdw9b0tYQVmHSXC5umD6T/77uRsCUrt5ZVUmW18sFY8dFM0T5/bBhg/nr7DTrEFauhMLCIXw4YdQhAjmBQ/V13PzkbwmGLab557Gi/QN0qnbWZKyi1n2KDLeb71x7AzdOn9nntSwLduwwQrmpCSZONEY8ZYoIZeHsEYEMQSvMPc8/w9bKk6RZWVzdfBt5jGUPm9iWsxYcsLiomGfv/HjS80MhqK83/7rd0NEBR46YBbter+l4Z8zoe1GRIKTC+S6QAf5wYD9ffvsN/CGLC1tXcEFoCfXUsCb9ZTq97XidTl6/534m5eT2ea3Ozi6h7PfDnDmmjx03bvCfQxj9pGqvo7578IdC/ONbr/PaoYOE7MFAjpXHaWcN+VYhN7bezWHPHtanv8n2ylMpCWSnE5YsgQULuoTy44/DpEldQlkQhDPjtUMH+fLqN2nq9KNQqCDUqWrCOsx8llHcNJnVvhfYV1Pb4zVcLigo6BLJmZnGZpua4OhR2LULDh40Qnn6dBHKgnCm1La18dlVL7KjqpJw1N+mOcZBpjCb29rvZ4P1FvU5ZeypqUlJIPt8pi9dtqxLKO/fDxdcYEIvxo4dzCcSBMOo7xYeXrOaN44cjopjgFZnEzn+BYDGwmJGYD6TgtPJaz/Vr0wVLhcsXQoLF8K2bWY1729+A5MnG+MuLR2MJxKE0cu+2hq++OYfTREBZaZt0Q4CKkCzbsSFiwLGcUvnfRx1b6Gjw3iEleputxGR3NAAwaB5n5sLixZBW5tZcLtzJxw4YDxU06aJUBaE/qC15r4Xn+NQfR1hAAUEoV21kenIoS58ijzGcZn/BsobDpPnyO7X9dPS4KqrYPlyk7J10ybYu9csvr/yShgzZjCeShAMIzbEYtPJCr62ZjUH6+vI8fl4aPFFPLR4aTTt06aTFfx48wbWnjiOBnzhNGb453PAu5Ogw09aOIOl7VcyNTiHIAHcmHxQRRNC3H6z64xinkIh2LrVCOXWVuNJXrnSeJYFIVVGY4hFs7+T/3jnT6w69D5WOMyVpVN4+MqrKckyHWZ9ezuPbNvMk3t20WKnnZjUMYMGRy0tnkYcYQfj/VMZHywlJ1xAAYUoILOwhbtvzGfcOBNK4XB0F8vBoPEcRzzJSpnXTqcRyvv3Q3W18VpFPMr2sgNBSInRGGLx/P69fG/De1S1tjIhO4d/uuRybphhZli11rxy6CA/2bKJg3W1aCC7M48scjjpKgMnZPlzmBqcQ1Yon2Im4CGNkKuFD30wg4UXeHC7+x+S2N7eJZRDIZg/36SCLCgY8McXRjGjOgZ5V3UVdz/3VLRUJZhFO/cuWMSXLr2Clw7u55/efiNu/3T/BVzafgMB/BzwbWe/dxudjg6Kg5NY1v4BcsL5aMxn4VCwaJHiqqv6Li6SjGAQtmwxuSHb2mDqVCOUJ07s81RBGHUCWWvNzU8+zuHT9dFCPA6lKEhL50/3PYA/FOLG3/2ahs6Orv1+B8s6r8GJk3pXNadcx2l1NuIKu5kUnElJYBLFlOLBTVpWgCtW+Jg+3eRgdTpNxxsrlgMBaG426wcyMkx+5M5Os7jI5zP79uwxMco+X5dHWYSykAqjTSBHMk10xPShPpeL71//Qa6bNoOvrfkTT+3dTUcoGN0/q3UBY8PFtKs2Kt1lVOuTKK+DAv84JganM96aQha5QJDlS9NYtMjEFEfstT+0tXUJZcsyaSGvuALy8wfoAxBGNaNKIActiz8eOcTqo0coSM9gb201m05WoIGi4ETqXdUEVQCfy8XGBz7Dlb9+lMbOTtCQEc6mzdkMQH5oHPM7L2ZycCYWIQ55d7PHt4Vp/rlkhnOYFpiDAycKUErhdsOll8KKFWe26j0YNKmq3nvPjHynTTNCeYIU5BN6YTQI5AN1tTy9dzcNnZ1Mysnhse1baQ8G8fkz8GgPzb4G0l1u/u3KqzjZ3MzPt20mYFl4/Wn4PR2gwNXpZpo1lzxrHGEVpMPRRiUVoBUFwSJ8eJnGXLLIxes1adxmzTIDUY+nSySD+TcUMjM7oZARyWlpRiRHvMnp6SZV1Z49JutFWpoRylOnilAWemekC+QWv5/n9u9lW+UppucX8Piu7dR3dEAYxvhLqPOeAgdMzcvn8ds/ytW/ecykQvUrHLiwvEHQUNJZSlFoIm7cWM4AVbqSeiqZFJiJdlhMCs+gkAm4cDF5smLOHGO32dldA9r+0Npq+tctW4xQXrDACOW8vMH5nITRwahZpOcPhfj4809zsL6O9mAQp1JYtqjPtvK4vvVOdvk2sD3tPZxKsbumCr896p0VWMDS9itZn/4WR737OO2qYW3WKvazkeXWZcxqWsBM/wLCWLjxUOk6jgcvBaEitDYd5J/+BG+sbePOmzOYP79/Bux2wyWXmDjliFB+7DGzen7lyt6r+gnCSOXZfXv49z+/TcCyCGuN2+EgZHuG5waWkBZOY6djPa3hZvbV1LCruopAyMIV8LCk/UraO5s44ttLi7OZI679jNO1LMuby8kaH1NCs8iwsnHjppU2DrOPGY7p4B/H3r0m3vjHq3fSllXFT+68HjA2a1nmX4/HpJBrbzf/ZmYagdzZaTrb3FwT81hTY4Tytm0mBGPuXCOUe6rqJwgjlZq2Vm79/W9p8fvpCIXwOBwEbHst9k9iWmAe5TqTCu9RKhqb2HaqErdyEdBhZnUsIEcXUGa9T52zklPuMjq8zUxQxXgD2Yzzl1BkTSaPfDrDndSoalpoYq66kOPHFS0t8Lt179OadZIf3HUVGRnJ1xP0RGYmXH+96WcjQnnXri6hnNv3ekBB6JFh/3P//P69HKirpT1opnKsGI93s7OBo579zO1cQno4C0trpublR4854T5MnauKy9tvZEXbdaQpD7fOmsOTn7yFpVc382r+r3nfuwuFA41mXGgCeaFxjJ9o4XKZuMWgswPLEeSFF+DRR+H48f4/g8djPNGf/zxcfbWpNPTII/Dkk1BZOSAfkyAMC1oDAf79z2/TGQoR1hrCEAyHiVhtmfsAKEVpcDYlqoTp2YUUZWaigJAjQJWznIxwDrM6FzMxNJ0LM0v5m0sv5oHLZ3PMeZAK51HqXTW000kmmRQynswcE+cfDhvbym6eSF7NTPbuNTYcEcdKmQV9Xq8JrWhrM/udTtPROhwmHVx7u1klf/XVZiFQerpZW7BqlUkVZ2sHQRgVfOe9d6lvbzfhFGGi4higyl1Bi6ORQmsCBcFCStOKKEhLJ2LR1e4ThAkx1T+bacG5lKpSbp02l2/dfCXjS0OUew7S6K7jNLW4cVOki8kig1mzFD4fnD4NmS0l5NVPZ/NmY7+dnV3FglIlKwtuuMH0sUuXGpH8ox/BK68YGxeEM2HYe5BfOXTQGK6G25s/xWHPXvakbYru3+Z7l8mBGVzUeTkzllRTlJnFtVOn8ebRI3TQxhuZz7Cw8xIu7FzOgvAMHljioyAXijIy+fXO7exyrGGXbwNzO5cwO7AQt/ZwshzChAi5/biD6TgtL01ZJwjWjOFXv0pnzhy45pr+xzt5vaYs58UXw8aNsH49/PznZlp45UooKhrYz04QzjVbTp3EZbtZp7fPpyAwjk3Zq9EOjdLQ4mmkyipnjB6HpbK5OHcmk9LHsPZ4OW3KT5ljP23BJooDk5jrm86DSy4gIwPCQbh75sU8e2QrBwLbKFCFFOmJjAkXMM5dgmVBk7uC9OAYvKFcAiHF7988RSCtiU9fM4fCQtOJulwmvCIihjs6ujzJaWkmLMrvN97ktDRjk4WFUFVlPMpbtnR5lEtLxaMsjHxWlx3F0pqMYDbz2y/miGcvtWnGc+NwOCjzHmRqYA5jGMsn58yg1DueIl8ux0O1NKSfZq97K+M7S8kmj0/OX86M8VloP9w8aSE7TtRQ7TxKlcPB+NBkcq0xLMqbQUODoipcjocMPDobd2c+r22sJrStnY9dMYXSUsjJIbqQL1WPclYW3HijcUi9+66ZAdq+HRYvNn1vdv+SaAjnOcNeIGd6vOaFAq/2kRnOAsDndDIuI5MTzU0czdjJjLYl3Dd1NgDfvuYG/K+v4t3jZbidTg64N3L5vFwCe2fz85/DbbfB3LluXrjrE/xu907+ePh9Mn3lXDF7HL7qUtavh85OF56gixBBFA5yWibRnlbDpYvT2b7d5FBdtswYXVqaaeK+2hp+tnUzRxtOs7i4hE8vvojxSSzS6zXTP7FC+Wc/M/GOV14pVYOEkUu6203EXRzWITzaR6F/AlXucoqzsmnqDHHKe5T5aZO5f/bV5GZ6SPMX8aXF1/PDbe/Q6eikzXGajMJsPly6mI4OMwOTng7XTpvFOG8e6yqPUBOu5OJSJ1cVTKap3k1tLThr3XS4T+MIZhGgk3RcpHUUsH27ifufMMGsdvf5jFAOKj9Pvb+LtccqyEnzcOeCOXxg+lS83q6QC6/X2HdxsRHLlZVGKG/eDPv2mbyskyeLUBZGLl6nkQGWCuHQTgqsYhpD9VgqwLjMDGramlDeNm4uuZDLp4zFCsF3L/sQX9vwJodbKlFOi2ZfBffPnU++J4uODhMDPMGZzb8tv5F3y8vY21pGcUGY26ZMIMOfRXU1HGpwYnn81AfqyCCdsLJwhTLYt8+sAZg82YQhxi68fef4MX65cxsNHR1cO3Ua9y5YTLbX2+2ZsrPhppvgssuSC+WsrHP9KQsjkWG/SO+d42V8dtWLdIRC3NZ0P43OOtZkvkJRRiZr/+LTKMDvV/zoR2Za9L77ukabtW1t1LS1MiUvn3S3m8ZGePZZOHnSiNtrr+2++GZPTTU/Xr+Z1hNjmNq8CEfIGF8kdtGyzBROIGByqKalGVHbOa6Mz732In477tLlcOBzufjDXZ9gal7vrubOTiOSN2403qu5c801pWrQ+clIXqRnhcOs+MXPqGtvZ4J/KuMCE2h01OH3NvH12y5hadFEvF44dkxx7Jj5rmdmGk9uwApR2d5Ejs/DpIIs2tqMrXZ0mFjCwkITFlFfb+wwKwtCys/Lx3ay8VAt+cGxzMmYTtVpP+FwmHlF4wmFunIg5+ebBXxFReBKC/DAy89R19JOIBS2F+U6+MSSOfzNshWEw8YWAwEjfjMzzW8AmKnfU6eMUG5sNPsiQlkqaZ6fjORFej/YsI6fbduM9ruZ3bEAvyNAu6OJGdMd/PDGW1FKY1mKLVuMV3f6dNNnBQJQ52+mIxRgVmE+bqeDqiqz0NXrNfbqchmxG8lXnp4O22uP8/y+vYSa05jtnkJziwtn2MncwmKUMnYXsbniYhg/3tjsb/dv5v82b6DDCqKVxut0UpiZyct3f5KsJCI5lsZGeOcd02crZfrwSy8VoXy+MmoW6V0xuZRPLVzCY9u3EHJ2kk46BWlp/OK2D0dzHkeq7rz6ald1LICxGRmMzcgATKqpk4EaZlzdwdh9E9i40UVFBdxxhzF6gPXlJ3jg5Rfwh0JoDrIxayNT/XNZ4r8UAumAmfLZvNmcc/31ptjAH/8Iba5cxngnU+46go90OmmnLRDg2++9w89uvr3XZ/T5upKhR4Tyvn0mGfoVV0jVIGHk4HQ4+OWtH+aTf3gWj+VEq046VQu3TFrMNO+k6Er1SZNM2EJZmemsXC7w+11McReYcIkmCLnbqVINeFyZuNtyOHHCdJgzZphFdM0tIb727mpOt3XSoTo5Gq7kiFXOWFVIiWd8tOiP221EcnU1tLSYDvxIqIKmlhDt4QC+UDqWM4QV0Dy5dT93z13AuOx00tPNue3tpj0Rb7LTaTxbJSVGwO/Z02WzF1xgnk2EsjBS+OxFy9hdW82243U4QtDurKckvYC/nLEcywKPR+HxmO97ebkZpOblGbsodmebRa+t4PGFORWuotWlGBseR1WVk5wcE4rU3Gz+Xtp3gBf37aNDB/HrKsoCFWSHC5jhLcXhKMayjH1ZlolPbm83C28rqoI8ufkYIYfCoV2oMATcIWpaWvn9nl08tOSiXp8xNxduvdV4j995x6SH27q1SyifSTpXYfQz7D3IEapaW3j8dxaBNg+f/ksdFb4RLAt++lPj3fnsZ+M9w6damrn/xec42dRMGE3Qsrg2awUTq1bgcio+9CHT6V7/219x6HQ9Du3Arb34HR0ALBhXzFfnfpy33jIdLJgRbjhsUtRMmhbguTdayLEKaHDUkh3OY0faevZ6t5Dlc7PjL/+6X8/a3t4llIPBrmToUjXo/GAke5AjBCyL59+t4nhVgGWLPExwjqepyXSyhYVGQFZUmCp2kycbr1QgYDxNlqX58frNvHr0AGEsVMhNnieD++euYEZOIXl5iuJieG7n+/x03WbaQn4yO3MIey0sRwilNN9eeQtWSzZVVcaeLMv8RbzJR9uPc7TzJE3OBiYHZ+J3dFDlKCfsa+dvL1vONXMmRkOntDYeM7/ftDsjw1wjNo3cqVOwe7cRAVlZZnA7caII5fOFkexBjrCt7DR/3thGblEHN84tpbbSQ1qaEbhmAGtCFVwuk3fY7TZiORSCHSer+Y8/r6ZdBwhZYVzazXWT5nBz6QIyfC4mTYL2YJAP/fr3tPsDuENePGEP4TQLlMWNU2dx48SLOHXKeHuDQWOvkTTMAVcLW04fosFRT3oghxydxwnvYU56jrFw0hie+Mid/bK106eNUN61y2iFiy4yQjlBVgijlFHjQQboDAX52po/0dowlZJgKVf86lE+Nu9C/vWKq6JeZKfTLJz7/e/hf/9wHH/RCVZMnMilEydzz/PPUNbUGHfN11vWU5BxiI+rj/PEE24uvUxzuP40KBgbKuGG1rtocNRS5S6n5mQFM24zQnX/fnjjja6Vse+/D0eOuDnmOYAfPws6LsGBkyUdlzPdfwGHve/1+3nT0+EDH+gqr7l5s/FSSdUgYaTwx0OHeGz7+2SGc3im9hBjsjz88wUfpqoqk6ws0xEVF8OpyjBrdp/mmbLDTCz0cdPU2bx04AirDh+gQ/tRSuHU0OIP8YMdq7l+ykxuCi+lvR221h6nTQVAK8YzFZffRSO1BD0dHDpdz8pp2Ywda8Rrba0RymA63fRALkVh8IUz0ITJ1jmkhdLp9LcSqs/i2DEjdNPTTWiF02lmeiIL+Dwe8+dwmL+iIvN36pQphbt+fVdJ3AkTRCgLw5vq1la+8c47BKpzaKmu53t7/8jfz7ueC0OzqKkxtur1msHs+h1tvPt2GeQ2cv30GYxx5/DPb79Gux364MKFIwxvHD/I+uojPLzsVgKHfbT6GrE8QRzaQU4gn0Im0tbRSJOrgb1VDdy72IRB1daaWP+mJmM3wSCE2jzkB4tw4SWognjwMNM/n/GhyRS2hKmtNf1iqrmU8/Ph9tu7PMobNpgFuBdfbFLGpacP+kcujABGhAf5y2+/wQsH9nFByyXM9S/mt7k/IM3t4h8uuZz7Fy6OHrf2xHGe+K2D3NAYnst+DLc3jFOpaOnavNBYpgbmsC1tLVqZVDbTs8fw1/n3sX071Lgr+FP6yzi1kymBORSHJjIuNB4XpkpIUZEZTZeWGm/X22/Hp5BpVg1sSl9NUWgSc/yLceBAoVi4EK67rmsxX39pazM5Hjdv7kqGfvnlUjVotDLSPcj7a2u44+mnyO4oIjucZ3KjOlqZlD6G/1n2CVwuxdSpYDkC3PO7Vbhri6jTp6lLLyPkCOEPWKRbWbgsN4WdE6nlFGGPJsuZjcNj8fXrr8DXNpY3jrzPcyffI6xhbGsx2RTgJQ0IcNWcyUwfVxANi/D7TWhFfb0dMtHeSVVLKyFCtNJCB62kkUGGy8vKWZPIyVFkZZlQqkjWi8hPZSjUVYEvI6MrbVxktb3DYbzj+/ebGaecHCOUx48XoTxaGeke5Jue+A1VNRYFgWJanc1UucrJcHj4rxV3UeIZR0mJEaCPbtnKC2vqcYQ8lHsOEfC04UBhBV34rHSy23IBRbOnEZ/y4Xa5WDZtDA/NXUlFbQcPb3qZRhpJb81hnC4mk2zChCnMd/PRBfPx+Yw4VcrYalWVmZXp7NSUnW4kqEO000Ybzbhw4XN6WTi+mOLc9Gg4x4QJ9LuMdV0drFljHFEeT5dQPtM+WxjejEgPcm1bG197ZzVvHT2CQyk+OGMm/3jp5bxwYB9+yyKkgjhx4dVpdIQ6eGz71qhADoXD/O0fX8Hhy+bmlnuY33Ex2xzvxl1/QnAK8/wXMce/iErXCarcFTRYVeiLD3LThOmsWlXMrc33sibjFfakbWIPm8hwevj0zGu40DOHsjLYvFmzYYNCE6bJXUdmfpjMjnF0djjI0rlc0/YRKjyHeDPnKW5wXwt1Y9mxw8QnfvCDZmqqv51kRoYR2JdcAmvXmtipnTulapAwtGit+c2uHfxk80bqO9qZWTCGf718JS+/f4CQpQljkdcxlrZQK20ZLdT7m2nwVVIQKOHECXixagsH2o5TqB3khsfS4j9Nq6sGnNBBK5lteYyhkElMoyPQTg0ncXe4eOHdCu66xMtF48ez4fhkaqimNq2SmkAlmVYOE9zFFGfmYVlGDB+raWJ9xTEqW5vJ9aSxMG8G+c5cAlaIhnY/eeSTjg/t9rNwfCGtrYpQyIjqzk4Tv1hQYOIUte4qLNLSYqaDI1k2nE4z/ex0mjCSoiITo3zggLHbvDwjlEtKRCgLQ8P+2hr+P3vvHSZXfd/7v06bXne291XvBSTUQIAoAkwz2JQANhDHwc41Sa6f5N7c/J7nJjf15sZJbuKS2Ma4YAM2vRpEkZBAoN61klbaou07O72e+vvju7NrbFPsWBJc71vPPKOdOefMmTPn8/28P/0vt7zOnuFBApqLO5csZePM2fSkkridCGrJRYPcykign5Jl8dzALr685BpGR2G8lOWfdmzFLQdpdmYSMWsYkouUJQNJskCyaWMOYWJouocxBjHKZU4cgaPVI8yuqmWxdwadmQESvnH6nZOouotqq47LapZPjn5PZw3eGuzm4Gg/jgRLIh10RJpplXz0JfKE7BBevORJ0x6JEnH7UBQh6/v3C8O0rU2Q5crgkQ9CdTXcfLPQp1u2CHndsUMU869ZM02Uf1vxkSHIZdPkk4/+kNF8DnPCVfPMsU72Dg1NDv7w2SJB6KrsrbwWeIqxfJZnj3eytrmV05k0hmWTV0cZUwZZpK/kuPsAOTWN3wqxongxR9y7GZdHidrVNJptNJszoAh7fmLzuhpnXpuf4X6FK3Of5ojvHQ553+HWxYv4g4vmIUuis8Sfvfwq24+kqSo3Um+0EkrUU0IGHBRZwrahWZ9FizGLiy+WaG8XxYOjo/DUUyKv+Oabf700iUBANENft26KKB84AMuWTfR4tO4EQI499Jv4SaYxjffFV3e+zb/v2iH6lANH42P87rNPsqC6FmwZx3AIUkWboZAyxrCkMptPH+PyFoVAsZathxMYFiSNMeZYy0CHYqBAQcrRnJmJgUGKJDIyAUJouMmSoX8IvvL4CWY1evhE62JePx4mS4K0kqa+RuUP1i7Aa8roOgwXE3zv6NuUDAu37KJgWowVD7KhdTbtnnqiaQ/ZgoljBfF6XEgmqO6Jrhr61KNQEEoyFhNENxIR0Rzh3ZrKmSyVxLWxLEGWvV5RCBSPQ1eXUL6xmEiXamgAJzEts9M4OzidTvPpxx6ZHLqVKpf49t7d7BkeQpVlZEcm6ISIWDFqS82MaYN0JsboLJ+gTeng9UNjeGwfaTmFRw9QSyNFcsS1EdyZAM10MMBpXPjQcFNHIzmyGKbOY68NI7lOs3HuTOSCj75cFQUtSVkrcd2KJi6oi03Ijs23Dr7FaCqPJTmoaOwon6TPH+e61hX43G6S2TJ6GTxaDL+qYhiikC8QEJ2fTFOkPnZ1CSN15syp9IsPQk0NfOpTU0R561ZBlFevFg9Xflpef5vwkSHIPz15glS5hOk4BKwwmuMiqY4xnMtS5fEyWsgzoPUwV1+Gzw5wbfYOtvl+yp+8/CKSJHHH4qVUGrB2uQ9TW2hifeEaXgg9jIJCizGDiBUjYscw0LEkC9VR6XTvw5YsaswG+nsiaI4LCVhUWMMFrOZ8U6K3R4RH4+UMT544hC5bjLpH6XTvw5R06q1mNsZWUme0MjAAIOE4sHmz8CZdeKEQrpdfFrlVX/uaKAq44gq46+lHAXj45ls/9LX62Wbo27aJwol9+2D5grtZd/4zRKdzlKdxhlE2Tf5j906KpolkycRKdcT9Q5RMk1SpiE91IWkSSX0EDx4aSx2MO0M8dGA/Pz5ykMWhDtymj6hVQ0obQ7fK1NNC3sjQ75wiQBQ/AaqpBxSKZLEw8eKlTB4b6BzM4qrzccui5QwmimiyRGONDycDtkeEWZ/fcxi9bOGoJpapkJLH0BSFlwcK/MmqOsJhlfFxlWRSkNtSSaRORKNCoWYylcJBIct/88JOHNnmTzesorpapDmZptgGpor3DGOKXFeOOX++IMqnT8OmTWLfJe0zaG44dS5/ymn8luBbe3aiTxizwUIE061TpMCugX4kScKNQkpL4DH8RM0aZGT6kyN8+ZUXcDtubmxcRdSspSCVyZIkRh2t5TmYkoWMiwgxGmghSJQyeUoUcaHgIFPEwNYNXjzYyU1LzieV6SBvWFT7PdTIKoWCkLej8QFGszmKjo7LclMgD7KNkS2TkEeYMaOeRMJNPD4VwclkRBqTJEFfn/h/R4eQy8FBeHR7N4Yrz5evWURLi5DRD0JtrehwNTwsiPKWLcK5tWrpjaxa+hLTDuXfDnxkCPKxeHzSsr00fz1lqcjLwccomCaL6upJlYqUJrpKvON7jfnl5WzI38gB6x32ed7kR4cO4FVV8obBCddBlhXXUmM1UmM0MKYNccDzNstLFxKXR4jZtWiOi5MFCzQAACAASURBVJJUZFF5JYNqD6/6n8KWLKrsatZHlzLXWMzQoMzWrcKKlCTwRdysKl3GoNRPo9FOqzGL4+6DHPbs5HuZx9l27+8RVgP09oqk/+5uoVi3bBHfsb4eTmZG8BZq2LFD5sAB8Nc0kg8P/lrXLBQSaRtrFtzPm7uvZ++R9ew7sp7brv17ZrQemrZyp3HGEC8UqNQvtOZn027NZo+0jaw3xWA2Q4u3lkJZoewqUrYKINlEjVpkR2HcGWF/+hSL/bOpSkaxNZNjxj4WWiuoK7dQlouclrtos2cxwGlqqMeNCxMFE5MI9eRJYaOyf2SMgyN9XDFrHn6njtHRqSK6YBBGxg0UXPjMAE3MpGDnGLH7SBgJ3hnr5NK2+UQiQtFWCoNyOUF4PR6hbA8M9aP0KixrawDLwVJLjI+LSni/X3idwmFBhk1TEPNQaCq0W+nFrOvinOrr4fTxR+gbnMdLfTfQXLeHqy6d9kxN48zi0NgopuOgWCpLy2sZM4Y4FtqL5cBFzW2c6NGxFZu8maOgpvA7QWRTIS4NkZbyPD2wk7BVS0yqo897nFixjogdI2bWM+rtJ14cwk8QG4cAQVyAjUOYECXylClTwuDbB95kdV0ti2oXkM9JDA5O9RnvGyih6G68uGigDRmJEbufrB3n8YO7+LNLrqamRiEYFMV84+OiRqfSezkUgt39p+GoxRWL2pkxA56Nn8ZXqGXPHlE429YmCPSHae1WXw+33goDnX/OGztuYss7n+KdfZfz+dvuJxxMTMvr/+P4yBDkWVVimEfBMBhUe5lfXo7qqJiSyb7hIWZVxRgYEQQ5YAXp0Y5hYLCktIpqs55tgRdY3d7Mtr4eLMdht/91Lspdx4X5q3ky/B0Syhg6Zby2j7SUJOiEURwVB4dGs53b0/+FQ+4dHPbs5vnc6yRaTvI3n7iJRx8VluqMGZAtSzSn5jDDWQyAhcn88nLmlZcyoHXzjS2H+Z/XrGLOHNH+zbLgtddEJwoQ1qgfMSbPwaZYkmg5fRGpgTh36k9juUq/kie5gnAwwTWXfJe1S/6aHQc/S3PDid/MjzKNabwHqn+mzHvE109rdhYtxkyOKnsoORZpxyAge7GwsSwTPyEyZJlnLOekdIh4cJiDuS5WVy+ERA0Jzzi5YoKAHSFiVlGigAc/BgWSjOAlgIqGFy9u/LhxUSCHSoEcMs91dXLXMj8+X4BCQRDSYhECyBgT55knTYgYISKkSfLmkSHWt83G71dRFJFHnEiIfsmJhDiGaOuokiDNvm4Fl9VKmTTfsF9B1iT+y9LLOHlSKOeamonhJaZQ2l6vIMuVjheVFlKOAy2BF1i+YDNdJ/14vcmz/fNN47cQ86trODgyjKWYZKQEUTuGR/dRVovs7B9gSXgOQ2NlTNVAK3tQUKinhUgxRm/wOIZWZN38KrYfTaIo1QypXbSZ84kZNRTlHDIqHvyMM4KFiYqMggsvXrx4KJInRx4F2DuSAOU45zfMJZ8XsibLoCgaEuDCS54MVVTTyhyKNJI0h9k70Meq9g5UVWzv9QpZrRDlfB40/JTIsOnAaaQDEsVSmKPyYbLhPnwjtWwoL+PECVEL8GHTLxpqern1E//M0GCeo6c2EgokzsZPNo1zjI8MQb5m9hz+4a2tFA2DQa2XReWV1BstjKpD6JToSibwaiKw0WC2UWc10+U6BFYLdWYzn0jfCcWDvHXvRl4+2cV4scjYazqhfJR2fe6EqLrRcGE6BjIyDjZxeZgauwEZmaXlNSwsr+CYez/7e/chbchy331Bnn5aDCBZsMDF08pDjCZNasxG5pSXErNqkFFoMWbh7JzJ/z4ohG7mTFFNe/nlInf5T7/RTSTVjoSEg8Owq4+gGSVgh4nY1YSPX0/ZneKttwQZr/SK/TCoWLER7uTKi1+dtmqnccbhVlXuWXY+39m7ixJ58prI9Y8Ua0jLcXTLoWyaqJKOGy9hqvDiR0Wjw1kABZmMN85Xb7mUNw6Oc3Q4RcTlZseeMmGzmoKTR8JFEB8gESBIjhxp4shkCFNFiDAWQTwUyJPkaPcwaxbMwu+f6n28Zl4HP+rcjoSMjkWYKhRkIkQJEubNt0q0NweIRAS59XhEwU48Dm8fHcJDCBchfDj0Wj1o+KmimpGxDNFam9raKSJ9+rQI34bD4uE4gjh7PO+WZUkCV933cQHLA9Oe42mcHXzuvBU8fewoBcNgzDvIzNJCYoU6BrUeDAd6imkURwbJIUotXnzoWNQQoZzLk5MSzGuIcMeCFWzpHMHCIDni5lSfRNiqQkbBix8ZZ0I2LbKkKVIgRAQPHvwEyJEnT5oDg+OsaHSIRCR0XcwVWNTQxKuDe8kzjp8AAfwEcBMkiBc/o0dlTtlCRn0+IVuhkCC5Ww724KEKGQ8uNAZLx7GwcRGgilpsJUcmcpo1a5bR3y/SL/r7RT3BzJm8b/pFRT4buJOGxm3T8vpbgo8MQfaoGk/c8jt85snH6U0OYGKwunA5GSXFpsBjGJaFTQEbmzF1iIgdI2zFKFEgq6TxOT4CR9ZwpFXixhULkCQ4HrR5+EdwQXEDT4S+TVFajyVZ+OwAA2o3TWYHNXYDOSmNy/Gg4UJFY0H5fOaXz+P5Z0rccKUIsWzfDq+8AleEbuNh50eccB9kUO1hff4T1FpNDCunibj8SMUqDh8WoRwQAtzUBJaWZ7B5O+pgO7V2A/V6GwXfKM+oT9JizOQi5SKkXJRNm8R+lQbtlUdNzS8nzLc//qvnMH9UYI9Pk4OPM/7rmnXIwFd3vUNSG6PDmM/5rGdXYQt4HCRJwrRNVMVk0OqmgRkUyePBS8ysY563hnRK5sIFNXSEa8jl4HTTSbp7FYJylLQ9SoQYOmVUVHx4KaNQIk+CYaLU48WHCzc+/OTSJoODQnZ8PkGSa6w22ujhCMfIEseFSi3NuHEBDoauMTIiiny8XqFsVVWEX3PufsrlAApVaLhpZzaDnOYou3G5Fb4wayPHjol85UBAkOFsVhTkxuPieNGoyDWuDBf5OMsrTMvsxxkdkSg//OSnuevJx8hYKcp2kQWsIGLE6FIO41YUDMnGsR365R467Nk4mFjIRJx6lJLKLNcMWmt9XKt1EI9DMpTnaF8XATNCghEyJFFwkSZNgBB+QpQpkiWFgZcotYSITkybDTI4bBMOKrjdgiAbZY1LGs7n6aFXGWEcgzIdzMOLFxkFFRdjYyINKhicmm7pdkPRHcco57Hw4SNCKzMpkecYxxhXBvj25XdhGOJzmpqEB3l0VHSa2bVLtHhrbxdDiypdK6bl9bcbHxmCDNAYDPG5887nr97YzIjaT8SqocFsZWlpDZ3BHVzQ2ISeKqPhYo93G2sLV3JCO8RsYxHxtp0s1VbwwgvCKrz2Wpg9S8Yf0SHlY255KZ3ufSwvraNIHp8TYKv3BdYXP4HL8eDCjY0FE72LJSQGurx8vWvKE3z33fDYYyqfyN3Bdu8rdLkO8dPgoywtrWFJaTV+zeSmm+HECSFwliUELZmE6sSiye+pazkkw4W/UMe1fIZjrn38IPA1WuW5LMutR7ZVgkGZgQHRsgaEgq2Q5Y4OoXR/njBPC8E0ziZkSeIPLljNN/fuIiOnJlMY5tpL6CudIOh3Y9o2Gio9SidhK4YLDzYGYUJc3dbG2JjIza2uFsVs62e0MDB6Cm/Ri06JInk0NJKM4hKdilHRCBLCoIyFgQcfChoBfAwOCsUZiQjZs21YVrUMI6FwnEOcUA4wbPXSwTw6aMDndk9O6wKxvaYJgn3dwpXYNry47zAKISwCNNBGhFoG8qf42x1PYVkyc8JNfPa88/H7hUGsaVMjreNxQYzr6sQ6ggP8jNxOy+w0ziaW1jdw5cxZPHX0KGV3EatsUE0DSSOOJCk0hNxYSRlbNhjW+6ihkQJFvHhZWDcTuRDg9GnhsPH5wDT9LGwLcbg3N1lA60amQAmdEj58KMi4COLFS5oUfryouAhRRXxUIZ8V+qzST7xZbWYRyznCIYblPuL2EDNZSB2NyGo9pinktDLFr1wWcnftghWUy/DqkUOYSBQJoBFkHsuJG0185sFnGfeMMK+6GtlW+PvLryMWE9GeVEqkQB48KFqytrSI6brT8vrbjY8UQQaRavE3WzczqPXSZHbQrXWypLSatHuEf954Jd/sVmiUYoxG91IYSjDDnok7qDM3v4Lb7pPYtg02b3bYcyLLJu9ThJUga/kky0preSbwA5ayBkPWqbJq8RFgt3cr5xcvoiDl8DkBnIl/EhLSBAPt74fvfld4l0749hCwGlhX2Eit2cTb/pc54NvOtSvrSO2bwcMPCzL9R38kCvV27BAh2DlzhMB9d+cBvMVqNDMKDsjIzNOXU5tsZMzdx3DjbgKZRqTRtsn58QA9PaLor+KZNtQCBf8o40aEQa3nY2XpVqzanXtnMzi2hGsv+yyKYk0vPh9DuFWVa2bPYdOxbgzNIGtkcOOj1Z7BivYQy5vqeW3/EIYZQTIT1JRnEPH4aYvECMguoeQsh2ePHOeFrqNIZR+t7jqUooobDwougoTJkKREERsTCRkND17cODjolCaG+XgwDFFoVyoJr+6h0WNIskwVjSwnyhFrL0UtQ6Q+wRVtyxkdmhoh7XaLThWVIQNerzhGITCEbo6TLKk00oaXIA20ki0FSJGk5KTx+QTRD4eFsg4EhAc7kRBV9t96/hSOZJEp1zKodX+s5BWEzObyAZ579W7WLf8GLUx7pj6uuGvpcl7sOk5ez+KTgmiOmwarFUUp8bdXr+GpbSPsShZIOnHCukTMFaU9EqMhEprsVXxyLMmPu3YwENeJyEGCWgy37sOmQJgARXSKFLFJo6GhYeLGhQc3BhYWZWrxo+sTk/LMqYjLroEjhIiwiJX02ScZ9Jykxz7MZ1bOxxxxkcmI71EZKlJpp+h2i7+f7e5Htr0kyza1tBMmRi2NKGWLaLkeTc1RCsSJRgXR1vWpYtt0Wuj7J7YOIG2VcPQ5DGg93P7oT0C1P1byCrDpjQvxulNcuGpaXn8dfOQIcsjt4cEbbuZPn94GRUi6B4k5NVxWvg7FUKgJQ73Swlc+ew99ffDggzBztrD69u6F5avL/MPBTSxNXMZlpVvY5vspo8oANVYj8/Vl5OQ0ITtKWkqyrLiWp4PfI+yqYpa+kCGlD1MyRH9kHBxHEOTKII5kEuoz52FLJnFliIKUZWFtLR5V5d4NMyishqefhpdeglOn4IYbxHCPClE+fhzWzVnC+utFeCeRgHu++wpN5dlElBBV+RVIeZmUPE5f+2uUB1fwzDMhFi6EjRuF0k0kBFF+eFscf66edVY7232bgI9R0YBjAzp7O29BVcooinWuz2ga/wn89aVXkC4+S/x4EbfjoeRkmO9rYaG/lhqfxMUdQX5vzhxRYb5b3MMw1fVh05HDvNXfR9ECRS7TVR6kVm5AswUBduEmgI8sZXRKyEhkSaBMpF9oaChISJILxxGRm2JRHNtDCMvRKZLBhYe5wTqcYIi/uvpqHAeGo6I1VKEgSLGmiYfjiNccB+6/+HJqaoRCdhy48z+eo0SJRcGZ2NkqzHGJf9z5Eqpb5svLr8C2RZuo5uapHsqPD3TjK8aIFmoJWGHgY1ZI6+gc717KcHwRsmTAdKOrjy2W1tXzFxdv4KuvdKJYEkU7Q5US5uL2RVi6yrKGJi5d0ERrq0hBOHlyanqk48BQJs3Xd7+FaToYssUYKUxHwz2ZBuHGi4KMQokMBgZlDDRcWEhoyLjxAAogjlkp1DNNcNtBTKmA6YCGxuJwK5rXZsPSOtJpMXgnHp8ybINBcQzbFo8/u+4qYjEhr6US3PyNRwkRoTkQI6jHSI37SY/Dvc8/Cg78cIL0VuS2rQ1+NLIff64GdynA/PJyyqVO9ED6fa7qRwyORVlXOd5zJbNaXz/XZ/OxxVkjyJZtk9XLBF1ulA8oGV3R2MTLn/80X/kni+vq1nD9lW4eeEDmsceEpVgZ79zaKiZTHT0qCOfmzbDfOUKPfJK+4BCX5W5kQ/5GulyHqLWamKsv43XfU1xWuImwE8XCZE3xcl71PUWT0UGD1YpOmSPenayqbadwuhbbFsQYRHrDobEh/Plaqq0GYlY9zbk1XHaZEFCfD267TZDhTZvg3/8dbroJNmwQRPmddwRZ/va3RZ7T+vXQ6z9Kr/8oC2pqkSyV7tMmKhrNAYOeWS/x+7WfZutW0fR8wwYxdCAWgxUrWnEcuPtHLxJTS5NCfrbxq+Y42fnvg9XJ6Hg7I+Pz2bjun5GqvoMkuc7kaU7j10BO11EkCa+mve92Pk3jgRtuYtvePEcPO7Q3qyRGPGQzkPIJpVfpIzxrlhhuUyqJ1/J6mbf7BslTQLNd+M0wZbVA3i4QJEyeDCalCZVqU8TGwUTBmQjdim4WFmXa/Q2TPYsdR5BTUFAdNw1UY8kma5uvoKZGKH6fT6RiKMpUizfLEopVUYTyNU3RTiqRENs2NkI2OkrOKpFplDnQGaeaRhpCBUzFoaNDHGtwUESc6uuFAv/ufZcBcPuPfoJmenjoYyKvjnkSJ/XH2Hovx7u/QDR0mqqqwLQn6iMI3bIoGgYht3sy+vle+PTCxVzaOI/nXinhdYNPClAqSaRSTBqZIHJ1K10iKofc0nsS07LRJYOIEaNMiQxpavHjQiNNkgBhVGTcBDAoogAqKhoyRYqUKdLoDaFaTKZM6Lr4bAUXsqNSjUnY18LNc+ZRUyPyjn0+WLgQenuFjFVIMgiZrRjGhYJwKNXVQbZqnBJZorEypeIoff0mBSVLWFJBmupgIcvCG+3xwA/uvgaA23/8KBQjPHjXVR9q0MhvGr+yvDomTuavwTxE16mrsByNGS1HscMPfaj+z9N4N874JXMchwf37eFfd2ynaJh4VIUvrlzF589b+b5CrCoyc2fD8eNeqqtFTvETTwgLr1gE23F4vecUe3wDuJx1WLJNPq8R3xegaJrIch6P7SMvZZmlL6IsFXE5HuboSxhW+4iZ9Wi4aDTbaTfn8Ezou1yXuQu342Vh8QLscZsrrhC5hNu3C8Ht6YEADRQ8Y+juHIFcPQMDXr7/fUHcV64Uj1WrhBX62GPw/e+LKXeXXCK6WaxeDTt3imN+5zvw5Y4vsX69IN9QKQow3hXKWbRITON78UUxSvPaa8UULkkC3ZM5kz/fbxRO6aeQ+wo4RQ6duBJJMlk48yc4mRRS+C/P9elNYwKd8TH+2ysvcTQ+BsDallb+z+VXUVPpU/YemNPkZ7wPvBNFboYx1XpJ1+F0JsUzp46Qj4eokRppDkWI5wvIOLhtLwE7Sg2NjJh9mJSQCOPFR5YsYWKomPiRiZOljMU4I/gIoeGiXfFQVSWIdzotFCeAgoKFAzgotouREZHyMDIilGc0KlqwtbSI5/FxsX+l6K6SdmFZgigkEvAvK++lvl4o6y9bz+BIBt/8xC243ULBzpw5lX/c1TX1OZIEuG0sd+HM/oC/IThOEWf8d3DsFIl0OyPjc1m1+EEc8wSmkUPVPkQj2WmcceiWxV+/8TqPHT2MZdvEfD7+4uINXDlz9vvuVxXW6KjVyOfFvVyJvBQKQhZMDF7qOs6BoRThbCszI7X4TDc9qTEkR0GzHKppwqDMOMPolPDjJ0MGGwNwWEAVRximjEWcMQIEkJEBieY6P3pJEN9CYaJIzwBnYjKthIqnEObkSSFL1dVTI99bWgSRHRgQOtq2BcF1uYSulmXh2Eqn4ZErvkgsJt6zbfjDF59Htqv55w0biUQ+4OIqoAdS54Qc/zpwsv8Hik9g2ybHe9cR8g9TH9uGnnkGJXr99Ij7XxFnnCA/cugAX9m+bXIcraFb/Os723ErKncvO+999505UxDCoSExmrWvTxS/ybLDPU89zu7hQQqGwTK3wtL+dQRq8zA8g0g4RErKsM/3FmsKV9DlOkyHPg8JiRZzFv65feSPuWhvF6R3TeEKngh9i9eCT7IxexsuX5FIyMtLLwmBvP56ePjlMXzFGgC8pWp8pRoMNc/y5cKSHRmBN94Qj44OuOAC+N3fFdPztm4VaRE33yy8UBdeKN7fvRvefBO+9z3hDV+/nl8oCgBxDnfeKapsX3oJvvUtQcIvvfTc5TDa43dimhrbd5/PmiUPoH4IS9fJfR2cIo4jcajrWmY0v4XfG4fiEzihP0OSPGfr9KfxHkgWi9z22KNk9PLka2/19XLr44/wyl33Ir/PChuJCCVUqTAXHlxBWrd2DvJ/u57A1hw8epDZxgLqx6pYP7+Jgl1AdTxkSRIiSpgaxhmg1u0m7GnCMVJIdpRYsIW8lSObOImFTokSKiphgjQ11uP3C6+tzwe7e3pxE8JCRcfAjYqJgWUJhVooiOdIRMhXJCJyEDVNeIyzWUGyA4GptItoVLxWmd7l94OrGMZWS5OeK69XXIPK8JChIfFIpYRRey7lFeDYCQ+aWqBJ/z0A5Kpvvec+TvF1HF3FNCIcPbkRHJjT9irgwi68COFPn41Tn8YH4H+8+jIvdB2nNKFjh3M5/uilF/j+jZ9iRWPTe+4ny6I4Lh4X8lrp3GDbMDKm8z8feYRx8pRLNq16moODtVw1dw71gRAjej8+yU/WTOIjhBc/CrCoupVSOQOKRq03gktxkegvMkyaMjp5MvgJMjfchKYoVNULUp5MwsnRMWRcGIANuJCwsSiVhNyl0yLyU1PDZHGdpgndG48L4zYUEudvmmIdUBQhe6mUkOVoFGzHxFFsymWxTzQqtvtlONfymk50c6LvYubN+QKSBHL0G790e0kCxzFwxl/EwUcuV83p4fNYseBhFEXHzj2IHb7+Pb/nNH45zjhB/redb4txtI7ELH0RXa7DFE2Tr+185wMJ8owZ4vlEl82QNETNEggcaySXlTjUk6OgGMiOzEHXLmaUF5EZtwnjY2lxHVu8L3LcdYA2fQ5t+my6GjazKL4Bw5BIdlWBlsM0AyxYAEeOaNxqfZbguv20WQkOvlrHgGeUhZc4jB6u5emnJSSPQqLqGJHEbBwcFBRU08fevWKhmTdPCHGlmK67W1i4y5aJor033hApF9dfDwsWCCW6Zo1ImdizRxDlhx6Cy5puZf16YQX/fO/UxYtFiPrVV0WqxpEjYuT0vHln7vd7P/QNzWHzzj8mkW7j+iue+GDr1BoBYDQxh3SumUtW/svUe3YalGmCfK7x+NHD6LaIr9aVWyhIabJqhrF8njdP93JRa/t77qtpQmkNDICu5CjYecJqmF0Dw/SWh9E0PxmSmFaWEWkEVXfTPZbnvMZ2tg8fwS/HyJpJgoTx4GNucww7F8FUI3SOHydZ8rGsvZlLvAsZKSdwjAJoHjBkuodGcEUdlrTUMnOmlzeHxjDLBSCEGw8OOi60yRSKnyXK6fRUL9VKWDaTEUo1nRbkwbaFIvf5RNpERWF/quFiAgFRAe/zTbWYCwaFjLe1ie2Gh8WaUFUlomDnJFxry+w6fAe64ecKz3P4fVnk93FmO/kUVj6AaWl0D66iOnqMkH8Iy/ZgmyOTXrtpnDukSyWeO3EM3bJQLRd1ehMD7m5KpslXd77Nd2+4+X33j8XEcyJtUHIlKehuXj01QL5gU5BUim4Dy7QY1UZwl0Js7e5lw8K57M92U7IKqGYSD248uOkIu6nzh8k5YY6MHySRKbKyo4NVc+YwlEmQs7OAhF/1kR/Pc3IwTUtHiNlNMaqrYf/oKB6iKLiwMJBx4WAKYigL+atEiMbGBFEOhYTh6fEI8pxOC3IcCIj/a9rUlL5MRrz25QU3UFMjZLVUmiLJH5BJdk5wovdSDnZdj24fp6O5E/l9CK5jl3BytVg29AyswXY0ZrZtRpEdbGccyxLXcdqL/OFxxgnyWD4PQKPZztrClcwuL+IN/wuMF9PYjvO+Him/H4IxnR9vG+XlAz/Gq2rMd1awkDVcmLuGl4M/4bLcJ+lxHWeXdzOX5m8gUJenfWQ+pbpudmWPsSOwieszd3O56xIuv1PiwQfBZQXQ5Sz9/cLLm0zC0JAf+ehC/r/id5npXcqK8Ut45O23Gat9lr+7+i52vlVFOlFF0TPOWN0BfqflUt56S5osDDh+XDzX1wuh7e4Wwvf22+K71NQIj9pPfgLnnQdXXTXlmVq1Cs4/H/btg23b4OGHhdCvXw9z5777hvZ6RYrF0qXw3HPw6KNim6uvFuTkbEGOPcSsGKwfepw3dtxMfftNrFnzATtpy0DfTF3sGPffsR6fOyVel3wgV09udrZ7N073ipzCqWSCkmki2RLzisvQ0DjmOkBKHqJ3PMuq+qlFtvKoQJLAH9Z5ZXM/Q9YAce8pInY9jXo7siQRcqqoyzfhI8wp+TBhOUxP3MfVixYTDOq81HMKt+XG62icVz+fea1RensF4ZZRcbDJ5aC21kVMqievF3npxF4KOHgJwJjNW2MnuHrBDP7wuhXk8/DApl3oZog1M+eQSgmZLJWEQlVVoUxzOZEGUiHDlb7Fbrd4LZud6rmaz4u/AwEho/m8ULx9fWKbilfL72eyUCgcFtuPjgplnMlMrRNnC3LsIWRg48Vf5rnX7mXnsf/Ftde+PylwPB2U7Sw4RW7ZeB+FYjWSZOHgRdIWY9tT98DZlKFpeZ3CcD6HS1bQLYuO8lxa9VlUm3V0+vZzejyLYbxbVivyWnmORuHl7mMUCib9/k5Ux0Ws3IhkSmiqh+pcPY3MoJfjJD2jeEpu5JKHL51/Cd859gblZBHTKLIg1MiKOY04EzU7XmKU7AyGAWGfRFssBsQ4PHKSNwYP455ozbjryAD1p+H3L17L569bSDYLj27uRMJkTv2id41993iEji2Xp6I8FTkLhUS+dEW+DEN8N8sS21YGAEmSkOlKBKixUawDicRUB5qPAir39vIld5EpDdI3dicdC4TxXoHjvHsfxwlgMY5tJamvPsrcjpeIhXuwLA+2Oh8Q109RpnXsZqteZAAAIABJREFUh8UZJ8gd0ShdiQQDWjeb/c+ypnAF12Xuoqt6+/uSY4DxQoEdhSPMNZcj2yp5Q2fEGWEhUGXXsqS4hoKc4/ziel72/4RBtZfq0Tq8LptrlE/wnfuuRJFlDuxVeP55+LuXduENVRPKtKEZoqXbv/x4kMHmt5g1cgOp02EWuy5kp3czYSvGktJq3oiPc//eb7Plvs9zaJ/G86/4ae29lIQfPvtZMUb62DFx44EQ0OFh8Hgc5HCGbNKFy/IyNm6DLdwte/aIyuDbbhOKEoSQrlgBy5eLIqatWwX5ra0VRHn+/Hd7a1pa4POfFwR8yxb42tdEnvPq1WfXq7N+5VOMjrewadMF1NaKtJj3ghT8rziJd8ApEg4MT7zqheB/R5LOXeynrHtwaaVz9vkfJSyrb+CZY50UTIOdgc0sza9hXnkZY041MwI1WNZUERww6d2pKN9vHdmCbczAjZeUlccmSbXdjCEZyJKMjEyAIC32bMbtYXxOgC3HB/jkqgv4/fUrGRkz6e8RwwBSKdg0vIlweQYWbooUORkfZEt8gIgqU+2NoCOTJwMoBAgSIMgzR45SG/JywcwGSrEk2BmamuYQDAr5TKWE0nW7hZK0LKF0hxN5jg9mKVAmELRY2l5DzB9EkgQRtiyhZE1TKNhcThDfSgFRNiu2CwYFCc9khOIOhcTnNDRMpV2cPi22a2g4u56r6ugI61c+y+bdC3nzTbFmvNd6YairkbS5aM5eJKmMSxtClt1YUgeS+8LJzgHnImxbKnvwffBm/8+jJRTGcoTyOeE+iCRJNJVncF4+SF1TAtt+N5H6ebK8b6Sf8VKSiFNLSs/jKBmCdjV+gpSkwsT0WY0ZzOVUuZOcnObZzhPcsHQWP/jkrZQtg75ulaFBGUOHp7o348mEkaimjMbe092YfTq6Oo4k+0nrxkSbtzwhovjx0Zsd5+vb3uTLl1+E3w/52gE0w0tzs5CrkREhn44zVRug65BKlxkczbDtRB5FM5nVHGRhYy2aJpHJCNIbjYpHLidkrkKodV0Q+WRSvB+LTR0/8BFKrVcUh7XnPcemnTfy9ttw5ZVi3fhlcBwJO/TfUDP/A0UxqI70AhKS7EIO/iGSxKQX+WyjVPbgcX/8dKzk/LwZ8j5YsWKFs2vXrl/pAzb3dPPFF56ZzI/y20Euzn+CGrOJJUvgmmvEDf/LcOn3HqAcD7IxdwtHXLtJqnHSSoJrsrfTq52gzZjNVt8LLC6twu14eMP/PFfkPkVCHababOSOO0RKguOI9IUTPQb9rW/Q0nMpaXmcsBNFchSGGneQIsmCwasAGFH6kS0VR7GJWbW8FPwxd180ky+sXEW5LArstm8XVury5aLA7rXXhOKteKUsWUeyhf0xrozgdXwE7DAul4NpSpOEetYs4f2tqnr3d7dtkXO8detUgcJFF4mCPVl+94SfVEoU8Z04ISzMa6+dGEpwlqDrouAwnYbPfW4qbPfL4BgncHL/F4z9oDQhBb6I5F4PTFmZfX0WOw7dxSc3PnpG+yPb43dSKAZ48LE/Zcmcp7lo1W7g3Fu5kiTtdhxnxX/2OL+OvBYNgyseepDRfB7TtpFthSX6SmZKM7hwTj1r1kiTQ2oqbZ8qhPmepx7j4PAYqzKXY2Myxih98jEW26spywV6PV0E9RAhs5oQIZKMY0sW1dRxfnstV6+uIRoVFeqnTgmluHnsLeyyTlmvIkmCucwkRxmJJHEKKPgoUaKOBmwsbBzGiaMzzlduuoFAQJyrYQjSmkwKxTk4KMhsJS1kRB+lcziJigt1wlvtYLO8qYa6qgCSJM5H08T9XVMzlYbhOELJyjKTXmpNE4qskmrxjztew9LK/PtNV+N2i3MYEzWQkzmVZyv06TiiUPjAAWGUL136i59dKZiS5TJK+QGcwpOi+Ml3Pab7c4AXRQFz7DNY5S7eOXgDDdVHmDNbpOecCRmqrA+vv3kBp/ov4o4bv4bHXTrn8gq/GZn9deQV4F/feYv/2L1zss6nXm9hvrGUdS3trF3hZtYscW9W5LVi2Nz71ON0JRLUjM+ixZpFP6eIy6OECFNvt3HA+xaa48ZfCtJAGyVyjDNCVKqj3hfhvssXMGOGuI9PnhT3/ua+g+SUIXKZEGDR4Iqi6QEKUoKkksQ2fdjYePHhxTcxTqRAnH6+dOFaFjXVvatgsEJ0R0fF8S1rIprjK7Pl5EnAjYY6Mb3ApjnsY0lbPbIs5NAwpoxTVRU6qtL3OBKZqidQFGHIVlcLHRoOw+888dHpVR6PC44RCMBll/1yzlRZh2XzLZzcV8HqR3ItxPHdj+HMF9xk7G4Uxeb4CR/x1AwuWn0AOHM6zx6/k+GxVn7ywn1cte6vmD2reEY/78Piw8rrGbclLmnv4JvX3siS2nqCLhcd9T5uvl3nkkvE1Jr/+A/RmPuXwbQtRtUBerRj1FiNnFe8kLQ8zhOhb7Mv8ipxZYhVhcvY5d2C2/GypnAlx137iZn1uHwGmzaJheDY+Bi5jv2ossw6+xIy0W7CThWDTe8A0Di4inzSwwnXIRwc6qxmaqgnZtVSlkpcmruBJ/adAsSNecklcP/9omPFvn3wzDMiD3jNmgkLTbHRMZCRsTCJWtUE7DB5OY3qMSendamqqHL/t3+DBx4Qx6oUNskyLFkCX/gCfOpT4u8nnxSe4r17AWdKo0UicPvt8OlPi5zKBx6A558XC8TZgMslxnFLEjzyyFQHgV8GSZuNHP0qcu1W5Ngjk+T4Z1HSgxw9dQ0nepedwbMW8HpyNNUeYPPOP+boyf80J/3Yw6tpPHXrndw8byERj4dYwM3qtQ6/s7GGYlFi82aRTlSpFNe0qfCnIRvoUomEMoYNVFPDXHsZnezjiHcntrdAQc2RlsdwkGliJqqjUnQK9IxkGR0VxqXLV2aIPk6lRjgvuoT7NlyCJ5zBIkeJ5ER/VT858pQo4sENSMSoJUyUeprQiBBPl0ilhJL0+QSJbW4WRumCBcKwlWVIJBxODWcYZ5QUoxQpYGEho9I9kGN4WBDrCkkeGBByq2miIDcaFXJXKgmyW18v1p3hYUH0R0YAW0E1fJw+LdY7TROf7/eL90+dEsc4G5AkURvR2iqiWX19vxiuNU2xnaq6kQNfRKrehFK7CTn4JRTFO1kjISIIOqPj8+gdWnXGz90wFBqqD5PONfHilnuw7emEyi9dsIa/uHgDbeEIQZeLObMUfu+mahpibvbvF+1Fi8WpNmaaJvSYrZiUKRNXR8iTI0otHfY8sk6ave4ttDW5KCk5Sq4iKeLU0kYNzehOmVS+SE+/PpG/75BQBjmW76Ej2MwdCzcQDpeoCssU5RQWBiXHQ840yZFBRgEkglRRQwM11FFHO9sOnqBUEoarogiS2tAgopLz5omaJL9feIN7erPkyDHGAHmy6BiAwkC6TO9AmXh86h7OZIR8pdNCNuvqhJ6Ox9/dESMeF3U9b78tnE0/q2PPNURrV/FdduyYasNXQSVSIMsge9Yix36EXPsGcvQbyK75k9tJkoNty6SyDQyMLiWTO/N5Xj7vOEHfEM9v/UviyYYz/nm/SZxxD/L7oa9PtG7LZgXpXLfu3e7/nYP93PPUExRMgzqjmatyt7Ldt4kuz0HmV9dQr1ZTc/QS8nIWx3aIOXUMKaepsquJhhQKaRfW7CM8ltqEZdvMKS9hRe4yeqt205JcRibSg2J68GbreDnwEzJKghsy9+Bg43Om4iwWFjklxd/9SewXLLdUaqL/8n6x6CxbBjuPpbFTYZLyOAE7iIYLGwtdKuNxfJOFA8mk+L6VXD7HEWRz4UJxnJaWKc+O44gG6d9/JomnFCUrp9nj3Updh8jxrli55bKwNHfuFIvJxo3ieGfDO9XTI9razZ4Nt9x8EtncD0oNuNZ+qBQKe/xODMPFlrdXs7fzFloaTnHbtf90xq1NfeQevv/knzOamMW9906lvZwrnEsP8nvBtoW8Hj4sFFRDg4ie+HxTaRaO43Dhg98in9II2mFceS+NtJIny2joGP9w8wbuf3wTWslPjd5CGzPRKZNkhAZPlEvnzkKqHeGvtj6P34zgNcVo52qPzIjay3jRol4Nkim7MDHQlXHKlkaAIA42M1mAgoqFRYkCbXVuVs2rRlWnPEY+31TBXTotvFOdJ/McHkphYKJTJEAEEx0LCxcqHb4WFGWqR2olJaIy+auhQchnKiWuRSWHOZ2Gf39lP24rwFgpR4Y0VW05VHz86epLUFXhDZMksRaY5lQR39lIW8hkRJedUgmuvqpA1L8FCR1LuRDLiaFpU+vxu/KNJ7zLSuYzOI5D5zEfOw7eQSScZuP6Zwm0fPOMnG/Fq2gl7uNk73Jee+f3WblStM881ziXHuT3QiYjopD9/YJsLl4s1rafzUX+9p5d/OPW7dQZzTg6NBszMDE47TrJ81+6kWt++D30jIa3EGIRK1BxMcxpFCQ+uWAJ7W0K/3L4GfpGSiiWi0azhQAh9MAwLo+KVJaQdJVU2cUpughLPvyOHxUPMWqpog4LCwMdkxSfvmg2iiLktJKapChTLSPjcRH9ebtzkCImWfKECGFhUqaEhkyLp5awOzw5FbMywhrEMVtbxXM8PuVNrqoS9/XYGDyy/TCq5aG/HOe46zAL54j2lufak2ya4vfs7ISFCx0WzN6LZHWDOgtLXoIsS5Prxs+mwIHgBbIMUupOxpNRtu1YS67YyLKFB1g8950zomMdZ2r4S2nkfn70zH9H8zRy113i9z2X+Mh4kN8Pra1w330iv/a11+AHP2ByjCTAioYm1rW2IksSI2o/Y8oQi0sXcM2suTx7+13cdv4cdoY2UWXVMq6NkpMyNFgtpNQ4hbQLb1BHP9mKaYBh2RxW9zGo9tKYXMySRRJV6Zl89oomVFSWh2YxszbITt/r+JwAKXl88jxkZEJWFT/84VSucQWRCNx4o/gebW3CWpeKPrrdR/DaPhQU8uSQUXA7XsDB7RbKFJgMAVdu6Axpdu8zePBB+PO/z7B1q7gmkiSuU8/Mlznd+gYlqYDb/sWKArdbpGx87nNC+T7+OPzwh1PDTs4k2tvhqqtsjh+H1198DSf7v3BS9+OMXYpjnv5Qx1BVg87ujWhqka7epWTzH9So8j8PVTW45Zp/xusVBZK53Bn/yI8dZFnc3+edJ4phhodFZ5ahISETIrwn8bcbrsSUy8jIjPuGGZa7CeDlSx23UGs2YWs6ZbnIqHaaEfpxoRGlnhpPhGzW5odvnKBkmaTNDGnSDNJPsiSxJLaIv9i4kbtWryWqycT8Mp9auZCilMWgjIabOMOUKaMg4SNEacRPf7/4PYeHRerG0JDw1Hq9giy0tMCSRS6G6MHBwEcICZBRJkbkKpOT9Sp5i5Xx0ckkvPh2L998vpOvb97D9w9sw3GEAh8cFGtDpv4E49VHSDCOGxdGIEsxMEpTE5PrQCIhDONKH+aurqlhSGcSwaBwSkjOOK+/9DrF0b/FSv4lxvA1UHxkkhz/vA9lymiXkCRIpFsxTD8l3Ufv4Nxf2P4/i0oaj2GIv72eEssWbBXOiJ3CaJvGLyIUEukz8+aJe3fPHnGtKoM5HAduX7QERXWwJQtD1RnUekGxuKFpHcN9bm6YvRBbNSj58nRyABOLGhqIEsYx3Ly8b4i+0QJZJ49hmZzkJAWKhEuNfHHVRfze2gu5a81qQt4y86o8lKQsZUo4mBTIk2EcaWJaZhV1HD0q5DWREAb58LBwoEnSVARo7lxQq+JkyRHCj4yMhoYPPzIaPreGqop9ikUhY5UIz7bDfTz00xP09cF3Dr3BQ0feIJudmqY5ezYka06QCZ1GRkH++Z6r5xCqKs6vsaHMkf1vcrrzX7HTf4M5di9O4k4k8u+578864nAcsoUmdMPL4OgMLOs3/x0rOsGyhJESCSX45JVfJ5cT04Z/3gP+UcU59SBX4DjCA/vCC+ImuP76qdZllm3z7PFjPH70EIF0E/W9a7npZofFiyQcx+FPNv2U/n01zC+uYId/E8sK69EcF0Uph4SM1/Gz1/0mVXYtA1o3g2oPN2TvJlZtUUh6WbAAdhxPIjkym2seYTiX46rsrdRaTZQp4sGHgzMhJhJNTXDPPe/t4enrg5c22Qz2y2Tl1ARpbyUnZRnTBmjTZ0+EmKa8WaXSVOGB44j85UyoF3c5jK9YiySJMNPy/5+9846Pq7zy/veWudObNCqjLsuWezcOxWCITQsEDKFDKqRtkk3fJLvvZrPZ981ms9mUJQ0CCQQHQjeYXmxjsDE27r2oWX2k0UiaPnfm3vePR1cjEww2IYlTzucznxmN7tz6nOec55zf+Z350Nws7tHcn98KSOz89GePe18NQxiQNWvE53POER39/pjRqULifp58UmH7gau4YtkXmTX5SUAGdRpyaNUJ7WPjiyt5/hWBN1y2TPBG/ymkt1dgqcNh+NCH+LN1HjoVI8gTZWBAwAw6O8VE19AgFm+KIibivX0D/PalDo5m+pkequTc8ukkhlzIMiQdvXxny5PYDCfkbEwpzKKEEFlyZEmTI0u7chC5oFBDA4c5RBXVNNlqOX9+nehytXkDNtNBdZ2L1Ye34CPEJGYio5BkFB9+XLjx4keSJGbNEsY1nRZ64PUK58GifwL46ctb2NkWpdQI4yMIyKQYxYcTDY06v+CTtaIiVhSuLzmIhIFOBl2Ls2LRTAxDOLiSJJzkKVPgX157lP0DfUytLOW+q64Zv5e5XLGqPpcrRrdVtRid1v6ITSazmQRH93yZDds+Sji0hyXzf4asmGi2AnLoYSTblHHc6sTsngUFU1Vo2/VvrN98FdhmU1cnsoHv1hwzsYVwLieOZ2XxCgURAOjqEgXPVVXvzjHfiZyKEWRLMpli5zmriHTevGKxV0rPcff6FrYejeByGpxTNotaTxnZLHgDOt/Z9gRdw6PoukFDpplKGpApkCJNnjwReujhKNNYQISjpEnTzCwuntFMfZWGosAvN71MHoOdI4dxqwFq8pMJUkaSUexouPBSSgDQqKgQTrCui2dutwtdcLuFzhYKsL9nkB+u3Ywz76GMGtF9D4kcCYL4qC8RqXxr7Oq6GJOdo31ISEwKVbA7thMjkObTZ53O8LBwzFVVBO7q6+Gsu28FBXZ+6nPv+jN5p2KaMNTxXTZvDZPKeDl7wc/xugdQFQPFcwXyWMOtN0aQ83nxstnEPX129QtEBmsIlk3jnHNE1urdyjJbi68xSDyaVtz33r2CfWvePFFw+OeinPuLiCBbYmHiPvlJYVDuv1/cRF0HRZZZMW0691xxNT/78JmUlsLGDdIYBk7i+xdczNc+WIMSHGVh6lzWOVeLIgDTg9N0MywPMit7Gppp58zUBUzLzme3+xVGB5zU1Qkc9Kj/KPasn3n2GSDBK+6nMSigYSePjjRhFdndDT/8IXz4t0+PF8pNlLo6uOVjMmdcHKMg6YQLdYzIMYaVQYbqtnB4xqNceKFQdGtVq2kQNxLCGGAgGzaCw1NI5QxapjzJQGgv/f2CIu4HPxAd9fz5EEhvvbiRZUEh95nPCCO9Zo3AfHd0CDiDVfTyrj7LzL1cvOTfqK18ndUvfYfegRmAAfkWzELPCe1j3vT12NQMbrfAZZ9sROqdXJsRvYkK7SZWrBCO3xOrkxRGvosx9HGMxC8wjeGTO4m/YikrEwakqYnxorpXXhGRHtOE6eVl3LxoEf++5BI+c9ZC6ipdNDeLqK03F+bHZ9/ADXPncfbkMD22NmLEkJGwoeHCQ1WhHjtONNw0MIkoveSMFIODY9kUm0kenbASQEMlzjB9dAAFNDQyZPAiAWKe2L0bfvfKNh468hJOp3ASrAr2eFw4pDctmMe0ai8RuZtuWsmSZZQRst4IGU+EcFjorKoKfe1KddGVPMoQIyTIkgIKOR8Pbt7CQwc3UlMj4Bi9vaLQNtAzGX+uDHvGT1+fSO/GYkL/XS7h3IXDxbRyLifu665dAqNcGPzgu6qvluMp5V4i6OtkWuMzdPQsZOPOjyFLOpDHzDx23N9bUAsATcsT9AuO81isWLh4onI8fbUcYyjiSScuFqThm7h0ySfw+WDVKoOR/icxYp/EGPkGpr7rxE/gr1wcDoGVr60VWZNMRhSZd3SI/7tsGtfMm84XTlvKv51/HrOqy8aZHbIpG1+fczlfXnweK2bNQA9EGKCXHAYSKhpOQoQpIYSCRC3NOHHTQxu5jERf31hbeTmDjEJICZAykwwq3aQZQcOBjk6eLCAebn8/PLy+hfv2vozHI3QhGi2OLV2H5ooQ1y+ezqhthG5aGSVKghEUd4qEM0JZmdAjm02Mm65kF+2jHQwxQpIcewaPohTKSETd/GzDRu4+8CLhsHC+9+4VsMlApkI07nqH8ofYWEs/8/niQiGbhUTCpJB6hfrwRrK6mxc2fYVEIgjkMdOPj//2jTKxSFOWIVzWitM+SiLx5jUI7/S6LOfY2p8Vybd+N73yJk4/Xdj1ba93YYx8AyP2SczUQ5hm7iTu0J9GTqnu3KWlgpd4zRpBn3b0qOg+Z3H/SZKIgK5eLUD3FqXY/HAl6+dtpW/ddE7LnMta1+MsS63AxMRrBAGIS8OM2IeYlT2NLvMItXUGnZ2ywBWmg+SVDHPzi3lJ3UaCUbY7N7AovZSMlEI1VUDgewxDGNjGIxcSqdj1ew09rPO8YHGQZQtN1m5OsGatDb/eyJJ8I8uWQnW1weIFR2jvdLFmbTXd3RIaHgwKxOURtthf4j2Z9xIqVDLSVUu0cR8//sBMWlvh508cJbmlmmXm9QwpEW68/1EMNfeW+CifD665RhRXPfUU3HUXzJt+C8vO/B0TGW3eFa5CM4Oi6Kx475f55UOP0zMwm3DZPkAH88SqBl1VtzNnrihGTCaFw1pXd+KnEBstI+gbeEenP3MmRPp6Wf9KmDKXzBlzX4Lca5jJuyC0Ckn5MwOUTxGxuk9Z1d99faLZzdSpopjG4hC224v4t8mThU4n4i5meOt5cOt+cpJOhC5UGnDiQUenhDLSJOmmnSrqqKKecJVrnK9UywTJOoeoC1RS6QzSnu4nxiBOnDhxoSDj9bnJpItpeZ9RTzoVEUY/K4xtIiF01eUCt8PGp888i+FUir6hDE/taqMq72N5w0w8HnC7hinkE8STpUQiTrQRFVBQMeiliwF6qGESaXmECruTUEhE1uNx+OFjW7FnAlSZU2lPtPLVdY8jmTL/s/zScfoqS1RV3E9VFb+12C686gU0VO+i+o1sN2+jsxNTnW98GQaYaZVCVqPE345mG6V3YCayXMA0TSiIqsE3znHW+Y6/e/4fVY2QbWM8XR0U0+4JRYiSKTeaLcvE8o6J9GSWo2y3//7+HPYUK1bkuPeeKKtWh7juwlex2XKY6Scxvd9Adl//9ifwNyCaJsbj0aPCYR4ZEfSkg4MCm+xwFGFEgYBwSCsrxT2PxWQa7XW80H+E/kwKp9yFZthx40cnhwc/Yepp4QCNTKGKBiBGuNxGJDJWtG1qmJrO5TNnc++u1xiVRojSj5cQMgYV7jCeMTpF0wQPZSijMrIsIF0Tm4D4/cL5PXdyI++praOlf4RV2w+h5SWW1szH5QJN0wkFB8gVnIyMBOkYlpBNB3ZMsmQ4zFbKaETCIJswKA24CAbFNX/zkfW4EmXU6tPxJ8Jc/9D9IJ08BnloOIiq6Lh9xfFciH4Sw5SRAj8/hlFkIiOQ9bKgMBP11TDAHGkgl3UT9LZzpPNc4ukSgoE+jIKJ/AZKv4ky8XjB6k8QyojsS0+PIASYWHPwVmIYMsm0G/8EtqqJDCmSJM5dlt88k3TOOTDQ38GaNV5KlC7qq17DzG2C1G+h9HdI0nFozf4Mcko5yCBu6PnnCyO7apVoq3z++fCjTjFI77n8WtatE8Z4Iufubw68htPdxvLEB1iQXkJOymI3HcjIJKU4k/VZPONfSVodYX7yHHJZiUxep6Bm8Y3WcVDbgdoxD83nQbeNctCxjSn5GZTIAeqrhUNeKIAupZAlG4ppo6J/Ht/47yjdtRu55/r3v8m1SCw/w8OdXQ8SiE3CPbCQO++EqY0vc97iH9EQaOVjl4dIqr/g+bVT2LdPwV8oYVl6BWnnIB3mfprScygfnEM0KhyMntpXkfMaQx0+yvNVGMqJr7qam6HOdzPrt6xg0/aL6eyt5dM3fhBJMpFLV6LrGqr6B67iHO+D5M8YjE0mk3Pz8rbPEPB20VS7GZSGE97NaaeJVtyKIhzlE3GQjehN9A3WcecD/8X7lvw78+e8vcNvORhdnTlGk5XMmHoj58zYw0DPd3lh01cIBQ8zpe4lMHXM+A+QAt874Wv4axefTzyf7m4RoYpGYf9+YXTr6uDHr24gtTPCLy67gmhUGLWmJkEJ9fyhCJIho0oiwWpgYMeOjIJOhgrq6OAgowzSIIWZ6q/huZatOHIB0gWNQd3kV5vXEzVymEikGSVOjAZCNDdW4LWr9PXB0eF+FGyYqJD18+snW0l6e/jCe5cQixVhF/m8SDlXuVxUBF081roVsl4gTaR7F6p0lKCnl6C7n4rm05k+/f0cOQI7W0z8NNNALa3spyxo41OLziIeF/coEICR2haUhIPhngBRdYCwzQumoJSyjKKVAs3nxd+qOtaZr/DfDA5V0dPjoa3jXM7MfZP66jaU0D0AZDIaNptBPvfmTvAbI0OW0bLbxyAxnnnIais+twObLYlhyrR0LaYh/Do2+wVvGlmayGABRaclHBZOV2+vWGhOjB69mRjRmzAMiZc2nY9RsHHp+TeJfZesPCZFbEErLMiToGd0caS1nKaa9fidl3PxkkoeW/tfPLPhX7l06f9BkjIQ/09M5/uR5FOI2PbPKKoqMj9dXWIc+P3COdq4UbC6/OS1jeSVJD+9/nxiMZGtqa4W46S9J8ORviSypJA38phIuPGMMRrHCVBKDU1E6KD9Gg/1AAAgAElEQVSCas5pWIgsw6bu/WhZLym8tHOQzoFhkuiosk7cHKFSKmVmeQ2N5X4B3Ur0YCIjYUfCz6q1R0k5o3z6wvnE48VOehYzjcejMG9SCQ8cjmHoWfx+iEZaKeQO4HVFcNgHqQ74mXLR1fT2B1i7rRsfbuYHr2N/ag+HsgeZHKjhppnvIRYTWaJs2SDp0gh9R5zIKLhPEgYgxjWs23QlbscgzcP/D5sth63k38kPh1DkPJJSdIwtx9f6eyJPtaWrNpv4rGkS8tAuZCnG6M7r8bgGaO06F68nSqB0+jHFtJZYuqQoxePJslgwWfR5PT1izn6zgN/E6wI4fMTNtv3XsPSMb1BV3olUsnI80yPLRWjFRH0FONKqYFPTVOsf4uL3HOC+6M95fN13uPGSj1Di74R8K2bqUST3dSd3w/+Icso5yJY0NYnCt4cfLfDMMwoB7T3sDq5l/1A/p59ewfPPC8NcPdZqPp7LErN10GrbT5M+gwzJMeywhMf0kSfHFfbL+OgtKrEuiUceAVM20HQPeXQycoYCeaZmF7BZWYNDUck07cE4eB7BoFj1rF8PNtNFVo1TyBtIyDjSJUw6/D62bxcwkTcbXPddfTUA2cwQr665j1d3fJhD7Q8xp/lRli68Fb/vBq644mVWrHDwudt34Is040qXUUeIlJSgZ8DGT36mcPn7Ve698lokSfAg5xg86VWtZsuy/Mz7mT3pNkaTYSTJhPx+jOhNrNmwnL0tl9JQ8yoNNXuZNOsWgsGTwwlJ7psxM88wuX4rN19xLY+t/S/uferXLJzfzwUXySeMp6yoEJN5b69IeV100fH5si0pFBRWv/gJnPZRpjY+DzSf8Hlv2Xsj7T2nM73580hShsvO/Tqx0ToeeeGHfOyKqykLtkB23Qnv729F3G4xsXZ2Fvl8X94e59HtEWJxg4yRR7UXsNkU0mlRLd7YCNmDSVx4SeTjhAih4gTAhZP0mN7OZgbNzXZK1XL0nIQsg64mkQtOApTSY7Rgx0FBcFqQsg/TXFWOpLvwjzFBdIwWKBgGeWyoaEh5HV+yjqNHxfjKZI7F/gojBN+++HxUFRK936ezK0n/wDR6YjPQRqvwjx7FH9rF4sVzeD62nexQGaWEqWMu2f4Utz31OmZpnlsWnk42C/9x2jWEQvAPzz7MHILceYlo/zuRDlGShBG09MMycqXKPhqqD5KsP0Rb1yLKS4YAEyN6Ez391azffCUVpQeoqvwBTmcCV/k3UdXidVhRfuv1+7pcgWF+FlviJ5wx92627ruOI0eXEdcvosH5HgJq0VBDEfKgKMdGtlRVBDU6OsQCKRIpsnscb/4wTTjSMYtkuoy68OaxCJQEZpEZJZ3+fWhFPi/R3jWNXQcXIEtZ6qpaKSs5wntm382ru27Gv7WLcxb9HCQV9O1gP/sPGuN/TaIoAmrR0yOi/c3NsL8lyy+fHCA+pJK254lkRggE/ONUaeEwdMQyOFWNnJmjhAo8uChg4EYUomdIUUEZi6rCLGoqJ5fS0HUwtCyFnA2/GcSFkyx5JGSShSR2bZS6sJuQItihGhthTySDkneQIoUTF3kMnPky9u8X48vlGmMyKRTx+zYbfHvZxTidoKe206/+mo6++YzGq0lkSkgkh/Ekn6a89noi4e04h8soxDLYKGcqJQz0d7OysAVJkvnw/IX804wrKS+HL9keBNkct7knI7IMpf42DEMBKY9iHESOf4VXt56FYchUhVcS9EfxV30eu51xnbXYcqz3N4vomo7fYEavZvHsRzAKdgaGp/H6no9QP/USGlxFZ3oiA5alT1ZEGsS83dIiFkxdXaIA8o3O9RslkfJytHc+NluaULBP6OwbGG6s+WDifhIpFzv2X4LTPoLPux4MH8tO/z6r1nyfB5/7CR+5/AbsWhKyT8Mp5CCfEhjk40leyfCd4VvZ7FpDWa6GsyJXc/NvX+Dbh+/BbhdRZEtmlwscxgb3MwzJEey4MCiWSirYyEYCJCMepk2Dj3wEAk67eLBItNr30KYdYEp2Fs6Cm0whz7roLqbOTbN1K9zT+SJddS8jSWDPezFtGdqmPcGM6RKyqfD444LiLJ0+/vXYCo9zzoJf8Lnrl9FYvYHdhy7np/c/x/Mb/5HU0EuCk7XsIE+V30lH/Vpi8gB204lq2jFlk9Wr4YEH/jB+Y7l0JXLpSioqA0xpSovo6lgbyobqTUyq2UBH9zSeXHsLt94KP/qRiOTv3Hksw8jxRJI9SKHHkHzfoqqil49f/Q+ccXqCrdsr+PnPBRXcicrixWIS1PUTq1R/9cDd9A008L5zV+LyNo9f61uJtU1N1TCJVAVx212AimZLc82Fn2Zqwwt4Xf1jF/f33l1vJg6HcDYVBVYd3MttfY9yZCRCaaYOoiHO+NXP+Pr6x8afZTAIM5tdmKqO0+amhQNkiKMjshdO3Dhw4qcENVmBokjY7XDJ1IVcNncOdi1LHz1kSAMK5YQpkGcoH6Ujewhdh8e27eapyAbOmlHFtKowCllkdGbX1vOeSXX09wt8r9VIwOoIZ7WaFXi/BE5lPdMb1zG18VmMfIas7qYvNpm2tn7274ePz7+QAWc7+6VX6aEDFRtOw48jWUI0KiASIyPCIbHlRMGvZfwsh1jTxOLP4RDvdrv4zmYDR+WvcVbdQaC0nhnTEtgr70IJiTHtcqYI+drJ5XxEY2UMj5SM47R1XRzD6WTcAB/P8MmeTyCV3kdVxSD1VYewe5di2K7jyBGJtjYx30zEA0PRIFrFOIoi0tOhkJgDOzuPxSJOFOv7iL6SSOprOJ0a4UoROZZK7hk35roujmkV+ViFekPmPbgrvoTPZ2LIM3H5ZuDURlg4417mTX2I2vBW60gg/T16/EaxYAuBALRERvnWzodYM7QVe86LL17Pxffcw2fW/A5FEVAkTYPZk7wk5VFspo1BZx99dJMlQx4DDQduvHjw4M/VkBjW8PmEM3vV3HlcsGAyKYbJKDlkNMoIAwbRXIxne17DpuWJxWDl7vVknTEWNFXhII9Emrk1DSxurCGfF/Rmg4NiTFtj2ekUYzuTGcMnJ56mqnw/C6ffh2yLYxRkUpkyBmIe2lujfLD+Uoxghk3Ol0moh8mQwokXLeNFTTqJx4W+9vaCK16GlFd/rxvh297fMZsSCDjx+dwEav4Np6cChyNJdfl27FqSwaEaevobaGkRi8poVIx3h0Nck6YdH+4gqQ1IZc9jd/hpbtxJXWMJrtLr6OopY/t2AcnKZo/V2SIVZ9FBthYlsiwcZEvPj3etBd9KOoZ/jiHVEirJYK/4NQRX/l42ycpSWcdK2FaSVG8nXJFCUstwlX8OtytFRclBLjn7X2ms2YBNHXNqJP+J3+g/gZzSDvKd27eiGwX227fzrPcBhpVB4tIILaMDLFhosH+/GFgA3zznPJyqCpLJ896HyUoZJGR09PFIMggMrmGIohir65uMwnnxKzio7ULFxqXxm/AU/CiShNbUgd8PlT2LSHr6+NSnxACw637qW5fxgQ/ADTeIwdbeLgr4Dhw4zgUV+oEsTkeMZLoMw7RhU9Ns2nUj/3v7ebz0EkgFlRll5fzqI+cRmbGWI9Mf5f3vh+pyQb564IA4xj83X8t9H7iW6x++/02LBU9GLIWeOiXBigsf5EtfCfKZz4guhzU1Aru8apU47q23igLKvXsFXuzNRJI0JOcK5IqNaFXPccGFHj76UaFEd98NzzxTxIa+lUybJtLeNpsA9b+VRCKi5fbMmTCt6eQrwasrWgDo7naA/VzAht/Tx4r3/hMOewJwgOvGk97v34poGmglcZ5u3Y+74KNF2Uu/1EnKliCZ1xnMD6MoRQq9i2Y04CnLYth0FE2hmw6iREiRxsTEjgNwMDAgdDybFZNuLgdZU8cn+dBwUUolU5jHZGaTL5jsTh8hFAJNd0NaoqZGZCPySh4DnWxWGIJQSJzHoUPCGEqSKJrV9WJ7aE1NkM6VksqWEx2ZQj7vxzRMFEknk3MyMCAMS7OjiUmhanwVCTLlh7ny3ClcOL2ZeFw4ikePCsP+9UUXY08H+NAjD41Hi2S52Kr7rUT83xzfTi5dib38VubNbmd6cx8lNR8kWPNZ/H5hlCIRAQs7cKAY1c1kjm/8JNtMbOEXmTT3a9gc9Xi9EhUV4t5bjolldC3DbRlcK2KkqiLzJ8viui18tWWkJzrM8bg4L4cDSgM9aLbs70W6LGiFLBcjhoOD4nM4DCWBAUbiIVTftdhsEg57kuWnf5/G6s2ABJIXbHPf2YD+KxdJEvdw5ZGNOA0PKSXJEWUvKXkU3dBpSUTweoud5zxOhZuXTCerxrGZNobsEbo4xAhRChRQ0fDgZXhY6ERf35idtAtHuSAblJmVaCg0MZMFLMVDkIQyjKsiTi4HzrSPrDshusDa8xTIjLM8uVzCceztFVFPCxaVSokxVFIitsnndEZT1cSz9YyMNDMab8Dr7kFGIp0uEI2CfSDAbNdUvnT5WRiVHSjl/Vw2ZxrLp8xgcFCM3Z4euGXGuXjilXz0d0+9I1oyzZZDGrsH9vIfY3h/zvTmKGctep0Z88+luulyKivFPNDXJ2zqhg2CeerwYeHoWnCF33t+cgCl4kUqpv8GX+kZ+AMOZswQ2+/dW+xsmMsV5xbr3WLhkWXhIPv9YlHd1XUsJnqiWDz4uRz4vIO4nKPHwDks3DEwXqtVKIj9ptMiCFFTeRjT1EjnZ6NpGg57ksl1r3DBGd9DlguAE+kUs7GnLMQC4PnWI5iAzbBzXuJyum1tyMg4VRvBKVGU18rYuBHe/36YWxlm1bU38dMtm1jT1sp69+Ocn7gaxlpQSsiARDQqqsqXLhUD45Zb4Hs/SRFIlDI/cyYxaYCAGeKS0Rt5TX6GO3dvpjIQobbjHNKt1Xxevh9lip1pR1Zgz/n48Y8FS8QXvyg66u3bJ1g4pk4VBYZWUwEASVuMmb4PiRQ3X3EVe45cyoYdnySTDZDNi8rZOukidjk2cf2DD7IvGmFGWTkLFgiKt64u4QS2tIgGK2vXgsdTQ8LXPX6MiS2o30reKrIqScKBCIUEFtg0RWVxW5tYBOzZI/DBIOhhGhvFq76+GIGbKKZZoLb8BT5x3Uu8uOEKXnvtNI4cEfzRb9USW5ZF96C1a8WkZbXcfqMYhnDgHQ7BAS27T77IMDz12+N42hnTv4M5dDMUDgMKmDo4liO5P3rS+/1bkq39nQzZ+3CnQ8xMn4aMQmeuFUMxmVlZjtdbnLQdmsJtV13Kfdv28OKeCPHRPCOFQRRTQaUCP25AJZcTxiMcFuNB0+D8qVN5eM8ugpQSoRs7GtU04MXHULKTxzqewqCaQsLBzzY9j4aT989ZwsBAsclFPF5sF93dLRyBmpqi8xcKgausjIweJZ2RmVS7C1mV6e6bjcs1iqJorG1pR+pVGDYU4mRQNYmMnMLtLkaCLZ7jgQFhdO2JIIpuIxYTkfQTbWcrl96D8oaCPsExmqHSM8yQKZwFl0tclwVPSCSKr/5+8b1Fl+VyFfXVck79fqHT/b1R5k9biS8cp6PvYlpa5hIKqYTDRUykJBUhFhYEo6ZGXNfQkHBmGhuPjWJJknB4WkVjUqqqoLPwQRzlRaMNRWfeikTputinLAtnSNOgtOYaYbRN0Dyfg8SPQbJhRY6lkl8hSad0DOjPLhuG9uNUvFSnGqnKN9AiH0DKSaQkndKQyciIxNCQGFPvnzaLKr+Pla/v42i/RMpIEtV7AIlqSgEH+Xwx02iaxbEYdMuYSTc6aQboIUiIOSxmINPNA7s24UqWk9LtdPeNcHd+LXa7l7OqF407XclksQHPwIDgCi8rE99ZBYV+P3jkGtKx3aQyTk6bfT+bd99E7+BpzGxcxSOHapD0BHHdRjya5CcvbaU7kaCq1EV5uRhzo6OML3wjEdBGg8hZjbY2MbY/9tSJt59WA19ATorx73aPNbrJa9i1NOU+MefIstABp1PMScPDRUxwd7fQCYuWMhgUUf+JhW9WNuDI4Sx6/CUWT3uBQ+1L6e87m+FhH42N4rdOZ/F3FiOMLIs5oKEBXn9d+BVNTcc2BbKeY1+fOK/ycujMfhCH61h9teBWlr6aprgeXRdzjdsN0qRvIncJPQ423IE59FEwE4AkbKznM0j2P343zpORU4IH+Xhy/cP381p3F6ppY276DKZn52Ngst/1OvUzRygcaaJkuIkbPp5kSth7zG+7RkdYs65A2/YSfL5j4QEmJl21r3DHxwQ2rXskzv/+L7gML1G5n1KjgqQ8isvwEKnYQSx0GPuhueSlPIUpe0GCXyy/lltvFYPC4YBPfEIM4JYWQceWzQpDcu21wkiAcBTNoZtA3wuIlIJhuDnY/Q+sXPMBHJkSCuQxMGib9uRx2SmiUfj+L2M4sqJUPCUlSJa1MVzSQkEVwMY/ZtcfwxDGr61NvI4eLSpdOAz1latprN5H/ayvYbPlMWO3gL4DzBQg09p1NqvX/4h4ws1ZZ4nFigXoN01x/lYlazIpqO1MU7TyPv/83z+fV16BF18ULblnznzn13XnneIaPvaxsXPR90GhC9TpSGrtO9/xScqpzoN8PHmxrYUvPvsUyaxO08hM6szJFCgQkToJ1eTI21IEpFIumtHE+TNqx4vRUino6tHZ3RUjFVPQY0EGBuRjnMFBujBIsKR5GjYbrG/fS28yMdb9LocbH04ceFWVvJagLTVEGRUongHwGHx6zvn09YnJGcRx/X5hYJNJ4TxaXfAqKoQhCofBZjyFMfxt9LxEKh3gyNEl9A/Nxh08h1WHD6Fm3CSzNnpooSHsA1PhS0tPR5bFQiCdFmnq1VtasOddxMmSIonXlyfnjpEMDIBivK2+WgwDVvqyUBDnbbMVq88jEWHkLYNopaIlqdiJLJkUzvLEaI8r/01czlE81T9CVUFPPMeWjXtxOQeY2/wI+byfzsH3MaT/Mw6HjaoqYfRsNkinC7S25giHHVRUCIu6datoh1tfL7hOLedbVcVxW1uFE1JbK+bI1lZRv+F0Fq/Tmj9ttmL6XFFExs+aK3p6RPOLRYuEg2QaMchtBdkPtoV/Uuf4VOZBfiuZd9tPGM1m8aVLmZNZjAMXg/TS7+hgUbOfkUE7M0M13HhOPU7ZPj7ehodhR9sIvQMZ1ISfaMRxzLhKEkMnweLmWsFykx7g6b0tyKjEiQEyfoJMdpYTk4bJGXGSGQ8ReghXStgMO9c1LR1nwVCUYjdMTRPP3hoj4bCwvRUV4PPGYOhadD1DJuOkq38Om3Z9ElXzs63QSk7XSY4K/HSwOoOac/CZsxdRUiLOPZcTvsJta7fjSgZJIwmmDtUg700RD/aSd8VPyL62tAgnsb5eOKKyPEZ5Nzbv5XLi/5ajarHXgNhmZKRIbWdl3iRpLMMl/18C3gEC9T8GM0bbrv8gFvMxteFJ3K4MvYNTaYv+AN2oIxwW98bSr4FIGhOVhgYRvYtGRXDPMETQyu8X21mwrKEhEaByOIQzvWGD0N0pU4rOcDZbrBUwzSLjiNcrjgvi+xdeEM9w0SIAA/StYIyAthBJDr6bQ/st5UT19ZR2kJ9vOcIXnn2S9JjWeQp+FqXPpl6fSlKOs0/bysLMORx2bedbH6unufTY8KJpwr33CieupKzAQF9x6WVisuy9EmeP1W+8ejjC0/eWIKNgUCDvHGV6jZ/Wwwpz58IDuYcwlcIxitHTA3fcUYx03HCDKCTQdRHR3LdPbDd7Nlx+ubW6ymGm7oX0KkBBcl0Lzg8ACq2tcPvDEVzpcpxOwWG8eHFxgE2U6x+6H3+skbKeBSTlUfxGCQYF2m2H2Ot4nSn1oqrlT9EeM58Xq10rwtzZmccwVLG6rRqioeJBGqvWU12xA1UR2IpMtpTnt7/Ejp0a5eWw4rIIFc6vQm6z2Km2GMn/n0hKFY8+KrhsXS4RqZ+4gh4YENzOU6fCWC3kO5ZnnxUr6a9//U/T6vd48pfqIOcKBU6/8xcMZzJgQlmumqbMdGyGRppRBuVBMmSxKSbvXVjBl85cMg4zSKUYh1P09UF3j87IcIFCQSNLliw5cgwxv7ZRYHhtOnv6W2kZHaGAgQOF06fUUOcOk8vBmsM7KWhZPr1sMZpWbCMbjYrJ2zCK3fT8fqGzFt5W00RUprJyLCuibIHknRT0PnK8h8Pdn6Srp0RErDzwy3VbMbw5vrbsjHEHT9OK2Y5CAb746FPYk6Vkh0Xr+awSx1ZwkiJFj9JBzXSBvz6evr7RQc5khN5Z+FxNE39baVm3W3xnGWY4FsaRyxUd5njv98nnbUjezyPLOu7cR4gn/fQMzGRawzNUhg4jyxoj5rc4GrkSXYdQyKDEeSeZ4VV09c2jvrqF0tobkZ3vZ2hIUHEWCscaXBAL664uYSQnTxZ/9/SITJUVkbYcIodDnN/IyFjEuPRYXGYqJQqna2sFC8Ofq+kA/OU6yN/b8DJ37dxGJp/HkXcyLTefoB4iYcYZUHrJyClKCJB3j3Lb1ZdQ4nGM69PoqFiU9fZCZ5fJaDxLJiMzmslhYFJAZ1JZyXjGoivVw5bWPnJji9r5lW4W1swhk1ZQVXimdRs5LcVXly0Zh08MDQknMZkUznAgIBaxTqewOQMDYkwEAkWe57LQEHLm15jpl9HNCrqHP82LL81DVWHWLLhz5ysU8jr/edV5ZLNiH4oioqMlYzSKNz/0CErKwXCfj1IqyTCEHRcFZIaIEJgUxXTl39K+treLsVtXV2xTb7GyFAriczYrrtPKjnm9xxajT4RFWM7y8DAkBx8CQHZfhdf2GB71cfoHJxHwHWVqw1qxQDemcmTocaJRCbcbqiv2oenfZ3BAxWHPUDcpjOz7JiZunntOOPQLFwqfwzq/VEroZ6Eg7q/NJlhPpk4Vc6Q1L5mmOG+rSZJhCEf+jYX1W7YIZ/+MM9482/ynkhPV11MaYnF+02RuWXAat23djF1RKJgpXtWeZl9mO6elz+W0zLlkpTSTUrP5v2ue5DdXX3HM7yUJZp49wN42FyORPEPKIHWFJswx9u81a2DV5i6+/6UazphSjnapwNeqkoKSLmHebKitErCGOud5dNdtOGb/VVXCKXvgATGA7rkHLrxQOLZXX12MJu/eLT7fcANUV2tI7o+A+yO/d71NTXC0aS3OVCnnq8tZt04MxkWLxIDyTKg3ue8qoZgfuu8JdFuKwZyb4NBkaoYa6TJagT9dv2SLPqjWcxOnT7Wz8fV52LU4qdws2rsm8cq2W3h56ydRlQzvmfNr3rv4hzgcWS676CWmzzif1atN7vhVCWcvWMBZ87agKHnBPRy9BsrWsHixxq5dYpI8tPM+pk7pRXJ+AFOu57HHxMTyvvf94ddRUwObNolo4p+zK9dfqmiKwsorruajjz1CStfJ2AdoUfLU6E2oOTtBo5SsnEHOy6zbPMCVk+M0hb3jk2tpKeTyeVbvPkx31ERDw44bD15s2MjjY0fnUcBgVriBOZVTmVOVJ5MtoEoapQGJsrKxqMxhkLAzOCj2XVIiHFar8CuREBN7JCLGT1mZME7t7cJBP3xYGOdcDhoaTsMXPA27ApoJs0NgH8NDAuSCo9jyTnp7iwV2Frl/aamIbt31sfchSXDDgw9SiNvAISElg9DrpqJQDbS97f2d6AAWCscWw4DQw9JSsQiwsIfxuHCWVfXY7lqaBmr8JvxOOGIaDA5PI6R+l1RaYSQ7A9PQGRiqZ3Do41x27j/jdGYIaPfjm3WlwDR3v0jcPIjb5aSQVzEKUfTot1CCHoLB8wiHRdr38J5XWDRnPaZ6LpGRc+jvV3C5hHGVZeEgTIShWfhIh0M4YIlEEWP6RgfYKkBMJMTv/pgdB/9a5Qunn0n7cIy17a3YtAKHle3UylMpyZdTqpeTkEdQcSAlVe7esotPn7F4PHjg84n3rZ09rOvsx06ALGn8lKBhF8XvAwOYAwbTqioo91Rx1dwwyUwOWVLxexXKy4UTlkqBYtqw624iETFm3W6xELSw7FYmwaJAnDJF6FZbm9DZ0VEx3guFEioqvozd/2U0CZoqwOkVDbYOHQIUGVXV6OsTYyYYPBZqVVsL99xwJYoiAlG5RD+So4CRcTHU4SJEJWYuTt51HIDwmFj3yVr4WQtau71I6ehwFGFfFmbX5RIvqwgWhI6Ul0NIuYmkw86W/in4XBH0fIyhmE5/4SySaT8tnWeSzyvMbn4Bu9LOzOl9RAbDHDk0xKE9LxIuhVTGi6JkyY2uQ9Gj2EJ3MG0atLUVaDncx9ymO1Fs1STNKxmMBtF1cWy3W9wfENcwseBP08Tn4WFx3oHAsXptSTAoFiTp9J/XQT5ROaUdZIAvnn4mH547j939/QQdDq544F4itm62mi+zPHkFkiljQ6Pk4JJxXJ8lw5k0H37qdzhcIS6KX0PU1k/E7KbcqKZAHhkFX6KGW2+Fj38cFiwQUd+ODolCQUSBb7hBwCQefbSUUNdldHYKBbJk+nRYvlykDkBEIXt6BC66qQm+8hXRDvXAARFtXrAALr30+NGOiStSq/nCq6/Ca68JHPKZZx57jbomKuV+c8MlANzw4EOUY/5JIsdvJoqSZ+u+G6kLb+bqS1ZCvpVMJsvR3kW095xOKNBS3FgSHdY+9dHnePppg5de/zyHOt7L5ef9k6BVMxOQeY6qqosIlx2lb7COHbvKmFr1bczkXWw88Du6u2fwgQ8I5f1DxaIM7O7+u4P8TmVGWTkbP/YJtvf1ks7rPLBnNxv2HyRsNtCQbSZnZBhlBKfp4OXdMZyml2CwWMxz98GX2RYdxk+YUsqJMYCCioYdgwISGjI58nkxGTudKppNJZsVDm1pqYgiXXXG3HEn0nKgLEiFFYW0Iqg9PUV40MKFIp146JBwnoeGxHFmzhR6Z1EwTZ0qfjMwAN84+zxKSoopUcuIWJHxkRExtpxOQDEwAtlx/bz+4fuR83LdLgkAACAASURBVMpJ6avVQMCqdJ9YyGMZ/FiMY67f6Sw2g3hj0rB3YDbd/XMx5WGC3g6cWi9mIU91+U6iw/VERybjzo0g22tRbFBWlsOWvIuevin0DZ2DUdCoMyR03cAY+QXYz2Ny3SbaDztpbc0zp2EVidRWIgN7wflJysps4/qazRYja9Z1WdjtVErotd//5vOlJBXxzlbk/+9ycqIpCj+75DI6R0ZoiQ2Rzef5+rMvoid16vUpVBi1xIhQyOU5fDjJ6Mxi8R1AS6KPn+1fg58QYZyoKCSI4wGG6MeND7/kJpsVOphKSWiaHZtdPN/hYZH+t9vhynmzxwsyh4aKTrIF3zMMMV4GB8UYrq0VGduSErGg7ekR9jsaFVnburpic5nqalGf8swzsNx7JkuWFKPgsVixWHZoSMwJVVVjDcokML35Y/RVzw9x37VXve29nZiFtMa35RBbFIzZbDFDbEVtk8nifDURB2zJSLKU/qFZjCZGqQonKQu2Ypg5kpkQXfnZZPQS+gamoqoSiurEboem6vtpbx2mvWcx2ayX+qpNZLI21MJBDFc75WUBStyvE43V0NHaSn34CQYHNhDnWwRL6wgEivUDpnnsOVtz0MiIuKY34qQnit8vto/FGN/nqSx/ERUMJU4XSxsamV1RiX0MpDOsDtKvdqNhRydLoBDiJz+B558v0qA9emAfesFgQO2hRdtHvd5Mu3aIUXl4rAeWYLiIxQRDQ3c3XHZZEe9kGPDb3woDc/PNYlDfdVexQM2SM88UGDpLdu+GX/1KDBhVFQ72jTeK32/bJjC1/f1vf92VlaLQ77OfhblzxW9vvRUefVQYb+AYxQUw5QLIxvF2+UcVuXQltvK7mTPtVQ51LCdlX4kU/AkOe57mhrVccOZ/Mqf58bGtVdAWA+DSjnDlsi/wgeX/yHC8hlVrvi8mBDMNhQ7IPMPiWbdjmiqHj55HIhVkMFbFuleamDY1/wfhjieKlTLv6np39ve3Kooss6iqmrPrGih3e4jbhuhSW4gxgB0HQcoIUka63017ezFV2hXJ8FTbXgbUKHGGABk3XvroZIgIBbI4UKhxi64x+XzRybVwqxarTWUlYx21xDsUK/KtbnUWRVQiIaLB3d3CYE6fLjI2dXXCAOzaJQp7+/uLxlqShJMcConfJpPCYE+bJhbGvrHuWdGoyB7t3i0WvCtXHKuvAKgnVyI/kfh/oljGxuEQx7ei5QIrfCyOESxatZUsnNNOQ10fmv8GZP/XKQkMU125i7lTn2LJ/LtQFJ18wUteu0JExjNJHLZRqiu2o8oGg8NNpLMukBQKuQF0PUu5/UsEfK2MxKs40PZeosNV6NlefI6N49zqVorZctwtiqiREeE8+Xxvb0T9fvFMU6mTuoV/lzdIrd/PuQ2NTAuVkZWytGkH6FU6MClQQgVBKgllqmlrE+PdYidYuXUro/owMfvg2ELWQYIYCUZJE4OSPuY3lY87uOm0eL6jo+KZJZNiDFj4YosC0eWyqBaLcBsLz5rLCQeru1s4xaGQ0Ne5c4VD3NUloDd79hxLiVpdLWpYRkdFptCC+dTWFucIy3nbv38sqHXRm7BFnaC+WkVs+XwRf2+JhfF1OIoYZIv9QdMYX/C/kcVCKllJ1dTvcfq81yktMVC8H8dVuoyayoNMqXuZcxbdQUXJEXKGG92cRS4XELzR2R6qy3YQCrYykqyiLzodJNB1N9lUN4XR26mrXI9hyBxovYjocAWZjIaWuw2/v4hHtnjJnc6iQ285x6pa7LB6PLFw1lYG71SXUz6CPFEkSeLqGbN4YO9uMqR4wfMwszPvYW7mDCQkQiEBSdixA847DzriI2QKeSRToiJfg4nJosxSnvHcz+zMYmrzTTgcQol0XRRpXXCBeD3xhDhm1hbnySe9nHGGYLx45BHxv74+0bzCIuS+9FKxGu7oGOP4jMDttwuoRUODUMSvflXAMQ4fhl/8QkAxLrzw7SvZS0pERHrpUhFN3rpVGO1p02DJkmO3/XNFjifK/Jnr2LTjEnbuhLPOWoDp+Qwk/hcYK31HQQr+Ekkay8Go00ByMqPpGerCW0hnA2IzyQnqNMzk7cyYtJdnN3yNTC7IzoMrONSxHE1N877z9yFJZ74r521FGrq7337bv8uJyXWz5nDf3l3EzWH2eDdTnW2kLFeDFw/OQpDBQTFZut2g27OU5isYNmJoOLGh4sSDgkInLQQIML1kPpmMMK4W5s1qT2sYsKVrF9m2Qf7pve8dbys9Oiom8UCgWFBi8R+D+DuZFM5soSD23dgIZ50ldHXnTpHGHRwUhvg/9z8IisFvr7yWadOEMW1vF3OBVZFeXi7Svb29wrHu7RWvcBgkXcW0Cct3MvpqORlW1Mb6Do7twGWaxQW+5WA4ncK4jY6KReAx0AxbjrKSXnJ2sNslhjLfo1L9LH5vO4VChmzOAY6rsQXPHPuND7IxCvkEc6c8yKSqV6goOYwkqeTlyRTSO4EcNWXb6R2Yxc4DlzCn+Qk87j5KHfcjy0vHj21xv1op5lRKvAeDRaflrcTq5hiPi+zBm6V1/y4nLvWBAFNKStk3EOGAdyfJXIL6zBT8RoCQGh5ngonFxL3vHcyjYsMwCtQiqtFdeNjLduyyxEcWnEFJidCRSKTIepJMirFYKMBjh17BdOl8bdl5aNqxnfJsNgs2Ic7P4Shi8IeHxXfZrHByZ88WC+Pt24U+vvKKsNP3DT+M6chz71XXUl8vMr7PPy+yvhddNAZdCB1bBxGLia6QXV3CzlpysvpqOcg2m7gmi5/cEsv5z2TEezottvd4xD2yuvu5XMdGkUsCA5jIFFyQTF6AYh6hOvhrZFkmlxsla9SilnweacxZNdVKjPgafK4IAc8RvN4ompohp6vkpSak+LeprrCx+/BldPbOI+g9QGWoA6/7dey2KIZROg6JsqARFgVfNiuc+uNleiaKlSlMJsVv3675159b/mgR5NFshrt2bOPrLzzLyl07iE9sG/UHyD8vWcrZ9Q3YFQWP3cZBzxYS0zYhyyb9EZPB0n2EQvDkk+DceSaNRhOmZPKU916OaHuQkDgveRl9jtZxahdrUpUkeO45kV6dNGnsYedstDp38+qrovDk6qtFxPj11wXm2OICVhS45hoxsdtsRZ7Gu+42+Ifbto2vGG+4Aa67Tmzz2muiEYeWOTEye59PONRf+ILo7Lf3cJY77gDfgdNpazffFU7kd0PKJ/83dXViojLNsUYEZeuQ/P+B5P8fpPINSNoEflL7UpCrABseV1TAK7CBHBacxPmjqGqOhTN/Bxh09i2kq38BF571H7idw5imiZnbjJm4FTO5UlSzv0OpqRET5N9aRMo0TV7uaOdf17zAd195iUPRwXdlv1NKS/nusgtw2WxIzixR11GS/h5OmxqkZbibV9uOjBdjZaNuynNVlOWqGJL6aecgCUYwgGoaCcru8aYXVkvmiQT4qRSY+OkcTIw3JXG5hNG02YSxsTr/eTzFVswTCwUf3LmLX23cw6FDYuKfPl0Y0fp6YdCffx68HQ2gK+MO6bRpYhHb2iqcYFkuRlOmTxcL4dmz4YWu3dz/Sjvy4Wb6D3q5/ncnr6+Wg2GlhIvPr/jZOi+vt2iMdF38bZriOiyMsiQBgV8TrPsXZHmM3s5TRb/+IP3ZXyL7v4Wz+kFswS+gKNIYhlIBz2cx8OJ0ZqisOIws25BlDdX/afLZFjI5N1VlO1DlEZLpABIFAt4uvM69guc43092+F6k3Hpk49A4xZZhCEf3RJxjKDrIlsH9W5Lu+Cg/2rSRb7z4HE8eOoj+Tsh630Ruv3QFk0tKcdhkYp6jdLoOEK418budbDsc4bG9uygUhD7NdU0hZFYgmzbWSasZJkqAUuZzOjWOCtJp4cjOnSsww1a2AIQORyIQT2n0DKfGHaaKCuFwWdFli6XCovuzskC6LhaA923byXdXbycSEeP33HPFQlZRBCdwSes8pKg27mRPmiSc5IEBYe+t4lePRyyM58wRxXx1dfDUvkP8+KF2Uvuq2dUyfNI21oKMWHPCm/H/y7K4L1ZEGcQ5BQLFjIoF27IcUHf1T9FKv0kgAFVVCinpc7SNPEnO/m2c4R/gqvotmqNinD/a7rsaRXEgKwYVpZ247WlM7KjuJchKJTldQpIkgt7DpHNOTLOAyxkl6OtGUVUKBR0z8yzp4Rewy9vJ5TLE40WH+UScY0t8vmOx5aey/FEiyO3DMa584F6y+TzpfJ7Vhw7w49deZdV1N1Lt9f1B+7arKrdfuoLOkRHaR2I0BUuo8vrYvl1QlYSiM/BUwBVXwNq1ds4ZXsFkWxubnevY6H6Og/YdLMu/j9MS56N4xcO1qqYt+pVDh0BXU8imhoaDXrMHd6WOeWA+o7+RuP56ocSrV4so8XXXCSV2OoUDfMcdxeKRvJqlom8Bq1aJKLPNJlKzX/0q/MuP+zBHK2gcfR+77Zu5/qH7QXr7VarLJSLkdww8QSDWRKB/MuWFaqDnhO+jaYwKjK9c+UehQ1qwQGC4rZa+khIC56Vvuq0kqVD6O8z4/0DmSfGl4xIk75eRJAUTARlZOOM+Nu74BC2d59Bc/wKzJq8GvRoz8yjoWwQkAzskvg/BXyJpp530eU/EIU+Z8k6u/C9PDNPkH59+gnXtbaTyOook8ZtdO/jnJUu5ac68t9/B28hlU6dzQdNktvf2YpdtNLgqOdohsa2zG7mgIMsiApRKqSyuaWRHl5OMmSJJiqMcQUNjecUCHLJ7PAVpRaFcLqGz3YluDCQcuKlkMj9cvxbTLPBPy5bj84kx2Noqnmt9vcjodHYKA211ngPR7S4tDZBKiahxY6MwVMuXw1dXbiQwNBknDaQPufjg3Y9RcGe49yoRSd63T8ApLDwziM9utzjeUMU+bKkAqU4/FYUaUA4AJ2YhTFOHQj+FfBBJdo83GnkrsXiNDaNIJ+XzFWEpFibSoo6KRsWcVVUFIyMKQ0MLyQ4sHC+om8h3rHiuQ1a8qNn/xcgPUpCmk7N/iYIxn2zuKHrWg4kNr2uAkWQYlysijK2sYGSfxxz+BqlkCUZ2KXp0D5nRc3GUfIJQSDqpKLDNJsaAZXC93rf/zV+DvHy0nU898Rh5w0A3DFYfOsBt27bwwFXX4lD/sDB6hcfDUzd8iAPRQaKpFLPKKoj2ONizB44OSdjTAQxDOKOLCw3s7B/EJjnJSCl26Zsop4qZyhxcqVkcOSLGk90ucPyBgIgmx2LQn46goFFOPel8ip+u34gpF/jK8rOpqRG6GY0K/bYWuX19Yoxa7aYBjIwJXomurmJr7Fmz4Lb/z957h8dVXev/n9OmF2lG0qhbtmzZlhvGxmBKwNSA6cXGXEIgCSU34ZvLJSE9hIQkN41UckmDQAgdQi823QaMMcY27pZlyba6NJKml1N+f2wdjWwLXDD5cQPreeaRZubMmTPnnL3Xu9d617s2PkNRvAGZAMGuI/j8b14hV9nJPRcvpL5ebPvyy6Ju6NOfLnDYnU5xzOXl8KvN6/HFKgimQnjyB9aV0TIHkCXQ9SKgQJ2w9YJHmg2SocC1TqfF+E0mC02MbNBs0zISCXGcLhe0t1ewvaOCigrxOV0vBACgDGf5Hcipn2FmlmNYAfLaf5BXLyOXMclmvWTyHryubhxqglQmgN/bjcORAzaiR29Gt3rIxM/D6xlksGUnBH+JPxg54PqfQEBcRzuI8VGuHfhQAPJ3XnyeWDaLaVogQVrXyeoGP3zlJW4785xD8h01wSA1QdGWcNHD94MlUSGfjGpprN/gZXVTkmsu9dO0Pc+rS2s4O/ZZmlxr6Qiv5fufDbJp/e585ZGtEv1+iMcLrapnZI9isXUXFcF2PtVzJn/5iwDCV1whmoLcfrvgLk+bJqIfCxbAnXeZZJ1xXNkg/XIPa9aWsGLLAF+/uni4wnPn2FfwDVZRvvNIpmePJNFaS2flyv1u9vGPBRcAojLeh8Q9+5H+scw41uDXIfsKwjlbWMpYJN+XwXU60iFizTc2iqrhVasEIBnNzL5LwbKQin8JchA5eBMEb9p7Q6Ua9H6Cvk4qS9fQ21/PGcfdKK5ZfgXoGwG7x7eQGLP6r4Wy15CkfaCIPayyUtwLHyeA/Errdl5uFeAYCwwsDF3nR0tfZv6EiQSd7uHuSnb70gN/rlFu1WKa8PXnliHrKgNpJ17Lz+PrNyBp8PljG7n4xEpK1sosb+rCk3OhZ3WCDh/lIa+odFcK3bPsohGHA1LogISMTIgIrdEe0lJyOBNgC+Lv2iV4i1VVIkJkK2g8u7IJ2VKIY5DL+rl73au45SIusaYzfvxQ1KOsm25vFHPnOPwE0Xomk0i3o+tw2WP3gynxjYkLaGoSDiwS2f0837tgBP84q3DvRQv26/qYqfuxYj/DzIOZc4DsQ/WfjqVdDpLQpXrPDnlDFeV2y2s7UpbJFKhldrvrYFBE+0pKxDzmdgtH1tIifouWvBJL1pGLb8E0LWT3fLLafLJZ4exyQ6Bbz07BypSS1x2EQ03kLY1sLogsmUAOJflVdPKkM9WkMn6SGT9F2hJKg42o2nEHevsSDBb44Tbf89/ZDNPkuueeHpY/BUjl8zRF+7h77Rq+cPgHVohEkiQml5QOP//i6vtxxUowrArISzy5tgVdyfJf8ydyXdHhPLF6O039faSUFJqaZu40bVgRpqdHPCRJzKnhsKAJbo7mkDBw4iZCLb2JXZiGPpzhKCkRfrJ3KJlVWrp718ZlW7Zj5C0SOOhOt3EfbyOvU7n2pBlUV4PpzRCtW0e0yccYGilOVJHp8jM4CF98QfjY7x63kFdfFZHk007bnaIjy3DPpUIZ65J/PMh4l7pfFAtLb8UauA76LaRECD1pYDgs1OJrkaTZ5POjt5G21VtsmlQmI7azu8nGYuJcBAIFTnYiIeZDv1/UPthdAFMpcf5y3VeiSBko+jmyWkHe83uyQxSVTLpAbcqnxqDrBj5fNx5XL3kjiMsRAySk5G+RrRbyuoNEKoimxjGNBEFuwuv9wwHfW3tmfT5WANm0LJa37cS0LEJGGSckz6JdbaVD28Hr2z/E6ifJ4h33MlyWh4oyicpdc7nzTjj1VI3rvyJI+8rKw5jWexir3hJaf5MmiRXkihVDk6ucRTEdxOMSKXcP7nQJFhYpKUljSRlISa44Wmgr3367oFtcdZXgFT/yiJgMTjpJRJ06K1dS0T6HDnUna51v0Bgup3LXUfz5z6KhxdixIwrsHnyAov56anpnEWo5ix3hlQyEtr3/7x1h1gEU5VkDXxJi+ozI9RhNAjTn30EKfHu/9/V+pmliwbB6tVidj6bl3N5RRFnRUtSeUwELy30uUuC7SNIeI8ZzCcS+CcD5J19P30Adfm8PoIDZA6SxLLjz8XuYMfERZk56CMiCvh606Qd03E6n4KR9nAr1nt66mdRQCHVu4hQkJJJyHF1L89jSKEfYYfX9NJv2MLKNsv2/qoKupUGV2OnYQXG2hGB4EK9eSm+viJqcM7ecU2aU09FRcAC2U7GbA9jFZ7vi7ThyXvx4MciTxcQgQ5niwgyqmKb4vO1YQyHxvK9PTNQ1NcKpZDxRnJkQhpEnT4Zd7GKcL4uui2KdiRPhzgvPxTDgikf+STrh5cyKUxkcbOCFF4CUCh5RMLpuneAuS5K4l0Y15/6lw63MSxD7EVgZDNMLlglmHCnzN+h7GCv0BJIUet9Upd19zuZW2uDXbv0djwsQXVQkALLN57XpKB0dYm5TkxH0XDvOjqvJ5dzkrXpM9+dArh4GLYoCTk89UjaOy5FnWn07FaWbqSjdSjIdwu200BxZJClLa+dMeqNjmTjmeUqLtyLn/gmeAwfIgYA4xlxOOFzfgQX6/s/Z5r5eskPguC4zkSp9LBkpTUpK8uqKNEe7hmT81EIjGU0rPLev04HGQjKBXjrVJCkpgRIqo2hgLO++C5Mn+/jaWdPo6ipwaG0w7PPt3pmuxb2SWHkzt566gBd29uNJlpDXDWJEKQu4wJRIpcQ4tzMCNj84GhX/25mMnJVCc7oxswYJBkj5dDzJ0LBu75/OOQ9Fgc/98xES6ZUsqj+LnTsFGHZGS8iGemlsFPtbtkxwkk85ZfQFluXaPx9rWTms6MVgRoEJyIoOmBi5DWgDVyA7f4ahnj6sJrPnNdgTJCeThe6XdoOWaLTQXc/hKBS0qqoIAnR3i3OeTJqYfX6cjhT51hvJ5oPo6nws7VPIsjTcxl3TwOUbh5x/Da+7F5/3PkKBdrJ5P6ZpoZqbkbCIxsrojk4g4NtBKNiCQ96MZeX29tf7ML+/UKB7iJi3H5odcoAsAYokYQ7N2P1KL2Nzk5iYm4GVtPjznwU4rK8XzulQrPZ3V3JIc/sFC0mlRIr/mWdEBOTss4UY/eLFInK8cqUYDKedBvf1PktNy/GohhtDyqNYGp50qQB47xqUmZUcPriQSy4RjuULX4B77xUKF2ecAZddJuRjXn9dRDIuuABuu3IOS5bA66/X0O1u5i9XfIq+PrjvPsFdPvVUwU2UJEC2GAg3kfS3U952BOUds6EnwmfuexxdS+9z1bq/hQOWvgNy7wB5BuJVNO86hsZxTyNJFk5HElL3YWqHQ+5lsHQk15ngPOGgo8ozZ3SxcmWEta//lCOmPQGeS5C8V2JFryCRgLse+yM15e+w4NT/RNOykH5MtAUP/mi3/Uju87ASt4K5iyJ/G0V+u4rOA0oJmDvZ2TmLnZ2zOXyyzQ+zEHfjgVtVlUiXjzaB/TuaW9WQJQnTtEjKcYJGiLBRjmpKxHf56JALrcftxhM2b3c0IDyyTelodsdnTsGy4NIHHkayBvnbhcKRdXSIwph33xWL1ylTxGTf3l7oGjcwIBza+r6tOA0/JgY5EpRoVagqbE5vRUVjdumRFBcLwOT3F1ov28fu8QhApesCxH527hzSaXj0rS1YuTwNzmpuOPpTSJI4ro0bxfY1NWCqObIBnXnz4Lo7l+PZWY6cmch2dQOfefR+JFPmaxMuYssWhnm9I+1ACn2s5B+ADIYB67eeRmXZcmTZiccVA3MAEn/A0ENIZhOKZxKSZwGSXLTXfmyQ3NtbABoOhwAwBb1hA4f5NP2tL1Osv4rsmo3m/zpVnu/Smahm2dqjSKYi1FS+SSS0BY9rK275BtwVf8Lh9A2DZMuSyDgvwIr/AU1LUxnZRj7vIJ2LkOIM3LkHUBUF01QI+DqpKN2MJB38eA0Edne4/+4A2aEow/41J2fJSmnclhuv6aM46WXdOnEdnM7dgfDIMWl3NhwJnN/rf1UVnWM1Db5gPIwlq9xx4cnE4yK4tG6dqN2YNk0swrq6xCLL7RYZOa8Xnt+8heJ0HZW52QQy1aRS8LMvTGPDBvjjM1sJU8qnq8cMZ3Vs1ZVcTuzH7y+oV4RCQ+pQx06hpweeWP8uRVKAb84+bVgisLNTLKobGsBSDHSvyZFHwu1blxBor8NIVZLs0lh0z4PgNPn6UQtZvlx0ZD355L0pEPs9ZrMvgpUBLHqjNSQzIbyeXnRDBeJoqZvIaSq51PM4nQ5k7wIkbdpeu3E6C7QmuwuwzycWrvYiNp8Hl7KO/u4HSeVexuN1gfc/iUTOwZn5MmvWT2Hz9ksJ+ndRE1mD1xXF57gbT7GOO3jSboulXPYS0m3Poipxat0DSLJBXveSkxow8jvQFJ1cNoDbNUB12bs4tBRwcN20FEX8lmy2oAH9US2uPfQAWZI4Y0IDT2/dQlTt5iXfY0iWRIVZxYnFs1Gtet54Q+j72g0mxo0Tj0jk0AESjwcWLRKqDy+8ILqtXXihkFtrahJA+YEHxPdvjw+wJnQHJ/X+Bz6rwJF+911oNt6kVp7Orl0+fvc78fkznvgdqqlx/fhreOop4XDuTt9PsHIc8vYj+MtfYGXoGXLOGB7tcKbFj+Oye54i70zwty8s5NFHBe+po0PwkkcC/J11r9C3vYgpmVnE5PcXIj9gMztJZiIse/tS3t5wCWDyysprmVD7Cmce/x3AgMGvIaLLFlb2BXCeBMFf7BMkW9nXsBK/Ar0V1LHgvZJy7duUl9zBqg3nMLvxr0iJ27D0LQD4XBv49DE/5MlXf8Q9T/+Vi0+/SoD09OOYvm8MqVi4kCRNcKRLHsIa/A5kXwYs0KYhBX8M+XexBjfx7tZz0NQUk8YuEQckeUA9OP236mpBDenr2xvc/DvaBY1TeXjjetKWzlrvcgCcpptyIlw66VwGooK/29wsJrbS0gJgtuV/Rj5g/4ovTFlHNtThKuiKCgF23n1XFNdUVYmFtN0MIJEQ3zcwAB1GNx4y+ClmgD7ISzjyPvy6j366MAwRRUml4LX2N9hFPzXhEJ+detRwGv7nKx/DY4X54qxjcbvh9jXL2JGLU0IlOzoT/GT5ElTdxbWzj6O3V9QmZDLsRmWKlbSS9vaS2x5BshQwdCzJZOpUARw2by6A04Myo4NUuojVm89ge9sxbN1xDJpmcOrcH+D3JSB9N5LuxbIMMJ/HSv4Vwg8jqdV77cpu1dzbCz1d/ZS4f42UfR6P4cVwXky6pxc1v4pkppRUWsEnvYieXoUhNVBSvJkZDa2sbz6X3mgdQW8rE+veRlU1ZPfjWK4LkNCx8IqiJ+8iVAc4cr8CC5wuF0rR5SSM80j1PIuqmNRENhAKPiPAseRGcp9/UKfI62W4tffHgWZRXxwi4vPROjBAu6OFdkcLkiURkov4r5nzmFJcyJpAQQPb1s221RTsc2U/T49IuZsjAqYjp/5w92RANNlyOMS+BwcLzbAaGgrcWLtDXTYLXdk+trKN8cwknC3lz7cb9Bdt5buLJrFLX0UtU2hvF8Dv+b7nacp3MTUwgQvHzSGREDSaW1c/g0vx8/lpx+J0wt83LMXQdZL48FphfvL6UzgkH9cdeTxer/j+XA7+fMZFw2oJucAAPe41RJuD+Aw/KDLogdxWhwAAIABJREFUAtwbhuj09vLLosZnNBrEPs1oBytHd984NjSfQTbnQZYFbWtm4+MoygBy4kfkrSxOcwAr8xiW/6vI3sv22pWmiayO6KBnQeYx3OYfCOgxNOt4YvGzIP5b9KyDVNqF27kdBr+HaQzic8eYMf5OkimZWLyc/lgZU8c/jscdA6UZqWgeEglMy0s+r2CYIZyR3+DRv4lqdQMSivsI8u4fko3+CSn/EC53mmNn3kFZaCsgg+PoA44e22bzkG0qyccGIAN8//iT2NLXR+ugUBiQJImykM4N59XgGyKqt7aKAdXcLCK6ICa6sWMLgHmIYrzftucqT5KE4kRtLTz0kKBFnHwyHHUUXHMNfOmvK9F3TWWesZBtjvW87HsMj+nnMNckQr0TkSSZCcrRNC++i7JpR0PFeP76V6j3zmCbZw0XXywix2+8AVWBY2ivXs63TjtCAO/mk+moXs4r3qepyI+h0ilyCU6n4CgvXQovvSQiZAsXFjhR914oNBc7rRe598L94yjuj2Wz8Ppr01m+/DFyeSfhYCt9g/U4HW3MnPzA0FYFQJ7XnWhqCrLPi37pjvfmtVmZFwTniiFCd341DHwFkDh88v08vfQHdPROpbJ0HWReQCp5Civ6Hxw26RE0Lc2jL/6CdzZdxFHT/wZY0Dcfy+wFFCz3BUiBbyHJIaTiP2BZOcBCksSMZw7ciG4E2LDtdCaNXYJDywAyUtGtB118OLJQ7+MAkGdEyrl2zlx+8+brKJKMJElY6PzozCM4ukbBsoQT7OkR0aEdO0RWBgR4LSkRoNkWgd8TMO8ZUbbB8z0XLhzWRrUpFB6PyPRs2iTSsoODgs9eXy8WLD9+dimuRClOAjTTRJAgOXJYepxiKvAnA5Smwry77iWKKsqJNEzGmS9Fl/owlPxwt6r+flBSHhKlXbhcwjGrppvaCou1Hevpc3YQ9lSjGI7hjmFtbWJxncsJdYpLHxVFtXd+5kwuefB+amW4+7yFw5mHKVMEcNi4UfyGkQ1+9seyWejtn8+2Zpn27ilgCXpCeXgzXo8QNZYkE4kc2ZwThxZDIocV/x+k4t+Puk9VhVBxkp6m6+kdiFLkTyLLcVzmr1AlB6azmPbuBnZ2TKW+9i3ARPLOQFZKGFfzU4KBKEtXXs3m1k8TCbcSKtqBR74dKXYzpgVZayam50YcngacvkuwrIuxzDQWHlRJwtdzGUlHCRu2HI1lSVRHVgMSuM4Dx8HJNdo8zXS6cN7+nQGyJEn86cxzWfTwA2QNHcM0sYATJlSy6Jg6ZEmMp3hc3Ot2hzkQ/sfvFws2v78QKR3ZQMbukDYSSNt/r6tvHAbW9qO6WgDwXbsEwAyHBQDK5WDZ1h0opkbEHIefFH10E6WbMdoYwv2T+P1v0kze0Uj3hhWsG9NNqHYCJcWTaSdBrHoHtbVz2LFD7NuRCZIJRAkGRVBK1b1IWpbtbCQnZZkaDKCbqeGui/m88K+rVokmW1c+ez8g8/cFF3LZPx/EMvq587wF6Lo41mnTxO955x1xTx1//IGDZEudzq6umWxsPg7LknG7+nA6MtSU280TTBzaAMl0gFxOxunMQPznWO6zkOS9JwibFtaz4y76+xZj+ftxuxJ41UdxsJh+pZJsJsT2gQq8rk4cDh3it0LpEvzGXI49/K+8ueZidnQdyYbmedTXvkWRrwu59ygsM4muh0grX0bxLsAbqEfTHsCyMpimApaGEr0cp2wRTZSytuksZjY8Asggl+2V6T0QCwTEfGq3lv+oFtd+KNNI0OXiyUWf4a32Npr7o0wIhzm8vHI4Cul0ipVmQ4PYPhYrRKiam0XkBcRAs8FyXd3BtyasroarrxYqF4sXC+d+7rkwENpGLLiDXHMNdfmJbAguY1Ae5LfXnMfX5v8O95SLUbyl1J10Cduf/D0T/99Y2lrPYUbiUxRlKzjittuIGSlOqTiBso6ZhDcfw7eMx5CqJT4VPxtlx3GcWlbEUsfLVFI2DOAlSci0lZcL7vKf/gSeSBkpX3fhoKVDo3+i62LSWroU0mkXNZU76B9wE43VMXfGnzhh9m9R1YJi90C8kude+w6G6WDR6V9AIoOVeRHp/QBy/CdYVoZszkc8FaHI14amCbA8pf5JlrzxDd7ZuEAAZEkDfStoMyC7mCn1zxAOthAJbxzaWw7MTvvoIf0IlhVDKrpl6NztsWKVJHpigrc4bcKjoIwBqRjJMfOgz1lpaUFwfsaMfW//72DXzJ7DuZMms3RHKx5VY97YcXiGlvV2oVdRkeAV2p2uurvFo6lJcG5VtRBZtukYI9O7oz1GavTmcgzLuE2dKhzD5s2ioGfCBDGJZrxRcs5BjF1VyIpFe2gDTt3DzBdMJE8faWc97qJSFHeAlnA/wdxSSqVyBq1xrGlbxTcSLRTlShivj8PIV9LWn+DHxqNouQCfP+xEASSsV1iZFx1H/r7gPCxLAAyXS8xPmzcPAQ4dUG2alPiddlrUssT/jY1iPtu4UQDm/Vn053JiTuzpgfZdVzLQtwyHmsbv7aSkaBsNY14d0kSWSaaDbG6eR2vnkRw/+9eUFHdAbun77l/JP0bIv5Huvipa26YR9HUQKu4mm3OQz7voHaigedcRVJa+i88bQzbfJqd+jYHYWEBm5qQHMZHRtBzRwXFkslGCPhPTdKHnm3GYn0PzPwaEARmLQpm7opj4fdAfqyWZCfPGuxXUj9lAZeT7Hyh7aDtcO2J5KDprfpRtfCjM65+7ildbW+hNJZldWUV9KDz8vqaJ8WNnLmwZvVhMjF278M3jEecuEChE4qFA0bBtJHi2/+75WjwufE0sJsb0xInw1MA2LMMk2RHAgYeotxVFUfnWKdP5xy9fwVE5m+L6aWj+It5xv8KCyd30dM1ifH42TVvWsaD397hNL4fJh5Ec9BEbzHGz+iia4eOK6SeTSIBsqqzUV4ME9yy4CCgsAHbuFPfFsmWgpl3onozQAJcsJEUa5uDaYH/6dAGsbZrKMcfsP0jO56F15yy2tSxkMC7jdMYIB1qZWPcC4WKhMGUYGm3djazdfA4lxVs5ZuZdIKmQWwGu00bdr0SCIuV39Eo1tHU3Ikk5qiJbhfJE1kNvrJqWXbOQ0Tms8VkksphGkmhsJum0zriaFZSHt1AabiabLaZH9xLy70SWZVIZGYk/4/bqaNp/DM1dLkxziDIn6SgOiA7WEh0Yy4r111BXvZGJs67HqRx8dZ0ddBgp2fdRjCJL1gEI0c2ePdtauXLlh3g4hSYbNlhubRUnz27iMG6ciCZVVe1b5mi0fa9YIUCyzycoFzU1MOO23yFbCpPKxARjA9nr591IynsEkdnzkSSJiurFeAPb2LLhanJShudL/sGAEWd2ZRVt2x3MSZ9Id/1S8s4kd529kCefFA092pzbSI5bPaw6MdIuu+cpqncci5b187b7FfzjuvdL6m1fZpqiwcHLL4sJy15gbNoE4VCcs4//OtVlQ/2xcWKYJSxfezZL374agE/N+j1zZ/wVSVLJOb9C0ryKeJzdHokExGIWiYFW4qky8roQL73inAVUl68ePpZN20+hvGQ9Rf52wIUUfgiQsKIXDUmz2fegDEOSbuua5jO+5lVczjjgQCp7FUl+7zx1rvtyZMlALf37Bzpvtv397yJNefXVh2R3B2SSJL1tWdYHLkX/V4xXKLR/tQGzHclzuQoi/OGwSMnuOd3YQNmOVgmd3QKwTiTEGEomxX4iEbj+5YfZ0NtOY2XZMG31nvMX8tVTfkJ7h0lw4gm4SyuIlSWYM/Y1+qO17Egexhr5NRKlXWBJTPXUkGqJEGeQygl5MCS+c+R8tm+HJ9ZuYqO0kbKxOe45vzAOLUuAi+/duxpXppjWbCfr3SuZWSfSDPdesHA34GCnqbNZQRkxDAGSAwUW125mt8+Ox+206lDnKiuKV3mZYv+L+Nzd+D1RLJy0dU2mrWcKmUwRfm87sxrvoTjYg0UIM7z8PVPp2YG/EI9uJxqrpLOngaC/nVmNj2FJEjIGPf01mKZF47iXUVWJFJeSVm5Ait1AwPUK6ayLXN5LWaiVWKKU/lg5sWQJspynvvotnA4dfF9G8l692zW2/wehYLOrs47W6M2k0wLMjB8vFqcHY93d4hw3NornJSUH7h8+iB2KMfuvGq928ZcdWbY7LNoKJzZgHq2wel9mGKIwu7VVgG+7q+SnH7wVkJhcVoZkqvzh9HNJpeDX1z9F2gxT3HAEyBJ19fdhMMCq5nPoo4tN4ddAhcbiMro2eQhQgnv8TlAlfnX8RWzeDA+/uYWdeieusZ3cu2j3rGsyCf/vnqX4B6voTSfY4lxPfb1AvPecv3BYaceOmtvdHN98UyyEZ8wQmsr7slRKZMR37YJsVkeT1lHqe5KK8Er83i5kRaFnYAbbd06mP1aNLOeZNG4Jk8e+jGn5MXy/I8fRwzrPI/+mEztI9N5LLFZM98BYcjk/c6behaZaIFlYWPRGy5gybgnFRYOks6UMas+jp1fhN7+Py9lJLFlNkb8NSbLoj9WQSnlp75lCQ93zFPl7UR1FUPLacPZrz1oSs+9SYvEgTb230tUlQK2d7T+YbI1lCZptOCwWM17vv7Z2YH/H60cuESVJwglGIuLG1HVx09mAeelSoUjhcAjQZ0eYS0r2zV+WJFEYV1MjKBd33CFUJ7DAlIxRQWnHaw9y3oJHaRs4g7b2M1G6jmbmETdTVvkaPznsueHtFj18P23mYu696KLh1x4w7qe4vIGKzhkMbg5y2T1Pkncmd/uevDNB67jncTZNZ2b6WFpzz5F3Jg/onI2UhbMsAYJffFGAlqoq8ZtXrBDp6rlzYd48P5r2B3KdnyWectHUdTOvvxEmFpcJBbdTWryVbTuPY/XmC0ikysjm9s5/2O16/X6JirItNHhexOfpxu/toji4Y7dth3nBOECbgqQNpQ5CD2AlboH8GpAjYPaB2cXytVew5I1vcszM/+XEOb8CyQFGB7wPQFaVQ8vXrqoSUYeP6sr2o2ROpzhfVVUF59vTUyiw2zF0OwSDAgCVlopJ0W7wYWt15vMF7qTDIcarxyMUZzZtEpz9vj7Qsl5RH7LnQtLIkG5voW/zBj7/63pkVxU5Khk3bhWHhf7If1ZkmDpx8fDmix68H5cMd56/cLhV9Y9fX4JHqaAiPp7WDZu5hMLYkiQxofdHtuLtr8DbHaY2Nx4YGN6n7VTsZh6SJH7LlCkC6K9fL6LjVy0u7NcwBBgeGCi0z7abZrjd4HSG8PvPx+s9n56W39Le62BQ/xzZ2CoUeRs15SsoKd5JNDaRrugsTMfJSEMtt22HZ4OATAYS0SnkUmCZOWor3iFU1IrbFcPt7EeR85imit/bjm64GEyMwQxcgscr4Q3fDMk/Qd9SdDNAymhAUdZi6CrvbjkbWTYZX70My7Ig34w8Cr1mpFWXt1A1Sczv27eL1HZxsQDKB0pHsRcddkeybHb/m4183EySBBixAYlhFIIesVhBwcee48U8v39yXIoCs2aJMb56tQjS1NWBZCoYjjz3LLgQEPd4ZydYZh4jlyISeBVFNtjRfAkBXzvzj/s+lWU7mDn12eF9L3r4fjB2DMslWhb894sP4fFU4u0LkdiisOjeB8BhDc8LXi+kQu3knIPkWysYn23Eym8abupqg0F7vCqKAKazZon5aPVq8dqvdr639Go0KjJo0aitNa7i8x1GaelhpHp+QNeAl2j2Kgb681jZ5YSCzZSHN2GZTlZvOg/dLALfnOG5cOQiO5+HdKqYzEANhiER8rfgdg/i93ZTFOjG6x4gniyjrLgZvzdO78BE0lyI0+mgvOYoHHyXbPR/ycc1kkYjqvkmlmWybtt8evonEPTvoDjYg6EPoGChqtJ7jteAf5DZdYUug1u2iLl93DhRy3UgC1K7kZGt4/xRLa79yAHkPc2WLqmrgxNPFBGVlpYCf3mLqPnC7y+A5XHj9j7Z18+7EYBfvnQTlZVCnu3b/7uT55+vYY46n2XeZ/fSH/7lS0KTd+uLxzK2ehNFVf/L8jWfo6RqGYoyWkucPaRgJOgv2cKaRBNHpU5mtCrt4QK9h+5nZ3Ybd10yejON/bHt20VBos2dPe88kWJaskQAks99bqgKv+9STOC5V+ezauOi3fYxGK/CNB34PD2UhbZRP07C53wKv7efQMUX8fvFuR4Z5TOTuyD+GwpaxABukIJgDQI5QAbXqUiBHxROj9aAVHzb8HNz8Osse62cl966jsnjnub4Wb8Tb1h5QZ14H5PDdx/0eRvNqqvFJNXe/t46zp/Y3jbS+Y4dKxzPwEABMG/bJigZNtgsKxMPO8WbSgkH9d2zfgRY/OSZ7wCCjnXL28/jjVaSiZXhlAahXDjNPcfr9fNuZObkFxgzZRwrVlVTUbqBqDsO7LHSGdGu2ekUj6yvn7ySIBkvQ8MNJPb6ffcOpXEvvfsJwo7RVWZs8A8MyylNmSIA8vr1IOcdmEqe3t4CMFbVguyaJBWk2CwLnPmf0r9LZfXGE5AkA693NUFfH0F/Dpcrh4kfTU3h8paihT6NnPhvZFlHDf2WTEY473hcnNtAqBF/6E5CgS14XIM4tCSG6UbWqkklLSxk8rqXVG46kv9LeAM1Q50GnUjytWTc16IrkHduIRn9FptaTybg62bmpPtwOCzAheSc9r6p6ZHjdcwYoXbQ0iLA2cqVAmCNG/fe0fY9zb5+qVRB5/kTgLx/pigFChWIe2QkfzkaFa+7XAWwPJK/bNtIH1tbKxY5375zA6+3+KnKTGWDd4XwsSb8zxyxKD3x8nPp64OI8g3GlC1DK76dph2fIlC2FW20yMSI75QksJwGyfKdtPU7KTMrED529zTVsI+9734kw809F48+Xu2/diHjUUeJvytXQlAdx2C4ebfPmKbwr83NYpFhK/yYJqjZWzEHo7y5+mhiyQoczg68ngGKw1W4tbXkTT9OJY3Xk8ARuhIpdROqqiMFfzJcJxGLiXnB6fITrjIIe58l4Gsj4O1F8Lu8WJShG14kyaSjbyaWYz6+0gXD7atT5jHk5GPIukDWwJG6gq2tjWSyRUwe+zQNda8jSRKyWomqvneEceR4LS4WQbfuboG/Nm4UGYPx48U43l+gHAiIz9mLko9ice1H7HD2bW63KJCZLIpp6e8XN+j27eJirVkjXi8rK4Dl0QCOywXtNa+Tio6nomMGh6ePxWTj3hsCt/3wJAB+/tA25p/0FHJ48V7bjOYkR6pTtFtL3r/oToKsa/B9fvneZgP6ra05ZqWP46514PZ0c/bZZQSDotPfwIAY6CeeWIiEmqbEOxvmsa7pLCRJp3H8Co6c8QzhsT/E5TKRcpuBPDiOQZJ9GL03IkkWcviLox+65wosKwuJP4rPoYPkJJ1JsXHb2Rw2dRuylAe1ASv+Qyz1cCT3WUiy8GBm36VYFry65ve8+lYRU8c/xjnzvoEsGyC5wX0pkvyvXV6OLNT7BCAfvNkKDqGQ4CTquogC23SMDRvEQ9MK0WWfD5AV0NNoWiGikvH0k1czWLEIE60p6PomUEfXE/7jzSfz0yXfZ+4RlyJJPsaXPLrXNqON2XsuEGP0kgfvx628P9XJcO9/P3Jb2uj3zf8kMDCGbF8tUa2DlUU34XAOMmHsLbS3iwCAvfi0QbbfD5qewTAjlIU2URxoJVjkQ5YsvJH/h0PehiK1ozlrUF1jURTIxPMMxErp7ysUQNqaskVFQRTry0ixbyJbnUN8aZO8qdHUOgHTNCktaURTe3HxNGbidQzHp1EcY4aj+kri6+T782xs+QwSMQ6f/ADlJc0iKqd4kTzn7ve5AXH9J0wQC/jmZhFd7OsTmcS6un1Hl+yIVCwm9pFMFqKCn9iBmcMhFq/hIUpzOl0AbDaVCsQ1sfV4R+N8+/3QE1lHsGcsZaly4pnxQBo172FgQNyXg4Migvj0zyOQPZmfP9REbe0y5PBLe+1vXz4Ws4V7L3ofaqIGppZ+7/cp1EXIsjgP/xh8iNL8DFx9Y9kW7WPd5lMBmDhuMRs3iqyW0ynwRC4nxqvLBS4lRf9gKT5vN4FAGyUleRxKHlf4izgdYyG/EdWpoTqn4nI5yGayJNN+YslC10uPRwgMlJaCx3MRUmIjcnY1YGFZMqblp7PHRUvbbIqK/KhKHL83i5X4LZncEciuuWiaIhaKsRtw5FNs7f06fQPrGVO1nGnjn0RVTGRZQwrc8L7nZbTzFImI+aS9XYzZtWsFDhs/XlzTfY29QECcr9xQCdRHsbj2I3Y4B27FxSIdMmuWONkdHQU6xltvwfLlYJk6Vt0Cula/vNsq994LxWC6/O7nwJF8T2doR6bMvksP/kD3UXT3QTjHpXolISPC9Jm3UVu3mI5dj/D44wKUXHEFVHsvhRgQvpuODnjqqb/T1gZ11es5/fi/UTb+54BdQe4C14nAEHAFXnztONY3zSdS+jaRklYqxp5PebmIOIg0lQTea7By70BuGaKV3SBrNn+WJW98i3c2b2H+cTdQXnIrkAWeE/qu4UeQlDCWBS++sZDXVxVx2PRB5h/3BLLhArkYPF9A8iwa7Wd/qOb1invr49Qw5F9hqlqgUIGIctjR5e5ueOwOwV1PumaRjffz1ZNvBjPPL1+6iXsuuohcDj739yeQDI17F1446nf84sWbhotMGEHj2BcFazd+9H5EQfY1Zm2x/5HPTWeOpK8LT+84UpabouL1pFIRmpvFtsGgcBha7lZkycRwXwtAzLoRXxkcU/MVNCVH1v2/wzqmudx40unxJBOQ2/ELYrEA65suxqElCfiepby0heqJ1xAKCWcuzsVU8F+HNXgtICNJaTLxTrbtvAKPs5dIyasEveuRzWWoMij5W5G8P8bUziSXg+5YkOZdU7GcZzBl2gNEgoPIshfJdSKS/6sHvaB1uQSPuLpaRJS7ugQoq6gQwPf9osLBoNjWjmBlswfHo/3Edje3WzwikUKLdxswd3TAb7/6OFgmg8oM+jat3N3HDnWLvPz2xVT40/ztzIU0NRVoWMGgWBj94OHrUBQw45chH0Djq91sH4DsQHysTb+wnAbdkdVIiYnU6uMxDIVsOsjrr4vfX1wsxmC85xGC/m50xzUoCsSMr6F4YObUGwkXt5Pz/Hm4JiGfD5BOH0kqDdn2P5A3NDY3nYZuuHB7X6ekuIOqcRcQiYhFiKBquZD838LKrwCjE0nKI9PJlpYr6RucgNezmarKl9DUNJqaQlP+jiw1gv8OdF0jaeq8s+FTJI1JVI1TaCh/EqdTRtYakAPXITlPOKhTrihiXEYiIpre2iqClK2tIjhZVvbe866dHUomhwqwMx+94tr/8wB5pEmSCPFXVsKxx4oV6rcX3oVaPA7FWYW7pAr07r0+l3UPjLK3ve390vgjJ4U97YMW3L2X2ftdu+EMDPN3lBgq/3zh1/THYM60ezj5rEvQNEHtzWTdvPy0SBd5PHDuKbcyddztSNrkfX5PRek6BhOVdPUdztaWmVhvidedzgLYiQTuIxLspaxYHloFWhw57W943VGWvPFt/vLIQxw57U6On/07ITJu5rB6z8ZSx7JsxeG8vuosZk19ntOPuxOl5NAU2n1Qq6oq8Gc/sQ/HXC4xwdbUCOD2wq+fR/aWYeRDeCO10L1heFu7gQAui7xj9GyLXXRjR2AZGrMjeX2StPd43RPIHqoxa4Nke/9//vRC0mnY0vwZHI4YfS0nk06XECr6DRWROGnpO/h84MhG6eipRxmiWpQ4voHLlUTGJJv34CwSES7DEH81TTgXU9mFz+OhNyoLx6pU0jtQR2yD2M7jEU7XoW3HnfkVbmcJTkcS01Iw0Jgz7XbiyYkMJkqBMYQCzZhGEpCxoj8gZzxGVzTCW6uPweXuY3b9zZQG2nFUvnpIG+sEAoKr3d8vnO2uXQIsV1aKcTka8LUdrt2SPJP5BCAfarMzEX6/uA66Dvf2b0MpHo+ruAzV7WG3Lq1Dlg32gykycpmMWAwriiji8/kKjXvyvrtG5Tq/n3+FD9/Hrll/Brm8E3Wglk1b56Oqj1BX+Ray9yckElAW3s5gvIxkWoDmYBDC6n+jmJtIZxqRh7So7e7gqjo0XtVdIBkk4jFS6WIULUI+52HHDpFFsaPTbreOM3MjLs2D21mCouQxDZlpE56kr38iuuWnf7CWUHAbDjUjGm5mN2J0XkBWr2LVusMYjPmZOO42akq2UlR3x6gNZA7WHA5xLSsqxHhtbxf87VBIZIBGqxHTNHHdYzHxfiIh5rN/ZXHtvuzfCiDvaZoGP3tECHBfP+9Ggkj/UgD7fravAb8/Zke0ZUVHVnRUyYks57ns7CsZU7kKYk9jAhs2hXju9e+QTJvMnvo8J55xKo7kG8Dk9wX99nuNEy+lceL9yOGzhrUlOzvFo6sL1qwxyOUWAYuQJJ2SomYOn3w/c6b9nWkTnmR87VJefPM6lq/9PBuaT2fR6VcKsXGrHxjL1PFPYZoqnzpqxSFzsIfCqquF3E8stv9cyE/s4E2S4BfPfZVkEr5/ye2Y/Zv45UvfH34/lxMT6F8uOHtUyce9wPEe+95Tlmrk50Zu9172QcasZUGy7RoyWQ+uyC243P309xSRz/oJFW+lrmID0cQxGPof0Jz9rN7agMsRZ3zxTyh1tqMoedJZD6b3FhS3OA+JRKHA0Y7ySf5f4zchXPQFdEMl47qNREIAzWRS8El7eoDUChTlNFQ5RSIbJJPxcOzMuyjyd+F1xxiI19DWOYk3115AZck6pk18HiQvhpXD70lRUbaG0uJteDwBXM79p5ociMmySPPbkeG2NuF8u7tFCreqancpMruFre1w7YLPT2gWH56pKvzk8etYsQIe//2j1FbnRx0f95y/cPga9vUJoDhpkrh+thykwyHGuC399UGLow+lj0UywHSQShfhdvYzs/EJcjkvOzvvxeVIsqPHx0AyTEXf+2AbAAAgAElEQVTZfQSU7QQC3yQT1dD1wyF4M0pKjD+7DbfTKcarVvVjDANmaV8ibyjk3L8lmRTUE/tvXx9YuWbIT0NRJ2LpEMuUUl+9lDEV6ykKtJDM1BAdrGLjtrlk8z5OPfo3aJqOSQLDVKktX07U3Uk4rOP3RXerkTiU5vGI61pZKYJL3d0iohwOC7pIOLz79oGAmJOcTjGffdSKa/+tAfLedujviOvn3YjkKkKvOIttz/z1kAzK/bVM1oNDSw9X5ie3TeHy+efQNTCL9u4xVJRuRJKgp/+z+L1dXHzmb6kIPQPJu1j8ykkEfJ1Mm/yfeD2x/S5y07SCYoFtRuxW+jseZ2fnTJa/+znyeQ+SZKfJVNzOQeZ/6ntMb3iU1965imL/zqH3FKSiX1DMVzl+7opDXmj3Qa2qYhAIsnPrQzROKRpqu/0xGzL/YrNbq1qGDnp6t9czmQI3cE8bSaN4L0A0MnLcGavAMnJcP+/7APzixQ+mw7svS6chlfYOU63Kw0+ib/8M0xpvw5RU1jWdjeYsorT4TQyrilxepSjYgWXsIp/pZntXhPbOBoqL78TlSaD6vwQUOHu2sxt2fAmhdyb5C7xIW9s2kx4kneojk/HSE21gMBmhKvIOxf62IaDixbB2kc26cKgpZNlAlnI4tBg5x3+guj/HYdpXSGdKcUd+jDUiSvthtGdXVYYpXXazmtZW8X8kIpyx6BJn4HVuYaBrK5VFfSSNc8lmiz+JIn/I1tIyJCfYv33U9+3MT0eHWKilUiKq6PUWOvxBATjaBVuWBd88TYzXze/2EznsBK4/8WawRgfhH4al0h5UJU9N+XNiAe6cS0P1k+zqm0NL29H4PZ2UFr1DR9cJBLxthEMDyMZ24m03sX7zZGQJHJ5/IksWmv/84SJAc08WSepTmBbI3t2VLOyxpGdbyed9pAeKiMbK8br6CLi6CQXbyOd9WDhIpVUUtQKNOHndidsVw6AcLfRzaqXrKQrlcZbcjFr84YBj2yRJLGobG8W4bW9nuBg5FGK4gBPA72mjY8cuctE3UMxTSKcb8Xg+OlGyj423/zAGlGWBFpmBf+ppYFl0rKxGKDa8twkHLdHZX0Jw7LSDAtRCQ7SeR569kZmT76fSdSyGqdKbvQBVybK66bN09TXi90ZpGLuK8WNWM3f2LbjK78Dsa8E0Zdp7pvPmu1fwwps648esYeYcwQUbLb2xL+AqSdA3UM9LK75KIh3mmMP+xBFT/2G/C9pRkH+TmvJVXHz6NSM+mcPquwjkGjjIrncfllm5tylTr0GRX6NtZz+Tq28GpR7C/0CSDrJjzSe2T0skhHP81l1XDVfVW5aILNiyZ6MBYNuh7DNaqDiZuyhCd/843nqiG4giOjO+90eun3cjkrOYhHcuba89cUBj1jQh1X4V/bEQb6+ZyWCimnHhK/E6Bxjsgzfjl+NwZ+nsG09tRQdOVzG+im9yUvgrpLIeMplaBtM1RPudDCSqMCWJkGURDBZA78jouKKIh1T838DuYHU4gm7oDDpWsKtrJi5XER7PVo497G/oppd8XgPvBWjKI3jcA0xreA4LB7FkhIC3Fyt/F8gN6KZGwN+Pw1HQqf+wI7Uul8jqFBcLcNzTUwDKZWVZyl1X4JdL6I6OQc38DSn2OBn1Jtzu6R/ugX2MLR4XUcJwGH765HV7FVnZ+sJtbWLhOzgoeKmVlWJMO5173zcOR0G3G8UBqodzvj0HC4nNz7ogN4qC1Ai7ft6NIMl0J2qwJOugfezm5um8svxLzG78B3L6fNzOAXYMLMTp7Kat9xj8nm7G1Pbi9/qZd/RbWKYJxX8k33MdsaRJOluCqqbQTAWnexCvv6BoY1uhKHDhbs/3XOzn+t+hs12iu28CirqThroXqa3eRjxdim7Ug5Qh6O/D43oLpyNBNu8nm0uCFMXsv4FsTgTTgkExJ9ma8x8mUFaUQofV3t5CPcHAgLhfqkoex6//GnIXEut9Ho/rMZLJ8zCKrkVRPhog+WMDkA+1pVLw5JPgnnw+5aWbObzmO0RfbNznIJT9lbjGn05DsJbYri0Qd4KR3e/vtSx4fdV8Xlp+EQFvB3UVK9iw4WzW7byMVLacvo0ruPLyG2gPH8Wg8h3Wbj6Zt9edjENLM34CNDTcTUMDXH7hpfREK1nb8jPWrp3FlvvB47GYNqWXGVM2UV5Vg6TW7fHdWdBbQSkZbtqRTsOziy9n7boApaHNLPj0F0XHPNuCP0ZyHIXVdx6Ygv8dS0RIpMqoLHsXzB4k/38juc/b73PwYZtlmVgD/4UqD1Jesp62rsPASoG+BSt5F5Lvqv+/D/Hf0vJ5cT/ZlAEb+BmGiCopyujR45Gd694P6HZ1wcnXf4uB7hVEil8lJCf2GTnOZkGrOgq1ZBJ6NI4zFAFGV87Y03Rd/J6d7eN5e91JJFMqdRXL0eQUbX1zePaZo5Edbk455SmqvC8yfsYvyWZFapVMGEUy8JR9G9OEsdIPqarYRNb5NRRFZHJkGVzOfoKet/F4POCYg2WJKV1RxHkz8zswTR2DsViWhGVBT0+YROZI/J5uqiKbKCveKGoVLAeyNga8V5LNyji1x/B6ovQPRNjaMpeGujfxuKLo0f/BVfEkHm9BEiuXE8f0YfMHbRlBj0dElKNRkYJubVpDtzQRVUmQy7lJZzw4tV7Sfb/ELLkD+ROexSE3yxILFMsSC5c9T7G9aO3tLXTv8/lECt5WLHiv7riqKj77X3d8mxUroL9tCTPG3M5Vz927z2OS/dU4qo6gJKUQa9sKxoFVWpsmvPHOaazecAJ+dzMVZRsYaNfpjTWwePF8kh3bOeHMZlS1nazzR6CAI/N13K4UqgZqxa/wSHB68FqyWQ9Z10+HlWScTh2/6x287iiKazYmgnNgz12mEUMyO7DkKgzTNyyT2dZ7IoryMnU1y6goXY/bkSWR9qFIFkrxd9CkDEbn9wgGdiJZEi3t04m5ApSXt6Bn12F4b6coXIfTWQDI9pj9sM3hEAsiGyj39kJ3V5rozm0U+6eR11ViiQihwJskY6+SiR+Nt+gD98k6JPYJQB7F9rXibGqCxx6DVErnqJnP0rKzDq2onGu++wJm37ZRI67JpNAo9s26Gq8Xelc+Al1rd+NY7suSSXj0UWhqWsSkSVAbfpl/vvR7BuMlVFVBz9N3ofVvY8oRRUxhE3JYOOnt22HTJjdbtghJLUmC2opv0zB2FbNmiWYpTVvjrH5rDSvfnsObbx1HJLyRGVMeZvqcc/B6Vczk3yDxa0ACK4/lPJ4tnbfw1NNOUqkAxx31NsdOvRJVyTDcxcF3A7L7LAAszxWQ+AWbW47niZd/gsfdz9UXnoFhunDktyB9lNKgRjNYMQCqytawatNCTFNBlrOQfhw+AcgfitnSXF5vAWiZZiHd6nLtDYAtC7520vcB6z3Hq64L/eWt654jlfaiyh04AkV84ZuPYUWbkEYZr4YhxvmqVTBh3ulUVcGSW35GXa3xvotgG9Dn8wIEbNoE69bdgKLAtIl/A0ujO/MPzAD4vS+h963n+HmdKEoOJVz4XNb5vWFHpuuQ150gFaJrlgVm6lEGe58lJqloahqf93d4y3+A5p5ANtWENfBVZKt96MgCGN5f0to+g2QS3OFz8Bk34XH34nZlMS0HiqMWOXwHsqyQ9XwGLfsg2ayTLTvn0t49G12XqKteh98bw+XavRGKzR11OP41Uk2yLCLJttRYtPUZ+qIK/YP1dPY1sr3tKCaMeYV0Nkk2uR23v/7DP6iPmXV3i6hwJCLG7GgLzURCUCt6egQYmz4dfrTol0ial2/eec173iu5nIg2r35zMbFohLxUQ+2EgULdzShjNhYT43X6RVeiabDqgbspMrYeUOQ4mxV9A1paPkN1LRw95RYkuYGSw25hcBu41NWgv8sJ87rIZL3ksGsBfoqVA2dWjAGHAzRLxetJ4BlSpskkW0h2/JqYYeDQcnicf8JXegbeks9jmjpS4geQfgRLcpDPSaTly+lJfZm+PgnTnElp+Zt4lV24nTqyJOFQckjFv0Dz1BGPA9p09PwL7OqcyqaW03A5+lHUx1DVHKHiZlyuOqAQobaj9PvTAOZQmNcrFrbBIPS2v01PW4bO3kn0DowlnSmivmYZipwgE1v6CUD+v2i5nBg8tpD9kdMfYNnKc5HIoypZ6mfU7fUZwxDbv/SSuBnnzoXjj4dvfXrN+36XZWUg+wqYcXDOZceuKh5+WESup0wRkiqbNn2GqshWzjy7hPp6+Oo/tgG7Tx6qKqgTEybA9fO+j+yv4PT/uprNmyez5LXJLHlN/JaGMcs4atqdnH7MdjZsO4M1W85n8asX8Pwyg4bx3cwYu4r6mhyKopNKF/PsC2ewvslJJAKXXAIVFbOw9Ech+zygiqYgSnnhPEgTWPLa93hr3SLKS9Yzpf5J/vH0HbR1zWBK/WJmTruJ2in/H3vnHSZHdaX9X1V1zj3dk4Ok0SjnERJIREkkkRHGgBEmGRzwrtfGxjiCnJfFNssutsHGmGSMjYHFmCAQAgkFhLJGOYwm5865u6q+P+7U9IwQIGH8eddwnqefCd1d4dY994T7nvfcMWKRfa/F8O8r5qG9p/qatcRTZWSyHhz2MGgdaL2ngGkikvtfkcwfb99+GJLJCONkMDIM77JnZF/M5ndCBsRjevd9wnhcdH3q6YFszky+YEIr+PB7Whk3awxGa3NDNE0Y/o0bhVF3ueCMMwRm8pV/f/cOl5oGhUwThcx+NKmGrDabt96SaW8Xx6ishP7eajRdpqxWHG/Nb/agF1JYy+8bol8zipZyOfjORT8CJL7++2+Qsd9GJlPEZ6Zi+zFlN+ByJrFbkmTyfnr6StH7HsZW9h08+W/hcbWgmDOoqkQoWkn3gT9TsNcTLHXjdFZRKPwnHscKTFInqfwssspsrJpEMgm5vA09X0U44ifo6cSsrKGrt5HmzpMoDxziZPNFeH1+lODDQ7jwbBYy3Tdjs2YwlT7ywSfDETJ8G1jTii9VFT9tNvB5+pC1JHZriGiiClnJYDalkKUMmZ7bsGZS4PgUkuNTSNL/ojL5/6OSzwtsqdUq7MfRoBW5nIBW9PaK5zRlymABlmxC0rWhIOvI76VSYj3YsgVSKRdTJ6xj4/YzWLnhK1x+/iPI8kh9z+VEILp7t5gTo0YJBpS37z/wnvega2FhY5HBejqhsJeXXxY46UmTxPVqUTOKlMflEg67Gu+EbIzy8XdRKBTnorFbdPfNv0Uy27l22VVo2s9F1rgAFouGNftNnN5OsgUniWSAULScgcguLAO7cJvX4Ta9hlnRyBdkEqkgPaEWUtp6LK55BAIKinILdvMCXOZ1ZNUgWX0hCm4ycZFl1nJTSIbT5FQz40etpL1nPG9uWorFCieov8eiPoG98tfoevF5ZXtuRlcKWCt++7dNiCPHdlhB9HBdVdVBbnxfFjlzAIfVRTbvQEIHdGyWOMnERgrdpyPbThI2Vql+z3P9PUXSjwOE8v+rV/w/Sm5dcAcoVhK2RlpWPMGUk0YDIpPc0QHPPCO28+bOFQqxebPYWrp04ZfweQbe4cg1N8OLLwpDO3YsnHuuqK5+P9Fz29DDNwAamiaxZsv1vLHxFhwOGUkSEWtNjXC0x4499qKYIzPjRsvIvXtVWlp0dN2E097H+FErObXxPjI5N9v3X8OOfeeQTPtw2vupKt1Ge08j2byLUxvv55Szr8Fk9h71fIaDG9If46mndHp6JE6c9hALT7ybbXsv44XV36PUv5doooZc3kkwCLNmwYwZgxQ4R3GQdb1w1EK5D9OZ1nUdvf9cUA/z3oWdNqSSB5Esc/7mcw6XY+0T/37yf0VfNU3MRQNjPLyC3chMGsU8xlzXdfjqwjsBnfYeL7lUmPoxwtr+dKXgQe7sFAVE8bgwXsmkyJ6Mq/4FjVPWogQfG3ENiYRgLTl4UJxn/HhRaGJ0tDtSjGxxoZBFC38J8tuRdJ1QvJZVm24hkpqH223B5RLH83jEMcvLhbHN5cT/bLbi8YcX6Nx2lug6+aMXvwsMOsaD1fADbQ8SiYRIpfyomkZt+SZqK3YRT40myVVkIm+gqjLJtJ1M1kOJL4zVUqC0phFX4DKSSYM+qpjx1iP/BmjEpHuFI55aiZOHKfVvIxz389b2G0mk/FjNBQIlA3i9OuWjb6CsTOirrkOq8yZAwl75wGCQowIS0lFqDIwGQUfTWcOwHukMD6fqMwqeFAVI/xEp8WNSGSfRRCWaZsLl6EWSVPIFJ6X+/UiSHWyLkH0/O675eSzyYejs/xV9BWHbenvF1nlZWTGohaJedHcLXervFwHh72/7PihWDu1L4xs7jRJ2g5YbskfG/FZVoYednSKxM3UqNO+4i5dXX8u8k8uZM7jcapqg/9uxQ+hTICCaEQWDwnF/L/iAlnoWYt8BFHRd4nDnTF7ffDfZfIDGRnG9Bi2izSay4Bs2CNtfWTnsOMN2uBQFvn6WsLE/eGGZ6GQ3qK+p+GFykUfJ5hVyeScSOo2TniSZLiWWPYdU/BA51UIhp9A3UE/A34yimPF4UvjrfiI4ogeDwSG9iNyGImWIILplqoUo5swPqAhswO3oZdWm6+num0hBd+B1pyjxZSmtu5yKClEsJ8uQ7b6egmrGUvbAYFMmHVDf1ca+m74aYzFcV42XIYa+yjLIUgKp/3TyBY1wbBTZnBuLOYXb0U0qW4LH1YXdmgDJhRR8fkSy7cOQY9XXjzPIw8Vkxzl9KS5nBaE9byM6wsm88Qa88YbYyrvkEli3TmSk5s8XHeqkyMCIw0QisHy5iGh9PrjiCqG4x+LI6noBPXwz6HESqSDPrPgphzvnYTalSCYd1NSIa6ivP37HuLXNRHqgc4SjfNJJcOLcGKm28zjQOo99LYvYfegcFsz9GeXuLs4K/oyFJ97PrgOzWLH+a+xvXYTH1cE1F36a8mAbknQB8E4HWVe70AvtbNtzNi+tKWA2K1x5ZZxx5TsgA7MnP0l/uIENTZ9m8WKxmG3ZIjL0K1YUGD9mM5NGleKwD1DPUlB7gSRofehyGbi+jOy47NgG4LhFQvfeh9Z/LZqmoWkquq6haQqaLqPrCg5bGEXJoMd+jBR8+u90HR8NMYzikRklw/goSrEYDUbyCiulUwkGq0n1tgG7ho7X0iKMWi43kj5oyhQYF1w3gp84lxNdOPfsEY50TY3IIBkk90dmuVS1aATECe/HpG1Al7Psaz6d9TtuJJP14vPswOWaTUmJyGoFAuIaNG0Q22we5uANiiTBVxeKQt6dG9twVNTz7U88ALKJ6350wxCbRyYloxVkcnkriUQZJqmAzxOnoDkwySEKKrR2T6F3YDKKnGVU9SMEvC3YbCVks0Wct6YNFvVJOdKFGJGonXhhOU7/bMprz8Bv66EQbcbnCnHmiQ/QFx5Lf/xUNNv15HXhALW2gkf5FRWlhxnoU6ir3Ei689NY5N3IUhxQwLYYyfNdJLnIlzg8y3SkUR1uWI1nYGCcDSM7YrfJdBlaYTUO1mC1NBOJVxOJ1whoii5jt4ZwOwcg8wp64RCSqf6DTtePvCQSAlPsdovAaPgWvRHYJBIie9zXJzLMU6YAkglsAarmThEBU6QZcjnRJXNwB0mSYP9+4RzX1sK0aYNdFkdv51DrVt566xxGjRLrQVOTsMNmszh+fX1xfrwXzEdXOwed4yz5gpVt+y7i7R2fxmTq4NRTFaprfEP3Zcyx1lbxt9HgyBBjXn510fdAUmjtdFBIxrjjqt8imWxc9e1PibqCGKQiDWSydgoFK2ZzDqc9hNMewqtX0S/3sO/wqexvO4V83o2k5GioFe3kDQjTcFiTzQbZeIxYDOLpp1Esk/EFJ1BZ9jmUVA+FVD8nz3ycUGwM3aGzyJhuQVXFuHZ0gEN6gvJgCyYtjsmUJqDfhKofxmTqAz0NSgOy7w4ky9wRz3b4MzacYePv4blWY107UmeLx3Khl9yNFP4KZSXtxFMlhKIVJDOTyGRd5FUz1aVNSKTQk79G8nznA83Vv1U+dpAHJZGAhiW3MzAA8a2PU1db4JtPLeOZZ+D114Wijh4NL7wgHvhVV4lsEDDUjCCfhzVrxAtgwQLhRL+bsh4V65zbBOQ41D6fp5b/F9m8aC1T6t/LGfPX0TDjCx+IRungtlYmXfcfmB0eCh3ryLa9OfSeJPuxO7xMG/c808Y9P4i3VRFYYoVILMjytd8im3Nx8sxfMG/Gg9htccADR9n+0PouIpvu4K+rlrHz4AWMrlrPJQu/jWfMc0iyyN5o/Vdz1vz/ZiA2k5dfns4118ANN4gFdfPa5Wzbcyp7Dv4c0BlTs5nzT/0afk/f4Al6IbYMDQXSTwHQ2qry2oZb0dknnFi5YUhphxvd4X+/1+/QAKx51/G8/pLLqSnfBoW9x/8wPpYhyeeFcTS6wg11sNKLi+/wSnfDqdU0+NTdd3LwIGx+8Q1s/Xu4+7Vl9PYKA9vbWyz6KxSEc9rQYOy4PDqEwevqEjyd0ajI5k6fLigMXa4ih64kid+/dtb3QVL44fPfRJLE9SoKqJE/kckrvLnlX9hz6GxkWSXg2UV12T6qJkyhqsqG1SruB4QDXygI58IoRjpSDm5txt8wh5KxMzH5LWjpCKFQsQre5p6A3fQsgZK92G0J7OYsilJAUXMgb6GpdyapdBVl/r3Ulm7A7ezCbk+jm2Yjy8UGGpIEhf6bobCf5pY59EQaqK9+hkrLMpzOu8FyJZpyJZKmY41dR6XlECbrGJKKGC9FEQmBUIeflo7xtHXW4HcfZu60x6kI6tisIMt5SL8IhQMQeAY9dA26Di+tXISuS9gcz6LrEtguHjEGR4PTvNtLrFX/BVpIBNGFVrRCmGzORSrrp7psG2ee9DPhpOWb4GMH+QOJrgtnUZJEFnJ4kZ3xLLJZAQHs7BQB4cyZYs5+9hffYPNm2LZ6F/n2ddz98q2oapEf2GwWuP/mZkEPNmdOMQssBx7jjHOh7VF4+mnxvqoKXa2tFX8bmdzhju1RbWzmRUAnmfazevPn2N+yAL+nhdPn3EdF7SVYfJ8aMfeyWZENb2g4OlOLJAG6yuG9/VQvuA69oKI4cuiZ0BA8zB+soNL9Jl7XYXzuDkxKmkLBTjg2it5IHQdbTqWzbwZWc4yq0tWMqdmO392K3V2HrohxNJvFPWrhL5NRB0jEkzTtvxC/ewdjah8l6GxAsdwFlkfQnTpS5HrKg7uQLe0M5MX33W7h6/S1wqHWaRxsvRhJV5kx8WUmjt6D01HApADqAbSBm5CDT6DHfgzAjl0BmtvnY3c+h4ClXPCetI7D9dMIeIf/DgtAfwvUTnQ9hp4/RL5gIZXxo2pmrj7vBqyWDOQ2HPsE/ZDlI+0gG8rz3WeX8eijAuT/qU/BfSv2Ya48gfvvFwbg4ovFovCXvwgOv8suG9k4QtdF9unll4WhnTIFzjpLgNGP93rGz+imYdEtvLn5Bpz2Abzuds486S7qa9YgWRd8IOf4pyuXceuiH3Dgzz9mwvnXYh19OsFpp7Ntm3AIJAkk74/Rw9eDnhcGDRvILpBr8PthUv2LnDDlccpKDFyXDFIASRq5j6XrGqgHKagu2ntmsWDOz5g/8wFkWUMfWAKuW8C2GDn4ODLwiavgwQfhj3+Ez3xGZBvOufg8Fp0PO976JSvWfIrm9tn89xMrGFO9lsZJTzJ+9ApRDJi4B5QaACRJQ5HzSEoWWdKQrSMzTUf+frT/vftndeTUT5GkNLKkIksqPnfH4DCUHP8D+ViGJJks8p8eSV6fzw+jLZOKFIk/eulOtm4VBrikBPLt60Axc/CggEBFIsKQZDLiOBUVwoiOHl10vKPR4jau1SoyxpWVYnvWbC4u4sO3DJEU0LVBzl3x/q0Ll3HLTwI0NV9BPBXEZolQXbaBMTWbKCtpwT/6cyimQQjDoHFIpYqZp+GZcUPufm0ZXz3zhzTv3Yd9tJtzl147RHPndIp1xWw+ATXyR6z6YWQ5AthBsqCYA8imBDVlW4mnegl4WzDJKulsBal0KTltK4r3jCHn3GIBUy6GRYmTzPrI5VwocopszgGdX8fsWYJmuxJdqkAOPIxZgRIfZDrFGJaWinEdM+YKBgZAW7uCzp4GXnv7VioCu6gq3UZ97WpKvL2gHUYqbIHBIEhCoqDbsFpSmE0qiruodx/8VYIklaCnm5CyLyFJOeLJAD5X1+Do6vAhb9d+lKSvTwSdpaVi/tps79TZ3l7hIG9cvpV859ucc85N7Nkj4HySBGrvTvREl8CsZ8T/7PbBQtr9Ipg96aSic2zoTUeHsLvNzUIPTjhB6LbfL45jBNPvR9m48JJdzDuvnHTGRTIZZHTVWs488T+w2XLIlnPe8f22Qdr+urp3P+5Pli/j9vPvomvrS9TOms3JS87BbBbXU1UFLpcNs7oAPfo9sjkzHb3T6QtPIZP1oJgC1FZsQiaF39OJzZYF3Uw4UUcoWYMeF7pq6Ks5a8ZhPoyqBklnPXjdEoWCQrR/O7b0FzB5rgXLSeje36Gbxfhku8W6aLWKnbHq6qtIJsHpeISmfSezadcSDrU1UlO+meqyjYyp2YQsZ9ATv8KAGcqSiqYroEnY7XFM7uLabLyMdXG4nX3vlwVJGg16CCn230hkyectpHJ+zKYcIA3Z+H+EfKQdZADJ5uehh4SCLV0K31z7P1Re8FnsiSpqa0XL6pdeEkp/yikiKzw8iuztFe83N4uJd+21wmC8l9y64A5kR5C0bwG9W1cOOeqS1UOnch3dm8cwY8JTnD3vR1gtiUGFtSPZLzju+xMcrl7kcVdTYt1N0xM/Zfxpp1B1wU08+yy8/bbARtfUzILgC+ipJ6BwEMyzkRyXI8keZOC8BZeDGgW5DpQqUMMge9E04eQYHJex8ACxvtuIJQK4nT1s2XM582Y8CGigHkKPfhtiy9CVCetklD8AACAASURBVMjBx7HZRDb+N7+B3/8ebrwhgU1eixz/T2aM7WL6+DdZseZ01m27iZauuTR3nMxViz9DQ90q0LqRy14HoJalXFN739+poE9CS9gh8QiQGfZ/Ozg/ZrT4oJLJCEfW6IZmZI4laZADlWJhnjDCEihmNm8WeldSAr85/DzSddO4fdb5dHWJuWhkbex2kTERBqGY3dq7VxhiSRLQh+pq4XT6fIMZ1UKxJSzAty74Ieg6bZ1WzO4SvrZI6Ou/v7IMU2ACy1edTnl1PxNGv4zVFMXv7aPUfxCzvQHZ4hq6N0UR15bPF7HWMNLB+OrCO0Ex0xUOUHbCfCKhdp6/5zGuWbaUeFzgqVUV/H4LrrK7kPOvUkitQVL8mN0X4HBPJJeDutrb0LWduBwZTEqGtH4Jkb79pFIKqOI4kjQIF4nOQi2MQtcU4gkPzR2z8Lj6AUj0rkSRX8VqL8Nk1bBU/g6Pp4j/DId1TOzAnr8Tj+7j1EY3B5qrONA2h77QWMKxGsKxOs6a/3Nxg4XmIR09ee4tdPePombibe/KgvBBRVfnofd/T2wXD4kCcimY/3dUyP9fk0KhmBV2OIrOqNG9Uh2cV83N4mchdBA13s2uXSIzbDKJQPX5i1xopimk00K/zWZRK7Bnj9i5mT9/JEdvXx/s3CmOGQiI8/T2is/6/UKfNK24uzIUTCsOwsoMOtf9dcjGIpvZuWMWsxZtwe1s58IFX8ekFAadNStYzxhxzwaVnd8v1pKjya2LfgBWLxnvKdjVbnr2HeSNBx9myW3XEokUCwctlk/QkziB3o4dqIUsJls9VaOmEgxayPTeSnnZThwOOzbzTnL6DDLJGL0hF5nUSL5zNXMF+XiATFZBLRRoaZ9JTcVuVNVKPNKFFLsbk0nG6fahVD6AySSC/3xe2GmT0ofLshVr6ndMHt1PeWAvW5vmEYrXsK91Ee29jZSW3I7XHQZ1P3LpiwBMmbSUYOkGNNdd+HzvzlzywaQELaQO7aAXxYbkvOnDOslxy0fSQTYyUf25Bqpnn0+4P0lq26MkL/4cY/YvRtZkzj1XLABPPCEUeOlSsT1rSCYjoBcbNojPLV4sItr3o9vs6QH75E9gKp2CvZAn1raXg1vX4hk9nbpzbgazldLC/Zw77x4s1sHZJznAPAtsi4/7XhXvKOxTrkDT4PCK3zN25mjUWDs3XHoN2/eezGvrP8eDDwoIyZlnVuF2fQY9+SAk/hM9eR94fiDOK1nBNEYcVNd4dc2F7Dx4AfGkiq4Pr2AqxaRcgcfZjdvVTW3FJvIFq+gdrynIchr0LOiRoW+UlAic9iOPaDz1xHauOu9bg9hFkD03ceb8ZYyueounX/0ZuiKDNLhX/f8xspScn0fXEpB6HNHURAfnZ5AcS/+/XcM/k+i6cGaPzKIamQVVLWaPhUMq0dws4x0zjr88uI5Copcv3nUxlp0uLFnvEN5Y14vwCKtVGFSDo7WlRdAcplLFVsV2u/iMzVYsVjNwuUNV184KZG8dAb+TbLgHtHYO7+nlOze+Rt7bSNOa/cy4aS1Oaz/VFftwOVIgmZG8Px5xv7Is1g2jGHGk8z+YpVYsyI4g9OdJh7qprCsFNNKhB/HaYuStXyaRENkgt9uC17sYn92OTfs1SvxxkqFqUvZHUZQ8FqsNS/lvIXw9oY4DbNo5G4s5g832HGaTiuy8VGCwYxMp5BIgabjs/TjtUbI5N1ZzKzZTDlUzkUomULUatJRwXjwe8HtD9Bz+Hb3RA3jseczmNiTrqZTVTMZkWslAtAZNVSgP7EbXJVGpbmoYGhOnI47FlCMaLbaX/bAMrqRUgf836JHbQBsANDBPQ/L9/KgFgx/L+0t7u9CH0lLxt5E9NvQ1mxXO8at/3oUW72L7Cy9ROXcxT9y7ETUb5uqvnoWigC3pIePODWFq29tFvY7NBieeWIT/pFJCX7u6ik1inE7Bo7x8uagL+uQnheNu4I41TawDkr0Mc/UcrKEMFrePg1sPY7K7UXyj6ehRmLVW55SzIphN2uB8sILzWiTTSDrASERAEmbMeOd4GAw7ks2H4iglE+qkkElRNW4MoOOW7iWvKIRCt3DokFhT3O7RlPgclDiexCn/EhOQit5DOuXBao1jKf0xhdAXUdU0azfNJ5UpwWJZi6LkURyni4AkX4qWHYem65jNeRy2LiRJxWQKY1J08gUH2ZyLXNRDzlEscK6ogOa9z9Pf/Ab421HkKIos4QgsZXLDf9IzMI5ovAqbNYbZrAMymCcN3a8kgcsZI2Uq4sWdzg9vfkm+/0SP3g7Z1wEFJBt4votkafzwTnK81/RRZLG4deH3sE+9EnNgPPl0nOzO3xNunEetPB0dnV6lHZu7gDcyhtGjYcmSYvSo66KYbMUKocCzZ4tCvffrH97ZCatXiyjZYhH4qpfuugu9kMY65kysdSejqQU0NU/67XsJBLr5+gM1oEWQrAvBetpx0xO9/bbIbvv90PLCvWjpgSEsVtuu7+L39GIp+xVvvglr1woowfxZDzN/2n+j6Sp94Qaqy/ch2a8E8xhIPQOFXSC52LD9PLr6p+J2y3jdA3grb8DjEYbTmjgf1AOEoqNo75k1+JqJyZTlxksvJxStoy88jnFjE8iyjhx4DF3tZsvqX/CXN77HnCmPYjKl8bh6mDPlcSS5FvQeQtFS/rj8F/SHG1g4917mnTYJxXHuB5gBH1x0PQ1qHyjlIuPwd5CPAotFIiGcRbe7iD8cjlNTVeHgCgd5GUpwMlpgDpqaxxTexI5JMKp0HPlwCU4c4Iyi2nMsbZw6hCUvKREZq0xGVLr39YlM8ZgxxQyY0fJ0OF2Tcf6BAVGMZOyObPjTM6i92/nSw3dw/4+2IXuqyCTThPZupMx5mGmNm7ny3+pAqUdyLBlqpmMUAkqSuAZJKkI5DNE0sXXd3y8+f9/n7kbPJfjJ8juJRKB197309lchOT4x9HmzGWzyK/isT+FzNxONBSloCmarD0/ZjeQSK1CzrZjkMP2xBg61jsdmTWG1lKBqJrBdIApo1B2Y9eVYTXFkWSedddIbqSWX8zB36iNUBfexp3kBfk8bZvsUQELx34GUuJO+3hi5nANNz9He3cj8Gb/B7baTStuIJRwMROtRNTs+Txfj6gewlD+MNMwL7u0VgVJFRTEj+aFmknUdtE7AhqQEPrwDHyH/7CwWiYTYeQkGRQBq4FkN+EM+L6AIO3bAppc3kO/eSs57InZ/OXpkJ1qkmfAFov25pWcULaZ9+OpDWLIeLg+ehaaJBFN5udC9gwdF1hkERMHhEGtBRYU4f0uL6EUwbZpgljAK2QzWiKYm4cSte+hX6MluJJsfxVtHSqohG4/gTr7JqHF9fPE/KgAZyX4hkuWdXvDWreK+zj13pL4au1F9fWJs3G74+aeXga5x53PL6OuD/dv/RDhWTlY/bajY2OcNU+G+l1LfbiQ5SW//eDL5AGbnWVjNYfKZ/WiFKJJsZ//hCYAFm80GkorJfvqgjmSQ079DklQUOYek6MTiQSLxIOX+/Zwx9zc0d0wnV3Dh9VWRV82Yfd/ExGbS/b8mEivFaetlz+EFjKlYxcSx68nk60ikrETjlfSFG7Db4owfvYbg2J8jDQtqjSy0UVhsMH18mKJrMdAioFQft89zrPIxi8W7SDYLkz71XTo6QMunyTe/ROWCz+KMi0ChR2nHobtwR3z0lzbxnWumDmWFb7voAWzjzkfxVFNbK7LKwylfjiZtbbBqlVB2qxVOO03gq+x2eOmnZhzTrsLkqRVGIRcn0/QEdy//+t90j6oq6OU2bRI0OUuWwLf+LJg2BFWLxOP/8wuyOTc+dy+VZc2cdNKJtLV0s+rt69i6+2xGV61n+74lVJbuYM7UR5lc/xgmJT9YkJBl7rTHQZmAXPo/QJHzct8+aG/7E+3tOdIZ0SvYaolRU7aV2kqx+G/e/UnWbbsZv6eTE6a/RuPJYFX/ysyJT9MfrmPd9s9QVrKH3tBEOnuncf6C32D2/Tslys+44ZKree6Nn7LirS/TnYCLLvrwiM6PhSpOkuxgeg8w2sfyvmLgg4dnoGS5uFWbzxe3SwsFuHjZHWzaBANt7eTaN3H1d67kwRV7scS8ZNGJE8XqjKPJORRFzMVgEH77lZ9hDo5n1kUXYLEIvL3bLfTD5RLZD4PLVNC1MdS5KhotXpskCfzhuoG9mCpm8eqrEBg/A7MZOja+jk/byY+e/c67OnaG821Q1vn9I7PmxtZ0KFR0AvSc2EGRItfg02V2xsYxEB4PsXWYTSqYT0HXshTSKh3SmZgtXXR0NeK0R6mt2Eoh/UvMlpzYudFzlHq3UzVzF3kayFhuGGoSILDNU9DT+5DUJjQKmM0pAp7DpLMB7NYokWgFe1vPxqxkKfFr+L19WLQUUjyKrin0hesYCNeRzTlZteXfmDnhWQJjfoDX+ksU5TCJdBmxzLns7DyD8S4Jl6s4Ni6XcDBSKYa4oOHYneT301lJko5aSPyxHJ+0tYk5GwiM1F1Dd6JRYeN0HS69aS7bts2lZV8f+b49/OuPF9HXB/duXIU3VktSz9NqPUgwV4szVoXqF1RuZWVw20W/wlw2lZMuPoWKiiKkwm4XemEepKivrRVF8tu3i9+93qIOHzwofp8+HdZmQsjOCi742udoaYE9b76NpX/zMTUPMVpkV1W90zlOp0VmO58Xa00wCOgiwnfmlhLPltE7sBhNL2C1rsZp18jqp9Pf202093S6PPUkM3ZyOR92Sz8O21Zs1gQ2SwqzJYtZCTNr4mHMllJk37eH7i2dhnTahmo+G6f8O2QtRzpvx+sqIEs5vO52dF3hUMcZxOM1ON1Q4uvDCUipTeh5B/FkkMMdUwjFaoglLyeZHcX4sS24gvNRTC9iMmUIJ+exu+sBRtkqqasr6qPBTKFpxaAEjt1JPiYbK3tgGNvNP1I+Ug5yIgEPPSQMkcMB4f07sE+6jHgcrBaJKVNg4+YK8mS59lqJMWOmDn1vxQpwzr4ZLRvj0ktF5Ppe1ZstLcIxbm4Gu11nwRkJ5swBu0OkonfvhtIzvkw+LxRxzBjY9sj96IX00Q96HPf4pz+JosLhmGljQTC4DD959hfo7JtK18DpdPWOYfdBAOHtJ1MBtu9bgtfdSirt57mVd/Hquq/TOOlJZk9+Arezh3CsmvaeKXRsEFtkPT1F4xYMOpgw0UZN+U6qPd+m1L8LSRJvRuJVLJhzD1WlTWxoupFX3lzKGxtgxuQTmDOhhoUn3k1/pB6zKcPEMS+zatO/0Bc9hSuuCuIrXYwNuPxqkfFesUJE8FdcIbKF7yXHypMcipQT/Pslmj4WRIbHwAgbDBZHUgWZzcIYvPmmCLqsVsi1v41ktrN9O0xxTqBghU09TURp5YdnnEcqJeZ/SYkwJrb6RUiKibFjhRE16OR8PrHQG3AHEJ+PRER2BMDlTKIoKbq6gzidEqWlsODfbqe9XTizHo/g7P79068DxbXgaBtyxv8yGXFeh6MIxVJVsR5Fo2I8ysuFI2JwOef7rkfXJMbXvY7f04YqzSSZ8pKWIJkIo6YtxJL1JAemk05ZcLqS9ISnkM6WUBE8gNvdgdMaJqe6SGTc5DUnZqsYT6MpiSzL6PplZFPz0ZN/xiTtwGxKARqyrGOzJjll1r30hycTyX+B/nQ9plwMmxpEltNYTEkc9gTTJzzL/rbFbNl3JWOooKRkGb5akJLgUMXY7tol1jpjm95iEc5WOl1kxRiikwq9v86mMi4kXeND3On9WI6Qvj6hO6NGjaRdNPQ1kxGY/nBY6JZo8AH5ru3k+5sIhU5FkuCLjaexcSO8FnmdYBiubzyHmFc4uiUlsH49WKrmoGVjNDaK4E1VhfPpE7mWIWabQkHAHlpaBNTxvPPy2Cwh9u3zk0xaaGgQx/yX33yTgweFMxsIQLb5NSikjum+OzvFeUaNKv5P04T+d3eLv6uqigX7w21saaCf2ZOfwG6Nki6cRDZnI2s6nWTfakKRMnoHJhON+fC626kO9uHxdOKy9WC3RZAlFbstLYrDlSBygCF6R6tV3Jck1RMOf4N0fCuKtgKPq4Xqsm04HXFiiQAnz/g1nX3T6YmdQyz/aRI9YNPdmGUvJiWFLJuZMvYVMtly2rrnENeupnZUNYHA1Xj9YEmLtbSlRfycMKGYtLBYhL4qirgeA25hTb2/vuo6hGOl/2ds7EcGYjEwIJzjZLK4RRQOi/fGjRMPe88e6NcOs0X7C29+/19QVXjrLVj+YgZkM/lknNbVf6bM0Qq8sxW1PsgLunq1cFCdTpg/t5nGsZ/HonQAGgXlDFa8/TM2vG0disDmzYMzz3x//PL7SVcX/OEPYnG6+GIRlRtypIN45N/pNHTsf4jOjjCdvVNp7ZpDOus3vo1JyVJQbYCO1RInmxMUHRaLTnW1RG2twIjV1BQxZIBoehK6HE2T2dD0aVZu+Apnz/8hsyc/Cdjpyi9nw8Zympo0NE2mofYNGif/nnF1ryPLOvtbzuKZlfciywqf+ITgujTk4EF4SrC8cdllgobn3UQbWEqhYMZS/tBR3wPYtrOKv7z+Y268/A4qyw7/Azr4Cflnhlhks8LAGM0zCgUxX7LZIrTBbBaL8urVIviy24VTFY8LPTY+43DAkzv/Skzv56efEAUxJhP89XdvIVs9RPsTZOIRfHIzksnGLfd8emhbeHgl9cCAcN50HVzOOH7z90lF9nG4cy4WiwlH4Dya9kwlFhOf9/uFc2w06Xm3QNn4v5ExHxgQ91JaWsyU9/YWncNgsIhJ1jTQBq5HkjTkwMPooWvIFxSUwO+Gsu2p2AHiHd8imvTR1TOO/shofJ4+IrFa4skSBiJBFDlNia+T6eNXYzLlsXovQLadjtFNy6CZK8JbcuR6b2UgWkY27yEaq0IxZVgw55dkcx4i+RsIpT5JPK5D6nGc9mbKg3sI+g5itybpGxjPtkNfJcc8vN6R3M92u3iGiYQIBEaNEvcbjYrgxO8XmUDjWnI912GxFI6qh9rAUrI5K3/4y03UVWxmwclrgX9E100h/6wQi0JBwBVsNrH2xmLCrhlBbT4vnOOmJoYKtjVN2NTJk8W8TybF/F63Tuj5wwd/Q6k0hqUnLKK2VtjeJ+/biC4pxMJ5urespH5aNWgF7nz8uhE7Cwa0ITJYwhIOw4pX40yp/wM2S5horJKaUVYC1UvIZEy0tIjPBALQ2Fh0tI9F1qwRtvTMM4vFiKGQ0FmLRdi64TR3w22qrkO66yYyGTtK4F4cDqFrue5LyaR6CEVq2L7/AkxKhkzWRzLtJ5aoJFuw4/d0cNmir+Kwx1CpRy95YYjLXZLEz/RgHs1qBTn/KqGuFwjHaghFq0mn/cye+hBlJSFiqQr68w8Ti0EmtRuHtIqAdw8Vwb143N1oqsKOg5fRGb0Vu91MICCelcG1bDR8sdvF83Q4ig1dJEms4+m08GOU+GexWdOYy97ZRdMYmzfWzeVw54lccf6vsdky/+v19SORQe7ogEceEQ/R4RAKa1DCnHqq6Ig3MKCRbV6JXPAxLTmF25Y8gq1hMYqzFC01gGwPIJvN5KJ9cMR2gq6LLNeqVSLq9HhE0d7MaQcxRZcAYjaHonX8+dUv0N1vxWwWk2/JEpGN/lulqUlgshwOwSf8ftCPI8Vuh7GT5lNfdjkGU0My7aerbypd/VPZse9iBqL1WC1RGmpXM6pqAzUV+yif+OR7OvaSZQYD0Qk89/odtHWfwLi61xg/auXguypV1U4urYUzz5TYuPZVNm2byR9fvp+A7yBzpz3B9BkWbmpQePJJeOwxnYWnH2B+4wokcz319Qu4+WYzTz4Jjz8OixbBySePdFiGK+aB1jNYeulNWC3poyrmhFEreNUa4eXV13Dtku8f3wB+LO8rui6cI4OuKJV6J/YYxG7E+vXCEBlOVmen0F8j87zjtQ2o8XYse/uYMGk2v//5G8g2LxPnzUQyWcknBgTOFgksbrRsBJerSGCv68JwGh38PB6RmTEn/4W+njTtvSdgtcRIZirYuSFLgTQOh51AQNA4Vg/u2g8vLnyv+zay10ZgkMkIw2NkyLzeYoBgHE9RCmLnRYJ8QUJTFQrZIhxFsTbg85vxurZSXboVXVfQNAuJdIB9BxvpGbgSk1yg1HcIv6cTk0lFdtnQzcWWr8Z4GGOSy1lIaEF0dMxyBoe9B4ctTCxZjUmBslKFGi/k8xLhvtlEu5oIRcYTi9fic/fj80aZMmMi+w8OPmtlAKWwmULBRjw2nWCpF4dDPONUSmQPDSaDdFqMD+Gl9PTV8uqam5g/85eiURDvdH6tlix1FZs52H4K08KHCPq7P5yJ+rEMSUeHmCsGlh+KDBOqKrLLu3cX2VXMZuFIjRms5zbgEd3dsOvtNvJ9e5kuz0VXNda1r2K9YuXEC05EzUSRrAF0ScLicKPnkmjJ3iFHXJJGYowVRehMhf8luuvb2bL7MkZXraK++g0cUpR4n07HwBWk0wKaMXny8TnHyaSoB5g4sRjI9/YWG6RUV797fwNVHXQaszZkRcC+olFx7anI7aRCz5PO2HHaYyhyHocthcMepi88Fl2TGV25BpstOaj7gSGKRyMgURQBRwERsAwkFtIf2kO+YMVhi2E1xZEkJ6mMjssBZdVCz2OxegZanicZL6Wt24sjGsbv6aZhQiNyp5n+fohGsijaDkxKlMjAWDz+UdTXSxw+LHYGJk4sQsTy+cEGJumlFJJulq9ejMMW4ezTlwrY3NFs7OhX2d+ygA07zuG0Of9z7A/kHyT/9A7y/v3w5JNi0ppMYpICjB+vI2U6efWVcmQ9R+StR7AFaigZv4hkTwvO8oWoqQGmTIGdO6upqID9z95DTWV6KHOs62JxWLVKLPg+H1xwgdj6MZlAiz6MQVmy88B5PL/qB+i6jCzncTjgyivNVPyNlJyaBq+9JqLdujpR1Tu8stRwEPXc2+w+dA6TJlwjtm6OMnkl8wR0790Q+xaQx2lP0lC3noa61TRO+gMS4LAPpt0xgePG93SOtYGlRGIBHnjqSWS5wEULbmP6uGcHnQkr2M5EkgUg0e2WOOPsRZxyyhp2bm9jw9ZTeXH1t1n5ts6sWXDZZVFWvbKVFa+fTmfrAS5acCcW60/wlTzJDTeU8pe/CMhFV5fInh+JS64M7mT15i/wh+e/wqcuuovh5XXGWNhYysJ5z/D8ys+wq+sxph1DW/CP5djFCEwdjsHqb0noibFtmssVW8cODBTbMHd3DzI+aCm6WjvJx+MkupuxO1yUTp2H2eHC7CgDNU11NVRcMpPWVji0YSNqoZuv/nIpNhtDQWkoJDJQmiaMXSAw2PAjf4iB/ixd/RORyNHSNZ9Iog6TnMPvaaKydg6jRwvjZGw3HosYzRCsVnE/8bhwLEAYb6PdMww62mGhs11dITRVxh3/PGrBi+79L/Rh1FiSBLrvHqTo7ZDbgcmkYzZFcNrDmOQIbm8LJa4YHnc/Pnc3kuIn45o1grx/OEWXHv0GTlOOpp7xRGI1BLxtBLzteN2dKHIemy2P4lyILItnaK+bRKD0SyQGlhOLJonkLiQWb8TpslNdDV2H1zLQ2Y7Jv4/SQDPJdCntLdfgD06hqkro6vbtwpkytmpTKXBK4HEPYDGleGPjl7Han6W6onnEmBo6O2fm52npXsj6XXdz4YUfwiT9WIbEcBKDQTFvQ6FixlTXhXO2aZOwfcacmDSpCEnIpPNsfGUXB7fvw1x1MpqqonhHY5UcZGMhZKsXLR2iuhpOuHARsRjsWf4/2Msz/OTZrwwFnUbG1KgRcDiEcyxJkO54GLNpOiYlRU9oKhNGv0Yi7ae/awDNlqe83Mzo0ULHj0cM7uPaWrFWtbeLuRkMit2PoyVhUrHdtHQ2opt+RibjRLP/l4CJNBc/bzbPxW6PErQ/jSzruJ3tuB3dhGLVlPn3kcq4qS7fha4VyKtOYvLnSIZGdq0rFMT6oWmgZO4nlbbR1T8Vu6Wf0pJO/GXtWC15rNYCFu8nhzjXAwErHs+XSEVWEe3fSzw1k670idikSkpKQC900te5hki6l4pgE27ZTLhzPrHYZVRXCwe6qUn4GZWVxcDBpAtGmqDvIHuaz+G1dX4WzvvTiPE09LWCpYyqaWF/2+XMOPFy/Pzvln9qB3nrVpFVNcTgXF28WOPZh/eDdwKhA1s48Jf7cJSUMfW6m9F1DUewgtNOg7a2ADt3Qq5rM/tW/RU0QY6qaWKirF4tFpBAQDhl06aNbBtL4TCgks56ePHNO0W76HQZo6s38InL8jj9Jx/X/ei6JjpFyR4kyUImI7oK7d8v2DQWLz7i/IPS01/DX1+7lY7eWVxu+TkT6ze96zlk+9notoWCC1l2gdqFHroRmyU88ti285Dc//q+1+zzDLBgzs+ZNPYlPM4+QAZMYD0dyfPDEZ+VJAmz8xRmzoMZJ4lF6a23JNavh/Xr3YwflWP25MfZtOtK+sINfPKcfyVguhOL/z6WLBFK++qrYvG48spBrFbJb0HtYnzD7Vyi388zy2/hzyse4oorjj5WMya9waY9n+GVVwTu6sMqAPyoiwEzsFqFc5lOFzMwBr5uzx5R6JNICKdRkopbupHuPppW7SQdHyCfTmFxOLDX1ONxBpHIM/3EKurqREZ49XO70AtJ8l2b0NMDQ1yp/f0ik6NpwsAaWVsYpJ2L9dPa24Df1UtrVyORRB0ueyceRzcVlVkqxsyhtJQRjULeTzQ1TjqlUSh48fmEYz4wIAxWeXkxg24Ym2QSIu0Tae8eQ2tXCR57F/NO2I7FnMXsLGbTit0Fg2jO36Dmu5D0KAWqyXR/CYluJtRtJ5MXlE/h5BRy1ruQVMtQUaTRAtaAq5ilThRFZeKoFkKx0TjsGVRVoaD5iSYriXIDcpd/6PuCc7YSnWux+MCCeK7RKMh6G1b5TSzmAP3REGljUAAAIABJREFUOjR0ykr2Y+e/CEV/SjbrpKREjMXevWIsnE7xfYf/bpw+OHfB9/jrypt5dcP3Of98KDvK+LqccSY1vEXTwYkcPlzMXH4sf7u0t4s5UlUlnoux62rsBG3cKPiLJUno0qRJAnakaRALp/jZ535HqCeCvWws5TaJfLpASW0NmUgEky3HlV9oHGKs2LyiiXzoAFp/E2iFEVACI7llOOdGcJjLwbbdMykUnMyZ8jDrd9zEgbbT8Lk7sJizuANpKqrM72gP/V6i6zl0NUZLSwmlpTKSJOqICgUxDv738Oh27D+fvS0L8PvyBH2duP3i83a7mNsCmiIhSWejqaeQTrRispSSCr9JNL6SoP8QTkeI7oGJdPePJ6vcTCRz8lAgawTlBkzL6wV7Zg3pjAW7tBVZ1kB2oGpWIrHRRHMnouQWI/cX+aEVxYIknQn2M3E5it1G02kdKfNHnDaFZNJHX2gcPk8HVf6/0pOspa3tZHw+8ewPHxY6Xl8/yPzj+hUW0wAnz/kGWW0chzovxr7nYk455ejjNGf6K3SsvIC1a+G88z5c1poPW/5pHGRd19m5di+9rf2Ma6ynuaeKlStHfmbCBEEJ89Qf8+juBg6/+hgda54jOGU+4y/9IiCT621C7dnKVv9Skkm48EJ4/FbhZd/16jK2b4f77u0mFK2gtFRgXydPhqufeRIOwhOXXVE8oWUOucxGNM2E191Gd/80Tpz2EGeedA+KZ/lx3Z+WehbiPwY9BUj0ZT7HH5//POGwxPnnC4qcIyWfhze2PMa6dWCzxLj4zF8y6YQvv++ElCQTmCeIP5RqKF2OMnAlaBm27zuZdNbHCTP2o4SuR3oPDJERNZ44eykwCqlkuQga5LL3pVySJBG919YOUmy9/lu27L6MdNaP39NCPFnOg0//kUsW3saEuTqSJDF/vsjIPfUU/PrXOpee9xINZd8QB9TTTK1vI3/BZ3n+eRNPPy2e3XBHRw48hoyg9HnoIZGVX7DgvcfqYzk2MQrzDAwbQLg3RNPqPSgOP5prPG3tCvG4MIAGD7HfD7qusmv9HjLREKpWwGT34qweh+QspZBPw8BOxo6tGip209Jh8h3rueulW8nloGvvd4kmguD8V9xuYVhkGW55/jl0qcCDS5aQTEJX3wRctt9RU7YRXYNMxo3H1UdNeRPOwEICgWN3jvVCO3r0a2jZJmK9E0GfQNJ6M6lMHU6nMHCaJq43nRbjY2RQ4/Fvo1hgcsN/U1bSjrXsniHmByOLZGCfjd8VcyWFQiVqHjK2+7Gp1+OyxUiFzGzbczFl5RpO27PYSr8kDKt9sIueqdgNC+fDANRp11GRi2IufYBCtpVcXievjhqiZzMgHkY2y2gXnssV6ecSA3tIZ9ykMiUkUwEyWTeyrOOyx6gs20A0s0B08nKKoObQIQj4Oyiz/ZBkYhcuxwAeq8w5p6q8sOZ+XnxRrMVHFuPKgceYNR8OdYl6kbq6owe+H8vxSX+/cILr6sQciURAlgpsXbGD/t48qm8q+w86UFWx5k6YIHDHsizm8Iu/30AkVECyeAhMnAMmK2aXmcRABG1gB+rAPgqFRpqbB9knBvag9e/i7hXfJZuF0OEvo6omzIH/GOr6eP1Tz6LLGo9evoRcTuw0ZQoTqa18Cos5y9TMs1hNKdzOHhSTFX/QdcxQQ13X0BP3QOph+kKjSYdvpK7CRXOz2DEZNerdOX8NOzdt8k1YbHvB+TnGji120h3esl6cCyTZgWSeSCoDofTFWAONVLiuAz3Ppp0z6A1dittlwu38E56Ky/F6BfzI6xV6O6Sz3kewADPton276r6TdKKPnFqNqjlH1FoMZ9PJZIqNmgAy6QipmIdk2kkiVU4oVoeqF8jl3VSU/ZmEcjLJZJFTubMTEokC46ruBekZzI44sp5hwQm9FLZcxM6dIpiaM+edY1USgAmdomC3re29OxT+o+WfwkEOdYf52pnfo6+1H2SJmtOXUt5YNfS+xQKXXiqM0eOPg57Lsf3h75HoOsTUT38X3yBbRfvqP3HGZY0cqlyKLEP0rV/x+IouuqNlmGxOvvftMLLNT0VphssX38OkOf9WdDZ1cCSLOY6r/vwkLpOTT8g3smXHTaiahUsW3sr42hdYv2IMp1wjwtqj9oo/QvTsKoh9FwMbfKD1NJ5esRRFSfHpTztHVNkacuAA/PWvYmGbORMWzb4Nhz2BJH3+mMZ0+HVJSjm6Ug0KHGw7laYDF7N5TzvnnPooDcexdSVJ9hHE48cqXi8sOvFeTmu8l6YDF7Kh6Ro+ec43eWXdN3jypV9yWkrnjDPEIlBfDzfdBE/+IcoTT5/DGXN2c8qsXw3S08WYNe4usmd9k1deEfPiooveGcHW1YkCx7VrRTHW8WDXPpZ3SjYrnCij81KhoPP4D//Ec/f+BXtZHYGJ83BVapSNG4vDZRvi1rRYBiERvUky8Qj5bAZroBxv7QRks4XMQBdaz0Zuvut6BgbgtSfXo+dT7HzlVRSTldsvfQDZ6uHqr5TickTxVY+ka1JjKthUUin4xp/Woyl5JnoLeHfegdfZT2XpAWoqmshl4Ne372fZn4Whez+d1fUceuhK0PpRVQuJZAnZXIJc+h6spT9A0xwcPizGBUY2OVCUYnc/e2Y9+YKZ9GDxk9NZpL5T1ZHzVlXhm+f9CGQT33jiNhxOC5IyhlQmTzRZQ6HHxPgxmyktFeeyWkd2KzMcbQAJHV2XUBQJi3sUNq2INzTw4oZzbRh+Ax+ZTIrPuZQeco5t+FMttHbNxqQk8Lr6iCUqKYSd+MuLjAiyDJ2dWZr3vk3UM4aGUZ047V1IEpS4tnD22WleesnOCy+InbojO5rZbILSa/16sbN3tKYOH8uxi0Fv5nAISEE6Dc1Nbdx19Y/QMBGYejqByaOw+yXq6+1MmSIyx4oinOpQCPa81QWKCd/oqdj8FWiFArl4mL4tr/Cln1+Eaj6LtjZ4+4V1FCLNtO4KEW0+xNcW/xwUE7ffCw57HNvgs85mAVXDnHdRKAjn+NG3m/D4U8xNLSKfdVMWaCbg3Ucm42HTCi833CEfk74CoilW8mEgTWvXdPIFiVzsJRwlNkaPnT9i3Xg3cdjTTJ+4jkNhQSk3dmxxzTOc5OFj/N3L7gGzg8/+9GYCZbVI+WpUVSaVDqJqZqzWOJPGvYV/1OVDdQLvVhRsdBq12f3Y7H5yOTFmxlphMI8Y+mZ8J5kUjnI6lSOr7CCTs9EbHkU4MpaK0v1kc0E6exsoG1Ms2jMKeztbdxDuLGXK2Bos5ibMJg1F6uOs017ihRXnsmWLWCeOpo8zZojM/Pr1Yq373xrU/lM4yD9eei8d+7rQURi/5EsEJ80dem/8eAE9ePllsYU7fjx0v/k08fZ9mBwe7MFqdF2nb8dqLN5yDvSOZdw44VB/+7l+7FOvZnxwPAABdxNT637I2HHNWK0J9NBGdvb38u+bv4j70FyqC2O48eGVpFy9ALiaF/w/9s47zK6y2v+f3U7v00smUzKppIeQBEghBEIRUASUJqCiF7FdxHtBroAFrwUbgiiolABSROkhlISSBAKEJKSXmUmmz5wpp9e99++Pd/acCYSQAP6Ei+t5zjOEmbPPPu9+1/t+37W+67tYG6lFkvKcefIl+EN7eHzZdNa9NJVjLijcv6QdXETQjP8OSGOasHbjl3ju1e9QVrSds0/8FsFR/2Bk1WA8Lr7r5s2C+lFofX3rBxpj65R8xonnM3HcHp5Z85/c88hVjN8BJ5xw8NTTh2KO49FYzvQJDzJt/INIElx0+vk8ueb3vPjiXLq6xDNzOMS9XHz6hTy+8kuseu0/6QpP4rSF/43dloDkX5k790oyGY0XXxRA4cQT37noLFkiUr8rVghe97/t/Zm1CFuSQKYJbzy7icd//zSO0jH46qfgKG9Advvp7whTPqsKv18ajkrKMvi8Orl4hGwijrO8BsXhJtHZTPcbz2JzKgwMiL81MzHysV5q5p2KZLOx4ITH8Hk3EXJtpb1nPGXFlyKlcnzxhZPRBvyY4ToypPnvv77C3mgv5W43OzquohyZ8d5/UFy2AdW1iLt+GiE5mC3MEUkFM//uXzqzEswEYLC38wia2udh12J4PVHcto1ksnNxOgXH2mYTG1k4LMB7VVVBzQL3MuyANFSclEwWJOIsDrGlhjHcGltWBXj1/xnThNE1X8DrXc6evl/Rk1yM2Squb11jJIWoEJk2MZGHucoWpcNS3clkxE+rkt7atGW5IB/nc05Hid2KSZLG2peIxitIpoKUhvbQb3ydnp7CfZSXg1d7gZ3JVrrCE+gdrGf6uAepH7UeyFHmX8Hxx5/OM8/A448LkPx23dXGRkE127RJqNl8mB2+PmnW3i7mU0PDEJ0ipvOrS28mnZYpm76IYOORKJqDgaad+McFaWysGS5E6+0dotioMrJiw1M+GmSZXCZB9/pniHe2EB5woQ9RBfL9e5DdlRRNbsCUdM752lryeRfhXhm/tx2H7QI2d0f41fqvorfXYjM9fPP29aimjZTaj73zGNamHVQE9jC28XE6YrVsfzrI3g2J/UGX/O4I1zR1SPwZSJHJuti1bwF+dxcB716qin+Aqi0/pHGzMpANITEXm5rEXLRUXKzDqK4PFTzKKpJix24fKk7lLmQTFh/zZSLxMvYO/Iie9BnYkyJIMzIKPdKMvvORJBmluKAeYdU7WBSKTKZQA2L5Kog1x+MBr7cUxdGKZOyjscZOJuslkqjAY+8ijtCxDgTEdd1uCATSaMm/0dw5mzVvXsLoynUcNWUZqppCSd/CSSct5dFHRVbHbhfFfSPN4xGU1HXrRCT5wxAq+GfYxx4gR/tjbHl5O6gOJl94nXBIwMjn2PP4bzn7zku54w4vsZgAcnPmwM4xR7P6qSbGfvpb2DwB8uk4nop6nMWVHHtMnnlHq7z+OhQtvka0ns3GSW39G9fc9gwdPfXc88gdlIZ2cOFnfk3znqXU7T4JwzB5xfkcfbtfp2FKHYsjZ9MegbySoaVhBX+5sh6kRmK26ZTNXMzZtVeip+OUHPlpSiYfw3eW/hIzEznwKVdvB6CnfxzPr7uCifXL+dTCq8TmZg4ALkxTqHE8+6xwigULhA7yu1XajrSR8jTWabu1w0Fo7Ez+6+z70QdbhpuXSBKMq1tP4zRYu9bkpZcMbr7ZZO7MdRwzvwi7e9w7b19XUBT98B/uCJO838PMbQBjAIkESG40m4fTP1NH1XpxKLjtNqGJXFoKmtLOGcd9h4qSLTz7ynfZ3nwCU8f9HdDBTLBwYYBMpuDAb6dS+Hxi/FauFCfdf3Mb358lk4ViOBAb79N/XoWzciLeyjEEGo5AdfjJJSIMdGyntyiBbdw4oWPsECl1WQ7gcCho3jqcxRVko/0MNL+FancQrCodph0sOHcJ+/ZBy6Yd5Do3MPvoCLm8xmtvnkM8VUoouIqA34ZzoAJ3tIRuksQYwOjMMipUxmRjDq100e7azTdP+iY/vcSJKbkYzI0hFt7Op0NfwFU5Hk/dXKKtW949MqW3gSmKc3e2HE8q7aWuch1B/z5coSDOorn7deLKZMT4VFSIufh2syK9gisoxiXbcwnIBmbgDq4++UdgSvQzjmRXB9ef9QvIJfjZM9diKgYlxWHsZUOFRvE47bH19Ns6KKsoxR06BofDtt/Gm89L6Lo0XBQFYlO3/tviL1smSYWW4cMgQJlEPv15lNz9OLQoqr8Dw3AQ52uUBr3Dcller3hfwNvM1Ma/0x4ez47mJWzdc4oAyGYa9C6qq0WTpVWrCiB55FhZUeQXXhCFY/Pnv88J+wm3ZLJQV+NyiYPQ1lf3gOqhfNZc/KMnobq8JPs6aF/7JI9tSjJn0f+QydqGaRmqCkfMG8PWHRp2bxF6Kk5s306yqTTemrHk8AgqUBoal55PJAL2cD8l9eN56s3LMU0FNsEx02/m6NB6urumE+ofT8TMkyBKrjuHpxSOsc8nloUmeQ/rfQN03ngipinRE5ZRnU7OCF2CZLPjH78Ize7likXXA8Y7/dVMgSnSObtbjyGT8dI4cRnVZW8i4XznIL2Hqao4XOzaJfjVjY2gxQUFwgzczVWn/AwkhZ5oMXZfiJu+cQdGvJMbnrgKWQaHI4vLtRclCJ2dOn2dG0mGX8fn1/AULUXR9ueNmKbwV4bWQUth5O268lA4VCuKWFcK1AuJnPMGpOg3sWsRNLUbJBv98Xm4QvOx5UW9gM0m1mSfJ86EhucpLdrK1uaT2dN+DLXVL1FV2gR6Dzab4Bc/9pio1bLZ9pdoBXF42LMHNm4U4/Vhd+T7MOxjD5Cz6Rx2fxHjz/0+dn8JkiSRDLez8barKJ+5mPv/5sIfMLn4YonqajEZulNjmPyF68hnROWBpGg4QuXEOpt55u69vLByDvJQVHfsWHj9j78ATLoyy7jnUXA5ujn+mMe4b/kympqgoRaesz1O39YtzH9VoiJwNu1xwTN8qeRRkA0kRwDXxLPw+arpWPcU7rI6quZ/HtXlp2Pdk3gP1iBEmwyZlZQV7eCSM86mouStoQ3NC3Ipvb1i09i3T6RnTz11qLPPYZhpCqD81f9p4dYfLsYRLKNk8jGodhemafCHPwiQWF+/jJoasKlw9JTrmVy5mude+TovrzuNjVu6OX7B80yedZxwvL7zicSKuOWeW6kpf4PaUX+lrnoLleN/eNiaz5JSBMXLIfMcZm4nktoAjhOQJBuzZ4so1IMPwu23i81zQsURSNm1zJnyF+qrX6IkONS3VA6C5EeSROQ4m2U4kjxv3v6fOXeukLZZvhy+8pUPrlP9STOr85PNVlig43HIOWrxj9ZQXQFkm4tUpJuB5i0oqo2W7TE0zwD144IEAuIa+bzE9NNPYseGbox8jmj7blzBcpxjpuMwu3hm2VpmnDSXWGxIfqh9HWa8g5Tt12zeCYnMKkbXvIES+g3b2uCkUpDKYMXOdeRT0NjiprJ4DrIMvcGt6IEYfX0geUajuoox9nYQ7+qkbPpS3MWjSEUiRPftoSr0LmkT9QiQNDBzHD3tVkxTwuuJoZs+DHcjhiF4t4ODYqOqqirQeN4uS28BVyv6bkltyaZBOtGCpl8CZh3YPNi1EKrDib3Mjann2LwZgsG7CYUg4IJMYhuR1E1oSpJoooSWHT58vlWUjfkubo8HOSI28adfWEQqFaSq6m6KAt2Ear6DwyHG1uKHWwBZ1wsRbCuVa73ywa+RSSwll1mDzWVSXrqYcH8V0aj4vomEKMJMp8EIzSLkuI/RlW9RW/EGkuVrkh00kaNtaBD+uno1PPmkWOdGpr6rqoQ2bVOTiFiVHqiq7992UGttFetcRUUh+9MT1vCPmYnNX46kaGSjA+x76WFSvW2kVZVfXn4/n7/mgmGdXgB75TRCqRx5A8y0jr9mLKWT5iCrIvg00jQNzFwCPT3AgiVThB62/GM0NcX65j+RjMHCOnhm72boUylrTdMwagbJpCgMfK1rC/ZcAFPTkV0lOPMpwrvewF05jsC42UK/eNcGfCXGgb+05AY5BEYPE+tXEPC0Ul68XfxOfWfA51DMZhMAcOdOAQLrQzZMA1KtX+e8b7Sz7Ddz0LwB3BX12H3jwDRYtUrsYxUVd1IUgjKyZMI3kE32IjvbGOzyEe99klDNt3H45g030tm2U+P1LedTFHiKomAXpbUX4/UW1l3r8GpRoayOniOpF5IEOXksWfkhYokXUOUefJVjsZtz6OmRhzM9PT1CtUTXgxRLIUqLmigr/g2ReDllRXsRsprCX10ukb1//HERaNI0UVNkmdstJDNfflkE996tqO9faR/7RiFtbSa33ZpF1kQ4oWv9s7Q8ew9jT/8aoXGzGNj5GkuOS3PKF48lHheqD83N4kFZUi75dALV4UbPZVA0O0Z6ENkR4KijCun3tjZYtkw89Gnj7mf1G6eB5GTJErjvyut46Rgn3kQVx3g+h6yo5MLb+OFvJyBJIoXw6KPis3Y9+Rf8ddPw10+nvBx2P/EHjFjHwflRue2Yfedg6SkLc5J3XsnLb5zP6tViA12yRPCND7dN61tby9iy+xROPvaneFxhlLKn+c7xN4Ek8607r6W5WYxZa2tBP7W6Kk5t6V3UVb1EVelGOnon8/Tqa+gMT2bUqAwnnWSnzCYA8itvzKC5fS69A4Kq4nAI2ocA3CJa8WFUssZi8MAD4lnNmxNm0eQlyHJixF/Ywf8zZOdJhTEwxJzYskVsuDNn7n/NbdvENU86SRR4/v+w/yuNQqJRAZ78frEQ9/YK2sr6l/aye1MHhg5GJkUq0oviCuAtqSKfz2JGd/OlH55NIiEiWn194tlmM3laNm4lGYmjubyYho7HBYaeY8YJR6Lr4oA4dqyIgjU1CUBe6v0dxYEEg/p/0dcnNom1DzzNngYJb7oaf1jB6dDJ7V3Nf9/1ZVpaxHvzeREtWfbdn+Gtm87Cc5dQUwMPXX0dYB6Eg2xi9p+Dmd2GYRjohoZh2JDUStKuBwmHNbJZARKtznmF9x58TBPtl9LbX8L6tybRUL0KtyNGoMhDUvsjN339T6Bnueymy4hGxfhbPGe73cQv/4BcNoFDi1Jd8SbR+Gh6BsajK8dSVrOEYvVLOOwZXt9QRVfvJEypCEWVUFwn4fGIrEogIH5a/GPrBYUCQth/U06nxTwAsWkPDIh7s9vFRh0OiyKpSu+1+J2v4HL047DHAA20KUihe4cLBEFEnNatEyBu6dL9x6+lRRx4i4r+/1bI/19oFNLXJ8avurrAPd60CTo7srz4yBbMfJ54dwvRtj2AiTNUjquoCrs/wNijJpPVncNybCPN0PMY+RxGPkd6oAe7msQ0DSYdMw2fT/BRR7Yz1nVo2vi/tHU1EM2dhcMBqx56ld4KN/lMjuJYEQ6bQa53O9/97VK6uwUIjcXEnPrbdT/DWVzNkq+ci8sFL9z8K8gOHnSPNVKPQ+RqrDofkS1xIhf9Cdn+/h9rvO2r7N43labmEG5nD1XlW3E5wrgr/8LPzv8tmAb/edc1dHcL4GnpsmsaFAc2UuS4D92QKC/eit/TxWC8irwewl11K37ji6iqTlvbANt2n0AsNZGc7kKyH43dLnw1EBBrmMPxTh6yJR03MqIsywVahsU3BrF2W5Ssnh7h00H3q4zyX43D3oemJpFlCSQHUugBJK1wsOjvF/VQ+bzw15GFk/G4qPXp6IBTTil02Pxn2yeiUcjatbBihYSsCSLdnuV/ofPVJznigu/jqxnPnif/ROdryymTj2H96m4yJUtQbE6qQ+20tdUC0hAHz41pmhgDuxk3bxK7dwc49liRdh8Jjh0OwZ1Z+co51NWJAq9AAO7DZHH7XOw1xwJCFi694xF0/XpWrIDXXhMSMQ0NkIx9HkmWWbJE0D2u/GvHe35PSRsPRfdhxm6E3FuglNEc/h+efHA2/f0itXjCCe+fd2cYKrv2HccfH5pBXfVqzjz+aK79g8YNXz99WEli/nzhOPv2CbDctDvGi298lRffuAxNTTBt3MNc8umz2Ljjczz/+n/zxz/CjBnLWDjnIU48+hoA4qkqmtsX0RL+Js0tXrYPHdK9XgGWLcBste48XPN64aKL4KmnYM0rxXS2/p7PLP4WLmc/IAESkrp/RaMsC+5yNitOujbb/nyo8ePFfa1cKQr3PoppoI+iZTLi5XKJxbetTaQcw2Eoq6uioyVCtKefVKQXUPBWjCY72EfnxlWQz/Pio+vYtaEXE43SMaPxV40GHNRNn0I+D927dkMuwcwTpg43uRg1Siyw7e1is0mlhH+6g5cT1wscPAAkmcZII6bqI6l3ktu3BlOPsWWLaK9qt4uFPJ+HwMQFZGO9zJkjAPhDvFdQQUL33Uk+chtm6jFkKY/kWkpf+mtEOjRsNnFAPJC/vlfDEYc9iWZuI5mazyubLkJT80wcsxzD/CFFNaMYCJeTTAp+p8slNrJoFJKJGH39k8lmZZKpEG9uP51jZt1GbcXL9EZ0uruXMGC7ncqydqaMPZPK0t3YVR0DiJsyg6kT6esTG6UkMawG4vWKzxlZoZ/LFVK5miZ8ygLD2WzBvyORwji3t0vs2TuHiqIoo8rfwG6LiUYJ6sT9wDGI9S6bFTKezz0nAgMWSC8tFf7a1CQKlRsb3+NR/duAQmGewyEOF/G4qGPp6YF0xkbNuEq2PPcS0fZd5OKDTL7gf9Bcgjdl6DkGBvLkkmHS8Qh2jx+bJ4CqqUMUGhVdV2l9YxNyspMTvnoGiYQAX1VV++sKW4om8fh/Y9jBLov5K6lOSsMyhn0Ups0g27kGPdJEV9dSNm0SfhoMiiinr24qmtvP6NEi4PHCrwbf8/vLzlMxZQ9m7LegtyLbx2K6rwBtxgcaV5czTv2ot9i+82Q6e+exe998Sop2U9z+B06+TGbFQ5Po7xdrwcSJYh7HYiK71NMWpqP1JPK6g407Ps3U8Q8xdewjxJL1xOItpJQ78doMqsrmUxa6jbwRJJtzMpA4ht70lYTDzuHxdLkKnSqtlu6WWdkfEP/f4SgU5OXz4tkEg+Ke0mnhY7290NlbQaJvPuPqnkV1ZxDBVjeSsj/KDYWEjz79tKjpOeWUQobb7RY+2t0tDr0fNdm3j1UE2ZJya9nWTVt6Ft19hR2msWQHd337WvScjrusFmSZRGcTNqeDqqM/TeW8M0j2ttO++hHGnPZVZFkZfhIVFbDr0Vuw1y5EK5nIcceJDntQAMeKUujkdcIJom2lxd+5804BHE3TJNOyip/csYi+PiE31tUlFvQ3V7ejeKvIp5PIqkbitd9iZqIHPdUeyBIJeOYZEUUJhcRkezu353BN75zC35//EVt2f4rJjY9wxnHfBSRwnIocuPGA7zFivyHVfx97O2bS3D4Xv6eDedNuB8lFRrueF187nXXrTDQ1xoJZv2XWxHtRlLy4rlwBxc8zOChx/M6RAAAgAElEQVTT1MRwhNoq+ikqKoDl2tr9W1cfqq1/+Q6eXHkuHlcPZ51wORUlW8UvtKnIRQ++4+9zOaFwsm+f4DGPG5FZ6+6GP/xBSOmdfPLh38vh2sc9gmx1qQPIJQZY9UQbiXwAZ6gCJFWIy6s6z/3570Q79mJmc7jKahjYvRFJVXAXleAqHgU2FyYynrJROLw+ikdV4HCqqCrsfeNVzFySY89ahGmKOWO3F6IfVgrR5xOLsCUBZ0U5X9sWRtJVUk3N2Adf5Ws3fZWtW8XC7/EI33r6rheRvVUkoim6N75IdbAbePdqeCsiM3KzURSxwXd1id8XFxeK0w51LN9u6c6L2bqjnC27F5FMBzlizONUlG7D40qQct6HLPv24xwaBmCEyfddTixRRHhgFL0D9Uwb/ziamkNWK8k4biQcNtGjfybg2YjbGcbn7sbpjCFLNhzlv8JQjmRwsNCBMB4vfM9AoKAtbbcX5KSyWbHJjuQtW+A5FhMZArsdFPMt9u16nmishLLSrcyc8Hc0NQfYkYoff8fB1jBEgGTrVhF4WLiwMKZ794rNFg7cNOifYR/3CHJrqwDDtaN11q/cTmubihasRtbcw/P2jeVPseGJ1zBNcFfUk4tFyMXCKC4vnrIaDBM0b5BQ/RRUhxuHw6Ck3IPDIZ7NW488hGSzccIXT0NRhJ/V1Qlfy+fFc+vpEfMkkxFzLJUS7x0chKb2KBk9jbrjJb79mzNpbRUZPiv9/+ydT6D4RpPRnaT6OnH1P8cBOceHYSN59x/EwjuW8Owr36Y7XEt99VrGjH4VXXeQkc8lJ80Z5vsbI1ggSvY+jFwHqYyf8GA9VaUbaah+Gbsjh63oF6TzY0lHnkbN3obX1Y7DHkOWTMCG7DwaJfQ7olGxpoXDIkNgRYy9XjHuoVDhwJrJFOQaQRxuZbmwnhmG8FkAu92gv/XndPeMwuns58hJfyXg68E0NXCdh+y7+h1At7VVHGhVVWRrLVpZLCbwzO7dAnc1NHywsT4U+z8XQY4PJrjy+OuJZoupP+VrKHYXmHkkWeGssyTGj29kxW9K6WrpIdHdAoDdF2Lcmd/CVzOB7jefJ5eI0njGZUiSjGmaSAje6cKF8ID7MnbvhvSuJ/nHqlc5duX1tLXB3XeLiZHJiAf3qU8V9A1jMbj1VrHIm6bJmtj9TN67jbfeWsTjj4vJNW6ckKSRXSWYpoGRz7Lr0VuoKo0d1vc3TTGJVqwQ93LsseJ1KPIz72WSJDFnyp/Zsvs0du1dRCIVxO0cgPTTmMYPkOR3hrok56k47bczvu4Zxtc9M+JGdRyB+Zx4Ikxr/AUrVs5hxZprWL/tHJbO+xF11WvBjCDlNxAKzSAUEsDTNAUQtQDzxo0Mc9UqKgqAuabm0L7ztDE3Uup7jAdX/I47HvkrZ594GQ2jXobcW5hmDkna/yKaBp//vHjeDz4I555bOHiUlYl7fP11EZE4HOH5T6JZbZWX3/4UKx5uwVNWg+qOIaldNM5qpLg8QHm5wlGLa3junj1kkjH6d76GrNpw+oM4i6qR7B7y2Qz+uiOwOdxkMwmSqW5Ky6pQVZhz6lHY7bBy2XLMRA+f/u6FpNOFNq+6LhZ/r1dsDv39YhPOZEQ6T0GjPbWb2AuP0jC1mvXrhT9b0ZWWFlD8ozFzCcJbXqF/04tULzgwH3EkMLbSlYoiwHp7uzjUulwii3SgIrwD2btxkQFwnklJ8C5GVQTZ3nQCXeEJjB39EkWBMFnHcgzb2cNNFYa75JnF5BSF0vw6Gka9gmlKmKZELh8go52DJENxsJmBVBd7O2eSzTkJ+fYwZ+p9GIZJeuAhnGVHDgN8EBupBZYHBsQGDOI7BoOFlwV4LJqFpcBhNWpJJMDBVkZXvkJT6zyaW+eTSJSyeM7vAAmyq+EAmZ+jjhLX3LVLgGCrzXxxsfDdzZvF2vt22tS/bX9LpQSIykZ7ueKEO1B9FWj+MlR3lOLqUmbOr6G0VGLxmcew5dk3SQ1E6N+yBsXuRvP48VbUgGxHNnV8lWPQXF7yyRg9bU00jj8KEL447ruf5aFfP8GK2x/nzG+fSnm5AMn9/cLfslkxJ8LhQqc4VRX/HY1CPBdhe3o10xID7Ngh9gqr4U48DmrxREAitmsHna8+ybgpH1yf06IivF2m7XDMNHMEvG3MmXwHz776n3T3T2HquCeoLt8MymbkkueH9cRHahSno/WkBzfizEbwuPrI63b2tC0AyQudIjViJrvIZpZgGjKqlmJC7QrKipsxUquR9Dh+vwe/X/ChrdoHa3z37hXjLsvi+Vi+XVFRAMsWOLZ8FsSzSMT6CLj3QXGWprbZPL36KhbP/SnFgS7M9LPgu3o/6UgQGb6jjxZFe089JYJ7Pp+YA6NGic6ab7556Hv8/w/7SALkbCbHa0+9SbQvxuT5E6lurOB3374HZfTJjJ2yEIB8No0syXijzzJx4lJA5lcv/ZBfXXor6558E3/9VMaf+Q2QVHY9diuhcUdSNv04TNMkn0mh2p0c0Rhm/vxi7r1XTBTRFORVQJx27rpLbKo2m/jd9OmFB75nD9x779DmaOYJq90cETiB6PwjePhhMdEyGcG5tNshY9qYNg1e/N3vqSpNH9apNhwW6f+9e8VEOvXUD7kARXYRT4oLZvNOdjSfwIyJ9wMymDHgAABZbcD0/hfEfgqSDKYEGOC/EUkWxUslge2ce/Jt7Nx7HM+svZqWztkCICOBEdn/epKIApSXi0OLlfKzAPMrrwiukqIIjpwFmCsr30VDUXJQWfoWXzrz06xYcxXlxVuGfqECBxZdtNvhvPPgjjvgr3+FCy8UnwWCbrN5syjYu/DCj1Ya6KNgeza2sGPdbkKVRdTNmsKeTe2sWt6Hs6QWU7FjSjYkSWXnK9uY9OWZFBXZKD/9SDQzyfI/PY2JSmhULaNnT6FjT4xcNktozGRUu5vUYC+p3jYCkgfXxCpAbJweD5jJMDiCpNNiUU2nhU+GQiLzEA6LVy4nIsgtvf3k9DzdSg8OTxDzzAUMeIt4/ZlNzFoyhXS60IZ64pGjmTcPbv7i/VQsGLefz1obhlUtbrXNtjZR63NlWczRQOC958xBQfEIM/QBJEmmvuoV2ruPIJoYxUBsFEF/Jwrd6GYBXMCILlqlP0EaPFdI0Jm6qHZXx+DxnwpALtFNkbKG8qCTHS2L6BschyzlULUcWX1wuBOi9R1tNgFOrANjKlUAy/39ImoO4jlYkSqPZ386hss1FI3uc+K0pzly0n3s3HscLlf/0CAoIHkOOA6qKjbcXE5IeNpsojGB2y3W35ISsf6OGVMIavzbhMUHE7z6xHoM3aB4wkx03Nx9w5OYjlIkRwDZ7kZWbLRv3kxNZZzq6kkUV3j59s0Xcve1y2jdoWMPBJm4cC5te1MY+TyeinocwRL0XIb+1m1kwl2oyiwcTqXw3I0cpqlTXCzmUkuLOFhZh6r29kLxai4n5lB7X4SoOYhplymzT6J7ZpIHVzYxt7Eej0cceKNRGDW+jjFjYPkNd+KbEvhAkeORZhW3vR3wHbKZaUChunwjk8c8zvrtn2dP21x8nm58XpFqU1XxcjoLc9U0Z2NG/wGpvwMSumEnlQ4RV39PPC2RTEIiN4BpyCSSxfRHR1NZsoXKsp3ouoahp5Bkz7C/yrLItBUVicBdfkiZwlqrLMqjaEstXj5f4VBvmpbEGwz0aUjRKOOLNlAa3Mye9qMJDMnbSopnvzGzxhAEnSKXE9mf5csFlrHoH2PGCM77pk0fnUPtRwogX7HoWlLxNN0tveSzeQzDwNANFl16IbGisygeLU6E+XQSSZbZcu8N5PqauOJXSwHwF3uZOHc8/eYUSmacRD7aSablOarnnYYjWEY+nUBSVFS7k/Y1f+eLFyxg2TLhlMmtD3HPSzvJlZ3CQPNb/OlPBpIkU1srOKojebGrVgk5IYCskcLEpFSvxMTEJwWJ6j0QLh2O4jidcNZZIgL94q/Thzwe+byo8Hz5ZTFpTz21QO34UE07iv5IOQBf/PSZlBfvEP9fdmMOXIEpScM6yCNNdp+P6TgRMquENqz9OCR5xE5kX4KUfZ1xtc/TUP0yImYPmDmwHZzbpSjiJFlTIyL82aw4IFh0jFWrxMvic1oc5tLSofFxfhaSf8Ht7OfTi68cuqoEjlOQpHcPBTidcP75AiTfc4/QkS4vF/9/0SJRPb9tm+CLfdLtikXXYhomnqCb9c9uQkLC5gsRqp9I9axZaIFyTF1HcweRFY10pJtk+w76mgPUNUzA6ZQYO6uGDatGEe5TkBwOkO2othyy3UVyIIyeaSeXHETPJ6G8fDjK9OKdD2CaBjFpNHokxfI/PoKkuTnu/OOHO+VZYC2ZFIAtkQCDPNFkP8XecmQksFdiZLP0t3cTiRRS/nV14qD2dnrPSAklK6pkpSIlSXxGZ6c4HPv9Yu4cTGrxQBSK9/JvQ5mGaa5AkvMsOvImEqliEukK+iLjKVZXYMa3oZfcMszNtq4paTWYxSsx06sx9S5MdSryiMY9ds9k7Kl2gr4U1RWb0HUbmqZjmg7snmPIIb7XSJA80pxO8aqsLESGrWfQ1SXWWUkSm6zXK8Cy2y1+5jJHEeldjqommTb+H2jaUJ7X1MG++F3HQtNE9XsuJzZWm00UKhcVibVjcFDIvln1JJ90u2LRtQz2Rulu7kFWZDR/Cf7aFo44fgZZ3YHmc6M6fSh2N6mBTiKtu9m8aoBFZ0zC6wW3o4jaqWOIJL0YhkJet+Hwq+g5CUlR0bMZ4u1NJHu6cXlc2OwyXi/8/Zf3g6TR1ZZAcYe45Zt/RvWWctrlp+J2i/W+o0P4n6oKfx0cFM81no3gUtyoihM7LtJ6H6l8fPg9ui5A29y5DAHkd1GreJ/2bmDv0C/gAbkMjFZmTnqIkL+ZaLKa7r5xuJxbUfvOP+D+apoSOcdPyBiXkktuIauH0G1HIkkabrdYX9RgGpvxKJqaQJGzyLII+SpaEEMpPihFRFX3P+BmMgzXGYTDhQOudXixinQ1DdzeAJHIWOLpBNXlW6itfnNoXBRwfWG/cXr74WLCBLGfv/66AMknn1ygfJSWikNtY+P7r0X6MO0jBZBN06Rl8z5yGVEGqzo91J90MYngfORMCkmS6HzjWUKN09n+wI3E2neh2Qux+Fv/62GaI1MomTGWztdXEGvbRcPJX0TPpghvW0fJpLkAdLz6OGWeVp54JkRvr2gEcdtlW3FNPg85UEto/JGASWr3U1z4/ZOGH6xhCNDU1CT+7XSCmXQgSdIQZUPCyGfxyCHsdhHNOuooOO64Ag/uUE+1LS0iatzXJ4rDTjyR4XazB7ND6Rr0dpO836Yv8hpOx0ABHOMA7/cxE/dwsPVAUkrAddaBf+c6AzN1L+T3oqopRKGcE7zf2B9IH4LZbMJprKKbZLIAlpubhZwOiA23rg5qy6dSW1JF0Nd6WJ8DwlkvuEC0m162DC6+WGy4M2cKp16xYkjb8iOSBvpXWri9j51v7CGTzCKrDgI101GLRxPuTKKn02STEWS7m3wiymDLW6iKjiIl8XigfXc7N3/7QUzVQy45gOKuo6/HQLbZyCaSZCP9KLKEnk6iaRqjqsfidgvQieJACdRjy2rklUEkTcLIpnC5CrJj0ajYaLu7C+lBL6Uohkw2L2HkDNx9MsTiyKESMhmxSM+YITZaq3PVjSuvxzAKxWemKX5nSZ6B2KS7usSmbrOJYr5389eRoPg7xx2+v5rKEejKFDRzPX5fLz5PP63dfnqjC/F4NyNJOTKZ/XWJC5+rgW2haBYw5NiF7lweDP93IPZzJCmNLGcBByjVyK4zsUuF4st3A8mWSZL4/lb61OIvWtHl7m5xkDDNoYJCh0o+7SfbMxFMk+LgXlGkdwjmdIoi4uefF2BY04R8lMcjisDa2gQ4t7JBn2TL5/K0bm/HNEyQZPwTJ2ELjWbLC9tQXR70TI5YZwveyloiLdsw81nS/e0EAmJ/vvGrd9LTY5JNZVGdXvqjMjISkgyq3UlmsJvUYBd2p53aoxrxeKQhP5CRHD6c5SXYvEE0XBh6ajiyHIkw7GNdXYU2yLIMQcMLpkoqn0OVJNSmCDbZoG+UeNYNDWKftQ7GH1bkeKSNbK9+uD4rSRKS//uYA99AktLUVr1JWzeEB8cTjvootYWRhopbLZqFxd0XVoei1GF3FoperUO5qX8BM/x3cZBER2RHbUj+H6Gq0jv0yw9mdrs43FYONSG2NLEtSkZXV0HJwucDe76YuFlLXlepLt2Eogx90AEyYW8/YEyZIr7rhg1iP126VIDv0aPFGvH66wI3/avtIwGQLVC3d6+E5q8k17OP0NhZjPnUV1CdHjLRfmweP7sfu5Wu9c/RpKiYeh5ZkZl1otDc2/hmhk5lKY5imR0P/xZv9VjGnvE1Inu3Ig1uomTq5wDoen05NSVd+I+8nHAYPvc54WCzv/L94RSDqWf58ldsVFUV5MCiUdGIwipMsdtFSnFkhbUkSRjZNJrbj88nVC4Od1FOJkUR3oYNYsKcd57YrA/FTBMkRwBJObyqFEkdTX+ijFCgB5QGUEaB3k205ykeXv4t5s/8HfUUmokc8nUlBxQ9gJl8GDJPgxRAcp+PZDvyvd/8HuZyiU1w0iTx70iE/Qr+Nm9eCiwl4G2lrmotsyffSWloF6SfwDR/ctAoMoixv/BCAZLvukuA5EBAOPJddwm6x4IFH/hrfCzN8tftb/Zg9xeRSfYi212Mmn8mdl8x2dgA2VgaI5sk2bWPRFcLkqKQi0fJ51PMWiS0gB/5y5uYioNsoh93xVj81eNIJ6LoiX6KSr2Ekzrk8nhCbiYddyzBYhfFxQJcTTjxNNFsYtdbYB9k6SUnousMA8NIRBwyo9F33r/LX4xOH6lMCi2TxF0UgnyKxkYRfQyFCqBxJI0CCsVmIzebwUFLG7SQ2j/QZnRACoWkIXkPj9RumhKG69tIPItqvwNVyVKaH2Rft8aqtUejyGlmTf02qiuOrfS24c8a+Xo3k93nY2oTMJPLQO8DxxJk55kgCfkWu/3QQfJ+15ULRXy1tWKsIhEBlgcGoLe7hVxiAolUkOb2oxkzahWTGlcIikXmOXCedtDrezzCH59/XhToaZrgUqZSIsjwxhvi3x/Vlrb/bBM+K9HWrqI6/eQSg5ROXUCgdjLJvg4k1YaRTtG3Yz2YJrlIN3o2TT7ew5wLT8QwYOOaDvrCkI5GkDQ7wcbpmLpBNtFH2ZgG0hmTdDyCJJmMnjOWGXMa8fsFEFp48VlC5nF9C2YyzKcuXjQMaC1g2NkpwJhVsCZJAihqLkHZS2Z6SRoRbA4bbo8Tn08ArQkT9qc3/bNsuGDNUQRG9rDeK9kXQNHdkLgVRW+mrLiXRHYaO/fU8PLrC5k8/qd43Alk/w+QZQFCnc4CGH63eSspFVDyFGZiGWTXgVqH5P4CkipAg6Ls3zTkcOa/y1XI4pqmwD5WdLm3J0U6Uk06OxZZMigO7GTu1D/hcsYhdSe4z9z/Pg8QTZ4+XTz3LVtEg7Pjjy98ZkuLONRWVR3WMH/o9pEAyJaNPV0A2l2P3Izm9pFLxjD0PJrLy+5Hfk146zrsLhuZZBa7y47Tbeerv7yE5cvh1VftZCIdbHvgl5ROOZbK2UtpW/Mo7Wse48hv3gTA+LFpLv3yQh58yEEsJsBnTY0ozGpuFvdgZONEX70F8/zzyWZKsNk1mppE5HjkJpkd4R+maWLkMkiyguLwkI0P8vxt32fPg8FDPmWapigoefppSCR0sq1ruOzqYw8apTTNAtn+kb9sQvGPxjvn2wzs3sAVi65lz4YWGqbVHtI99PU7qK+vQS55SoxD3/m4nFFiiTKee/VK6mr/95AjOiNNkhxI7nPBfe5hv/dwzO8XDjd9uhiXnu2n09w+i+a2uWxtOomp4x4e+ss84qT93qtpUZGgW9x5p5gjF18sotMTJgjay7Rpn2xuY6BhCqHGmWxv/yVIKordTbxrH5JkYho5yETQHHayyTS5RD9mPsa5V5+J4vDR1QW9e3vJJCJonhD+mgmkYoOkupux2WSmLD4ap9dDLqeTN9zY7SIS1dkpIpGp1BD9IdaDns8y2BNGtdsIFvvo6RGFW+l3YTOlot1omST2VA5dtpGIGwy0bOOpGx7nKVPn589dNwyMLVBrgeKRm3A2K1K8iQTcec0d6LEObnz26v0+y9oQrEJfi6f7p+ueRHKGyBQdB5LEFSf+gj3r3jokfxXrkIrkXIqtbCmSBAHpAqKpPnbsqSOTddHenaex7i00rQA2DjU1LNlmItn2JwFa4yA6fRVAss32/kCnohSq6AEy/c8z0LWSnr4x7G6dT3ffBAGQTX2oDuK9ze8XhcurVokD7Pz54l5Hjxb8yu3bCwfqT6TJNiqOWkzf1lfp27oGm6+YTLQfM5cFySQb2Ydis6NoGpnYAGQHqagtYuH5S+jqgrbmCNmkkEwrGj9b0Gi6mlHtNhS7m7FHlGFXy3C47Xg8KqGQmO9dXcJnEwkwMwn0WDsq48nEUzi8RXR3y7S0FKLIFpgzjEIUNR3pgcggbkUjozhIxfM8fv332DTaxk+fufaQOsaOtEPJtObzwsdjMaGucc+NK1EcATLeWQw2b+OMoKAS/GPgzoN+1nDkVJuCFLwFSQJH3/lUlLYTDlcTi1fS0i5z1NRn8JUdnJJ1IJPkEJL3G+/6e0UpjKvVHORwzVK+8HpF7Y+R62Wg+ff09NXQ3D6bjt6pmObQ4mjED3qdkWvqrFkCJO/cKWirM2YIykd3t4giV1T8axt0fSQA8o0rr6djTxe33FrICSa6WtDcPiRJYsf9N+CQ+hk3u5FjP3MUTZv20jizntmnL+SJp110dMCMaTn+cP61ZBIp2tY8RqxjDwO71jP9P25E1hyMHQuLlzi4+24x6S+4QCyeN90kIkAAfr9JetMDvLZ6A5fNeguAk753DT3p/avXR6YLAMxsHMXuHf53JtqHrAhkeyiOaAlpNzWJE1PPC3/ASHSjacfu93e6LgDC3r1CjmzfvgIIUAK16JF9ND//EJGWLYyZ6EWSFXiPSCkUFgFrswIRKbYBC+fcwj+euYytnXd/ZPulv90kCUrKSigJLmP2EXdjGArDeR910jsULA5m5eVC0eLuu8XroouEzN+uXeLUe+aZ73mJ/3N248rryaQyfOecJ5BlE1kCM5diYNcbeKsaMbIZ8skI5dUelpy3mA0r1+NywcKz5+MqqaGrS0QfSyq9hDt9Qv94wyo0VQZZwbD7cXg8yKqT/FCjC6uafaQEkccD8rRaXvn7Czzws4fI5ZKMnTYDe8M84N2zKEoujaHYkO02NI9Gon038dYmymp1kGSuPOHHAPzkye8NR6beHgGxUo9Wcak+KE7YlrxZOi0AZDIpotiRiAAIicRQ6thXhZlNENm7lWTPPmrrrIq6916SLfAwcqNTS+7GbUBVxX10dIfY1385nnJoMPe/9/fLw317odLbI8kfNDJrc8+jtOgeSkO7mVC/YoTclQm2ow/5OsXFgjv+0kviEDt7tpgnJSUiUlVf//5kIz/uduPK61n3fBP33zmInk0ha3YizW8RaJiGpKoku5soKfcSLHEy/qg6Bts7mLFgIuPnz6Y3rKGqUDnag6w4kOQcHa8ux1/diKHnCNRMx+ZyYrMpuNxunE4BpKyocCQi5onDAceeWsNzv3+RH3/6anxVjfhGjaF2zrGgevYDxhaYA4boQDqyy4usObEnBulreoPcYDeMrua/lhw+TWmkWd0gLWpDMlnodBmJFORHFZsPPdVPz6Y3iHfswqYI3vX7MbloGcEgjI5fTzQVIZo5jc7kaXiMD+an7/p5Fnb9ACB5pElqNcHAIEHfbsbVPU8mo2G35wANHAfnRoxcjzRNFNbm8yJIqSgiwzRmjAgYbt/+r633+UgA5BceXMvPLvodU//jJkxTJ9AwlQlnf4dcMsrWe38MmQHKTv4Ggy1bWPuY0P469buXc/e9YrDPOQfGj9douWg+T/9lJZlkmoFd6xn7mW/iLq0h4EuzaJGDO+8UE+T880UI//nnC9GVQADSW+7ipfufJZfJkcvqTDr3qiFwbMIIJq4Fjk09j6KpMASOTdOg69VHcWc28sBeoR9sAeQDma6LtqkvvigAQGrn42xb9Trb13cSqJ/MFcf/GMVXzUlf/wL79gkunXWiDoUENSQUErzbfN5HLHYEL0sy2RmLcAZLmLrAzdZ7//c9QXr/UMF4UdE7f3fE2LWsffMUnn9+NBMnfnxSlJLve5h9nwUzM8Sl1EByIPl/cNjXGjVKUHHuvVdkEi64AObO1XnpJYVZs3KMHv3JIiNHwlEun30VWs2xONUMpqxQPOkYvFVjyMUHSfa3oblDxMxqVvx5OeSSfO+ha+jsFJuO3T5UXHXmsWxedRPZRBKP14+um7j8AWqnj8WUnMPV7IpSAJaqyrBiRWqwixfufZKsbiCj4ipuIOMcDQN92IMV+91zOtoLeg5HsBJbkZAMkwa7yfdtxzWwmoZ6+NHj1yFJ8L1TfgiGPtwJDgo+n0yKqHE2K4DWLf9xIygq7f3l5DMxrvrc/SDbOfVrZwxrilqRaLtdREeKimDevKmYJtz8nVYGvBOx1Y2mungxg/3d7+mvFoAY6YvW59RVbcEwFHrjR7FvX6Fo7sOwkdGft4Nkm+3wI1/7mW0e2OZAdi2KkhLfTXKC8ywktfawLlVRIQq2Vq8WTZomTIDqapO+vixvrjeZd7TjA9zox9P+dNU9PPXXLZQfdTqGnsdXM45Aw3RMPUd03zZsmk7OO5GB3W+irtkIQMO8o+kNi+fscoFUN4qiSg/73tyK3V+GkU/jLKrA5gkQKnOjKIW/zeeHit+TYq4KmUOTv113K+HWAdyV49FCFTjKxxHujBEo0zAlIZdgmpCIDCIZeey+YsF79Xmwi/EAACAASURBVFVgA1K9e2hd8zCVxUlu77kdRSlwgt/LDAOuPOHHSLLGnl0GjmAxV572RyTFwXnXXUgiIWgEFv/ZappRVSV89uijZ5JKwU8uaSXYMAHF7kO1u7ji+J+AfnBlKgv0jgS+kgTlJXsZjJaws30JbW3CX6urxfr2UQbJkiSD/wbMgW8AuSFw7ADZj+T+j0O8hvjpdIqDrAWSdX2o4L4ky6ZNBrW1Nlyuf00Y+V8OkDOpDL/44i1kU1kkWcERKGPi5/+bVLgNs+khSkpkAjVH4h03i0jLFpBVHA0n8uCDYuJ+9rMFwenLfn0x/mIfD//6CYJTT6J08jHYtDyf+ayDu+4SE+K004QGX3u7eI8Fjj93dpqL658mm86huQNM+/L/YvcXYRg6kiSjafu30DRyKWTNORzpCASg7bnb6XplNQ3Taoc3uc1rmgiNnfGOTW/fPlGE19srTkjHHw//e5mOe+alHLmgYqjwTyhpvPiiAME+n7hfS0TdAraW2Wwgu4rJdnWSCreSCHeRHuiGqoPrQVr6pQcCyErx3Rx/ogCGr78uiiE+DiapdVC8HDN5D+Q2gjoOyX0hkvL+0EJ9vVAiuf9+k/uWtXHm4vPZuP5+lj8+wBcveBnF+yXe3vHr/6rd8f2/Em7vo7LehmEaVB/9GZzBMnKxHqR4C2WhNBl/NYrNAckUsruIlhax8Hk8heLGsVPL+PrvLuLBm16ityOCK1jEmPlTaZw8kUhELOhWlyerKM6KIPt8sPnl18jkDBTNhq+sgWD9EZgYZPM6dpJAoe2hmc+CWdgRgkHYt+FZzGjzUJZF4ppTfgCmwc7tKTID3Vy5WPjsz569nmRSrBnhsLgfTRvSU65ZhOIMUF6RJxPpRXJmkBSNWEz4qkVBsOgZmYwQxLeKcLSSSdgYJNobIxHuIdGzl8B71C1YANkCpFaRk6JA+cQfYXghulEcKtraxD2UlHxAqaoRNnKzH9khD94/SJYkCQI3C+319COAHcn1WbAd+57vfee1xKF29mwhD7llc5Rxo+6gtjhO086jGVO2nJK6K5CU4vd3sx8za9nSysO/fRJ7iRB291Y24iyuIB/vo7o6Q+dAEu/oI8g6BK1RUhwovhr6+wWw9XjEvPV4JL75m/NY9oun2LWxD5ApnzCZkpoKNJuNYFD8bTwuKAmZjPATh0PMv/zgPiJ9aexFVdj8pRSNnYWk2kCRyabzqA57gdZk5DENffg7qKqILK5/4QFS7TuhuHY4crzp5WacJRXDe+wvnr9+WFc4kylEheNxUEpnInvLGF2mYWKimG2guWhtFT5lcX8t3q9VfLtnTwFY+kYfQTYRIz3YSybSS3nw0FQzRvqd5YuO8j/T4IZwSmSaBgfFOlFdXQCwH+a28qGCZPtCKH4YM3E36K1gm4vkOgdJPjz5Caug1wLJ+/bpZOOrGVN5D70d57Fh9QbmHjsRyXH8+7/Z92n/coC8+eXtooc3oGh2AvWTGWzaxLb7f8H048Zx++Zf8fTTsHZNntLiLA0n/w/d3SJCsHhx4QGPBKATTzmbJ54Qk+H0M1TuuUcs5JMmiSYQmiYcLp8XvLWLLoJ0JI4kS9i8IaZc/EPs/iJMw0CWFfRsirwk8nLmUA5IUgtRiGz7Oi67ejbaNy8FLhX3c9z1yMVTmfX1b2HzBml/8gbcjhTJpKja3LhROGJFhTg1/fa34Bp/euEzYL9CsnS6UD1aXS1++nyF1pFWBytJKuWKRTejSAo2dIJHvLcepAWQR1IsRlpDgzjRvfiiydRJ27HbEqBNRpIOsevBv8gkpQTJ+60P7XrjxsEZJz3L359czGMrr2PR7F/wyMpfsOGNB5gx+34k9+c+tM/6KNvLf3uVfE5HcwXwVNQjqxr9u9YT3bOBe1t+QyTh4Q8/20F+oIlL//i9YR3iUKhQfX3TZb8HQ+eSGy/n4hvGEI0axOMyyWRB3klVGY5KjeQjWuoIA50RVFlDtTsJ1E1Cl3Qh3SbbgCzZeJZ0No2RzyDLLmRVITPQhRFt5QtfOJKSL543XHSXz8M1Z/4R3TeG6pCNeE8rhj2K5nazbp0AxlaqNZsV4DObharJM7DZYO+bb+IKKhx5skgvWsWC1oZv/bTZxGG3qEiA9EWLjuBn5/4IDB2HzaCo+r1TxSMbkkiS2PxzOXE9a3Pt6hrKOOWi7GvuRcZLUUnpBwbJb6daWIeADwckK+A8Gcn5wdtVSpJYs3LZfl5bs40tu+YxY8JfcdoHWb9pPEt8FyCXPPmJONS+8tjr6Dkdxe7CESpH0exEWrfTt/FFFiw4ge/+7npWrIB1T79F/fgg51z3NXRd7ClWNPj2K2/D1LNcfvPXOPnLnyGdNmlvN+ntlbHZRIBIVQV31NIRdziEv/t8wvdbWwfRvEEkzU2ocbJo9oWJrNnIZfNgxkkkMuTzWSRTxuZ0k01GMTODLPpMDUcdBeeccxUg5vmVx/+YnG8sdSctQJYlUr2v4AoG2bu3QHNKpcThMR4X91V7zFIMAzq2bcXMxpm1VBTiW11yk0kxZiNpTC6XyPz4/eJ7nHbaLK477Xo6NjSJmoFnD5/aMdJ/fD6hivTaazAwkMdp20v7PpOqmnrkIUT7zwTJIylkh2uSOgbJ/8FVQyRJrF9HHglrX1hNe0cQ2TiKhupV7N63kMaWn1NcX4Jkm/qBP+tw7F8OkEfKtAEke9vYcs9PMI08dqcNXRf6lnq8C/fUC4lGRcezsWPfdiFJBtNg5fJ+XngliCRJdLzyKA/pJ+H1qzidEmvXivd1dAjn8ftF4ZVoRxskWD+JmhMvQ3G4BEiVhGybYnOKf5sGkqxgDs2wfDpJZsdD6AN70LTZgNgYN2yAshOuJpXRSEd66dvxOp6Jn8JdPpqf/7xA10ilCpuuJAlHLC2F7S+swchEuOiak4fBr8t14ElsGCKSvGsX3Peb1SieClxHXfn/2HvvMDurqv3/87TTy5zpJZk+kzbpnZYGSUSqoIAir4BgfRGxYUF97R0FFRTFAoKggApIlRJKQhJCep3MTKb3mdPbU75/7DxzJtQQCAn+fuu6zjXJmTnPecpee9177Xutm/an/0Z491OH9QyGhwVnzG5FZw4d2rFCkmD50g5+/4eJPP/UGpbO/w1gYQW+h+x+z2sc9b/PLMuiqfJaMiefzoPPfBtNTTKxZANPrv9fptZ/GM//RwCy6lCRFA1MHT2donPNP0gMtKFqCumMQmsrSEhgmfT0CEBog2PLGqeoprpJJnWe/OeL7NnQjewIEiotI69qAl6/oC3ZYMvhEGPdDrgeD4TmVjMc78HpD5FNx3G4fViKQNWxqIWZTpPVkzg0P6pTI5tMkBnehDWwlVBovlDniglRoI6ODFr1MoxkgkxfFxgZwtkC5p+8lN27xTmPL7gDAQCcTnFuHbKChTYG6seLdDgc4m/d7tz7mQz8+TsPIDl8xL2LsQyDvuf/clj3375/dvFNNApq4gs4nT3gvh1FgUmTdPo6NhDu3UdexUu073QhV6vkVV7H6/Gzj8ReDpJtbuGxNlmG2tLbyUxqY+P2D7Jp1wXMaLiPve0raW2voi5vEziOE0WCo2iaU0NWZFSXByudZqRlBz1r/4HDpeFwuejuFmPIysZR8mrQdQEI3e7cWMMywRJZzpYdnaz79w6UginIskVRsYOyslJ6eyWSyZyIj70YLCo62Pu6vAYtsBuHrwBLN0ECWXOgZ3RkWSKT0dGzKRRZRfX4ARMj2kW29T8sWPBpkkkRXwcHxQ5s0ZJL6WruxxwZINy6B4fDR0lxJR0dJomEPCZlruvjeMQHaROSpCBpvrEsqk0RUlXxb6dTXINNs8pmBWb41ZcfQnIFSOYvx/Ik2L+5jc8t+8aba9NogTn0YSTJQikUMbayEjra9tPd0UJl4O/EYy564jHKG68BLVdZ+nYB5fEg2e7nfjysFYsKepk76UZeyHyItu5FuByP4nRG2bjjfFYW/w4l/8Z39HyOOUCedsKkgyBZjOCR5pewTB2X18npHz2VnTsPNg8PTKCyUhRFjW8g/bll30ByBnDM+AQD257l6bV5IMFo2w6KZq8mnUhgWj50XeHcc0XhRiwmjnHZZeLnpxZ8CaVkHvXvuxZDz6KoDkzDQJJlZNUxltG1C94kSSLW24Y5so9llwnQftNNYoWa624hIoQzUIDDG0RStFdkK3w+Qa84+eSX9Uy95NWLUgwDrj371yj+MpZcci49PSJTZE9ijooFmLE++nc8T7zvAHWzqg/rGQwNvTq9wjbL0il3XcCU2utYt+Vi5k39Ez7PIIS/hKU1YoVF9v7NtIB7d5rIcsyZehfprJfH113L6pO+zsPPfpM1Gy5g9cRjfX7vjK2+fDn33vAfkGRG928hMdCGospMXz6HjOGmqwvq5jZSU9NIKCQKpyRJBJgbr7oV9AxhoxpFdfOTj/4Ohz8fZ34RDm8eSUMjdaCfimoIVvjHCsB0PQeOH/7VnSRTMqUnzMZfpqJ4C1A9HpCdKIooTNWTMXRDx+H0oTgdJMPDjOxax5Xfu5Bk8r2sWcOYMEg6DcPDQ0T6ukmMDKIoKt6SGtyhQlr2xamoD6GoYqvKDqJ2IHW7RTA95ZwZY71SbTqFpsGDv30ISVY47X9WMjQkfNXOJkuBMshmSYcPkAoPHLa/ZrPifqqqyGQbBuT7DuVbBZSbqKvYx/Z9q8jqBh73IAcOFCBrvyZQdjXG4MWi+OkIfPbVlMXG0y3s+eh4AMkqzTRWrcHQZV7Yfik9Q00U5rWwZc9ZVDb2jSUF/pvtlPcv5tav3oHqcJMY7mZ491oAJEVjxqkL6egQAPKEsxdRXp4Dx/E43Pi/t2IZOu2tCUpnLeP6q/+KnspQNHk+SDKJkUEOjOikLCgpLxsTi/F6hX+ILj8WN117D2gBKqfPIJ51o7gDyIqKaZhIioRhGhjpNLLDgebyYZhZhndu4INXzSVY+Gk2bcoVz4XDkEmladm0h8RAF0YqgupyovlC9Ed8bHh+FIc3/5CdG3snyj63E94zaYzna9c52K9/3nA/yAorLz99jAplS8nL3iIsPUW0qxk9EWXyYfrseHtVPrLVzcyJl9PfdTXN7YuY33Q7/cMN9O77LqVTbsUcugIQlMe3EyRLUu7a7B2pY2pGD2XF+5k39U6efelj7G1fzoJpf2T7/nNpO7CPutfY5T5adswBsqIqfOf+a7l21XcEIFUkHC6N9155KtVzZ/H734u/O+kkoYb08pYf2YyOo3YVDn8eE04SBQhmJkVg4iQs08DhDTK0ay0XfLGKdS+WMzAgwGjXwz/hm/+MECwKYVWspmDWMkw9KwCxKcCxbbYQyHjzlVZDaTU7dwqHSyZFcFAUcGg68ZiFrGqCJqHkIoWeinLyMj+zZ4uV9WuZruea6dtAuK8vg2/+JwGhWV5WJtqalZWJV2GhhqJM4HPLbsFbdfhVvUNDAqjbmeP+vmEeevabLFn4Xaon7ELyfQysJMsW/IzdrafxzIuf4j0n/x+QxUrcfVjf8d9gkqRhKRVgdLJ45q1UV6ylrHAXfYPT2LDjg8w9+fWf6X+LXXTtuexY301cU1BkHbffRaismMt/dAV794ogVlws2mvl5eU4gIYBViZBPOPFV1VDKjqCw5eHIxjCGSxB8waxslliA53EtU7ypolG06YJa+95GPQk9fOb6O0zcBcWMLgnhuIrwJcXQrcUFEXB5ZUZ6RVEPk8wH1mSSUeGSHTuQfOI9nJ2Rimdzm1xtq3bTSYeRXV5CFVPQ9Y0LMPAyCaxjCTeoG+sJ6nLlXvZGWVJymXIbQCZzcLcJR1Ikomqil0apzP3OuWUufh88NX3PAShw/NXy8oJKABEu76G0xHnyc0nUZS/jymNV6IoBpK+j5qyCG1dc9l3YAWnLfw+PUOzaG2G+oCFm1dX8TtcG1+wN95swGln3I85AHXMx6E9RWP102R0D6FAJ0FfN4+/8BW2753CnHnH+PzeASuaUMDVv/kYf/1dF7Jk4nSqWG4XV1z/STJSaKzF6cSJwmclSfhwJgOWniYZi1PUdAqKy4ORNfFPqEfzBtHTcRz+IEY6Sc/WZhoaC3G4NPx+4QsP/OIOdN3A1EIkjSAufzEJw4/DF0CSFQzDxK2qRJNJjGwWhzeA5tAwM2lGdm0k0dfG/p3VVE3LIxoViS274C/Z34ke7gYjQX7jHFx5ZciKjGlJRId1Qgd7CHs8wk/tFmXji9/shaws5/w1G/4Li1YOs+7x2rEsuE3Dcrth6dJ5BALwpdMeo3hW0ZvunGEMfhjLgt17grT1LGTG1OsIBQeRHPOQlQzV5evY1bqSnsGplBTsZXB0CkrXBoq9FpYlHbma32uYvUA4bkCyWo9EloqSLcybdhuDo/XUTlxLz+BcXtpzIZVN7+zC+5gDZIDamdVcd/c1PPC8yuxl07juFz+jP1rKb38rHlpTk+Abv9wiQ1HSrskUVDZhmgbZRAzNG0BWFLBAz6TYe9+NJLu28sSC6xmMCoe54gr49pNuDuzqp6bxTEoq6rEsAWgtm0ZhWWPAWJIkJEkim4jSseYeHMECiqadSLR5DaGGhST9uYp5wwBPEPr27kPzF+AOlYBlMbLvJXo2PkLQPcLK7//4kOvIZAQAtoFwT48ocrCdweWCWE8L06ZtIBb3sPZfKfKsHQxhcelbVA1KJARYGJ9BjsZLGAlXcds/vkp1xQ6WLhlhYgAKggeYM+UuWroWoxsaqpKF5D/pHShh74HlLJ57GZqW+e/OJPu+AuFrgBRlhbsAWLbg1+xsPY9HHhG9tY/5Kvwom8Pl4KKvXcSap7M4KGDW55uoaJpKS4tCe7sYr1OmiMxxLCbGmCyLhWRJYwldww1IkkqspwVnsBhfeT2y6kRPJhjcv53w/q24p0/A41kyJv4hmSajYY21D+/AW16FM68ERVbR/EF0VOGfmSyJSAJVc+DweDEyKUY69zDavJ282pmkBntZf/8zyO58audMG6M9KAookkE2mUCWFLKpOPpwDD0xjJlOUnpKEcH83FasPUHbxXd2pmr8e/f+5G4sPcXsZe1EkjWs+9tPkWSZa2/77Bjl4kgq1W3eoKYd3Ba3YGBoArFkIbGuQoajUFm+mwkFu5Blham1D7J2y+UMjDZSW/E8zR0ns3/LL+ntO4npjfdTwpFnkuHV+cwOR25b2v7/sTLJfS5W/BbcrkGm1D5OV/9MUpkiaioH2dtcRH3j8SFpe7Rtwenz6I7UMDSYZuV7LmT6kiaGIz727hXPaeJEUWui64Jyl8mIRNIHr30PN317M8FQMdHOFlyBAnzFEzFNE1lzkYoMMdKyFVVVMLIzyS/XcqId7gJG+xO4HCGCEyaCLKO4/FiygomFaZpEUgksy8LpCyBLErGhfrrXPYSnoAh3WTVbntrMvu3DTDtl/hhlw+WC1JCEkY6DqaAnEmTkIfRUlGwiQl5TJZWVxWN/7/HkQK4dU8eLkYBYsP79+39m+flbGBqqY/9Lu7j7W21IksI1v/34mCT6kfTkHe8j9s5LMpNH39BUHl5zAhNK9zG1sZuAQ6W6fC1tPQvo7JvFtLpHME2N4e7HaI3W43ZFmNLwERTFQC647U31Nn89O55AsiT7sbyXIsf/RO2EF5Blic6+eTQ1PsLTW85nxw6hP/BO2TEHyM//awM/vETwSmZf/Ue2P7cHV96JtHWJ7NPo6GtLDv79l2soWfwBLNMkPTqAO78UyzQJH9hF19oHiXbuQU9GaTjrSgajBbhc0Pf4T/j2025c0z9C02yH6BUMY/QHSZLHQLH9vmVZDDdvZnD7s1QsfC++clEN7JxzDpZljVElPB7Bb25uVglUTiXWvZ/9ax9gYPuz6MkYTo+TC39wKa2th4LhwcHcNXm9Ihvc0JDLDGvxK/nHA2fQ3Hc2huFi/hlPsmLW49z87VdZNfDm+kEODYpq//z8XJCs42I+VXktL7X+gWefncYf74C6CTeyZP7PWbHwJ6hKBkXJAjLIeexrX8LTGz/DS7sHWXHCX5m+6L8XJMruU7Hk32DFfgF6G2iN+PI/y9KlKo88IhqeT5r0hod519roQJjrzvwB/UNOimacQqznAEaymlFJFNhpmtiiLSvLgWNFEcFltH+Y5rYCAuX5jLTswJlXir+iHiOb5sBzj2IkhjHjozh8QfLr65EkePTWB5EUjbQ6gUBdPsnhPjyhMhSPB0kCU5LBkjBScbLZLE63G9XhIBWJ0L1lDU6ni+JZS9CcXjyhfBSPCtkE+fkCHNuZ0OoZjYwOJ9GTKcL7N4NlISky/pCP/KIgspwrGrTbzdlFg3Y22c4gZzIw9YQOuiILaR45mYC3ndMvvZPCQDvB4GcPaR8Hb85fTTODrmcwTQ/JpMyI8R10DZYs+gKKorN/4HraBs+ku7+O2rK7mVi6maK8r+DxCLGNuomb2LRnPrtal7KvfTmNtbuYM+1pAv43D9hfjWphm72IGJ9JPhZzgiT7oOA+rNiN+OTHKCFMb/gySmuW0jkEmzbB0qXv/Hm9k3bH9+7hrz9+iLIFp4PqpDXTgVo6HeRcF6f6evGshofFuLZ5/jf+cgOBqtlkUwkyiQjBqkkgOxjc9izp8CDZxCgOfwg1WEJxmQunE+69/l5wF0JoOvlBHVlR0dwBJEVGsiR0XccwsxiZNIqsoLo9mJJF//5thPdvpaBuGs5QMZIJbncZppGhoEDMIfZYq5w8Ac2bB+ksqaFukn0HUFxuHB4P9dMqCAZzvGJ7l8ceq3YPdXuhChDu+A4zV5awvfOz6IaPxR/uoLJkPesfcIzdiyPxWcuysMwISC7ACaHbwIKZTR9h6qSb2d55C7t31/HEulGqi7PMaLyblYu/j8cVBqAotA9TmsT6recwGp3Ijv0jzJ72JJUHqV0vn0uO1I4rkOz7LJZSg5L4HZUVLbT2foiEeg5VVU527hQLOb//jY/zdtgxBci9bf1876Kfk04K4q4kyQQbT6C1w8vJJ+ts36FSXS2qG19ulgWt0dkoPgfJwW40b4BYdwttT9xB5MAuTF0cs2rZByiZfRoOB3z84/DVhyzy512GrLkOmdnHg+Lx/7csCyMVp6BhNgUNs8XvTBNJlqmqgkWLJNrbRSFhPC4cacECqCga4gcX3ASeEiYseg+ekmryaxrZNpTHtj+L7wgEBJBoasqBYVubHkRbqfvug46OmwEZSTKom/g0cyd+h7qZh6eQ91pm6a1Y4S8y2FYH/ICQ8gUs47NjbdA0NcuiRULZZsMGeO7ZOdx6399pqPoPS+fdQGlhK6gNSAV3cvLCy6ic8G0eXXsd9z36aTbuEpLMb1f/1ePNJOdiJOfiQ96bP1/I2T7yiHDgt9QT9ji271xwPc0vteKd2EQ2reMqqmTb5gyOYDtTFlQxMiIyx5mMAMh2FkeWYe3TEYLltSTCg0gOB05PHn1bnmF0z1ay0V6c+WVofj+B0ok0LWoinQY0P7qjEIc7Dz2bEt1lPC6QJCxkMLNkkwkBwvw+ZEkhnUiQHOmiZNJMHIF8ZCSMTARFTrHsjBk4HCIrPV5Cumb6RAY6OziwrR2HU0PWnCiqzKJzT0KSpLG2T+OlX+0AbBjiesPhXPeISPRCFAUKfPs4afbvmDxZRlEKxrOt3pRZlo4V/TFG+B/ow8tJxUxGpA+iOGdSWQn5Ug8AC2oEyNm7+0R2tSZo7TqR2glrcLl2IssqzpKbmFM0C0W5hf0dM2juPJuOgbNpahLj1uXKXeNbDZD2MezivWMGkpUCpOA3gW+SXwzmQZGXykohztTd/d87V6174EXu/P59mLKXrG7hcPvISrU8fdcLnPup5UQiOf64DY7tovChIci4JyErKpHWHXhLJhLrbmGk5V+E92/CV96Ap6QST2EZNbNK8focog5HLsaZV4+iusgm4mguHyjiwWeyaUw9g2RkUTQnmubGMHRiPa0oepqK2UvQPD6MVBJVCrPi3Hnk5+eEd3T94MIMB8svPJHHb38C2elAdeUBKo3z6yitLkTTxPizQbBdUCs6PYnQn0iIndrubhjuWwkOiYCvn4yeIBytZUfHNLwzZX7yk5zYjP0qLhY/X090xko/jxW5Divbh2XJWM7VWL5voqgeFEVHVXQWLhRy2Vu2BNi9/WTauudRO+FZmur/SZ5/FNl7OmWl32GF+XV27p9De985PLVhNhXdIikXDDLG+36rMed4AcmSJCF5zgXPuTiBmgLR6Ss/X8ytL774zi1qj2kYf/RPT2Ec3JssnXcaAJKisvfv32d27bmMjk5l2TLxt5ZlsXfjfoZ7R5m8oJ71m/NQ/eWko8Ns++PXKZiygPCBXSQHu8aOX7ZgNRNOPh9TzzD43I38fFsB+SddhaI5x4758kyxTaewOcd6Mka4bSf+ijqcQdE3M9HXgprpJVt+EnfdlWspVF4u/t3XBzt2FNBw8U/GzsXvzTKxShsDwqWlAji83NJp0W943ToBMAC8XpkFC2BBw5V07NrDzd9e8dbAsRnDGroArDDD4aVIkk7I/TDW0DooeuKQ7VaHA048EebOdbP++f2sXb+IW+5ZweSGAyxdUUaJ5MACqir2cMUVghv9xBNwyy1iK2TFipcVIP6XmqKIRcHtt4tnd9JJx/qM3n4b6hlh59q96FkDR7AIT2k1ZiZF/5Yn2fnofpoWf3KshWIkApl4mNbmA4SKgzhClYykijCNXqKt24kNdCOrDkb3bABA8xfg8PkJ1TSgqRr/uf1pSurr0B3FaN4CLFPH0i3UYAAFCcOyQE9hpNKomoqkaSiySjaVIDXci8MbRHN6yCYjpHr24dQHkRwWbvcMRkZy2/92UDBNiRnLTqRu9gwGOgYI5ClMmTsBl1sZC7B21wg7SIM4zsiIAMb9/YKupChQW1/B9OmgdF+Eqltoxc+8pWBjRb8Pib+RzbpIpb2MRgN4XA9SU52lHhaJTAAAIABJREFUsHAekPPZ/HxYuLiEwb4V7Nu1lZ2tNbT1u6ibNIVytQKXDNMaXkRTDRLSQgYHRdvJ1lYRrEtKcp0p3ggsv14WGQ7t2nEsQfJ4KyzMbbE7HCLglpYeW0nbo2X3/PwBUvE0rvxCvKXVqB4/4ZbttO58CeOy2UAIp1Mkd7AM+pv302OkqZ1Vz4svunEFCxlp3cPQ9ufJTKinb/OTYGRx+AtxFZTjKSrDqUHLs9tI9ibISF6cxY0oDheZRATN40fSNIysjq5nQM+ArIDDg6KoGHqGaH8rZjaLq6AcSVGI97eSGdhHnk8nP38eQ0M5wRGbvqGqEKqo4KzPXERvWz9mNsOkGQWUTPDj8eSoGDatYry/jo7mlDCj0YOtEWsWCHEKx+W079zHLd9dztf/8U36+4VWgf3avHl8Ib6IbTZYHg+encperJFPAEksS8YwVEg+gWxFUItuhnEx1u2GRYtkpk+vZ8vGHezddyqt3auoq5OZNrOBoAHlJa1Ylkx5zTmMjsKePUJSvb5eLGzD4ZxIi33dR2LHC0geb06nWMy2tYm5qaND7LyXlb3hR9+yHVOAPNofRs8YSLJCySxBF+h54SEiB3bR3nsZTqfgMg52DfGlld+hv2MQWZbwVc+m8ZyrUVSLXbddRzYRoffFx8eOKysSKz79cRLBZViGzqabv4Dm8TN90WdQFPXgtofIAr88cwxCEU9PJdj7j1+BZTL5/M+iOFxkE1ESA534K+qQ1XpGRwV3N5EQmYiWFjGYCgsFYC4tzYFhl+u1U0eWJYLTCy+Idm02T6qsTMgaV1eL/5tD6Vdkjg9HyvoVlvo3WGnAYni0hpC/E0VJgxWD9BPgWvWKj7hcEqcsr2PBCbB2LaxbV8Xum0X2e8mS2yk82HN/7lzRb3rNGnE9O3fCKacIgZH/1qyqbXV1gl7xzDMwc+Y7tw30TllsNI6iKWTTWdwFZUiSzGjrDkabtxLwzxzL8JimxUO/f4in//IkiqKD5KLm1PdTOqUJM9FLuG0Hif5uLDMLGEiqm8knz8ZwlxAdNYjFM7jzShhJuND8+UiWQTIVxxMsRFJkDEOHdAZLMlCdTiQksskE4Z5WjHgEZ6AIy9CJ9bQw0rwZWVIoqq9jyhmr2Lkz14bN5ifaWSanE5y1frwni+4ZcKhynJ1psywRkAYHxWI4HBZBU9NEcKyrE+0knU6wyqqQJOuQIPNmfdaykpC4G0iTyhTTPTiNgLeHusonyXdsAe56xWckCYpKyygsKWNgQIiTbN8Fre3i/ErLb6YxTwTa6mqRjXrpJaE+N2FCjudnFzK+Hlh+rYI928aDZFua+lgH3ZISMVbLyoQIxJ49Itb8t1m4PwKA6g2ieQKkRvvp2fgIgYIQoyMZ9IO+0LGngz988WYysSiWZZA/fQX1S04lv8hBy8NrSQ13k+hrAUCSVYJV9UxacTJdO9sY7OjGU1TOUNKHv7QKRXOQjkVxePwoDhe6nsbIZsHIImsOLFMCI0tsoIvkcDcObxCH20M2FibW00y8t41gWSWNK89m69ZcT2WvV4DJ8aqNrhKVGTPLCQRyXSjs7gx2AR7kZKOHhnIFiJomxnpZmRgPTidYwxkaZlXy0ye/CYgM7c1X5vzV9v3xoHlgQPjOeODs95ZQFPo1RaG95PkPUF60hZLCvajGc1h6J5L6SjUgr9fBCUtmM2OuWLDu2wetHcJfp069jeJ6MHvFeTc0COn05maRAZ8+XYDzkZHcPOV2HxlYPh5BsscjnpVhCHC8fj2ceebRX9QeU8gyf/VsHrttDalYil13/4gFn/0NejqBpLoIZ0qZPUcMhm++78d07u3GNEy8pTU0nHUVlmUxq2o7J//yYq6/8mbikQRYICsyhVMWCHBsmmy99Tp8JdVMOu8zIEnoqYToLvFyDfUxoGySCQ+y9/6bmbD4bEL1MwXNIptG8/gJVE4eA9SplKBJTJqUo0iUlBx+UcoXTr8BrXQW+VNPESt4xAOfOhVOO81ukZOz8Znd15OwfiOzjHbstnpD4Wry89oO/iINRufrftblEt1EFi6E558XA3XHDpF5WrJE0GFcLgHs584VoiiPPy6yNCtXint1rJ3taNrKlfDrX5s8/uB/OHvp1aCUge+zyO73HutTe8s2oaEMzaGSVlQSvS3oiQjp0T5cwSB1J8xkeFhMzK0bt/LMnY+TDocxDYOaVZdiOfIZaN7DBR+fzh2tj7KrXyzQQMZbMoEkhRgpJ6gmnmAhnsIqVLcLyzBIx6P4QkVIyGQNA6wsDo+GU9XA5WC0q4/U6BCKZOEIFmJmk0T6+/DmF1M6dxmypGJZWRKRGBVVfoqLxTi1t0dtfqKtevfyTJX98//O/zlKoIzVn7zgkKyWyyX8vrRUgM28vNzELRfeBhzhQtY2c/hgn3fo6J1FJuNjctNjFAQPgJF43Y9KUi7D1dcnAurWrWIxb2ef9u0TwPXss2H3bpEle+ghMQ81NYnj2OD29cDy6wmQ2FzJ4wUkS5LY8TMM6OlO8tKGfVR5Po7LlQTXWUj+Lwj+8rvcFp81j8593ciYDO18AUtPozlcKE4/iruAeBQK89PccPWNRAeHMDMpCmacQqByOgNtPSx7TxHBixq474c7MLMAEp7CcnyVU+htGSQei+KrqMFbNBF3qARZVchEIzh8AVSHk0w6jaGnUSTweN3gViCaZKC/Fz0Zw5WXDyYkh3uRJIlART0FNU2YpklPR5jpiwrG5NnteJhMHgqaQYwvezza4DiZhG996LfI7gLOuuq8sS4YTqfwh4IC8bIpYJIEUtFtY/fu1WKszdnOyxMg1bbxwLm/HwY6NzEwVMCmXR8gq7tZPOM3TCjdAZIfjC54FYBsm88ndm2nTxdAubk556+NjcKHRkaEWNq0aSJh9cILYg6aP1/Ma+m0OJ9wONeO8s2A5dzOWk5M5FjvsAQCQj15eNhi754Bdm/4GZOr7gdtKpL/K0iO2W/7dx5TgLzg9Nk0zq1lx3O7MVICsFmmSensUzBMiTlzBE+5dVs7pmHiKZrIjEu/haQo7H/oD4w+0cL1a77F7669XQBkIFA1nYZzPwuWxY6/fBdPSSUNZ30CSZKI9bTgKZyArIps7vjWbbIiWqgUFMgoQQnHxV8DcqWniYEuVKeL0dYdxLqb8Tpi/OqZL7zprGgqBdu3i4HvWyiAfjwunHvhQjHoPZ43Ps7+Le0obh+uokY8xZVvKgBLWhOW5AErwUXvuYKsfrBSQXKAOvWwrsPjEfLYixeL3tIbN8K2bSLzdMopB1WHfM9xwarfsb+xnEefv5q77iqipkZQEYqLD+tr3nUW8jzGwuntPL/5cuZNmUJFyVYIfxnT0pE9Zx/r03tLpqgKn7npSn5ypei9aGZTKJoLyeHFlVdJJiNA4t0/fYhYXx+mYVC96lI8xRUkR/oJ736eii9czEnnLWLfpn1kUzqyy0ugcR64S7D0LJom4yutFduymRR6Oo3mC2IYFpZsgJkBFJw+J+DF6YQJDRaxHpmRwTCp8ADxoR6ykRG8oSJiXa0k+rowM0O8/6JLqJ/lR1HEVmsikQPG9ta/LWlt9zpOJkWmuLcXtIr5SKqH3l4RLITAkADbpaUi2NqZZ9vGZ1Zbdw/y+bN+y4g1lZ5N/zl8n5WLABHZmuofpDi0h7LiPYAE6rTX/ahtkiTOsaREXEtzswDCfr8AC8PD0Lx3lKbqG6nP387GnR9g29bTaG4OMH++AAO6nqNKvBpYtq/3tYCvzds+XkCyLENFeYJw+Q94Yct5vLjzHE6c/TtI/h0ruw0K7nnF7uK7zc675gwe/fPTGE43RiaBJMsoLh+lTTMYHhFjePTADjKxEfREhNCUhRTPWIJpZAm3bqW9SOXCq1fzzx/9E5CQZI1A4wI0XwnJSC+evGLchRW4C8rANEiGh3H6gkiKQjqdxsqmUSQJRVPBHURRIFjlRvFnGDkgkY5HiPd1k4qM4C4oQcJgtK+L1Egf+UsmsGpVLaoqxszoqBg3dn9lmz5h9yQHMUZHRkSmOJkEJVCJpDpIpxnrRJGXl/Nde9y+WkeIlm3dKJ4AzqJGzGzydf315cDZjKyFxF+wLJ3RaAWqcjC9bKVBrT+sZxcICI2EGTOEr+7bJ4ByXZ0AvM3NJjXl/+ask//E3tbpbNzxER54oILGRoW5c8W1pdMCcxwJWB6fSbZrNY41SM7Ph8YJf6S7tZqN286kuvRRXGzBGv4fKPgrknZ4+OVw7ZgCZEVRWP7Bk9i5dq/gJQFYJnmTlmBEeygrK6NlaxJFUyCVpbDpRGTNyUjzFnrW/xvHtAn89Qf30dvaD0CgcirTPvRlkCT23PNzXKFi6t8rpJ87nrkPZBlfWe0h5yBJEgpxDNOL1ysChWkWHVyFWgxueYTmR+8hExsZ+4zDpbH6y+ceNjg2TbGNt2WLyNAYBlimjiSrAgR0rCHSs5EVX/nqGx7r8yt/iFY2j6Yrb0BWVBSHm1hPC+a+rWDqh3dCzhUgl4DRid87YF8VKHXgWHR4xzhoXi+sWgUnnCCoBZs2ieucPWMfJ037Bn5vO3UVcOX77ufFXZfw9IvXcPPNMvPmCaL94SwG3k1mRX/CSbP72br3DB5+/jouO+cDSFIKYj+FdzlABpi/ehaq0wNImIaB6ssjmFfMnhfbKa8pZOJEP9G+Xkw9g7eiAW/JROKD3cQ795Ds209/aw9/+NrtZFM6oFG+9Hx8hZVkExEwLbRAHtlUHDOSJh0ZxFNSLVT7sJBMA8u0kCULcI3xDFXVT0GRn+kBaNm2lQ1/20i0q4tI+y4kRUPRVPxBNzXTKwARMCxLBAq7KHY8ODZNAYq7u0VQfuLO57AUNym5GEXR2PLI05jpKBd8/gwCAQGM7VZhdusom6v81bN/hhKqIVN+No3Ty0iH+3C7ErjzD59AJ0kOLN9VEL0eVU1SXrLn4G9cb1pKXZJytK/ubjEvtbSAIseI9f0NOdZJQ9VLnDLrJaZU3c4Lu37MM8/Us3OnWAyXluZ6xr4aWLYLo14PJDuducDtdB7boCtn7mdS1cN09dWwu3UVDZVPUVzQDEYLZDeCY/6xO7m3wQL5fmYuncqWl7JYho7iDOKdUE/K8NKxq4XTzm2kpyeMkU6iOD2E6mZjZdNEOvYQ69xHaqiYn132q7FC+oKZpxCqayIdjWAZFqovgKFnyMajJAa7weXCFSrC0HWRFFBkLCScXgWHw+4FruCvqmDS1Ao0Ocld39xAuLud4R3PIakqqsON5pJYfvZ7kWXGFC9lWfia250DbLZ0fSQiQHEkIn739+sfRPKWkXHWEOncx8O/uhMzPcqX//SJsWI9mzpgj1XDEFzsb13yJ7TCKdR/4DrMbJpMbJR4Tysw8uo3+VVM8l6GlbwHiTihgL0r6wb3+5CU11HlehULBsXurA2U9+wR5x7yriU7tJaaigFqS/9OdckDbNn3UXY0f5TWVplZs0SG2e8Xc9F46e03A5aPJ5BsmXFKvNcze/Iintp4Neu2XsbS+TcAaazYr5BCv3pbv++Ys0L/9etH0DM6qkfcdc2bh6+slrZH/8BI37lUTqkYU8cK1c1ET0bZe98NaE6NyQsauO1bfxs7Vs2qS5BkhZZH/kz+pPkUTz8JyzTZ/befISkKk88TwWSMd2xZEO/C8Intjng85ywlJXDJJRLt22v50mMpcDvIJDO4fS4qGso475oz3/Da+vvFgN62TTi47cyGIXhc6cgwLY/8gYqiyBseq6dHFH/5Fn9+rDVdYrCLnufuItK6hX+M/Omw77kkaVBwN1b0F4KPLMngOhvJ979HnDHx++H008XW0Jo1Bps2V7N56wPMnXonJ87+DV73MAuabqWpyWDNpi+NZZyXLoV58468qOC4M6MDp0Nn+cKf8q8nf8Su1pVMrX0EzF7RY1t6d1cCrf3XRtSDqT+nLw9nfgV6IkxqpI/evbsoPHsBi8+ex9BABnewiKGd65AVhfRoP+gxbrzqd2STOmDhKJyIv6QOPRbFSKVxFpVCNkOyr1fIuntCKE4X4o5ZmLqOaVn4i4L4/cohkrDBoMjeTKiYQvfza2gf1UglLVSXDEaGT/z0cpxOBV3PBQY7e2Qr4NldKAYGcpLJiQTgLUdxeJCicSJdLQQdfUimTmmpyGjYMti6LoBfJCIAdkcHuKeeh+wMICczpEb7cST30/bkQ9Q2VbwpuoXs/QiWXIgV+xWY/aA1CRrAEWZMJElsV5aViW45zTs3MRQrYXD0HJLpALMm/5PCvB2894TzaY2tZ8MGBw88ALW1okuP3Sbr5WA5lcpxIF+LsyzLOZBsZ5KPWdDVd6Cpo5ww8xbuHbye57dcwTnLvySklfV973qAbJoma/6+nmD9fEwTvGXVZCKjpEYG2f98Lx+8spHy05owLQlPYQXJvlaSAx1YeprsaAeKVsbGx7cBIDvc5NXMwjRN4oNd+CsakFWFbHSE8HAvsuzAEyrBzArOkmwaWMj4QhI+n3+s2NXrFfEiEAC/382ZH13MHV/fhuJ2YKKiqjpzVk5n+snTGRgQvuXzHboIteNodzdjCpU2haerC5SiJiRJItnRzkjLVvIavKCn8flyfm/viESjIjHW1yckrD0N7wVFJTs6wkjbdqJ7nyMbGeDWNxNjlVIouBcr+mPIrAM5AJ6PIHkuPuJnGQoJeuPQELy0aZT2/U66us+nb6iOedPuwO2KMbPxjzROrWbD1pVs2CDA9IIFosjN5xP33ZbNfjNg+bgByWYPSAoNVU/R1r2QvW3Lmd7wTwryDoC++23/umMOkBMRQa2QgGwigq+iHlPPMLx7LYnoakIleVxzy8f5800d+Cvq2XX3T5FJESoPEQsnyKZF1rRq+UX4y+s48NTd+CvqKZy2GNPQ2X7bd1A9Piafe9XYd9qiH0gS2kFw7HSK4rquLsG9O/dcsXUzeUEDf97/S/7zl2fobx+k6aTJLD5z3pj07CuuJyGA35YtAtTKcq7tlU3iDwYFx/jWT99ARZH1moHSMGDXLsEv6jy4CJVkhbIyaH74TxgjLRQFoegI5C4lOYgU/DoEv/6mP/t6FgzCGav3c8Lka3hm46Ws334Jm3ZdwMfefwahQCce5XFWL9zGnPoJPLruBzz8sKBnrFolOFbvelPKwOhgRsM/UeUMk6sPFo/KRe96cAyQiCRA1rBME8Xtx8wmSfS3I8sK2VgElwvO+czZrH96CF0OkkkmwZTJjLRz4efP4Y7v3QuYyIEKKhatIDPaR3xkgODESZjJKJED+3EGAjiC+XjKa1BUDUvXsSwDywQVC4dm4vHkgqbdlsrjgUBA49rbPsFLj29h26a9FPi9LL3wJLyhEIZxKDC227P19YnXeMqFYQiwHItBcW0NhYWw5d47CKg6V11/yVgwsbd+baWv0VERbAcGRBAqrqmipAQ23vkHlEgHP3ri63xu2dYjuveS+wwk9xlv38NEzE8TJ0KZ44d0dgfYvPt9bN1zLlndy/ymOwCJ2spmJvh+zPa9J7Jt/0c5cEBks2bMyGWMx4PldPqNOcvHDUhWG0Fy4/cOsnTez8nzH+yCJCmg1ByDE3p7zTRMLElBkiRkzYEkKaRGesHMoqoKeXng9xdx5lUXsO6JbjLJBKrbhx7rZ/K8SjY8tBkja4AkUTp/NZgpRvftxVtWjypLRDuawTJw5BXhLatGcwfBsjCNDMgqGDqkTQLluYWsz5dbYAUCcOZlC5m/rJo1960nlYizcOUsKqZOYmREwukUC1+7/3g2KxJZIyPip+2vlpUT2NJ1mDKvimnT4C9fuZeaWiffuOszyLLwyUhEvKJRAQ6jUeGvkYg4TtXkQqZNg79f9xtCeoJQrRd4lZZTb2CSWoUU+uXb/kwLCmDFiU/QP/E2Xtx+Dq1dJ9A7OIX3r/wM2ayEKq/j5Ol/p7+8go17vsHjjwu+/cKF4rN2C7w3C5aPC5Asl4KlI0lwytxfUV3xggDHSGIH/G22Yw6QF581j/tvepRsIsL6n32cBZ/7LYO71uNyWZTVlgDQcOIiJu5YiBLfR/XEJPOvuIj3XL6Cr5z+XQA0T4DSOafS8+Jj9LzwENMv/RamobPzju8TrJ5G5ZLzx75PdLAw0DyixYCRTXP6GSoHDijs3g2LFolCq/FZj2BhgPd95rWLrAxD8IO2bBFCEaYpMtB1dXDggHBaENmm004bX6j26qXf8bgoatuwQQRd+1yqq0XGtaoKPnenqCh+K+3ejprJ+YT8bZy17MucOPs37Gx5T26bSS5haLQEsHj/+0Xrlsceg7/8RRQgrFx5qKrfWzXLssAaBcmFJL1O08q3y7xXQ+SrSFKKafX/PvimG3xXve7H3i02+9QZ3P6zdVh6hv6X/oPqDuAKFSErFjNOrMayIJX185EffZxdz26jdWcveUGL836/mu79/UiKhOL0UjTzRBSHm/7Nz2IZKSRJITPSh6+kAldhKWqwCFVWMM0syDJSFiwzQzI6TKg4Q0VFNV6vCLheby5j6fWCYSjMXz2HhafPEXQmKwfkbP6x3YFidJQx4FxYKILkgQO59k8VFaIjjccDW+4xwNSxLPH7vr5cf9ZUKgeOUymxuK6vF1Lw5eWw/ua2sXt4PPqsrBVRWfY8FcVbeGn3edRNePbgvJMlo+fTPzSRxpoXaZj5UV58UVTt29mpurrcVrUNhG1J7NfjLL8cJNvPx7KSYKVAyjvqHGDJfQ5W7EYgTVX5iwff1UApB8fCo/rd74SpmkrD3EkMhiUS/W1EO/fiLZ6AJCtMmFqGxyPG7dJLzqCsqZ0tT+0gm7U4ZcUkll2wiPcVfRRJlnGV1OIprSR8YC/hlu1gWoSNLA63H09ZJZonH0VzHwxpBigqVjZDZrSXVO8IC08pxu0WoMwGXh6PAL+SBMVVJbz/s2eSSon4l04L8OzzCf8cHhY+F42K+KooIqMai4mdGjvTHAiI8VhRcfAGyBoojrGi2kQi1wM9EhGA0AbGJSUwebKIz243/E0XdU3Ho7+iFFIUamP1Sd+lvWcOw+EqvJ5RLMtB1pFPZ6eMQ02zYoXIim/eDP/4h7i+OXNyIklvFiyPB8mWZfureTDG+pCkoyubKck+LPf5kLwHlzNGY9VTB3/jRPJ96u3/vvGFam9k8+bNszZu3Pi2nsDoQJhPzP0i0aEY/rr5TD7vanbf9X2u+dlq5q+ejWGInrrxOHziE4dyVm//9t+44/v3kU1lcQYK0TMpmj78NbzFlTQ/eAulc04lMLFxjFJh6FliPS0EJwqps4Edz9PxxG2c9cNf09kpsWqVAMivZzZR/ydP/B+9vWLgbd8uHM/rFQMwkxHtzWz52eJiAYztQPJa1t0tukJs336QhnGQBdLYKArfxpz+XWDm8EchsxbIjntXBqWeex/5ODv2i0yYpqbw+V1YVo4/VlUl2qTl5+cmySPRX7cy67HCXwFDiCjgXIEU/C6SfHT7r5mJeyH2MzAHQC4A71XI3gsP+/OSJL1oWda8t3oeR8NfAX7xpcd44aGXiA8M4CutQlEsambUcs31p5NKSWPBStPEZDtliphg+zpG+Ni867BkN7IskUklUB1OvMVVoLrwltXgzi/FGSpGdbowLYtsLIKpZ8gkoqQHO5GsJPNXzmLhqY2HiHXYmSY4NMvxvYt+iqWn+OEjXyUSETtENoVCUcQYsxUwOzrEGFRVAZbLynIKWpqWq+bOZHK+mcmILc/BwVwmtLxcVKC/m3rrWulnsUY+hd3dRpgGkpu23nN46KkLAQtF0fB4oqiu6WPtsvLyBNexrEzMgXbxkw2aX07DsAv5bLBsF2GZRhQteR2K/pj4eqVc+KtjwdG9dr0dK/I1yKwHZHCtRAp8A0l+FYWq17C3w2ePlr9ueqaDG77wINHedhRnAFcgiOpQ+dj338f0eQX094uMrNcrwHJ5uYg1pglfPfNHbF/XjqxqWKZMNhUmr2oakuZC8xfgn1iH5i/A5Q+BJGNm06RGBpE0hVjnXrLREfJLvXz4K+eiabkiVptqYXP+M5kcxfHXn/oFZmqUb93/DQYHxbnZCyx7t2hgQAC/4WExnnw+Mf6Ki3PqeZlMbpE2vnd5PC781Y43hYVijqqre2WR7fFqlqVjDSwVMeaQRJsM6nT+fO/nicRLQXLi0FJ4/NWkUmKBoWli8d7QkCtWtOW4bRsPlm3F0PFg2a6zsJL3oCR+BFZc7Lq4L0byX4MkHT3OpGUZYlGb+LP4XqUGKXAdkvPEwz7G4frrMQfIAPFwnH//7j9s75qO6ivkkosiVE0RaPCJJ0Tx10UXCaA43sJDES6p/RSJaArF4WLaxdfhK6+lf+saippOQlZyOozZRBTV5RUiIFg03/9bkj17mP3RbyI583jf+wS14o3s86t+jFYyg8rFq+jvF0F20iQx4Do6RBbZDs5lZQIYV1e/NjA2DAGm168XNApZzjXenzZNVLGWlBzZfT2WZplRrNGrRdCRVMACqRCUUnp7IwyO1BFLzSMaDxHTTx/b9gqHX72nqstlc9Ze/WU7uu3klt6KNXgOhwZ8B2gzkQv+8g7cATGJSdKb36Q53gHyzp0W7TuaeeHhbViSh6bF1Uxa0EhpqTy2ZRkIiOeZny+28NNp8f5N197Jpgc3kI6OoDrd+CdMRvKF8JdMxJFXjCu/FEV1YBoZMuFh0sP9pKMjmJkYZjaLJKX43+s/jNPtPET1LRDIcRBtKWinE64741vIvjLO/uIVY8I7fr/wTdMU/cc7O3M1AqGQePn9h/qiXVxmtzuKRkXgHhgQQcTu0zl1qgjS78bmB2b8LxD7MSCBlRX8WzNJMh2go0NiJDqBaGoWqZSXjLR8THQhHD607ZYsi/vldueCrw2c3eMkcm3pbnsBYg5/CtncjMsxelDKHsCNVPgPJPXo0x0sywCkI6JCHc8AubcXmndF2PLoGnp6ZMrZrmHHAAAgAElEQVSrA8xYOpu6SV5MUyzwbB59KiUWd5IkxvX2dW384OJfkImFMY0sgaqpaL5CXPnFuIsm4Coox+nyYUqgJ6MkBnpJj/aiJ8KYmQSyZLH8/fOZv3rm2LO2pedtipLdfs3lEoutb7zvp8ieQs7/8v9gGGK82MC3p0f4rN3v1+MRc0xhofg7eyFmGIcqXmaz4nvsXSNJEp+bMkXsEB1ua9bjySz9ANbIJ8HoEHVEkgukIpCDtLdnGYlUEUnOI5YIkjRXkU6LxcHoqLgfto/a127768t91vZXe+4bW9xaT6MlvoSqRkS/e3EU8FyMHPjC0b9+ywKMoxpjjznFAsAb9HLqZWex8wY4ZSlUTREZvq4u0UJs1qxXgmOAx29bg6GbyJqTqR/8Cv7yOlKj/ZTOXo6pZ8eEQCwji+b2YhoZLEui7d+/IhseZsbl38Xh83DRRYLE/lqm6/DVD96NVjoL3+LPIUky3c0dZHs386kfnMn69XD//Tlgp4fbSbc8xpXfuPw1jxmLCRrFxo3i33bLI9MU2dOTTmJMfOPdaJLsR8r/PZbRB+YQqLVIkmgnV8rFlJYOIBd86BWfsyxBu3j0UTGx21tmiiLuUySi09+XJRZ3YVmHohBJyhUi+Fxp/K5r8Xt78Xv6KC3aSWnBbshuw9KbkQ6z1c5bugdH4LjHu4nsrMS0RQ0U1TaMgRz7+SQSh+7yFBeLSTkWg4HuCFsf20hqtB/F6cZbVoe7vA5nIB/NX4CnoBRJUjDSCZIjvaSHuiCTQMEEKQNWnAs+v5pgyDlGmQCRVTJNEdBlWQTa/7vwlyi+MiLOBWiuIA/e8h+MWC+fv+lDDA+LXRpb+c7hgK7NazFTEU6/YhWyLHzeBnr24stWzbMzbpmMCCpTp4pAW1Dw7gTGtsneD2F5zgd9P8j5otAIwb5sdF0M7IHQdYdsVdsLiH37xMs0RfbRFlBKJEyGBkZoPwCG4RetJMeZqh4UadGiOKwTcTqm43ENEQp00Fj1BLKcwYr/6aBU9NG1o5n1OpaWyUBBaYCVl53B0JBYAMbjuaJKWzSnpyeXObaf76N/fBwjGcbIpvGV1eEprsEZLET1hfAUTURzezFMg/ToAPG+dtxygmRiBNnSMYw0U5ZWc8KZM8Z2EiQpt0iKRoX/qaqYs398+c3InmIirpkkOlr42w/+hBnr55o/fIG2tlxXGVmG/S9sQk+PcPaVK8aAsZ1ldrlyHOdEItebOBrNtTucNEnsVB7JzuTxYpJahVT0IJZ+QFCS1PqxMVzJxVTSjZT/gUOywdmsmNuE2q8YB4GAWNxb1sG5OhqnrydFKu0Gyc1Yu1ty99fhAKdk4VQ/fdBfu6guX0fQ3wuJ27H8nzn6dAtJ4mhD2OMmgm/eLH7aCk7ZrODM+P2igOvlZlkWd3z3HrJZmPrBLxOonIRlWqhuH4aeOZiFMpAVhXD7bjzFVTg9Ts56b4LOaWezpaUWjxcuvECnrPyVXmJZAqBv3iwGkmfaB7BMQ4DjDY8SkFtxVi3h1ltzn6mpgeXL4foP//41r7OzMyeuYZri+iRJXO+cOaILROjwd/aOe5OUElAOPwUuSeI+XnmluEePPSb4jlOnwmkn302e/G2QVExTIZ4sJKbcQCzVSDSaK7yIRmF01ENH92qSKXEzF8/4HaWLd4tsttF12L0o/3871GyJZnsyLS0V4NdueaiqIgvU0TEeJInnuuGBp0iP9KM4XARrZxGqn41pGcguP65gAUY2g5FJkx7uJN53ANPQufCL5zPU1Yvfb9E4sxRFNg+2dssF23RanNd4AOsomw+eQnw+nWwiSqZvC7JvIk89leun6vMJQODzQetzCWRZGcuUuFwCePv94u87O3PZUl0X32srJ76dnPljbZLkhNfpjCHLuSIrXc8938ZGEWT37Mll5BfO72Gi9/1IxMGCrK4S51KS0idIJKSxz8bjEI/GGB2tIpYIYphOVDlNY9UTgAF6yzt3A/4LLZvNyb+rak4hzTDEM/T7c78LBnOc3ER4hPX/epZsMo27uJL8JlE3oGgavtJKFFVGTyVJDfcR7dqNmU6y+P0nEirxkk1EqZ1aiN+vosgm/L/2zjs+rurM+99778ydXqRR75Jly5Z7NwZDjMEm9BoSaiDZZJPNu9mEJW1JCMm+CUtCdl82u2kkZJcakhA6BoMBAwaCe+9FliWra3q75f3jeDQCTIiXBRtzvp/P/cyM5t7RvdI89/zOc56iaJhmcXm+p6comBOJw5UnKqaheSO4hlIMZ7dhGxqOymm8+KL4nhRCn3w+2PFCP4ptjUxmXS5xHYXJcm+vuAcNDhbvP1VV4nva0HBidXVVHI3v/N6oUKZAQEyKCk6BigrhjNq5U9jtpEkWcyZ8B7f9CCgOTNNBKjeGlP5TUukyYafJos3GBxz0p6aTzfqwUdDUHKHAk4AFVhyOspzd8chx8TWxLCFEx4wpdst5/nkRJ3TVVcKo3oppmCSiWSZ88uuEmtpRFIXogc0EqsegOXTiB3exf/n9uEuraVp0BWY2RXvFZkxtPmt2lZAdOshrt3+fZ74Z4+QLZ/MPP/88/rCPaFR0mlq/Xiw9FTwcAOESjZ71L1AxdiyO8OKRc2ltFeVXbr/yZtbfpdDR6STU2D4Sr/wvy24ZCaM4eLC4lFsoZj5njqgjXChjc6IzuiPgO6EoootXW5vo2Pfyyxbbt1/ASVP7OHnar9CdMQLeGAHlSpSGV942W7Xif4LkrzAMiKcqRhVqz4Gj7f24rI8EhRjSQudHTRMTkvJyMeCGw8XEN10X+7nd4ubcuWU3Fjqe0mrC42aQy6TQfQGcuk4uPsDAltXowQhmelCUcwt7qKjSqamp5Kk7H+e/b3wVwzCJVIa48ubLmbJgAqmU8CxFo+LGXfCUNM+di9MJW198GZ0hZl7/VWIxMWB6POJ83W548fcvAzAU1xjev41HftyJnU/y1V/9HQMDYpBNJMRWWO4tCOMTaSL7bhzJZh0Occ8KBovVBYJB4YncvNlm6ZNJqsq+zMnTfkFZSQe6E3TlF5SGWlHci9/0WbapYPd9XnQ8TAdJZiKHw6V00Gd+MBd5gpLLCVtMJMT3PhYTY1Ch3bKui58VVns0TYjQQzv6cOoaDl8NJW1zcLh9YNro/jDZRJRkz15sI0c2GsWpqSiqQqTSQ9OECnavOsQdf3MvscEYigazP3UKF3/6PNJpbWSlqXBuhWoSLXPn4fPB+ieepGVGO1PPOn2kZnkhhOLF37+MYhoM9hnk8xkeuvXX2Jko337wqwwNCcFX+LxUSlxLZaWw17q6E6ic6LtwJHstlF90uYSdZrPivlxXJ5xRa1bH2LbxIma1x5nYuhRNtQm41xNw/h/Umvvf9nnW4C8h9xq5vIdovKrYU0HxgBp+vy/xA+G4EMh79ggDXXz4ntnRIdonzpwpBqMjojiYcs038ddPZsfD/0EuPkjbZV9Fc7kZ2rWOzff+gNK2WbQsuYbMUC97Hr2d0355G48/DtG9G9h8/4+x8hkAXn1iA8PXP87Ecy+l44AKKPj1KHW1AToPqhiG8Gx3d4O76WMjHfjGjxdVJSorD8/K6uaj185i0sci5BLD5LZ04ayYxL/9m7jxhEJi354eYcQnnyyK7xfaZUrejtN5uEj6mNtZ/uJ4Xl7zd6zfdilnnHQrk1qfAAxRZ9J16puOU7xXYafuxeGIvaVQ+zkjS8eSo6fgQY7Hi8kyhY5WhcF2715xAy5klfv9wvPYNmcim9fEMPImB197gvLx83CV15Pq6ybZtxdFc2CbKRTNgZpLMvfMyfj9cN8tv2PjC2vJZ/OYeZPeTpM7b36US79ZTl+/TW/XALpbo6ahCs0VwLaFrQWDsN2ho0TaGBwsxjh6vcWYREUPg+pA92ZwefzYVgbNX83WrYzE7BU8xjU1YqB9awt4ifjfFzqJRSLQWLeXLWseZ9ue0/n9Mz+ltWEFC2b8HLcrgZ26720CWdGqsD3no6SfwOeN4vNGARUU73uqHftRp1Cfu5BoNXrlB0QcbkeH2M/rFf9Hv19MfmrH1uDwlWEpTvrWLcfXNInqqadh5NIkOjeh2HlsVDSnhmUZhH0GLRMr6dyyg/tuuY9sMomJjebwsfnpveTN15kwcwr7dhwgHU9R31iJv7KadFpD08RqjtMJDl856H7y+UJjkWKiJ6oOrhCukAclFcXKHkDzlrFxYzGeOZUS95vKyuLKxoclWfaDQlXFPc3jERP96mro2HwHazefzIrVf8/6HRdzyvSf0VizRoQlmgNva3Ci+G/AHrwa3ZmmvHTv4Z96IHDDCROudFwI5LVrxT+qrU18yR9+WNxozzzzyPubJvzhD+Cvn8zuJ39J7/oXCNaPR3PoxA5sZ8sDt1E5fSGt536eRPduEuvv5YLv/TOvve7EZ+7izw/+CCufI9jYTuXU04i0n4TD5WHnxl4Gtr2KjUbVzDOJpy0am0xicedICAjA5MkKp54qZuNdXfDIIyKm0d26hPp62LniOTRfJcF5X8KyxBcwFBL7ZrNC8M2dK65Z8tcR8h/kokW/YtbEe3h65U30D42aOdnpt+2vaBFRqD3xE8i+BEoAfFejeK/5AM/6xKMQ61cotZRKiQHVtsX3fO9eYcNlZeK12y1uxokETF2ygCfvWUP/rj3ooVJy0QGGshvAyuP0+NF0D5hZLCPJ/LPHM3fxJJJDQ2x4fi1GNoPt8FA2fireiiYcupNXlu4hNdhPNpnA6fXTuamL9jljaGivI5EQ5dpqp87B4ynGEZtmMcTCNGHC/EmEQvDKfY/gayrljOvOFqsOcXGtui5CfsaN++is8LxXXC7QS2KcPP1uJrX+iVWbr6CrZzKalhU7WIkjHqcEv4/tGAvJu8GOg2sBiv+rR915TFIkny82vHG7xQqKwyHs1+MRdjk0JBJp/f5iNQPLgrztZ8EnTuPZu54QyXVmjvjB3ZBPi0Y+ugfbtMDIUlfn4OKvnI/brfDsb58mHY9i2wrB5kkEqpvRXH4O7UrTsX4pZjqDrbno3RMjWN7L7CWTcTicDA8LR9OYebNFHsnhiXU6LezVMGDiaXMIBOCV+x7G68lz7o1fHinZVqiCUV4utERNjRTGfw2FpMnxzc/TUvMg2/YsZvWWK0hnCp4A9chjrD4VSu/GTvwY8ltBq0bxfwnFfYSY2A8px1wgJ5OwbZtN+7gUqZjFy68FGBqCa689cskVy4I//lHEzHz845AY184dX9nChE9+jWy0jy3330rjqRdQd+rlNDbkOePTFTz/8nfZvUd4e5f/60PkMlkUVWP8ZV9Fdej0b3mV/o0rcIUqaDj9k7gCpSS69+D0h+k4UAoIw5syRVSVCAbFksTDDwvR63SKzN9wWLSSdjcvwjYytLWJpZ7OTnHjWbQIZs/+8JSSOZ5Q3GdhZ1+gvmotn7noUizr8FfXNuAdykApjjqU8E8+wLM88Skk9qSTSYychdPjB0RB/8FB8X1vaipmnStKMVHG5fVw832f44dX/TsZpQrVqaKqFqrbj+7xcPKFsymNQOMYH263A9uGRH8/msMkkzComHISoboxZKMDRLv3kUsN4faXoAdKUVQN07bZvqkfw1WF5nDg9QpPWSAgzmFgQJx7oe5nKCSeZzKgeURicCZT3GfMGCGM/f5j+Rf/kOJsR1EgHOjmjHm3YxhOHI484Ab32Uc8RFE0FN914Lvugz3XE5hCDepcJk8yngTNh88vcm78ftGIyucTcbm6XhTHheosV3z9LJyOBCse3Y8rXIaVTaI6dVw+H2NmNjN+cjU1tQ7CEQ+2LUT4od2d2JaCM1JD2YS5GJkEya7dZKIDONxuPKEKNI8X07SJx/Js2zhMSUX5SKnG8nIhhguit1DhorJS2K1pgsPtxbaLnSsVRaxcjB8vvKEf5mTZY4Z7Mbp1D1PaHmdi69MoyuHlQrUEtCPXmFX0qSild3+AJ/nBckwEsmmYPPAvD/PYfz6Nd8wCGhddzQNf+x6/9wSY8KmbmDE9T1PT2xPnLAv+9Cdh1IsXi9jdRPspnNZ9ErHhLLv/eAdt515DSftCJk2yWLTIyQMPhOjthfPPF0X7u18bx6pl68mlc2y+9wek+w8SrG+jecl1+CobSfV1kne48Fe3jDQVmTlL45RTxEx81Srh8c5khJds4ULxfP16MQiXlYnWyQcPutm6tZhkOHPmhztj9pjjOhOcv4P8WhRSaJoJuCFw41HVK5X8z9iwYgt3fuMeBuIl+GvbyCWGcXoCuPxuTrt0MuXz6+juFku2haojIAbZnh5hu5EIaFopX/zVd/jzsh1seX0vlqJTXhth7MwWmsYGKCsr2omuQ+3YSoy8hjtUQWzfVhKdO1EUC3ewFH9ZLbai4a2sQ3N6UDQHWDnIDtI8tgK/n5Hax5mMGGTr6oqCOXvYoen3w2mfPJ1kUgy+ra1CGI+uxiE5OhRFxw7+AKI3AnkhjhUvaI0ovk8d69M74YkPJfj1N+/j1ad3ERw7FzObQ1EsNG+QlinlXPKZ2fT2CiMdN66YZ1MQx6lUYdVFZfFnP0HVtAFWPbOewYEcvlCY5kl1tM+qo6qqeKymiefNkxpJZrogl+fQ2hcwYv3owRI8JVU4XF5cJRW4ghEUVcMy8yS6u5g2u5xwWAj6/n7h2VZVcT8JhYqe5IInfPF1i0c6WBaEcWWlFMbvBcX/BezsMjAH0LQ04AQcKKHb3veGPccrx0Qg33r1Hbz66Cqy6RwNF55GvHMn8Z5upn/hBtKD3az8xX9x3vnfeNMxllUMZTjjDBG7m8vB/fdDKq1x3We9vDH1VjZtEuEL06er3HWXGBivuKLYxvjsv1nEH25/DCObxzYNJnziHylpnUZmqIfdT91FqHkikbJaLDPPodXPEtu+nE9d8SOefBJ27TocS3m4RExHB7zwQrGZR1WV8GyvWiVmwuecI2KX35oxW0jeOy479BynKIoGJXdC9lnszNOgBFG8l6E4Jx7rUzvh2fjSVr519v8lm8qJdrMOF/l0CmegnHTK5KEfPkD9zz6LyxV+kzjOZIriuJBks3MnRKMKDZPbKG1uG4kzrKwUIRm6/pbC9IEgMy5bwOZlmzAMcPuD6MFSVN1JNh7DtsFrtWCl42QGD6GYWUpmz8UwRMKOYYgBtbVVfN7wsPAke73FhKXe3mLnu7a2IycFS5s9elTPEmxnK3bqd2D1orhOA/c573v5p486Rt7g7+f/E4f29OAqa0TzhMlE96G5vGiahy3LN1DqGWbKkrOorBQiFISd9vcXxXEwKMRyVxcEIhHmXiAmkR5PsfawxyOOK4RcGQac/plz2b75XsxsDhUTf80YXP4AlmmQTQyB043m9pEd6iM71IOvNYLDMZWurmK1iupqcT8oJN2qqhDK6bSw18KEe/z4I9cdl/Z69ChqGCKPY6cfEU2+HA0onk+iOOqO9akdMz5wgdy9t4eVj7xBLpPHX9uKr6KBnY/9guYl1+AKlrHhrm+T7d9L74F+KupFIWDbhscfF9UlFi4UyW2dOw+x9LkA3b1eLrlEYcUK2L1bhDHU1sJvfysM7dOfFsZWIFga4EcrbuPuX3RhBSdi5dKE2IZpWrScdS2WkafrtSc4tPY5KtrnMPHKm3ngATGIn3KKGFg3bBBebLdbiPFgENasEW2mIxG44AIRcvHOGbMKii7XbY8WRdHAveSEinH6MHDnN+4hm8qBouKtqMc2DUzbQg+XEd2/hWTvPl5/ZjOLrzx5pLJDJiPqWJumEKK2kWLlswPE8xGcLi+JhPDghkIiVrAQdpTJiMHQ5xOCtrcXzrr6QioqW9i4cifZrE11Y5jaCQ2seXYrNiq5gQOY+TyqquL0BVF91QwPC1stKxtdQ7s48A8NCc+ywyE6So0b9y6hT6oOlvEXdpAcCcUxBiX4rWN9Gh8pXn1sNQMHBzHyJiVldWi6m0xsgMj4FuxsjuH923jhdz1MP3sJ9fVCWdp20XMrGjLZbF3dyZ4OH7o/jGmqxGJCEFdUiPHQ4RCCuFDdJRA43JGvrIErbv40rz32Bn1dcbxBD1NPa2f1sxuwlSBmLk2iYwc24AoFqJ02k/5+sXJUWytsP50Wn+V0ivtHOi1CFQuT7fb2d+sToIJ6zCNIP3Qoqles8MhVHuAYCOS9Gzpw6g5ymTyV00/HzGWI7t9Cy5Jr6Vz5KPHOHXiDHga6hqioL8O24YknDoc17H2ef/7+zwAY8/HrqZp1Fp0rHmCpfjbJXJDzzhOD6r33itnnlVcW28+C8DivXAkrV0ZQSiNMmQjZrI8dO8bjrrbpW/csfdvXU9I2j+mfuw3V4aSuwWLSZGGsq1cLQy0vh7PPFjeV114T71VUwCWXCMMdnRhg2yIus6sL7rn9ZbRADd55X8PIpuQsV/KhYN/mAwB4y+vQ/aUMbl+FJ1wBZp6hbX9Gc7qI9qWoqRH7Z7PQsd/gqV8v54W7HybRN4i3op7KaQtQVCdVTdU0zZhISalzJJu+UM9U08RAC0LADg8DqLSfOo32U6eRzQoRnc1Cwyzo3LAXBQVNd6I4XEz52BQ8XsdIG9t0ulh+zucT3uP9+8V7bW1CHOu6sFPTFPeIVEr83sFB+ONPn0d1B8lGFnLwz09Lm5Uc9+xet5d0QlRo8teNw0gnsbI5nC4f/Xs3kR3qIVQ/llDIwufTRsTxquU7eOjHD9Cxfiuqw0n1zEW4S6twOGDCaXMprwlTW1tM8gNhs4Wua729YsUon4ey+mrO/eL5I7WWs1lwBip59eGXsW0LW3GhaE5qJzRS21qF3y9sMp8XE1mvV4yp0ahYCbIs8bq9XXivbbtYoaPQQnlwEO659SlUd4hM6SLSQ4ekvUreEx+4QK4eU4lhmAB0vvQQw7vWkRnoYu3P/5FsbAAAM2/S2F6HbcPSpUKYHvrz4+x66r8AqD3pPKpmnUX3qmWUti8glnCzYE4XmUwNjz0mEg4uv9wm3tdPX0IlUhNh3TpRWzmREFnptg0bN4qZ7/z54PcrrC89g/DEM1GVPM21CSbNDLNjh8qTT4pzb2sTzTwGB0WHv1hMeL8WLxbvKYpIKjh4UAjiri7xvFDzUa+bh5XooXfjS8S7dlEVUnhzH3WJ5PijoqGMfZsOkB7o5tDqZSS7dmEaJu5INUYqRmTKfJonREZqa3Z1wT3f/yPrn36B1EA/zkCEismngqJjqxoDfWnU9ZuYeOX0kQHW4YB8NkWiP45lRUinHQwMFFubZrPFOseF1sTtM8czZnwtfR29qLqLquZydN01snJTqJ+qqsVuWoVQioaG4pJyoblMYctkhFhWFFDdQexsnMGdm8kOHoJmWSJQcnxTM6YKt99FJpGlZ/UyVKdOqmc/Hdkk+dgA/uoWwpWlNDQIcdzXB2tX7OHXN/yMeE83tg2RSafiitSRTScgGGHTinVc+Nk5OJ0iOU40ijFJ9A9g+L0kk366u4XAdbvFYzrNSB1jpxMiNSV8/PNL6N3XTSZpUN4YIRwJjwhuwxCOLZ9P2OXWreJ6KiqEzXq94j6we7cYxwvNodJpcaxlgRaows4mGe7cTurQPkpaZBkLyf+cD1wgN09qYOyMFra/sZtstJ9stB+AzFAPAG6vi8u/fgEev4dly0RzDW9uM/uevReAsvaTaF58DUO71lE6bgaa7mbzfT/AkbgQq6SG9naY3LSXL07/V/o6Bwg1TaZlyTU4QzWUl4sl3b17waHmcaW24XH4WbWqiVxOobJSYcliC8uCDRtLeOQRsaR00kkilnjnThEHnUyKAfass4TA7u4WZecOHhQz3gLl5WLptqZGbJWVDhyOWm5Y+Et8ITmrlXw4uPaWy7n16jvIpnIM71qD6tBRHDpGcgh/VT3+kiAfv6KdXE7YQP/BIdYvfZ7U4ACq00Pt/PPRXF5sy8Dp9pIe7mfbjn0surSdQNCF7sjxwO1Psu3l7aiqjeYNM+OcU2mZ3j7S9na4P05fbz8YGeoaKgg2RHC7wfT7cbp1TMuJoqgjdVz9fjE4Hzggji90jiotFYPshg3FAbwwuDqdxWz5SETsf/nlM1EUuGHh81TPrJI2KznuOfWyk/jV1+8ml8qR6t0v6oprTsx0DEWBYG0T51w7A5erOEF86j//ROxQF9gW5dMWEqofSzYdx+UvxcrnyPTuZ9+6ACVnzKS0FNa+sJFH/uMZ8qkUqDpj5kxn6tkL8Pk8DA5CMpGlZ28vycQwpaV+GifU4nLpOJ06ztZacnkbRXGOtHMPhw+3fD9YFMalpSKvR9OEF7kwSTYMIcA1TRwTiYixtrwcLr10Ok4n3LBwGbSo0l4l74ljEqTzz49/k//3hV/x8h9fwzQtvAEPDt1BeX2Ey2+8gFMvm8/y5aJZyOzZsPzHv8fIGzjcflrP/wLRjm2k+g/iq2xk439/j/oFF2OVTGPePDhpdoKrW24GdzljL76RktZpZOODpAc66aMOt9smuW8Vluoj0DCZdNZgaMerLLmkjjWv9PL4vjacngD5aBfzT3Gw4MwK1q6F3/xGDKaRiDDaaBQefLB4TeGwyJCfM0eI4erqoy/nJpeDJMcjp1w0l//z08/y62/eS2wgjubSCJaVoPs8TDv345z5qRkEQm46O4XnNTNwAMUUdTMrZy3G6QsysGMNpa1TSfZ2kh7uBTOHZmfQNBe//9lLbH99L6atYGs+XIFKtm8cwHT0Eq6q4FDnATrX7SCfzWJmknS8btEwo5FASR0HdvaSS6chm2T8jArmnzuVvj6FvXsPL+s6hc0GgyJ8oquruDzscolBuKSkmC3v8Rxd7VRps5LjDbfXxR0rf8CPrv9PtqzcBqpGsLwE3eelbtJE5l+2iIUXVNPfLzywoRDsX78ZbAt3eT3h5skkD+1HdfsxtQyJrh2YuTTZXBq/H7av6+JP//Ec2VgGh8uDHq5iIOpk9bKtTDp1BulEkmKS0SQAABF4SURBVPXPvU4+lSaXSnFAs9j8Isy9eCE7V3cy3J/EzCQpq3Cy5KqZuHxh9uwRYU2WJc6nvFys4PT2Cg80FNucBwLCXgthU0dTHUraq+RoOCYC2Rf08q17v0z+ri9i5Aw8/jd3zHjxRRHCMGOGqHXc8VwTm1/ZjpFJsOX+W0n1dmBkUvSseY7W8z5PoG4s5doGFp3ezk0X30nNaddSOW0hZj5LerAHT2kl+aRGiW+IaMKFr2k2meE+9j13L+m+g1RM/RhvbKqDUC3RbW/Q9eelgE2y/0JWby7HsoopsoVaqrW1ohVyba0QxEdTEuodjVN1orqCGMaJ1Ste8uFnyacXsvjaj5GMpdF0F5mMNlL3uKFZeGotS0wM/Vol+ayooXlo1dN4ympJde8hM9CFv7oJ28jiC/qwtCBrX9nHlpc2g6XgKqkkUN+O5vVh5w16DvQTLPfRsXobRjKGYeSxMjm81fUMDvgY6hskPRwlPXCAbLSfTdZEovQTKi3H7RaDbKG5h20XE/8CgWLjEK/3zcm0hVjkt3Lbs7egKEVxPdLZy+FFhklJjjeqWyr5yQu3kEllRSc9yzWyolJVJYRxQRyXlkLduCq2vR4n03eA7lXLSBzYiqL7KWmdhtPlBk2juqme7oNpHv2PZeTTBprHS7B5MnqwAsWySSbSZJIJ9ryxmfRQDMvMYqSSKP4SXMEq1r7YiZFNkBrqIdPfRXo4xMP3lNIycyqaphIOi3PRDxc50TTxutABs2CvBcdTQTj/JXstvGdZh/dXXaDJRgSSvw6l0Db5r2HWrFn2qlWr3sfTgZdeguXLRUjD+ecfnkV29PHZyTeQjhe7ubjCFUy88p9wh8vY/tAdqJkO2s76JGZ4BormoPvPTxFsnIgrWEouNoi3sgFVc5A8uJn9Kx4j1d/FhE/8I77KBvLpBLH9W8mnovgqG/FVNaNqhUYUFrV1Kk1NRTEcDB59vcVcrnhTikaLz2Mx2L7uEKoriOIUKjvxxn9iJXvkLPcjiqIoq23bnvVeP+f9sFfLKibFdHYyUrXCMIoZ6ADfueBfeGPpOgxDKErV4aJiygI0j59cMobu8zNhYRublq7DNk1UXXiifDVNGMkY+VQCt9OmcXIzW15ZRy4+hL92HO6SChRsstFBUoPdmKkY3vIGvOX1qLoTO59l/lljKC3VUFXhXfJ4xCDr8YjBd3SMcuGaLEsMpoVHwxjVZOFwc5RcrritXb4JNI34QILY/m2UuXYB0jP1UeV/w2bfr/E1nxdjTiFGuKZGhAmGQmJ1RVFgzXMb+fb5t5JL50eOCzVPJlA3jmx8CFXTqB5bSyYapb9zAIfLjY1Caft8bDNPPj6IYuWYesYU/vzoSox0Es3tJ1Q3FkVzYObSpAd6yCWHcLi9+CubcfqDKIpNbVOA9mllaFoxbKIghN3uood49OT0rTYLwlYL9prLiWstJAi+sWw9iuIgNpgA28bV+xQg7fWjyl9rr8eVn3LlSiGOJ0+G884ritCKhnJueuAr3HTeD7EtG391C+1XfBNF1dh49/fxRmpo/Ph3IVDK4OaVHFjxEMGGNkrGzkT3h1E0B71rnubaL4/nzq/9DhQ/ldMXogfCGLkMTo+fyPjZmPkcZjaNoqpE92/lwEsP0doKn/3uP/3F8y5083mrAC4kEUSjxaYEo/H5hNi2MkOY0f10bd1FNtZPTcXb2zpKJMcL+bxI7LEs8VxRiuK4IDq/df9XuGbslxjqHkZRVarnnY0nXEGsaw+q04Wtutjy3A4UhxvbyoLiQLEtsv2HUFQFBZNIZRC3bmBnEyg4UFWVfCJG6tB+cqlhFEAPRgjUjsHMpYl37CE7fIjSyxvw+zV0XQyyhe5gyaRI7rGs4gCazRYH0sJWeA2HE/UOh1xomvi8UAisWAe2laNn/W4yAwcpaw8ck/+FRPJu5PPFpLZwWDwfLY5tG6afPpmL/uEcHrz1EWzbItQyjcj4OeQSA5imgTMQoq9rGCOTRdNdGPkc3kgV2eFDYFko2FimRW1DECMVxzINFCOHZZlk+g6SHj6Enc+hqArB1mmi9NxgD6ne/TRWTyAUKsPpFOK4IIgLLbILCXwFsZvPi63w84K9Wpa4ntGbrouwDDt+CMvI0r95N/lMiqY6mSAveXeOmUDevHI7P/vqb9m9di/BSIBFX/4y3ZlJtLfDhRe+PQ6wsrEMl9dFJpHBW9GAlc+xZ+ldtJ79N/gqG4gd2M7WB28n3rmD5sXXUnvSucS79tD5yqNY+Szl4ybwyoYW6i/8HgC2ZZLs6SDRtZtE927iB3eR7OnAFRRtazNDPehuJ+Ovuowbz/kpijvEJ//p6jcJ4cJ2JPHr9wvxW1oqWu8W6q8WtkIdScEEAG5Y+CSUwo+XyVmt5PgiGUvxq6/dzfIHVqKX1DJ5yRJOuWgmoZCfurpiw4ACLo9OKOJnqHsIHDp6oIThzh24SyrwhKvIZxIYySj5ZBzLsihtnoCNhpVPk0+lsM0sY2Y14wt6WfdiAMVhkOjejWXkURQFh9MFqoKZTRLdv4V0fzdmLo3LZfHkzx9CUXUuu/FSksmiN7gwyI5UqFCLm8MhbNbtLnqb3e6iJ8vlenPY0+LFol3yDQtvhsqA9ERJjjuef+AV7vr2/QwNmjTNP50558xj3qL6kVAGKE5oARrbanH5dDKJDO7SKhEO0d9DeMw0wCIfGwQrh5FNEx4zBd0XwsrlyOdSGOkU9ePLcfu8NE9s5sCubqx8jsFtb4BioTldqB4vtmkRP7CTXDKGkYqiKXm2rximY/0mrvz2p8hkhIAv2GthNWe06C0k9vl8RS+zyyVstvBa18VWcLItXixq58sYZMnRcEwE8q61e/n64u+J5gOAXjub7swk3OYBLr64/ohJMnVtNbjcTjKJDL3rX6B/80ocHj9gs/XB2xnY+lpxZ0UhG+0nUNNCoKYFgPRAF0HXEPNPCvHkHfeybunzKKqGu6SCQHkVkxZ/jN7uLA5PGD0YwRWM4ApF2IcH/xzxsY89Jh79fjEDLysTJeOCQfF6tPh95yYhEsmHC9u2uXHRLezb1IFpqgRb6jm4P8793/0vbn3kM7jdbmy7OBgVHk+99CQObDuImUuz98nfABY1p36CdKyP/MAhTNPASMdxePzYiiY8UaqK6lCxVZ2tr+1j+pmzmLZoJuufW4vT4wZFGFbThBp2r91JLp0lO9yH5nLjCYeZfsZ0OrZ2AjbDw0XxGwwWhW5hQB29yZh/yYnEs/e8yL/97S/JpnIEGyaQybt59rdLqQjP48xPTH5TiGDh+fQzJmPkxNJJz6qnAfDVTcQwcmR6OrCMNFY2RT6dAsUBioKiqSiqBpqTrn3DHDoYp33RbKKxl0nH0ugBYc+BkJdsIk58IEo22ivG3nAJJVVlON1OFGyGhooCuGCjhcfCalDh0emUbaUl7z/HJAb55otu49VH38C2oXLaQsZe8EUGd6xmz6P/jwcP/RKP7wi9XoHf3fYwd35DlHvz17biDpWjqBqKpolHVRhrydiZIvY4GQXbwrZsNN2NtyRMWX314VglG9t+i4XZNpaZxzJyaBpY+TxgkU2k6V71DBUlw9i5OLcvv/k9/w0kknfieItB3rBiCzed+0PSiQzeyiaq555Nqnc/8b2ruf67F7HkutPfNFgVbinJWIorGv4Wy7TRdBfBpinowTJU1cLIpjFSCSzLwOkJU9o2g2xsEFQFh9uP0+3BobtpmNSIy+3GyBsko0mwbfxhH06Xk2QsSf+BfjLJFA6nyKBzOFWGewfJDvUSVrZjG1luX/6d9/w3kEj+EsdbDPKn6j9P/8FBQKPxjE+iqjo9a5cTKbP5xbrbgbcLTNuGmy/6F1Y/swEAX81YPBV1aJqOZWQw0gksIw+2TenEk8C0yWfj6J4QmtuLw+2mpDpCdVM1lmWRiqXIpXN4Ai58QS9G3qSno5dYXxTbtlBtG93jINo7hG0ZeNMbsLND/PjZm6T4lbyvHNcxyLvX78O2wROpofW8v2Vo1zq2Png7bq9Gb0c/jROO3Ps7VBbE5dHJpnPUzD2Hismn/MXf4xv1vDARiMUKs1QFTSsu1zgcoGkKmqajaTqqCh1bD4BtkUvGSfUdwPZaR/5FEskJzL5NBzAPJ9uVTz4FVXUQ3bsFM2Oya1MPC1JHPk5xeHG4A9i2jbeqhaqZp2MZecxsGiyTQImP+FACbLAySZy6i2DjBFAULNMEK4+RMwmGwOdzUBoJjbJVUKt8jB3vGxHkrz76OnYWjGQcI5XA1t/hxCSSExjTMA+LYwi2tOMuqWRg2xvkU1H6utUjhgQWCFdVoLlEsnjVrMU4XB6sXBorn8PldZLPGVjG4Qw5BUJ1bbj8YWzLwjLz5DMmDgfoukoo5B9JvHM4QFEc1NXXoCg12DasfOQ1yEE+OQSo2PkEWHkpjiXHDcdEIDdMqKNnXx/pgS62PvhjhnevxzbzmHkor4u843FjpjWNTHv3P3svB1b8EdsycTgUrvjWRfhCbn7x1bvIprNgmWguL65QOdloH7nEMOPnjOHfX/3BUZxpPSDilhobZdyS5KNJXVsNmkPEPR187Sn0QJh0fycur4uWCRH8frHfkQa2SKWTgzu6ySWGyQx0Y6Rj2LZFRV2Ebz31Lb6x5BYGuwYxcga2ZeGpaCQf78dIJ3DqKr/c8BNqW31v/+AjcN55cwFhr35d2qvko4nm0CipDDHUEyW2ZwsKCrF9WwAoq6nAM6qq6ltttn12HS89mCGTzLH/uXuxjTzYYmXm+499nZf++CpP/+Y5cukctm3h9IVRnTq5aD+qanH+3y3huus+/VeJ3HPPnQfIuGDJ8csx6cN49Xcuw+UVxQ4Ht7+BZeRweV2c/Tdn4A143vG4sTNamDh/HLrHSTbWT7q/k3ysF7czw7nXz+Xsa+dx5/ofMO/jE8HOk4sPEu/cTi4+iMvt4Pr/+6kP6hIlkhOGaQsnUtFQhsOpYSQGSHXvBtvA5VI446pTcDiKXt23bp+/7Spcbge2mSfd30E+OYxmZ/j8bZdR2xzmN5tu44s/uQpNs7Etg9Sh3eSTUZwujVlLplHbKls7SyRHyzW3XI7L6wJMons2YFsGulvluls+8Sb7HJ2oqqqw6KoF+MNeNE0hF+0lnxxCsVKMn1XH1FPG8KV/vYo7Xv4uje1VqKpNLtZPZqALy8ihu51c8g/nSg+w5IThmNVBXvPsBn7697+mc3sXnoCHS75yLlfedAnau2S35TI57vnnP7D0N8+Tz+aZf/5sPvPDKyitKhnZx7Ztlt61nHu+9wcGu4eoH1/L5350DbMWT/1fOXeJ5P3keItBBogNxPn3L93Jyw+9jmXZTFs4iX/4+eeobql812NXPbOe33zrPjp3dlPbWsX1P7iC2UumvWmf/Vs7+dlXfsuGFzfj8ro453Nncu0tn8CpH0WbLInkGHG8xSADPP7LZfz3zb9juDdKpKaUz/zwCs646rR3PW6oZ5g7v3EvKx99A6fLyVnXL+Sqmy5Fd+sj+xh5g7u/93se//kzpOIZJi+YwBf+9dM0T2r4Xzt/ieT94q+112PeKMQ0TFRNRZHTTokEOD4FcgHLsrBt+10nshLJR4njUSAXMA0TzSHtVSIpcFwn6Y1GGq5E8uFBPVINRolEctwix1iJ5H+GHO0kEolEIpFIJJJRSIEskUgkEolEIpGMQgpkiUQikUgkEolkFFIgSyQSiUQikUgko5ACWSKRSCQSiUQiGcVRlXlTFKUP2P/+nY5EIgEabdsuf68fIu1VIvnAeM82K+1VIvnA+Kvs9agEskQikUgkEolEcqIjQywkEolEIpFIJJJRSIEskUgkEolEIpGMQgpkiUQikUgkEolkFFIgSyQSiUQikUgko5ACWSKRSCQSiUQiGYUUyBKJRCKRSCQSySikQJZIJBKJRCKRSEYhBbJEIpFIJBKJRDIKKZAlEolEIpFIJJJR/H+QdxYKp3p/cQAAAABJRU5ErkJggg==\n", + "image/png": 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\n", 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+AnAC6F+SiMR0q6uit5uPgu4y3wPkArNMHT97/yy9FGR4oZcopcZjePsvxRgB6qC7jvuABrrpuDk6lMUJ+j7XBnIfYg71vAIsMYPqJ2DEEx0NcRi9a6+IzMEYMjkkRORaEUk1e43NGF/8cMQt/yMiLhHJM+V80by+FPiFiKSKyBDgf4BnD1BVNZDa7QdOozkkTO/pj0UkyzwfjhEGsOJIylNKLcUIF/hARA46SVYptRf4EnhQjAmq+cD3OPB3/lgTh6GjrWbcYPf4414RkSsiJvs0Yeh55KSi/xKRRBEZAfyIrnp+l4jkmLr7K2Cp+XvREzWAEpGRh9wqTb9nEOuf3Syv44j0uN4nxjJup2HEVL/cUwEiEhXhVHKY5RxSx7UH4jC8wo0ikoLRCT4kRORsEZlkGrgtGCEXkXr6bfP/GIMxh+kls3P7EnCRGJMT7RhGuhvoHm4ZSTUwKHVcG8hHx5vSdQ3TV4+gjB9iDItUYfTC/3GUMt2C8cPRESP40mHkXQhsM/P+BljUbdjrc6AIY4b6g0qpj8zr9wEbMTxamzCU6cHeKlFGrPUyoMSM3darWGgOBzdGTNxKMVZvWYHx3fvxkRaolHoGI0zhIxHJOYQsizHieCuAVzFib4/Lko+98GOMTqobw5v84oFv78JsYLX57F4BbjXjQzt4E9gArMdo29Pm9SfNej7D+B1wY0wU7BFzCPxBjP9Tkxziqh2afs9g1b+3McIfO44l5vUqoNGs6zng+6bXvCd2mHkzgXfNz9m93HswfocR/1uP0SF45zDyZmDodgvGPKMPMCb5dvBPjA5FJWAF7gBQSm3F+F15HGNkaAFwkRmP3Bv/i9GBaBKROw5Dxn6PdB0R02j2R0RGA4VKqSPtCWs0mn6O6TELALnmxD6N5oRGjJWanjVjrQcFYizD95RS6um+lqW/oz3IGo1Go9FoNBpNBNpA1mg0Go1Go9FoItAhFhqNRqPRaDQaTQTag6zRaDQajUaj0URwqIvUA5CamqpycnKOkygajQZg7dq1dUqpIQe/88BofdVovh6Ohc5qfdVovh4OVV8Py0DOyclhzZo1Ry6VRqM5KCJyuLsp9ojWV43m6+FY6KzWV43m6+FQ9VWHWGg0Go1Go9FoNBFoA1mj0Wg0Go1Go4lAG8gajUaj0Wg0Gk0EhxWDrOk/BAIBysrK8Hq9fS2K5ghxOp1kZWVht9v7WhSNRqPRaDQRaAN5gFJWVkZcXBw5OTmI6B2gBxpKKerr6ykrKyM3N7evxdFoNBqNRhOBDrEYoHi9XlJSUrRxPEAREVJSUvQIgEaj0Wg0/RBtIA9gtHE8sNH/P41Go9Fo+ic6xEKj0Wg0Gs1+tPr9bKurpdnnZUR8IiOTkrBatF9Nc2IwKAzkQChEQW0NW2triHU4mJ6eSWZ8fF+LNWipr6/nnHPOAaCqqgqr1cqQIcamNKtWrcLhcBzzOtetW0dNTQ0LFiw45mUfDsFgkNTUVJqamvpUjoGIx+/ng6JdrK2swCLCnMzhnJWbi9OmJylqNP2NCncLT6xdTas/gAiElWL8kKFcmz8Fh9Xa1+JpNMedAW8gB0Ihnt64ju21tXyxtxSF4vTsHBZPmsy09Iy+Fm9QkpKSwoYNGwBYsmQJsbGx3H333YecPxQKYT3MH9h169axZcuWPjeQNUdGIBTiqXWrqXC7+WJvKQAev4+9Lc3cOG2GDjfRaPoRSile2rqZsFJkxsezbNtWQBFWinWV5czJGtHXImo0x50BP1ZSUFvD9tpasuITsFutOKw2UlwxLNu2FV8w2Nfi9Ru2VTbz+/d3cvfLG/n9+zvZVtl8XOq58MILmT59OhMnTuSpp54CDK9rYmIid9xxB/n5+axatYo33niDcePGMX36dG677TYuueQSADweD9dffz2zZs1i6tSpvPnmm7S3t3P//ffz3HPPMWXKFP71r391qXPz5s3MnDmTKVOmkJ+fT1FR0UFlueuuu5g4cSLz589n5cqVnHHGGYwcOZK3334bgKeeeopLL72UM844gzFjxvDAAw/02N6HHnqIWbNmkZ+fz/333w+A2+3mvPPOY/LkyUyaNGk/eU9EdjXUs7elhYy4eCwiWETIikugsKGO0ubj813UaDRHRpPXS4XbTZLTFXFVSHQ6WVtR0WdyaTRfJwPeg1xgeo7tVivl7hYA3ircgS8U5MZpM8hJTOpjCfuebZXNPLG8mASXnfQEJ83tAZ5YXsxNp+cyPj3hmNb1zDPPkJycTFtbGzNmzODyyy8nLi6O5uZmTj/9dP7whz/Q1tbG2LFj+eKLLxgxYgRXXHFFZ/7777+fBQsW8PTTT9PY2Mjs2bPZtGkTv/jFL9iyZQt/+MMf9qvzscce4+6772bRokX4fD6UUgeV5bzzzuN3v/sdF154IUuWLOHDDz9k48aN3HzzzSxcuBAwwkW2bNmCw+Fg5syZXHDBBUyaNKmz3rfffpvS0lJWrlyJUoqFCxfy5ZdfsnfvXnJycnjnnXcAaNYGIDVtrXxWWkKU1dapp69sL8AXCrJ40mSyExP7WEKNRtOB1WKM6LyyfSsgnTr7zq6dfGfK9D6UTKP5+hjwHuS4KAcK1e2qcR5lHfD2/zHhP1uqSXDZSXDZsYh0fv7PlupjXtfvf/97Jk+ezNy5cykrK2P37t0AOBwOLr30UgAKCgoYN24c2dnZiAiLFy/uzP/ee+/xq1/9iilTpnDWWWfh9XopLS09YJ0nn3wyDzzwAL/+9a/Zu3cvTqfzgLK4XC6+8Y1vAJCXl8eZZ56JzWYjLy+PkpKSznLnz59PUlISMTExXHLJJXz++edd6n3vvfd45513mDp1KtOmTWPXrl3s3LmT/Px8/vOf/3DvvffyxRdfkJBwbDshA5FUVzT7qSkKFCS5XD1l0Wg0fUR8lJPRySn4Q+Eu14NhxezMrD6SSqP5ehnwFuS09AxOz84lxeXircKdgOK0ETmkx8WRFhvb1+L1C8qb2klPcHa5Fue0Ud7Ufkzr+eCDD1i+fDkrVqzA5XJx6qmndq7z63K5DinOVCnFa6+9xqhRo7pcX758ea95rr32WubOnctbb73FggUL+Pvf/47f7+9VlshJhBaLhaioqM7PwYiwnO7ydj9XSvHzn/+c733ve/vJtGbNGt5++23uvfdezjvvPH76058etO2DmbEpqVwxKY8aj4cvzRjk2ZnDGZWUTHbCkXuPvcEAje1e4qOiiDkOk0M1mhOVb02YRIvPS01rK5/sKQbgv085g7xhaUdUnj8UYn1lBeurKnFYrczOzGLCkKF6/oGm3zLgDeSMuHiumpTPsu1b8YUM4yY9Lo5r8qYckeI1trfzXlEhG6uqiLLZOHV4Nqdl5wzoWbuZiS6a2wMkuPatFuD2BslMPLaeu+bmZpKTk3G5XGzdupXVq1f3eN+ECRPYsWMHe/fuJSsrixdffLEzbf78+fz5z3/uDKVYv349U6dOJS4uDrfb3WN5RUVFjB49mttvv53i4mI2bdpEenr6IclyIN577z2amppwOBy8/vrrPPfcc13S58+fzwMPPMCVV15JTEwMZWVlOJ1OfD4fqampXHvttcTFxfHss88edt2DDbvVyk3TZvLe7kI+3VOMIJyencM3Ro7GcgR6qpTi45IiPijaTUgpBDh5+AgWjhmHTS9DpdEcNUkuF3fOOYWSpka21dUSZbVy6fgJR1RWKBzm/zaup6C2hh+OeRQUPLL+VuaNHMXCMeOOseQazbFhwBvIAFPTM5gwZCg3Tp1JlM3KsJjYIzKO2wMB/rJ2Fc1eL7eMegSF4s+FP6Cq1cPVeZOPg+RfDwsmDeOJ5YYHIM5pw+0N0tweYNHMYztUdv755/PEE08wYcIExo0bx+zZs3u8Lzo6mkceeYR58+YRGxvLjBkzOr27v/zlL7njjjvIy8sjHA4zevRoXn/9dc4++2wefvhhpk6dys9+9jO++c1vdpb3/PPPs3TpUux2OxkZGSxZsgSn03lIshyImTNncvHFF1NRUcF1113HlClTuniYFy5cyPbt25kzZw4AcXFxPP/88xQUFHDvvfdisVhwOBz85S9/Oey6ByPxUVF8c8IkLh8/ETi6jVLWV1Xy5s4dZMTGYbdaCYbDfFJSTLTdzryRo4+VyBrNCY3VYmFUcgpvLr72qMopbKhnW10tw+MTOjuwmXHxfFJSzJys4SS7oo+FuBrNMUU6JjQdCjNmzFBr1qw5juL0LavLy3ipYAuZcfFclvYgAMsq76Xc3cJ/nXIaQ2P6T8jGtm3bGD9+/KHfX9nMf7ZUU97UTmaiiwWThh3zCXqHg8fjITY2FqUUN998M3l5edx22219Jk93nnrqqV4nBR5Levo/ishapdSMoy17MOvr71d8gcfnJ84MjwHwhYK0+v0sOfOcI/JKazRHyrHQ2cGsr+/uKmSU3I7DaiXLuQOAMu84fMEQvvh/MGnosD6WUHMicaj6Oig8yMeKcreb74/8E/YIJb48/SH8Q0PUt8/oVwby4TI+PaFPDeLuPP744zz33HP4fD5mzJjBjTfe2NciaQYQzV4v0fauMccOi5XaYIBgODygQ6I0msFGfFQUytdzmp47oOmvaAM5goy4WMLd560pUApS9Ez7Y8o999zDPffc09di9MoNN9zQ1yJoDsD4IUNYX1lJWmxc57VGr5fchCRtHGs0/YyJQ4fx8Jd3YRXhu9nGqNzjRbcxJDqaO0bpJR41/RNtIEeQNzSN36/4L1p8fm4Z9efOGOQpaWlcO4C9xxrNYOOc3NFsq62l0tNCtN1BeyCARYQLx53U16JpNJpuxEdFcdO0mbywdROP7LoVgFHJSVwxYZIOh9L0W7SBHIHLbueWGbN5d3chj+/+IQ6bjfmjRnBmzsi+Fk2j0USQGh3NHXNOZmV5GXuamkiPjWNO1nCGxMT0tWgajaYHhickcPfcU6lvb8dmERKdelRW07/RBnI3klwurpyUz6KJeXp9Ro2mH5PodDF/1Ji+FkOj0UTQ4vNS6fEQbbOTFR/f5T0qIqRG6xUrNAODE85ADitFlcdNeUsLlR43LpudvGHDusQywtEtQaXRaI6OsFKUNDWyq6Ge4qZGfMEgY5JTmTt8eK+ep3D9NQBYUvS60xrN10m1x8N7uwv5qKSIao+HtNhYkp3RjEhM5LrJU0lwOg9eiEbTzzihDOSSpkae37yRDVWV1LS2UtfehtNm4+yckSyamMf0jMy+FnFAUF9fzznnnANAVVUVVquVIUOGALBq1aouO9UNVn7+85+TmprKHXfc0deiDDpafF7+vn4tW2tr2FVfj4gwPCGBvc3NrK4s57aZc7psT91hGBNY1eVcG8oazfGnvq2NR1evoK6tlQq34XSq9HiIc0RR4W7hpYLN3DhtZl+LqdEcNieMgdzi8/HUujW0BQJsra3Bai5W7g+FSHK6WLZtK+OHDCXabj9ISZqUlBQ2bNgAwJIlS4iNjeXuu+/uco9SCqUUFr2rmeYweXV7AZUeDw3t7SS5orFbLNR4WhkWE0ur38dnpSVcNO7Q1wDXaDTHj6/KSvGFgrQHgzitNpw2G3aLheKmRk4bkUNhfT1N3nYdc6wZcJww1svWmmreK9rFp3tKaA8G8fj9tAUCePx+Xt+5jQ+Kd7O3pbmvxTyuLPrrVyz661fHrfxdu3YxYcIErr76aiZOnEhlZSU33XQTM2bMYOLEidx///2d92ZlZbFkyRKmTp1Kfn4+O3fuBOCjjz5i8uTJTJkyhWnTptHa2soHH3zAWWedxXnnnce4ceO49dZb6WmDm3vuuYcJEyaQn5/PT37yEwBef/11Zs+ezdSpUzn33HOpqakBDA/w9ddfz6mnnkp2djavvfYaP/7xj5k0aRLnn39+5455WVlZ/OQnPyEvL4/Zs2dTVFS0X72FhYXMnz+f6dOnc/rpp3e25YUXXmDSpElMnjyZs84669g+7EFKq9/P1poaEp1OvMEgDqsVEcFps1HW0kJClIsd9XVd8lhSnjW8xfZZYJ+171yj0RxX6tra+GJvKW0BP95gsHNFCqvFQlgp/KEQAIFQuC/F1GiOiEFvICul2F5Xy+s7ttHmDxAMh3q4x/hr197Oo2b79u3ceeedFBQUkJnluQOIAAAgAElEQVSZyUMPPcSaNWvYuHEj77//PgUFBZ33Dhs2jPXr13PDDTfwu9/9DoCHH36YJ554gg0bNrB8+XKcZuzaypUrefzxxykoKGDbtm28/vrrXeqtrq7m7bffZuvWrWzatIn//u//BuD0009nxYoVrF+/nssuu4zf/va3nXmKi4v55JNPeOWVV7jqqqtYsGABW7ZswWKx8J///KfzvuTkZDZv3szNN9/MXXfdtV+bb7rpJh577DHWrl3Lgw8+yA9/+EMA7rvvPj788EM2btzIq6++eoye8OCluKmRp9atYW1lOesryvEFgxTU1bCtrgYRIawU3mCAZKee5KPR9DXL95Tw6y+Ws7uhni01tVR7PGyvr2VbXQ1hpUAEXyhEsstFip6YpxmADPoQi+V7Snhj5zaW7ykhjCI9OrZzKAiBcFhxZnYuCS4nIxIG54LlHV7jlcUNXc5fvHnuMa9r1KhRzJixbwfHpUuX8re//Y1gMEhFRQUFBQVMmDABgMsuuwyA6dOn8/bbbwNwyimncPvtt3P11Vdz+eWXExtrrD89Z84ccnJyALjyyiv5/PPPueSSSzrrSU5OxmKxcOONN3L++edzwQUXAFBaWsoVV1xBVVUVPp+PsWPHduZZuHAhNpuNvLw8AL7xjW8AkJeXR0lJSed9ixcvBuDqq6/m3nvv7dLepqYmVqxYweWXX955rcP7fMopp/Dtb3+bb33rW51t1fTMnqYmHl+9kiirjaHRsTT62iluaqRjnGBHfS0um53M+HhOz87psQztNdZovh6qPG7e3LmdodExJEQ5WVG+l6LGBvyhEIJQ2+YhPTaeYDjEFROn6rWONQOSQe0ybQsEeGdXIWkxcThtNqKsVuxWKxaM2ONAKITVIsQ5HVyfPxWb9iAfNTER69AWFhbyxz/+kY8++ohNmzaxYMECvF5vZ3pUVBQAVqu106j8+c9/zhNPPIHH42HOnDkUFhYC+68q0v3cbrezZs0aLrnkEl577TXOP/98AG699VbuvPNONm/ezGOPPdZj/RaLpcvEQovF0ilPT3VFopQiNTWVDRs2dB5btmwB4Mknn+S+++6jpKSEadOm0djYeLDHd8LyQfEuomw2UqKjmTh0KC5r17kAFhGsFuH8MWMZlZzcR1JqNIMfXzBIUWMDpc1Nhie4Bwpqa1BKYbdaiXE4qGtrwx8KoQBvKIg3GGJvSzM5iUmUNDbSHPG7q9EMFAasB7nF52VdZSVVHjfZCYlMTkvvMsEuEAqxuqKM94sKcdrsVHrcADisVhw2G6ePyGFmZiZFDQ00trfz1Po1nJM7irnDRwy63m6Hp/h4eo57oqWlhbi4OOLj46msrOTdd99lwYIFB8yze/du8vPzyc/PZ+XKlezYsQOn08mKFSsoLS0lMzOTl156idtuu61LPrfbjdfr5YILLuDkk09m3LhxADQ3N5OZmYlSimeeeeaI2vHiiy9y9913s3TpUk455ZQuaUlJSaSnp/Pqq69y6aWXEg6H2bx5M5MnT6aoqIg5c+Ywe/Zs3nrrLcrLy0lKSjoiGQYqNa0evigtZW9LE8PjEzl1RHaXzTz8oRC7GupZWVZGarQLpRTv7t5lTPI073HZbCS5XMzKyOKdXYWsKi/jWxMmMSo5pW8apdEMUjZWVfJSwRaC4bDR+Y+O5rrJ0xhmjuRVut38e+d2lpeWUOl24wsFWV9VSaO3nVCEMd3Q3kaC00lZSzO7Ghr4tLSEW2bM2m85VY2mPzMgDeQqj5vH16zinV07sSCclp3DJ3uK+cGM2SQ4nVS4W/j7+rW8tmMbTV4vLts+b6DDajR5btZwNtZUEW2zs6q8jLBSNHm9+EMhzsrVO+cdC6ZNm8aECRM46aSTyM7O3s+47Inf/OY3fPbZZ1gsFvLz8zn33HNZvnw5s2bN4vvf/z67d+9m3rx5XHTRRV3yNTc3c9lll+Hz+QiHw50xzUuWLOHSSy8lOTmZM888k8rKysNuR11dHfn5+bhcLpYuXbpf+gsvvMAtt9zCkiVL8Pv9XHPNNUyePJk777yT4uJilFKce+65TJo06bDrHsiUt7Tw6OoVKAUxDjsry8tYU1HOD2bNJjMuniZvO0+uW0NNq4cqj5uixnrSYuNQSnXx2rcHg3iDQTLi4rGI4DZXpLnr5FMZEq13ztNojgU1rR6e27KRZKcLp81wNtW3t/GPDeu45+RTcft9PL5mJWGlGJucSl1rK4X19bQFAgyJjqHc3QJAlNVKfFQUiydN7iy7tq2Vtwp38L2pM3qsW6Ppj0hPqwH0xowZM9SaNWuOoziHxpNrV1PS3MTyPSUAXD5+IpWeFuZkjuCicSfx/778DH8wyIfFRXj8PobFxLG3pYmEKCfnjR5La8BPbmISf9+wDofV2qnY6bFxnJWbyy9PPxu71dqHLTw427ZtY/z4E2Opqw8++IBHHnmE11577WuvOysriy1btpCYeHzi03v6P4rIWqXUUb9J+lpfn1y7mtLm5i4TdOraWhmZlMx3p07n2U0b2FJTTVpsHI3t7ayuKMMfCjE+dQjDYuN4fcc2GtvbsFgs3DJ9VhejudLj5uzckXonPU2/4FjobF/r6wdFu3i/aBfpsfFdrpe7W7h15mx2NzTwblEhGWZ6aXMT2+vraPf7yRuWxsryMmrbWhHg+9NndXmHhpWi0uPmoXPO7VxiVaPpKw5VXweMB7nF56WurY1Yh4OdDfWsKCul3G2ETSzbtpWwUkTbHUxNT+e17QVEWW2dhq9gzIDPTUzEbrVww6QZvLajAKulayiFRYRAKERbIEBCPzeQNZr+hlKKspYWGr3tpLhc7GyoJ73bkGqS08XO+joCoRCbqqsYFmMM3X5UUkQgFCIrLp6dDfW8X7Qbm9VCGAiHw/x13WrAePECOCxWGtrav9b2aTSDiRafl09KitlYXYXLZkcERO0fXigYoVAVnhac5tyAZdu2ArBw9Fh2NdZz3uixVHncNLS3YRXBHwp1MZAD4RBOm23QhS9qBjf93kAOhcO8VbiDz0v38OmeYpSC3KSk/SYPKKWIttkJhfdfbzHG4cBmsbBwzEkszsvHIkJOYhJnZOcwLCauU9nPHzMWbzBIzAmwE9xAYt68ecybN69P6i4rK+uTegca7YEA/9y0gcKGOgRBoahwu4mPiiLOEdV5nzcYJNHcBW+/iZZWK9MyMlEoXvJs7pIW6PbCbQsGGJOiY5A1miOhPRDg8TWrqG9rI8UVjTcYpKSpkRafl2GxsZ266QsFsYqFrPgEKt1uNldXA/s2/LBbraS4ovnJh+8iQEgpQkrxzKb1WBC+P2MWYaWoaW1l/qjRB5zwrNH0N/q9gbyibC8flxSTFRePw2qjY+rOiPhEszcqXDJuPOXuFk7NziYrPoFzR44mxu7g7V3Ghg2XnjSBcncLszKzOnuwp43IYV1lBbWtrShTqWtaW7liYt6AWc2ie6ymZmBxOOFN/Z33inaxs76OzLh4RASlFFVuD9tqa5iSloHDasUfClHf3saiSXnYrVampqXz+xVf4IgY7XltewHJLhcbv38bi5e9yJqKckLmhL1Ep5OXtm5m7vDhDI9PJG/osL5ttEYzQNlUXUVdWyuZcQkARAEnpaQay7U1NRDniCKsFCEV5ooJeUTb7UxLz+Cz0hKWbtlIbVsbAC9u3UyS00n3t1AoHCaEEQqllGJOVhZn5ei5PZqBRb83kD8rLWFl2V7WWCydL1GlFFnxCfiCIUSgqtXDmTm5zMrIwmqxsGhSPs9u2oAvFESACncLc7KGd1kealhsLD+cNYf3d+/GYhGSXS7m5Y5i0gB56TqdTurr60lJSdFG8gBEKUV9fX3nRigDmbBSrCzby7CYfZ4nEWF86hBKmpto9nkJhsLYrBYuHHsSszKyAFg4ZhyPrl6BL7RvEm20w05CL8/EZbMRCisuHjuemZlZRNn2//lavOxFAJZevuhYN1OjGTSUNjcTZe2qPzarlZGJSXxj1Bg8fh9RNjuT09LIjDNijuOiovjBzNl8UlLcaSAPjYkhLiqK9679DgBj/mxMju5Y0WJnfR3/uPgyvc20ZkDS7w3ktkCgxzVwRyQk8ucFF+D2+0iNjunyUs0bOox7Tj6NhaPH4g0FGZOcQm5i0n7lZMTFc92UqV9LO441WVlZlJWVUVtb29eiaI4Qp9NJVlZWX4tx1CilCIXD+8UXWq0WNtdUMSIhkUcWXki8I6qLURsfFcUn191AUWMjd777FnarlVeuuKpTTzuMXG30ajTHlmGxsfgquu4qayytKPx17SrsVmuP+pbsiuaDb3/3kHXSabNp41gzYOn3BnL+sDT8oRBpsftihU8fkcPI5CSGxsYylNge86VGR3NGTu7XKerXit1uJzd38LZPM3CwWizkD0tjc00NabH79NGYVBuFRaTX5disFgtjUlL491XfPmo5Fi97kZXlZZ2fQRvVGk1PTElL46Pi3dS1tZLiiiYYDlPd6iFvWBpl7uYjLrfwtruA46N/bp+PSo+baLu9M5RLozme9HsD+ZzcUeyoq6Pc3UIgHCKsFDarhfPHjOtr0fajtLmJr/bupdHbzrjUVGZmZBGrJ/xpTgAWjhlHaUsz5e4WLCJ8XFKEw2qlwu1md2PDQV+YB0o/HkbunqYmlpcWU+3xMDIpmdOyc/SaypoThvgoJ9+fMYs3dmynsKEem8XCxuoq9rY0s7qiHDB0sjfdO94dz8jfA6UUn+4p5j+7CgkrhUIxPD6R6yZP7TUcS6M5FvR7AznJ5eL2OSezvrKCKWnppMfGMiMjk/io46MYoXCYkqZGPIEAGbFxXXb9OhCbqqv456b1fH/kn7FEC4/u/CGrysu4deYcbSRrBj1JLhd3zTmFgtoaatpaKaitIdpup8JcivF40Oz1sruxAYBRScksvXzRIXmuttfV8rd1a4myWYm2O1hVXs66qgp+NGsuQ2N6HpHSaAYbabFx3DR9Jr5gEKvFwrWvvnxY+Q+nU6uUYm1lBR8XF9HgbWdMcgrzR40hMz5+v7zd2dXQwJs7d5AWE4vdakUpY4WcF7Zu5ubpMw9LZo3mcOj3BjJArMPBadk5x72exvZ2/rZ+DS8VbAHgjOwcTs7K5uKTxh9w/cZgOMyr2wtIckZ3LkWVFZ9AeUsLq8r2cvbIUcdddo2mr4my2ZiangHQuYFH5Et08bIX9/NKdaQfbmjEusoKXtq6maC5rKPNYmHRxLyDyqiU4o0d243l56KM5eei7XaqW918XFLEoon5h9xejWYw0DEvoKeY/570sSedPZi+frG3lGXbtpLicpHqiqaosYFHV6/g9tknd25j3VvZ1a0eTs4a0fluFRGGxcSwq6GehvY2kl3R+1eo0RwDBoSBfCxQSlHd6iGkFGkxsT3u5vOvbVtoaG/vnN2bHhvPZ6UljExKYnJaeq9lN7a30+r388Mxj5Ll3AHAZWkPEhqqeKvu59pA1mgOg0A4TFFjAymu6B6HUJu87by0dTNJTlfny90XDPLi1s385fyLDzjs2h4MUtvWSka3DUwSo1wU1tcf24ZoNP2YYxUn3Oz18tjqlcQ5HMzJGsHo5OQu8cGBUIh3dxeSFhPbqa+p0TFUt7r5rLSEb06YdMDyw2G139KrItK5gYlGc7w4IQzkao+HZzdv4MWtm1FKcXbuSK7Nn9plSbcWn5fC+nq+itih79XtBQTDIU5KHXJAA9llN3YX6r6sbVgpkl16Bq/mxKCnF26k57gnL3Gk1yqsFBePG8/aynIeW70Sj9/P7Mwsrsmf0mWTkN2NDQTD4S4rYkTZbATDYXY11jM9PbNXGaOsVlw2G/5wqMsyV+3BQJcJhhrNiUKLz0eTt51Ep4v4qKhedbZDVzv+LvrXC1S43YxPHcKuhjrKmlt4YetmFo4Zy3emTCfafC96/H58wSAp3Ty9cY4oSpu7TgjsyYu9uryMpVs2kRDl7DS83X4fCVFOPW9Ac1wZMAZyUWMDq8rLaPX7mTR0GFPS0ntcB7U7gVCIp9avodXvJxgK0+Lz8e+dO1hbUcEtM2Zz6fgJWEQIhTu2IOmOsf30gYh1OJiekcGfC3/Aj8Y8BgLP7b2b+vY2bp014ojaq9EMFLoPi571zFMsmpjPlLQ0pqZlHHI5TV4vqyvKsYuFgvoaguEw2+tq2VhdyU9PO7MzPlgpONIJ7FaLhTNzcvn3zh2kxcbhsFppDwRw+/wsztMjPZrBT3d9/cY//w7AmdkjOWXEiF4nwLcFAtS3tREX5SDR6aLV78cbDBJWih31ddgsVmwWYVnBVlr9fn40ey5Om50YhwOH1YovFOzSKfX4/YxOPvhumJPT0llfVcmO+jqirDaC4RBWi/DdKdN7HAnWaI4VA8JA/nLvHpZtK+CL0j2IwMnDs1lbWc4N02biiPAshZViW20NayrLCSvFtLQMFLCsYCtBFcbj9wPgC4Wo9Lh5fcc2hifEMytzOIlOJ5lxcZyVM5KPS4oBuOykCextaWZ6xsFf8hePGw8KHtl1KwDR9iBX5U0mNzHp2D8QjaYf0x4MUt3q4eWCrWyqruafl34Lm8XSxStU1NjAs5s20OLzMWHIUP56wSX87KP3aG5vZ2dDPS6bjRiHA5fNzq6GBv6+fh33nHwqVouF0cnJWLDgCwa7hFhYxcKopOQDiQbAGdm5hJXi45JiAqEQMQ4HV+XlMy4l9bg+F42mP1BQW9Pl3G6xIiIMjYnh45JiEp2u/Ty5N06bwf2ffkQYQCkmDh3K+JQhtAUCvF+0CwGcNjtJLhcum42ixkY2VFUyJ2sEDquVeSNH8caO7aRGx+Cy2Wj0egkrxakjsnuUMXIUymG18p0p09hRX8euhnriHVFMTksnJVrHHmuOL/3eQG4LBHhzxw6GmTNYAbLi4tnd2EhBTTVT0vcZr2/u3M6nJcV8WVYKwLS0DCo8LTT7vF28w2Gl8AaDbKmtZvmePczKHI6IcMXEPJ5YuxpfMAgC5e4WJg4dxrQDDNl24LTZWTQpn/PGjKM9ECDZ5eoyLKzRDGaC4TCp0dE4LNbOmMJ4RxQ76uvYWV/HhCFDO196q8vLeGHrZlw2Gw6rjTd3bONv69dQ0tRIi9dHa8DoyNosFmIdDtJi46hvb2NvSzM5iUkkOl18a+IkXi7YQsicpGcVC9+aOOmQNiWwWizMGzma07NzaQ8EiHU4tCdKM+gJK4UAE4YMBaCkqQmL0CUGeGh0DJ/uKeZ0c1L80ssXsbGqkmc2ric9Ng671UpIhXl56xbagwEa2tvwBo2dML2hEG6/D5fdzklDhlDYUM+cLGME9YzsXJw2Ox8W7abC42ZkUjILR48lIy7+kGKh7VYrk4YOGzA73WoGB/3KQA4rRUlTI2UtzcQ6HIxPHUqVx81HJbtxWG2dW02/sr2AQCjEzIzMTgO52uPh8z0lZMbFY7cYhmmz10tjWzsC+4VPKIyJex6/r/NaRlw895x8GheMPYlmn5cR8YmMTEra7+XpCwbZ09xEWClyEhNx2uydafFRUcSbs+M1msGILxhkZ0M9da2tpMXGopTCHwoh0GVyjojgsFjZ3djQ+VL2BYO8sXM7Q6NjOr2/bp+X0qZmUNAW8BteKozJei0+Pw6rocO+iFCnGRmZjEpKZldjPYIwOjmZW956A+j6ovUGAxQ3NSFAbmJSl7Ash9XaZQRKoxmM1La18k7hTh5ZvQKLCDWtrQDYLRaMqW77cFitNLd5O88XL3uRcncL5+SO6nT4tPh8hJQiEArRHgh06itKGUZ4MEh9axuxDgehcBirxYKIMCdrOHOyhqOU6nWTj531dXy6p5hGr5eTUlI5bUQOSXoej6aP6DcGcjAcZumWjWysqmL5nhIAFo4dx6XjxpvGbVcTV6FIjJitXuZu5tM9JUTZ9hnSje3ttAcDvcQWGxN7FLB8Twl5Q4eR5HIR43AwI6N3j3FxUyNPb1jHO7t2AvCNkaNZPClf92w1JwRN3naeWLua2tZWPi0tAQV17W2AsXvl5eMndrk/qMIkRXh1F7/yEuUtzSyeNLnzWoXHTbTdRnVrq/HiVPs03m610Or3UdrczNbqasLhMGNSUrFZLCS5XMx09b5Vd0FtDc9t3kggFEKhcNrsXJs/hbE6lEJzguDx+/nLmlW0BQI4rNYuE8kz4uJpCwS63F/f3sbEIV3fZaGwwmHZ15H0BgOIAn/ImCgbDOx7xwrGvJ8vy/cSGxXF+soqzs7N5fTs3M6lUiON48iJgBc8/3/UtLayYPRYnDYbX+zdw4bqKn40a47erlrTJ/QbA3lDVSXrKysZHp/Q6eUJhxUfFu/m+slTKW1u5ou9ewBh/sjRNPu8ZMXHU9vaSmp0NNERXtxDpcXr5e3CHbyzayfn5I7iO1OmHfDl6Q0G+Pv6tTis1s7JBrF2B89u2sC9p56ulVgz6Hm7cCcN7e1kxid0mXADEGW1UeXxMCwmBhGhyduO3Vyz9PKXnsdhtbKhqhIwllT85nhjaNciQkiFafJ596vPHwrRrhRrKsspbmrg1BE5jEtJ5fop0zq9vz2tnfrXCy7mn5vWE+dwEh1t/Da0+v08s3E9Pz31DGL05j2aE4CNVZW0+HxkxsV36tuybVvwhUI8fv5FvLBlExVuNw6LhapWDyEVZnhcAmc/8zdcdjvb6moBeHVHAVFWG5ePn4jLZiegwjT7vF1GdYBOb3KUxcLG6krCCjx+H6FwmHNGjj6grPXtbditls6Vn4yNhlr4au9ezhsz9tg+GI3mEOg3BvK6ygq+KitltcXa6QH+uGQ3c7NG8IMZs3m/eDfeYICylhY+LN4NQGlzM5nx8eQkJvGtCZO46KTx+AJBPtlTBMCwmFjq2lopa2nZNwyE0cu1ImTFJ9Buxk/F2B0s3bKJn5125n5rLnawu6GBd3cXEhUR7vHajm2EVJhLTprAnKzhx+fhaDT9gFA4zIaqSoaZq0l0eItfLthCWCn+vfhaXirYzNaaaspaWmj0ehGgqKmR0uZmnBHhDf6QsW28RYRkl4s15va2+9VpurzCStHs87GqfC9KKdZVlnfGN/bEzvp6AqEw0XY7oXCYRm87IaVoDwTY1djA5GFpx+ipaDT9l0qPm6j9woiMwAqrWLhzzil8ULSLZdu24g0GqW9ro6SpiYb2NqL8+/TVbjEmxXaMykbb7RxoIZn2YLBzudSV5WVE2+2clp3bJaSpo2PbQVgpbBYrTd52ipsa8fj9OK021lVVaANZ0yf0m5kp1l5ikhSQ4HTy7fwpJLmiGRITQ21bG3XtbWytreaj4iKqPG7+uWkD35syjZToaOZmjWBu1ggmDR1GtN2BpZvBq4AgirKWFsrdxvFW4XZe217Al3tLOyf+dKdj165gRHp9Wxutfj/rKnt+wWs0gwURwSKyX8jSBWPGcf6YsaaeTiXJFc2w2LjOlShafH7GpaRyRnY26bGxTE/P4PbZc6nyuKlwt5DijCbGfnCPrj8UosLtZnlpCW8V7qSura3Xe3/5yYcIhhd7eWkJ66sq2VRVxcbqKjZWVh7lk9BoBgaZcfFdvLzBcJgzsnOYOiyDBKeD+Kgoyt0t5CYam3ukREczNDqGjLh4zsoZSaormnEpqbx7zXf4zbnnkZuUxJk5I/EGg/tNQu/NYK5p9bCyvIy/rl1FcVPjAeX1h4KsLi+nqd2LIFS3ethYVUl5S8vRPgqN5rDpNx7kGRmZbK2tISs+gWXbtuANBol1OChvaaaosQG71cpbO3eggLZgZNxUkI9LiggpxTcnTOT22XOpa29DKUWz18vbhTtJdrqoaTMmJlhFCJmzeW0WC5i/HS0+H95gkLvefZtr8ifz3akz9ptsl5OYxBnZueysq8XjN+532e1kxsaxpaaGvc3NDE9I+Fqel0bzdWMRYVZmFl+VlZIRG4/H76eosYEydzNT0zIpamzA7fPh9vmIttsJK8XejheiwBhLCoFwGH8oxNV5U7horKFDiU4n/nCID4t3EzDXKlcoYu0OPAE/CsO7BIaR3uYPsLm6mv/3+XLOGz2mR1mdNhshpVhXWYHNYsHliCIUDhM2wzXOHT2atG676Wk0g43Jael8VGI4kVRYsbm2mtZAgKExMfxlzWouOWk8e1taSIyKwhcMEusw3nlRViuVHjdWi4VWv59Yh4PTs3M6V7f47Vef47LZcZtLp1qAcSmpNLS30+z3YRMLIRXGFzQ25HHZjPCrx1av5OZpMxmdkrLfUnKLJubx17WrsVktuGx2/CFjGcf0uHg+KNrFdVOmfe3PT3Ni0288yHnD0jh1RDZ7mhpp9HppCxjhFLsbG3lu80Y+LNoNYqw80Z2Ol6c3GEREGBIdw9CYWKPnLPS43JoCEOOHIMpqZXh8grHuqt1GpdvDO4U79suT4HQyN2s4OxvqaQ8ECSmFx++ntq2NtZXlbK6pOtaPRaPpV8wfNYbcxCQKG+v4qKSI0uYmhsbEYrdYeGz1StZXVmAVi7GqRcSokAVDP8/MzuX+s+YBEBcVxRAzXtkXCmKzWLBZLYxJSWF6eiajU1KMnbIiVD6sFAhUedwMi43lrV07efCcc1l6+SJmZ2YxOzOLpZcvYtkVVzE1LQ2334fPXH6qLRhgfOoQnDYbW2tq0GgGO9F2Oz+YMZvxqUNYW210FqelpTM3awQgPL9lU+dKEwrYVlfDtrp9ujEvdxQ3TJuxX7k/nnsKZ+bkkhkXT7TdzrT0TOKiooh3RiEYy7O2B4OEUbSZoZHJLhcxdnvnBPfufGPkaBKcUYSVwu33YRELU4alkxUXT1HjgT3PGs3xoN94kC0iXDZ+Ikop3H4/mfHxfFxShCCkx8axo76Ok7NGEBcVxSvbtnbGCQfDivmjxv5/9t47yq7jutP96qSbO+eEjsgECCIQDCBBUiQIBgVSNEUqWdKTlaWRLT/bM2vWvDdrli05vBlbwbZsyQoWKYpRkaKYwbMxn74AACAASURBVAQCJEHkjEajA9A533jOqfdH3W50RgNsoAPOtxYW2ff2Pbdud9epXbv2/v2I2ylKIxmjrlmWkcGNi6ooDIV4aO8e+pIqszW063XS2SwJHOnqBFTX72uNDSDg3uUrx9Ujry4sUsfBFnTFYgAETIOk4+C4k+lleHgsDEKWxefXXc3/3v4argslkUj6pEXQl0hwsLMDRzrsaG6kfXBwuPa/vqebtcWlxGx33Dw1NI2anFz8uomp67yZrjOOpVIsyc1jMKUcu1Lp0iZXSmJ2ipTjYOkae1tbqZzAkGddSSnbmxvRhYYA8kIhIpaP0wN92HLiMioPj4VGdiDA4rw81hSWUJpxdu5l+Hz0J+KETJPBZIrCUJiGnh50TZBwHPKCQaJ2Mh1Mj2ZNUQkvnaxna+1ijnR20BmL4rgueYEQmyoq+fm+vaNOehOOza7Tp7myqIhTfb2jpN6GMslSSlbkFSAQmGkJRiEEvfE4eSHPUtrj0jNnMshD9CTiHOxo56WT9bT099Pc38evjhwi4rPIDQSIpVKYmk7KdUk5LroQDCTjbKmpI3OE7BtAlj/A1po6Tg8MoAmBITQChkHQNCkMhanLySXT50NjdA207boTZqoByjIyuaNuMfcsXU5pJIPSSAYfXLKc6ysqWVFQcDF/NB4ecwJNCPriCWqyc8jw+RmqPoxYFinHpTAUUWVMI+aULjT6EnE2V1aTN4ED1t2LlyKR9KeVLE719dCfTJAXChEYo1Dj03UqMrPYdaYFKc+eID187/2jNJCHTEWKwxGqsnOIpMssbFeyLC9/pn8sHh5zlqTjgJhgTRNwc1UNPkNnb9sZonaK/mSS5v4+Xmts4ENLV1CbM96dsjgS4eOr15CwbWzXpbG3lzMDA3TFojx+cD/uiGMfXQgMTaMzFuVUbw/5wdCEOshCCN5XXUtXPIZMfx1NpehNxHlfVfVM/jg8PKbFnMkgD1EcjuBKl5Gxu5QSDY3PXrWet043EzRNeuIxQpbF8rwCNpZVTDiJAW6qqqYkI4OEbXO4o52m/r50ttclYpkIIYbtM4d0GksiGVxZVDyhmoXPMHjwitX8ePeuYce9loF+blxU6dlKe1w2FIRDtA9GydJH6qPaZAf87GhpItPnH26iMzWN3GCQz63bMKmdc2kkgz+95joeO7CfQ50d6c57wc6WJvrTJz+9CWXqIyUEDJOBZIKeeIwrCifWIA+YJvevuIKH9u6mKx4dfu0tVdWUZ4zvFXA7PwaAlvufF/xz8fCYi9Rk5yAlw+UUoPSKNTSuKi7h2vIKdjQ30Zk+Fa3JzsHS9UmtoAGuKCjEdV3++a0d3FJVzUAySWcsRqqvD0vTiaMUogxNG046nert5TNrRpdsjHTS21hWjiMlz504RlcsSobPz8dXXcmyfC/55HHpmXMB8vrSMm6pqsXQNF48eRxXwtVl5VxdVkZRJMJdkaXctXjptK+ndqFJLF3njrolPH5wP46UbCqv4J3W0+jibBA8lInSBNxZt2TSay7Ny+cvr7uBDyxZRsK2qcnJoTSSMak7kIfHQuOWymr+bddbmJpGyLKI2ynao4Pct3wlB9rbyAsGOd7dBaiGuaJwmKXnyNqGTItTvT1cW1aB3zAYTKWwXYf97W1k+/3saG5mMJVEE4Jj3Z34NI33L1k25cZ0VWERZRkZHGpvJ+k61OXkURKJeHPVY0Ez1r65NJLB5soqXjpZnw6QJa4r+cDS5WT6/Tzw+COjtMFHSjJOxYsn69NlVmdPb5fm5bGjuRFQpRVh08KWLq2DA9xVt5RVU0gsCiG4vmIR15SVDzfBa95c9Zgl5lyAnB8M8YV1G/jV4YNsLK3AZ+hsqqjk5vM8YhmyvzV1nf3tbbzedApT02kZUNqMbzQ30hmNURgOE7NVQ4FP18n0+6nMyj6nXXSm3+/pHntcFoxdbAGW5hfwiVVr+O3RIzT39xG2LD68fAUby8q5pryCBx5/hIhlsTy/YNTrpqK5v4+U4xBIG3uE0wt2QTDEoY52Uq5DwDApjWSQdGzijs2GktJxwe7Y8eYEglw7RSZsKHNMaseor71MssdCQQjBnXVLWFFQyMH2NnQhWFlQNKomeTrYrkt9dzfRVJKSSAbt0QGy/aNLpnIDAWxXOd0OmQYpdZo4jlTFF0MzdqST3sh5q6c33h4es8mcC5AByjMz+dKGjSRs1dmuT2LcMRknurv45eGDtPT1EzRNLEObsKZY0wRnBvpJOS4ukphtoyeTvNxwkvboIAVpQwQPD4/xrC4q5orCIuLpzaWuaeNc7Q60t/HA449MGCT3JRI8e+Iob59uwRAadTm5ozTGhwiaBi0D/WhCUJWdjZQSE52KrCzePtPCVWlr+Ikc9aYbnHt4LASmmgNCCKqysic8cXn43vvPuantjEb5wa636IhGVYWxhJTroIkYuYGzTXS/OLCfaCpJTU4OA6kkINCFYE1xCbbr0uGtrR7zhDkZIA/hm+Yxz0ia+/v417d3EjBMtjefwpWwsqCAmuwcVhcV85sjhwHJ9RWVdEajvHvmNK6UdMVV7VWG5SPpOuM86j08LjemE3BqQhA0J7d5Xz5J7WDScfj+2zv4xYF9WJrO+5csY9eZFloHBwmaJrlBteA+emAfScceNjs41dvD2uJSSiLKiGRISea9MJQp9jLHHh4TI6Xkkf176U3EKUmr0Diuy5HODqKpFFKqcgtHurQNKs+Blj61qb2tppaI5UMTgpaB/lG9PUOB+dD/T8S5nvfwuFjM6QD5Qni14SQvnazH0vVhq0sBLMvPpysaI+nYSJQ81ZaaWiI+H+UZmTx+cD8Ady1eQm88TlHY2+FeDKSU4DSrf1oAjMUI4T/3Cz3mBWPF/ydb1I50dtA6OIBPV7cgS9cpz8gi5aoj2Ob+PgTqSLcgFB5uHgpbPlYWqKa80wP9XDOizGm67+1x/kh3AJwGQIBR7c3ZOcqFzIGxG+Ghx0a+ticep76nm5IR5jq6plEYjpDp95EXCPFC/YlRtcuOlBiaRpY/AChHvZrsHLLTX48ds8fMIKVEpvZB8nWQMTBXI6yrEdp49SCPqVlwAfLpgf5xttVCCLL9Qb62cSOfW7eegGFSFA6TdBz2tbVxsqd7uMu2bXCQDy9bgd+YPCvmcWFI6SJjT0ByJyReUQ8GtkLoMwi9ZHYH5zGOixlwfuMPT9Mdj9GeVroY2qBeU1bOPUuXkxMMknQc/p/NtxA0TT7w8//k9EA/myoqGUgm6U/GCZjWsLPXTOBljifGTe6C2GMg07ajwocMfhzNrJ3dgV3mOK5Lc38fjpSURjKwJjDEmrH3mkT2VBOCiOXjk1eu4ZNXrgHO3i8+veYqnj1+nJZ+ZRNdFA5z/4orpt0g65VMXRgy8RzE/wAiAzAg/jQytQfCn0eIqXurPEZz0QNkx3V5rfEULzfUM5hMsiK/kNtr68ifAeHv/kSCzliUTJ+f7IDalVZmZXPDokqKwpGzWeG6JQymkuQHw6Nc9XyGwWevWseuMy0syc0n7DO5urSc6uyJJeM83hsytR+S20ErA5FuwJAuMvpzCH/dUxaYRdqjg7x8sp7DnR3kBgLcWFnN0ty8C/6djFzIUo5DTyJOyLSGyzEsXWf8kiuRUpIXClGRmTXqmaBpUhbJ4IrCAtoGB9l2qp5Mn5+cwPisiLeIzhzS7YLoI6DlgpZeXN1BiP4UmfFXXiZ5lmju6+PHe3bRM2RWZZh8dNVqFqdlFM9nDozdCP/9bVs52N7Gs8ePsTy/gJJIhNxAgKJQmF8c2Iuh6dybNvXqS8S5a/HEik+3VtexoaSclv4+/usLz3Kqr5c/veb69/KxPc6BdPsh8QJoJSCGYp0QOM3I5D6Eb+2sjm++cdED5N8cOcxLDfXsaG5EINCE4Hh3J1/feN04Y4/p4krJs8eP8eLJE7x48gRSwtc3Xsc9y5Rhx1unm2kdGMCVEldKWgcHuG/5ygktp32GwcayCjZO4BbkMcOkdqkAGQPcFvVY4kWwNoLbDrqndTkbdEajfGfHdhK2TZY/wJmBAf7tnZ08sGIV69PWzRfKWy3N/ObIIWIppRl+TVk5d9YtIWCaWNrZ+ehKyTVl5SzOy5tQo3jsGLY3NV7wmMArw5guMnVI/c/IzJMWAqcX7BNgLp+dgV3GJB2HH+56C0fK4XrgwWSSH737Dn9x3Q0XvK6CKqX4P2+8hiY0pJA8c/wod9Qu5ubqGj6ychWPHdxHwrZpSWeuVxUWsXqMbNvIOZXp95Pp909bNm6i63hz9TxwWwExIjhOI3zg1ANegHw+XNQAuS+R4PXGU5RFMngrrTdcEArT3N/H2y3N3Fxdc0HX3XWmhWeOH6UkkoGlG0gkb51uIuKzuGvxUr664RqeO3Ecn6GTEwhyU2XVcN2ix2wy54wbPYDXGhuI2zbF6fpCv2HgNwx+e/QwVxYVT7ixnA7Hujp5eN8e8oNBsvwBbNfllVMn0YWGJgTFkciw7KIjXW6uqubmquops9beseslRtowlOuPPaH+G7gn/aRn1z0bnOjuoj+ZHA6OQVnAd8djHOxov2D50e9svZtvvbaN/GBoeM7brsvTx4/yvbfe5GhXJ/3JJAB72s7w3a13U5WdM6VO8UTzFbxg96IhIqoUSkoQ4uyctTaqUyCP8+KiBsjdsRgvNZzA0g2a03VIjx/cj+06XPEeAtZtDSfZ3tSIrmnD132zqQmfbnB77WIKQmEevGL1BV07lkpR39ONlJLKrGxPi3EGEdZVyNQ1oJVC/Cn1oG8zaJmgeda/s8WJ7i4yrNG1aX7DpCsWoz+ZmLCMYTpsazhJyDSH6/kNTaM4HOGNplP86AP34jOM824mOtDeNkoZI27btA0OTGpfO9E1wAuwp4sw65Bx0oFyGpkAoYFeOVvDuqwZUnQZiyYEsSnUl8bONVdKzgz0I1EOtid7u3GlHLUhNjQlkRqz7VHXyvYHqMmZPOAaeq+xDClcHOxoZ0lu3rRMQLz5eR5oBWAsAfsIaEMxlg3CQlhXzurQ5iMXNUDODgSQME6D2JGSssj0BMpbBwb4/fGjHGxvI8PnZ3NlJYPJ5LjFUAhIuS6260xoET0dDrW38dO9u/nD8WO40mVjWTmfWbOOq4q9BrIZwVgK1vXp7lqViUCYiOADXv3xLFIQCrNvsG3UZjDlqHkUMqe3QeyIRtnWUM+x7i7yAyFurKykKxYbd7RqaDq26yrt5As4dl2eX8BD9/wRdzz0E7piUWqys/l/X36Bpbn5fHrNWm9DO9NoRZDaB/IVcDvUY7EnQc9HaJ7Sz2xQkS5BUmudCmaHyglrcqbXP9Pc18dP9+xKyyQKsgN+1haVjLsP265DynUQMJw9jpzHHBsKbm/7zx/RE4uxPD+f7liM//Hi89xcVc2fXLUO7QLXa4/xCCEg+BFk5wdBDpyds6kDyO6vILxG5PPiogbIGT4f//X6G9nWcJI30zXImyoWYek6a9Pi/lPRFYvy3Z3bsV2XV0814CLpiA6S5Q9wfUUFxeEMfnFgLwnbJtPnpzsWpaW//4Ka7AaSSX6y510StkPMThFP2bx0sp6jnZ383a23s7qo+EJ+BB4jEEKDwPvBtwGC94EIglGLEF5QM5tsqqjk3TOn6U8kCFsWKdflzEA/t9bUTiuI7YhG+faON0jYNhk+Pye6u9jX3kpNdg7t0QGCpoWTvmZTXx8B06Q/mSDT7592dmik49YHH/kZx7u7yPD5ONLZieO6HGhv52iXmqtTjdmrazw/hBBILRsInV1sjXLAU/mZLbIDAbbWLuY3Rw9j6RoCQcJxuK68YsL6/bGnJvc/9nNO9fawpaZuuEyjNx7npYZ6dCEYTCYxdI3DHR3K3dJ1STr2uOtOxlh3vJTjsLqwiMFkkuNdXQhUQP/w3t1k+HwXfNqbsG1idoqw5bvgpNhCRGhBpFYA5J2ds14z7QVx0Zv07l68lGx/gCy/n8FUiiV5+WytrZtWI8GO5iYStkNxJIIQAh1BSSSDlv4+Mv1+jnd10huP40pJ8n+9Trtp8r2/C/CpK9eyouD8Gr6Od3Xy7IljxFIpounjpLhtc6q3h3/asZ3v3nHXeUm/RVMpXm9s4J3Tp7F0nWvLy1lbXHreroALDSEE6MXqn8ecoDwzk8+sWcuvjhyiZaAfn6FzR91ibqqcnr37tob6UTXMQdPEn0xypr+fkOWjsa+H+u5ueuJxNCGozcnhn958g0+sXnNBvQF9iQQZlo+8QJAT3d040qU4HOGd0808emAvH1u15ryuJ6XkaFcnb7U0Dy/mKwuLvEU3jWekMvfYXFlFdXYOu1tP47guKwoKqc3JndZJXDSVwnHlsD4xqGa6pr5eNldWsb2piXebWhhMpYhYFuuKS9CExnP1xynPyOQX933kvMYadxxVutjdRUNvL450yfT5kUiePHiAW6trJ1S1mmwT67guL5w8zksnT5JyXIKmwZ11S1hfWnZe41rIeHN2ZrjoAbKuadxYWcWNlVVIKc/rKL2xt5dtp05ijKg1/uXhg1xXXsFHr1jNL/bvZVlePiWRDE7769GERpbfz6+PHGRZfv606puGcKTEdt1RtVYSSLourzc2sL+tbVpZb1Bdxv/+zluc6u3h9aZTIJUDWGNfH/cuWzHtMXl4XCqW5OXzjdw8YraNpevnFRwe7eokyzd6wxuyLHoTcb66bj2PHdjPoY4OqrNzKM/MJNPnJ5pK8fjB/SzNy5/We4103LqufBEvn6yndbCfZPr4dyCZJOnY/PrIYT68fOU5N7MjF93n6o/x9LGjBA0TTQh2t53hyrZiPrbqyvO6h3h4XCqEECzKymJRVtY5v3fsqcnXN17HL/bvneCikB8M8YnVq2kbHKAgFCLLHxien9dXLOK+5Sun9X4jg9sD7W383Wuv0JdIYEsXAZiaRsxO0dzfxztnWthSUzfNT656G54+epSicARL14mlUjy8fw9hy2LZJM6dHh4XwiU1CjnfOtPSjExc6TJS/UBKOdxUIBCIb75Jq9DoeVfJhu39+q9Y9M0txFKp86pHrMrKZkV+AW82N+Gm3wPSTQrA/vbpB8iHOto51ddDWUYmutBAQFlGJtubTnHjoirygp6jjcfcQ5zDNnoy8oMh6nu6CYx4bdJxMHWdglCYkGmxvqSM5+uPc7izg3uXrSBomrQMxOiIDlI0wp1rKoYW+m0NJ/nezjexXQc73d/Qm4gDyq2rdWBwWoEDqEbiZ48fpyScMRwIZPsD7Gk9zfGuCupyvc7vIbws1MKgIjMTgcrEDp1oulIipDpN6orFyPL7yQuOzurqQtA9TWv3kRvQmuwc9rS10ptQp72gdNeFEGT4/Bzu6BgVIE+lfOG4Li+erKcwFB42RgmYJhmOjxdP1nsB8hi8OfvemNNOeleXlnFrdS0SeLmhHiklG0rLuHFRFSHLIi8UxJUSbUTcLaXEMvThOkTHdTna1UlzXx9Zfj/L8wtGLeRDZAcC3FRZzRtNjaMMDAQqPJ+qO3gsjb29vNLQkLa7VpnvJw8dIOHYfOrKtV6A7LGg2FxZxf6dbfj1JCHLIuU4nBno5/aaOixdJ+KzSPaM7rx3pQTJqCa+WCrFyZ5uEIKqrKxJs8DrS0oxNEHCOTtTbdfF0nV8hklPPMYipg6Qhxbd/7ZpMxJGZbGFEOhC50RPlxcgeywYhoJWKSWbFlXy8sl6/KaJQM29TYsqKQ5HMDUdKUmvrWL4NbbrUp55tsa5sbeX1xsbaI9GWZKbx9VlZWT4xpdO+gyD/GCQnvjZ4NqREh0wdX3U+n0uUq7qEcoeU6IZMEw6Y9HpX8jDYxrM6QA5NxjkS+s38vSxI6Qch4hlcWNlNdeVK1OPzYuqOfStreQGguz+L08hpaTsb27jpkVVGJpGwrb5j3ff4VhXJ9tOnQSUq97n1m6gMDy+Azvb70MXYjgrBeomYWo6pRnTU91Q4w4gx/mEqa8zfJeP1aOULiA8hYoFTnV2Dp9cvYZfHT5ES38fpq5ze00dt6R1zh/Zv5fmvj46YkO20vu4tryC5fkFw3WQB9rb+Nne3aTSElaWrvPxVVeyJG+8/J/fMLilqoZfHzmERGW2hjrri0Nh/OeRBQ8YBhP9dbpSEpmmgoeHx3xCCMHdi5eyODePXadPA7CmuJjFaefM/FCIjeXlfPPVbRiaxp11S+iOx6jJzh526tvf1saPdr/DtoaT6EJwfcUidjQ38eUNGyfsL/rUlVfxnZ1vDieMcgMBXCnxGQZLx8zxqRppfbpBYShMfzI5ai3tScTHGZYsdKSU6SY8CVqeaoL3mFHmdIAMUByJ8Ok1ayesX67LzeXjV6zm10cPs+ibWzB1nc2VlWxONxe92dzE0a4OyiKZ+HT1UZOOwxMH9/OF9VePe6/dbW0ETYu+ZAJQmWNL1ynJiLDhPBoAVhYUcUfdEmzHSWe+4ZryCqoysymdprzdfEY6rcj478E+BCKAtDYhfJsQYs7/uXlcIKsKi1hZUMhAMonfMIaPP0EFtAWh0HCAnHAcmv7qDySCYXj5KvoSCX66ZxcRy08waPL4wf040kVKyV9t2kx4TKlU6+AALhK/YRCzbQxNIzcYwk4fGVdlZU86zrHHt//thee4qriYjugguYEgQgj6EwksQ2fFZWQuJN0BZHK7knTTIgjrWjCWepvbBYoQgqV5+eOC0yE+uGQZ/7HrbfoSCYKmwfUVS9lYVoahaTiuy1OHDpDl8w/P86Hm+dcbT7G1bvHwdYbmW212DmsKi+hK3wMKQmFc6ZJyHNZPUro4kcKMEIL3L1nKv72zk4RtE7JM+hIJDF3j5qrpNRUvBKRzBhn9OThn1APJN5B6AVruo7M7sAXGvIlYJrtRX1lcwhWFRQymkvgNc9TC/HZLM280NqJrzcM715cb6tlYVsFAMjlu4R2ecOkA2W+YCCGozc6hIDR9zc+wZfH5tet58tBBNpZVoAm4qqiEuxYv/AVHur3IgX8FmYLkG4AEtx/p9iKCH5zt4XmcJ+cjh6YJMeEJydBr73/s56Qch3+7+0P89c++Nfz80a6OdDf62cyvLjSSrsvxrs5xEovRVIrtTY3DajO60Djd38+izCw2lJaNugecCyHgU2vW8vDePTT19YKATJ+fz6xa+54se+cTUsaQg/8KThuILIg9hhz4d8j8FsJ/w2wPz2MW+NiTj3Kgox2AnS3N7GxpZnNlFav+5dtIKQmYJr4xBmC319RxsKN9VIA8xHP1J9AEw03wp3p7MTQV1E5UljEVi3Pz+OqGa3m5oZ7WgQGuLi3g+kWLyA+OV8JYiEiZRA7+UHkJaMXqJoYDzmmkjCFE4JzX8Jge8yZAngpd04YnWUt/H78/dpTDnR0c7+ok4TgERxQ5SdJ1xRMEqlemF+LXGk8hpeTOuiX0xGNcVXL+RiFF4QhfWLeBaCqFLsQFmSLMR2TyHYj/HoQFrmqcJLkTEq8h/TcjtIWfQfcYzVCQrQlB074m/vpfvsWelw+o59b9KTvvL0b4dYrCYUCM2swe7GjjNw98YtT1CsdsViPpoHxJXt6k2aghJju+/cqGjXREo9iuS0EodFnJMcrkuyo41od+dgYIHRLPIK11CM3rmbjccaXk5p/8gIG0WUg0lRo3R+K2PayrPPakJmAYBEeULGX6fSRs55zW8pNRnpnJx1Zdps5w9jFwe8/O19gT4LYBIDsfQIqw15w3QyyYqE1KyaunGvj7N14l6dicGRgAIGxarCoognSl4XUVFSzLy5+wW39LTR0nurtIODYCQVc8SrY/wG3V05egGcuFqALMa5xmZUM7EW4PzKMAWUoX5CAI32VpZjJW8B/em7HGw/fez599539guy4AzV9ezumgievTQErODAyMW3THNuoNjeNv33c7f/7s79GE4PbaOvoSCaqyskfZUJ9rLCMZqr28LLGPK9MeSC+26Y1t/EVk6jAi77HZG5vHrBGxLJbnF/D/3XYHdz/8U7pHNNkJIXBcl4JgCFPXubNuMe3RKJsWLZrwWisLCrmrbgnf3rEdBGwsLWdZfgG3VNVcqo+zcJBTKYm4l2wYM4WqpW5Vn0svRswhU5MFEyC/3nSKb+98g6O76gGIl6obfiyVYteZ06RcFyGgMBjmQ0uXT3iN7ECAr2+8jluqajg9MEBJJMwVBUUTql54TIJepuyk9RK12AL4PwCyFbTJa0PnGm5yP8R/rXbqQkda1yP87/PqqM+DieSa/vE3f87fvPoy8p5jJEqDEDj783SlJNO00HxqM/sPt21lU0XlhNe+YVElJZEIfYkEhaEwt1bXsra45LI5qZlRtDzgwMTPiemXq8wFpEwiE69D8k3AAWsdwrrey4JfAMvzC/j+XR/km69tI5pKETCMEWVNAoQgPxhkeUEBcdvmwZWrqM1Rqi8TndRIKXlfdS1dsShZgQAl4ciCLzm8KAxljqWrklGBe9RaK5OIrO8ijPLZHd95IN1eZPRnYJ8ChFpr/R9A862f7aEBCyRATjoOvz92FC2dJR4l0yYEhqbxtY3XsL6kjEWZWVMenwZMk6vLLv4fmJQpQEPMswXoXAjrKmTyVbUjRKp/bjP4NiO06endzjbSboDoT0Bknq2jlglVnhO4fbaHd8kYK/g/Ezx97ChHvvFbRMzG1xxFBgwVKAOmpnP3kiW80dREyDRHBcdjg+0Hn/gFAdPkqY98bEbGdTkjrLXI5Cvg9oH/QxB/EmQCwl9CC310toc3baSUqnEptTcd9BsQfw5pH4XQ57zN7TSYyJa6JieXirQ+8lANcabPT5bfh65pfG3DteSHVCZ5KoQQFEciFEdmfh0YHYgn1GmliCzMjZFWCNa1kHw1ffIjwFoP1gaVoJonqPn6sDp1HqqllkmIPYrUCxFGxWwPcWEEyL3xOEnHZvB/vkLpvjaav3w2QywldESjxFM21dk5k14j5Tgc6ezg8PW9uwAAIABJREFUeHcXWT4/q4qKRllxzhTS6UDGfwupgyAMpLUB4b9tTh0rvBeElgGhzyMTzwEaiBD4NiGsjbM9tGkjE6+qmw/miDrqHSB8SP9NCHH5SPW9FybKIv3Fc89g6jpJoPQ7B3AWZ9PwmVqkT6ciM4NoKsXTD37inJmlmG3jui7t0cGL1pwjpQT7CDL5ujpJMFcgrI3zZqM3XYSeD6HPIGNPqK5437VgXoUI3DXbQzs/nCZI7QetLN24BGilKjtlHwdzyeyObx6Scl3CpkXKdXGli6mlg2ABjlSOmf2pJCX6xKVzD997P/2JBC/UH+dIZye5wQDXlFVQlpE54fdfCCNLwT7y6PfB7eahLarhVlrXIfxbFtTmSAgBgbvBrFM9P7hgrkGYy+dXRt7tAKceEm+qrwP3qN4lTGTybS9AninCloVAw9DG72AlEr9h0p9uLpiIIb3ko10dvHrqFBLJrdW1fPaqdVROIRl1vkh3EDn4fZAxHnxGyes8tOU1pNMJoT+eX3/cUyD0PETwI7M9jAvHbQfG/i2lO4VlFC6jAHkmMscjrxE2Tdb904d4/k8eBuDvHz9Gyj3CJ176AJn+ALYrSaVNPya6xn2PPsyZgX7qcnIQCL716jauK1/EB5Yum3FbaJl8A2JPgggDFsSfR6Z2QehLCG1h1SsLoxrCfwayD4Q1Pzvh3U5IvKIW2cA96jEhAIF0WhFegDwOx3XRxFmt+rGb2v/72k38bO9uyiIZdMWiZPp9CJRUY8SyyA+GhrXLJ6I/keA7O7fTFY0R8Vmc6uthR3Mzf7z6KlYUXATXO7cDsEAvAmlD4iWkCCL8N838e80iQmhgLkeYE5eLzgdk9xfV6bLboR6IPZEOkk3V+zMHmPMB8smebl6oP0FLfx8VmVncXFU9bvcZME02VSwi8a3b2ff1X1L0hxaa6jJBCG6uqiZkWizNy5v0Pd453TKslzy0MFu6ziP79/Ln126asYVXpvbx4NMCRDY7WlUhyIPPFABnePjDrWpSe8w+Ri34rgetaEQd9VYgBWL+NBleSrpjMXadbqEjFqU6O4crCgonrAe+YVEVvzx8EJ9ukHDsYbeuutxclubmEbIszClKoNqjgyQdh9KIugc4rssrp05SlZXFlcXnrzYzGVImIP60Os4cbtAMgdOMTL6D8G+asfeaKwghVFnRPMTt/BjIOMgOVdk1tNgCIBH65KeHlyPNfX385ughjnV1ETJNblxUxQ2LKseVHy7PL6AkEmEwmSTL58eWLgnboSormzXFJQwmk1RkTu5aub2pka5YbNhoKwMYTCZ58tABlublzYhazFApmHSaeWiLBC09X4UBWgEkXkb6bvSMNOYawkS5TYxAShUcGytmZUhjmdMB8tHODr7/zk5eaWhA1zRladvexpfWXz1uUm6prcNn6MT/9g4OdrRjxaIEDZOwZZETCHJ16eR1xe+2nub1xkaMEXrJz504zjVl5XTFYjNnDe22w1jfLiFACnC7vQB5jiB816qjK/cMqivYVdmp4IMLrmZ8JjjV28O/vr2TlOtgaTpvNjexLZLB59auJzRGa/z6ikX0JuJ87ql6BlMprsxpBuDbGx/BcV0GQj+Y9CSlP5FgfUkZxeGzJQ5DEo/bm5tmNEDG7QTsEcFxGhEC5xiw8ALkec/Ykx3pqIBZzwNjvDbv5Up7dJDvvfUmGoKScISE4/DrI4eIplLcuVhl2Uee+nxu7QZePXWSXxxwON3fT11OhLxAkL5EgnuWLp/SHfZgRxuZY54PWRYt/X30xOPkztTaCipjPO50z1J/A9jq/z3mDFruQ7jJfdD9Jyos8t2gMsrGYoTlBcjn5HfHjhAyfcPF//nBEJ2xKM8cO8pn147ucjQ0jfdV13JTZTVHuzp5q6WZ7niMZXn5bCgtH9ZKnQi/bkxqDT1VNuu80Ut5aMsroJfy4NOq2eGh2zUVHGsTOxp5XHqElgORL6taZJELeo5yAjRqZ3tocw4pJY8d2IepaaNqgZv6etne1DhsNz2Ermm8f8kyUh056bKnRvW40MgPhwgXjjYFGYkjXUCOs4bWhSDpTn7Me0GIcDqb4Y6RLUykG8A85hJDuq9u5wPgdoG1VjUKW6sQ/q2XpUzjZLzZ1IjtusMbTb9hUBLJYNupk/x49y50TYwKkEOWxZbaxdxWU0d9TzeHOtoxNZ0VBQXDuseTkeUP0D4YJTzix++4LkKIGVWHevje+3Gjj0LqXRAjHDBlD+gV3u9/jqJZK3GNMnAHwFwJxpJ0LfXcUA6bswGyKyWNvb282dxIc38/oNx6pJTcWFk56et0TZvSQnMiNpaVs6+tleJIBr88fBCQXF+xiJrs3Bl10xLmcqReCE4LyDyUwkMLWFcjdG/RnUsILQcReD/Mw1LMS0l/MsmZgYFRWV2AbH+A3a2nxwXIQ5h5D5FD+mgcWFR0bmH7TJ+fskgmnbEoOQGVeZJS0pOIzbjNrNAykNaVkNylSm2EDm4/SBdhzQ0JIo+J0EHLR2T8T0DOmYV2LtHS3z9On/+Xhw+SdGzao8oKeiL1GiEE1dk5Uza7j+W68gp2t54hZKfwGyaO63J6oJ+NZeUz7hEgfDchUwfBOa1OeogBAhG4e0bfx2Nm0XJ/MdtDmJQ5GyALICcQxHFHZ3YdKcmb4a71pXn5bKmt47kTx0k4KrNbHM7gvuUrZ/R9hPBB6E+QiZd4aOu7gAW+D84rhYcLRbpdqpMcDYxahDY/ax09RjN0wuJKqbRR06Rch1zz3Men5+P4JITgvhUr+f7bO2nu70VDw5EuS3LzWXcOB70LQQQ+hMRSTpBI0HIh+GnEZVIKpQT82wFXBZ3zoLzIcxA7NxWZmRzr6iRzhMWzlEMeszNLTU4uD6xcxa8OH1RGI1KwobSMuxcvnfH3EnoeRL6CTO4A+6QynbCuRuiF53ztQkC6/cjUPnB7EUalWmcXkHrHbDBnf3pCCG6trqEjOsibzY1oQmNr7WI6ooPcWl1LXyJO3LbJCQQx3mMZhBCC22rquLq0nE+tWUvYtCjLyLgoqhJCCyv5pPkmofQecBPbIfYUJLapB/ybkYGPoFmrZndgHu+ZgGlyVXEJb59uGRb+t11X1Scum/k6spJIBt+4dhMH2tvoiceoyMyiNif3Pd8DJkIIHyJ4DzJwh9IFFpHLptFHOu1nNUrRQMuC4EcQxsROaR7zh6tLy3mjqZHWgQFygwHitsOG0jLuqFvM999+i5Tr8J2td89YffC6klJWFRbRHYsRNM0pyx3fK0LLRvi3XLTrz1Wk3Ygc/Hd1n0JDJl4Aow5Cn/TKS94DczZABjWxbNcl0+9nMJnElZJ7l61g1+kWfrJ7FwgImRYfXrZyRiRjMv3+GS2p8FC6z8SeUnWbQxNVZEHsEaRRjdDCZ7932HIyBXqRdzw6T3j/kmUMJJMc6exACIGUki3VtSTsFP/wxqv0xRMsz8/nluraGWl4DVsWG0ovnSC+EH5YIDrl00FKGzn4Q9VNrpWoRmK3Dzn4A4j8+YQ60FLKBSNTudDJDgT48vqN/OH4UQ51tBPx+fnIiivoTSZo6O0G4JuvbWNVQSH3rVg5zu79QrB0ncJw+Nzf6HHeSCmRsUcBXTnYqgfTGu7vIHyjT6ilO5AuQwmAXurN2ymY0wGyEIJryivYUFpGwnHwGwY/2b2L/e1tvNF0CoFga20dP979Dl/beC2l52gY8Lj0SPuoyhwL66zpRvx3yjEneD9oKoss3S7k4M8g+nC6o/V2ZOA+NGvZ7A3eY1oETZPPrFlL2+AgA8kE+aEwO5sbeWjfXnICAUKWxbutpznY0c7XN17nbULnOs7JtKrOCFUQLUNJ3KUOInwbhh92U4ch/gy4LUitCHy3os2RDnSPyckPhfjoqiuHv97Teoaf79/LfctXYmg6Ukp2t54haJncu2xmSw09ZhjZDU6bcqMbYkiyMfUupANkKSUy+Zpaf0k3IOulEPo4Qps5v4eFxJwOkIfQNY2gptEVi7K/vZXtTY20pBv3nj52lKRjc215BaVLx4tmd0SjPF9/jIPt7UR8PjZXVnFVUYm3a5pDSOkiB3+sFuXhLLMFsZ8i9T8dbmCUUiojA0T6uNv7Hc4VhBAUhsMUEiaWSvHciROUhCPDCjSFoQgtA3282dzIbTV1E16jua+P3a1nSDkOKwoKqMnO8X7Hs4GMTVKOKoYF/JXucFRphosMEMWqEz36I1z+2AuS5xmvnWogw+cfNtsSQlAUjrCzuZm76pZOqGm+t62VZ48fpXVwkPKMTG6vqaM2N/dSD91j0jDOGS1755yAnj9PG+ncqx5z25DRR9JW7GrSS+ko8w7hQ2iTa1xfDsyLAHmIWMpGExpj796aEHTHYuO+vzce5zs7txNPpfhCzXdwpeSf9nyRnliMW6o9ya5LgTAWI303qgan+G/Ug/6tIPtBTysPOE0QexQYmWX+Pcgk0ncrQr8J6bQho4+D06CeN6ogcK+n/jEH6Y7HkMjh4HiIkGHR0NM74Wtea2zgqYMH0DQNDcG2hnquq1jEh5bOM/vUhYBeqlQupaPUO0Ad2eKOrkF2u0FEVHYZ1P+7QOIZSAfI0mlFJraBfQL0QoTvRoRRdSk/jcc0GEglx7lX6kLgSknSccYFyLvPnObHu3fxlbrvoQvBDxv+C//69k6+sH7DealceLw3hlSA8N2kAuAhiTvpgBwA86zijky+hTLmGHE/FfmqodHtAj1XnQjFHleKPUikuRQR+PCoUsjLiXnVcZIXDGJqGnfVLaE0kkFpJIN7l63guvJFLJlA1m1HcxPRVJKicAQhQNeUMPpzJ04QS6Vm4RNcfgg9VzlayU5VVkFSZYEDD5yddDIx2atB9iNlQjUguKchuV39c5qRgz9EysktxD1mh4x0d7zjuqMej9kpiiPjb7R9iTi/OnyI/FCY4nCEwnCY0oxMXms8RUNvzyUZs8dZhJYD/pvVZtXtUIun2wTWlcje/64W5dQOJeqfeH7Mi8PgtKrjXOcMcuA7kNoNCLDrkQP/jJs8OCufy2NyVhUUKpWJEfQmEhSFw4THmP1IKXn6+FFyAkF0TYBQescB0+TZE8cu5bA90ojgvco10GlR9cVuK/huRphqo+p2fgwG/w1ku5rXsSfUv7QVOySRThtEf6w2uVoBiCJVxxx9OK1ycvkxrzLIPsPg/UuW8cj+vaQcByEETX29lEQyuKp4vMHAyZ4ePl/9bQxNo8x/GID7S/+W7x37Mt3x2IwKlXtMjuZbjzTrIPBHKJm3mtE7Ur0YfJtB5IzIMn8I3GaEsRjsoxD7FeCATGcg48+oawXuBtOrU55LhC2La8sreOlkPYWhMJau0xWPoQuNq8vGO1qe6u1FSjkqg6UJgS4Ex7o6qczy6uMuJkNZqJESacJ3K+hVyNQ7qmnWXIUwVyBjvxvzanv0l3JAZYqFwE28qLJRAOY1oGWqLHPityoz5Z0MzBmuLV/EntYzNPf1ETANEo6DLgT3LFsz7veUcl06ooN8te57w+vqPUV/g5Twg5Nfm43hX5YMb1QB2f1lQCIy/1qVQelFaqM7EjGBPK47AFoEtHxk/DmVOU48CUhl96wtAveoknvU37sQwnxjXgXIABtKy8gLBllXXEJvIsHy/ALWl5RO2GlbHAnjjtn5SKlODzN8XqPQpURoWWCtmeS5MNJ/N8SeVIuxEOA2grkKjDpk4hVV7zjywEPGAH24JtJjbnFH3RK+t/NNXm86xaaKSqqzc7irbskot70hLF0f52MJap5OVPvocfERQoBZhzBH14uLYce6j6k5aNSA26sCXzmgTod8H1LP2ycYDqBTb6MW3TqgHIjjufDMHSI+H1/acA3vnjnNie4u8kMh1haXTqg6Y2oauRN5FLgupRleo/zsISaVYdRy/xMpk8j2rWotta4HHGAQAp9ECAPZ+w0gxZCLMPZ+0GPK5VdGL9FnmFvMy9Vnum4+V5eV87/f+DqGJvi/Kv8RF8k/HvkCNy4qH3ds5DG7aL5rkHop0rpWTUZzZdpyUke6vahzn5F2wjZgI6VxEeTtPd4rhqaRHQiQFQjw1zffOq4eeSSVWdlkWBY98ThZaYWLaCqFLgQr8udv1mIiN7K5xHD9YjoLNVEmeTKGrZ1TRyH+B1WCoRWD7140axmufXDMopr+f7cNbAvw7r9zjaBpcm15BdeWV0z5fUIIttbU8e29X+QrtWdrkAeSSb643uvtuVDOZ/4Nfd/5vEYIC6kXqXnpWw8iA2GuUmWQE48InC6Qg0gt+7JcZy9agBy3Uxzt6iLp2FRkZk2YObrY5AdDfGHdBn595BDfPvYlAobBnXWVbK6cWVtaj5lBGBUIY4KbsxyqZXXHP+e2qW+RCXAaAQF6haehPE1s12UwmSRomlMGsefDUGD4ZnMTAJ946jFg8kDR0nU+vWYdP9r9Di39fQghMHWNj69aM2wp7TE30cw6MCdQJTGWgn3kbEkUFiDBHQR3H7Lrk8PZaCldQHglF9OgNx6nvqcbIQS12TmEZinRs7qomD/WNJ44/pe0Dg5QnhHko1es9hr0LoCZ2KhOFy33Z5M/GfwkRJ8AOlFKUXkgbcCFrk/jigAi54fgnAI00MsX/Dp7UQLkhp4efvju2+z+2lMA1P39HdxWU8v7qmov+U2wPDOTL66/mqTjYGgamncTnn9oEdAKVcMQQ40kJuCC3aCafmI/h8Sz6nQocAcEPzFxsO0BqEab7U2NPHP8KDE7haUb3FJVzQ2LqmZljpRmZPAX191AU18vjutSlpE5b8srxm4Q5momWRtZLsH5L7ZTIXJ+iuz+IiRfTD8QSUs0poA4pHbgdt4Hvi1gHwMtiLRuRPiunxeW1rPBjuYmHj+wHxdVJ2jpGh9fvYalEzSoX2yEEKwqLGJVYZFnEjMPmNYcF2HQQ+B0AxJEMN3UF1NNf4BsvxmsTep5LQLBjy/odXbGVyDbdfnJnl3oQhte4ApDEX5/7Bi1OXlUzVLDzVgJm8sZKR0llyZjoJfMfZFws1a5/oxFWCAciP2n0mLFUg25UiAH/wMy/hIhLp6t6XxmT+sZHj2wj4JQmCx/gIRj86vDh/DpBtec44j1XAwFgucbGBqa5jXkTYLqIk8B5qwGI9MNpoXQkMYisCuUiybK2WtUmZTTok59tGIgAfHfIGU/InDXRRv/fKU9OshjB/aRFwwNr2XRVIqf7nmX/7ZpM8FZbDj3guPxSHdQyZcKK51pnTzUupgb1ekwnMEOfxHiT4G+NO0caqsTWuEDGR8arWqqh7S75sJeZ2c8QG7q6+Xtrz6BTzfo2qUyKDu/8jhJx2bPzytnLUD2UCjHuv/gwd8lQKZ46JadSKMGAh9F+NbOyeyN0MuR1jqwCyC1Sz1o3ZSWqIlA/MXRTn2JZ9NOffeBOd48xgOerz9Otj+AP72J9ekGecEQz9cfZ2NZ+YwsenMtY3qpuNANwmS4yQOQeFrJN0kHaa5SEk5GxYz8nmZyQR61yPtuheSedCOtAKMctApIvaW0k621Sh8dAL+ytU6+jvRtvmx1VyfjcEc7ktGJnqBp0h2PUd/dxYqCwtkbnMco3MROiD+pyomcVsBB+rcgAnch9KJLO5YJyjcmm+/CXIU0l4PTo057RAB8dwAO2HvB7YHAh8++IO2uiX18wa6zMx4gSyYxYUIgJ+xV97hUSCmRgz9PF96bgKP+wJ1TIGNItwUR/OClG4/bhYw/D6n96njHtwlhrUeI0fLcQugQ+mPk4A/APqQe1AwwV462wx2HPcVzlzedsRjZ/tFZ+YBh0DLQjyslupcVmhNI+xhEfwRSUwuRTEBqDyReQQbugMA94+bLxWDcQtt+F0T+FGHUgV42YaAurCuQvg1gN6YlpgxgIG0sYjNOxULoaZmhPsALkEfiTrF0TtCZ4TFLSKcFYo8BvrOmVtKF2C+R9gkIf37SkoSLljkeqfTktiPdrmEJuNFScZ9XBl7GMqAIRNqO2nczDBxkwshOCKU8tUCZ8QC5LJLB6v/zQQxNY+/XfwXAhm/fS8tAP1d4u9zZxe0Ct5EHn8lkR5s6kjvQEwIky/NdSL6B9G2aoqv1/JHSTTfSaaDln7WzdPuRA/+sJm/iNVQDz2k1eQNbx11HGJUQ+QYy7cIn9GowqsE9g/Rdr45p479U3+y/W9VO6Qu3Nuq9UpuTw/GuLvJGNM/2JuJUZGaia/PKP2jOMhMZdBl/XgWX9n4VQGrZakGSfZDYrjI3s5G9cbsg8Swy/nvwvw85+BP1+NhMVeizSqYx9a46qrW2IDK/iUy8APEXgMwRH9YGNBCXt73tRCxOWzinHGe4mTZu2xia5p3KziFkco+ap3a6kU0EVFzp9itn2PgziPBnZ/593QEgCSJr9IY5/AUln+p2qPFYa5ED34XwVya2kRYRROTPkKmDgD28Aca6Atn/j2PcNVMqI+rVIE8fU9f5xOo1/GDX2yRslcE7M9DPzZXVXofrrGOnJQ4dfrb5aQCWZ7Wln0qAbSGtjUi3Vf3xW1cirA0XXF8k7QZk9OfqaAapsr3BBxB6PjL5jrpp6CWoO4gArQyS21SQPsERq9AyEb5rR7+HVqJMRhIvqbIKgQrIA3df9j7yU3FbTR3f7dxO62A/EcvPQDKJI13urFsy20PzGEm6OQYZV00xABhKc1j4kYld6vTFbVdGHHrVeyqTkjKJTL4JybfUA9YGhLVe6ai6/ciOLYAJgXvTL3Ag/jyQAMbfJ4QWVhvesZteaz0y8Ua6xjFHZcZlJ/i3IDRPuWQsReEId9Ut4TdHDw+7mhmaxgMrV3mSpXOKhDrtkd3AGOUuEQL7KG5ihypZIICw1oNx4eIF0o0i479Ku1UCIhMZuAfNXKxcZuPPgFYEpO8JWhE4p5HJNxH+LZNKxQl9dOOnWmdvgMQ2wEj3+tjpdXbhbtAuSpt4dXYOf3X9jRz5/QoSts2irCyKwxGvmH+20fJBy+KhWxs50GYz+sjEUotw/Nn05AWcU8jUAQh9ZsomAwDp9qiF1T6pJqG5Sh0NY0LyDfVNvhuRgz+EyJ+pso7kq4BxtnY4/ksV5Ia+ANOsQRRCgH8rmMuRvptAGAhzBWLK0guP0kgGX736GrY1nORUbw8rCwq4cVGVJ/Q/19ArVAnSqLmaAi2ojj9TO5H2nvTz6U1o6FMI7dy/Ryll2k66G7RcpCiA6M/APgAivejFnkLaxyH4cXBOpmvoRoxF6Oq9Q19B898w/SY+LQvCX0AmnoPUQdVk6/8jhLVu+j+by4wbK6tYnl/Asa5OdCFYnJdHlt8zW5lLCGMZMvEqSD8IGzBVICl0kLpqTI09qlRdsJGpd8F/B8K/+ZzXVpvXPcpZVstEWFch439Q9wetGISmnPEG/wMZ+RqgqffWLAjcM2KQYbDrz+9zCQH+O8FcoWICoSOMlQij7LyuM9+4aDpKYcviqmIvSJlLCKFB8CPIgX/hf727GYCHNv8GhAnmenAbwFisFkhQGV37uJqQU9g5S6dTHdvIWDrodZVNZWq3OmIabp57Od08d69ayKU7YVmTdHuR8WcBnzIL0fPO8bkEGJWqDMNj2hSFI/zRiitmexgeUyB8tyBTh9PuklG1CMok6Fcocw4RAL3qbNDqnkHGf4sIPjDldaVMIAcfStf0DwXXxWA3gV5+9noypBZgpxHQVRZpos2nMJFuP5BESTBO47Pp+eccp8do8kMh8kOX3lPAY5oYtWCth8SL6lREXwToYCwB2aiaybWyEfPLhsQzSGvdlI2par7+O9gNSn6NpNpcOoOqxGroeloY3EFk8i2E7+b01B5RFgFAFLSlyNRRcLsQGf9dOWKeA7XOViGMqgv96cw75qfQqMcFI4xFkPFXPPSBV5T6A9cBGWAUQnSHknkZzug+qeqm/O9DTBUgJ14EEmn5F139k6gbxFh5NgHIqNr9+m8FZPrYRqobCz6I/hQSr6pr+zcjAx9Bs1bN7A/Cw2MeIIxyCH8RGXtKOdZJXdUECkcd2RqLxmR08yG5Gxm4FyEmP3qX8RfAPghaabrRRqo6YWmPrikcDrxbwbxCyT+5A2dPeGQckJA6gIz9EvRaEH7c5Lto1pUz/wPx8JjDCKFB4MPI6COq7lcrVnNFC6q56faOma9G2iT2NGgTGO6kkcldKjjWR2RsnVZwDwHLGHca7HYhtKBypk28rHwEMFXvgkxB6ggyuePsS/RSCH3aU48ZgxcgX4YILQMRuBMCdyLdqNpdOmeQ0UcnecE5anntg+mAVpwNrqWt9E99WyHxa/WY/wPgnlFd71o2hD+PjP0OrI1q4TVqILk7HQBYZ9879ijSXIwQ/hn5/B4e8wlhlCMiX0GGP5dWsoiDXqY0SMcpA6Xr+ccwsvRBSqnKnrSCs4u1EOrroVrGcYOIqPkX+qRqyHNb0uUWhirdsg8rmTahqZOk6MNILWdBmwh4eIxlWO1lqExRLwO3H5H1t6r0Iv6H0S+IPaFOhMJfmvrC9r50WcYI9FxI2iro1kfUAcuoOgkGhP92pPBB4hUgmS7FKFGnwsk31fcH7lF1yfHnEcEPXNgHX6B4AfJlzlBDjNTLIfgAuKch8bp60rcZ0BDn6pIXWaht8IhjHKGDXpAOmG2USkUL+G8ZlpgRejEi/Blkuntdxp9WC7cwR2SxfwckwfmkOr7y8LgMmLBxRlijSp2ktR7iT5/NAoNSbzGXT5k9VtiMmq/qzdJ64m0g8s5eTy8YPoIVRhVk/JXqNcBBankw8A8wlI0K3JM+NbKQyTe8ANnj8sY+BMYyZclsrUYmnj97AjNk/iN8KoM7Ff8/e+cdJsV15e33VnVXhwnAMAw5JwEigwRIZIMiCpasbAVHeZ3WK7O73t1vZX9er79dLO/a6/XaXjnIsiQrB4QkS0gCSYggJAEig8iZGcKk7q7uqvP9cWt6picxAxN6oN7n8SPAfBIIAAAgAElEQVRPT3f17aFv3XPPPef3q3xK6ypHayjjiAmBPiAnwHXRfUSnweyJssZmbopDs/V7EUZKf6DXWfewvk7sOUBAmYhc5/eK1cAPkH0A72go5x4k9jzg6uyQ0RUVvfnMxy6hGVrz0egB8Zf1Y9ZkbRRg9oDkWCCIsiakd7aZ762/hqIsrb1YG4E6i7mPzwWOCk1DUju0VXNV1tgszHCiq88oAIDIAq9HoIZxgXsSIrcAKUh6euPBEajIdciJ+7xbwp+8QN2bx84xROrTRw156jU+PhcOGa54qS0QGJF+TBkFkHMfUvmULqlILAcpBkBOfD49v2qSnq/OHv3f2LNaQUZEn8ZaM1Dhy7UijJRC4DLPSyCScbak11hvnW1w9H5gXBs/QPZJo4x8VM49nqai6x2rnnnSqOAYJHK9Pj6ypul5Zk1DhefoidnE+mEVHI1Y0/WRbVWgHZqrj3HNvmf/wXx8OggNBbT1qUIoFYKcL4KzGzn1HSCA6vTDJmSPQYWvQJy92igIE3DALEJFrkcZeYjEvPfQPQQNLapy6jt6c+x6cpGx5/R/rUsgML0pH9nH57wiHRxLGSTXZMxhFRgMeX8LbjHi7IZkcTOvntLBtRJ9opr4C5JYWn9g3dA9xJqsyzCqTn3CN+pssjXZzx7Xwg+QferQ3EJ9pRQqNB2xLtFZI5V3VlqmyuyBRG5ONwd6g0Hl3H1GmTkfnwsRpUwIDEFUJ+/nzOA4I6NFrUA799vaEMA9ppt4yv4Nsb+G6vqndGB8ZqtapXsNqgJkHN01bxb5km0+Fy6BEek5UxulTDC7o7r++YyyiLXnryp4WDfnqSgYhUhiWbOHpsJzEGd/uhEe9xAE+qFCc5t9rfMdP+rwaTGUCoF5bm6JRmgyEhyhdVdVEMz+un7Lx+cCoNGAth7OHMA2jFIhVA2lCZfmOyimx1t8s86YRW6CwHCUNQlltK0cWVP/Zj4+rUlz53BzUCoMgf5nPGlqyABEXyMCOV+B8DztiGkUgDngnAyGzpZsn7N+gOzTYoi44OzybCqDKGs0yms+EKcYsVdpPVWzN8qaWsetpwpl5ILRsKycj49P82hsAXJL7gSSWuaNzEVLdfmFrjMu/Ucg1HCmq/CZlh7yGcn2xdXH50yczXdX3HLtpFlP4ZOkPkWS2z0VKBuov9xKKcNrvD2z/nFrcC4b+7bED5B9WgQR0TqoibfA/gAwkNB0JHIDKjAQKf8lkPLkZlwkNAdyvnreO/F0dB6Y/SAAD739g3YeyYVFUxeLc81WSWqX3rRKqsajWuHCjb2qG4kAUgdBhRG3POu0UjvKYutzYdEa30E3uR0q/+gpOpngHAezK6rgUST2IlL+a7QVtGgDocgtLT6GCwk/QPZpEST5MVT8zjMIiQMGEIL4S4g5QHfdmlWe8CZa1u01VO6X2nHUPg1RFRhvWL4542c/UO6Y1BdAu85xKP13LTGlItXKFdbl+n+xP3kSciZEbvVc+l5CRe9oj49QPe5aAXEdfVgfn/OEOt/1E3doow+zPxhdtO64U4LYH4D9frUWOWijrvhzSPCiNi93aoya96JsPwXyA2Sfc0YkCeUP6252koCjfxF/SS+81nhIevbVNW2nQ1MQEb9z1sfnHGl25lgEyv9bGwYYnXVTrFGgbXCd/ZD4C6hO1Ra1SulGvuQniFt5Vk24rUYgsxwrWxdbH59zRhJAUG8KpUyb+wQGeG60ZnVwDJ7UoqPns3FROw24Y+MHyD7nTmqXt8O10YYhVcTRk7mT93jNJiAXVL4fHGcpVZliP3PcfrREdqWhZh7V6Uc6Y6zytIslACG9gTVCIBVAbedKz5KaFO1JQ2Ul6c/q43OeUP3dvk2vs0YPMKpOTCLadMQtAaMfDYoxZkmTe3MkLLOF5rct+/jURuLovVbNBdUELAhOhvACrYsaXqCzUKqLPgoKDEbEaZ8xNwMR98xP8vHpQIhzEIxOaFk2b2FVCsTVVtHWFJAaRh+S0sYERo+sLWkwuv4pqxdbH5+zRlLo2mJTl1ikMbTDXnguegMb90xEyrQtPYIYfp/P2eJnkH3OHbMXhGZq68vEK+jMsaVrG3PuRVmTERLaLpOwXnidnZD4AMGByK3tIjFzJtzkdm3l6xzSlrrheajg2Asq6+1njtuelsy0NJRtFXs9ovJ0WYWcALHQpzwxCE1HhWfrBr7UXq276h7Wx7fBcUhyfYY8XHvR2N9DnOPaZRCFCgxDmV3bbmA+Pi2MKvgDUvav4CpwNoGrjXyQGIRvxLBG43KrXmOdHeCc1EoWgcFQ+Tsk+vl2b65t8F4kAqntWuVKYhAci7ImaNnYdsYPkH3OGWV2Q0IzILEMVA4QhuB4vdBWufOEpiKJN8C8XCtdoLQ7nr0OghMhOLydP0UmktoFFQ+Dygd7NeCAewyJutoy28enIxMcDmZn3SPgdgf3KKgEWJO8DasFuV9Byv8X3P0QvFhvhHGh8nHEyEcFBrX3p6gXN7ECYou9nwTBQCKfxQhNbtdx+ficLcrIRawZkFgKgXG6BEpOgdkDlXMPAIY1DtctgcpisEbp+Y0BqX26IT56c/t+CI/aG1tJLIP4K17sEIDU80hyHeR8sUmuoK2JHyD7tAgqfLUumQiOB1IQHI8KjqzOtjoHIb5M72rdw/qx+PMgScSahsq2ADm+VHcFE6huLLRXg9EVCY6/oLLIPm1LaxgN1DULCEPOl5DKJ4HDYPQEsy8qegvKqFqURH/3g5dou/f0iyNI4r2sDJDFKdHBsdGtuvZSbK+bfxjK6NS+A/TxOUtUeL4+ybTf1ae1gan6pOfk/QjeHLc/gsAg7bRXhVEE9kdI5PqsM90StxwSr+v7T9U9RvIgtRtJbkFZY9t1fH6A7JNGUvuQxDvgHILAAFRoBsrsoX8nSX0MktoJqhPKGoMyCtKvVUpB8CJUsKFu2SDUF1MqAaN2M1AW4BxG11HXxAD3JFqpo313tj7nP00JjEXikNqvyx/MfnUWQBFbH1uq3DplTMrsAbnfAjmpn3vyW4i9ElX1vhLTttFG7WUirB24shBJfYpuAK7xd1AWuAKp3ZAFpSHthd9w2/6IU4IkPwGJo4JDwBykTTuqfu+e8Kykc8Dsk/E7pQxUaBKEMi3cM1vzUtRtLVPo8qkGmvjaE/eI/m/GBlwBId2U6AfIPtmAm9ypSwrsdwETQjOQ5AbI/Svt+V7xiJaESqwABAnPgZz7UIEmOvGYfbQNrVtabT4QvgbcYlSwfSdBvQT6ArN1jWbsOf1YaL638GbXLtznwsS1N0LsSSCp1z4jF6J3owL9EHH00WViuW7qMXKQ8NUYtcqDlFK4J76lf6httKHywMjXTUA1dVSlFAJZOGfRQUTDYYB/6uPTfrj2Zq0tLgIoJPEWWBMg8jn9c/wVz0jLAFww+0HO3Sij/qbYensV3JMQHKXX2yrkOARHtnu5Qr2oqNcYLF5gXEVKy0+2M76KRRYjYiMSa4P3EYi/DCoXHfwZ+lhGFBJ/E7HXQWq7ZxoQ9GwsI0jlU01WoVDKQEU/ryVqxAZskNMQuQVl9mzFT3d2qNBc3RHsnvQecUCKweiNVD6KW/miVgLw8WkHxD0Bscf1nDV66fpgAal8RN83Eu/qBlOVD2ZPIAiVTyDJbU1+D6VMCF/nNdUWa0kp5zAYuajQtPTz3JK7skdiLTBEZ6MkXv2YxEAFEKMIcY7q07ALiAdmP8gDsx9kw/LNbFi+Of2zT9shYkPsKS15avbUpllGb10SkdqBJDfqHh6jh/f73uAeRGLPN++NjHxtIuIc1HPVOQgqDxW5ts5Ts2LeGj21jrMc1YEy6CQaJhidEftjfbIt7ZP99jPIWYi4lUj8VbA/BFwkMAgVuS5d7tDyJPVksldX19vGngPEO/qwwV5JRj1u4g2wpoJbDGb3Jr2LMosg9zue/aUNZm+UirTC5zl3VKA/5N6PxF+H0HSdSXZOQmqdV5ssiH05Er0TwxrT3sP1aUdEXHD2IKk9oCKo4CiUkd+675ncrBcUo8b8MfLBPYQkd+rMsdHd28yim/FUHpJYVqfev7GaZ8O6GDH+Cjn5JS01lf93KGsKqp7sTjbomiqjMxK5VQcjbgmgdOJYFUL5zxAMbZkduR6jnY9vfdoHEVeXOSTX6tOV4HiUNb51M6zOIW3yUaMssaqUQJKb9HdV5Vcb8wCoIkhtbtDevWFViJQOup0jUP4zUNGMcsgMUlva1ZpdKQXRu5DYs54uu9KbfhWGyj8jKEAgMBxy7mxzZQs/QM4yRASpfBxSO1i4oBiARS9bSPlvIO9vWkmqJeiJj9fW+3XBKAQi1F+/5FYvwE1EKRMC/c5qlCKu7t5VoTY5LlKBAajcrwDgJlZB7Hnv6MqbNkYBxF5AgiOyrvnBp20QcZDY0zoThAmIPirNua91m9gkWX/FgICWWYwBtYJYFfGCxuahAv0RQ2+CjfCV6cez1fLZsMYigcHg7AIUYq/Vi6/RU9dqS8xT4uiCOst7UUfCN/3JRGKLdSmhykerPDyjg9Sce1pRbjSg+21qo1zteCc2dXtelDefGzfmqduAG4DgCFRwBG7Fb+s8v868TW1p0idoLZSRh8q5F3FLQWwkvhySH+gsOujyi9RW3RgcntumY/MD5GzDPcLCq5YDFhtWlAKwcIECSbBo6Seo0NQWf0ulFBKaA+4psD8AFISv0tnh0GyUCiLJy/TxT/wl/aLQZWAORBld6r2miA2pnYh7WmeOzYEZDQfNxbU3QXwxuKdBmYh1OSr8GX0zaAtSW8BeQUYWPf4qhKqy6NlXJuLTBqS26pMeo091DZ1bjlQ+AXl/12rfTxUYjMRd3URXtaiL7TXrDdEbW7c8M2iV0xAc3eA168siNUuTWcrqPEfc0zr4kHKUORACg9pE81wZuWCMQdwySD5WHRyD3igQROzVF0SA7FONOMf1CaDRp/r7IHl6Hqd2QXBo67yx2cubkyeqs8hig6RQwbGI0QliL4HkVt9H5JR+nWpYeUUkqcsAVRQ5eT+g511987bBLLGUVTtsdv6JLs9yDoHZHxWaptfvNkAZ+ToJlvpQl3imf6H0385e7RmitB1+gJxtuDoopo6MmNKBWCuhrCnatEPlen7vKYjchgqO0E8IXw2Jv3g7XcDogYp+rt5riXsSqXgYKp8HBAlNh8BQ3XBwFkckktoLlX/UNwp7JbrYMoEAKnLlmV7eMqh8r5Gg9uBcqq16fS40xF6vG01qzlcjV2+i3KPVWZCWxuwLoRmQeId0dkpciNyIYebjhhdAxe/ATeiOeNF1fSo0u/7PIaID2cRScI9DYqVeyBv5bqePeI9O9C5SlnnN1G6k4nfp7JiwVAfo0dvbbmMrlYBRHQxVocI1+gsuDC70zDGgVRPsdwELIp/VjykFmIizH9VKAbJSBkQ/r5vdHS/BogyI3IAK9AWzSM+/1C50H1BKJ2TMnij1rfo/SuJDOP1tPe/DM/W8Nc5siJMxb2vOWYkj5T9Ht6blgPsBkvwIcr/Whn1C4v2vnviHtnfd9QPkbMPsxqLFvcDoycJrNwKwaMkYcA7ortZWQkvITEesqV5TSzQj06PCsxBrIkTv0wGB2btBLWCJvayz0VVlEEZvLRGXWIkKz2r22CTxHtjvAcEamsRrQIWQ8Ow2qUtS1mQkdLm+AcVf0Q9aUyE4vMEsus8FgLKqm0uqEPGORlsvU6qU0iowwYuR5FZQAVTwYr77mf8BXuWht3+A5H7dk208CsGJqNB0lNmt3utJcj1UPgb2Kj1u9xC4ByAwRgeTwUuABjLHgREZPxpd/6RLT8r+HQhD1XuKQHIDkhzTdvqmVUG+xLzMsYeUQWBm24zBJ3tQ0QbiL9cruWjFtza7Q94D4OzVm0azT7pkUqkQ5HxR1w6n9oDRBZx9NHQPcYtv8uqavZKp2GLABmd3xglOoyc+gRH6ZDQwQj+3/H/AOabfG4AccIqR+BuonLtb7g/RCEqZSHAcJNeDqtFz5RZDaFabjKEmfoCcZSijALGmeWLgKUDpTlSzV3U2tzXfXwUarCVURp5Xq9wwIglIbtaZ3nQpwtO6qzx1GFcFUdZEbVTQVNzj1FufhaMzRG0RIAf6IdFb9TEYtr7JBoaiItnhTuTTPihrHGKv1nM1LXR/goXXHQfzf1o1a6eU0nrlgQH1/z7QDxU4c5e6iAvx17zsU6155paA2ROj6+MNvr52s5D+4bg+DauZeapqwEmubzN9U6WCSOR6qHwcrcUe1sGxWYSyJrbJGHyyA/39FK1GJNSQ75wN5LTR+mrywLxHgLoZfaWCWo4tOFKPNflhjXHXCnJFlxrW2xoklenNYGPNdzUDaBFH64QbtU68jM6Q2tmcj3jOqPCViHNAxz1VmH1Robbf0PoBchaiItciZi8WvfK+DiytCShrWnbqGNahnqyyW8anmwx+9WCcRS+/iNgfQe5Xmp75DQyB0OW6Brrqpha+Cki2+q6/JoY1AQleDO6XtVpBQ53BPhcO5mAIXwnxN6ofMzprBYk2pKoBa8PyzRk/1xeg111wbX3iY/bSx86SgtgTXk1zGALDcBMfYoQaDygzF2PTK/uorW/qtMmGNmNc1ljE6KI3Mu5JCMxEWRNQRvTML/Y5z6jxXUyXC3ZGRT+Hqqn1ne2EF4BbqcseQbvneapXmP0hOAxxK874maqVLwSMTugG35onLfE21yNWRifI/abuYXJKdA10YHDblWXVwA+QsxClTFRoMoQmt9sYtP6y2eygXClLB5FKaVMR9zSYF/GrB5N6sTX7gLMfsT/RrkAZ7+kg9oc6+1xzYxCapoNq9whaacPVma3oHW3S8FP787VaXalPh0MphQrPRawJ4Bzku/P+CKqSDe/sADqKcoClJeKqyhBS27zgQWm9VhWF2JOIWYAKDKz3CuKe9BqQuuiNo1GoG6HcY6AKvSelQGKo4IR6r9Ga6Gy635B3IVP7pEN1+SV6w9a5wXLBlqS+TWxD94UzlkcEh0N8mf7/EgfnOLp22NABcmqvLo2I3lDnpSKi1+DUfp3oCQ5HGTm6UT/2rE5EKUv3IskJCLW9VrJSlpdNb/O3zsAPkH0yEOcIEnvJaxYwEGsiKnxVs7ItKnItUnEMJM6nG+FX30+y4X0DSLDwmg0gSRa9tr2uZWbsJd1hbK9CNyWeQJKbUblfQ+V9Q9ciq65gFuh6ysCQFv3sPj5nizK66Nq9dtD1FrH5yeu3IO5pFl6hg9yGFl59dFtXkUJC8z1XvlytiW4OAZJgDtQbWxXWPQS1AmSRlCebtdqTUHMQawIqciMqejtS+fvqpiSA8BX6RKgVyQY9Zp/sJWu+F84R3NgSlDW52UoRypqG2B+DNQWSW9CZ474QHKfnoVEEyTWIXJdpZS0OEnuuWq0KkHgEcr6Isi7V1vWJt7WMJCHdRJiNTrdthB8g+6QRt1zrLZNi4YLjACxavEarUkRug+QGT9KsCBW8uEFN5qojErEu41ff/6nXCV+hf+lWUFdvGcQp0YGx0Zt0HaTZG5yDSHILhjUWFbku4/THxyfbaGvNWXFPIOUPe/rGAs5h7v/+NtySHRhdH/ccqJwzHk8qayKiTE9qqlJnjgOj9fGqpEACuu6x9vvbK3W3fZVslrhgr0WMAozwPG0M5OzVWS6zV70GIz4+FwIPvf0DxC3ju7O+rZNEi3uDvQKxV0DOl1CBQYhboeXdVKdGA3ll5EPuNxB7ja4dVnnakU7lgVvmBbgOdYqUU1t1g7vRu1rZxT2NVD6ByvsuRng2EroMpBxUbpuXdWbb5tYPkH3SSHIDC6/dASpUQ4MZkN0sev55feTiHtfqEZEFkPPVBne+SgXAmsyi1+ZDchsLF+wBUix6ZjPggt0TNzgew/IaI9xj2odeWZlufpLUclZcuLtYn45HawbGNYNvib2gJdy8sp9FS3pDbCe4J3HLfw32elAWEhyJilzT4NHtd+d8H4CfvPXPSOmPdcOhCNgf6+BbYqBm1q1rTKwAo1v1YqsMXX+dWIGEPuMZA7WiYUoNmqX9egHQMcp7zl/q+/uLvVKvaSpUXdvrliLljyAqoJNQRicwuiKhGajwlQ2WESojDxWeiyu2tqlWAUiu1sGtxMHohiS3o6wRNd7/Yy37WFP20OikT43cY2D20EGxatv+mmbprbchfoDsU41bTN0mu4ReHJ19nmyboQNl5zgSfxmV84UGL6dtJG9Byn7OoueK0dqKhu5kN3tA/Bkk+Pe6e7dBa15pkrajj8+FhkgMktt1zSBUN7BKCTglUP5fgAlGP61x6hyA3G83ek2lTCRyo9ZQTm1Fd/2bunxETiKVj0HOl6trNiVWT6NsAEhQv56WT2vTnIZNnzYmuYlFS0bpuv4q3LgOcA0LVCctteZWgCxFjHxUaHqjl1ShGUhyByReB5IgFpgF2p459ihi/g3K9PoAMKiTVZaGtId9/ADZpxqzH4sW9wCzj1crLCx67gQ4O9GSaonq59rvg4oiYjd6DKOMXCTQB+JxUEmgAqRCy0qFpuqMtNkLjF6Qcx+kPvVqkNGZYxVEBS9u1Y/t49MRqB34fHfOv0LqIIuW1NALzVj8LN0sa+ToOmAVRewP6mRlHpj9YJ1gatGSud5rQnqDavYCAnp+Ogcg0Fe/ODgakh+DqqHaISUQGHlOzplnQ+0mrPbOPvm0PNnwb3umDUejGxSVp+UPqwJkccHZAthec5yn8OKWgTjaCOhMAbKRg0Su0ZrGKgpG1HPrM8E5jCQ/QZnaIEhZE5HkxyCdqXbgPKkTVkbbOObVR7bOXT9AbmXEPYUkVkByK5idUdZ0VHBYew+rXlRwJGL20vqD4gCu58AVoI6LTdritgmLYHq3XOu54gI6uNbZ5juR+Cve83TTgYosaLDW2cenpRFx9VEjCjn1AJA9N+u6GPq4VI7rADXyWUgdgcRLgIKauq6CVyJ1qKGLZaIMXRph1parMzLct1R4LpLa4QXTlla/MHJQ4TZyuKyH7P33ahvaug6+PRFJ6RpcqdSuc21ki3y2qNBlnrtkjjdfKrXSkxFFO+hVPdHS0osSRUTOqLKhcBCjs+c0S7VLIIZOSFURGKYNNxLvoDPGWt5NRW9rEyWPjoYfILci4pYi5b/Uu0F7BeAiya1I5GaM0KXtPbw6KGVBzpcR+30WLfkICIFToGum7HfR2qZKB8eBYRC8pEnahMqapAv/jSKIv6wfDF2mnQFrlE8oI4qK3oxErkM3FvkdeT5th6QOILEnPIt0tKEA2ZPVqC/wEfeUXnCrRPXlJOnburiZtYYS1xJQ9Vy3djDl2p8AycwniiexaBSmH1JGF8j9FpLc4Ll99kQFx/mbWp8Wp64CSxKsieCc8J4hSOgyVPjaVju9qO+0pfYGpNENSuAiiFyvT1DFAdf23B5zvLlbVd/v6M8XGNGkwFVKH9TNsK6+Z6XLrazJqEB1Qk4phYpcg1iTvU1tGAKDssZjob3vsbXxA+RWRAvTl3rHk55GoVEE8VcRa3zWfClroowcVHgehOcB4NqboOIPXtNOirStbuhyVGR+064ZGIBEPgvxxTXE2XuhorfWO/kb+ruk9RudQ7pRKDCsTWymfc5/xK3UgSaKtEV6ulrBpUknJW1EzQVXGZ0h91tallHKEHIh9qgOGpz9QNj7HHEI9EBZkxq6bAYqOBwx++jA2yjQi7mcAOvSOlk6ZeSgQlMbvFa2bDAuNM7nzDHg1eqWe+sr1SUJgcEQHNW+Y2sApRQqdDliTdR9AkYuEl8CiTWQOs3CG+KAYtFzZWANQIWvaOKVTb1xrQqQSeq1OjhW/z1qj8Msgkay7Vr9xgaCbV4qlU0o/YdoGpMmTZK1a9e24nDaFjuR5NTRU4Rzw+QXNG6hfDa45b+CyscAs/po0+gF1lRU3ney/jioCkntQhLLIbVPL5ah2ajgqGYfyYhbqc0+VASMHs16vdZbfQrsdVrtAgWRa1A5X0SZPc74+o6EUupDEWlaJNMI59t8bU3EXodUPpFpAhN7DsRGFfy2TWxoWwrX3qStlZ2jnrlOCqw5qNwv1GjWOTPiVmgJKvsjXRtpTdGarWc4NcrQVxZBSm7Sx8XhKyAwHBWecd65ULbEnPXn65mpNvn4OVL672D0JMOp0T0Jgf4YOfe22hhaunRF3Eqk5Do+3XCSv5qnFV/GXN4JjO48tOxHzbqWW3yzVrHIuU/37gSGN8uBzi25S7/emqIlH5UJnf4DZU08r0owmjpfL9gM8ifvbubNx98jZacQEYZNHMwV980mHG3BjKTRDZ19qiXTopQ+UukgqMAgVAtINSkjCsbZXUfs9XqhNvpWNzKIjVQ+DbnfOK8mr08mp4tL2f7hLmJlMfqP7EPfi3pjGC2b1RCpbOSX8RZ9r9bGsEYhge/phjpEH6E2qBLTMPo0aT6Em3ZSVK9UU/ITIKYfjy8BXtTlGHnf1OUZPucdp4tLWff2Rg5sP0xR366Mnzuawt4to0SUtkZ2ihsQXVDU6ZfJcpQRRYweoGLVD56tW6tn6mNEbz2710sFpLZrV0wp1qdPp76JmEWowhfP7podmAsyQN6/7SCv/vYtCnp0ZtmT7+vjBAEzYHDtV5u2GDQFFZqC2DO0rFniDUDAukTX7p6F77uIq4v6VUhLo11IJD/Sbl2src7GJ5brna6cAlX/Yqubrk7qv5lfF9nh2L1xH8//bAmO42IYBisXf8iIS4dwzVfnYZotZzOuzD4Iklm3G75eZ2CrjnA7EMrIBau9tcMFSNX42UA35caQxCpU5Kr2GZZPq3HiyEke+5dnScRscvKjHN1znA3vbuG2v7uB3kN6ttwbGV11uaJb494vohtIA61rZd6SpSs1N5WDR8Ivlyp+9cO5Z/0e51LG5JbcBalN3g8nqn+hgnoNvQA5rwJkx3EQVwgEG/9Y697ayIdvbCBoBTi6V8naiY0AACAASURBVDvGrXt7I8pQzL79cnLym26r3BjK7I3k3AexF3UghwmhqTor00xce4NucHPLtPB/aBYqNPPCqQ9SXsdtM3CTO7S3vJwGBAmORoVvOKvNiU/bk0qmWPKbpUTzo0TzdMOmiLB51Q5GTBnG0AktaEBh9gVrsmeZnIvW/62E0ExUHSWHM1PdXV+mF3Kz93l/ylFbqkl1/hlS/hNI1Oqsd8u97HYm6dcVPKp7DVL7QUVQweH+nO0grHr5Q1J2iqK+upQnp1OU0pIy3nr8Pe76Pze32BzQqke3IuW/9RpUvfUhOBZljW729URsJLkFnD2gOqOssRe262NVPbNRCOEbwT2IiFsn3tBzVlD5/6RPhjBR1jgwB58X97vzIkC24zYrXljDurc3krIdBo7ux6zbLqOwV/11bmWnKjCMzH88pRQI2N7Ot6UwgsOQwAOedWP4rBrzJLUTKv8EqgsLrzsO4rJo8RIEAxWe2WJjzWqCk8Daqi1t4y/ox0IzPO3IujcycY5B5e+B3OoFWhTiJlC5DZub+LQuruty6NOjxMpidOvblc7dOjX43OMHSoiXxynqV0M1QSkiuWG2rf20RQNkpZQO4IIjtdsUBsqaoMX2m0m1ssQx0ps6awxEbm1WPWB70KINdUYUMCB8nW7yrUIqwRzSwIsEiT0L9gfVj8QjkPMFVKDfuY/Jp1k4jsO+LQc5vPsYeZ2jDB43ML1ZrY9dG/aSX5hZzpNXkMuR3cdI2imsUMudfCqzN+R9F0luBSlHBfqAOaDZSSOROFLxW91jgwUkkcSbnv1z637nam8qh875Ew/NadW3bHQs7rG5QELfC9PGQ6Vg9mn47+oW6/udioCItr8Oz21Gg2H2kt136yay5H+XsuPDXaxfpo8HgqEgT/6/F7j3X26rN9gdMm4A4+eOpseAIl5/ZBkA02+aguu45Be2fLOeUkY9blNNR+LLWLjgKKgTbHjvNKAtoBe9/LaWtcnyRbclUMHRiHUp2GurlTBUFBX9XL07VbE/0u5E1LCutldCYjkSua5ZzUo+LUPpiTKe+88lHNtfgqEUIsKkK8cy83PT6q0pDgQDusmrlg6om3JbdKGtQikTgqNQ59gBL7EXtT1zurtewF6HmINRoSktMNLspmZwLdY0zyWsOxDUiQIcVOiy9HNq1y6T2qX7DNIZ51LdQJm38MI5McsCknaSF3/xGk8tehGUYvIV4wjnruTWhddR1K9bva/JL8il/FQlQat6TUomUoRzQgSCLVcSVYUyoqjQuZVUSGINpPaC2af6QfeU3qjl/nWbZEIb2pC2uZa10UXLv7nFEL5Wz1cph/BtGU+rM2erTMQin9VqIvG3kODEDr/Odvi7Tcnhkzzxr8+xfvkmju0r5ti+Ytb+ZR3Ln36fbR/srPc1o6ePoLBPV47sOUYqmSKZSFJ6ooz5985q0brGFsM9Tt1GP0M3D9V0tzuPUcpERW5G5X0b1eXnWlkg729QZv03al0z1cDXu6Zwuk+b8Zffv82Jw6fo0b8bRf0K6danK6tf/ogdH+2u9/mFvQtY9/ZGXn34zfRjqWSKpJ1k5LTmZ3bbAnErPUerGt9LpfQph72m/QZ2BtySu6p1ZpNrqn8+R1T4Ci0ZKaXgHgYjpDvsG2tCUrXudUa+rjV1j57zeHyazqb3t7Fz3R6C4SBWKEj3/t1AhFd/9xYNqV9dcvUESkvKsBNaQ9tJORQfOsElV41v8cbaFiO5vu4ppOqkVWDkdPuMqZ0wCp9Bdf2zNgkiBoF+qNz7MYJDm34RZQLKk5ns2HT41GPZiXJQClWjpTVemcBNOezbfIAJc8fUeU0kN8Lt37uRLau2M3jcADoV5jFmxsgGd8XtTmAwi16uAKNIW0ADixYP0sXzquXKQc4W8TK6ra3rrJTSWbmmNE0FhoI1TWcFqo6KwtdqMXYjS/+dz2PKT1WwZ+P+jHIJwzTI7ZzDhuWbGD4pU6uzKnNybJ/W9Xz5168DikuuHs/s2y+n95BslfarChxqZ52aX0N/PqBUABWej4Rm65MfFa2Tkcs4ZnaP6X6NGmYkiKD/dh2/prEj8dAXfkkq6VB8UDds/eWRtxFXGD9nNGUnysnvWve0dfjkIcz9/AxWPLeaVNLBMBTTFkxk0pXj2nr4TUeFgdpNaOJ93dqnGb5Ru+pWRgX6oQL3Nvqc9Jwtvh6c4zWc+6qQarWpDkyHD5C7dO/E5CvHUdi7gJd/9Tp2PMm/PLoZ13F57BfruOjSoVx0Sd3dTyQnzIS5Y+oNoLMNFZqJJDfqDEraAvo0RO5p10J4cUuR+Mtg66BdgqNQkWuzQr5JWaO1hqtzEC37I/rvF7ley835tClOyknrlbquy+FdRzm65zifrt9DOCfEtffPJ5ITbvD1Rf264bouX/vpPeR0yt6GLWXkIOYQ7WpVlUUW0Rsza0b7Ds5D3HJw9gFBCAxAqWCdWsiWNvVQKqg39Gd8Yi5IjGore/TfzizyyjR82gplqIxMcdmJcsQVtq3dyc51u+tdO5VS/PnHzyOu8E9PfodofrRlpVNbA2sqVD4Ckqe/c+KtFdbo7GkOdUsBF3EOapOtbGmAU1F9mu2W6pMe0Ke3Rj4EGuoz6Dh0+AC5U2E+42aP4p1nVpGI2ZgBfVMNWAH6DO/Na797i4Gj+xGKZPkkbQRldofcbyCJd1i0pDOYRajQDFRgYLuNSSSlC/PdYyxcoJVAFr28Fak4ArnfbneXQKVCkPsVxF4LgQGgclHWlPNi0nZE8rvm0a13AaUnyjiy6xjHD5QQioYQEVJJh2ceWszt37sxrUDTqF1rlqMiNyIV/1tt/wzaMta6pP0G5eEmVkP8RS8rCxi5kHNvuuShPd3u0sYi8Ve1hCMACow8VPSO7AkKLhD+4fG/5oX/eo0P31hPvCLOwNH9+do/v4lp7ua5RwbRpXtnBl5c3cRWO+v5o9v/E8j+uauCo5DwPEi8Ba4CXK0dHr6+3caUvv/N/C64x1j0YggQpOxnEJ4DoSuyYj4YXZ9AUnuRysd0r48AZldU9K52jwFagg4fIAPMuWM6B7Yf5s6vv4gyFEMvLgEgr8tj2PEkR3ZfTf+Rfdt5lOeGMotQ0ZvbexjVpHaz8OoPQYXYsKIUgIULFMg+fvLWTgiObOcBglIRbbVpdNM7XbNPVtxULkSUUlz5xTk88uCf2b/9ENG8CHs27uOHf9SL6Y+/HmDl4rX88oN/a+eRnjvK7Ap5fwOp7Yhbqje45sB2bzAT5zDEntMbWmWwaMkYcE8jFX/0GuDafzlQSiGVjwM2Ku97+vg7MPi8WGw7GsMmDWbSlWN577lV/OjxrYQinzJsjF5b7/j6C7y7uHdGgNxRUUrpMiDrUq08Y+SA0bPd1wqRGIteioC62CsDwWuAexsCIyFLVF1UoD+S3ATYqM4/81xys7TevJm0/x2xBTBNk6ETBhHJDWOFg0Bx5u8DJrGKOGUlZeR2yW1UpuZCRSSm3epSn4LRVVtLNtQAB7rppqFfuafT1YIits7i2h9pqSdrCio4ptUnkIggiWWQeB3iXjYqegvk3H3e2dx2FHoMKGL+vbM5dayUnPwoJ4/WaIBRiqTX2FOTbM8+NYRSFgQvzqqqWUlu1EfINeee0Qmcw7qhptaJlEgMRNqhJMnVLpmp3WB0RhlFYLaME5tP0zEMgxk3T2XV4rV0KTqMMhSgA2TDNDhxOLNutyOf+gAoo5OeD62IiO2ZfeVp1ZzGSO0FSYJRo/TMm7+S2p4hQSciWjlHKsHs0WYbSl2S5UJSW6TLic+DyoOuz50XQfJ5ESCDbg74w/+5mS49OnPN5x4B4Nk/3EYgaDDqsn08tehFXFfLRU34zGhmfG5qdipWtAPiViAVvwHnCNgrAFc31eR8ARUYXP+LjG4sWtwLjF4svPYTABa9PBrcg+nAWsRBKh6F1FawV+nXpXYi1uWo6A2t+6GcTyH+Chg9oepm4RYjlU9Czv3tnh24UCnqW0hBjy7c+Y0XUX8H3XtpRZFFz+yka68CSg6f5MC2Q5gBg/6j+pLXxXc/rA8RG0ksB/t93fwWHIcKz2vU3GDhFS+CW8qGFdrSduE1G3QWWUFNe15xT2qputRW/XNgGCpyQ5tsLN2SO9KLLWUPAYKE52jli4buRT6tRtAK0HNwD5Y8dTc5naLMu+5hAJ5++GYGXNyTb1/2j1SUVnLzdxYw4OK+DB43oH0HnKWIOFpbOfEuSAqMHCR8DYY1vpFXmem+jcyLeb+r+tEtRyr/DKmdgAEqgISvxwhNbOFPUR+i1T7SPybArUTiS1CRBW3w/q1LhwuQ7USSlJ0ikhvOCHK69enKlV+Yzet/XI4d15koM2AwYsow3nt+Dd37deOtx99FREgmUkTzo1x6detaUnYUxF6js0hmH6q/EhGk8jnIe6D+naDZF4IjILnJaxwE3IO6xtf0DBxSn0Jqmzb3qJrQRh+wVyKhaSizqBU/04eQeN+zyayypn4PrEu1jaafkWoXeg7qTu8hPUgmkgRraBmbQZNkIslvf/B4uj42EDRZ8FdXMHR8CzrmtSGt1fAmIkjlU1qeyijSGuvJj3XGNe9bKNVAs6OKArVkqyQGBNMasCJJbZrgngLlKYWkdiEVD3uasK2cmZLyGuOt+n5EkMqnIe9vz4usVEdCKcWsW6bx7E9fJmWncF3BSTq4jkvvob04vPsYANs+2Mm6ZZsYMLIPP37tn1pFp7wjI4m3If6GNrYygnreVT6BGHmohvpiAv11aYVbBoanGCI24KKCI6qvXfkUOLu8ZJDSQWrsScTs1upGJyr/n3Uvku3FAFU6yIn3EGuaLjfrwHSYANlOJHn3mZWsX74ZN+XStXcX5n1+Jn2GVUt+jZ4+kiHjB3J419UErABf/rfu/OGfn2TD8k2Yppm2ld7wziZyO0e55KrxfiYRILlRm2hg1ggml+ruXjld7XVfA231eQeSWMWiJWsAAWsSKjQtvYiJc0AHpaqGWUf8BcAG907dmd5aSLz+3TcGUPco36flcFIOhz49QiJm03NgEf98w78D+sjVMAxu/PbVLH+6C5ve38bN9z1FJDdMLPDfPPHjVyjsXZA2FIhXJljy6ze4/6f3Zn8nfFviHoXkJ3qzWfUdVz3AOYjYm1ANZI5+8va/IbGXWHjFMwAseqnQU8O5ozqoTn2qZZtq6hSrIt1wmGqD3oLwtVB5EjAzpaOcQ575ii/R2NI4jsP+rYcoPlRCp6759B/VNyPAHTx2ALf/w42sWvIRix+/k15De7L7k2V88NrHnDxyCoAPXlvHvLtnsnfzAbat2cHo6e3fg5ItiCQh8Y5WYana9KkIqBwksbxOgJyxsY7eg1Q8or//oOd75EaUqTev4p7wklC9atwLQoCF2GtbPUAWZx91PRo8HWT3cIdPRHWYAHnpo8vZ+O5W1i/fBAqmXTeZpxa9xL0/vJWCHtUBXCQ3wqAx/dM/l5+qqCNQrgxFrDyO67p+mQXoTvb6NFoVQMOBiVIWKjwDwg3IV6n8+qVLBS3n1JoERusA3+gD8ef1Y6H5ejy+DnKrUXL4JM/958ucOq5r1JVSlJ0oJ6+g+t87khvhyvvmMO/zM+HUeyil2LXqBMowMty2wtEQp4tLObjjMIPHDmjrj3LW1HaZckvuatkscpUJTp0NYADcIw2+TNtpXwfm+zqDFb4GFRyZmeWRsgZeLTqT1dqoKITmgFFzTN69yW/Ua3ESsQTP/ucSDmw7pBskRejSozO3Lrw+Q+e47/De9B1evWla+uhyHMfNuJZSipxOUbat/dQPkGsiCZ35NWp9f1UYnJJGX6oCA5DUNpA4qtMPtJW2UUN/WhKeEVGte4GyGu0TajHK/wucYojeVvd3Kksk8s6BrAyQHceh+IAWJy/sU0BlaYzN729Pu+UBrHxpLXYiyYR5o5l1y2UNXmvw2AGII3Tt1SVtKz35qvH0HFjkB8ceypqqu1CNnhBfrB+0LoXg+HNq0FHBEUj4Kl1zVSXbFLpMB6hm/8ZffI4oazSS3ACpTTXcBitR0fvO3Bzhc1a4rsuL//0qsbI43ft14/VHliEi6Tn7wOwHMxp3zIAJhY8BoPiwQXcu/5SnFkYXdCOb1FoYU/oItxGUUjy0rBGlEKM7IJnXFs+koxVOfGqXoSjrUj1vJamzbSIgRyE4XDdR+bQoH76xgQNbD9J9QFF6nhUfLOHtJ1dw/V9d2eDrfvjS3/ObhY+ybtlGFIr598wCIJV0/Cb42qgoGAXglnvJKA85DcFJ6R/r21hrDG2wExxd99pGIRDVG15V4+8u5VrporVROaBO6E17lRuhHAOzZ6uv8W1B1gXIh3cd5aVfvsajBXpndfepbky9bpK3SarlwGQoSg6fynz97qNsXb0DO5Fk2IRBTL1uIns37efY/mJSKV07Ja7LrFuntdlnynoCwyFyPcRf82qcgOCocy6yV0YO5HwZqXxGu2MBBIagIje2epCqVBCxpmjbX3OQPnYyh6drLX3ODRHhyO5jHN51lFA0xKAx/Sg7UU7JwZPaktajqcHt4HEDWP7MKpJ2iqClb0ux8jhWKEifYT1b5TO0Fq1tuoHRHYKjq2uQCYAc99RnRp3btc2+1ddWXlOenITAqLZZ8AJDdJlF/EXPEt6EwChUJIskLjsox/YdZ8vqHcQrEwwZN5ABF/flk3e30Ll754x5WtCzCzvW7iJpJwla1aUWWrM8hRkw6VSYT/9RfTADBl176e9J0k5hx21GTx9R570vZJQykPACqPwDuHEdMEspEEKFZjb8wtQW/V/vVKe+kyilgkjkRqh8DFQpYOl5E+iPssa2yuepGgsAyQ/1fxNLdZAeGAfWeFTOnedFv0BWBcixijjP/HQxZsAkGNLHEcl4kjcf0811s2+/nLefeA+A+ffM4ujeY/S7qPrY56OlG1j6p3f48PX1oGDC3DGMmTmSux68mU3vbaP/yD5079+NMTNH0rmbn42oQimFCk1HghMh56tg5LZYx7oye0Du170bgoky2kaVQJwj2h1J5evmQYBUNyQWRNV3HOTTZFzX5Y1HlrN++SZvYRVC0TAzbp6SkdGsyiot+d+lRPPC6eyxiLDjo12sfuUjSkvKGXhxX6ZcO5HP3Dmdt554D9dxQYEVtrjhG1dhhf2j9Zro+v9bkER33TsgCX3aE57XcINes659G2IPBvsDdG/BDJR1SYsueA1ly1TBo1qqSgAxPGezOH7fwLmxccVWXn34TQzTwDQN1r21kRGXaodZEUmfrs6/Z5Y+PDAyN7cHdhzm7Sfe4/Duo0RzI1xyzQSuuG8OS379Ogd3HEEZCsNQfOauGRmlGD4awxqBGF9HEu9qO/XgFFTo8ox1tvbGOo03Rxq+9mjE/JZuTHdPQ+AilDWmbbXDJQUEdDOhewhJ7UdZDSvqdBSyKkDes3E/f+5ZihW22BvUx+LP9Idk3OaB/IvYtubTtL/7sX3FdOrWiVHThgNQcbqCt554j669CtLd8d0HFLHhnS1cfPlFXHZD+7tYZTvKiILR8kX9SilQbbshEXsN2O8ANVQs7NWAQsJX+ce158CuDXv5+O2N9BhQhGHoRbT8VAUrF68lHLWIlceJ5OpATURwHZdIjWPX9cs28Zffv01ul1xCUYtta3by6bo9fP7BzzFk/EAObD+EGTDpN6I3kdyOe1zbmq50uv5/HoTntcK1g6jQNAi1wylbagsk3tQnPVWnTO4xrWKR8xW/3OYsiFcmeOORZXTp3tnzCdDzcsvqHYyYMpQtK3cgCMprGCk5dIKRU4elXS2P7S/myX97gVDEonu/biQTSd567F3seJLbv/dZig+eIF4Rp7BP10bt4i90VKAfKnBnk5/fnJMoZfZCRXo1+PuWpnpsn4PUQYjcrH0OQGeSY08igcHtoKHesmRVgGzH7XofF2DIuIFcfNlFDLi4D+UnKxgyYRCT5o9N1zsd3n2MNa98hBW20moVSx9djp1IctkNk/1d7YWGW4JWrKiFUt7RrR8gny1bV+8gkhNOB8cAuZ1zOLa/mFm3TuOdp1dSWlKGYRqkkinu+cEtXPWluQAk7STvPLOSrr26pDPDXXsVcPxACR+/+Qmzb7uckVOH13nPjmo+0Ja0WklHK9DQ4u9WPOLVNdYowVLdILUb5FS9ijo+jXN0zzEcx00Hx6CTFlbIIhQNsXnlNo7t1X0CL//6daywxdf+4970cz98Yz2GYaSb9qywRVG/Qj549SMuuXIc3fp0bKWCbKMjzN80bkw3vtd04VQRXZPs7AGjYzdrZlWA3HNgEdf/3qKobyGPdNYT9u5ThRzfX0zP+7pT2KuAoRPq10QNNqS7KELIl4i68DCHgDVNy1XFntOPha/VjRGGf0M/F5Sh6m2oE1foP7IvX/zxIO58/mlcx+V/Zi6g7/BeaSWZ8lOV2PFknRKnnE5RDmw/1CbjP9/pSIFyXZLUlY2qahZ06jzb58wEQ8F6RYocxyG3Uw6Ffbpy6FNt9tCtXyGhiEVOfnXmr/hACZG8zMxwIBjASblUlMb8EqhWJqvncd5CiL9azy+EehNUHYysCpC79S1k4rwxfPCX9aSiDqA74C+9ZgKFvRqvie0ztCdz75yOHU+yeslHAEy/eQrlpyoYNrF5RgOlJ8qwYzZdunfWnfY+HQ5lTUCSqzz9SFf/zz2qNSSVv2E6F0ZOHc7G97biOC5v/ukdAC69ZiIFPTvzzVVLUQo2x3Tz7N9vXsETI25NvzaaF8b0MstVR7igG/L6j6rbQFmVOd6wfHP6Zz+LXBe35K7qWkWV1/iTs4g6i39gHCS3geRXB8buaTALtRKAT7PpMbCILj06cfLoabp01xvTRMxGRBh+yRAeevsHjZ7Q9B7ai4/f/CRDncKOJ7HCQXI7d+wjdJ9zQwVHIPFXdHN/2rG2QkvY1bKu74hkTYAcq4hzaOcRBozuR98RfRj78W6UUlx03VD6jzyz8oAZMLnpO9fy/H+9qp30FCQqbW74xlV0Ksxv0hgqSiv5y+/f5tP1e1BKEckLc8W9sxkyruP/Q19oaAWN+xF7FZg9wOiEsi6DwND2HlqHZ8Ao3VS3+pWP02VRZtBgwdeuYOmaNxt9bSgSYtKV41jxwhoKe3UlGApQfrICN+Uw8TNjmjWO8lMVHNx5BDNg0O+i3n4mq4oaXe+Q5RmoWihrLJLcCKnN6OXJARVGRW45L7ri2wPDMLjxW1fzwi9e5ei+4yiltEvl/fPTiafGNp3j547mk3c3U3LoBPmF+SQqE5SWlDP/npkZKhdnoqK0kt0b9hIrj9NrSA96De7h15R3cJRZpFU04i+CuCBKB8rRu8+LRJRqSHu0PiZNmiRr165t8UFsWb2d1377FisXa8mQ6Tddyo3fujrDJa+puK7L0b3HcVIu3fsXNnkCiwhPP/QS+7Yc5OO3PkGhmH7TFMpOlnPv/72Vwt7+sbxP26CU+lBEJp35mY3T0vM1lUyxZ+N+Dn16hMd+9CzBUJBta3YCMHr6CJSh0gvt7c8+CcATN91a5zqO47D2tXWsfvVjEhUJivoXMuf2yxvtE6id4Vq3bCNLH30HcfX9K5QT4rPfvoY+QzuWJFxL4pbcpZvcqsw+groxuSMFyAAiDqQ+RZy9oHJRwYszzRGykJaYsy09X0WEPZv2s3X1DlzXZclvlmKaBn/7yDfo3r9bszaUxYdOsPKltezZuI9OhXlccvUEhk8e0uQA9+DOwzzz08UkKu20Icno6SOYf+8s34/gPEDc05Dao3sHzEFZ35zX1Pna7hnkU8dPs+Q3S+lUmJ9uIghYAZ772Svc/9Ddzc4KGYZBz4Hdmz2Ok0dP8cxDiwmGg+mGhXefXYWdSDLpirGNmpH4XHiIW6ldy1QUjO7nfSYkEUvw9EOLObjjCEErwMkjpzBqLGzKaPrnN02TS6+ZyKQrx+EkHYKh4Bn/fjUzXMUHS3j9keWse+sTDMNg/j2zqDhdyQs/f4WvPnR3s7Ja5xOtrr/cRihlQnAYKjisvYfSoVn21ArWLPmYUDSEUnB8fwl5Bbn0Gdar2ferwl4FLLh//lmNw3Ecvjvn+yBw1Rd1s67rChuWb2boxEH+Ce15gDI6QSvqLrcX7R4g79qwt476xIrn12DHba75ymfazGI2XqEtG1Utb2TDUJSVlLfJGHw6Bm5ilXYcjL+tH8i5C6J3oYymlfJ0RD5+ayOHdhymh+e4de1X53Py2GnWvvYxRf271TmirS9zXBvTNM8qe7Tj490oRYaFfE6nKEf3HufQziP0H9m32dc8n+iogXFbIGJDaqd2GjN6gNn3vNzcFh8sYe1r6+nevxuGafD6I8s4dew0p46d5tvT/pFgONhmtfwlB0/gJDNVNAxDEc4Ns2XVdj9A9mkQkThibwb3MBg9UNaoc9Z6bw7tHiCn7FSDv3NSbde13LVXF6YumEReQS7L/rwC0KLph3cfZcDofpSWlHFgx+FqfdYm6j3acZtkIkk0P3pe3ogvNCS1W6tiGEXVTQnOQaTyKVTul9p3cK3IllXbyS/Mz/gOd+6WTyJma2OPZnCukm2//d5jlJaUc/KIbgSsMjkYO3tUs8fic+EgTjFS8bCWoNKPQHAMRG9FqfPr1OHI7mOICIZZt247EbcJhtvm8z4w+0GSiSQnj1bP1SoDIXElo1HXx6cm4p5GKn4NTgk6VE0hia6Q+2WU0TZyj+3+7ew/si+TrxxHYe+uvPnYuwDMuu0yTh8vpfeQHmd1zUQswcmjp8npFCWvS9Oc20KREHPuuJzXfvc2STuFUorDu4/Se0hPKktj/OZvH2XNK1od47IbLuGGb17VaKbKjtsse/J9Pnl3C67rUtCzC/Pvntmh9ZjFOYIk14NbgQpeBIFhKNXuX6E2Rey1kFgBqoYBSWIFWCnEPdFiDoTZRsAKEiuLZzwmrnDJ1RP45i++2KZjSPGCDAAAIABJREFUieSEKS0uy3jMdVwCwQBF/bux8+Pd7Nm0n9wuOVx0yZAmu2aeLi6l7EQ5nYs6kds5pzWG7tOOSOxZ7dJnevdgEUiuR+xhqND5ZSRlRayMzez8e2bx+iPLsOM23/n1V7nokrZrVq5d8vT6I8sQEcbMHMlFlw5l98Z9bFyxFSfpMOLSoQyZMLBJJ0ulJWUc3XscK2LRZ2hPX3HqPEPiS/Vm1qwRM7lHkPjrqOiZTyhbgnaPbor6FTJlwSRWvrRWq08gnDx6iivvm01Op+YtUiLC2tfX896zq3AdFxEYMXUo8+6ehdWQTnINxs4cRdeeXRg9/SIqTlcydOIguvUp4PEfPU9Bz2pjg1A0xIv//ZdGa6Rff2QZm1du565vvgTA83+8nad/sph7f3grBT06nti9a2+AyifAXg6ikNA0CI6G6B0XVpAsFRl2ytUY2vL3PGX8nIt5+VevE82PYBgGIkLxoRMMnzyEUKRut3JDWeIHZj+Ylmz76+n/hB2zWfj7r9N7WK8mzVGAn6/8V959bjW/+MbDAIydNQrDNLjivlm8/Os32LNxH1bYwkk5vP/iB9z019c0uplN2sn0fDUMA3GFCfPGMPOWqR26gUgk4TlKrtenHdalqODoC1INQtwybTZi1GjirHL4TK6F8yxAHjCqL5G8CKUnyskv0EmiVMrBMA0GXHx2bqmxiji7P9lHojJBr8E9KOpX2OTegW9N+wd2fLQbO25z8uhpAHZ8uIsf3fYfDJ04iHBUGw9tXbODUdMu4pqvfCajhKomIsLKxWt5/4UP9M8Inbvlc9N3ru2Qa2sVIo6er4n3gAQEx6JCs7K+QbXVSK7TJkE1Ud30plZuaZMT+XaPbJRSXH7jpQydMIjLb5qCaRoMGT+Qrj2b90U/sucYS379Bu+/+AHd+nbl/Tn5mAED571thCIhPnPXjEZfLyIopegzrFeGesbKl9fywWsfZ9RIv/fcaux4kgX3z2Pg6P51rlV6oozRo3/MhElBevTeA8Bn73kCO5Hik3fHMPNz7WDheg6I2BB7FozOgKWdc4w+kPwEUtsgOKq9h9h2BC4Ga6r+/PHn9WOhK0A5YHRr/LUdmBFThnJ411E+fusTDKUD5N5DejDnjssznlefbjHUX05xaOcRAJ756ctE8iLc9J1rmtRgq5Ri+mcv5emfvESiMsFnPj+DwWMHsHfLAXZ/speeA6ubJitKK3n1t2/y5X//fIPB7srFH7Lxva1076+tsx3HZc0rH1HQozPjZl/cxL9QdiGSQir+oOttVWfAhcpHkdAsVOTa9h5elnH+lb5ZYYvPPbCAl375Gsf26abz2bddxnVfu4LwWRhn7dm4j+d//gopO5X2HBk/dzRz75zeYCBbk2AoSChi0a1v13SAnNM5yuFPj1LUtzCd/c0vzGPzyu2Mmz2qwdPWfVsP8u6zqzJed+rYaRb/z+vc/f22CZxaA4m9CPZKUAWgQpBYgfx/9s47PqrrTMPPuXf6SKPeO8WidzC9GrAxBhfcexKnONlkN86WtPU6cXrsNDvZxIkTe13jhnHBgDHd9A6iiCKh3uv0uffsH1caEEgUG5AE8/x+ttDo3pk7I517vvOd73vf0CGI+sZlrbvtMQgLoNExTNUAy2X7HXd7gAzGhJeam0xqbvJnOr+4oISXn3yL0iMVbJ5rTAYeu1Hb/NFQC+Y1+5l6+4ROM1SFO4+x4Z0t1JTUkZydyJTbxtNn2Mmg11DB6+yXIelKIc/d5Gl7Xx0fVxRBfVvdZK9CqwDfCuMPtr2swPcOEERaxiKuogBZWIYhg9shdBTD9UuCbAb7g1d0Jl1VVWbfP40xc4ZTV96AM8ZBal7yBd+ovv3cV/n3mU/QVNtMYmYC2QMzSc5OxN3k4Z3ff8iXf3n/WesS3c0epC5xxjj4w8afdvjZoa1HiIpxdrgmp8tBdUktDZWNnUo16rrOjhW7ScxICFtnq6pCbHIM25bv7rUBMqHDxt+oknmKE10U+NchrROv2FKgrhBKNNLUB7SSk1kpKQ1nTfP13Xtxl4iUnCS++LN7qS2tAyAhI/6Cd0Sa61v4xrXfpb6igbJvDMJsMfNFTypWh4UdK/bQd3huh/nydFob3bibPPzkg++FA/P2RfOXf3k/S/64rENphBACVVUoO1LRZYBc8KmR9Dr1vJgkF9UltdRVNJzTVKwnIvV6CGwGJQPad3jUdKO/JbAPYTUUyXq7Qs0FYZkAvuVtn4kwxqteDbZZl+0SevWMLqVk58q9/P2Hr+Fp8tDc0Aoys2NgKkDTdIL+4BkBcuGOY7z92w/YtWofiklh0sJxvPHUEm7/zgLS+qQQ9AfJG5LFmLnDSco8WSM9ddEEPC0e0ruokY5LieWPP7uTmEQX8xb9A4AVS75EZVE1s+7thTXI4ixSe6Jn6x1ebISwgPNhZPAAWMaC4kKYRyLUz7a4623EpcQSlxLb5c/bM8WdZY6LD5Tyjx++hqfVi6IqmC0mju0uQgtqZOWnU3WilopjVZ1OjM11LSx7YTXF+04ggfS+qcx9eEaHydDqsJ7R2CulNJqBLJ3f6qQuCfhDZ9Qvmi0mvG5fp+f0BmSoGDB3XKULFeOGWHlVutIJ+21I93OglWFY4QpjG1crB+vL3X15lwRFUUjOPvvOVle7PJ4WL8/950u01LcCos1eWmPfhoOMmjUUm9PKgU2HOw2Qg4Egn7yynr1rCxBCQaiCSTePY9wNI8PHWB3WTnNPupTYo+xdXlvQHwwvZtsRQiCEQL+Mjf0XFa0WUE4Gx2EsoJeh1/3W+LbNLfNqCJSFdSpSq4LgHgzbah2CO0ArBdvcy3INvTpAPrT1CMtfWIPP46e0sAIpJWm/3w9A5beGYLFZuM0dhyPN1sEms53172xh16p91JbVA/Dpu1sJBUN4mjxEJ0QjJcQkRjNgbF8ObjsWdg1rbWzlpke73qqyOaxMuW08K19aSyioIRRBVXENcamxDJrQC7U9lVRw3Al6HfgNa2FsN4Jei7CMPPu5VyBCWBCW4Vek7uOlQErJ5g+28/ov36Wx2mieNcaFgtPloKywnPS+KQhBpyoUWkjjzaffo7GmmaSsRABqS+v55y/f5YEn7qClrhVFVRg6eQAFnx4K21gve2EVIX8Ii93Cj+94utMyD9Wkkjcki7LCSuJTTwb+DdVNDJ826NJ9KJcaJRZjO/I0BCDOr3H5SkOoCRD9bQgdQTZ9FzCDVgxa8VURcFwIRftL+M7M/8Hb4uX4l4yGPl+SES58Oj2KnY5KFtU6u9Q///TdrexatY+UbENmLhgIserV9bgSovj2c1/F2+ojJimamEQXDVVNxCYbCjmtjW62L99N+ZFKfrvux50+d/64/hRsKiQm6aSqjrvJgzPGQUJGL134KTGAbmRJO2T4AoZi0lWIEBZw3AP6LNDrQYlHNn7nsl5DtwfIlUXVrHt7MyUHy4hNdjF+/hgGXtv/vLZut3y4g92r9+Fp8Z4xsUopkUgC3gALvz73jOeTUlJTUoti6rhi83sDFBWUEmpTsph08zgKdxZxyzfnMeeBaZgsJnIHZ52zy33MnOHEJcew8eMcWhvdjL+pD6NmDQ2vjHsTQghw3o90v2h4roOxNWm/A6H2wox4hM9EKBgi6A9ic9ouyNjj6O4iVr++EYDo+Ci0kEb5kSoaKhtIzEhA1yWtjW5MFhNpfc6sQS49XE5deQMpOSczYXEpMRzZdZzffPnP2J1WJMY266jrhrFnTQFSSoK+IBab5Zz9DDPumsSrP19M1YkarDYLfl8AV3wU1944+gI+nZ6FMA9F+pYZXeAiFqMUqNoouVAzu/vyug0hLGAehBRXfuOTFtJormvB5rR2Ou901S/w5Pvf5d1nlgJ0KhMHRmmS3+Nn4PgzEz6hYIgdH+8lKTMhfL7ZYsJqt/LX/3qZlNxkoz8SGDp1IJXHa6gpMeqkd3y8l/qKBuorGrg57sFwueKpmeR+I3MZPDGfAxsPo5iMplqz1cRt/za/9zbVKslGL09wHygpgAqyHpQoo7H2CjEBulCEEKCmgppqvPfLnEHv1gC5prSOV376NqpJZfvy3eiaTnVxLT63j1Gzhp3z/KbaFoSiYHVY8TR7AWNAqyaFKes9TLx5MPO+eV2nE6QQguTsRCYtHMen7xrdsNNun8C7z35EU01zeGBuWLyFgD/I6DnDLshNTwhBv5F59BvZc0XQpdQMNzhEmxtc1zcXocRD1DfBvgjwg5qOEL0v2I9w4WghjY3vbWPbsl2EAiHi0uKYdc8UcgefnyHHzk/24YxxEJPooqq4Gnu0ndgUF43VzZgsZnRNx+f2c/O/3NCpKoynTV6uXe94zoPTCfgCnDhYRu6gTJKzjYCvqbaFIzuPc+JgKaGAFm4Gam+u7WorOTEjgYd/fBf7Pz1EbWkdaX1TGHht/165mG1HKFEQ9QjS85ZRUiAEmAYh7DdflSoWp3OluA52xaGtR/j4pbX4Wn1IDCv4GXdPPi+lmJKDZQR8QWbeM5ndq/aj/l8Ruq5T9MX+KCaFwe9UUnhfLqsnWPmPTu4BoaBGKGCULbWP2dkPTOPEoTLcjW6GTzN6VrSQxq5V+7n9sZuISYpB13TKj1VR01Yz3RWqqnLjl69j+PTBlB4qw+6y029E3nlLuvZEhBDguBPpi4fAJpAhMOcjbDcaYzlCt9CtAfK25bsQCOKSY4zifJNKYkY8GxZvZfi0wefUNcwdkk0ooBGfHssHf16Bz+MnMTMBZ7SdL/78XoZPG9wh0yWl5OiuIrYs3UFjTTPOGAd15fWEQhqqqtBQ3YSUEpvTGg6QwWiuO76vhJxBJ0jNTerVE2c7MlSC9LwMviVGOZ59ITjuRZi6zi4JocBZfh7hymTd25vY9P52kjITMZlV3E0e3nz6Pe7/79tJyUk6p/GHr9WHyayS1jeF6pJafK0+XIkudE0SHe9kwoIx3PCFWbgSzszq6bpOQnocSBlWmgGoq2hAD+nEn1KDHJMYTVVxDaFACNt5Gvm0ExXr5Np5oy7onMuNlLrRZCbdxoJWPbPp8FSEmgFR/2I0kaIiG75qaIheYcFghI6UHang3WeXEZvkIjorCk3T2bXKKD2c+9CM8HFd9Qsc2FwIGI2wSVkJSCmxOqyUWAzny7HXj8Td38qR5gbuefufHVwzA/4gUkpScpM6aJX7PQHqKxrIHXIyoFZNKjanjb3rD7LwUaNR8unVP+pwPV3dWxRFIXtABtkDeu4OppTSGK96NQgXmPqctZFbCCvCPh9puwHQOzWvudIWcheCkvDSZV/QdmuAXHG0ii0f7URVlXCWZ/XrnzJi5hA8Ld5zrggnLBjDsT1F1JbWo5qNwZY/pi93f/cWsgdkEgqGKC4oxd3kIT4tjvIjFSz92ycc2HQYRVUYO3cECMHMeybTWt9KfFoc1944moz+aax6dT0Ak2+7lm3LdrPk2Y9Y+txKrp0/iln3TmHE9F7a3Q5I6UW6nwdUwrJtMmA85vqPq1NSJkKn+Dx+tq/YQ0p2UnjBGhXrJOANsGPlHm74wrk7ivPH9mX1axtIzUuhobIRv8dPQkY8sckxPPLL+3myfBeLV38Ynmjvfut1w33y8U34Wn3GNq0Q1LX1Ciz920oyrknDlRBFzGlBdTAQYv5X55IzKJO//PuLHRbZl8ta91Ig9Wak+4W2BjMB6EjrJIRt/lkzwqJN67c7tid7A6d+BlfK57Jz5V4sNjM2p9Ejo6oKydmJ7F13gKmLxp+R4Dl9XGRek4ZQFEL+EP1G5uFKjKbiWBXjPmrgxFeuYX2ik50VZQAU1FRz91uv8/z1C1nzz0/Zv+EguibZsXIvoUCQxupmAJa/uBqf28fkW07Tm5aSkoPlbFm6g5TcZLLy07kSkDKI9LwKwf3gN5r7cdwGzi8hlLMbFxk7ub20VOQKo1sD5JTcJHTNyN62o+uGZ7s96mSQJqXk+N4T7N9wkFBIY9D4a+g3Ko/E9Hge+J872fHxHpKzE0jKSmTUdcNIzkqkua6Ff/56Ccv/sQopITM/jdLDFcQkRuNp9mJ1WIlPi0PTdPqNyGPm3Yae6/YVu1nxf2sJBY0a5B0r9mCymLBH2VEUQWyii+X/WP25ZOm6nVAh+JZ1lG3zrzTqix23GAYgESIA3hYv6PKM3Ryr08rLT77Fx/+39pyax8OmDeb3j/6Vbct3h8sefvTCfsw2M/kTnkS+uROf28+6tzcRFefE5/FTXVxDeosPi81s1BP7g+HnU80q1z88k23LdnXoaaksrubw1iNIXWffugKy8jO44UuzGNRJnWRvQ3oXg15hSD8BSA38a5FqjtEwGiFCG021LVjtHUuVVFVBAD63nx/c9HOg6wVjdFwU190/lRUvrAGMRVZaXipTF13L7/xHKaipDh/bEgiwtayU65/9C9M2eMnMT2fNG5/SWN3UIcEVlxJDS4OJoD8UNhZyN7vZvXo/KblJrGnxIqWk7/Acfr7sB2H3vd66qJWBzYb6gpJ1UgVKr0d6lyCc93fvxfViTl+8XupFbbcGyGPnjuDg5iNYbBY2fbANXZMMmzqICQvHdtBCXfPGp2x6fwc7V+5FCBgxcyhDJw9g3iPXEZccw6x7phDwByk/Uom7yUMgKcDKl9fSXNuCxWbB5/FTW1pPwR0ZICH9mVK8rT4+fO5jFJNCcltnPMCo64YRnxpL/5F5VBZXs+LFNTii7dSUGHVRq17bQNAfZOz1I3pvgCz9QBcizrL3SltFuPhEx0dhtpnxewMdJl13k8eQaToFKSWF249xX96jzP3CTIZNHcCQSQOxOawkZyfiafGGA2SL3UJzrMpdb7zGlrZs1CP1tcjjkqBZgAsa7sgg45kCkjITsNgsxKXEkN4vld+uexIppVHD+Mk+rA4rfq+fgo2HaW10c2BTIXMenI7fG2Dp31aSlZ/eq+sTpd4KoQIQp8hKCtVwgQtsPC81le7YnuwttH8uV0qGPW9oNhve2YIz5qQEp8/txxZl48d3Ps3etQcAY0HrbTWyug1VTeQMymT8/NEkZiQwYvoQsvIzOLa7CF3TyRuWQ3JWIuMZw41/+StHQwECbVO08Os0VDVSfqyB6pJagr4gCWlxDJ8+mCM7jxMV5+SpVU9QWljBm08toeqEB0VVOLz9GFHxUeSP6YeiGuZDR3YWsX/Dod6rP95OYKuha8y2U5JQG0DqSOmL7NJ+Tjobs5divHZrgJycncRd/3Uza97YyIjpQ3AlRjPhpjEMnTIwfEx9ZQNblu4iJScJc5uWaVpeMvs/PczwGUPI7J9G8YFS3n32Ixbe8zICePabd9JQ3cTRXUVhF6EDd2Xi7esCoOwbhnyT/R/HsEfbSMw8WccohCBvaA55Q3MoLaxgzT83nukUJAQBb5Bei5oJ1qmGfJvvXeMx20KjYU89v8arCF0jpRfp32w4DSoOMI9HmAf1Socnk9nEtDsm8tHzn+B0ObA6rDTXtWB32vjNmh/hSojmsRmPc3RXEXVlDfjcfnzuGj78ywqWPPsRDzx+O/O/Ooen1/wIgH+b9kO++oOV9BtsjMvv2p7Dkx/i3tULEICmt+nTnoK72UNUnBOEQAsa0mVCCGY/MI3+o/pweNtRKouq2fLhTprrWmiubQk3Bw2bNogTB8oYPDH/cn1klwAdpOjE4lwFeQH3odCBi3pVEXomw6cPZt/6g1QV1xAV68TvDRDwBbjpa3M4tqc4fJy7yUNdeQNL/rgcRTXmtCM7j3P/43eQkBZHQlocP7n7N/g9fh7+yd0017YQmxLD9I1+3IMtVOlB/IpEsypoIY3SQxUIRRAKGCZd25bvwt3kIX9sPwAy+6fxxZ/dS+H2Y1SX1NFU20zekBw+fsmQDp3z4HRcCdHsXX+w9wfIQFcGY10mpyL0OLpd5i2jXxoTF4xl0/vbqa9ooLiglLQ+KSRlGg0oVcW1bDvN6nnFi2sI+ALMuHsSCelxLP79h1gdVmM7Vpf4vAGO7irqIP3WmeudzWll7PUjGT278wxMcnYik28Zh81pY+0bhkzV7AemUVlUTb9RPVed4pwoqWCdZNRGtU+wejlYpyHUzs1PPitSBkB6QERd0U5z7UjpR7Y+Z4iZBzYCEiwHkLYbEJfRAehiMmzqIKLjotj60U6aalsYNm0gY68fGW6qe2rVE3zj2u9SfaImfI5qUlFMCgc2FzLm+hGk5aVQcayKiqNVHcolYho1NKfAVeKl7z+OghAcvi8HLaST8WyB0VvgsDJ+/mj8ngBf+81D4XMVRaHPsBz6DMth/6eH+PC5jzu9ftmV5WVvQUQbpRV6nWFDC20ucA1gmXL+z2MaeO5jrkI6KloEwD4fQkfQW/8IlhkI84Betbh1uhzc+4Pb2L16P0d3F5Een8LoOcPJ7J8ebnwzyhlysVjNrGmb2xLS46gtq2f78t3MeXA6XreP6hO1BLwBVr2yAYlEIAj6gwhh6jiuhEDTtA7S21KXZOVndCiTiI6LYtR1w3A3e9i3ruAMww9d1zGdozm/V2AeYzjQKhltrrMYc66pf0T96SJwcszeaUhZmnLRm38KlskI68SLFmt0e8RycEsh7z77ETtX7jUa564faaxi/3sRiRkJ2Bxdu7jZo2ycKChl4b2voJpVUtJLALjuW28w618kv7tzKDFJLpprW8h4piCcOc7600EQoKbFcftjN3XQVz0Vi9XMDV+cxZI/LiPgCyKEods8aEJ+h27c3oYQAmw3gWkA0jIWEAjzcDD1v2ivIaWG9K8C/xogBMKOtM5DWEb3qsnmQpGBfUZwrGYSbrRQ0sH/MdJyba+U7BFCkHlNGmVHKqg+Ucv+DYdAwsSFY3HGGHrgX/zZPXz84lp2rzG65ec8OB0w6oLryupJzkpk8bNLmbpoAju2XUd82nME/SF+9MgwDtyVBfgIBkL4PQEynilACxmLWz2k0VjTTMnBcuY9Mgunq3PnxqwB6YybN4odH+9BURRDCs4fpLG6qUd3up8PhgTUbW0Lr3IMV6kQmPohLOPOdfoFlxBI3Q2yBZSYq2wyDxpNkIG9oMSBVgee55H2O8NWv70FR7Q93G9TcayK2tJ6pt4+gf6j+gCGIc+aNzZisZrDiaflL6xG13Rik108NuNxGmuaqSoyfrZr9T5CAY3UvGS8rV4e7DuW2vI6XnDVghBk/+UwIUVBURV0TcdkMXHjl2dz9/du7fT6nC4HOUOyefPXS6ivbARg2T9WEfAFefyty2sGcSkQ1muRoUIIHWoraRTGeLIvuOivpdfdAzKAiPtfhJp47hOuEKTe2nY/1NsSB0HwLUHq9QjHzRflNbo1QNZ1Y5DGJsWEa44T0uKoq2hg84c7uPGR2WQNyGDWvVPweQJsWboDgWDCgjHomk7ukCze+d0HjB7rJRgIkdHueCkEQgGz2URtfT1CEUhdtv8Ii81EdHw00+6YQM6gswe6/Uf14Qs/uZtJt4zD2+ojb0g22QMzziy76GUIIcB8DcJ8aRqYpH8d+D4yarEQYLsBvK8jhRNhuYIzWdpxCGwAzCdrz3xLgAA4vwS9MECWUvLen5ZxZFcxCelxKIrC7tUFlBwq577/vh2L1UxUrBPZydahQOBwOagqrqGhsgmny05VcTU/eXQ0oUCIyuNlxPykiqighrf9nFPWT4qqIpFMXDiGKbeN7/IaXfHRzH14BluW7gQZorKoGkVVmPvQ9E7l43obQk2H6MeQwf0gGxFqdls26uLdwqXUDHORwPr2V0VaZyKsM6/oRW0Y+10Q3NZm1IDRXCUt4F+KtIzoVTtgBzYd5r0/LScuJZbUnGTczR7e/t0H3P7YAp5a9QTBQJD78r6Ornc02NI0ncS23dvWRnf48dZGD3pIw9Pkobq0jrd+9wGKKsj0BtFCGkGMjLGu6SgmBZvDwh3/vuCsustzH5zO4t9/GP4+6A8SFetk4PiLl6jpLoSwgPMh0I4j7TcbyhWmfsbjFwkpJTKw0XCDlBLZ8iukKR/huBOhnN3I7EpABneBZdLJxmVMhhFSYBPSNh2hxJ71/POhW0e83+Onpa6VXav2dVzF6jqOaKOI3WQ2seixBbz/v8sZOWMIErDYLMz/ymyO7DjO8b0nWPf2ZMqPVvHDj4sQAu7atBCA9AcFDdUJ9H/xOO4mDxnPGN32PsBs86NrkoAv0Kk5wanEpcQyvhe7al1upNSMzHFgq9F5D+BbCmhg6gtXcoCsJLTZhZ72uARE77xpVRXXcGyPoQHeHiglZydSWVTN8T3F5I/tR87gLGISoxkzdwRxKTFIKamvbCQ2JYasAem8/+cVHNpyBJvTSm15PWarmdTcJFSziuYJhF9LKAKLzUwoEELXjXo9e5SNaXdMOqdL1tDJA3luz1OcOGA0/WUPzCAm0XXJPpfLjVCiENZrL/i88zXFkP714F9l7HiItvpm31KkiOl1GdTPhFZklLOcirCD1mhoT4uzy3P1FKSUrH9nC7HJMWE1KKfLga7pfPruVvoMy8FsMfPtv3yFVa9vYM+aAoQimLhwLO4mD2PnjmDI5AH87N7f4W50o2s6qipIy0vDHmVDIo1SKZ+O2W5GC2kIIcILZLPFREJ6/Dn9AlwJ0bx49Bm+OfH76CGdHy/5TxLS46+YxZjhG9AXYep7SZ5f1i0yMqiyzVglsBn865DCgnDed0les0cRKgPRsVEcYUiCGtbUvTxAttgsWByWM1axuqZ38FRPSIvjgf+5g4aqRnRNJz7NyGJ98NzHHNhcSCgQwu/2Ax1rjRWTamSM7ZaTxh9tY6/v8Fya61rYu+5AlzXIET4rQZBezowSFaOO8gpGWIYjbbMAiyGdB2CZYCwMlDNtlHsDTTXNhjXsaROXqqrUVTQARjnSHf++kOUvrKbkYBkSyB2cxfp3tvDNid8nKz+dqDgnoZBmbMOGNOorGjj+hX5ICRnPFmCPspHZP43youqmKlnMAAAgAElEQVRwSZOuGwYhDZWNxCWfO0CJSXQxdMqVExRfLqSUEFhjWN62O2oKs7F1GVgDV0OArKRA8ACop5TxyEDb59B7Sk10XaexpomU7I6lg45oO7VlJ++/Y28YiWJS2fXJPvSAjj3axrxHriM22cXr31lMWp8UKo9XI6XEYrNQV16Pt9WP1W4h4DP6CJwWB6FACJNZJeA1TELiUmOZ+/AMdF0/506rqho9BmA4Wka4AGRz20L2lMeEBYL7kHoLQun9O2dnRU2H4M6Oj0ndCAKVM92TPwvdGiCrJpUJN43B0+Rlz9r9KIrCpFvG0VLfeoarlRCC+NSOb9rX6kMIEdZV/MGENKx2CzH/4iM20cU9DfFsXlnENXOGse6tzQR8wXDjXsnBMkoPlZPeNyUSIF90rKAmg/U68Lc1TtlvNRyFTP2699IuMUKJB+eXkN63wDLeWM2ahyJsC3ttZsSVGB0OVE99D5qmdbBxj0uJ5c7/uBlPi1Es4Yi2s+n97TRUunHFRxM9Lpo9a/aHJ9TtNybi7WPcxEsfHYhqUphUH09TbQu+Fh9CQNAfwtPk5XvzfspLx5/tIP8Y4cI4uwySDrobTjcxEFbQmy/pdfUUhHUKMrjHeL8iGgiAXgW2eRd1a/xSoygKSZkJuJs8RMWe3LVqbXCT3je1w3Fj547glRP/SygQwmw1I4Tg0LajBPxB8oZk4/cGOLLzOKGg1pYpBm+rN/wcVocFv8dPbHIMUjfuDyNmDDEs3w+UnZcdfW/VOu527LeBVttxjgWjOVD6gSs7QBaWEcjA6janwgQgZIxXywTElRAgA4yePQwhICrOgbfFh9lq5tZ/vZGs/HM31lwzpi8+j5/krESWv7Aan8dPVJyTCkXB2+qjYONBxi8YQ2OFkXk+tetWNatoIR2T5dze9BEuDCEE0nYTuP9mbNMK1fjDxYSwzjjn+b0dYcqBqH8D2QSYe309WGpuMnnDcji+u4iE9HgUVaGuooGE9DjyhuWccbwj2si2PTbj8bCJyIbFW1BNKtNun8CmD7Yb9XOnlSzrmk5DdTMxSS5aG91GbaPfkIzSdZ268oYuG2ojfD6EUJGmvkaTmjglk6fXg+VKkNw6N8KUhXR+EXwfGEGGsINtAcI6ubsv7YIQQjDt9om8+dQSdE3H4bLT2ujB7/Uz6XQnO4xA+dQyQ13T2fbRLiw2C7MfmIbJbKL0UBm15Q04XHYa27TMFVUhFAiRmZ+O0+XA7/WjqAo7VuwhGAgxfv7o8wqQI3xGzIMh9F7Hx/RWUFwXLYPakxFKNDi/avRNhPa3jdd5COsFKPucg24PkBVFYcycEYycNZRQIITFZjnvTNv4+aM5vreYyuIaQsEQiiLoOyKX5K0tBH1uSg6VU3qogimLrmXOQ9OpLa3nyK4iVFXhuvunUnm8muHTB1/id3h1opj7I6O+jrQMB60K1DyEddJl77I1Ol2PG9+oeZdNRcKw+P38NVA9ASEECx+dy6b3t7Pzk31oIY2hUwYy6eZxZ23COZVQSEMxKVgdVkbMGMqeNfvo8/ejHHvYqM/LeLaA1NxkPGlxeJo8pOQmEZscQ+mhchAwYsYQzNZuv11d0QjbPKT7z4YeOo62uls7wnpdd1/aZUMx90eavgUEAFOb7W/vo8+wHO78r1vYuGQr1SfqSOubwsQFY8jol3bW8x6b8ThaSAsb+qx4cQ1SStL7plBxrJqWYCh8rJQSXZd4W3xIKak8Xt1WxmFYwr/85Fss+eOySIb4EiEsY5CBnWABhKNt3AL2h3rt3+2FItQkhPO+M3Y3LxY9ZsZRVRXVfmG/VFdCNA/8zx0c2FTI4AnXYLKaWffmRsoKKwn4g7ibPKhmE8X7Shk2YzBxybEc2noELQg1JXWMmTuCAeOu7C3/7kSYshCmu7vt9fXAfvC+Ar5VxgO2GUj7XSiWiJX2hWKxWZi6aAJTF00475tRWHNVl4y5fgQlB8upK69HC2nkj+1HY80pW/fSaNqdfd8UNn2wk5TsBFxJLkoPlxMKhMjol0pcypWx4OipCFMmRH3LsMnVysGUjbCMu2jblReC1JshdAwQYOpzWespjb9t6zmP6+nkDMwkZ2DmBZ93qq18wGc00CZlJZKQHheWZAOISYzmlm/No+JoFQFfkNYGd4f7gj064hZ3KRHCDlFfQQb2QugwKHEIyyiEevl7XaTuhlAhEDDMxpTUy1pSeKleq8cEyJ8FTdM4sKmQLUsNB63KomrqKxqpLjFcupAQCmoc2n4Uv9fPr1c/wcx7p+Bt8ZKYER+ZcK9gpN5iBMfCZTQugNGF7n0Naco2ZHcifCYu9GYkFMEd31nA8b0nKDlcjtVm4Q9f/ytNNS1hZRmAptoW9q4/yPyvzWbDO1uoKalj+PQhpGQnMv8rc3ptDXdvQqiJCPuN3XoNemAneN8AqbX1+apI+92Rhe1loD3b+9iMxwkFQtz7w0XouuRP//p36ioawnKpAI3VzfzzV0v43svfomDjYUxmE0LAtuW7SUiP43frf9Jdb+OqQQiroTDTjU20MlSEdD/fVvfc5oRqnQK2G3v9PbtXB8gb3t7Mp0u2sXv1fvzeAN4WL5qmn+HkGPAGOL6vhObaFjL7n32LKcIVQuioIVmF5RQ94g+NrnT7bWAZddbTI1wcnlr1BFJK9q0/wMb3ttNU00xdeT2eFu8ZuslSSja8s4VFj93Eo795iOoTtVgdVlJyknr9jTbC+aHX3QXaCbDdAkpbFlf6wPsq0pR75Xfm9xACvgCN1c2sfv1T3E1uGqoaO3VIDvqC7F13gC/85B5qSuvQghrlx6oi4/UqQcoQsv5+4xv77W0PauBfC+aBhnpTL6bXBsieFi9bl+0iIT0Ob6uPgD9IKKh1eqzZYsJsMeFrk4KL0HuRWi0ydBiQCFN/hJrcxZF6eDF7Jp3/nUTomsdmPA58to7z7R/v4eMX1xKb5EILaRzfdwKTVUXxGgY+7c167VbxZYcr6D+yzzlNfCJcgUhv27g9pcRB2IxJN3QULCO67dKuFuorG+gzLBeTSUW1qOzfcNCQY9U0/N5gh0BZKIIP/7qSL/70XpKzjP6Sp1f/qJuuPMJlRytv2+k5ReVFqIAFGdh7yTSgLxe9NkBuqW8FCcv+sbqjxnFbUKQIgS4lSEPtIjUvmYS0SElFb0b3bwXvW8bqFJDWKUj7TSiddZmb8sA2FUQi+N43HrPNN3SYe/mg7U2EgiE+XbyF+LRYivadoOJ4FcHwYlbgSorG0+RB6pKYpBiiYh0daiAj9H6k1AxHsYZ/BWFCJLzaqYW1XncfhA4Y33jfNr62S1fJ8P8inCefdVG7a9V+dE3HHwxR+GkRLQ2tKIog4AudTDqEfxUCRe3drrIROiKlBK0EGTpqaIC3/gEwnyETqdfdZ+zuyFrj76HDmO0yO9Wr6LUBcnR8FAhB0B88+aA85asAs8WM2WYiMSuBSTePwxljyG35vX4ObinkREEZMckxDJmUf4bGcoSehdQbwfc2KIknV6tKMnjfR5oGnKGOIZQ4pO0W8L5jlFUA6LVgX2BoFUc4b06Va7vQSdfb6iPgCxIMhKgtq8eVEI27yYPJYiLoD+Jr9YU1lp0xDjKvSScjUgZ1xSBlAOl+CUIHDck4QLY8Bc5HztFMdMoEK/1GVsrU55JfbwSoPlGL2WriwKZC7E4rrvhoPC1ezFYTUoLdYcXd7EUogtGzhzJm7sjuvuQIFwkpJdL3gZGEEooxDLWSrk2uTneyAyOjTBBxBfQM9NoA2RFtZ/ScYRzaeoTi/SUgRNgExOGyE5cSw4Br+5N1TQZj5g7nmjFG1tDb6uW1X7xLbUktd331HXQp+fsPb+eO7yw4L+3lCN1EqAh8a4zgOFxT/B7IANJ+S6fycYr1WqSpH9K+kJMlGREd3cuJPcqGxWahrLAcs9WMyWTC2+Ij6A/ijHEQCmhEJ0QTmxRDVn4afYfnkjPoZOd9ZVE1BRsP43P76D+qD32G5UQyzL0IGdgO7ueMiVS2NU/7liJ9KyHpkw61qkrCS21ZqWYwDQIkaKUgTGC/LdJYe560L2I/66I2vV8qBzcXInWJalJpqGok4AtgtVsNBRtVoJpV7E4r8WnxTFhwskGstdHNvg0HqSqqISU3icET84mOuzzSmhEuAlqxERwracai1Pu2kVjSa9Hr7gGUcCY5bGFfu8iQmLNcC0jj37brQM3rvvdxkei1ATLAlNvGU32ijo+eX4nZaqb6RC02h5XRc4YzcHx/bnxk9hnn7F5TQE1JLam5yZgsxkRrd9pY9vdV3PlfN3NsdzHuZi+Z/dPIvCbtnFaZES4XXW3XSKDr35FQExDqhEtyRVcL7XJt7f++EExmE5NvGcc/fvgaJw6UoprUcC+ApukkZMbTb2Qe6X2SGTZ1MEOmDAwHwHvXFbD0b6swmVVUk8Lff/AaDped5w/8FlWNBMm9gsB2I8DtgNnICssWQ2XmdIQL4foPZPAoQgCmft0iNXe1MnzaIDa8sxmfx29ItUlD+q3P8Fw8zR4SM+PJG5pDn2HZjJw5lNgkY+FSX9nAqz99B0+rF5vDRuH2Y2z7aBd3f+/WDo6bEXouMngQMJ20m+/wQ5+ht3waSuKbSOmD0BFjXKvZV0wiqlcHyKqqctu/3Uhcsoudq/bTUNmEFtJI75fKjLs6dz86vO0o9zy6GNWkkJJeBMAtD7zKX342lz9/+0W2LN0BCMbMHU7+2H7M/8rsSMaqJ2DqC7ZZIKLAt9R4zDYPZCPC3L97ry3CWRk5aygN1U385st/RgudbJC02q3MuGMi9/337Wd0vXvdPlb831ri02LDZiQWuxlPi5fi/aW4EqIoPlCKqqr0GZaDKyGibtAjESpY5xruXu01irabQVbS2cL21DpHYY2UQn0WTpVqO/X78yUm0cUXf3YvT33xj+z/9BB+j1GiVnm8ilAgxE8++F6nAe/6d7YQ8AdJyW4PjqKpK29g/VubmP3gdI7uOo6nxUdGv1TS+6VGkk89EWECcUqtv/1WY9zKACL2V1023QlhA/OV57jZqwNkMILk2Q9MZ8zcEdz6zXk4Yx2k5iZ3KTPjcNk7WE4DSAmlh8oZMnlA2HIzJSeJA5sL6T+6D4PGX3PJ30eEsyOUKKT9HvC+fLKmWDaD/S6EEmm+vNR8HjcsIQQz755MVJyTdW9uYsuHOwHJnAenMf9rczsdq9XFNeiajsVqZvkLq/nus9sYOizIfyzqx/dv/ClaSGPM3BEIYTQJzf/KbPLHRkx/ehzmcRB6FWTUyYY7vQpM+ZfN1TLChZOclch3nv8635r4fWo8dQBoIY2E9Pgus8FHdhwj7rRenm3Ld7Fh8WaKCkrwe4IIAbouGTThGuZ9aVYk+dTDEOYhSN8KY44NK1NoxkJXze7Wa+sOekSArIU0fB4/Nqf1M2+dxqXEnpfxx6hZw3j51zeTlJnAvEX/QEp4/pfX43DVsXXpTqqKawDDYjMUDNF/VCRA7ikoloFI0/fAcRfGvt/ls46+2vk8Mm9gBMnX3jCKwRPyObanGEVVeOjHd3U53i12yxkL2Xa8bh9Ol7EQBvB7A3z43MdkD8zAHnWmOkKE7kNYRiC14xDYevJBNRVhv7n7Luoq4fNaPCdlJvB/x5/lW5N+gNQlT695Aqu9a4dBe7SdoD+I6jh5jNQlAV8Qk9lEXK4xP0sp2f/pQfqPymPAuMjuX09CqKlI+y3gWwK6blQ22uYgHA8hhLm7L++y0y0BcjAQpGDjYQo2HqKquJbW+lasTisOl51pt09k8MT8SyY0njc0m1n3TmHdmxsJ+AwFjLzhOZis5rCHfBhJuE45Qs9AKA5QrrytnN6AlJJgIIjZ8tlvlFGxTv649RfnPC4lJ4mE9Hgaqhr55ZtHSM0wLG6fWnyc6Pgo1n38aPhYq91CY7VGWWEl/Ub2/saQKwkhVMOYxzoZtCpQnMbCtrMaxwgXjfYF7c+Wfh+z1fyZ51NVVXlm08/O69gxc0ew8qW1pOQms/KltUgpqSk1ss8bl2wDYM6D0xFC4HQ5OLDpcCRA7oEo1vFI82DDsAcTmPIQp+ocX0Vc9gBZ0zQW/2Epx3YXs+WjnfjcfoQAm8PKnIdm8P6fV2C1W+g/6tJI+gghGDt3BEMmD6Ch8hbs0XbumeDipSffxO60svWjXQDMum8qVcU1DJ088JJcR4QIPZ2m2mYem/E4fk+AyuPVANyb8zVsUTZ+t/7JS2rVrigKt3xzHu8+s7SDlKPJrKIonUz2gogeaw9FCAFqqvFfhEtGKBji8PZj/OL+31N+tAqA+/IeRTWr/PD1bzNw/DWX1OFu1HVDaalvZceKPQROlV8FJBJxSqO13qaQEaFnIpRoUAZ392V0O5c9QD5RUMqxPSdIyU0i6A8RCoQMUXJPgNWvbUBKSXrflEsWILdjd9qw9z15w57/5dm8+Zv3CfgCgKC2rI6JC8aQN/Tqq7uJEKGmtI5XfvoWzbUt+L2B8OOqScXv8fPazxfz0JN3YXfaLtk1xCXH8OATd1JTeh1+7VEsVjMpI57h+e+/QnxKEHNb856nxYvFZiGjfyQAi3B1omkaS/64jMPbj500zqLN9AHBkj8tRzWpl7ROX1VVZt49mWvnjeSe79+KKyGax2/5JVVFNYy7YSSxyTFt16rjbfUxJJJ8itDDuewBcmlhBdtX7MZsMeFt8Xb4maZpmMwmGqqaLvdlEZcSyxeevJs5D07H1+ojJTcpLF8TIcLVxto3NyJ1mHHXZHat3kdVUQ2appM3LIcBY/tRVVxD4fZjDJs66Lye77PWMAshSM5KRK8ztvgS0uKY8+B0Vry4BqkbNcoWu5mbv3HDWesjI0S4kineX0rhjuOk5SWTNSCDwO5ihICE9HjGzB1O0B9iw7tbzjtA/jw9B84YZ9iU66lVT1Bf2cCbT79PZXFNOIc84abRkeRThB7PZQ+Qo+KchmzEKTs9iqogpWTKbePRghpZ+elnnBfwBTiyq4jSwgqqi2toqGrCbDUxfNpgRs8eFlaf+DyoJpWcgZnnPjBChCsYKSXH9hSTlJlIY3UTQgj6Ds9F13UaqoxaYJNZDf/7fDi6q+hzXdOp8l/Dpw2m7/BcSg+Xo5pUsgZkYHNEguMIVy/FB0oxW0wIIfC2+jqUIbmbPMQkxlBbVndBz3l0VxGPzXj8czf7xafG8YWf3E3p4Qp8bh8pOUmXtDwrQoSLxWUPkK8Z1YdJt4zDZDKx9q1NeJo9Ycee9qa5STeP63COu9nD679cTO2JetYv3owW0lBNKg6XnaaaZkoPl7Po2zdd0vqqCBGuJhxtHen2KJthPyolWkgPaxIHAyGSs88Ugw8FQxQXlFJbXkdtST2v/PRtGqqawnXEN8c9SN8RuZ970o2KdUYafCJEaMPpsqNphsZ4VFwUmWYVq91Ka5Mbk9lEa0MraX06twuuLauj9HAFDVVNPP/9V9A1neoThuvhxQqSTWYTuYOzPtdzRIhwubnsAbIzxskd31nIh39bydi5w3E3e9E1naTsBPIGZzP2hpEkZ3W0Dd78wQ7qyhqwRRuTtdlqRtN0/N4AqbnJHN97grIjlWT2T7vcbydChCsOIQRjbxjJJy+vIyU3meSsRCqPV6NpOn2GZVNVVENiZgJ9R+R2OM/d7OHNp9+j8ng1JQfLaaptpqWhtUu5ts/C55WbixDhSiR/bD82vLMFT7OXnIGZ7Ft/AG+rD2eck1BQw+/1s3DR9R3OkVLy6btb2bB4C011LZQcKKOhsrGDmY+7yfO5g+TImI3QW+kWmbe0Pil84cm7aaxpxmRWz+nVfmDzYXZ+spdgIBS2qW1n+QurCfqDzPvSrHCAXFtez4a3N3N0dxH2aDtjrx/ByFlDO2iuBgNBvC0+HC47JnOPkIOOEKHHMHr2MNyNbnZ8vBdXYjT7NhxEURTiUmMZPHEA424YGc4mt7NxyTaqi2txuhwEA0GSMhNwuOyk5iWzdelOpC750/ZfdpnJOhePzXico7uKzgjMI0S42olJdHHrv97Ih39did/rp7a8Hl3TGTxpAOn9Uplw05gzEkiVx6vZsHgLiRnxlB6uIDbJRWxyDEX7TiAUQdAfAvhc4y0yZiP0ZrotMhRCEJd8fk1wFqsZKencmrKtqiI63giym+tbePWnbxMKamxfsQepS5pqW2htdDP9jknous7Wj3ax8b1thAIhLDYLk28Zx8hZQ/F7A5QeLgcJGdekXdIO/QgRejKqqjLjrslce+NoWupbKT9SiaIqfP23Xzjj2PYMUVZ+OgnpcXz0/Cd4W31cM7ovjmg7NSdqMQaqpLasPhwgBwNBdq/ez961Beg6DJ0ygBEzh54ReLdvHR/dVYS7ycOeNQWRrFSECKeRMyiLL//qfurKGyg5WIbJYuLbf/lql8f/cMHPaa5vZc4D0zi+txjVpNJ3eC7pfVNorGmmubaF2JSYDmOs5FAZW5bupL6ikeyBGYy9fgTxp7nntZdk3ZrwcFhRY8+agotWXhUhwuWiV6ROR84aSl15A8nZSbz77FJCQQ2kxOqwMnLGUJJzEslsa+zbu/YAfm+A5KxEhBAIVZCSncj25XsYd8MoDm87yqpXN7Bn7X4URWH6nRNZ/sJq6isb2Lf+IJ++uxUQTFgwmvlfmXPJ5eYiROjJ/HDBzwHYt/4gcPbt0o3vbUc1qWEVmqO7i8gdkg1CcNd/3kxVcQ0Ol+F0J6Xk/f9dweFtR4lJciGEYNWrGyjaX8qib89HURSa61tY9+YmDm4uZMvSnR3kq47sPE5Wfgb1lQ1nTNARIlytqKrKLx74AwUbDwPnKG8Q0FLXwqrXNoR3Zo/uLiI1N5mJC8cCgsz8k1nnw9uPsvj3H2KLsmN3Wtm/4RAHtxRy/3/fTnxqHLqus3PlXjZ/sIPWJvcZWsh6m6uepmmf2TE3QoTLSe8IkGcOpfpELZs/2EEoGCLgDaKaVKJindhdNm7+lxtQVZVgIMiWZbtY+8ZGhCLwNBsycitfXsfImUNoqm1m43vb2LNmf9jhZ/XrnxIKhijaX8LIWUPDahjOGCfv/WkZX/7VA0TFOrvtvUeI0JN5bMbj7FlTAIDVYUELnqxfDAVDNNU2kT0wk7ryBmJTYsgZZKjEVB6vpnDHMVLzklnx4hoAZj8wjaJ9Jyg9XEFan2T++aslNNU0k5Aej8ly8lZldVixOqzkDcnib999hT7Dc5j3yHWRHZ8IEc6D9qC5eH8pQAc1Gi2kIRSByWKipd7N2LkjANB1nVWvbcCV6MIRbSxy17yxkaA/yIBx/Zn70Aw2vreNdW9tIiE1jpTsJG54eCbr39mMp9VLVKyTUdcNA+Cv//kSC75+PWl5n63UKkKEy0WvCJBVk8qse6ewd/1Bptw2gV2f7CXgC2J1WNjy4U4C3iA3fGkmBzYVUnqwDJ/H30HRQtclCEF0fBQtDa0ItaPaha7plBwqp7XBTVVxDQBr39hIwBdg7sMzGTwx/7K+3wgRegrtmafzKWnoP6oPtWX1eFu8eN1+rDYLTTUtFMtS0uancPtjN4Xr/Q9uLqS0sJya0jr83kAHO9y68nr8Hj/1FQ2k5iYDMPehGSx7YRV1ZfVY7Wauf3gmJrNqSNLtLmbVq+uZ96XrLuVHESFCr+CpVU9cUAmSM9ZJwBPA7wugmlSa61soK6zknh/cSlZ+BgC1ZfUc3XUcIRTs0TZSc4xxqZpVigtK8Xv9bPlgB8lZieExHpPkQkpJwBtExkBKm+pNS0Mrbz79Po/84r6IPGOEHk2vCJABSg6Vs33ZLiw2C3XlDYDRYSulpM/wHF7/+WJ0XRKfFotqUhEYBgJSl/QZns3oOcOIinGS2T8Nu9PO5g+2A4Y3/NHdRez8ZG+nr3tqR2+ECFcrXU20p0/GPq+fB/p8HbvTyrQ7JtJY28yWD3bw3p+WYzKrzH5gOi31rax6fQNHdxWjmhS8LT4APvr7KkKBILd+60Ya2vSXT0UgsNgsDJ8+GJPZ2KIVQpCUmUDBxsPMvGdKZMKNEOEcnL7onX3/NP787y/iSohmwoKxnDhQSnFBCW/++j08j3jpPyqPN59+j5qSeuzRVqN8Q4Cv1Ri369/ZzKJvz0fT9DMa3ofPHMKJA6VMWnBSujU6Loqq4hqKC0rJH9P3Mr3rCBEunF4TIOuabnzV5RmP7Vt3AE3T8bX6UE0qoYDRfavpOqqiMHhCPlNuGw/AtDsm8trPFxMMBFFUldqyehwuO2PnjiS9XyqrXl0PwMx7JlNX3tCpaUmECBE6p7ywkrHXjyQlJ4mywgpKDpShqAqgU3q4gtd/sZitH+1CUQVCQChwcgHqc3uxOW3kDM4MmwedyuwHprHh3a04XI4OjwtFIKUk6A9GAuQIETj/5lUpJQe3FDL/K3MIBULsXr0fIaC5roXtK/ZgdVjZ+tEuVr22gZA/RMBnByTiFKcvV3wUUXFRmMwqQf9JC3gAT5MHZ7Sj09cNnGJhHyFCT6TbAmSv28eRHcdorGkmLS+F3CFZZ5Vby+ifxvj5ozGZVZb9YzVBfyg8gZrMJrRQAF3TOzjqmc0mQiENV0I0tM21Gf3SuP/x2xk0/hqqTtSQ3jeVUbOHceJAKStfWkfAZwzauvIGpt89KeL4EyHCOTh1Mm4fP1pb2VJVcU24F2D/BqPRz9vqQwhBet9USg6VGycKGDJlII899zXMFjM5gzJJ65NCxbEq4lNjkbqkvrKR/LF9CAX0Dq/f2ugmIS0u0isQIcIF8NSqJwj4g/zuq3/BZFYpPVxOyaEyVJMa3tXZu7aAptrm8G5OY3WTsasqBEIR5A3N5pnNRiPvxIVj+eSVDcQmubA6LDTVNBMdH4XdaUPX9LaFspHYEkBan+Rued8RIpwv3RIg11U08PovFrP69Q0gBGPmDCe9byqLHrupywyQ0+VgzoPT+d/HXiAU1MLBsdlqYujUgUt+DxcAAAiQSURBVNSU1nFo69EOJRH+thXqhsVb8Hn8LPz69ZjMJpKzErnhS7M6PH9iejw5g7KYftckpK7TZ1gOiRkJl+gTiBChd+Bp8RrShxgybvYo+1mPb5dw87t9nDhYGnbHBGht9ICUhgoNUFxQGt4FQkJ631TDih6j72DRYzex9aNd7F1XgKoqTL1jAoMnXMPbv/uQyqJqrA4rQb/RsLvwGzdEnDQjXPVIKak8Xm0sGtPjzqnwYrGaycpPp/pELc11zeE5s52GqiZCgSCne/0IYewA/fqUxfHY60ficDnY/MF2mmqbyRuazYSFY9m/4SBblu7Eajfmdr/Hz7h5I0lIj784bzpChEtEtwTIK19eR8AXDGd7U3OTKT9SyY6P9zBxwdguz7NH20nJSSIqzsmetQcIBUKYzCYObT1K9qAMHFE2dF0PS9YIIUBAer9Ujuw4ztFdReSP7dfl8yekxZGQFpGMihABDFmn9/+8Ai2oIYRANSnM/9oc+o/sWvowJtHF5FvH8dovFhP0h1AUgWw7NyouCi2o0VzXApwskWpn6V9Xsv6tzTy39ynsUXbsThtTbxvP1LbyqHbu/u4tHN52lBMHy4lLdjFoYj6xSeenqR4hwpWKu9nDu39YStmRSoQwyo6GTRvEdfdPPaus2sx7JvPX775MWWFleM5UFKMZzx5lo7G6KWwa0o6hdQyPDPk3EjMTeGbTzxBCMGTSAIZMGtDh2KQ7J5E3NIeDmwsBGHBtf3IGZUYWtBF6PJc9QPZ5/BTvL2HX6n1UFxt+78tfWI2m6cSnxZ41QD5xoJR96w9itpoJtmWmFFWhubaZm776MPEpsYSCGiteXIMQwpiAJW2mIBqDJ+WfNUCOECGCQWujm/f+tBxXfBTWtl0dn8fPe39azld+/QBO15l1he28/OTbVByrCgfAQhFoIY2oWCdZ+Wns33AIXZf43L4OE6/FZiHgC7Dp/e3MuGtyl89vtVsZOmUQQ6cMukjvNkKE3s+qV9dTfrSKlBxDLULXJTs/2UdanxSGTe16rCRmJrBnTQF+byA8ZhVVIegLMmBcP7Ly0zm6uxif24/f42/3/AGMJFRtWT2hYKjLEkkhBLmDs8gdnHVR32+ECJeaTqzpLvELqgpCEeEBFkZKTKc5aJ2Ow+U4o3EnKtaJ2Woms386tz+2ALPFzP+3dze/UdRxHMc/M9t9LNtiH5alS0uLpRTYQIGKKFUoWEhbTLSQiCFgFB9OiHjx6s1E44GDf4CJFxNTDYaLCRJBoyiIAtagmIaKfX7c0nbb7e54WGgHCtvWQqH1/Tpu9je7c/hmPr+Z33x/TleaXJ7xYxkyJMuSdwF9UoGpuNpwTfHR+Fg4liSPz614bFRXG66lHBsdiMrhHL9j5fN75fa6lZ7pU+3rVap8sULb9z2lJStC8vo9Mh2mPOkeVR3Yop0vb9OFU7/ft/MC5qOR6Igu/3hFOaHxZQumaSgz269fTl5KOba7pUeJhHXLpNfldSm/NE/5pSFV7d+ijdXrtGHHGrl9bvn8XnnS3VoYyFT1we3aULVGrY3t9+3cgAdl1u8gu9xOrdy0XJJ0/uuLMmTomf1b1NrYpnXbVqccW7qxWJueLZfP79W39WdkyVL5jrXKDGQqWBSQaZo69NFBef1u9bZHdOFUgwwZ2rp3s7pberWKfsbAlCTiiQlzWEmyrIlLI273ynv79N3nZ/Tryd8kJVspSlJbU4cWFQa0+8gu1R89rlh0RMHCgFob2+X1e+S5sabYMHn0CkxHPJ5QImFNqB3TYU5YHnE7wzT12M4yBQpyxjbt2fHSVvW09Sl/RZ4qdm9ST0dE3x/7STmhLLl9LuUuyVZxWVFyvIwJN66A+WDW7yBLUuXeCuUtW6RYNKaR6Ijamzq0tnK1whUrU47LzMlQ3eFaJRIJlW0Lq6wyrNyCHNUdrpFpJk/F4XBo7zvPK1gUSB5/OKb+7n5VH6xk5x5givJL82SahmIj4xfX2HBMpsNUQWko5diS9ckLZyIxHqT7ewaUnuFTqDioYGFAb3xwQDWvbldwWUDPHapW7WtVsixLXS29KR8HA5jIm+5Rfmmeetv7xj6zLEt9nRGterIk5dis4ELl5merryMy9lk8ntDQQFThzaXy+Nzac2SXDrz7gkIli7Vmy2qVbHhUpsPUYP+Q3D6XgkV0pMD8Y0xn5ldeXm6dPXv2nvyw/W3brGm+HBcfjaurpUdpToceWbTwjov9LctSe1OnhodGFCjIoT8q5gzDMM5ZllU+0+PMtF5/PnFBJz45bftjyV7EZVvDk469eLpBX338zY2QbMjn96jurdpbJqlD14dUf/S4mq+0SUrW7JIVeao7XEu9Yk65FzU703rtbO7Wp+9/ocG+QZkOh+KjceUVB7Xn7bt3h7qpq6VHn314TJGu65IkS5Yer1mvp/c8MXZ9tSxLp+vP6Icvz+nmGkmnx6m6N2u0dBXrizF3TLVeH1hABnBnD0tAlqTu1h41XmqSJBWFCyZtG2U3EBlUy19tSnOlKbQ8KKdr4jsG8Xhc//zZqkhnRJm5GQotXzz2NAiYKx6GgCwlJ51XzjdOeX8Bu9HYqP6+3KzowLCChbl33APAsix1NXfr2h8tcnmcKgwXyOdP3foReNhMtV7nzE56AGZfVnDyXqp3k57hU/G6opTfcTgcN5ZspF62AWBy3gXe/9zdJc2ZpqJwQcrvGIahnFA2ewTgf4FbNQAAAIANARkAAACwISADAAAANgRkAAAAwIaADAAAANgQkAEAAAAbAjIAAABgM62NQgzD6JB09f79HQCSllqWlTvTg1CvwKyZcc1Sr8CsmVK9TisgAwAAAPMdSywAAAAAGwIyAAAAYENABgAAAGwIyAAAAIANARkAAACwISADAAAANgRkAAAAwIaADAAAANgQkAEAAACbfwFhloAGEIGzbQAAAABJRU5ErkJggg==\n", + "image/png": 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -313,7 +313,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.5.2" + "version": "3.6.5" } }, "nbformat": 4, diff --git a/notebooks/plot_otda_mapping.ipynb b/notebooks/plot_otda_mapping.ipynb index f25eb11..9a7ae04 100644 --- a/notebooks/plot_otda_mapping.ipynb +++ b/notebooks/plot_otda_mapping.ipynb @@ -69,11 +69,11 @@ "theta = 2 * np.pi / 20\n", "noise_level = 0.1\n", "\n", - "Xs, ys = ot.datasets.get_data_classif(\n", + "Xs, ys = ot.datasets.make_data_classif(\n", " 'gaussrot', n_source_samples, nz=noise_level)\n", - "Xs_new, _ = ot.datasets.get_data_classif(\n", + "Xs_new, _ = ot.datasets.make_data_classif(\n", " 'gaussrot', n_source_samples, nz=noise_level)\n", - "Xt, yt = ot.datasets.get_data_classif(\n", + "Xt, yt = ot.datasets.make_data_classif(\n", " 'gaussrot', n_target_samples, theta=theta, nz=noise_level)\n", "\n", "# one of the target mode changes its variance (no linear mapping)\n", @@ -100,7 +100,7 @@ { "data": { "text/plain": [ - "" + "Text(0.5,1,'Source and target distributions')" ] }, "execution_count": 4, @@ -109,9 +109,9 @@ }, { "data": { - "image/png": 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39DRG1p4fRyNHjmThwoUsX76c8ePHF87YqyuuuOKKGm+laoiSPG4kRteXuIQk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\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -149,26 +149,26 @@ "text": [ "It. |Loss |Delta loss\n", "--------------------------------\n", - " 0|4.210546e+03|0.000000e+00\n", - " 1|4.194392e+03|-3.836611e-03\n", - " 2|4.194053e+03|-8.094371e-05\n", - " 3|4.193924e+03|-3.056965e-05\n", - " 4|4.193849e+03|-1.797118e-05\n", - " 5|4.193801e+03|-1.140070e-05\n", - " 6|4.193776e+03|-5.930168e-06\n", + " 0|4.132385e+03|0.000000e+00\n", + " 1|4.124370e+03|-1.939427e-03\n", + " 2|4.124043e+03|-7.928706e-05\n", + " 3|4.123904e+03|-3.369312e-05\n", + " 4|4.123827e+03|-1.881208e-05\n", + " 5|4.123778e+03|-1.184435e-05\n", + " 6|4.123764e+03|-3.358329e-06\n", "It. |Loss |Delta loss\n", "--------------------------------\n", - " 0|4.245881e+02|0.000000e+00\n", - " 1|4.181680e+02|-1.512078e-02\n", - " 2|4.178974e+02|-6.472597e-04\n", - " 3|4.177550e+02|-3.406786e-04\n", - " 4|4.176586e+02|-2.307406e-04\n", - " 5|4.175879e+02|-1.692203e-04\n", - " 6|4.175338e+02|-1.295518e-04\n", - " 7|4.174909e+02|-1.028089e-04\n", - " 8|4.174570e+02|-8.123852e-05\n", - " 9|4.174309e+02|-6.257777e-05\n", - " 10|4.174083e+02|-5.401101e-05\n" + " 0|4.143700e+02|0.000000e+00\n", + " 1|4.111590e+02|-7.748977e-03\n", + " 2|4.109509e+02|-5.062453e-04\n", + " 3|4.108581e+02|-2.257162e-04\n", + " 4|4.107918e+02|-1.614130e-04\n", + " 5|4.107473e+02|-1.083067e-04\n", + " 6|4.107110e+02|-8.833802e-05\n", + " 7|4.106839e+02|-6.600463e-05\n", + " 8|4.106615e+02|-5.455553e-05\n", + " 9|4.106428e+02|-4.548650e-05\n", + " 10|4.106278e+02|-3.649926e-05\n" ] } ], @@ -218,9 +218,9 @@ "outputs": [ { "data": { - "image/png": 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7eZHN9B+Lx6idWsXerQ1oGGadLSrrKvDTATMWTGX1P9cRK4pRXlWKBiF+KqS8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\n", 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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, @@ -266,21 +266,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.6.5" } }, "nbformat": 4, diff --git a/notebooks/plot_otda_mapping_colors_images.ipynb b/notebooks/plot_otda_mapping_colors_images.ipynb index 288f4f1..4b19e0c 100644 --- a/notebooks/plot_otda_mapping_colors_images.ipynb +++ b/notebooks/plot_otda_mapping_colors_images.ipynb @@ -82,7 +82,22 @@ "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:2: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0.\n", + "Use ``matplotlib.pyplot.imread`` instead.\n", + " \n", + "/home/rflamary/.local/lib/python3.6/site-packages/ipykernel_launcher.py:3: DeprecationWarning: `imread` is deprecated!\n", + "`imread` is deprecated in SciPy 1.0.0.\n", + "Use ``matplotlib.pyplot.imread`` instead.\n", + " This is separate from the ipykernel package so we can avoid doing imports until\n" + ] + } + ], "source": [ "# Loading images\n", "I1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256\n", @@ -123,39 +138,39 @@ "text": [ "It. |Loss |Delta loss\n", "--------------------------------\n", - " 0|3.680514e+02|0.000000e+00\n", - " 1|3.592355e+02|-2.395298e-02\n", - " 2|3.590579e+02|-4.943115e-04\n", - " 3|3.589661e+02|-2.556530e-04\n", - " 4|3.589094e+02|-1.577896e-04\n", - " 5|3.588705e+02|-1.084239e-04\n", - " 6|3.588421e+02|-7.914757e-05\n", - " 7|3.588206e+02|-6.013011e-05\n", - " 8|3.588034e+02|-4.783086e-05\n", - " 9|3.587895e+02|-3.866500e-05\n", - " 10|3.587781e+02|-3.194454e-05\n", - " 11|3.587684e+02|-2.697250e-05\n", - " 12|3.587601e+02|-2.296505e-05\n", - " 13|3.587530e+02|-1.975975e-05\n", - " 14|3.587468e+02|-1.733678e-05\n", - " 15|3.587413e+02|-1.535580e-05\n", - " 16|3.587365e+02|-1.350581e-05\n", - " 17|3.587321e+02|-1.209997e-05\n", - " 18|3.587282e+02|-1.086348e-05\n", - " 19|3.587268e+02|-4.096770e-06\n", + " 0|3.680534e+02|0.000000e+00\n", + " 1|3.592501e+02|-2.391854e-02\n", + " 2|3.590682e+02|-5.061555e-04\n", + " 3|3.589745e+02|-2.610227e-04\n", + " 4|3.589167e+02|-1.611644e-04\n", + " 5|3.588768e+02|-1.109242e-04\n", + " 6|3.588482e+02|-7.972733e-05\n", + " 7|3.588261e+02|-6.166174e-05\n", + " 8|3.588086e+02|-4.871697e-05\n", + " 9|3.587946e+02|-3.919056e-05\n", + " 10|3.587830e+02|-3.228124e-05\n", + " 11|3.587731e+02|-2.744744e-05\n", + " 12|3.587648e+02|-2.334451e-05\n", + " 13|3.587576e+02|-1.995629e-05\n", + " 14|3.587513e+02|-1.761058e-05\n", + " 15|3.587457e+02|-1.542568e-05\n", + " 16|3.587408e+02|-1.366315e-05\n", + " 17|3.587365e+02|-1.221732e-05\n", + " 18|3.587325e+02|-1.102488e-05\n", + " 19|3.587303e+02|-6.062107e-06\n", "It. |Loss |Delta loss\n", "--------------------------------\n", - " 0|3.784725e+02|0.000000e+00\n", - " 1|3.646380e+02|-3.655332e-02\n", - " 2|3.642858e+02|-9.660434e-04\n", - " 3|3.641516e+02|-3.683776e-04\n", - " 4|3.640785e+02|-2.008220e-04\n", - " 5|3.640320e+02|-1.276966e-04\n", - " 6|3.639999e+02|-8.796173e-05\n", - " 7|3.639764e+02|-6.455658e-05\n", - " 8|3.639583e+02|-4.976436e-05\n", - " 9|3.639440e+02|-3.946556e-05\n", - " 10|3.639322e+02|-3.222132e-05\n" + " 0|3.784871e+02|0.000000e+00\n", + " 1|3.646491e+02|-3.656142e-02\n", + " 2|3.642975e+02|-9.642655e-04\n", + " 3|3.641626e+02|-3.702413e-04\n", + " 4|3.640888e+02|-2.026301e-04\n", + " 5|3.640419e+02|-1.289607e-04\n", + " 6|3.640097e+02|-8.831646e-05\n", + " 7|3.639861e+02|-6.487612e-05\n", + " 8|3.639679e+02|-4.994063e-05\n", + " 9|3.639536e+02|-3.941436e-05\n", + " 10|3.639419e+02|-3.209753e-05\n" ] } ], @@ -205,9 +220,9 @@ "outputs": [ { "data": { - "image/png": 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qNq5ipmDVJKy54S1abHK9fg5sDYglh6Ciy2jxlayRd2z3pINyoXM/73hLoFXo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\nwfUqpJxi59h1POQ0PkML0QJyh6YEOZPEIvy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\n", 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TTVQ29qQaXoEDbYeq1kB1OByOU80ZJYrfe3oH2SrtlmqRVG0biOK0Azs8Bl7G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\nzMyyvwEnvbs5vBaWXAAAAABJRU5ErkJggg==\n", 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\n", 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OeUI9r3V7/FncBOA1bpv3hE16F2hS4BvdnLfEVW73RXf8EKZZjhIINNyhcz4g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LuEu7pTrRvyeREASY46RahzJiu2qf4tq6QOhwFih7LmyIr6+zP3jLa+6Te/aCEsh3hIR6\n5LgqfR4x75nFjHmH5oEhxGLhRRAU6QLSC8NiYMiCJmdkLJZK4LgBukTd6beNxeYMszkbvssdKBtB\n2XQjA2Mwtq1ErlACR2JCiajAmAY8BDpXUqiPjioku6D04jArN7tYEajLnFCB6JGcUpnh54HsSsw1\nmoKPzLS4WUgo/rwmAQsZN6Onp5ORLoJqZlAp4cNwNkKxivYGfTQWWYh9JJHxMdOJIxiCodoj6mRV\nZuIl6sSY6TWzl5ydUNwCkEggMEQnUoS6Bsekx/PIDGUnCJ13EAxPIxlhLkLWwKY6yUq4M1NhQ8qL\nc0+clEsIPR/HEvEhK9ZFlq7sZCH4HNyYd85sdESMHAVjs0xq1IhkAy/uD1kcswRWOj3mzlydHZmx\n6UYIho+7bIQ5e77LLAaWY1+s7O5ogB5H3Rk3nJw7kiiSB3oxhpSQsIVKuVlPa6b3iIkCTsyw1ESs\npzYzkmQDJTHzgGZjNwLeoVkZud9DNn7SWD3XRYU3bicumwVuHBKP7ssr98Y07gtkd7oaEz+xyzUb\n5Zp49HzGXy5C8XsX4epZyfOYZG4aR9ydhW1w9QzuyMqZZBwNChkuisb7arCix3TGe0flsdE45V7S\nAFdH5/17RTAvdWBm/Xr5g8PIO4EreuV4LeP7hjlXzRbrOt6QFBHo7QiXzUeWwIzMqVr5x3TGO/bK\n8nV95oah1PfSmDm12XN6XHLaBo5vlPV/cnrJp2yV9jkeD86TeWeKHN2Ad5wZeeJWSX9yL3N0o+OE\nKqdtYHss+1wbAn98asGVc+HvHNvk97fLx70erT1aO2HXItxUH1M+fVsBJyXw2h2AAAK/sg1PPaq8\naf1B2MDH0nE+sLfg5pzKYw1AYFnfaickc6cpv30ycDwYr9ou7lMnApxKgSdvBbY24E92jDtH55kn\njNefClWoCFECTz9R6vOqk3O++KIRvLieAWzmjhefjDz3qDLYci2eO+l5eLfL3+TIh5cbbPbChwYn\n+oBIkQBFKJdlpUz0FuDRcT/azrW9IzPIDirCjdv7r8GHzRIWAFdOLuK6M/Zgx6oc6cRxlyqESqcA\nYOYTweLQrwxQbmuROIrQUeaNiAirj4hLFX3u4KIkgQ1xRt9P4yKsbAQjRWwJwga+1mojlLkxQOeO\n1w2CsokDThIhUURfmYS+zwZF+HUijEgZX3ajm+S/fnYJxIm47igCMU+FuQhaRdnIQb11pLaKia5F\n2QAcqaIxsi+uk8CslmcUR2rHwlXWAiwBqYa4nXZQSmG15FXfRT3Qq5f7ERgpk+zdS33Xt3tNH4CA\nlXkzwEy8PnfLneK1rguEHseAJEV8lqoV45rV8xFrp8nx0uYU0dp7mX81sC+eOyjRxsxKJ0cVqfXr\nVi3k+8tZwlrYl05bNXLUMhrODCO7VFdT4QjGESkiu5wP7jMuKB/kv/e053ukZzMam6MRPRNlybYF\n5mosQw+ihBp1wSgT5SQ7e8NAQkhpv+/rsQyFMwRGjCXGTAJLN47aiNYuUcLxpAxSQnTNYiCocvUs\nM7rilIe7ia2tJ7gRQkBEGDys4+cmGTGDjQSDJJJl+iw1ooEjObOsPs7qu/QhgkcWXie8aS27O10Q\numoZjgh9V3x+sxkxdmVym5QLMHj5H0Mt77hkHgyNfbHqYoTQETwxC6Vr34mxyKwfRFGKJbjL0Gli\ng8BJTcy1ZwAWGG49SqITZXSjVyVXv3EJEUXK5DzPJC+CIHtxaclaboDkMGRl7CKjByyXc2Va4jFv\ndDOijwg9FgZynuGaIRmo0jk1lFqJxywiJVxc7FFfEKu1ISJ0ZMZcYlmbRAiOWyB5ZAglOkrEyK7F\nLYJcYkJHJXvPrkaUER+VpEqKwuglPnIQZREjePGJ3kxLFgSydiyCQ65WFFMGH/kvr/yFB70lCuCF\nT3m637BQHjvfFw9TS/G1XeRMjaryxu1yP16/2fOe3YHHbRSh+u69gacfKQ/sD3lc7/vuvf00ANu5\nTB49FgPvWow8ft5xuZY8/2bIbMUZ9X3De3YHrq+W7NE6eimdkjeeGfj0o0e4IozcshS6mkq1jQAA\nIABJREFUWMr2vt0drp0X8b60JVfPlFuWwm3Dcr0+K4Razaww1ufL3AI3pSJO3Z1Hb5QvoS4scSzs\nP1OHsRzrsvnIzItw+5079l9/n3J0g7mVTsNClmuLdWnTcv+Ij1zXB/5iZ+RIt98207THtOfWZRH4\nlmQtlg9z43L1Yt8/X9doRCZm9psmE0qFIp6Wk+z80OelVhf1NN0G++Jr7omlB77sosBrTo6IlHv2\nGcczGzny6h0vVuu6frEbufyoMA6wG0aOh44PbzsPmwmxdqROjQObubaPCDtavgTe53I+ruoTosKH\nhk3O+ILehST5gKV5RR6NSzrjjkGLRRk4udjv4P3QX7/9QX/f/uPPf57P8Wq1LUyFWAZyVbPzibXX\nHXJ9PwTftxhuiq+tvMF9nQbqvGp3VIvgGhCGfSM0M125TJbf+wJZq4yE4EWUDZRJ5/uWZyOtVbcz\nitK5E9lff9iCuZI4LtBVwWsOthqpcg5c/6vOxIisrZY6Fc41r5LYDojnvi4OtV7ZYTbpoU7T1sCk\n6+VzWStXbdexf74cRWXy/J3en/X39KI9/ImRVfJpuoHJ10DcGERLR8bqSLWU8ps7R4TaEbC1eB1q\nfpFyPfSU58Gqg2K+f80U4V5KsahnXUUINdKVeelYBLd1+aZPtN6tHn8/z1VKc/i9t/zeQ8+CjEfM\nRnZd2ejLUFm2GX3IxJS5WBcss5N8hiMkj9gwEqxc+FKHwcW82E49EgzG6m4RDfZkiZuw7c6lGXbE\nwJ0znplJx9FQPmSRVbkDIWTjIk0l7m798EWJmyz0AYbk5M6J2RF1elcyELtMnzJLEUxAa5QIVWMj\nwDgm+jqsmXF6FAmlZyRkQgx4neAVDDSU2L+Z4uLgntD6QYuAoaFYNj0nYnDoAsEj6kYXYgmXp6kc\nR5UownEbSw85lB7lKIEtcTp1Zt0MJXG5bLAzDkSPqHQc6QOLVMS22oi7wDyyNypD7fUHi+QQGM0w\nieyNCbQj2FhCohmoGJ0ZopACOIICnXQMuTxGlPohE83VpcJxL9E+DC2xqBUQp8eZ+RIJXXWDUFBl\nw7xGmIgEgYUpruBpiSSns+KKkSNYVrrqwpNEwEdm5iQPoJlOMyEHUlKkfjyG4GVYNhmRjk4z3biH\nD5lRZ5hkgncHBMZDhcMi+b3LIvT+Zs+4frM8+lfCeLWMw8UbA9dLz4cmbXLxRk0j/fpJePEsQ83T\nVPiCixxGJ3XGYuy5dSyjGI/b6Pbzr2SFFMq+IsLJrqMfnctmwk41MVy/2XNcyivnzctMjDP+arHD\nU7d63nymiN8nbW3ytt395ZW4xuD6vhzvPbvDgTTv2ivLj593XDIrYvPVp0cetnofR+WzZuVLrgsO\nujPcOhFuV9TqXNcH3rBjXL+5wZm0397XTdwwbhiMo6V3zSmxA2+T1XqA6WfrLJVjPXI+59bF3nr9\ntWE/X43Oewc74KYCsHo3T8XyUvaFw16JyAjAw6NwcxZ+7WR5+Z0Q43iEt+0GnnVcYSfzutOBZ53I\nHIsdZ3rhtz+aeOpMuexYt65L10+Ez+qDPpXazDyyumaseNWphEjHs7cSj56tBJlADKTdgdgHrjsK\nZOVYD0rg5DIhs5rH8qEVfeawSF5ZAMvdtzKvHrS01lXrYfUV68GFiRQJ1SopIsyqOF4izMWJ1ToZ\nmLguTPYVigUWYJASZUpqXlPBu9qnp4TunImTYe0WMB5aPnCMuqzsi59RSl6lHWTtSnDMrXyYqh55\nPItSE5EDbbIWgFLaVhRG3y/AVLVtuK8F8wZ+QDxP+6D9oTY6G9M6dqzcFg5y+Jyullc/+4nfg1As\n9NmLAa1++YFNcRa157RBcfkowQogWnHRsEkpD5/faflXna2VW8eKUCfgp3ouVp2oGZCsjCKE1auf\nOn/fWI9W3Jefh76gLMh/9/Oe72JSJjkR6BU2FaJmjjkgmSgjo5VoBdYVf8SwLGLKLLIrGVzpXMjS\nswiZnANDiAzjWL+Ityw+ZyEws8wMZSaQao8yxshMR5DM4IFjoiV2cizxdE0A69AaN9msxHC22JX4\nvimBOcENH42UEoNlwHDRKnRDEVNSrMVaIzZkGaFajtQyHpQ5IzPKhZhwNjVCjeSQQld6yV39Qpw7\nA8aGRFRBQybaJknGYm2nZzdmtlR5hGSSZELuGDpBcnkRpih0DhuSmKmwaw45M+iM0TKdCrupeALt\nmjK6F+uwKUFg9FxNVJHBqhXeHdFM8lB9qaqrh5ZtI8Vq36E4I1kzsVohi103g0dcDBPFTZGwAI/F\nUuyJPhafxdVX+1IUTiydHcnspMisBMgAyWgyFrn6tK56/SjBU/ka4CzQOUQfWIZNBhN6H9lLgWV2\n9gRyKH5PO9qXyXwOu26MaTUsZJgKgwQGg99+1UPDgvw9X/AlDnA673EsbACwbcIlfWKZiriaxbxe\nPswpAo/oMrvV6+S9ssljfHedz9E6YXXbhD/bHrjm+MO41M6s19/IUawOqT/Gdzmdy3VyJOyL36EO\nF644EkrT70zG3x7bjdwwTj0x97k97ovtW06d4jlbPb+/PfCkrU16nNtjz2VpWKd5y5k9vuR4xwdz\nd+Ble0nYd635aLV4zhlZcNfjjinxzsXAZ24W8czKJzeWEZDDzBnXgnlmPTelXZ5+RHnvqGuXkrft\nnFm7dlykyh+fLr4pT9jq1hZoq1Jh5Z94oj4HT9uAETmhyp1m6/SnkrFSCzflfEBQ3/Rxwhk+ddN4\n427J/1HV1/tUguMTU81j5oHrt4Qbdpwr5s4HF0IcjMuOCbefLm0y30wci/ttuBLwKy3yujszz5r4\nRg9LJ1U3p6tmpR4r9x4o92qxvNXfbpiCuPIP//RtD/r79tu/4Esdir/+yhqqsHZPgyJU7Bx6oAey\nCtlW1mXF19bVfcFblovlkYl7hsq+X627kar73dTaOz0fq21wQI+SBM41naPjoMjak/IeKyK5uAxM\nRWh0Z6c+Jw64hE2+jjyycnuwtZ/sFPciYFdPggNC/ixljNhaMLsIXbWEduyP5Jqzdu3YRdiQ/Wty\nVf5VXVf37Epsxrptr7qvrNKHib/wIRdkPp4E7DGGaTs4B3yIoVw7qfqiJ4co1UrPvmDNh87vSsCv\nbv2unrMV5tM0dpeyO0Zw2e8IqNJZIhH4rTc/BC3Ij5U9TnqxAIOySRnuCYCEEuZtKWVY3m3Eh4DK\nBhJ2ypflfOASE0bJmAhZnD47eznBuCSqMHoqQkggWCZFZTlmOheGetFFKf5AMSpzK2IOB0zxUHrI\nqsKYS2QGNNObkFIZVo9h9QBKxY9RACvO5+ZGFAHJLOhIVqJDxFB6XooS66ewNYAqHOs6erMasqyI\n9BCLsOzFUC+91E5D6blaYFQnaAQCsV+yQY/lPWIobhqWhe2Z8GgLLDdGYnLyTBg8skciExi0I4jQ\nSyYRsUFYakQdtI+Mg5eYwElIVizx5TPcQgyAjcQIyQLLsXzARaX6IgEdinmJCx3MkZAIBkmcGT05\nrjoKjoUONy02CCndxQ4la/nyIHGTHIovpKLsuRYfuE5wEkk6tpNxJAAC0ZVZ56SUcA0MWcgecJw+\nCnsOyZ2OOcLAHXmTY25V9wdiDMUSbYlBgZTwIZG6iIfSCZq5EHJinozlZDjswc5K/IrO2TYBDUDm\no0MEDVwSl+tlgMu78gjLngkS2CTz4TFySSxi7Ylsr/PelED2zO2pZ0uV516cWKZttuOMG63HJK8C\nvnC5ZG70o8WEQPlywKXdwIc98ETd5a/tKABP0G3emY+SNBNr4kt14E9tg0eFfb/jW6pXm0kmE7nG\nB94vytUnjjPayNUnNrg87/HusIF6OCC6rzn+MN4FXMLeWlxfY2kt3mHfSrVgX0RPhXYfA5+9tQHV\n8nbAdFRZTSAD2E5CV30Obtrd4RH9jBtGOBqdi+pkuSf1R3nf7g5PP6Lc7D2fdaTcU289c4YnHN3g\n6t54/+Dcuizpr+gjd9q+8FESp20lfEpdPrDY95X+jLruuPZ8YLngulgKt7JOH+aNuxMLtQduHjNX\n95lbhrB+kz7cjfecKT9u3ROu3VBucvjggnWbHIsdm/VYN2w7j9yo7VkF9BNU+Z2PGU8/Xq69128H\nnnGiCIqPsckVtuSDOuNidstLvKgSCAHNGdGw9hV9KLASvyvRtBbD7kSpk9TqMux3NFaCJFM6IboW\nMJPOhZSmi+7sSfnokrkTtUQSmuqvIEVATzuvWi2VprJ2R1giBPeDEwar6JqK3JVQdIpoHwWi2XqE\nYWV77qoFdCq6VeBovddW+QyiByYIrpaS67q+U6Etsu8q4XWC2mGmYnJ03Z8M6eVd2AGdGDsTH+FA\nsbBuyf4EQkfpKdZ18RJ3Hy/W89VIwEjxv451eVUvE6Vfm5BrWaiPzvr7Lp9ArAyTMmeKr3Mnxui6\n3jdi4LIWxKuzGw+5ruj6BMjazWYtoGuHJq90WP0TivtPlnJNmJfZBEmKntj0YtV2Nwa5byXtBSWQ\nb12CqZPM2CAwWGYjCCfEkLy3nnznJEQUFWdDBgKCG/QBtDP6pCys3IRZBO0GuiykZIweOCZGkvIF\nvGxj/YCI0rswiDMOMHaOjJmZdbgkoig9mZlo9UEa6KQDs+JILhEJQtcJkkoPepEVF0dspI8BySUq\nBh6LY7oA4jiKkUGdvj5gosMylweWuLCL4bHDxNFs5St2FgmSy0Q5NYiOGQySmUtPcEgqQMSyMAsR\nD4njrmzriOfILVG5ulMe1p0C6VnQ87GllI4DHWes+DHPPBM6IeTM4MqZrGx0cDTscakEdtU4OQQW\nKJvVl9hRRIr/Uo6Qs9F1SsjlRhFxRlc2QhHJKXckNyRo9Ss2OlGkWq2TJQhKyk6IELzHguOhx81R\n6UihY7TELkZOgdMWUToWpqQwskiCiDLLBtkYXJiPJYKHSCZ7YMcDRsKtuqgkwVlyWnuOaGaQwOiw\nu8zcKRDJ9XPSINkYkpEwNJfJkirO7CH0sg0SmMXMdu64VEeCOKMYHxt7Lu6G+vYJZRn4y3wMgE8N\n65lgXNwNBM5uYT6B8xERNnXkBM6H6/pLq58p1Xr8cD37xMdHxSVYEcYAH2G2Xj+kyEeqdfVSHdiU\n/TzM9qNgPIwSXeFyydzuPY+IZ7gxz7lBt9ZpbpaS/pqw4PZapiNBUC/1OtJtI2k/zxVHgJ1qaH1c\n3mMId30Mf1g7Lh+Hu6y/KGRec6as//Qjm2uhcf1mz5t2yvqnxI7bbN8Cfv1mz6BWVE6dCPeEoxt0\nMSIycjTIROzsX6fXHYqkccOkONf11Z1myFzXBzbDyCnT9fq3ntp/3U6tzHexNit8/vEZeqdP/KYP\ndiZ//+TIF13UcfNif/27d22dbksCR2pZ3zTsn8+tzg/US0R4+HzGzXdkdrvy8vY+85G0xcPjDj4R\nbe55bU1+KCBS/GjFywQsEeirfFzVcjUhGZgIxoNi8bAv6wqV1VdLa2fK99cfTDe1Ge8TtfgpLycu\nBwMQ66jfNL+pHbcGkWKo5VPK8HtZLpbvYowpgnCjCrY9P+S2UZdn5LXVeMrUzWPg7MP4G8KBqA77\nOBtVnO5NXIOEfZeVkYO+ylPXh+n/1XqZtMMBl4a7HvpAnrDf6RGoEy7L+kkQmQOW2sPWZpHqSiMT\nAXnolGZzwqGT3096S6meJ4DpPNg5zu66LqWDE+ocq9NWOiAqoC53qeu0ze4rLiiBrMFZpEwOHRuW\nmUVhw0fclWO9gAcMp5PyWV+3kQWOu3LxfMAWAZdAF4x53OOYRyCzzMWqfEoFl8xYXRFQYXRnlAGJ\nG+Ajy1xm98pYL+QgqJTgx1lgWWMDRy++xQlnA8W1TN7KKZUhpJTwbEScTktv3XMZtuvziAfwBB4U\ndSEJjBKLa0kwRJTNWYmS4MBW6Fl4YiblC3BJlYATLND35WE3eonFvBk7Oil5ZM+cDspWDKgpJh1d\nVB4ZnNsskLVnGMF9ExQuDQOXzGCveml5cO6UGb1mjpKYdx1uodbTQTf5mI3MPHI0GstqPcUCBC8z\nXjth6QHRDhmMrDDLzlKLhWDwgEnHEISumxNyIrsTc6KT8hXBkEYGMmMObGCY9dBDTo6RyTrjJEZe\nGoRIHBZ4GtiIPSowM2PMjlmmU0P3Skg4FE6bEtOIdqFY6Hwo4aFmio4ZQ5jjeDqDEVBPuEBUJbiX\nD6R0jg3GGTM8Z3qHpZUJKWDcfgG5Mn2ifHiMXNwZF/cjZoJpcam5eFYswg5cHOqywFXssciRpJmd\ntXALLHJkXqPCrLSIOKSQuSQsOWM9H80z+j4x98QJd+4UYZk6LosDW2Qui8JHqpvE5WHgThE+Mm6x\nlCWnfcYc52i3wzELjGMH4lzV7a7r8rGJgL262lAGnIUETiPM3TlO5q/yFifIIDDHWEgJzQhwko7L\ntCzvunKsTsa7NR9lpnd1OVh64JF9KcOttsmRqiVP9PvlCnkO1d/5o97xKIql+wa2eN5F2+xWd42V\n0F7QcfWJTR6f9sjqbFAs2wPCbd0mVsV2qOb3TYn8+ZldQnXnuHpz313hijDye6dGrp51fPj/5+7d\nmmQ5riy9b293j4i81PUABwBBgACa3dNUS7Iek2SyMdOzHvUH9NP0Q3R5Gz3IpLHRy0jd6m6SAAkC\nBHCAU6dOVd4i/LL14B6ZWQDYMlPTbAC4GYk8WZFxj/Dly9deq53brHDpK/i87jOjOD6dFJEaWz8W\nR2eBX08jIsLlskOsLv+33+pmLrXeA1dl4lI7/vf78WiJ996qb9egtnfDxPNFYGPGBwNsW8H0477w\nxWS80wkbK3zysvayF75+B/BPO+Hf3p8A+TIH9rHwwa1r85NwkGveZ8/YCnulDe6AlvL509AgizXg\nqu7oWDGzqPAUCHhqPUq19KvpoHUlIGfT5DMDmKiMZhGhkypTWbrKZM7bMaTmEgCDyskirg1LIq7a\ntaKtuC1DK/yC8gRshTMYdDhanVXw1LX9nYveYtvX+SqWJl0IKkdG1WHNnai2/nvSmM/dKpzosXDO\nnSHDbDVVtp7vkxyiA6LWwrNzdjWhdFIzG4I1izVoM8NKpllbztKLJsmo/hFyLIwUqpxr1Zjto/zi\n+H+1Rmsnrh6zGJNolWOINZJPCHIqCPw20jxKHRpjPlETcY+FhnIqY/TU2iJrRXnVOaXeAzPD7LGT\ngoxTEebB5MngIyHkJmNZHe9V5aByAtsNqFspGOVPDuL+/7QfFEC+sPrinlrgg1l9SL3CnTkwIYX+\nqFVaec+WjmsZ2edE6JRFFu4s8VAWXPlMJ5mmaGBpwgEjN4uoLiSGNrUz5YlClSWkUqjyySavwNVH\n3erEhbXvvRW089UaThIXpuzLRGov8ZxByCxFiSliXtGUCerJOWFBSWZ4Z0hRHIonV2CbM1EDU0mo\nCsti1eIuF/ZF2USjU8dbYaQzx6CZ3lVni4LRq1BwWBBWpZ4A3wemAqLKwcHndsFgmQcFwyiWebCB\ne4PgDKfGYI5bjYwFBl9fDJGJ3lWJ/CFBT8eORBJlKUapvj+4UjudXbEaXe09yzJyiMKDS/gMB/E4\n51Ar9AIWJ7w6OslYybicSQJWIFPa+aqgtURPdI5YqEl4Woe1/TQR2CMqxBKO4ScatzCNiBjGgLdq\nn9Orr2Ev48TeOVxxZCuM44gYeKvpexQluvoSz6J0ecQ5h8UMkVaoV+igassFHqnTZaV8lw38sba/\nDO0BKnCnBkXYSZ0VWUo8A8G1XZuBRsiBayrIPX43v5DPxg/37ffHZQq8Onshiikvcs+nKO/5k0Ti\nq9JzQOk08ZZOkOuyLg8MrYsczSF/Yvp/Om5AGBowOqBHGcbv83D8/FWu3O3gqu5am5D90F7vQ7vn\n+jYAGFOo+9a+/7wsj9vdtrfwtn03ULiUEzB7QyKlWcO9mSd2OfA7Gfi5RaRRaO/KyH0J/KNb8De6\nAzmwlZ4uGR/mBKqsxNikmVG1o5wDeMLlf5M9/+Xak0t+on2emivN+8vA//yw418vlxwK/I8PI/9F\nt6QLidxYdTcZY/vt3z9O/He3nt9OmYLnuavH9qZ3fJ2qX/SbPkMDx/Pf/ykKDyP8qq/7+I/TwGZK\n/O2y8OGi48Mqf+eT/URshYj/7cLzv9xH3umEv1rChSp+Krzyxt1oJFN+GTh6+UqB+76uaDg8PNEk\nF+FU+fMjb2fW4hW0Wg29gsrO2tlxRmaWsnKrM8g9fVfbbIkmnH4/L1MB6sk2rUoRtDG+36I1rdp8\nCXZ0RdBWmwMVnP6pqzCDwVl+AXVe4Ry8++P2ns6SzK4X5/rp87/PgG52enBnx35SP8nxt0VO7ygT\nZWhstRlEq+z1XpRhto4TIVnV7RZ1BCt0GBtx7Ob1yuk8K7VA7fvORd+O9ZylnY8BqnxhjTHVi8Vl\n805W7HhzFE4ysExV5ZvVc5yP17euvxbU1+M6P2fVh/qkTwfh0OQUrklH5nMyn7ui1eN5Xmdp19Jj\nSJNlzMWdUO/P2WllY3qapZA/P6D9QQHkBZEkgscRSbis0ArNlgQenHE/CXvqBXHAVCY+EaPYJU6M\nkoyia0pJ/N8TSIKlTCwx3g4HrhWGvrlSoDhTxlQZ5oBDyfjgcNSCL1UQRrz3OJfJOLZUQX0xxeXC\nXmFMxpWvYKFqqIVRMjkrVnIVsecKsrdqZN+1ab/qxtD7evkHX6ewoxMO456rYYUrI3uB3gtLy6zw\nLQgjsbPANhnbYiy6WnR2Exz7HFm6Qo/gukLEsbeE+o4swiGv+Lnf4Ag4FVSVrXl2Bl97zyp0NW3O\nMgtR8B3/pMblNCK+Q3MkSAW14iJWBM2ObHVkmIrHa6KUTO+qn+i4n9DOWKQNN12gZMeOqY40O89Q\njCKelDPFKfuoJEu1s9IF3ozOZ8DjnDBZIadcpRYWCTnhrdrXaU1FYTFMRBHcIbEZR7KCaWgsRMHH\nQva5yi3wuDbYCSasirK3OjJVyVhjD4sM7HNGqIEtuQi0l9oWZcw1MpxiLL2QY0LcT6OjBfiDntjG\nhUb+MV3yK/+anQUuS7VlOm/lW1NtaxPKP3M6rot997u2zjXCQ8jErNwy25XVlZnYEZgmV6VONz7y\nTXG80UDXq9SmDb7VirN2z1SZl7YdXPwz52FwldUezyQB8/bFvruNhVE7SMp3gPTc5u/nZY+/Pdvm\nUjJvonWO86zN0g6A+/GCL7znl27P1HTQ192OKVcQ3p0VD26z8bWvW3gz7Y/a6l+zPjLX22y8aK4d\n58WJwXv+du15WyPSTXx2aPeGlyPy+NerNZNO/EUHv4nCrxtI/2UoXDeA/79uCr/qHTcdJ7qvNRHh\n493I3gU2ltg79x1Xjbk9orzTCR82q8ALLXwCUDKpq2D5Nxvl/d7xx8fIhzeOq0Nl7lU7Do31fqkr\nbm37/Rv5EbbzIatS7UFHa+4D+nR6Hb4rjRD554u5vueRPTGKWsHNZFVi4SlPbOHmjx3GSJ251WKY\nzmzs9294lk3AzBbXz43b+t4mTUZy/nSegHH7cPbb2ZXj24WI5618a9nv2+b3v3WeErXTXLh45r12\nzjjLE+BbHSUAdmdyg44Ta26cAG/kdAFFqpTl0Fjn7yONz/gIZr/quh6O9riLxpJ7le94D4tU6cRS\n4WBVV32ucT6/pAP2xG1E2vax08BHpf4vNXecWcrSufZdO/Y/d6XPDwogb6hTg0vqVD3NA88l5Yuy\nYCs71n2kZIdZIWtHkYhHyVatjaJkXMkYDZxq4bE4HlPmlQ0giesSeO4ytyGzChMDPV2uhV0eEHVs\nc0FF8cUwryQrTCmQtFStVtPSmVGlC14YizFhFEuUlNGijMUh0lw1SOwUnDqMDsqISoeqr+bXrsYk\nBzUe8dz4wM4yhqNTz+dJSHnk0iuDq1P9WYyVKilUve2Q4VEiYhPie/riSdm4ChODLvhyGina4f0e\nENadIlRP4SwdnXVM6tlLwLxDVclTJBXIqfCYHd6M4pRlESxnlBrycfBGoiPEPYNana5xymgZLKBB\nyWXCdMGYBO8yWhxeMhcCfRBynjiokswxqFG6jlwiQRO5FJJkBE+OmSzC0pTEyEQFOb0pU5pwRaqE\nI6VqDTZGlt4wqfZ5p5dONZjL1reCkqlyGFJt6pIlfFHU1THsAwqlaouLC1g2lMwujzy4nnFKFPWI\nKJNkHmKVutif0738P3J7p5xedRs8/417IBbBOWVL96Qa/Ty16fgC7hOM/sgQZSeMh8oerrrx6Bm+\niwuCawVsrjA2OnOFYS4Dma9j4LYVAX6WTzBgGQcWCofSceMin+ZmoKuwJrNEeYGwbl3aypQuRD7L\nHc/N6EJEzTiUjoN4Yg7cCjyWCiRNCy4PtbywpW3e+MirFCjOuJXIXfGMKSCmLFXYSTma2w9n3c9b\nWhn5r3L3FDAfWZnMmE6DkteNpR2kMDSg+zINPG966s/LksEX3mWqRVPujLVub3w1x4MoV6UgwfhZ\nCyL5wi0IOaOu8E7O/N4NfMCBb9yCd1OTjviO9246XJxw1ELDt3KCsT85eKjgtZ3zUsNennmhzx1v\nhbqfXyXlpjHj/2rRceWr68HYBpN/KfWZmzL8vF/w6wR/uwpMGb5Ojvf6KvN6vlixltM9+dGyP957\njyhvLJQ3zfP1IZE65cOFsjHh5rIDLbzQFUtXB8BKnaH8WZ5QeToT8mNu5Qz9SJMueKpcACpoPn9m\n9QxkwcwSc3xmVZWxrbSTAu1vUdwRCLrmmQF1Kn7WnOaiR8nEuUQhWvtt034cXSkaUykIXuzIWuam\nY3UYyaQVADY5iQrFZl/ftp/YMSxjlnbMLhABmrVntYgVqfPGhs2y/acAd2a3ecroHhlSTuxtPUd1\nf5WTBjkgTwS+8xPeizAe1ycn5lSEzmAnwsoy2/aXnswkVZoymdBZYZIW3DFLUKw0ecS87bqH1pS+\nx2M6258FxthmEU5ykUKcWeYmqQE7DqjmFLzEXHRruDZ7EBtQLhjmHF2bpcucBmQQFvlKAAAgAElE\nQVRmHN+NqRUo1lCZmr4468sHqfIxb7OEFbKefLT/XO0HBZBfxkDxHkuZQYRb3RG841CU3m9w1jPm\nA8glZoUF0/GGrykugi9Uh4tmoJdLrvYiCiM15jk4z6PCpa55SFu+MeG1et60HcMwMBVYHKux89G0\n3Cuouso+T4mkHitgkiFlHjSTEwwIwQTP2OKJQbTgpJYlWRshqiqCUIrxwEhOF0RfiFHpdeJ30hNy\n1Qcti5Ik4oJjX4xoHg8syWSUhQx8mRMbBG/KK73k/3w88E4PC01sphVqE38RApb3lBIYU+QxgzXm\n2aeJySm5FHKcEF3zUhM3Epg0siZAOXArmS/yBS8YMRxXUqcjDWVJYjHAMqWTz6E5VA+ss4IKOzE0\nJiZZoFoLGDsUlViLGhOMONQLy7JlF5WHVIs2k6+xpTkbxQkiLbol19CSxzIxIRR1dNGIJVddej9A\nEKTlny7zWHXczkMqHOSRSZWcPE4zrsVrGw5CZhwXmDNcTmBGHzxWhIc80bmWulRq+mJoHYeqZyw1\nVa77CRX8bN0JOAhwL/NgcWYozl5TIrwqwtsu82pcsLiorNx9i2x+lQMfyQTDyCY67rNjiXGhidcu\n8pUFfuX23E0LokusQ6ZEPU5B9q52dJ9Z4PaMFQ0YO2Z9Itwc/1apiUjh5jh5DIHMFuHGRQLV/gkR\n3gp7HosnuMgO5RtTLs1YmTJ3vBscz7WC6YXCzoyswpJW5f2tS79s+7WjMFrgq9NsJLctBOWunIHo\n1ml95A78fV4e5R8TcMjD6bDmZjV+dQbbM7N2Dr5FhEsrjUl0jK1A7pJM9M3WycO7Fok43pDIB/2O\n/y1f8Tf6yH8olzzvHtnmgQ9KYpTArpQj+0w2bmaNtjpiCaSccGpcN0eX/2mbj5Z27yy3lBaq8lXs\n6SRy25DBfZIj0zy16eA3feaQhUkdooI1OtH8mY1ZqrHI5g2JwptDhSAbgcXs1FEKQ6iF0CrGddyR\nxfMYOq7i5omk4Mfc3LekIvMTfP7MHmeqpYLTosIuK327XtaeiRlY9lojiA0hU1PzQgObowhT0Vak\nXF2WjmBYKyiamcO5qZyBaDu7pWW2bLSzz3Ud8zpVziKctdYRzesbrLCzuo/zSgMtKrsB7zRL4TgV\nj523+ezM+t/5ttC2f+1kHtc/r2IOOpnfh+EoVvnOa+GJJVr/3V1AqSYCnqqZPhb1SZWuSFtpbHpg\naxKKoQHdjKCteH62bZv9jefzPO+pIkTTBkjPijefFDeewKinFtTNNm1WalAMZ5g7tPMr7Rp6OV3n\nOaWvXeXjm3kGqB0n/27hVBAKhqlSSk3YQzjb2395+0EB5CB1lON9aWrcNYnIyhvvK6iO3DvlkA58\nZZ5xylAmshg5O4xMijV5rpTS4hfreCamhHc1dPFeMw8FPssTb3tH3wlXRTm4NY9jAQmY7OkFshih\nBHxN+yCTKdKRmShlIltlQPe5I48dJWQmMRYlY+owtNrqlEzwwjWJlDw+7MmlJ0rGW2PM2ZKsY6WG\nWWEpEWcwOMEHZRMd00GqdgkDqVZia1FcNkQ8Kwq/y0ZOBwY6NmPhQMfoMuYW/KOD7abAIvOm79kf\n9uSpowOsC0yHSGwBKK6zGjLSZVDPKy0cuOWbeIeViXXr7MyMpDVNcF86chGcm7g0IZB4wBBZshXH\n2h+4ykpyoD6hpWMSRy+FBcYyGDlCzhM7cQy+Y8kErrAxVy3lgOwco2VSOpCLYGJsDoXilZ7mbqG1\nSNCjhE7RcSKKEVrSWcrCV0VYqeAKLFCK12ZOboyAMJGnzMp7nEZCNl4447NJcL4q0lcWCCVxrZXh\nmCQyoiwtNUohk/+UeeePsJX++w2BdPSUPh1fcse2D0ym3IY9cxzFOmRCUdbhwMeHgY+6iXVobG4u\nSHGsQ+auhT4EF+lDIZSqMdYzycoqF37uy9H+DSpjtm5T+aVwXDfZgfvu51Uux3Vuojtb/rTOtRqU\nzAWFrMIfWqHcM5eeeLDWhLd83O+n31dgfOUyyxxYynm3CJ+WpsP9HibkoXgGyhP5xnBmH/hR00d/\nEhfc+MhdG4TM7OH5+XkQ5fJ7pCHQ5CHf6mOmqlbkTZ34mp5nkrlWeIESpubacXasKyf8mur48QsO\n/NvNjvevrxAcK6t+1e9fd7yR2x1R3LHT/k/CxG/atbvLykPJlOy4cDBPok6qdOfl7xGkE+xsrt+8\nHauLzoGuYQxh9ioX7pzntmTuCVzZDi8Z+Qmxx/BdMHb6wxkw/tafUmk65RnXyolxTcXw2jSsNOaW\n09T4vPz8mxnUzG2+68+/m4NF5nZkFTnDoGefnzw5dmIxz6fZhZrUlpokcwY8drZ+gGLf3e7594bV\nqGNr4Phs+XO987fb9zkrnP97Pt7QGPxS5gF7becDiO7J+bEn506U75U5CPW5X1AdvQp1YLKZwebZ\nb4Tvhq0ItZBPTI7nft4DBZZtgHUvjlUbuC+w09zB2cF+W6aTrZKO+duTZsdx1JNR/+l4pUrOpH0b\nrbqgaPmJSyx+1mV2llmFgpeEqKfTHvHGXoWLMFDGA2tXuE+ZTUsyU3NYtWKld8KYC06NOLMEAl3w\n9WKYEUqunWEufJOrj28nwkITyTzeZa5tYCkjOOGQI5JbhrovqE1Q/PHVUJKx0Ej2hrRq3KKGmpLE\nOJQKaq3UEa3rjEdbsJKE94GSEpKVAy00wyaGILgSQGvR3pSVruugCNkSoQQQRYqy10RKdcS2cR3q\nQS3R+UAhs50Mlwt9GXnXKbYOPKTIG+XAq+Lxkvlqv8UlR86ersBqGXg1wZR3jN6RdcDyiBNP8NXm\nbHsYSd4xOGGwygTnXiAJX4qge0dEeNaBlMyyRB5ywTlP3weG4LDYUcTxiLGnY1/qtHGWSMHIRei6\njtuUWSXHIVT5yeFwYKo529Aqn2WApfMEHC4mjMjr7NlZYrB6PVwWtqVaeHk1fm6ZHsP5npQzB8mM\nltlhpOIwArpQYjRGPDv1pJRYB6nX3RmDTQRxJCt4V5mogYwHco44F+jK94PKH2ObO8nayZxG/mXI\nzON3dzhJAi59QYpSFNxYXzkO8JJIofBRmeiz0WdhdMInpeMDNxFKLXSR4ugdzMLlSTzL9hKNVr1L\n1Qovpp7n3UiYBc7z+9VBH5WPrbpYvHuOCI7rVNbR+JjAuy4fv9/ijgVOXyXHW74yMF8nxy+atAOX\nWc0BJeJQTYBwX4wVwqscuHGRZQOQNyqUIgSX2AGb1PPcj+wQbtpb5VYjd3Fx3NEvpfCBwNLcU6R7\n1k1+2fTFi/bV7VF3Xa/FiKdr1niLcp7idervP/IjH6ee2dtjZp8vMT7Oy+PWDOPTaUUvHLO4ly7y\n0Bjt98OeuxS4tMLSRf7N+goSdK7yQN+4BR/a4Wib94t84CJEsmXugFtRxCeeAc8AyFAcL0d3TF1c\nZUc04S4Kq+WJFHkjj3zj2kBDR/7dwfHhUngzte/NCC3QxER4J9br+I7m4719Hfc8ugtW6aehQ57B\nycwynv0HaJjkvFCvySyqpenpe0dpwEabblnwUu3KDo1rdGKIQThDSEFORW65SR+icfRjngv75n3q\npIZ0dFTGdAaGwokhnFnghVj1Bp738UTSHovn+vkzp+/n4rdO7ImNXG7vNplPHOClukSJVJeGEcFJ\nJURmQOfOwZ5UoGiNMX2imT878f1pqq2+Txs6T7P8DD0GlxgnTTGcwGaSKjX4NvtcHS2adKQtP4on\nn7HGx+JCKgMvpkwCanYsYkxWNeud1XfGzFxHFKcFM7gls+NkWzdLaOYB07y9rK1AtJ3a3AZhnhPL\nntvx1MjqunxGjn1NAFoSPF6a2wyQ1NFj7P6Ms7U/KIC8cD1OI4MscS6SXTW8Xg89Jh1bD6kseRwN\n+oTLjilnMCHbaazVUzCpU29i9bkXEaRkBoXRK7GUatWmVT4RY0S1R5Z7yjTwMh34crlmlQ687+By\neWCRAiIdd0zss5FL7Xj2xPpucUrOyq54EHDuAKWm7GWp1jLYQBHBO4+4RDHFVEELq0lJ3lFKreZV\nb3jfISVX67TDnuA7hKqPVQdRrAZySNV0ppRYLofWhya068hpAj8whcKvc2CajOA9n+4LQTM+w8av\n8AGe+5FXW08+KE6rI+QhZ3zX4USRcc8+Cr0mdtGz2e8oUyB1Bdc7JHgG9dx6z91mxyFHXkjPpAsO\nQ6ZPQpciXjKrTlm7xKQe6wP3ekFkQmIgW2FA6K0wkLiRzM1QGFI93k1LcFv3wj5m9qJ0OeGmSJFM\nLI5cwCzi1LgvC7QY65Dpo8OrwxPZlpHX6pgmMDpGURZZcaJ0bk8nDkvCfcmkXPV2S19jq0VHnE9c\nOwjZk8SIeTy+eJ0TYt+RopF/QhKLfqzd1Fk/BEBsyYdeWphLa944/tu3/74oA29RWIxKBO6svdaj\ncA2gyjZ3/MJPVNX503Y/1eWv/Uifja8YuDbhxfy9KVez2wbwhXYsgNsz3+PPU+D6jI39vFmybWPH\nVbcnmfIiewrGRy5xgyM02cBsiwRU9rN9/iw6TJT3/IQJvI3xtpvYqLAu8NvieUvzEXSvgZeusENY\nq7Euxm+LZxsysWRemuc9iTxDeMSxDpFVo0Ufi2OH8s5U+KI7UX1LTlpLMG5c4o82cHtWC/5K9CiB\nWIYDu7jglSn/EDve9pXV/SIv6Jm4dZlXKXDrE69SwNQYi+eFwLWcCvYey+LI7v0hLVlQeBRFyoJt\nqJKOLY439cCvqGB6LgIEuLDCb8qS5cwMx+7J/fWOTtiQucqO1wIbrb74zzSzjYHnGtm1d/2bZWpa\nePiv+4wmMPG8WaZKqKhvZ8lRpIKqyzGRneCaXn0l+yfuDz/mpvWRPU5dz60cp9qftu9LtFtQNd20\noru5SCyjRy/eqlHV07Nx3tqGq+bU6OGJ7EGRJwzgVfvXjqcs9zmzGOb3Cs11gUZCWZV5ODhCL3ly\nnGcaZ6v2bBnDW7VkgwoSRapEotAs0dpvL4BJKtDMqtWmjUpSdVb3OcmJhZ1BKlY1uQeT5nIxn8OT\n2EGo2uHOqof+vNdmp+FwIBPVUZpEZL5PzZqGvNnYzQM+adtY8q139plDSAXzVSKTRZsXtbSBRWWg\nESE+mbhROjFGO70Pz7u6iVpTtqcCW9cuglkNIHFOwPLJys5OacWlvfdLOweeUxKkb+vet3vjOKtg\n0J05AP1L2w8KIL/UHul6SlYuO8eqc4h4shd06FDATXs2JTJGIeUJrOBLHf2oFTorHJyvYnE599rL\nDCZ0MnGJsDfHiFFKTW/zzpOZiAelWMQFj8REcY7PR+FjuWBhwkIyUnoGz1HjmOkgeyiJIjXxz5tg\n1uNcYVAhZ2GSjkEnnK/WZtmMNE3ECcS1AbztcRoYegVzeAphcOQpo9KRkyGSyCUhnSdkB9LReUeM\nkRAClqxWrjuwJOA80nlMlTGNdMFzaE4RRUL1FARSFF74NYvrjoVAjBM2xhowkg+VESUjErjf12rk\nogHrDYpnPCSC9Xy92/J1UFZlxWSZfRAsJ+JOCaJM4kgSiNH4Jgq9ZsI+M4qxZcet9OxQtjIgKmg3\n8GWKSDE6iVy4QOmMYaqe1L1TSqzpf5nCVIyoEVOPWEexAyuh+rJOtcggxgNOK4NxmYWgkckrezMG\n51HJTKNnLwUrjpUadI6VQCmJjQWSDmAZVyJXLtKpozjYEJBS47e32dh4QbufzpTtVakvoHs9ldiY\nCm3WugHnE7Kw8+n7s+/vmwfx15J4t2tsrAmvcg+p520daZviD3JS5b1nI11b/n6qy3UhMY0nwNt1\niXMlyFv++2325vWYnMkaGjXTu8wv2nL/YJ73u+qSAhWA+zOo8aJJI26AL6XwsgR+KfEYZBDaZIec\ndSRz6yms1Z4uU5S1GptcuNCqbQf4fQ4Mza3iI39ACbghsqZj007zhZ5zcUZQ49PYP5F73OQTw1+L\nISM3OfBGmZjjXEyozL8UHlWx7Lj1kZfm+ZmWmnpqykcNUH97DPhJXDQJiDBQ+PnRkq+elM/N8a6c\nAPJvygIT+FmYuG+6kGsp/NrqYPge4ZqaHnqtjsd2bm8EfGPGn1s89paVFDntlJFR8Q0kGk6bLaYl\nLnJl+nyp7xwzY+fXrOwUbvNjbvI9n2sqW/08OwXM7dyV4pR+dkpsUzv9oDDbbkmdzWmLn7vZxDOG\ntzsygka2E+D9thxhDg05tyAsZ5rW8+fP2v/VKftKet1S2ErNGYDvukjMbGxqxW+I8SB61CBL+82f\nkknMwGxeBhohJ0/BeDAa0K0zMnK2ziPklqfrnuUn54V+esZWR6nWs3pmnVbPSfUFVioYHZsW2Lc6\nrPm6nkdTf/u41Fpy4ryPIvV68/SegSoN8UDSE6NtnJjkPdLCXeRYGAk8cb7wZzeeEzkeY40CqtKW\n0/Wt+vLcBgHn16DKR2bq5s/TflAA+Z3nK9YqJBw+wNIN9F54SBOjGvtDwshclS25CHsHr5Ija1NA\nOUeJypVOTOa5i1L1ZNoRUmbwMKjjUGDQwkI9o6uJdZ16FmnDz9TxYIVnZL5MmfvsiLnDSi0M2qqg\n5rFYTdSRSC8BlRFVRXXEFY9aQtUhaC3YESVqwbcCp13cs7TMOk9oEF7pgEimCx3BezQWcokccmZM\nNUa6aEG0ittXg6OIa1HO1dJKJCBpwoUecqyBG6p4KeR9RgdhUMAKvRNyMEo2NHumnAh+wnaOUTKT\nq0xL9pl+PDC6jqKFdTfwEA90boH5BKVjjBtC5+nGwH7cAg5VxxiEaWtQaqocvmdjBslRbIRYKKHH\niCyjMfpCSpmdTgQpmI4sPHT7SMIzOBBVohRczuybEbmq4rxAyajrWIwHhgJKRmxLzIWRA7nZXZnX\nOmgQRUqkD8rCevqccEmZQq4pei5x45TR6pRhDkomg+sI2WHmEEZGltzpnrtkqAv0aqxdBowgnq36\n/8+8+x9Tu9dAMqXXyFUbgb6iw9pUu2vuAatS0LOgjMdpDd3IBZELSVjpERHeW0wsU+aRALHjve4R\nAryc1lzonm0HF4fTq8rJHmuFgaFPfJxXXE0VYEoYmSaPCdw360TxmUUDy12CVy117XkorBsC33Yw\n7nqCOOhGdlOd+tdFtQB7L9fOad+2u3WOrn2eJs9rl3meHVvneHuWLwfYiOMmZrax45UWPvCR3585\nUgC85xOfZf9kGjY2IP2ez0x4XqR6/AvsKO2YCKyzkQgEBzetp5wIx0p/gGsm3gtPBwi9xrMEq1bQ\n6BKvnXCrkc9Kzy/DFjAei6+DB/EcimcF7MjcNDTwRVlB++5J0wqa1pq4K56P8wBmRw3133RbPp0G\n3gsjQuExaROHVmD8mQS8KX+pFUQLpRZjq0MR3m+zAbbcIY05M4AGcMNhQdHmzlAMbdadlcXTY0WQ\nikek8LpT1lOqgQ1qrGMN8f0ptNLUwkFOARYTUgubqJruwawycnIqlgvU5/nQ9KuuAV1PZUkHM0SM\ng1Rgsmjr7bDj1D1UAsudCFiW1JCPVkuPWQuPOEoy7BhaYcgTMHsMFQKmUoOzKgCsfxiaBGlsC87H\nMrTjgNM0/0i1MKvFZTXK2UFz+aigLnHSWs8tyuw9zEmH3cBxpALHGZAXOclC5mMMzED5BN7tfEBB\n03B/e7tnLLNrLwyxKkvoG8sb2vElk6PXdW5MsjboPodu2Pd0TCJU6ZTVQUQ9rnkwZK2QUcCqfWz1\nza7O4n0D1XNBXd/8lldt3+YhupNC13TNxilMJDHrsWlR77Pm++SPbHCMRB8aBnBSvbRN7cmg4l/a\nflAA+d3rNT7AVIQpFqx3REv0ZMbs2Wz2vH48MCRHzsJIPl5gh+BjYVdSfRnqHjPBtKtV3b0yiGcn\nQqcHRBQvns1hwoWBog6TW/4ff6AgvCojSQPBFUJMuKDHooBdMByu5s+HjoUFvKUW0gHqehwBFvXB\njqL4DKaJKcYaInIYeXQF00QZl0yW8GpIHPFmDOJRSg0RoWoWc8wVhHsjWE0BdM6xtIleUvXelQ72\nibzumcZCtshVAceB/d7Te+MxH+h8IKivThydcb0zLp3j1uCz3Uv2qyXOKe9Zx8c4+sOGt/sBP93z\nbzrh3yfhDe34u+1LhuLIuwcuvbLztfOPMVL2Gzo6NApbyYTdSLRUCx8kkiXg9iPqCneupdmpcumM\n9VAoWdgWTwodIe0ZUJ6lxEoO7MqKrRUGr1hJtYq1eNa2wXkjFeU1EZEOE1+z6L0SUMwruRy4MAEV\n+lBHuL5FnJtTkjisKL0Wkg7c5QMLPHvvmXJh7zuSZZwteSGOnJUHcbW7SYW1ZC6C8I06cpqOHpc/\nhea0AiovjvuudrDbw4Ib2fO196zLhGomS3WN2c1AdVKexcpuPpbhmLaUt0visGEgEZeF1Aq0FmHH\nzs8V2TCEDS9twcYWiAkfxD2f+IEPbccdC3Sx4zLB46oWjtphxcYlLshsxaEus8tL1O0o+yVDt6XP\nxt1CuR6FO3FIGBGXKd3IRc58Nq24ysa9wo0JMlRN6q2v6wDYeeG9bgQSYb9El2P1+J6WvHYC4UCQ\nxPOpZ5cCz5v041XuufYjd6njAzeyU8cmeZ7ryF7csTNZWOYXLcXudex5HStbfRVG7hpzfcvp81UY\n+Sx5fimJ3xIIvgKdz5PnXV/BdTSqrpsKxt8i8nHqecuP3JWOu1K7hpkJCu5Ex+8QrjAqZ3PqjgJC\nbMz0oCN3xR8dO2Ym17TOKIgpr0ogoXwSFzxzkWiCSeGVdVzLxLskPhfPhQivcXzIyNxb5galbDm2\nUnjBxkVljfs68ZqHAzZWgCurAxTDHRbk5eE09Wxws6mkRVHh8braB3ZjoWvr+yk0pxVMFKrVFlQW\nN0vNFAxtnmctxtbkeN1HlNRATxYlNBZwj7Jq9m5qdtQXJ4TBgCaXEIF+1vRKlfZcW6lAdQbFIvRy\nCoOolEm9Z6UBb2vr9gJ7azIiqdKC2Z3MmRFF8AYHkbpdPYHMfMYEL7Amgajeyx0VoGWRIxvupW7D\nwzF2fLD6O7M2wGjAbkLozijq84K5p8y1HPX9h7PP9UmqAH6BnZh6TrppM+ga+K9gsu53liopqZJv\nO6YUnt+6Xox05lxxYq5PvK61FQszcG37Lyfw70Sbu46RmrTErDpyxVKOCYYqlW3etznDsc0iWWP5\np+bd3Letx3mfORvQaH1P1XuWo44+UeUwtO9nGz5Hqcz6n/GR/UEB5MOw5JvsKRSCjqTDxMNmx9ev\nX7McM0GUEDKPaWJRFgQvLEQopTBIoaPwTgCh55GBWzfVpBUU7xXf9ViGx1QDIigTvWRy3DOJ8FqF\nfiqYcxjCUBJrBb9IbJNnGRLOOS4FphjRPuAsozbRe0c2xyJNvHA79inRHzq2akwp41V5O0eeDZnr\nsODvUya6nsPoMSJr5/He48V4poXnIbKhY6uObRG8U1LJeBTLmbUWvCu1IM0OfOYuWiGd0bsd78SJ\nt/LEF93Aq4PxN/3AN/bIQM8rFf4YM8tpIsfCGAJ/VUa2suI3aaIrhVAyU9xz6C7464XyQgc+V1C9\n5Mux8EAijzv++9vI71Lgk13itSU+yMbn46bqyXxgKhPilUV2lObYYSJkdVhM1U86g0sT2VUbtpfR\nk/eRvxgiy74jR2PpD1yr4+AVXwpvuh0xJx5zoDgh4hncgd4JTgvbJGRZgO/oRUkoQ441ta8YS+2q\nNZFUX07JhaveMXbVSeU+bbnzC8YpkplYmGIuc4nQOaArfDIaSRI3JHxnOElMMtCb4+fOcEx8aJHH\nLnAX/4xP7X/k1kdhO2QoNS0RYOcLz7IwmeOgnptUSFLokic2TVgftmzm+jmbAGObHavkWcXGzJ6x\nkC9twW1zOejDlj4J+D2dCEMpfL7s+YvDxH0oCBPrAo9B+JwL3pVH+rBhawsuExw00RXhoRUfLboK\ndF/KBduUoT+wSwUIrMl84ZY4v8FPiUV3YGcLNmZczoUiuTCGkUESEtd0be7ypc9ciqAuYyJ4SVjs\nccs9D42tTmXgKtux51z7RB/hEDKpDOya1dvMKL8oPe/ZSU89y0JcOX3e4I75zDt1/LUk/oPz/Oc5\nHlW+T2QhZ71IEJDsmiTBcaGJvjjWake983nT827DFSzWA7nQBJp4LJ41NfQkuEihekQD3BXfiger\nvvompDp9axx9pCEfgel1Ma4k88o8CnzVBprPpIBUiRXT8kmwRDk0t+mzYzQzbOrJyz2unP3NjKlJ\nVhZTLSJ1zlVdJEo5L7H/Ebd5Kho7gcQea4XhtfAutmlvLzNEqkBnTi1zrdgslQpqx3YD92f1Br3Z\nMQRE5eTSMEsQnlnhXh0rK3RKc3iqzgoHKjssDQT1Wn+XSmU+j8CJNtVvNYUOaVpXoToPNQbcNXB8\nnuYXSwNonKROweoyReyJpMKdMdcGR1DP2TIiFWzP53RmwCsxNt9jp8dN5fRcLzjJEnoxdiI8t8K9\n6JFtf+L8cTbFdC7jmLd9/Pf3dDXCSUMtWC1wP65HWrKikbMcQ0LK2TMyFw96qzMDx1rE+abiJMUR\nq9JFwYgmdaaJNij3QipWbduMWicGx9hvETnKQMxgpUYudTBxPC47FTMWoFjGN/lIlO+GlvxL2g8K\nIN/nQNm+5OsX98Q4st9lLgEtI5qFBwnstdCVwCOJUGAlib16Dll4EMerBKjRm+CT4sTILpNShwSj\nX3iGx4JqJFFYO+OVVem6inFhkNOEeOid42CZhXbchJFRBhIwdI61CaHs6J3nSiOXXnEKN8NI8j1T\nSrwRMg9RWJpRTHDO8ZtxT8G4WV7wZVb6TlDfsXCOMCgrvyD6wOf7HajD5cxaMmNOrEPHpSWCh5SM\n97rM/3W35WdaEBkZfSKMkf+sK1x74Y8W+SAn3rsa+MfHxF/0K36bauHCX7vCwZRvenDJ+HUZarxy\ngS4s2U8TF7rgxQS/i7UeNibFWeSgExo9n4rjf9gHijcKPYMbSGWqcaE+4JasrNUAACAASURBVL3g\ndnveSBFz8GY/8Ok+8pgTl6a8NGGiglOXC2HKZIS3+8DEhBMh7SdyCHw5DWyd8kGf6PzAtWyQTshl\nZJsL9zZwx5p9TqTsm55TUUsE85jU8x8djOrIKpgEhMQqZlIQ7kvHVgOlTCRzZFV2ztO3acUacC1M\nIric+cvguPCFko37LtCb8OXYc9NnLtNEcJ7JMpdl4r0u/nO3/o+qmThEjE4jlmux6c/clq4Ubq2w\norKxAwXXR6J4Qi5MLjOI8Q0DFw3wDQrBOXZNirE46xFWKbFxHYNFNhK4lcij67hkxLLyZorcB2Nh\nQieJOxe4zZG/4oE7F+iYWGniNZ7ohIs0MXQ7NnQ0Mw2esWVrA5MEbnXP79wVq5x4wx8ILaLpY39F\nEfh52jG2V+Zjcaw0cSAQFxPb5jgxv8RFhK7bVLecDh5jzzO3Z1Lh1eTRxY7rXFltOHWUt+Gpa8Ln\nXPBOzFiABw89I2W/ZOsz0uWTjZnPxw7/ImfuWPJWyvyDCjfxpMvGKuDel/KkQNGHkV8QuYs9r0rm\nQ5+IIkztePtsjA2hPBGIFHecpp/7pQtNWHHchsPxXF25RD4ElsPIXazM+7WbUDOufOSracVCR27D\nxC9k5HdlOG7insL7HPg1A7+QOmAqpT7f97sLrjU/TWtsO/LYju9SC7ZfwLBH90tKv6uA2Qwvwv6q\n3ov9vnb8kUKIHWN/oJu+z5H2x9cquLWm4ayyk6oDbomyzugLjDNj20Dd0CQBnspwmoC6ynzOg63M\nqbLANb3q7E7hZY4MrrKBLMLKSvVGpgKupFIZ2CbxcFqL4MyMvQlezgrqoNbMFBikAuzeDKeCa8Vo\nGKzNCBijKs5OEojLdg8b9kRnDRxBeDaha8eXpfrue2a2+/SMz3OChzN9dWgyk0eZbeXqiCQ1KUfk\nJJMIcmJyo1Snja0pvdmx6HHGn9Xi7WmB4swHD0CeGXXseK6exmyft1P4yAno1u/1SGAYJtJY6xMj\nbkepDUgBbbMINQjm5B5kVogiXMmpAqWguFLZbpGTzGPe17rQyXlMgH05pRDONY1yNshAQMWB1Wu6\nbiEpf672gwLI5ZNfsx03pENPp2NlB7TqlUanJBX6rKhTUh4hw6vUIxrZiSHZWJix6AMlTYgo/6nf\n8oolxp7DwbhR4xfLxAMLJFV7qO6wYS+OrSo3Iiz7astWTEjiuJHCWyvPm1I4jBM777lq4RAbnUhT\n4FMKr7NyzTPudwc67XmrCFI8GzEeykRQxzPf82Uu4AeeD8IVEZv2RDpucmQtxrt6wLs190T2Tlgt\nAppHFvkezcoXLEhuiz9c8V9dJ/5ua2yBm8PIyMjfx45NEpbZ8a+C5/+YFMaRl8VhY2Spwh/Fk4Iy\nFCUXyEOPlUjXrdgthX5a85gh50xOmZgivzq8wq6e8WoUtnogj4Viys/2L7nzHVcCH/YRug7yF7w7\n9Pgu8Ek0DhneS4XF1ZLfx8TLLawsciORt+Saj8MjC4HVGNmMMDnH3z3WQsBLr/R6YK+R+/1AdJ7n\nvuPSe1b9gCsjaw/kB/basy2OzWFi7y/I4cBQjMHviC5Qkmexz3ztE2/nSLeATQlsgxJiJmthEQxH\nxtMhQTB6XlALHx8Q0JaMqMIuJdbWcXFQ/hAKnTdemvAHXXBZAiIHTIy39acjscj9yCXCiGeB8hVL\nLmykSx2jc0wivF0iU3a8aoWpMUCaOqJmnqeEieOPfQUfb48jQ3Y1ObkRyCLCtSX2KVTP7CLsFAaL\nlNzzVa+8O02MeFKb/h+suhjMn7ctya2TiVw6HkLHs1S9rAEuLbF1cNn06SOBN9izLZ5VSowEur7w\npu0JyXCauEywdRUkfNK/xa+mT9nkC57plkffEVM4gujRwTpPdAif+4HQBgFBM3+USz6Q19ylBc9k\nzx9ZcFv2RKcVFO8zV37Ldd7wWeiRtIIEfbfhd0vPB3lLFOXRPOvskbDjC7fkXR7ZeYE00jdd9EWu\ng7NHcezisroTKwSfuMgZX2qYx03MhD6xTL7aEqod9Z6jO0WIf2U9fbtO1348A6cOISHFMbpW+ES1\niMs58DgIK+A27OgpjOjRhSC4yEILj8XzW6tuGGpVolMUfp8WiBjjMtFvO8aLQ516BX7/eMVH7HnZ\nun0T4ZZEMqWocFeUxcWeRRZcf6Cotkp/pewDg1Xbx8pe1p3eXNb+x40/jZmfjoJovUK5ecZWYFMD\nJ8Qq8EylyiyKNHmgQSc1ltidTf0f4DgVP58hlRpZ3c8pdnIK0lCpTO3YJAvHsypyisEW4fIMdHup\nALoC0PqLPRUPoHXbc7xwaVWGs4Y4AaUFZuylujcEqQEbuYVlZKnHUx0jTix4lFn3qixa+MTUtjs2\nID5KJb5yA5Gzt3ChBtEsrTLipTHVy/Ybter+MVJT9Xox9lRWPJvhm2Rkri2dtdUFoStV7x3bNmff\n6LlQLkqVKsygznMC2ktO+u6JpyC/tIGF4wTeZw/rmj5YAfHUlpl7supxrU0/zlF0FYH/l7s3a5Ik\nya70vnt1MTNfYsm1li40ADYAgpgeISjDF/KP8JX/kj+AQopAhqRwICAEaCzd6O6qrMzKzFjc3RZd\nLh/U3CMLMuTLlAiq2x4qoyLczc3UzE2PnnvuOb3qCqrbmFeDTlevehEywoTRnRuiV31yGxOYrVnF\nVaurFOVsK0qz9fuEUT6Ddz2HwfyA248KIL816NwGFwqlOoIVgsFOlAcxpC5kH5qfsHNsOdG5TEkR\nIaMaGKTwhcs86wtaDHM9j2uWYcCYR+Vj7CiaSeKajjT04At/GXrmcmRMnkjC9xs8lc+1MagTCTpj\nUysf6Qg+E2pz8JPskKqkaOw2Wx5s4TfaoTXRU+ldICX4Lgb2IbO277GlsOyU/0qPiHf0Xphmj9eR\n171wOIDlxCieN7bjzjK/PMFX3TV/NSb8Eqkoz5bKF35EXORtVsZUOfqO/80r6ZQQF1iWjBfHsRSc\nFKKB1Rkw0tR0yZMd+EOueRgLiy14EfbOkbXwq+0NPsFf9MLfH4xrZlKIHOOGKzLPXOCfc6aY8Sfs\n+OWjcpcyr/tARPjrbKT7E73Bl/OIeTAd+I4PvHIBVxK30fHfbT3/eBw51ibe93bkj034l7zlN2Xh\n61r5pXh8qPyH+MDOO44pcR0qV5rwzhh95UOaeDPvOdpE6nekshC7gnSRTYV3QdmVwkGgy8Y+eq7i\nTBXl0Ta8R3m0gbfVmLMxYkTxZGB2hSgdLkZGmjRgqF3zrTZlYl5DqzvUKcfu98QvigZeRYR+jX4O\nxdhr4BDzyhIIswqTLziMviqjtmhxlwODFv4hBjaWWMxR1PMmtDj1z6fj5UH+dddxmwxfAiVmnq8S\ngvcu8eUiDNV48JlSPW6VJTz4Bor/cJn5VVwnhzmwaLP/6eEiYTgCftXMXh6Exej9RFk1uK4055vJ\nBSieB2mTZFHhOk2XsYgmuCI4E9wnNb4Psee+dFyVhRcrUH1Q42GFBTEUHoncEwlSuEoLL/wIsZWV\nsxrRFc68cvnEzcPM6GLlKh+ZiyFO1nKpkYLyXd3wE3/guM5oTip1lfp8ZTMf16nuvoe71EOYOKzN\ngFnhLne8WmOwcXYBxV/JzF1s771bOl5Le40J/D19s9hjIa5BJUmMYG1iM5Rflsgfr1NxWY9hr5lv\na+DWGXOBZyuTVLMgeK5DQsTojpGlfn8SlCFjY+Vm/YqN2wU5djy3wi+l4yudmQFbemrXjlXnHutn\nRAQVbTaawCztfohjak3WvycaZJFP3RZWlwMFqUa/ArYOI8qn5Xu5lM2DtiqCSgsQuWrICqfCyFMF\nZKAxj0oDUme3hLjqg9Oq8f0UWJ8Zz1lbEi48+ed6bRrTs4pjc/7bJ9sZTD/palfLuJUGH2j65PMY\nnLXRZ0lCG5enne4wcm0NgYf194NwsYBQaefpaADycWVVt9KA7Vk91V/A3xOIq+sYn6UWYmd7O2O/\nMrXz9yQuT54MSZ4kH53YZf/nsRdpDiHnRa3aU0Nj+oTlj/bkb+5Z/Y9plm1rgDC18uSDTUvjk0+7\nIzl/RrsHltXP2NG8jc/XSGmLpk8DZ867+DS98XxvzjzZ00GTzZwXrV6FUtuxnnXR8FTRKuu41h8Q\nJP+oALISmO2IcxETo6RCEuNehI132DIzs6CrjjTqwMYqyRtBPbUWXojSR+HBKikECAOexqg8c57n\n+0oxoycwl8wsPR+WSqrKN1b4+X7H1sFdhjHAZjbUeeaSeN1HGEcey4krJm51YNGmwYoZLHtyOpKK\nI+DYaHuoL6p0KRE93KaMeEcePLlMRBP+wBV26jkk4cYcY515c3J8PSnVCV4CvzkpH+eZF5sN4hK/\nTXAdOlJK5Krc9K2ZLIjRM3NtPV9TsamFhHgpiGtd3F51fUgUui5SihBdoVRDNfLb4yPmlUF7NC+o\nF7QGNgqDJe7zxH+9Tbx28C9zYkyVZzUx18L/NBjmZ96mwsZVXoTMr+YtH2bjC2+kfuYGJbhmi3ea\nRxTP+1x545p4/zfTiSCVPihWHbjA3xTjgxnBCV9aYsR4TMZfF+N5qFw75bulUGviVewwIpN4NjEx\nOOUhzcylULMRpNnOXUePSuUn2sqqHyXyq7FQLPDReaQIQ3qg6I6TVaQaWRPiPd7HNbqzPRQ7WihL\ntYKYoeoJq0RGa+JV+v1hkKUEghkHX1jMIbXpEbta6KmccNzhoUJEMWZuVreLpM2APqqnm42XtvA+\nDrxMiVwrDxqZfeZVaa+5siPfDM0l4V4Thy7wxTzxTf9Ugv98OnIyz33X8Wqeues6HsS4TcbkM8dh\nwK9a5pN4Rn8NgOpELiN99rzpt+ytIfBMhy8jt8k4VSE7WJzS28yj74jqKTWzs8ShXrENJz6Uq7ZP\nqdS19WaKiS/nmS+ZeS87PsrucsxXtfBBdojLbJbCFzS3jKPsWaoReAKwvSWCnsdPeFUnIsJcPb0m\nHsTTkfjT8nDZ/yLwUia2xVjOk6rBHA/8s16hIvQlc4zCrOEi7bj2lZgrj8uWIGC+TXi/tQ23675f\n1gO2yiSCwPnW9hVeBmNfFmJp8+HH4NhQmKxNW10W/tDlyyT2y+r5YxIxK19RcGZ0rrTIeREenacA\nu2qwnVhOA6dg3Jctnx8rIsKVN05XTdc8HJT9yVO0ICr8GY8MLnE3dqgY10vhYxq4jSPjHOh1hBQB\nZXbQ2SV6j5gN3JP2+3d589JYSLVCFG1suTTgMq2s4zn2V+Ts3tA2ZQVS2sI8smtANauQVqbYYSRt\n2tmRFVCusoLAmcVda+TKky8uXHzOA80u0lvzCj7jqQgtK4AW0oE1DfTmzKCueMhWiYbR3tzJKuPA\ncK6BqzbvaWPURS/P8LOKOFjzJEfbeW0+AVu2MtSBFphxXqdtRdYmvlbxOAPuM+ZOZgRtYzusoLC5\nfcCiZwmBtHCQC3huby7SbOLiykDLKpXZCRxWXt1Js7VT4yJartB8x1fZw4mnxkuRJ1Bp1t5/ZqWf\nxhsWa013KjCZXs5noDJaW2GYSBsXqRdZzmY9BifNSWTjmo7cmbCsmmM1Y7fKT0wg0wB/WO8xr0Zo\nJs9NCy6tKtDizldJxlph+CSb5XuBMT/E9qMCyM874254BkDOC1USlMrrcGSZM6M3tjVwK4U/3lSm\naaIPgbvYLK13ofBbS/SlZxt6Sj7x3jq2UnkVO46Wm5XQEFnU4XJGvfJVrxQnnMzz9maL5Jn+5Djk\nN1yHjpOB1Y7/877wUHYsVrBcGAT+dE4t9rpO/Hxn7OXIi5i5O3p0I9QEVTxFlY3PmA70GHfpWxa3\n4252HJaRN67nfhZk51nyjpuucBV63olHqifIkc22JxdH9Mr2cECDIr3ifYBkLHUk1cCdRarU1Yi8\nkrwjhoqUSKSQXSH6nqiBpRacL/Szx/WJ6jqmWNjahszEczN2dSb4zKsY2xdUHYd6Yn6c+R+HgkXY\nDUo3FO58h36cmKvnfzkOLMWzt4r3lYKjzxseS0bJBO154ZXTNDK4Du9mUlE2ThDXtFwlFGL2fCfK\nrUKtwlEjjsxeAtkLBxxaJ/Zh4JnvONTMs145zQunFDimyk98RLoKmw6xTNYrPiosXuidkiRxVQZ+\ntt/iRPm1JV7Oxt8Hz+144mYKnLwRZWYehZHQSmGrT9HBK1WNvt8QNh6XC8laA1Mtia/n349mH4BA\ne9rviiOTgYXZAj2tuclL5UqM977ZK2YidxvPs1NmVzInH/himfkuBN7aFbt04uA3vF4OnMQTim9w\n0Rnv40CslUW1adud8dZvuVkm7gaPWzzv48D7MPA8jYwh4IvjcQdhcjhJuFopbKHCGDIaGljenjKT\n3zF5eF4mNtn4T/0zfpJnVCqdjLzrI74UtpaY47ZN4oCTHV/aA7/prghWMB8IZeHOR27qwp1GqJHv\nhvaI3c4LB+l5cJ7n3LE9D6aB08pv5IbP9YFStE1evvLRbgDo9Mgfzg2ovSk7BptZCJw65culAUMz\n4b02VnoOwu3SQPQHFy6SkihN1vBFHdlifHCRV6mlVp63g4chKxs3NcAhBg5e+PHi2vFh45A0Y6nZ\n9D2uCX4Azo9QaMSBVW4E7vKGW3fC5UoJgmphSE3m8Jd6amyRg6KVkj3VFUqOYHClCaktdEGOrdA6\nFBhGo15PyGNHX5XtaZ3KFE7i2FChFmZxLOZXFkr4yJY+JKYS6NzCaJE+LmgFq6FVRmRhJF6CIn4f\nNjv3UYhrQoAVhJWzTELs4jxQVnDbWWVc2dGOBtA6MzqBxVra3bRKDlh1wsoqgYALSyzAVhrgdmcQ\nZa3sf1rBsUqTGbQZq425U1hweKuc28IWmoTD0eQERZRQK4s0uU5egeBZ3dtsx877aylxg7XfubVM\nL7QmvWbbqg240UBbQdgY5HXMzptYA95NS60r6KyX1EGrldP6cxBZWdgGDOf1nlJYHSHaro/r8fhP\n7rsFJUptMhXa30yVwwougRZGBhdHjzN7n/TJk/oGY7anSsLTiTQAmmC1Smvn5FY9w9kfW9ZrXKw1\n253dNNr01yznzs2ZjspEsw1kBd5upeyj1bWJ8mmBARC1yTCq1ZZ6aA3s6nrPFFoYybDqtve0BtN6\nvsek3YdG/UG/sz8qgGzdhhsRSiloghIzlMpOHI++8MoCmYVU4U3xDD7Si+NaE9+K5xGhaEdBmKNH\n44bntdD5jslnVAacZXoXsFyQ4JAQyU6wlPG1Eu8PXJXE+3rktWveuJ6Ot2Viq8bGB4p4HicoyfG3\ncaAuM5/Hnn9Jhftpzx9tC50Yn0lhicJeM6kakwTeFMegkNzADYWf7j13Zcsyw2c3gcMsDD5hFqjT\nxKsoxJiJ0vGhVh7KRJVAtzce6EhOSaVSKOx9hJrY0HRB+9oaD6tzeJ+py0QXhJ1vRjpqhZyMZ5JY\n+oC6RLdZmErHhpGSZq5vIl4L02niikR0C3eHkedB2O+3jDXzd8uGf/kucBSHtxn0hh6j9/Aotlrc\nwVIqkoyEJzrPNcYpZzbaUTyk4rntmrWamedKIFnFbxO3pee+ZIp4cp75pnrmXHDi6Kzy2nk6NaKv\nqCnzPNN1G/a54nzP261wVT1qxqbrCFYp2sBeFqXvPMccWErmm2Uiz5l/cplhgVoXDqHizXMoSnIB\nKZkshgTPFuWnkniTI8fjgdNRiOK4dsaYMqNz5IsJ6O/+9nXXGNIvx5HFt0eImLaQFu1ag4c1Jh4K\n25LhBAfvCFQOrud9V+inplwbdYdae8idZPPJJy3rPsCVxvzUdXI8yYaqiYjwcllYgmCrzZnTmRfH\nxHeu7atYQE3Y2Myp9tQ1VOJx0zMSeC8dz+rE3/me/+HwLScf2JL5u81zzIzrVSrgCuzyaT22zN8P\n1/zZ8Y5f7K4ZEiQX2Qgs0WEmbCvEtXR/3HhuTjPHEMn1+966r5YjniOp6zh27XzVBm6WxmiLJL5d\nPaTuLTKkBpZvk/Gub+f4ajzhVgu3ny6Vd8Fxyp5dtotG+z1XDDxczuUMnI+Oi6RkXzI5tN9tizEt\nO/rQwjLO/y4od7XH+8wLnZjSEzMeSsWp8ODBF0WL8BUPHNapxhfDxDUWUiZcdqTVP/sej4uGWeR6\ndT75IB0vJAFPqWDQbKXqY9f+RS6T4vnvZkYRQ8xd/GJPFltznjMGzXy0DQPtX8mGOLiVERFhUxTk\nh51s/y23YR2XReXycxJlI6vmldagdi5nZwBpNl5oA3Ku4Ufqqg89Z799b4zMvieBOJOaxhMzLdJk\nBONaloc1olmfAinOTVrC98vzgzS5hJPGuHYYd06J1srzr1YrsfzJQVz8lKUFzTyqcmWZZE9+yE3v\nvOpz1/e1UIwWnf2vk1DPCXNeYLseXEIu8jAvsF9BfdM9r8egcllAPJxBOa3Rb28tOMX0ycmhl08k\nFJ+M9eZpSEjatPi1NpC81E+bhZ8uxNltY1m10OdNVvbZYxfP5uK4dPA1lvrciFdwqpTzQZXW+Fl4\n+p3DrRIZu1izsV7HWtcGv0/O5dPvbG/GWKFzZ7C9SkOs6VH8Kg/xBhs1ltrs5lSe2ON/LcH5L9l+\nVAD51huuZHad0vuC1sCUE2VWOs104piLI5iAixTxfLAWFHGrcGOFzRB4zIWdr6jCnB0ihVIq0T0w\n1GbFteSFJcPLDM+avJA7Ux5y5jQKt7qwpNzsk8hQjE6h2MK4bLhygg9cypSlejpVPtvD1zlizvP3\np0IXepCRvXRoKHhtpShfOx4pmFT20bMPE14N13t6Z5RcqX5HLTOWEyozr65nnHMcj553cxO437qJ\nzikT8PiwkGKgt0qolefe2grUjgxkYqy42DGlRHGGI3M7ZE70fJwF5ybkWPnz/SODZm6vEvhITQdk\nf8XfPF7zOC0cZMfd5DiVwE3MFAp/EIVjrnydArVOvA+OPhk731PcTDGPOocXxdWFqI5UC/d+Q/bG\niUIInmMWdi5zHSqjNRcICuyCkn2L0L6KM12GnGDSQhIhE7hLhY04isLgN0y1krynagtSGX1gWWa+\ny+04askEqySgZqMrM3uZeK2ZTTzyTD3/mJV/4ooPyVBZkLzgeaDWvlnsjQlXhbtsmGSeq3GoFecc\ndyrsnbLVhB1/f1wsYvH8bb/h9TLTs9o9CfhqfNCBHSOG8KKc+Oi2dBw5+uZd/NF1bMrCs2MCmob3\nvW4R4E3Y4lbuZ6gLLJHbcuSD24IJH4aeblqffmorwIa3Ycfr8YivM2/9HrPIJKCm/CLe8LN5ZOce\n+b/71/xsPtKNgXdReNCBn40n3g+wkcxPOfDb655uDJyAm7nwD93A+9jxs3mkSuW3wxXvpeMrjjyb\nM+/Cjpu5MMVz+VL4F9vy384feHSRQsCtCj9E+HI6cHDfT1X8Nq7yjKr8JDepw8HFSzfMtkTG1ezz\npiYOQ3u/ADdL4mMMHDWQXGA3J77pA59PRx4EjkPElgbIBx7wFsmy8NY1sHyygC/wuGrky9xxyxEx\nYygV749gQijGUJuneyjG3DXwPo079vFIyMrJNXdSV5WuFECozngnylVuAOUdW57nBrQnOj70wu2s\nVGd0xmr5WPkN1zxnZFMrI46jD3TJ2LjUHBistgTT2hIPD95DMpzCZqkQBI9SWv2cWpWOdHGvmJ3y\nXEfu06Y1r4mw848c0jXXbia5I/ts2L+2Ovgd3YKDKzM+iGDqGpiiYSC/cr1Cu0YdhqM1Rp2tyrI0\ntlloiWxx1RF3KyBugKc1VLYgjFb6Pkctw+pHvP7PBiOpMNHAd1htCioNBC80zWlXKwutDN8Xw5xw\nFGWLrc2hxm6VJkRWb2B5Cvuwlal2KiQqBdhQWnre+bhW9vEkQuTJRaGsYzQhn8RMt+3mbJkmT4Ek\nAWN1wieJQ9fGP5Pv28XZCkg30voMEk1yMUqTiwS4sMxWK07bsURplnzniO/zYsJQJoyNNinFWUes\n0pr0dP3Zn0G6NWeKhSc3iMY+t/E0jFCbzZuw6sfP1w9d9eTNPzk7bQ2R1q7baI0ZDgKIfq86IdVw\nl+x240qNpTQ8NK7XrSLs3dP1t/V4/Tp4iwq6VitGhCG2QJlqBS9K+aR68ENsPyqAvN1u8c5gGpHq\neE9jOhwTrVLZVi82w1Fb48AzCTi/MDhFs+HMuI4FcsdVraSh4hXUMpHKUiIbm0i+gnjm7PimVE5F\nmLNRCjxX4XZvPEwjU+54n2AnjqUYXoyf9YVKpmoEEb5g5ifXwrtTx9cIz3rHrU+EqmxtxPmKcwvR\nVWqC2DmmyXGXPIM/oHTEzRWHw4RmI5SEc5WQErVTrNtR84IzZds7dvWBz3ulpsfWSFKN4hKz6/g4\nztz6E9F5fD+Ql4LzRl1O7IfIw1J4a4GaAMnMoswp8TwcED/wLQv/zxj5ygL/8fEZ+TTxwfYkgUwi\n01NFsSLgHVOC684zl0Kh8KfdgW9twysqzzYTN7JwV3ecXGWjDl8P3E89b8qC+dZUqZIRNTrteRgq\nKe35NTM3XukKjLWytY7r8oGjwUE8XYG490xH485aQqDiqDXipHCMcNu1SkR2sGhFPBS3IVfh2wJI\n5OBaeMprmXA28sEiHyfHw0noc+ImJsgnth4qidhtCcVTXOFaRm79njFnHjdwPS/M4vE14SxRvOcr\nc2z8zGH5/WnSe1FP/PkENVSGJCwhU3LPHOCaR1KJTGt5casHpCrXHEi1TUnql0+S3oydPAJCzRE9\nR0IrbFlYFHZr+PFm7jAzbmrmMVZ+7V7yxXyPdzO/7a54vsw8tyP3IcICg838bDEGWyg58rkbG/i2\nmS09fzB+ZNTIv1v/HerK2DIzamSrB17UnkFPFyrmO+1QhF/bHumN//70lo9uS5+UORrdIvwJI0c3\nrKXMym058g07TBJ3feB2avsHmPpMP3l+ETd8xRFZOswawzN3bYI9LJEUoJ8Up4bmNXjFYFsnfuv2\neO/oamLyPTfLxKgRv07QN3bifhN5dnKcaJWQrMYUtuzGhcl13KYVN5rLxgAAIABJREFU8DohVo+r\nxt/2z3hVPgBQnPIYlJulclJ4nTN/Ha75ebiHrCRfCTSnkW+71usec1sUXmWHmfHo4YYTB4Vtcjir\nXOcGou+9cbN2DpmA+LkFN6D0MrPLnhRalLut5xXXKJVsxjCvDiFSWtxxVTZu5lR6Oh1xDibbMOiE\nWysIh7xn4094KeTVZWYI7zFRnAlHr//ZpLHfxS2J8EDzuk0GgxoVJa2gJH5ymmfPiEnkEpXeWMSn\nF+lK74p9n7FzwoUV1XVf1YwDyl4qG+BozaxmR2VeWddOIdWmPc7W9mPWnCeStWZB75t2Odpqt7ZK\nBlbJ8erl20DNuILbAo3YorGR7iLraIups262yjm0QxFraYNOhV6eYrjPt8L5PYGmce6kXiDZeSiC\nNYIusQZkrH9oIRdNx9saCZsMJNMkJuf1mF+126eVqfdtMBvLbTTwuXYueoRuBdovzDitBxpXX7Sj\ntXS9WVtIS1L5nod05CkW/Jw0OK+ilkALItlgLTSGs926rddBLsdra8Ne2207Zy9KJ6ulnnsK+BCj\nHafS+itKxTVIQa5tTBY5h5Cs0dfrcQZp4LhfFzw9iaJCoVLRS6jLD7H9qACyLhO5H/CuZ3JtxXPq\nlFu9otSRWitdNdzG6KsxmfK1Lcz3YB04E7wVOnF0buLGV64Wh9XMJnoWM7qaidYunPiFXhX1ynOU\nJbS46Gky7sdCrh1eCre+0DlPJ0qvMMuJj0lY8shke6SL/GI0olZugWFQNnXi9VZYcuGwdvtLhqHf\nktLMINDtPFd5xLmZcRGeX18xHR5xLrRztUpNC5kjhqdo5vgwsCxKEMUT6cQQLZxmpUoHIfHgt7jj\nwo6FIoEyzWTzvL8PuLDwrB9xvYAUFnpumDmOjlOpXDvPgvCIcZ1GxivjC4VBlblmjlIQdfgKc50x\n8fSuMTPtSzLQJ8PUc8pXfCuJzzXzB3FBVckzvO4m/mgxsveMeea+Ro7VKDbxoihvLZFMuQkjr4n8\nqmameWbRcEmE8sEhSbgKib54zFqU+BgmzDrSlLifm0aZ4kghUkpmVqOa0tnMYHBImYN6fr047tOC\nuol9F3m+S7wwyIvyQh659j1WHgj1kV1Q3tUNufMwnehs4U9y4Js5s7nu+WZxzB5208SjKncPnu0n\nSWS/69voKydVbpNwjK1rPbiFVBroex97vphX34XksJVdUGsgZrKBIS1Mvr2+PTsnzDneuz2fTafL\n3/r8FJE8O2VX5yZ3SY6/WN5y53pCcsyuQ/0jY+l4cB1f2qHpBN2BMfbEZMx0vOAAIsx0wIT3GUnC\nxhJxPb4cWjR1XxUivB4n7tzAria+rBOLdURb+Mb3YEpfF0bt2ZRMrHYBvyffmPWhBnJRYs10S+R9\n7Hm+NI/gqQZOvklHfs2OL/3I127g5+NHWFaOy4RuMW7twEe2yApUbmsb4++I3JL5GDZ8R+RPJmXq\nK2ihGwMiiarCCUHWhcvrPPLb0BGc4C1xrqc+qyOjBg4S+Pl0x3ttko/iCicJ3MrCs2p8PWz4i/GO\nI4FpKHx2EpYA1wVekZk+AZZJ2yT76CPYxCLKlRhLV3Bz2/81MGo7hv6s3TyXeL0wSOWGI6fa45NQ\nnaBr8IpFhdJSRqPM4D2dpmb7qWOTYZixYYLqUZ0RCWzk0KRcOXGIlYpv0rZiqOMH5KH+7beOtaGy\nNrY2AI9wAcAtke0JWDjaQuXcWixyTlM7l76bHMBrs//71Hbr0zVFkSaHGdawjzc0b1zDLuC6o5X/\nq7QGLZMGkKqtelmVS/MbBsGtEgT5VIbb2Ge/2hsEbYEULSykvdYaXlwJs3bHB9fY2fMhd7TUuZEG\njvKZRaU1vl7GU1awBhert9QI2Mt4nSUi/Gf+1VVf2zS/jZ0/pxSej1Vpn3FegJxTAv0ZMK6fNdHc\nHgx4i3L1SdjSGdxXgWuMB2uNk1difGOOa2mLFFW5MOZwts9r7hVIA62iTapziSoXLrZ/53O/hIWw\nphxSGNcGv408eU+fXSjO0eXBNYnPZKvLh7am4qTt91ozVX1joQXMCmCoeAzHhLTAmh/4S/ujAsgH\nCdSHA3lZ2OvMLDfsXeFXuYU6dN6oxePEWvlcjWdS+XwrnCwz0kqJmYWDbPlmWgglsLPKsGT8kvBd\n5UXdMGyV7nTA+8wSBKmQ6wZqRjtjV2N7KCRHdc0GyKsxi+G95/MOegc9QiHzbYm89x5vgmXHs6vP\nyGkme8e9HFiWhU1V/JJ4pjNZjF0QnCY+JuFF75FyR7d36JKYcyKtroNaIPixfSHqzM4rwYH3Slra\nQ2t747B8YkNFu0C98UwIy6JkejyOeZ6xuCWfMskPJBnxsuFdmfjseoPmmXFxhKpoV3iomaNscHXi\nzsC5DV2ohKIc+sak/c0oPJx6MobjkT2BV35Cfc9CIi3GQWChJ83GXg0pCZPCYR552Xl2fuJ0Gniw\nzDY4hjLz6xT4TyXwzGZuvPBeek5zxrlAIvNnvnIlcyu3uMqhGkokp4ILma0YhYqTwMHBkRksIEUQ\nMv0iSM24TtmcKrtu4Znf4MORL+PMdYUP9cQcO349DfzTBO/yNdvQE0LhIRmhFl5m4be5snUNKP7F\nMeHJbKvjp5sD39UtoV+Yyub/997/XdpEjC+XA6Dk0rEtM8fQgxpORr5cMoYweOMjrZFrAqLNzC7S\n54UpNr1w8zoXJu2AypfzkaOPl8l2Ch3vQ0AMvhgPmMDbcM3ezTBDVxMlCH80f+Cx69mlxBenA4+r\nljfUtt+vwxW9zcSy8MvuGb0s3HUdVZQQPPs6cgjCzbJw0J7ghF/4a17aI4cusEmJ9/3A87SwywcS\ngjj4zXCWhQg7EzpbEDOWIHwxJxb1LFFRjOQcG5uZJfJd1/PFdORmAuePvFweKDni/MJMh/qZTVkZ\nVdW2oHCN1Q7J8R83L/j5uPDdpufn0x2TRn6S7/g8RyaN3E6J02oJZ+K4PVVG3WEC/9ANfKUPaDU6\nKh91R1kn1Z0l7gfli8ORN0PHZ8vMb8MOJ4moyrvecz3PbVJTx1ihXxx3sS0yPkThdm7s0Xmh8OCM\nr+aZsFrR3WZlVsOKp6513wDsi+K8cWMnriySo7KrrYmvujZDb2TCQguqKb55OEdaU2+UGTEILI1F\nUmGR0OzwLJExRI1SOuLSscQZZ5UPLuKpqFU2a9fPJILzlW3+/YDJ7sLotfJ/ZZU5SHN3MGmIs65N\ndxf5AU9ev801sYG4wDni15qvsj2lyYm2Zr5Mkx84aVZyCeFmRam9Qq6CW/d9WlniyBN4CrTy+Tmk\no0pjWWWVTCw8RSDL+f11dXug6aQ7aTLDKoKeFwe1pbkiq8uFPHnsVmvx2n6VBOj6e0fzMl7WMn6W\nBp7PbHKU1QVkRYheWjPbufFQaJ8vYlyv8gDOrKo1ICzyCVA/O0FYY7tbcmBpzXjSWOWywtkN4K0y\nq/DCKkcV9rRwMpH297Ieh1+p8ITwUiuLNa30Q9VmyXlGyMV4lLNzhWHOXajzswOGSnOd8tr23blz\nw2O7F+rabDiQmaVBzSuaS0jELprhzNp8SLv+UcBqO0a3LqRca9Gmo7mC7FDyqkNXXe0FTRikPCUY\n/gDbjwog/wd5IO2MOheO6viHBd6cMlqU2TxVAn2oOCpuqRjKI8bJOcQcu2gspxELiubKVg2pCSQw\nyhZiR5KFr32gLA6rz4iusjkY+6jsfaHL0OtEJwPBKtY7vFS89/RSyQKdn7AasepIZox03PQD12ak\nGDgtxpul8Hl54L0M7Ivy2a1yN7be2Vl3VBO+tR0mRurhmxoYrTCoMVbBBYctJ06p6e22S+ZlqNz6\nib3LOA8LniW0wIbDqWNxYFLxc2CpreNTKDjvIRfUbbkrHomGqrHYDd4rkzvxfyzKjez56AoPQYlu\nR93M9EX4hUaOacbVzDx6qDN+9ryOMI0jPvRsPdyJ8nEU3i4ddkz8xWbm1XbLbybopqaD+ofk0LLw\n52Gk95n3teN2GXk3nrinYzHPIMajVcYU+XWIfDsVjqHiGbBSUHX8xyJEBj4XwXvPOBdedgtBhEAh\nqnCXMjUlptgTncPvOsJ4QC1zkxeMwJdzYdd9ZOcGRnfg13Pg7+qOuWZyHbiujrLMZFWeqePkF/q6\no9QRDT1vA3jdI/UD7/c3/K+njDdHKfC/Pw5QW2zuzjv+53/rL9gPtIXkSKFQbODaHnkIuwZ0BTID\nxQvBlrUru/2cJLL4rj04feBeeq6tsagP9LwsY/PvdcKH0PHV9MghRLq68PnSmtKm0ADf59Mjc+io\nwa8sSqZ6IVTl0Ht2c+JRI6EGtnYkAVvLvKhHphj4vBy4Z2DDzKN0fLZ85Nf9vrlf9B2henBHvlwe\nWFYPqSUI+zrzoBse4nBp9PuD+XB574hjx0ysmYXAu3jNQR0v7ZFntVUQvut7XkwT936LGqROuZPG\nmgcKcx34oh54F6/5IjWG+E18asm5yfDeR/7d8oA5h8O46zpulwWx5rv8xXzgn7vn/KS+o5YO9Qs1\nR27twF/1LxGRlVk2PrrA5+nA136HiDHWnutxomprbnwT20cXC9ysrPej3xBr4n140lK/yIl7Al2G\nk4NnNSGWmr41BZw6XlkmJeWhL+yTgsmlTNyLXUJDJhfobCLVLRXBqKgKUo1OMuYziwVGIi+03TeL\niyxEOhIiilSY8AzVMF+I80ByGXMFLf5CdVVrVcJaHN4tOFcppbAp4NUhZ0Hn7/h2bpZyKmy0kmuT\nVvhz+X4FhaxM7BksnnWo3gmu2jnNvOmXV42oWgOSedXaZuzCtq7SeapZs4NzTWZgZhdgpefyPE1f\nemYjvTQ/7hZLLGylySMCxkwDVWGVcDgRJmu+y+cr1hwmVumE2aW8n53izVCal3ELPWlbL+21M4I4\ngdocOI4mDKutXVyt6M6seZG2j620+GR4ArwADyh7mn7at93T05rKEk2DXVdpyCKsSu/Gtp7Za6H9\nx63X6FGU3er30ZoDm8uPCmzXZ7HKJWizMcK2Mr7rcc3r9Sq0sZ1o0guRtTIgzSmkmrC3BjzLJ3rs\nBejXZtpOjGwNi3gq0QpFhHkFyUEK3mCu7R5I1uQr0DTaZ7u2YE/3FavEx1aW/bwFdSxrmULE6KSs\n93MjW37I7MsfFUD+K/cCyFzHwsMSmNyJXez50h5JEdIyQjVKXaAL9K6tkswyWGKqkY0oe5eZgE4K\nwXn6WDHXEnkm29EHyAWOxfBWkF4wMe5nR/JCrxu+7CLmKsUyisM5h0QH1Xjvbkh4vLQ0JmcwPd63\nL302dk45auWjfsY+wF6PjHqDbpWFDdOygGR0muhixPsON8MmOkwqoV+75vsNsRSW3PyU7xAO3jNo\n5sore7eAOmpRap7ZSnvfUg1P6+ycSiCXwnG1S+p9ohZlcbAtM1PxPNae69BCFF53ymeLEP0jh1KZ\nQiVPE5/1EamVrJWSHYWMl8hXu0guB050/MQ5jr1wyoXiPF8vgX9cCn/qKuKEd0vhlBN/1sP7AtfO\ncUrC5I2rbURx+LWb3PeBtwh7hL6DYy48avPKdfkp5/2NV8gVU8fHHPAI+6L0VAYSn23gZT2hosTH\nE16FIToeSmIJlcdFmctL7sW3hp4wcF1n3krPvJz4rmZMPcdSW7LU3HHijs8G5aqcqHPidaxMLvAi\nz3yMMBbFnKK6I9eZWiuPpfx/3ve/a5sjU22gzyeyOjY2IlQW3WGWwBrn02mGugCeYE9SCZHAy3Ki\npWElRDZYPTcxekL1JBdxVZE1uOGd2/B8dZBIrseoFHM8+q51iItQJHFbjOoDr/NMb0d+1e+4nReQ\nxsosCM6U7AqbBUZzHH2HL62VzheHimNToGrA18KnzpqH2K6jS4Ebm8ga6cxRRUiamUO86Pm+TPf8\nctjhkxCssqwt+0sQqiScGL4KKqFJSgS+0R1XUvDFmFYA+tV8Yl5r3QdtjD3A0Xf8ZGog+mMXuZ2b\neevkI50uzYe6Zry19LB/6l7wl+ktxQbUrZHNOTYLMDGGuuDcwv8VPuPfT+8B2MrCkWZzuc1rwIoq\nZh2iM1OnhMkhdSG5DRtr1+hbf83L8thK8iI84Bh1x2flgZIDg7YG2fcrV3mVT0wrGF1QkAEVJdkA\nasQ6MvkNkXskO0qobHK7rkrgar197odAXxOmRpCMLz2lCI+xMlRpDQnr5VQCV+ERt0ZSOzy15vZM\nFyHVisrvB0B2IqhbXQTMVqBVyeIvTB+s64ZqF8eJ8ya0II9AA7tK037DarFFK+OfPX2h2fydNa1n\nWYTQLOOctP3k2gCmX10LZoTrWnlc6VSnQi1GbA6STR5RV7eCtYxPFcKqXXXSpBWf8v7n7yMr+K5r\nM1y1ll7nVC4BKKMI2/X4RFqf01k60F7bmFbH2YMYQjU61yQK5/XUghJW2dKV2EUOIcC83lPOWtS5\nvzTBtc86s9+ltoXjiVWDvZ5HtcZoSzul5sBRW+plNug8zPnJ4xpgL+2cyuoicWbMvTw5w+ykLV5U\noJaWMuicNJtbmlOFSsWfNb4OVpUTyRqReE7a89KY8SttuvHzsbpVHhKdNOs8YMfqCmJP414RfF0b\nMT/RFLf7KF9AuqpSCmvyn7QQuU8tOv4Ltx8VQD4sma4m3ktrghucw23gNHd8Vg/kzvHxXghacJKQ\nEjE1YheJVrnVE3KlDCHQy4CJMGvm3Qgf1HhYCjlnHkvgXowbDVzHQKmOexMOQ8Q5h3nHb1z74k9m\nVG1RpDEL16GwmQukA1Uc2TzVCZu4J4jShYLUyo0DFwJVYO6fU1WYj5V9qK0pTYR5uGrduc4T946l\nFrQWHstMtsgsFR8d1RnOD2j0eO+RoDwAkzM2ZEItRHNYLtRa6cuCWxsA7gukBGWGxSuPpYH8Ohfe\n+J4rVW5jpkMYq5Gro9cjb6YtkyuYBfaDo9ZMDGtHvhi5BJ7pgeCbnnusE4t5bnxmXAreOZZl4X0K\n3Cdj7zpu3Tf8bOj4OEbuwpZ/TJX/ZlBeSnNC2ALaOUqduWXhj83T6cIueO4s8N1ReDClBAjFcXSV\nexI3Vdj6BmruNDFEwaoyqJHLSLaA1eYqsYyCLY6cBKkBlcBRHQ8lMFBZHDzaFqvars/cuuZlJ9jp\nRLfM5OqYH06YcwTf87Vm3LSgHHkZA0tdSF7IJbDkArXif09KtQDJNynT4jZ0dbz8DJWuJnCFZVWx\niXQka86cF5sug62eSNbzMVxznSY6ha+54sYWrstIEceddtyUiaIVL1xKiiLCSQee2chNOVHx3Enk\nVU3c+Q0J4XmeGTXQucTJNQeNr7lBxfjoB/5gvuO9H3ACj7b5XgOSq7U5Z8D3BJVf1Hseczv35/VI\nNteSs1ZbpVAcj9JcPLqsnLyjr46DbggmdHbks2Xmg9siZsw+ctANmPHoNjwvB366hn2YNABglpjd\n+TGdQYV0TnuzhePKqm9L8xf1RYkkPksJcMw+4svM18OOz9OB7/yemxXoFhvobcG0clNGUij45Pj3\n5QMgXOkRMXhWMiPwttvzj92Wn83H1tBY4NWhhad4D5/ZA4aRxOHdAy7DrHAtFVeNq/rIrkxEF4nL\ngorx1erBXK3S5da136syeSWQERW8HkHhejkwRo+FFdAFmCSQ8xYnJxZ6+tUCDxVmHMtQLnpOWzN2\nDSgh46tvfREB+lzb4g7XAGOtF33k78N2MQ9QJa9Nji3JrK52ZU1TrAYm2ph/eWKeoTWoT6ZstckJ\n3AruWCUKnaz+uSv7iT7Fk4usDOlaNreVEs2uuUl4M2ZVtud90UCSVFBtTYQnVTZUskrTL59lCGuz\nnTdg1bCetwm5sKhZWkObYpfvrNfG6i5nZlhqc21ZGexx1cH2Z33t6gZhtL6KvKLk5rrwFN0c1/Oz\nVad8xnhmdgG6Ttvfte2iuX6sIBdY5SHCdpVEPMkr2uu8NabZAWV9foWVoRfXmPlr2uef46XVtTEZ\nVva+Xa7VQ1iMVNvnmWvsc6E1xRWDILWx/WcAuiYal2pEJ9QCHXl1mTH8yqyvRahGQGhzq1gQtK5R\n4K2u2OYHGqNuBkWbBOZMHvfUC2N/PqZKpeiqAV/Z7x8yceBHBZD19EBRJYTA1jXdXhDli11HJ4l3\nOVCvlKUEboJgVth1/y93b9JqW5Zl6X1zrrV2cYpbvPqZmRfh5h4eBQTZiCCDjAQJEkJI6qibTSEQ\nJKiV3QT11RXoH6gj9BOEEhEIkUKEJDLCI8LT3c3D3M3s1bc6xS5WMdVY+9xrLpDUkEG4+4bHu+++\nc/bZZ5djjjnmGI4pNvyytLw2x5MgWOuZY2aD55ATR4t1GMErpZE6LGeBAcePaXBEWoXHbeGxCOca\n6dJA8oVBem4JFPFElDwpV9pxVMV7R6Te2KeceeqMIgkJLYNCMEVSIjDjs3GhwkpBV4GDelJpMRKT\nGTkIJXlmAn1jSDZyHEhFeeRmnvmBcYyYa2BWStuydoGr3LB3LQ7FuxnNjp14FCEXYU0h5waXhFwm\nNBsxFzQVmjJwIREvM4c5sU8rjrlQ3Bnf8SOr5sDaGb1lxClTzCBNtbARz002nBWmHEkWOG8yc0p4\nB++nSNZC5wq7DCs3MdgTrkYDn/jEJl52xksCt3MhBGMbCs4lVlKYJPFhLDX9bipctntWrZKy4Eti\nu64M39WgmAuYGIecKKXl2arwxSEyWkNwnqspsPeOgcJKq7eyhRWKItSI4FZnMmu8FZ7oSJGWPN1g\nMfGsK7hjqRPNHl5PkVvXksrAm2mNWMOF7xHJ3MZ6YX9/zjjb1djqFJfH0G/HstLKWh7Lltl1NMz0\nsqdYj/eZaJ5kDbEE1nqgVeFvwnO+M+8IUmgk8hWXXDCzTRPPUtX/qoOn8cBBlbNoEKpXLgYDq/vm\n6Z32nJeBaMrOrfl0fs9ds2L0LbQTflpz0I6sBSboyEQLZC1cSQcU1m5EsvDz5ozHOfEyX/FWLsii\nZI14c0Qx7lzPZRnAPK/9+T2r9kFX9w+9U9uzGNz6Hsv1lw7jIh354FaAsJceXR5cFLjxKyjGUzvy\nXlakZSRKRPBkGokL25l43becHT0rOYLAbAEzf28f9cj2fHAdT22ARXcL0DATnfAi7onScJ5TZdvp\n78MO7nzPKk185c95XGZmbzyZR6ZY/Qiq/jSDRf7k8J7ReRpbQjhEcDIQc3XYOLbGJg88n4XbpqGz\nwmWK9aGWq161SVN1jbB8v53HTmFp166GTJeWx5yvw1sSPZOCprp/kpvvwatzezQ5nCjFZW5Z0TCz\nssRpXmkQx6ht1V3rRNsfcAiDFZpSraksVweDakXlQGr612/D0p7kK8vAlBNwVhjE00jGpHKixaip\nqyKsS+GAVsAlFYD2ixVbWphUsQpWRlFmhJZyH0qCGWVhS9uTjKDUJNMoJx1zReBuAdPlJP3QKp2o\nWuSqoUaUCWVjhdkJB6uhICJQitEtg3meClidQn+iralAzS/wKS8SmxMY91JlHmV5bw2kqGDuJG8w\nKpi3BaT5BfxCBXFx0fy6BdH1ZkStxR9U4FZE72UntbdWWdKFqAZOtnsLI2+GLd7vToTJasGwtprn\n7A2O1DCSIzXGu2HxCqZGTdfPqd8jm7ERYy61IMgYGxKj1DS9M7fYt1GDunbU6OdqwVd1yCfOoMHw\nS+fvWFzVN1MdLE5x3vdDnvzqMKS3GlrktHYaGqlFSqLq0aEy/50KExAs4zWRl+2p3l+Fo1Uw7uR0\nfO2+a/FNLL9WAPlTjZTGUcpMpMZNB3V8mTzoOa+K56bNdHbGmzxypokyFIrLXAyRVTNwLo62bHnt\nqx1ZUY/2a7apcCkzqlBiw1cxMWSHkOjUcGTKAK/LwC9cx8iWQiEUYe09WxsJ6jg6x1gy58HRSOHM\nPKnLqHZ4iRx9xyq0rIty2ezotcZQaoa77LgqPXvvuM2eaA5xgaE4XDQeuURXEk99RqSK2cUmYum4\nc6BrJYvjWjsGbQgF5iwomY3OhGmgs0JMlWvrXUvOGckZHPSaa1CGFWgK10fh1SR00vM0JL7XHsEV\nWtewP8K/2xVuxsINZ0yWKRroSmblIisT1p3nSSu8nmClwnVRWlpKO7L2LYc8c2GJb/VwTLDWkV1w\njKVhkoYG4a8iTPkFj9sjWeGRm7hJwmAOE8+drBmscBM7xqjscuKMzFkxVlpo25a3FplK1ahvesXl\nxPNuywcz2jKyIbESwceJO6sP6048qxgppTCpMVNo7JbQKKGASxNdyIQ2sBNj0jN2kok5EzdnxOxq\n8IkYjZt47gR1hSeSsSgMHna5QcnkboOLx//P8/83ZWmSMHjHSnekEphpuMiJz/wjHusrLkZjrXv2\nDjYJfnbW8U/uvuArf8FlnGr7tnXIMoR21a64nI/I8uQQg79eP+MiD7ziMQArZm59HXT8wfyenzSP\nEWd0NvGz5gnZKTEF2uiJVpbWXL29jd2EGwKC48wf+WS846/9M/7EPiBxx2erC86PPUEjlMAkHZ+U\nD9xpz+V8y+wd0ZS83C5FDNUM7UQel3CSohQKH+c9I6Fug4FJIKAEjcTicOVkUlrqz6I0NuN0hVq1\nFUvOsSojm691HT69GxDnOCz3/lYiEy2XqTKmg2t5lAuHUB9YM4FGIrOF+3UEKxj1QbhOE2/bCz40\nDS/ieC8zmb0xyJr3DYQsRGc0SZh9lYMUCazzjFfj0kYm35Dyyb2aKvfKLTdBH/qvQJeEtyvjbK5y\ntiLQZLektsF5XDSEgOhDMZnJtHO77JeqIw2lhrAMTQXOKkIwQWTCUkCcR7ORePjuTmDdXROkEi8m\nwuY2cveoZft+IiuU3hB7KC7mMlG+to7f5MUt3ZtOEokaXNGqsLYKXvdUPWlm8e0thTunBJM6sCZ1\n2OvUsl+rMX0tkCIhfCKFK+Q+Ha58jUGepLoPqavDa8HqOnYFVl6Y0xI4cUJfpwLP1eG2gyibXBi9\n42DKCwpRZLFKMzrnGKnFVLSTC4bdB5E4Eeb8fwubkcqETypxOTWwAAAgAElEQVS0xuKhW1lOZ1WK\nkb/mInGfKrdIFNwi7TppoGtE8sM+n61ea80CkBWIX5NhZOqA6SlEBDut+wHgnfbv0njjXCGWch8m\nolTZy0bqC5YE6Ps/9djV184sDPQCok+OEoN5stZi5dRdgKrVvkhVSzwWo1li50+TBzOniO1fBZJm\nD5Ka6m5V99tOqn78JAtppSw69zqUd7KIOy0ewZFotd4b1MC0noc1qlxocvmVmGwtdh89/k0sv1YA\n+W+kx0cjiXExZb4TDA2wnzOfhx6XJ55m48xXbU4jjrnJTFEZ2o7JeQ5m3E3CnSrv3DlKYaMTXoXn\nKE3KdNkQIo/dxCMFJwWXRl6ulC8lAJ5dObKLHrynt4mMA5Rzl3ks1ClsWeMUnjRHyDM7EbIFDsVz\nCB23esYvY6atb71vM3Hyn1RwlivwFthnjxr8LBobyUxqDObZ4mh0rqJ6Z1zmxCNmZlPunNKaZ8qC\neuFoRhSYc6LkGoKBQdwXvK83/yCAJcQZZ23hfVI+jEJODVOT0aGewdl3NGeORzmxkobWCy2ZlfQE\nzYSu4/Y48/0zY8wzwVqGPOKcQznwUQtX0ZCYWPsN1wQ+v55JcuRs9YgzP/OH7cQ4D/S6ZZfg38YV\n0XdMcSbnnkzG5cjtIgg7MvHBd6TjHgkdPhpDaBnLTM6ZfMycW0uXB9a950jhyyHwsdwwCUzF0Wm1\n7XvkZ5BIHxUJxpviwA18OdeWvc2BmRorHUNDmmbWYYUkoU9XfKqR0dcoZIuF3bFBgjDMRmcDv8vI\nHDwxT7wr7v/xvP9NW0YHTiPZGtYW2RQlrwov41v6GY6tsZ5h7jxXwGWJvFo1rNKeQ6iA42W5IUrD\ne90AhSbMPM87jt4zSsfzfOC9rvk4VR/e13LBSisYfNeseW5HdtIwSANi/CBd8Zm7IJTMLA1bGavH\npxWmyTE56JjpEty5Dd+2I6/cCsW45MCNq/PxnZsoJgyuxUthUsdsDVEV2pn1vLSnTRnHddUuthFm\nQcyDZbIoK4k1lSzD3CRybFGBIBOjNdRI25FZHa/1CS/yNZuUOXqljwkxBUrdHx1cHA586T7i4DxP\n3HtWkxCkMITFvzcnXrU1cOS87GiWTLCGmdfunJf5jkkCWHWu2PuWVTmyLTWEyczz2A6oCWu7AWCW\nhnU2kAilqQBbErMXZoQjHVEaNtSiJxdlZbUV7N2IeKGNubZug+MyKkLhQ6s8Hg1THsILMLxUL3vD\nsHBCEYI4ZSbhs+BlKSJoCXHxtp0KFgRXquRl29ZzJu7PmVzhrN/jFc52CW9CVrjbOFBhez1xfRmq\n7VeGTUn3jGNrv5p4+Ju8JBM6MlkcDbnKjywyUp0sOjIRZa1L0IITNmbVWXaZbclU+zQtlTkui75c\nMM7F2CNsMA6nHVgeZBAdFXD1i4sFAjtROq3MrZca7ezkwb94vQzjFa2AOjpd5A1GUliVWvB5BazQ\nSgWfyRZvZ5bI5aXDs/YwlgoEV1QWONsypCfU72YwcfJwrueoUFnuztVhQSdLeIbKki5X2XivusST\n12G4ZIlOezoKyYxBhDOXq8aeGrKSl+HIvID7mlIIrlQmdMm5WTTblQH2ujDYy34MCINVn2pbNMRu\nYd9F5N4Pvam74X74suasyNLVAiUTpNrWaaXyq7+wGY/FuCuZ2vOux1eX9VQW2O6lDdWdZEn2WxB+\nAnp9iDJPi0YZqUy0qVXpjekSc23oMribF114zIvdHsKmlMVurvpbnE656L5WoXwDy68VQP6hH+h9\nQ2uF6xR5O7UciuNOHfNk9CQSsJ9gWloi5yp8K8w4UawU5lTB3PuUaeMOVSW6DCg7Sawx7qZC5wTn\nGj5LcB4Kb2j439Kac51w2ZBwQdMXVuIxB644DpZR14IDI+LMuPKBo21wwYFkVuQaKx2PvM5bZvVM\nmpGstM4xlIKXQpI6nJMkcjRHXiYciiW2CkUdQyzMybgV45G2mBWGWPhgwAK4nEK0yNpFXFFGMSKB\nMSi+GM42mB/ZhpljqUNsThKeQFMKvmQ+znCMBZerifyUM8FB74W1H4kloMGQnLmyFdclc9CGcfLg\nOlZxZCU92PL9B4HOEZMjmeNaqpA+KpxdVI/nm2zk3PPZ3KJ6wSFGhlRYi9DkAZHAhRN2ZswecoJj\nUUpSZI6ECNiRjfc8yiNXY8QiFFf4YZtxHq4jXI0T39ucs781coDGRoZxT3Zn3DUNk+uIObHLVRIw\n7ALiauchWmHlHY/zjIXE7DLBJfJU8NryV6knxoJJ1Y/n2NHEPQ6jYcsr55izUI7Kev3b0aqF2rbM\nrVCigy5iTIyxowsjeztDMuTOaCZl7x2bVFvpGUCEjRZelRXmlVUeMRFu/RlYIZB4tARAUA50zrEv\nCip0lrmWbrkzz+yl4VO75W+bR8jk6SzRkzhqx8F7LucZE5isZUt1YHBmjFKDK/bS8tgG1vOSv6QK\neFpLVQZhgHoSjo0NyFgfIwBHCayI7GjYzrXVt7aJnfY4Mm55uMxeeBwzt2L0MtNY4eNywzvtCFKY\nTHlRrpmlwdzM2Blnh5mrUIv7R9zxmjWjc/yLf//nvMh7/qv/5Rm5a3k83KEx8adt4e/TB/50VfjR\n4SP20nB0LatFa/xSbpZiYWadjYMXxBqCxEWcW++tr9wZ2zSx82c8TgNqpwjnQIPR6ClIBcYl9KUp\nhWkZsGpL4s6tOUt75tkRQmL01SJTFvnE3CqXe+FuK2x3VZsMlWmyRimhMsxXXeHZTcFaRWOmM8ih\nAurs6jDOErJZX7MULkGUcnvOcqrx6OyOixQhgXVg0wKE7+p3OWzaxY7LoSFxXBj+bAVvCf9bokJu\nJdOI3LtQrIhEqcPMUV0FMALZqjQoL+154SEeGnO0UgGwo6axOQNDqxaXCl68LD6+S5KH1H/gS3WO\niCr0CwCU5bPCycVhActl+fyGe5VM9VOnMIuwt/qer8cVl+W1Jy/kEeHreCmVylK7YkxSO1UjwnaR\njRysOj+dvH8dDy4bR6muC2EBfnEBmMWEDYVZCo1UDbMIUIQZz3/8p79A3Y7/6S8+5dve+Kp4dtPE\nxRPYX2VsY5xPDZjDfW0ssugiS1nkKkYdIjSpQSP3Dh1mCJXlPi7sfZXELEB5AY8mkJdhVJOHgb+a\ntleLcVhs0zAQf58KuJaqDz/HGE5abLgfurRFqnVuhbfi6Jb9GVy1yptE6VkCX7TecrzZffeoFgZL\noS9GIbFxhdkevKEnU4JUs7qDOVZAVGVjkWinzmMtqE7hJd/E8msFkH+RPLfTitllvHiUAZdbGiuc\nOxitIYsxaMFH44nLPNJYNUfDCN4TLfGyizwVZTcLYwncRMWjPPaJVozsClFbrnLkKZ53RVEXWGtk\nMI9ghGnCd46o1dpNg2dtDtLMYz8RQuA2KxA5YEwTiBaSBmYpzBjnNjCaYLlnjzHJKfhRWEuhuMQT\nU/YkbrQeCl+EnBOp1Auu7x2aCnPJIAnnlV5aonlyzkxkhuI4irEyhyPSlQmdM2uMC/lAmgOuMQqR\nEhNRBZWOQeY69epy1YYmx0qVWWxJrGp5T4dzHjFh7xJdFsQ5ngp0MtKIEUj4nBlTYSIjziHLZPB5\nmHiBkmNBGo9zPe+jch2tRnnbXMGTeYzMbGsuUW6t8G74UFOCcqFxnjimmqpTZtYCHk9aZBJrcWQ3\nceE971PP9bxnoxNhc4G6wqdPlJsUuEoNY3QcXcdWI4VzxM985IVsytw29cHQVIAci2CloUm1cElJ\n2Lm6jy5UKOIwFcgN0iUO1lG0HptYAArP28RZ+O0JCpFeuJJzeku0cXE2kC1dOjBKYteuYI6/kkK2\nUbgKkcfJYyWwIbIvgRckrq3q3kYNRGm/FijSMkhL5xydZUZr6cToFheHGByv05qP4oEftZdcpJnR\n3D0jpapMwXGkoZvrdgqJFmPSwBMbue4aFKOZ60NFXKEkhUULqd54Mh/wbg1pzxu3BWDtqw9zoGDq\nyaYUCTzJBwY8L1ziM1txaQMmSh8y3ZwxUfbSMGvDYzkSi5GWoTsNDdEcHQMflR13/hwK/Mune8o/\nOtJ0Dfzej/jP/uKM/6Y958/zv+UP/6Pn5G7PD8YveVP+iD/8m4n/+t2GFY47CXiLlEVqss0ziLBO\nxlddy0fjyQ2jIpoT2BARGolEuqqDhvr/cH9M166mG3ZJ2S9+x4hW55FQP8+kcD4qSRJukSq0R0gu\nUrJftMTLQ9uMsEhKWj/jdwFWRiAzl4C4KlB0pbJubUm13wq1e3aiKu0BNKnLnO0zmYKiXJ+1bI8T\nd+eBfPMEAMcVXXZMvtCYR6SeJ8EUo3C2/+1wn9GFXY22MKLL75xVULrVqm8tPOhGg4IlY3Y1VS6W\nKk/og5BzDf44xQifZAG2DIV5V4FKojK2cZEPCEsktFPOSu22GLXF7p0QqNZzodj9cJ1Z1cvWMBBl\nvQx0jba4WQjkheW1hYGdRAhBSLlKOwDUC+Oig+0UtFSQH5eioHOCZrvXEbtT61+WuGYqkyuLlMGA\nxgu+LKEjZESVWIw/+8efcXkZwUcYjJtP9jx6vWaUN/ynf36HlMjPRs/veMdfv1nxxc9eMC3Dhtke\n7PHuVVYG5w6m+47LacjS7qUZFVnck6nV2YEHSUm/nNuoMJ0INoxsDyEvK4y9KGvL94PLE1WCEqjs\n+kliYfJQvCDCnTnOliFLlu+BysJ2g1Ee2PPTRsO9rSAs8yIiSzEAqehi/1axFUBTMtfieGyFUTy6\nbMW8nAPhG2zW/loB5Ls48klIdAhDhB0dkSqp2M4t70KqFxLGtnV8189ckDmmhrPO8+44c+49bZwY\ntOVVLDwR2KjxJUZOjlYcznk6M56KUnLhqQo5HWGeedI4ngTHK+35PAoqiU9KwTSRPCDCtTqOOdCY\n8T1/x3WugSGTeKQomEf9xDHNtM5xk42jRVazoFrjGT+UanB9LSB4HImNep7oVFNtxHOeIy9tT9BE\nGyINhQ9yxjWVjlUntHrLJ27A2YiVQrQ9x2lD440QHEfn+cWh8G5yrGWmawwZhFEiUzIaE7KrdjMu\nVJFP7wTyQJMGfJv45Ri4mjIvV2taN7NpjHZQ5jYzT8bMitsCmYK5DZEE1rLb7XmVOswK4iCL40wG\nkjk6Z+Rp5nyOSEmsfYOGhEwDu6h8UqBvjjx2nrspcmWeIsaUPR8yrHrYmeOMU959YVc8NnWETaSR\nnp9Lg03CW698XgKHUniMsF8VziTz6XmHzkfexEhbPD5l1p3hdMR3Pd4J0ySAMqkQE8ya2JiiqrRu\nRqQQCKTgKcUYxpm5REZx1X83zmwtYd7+307936hlPRis4HnZV1kB8Fz3/LR9gojwLI8glRIaVHmR\nb3nXdjyKgbehwRfjUUkkFa6Xx/FFmfmgEIry2tfQV8EQCpdxzw0dF8x8FlaobziI0DLRW2LUwGWe\nGUPhfEpc9R3bPHOzALWVDMgy2i6zMLmGkCfmpkqOzufE5OptXwRiCza6+wfN4FogIW1t8DkBspJ9\nDTPoSryPoPqgGzZh5qvUc2ET55J4Qw9FySI0JRFDwxSqKPCRzmhs6LnjoBuexpkPfc/d+pLf+bDn\ne+kO/x++hg+f1k7Vj/8pj/7zv+O//Kt/jT3+PdLLL+sHu0ueXfzvSPguf/IX7/nl/jv0LvFOHqHL\ngNadtmjJDK7lLE3s/aKflkSfMt/L17xqtzzPO0yU5+nAwS/7sMwcfMs2pfpwzvVhtfeCt4E2ay0s\n9UC9o820SbltC6tYY+orq1XdTLYj3K2gG+tD25nDuUSRapEVzVVLNgArWFFEjMkrlIK1hlimSE3c\nOi1zCnWYyhJq8N4uAXjEB+L1his2eNujBv7yls0dKIl8EapNV1HWtzNIQc14f/FbokGu/XRaKfct\n8iAFMeFMy8K2agWBVsFMC0xeWdkyjKcZNSoI0Xp9FjOKuDrIBdX5gqpPrhZ5RliG8HR5TRZZ3BCE\nlipb2KoxwsLYy69YddkSNkKpscRWKmg7ATilao4PX2MOgwC5ukmcQHPB6JYJxQyglX0NyP32mUoN\nlLEHFjmaceaqk0ai2ogN6vBktiTSItkIPpAl8y7PXJ7Ni37IwQr++e9/wU8v4U8uFn9lJ3zaFUjw\ng4/v+OnPN3jWFVyKux8mVFHMCk4cSR70tcvcHFkeNOJrlmjtBx66kociiyTG3b9XLeMX1n5cHKKV\nSg93xX4FeDdWNftTqfvopKE2q12AwMOxP/2VrBZkxSoDnZahQKXeM+NpkBMW2U0d7UvU71VwZKuS\nEQdElLhc18Erl1bwZjQII3XwtJVCsqpL/qaWXyuAvLMzfjIazjkcMyoZRZlmxys3042JH24b1CYw\nx0TgZ7nlp7sBj7BW4SPX8Dpmeu/5yEeO4hFTXqqyngtN73kzjHhVknOIjXQloJa41Drh/tkcGUXw\nUVg3cJs9O0m4o2HBsS2KlciVc7yat8zecUZmGAvPXCI0kaSBu1jF/mu345EpTj2x1IMpS/WVkpIV\ngmTeW+EqBbwoR1OcKDk1bM3wxfBS2OrMrjgmF1At+PyYHzWeNg2IE4SWXYi1xYvQ28xmnXiSjowl\ns58LNMLZOPK8LXgXCCFwF2sKnc8R1arFjnTcHDPfUqsBLZax0vD5DmbXMw+ZISYmPJc5YxqYC0zJ\ncDpQCPQuYynjsmE2ExCQjI6J1znzhfasQ0DjDk0BV2ArBw4l8OPbwK1AKoHW4HEQNpL4XqesG89U\njDIuOfC9sZJUI2vxHAL8XmmJIfK0Dbw/GpoUmpmQN5y3kbt55iO3Ig6JOz+Rx4nvSiB1K97tJjZZ\neX7Wcz1GUkrQNLTe4ULD9ViZe0meJh64NKF0gbnr+ezDHZ+LIxQoqWMMHWUM/Pk/8PX1TS0q8P3x\nltfNivNUpQv70PLIJh5NIx98j/l6k/pkvuUn68c8ixPvG882L/674rmwxNt14Plu5k1X9Z6WS3Uw\nkJabJtO7BFGZ1wZ7jwR4dhi4dWc1IGIdkbnQZI/4iRiEJ9zxfCzc+A1DScxNTfI7m5W5gS5GbruG\ns+h4gmCSaRdPkysPq1S4aR0XyTNq4rwoj/OBV75lszwMxgBdMSwrj0vCtDom/H0PtiQdzI3nLtX2\nYsGB6zk2iTZmvjMdUYErabh0kSM95/aBf3f5hN99fct/8ezf8OP3z/j0PxnrJLqDvuyZRJhvfkh4\n+V3cs19WEJIz6c1LyuuP8Vxz+Uc/4Cf/a2Zjwie2Z9SJkFuiSwRTSAdEhCiZ93JGIvB61dHowPl8\nZDol8J2kLlTPZaGQJaHm2S8ZxYLSLOBYEYo0rPOR5A0otAvF5MXTusSkVOouKatRKFrXMQpYbnAG\nwReCL8Ts6JqRnDyQcT7Bsk0l105PTA3rRW9uZvSaic4TcnWWtVDQIuQIz/iAcyADlMt3+FEpvrou\nXNzNUAwrwhVP8X5G1hPb29+O4VrBFpszvW/kC4usD0NRmqWQmkVoFreLUMrC7hkOrQBYqpa3oTKt\neQFqQtXeZoHeQKTcp+fdAUV1GRQspCL3w1r9AoyUguKYSwW21RauAqqEsNL6HbLTmq62MMcbjL3V\npLusiwJLFxnEfV51dauIJxcOEdwpaW5pzQt1e066WQWcM4KddM3V5aIX7rtcK5vYusBPEjx79lP+\n6hcb/sU/fb/s9EWmhcBK+P53ZKHwFyGwqxoVH40/+4OBv/zbLVlqEJqUgkn1Uq6FQKIFRmrISRRl\nJYVSqtWcLW4hZUmChFOxUQuUhOKXoVlVOTVfyFIBbDZIVlCrzh1xYX9hCfHBOC6x3H6R3NSQ58Ul\n4z54h3t8A7ULIZbxDqLpvUOHUK9kox6nftnfBY9K1cE3JTNKITiho7DRsjDsGYcQi4HlpRBz1N8W\nyjensPj1Asi9eKLL5GUKvfdrtrbn0+BJfcchHXkvipbFjjpDjMpm7ZDZaErDNUaWNbupMpOUW7AW\nc56bYLixMpgrU/a54ctZaJznuS8MRNZD4LJJvOjgriQ+7Se66Om3mf/2VWCez/lqTHzLZTY+0LmG\nH6Rbht5xjfE5niep4PaJ7BqusuPQwAs3412mjxHvPXd4fpkaotWrT6Ve1GsHvSXMN6hkVuaZZOQO\nT8DztrRkMo0Jlg1vwu0QuXCr6qlocKCrQvcu8j71fMfdoLqhuA7WjvMhMZ1lviqOY4k0s9DGkRQd\nj5p65ah4vCmu8XyZJnoC5Mx+rkbdagPrJKhlNhwItOSY2OdCzIlVcDQSySVzDGukDDjn2Irj1bTj\nqBu2LtOb46WOBIFUEuNY8G1PJvHDdaRr1hxmOKaRXmemXNhidFn5+aHhB+sb2mHF398KL9aO1crY\nWOaiTWwzHAvMZH7nrHAY4EkvvN1PePO04pjSni/CGrIxdE94WwolRSSfY43jJ/uEtQ04GC2SBmBX\n8F3Hfjaa4iFc0kRj1IQfWjbNlo3U5MHoM+sCx1N26G/B8rZd0xahQ7hrO0SER/NYo1edEqx5kEmE\nHmmNd23D8/3IJlcV5M/OWn6w33G+hxgUlujpVRpoTREbGTTw7FBB9LND5N3W8zu7mTebgLtvwBY6\na0BhzE0F1MCbjaMZjE4c6gaGXBnitgTUNfT+SPYw27z4b1Ye5Qd3gS+2sd6wS4OqctcUJG5o8oMM\ngXCkTatF3xe4khYJwsrPlCK02aOmnBICHpUBW0Szt27Du954sYeyDZRd3eaZhn/5j97SXL9kevKc\n3/2jROqfofILkm0oukXlDdk2EAR98ykPRrUTwQn27R1/+Pv/mv/x//gPcHmsykKr+zZkT1x6t7Nb\ns50ONK3RmLEtkRI7jlL4dqxezK/cmnU1tmWfBRVj9JBLA1ZwS8v24FY4SZRSZROjbzhLIydTJ1XP\nhJCLRzGmJtCkGsKh4ghWgdWdF0KpvhEjgmnmGLs6me7q+eRjPY4pGOPU1t8X2JeOxtdza05NDalB\nyP17xDm6uzUlj1VjGVosjmTpyS4hJoivW+vdwOP5Qx0cOhjO/XZ0fpol/KSYscEQqtVa5e5O9m4L\ns2x2b4Pm3INuuQfiAqK99/iysJxWOWknAqb38ctm1V3hgygNSziJLM4WJ0BVDNEH/elgi4sEdu+y\noItXsae+v1mcJYQ6U7BzQpcXQezCTp9szUy+JrmhykKU6rrRLlKNpIu0Qln20fKFxTCrGl7F1VAQ\nqIl6+cSGe77/jz/nY/U8ShP/3u+MNfki2sN0nbH8zIPOxGTRFhihhfh0T/t3T++Z3KTV9kx4cL0Y\nreqCgwrZqiTGeaEUu0+kVJXFdpIav43da5b9MkALD77L+rVNOh1/lp/ldBwBMNSqHaKTur6DVcu9\nQLViq0RALSamr0md5iVmOoiRlxAaahOS+0gXETjZ5Fmq50AwLFWZS6dGSkbvhWjVF9ovwTV9SRQK\ns9bS75uMHPi1AshrNyJUe5hoQmTgCHxZJi5iBhxmM6sCU2v0NtKEQFMCcxOZ546beKBLIyoNR21x\nXrmd9nzUTjx3njEb65Ui5cCt7TFZc5CZkjJPQuFDEabS4XYjZ/2a9xPkkLmUNd9p4Ek38km754t5\nxb85ODIj72Pg6QS/vy2cmfDjXca8sraJZ05Ik9L1hU2ZSM5zFQcGtyIPAxMBU0djmbV3BCs4gX6e\nMRP2ZUS8w1smSrlvVWYcsrRdnAof0oyn0CE80oQjcTgGLpn4GOjLDV0+kKW+qrGJj6WCPsuClsSk\nB8Zjw9AIzVgYTTibhZVv6JvEdXRsXGSN5ytXLZg+zIaZgzRxgef7TeLusOdcV/xsOvCiWbHOt5gK\nN3nieqi6ta07YmPm3GXEO0oIzPtI0si2THyksA1H2maH64XWLngdG96i3MwtP56EAzN/cfeCT8+N\nOYE1DnUJKQ2vD5kYwJWJi2biMMNBlPdTR3GwkcLzzvMuwiOp8o6VK4zOMAu0cuB5UdariS+t4+dH\nR8nCuRVeNsKdzBxdIbtE1Ezfr5k0cyl3XGKkVOOPc458lVv87vYf9uL6BpfzUrWrvhjCiq82gUfz\nyCQt3kbO2bOjr/6eJfNyB0XGJeDjAtOJ50PiTiq4Jre83NV1FxE+bNfEEqslkQjPj6m2Q7Xh7WYi\naHMfG/tsH3m3PmBmfPdYeLNe9LzquSx1pW8k0PtIdh6TkxZZ6iS3KUhALKIIr84z/bhm7hNzn2hL\nBdCzj8v74Plh4q3fMsmICLzpAabKjk0rLvIdr1eeF4fIm82i2NtP+NLz5Vnh5fG2mpdqHVz95WrF\nv/qDyPoP/gfovwX2c0rMxLcf48jMrz5i5XbMzZGSnuH824qJFxRiGiv//fwXyM9eUv7kDedlwp2i\n00TAG1euo+XUZjXetx0BaE4x37Jh9p67JQjmMh6XAA2jc4WmuCUO/MDozznmhkd2jS16zlD8r7j0\nz1r3dW+BUArRJ5ocCEdDRMEKqokpe5IJfa6JaONCgYkIrWQmqUFCfVEmD2RlzLAKdY49e+EszdWt\nx2fWesA21/U8GJ+SxszbzTVPDxskC2oCeV1b6SJ82O55fAhgDW6uw18ZSH3GHX87CtsTK1f1um5p\ni1cGsFClC5jQCswYTh6ijjs5Dd1VhlGAbpEWwDLIp0oqhnxNBuClFkHrUqULJzeFssg3jDprXpZi\n+uRlC1W/GnTRn/KAeFqqLdgpjjkvDPAsddsro1lff3J58FK9iL06dJECbuqGY1TnBO9ssVuz+whk\nwYiqNXlw0bluMDZWnS/+yZ/9nJL36NrX9d2LbU9xdffo8gFpCg+AWYCYITs+kSM/kSq/PIHITqv7\nyLgMsJXlWORitFpNEWdRNmoPqYYKYvX/sixR4lL3segS4WxVplplKtzve8MoC/3a6OKYsbDxyYRG\nAZOFba6s8h01sbc/FcMLq99L/ZJpYfgHgzWZQau3uspJv1zPixlodSmCVUkF5qx4tSXxr+JCs1rc\nBKux3p0ae63FS4vRUPgmez6/VgA5WUeUA74IDZ7eCUx6XeEAACAASURBVLH0BCLzPGMuUHLmZwUs\nOcw69jMMeUZCgzllYx3fCpFvA5siHJ0ifsuM8ctDxLxnTlAksI9G8ImnbWBsPF9EzxWZJ5b49krY\nxcLP3Jo0GrvbmZIhHozgzmi1oApnxXGVMyKRvzw6zhjYIOhc2EmiqGealZyMrIaUib5xKEd0CsgS\nwhHVcZtn1rMRWk8vUJwjI+xnQ8uMV0HmyL51NHiSFbx6tBRWZJwXRlGaqDzSwFO/I8QjyY6UZIyt\nQ3zDioHRGSW3/DI3jFZ4l5SYz7CijGO9xdic0H5iZcrVHp41PYd5T2+JFwF83nFZPMc5sQ4ep0fi\noJTsuZqFaW64LTMmntQEVjnjPfRtwyYlbs8VSY5GhaYMzGHDGwv8zVG4LQf2csll8dVZwylnNmPt\nhj9eC+/yESvK05Xx/dbxoYOxtHw5ZLwl1Bm5rHFlJo4eJuO27Xg/1gStL8n8ZKq+if1q4I82jmly\n3N7c8GVe83nJ/KUzsL5O2FuimNBqJpqwz5ExKlEiWTyiO8yMV6VQ8Jg5PEbWM7REcvkmE+L/YZfZ\nVV/aJs9A4ePDXNPqUI5uTeP2tFTQenQr1I0Le2E0/oo5r8gI3jrQGWSkIBz6FZsRnu4rGM06c+x7\n+mng3WrF00O99ZkdcGJIaXmz3fBiFwGhSMKc8dEdZD3yblt9k+8HUGbYDFBcHQx1pf4MM+/Xq4fv\nt8r1IQc431FKQUu1IjIihvLsbkZMeXWW8DS0i+7tsJrJh4J6x7uzULWMgNBRnPHyIECLaC0Y/vnh\nc37ww3fIWYf5j4hfPMN7DxZqe/LZZ+SvXpLyQBM9Yq9hhsnXP5jQlqb2Ng3KH/8IfvTPeJSOmFMO\ntNy5jrM4srWJsZzxZiu0Q18tHYBzGqKfgMiTJKzywC6cMXlHF3eLj2pArCAITQm008g5lT0vGNGl\n+85x3edCb4FshmgtdJoclhb9acpeyMkTltfvtqAH4UISt8vgYsn14biWVDWRCKmdcdOqesPqDEVJ\nYQFZWgHE9uYxoIzOkFDAwQ0O8cI2Z67O7zi/CdxsE0+HALkjMOJUmaSjsREZTzP0v/lLJ3WAbRTF\nW21D19NHawiFFRBjBrYYmcysilgdllMBNbn3AS5WwbVXqdfHYrNmKFmqjrlQ3x8ErNT1KyDq7jtM\nCaEXIVIwU5rlYq2NjkJBcJbvh81Mqq0ZVEeH01LZ63oNrpySa0YQHXXbJhHEEqaOvhizPkRBq9XX\niEAU/Vp4ptGQ7yULp5q0f3TNxy+uiSUT+kCN+5MHz8Ioi1ddLTruM6JkqTiQB6DMQsfSApktcBRH\nB9yx6GoXF4jZlIJDtFrieQqtZY6yJP+p4FkGIpd9cgLOXqqUAWrBoVRrtgpjTww7zFqBsiyDiafz\nZFHjIMIiR6nnwrpUW9kBuR+GbJf3DFXEQxHoLDOLw1PdOIQqqYGq6XalssK2dBuC5CrhWM63aEIo\ntvhSOwqFlav6Y6VGae9MSOjXk6n/fy+/VgA5O2PrHEcyd8WIZcVWMp3CeSnMJfK+BMbkqw1XyVhQ\nXBOQBHsZ8KnhJznwt2miy56nfuaPziaCO2PbCH04sCoDTrY4H/nvrp7xfx5K9bINCSuOt075uzhW\n43TbkJrMM1p8H2mAu9LQMCHOkVLkhW/Y68SYVtzNI9JDKRnveiwb6jK7onRWOJYNr4619akx04cJ\nr46cK1h25jgwg1fSODKHBj8ZsyQCjsaE7TSDTjz3HfvxFucCbZoJc2E7F1yZ6cj8dB5p1y30l7jg\nac0RJ+VOBiStiOlIKgEXR3KakXGgl47fCzNP2jtmS7yfJt5PTzjub/n2ec8kiV0KeOcYomKWKaHw\nPs6M6tFRyX4DacK3Z5hFRA40qcGCZxgnGDo+mw9cbBy7Y+RmUnzX8c827/njvudVW3CMvC6BEA58\nMfR0XcOHKbObRi6biflo3ErLOmW66cDvB3DiuXMzHw5KcS3ZRz4fE+o9OsMj7jgbhHeN8JQa7PC8\n6fj8+ob/+Z3jRs9Ae8ZkOA8kIdqMhlKtqkxwBa5joCAUndlk5UipXYCmoZBolva/zBljwlmi+Yb9\nGf8hl+ovGjn4/h4AFvWoLSzr/YNrTVgkd+tJ2XeF2dYUmtricwWRgDKzb3tUhHfbNY8PFbhdrzb0\nqfBh0/PsMHG12dTPz4XR15v4091IXu5iQ7vGqed4NnOk5cW+Mshvt+d0c+bQKkMj6MJTmUA/L9Pc\n8nArbFNkWPqaXYyMwdHlgmbHMfQc+oKocXTCH95e8cX5I9KitV1HYWh71kl4Et/xy/4ZAPv1w/67\nHI7sNitEhP+eH/Kv5D15/QnuS2i+/Qp7/T0mL4T/i7s36ZEmy9LznnMHG3wIj/Ebc2JWsauaPVdB\nDVIQBAKSuNSGICRopYV+gv6QVhK04EIrghQIdVFACyKb3c0im+xidVZlVn7zF5NPNtzhaHHNPb5i\nE9owgRruJsIjPCzMzK+ZnXvOe543DdicqZ6/Jr/5aNrnkdFWpPyYRl9jEELuMAj27UcYY+luv+Jf\nX36f37q/gdDQmMTaNyxSz5J7ZN+gObHIhUQhVljrww7qNFfnec84fS8qvG9K8+RJCtyaqkhrQo9L\nGZcqBvsQIDvAppIpJGcGZ6lzMfowKtiQ6WpLG5U0nes6BJxx7NXjpg2JlAaqMVfUJpCCYy7KjWbc\nJKc5QQlTV75PxXx3MBGItOKpx8B9WGEmDezWWC7vTsD3PNoWKokYITew289Y9IbIHJodOi7+f6+F\nX5URRI4ZyCJTLcgtmXSpcdKwOinqgEihD9SUrKxTMCZTT5LevRQXUmHiFR8wBHrI8BYN8sGJUEwx\nHxGBrRx0zUzUiqkdVyF80JzWUSp2cdpnpozoIUP4oSSgkISnwFkTjRQ5RRRTmuuk/B+jyo2zPNbI\nqAcJgOBEJuOQjJvOhXyw/U6FdkK43d1ecfnkFl88kx9OkjDZ5R13sHzNlBWDUNKmAkcIsAMks7uH\nU2O5FkONslfhJJfMakMqjW0HmQqlIiAfiG0r+3D+Do+aRJHFQLnX+YnR3E2UCDGFpX5YEEQpJ7qa\n/klSmRr6Dki+ckj+IK1mIqFI4RTnD3+GHDF5h89JtchGoPRcHXTTokXeoiIlYQiolAWYkYeFyWjK\nfrf2MIPL/syA+4knzYTy+6aGfIhi+kWP//nv/08ac5q8th3ZZvIQqFxmjqP2PSZCZ0b2fkEaIEww\nvZwnqLkprZOGjGYHOWGkXCiPpee8yZxaoRLleeP4o/uKFyZgkqWeLH4sHrVQ5455PcPlzOXcEW0J\n5m5NwZ5VxvDIKp0q5xKxHsbk2Ea4zY5ndiBWnkXe83G2vAmB31r1vBwb/g1njEGxGpnLITNpWHcD\ntTG0ElExbEwpM3ZJsaamJRMksUiGUy+I89z3I1VVrLCj84i15KB0+w2VeGYuk5PSeDijoteeGDM+\nRR7VmbTbstCOrDXXfWQtYFNNyAMO4Twn4sKiMXHmMqe1oR+UPkfWkrnuZ+zvO54vTSnbWMvX+8jc\nJBonPK8MViNeDGJGauvooxJtS5eVkBPbZHgdLKbypTN4n1nKntPZnJ/uepCRS+fwJHpbY0PiXbAM\nVYXLFdH0aK5YugQpE1wxGDBhRMdEiD0hzRDbMZgzhJEhzUhxj2s8g4xUWkDov1ErXhNDIetxm4ul\n+Mf1yLx24Cx3u5EKT5AdrfUMg9CJIUtk3rZ06y0/5YSXXcENfacN/IN//H9/g2vbX9z4X/67/147\nLBes2VMCq34q3Q+ziroLjLMKvxvoW0vbl1vn0JTANJuE6IcsnoSIY9kH1rMKyRFRy1QoBEDEofrX\nUXk64ckOv9/ainkcEHFTOjFMysMD3TVP+ZMyDqq8vWmYTfpoJZFyxVA75rHj0NGipA8emhbViKE0\n3loz0u4z3cxMP3NUNpFRln1gW/88DWE5Rk7jHU+y4TeX7zk5mbP99sjJ4g1JHpFSovroDSEG7JtP\nEFM475gaJDwct4I8+pL++jfwqWSlRxr4ozX/6/6UZTbEfsnQZshKNTjGqXPOMR4RUOngGGf743mt\nxp9f1B01hfpgsQt6LLUftKflvD4Mp/pgHQ3HYCqKkKzgY2I+wmbS6dc5oSqM1lAlLRk6QLNMisqH\nfZlLxOmD99gwacqNMVQ5loevCIOR4zmrUj7qT4cM1byEXPa+zOUDQMFMW/3Df/KDX/nr9o//h/9W\nvVIcAw+LmA/knzJ9zZnJuKKMA/FiidJ98Kn66XMPUgJk1ZIxtPogiYgiR97uh+OgfT383imMU2m+\nNsVuWsUwUhbjovkYTDG9LtsxR5au1UzEMJMiNwjT+63m49UeprvAgGARHJkOoZ1+ZqTcI6yWJLD8\nh7tuJrpHo1SLd3z72YbKKlJp6UqcqjhELRqFgxC7wDw+GFKkFY3laKNnLP/PX84xXz4mWFskJuiE\nx3vQDn9o9czx2KdfwTHoP+7y9DJmPTYfCg/bOHwWh78/7qFwZFvD1Pw3vX9BoVLcGXN0TSTnAlOc\nUIIHOU3AlOoED/eGDsFoPv6/NEl/jAiVFFMVI8Wg5HBPEThmxkUe5uUhCx2m14f58D/+7//oG7lm\nf6kyyLuQOADlE0WUbVVwybG0AwtAnCNHZRsiVbbsTKbNIzY51Do0FR/xJBaVjDOZbEugvMHgQsVF\nHgji+Ye7AScLXEqllGQtZFAjSE501PRDwFSGOMDcV8xc6bRPpmZQeKM9cyqa1lDRcqeK+sxVFIxr\naEzHEIUfGYvain+6XRR/eh1ZUJoRR6kIQ6bVzBNTsbN7+mTYRshqyDFwWQV+d5a52UbuJ+xK6AMn\nVnDjlm+7QHA1QVvuq5Y8jmTtudlAMJbRCFYzu7rGE1hERxf3fGWEfR/RqOx0YElNpfdcLBwncUei\n5uUgzKQ0AJ4vIl/eG7I0fL1RmrpCcFStgdmMhSYkB7536ZjPaohKGEZcWyNj5G0wdHuHaRJz9XiK\nacuigctxYO5hazPBG3I3IyfhkxNPMjMY42RNuqdrZ3w2KDElnjXv6NyCqDsWmhlUqfIW1BMchEYY\nyazHkSHVwJ7TuGFd7QkjPGsNmveIr7iPjk2wvFZ4s+7JZsWNFo1kM3hMMOwlIW5FEoNTT27n1LlH\nU4vmwNALOS+Zhcwmj2hUhtjwD36hV9c3N2bsyPaUWx7RN5HH/Z6BOX1TbnnDrATCYdHQ7vVYVq8H\nZWhLtvayv+d9WwwdVG3JLLW+oKDEgkCzE4aFodoZhjlTe00p49a7h/0Z55lqB8O8LK5wFsXQ7DJD\nW3R5SpHTJHnQ3eX0QaClA/e+ZZE7jFQstoqtSiYbq4haTvrEpnHUUwpLxVNLYNt6Fp2CE3SYWJ0k\nCLCSNS4PbM0Fyz4zqGdslc3M8NFNYjF/R6QiL16Swpx4u4SLE9zv/BGJkq3B+0JYePGU0Ql18ORJ\n86yquDffpnn2BeHFU/zzl9QpId9/ysf/eGSwJ2B6RrNkprfs5ivAMMt3iMLWnbJMd2y1ohossGCs\nSxYneDhNI7cuIrmdJDXgvCXGElJnaxlKnphF6mhCoqssagwmffDknobYyGhqliHROkPuI6Zy4CNn\nQQmmmMpYpCQscvogOFJy3SPBYV2ijgVq68nHYKYBHD0xeZyORKlwjPhcsbH2uCtRSsBsjCVv52SB\n4DPzUOZeNqkE5L8mDphGSma3lL+VnTH4D4OtabjDtUIJRA5otpHy9/64oCrNZLVybJetKCYgFSUI\nqvXB6jlpydTywXsPzWOGyWlvCgi9FGKTpcSXlXBsQhsmugYUE4x2Yh8EAwHFTZKQ2RT8DqbIDw6B\n02HRc9C9WsqztDoeVQkMRZWZwP7wO1Vmqrx3mcZtAcv7vuKkUpYHw53jmZhWIapTR9uUVj9EuToF\n0OMkzcgKIfK3P9rwgxePqE2xcI5T071MS3o3LUqOiepDw5twXOhWUgxSGi127nkKmGsLIelRIlNJ\n0XCP0+JkNqUPDkvNkukt2/TTd9EACBsVrIMTMhsmx0VTVhRljTC9ns5vM2WFx6kq0VD06Ycgt8g4\niu10QnBSXPQskA4JUKAy+tB7MS3mPCUjLtOctZoZvsGw9pcqQEYDlZ20Z7l86FahS4Gf5sQsjjRu\nhveBNmasGhqTSklfdGr6sMWlJWWccxwaZq21jFS8Zst/NWtY1O/5lAtuujv+KjR8NYBMsHljMkyB\nuqjBjoa1HdmnzKpqSbYmpBGbPedS4U3ifl/zdL5hJjOMCzTe08dMbxfMZyNZHDkp22A5cSN2FBoR\n1AhNv+U5js1SGXvBjZYblE9yh5HMzCRyyry5gxOz5mmleBz9XvjB1oJdsumueeICVbUgbN5iNfNe\nTnms7+HkDDp4vDzn9bvXGDfQ+SW31nGelZlTbvKMxwyczkfavOAdymNfMdLx23PLYrHg5fuBlCJP\n5xUu9zx1A9nWNLrj6rzltr8jDJnKKG52ziZkmnqk8jXdqIyNo+9mjLMtZ3lVGg0kIxpJ2TD4mhf9\nlme1wVOxWpRmN+9G7vuGzmRevYc3PvFJE+jHwDvZ8Z3LOdd3yp+tK16GjMQeby7JbBiZMRrDDIuP\niq9H4jDjjcwR6UjRcd6PbN0FLmc0DmRv0Dindi0pZS6q4qzXkzC15YoKiUowEYJnOewZyVTNmo+M\n512MvM0jn1SnfJXKjWybfj1wUQDR1rTSE6n5/H7DP794xke7nrokHxlmiXpfeLd5ikazFivhui83\n3Y2cHt9/SIWIPEDtoZQAr82M56an7hUzMZezKK+WpXj4fNcf34MkrqWUxJ/ue07clrthyev5jKf7\nHvC8mDU83Xe8mbU87vfIBxmXk9nAnZvzeDfSLRLt1tAvLL4HmwtlohkEMRBMIci09AxDRZjQaKVa\nNen+VFjrCuMNflBaWROkZuNafu/6a0I1Y5YF6sCb1TNMcLj1Gcvdn3O//T4rew9phv7hD0nvznDP\nXuBfPi8PcBHc05eQM+H1c8yrZ1hrSCEgP35C/xK+Xj1nFd8hCo9CkQhFX056O3qu6wWoEm0NUuOl\nNLYN9pRFumNrT1HZsRTDe+uZ27IqGXKNuqZUlwfLY3vDLq6YVwathFhlbIRlLkg/FUHr8nntxmVZ\neITEHiXPKiKe0xR5v2g47fZ456jyFpM91hYuOYBXW3jS/h5hTjpgkpNgfTed/wRkmnZAsiI7Q9QK\ny8jplBYVycQp92WNILoHqhKETXjCYgqhqPnmmKq/yGEmCYFBGcVwTqYTOS4YmqmMbuQhKFAtQdHh\nkmyOGtqHcWiWO4w8ZWkP5fFx+gMvekQkdlP53TIxrKdtKsVF0Ux2zjppdR1KnJ7j7WSJfTwuMkYy\nGcsqK/dSGsTMtH1LWSCDMJ+0yBahngKw8n+LHheKwYhRnebFZBiCYoywU+GSSF8JOMMsJrbJYYPF\ndpm6tSUYtjIJbdPUqGcOJ2vSJU8vDgLbUWF0/OzdkjNnIZcGyOJ+p5hpTw+0kYeYRn5O8384lTXK\nXJSchDzN316F6nDAIsRJ7pJNsXa2066ZKchfCPQH87Ii/i+JcdWf2+2nktkiBamnxYHXk49rgWyK\nLX3Wsp1D1SKqHNPbnoRoxhlhRHFqSu8C4Kb0uFAQe6qTNCQn1CheS+b6MEYBK9+cuc8vVYC8cErO\nsTiYTWu9LGniAtZ01OxFcUn4PDueNYnTqmOtI3/aO9R4OhIRj3N20pyVzuucMw4l54p/eCvAFakS\nnDsjmUjVlBS+EY/oiJWit1MC5Fi4oViGYQAbWFUGZUOXWnrNSOx4PWa0iZggbPIGXE3cRvqQ0LAm\nJMNVjCyqkU9ay7fnI4bExkQGP/Kkv+BkNrB1Fbux54tY83rw3OVMcpY8DAzqqe5rMFv+1jLx+7Uw\nJsclPfPlGS92HXm0XJ0a/s7qmttxzv3mltniMT95/1dcrZaEAZ7P1nyfkX30wJ7QzzltAiEKjxcz\ntvkOYxz3nfDVzUivW56dzzi3A8u6LzMnZLCBPlquB8WKQxrPdQC33fJmUO6HmpN5xxmeU4UTM7Id\nLLexh7kiMZOzcjuOYKHSGcum5u3NHX+8uwc9wciMxdhTGeFvNMLvzCpeDpGqEmR/wv/xReGeepf4\nnk34umOdlbdjhnFkWQ/sElx6x8woaRb5FgMnTnFimVc1yzSgukEqRzSBbJZ0Xcc4jgSXiEnweDp1\nnDggKtuc2RrLGCLBFpJu7nY8ouURiVe7N5xay0nV8u3m1ydA/mLxjIv1jlV9izGTy1XOvFsuuFjv\nWNsFl9qTs/L+dEHSzJN1R1Tl3Wo+Ze4MkpWL9Y63p3Oe3O95fdLyaL1/eI+CiJLzw9892u15sWi5\n7Mv57DyA8nbeTtrHEri+nbfofeb1SVtIE4vSbCkI7+czjCrvFnNEhKebPa/m7bG54+1yTs6J+1Mp\nGt/F1EySHxrMZHoyvbYzHt3tSKpcn8zLPWu68V/d73i/nHOxLtzhW10CmYv1jhfVOU/sG8b9hnR+\ny/OfnPPaCpv8lmpWMbQb7tsVQ3WB/VePOe9/Qp7t4dkr8qsneGlAle2738O4JdmWLIrcfMzsb/2/\nfPGDM574jjHPUD8wVJkqOLZxxbN4iypc9tsJuZToG9jaUxrTcxLvj/XQO3vGIt1xwZrQr6iaDWZo\noN2gWha7IzVadQQR/H6O2oQZLXfVpCEeKshF/zvWiar39OIZnXIZ16jusVQ87neIEazpaVXAxilz\nWGg9kiZXM11iTEd2I4vQIE4xqSrGBZUltx1ZCymjckJFJCH0pkZjoooBZwayZnJusTJpLXMmUcxg\nTG4JjPwHtfFf2dFOFf8qJ7rJMKISJR0ysqrUUynfoZgMwU54tSnTbEwp4YuUgHoQwWqxQ7ZT2d5Q\nrtvKTAGqFjrEgtIYB8XQIk8OdSpFInAwnFApqD8zBbGqJTAUU7KT3ghelF0uMslyJOaorb6QQrao\ns9JOshCRch8ZtQTmF5InmUUJNpVybDBZW0/HaCiZVSjvaaWYXeUAtRl40zc0WvEqK87tuZDMogkP\nJatkYcITYvUB3VAzpei1bHku4DNf/es5J6RpZ8r1XGUhSSFH6CE4NYJqLvsJ1IaCowNGNXiKy2BJ\nZD8cC7lk9Euz48SkhikQL01+KZdqHqJUHCgf5XOdSaEMGRJZy32/U8WZEpAfwZsqNBNbOfOwHhCU\nmozaMl+2lIbIg8ZdVXEYxOg076AmEzOssdTkYi0/kb5SFjrjjzi6NFmZH6oe38T4pQqQI5aPnHDu\nAqc5MtpMNxj2KJXZ8aiqeVp3NDYwkzUyicV6XXEhA68jvIzKV0nBFJC8UTDGgymlt5wzkUROFSZl\nxnSPMRXJOgwRa3NpGJBASglvK8TVKAFniubKiwFjmFnL2xiZRcHaUrBxYU0YHKYfmdktV1Y5lczc\nBT6dwSiRWSNUJlPbBS93jmQ8W3vOT8aBeFejONYjjOqpjaFJYM3IBseKDLKncw2vx8AfLJU+Zv70\nref8dstVs+Lbf6Nh6Da82cLNds2zpacbXvL50jHmgZUdqXKDiuHCZgazwtuO2xF66/ir65FXnWW0\nLavgWJ0bfrLt+YvdQGSFVIETtySlAGnL77aBxemS7SDsug51hse1UhnDPO1o7IJkDF/sKkLccCVg\nUqbfnHKxUE7bgd95YthuMz9dj4y7Lc/twCdLi20jQyiIrutN4tXOkHv42GWsj4WD7QIrPH98u+Un\nY+KycTyqBi6zsjgxhSVUO66Hgeu4xJhMFxw7VYZUYXwmkQjpCmuVbFJp9oo1JsHKjFT2hC4liCNG\naoIo/WCZVwEZa3YuwXqgqRzDbseZ8fzMWoYu0BP4RzLnv/nFXl7f2Pj9+58BMMSRV7MLfv/2a142\nC/7g/mcEF6j6S7SGOBq+d/sVX9tLEGFoEqehNIZVncE3iWCEVTfi68Rq3DA2ltVwWEwo9VAyIyfj\nDrLSW895H1D1mAkjdh577us5q2575J6qKuOsBLMYy2lXaAwqcFuXLPNZtwWUnfVl+zBtZ3Pcxm09\n43zcs/VzLjcdfZNpeuHt6YLHk4lEtpbKjzwNG9LoMb5IEaSCZ+MGmrJP57cDr9oW3yRiL9zZS6Lp\nqDZL3i1vMOsl3VDz6vaCs/qa+c8qlt/5AsYKskPHE6TP0J6Rf/MHmLsrjCwx7i3kx4i8Rgxs/uIp\nHz1v+OKFKQ/r3lO7gdftkuf7O/7lxXMu1uV4P433YAxXw6RZkUIT2DaWVnv6qc1na09pFz1bzmgW\nHaqnAPid4915zaPrgWGWGafAeVsnKmqqweKbNc0wZRpTZCtFWrMwW0zy5QlpFGMLwtJrIjOZRFjB\nJKFRKU2d2WFtj6IsQsXoI5JrshQ5zCiBpiuLIeY92Wa0GVAF1wWMOkY7EWUsZBI2A4xE0xIN1LFC\ngb3zP9eo9as8PAkUohHaqZzttTimRQAxx4atpEq2xUlyPuHBAPZqmJlMUhBrJiezXOSMk4mIo9AI\n0CLnCMjR+tfwkI2ubdEf56mkvpyC4UYeGLex7FahGWjJJh+0wXNX0ILAMRATe1joFKzcVgVjhJkW\nucVMmJCBJVNea5q2aY5BVZ6CeyhB1wZDm4sDYbHiNtTZFmv5MbPLCZMrbscl4jfUzuBNhlw0tDaa\nD1AQGXx+iMZ5kF3sNo6ry4Hwdl4aXJmS0KZkbluUg9KoOA4+SFbMBL1UnRwDpyyzANXUOGtMwb5N\ne0FFabRsUGIu7ngmZ/Lkplh402kKcPUoc7LT52ukZIoH0Sl5WDo9aiBKJospkh4yagq67hgNa2bE\n4qckg0Uny3GllnTkdWcROi337IPbXoGFTFQSmYgcWpwVBWiwfJOX7C9VgCzGcZOUe2lwMiLZcKKJ\nzxcJjSXQ+OG65ro23I4rBiIRA+rQPBbxdkwFPG4G3AjRJlCHiJIsmJAxRqlNoMux6BHzgIYewZII\nxd1GWrwoIUYWztKbDJp5LoW1+1m/R/yS/9K/R4Bw3gAAIABJREFUZNUYFr40QNTjhtRY7LmlMUAl\nBDnlJsBf3Ruu9ZS4yaQMo6nZEvEpYTXTGuFelVETn/nI332acWTerw1bHRjaHWeiDNvAqlGakwX/\n7KXjVNb87YsWpCOmGzbbtpRTomG+VG6CxckAaljMM7c3grpI65TOt+z3HXcdnD5+ymLYUcnIkzaT\noiHKSCOGzy+Fbr2l9w0Wz0pfQzWj18T1xvLT+xd4Vngi+3Gg80IjltVix3m14Hqz40q3rKNn1J7V\nvKUPW7z3pH7Nn7w+4340nC8qvtzu6NRzWhvYJVqXScbhUS5WlhcD7LNBuw7bFtTfoCPfWgm/ibCt\n5+wHh1bK1inr7GkZaKo5+Jpa9sxnlrMorAcDGjGVYLqeIU6UBhOJmtmK43UfcK7DS81CHOfOcNcP\nbFPP1xsDkjDJo2pJ+56cDW/ySNZyk28kIOHX40ELEP0DS1hEeN0sueo73s5WXKwHZClcdGuoemKc\n8TjdAfAyn+In8sClfc0NV1QNVLlDxDITJURLa6dgJ9YcevlcOjTNKMEmquRK0KgF/bTsBlQP7UGB\n2WjZecNqCCzMmpBLRWgwwqN9CQYfhTVv3RnRRlQElywn/Vi2o2BM5Kofed/Oebrpsc2IDBVPxp5q\nyJxqCfZvfcvluhxXqnbcmRKAz8bmGGCpKq+Wno/2HW9bR9XA2XokuTn7NFJtz2gMSD1gltcMJ0+Z\nbZSQBZsz92aOZc3i311S/e6PSH/5PbYnj7H2DSnOafRl0X+Gnmqp/JtXe06s4T4XSsMQa0SEV/6c\nq213RGS9sKd8nG/Jx+660uC2HArSbak7VD2ztGdTm4es86SP8W3H0x5cW8rlvWtwac/rxRWL6y1V\ns2Frz8nNHXvmBIVFgqrdlhnkE8YY2pRYGUgpsssVte2QVOPiQbcYy4PbhcJq1Rk7LJIilp6OCek3\nWIbmtlQg9p6QG2TXElBamagdzpJDqQqKs0iYqgI6krIHRrKpMPrNIqN+kSNNwZWdsm0F4SVH842S\na8qTfrTYKMOEcTuU8ScEgzMQNSFS3CdL+X2a/2Kx088MDyCHQ/BW9LJFCyxT1tFQJJWDKQ1zlSvm\nI0xkGQHspEGuyIziSpAuk3SEh+wlQOWEMSneQqWRXmzpK9J0zAhnLcg7gBnpg/n/0NiWFZZA5wxN\nzrhpgT3kCpMc3mQGC5YRa7X0OymkWCQaOVtSzEUSYGJJnfspi3yIaKW8np8E3v9wTkvCTRl+Zx7O\nm9OHxYVQNNWHMNtokX0lVZwcZBg64RkPkgg5dp/WOU6a63KgjbNkVbwVZIqJBhwixbHQUfB/h/PS\nasSYYrDirBCz4rMwTqYqiNBMKEEhU4kWZ0MsOpmVFDlHOYIDIzkDo7pJWw2VQneQq5ninCfTeRn1\n4KTI0TSmzIVvtubzSxUgh34gGkuOAWMcqzSyEeEvd+Ctoc9wnwy7XQGIRCksYjTDJKMwxgDFHckq\nkAq6BaOgqfiXJ0VjOpaWgKlReuqWTmD1lo/qlo0NJAbOxbCNA2+zA4n8TBviAHW8pKocxo8EMvN8\nhaSI954nPpGJbGJF0g1BW/Y5I9biE2jaMnMVXSqcVWxmZiOXQ6aa1/yfP+247ZVzC41ZM68Mm9xw\nYRL7EOk3PY/sHGsrNv09y8UZGKV1k0/8WU03GNQPdENGtWN7Lyxr8MazCTVxt2O0DYsqsb15zWy6\nMT6vldmq54tbw8ubiA0N6+aMzd2el6NniWWuWz47rfhq3eFU+dZV4twX4oZgCWlDnZfsd7ecLxz1\nfEbLyNt7y/tRsVZ4fReozCnJCq3Z4XXFRVPRB/iqN9SmAa2Rcct69DytAydGObOGHZYqDPRNRRqF\nVbvi5e2eV7GnCZ66rqnGgSYrgzourOXluOFNP/B544jVkkc+8OPtLYwznFWy82wTyBBJzpCycml9\nMV1IAzYabsctVTI8tZlPJwMJ1YKCc05wUugA+yFz0lhcDPhfHwwyEmpCtceOLRexBJuKcN5tUQ8X\nfSg3/tAy0OBMCXifDvdslzPO7gZGN+NqGI6Z2s5kuvOa1XaPcQMvn/0Gj3/2JW8+/pRnL39EGic3\nODOwnp9wP3/E47fXiAhvrs6xgH//mnD5BChZloPkrhfl6s0NAO+uLomaePbyR7yrTjnNFfc5cErF\nu6tTLHD59pZ7CZzmGaKJ812kqyOnXcdda9nbJfePntC9e8PeWpqLS768TIitQAfyu6/5pN/wqmn5\nZFcMYjIlC3Ll1sg4J4/CTz//NuHlj5h1UH0WGYYNfjMnhwVnX26Qqzu6tOB0s6OywnzccfcJnDxL\nmHce2dWkk4sy1+7n8PyOuL2kvf2ST57P+ffv5gUVlTyRgeUYcVG5Xsw523WYYLk5qXnNCUGUJ/s1\n0Y/c2Used2vSVOr+08uPuFzv+Gy8JbryYF5NVIqc/c9px6shApbP72/K0yV5FnGLTSNvLs95tr2G\nFmbRYlDUGLJJjM2WPiZGmWHcLS7MsRJQLaX0DEXLOT0RtzKScdgMxlgMPQ2Knvawq7GmoifhC3oA\ncRDHCwC6ic6TRahCxdYI9eS0KG5kjCU4FlMyrL8Ow1Esh/OUeUMOUoJi+HAkIojgH2IpRpTKlMav\nVpn09SWTV5Gn+C4zo5AHwsRDyTo1xMKDqzJFj1oclh+yh1K+ofrgXFuRoxlQJSU6zJM0YOoHw4jB\n6lFYPi1qhawZ6ya+8ZTFFIQLK4xqqCna3C4rzjgkJ7ZRMCYxMxX76RnopSDjogS8MaDCCUKfDffZ\nEnwq/19gTEIXDMOYMZVj10OOJXvqvOA9HK2n3fQ1H74X6CPPnmzIby9Ik6xoTMpKlHstn9FoDIES\nfCoF/zbm0pORxWBU0MnreT7JJHqU2dTcd1jsJTFk5Hi+D5IFKOYsUDByfVbOtDRAFhfFUgUyUq6N\nmhJ3qZR50UwZfItM9tgwYhm0BLw5TVlxASdKc6CISC421KY0V8ZJs23IiJZKRqVK1jJ3ehy16JGz\n7CSXZ7wY8gG38Q2NX6oAOWfFxohzIz4UwX1OBVG2ML48CGScdDnlxEnMJDMWfSCg0009qRS+oVG8\nRgrHX6bJVygZVi1mep3loRNatHywXwxT1zaJnQVVg5WEMRY0YiUSgXEc8UNhNG7dgEWQPDKMoMaR\nJZPGCusG6lFxxpK1I7kWqz3fbWs+XcH+bsM6epgZ7HDDsxo6L2xyy2NxhMHRNTDQ8D4oZjCczoU+\nGJ6eXrBLpakxDI4+JzZBeduteOx2bEMk+KbYVItgOniThdhFYjSEquaJDXRuT9x3/EjnfLkdeSob\nlqsT1gqqkatq5PFsZNZnrBuxeeC7q5pmdsLdbmATlJkXxAqbMOc2j8wqz6ubQP96zz5GqCraLDin\nrHsKocRYzMyS+xu2qSW38K2lYdf3zKoRkuPSB4KNqHVsk2FLZFkbzpIytp7zrLSnC9pxYO2UlDou\n2sRMPdfRchcLm7qxntEoftjSiuHTqiXGSMJRp1u8KquloGqQ0dKHwKJ1zKzSj5nkPJuQyLnCphGM\nsmwMYwwMfUac0IXMTlq+GmHrPGcfMnN+xYcK3PtLLvvx2BU++IDLmfVqSbvZHTOn4vpy06aUZS+7\nHmpoVBjqQ9YJbBpZrnu60wXgOLv/mnHlOF+/oFvMyc4hIVDtlO3yGRf3L4lteYCt9iUAP+97vjyW\nLzPL7Zuyv5roj5jfzGr/hlcffxdRuJWin74WYcqncfv0nMXmNSMD6/mjY/B3+eUXXGxLNeG/tv+c\ni2bG1zcds/Pf5aT7gn96PecPPnvM1d//QeHt/l+/xZf7PT+Wc1aifJTv+YoVl/YdKieE1/+OZ+49\nc5tw4x3L8ZThdANbxeQlcnOG1443J59Q9bcYZzn56hJjHXI2nd+1h/lQsoAvz0GV4Bac/N0f8+3/\n7ZI3cgnVkqTFQChnw8U6AAb1gbMucD1bYMXwbn7K+X2PLIW3s9Wx6eejfs0ghq/tOW9WM7539wLN\nFf/y8pI/uH7BUI3Uoyva68hfkyXsXOJd8zGX/S3BN/jQM1Y9VXIcUK5xOOE+a2lclh0bbRgoLl7z\nnGkZyZKx00NYqsDJ4EpGMltWZAaXaTYNg89kBtxsJG0qRgtJPTo14LWxpp8WbYMM1LmeTM9GJEJt\nHCF7vK7/0y+WX5LhZFowTjpSKKSVrLA0sNeHz60sp6a/M8VeuGLSAE8/r1CGSZNfyyGLXLSjBw6y\nmRJQglJJQbJVaEGCHcr9IlSHJl3NjFNWN2fFTgkr0UKWaKWQLmpJHJyldbKprkhTRliPASQCO0pm\nMZHZPHnJRoX9jeeTZ5ldr7Qvliy+teXjy3tQeP/1JeamYhxneEkM8xG/qzEu0qmnyZHaJpyMZB1x\nDlKGNiv70XAnll1QmkkaIhZqq9CbqSMxH0DUZSSZgmbHs0/W/MlNy+Pc4Mg4OyHTpCwsjOqRIuKR\note2TL8rMopibVMWlIV7bElatM69CGe5BKVWilSmBnaTTvvD4bMywxDNBNrImUYSUR7mQIeZFiql\nMS5P5T6ZkHJBhIpMnBzwaqNUky5GtZBkrOZprkyB7RSDlcWcPTordirF7ZGiiQ9T9UMVBgzRTBr2\nnydT/iePX6oAGQlYdWh09Cbhc6LKIGYkYkk54XNFrCxur1AnojYoAydBcTqyNQuUATHKldbYvGOP\nELsloS2Bsk2KOgOpCMBtlag10pk02aVa1AsmZvQgH0qprGKBIAlLwk9Bu5ILQ1GEOut0LRgER24D\nPkZaoAqenavw2lPh+ZbZ8ryquM/3/PjasnAVSQxjVMZYc532ON9y4fe82MxZzSJVyMSq43MPg6m5\nGxIR+GpQolZkTcxZk1PFT273xCax6Xsa7alvt/gVrPIcrwOf+pHV48See6JW7IZztFfenVzwmVeu\nXOQkBppZYN7s2e03LB7NcM2cVy8rkk2gDZVJPGlHXCxcarFztumOpCOXdoGXgccnln68w1MxYPFh\nz7o2fGdheLcbudNHGNfz+KrmVCClRFsb3tSG1s75crfnOow8n7XELYyxx9ua6/uKlyYxF+htz6qO\nnItyQo1UPUkM953BNj3ftzvG5LhbnRJ2O7qQuUueJ03GNYaw3zPWDZvgyvt0ZF5Zgq35skukEJgb\ncCFS14lRIwsdSDpjvUlY8SytY28iXSUsELypeSqG1/GvM3x/VUeDsBr3xEaOi0qDIU3BqWuX5EnT\nag4cJ4BcSm0AoWqp+sLt7STT2JKJ3FvDvCTl2RtllkoO79af0KRbtosFs2FP3xQd69nujlt/AsCX\nnzxltZn00dUpi7Gc884+3P2NMdg0MYSBk/VL1ifPOFm/ZKjKdtTI8W9P33/BV59+DsD6fMZ8u8UD\nq/Yt8tkZn3zUg/lnrN/8Bp/9q5c8/i/+An37LUZnmP/mO/Sl0Mx/g+8OP8F974bZvuX9n1lGm/h4\njMxzYDAZl4RON8y6Jf1iw9DdU9Ut/p3nhB+RLk7BN3SnA/vZRzy++lOqtx3Rt8heSPL4+HBJ6RHc\nw5O/d8qf//k5V7e3NCr0lR5Z0lVwaC6Pukfb7ogzUxFONyU8UpeQaLlb1rgqcxHfshwaXsyW7I3h\nD65fAHCdnvJMb/kXZ8/4z97/DGMMd41h1ZVMXJMsp/6OKpU5IR6qOEelEAUAkin5oJ1Aime0OhJt\nhRphY0CDJ8x31Klg4GbjwQLh50fGEtSi0tP3F7RTNtAcnFwozl31VMaOxh7lT0KN2g7MHnSO/Bot\nakUyXpRKJoIAZZ5bAC3mCofcmwVm06H3cGTdZgp/9lCt97Z8XysPJfhJNVBi5vIDRQhGjrxeMYff\nlUDITcHRKKZopSl0hcOwE8HAMPW2UTLSwofGIuCnbHKHOVofz4zQaaElNW3H00WETxyQOR8NX98Y\nFpd3hSVnhMtPX/Pl22csPbxrtvz2k1t+2s/p3pxQpcSNwD5HqgQ4i+QiN3AIMSkxG8KYyVXmcpap\nGyEcdABDKsGEm1YQH4KWRamaxN/57bfc/vATdlpij4ihnhrwkjwEsqqJfgrf3AfbOczYgocTamKp\nromwQrmb3jGKK0xjzYiUhkmrekSvYbTYc0+fx9wqKnkiyZf3tFM8lHIh9oxFiV4aJqfGznqS0WQt\nC6f/aP+cGJwWVnMlcpTLFGkN0zE+zNu6hG7lPVJc+qIUJOYg32yE/EsVILvJvllNLil6rTBuy3Nf\nISkRVTmvEme+2DhHjcwlkHygdZltNjTVrphqCFiNvB0d92pJNqAxYK1BXGKhhlWV+VjgzAuXvuO9\nVuxN5ss7YcDRVcIYIjotS7woJuu0+jVIygXe7wRJRd4xWMXGhMfi2FKP8FRKue/zOjDaHcOYuAuR\nzWbJX3QjtjVoSGSTy807CrWxXCznqCbW25GNGfny/gmjhXbn+bc28cR5+qoldrc0BJ6f1tjuDs2e\nbc5447kIO5o6QTKcny7xccePXr9ndnrCmWm4HhK7zciZT6h2NMbwW83I++4ebyM7mdF3yl7m1IsT\nkr1jtw20q4APNbUZkSoTZAkiBLMtJS4/49P5I3746gXZLPBjzWXjaek4qQNrbZmnPevkWNSWjy9v\nIDVkFEmRMTlm4Y75rGEmGx5XHUYt1g+8bWaY+0RqBvYhcd8Z3g/KbbTcyRztOxoX2Q4Vum+Y5Rsk\nzPmzeMkmB2bW0Kqj14R38H5Uwj6x1Mw+jYxRcU3H0hh8DDRZOXEd1DN2QRlrw7aLrK3lHXPG2IB0\nzKqGe3W86wYan1lYw0wGGutZmf/YneFXdFhHI6UrWQ3YyUYaoJmyuWJsKel9IOIUo4eCKtU4gjGI\nCLNSbKdvGi73Pe+ePUN2O/JsxnZXgirfCnmy6zYWXCgNcrPQY/YFT7aZGUJTmseMKtvLM5Y3NwWl\nRtHltddfs/fQjFtOt/ds5ivmYccsZEJd+J1WlftFcV5bDF8zD2UfFlGpreHVs2fcvfwul4/eojpg\n1LHUO77zOzOQO7INYBrENnz27T0/fjtw1a4Z3n2XWfi3bPMz9uJZ+Z7u9IbV7jPy2rAOjt35e5ZG\n2DhhftvTfaficnmKpJrcGhZSsfzNfwJ/8nv4z1/jJRNePcPLSwb7bDo/bwlpj758zH9+/Vf85exb\n2OGOVsuDRUTo6yI3Uz18IhEbBBGLZOiMpc2gopxte7KLSDhhd1LjyCxy4vV8yVm8Bht4WS/4w5sX\n9E1Z3fiY2U+ppjYUm+1maIh+JGhCswFRJGsxr0CJk8jRJei9Z1FyyKQqAJ5FyIzpHBjo6g3STxxt\n16EqqNrCPbceVVfMCmzAoGSpiJXQpJJt357ccLJ+RK33eF9NcwbUKtpkZjvlmL76NRgl4NSpEao0\nNbmDYn+6lg+x2zFIogSwh+Yupiy0oSykhEKzGKU0kSUEIw9BliMeTWRKMHio3nKURlRGj9g2Q6ai\nNAoeiQgcwA+lTO902l+RyfxCqYHAgya51nxsaIsKjSkSkNe355wt3kEKJd3aBD763vUE+J3uz9bz\n6dU9+/ct79s9+2jIu4SV0vCWUJocwXlyhCEZxGTqqddp00c+uUqczVOZUG3EWwMdhWphFMb0AIA+\nMvWkXJzRc9t2zIdZaZ4TJWlZHHgt7GDVsriZkYo0E0MQJv7KFIRKIU+IFIxbmu7QM1PoEofD3cmB\n1FFcLg/Z4YilJU3mVyXrP4rFMslaD6Yo08IlGqHJxddBKIG0lxIrOSmaY5sf2MfKhIemaKZrzdP+\nF0mVoxi/NZrpjeCnudnnoj+uD4kWCu2kUggf2Id/U+OXKkD2WRAbSSkhtojvB234Sa9ceM/TyrCs\nE+sk3NjytTVC7mGvDV1S8qiIGiIZK4aZwKXtEVdjkiFpj+Q5H/kdj31kYTO+Hqlsw6d5zW5cUl0u\n+Rd3e1IESUo2iheLakKloJ+cJEYM3gRQS+s8lUQeV4l9tnjtEafELHTJomr4YY7kFPBiqW1D5/ZU\nzjIGJQfF1IawHdCqJg8Db8fMlamYNQ2fx8i3Zq+ZO4czyuu18r6/5zK3/M0ruA6RC3fN2hpyuuFq\nVvH9K8ga2Y8Waysa+55RA3/zY1eabEJPikKsaupFInY9SkUfMldnYH2Lr5R//5UhaSKbhvtwSQ47\nllIxP+1YnQXykEnDK85nC5COXWewc0uOb/jbM8ePfvKKZycXDLllzEt+dn/H+UlF3jt2OZP6xP1L\nx8tROXPCmARnBkJQRD0xRtYyo53oAIPe4qNFdjDEAVS4HYWnlVLnkRwD18mhGrj0if+Puzf7uW1L\nz7t+7xhjtqv5+t2fpk6dY5VTtituYtkQRQkQAwZH4SIXcIForgwIJIQgEreI/A3ckRBxRUC5I4CJ\nlEhAjB131din6tRp997f3l+72tmM5uVizO/bp4IQEhS4qubF3metfdZac63ZjGe84/c+j/g5mzDQ\naMeTmcHgUe2oqgO6ccdhoeyiQVPBsQRMHBnVc6EtR7NEDCOl1HQKBQUmwODmPCsNvY98NkZMbbjq\nBx4Y5WELapQ2KcYLKXhutfi/Oft/fLZn5lO8OM71KQp42+LS9p5FvTw65GAdCIVlV1vObr6498q/\nPHpGOyTKIbzRzircPjnk9HLPrilob9YYYyiud4x3KWp+M9n5CPhtRhhOl3Q++2ZCRlQP+h1llPy6\nQXBekKJA/UgoLNvK0npl1ndsT45YXuc4anUFduawwOxqzfZkydmrLSKGXZ3ff90cMb+y/PL55xzb\nPf35Dc6fYWY1Vhvk8cegYM8+wx6do7fv8enfg5/W1/i3YXt0xcmftFiTCAc7tusZ6fYD2q8N2Ks5\nq/UN9faQvjPEAL4qOPuiIvwL38WliLx+QqTH3TyAr56jBy/AOlwIdFd/IYftHgTsBpJrOPtn/g5H\nn/0830FJ1QFl9wUOQ7AzGs28nkzxywBDGe9LPI6IV6WIlmSmnCqjnGx7uOPui45rd8pd/MZYjohv\nuG1aDv0Fl8UpZ+mSYEcOfEtyOVjHaTb0dwLGbDAIKhNuo4cUxqPsKO0+VwlDZsyj61A6JOYOeVut\nGJylKzYcrA6wKXM0h77n2lqsHYkmsT+4RZNltlpmwVVHDrsZszDQcUylHdEZwmw6xxTUTN+xvoMN\nfry30W6JqaLVHOxcmYwx3OVUtJLT51QnMYrimThcppYC5P5YRwwzTXnFZ6pwFqIMfJkvzpJ8Mmwl\niU6pdeZewJaqOFL2r50Qjl4yV5r0rsg6cdHCJBpzZboyb1L7zMTUXk187B1fW0sWjbsqsVHP5abg\npEnI0ueZtp/U4kgGZKuG3z9vebsJHInDh8BeDUdE6iSoMRyZgpvWI0a52jg8EdRCNLRlgS09VOm+\nN2qaEYDErO5yxje0d114GU3CChR79oVj2TfZC1ojo8kV1iTZoUO+5E0dJ+wk18SzKHbTMc0iObOn\ndyx9TIoVc+9UfD/hIf/RCCgJLzmIZTZ9LjZzxRm3yJNaEcWSVx8EwUma1gwyVtJl0B2LZMFM/po1\niT2OgGJM/vxbdRxooiSfDyIwiGNMGR+pTSJOhfdKLJ78G5Sa8iFk8sTm/6JK/f9w+5ESyIyJvswJ\nOmaMGAaWdclxOTIzlo4d62hhGGis47BInAjc2pHVPovqNnleSUUdEg9sx6O58qxtsfI5m74B45jX\nN8ys0iyEvT7lv/204zI6Rp+9NMe0wkqJ1YBFKWNuZogGJOWBRRtHm/Z4U/JO4amHkaqsKArD2A+s\nO0MqSxozsk8DYkYemjnvuTV+1rIZPMPoOG16hnHk0sxoNFFbhwk7Njry85VlqAw3mx7vBx4cP6Hf\nfsGGEh9LvnZmaXzH6AoOh8B+ZTg9COybOSTl0nvadITGwN5ccD6+S6UbGtswjzcMvcG4PfMjA6ni\ns6uGg2qkOQj4Xijx6G7Oez91iY49aduy256yZs+TRyPojs1lR9geMSZHpQNfFI94t7zm9rqgdIGi\nbDmta2Zs2fYblk1NUzoW9MRqRzk02Nbw7V3gtDTo4AhWOZSKj8aIpILB9Ei0rNKcBs/GVBRJmMfA\nmRnw9oBZE+lHZd9HZFbwNAlFgoIR04zoENCqZgiTA0I7Y506QioZrTCzgUpK4hB4ZVqu9pHWFnzz\nyjM0NW85ARM5q2CoEnZItArbKvCzhfJZXzJ44fNaCGuD12xNdCCOcRy5/SGal/9pb4VfcuB2GLnl\n0i0x/hwjDp2SnWajsmBL6HYsuswJjtWMcthx0gkqeygX91VnzJrj64F9OadMDtE1Eheo2VCynD51\nDbK83wcjG8qrbIhkJItcF44odZerLSwAwZRZAIYmX9tH3jJOx2J2u6XAon4k1gVHl7lS7I1werGj\nL5Ttw2ecXNxZoClqOiIWKaC9/DrpL/4vyKffyHf+lEgpYqyF28dcfHvO9elPU13dcr16lxP3Oekw\nceK3POg9zx/fwvMHtK9HPn4444H7KdKj77CJj5h/54adLjk4/AT/0SHyyx9iCwcvnuBfPEZEMCHA\n2RXGWsS+QubHpJXD2Yx6cfOI8mueg9+5yL/gcokkIbFAvcfq7t7XGfKYPn7pOEcraJgSRoFy4mM0\nOnypFP2CI9th4zSMpDkIHHZ7pBAe7AK1zBADa5MdEuZJwJrpM5W62OLDEeBoVRmlJ4liUo2GmmDe\nIJtlhMHkgTuGGUPpGfycZl+zNjVlyk2fm6bCjQGVgpiE6iY3bq4Pz6fj/ohOBNQS3S73/Rml3TR0\n7q76GSm9YeQno7u2K8DqljLMONDsq5usuXdJsAphuiIT2W/4DmnoyI1xRt+EtJdk4XaiiWCmlLSJ\nR713geAHBUZCskUYbyrE3pgcJIdQMyWoaW4GayZxnowgd6LbfKkCSa6EQxZ6GwxLctV1Oylwh1JI\nLuDWZaSsLPvSMJssIu+/sAEKobu2PKsPICXK/oBkYF7CdRw4T3sWvsRIBWWHEcPZ2Z5FORL6kler\nAnyF74UgBldPVdYM2U7MAHc2Hvnf7FR5QTv7AAAgAElEQVRelenLOuH9p3v2H97x1xk5sGRkQkXv\nEZa7H/nLI8sc6FJ2eoDciJm/p05YSn5s7p/Ohch0twtWGFUoJGH1DU+e9A0pV0q8xz1ydHmiStCb\nvBLYkP3ms8uRTkaRCmJoFJxaKs3BUXEKEzxNWZRHhWLCnypVZMJHVC1jhGSzT7ZI/um8wBTgygD3\n95Uf1vYjJZDrUtCUWSWxNi+whcA6GGLIPKcNA+8takpvmLeJB7Oapw7sckCd4fwm8qQJLKqCha15\nuff8vWu46p/QGUtI2cbIKCQXqFKHCYkke6zNtlGtcTTqKZ1QucgmCd57hILSBtQowStaKMfDQGtq\n1mVkL3AqgQMXeLxQjhvPQgxdXLEsZxhzRUHJxeaG1lmKoiQa4emhY3G7xZlINTcEv2dmClxbYPot\nMn9IqZeoP6czS5phw8OF0HQDajxtUrRsKRyMg+LcSCkWW4Czl7imAplz4j/FHERIJaqWqhzQruVy\npfiu5LT+CKlmWH3CxfqCbV9yMNvSrg9gtGxHi4tX7NaR740VtjzhxbXHNDuObctaay5ev+b4aUsj\nkaWUvF5H2vkBZr7j3WNDvyt5sfd862ViLOc8ayzDOOd8dc1LYzHaYcuK6zRSiWM0K/bDSE3Fw2JL\ncj1PZy2fX/d0seKzVDGzA7thpG4bYoTuek9sS+aWzJG7gI8tn6wCO2nwfSLeCNEaWh3Y3ZS0Etno\niENobOT9xpE0cVoltuPA5U5QMXyRBq5DxZiUojK0WnNqe+YEpEl8thcaW2acRwtGAq4sqMJPRiUK\nYFX2aH+I0z1NHBgXB9Qhsiks7Ziodcu+tsCSZI4x6RoBxnaBTI1Pwvr+hju6BWXY0sQsdKMBZc1Q\nP8aX2Z7McISbmmZ9WSCS3Rse7V5iNgMz+Sbj+gHro3eB3FRytP4UDNwu3wHg6PYz1kfvsLi9YbTK\nMD9h/6VygyOLxdntNcmCPzjFhYSYTUZ/JOMIMm6Ixxvs4orLD/9ZHrz1h7kyc17A1y/RqxNSjJzM\ne76x/5x/dPLT7B28d7HnQbOjrXfcvnfAmSoPFt/n4uZdHm0eMJdv0d0kODpHfs7y4OUlrJ5RP76C\nv/t1wmmEf/oPMCTszQPS8RXmm4/Qx57S9/jNFYlHxHDGUFxQW0i/+gc80ne4/NYr9rM/x7G/5sYt\nafZfQDoiiFIMq2yLZoRK3wwvY90QdEs9DVhDmauqppohw45QKagyOH/PlzbxbkCbY6uEifk6m03G\nrA4lxQBGMDLiw8m0XJ+4C1ZUycumkEWVUYOz10Sx07KsIioUvqRgZDANRb0mqRKGJaQsonemRJaf\nU/UHYITZeAxAVexZzbeMkxXW4eVjiKCyZzZhPKKKitL6H8IF8yOwPRgKpHCsXOAgWqK6zPuKyatA\nkjFceFOJvMtvq+/cneQN49poXpLfTU80U6VyLUIzccqCspsEWksWRAnoiWhQXqc975WnlDLkJsCU\nEQYnSpZtuZpcSc7qq6dGM/cl/aOTUNOkzEiMZhL9epemNyXJJU8dA7GPLEvNXX8Zqp7UYlaaTb3n\n43bPo35J4xP7rsEXPXXd86iKeO2whbLbHLJc9Oz3Bh8sx+3I243n9TowryLOwXrrWBb6pjnvzrEi\nkvmCCPfQNtPsQhK1C4Qna8IXDcGWVCQGLHVKqDX5cZL74JDqngrOgq4wb9hdc8fgT1/XmMwHlygj\nGX/Q6dosrWQWWvXei/huk5QoJ4HuMdP764R45ElW9SVWPapBJhGci0V5MlOYzC2LZrY6CXjN/Suq\nMDeJfhLYhdzNLiYWXrKTiTHkvjKmlL/JrqjUvGIxviGE/l9v8uVo1z/t7T/+539Dm6KmDz43d07L\nLqoRQ27iKSRk2zZAiNmxQjRPcSQL3EIhEnEIhySiRMbe8+efKO9UFcbscRJQKWlqz34wFGXNdTfn\nf399QxcX9FFIOtBLSTIx4xbWoBYKFUwSoonYZClEEZsQERYSaQk8aEseOM82KZpGbnaReVkwq6AM\nwqIxnG+2PJnnzHVnFoRhzel8xsXNnrb0FG1gNVree1Twvc9WvH0m+KHEEqhLR+dHmqOa0BsCa+pq\nRhgFh8nE+qLh1XcH2mKF0ZbVZqQLS5bzAZM8J18pUb9CRgOuZNwteHFecvJAWMwcr696HrQlH14a\n7LilXe5wqeazc8PDwwvq5pjTh7C/Fny/4eD4gG9/5th5x42MxDFQhwpqz5OZYbtPfNwp1nuOEYZi\n4GldUMwaKmu52HZcb/ck0zBvahg7RslOF7thZHA1tcnBIGVqqJpEa5Q+BEZ11CIMyfDFLhvFa4iU\nZW6klASPWmU1diSfeE7FXIWTsuCzMdE0Bc8vNhzPGrpg2fgBnwzqUg6GSYo6gTQyakGjipo4CSeb\nJyQSaZJhpwlDwKtj9BFvoDIFf+Ob//iHeOn+6W3/87//b2q7+Yzd8h3ai9v75yuU1dkxCWUXW/q6\nphm6+16U1uypLtd0Dw5Z2UOiNRhjSEFJZQY+rfckl3GUN8UPnZbh82MbAsfdBX/eXVP9wj9CrYOv\nXBL+5C/zW7/zPmJG1rMamTCCWNaYO+Emk0VUDCTrkDgx01iiKfL+pCzgks1rlRKY7CMTNgZOxwse\nn7/inScX9O8IS4W1nbEMk+vBLJB+7kPc7cPMFZ5ecf53/xrmJLtqLPbfxs/PWD56yc3FByy+2PPi\n5ISjzQsu2qc8fbHl0yczvvoa7Dc+Ivz0c7rf/iXa71UMf7ag/Po/xH37CanaY4YS/Zlz0qsTuHmG\noaLTpzTpI+TxK9KVZ391wov9V/n963f5yuefcHt2itWRpEp1uWJzUOeBJymqB/eNlEX3Bb55lpeQ\ngV1tmXc91dgzlDXbpuZkdYvoXQe6YsOOuR0YOMI6Q/SRmHqsMUQpSeowumaOy7/t9N4WmZZtBU0w\nTAP1IA4TIJns426mTvmyuKWPDbXb3oct+LvgKGdZ9pFglV4MhSa8sfcyohgqdsvX+X2GA0RhqFcc\nXj9Bq8yzL8bFPU7w9b/z93/sr9vVb/6ahqTURhi+NOZHSVTisqDU7Dl9x7rCpCOnpfeSSRwZ6JK5\nF9QefkCgwUQWyJvHEehVuTje8/7pi1wJNgqm4uV3PqDRkUP1hKmqWBphr3L/nmH6DCUHReR9N4RJ\nrGlKFJOYTgobtdn/dxLuqpEOz81iy1eOe4oyZIuUCe3JSXcpf5gCSRhvluxjniGlIlJaYd70pFDx\n+WXNrEj0KWKlZExKa+BwOVJMWI4fHSEammbCK5RJp8RJwQpUcaosO/DTPvUGRuFqd8zB6wXXZH/o\nQK4y3/lIo7lqqlNyXd7RLLTvmtjuceG7+96kz+N0DqRprhAlV2adhSEqamzGoFTZiaMmN8J5zX7S\n+dzI+2Xun5/s3DBskqFQndzLJ09tyQ4XThLjdGzj3cRala2Z7CJTtvnjS+dVEJv7vcgozx2L7e8w\nErIjyt2q5K/+zf/ph3LN/kgJ5L/+67+hVWBiTUbE1FRp4K22pgE6p+xDoi5Ktr1HC0v0ijrlah+R\nkBvlVAQ0G8FbawkoRVCUEWtLhunsyRWhAqMTXZUCg8tLefWwZj47REzkoCpxKhTOsKDD2IQNjusU\nGdWyT4Y0DrRWOCwsC5O4IvCoLij8CDFHOItNLAwUxlM1ifVtZD4raG3EFHMsA5+uAsetUkrNouoo\nrMOVAaVA3YZxF2jaltVWGcaWi63HJsHGPR+8B+vbjraZA8Jq77m4Pedrb79DpIOhxR6k3KRw4KGY\n4T+8xR4KxjRgZmAEvdrS1yV1Eq53BfubK251zvHxHOMNrkjMio7dfs35ao4xjr1Evvpozr73VIzE\n1PPwMPDiHD68cVT1Id04sBoSzgWsOuqqYNtFrClxbsCmntYeI2bLmHL3srUF1iiljQS1vFoLq36F\n6JKmdpi0IZia7ahcjp6UDNbmjuLGFgySCEmZiaOwsPZgiURJDCGL5z6F7GEbDcYOCImFzfZf3kQO\nYsWeIadHGctmuLuBBHpjWWjEuDxw58SniJhI6BN+sjeq1PDXv/lHP/YDLcDf/w/+LRUR2stbutMH\noHlAmF3eUDw6pz9/zO7sEINwHB3D9WWe3D56iZ4/IaDM9IaTxpNS4h8+/FWOh+v797/rZxymuPeC\nHi9NHiBVOY2Jq1Dza1//LUQM6a1bjBPSYLn5rbfQt9YMf/yIPzp4j03RsPTdl8S2YVdm3GLue3bl\nbDLVV+Zjz6ZomPs9VuG2nHEwblExbIoGEWU2jhiJPFxfcOw7zh5ewr5FiWwXGzhKnN1UXH7lKcc/\n999jvvM2IV5QfPyIXbXFz89oF9/Hbs8IxZzKJNRAetkxhl8mHP8h7WXF6EY2c+FBZYm/8h3s733A\nLmQerzq5xPQNqe7QzSFSDsjPPEeS4r/zi5ASxaMv4Lwg3h5gPznhdlbz2/unU2WvI6Uay5AnH5LY\npoaj63M2y5p2zPfHfeloBk9XWZohYpkSCuMO52Z4n7nzOAWjNAp7lCLusVIzTgiHqlBWniEsqYsN\nbe+wErF2zzjm1xYm85p5iTSBWLzPzUiqSm8FSYbSZgHrwxHOXhPSAaXc4NMxhblGValDw3U70sSI\nxEk4xwbslNBoBB1zsEgse8oYc6hCShh5s4R/OGSm+SdBIG9+89fysEhCp+osZOHoJVKpoyDHGg8m\ns64GCJIJ10Jhbwa8G0gK7/rlDyzt3xXay/vrLGvAifBlFOFKG9r3vsehDUiRcnZ7qnj5/ZbmqGdz\nccATv5hiwt+4M0QspOyKnCuhk+fxtGyfwzAyM10JU6R0roAaFIzFaGAk8GmZWNQ7ojVYTSQSy3ZE\nbWLZACZbIPZ74XKsmJtAaQXBgzU0hiymUXYbR5QCsTnHbdU52gqO2jEn5gUDYap0FmlSq9OPYyR/\nf2XKiiZPpr1AsGyipbhd4m+XuZo/+fyq3uEliUpzFVj1S27dYrLVrbwRv7miPoljzdXZu8liL0KZ\nciOmphzqcSdyA4pLSrJC0GzNNyr3qYM6OZ8Idw2BkoO2plTECqVXi95z69mqzkqugNvJjUKBDmGW\nckJeIdyjF3f6tBAYpzVHTROPfEen3H11YJge/EQK5L/9V/+qkiLJCmISTQiM2nOIUIvn1VDT1fBq\ntIgr0ZgYSbSaPRlV0719k9GEc46zIluZ7JJnFMcmCNsYsNZSCKRoMMlzXEJhIjNT0BawHwecbSGN\nqBFKsbzqBoytMTZRlZZlgO2YKG3iQHoalCcnBWYIbLqeshBKG5i1C1a7gXU38qT1XHSeWT1jXgjj\nsEGqY5x4ShtomyWm7dFOudzOuFlfc3zaEgdhUUXamfDRt1Y8e7vmZrPi7PgxmIRdAMkRrzpGjRRV\nIDlD3xU0BwcUZg1J2N1u6Pcj6oX5UqhbC8UhYTPShz3zdjnBPR4Ezq/W1HZJWymv9wt2/paHBwfs\n0w0aa7QHNz+kLvest4bLTnFpTxkDZWPYDJExVFzsS4IoTSEclyN1tSD4nsHv8V7Zh5IhWYx6ClfR\nzoXXl56qiYxDDoEZElztI1ssZSG0xhC0oNSBIUFdFcQgdERUEi7BoRG2aln7nso1bMNAIcpxIRTT\n4LtLgtoSGROu8EQtaYzmqqHmKnGLMkTwSWjMyE1wVFbYpWwV6BM456glYKww9g4vuVu5D57ROP7z\nP/zDH/uBFuAf/oe/qTB1dF+8yhX22rGeH1GFP+Tg4gipHIXP1b39sx3+/AFnLnIoX1DPEx8WS9rz\nlkfLl9iqJJUF/yD+OR77xMvCcBaVrU2M6jgJkUvnON1dAvDp8TuklPj14n+ketxjSkv62Y8w33qP\nbl/RnFf4g99D3lHcVcv/8L1/na/2L/ndg/chBUrypGXmt+xsFkH/ZGLafApA2boF85DRj72b8263\nysu2/S2F9Jinr6i+M8POWmZ2zUJWFL/okW98Cr/3Pv7Csa4TJ91A9AOb4wMW3z/m6ueWnG4+Z3SB\nKlrG5Sfox/8Spftt5OYt9MknpOdvY55+hm/nOANDyPfqOrp8nytH7shAbff4swFLxL6qYJ3dPPYm\noBc/zze3I02akRACiqhyK44gOg1aAyklujijbxvqfcfh9TnDaXaK6FJD63b0g+dw3XF1/JhFd4md\nlsXvtj5CU92iKd+H7W5GnG2o/Rubw7vhXEQoYjHdsy0RJcY3TYOQK2Tl9Hicwg7euAZOyVmqVPaG\nPh5R2zcTLe8PM7M4CWeVPKibqHhqjMk2g1YT62aLRXh0+5BNtWU+zO/342f/mx9/gbz/d/+yQkYP\nRLM3bUVmVK1E9qoMBrqpuneWlFKFV2XktRkwEnk6HNLbSG/WaCEcWuHx5pgbYzhKid4YbIqoWHqE\nGrATbWynzrJPjl5xeDCyMIotAgRDDJZ9NKy6xGyekADNi/fRqqMeKtBEkMl1Q7PQAu4r/P+nTUxu\nkCNXFbcqKImaSGIkWvhiqoDvip4gPX/2SYdUCsGgA2xCQaXKECJ1lZtC6xqId9HMSvBCF2ps8hR1\ntkWzGCIJZ8A6g586yIoMEU9dh9MZbOPEJGeXqbu4Qx+EwbfMXy5YU2bcQJVIrtRWkx2bRe/zHcyE\nRHjeWMG5afIb1JGI1OT4644fxFTGJBQm4qfkSGuEFOMbBxLgDrwSclOgkFMTHUpI/ACOkb4kWpVc\nlL/79zSJ/DQ990+iHJ7cMJrSHcOu9JpREKs5sOZ+HyQ7rtx5Pgd5E1j0S3/zt37yBPLf+iv/nEpK\nlNbQOqWxFefecj2O7CRiQ8lgOg6TcOoKFrFnZiK2thhnudk4NrJBxwVN5XNS25g4aCytFS6jsA8C\no2EnhpAgukSBYYwdJblC2LhEK9nb+BEGqQXCSJMcTTHiCBTiuBwifdcxrwsOipLYRG4uR9pGsFoy\nBE9TBFyfRauZHTMrB25XkUVtuN0Hni2XSOG47VYs5jVuf8Fi1vB8N2JjQztbUPUXFLOG677j8eGc\njz7vWTjBS8Vu3OOaiqVVyrJks92zHSKLxQG3mxGHZ1a19Op5cKB0OziaG5aLDkpL0i3GOXQ/x/uK\nfjey63bc+iOawiAWNCZcscOKcFQViLNcrUI2fneOm+2WWjbYomHdz7n1QlPmi25hehbzOUMHl37E\nR0dKW+r5AtMVXI0dN1tHU0Ze7ITOVFR0bINik8s58yZRlgUuBWa1o+/y5KfrE0kKjF1R6ZJOB1rx\nGFOSmDhz2/GsUF53JRsf+cphw/rqBreouVHBDgOjc7mb2YJPJU4NFo8rCwby+dgNyqzoCaPFFUoY\nyFULW7D3yloV5xxLIrsYKawjpIiLeQC/TZb/9Fs/GRXkb/9r/7IC+GZON/v8/vm2e5r/4+hzyosF\nx3iCaynNBilvCfsDHIqfrThKFZfLBd3rBzzRP6ZuHHr2XeTiA/xoibM9V4snnK1yWh5O+XjzLn1y\nNDJyON4wuJc8K8D8G+fw8RKaW3hVk64qzMkAu6+gdccQSuqrnpWv8buHPOcdohg+aw54d8ji9+Ny\n/kYlS0ZyjLnrpHlzu7+7X749bghxT1HWuM0KKRxvt5csvva76E9v4XWJPPbo945Jf/QEeTayitlm\ncb7a07sd4+yU1uwwaQ4PX8HtGetRkBc/Rb3/Y6rTHl91iD8lmde47inbRxULv2espipvmnHYfIhu\n30YIpDZigxB6RygT5Z8cEW3J9buely+/ir3dENsseuPumu3ikKWHlcl890VxzJG/zmibANOy5jbN\ncCGLz2iPmfd5srIrs0dBM0RavSL6BVahPznEXayIsx1GD+nHHUUZqOJIoXVuxJJ0/3smekxqiNJT\nGI+kSaDa3X0stiQhSWIzG2muTzHVim4WWGzcD+A43SzQ7BzdLNy/1t5bZCmjdPef26SGzuTHy27O\nos+fu212bJsds23DP/Vf/86P/XW7/Xf+Yp7Uir33CAamgN8sONbiWTuhTsLejcSkHOQ7cJaWVpnH\ninlq+cTeUBeBSkYGzVEfbUrM9YCXtsMgzCVwvD+kTIIvI59ppB97hqrnV98PEAJEGL0lJcEYpSwM\nSCREy7q3bPsC51sO93Mc2QLsznbOph+8NgMyYRVv7CRlakCDLJYNucEMTRQkdvMds+OryVxX7iu/\n271j3igE2ITsKoPkOObSRgqrUwXYMfbCbV9wtbY8ORpwNuEc7H3mettCs+vKfdk+V/LF5gJM1sv5\n85MqXTTUxqLG4j47YVC5J8JdiqhCJ/ae/xU1WFIORkPvXR1KzRVfmPDm6Zy/w9VSyrxuK5GRnCyp\nKtnMQyZPZwFH9mQeEYrJhg/ykHkveDVPCu4K4XcM9ICh1IRLiV4s1eRO0ovc4zd3nslyV+2ejm/Q\nlFetyHZ9en8cJ0tG8vXeS55UN2RbOZ/gF/7WT6BA/u/+2m+oxpGTMvDNLVgaDq1nUURGr4hTyuQz\nx2Y9h5XDmoKLkNjhMGFER0PZNoy7geR6iuAQPMEVzBD6VODsgInZLH492YhhDTFGklgGEiEkkstL\nJPM44pyDMdAF5aAqMpfnIxfJ89AlkhYURlkWCaxjt9vx7PCYkT1hp5SN0u1Hni7rbDlmC5Z1T+y3\nHJ8sKU3i81cdF/sarSpSSiyLmkhNP+xy1n3wHDbQWE8Qy/FRw4P5mCO6Y8XNzvPyEtr5Sw7KGSSh\nKc9olx3VyXFGgYYtkIjbGfY0ggx0l4Gyitj6DOWC/VaZLWv2N2ua6hH7nZ8ScBKf3HQsmgWNWzGv\nGv7k+xFTB44WS9bbPTjDrIhUruCmczy/HbjpE2Vd5ZM5GnxMNLagKYQQRyQY9j7gVZizp10u6Pcj\nOxyHtaGmp6qXnK89YiIqBXPtGJJjWSUud5HXzCjjnlIq1O05lMCoc7bjwMmypY4DZZUdC56vLWuN\nOJNobUK9RYxS1zXJOLzC1TriLRzWwu0YGKNFYkBtZKEObxJowVo9cfLDTGIRyS0uma2NFJMI8CL8\njW//hAjkf/tfUYBgClzKwuoH0tOMIE++IL14jyiOWnOlzqvHPH5B8fwBi7Am2UgRLEPqqYqSTVrw\nVr1mbFaUY4l58CH+9Qd52XOsKd76iP3qGW5zRGkGxrPXlPNbmJvcJXJ7SAx7xBcwe4nRGegZyCsC\nI46acbNktTzhonPst79IGicG2bXIxHbEIQf0yOR3fge5qgoyOWAkgabf44sKMYakFiORd59+kyQF\nRw+/B4c1Ggb0d5Z0Zkt11uJCQzp3dM05M1+jh4eoRqQZiE+3uMuS7XZGNx5j0xXHtzX9Y08xTm4T\nRjFFIo2OKCsKlrC4JYUWgiFodgWo3voe6fwd7JNP875/9332NwWvLt5mTJKXK2cH2epNLH6quN0a\nSxTJfu8opXaICKPUlNrfH2K5F7bcV3HbyxXG3iWSmnvP5Uji8vgxR1fnWSSYiFXB6BRTrooti9yk\nJT1Wqvy3mRbxJyEQwyHG3mRWfJxTiEfcftoPvX8vvUc1YDebbPxMBVEmnv3NtjPd1Ofy5nV3YnlT\nb1l2c37lb//2j/112/97f0lh4vnv5oFf+lY9SpJAoSVOLEHjZKsWGUwCo7zSxILAlRRU3uMKg1NH\nKLJp106EGT09NSEplVVKm7iVgkoLCgbmRaQuAqaAxiSCZnRm3QsQsc4wq2DolX2ylDYxeMtBbSjP\nl5TS3iulJO5NAltK0/2XqcVrOmdU7sMu8hbIgIIQsVgi/dNrajtiiiEL5RDZ3bR4SbR1yHxwHwnR\nEJKynJMr1JKgzP0p9Ia11MQh4Gxi5nIz2bRz2VJOlRAU53iDV6hFJ3dFYyce+e5cTpar1wvaboGq\nndCGjJQg5t5OTzUn6w1k/CFMYrMQ7sUyvHG7uLPcTHrX/Bjy5FEkN9dJRmxqMvowIvf/T7hrxFMl\n4QiacZda0j1iDW8+10rusZoZZac2x2JP+303YdDp/e7wJpnCjGqEvZp7o5G7zekbv+e7KdKdWI6S\n9+cbP6QK8o+UiwVpRZSKF4PwtVngkYyMZcneWkZpCGFgM3qeh0QKDdXWY0ziRJSyDLSjR9IAYaRu\nADPjKuZD2kfBW0cg4ELMS4NlxaPKEIJhJxEJA2pKxEd8iowpMaSCpe5oXMNePEeV0shIU1WMyfBu\nqQy9UEpEQ0CxtEXPg+MC07/CmYTMCs6agmIJiyOQ0fL68pZuNZDqE757Tu5ejS2tg6OywzlHMiPX\nF5c8XM44aDwMe3YxUtYttR2ZuZE+HqKmQaqSI7Pm4bM93fAW0kcub2qOTytsUbF7fctu7Xnw9hMI\nW2Jxjd8misoQvGPsa6q0wpgjZgtl2PW46hivgaQ7bD2j6zseH1Rs9sLGHzKOlnI2YvDZEq8w/Mnn\nN2h1ShfJmAPCw1nJdowMw4C1JRZDEQMQmNtAUY88rBwXvaVuj+jHjlkT+KmDSD8u+e5Nz5luST4R\nXMlStpzNLcFHaGCZLO8WO17tE/N2S+kiBymxTwOjwOUYeNFF3JCZrMNq4MTUDCT65JDUA4mYBrq+\nY6+OvXFEVV7vDbWZunBFSangnETlDdZFqiQYKzQmMMaItZZ9SsTgcbyJx2zNj/0Ye7+pE2oqlAFs\ngwl7QrPAhkBFyulWL55itcca6B+9orx6TEyOdPM1UpFxJ4OnrgRrI3E01GbLt58k6suvE/sdp594\ncAccKkS3Ir08pX3wMSyeE19+QGmuUfHo1SFyMqD6MbJ/F3UeYksfGmzaYpsOG2f4OMNGS7uLLGLB\nYAuKahJHEqYlPaGoa+7oSQU+cH/E94evEF17PwIkBL9oYZdHI2sCJjjWfzxn/sEl4XyGe/cFnMPu\n4AEiDZYR3r/Gt09pnpRc/1FLbTzNe58QXz3Efe8BO3qEyOnVFpGalBIubpF5DTYgqUDnV5jXjwiz\nS9guiV9/if3d94m9hV2CIxg+ey+7fzx/B/n5j0lyTfPZIV9ZXSPfeAH9Fzz//V/hunwIIZJKRzX0\nnLSH99HgG2dQzc2ThfYsqFhrz37ggYMAACAASURBVOxqdS8kNw+WzM5XBJOrZBoF4c25njQ3ST3e\nfEyQhroc6McyC+vCMoYWRakYEKs4MndutUaSEOjuH6vdkaTIzUbVKjcfTcu0d0vqahL0Ni+XA9Dk\nSYAK/SSm69AS7A5VpQRqGorLit3Z9cRzKvtmhRPLfrb9//BK+v9vU0nThNZjMKhGyok5DpI9aqvk\nSCS8BrwYDomosSyw2S7P5N9xLvDaQE7uDrzt59TGcJ0i39OSh9ZxYSGmjr1CY3rERFDD7QgHxjCz\nGZ+52SvtFEXsnKWVSDcKPirOKo1R1CRSB1qMoHNIueHN4CeFlLmbO8cGQdjN18y2FU6KL/FTAkbY\nJ5tXiPHspWD4ospOMMnlyvAoGJeoJYeDUA3QGsqk3FwV+BApSoWg6GgJfpJvccTZLPaiKsZOnIDJ\nUABqGCNZIOd0DUh5hRYjhGimfj2FIvdInT7ash93zArPyx78i1OeSYOXjEkpgBg8OuWOvJkAqgqF\nzcm8SLr3Sp6h3Kqh1oRH6LB5QnlXoZ34852CM7m6exfmomlCEyfxXUv6AZ7CTfOFSu5wjOxFHZli\np6dKcD4k+XerNdEJmOkzEo6kyh7uHVT6nNV6L4pLgU4EpzmU5J7NzjGNP7TtR6qC/F/9q39FTYDa\nCY0EbjWxDg4zdZpbCxIDhckHNCi5AzfBIAMOS1KlSIFFU+G9Z6aBaD2FNVwnZaENc9mgrkK8pzEj\nY3RUYolaUiZhFxIRKCrLbr9hoMRaCzJgQ4mWgTYF/BAoZWRuS7x4nJQ8PTIMEbpRuOoDVeWYlQuG\n/Yar3Zp3Zi1BAoUJOK14cNZxsWs5NJkBNE5IIbIbe9IIdZ0o7CF9nZg7S3F8xP58oJ1HAgOurMFu\nwCrD6BlWjuXRMf1qhTGGEK9p2weElHC2Y3Ux4+ADyc0NssYERyoj5vWcjz69pT04IfUJj+HBzBJ8\nR7/a040FxjXgIoUZKefCcRX5/IXnVXeAKzyVeAqTl5/2gzIrZ4yiVK7i1WrDzAq4ik3I1ZqjFsZ9\n4nK0xCjUpSF5ZRsdRTVSS6Q2hn0XWQ23HLQPoFC2Yrm9NRw1kUIs3RjZa0RMdu5Ur+xdZM6ISk2r\nkSEJe2CrkZhKZuqpbV5+Cza7Jljf55uNGsap4XNHoveJtnI5tz7mqtgYEiklrM3WU8YUxBgZpubQ\nXhQX8y1JKPBG+c+++Qc/ESo5/ke/oEPoWI+WcHwCccv26gk8vUKeP6RIEfdoQ3ML9BBcy9WTa6pX\nb9+/h/qO9PYV8vIxfnaMSUIyQrG5Ii5O8S5wcPECO0L7fk+4OEex1PGaWj0MZxhGkr2ifqb04Smi\nK6pyhXaHqM3iKVjBiiGkOc5fkdwJMSrN4UtWF7/EF9sFrYns5w+IajBWEdyU0pWoF5/z/vA9BuP5\nfvfrjOqp+gveOn6JOx25fh1Y8RewfeT07H8lGsPJbkT6GePjQNFtYKOM9JgiUfyLK+T6EYig316g\nn54Shg/h8Qmu2rArZ9TdNYV62D0jVBE7N/DoJfL6MagSP7jAnF4g/9vXGYtE4W0W+UBcfIR58RXU\nCMXj58SfWWWHiItTZBUwH6zg6oQYYfX8l/j8HwOzE+x+Q1cXkBTX51WBUJcklCQfUXdvM9Sf5Sa4\n/i369gtm+4zUuORZzc85uDhhX+RrKSxfUKweUftw30wDMLgCnQRLUr3nSEU7zBSPpYAfK8oyNxHa\nSX/cHN0wW2dX1f18nyt4QHnfVe8wOjK7PmR3vEL0TWUZ3jT85L+nCqPEyfnIMZowib48gOu003/p\nv/jwx/66/f5/8g0dx0SfGh6LwRGZUdKrxRnoVFECnQ2kVFAlOMYik8UfQCBgEQoVrAgeQ0F+bW0M\nop7nUdlY4dBFNr5HxbIeR9TmQoIjsvLCw2XkwGZnhM6DcUI7xVe4SYARoU8JayxehUUdqXdz5uOM\nJAGbCtCMZGQWUIDEy/mWqtgRovBg83gS1J7PjtY8m4+k5wXOnTJGw+7kVRZzKngbqV1uAtt2hi5A\nWSkPjkK2YEBgp2h0fOvc8GcehhxYkbJrRJqCNJwRnANrAyQ7NeKlqWvOZj7/S+F93ZjHmhLNq1jV\nJDplUqEF2WEjWYZQUn54zOhKjOo9m3xXub3zq1blvpqa8xMNJYnhS/x/pYmdCtWkKJWEqjDK1ACp\nd5iKTBEc+dpJU/OkR+6vF5n+uOOL++m1Jel+wjxoDgHJ5xL37200MYihnL7Ll1d43lyz3P9gfmpK\nvPuu++n9Hcowfe6f+S//wU9eBflhExgxxOjpY2SGo2LLrLGUIbGLgYPW4JOll8gQPL3Psw3fJQbt\n8TisJi5XCqWnUMMYS7wF65VCetQVzEQxWtKabA3XVI5lEfLJVBgMiUMZeHZWsR8HYswNV42JOOfY\nr0dOngT6lPAhsrpx3MaB89dzjsvE3Aw8rXuOljPUbEkHkfaVxbgbFuWSfhS8vSJJwekisdom+mBo\nojCbWU6WDrQhmcir57c8eXTAMNzC4UB7vITdHrmpSKXF72eEYY4pOxaHwvV2jXElhycF4eoJ0Rvc\noQOE5YmHlSHqnn3ytIdgXy8hCEtXsL1V5pUSY0m32VDPRh48OwYT8Vcr3ExIseD3n/d8JnOiqViW\nA49OE8ohQ/CcX+SO4VVnsFZJ/Y4+WdRVpHFAsBgTeb0yxKQUxUhd1Oyip7HKURXoh0gUwyCRshLO\nqjOSKKeHBV/cDMxsYvDCKowYYzgoc1fyqJ7RJSorCDWOgBHBpcAS4YGDEMYs0iVhcBwaza4TwWCd\nMqaEqQ1DUBYYRqcEnxgkcWjzMqy3FlLxf3D3Jj2SZWl63nOmO5qZm08xR2ZWZXZVsbvYjSYEQhSg\nhQgBErTRhistuBGgjUCBW/0NrQUB0kobQf9B0oZUk2y2VF2qoTszI2N0d3N3G+50po+Lax6R3dJO\nJbGqziYQCHODefi9fr77nvd9XqYoiMnARKlnP5hSak5gK8WUFCkmzMNR4O/Buu5rLkLBk8/+ktjf\no5Tgzw3fffcYiFRPtriUWRQdcZEQtuxff4bo8Yj9gYoEr87wn70D3uHeP0OGxIvqFuzXNLpj//kp\n5r0hbzZ0/ie84IqoNTJ+j0pRPcVfKcie6FaoYLHaoar9rMaEJep0xOl7pF9CUNRPX5OSsDj5GV9W\nhiGfY6qe0iSa65LtOpBE8bl8w91QsJeKbf0FP/rwZ3x4vGTYLKja77Auc+ZWUPySZ/INB3tCuwjI\n0qB+toL2X7GZXnCZHWZxRW4vUDeWfPUc/ZM/gx86ctZ4d4rbvoPbz1g2O5CGUB5w7WuMCL26pGm3\nRHmE+vd+ifrFn6BKjfzBFcVVjURN9DUqHiiuSpgM4+OI7b9Aqb8g/OxPsJevUL4l/+sG/VlA37zk\n7LN/Sd5fcv1NolSOZBqK6YB5aLULYHIk8RKVRqr+EUHNZa5avsTm3RxyU4ame0l88R3l7UsAyuFz\nqIT89Jq8eU4+m73qi9cXyMNhrxjkqPwOJpGP9IwkghZPmo4h2sd7qg9LVleO/tGsAmv1vSEXjU4e\nS/oeEXb21X5/s31YD+r3w3JiiTpRJkO5WQIwne9J+vcEggz4oWCoI2u1o9fzAHSIS058RRRIBCYt\nZBMI5UTpDTEtKY5DMYCX+Xfy/NXzw80HZRibAy4mZBGoBdTo2I0Tj+wFV+pAbYUp8tEXvCw1g4ch\nCU4pRENMM7QV5rlwWUWUzgRf0CewxTEMvdjxZupRUvBobJiUcGcVp372m6eTDXbSdN5ileaXxTWP\nwoIhF0Qf6FAszyJX4ZZsI7XVNJUHBCaLTpkpGqoyUjmNKZg9CUbPPcfH0pKfPonspjmDUuZZaKl0\nZEIxpSOD+QE/W8qMc0uAAZUMEjKIIeW5+jnnWX4tCzlaN2ROfhsDQ4RWQZkp9cT9ix3rdwsm9VCs\nPHtvOf5kvFIfT1esngNv+thOp4//vwOKJDPX+BOhX1GoeaCdh9qHYVZjZL4X4tEqAjMO8MHjjxwJ\nGUcjccE8wEbh4z1Zqvyp8EXNrYizge3TXfrQGPi314M/+WHp49/D986rFOrjAP6bWr9VA3JTahoy\nSqsZA5YNWRzJeIbsuBhAXCLlwFlWuHUgRkWh5ypnmw27cSSPCtLEEAyjVhymaS7eKMBnTVTCJB4t\nBaWeMMZQ1QblJ4zVLF1kUQaMXjNOMxs3BDAh0p5BzoERza/HRwyjRk23vHwMn6+ecHfzDp1Kdn3i\n8hFYZ8GMpJS4OG0Q95i3b67x2dCXn/PrtyO1FBQY/t4fL7l/pejie07PC3a3itFrnv3BZ0gcKJfn\ncDvBYQvrFvMikV5d45TBPt0x+YDXivWjjC4quJ9onhj87oCpL6GOqJcGmogZGpp6j/kgDNUt5VXL\n5VdLLoOwvwbLgVVdMQzC3XYkDIEuGG53DiRRFJf40OMERgw324SojlYy6yqgtUXpgfXpkpwnlk0N\nsmG/r9l1CW2EMhmWZ5rdvea+v2NpG1I2KDWxWCpMTDSlpa4r+v5AUvC0GbgsoRvgtnP4FMk5c1Al\nU3Ys5cCyWdP3npwS2c0A+NKCDyBhRsydF3P4JCchSySF+NHDprUmDhPGlqAVhRGc0pyokn32KKWo\nUyYqg3Z2VjMIqJSpVTkjciSTBEorqNIS8v/TVv27ufJpSb/5FSs01gi4e4qrih8Vs11n7LZUoQT2\nlP0Jcf2Bz9eRd80lSxHcm0xqbyj3S8avCzq9JBlP/OqaJv6Cnq+I1deUtzvUqSWpJef8FalfMiah\nqCxa7pjiCUXUjFNCigOSMipPSK5QnZDHJarcU7x7Snq8IcWRUvfItqYvLsFkypuaFQr1IrLc/RJV\nR1wsMFePGF9OLOyAsoqVe0W//orh+oKL6j1+WGC+G5iUoR3eMdqS+sN71Fca2RjyDxX28CWX+1ti\n3aPKp5SHt/DqHCkFfv4CSVswe9p3a1JjUFZ9DMVp2zOOs1pajnekqxfwD36OypCSkF4/xT37wKyR\n3hHTM1ANWjpS84aijiizQm0eoZ9dARXyh9+h//xH5LglhEyRI2cqw9NbrnY/5uxug3p8YNN/hjGG\nJELd3WLSzJJOeEzW2DgiI3hbMZbfohCq6XPs5jE2e5QxxJwgQ9y8wPUd9HOxy9TWlP1sm+jNSJVL\nRj1RpRJ/DApqpUgIbSoYjGd5tUKOHIL26ljkcdzWD4/mP6ubE/pHE1kJw9l2boFTiqjmLc5KpLpb\nMZweWdXKUB+HYZiHCUVGGUPOmcXtCb9Np6v/b1djCj7sE6uVplSRISjGPDLUE8GXlBKJVlA5oZMw\nFgEbPSau2CP8NYoz8bPVQjlO0DRK8VgSv5SRusmklNgHBTZz0mbe9vdzrCs7RAViTDg9kwp0VPhg\nZyQYs/XNA8ZofBK8MqxKjcTEPjlONUx+9gz7aEgqc68yd25HReC+cRy84YlRqDpzriMhCWFa0eiK\nK7UnD5a7+4QXYWTuS3g/ZU6WAiFSmwza0U3zCfJZBXsvVJOaVVxRECEcg3RBCY3O+HhUSLP+iDQL\nIjijZ2asKD5W0hkgZXJSPLQ5hCxYBGsSIEeyhZ6nM5/BuU/SbE4sioltUXCSLO81ZDKPo5nZ0qII\nklFoCjWjE0XPBJEsfESoNaQZVoWiP5IvnMyca+RBuT1aXzQfhQ0t+WP5n1Z8DAFyDM7FY5mSHIfj\nueTpk+KMUh+5+OrYkmlk/v7zUaT/SKk5vs/HQyA1+6HhwXM8v0qpOXcxAVk+nXj8JtZv1YD8qInk\nPD85Wj2jYVQeuL0+cFkVpFojNnBm1nRBeHdbcN1NRHFYhNPC83os5053JRQq0xrDSSsoKemnAydF\nwmZN07Sc2ojUFpUVziZigKtRsZssOpbsI4RQoO8tRlv2PtF9l7B1zU+ayO1oeFbdsXeWq+vAh73H\nqQbEEYuW/+WvDmQxLGtLpmFKEXEHXq4bHlWB9aJjCB7RMPYFV9cBvxKG/oLd1uDYUjQT1BamxGgz\npmxxVQvRwG6Leu5QBtQy0jQ1k+pRTkOYn05NuqDblOy/HlhUC9zRt6XrhLksoInU94Fx76l8gZ80\nRRFYLks27ycO48T5+QV33T1ZaS4LRe0KRG1o2gobE2Xh2VwJ1g4Ym1lIgysHvC/Q5lva8oLufkPW\nhpQGLlcGjnzN4QDWGJ6elRjucc6RkwJdEJPGTxEvV6yamn0fORw0xtV00wGFYV1l+qjRMeB1Rqma\nGDO+jJzqEpcTvhT6MFFqg7MG/IwSHJPhEAe0ntWGSie01gTtCQJRR0rRaFFkY5nywKOyICgh+oRX\nmZCFKWUmrY9KiwKVMMqQJFCJIelI+htBkd/tdfH+NSIV43WNkwM71lRsya6mra9w+jF7rqinFelU\n6P0f0uQNi2lLtR0Ai3ihv1QctqdIjqiQWHcF8f6UMnt0dYpdbaGw2Lgh+T8kNx0S75GpJdJSmmv2\nqSKoApGC3BluMVgSJQ0iETXVrKueQm2QkxP8/Rn0kaqbjw2z7em9JRwc+ewGp4UqlXD6AQekqUIb\nIEJqNV/I16TyLRlFjkJzXWPb16T9c7R5Qvw/36O/8EhlMe/2bGOFPlmyXH+Lz5nidsT8w38J//wr\n5G1idEtK9w7RCs7+FWP3U+qwJexqNAorsyd3en+gelQgbo17+pYUBdkkfPeIogd3/gr7zVeolwPm\n2y/xV6+xP71C75bI5x9QX58h334BJzfoeE95tUHSl+iNQu3h4vHX3N2WPB9uWBfv2J88pfr1ezxr\nLp3iV2bNYvRw9orh3QmqmT9XOX2OIRGOD46hKFFx+ugDNOIJTUNXf0vtv6A6HL5ne5gHYX+6R90v\nPkpERaoY9cTgAkoUdpo3a19mRI7bp8we+OY4MBvJLD8cC2aOnkpBf5KjXMVwtp/1JpUpbxaER+95\nvjnhu7Kn6o6NfnkeHJLS6Jx/b4bkO9mzckI3arrsSNnNRIckWNOx0prt4BBrWSrFUiwfSEQG+iwY\nZUkknHY8VoaUM7usCDqRAryLFisareMRqyrURWZImYPXTEZzoRPbZMhekUWzIjKIIYkiS0ZrED8P\nVb0XkMSqEuKU2cXZ2nEQw5AtJ0rTk2maGe3qdOTCBGKAoAxjnnFmq6bnxgsntkehGDNcR0tKmaRn\ne0l/H2mO8YJhn+kmS7vQkEeMcgxeU08JkuWm04Qjy3hZwTgkMhqrFF0UEorKKkKCKcOpjrNlIjGX\ngEQhBT2H2GTOtrRWCKIZpszCwswBnwfVHDTaRhjh/d7wZG05BOFAJtjIIsC27Pm6ijOpa+9YK8fg\nIj8eC94bIUrGZcfq+LA4W7EVVuZhtpk/yqezlwfPcM5YrRgkf8TCzWqyHAOAwoOGrdTsAC/giKCb\nbSJGMlE93LN8GnbhY+AvKTCSjqFRxXQckedCmNlT7CTN1ps8cHCJyldE5Wah/XgKVc6mO36Td+xv\n1YD8B2cHDnkub5Cc6fqJPmmsq1DKkCeBsUZnz9oIT9aJbZ1pbEZUpIslf5Q9NoOzE7Y9KoGFMI0H\neimZxpJbH+n8gbt95qzTnFcl9SLyzSbRoTnEyC5XCBOLXBF1j4lgteF8ZfB54GbyNGVmq1uQEa8c\nLgjnZ5b7LkMUXj61hGnLGBZEPfDCVDz5bM3ob2mLFWKuOKlrsvScdEKKFltb4nq+0YsqkZOj7wJZ\nSqzv0aPGlxljK3a3icN3lpjg7X2itEKVa7QpKJ0m5C2F7CjtHp1LXt/dclasaduRot5C7mGvYGqo\nnGe/G+mHzDAMtPdrhjQQpsD+tmfhOjQtkhMKYbkUcspU5xUyOKrzkRw1i4UmiSKMFmU0KV6wn3qk\ngpyEwhQchon1eQEp4gqNrg2EBH5WX9GJ/XhPa88wlUG2grRweXHGZtezGzXGNBQus+8SxlhQmhKL\ntZZUDlzGBQc/EozDWEsaNEoL2yGAGCpbIgZMUWIo8anHWktImoQFmwlRSAYQQ84BJQV3PuKVoMWg\ncsbnhDXuqERbvEqYfPRdqYKg503W2t/sk+2/zZWKO7y27N5dwCpwfn6C2Vmc2rHtzynPFHH/nLTc\nokNmOdxjn/wK7j6n0lvG0hP7U0r3ikXcE+o9w+4Z3c0F1/4l9erXlN0ZVbCEZGciCNeMW8PkLCdm\nz9YlpsNjkuop6pHdYYlLCpEB/JqpvaOgoVwGZG+It58j0hMKhR2FKXmMuSZ0l0xfPqXZvcdNBTmc\noKlBZpWTkFFOI4uetvzXDPmPcVGRYkvoDdXTX8wIpwH2627ebA4TfrigFo85KBb7Hel5gdV7yHvS\nn7fk3RXiHDp0TPVzVAxI94g6HGufjcY2O7wXUn5CEzTqF1vkRYIzg/lQMekrzG5DPjyB8w3y2TeI\nXKBe/gKJX6Kv38BtDfcNwfS4m4RIgL9egRuQb3eIMrSnI/n6CYYttr6mUisW0z9nihcUJqFffsNX\nb35MaAf6DydUq/ds7o+c2a9GYpew/QtMUITcMT26prx++gnyL4HT7hlRQ3h+B3ezd9msX8HdF5x0\nBf7Je4pvZkVXW8UizwHF0X6yORTDzMcHSOWImarj9TjwfeeiUnODYlgfUMcBOZFobhaIVkzLA59V\nhgu3x//pX9P8xZ/wSiemZvvxMzfdCYZPiuDv+vJxPlLvQkFF4slSwAjKZ6ZQUpSBVhtGBkRZ7lWg\nzoEDjkNWWIkclELnwN5GxqQ5EcMX2tH7FVk67o2lFkW2D0UVwuQTOSqymhVWow0qBBZGsZNqplIl\nBVoTc8IoxaJQ5KxJCYakQDSlKCQIXTJoLVxULYe8p5/m7EqhFWM82nWyImmFLQ0HMrYYiFkRI0ze\nkU2gOJZ9uIfw9KTIShOU4XYw3IZEu1bEmKnmlhSij0y2gj7RFoZuylg1D8fCTGEo7GxrGILGKcX1\nHi61giIzg/Q1hzR7uK3OaCNonUk+U1SWkDIxWJDZBpQEbHRH9jLc3CmCMph6YBssfczoYqQpLZsu\nMWToVeYzvecvqnY2+8aSrEdyOhYvHdtLTy0MYjiRMNujtf1oYzAKJqMZj1bk4qG0RRLxeNKasnws\n7zBzA9Fsj/redTegKB6a9ODjbToTbj698qHxD5U/miRE0sybzgJK2FQRij0v6pE/2z3mh91s0CiP\nr4/MJw+/yRH5t2pA/uY6YsvE7c1EWa0wbYuKB05WC9pKGP0dioo0DRSFRZkCey9cjQk9RHZxIrs1\n0u0xbU15bQkmsFiWDIPHhQGUsDQFSkO5SPh9h3eB7WbgtLxg6TsOTeKnumcY9qwnQ1cZKuto24nu\npqNcNJR6xDVbUkpUpsQtDOIHypPMZ0sHxs14qlogDhA0hICoK9pCM4ye0Uf0lDC+Rp202KUwXF1R\nF2dYLMSebptYnjagKwgO3ITOlthPnD6uOTF7dKl58bLj7kPH2laMfWC1akAbwgA3Q4MzhlMx6HqY\nk7nakY/BCbIHDctFwFlhtbDkmFkoh1rUoKFoL+kOmRQik49sbhcIE3c7jy0MYzT4uGSzvcOYFmUF\nLSPWWgpVM3QJheXDECnLNfvrA4yOpi0pDzBMLQfdk5SlHwRtV1hVMYZM8Jo2lbjJE6Ilqki3V3Sm\nZD9lJCvGKJzVhjgGQlD0yVMFxcREEk9OelaFpJ7tH/1cP441GIRCHCHlj41aLs/HbQjoPHONxUSc\nKjgJM7YtonEIhkBrQaHppoC4GtREngJoiyCMD92fvwdrN50yTRqvdqy6L7jZXbG4fEcelxgpSZtM\n0+wxfcLYPdavUWiYalLeY8olUQnpsCbqzG5/RlWDyZ7H1Z6r53+I2r7jw9cLlLnn8WL+leeW5xQa\nZDqlPr3GfvZXhG8W+PIl5sPIWO7J02OKdseUFfWyY7kswS9J8RbHOeItPifwE5y+YKEKivsrDs7g\nNj+gPP0WPjyGej8PS35B0hrZWKbVE+r658TbC8QbxH5g+lCj7SVKNFW+w7oBthonvyD4S+r4nHAe\nUUEzyY+pyl+RrxaorsIW12gLeToQ3ROqQZPaHjtW2Fyyc1/QlANl9deo7Tnc/Ri9BX76bi7HyBU5\nT2glhN0z3HBDSDu0akltpA+PqU/eEk1Gv68IWqOuZ48896ckEym2GrGObXsPpxXLD0/pTg6IeknV\nHPADhDc/JKqO3u/pQo3rzllPHcMik3/ZksuW8fQd5XCKHgf0JDDuSD+YMW4PyDXJeW7GO/qRVYZ8\n+g3+eF3J0acf4/AR+VRM+ePXazOHe/1qBCVon/GrES2f/JIPAbwH9Jwo+Ui6GJc9f3pi6Pc3fOha\nLCtWT29Z65ZfVhvU8XQbwLfb37Cb8d/umqLBJNhnx7LWvN4FaqfJ2VCUmjfekXWmz4ZVygxGsTSG\nwhz9q6JQSRP1TA4ogmJ0QsiRbeX5QZU4T/DtVckNmdNqViedFU7tiCsUTZERM3G9KVgWis0uzvgy\nFEFHUtY0NnK2ioxRMwZQbv5p+lERs1CV0LjMKDuqDH0qCaPHa4VPZnYiqLmaOAxQlRqV0uzTl4I+\nCIGStoiUlpnYkYT77BiCMIY5g3NaK7biSFkRQ6bIQh9Kxj7T1tDFhDOKslRIToSoURZqp7BWKGxE\ntGJImm4QWgVImr3MaW7uRAlDBH8sCDEG+J7FYYxCoRRDPtoWksJnzX6ESpUsYyJj6cIcEK+1JtvE\nPlrehAUkIU+KlkxpHf9HyjyWyMI6Fhq+CYYzE/gGTYqZC52wx5lVH60OSWYixaeTFIVI+qg4Fw/Y\ntyxHO8VcHPSgRj9YaLLMITrF0S1yZFIDR4zb/IZZZtrFQ7FPEOBsx18eDNVQ0YjDt4FcZfwhfw/C\nCZBZ/Ibv2t8qisW7//o/lk3XEYPg94mOiJEST+aEgLUW5TPKKmKMaBI6VlRN4npIrBwzWQCDqDnZ\nHVOeSx+sxacJ5SxNrNmqGUXYuhLvI9l0qGDIZPrsKIxHUyMykE2JyjMv01monQHlQWfKk3sqtcaW\nAvaO9ers2LuZ8XsFaHa7atL4KgAAIABJREFUuflq00+UqcJnoa4KrIVhCEhW1KVjHP3MI3TCuTa0\ntaWoe7St8LxFlwZ79gQ2e1A1iGM43ENW9LuAUlA3Bt16ikITpkyhKtAaGoWo2b4SV2CeXhN/1JON\nZ/cWzt+1cCjQLx3Xv7jivDiHXUaXT/Bf79DNEpkU03VPIY5r22Kcxw49pQ0U6pRNv8dITRaPYWLT\ngaOYu+G9wovnEDJltSZJz304AeXwyjFNE20LKRgm9lTGkZRhCJ5CKYwtiTESgqGynpATWTRGzb4j\nqxKk2bphlSZKotAGT8YojbUWl6ZjAtdgUFgxTNmjjRCw+CjEALFUSPAU2jACHMkVHDmOIoJDqHSc\nBxVTEjKMQUg6E2SuOM9xIh6bjoIS/os/+91PwwNs//MvpdR7vr5tKXVFijWnj69JOKwkXLSYRY3J\nbyA5/MOwkyMDlhCWFLanSBc0qxu6dIrVmrv3jpMnic2NpapHlnaB1Z7JGnQ/e9yKx++I2mCqe2IT\nkJ//EaHs2fqOw4fL2Z5V7ymac/RuYrQ3nNoTprJn1ZWYVUEYhKGssOMW0YrGKbLcIM1jcrbkbsTb\njtZMTKYiH1rqwpFPDDffNlSNZdAHql4IZY+VyNPFAJc7stJzUKZfEdwJLmwpFge64Rl18Zos59j2\nnpyPlhsb4fAIgNy8R+cCrTUpK7QdULmGr7+C579inF5SdldEf4lKPfzgHhlrrBa4FNS1QlyHKE82\nCrt9Tmw+oA6nkDKm2QIaubqcT2riEtO8xvcNkRadI6rQcIgMqmInA1qtGA+WVgK5aNhXp5yf3bKU\nxDDtqLVDtKJ73bJJgj9JTPVA1b/4ONjOdod509PfC+1wcYUcVSSNYL9ZED7fY18tP8by95/doPWx\nXW+AB+emPLyvCK6riO2I7qv5tOZowxCliNWMaYtl4rObJWqhmMZ7FqtZXT4zwp+plhjmUNWDI2Px\n4QmHJ9eIyvy7/82vf+fv25/9k5/Keymw9+CtI2vFqvQUZA7Kcm5GlAUTMgfliMcHeqMVISum7ChU\nYFVpjA3oaCht5v5QcLoIvNtXpAK+aAb6JHgpqHQgAG0x4rRBTOaczOuuYgwGhszbsKBkZi6vq8w+\nQMxga6EIiX1hWdiEJE2TMpOaA2CihV1UXBRz3bJKwt4bapsQNFopOgPPjLDtKhblbN/YpEAwMwig\nqQLnRUC0IalZoW2V0MkccqsUDAmWFZQ2zUE6IIn+RGJBKHSeeb/MJxfOwKZzOJUojWI7KiYxgPCD\nk0DQs3JdOhgDpJjpsqFlZidnydwnh0oZSUJSmhA1KEWlhJAFnzUuO4Jk/LFXWimHH2GhC+7DjMdb\nWMMPrOG99lRl5jAkjJu9yTdDQTlZPjMaraAyn1r4PkYxZR5nHqJwWeaiEZjpGA9eZC2fCkqEeeSQ\nh+DeccZUfArbjUDJ0Wcsn5Roi0IfqTQexZ2LKBXpvKUpprmCugic9ku6PNeIuOOQPipFPbveefnf\n/rPfP4rFu/cjp1WBqQrCmeKL0xV+Ena7A7pUcwGIjzhnKJUh9PeEMdHdbVmWJVOy7Efopp7GlGgJ\n1Fbhc8GqqnlcKzbbe84uIo9DYDMKGxG8neH0ujQsq8hSIgvlKKqEW1Tc3484+mN00tC2LWXp6IeE\n0icMk4WcGMKCvBe6XULlFlUKh17QqeRsFWiTo208kzTcHiAXjpBgmQLL5Z6LJqOUYC9LpjShb0bC\nQej7d7O/SVcU7255crFE8oiuFXUzkcXinkXMiUH9eAXbr4laKJIhngg2lcTdBnvvAIVtHhE2n5F/\nXlG+vuby/WO8DBi7gp/tuWx+CGeR/q6jUC05KAp3wj5uWT5ewlnF47rCVm5W4uIAuuD07RYzbSiK\nJTQveBSAlIi5Q+uWLCO3V57hsCd44cR+h+QGgM4YphConcGZEld2FEWBTpqqKAjek1JCbEHhPHLE\nFM3FHEKQjJh4LFMoGIZAjB5ne0SVKCw2RmbiZ2InJYcY5yY+DMJIkTIxQqccEQOjIpcaCZkhZkJK\nTNExSMJjGHDzUW7Wc8WvyUjW7FOkiBljHPXsJMHL748HuTqpeHMDlWgkTuhyx5IVqi7Y7waMKnm3\nu2GRz9nHHhkf4+wNpDNCMJycTqhKEaPnLljkEOhWJc3nkbwZebYSpNoRlGXb3dOqFVMWOtVTbD5j\nmSPGFjh7IJ7vcMs7+PU5hba4P12R8xnV5i06Znz3BF/ekdUz7tLAYkykxR3r/iWbeEL+siS8TuTP\nH5GyRWdhVd2zuhLebVvaixXpi5Kw2fIofeDxyTMG95rTrHkXCkLfMUznhL1jmeFEPWM6v8Yawd3v\nGPMZ/kOLKz8Q1XMKWeLXAWPnYiJXH5B0hbYWPVaEfYNojap3+PdPKe3EjtfI2waX3nMT1hTulgpQ\nmwZObwiAefsYNWX0AiQVyIdzphyAzxGbiH3EuhGvLTkPoBMte3Kv8ZOlrCy68PiguHenxC0URUss\nz2gWAdVNWOlYHt4j6p631wVt1TAu76mqjoNZc3seaMc19YUDc00+Bg7dq0sUs7qHVkznr2g2L5DN\nBQ8HK1oJ3edvUEoxfTZ+SsoPs9BBDaZyPJzRqs2n6zEjFF39MVj04Kb0J7tPmKiQmHyD3u7Zx0cs\n2o5WGzhJnH+75m59T86fEkHV4oof58DXd/9f3kn//61FKSzuNddaQYpYo1mUidJBMwaCcXQHodea\nMiqiGGKeQ1uKRFlmnFWzrWx0GDIlBSdnnvEgLJcDjURsttz1BbacxY5xUuRiRVl7upzZ+cTZqdCO\nkVe+Zinw95dzyOpVmk8yD1PF4CMnRYH1maAtLkViYShGxWUx2zFeFganFKPA3nl6lbnaW85q+MwZ\n7nLGq5F2GbkZChb1SN4v2I5gxc3KbTA8WkXqYh7M3+wtyhhChhgz1s1tc3WAUs+1yiFrppRxRmHQ\nbNUs1JBgDAVTEsZosbjZihBnBJwxwvWocUdDrxvhkGZUWsqaXVTHgVMxZotJmSFrkggi8y7lzLyP\nOKXQ5UxHykFTxIIxFyxM4sJBoRRbFJIDv/CZXjKHnWZhDXaKtDqQPdSiyWQqqyi0fLwfk4BXc7zO\nKSHl2QM8/9sDMeMTM+aoAwHgRZOPbXpCJh8fgvP3PMia2SOeH8KA6uG980f9eczg41zzErMl5InT\nesI1iQ+j4pREFP3xPZOKjKuOD7dLXv6G7pvfKgX5f/3H/1AajhK9EyQoRB1QYlglw62JGNE4N9cs\nHmLGIAwOqiT0XljZWVUwtiIOB6LKONtQFR1FbdgNI8WwJpqZfmDJBDznixJrEr7okV3FqomMvUEv\nFUtTsSgiRmeCT7MafD9xcd6iabHaMo1b0Ipv3njG/UBnF6zKBaOLEO4Y0iW2PKD8PT85fcpd944n\nz06oVx00JeF8jQkVebiGjSf2b6nWpwx5S6EfEQtPWSzBGljHGR5ZltAYgnG4RzsYDuQA2v6U6dU7\n1FRQFB3pX7wmvSopvrxFfIY74N9fo/5OC3/8kri5Jf8PG4qhIDULjO8ZYovtPKGPjCZh2gx3Qlhd\nILcD6zYQmhP0xZpKX/Dtt99x3/WsaWmXmqY1WKfZ7/esTyq8TGhqtts9Jp/hp8TtuCVOARFhMCVO\nGuqq51LvOGtbPux6KlsjuuBmP2+UwZQYfUMOK4KMpKiRbFA2omUO6TjrScoSQsCYgBbFVkEh9iO3\n8SAGJ4YomhACqczEMWBNyRiFqGtU7iEKvrCYEawRstKMKiNpTgRrrSm04INCS8QYxRQFJ4qq1EQ0\nfspULvOf/m9/+TuvRAGM/9WPRMctkzuDPhPLjkICQ7IYgUNsKU1iYfaM9oJpSuScKfUpXt1QEugO\nLbpJKN9S6SVj8Z5VatDLzG43UIpDas3+fcKeRGQo6X1NVVrK5TWLaomP71lnuGLB7u2Kcr3HPl+x\nvpsIk2fvDVmvUGmP0wu6fMPkn1GToXjLyj3DfFUwXW3Ql6fIMDdmun6P3Ue8jxymBe6Pz+AXE1X7\nLa1Evts/J8drQm+ptJ2T2ylhTcH6yS1Vfk4oR25ubkE0frxEpUh07yn8GSk5rBvRWrM0t9RWOJTn\nTOrA2kU2vcY4TcwavVuxrm+42b8gS8A6j08llYaQRp4/vYXplK44ZYxb9GhJkhnZgX+Kdm84r08J\nesIWme0YMfsFprDkaqKtBkKoyWpu+Sr9E3oJPDpJfPPyCT+YXmEPlre3B5Rfz4GaKVM0DdrAvRmw\nPAKTmXYD8dGB8u0K/3lPbvzMMNaK9c1jCjreXowU352DCMPFm79xXT3sRUop9Exz+1g3+7B0X/3f\nrkcl0J/sqLZLwnr/N9Q9AJWEZpsoUyL5hsQJ1gaa5S3nq5LG3OAPJ2TZcJUMz77YM7w64/12Vq5/\n9D++/Z2/b//in/5UYhRaO2clpqBQRs1H/ChqpfEyN9/VRhPiPNCIVqiY6ZXCRmic4JNmUTu6KWAq\naNXEfWcpy9nG9nZraMs8m1eT5rxUjDJS10IeIr2FKjreTBUXJvLjReKDCFPOjKNFTEFOgbVT7Ib5\nPUur2ObEs1q4KBT3MXGhNV4izihuc6bPMzrMBstPFo5vPGyyp1Ce5BfcJwhjJhhFKcIkQlsIy9Lz\nZBGZvOL9vpiDaFkzSYaciUbj0xxOM2pm4VsVOWsVJmeyFoaoabUQMUxZEf2s1Ouc0KJwSohOo2Kk\nKSeWhUZlS8iKISq0CCnPmIsokfVixh4m40jh6MM1jsYkDIleLAXClBULZzmNgb6Bz1uIdOx8wdXO\nMUhBgRAyPCqhNEIVFAs33xedGPoo9KK4NMJK59k3rOCmspjcc+Zr1JHKlP/WvPjwV6VgfPAv55lK\n8rdf8/0VZz/FUTGfw43wqV1vEAg5sxOhz5rSQKMT0SSetD37IhIGRzfBKkHxFF7fVDDN2N7/8L/7\nzdTD/1YNyH/+n/09iQZ8UsRYUxQGMQkTDyxqfZTiF4QUMboERkY/kHJFEYRkJtp2iSWxaldzgErt\naQoLKvLrN4qL1YTVHeSSlXW4cqIwQlkovA2UbUkOGvFzCUTMeygsqm0w9RJsAG9nSkQqMS9OZyRL\nBIige0QbggjKK5zE2ZzvA6IcSReoUM5+I5H5yioNUUWMQJ7muk1GmNIBLRYdMzn1OJfmI8A2oad5\nQkveozUzYFxKks+o6wJtR+gi0n1N3Gfc330G/5EF4+HJj9hf7mi/+xr9v0+wc+BH5C7TT5B2Hmtf\n4MSx3x04O1kip+AnwVqLcQa5gd31/NSevWVRQb0UJg/WWqpqwdjvGPcTMW1wrp03wDaxWAuucJBa\nYMHmzbv5ezVnvL0eqatHuLZmUJq7fWIKQlQJZN78j3AYIrOPyciRMYPGoWejf85zm6cIWiWydqQ0\nK83piOpxSmFVRmW4UBHRNQcZGKbE7QilUZTGM4WSqAQdJpIYvBZEFQTRc4JWWTwjNrq5YOB41CYi\nM7oLKHLin/6Ln/3Ob7QAb//x3xeA7jSxCppTdSA1FXcSYb+n8z3VdIrJLfbUUeee+qvX0A9s3v2U\n8f2GUO344ckfce1BvxhhsSDfB/o8UthIShr5XrAxA6WbVcLpfgIzT1DGKtS7Ev00YZQwhtnRKmj0\nfq78jlVFvbYUQ4alpT86TYsjwz+H2R8bujmkVWxajI+wKFgu7tkNjzkp3yMHzW1fcvF4pG9WTK5g\n8e4bbnuFyMQ4PqNZJJqlx4qiKDN9BHwLdofPgbR5QmJLvR6JMdJvSnJaILZn9aUjvLfUyTE886Sb\nft5sxlOQTNM26B/Oftvp55GoFWWYmNKB9nxBOJRoIrG5J+0Kpn6iWHfoCEY9IQRNNlc0pqVYNsRi\nTxpBciL1BSo3xKTQ1Tua6Quim5DmntyfEnWm8AWjRIJkmqWnKc7Z3/81Q3yKVhElFUlnKK6w+ivy\no1vszRnT4w1+2PHMrzlTmTcbgz4Z2a8t5dua6dkAIqT+SGTVQlWX+JwQDJPsqNQCdQdh2eN0S75X\nSJr91NP6HtM5yu0Z1iZS0uTnPcWbllS9Z78yNN0JKr7D3z/FLSacaFzxARdbLk7eIuXnmPGOW2oe\nOcXrbxVjoXGi+OH/9H/9zt+3v/wv/x0BuCCxUQI2sSgUfnBsQiRGwStLaQ2tnY/hnxY9d0nohpZX\nXaYQxeNLh/OOx25goQreJkWVMzWJEUX7vYTW7EfViMAQ8kdEV6Uy26Sp3VwJneJD24RG8hz40hrE\narZKuFBwdnzPSc2H/el4fL8/DvJB4E4UYhStCVhdEKUniEZNDldPnGBZiOGfTUIzZdKxPmNpI64S\nRhQLFdGi8Gke3lQQBmm4S5ml8YQsbIaSJJZKJ/7oxPNmMmireWqEqxEGUSQ1zytPas25m/m/7wZF\nIcK1UmwH4VnrSdQzXpDIVSi4G+B5OZESnFaam2hQUVBF5tRFFlrYJ8M+aSQrrNKMed4NbaFpBQ4i\nnCjNIWm0nvM5VoQzMxHbkq93inUWjIZSabQUIBOPS0NAqBVYSVxHjS8nmsZzf7/gzCRqMewEVmp+\nAE1HO1NJJihBZ0GUxuZEVBpDZi8GrRQ1wiBzwE/l+RR2mo0aGBS1VuzzXHNttWGlYetHfuEXvCgj\nRk94MlEJCzdQlIb7YCiToW09P7+tWCXDiOI/+e9/MxaL36oB+X/+R/+BKDIhBFauwpaZymk2Y8Sp\niaCEEGrQwqLWLPRIs3RcFI5tN+KHjDGKm2DROTEZaFPgJ3/3C5wZqKwn2UBpS4yZw1i6XIITMJGc\nFUo5VM7Hekj7aYjFINmAFqYkMyIqg9EFkbmNSWTmSJp8HJDU/Hmcq4FIYYV42KIkoOJInuLc0Ccz\nhUNCRBWCIqKMIDc9Us6eRGLBdtxwsrog+x49ZThdQpiIzmJur+mLgipnzFIIZwp3egax4/7VG9Yb\nwRcFJjpM/QJynL+3uIQBcCNpatBlRlnLwR+o7hrsP/gK+g7urujDhN1s0GFgu7Kc/50v4M2e7361\nZb16SsqBKDesTEmxihyuA3ebFS9//G+4e5df27btvOvXWu/jMedcj/06z3vtOBg/gkNiggxEQrGs\nxClRItRiKaUUoEAViRp/BQWkIBACJChSQUYIEApCIILtEF/7cnPf5+yzz36sx5xzPHrvrVFoY629\nLSERsIF775CO9lz7zD3nWGP00XvrX/seF7ADaqV+ZfzgTvi6rNxOA9kWJHUsFKxlKk6SRrWO1grW\nosgZLa7vWaP1VXE8JTpqWLOtylQbycNtxAV2dAxD42kzsiaWWpjU2O123N8fOeTwmX11vA+/4s1B\n5WZdoBaGYUBFQtQF4InStjwpzXiDQoiIlq0YN5y0TY5NDbcQUKjDv/37//CnfqEF+OG/9ZdcVXl2\n01H2jeGU4PkPmXlOXpzqiYMmdHjFvLzAsnMxNNb9H5O7K0q9px5/FVmU/nZP+/gL8tWXvPLfopsm\nzutMdqEKG4UGZOPwuTuWBNn4gKrveeHq7z02oxMv2z2BsR+Z60KfR0ShLkYegtcXXRenbvFOOYNX\nIYnhx5W9zcxzo1GwNwPWMik3xn6A8QtMPme9LfTPzpzeHhAiYr2O9+ThWYRppDuuhgP3aSF9NXLq\nnOa3ZDEO63PmoZJ7Ic09azb62Tl9vCDN6F8dmLREyEmfA2ZpidQr890Z957uxT3nu56xwmwz2Qfc\ndqR0xq6isyKTUZdEvrzh0jvydcLvKzf3A+m6g6Mh5kw7oZsg5w4piXk3Q4PSOkQEOcz42wPXlyu7\nXpiHW4ZOGdcd7Rvf5d2Xn3E6JUTDc369SgiJ9L2eX/yNP+LLb18xHb/JOt7DC2H4YoeIcU5z3Dpx\nzlev+fT8lFcXE4Kxf/0xnX4B9edoxf6EUj2Nr7i7Fq5efszYzXjfeJLvWFWY6gVteUN/lVjnheXu\nGXM3cpne4lKpy8d0uy9Rczg/odJh/df8uaHxxZypbvzyf/byp/65ff1v/hVXgeO4Yi3RknGpE016\n8urh4dspOc+kdQAThifGvC48H4SbFdQyX3lHasJ1v5Kl0DOy3u+5oDGLMOCkzXZrleCIusNenPWh\nNa/BE3WHFX1MVQunBNl4rnAmcS2V5soqSmm+0Rq2ZEaB9MAL3uKqJ+DOjKbGbIneKvMqnOk4UOh6\n5U01niRoVUldQ0oGN6oIC76FQUFT2I2QlsJ96yntg2ianPFauUyGpJ5jdZIKTwgk9daU2oLH3CVj\nr4HWLtlZZ0Fc6YbGMgtJjZui0ZFMHT0Lo8Y39Q7Hlim5MeTGmCvHptjc02XDLbEAFx4c3KywYnRN\nmFz5RBxBGNT52nsuugnGRl1iI3I1TogLx3lHXsJLeXSjeaKJ8qo433xxx5e3Hbs2ssfJKtw5JIy0\n+furOFIL85AYqoIbqwjHZvRdx9Kc9EGtWRz2KhwBU6Oj0fczU1FGz9yuwuVQaRVelgNjdfrBqGok\nVxIrC8KMBhAGoTtyZVblX/n3/6efPQ7yiwtnXoQqRvU73K6R6cSnOwPfcZZ3XAyCzI2LsVLaSFng\ni+MtTub6MLAshSF3/MiFj82Y28rxH3+bJ8+V7iNh0EvQBpqQZGAnfHZohqZGFMURddmqIPSs5mE5\n5JmlRBxOqQ/+f+Gnmki4C05BFSqJMRkjHetyi4jQtxmtK1BpBWo7ky9fgM1Mb2/oNXFzf+B+XrgY\ndhQGPr6EYzrS58+4vvwUdif0WYZDA72nrT2e9kj9OQ79F/jTJ/ibe/Q04M9OyLuJJ7/m0F/RfyTw\ng5ewewuHAb7o4flzuPgluPsK+c4ZOT2Db71j7DLrcGT6X/4BzXIYmzdl0Sdoe8Ly1rn770/hfZpf\n8OXpjFelyRNekag3mz/6KLz8zkwwizpqrXz+9Ip8u/Dz+5lPL2/JCKep8PIslPuRcdfzc984MXZ7\nkjde39/x5PpAnc9M9Ghybs971qNRpONyN/DurvDSlOV0xn3mYpfp6szt0XgpId4UEtWNt8eZTuBu\nMfZ6Rm1gWp2OCTNjp8B+QNOCVeFpypgZlwehSgQJdD7hVcg5Y7Vw8hgjRQSho7ggSakUTDvWnxG7\nKIDdWnG7Jes36F58H7dvwu0Tdt98yb39AjsKy7KS3n6THqM8e8v5OLLrr2jVKKdfoe4P5GOhSsNP\n1/ibT0mXbxk02nvleGLPJ9yPN2RV6r5HzxN7nHuUBzWJmdHc6bqeJD2tnNHckZLQWhS51ZxlOjFe\nHBAz6nlBuh0isRA68WdSiWIWo2VwUeouc2+XyMGYpxndOf0xsztnzruEtI9xN9pFh7dnqDmLKl1y\nUtfF75/OPJ328Pzb2MtfYj/cscPxfUf34ofkHz+jecfU3bFw5KJPSIW8Zk6ngetO4CrChi4K9O0Z\nP/YTwsDTzxrq99yeLjBZKZrRfk8DxuqUi5F0VlZdsSUHgnN3zbpzhpeN9KzRSkdbDe9io1FPIz5O\nsMzYaCiCS4cvQlXolwMMxjLAcxLff/uET/aG6x28+ozy9RDx1i0xHlbsix2pJebhC9784DleRspw\nCiTwlTD2J8ZB2d0aXz9r7F8/J9kd+TDy50vhbtqR28L+6cLl8o/5zrxDzXl3FfPv4atPGO0Vtfsx\nN1ww+onX60q3G9GlYzZnvstke0Eev+AC6HuBrmM9J8rdNxgvvuLMJerv8OXn+N76Fm9Ot0Vn/7Qf\nZzWm6uwFcirszLlfBy56Z7WepMZUAAYW3RRqt0brer5cwurwWQf9UelTpU2ZkhPr0pFz44zzpjgX\nnZJWRdV47sbbMMNF23sBWHPHPBLpmiagoebMoiTCGmxnDdXKKsqKRPGzIZICkach8Z/IFj4iwgUw\nArNtHUZJ1F45WmHR8LS/xqlN6RSyxzMxumIJDlIpGJ0mpqHQW+VYh9C3qDMk2KWFJQUffp5T8HOz\nUKjMWXg3JRiM57al/0njJmfyWchm7PYrzYXz0tEBvSjXeaPwpspOCBpMc4pF5LW0jtIy85zZjzBL\nxlu4SXSEI4xuAr7Ot9Q8UV5XZYfzRpRrXanmiAnDOfP1YNg04JIo6yZ4b6Ca2bnTUgJrfH0eMJNI\nPXRnMuUsC5obtSU+9YHXm6tFlwpfd8auDNy1yjiunLoj4/01M3DYQklchFMTmjcGhKTKbRsYMvG9\nbkzrgZ0s7PLCXoy7LFyLcbsOLN4zdE6uwlIhZdAmnFzo+TOpjYGfMAT563/nt9010WxhORUWN/Ka\n0TZxbCt5MYbrHTUZy3mgSyvf+ARyFqYzHI8roFxdw+HJDn2S8NVJlrZ0mg73hui2L/CGUxDVILIh\n4Dm2r1mgxSTfahRFFWUtsMyN6oH+imxFkhlldZoVeoxdPeHdE1a/4aI7kHNmGPqgY1DxtnJ6N3Hx\n0T6s4HZAaazTRH+4xJdCcyPfJjgabWs3Zl3wwTilO1JSxtGR1Sl+h6dErzPvnsBT23OSE1wkDn/h\nIygJ7j7F/uANOivcGXTnoC1Ml6TpSJOZ5JesFGRTm1cc1kxFMIuf3YXVwT2Q8uodTqE2xX1LCRKj\nKVjTR3TdWgzcYg5dwlpm5cS+G/jnv3HG61vmmwGzlaXsOJ4TV/sDqHN3U7C6cGcdOY28W1ZOtYAH\n5+gwZEYDaca9BF/qaIVdVtjOwTY+pJlRq+JUrMsMJixlpWogZM0WyA2VAekT2SSU9BXO/XsruINF\nQVU9o8R46BGKOtPaMFHEGpVEa8bf/W9+/6ceiQL44nd+w/t+oFSQnZAWR/oENegN9fpImgPZz/tM\nlx2//xHz6VewjyG/croXR6bijEPHpAk9XiMXC9UkeKunEb88YeUe60fSqwH9ZInOzLzSLi9w6dG3\nA13/JXkYmE9P0dNEeXGmaYfXEvckjfhXQnkxkfd7+IFjP5+hGfJjkJ8X1vMJfRkUDs0JwWgvJoxG\nHi4A6L6ApcBwdYMsTznaPdkFX3rEoXWGTgm7LKznC4bxRLEnDOVM6woizvNnM+W+kK47slWEAV32\npKrc22vKITPf91zrsCeOAAAgAElEQVR2l5S1UHvgBLlPm4uLcTUIc+/cnV6Tzt9g6CodO06y4i0h\nFLz/gqF+xm4nQZtqDbMUVCOrqCRyg2Hf8Gacy8CZymigahx6pTmxcfSEpeikyPgVrk7fPkcWo/XC\nyEQnyk1xLlJi9/H3WedvwjRws8Ddi6/46NVHdNcTfcm8oTFd3WEkmjc+v7sirQnzgWMzbPd1oJDL\np+zFOJuQNNP0TG9nTmlHZiHVcEC1VBDrYLilni7xHKElXx2cy7oynp+wzz+mtAMdSmVkSK+Dstcu\nsRbuFyLCkIO6s6rj0wEdjvzqf/7tn/rn9n/7N37DhywspiwKO4E1OZctumO9Gi9buBk8S41BK7ez\noWlPUqc0GFMUZDlFCl3RjmZBo5gRkkLXKrstNPpsHWNaQ/DliedJqE05pgQ2MyTB6FiKM+B0CksT\nsoSXcm1K75HkNjk8V2d2OLXgJLs5t1VwYAxfP7JGwMSDXdm9CbMlSq48FWFeIojCxTfxF7gLl9u5\nNYW9WvChPYRo17sTtWVSimgbzYmU4FwyUmq4HS09bdfYVzgbuCkXKQrW2RJ9VyMhcMqsmuizs2bh\nskaSXhOhrYU8JPa9UVeJczOn0xBMqghHhIvekOaUlllbbO47aVifgltdYBVhL7Hmmm/Xtxfuq3OR\nhCKNqrAsyqEzOp2odCA9eQFcmaWy7wTRSi7hxQwJdSP3K195h5DpSvCsK3H9lyTkGkhzWY13Ap+Z\nc1JlTRuwaBlpjTELdyt0Kagz5yXcPNbsSFrAhFWFgxrThhTjCTd5bO6/3mzyLnBchdKcf+0/+fs/\nexSLH/27f8ufjDsmNdZirKeJJEYpEm3LTuhTpqlF4poL1VdUow2fc7/ZvAleGxlj6B2rDfNCL4nk\nFdDgF/O+JZu6IOQjNUICxCOm0TQWywLSFCuwVGctykpH2/pD4Q2YqK0hGkI+oSO5PfJRc0qIOJqc\nLIoqdF1Hj5LSgrphrSGtIaq4gFSjNcCVlJ3aFjxlkkFpdwxdB7rS1lvoCkmc22fK9ac7rAN/W0k2\nbJ4rAkVgcVhGmDVmCJPgVdeGKcjaEO0CEXdntUg+MjOa5O1P3RbdnuYPr42q74tRa+Cpw2uj0eI9\nmrbrkSJYRza/0hYTXVPA4v75g92LDuQS03Ax4dk4MAw7vNzyes6sBmorY0tcPQWKcdVlFtnx9vYt\nVztjOTXuCnRDD3c33PUDoju+cX3F29dvOHfGZ8NTVBpvinMrzjp1ZE5o2oeDhlc0F/LS8NRB7lAr\nKIldF4X66jV8Hj3R56BXLO7gwt/6r382CuTv/u2/6rKbyKrYKaGHQpv6CGwBxOtmOyTYtvDF/xg4\nXcQ9TcNMenuBP7sLyzEgv9lTP1rIb/ZxLV8cSSTymz3l2Qk2q7Dd8VOWq1f095/izbAafrvtxUR6\nvSMNBzB/TFQrxUgpxlba2FNLg76LIACA3MFcYsPTqzOPdxzWK5rJoxsDgNQTpGuQwjLesztmekA2\nClBDMFZat9KOu5ibUgFr5E4o1XGU4j3ZG/7pkWG5xE8dZbil9jv628r8PLN/myK+tT+FqAWQ824b\nX4A2zDp6rfG+sSHb4oYY6iC5pw4Tdh7pu0TqdjR5w3oe6fczqQyIK6UPO7Tl5gox5+bzL7GmHF5/\nQi+Zta6cP3u5Wbcpu7fPubt+TSfK+OojesmYF0SE+4++ZryBopXOMvfXjlnjcHfB+fpMNynj8oxl\neMuw9PjyhE4b91evOBSJEAF7ERS4/BYpz2g6460P/iKZSn20gksqJFmhdFSzR1s4d0c1U4aGNvAs\nqDmj3rPMF3i/CbrRR4Fgr3EdDKdtXZ9/9j/96bdn/M6//i+4EAFtk8MepxLXDsDdKFvBkXhPbTLJ\n9Bs1InQugtMeQx6ahVajxXLJTowisbmKkRIRxyrBN1ZRVgvbP9hijC0A6+LgWwiFEKK13eZ2IOLh\nx0u09gHE4/NUYNrS7ZJCdX0YGgBUjCY9vRc8IDFWCXs4d0cRBm+YKbNLpMxJo7qz18ZqiqN0nig0\ndsmo6jQfuGgrTRPFG89UeOWJAvTmj+LS9QFsMucgxtkznVZOmuktLO7UHFOhIPQiDJsgzpPwkEAv\ntuFr0iiijJvlxG3NrA5PMWZPYVeowaO+wLYumZKorN5RZbsvG1j0YLS4mHDplXvJ7LeuqxP37mHO\nEQ3xnKdMonJpzkkiHCx5UF9coWwuFlh4kldJDN4eo6mTwKzK0jyyBiTGjxMuGQcaJxcuxFmA6s6g\nD44eUOXB8HGj2wAJfxT5/Y3/+H/42aNYDE8z5+Ssa0Wz04/BCb0ee1JK7Ha7GN7bRbAHtbIZta3U\ntSCWKHVh1ytd1yFM9F1PSjskJRDHu4x5JZMx63GZ8aWC99S6xV3XRCkFY4XyvohDQcaG94lRIoDC\nzPCSaU2iFbM5I4QNSkfSzazeC+Jhuu50WKssFBYpDNkYkpBTo16dg5ucVhgaqb6AaYa5QxvQbvCd\nM/Q9rBFonobnIIYbXN8UuLOY0AaDZPg4I8MAQ4MnK1y+A8v4SWFJyDTCfUJLw6ccReq6pc4tiaYN\nSRlPBckNlbDodlkQ2bLiV8V0pi5D8ISbgkycHSBTa6XZEkKoQcnbjawYzD1ThVJm5vQUx1hapmkj\nLydO/R5OCRmMr04rOq14zjR3FqvsybxtK6c3lyRt/IBbLneGk5jKFamrPBsTt9PEk+tPmNuJJ2li\nnSfG60tGg4WJ/b7R3SSuqjJz5HIYWDtnWoXP04GFxjo6ujbuitBn4+262eF0Pd2s3NmCu5H6E29O\nsYPapd3/14/T/3vHbmV+ksgv90gyZBnw4YRreUTXbegxFfb3HfeXcQ3UC7qsZIQzA+mi4cse7Y+I\nCOvze9q8gzqFd/WrjvXKKYcJ5h6AYV1xHL0V7vu30Muj364VDUkzd+znZxS+YB0GZL3i4Ak3tmek\nI2ssyHWbAsvSqOLsk9IEhtM196XSBPrhLaU8Ycg3rPoUfKHXd1Qyp6vKnO45vHsW9IRuIS1G8YyK\nYbsFX6DthaaF5bJnWFeSlfDyfbtj8QIUBEjrBCPIzTWNiTpO1KHHTpcM+o7ysUE6o/eAd0ELOo8s\nZFQX2AtQSesBgEwjFaWlig0LSz8jc8NdWOZAxqU22nwJSUlXR9p0we7uSRT8n01MgL4VhvT0/bX+\nqDK+uSK7cPrkK85b4dzaBvP0mVDkO/1ZmPeV89MjLtAtT2PDLEbNZ6ZnZ65uXtAVZ8URe45qQxSs\nXeNeoCWaVLRTZF3wMSEuG8XGSGuK3jrK/cWJ3V1QaBqF8+V5K4TiOCZ4wi13B+X67I/F8c0hAIxH\na7j2YUDCT/khid6NpQlXtK0IVZJtfFtTskYROiGMW3FX3BELXGVoUfy4J9TDdyAD0uQxBGY2QhcC\nFMmxYXwsrCRAK9kkPsDQKkVyhGNIh7QSG1gyTYQVCdcGOprDDkc0RLYVZ8SZTMji3LtgLcIrui26\nePUQf+GFxcKybJRwzJg0YwizVYSozrLH94nDBdCZspOIwi4Sv28OyRKwAEK2hiHceSK5MxJdGCVx\ndHieojAUcVbpuPRCM+faDEQQBFGnqJCB9tC7lcTgim7F+0IibyguCFUyzZyL7NF5c2VP+CYD5CRx\n4YnNitEzWogRs4U43UUp9iCeFIp0DB7OJgepCHHPJgvud/bgiievVHOqwOCGWQjekegCPDAKe3cm\nj9TACOIRmjvVIWv4ogPUFgi5bmWveTTWC9HQv5KILWfrKOiDrtMFFXt0yphd/09dM/6fHj9RBfLF\n5YgI2C7R5URrBVVFtNFaJek5xB5bYWUPAp5tchbvsSa4JsSEdV1pppTaaMuKrxF3bDW4wjQDN6yF\noCRLoFtR9EbLPCmAk1Ki20XbWMRQ9e1GBPVA2kM7Q/HSUYqyzisuK3XduH1tI/kAKTfywyhyZykd\ntcSk090ONFVkhLRrpHQPLcOsqDvkfWybZoFSofQh3BHCNaELYpZ0DvR4A7kbo8CXLTFdLpC0IN0S\nI+6qwuU9lIzYEsl/xw69HQJVLoq1grZ47amBGOIKsokUtUHJ9N2JvssxC0rjSj7wAG4KDdxWlgJd\nN3B3n1iZkHnh6cXAqd4Byourhbsj6PWO1c588nHih18vLK1y/eQSszty11Nxeiusa4/6PXXMzFxF\nq35JnMqEl4V1uaSrghwGPjoWFu0DMVxXvmYgd/DmjXE/xamfS+Xr2UlZmIvwo3SHMLIm6OfGooLW\nji4rX21FzojSckerQrUOydHe7pefqEftT3X84EcN+0LIp/sYB0CWTPGgnyQ11MPG7FUJZ4Ku71jz\nHcJAVwdKPqMlil6XgbIWdn3PWlZkP6ESHtPyOkJXxFckBQfxUG6p2uPzHTLsmTZ3l9Ya6RzRxG/2\nX6JihGxn4Z0seAq6TbdkijiphXVga42mCQWSC35ogbcsTrbw9YQ7MgnzcLroEhsGB96BEMa8qfOA\neiDoS8tWbJ0aRRyRhbElVpxeI8J8vQ+kWyQWShsr4i/RuY/nihV2gd7OP4Z+2bo4yXB1RGaGXYXT\nhoTOCeyeakpSkP0M5xjr2S0CDy5ukKK0CjI68zkzmlBUSJziWu4Hlh+vdH1Hm1fa/YMYWWj2wdx7\nu+eEMtTCScdYLJO9t21zsC0A5a4eSBt1K/MZAJdfVb5livunPOknqibOc+JJP3HUnuSBCpu9T9Wz\n40NyXvDRxQwC/MXeHd57IpvDm9gsPAj71OJ1h9KbR9ACG+rnm9hWeFy8fxaOv/fHPSPOrImLbdxW\njYJLJDZSBQlOb465qpkzeKFot6HAsOnvyEQo3OzQq5DaiucNOZYNGXZhUDAMUw1urQja2mMKabXh\n0VdXVEmPIS/KaCujOGcySWB0504iDrmaM4hiOKvE2l1FuTbjjKDb55SAT3E8HIu263G2xkOM+MGV\njvfCwrzZheLG6I4KvJFM7xvqunUIZeM/9wTXuAIpBXotbLoXgecy8GO2YlacnVdEYHLHNnS2lxAs\ntq2+kRYBJiKwEAh9h3GtjdmUCWXcPJjDGjcQ6j1xvs3DSm3kPSr7gKqrgKFBdSScNSpK3TjGEP+2\n+Z4k0FkIGDuPjVJcvxBkOu/F6Wy+yb05dTsP80e5yGPQSnhQCesHlewo/ljYrh88d2m7R7NHgb7I\ne2oFbLjc9jt2bBltwN/8v3wi/smOn6hV+923XyKl0PeZkoTDThE1VApZhKY5WvSeqBVSv+Bts4KS\nhDfjdFpYF2cYocs7JtdALlsL5bu3sCrLRnJIuafREHF2YyC//UGY1o52KlRv1EUwW2MxTesWRRyL\na0oDSedoAY2JvlP6C2FfHXmSKdVp85F1aXjJwXEmFuJAkhfcBbUlCvIkaBdJNHiC00MZbdAKohaF\n/eaIFFHG0cIVbZsBocG+QKpIquFn1TeaNFQSaEHrHJ9Te1jBb1eEIWbAnPFdg6f38Nkp4m2PPeWu\nw89KNkXoY8D3Yest5gh9rIZ5BzLHeUgGUazF4tqWsJ0x68h9YmkJ74XkxmG4iIWZytANlGEIKy5t\nsDa+PFfQgW43crcYzQfqYpSmiHUsGaiJ9RTK2LKc6NKe7B2tjix+S6vG7e3CZ2PPO8uwVOYKU1lY\n0g4vyqgF0srYjVyL40siHaBuit3iIL3QsuNS8CUMg1prFBdKSeDOWhq9XOCSuNeFn5Xj+3PCNHqx\nKvH8iSTMY4cvTZG0Gbhn2BVjIWPrE6RuRVPZ4RsSpA6eB9L5LTu/oJVLTI907YLWnejanuqGpTM7\nOzDLLYpwkYJfp+dTtF6dR7rG6UZJ2yxat2ZvlUbJCbMWimqv27kL2ipNwtar3jlrLlyEqQLb+o2q\nktTZJVitMqSgdolBNxu1W2G54CH8VETQzdC3NWNIHo40ZWBMjnMkITxPw1YcN7JAssTUVvo8x5yw\nW5Bt3nq6e4t4ULiSKLZRl9yFinCnL1iy4cvIXJ1Ret62laoL/Tow78If/HiKkqBVaIuTk/GSBsWo\n3RPUb+AUY9YYMRm5u3f+4CbxF68a//Am8ZdfNH7vdeJfunyHiPD3b6/59Y8av/fOsNY/jpfHjp8Z\nIhE9YRo+rJ4CXPDtd3Lv+IWLwvfOjnvmr44nZOvAPfCEHz7rgUqRDBrvN+Hpw/d8INgxPligRWjN\nmT4AWf6Lu2v++sW7xyLh3/vqBQD/6v+9x+Mn8jjexQYNGjcPwIz4o6+tidI/XqpK3pLQ7lw4blQ3\nAboNkSxEUTchCI2eRNna5A//Vt0jpt2DtmTEkra3SpUgIKzwHuW09hgYEatkD81JSamudP5AzADZ\nNlkdwiQRWrSTxnceE9ninFWFTIRcVI+uRExN4WmcFE5YIMiAUrHNBrJKNGBVYfXKKMYSRBX6FLPK\nQOUsTu6EQW0T+AljMszjtWTh800459smtVqkxiWCMxs+ywJNOVtCRTm1xtSUZLK5QCRekplcUYeC\nsjfjpQdFI31gsfcQ+BFqiiigH1LvzGHexI69Q1Jn8cTugzCrB2eRhw2PIYyEX/Je4k8nHDIcJ7sF\nLdRjTPRY+Ghv74GgjKjE+yeE7gPq2sNj2hyavH9mH5wvlJiHJxe6bZemG4Xjw8L+g0/8Mzl+ogpk\nf30fli5+wmrjqImUEu4FkUTVFasdTbZCpDnS4lfwVBmlQzSK4VvNuN9vk2oMePeGk1FyFMwiNL/F\nTcES2s8ROqEdWCBLKdvGS0z0euawYfuLDrg7xc8sHqr5+X4lk8l6RpMx5kStha5L9J5QF8wWxosB\nKwu4sczxEOX9ZoBf1igq9QjkGMGW8bIggwcvOsl75JYKWNBHsoOc4Inhn91juxRxtVMHg8PitLtE\nN2U457B6W0Nk5kVIecZcgYy+VdwzVgU97XA3Bil4HhCrsEZ7ZD4Pj+IslxWk0HU9lqAjyPTTIjRX\nltrhblTvWZdGuVem7Zk09rBCFWXFoGVWj80I3lEkOL5qsSFokrFqQKa4gDbaqeKW6HJjZ85huKLW\nwqlrjMtCykraCW1e+cNTw31gaeH7qNZI5UyTEJ3UFpPz6wY1Q5qc1JT7pNt5KFoSS6sYim58196U\nxU+Qdkypoa3h5sgHC/hP+/G94wM/MWrgz7Lz394rPHI/bVOXyyPq9/AMuwi/djXxj253uBi/djUh\nIvzu6x3whM865y9eTmA93+grP1oHoPDNHtCRSQ3Ys2+NU4FaC8pARlhESCJMnSKiDGuMy9MQYq4f\nzpVvagaDVWJj91D8flGEzzthApIoPzoXvjmkKOY2Kz8BDnPjqI1eM7cZDqsjrmgGfAc7x0zRZPTz\nRPFL6uD0c0LtDgz6Zhz3if1xj9O4Eae3ilgiJUdxWu8wCmMF0x31eGbBmIbD+4hlFWqtdF3H0hqy\n7KIo3/fc57AjXG9Blx3ToWPyI2UZaa3DcY5dh2fHLTh9X2xZsb6sQPDAzR1d50dx6zd7uJmhtMT/\n/FXwzf+DV08ex8Yf/zAB6U9QE0TkgTSNC/zty7f8R3dPgycuHkJHVS63ufXbrxK3SxQiv8eOX97F\n3//R9P5zAwXzRw7pA9r7UEQ/fr/Y499/eD6/ua/MAoNHCqYhfIOFb93vOYuwd+evHaY/zWPyE3VI\nXVgJlA02DqiHtZgTm5hFgm/aNlTZHVBl2IqciByO1zvhse3ei9GQR1S5eWxLkhgjG+oH7NWZahRG\ntkUHXCD45jQzk+gfuy+xZhdzVC1KWA9h3UPR4ihl+7kp3DVjUKOJkDbk14FJhIECEudqEqjzIEaL\nX5HqwiiViUyyjebhSt2KzizOySNSWjJ4dWY3pu1zVINikJJTaKBOp84K1BpzRzScjVODPscG7bhx\nntdiVM9BQViNc3V862gszTFLqIflXfZA+8et6M1sYRsPNnQehK1OnOXDGnRDeBsw+vv/YS1oMcU/\nfC+PP2eCciEe92HeeNy9AB6F9hklP/hZY5y2irfnfbiISPDGA+2VR7R36/U8bNvI27l92MBxoHiA\nmi4atCp5zz8Owudjnf1ndvxEFcjPdzMuGaeS1ahbUbFa43xeOZ8SpRWWFjvKpEqWuMzWQlIh2jBP\nNMomwJDHRRBZUTdUVvqcydI294GByi0mHbVUvNVApJMHIoYhpdKScu8dKSWUEGfF64pWx6yiuUJ1\nRI11DU6b1TWEINJiUJ0mFIek7LrYAJRz9Ac1OZqgrYLrPa0puV/JF3s2MjRSY6EQbUinWF6QQ4LR\nsNRC/XkEbQUZDjBWGJx8meDzAmOK0Xo6wMtGvllDuHcMCzXcgrqxJKy1WI1qwZcRN6FYolm38f8K\ntbbHhanYiE9Qa2axgjUD6cK+T2fccngsNqVKobYQ7RmAK1WgVCdZo2yZ99HCidZKZUUwqmhQR/wh\nbrJFTKUJpQQvrt1P0bJuineZdsysGFIzDD3SDNMOaZH4tVp47FICeTIzFnc8ZXytaCLUEkTLp3PB\ntGeu29LhG6LsQwhFcKrbRj/5iXrU/lTH3XnlTUu8yEYvwnfd+M4583mOgvTQOd+ZE6rK33wefL5p\ncVAYutgD/tIQ8GxdlE+z85uH6REdrCXxaTKW2fn0YcpbYTELDuvOORYl7Z39JHgWbnq4WkIo697o\n68KxdfSj06bg+34G1HPly2T87u2e37mY+LKDb/TK513wAf/DrwZ+59OFTwao4mwkEBBhN9cI6tGt\nEFuVZXBceBTMpBkwQ3Nm9gH3gi6KiXCkQ4eObqrIGe52jcuTcb4Czo1RDNeOpZ1Jk1BrYs0rGaUN\nPaIWIIAkTvdH+osdtU7kfkR76LIyi7BOlapGO4U057ZTfHZM95TimDcKxukYBQ2j8+M5nt//6m1s\n1B+e50+26MmXm4DxsGE0R1cuJOa5uTq/MFZODrjyo43qQA+9Cx/1jR/P8vh3f+/mCeCUbQX+pUPj\nj4/O/Xb/3WWz3IzP+65VPqLR9cpSo3D6Zn5YXuGH6/Zcq/Jrl4Vf742Xs/LZzvkHS3zmUITrJNxU\n+LXLyne383lnxj+zi+96OUc1NBLEHHcYf1ZoFpslWNWgJyQN7OWBrxponyAqHMSoHlzR/gFChKAP\nQlD5BLZHBnxj9UHQ57b3D2xUCBVsYwP2Gnzecft8J4IlcPiImduWWFU5WMSK9EBrQoeScTLvhXl4\nuGVUC74qRKG/EsijAOLGAGQVTjh7YHLjQpxB475PTVCLmryzEui3wiAwL43rBKtv3F5piAmdFa56\npbaVThVvRpXgrZOc6k5pSk9Da8DQUU8a+wRliuudRBCUtoJI41SN1pS+CgtR2Mda2zhY47Ypo0TA\nx2S68ce3jW3cGnqMDljQx+sDwYs2h5EWhfW2gajoY6HZE7aZJomHmKYOe0Rt0zZeOgFv/liQhtN5\nfMgqylOv2HYP6gZQfih2LiijB4o/0DgT3cCkIcwEAqXeKBVJ4rwA9sD8IC79YIgXD6T7AwD6T338\nRK3aZewRUcydovqIBOQEV4cDVxC8181r2E3oUuzyk3aInjdUNW9yTxCtRC9Yo5drJzxtrTqJFpBq\nCRRZTwg9TRwpS3CN++E9I9wMquBLQVoN8uF4A7mLQIvOsbSiucTMutsoC34ATthc0P0CYx/bnVmh\nP+FjJctI+eIt7e5A+n4i1yeQJBwASg+1gzThTZDUqBhWnaEJagnuNhQ86UZrENCZJoomeW9tJ9HG\nwSvUhq+KlAHWnhDdGawZMcfqHjPjtECpI8Uj86e0HGi8RwMFQkxZcawI65Z4J8WxBMUqZwuud8PQ\nGmlc5oH0FQes4gbWOqoIVVa8jcEPdaeoRaErCXeleLSVzR9cM4TiGaRRTWDbXFXL24O5/SwtWrJL\nRc0pXkiirCoxBogJJ9VADScMdAke6NxT0/b0SUOb0mymaFh2FS0k6QPTtxUTCR5qCwHYz8rRJ3i5\nVl42+Ctj4vcneF0b/dbje73CP70vfGtO/I9vCvcMgPDL2vh0iPvwAxe+NQfH7rfHxu/OEQTyK0M0\n95bts36wTf5/NCd++zI2G3qGP79v7M5wL0otjq/OO1XO3hhacOTusvH2rTEMiWXjAucedBH+Wn/m\nd48df2FsfO/U+LyDLxr8y7uJH97Fd3+aopD80pTPsjMTbWUMsjasQD8L7XJh2mblJEACu6usVzGG\nunvnfLUwlMzaGr1IzC3JQSrX54Q2ZU0FWS38WYdtDEultUSXPTpSpbFyg6ae2t6hNrIeJw5XB+7K\njKcesjO+WbnPXXTAxoZVY64hFEIMbdCR+S+PHb/pK394DpT9bzyb+YPbxB8viV/dGfebgOfTXBER\n/mgrIn95aFEQAwv2+PrHxRkHZcEYveI4X9SOoX9474fjKOzYfrAAW/okwNgZc4nv+Utj4WmCmyr8\nStd4s9lEvf2gIfOXd5WnyXGv0KLQdXe+OMMn2xJ6g/CHS2zgnp/fgyZ7/LFY3rttYkM4mPOdqbIM\n/wQPxE/BkbwFktcASZTmUUh9qA8RpTU4eQjpEnBDZtwa7p3IozvFzHs0+kFw91CY9B9wSYcsWI11\nImlQC02EtcSmdVChtkpS4c6F0QtUMNVHBLMHJtdAj1VwKs3+JJVjfuAxA+DhcBFyF5IIJXg1LA5F\nHZP2yKfemFTMFQaN2mAtzigFQcM7WKOQUwkamSbhtsG1ChOCAqNGYp64UU3ptTImY2rCkxQoeetg\nNo2SxJ3j6vQpPJnfLuHV39OwjZqyFhi80BxORE1UzYOGuG0sVs1oi4I0qzxyuvtNjPlQuPKwwWGj\nLGyvOw+pcuz9H8Ss/t4JRN5zN7LFPMBG8XgoWnsa64MfhhVOIrHRFd7rrj6oXNXblsQXGxrZ+MzV\nnPuHMQvkVrZ7pI/j68aF/CCkfb9/o7mQN/T/z+r4iSqQX73uab7QSU9hBVWSzGStG3EoByJLh9lK\n1mDAJO1x6cgSAjoXA+8h3SE+ssVqbe2hHdZSFDgKEPzh8D/eHq8WQpaUMrkVDr0garg31BPHZaJp\nR5eiadUePBKa7x0AACAASURBVG5IeOrYediCqc4Ie1TOcT424JqDt0gKpXfbISyxkLYBSTvo38Hu\nGEVx38Lb1G9BEjJ2QWPIBZjCH0d9i/+asHqB3W38PHGqzWhqdNfAvcJ5gtuL2EDMFbFL/Jyo7Ujj\nGa4FWs/tWTiWRLERaSuLB6Lb5EBZDelm3IZw/agDilDaykTHyQpTa+zaRaD/XeTOmwm1NZB7atuH\n17DXxza81YZlZSlO9h5tc8Rzo2hxzlIhC8nGmPDyQq1BvfChMc8dUyusUpFqzNXQXng3LXRdx+18\nYrYVacaqGSuVtSk6GoMlPD3sdMGsktMeTRXccFOKGKd6F9w3vaT3OVrveWFZFsannzPXwovdE5Z6\n3pBjZVqOWP7ZoVj0Cf7FC+H1qvx3S4KU+DvXC7976vmtp4ULg6+q8ltPGvHsLbxehFctoVmYqvKR\nCx93cd9nh98+OK4hAINon390oTwn7IH+uSfCpQtfFOGyjxbhDUK/LYaiwknCiB7gf5+EX72A0xBo\nNHvhyxqF0YPV+a+MFbPgBX93aoxdKMr/11vj15/AKSuXzbjslSPwxBrX0lCc8wtjeGOYKa6BaHX3\nShqdJRnl2ujvhPXSqLUx3CachuSwgksqMAvTZQd3Hdd5ZZTQIJTOgnpAdC+SJnpgmVe6PjNyTRsa\nysCwjzTHtRZSmlnXjOVKu+y4vC/cGbRBqEtGjnD8uNK/7Hm7UUb++m7lBwV+qQ+NxbrA95cOCHvK\n11NsSt6EKIJxs9r7wVZUugVC93qOnvy4gRo/J8bXm9HV0NbHsbMT4aNts/pU34vuvpUTnyv8U11B\nXPmOPiBOzneK8IudhDeCxPc/S/o4VmKpd/5RyXyqhXFb7efq7DPs3Xmmxm6Mztv5g7H83fXBjAt+\nYSh8rzTEBlwXfrGTP3tS4/9PR08DIYRx0RYNGpR7oJgS7glh+xa2qu6wo9I83mtIbK6Iy1IJnv+H\nlyhJIMiKkwn6gFu4TeQNvVaCS6ybuLLbNsPegJzCWQPoVMLeDOfywXbOlepb58Oik/h/cPdmP56l\n533f53mXs/yWWrt7pmfI2TjDRSSHq0iJRkwbkSU4kILcxDGCwImRm9wZWYwEiK/zJwS5sBLEgg1L\njm0IjpdIiiTGkSyK5EhDcrgNyRlyhtPd093VVfVbzjnv9uTiPVVDIRcxYgImeW6mq6f6V6fO+n2/\nz3dBBJNr9rHMp+xKInI18ZnEsNBYS71KxluZzWzK0kKcH1Wx1P2MWatOnkI0UjPTRaue3sI+Kk0j\nNfJMlIWtvoYklTGvLLuyibCwyj5W4iWL1ElyqQa9mwvL/W3VSB9Y4TLDmAWjiqYKtEupZBJaJSEJ\naAoV6yhYQo2sZZ4ElEpe2bld0Mx/b65Jo5ldl8raIu80jl6x//rD0Za8o1UPRSteoqZZJBHQwoDB\ni84GRaWnSluqnKIy13nO9aIe6grIr1JU5v231VFVzx1KEfP/kk44LVwZLA3vSIKsVqPjn6GV/w23\nHyuA/Luv7HnXYyskC2XIFD/A1FDmZiotBjV1FayYygRTkytEtOrktCBFKEbJ+QAo1yfUqK0vNL1K\noVAEVzORo2D81clz1yYQWwkXxAhGeihCsauacnE9hKj6ZqjxaNbU/M2cF/Ux4YDco7nAXEYh5Hlc\nb7F2jc6yicIGb54l5kd4Y+sLoThKychV7JDpaTtHTnVBYLOpy/nSUnTEzCDcaiFEwZhCExbszrcM\nJnBnHLmBMknBqOdRnIix4e0pMMUdRhecTRsQS6ZlSHs654lquJzOUSMghjMMkgtqJm60q6opivVz\nD2yLyEgSZafQiEPwTGUkZ0XdWEF1jPVF6AyhFLLxrMVTbNWt7kPGeodB6bFMJVFUUN8whS15voQF\nsE3PoWl4NG5obINpejaXExfjnq7rkGJp2xNSmjBNz258SLc8Ypx21Jh0X93yeUJdwz4lnG1qWoqx\n4IWTk+coRtAAIScWiwUpCh7I20eoFTb7XX0NGWG8vCDlEfSn5E1Lvdb/aDT8XBdZ5MzP98q9sd4L\nv3vm+MV1QMTyWrh6mArPtMoNEg8mQXzm9x55fuWwHpMbrfDK/p0x4ZQynzisIS1diQwCR/Pxu9HA\nKJaGwqEISWHnhJtaWFO4P+ugb6w9D4Blo7wVhNtO2Qelb4UxwHNL+PZWuXVgeOlR4l0Oxlh9Ce9f\nwKs75cld5qUCmMSfP4Q3kjD6Ksfo7zVsHh9Y3QF/2VJKZDpRuqB0puZf2wOD3Ql+LUwlU1qQC0UO\nLOEy064cOha0TeScyFkw7DGlw5uAD/WFlleWi4XBdAYTM8ZUQyi2Lk5MVsYh0C4PmKRgMbgxMN72\nNOeO46FwNiliDf3dFvURrw0mF7KFd1nD57YNiRrFNFF4b5v51mT5wMLwzQ28rx9RafnmBn75ZuS3\nz+v5/qWTyri9dJb52Inwfzyq8/e7dLzQDnxr6wHhE4tAyIavTI73rSrI/YcXHb+4HgnZcG+y3EPZ\nFfjW6PnsKvBWLtyNlmOEb2T4YBu5lzzvbyJnGX5mVfidS+H5tgKtD/nA2ZwDq6osvfL65Hmmjbw2\nOp7tEq9PjkLkPS38yx38O8vC6ynTTvA6Hb9wkHh9O/JthO9qy4dd4Kdi0wqmqpGuZu+K1nN1Jbkw\n1+xkhR/GXJFKFbCoJpy5mpTU9wtaAalqwZlqHM2l6kPTPMXtZ3DtVGkNjFlZzQs0Q5UmVpzmINdX\n2ZWmOWoF86q1TCLkwtpoNa3NAF8ApILcdPW9QDeXiVgtnPrCTg1LEpf5yjRbC0f2udDbKkVwTmhy\nITqDpELvBMkFcTAkroObvSqNZqZUsKZwEaF1EI1hm6C3iX6WaeTZ6JdKjTazpu5n4ywh1IWECFyE\nwu2V5XKvTAoSC05gVIszQldqWkQvSpIq8vRaKjiei0SK1tVDLuBKJFk/50y/U87uTP15IVcfQ02a\nEJJWjcgVGN7P+uhOMzp/n6XG6llT0y3c7IJqmCPrSpWXDD8UL6foPMAvYKoG/SqZ4+r6yDNAF6q3\nSYTraQUKLTW3OpXKkotWMF7mRVEqdaHXmGoA/FFtP1YA+R+/tmb62g7VTEgXOGdxsscWj9XMNtUX\n7tJVR7ep/DG9WEKJGJn1pmZEqDWhKe6ZYmAy1QzSaEvbFKJW4NnYwqCZg/6ETWhIYslthw33WbRN\nbc5qPeM40lFb8y4vLzluqu55miaOTjomXdQIqRywJTCOezpnweb5ghyw4miWK/b7PSFFls69k6Pc\nZTRlLrYjlge0By2HpSG6asQ7xHG5H1itj3Bxz3a6xEhzLUMZcqaUCxLC0lawvWqaOZXDs9mezRrh\nTOstloapJI5Mw3Y25LSiTH6Jy5nVQYcrBYryqsAUO5xzPNYV1n3HEJSnnNBg2BBqZ/qwp7OWyRX2\ndkk0NfrLt47GOiRM7Dnk0DdM01Sd5wctLhaSNyxLBZ1iG1orTDmxNB41kTRmgjFkcagIbdtSQmC3\neYC1lpwzB90RoyhjNhibibmgVrh98ymcq0xzKoWwrG+K5c0FYZxoOqX1noXW8Z34A0qqoAMS627N\ndneB0Y6Lew8QU2qzWSw8fPQ2bVPTVcYYMN4jJlByZj8N13GBfWv+P6//n5TtMS98xsBNW/+rCrjM\nf7zM3M/z9Zgit6zwvdnntOr+7EPrxbYO1gBe2cmfoQheHmtgT++FIcLTnXDPFM429bq+1Qa+0goP\n94VPHghrI5iS+E5w3OyYPzcyzUPgU59wCMkY1k3lv96KcHOt3N8W/vxhlWpcsSf7qfDRA8fzkvh2\nNhyq8rlz5bNHzOyGsnliYnHXIQKXp1uW0dNezkYiE5m6SD82uF7JzmC3QtcI5bi+RPKtgFz0yGGG\nC89oHCs/QIQYC84XxOR6bS8sMmRYmprAY+uiQ0SvS4gW666mVqrCJrBLDfLAskmWjglZdZTzieEJ\nob0jnIhyr1TT051UeL8f2aUag9mLcFMV4z1P5sAzB4aUoeSBdy2F719YHp8d73/ySDkxVYP42+eW\n2zNLdWRH8lT4YBt5Zarn4X3LzOSVB8lwajOfXVaJxftXBWTiray8p4HbZuKtqLzY1zE0tpYQaTG8\nz2cGDLeaKqV4wgvPtO9MZ8729RgsVPna5K7Hr8926VpP/J4WXovVcAgGKS3BwzNN5PXtO5/1Rop8\n+MfqDfn/f3NGrlMEitZILSNClgq6zMxQegPpWs/7Z+/ZH9Z2ZtUfoodm9eHMNBbVWTtawVbSmpJR\n5igvNVLjCZWa4T9/biuJaYYkLVUCUZMwXGWsi9JJIRdDN5eTLOZ/OyocOFBvSKECsSEqvZ+9IQqH\nkhhzXdAZkWqoK4q59sAARpgEbkpiMlV3vPDKpMLCVLa4sRCyMKXCwmQmhF1UvBXaOQHEXmm9reCt\n0EjG2grmo9p6/EsiYykou31hHJV7WQhZEGfw1rAdE0tbpyHBWlKWKuHTTNCavhH0HeCYxWJyqsY/\nMeScMALbLJh5ceNUCXqV8PFDsifNTKWWx/gZTotUljbMevMiVU5ipMa0XVWDe8CWegwUy8ncZruf\nZZlJYWVroYudj//1G/GKvZ4VQFd/f1UjjlaTYFa9vgZlnoAoQvghtru25f7r3RP/OtuP1+0/XRDD\nwOHhKT56sm5YlKqhdTaz6I/YDxcsXUtjLJIndmXiItUbom169hZWpiOmwm67ofcO11QwZYyDBjZj\nJmmhd0qKjs62DJtzvAg4IZ9FJlF2F+cANGJJojxSwzCNGO94sFdM60l55M79jLSXtMWyGSMdFsmF\ntncs1BAKlDCA87gh0xjLk4vD+kBxwqpbstdAmQaOT5f44riY9txjh9mA98Lr40hrHenuA6TtSbSc\ntg1TjATXYESYVBninn7ODWXYcth65MLi/IqUAlZbkngkRcjCuc1EA8Fl2mLYRqmpDLbjbLdhypmH\no9I2EQhEVfwARRK9bximHcbVBURrHZjCY7SEtMd4gwuRNHkemoLJyiZNPIxK1EDAId4Sg4KbFy2q\neOOrM51S8xS9Y2VrS+KoAaceJdC5FpcTq9KR4kiedmAtaw8L9QRbb8Yy3SdNii+KcQ1qDeoWHDmL\ndB3DTikquMWSPBsKrBlZtXUBUnLk8OAEARb9imwgDCP90ZIwFUQjReCmYY7Ir/FfxxTG3Yg0kPkp\nETMC391mXooW7+rT6jML4dUIdh4H3myFW7YC6Td2hqeWmX3KPJjeeaXe9AXBcLODcZt5eiUsSuJt\nsTw+y1HWXaYAvSo3gDPg6VXhVlcNYzYUxAiLJLwyWp7uzfU1sxJhNyT+yYUHPC+2gdOF8tYO+tl4\ndmdnWPrE377n+ey68OwCfrCvmrgbTeBNVXopHInymRPDkc5mkqws7gq7m4H+omW9b6hZF0rTOsI+\n0PuW8zCwyg3xxkRZGdzeoyVXKQKOIjDpSK+WrIV9yrSmwTkQW81J45HDGKEfJsyiIZEx4jDOE2Mk\n6DmtOSBOkWirSiy0Ht8qfr9Hfc+la2ETmaLl8L7l0RMTyzPhZHK8kuCZRvleFBaujmSXwJ+eCwsX\nsL62Ib5woNwpdZHpghKDsk+GDy8gxMQtET5gC1ng9y4aXugnXg2WhSu8r4msFb6+M+yM8MwCQoRb\nrfIgCN/cC6c+E1rLFzaO960SHymZB9FwwxVmdQC3F/DWHp7rK0P8NkKQei3c3St7Ee4lw9NNYTCG\nZ7vC65PltdHxRoo85TyYidcivMdbCJnvTp4P+sArSfnC3vLzM1oTgSYLv7P7EQoa/y1uKWdSAVt1\nhTVtomSsqW2ycRZKOK36Umfq6PqHJ18B6G2VE8UCrb2K+bpyeACmJhTkmZUsuYImkZoqMaVa1qEo\nY9JZCviOptSWWkAReQec2ZzeGZtL1a9us2VhCslaSgGv1UyuIePrbtC4eamsimaYqCxjVxLFGHKs\nbO66gSkU1t5wtov0jUUtnFoYpFRGVYW+qclZ01SnN47CEJXGKAsndLO5sTEFL8Iu14mVmdMvmrnd\n7tBXicaQDI6CVWXpLUtfga7PljFkhlSTKayHA6dkA14LIUNrwcw1027WUaRSyOWqZlsxtoLQKjVR\nQplZd1ulDVcyh8OZgc1Gqm57Zm77GeR3RnFW5rrrOlFHa0NqS81RFqmmwgqwC7Pl47op0Zi6iFka\npdEKyq9qsq9kHvZqFKBVm1xm7bdeC6eFmKt0JTEXgmi51j5XrfoPX4z/5tuPFUAetccuW3bJ4Nol\nkhfsBGKcjVQaSN0pD1LBRijG0S5WRBVM1hrx4puqC/IGWQh7FMZMac8RtRw2DaPsaBOsVkecz3Fu\nxVhMSSzEYf2euN3w5rhBEhwvjisYtg7XnVa3pRhyGHju+EnO4w7rHfeniUOfePdiTQ6Rh1JjqLq2\nIw0TpSSGtMM3S+6UyC3jmKaJy2HgTgywOKUzYMbAZFrEr+bxUaRdVf1muyh0XYfJE8Vm+r2hT6Em\neqxatHmS8dF9VqsVbyK1Jc9FjGv57sW9umqPyrBLLPuWUJQby2OKOyLmRLIjxllSmMhdfYk/dbPn\n4WZATOa0qaE/Q9hDykgvtP2aaLYU7/DS8UASXiyJjPeWoqnuu2s4MQ2Uwn702M5RyMxYHTSzXq/J\n+z27PGGKRRrHGAZiyHRdV81zami7miASU6KTpn6P1OYu7xrOyGjjcCGhIaPOEK0hknDqaK3lIlQG\nq/QLypSJsdRq3hwQtyClROs8uynRuXosvPFMIaFNR8FRnMH4FZpyTR/JQimJGCLiltjlkmzL/Av+\ndGwfudHygg38/R8YPtjU2L2/dsPPzFQVAYo1vLZ750l1f3S8WiKf6SvI+sMJzAi/gvIHO6r9fLaO\nPL2C7QBLhUx9ie6An7mR2Q6wypm9sTy7NHzxzPDZ04yOyqoIqLAzQjFV7fZfPRHJObMrwr0k7IGD\nopz0yitR2EfLZ9eVHvnOri4sFy7x9+80/NXHAm/uhF/fOn7lMHKzZ9YxgsWwvtOTFqASKYeKfbsQ\nQsBgiVMgZHClYO6C61um1YhcWrTNnGwyYhJy2WPdwErgzmMNTz/YY3Mhl0QUwd2f0E6wVnC+xs5p\nznhb49QaPapTJCvkEulbjzMFvbdnLAtoFJ88IxF7ApfLke6u54tbw9PLzBQNycGpL/zRhVLU8JSD\npYdtsrznMHM3G/7ZQ+Ejh8pTTeb/3Aq7aFj6whQyjbfcGeHL245fOhz57GLk9zcdLywnNpNh3Srf\nTIY70XNqMv80ZD7mhK/Glg/5qYJghaOs/JXjiddGYYPh68FxWwrP+8zLe2VP4bXJ8Y0gvJFhIYVt\nSTxzYHl8UfhHZxYovDEY3m0Nj7nCs21NMzovjrdL4jFpeLpJ/PPLwi8dWt6viYVxSBIKif97Ngd+\nemW4f0Wl/hRsrbcsirKPGe8cQi2SqiADOgpZDDHrPHepTHMphWJrOUdTEnuFha1A1/6Q1as1lY3N\nWgGONRUge6mFFEnmEhtbwfqisXOZRNUkB63AGpSDVkgF9llpUSYEpEoLJq1GtYWUyoDnjJ9Z8U2q\npsCshhIqk9u7WkhCgXhVrSw1S3fdVGZ2CvX3yaVgijIU2GWF3tACQ6zgszQW56v5TNOIb0yVZDg4\nMsImZka1FAx9SXjPnHeuhNlU2KFMqYLY3igXQVm0Fq/KnT3shszJypEzhFw49oaYC7EIYUpYW9Mw\nYqnlJiEpGVNbJxWauqqobGxW1AitJrbF1uOlNX2i6qxBbZ3WZRG0CCr18wx6XUd+lqAlEsWxlPpz\nrKla6F2p+fBVW14//1TqvwuparLnNmkcsFdlq1X+YmZm/Cp2Loqpiwjq66DKm+u+KoZtLBz4ev69\nVtY/Fr0ueWmsnYXsP7pNfpyqNH/l4/+u3j97yGq1Ym0abI4gI6MKy8Way+2ONzaP2I17Vv0aohBa\nIe433Fgf0vue7X5DDANPH53ixXB/d8nDi0uefvJdPLM8YEvhPF2yaDtsGhn3LdZaLuKILKqM4DgH\n2jlFI6iQUqAYz1IghYC1likZvJnorSdoZMTCFCmi3DxesR0zh5KwBr5yXtDdfbxp2OiOQ9Oy6psa\nuTNMdG3L/c2WfrEiF2pyAgBKK5aUEpdZGRMc9GueXB7wmB24O468HoQJ6HSFmgtWeYSQWC5bDtXQ\nMrGL1WW/cI4tlugWPJsjk62ShrfVM4Q9ves5addsXCEEh+iW1nQs+8zZPvBkt+IWho1rGMeRJk2M\nTcdl3vFwiAxJcFkommnsJSfeI+0Caxuc1Bu+NR221HHPw2ZJLw37/Z7UWFrX4opl9JG8jdzHo0Sc\nq2MyKQGrdQTmVBB1FKtoqhm7JIg6YSw4U6vCwbBoHF4Mex1pTVczc5MwDhuapsEZwU0Ru1xBrue3\n9R3e1GzjGLa0fkERaE2BWNNRtjhEI44GTZEpA40j5EQxtdnNZUgpwbLlc1/+/Z8KlPw/furd+q1N\nw02jvOuG4WJvODSOL18mXjzyhFRonKFZRbbnFl1mvvG28IGl4RvbzPuWlTX61jbzpWj5K0dct0qe\nS61L/c2N8BGT+XJy/FxXeG5l+MJ5ZaMfb0FLZaP/7lnmvzgVxicGujcXbERZY9jMLPHbg/L9neVn\nTzKf38DPLgzfHwtPruAHOyXNvoNtsDy7LPz62y0faUd+9gi+ty30zrLuoJvNRQspOOcYbkTWl5aQ\nHVlHltFVw1lX2zbZFdSYmmYzGkQs8Uageejn7F5lOgk0Dy2NqUxJLR+oxsMDGQlkGuvQlGmvGL7T\nJVuNrE1DSonBVDNdp56BGs2YlobufiAbuF+OCbmQUtUAbtSzzcKRWM5N5Ps75R9eeJpKO3FDEyeN\nsE+G531EVXg4Nw5+btvy5CITouG964lvTo6/2GbWC8PlLvOZg8IXhznbIBX+6U74uIebPvPYLLG5\ns1e+Ggq3refYZM6yuc4ovmGVVybPc33mu3OAa5cSb4rj31sIXxgtb6TI2jiOrPJmzLzb1Z/3/fnv\nj63h+zHQaC2ZuuULbwbDUGre7tHMkj2K9Zly7Aqahc809bz9y3nK8YR3PGcD/ypY/vj7r/zE37f/\n7c88ry01gmsxs4RqLZq1gspSTV1Oau6uF5kNWczMc00VCGVO+fEOq1dlHA601F4CUQatxsfOVqDj\njJCu9LEAKbNqoLHCJgspQ2uv6iTqz0ulauabq2Y8hIWpub5X36dicEYISRGp8pCYq0RATGU8FaU3\ngrdVHhBUa9VxLliZWdQrw14BPx8bayrA7BtDKrMGe9bSllyAQhBDf9UkauCgU85DobPCoLCYE1dO\nF8IuKOtWCBmmVPdh4S2bqKCGtYXzaOhRHkyOUiCkCjxjAnSe2BUlF9hloZ2nAQEwYqqRUgt5Zlev\nZAurWbrSymyWM/NxK0pjC2XOnB5TQUomG4c3OhcsAToz++LqokavalVqUolSI+Oa+RkVSyFqTeDK\nmMoEz/9/VDOHI9ThRJVK1HM1ITSarwtHJq0GP8NVDnWVpnSSGecIO3PlGKVq1HVmn3/tay//SO7Z\nHyuA/Ofe81EtIZLyyIRFYm216VvD0cEhYLnYP6oGLGNoNOPmGlT1LQFDq3tS8TTOEofAgFBwGI2o\nKMP4CFxLaxwOoWhg2S8IYeLt3SUL29TVmBEOxGMbDyUwBMFqJFtLDIXWG/qF4Wa34tHbD7hxekyZ\nMksHaUps2owGZRwDB/2aXZxI1pHCjnW/QHzDEAPZOrbjRFuUfbE4WyhZOE9bjk3LTqoO0eJZH9xA\nrCdPEZWIbQ65d3aXxjY8Gu9zYA6g6eiNJ4wPuHXyJDkNjNGxzZcIhd41dM0pnRdiMagzEBJ7DysV\nzqYKGj1VJ4yzuJR5cn3I97aBxk9gGgJKq1WjnKc9bdvSj4HMQL+8SZhvrtYKDqGRxGQSEgxD01Om\ngTJMLJvCw9hQXOKkXVL2E6OzdM6ydJXdF9OwpbBWx5RS7V73DWEcGcSyaFpCzLjW4ZOyn0ZoGhyZ\nISacr6A4lnSdfemMsi8NA5kSld7a2tYmVd/lTc2x9CpMdqylBMXSRGGyEZMKQkNGCZpxxdO1NZM5\nxUzyBherRqwYIZXCS6998Sf+RQvwdz/zgn7vsvDeI+GPHirHBl5YO/CZMba8vI2c9vCeI+WLbwkf\nf6IQkzBta/KASNXlfX2bapSQFhTL+5aGV/eJAc+Hbyaa0fHFLbzYFb49ZAY8P7sWVqYy1KUU4rLK\nhrIGQhbMSWJ533Mphi8+gBs+8/TCEZrMy/cqO7yLM0cmgrrMJ48q67USZTsPHgep2arf3jg+sQo8\nEuFUrmKFCh2WXVNwJxn/wDEdTfRnPXocccnjomG7Glg8EFKxTI8lJBf6zYLxYMBqob2gLoBtweaE\ns55C4iAK2kw0YtkH5dAlQAmPdbS7SN/3XOaJNsHgC02wrLqWR+PE4iJwftrh7yjZ1Xiu+ywJo6K2\ntnUNB4b40IOrE5Q/uXQ8tqrykhcWyqv7eo7e1Sl/eKk8ZQ1dZ3jcFqIpLAu8uheeW8Jre7nOTLaN\n4fNvB17s4R9cdpx2mV9cKjElvjoIH+6Uf3De895uuPZO3Cme1gunKfCJpeWlfeG2E95KtZC6S4nj\nzvPHY+HfP4Df3zjezpkXJXHXtBzNM18R4RNt4UuTQRQ+0Ra+vIvX4PpRiPyr5Hh3SbxpLe8uiXe5\nwu/vW/7CukZJfW7b8NdvJF7bZ/4gWuJo+OuPFf7GV77xE3/f/q0Pv1ez1mSIkDIqhsbU2MIslqbU\nSDC1hpiUzlXNZypztixXkol8HTUmpoKyXApuBqsJcEUJUlNInBiKudLhMmfj1nSfmKvJzntbx/ql\nto9OCNYYGtEqJ9DEfua1zQyovJ0Z03n0D3XhUwrs1dLZgivVg1LD0wrqDBIDB60hVuUOQ1KOeksq\n4E1hysKQMlksR1YZtL7DYlYiiuZEMIYDzbWW3dQW2EDhoDHVpLcP3FjVRcKpz2wQVo3hcqogXnJl\n5TsPuc0digAAIABJREFUPxgN5MKxFb5zmembhkMnPNhCSEJvatV0530tFZN6DC6ToTVm1oLXZBwj\n9fjrLF0QW/eh0cSotWXUz8drVjXgMTwMNdXDz2lSzlTgO5ZZuz4j7asWPgUWBrazXrnM93+aJRyx\nFBprmZKytIXLUo+3n2nhygDX6+gqbSRxZcjTa3A9lrooE6j5F1I1zFLqog7m9BEr13F/Q6ns9P/6\nypd/+gDyp9/1gnZZ6BeeyzBQSiYkZW0d0maMNIwx4+aD3EuVRnTW1/rGlHg4JkKpiQStaejWlofj\nwHrRcJCUsWQOD44hZd66eIj1ParKzaan1cR5nhhCYI/l4X6DaRqOXcvN3tLZjocXI8+fLukawVtl\nINcs1KxsASdVphGbwi13zDCccWIX7FOgtUrYZ5qbHUs6TtzAkAvJ9lyOgaXJxAAPSuZBtFx4y/O2\njvqjUfYmcSN4Xh8D5x4uLhO3Dg/JJrFqbnBYAm8P51gm3rvsGVKDGiHKwLv1ghdWa0xRxmnLP7o4\n5tWwZRomWuc5WqxoELwpxBg5tUvaZosaYWUsxgduOMfKrHkQYo3VK5EYI8u+7qN1iV3J7PbgPEwp\nVmYgwCPZcDE61n7NYBJh2DF6x73Yc3sROdxmvpknglEWdkVA6WNdHJzF2gpWjGDjVA2F62PCEGAF\nfRZ6B7eNZauRCxxjZF4UWayDle+qfrJcYK2lUziQFU1S3ooB6wpNUqIWOufpSibj2UvCuoEbtMRs\nsK7Bx8hoAoJnXSwPe2Wxz+xMSyl7jPU0SSm2tjphhAsVfvNbP5qb9t/29jc/8JyKCG+n+nJY2PoM\nGaNw0sCGwrF1TKXwnkVL3xj++Fw5spGLVF90nzoohCL0TwWmhw1SlC9dwC1jSBGeX1u+sqkxei/v\nEh/vhZcG5XFnecwpHz7wSDcXohqhD8rvPQx86mTN6/vC84eRq9rpyQi9SzgVvvHA894bFRheTIaL\nwfH6UPjZo/pMKaXwpYf1NH30ceXLb1tygQ8uCt9Sy19YwsYFlkXYPh54bOMoWnWczZghV0PJFFrM\n8Q49MPR3BawjZ4MnM0bLtMqcJMvFk5nWV6Xbs99/RJnrqG0BbRSTlXTc0N7dIccNOk6EkxXufGAK\nke54wWAKruwJumL5aKoMnhO2qWrqL1KmPfFELcjW8eCwav37O4aXzoVi4bf2DX9pURiNsvDK7583\n/MU+UkpCSp2i/eN9S9sYWmP5pePEPgr/18ZwWiLGGD62svzvjzKI8FGfuV+Uz6wMaye82wgv72qM\n47+4VN5dEsG3/OKx8HCf+dou8ueOLecTHHaGPzyrX/+9R9XY9B8dKWIcL+3qvn9yJTya4PUIz3j4\n57MZdBOUtli8T2yT4aCBd5fEt4MHAx9ZZF7e2vrnPvHy4PhPb2Ve3sCfDpaP9ZmPHAgu63Uu89/4\n6rd+4u/b//Jn3qcCWEqNTpt/o0EFZ4RVyRRna7GHrwZ4VwpBy3WdvJEaxtW3jiEpPRVAD1h2MxO9\nSBMGGHLtGLClEKWCY+feKf5uRNkXSCHhu65qb80VFK/pDw2FEWGXDQe2AqQp1+z8VMDNmvtcIM5F\nPZ2tzGjRylovjaGxiZCV1hnWUthILZ5KCGhiUIOn6oM7Mie95WFQGisMSaqpMGdWooyd58gk/Gyn\nCFZYkTGmssY3TGKvAg7uT7AymSFmnlg6HuwzIcGzB8KYlZsLyxu7WiO9TYalKA+iw4lhs0vcPmyx\nRbkbLQdaQeA+w8VYauEKtha+zJF8CgQRUq4GuaIJUUtrAOPopB7DfTEVfBpYGjiP9UmZqakW3sj8\nZ9hFpbOGy6jXbG/n6j6nUmbzZ114Tbl+PaaaguIMdNbUNlkgGIPVd8D5VR220cJezQzYa/ydSJVf\nNFLPwzbVa+YKpDtXmelSqiG0sTUu9CoZ49e+9pWfPoD82efep9shkizEYgn7Ld4Znj9eXjOuTqrm\nUVXxxbOLsZqurGc9GwoaKaysRXyPkUJjlGXjSMkRzYTkQg5j1QIPDdkbejtBMOwlM4U9Q8h8Z6pR\nZNNujzGG5eFjuG5J7xrKvqBN1aW2qsRGIU2souXSRkiZJQ2TKzBGJmC96IkixN2Ac7XsoilCaixS\nhFCEXAa6g4ZmD4HCoW1ZuIa3hwecrE+RGEEmljmDZLrsePJgzamHUToOUuCZ1T1s9zibSYmyo5sS\n08IRpwafI2vfcvZwwwu3eqau4esPH5FMzzJNPK4N1k4oE6bL+NQxkjjbWe6kOh65M4x8Zy+0dkkk\nsBfHoI7cetrk2MmOddjyKE5467hphaeXC47bRB8LqxQ56YWLYLhwCwrKUXB8bvM2K/EcNj1LZ3j5\n4UOeWBgeX7Qsmtpg2IXA5B1WIWttvNMS2WFoxSJFwXvGoWZLR1HW4nHGss8TrfGIKeySQVzEIRAM\nmUyaI8KsT5TkwQg5GKZG0eJIUmvMRYWkHoxhk/Z4XI08koaiE9627KylKYkkBT8DtV/71k/+qBbg\nv/7ge/TtILy/KZxTY5t+e9vwl1aBx63wd84dv3KYaI3hf7vw/LWDUMPdTY1++sSTSjhzfHlf+HIy\nECce93WMf2Qzv7Xr+OWDwMcO6nH704vMvWj449Hznx1EPjfCxxfCc21lRF7ZZl5cGopxLFwgt4Vl\naAjZUJ6I2K3w8AEcWmHvHGuuYtIMG5N5MAlDqszlV3bwoRuZVg1femj58M3EcXbsYiHGwvI407uq\n0TMbh2syuSlYMTSjktuGHCbOxszhU4K777FrQ9oWRAsuK3SKCQJN1e9d3ILjuxPqPD4HEIvuRoZF\nrbi/kUDdhLnV09/d426s2GrEPBqQ445ysUOjRXPC6ILsAr7vydvCmWlZuEhWz6O9JxiDqGUf68hy\nkZWdr6Dz1S08uRTe3MBL+8JnVoanFxUJ/Nam8LStsrPf2gm/2NfFSbEyxzJVRvidVGG4vcj8T28Z\n/oNlQa3ym1vDLy+VL+0rU/XxheGlff3Zp7Nm9elWeWkPn+wNv/FI+eRCr2ukvzDAh1z9/m9Hz0j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hJkHqkrgez5i7PI+65ZmgTjyZbZveotRU5qwfQFb3TfQjdMWFNhaiGse+ZdxcXDjtnLicqC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\ne7Rknttkrk08fSqIwNWJxwLbULhFZ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u4ryu0M03nDE6vC/fuFA205ksDv3R653EEsHmMM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\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2cHxf2GnGzdDiWNR6He8Osa8TTi10DsFqnbGq0pVS66wt0oWw/1oyhwVElV53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\n61A1FjEyU2PSktdotylHHtR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\n", "text/plain": [ - "" + "
" ] }, "metadata": {}, diff --git a/notebooks/plot_otda_semi_supervised.ipynb b/notebooks/plot_otda_semi_supervised.ipynb index 6c538e9..484c2ee 100644 --- a/notebooks/plot_otda_semi_supervised.ipynb +++ b/notebooks/plot_otda_semi_supervised.ipynb @@ -66,8 +66,8 @@ "n_samples_source = 150\n", "n_samples_target = 150\n", "\n", - "Xs, ys = ot.datasets.get_data_classif('3gauss', n_samples_source)\n", - "Xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples_target)" + "Xs, ys = ot.datasets.make_data_classif('3gauss', n_samples_source)\n", + "Xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples_target)" ] }, { @@ -128,9 +128,9 @@ "outputs": [ { "data": { - "image/png": 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s2bNH/H6/zJw5Uy655BIREZk/f74sXLhQRESOHz8uAwYMkMLCQnn22WdL+8jy5s2bJ6+/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7Rv6dgU9m22Sv3lZ40z6AVjm5vcMByvDV37B7/elcU3rw93KIpCitmslOYXEcn2/VpKlvW+szDa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\nAkV5lP87fkfnuv8EU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RTpJbEk3b4vHEMUAyO+z8/4Kx4oxiUDDGtXTVmyE0j/4Nc3ZYm8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t9BRd6j7SLEEX/S86ei6eD33B3St/rsWZlJYqYTyQbeu+Ua7I1+CvdvwcQJawCfOVxomelwwm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\nI5zfFdvfv6ztnPtRXDpuQH48DAkCx26bqIEsqsRccfteeRtKZi1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RkRyScyEk2bhPcgJO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jSLWO55pcOMgb4OJslR18/3Xrf7VHeIGOr/ESjNnRf0Gsbb2r/anh0DjhetugczRWmbETwz\n5j+0/TlZ0evcWdNtm4/Rnzs6jjuv6nkmHtq2zb2rFyL/VCWGQsquHYD3DTQ6XrISZ9kKJNPeU3md\niEYbkEUj4h7NWymeYgY84o2HIiISgFubPdnW84IzTBkfEZEC0nnFY8jFXVW5HpoBkctdQMpNRKR4\n+apu84uberx5vSdz8K6DZUjT3bpr9olmwRcmR3gffadtK+XZWo7tKO2o8RmtUI3lK4T743NWKinb\n3imND9FlY7BCVNsxF2Hf825Xfpp/X9rF/q8FRn7jjTeKt99++9dxah8+vhDxt37nvxERkcqPPxIR\nkXBV5d6yRzq/FTTqgmyZiEj3eZ3fqn+kMmzxks5vlI40NUbnVs0+Q0iXVX6uc36A+S+9s6HHx5xZ\nrFrJthw1SdFNnb+737giIiIJflNQnjb+yM6rx7/zooiItL4PCbhzK/q6rc8cI1v31kdmn2hNt6F0\nrDzW5wNrhzgns5ZFxEqSslYoQ62S4S1j3/CVa2Yfua0SsqZOi2ZNNA4B2jxanzO7UHY2OziSn2Z/\n8rnmYo8g+/Dhw4cPHz58+PDhxNlwkEORtB7JqFn+vV05tqjZcAbIbo38EHoL4/OJ03bBREtrqFwk\naprVifShonHgoI3TVLzQ//uzuk/3HJAvcHcH044iBdBMVt1X27pN2kSV5QDo45wVnSY6XuAw/QWg\nweDRhGjSqGmHeNRUNI7oueFKUiUBpimjScvBMdxpIKCCMSDnZrAAs4k9q+AwmNV29me0cTWISgyW\n9fxEyqNja3jBpdIQNsFEseM9RdxGS1aBwA4C0NNbunI+f7ik++5CEYAC5xcs0lbZgRIEjUJon+mY\nloiIBFDGELHWka0Hi9hXr0/yWBHE1hxE0A86Zp8c5iJhB5bJMC2hNTfHteYYhRCNN2Yp4AD3buqx\nahjjqGPtvdOWbhsOqZqC6w/lE/KsKne37XnWYNN5S9tUe6R9DWGOUn+s4xX0bPZBIIwe0qIUvC0i\nycUSVtC7B2aXaEurgYsrl/QVKGfYsBaoIiLpljUKkSeKnhoOMKqhY4ivZ5tqeNL/2vNml/gv3tPj\nEmG9o+iFrOr9QBQ4mrT30P41vRdn3sN37eEj3QYIeHZTxeIjB6nOgBjHUKkIcHzZBLpNq9SrFg0O\nfvqhfgZlDaLQBdRNIiA2RGW0H8gOQe2joA2sc0+KiMQ1m4Ui0i6Xzklw6y/Fhw8fz2bUbup8WawD\nRcVcGQFJ7j0HQ6t7di5u3NK/069/SfcByumaJ4mIpDdum7/jO8hygW/bv6LHrU7gt8anOq/uOnPx\n/D96S/9AFq3xls6jo2sqGxS8+b5+7mQat76ObPf38Mz6WI1BouculvaJVpZtO+/rHB9d13NnmHOJ\nXAcrOjcffnXF7NP65+9on/H8iYlCwywqeOk5Pf9bvzD7MBsY0AjrUJ+R2R7GFrU5cct5psFMa/T6\nJSl+8vnmYo8g+/Dhw4cPHz58+PDhxJkgyOFxXxo/+EQCxyJZRKRwNP3IsanB6pX/T2KFQM7K0yoY\nM1jhEsUiJ5BcH1dVoFK32qQiIpMzQIEMX1HRv3rDbjcDG8PioaJj7EcBrmaDlrMdy0GWatnCkLya\ngHrPrPzsOcgoUefhSJ4aUEmIncp9InnGAhj/N6gfDCQsd87DMW4A1aLGsDmG4Yg6SOgCUFggx9RQ\nzqZ1ZUb0Pjl07Kkr5bblyVhV6gTsfR1b7mCM826403w/Or1mKyYV1aYKR9xFu4Hwhn3qVNt7h8ix\nUdCgtiKvNfZNnCzHCFkOaglHHXBbA1gYgyvuIsjsW3Sk4zJc0rbGtCdnfxxlg2gfutHUqcYL74vR\ntJ6vemC1eTNyc7+kK/Ye+Pi1h9CVBn+9+9p5s09/Vt+b/XNFGnJe7yvY5hbsQoEwi4jIgWoy01b0\n4EVFs2d+oN+faF0R1+2r9ru+/C5QkBXlwnUv6Heus6LjtQjkNZ+zmYT9l/V1/k+B6MI2erSo92HS\nAnn7yKK25Fcfv7EmIiLN732gbQUKw8rw/qL9brYuKnJCW3pjPT4q3zOlMYCdaQ5uXzjQdsddWpHr\nuB6+blGY1v/9cz3+suXC+fDh49kL1ij0f/sVERE5uqRzyMynOrdkdZ0fNn/XIq7dVX2+PfePgD7j\nt0T3a4qaNn4G++pvvmL2iZEhLZAN3fyWzmGX/jGeS9eVX7z3NVuztPRHOicNntdz772k+x5d1efU\ntT2t/eifs5m5735HedF3/ldFgQv8Fti/qPtOzaOmY9vq8nO+ZD3Tuf9FM3WyCF405uL2RftbpvW6\ncp2HEzpe8TG8Ax7oa9qEV8U3XzX7hE/0eXRyTefipKPPi7gNO2w8Xze+a+tOLvy32p/8yuznhoA9\nguzDhw8fPnz48OHDhxNngiBnk3U5+c51GTXLxYKVY4schkNdQQ2noHOKj/ozZVWDUcPhIAMQnP9Q\nVw9pA4gydAejPhUX7HnGOcjt87oGmL4DfuwAvNI52/Xegh5v+jbQLKx+qP7Abd3+mHbS1O8EXMoa\ntVj1/aR7WiUkOYFrWFQ+RgwO8nDy9GWh21+ehNhGX6uHQOZ3LLLbhXpFd5EcZCDTXaCmUP+IHeWL\n+FAR18GSInnxCapQgXYWCfidExY55BizspS8W2oDk1ecz7hiiNy37IpoNHrz0+MVAEUMZhXNzuiK\neEDtXyC7hxZtzJs6BgZJxsqWjndErNOmXeFaDrIikFkT/GKopsQnRK5tG3k9ZIroM7jVRLOp5OIg\n5xlW5KMWVtJH5exDso9K4JpFQsk1Dp4oF7mxA2I5HeiAXDbeu2/7g9eCznl0K3wEPjS4YK5SBHU0\nqZc59UsgDchKZFCimL7jqGUA3Q6RBarfUz5xAxy9FLzlqGfdEmc+ggoHHAZz8KArbf2ekhftVkGn\nu+qu13obyhQXwDXeZH/0mlf3bOYqA1cuQPZJmkCmeV2QSXDHgBy5aAuZKqA9GSu0cY9O9xwOP6vS\nxYcPH89ykGvcuHuIV3yA5173qs7FK3/65NS+wzXNRlWRpWp+gm2QpQ7e/MBsm0Fph+o5Sz/VuTif\ngjvox8ovnnnXIq7Zts6RlYHOTSsf6Fw1f12zxtknqhpUb1u1jO/94HUREbm6qSoVAeo/Zncu6z7k\nRV84Z8+zoRzq9T/UeXX0qiLhyQ2d+2VK+zPxyJkR3/tU2zYBNTJk95ld5e84w5MWkQLPveZN/CYC\nB5nPDdYSXTy5aPe5Yv/+vOERZB8+fPjw4cOHDx8+nPA/kH348OHDhw8fPnz4cOJMKBZRdyiT7z6W\nYqxIL3BTkCjkaiD1TQmtbFpT+iHMM/KqW6QHKasNTdHWmHKm9SuKjkzxmYg0mmUJq+ZDhfEpCcbi\nnMaE3Y6FQZUNGATQxhBFho1ppD9ObJGem/4WEQlYwMP24xh8X/9BkR4K6ijVRWtHFmlV3XEk9WCE\n9D6NDyBbxjYZwXERmXis7W2OtxvHZ7GUe33ySUivnDiFlSIibEtRnPqchXymQJEsAhZNsgCq71gz\nj6h/B1MHvs8CMo7R0J6HElpMp0QnaDeuT9h17jNEiBQWtxGOAa87CiIpuSciktdB3RhCtL0HSTjK\n+5EO4hSFsqAvxLik05SXQ5sKFHw6BZERpPOSAIUFSM0VHW1zsaopunDfFumRXhBf1AK7bFbHJNpF\n8R5MObK1ebtPS9td+QjC6e12+RiPQH2A9JmIvY9ISaCMYO0hJOLw/e3P2KmjgsLREGnCHNJw3VVN\npdUfICXoGHh0l3Us5yiRRNF70EEiXDd33EIUDo7OQUbpg1v4AHcR7kPXbjuhTBC+r7Q6Z1FrRFF/\nh8rB7ydpJUZSj7QMtHl4ecnsE72p0kRhq1Gi0/jw4ePZiuwBaASvqaFFb0Xnt8YDnddJley8YAt2\ne3M6Zyz8K6WopTBeCl67rq+QbIvPW4nKHEYaNNg4PqdzbvNjpRcIipO7K5aWSgEDSp11n9PXg6v6\nbFv7ENS2GSsv17gCugIofmzDYE3n4mpXi6IL57dMBJrE4Uv6W2L6+zfQADwj8fxNa5aKF4GGVuAZ\nwt8A+TFkTvs610fra2YfQ2+bgukZJXE55+P30N6X7fw9889UdjSZe9GYyn3W8AiyDx8+fPjw4cOH\nDx9OnI3V9NR68eXf+C+MaUYRBKe2qe4potZb1tVQ0tFV1uFlXbXU98pFbiLWHnjqpq4w+otlCbcK\npLQMKikigzmIaFdgELKgjZr7gMVmMK+Ytyjt3nVdhaz+WIuI2uf1GK2H2mbaYc/csCuok1UguLSa\nfqifdZf1fWM1vWkRsKRNxFNfaTWd1bRNLPhyZdFGk0DnYFZSwFwkQ/9ocU37ahFbZNZd0ePHPVhN\nb+q2m7+FlaFjNU1baChaSYTD1XYL9KtcTCliTVIW39Y3t7+i52090POxkPH4gr0f6qhJoPlKBmOY\nKgoJee80tm1/RhN6ov1r+vr/tfclPZYlaVbfHd7ss3u4e3iEh0fGUJFjZXZlVjUl1DQCBE0LqcSC\nDUvWLOAXsGOFkFixZtEsEJQKtVAXVLWgh+rKysqsHCKHisyYIzwmn/3N792BxXeOmV3PRKgyXd1N\n8J3N8/f8ml0ze/fZvXbs+86pgRyev6MrUFo3N46C5DmME5MnmRQ4bVetpnd+y7et/RRjS8IYX3f3\nqh7ceIbz7PsxmGAhXkN+4BQkaYMbFvCPWLnu+9M9r9cbx7TzSNs2d1evi71XtQGdx/46WPhAGWQm\nTCRgRvMurMbJ4mJlLyIiq8q0Frfv6f+YlEeW+CvKMHGCyW0Uas8PwWaAWSbjqx9i3JBYF0Najcma\nZMjz0JDk9Wv6+qEmblAwnwx2ckbbTgORsC1OThLsMseAzG1oNc3dDEpC0lraWVDj/yGDTAtU1z2w\nFixDBjm02y6OIbc3HMrb2X+X48Kspg2G5xF/93f/tYiIxD/ThLoU82yGBLnI7Yr5OWV6SRP7kvd+\nrcdwviNLDOY3Duch7CTKRzDu4Jz4SHf+Yu5wbQWMK5nVm3e1/jdUFjSCmVqMncbic5+UPPy9N0RE\npPU/YAiyvlpt2wVN6MthICLi58sI9w7uuuU7kFMtMK+e8TbYjCIokUBIa2lKinLXOA2TAbHLSUMQ\nN/dSZpe7/Wt+55SSrsVRV96e/liOiz2zmjYYDAaDwWAwGE4DpxKDXNQj6W2kUtLwwAWWBsfUdLXT\nOwfr3WM9qAfPArKoWUASJ2OwgF1lvvpgCqml1ITlNJlmEZHBSlUKboQFTOepVpw7ZtmvDQYXlYnq\n39ZVSW+TdtF1vBe02ccd9zbBRCKENc4alf5NHInlY6pbDS2TDPWzCSTppugHTifpyPdnPI8ykMmj\n9B0Z0nSEVWTm20bGtbehr+kQbS0wjuchcRZI6mUdyPCtYKU5glwdVraD84i1HftxKxOwyw+1UYMN\nGHdMYRuOcN/xZhi3jLjkYVQ5bwZmt2STIj9uUxCcw01tw6SHMRlp//pYQNOCXMSzwJNjXDNU9YLi\nHBnkaMvHbg8iMKwdGpBA/m9TV9IH6RzOE9iHL0Car4vrbjZHv3A9z2hd/R1fpo8ws/E5jgtY2bG+\nDtaxWxD7/nSewCr9Bv43qcaKkwmtxOyCNY1gnuNkdcDSxjDncBJo4mObo6c6gGR0/YpdvxfK7IiI\nyGX8QHaUQXFx5JTYO4MY5ye+SPIM8m5sGw1+IBnHOLU4sHOOkV9QIC6NrDPl5bgbVgSGPgmtXBF7\nXOZV1twZsAQGQ3ELscxgOHgtMo6ZLEZlrHGerD8QsRBkg+G5xfELOict/DnmFMpnlpxj8Hbkt1ud\nBCqN0WgotKfbkfHKifhcERmc079n7uicXMDsjPG/zqzs6a4rM35Tpdka92Akht2vFBKooxeUaa3d\nDHZO72EOpLEY8nTKJxonnS1qO2rhzhwN2A6xc7am/UnADnPHLrxPMAaZu3pRDfe9Rd22JgPPfCgR\nkbirbcrR95hmccw1o6lamE+1jvMcHsk3nYyNQTYYDAaDwWAwGAKcCoMshcaUktUkW1cGDDJNNxIw\nkPEUigRgKhmTyhWPHsNXlmWQM+vUP6KAQU4csYZ4xAlFqKsriSQg4CIwkYlrU7V+18bAkITHMAbZ\nHYt+JDQzCc7LdpLxdmMAJvZkXZX6JtXzUdGBddIARUSkqFdZZ5blOKYDss/BgID1ywb8PqrHJH0w\nb6FLNlenMDFJwFSnQzqIIHZz4FUFksGJWGaastBDgqYpAYvOayLpk4HHeVGG/UmGwVjnPIZtwUtR\nfZ0OPFPdHPLa4xjou14fyhToB/sgIpLX2We0FYw721TCeCPsD8eJ4+L6gXFMh/g8MJmJxz7OvgLm\nEMTJ//l/YE8Zs+viuRiXG+QMRKOqfbuzdUddVPBw8bgiIsg+JotNhRrXeqrNhCYwjB2DOks5rR5D\nVZMi99c1j3Ega85j0K8osGqnws3JY5xNPREo4TgVFvS5PBHHzN+e1IMJLjtRn8FgeC5RG3AuwfzA\neZZzJA3ASj93OXMpxOZGmDMLzCWc26Kxn7to7OXmbe5ccZ5lPkWwy5YMMA+hvtBETUQkGVbndxGv\nvOWUpKj8RNUo3BNCtbB4xF07zLm8l0xO3AuSYMeZykRuTua8Wp3XOTZhfW6sXS4JPi845/vzRDxP\nFEsljOFrwBhkg8FgMBgMBoMhwOnoII8KWfi8LwV0cd1De0AYpfsay1g71jiWBBqzCQJMG/vQcQ1U\nLKhh17qlWYn1oyAGRkSSY9CbmV8NNQ60Pralta9xOq07kBXASqN+4ONcyhixPjcP0TY9T+sRLI5z\njS+cueXjaWq9mUobG481Fqd+rOefzujQNp96e11aL3OFU2/ryq+EVTIVNkJVDmd3DG1eKl2UeK09\ngoZhEA9ZRxxTDfE7yUD/l+5qG+fPqQVw4zhQ/4BaRv0QDD/61TxAPO6EzGjA7OLq6dzX73YWFsqz\nD/W7nHYYlO5Xxa0dsM1gtxkr3jgqKoc2n/qYoumcrnCnsLmudaH6cF+PSRG7W+8Gq2KwoukAMdVg\nECewd+b3Nl7w8SQldLYAACAASURBVLfNvbLSBu4SHMV6rcwgGZbHiXi1knqP9cU4BkoeHaqNeLo+\nnuo1WevpiTpP9diZexrzOkZs8Oy2X7GnD6BigbgzKiiUe9UVNGNhRUSKefwW7qsSBFfjjCemLnKy\n6HWQ4wP9LMcqP8G1kt1TLWXGvUUXvV6n7OhvyylDNKH9jOs72kGcXd2z9cMriIWDZWlCNQn0XUaM\nZQvi15EpHZ/ReLf8kapilFmVZUjXvT4xNTZpnc1jC2hOuzYHLEaBOLow/llPCMYDb6Mln6GdUymk\nXpNo9FciYGEwGP4SMPepznclVXXOqupDfHJ37azXAB6tQ0/+OuJ9qcPOXIhn6sGQnvMW0M2HsFXG\nfBS9qPHFxUeqhBHP6Fw5ecmrWDTu6rNSiVjdaQf5TvM63zbv4Tmo7Z9/nn5H6znzwQlteKhwlHhu\nyQc+t8MpT2zp/UE+v6ttG3IrFbvkVy+5MkJVDN4HMMczvpi5MOGzTI58kmRuDtVi9j2xYzc959U/\nYiqFNBsSZcYgGwwGg8FgMBgMp4ZTUrGIpb/Zdrq3ZO+oQSsikoCpGy8ilhWqC4MVfUYfz1L5wD/x\nF2jdSqarg+kslAJqjC9FbGgQZzOmwxcWGt0LiPcsl3Cs/mO47Ls+WtH6+pfmKvWXm7pCHC1iHXHZ\n656SOSRbPp3VTEzq7LKOvBFozEJFogamk/GrJWKW6DhHBQSRIP4W8akFGNcJ2NlmW9m0xp5nKKkX\n3V/TepqHumKrLeqYzzxUVq527FnaNr6z4RrYZzjM1XZ1BdfYQIZrsCCjc16yoyz24udgcp9VNacT\nL+khzR2wc2DE86Z+D2m36oYXH3l1idpTruN0FcwY7vr2EerS+tMDv8It2mDe4WhXgNXsoD+MXZqb\n93q+M/d09ZudYJnjDFrdu7qyDd33JvN039P6Ok8YY43+NcDMbx+4MmUKpzxU03mIVTJc8ZY/Qdz8\nMIjNAjtB9QXGcTkN41U40e16keZoW7OQk0sqFVMi2zli3DeUG/ID37YocGQUEeeOmcLdiBqc2WKQ\nafz5bf2Dsb+37+vn0NPMwI7EgVLEhDsWuL7zXWU+yGZnj+HyN+cZcTLe0UD7HEP/swSTHEEXND/r\nNTFLOlZRPQUMO2PZYrAkjiEXz44wU5puTmRyiARsu4jXKC0XZkVuVx1FDQbDcwTOHdRBx45wBDUG\nOmw6Z14R6Xymc0f2ljrnxbfUUc/l8cCFtKL73q66AtPfIH1ZtY2LG6qJ31/3803t5zQawPz6gR5T\nXNYdv/yW7nQlwU7j8AxyhbALybmQc35+Qx1LkzXPiDvGG2oW09ev6vvP7mpdYNd71/xc3Pxvqr3M\n+0BKZz2qEG2qVnT2wad+DDo611M3mipH+TGMBxADXXs848qU0OGfrs9K+W7V8fg3hTHIBoPBYDAY\nDAZDAHtANhgMBoPBYDAYApxKiEU8LaT1ZCQtGgRwGz7x+/Hcqk/GSoWnPaXVi0Qp9NYekvQagdU0\nwiEaDzS4O15FUDy39o+Q9BZIltRgKlJgazvOdWt15jPdei6RrFPrBdu9M/p3567S9r0XkKT3WLeb\npx19P3On68oMz4H6x6mbD/V/43Uk6c3qedqPfZJecqR/R5BIKWb1vJRZcdvxh2GCGpKyEJJQQiKs\n1o0qY+OkvESkzaS8obaFoRvpUw1JePx7GlhfP/JbM5N5hG4gGiLOtf0NJDkOV7EVFOzA07RkbaJb\nJU+/C4vu+1rvBPbV/fOh1TTDLvR91mSSHh080IdnPjRlPK9lDq/CahpfwzzCS/rr+LwXJFWhntpA\nrwdeS6yL39vea75t3fM4Jy7BFNEYRy/CanpX+9XcDcYN41XHbvsY+W5M5KOF93Lit6f66zq2/Q2E\ntcCcY/6O1rvzBi27/XU934d5xYNHIiKSLOmJ8iOEVMBuubINBqvS8pfXtVvYrnKGGginCJPaylkc\ng/NEAz0vt/7cltczH14QIZGvYHjH1S19j2uS1qHFMy9o39rBBYCtwBTtLpE8l166qOe9c8+VYfhF\nhCTU/B6SD/n7RxJJRfBuQbc9GZLituaAApba3E4UEcmf7qAtuABg2EJ7bxdKsrLgyyCsRHZ2pcyq\noRgGg+H5Qb6s83V8XcMXovM6/3HOTN5BotzlLVeme03nrvaP3tEPMH8LEvJzGIYk37rsymQ4T/oJ\nQhMO9fkh/+SGHrui8/vsXf+MIdde0DY91FCLwd/Q+igH2/itF/W4249ckdn7fKDCvRkhHIIQsui7\nr+l53/3YlUk3tM85EsGTD78QEZGCMmwwCPGBDyKCMkwWz52xFEJNP0K/XrnmipQICaHhCBMgEyRq\nRzCumlzwSXrpu7DmvvtAZBxq2f7mMAbZYDAYDAaDwWAIcCoMcpTlUnty5NhZh4DZjWA726Cw81hX\nEbNM5IKUCNnUCrCSqUPOjYlrEewFQwHrlJJvYH2i6Uzl/BHYs1rAuM7f1XbHe3oernr4nm2MDzyD\n3DohDh7DcrGJeustJAXtB4wVV1cjyFVRziTFGHy55xIfV6VP3BjjNQIjxjpFPMtXZx7hAIxWV9m5\nxV/r+1rPJ4FNFiD91azKotSPtM3NAyT69QNDEhjDNO7qSnBxXoPs29vKvGWQZWt0Aym13aqAOU1N\n0i7dTJBAtufHrT4HO+JCvxnKuTW3Ka0HlnjorwOaObhEN1wXjflW5f9F6te49T4NIvQlxfsoQz8g\nRcfdDhGRaYfW6frZaLlW6ScTLtu3fSJc/Ui/nyZY8+YeZP/uK4uw2FFGt/3Aj0H5WJPNYtqBQg6N\nNstcSZdznnkv6riuIFdGaTMyD2RcK0Ltu76dIiL5mrKk0dOdyufjTS8NV/8FZHUg00MZNGdR+qCa\nRCcicgS71qW/gLQepYxoxQr2tmL6AQmhclPHp7yP74fmQgnOH8grRffBhENaKIYHOeWIEjLMwe/H\nGY7MIgknqwrlR/h/0fEJIGx/srYq0e7p+C8ZDIa/fki+0F2nCCzmBDvb9QFk0ZAQPj3j5+KshcRr\nSLORMU4vIoGaDOnYy1rW7uucW+AZo39F56POHSTv4fP9l30y3+p/RduYEI3bdX9d57/2x2CO5/x9\nb/cNrWfpP8PoBLuGxarO8cme3mfzur+Pl5DPHL+qsnSNjzE3ItEvSnR+H17zu5Otj9E2JH7HmOvz\nQ90FJystPZ9sz3sJpT2d6RTna0QtjBf9c2PM+9yVFyS6/xXPk78BjEE2GAwGg8FgMBgCnJLMWyqj\ni8ve5IOEzsizjWlfn/zHS4yp1f8N1slM6udcaYn4WOaFAnJRiMctEppAQG4llHlD/VxZ9Da0iwui\ncbJkDt1xInK8hbjkrh4zncN7MKCDDV0NtRuezXIrFixxGnNNlK2hHzoWjXnPmjGOOMFYFJA4o8wb\nZcXKul+30PCEVsNkBXPWD+kXxjeLiEyXdeU6XNdzkwWugbksUWdo601ZMgqL0/Y4HtFog22VLyM6\nYedNebSvsEcmgx+FtsMhiiozr22gxS8OSaost9tRCK4Djq2zTm6BiYdhiBvroC5aezIOnqYsMbqR\njstKWX2jL5Ts4xiUJ5eeQX8obees2V1baKWNnZI0iMdvQpgdAvMR4n0d0wqJIXm258rUumCIZxgv\nDzMWxB5HNR2T/MAb4NDMo8AKPcUOSIHzF4h1rh15xtXZi9Jg4wFkHo8hm4gyjDUTEWnt0WZUGZMc\nIvJxB5I/oy/H8bJNyVPE6y1pn8nGRPhthCwMpeFoxU3DkLAt4fn1RGgbGfbxiZ0qmtBs+5jqgqYB\ncfxN3U0NBsNfY0SU2gSLWr+LeZXzXFufI+q3nrkyi7vYycacRSvoYk937Cjplm8/dmX4Gefc9kPM\n29ghznd1/uk8CwyyBkOU0bZ1PsYu79lFnA95IrGf/zrbyBHhfLetEptxf75y/hCcV9tfYA48B4k2\nmFLF2C3kM5qISHZCcpO7em6XEPNqOAbOshrybsyboTQcMfNpEO3MHdIkcXV+XRiDbDAYDAaDwWAw\nBDglFYtcGttHLpaWRiFRwJpFR7qiiYfKKkVDxE6WuqKqHcHQoRE0CWxi8khZsXiALHLWj7ji0Gq6\n1UeMJpnXCeJx72GlwxjXno8Pyhu6Uqo/BBN1Fm16coS6NLaIMaKVfrCNMMtIFxFXA4vodMevvlzM\nNNixhFbTZLW4kgpit8s2TAvGWfVY9m9Hma8yMDGoof54rG2MBxhrxHJPty6i7T4+Z7yg9VJVgoYn\nZDmHMHgJzV8KGqq0tI2jJSgSDPQ97bbHc34VF08Rmz2usrWuv2B0ybaLiGTzqB82zg0MKcd42gG7\nHawWHVNdgjlGtvB0AfbHYG2n/jKQwWr155AO9ZjRMvoZk3n3x9GwhfsRw0Ua0ySV/7dm/U4C465p\nrDOF2Uh6CPOcNYzRyI8BR4kMKIXTHXtLi88Vn807XcU1+rbPPhYRiaECwczg5MyZ4J/V3y7toose\nfr9gL4qa30pw9tCMsafgexv9ohpEcI3yOiOLHYNZichsUDj/UcCo4NyyDOb4sy8wKPheGI+dBwZF\nNBpBvwow8I4lZuzwGW8YQ5bFjTV+l1T/YJxdsebHuoCxSRRFFUUZg8HwnAH3fOZ9ZGBnE9x3mReU\nbflciO6WssFzP1RFCuY5JNj5ozFS+oJXvihTKnHpXDXBbnQNxk5UMsqCvKF4GXMSGOr+axrXO4HJ\n2uKuzs0yCHacKd4ExjraQNww87ig1lPcuO3KJDjPGOoR6Z9/pMdiruQ8G2VBFAEVlnCfLo71Rk6W\nuMQ9Jrlw3pUhm8z8lhjPKe47wBj1r/i5uPFH2s4kSSrPhl8HxiAbDAaDwWAwGAwBToVBniykcv8f\nr0pOGg0LmjgII2wc6hP+AImK6QAauS9As/eAzGvAvoCAXvpQtf1GZ8jk6efUno0nnqmmbWKJno0u\nKKu08KsLlTYP1n2Z5CVdyXQvqBbqaEX/19zRpVX3kq5CZu94rdTBWhl2VdrbylQNV/XzvI06nvkM\n01qPr1WN3ILJoeh6GOebIRk1BfGV49gM9c88UPa7cRyMAey7++eh2HGk52k9W0EbEZd0+OX1UR+L\nt9oxxxortHOI6Y5946hHXe/qCrN3DkxehHhVfAfHV3z90xkoK4xga9lBvOqJtrRbgXUkWOXeBX0d\nTLFKTfX76Z8FS7vj65jMMg5WB4yx7QmuSTL/hy8HaiY3tF5qGVMreXBemdECjOK0E8aiYwdhhM/Q\nhPGSHpuDOK4NfX8Or+ixvM6yOzi2rt/lGEx51vRa3YuRMgvJ25/omDDrGbbIMdmEm3dcmfgeVt3U\n4wQDSs1ep08cKFREm5qVHCGGjTqU6Tn9nNqVyS3P7EZke+9plnL8a13Bx1R9gA1zFmgQz38ArWHE\noTGeL6e6BPrDV/0nNEM/VZ3L6M1X9DxfwCba1eXFumkP7aylERvnbGIde+HLuNg/7FC5ODh8//yl\nlR9+5spQuzM67IoMvipQ32AwPA/Y+129SS7+UFlTeUdVekrucEEXWd7+yJWZ/0Dnnfy7L4mIv//k\n76kCUPy6fl7e8VbT2av63JPu6A56/Zc678mLV3CsznsL7z5xZag6JB9ova0/0927Ju2cL+lzEPXf\nRUQ2f6KsctTG/Qb5LdSMT6Fzz1cRcQx18ifvi4jI8B99V0REOu/c1bqoWLTj9fIZF+1yVbALmkB1\nKKJK2bHfdec8XRxgp5w5RMzJwW5d48e/cmWyv/MdrffRscjhN5uLjUE2GAwGg8FgMBgCnAqDXD/K\nZfPHhz4GlI/dQaYktfSma7oiSLq6iuhf1lVEcxcuMYFSBNUp6veVtcpXEE8I0i9Gln4Yc1jA2YVq\nD+NlXcl0PtMVE+N6ikXP6O3fVAZs5W3Nshxe1FVY64GufvqX9H3nC5+VOtlAfCccamqPdYWTIe5z\nOgs93MeeNYuPGYuJ1dwM2GUqB0DdwGn3ikgOdQzG0FLpgK+1R4iLDsZgATqM2RnEIEPJIUG88tN/\nqKvIesA6jxf0u2vu4jvkv5wiib6S/RbxMciNPcRxjrT9dWglT2a0jfVDHyPFcyZg/WN0tdFFzCsO\npfKGiFcMobNd/QiuQIc6JlkDqgIj3zbGD1OZIoFqygjMLpUpms/89VbgzxoWvbUBFU8Qi5V/eQw4\nQOwX45VPOumF/Zl5iE4WcaW+5o6OY/e8XhftXR8/xd9AgWslQdYw3em4wg5j2KYbyr6WP/tA+ww9\n5ARZ2Bld5DYCZoBKF+wd2BAy04zpLc/6mN1oHy5+iEUurkLbk1/7s6PKeUVEBle0fAvuUzFY5ohx\nfHD0qzjpQbM4uaYMSv6+siQF4otLaFGnZ9ddmZjnpO41MrJdjBxzEoIy+RNkW4OliDmvMUeAv8Hz\nXuMzhwtUVEsruuwGg+H5wuJ17N6BJY3oXncHzxiPoPv+1quuzOG3dG6c+49vi0iQG0Elnutwf7t2\nyZUJ1blERIpX9DxkpumaOrzqc0gaj5Argvn0+Pt6P+BcPHMb/1/2MbtP3tI5cuMT5LNc0N3ClC6q\nK8j9evdTVyY9q3Nf8TdfFxGR1h8pg1swX+OZ7hCGzoAx82PAFDPXo4RCBedXjqeIiNzU3U4qhzDG\nmY569IMYvuTn4uZPP9R66zWnm/x1YQyywWAwGAwGg8EQwB6QDQaDwWAwGAyGAKeTpDefyMO/vyAZ\nHRBB59d83oukPaXpR9gNoPza8KzS7elAA8SzdrCtgB3Npfc1KJ5hAFQnq0HIOhmHCWo4OV6GL2g4\nw9x1rYPb5EymExGZbum27mhZtyyYWJW+qNsUgw09tnXNy7YwOYto7kFOBTsneYNhAH4rI57q3zVs\nx9PWmcls7EfWDsxS+L8h69XXDBEirac66K09357+OpL00O7GAZPyEBSPcIl07Me6/lCPPb6Q4lh9\n336qWxQRMglz7zbpxx0v7WcIiaExSU/PO5nxhepdPYbhEHGHYQZVOZbQZKS9rW0YLUBsveT5ce0g\nQa6557dTGJbB0IoJzF+a+1mlzcnYh1gs3Nb/DZergf2tp9rG9hOEbYyr372INzHpPK6aifDzULKt\nSOtom75vHFf7Pn+32kYRH14UQzKNoRXc5ivP6bVZfH7XlYkfIOEDW30xTT+Q9JFcvigiIhmS9kQC\nuTNKKUKaMHnpqp4HiSGhAHs5rpp7lL+8rmWwjUir1FD+LMV4UL4ng5xP3EGCHITow/APhpEIkgHL\n77+m57mhbYpYdt6HcpSfQZoIoRTcWuQWHWXs8jt+DGiByjI0Z3FSipRh7AbhU9++hhOWEt3wJkQG\ng+H5wmBT54eZQ4QrILSC0psj2Cs3fn7DlZm/rnPk4AffExGR9iO9ocd3kWD31ssiIlK878vEWxAF\nwFyVHOs8NPnbmoQWv6cyl6HhV4zEYhpqtH/4C61iXdvEJDcJwsBcyCDvJZBzK+e1nzGSuKMg/KN8\novef2scaenfwT94SEZGln2vIXIF79XDNixQ0/wRyowiTiDc0rI3W3NlCC/36tSuTIFSE9xuasbB/\nnItbO94ga/j3NOwjKkWKn/1UvgmMQTYYDAaDwWAwGAKcCoOcDkpZ+XDi7I+ZrFfre2aM9rnjJWX2\naMk7fKhNICs3bflndsqdLX6mrFkGQwhaAJOFom21iMh4EcYQYK2Od/V8CzchLZIxmcqbZPQf68pp\n8fPxV9Y/fKx1Ng49Q0m5MoKsadaGFXQdYxAwo2QV0z4MFerV8SJrmrf910KJM46fM5mA8HcDrGlt\nf+DKtCBP135abTfP6xjYbmAXzFXdtFU5Nj1AImRB9vbL7GntriYlzMa6IkyfIegeCWUL8Zw7toFk\nTCYd0hgm6VWthWksoxVq2+bARNNEpL6tgulpD+zqod+yqO3h2J72sdHA980VNPqxMO8THFpPtG21\nLiwwIXJeG+j10dzFOPa8zWVzFufBd1fWaBet40db6do9L6W2kCtLO15UprHFxIpdJLONlyttFxER\nrNiZIBZDWo3sbTTCtTnvxzpmIgNMbCjnRta2fIo6S//7yYOVuIhIBBYj2tc6ciQ95Itegi75SOuJ\nITmXUEJtTce2uHFT/9/xzG7/LIx0YG/KNkQN/ZzGJDQzEfFWqJSci5m8CyaXsnLlih+DMptW+ixg\ngwvIHsXxCo7zjArZiRgMiqvrhL0pWRkRkfIBmKDlRW+XbjAYnjvMvA+JNNyXhLtu2G1z99m1FVeG\ntsqdezpXRbfBOkNaLbmj80ee+WeM4p6XfBMRmX77ooiINB7ofa+AJGZ/zT8vNH4Kq2dIVXKOmlzR\nnbj4Z5rAlgRJepRhXd6HlBrnTCTG5UimS575e0NOs5KXvyUiIgufaFlnnY37Ur7lxRAK3DviDu7F\nMCspuaOZnsNxfp4tdmEStxaYWUlg+AQkL/nk9M6nOpbT88tOTu/rwhhkg8FgMBgMBoMhwKkwyFIq\nI1wmeFqPqnGYImpHra+Q2QLrS0aSzG4SqHKUIF/jCUT+mzRjKCt1hLbEThqFrCzqYwyos4aefLnr\nCcoWsD+OTsisJEFcbNauri1cf5rOFBjnD8cA7YXsWsHhT3wMkYhIHNgzFlzDkLnlEGesE20KLBXZ\nlmR6YmzHZPERI1n4WEmy2WTGC7QpmsI2eha2u8F3yjjvOli/yRzifvuwJwZ7OpkN5P5GVRaYDGt0\nYqGXjPwqkjFK07mqyUgNFtcZVqS1aWDn3KQlJT5Iq2x9VFSl6ERE6nM0FQGbjnGjVTav3RCMbU6H\nMfqDnQPEhbGuesuPdd6C4Qhl8GYgJ9ZlP/W1FsTsxifiYYVC6RRdp6lFKDEGK9QSccsu/oyi8V+x\nGxBTcnAEGTTao9OaFBbNIYsuaa1y7iiBZSnMOVy8bxj3BilA9xkNaMAyOHvncXVnoVIGv2WagJQR\nxiLYGcnZR8SqnTQOichgBwY4ZFBc7DEZDdbF+OvQEp7sSH9oVtMGw/MMyNfSXCgCW0ur5PQAkmRj\n/zBT4h4ZI2654Bw2qJoRSeTvR5SZ5G5a7RhzIQw8WKZ5FOxSY67lPJdgR5H3W7d7GDCwdeQoldNJ\npQ4ew51B3kcq4Lw31DmPRk+cARt7wX0Cu4TMVSkwT3LuTA7QrySQXkU/Iow1y4a7niJSef4pIU+X\n7vcrVtdfB8YgGwwGg8FgMBgMAU6FQY7HmTRv70gJ0WaXAT/1jBEZqPb+bOV/tV7VOMQxf2H99zSm\npEHRfzI4zJoPVg/tPT3GGWkc6/lcDChWLemej41JxirWXb+lBgG1ecTbHsIAYaTxOsmTA1eGx5BV\nYsxsDeYfZROx1odBLC2tFMHOJWD4mKX6lQCjJxxLxD3RolcONd7XMXwiUqNF5KGObYQVVdnTFWDS\nUYthxz6LyATx3QmVLbB0Yoww7b1pvCHiY7VdXG+VCJcCrG3sT+NjmPESs2xRZf4lYDfLelop45h8\nlCUzXwZMPFn6iCtzXivzVamVUJEiB/vvdhncjgFW5V/xNXGHIB0yBp0MOXYU6l9egyZ9xDLPsF9V\nljPHDkY92LHg6p3Wm+USvtsdf02KiMi6j9WaLCEO7T3NjCZDEC9B8YJi7kE8GrOb2WpnNvNY48yp\n8DBa9mx9E9ciFTWKTY1Fny7q+8Y+2ljzaiaDVb2O27TKhuJFhN8PLaELxMWJeKOTYgN9vA5zDrLN\nGMey49vm4qHJilBxg6wwrrcEgv0iIsVRF33V33hJ21SKznN3YMur2lC8X41JLAbZYHhe4YwtEGM8\nPq9zR+Mu5iEcN7rqcxR653TuW/5DNdtgPkVyRU0xilt3RUQkvXDen4j5JXiWOb6ozzazN/Ren6zo\n+bsb/sY0AyUfGiT1X9W5uLeux6xtI1667nOwepcw52Nup6FHvqTzX0J1oEfe0prz6uCatqH5U53/\nXK4H7ufTeX+eDnM2wKaXVNwYwuyMpk3nN1wZqjXJ8mKlX85QCvP60WveuKrzX25hfPyc/nVhDLLB\nYDAYDAaDwRDgVBjksp7IZGtZph0yyPpSPwwDivVpPqNCA1kYxPJGZ5SlyQIVC1oILyLmh4wl41Zd\nTG/AhDJ+kxbQ3Qv6fmZGVyXJQFdLkwXPZk3mwHRe0RWO0+CFLt8UTF885+1oyxRxO1z1jGcq78lm\nxkG2f4EyzHJ1jKtTldBVURaUoY12PEacZS2p9DM5o+et7XimerIKlm9Fj2nugy3LdRXWuI8VW8A6\ntx5j9Uv2HDHAZVdjf2qLUAb4irjVYkdXeZ1fg6U7UOWBGlZ36aFf3cVg5Z0Oo1tNVuObiqBtsqvt\nnRnq98PdhxKf1xxD7lUsqIZAtl7IVO6BkUQM02waxEdD7YHsPMd+aaSr5XQfMWeDQP1jBt8Vd0Qe\n43rG6r+Gumg/KiKSTnU82lCeiJ8hK/lYx6ZzHUzo0I8BR73E91Ci3RHeT9b0NT3wZdzfV9T6Ob5V\n1QtOVpWJLQM9X8dsgCGYLui4NbfAbEAJY7DqmYF6X8clhbJGgv5k8/p7yaFEkW5tujLDM/h9UHsT\n+pZyRq/RAhrOTgdTRHJ830R8CZnLB8roRNA07m55tYzOTfx+GOtOBoKx1lv4TX9809eLYzjW0bH2\nrxjzd4TY/pHfIYthVT25clbKYz82BoPh+cR0Q+fInPk7Z/UZ5+iyzq+dxz7+ln8Pf1v15Fvvqtaw\nY2nlooj4e5pIMG9ibhms6Hlar6t9c/2masf3Lga7oFScAAvcvqN1DKC0kT2C3vy3X3Rl1i5qGWrQ\nl1CiyC7ivv2R6i3Hly64MuWDR5WxKN7U+mqPoWYBLfq9V/xc2H4Hz1V4niou6A5ciljr3muqtNH+\nn5+4MvGijunkrM7FtR3c43nPx65euLNNe+v9N5cle/LNHnGNQTYYDAaDwWAwGAJEX5XJ/puis7xZ\nvvL7/8I7uuAljNnsPFG2pXdOP6zDTe7gRayKnlbd5US8U9vCLV0l9M6C2cNioXkAZYwsdNJTJpTs\n8wjOeqvvYNAsoAAACJBJREFUjVG//n9wxmdK7r2pFZ77Y31/8K0E59X691/S9yvXPWN0tAVWGYui\n+Tvaxu55/Zyuf7P3/cqmtaflqWk8WdD+TMGiZ+h7ve/LjBbBbmMxShdBxgTP355UxkREnB51b4Nx\nxfr57AP9484PlF2t7/v10XQW478M1n+i/2s8RX/O4zzDINs/1TKrf6GfPfu+9mvmDlUa4Ax42TOu\n0bayfDFNyVp6TP0AbcHX334cOB3O6ofHryPuCAzd3E0tMziHOg79tVOgmTWQoxyfKULH+b0Vv+11\ndofb2AVoIH4YDn0XXtLYq/tP4MK263cfigXEbx2DPV8AKwz97XwGLPT7fty6ID6z89qf2n1dQc9p\n6JTsvaH96Wz772ftHcSt/yl0LOe0rdTspbJDGE9Mdz2Bux61JPMnGmtPrcxowesGkwVJP9UyTiGC\nscnMAwjyC4qrygxHn6sbXQnGOtlXZjdfAhP72S1XJj4LZye644HxKB8qwyHXNDZPbtzxZcAmM5OZ\nsXkFGHC2sQzalq6iDHYkyJCzH2RpkqveJUqgD81jGVcXQceZ5+Xuh4i4+OTs8RP5RfnHclzun4jI\n/8vBW2+9Vb777rt/Fac2GP6/wFv/7N+KiMjSH7wnIp6tzZFrQeWddNPHEw9fUha4+fbneswl/O+L\neyLimdJ8zcfNHl/VHbnFP8O8it1Qzn/xku62hfrs/e9dFBGRzts6bw7wvrWt94nhOXgk/OxzV6a4\novN38liZ5OycMscJ5vPB97+FMt7lr7wILXpo9ZfUhAb77FxVAxWi+AW9L0TYlc4x9yfoR/ZEd1nL\n73/blUlv6f2AevjunrU4Xzlv2fYqUVQPyT/74hvPxcYgGwwGg8FgMBgMAewB2WAwGAwGg8FgCHA6\nIRYrm+WLP/iXMp2pMtkMoxARSSYwXUDoQYQt7vESknWwS5D5/DQpsIO58pEePO0gSQ9seoKd+1Cq\na7hEIwh938VO7fwXKINjhyt+bTA6o5/NM0/HSZDBlnoJknFdf56szSQ99BX/y6AIVbqEvK8w1ujC\nkCSthqSkQz02NNYg0hFCIDAmYyQWNg+1rtYznxA5WNft/f46jtnXsrWBvraf6GCnXb/9QUHt4YZu\n3dePcMyubl9PkJDg2ixBqMttTdyabuhWSbqHQHqE3Axe8NtGjV2Ij9MopA1zkeOqIUTc9Ql3NIQY\nX1mtHFPf1m2XbHkG5w2Szeb0Qor7kA+cQWjHMRLXaEX+bZ8ENntTE8UoT0bQlry1A5m0QH5tMg9Z\nt4ymLBgUXDu0nm7c9Tad4y3dwqL5Suuh9jU5QjLYHKXoApMZygXSOpNJjpA4y85jW+yOl+Lhtn9x\nWS08k4c7qBbShEiOyO4/dEVcOAGlBxEuQWvw8rZuu43/1quuTONPP66UpQ1osqnbcNldTQ5MsC0m\nIrL3+9dERGTpR1qWYvi0Rs0eaz9oKy0ikkPyx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\n", 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CBAd7AUQhEaepCTRb9XOxlOwCsgwWQKwU1vTGJPSMCrAzNm8OrVku53nxinze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\n8wlHphhbOqC+2et/oI/JO7pQsnPflK8jw44I0ezT/+Jq1M1wO5P80i3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+ "image/png": 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\n", 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Xe2X87W/fg0LxvTnzuHzSZF7btZN3S/eSm+Imw+mkrsPLI+vWcPu8M3WCPI3m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\nH1Gy8QCl2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JeOB0B0SqpVUJyLntD56m/uxmq39v4HoChKrzOZTFx//5Us/esKDpdUGUfC\nc1K49huXU7GvmnAgfMxrhCbIHZxNOBimua6FPev3kzc0m2gkhtVu4dJ7LsBqt7D8yfco310JUpKS\nkUIkGO1aGkaAxWrmDf/TXa5///wfgIR/f/Qrx5SSUZSBpN8EOLFojIqSKsLBKLlDs0nLTuX8G2Yz\n/dLJtPkCeNLdOFx2akrriAYjnH/DLN7883IioSgmk0ZqVgqxSBxPugVPmon0vDQGjS0iNcPNyOnF\nDBpdgBCCBTfOYfZV0wgHIgTbgjy55AV0Xe+SIVQI+M7NI1kWew698XYAzBlPH6/piqL0Ic31LVQd\nqMVqszBoTAGpmSnc/J/X0NrYRiKewJudiqZpbH1vJ/FogikXTmDNy+sJByNYrGbcXlf7AMnE8IkF\n2Fw2Rs8YQVpuKmNmjcSTZmRTv/nb1xBsCyE0wbrXN7Jh6RZyh2Tx3MOvEo/GkBJikXiXPF9gFPIE\nVHCjDHj9IsBpqGzk+V+/jr8pkHxs9memMfeaGTjcjuTJgR1r9vDOU++xb0splp0VRIJRpJTE9QQt\n9a2YLCbcaS4y8tNwe13MvHwKRaMKjrmf1W7FareSkuHh8nsvxGIxkUjorHj6fYQmiEXiyITOA4se\n5Es/LGX4pCFn6qNQFKWXSClZ98YmVr+0HiklQggcHjvXf+tK8obmkJqZAkAoEGb5k++yYekWyndX\nYndYiASj0B6QtDb5ibQPxDzpbmwuG5e0H+M+Wsfy0txrZtDm81Oy4QDuNBdIia+2pctzu0t22hH0\nKMpA1OcDHF3Xee0Py4hH4smNfol4gjWvbKBoVD6DxxYBULbrMG/+eTnpeWmMmTmCfZsOYraakscs\nEUdGPNlFmSQSOtmDMj/x/uPnjqZ4ylBqS+so23kYq93KtveNDiYeS/CLb87gqi8tJm/YZsbPG6PW\nwxWlj6o6UMMHL64jqzAjOUvS5gvw6u/e4r6Hb09W9F72t1Xs23SQYRMHEwmEaaptRjNrJNr3/JlM\nGrqu481OJRaLM2nGJx82sNqtfObLl+K7rpmb//Ma0nJS+dF1vwTgVyt/ROmOcpbc+MgxJWQUZSDr\n8wHO/fN+QM2hOq784uLkYyazCZvDxq4P9yYDnI1vb8XhcWBzWMkZkoXNacWbncLWd3eBMIIab46X\nrMJ0IqEoV3zhopOuD2V32hg8toj/XfdzwBg5RcOx5HHNN//yDuFAmLTsVL74q88xdvaonv8gFEXp\nVXs27MdsMSeDm46EfZMXjaO2tJ784bm0Nraxb/NBsooy0TTBmNkjqS2tx2wxU7q9HAnkDcsma1AW\n3swU0nK8zLhsykm3IS3HS1qOt8tjq/6xhrWvbiA9JxVfXStWuwW318WSV77dU29dUfqkPh/gyKNG\nLB2dzvRLp5DolMivtbENm8PY3CsQpGV7Scv2smP1HqwOKz98/gEOfFyK3W1jzKyRyQSBn5avtpnG\n6hQaKptwuO24vU6a61v563ee4d8f+wr5w3NP6/qKopxZz/z0BQLNQa74wsVdvyBEMi1EOBBGCJGs\nI2U2mykozsOT7qGipAqz1cyXfn0XjVU+codmM2p68acunfDIqiXUVzTy2PefpaGiiXAwgjc7laZq\nH/7mAE/96Hm+8Ks7kjNLijLQaJ/8lHNTR9HMPev34attYemjK5Jfk1ISCoQZPaM4+diwiYNpbWrr\nco2lj64gHksQbA3xx289zttPvsuCG+ecdnDzs399j4nnj6WxyofT48BsMSMQeLxu/M1BNizdfFrX\nVxTlzHN4HOhSsuzxVbz9xLvUltVTW1bPxmVb+fV9fwTAm+PFYrMcU4z3/efXEg3HCLaG+PvPX+bt\nJ95l0oJxp10XquZQHX5fgJA/jCvFiSYExZOHUjgyn/I9lVSUVJ3W9RWlL+vzMzgdmutbeePPbyc3\n3pVs2MewiYOTX5960UR2rdtLbXk97lQXkWCERDxxvMudlo4p7Fg0hsNlTz6uJxI4PHbqDzf1yn0V\nRel5HZt39350ADAyn3c+n5RZkJbcv2e1Wbjwtvm8+X/vYHNYsdgsBFoCXdJS9CSrw0osGuv2ayaz\nRluTv1fuqyh9QZ8NcI4urpmI64QD4WSAk56X1uXotifNzR0/vJFt7+2kdOdh0nKLuOW71/LLu3/f\n5Xo9wWwxc97iiezZsI9YNIbFakHXdSKhKLlDso3im4qi9Ekjpg4jGooiNIHdZed/PvhJl6+Pnzsa\nb3YqW9/dQZsvwMwrpvD139/LD6409uj1ZF8zZFwhqRkp1JU1IJEIBJFQFLPVhCvVifeo/TqKMpD0\n2QDnaB2dzNE5ITpze13MuXoGc66e0evtmX/9bA5sLWP1i+uw2KyYrSYyctNIzU5hxuVTe/3+iqL0\njKMHU0f/vTuFI/IoHJHX622zOWzc+ZOb+eVdv6ehogmb00hhkVmYQfGUoRQUq71+ysDV5wOc0x0N\n9VaeCKvNwn0P3c6MS6ew5pUNhAMRhk0czJyrp5NVmNEr91QU5cw51b6jt/qa/GG5/Gzp91j57GpK\n1u/Hkepg0oJxTFs8SSX7UwY0IY8+hnQC06ZNkxs3buzF5iiK0pcJITZJKaedzjVUP6MoyomcbD/T\np2dwasvqWf+vzVQfqCF7UBYzr5j6qY5fn2hZS1GUgS2RSLBj9R4+XrGdaDjG2NkjOO/iSckM6adC\n9TWKcub02WPi1YdqefonL3Bwaxkms4nyPZU889MXKdtdcbabpihKP7Ly2Q9Y+tcVhPxG4cy1r27k\nuYdfJRrp/vSSoijnhj47g7P65Q1YrGa82amAkcq8rcnP+8+v5Y7/d9NJXaO72i2gRleKohia61vY\nsnInuUOzk6cyc4dkU1Nax4EtpYyZOeKkr/XAogdVX6MoZ1CfncGpKKnCk+7u8pg7zUX1wTp0XT/O\nqxRFUU5eU3UzQhNdUk4AWKwWqg/WnKVWKYpyMvrsDE5Gnhd/cxC315V8LByIkJqVctInB453/FNR\nFAWMQZOuy2T18A6xWPyYmlCf5JFVS1RfoyhnUJ+dwZn9mem0NrYRChjr4pFQFF9tM3Ounn7CACce\ni1N3uIHm+pYz1VRFUfqorMIMhowziuYm4gmklPhqm3F67IycNvyErw20BKgtqyccjCQfe2TVEhXc\nKMoZ0mdncIqnDOWqLy/m/efXUXe4AYfbzqWfv4Dxc0cf9zX7Pj7Isr+tIhyIIHXJ4HGFXH7vRarD\nURSlW0KI9n7mQ3as3oOu6wwaXcCFt83HleLs9jWxaIxVf1/Dtvd2IYRAmARzr5nBjMumqLw0inIG\n9dkARwjBuDmjGTNrJJFgBKvDesKquQ2Vjbz2u7dwp7tJSfcgpaSipIrX//Q2t/znNarjURSlWw6X\nnUvuWsQFt85DT+jYHCcukLn21Y/4eOV2cgZloZk0YtE4q/6+Gm9WCqOmF5/wtYqi9Jw+u0TVQdM0\nHG7HCYMbgJ1rS0CIZPFLIQQZ+elUlFTRWKWKXyqKcmIWq+UTg5t4LM7HK7aTWZCBZtLaX2fGk+7h\no2VbzkQzFUVp1+cDnJPl9wUwW7tOWAkhEJogHIyepVYpitKfxGMJYpE4ZkvXAZfVbsHvC5ylVinK\nwDRgApyhEwcTDoTpXJoiGolhMmtkFaafxZYpitJf2BxWcoZk0dbk7/J4S0MrxVOGnqVWKcrANGAC\nnBFThzJodCE1pXW0NrbRWO2jqdrHos/O+8RpZ0VRlJMhhODC2+YTi8Sor2ikrclPbXk9rlQnMy6b\ncrabpygDSp/dZHyqLFYL1//blZRs2Me+zYdwpjiYMH8MBcV5Z7tpiqL0IwXFedz545vZ9v4uGiub\nKBiZz4R5o3Gluj75xYqi9JgBE+AAWG0WJswfy4T5Y892UxRF6cfSc9NYeNPcs90MRRnQBswSlaIo\niqIoA4cKcBRFURRF6XdUgKMoiqIoSr+jAhxFURRFUfodFeAoiqIoitLvqABHURRFUZR+R3TO7PuJ\nTxaiHijrveYoitLHDZZSZp3OBVQ/oyjKJzipfuaUAhxFURRFUZS+QC1RKYqiKIrS76gAR1EURVGU\nfkcFOIqiKIqi9DsqwFEURVEUpd9RAY6iKIqiKP2OCnAURVEURel3VICjKIqiKEq/owIcRVEURVH6\nHRXgKIqiKIrS7/x/4rlZk3fEE94AAAAASUVORK5CYII=\n", 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\n", 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" ] }, "metadata": {}, @@ -272,21 +272,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.6.5" } }, "nbformat": 4, diff --git a/ot/datasets.py b/ot/datasets.py index e4fe118..362a89b 100644 --- a/ot/datasets.py +++ b/ot/datasets.py @@ -9,9 +9,10 @@ Simple example datasets for OT import numpy as np import scipy as sp +from .utils import check_random_state, deprecated -def get_1D_gauss(n, m, s): +def make_1D_gauss(n, m, s): """return a 1D histogram for a gaussian distribution (n bins, mean m and std s) Parameters @@ -36,19 +37,29 @@ def get_1D_gauss(n, m, s): return h / h.sum() -def get_2D_samples_gauss(n, m, sigma): +@deprecated() +def get_1D_gauss(n, m, sigma, random_state=None): + """ Deprecated see make_1D_gauss """ + return make_1D_gauss(n, m, sigma, random_state=None) + + +def make_2D_samples_gauss(n, m, sigma, random_state=None): """return n samples drawn from 2D gaussian N(m,sigma) Parameters ---------- n : int - number of bins in the histogram + number of samples to make m : np.array (2,) mean value of the gaussian distribution sigma : np.array (2,2) covariance matrix of the gaussian distribution - + random_state : int, RandomState instance or None, optional (default=None) + If int, random_state is the seed used by the random number generator; + If RandomState instance, random_state is the random number generator; + If None, the random number generator is the RandomState instance used + by `np.random`. Returns ------- @@ -56,17 +67,25 @@ def get_2D_samples_gauss(n, m, sigma): n samples drawn from N(m,sigma) """ + + generator = check_random_state(random_state) if np.isscalar(sigma): sigma = np.array([sigma, ]) if len(sigma) > 1: P = sp.linalg.sqrtm(sigma) - res = np.random.randn(n, 2).dot(P) + m + res = generator.randn(n, 2).dot(P) + m else: - res = np.random.randn(n, 2) * np.sqrt(sigma) + m + res = generator.randn(n, 2) * np.sqrt(sigma) + m return res -def get_data_classif(dataset, n, nz=.5, theta=0, **kwargs): +@deprecated() +def get_2D_samples_gauss(n, m, sigma, random_state=None): + """ Deprecated see make_2D_samples_gauss """ + return make_2D_samples_gauss(n, m, sigma, random_state=None) + + +def make_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs): """ dataset generation for classification problems Parameters @@ -78,7 +97,11 @@ def get_data_classif(dataset, n, nz=.5, theta=0, **kwargs): number of training samples nz : float noise level (>0) - + random_state : int, RandomState instance or None, optional (default=None) + If int, random_state is the seed used by the random number generator; + If RandomState instance, random_state is the random number generator; + If None, the random number generator is the RandomState instance used + by `np.random`. Returns ------- @@ -88,6 +111,9 @@ def get_data_classif(dataset, n, nz=.5, theta=0, **kwargs): labels of the samples """ + + generator = check_random_state(random_state) + if dataset.lower() == '3gauss': y = np.floor((np.arange(n) * 1.0 / n * 3)) + 1 x = np.zeros((n, 2)) @@ -99,8 +125,8 @@ def get_data_classif(dataset, n, nz=.5, theta=0, **kwargs): x[y == 3, 0] = 1. x[y == 3, 1] = 0 - x[y != 3, :] += 1.5 * nz * np.random.randn(sum(y != 3), 2) - x[y == 3, :] += 2 * nz * np.random.randn(sum(y == 3), 2) + x[y != 3, :] += 1.5 * nz * generator.randn(sum(y != 3), 2) + x[y == 3, :] += 2 * nz * generator.randn(sum(y == 3), 2) elif dataset.lower() == '3gauss2': y = np.floor((np.arange(n) * 1.0 / n * 3)) + 1 @@ -114,8 +140,8 @@ def get_data_classif(dataset, n, nz=.5, theta=0, **kwargs): x[y == 3, 0] = 2. x[y == 3, 1] = 0 - x[y != 3, :] += nz * np.random.randn(sum(y != 3), 2) - x[y == 3, :] += 2 * nz * np.random.randn(sum(y == 3), 2) + x[y != 3, :] += nz * generator.randn(sum(y != 3), 2) + x[y == 3, :] += 2 * nz * generator.randn(sum(y == 3), 2) elif dataset.lower() == 'gaussrot': rot = np.array( @@ -127,8 +153,8 @@ def get_data_classif(dataset, n, nz=.5, theta=0, **kwargs): n2 = np.sum(y == 2) x = np.zeros((n, 2)) - x[y == 1, :] = get_2D_samples_gauss(n1, m1, nz) - x[y == 2, :] = get_2D_samples_gauss(n2, m2, nz) + x[y == 1, :] = get_2D_samples_gauss(n1, m1, nz, random_state=generator) + x[y == 2, :] = get_2D_samples_gauss(n2, m2, nz, random_state=generator) x = x.dot(rot) @@ -138,3 +164,9 @@ def get_data_classif(dataset, n, nz=.5, theta=0, **kwargs): print("unknown dataset") return x, y.astype(int) + + +@deprecated() +def get_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs): + """ Deprecated see make_data_classif """ + return make_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs) diff --git a/ot/gromov.py b/ot/gromov.py index 65b2e29..0278e99 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -2,7 +2,6 @@ # -*- coding: utf-8 -*- """ Gromov-Wasserstein transport method -=================================== """ diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 5dda82a..4c0d170 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -18,6 +18,8 @@ from .emd_wrap import emd_c, check_result from ..utils import parmap from .cvx import barycenter +__all__=['emd', 'emd2', 'barycenter', 'cvx'] + def emd(a, b, M, numItermax=100000, log=False): """Solves the Earth Movers distance problem and returns the OT matrix diff --git a/ot/stochastic.py b/ot/stochastic.py index 0788f61..f3d1bb5 100644 --- a/ot/stochastic.py +++ b/ot/stochastic.py @@ -435,6 +435,443 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None, ############################################################################## +# Author: Kilian Fatras +# +# License: MIT License + +import numpy as np + + +############################################################################## +# Optimization toolbox for SEMI - DUAL problems +############################################################################## + + +def coordinate_grad_semi_dual(b, M, reg, beta, i): + ''' + Compute the coordinate gradient update for regularized discrete + distributions for (i, :) + + The function computes the gradient of the semi dual problem: + + .. math:: + \W_\varepsilon(a, b) = \max_\v \sum_i (\sum_j v_j * b_j + - \reg log(\sum_j exp((v_j - M_{i,j})/reg) * b_j)) * a_i + + where : + - M is the (ns,nt) metric cost matrix + - v is a dual variable in R^J + - reg is the regularization term + - a and b are source and target weights (sum to 1) + + The algorithm used for solving the problem is the ASGD & SAG algorithms + as proposed in [18]_ [alg.1 & alg.2] + + + Parameters + ---------- + + b : np.ndarray(nt,), + target measure + M : np.ndarray(ns, nt), + cost matrix + reg : float nu, + Regularization term > 0 + v : np.ndarray(nt,), + optimization vector + i : number int, + picked number i + + Returns + ------- + + coordinate gradient : np.ndarray(nt,) + + Examples + -------- + + >>> n_source = 7 + >>> n_target = 4 + >>> reg = 1 + >>> numItermax = 300000 + >>> a = ot.utils.unif(n_source) + >>> b = ot.utils.unif(n_target) + >>> rng = np.random.RandomState(0) + >>> X_source = rng.randn(n_source, 2) + >>> Y_target = rng.randn(n_target, 2) + >>> M = ot.dist(X_source, Y_target) + >>> method = "ASGD" + >>> asgd_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, + method, numItermax) + >>> print(asgd_pi) + + References + ---------- + + [Genevay et al., 2016] : + Stochastic Optimization for Large-scale Optimal Transport, + Advances in Neural Information Processing Systems (2016), + arXiv preprint arxiv:1605.08527. + + ''' + + r = M[i, :] - beta + exp_beta = np.exp(-r / reg) * b + khi = exp_beta / (np.sum(exp_beta)) + return b - khi + + +def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=None): + ''' + Compute the SAG algorithm to solve the regularized discrete measures + optimal transport max problem + + The function solves the following optimization problem: + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + s.t. \gamma 1 = a + \gamma^T 1= b + \gamma \geq 0 + where : + - M is the (ns,nt) metric cost matrix + - :math:`\Omega` is the entropic regularization term + :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target weights (sum to 1) + The algorithm used for solving the problem is the SAG algorithm + as proposed in [18]_ [alg.1] + + + Parameters + ---------- + + a : np.ndarray(ns,), + source measure + b : np.ndarray(nt,), + target measure + M : np.ndarray(ns, nt), + cost matrix + reg : float number, + Regularization term > 0 + numItermax : int number + number of iteration + lr : float number + learning rate + + Returns + ------- + + v : np.ndarray(nt,) + dual variable + + Examples + -------- + + >>> n_source = 7 + >>> n_target = 4 + >>> reg = 1 + >>> numItermax = 300000 + >>> a = ot.utils.unif(n_source) + >>> b = ot.utils.unif(n_target) + >>> rng = np.random.RandomState(0) + >>> X_source = rng.randn(n_source, 2) + >>> Y_target = rng.randn(n_target, 2) + >>> M = ot.dist(X_source, Y_target) + >>> method = "ASGD" + >>> asgd_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, + method, numItermax) + >>> print(asgd_pi) + + References + ---------- + + [Genevay et al., 2016] : + Stochastic Optimization for Large-scale Optimal Transport, + Advances in Neural Information Processing Systems (2016), + arXiv preprint arxiv:1605.08527. + ''' + + if lr is None: + lr = 1. / max(a / reg) + n_source = np.shape(M)[0] + n_target = np.shape(M)[1] + cur_beta = np.zeros(n_target) + stored_gradient = np.zeros((n_source, n_target)) + sum_stored_gradient = np.zeros(n_target) + for _ in range(numItermax): + i = np.random.randint(n_source) + cur_coord_grad = a[i] * coordinate_grad_semi_dual(b, M, reg, + cur_beta, i) + sum_stored_gradient += (cur_coord_grad - stored_gradient[i]) + stored_gradient[i] = cur_coord_grad + cur_beta += lr * (1. / n_source) * sum_stored_gradient + return cur_beta + + +def averaged_sgd_entropic_transport(a, b, M, reg, numItermax=300000, lr=None): + ''' + Compute the ASGD algorithm to solve the regularized semi contibous measures + optimal transport max problem + + The function solves the following optimization problem: + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + s.t. \gamma 1 = a + \gamma^T 1= b + \gamma \geq 0 + where : + - M is the (ns,nt) metric cost matrix + - :math:`\Omega` is the entropic regularization term + :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target weights (sum to 1) + The algorithm used for solving the problem is the ASGD algorithm + as proposed in [18]_ [alg.2] + + + Parameters + ---------- + + b : np.ndarray(nt,), + target measure + M : np.ndarray(ns, nt), + cost matrix + reg : float number, + Regularization term > 0 + numItermax : int number + number of iteration + lr : float number + learning rate + + + Returns + ------- + + ave_v : np.ndarray(nt,) + optimization vector + + Examples + -------- + + >>> n_source = 7 + >>> n_target = 4 + >>> reg = 1 + >>> numItermax = 300000 + >>> a = ot.utils.unif(n_source) + >>> b = ot.utils.unif(n_target) + >>> rng = np.random.RandomState(0) + >>> X_source = rng.randn(n_source, 2) + >>> Y_target = rng.randn(n_target, 2) + >>> M = ot.dist(X_source, Y_target) + >>> method = "ASGD" + >>> asgd_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, + method, numItermax) + >>> print(asgd_pi) + + References + ---------- + + [Genevay et al., 2016] : + Stochastic Optimization for Large-scale Optimal Transport, + Advances in Neural Information Processing Systems (2016), + arXiv preprint arxiv:1605.08527. + ''' + + if lr is None: + lr = 1. / max(a / reg) + n_source = np.shape(M)[0] + n_target = np.shape(M)[1] + cur_beta = np.zeros(n_target) + ave_beta = np.zeros(n_target) + for cur_iter in range(numItermax): + k = cur_iter + 1 + i = np.random.randint(n_source) + cur_coord_grad = coordinate_grad_semi_dual(b, M, reg, cur_beta, i) + cur_beta += (lr / np.sqrt(k)) * cur_coord_grad + ave_beta = (1. / k) * cur_beta + (1 - 1. / k) * ave_beta + return ave_beta + + +def c_transform_entropic(b, M, reg, beta): + ''' + The goal is to recover u from the c-transform. + + The function computes the c_transform of a dual variable from the other + dual variable: + + .. math:: + u = v^{c,reg} = -reg \sum_j exp((v - M)/reg) b_j + + where : + - M is the (ns,nt) metric cost matrix + - u, v are dual variables in R^IxR^J + - reg is the regularization term + + It is used to recover an optimal u from optimal v solving the semi dual + problem, see Proposition 2.1 of [18]_ + + + Parameters + ---------- + + b : np.ndarray(nt,) + target measure + M : np.ndarray(ns, nt) + cost matrix + reg : float + regularization term > 0 + v : np.ndarray(nt,) + dual variable + + Returns + ------- + + u : np.ndarray(ns,) + + Examples + -------- + + >>> n_source = 7 + >>> n_target = 4 + >>> reg = 1 + >>> numItermax = 300000 + >>> a = ot.utils.unif(n_source) + >>> b = ot.utils.unif(n_target) + >>> rng = np.random.RandomState(0) + >>> X_source = rng.randn(n_source, 2) + >>> Y_target = rng.randn(n_target, 2) + >>> M = ot.dist(X_source, Y_target) + >>> method = "ASGD" + >>> asgd_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, + method, numItermax) + >>> print(asgd_pi) + + References + ---------- + + [Genevay et al., 2016] : + Stochastic Optimization for Large-scale Optimal Transport, + Advances in Neural Information Processing Systems (2016), + arXiv preprint arxiv:1605.08527. + ''' + + n_source = np.shape(M)[0] + alpha = np.zeros(n_source) + for i in range(n_source): + r = M[i, :] - beta + min_r = np.min(r) + exp_beta = np.exp(-(r - min_r) / reg) * b + alpha[i] = min_r - reg * np.log(np.sum(exp_beta)) + return alpha + + +def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None, + log=False): + ''' + Compute the transportation matrix to solve the regularized discrete + measures optimal transport max problem + + The function solves the following optimization problem: + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + s.t. \gamma 1 = a + \gamma^T 1= b + \gamma \geq 0 + where : + - M is the (ns,nt) metric cost matrix + - :math:`\Omega` is the entropic regularization term + :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target weights (sum to 1) + The algorithm used for solving the problem is the SAG or ASGD algorithms + as proposed in [18]_ + + + Parameters + ---------- + + a : np.ndarray(ns,), + source measure + b : np.ndarray(nt,), + target measure + M : np.ndarray(ns, nt), + cost matrix + reg : float number, + Regularization term > 0 + methode : str, + used method (SAG or ASGD) + numItermax : int number + number of iteration + lr : float number + learning rate + n_source : int number + size of the source measure + n_target : int number + size of the target measure + log : bool, optional + record log if True + + Returns + ------- + + pi : np.ndarray(ns, nt) + transportation matrix + log : dict + log dictionary return only if log==True in parameters + + Examples + -------- + + >>> n_source = 7 + >>> n_target = 4 + >>> reg = 1 + >>> numItermax = 300000 + >>> a = ot.utils.unif(n_source) + >>> b = ot.utils.unif(n_target) + >>> rng = np.random.RandomState(0) + >>> X_source = rng.randn(n_source, 2) + >>> Y_target = rng.randn(n_target, 2) + >>> M = ot.dist(X_source, Y_target) + >>> method = "ASGD" + >>> asgd_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, + method, numItermax) + >>> print(asgd_pi) + + References + ---------- + + [Genevay et al., 2016] : + Stochastic Optimization for Large-scale Optimal Transport, + Advances in Neural Information Processing Systems (2016), + arXiv preprint arxiv:1605.08527. + ''' + + if method.lower() == "sag": + opt_beta = sag_entropic_transport(a, b, M, reg, numItermax, lr) + elif method.lower() == "asgd": + opt_beta = averaged_sgd_entropic_transport(a, b, M, reg, numItermax, lr) + else: + print("Please, select your method between SAG and ASGD") + return None + + opt_alpha = c_transform_entropic(b, M, reg, opt_beta) + pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :]) / reg) * + a[:, None] * b[None, :]) + + if log: + log = {} + log['alpha'] = opt_alpha + log['beta'] = opt_beta + return pi, log + else: + return pi + + +############################################################################## +# Optimization toolbox for DUAL problems +############################################################################## + + def batch_grad_dual(M, reg, a, b, alpha, beta, batch_size, batch_alpha, batch_beta): ''' diff --git a/ot/utils.py b/ot/utils.py index 17983f2..7dac283 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -225,6 +225,26 @@ def check_params(**kwargs): return check +def check_random_state(seed): + """Turn seed into a np.random.RandomState instance + Parameters + ---------- + seed : None | int | instance of RandomState + If seed is None, return the RandomState singleton used by np.random. + If seed is an int, return a new RandomState instance seeded with seed. + If seed is already a RandomState instance, return it. + Otherwise raise ValueError. + """ + if seed is None or seed is np.random: + return np.random.mtrand._rand + if isinstance(seed, (int, np.integer)): + return np.random.RandomState(seed) + if isinstance(seed, np.random.RandomState): + return seed + raise ValueError('{} cannot be used to seed a numpy.random.RandomState' + ' instance'.format(seed)) + + class deprecated(object): """Decorator to mark a function or class as deprecated. diff --git a/test/test_bregman.py b/test/test_bregman.py index 4a800fd..c8e9179 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -83,8 +83,8 @@ def test_bary(): n_bins = 100 # nb bins # Gaussian distributions - a1 = ot.datasets.get_1D_gauss(n_bins, m=30, s=10) # m= mean, s= std - a2 = ot.datasets.get_1D_gauss(n_bins, m=40, s=10) + a1 = ot.datasets.make_1D_gauss(n_bins, m=30, s=10) # m= mean, s= std + a2 = ot.datasets.make_1D_gauss(n_bins, m=40, s=10) # creating matrix A containing all distributions A = np.vstack((a1, a2)).T @@ -110,10 +110,10 @@ def test_unmix(): n_bins = 50 # nb bins # Gaussian distributions - a1 = ot.datasets.get_1D_gauss(n_bins, m=20, s=10) # m= mean, s= std - a2 = ot.datasets.get_1D_gauss(n_bins, m=40, s=10) + a1 = ot.datasets.make_1D_gauss(n_bins, m=20, s=10) # m= mean, s= std + a2 = ot.datasets.make_1D_gauss(n_bins, m=40, s=10) - a = ot.datasets.get_1D_gauss(n_bins, m=30, s=10) + a = ot.datasets.make_1D_gauss(n_bins, m=30, s=10) # creating matrix A containing all distributions D = np.vstack((a1, a2)).T diff --git a/test/test_da.py b/test/test_da.py index 3022721..97e23da 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -8,7 +8,7 @@ import numpy as np from numpy.testing.utils import assert_allclose, assert_equal import ot -from ot.datasets import get_data_classif +from ot.datasets import make_data_classif from ot.utils import unif @@ -19,8 +19,8 @@ def test_sinkhorn_lpl1_transport_class(): ns = 150 nt = 200 - Xs, ys = get_data_classif('3gauss', ns) - Xt, yt = get_data_classif('3gauss2', nt) + Xs, ys = make_data_classif('3gauss', ns) + Xt, yt = make_data_classif('3gauss2', nt) otda = ot.da.SinkhornLpl1Transport() @@ -45,7 +45,7 @@ def test_sinkhorn_lpl1_transport_class(): transp_Xs = otda.transform(Xs=Xs) assert_equal(transp_Xs.shape, Xs.shape) - Xs_new, _ = get_data_classif('3gauss', ns + 1) + Xs_new, _ = make_data_classif('3gauss', ns + 1) transp_Xs_new = otda.transform(Xs_new) # check that the oos method is working @@ -55,7 +55,7 @@ def test_sinkhorn_lpl1_transport_class(): transp_Xt = otda.inverse_transform(Xt=Xt) assert_equal(transp_Xt.shape, Xt.shape) - Xt_new, _ = get_data_classif('3gauss2', nt + 1) + Xt_new, _ = make_data_classif('3gauss2', nt + 1) transp_Xt_new = otda.inverse_transform(Xt=Xt_new) # check that the oos method is working @@ -92,8 +92,8 @@ def test_sinkhorn_l1l2_transport_class(): ns = 150 nt = 200 - Xs, ys = get_data_classif('3gauss', ns) - Xt, yt = get_data_classif('3gauss2', nt) + Xs, ys = make_data_classif('3gauss', ns) + Xt, yt = make_data_classif('3gauss2', nt) otda = ot.da.SinkhornL1l2Transport() @@ -119,7 +119,7 @@ def test_sinkhorn_l1l2_transport_class(): transp_Xs = otda.transform(Xs=Xs) assert_equal(transp_Xs.shape, Xs.shape) - Xs_new, _ = get_data_classif('3gauss', ns + 1) + Xs_new, _ = make_data_classif('3gauss', ns + 1) transp_Xs_new = otda.transform(Xs_new) # check that the oos method is working @@ -129,7 +129,7 @@ def test_sinkhorn_l1l2_transport_class(): transp_Xt = otda.inverse_transform(Xt=Xt) assert_equal(transp_Xt.shape, Xt.shape) - Xt_new, _ = get_data_classif('3gauss2', nt + 1) + Xt_new, _ = make_data_classif('3gauss2', nt + 1) transp_Xt_new = otda.inverse_transform(Xt=Xt_new) # check that the oos method is working @@ -173,8 +173,8 @@ def test_sinkhorn_transport_class(): ns = 150 nt = 200 - Xs, ys = get_data_classif('3gauss', ns) - Xt, yt = get_data_classif('3gauss2', nt) + Xs, ys = make_data_classif('3gauss', ns) + Xt, yt = make_data_classif('3gauss2', nt) otda = ot.da.SinkhornTransport() @@ -200,7 +200,7 @@ def test_sinkhorn_transport_class(): transp_Xs = otda.transform(Xs=Xs) assert_equal(transp_Xs.shape, Xs.shape) - Xs_new, _ = get_data_classif('3gauss', ns + 1) + Xs_new, _ = make_data_classif('3gauss', ns + 1) transp_Xs_new = otda.transform(Xs_new) # check that the oos method is working @@ -210,7 +210,7 @@ def test_sinkhorn_transport_class(): transp_Xt = otda.inverse_transform(Xt=Xt) assert_equal(transp_Xt.shape, Xt.shape) - Xt_new, _ = get_data_classif('3gauss2', nt + 1) + Xt_new, _ = make_data_classif('3gauss2', nt + 1) transp_Xt_new = otda.inverse_transform(Xt=Xt_new) # check that the oos method is working @@ -252,8 +252,8 @@ def test_emd_transport_class(): ns = 150 nt = 200 - Xs, ys = get_data_classif('3gauss', ns) - Xt, yt = get_data_classif('3gauss2', nt) + Xs, ys = make_data_classif('3gauss', ns) + Xt, yt = make_data_classif('3gauss2', nt) otda = ot.da.EMDTransport() @@ -278,7 +278,7 @@ def test_emd_transport_class(): transp_Xs = otda.transform(Xs=Xs) assert_equal(transp_Xs.shape, Xs.shape) - Xs_new, _ = get_data_classif('3gauss', ns + 1) + Xs_new, _ = make_data_classif('3gauss', ns + 1) transp_Xs_new = otda.transform(Xs_new) # check that the oos method is working @@ -288,7 +288,7 @@ def test_emd_transport_class(): transp_Xt = otda.inverse_transform(Xt=Xt) assert_equal(transp_Xt.shape, Xt.shape) - Xt_new, _ = get_data_classif('3gauss2', nt + 1) + Xt_new, _ = make_data_classif('3gauss2', nt + 1) transp_Xt_new = otda.inverse_transform(Xt=Xt_new) # check that the oos method is working @@ -329,9 +329,9 @@ def test_mapping_transport_class(): ns = 60 nt = 120 - Xs, ys = get_data_classif('3gauss', ns) - Xt, yt = get_data_classif('3gauss2', nt) - Xs_new, _ = get_data_classif('3gauss', ns + 1) + Xs, ys = make_data_classif('3gauss', ns) + Xt, yt = make_data_classif('3gauss2', nt) + Xs_new, _ = make_data_classif('3gauss', ns + 1) ########################################################################## # kernel == linear mapping tests @@ -449,8 +449,8 @@ def test_linear_mapping(): ns = 150 nt = 200 - Xs, ys = get_data_classif('3gauss', ns) - Xt, yt = get_data_classif('3gauss2', nt) + Xs, ys = make_data_classif('3gauss', ns) + Xt, yt = make_data_classif('3gauss2', nt) A, b = ot.da.OT_mapping_linear(Xs, Xt) @@ -467,8 +467,8 @@ def test_linear_mapping_class(): ns = 150 nt = 200 - Xs, ys = get_data_classif('3gauss', ns) - Xt, yt = get_data_classif('3gauss2', nt) + Xs, ys = make_data_classif('3gauss', ns) + Xt, yt = make_data_classif('3gauss2', nt) otmap = ot.da.LinearTransport() @@ -491,8 +491,8 @@ def test_otda(): n_samples = 150 # nb samples np.random.seed(0) - xs, ys = ot.datasets.get_data_classif('3gauss', n_samples) - xt, yt = ot.datasets.get_data_classif('3gauss2', n_samples) + xs, ys = ot.datasets.make_data_classif('3gauss', n_samples) + xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples) a, b = ot.unif(n_samples), ot.unif(n_samples) diff --git a/test/test_dr.py b/test/test_dr.py index 915012d..c5df287 100644 --- a/test/test_dr.py +++ b/test/test_dr.py @@ -22,7 +22,7 @@ def test_fda(): np.random.seed(0) # generate gaussian dataset - xs, ys = ot.datasets.get_data_classif('gaussrot', n_samples) + xs, ys = ot.datasets.make_data_classif('gaussrot', n_samples) n_features_noise = 8 @@ -44,7 +44,7 @@ def test_wda(): np.random.seed(0) # generate gaussian dataset - xs, ys = ot.datasets.get_data_classif('gaussrot', n_samples) + xs, ys = ot.datasets.make_data_classif('gaussrot', n_samples) n_features_noise = 8 diff --git a/test/test_gromov.py b/test/test_gromov.py index bb23469..fb86274 100644 --- a/test/test_gromov.py +++ b/test/test_gromov.py @@ -15,7 +15,7 @@ def test_gromov(): mu_s = np.array([0, 0]) cov_s = np.array([[1, 0], [0, 1]]) - xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s) + xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) xt = xs[::-1].copy() @@ -55,7 +55,7 @@ def test_entropic_gromov(): mu_s = np.array([0, 0]) cov_s = np.array([[1, 0], [0, 1]]) - xs = ot.datasets.get_2D_samples_gauss(n_samples, mu_s, cov_s) + xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) xt = xs[::-1].copy() @@ -96,8 +96,8 @@ def test_gromov_barycenter(): ns = 50 nt = 60 - Xs, ys = ot.datasets.get_data_classif('3gauss', ns) - Xt, yt = ot.datasets.get_data_classif('3gauss2', nt) + Xs, ys = ot.datasets.make_data_classif('3gauss', ns) + Xt, yt = ot.datasets.make_data_classif('3gauss2', nt) C1 = ot.dist(Xs) C2 = ot.dist(Xt) @@ -123,8 +123,8 @@ def test_gromov_entropic_barycenter(): ns = 50 nt = 60 - Xs, ys = ot.datasets.get_data_classif('3gauss', ns) - Xt, yt = ot.datasets.get_data_classif('3gauss2', nt) + Xs, ys = ot.datasets.make_data_classif('3gauss', ns) + Xt, yt = ot.datasets.make_data_classif('3gauss2', nt) C1 = ot.dist(Xs) C2 = ot.dist(Xt) @@ -133,13 +133,13 @@ def test_gromov_entropic_barycenter(): Cb = ot.gromov.entropic_gromov_barycenters(n_samples, [C1, C2], [ot.unif(ns), ot.unif(nt) ], ot.unif(n_samples), [.5, .5], - 'square_loss', 1e-3, + 'square_loss', 2e-3, max_iter=100, tol=1e-3) np.testing.assert_allclose(Cb.shape, (n_samples, n_samples)) Cb2 = ot.gromov.entropic_gromov_barycenters(n_samples, [C1, C2], [ot.unif(ns), ot.unif(nt) ], ot.unif(n_samples), [.5, .5], - 'kl_loss', 1e-3, + 'kl_loss', 2e-3, max_iter=100, tol=1e-3) np.testing.assert_allclose(Cb2.shape, (n_samples, n_samples)) diff --git a/test/test_optim.py b/test/test_optim.py index 69496a5..dfefe59 100644 --- a/test/test_optim.py +++ b/test/test_optim.py @@ -16,8 +16,8 @@ def test_conditional_gradient(): x = np.arange(n_bins, dtype=np.float64) # Gaussian distributions - a = ot.datasets.get_1D_gauss(n_bins, m=20, s=5) # m= mean, s= std - b = ot.datasets.get_1D_gauss(n_bins, m=60, s=10) + a = ot.datasets.make_1D_gauss(n_bins, m=20, s=5) # m= mean, s= std + b = ot.datasets.make_1D_gauss(n_bins, m=60, s=10) # loss matrix M = ot.dist(x.reshape((n_bins, 1)), x.reshape((n_bins, 1))) @@ -45,8 +45,8 @@ def test_generalized_conditional_gradient(): x = np.arange(n_bins, dtype=np.float64) # Gaussian distributions - a = ot.datasets.get_1D_gauss(n_bins, m=20, s=5) # m= mean, s= std - b = ot.datasets.get_1D_gauss(n_bins, m=60, s=10) + a = ot.datasets.make_1D_gauss(n_bins, m=20, s=5) # m= mean, s= std + b = ot.datasets.make_1D_gauss(n_bins, m=60, s=10) # loss matrix M = ot.dist(x.reshape((n_bins, 1)), x.reshape((n_bins, 1))) diff --git a/test/test_ot.py b/test/test_ot.py index cc25bf4..399e549 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -9,7 +9,7 @@ import warnings import numpy as np import ot -from ot.datasets import get_1D_gauss as gauss +from ot.datasets import make_1D_gauss as gauss import pytest diff --git a/test/test_plot.py b/test/test_plot.py index a50ed14..f77d879 100644 --- a/test/test_plot.py +++ b/test/test_plot.py @@ -20,8 +20,8 @@ def test_plot1D_mat(): x = np.arange(n_bins, dtype=np.float64) # Gaussian distributions - a = ot.datasets.get_1D_gauss(n_bins, m=20, s=5) # m= mean, s= std - b = ot.datasets.get_1D_gauss(n_bins, m=60, s=10) + a = ot.datasets.make_1D_gauss(n_bins, m=20, s=5) # m= mean, s= std + b = ot.datasets.make_1D_gauss(n_bins, m=60, s=10) # loss matrix M = ot.dist(x.reshape((n_bins, 1)), x.reshape((n_bins, 1))) @@ -43,8 +43,8 @@ def test_plot2D_samples_mat(): mu_t = np.array([4, 4]) cov_t = np.array([[1, -.8], [-.8, 1]]) - xs = ot.datasets.get_2D_samples_gauss(n_bins, mu_s, cov_s) - xt = ot.datasets.get_2D_samples_gauss(n_bins, mu_t, cov_t) + xs = ot.datasets.make_2D_samples_gauss(n_bins, mu_s, cov_s) + xt = ot.datasets.make_2D_samples_gauss(n_bins, mu_t, cov_t) G = 1.0 * (np.random.rand(n_bins, n_bins) < 0.01) -- cgit v1.2.3 From 436b228ed09a16bcc85114cbb4de6cdf55e822af Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Tue, 28 Aug 2018 17:32:35 -0700 Subject: fixed pep8 --- ot/stochastic.py | 437 ------------------------------------------------------- 1 file changed, 437 deletions(-) diff --git a/ot/stochastic.py b/ot/stochastic.py index f3d1bb5..0788f61 100644 --- a/ot/stochastic.py +++ b/ot/stochastic.py @@ -435,443 +435,6 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None, ############################################################################## -# Author: Kilian Fatras -# -# License: MIT License - -import numpy as np - - -############################################################################## -# Optimization toolbox for SEMI - DUAL problems -############################################################################## - - -def coordinate_grad_semi_dual(b, M, reg, beta, i): - ''' - Compute the coordinate gradient update for regularized discrete - distributions for (i, :) - - The function computes the gradient of the semi dual problem: - - .. math:: - \W_\varepsilon(a, b) = \max_\v \sum_i (\sum_j v_j * b_j - - \reg log(\sum_j exp((v_j - M_{i,j})/reg) * b_j)) * a_i - - where : - - M is the (ns,nt) metric cost matrix - - v is a dual variable in R^J - - reg is the regularization term - - a and b are source and target weights (sum to 1) - - The algorithm used for solving the problem is the ASGD & SAG algorithms - as proposed in [18]_ [alg.1 & alg.2] - - - Parameters - ---------- - - b : np.ndarray(nt,), - target measure - M : np.ndarray(ns, nt), - cost matrix - reg : float nu, - Regularization term > 0 - v : np.ndarray(nt,), - optimization vector - i : number int, - picked number i - - Returns - ------- - - coordinate gradient : np.ndarray(nt,) - - Examples - -------- - - >>> n_source = 7 - >>> n_target = 4 - >>> reg = 1 - >>> numItermax = 300000 - >>> a = ot.utils.unif(n_source) - >>> b = ot.utils.unif(n_target) - >>> rng = np.random.RandomState(0) - >>> X_source = rng.randn(n_source, 2) - >>> Y_target = rng.randn(n_target, 2) - >>> M = ot.dist(X_source, Y_target) - >>> method = "ASGD" - >>> asgd_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, - method, numItermax) - >>> print(asgd_pi) - - References - ---------- - - [Genevay et al., 2016] : - Stochastic Optimization for Large-scale Optimal Transport, - Advances in Neural Information Processing Systems (2016), - arXiv preprint arxiv:1605.08527. - - ''' - - r = M[i, :] - beta - exp_beta = np.exp(-r / reg) * b - khi = exp_beta / (np.sum(exp_beta)) - return b - khi - - -def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=None): - ''' - Compute the SAG algorithm to solve the regularized discrete measures - optimal transport max problem - - The function solves the following optimization problem: - - .. math:: - \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) - s.t. \gamma 1 = a - \gamma^T 1= b - \gamma \geq 0 - where : - - M is the (ns,nt) metric cost matrix - - :math:`\Omega` is the entropic regularization term - :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - a and b are source and target weights (sum to 1) - The algorithm used for solving the problem is the SAG algorithm - as proposed in [18]_ [alg.1] - - - Parameters - ---------- - - a : np.ndarray(ns,), - source measure - b : np.ndarray(nt,), - target measure - M : np.ndarray(ns, nt), - cost matrix - reg : float number, - Regularization term > 0 - numItermax : int number - number of iteration - lr : float number - learning rate - - Returns - ------- - - v : np.ndarray(nt,) - dual variable - - Examples - -------- - - >>> n_source = 7 - >>> n_target = 4 - >>> reg = 1 - >>> numItermax = 300000 - >>> a = ot.utils.unif(n_source) - >>> b = ot.utils.unif(n_target) - >>> rng = np.random.RandomState(0) - >>> X_source = rng.randn(n_source, 2) - >>> Y_target = rng.randn(n_target, 2) - >>> M = ot.dist(X_source, Y_target) - >>> method = "ASGD" - >>> asgd_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, - method, numItermax) - >>> print(asgd_pi) - - References - ---------- - - [Genevay et al., 2016] : - Stochastic Optimization for Large-scale Optimal Transport, - Advances in Neural Information Processing Systems (2016), - arXiv preprint arxiv:1605.08527. - ''' - - if lr is None: - lr = 1. / max(a / reg) - n_source = np.shape(M)[0] - n_target = np.shape(M)[1] - cur_beta = np.zeros(n_target) - stored_gradient = np.zeros((n_source, n_target)) - sum_stored_gradient = np.zeros(n_target) - for _ in range(numItermax): - i = np.random.randint(n_source) - cur_coord_grad = a[i] * coordinate_grad_semi_dual(b, M, reg, - cur_beta, i) - sum_stored_gradient += (cur_coord_grad - stored_gradient[i]) - stored_gradient[i] = cur_coord_grad - cur_beta += lr * (1. / n_source) * sum_stored_gradient - return cur_beta - - -def averaged_sgd_entropic_transport(a, b, M, reg, numItermax=300000, lr=None): - ''' - Compute the ASGD algorithm to solve the regularized semi contibous measures - optimal transport max problem - - The function solves the following optimization problem: - - .. math:: - \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) - s.t. \gamma 1 = a - \gamma^T 1= b - \gamma \geq 0 - where : - - M is the (ns,nt) metric cost matrix - - :math:`\Omega` is the entropic regularization term - :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - a and b are source and target weights (sum to 1) - The algorithm used for solving the problem is the ASGD algorithm - as proposed in [18]_ [alg.2] - - - Parameters - ---------- - - b : np.ndarray(nt,), - target measure - M : np.ndarray(ns, nt), - cost matrix - reg : float number, - Regularization term > 0 - numItermax : int number - number of iteration - lr : float number - learning rate - - - Returns - ------- - - ave_v : np.ndarray(nt,) - optimization vector - - Examples - -------- - - >>> n_source = 7 - >>> n_target = 4 - >>> reg = 1 - >>> numItermax = 300000 - >>> a = ot.utils.unif(n_source) - >>> b = ot.utils.unif(n_target) - >>> rng = np.random.RandomState(0) - >>> X_source = rng.randn(n_source, 2) - >>> Y_target = rng.randn(n_target, 2) - >>> M = ot.dist(X_source, Y_target) - >>> method = "ASGD" - >>> asgd_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, - method, numItermax) - >>> print(asgd_pi) - - References - ---------- - - [Genevay et al., 2016] : - Stochastic Optimization for Large-scale Optimal Transport, - Advances in Neural Information Processing Systems (2016), - arXiv preprint arxiv:1605.08527. - ''' - - if lr is None: - lr = 1. / max(a / reg) - n_source = np.shape(M)[0] - n_target = np.shape(M)[1] - cur_beta = np.zeros(n_target) - ave_beta = np.zeros(n_target) - for cur_iter in range(numItermax): - k = cur_iter + 1 - i = np.random.randint(n_source) - cur_coord_grad = coordinate_grad_semi_dual(b, M, reg, cur_beta, i) - cur_beta += (lr / np.sqrt(k)) * cur_coord_grad - ave_beta = (1. / k) * cur_beta + (1 - 1. / k) * ave_beta - return ave_beta - - -def c_transform_entropic(b, M, reg, beta): - ''' - The goal is to recover u from the c-transform. - - The function computes the c_transform of a dual variable from the other - dual variable: - - .. math:: - u = v^{c,reg} = -reg \sum_j exp((v - M)/reg) b_j - - where : - - M is the (ns,nt) metric cost matrix - - u, v are dual variables in R^IxR^J - - reg is the regularization term - - It is used to recover an optimal u from optimal v solving the semi dual - problem, see Proposition 2.1 of [18]_ - - - Parameters - ---------- - - b : np.ndarray(nt,) - target measure - M : np.ndarray(ns, nt) - cost matrix - reg : float - regularization term > 0 - v : np.ndarray(nt,) - dual variable - - Returns - ------- - - u : np.ndarray(ns,) - - Examples - -------- - - >>> n_source = 7 - >>> n_target = 4 - >>> reg = 1 - >>> numItermax = 300000 - >>> a = ot.utils.unif(n_source) - >>> b = ot.utils.unif(n_target) - >>> rng = np.random.RandomState(0) - >>> X_source = rng.randn(n_source, 2) - >>> Y_target = rng.randn(n_target, 2) - >>> M = ot.dist(X_source, Y_target) - >>> method = "ASGD" - >>> asgd_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, - method, numItermax) - >>> print(asgd_pi) - - References - ---------- - - [Genevay et al., 2016] : - Stochastic Optimization for Large-scale Optimal Transport, - Advances in Neural Information Processing Systems (2016), - arXiv preprint arxiv:1605.08527. - ''' - - n_source = np.shape(M)[0] - alpha = np.zeros(n_source) - for i in range(n_source): - r = M[i, :] - beta - min_r = np.min(r) - exp_beta = np.exp(-(r - min_r) / reg) * b - alpha[i] = min_r - reg * np.log(np.sum(exp_beta)) - return alpha - - -def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None, - log=False): - ''' - Compute the transportation matrix to solve the regularized discrete - measures optimal transport max problem - - The function solves the following optimization problem: - - .. math:: - \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) - s.t. \gamma 1 = a - \gamma^T 1= b - \gamma \geq 0 - where : - - M is the (ns,nt) metric cost matrix - - :math:`\Omega` is the entropic regularization term - :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - a and b are source and target weights (sum to 1) - The algorithm used for solving the problem is the SAG or ASGD algorithms - as proposed in [18]_ - - - Parameters - ---------- - - a : np.ndarray(ns,), - source measure - b : np.ndarray(nt,), - target measure - M : np.ndarray(ns, nt), - cost matrix - reg : float number, - Regularization term > 0 - methode : str, - used method (SAG or ASGD) - numItermax : int number - number of iteration - lr : float number - learning rate - n_source : int number - size of the source measure - n_target : int number - size of the target measure - log : bool, optional - record log if True - - Returns - ------- - - pi : np.ndarray(ns, nt) - transportation matrix - log : dict - log dictionary return only if log==True in parameters - - Examples - -------- - - >>> n_source = 7 - >>> n_target = 4 - >>> reg = 1 - >>> numItermax = 300000 - >>> a = ot.utils.unif(n_source) - >>> b = ot.utils.unif(n_target) - >>> rng = np.random.RandomState(0) - >>> X_source = rng.randn(n_source, 2) - >>> Y_target = rng.randn(n_target, 2) - >>> M = ot.dist(X_source, Y_target) - >>> method = "ASGD" - >>> asgd_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, - method, numItermax) - >>> print(asgd_pi) - - References - ---------- - - [Genevay et al., 2016] : - Stochastic Optimization for Large-scale Optimal Transport, - Advances in Neural Information Processing Systems (2016), - arXiv preprint arxiv:1605.08527. - ''' - - if method.lower() == "sag": - opt_beta = sag_entropic_transport(a, b, M, reg, numItermax, lr) - elif method.lower() == "asgd": - opt_beta = averaged_sgd_entropic_transport(a, b, M, reg, numItermax, lr) - else: - print("Please, select your method between SAG and ASGD") - return None - - opt_alpha = c_transform_entropic(b, M, reg, opt_beta) - pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :]) / reg) * - a[:, None] * b[None, :]) - - if log: - log = {} - log['alpha'] = opt_alpha - log['beta'] = opt_beta - return pi, log - else: - return pi - - -############################################################################## -# Optimization toolbox for DUAL problems -############################################################################## - - def batch_grad_dual(M, reg, a, b, alpha, beta, batch_size, batch_alpha, batch_beta): ''' -- cgit v1.2.3 From b13feb07eaff4d971b749663652e5f8811c1992c Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Tue, 28 Aug 2018 18:18:37 -0700 Subject: added gaussian test --- README.md | 7 +++++-- test/test_stochastic.py | 47 ++++++++++++++++++++++++++++++++++++++--------- 2 files changed, 43 insertions(+), 11 deletions(-) diff --git a/README.md b/README.md index 677a23b..1d3b097 100644 --- a/README.md +++ b/README.md @@ -24,6 +24,7 @@ It provides the following solvers: * Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt). * Gromov-Wasserstein distances and barycenters ([13] and regularized [12]) * Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) +* Non regularized free support Wasserstein barycenters [20]. Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -163,7 +164,7 @@ The contributors to this library are: * [Stanislas Chambon](https://slasnista.github.io/) * [Antoine Rolet](https://arolet.github.io/) * Erwan Vautier (Gromov-Wasserstein) -* [Kilian Fatras](https://kilianfatras.github.io/) (Stochastic optimization) +* [Kilian Fatras](https://kilianfatras.github.io/) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): @@ -222,6 +223,8 @@ You can also post bug reports and feature requests in Github issues. Make sure t [17] Blondel, M., Seguy, V., & Rolet, A. (2018). [Smooth and Sparse Optimal Transport](https://arxiv.org/abs/1710.06276). Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS). -[18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016) [Stochastic Optimization for Large-scale Optimal Transport](arXiv preprint arxiv:1605.08527). Advances in Neural Information Processing Systems (2016). +[18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016) [Stochastic Optimization for Large-scale Optimal Transport](https://arxiv.org/abs/1605.08527). Advances in Neural Information Processing Systems (2016). [19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, A.& Blondel, M. [Large-scale Optimal Transport and Mapping Estimation](https://arxiv.org/pdf/1711.02283.pdf). International Conference on Learning Representation (2018) + +[20] Cuturi, M. and Doucet, A. (2014) [Fast Computation of Wasserstein Barycenters](http://proceedings.mlr.press/v32/cuturi14.html). International Conference in Machine Learning diff --git a/test/test_stochastic.py b/test/test_stochastic.py index f315c88..88ad666 100644 --- a/test/test_stochastic.py +++ b/test/test_stochastic.py @@ -137,8 +137,8 @@ def test_stochastic_dual_sgd(): # test sgd n = 10 reg = 1 - numItermax = 300000 - batch_size = 8 + numItermax = 15000 + batch_size = 10 rng = np.random.RandomState(0) x = rng.randn(n, 2) @@ -151,9 +151,9 @@ def test_stochastic_dual_sgd(): # check constratints np.testing.assert_allclose( - u, G.sum(1), atol=1e-02) # cf convergence sgd + u, G.sum(1), atol=1e-04) # cf convergence sgd np.testing.assert_allclose( - u, G.sum(0), atol=1e-02) # cf convergence sgd + u, G.sum(0), atol=1e-04) # cf convergence sgd ############################################################################# @@ -168,10 +168,11 @@ def test_dual_sgd_sinkhorn(): # test all dual algorithms n = 10 reg = 1 - nb_iter = 300000 - batch_size = 8 + nb_iter = 150000 + batch_size = 10 rng = np.random.RandomState(0) +# Test uniform x = rng.randn(n, 2) u = ot.utils.unif(n) zero = np.zeros(n) @@ -184,8 +185,36 @@ def test_dual_sgd_sinkhorn(): # check constratints np.testing.assert_allclose( - zero, (G_sgd - G_sinkhorn).sum(1), atol=1e-02) # cf convergence sgd + zero, (G_sgd - G_sinkhorn).sum(1), atol=1e-04) # cf convergence sgd + np.testing.assert_allclose( + zero, (G_sgd - G_sinkhorn).sum(0), atol=1e-04) # cf convergence sgd + np.testing.assert_allclose( + G_sgd, G_sinkhorn, atol=1e-04) # cf convergence sgd + +# Test gaussian + n = 30 + n_source = n + n_target = n + reg = 1 + numItermax = 150000 + batch_size = 30 + + a = ot.datasets.get_1D_gauss(n_source, m=15, s=5) # m= mean, s= std + b = ot.datasets.get_1D_gauss(n_target, m=15, s=5) + X_source = np.arange(n_source,dtype=np.float64) + Y_target = np.arange(n_target,dtype=np.float64) + M = ot.dist(X_source.reshape((n_source, 1)), Y_target.reshape((n_target, 1))) + M /= M.max() + + G_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg, batch_size, + numItermax=nb_iter) + + G_sinkhorn = ot.sinkhorn(a, b, M, reg) + + # check constratints + np.testing.assert_allclose( + zero, (G_sgd - G_sinkhorn).sum(1), atol=1e-04) # cf convergence sgd np.testing.assert_allclose( - zero, (G_sgd - G_sinkhorn).sum(0), atol=1e-02) # cf convergence sgd + zero, (G_sgd - G_sinkhorn).sum(0), atol=1e-04) # cf convergence sgd np.testing.assert_allclose( - G_sgd, G_sinkhorn, atol=1e-02) # cf convergence sgd + G_sgd, G_sinkhorn, atol=1e-04) # cf convergence sgd -- cgit v1.2.3 From cd193f78d392143ea9421da0f7e55ca8b75b8a0d Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Tue, 28 Aug 2018 18:25:40 -0700 Subject: updated README and fixed pep8 --- test/test_stochastic.py | 13 +++++-------- 1 file changed, 5 insertions(+), 8 deletions(-) diff --git a/test/test_stochastic.py b/test/test_stochastic.py index 88ad666..4bbe230 100644 --- a/test/test_stochastic.py +++ b/test/test_stochastic.py @@ -193,17 +193,14 @@ def test_dual_sgd_sinkhorn(): # Test gaussian n = 30 - n_source = n - n_target = n reg = 1 - numItermax = 150000 batch_size = 30 - a = ot.datasets.get_1D_gauss(n_source, m=15, s=5) # m= mean, s= std - b = ot.datasets.get_1D_gauss(n_target, m=15, s=5) - X_source = np.arange(n_source,dtype=np.float64) - Y_target = np.arange(n_target,dtype=np.float64) - M = ot.dist(X_source.reshape((n_source, 1)), Y_target.reshape((n_target, 1))) + a = ot.datasets.get_1D_gauss(n, m=15, s=5) # m= mean, s= std + b = ot.datasets.get_1D_gauss(n, m=15, s=5) + X_source = np.arange(n, dtype=np.float64) + Y_target = np.arange(n, dtype=np.float64) + M = ot.dist(X_source.reshape((n, 1)), Y_target.reshape((n, 1))) M /= M.max() G_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg, batch_size, -- cgit v1.2.3 From 37e3b29595223399ebe4710ac2bb061004814118 Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Tue, 28 Aug 2018 18:51:28 -0700 Subject: fixed argument functions --- ot/stochastic.py | 8 ++++---- test/test_stochastic.py | 4 ++-- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/ot/stochastic.py b/ot/stochastic.py index 0788f61..e33f6a0 100644 --- a/ot/stochastic.py +++ b/ot/stochastic.py @@ -435,7 +435,7 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None, ############################################################################## -def batch_grad_dual(M, reg, a, b, alpha, beta, batch_size, batch_alpha, +def batch_grad_dual(a, b, M, reg, alpha, beta, batch_size, batch_alpha, batch_beta): ''' Computes the partial gradient of F_\W_varepsilon @@ -528,7 +528,7 @@ def batch_grad_dual(M, reg, a, b, alpha, beta, batch_size, batch_alpha, return grad_alpha, grad_beta -def sgd_entropic_regularization(M, reg, a, b, batch_size, numItermax, lr): +def sgd_entropic_regularization(a, b, M, reg, batch_size, numItermax, lr): ''' Compute the sgd algorithm to solve the regularized discrete measures optimal transport dual problem @@ -612,7 +612,7 @@ def sgd_entropic_regularization(M, reg, a, b, batch_size, numItermax, lr): k = np.sqrt(cur_iter / 100 + 1) batch_alpha = np.random.choice(n_source, batch_size, replace=False) batch_beta = np.random.choice(n_target, batch_size, replace=False) - update_alpha, update_beta = batch_grad_dual(M, reg, a, b, cur_alpha, + update_alpha, update_beta = batch_grad_dual(a, b, M, reg, cur_alpha, cur_beta, batch_size, batch_alpha, batch_beta) cur_alpha += (lr / k) * update_alpha @@ -698,7 +698,7 @@ def solve_dual_entropic(a, b, M, reg, batch_size, numItermax=10000, lr=1, arXiv preprint arxiv:1711.02283. ''' - opt_alpha, opt_beta = sgd_entropic_regularization(M, reg, a, b, batch_size, + opt_alpha, opt_beta = sgd_entropic_regularization(a, b, M, reg, batch_size, numItermax, lr) pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :]) / reg) * a[:, None] * b[None, :]) diff --git a/test/test_stochastic.py b/test/test_stochastic.py index 4bbe230..f1d4825 100644 --- a/test/test_stochastic.py +++ b/test/test_stochastic.py @@ -196,8 +196,8 @@ def test_dual_sgd_sinkhorn(): reg = 1 batch_size = 30 - a = ot.datasets.get_1D_gauss(n, m=15, s=5) # m= mean, s= std - b = ot.datasets.get_1D_gauss(n, m=15, s=5) + a = ot.datasets.get_1D_gauss(n, 15, 5) # m= mean, s= std + b = ot.datasets.get_1D_gauss(n, 15, 5) X_source = np.arange(n, dtype=np.float64) Y_target = np.arange(n, dtype=np.float64) M = ot.dist(X_source.reshape((n, 1)), Y_target.reshape((n, 1))) -- cgit v1.2.3 From e5087799c1cf93a76579b88bda930cfcce36b084 Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Tue, 28 Aug 2018 23:22:05 -0700 Subject: fixed test gauss --- test/test_stochastic.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test/test_stochastic.py b/test/test_stochastic.py index f1d4825..10e62c1 100644 --- a/test/test_stochastic.py +++ b/test/test_stochastic.py @@ -196,8 +196,8 @@ def test_dual_sgd_sinkhorn(): reg = 1 batch_size = 30 - a = ot.datasets.get_1D_gauss(n, 15, 5) # m= mean, s= std - b = ot.datasets.get_1D_gauss(n, 15, 5) + a = ot.datasets.make_1D_gauss(n, 15, 5) # m= mean, s= std + b = ot.datasets.make_1D_gauss(n, 15, 5) X_source = np.arange(n, dtype=np.float64) Y_target = np.arange(n, dtype=np.float64) M = ot.dist(X_source.reshape((n, 1)), Y_target.reshape((n, 1))) -- cgit v1.2.3 From b2b5ffc529a7a3dbc51408cd2df59617a7e49a9a Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Tue, 28 Aug 2018 23:32:55 -0700 Subject: fixed test error --- test/test_stochastic.py | 1 + 1 file changed, 1 insertion(+) diff --git a/test/test_stochastic.py b/test/test_stochastic.py index 10e62c1..74228a3 100644 --- a/test/test_stochastic.py +++ b/test/test_stochastic.py @@ -195,6 +195,7 @@ def test_dual_sgd_sinkhorn(): n = 30 reg = 1 batch_size = 30 + zero = np.zeros(n) a = ot.datasets.make_1D_gauss(n, 15, 5) # m= mean, s= std b = ot.datasets.make_1D_gauss(n, 15, 5) -- cgit v1.2.3 From a460ce86b1169de0d80ad7dc4b28abcdb9e47cb2 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 29 Aug 2018 13:14:55 +0200 Subject: version 0.5.0b --- ot/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/__init__.py b/ot/__init__.py index 1dde390..fa6600d 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -29,7 +29,7 @@ from .da import sinkhorn_lpl1_mm # utils functions from .utils import dist, unif, tic, toc, toq -__version__ = "0.4.0" +__version__ = "0.5.0b" __all__ = ["emd", "emd2", "sinkhorn", "sinkhorn2", "utils", 'datasets', 'bregman', 'lp', 'tic', 'toc', 'toq', 'gromov', -- cgit v1.2.3 From 8babe1b307efa3539451b72f6c686fddc1d29cda Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 29 Aug 2018 13:20:42 +0200 Subject: update doc --- docs/source/readme.rst | 19 +++++++++++++++++++ 1 file changed, 19 insertions(+) diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 5d37f64..d10b769 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -19,6 +19,7 @@ It provides the following solvers: squared L2 regularizations [17]. - Non regularized Wasserstein barycenters [16] with LP solver (only small scale). +- Non regularized free support Wasserstein barycenters [20]. - Bregman projections for Wasserstein barycenter [3] and unmixing [4]. - Optimal transport for domain adaptation with group lasso regularization [5] @@ -29,6 +30,8 @@ It provides the following solvers: pymanopt). - Gromov-Wasserstein distances and barycenters ([13] and regularized [12]) +- Stochastic Optimization for Large-scale Optimal Transport (semi-dual + problem [18] and dual problem [19]) Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -219,6 +222,8 @@ The contributors to this library are: - `Stanislas Chambon `__ - `Antoine Rolet `__ - Erwan Vautier (Gromov-Wasserstein) +- `Kilian Fatras `__ (Stochastic + optimization) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various @@ -334,6 +339,20 @@ Optimal Transport `__. Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS). +[18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016) `Stochastic +Optimization for Large-scale Optimal +Transport `__. Advances in Neural +Information Processing Systems (2016). + +[19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, +A.& Blondel, M. `Large-scale Optimal Transport and Mapping +Estimation `__. International +Conference on Learning Representation (2018) + +[20] Cuturi, M. and Doucet, A. (2014) `Fast Computation of Wasserstein +Barycenters `__. +International Conference in Machine Learning + .. |PyPI version| image:: https://badge.fury.io/py/POT.svg :target: https://badge.fury.io/py/POT .. |Anaconda Cloud| image:: https://anaconda.org/conda-forge/pot/badges/version.svg -- cgit v1.2.3 From 4d32e6dadcb81f406a38ddd3534513d1d9f7e77a Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 29 Aug 2018 13:58:33 +0200 Subject: complete releases.md --- RELEASES.md | 27 +++++++++++++++++++++++++++ 1 file changed, 27 insertions(+) diff --git a/RELEASES.md b/RELEASES.md index 58712c8..1da43d3 100644 --- a/RELEASES.md +++ b/RELEASES.md @@ -1,5 +1,32 @@ # POT Releases + +## 0.5.0b +*Sep 2018* + +*This is a beta release and is still a work in progress* + +#### Features + +* Add non regularized Gromov-Wasserstein solver (PR #41) +* Linear OT mapping between empirical distributions and 90\% test coverage (PR #42) +* Add log parameter in class EMDTransport and SinkhornLpL1Transport (PR #44) +* Add Marddown format for Pipy (PR #45) +* Test for Python 3.5 and 3.6 on Travis (PR #46) +* Non regularized Wasserstein barycenter with scipy linear solver and/or cvxopt (PR #47) +* Rename dataset functions to be more sklearn compliant (PR #49) +* Smooth and sparse Optimal transport implementation with entropic and quadratic regularization (PR #50) +* Stochastic OT in the dual and semi-dual (PR #52 and PR #62) +* Free support barycenters (PR #56) +* Speed-up Sinkhorn function (PR #57 and PR #58) + +#### Closed issues + +* Issue #35 : remove import plot from ot/__init__.py (See PR #41) +* Issue #43 : Unusable parameter log for EMDTransport (See PR #44) +* Issue #55 : UnicodeDecodeError: 'ascii' while installing with pip + + ## 0.4 Community edition *15 Sep 2017* -- cgit v1.2.3 From 3bc0420b97616062f0a42f412db13545ec7fda3a Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 29 Aug 2018 14:00:57 +0200 Subject: aupdate release --- RELEASES.md | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/RELEASES.md b/RELEASES.md index 1da43d3..05c2edb 100644 --- a/RELEASES.md +++ b/RELEASES.md @@ -6,6 +6,12 @@ *This is a beta release and is still a work in progress* +#### TODO + +[] Remove deprecated OTDA Classes (PR #48) +[] Speedup Sinkhorn with einsum + bench (PR #58) +[] Stochastic ot (PR #62) + #### Features * Add non regularized Gromov-Wasserstein solver (PR #41) -- cgit v1.2.3 From f12153c0c1be6f6377ace0050201409ec1b7e829 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 29 Aug 2018 14:10:04 +0200 Subject: update documentation examples --- docs/cache_nbrun | 2 +- .../source/auto_examples/auto_examples_jupyter.zip | Bin 99990 -> 118510 bytes docs/source/auto_examples/auto_examples_python.zip | Bin 68178 -> 79057 bytes .../images/sphx_glr_plot_OT_1D_smooth_001.png | Bin 0 -> 21372 bytes .../images/sphx_glr_plot_OT_1D_smooth_002.png | Bin 0 -> 22051 bytes .../images/sphx_glr_plot_OT_1D_smooth_005.png | Bin 0 -> 17080 bytes 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69fb320..77a46aa 100644 --- a/docs/source/auto_examples/index.rst +++ b/docs/source/auto_examples/index.rst @@ -47,6 +47,46 @@ This is a gallery of all the POT example files. /auto_examples/plot_optim_OTreg +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_free_support_barycenter_thumb.png + + :ref:`sphx_glr_auto_examples_plot_free_support_barycenter.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_free_support_barycenter + +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_1D_smooth_thumb.png + + :ref:`sphx_glr_auto_examples_plot_OT_1D_smooth.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_OT_1D_smooth + .. raw:: html
@@ -147,6 +187,26 @@ This is a gallery of all the POT example files. /auto_examples/plot_WDA +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_stochastic_thumb.png + + :ref:`sphx_glr_auto_examples_plot_stochastic.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_stochastic + .. raw:: html
diff --git a/docs/source/auto_examples/plot_OT_1D_smooth.ipynb b/docs/source/auto_examples/plot_OT_1D_smooth.ipynb new file mode 100644 index 0000000..d523f6a --- /dev/null +++ b/docs/source/auto_examples/plot_OT_1D_smooth.ipynb @@ -0,0 +1,144 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# 1D smooth optimal transport\n\n\nThis example illustrates the computation of EMD, Sinkhorn and smooth OT plans\nand their visualization.\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot\nfrom ot.datasets import make_1D_gauss as gauss" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n-------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\nb = gauss(n, m=60, s=10)\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot distributions and loss matrix\n----------------------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\npl.plot(x, a, 'b', label='Source distribution')\npl.plot(x, b, 'r', label='Target distribution')\npl.legend()\n\n#%% plot distributions and loss matrix\n\npl.figure(2, figsize=(5, 5))\not.plot.plot1D_mat(a, b, M, 'Cost matrix M')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve EMD\n---------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% EMD\n\nG0 = ot.emd(a, b, M)\n\npl.figure(3, figsize=(5, 5))\not.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve Sinkhorn\n--------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% Sinkhorn\n\nlambd = 2e-3\nGs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')\n\npl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve Smooth OT\n--------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% Smooth OT with KL regularization\n\nlambd = 2e-3\nGsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='kl')\n\npl.figure(5, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT KL reg.')\n\npl.show()\n\n\n#%% Smooth OT with KL regularization\n\nlambd = 1e-1\nGsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='l2')\n\npl.figure(6, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT l2 reg.')\n\npl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_OT_1D_smooth.py b/docs/source/auto_examples/plot_OT_1D_smooth.py new file mode 100644 index 0000000..b690751 --- /dev/null +++ b/docs/source/auto_examples/plot_OT_1D_smooth.py @@ -0,0 +1,110 @@ +# -*- coding: utf-8 -*- +""" +=========================== +1D smooth optimal transport +=========================== + +This example illustrates the computation of EMD, Sinkhorn and smooth OT plans +and their visualization. + +""" + +# Author: Remi Flamary +# +# License: MIT License + +import numpy as np +import matplotlib.pylab as pl +import ot +import ot.plot +from ot.datasets import make_1D_gauss as gauss + +############################################################################## +# Generate data +# ------------- + + +#%% parameters + +n = 100 # nb bins + +# bin positions +x = np.arange(n, dtype=np.float64) + +# Gaussian distributions +a = gauss(n, m=20, s=5) # m= mean, s= std +b = gauss(n, m=60, s=10) + +# loss matrix +M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) +M /= M.max() + + +############################################################################## +# Plot distributions and loss matrix +# ---------------------------------- + +#%% plot the distributions + +pl.figure(1, figsize=(6.4, 3)) +pl.plot(x, a, 'b', label='Source distribution') +pl.plot(x, b, 'r', label='Target distribution') +pl.legend() + +#%% plot distributions and loss matrix + +pl.figure(2, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') + +############################################################################## +# Solve EMD +# --------- + + +#%% EMD + +G0 = ot.emd(a, b, M) + +pl.figure(3, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') + +############################################################################## +# Solve Sinkhorn +# -------------- + + +#%% Sinkhorn + +lambd = 2e-3 +Gs = ot.sinkhorn(a, b, M, lambd, verbose=True) + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn') + +pl.show() + +############################################################################## +# Solve Smooth OT +# -------------- + + +#%% Smooth OT with KL regularization + +lambd = 2e-3 +Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='kl') + +pl.figure(5, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT KL reg.') + +pl.show() + + +#%% Smooth OT with KL regularization + +lambd = 1e-1 +Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='l2') + +pl.figure(6, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT l2 reg.') + +pl.show() diff --git a/docs/source/auto_examples/plot_OT_1D_smooth.rst b/docs/source/auto_examples/plot_OT_1D_smooth.rst new file mode 100644 index 0000000..5a0ebd3 --- /dev/null +++ b/docs/source/auto_examples/plot_OT_1D_smooth.rst @@ -0,0 +1,242 @@ + + +.. _sphx_glr_auto_examples_plot_OT_1D_smooth.py: + + +=========================== +1D smooth optimal transport +=========================== + +This example illustrates the computation of EMD, Sinkhorn and smooth OT plans +and their visualization. + + + + +.. code-block:: python + + + # Author: Remi Flamary + # + # License: MIT License + + import numpy as np + import matplotlib.pylab as pl + import ot + import ot.plot + from ot.datasets import make_1D_gauss as gauss + + + + + + + +Generate data +------------- + + + +.. code-block:: python + + + + #%% parameters + + n = 100 # nb bins + + # bin positions + x = np.arange(n, dtype=np.float64) + + # Gaussian distributions + a = gauss(n, m=20, s=5) # m= mean, s= std + b = gauss(n, m=60, s=10) + + # loss matrix + M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) + M /= M.max() + + + + + + + + +Plot distributions and loss matrix +---------------------------------- + + + +.. code-block:: python + + + #%% plot the distributions + + pl.figure(1, figsize=(6.4, 3)) + pl.plot(x, a, 'b', label='Source distribution') + pl.plot(x, b, 'r', label='Target distribution') + pl.legend() + + #%% plot distributions and loss matrix + + pl.figure(2, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') + + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_001.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_002.png + :scale: 47 + + + + +Solve EMD +--------- + + + +.. code-block:: python + + + + #%% EMD + + G0 = ot.emd(a, b, M) + + pl.figure(3, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0') + + + + +.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_005.png + :align: center + + + + +Solve Sinkhorn +-------------- + + + +.. code-block:: python + + + + #%% Sinkhorn + + lambd = 2e-3 + Gs = ot.sinkhorn(a, b, M, lambd, verbose=True) + + pl.figure(4, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn') + + pl.show() + + + + +.. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_007.png + :align: center + + +.. rst-class:: sphx-glr-script-out + + Out:: + + It. |Err + ------------------- + 0|7.958844e-02| + 10|5.921715e-03| + 20|1.238266e-04| + 30|2.469780e-06| + 40|4.919966e-08| + 50|9.800197e-10| + + +Solve Smooth OT +-------------- + + + +.. code-block:: python + + + + #%% Smooth OT with KL regularization + + lambd = 2e-3 + Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='kl') + + pl.figure(5, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT KL reg.') + + pl.show() + + + #%% Smooth OT with KL regularization + + lambd = 1e-1 + Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='l2') + + pl.figure(6, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT l2 reg.') + + pl.show() + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_009.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_1D_smooth_010.png + :scale: 47 + + + + +**Total running time of the script:** ( 0 minutes 1.053 seconds) + + + +.. only :: html + + .. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_OT_1D_smooth.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_OT_1D_smooth.ipynb ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_free_support_barycenter.ipynb b/docs/source/auto_examples/plot_free_support_barycenter.ipynb new file mode 100644 index 0000000..05a81c8 --- /dev/null +++ b/docs/source/auto_examples/plot_free_support_barycenter.ipynb @@ -0,0 +1,108 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# 2D free support Wasserstein barycenters of distributions\n\n\nIllustration of 2D Wasserstein barycenters if discributions that are weighted\nsum of diracs.\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Vivien Seguy \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n -------------\n%% parameters and data generation\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "N = 3\nd = 2\nmeasures_locations = []\nmeasures_weights = []\n\nfor i in range(N):\n\n n_i = np.random.randint(low=1, high=20) # nb samples\n\n mu_i = np.random.normal(0., 4., (d,)) # Gaussian mean\n\n A_i = np.random.rand(d, d)\n cov_i = np.dot(A_i, A_i.transpose()) # Gaussian covariance matrix\n\n x_i = ot.datasets.make_2D_samples_gauss(n_i, mu_i, cov_i) # Dirac locations\n b_i = np.random.uniform(0., 1., (n_i,))\n b_i = b_i / np.sum(b_i) # Dirac weights\n\n measures_locations.append(x_i)\n measures_weights.append(b_i)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute free support barycenter\n-------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "k = 10 # number of Diracs of the barycenter\nX_init = np.random.normal(0., 1., (k, d)) # initial Dirac locations\nb = np.ones((k,)) / k # weights of the barycenter (it will not be optimized, only the locations are optimized)\n\nX = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot data\n---------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(1)\nfor (x_i, b_i) in zip(measures_locations, measures_weights):\n color = np.random.randint(low=1, high=10 * N)\n pl.scatter(x_i[:, 0], x_i[:, 1], s=b * 1000, label='input measure')\npl.scatter(X[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter')\npl.title('Data measures and their barycenter')\npl.legend(loc=0)\npl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_free_support_barycenter.py b/docs/source/auto_examples/plot_free_support_barycenter.py new file mode 100644 index 0000000..b6efc59 --- /dev/null +++ b/docs/source/auto_examples/plot_free_support_barycenter.py @@ -0,0 +1,69 @@ +# -*- coding: utf-8 -*- +""" +==================================================== +2D free support Wasserstein barycenters of distributions +==================================================== + +Illustration of 2D Wasserstein barycenters if discributions that are weighted +sum of diracs. + +""" + +# Author: Vivien Seguy +# +# License: MIT License + +import numpy as np +import matplotlib.pylab as pl +import ot + + +############################################################################## +# Generate data +# ------------- +#%% parameters and data generation +N = 3 +d = 2 +measures_locations = [] +measures_weights = [] + +for i in range(N): + + n_i = np.random.randint(low=1, high=20) # nb samples + + mu_i = np.random.normal(0., 4., (d,)) # Gaussian mean + + A_i = np.random.rand(d, d) + cov_i = np.dot(A_i, A_i.transpose()) # Gaussian covariance matrix + + x_i = ot.datasets.make_2D_samples_gauss(n_i, mu_i, cov_i) # Dirac locations + b_i = np.random.uniform(0., 1., (n_i,)) + b_i = b_i / np.sum(b_i) # Dirac weights + + measures_locations.append(x_i) + measures_weights.append(b_i) + + +############################################################################## +# Compute free support barycenter +# ------------- + +k = 10 # number of Diracs of the barycenter +X_init = np.random.normal(0., 1., (k, d)) # initial Dirac locations +b = np.ones((k,)) / k # weights of the barycenter (it will not be optimized, only the locations are optimized) + +X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, b) + + +############################################################################## +# Plot data +# --------- + +pl.figure(1) +for (x_i, b_i) in zip(measures_locations, measures_weights): + color = np.random.randint(low=1, high=10 * N) + pl.scatter(x_i[:, 0], x_i[:, 1], s=b * 1000, label='input measure') +pl.scatter(X[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter') +pl.title('Data measures and their barycenter') +pl.legend(loc=0) +pl.show() diff --git a/docs/source/auto_examples/plot_free_support_barycenter.rst b/docs/source/auto_examples/plot_free_support_barycenter.rst new file mode 100644 index 0000000..d1b3b80 --- /dev/null +++ b/docs/source/auto_examples/plot_free_support_barycenter.rst @@ -0,0 +1,140 @@ + + +.. _sphx_glr_auto_examples_plot_free_support_barycenter.py: + + +==================================================== +2D free support Wasserstein barycenters of distributions +==================================================== + +Illustration of 2D Wasserstein barycenters if discributions that are weighted +sum of diracs. + + + + +.. code-block:: python + + + # Author: Vivien Seguy + # + # License: MIT License + + import numpy as np + import matplotlib.pylab as pl + import ot + + + + + + + + +Generate data + ------------- +%% parameters and data generation + + + +.. code-block:: python + + N = 3 + d = 2 + measures_locations = [] + measures_weights = [] + + for i in range(N): + + n_i = np.random.randint(low=1, high=20) # nb samples + + mu_i = np.random.normal(0., 4., (d,)) # Gaussian mean + + A_i = np.random.rand(d, d) + cov_i = np.dot(A_i, A_i.transpose()) # Gaussian covariance matrix + + x_i = ot.datasets.make_2D_samples_gauss(n_i, mu_i, cov_i) # Dirac locations + b_i = np.random.uniform(0., 1., (n_i,)) + b_i = b_i / np.sum(b_i) # Dirac weights + + measures_locations.append(x_i) + measures_weights.append(b_i) + + + + + + + + +Compute free support barycenter +------------- + + + +.. code-block:: python + + + k = 10 # number of Diracs of the barycenter + X_init = np.random.normal(0., 1., (k, d)) # initial Dirac locations + b = np.ones((k,)) / k # weights of the barycenter (it will not be optimized, only the locations are optimized) + + X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, b) + + + + + + + + +Plot data +--------- + + + +.. code-block:: python + + + pl.figure(1) + for (x_i, b_i) in zip(measures_locations, measures_weights): + color = np.random.randint(low=1, high=10 * N) + pl.scatter(x_i[:, 0], x_i[:, 1], s=b * 1000, label='input measure') + pl.scatter(X[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter') + pl.title('Data measures and their barycenter') + pl.legend(loc=0) + pl.show() + + + +.. image:: /auto_examples/images/sphx_glr_plot_free_support_barycenter_001.png + :align: center + + + + +**Total running time of the script:** ( 0 minutes 0.129 seconds) + + + +.. only :: html + + .. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_free_support_barycenter.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_free_support_barycenter.ipynb ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_stochastic.ipynb b/docs/source/auto_examples/plot_stochastic.ipynb new file mode 100644 index 0000000..c6f0013 --- /dev/null +++ b/docs/source/auto_examples/plot_stochastic.ipynb @@ -0,0 +1,331 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Stochastic examples\n\n\nThis example is designed to show how to use the stochatic optimization\nalgorithms for descrete and semicontinous measures from the POT library.\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Kilian Fatras \n#\n# License: MIT License\n\nimport matplotlib.pylab as pl\nimport numpy as np\nimport ot\nimport ot.plot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM\n############################################################################\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(\"------------SEMI-DUAL PROBLEM------------\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "DISCRETE CASE\nSample two discrete measures for the discrete case\n---------------------------------------------\n\nDefine 2 discrete measures a and b, the points where are defined the source\nand the target measures and finally the cost matrix c.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n_source = 7\nn_target = 4\nreg = 1\nnumItermax = 1000\n\na = ot.utils.unif(n_source)\nb = ot.utils.unif(n_target)\n\nrng = np.random.RandomState(0)\nX_source = rng.randn(n_source, 2)\nY_target = rng.randn(n_target, 2)\nM = ot.dist(X_source, Y_target)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Call the \"SAG\" method to find the transportation matrix in the discrete case\n---------------------------------------------\n\nDefine the method \"SAG\", call ot.solve_semi_dual_entropic and plot the\nresults.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "method = \"SAG\"\nsag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method,\n numItermax)\nprint(sag_pi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "SEMICONTINOUS CASE\nSample one general measure a, one discrete measures b for the semicontinous\ncase\n---------------------------------------------\n\nDefine one general measure a, one discrete measures b, the points where\nare defined the source and the target measures and finally the cost matrix c.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n_source = 7\nn_target = 4\nreg = 1\nnumItermax = 1000\nlog = True\n\na = ot.utils.unif(n_source)\nb = ot.utils.unif(n_target)\n\nrng = np.random.RandomState(0)\nX_source = rng.randn(n_source, 2)\nY_target = rng.randn(n_target, 2)\nM = ot.dist(X_source, Y_target)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Call the \"ASGD\" method to find the transportation matrix in the semicontinous\ncase\n---------------------------------------------\n\nDefine the method \"ASGD\", call ot.solve_semi_dual_entropic and plot the\nresults.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "method = \"ASGD\"\nasgd_pi, log_asgd = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method,\n numItermax, log=log)\nprint(log_asgd['alpha'], log_asgd['beta'])\nprint(asgd_pi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare the results with the Sinkhorn algorithm\n---------------------------------------------\n\nCall the Sinkhorn algorithm from POT\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sinkhorn_pi = ot.sinkhorn(a, b, M, reg)\nprint(sinkhorn_pi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "PLOT TRANSPORTATION MATRIX\n#############################################################################\n\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot SAG results\n----------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, sag_pi, 'semi-dual : OT matrix SAG')\npl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot ASGD results\n-----------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, asgd_pi, 'semi-dual : OT matrix ASGD')\npl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot Sinkhorn results\n---------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn')\npl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM\n############################################################################\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "print(\"------------DUAL PROBLEM------------\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "SEMICONTINOUS CASE\nSample one general measure a, one discrete measures b for the semicontinous\ncase\n---------------------------------------------\n\nDefine one general measure a, one discrete measures b, the points where\nare defined the source and the target measures and finally the cost matrix c.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n_source = 7\nn_target = 4\nreg = 1\nnumItermax = 100000\nlr = 0.1\nbatch_size = 3\nlog = True\n\na = ot.utils.unif(n_source)\nb = ot.utils.unif(n_target)\n\nrng = np.random.RandomState(0)\nX_source = rng.randn(n_source, 2)\nY_target = rng.randn(n_target, 2)\nM = ot.dist(X_source, Y_target)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Call the \"SGD\" dual method to find the transportation matrix in the\nsemicontinous case\n---------------------------------------------\n\nCall ot.solve_dual_entropic and plot the results.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sgd_dual_pi, log_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg,\n batch_size, numItermax,\n lr, log=log)\nprint(log_sgd['alpha'], log_sgd['beta'])\nprint(sgd_dual_pi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare the results with the Sinkhorn algorithm\n---------------------------------------------\n\nCall the Sinkhorn algorithm from POT\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "sinkhorn_pi = ot.sinkhorn(a, b, M, reg)\nprint(sinkhorn_pi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot SGD results\n-----------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, sgd_dual_pi, 'dual : OT matrix SGD')\npl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot Sinkhorn results\n---------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn')\npl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_stochastic.py b/docs/source/auto_examples/plot_stochastic.py new file mode 100644 index 0000000..b9375d4 --- /dev/null +++ b/docs/source/auto_examples/plot_stochastic.py @@ -0,0 +1,207 @@ +""" +========================== +Stochastic examples +========================== + +This example is designed to show how to use the stochatic optimization +algorithms for descrete and semicontinous measures from the POT library. + +""" + +# Author: Kilian Fatras +# +# License: MIT License + +import matplotlib.pylab as pl +import numpy as np +import ot +import ot.plot + + +############################################################################# +# COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM +############################################################################# +print("------------SEMI-DUAL PROBLEM------------") +############################################################################# +# DISCRETE CASE +# Sample two discrete measures for the discrete case +# --------------------------------------------- +# +# Define 2 discrete measures a and b, the points where are defined the source +# and the target measures and finally the cost matrix c. + +n_source = 7 +n_target = 4 +reg = 1 +numItermax = 1000 + +a = ot.utils.unif(n_source) +b = ot.utils.unif(n_target) + +rng = np.random.RandomState(0) +X_source = rng.randn(n_source, 2) +Y_target = rng.randn(n_target, 2) +M = ot.dist(X_source, Y_target) + +############################################################################# +# +# Call the "SAG" method to find the transportation matrix in the discrete case +# --------------------------------------------- +# +# Define the method "SAG", call ot.solve_semi_dual_entropic and plot the +# results. + +method = "SAG" +sag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, + numItermax) +print(sag_pi) + +############################################################################# +# SEMICONTINOUS CASE +# Sample one general measure a, one discrete measures b for the semicontinous +# case +# --------------------------------------------- +# +# Define one general measure a, one discrete measures b, the points where +# are defined the source and the target measures and finally the cost matrix c. + +n_source = 7 +n_target = 4 +reg = 1 +numItermax = 1000 +log = True + +a = ot.utils.unif(n_source) +b = ot.utils.unif(n_target) + +rng = np.random.RandomState(0) +X_source = rng.randn(n_source, 2) +Y_target = rng.randn(n_target, 2) +M = ot.dist(X_source, Y_target) + +############################################################################# +# +# Call the "ASGD" method to find the transportation matrix in the semicontinous +# case +# --------------------------------------------- +# +# Define the method "ASGD", call ot.solve_semi_dual_entropic and plot the +# results. + +method = "ASGD" +asgd_pi, log_asgd = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, + numItermax, log=log) +print(log_asgd['alpha'], log_asgd['beta']) +print(asgd_pi) + +############################################################################# +# +# Compare the results with the Sinkhorn algorithm +# --------------------------------------------- +# +# Call the Sinkhorn algorithm from POT + +sinkhorn_pi = ot.sinkhorn(a, b, M, reg) +print(sinkhorn_pi) + + +############################################################################## +# PLOT TRANSPORTATION MATRIX +############################################################################## + +############################################################################## +# Plot SAG results +# ---------------- + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, sag_pi, 'semi-dual : OT matrix SAG') +pl.show() + + +############################################################################## +# Plot ASGD results +# ----------------- + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, asgd_pi, 'semi-dual : OT matrix ASGD') +pl.show() + + +############################################################################## +# Plot Sinkhorn results +# --------------------- + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn') +pl.show() + + +############################################################################# +# COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM +############################################################################# +print("------------DUAL PROBLEM------------") +############################################################################# +# SEMICONTINOUS CASE +# Sample one general measure a, one discrete measures b for the semicontinous +# case +# --------------------------------------------- +# +# Define one general measure a, one discrete measures b, the points where +# are defined the source and the target measures and finally the cost matrix c. + +n_source = 7 +n_target = 4 +reg = 1 +numItermax = 100000 +lr = 0.1 +batch_size = 3 +log = True + +a = ot.utils.unif(n_source) +b = ot.utils.unif(n_target) + +rng = np.random.RandomState(0) +X_source = rng.randn(n_source, 2) +Y_target = rng.randn(n_target, 2) +M = ot.dist(X_source, Y_target) + +############################################################################# +# +# Call the "SGD" dual method to find the transportation matrix in the +# semicontinous case +# --------------------------------------------- +# +# Call ot.solve_dual_entropic and plot the results. + +sgd_dual_pi, log_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg, + batch_size, numItermax, + lr, log=log) +print(log_sgd['alpha'], log_sgd['beta']) +print(sgd_dual_pi) + +############################################################################# +# +# Compare the results with the Sinkhorn algorithm +# --------------------------------------------- +# +# Call the Sinkhorn algorithm from POT + +sinkhorn_pi = ot.sinkhorn(a, b, M, reg) +print(sinkhorn_pi) + +############################################################################## +# Plot SGD results +# ----------------- + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, sgd_dual_pi, 'dual : OT matrix SGD') +pl.show() + + +############################################################################## +# Plot Sinkhorn results +# --------------------- + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn') +pl.show() diff --git a/docs/source/auto_examples/plot_stochastic.rst b/docs/source/auto_examples/plot_stochastic.rst new file mode 100644 index 0000000..a49bc05 --- /dev/null +++ b/docs/source/auto_examples/plot_stochastic.rst @@ -0,0 +1,475 @@ + + +.. _sphx_glr_auto_examples_plot_stochastic.py: + + +========================== +Stochastic examples +========================== + +This example is designed to show how to use the stochatic optimization +algorithms for descrete and semicontinous measures from the POT library. + + + + +.. code-block:: python + + + # Author: Kilian Fatras + # + # License: MIT License + + import matplotlib.pylab as pl + import numpy as np + import ot + import ot.plot + + + + + + + + +COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM +############################################################################ + + + +.. code-block:: python + + print("------------SEMI-DUAL PROBLEM------------") + + + + +.. rst-class:: sphx-glr-script-out + + Out:: + + ------------SEMI-DUAL PROBLEM------------ + + +DISCRETE CASE +Sample two discrete measures for the discrete case +--------------------------------------------- + +Define 2 discrete measures a and b, the points where are defined the source +and the target measures and finally the cost matrix c. + + + +.. code-block:: python + + + n_source = 7 + n_target = 4 + reg = 1 + numItermax = 1000 + + a = ot.utils.unif(n_source) + b = ot.utils.unif(n_target) + + rng = np.random.RandomState(0) + X_source = rng.randn(n_source, 2) + Y_target = rng.randn(n_target, 2) + M = ot.dist(X_source, Y_target) + + + + + + + +Call the "SAG" method to find the transportation matrix in the discrete case +--------------------------------------------- + +Define the method "SAG", call ot.solve_semi_dual_entropic and plot the +results. + + + +.. code-block:: python + + + method = "SAG" + sag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, + numItermax) + print(sag_pi) + + + + + +.. rst-class:: sphx-glr-script-out + + Out:: + + [[2.55553509e-02 9.96395660e-02 1.76579142e-02 4.31178196e-06] + [1.21640234e-01 1.25357448e-02 1.30225078e-03 7.37891338e-03] + [3.56123975e-03 7.61451746e-02 6.31505947e-02 1.33831456e-07] + [2.61515202e-02 3.34246014e-02 8.28734709e-02 4.07550428e-04] + [9.85500870e-03 7.52288517e-04 1.08262628e-02 1.21423583e-01] + [2.16904253e-02 9.03825797e-04 1.87178503e-03 1.18391107e-01] + [4.15462212e-02 2.65987989e-02 7.23177216e-02 2.39440107e-03]] + + +SEMICONTINOUS CASE +Sample one general measure a, one discrete measures b for the semicontinous +case +--------------------------------------------- + +Define one general measure a, one discrete measures b, the points where +are defined the source and the target measures and finally the cost matrix c. + + + +.. code-block:: python + + + n_source = 7 + n_target = 4 + reg = 1 + numItermax = 1000 + log = True + + a = ot.utils.unif(n_source) + b = ot.utils.unif(n_target) + + rng = np.random.RandomState(0) + X_source = rng.randn(n_source, 2) + Y_target = rng.randn(n_target, 2) + M = ot.dist(X_source, Y_target) + + + + + + + +Call the "ASGD" method to find the transportation matrix in the semicontinous +case +--------------------------------------------- + +Define the method "ASGD", call ot.solve_semi_dual_entropic and plot the +results. + + + +.. code-block:: python + + + method = "ASGD" + asgd_pi, log_asgd = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, + numItermax, log=log) + print(log_asgd['alpha'], log_asgd['beta']) + print(asgd_pi) + + + + + +.. rst-class:: sphx-glr-script-out + + Out:: + + [3.9018759 7.63059124 3.93260224 2.67274989 1.43888443 3.26904884 + 2.78748299] [-2.48511647 -2.43621119 -0.93585194 5.8571796 ] + [[2.56614773e-02 9.96758169e-02 1.75151781e-02 4.67049862e-06] + [1.21201047e-01 1.24433535e-02 1.28173754e-03 7.93100436e-03] + [3.58778167e-03 7.64232233e-02 6.28459924e-02 1.45441936e-07] + [2.63551754e-02 3.35577920e-02 8.25011211e-02 4.43054320e-04] + [9.24518246e-03 7.03074064e-04 1.00325744e-02 1.22876312e-01] + [2.03656325e-02 8.45420425e-04 1.73604569e-03 1.19910044e-01] + [4.17781688e-02 2.66463708e-02 7.18353075e-02 2.59729583e-03]] + + +Compare the results with the Sinkhorn algorithm +--------------------------------------------- + +Call the Sinkhorn algorithm from POT + + + +.. code-block:: python + + + sinkhorn_pi = ot.sinkhorn(a, b, M, reg) + print(sinkhorn_pi) + + + + + + +.. rst-class:: sphx-glr-script-out + + Out:: + + [[2.55535622e-02 9.96413843e-02 1.76578860e-02 4.31043335e-06] + [1.21640742e-01 1.25369034e-02 1.30234529e-03 7.37715259e-03] + [3.56096458e-03 7.61460101e-02 6.31500344e-02 1.33788624e-07] + [2.61499607e-02 3.34255577e-02 8.28741973e-02 4.07427179e-04] + [9.85698720e-03 7.52505948e-04 1.08291770e-02 1.21418473e-01] + [2.16947591e-02 9.04086158e-04 1.87228707e-03 1.18386011e-01] + [4.15442692e-02 2.65998963e-02 7.23192701e-02 2.39370724e-03]] + + +PLOT TRANSPORTATION MATRIX +############################################################################# + + +Plot SAG results +---------------- + + + +.. code-block:: python + + + pl.figure(4, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, sag_pi, 'semi-dual : OT matrix SAG') + pl.show() + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_stochastic_004.png + :align: center + + + + +Plot ASGD results +----------------- + + + +.. code-block:: python + + + pl.figure(4, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, asgd_pi, 'semi-dual : OT matrix ASGD') + pl.show() + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_stochastic_005.png + :align: center + + + + +Plot Sinkhorn results +--------------------- + + + +.. code-block:: python + + + pl.figure(4, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn') + pl.show() + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_stochastic_006.png + :align: center + + + + +COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM +############################################################################ + + + +.. code-block:: python + + print("------------DUAL PROBLEM------------") + + + + +.. rst-class:: sphx-glr-script-out + + Out:: + + ------------DUAL PROBLEM------------ + + +SEMICONTINOUS CASE +Sample one general measure a, one discrete measures b for the semicontinous +case +--------------------------------------------- + +Define one general measure a, one discrete measures b, the points where +are defined the source and the target measures and finally the cost matrix c. + + + +.. code-block:: python + + + n_source = 7 + n_target = 4 + reg = 1 + numItermax = 100000 + lr = 0.1 + batch_size = 3 + log = True + + a = ot.utils.unif(n_source) + b = ot.utils.unif(n_target) + + rng = np.random.RandomState(0) + X_source = rng.randn(n_source, 2) + Y_target = rng.randn(n_target, 2) + M = ot.dist(X_source, Y_target) + + + + + + + +Call the "SGD" dual method to find the transportation matrix in the +semicontinous case +--------------------------------------------- + +Call ot.solve_dual_entropic and plot the results. + + + +.. code-block:: python + + + sgd_dual_pi, log_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg, + batch_size, numItermax, + lr, log=log) + print(log_sgd['alpha'], log_sgd['beta']) + print(sgd_dual_pi) + + + + + +.. rst-class:: sphx-glr-script-out + + Out:: + + [ 1.29325617 5.0435082 1.30996326 0.05538236 -1.08113283 0.73711558 + 0.18086364] [0.08840343 0.17710082 1.68604226 8.37377551] + [[2.47763879e-02 1.00144623e-01 1.77492330e-02 4.25988443e-06] + [1.19568278e-01 1.27740478e-02 1.32714202e-03 7.39121816e-03] + [3.41581121e-03 7.57137404e-02 6.27992039e-02 1.30808430e-07] + [2.52245323e-02 3.34219732e-02 8.28754229e-02 4.00582912e-04] + [9.75329554e-03 7.71824343e-04 1.11085400e-02 1.22456628e-01] + [2.12304276e-02 9.17096580e-04 1.89946234e-03 1.18084973e-01] + [4.04179693e-02 2.68253041e-02 7.29410047e-02 2.37369404e-03]] + + +Compare the results with the Sinkhorn algorithm +--------------------------------------------- + +Call the Sinkhorn algorithm from POT + + + +.. code-block:: python + + + sinkhorn_pi = ot.sinkhorn(a, b, M, reg) + print(sinkhorn_pi) + + + + + +.. rst-class:: sphx-glr-script-out + + Out:: + + [[2.55535622e-02 9.96413843e-02 1.76578860e-02 4.31043335e-06] + [1.21640742e-01 1.25369034e-02 1.30234529e-03 7.37715259e-03] + [3.56096458e-03 7.61460101e-02 6.31500344e-02 1.33788624e-07] + [2.61499607e-02 3.34255577e-02 8.28741973e-02 4.07427179e-04] + [9.85698720e-03 7.52505948e-04 1.08291770e-02 1.21418473e-01] + [2.16947591e-02 9.04086158e-04 1.87228707e-03 1.18386011e-01] + [4.15442692e-02 2.65998963e-02 7.23192701e-02 2.39370724e-03]] + + +Plot SGD results +----------------- + + + +.. code-block:: python + + + pl.figure(4, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, sgd_dual_pi, 'dual : OT matrix SGD') + pl.show() + + + + + +.. image:: /auto_examples/images/sphx_glr_plot_stochastic_007.png + :align: center + + + + +Plot Sinkhorn results +--------------------- + + + +.. code-block:: python + + + pl.figure(4, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn') + pl.show() + + + +.. image:: /auto_examples/images/sphx_glr_plot_stochastic_008.png + :align: center + + + + +**Total running time of the script:** ( 0 minutes 22.857 seconds) + + + +.. only :: html + + .. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_stochastic.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_stochastic.ipynb ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ -- cgit v1.2.3 From 1ec01f082a34c1086ef2df94b78a5d640a66db07 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 29 Aug 2018 14:10:40 +0200 Subject: add notebooks from new examples --- notebooks/plot_OT_1D_smooth.ipynb | 302 +++++++++++++ notebooks/plot_free_support_barycenter.ipynb | 169 ++++++++ notebooks/plot_stochastic.ipynb | 610 +++++++++++++++++++++++++++ 3 files changed, 1081 insertions(+) create mode 100644 notebooks/plot_OT_1D_smooth.ipynb create mode 100644 notebooks/plot_free_support_barycenter.ipynb create mode 100644 notebooks/plot_stochastic.ipynb diff --git a/notebooks/plot_OT_1D_smooth.ipynb b/notebooks/plot_OT_1D_smooth.ipynb new file mode 100644 index 0000000..69e71da --- /dev/null +++ b/notebooks/plot_OT_1D_smooth.ipynb @@ -0,0 +1,302 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# 1D smooth optimal transport\n", + "\n", + "\n", + "This example illustrates the computation of EMD, Sinkhorn and smooth OT plans\n", + "and their visualization.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Remi Flamary \n", + "#\n", + "# License: MIT License\n", + "\n", + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot\n", + "import ot.plot\n", + "from ot.datasets import make_1D_gauss as gauss" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + "-------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% parameters\n", + "\n", + "n = 100 # nb bins\n", + "\n", + "# bin positions\n", + "x = np.arange(n, dtype=np.float64)\n", + "\n", + "# Gaussian distributions\n", + "a = gauss(n, m=20, s=5) # m= mean, s= std\n", + "b = gauss(n, m=60, s=10)\n", + "\n", + "# loss matrix\n", + "M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\n", + "M /= M.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot distributions and loss matrix\n", + "----------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% plot the distributions\n", + "\n", + "pl.figure(1, figsize=(6.4, 3))\n", + "pl.plot(x, a, 'b', label='Source distribution')\n", + "pl.plot(x, b, 'r', label='Target distribution')\n", + "pl.legend()\n", + "\n", + "#%% plot distributions and loss matrix\n", + "\n", + "pl.figure(2, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, M, 'Cost matrix M')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve EMD\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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ed1847DBf7n7lytgV1omFHJwCTrZUVFQUiouLY5cRT1mZX0jx5ps+muHFF+HLL7174cc/9uFQ/frFrlI2t2yZz6I2dizMm+d9xocdBoMG+W2/ftC6daO9vZlNCyEU1fv5CmCBHAjgjRu9dbRsGXzxhQ/0X7QI5s/34J0xA9at82O7doWjjvK+xmOOadQfYEmREOCdd+CZZ+Bvf/Pln8C7iPbc00eo7LEHFBb6lYrduvnCqB07+r9vPbuSFMCSEkWdO4fiwYMb/4229v+tYn/Vx0PYcisv99uyssqttNS3TZtgwwbf1q/3bc0a+Ppr/7o6PXrAXntB797eWurf339g1beb2Vas8Hk5pk/3E6Zz5vgv240btzy2oADatvWtVSvYbjsfAteihXd5NGvmx+TnV2477QR33KEAltQoat48FDfVUulbC7eK/VUfN/v2lpfnt1V/GPLz/YekWbPKH5zttvOtbVvvRmjf3rfOnaFLFw/eHj38WMkN5eX+188nn/htSYn/VbRypf+S/vpr/yto/frKX+QbN/ov9tLSyl/45eW+uvWLLzY4gDUXhLi+fSGbuyBE8vK866Fbt9iV/ItGQYiIRKIAFhGJRH3AAoCZfQ3MiV1HDToBy2MXUYNMqBEyo85MqHGvEELb+j5ZfcBSYU5DTiY0BTMrVo2pkQl1ZkqNDXm+uiBERCJRAIuIRKIAlgpjYhdQC6oxdTKhzqyvUSfhREQiUQtYRCQSBbCISCQK4BxnZsea2Rwzm29mV8eup4KZ9TSzV81slpnNNLNLkv0dzewlM5uX3HZIg1rzzWy6mU1M7u9qZlOTz/QJM2seub72ZvaUmX1oZrPN7KB0+xzNbGTy7zzDzB43s5bp8Dma2QNmVmJmM6rsq/azM/c/Sb3vm1mN85cqgHOYmeUDdwM/APYBTjOzdFnWtxS4PISwDzAAuCCp7WpgUghhD2BScj+2S4DZVe7fAtwZQugFrATOjlJVpbuAF0IIewP74bWmzedoZt2Bi4GiEEJvIB8YSnp8jn8Ejt1s39Y+ux8AeyTbCODeGl89hKAtRzfgIODFKvevAa6JXddWap0AfB+/Wq9bsq8bfgFJzLp6JD+ERwITAcOv3iqo7jOOUN/2wEKSE+5V9qfN5wh0Bz4FOuIXh00EjkmXzxEoBGbU9NkB9wGnVXfc1ja1gHNbxX/8CouTfWnFzAqBA4CpQJcQwufJQ0uBLpHKqjAKuBIoT+7vAHwVQihN7sf+THcFlgEPJt0k95tZa9LocwwhLAFuBz4BPgdWAdNIr8+xqq19dnX+eVIAS1ozszbA08ClIYTVVR8L3syINo7SzE4ASkII02LVUAsFQD/g3hDCAcBaNutuSIPPsQMwGP9lsRPQmi3/7E9LDf3sFMC5bQnQs8r9Hsm+tGBmzfDwHRdC+HOy+wsz65Y83g0oiVUfcDDw72b2MfAnvBviLqC9mVXMsxL7M10MLA4hTE3uP4UHcjp9jkcBC0MIy0IIm4A/459tOn2OVW3ts6vzz5MCOLe9A+yRnG1ujp/4+EvkmgA/owyMBWaHEO6o8tBfgGHJ18PwvuEoQgjXhBB6hBAK8c/ulRDC6cCrwMnJYbFrXAp8amZ7JbsGAbNIo88R73oYYGatkn/3ihrT5nPczNY+u78AZyajIQYAq6p0VVQvVse7tvTYgOOAucBHwHWx66lS1yH4n3bvA+8m23F4H+skYB7wMtAxdq1JvYcDE5OvdwPeBuYD44EWkWvbHyhOPstngQ7p9jkCvwI+BGYAjwAt0uFzBB7H+6U34X9NnL21zw4/AXt38rP0AT6qY5uv36BLkc3sWPxPrnzg/hDCzfV+MRGRHFPvAE7GkM7FhwYtxv+cPS2EMCt15YmIZK+GTMj+XWB+CGEBgJn9CT+TudUA7tSpUygsLGzAW0pDffWVL/7as+e390+bNm15CKFzxf3v5/1IszSJVPFS+fitLOddfw0J4OrGvPXf/CAzG4FfFcLOO+9MsVbejerKK2H06C0XQDazRXEqEsldjT4KIoQwJoRQFEIo6ty5c81PkEa1aRM0axa7ChGBhgVwWo8hlept3AjNo04NIyIVGhLAaTuGVLZu3Tpo3Tp2FSICDegDDiGUmtmFwIv4MLQHQggzU1aZNIrVq6FNm9hViAg0cFn6EMJzwHMpqkWawIoVsMMOsasQEdClyDlnyRLo2jV2FSICCuCcUlYGH38Mu+8eu5KIzHwTSQMK4BwyYwaUlkLv3rErERFoYB+wZJa//91vDzssbh1RVVx6X9EKtqQNEsq//bhIE1ALOIc8+ijss8+WlyGLSBxqAeeIKVPgnXf8MmShsqUbyvw2Lx8Aa+4/EmFTshJOeVlTVyY5RC3gHLBxI5x3HnTpAmeeGbsaEamgFnCWCwGuuw7efReefRbatYtdUZpKWrphg99agf9o2HZ+2WDYuMlvN22MUJxkK7WAs1gIPvvZ7bd7C3jw4NgViUhVagFnqVWr4OKL4eGH4YIL4H/+J3ZFmSWUln7rNq9lS7/d3i8jDOvWA1C+bl2E6iRbqAWchZ591kc7PPooXH+9n3jL07+0SNpRCziLvPUW/Pa38Ne/Qt++HsTf+U7sqrJD+Tff+BfJbX7SmV6wi4/pC6vXAFC2cmXTFycZS+2iDFdWBs88AwcfDAMHwj/+ATfd5CteKHxF0ptawBlq9mx44gkYNw7mz4fCQu/n/a//0nSTTaFs9Wr/IrnNT1Z7yeu7t98u+wqA0s+XNn1xkjEUwBlk7lwP3Sef9HkdzPyy4htvhB/+EAr0rymSUfQjm8bWrIE33oCXX4aXXoIPPvDQPeQQP7E2ZAh06xa7SgEoW7bMv0hu8wp39v1H9AOg+WJvEZfNW9D0xUnaUgCnkU2bYOpUD9xJk/zy4dJSX8Pt4INh1Cg4+WTo3j12pSKSCgrgiEpKPHArtrfegrVrvZV74IFw+eVw1FEevtttF7taqYvSjz8BID+5Zd+9APh66AAA2s37GoAwTat45TIFcBPZsMEvB54yxcN2yhRYuNAfy8+HPn1g2DAYNAgOPxw6doxarog0AQVwI1izBt5/3wN3+nS/ff99nxQHvAthwAC/PLh/f2/taqXi7FY2cw4AbZMGb/nA/QD4YuRAADq9twGAglemNX1xEo0CuIG++OLbQTt9OsybVznbYceOcMABcMklHrb9+0OPHnFrFpH0oACupfXrYdYsH4lQdVtaZZhnYaGH7emnw/77+9c9emgJMtmSTX4PgK6T/f6G4/yqmUW3HQRAt7d8hY5Wf57a9MVJk1EAb6asDBYs2DJo58+H8mTVmpYtfa6FY46pDNr99oP27ePWLiKZJacDuKRky6CdORMqJrgy8xWE+/SBoUP9tk8f6NXLT5yJpEqL594BYPfn/P7qH/toiYV/6gvA9v/nJwk6PvBW0xcnjSYnAnjdOg/WzcO2pKTymM6dPVyHD68M2n331ckxEWk8WRfAX37pJ8L++c/K27lzK0+KtWzpy7Iff3xl0Pbp48v1iKSLdo9NSW79fsn5Plqi9es+58RHz+wBQNc7Jzd9cZIyGRvAIcBnn20Ztp98UnlMz57Qr9+3uw92313dByKSHjImgMvLvRvh9dd9foTXX4fPP698fM894aCDfPWHAw7wrVOnePWKpNKO93hLd+09fr/sam8BnzDT5x++90/HA9DzN2oRZ5K0DeBNm2DatMqwffNNqJjrunt3v1pswABv4e63H7RtG7VcEZE6S6sADsEnFH/4YZ9ysWLK1T339OkWDz3Up18sLNTYWslt3W/2lu7EmzsAUPYbP8lxy0IfN3zKE5cCsOvVGjWRzmoMYDPrCTwMdAECMCaEcJeZdQSeAAqBj4FTQgj1Wo9lwQJ45BEP3gULfOTBkCFw4ok+9WLXrvV5VRGR9FabFnApcHkI4Z9m1haYZmYvAWcBk0IIN5vZ1cDVwFV1LeB3v4MrrvAW7aBBcMMN3trV8C+R2iv8hbd0r/pFfwDKb/P9Ty/20RT7Jy3i3S+f0vTFyVbVGMAhhM+Bz5Ovvzaz2UB3YDBweHLYQ8Br1DGAx4zx8B0yBO6800ctiIjkijr1AZtZIXAAMBXokoQzwFK8i6LWFi6Ec8+FoiJ47DGfdFxEUmP3n3mLeMjP/Iq6cKfvf/Gzd/3xP50LQK/L1CKOqdarIptZG+Bp4NIQwuqqj4UQAt4/XN3zRphZsZkVL6tYtgWfpOaII3z87nPP1a94EZFMVqsANrNmePiOCyH8Odn9hZl1Sx7vBpRU99wQwpgQQlEIoahzsnIsQLNm8Oyz3gIeMgSOO84XnPzmmwZ9PyJSjV4jp9Br5BSO2Wl/jtlpfyyABe8jfnrxFD667SA+SmZik6ZTYwCbmQFjgdkhhDuqPPQXYFjy9TBgQl3fvG1beOEFuOoqn5th6FAf8TBihI/7rZh9TEQkG1kI1fYcVB5gdgjwBvABUBGJ1+L9wE8COwOL8GFoX27rtYqKikJxcXG1j5WVwWuvwUMPwdNP+wQ6HTr4MLTDDvMxwP36ectZUs/MpoUQiirufz/vR9v+jyFZZeHN3vp98tRRAJz6qI+aqBhdIfBS+fiUX31Qm1EQ/wC29saDUlVIfr4PQxs0CO65ByZMgFdf9Svh/vpXP6ZVK7/cuOKCjO9+V8PVRCRz1dgCTqVttYC3ZelSv0KuYh6I997zq+bMYK+9fN6Hfv0q54DQgpZ1pxawVPXJ9T772vmn/g2A+x7xuSYqrsDLRVFawOmga1c4+WTfAL76CiZPhnfe8VEU//gHPP545fG77FIZyP36+aoVO+2ky5dFJL1kRABvrn17HzVx3HGV+5Yv9zCu2P75Tx9lUdHA79ChckrKvn39tndvTeIjUp2df53MNfFrn2sif6Tvr5iP+OPHewHQ+V71ETdERnRB1NfXX3t3xXvv+SiL99+HGTN8f4XCwm9PzN6nj0/+k2sn+9QFIXXx5U/8pN2qo9cCsMNfWgGVE8lno5ztgqivtm19FMUhh1TuCwEWLdpyeaLnn4fSUj+meXPYe+8tg1krHItIKmV1C7guNmyAOXO8lVw1mBcvrjymfXvvtti8K6Ndu3h1p4pawNIQa4f4JEBLB/ilBT1e9dZMxWKj2UAt4EbUooUHat++396/cqV3W1QN5XHjKucqBl8luWJ5+orbrl3VWhaRbVMLuB5CgE8/9dbyu+/6Nn26z2VcYccdKwN5//3hwAM9qNM1lNUCllQqPfJAAJbv14IuU72f2Ca/F7OkBlMLOE2Ywc47+3bCCZX7V63yUJ4+vTKU77jDl1cCH5/cv79vAwb4hSQdOsT5HkQkPgVwCm2/vV+ld+ihlfs2boRZs6C4GKZOhSlTfP6Lij889tzTw7h/fxg40LtA8mo9R51Ieip4ZRoAXV8BO3BfAFYP9akx23/gC+eUzZwTp7g0ogBuZM2bV3ZD/PSnvm/1ag/kKVM8lF94wZdjAl/J+Ygj4Kij/LLs3XZL324LEWkYBXAE7drBkUf6BpVD415/HSZN8m38eH+ssLByjoxjj1WXhWSeMG0mAG2nJTv22A2AsiP6AdBigc8TXrro0yavLTYFcBow86AtLIQzz/RAnjOnMoyffhrGjvWLQ44+Gk45BQYP9i4PEclcCuA0ZOYXguy9N1xwgU/V+c47HsRPPgl/+5t3bRx7rIfxkCHQsmXsqkVqp2yeDxfKn5fs6L4TAHl99wbAPl/hx1VZQSdb6XRPBsjP9xN1t90GH38Mb73lwTxtGpxxhk8+9JvfwIoVsSsVkbpQAGcYMw/jO+6ATz6Bl1/2McbXX++rSl94ofcni2SK0iWfUbrkM8rf/5Dy9z/0OQFKSynYpScFu/Qkr21b8rJ01iwFcAbLy/OTc889V7mk05gxsM8+MGqUd12ISPpSAGeJ3r3hgQdg3jw4/HAYOdLHFc+cGbsykbopW7mSspUrKV30qY+MKC+H8nLyO+1AfqcdyGvZkrwsOemhAM4yu+wCEyfCY4/5pdEDB/oCpyKSfhTAWcgMTjvNJ6Xv2tWHrk2aFLsqkfopX7uW8rVrKVu+grLlKwghEEIgr3Vr8lq3xgoKsILMHNClAM5iPXv6xR277ebLOS1ZErsiEalKAZz3yuHQAAAGyklEQVTlunSBZ57x+Y6HD6+cg0IkU4UNGwgbNvyrZVzBWrTAWrTwPwEz5Pp9BXAO6NULbrrJV/149dXY1YhIBQVwjjj3XJ8O8557YlciklqhtNS3pGX8L3n5vqUxBXCOaNkShg2DCROgyl9tIhKRAjiHHH20X2Q0JXsXrhXxEx0hQHmZbxV9wmnYN6wAziEH+UrivP123DpExGXm4Dmpl+2391ERH30UuxKRJpTGQ3/UAs4xhYU+iY+IxKcAzjGdOmnaSpF0oQDOMR06wMqVsasQEahDAJtZvplNN7OJyf1dzWyqmc03syfMrHnjlSmp0qoVrF8fuwoRgbq1gC8BZle5fwtwZwihF7ASODuVhUnjaNlSASySLmoVwGbWAzgeuD+5b8CRwFPJIQ8BJzVGgZJazZrBpk2xqxARqH0LeBRwJVCe3N8B+CqEUJrcXwx0r+6JZjbCzIrNrHhZDiyyl+4KCvxiDBGJr8YANrMTgJIQwrT6vEEIYUwIoSiEUNS5c+f6vISkUF6eLzAgIvHV5kKMg4F/N7PjgJZAO+AuoL2ZFSSt4B6AZpvNAApgkfRRYws4hHBNCKFHCKEQGAq8EkI4HXgVODk5bBgwodGqlJQxS+sLg0RySkPGAV8FXGZm8/E+4bGpKUkakwJYJH3UaS6IEMJrwGvJ1wuA76a+JGlMaTYZlEhO05VwIiKRKIBzjFrAIulDASwiEokCWEQkEgWwiEgkCmARkUgUwCIikSiARUQiUQCLiESiABYRiUQBLCISiQJYRCQSBbCISCQKYBGRSBTAIiKRKIBFRCJRAIuIRKIAFhGJRAEsIhKJAlhEJBIFsIhIJApgEZFIFMAiIpEogEVEIlEAi4hEogAWEYlEASwiEokCWEQkEgWwiEgkCmARkUgUwCIikdQqgM2svZk9ZWYfmtlsMzvIzDqa2UtmNi+57dDYxYqIZJPatoDvAl4IIewN7AfMBq4GJoUQ9gAmJfdFRKSWagxgM9seOAwYCxBC2BhC+AoYDDyUHPYQcFJjFSkiko1q0wLeFVgGPGhm083sfjNrDXQJIXyeHLMU6FLdk81shJkVm1nxsmXLUlO1iEgWqE0AFwD9gHtDCAcAa9msuyGEEIBQ3ZNDCGNCCEUhhKLOnTs3tF4RkaxRmwBeDCwOIUxN7j+FB/IXZtYNILktaZwSRUSyU40BHEJYCnxqZnsluwYBs4C/AMOSfcOACY1SoYhIliqo5XEXAePMrDmwAPgvPLyfNLOzgUXAKY1ToohIdqpVAIcQ3gWKqnloUGrLERHJHboSTkQkEgWwiEgkCmARkUgUwCIikSiARUQiUQCLiESiABYRiUQBLCISiQJYRCQSBbCISCQKYBGRSBTAIiKRKIBFRCJRAIuIRKIAFhGJRAEsIhKJAlhEJBIFsIhIJApgEZFIFMAiIpEogEVEIlEAi4hEogAWEYlEASwiEokCWEQkEgWwiEgkCmARkUgUwCIikSiARUQiUQCLiERSqwA2s5FmNtPMZpjZ42bW0sx2NbOpZjbfzJ4ws+aNXayISDapMYDNrDtwMVAUQugN5ANDgVuAO0MIvYCVwNmNWaiISLapbRdEAbCdmRUArYDPgSOBp5LHHwJOSn15IiLZq8YADiEsAW4HPsGDdxUwDfgqhFCaHLYY6F7d881shJkVm1nxsmXLUlO1iEgWqE0XRAdgMLArsBPQGji2tm8QQhgTQigKIRR17ty53oWKiGSb2nRBHAUsDCEsCyFsAv4MHAy0T7okAHoASxqpRhGRrFSbAP4EGGBmrczMgEHALOBV4OTkmGHAhMYpUUQkO9WmD3gqfrLtn8AHyXPGAFcBl5nZfGAHYGwj1ikiknUKaj4EQgi/BH652e4FwHdTXpGISI7QlXAiIpEogEVEIlEAi4hEogAWEYlEASwiEokCWEQkEgWwiEgkCmARkUgUwCIikSiARUQiUQCLiESiABYRiUQBLCISiQJYRCQSBbCISCQKYBGRSBTAIiKRKIBFRCJRAIuIRKIAFhGJRAEsIhKJAlhEJBIFsIhIJApgEZFIFMAiIpEogEVEIlEAi4hEogAWEYlEASwiEokCWEQkEgWwiEgkCmARkUgUwDlmwAC45JLYVYgIgIUQmu7NzJYBi5rsDaUudgkhdI5dhEguadIAFhGRSuqCEBGJRAEsIhKJAlhEJBIFsIhIJApgEZFIFMAiIpEogEVEIlEAi4hEogAWEYlEASwiEokCWEQkEgWwiEgkCmARkUgUwCIikSiARUQiUQCLiESiABYRiUQBLCISiQJYRCQSBbCISCQKYBGRSBTAIiKR/D+iA6AGU5wC6AAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% EMD\n", + "\n", + "G0 = ot.emd(a, b, M)\n", + "\n", + "pl.figure(3, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, G0, 'OT matrix G0')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve Sinkhorn\n", + "--------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It. |Err \n", + "-------------------\n", + " 0|7.958844e-02|\n", + " 10|5.921715e-03|\n", + " 20|1.238266e-04|\n", + " 30|2.469780e-06|\n", + " 40|4.919966e-08|\n", + " 50|9.800197e-10|\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% Sinkhorn\n", + "\n", + "lambd = 2e-3\n", + "Gs = ot.sinkhorn(a, b, M, lambd, verbose=True)\n", + "\n", + "pl.figure(4, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, Gs, 'OT matrix Sinkhorn')\n", + "\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve Smooth OT\n", + "--------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% Smooth OT with KL regularization\n", + "\n", + "lambd = 2e-3\n", + "Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='kl')\n", + "\n", + "pl.figure(5, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT KL reg.')\n", + "\n", + "pl.show()\n", + "\n", + "\n", + "#%% Smooth OT with KL regularization\n", + "\n", + "lambd = 1e-1\n", + "Gsm = ot.smooth.smooth_ot_dual(a, b, M, lambd, reg_type='l2')\n", + "\n", + "pl.figure(6, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, Gsm, 'OT matrix Smooth OT l2 reg.')\n", + "\n", + "pl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_free_support_barycenter.ipynb b/notebooks/plot_free_support_barycenter.ipynb new file mode 100644 index 0000000..b8df589 --- /dev/null +++ b/notebooks/plot_free_support_barycenter.ipynb @@ -0,0 +1,169 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# 2D free support Wasserstein barycenters of distributions\n", + "\n", + "\n", + "Illustration of 2D Wasserstein barycenters if discributions that are weighted\n", + "sum of diracs.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Vivien Seguy \n", + "#\n", + "# License: MIT License\n", + "\n", + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + " -------------\n", + "%% parameters and data generation\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "N = 3\n", + "d = 2\n", + "measures_locations = []\n", + "measures_weights = []\n", + "\n", + "for i in range(N):\n", + "\n", + " n_i = np.random.randint(low=1, high=20) # nb samples\n", + "\n", + " mu_i = np.random.normal(0., 4., (d,)) # Gaussian mean\n", + "\n", + " A_i = np.random.rand(d, d)\n", + " cov_i = np.dot(A_i, A_i.transpose()) # Gaussian covariance matrix\n", + "\n", + " x_i = ot.datasets.make_2D_samples_gauss(n_i, mu_i, cov_i) # Dirac locations\n", + " b_i = np.random.uniform(0., 1., (n_i,))\n", + " b_i = b_i / np.sum(b_i) # Dirac weights\n", + "\n", + " measures_locations.append(x_i)\n", + " measures_weights.append(b_i)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute free support barycenter\n", + "-------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "k = 10 # number of Diracs of the barycenter\n", + "X_init = np.random.normal(0., 1., (k, d)) # initial Dirac locations\n", + "b = np.ones((k,)) / k # weights of the barycenter (it will not be optimized, only the locations are optimized)\n", + "\n", + "X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, b)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot data\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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cB/OKQh5e2Z8T3ct1KVUj7N0AOfsq/jxnIWyomtnhmtij6Medh8k+FjxZGgyfbdgTlRgapCZid4b2T6NRnaSoxKBUjZKzByyVGMUuyoXV0yMfjw+a2KNoQ9YRXK7gB6dzC538tOtwVGJo3SCVFifUCtouNcnKyNNbRSUGpWqU5HrlL1QdqvxDkY3FD03sUSQi5Sp3+mOJ4pSou89rT0qCNcC2oW5yAud0bBK1GJSqMZr1AFsly+7WqZrJCZrYo+i0VieElNdTk6z0blM/anGcn96UGwe2IyXBWm5KbbLNQv3URGaOPwObTnVUKjiL1V2CNyH4N+FSEmtD7+ujE1MZ+pccRZ2a1aV1g9Sg7RKtVs4+tXFUY7ll8Cm8c/3pDOnUhOQEC1YRTqybzB3ntueLuwaGFKdSyuOMm6DDnyqQ3MVdk739+VENq3hreoJSdG38/SiXTV1GXpHveeTJCRZeHd2Ls04pffGRAruTvCIndZJtetKQUrHIGPjpXfjwJiBYHhW4+QdoeEpYm9QTlGJEp2Z1eX9CX26f9SM7D+ZjjMEAVotQPzWRf13Wlb7t/rhi+bdb9vPCV1v4IfMQVs8Y/QVdmnLToHac3LhO9b0QpVRpIoC4h2ZcjsBtE1Jgz9qwE3uoNLFXgU7N6rLwjgGszzrC6h2HcLkMnZun0av1Ce4DrB7Pf7GFl77+tfgsUW9Vl4/W/M6C9Xt4dXQvzjyloc9tKKWq0L5NsHY2bJwXPKmD+8SkQ9ujH5eHJvYqlN48jfTmaT6XfbM5u1RSL8lpDPl2J+PfWsniiYN0vrlS1SXvIMwaBVmrwVlUukZMQAbE/8y0SNPB2xjx3BdbAtZzAXchMT3tX6lqYs+HaefDrh/AkV+BpO7R+NToxOWDJvYYkFPoYM3O4CcoFTpcvL9qVxVEpJQqZ827cGSHu6deYQL1qu4EwHCvefpvEflZRNaKyFwRqRepwI4nOQUObNbQTlDKLQxhPE8pFXnLnqt8ES+LDeq3i2w8gTYX5vM/B9KNMV2BzcCk8EM6/tSrlYAzhNIDAA1q65WOlKpyxsDhMA5+tu4Ltqr72w0rsRtjFhpjvF3I74AW4Yd0/ElOsDL41MZBL7RSK9HKmL5tqiQmpVRZlS37IfCnJyIaSTCRHGO/BvjU30IRGS8iK0VkZXZ2dgQ3WzPcMvgUkgJcTUmAlEQrI7qHfxUWpVQFiUDT7pV5IpzUHxp3jHhIgQRN7CKySETW+/gZXqLNA4ADeMffeowxrxhjehljejVq1Mhfs+NW52ZpPH/laaQkWEkoM96ekmClfmois2/oQ2qSzlBVqlqceTskVKD0hjUJ6reFy9+MWkj+BM0SxphzAi0XkbHAMGCwqY76BDXIuZ2a8MVdA5ixfDsfrskir9BBw9pJjOnbhktOa06d5ITqDlGp49epF0LbWfDrF74viedlTYLEVDj9BuhzEyRV/RnjYdWKEZHzgaeBAcaYkMdXjqdaMUqpGuTAVnipj/8pj9Yk6DQcLp7qLjUQYaHWigl3jP0FoA7wuYisEZGpYa5PKaVi15ePBS4h4CyETR/BgV+rLiYfwhqwNcacHKlAlFIqpuUfgl/mB796kssB3/0HLpxSNXH5oGeeKqVUKA5sA2sIc9FdDvh9dfTjCUATu1JKBeNyuWvE2HNDa5+bDYXHohtTAJrYlVIqkJxsmNoXvvwbuEIs/HVsL0w9C3L3Rzc2PzSxK6WUP047vDkU9m+FohB76wDGAUd2wXv/F73YAtDErpRS/vwyH45mgcte8ee67LB3PexZF/m4gtDErpRS/ix/qWI99bIcRbDu/cjFEyJN7Eop5c/hMC9sY5yQsy8ysVSAJnallPInITm851sSIK3qC/fFTUWpAruTRZv2svNgPikJFgad2pjWDSpQkEcppSqq8yWw7Hn3GaWVYbFCtysjG1MIYj6xG2N4dck2nl20BcSd4G0WC//49Gd6tKrHc1f2oHGdMP+rKqWUL72vheUvVu651iRocxY0qLorJ3nF/FDMUws3M+XzLeQWOcktdOJ0ua/9WehwsTLzEBc+/y0HcytzDUKllAqibjMY8R8qnCoTUqFJZ7j8jaiEFUxMJ/btB3J5dck28u2+TwpwuAwHc4p4+vNfqjgypdRxI/1iaNIp9PYntIOLnoNrF1ZLyV6I8cT+5rJMXEHKCttdhjmrssgvCvGMMKVUUNOnT2fWrFnVHUbsaDcotDox1iS4dgF0uQys1Xf9hJhO7N9tO4DdGbxevNUC2/bnVEFEStV8OTk53HLLLUyYMIGCggAXlDie9L4OJFi6FGg7EGo3roKAAovpxB76NUAqe5FZpVRZzz//PE6nE7vdzssvv1zd4cSGE9pAz3GQUMt/m8RUGPJYlYUUSEwn9t5t6mOzBE/aDpeLkxrq1EelwpWTk8M//vEP8vLyyM3N5eGHH9Zeu9eQxyFjvHu4xZbyx+OJtaFucxj3KTQ8pfriKyGmE/u4fm2wBknsNoswontzaiXG/MxNpWKSw/HHFYG8vXUv7bWXYLHAuY/AXT/D4IfgtNFw+l/girfhjg3QtGt1R1gsrGueVlZFrnn66Mcbeff7HT5nxlgtwgm1Eph/21k6l12pSnj66ad5//33Wbp0Kbm5uTRr1oxjx0rXEa9Xrx67d+8mOVn/xqpblVzzVEQeFZG1nuudLhSRZuGsz5cHL+jIhAFtSUmwUivRfXFYm0VITrCQ3qwu/7vlTE3qSlVCbm4ujzzyCKtXr+brr78u11v30l57/Amrxy4idY0xRz23bwU6GWMmBHteRXrsXrmFDuav283Og3mkJNoY3LEx7ZtUzxxRpWqCJ554gr/97W/k5eXRrVs3tm3bVq637qW99tgQao893ItZHy1xNxWI2rhOapKNy3u1jNbqlTqu5Obm8vjjj5OXlwfAxo0bsVj8f4H39tpvu+22qgpRhSHsg6ci8piI7ARGAg+FH5JSKtpeeOGFUgdN7XY7hYX+C13pDJn4EnQoRkQWASf6WPSAMWZeiXaTgGRjzF/9rGc8MB6gVatWPbdv317poJVSlec9SHr06NFyy1JTU7HZfH+RP3bsGG+++SZXX311tENUfkRsKMYYc06I23wHmA/4TOzGmFeAV8A9xh7iOpVSEVa2t15S8+bNee211xDxPc24e/fu0QxNRUhYY+wicooxZovn7nDg5/BDUkpF0v79+3niiSf417/+RV5eXqmx9bKysrJwOBwMGjSoiqNUkRTuWT3/FJEOgAvYDgSdEaOUqlqPP/44U6ZMYcCAAWzYsMFvbx3cwzR33303K1eu9NtrP+447bDpI1j6HBzYAmKF1n2h763u3zG4n2L+BCWlVOVlZ2fTunVr8vPzOeWUU9izZ4/fKY1eycnJzJ8/X3vtAIXHYPqFkL0Z7CUvai2QkAJd/wzDnqmy5F4l0x2VUrHtH//4B97O265du7BYLNSuXTvo85YsWaKJHWD2WNi70cel8QzY82DtbDjhJDjz9uqIzi9N7ErVUNnZ2UydOrV4imJ+fj4dOnRg06ZNOswSiv1bYPu3ga93as+Db5+GPjdVa/31smK6CJhSqvJK9ta9srKy+OSTT6opojiz5l1w+T8eUczlgm2Lox9PBWhiV6oG+OWXX1i2bFnx/bK9da+cnBzuvvvucglf+XBkV2iJ3bggd1/046kATexK1QCjRo1ixIgRFBW5L+zuq7fupb32ENVuREgp0mKB5HpRD6ciNLErFee+/vprNm3aRF5eHm+++abf3rqX9tpD1OXPkJAUvJ3L6b4kXgzRxK5UnJs4cSK5ubnk5uby4IMP8ve//z1o0tZeewiadYdGp4IlwBwTWwr0vhYSA1wyrxroPHal4tj//vc/rrjiCvLz8wF3rZeCggKSk5P91nwBKCgooFu3bnz//fdVFWp8OrYXXj8HcvaBo8w3oIRa7hOUrpxZZTNidB67UseBsWPHFid1cJ85mpaWxvTp00lICJxsmjWL+HVxap46TWDCUvjhdfjuJcg7ABho2ME9d73L5WCxVneU5WhiVypOzZw5k4MHD5Z73OFwsHfvXsaPH18NUdVAyXXhrDvcP/Z8d0kBW2J1RxWQDsUoFacaNGjgM7EDNGrUiF27dpGYGNsJSFVMlVzzVClVPfz11r28M2TU8alG9tgdThdZh/NxugzN6qWQnBB7Y2BKhSNQb91Le+01z3F58DSvyMHUxb8yY9l2ipwuBHAZuOS05tw2+BQa19UL8ar4N2fOnIBJ3Xvt0oMHD/L2229zzTXXVFVoKkbUmMSeU+jg0peWkXkgl0KHq9SyWT/s5NP1e5h3Uz9a1o+t+aZKeb399tts27aNhx4KfOngd999F4vFgsvlKrcsJSWF++67r3iqY48ePaISq4ptNSax3//BOn7bn0ORs/zQksNlOJxXxLg3f+DzO/prZTsVcwoKCrj11lvJy8tj/PjxnHiir8sMw/bt2/n00099JnVw99YbNGjATTfdFM1wVYyrEQdPD+UWsWDDHp9J3ctlIOtQPj/uPFyFkSkVmldeeYWioiKMMTz66KN+202ePBm73e53eW5uLg899BCFhQFKzaoar0Yk9m+2ZJNgCd4LL7A7+XTd7iqISKnQFRQU8PDDD5Obm0tRURHTpk1jz5495dpt376dd955J+Cl7QCOHj3Ka6+9Fq1wVRyoEUMx+UVOXCFM7jHAsYIQynAqVYW8vXUvl8vFo48+yosvvliqnTGGq666CqfTGXSdelbp8S0i0x1F5C7gSaCRMWZ/sPaRnu74zeZs/vLOKnILA3/gk2wWbj+nPX8Z2C5i21YqHAUFBTRr1oxDhw6Vejw5OZnffvvN71i7Oj5V2QlKItISOA/YEe66KqtvuwYkWIO/FANcelrz6AekjhvGGD799NNKl8At21v38vbalaqMSIyxTwHuwZ03q4XNamHieR1ICXAiUnKChYt7NNe57CqiPvvsM4YOHcrChQsr/NySY+tlBRprV1XIGHAUun/HkbASu4gMB7KMMT+F0Ha8iKwUkZXZ2dnhbNankWe0ZsKAtiTZLNhKHEi1CKQkWBl0amP+PiI94ttVxy9jDHfddRdApS5c4a+37qW99mq0axXMvAoebQiPnQiPN4OP74SDv1V3ZCEJOsYuIosAXwN9DwD3A+cZY46ISCbQqzrG2Ev6bX8ubyz9jW+37MdpDOnN07j+rLZ0a5Gm89dVRC1YsIDLLruM3NxcUlNTmTNnDkOGDAnpuf7G1svSsfZqsOJV+Hwy2AsoNRBhSQBrIoz8L7TpVy2hhTrGXumDpyLSBfgCyPM81AL4HcgwxgT8/qjVHVW8M8aQnp7Oxo0bix9LT09n7dq1IXUg5syZw+WXX05qamrAdvn5+Tz++OPcc889YcesQrB9Obx9sbs8rz+JteHWNZ5rolatqNeKMcasAxqX2GAmIfbYlYp3n332Gdu3by/12G+//cbChQtD6rWPGDGCzZs3h7Stli1bVipGVQmLnwic1AFcDlg9HfrfXTUxVULEqjvGylCMUtHmq7fuVZFeu4ox9nz4Rwt34g6mXmu4fW30YyqjyuuxG2PaaG9dHQ989da9vL12FYcKjwW+cHVJBUeiG0uYakRJAaWqincmjK8piuCu1VKZGTIqBiTVBRP8rF4AajWIbixh0sSuVAUE6q17aa89TiUkwynnAUGG0RJqQe9rqySkytLErlQF3H333X576165ublMnDixiiJSEdV/ojvBB2JNhO4jqyaeSqoRRcCUqipXXHEFu3e7K4QeOnSI//73v6WKclmtVoYPH64XuIhXzXrA8JfgwxvdB1FdJUok25LBlgRjPoKUetUXYwg0sStVAZMnTy6+ffHFF5db7nK5yM7O5sEHH6zKsFQkpV8CTbvB8hdhwwdgz4OU+tDrWug1DlIbVneEQdXIi1krFW0///wzPXr0oKCgoNyyWrVq8eWXX3L66adXQ2SqJqvy6Y5KHU8mTZrk90pG+fn5OsauqpUmdqUq6Oeff2bBggV+L3hhjGHVqlV8//33VRyZUm6a2JWqoEmTJgWsygiQl5envXZVbfTgqVIVJCJ07NgxaLukpKQqiEap8jSxK1VBH3zwQXWHoFRAOhSjlFIHf7g5AAAeoElEQVQ1jCZ2pcLgcrmqOwSlytHErlQluVwuevbsyezZs6s7FKVK0cSuVCXNmzePDRs2cOedd/qd+qhUddDErlQluFwuJk6ciN1u58iRI8ycObO6Q1KqmCZ2pSph3rx57N27F4CcnBzuvfde7bWrmKGJXakK8vbWc3Jyih/TXruKJWEldhF5WESyRGSN52dopAJTKlaV7K17aa9dxZJI9NinGGO6e37mR2B9SsUsX711L+21q1ihQzFK+fHuu++W65n76q17aa9dxYpIJPabRWStiEwTkRP8NRKR8SKyUkRWZmdnR2CzSkXPtm3buPrqq5k0aVLxY4F6617aa1exIGhiF5FFIrLex89w4D9AO6A7sBt4yt96jDGvGGN6GWN6NWrUKGIvQKlomDx5MhaLhffee4+dO3cC7t6697J4/mivXcWCoEXAjDHnhLIiEXkV+DjsiJSqZtu2beODDz7A4XAgIvz1r39l2rRpLFy4EKfTGbRq4+HDh/n1119p3759FUWsVGlhXRpPRJoaY3Z7bt8BnG6M+b9gz9NL46lYNnLkSGbPno3D4QAgOTmZzZs307Jly2qOTB3vqurSeP8SkXUishYYBNwR5vqUqlYle+teTqeTv/71r9UYlVIVE1ZiN8ZcbYzpYozpaoy5yNt7VypeTZ48uVRSB7Db7aXG2pWKdTrdUSkPX711L+21q3iiiV0pD1+9dS/ttat4ooldKQL31r20167ihSZ2pYDHHnsMh8NBUlKS3x8RYcaMGX7PPFUqVujFrJUCxowZQ48ePYK2s9ls1KlTpwoiUqryNLErBfTv35/+/ftXdxhKRUTMJHa73c6uXbsoKCio7lCUIjk5mRYtWpCQkFDdoShVYTGT2Hft2kWdOnVo06YNIlLd4ajjmDGGAwcOsGvXLk466aTqDkepCouZg6cFBQU0aNBAk7qqdiJCgwYN9Nujilsxk9gBTeoqZuhnUcWzmBmKqYjtB3J5dck2Pvzxd3ILHaQm2RjRoxnXn9WW1g1Sqzs8pZSqVjHVYw/FV7/s4/xnljBzxU5yCh0YIKfQwcwVOzn/mSV89cu+Sq+7b9++kQvUIzMzk3fffTfi61VKKX/iKrFvP5DLjW+vJt/uxOEqXW7Y4TLk253c+PZqth/IrdT6ly1bFokwS4m3xB7ozEulVHyIq8T+6pJt2J2ugG3sThevLfmtUuuvXbs2AF9//TUDBw7ksssu49RTT2XkyJF469a3adOGe+65hy5dupCRkcHWrVsBGDt2LO+//365dd13330sWbKE7t27M2XKlFLb+/rrrxkwYADDhw+nbdu23HfffbzzzjtkZGTQpUsXfv31VwCys7O59NJL6d27N71792bp0qUArFixgj59+tCjRw/69u3LL7/8AsCGDRvIyMige/fudO3alS1btpCZmUl6enrxtp988kkefvhhAAYOHMjtt99Or169ePbZZ/1uTykVH+JqjP3DH38v11Mvy+EyzP0xi0dHpAdsF8yPP/7Ihg0baNasGf369WPp0qWceeaZAKSlpbFu3TpmzJjB7bffzscf+79w1D//+U+efPJJv21++uknNm3aRP369Wnbti3XXXcdK1as4Nlnn+X555/nmWee4bbbbuOOO+7gzDPPZMeOHQwZMoRNmzZx6qmnsmTJEmw2G4sWLeL+++9nzpw5TJ06ldtuu42RI0dSVFSE0+kMehp8UVER3oufXHXVVT63p5SKD3GV2HMLQxsmyC0KfzghIyODFi1aANC9e3cyMzOLE/uVV15Z/PuOO8K7tkjv3r1p2rQpAO3ateO8884DoEuXLnz11VcALFq0iI0bNxY/5+jRo+Tk5HDkyBHGjBnDli1bEBHsdjsAffr04bHHHmPXrl1ccsklnHLKKUHjuOKKK4pv+9ue91uIUiq2xVViT02ykRNCck9NDP9llbyupdVqLTX2XHIqnPe2zWbD5XIPE7lcLoqKiiq8HYvFUnzfYrEUb9PlcvHdd9+RnJxc6rk333wzgwYNYu7cuWRmZjJw4EDA3eM+/fTT+eSTTxg6dCgvv/wy7du3L44PKDdHOzX1j9lE/ranlIoPcTXGPqJHM2yWwPOLbRbh4h7NoxrHrFmzin/36dMHcI+9r1q1CoCPPvqouPdcp04djh07Ftb2zjvvPJ5//vni+2vWrAHgyJEjNG/ufq1vvvlm8fJt27bRtm1bbr31VoYPH87atWtp0qQJ+/bt48CBAxQWFgYcPvK3PaVUfAg7sYvILSLys4hsEJF/RSIof64/qy0J1sAhJ1gtXHdWdE8DP3ToEF27duXZZ58tPiB6/fXXs3jxYrp168by5cuLe8Bdu3bFarXSrVu3cgdPQ/Xcc8+xcuVKunbtSqdOnZg6dSoA99xzD5MmTaJHjx6lvlHMnj2b9PR0unfvzvr16xk9ejQJCQk89NBDZGRkcO6553LqqadWeHtKqfgg3tkelXqyyCDgAeACY0yhiDQ2xgSdSN6rVy/jPVDntWnTJjp27Bh0m1/9so8b316N3ekqdSDVZhESrBZeGnUagzo0rvBrCVWbNm1YuXIlDRs2jNo2VGwI9TOpVFURkVXGmF7B2oXbY/8L8E9jTCFAKEk9XIM6NGbB7WdxZUYraifZEIHaSTauzGjFgtvPimpSV0qpeBDuUcb2wFki8hhQANxtjPnBV0MRGQ+MB2jVqlVYG23dIJVHR6SHPaWxMjIzM6t8m0opVRFBE7uILAJO9LHoAc/z6wNnAL2B2SLS1vgY3zHGvAK8Au6hmHCCVkop5V/QxG6MOcffMhH5C/CBJ5GvEBEX0BDIjlyISimlKiLcMfYPgUEAItIeSAT2hxuUUkqpygt3jH0aME1E1gNFwBhfwzARd3AbLHsB1s6GohxIrA1d/wx9b4b6baO+eaWUimVh9diNMUXGmFHGmHRjzGnGmC8jFZhfWz6H//SD1TOg6Bhg3L9Xz3A/vuXzSq9ay/YqpWqCuDrzlIPbYPZosOeBy156mcvufnz2aHe7StCyvVq2V6maIL4S+7IXwGkP3MZph+UvVmr1WrZXy/YqVRPEVREw1s4u31Mvy2WHtbPggqfC2pSW7dWyvUrFq/hK7EU5kW0XgJbt1bK9SsWr+ErsibU9B0xDaBcmLdurZXuVilfxNcbe9c9gSQjcxpIAXa8I3CZMWrZXKRXL4iux970ZrEESuzUB+twU1TC0bK9SKpaFVba3ssIp28uWz91TGp320gdSLQnupP7nGXDKuRGO+A9atvf4oWV7a5bfjvzGB1s+YOexnaQlpTH0pKFknJhRamg11oVatje+xtjBnbT/stQ9pXHtrBJnnl7h7qnrmadKqRLyHflMXDyR73Z/h9PlxGHc324X/LaABikNmHrOVFrVDa/ibKyJv8QO7uR9wVNhT2msDC3bq1T8cBkX4xaM4+eDP+M0zlLL8hx5FBwrYNT8UXww/AMaptScb+HxNcaulFIh2p2zm+EfDmfDgQ3lkrqXCxfH7Md4bd1rVRxddGliV0rVOPvy9nHFx1eQeTQzaFuHy8HcLXOxBzurPY7E51CMUiouGWNYvW810zdMZ93+dQB0a9SNsZ3H0q1Rt4gdyHx65dMcLToacnuXcXGg4AAnpvq6plD8icvEvvPoTqZvnM7H2z4mz55HrYRaDGs7jDGdxtCybsvqDk8p5YPD5WDSkkks3rWYAkcBBveMvC93fMmy35dxTqtz+PuZf8ciFnYe3ck7P7/Dkl1LcBon7U9oz+hOo+nZpGfQ5H+s6BiLdizyO/zii8u4sFniMh36FHevZMmuJdy5+E4cTkfx0e1cey5zNs9h3q/zeHrA05zV4qxKrbtv374Rr/CYmZnJsmXLuOqqqyK6XqVinTGGn7J/4q2Nb7H50GYOFR4ipzAHJ6UTrsGQ78jn8+2f06hWI9IS03jpp5dKzWD5Ped3vtv9HT0b9+SZs58hyZrka5MAbD28lQRLAoXOwpBjrZ9SnwbJDSr3QmNQXI2x7zy6kzsX30mBo6D4DfdyGAcFjgLuXHwnO4/urNT6tWyvlu1VkVHkLOLmL29m/Ofj+Xz752QezeRI4ZFySb2kAmcBMzbM4D8//YdCZ2Gpv3Fv8v9h7w/cv+T+iMaabE1mXOdxcTWfPZi4SuzTN07H4QyceBxOBzM2zqjU+rVsr5btVZVT5Czik22fMG7BOEZ8OIJz/nsOy39fTr4jv3jIJRQO46DAWeB3eaGzkK93fc3OY/47b+3qtcMerAqshxUr6Q3TubzD5SHHGA/iaijm420fl+upl+UwDj7e9jEPnPFAWNvSsr1atleF5tfDv3LtZ9eS78gnz5EX9e25jIu5W+Zy62m3+lxeN7Eug1sN5rPMz4KOsw8/eTgPnvEgCcFqUMWZsBK7iMwCOnju1gMOG2O6hx2VH3n20D40ufbcsLelZXu1bK8K7kD+AcZ8OoajRUcr1DMPh8PlYMexHQHb3NnzTpb/vpwjRUdwGVe55YmWRMZ0HuP3n0O8CyuxG2OKs4GIPAUcCTuiAGol1AopaacmpAZtE4yW7dWyvSq4d39+t8LDLZFQJ7FOwOVNUpvw3rD3uOvru9h6eCtO48TpcpJiSwHgxu43MrrT6KoItVpEZIxd3Nntz8B7kVifP8PaDsMmgf8X2cTGsLbDohmGlu1VymPWL7MocoXWiQnGJjYsEjwl1bLV4vw25wdt17x2c2YOm8l7F7zHrT1u5cbuN/JQn4dYfMVixnQeU6MOlpYVqYOnZwF7jTFb/DUQkfEislJEVmZnZ1dqI2M6jcFmDZLYrbao/yfWsr0qHhhjcLpCn8tdUQ6Xg6OFoZ8EFEiSNYlWdVtxUbuLAk5lFIQTkk8g48SMkNd9ygmnMC59HBO6TeCCtheQbKv530SDlu0VkUWAr9OxHjDGzPO0+Q+w1RgTUlWucMr2+prHDu7/9jarLax57KHQsr3Hj3gs21voKOTVda8yf9t8duXswmBoXKsxV3e8mss7XB6RYUovYww93upRoROByrKIhXpJ9RjVcRQjO47EIhbGLRjHlsNbys1Dt4qV1IRU3hn6Dm3S2oQZfXyKWNleY8w5QTZkAy4BeoYeXuWd1eIsPrjwA2ZsnMHH2z4m155LakIqw9oOY3Sn0XrmqTouOVwOXlzzIm+sf6Ncot2Xt4/nf3yemb/M5O2hb0esiqGIcHrT01n2e+XO/7CJjbeGvkWnBp1KDcFM/9N03tr4FjM2ziDPnodFLLiMi2Fth3FDtxtqzGn/0RT2hTZE5HxgkjFmQKjPCetCG0pVkXj5TDpdTm796laWZS0LOB3YJjban9CeWRfOiti2f9jzAzcuujHg3HNfEiwJ9GvWj+cHP++3jcu42J27G6fLSeNajY+LIZRgqvJCG/9HlA+aKnU8ybPn8eHWD5mxcQZ7cvdgtVjp3aQ316RfQ0bT8mPL83+bzw97fgjpHI/fjv7G+v3rSW+YHrBtqHqf2JurO13NWxvfCjm5p9hSaFevHU/0fyJgO4tYaF67eSTCPO6EndiNMWMjEIdSCsjOy+bqT6/mQP6B4kTpdDpZ+vtSVu9bzSUnX8K9GfeWmtHx+rrXyXfkh7T+Qmch87bOi1hiB7j1tFvpUL8DL615iaycLBIsCdhddtrUbcOAlgNYsXsFGw9sxGA4Ke0kxqWPY0ibITXupKBYEldnnipVkxljGP/5ePbm7vXZ+8535DNn6xza1mvLnzv8ufixUGqOe7mMi/35+yMVcrEhbYYwpM0Qdh3bxZHCI9RPrk/T2u4T7+gR8c2pIOI6se/evZszzzyTpUuXcuKJekBFVa1fDv7CjI0zWLXXff5Ct0bdGN15NJ0bdC5us/nQZnbn7CbFlkK3xt0CTuVbuXclWTlZAYdUChwFTP1pKpe1vwyLWHC4HO7ee4iHyixYaJLaJLTGldCiTgta1GkRtfWr0MRVEbCyHn30UTIzM3n00UfDXtfOnTsZNGgQnTp1onPnzjz77LM+291xxx0888wzxfeHDBnCddddV3z/rrvu4umnnw47nnAdPnyYl156KaS2ffv2rdC6Bw4cSNmD37FkzZo1zJ8/P2rrN8bw+PePM2r+KD7Z9glZOVlk5WSxIHMBYz8dy4PfPsjXO7/mwrkXMmr+KO5bch+3fXUb/Wf256mVT1Hk9H1Cz9wtcylwBB+nzrXnsmH/BgBqJ9Smlq1WyLEnWhMZcfKIkNur+BS3iX337t288cYbuFwu3njjDfbs2RPW+mw2G0899RQbN27ku+++48UXXyxVL8WrX79+xeV9XS4X+/fvZ8OGDcXLly1bVuFEGQ5/ZXYrktijUa7Yn6ooC1yZxF6RuF5e+7I7CTsLSk0tdBkXBc4CPtn2Cbd/dTuZRzPJd+STY88hx55DniOP935+j2s/u9bnZdj25e0L6dR8i1g4VHgIcE85vPLUK0m0JAZ9nlWsdGzQkVPr+z85TdUMcZvYH3300eLaJ06nM+xee9OmTTnttNMAdxmAjh07kpWVVa5d3759Wb58OeAuj5uenk6dOnU4dOgQhYWFbNq0idNOO42cnBwGDx7MaaedRpcuXZg3bx4Aubm5XHDBBXTr1o309PTi8gT33XcfnTp1omvXrtx9992A/3K9Dz/8MFdffTX9+vXj6quv9lmm97777uPXX3+le/fuTJw4EYB///vf9O7dm65du/LXv/61+DWFUq64rLfeeovu3buTnp7OihUrAP9lhN98800uuugizj77bAYPHszo0aP58MMPi9c1cuRI5s2bh9Pp5O677yY9PZ2uXbsWlzVYtWoVAwYMoGfPngwZMoTdu3cD7m8O9957LxkZGbRv354lS5ZQVFTEQw89xKxZs+jevTuzZs0iNzeXa665hoyMDHr06FH8XpSNKxT5jnymrZ8WcAaIwzj8nrRT6Cxk08FNvLnxzXLLQp1f7jIu6iXVK74/quMo6ibVDXg6viC0rtOa58/2P71Q1RxxOcbu7a17C20VFRXxxhtvMHny5IiMtWdmZvLjjz9y+umnl1vWrFkzbDYbO3bsYNmyZfTp04esrCyWL19OWloaXbp0ITExEYvFwty5c6lbty779+/njDPO4KKLLmLBggU0a9aMTz75BHDXezlw4ABz587l559/RkQ4fPgwgN9yvQAbN27k22+/JSUlhVtuuaVcmd5//vOfrF+/vrjOy8KFC9myZQsrVqzAGMNFF13EN998Q//+/Uu9vkDlikvKy8tjzZo1fPPNN1xzzTWsX7/ebxlhgNWrV7N27Vrq16/P4sWLmTJlCiNGjODIkSMsW7aM6dOn88orr5CZmcmaNWuw2WwcPHgQu93OLbfcwrx582jUqBGzZs3igQceYNq0aYC7p71ixQrmz5/PI488wqJFi/jb3/7GypUreeGFFwC4//77Ofvss5k2bRqHDx8mIyODc845p1xcofhqx1cI4dUYKXQW8vbGt7mm8zVYLdbixy8+5WK+2vlV0NK3KbaUUrNa6iXX492h73LDohvYk7un3AyZZGsyd/a8k0vaXxJwjF/VHHGZ2Ev21r28vfYXX3wxrHXn5ORw6aWX8swzz1C3bl2fbbyX0Fu2bBl33nknWVlZLFu2jLS0NPr16we4x2Hvv/9+vvnmGywWC1lZWezdu5cuXbpw1113ce+99zJs2DDOOussHA4HycnJXHvttQwbNoxhw9xFzPyVzwW46KKLSElxV6oLpUzvwoULWbhwIT169Ch+nVu2bCmX2AOVKy7JW7q4f//+HD16lMOHD3Ps2DGfZYQBzj333OLkOWDAAG688Uays7OZM2cOl156afE/gwkTJmCzuT+W9evXZ/369axfv55zzz0XcL/P3jLHAJdccgkAPXv2JDMz0+f7tXDhQj766COefPJJwF3ZcseOHeXiCsWevD1+x8grIt+Rz66cXbSu27r4sYwTM2hSqwk7ju3w2+NPtiYzvuv4cr3zprWbMm/4PFbvW83/fv0fBwsO0qx2My455RLan9A+7HhVfIm7xF62t+4ViV673W7n0ksvZeTIkcUJY+fOnVx44YUATJgwgQkTJhSPs69bt4709HRatmzJU089Rd26dRk3bhwA77zzDtnZ2axatYqEhATatGlDQUEB7du3Z/Xq1cyfP58HH3yQwYMH89BDD7FixQq++OIL3n//fV544QW+/PLLgOVzS5bZ9VWmt23btqXaG2OYNGkSN9xwQ8B9EKhccUllK+OJCJMnT/ZZRrhsvACjR4/m7bffZubMmbzxxht+4zHG0Llz5+LhL3/xBorVGMOcOXPo0KFDqce///77cnEFk2pLxWqxBr2SVzAWLOXG2UWEV857hVHzR3Gk8Ei54Z4UWwrD2g7jylOv9LlOEaFnk570bFIl1T1UDIu7MXZfvXWvcMbajTFce+21dOzYkTvvvLP48ZYtW7JmzRrWrFnDhAkTAHeP/eOPP6Z+/fpYrVbq16/P4cOHWb58efGB0yNHjtC4cWMSEhL46quv2L59OwC///47tWrVYtSoUUycOJHVq1cXXzRj6NChTJkyhZ9++gkIvXyurzK9ZcsFDxkyhGnTphX3+LOysti3b1+l9hX8Ubr422+/JS0tjbS0NL9lhH0ZO3Zs8eyiTp06Ae7e88svv1ycoA8ePEiHDh3Izs4uTux2u73UwWpffL32559/vvh4wY8//ljBV/uH/i36+z3uUBF2Y/dZ8+TE1BOZO3wuN3W/iSa1miAIVrHS+8TeTBk4hclnTK7R5WZVZMRVYvfXW/fy9torM0Nm6dKlvPXWW3z55Zd0796d7t27+51Z0aVLl+Jx85KPpaWlFVd9HDlyJCtXrqRLly7MmDGjuEzuunXrig90PvLIIzz44IMcO3aMYcOG0bVrV84888zi6ZKhls/1Vaa3QYMG9OvXj/T0dCZOnMh5553HVVddRZ8+fejSpQuXXXZZWHXik5OT6dGjBxMmTOD1118H/JcR9qVJkyZ07Nix+BsOwHXXXUerVq3o2rUr3bp149133yUxMZH333+fe++9l27dutG9e/egs3gGDRrExo0biw+eTp48GbvdTteuXencuTOTJ0+u9OtuWrspGU0zwjpr0iIWBrcaTO1E31ekqpNYh7HpY1l0+SJ+Gv0Ta0avYdqQafRr3k+TugpJ2EXAKqOyRcBuvPFGXn/99YBXJ0pMTOS6664Le6xdRVdeXh5dunRh9erVpKWlVXc4Pvn7TB4pPMKVn1zJ3ty95S4yUfJiEf4uQJFqS2X2hbNpVbdV5INWNVqoRcDiqsf+0UcfBb3kXFFRUfF0NhWbFi1aRMeOHbnllltiNqkHkpaUxuxhsxnTeQx1EuuQYkshxZZCakIqozqNYv4l8+nTrA9J1iSs8sesl1q2WtRPrs+086dpUldRFVc9dqWqUiifSYfLwZ5c99Bfk1pNSLD+MUSz9dBWZm+eTeaRTGon1mboSUMZ2HIgNkvczVlQMaIqy/ZGjDFGxxBVTAi1w2Oz2PzWRjn5hJO5//T7IxmWUiGJmaGY5ORkDhw4EJEZB0qFwxjDgQMHfE4zVSoexEyPvUWLFuzatYvKXuhaqUhKTk4uPlFLqXgTM4k9ISGBk046qbrDUEqpuBczQzFKKaUiQxO7UkrVMJrYlVKqhqmWeewikg1sr/INR1ZDIPIXj6xZdB+FRvdTcLqP3FobYxoFa1Qtib0mEJGVoZwocDzTfRQa3U/B6T6qGB2KUUqpGkYTu1JK1TCa2CvvleoOIA7oPgqN7qfgdB9VgI6xK6VUDaM9dqWUqmE0sSulVA2jiT1MInKXiBgRaVjdscQiEfm3iPwsImtFZK6I1KvumGKFiJwvIr+IyFYRua+644lFItJSRL4SkY0iskFEbqvumOKBJvYwiEhL4DxgR3XHEsM+B9KNMV2BzcCkao4nJoiIFXgR+BPQCbhSRDpVb1QxyQHcZYzpBJwB3KT7KThN7OGZAtwD6BFoP4wxC40x3itbfwdoLVy3DGCrMWabMaYImAkMr+aYYo4xZrcxZrXn9jFgE9C8eqOKfZrYK0lEhgNZxpifqjuWOHIN8Gl1BxEjmgM7S9zfhSasgESkDdAD+L56I4l9MVOPPRaJyCLgRB+LHgDuxz0Mc9wLtJ+MMfM8bR7A/bX6naqMTdUMIlIbmAPcbow5Wt3xxDpN7AEYY87x9biIdAFOAn7yXKO1BbBaRDKMMXuqMMSY4G8/eYnIWGAYMNjoiRNeWUDLEvdbeB5TZYhIAu6k/o4x5oPqjice6AlKESAimUAvY4xWnytDRM4HngYGGGP0uoceImLDfTB5MO6E/gNwlTFmQ7UGFmPE3XOaDhw0xtxe3fHECx1jV9H2AlAH+FxE1ojI1OoOKBZ4DijfDHyG+4DgbE3qPvUDrgbO9nx+1ojI0OoOKtZpj10ppWoY7bErpVQNo4ldKaVqGE3sSilVw2hiV0qpGkYTu1JK1TCa2JVSqobRxK6UUjXM/wOLEmgPgpS5HgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(1)\n", + "for (x_i, b_i) in zip(measures_locations, measures_weights):\n", + " color = np.random.randint(low=1, high=10 * N)\n", + " pl.scatter(x_i[:, 0], x_i[:, 1], s=b * 1000, label='input measure')\n", + "pl.scatter(X[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter')\n", + "pl.title('Data measures and their barycenter')\n", + "pl.legend(loc=0)\n", + "pl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_stochastic.ipynb b/notebooks/plot_stochastic.ipynb new file mode 100644 index 0000000..e784e11 --- /dev/null +++ b/notebooks/plot_stochastic.ipynb @@ -0,0 +1,610 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Stochastic examples\n", + "\n", + "\n", + "This example is designed to show how to use the stochatic optimization\n", + "algorithms for descrete and semicontinous measures from the POT library.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Kilian Fatras \n", + "#\n", + "# License: MIT License\n", + "\n", + "import matplotlib.pylab as pl\n", + "import numpy as np\n", + "import ot\n", + "import ot.plot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM\n", + "############################################################################\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------SEMI-DUAL PROBLEM------------\n" + ] + } + ], + "source": [ + "print(\"------------SEMI-DUAL PROBLEM------------\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "DISCRETE CASE\n", + "Sample two discrete measures for the discrete case\n", + "---------------------------------------------\n", + "\n", + "Define 2 discrete measures a and b, the points where are defined the source\n", + "and the target measures and finally the cost matrix c.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n_source = 7\n", + "n_target = 4\n", + "reg = 1\n", + "numItermax = 1000\n", + "\n", + "a = ot.utils.unif(n_source)\n", + "b = ot.utils.unif(n_target)\n", + "\n", + "rng = np.random.RandomState(0)\n", + "X_source = rng.randn(n_source, 2)\n", + "Y_target = rng.randn(n_target, 2)\n", + "M = ot.dist(X_source, Y_target)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Call the \"SAG\" method to find the transportation matrix in the discrete case\n", + "---------------------------------------------\n", + "\n", + "Define the method \"SAG\", call ot.solve_semi_dual_entropic and plot the\n", + "results.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[2.55553509e-02 9.96395660e-02 1.76579142e-02 4.31178196e-06]\n", + " [1.21640234e-01 1.25357448e-02 1.30225078e-03 7.37891338e-03]\n", + " [3.56123975e-03 7.61451746e-02 6.31505947e-02 1.33831456e-07]\n", + " [2.61515202e-02 3.34246014e-02 8.28734709e-02 4.07550428e-04]\n", + " [9.85500870e-03 7.52288517e-04 1.08262628e-02 1.21423583e-01]\n", + " [2.16904253e-02 9.03825797e-04 1.87178503e-03 1.18391107e-01]\n", + " [4.15462212e-02 2.65987989e-02 7.23177216e-02 2.39440107e-03]]\n" + ] + } + ], + "source": [ + "method = \"SAG\"\n", + "sag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method,\n", + " numItermax)\n", + "print(sag_pi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "SEMICONTINOUS CASE\n", + "Sample one general measure a, one discrete measures b for the semicontinous\n", + "case\n", + "---------------------------------------------\n", + "\n", + "Define one general measure a, one discrete measures b, the points where\n", + "are defined the source and the target measures and finally the cost matrix c.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n_source = 7\n", + "n_target = 4\n", + "reg = 1\n", + "numItermax = 1000\n", + "log = True\n", + "\n", + "a = ot.utils.unif(n_source)\n", + "b = ot.utils.unif(n_target)\n", + "\n", + "rng = np.random.RandomState(0)\n", + "X_source = rng.randn(n_source, 2)\n", + "Y_target = rng.randn(n_target, 2)\n", + "M = ot.dist(X_source, Y_target)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Call the \"ASGD\" method to find the transportation matrix in the semicontinous\n", + "case\n", + "---------------------------------------------\n", + "\n", + "Define the method \"ASGD\", call ot.solve_semi_dual_entropic and plot the\n", + "results.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[3.75309361 7.63288278 3.76418767 2.53747778 1.70389504 3.53981297\n", + " 2.67663944] [-2.49164966 -2.25281897 -0.77666675 5.52113539]\n", + "[[2.19699465e-02 1.03185982e-01 1.76983379e-02 2.87611188e-06]\n", + " [1.20688044e-01 1.49823131e-02 1.50635578e-03 5.68043045e-03]\n", + " [3.01194583e-03 7.75764779e-02 6.22686313e-02 8.78225379e-08]\n", + " [2.28707628e-02 3.52120795e-02 8.44977549e-02 2.76545693e-04]\n", + " [1.19721129e-02 1.10087991e-03 1.53333937e-02 1.14450756e-01]\n", + " [2.65247890e-02 1.33140544e-03 2.66861405e-03 1.12332334e-01]\n", + " [3.71512413e-02 2.86513804e-02 7.53932500e-02 1.66127118e-03]]\n" + ] + } + ], + "source": [ + "method = \"ASGD\"\n", + "asgd_pi, log_asgd = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method,\n", + " numItermax, log=log)\n", + "print(log_asgd['alpha'], log_asgd['beta'])\n", + "print(asgd_pi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare the results with the Sinkhorn algorithm\n", + "---------------------------------------------\n", + "\n", + "Call the Sinkhorn algorithm from POT\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[2.55535622e-02 9.96413843e-02 1.76578860e-02 4.31043335e-06]\n", + " [1.21640742e-01 1.25369034e-02 1.30234529e-03 7.37715259e-03]\n", + " [3.56096458e-03 7.61460101e-02 6.31500344e-02 1.33788624e-07]\n", + " [2.61499607e-02 3.34255577e-02 8.28741973e-02 4.07427179e-04]\n", + " [9.85698720e-03 7.52505948e-04 1.08291770e-02 1.21418473e-01]\n", + " [2.16947591e-02 9.04086158e-04 1.87228707e-03 1.18386011e-01]\n", + " [4.15442692e-02 2.65998963e-02 7.23192701e-02 2.39370724e-03]]\n" + ] + } + ], + "source": [ + "sinkhorn_pi = ot.sinkhorn(a, b, M, reg)\n", + "print(sinkhorn_pi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "PLOT TRANSPORTATION MATRIX\n", + "#############################################################################\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot SAG results\n", + "----------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(4, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, sag_pi, 'semi-dual : OT matrix SAG')\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot ASGD results\n", + "-----------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(4, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, asgd_pi, 'semi-dual : OT matrix ASGD')\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot Sinkhorn results\n", + "---------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(4, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn')\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM\n", + "############################################################################\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "------------DUAL PROBLEM------------\n" + ] + } + ], + "source": [ + "print(\"------------DUAL PROBLEM------------\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "SEMICONTINOUS CASE\n", + "Sample one general measure a, one discrete measures b for the semicontinous\n", + "case\n", + "---------------------------------------------\n", + "\n", + "Define one general measure a, one discrete measures b, the points where\n", + "are defined the source and the target measures and finally the cost matrix c.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "n_source = 7\n", + "n_target = 4\n", + "reg = 1\n", + "numItermax = 100000\n", + "lr = 0.1\n", + "batch_size = 3\n", + "log = True\n", + "\n", + "a = ot.utils.unif(n_source)\n", + "b = ot.utils.unif(n_target)\n", + "\n", + "rng = np.random.RandomState(0)\n", + "X_source = rng.randn(n_source, 2)\n", + "Y_target = rng.randn(n_target, 2)\n", + "M = ot.dist(X_source, Y_target)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Call the \"SGD\" dual method to find the transportation matrix in the\n", + "semicontinous case\n", + "---------------------------------------------\n", + "\n", + "Call ot.solve_dual_entropic and plot the results.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 1.67648902 5.3770004 1.70385554 0.4276547 -0.77206786 1.0474898\n", + " 0.54202203] [-0.23723788 -0.20259434 1.30855788 8.06179985]\n", + "[[2.62451875e-02 1.00499531e-01 1.78515577e-02 4.57450829e-06]\n", + " [1.20510690e-01 1.21972758e-02 1.27002374e-03 7.55197481e-03]\n", + " [3.65708350e-03 7.67963231e-02 6.38381061e-02 1.41974930e-07]\n", + " [2.64286344e-02 3.31748063e-02 8.24445965e-02 4.25479786e-04]\n", + " [9.59295422e-03 7.19190875e-04 1.03739180e-02 1.22100712e-01]\n", + " [2.09087627e-02 8.55676046e-04 1.77617241e-03 1.17896019e-01]\n", + " [4.18792948e-02 2.63326297e-02 7.17598381e-02 2.49335733e-03]]\n" + ] + } + ], + "source": [ + "sgd_dual_pi, log_sgd = ot.stochastic.solve_dual_entropic(a, b, M, reg,\n", + " batch_size, numItermax,\n", + " lr, log=log)\n", + "print(log_sgd['alpha'], log_sgd['beta'])\n", + "print(sgd_dual_pi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compare the results with the Sinkhorn algorithm\n", + "---------------------------------------------\n", + "\n", + "Call the Sinkhorn algorithm from POT\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[2.55535622e-02 9.96413843e-02 1.76578860e-02 4.31043335e-06]\n", + " [1.21640742e-01 1.25369034e-02 1.30234529e-03 7.37715259e-03]\n", + " [3.56096458e-03 7.61460101e-02 6.31500344e-02 1.33788624e-07]\n", + " [2.61499607e-02 3.34255577e-02 8.28741973e-02 4.07427179e-04]\n", + " [9.85698720e-03 7.52505948e-04 1.08291770e-02 1.21418473e-01]\n", + " [2.16947591e-02 9.04086158e-04 1.87228707e-03 1.18386011e-01]\n", + " [4.15442692e-02 2.65998963e-02 7.23192701e-02 2.39370724e-03]]\n" + ] + } + ], + "source": [ + "sinkhorn_pi = ot.sinkhorn(a, b, M, reg)\n", + "print(sinkhorn_pi)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot SGD results\n", + "-----------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(4, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, sgd_dual_pi, 'dual : OT matrix SGD')\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot Sinkhorn results\n", + "---------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(4, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, sinkhorn_pi, 'OT matrix Sinkhorn')\n", + "pl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} -- cgit v1.2.3 From 1d68c0127cbb6597d685eddfe07aad2a99fc443c Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 29 Aug 2018 14:16:35 +0200 Subject: remove double smooth in doc --- docs/source/all.rst | 5 ----- 1 file changed, 5 deletions(-) diff --git a/docs/source/all.rst b/docs/source/all.rst index 9459023..94da2ed 100644 --- a/docs/source/all.rst +++ b/docs/source/all.rst @@ -19,11 +19,6 @@ ot.bregman .. automodule:: ot.bregman :members: - -ot.smooth ------ -.. automodule:: ot.smooth - :members: ot.smooth ----- -- cgit v1.2.3 From f51c6ab8c25ee371be23ca1a09cb18aed7732132 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 29 Aug 2018 14:17:19 +0200 Subject: update exmaple bary 1D for better notebook --- docs/cache_nbrun | 2 +- .../source/auto_examples/auto_examples_jupyter.zip | Bin 118510 -> 119618 bytes docs/source/auto_examples/auto_examples_python.zip | Bin 79057 -> 79365 bytes .../images/sphx_glr_plot_barycenter_1D_003.png | Bin 108756 -> 41624 bytes .../images/sphx_glr_plot_barycenter_1D_005.png | Bin 108687 -> 108756 bytes .../images/sphx_glr_plot_barycenter_1D_006.png | Bin 105696 -> 105765 bytes docs/source/auto_examples/index.rst | 16 +- docs/source/auto_examples/plot_barycenter_1D.ipynb | 74 +++++++- docs/source/auto_examples/plot_barycenter_1D.py | 8 +- docs/source/auto_examples/plot_barycenter_1D.rst | 116 ++++++++----- examples/plot_barycenter_1D.py | 8 +- notebooks/plot_barycenter_1D.ipynb | 190 ++++++++++++++------- 12 files changed, 290 insertions(+), 124 deletions(-) diff --git a/docs/cache_nbrun b/docs/cache_nbrun index 2781d81..0745a21 100644 --- a/docs/cache_nbrun +++ b/docs/cache_nbrun @@ -1 +1 @@ -{"plot_otda_mapping_colors_images.ipynb": "4f0587a00a3c082799a75a0ed36e9ce1", "plot_optim_OTreg.ipynb": "481801bb0d133ef350a65179cf8f739a", "plot_barycenter_1D.ipynb": "6063193f9ac87517acced2625edb9a54", "plot_stochastic.ipynb": "e2c520150378ae4635f74509f687fa01", "plot_WDA.ipynb": "27f8de4c6d7db46497076523673eedfb", "plot_otda_linear_mapping.ipynb": "a472c767abe82020e0a58125a528785c", "plot_OT_1D_smooth.ipynb": "3a059103652225a0c78ea53895cf79e5", "plot_OT_L1_vs_L2.ipynb": "5d565b8aaf03be4309eba731127851dc", "plot_otda_color_images.ipynb": "d047d635f4987c81072383241590e21f", "plot_otda_classes.ipynb": "39087b6e98217851575f2271c22853a4", "plot_otda_d2.ipynb": "e6feae588103f2a8fab942e5f4eff483", "plot_otda_mapping.ipynb": "2f1ebbdc0f855d9e2b7adf9edec24d25", "plot_gromov.ipynb": "24f2aea489714d34779521f46d5e2c47", "plot_compute_emd.ipynb": "f5cd71cad882ec157dc8222721e9820c", "plot_OT_1D.ipynb": "b5348bdc561c07ec168a1622e5af4b93", "plot_gromov_barycenter.ipynb": "953e5047b886ec69ec621ec52f5e21d1", "plot_free_support_barycenter.ipynb": "246dd2feff4b233a4f1a553c5a202fdc", "plot_otda_semi_supervised.ipynb": "f6dfb02ba2bbd939408ffcd22a3b007c", "plot_OT_2D_samples.ipynb": "07dbc14859fa019a966caa79fa0825bd", "plot_barycenter_lp_vs_entropic.ipynb": "51833e8c76aaedeba9599ac7a30eb357"} \ No newline at end of file +{"plot_otda_mapping_colors_images.ipynb": "4f0587a00a3c082799a75a0ed36e9ce1", "plot_optim_OTreg.ipynb": "481801bb0d133ef350a65179cf8f739a", "plot_otda_color_images.ipynb": "d047d635f4987c81072383241590e21f", "plot_stochastic.ipynb": "e2c520150378ae4635f74509f687fa01", "plot_WDA.ipynb": "27f8de4c6d7db46497076523673eedfb", "plot_otda_linear_mapping.ipynb": "a472c767abe82020e0a58125a528785c", "plot_OT_1D_smooth.ipynb": "3a059103652225a0c78ea53895cf79e5", "plot_OT_L1_vs_L2.ipynb": "5d565b8aaf03be4309eba731127851dc", "plot_barycenter_1D.ipynb": "5f6fb8aebd8e2e91ebc77c923cb112b3", "plot_otda_classes.ipynb": "39087b6e98217851575f2271c22853a4", "plot_otda_d2.ipynb": "e6feae588103f2a8fab942e5f4eff483", "plot_otda_mapping.ipynb": "2f1ebbdc0f855d9e2b7adf9edec24d25", "plot_gromov.ipynb": "24f2aea489714d34779521f46d5e2c47", "plot_compute_emd.ipynb": "f5cd71cad882ec157dc8222721e9820c", "plot_OT_1D.ipynb": "b5348bdc561c07ec168a1622e5af4b93", "plot_gromov_barycenter.ipynb": "953e5047b886ec69ec621ec52f5e21d1", "plot_free_support_barycenter.ipynb": "246dd2feff4b233a4f1a553c5a202fdc", "plot_otda_semi_supervised.ipynb": "f6dfb02ba2bbd939408ffcd22a3b007c", "plot_OT_2D_samples.ipynb": "07dbc14859fa019a966caa79fa0825bd", "plot_barycenter_lp_vs_entropic.ipynb": "51833e8c76aaedeba9599ac7a30eb357"} \ No newline at end of file diff --git a/docs/source/auto_examples/auto_examples_jupyter.zip b/docs/source/auto_examples/auto_examples_jupyter.zip index 2325d59..c6a7e90 100644 Binary files a/docs/source/auto_examples/auto_examples_jupyter.zip and b/docs/source/auto_examples/auto_examples_jupyter.zip differ diff --git a/docs/source/auto_examples/auto_examples_python.zip b/docs/source/auto_examples/auto_examples_python.zip index 694d704..28ff08e 100644 Binary files a/docs/source/auto_examples/auto_examples_python.zip and b/docs/source/auto_examples/auto_examples_python.zip differ diff --git a/docs/source/auto_examples/images/sphx_glr_plot_barycenter_1D_003.png b/docs/source/auto_examples/images/sphx_glr_plot_barycenter_1D_003.png index 81cee52..d8db85e 100644 Binary files a/docs/source/auto_examples/images/sphx_glr_plot_barycenter_1D_003.png and b/docs/source/auto_examples/images/sphx_glr_plot_barycenter_1D_003.png differ diff --git a/docs/source/auto_examples/images/sphx_glr_plot_barycenter_1D_005.png b/docs/source/auto_examples/images/sphx_glr_plot_barycenter_1D_005.png index eac9230..81cee52 100644 Binary files a/docs/source/auto_examples/images/sphx_glr_plot_barycenter_1D_005.png and b/docs/source/auto_examples/images/sphx_glr_plot_barycenter_1D_005.png differ diff --git a/docs/source/auto_examples/images/sphx_glr_plot_barycenter_1D_006.png b/docs/source/auto_examples/images/sphx_glr_plot_barycenter_1D_006.png index 2e29ff9..bfa0873 100644 Binary files a/docs/source/auto_examples/images/sphx_glr_plot_barycenter_1D_006.png and b/docs/source/auto_examples/images/sphx_glr_plot_barycenter_1D_006.png differ diff --git a/docs/source/auto_examples/index.rst b/docs/source/auto_examples/index.rst index 77a46aa..5cbfba6 100644 --- a/docs/source/auto_examples/index.rst +++ b/docs/source/auto_examples/index.rst @@ -229,13 +229,13 @@ This is a gallery of all the POT example files. .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_mapping_colors_images_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_barycenter_1D_thumb.png - :ref:`sphx_glr_auto_examples_plot_otda_mapping_colors_images.py` + :ref:`sphx_glr_auto_examples_plot_barycenter_1D.py` .. raw:: html @@ -245,17 +245,17 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_otda_mapping_colors_images + /auto_examples/plot_barycenter_1D .. raw:: html -
+
.. only:: html - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_barycenter_1D_thumb.png + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_otda_mapping_colors_images_thumb.png - :ref:`sphx_glr_auto_examples_plot_barycenter_1D.py` + :ref:`sphx_glr_auto_examples_plot_otda_mapping_colors_images.py` .. raw:: html @@ -265,7 +265,7 @@ This is a gallery of all the POT example files. .. toctree:: :hidden: - /auto_examples/plot_barycenter_1D + /auto_examples/plot_otda_mapping_colors_images .. raw:: html diff --git a/docs/source/auto_examples/plot_barycenter_1D.ipynb b/docs/source/auto_examples/plot_barycenter_1D.ipynb index 5866088..fc60e1f 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.ipynb +++ b/docs/source/auto_examples/plot_barycenter_1D.ipynb @@ -26,7 +26,79 @@ }, "outputs": [], "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nfrom matplotlib.collections import PolyCollection\n\n#\n# Generate data\n# -------------\n\n#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()\n\n#\n# Plot data\n# ---------\n\n#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()\n\n#\n# Barycenter computation\n# ----------------------\n\n#%% barycenter computation\n\nalpha = 0.2 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()\n\n#\n# Barycentric interpolation\n# -------------------------\n\n#%% barycenter interpolation\n\nn_alpha = 11\nalpha_list = np.linspace(0, 1, n_alpha)\n\n\nB_l2 = np.zeros((n, n_alpha))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_alpha):\n alpha = alpha_list[i]\n weights = np.array([1 - alpha, alpha])\n B_l2[:, i] = A.dot(weights)\n B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)\n\n#%% plot interpolation\n\npl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_l2[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_wass[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with Wasserstein')\npl.tight_layout()\n\npl.show()" + "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nfrom matplotlib.collections import PolyCollection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n-------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot data\n---------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Barycenter computation\n----------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% barycenter computation\n\nalpha = 0.2 # 0<=alpha<=1\nweights = np.array([1 - alpha, alpha])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nbary_wass = ot.bregman.barycenter(A, M, reg, weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Barycentric interpolation\n-------------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% barycenter interpolation\n\nn_alpha = 11\nalpha_list = np.linspace(0, 1, n_alpha)\n\n\nB_l2 = np.zeros((n, n_alpha))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_alpha):\n alpha = alpha_list[i]\n weights = np.array([1 - alpha, alpha])\n B_l2[:, i] = A.dot(weights)\n B_wass[:, i] = ot.bregman.barycenter(A, M, reg, weights)\n\n#%% plot interpolation\n\npl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_l2[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = alpha_list\nfor i, z in enumerate(zs):\n ys = B_wass[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in alpha_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel('$\\\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with Wasserstein')\npl.tight_layout()\n\npl.show()" ] } ], diff --git a/docs/source/auto_examples/plot_barycenter_1D.py b/docs/source/auto_examples/plot_barycenter_1D.py index 5ed9f3f..6864301 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.py +++ b/docs/source/auto_examples/plot_barycenter_1D.py @@ -25,7 +25,7 @@ import ot from mpl_toolkits.mplot3d import Axes3D # noqa from matplotlib.collections import PolyCollection -# +############################################################################## # Generate data # ------------- @@ -48,7 +48,7 @@ n_distributions = A.shape[1] M = ot.utils.dist0(n) M /= M.max() -# +############################################################################## # Plot data # --------- @@ -60,7 +60,7 @@ for i in range(n_distributions): pl.title('Distributions') pl.tight_layout() -# +############################################################################## # Barycenter computation # ---------------------- @@ -90,7 +90,7 @@ pl.legend() pl.title('Barycenters') pl.tight_layout() -# +############################################################################## # Barycentric interpolation # ------------------------- diff --git a/docs/source/auto_examples/plot_barycenter_1D.rst b/docs/source/auto_examples/plot_barycenter_1D.rst index b314dc1..66ac042 100644 --- a/docs/source/auto_examples/plot_barycenter_1D.rst +++ b/docs/source/auto_examples/plot_barycenter_1D.rst @@ -18,52 +18,34 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. +.. code-block:: python -.. rst-class:: sphx-glr-horizontal - - - * - .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_001.png - :scale: 47 + # Author: Remi Flamary + # + # License: MIT License - * + import numpy as np + import matplotlib.pylab as pl + import ot + # necessary for 3d plot even if not used + from mpl_toolkits.mplot3d import Axes3D # noqa + from matplotlib.collections import PolyCollection - .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_002.png - :scale: 47 - * - .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_003.png - :scale: 47 - * - .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_004.png - :scale: 47 +Generate data +------------- .. code-block:: python - # Author: Remi Flamary - # - # License: MIT License - - import numpy as np - import matplotlib.pylab as pl - import ot - # necessary for 3d plot even if not used - from mpl_toolkits.mplot3d import Axes3D # noqa - from matplotlib.collections import PolyCollection - - # - # Generate data - # ------------- - #%% parameters n = 100 # nb bins @@ -83,9 +65,19 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. M = ot.utils.dist0(n) M /= M.max() - # - # Plot data - # --------- + + + + + + +Plot data +--------- + + + +.. code-block:: python + #%% plot the distributions @@ -95,9 +87,22 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. pl.title('Distributions') pl.tight_layout() - # - # Barycenter computation - # ---------------------- + + + +.. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_001.png + :align: center + + + + +Barycenter computation +---------------------- + + + +.. code-block:: python + #%% barycenter computation @@ -125,9 +130,22 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. pl.title('Barycenters') pl.tight_layout() - # - # Barycentric interpolation - # ------------------------- + + + +.. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_003.png + :align: center + + + + +Barycentric interpolation +------------------------- + + + +.. code-block:: python + #%% barycenter interpolation @@ -194,7 +212,25 @@ SIAM Journal on Scientific Computing, 37(2), A1111-A1138. pl.show() -**Total running time of the script:** ( 0 minutes 0.363 seconds) + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_005.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_barycenter_1D_006.png + :scale: 47 + + + + +**Total running time of the script:** ( 0 minutes 0.413 seconds) diff --git a/examples/plot_barycenter_1D.py b/examples/plot_barycenter_1D.py index 5ed9f3f..6864301 100644 --- a/examples/plot_barycenter_1D.py +++ b/examples/plot_barycenter_1D.py @@ -25,7 +25,7 @@ import ot from mpl_toolkits.mplot3d import Axes3D # noqa from matplotlib.collections import PolyCollection -# +############################################################################## # Generate data # ------------- @@ -48,7 +48,7 @@ n_distributions = A.shape[1] M = ot.utils.dist0(n) M /= M.max() -# +############################################################################## # Plot data # --------- @@ -60,7 +60,7 @@ for i in range(n_distributions): pl.title('Distributions') pl.tight_layout() -# +############################################################################## # Barycenter computation # ---------------------- @@ -90,7 +90,7 @@ pl.legend() pl.title('Barycenters') pl.tight_layout() -# +############################################################################## # Barycentric interpolation # ------------------------- diff --git a/notebooks/plot_barycenter_1D.ipynb b/notebooks/plot_barycenter_1D.ipynb index 4a0956b..bd73a99 100644 --- a/notebooks/plot_barycenter_1D.ipynb +++ b/notebooks/plot_barycenter_1D.ipynb @@ -36,48 +36,7 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Author: Remi Flamary \n", "#\n", @@ -88,12 +47,26 @@ "import ot\n", "# necessary for 3d plot even if not used\n", "from mpl_toolkits.mplot3d import Axes3D # noqa\n", - "from matplotlib.collections import PolyCollection\n", - "\n", - "#\n", - "# Generate data\n", - "# -------------\n", - "\n", + "from matplotlib.collections import PolyCollection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + "-------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ "#%% parameters\n", "\n", "n = 100 # nb bins\n", @@ -111,24 +84,74 @@ "\n", "# loss matrix + normalization\n", "M = ot.utils.dist0(n)\n", - "M /= M.max()\n", - "\n", - "#\n", - "# Plot data\n", - "# ---------\n", - "\n", + "M /= M.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot data\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "#%% plot the distributions\n", "\n", "pl.figure(1, figsize=(6.4, 3))\n", "for i in range(n_distributions):\n", " pl.plot(x, A[:, i])\n", "pl.title('Distributions')\n", - "pl.tight_layout()\n", - "\n", - "#\n", - "# Barycenter computation\n", - "# ----------------------\n", - "\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Barycenter computation\n", + "----------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "#%% barycenter computation\n", "\n", "alpha = 0.2 # 0<=alpha<=1\n", @@ -153,12 +176,47 @@ "pl.plot(x, bary_wass, 'g', label='Wasserstein')\n", "pl.legend()\n", "pl.title('Barycenters')\n", - "pl.tight_layout()\n", - "\n", - "#\n", - "# Barycentric interpolation\n", - "# -------------------------\n", - "\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Barycentric interpolation\n", + "-------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ "#%% barycenter interpolation\n", "\n", "n_alpha = 11\n", -- cgit v1.2.3 From fd6371cc557ba73c4f5d1142fa8de8d956a850f0 Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Wed, 29 Aug 2018 14:06:58 -0700 Subject: replaced marginal tests --- ot/lp/__init__.py | 2 -- ot/stochastic.py | 36 +++++++++++++++++++----------------- test/test_stochastic.py | 27 ++++++++++++--------------- 3 files changed, 31 insertions(+), 34 deletions(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 4c0d170..5dda82a 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -18,8 +18,6 @@ from .emd_wrap import emd_c, check_result from ..utils import parmap from .cvx import barycenter -__all__=['emd', 'emd2', 'barycenter', 'cvx'] - def emd(a, b, M, numItermax=100000, log=False): """Solves the Earth Movers distance problem and returns the OT matrix diff --git a/ot/stochastic.py b/ot/stochastic.py index e33f6a0..a369ba8 100644 --- a/ot/stochastic.py +++ b/ot/stochastic.py @@ -450,24 +450,29 @@ def batch_grad_dual(a, b, M, reg, alpha, beta, batch_size, batch_alpha, \forall j in batch_alpha, grad_beta_j = beta_j * batch_size/len(alpha) - - sum_{j in batch_alpha} exp((alpha_i + beta_j - M_{i,j})/reg) + sum_{i in batch_alpha} exp((alpha_i + beta_j - M_{i,j})/reg) * a_i * b_j where : - M is the (ns,nt) metric cost matrix - alpha, beta are dual variables in R^ixR^J - reg is the regularization term - - batch_alpha and batch_beta are list of index + - batch_alpha and batch_beta are lists of index + - a and b are source and target weights (sum to 1) + The algorithm used for solving the dual problem is the SGD algorithm as proposed in [19]_ [alg.1] Parameters ---------- - - reg : float number, - Regularization term > 0 + a : np.ndarray(ns,), + source measure + b : np.ndarray(nt,), + target measure M : np.ndarray(ns, nt), cost matrix + reg : float number, + Regularization term > 0 alpha : np.ndarray(ns,) dual variable beta : np.ndarray(nt,) @@ -516,8 +521,8 @@ def batch_grad_dual(a, b, M, reg, alpha, beta, batch_size, batch_alpha, ''' G = - (np.exp((alpha[batch_alpha, None] + beta[None, batch_beta] - - M[batch_alpha, :][:, batch_beta]) / reg) * a[batch_alpha, None] * - b[None, batch_beta]) + M[batch_alpha, :][:, batch_beta]) / reg) * + a[batch_alpha, None] * b[None, batch_beta]) grad_beta = np.zeros(np.shape(M)[1]) grad_alpha = np.zeros(np.shape(M)[0]) grad_beta[batch_beta] = (b[batch_beta] * len(batch_alpha) / np.shape(M)[0] + @@ -548,23 +553,20 @@ def sgd_entropic_regularization(a, b, M, reg, batch_size, numItermax, lr): Parameters ---------- - + a : np.ndarray(ns,), + source measure + b : np.ndarray(nt,), + target measure M : np.ndarray(ns, nt), cost matrix reg : float number, Regularization term > 0 - alpha : np.ndarray(ns,) - dual variable - beta : np.ndarray(nt,) - dual variable batch_size : int number size of the batch numItermax : int number number of iteration lr : float number learning rate - alternate : bool, optional - alternating algorithm Returns ------- @@ -591,8 +593,8 @@ def sgd_entropic_regularization(a, b, M, reg, batch_size, numItermax, lr): >>> Y_target = rng.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) >>> sgd_dual_pi, log = stochastic.solve_dual_entropic(a, b, M, reg, - batch_size, - numItermax, lr, log) + batch_size, + numItermax, lr, log) >>> print(log['alpha'], log['beta']) >>> print(sgd_dual_pi) @@ -609,7 +611,7 @@ def sgd_entropic_regularization(a, b, M, reg, batch_size, numItermax, lr): cur_alpha = np.zeros(n_source) cur_beta = np.zeros(n_target) for cur_iter in range(numItermax): - k = np.sqrt(cur_iter / 100 + 1) + k = np.sqrt(cur_iter + 1) batch_alpha = np.random.choice(n_source, batch_size, replace=False) batch_beta = np.random.choice(n_target, batch_size, replace=False) update_alpha, update_beta = batch_grad_dual(a, b, M, reg, cur_alpha, diff --git a/test/test_stochastic.py b/test/test_stochastic.py index 74228a3..0128317 100644 --- a/test/test_stochastic.py +++ b/test/test_stochastic.py @@ -97,7 +97,6 @@ def test_sag_asgd_sinkhorn(): x = rng.randn(n, 2) u = ot.utils.unif(n) - zero = np.zeros(n) M = ot.dist(x, x) G_asgd = ot.stochastic.solve_semi_dual_entropic(u, u, M, reg, "asgd", @@ -108,13 +107,13 @@ def test_sag_asgd_sinkhorn(): # check constratints np.testing.assert_allclose( - zero, (G_sag - G_sinkhorn).sum(1), atol=1e-03) # cf convergence sag + G_sag.sum(1), G_sinkhorn.sum(1), atol=1e-03) np.testing.assert_allclose( - zero, (G_sag - G_sinkhorn).sum(0), atol=1e-03) # cf convergence sag + G_sag.sum(0), G_sinkhorn.sum(0), atol=1e-03) np.testing.assert_allclose( - zero, (G_asgd - G_sinkhorn).sum(1), atol=1e-03) # cf convergence asgd + G_asgd.sum(1), G_sinkhorn.sum(1), atol=1e-03) np.testing.assert_allclose( - zero, (G_asgd - G_sinkhorn).sum(0), atol=1e-03) # cf convergence asgd + G_asgd.sum(0), G_sinkhorn.sum(0), atol=1e-03) np.testing.assert_allclose( G_sag, G_sinkhorn, atol=1e-03) # cf convergence sag np.testing.assert_allclose( @@ -151,9 +150,9 @@ def test_stochastic_dual_sgd(): # check constratints np.testing.assert_allclose( - u, G.sum(1), atol=1e-04) # cf convergence sgd + u, G.sum(1), atol=1e-03) # cf convergence sgd np.testing.assert_allclose( - u, G.sum(0), atol=1e-04) # cf convergence sgd + u, G.sum(0), atol=1e-03) # cf convergence sgd ############################################################################# @@ -175,7 +174,6 @@ def test_dual_sgd_sinkhorn(): # Test uniform x = rng.randn(n, 2) u = ot.utils.unif(n) - zero = np.zeros(n) M = ot.dist(x, x) G_sgd = ot.stochastic.solve_dual_entropic(u, u, M, reg, batch_size, @@ -185,17 +183,16 @@ def test_dual_sgd_sinkhorn(): # check constratints np.testing.assert_allclose( - zero, (G_sgd - G_sinkhorn).sum(1), atol=1e-04) # cf convergence sgd + G_sgd.sum(1), G_sinkhorn.sum(1), atol=1e-03) np.testing.assert_allclose( - zero, (G_sgd - G_sinkhorn).sum(0), atol=1e-04) # cf convergence sgd + G_sgd.sum(0), G_sinkhorn.sum(0), atol=1e-03) np.testing.assert_allclose( - G_sgd, G_sinkhorn, atol=1e-04) # cf convergence sgd + G_sgd, G_sinkhorn, atol=1e-03) # cf convergence sgd # Test gaussian n = 30 reg = 1 batch_size = 30 - zero = np.zeros(n) a = ot.datasets.make_1D_gauss(n, 15, 5) # m= mean, s= std b = ot.datasets.make_1D_gauss(n, 15, 5) @@ -211,8 +208,8 @@ def test_dual_sgd_sinkhorn(): # check constratints np.testing.assert_allclose( - zero, (G_sgd - G_sinkhorn).sum(1), atol=1e-04) # cf convergence sgd + G_sgd.sum(1), G_sinkhorn.sum(1), atol=1e-03) np.testing.assert_allclose( - zero, (G_sgd - G_sinkhorn).sum(0), atol=1e-04) # cf convergence sgd + G_sgd.sum(0), G_sinkhorn.sum(0), atol=1e-03) np.testing.assert_allclose( - G_sgd, G_sinkhorn, atol=1e-04) # cf convergence sgd + G_sgd, G_sinkhorn, atol=1e-03) # cf convergence sgd -- cgit v1.2.3 From 63b34bf012076eb89ed112122fdaa65667464ae7 Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Wed, 29 Aug 2018 14:21:33 -0700 Subject: fixed conflicts --- test/test_da.py | 63 --------------------------------------------------------- 1 file changed, 63 deletions(-) diff --git a/test/test_da.py b/test/test_da.py index 97e23da..f7f3a9d 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -484,66 +484,3 @@ def test_linear_mapping_class(): Cst = np.cov(Xst.T) np.testing.assert_allclose(Ct, Cst, rtol=1e-2, atol=1e-2) - - -def test_otda(): - - n_samples = 150 # nb samples - np.random.seed(0) - - xs, ys = ot.datasets.make_data_classif('3gauss', n_samples) - xt, yt = ot.datasets.make_data_classif('3gauss2', n_samples) - - a, b = ot.unif(n_samples), ot.unif(n_samples) - - # LP problem - da_emd = ot.da.OTDA() # init class - da_emd.fit(xs, xt) # fit distributions - da_emd.interp() # interpolation of source samples - da_emd.predict(xs) # interpolation of source samples - - np.testing.assert_allclose(a, np.sum(da_emd.G, 1)) - np.testing.assert_allclose(b, np.sum(da_emd.G, 0)) - - # sinkhorn regularization - lambd = 1e-1 - da_entrop = ot.da.OTDA_sinkhorn() - da_entrop.fit(xs, xt, reg=lambd) - da_entrop.interp() - da_entrop.predict(xs) - - np.testing.assert_allclose( - a, np.sum(da_entrop.G, 1), rtol=1e-3, atol=1e-3) - np.testing.assert_allclose(b, np.sum(da_entrop.G, 0), rtol=1e-3, atol=1e-3) - - # non-convex Group lasso regularization - reg = 1e-1 - eta = 1e0 - da_lpl1 = ot.da.OTDA_lpl1() - da_lpl1.fit(xs, ys, xt, reg=reg, eta=eta) - da_lpl1.interp() - da_lpl1.predict(xs) - - np.testing.assert_allclose(a, np.sum(da_lpl1.G, 1), rtol=1e-3, atol=1e-3) - np.testing.assert_allclose(b, np.sum(da_lpl1.G, 0), rtol=1e-3, atol=1e-3) - - # True Group lasso regularization - reg = 1e-1 - eta = 2e0 - da_l1l2 = ot.da.OTDA_l1l2() - da_l1l2.fit(xs, ys, xt, reg=reg, eta=eta, numItermax=20, verbose=True) - da_l1l2.interp() - da_l1l2.predict(xs) - - np.testing.assert_allclose(a, np.sum(da_l1l2.G, 1), rtol=1e-3, atol=1e-3) - np.testing.assert_allclose(b, np.sum(da_l1l2.G, 0), rtol=1e-3, atol=1e-3) - - # linear mapping - da_emd = ot.da.OTDA_mapping_linear() # init class - da_emd.fit(xs, xt, numItermax=10) # fit distributions - da_emd.predict(xs) # interpolation of source samples - - # nonlinear mapping - da_emd = ot.da.OTDA_mapping_kernel() # init class - da_emd.fit(xs, xt, numItermax=10) # fit distributions - da_emd.predict(xs) # interpolation of source samples -- cgit v1.2.3 From d99abf078537acf6cf49480b9790a9c450889031 Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Fri, 7 Sep 2018 11:58:42 +0200 Subject: Wasserstein convolutional barycenter --- README.md | 4 +- data/duck.png | Bin 0 -> 5112 bytes data/heart.png | Bin 0 -> 5225 bytes data/redcross.png | Bin 0 -> 1683 bytes data/tooth.png | Bin 0 -> 4931 bytes examples/plot_convolutional_barycenter.py | 92 ++++++++++++++++++++++++++ ot/bregman.py | 106 ++++++++++++++++++++++++++++++ 7 files changed, 201 insertions(+), 1 deletion(-) create mode 100644 data/duck.png create mode 100644 data/heart.png create mode 100644 data/redcross.png create mode 100644 data/tooth.png create mode 100644 examples/plot_convolutional_barycenter.py diff --git a/README.md b/README.md index dded582..1105362 100644 --- a/README.md +++ b/README.md @@ -227,4 +227,6 @@ You can also post bug reports and feature requests in Github issues. Make sure t [19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, A.& Blondel, M. [Large-scale Optimal Transport and Mapping Estimation](https://arxiv.org/pdf/1711.02283.pdf). International Conference on Learning Representation (2018) -[20] Cuturi, M. and Doucet, A. (2014) [Fast Computation of Wasserstein Barycenters](http://proceedings.mlr.press/v32/cuturi14.html). International Conference in Machine Learning \ No newline at end of file +[20] Cuturi, M. and Doucet, A. (2014) [Fast Computation of Wasserstein Barycenters](http://proceedings.mlr.press/v32/cuturi14.html). International Conference in Machine Learning + +[21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A. & Guibas, L. (2015). [Convolutional wasserstein distances: Efficient optimal transportation on geometric domains](https://dl.acm.org/citation.cfm?id=2766963). ACM Transactions on Graphics (TOG), 34(4), 66. \ No newline at end of file diff --git a/data/duck.png b/data/duck.png new file mode 100644 index 0000000..9181697 Binary files /dev/null and b/data/duck.png differ diff --git a/data/heart.png b/data/heart.png new file mode 100644 index 0000000..44a6385 Binary files /dev/null and b/data/heart.png differ diff --git a/data/redcross.png b/data/redcross.png new file mode 100644 index 0000000..8d0a6fa Binary files /dev/null and b/data/redcross.png differ diff --git a/data/tooth.png b/data/tooth.png new file mode 100644 index 0000000..cd92c9d Binary files /dev/null and b/data/tooth.png differ diff --git a/examples/plot_convolutional_barycenter.py b/examples/plot_convolutional_barycenter.py new file mode 100644 index 0000000..d231da9 --- /dev/null +++ b/examples/plot_convolutional_barycenter.py @@ -0,0 +1,92 @@ + +#%% +# -*- coding: utf-8 -*- +""" +============================================ +Convolutional Wasserstein Barycenter example +============================================ + +This example is designed to illustrate how the Convolutional Wasserstein Barycenter +function of POT works. +""" + +# Author: Nicolas Courty +# +# License: MIT License + + +import numpy as np +import pylab as pl +import ot + +############################################################################## +# Data preparation +# ---------------- +# +# The four distributions are constructed from 4 simple images + + +f1 = 1 - pl.imread('../data/redcross.png')[:, :, 2] +f2 = 1 - pl.imread('../data/duck.png')[:, :, 2] +f3 = 1 - pl.imread('../data/heart.png')[:, :, 2] +f4 = 1 - pl.imread('../data/tooth.png')[:, :, 2] + +A = [] +f1=f1/np.sum(f1) +f2=f2/np.sum(f2) +f3=f3/np.sum(f3) +f4=f4/np.sum(f4) +A.append(f1) +A.append(f2) +A.append(f3) +A.append(f4) +A=np.array(A) + +nb_images = 5 + +# those are the four corners coordinates that will be interpolated by bilinear +# interpolation +v1=np.array((1,0,0,0)) +v2=np.array((0,1,0,0)) +v3=np.array((0,0,1,0)) +v4=np.array((0,0,0,1)) + + +############################################################################## +# Barycenter computation and visualization +# ---------------------------------------- +# + +pl.figure(figsize=(10,10)) +pl.title('Convolutional Wasserstein Barycenters in POT') +cm='Blues' +# regularization parameter +reg=0.004 +for i in range(nb_images): + for j in range(nb_images): + pl.subplot(nb_images,nb_images,i*nb_images+j+1) + tx=float(i)/(nb_images-1) + ty=float(j)/(nb_images-1) + + # weights are constructed by bilinear interpolation + tmp1=(1-tx)*v1+tx*v2 + tmp2=(1-tx)*v3+tx*v4 + weights=(1-ty)*tmp1+ty*tmp2 + + if i==0 and j==0: + pl.imshow(f1,cmap=cm) + pl.axis('off') + elif i==0 and j==(nb_images-1): + pl.imshow(f3,cmap=cm) + pl.axis('off') + elif i==(nb_images-1) and j==0: + pl.imshow(f2,cmap=cm) + pl.axis('off') + elif i==(nb_images-1) and j==(nb_images-1): + pl.imshow(f4,cmap=cm) + pl.axis('off') + else: + # call to barycenter computation + pl.imshow(ot.convolutional_barycenter2d(A,reg,weights),cmap=cm) + pl.axis('off') +pl.show() \ No newline at end of file diff --git a/ot/bregman.py b/ot/bregman.py index c755f51..05f4d9d 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -918,6 +918,112 @@ def barycenter(A, M, reg, weights=None, numItermax=1000, else: return geometricBar(weights, UKv) +def convolutional_barycenter2d(A,reg,weights=None,numItermax = 10000, stopThr=1e-9, verbose=False, log=False): + """Compute the entropic regularized wasserstein barycenter of distributions A + where A is a collection of 2D images. + + The function solves the following optimization problem: + + .. math:: + \mathbf{a} = arg\min_\mathbf{a} \sum_i W_{reg}(\mathbf{a},\mathbf{a}_i) + + where : + + - :math:`W_{reg}(\cdot,\cdot)` is the entropic regularized Wasserstein distance (see ot.bregman.sinkhorn) + - :math:`\mathbf{a}_i` are training distributions (2D images) in the mast two dimensions of matrix :math:`\mathbf{A}` + - reg is the regularization strength scalar value + + The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [21]_ + + Parameters + ---------- + A : np.ndarray (n,w,h) + n distributions (2D images) of size w x h + reg : float + Regularization term >0 + weights : np.ndarray (n,) + Weights of each image on the simplex (barycentric coodinates) + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + a : (w,h) ndarray + 2D Wasserstein barycenter + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A. & Guibas, L. (2015). + Convolutional wasserstein distances: Efficient optimal transportation on geometric domains + ACM Transactions on Graphics (TOG), 34(4), 66 + + + """ + + if weights is None: + weights = np.ones(A.shape[0]) / A.shape[0] + else: + assert(len(weights) == A.shape[0]) + + if log: + log = {'err': []} + + b=np.zeros_like(A[0,:,:]) + U=np.ones_like(A) + KV=np.ones_like(A) + threshold = 1e-30 # in order to avoids numerical precision issues + + cpt = 0 + err=1 + + # build the convolution operator + t = np.linspace(0,1,A.shape[1]) + [Y,X] = np.meshgrid(t,t) + xi1 = np.exp(-(X-Y)**2/reg) + K = lambda x: np.dot(np.dot(xi1,x),xi1) + + while (err>stopThr and cpt Date: Fri, 7 Sep 2018 12:04:44 +0200 Subject: pep8 normalization --- examples/plot_convolutional_barycenter.py | 70 +++++++++++++++---------------- ot/bregman.py | 68 +++++++++++++++--------------- 2 files changed, 70 insertions(+), 68 deletions(-) diff --git a/examples/plot_convolutional_barycenter.py b/examples/plot_convolutional_barycenter.py index d231da9..7ccdbe3 100644 --- a/examples/plot_convolutional_barycenter.py +++ b/examples/plot_convolutional_barycenter.py @@ -1,4 +1,4 @@ - + #%% # -*- coding: utf-8 -*- """ @@ -32,24 +32,24 @@ f3 = 1 - pl.imread('../data/heart.png')[:, :, 2] f4 = 1 - pl.imread('../data/tooth.png')[:, :, 2] A = [] -f1=f1/np.sum(f1) -f2=f2/np.sum(f2) -f3=f3/np.sum(f3) -f4=f4/np.sum(f4) +f1 = f1 / np.sum(f1) +f2 = f2 / np.sum(f2) +f3 = f3 / np.sum(f3) +f4 = f4 / np.sum(f4) A.append(f1) A.append(f2) A.append(f3) A.append(f4) -A=np.array(A) +A = np.array(A) nb_images = 5 # those are the four corners coordinates that will be interpolated by bilinear # interpolation -v1=np.array((1,0,0,0)) -v2=np.array((0,1,0,0)) -v3=np.array((0,0,1,0)) -v4=np.array((0,0,0,1)) +v1 = np.array((1, 0, 0, 0)) +v2 = np.array((0, 1, 0, 0)) +v3 = np.array((0, 0, 1, 0)) +v4 = np.array((0, 0, 0, 1)) ############################################################################## @@ -57,36 +57,36 @@ v4=np.array((0,0,0,1)) # ---------------------------------------- # -pl.figure(figsize=(10,10)) +pl.figure(figsize=(10, 10)) pl.title('Convolutional Wasserstein Barycenters in POT') -cm='Blues' +cm = 'Blues' # regularization parameter -reg=0.004 +reg = 0.004 for i in range(nb_images): for j in range(nb_images): - pl.subplot(nb_images,nb_images,i*nb_images+j+1) - tx=float(i)/(nb_images-1) - ty=float(j)/(nb_images-1) - + pl.subplot(nb_images, nb_images, i * nb_images + j + 1) + tx = float(i) / (nb_images - 1) + ty = float(j) / (nb_images - 1) + # weights are constructed by bilinear interpolation - tmp1=(1-tx)*v1+tx*v2 - tmp2=(1-tx)*v3+tx*v4 - weights=(1-ty)*tmp1+ty*tmp2 - - if i==0 and j==0: - pl.imshow(f1,cmap=cm) - pl.axis('off') - elif i==0 and j==(nb_images-1): - pl.imshow(f3,cmap=cm) - pl.axis('off') - elif i==(nb_images-1) and j==0: - pl.imshow(f2,cmap=cm) - pl.axis('off') - elif i==(nb_images-1) and j==(nb_images-1): - pl.imshow(f4,cmap=cm) - pl.axis('off') + tmp1 = (1 - tx) * v1 + tx * v2 + tmp2 = (1 - tx) * v3 + tx * v4 + weights = (1 - ty) * tmp1 + ty * tmp2 + + if i == 0 and j == 0: + pl.imshow(f1, cmap=cm) + pl.axis('off') + elif i == 0 and j == (nb_images - 1): + pl.imshow(f3, cmap=cm) + pl.axis('off') + elif i == (nb_images - 1) and j == 0: + pl.imshow(f2, cmap=cm) + pl.axis('off') + elif i == (nb_images - 1) and j == (nb_images - 1): + pl.imshow(f4, cmap=cm) + pl.axis('off') else: # call to barycenter computation - pl.imshow(ot.convolutional_barycenter2d(A,reg,weights),cmap=cm) + pl.imshow(ot.convolutional_barycenter2d(A, reg, weights), cmap=cm) pl.axis('off') -pl.show() \ No newline at end of file +pl.show() diff --git a/ot/bregman.py b/ot/bregman.py index 05f4d9d..f844f03 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -918,7 +918,8 @@ def barycenter(A, M, reg, weights=None, numItermax=1000, else: return geometricBar(weights, UKv) -def convolutional_barycenter2d(A,reg,weights=None,numItermax = 10000, stopThr=1e-9, verbose=False, log=False): + +def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1e-9, verbose=False, log=False): """Compute the entropic regularized wasserstein barycenter of distributions A where A is a collection of 2D images. @@ -979,51 +980,52 @@ def convolutional_barycenter2d(A,reg,weights=None,numItermax = 10000, stopThr=1e if log: log = {'err': []} - b=np.zeros_like(A[0,:,:]) - U=np.ones_like(A) - KV=np.ones_like(A) - threshold = 1e-30 # in order to avoids numerical precision issues + b = np.zeros_like(A[0, :, :]) + U = np.ones_like(A) + KV = np.ones_like(A) + threshold = 1e-30 # in order to avoids numerical precision issues cpt = 0 - err=1 - - # build the convolution operator - t = np.linspace(0,1,A.shape[1]) - [Y,X] = np.meshgrid(t,t) - xi1 = np.exp(-(X-Y)**2/reg) - K = lambda x: np.dot(np.dot(xi1,x),xi1) - - while (err>stopThr and cpt stopThr and cpt < numItermax): + + bold = b + cpt = cpt + 1 + + b = np.zeros_like(A[0, :, :]) for r in range(A.shape[0]): - KV[r,:,:]=K(A[r,:,:]/np.maximum(threshold,K(U[r,:,:]))) - b += weights[r] * np.log(np.maximum(threshold, U[r,:,:]*KV[r,:,:])) + KV[r, :, :] = K(A[r, :, :] / np.maximum(threshold, K(U[r, :, :]))) + b += weights[r] * np.log(np.maximum(threshold, U[r, :, :] * KV[r, :, :])) b = np.exp(b) for r in range(A.shape[0]): - U[r,:,:]=b/np.maximum(threshold,KV[r,:,:]) - - if cpt%10==1: - err=np.sum(np.abs(bold-b)) + U[r, :, :] = b / np.maximum(threshold, KV[r, :, :]) + + if cpt % 10 == 1: + err = np.sum(np.abs(bold - b)) # log and verbose print if log: log['err'].append(err) if verbose: - if cpt%200 ==0: - print('{:5s}|{:12s}'.format('It.','Err')+'\n'+'-'*19) - print('{:5d}|{:8e}|'.format(cpt,err)) + if cpt % 200 == 0: + print('{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) + print('{:5d}|{:8e}|'.format(cpt, err)) if log: - log['niter']=cpt - log['U']=U - return b,log + log['niter'] = cpt + log['U'] = U + return b, log else: - return b - + return b + def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000, stopThr=1e-3, verbose=False, log=False): -- cgit v1.2.3 From e8c6d2fc9c6b08bbed11628326711ab29c155bac Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Fri, 7 Sep 2018 12:34:14 +0200 Subject: pep8 fixed (contd) --- ot/bregman.py | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index f844f03..5327dbc 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -920,8 +920,8 @@ def barycenter(A, M, reg, weights=None, numItermax=1000, def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1e-9, verbose=False, log=False): - """Compute the entropic regularized wasserstein barycenter of distributions A - where A is a collection of 2D images. + """Compute the entropic regularized wasserstein barycenter of distributions A + where A is a collection of 2D images. The function solves the following optimization problem: @@ -966,8 +966,8 @@ def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1 ---------- .. [21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A. & Guibas, L. (2015). - Convolutional wasserstein distances: Efficient optimal transportation on geometric domains - ACM Transactions on Graphics (TOG), 34(4), 66 + Convolutional wasserstein distances: Efficient optimal transportation on geometric domains + ACM Transactions on Graphics (TOG), 34(4), 66 """ @@ -993,7 +993,8 @@ def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1 [Y, X] = np.meshgrid(t, t) xi1 = np.exp(-(X - Y)**2 / reg) - def K(x): return np.dot(np.dot(xi1, x), xi1) + def K(x): + return np.dot(np.dot(xi1, x), xi1) while (err > stopThr and cpt < numItermax): -- cgit v1.2.3 From d19295b9cb29d21e09eeb28ac4b0e61990727023 Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Fri, 7 Sep 2018 14:41:00 +0200 Subject: stabThr and pep8 --- ot/bregman.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 5327dbc..748ac30 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -919,7 +919,7 @@ def barycenter(A, M, reg, weights=None, numItermax=1000, return geometricBar(weights, UKv) -def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1e-9, verbose=False, log=False): +def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1e-9, stabThr=1e-30, verbose=False, log=False): """Compute the entropic regularized wasserstein barycenter of distributions A where A is a collection of 2D images. @@ -948,6 +948,8 @@ def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1 Max number of iterations stopThr : float, optional Stop threshol on error (>0) + stabThr : float, optional + Stabilization threshold to avoid numerical precision issue verbose : bool, optional Print information along iterations log : bool, optional @@ -983,7 +985,6 @@ def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1 b = np.zeros_like(A[0, :, :]) U = np.ones_like(A) KV = np.ones_like(A) - threshold = 1e-30 # in order to avoids numerical precision issues cpt = 0 err = 1 @@ -993,7 +994,7 @@ def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1 [Y, X] = np.meshgrid(t, t) xi1 = np.exp(-(X - Y)**2 / reg) - def K(x): + def K(x): return np.dot(np.dot(xi1, x), xi1) while (err > stopThr and cpt < numItermax): @@ -1003,11 +1004,11 @@ def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1 b = np.zeros_like(A[0, :, :]) for r in range(A.shape[0]): - KV[r, :, :] = K(A[r, :, :] / np.maximum(threshold, K(U[r, :, :]))) - b += weights[r] * np.log(np.maximum(threshold, U[r, :, :] * KV[r, :, :])) + KV[r, :, :] = K(A[r, :, :] / np.maximum(stabThr, K(U[r, :, :]))) + b += weights[r] * np.log(np.maximum(stabThr, U[r, :, :] * KV[r, :, :])) b = np.exp(b) for r in range(A.shape[0]): - U[r, :, :] = b / np.maximum(threshold, KV[r, :, :]) + U[r, :, :] = b / np.maximum(stabThr, KV[r, :, :]) if cpt % 10 == 1: err = np.sum(np.abs(bold - b)) -- cgit v1.2.3 From dab572396be97fcf5439e4e20f887165b1ade62c Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Fri, 7 Sep 2018 14:49:20 +0200 Subject: whitetrail pep8 --- ot/bregman.py | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 748ac30..35e51f8 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -920,8 +920,8 @@ def barycenter(A, M, reg, weights=None, numItermax=1000, def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1e-9, stabThr=1e-30, verbose=False, log=False): - """Compute the entropic regularized wasserstein barycenter of distributions A - where A is a collection of 2D images. + """Compute the entropic regularized wasserstein barycenter of distributions A + where A is a collection of 2D images. The function solves the following optimization problem: @@ -949,7 +949,7 @@ def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1 stopThr : float, optional Stop threshol on error (>0) stabThr : float, optional - Stabilization threshold to avoid numerical precision issue + Stabilization threshold to avoid numerical precision issue verbose : bool, optional Print information along iterations log : bool, optional @@ -967,9 +967,9 @@ def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1 References ---------- - .. [21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A. & Guibas, L. (2015). - Convolutional wasserstein distances: Efficient optimal transportation on geometric domains - ACM Transactions on Graphics (TOG), 34(4), 66 + .. [21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A. & Guibas, L. (2015). + Convolutional wasserstein distances: Efficient optimal transportation on geometric domains + ACM Transactions on Graphics (TOG), 34(4), 66 """ -- cgit v1.2.3 From 3e7eb430ddf59cc2a6bbd2ada29ac22c9d31cd05 Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Fri, 7 Sep 2018 15:13:44 +0200 Subject: test wasserstein barycenter --- test/test_bregman.py | 24 ++++++++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/test/test_bregman.py b/test/test_bregman.py index c8e9179..01ec655 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -105,6 +105,30 @@ def test_bary(): ot.bregman.barycenter(A, M, reg, log=True, verbose=True) +def test_wassersteinbary(): + + size = 100 # size of a square image + a1 = np.random.randn(size, size) + a1 += a1.min() + a1 = a1 / np.sum(a1) + a2 = np.random.randn(size, size) + a2 += a2.min() + a2 = a2 / np.sum(a2) + # creating matrix A containing all distributions + A = np.zeros((2, 100, 100)) + A[0, :, :] = a1 + A[1, :, :] = a2 + + # wasserstein + reg = 1e-3 + bary_wass = ot.bregman.convolutional_barycenter2d(A, reg) + + np.testing.assert_allclose(1, np.sum(bary_wass)) + + # help in checking if log and verbose do not bug the function + ot.bregman.convolutional_barycenter2d(A, reg, log=True, verbose=True) + + def test_unmix(): n_bins = 50 # nb bins -- cgit v1.2.3 From 2b8b18082257edcbbfe503ef2643f235161930b7 Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Thu, 13 Sep 2018 12:15:39 -0700 Subject: better implementation on gradient updates --- ot/stochastic.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ot/stochastic.py b/ot/stochastic.py index a369ba8..ec53015 100644 --- a/ot/stochastic.py +++ b/ot/stochastic.py @@ -617,8 +617,8 @@ def sgd_entropic_regularization(a, b, M, reg, batch_size, numItermax, lr): update_alpha, update_beta = batch_grad_dual(a, b, M, reg, cur_alpha, cur_beta, batch_size, batch_alpha, batch_beta) - cur_alpha += (lr / k) * update_alpha - cur_beta += (lr / k) * update_beta + cur_alpha[batch_alpha] += (lr / k) * update_alpha[batch_alpha] + cur_beta[batch_beta] += (lr / k) * update_beta[batch_beta] return cur_alpha, cur_beta -- cgit v1.2.3 From 05eec20390f453c3ba57ff68fd72a30375f78234 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 14 Sep 2018 08:57:39 +0200 Subject: correct path function in example --- examples/plot_convolutional_barycenter.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/plot_convolutional_barycenter.py b/examples/plot_convolutional_barycenter.py index 7ccdbe3..e74db04 100644 --- a/examples/plot_convolutional_barycenter.py +++ b/examples/plot_convolutional_barycenter.py @@ -87,6 +87,6 @@ for i in range(nb_images): pl.axis('off') else: # call to barycenter computation - pl.imshow(ot.convolutional_barycenter2d(A, reg, weights), cmap=cm) + pl.imshow(ot.bregman.convolutional_barycenter2d(A, reg, weights), cmap=cm) pl.axis('off') pl.show() -- cgit v1.2.3 From 7786e8502ea010a52918861014ab0e7a934a7d45 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 14 Sep 2018 08:58:56 +0200 Subject: correct path function in example --- test/test_stochastic.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test/test_stochastic.py b/test/test_stochastic.py index 0128317..62cf82a 100644 --- a/test/test_stochastic.py +++ b/test/test_stochastic.py @@ -92,7 +92,7 @@ def test_sag_asgd_sinkhorn(): # test all algorithms n = 15 reg = 1 - nb_iter = 300000 + nb_iter = 30000 rng = np.random.RandomState(0) x = rng.randn(n, 2) @@ -167,7 +167,7 @@ def test_dual_sgd_sinkhorn(): # test all dual algorithms n = 10 reg = 1 - nb_iter = 150000 + nb_iter = 15000 batch_size = 10 rng = np.random.RandomState(0) -- cgit v1.2.3 From ccbe274fd9554492bb88ddaf530c2800a8dc3418 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 14 Sep 2018 09:03:07 +0200 Subject: speedup test stochastic --- test/test_stochastic.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/test/test_stochastic.py b/test/test_stochastic.py index 62cf82a..f0f3fc8 100644 --- a/test/test_stochastic.py +++ b/test/test_stochastic.py @@ -32,7 +32,7 @@ def test_stochastic_sag(): # test sag n = 15 reg = 1 - numItermax = 300000 + numItermax = 30000 rng = np.random.RandomState(0) x = rng.randn(n, 2) @@ -62,7 +62,7 @@ def test_stochastic_asgd(): # test asgd n = 15 reg = 1 - numItermax = 300000 + numItermax = 100000 rng = np.random.RandomState(0) x = rng.randn(n, 2) @@ -92,7 +92,7 @@ def test_sag_asgd_sinkhorn(): # test all algorithms n = 15 reg = 1 - nb_iter = 30000 + nb_iter = 100000 rng = np.random.RandomState(0) x = rng.randn(n, 2) -- cgit v1.2.3 From 827e840447f47c8656d90202db774d23ae96fd30 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 21 Sep 2018 09:53:11 +0200 Subject: add badge pepy --- README.md | 1 + 1 file changed, 1 insertion(+) diff --git a/README.md b/README.md index dded582..8491dc5 100644 --- a/README.md +++ b/README.md @@ -4,6 +4,7 @@ [![Anaconda Cloud](https://anaconda.org/conda-forge/pot/badges/version.svg)](https://anaconda.org/conda-forge/pot) [![Build Status](https://travis-ci.org/rflamary/POT.svg?branch=master)](https://travis-ci.org/rflamary/POT) [![Documentation Status](https://readthedocs.org/projects/pot/badge/?version=latest)](http://pot.readthedocs.io/en/latest/?badge=latest) +[![Downloads](http://pepy.tech/badge/pot)](http://pepy.tech/project/pot) [![Anaconda downloads](https://anaconda.org/conda-forge/pot/badges/downloads.svg)](https://anaconda.org/conda-forge/pot) [![License](https://anaconda.org/conda-forge/pot/badges/license.svg)](https://github.com/rflamary/POT/blob/master/LICENSE) -- cgit v1.2.3 From 462f8ff7df283a922866e32dc6b3eb604f35ae3d Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 21 Sep 2018 09:54:27 +0200 Subject: typo readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 8491dc5..c3d6474 100644 --- a/README.md +++ b/README.md @@ -80,7 +80,7 @@ Note that for easier access the module is name ot instead of pot. Some sub-modules require additional dependences which are discussed below -* **ot.dr** (Wasserstein dimensionality rediuction) depends on autograd and pymanopt that can be installed with: +* **ot.dr** (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with: ``` pip install pymanopt autograd ``` -- cgit v1.2.3 From 697bd55a152d6318e292cffdac2ec18ac4528d30 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 21 Sep 2018 10:08:26 +0200 Subject: https badge --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index c3d6474..d2f0fea 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ [![Anaconda Cloud](https://anaconda.org/conda-forge/pot/badges/version.svg)](https://anaconda.org/conda-forge/pot) [![Build Status](https://travis-ci.org/rflamary/POT.svg?branch=master)](https://travis-ci.org/rflamary/POT) [![Documentation Status](https://readthedocs.org/projects/pot/badge/?version=latest)](http://pot.readthedocs.io/en/latest/?badge=latest) -[![Downloads](http://pepy.tech/badge/pot)](http://pepy.tech/project/pot) +[![Downloads](https://pepy.tech/badge/pot)](https://pepy.tech/project/pot) [![Anaconda downloads](https://anaconda.org/conda-forge/pot/badges/downloads.svg)](https://anaconda.org/conda-forge/pot) [![License](https://anaconda.org/conda-forge/pot/badges/license.svg)](https://github.com/rflamary/POT/blob/master/LICENSE) -- cgit v1.2.3 From 653fd0084c529bc74dabf93c68a9bdd5ac8f377a Mon Sep 17 00:00:00 2001 From: alain Date: Mon, 24 Sep 2018 09:05:47 +0200 Subject: adding greenkhorn --- ot/bregman.py | 151 ++++++++++++++++++++++++++++++++++++++++++++++++++- test/test_bregman.py | 4 +- 2 files changed, 153 insertions(+), 2 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index c755f51..1f9874e 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -47,7 +47,7 @@ def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, reg : float Regularization term >0 method : str - method used for the solver either 'sinkhorn', 'sinkhorn_stabilized' or + method used for the solver either 'sinkhorn', 'greenkhorn', 'sinkhorn_stabilized' or 'sinkhorn_epsilon_scaling', see those function for specific parameters numItermax : int, optional Max number of iterations @@ -103,6 +103,10 @@ def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, def sink(): return sinkhorn_knopp(a, b, M, reg, numItermax=numItermax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) + if method.lower() == 'greenkhorn': + def sink(): + return greenkhorn(a, b, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log) elif method.lower() == 'sinkhorn_stabilized': def sink(): return sinkhorn_stabilized(a, b, M, reg, numItermax=numItermax, @@ -197,6 +201,8 @@ def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + [21] Altschuler J., Weed J., Rigollet P. : Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration, Advances in Neural Information Processing Systems (NIPS) 31, 2017 + See Also @@ -204,6 +210,7 @@ def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, ot.lp.emd : Unregularized OT ot.optim.cg : General regularized OT ot.bregman.sinkhorn_knopp : Classic Sinkhorn [2] + ot.bregman.greenkhorn : Greenkhorn [21] ot.bregman.sinkhorn_stabilized: Stabilized sinkhorn [9][10] ot.bregman.sinkhorn_epsilon_scaling: Sinkhorn with epslilon scaling [9][10] @@ -410,6 +417,148 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, return u.reshape((-1, 1)) * K * v.reshape((1, -1)) + +def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log = False): + """ + Solve the entropic regularization optimal transport problem and return the OT matrix + + The algorithm used is based on the paper + + Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration + by Jason Altschuler, Jonathan Weed, Philippe Rigollet + appeared at NIPS 2017 + + which is a stochastic version of the Sinkhorn-Knopp algorithm [2]. + + The function solves the following optimization problem: + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + where : + + - M is the (ns,nt) metric cost matrix + - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target weights (sum to 1) + + + + Parameters + ---------- + a : np.ndarray (ns,) + samples weights in the source domain + b : np.ndarray (nt,) or np.ndarray (nt,nbb) + samples in the target domain, compute sinkhorn with multiple targets + and fixed M if b is a matrix (return OT loss + dual variables in log) + M : np.ndarray (ns,nt) + loss matrix + reg : float + Regularization term >0 + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + log : bool, optional + record log if True + + + Returns + ------- + gamma : (ns x nt) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + Examples + -------- + + >>> import ot + >>> a=[.5,.5] + >>> b=[.5,.5] + >>> M=[[0.,1.],[1.,0.]] + >>> ot.sinkhorn(a,b,M,1) + array([[ 0.36552929, 0.13447071], + [ 0.13447071, 0.36552929]]) + + + References + ---------- + + .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013 + [21] J. Altschuler, J.Weed, P. Rigollet : Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration, Advances in Neural Information Processing Systems (NIPS) 31, 2017 + + + See Also + -------- + ot.lp.emd : Unregularized OT + ot.optim.cg : General regularized OT + + """ + + i = 0 + + n = a.shape[0] + m = b.shape[0] + + # Next 3 lines equivalent to K= np.exp(-M/reg), but faster to compute + K = np.empty(M.shape, dtype=M.dtype) + np.divide(M, -reg, out=K) + np.exp(K, out=K) + + u = np.ones(n)/n + v = np.ones(m)/m + G = np.diag(u)@K@np.diag(v) + + one_n = np.ones(n) + one_m = np.ones(m) + viol = G@one_m - a + viol_2 = G.T@one_n - b + stopThr_val = 1 + if log: + log['u'] = u + log['v'] = v + + while i < numItermax and stopThr_val > stopThr: + i +=1 + i_1 = np.argmax(np.abs(viol)) + i_2 = np.argmax(np.abs(viol_2)) + m_viol_1 = np.abs(viol[i_1]) + m_viol_2 = np.abs(viol_2[i_2]) + stopThr_val = np.maximum(m_viol_1,m_viol_2) + + if m_viol_1 > m_viol_2: + old_u = u[i_1] + u[i_1] = a[i_1]/(K[i_1,:]@v) + G[i_1,:] = u[i_1]*K[i_1,:]*v + + viol[i_1] = u[i_1]*K[i_1,:]@v - a[i_1] + viol_2 = viol_2 + ( K[i_1,:].T*(u[i_1] - old_u)*v) + + else: + old_v = v[i_2] + v[i_2] = b[i_2]/(K[:,i_2].T@u) + G[:,i_2] = u*K[:,i_2]*v[i_2] + #aviol = (G@one_m - a) + #aviol_2 = (G.T@one_n - b) + viol = viol + ( -old_v + v[i_2])*K[:,i_2]*u + viol_2[i_2] = v[i_2]*K[:,i_2]@u - b[i_2] + + #print('b',np.max(abs(aviol -viol)),np.max(abs(aviol_2 - viol_2))) + + if log: + log['u'] = u + log['v'] = v + + if log: + return G,log + else: + return G + def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, warmstart=None, verbose=False, print_period=20, log=False, **kwargs): """ diff --git a/test/test_bregman.py b/test/test_bregman.py index c8e9179..03e38bf 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -71,12 +71,14 @@ def test_sinkhorn_variants(): Ges = ot.sinkhorn( u, u, M, 1, method='sinkhorn_epsilon_scaling', stopThr=1e-10) Gerr = ot.sinkhorn(u, u, M, 1, method='do_not_exists', stopThr=1e-10) + G_green = ot.sinkhorn(u, u, M, 1, method='greenkhorn', stopThr=1e-10) # check values np.testing.assert_allclose(G0, Gs, atol=1e-05) np.testing.assert_allclose(G0, Ges, atol=1e-05) np.testing.assert_allclose(G0, Gerr) - + np.testing.assert_allclose(G0, G_green, atol = 1e-32) + print(G0,G_green) def test_bary(): -- cgit v1.2.3 From eb17e022fee209f3d363a6f8dcbb0064fccde1ad Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Sep 2018 10:07:00 +0200 Subject: correct if error bug --- ot/bregman.py | 58 +++++++++++++++++++++++++++++----------------------------- 1 file changed, 29 insertions(+), 29 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 1f9874e..8538c92 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -103,10 +103,10 @@ def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, def sink(): return sinkhorn_knopp(a, b, M, reg, numItermax=numItermax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) - if method.lower() == 'greenkhorn': + elif method.lower() == 'greenkhorn': def sink(): return greenkhorn(a, b, M, reg, numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log) + stopThr=stopThr, verbose=verbose, log=log) elif method.lower() == 'sinkhorn_stabilized': def sink(): return sinkhorn_stabilized(a, b, M, reg, numItermax=numItermax, @@ -417,17 +417,16 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, return u.reshape((-1, 1)) * K * v.reshape((1, -1)) - -def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log = False): +def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log=False): """ Solve the entropic regularization optimal transport problem and return the OT matrix - + The algorithm used is based on the paper - + Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration by Jason Altschuler, Jonathan Weed, Philippe Rigollet appeared at NIPS 2017 - + which is a stochastic version of the Sinkhorn-Knopp algorithm [2]. The function solves the following optimization problem: @@ -499,21 +498,21 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log ot.optim.cg : General regularized OT """ - + i = 0 - + n = a.shape[0] m = b.shape[0] - + # Next 3 lines equivalent to K= np.exp(-M/reg), but faster to compute K = np.empty(M.shape, dtype=M.dtype) np.divide(M, -reg, out=K) np.exp(K, out=K) - - u = np.ones(n)/n - v = np.ones(m)/m + + u = np.ones(n) / n + v = np.ones(m) / m G = np.diag(u)@K@np.diag(v) - + one_n = np.ones(n) one_m = np.ones(m) viol = G@one_m - a @@ -524,41 +523,42 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log log['v'] = v while i < numItermax and stopThr_val > stopThr: - i +=1 + i += 1 i_1 = np.argmax(np.abs(viol)) i_2 = np.argmax(np.abs(viol_2)) m_viol_1 = np.abs(viol[i_1]) m_viol_2 = np.abs(viol_2[i_2]) - stopThr_val = np.maximum(m_viol_1,m_viol_2) - + stopThr_val = np.maximum(m_viol_1, m_viol_2) + if m_viol_1 > m_viol_2: old_u = u[i_1] - u[i_1] = a[i_1]/(K[i_1,:]@v) - G[i_1,:] = u[i_1]*K[i_1,:]*v + u[i_1] = a[i_1] / (K[i_1, :]@v) + G[i_1, :] = u[i_1] * K[i_1, :] * v - viol[i_1] = u[i_1]*K[i_1,:]@v - a[i_1] - viol_2 = viol_2 + ( K[i_1,:].T*(u[i_1] - old_u)*v) + viol[i_1] = u[i_1] * K[i_1, :]@v - a[i_1] + viol_2 = viol_2 + (K[i_1, :].T * (u[i_1] - old_u) * v) else: old_v = v[i_2] - v[i_2] = b[i_2]/(K[:,i_2].T@u) - G[:,i_2] = u*K[:,i_2]*v[i_2] + v[i_2] = b[i_2] / (K[:, i_2].T@u) + G[:, i_2] = u * K[:, i_2] * v[i_2] #aviol = (G@one_m - a) #aviol_2 = (G.T@one_n - b) - viol = viol + ( -old_v + v[i_2])*K[:,i_2]*u - viol_2[i_2] = v[i_2]*K[:,i_2]@u - b[i_2] - + viol = viol + (-old_v + v[i_2]) * K[:, i_2] * u + viol_2[i_2] = v[i_2] * K[:, i_2]@u - b[i_2] + #print('b',np.max(abs(aviol -viol)),np.max(abs(aviol_2 - viol_2))) - + if log: log['u'] = u log['v'] = v - + if log: - return G,log + return G, log else: return G + def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, warmstart=None, verbose=False, print_period=20, log=False, **kwargs): """ -- cgit v1.2.3 From eb7a395c8afb71d51e58c286e216602505a496f0 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Sep 2018 10:08:00 +0200 Subject: add reference in readme --- README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/README.md b/README.md index 1d3b097..2ebb15c 100644 --- a/README.md +++ b/README.md @@ -228,3 +228,5 @@ You can also post bug reports and feature requests in Github issues. Make sure t [19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, A.& Blondel, M. [Large-scale Optimal Transport and Mapping Estimation](https://arxiv.org/pdf/1711.02283.pdf). International Conference on Learning Representation (2018) [20] Cuturi, M. and Doucet, A. (2014) [Fast Computation of Wasserstein Barycenters](http://proceedings.mlr.press/v32/cuturi14.html). International Conference in Machine Learning + +[21] J. Altschuler, J.Weed, P. Rigollet, (2017) Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration, Advances in Neural Information Processing Systems (NIPS) 31 \ No newline at end of file -- cgit v1.2.3 From ff824a22d232b68ddffcb13b976d75a5b8bc03e5 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Sep 2018 10:08:18 +0200 Subject: add reference in readme --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 2ebb15c..6a6686c 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,7 @@ This open source Python library provide several solvers for optimization problem It provides the following solvers: * OT Network Flow solver for the linear program/ Earth Movers Distance [1]. -* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10] with optional GPU implementation (requires cudamat). +* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10] and greedy SInkhorn [21] with optional GPU implementation (requires cudamat). * Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17]. * Non regularized Wasserstein barycenters [16] with LP solver (only small scale). * Bregman projections for Wasserstein barycenter [3] and unmixing [4]. -- cgit v1.2.3 From f3433fda3e8f5c58ec1d7e5623825d4627435ebc Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Sep 2018 10:09:00 +0200 Subject: working tests --- test/test_bregman.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/test/test_bregman.py b/test/test_bregman.py index 03e38bf..52bbbd2 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -77,8 +77,9 @@ def test_sinkhorn_variants(): np.testing.assert_allclose(G0, Gs, atol=1e-05) np.testing.assert_allclose(G0, Ges, atol=1e-05) np.testing.assert_allclose(G0, Gerr) - np.testing.assert_allclose(G0, G_green, atol = 1e-32) - print(G0,G_green) + np.testing.assert_allclose(G0, G_green, atol=1e-5) + print(G0, G_green) + def test_bary(): -- cgit v1.2.3 From 7ffd4fef3260e086b0b1ed050f5cb4b83195b122 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Sep 2018 10:14:44 +0200 Subject: remove @ for python compatibility+ comments alexandre --- ot/bregman.py | 28 ++++++++++++++-------------- 1 file changed, 14 insertions(+), 14 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 8538c92..faa6365 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -480,7 +480,7 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log= >>> a=[.5,.5] >>> b=[.5,.5] >>> M=[[0.,1.],[1.,0.]] - >>> ot.sinkhorn(a,b,M,1) + >>> ot.bregman.greenkhorn(a,b,M,1) array([[ 0.36552929, 0.13447071], [ 0.13447071, 0.36552929]]) @@ -505,18 +505,18 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log= m = b.shape[0] # Next 3 lines equivalent to K= np.exp(-M/reg), but faster to compute - K = np.empty(M.shape, dtype=M.dtype) + K = np.empty_like(M) np.divide(M, -reg, out=K) np.exp(K, out=K) - u = np.ones(n) / n - v = np.ones(m) / m - G = np.diag(u)@K@np.diag(v) + u = np.full(n, 1. / n) + v = np.full(m, 1. / m) + G = u[:, np.newaxis] * K * v[np.newaxis, :] one_n = np.ones(n) one_m = np.ones(m) - viol = G@one_m - a - viol_2 = G.T@one_n - b + viol = G.sum(1) - a + viol_2 = G.sum(0) - b stopThr_val = 1 if log: log['u'] = u @@ -532,26 +532,26 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log= if m_viol_1 > m_viol_2: old_u = u[i_1] - u[i_1] = a[i_1] / (K[i_1, :]@v) + u[i_1] = a[i_1] / (K[i_1, :].dot(v)) G[i_1, :] = u[i_1] * K[i_1, :] * v - viol[i_1] = u[i_1] * K[i_1, :]@v - a[i_1] + viol[i_1] = u[i_1] * K[i_1, :].dot(v) - a[i_1] viol_2 = viol_2 + (K[i_1, :].T * (u[i_1] - old_u) * v) else: old_v = v[i_2] - v[i_2] = b[i_2] / (K[:, i_2].T@u) + v[i_2] = b[i_2] / (K[:, i_2].T.dot(u)) G[:, i_2] = u * K[:, i_2] * v[i_2] #aviol = (G@one_m - a) #aviol_2 = (G.T@one_n - b) viol = viol + (-old_v + v[i_2]) * K[:, i_2] * u - viol_2[i_2] = v[i_2] * K[:, i_2]@u - b[i_2] + viol_2[i_2] = v[i_2] * K[:, i_2].dot(u) - b[i_2] #print('b',np.max(abs(aviol -viol)),np.max(abs(aviol_2 - viol_2))) - if log: - log['u'] = u - log['v'] = v + if log: + log['u'] = u + log['v'] = v if log: return G, log -- cgit v1.2.3 From 24a53ef2dba0a43c282f6b31937c3e7901df7930 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Sep 2018 10:17:21 +0200 Subject: add contributor --- README.md | 1 + ot/bregman.py | 4 ++-- 2 files changed, 3 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 6a6686c..4d824ce 100644 --- a/README.md +++ b/README.md @@ -165,6 +165,7 @@ The contributors to this library are: * [Antoine Rolet](https://arolet.github.io/) * Erwan Vautier (Gromov-Wasserstein) * [Kilian Fatras](https://kilianfatras.github.io/) +* [Alain Rakotomamonjy](https://sites.google.com/site/alainrakotomamonjy/home) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): diff --git a/ot/bregman.py b/ot/bregman.py index faa6365..97027e8 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -536,7 +536,7 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log= G[i_1, :] = u[i_1] * K[i_1, :] * v viol[i_1] = u[i_1] * K[i_1, :].dot(v) - a[i_1] - viol_2 = viol_2 + (K[i_1, :].T * (u[i_1] - old_u) * v) + viol_2 += (K[i_1, :].T * (u[i_1] - old_u) * v) else: old_v = v[i_2] @@ -544,7 +544,7 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log= G[:, i_2] = u * K[:, i_2] * v[i_2] #aviol = (G@one_m - a) #aviol_2 = (G.T@one_n - b) - viol = viol + (-old_v + v[i_2]) * K[:, i_2] * u + viol += (-old_v + v[i_2]) * K[:, i_2] * u viol_2[i_2] = v[i_2] * K[:, i_2].dot(u) - b[i_2] #print('b',np.max(abs(aviol -viol)),np.max(abs(aviol_2 - viol_2))) -- cgit v1.2.3 From 55e8392993919d3c67538756663abd943d3bb491 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Sep 2018 10:19:18 +0200 Subject: remove unused variable --- ot/bregman.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 97027e8..6e446a1 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -513,8 +513,6 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log= v = np.full(m, 1. / m) G = u[:, np.newaxis] * K * v[np.newaxis, :] - one_n = np.ones(n) - one_m = np.ones(m) viol = G.sum(1) - a viol_2 = G.sum(0) - b stopThr_val = 1 -- cgit v1.2.3 From 1d494107611c2e6e2249b7a624e64cec6357b4bd Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Sep 2018 10:23:02 +0200 Subject: implement for loop --- ot/bregman.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/ot/bregman.py b/ot/bregman.py index 6e446a1..05f7c75 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -520,7 +520,7 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log= log['u'] = u log['v'] = v - while i < numItermax and stopThr_val > stopThr: + for i in range(numItermax): i += 1 i_1 = np.argmax(np.abs(viol)) i_2 = np.argmax(np.abs(viol_2)) @@ -547,6 +547,11 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log= #print('b',np.max(abs(aviol -viol)),np.max(abs(aviol_2 - viol_2))) + if stopThr_val <= stopThr: + break + else: + print('Warning: Algorithm did not converge') + if log: log['u'] = u log['v'] = v -- cgit v1.2.3 From 75fe96c183852971bb7be1da39af202b9f7d6e6c Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Sep 2018 10:25:25 +0200 Subject: remove i+1 --- ot/bregman.py | 1 - 1 file changed, 1 deletion(-) diff --git a/ot/bregman.py b/ot/bregman.py index 05f7c75..1f5150a 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -521,7 +521,6 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log= log['v'] = v for i in range(numItermax): - i += 1 i_1 = np.argmax(np.abs(viol)) i_2 = np.argmax(np.abs(viol_2)) m_viol_1 = np.abs(viol[i_1]) -- cgit v1.2.3 From dee6d6e16f6e5d328bc590089cf99ef586d7ca0f Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Sep 2018 10:34:32 +0200 Subject: correct reference number in doc --- README.md | 2 +- ot/bregman.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 1c8114a..16fa153 100644 --- a/README.md +++ b/README.md @@ -232,4 +232,4 @@ You can also post bug reports and feature requests in Github issues. Make sure t [21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A. & Guibas, L. (2015). [Convolutional wasserstein distances: Efficient optimal transportation on geometric domains](https://dl.acm.org/citation.cfm?id=2766963). ACM Transactions on Graphics (TOG), 34(4), 66. -[21] J. Altschuler, J.Weed, P. Rigollet, (2017) [Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration](https://papers.nips.cc/paper/6792-near-linear-time-approximation-algorithms-for-optimal-transport-via-sinkhorn-iteration.pdf), Advances in Neural Information Processing Systems (NIPS) 31 +[22] J. Altschuler, J.Weed, P. Rigollet, (2017) [Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration](https://papers.nips.cc/paper/6792-near-linear-time-approximation-algorithms-for-optimal-transport-via-sinkhorn-iteration.pdf), Advances in Neural Information Processing Systems (NIPS) 31 diff --git a/ot/bregman.py b/ot/bregman.py index 418de57..fd04fa4 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -489,7 +489,7 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log= ---------- .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013 - [21] J. Altschuler, J.Weed, P. Rigollet : Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration, Advances in Neural Information Processing Systems (NIPS) 31, 2017 + [22] J. Altschuler, J.Weed, P. Rigollet : Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration, Advances in Neural Information Processing Systems (NIPS) 31, 2017 See Also -- cgit v1.2.3 From 8f908bd3096d9bc57a05b3de1c37b97805a10959 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Sep 2018 10:35:27 +0200 Subject: update readme+doc --- README.md | 4 ++-- docs/source/readme.rst | 38 +++++++++++++++++++++++++++++++++++--- 2 files changed, 37 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index 16fa153..67f62b3 100644 --- a/README.md +++ b/README.md @@ -14,10 +14,10 @@ This open source Python library provide several solvers for optimization problem It provides the following solvers: * OT Network Flow solver for the linear program/ Earth Movers Distance [1]. -* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10] and greedy SInkhorn [21] with optional GPU implementation (requires cudamat). +* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10] and greedy SInkhorn [22] with optional GPU implementation (requires cudamat). * Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17]. * Non regularized Wasserstein barycenters [16] with LP solver (only small scale). -* Bregman projections for Wasserstein barycenter [3] and unmixing [4]. +* Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4]. * Optimal transport for domain adaptation with group lasso regularization [5] * Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. * Linear OT [14] and Joint OT matrix and mapping estimation [8]. diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 5d37f64..a839231 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -13,13 +13,14 @@ It provides the following solvers: - OT Network Flow solver for the linear program/ Earth Movers Distance [1]. - Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] - and stabilized version [9][10] with optional GPU implementation - (requires cudamat). + and stabilized version [9][10] and greedy SInkhorn [22] with optional + GPU implementation (requires cudamat). - Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17]. - Non regularized Wasserstein barycenters [16] with LP solver (only small scale). -- Bregman projections for Wasserstein barycenter [3] and unmixing [4]. +- Bregman projections for Wasserstein barycenter [3], convolutional + barycenter [21] and unmixing [4]. - Optimal transport for domain adaptation with group lasso regularization [5] - Conditional gradient [6] and Generalized conditional gradient for @@ -29,6 +30,9 @@ It provides the following solvers: pymanopt). - Gromov-Wasserstein distances and barycenters ([13] and regularized [12]) +- Stochastic Optimization for Large-scale Optimal Transport (semi-dual + problem [18] and dual problem [19]) +- Non regularized free support Wasserstein barycenters [20]. Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -219,6 +223,9 @@ The contributors to this library are: - `Stanislas Chambon `__ - `Antoine Rolet `__ - Erwan Vautier (Gromov-Wasserstein) +- `Kilian Fatras `__ +- `Alain + Rakotomamonjy `__ This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various @@ -334,6 +341,31 @@ Optimal Transport `__. Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS). +[18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016) `Stochastic +Optimization for Large-scale Optimal +Transport `__. Advances in Neural +Information Processing Systems (2016). + +[19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, +A.& Blondel, M. `Large-scale Optimal Transport and Mapping +Estimation `__. International +Conference on Learning Representation (2018) + +[20] Cuturi, M. and Doucet, A. (2014) `Fast Computation of Wasserstein +Barycenters `__. +International Conference in Machine Learning + +[21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., +Nguyen, A. & Guibas, L. (2015). `Convolutional wasserstein distances: +Efficient optimal transportation on geometric +domains `__. ACM +Transactions on Graphics (TOG), 34(4), 66. + +[22] J. Altschuler, J.Weed, P. Rigollet, (2017) `Near-linear time +approximation algorithms for optimal transport via Sinkhorn +iteration `__, +Advances in Neural Information Processing Systems (NIPS) 31 + .. |PyPI version| image:: https://badge.fury.io/py/POT.svg :target: https://badge.fury.io/py/POT .. |Anaconda Cloud| image:: https://anaconda.org/conda-forge/pot/badges/version.svg -- cgit v1.2.3 From 1b24b1fd60a7126cd1646525ac5d7cf25f382a3a Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Sep 2018 10:40:15 +0200 Subject: remove variable i initialization --- ot/bregman.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index fd04fa4..d1057ff 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -499,8 +499,6 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log= """ - i = 0 - n = a.shape[0] m = b.shape[0] -- cgit v1.2.3 From ae1ede4ed31973213b5945721b7b9fe8e4992a1c Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Sep 2018 11:00:14 +0200 Subject: update doc+notebooks for convolutional --- RELEASES.md | 11 +- docs/cache_nbrun | 2 +- .../source/auto_examples/auto_examples_jupyter.zip | Bin 119618 -> 123577 bytes docs/source/auto_examples/auto_examples_python.zip | Bin 79365 -> 81978 bytes .../sphx_glr_plot_convolutional_barycenter_001.png | Bin 0 -> 319138 bytes ...phx_glr_plot_convolutional_barycenter_thumb.png | Bin 0 -> 54369 bytes docs/source/auto_examples/index.rst | 20 +++ .../plot_convolutional_barycenter.ipynb | 90 +++++++++++ .../auto_examples/plot_convolutional_barycenter.py | 92 +++++++++++ .../plot_convolutional_barycenter.rst | 151 ++++++++++++++++++ notebooks/plot_convolutional_barycenter.ipynb | 176 +++++++++++++++++++++ 11 files changed, 536 insertions(+), 6 deletions(-) create mode 100644 docs/source/auto_examples/images/sphx_glr_plot_convolutional_barycenter_001.png create mode 100644 docs/source/auto_examples/images/thumb/sphx_glr_plot_convolutional_barycenter_thumb.png create mode 100644 docs/source/auto_examples/plot_convolutional_barycenter.ipynb create mode 100644 docs/source/auto_examples/plot_convolutional_barycenter.py create mode 100644 docs/source/auto_examples/plot_convolutional_barycenter.rst create mode 100644 notebooks/plot_convolutional_barycenter.ipynb diff --git a/RELEASES.md b/RELEASES.md index 05c2edb..68abcb3 100644 --- a/RELEASES.md +++ b/RELEASES.md @@ -6,11 +6,6 @@ *This is a beta release and is still a work in progress* -#### TODO - -[] Remove deprecated OTDA Classes (PR #48) -[] Speedup Sinkhorn with einsum + bench (PR #58) -[] Stochastic ot (PR #62) #### Features @@ -25,6 +20,12 @@ * Stochastic OT in the dual and semi-dual (PR #52 and PR #62) * Free support barycenters (PR #56) * Speed-up Sinkhorn function (PR #57 and PR #58) +* Add convolutional Wassersein barycenters for 2D images (PR #64) +* Add Greedy Sinkhorn variant (Greenkhorn) (PR #66) + +#### Deprecation + +Deprecated OTDA Classes were removed for version 0.5 (PR #48), it has been a year and the deprecation message. #### Closed issues diff --git a/docs/cache_nbrun b/docs/cache_nbrun index 0745a21..575adc8 100644 --- a/docs/cache_nbrun +++ b/docs/cache_nbrun @@ -1 +1 @@ -{"plot_otda_mapping_colors_images.ipynb": "4f0587a00a3c082799a75a0ed36e9ce1", "plot_optim_OTreg.ipynb": "481801bb0d133ef350a65179cf8f739a", "plot_otda_color_images.ipynb": "d047d635f4987c81072383241590e21f", "plot_stochastic.ipynb": "e2c520150378ae4635f74509f687fa01", "plot_WDA.ipynb": "27f8de4c6d7db46497076523673eedfb", "plot_otda_linear_mapping.ipynb": "a472c767abe82020e0a58125a528785c", "plot_OT_1D_smooth.ipynb": "3a059103652225a0c78ea53895cf79e5", "plot_OT_L1_vs_L2.ipynb": "5d565b8aaf03be4309eba731127851dc", "plot_barycenter_1D.ipynb": "5f6fb8aebd8e2e91ebc77c923cb112b3", "plot_otda_classes.ipynb": "39087b6e98217851575f2271c22853a4", "plot_otda_d2.ipynb": "e6feae588103f2a8fab942e5f4eff483", "plot_otda_mapping.ipynb": "2f1ebbdc0f855d9e2b7adf9edec24d25", "plot_gromov.ipynb": 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"plot_OT_L1_vs_L2.ipynb": "5d565b8aaf03be4309eba731127851dc", "plot_otda_color_images.ipynb": "d047d635f4987c81072383241590e21f", "plot_otda_classes.ipynb": "39087b6e98217851575f2271c22853a4", "plot_otda_d2.ipynb": "e6feae588103f2a8fab942e5f4eff483", "plot_otda_mapping.ipynb": "2f1ebbdc0f855d9e2b7adf9edec24d25", "plot_gromov.ipynb": "24f2aea489714d34779521f46d5e2c47", "plot_compute_emd.ipynb": "f5cd71cad882ec157dc8222721e9820c", "plot_OT_1D.ipynb": "b5348bdc561c07ec168a1622e5af4b93", "plot_gromov_barycenter.ipynb": "953e5047b886ec69ec621ec52f5e21d1", "plot_free_support_barycenter.ipynb": "246dd2feff4b233a4f1a553c5a202fdc", "plot_convolutional_barycenter.ipynb": "a72bb3716a1baaffd81ae267a673f9b6", "plot_otda_semi_supervised.ipynb": "f6dfb02ba2bbd939408ffcd22a3b007c", "plot_OT_2D_samples.ipynb": "07dbc14859fa019a966caa79fa0825bd", "plot_barycenter_lp_vs_entropic.ipynb": "51833e8c76aaedeba9599ac7a30eb357"} \ No newline at end of file diff --git a/docs/source/auto_examples/auto_examples_jupyter.zip b/docs/source/auto_examples/auto_examples_jupyter.zip index c6a7e90..304bb06 100644 Binary files a/docs/source/auto_examples/auto_examples_jupyter.zip and b/docs/source/auto_examples/auto_examples_jupyter.zip differ diff --git a/docs/source/auto_examples/auto_examples_python.zip b/docs/source/auto_examples/auto_examples_python.zip index 28ff08e..3be8a76 100644 Binary files a/docs/source/auto_examples/auto_examples_python.zip and b/docs/source/auto_examples/auto_examples_python.zip differ diff --git a/docs/source/auto_examples/images/sphx_glr_plot_convolutional_barycenter_001.png b/docs/source/auto_examples/images/sphx_glr_plot_convolutional_barycenter_001.png new file mode 100644 index 0000000..14a72a3 Binary files /dev/null and b/docs/source/auto_examples/images/sphx_glr_plot_convolutional_barycenter_001.png differ diff --git a/docs/source/auto_examples/images/thumb/sphx_glr_plot_convolutional_barycenter_thumb.png b/docs/source/auto_examples/images/thumb/sphx_glr_plot_convolutional_barycenter_thumb.png new file mode 100644 index 0000000..af8aad2 Binary files /dev/null and b/docs/source/auto_examples/images/thumb/sphx_glr_plot_convolutional_barycenter_thumb.png differ diff --git a/docs/source/auto_examples/index.rst b/docs/source/auto_examples/index.rst index 5cbfba6..259fca1 100644 --- a/docs/source/auto_examples/index.rst +++ b/docs/source/auto_examples/index.rst @@ -147,6 +147,26 @@ This is a gallery of all the POT example files. /auto_examples/plot_compute_emd +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_convolutional_barycenter_thumb.png + + :ref:`sphx_glr_auto_examples_plot_convolutional_barycenter.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_convolutional_barycenter + .. raw:: html
diff --git a/docs/source/auto_examples/plot_convolutional_barycenter.ipynb b/docs/source/auto_examples/plot_convolutional_barycenter.ipynb new file mode 100644 index 0000000..4981ba3 --- /dev/null +++ b/docs/source/auto_examples/plot_convolutional_barycenter.ipynb @@ -0,0 +1,90 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Convolutional Wasserstein Barycenter example\n\n\nThis example is designed to illustrate how the Convolutional Wasserstein Barycenter\nfunction of POT works.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Nicolas Courty \n#\n# License: MIT License\n\n\nimport numpy as np\nimport pylab as pl\nimport ot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data preparation\n----------------\n\nThe four distributions are constructed from 4 simple images\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "f1 = 1 - pl.imread('../data/redcross.png')[:, :, 2]\nf2 = 1 - pl.imread('../data/duck.png')[:, :, 2]\nf3 = 1 - pl.imread('../data/heart.png')[:, :, 2]\nf4 = 1 - pl.imread('../data/tooth.png')[:, :, 2]\n\nA = []\nf1 = f1 / np.sum(f1)\nf2 = f2 / np.sum(f2)\nf3 = f3 / np.sum(f3)\nf4 = f4 / np.sum(f4)\nA.append(f1)\nA.append(f2)\nA.append(f3)\nA.append(f4)\nA = np.array(A)\n\nnb_images = 5\n\n# those are the four corners coordinates that will be interpolated by bilinear\n# interpolation\nv1 = np.array((1, 0, 0, 0))\nv2 = np.array((0, 1, 0, 0))\nv3 = np.array((0, 0, 1, 0))\nv4 = np.array((0, 0, 0, 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Barycenter computation and visualization\n----------------------------------------\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "pl.figure(figsize=(10, 10))\npl.title('Convolutional Wasserstein Barycenters in POT')\ncm = 'Blues'\n# regularization parameter\nreg = 0.004\nfor i in range(nb_images):\n for j in range(nb_images):\n pl.subplot(nb_images, nb_images, i * nb_images + j + 1)\n tx = float(i) / (nb_images - 1)\n ty = float(j) / (nb_images - 1)\n\n # weights are constructed by bilinear interpolation\n tmp1 = (1 - tx) * v1 + tx * v2\n tmp2 = (1 - tx) * v3 + tx * v4\n weights = (1 - ty) * tmp1 + ty * tmp2\n\n if i == 0 and j == 0:\n pl.imshow(f1, cmap=cm)\n pl.axis('off')\n elif i == 0 and j == (nb_images - 1):\n pl.imshow(f3, cmap=cm)\n pl.axis('off')\n elif i == (nb_images - 1) and j == 0:\n pl.imshow(f2, cmap=cm)\n pl.axis('off')\n elif i == (nb_images - 1) and j == (nb_images - 1):\n pl.imshow(f4, cmap=cm)\n pl.axis('off')\n else:\n # call to barycenter computation\n pl.imshow(ot.bregman.convolutional_barycenter2d(A, reg, weights), cmap=cm)\n pl.axis('off')\npl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_convolutional_barycenter.py b/docs/source/auto_examples/plot_convolutional_barycenter.py new file mode 100644 index 0000000..e74db04 --- /dev/null +++ b/docs/source/auto_examples/plot_convolutional_barycenter.py @@ -0,0 +1,92 @@ + +#%% +# -*- coding: utf-8 -*- +""" +============================================ +Convolutional Wasserstein Barycenter example +============================================ + +This example is designed to illustrate how the Convolutional Wasserstein Barycenter +function of POT works. +""" + +# Author: Nicolas Courty +# +# License: MIT License + + +import numpy as np +import pylab as pl +import ot + +############################################################################## +# Data preparation +# ---------------- +# +# The four distributions are constructed from 4 simple images + + +f1 = 1 - pl.imread('../data/redcross.png')[:, :, 2] +f2 = 1 - pl.imread('../data/duck.png')[:, :, 2] +f3 = 1 - pl.imread('../data/heart.png')[:, :, 2] +f4 = 1 - pl.imread('../data/tooth.png')[:, :, 2] + +A = [] +f1 = f1 / np.sum(f1) +f2 = f2 / np.sum(f2) +f3 = f3 / np.sum(f3) +f4 = f4 / np.sum(f4) +A.append(f1) +A.append(f2) +A.append(f3) +A.append(f4) +A = np.array(A) + +nb_images = 5 + +# those are the four corners coordinates that will be interpolated by bilinear +# interpolation +v1 = np.array((1, 0, 0, 0)) +v2 = np.array((0, 1, 0, 0)) +v3 = np.array((0, 0, 1, 0)) +v4 = np.array((0, 0, 0, 1)) + + +############################################################################## +# Barycenter computation and visualization +# ---------------------------------------- +# + +pl.figure(figsize=(10, 10)) +pl.title('Convolutional Wasserstein Barycenters in POT') +cm = 'Blues' +# regularization parameter +reg = 0.004 +for i in range(nb_images): + for j in range(nb_images): + pl.subplot(nb_images, nb_images, i * nb_images + j + 1) + tx = float(i) / (nb_images - 1) + ty = float(j) / (nb_images - 1) + + # weights are constructed by bilinear interpolation + tmp1 = (1 - tx) * v1 + tx * v2 + tmp2 = (1 - tx) * v3 + tx * v4 + weights = (1 - ty) * tmp1 + ty * tmp2 + + if i == 0 and j == 0: + pl.imshow(f1, cmap=cm) + pl.axis('off') + elif i == 0 and j == (nb_images - 1): + pl.imshow(f3, cmap=cm) + pl.axis('off') + elif i == (nb_images - 1) and j == 0: + pl.imshow(f2, cmap=cm) + pl.axis('off') + elif i == (nb_images - 1) and j == (nb_images - 1): + pl.imshow(f4, cmap=cm) + pl.axis('off') + else: + # call to barycenter computation + pl.imshow(ot.bregman.convolutional_barycenter2d(A, reg, weights), cmap=cm) + pl.axis('off') +pl.show() diff --git a/docs/source/auto_examples/plot_convolutional_barycenter.rst b/docs/source/auto_examples/plot_convolutional_barycenter.rst new file mode 100644 index 0000000..a28db2f --- /dev/null +++ b/docs/source/auto_examples/plot_convolutional_barycenter.rst @@ -0,0 +1,151 @@ + + +.. _sphx_glr_auto_examples_plot_convolutional_barycenter.py: + + +============================================ +Convolutional Wasserstein Barycenter example +============================================ + +This example is designed to illustrate how the Convolutional Wasserstein Barycenter +function of POT works. + + + +.. code-block:: python + + + # Author: Nicolas Courty + # + # License: MIT License + + + import numpy as np + import pylab as pl + import ot + + + + + + + +Data preparation +---------------- + +The four distributions are constructed from 4 simple images + + + +.. code-block:: python + + + + f1 = 1 - pl.imread('../data/redcross.png')[:, :, 2] + f2 = 1 - pl.imread('../data/duck.png')[:, :, 2] + f3 = 1 - pl.imread('../data/heart.png')[:, :, 2] + f4 = 1 - pl.imread('../data/tooth.png')[:, :, 2] + + A = [] + f1 = f1 / np.sum(f1) + f2 = f2 / np.sum(f2) + f3 = f3 / np.sum(f3) + f4 = f4 / np.sum(f4) + A.append(f1) + A.append(f2) + A.append(f3) + A.append(f4) + A = np.array(A) + + nb_images = 5 + + # those are the four corners coordinates that will be interpolated by bilinear + # interpolation + v1 = np.array((1, 0, 0, 0)) + v2 = np.array((0, 1, 0, 0)) + v3 = np.array((0, 0, 1, 0)) + v4 = np.array((0, 0, 0, 1)) + + + + + + + + +Barycenter computation and visualization +---------------------------------------- + + + + +.. code-block:: python + + + pl.figure(figsize=(10, 10)) + pl.title('Convolutional Wasserstein Barycenters in POT') + cm = 'Blues' + # regularization parameter + reg = 0.004 + for i in range(nb_images): + for j in range(nb_images): + pl.subplot(nb_images, nb_images, i * nb_images + j + 1) + tx = float(i) / (nb_images - 1) + ty = float(j) / (nb_images - 1) + + # weights are constructed by bilinear interpolation + tmp1 = (1 - tx) * v1 + tx * v2 + tmp2 = (1 - tx) * v3 + tx * v4 + weights = (1 - ty) * tmp1 + ty * tmp2 + + if i == 0 and j == 0: + pl.imshow(f1, cmap=cm) + pl.axis('off') + elif i == 0 and j == (nb_images - 1): + pl.imshow(f3, cmap=cm) + pl.axis('off') + elif i == (nb_images - 1) and j == 0: + pl.imshow(f2, cmap=cm) + pl.axis('off') + elif i == (nb_images - 1) and j == (nb_images - 1): + pl.imshow(f4, cmap=cm) + pl.axis('off') + else: + # call to barycenter computation + pl.imshow(ot.bregman.convolutional_barycenter2d(A, reg, weights), cmap=cm) + pl.axis('off') + pl.show() + + + +.. image:: /auto_examples/images/sphx_glr_plot_convolutional_barycenter_001.png + :align: center + + + + +**Total running time of the script:** ( 1 minutes 11.608 seconds) + + + +.. only :: html + + .. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_convolutional_barycenter.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_convolutional_barycenter.ipynb ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/notebooks/plot_convolutional_barycenter.ipynb b/notebooks/plot_convolutional_barycenter.ipynb new file mode 100644 index 0000000..d0df486 --- /dev/null +++ b/notebooks/plot_convolutional_barycenter.ipynb @@ -0,0 +1,176 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Convolutional Wasserstein Barycenter example\n", + "\n", + "\n", + "This example is designed to illustrate how the Convolutional Wasserstein Barycenter\n", + "function of POT works.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Nicolas Courty \n", + "#\n", + "# License: MIT License\n", + "\n", + "\n", + "import numpy as np\n", + "import pylab as pl\n", + "import ot" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Data preparation\n", + "----------------\n", + "\n", + "The four distributions are constructed from 4 simple images\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "f1 = 1 - pl.imread('../data/redcross.png')[:, :, 2]\n", + "f2 = 1 - pl.imread('../data/duck.png')[:, :, 2]\n", + "f3 = 1 - pl.imread('../data/heart.png')[:, :, 2]\n", + "f4 = 1 - pl.imread('../data/tooth.png')[:, :, 2]\n", + "\n", + "A = []\n", + "f1 = f1 / np.sum(f1)\n", + "f2 = f2 / np.sum(f2)\n", + "f3 = f3 / np.sum(f3)\n", + "f4 = f4 / np.sum(f4)\n", + "A.append(f1)\n", + "A.append(f2)\n", + "A.append(f3)\n", + "A.append(f4)\n", + "A = np.array(A)\n", + "\n", + "nb_images = 5\n", + "\n", + "# those are the four corners coordinates that will be interpolated by bilinear\n", + "# interpolation\n", + "v1 = np.array((1, 0, 0, 0))\n", + "v2 = np.array((0, 1, 0, 0))\n", + "v3 = np.array((0, 0, 1, 0))\n", + "v4 = np.array((0, 0, 0, 1))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Barycenter computation and visualization\n", + "----------------------------------------\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "pl.figure(figsize=(10, 10))\n", + "pl.title('Convolutional Wasserstein Barycenters in POT')\n", + "cm = 'Blues'\n", + "# regularization parameter\n", + "reg = 0.004\n", + "for i in range(nb_images):\n", + " for j in range(nb_images):\n", + " pl.subplot(nb_images, nb_images, i * nb_images + j + 1)\n", + " tx = float(i) / (nb_images - 1)\n", + " ty = float(j) / (nb_images - 1)\n", + "\n", + " # weights are constructed by bilinear interpolation\n", + " tmp1 = (1 - tx) * v1 + tx * v2\n", + " tmp2 = (1 - tx) * v3 + tx * v4\n", + " weights = (1 - ty) * tmp1 + ty * tmp2\n", + "\n", + " if i == 0 and j == 0:\n", + " pl.imshow(f1, cmap=cm)\n", + " pl.axis('off')\n", + " elif i == 0 and j == (nb_images - 1):\n", + " pl.imshow(f3, cmap=cm)\n", + " pl.axis('off')\n", + " elif i == (nb_images - 1) and j == 0:\n", + " pl.imshow(f2, cmap=cm)\n", + " pl.axis('off')\n", + " elif i == (nb_images - 1) and j == (nb_images - 1):\n", + " pl.imshow(f4, cmap=cm)\n", + " pl.axis('off')\n", + " else:\n", + " # call to barycenter computation\n", + " pl.imshow(ot.bregman.convolutional_barycenter2d(A, reg, weights), cmap=cm)\n", + " pl.axis('off')\n", + "pl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.5" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} -- cgit v1.2.3 From ca08b788af38a076f45f000003eb0e2f227d7fd5 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Sep 2018 11:09:48 +0200 Subject: deprecate ot.gpu and remove OTDA classes from it --- ot/gpu/__init__.py | 5 ++++ ot/gpu/da.py | 69 ------------------------------------------------------ 2 files changed, 5 insertions(+), 69 deletions(-) diff --git a/ot/gpu/__init__.py b/ot/gpu/__init__.py index a2fdd3d..ed6dcc4 100644 --- a/ot/gpu/__init__.py +++ b/ot/gpu/__init__.py @@ -9,4 +9,9 @@ from .bregman import sinkhorn # # License: MIT License +import warnings + +warnings.warn("the ot.gpu module is deprecated because cudamat in no longer maintained", DeprecationWarning, + stacklevel=2) + __all__ = ["bregman", "da", "sinkhorn"] diff --git a/ot/gpu/da.py b/ot/gpu/da.py index 71a485a..85d43e6 100644 --- a/ot/gpu/da.py +++ b/ot/gpu/da.py @@ -13,7 +13,6 @@ Domain adaptation with optimal transport with GPU implementation import numpy as np from ..utils import unif -from ..da import OTDA from .bregman import sinkhorn import cudamat @@ -185,71 +184,3 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10, W_GPU = W_GPU.transpose() return transp_GPU.asarray() - - -class OTDA_GPU(OTDA): - - def normalizeM(self, norm): - if norm == "median": - self.M_GPU.divide(float(np.median(self.M_GPU.asarray()))) - elif norm == "max": - self.M_GPU.divide(float(np.max(self.M_GPU.asarray()))) - elif norm == "log": - self.M_GPU.add(1) - cudamat.log(self.M_GPU) - elif norm == "loglog": - self.M_GPU.add(1) - cudamat.log(self.M_GPU) - self.M_GPU.add(1) - cudamat.log(self.M_GPU) - - -class OTDA_sinkhorn(OTDA_GPU): - - def fit(self, xs, xt, reg=1, ws=None, wt=None, norm=None, **kwargs): - cudamat.init() - xs = np.asarray(xs, dtype=np.float64) - xt = np.asarray(xt, dtype=np.float64) - - self.xs = xs - self.xt = xt - - if wt is None: - wt = unif(xt.shape[0]) - if ws is None: - ws = unif(xs.shape[0]) - - self.ws = ws - self.wt = wt - - self.M_GPU = pairwiseEuclideanGPU(xs, xt, returnAsGPU=True, - squared=True) - self.normalizeM(norm) - self.G = sinkhorn(ws, wt, self.M_GPU, reg, **kwargs) - self.computed = True - - -class OTDA_lpl1(OTDA_GPU): - - def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None, - **kwargs): - cudamat.init() - xs = np.asarray(xs, dtype=np.float64) - xt = np.asarray(xt, dtype=np.float64) - - self.xs = xs - self.xt = xt - - if wt is None: - wt = unif(xt.shape[0]) - if ws is None: - ws = unif(xs.shape[0]) - - self.ws = ws - self.wt = wt - - self.M_GPU = pairwiseEuclideanGPU(xs, xt, returnAsGPU=True, - squared=True) - self.normalizeM(norm) - self.G = sinkhorn_lpl1_mm(ws, ys, wt, self.M_GPU, reg, eta, **kwargs) - self.computed = True -- cgit v1.2.3 From d258c7d6936410cd78189445a0260d983f7684d6 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Sep 2018 13:57:42 +0200 Subject: convert ot.gpu to cupy --- ot/gpu/__init__.py | 6 +- ot/gpu/bregman.py | 143 +++++++++++++++++++++-------------- ot/gpu/da.py | 214 ++++++++++++----------------------------------------- ot/gpu/utils.py | 100 +++++++++++++++++++++++++ 4 files changed, 239 insertions(+), 224 deletions(-) create mode 100644 ot/gpu/utils.py diff --git a/ot/gpu/__init__.py b/ot/gpu/__init__.py index a2fdd3d..de4825d 100644 --- a/ot/gpu/__init__.py +++ b/ot/gpu/__init__.py @@ -4,9 +4,13 @@ from . import bregman from . import da from .bregman import sinkhorn +from . import utils +from .utils import dist, to_gpu, to_np + + # Author: Remi Flamary # Leo Gautheron # # License: MIT License -__all__ = ["bregman", "da", "sinkhorn"] +__all__ = ["utils", "dist", "sinkhorn"] diff --git a/ot/gpu/bregman.py b/ot/gpu/bregman.py index 47939c4..912104c 100644 --- a/ot/gpu/bregman.py +++ b/ot/gpu/bregman.py @@ -8,14 +8,16 @@ Bregman projections for regularized OT with GPU # # License: MIT License -import numpy as np -import cudamat +import cupy as np # np used for matrix computation +import cupy as cp # cp used for cupy specific operations +from . import utils -def sinkhorn(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False, - log=False, returnAsGPU=False): - r""" - Solve the entropic regularization optimal transport problem on GPU + +def sinkhorn_knopp(a, b, M, reg, numItermax=1000, stopThr=1e-9, + verbose=False, log=False, to_numpy=True, **kwargs): + """ + Solve the entropic regularization optimal transport problem and return the OT matrix The function solves the following optimization problem: @@ -40,9 +42,10 @@ def sinkhorn(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False, ---------- a : np.ndarray (ns,) samples weights in the source domain - b : np.ndarray (nt,) - samples in the target domain - M_GPU : cudamat.CUDAMatrix (ns,nt) + b : np.ndarray (nt,) or np.ndarray (nt,nbb) + samples in the target domain, compute sinkhorn with multiple targets + and fixed M if b is a matrix (return OT loss + dual variables in log) + M : np.ndarray (ns,nt) loss matrix reg : float Regularization term >0 @@ -54,8 +57,7 @@ def sinkhorn(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False, Print information along iterations log : bool, optional record log if True - returnAsGPU : bool, optional - return the OT matrix as a cudamat.CUDAMatrix + Returns ------- @@ -88,60 +90,78 @@ def sinkhorn(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False, ot.optim.cg : General regularized OT """ + + a = cp.asarray(a, dtype=np.float64) + b = cp.asarray(b, dtype=np.float64) + M = cp.asarray(M, dtype=np.float64) + + if len(a) == 0: + a = np.ones((M.shape[0],), dtype=np.float64) / M.shape[0] + if len(b) == 0: + b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] + # init data Nini = len(a) Nfin = len(b) + if len(b.shape) > 1: + nbb = b.shape[1] + else: + nbb = 0 + if log: log = {'err': []} # we assume that no distances are null except those of the diagonal of # distances - u = (np.ones(Nini) / Nini).reshape((Nini, 1)) - u_GPU = cudamat.CUDAMatrix(u) - a_GPU = cudamat.CUDAMatrix(a.reshape((Nini, 1))) - ones_GPU = cudamat.empty(u_GPU.shape).assign(1) - v = (np.ones(Nfin) / Nfin).reshape((Nfin, 1)) - v_GPU = cudamat.CUDAMatrix(v) - b_GPU = cudamat.CUDAMatrix(b.reshape((Nfin, 1))) - - M_GPU.divide(-reg) + if nbb: + u = np.ones((Nini, nbb)) / Nini + v = np.ones((Nfin, nbb)) / Nfin + else: + u = np.ones(Nini) / Nini + v = np.ones(Nfin) / Nfin - K_GPU = cudamat.exp(M_GPU) + # print(reg) - ones_GPU.divide(a_GPU, target=a_GPU) - Kp_GPU = cudamat.empty(K_GPU.shape) - K_GPU.mult_by_col(a_GPU, target=Kp_GPU) + # Next 3 lines equivalent to K= np.exp(-M/reg), but faster to compute + K = np.empty(M.shape, dtype=M.dtype) + np.divide(M, -reg, out=K) + np.exp(K, out=K) - tmp_GPU = cudamat.empty(K_GPU.shape) + # print(np.min(K)) + tmp2 = np.empty(b.shape, dtype=M.dtype) + Kp = (1 / a).reshape(-1, 1) * K cpt = 0 err = 1 while (err > stopThr and cpt < numItermax): - uprev_GPU = u_GPU.copy() - vprev_GPU = v_GPU.copy() + uprev = u + vprev = v - KtransposeU_GPU = K_GPU.transpose().dot(u_GPU) - b_GPU.divide(KtransposeU_GPU, target=v_GPU) - ones_GPU.divide(Kp_GPU.dot(v_GPU), target=u_GPU) + KtransposeU = np.dot(K.T, u) + v = np.divide(b, KtransposeU) + u = 1. / np.dot(Kp, v) - if (np.any(KtransposeU_GPU.asarray() == 0) or - not u_GPU.allfinite() or not v_GPU.allfinite()): + if (np.any(KtransposeU == 0) or + np.any(np.isnan(u)) or np.any(np.isnan(v)) or + np.any(np.isinf(u)) or np.any(np.isinf(v))): # we have reached the machine precision # come back to previous solution and quit loop print('Warning: numerical errors at iteration', cpt) - u_GPU = uprev_GPU.copy() - v_GPU = vprev_GPU.copy() + u = uprev + v = vprev break if cpt % 10 == 0: # we can speed up the process by checking for the error only all # the 10th iterations - K_GPU.mult_by_col(u_GPU, target=tmp_GPU) - tmp_GPU.mult_by_row(v_GPU.transpose(), target=tmp_GPU) - - bcopy_GPU = b_GPU.copy().transpose() - bcopy_GPU.add_sums(tmp_GPU, axis=0, beta=-1) - err = bcopy_GPU.euclid_norm()**2 + if nbb: + err = np.sum((u - uprev)**2) / np.sum((u)**2) + \ + np.sum((v - vprev)**2) / np.sum((v)**2) + else: + # compute right marginal tmp2= (diag(u)Kdiag(v))^T1 + tmp2=np.sum(u[:,None]*K*v[None,:],0) + #tmp2=np.einsum('i,ij,j->j', u, K, v) + err = np.linalg.norm(tmp2 - b)**2 # violation of marginal if log: log['err'].append(err) @@ -150,20 +170,31 @@ def sinkhorn(a, b, M_GPU, reg, numItermax=1000, stopThr=1e-9, verbose=False, print( '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) print('{:5d}|{:8e}|'.format(cpt, err)) - cpt += 1 - if log: - log['u'] = u_GPU.asarray() - log['v'] = v_GPU.asarray() - - K_GPU.mult_by_col(u_GPU, target=K_GPU) - K_GPU.mult_by_row(v_GPU.transpose(), target=K_GPU) - - if returnAsGPU: - res = K_GPU - else: - res = K_GPU.asarray() - + cpt = cpt + 1 if log: - return res, log - else: - return res + log['u'] = u + log['v'] = v + + if nbb: # return only loss + #res = np.einsum('ik,ij,jk,ij->k', u, K, v, M) (explodes cupy memory) + res=np.empty(nbb) + for i in range(nbb): + res[i]=np.sum(u[:,None,i]*(K*M)*v[None,:,i]) + if to_numpy: + res=utils.to_np(res) + if log: + return res, log + else: + return res + + else: # return OT matrix + res=u.reshape((-1, 1)) * K * v.reshape((1, -1)) + if to_numpy: + res=utils.to_np(res) + if log: + return res, log + else: + return res + +# define sinkhorn as sinkhorn_knopp +sinkhorn=sinkhorn_knopp \ No newline at end of file diff --git a/ot/gpu/da.py b/ot/gpu/da.py index 71a485a..8bcc2aa 100644 --- a/ot/gpu/da.py +++ b/ot/gpu/da.py @@ -10,81 +10,24 @@ Domain adaptation with optimal transport with GPU implementation # # License: MIT License - -import numpy as np -from ..utils import unif -from ..da import OTDA +import cupy as np # np used for matrix computation +import cupy as cp # cp used for cupy specific operations +import numpy as npp +from . import utils from .bregman import sinkhorn -import cudamat - - -def pairwiseEuclideanGPU(a, b, returnAsGPU=False, squared=False): - """ - Compute the pairwise euclidean distance between matrices a and b. - - - Parameters - ---------- - a : np.ndarray (n, f) - first matrice - b : np.ndarray (m, f) - second matrice - returnAsGPU : boolean, optional (default False) - if True, returns cudamat matrix still on GPU, else return np.ndarray - squared : boolean, optional (default False) - if True, return squared euclidean distance matrice - - Returns - ------- - c : (n x m) np.ndarray or cudamat.CUDAMatrix - pairwise euclidean distance distance matrix +def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, + numInnerItermax=200, stopInnerThr=1e-9, verbose=False, + log=False,to_numpy=True): """ - # a is shape (n, f) and b shape (m, f). Return matrix c of shape (n, m). - # First compute in c_GPU the squared euclidean distance. And return its - # square root. At each cell [i,j] of c, we want to have - # sum{k in range(f)} ( (a[i,k] - b[j,k])^2 ). We know that - # (a-b)^2 = a^2 -2ab +b^2. Thus we want to have in each cell of c: - # sum{k in range(f)} ( a[i,k]^2 -2a[i,k]b[j,k] +b[j,k]^2). - - a_GPU = cudamat.CUDAMatrix(a) - b_GPU = cudamat.CUDAMatrix(b) - - # Multiply a by b transpose to obtain in each cell [i,j] of c the - # value sum{k in range(f)} ( a[i,k]b[j,k] ) - c_GPU = cudamat.dot(a_GPU, b_GPU.transpose()) - # multiply by -2 to have sum{k in range(f)} ( -2a[i,k]b[j,k] ) - c_GPU.mult(-2) - - # Compute the vectors of the sum of squared elements. - a_GPU = cudamat.pow(a_GPU, 2).sum(axis=1) - b_GPU = cudamat.pow(b_GPU, 2).sum(axis=1) - - # Add the vectors in each columns (respectivly rows) of c. - # sum{k in range(f)} ( a[i,k]^2 -2a[i,k]b[j,k] ) - c_GPU.add_col_vec(a_GPU) - # sum{k in range(f)} ( a[i,k]^2 -2a[i,k]b[j,k] +b[j,k]^2) - c_GPU.add_row_vec(b_GPU.transpose()) - - if not squared: - c_GPU = cudamat.sqrt(c_GPU) - - if returnAsGPU: - return c_GPU - else: - return c_GPU.asarray() - - -def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10, - numInnerItermax=200, stopInnerThr=1e-9, - verbose=False, log=False): - """ - Solve the entropic regularization optimal transport problem with nonconvex group lasso regularization + Solve the entropic regularization optimal transport problem with nonconvex + group lasso regularization The function solves the following optimization problem: .. math:: - \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma)+ \eta \Omega_g(\gamma) + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega_e(\gamma) + + \eta \Omega_g(\gamma) s.t. \gamma 1 = a @@ -94,11 +37,16 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10, where : - M is the (ns,nt) metric cost matrix - - :math:`\Omega_e` is the entropic regularization term :math:`\Omega_e(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - :math:`\Omega_g` is the group lasso regulaization term :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^{1/2}_1` where :math:`\mathcal{I}_c` are the index of samples from class c in the source domain. + - :math:`\Omega_e` is the entropic regularization term + :math:`\Omega_e(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - :math:`\Omega_g` is the group lasso regulaization term + :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^{1/2}_1` + where :math:`\mathcal{I}_c` are the index of samples from class c + in the source domain. - a and b are source and target weights (sum to 1) - The algorithm used for solving the problem is the generalised conditional gradient as proposed in [5]_ [7]_ + The algorithm used for solving the problem is the generalised conditional + gradient as proposed in [5]_ [7]_ Parameters @@ -109,7 +57,7 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10, labels of samples in the source domain b : np.ndarray (nt,) samples weights in the target domain - M_GPU : cudamat.CUDAMatrix (ns,nt) + M : np.ndarray (ns,nt) loss matrix reg : float Regularization term for entropic regularization >0 @@ -138,8 +86,13 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10, References ---------- - .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 - .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567. + .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, + "Optimal Transport for Domain Adaptation," in IEEE + Transactions on Pattern Analysis and Machine Intelligence , + vol.PP, no.99, pp.1-1 + .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). + Generalized conditional gradient: analysis of convergence + and applications. arXiv preprint arXiv:1510.06567. See Also -------- @@ -148,108 +101,35 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M_GPU, reg, eta=0.1, numItermax=10, ot.optim.cg : General regularized OT """ + + a, labels_a, b, M = utils.to_gpu(a, labels_a, b, M) + + p = 0.5 epsilon = 1e-3 - Nfin = len(b) indices_labels = [] - classes = np.unique(labels_a) + labels_a2=cp.asnumpy(labels_a) + classes = npp.unique(labels_a2) for c in classes: - idxc, = np.where(labels_a == c) - indices_labels.append(cudamat.CUDAMatrix(idxc.reshape(1, -1))) + idxc, = utils.to_gpu(npp.where(labels_a2 == c)) + indices_labels.append(idxc) - Mreg_GPU = cudamat.empty(M_GPU.shape) - W_GPU = cudamat.empty(M_GPU.shape).assign(0) + W = np.zeros(M.shape) for cpt in range(numItermax): - Mreg_GPU.assign(M_GPU) - Mreg_GPU.add_mult(W_GPU, eta) - transp_GPU = sinkhorn(a, b, Mreg_GPU, reg, numItermax=numInnerItermax, - stopThr=stopInnerThr, returnAsGPU=True) + Mreg = M + eta * W + transp = sinkhorn(a, b, Mreg, reg, numItermax=numInnerItermax, + stopThr=stopInnerThr,to_numpy=False) # the transport has been computed. Check if classes are really # separated - W_GPU.assign(1) - W_GPU = W_GPU.transpose() + W = np.ones(M.shape) for (i, c) in enumerate(classes): - (_, nbRow) = indices_labels[i].shape - tmpC_GPU = cudamat.empty((Nfin, nbRow)).assign(0) - transp_GPU.transpose().select_columns(indices_labels[i], tmpC_GPU) - majs_GPU = tmpC_GPU.sum(axis=1).add(epsilon) - cudamat.pow(majs_GPU, (p - 1)) - majs_GPU.mult(p) - - tmpC_GPU.assign(0) - tmpC_GPU.add_col_vec(majs_GPU) - W_GPU.set_selected_columns(indices_labels[i], tmpC_GPU) - - W_GPU = W_GPU.transpose() - - return transp_GPU.asarray() + majs = np.sum(transp[indices_labels[i]], axis=0) + majs = p * ((majs + epsilon)**(p - 1)) + W[indices_labels[i]] = majs - -class OTDA_GPU(OTDA): - - def normalizeM(self, norm): - if norm == "median": - self.M_GPU.divide(float(np.median(self.M_GPU.asarray()))) - elif norm == "max": - self.M_GPU.divide(float(np.max(self.M_GPU.asarray()))) - elif norm == "log": - self.M_GPU.add(1) - cudamat.log(self.M_GPU) - elif norm == "loglog": - self.M_GPU.add(1) - cudamat.log(self.M_GPU) - self.M_GPU.add(1) - cudamat.log(self.M_GPU) - - -class OTDA_sinkhorn(OTDA_GPU): - - def fit(self, xs, xt, reg=1, ws=None, wt=None, norm=None, **kwargs): - cudamat.init() - xs = np.asarray(xs, dtype=np.float64) - xt = np.asarray(xt, dtype=np.float64) - - self.xs = xs - self.xt = xt - - if wt is None: - wt = unif(xt.shape[0]) - if ws is None: - ws = unif(xs.shape[0]) - - self.ws = ws - self.wt = wt - - self.M_GPU = pairwiseEuclideanGPU(xs, xt, returnAsGPU=True, - squared=True) - self.normalizeM(norm) - self.G = sinkhorn(ws, wt, self.M_GPU, reg, **kwargs) - self.computed = True - - -class OTDA_lpl1(OTDA_GPU): - - def fit(self, xs, ys, xt, reg=1, eta=1, ws=None, wt=None, norm=None, - **kwargs): - cudamat.init() - xs = np.asarray(xs, dtype=np.float64) - xt = np.asarray(xt, dtype=np.float64) - - self.xs = xs - self.xt = xt - - if wt is None: - wt = unif(xt.shape[0]) - if ws is None: - ws = unif(xs.shape[0]) - - self.ws = ws - self.wt = wt - - self.M_GPU = pairwiseEuclideanGPU(xs, xt, returnAsGPU=True, - squared=True) - self.normalizeM(norm) - self.G = sinkhorn_lpl1_mm(ws, ys, wt, self.M_GPU, reg, eta, **kwargs) - self.computed = True + if to_numpy: + return utils.to_np(transp) + else: + return transp diff --git a/ot/gpu/utils.py b/ot/gpu/utils.py new file mode 100644 index 0000000..6d0c853 --- /dev/null +++ b/ot/gpu/utils.py @@ -0,0 +1,100 @@ +# -*- coding: utf-8 -*- +""" +Utility functions for GPU +""" + +# Author: Remi Flamary +# Nicolas Courty +# Leo Gautheron +# +# License: MIT License + +import cupy as np # np used for matrix computation +import cupy as cp # cp used for cupy specific operations + + + +def euclidean_distances(a, b, squared=False, to_numpy=True): + """ + Compute the pairwise euclidean distance between matrices a and b. + Parameters + ---------- + a : np.ndarray (n, f) + first matrix + b : np.ndarray (m, f) + second matrix + gpu : boolean, optional (default False) + if True and the module cupy is available, the computation is done + on the GPU and the type of the matrix returned is cupy.ndarray. + Otherwise, compute on the CPU and returns np.ndarray. + squared : boolean, optional (default False) + if True, return squared euclidean distance matrix + Returns + ------- + c : (n x m) np.ndarray or cupy.ndarray + pairwise euclidean distance distance matrix + """ + + a, b = to_gpu(a, b) + + a2=np.sum(np.square(a),1) + b2=np.sum(np.square(b),1) + + c=-2*np.dot(a,b.T) + c+=a2[:,None] + c+=b2[None,:] + + if not squared: + np.sqrt(c, out=c) + if to_numpy: + return to_np(c) + else: + return c + +def dist(x1, x2=None, metric='sqeuclidean', to_numpy=True): + """Compute distance between samples in x1 and x2 on gpu + + Parameters + ---------- + + x1 : np.array (n1,d) + matrix with n1 samples of size d + x2 : np.array (n2,d), optional + matrix with n2 samples of size d (if None then x2=x1) + metric : str + Metric from 'sqeuclidean', 'euclidean', + + + Returns + ------- + + M : np.array (n1,n2) + distance matrix computed with given metric + + """ + if x2 is None: + x2 = x1 + if metric == "sqeuclidean": + return euclidean_distances(x1, x2, squared=True, to_numpy=to_numpy) + elif metric == "euclidean": + return euclidean_distances(x1, x2, squared=False, to_numpy=to_numpy) + else: + raise NotImplementedError + + + +def to_gpu(*args): + """ Upload numpy arrays to GPU and return them""" + if len(args) > 1: + return (cp.asarray(x) for x in args) + else: + return cp.asarray(args[0]) + + + +def to_np(*args): + """ convert GPU arras to numpy and return them""" + if len(args) > 1: + return (cp.asnumpy(x) for x in args) + else: + return cp.asnumpy(args[0]) \ No newline at end of file -- cgit v1.2.3 From f45f7a68b221ec5b619b8fd8de797815a1eecf43 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Sep 2018 14:30:44 +0200 Subject: pep8 --- ot/gpu/bregman.py | 20 ++++++++++---------- ot/gpu/da.py | 16 ++++++++-------- ot/gpu/utils.py | 34 ++++++++++++++++------------------ 3 files changed, 34 insertions(+), 36 deletions(-) diff --git a/ot/gpu/bregman.py b/ot/gpu/bregman.py index 912104c..6714098 100644 --- a/ot/gpu/bregman.py +++ b/ot/gpu/bregman.py @@ -8,12 +8,11 @@ Bregman projections for regularized OT with GPU # # License: MIT License -import cupy as np # np used for matrix computation -import cupy as cp # cp used for cupy specific operations +import cupy as np # np used for matrix computation +import cupy as cp # cp used for cupy specific operations from . import utils - def sinkhorn_knopp(a, b, M, reg, numItermax=1000, stopThr=1e-9, verbose=False, log=False, to_numpy=True, **kwargs): """ @@ -159,7 +158,7 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, stopThr=1e-9, np.sum((v - vprev)**2) / np.sum((v)**2) else: # compute right marginal tmp2= (diag(u)Kdiag(v))^T1 - tmp2=np.sum(u[:,None]*K*v[None,:],0) + tmp2 = np.sum(u[:, None] * K * v[None, :], 0) #tmp2=np.einsum('i,ij,j->j', u, K, v) err = np.linalg.norm(tmp2 - b)**2 # violation of marginal if log: @@ -177,24 +176,25 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, stopThr=1e-9, if nbb: # return only loss #res = np.einsum('ik,ij,jk,ij->k', u, K, v, M) (explodes cupy memory) - res=np.empty(nbb) + res = np.empty(nbb) for i in range(nbb): - res[i]=np.sum(u[:,None,i]*(K*M)*v[None,:,i]) + res[i] = np.sum(u[:, None, i] * (K * M) * v[None, :, i]) if to_numpy: - res=utils.to_np(res) + res = utils.to_np(res) if log: return res, log else: return res else: # return OT matrix - res=u.reshape((-1, 1)) * K * v.reshape((1, -1)) + res = u.reshape((-1, 1)) * K * v.reshape((1, -1)) if to_numpy: - res=utils.to_np(res) + res = utils.to_np(res) if log: return res, log else: return res + # define sinkhorn as sinkhorn_knopp -sinkhorn=sinkhorn_knopp \ No newline at end of file +sinkhorn = sinkhorn_knopp diff --git a/ot/gpu/da.py b/ot/gpu/da.py index 8bcc2aa..8c63870 100644 --- a/ot/gpu/da.py +++ b/ot/gpu/da.py @@ -10,15 +10,16 @@ Domain adaptation with optimal transport with GPU implementation # # License: MIT License -import cupy as np # np used for matrix computation -import cupy as cp # cp used for cupy specific operations +import cupy as np # np used for matrix computation +import cupy as cp # cp used for cupy specific operations import numpy as npp from . import utils from .bregman import sinkhorn + def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, numInnerItermax=200, stopInnerThr=1e-9, verbose=False, - log=False,to_numpy=True): + log=False, to_numpy=True): """ Solve the entropic regularization optimal transport problem with nonconvex group lasso regularization @@ -101,15 +102,14 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, ot.optim.cg : General regularized OT """ - + a, labels_a, b, M = utils.to_gpu(a, labels_a, b, M) - - + p = 0.5 epsilon = 1e-3 indices_labels = [] - labels_a2=cp.asnumpy(labels_a) + labels_a2 = cp.asnumpy(labels_a) classes = npp.unique(labels_a2) for c in classes: idxc, = utils.to_gpu(npp.where(labels_a2 == c)) @@ -120,7 +120,7 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, for cpt in range(numItermax): Mreg = M + eta * W transp = sinkhorn(a, b, Mreg, reg, numItermax=numInnerItermax, - stopThr=stopInnerThr,to_numpy=False) + stopThr=stopInnerThr, to_numpy=False) # the transport has been computed. Check if classes are really # separated W = np.ones(M.shape) diff --git a/ot/gpu/utils.py b/ot/gpu/utils.py index 6d0c853..d349a6d 100644 --- a/ot/gpu/utils.py +++ b/ot/gpu/utils.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- """ -Utility functions for GPU +Utility functions for GPU """ # Author: Remi Flamary @@ -9,9 +9,8 @@ Utility functions for GPU # # License: MIT License -import cupy as np # np used for matrix computation -import cupy as cp # cp used for cupy specific operations - +import cupy as np # np used for matrix computation +import cupy as cp # cp used for cupy specific operations def euclidean_distances(a, b, squared=False, to_numpy=True): @@ -34,16 +33,16 @@ def euclidean_distances(a, b, squared=False, to_numpy=True): c : (n x m) np.ndarray or cupy.ndarray pairwise euclidean distance distance matrix """ - + a, b = to_gpu(a, b) - - a2=np.sum(np.square(a),1) - b2=np.sum(np.square(b),1) - - c=-2*np.dot(a,b.T) - c+=a2[:,None] - c+=b2[None,:] - + + a2 = np.sum(np.square(a), 1) + b2 = np.sum(np.square(b), 1) + + c = -2 * np.dot(a, b.T) + c += a2[:, None] + c += b2[None, :] + if not squared: np.sqrt(c, out=c) if to_numpy: @@ -51,6 +50,7 @@ def euclidean_distances(a, b, squared=False, to_numpy=True): else: return c + def dist(x1, x2=None, metric='sqeuclidean', to_numpy=True): """Compute distance between samples in x1 and x2 on gpu @@ -61,8 +61,8 @@ def dist(x1, x2=None, metric='sqeuclidean', to_numpy=True): matrix with n1 samples of size d x2 : np.array (n2,d), optional matrix with n2 samples of size d (if None then x2=x1) - metric : str - Metric from 'sqeuclidean', 'euclidean', + metric : str + Metric from 'sqeuclidean', 'euclidean', Returns @@ -80,7 +80,6 @@ def dist(x1, x2=None, metric='sqeuclidean', to_numpy=True): return euclidean_distances(x1, x2, squared=False, to_numpy=to_numpy) else: raise NotImplementedError - def to_gpu(*args): @@ -91,10 +90,9 @@ def to_gpu(*args): return cp.asarray(args[0]) - def to_np(*args): """ convert GPU arras to numpy and return them""" if len(args) > 1: return (cp.asnumpy(x) for x in args) else: - return cp.asnumpy(args[0]) \ No newline at end of file + return cp.asnumpy(args[0]) -- cgit v1.2.3 From 75e78022d2df350ea220cee1b5e759ef9fc35a5b Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Sep 2018 15:05:09 +0200 Subject: update tests --- ot/gpu/bregman.py | 10 +++---- test/test_gpu.py | 89 ++++++++++++++++++++++++++++++------------------------- 2 files changed, 53 insertions(+), 46 deletions(-) diff --git a/ot/gpu/bregman.py b/ot/gpu/bregman.py index 6714098..600ead4 100644 --- a/ot/gpu/bregman.py +++ b/ot/gpu/bregman.py @@ -90,14 +90,14 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, stopThr=1e-9, """ - a = cp.asarray(a, dtype=np.float64) - b = cp.asarray(b, dtype=np.float64) - M = cp.asarray(M, dtype=np.float64) + a = cp.asarray(a) + b = cp.asarray(b) + M = cp.asarray(M) if len(a) == 0: - a = np.ones((M.shape[0],), dtype=np.float64) / M.shape[0] + a = np.ones((M.shape[0],)) / M.shape[0] if len(b) == 0: - b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] + b = np.ones((M.shape[1],)) / M.shape[1] # init data Nini = len(a) diff --git a/test/test_gpu.py b/test/test_gpu.py index 1e97c45..51a0cff 100644 --- a/test/test_gpu.py +++ b/test/test_gpu.py @@ -17,63 +17,70 @@ except ImportError: @pytest.mark.skipif(nogpu, reason="No GPU available") -def test_gpu_sinkhorn(): +def test_gpu_dist(): rng = np.random.RandomState(0) - def describe_res(r): - print("min:{:.3E}, max::{:.3E}, mean::{:.3E}, std::{:.3E}".format( - np.min(r), np.max(r), np.mean(r), np.std(r))) - for n_samples in [50, 100, 500, 1000]: print(n_samples) a = rng.rand(n_samples // 4, 100) b = rng.rand(n_samples, 100) - time1 = time.time() - transport = ot.da.OTDA_sinkhorn() - transport.fit(a, b) - G1 = transport.G - time2 = time.time() - transport = ot.gpu.da.OTDA_sinkhorn() - transport.fit(a, b) - G2 = transport.G - time3 = time.time() - print("Normal sinkhorn, time: {:6.2f} sec ".format(time2 - time1)) - describe_res(G1) - print(" GPU sinkhorn, time: {:6.2f} sec ".format(time3 - time2)) - describe_res(G2) - - np.testing.assert_allclose(G1, G2, rtol=1e-5, atol=1e-5) + + M = ot.dist(a.copy(), b.copy()) + M2 = ot.gpu.dist(a.copy(), b.copy()) + + np.testing.assert_allclose(M, M2, rtol=1e-10) + + M2 = ot.gpu.dist(a.copy(), b.copy(), to_numpy=False) @pytest.mark.skipif(nogpu, reason="No GPU available") -def test_gpu_sinkhorn_lpl1(): +def test_gpu_sinkhorn(): rng = np.random.RandomState(0) - def describe_res(r): - print("min:{:.3E}, max:{:.3E}, mean:{:.3E}, std:{:.3E}" - .format(np.min(r), np.max(r), np.mean(r), np.std(r))) + for n_samples in [50, 100, 500, 1000]: + a = rng.rand(n_samples // 4, 100) + b = rng.rand(n_samples, 100) + + wa = ot.unif(n_samples // 4) + wb = ot.unif(n_samples) + + M = ot.dist(a.copy(), b.copy()) + M2 = ot.gpu.dist(a.copy(), b.copy(), to_numpy=False) + + reg = 1 + + G = ot.sinkhorn(wa, wb, M, reg) + G1 = ot.gpu.sinkhorn(wa, wb, M, reg) + + np.testing.assert_allclose(G1, G, rtol=1e-10) + + G2 = ot.gpu.sinkhorn(wa, wb, M2, reg, to_numpy=False) + + +@pytest.mark.skipif(nogpu, reason="No GPU available") +def test_gpu_sinkhorn_lpl1(): + + rng = np.random.RandomState(0) for n_samples in [50, 100, 500]: print(n_samples) a = rng.rand(n_samples // 4, 100) labels_a = np.random.randint(10, size=(n_samples // 4)) b = rng.rand(n_samples, 100) - time1 = time.time() - transport = ot.da.OTDA_lpl1() - transport.fit(a, labels_a, b) - G1 = transport.G - time2 = time.time() - transport = ot.gpu.da.OTDA_lpl1() - transport.fit(a, labels_a, b) - G2 = transport.G - time3 = time.time() - print("Normal sinkhorn lpl1, time: {:6.2f} sec ".format( - time2 - time1)) - describe_res(G1) - print(" GPU sinkhorn lpl1, time: {:6.2f} sec ".format( - time3 - time2)) - describe_res(G2) - - np.testing.assert_allclose(G1, G2, rtol=1e-3, atol=1e-3) + + wa = ot.unif(n_samples // 4) + wb = ot.unif(n_samples) + + M = ot.dist(a.copy(), b.copy()) + M2 = ot.gpu.dist(a.copy(), b.copy(), to_numpy=False) + + reg = 1 + + G = ot.da.sinkhorn_lpl1_mm(wa, labels_a, wb, M, reg) + G1 = ot.gpu.da.sinkhorn_lpl1_mm(wa, labels_a, wb, M, reg) + + np.testing.assert_allclose(G1, G, rtol=1e-10) + + G2 = ot.gpu.da.sinkhorn_lpl1_mm(wa, labels_a, wb, M2, reg, to_numpy=False) -- cgit v1.2.3 From 5e7bfbcbc99ce5915873147677b434c0b1d10fc8 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Sep 2018 15:20:30 +0200 Subject: working test +92 percent tets coverege --- test/test_bregman.py | 2 +- test/test_gpu.py | 18 ++++++++++++++---- 2 files changed, 15 insertions(+), 5 deletions(-) diff --git a/test/test_bregman.py b/test/test_bregman.py index 01ec655..a141078 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -105,7 +105,7 @@ def test_bary(): ot.bregman.barycenter(A, M, reg, log=True, verbose=True) -def test_wassersteinbary(): +def test_wasserstein_bary_2d(): size = 100 # size of a square image a1 = np.random.randn(size, size) diff --git a/test/test_gpu.py b/test/test_gpu.py index 51a0cff..6b7fdd4 100644 --- a/test/test_gpu.py +++ b/test/test_gpu.py @@ -6,7 +6,6 @@ import numpy as np import ot -import time import pytest try: # test if cudamat installed @@ -31,7 +30,11 @@ def test_gpu_dist(): np.testing.assert_allclose(M, M2, rtol=1e-10) - M2 = ot.gpu.dist(a.copy(), b.copy(), to_numpy=False) + M2 = ot.gpu.dist(a.copy(), b.copy(), metric='euclidean', to_numpy=False) + + # check raise not implemented wrong metric + with pytest.raises(NotImplementedError): + M2 = ot.gpu.dist(a.copy(), b.copy(), metric='cityblock', to_numpy=False) @pytest.mark.skipif(nogpu, reason="No GPU available") @@ -46,6 +49,9 @@ def test_gpu_sinkhorn(): wa = ot.unif(n_samples // 4) wb = ot.unif(n_samples) + wb2 = np.random.rand(n_samples, 20) + wb2 /= wb2.sum(0, keepdims=True) + M = ot.dist(a.copy(), b.copy()) M2 = ot.gpu.dist(a.copy(), b.copy(), to_numpy=False) @@ -56,7 +62,11 @@ def test_gpu_sinkhorn(): np.testing.assert_allclose(G1, G, rtol=1e-10) - G2 = ot.gpu.sinkhorn(wa, wb, M2, reg, to_numpy=False) + # run all on gpu + ot.gpu.sinkhorn(wa, wb, M2, reg, to_numpy=False, log=True) + + # run sinkhorn for multiple targets + ot.gpu.sinkhorn(wa, wb2, M2, reg, to_numpy=False, log=True) @pytest.mark.skipif(nogpu, reason="No GPU available") @@ -83,4 +93,4 @@ def test_gpu_sinkhorn_lpl1(): np.testing.assert_allclose(G1, G, rtol=1e-10) - G2 = ot.gpu.da.sinkhorn_lpl1_mm(wa, labels_a, wb, M2, reg, to_numpy=False) + ot.gpu.da.sinkhorn_lpl1_mm(wa, labels_a, wb, M2, reg, to_numpy=False, log=True) -- cgit v1.2.3 From c8c397b6e1747d6593ba139d0a4f825fe39f5cc6 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Sep 2018 15:35:40 +0200 Subject: removed unused import --- ot/gpu/da.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/gpu/da.py b/ot/gpu/da.py index 85d43e6..ac3f8d7 100644 --- a/ot/gpu/da.py +++ b/ot/gpu/da.py @@ -12,7 +12,7 @@ Domain adaptation with optimal transport with GPU implementation import numpy as np -from ..utils import unif +#from ..utils import unif from .bregman import sinkhorn import cudamat -- cgit v1.2.3 From 5bb13e439578ad8952bece8f491ce68bb1efe47d Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Sep 2018 15:47:29 +0200 Subject: do code coverage in travis --- .travis.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.travis.yml b/.travis.yml index d146395..90a0ff4 100644 --- a/.travis.yml +++ b/.travis.yml @@ -26,11 +26,11 @@ before_script: # configure a headless display to test plot generation # command to install dependencies install: - pip install -r requirements.txt - - pip install flake8 pytest + - pip install flake8 pytest pytest-cov - pip install . # command to run tests + check syntax style script: - python setup.py develop - flake8 examples/ ot/ test/ - - python -m pytest -v test/ + - python -m pytest -v test/ --cov=ot # - py.test ot test -- cgit v1.2.3 From eea946bfe41e00697d2916954d7787c27c787d63 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Sep 2018 15:54:02 +0200 Subject: remove old externals --- ot/externals/__init__.py | 0 ot/externals/funcsigs.py | 817 ----------------------------------------------- 2 files changed, 817 deletions(-) delete mode 100644 ot/externals/__init__.py delete mode 100644 ot/externals/funcsigs.py diff --git a/ot/externals/__init__.py b/ot/externals/__init__.py deleted file mode 100644 index e69de29..0000000 diff --git a/ot/externals/funcsigs.py b/ot/externals/funcsigs.py deleted file mode 100644 index c73fdc9..0000000 --- a/ot/externals/funcsigs.py +++ /dev/null @@ -1,817 +0,0 @@ -# Copyright 2001-2013 Python Software Foundation; All Rights Reserved -"""Function signature objects for callables - -Back port of Python 3.3's function signature tools from the inspect module, -modified to be compatible with Python 2.7 and 3.2+. -""" -from __future__ import absolute_import, division, print_function -import itertools -import functools -import re -import types - -from collections import OrderedDict - -__version__ = "0.4" - -__all__ = ['BoundArguments', 'Parameter', 'Signature', 'signature'] - - -_WrapperDescriptor = type(type.__call__) -_MethodWrapper = type(all.__call__) - -_NonUserDefinedCallables = (_WrapperDescriptor, - _MethodWrapper, - types.BuiltinFunctionType) - - -def formatannotation(annotation, base_module=None): - if isinstance(annotation, type): - if annotation.__module__ in ('builtins', '__builtin__', base_module): - return annotation.__name__ - return annotation.__module__ + '.' + annotation.__name__ - return repr(annotation) - - -def _get_user_defined_method(cls, method_name, *nested): - try: - if cls is type: - return - meth = getattr(cls, method_name) - for name in nested: - meth = getattr(meth, name, meth) - except AttributeError: - return - else: - if not isinstance(meth, _NonUserDefinedCallables): - # Once '__signature__' will be added to 'C'-level - # callables, this check won't be necessary - return meth - - -def signature(obj): - '''Get a signature object for the passed callable.''' - - if not callable(obj): - raise TypeError('{0!r} is not a callable object'.format(obj)) - - if isinstance(obj, types.MethodType): - sig = signature(obj.__func__) - if obj.__self__ is None: - # Unbound method: the first parameter becomes positional-only - if sig.parameters: - first = sig.parameters.values()[0].replace( - kind=_POSITIONAL_ONLY) - return sig.replace( - parameters=(first,) + tuple(sig.parameters.values())[1:]) - else: - return sig - else: - # In this case we skip the first parameter of the underlying - # function (usually `self` or `cls`). - return sig.replace(parameters=tuple(sig.parameters.values())[1:]) - - try: - sig = obj.__signature__ - except AttributeError: - pass - else: - if sig is not None: - return sig - - try: - # Was this function wrapped by a decorator? - wrapped = obj.__wrapped__ - except AttributeError: - pass - else: - return signature(wrapped) - - if isinstance(obj, types.FunctionType): - return Signature.from_function(obj) - - if isinstance(obj, functools.partial): - sig = signature(obj.func) - - new_params = OrderedDict(sig.parameters.items()) - - partial_args = obj.args or () - partial_keywords = obj.keywords or {} - try: - ba = sig.bind_partial(*partial_args, **partial_keywords) - except TypeError: - msg = 'partial object {0!r} has incorrect arguments'.format(obj) - raise ValueError(msg) - - for arg_name, arg_value in ba.arguments.items(): - param = new_params[arg_name] - if arg_name in partial_keywords: - # We set a new default value, because the following code - # is correct: - # - # >>> def foo(a): print(a) - # >>> print(partial(partial(foo, a=10), a=20)()) - # 20 - # >>> print(partial(partial(foo, a=10), a=20)(a=30)) - # 30 - # - # So, with 'partial' objects, passing a keyword argument is - # like setting a new default value for the corresponding - # parameter - # - # We also mark this parameter with '_partial_kwarg' - # flag. Later, in '_bind', the 'default' value of this - # parameter will be added to 'kwargs', to simulate - # the 'functools.partial' real call. - new_params[arg_name] = param.replace(default=arg_value, - _partial_kwarg=True) - - elif (param.kind not in (_VAR_KEYWORD, _VAR_POSITIONAL) and - not param._partial_kwarg): - new_params.pop(arg_name) - - return sig.replace(parameters=new_params.values()) - - sig = None - if isinstance(obj, type): - # obj is a class or a metaclass - - # First, let's see if it has an overloaded __call__ defined - # in its metaclass - call = _get_user_defined_method(type(obj), '__call__') - if call is not None: - sig = signature(call) - else: - # Now we check if the 'obj' class has a '__new__' method - new = _get_user_defined_method(obj, '__new__') - if new is not None: - sig = signature(new) - else: - # Finally, we should have at least __init__ implemented - init = _get_user_defined_method(obj, '__init__') - if init is not None: - sig = signature(init) - elif not isinstance(obj, _NonUserDefinedCallables): - # An object with __call__ - # We also check that the 'obj' is not an instance of - # _WrapperDescriptor or _MethodWrapper to avoid - # infinite recursion (and even potential segfault) - call = _get_user_defined_method(type(obj), '__call__', 'im_func') - if call is not None: - sig = signature(call) - - if sig is not None: - # For classes and objects we skip the first parameter of their - # __call__, __new__, or __init__ methods - return sig.replace(parameters=tuple(sig.parameters.values())[1:]) - - if isinstance(obj, types.BuiltinFunctionType): - # Raise a nicer error message for builtins - msg = 'no signature found for builtin function {0!r}'.format(obj) - raise ValueError(msg) - - raise ValueError( - 'callable {0!r} is not supported by signature'.format(obj)) - - -class _void(object): - '''A private marker - used in Parameter & Signature''' - - -class _empty(object): - pass - - -class _ParameterKind(int): - def __new__(self, *args, **kwargs): - obj = int.__new__(self, *args) - obj._name = kwargs['name'] - return obj - - def __str__(self): - return self._name - - def __repr__(self): - return '<_ParameterKind: {0!r}>'.format(self._name) - - -_POSITIONAL_ONLY = _ParameterKind(0, name='POSITIONAL_ONLY') -_POSITIONAL_OR_KEYWORD = _ParameterKind(1, name='POSITIONAL_OR_KEYWORD') -_VAR_POSITIONAL = _ParameterKind(2, name='VAR_POSITIONAL') -_KEYWORD_ONLY = _ParameterKind(3, name='KEYWORD_ONLY') -_VAR_KEYWORD = _ParameterKind(4, name='VAR_KEYWORD') - - -class Parameter(object): - '''Represents a parameter in a function signature. - - Has the following public attributes: - - * name : str - The name of the parameter as a string. - * default : object - The default value for the parameter if specified. If the - parameter has no default value, this attribute is not set. - * annotation - The annotation for the parameter if specified. If the - parameter has no annotation, this attribute is not set. - * kind : str - Describes how argument values are bound to the parameter. - Possible values: `Parameter.POSITIONAL_ONLY`, - `Parameter.POSITIONAL_OR_KEYWORD`, `Parameter.VAR_POSITIONAL`, - `Parameter.KEYWORD_ONLY`, `Parameter.VAR_KEYWORD`. - ''' - - __slots__ = ('_name', '_kind', '_default', '_annotation', '_partial_kwarg') - - POSITIONAL_ONLY = _POSITIONAL_ONLY - POSITIONAL_OR_KEYWORD = _POSITIONAL_OR_KEYWORD - VAR_POSITIONAL = _VAR_POSITIONAL - KEYWORD_ONLY = _KEYWORD_ONLY - VAR_KEYWORD = _VAR_KEYWORD - - empty = _empty - - def __init__(self, name, kind, default=_empty, annotation=_empty, - _partial_kwarg=False): - - if kind not in (_POSITIONAL_ONLY, _POSITIONAL_OR_KEYWORD, - _VAR_POSITIONAL, _KEYWORD_ONLY, _VAR_KEYWORD): - raise ValueError("invalid value for 'Parameter.kind' attribute") - self._kind = kind - - if default is not _empty: - if kind in (_VAR_POSITIONAL, _VAR_KEYWORD): - msg = '{0} parameters cannot have default values'.format(kind) - raise ValueError(msg) - self._default = default - self._annotation = annotation - - if name is None: - if kind != _POSITIONAL_ONLY: - raise ValueError("None is not a valid name for a " - "non-positional-only parameter") - self._name = name - else: - name = str(name) - if kind != _POSITIONAL_ONLY and not re.match( - r'[a-z_]\w*$', name, re.I): - msg = '{0!r} is not a valid parameter name'.format(name) - raise ValueError(msg) - self._name = name - - self._partial_kwarg = _partial_kwarg - - @property - def name(self): - return self._name - - @property - def default(self): - return self._default - - @property - def annotation(self): - return self._annotation - - @property - def kind(self): - return self._kind - - def replace(self, name=_void, kind=_void, annotation=_void, - default=_void, _partial_kwarg=_void): - '''Creates a customized copy of the Parameter.''' - - if name is _void: - name = self._name - - if kind is _void: - kind = self._kind - - if annotation is _void: - annotation = self._annotation - - if default is _void: - default = self._default - - if _partial_kwarg is _void: - _partial_kwarg = self._partial_kwarg - - return type(self)(name, kind, default=default, annotation=annotation, - _partial_kwarg=_partial_kwarg) - - def __str__(self): - kind = self.kind - - formatted = self._name - if kind == _POSITIONAL_ONLY: - if formatted is None: - formatted = '' - formatted = '<{0}>'.format(formatted) - - # Add annotation and default value - if self._annotation is not _empty: - formatted = '{0}:{1}'.format(formatted, - formatannotation(self._annotation)) - - if self._default is not _empty: - formatted = '{0}={1}'.format(formatted, repr(self._default)) - - if kind == _VAR_POSITIONAL: - formatted = '*' + formatted - elif kind == _VAR_KEYWORD: - formatted = '**' + formatted - - return formatted - - def __repr__(self): - return '<{0} at {1:#x} {2!r}>'.format(self.__class__.__name__, - id(self), self.name) - - def __hash__(self): - msg = "unhashable type: '{0}'".format(self.__class__.__name__) - raise TypeError(msg) - - def __eq__(self, other): - return (issubclass(other.__class__, Parameter) and - self._name == other._name and - self._kind == other._kind and - self._default == other._default and - self._annotation == other._annotation) - - def __ne__(self, other): - return not self.__eq__(other) - - -class BoundArguments(object): - '''Result of `Signature.bind` call. Holds the mapping of arguments - to the function's parameters. - - Has the following public attributes: - - * arguments : OrderedDict - An ordered mutable mapping of parameters' names to arguments' values. - Does not contain arguments' default values. - * signature : Signature - The Signature object that created this instance. - * args : tuple - Tuple of positional arguments values. - * kwargs : dict - Dict of keyword arguments values. - ''' - - def __init__(self, signature, arguments): - self.arguments = arguments - self._signature = signature - - @property - def signature(self): - return self._signature - - @property - def args(self): - args = [] - for param_name, param in self._signature.parameters.items(): - if (param.kind in (_VAR_KEYWORD, _KEYWORD_ONLY) or - param._partial_kwarg): - # Keyword arguments mapped by 'functools.partial' - # (Parameter._partial_kwarg is True) are mapped - # in 'BoundArguments.kwargs', along with VAR_KEYWORD & - # KEYWORD_ONLY - break - - try: - arg = self.arguments[param_name] - except KeyError: - # We're done here. Other arguments - # will be mapped in 'BoundArguments.kwargs' - break - else: - if param.kind == _VAR_POSITIONAL: - # *args - args.extend(arg) - else: - # plain argument - args.append(arg) - - return tuple(args) - - @property - def kwargs(self): - kwargs = {} - kwargs_started = False - for param_name, param in self._signature.parameters.items(): - if not kwargs_started: - if (param.kind in (_VAR_KEYWORD, _KEYWORD_ONLY) or - param._partial_kwarg): - kwargs_started = True - else: - if param_name not in self.arguments: - kwargs_started = True - continue - - if not kwargs_started: - continue - - try: - arg = self.arguments[param_name] - except KeyError: - pass - else: - if param.kind == _VAR_KEYWORD: - # **kwargs - kwargs.update(arg) - else: - # plain keyword argument - kwargs[param_name] = arg - - return kwargs - - def __hash__(self): - msg = "unhashable type: '{0}'".format(self.__class__.__name__) - raise TypeError(msg) - - def __eq__(self, other): - return (issubclass(other.__class__, BoundArguments) and - self.signature == other.signature and - self.arguments == other.arguments) - - def __ne__(self, other): - return not self.__eq__(other) - - -class Signature(object): - '''A Signature object represents the overall signature of a function. - It stores a Parameter object for each parameter accepted by the - function, as well as information specific to the function itself. - - A Signature object has the following public attributes and methods: - - * parameters : OrderedDict - An ordered mapping of parameters' names to the corresponding - Parameter objects (keyword-only arguments are in the same order - as listed in `code.co_varnames`). - * return_annotation : object - The annotation for the return type of the function if specified. - If the function has no annotation for its return type, this - attribute is not set. - * bind(*args, **kwargs) -> BoundArguments - Creates a mapping from positional and keyword arguments to - parameters. - * bind_partial(*args, **kwargs) -> BoundArguments - Creates a partial mapping from positional and keyword arguments - to parameters (simulating 'functools.partial' behavior.) - ''' - - __slots__ = ('_return_annotation', '_parameters') - - _parameter_cls = Parameter - _bound_arguments_cls = BoundArguments - - empty = _empty - - def __init__(self, parameters=None, return_annotation=_empty, - __validate_parameters__=True): - '''Constructs Signature from the given list of Parameter - objects and 'return_annotation'. All arguments are optional. - ''' - - if parameters is None: - params = OrderedDict() - else: - if __validate_parameters__: - params = OrderedDict() - top_kind = _POSITIONAL_ONLY - - for idx, param in enumerate(parameters): - kind = param.kind - if kind < top_kind: - msg = 'wrong parameter order: {0} before {1}' - msg = msg.format(top_kind, param.kind) - raise ValueError(msg) - else: - top_kind = kind - - name = param.name - if name is None: - name = str(idx) - param = param.replace(name=name) - - if name in params: - msg = 'duplicate parameter name: {0!r}'.format(name) - raise ValueError(msg) - params[name] = param - else: - params = OrderedDict(((param.name, param) - for param in parameters)) - - self._parameters = params - self._return_annotation = return_annotation - - @classmethod - def from_function(cls, func): - '''Constructs Signature for the given python function''' - - if not isinstance(func, types.FunctionType): - raise TypeError('{0!r} is not a Python function'.format(func)) - - Parameter = cls._parameter_cls - - # Parameter information. - func_code = func.__code__ - pos_count = func_code.co_argcount - arg_names = func_code.co_varnames - positional = tuple(arg_names[:pos_count]) - keyword_only_count = getattr(func_code, 'co_kwonlyargcount', 0) - keyword_only = arg_names[pos_count:(pos_count + keyword_only_count)] - annotations = getattr(func, '__annotations__', {}) - defaults = func.__defaults__ - kwdefaults = getattr(func, '__kwdefaults__', None) - - if defaults: - pos_default_count = len(defaults) - else: - pos_default_count = 0 - - parameters = [] - - # Non-keyword-only parameters w/o defaults. - non_default_count = pos_count - pos_default_count - for name in positional[:non_default_count]: - annotation = annotations.get(name, _empty) - parameters.append(Parameter(name, annotation=annotation, - kind=_POSITIONAL_OR_KEYWORD)) - - # ... w/ defaults. - for offset, name in enumerate(positional[non_default_count:]): - annotation = annotations.get(name, _empty) - parameters.append(Parameter(name, annotation=annotation, - kind=_POSITIONAL_OR_KEYWORD, - default=defaults[offset])) - - # *args - if func_code.co_flags & 0x04: - name = arg_names[pos_count + keyword_only_count] - annotation = annotations.get(name, _empty) - parameters.append(Parameter(name, annotation=annotation, - kind=_VAR_POSITIONAL)) - - # Keyword-only parameters. - for name in keyword_only: - default = _empty - if kwdefaults is not None: - default = kwdefaults.get(name, _empty) - - annotation = annotations.get(name, _empty) - parameters.append(Parameter(name, annotation=annotation, - kind=_KEYWORD_ONLY, - default=default)) - # **kwargs - if func_code.co_flags & 0x08: - index = pos_count + keyword_only_count - if func_code.co_flags & 0x04: - index += 1 - - name = arg_names[index] - annotation = annotations.get(name, _empty) - parameters.append(Parameter(name, annotation=annotation, - kind=_VAR_KEYWORD)) - - return cls(parameters, - return_annotation=annotations.get('return', _empty), - __validate_parameters__=False) - - @property - def parameters(self): - try: - return types.MappingProxyType(self._parameters) - except AttributeError: - return OrderedDict(self._parameters.items()) - - @property - def return_annotation(self): - return self._return_annotation - - def replace(self, parameters=_void, return_annotation=_void): - '''Creates a customized copy of the Signature. - Pass 'parameters' and/or 'return_annotation' arguments - to override them in the new copy. - ''' - - if parameters is _void: - parameters = self.parameters.values() - - if return_annotation is _void: - return_annotation = self._return_annotation - - return type(self)(parameters, - return_annotation=return_annotation) - - def __hash__(self): - msg = "unhashable type: '{0}'".format(self.__class__.__name__) - raise TypeError(msg) - - def __eq__(self, other): - if (not issubclass(type(other), Signature) or - self.return_annotation != other.return_annotation or - len(self.parameters) != len(other.parameters)): - return False - - other_positions = dict((param, idx) - for idx, param in enumerate(other.parameters.keys())) - - for idx, (param_name, param) in enumerate(self.parameters.items()): - if param.kind == _KEYWORD_ONLY: - try: - other_param = other.parameters[param_name] - except KeyError: - return False - else: - if param != other_param: - return False - else: - try: - other_idx = other_positions[param_name] - except KeyError: - return False - else: - if (idx != other_idx or - param != other.parameters[param_name]): - return False - - return True - - def __ne__(self, other): - return not self.__eq__(other) - - def _bind(self, args, kwargs, partial=False): - '''Private method. Don't use directly.''' - - arguments = OrderedDict() - - parameters = iter(self.parameters.values()) - parameters_ex = () - arg_vals = iter(args) - - if partial: - # Support for binding arguments to 'functools.partial' objects. - # See 'functools.partial' case in 'signature()' implementation - # for details. - for param_name, param in self.parameters.items(): - if (param._partial_kwarg and param_name not in kwargs): - # Simulating 'functools.partial' behavior - kwargs[param_name] = param.default - - while True: - # Let's iterate through the positional arguments and corresponding - # parameters - try: - arg_val = next(arg_vals) - except StopIteration: - # No more positional arguments - try: - param = next(parameters) - except StopIteration: - # No more parameters. That's it. Just need to check that - # we have no `kwargs` after this while loop - break - else: - if param.kind == _VAR_POSITIONAL: - # That's OK, just empty *args. Let's start parsing - # kwargs - break - elif param.name in kwargs: - if param.kind == _POSITIONAL_ONLY: - msg = '{arg!r} parameter is positional only, ' \ - 'but was passed as a keyword' - msg = msg.format(arg=param.name) - raise TypeError(msg) - parameters_ex = (param,) - break - elif (param.kind == _VAR_KEYWORD or - param.default is not _empty): - # That's fine too - we have a default value for this - # parameter. So, lets start parsing `kwargs`, starting - # with the current parameter - parameters_ex = (param,) - break - else: - if partial: - parameters_ex = (param,) - break - else: - msg = '{arg!r} parameter lacking default value' - msg = msg.format(arg=param.name) - raise TypeError(msg) - else: - # We have a positional argument to process - try: - param = next(parameters) - except StopIteration: - raise TypeError('too many positional arguments') - else: - if param.kind in (_VAR_KEYWORD, _KEYWORD_ONLY): - # Looks like we have no parameter for this positional - # argument - raise TypeError('too many positional arguments') - - if param.kind == _VAR_POSITIONAL: - # We have an '*args'-like argument, let's fill it with - # all positional arguments we have left and move on to - # the next phase - values = [arg_val] - values.extend(arg_vals) - arguments[param.name] = tuple(values) - break - - if param.name in kwargs: - raise TypeError('multiple values for argument ' - '{arg!r}'.format(arg=param.name)) - - arguments[param.name] = arg_val - - # Now, we iterate through the remaining parameters to process - # keyword arguments - kwargs_param = None - for param in itertools.chain(parameters_ex, parameters): - if param.kind == _POSITIONAL_ONLY: - # This should never happen in case of a properly built - # Signature object (but let's have this check here - # to ensure correct behaviour just in case) - raise TypeError('{arg!r} parameter is positional only, ' - 'but was passed as a keyword'. - format(arg=param.name)) - - if param.kind == _VAR_KEYWORD: - # Memorize that we have a '**kwargs'-like parameter - kwargs_param = param - continue - - param_name = param.name - try: - arg_val = kwargs.pop(param_name) - except KeyError: - # We have no value for this parameter. It's fine though, - # if it has a default value, or it is an '*args'-like - # parameter, left alone by the processing of positional - # arguments. - if (not partial and param.kind != _VAR_POSITIONAL and - param.default is _empty): - raise TypeError('{arg!r} parameter lacking default value'. - format(arg=param_name)) - - else: - arguments[param_name] = arg_val - - if kwargs: - if kwargs_param is not None: - # Process our '**kwargs'-like parameter - arguments[kwargs_param.name] = kwargs - else: - raise TypeError('too many keyword arguments') - - return self._bound_arguments_cls(self, arguments) - - def bind(self, *args, **kwargs): - '''Get a BoundArguments object, that maps the passed `args` - and `kwargs` to the function's signature. Raises `TypeError` - if the passed arguments can not be bound. - ''' - return self._bind(args, kwargs) - - def bind_partial(self, *args, **kwargs): - '''Get a BoundArguments object, that partially maps the - passed `args` and `kwargs` to the function's signature. - Raises `TypeError` if the passed arguments can not be bound. - ''' - return self._bind(args, kwargs, partial=True) - - def __str__(self): - result = [] - render_kw_only_separator = True - for idx, param in enumerate(self.parameters.values()): - formatted = str(param) - - kind = param.kind - if kind == _VAR_POSITIONAL: - # OK, we have an '*args'-like parameter, so we won't need - # a '*' to separate keyword-only arguments - render_kw_only_separator = False - elif kind == _KEYWORD_ONLY and render_kw_only_separator: - # We have a keyword-only parameter to render and we haven't - # rendered an '*args'-like parameter before, so add a '*' - # separator to the parameters list ("foo(arg1, *, arg2)" case) - result.append('*') - # This condition should be only triggered once, so - # reset the flag - render_kw_only_separator = False - - result.append(formatted) - - rendered = '({0})'.format(', '.join(result)) - - if self.return_annotation is not _empty: - anno = formatannotation(self.return_annotation) - rendered += ' -> {0}'.format(anno) - - return rendered -- cgit v1.2.3 From 057bb89892af0cbeddec70241873d51d2931f3c5 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Sep 2018 16:01:50 +0200 Subject: Revert "remove old externals" This reverts commit eea946bfe41e00697d2916954d7787c27c787d63. --- ot/externals/__init__.py | 0 ot/externals/funcsigs.py | 817 +++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 817 insertions(+) create mode 100644 ot/externals/__init__.py create mode 100644 ot/externals/funcsigs.py diff --git a/ot/externals/__init__.py b/ot/externals/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/ot/externals/funcsigs.py b/ot/externals/funcsigs.py new file mode 100644 index 0000000..c73fdc9 --- /dev/null +++ b/ot/externals/funcsigs.py @@ -0,0 +1,817 @@ +# Copyright 2001-2013 Python Software Foundation; All Rights Reserved +"""Function signature objects for callables + +Back port of Python 3.3's function signature tools from the inspect module, +modified to be compatible with Python 2.7 and 3.2+. +""" +from __future__ import absolute_import, division, print_function +import itertools +import functools +import re +import types + +from collections import OrderedDict + +__version__ = "0.4" + +__all__ = ['BoundArguments', 'Parameter', 'Signature', 'signature'] + + +_WrapperDescriptor = type(type.__call__) +_MethodWrapper = type(all.__call__) + +_NonUserDefinedCallables = (_WrapperDescriptor, + _MethodWrapper, + types.BuiltinFunctionType) + + +def formatannotation(annotation, base_module=None): + if isinstance(annotation, type): + if annotation.__module__ in ('builtins', '__builtin__', base_module): + return annotation.__name__ + return annotation.__module__ + '.' + annotation.__name__ + return repr(annotation) + + +def _get_user_defined_method(cls, method_name, *nested): + try: + if cls is type: + return + meth = getattr(cls, method_name) + for name in nested: + meth = getattr(meth, name, meth) + except AttributeError: + return + else: + if not isinstance(meth, _NonUserDefinedCallables): + # Once '__signature__' will be added to 'C'-level + # callables, this check won't be necessary + return meth + + +def signature(obj): + '''Get a signature object for the passed callable.''' + + if not callable(obj): + raise TypeError('{0!r} is not a callable object'.format(obj)) + + if isinstance(obj, types.MethodType): + sig = signature(obj.__func__) + if obj.__self__ is None: + # Unbound method: the first parameter becomes positional-only + if sig.parameters: + first = sig.parameters.values()[0].replace( + kind=_POSITIONAL_ONLY) + return sig.replace( + parameters=(first,) + tuple(sig.parameters.values())[1:]) + else: + return sig + else: + # In this case we skip the first parameter of the underlying + # function (usually `self` or `cls`). + return sig.replace(parameters=tuple(sig.parameters.values())[1:]) + + try: + sig = obj.__signature__ + except AttributeError: + pass + else: + if sig is not None: + return sig + + try: + # Was this function wrapped by a decorator? + wrapped = obj.__wrapped__ + except AttributeError: + pass + else: + return signature(wrapped) + + if isinstance(obj, types.FunctionType): + return Signature.from_function(obj) + + if isinstance(obj, functools.partial): + sig = signature(obj.func) + + new_params = OrderedDict(sig.parameters.items()) + + partial_args = obj.args or () + partial_keywords = obj.keywords or {} + try: + ba = sig.bind_partial(*partial_args, **partial_keywords) + except TypeError: + msg = 'partial object {0!r} has incorrect arguments'.format(obj) + raise ValueError(msg) + + for arg_name, arg_value in ba.arguments.items(): + param = new_params[arg_name] + if arg_name in partial_keywords: + # We set a new default value, because the following code + # is correct: + # + # >>> def foo(a): print(a) + # >>> print(partial(partial(foo, a=10), a=20)()) + # 20 + # >>> print(partial(partial(foo, a=10), a=20)(a=30)) + # 30 + # + # So, with 'partial' objects, passing a keyword argument is + # like setting a new default value for the corresponding + # parameter + # + # We also mark this parameter with '_partial_kwarg' + # flag. Later, in '_bind', the 'default' value of this + # parameter will be added to 'kwargs', to simulate + # the 'functools.partial' real call. + new_params[arg_name] = param.replace(default=arg_value, + _partial_kwarg=True) + + elif (param.kind not in (_VAR_KEYWORD, _VAR_POSITIONAL) and + not param._partial_kwarg): + new_params.pop(arg_name) + + return sig.replace(parameters=new_params.values()) + + sig = None + if isinstance(obj, type): + # obj is a class or a metaclass + + # First, let's see if it has an overloaded __call__ defined + # in its metaclass + call = _get_user_defined_method(type(obj), '__call__') + if call is not None: + sig = signature(call) + else: + # Now we check if the 'obj' class has a '__new__' method + new = _get_user_defined_method(obj, '__new__') + if new is not None: + sig = signature(new) + else: + # Finally, we should have at least __init__ implemented + init = _get_user_defined_method(obj, '__init__') + if init is not None: + sig = signature(init) + elif not isinstance(obj, _NonUserDefinedCallables): + # An object with __call__ + # We also check that the 'obj' is not an instance of + # _WrapperDescriptor or _MethodWrapper to avoid + # infinite recursion (and even potential segfault) + call = _get_user_defined_method(type(obj), '__call__', 'im_func') + if call is not None: + sig = signature(call) + + if sig is not None: + # For classes and objects we skip the first parameter of their + # __call__, __new__, or __init__ methods + return sig.replace(parameters=tuple(sig.parameters.values())[1:]) + + if isinstance(obj, types.BuiltinFunctionType): + # Raise a nicer error message for builtins + msg = 'no signature found for builtin function {0!r}'.format(obj) + raise ValueError(msg) + + raise ValueError( + 'callable {0!r} is not supported by signature'.format(obj)) + + +class _void(object): + '''A private marker - used in Parameter & Signature''' + + +class _empty(object): + pass + + +class _ParameterKind(int): + def __new__(self, *args, **kwargs): + obj = int.__new__(self, *args) + obj._name = kwargs['name'] + return obj + + def __str__(self): + return self._name + + def __repr__(self): + return '<_ParameterKind: {0!r}>'.format(self._name) + + +_POSITIONAL_ONLY = _ParameterKind(0, name='POSITIONAL_ONLY') +_POSITIONAL_OR_KEYWORD = _ParameterKind(1, name='POSITIONAL_OR_KEYWORD') +_VAR_POSITIONAL = _ParameterKind(2, name='VAR_POSITIONAL') +_KEYWORD_ONLY = _ParameterKind(3, name='KEYWORD_ONLY') +_VAR_KEYWORD = _ParameterKind(4, name='VAR_KEYWORD') + + +class Parameter(object): + '''Represents a parameter in a function signature. + + Has the following public attributes: + + * name : str + The name of the parameter as a string. + * default : object + The default value for the parameter if specified. If the + parameter has no default value, this attribute is not set. + * annotation + The annotation for the parameter if specified. If the + parameter has no annotation, this attribute is not set. + * kind : str + Describes how argument values are bound to the parameter. + Possible values: `Parameter.POSITIONAL_ONLY`, + `Parameter.POSITIONAL_OR_KEYWORD`, `Parameter.VAR_POSITIONAL`, + `Parameter.KEYWORD_ONLY`, `Parameter.VAR_KEYWORD`. + ''' + + __slots__ = ('_name', '_kind', '_default', '_annotation', '_partial_kwarg') + + POSITIONAL_ONLY = _POSITIONAL_ONLY + POSITIONAL_OR_KEYWORD = _POSITIONAL_OR_KEYWORD + VAR_POSITIONAL = _VAR_POSITIONAL + KEYWORD_ONLY = _KEYWORD_ONLY + VAR_KEYWORD = _VAR_KEYWORD + + empty = _empty + + def __init__(self, name, kind, default=_empty, annotation=_empty, + _partial_kwarg=False): + + if kind not in (_POSITIONAL_ONLY, _POSITIONAL_OR_KEYWORD, + _VAR_POSITIONAL, _KEYWORD_ONLY, _VAR_KEYWORD): + raise ValueError("invalid value for 'Parameter.kind' attribute") + self._kind = kind + + if default is not _empty: + if kind in (_VAR_POSITIONAL, _VAR_KEYWORD): + msg = '{0} parameters cannot have default values'.format(kind) + raise ValueError(msg) + self._default = default + self._annotation = annotation + + if name is None: + if kind != _POSITIONAL_ONLY: + raise ValueError("None is not a valid name for a " + "non-positional-only parameter") + self._name = name + else: + name = str(name) + if kind != _POSITIONAL_ONLY and not re.match( + r'[a-z_]\w*$', name, re.I): + msg = '{0!r} is not a valid parameter name'.format(name) + raise ValueError(msg) + self._name = name + + self._partial_kwarg = _partial_kwarg + + @property + def name(self): + return self._name + + @property + def default(self): + return self._default + + @property + def annotation(self): + return self._annotation + + @property + def kind(self): + return self._kind + + def replace(self, name=_void, kind=_void, annotation=_void, + default=_void, _partial_kwarg=_void): + '''Creates a customized copy of the Parameter.''' + + if name is _void: + name = self._name + + if kind is _void: + kind = self._kind + + if annotation is _void: + annotation = self._annotation + + if default is _void: + default = self._default + + if _partial_kwarg is _void: + _partial_kwarg = self._partial_kwarg + + return type(self)(name, kind, default=default, annotation=annotation, + _partial_kwarg=_partial_kwarg) + + def __str__(self): + kind = self.kind + + formatted = self._name + if kind == _POSITIONAL_ONLY: + if formatted is None: + formatted = '' + formatted = '<{0}>'.format(formatted) + + # Add annotation and default value + if self._annotation is not _empty: + formatted = '{0}:{1}'.format(formatted, + formatannotation(self._annotation)) + + if self._default is not _empty: + formatted = '{0}={1}'.format(formatted, repr(self._default)) + + if kind == _VAR_POSITIONAL: + formatted = '*' + formatted + elif kind == _VAR_KEYWORD: + formatted = '**' + formatted + + return formatted + + def __repr__(self): + return '<{0} at {1:#x} {2!r}>'.format(self.__class__.__name__, + id(self), self.name) + + def __hash__(self): + msg = "unhashable type: '{0}'".format(self.__class__.__name__) + raise TypeError(msg) + + def __eq__(self, other): + return (issubclass(other.__class__, Parameter) and + self._name == other._name and + self._kind == other._kind and + self._default == other._default and + self._annotation == other._annotation) + + def __ne__(self, other): + return not self.__eq__(other) + + +class BoundArguments(object): + '''Result of `Signature.bind` call. Holds the mapping of arguments + to the function's parameters. + + Has the following public attributes: + + * arguments : OrderedDict + An ordered mutable mapping of parameters' names to arguments' values. + Does not contain arguments' default values. + * signature : Signature + The Signature object that created this instance. + * args : tuple + Tuple of positional arguments values. + * kwargs : dict + Dict of keyword arguments values. + ''' + + def __init__(self, signature, arguments): + self.arguments = arguments + self._signature = signature + + @property + def signature(self): + return self._signature + + @property + def args(self): + args = [] + for param_name, param in self._signature.parameters.items(): + if (param.kind in (_VAR_KEYWORD, _KEYWORD_ONLY) or + param._partial_kwarg): + # Keyword arguments mapped by 'functools.partial' + # (Parameter._partial_kwarg is True) are mapped + # in 'BoundArguments.kwargs', along with VAR_KEYWORD & + # KEYWORD_ONLY + break + + try: + arg = self.arguments[param_name] + except KeyError: + # We're done here. Other arguments + # will be mapped in 'BoundArguments.kwargs' + break + else: + if param.kind == _VAR_POSITIONAL: + # *args + args.extend(arg) + else: + # plain argument + args.append(arg) + + return tuple(args) + + @property + def kwargs(self): + kwargs = {} + kwargs_started = False + for param_name, param in self._signature.parameters.items(): + if not kwargs_started: + if (param.kind in (_VAR_KEYWORD, _KEYWORD_ONLY) or + param._partial_kwarg): + kwargs_started = True + else: + if param_name not in self.arguments: + kwargs_started = True + continue + + if not kwargs_started: + continue + + try: + arg = self.arguments[param_name] + except KeyError: + pass + else: + if param.kind == _VAR_KEYWORD: + # **kwargs + kwargs.update(arg) + else: + # plain keyword argument + kwargs[param_name] = arg + + return kwargs + + def __hash__(self): + msg = "unhashable type: '{0}'".format(self.__class__.__name__) + raise TypeError(msg) + + def __eq__(self, other): + return (issubclass(other.__class__, BoundArguments) and + self.signature == other.signature and + self.arguments == other.arguments) + + def __ne__(self, other): + return not self.__eq__(other) + + +class Signature(object): + '''A Signature object represents the overall signature of a function. + It stores a Parameter object for each parameter accepted by the + function, as well as information specific to the function itself. + + A Signature object has the following public attributes and methods: + + * parameters : OrderedDict + An ordered mapping of parameters' names to the corresponding + Parameter objects (keyword-only arguments are in the same order + as listed in `code.co_varnames`). + * return_annotation : object + The annotation for the return type of the function if specified. + If the function has no annotation for its return type, this + attribute is not set. + * bind(*args, **kwargs) -> BoundArguments + Creates a mapping from positional and keyword arguments to + parameters. + * bind_partial(*args, **kwargs) -> BoundArguments + Creates a partial mapping from positional and keyword arguments + to parameters (simulating 'functools.partial' behavior.) + ''' + + __slots__ = ('_return_annotation', '_parameters') + + _parameter_cls = Parameter + _bound_arguments_cls = BoundArguments + + empty = _empty + + def __init__(self, parameters=None, return_annotation=_empty, + __validate_parameters__=True): + '''Constructs Signature from the given list of Parameter + objects and 'return_annotation'. All arguments are optional. + ''' + + if parameters is None: + params = OrderedDict() + else: + if __validate_parameters__: + params = OrderedDict() + top_kind = _POSITIONAL_ONLY + + for idx, param in enumerate(parameters): + kind = param.kind + if kind < top_kind: + msg = 'wrong parameter order: {0} before {1}' + msg = msg.format(top_kind, param.kind) + raise ValueError(msg) + else: + top_kind = kind + + name = param.name + if name is None: + name = str(idx) + param = param.replace(name=name) + + if name in params: + msg = 'duplicate parameter name: {0!r}'.format(name) + raise ValueError(msg) + params[name] = param + else: + params = OrderedDict(((param.name, param) + for param in parameters)) + + self._parameters = params + self._return_annotation = return_annotation + + @classmethod + def from_function(cls, func): + '''Constructs Signature for the given python function''' + + if not isinstance(func, types.FunctionType): + raise TypeError('{0!r} is not a Python function'.format(func)) + + Parameter = cls._parameter_cls + + # Parameter information. + func_code = func.__code__ + pos_count = func_code.co_argcount + arg_names = func_code.co_varnames + positional = tuple(arg_names[:pos_count]) + keyword_only_count = getattr(func_code, 'co_kwonlyargcount', 0) + keyword_only = arg_names[pos_count:(pos_count + keyword_only_count)] + annotations = getattr(func, '__annotations__', {}) + defaults = func.__defaults__ + kwdefaults = getattr(func, '__kwdefaults__', None) + + if defaults: + pos_default_count = len(defaults) + else: + pos_default_count = 0 + + parameters = [] + + # Non-keyword-only parameters w/o defaults. + non_default_count = pos_count - pos_default_count + for name in positional[:non_default_count]: + annotation = annotations.get(name, _empty) + parameters.append(Parameter(name, annotation=annotation, + kind=_POSITIONAL_OR_KEYWORD)) + + # ... w/ defaults. + for offset, name in enumerate(positional[non_default_count:]): + annotation = annotations.get(name, _empty) + parameters.append(Parameter(name, annotation=annotation, + kind=_POSITIONAL_OR_KEYWORD, + default=defaults[offset])) + + # *args + if func_code.co_flags & 0x04: + name = arg_names[pos_count + keyword_only_count] + annotation = annotations.get(name, _empty) + parameters.append(Parameter(name, annotation=annotation, + kind=_VAR_POSITIONAL)) + + # Keyword-only parameters. + for name in keyword_only: + default = _empty + if kwdefaults is not None: + default = kwdefaults.get(name, _empty) + + annotation = annotations.get(name, _empty) + parameters.append(Parameter(name, annotation=annotation, + kind=_KEYWORD_ONLY, + default=default)) + # **kwargs + if func_code.co_flags & 0x08: + index = pos_count + keyword_only_count + if func_code.co_flags & 0x04: + index += 1 + + name = arg_names[index] + annotation = annotations.get(name, _empty) + parameters.append(Parameter(name, annotation=annotation, + kind=_VAR_KEYWORD)) + + return cls(parameters, + return_annotation=annotations.get('return', _empty), + __validate_parameters__=False) + + @property + def parameters(self): + try: + return types.MappingProxyType(self._parameters) + except AttributeError: + return OrderedDict(self._parameters.items()) + + @property + def return_annotation(self): + return self._return_annotation + + def replace(self, parameters=_void, return_annotation=_void): + '''Creates a customized copy of the Signature. + Pass 'parameters' and/or 'return_annotation' arguments + to override them in the new copy. + ''' + + if parameters is _void: + parameters = self.parameters.values() + + if return_annotation is _void: + return_annotation = self._return_annotation + + return type(self)(parameters, + return_annotation=return_annotation) + + def __hash__(self): + msg = "unhashable type: '{0}'".format(self.__class__.__name__) + raise TypeError(msg) + + def __eq__(self, other): + if (not issubclass(type(other), Signature) or + self.return_annotation != other.return_annotation or + len(self.parameters) != len(other.parameters)): + return False + + other_positions = dict((param, idx) + for idx, param in enumerate(other.parameters.keys())) + + for idx, (param_name, param) in enumerate(self.parameters.items()): + if param.kind == _KEYWORD_ONLY: + try: + other_param = other.parameters[param_name] + except KeyError: + return False + else: + if param != other_param: + return False + else: + try: + other_idx = other_positions[param_name] + except KeyError: + return False + else: + if (idx != other_idx or + param != other.parameters[param_name]): + return False + + return True + + def __ne__(self, other): + return not self.__eq__(other) + + def _bind(self, args, kwargs, partial=False): + '''Private method. Don't use directly.''' + + arguments = OrderedDict() + + parameters = iter(self.parameters.values()) + parameters_ex = () + arg_vals = iter(args) + + if partial: + # Support for binding arguments to 'functools.partial' objects. + # See 'functools.partial' case in 'signature()' implementation + # for details. + for param_name, param in self.parameters.items(): + if (param._partial_kwarg and param_name not in kwargs): + # Simulating 'functools.partial' behavior + kwargs[param_name] = param.default + + while True: + # Let's iterate through the positional arguments and corresponding + # parameters + try: + arg_val = next(arg_vals) + except StopIteration: + # No more positional arguments + try: + param = next(parameters) + except StopIteration: + # No more parameters. That's it. Just need to check that + # we have no `kwargs` after this while loop + break + else: + if param.kind == _VAR_POSITIONAL: + # That's OK, just empty *args. Let's start parsing + # kwargs + break + elif param.name in kwargs: + if param.kind == _POSITIONAL_ONLY: + msg = '{arg!r} parameter is positional only, ' \ + 'but was passed as a keyword' + msg = msg.format(arg=param.name) + raise TypeError(msg) + parameters_ex = (param,) + break + elif (param.kind == _VAR_KEYWORD or + param.default is not _empty): + # That's fine too - we have a default value for this + # parameter. So, lets start parsing `kwargs`, starting + # with the current parameter + parameters_ex = (param,) + break + else: + if partial: + parameters_ex = (param,) + break + else: + msg = '{arg!r} parameter lacking default value' + msg = msg.format(arg=param.name) + raise TypeError(msg) + else: + # We have a positional argument to process + try: + param = next(parameters) + except StopIteration: + raise TypeError('too many positional arguments') + else: + if param.kind in (_VAR_KEYWORD, _KEYWORD_ONLY): + # Looks like we have no parameter for this positional + # argument + raise TypeError('too many positional arguments') + + if param.kind == _VAR_POSITIONAL: + # We have an '*args'-like argument, let's fill it with + # all positional arguments we have left and move on to + # the next phase + values = [arg_val] + values.extend(arg_vals) + arguments[param.name] = tuple(values) + break + + if param.name in kwargs: + raise TypeError('multiple values for argument ' + '{arg!r}'.format(arg=param.name)) + + arguments[param.name] = arg_val + + # Now, we iterate through the remaining parameters to process + # keyword arguments + kwargs_param = None + for param in itertools.chain(parameters_ex, parameters): + if param.kind == _POSITIONAL_ONLY: + # This should never happen in case of a properly built + # Signature object (but let's have this check here + # to ensure correct behaviour just in case) + raise TypeError('{arg!r} parameter is positional only, ' + 'but was passed as a keyword'. + format(arg=param.name)) + + if param.kind == _VAR_KEYWORD: + # Memorize that we have a '**kwargs'-like parameter + kwargs_param = param + continue + + param_name = param.name + try: + arg_val = kwargs.pop(param_name) + except KeyError: + # We have no value for this parameter. It's fine though, + # if it has a default value, or it is an '*args'-like + # parameter, left alone by the processing of positional + # arguments. + if (not partial and param.kind != _VAR_POSITIONAL and + param.default is _empty): + raise TypeError('{arg!r} parameter lacking default value'. + format(arg=param_name)) + + else: + arguments[param_name] = arg_val + + if kwargs: + if kwargs_param is not None: + # Process our '**kwargs'-like parameter + arguments[kwargs_param.name] = kwargs + else: + raise TypeError('too many keyword arguments') + + return self._bound_arguments_cls(self, arguments) + + def bind(self, *args, **kwargs): + '''Get a BoundArguments object, that maps the passed `args` + and `kwargs` to the function's signature. Raises `TypeError` + if the passed arguments can not be bound. + ''' + return self._bind(args, kwargs) + + def bind_partial(self, *args, **kwargs): + '''Get a BoundArguments object, that partially maps the + passed `args` and `kwargs` to the function's signature. + Raises `TypeError` if the passed arguments can not be bound. + ''' + return self._bind(args, kwargs, partial=True) + + def __str__(self): + result = [] + render_kw_only_separator = True + for idx, param in enumerate(self.parameters.values()): + formatted = str(param) + + kind = param.kind + if kind == _VAR_POSITIONAL: + # OK, we have an '*args'-like parameter, so we won't need + # a '*' to separate keyword-only arguments + render_kw_only_separator = False + elif kind == _KEYWORD_ONLY and render_kw_only_separator: + # We have a keyword-only parameter to render and we haven't + # rendered an '*args'-like parameter before, so add a '*' + # separator to the parameters list ("foo(arg1, *, arg2)" case) + result.append('*') + # This condition should be only triggered once, so + # reset the flag + render_kw_only_separator = False + + result.append(formatted) + + rendered = '({0})'.format(', '.join(result)) + + if self.return_annotation is not _empty: + anno = formatannotation(self.return_annotation) + rendered += ' -> {0}'.format(anno) + + return rendered -- cgit v1.2.3 From 6a69eddaff07f9bba2b081aa9d7e26b130b441f9 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Sep 2018 16:12:05 +0200 Subject: correct typo deprecated function --- ot/datasets.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ot/datasets.py b/ot/datasets.py index 362a89b..e76e75d 100644 --- a/ot/datasets.py +++ b/ot/datasets.py @@ -38,9 +38,9 @@ def make_1D_gauss(n, m, s): @deprecated() -def get_1D_gauss(n, m, sigma, random_state=None): +def get_1D_gauss(n, m, sigma): """ Deprecated see make_1D_gauss """ - return make_1D_gauss(n, m, sigma, random_state=None) + return make_1D_gauss(n, m, sigma) def make_2D_samples_gauss(n, m, sigma, random_state=None): -- cgit v1.2.3 From 4b0517607fa7316fe263b3894df9d30a5cdb133a Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Sep 2018 16:21:13 +0200 Subject: Update issue templates --- .github/ISSUE_TEMPLATE/bug_report.md | 36 ++++++++++++++++++++++++++++++++++++ 1 file changed, 36 insertions(+) create mode 100644 .github/ISSUE_TEMPLATE/bug_report.md diff --git a/.github/ISSUE_TEMPLATE/bug_report.md b/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 0000000..7f8acda --- /dev/null +++ b/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,36 @@ +--- +name: Bug report +about: Create a report to help us improve POT + +--- + +**Describe the bug** +A clear and concise description of what the bug is. + +**To Reproduce** +Steps to reproduce the behavior: +1 ... +2. + +**Expected behavior** +A clear and concise description of what you expected to happen. + +**Screenshots** +If applicable, add screenshots to help explain your problem. + +**Desktop (please complete the following information):** + - OS: [e.g. MacOSX, Windows, Ubuntu] + - Python version [2.7,3.6] +- How was POT installed [source, pip, conda] + +Output of the following code snippet: +```python +import platform; print(platform.platform()) +import sys; print("Python", sys.version) +import numpy; print("NumPy", numpy.__version__) +import scipy; print("SciPy", scipy.__version__) +import ot; print("POT", ot.__version__) +``` + +**Additional context** +Add any other context about the problem here. -- cgit v1.2.3 From c531fba46d9ea703a8a5ae270efe44466d54a593 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 25 Sep 2018 08:19:03 +0200 Subject: update contibution.md --- CONTRIBUTING.md | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index 84ef29a..54e7e42 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -17,7 +17,7 @@ GitHub, clone, and develop on a branch. Steps: a copy of the code under your GitHub user account. For more details on how to fork a repository see [this guide](https://help.github.com/articles/fork-a-repo/). -2. Clone your fork of the scikit-learn repo from your GitHub account to your local disk: +2. Clone your fork of the POT repo from your GitHub account to your local disk: ```bash $ git clone git@github.com:YourLogin/POT.git @@ -84,7 +84,7 @@ following rules before you submit a pull request: example script in the ``examples/`` folder. Have a look at other examples for reference. Examples should demonstrate why the new functionality is useful in practice and, if possible, compare it - to other methods available in scikit-learn. + to other methods available in POT. - Documentation and high-coverage tests are necessary for enhancements to be accepted. Bug-fixes or new features should be provided with @@ -145,7 +145,7 @@ following rules before submitting: See [Creating and highlighting code blocks](https://help.github.com/articles/creating-and-highlighting-code-blocks). - Please include your operating system type and version number, as well - as your Python, scikit-learn, numpy, and scipy versions. This information + as your Python, POT, numpy, and scipy versions. This information can be found by running the following code snippet: ```python @@ -165,8 +165,8 @@ following rules before submitting: New contributor tips -------------------- -A great way to start contributing to scikit-learn is to pick an item -from the list of [Easy issues](https://github.com/scikit-learn/scikit-learn/issues?labels=Easy) +A great way to start contributing to POT is to pick an item +from the list of [Easy issues](https://github.com/rflamary/POT/issues?labels=Easy) in the issue tracker. Resolving these issues allow you to start contributing to the project without much prior knowledge. Your assistance in this area will be greatly appreciated by the more @@ -201,4 +201,4 @@ method does to the data and a figure (coming from an example) illustrating it. -This Contrubution guide is strongly inpired by the one of the [scikit-learn](https://github.com/scikit-learn/scikit-learn) team. +This Contribution guide is strongly inpired by the one of the [scikit-learn](https://github.com/scikit-learn/scikit-learn) team. -- cgit v1.2.3 From ee8ed4fa101861eec9e578f09aee4367af593af1 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Sep 2018 09:41:22 +0200 Subject: update documentation --- README.md | 10 +++------- docs/source/all.rst | 6 ++++++ docs/source/conf.py | 2 +- ot/gpu/__init__.py | 22 +++++++++++++++++++++- ot/gpu/bregman.py | 7 ++++++- ot/gpu/da.py | 8 +++++++- ot/gpu/utils.py | 15 +++++++++------ 7 files changed, 53 insertions(+), 17 deletions(-) diff --git a/README.md b/README.md index 5f37ad6..e49c8da 100644 --- a/README.md +++ b/README.md @@ -14,7 +14,7 @@ This open source Python library provide several solvers for optimization problem It provides the following solvers: * OT Network Flow solver for the linear program/ Earth Movers Distance [1]. -* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10] with optional GPU implementation (requires cudamat). +* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10] with optional GPU implementation (requires cupy). * Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17]. * Non regularized Wasserstein barycenters [16] with LP solver (only small scale). * Bregman projections for Wasserstein barycenter [3] and unmixing [4]. @@ -83,12 +83,8 @@ Some sub-modules require additional dependences which are discussed below ``` pip install pymanopt autograd ``` -* **ot.gpu** (GPU accelerated OT) depends on cudamat that have to be installed with: -``` -git clone https://github.com/cudamat/cudamat.git -cd cudamat -python setup.py install --user # for user install (no root) -``` +* **ot.gpu** (GPU accelerated OT) depends on cupy that have to be installed following instructions on [this page](https://docs-cupy.chainer.org/en/stable/install.html). + obviously you need CUDA installed and a compatible GPU. diff --git a/docs/source/all.rst b/docs/source/all.rst index 9459023..1eaf3b1 100644 --- a/docs/source/all.rst +++ b/docs/source/all.rst @@ -48,6 +48,12 @@ ot.da .. automodule:: ot.da :members: + +ot.gpu +-------- + +.. automodule:: ot.gpu + :members: ot.dr -------- diff --git a/docs/source/conf.py b/docs/source/conf.py index 114245d..433eca6 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -31,7 +31,7 @@ class Mock(MagicMock): @classmethod def __getattr__(cls, name): return MagicMock() -MOCK_MODULES = ['ot.lp.emd_wrap','autograd','pymanopt','cudamat','autograd.numpy','pymanopt.manifolds','pymanopt.solvers'] +MOCK_MODULES = ['ot.lp.emd_wrap','autograd','pymanopt','cupy','autograd.numpy','pymanopt.manifolds','pymanopt.solvers'] # 'autograd.numpy','pymanopt.manifolds','pymanopt.solvers', sys.modules.update((mod_name, Mock()) for mod_name in MOCK_MODULES) # !!!! diff --git a/ot/gpu/__init__.py b/ot/gpu/__init__.py index de4825d..9de2c40 100644 --- a/ot/gpu/__init__.py +++ b/ot/gpu/__init__.py @@ -1,8 +1,28 @@ # -*- coding: utf-8 -*- +""" + + +This module implement GPU ilmplementation for several OT solvers and utility +functions. The GPU backend in handled by `cupy +`_. + +By default, the functions in this module accept and return numpy arrays +in order to proide drop-in replacement for the other POT function but +the transfer between CPU en GPU comes with a significant overhead. + +In order to get the best erformances, we recommend to given only cupy +arrays to the functions and desactivate the conversion to numpy of the +result of the function with parameter ``to_numpy=False``. + + + + +""" from . import bregman from . import da from .bregman import sinkhorn +from .da from . import utils from .utils import dist, to_gpu, to_np @@ -13,4 +33,4 @@ from .utils import dist, to_gpu, to_np # # License: MIT License -__all__ = ["utils", "dist", "sinkhorn"] +__all__ = ["utils", "dist", "sinkhorn", 'bregman', 'da', 'to_gpu', 'to_np'] diff --git a/ot/gpu/bregman.py b/ot/gpu/bregman.py index 600ead4..978b307 100644 --- a/ot/gpu/bregman.py +++ b/ot/gpu/bregman.py @@ -16,7 +16,10 @@ from . import utils def sinkhorn_knopp(a, b, M, reg, numItermax=1000, stopThr=1e-9, verbose=False, log=False, to_numpy=True, **kwargs): """ - Solve the entropic regularization optimal transport problem and return the OT matrix + Solve the entropic regularization optimal transport on GPU + + If the input matrix are in numpy format, they will be uploaded to the + GPU first which can incur significant time overhead. The function solves the following optimization problem: @@ -56,6 +59,8 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, stopThr=1e-9, Print information along iterations log : bool, optional record log if True + to_numpy : boolean, optional (default True) + If true convert back the GPU array result to numpy format. Returns diff --git a/ot/gpu/da.py b/ot/gpu/da.py index 8c63870..6aba29c 100644 --- a/ot/gpu/da.py +++ b/ot/gpu/da.py @@ -22,7 +22,11 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, log=False, to_numpy=True): """ Solve the entropic regularization optimal transport problem with nonconvex - group lasso regularization + group lasso regularization on GPU + + If the input matrix are in numpy format, they will be uploaded to the + GPU first which can incur significant time overhead. + The function solves the following optimization problem: @@ -74,6 +78,8 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, Print information along iterations log : bool, optional record log if True + to_numpy : boolean, optional (default True) + If true convert back the GPU array result to numpy format. Returns diff --git a/ot/gpu/utils.py b/ot/gpu/utils.py index d349a6d..41e168a 100644 --- a/ot/gpu/utils.py +++ b/ot/gpu/utils.py @@ -16,19 +16,22 @@ import cupy as cp # cp used for cupy specific operations def euclidean_distances(a, b, squared=False, to_numpy=True): """ Compute the pairwise euclidean distance between matrices a and b. - Parameters + + If the input matrix are in numpy format, they will be uploaded to the + GPU first which can incur significant time overhead. + + Parameters ---------- a : np.ndarray (n, f) first matrix b : np.ndarray (m, f) second matrix - gpu : boolean, optional (default False) - if True and the module cupy is available, the computation is done - on the GPU and the type of the matrix returned is cupy.ndarray. - Otherwise, compute on the CPU and returns np.ndarray. + to_numpy : boolean, optional (default True) + If true convert back the GPU array result to numpy format. squared : boolean, optional (default False) if True, return squared euclidean distance matrix - Returns + + Returns ------- c : (n x m) np.ndarray or cupy.ndarray pairwise euclidean distance distance matrix -- cgit v1.2.3 From fa7f3ddbed0267edf634e359ce5b3a335807af3c Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Sep 2018 09:45:21 +0200 Subject: correction import in ot.gpu --- ot/gpu/__init__.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/ot/gpu/__init__.py b/ot/gpu/__init__.py index 9de2c40..0187a4f 100644 --- a/ot/gpu/__init__.py +++ b/ot/gpu/__init__.py @@ -1,8 +1,7 @@ # -*- coding: utf-8 -*- """ - -This module implement GPU ilmplementation for several OT solvers and utility +This module provides GPU implementation for several OT solvers and utility functions. The GPU backend in handled by `cupy `_. @@ -22,7 +21,7 @@ result of the function with parameter ``to_numpy=False``. from . import bregman from . import da from .bregman import sinkhorn -from .da +from .da import sinkhorn_lpl1_mm from . import utils from .utils import dist, to_gpu, to_np @@ -33,4 +32,5 @@ from .utils import dist, to_gpu, to_np # # License: MIT License -__all__ = ["utils", "dist", "sinkhorn", 'bregman', 'da', 'to_gpu', 'to_np'] +__all__ = ["utils", "dist", "sinkhorn", + "sinkhorn_lpl1_mm", 'bregman', 'da', 'to_gpu', 'to_np'] -- cgit v1.2.3 From 287c4c0747ea5341c989c0c85daf0ed1d5a7cdf0 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Sep 2018 15:43:03 +0200 Subject: update realease.md --- RELEASES.md | 11 +++++++---- ot/__init__.py | 2 +- ot/gpu/da.py | 4 +++- 3 files changed, 11 insertions(+), 6 deletions(-) diff --git a/RELEASES.md b/RELEASES.md index 68abcb3..57ea61d 100644 --- a/RELEASES.md +++ b/RELEASES.md @@ -1,10 +1,10 @@ # POT Releases -## 0.5.0b +## 0.5.0 Year 2 *Sep 2018* -*This is a beta release and is still a work in progress* +POT is 2 years old! This release bring both numerous new features to the toolbox as listed below but also several #### Features @@ -12,7 +12,7 @@ * Add non regularized Gromov-Wasserstein solver (PR #41) * Linear OT mapping between empirical distributions and 90\% test coverage (PR #42) * Add log parameter in class EMDTransport and SinkhornLpL1Transport (PR #44) -* Add Marddown format for Pipy (PR #45) +* Add Markdown format for Pipy (PR #45) * Test for Python 3.5 and 3.6 on Travis (PR #46) * Non regularized Wasserstein barycenter with scipy linear solver and/or cvxopt (PR #47) * Rename dataset functions to be more sklearn compliant (PR #49) @@ -22,10 +22,13 @@ * Speed-up Sinkhorn function (PR #57 and PR #58) * Add convolutional Wassersein barycenters for 2D images (PR #64) * Add Greedy Sinkhorn variant (Greenkhorn) (PR #66) +* Big ot.gpu update with cupy implementation (instead of un-maintained cudamat) (PR #67) #### Deprecation -Deprecated OTDA Classes were removed for version 0.5 (PR #48), it has been a year and the deprecation message. +Deprecated OTDA Classes were removed for version 0.5 (PR #48 and PR #67). The +deprecation messagehas been for a year here since 0.4 and it is time to pull +the plug. #### Closed issues diff --git a/ot/__init__.py b/ot/__init__.py index fa6600d..b27541d 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -29,7 +29,7 @@ from .da import sinkhorn_lpl1_mm # utils functions from .utils import dist, unif, tic, toc, toq -__version__ = "0.5.0b" +__version__ = "0.5.0" __all__ = ["emd", "emd2", "sinkhorn", "sinkhorn2", "utils", 'datasets', 'bregman', 'lp', 'tic', 'toc', 'toq', 'gromov', diff --git a/ot/gpu/da.py b/ot/gpu/da.py index f5e7daa..7f7b2b0 100644 --- a/ot/gpu/da.py +++ b/ot/gpu/da.py @@ -133,7 +133,9 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, # separated W = np.ones(M.shape) for (i, c) in enumerate(classes): -<<<<<<< HEAD + + +<< << << < HEAD (_, nbRow) = indices_labels[i].shape tmpC_GPU = cudamat.empty((Nfin, nbRow)).assign(0) transp_GPU.transpose().select_columns(indices_labels[i], tmpC_GPU) -- cgit v1.2.3 From 49af12288b4a6e45d1ff85f3e39d8d76839b2d5f Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Sep 2018 15:46:31 +0200 Subject: correction bug merge ot.gpu.da --- ot/gpu/da.py | 18 ------------------ 1 file changed, 18 deletions(-) diff --git a/ot/gpu/da.py b/ot/gpu/da.py index 7f7b2b0..4a98038 100644 --- a/ot/gpu/da.py +++ b/ot/gpu/da.py @@ -134,23 +134,6 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, W = np.ones(M.shape) for (i, c) in enumerate(classes): - -<< << << < HEAD - (_, nbRow) = indices_labels[i].shape - tmpC_GPU = cudamat.empty((Nfin, nbRow)).assign(0) - transp_GPU.transpose().select_columns(indices_labels[i], tmpC_GPU) - majs_GPU = tmpC_GPU.sum(axis=1).add(epsilon) - cudamat.pow(majs_GPU, (p - 1)) - majs_GPU.mult(p) - - tmpC_GPU.assign(0) - tmpC_GPU.add_col_vec(majs_GPU) - W_GPU.set_selected_columns(indices_labels[i], tmpC_GPU) - - W_GPU = W_GPU.transpose() - - return transp_GPU.asarray() -======= majs = np.sum(transp[indices_labels[i]], axis=0) majs = p * ((majs + epsilon)**(p - 1)) W[indices_labels[i]] = majs @@ -159,4 +142,3 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, return utils.to_np(transp) else: return transp ->>>>>>> master -- cgit v1.2.3 From eaa05d1411cc8b365479fb239acf4e774c68037c Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Sep 2018 15:50:34 +0200 Subject: cleanup tests with plot --- test/test_plot.py | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/test/test_plot.py b/test/test_plot.py index f77d879..caf84de 100644 --- a/test/test_plot.py +++ b/test/test_plot.py @@ -5,10 +5,18 @@ # License: MIT License import numpy as np -import matplotlib -matplotlib.use('Agg') +import pytest +try: # test if matplotlib is installed + import matplotlib + matplotlib.use('Agg') + nogo = False +except ImportError: + nogo = True + + +@pytest.mark.skipif(nogo, reason="Matplotlib not installed") def test_plot1D_mat(): import ot @@ -30,6 +38,7 @@ def test_plot1D_mat(): ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') +@pytest.mark.skipif(nogo, reason="Matplotlib not installed") def test_plot2D_samples_mat(): import ot -- cgit v1.2.3 From 642fd5b7707c4bdce72b883c666ffdae89fde76a Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Sep 2018 16:04:19 +0200 Subject: speedup tests --- test/test_bregman.py | 4 ++-- test/test_gromov.py | 10 ++++++---- test/test_ot.py | 12 ++++++------ test/test_utils.py | 2 +- 4 files changed, 15 insertions(+), 13 deletions(-) diff --git a/test/test_bregman.py b/test/test_bregman.py index b0c3358..14edaf5 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -118,12 +118,12 @@ def test_wasserstein_bary_2d(): a2 += a2.min() a2 = a2 / np.sum(a2) # creating matrix A containing all distributions - A = np.zeros((2, 100, 100)) + A = np.zeros((2, size, size)) A[0, :, :] = a1 A[1, :, :] = a2 # wasserstein - reg = 1e-3 + reg = 1e-2 bary_wass = ot.bregman.convolutional_barycenter2d(A, reg) np.testing.assert_allclose(1, np.sum(bary_wass)) diff --git a/test/test_gromov.py b/test/test_gromov.py index fb86274..305ae84 100644 --- a/test/test_gromov.py +++ b/test/test_gromov.py @@ -28,7 +28,7 @@ def test_gromov(): C1 /= C1.max() C2 /= C2.max() - G = ot.gromov.gromov_wasserstein(C1, C2, p, q, 'square_loss') + G = ot.gromov.gromov_wasserstein(C1, C2, p, q, 'square_loss', verbose=True) # check constratints np.testing.assert_allclose( @@ -69,7 +69,7 @@ def test_entropic_gromov(): C2 /= C2.max() G = ot.gromov.entropic_gromov_wasserstein( - C1, C2, p, q, 'square_loss', epsilon=5e-4) + C1, C2, p, q, 'square_loss', epsilon=5e-4, verbose=True) # check constratints np.testing.assert_allclose( @@ -107,7 +107,8 @@ def test_gromov_barycenter(): [ot.unif(ns), ot.unif(nt) ], ot.unif(n_samples), [.5, .5], 'square_loss', # 5e-4, - max_iter=100, tol=1e-3) + max_iter=100, tol=1e-3, + verbose=True) np.testing.assert_allclose(Cb.shape, (n_samples, n_samples)) Cb2 = ot.gromov.gromov_barycenters(n_samples, [C1, C2], @@ -134,7 +135,8 @@ def test_gromov_entropic_barycenter(): [ot.unif(ns), ot.unif(nt) ], ot.unif(n_samples), [.5, .5], 'square_loss', 2e-3, - max_iter=100, tol=1e-3) + max_iter=100, tol=1e-3, + verbose=True) np.testing.assert_allclose(Cb.shape, (n_samples, n_samples)) Cb2 = ot.gromov.entropic_gromov_barycenters(n_samples, [C1, C2], diff --git a/test/test_ot.py b/test/test_ot.py index 45e777a..7652394 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -70,7 +70,7 @@ def test_emd_empty(): def test_emd2_multi(): - n = 1000 # nb bins + n = 500 # nb bins # bin positions x = np.arange(n, dtype=np.float64) @@ -78,7 +78,7 @@ def test_emd2_multi(): # Gaussian distributions a = gauss(n, m=20, s=5) # m= mean, s= std - ls = np.arange(20, 1000, 20) + ls = np.arange(20, 500, 20) nb = len(ls) b = np.zeros((n, nb)) for i in range(nb): @@ -207,11 +207,11 @@ def test_warnings(): def test_dual_variables(): - n = 5000 # nb bins - m = 6000 # nb bins + n = 500 # nb bins + m = 600 # nb bins - mean1 = 1000 - mean2 = 1100 + mean1 = 300 + mean2 = 400 # bin positions x = np.arange(n, dtype=np.float64) diff --git a/test/test_utils.py b/test/test_utils.py index b524ef6..640598d 100644 --- a/test/test_utils.py +++ b/test/test_utils.py @@ -12,7 +12,7 @@ import sys def test_parmap(): - n = 100 + n = 10 def f(i): return 1.0 * i * i -- cgit v1.2.3 From 84022d1e07f3f4cd3e54ff406f46f3b5e2261bf2 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Sep 2018 16:23:59 +0200 Subject: add coveragerc for better coverage estimation in ravis --- .coveragerc | 6 ++++++ 1 file changed, 6 insertions(+) create mode 100644 .coveragerc diff --git a/.coveragerc b/.coveragerc new file mode 100644 index 0000000..2114fb4 --- /dev/null +++ b/.coveragerc @@ -0,0 +1,6 @@ +[run] + +omit= + ot/externals/* + ot/externals/funcsigs.py + ot/gpu/* -- cgit v1.2.3 From b6977571c1788152b85b756f2a09b1f47b99aac5 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 3 Oct 2018 08:27:36 +0200 Subject: update releases.md --- RELEASES.md | 31 ++++++++++++++++++++++++++----- ot/gpu/__init__.py | 2 +- 2 files changed, 27 insertions(+), 6 deletions(-) diff --git a/RELEASES.md b/RELEASES.md index 57ea61d..93af09c 100644 --- a/RELEASES.md +++ b/RELEASES.md @@ -4,8 +4,29 @@ ## 0.5.0 Year 2 *Sep 2018* -POT is 2 years old! This release bring both numerous new features to the toolbox as listed below but also several - +POT is 2 years old! This release brings numerous new features to the +toolbox as listed below but also several bug correction. + +Among the new features, we can highlight a [non-regularized Gromov-Wasserstein +solver](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov.ipynb), +a new [greedy variant of sinkhorn](https://pot.readthedocs.io/en/latest/all.html#ot.bregman.greenkhorn), +[non-regularized](https://pot.readthedocs.io/en/latest/all.html#ot.lp.barycenter), +[convolutional (2D)](https://github.com/rflamary/POT/blob/master/notebooks/plot_convolutional_barycenter.ipynb) +and [free support](https://github.com/rflamary/POT/blob/master/notebooks/plot_free_support_barycenter.ipynb) + Wasserstein barycenters and [smooth](https://github.com/rflamary/POT/blob/prV0.5/notebooks/plot_OT_1D_smooth.ipynb) + and [stochastic](https://pot.readthedocs.io/en/latest/all.html#ot.stochastic.sgd_entropic_regularization) +implementation of entropic OT. + +POT 0.5 also comes with a rewriting of ot.gpu using the cupy framework instead of +the unmaintained cudamat. Note that while we tried to keed changes to the +minimum, the OTDA classes were deprecated. + +The code quality has also improved with 92% code coverage in tests that is now +printed to the log in the Travis builds. The documentation has also been +greatly improved with new modules and examples/notebooks. + +This new release is so full of new stuff and corrections thanks to the old +and new POT contributors (you can see the list in the readme). #### Features @@ -26,9 +47,9 @@ POT is 2 years old! This release bring both numerous new features to the toolbox #### Deprecation -Deprecated OTDA Classes were removed for version 0.5 (PR #48 and PR #67). The -deprecation messagehas been for a year here since 0.4 and it is time to pull -the plug. +Deprecated OTDA Classes were removed from ot.da and ot.gpu for version 0.5 +(PR #48 and PR #67). The deprecation message has been for a year here since +0.4 and it is time to pull the plug. #### Closed issues diff --git a/ot/gpu/__init__.py b/ot/gpu/__init__.py index 213578c..deda6b1 100644 --- a/ot/gpu/__init__.py +++ b/ot/gpu/__init__.py @@ -9,7 +9,7 @@ By default, the functions in this module accept and return numpy arrays in order to proide drop-in replacement for the other POT function but the transfer between CPU en GPU comes with a significant overhead. -In order to get the best erformances, we recommend to given only cupy +In order to get the best erformances, we recommend to give only cupy arrays to the functions and desactivate the conversion to numpy of the result of the function with parameter ``to_numpy=False``. -- cgit v1.2.3 From 27f0a9fe625f6fa8123e0e7364a4c0c0e10d5702 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 3 Oct 2018 08:31:21 +0200 Subject: small release edit --- RELEASES.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/RELEASES.md b/RELEASES.md index 93af09c..a617441 100644 --- a/RELEASES.md +++ b/RELEASES.md @@ -19,14 +19,15 @@ implementation of entropic OT. POT 0.5 also comes with a rewriting of ot.gpu using the cupy framework instead of the unmaintained cudamat. Note that while we tried to keed changes to the -minimum, the OTDA classes were deprecated. +minimum, the OTDA classes were deprecated. If you are happy with the cudamat +implementation, we recommend you stay with stable release 0.4 for now. The code quality has also improved with 92% code coverage in tests that is now printed to the log in the Travis builds. The documentation has also been greatly improved with new modules and examples/notebooks. This new release is so full of new stuff and corrections thanks to the old -and new POT contributors (you can see the list in the readme). +and new POT contributors (you can see the list in the [readme](https://github.com/rflamary/POT/blob/master/README.md)). #### Features -- cgit v1.2.3 From 87930c4bcddfded480983343ecc68c6b94bcce14 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 3 Oct 2018 09:01:41 +0200 Subject: pipy release --- ot/__init__.py | 2 +- setup.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/ot/__init__.py b/ot/__init__.py index b27541d..b74b924 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -29,7 +29,7 @@ from .da import sinkhorn_lpl1_mm # utils functions from .utils import dist, unif, tic, toc, toq -__version__ = "0.5.0" +__version__ = "0.5.1" __all__ = ["emd", "emd2", "sinkhorn", "sinkhorn2", "utils", 'datasets', 'bregman', 'lp', 'tic', 'toc', 'toq', 'gromov', diff --git a/setup.py b/setup.py index 3066848..c895034 100755 --- a/setup.py +++ b/setup.py @@ -55,7 +55,7 @@ setup(name='POT', 'Operating System :: MacOS', 'Operating System :: POSIX', 'Programming Language :: Python', - 'Topic :: Utilities' + 'Topic :: Utilities', 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', -- cgit v1.2.3 From 07da88cc39f5c7f778bcb8bfc16ecc95ffaa0cb9 Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Mon, 15 Oct 2018 15:20:22 +0200 Subject: fixed doc for stochastic and plot_stochastic --- examples/plot_stochastic.py | 11 ++-- ot/stochastic.py | 150 +++++++++++++++++++++++++------------------- 2 files changed, 91 insertions(+), 70 deletions(-) diff --git a/examples/plot_stochastic.py b/examples/plot_stochastic.py index b9375d4..742f8d9 100644 --- a/examples/plot_stochastic.py +++ b/examples/plot_stochastic.py @@ -21,9 +21,9 @@ import ot.plot ############################################################################# # COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM ############################################################################# -print("------------SEMI-DUAL PROBLEM------------") ############################################################################# -# DISCRETE CASE +# DISCRETE CASE: +# # Sample two discrete measures for the discrete case # --------------------------------------------- # @@ -57,7 +57,8 @@ sag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, print(sag_pi) ############################################################################# -# SEMICONTINOUS CASE +# SEMICONTINOUS CASE: +# # Sample one general measure a, one discrete measures b for the semicontinous # case # --------------------------------------------- @@ -139,9 +140,9 @@ pl.show() ############################################################################# # COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM ############################################################################# -print("------------DUAL PROBLEM------------") ############################################################################# -# SEMICONTINOUS CASE +# SEMICONTINOUS CASE: +# # Sample one general measure a, one discrete measures b for the semicontinous # case # --------------------------------------------- diff --git a/ot/stochastic.py b/ot/stochastic.py index ec53015..4795d88 100644 --- a/ot/stochastic.py +++ b/ot/stochastic.py @@ -12,16 +12,15 @@ import numpy as np def coordinate_grad_semi_dual(b, M, reg, beta, i): ''' - Compute the coordinate gradient update for regularized discrete - distributions for (i, :) + Compute the coordinate gradient update for regularized discrete distributions for (i, :) The function computes the gradient of the semi dual problem: .. math:: - \W_\varepsilon(a, b) = \max_\v \sum_i (\sum_j v_j * b_j - - \reg log(\sum_j exp((v_j - M_{i,j})/reg) * b_j)) * a_i + \max_v \sum_i (\sum_j v_j * b_j - reg * log(\sum_j exp((v_j - M_{i,j})/reg) * b_j)) * a_i + + Where : - where : - M is the (ns,nt) metric cost matrix - v is a dual variable in R^J - reg is the regularization term @@ -34,15 +33,15 @@ def coordinate_grad_semi_dual(b, M, reg, beta, i): Parameters ---------- - b : np.ndarray(nt,), + b : np.ndarray(nt,) target measure - M : np.ndarray(ns, nt), + M : np.ndarray(ns, nt) cost matrix - reg : float nu, + reg : float nu Regularization term > 0 - v : np.ndarray(nt,), - optimization vector - i : number int, + v : np.ndarray(nt,) + dual variable + i : number int picked number i Returns @@ -93,14 +92,19 @@ def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=None): .. math:: \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + s.t. \gamma 1 = a - \gamma^T 1= b + + \gamma^T 1 = b + \gamma \geq 0 - where : + + Where : + - M is the (ns,nt) metric cost matrix - - :math:`\Omega` is the entropic regularization term - :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - :math:`\Omega` is the entropic regularization term with :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - a and b are source and target weights (sum to 1) + The algorithm used for solving the problem is the SAG algorithm as proposed in [18]_ [alg.1] @@ -173,21 +177,25 @@ def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=None): def averaged_sgd_entropic_transport(a, b, M, reg, numItermax=300000, lr=None): ''' - Compute the ASGD algorithm to solve the regularized semi contibous measures - optimal transport max problem + Compute the ASGD algorithm to solve the regularized semi continous measures optimal transport max problem The function solves the following optimization problem: .. math:: \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + s.t. \gamma 1 = a + \gamma^T 1= b + \gamma \geq 0 - where : + + Where : + - M is the (ns,nt) metric cost matrix - - :math:`\Omega` is the entropic regularization term - :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - :math:`\Omega` is the entropic regularization term with :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - a and b are source and target weights (sum to 1) + The algorithm used for solving the problem is the ASGD algorithm as proposed in [18]_ [alg.2] @@ -195,11 +203,11 @@ def averaged_sgd_entropic_transport(a, b, M, reg, numItermax=300000, lr=None): Parameters ---------- - b : np.ndarray(nt,), + b : np.ndarray(nt,) target measure - M : np.ndarray(ns, nt), + M : np.ndarray(ns, nt) cost matrix - reg : float number, + reg : float number Regularization term > 0 numItermax : int number number of iteration @@ -211,7 +219,7 @@ def averaged_sgd_entropic_transport(a, b, M, reg, numItermax=300000, lr=None): ------- ave_v : np.ndarray(nt,) - optimization vector + dual variable Examples -------- @@ -265,7 +273,8 @@ def c_transform_entropic(b, M, reg, beta): .. math:: u = v^{c,reg} = -reg \sum_j exp((v - M)/reg) b_j - where : + Where : + - M is the (ns,nt) metric cost matrix - u, v are dual variables in R^IxR^J - reg is the regularization term @@ -290,6 +299,7 @@ def c_transform_entropic(b, M, reg, beta): ------- u : np.ndarray(ns,) + dual variable Examples -------- @@ -341,10 +351,11 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None, s.t. \gamma 1 = a \gamma^T 1= b \gamma \geq 0 - where : + + Where : + - M is the (ns,nt) metric cost matrix - - :math:`\Omega` is the entropic regularization term - :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - :math:`\Omega` is the entropic regularization term with :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - a and b are source and target weights (sum to 1) The algorithm used for solving the problem is the SAG or ASGD algorithms as proposed in [18]_ @@ -353,15 +364,15 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None, Parameters ---------- - a : np.ndarray(ns,), + a : np.ndarray(ns,) source measure - b : np.ndarray(nt,), + b : np.ndarray(nt,) target measure - M : np.ndarray(ns, nt), + M : np.ndarray(ns, nt) cost matrix - reg : float number, + reg : float number Regularization term > 0 - methode : str, + methode : str used method (SAG or ASGD) numItermax : int number number of iteration @@ -438,40 +449,40 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None, def batch_grad_dual(a, b, M, reg, alpha, beta, batch_size, batch_alpha, batch_beta): ''' - Computes the partial gradient of F_\W_varepsilon + Computes the partial gradient of the dual optimal transport problem. + + For each (i,j) in a batch of coordinates, the partial gradients are : - Compute the partial gradient of the dual problem: + .. math:: + \partial_{u_i} F = u_i * b_s/l_{v} - \sum_{j \in B_v} exp((u_i + v_j - M_{i,j})/reg) * a_i * b_j - ..math: - \forall i in batch_alpha, - grad_alpha_i = alpha_i * batch_size/len(beta) - - sum_{j in batch_beta} exp((alpha_i + beta_j - M_{i,j})/reg) - * a_i * b_j + \partial_{v_j} F = v_j * b_s/l_{u} - \sum_{i \in B_u} exp((u_i + v_j - M_{i,j})/reg) * a_i * b_j + + Where : - \forall j in batch_alpha, - grad_beta_j = beta_j * batch_size/len(alpha) - - sum_{i in batch_alpha} exp((alpha_i + beta_j - M_{i,j})/reg) - * a_i * b_j - where : - M is the (ns,nt) metric cost matrix - - alpha, beta are dual variables in R^ixR^J + - u, v are dual variables in R^ixR^J - reg is the regularization term - - batch_alpha and batch_beta are lists of index + - :math:`B_u` and :math:`B_v` are lists of index + - :math:`b_s` is the size of the batchs :math:`B_u` and :math:`B_v` + - :math:`l_u` and :math:`l_v` are the lenghts of :math:`B_u` and :math:`B_v` - a and b are source and target weights (sum to 1) The algorithm used for solving the dual problem is the SGD algorithm as proposed in [19]_ [alg.1] + Parameters ---------- - a : np.ndarray(ns,), + + a : np.ndarray(ns,) source measure - b : np.ndarray(nt,), + b : np.ndarray(nt,) target measure - M : np.ndarray(ns, nt), + M : np.ndarray(ns, nt) cost matrix - reg : float number, + reg : float number Regularization term > 0 alpha : np.ndarray(ns,) dual variable @@ -542,24 +553,29 @@ def sgd_entropic_regularization(a, b, M, reg, batch_size, numItermax, lr): .. math:: \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + s.t. \gamma 1 = a + \gamma^T 1= b + \gamma \geq 0 - where : + + Where : + - M is the (ns,nt) metric cost matrix - - :math:`\Omega` is the entropic regularization term - :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - :math:`\Omega` is the entropic regularization term with :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - a and b are source and target weights (sum to 1) Parameters ---------- - a : np.ndarray(ns,), + + a : np.ndarray(ns,) source measure - b : np.ndarray(nt,), + b : np.ndarray(nt,) target measure - M : np.ndarray(ns, nt), + M : np.ndarray(ns, nt) cost matrix - reg : float number, + reg : float number Regularization term > 0 batch_size : int number size of the batch @@ -633,25 +649,29 @@ def solve_dual_entropic(a, b, M, reg, batch_size, numItermax=10000, lr=1, .. math:: \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + s.t. \gamma 1 = a + \gamma^T 1= b + \gamma \geq 0 - where : + + Where : + - M is the (ns,nt) metric cost matrix - - :math:`\Omega` is the entropic regularization term - :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - a and b are source and target weights (sum to 1) Parameters ---------- - a : np.ndarray(ns,), + a : np.ndarray(ns,) source measure - b : np.ndarray(nt,), + b : np.ndarray(nt,) target measure - M : np.ndarray(ns, nt), + M : np.ndarray(ns, nt) cost matrix - reg : float number, + reg : float number Regularization term > 0 batch_size : int number size of the batch -- cgit v1.2.3 From 47daf05106a99b8e1278e1b77328e9e4542c0c32 Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Fri, 19 Oct 2018 12:27:22 +0200 Subject: add -tdlib=libc++ argument --- setup.py | 4 +++- 1 file changed, 3 insertions(+), 1 deletion(-) diff --git a/setup.py b/setup.py index c895034..7d42d8e 100755 --- a/setup.py +++ b/setup.py @@ -39,7 +39,9 @@ setup(name='POT', sources=["ot/lp/emd_wrap.pyx", "ot/lp/EMD_wrapper.cpp"], # the Cython source and # additional C++ source files language="c++", # generate and compile C++ code, - include_dirs=[numpy.get_include(),os.path.join(ROOT,'ot/lp')])), + include_dirs=[numpy.get_include(),os.path.join(ROOT,'ot/lp')], + extra_compile_args=["-stdlib=libc++"] + )), platforms=['linux','macosx','windows'], download_url='https://github.com/rflamary/POT/archive/{}.tar.gz'.format(__version__), license = 'MIT', -- cgit v1.2.3 From 0702dbccf08d333b668aeab5621a2db0c8d33deb Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Sat, 20 Oct 2018 01:11:41 +0200 Subject: platform dependant check --- setup.py | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/setup.py b/setup.py index 7d42d8e..a19255c 100755 --- a/setup.py +++ b/setup.py @@ -24,6 +24,12 @@ ROOT = os.path.abspath(os.path.dirname(__file__)) with open(os.path.join(ROOT, 'README.md'), encoding="utf-8") as f: README = f.read() +# add platform dependant optional compilation argument +opt_arg=["O3"] +import platform +if platform.system()=='Darwin': + if platform.release()=='18.0.0': + opt_arg.append("-stdlib=libc++") # correspond to a compilation problem with Mojave and XCode 10 setup(name='POT', version=__version__, @@ -40,7 +46,7 @@ setup(name='POT', # additional C++ source files language="c++", # generate and compile C++ code, include_dirs=[numpy.get_include(),os.path.join(ROOT,'ot/lp')], - extra_compile_args=["-stdlib=libc++"] + extra_compile_args=opt_arg )), platforms=['linux','macosx','windows'], download_url='https://github.com/rflamary/POT/archive/{}.tar.gz'.format(__version__), -- cgit v1.2.3 From 15a062d55ad5a14d351a4f0c57d4d7359011f510 Mon Sep 17 00:00:00 2001 From: Nicolas Courty Date: Sat, 20 Oct 2018 01:16:31 +0200 Subject: forgot a '-' --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index a19255c..bbcaf04 100755 --- a/setup.py +++ b/setup.py @@ -25,7 +25,7 @@ with open(os.path.join(ROOT, 'README.md'), encoding="utf-8") as f: README = f.read() # add platform dependant optional compilation argument -opt_arg=["O3"] +opt_arg=["-O3"] import platform if platform.system()=='Darwin': if platform.release()=='18.0.0': -- cgit v1.2.3 From f9995cf190b908933d6ababa2eb2bbf46431f997 Mon Sep 17 00:00:00 2001 From: aboisbunon Date: Mon, 22 Oct 2018 14:40:51 +0200 Subject: Corrected the Sinkhorn prediction images The prediction images for Sinkhorn used the ot_emd object instead of ot_sinkhorn --- examples/plot_otda_color_images.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/examples/plot_otda_color_images.py b/examples/plot_otda_color_images.py index e77aec0..62383a2 100644 --- a/examples/plot_otda_color_images.py +++ b/examples/plot_otda_color_images.py @@ -4,7 +4,7 @@ OT for image color adaptation ============================= -This example presents a way of transferring colors between two image +This example presents a way of transferring colors between two images with Optimal Transport as introduced in [6] [6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). @@ -27,7 +27,7 @@ r = np.random.RandomState(42) def im2mat(I): - """Converts and image to matrix (one pixel per line)""" + """Converts an image to matrix (one pixel per line)""" return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) @@ -115,8 +115,8 @@ ot_sinkhorn.fit(Xs=Xs, Xt=Xt) transp_Xs_emd = ot_emd.transform(Xs=X1) transp_Xt_emd = ot_emd.inverse_transform(Xt=X2) -transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) -transp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2) +transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1) +transp_Xt_sinkhorn = ot_sinkhorn.inverse_transform(Xt=X2) I1t = minmax(mat2im(transp_Xs_emd, I1.shape)) I2t = minmax(mat2im(transp_Xt_emd, I2.shape)) -- cgit v1.2.3 From 93db239e1156ad1db8edbb13c1ecde973ce009c0 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 19 Nov 2018 11:17:07 +0100 Subject: remove W605 errors --- ot/bregman.py | 18 +++++++++--------- ot/externals/funcsigs.py | 46 +++++++++++++++++++++++----------------------- ot/gpu/bregman.py | 6 +++--- ot/stochastic.py | 20 ++++++++++---------- setup.cfg | 2 +- 5 files changed, 46 insertions(+), 46 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index d1057ff..43340f7 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -370,9 +370,9 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, v = np.divide(b, KtransposeU) u = 1. / np.dot(Kp, v) - if (np.any(KtransposeU == 0) or - np.any(np.isnan(u)) or np.any(np.isnan(v)) or - np.any(np.isinf(u)) or np.any(np.isinf(v))): + if (np.any(KtransposeU == 0) + or np.any(np.isnan(u)) or np.any(np.isnan(v)) + or np.any(np.isinf(u)) or np.any(np.isinf(v))): # we have reached the machine precision # come back to previous solution and quit loop print('Warning: numerical errors at iteration', cpt) @@ -683,13 +683,13 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, def get_K(alpha, beta): """log space computation""" - return np.exp(-(M - alpha.reshape((na, 1)) - - beta.reshape((1, nb))) / reg) + return np.exp(-(M - alpha.reshape((na, 1)) + - beta.reshape((1, nb))) / reg) def get_Gamma(alpha, beta, u, v): """log space gamma computation""" - return np.exp(-(M - alpha.reshape((na, 1)) - beta.reshape((1, nb))) / - reg + np.log(u.reshape((na, 1))) + np.log(v.reshape((1, nb)))) + return np.exp(-(M - alpha.reshape((na, 1)) - beta.reshape((1, nb))) + / reg + np.log(u.reshape((na, 1))) + np.log(v.reshape((1, nb)))) # print(np.min(K)) @@ -899,8 +899,8 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInne def get_K(alpha, beta): """log space computation""" - return np.exp(-(M - alpha.reshape((na, 1)) - - beta.reshape((1, nb))) / reg) + return np.exp(-(M - alpha.reshape((na, 1)) + - beta.reshape((1, nb))) / reg) # print(np.min(K)) def get_reg(n): # exponential decreasing diff --git a/ot/externals/funcsigs.py b/ot/externals/funcsigs.py index c73fdc9..106bde7 100644 --- a/ot/externals/funcsigs.py +++ b/ot/externals/funcsigs.py @@ -126,8 +126,8 @@ def signature(obj): new_params[arg_name] = param.replace(default=arg_value, _partial_kwarg=True) - elif (param.kind not in (_VAR_KEYWORD, _VAR_POSITIONAL) and - not param._partial_kwarg): + elif (param.kind not in (_VAR_KEYWORD, _VAR_POSITIONAL) + and not param._partial_kwarg): new_params.pop(arg_name) return sig.replace(parameters=new_params.values()) @@ -333,11 +333,11 @@ class Parameter(object): raise TypeError(msg) def __eq__(self, other): - return (issubclass(other.__class__, Parameter) and - self._name == other._name and - self._kind == other._kind and - self._default == other._default and - self._annotation == other._annotation) + return (issubclass(other.__class__, Parameter) + and self._name == other._name + and self._kind == other._kind + and self._default == other._default + and self._annotation == other._annotation) def __ne__(self, other): return not self.__eq__(other) @@ -372,8 +372,8 @@ class BoundArguments(object): def args(self): args = [] for param_name, param in self._signature.parameters.items(): - if (param.kind in (_VAR_KEYWORD, _KEYWORD_ONLY) or - param._partial_kwarg): + if (param.kind in (_VAR_KEYWORD, _KEYWORD_ONLY) + or param._partial_kwarg): # Keyword arguments mapped by 'functools.partial' # (Parameter._partial_kwarg is True) are mapped # in 'BoundArguments.kwargs', along with VAR_KEYWORD & @@ -402,8 +402,8 @@ class BoundArguments(object): kwargs_started = False for param_name, param in self._signature.parameters.items(): if not kwargs_started: - if (param.kind in (_VAR_KEYWORD, _KEYWORD_ONLY) or - param._partial_kwarg): + if (param.kind in (_VAR_KEYWORD, _KEYWORD_ONLY) + or param._partial_kwarg): kwargs_started = True else: if param_name not in self.arguments: @@ -432,9 +432,9 @@ class BoundArguments(object): raise TypeError(msg) def __eq__(self, other): - return (issubclass(other.__class__, BoundArguments) and - self.signature == other.signature and - self.arguments == other.arguments) + return (issubclass(other.__class__, BoundArguments) + and self.signature == other.signature + and self.arguments == other.arguments) def __ne__(self, other): return not self.__eq__(other) @@ -612,9 +612,9 @@ class Signature(object): raise TypeError(msg) def __eq__(self, other): - if (not issubclass(type(other), Signature) or - self.return_annotation != other.return_annotation or - len(self.parameters) != len(other.parameters)): + if (not issubclass(type(other), Signature) + or self.return_annotation != other.return_annotation + or len(self.parameters) != len(other.parameters)): return False other_positions = dict((param, idx) @@ -635,8 +635,8 @@ class Signature(object): except KeyError: return False else: - if (idx != other_idx or - param != other.parameters[param_name]): + if (idx != other_idx + or param != other.parameters[param_name]): return False return True @@ -688,8 +688,8 @@ class Signature(object): raise TypeError(msg) parameters_ex = (param,) break - elif (param.kind == _VAR_KEYWORD or - param.default is not _empty): + elif (param.kind == _VAR_KEYWORD + or param.default is not _empty): # That's fine too - we have a default value for this # parameter. So, lets start parsing `kwargs`, starting # with the current parameter @@ -755,8 +755,8 @@ class Signature(object): # if it has a default value, or it is an '*args'-like # parameter, left alone by the processing of positional # arguments. - if (not partial and param.kind != _VAR_POSITIONAL and - param.default is _empty): + if (not partial and param.kind != _VAR_POSITIONAL + and param.default is _empty): raise TypeError('{arg!r} parameter lacking default value'. format(arg=param_name)) diff --git a/ot/gpu/bregman.py b/ot/gpu/bregman.py index 978b307..3031ed9 100644 --- a/ot/gpu/bregman.py +++ b/ot/gpu/bregman.py @@ -146,9 +146,9 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, stopThr=1e-9, v = np.divide(b, KtransposeU) u = 1. / np.dot(Kp, v) - if (np.any(KtransposeU == 0) or - np.any(np.isnan(u)) or np.any(np.isnan(v)) or - np.any(np.isinf(u)) or np.any(np.isinf(v))): + if (np.any(KtransposeU == 0) + or np.any(np.isnan(u)) or np.any(np.isnan(v)) + or np.any(np.isinf(u)) or np.any(np.isinf(v))): # we have reached the machine precision # come back to previous solution and quit loop print('Warning: numerical errors at iteration', cpt) diff --git a/ot/stochastic.py b/ot/stochastic.py index ec53015..1376884 100644 --- a/ot/stochastic.py +++ b/ot/stochastic.py @@ -418,8 +418,8 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None, return None opt_alpha = c_transform_entropic(b, M, reg, opt_beta) - pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :]) / reg) * - a[:, None] * b[None, :]) + pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :]) / reg) + * a[:, None] * b[None, :]) if log: log = {} @@ -520,15 +520,15 @@ def batch_grad_dual(a, b, M, reg, alpha, beta, batch_size, batch_alpha, arXiv preprint arxiv:1711.02283. ''' - G = - (np.exp((alpha[batch_alpha, None] + beta[None, batch_beta] - - M[batch_alpha, :][:, batch_beta]) / reg) * + G = - (np.exp((alpha[batch_alpha, None] + beta[None, batch_beta] + - M[batch_alpha, :][:, batch_beta]) / reg) * a[batch_alpha, None] * b[None, batch_beta]) grad_beta = np.zeros(np.shape(M)[1]) grad_alpha = np.zeros(np.shape(M)[0]) - grad_beta[batch_beta] = (b[batch_beta] * len(batch_alpha) / np.shape(M)[0] + - G.sum(0)) - grad_alpha[batch_alpha] = (a[batch_alpha] * len(batch_beta) / - np.shape(M)[1] + G.sum(1)) + grad_beta[batch_beta] = (b[batch_beta] * len(batch_alpha) / np.shape(M)[0] + + G.sum(0)) + grad_alpha[batch_alpha] = (a[batch_alpha] * len(batch_beta) + / np.shape(M)[1] + G.sum(1)) return grad_alpha, grad_beta @@ -702,8 +702,8 @@ def solve_dual_entropic(a, b, M, reg, batch_size, numItermax=10000, lr=1, opt_alpha, opt_beta = sgd_entropic_regularization(a, b, M, reg, batch_size, numItermax, lr) - pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :]) / reg) * - a[:, None] * b[None, :]) + pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :]) / reg) + * a[:, None] * b[None, :]) if log: log = {} log['alpha'] = opt_alpha diff --git a/setup.cfg b/setup.cfg index b2a2415..24512d2 100644 --- a/setup.cfg +++ b/setup.cfg @@ -3,4 +3,4 @@ description-file = README.md [flake8] exclude = __init__.py -ignore = E265,E501 +ignore = E265,E501,W605 -- cgit v1.2.3 From de04afc0f9f01fc09a3a8138865eacc0b6f4415d Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 19 Nov 2018 11:18:29 +0100 Subject: update flake8 parameters --- ot/gpu/bregman.py | 6 +++--- ot/stochastic.py | 12 ++++++------ setup.cfg | 2 +- 3 files changed, 10 insertions(+), 10 deletions(-) diff --git a/ot/gpu/bregman.py b/ot/gpu/bregman.py index 3031ed9..978b307 100644 --- a/ot/gpu/bregman.py +++ b/ot/gpu/bregman.py @@ -146,9 +146,9 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, stopThr=1e-9, v = np.divide(b, KtransposeU) u = 1. / np.dot(Kp, v) - if (np.any(KtransposeU == 0) - or np.any(np.isnan(u)) or np.any(np.isnan(v)) - or np.any(np.isinf(u)) or np.any(np.isinf(v))): + if (np.any(KtransposeU == 0) or + np.any(np.isnan(u)) or np.any(np.isnan(v)) or + np.any(np.isinf(u)) or np.any(np.isinf(v))): # we have reached the machine precision # come back to previous solution and quit loop print('Warning: numerical errors at iteration', cpt) diff --git a/ot/stochastic.py b/ot/stochastic.py index 1376884..959c6fa 100644 --- a/ot/stochastic.py +++ b/ot/stochastic.py @@ -418,8 +418,8 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None, return None opt_alpha = c_transform_entropic(b, M, reg, opt_beta) - pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :]) / reg) - * a[:, None] * b[None, :]) + pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :]) / reg) * + a[:, None] * b[None, :]) if log: log = {} @@ -520,8 +520,8 @@ def batch_grad_dual(a, b, M, reg, alpha, beta, batch_size, batch_alpha, arXiv preprint arxiv:1711.02283. ''' - G = - (np.exp((alpha[batch_alpha, None] + beta[None, batch_beta] - - M[batch_alpha, :][:, batch_beta]) / reg) * + G = - (np.exp((alpha[batch_alpha, None] + beta[None, batch_beta] - + M[batch_alpha, :][:, batch_beta]) / reg) * a[batch_alpha, None] * b[None, batch_beta]) grad_beta = np.zeros(np.shape(M)[1]) grad_alpha = np.zeros(np.shape(M)[0]) @@ -702,8 +702,8 @@ def solve_dual_entropic(a, b, M, reg, batch_size, numItermax=10000, lr=1, opt_alpha, opt_beta = sgd_entropic_regularization(a, b, M, reg, batch_size, numItermax, lr) - pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :]) / reg) - * a[:, None] * b[None, :]) + pi = (np.exp((opt_alpha[:, None] + opt_beta[None, :] - M[:, :]) / reg) * + a[:, None] * b[None, :]) if log: log = {} log['alpha'] = opt_alpha diff --git a/setup.cfg b/setup.cfg index 24512d2..aa0ff62 100644 --- a/setup.cfg +++ b/setup.cfg @@ -3,4 +3,4 @@ description-file = README.md [flake8] exclude = __init__.py -ignore = E265,E501,W605 +ignore = E265,E501,W605,W503,W504 -- cgit v1.2.3 From 42a501c5d839c010bbfa3a4440b43cb4f9775fc7 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 11 Mar 2019 10:39:03 +0100 Subject: add test sinkhorn+log --- ot/bregman.py | 14 +++++++++++++- test/test_bregman.py | 25 +++++++++++++++++++++++++ 2 files changed, 38 insertions(+), 1 deletion(-) diff --git a/ot/bregman.py b/ot/bregman.py index 43340f7..013bc33 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -120,7 +120,8 @@ def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, print('Warning : unknown method using classic Sinkhorn Knopp') def sink(): - return sinkhorn_knopp(a, b, M, reg, **kwargs) + return sinkhorn_knopp(a, b, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log, **kwargs) return sink() @@ -499,6 +500,15 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log= """ + a = np.asarray(a, dtype=np.float64) + b = np.asarray(b, dtype=np.float64) + M = np.asarray(M, dtype=np.float64) + + if len(a) == 0: + a = np.ones((M.shape[0],), dtype=np.float64) / M.shape[0] + if len(b) == 0: + b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] + n = a.shape[0] m = b.shape[0] @@ -514,7 +524,9 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log= viol = G.sum(1) - a viol_2 = G.sum(0) - b stopThr_val = 1 + if log: + log = dict() log['u'] = u log['v'] = v diff --git a/test/test_bregman.py b/test/test_bregman.py index 14edaf5..90eaf27 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -81,6 +81,31 @@ def test_sinkhorn_variants(): print(G0, G_green) +def test_sinkhorn_variants_log(): + # test sinkhorn + n = 100 + rng = np.random.RandomState(0) + + x = rng.randn(n, 2) + u = ot.utils.unif(n) + + M = ot.dist(x, x) + + G0, log0 = ot.sinkhorn(u, u, M, 1, method='sinkhorn', stopThr=1e-10, log=True) + Gs, logs = ot.sinkhorn(u, u, M, 1, method='sinkhorn_stabilized', stopThr=1e-10, log=True) + Ges, loges = ot.sinkhorn( + u, u, M, 1, method='sinkhorn_epsilon_scaling', stopThr=1e-10, log=True) + Gerr, logerr = ot.sinkhorn(u, u, M, 1, method='do_not_exists', stopThr=1e-10, log=True) + G_green, loggreen = ot.sinkhorn(u, u, M, 1, method='greenkhorn', stopThr=1e-10, log=True) + + # check values + np.testing.assert_allclose(G0, Gs, atol=1e-05) + np.testing.assert_allclose(G0, Ges, atol=1e-05) + np.testing.assert_allclose(G0, Gerr) + np.testing.assert_allclose(G0, G_green, atol=1e-5) + print(G0, G_green) + + def test_bary(): n_bins = 100 # nb bins -- cgit v1.2.3 From fea0c38c4260788c0359547f7caf75a3d92a2b42 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 11 Mar 2019 10:51:10 +0100 Subject: pytest-cov workaronud --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 90a0ff4..50ff22c 100644 --- a/.travis.yml +++ b/.travis.yml @@ -26,7 +26,7 @@ before_script: # configure a headless display to test plot generation # command to install dependencies install: - pip install -r requirements.txt - - pip install flake8 pytest pytest-cov + - pip install flake8 pytest "pytest-cov<2.6" - pip install . # command to run tests + check syntax style script: -- cgit v1.2.3 From 950efdc2644880fff9119e18f66e9825678fefcb Mon Sep 17 00:00:00 2001 From: vfdev-5 Date: Tue, 12 Mar 2019 15:26:17 +0100 Subject: Minor typo fix in examples. Fixes #77 --- examples/plot_otda_mapping_colors_images.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/examples/plot_otda_mapping_colors_images.py b/examples/plot_otda_mapping_colors_images.py index 5f1e844..a20eca8 100644 --- a/examples/plot_otda_mapping_colors_images.py +++ b/examples/plot_otda_mapping_colors_images.py @@ -77,7 +77,7 @@ Image_emd = minmax(mat2im(transp_Xs_emd, I1.shape)) # SinkhornTransport ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) ot_sinkhorn.fit(Xs=Xs, Xt=Xt) -transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) +transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1) Image_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) ot_mapping_linear = ot.da.MappingTransport( -- cgit v1.2.3 From 9cd97796797b9b2853c6458a7f4e9347bb212978 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 15 Mar 2019 11:56:10 +0100 Subject: update notebooks and documentation --- docs/cache_nbrun | 2 +- .../source/auto_examples/auto_examples_jupyter.zip | Bin 123577 -> 122957 bytes docs/source/auto_examples/auto_examples_python.zip | Bin 81978 -> 81905 bytes .../images/sphx_glr_plot_otda_color_images_001.png | Bin 144957 -> 145014 bytes .../images/sphx_glr_plot_otda_color_images_003.png | Bin 50401 -> 50472 bytes .../images/sphx_glr_plot_otda_color_images_005.png | Bin 234564 -> 326766 bytes ...phx_glr_plot_otda_mapping_colors_images_001.png | Bin 165592 -> 165658 bytes ...phx_glr_plot_otda_mapping_colors_images_003.png | Bin 80722 -> 80796 bytes ...phx_glr_plot_otda_mapping_colors_images_004.png | Bin 541314 -> 512309 bytes .../images/sphx_glr_plot_stochastic_005.png | Bin 10677 -> 10677 bytes .../images/sphx_glr_plot_stochastic_007.png | Bin 9563 -> 9483 bytes .../sphx_glr_plot_otda_color_images_thumb.png | Bin 51085 -> 49131 bytes ...x_glr_plot_otda_mapping_colors_images_thumb.png | Bin 58315 -> 56216 bytes docs/source/auto_examples/index.rst | 2 +- .../auto_examples/plot_otda_color_images.ipynb | 194 ++++++++++----------- .../source/auto_examples/plot_otda_color_images.py | 8 +- .../auto_examples/plot_otda_color_images.rst | 21 ++- .../plot_otda_mapping_colors_images.ipynb | 192 ++++++++++---------- .../plot_otda_mapping_colors_images.py | 2 +- .../plot_otda_mapping_colors_images.rst | 77 ++++---- docs/source/auto_examples/plot_stochastic.ipynb | 44 +---- docs/source/auto_examples/plot_stochastic.py | 11 +- docs/source/auto_examples/plot_stochastic.rst | 97 ++++------- 23 files changed, 298 insertions(+), 352 deletions(-) diff --git a/docs/cache_nbrun b/docs/cache_nbrun index 575adc8..6f10375 100644 --- a/docs/cache_nbrun +++ b/docs/cache_nbrun @@ -1 +1 @@ -{"plot_otda_mapping_colors_images.ipynb": "4f0587a00a3c082799a75a0ed36e9ce1", "plot_optim_OTreg.ipynb": "481801bb0d133ef350a65179cf8f739a", "plot_barycenter_1D.ipynb": "5f6fb8aebd8e2e91ebc77c923cb112b3", "plot_stochastic.ipynb": "e2c520150378ae4635f74509f687fa01", "plot_WDA.ipynb": "27f8de4c6d7db46497076523673eedfb", "plot_otda_linear_mapping.ipynb": 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"51833e8c76aaedeba9599ac7a30eb357"} \ No newline at end of file +{"plot_otda_mapping_colors_images.ipynb": "cc8bf9a857f52e4a159fe71dfda19018", "plot_optim_OTreg.ipynb": "481801bb0d133ef350a65179cf8f739a", "plot_otda_color_images.ipynb": "f804d5806c7ac1a0901e4542b1eaa77b", "plot_stochastic.ipynb": "e18253354c8c1d72567a4259eb1094f7", "plot_WDA.ipynb": "27f8de4c6d7db46497076523673eedfb", "plot_otda_linear_mapping.ipynb": "a472c767abe82020e0a58125a528785c", "plot_OT_1D_smooth.ipynb": "3a059103652225a0c78ea53895cf79e5", "plot_OT_L1_vs_L2.ipynb": "5d565b8aaf03be4309eba731127851dc", "plot_barycenter_1D.ipynb": "5f6fb8aebd8e2e91ebc77c923cb112b3", "plot_otda_classes.ipynb": "39087b6e98217851575f2271c22853a4", "plot_otda_d2.ipynb": "e6feae588103f2a8fab942e5f4eff483", "plot_otda_mapping.ipynb": "2f1ebbdc0f855d9e2b7adf9edec24d25", "plot_gromov.ipynb": "24f2aea489714d34779521f46d5e2c47", "plot_compute_emd.ipynb": "f5cd71cad882ec157dc8222721e9820c", "plot_OT_1D.ipynb": "b5348bdc561c07ec168a1622e5af4b93", "plot_gromov_barycenter.ipynb": "953e5047b886ec69ec621ec52f5e21d1", "plot_free_support_barycenter.ipynb": "246dd2feff4b233a4f1a553c5a202fdc", "plot_convolutional_barycenter.ipynb": "a72bb3716a1baaffd81ae267a673f9b6", "plot_otda_semi_supervised.ipynb": "f6dfb02ba2bbd939408ffcd22a3b007c", "plot_OT_2D_samples.ipynb": "07dbc14859fa019a966caa79fa0825bd", "plot_barycenter_lp_vs_entropic.ipynb": "51833e8c76aaedeba9599ac7a30eb357"} \ No newline at end of file diff --git a/docs/source/auto_examples/auto_examples_jupyter.zip b/docs/source/auto_examples/auto_examples_jupyter.zip index 304bb06..88e1e9b 100644 Binary files a/docs/source/auto_examples/auto_examples_jupyter.zip and b/docs/source/auto_examples/auto_examples_jupyter.zip differ diff --git a/docs/source/auto_examples/auto_examples_python.zip b/docs/source/auto_examples/auto_examples_python.zip index 3be8a76..120a586 100644 Binary files a/docs/source/auto_examples/auto_examples_python.zip and 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259fca1..17a9710 100644 --- a/docs/source/auto_examples/index.rst +++ b/docs/source/auto_examples/index.rst @@ -229,7 +229,7 @@ This is a gallery of all the POT example files. .. raw:: html -
+
.. only:: html diff --git a/docs/source/auto_examples/plot_otda_color_images.ipynb b/docs/source/auto_examples/plot_otda_color_images.ipynb index 2daf406..103bdec 100644 --- a/docs/source/auto_examples/plot_otda_color_images.ipynb +++ b/docs/source/auto_examples/plot_otda_color_images.ipynb @@ -1,144 +1,144 @@ { - "nbformat_minor": 0, - "nbformat": 4, "cells": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, - { + }, + "outputs": [], "source": [ - "\n# OT for image color adaptation\n\n\nThis example presents a way of transferring colors between two image\nwith Optimal Transport as introduced in [6]\n\n[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014).\nRegularized discrete optimal transport.\nSIAM Journal on Imaging Sciences, 7(3), 1853-1882.\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + "%matplotlib inline" + ] + }, { - "execution_count": null, - "cell_type": "code", + "cell_type": "markdown", + "metadata": {}, "source": [ - "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport numpy as np\nfrom scipy import ndimage\nimport matplotlib.pylab as pl\nimport ot\n\n\nr = np.random.RandomState(42)\n\n\ndef im2mat(I):\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\ndef mat2im(X, shape):\n \"\"\"Converts back a matrix to an image\"\"\"\n return X.reshape(shape)\n\n\ndef minmax(I):\n return np.clip(I, 0, 1)" - ], - "outputs": [], + "\n# OT for image color adaptation\n\n\nThis example presents a way of transferring colors between two images\nwith Optimal Transport as introduced in [6]\n\n[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014).\nRegularized discrete optimal transport.\nSIAM Journal on Imaging Sciences, 7(3), 1853-1882.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport numpy as np\nfrom scipy import ndimage\nimport matplotlib.pylab as pl\nimport ot\n\n\nr = np.random.RandomState(42)\n\n\ndef im2mat(I):\n \"\"\"Converts an image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\ndef mat2im(X, shape):\n \"\"\"Converts back a matrix to an image\"\"\"\n return X.reshape(shape)\n\n\ndef minmax(I):\n return np.clip(I, 0, 1)" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Generate data\n-------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Loading images\nI1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256\nI2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n\nX1 = im2mat(I1)\nX2 = im2mat(I2)\n\n# training samples\nnb = 1000\nidx1 = r.randint(X1.shape[0], size=(nb,))\nidx2 = r.randint(X2.shape[0], size=(nb,))\n\nXs = X1[idx1, :]\nXt = X2[idx2, :]" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# Loading images\nI1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256\nI2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n\nX1 = im2mat(I1)\nX2 = im2mat(I2)\n\n# training samples\nnb = 1000\nidx1 = r.randint(X1.shape[0], size=(nb,))\nidx2 = r.randint(X2.shape[0], size=(nb,))\n\nXs = X1[idx1, :]\nXt = X2[idx2, :]" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Plot original image\n-------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "pl.figure(1, figsize=(6.4, 3))\n\npl.subplot(1, 2, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.imshow(I2)\npl.axis('off')\npl.title('Image 2')" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "pl.figure(1, figsize=(6.4, 3))\n\npl.subplot(1, 2, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.imshow(I2)\npl.axis('off')\npl.title('Image 2')" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Scatter plot of colors\n----------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "pl.figure(2, figsize=(6.4, 3))\n\npl.subplot(1, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 2')\npl.tight_layout()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "pl.figure(2, figsize=(6.4, 3))\n\npl.subplot(1, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 2')\npl.tight_layout()" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Instantiate the different transport algorithms and fit them\n-----------------------------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# EMDTransport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# SinkhornTransport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# prediction between images (using out of sample prediction as in [6])\ntransp_Xs_emd = ot_emd.transform(Xs=X1)\ntransp_Xt_emd = ot_emd.inverse_transform(Xt=X2)\n\ntransp_Xs_sinkhorn = ot_emd.transform(Xs=X1)\ntransp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2)\n\nI1t = minmax(mat2im(transp_Xs_emd, I1.shape))\nI2t = minmax(mat2im(transp_Xt_emd, I2.shape))\n\nI1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))\nI2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape))" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# EMDTransport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\n\n# SinkhornTransport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\n\n# prediction between images (using out of sample prediction as in [6])\ntransp_Xs_emd = ot_emd.transform(Xs=X1)\ntransp_Xt_emd = ot_emd.inverse_transform(Xt=X2)\n\ntransp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1)\ntransp_Xt_sinkhorn = ot_sinkhorn.inverse_transform(Xt=X2)\n\nI1t = minmax(mat2im(transp_Xs_emd, I1.shape))\nI2t = minmax(mat2im(transp_Xt_emd, I2.shape))\n\nI1te = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))\nI2te = minmax(mat2im(transp_Xt_sinkhorn, I2.shape))" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Plot new images\n---------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "pl.figure(3, figsize=(8, 4))\n\npl.subplot(2, 3, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Image 1')\n\npl.subplot(2, 3, 2)\npl.imshow(I1t)\npl.axis('off')\npl.title('Image 1 Adapt')\n\npl.subplot(2, 3, 3)\npl.imshow(I1te)\npl.axis('off')\npl.title('Image 1 Adapt (reg)')\n\npl.subplot(2, 3, 4)\npl.imshow(I2)\npl.axis('off')\npl.title('Image 2')\n\npl.subplot(2, 3, 5)\npl.imshow(I2t)\npl.axis('off')\npl.title('Image 2 Adapt')\n\npl.subplot(2, 3, 6)\npl.imshow(I2te)\npl.axis('off')\npl.title('Image 2 Adapt (reg)')\npl.tight_layout()\n\npl.show()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } + }, + "outputs": [], + "source": [ + "pl.figure(3, figsize=(8, 4))\n\npl.subplot(2, 3, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Image 1')\n\npl.subplot(2, 3, 2)\npl.imshow(I1t)\npl.axis('off')\npl.title('Image 1 Adapt')\n\npl.subplot(2, 3, 3)\npl.imshow(I1te)\npl.axis('off')\npl.title('Image 1 Adapt (reg)')\n\npl.subplot(2, 3, 4)\npl.imshow(I2)\npl.axis('off')\npl.title('Image 2')\n\npl.subplot(2, 3, 5)\npl.imshow(I2t)\npl.axis('off')\npl.title('Image 2 Adapt')\n\npl.subplot(2, 3, 6)\npl.imshow(I2te)\npl.axis('off')\npl.title('Image 2 Adapt (reg)')\npl.tight_layout()\n\npl.show()" + ] } - ], + ], "metadata": { "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", "codemirror_mode": { - "version": 2, - "name": "ipython" - } + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" } - } + }, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_color_images.py b/docs/source/auto_examples/plot_otda_color_images.py index e77aec0..62383a2 100644 --- a/docs/source/auto_examples/plot_otda_color_images.py +++ b/docs/source/auto_examples/plot_otda_color_images.py @@ -4,7 +4,7 @@ OT for image color adaptation ============================= -This example presents a way of transferring colors between two image +This example presents a way of transferring colors between two images with Optimal Transport as introduced in [6] [6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). @@ -27,7 +27,7 @@ r = np.random.RandomState(42) def im2mat(I): - """Converts and image to matrix (one pixel per line)""" + """Converts an image to matrix (one pixel per line)""" return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) @@ -115,8 +115,8 @@ ot_sinkhorn.fit(Xs=Xs, Xt=Xt) transp_Xs_emd = ot_emd.transform(Xs=X1) transp_Xt_emd = ot_emd.inverse_transform(Xt=X2) -transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) -transp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2) +transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1) +transp_Xt_sinkhorn = ot_sinkhorn.inverse_transform(Xt=X2) I1t = minmax(mat2im(transp_Xs_emd, I1.shape)) I2t = minmax(mat2im(transp_Xt_emd, I2.shape)) diff --git a/docs/source/auto_examples/plot_otda_color_images.rst b/docs/source/auto_examples/plot_otda_color_images.rst index 9c31ba7..ab0406e 100644 --- a/docs/source/auto_examples/plot_otda_color_images.rst +++ b/docs/source/auto_examples/plot_otda_color_images.rst @@ -7,7 +7,7 @@ OT for image color adaptation ============================= -This example presents a way of transferring colors between two image +This example presents a way of transferring colors between two images with Optimal Transport as introduced in [6] [6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). @@ -34,7 +34,7 @@ SIAM Journal on Imaging Sciences, 7(3), 1853-1882. def im2mat(I): - """Converts and image to matrix (one pixel per line)""" + """Converts an image to matrix (one pixel per line)""" return I.reshape((I.shape[0] * I.shape[1], I.shape[2])) @@ -168,8 +168,8 @@ Instantiate the different transport algorithms and fit them transp_Xs_emd = ot_emd.transform(Xs=X1) transp_Xt_emd = ot_emd.inverse_transform(Xt=X2) - transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) - transp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2) + transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1) + transp_Xt_sinkhorn = ot_sinkhorn.inverse_transform(Xt=X2) I1t = minmax(mat2im(transp_Xs_emd, I1.shape)) I2t = minmax(mat2im(transp_Xt_emd, I2.shape)) @@ -235,11 +235,13 @@ Plot new images -**Total running time of the script:** ( 3 minutes 16.469 seconds) +**Total running time of the script:** ( 3 minutes 55.541 seconds) -.. container:: sphx-glr-footer +.. only :: html + + .. container:: sphx-glr-footer .. container:: sphx-glr-download @@ -252,6 +254,9 @@ Plot new images :download:`Download Jupyter notebook: plot_otda_color_images.ipynb ` -.. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb b/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb index 56caa8a..baffef4 100644 --- a/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb +++ b/docs/source/auto_examples/plot_otda_mapping_colors_images.ipynb @@ -1,144 +1,144 @@ { - "nbformat_minor": 0, - "nbformat": 4, "cells": [ { - "execution_count": null, - "cell_type": "code", - "source": [ - "%matplotlib inline" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "\n# OT for image color adaptation with mapping estimation\n\n\nOT for domain adaptation with image color adaptation [6] with mapping\nestimation [8].\n\n[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014). Regularized\n discrete optimal transport. SIAM Journal on Imaging Sciences, 7(3),\n 1853-1882.\n[8] M. Perrot, N. Courty, R. Flamary, A. Habrard, \"Mapping estimation for\n discrete optimal transport\", Neural Information Processing Systems (NIPS),\n 2016.\n\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport numpy as np\nfrom scipy import ndimage\nimport matplotlib.pylab as pl\nimport ot\n\nr = np.random.RandomState(42)\n\n\ndef im2mat(I):\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\ndef mat2im(X, shape):\n \"\"\"Converts back a matrix to an image\"\"\"\n return X.reshape(shape)\n\n\ndef minmax(I):\n return np.clip(I, 0, 1)" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# Authors: Remi Flamary \n# Stanislas Chambon \n#\n# License: MIT License\n\nimport numpy as np\nfrom scipy import ndimage\nimport matplotlib.pylab as pl\nimport ot\n\nr = np.random.RandomState(42)\n\n\ndef im2mat(I):\n \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n\n\ndef mat2im(X, shape):\n \"\"\"Converts back a matrix to an image\"\"\"\n return X.reshape(shape)\n\n\ndef minmax(I):\n return np.clip(I, 0, 1)" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Generate data\n-------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# Loading images\nI1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256\nI2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n\n\nX1 = im2mat(I1)\nX2 = im2mat(I2)\n\n# training samples\nnb = 1000\nidx1 = r.randint(X1.shape[0], size=(nb,))\nidx2 = r.randint(X2.shape[0], size=(nb,))\n\nXs = X1[idx1, :]\nXt = X2[idx2, :]" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# Loading images\nI1 = ndimage.imread('../data/ocean_day.jpg').astype(np.float64) / 256\nI2 = ndimage.imread('../data/ocean_sunset.jpg').astype(np.float64) / 256\n\n\nX1 = im2mat(I1)\nX2 = im2mat(I2)\n\n# training samples\nnb = 1000\nidx1 = r.randint(X1.shape[0], size=(nb,))\nidx2 = r.randint(X2.shape[0], size=(nb,))\n\nXs = X1[idx1, :]\nXt = X2[idx2, :]" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Domain adaptation for pixel distribution transfer\n-------------------------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "# EMDTransport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\ntransp_Xs_emd = ot_emd.transform(Xs=X1)\nImage_emd = minmax(mat2im(transp_Xs_emd, I1.shape))\n\n# SinkhornTransport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\ntransp_Xs_sinkhorn = ot_emd.transform(Xs=X1)\nImage_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))\n\not_mapping_linear = ot.da.MappingTransport(\n mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True)\not_mapping_linear.fit(Xs=Xs, Xt=Xt)\n\nX1tl = ot_mapping_linear.transform(Xs=X1)\nImage_mapping_linear = minmax(mat2im(X1tl, I1.shape))\n\not_mapping_gaussian = ot.da.MappingTransport(\n mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True)\not_mapping_gaussian.fit(Xs=Xs, Xt=Xt)\n\nX1tn = ot_mapping_gaussian.transform(Xs=X1) # use the estimated mapping\nImage_mapping_gaussian = minmax(mat2im(X1tn, I1.shape))" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "# EMDTransport\not_emd = ot.da.EMDTransport()\not_emd.fit(Xs=Xs, Xt=Xt)\ntransp_Xs_emd = ot_emd.transform(Xs=X1)\nImage_emd = minmax(mat2im(transp_Xs_emd, I1.shape))\n\n# SinkhornTransport\not_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\not_sinkhorn.fit(Xs=Xs, Xt=Xt)\ntransp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1)\nImage_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))\n\not_mapping_linear = ot.da.MappingTransport(\n mu=1e0, eta=1e-8, bias=True, max_iter=20, verbose=True)\not_mapping_linear.fit(Xs=Xs, Xt=Xt)\n\nX1tl = ot_mapping_linear.transform(Xs=X1)\nImage_mapping_linear = minmax(mat2im(X1tl, I1.shape))\n\not_mapping_gaussian = ot.da.MappingTransport(\n mu=1e0, eta=1e-2, sigma=1, bias=False, max_iter=10, verbose=True)\not_mapping_gaussian.fit(Xs=Xs, Xt=Xt)\n\nX1tn = ot_mapping_gaussian.transform(Xs=X1) # use the estimated mapping\nImage_mapping_gaussian = minmax(mat2im(X1tn, I1.shape))" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Plot original images\n--------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "pl.figure(1, figsize=(6.4, 3))\npl.subplot(1, 2, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.imshow(I2)\npl.axis('off')\npl.title('Image 2')\npl.tight_layout()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "pl.figure(1, figsize=(6.4, 3))\npl.subplot(1, 2, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.imshow(I2)\npl.axis('off')\npl.title('Image 2')\npl.tight_layout()" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Plot pixel values distribution\n------------------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "pl.figure(2, figsize=(6.4, 5))\n\npl.subplot(1, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 2')\npl.tight_layout()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } - }, + }, + "outputs": [], + "source": [ + "pl.figure(2, figsize=(6.4, 5))\n\npl.subplot(1, 2, 1)\npl.scatter(Xs[:, 0], Xs[:, 2], c=Xs)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 1')\n\npl.subplot(1, 2, 2)\npl.scatter(Xt[:, 0], Xt[:, 2], c=Xt)\npl.axis([0, 1, 0, 1])\npl.xlabel('Red')\npl.ylabel('Blue')\npl.title('Image 2')\npl.tight_layout()" + ] + }, { + "cell_type": "markdown", + "metadata": {}, "source": [ "Plot transformed images\n-----------------------\n\n" - ], - "cell_type": "markdown", - "metadata": {} - }, + ] + }, { - "execution_count": null, - "cell_type": "code", - "source": [ - "pl.figure(2, figsize=(10, 5))\n\npl.subplot(2, 3, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Im. 1')\n\npl.subplot(2, 3, 4)\npl.imshow(I2)\npl.axis('off')\npl.title('Im. 2')\n\npl.subplot(2, 3, 2)\npl.imshow(Image_emd)\npl.axis('off')\npl.title('EmdTransport')\n\npl.subplot(2, 3, 5)\npl.imshow(Image_sinkhorn)\npl.axis('off')\npl.title('SinkhornTransport')\n\npl.subplot(2, 3, 3)\npl.imshow(Image_mapping_linear)\npl.axis('off')\npl.title('MappingTransport (linear)')\n\npl.subplot(2, 3, 6)\npl.imshow(Image_mapping_gaussian)\npl.axis('off')\npl.title('MappingTransport (gaussian)')\npl.tight_layout()\n\npl.show()" - ], - "outputs": [], + "cell_type": "code", + "execution_count": null, "metadata": { "collapsed": false - } + }, + "outputs": [], + "source": [ + "pl.figure(2, figsize=(10, 5))\n\npl.subplot(2, 3, 1)\npl.imshow(I1)\npl.axis('off')\npl.title('Im. 1')\n\npl.subplot(2, 3, 4)\npl.imshow(I2)\npl.axis('off')\npl.title('Im. 2')\n\npl.subplot(2, 3, 2)\npl.imshow(Image_emd)\npl.axis('off')\npl.title('EmdTransport')\n\npl.subplot(2, 3, 5)\npl.imshow(Image_sinkhorn)\npl.axis('off')\npl.title('SinkhornTransport')\n\npl.subplot(2, 3, 3)\npl.imshow(Image_mapping_linear)\npl.axis('off')\npl.title('MappingTransport (linear)')\n\npl.subplot(2, 3, 6)\npl.imshow(Image_mapping_gaussian)\npl.axis('off')\npl.title('MappingTransport (gaussian)')\npl.tight_layout()\n\npl.show()" + ] } - ], + ], "metadata": { "kernelspec": { - "display_name": "Python 2", - "name": "python2", - "language": "python" - }, + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, "language_info": { - "mimetype": "text/x-python", - "nbconvert_exporter": "python", - "name": "python", - "file_extension": ".py", - "version": "2.7.12", - "pygments_lexer": "ipython2", "codemirror_mode": { - "version": 2, - "name": "ipython" - } + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.7" } - } + }, + "nbformat": 4, + "nbformat_minor": 0 } \ No newline at end of file diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.py b/docs/source/auto_examples/plot_otda_mapping_colors_images.py index 5f1e844..a20eca8 100644 --- a/docs/source/auto_examples/plot_otda_mapping_colors_images.py +++ b/docs/source/auto_examples/plot_otda_mapping_colors_images.py @@ -77,7 +77,7 @@ Image_emd = minmax(mat2im(transp_Xs_emd, I1.shape)) # SinkhornTransport ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) ot_sinkhorn.fit(Xs=Xs, Xt=Xt) -transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) +transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1) Image_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) ot_mapping_linear = ot.da.MappingTransport( diff --git a/docs/source/auto_examples/plot_otda_mapping_colors_images.rst b/docs/source/auto_examples/plot_otda_mapping_colors_images.rst index 8394fb0..2afdc8a 100644 --- a/docs/source/auto_examples/plot_otda_mapping_colors_images.rst +++ b/docs/source/auto_examples/plot_otda_mapping_colors_images.rst @@ -104,7 +104,7 @@ Domain adaptation for pixel distribution transfer # SinkhornTransport ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1) ot_sinkhorn.fit(Xs=Xs, Xt=Xt) - transp_Xs_sinkhorn = ot_emd.transform(Xs=X1) + transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1) Image_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape)) ot_mapping_linear = ot.da.MappingTransport( @@ -132,39 +132,39 @@ Domain adaptation for pixel distribution transfer It. |Loss |Delta loss -------------------------------- - 0|3.680518e+02|0.000000e+00 - 1|3.592439e+02|-2.393116e-02 - 2|3.590632e+02|-5.030248e-04 - 3|3.589698e+02|-2.601358e-04 - 4|3.589118e+02|-1.614977e-04 - 5|3.588724e+02|-1.097608e-04 - 6|3.588436e+02|-8.035205e-05 - 7|3.588215e+02|-6.141923e-05 - 8|3.588042e+02|-4.832627e-05 - 9|3.587902e+02|-3.909574e-05 - 10|3.587786e+02|-3.225418e-05 - 11|3.587688e+02|-2.712592e-05 - 12|3.587605e+02|-2.314041e-05 - 13|3.587534e+02|-1.991287e-05 - 14|3.587471e+02|-1.744348e-05 - 15|3.587416e+02|-1.544523e-05 - 16|3.587367e+02|-1.364654e-05 - 17|3.587323e+02|-1.230435e-05 - 18|3.587284e+02|-1.093370e-05 - 19|3.587276e+02|-2.052728e-06 + 0|3.680534e+02|0.000000e+00 + 1|3.592501e+02|-2.391854e-02 + 2|3.590682e+02|-5.061555e-04 + 3|3.589745e+02|-2.610227e-04 + 4|3.589167e+02|-1.611644e-04 + 5|3.588768e+02|-1.109242e-04 + 6|3.588482e+02|-7.972733e-05 + 7|3.588261e+02|-6.166174e-05 + 8|3.588086e+02|-4.871697e-05 + 9|3.587946e+02|-3.919056e-05 + 10|3.587830e+02|-3.228124e-05 + 11|3.587731e+02|-2.744744e-05 + 12|3.587648e+02|-2.334451e-05 + 13|3.587576e+02|-1.995629e-05 + 14|3.587513e+02|-1.761058e-05 + 15|3.587457e+02|-1.542568e-05 + 16|3.587408e+02|-1.366315e-05 + 17|3.587365e+02|-1.221732e-05 + 18|3.587325e+02|-1.102488e-05 + 19|3.587303e+02|-6.062107e-06 It. |Loss |Delta loss -------------------------------- - 0|3.784758e+02|0.000000e+00 - 1|3.646352e+02|-3.656911e-02 - 2|3.642861e+02|-9.574714e-04 - 3|3.641523e+02|-3.672061e-04 - 4|3.640788e+02|-2.020990e-04 - 5|3.640321e+02|-1.282701e-04 - 6|3.640002e+02|-8.751240e-05 - 7|3.639765e+02|-6.521203e-05 - 8|3.639582e+02|-5.007767e-05 - 9|3.639439e+02|-3.938917e-05 - 10|3.639323e+02|-3.187865e-05 + 0|3.784871e+02|0.000000e+00 + 1|3.646491e+02|-3.656142e-02 + 2|3.642975e+02|-9.642655e-04 + 3|3.641626e+02|-3.702413e-04 + 4|3.640888e+02|-2.026301e-04 + 5|3.640419e+02|-1.289607e-04 + 6|3.640097e+02|-8.831646e-05 + 7|3.639861e+02|-6.487612e-05 + 8|3.639679e+02|-4.994063e-05 + 9|3.639536e+02|-3.941436e-05 + 10|3.639419e+02|-3.209753e-05 Plot original images @@ -283,11 +283,13 @@ Plot transformed images -**Total running time of the script:** ( 2 minutes 52.212 seconds) +**Total running time of the script:** ( 3 minutes 14.206 seconds) -.. container:: sphx-glr-footer +.. only :: html + + .. container:: sphx-glr-footer .. container:: sphx-glr-download @@ -300,6 +302,9 @@ Plot transformed images :download:`Download Jupyter notebook: plot_otda_mapping_colors_images.ipynb ` -.. rst-class:: sphx-glr-signature - `Generated by Sphinx-Gallery `_ +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_stochastic.ipynb b/docs/source/auto_examples/plot_stochastic.ipynb index c6f0013..7f6ff3d 100644 --- a/docs/source/auto_examples/plot_stochastic.ipynb +++ b/docs/source/auto_examples/plot_stochastic.ipynb @@ -33,25 +33,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM\n############################################################################\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print(\"------------SEMI-DUAL PROBLEM------------\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "DISCRETE CASE\nSample two discrete measures for the discrete case\n---------------------------------------------\n\nDefine 2 discrete measures a and b, the points where are defined the source\nand the target measures and finally the cost matrix c.\n\n" + "COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM\n############################################################################\n############################################################################\n DISCRETE CASE:\n\n Sample two discrete measures for the discrete case\n ---------------------------------------------\n\n Define 2 discrete measures a and b, the points where are defined the source\n and the target measures and finally the cost matrix c.\n\n" ] }, { @@ -87,7 +69,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "SEMICONTINOUS CASE\nSample one general measure a, one discrete measures b for the semicontinous\ncase\n---------------------------------------------\n\nDefine one general measure a, one discrete measures b, the points where\nare defined the source and the target measures and finally the cost matrix c.\n\n" + "SEMICONTINOUS CASE:\n\nSample one general measure a, one discrete measures b for the semicontinous\ncase\n---------------------------------------------\n\nDefine one general measure a, one discrete measures b, the points where\nare defined the source and the target measures and finally the cost matrix c.\n\n" ] }, { @@ -202,25 +184,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM\n############################################################################\n\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "print(\"------------DUAL PROBLEM------------\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "SEMICONTINOUS CASE\nSample one general measure a, one discrete measures b for the semicontinous\ncase\n---------------------------------------------\n\nDefine one general measure a, one discrete measures b, the points where\nare defined the source and the target measures and finally the cost matrix c.\n\n" + "COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM\n############################################################################\n############################################################################\n SEMICONTINOUS CASE:\n\n Sample one general measure a, one discrete measures b for the semicontinous\n case\n ---------------------------------------------\n\n Define one general measure a, one discrete measures b, the points where\n are defined the source and the target measures and finally the cost matrix c.\n\n" ] }, { @@ -323,7 +287,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.7" } }, "nbformat": 4, diff --git a/docs/source/auto_examples/plot_stochastic.py b/docs/source/auto_examples/plot_stochastic.py index b9375d4..742f8d9 100644 --- a/docs/source/auto_examples/plot_stochastic.py +++ b/docs/source/auto_examples/plot_stochastic.py @@ -21,9 +21,9 @@ import ot.plot ############################################################################# # COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM ############################################################################# -print("------------SEMI-DUAL PROBLEM------------") ############################################################################# -# DISCRETE CASE +# DISCRETE CASE: +# # Sample two discrete measures for the discrete case # --------------------------------------------- # @@ -57,7 +57,8 @@ sag_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, print(sag_pi) ############################################################################# -# SEMICONTINOUS CASE +# SEMICONTINOUS CASE: +# # Sample one general measure a, one discrete measures b for the semicontinous # case # --------------------------------------------- @@ -139,9 +140,9 @@ pl.show() ############################################################################# # COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM ############################################################################# -print("------------DUAL PROBLEM------------") ############################################################################# -# SEMICONTINOUS CASE +# SEMICONTINOUS CASE: +# # Sample one general measure a, one discrete measures b for the semicontinous # case # --------------------------------------------- diff --git a/docs/source/auto_examples/plot_stochastic.rst b/docs/source/auto_examples/plot_stochastic.rst index a49bc05..d531045 100644 --- a/docs/source/auto_examples/plot_stochastic.rst +++ b/docs/source/auto_examples/plot_stochastic.rst @@ -34,29 +34,14 @@ algorithms for descrete and semicontinous measures from the POT library. COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM ############################################################################ +############################################################################ + DISCRETE CASE: + Sample two discrete measures for the discrete case + --------------------------------------------- - -.. code-block:: python - - print("------------SEMI-DUAL PROBLEM------------") - - - - -.. rst-class:: sphx-glr-script-out - - Out:: - - ------------SEMI-DUAL PROBLEM------------ - - -DISCRETE CASE -Sample two discrete measures for the discrete case ---------------------------------------------- - -Define 2 discrete measures a and b, the points where are defined the source -and the target measures and finally the cost matrix c. + Define 2 discrete measures a and b, the points where are defined the source + and the target measures and finally the cost matrix c. @@ -115,7 +100,8 @@ results. [4.15462212e-02 2.65987989e-02 7.23177216e-02 2.39440107e-03]] -SEMICONTINOUS CASE +SEMICONTINOUS CASE: + Sample one general measure a, one discrete measures b for the semicontinous case --------------------------------------------- @@ -174,15 +160,15 @@ results. Out:: - [3.9018759 7.63059124 3.93260224 2.67274989 1.43888443 3.26904884 - 2.78748299] [-2.48511647 -2.43621119 -0.93585194 5.8571796 ] - [[2.56614773e-02 9.96758169e-02 1.75151781e-02 4.67049862e-06] - [1.21201047e-01 1.24433535e-02 1.28173754e-03 7.93100436e-03] - [3.58778167e-03 7.64232233e-02 6.28459924e-02 1.45441936e-07] - [2.63551754e-02 3.35577920e-02 8.25011211e-02 4.43054320e-04] - [9.24518246e-03 7.03074064e-04 1.00325744e-02 1.22876312e-01] - [2.03656325e-02 8.45420425e-04 1.73604569e-03 1.19910044e-01] - [4.17781688e-02 2.66463708e-02 7.18353075e-02 2.59729583e-03]] + [3.98220325 7.76235856 3.97645524 2.72051681 1.23219313 3.07696856 + 2.84476972] [-2.65544161 -2.50838395 -0.9397765 6.10360206] + [[2.34528761e-02 1.00491956e-01 1.89058354e-02 6.47543413e-06] + [1.16616747e-01 1.32074516e-02 1.45653361e-03 1.15764107e-02] + [3.16154850e-03 7.42892944e-02 6.54061055e-02 1.94426150e-07] + [2.33152216e-02 3.27486992e-02 8.61986263e-02 5.94595747e-04] + [6.34131496e-03 5.31975896e-04 8.12724003e-03 1.27856612e-01] + [1.41744829e-02 6.49096245e-04 1.42704389e-03 1.26606520e-01] + [3.73127657e-02 2.62526499e-02 7.57727161e-02 3.51901117e-03]] Compare the results with the Sinkhorn algorithm @@ -288,30 +274,15 @@ Plot Sinkhorn results COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM ############################################################################ +############################################################################ + SEMICONTINOUS CASE: + Sample one general measure a, one discrete measures b for the semicontinous + case + --------------------------------------------- - -.. code-block:: python - - print("------------DUAL PROBLEM------------") - - - - -.. rst-class:: sphx-glr-script-out - - Out:: - - ------------DUAL PROBLEM------------ - - -SEMICONTINOUS CASE -Sample one general measure a, one discrete measures b for the semicontinous -case ---------------------------------------------- - -Define one general measure a, one discrete measures b, the points where -are defined the source and the target measures and finally the cost matrix c. + Define one general measure a, one discrete measures b, the points where + are defined the source and the target measures and finally the cost matrix c. @@ -365,15 +336,15 @@ Call ot.solve_dual_entropic and plot the results. Out:: - [ 1.29325617 5.0435082 1.30996326 0.05538236 -1.08113283 0.73711558 - 0.18086364] [0.08840343 0.17710082 1.68604226 8.37377551] - [[2.47763879e-02 1.00144623e-01 1.77492330e-02 4.25988443e-06] - [1.19568278e-01 1.27740478e-02 1.32714202e-03 7.39121816e-03] - [3.41581121e-03 7.57137404e-02 6.27992039e-02 1.30808430e-07] - [2.52245323e-02 3.34219732e-02 8.28754229e-02 4.00582912e-04] - [9.75329554e-03 7.71824343e-04 1.11085400e-02 1.22456628e-01] - [2.12304276e-02 9.17096580e-04 1.89946234e-03 1.18084973e-01] - [4.04179693e-02 2.68253041e-02 7.29410047e-02 2.37369404e-03]] + [0.92449986 2.75486107 1.07923806 0.02741145 0.61355413 1.81961594 + 0.12072562] [0.33831611 0.46806842 1.5640451 4.96947652] + [[2.20001105e-02 9.26497883e-02 1.08654588e-02 9.78995555e-08] + [1.55669974e-02 1.73279561e-03 1.19120878e-04 2.49058251e-05] + [3.48198483e-03 8.04151063e-02 4.41335396e-02 3.45115752e-09] + [3.14927954e-02 4.34760520e-02 7.13338154e-02 1.29442395e-05] + [6.81836550e-02 5.62182457e-03 5.35386584e-02 2.21568095e-02] + [8.04671052e-02 3.62163462e-03 4.96331605e-03 1.15837801e-02] + [4.88644009e-02 3.37903481e-02 6.07955004e-02 7.42743505e-05]] Compare the results with the Sinkhorn algorithm @@ -448,7 +419,7 @@ Plot Sinkhorn results -**Total running time of the script:** ( 0 minutes 22.857 seconds) +**Total running time of the script:** ( 0 minutes 20.889 seconds) -- cgit v1.2.3 From 325f02ccdb2718d84283076bebb7c6a2f1e99e52 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 15 Mar 2019 11:56:42 +0100 Subject: update notebooks --- notebooks/plot_otda_color_images.ipynb | 20 ++-- notebooks/plot_otda_mapping_colors_images.ipynb | 14 +-- notebooks/plot_stochastic.ipynb | 149 ++++++++---------------- 3 files changed, 68 insertions(+), 115 deletions(-) diff --git a/notebooks/plot_otda_color_images.ipynb b/notebooks/plot_otda_color_images.ipynb index 6499daf..e2bd92b 100644 --- a/notebooks/plot_otda_color_images.ipynb +++ b/notebooks/plot_otda_color_images.ipynb @@ -19,7 +19,7 @@ "# OT for image color adaptation\n", "\n", "\n", - "This example presents a way of transferring colors between two image\n", + "This example presents a way of transferring colors between two images\n", "with Optimal Transport as introduced in [6]\n", "\n", "[6] Ferradans, S., Papadakis, N., Peyre, G., & Aujol, J. F. (2014).\n", @@ -51,7 +51,7 @@ "\n", "\n", "def im2mat(I):\n", - " \"\"\"Converts and image to matrix (one pixel per line)\"\"\"\n", + " \"\"\"Converts an image to matrix (one pixel per line)\"\"\"\n", " return I.reshape((I.shape[0] * I.shape[1], I.shape[2]))\n", "\n", "\n", @@ -238,8 +238,8 @@ "transp_Xs_emd = ot_emd.transform(Xs=X1)\n", "transp_Xt_emd = ot_emd.inverse_transform(Xt=X2)\n", "\n", - "transp_Xs_sinkhorn = ot_emd.transform(Xs=X1)\n", - "transp_Xt_sinkhorn = ot_emd.inverse_transform(Xt=X2)\n", + "transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1)\n", + "transp_Xt_sinkhorn = ot_sinkhorn.inverse_transform(Xt=X2)\n", "\n", "I1t = minmax(mat2im(transp_Xs_emd, I1.shape))\n", "I2t = minmax(mat2im(transp_Xt_emd, I2.shape))\n", @@ -266,7 +266,7 @@ "outputs": [ { "data": { - "image/png": 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TM37Z9/kGO+GNzviNMMWy8fsOjE/S8F6/5Li+3Z+T8vJ+Jnc8rw2PDh1fN59yLAOfjHM+kC85ry+/U5/SkHgtNDyTO94cJnQIMcDSjVl9y/zMacfXziCGuqDOkLcHik8sL/nde3s8YhNueOCoTUy85cPzjSP2a68ckPpzLM65Zs4/vzjn35jNWbLi5W7g8aa4P0pvdKWNfWYWG9Kq5+nWSQSOFb7+SHhpWbY8iOXB/MTZwEcOAp84K20avz8WEy92xtfOJnzs8pJ5fZC//5HI37vV8/zNxNAo4ByEyEsL4WDq/MwbgY92pfTQlRYutOPnlxGfGnFwNAIMfGAmWBIIZaL35XO4D+afXxTsZH4n8/dC5u8LBUdVGXygwXDZcD9EWCsyg4D5xmKwPcxtthdAUNHK5dDP2uaO376xomTAzCAXa4Kqrs/RixOkaNlSRn/Uy/LR2jIoWNVwUs6YjMpAAtc7zj8qXtHlDp7QIBvX29qigjOI0zBes5BUMC9KwXYfpq2+Gfui/m1tt/m1dn+p3vHdRzddunN2dXcfqip5VOy2hHLsv3HhqLCJs3aFjX2QqtVtbHOvTkttA5R7WV1962t6wLEXYdX3XAtGysaRlRcS2epLveNj6YCvtnMAjqzDKPI3vujH5ZKNo9rPT3jVeElI23N9M0nken3pfb2c8Vya8lIwnmyWsIy8JMYT3nGt+uh/2fd5srkoR1o1POGJJ+LAL/s+b9T7/Jzs8Yxd1u/G+2xA3ZjFFTI0zLYE4qIKwHfMGzrdyNSzseWxvrRrZmX7p48O+ecnJ3zrwYQmeXUzLPjG1ng1Tdb7Pjk1XhmAIHxof1q4eLny8bbk9COzuP7+yW6zbPz+3gZOk/KBxni2S2xjnPGO+Nr9CTdu9tzOA5Ot5X/vVs8fPI48X2fK/+qy/J+LMYsGZH6pq/3ROd98VNp4ECJ/53bmh64scU+8Psx5ry5ZrV3b6aFQ6GEn8yN2Mv+llfn7QsFJ7pjGcRjbuIFMKr+FIuwyurHKNN/dEZW1T1ermyu7Y1Y6NKWEiKGe8WqFoJ4JNTyDklA18MIJMSsPU6a4Xprs4x5kpbhoBGw8vzsqXiw2qoW54sVa4xiumY2bVFEvR0vIhqxMdT7pyDKqECFmKZweEXDDRDDARddl4XvVNW9HxWrrrR6j9jPFapWLboeSGXAsKy6JwSB44SllT0hQJDtGWZa2uE9oOchGjzFEi1VHYM27GbcrvCZnyPkOpa6QtKuVx8HQ0j4xghcWk8oopvkO992DijdSmaX1owmu3u1cSGe4wIf9kpsmXBmEU1OuJnjT4GoqJmlxOAvCI97zgjV8mAsuXblMRhLhAzlxHuBGaghNebO93k+4kVuutGc8Rc+wajC8vPSBf93tcSyZpxnoujIjXKox8cSLPuGIxEQyS5R5s2S1bMgKj5PoCBxI4qW0z56ktbxfiPHlsQwKn0kTJClP1N8vZOOVsEd0543aEwJ8y8EEpwwAF6nhfQdTDuw2ljIXSegQfva283VzB4VrwbjMTkvL03ubmfGvnV3w1CTwWteSFB6JKw5aZanCy93Ay8C8CTTe8sluhUhEvOcPXWlxgU92wnGdI33FPpwk46OH9ZnSwJE5vThf28LHLpfMRdlrImerzKNty2urFc+9mfnyvc29/9eXsLwMUHly33PVWIQ5LvCUL8hbLmjuclc/yNjJ/E7m74XM3xccnMl3/aTDhny25nzw9haYt8L2tayVHlVGQ4RoMTeMW42/1xyVt7AOOBu3DVR2uTsiTs6jclJukGrYRDbdYbFJd1iU1nyUu6w6m+veKD3u5eG3vHGNjYqK+0ikzus2bCtwIuXa7haY7XPdvY05ZNlQtt1LlNVaKdMtHo1TlKLEHW0dydVJFKuRZuvzOqRKRF5b3ipPZ3RpiQiatyx5dX+1ojid/fyffKCntT/y7d95h7w/nRMvqnFl2FzWpX/uDA57WxFp47EGUd5MZQY3156UA4sq1+Pvi7rP7C2OuUCY4lzWvt9zZ+HwmHW8kCbMmyVDVwaQg2TcTOXlN926I33syHUgAFjW53jCpr1LlAl5vW5eBxxfxTp5KGr+E7rin+V9vk7PeKnf44UY+LJ8QZdaGlvhLtTJIzNznpOWR4fujmvqEBp8/f+fnS/4mnl5A18PKy4HOKHFMqzyijY3PNWW9vzCRebxJqyjdn7+MvPNe6XNr6cSXfN0LBf/fG88026iIV9brZiL8q+7xONReLQtA9Gi7/ngXNbm/fdNlaAbeZ90xU/QirJi4If+0a8+0PIOO5kfl+1k/ksr8/eFgjP97r90h/D7OxCBgTsUAa/E5DECZyTwIhnqA6Mi5Grp0OI3KvurlJDz+nub3b8NrUqA1Qgo584w6vX+Y4idbLaDO4nJ2/uVdaNSUo6TqzVoXDYeJyC4+pqEO24/bqNbHCDJJawbMpJLNBKwNvniqbiGXHFPm2iorbbmMTqsLnPJKLZ1fEeyr4nWI0YlBraivLKDZFQCzp0m0eLOkvV24zUVdQ1i3rjQjJIO4Nbf/88e6Bf+f/ut3+EAN3LgMR24kZt33H5b3l/NE65ZR6jSNQae2haBfl/hLMMiGVd0wEw4z2XbgDNXOM/wemq4Zt1nnW9f4dm+4f2xY0jOQoRFMqZW7t24/6K+6K/owCLbelCZ1rYtkPVLfcRjuuJGKtc7FbiF8V5drJctKbP1Q5xjK9vePVgsUfY9c1avdzbARYCZJ0jGK9UC+wSF9BjpeZ49rqdET8eN2KzdBMeVw3HDwzpqxjLMbMW0yrtawsJAGpTLQdkLm+tpE58l728MwqEl3LREEm7hxnLJY5Py0r/set43LdcQAgwDXB0uUVVOreEgdQiZP/KP/ukDLe+wk/mdzN8bmb8vXFRoHTgBUSkKSbWErJUEERijkFC8WhQK+dVBiqLjWgZdy4brhmsjOjp+RiZLVQ60uE60um6ySBW3MryPDiP1Yvlx3ZBvUSWLo6m4WVSEvkZ0cRdPqJxso5iUh1dwtHB6qnIjIiCKi5dcMjUvkANOaatoEaJUyb9QlcN1s8rxs2jpA0aXW11fHFwlt45oJf/mDXmYvMmVU88tvmX5opB9vebWwUu+Ha2WmZHDk0elUEFq6Dzr424ph0JRgrRchHiVBYqOKurE6qKU6ot/kPGal4ddBG5PMxNJrC4q6bpu0+z12LIhTzpyH/j0snjAn5osOe+NSczMUuZGjtz0wPut53YW5jHRpMwkKq955LhxBpxJ7bbzPnA7l3wZAz2v5YYr2jPK+2PacyNH3hc7HDgT5UgdSAwoN90g97yeWt4XV/xmH5mKgGU+K3ZlnOlaYpGMlUdu5HKqKzpwMwcmZE4l4Cp8QJc8mybrweJGbllKGSwah1OUY8ngmV42L38Lwr47Z2IQ4KAq0Wd1kNj3yHu854lYZqiv+B4zW3FNEy/1ezS24pgMVnvBoMnGrRqleJXEK5dl7n9974I0KC8uI4+EBCHTJlgZfHqpPDXJPIKzqlFC07a+p6q8v0em6zwve03Da7kcv8mJlI3zOOHQVxwPl1XeH3jdBtjJ/E7m743M3xeZjKVaVQrBtVgPkJE8ZYhYieAxLSqHCMHKR0Sw0eKjNVxZdf1dZEPCCnW7vKV6jOtcpViODEQUq5FZLiVEGS0fKcznotwA6kIez0nl4IgiVrc3q/vU9pghoqgaYiW6q2S227QXtohjdR3r5WWbXI83XkPh/tTrE8A21x7RdX8WhaP0hasVha32M7Vvdd3vslZIxn4XEfpQlotq2S8aaqyPPV6Hqt7RlsSWCyqUSC+0KkpWP6McaKQJAYsBCWMyR9tY6B5gNHs9ca+nnQ1reY97fVkenRids8sWEch9IGblmYMLnjm4IGblPfUl10ng2JyJOINljs2JWZFsxKx8IJTZ3I3VZrY8BeYx0VvmIhumA1OE95CZx8SFleOvsrLKyjwmVlnLiz4HrkjitdzwuJVjq8JchUnMDCgRZa5l5nwtlgFirsK1mJmHFVPLnBMwK+vLDFm4cOM0B84JZTppmZVHxMssuhPKi54yC75iic7LdXU4QcvLf0Lmq2wBIkzIHOD0AkGFF33Cy1G5wsBA5JU8QS3zeOgZiFzTxCOVdLoSY2bOzJyPyxxT5yBmLvspMQSut84sOhYyXZXPZ+YDl4MytOUl/enl5pm5Rg8qTNvMC8vM3qR+msy0cc7yhCPLNAgwPFTyDjuZ38n8vZH5+8KCM0b+6Bjysz1LH90+1VKzVliqpik1z4tnWc/4NYNsRVBRjDhYtfDAVsbGcQMAdaKXqJ0kpUXmJbzaR14yMACNWAlh385ho1ZC2U0qcbo0X8fl3Mmvlep2Gq0l4OCytgAlg0ixEo0WK6+mjSL3m/0SxXIjNeeP4yUlTi7KjVMiu7zyhoRRdzFwH715hXycq3Ln1TUkStaiOE1dER+q5ac8GOIlDqoQsDeuMnNAcrk/KgQJhcNEuuNeBjY5jLYTGCYvrjnziFjlDPHZWTIfNIzyPrs0LvYSzWoj70O9EddCYnAlZiVIRpYbwvhNb7ndOU+EgYsh8kToCXkj78mEi77lKKwIDscYr3VlBv1EGIpAA3uiPBo6FgLPdi108H7puekNt8colZx51ZX320CThZk5l+5MzGkTtDX9wY1Vw5NxYJmUSYYpQsvA49azyqMZHxZQlykWB4YcOFJnYOAE40O+ImtihUFY8XpqGJD1oFDgvJymHMcSMnN7aIGeufW8mfYYukQTMlcs8eow5VA7rtuSV1ILfcPKA70Uc/1SjBeHPUSEV/KE5GACp6rc1Mj3yi2u5cSrOoFq/t/3zBmRRbbiCa+tepyBw5g5oTznH5gpTQr0IeHZeSSV9j7WUpN7bnK7XLGORCa40fZ7aytvssUXQ+TuOXYyv5P5eyHz94WCgxgihZ0dPDA6U0reFVnnVfE64xcqMVaVYRgws2JU8epCkpI/JqUyAActtFevx9HsqBlp5M1UrSPkjDt4DRePomQT8jCgoZaGEMME+pxrUkEviQe98nSqu81yrhFAFGuHl6zLAYEhs4zQJHBTchrDynWTu6f+H6rLxiqBeRz8fYv4Ii53KE54WTbmn3GEiJGkMINFpCohNR+QyR37uvkmIstLmYTgpU87HNEAOaGUe+UuaFXU3H3d9kzCEKyWttDqe/IcsJxZtYoNCaplqbSIdbsTXiLafIz6clTvD5H9QtCsiry/7C2PrnqoPnF3IUgmUJToi6GhD3DgA6cEDixze6UcNonr2ZlIwqPSZmcpgcuaXOxqs6APsLLIaVIeiSuueHETXPTlmACPxgWDG+d9CyR+Fz2natwaMvNgnOfMSW7ZN3guC8fAMgmdljIgz3pk35STbDwZO14aIk+EgQRMcG4MM56UJcnh4xZ4vyQWQ8sbPvBkTISsxBoqe1EHq0+JcTUr+5q5qgOxOisXbGT0SGGgW3MxenGOLJNQjsIKz8IzKfFxJkQBLPMKEcy5PbRcr2Z7gNXQ0GvmEakp8XXg2aHlEOeKr/hneY+pwFPNgk8PezwVLnmx3+PpuKj3zBlq208kc4RwhBFJ7NODwHmvHOQVL7aHHOdlkXcvvoE75R0OspeM6xR5H3wrHOUBxk7mdzJ/L2T+vnBRYQpW3EKYMpgxmJXsv6GGDQdFLRBcELQoFymv+TsuYz4W2SQHDAEPymDKVAIBYQ+DUNwvHhQzI5tgZkWxiVoUFjPEFBMhxohq2TZUt4sEK66YGIhqJdRZSx4XFxArLhixqrHW60vueLTyXYqVQ4NVPous/wdVwppfU45d6nSVbVTL/hOsELu2XGiqigZDzNbuuo0rqLrQqqtLTBGXNefFtChaA6U/R1eYa+UIrS1pVvpBipvLtZSwGBSCFbeVRyNHw0xoJ5GmaTAzbBKQJjBxLfkttIbd1z7XYJgZUUqfuxSlMwmkhyAvSOcBFbjWrugwXstTXstTXh+mrCRy6YGVRKaN8x5ZIigHDJwNSrISLeIqXHoAFy414JaZTnq6CK+yx/vyioNh4P1pSaexWBNN2IuJk6ZhP/asaEiqdMG4bpkcYa6JJ2KpfXM9ONdtRU9gZso0DMSgPG3wZm7Zw1iK1bQCyh5G9sKFu+0Nh9bzDr1YAAAgAElEQVTzWm65NGOfls+kwLWwZB6Mi76hTXDRNwScZ+LAM7G8cN90o8nwXJpwSOZNN47NedMDX5FXvJyKm+LSjSlldrxfrZ1XdSBPEnmScHHmoWOuUj8wDx3TDFN3pu481SyZWualPCULvELkiiVMnFsY05BZeHHRdpWt1wu8nApr4oUQaALc1MDtJuAGR5rYj8WS63srpjFzfhC40l5gRFxK1IzvrUh7S/Jshe+tiASWsSTnvBXL8x3vjzf0F4ydzO9k/l7I/H0xHS6mqZIcrlsniSsZeoMX14dX5cWDUioiSHH/iBKyr2tS9e4EKS6ToigrKkqnmdaVQfNdfBCICNGFTmStYY78kULW3UQKJdkQlJNuQsudWt7By0BdyieUdowKQqKQkSUXF5KprsPiU+H9oqIkz8XSA2wP5yNHSby4fKZJOQ+Zada1C2x7+89KiLj1uWNhENZGx7xxHVlVakY34VinSlVL/hwU8oboLAhmiroUi9qQcVPIqWSKzrm4CWufwpg1uaAnEytPyrMXBdWdRgyRxLoA1gMOESFJyX79Sm6J1Qd+0CT2PXEWitnO3bkQ42woOSQcJ0rieOi51Roo3B6MA+uZ5Uw7ALHnIGfeaJVrndOp4po4z5GZdrjDk37B1S5zM5QZ1SKW+3gmhctwiq1Ji+cSCXWmdyKBw60w3adY8hla9mPPyRA5iAPiwnTIHDSZkxwYQmDw0vq5Km2C/ZC4GVpeosweXx8mPCllhnk0+koF5qq8RstRLoPH7/YV/zhM+ZrUs6zbTsLGZTkxR9LG9dFSSKDtlltzSmTQ4ioAINVBoFkxzbCnAx2BKYm5ZV7vjSs28Kk84aqVfa7UyBoRYS8Zh9JzawaPX2b6kbM2WZEXkzJTRcmVMHuCcVAt1HfLezsI0JBdOBxKLq30EMg77GR+J/P3RubvCwXHzDAvhS7NlMadJEZ0GKrro8uJaIonJ1QrA0BNIICkMvDGVOpHqQidgqZaMduMLjuqVpQpHxWOYh1a5kxIxZ0TBDpxWjH6nEpV8UKMWUf4BBeybbg8qNFLUXhSzmuybpZCCiq5bIolJ5GwarXQVC4hICXSK2dMbV2mwsc2ejnnIA7JiQg5CE0u/mfyyAMqSsg6hJ1NJuGYNpXPt+tfQU0L6FQ/X91mXCuglMguMyttGSOlxJEqtH31r6acqpITcDJigcEd1DZB4hsPW3WjKXjJ8ZMpOleu7RQVhqxEMSxs7fiAYt50TFPi0uBIFhx7Lj73AVbtwL4IL3V7PBIXdNm4Gi7W97MdlMtJ4WDt21AslhRT7Auzlse6FSdBMMlcxiJX2ZV96TmrCRvPYyRJT8hl5vSEX/Bpm/Oh4ZSXvOE9ulyHoyogbhxoR0JJ9WW/1yTeFGWWE2epKXwuy5z4hBwymcQhkHTB7X5Ko4nT2PJ4XwrxXbUO14QnLeGlXgaWLsNpbOh6OCJxG6PLwntYchoDV7NxEuE8GViZRS+GwFDf8Y+z4iUt3Iv31VpFGVub9ifVPfBS1/J+6QHH62DrpfgKDQMzc3qEJ+PAishjJGbSc+YNkjPv044XmTAVuO3G05dDcdFKYjHraS9qfE1XM69sie0Y4WjLGam9RFZ7SHvJ4nCFuzNZKsuJMz+dPBTyDjuZ38n8vZH5+2J6YCasKqk3qLIKymBOZ8V9k7W4jDJACLgoWZVUuSQZKXWYRPGmuGWyCY0rQYsLxDJgurbcZBMkGF5rYJgoKUjl8yhRiwumsRLOp8GKK0qLWyfbJtJJTMEaSqXxGkElY4TSWDVb8Vo9VoMRpLjZOisK1nZOnWxCX0m9qoWlb5SIriCKhOJmS1p4PYpUd1g5TrRNhFJf2e7uXtrNxn23iRQrLrEcNhFgHuSOSDSr0WCjZWeMyhIRsEDPJkori4KFau0aXYhCclmHDmYt26qG4qKyQKhuwGi2dlNpjVZDS+HRh8FFpUl4xaeAMtXELRPODF6PAcnGJcbcEsvcQG5IYiw08KZNuQgwoBw4yBBIEaJmbjfKM6sVUwcTZ97DpbecmxNt4EbT4qG86A/TikYyi2AcuhM1815OuQjwpK5YGhDg2J3sSmwSCSXaAK5cxshNPWbhUy6kJTap1pJTZnQc5My5T1jUjPGHzYL36JLr+ZzfiAcsIusXrIgwWOJlJpy6MQ89T/oFh1o4dwehRyJIKLPpXLl2c0vsh6LIX2tWzC0xt8THJ/vsa8JTiSk86VvaBHvizIfCo7joG75cem5oS1Yhi3ErTUlWXviSDR0iksv3ac40DKQUaRkwU14lMJOOfTpu5cibTHlhYqz2yox3Met4IU/X8n5LIoawL85yPnAx65HpEl1NEDLSTWhXRrPcyPvF4fKhkHfYyfxO5u+NzN8XFpyVO9HKACjuNKPeNRKPRBHPlRCs60IGwYsVg3xn8j2TwsNxKZyQXHPkQI0+UrnDtUTdVtf5dEpCuYzTayEQuztJIWSt+XKKpUPGNrojo1LigTxyccgMlKR6QE0sU6xMoo5lr8TeAF5Y8wpr4rNWUyc1xwDZ1+HuawULQb3koOklr113gziWShSUByGn6k5yZ0EiihBd6aW6zqoShDsqRV0rLiTdqi9VI8ME3EvJexFBqtbt7gQv2XOCFFddRmjEGDSXvEGU7JqpUdqhnHMgEwhFYaoFOEMWUGOVBkxrviAe/Bf+aYSjvuNSAuaZ42oB73B6AjEJe0VlpFM4iWVG9N7lBW82U6arng5YYgwWuNZf8Eqc4nLBy5MZ59KwiCViol3Aahq4tloByjwnIHAybWnSgk6MCzvkeHXCRZN5tjlk3ztCWnArCsd94rRvOGvhMA0QM1PPBO+Z5oGh6RnSnCmZQSZ0secCY9opDC0JWOJcCkgcuN6VlPV9YTMU0ntWZlpekvueuVhbTMvj8h4tpvxZTdA2rdvMcuYVmVSOWuIlnfNkf4loRiIssjENA2R4Lu8x0cTT/YI3Dc6kWBtVSibuqSWmqVgNuyAsPVfzOaxMKDZLpxmUVpyplVASd2e/+q+vL5yLMOGkER6/dA5Dz+Vex+QicE06kjp7OXFl1XM7txxpx+0Mx3HBgoa2L1d8updoF+Nk6LOT0j2I2Mn8TubvhczfFwrOngRWZKJBnwrXA8qs32tIHqJlENeiCLlQksDViKhN5fC6vG6rCpZ9zfVQKwpS4bIUBWUsACkYaHXMpGI9USqRlhpxZU6hXxXlIld/o0nNmQPgxZ1TyjoIJiW7b0mOd0cWnnW9raJh1Tw/ZELVYhMCnkqfpEwKslbm1sU9tURNuWsx3HjphyYLXssbGILapnZVI6BWrDfkEskUvIS3i5ZUgIkSPuiqhKrkjUVIM2kTDw4lbLy6xiSXiLjso3LqZMmIKlFYW6vUnSSJYssU+pQJurHyLM1LGYhqScrKmpv0IOPaoJxmY64D56JrhbWvCSxFhIGGKQnzjHQ9fRBOm5Zp37MIc/b8kinOIsJpbmlyRjTgEZ48P+Nkr6Hpnak5njOLGJn2PUNrLJjR5AUpzxhiB+0SVqDScjUtmQ7OuczoraGTE1aTQAMklF6V7BOuDOcsWsVoQZzb05a9DKvUsB9WHPiS27ZHtPNNenwHj9VtmhSzzC0/4lhuc60vI97LesCenDIdlMjAy03LOSXC8epQQlbfDJFmKBOImQzsMXDmgd81nOIKlx7Zt46ZlEHhPAWuaYnc6YIy9wSiXPcV57nko5pLCdk98p6VGVc6YYhCzDANA5lE7sP4gJOt1HATEQ7zwJvByANME6RFIMuKSVZml6HUaKuTkKUoyxqyfCvtMY0brsTtoEw948uGqS6KvMumztCDjJ3M72QevvQyf18oOB60VAoXIYjXXDNe89bopoRCdVOV0gTVYmCluKZWC4f4mjmy5uJI0Dt8cTH5liLEeuAu5QtKErwuKJZKyLOTC3lWS6h4TmldtNKqFWWb95KBqFaz82ZEtPByvFoh7nIxFgK1bqU8CHXgBxXDx1pOoeTlKZwcR4LWOlC5hIWP2YhFoZZgAMBKFuFEJmYBM4acSJWYrTUqTVQIOW9qQlGub1SokpZjlDpUgigM1UKUUi5RXLlY2fBiZzOF5BlRK1YxhyCCZeiEsnys52Ult86YAXnMam1Ssn9mFFlXD35wkb1GuTm0GO4DZzHQDo6rcS5T5r7ghh0BMMSBq8syozu3OY0uOGVeDzbQ5UijC06C0S4iZ7FF+xJ2exrgPavbvBEOuLQWSWCScVqenUz54GrB45cX/NLBo3xwcclq2rNsIawiV+U2J/EQbMnRYuDmLLK/GuhyYhGVsTzuYurMPHG8WnIQnG6YcjJ3wtIZmgmHl3fOyBYxEsXL4CILUprxyqQc66jrYCivpTemM64tVpxMW4IvuGimPLZactlrCUclE0PmJM1RlpzWoqyJBjKcyoorQwZL3FZhaUYaFNXMLJcSfweeORNDU5U3Fw57GEw4tTJj2c8B8UAbV1zkSEAZekMkI2N5EVcWWZmFgZn3dB5pNbH0iEtm2mVWsaGlq++JYuVcubJiwqEu6WSPZVcGkL2h5yIo7XD2borilww7md/J/L2Q+fuCg6OqBC0GMSofpbVAtFDcSWO4M8WaY0EJIRQeiekdfBIbw7lDwE3LB+itKFCxhocXi4CQKoclVb6LmzJoIQSL6jocPG/VrbImluUiNdy7rHcrnJ+xGjk18R6ASVhzb8QqT8eUFDeZmXXMaGwlgqwRQ9TR2oYx5HuM1ApqDFK5NaHyccY2B8MsFu6SQOuKYfQxMIiXPmArU3PFYIXLM1jhAiWpQflSeEDrfDUi5Eq0huqqq9wcVQobqbY3hECs54lW1nuAoKBRiDEwCcpeE9izhlmMNNGYScDMmKhiJgQFHoI8OKsIUwaaWkvHKL/nOCc2Y25ntEWVZz9dciWdIyEjITO3UxoZOOKE/bTgeNkztzMaGfhEc5VFLArpIjjztOAKt7kILVe44FOTPX5zuseVfM5vtlOeyEsWUfhMM+eZ1ZKpr8hDg61aOnPaQdlPCy7yAVnKQDIfEr/ZzrgV9/hEc5X9lAldiZa4NZ3QpTJ3jcsJ2VqaZcOlzbm0Obf29vi12TUimUd8geeWZauoGNmMxxcLLnXCZZwjpszywKKNHC5WtBI5HnpOMbxVXp9Oud0ozRDo5zBMWlwnzBwWTeLxfMmRZz41OeIsOOcxlpdwEA7H6rvArWhMbeBWNIaQWYWSeDIkOOzLByAkZ+ktl1oyyeYItxsrk7LYc5w7TJwLNTxAG3qyZBrvmeSMB4i5YxGUliV7fslclsQcObBLLvKMA79gz5YcxXPMhP0Mi+bBl3fYyfxO5u+NzN8XT4/X+lK4ElQo5BZh8IFAiTaSar3IjPWaxkE2E7REV60tPxR3SmslzHiIMHGp0UyyDs2OYyI8ddox1K0mtovBqkXHUaluIKkuMGrodN0mZujEmCTh3DJtVrYTKbsX3o4BeSTr9qlm9xU6LS4owwuvqF5jH0o9Kijntgy9OYON7q8SSp1xbIu7UmLBtAZZGtYIKTlILm1A1tmT2wSdZZRiCTO1UkTTM60GejJDtdhsLEwlTBxK/alMSdalKE07FtCspR4wXEqI/opM0FAj04oJ06rVZxAnmFKDzlAZQJ0JwpBSiZoT1iHrDzJcnaUCbuDGwpSUW87iBUec4aJcWsu+d2gcSHm6JofftMhx7jFZodrzGbnKE34THyJf7aeYrng97nOUOyz2dHmKxo4Tafia4XVW0rBqhK/IpwC03vGSXOUK59yatIgLh3rGG7rPKsJNLbkvzmzKuTXckglfs7rFa03LNyxe5+fn1/jIxcm64jvVunizmXC1v+Tl5oCJdDy+PGWxjFwNr/Gr8VG+Jp8wt1P2V0LyzHzoeGF2yCOrMmufiLPfZV5qZvz6/IgPrhbcEji2cy5zi6FojgztgoOuJw4l2/d5mPJq3Oex4U0WUbnKgt6VRgMLDTy5uOD5Scv+CjSumA1UWYRHc+ZWKMnGxMGbYkqXzljW6slRVyQ3ZtoRh4bj5pLVEBmiM+t7SMXifCwXvBFmzGTFTd3jOJ0DsF9TRwziaBAmukQlMI9l1jolMaTExZgM5CGQd9jJ/E7m743M3xcWHKnWiTYWLk1JUqeYxTVHZrue0xj2vOaEOJgpolbqF+lWpI8qjSpqQhOKRSOYgTit6jrZn1dFxsaIpjX/p0RbiYPEgGQnUi1IUiw2EoyJGH0QWjE06tqSJKF8mqpsje0G1hab1kqywByKxcNNMVMmWhILhlCsP10cr8cwU7wtFq5oRU9dW61qH041oFZSiLuUmh/F01csQo0rOW7aOdaoGpMaKtSaVZXMrCVNt4Ri9ZKgTM2YNkLTKDGAi+JWrTZmNAGCCbSxHEtKuzZWOQqZfEzFbIVsN0jECfQYk7bkwYnm7D8EM9rBpySfci0vMV2hrkTpycxJTMg+Wct79gm3Yi1K6I7lIouWheRTHtNL3Nu19Sz5hCN6TJWY20Lm1imIc5idhcwYdErrHU0WBpswka5o0IXUxokcl8RlHHGlW3LNz5jYikc4Y54TJ2HGkXc8P9nnvcOS07hXXMlmnIQZtyYtV/KwlvNlrZ+jccBF+Uh6g1nueSMe4arcbKdkpjySelBlj4FLa/mN6SPs5yUfGV5jbqdcTpSlRPbtAoB5uOATzdXC4xDjyItL9/cvXmMRlWurJV0UWonM8sDTywUnbcscZXEQ6FSZWZHxmUNwOEpwRlsmQIOhy4hbmWhkN64gXM9LDlPiKJxzrnuE2DFnYL+55KA9Yxo7FpOWeeroZMKjeVHc5FXecxC6cVbsxpCVlbfk1NKlhkMTRBJzTzyi98Ur+gvGTuZ3Mn8vZP6+eXpEhI58Rwz8ZxWTrHyXiYa1y2YMeV4XcayDc66du71fL86g0FdSWxeEada1JSSE8lCtQ8VrBmORks1YRGiaZhO9NEZrjZagSuAt5Q+Kayr6RlGKMVa3mjGZTNbXZmb0tYSCmdGwaXMOmwimVmyjfNx1bWbGoNCKrdu91E27QgjrPtlu/9hn4zbbx8/tRhEZ3Xpt2zKRwLTZ9FHhIG0Uo1FhHHQT6ZVSIsZYCIDNnfdLVdmzeEdboyYaK5/xnowy8jBgNlxy6oHpsPHVx1pvLHpH9Fz/d1zvBxp6GnpEhLl0QCilP3pocE7DXt0n0w6Jdki8rlPesD1uS6nv84Id8bu6G2hyLDf1OBA8ED0zS0LIDbgzS0LrGZFS+HD8DNFJ1hOzsvSGpTck62lqZOMT/Ql4LXHSRuZkZp4INUXCShoGnfLp6QxNyqBTnlqera/1NOyxjIVv9mWrN+miMBmU5FMsGxo6Um451DNet30+0r/OTMqs/8XmgFRleZrgPLRkAl2e0uUpKUOXp3hu0ez0zbwEKdRgg89MriCuTDSxUkM0MDFhrsZj9IgpTQ0uOA0tliIhOyE3uBcehgxG0owMMBHhYMhcyrRaioFsaAocDgEZynMS0oRJ6DnQJQc1emavlql5WOQddjK/k/kvvczfF9PhkuNGMLzUn9oaGMvHauRTqeXkXhL3uRfXTAIsGORSJNNqiPKovblXknI9RgyxuJz6BFHXxcMGoJR+KFFAljfKVTmO11lDLJFOCILXkGmnCUWpyTkz4ESzkhwvF3J04a84WYUhZ0IIJS8PFAuQFPdaNtaDvQjE6obChFjrVQ2UDM4aAyD0norGLSXEG6CJsSRYymPklhDDhksk4iWJsQqhmlsnozvOS2i7atHk1RWp7RvcUXFmgHuqSlCJrQLH3CGUaKqsApUzNCToRSEVArmI0nsGcWIp416tWk6DEQVSSuRc3GdB8lZh0gcXDc4q7KE4TVoiYjTSg68QGhqFi2wsZcJVOafzzIXsl4RYnrnJPlMdCEPmLEzYH5ZMcybXIrXuTpTMFb/kwidMk3M6U+arjiUTOho6gQufwFDcB8/GY47SCtyxyYoBCIvI+QxsUfJW7PnAvHP+f/beLNayJDvP+9aKiD2c4U55c6rMrLmb1WyqRZE0J1EiTUkkRROGLRA0DcmTQMvygwFDD4YsySD8Sj/agC3DNmzA0GBIEAwDBkyQEmVRImWSTTYpdqvZXVVdU855xzPsISKWH2Lfm1Ui9SAQUmUVcgOJzMq6eU7sff6zY+21/qHJGSeZK7kM63sqnriKvdixdjXePGs8B2nN2hXPo1NtqGOic3OuxBU3ER6pp02JwT8tbjEj5AqXI4MPLDKMLnDuZuzlLc5qnBj3/ZyDYSghsLkFgxvpnLWr2dSglI3sSkpISd9FvWOp55gqMuaycpeLI7nLHNgZ3md2DbxlhJLTFnMmu8gLdkZPoPOeYLB1DsHIUwBikzds1YMZ5mHY1vQCkqZGQfR0TokusaBYQoxSUUnGaSLVUI/yqcM7PMf8c8x/PJh/NgocX6i4KZY/Z3NF128OKe7cZAE/ZX5k9YW6O/FpmOTgGaX5UIjmRR0YRBjtaTimIWVDDoVEW4uj8yVaIGLFcI5CSI6TPHycnjSqyZMHPhTboHIpI6/EEaXwaJJIcV72ZbRVpOqGz0UBBYWMK9O5VBM3SKzIAb0JwaWSlG46fYlLUVAB4oQYI0mMRqdiSpSc0tR+fepdc5F7BRTH5KmwueDyIBBE6S2jGZyUckwEqomQPNp0DrEY9F28ppBJpqgUTg5TByqlVHyBpuLJ+en8stEbBINq6nSZCpUqnlRCOtXhJnNEiRGJmWg2Ddk+2Uet5ebQ5xofEpscqKTHx4D5AUFYL4zlJoEZR7rLrm1BoLUNZ27BLPXcc3sc2IZtCKhlTidx6s14wj23S0vPaBUjQj30bK3mceW50a95qzmgCiuG7PHRsW+JO/Gc98M+0PNk2AUHB31Ephvn2cyAkfN+Ds0aemVmiVh32FBztoC8bVAcO2nD6cyhg0GGroGucjRdIokni/CCneKzEl1mKzN2Yse+PWLrAjapKutR6IPxgp0CRo/wxO1yc9xgGDG1zPOW87plI3VxCo8tx8GzP8ZC5zMYnBH9RNC3HkRYJuPUlwehWczI1Nndy0PB+6SMTOIIqeB9YYaQWPmaeUwF72MZrwySGM1TpxESZP/UNmGILTUDrRlEpdPAsjlnxuYS78uTkSjGduFoBv3U4B2eY/455j8ezD8TBU6euDTelzGLt0zRU2dcLrwUdzEKMiVI+X2gXEw3dWeeZiTBoFx6yZgIOvm0ONUPBVvmEi3gKEWHK+x+MSNO+VZl8xe8TGMVDJHivJgvNnhVNOXSgZj4O5UpIxnvixX2hQrci5TuRpoIz6LT+kvFDbkIhXJPYheL4H2PqJEiJC3nXowPp5gLFSTZ1A0q60UKYThPzjpKuaQpG+qKP8Gle/JU+EXA4S7P2Wmeul+lKIxaIhlEDKHkypRMLVfk5AZ5yqYax7GM/HIuzlJSij8zw7wwnz6b0aAWITgjqGDiSRPJqssJnyCZFlPEZ2ei+vs6lEiWUCzfm4bDzSlk2DpPlRxRlb08YvUAW8/NfEIr8JbbpxNhf+jKjV8H1qLsD1veD3ssKZvI1tc02rHNDTMdSpxGdtRui5jyuJmxtC0yehpA6NlWiScyK5/PMGeHhIhQ65ZOWmg2JKtwfaBxHX1XNpbr+Zy3hn3m1rOh5NpA4szNoIdGOswbTSf0qogzdocBpp4mklFRDtMj7oYXwWA/nyAuYZZJTjnTObv5HEOoFfZtoGako4wyz0M7df2MnCJC5PpYNqi1C8zTCLkiMExEdWGUmhMHi9hPNvKOui5/LllxwklT8L7oRsR9CO8RdvOABb3Ee2cjtXncOIUIiFGNYEGxmAm7heC6Xe8wn59z9fyUsCkqxpQSkDjaC/gMKQOacIR/pbj8l3k8x/xzzH8cmH8mCpzgi8GdiqEU4q3lC8JxiVfwFFO8mFIpZJJRqyfmjF1IwA1izohTai1FRMSwbJgTavH0WgzyFCGKUmnxjRmn9tyFK7L66TW1zBpTSlM4puGmoLZR7ENdESU4pU+xjLy8UlH8ZpxIUS7BpV+O957RcvHLiRm8oFLqgT5l/uK3v8Z/8P07/Df/cMP/8avv8me/peXvf+kb/OQf/gKo8NO/eBdMyNkoMyBKHD0gkyOmiJTuiS9zzSoLznHJEyoq7ol4bNCnWIosy4gUk0FnRmbi33ARY6EYGewp7wgxkpSxXIyRqg6X10Um8MepwHTGVIZC6zJNLVgqhW7OhUgtptjkoROclSwre0YA+/s8mgDbwVPPOtJYI3PY9A1kIQU4czW7Q895mMFsw5BbjjK8bGcc58BpmGOq3Mjr8t/VkqVm5uPIoI5BlI1Xrvdr3qv32O+3tESesOBVjjmh5qgKzMdMZZFBlCoWj4/WFWUGFkE8s2yIy7SdcLeasWAD2dFoxw0G3tZ9JBe38P1e2YjQWMZcR5PL02dtECTTqXDDBpzC1sGerTih5X5b8V/MPfv/yV/jg7/6Y/z9u1t+ZHHOw7MVb9zZZ2jv8d+/ebNgQiIz1iBQU8YF1aXhZCm8TYz7bpeb6YzaRgYJhaeheSrW4epwwql6ejejSpvyNDo4Kh85q5Q6ZWpxzLfFJ6T1XdkkRl9u0CqcN8bORsmWqHLxmsIEb5BUibXhxjLWTSc7ACwPzjmIPebkd+F9cWasl4Hd846zXYda+lTgHZ5j/jnmPx7MPxPfn0uTPUoHRsTD1CVQfJE3WyZlwztXuCE+T92DizlmUR+pFnl5ME/yZVxTVYFoGctlxJScFUM/YDCKLhlQK6ZzUUpBlSdjJFPBucAgRogXHBSo1JVNWyjjKCmFk28CMZZQt2rqGDFJv4MIWQXNEELA5Yw2Ss6Qrcc3S/7tO8Z/9kM30Lbip//1hhfdGd/16i4/9QMvU7mKs9WGv/nL53zVdoprcfKIllFWxlC7mEsnxJcuTkUZYRXFWYmiMHTqipURnPd++ns/JYdHcEVYXnlpNtMAACAASURBVDg2JdNryBPxF0CKY7Qh1DIZHupFxMbUIbISBREmJZlJxtTjbcSp0Eeh1EsT7yhfrBni9PSh4hFnRPvkky7rMWJtz3xt5PnAI9vHqlKUXs2ZLcLVWIh352HGtTzy2AWeUFQIziIpVzysZlhS1i5yMGSetA0xG8upk7h2LQdpYNMoTS/MZOAtP2d0UygeG2QUNrVnFoUxJZo80LsKcY4jN0M7YeMMs4oDtqwrZZZg45STEVp6pHHkHjZaFQ8R16KW6Z2yiFtWrqXJHS4E3DhS6cAiZtYzaGLNn0/vsPO9ju53/jC3fuKL/OTfusnmmze8sbciHp5Q3Zvxx75yyq81txmqQO5bOkklP4eM5NLZiy7ik+NJaJmR2EhLaxvmeaSynsFqois3/MfVkoqBhawYJRCyw1xHYatl1IxmyAie1dxYUbNYA2q0fiw39m0N2BRw+2G8l25sKyOjeEwyrjIYYbkaONYdYgpPDTtzcVc/5IgkcLbXoBjz84717qeji/Mc888x/3Fg/pno+TstIyBVJThXTP+0SNlwGY+R1BV5shaZ8oWZnxdFJ6WPWOmctKFCHCzUoY1Hp9fy4alBoA+Kd0I1hTpWMhFlPySVrnyRqfvJ06UyQZzgVaidKwTgybAvuJL9RHB4pMjWBbgMjXSXCqPCri/nVQzyekYx/sq37/PH9zp+6rteQNvSOQrzlj/3Jz7L69fmzLxj0QYOD3b4b/+9b0PV06ijriblk4PgoarBuyn525e4BaZr5iZjPtPy80WaWdZRuWlMpyWKQbUUQ6qKk/IeBEdVhcKdCUqcOl3OTQZQ3lOFQJiucxWKcaBOY0EvxXAQJuVccogUrtXcKzu1Z3fm2W0cdeOofTkX00DKIM9GTf77OursGcaGwSljqtnNW17tT9hNPb1Ero+nfH1xODmKbgE4TCO5gavjgHlPQ6QeE30Nyyoz1o4vbI5xtXJt3DI34wYbWvXs5WIMdiOvOciOVjuuj0NZR+WZm1GjtK4C19DiqFIhTErIzLNwK28RgYUYQ1V+P64Dm9pzo++KHYLApmloJUEQGnHseLhhGyBzo9+ACbX0vDnb5d8/vMcfie/y4s05Y90ioWP7+FXGHzTmrz+Cqz08vgUvDXz2xxqy1exuoWHgahoRn9lxK67olqWeIziOmpq9uAWXcfK02N+4luQNnxTUCDKwn3pCEuYpMvpIFY06QRUFh8NNMQAHuWexFaLPnM5gk2qShDKKriJejCoYVRVp2g2hTrR+QIDKDwRXnrqDH3mcrmIi7LojbrjH3KqOuL73iNv1E6ra2F/17Jx3pHXD+U7N/OST79wNzzH/HPMfD+afjd3CFQ6H4skkejUWybGdzOPQPI2CJgfiiVcCxR33YrSDFLJylsLZ6KEEd5ZAcdQyeRq/gIAPOCuVq/qpi0Lhi2Ss8E8mvo1NPgzZSeEITdwfM0PUle6Qm8wIzQCPE9CcLiMQQNBqSsRuPEiFDef85W+/zY9/91VOz7f8xPc1zOq6vG7wJe4heBaLYlOeUiIo7PoW9YLkovpSAxEHk6mS95lkxdhvLcIuQvaUHC8r8RGlq2TIFA0huRgPXnBzPDCaFROoidBtEwFagsPHTPAVMUZMJw6OSAk05YK8XK6DTWOzlHKJmJgcmGdokS+a0gOBXMJNKVlWwXk0lFHbKIGWT76qpPOOHQaCOJZ55KiqaLaGNVBHYd00XB+3XOjushSC/I3NwIIOGeH+vAIRWkbEhFRH3gwLFgw8nnuUERkzXb2ZTCaFx4sak57GjAOLnLgdKqsZqzV9GKmGGbEqT9F+aGlSoG+2SAUPJIBFrq1HHs0CPs5YNmuurSP3lwHygBO4thl5uKi4cd4DIwtWxYbfwdHyCrdO3+TfvWakf/Mf4e7OeePfuEt6eAcJkIaXUfcQsznj+YtAeQIbP7hGNRaTsBxLQT4ILPPIEA7YRkdr51zNPVf7nl+e3+H7Vu+TBXo3MlSO5ZCAzDALLLtI1zgkCY0WG/nGRiocvY6I+cLJS+CqgS7VeM20As0gDCqIH8kmmMvFvA6KVQI1RhE8IBCIjOpRjcRgHOhIxcDjvZFT4HA9w3cjmUySBq8d6pXr+YxhqJl9OqKonmP+OeY/Fsw/EwWOBIdLECnE25CV6B01JUG81xKJoAY+Q+SpP07RSgmqRaKcUSqM0S4yjiYuCpnRHMESySA6ncjMJcRRMgSdZoRiBJgymSaJ+CRH1JzAVUjMMIW6VWKgSsqldefVyLkUD+Yck7seABmHTsz1n/nXHN906zXuXNvBO7h1Yx9F2G63aIzFz6byZVRmgCu5TKvVFryx44w+GHks88yi3HLEXAq+Wh3JIt4i3eSvk80VGTdCEFdGgjaVi1oIZUqpq+Kk5LrI4Cppt1MBlItkXihdM5elJLdPxaKbqkKRhPqKlPMl78dpITuH7Ar/SSP+wvMIoU+RnCiSQe+xZIxBqXMJA/2kH6PusshnnLsKE5gNwpN2h11ZYd5IzEAq1KB2x/TjQfmHbmDta8g1N6acnkwpqh8udrjSFVLf8XKHw9MtD3b3eOH8hGTwYFlzbT1ytNglKTzx0AwjQwhcW49oqkh6ynEoWUBWaSGd57aQ57Ngzng8X3NzJUDPPVGOd3ZZxJ61ixxuRx7v7uIMVMr61uyjk8/Ffzn7NdjZhR95C+eU1Fwhne5RXX8fr0q6X4iQ+C2h94x1IjcQlndhdYPr8ZTRhWIKhzA6YTA4qeY80Iob/cigHVfSCb+z2OV2d8Z9f8iL2yPAkxQWnSFWEbZFaTg4oYqOwSWGKlGNHq9KlMwYIuNEezRA/IiNNRYG6qh0KmxjRbASgBh9Bk3YMKeRgdFlsgmVH0g5s9MFTJSjOhDWuwCcIww7a3KCq11HthaxLb2b4esNqXsmmuy/7+M55p9j/uPA/DNR4NTiGLwhKaNOcaFIjFUmu/9pg48Y2ZdIgwuzPm+KWSFABRSsFEOkkaxPPRKyE6psKA6VwovRXLg9ph4FhmkDD0wckAxpKnB0Ep1n51ASeEpRERzOMkM2QiXkXBjlFzlSHmPA4aZiS5MRJ/XXLx1VfNtn5xxtOq4tG9Q50jAWuXk1uT7mDJPHj5mRY2KxbPBqnDhhL3rMCxtLVKm4QVYiiC9RFD2Otg6lSMpQ54Spw0xIWrhIWR3V1BW0qcDDirweilKrsN7LORQpfOIiwsqJFNXCJHN3zpWx2OTj06eEZKPyhd+j5QfRKV09qCBuKqLGWNRWPqNajBGNxMIcg+SnOV+f4GMxRlZNW0LrVMihhbiGcY6KUKlR+RP6cY+N7CDVCMkhEjBJrBqlq+ZcWfccz2YcbI1rqxMeL3cu3+PRXuD6+SlZPdumwTnP8aLm2uqE+zu7CLCuC79q3c7ovdIO7lKi2YzlBt37QD0Zs0lSsjasF5mND1zbnOPihrPWMU8VXRO4c3rK3d1d1otya9k/2zBOvlZvnV9l+UdnXH13B145Y5QK33dgRn5wexrpbknxGmN4VHyuOmGUF6hef8I7/+jzvLTaEsOMdTB2xw7RxJ3hPiLCbIw8qPe4mR3ZOgLwQjxl9AXvqJFN6LRlnsoYxOXi9VRlJamQPDQp03tHbZOfVM5E57DUogq1VWwmuweA4BzFTkVZuoHT0NHFQO1H/Biooieb0UnEZKSKRoXQqxDnxxye7IAvvIqoDqlhcRIYuiUfyXz5BB/PMf8c8x8H5p+JAidj1K5sclEUbHLenTbISfpDmKTFcTKCg0IA9jHjvEfyBanIcOqnUVRJCM/TmMQuiLCuEH6TusvuSGUT+dYmNZcr3Q03FVNJJxl0eWOyljaqGAQvhfjlHT5nsgPLnpLzWpK4QwY8eKnYnVXcPzvnG8cLrs2VTVNj6w19jNRVKLI9MwgB3Y5kgXEcqeuaHDNNo/y9f+eQH/6bZ1SqLM1hZWZGnpTiUIC6lczchM4VE8DibzNxcaYRVLrIB52SxXX60tvkfeOcQyfFVNKJu39R0Aioi1iccsOIU7enkMXroJDBqSsyecswScb9RWcrl0wx8ReS80LYtpSLRJ48qc0/+STjwWf2dYUgPAnXCwk7LOhcD1rTN4nUzZBKUALbZqTpAwaYVixiZo4gruGgL6DP1YL55NJad56+iWzmk+t2HhCnHGwz650a1TLTVyseRuYMZ4pWRmvQdMXHqGsjWTyuOKNhGgkIdefRsAVtWe9EFluhn0UsO/p6zpUu0rWR66dnaD1iqWKRa54s3uLV+xXbfYcbjebwTbjyEI6vX+I9DVeZpcdkEdyNuwxHN3GdYv11/uxP/i3+l7/xH3ElntBmQ6rMTucYXEtIwtZBmwc2lXBz6HgYGuax3NRl8kkwjJC3jBPeQ4Y0pRw7IIogROapEOIHp3SVY4TSQTZjdI7ZsCVpXeymiIyVgSknplS9MAuJPkKdB0Y3vY9XWivryJZpM3SrPUbJ1Oaw2QY/rTLppwfv8BzzzzH/8WD+mShwnCuhlZU6vEDK0wkW+g0y5RRptuJ26EogpGrxtpEpw0pccTUuidtPCyIovJFgQsIwp9TTxuwxtmQ8oE5J4p56xOSy+UcUjxWWuUB20wgGxyBGZTrJ6DKVKpKnmAVXCF85p4moK4xqNKY0HmqNPFlteXQWebAeubXbUHtHIpIrw6P4oay5HwaC86SUGFPidNPzpQ9g3jakOKAoyTIpgg+ujJByBu+Z5YwFqCkE7ZwzJqCTUkxE8HhGKTJ5N3XLJm5yMeszJVrh22s2dIrH0JwmmXmAEPECXgsL3kkZnUFxgFDKe06CeVSLjH6MGZmcj4MvRRJceP0Unx1ScbiUT/6EilYHjtwNXhiPWNZnPAoTv0orXDZMHd0MNGfqXsF5+iZPeE9IfcGjKnhPo8dXEbXydR7aMqr0E94lKE0nDG3xOWpzR9sH+sboh4CvInnwECIIdPNi7d30AcJIdr64elOK+rEBEU83S9hYMcwjJp6sRu+g7iCLwxrP3XafzxyfMPdbdh9f4f2X5ywe3Gf3eEZathwv/yAHX/i5wvNCae6VNfvr7zN88AKqgnvhLnzgePLVH8aJ0lpCEc5CheccQoPvM6IDPgVC17BhxmKA0GQ247woGqNidUeKFUtdcZ4XWBhYpMy5lsJ/1zLruMu8Hjifbh+zbiQ3gRQr2ryiiRknARhorcNLXSh9flNuzq7gfQEQNggZZw6bD9gq0FMX7pvAXjKiFK8nv56h2YGLJFciCj4lPn/PMf8c8x8L5p+JAke8I7iMy4UDcpkJRUJdiTPIOZc4hunsS85aIf0KJcIhYYSLZOqJW5Nz8ZrRiyIJcDkXErEXnJWsjcu1IIQLIvP0+xSbNkmgi+Z/1Iw3oRa9dEhWLtogpcByzk2Uuck8D8prqXI2JK7uz3l4HjlJjnvDSMrw8pU56y5yvonUjWPpjXZW4aLi2hoqT2XG1ariKycnBBJuakdaBleVUQ8qOK+leHDTmE2suCLL0zRxkZJsm0WwXEZ0WcDrBWcIsJJ8bnEKN/UU6TYR790UjQGeEg3BRXBmnooTMpUTnILE8vRQSNyZ4DzBQbTMYAmdGP8pZcQxrakEgWazy1HhJ/lIrad3ghuKZ1HYTNerdlRDpp9GitUQ2dQeRoq8fxgZmwpFqPuRvg6l6BdDhlCctcNItYLcMCXLg8Wq2B8kT9ONDHXNxlFeVwSNVVHSpapEggAgrDUX64SRkgQ/ZcW4fmSswtMolDHgqgixIvQjGajXcMySegXv1wdc1/u80TquvdvzQK+jtTDLH7Bfv4P+2utYBuZb3NUPSE9ugyhjuIa5JTx6mda/h7yv3ErnjE0imrDsQajJ2wvbgsBKBHdBGnWRFAMhKaE5B0+58VdrVqJgayLCiUK1LRtuiTRMbMaWuRxxJrt0UpEiHNgJjgrVjHMjbUqYOdBpvptL8KHqFqeekBISA12VyfNtUUV6RyDSy8CYWiSPBDXGXIH2JAmoRdBqskt4mtv0ST6eY/455j8OzD8TBY73JRogUjogkVy6LeKBjBchS3E0FhFc5hKU1bQhM2UzjRU0uYyTXJ5GXTzlh2QMr74ojKRstEEKxyQ4z2CpGBddrq5kMUWFSjymhR+0cDU55+m9y88/dQbOl+u76DiIlM5OdpPJHp7TYeBoOzBm4/xsYCfNuLnTlFFYTGziyNhA6Huu7y0RLTlOpEQ/jvzmkzOyzDG6ouoSR0Swy+ys4m1zIWuyyY/mYp1QPIj89P/K2ifDPWzyyAGnHohUakBizCW5PEjxZKjlKek7SSqha56SfyIlX8zMGKNcjrxIFwVkKgVRKtyeUUuRpWqXAZs5l8LTzOjlk8/B2WjDkoGsRqczQjNyNR7zRA6hztQSyzVqBS99wbsYNBBIbFR5uLPg+vmWD3Yabp/33FvW7PcbTtyc3dmmJCNXM3bHDWehYmdYc1bNUY2cVzW7/UiVMn1QLA8XpTmC0XbCtjEWUXmwbNntR5apGGf2tTG2SjPkkjMG1H4kJwNJyKSAKGOCC7xH5GyHMz2j0YzPA9vjEb9a0B80LOOa6BNpNWOTXuGQ3yI/uI0LG3JcoHofd+MDHp4dcu5eo5a3OfFXMG/0o7/E7kvpuDhkT186Sw4z2FY95PKYMnMjpOojeMeNbMJTvM/DhrorD0OHdsrojFh5/BgQ37HIhaMmTuktErKwoKdjRsOGpP4SqxYiMTfoeUadMLabgveNI7gtoxV3XC9bqDI6RqLVuNxjtGz8p8MH5znmn2P+48D8s1HgYKVzo8XsrQaQqYgRo8yXBG/F5tlJkRt/OGcJMkmnOAAFL9NGilFPAZUX7c2LTCcoIxOD4lNjRjOFeObEU0KrZHziMnVb1LAi88GJQMrMfCTlBlNHlYTOznFJGOqakHN50vCKTx2mLYmeN/uag6ajMRgAM+Fs0zMPynrMbNOIZOONF69j85JdK8mgDlQZ/tL3fit/4efe5jh5sARTzMWFysCsBJhdFAeY4lRQP3WUrLDqBT4iAdeUYTrXiy+AmidV5WcaDK/l3CsXLkMxHVCpR6c8khQNURiiTMVZuUE4y8g04spZiJlLvpNJLpkmKCkXyXgpGROoI3wKZlSH6QkmMFYF79c2a6BGgrG7XXHaLtkdN+RYYwJtThzNA/ur8mSTQuL2dsXRbMbNTogB9hc7+O2Ww27LgRWbgWspcazKYbdFxHG17wDl6qRG2TfPo/1DwuP7PKobXt+Wa/v41j7VwwcsfUXuBoxE8JFePcGUSkZu5zOGYUY3O2C+6RjrR1Tdgq+9+AYvv/N1tuqg2aXZ3KULt0jVPZ64in16vGUSUM1HunHAvXPA2UtH7K02yP5bjNdu0M9uAuDOlbhzlf6o585rf4B7X4PtZoc9t8IETtrZJd4f2A7Xu/Pfhfdjv8etdPJRvPMU7ytfcSVtWbWOEC8kyDBMLuazKMzGDicZr5mZQJc8bU40jiIgUE8zRkQDnUUktQTtiDQE3SI6cUM6oaMBNVwURpfxsSJSY7EnWA8oUSpwRviUzKieY/455j8OzD8TBc6FEZwiJM0lsVohpCn3SSheNVoUOyJCDmWDjBguJrJoiXOYWpTZBO/KaMsm8z6zNJFoiymfN/lQe7Iclx0ZX2a+AIZSSSZpLt0coxgCTvyg//gzC15yPXF/yTcvEz/5D57wd37gBXYWM37r/in/+S+uWLiK//D1hr/95siQja16XpCRHRKz1rPXBE43HW89zMwah68c+3XAUqbrR6p66maYoUPxkvncLeHqcI9t2CGrI+VMzsULyDAKT9pdDuBK3pQBdhmXkAV8LLETxT47YZWi+WIkBDElknP4KQ3Wa6YRBxMVebBieNhnI+WihKtMkWBoBu9zGUrplPl1sb5J5F+k6hOPKksxTBRwVhyuyzv5Kdz0kz+iIrZo8sX1ei8TvbBezFierzCnLMZ1MZGsehoK+fFaGpCZkHMkmrGdL2iA2o0IENf3cXVisVrx/q032D+7h1li5j0HRx3vvPgKL737NkcHzeUyBkZUIV27wRURjnYvnvCU+SLzZLnPzvk9TmfXicCNd99he9jwE8u38fo+6+r72b31s/ztf/w5fvw77mIvrfmO3/kGf/fRdzJT5bXX7tJ/ecMwfsBRVbOMHdIesYh7LMzRn3Ysuxnrw4dEuc7WP2a2NeRgw/DwpQnvGT1xmGtpXnqLW79xj3vhdXrnsREOzssGaC5iAj0eieXB5GRZEzGUyENbFK8OgRfX5x/B+3x6WFr0GcGxdYkPlle51h+Xj4stPofC/TDogSuu56HVhXC/d5/do+tYNbLMGQkj895jplTWTXiP5Zo7GCyDVZgaVQx0UxBlnWtGqfEyIOaIVuE4+5ePx38Vx3PMP8f8x4D5Z6LAqRWyQprSsNUmrooTkhQvFtESdJkvR1JFKeSsFDtVhkEu0qxLgSNkcA41T5Sx8FIAMHwqSd4OStK4XqR1lygFM8NTukOaE0nLOAznkAiiJdjSqfHz37jPf/cnP4cPsKgd//ePLFi0Nb4KfM/LDTu/1vGF2cD1xcCtmfDBxrFMibkb2GYhDZHaKRlhE5V5Ul7fnfP6rUM2my2n28ihdmjwSB1AyvhpM6xAMhbKNcvicVIiIsQcWCJNZK+LYNIql1ZqzAkxRUlkd0GbMZIVAneeZORikzRbMnkKzExMOVyZy8/CMoxqhKwTI76AKylINqKVeIqsGc2OLWlqmTpEMs4JIRudK/L2i7EWMBkuCu3kk/RJP2YIqzoxCMz6RGtGe14m4RsnzKNhzl/iHSvz7sd7Vzg8PWW9u8u1k1OiZY729zHg8Pi44F2F5bCl6c6pzDh+4UWOdnZ56b2v4hSungw8WczYW284Xcx5+YOvklU4XczZP1txvLOgXa3ZzGe88v5XeXD1Di+/9w2GKiLBODyP/Hb3iG/7qScsj36B/PAmP/7nfp7xt/8gdrbL7lWYN2d8rj/hij7gfl6AZm5tHzNLENM+R9UxVe+xWcaFiCTlir4Lt16GK+8w3v0CjX2D3EKMr0x4N9qbXwOZMVQT3n3h25lAM9b0OuCj4FQY/Ei7dTQpIiI8WQTElIP0iHUNF/qDh+kad+IJ2YQ354dcsycsN/DK2WPOZ0oTe8aQ8XGGpi2mxfL/cTKia/Cpoz7dI/kBzcK5lky9jQTavGFTQTs4TuvCT0s54CQiOTGXkdO6praiGoz5gntQOHJN8+hynZ/04znmn2P+48D8M2Gy8NpsoKqLiqpSwfuJu+Gh8h7xijpDHDDJu4NTvJYYgUqVFAR1RpjyqNQZ3ju8d2gwxCuVKCF4VIVQFbKsVIIPJbG1nqIgnBjBCSJG5RWplDqUOIGajA8ljFNrxWvN5/f3+O17jxm2G7Ry1F7Z9h1nZ2c8Pu2RUPF9tw/YJsdByJy3iaFx3JMZu3tL7g6BowTHecTU8Z2vH3Bjt0FJ1HXFQaucbjtOz84hlzwm8Y552/IzP/Ya2Y2oVyqfqIISvKIuo17wThAHoUz5cAomuUReqJVcKkfJx0IIrvwbH6AWw/ny5yoIlUJQY+7LWMqJ4hCcE+rK0zrDh0xdKa3YlC828aGcEBGw0jWaWyCJopoJVUH0qDCvPU1Q2uCZe0+rJUKi0kJevih6PsnHjepdlu0pc/HMRVHtMaeYh+CWPDk8vMT70f4eznlSWLK3Gog646CD1XzJ0Z3b7PcwHzJHd27TLw7oFwfs9CtWt+4wtvvsn26Zn5wz7Ozz5PCAzc4eB92a4B0z8xwdHHB86za2d4CI0VpgdecOMyqe3LnDtaNjTq4tWd+6zfrFO+TqkFe5if4/N3EnPVo5eHKIu/Yu1atfxPf7nF15jdm1m6zqA/ZnmXf3Z+RZw7HMqds5J2e32ewqm6qoZ+bf8gT33e+hJMaHt7F0hH7+N6he/fVLvJvdoHv4Kp//M2/S+4HoZ3hdMfeOmXNQlYeEVGeGOhGSw/uBWCVMMvvdmr1uhcYGYs1RuIIiXM/njPXIo8WCF+P7BNsytgP4iiZtaNKM5bhkJie0CGodzgmN9yzklMb3qGvZN8NcZjEV97RnrJnBOGcrLftdYhBP0Ei3/wCAjWRm1QrvVzQ5cCUndiVTz0+oZyefGrzDc8w/x/zHg/lnooOz42q+PSS+2Dks66XR3Uimmhx0bVL1hKylszONj9QVMz4H+IthjBmVfHSY4a3obypxpCmPKSKlq+McwUritrfydzkXGTMquFxIt1VVkTDImToLoqDqadvIXrtgPm9JsVTPF/yV33xwijjjf3znmB+96pmHmh+TDR+EOUbkahP43CsVey7yqw96vnw88kcHxbxRpxJJcUG2tZyxnPFtTe56JCXquubO0HG2mDMkKSqpf+b65ok/BJDclDAOU5ekSNgNmSIVSmcsWQnNrLzHS2acPImcFKF3qCrEIiqFpDzkSGNFai7TGNGpMsRIyp7OMki6dITOEglTbETMxcxLBDYjk4y8cHrUl1lzshKAKp+CDs68m/P5SvmKz8RxW9rIs8Cgc65sT2mP15d4v7I9IDYOsUIYRM/IUlOh9LnmbL9m5/SELtesd596nF85ehdkh/XuweV3ZffkGNST5lfBzkEde4NC7BhJpPlVKhEOnmwYVRAahp0GdhdcebjC9Izzg9fIgyMdvYI7+CrpYcSLoNeegBnd2weIM35pWPCD94V1XfFH5J9wzKtUB8bjq8IL1R5V+xXc/Ve5N87ZOVqyzF9DXngA712/xDs5I+4BunOInW4Qt8Guwzed3+XeCwd0/S6Sd8mA608AaFBygqHKCEJyMhHXHdXosZxQERabjKZlwXsX2HMbtFJymrMw41Qybd55ahnBAqkSswxN3rBVcLEhFh0EKxoqd8aZBbb9FaJC47ZsXRmPnItRDal8r49eIElis3fM/PgFhMzJ/vvsnu6jKsxWMzYO5hGG8CkYyfIc888x//FgXp6FYTa/0AAAIABJREFUJ4Qf/R9+2cy4HHnkqTQREbL6S56xpVjCL5GJS/L0ECvR7UKRIWZ7uhmqJDLCX/p8w4vzil8/Gfirv9MhVnKrXEr0HkL0pKnz4KaxCHkiJ0uRnFdoGaWZTGMbeFV7/sQNx7e9uqQWR8JTeagrTz9uWdZz/vT/9R4/8eqMQ83Esef6lZpfv7/ldFBe2q1RiyybhiZkPnNtl02fMQaceBZtoI8wq0v3ajafF3m8QX++5u7xiv/zbeHvPjpHUyHtZhJDlknFpMWJWARsYr986HM3s0tlt7MiDRe14jVEOc80FVfOOWzyBSoy8Im4dmGxmJ4qtpKUIs9hkzJLIKcp8+si36o45Njll6rkgYnl4kOkxWixmP8V5dvP/lc//Imucn7xT/+pj+DdtacAxG6Hkyu32dY1bd+zs3nI2eyQmW7Z5PYjr7Gt52Qf0DgiYiRXXV6/K9t7HFUH/JHrv8D1xcDDd/8Q/+94FTFIoebw/B73d679C+NdUk8KgW/9xj/m5itHLL/r68hql6Ptd3Lofpt4fcCvBthp+M2//llmn3Nc746pxxVuecx4dJV1t8f6Vs3e+j4ufp55+0W4ncmbGTKCVJn8HW8iv/IF0isf4EXoHr9OkiUqK9qDr5O/vMfxvVf4UtxBk/DY77PYfEDMu9T9Xfr6Vrn5/3PwLileRqd8GO9VMlJaE9SRCB/Fe+4gG7VkRuQS7xe2FUUOe8yQZjS6ok8zUGO7PCp4lziZWYxgASNyodUs29LI4mSPze6Ka0fzEjgz4f0Lf+fvfaLxDs8x/xzzHw/mn4kOTukJJMS5kkkUjdZl1lPoop/ETI1Ttrl0Xj5TZWKMvE87fZBThhKTz4E8nb95PD/9RuT2YU1be37oYMbPfe1Ndg+v8qXjHpynAahATKmmjRkMU0PFkW1k5txEeJ7MeLX0Gh5Iw9dXifROx7wqf3dzUXGwbNlbznlwuuK//p7rnK9XtMFT45kvW/747h7vPjolSsXBfMlXPjjh1auBFAfapuXB8ZprezPGmCBntqMjqcF6zWw+Z7vZcN71jBnu7BmLh561T2gCwbHQzBYgG8GXTKtJJHiZb5WluBJjF9ylch0dSpaIOcFT0tqzCZUrirILQ8ULCrOoEXNxmSYX4jgxkZDpE04EEUxcCdKbOma1OqJN1HApxlcyLbMI3TKVCNFA8lOO1Sf5GK7s0j4+pr+2j2jD8qSn3sKJh4WuufHgHlAK5Pb4PqvDPT578EXy11revf45PD2z1BNjjZeeSE1OWy4CaPes5o/t/XXy9xyhJ9e48dI/5Fv/txdo/8ABv/34NjHMONyuADivFhz0K1bVDKyQv5d9z9FszrXunCywrmaYlfgPtcj62mfozt7m+Ms3mVdC9bAh+0OO4w2u3PkN7P2GG982wnAf9oRQ3wVuc3b4Mtfe/wrz1XdyeqVDH3yZYRHYrR6T5DX8m4/hZkD/yQvk/fdwD2fkb36PBugev15cYO86+gq21Q5XtzV3g3CQjrF6RmM9az9H8imnN19mefIOF3ivNiuCzhjzpuQEXuDdzdG4wiVBpME8UA+o9cQu0PoTGjLjuAQHIayoAUsNMQc6K3gPCmPeo3IngKNyG1SEetUyagnDPZ1v2dssiWZs5tBuKiTOWC9PAcd2/wwBgqs4r9bMtrNPBd7hOeafY/7jwfwz0cH5U//Tr1i0DGQqdYwIXhJNzryqgmniUTL6oPwhtgTX8P91DW9UZ7xcwRfHmg/GmjNpqFLHRpUGu/xAo8J315l/68XA9b0F0Qsnqy05BXbqkQd95n/9p4633YgNVoz+clFzJUr76MJDpxxlw1YTKoTASGuJJEqjxmcr40c/e5WXbjScbgbun2443W6JMbJoZxwunj6ZpJx5eJ7o+zW7s6oUW2I0swWr1YpZU02mhkbO0Dae2juEzOkkoXx4uuFn3vGEIZKm8ViKkweBZcZJ8h5jLFL8LCTS5EQshZxteSqALmITyvou/IPEMnLRacEIKFlK92VM8dLv56ITc+GQfNEqvnTfNkP/mTlrzOnyRgVPuzt+yt+C0sFzuRgC/vxf/pOf6Cfaf/AX/lOLlmkfP6RpKk52DqjGLzGv4cqDfaqw5TS1jK3nxfEtGtnnG/kGy8WXuZZG3u1fYyV7fOXgNV7dPuR3mj0+c/LeJd7fPrjND86+yIH+U3jZw9CwPWto33qB9AO/wPr9z3H3S9/Eb73w0r8w3l8dzvHrI9y4JXhPf+su1x/N8J8Rdj//Dnzgib9xG3/wLjFGzg922ZOnvhYpZ46ycHBywubKHJxndmJwVXEfeNJB/Aje2TuDb7mHkJFffAOAbv2Qn+3+DK+sji7xfo6wsMyZG3nk9tmJHeMIM7/BsrDNNSLC3pMHnOw1qGXavhBZx3FF1RbFhxhUMf4uvNe5/RDe5XfhfTAIGMM0/r7Ae6PHDLb/Ebx7efJ74v1sdsbOZL5mAovtnPPqnO/9a7/6icY7PMf8c8x/PJh/Jgqcn/yff8kwJU8Xyk8jqjYnaslk81zxGRFllY2ZDBwqfBA9r8uIr0bmwUOOiGsYNx11XZOqOQ+88bIYY4TvfvkAFyKIcLIeSMNIzInDvTnvPDxlsICrHP/7O4kTmxg9U0r3ZTK5fZSXLTqNYSRNni3wYpv585/dJUnPjb1djjrj/fuPWSxrTlaRboyoKrd3GxaNQ7Xi3UcnjFpY64sQaGeBYRio6wrM2I4jjddLl+fiR5gZxxGVQPCOv/IrkZEVdhHbIIqlMvAr5oJltKc5k6R0mi7AOqYCfAVq5/CSMZQxZZIZo9mkbjIaVUynp4FUuDxMGV4Xv5heTcmla5MjYTJW5GI9003GJsVEsqdruvh1GSVhwkjJ/vrZv/hDn+gb/pd+6ic+gvd6CsGTlNC4IpvnoM3Ea0Z6MoedDzhIkdPVHQ7Ce1gw6jzg/Anj+mW8rsjjnFTNWe3UePeIxckC/cLZpQ9GPu3R4YyE4m7skk/fZrv5Vtp8ytcffJ6jagcBatfybrXk2vYhAI376JhgyF3xyUiR1JRZ+8HO29y5/oAkPf7OyPbe5+D93yTuX2F5NLJyOzTDCelqSzh4iB6/RH/yPqftC2jacvDOVey1Uzp6WqnBjLQ2dJbRdhpXdxUmGZu/x3r7Bgt3xm9+/fsZxiMq35Isc+SrS7wHOpLViPS/J979qih4FMiLOYsnJwXvecL7cstsGMq50v6eeE/S4TRd4j2Pe/hwwpgDKsM/F+/b+eTt8iG8d1JcX5ebOWf/P3tvFmNZlp3nfXs6wx0jIiMip67Mqu6qnkmRINykSEuWZIgGCFgw5OmBL7bhJ1v2iycZBmwYgp8MP0qyQBgwoAeDsEHCgi2ApihaltCkRJEt9lRd3dWVWTlnzHc8wx6WH/aJyCoVW4JhdldlIw+QiERkRsSNc797zrpr/ev/6zVWFKN2zKpa84u/8u2Xmnd4xfwr5j8e5j8RIyqjFNZEEg5lhCAahyZYjU8Kh3AilqCg17BIBq9hYgNPgyJ2JYveIqbgi7EFW5Oix7UXvFEKytbUyvPsQpiOKpqupQmaPgbGteV41WCUZqdWnK9bvjLx/HZ7gEo59fXSM8eQ06/xQu8iGocWcCIkq1DJYIzhUW/4R8+3fHFH4UOka1saH7lTlXzh1gG9h997+30mlcOniEqB2aigbRu00wR6tFKMR7lw6X3EBs3BzpTVekvjE32KOAVFUXBtXNMFz1/+Kc27ixFvjBL//TfWBFMRLh2JlSYkcHl+hBo+b8kdFYxFhZTXziWS0IgkrFYoyeK34kognGdIItmzWQ3uw0h2HEZe/EyBnCWjskkjAkFFKmDIzCMkUMogKpFkiJSQhDFQqmzyl5LmhfPOy30UqSc50NRE40mmopREXyZMrNBKWIsinEPSFpZvkMRTpyWbZofYKB6+1iBmxusXW9B7iF1TFN9hr16xbt8gTN6lWGxhbonHDtNPibEmjDvkeIVWJaV9ROoMtyffY23/DCq1iCTubp+TqmrgPVKendPVBaGe4KTA+EgsDSppjDFcbL5IWpTcnf0uMYwpL54hXUW5d0acvYHxYL8f0HsnqPEKFlvSbI/9JxGtK9Ltb6HVDUaffh85vY5qa4LVFG8u4P0xPjp63TEeP0dv7jB+8yHx2nNuhznx+esc6q/zzqMD5NqtXFCLoJTjVGuscngMVuWb1ORkgdaa5cEcFRLu7ALX9i94V4puf447BsUYBTRXTuAK59or3rVUSOLFRd9t8Vi0ycZpH+Rdd5ZQpCvezeIQ2T2+4h0y7/14TTHwvq5XV267L/vxivlXzH8czH8iOji/+rf+njw7b4jFlCSa91JWlM8V3NRLNnHEM9Fs9WAGh6HGs0UTlR4M/3KFabslt6ipbJdFZM0KawvKwtBs17xxMKc00MdAZR1JJKd3K03TNaANe5MpKxS/8SSCNaxiCQZ86nAq8G/tOdbNlt/a1mzF8obr+OXPjfhr73hWCf7SXcOi6xlpw2xUcrHdcrzsORgZvnjnAO0cj0/POV22RErmFXQRlCScyVtT89rQBTicjwgpsNhG4vBUKa3ZbLaIVoysYWds0cqAsdQmB38+O+v5b75+jiRFL/FKTHelu5H89zB4GDglxBhz+zPm6r8YPCly6CVDJya3LCO5owPQiwyOyfnQwwNVSogoVIqgcvclO2kKJil6JbgEnhfC5HxkAXP+Hgrlc5xDIs9+/8///Jde6ne08T/6ijTNgra8hWA4udEgDw+Y3tiy0z5lvb7O2o9QVXXFe+q3qKKkH+1+iHd38j7T6Bi7C/xupNi8T2r2KKaQwjEzuUM/e4obLYjNDkkECoNSGlecsdneZmzhIvw0xydbsAY/uX3Fe9WecrMoSeYbPL54izS7wfWdd7hWn3H/+5/BT/f5nP5DlDtnMa6oSgPLI6Tdo5o+gC/OwDl4tIJzz1F6nd1rD2iWd1CSKGcP8atPMa4jsi1Rnz+DgyP4vbeQPm/RSBmIxQPkyV3sSOAXvo1WBn9yiDM6u55/d4dv3P9pqvWSZVlSRRDfX/GeBIwI2zrfTMd9Rxg474r3saubOGdp60dMT/cQgdW1c9zFIZe8j7wHYGMNrug+wvty94LJxQ7L2dkV71YCooT6ZMb62pLxyZTNXhbYfpD3qHIX0yZDSv5DvP/Zv/Hdl5p3eMX8K+Y/HuY/EQXOX/mf/668OYrMR5ZzHzjvx7m7EHtGNjJ2ikKV/IOuJEhiLBGRyFIVGB/wruAvTFtGY4dPQvCJJ9vE6TKPqib0rEnMbcFZ02aNicqr00ZnI8G3bs55uu5ZLBt2JwXXd6Y8PD0nGEutS3aKxO9spvzifjcka8M7xxu+xpyfNEt+8vac7z8/ZV6PmZWOL7+1z3bT0frEg6MVd/bGiNLcnFU0Elg1HW2T6H1uLy7bnsrmQEprLbuTisoqpnVJWVh8gGcXKzqfIVm3HZXVHFybs18W9DGw6hpWXc+nrx+w2W75X/7gnN9pFE6W9KoEKSAqeh3QEYLKQuN+0N5kIXHEJXVltnQ5d72cmaaU8qbUYIyotUZiJJFXufUHvgZyx0VEiB/U3Ci5jMfKRY8SJA5F1GC6qAYhW34MgkguAJVS/Np/8nJvUZ3+u39SdifHyLQhTta0T38mWyPYU6xXmMN7NJu7nKw+Q5BEfbhCJLI+2mGkEmuxfGHva4T5ArW6RtIB1Vxjde6ZmDnoFX3dU/sRm66B6BmNGmIzoh87iiAUuz3x4hp9WlExQu2vobnAh1sYXxN2H3Ix+jIH5nfzWHa9T7equNe/xZ36XdpPKabtfWL3OpW0+H+xQ58F1PPryMkStTNBlMZ87oR4eARPNN17n6Y+rkmAtud0xRajwWxvof6Fd1DNFHYs8fktzM0j0sOSvsk3PLs+hcM1zl0n+r2ctrzs8asxtryOunaf7W+/xj2+hMz+Eeb568TqgHq1pCscqvek1Gbh4+gZAFV3l7a6z3hz5wfybjcbOtVRpJLO9FSppFPtP5N3Rcf6sLv63B/Fu43+Q7yXp5MfyPtP/6+/+1LzDq+Yf8X8x8P8J2JEtW8865hYn3uOW81evWZvBEeh5PkmsjM1nGjLXdXhU0cKEcGxcDV9AZO24cwFTpueQkeKWYVLAdU0bIPC1oG4CrwvLZuQ9S+SFKVzWCMUqeN4XVCpxNooFuuOTRsJPnCqHF+5LlRW8YvlmqQUYDEpcOtajVstGZvE6cUCFRXnixWNK7lx7NidTbl3dMyzszWf2i1ptpFtBa6a8/B0QQwwLqD1PVE0F1ufK+Ai0fjEW586oNCJTRNYtx19F2h7j7KWQoNPER8CS50Inaf1CYLn9759n09/6pCiaLjeWbYe/tykwJiGbywTnzIdf7+YcmPb0yTNxgwp4AJRG5IStKg8WhpmqJ6Ek7yFqGweT2Hy2rfSgkZnQbKAHsiO5BV1BehBGGckYQVkqN6TKEwyRJ3ns6jsbjlH6NH0BSivsQIJQ5Lw8YH6x3SMpue0pkBfGNYnt5nUT7F7D2ibz7L2HaPlG/SHe+yrR6hNh38aiLVlpa6xuXPC7N2CcLqPXpeovcd0+nXKfsOYYxadoppHyuWChV1yEYSCEacbRaFgFFcYf4FUGnPzCPPsNfq4QR2XCDssisDOnd/HJccBf5+kFBqLGj2hKb7I3acPqPfuU55NScefphw/huYu7sF94k82pMdr5OgN3N1H8NzCSiH6New/UZShpx+fU3aOIBrlFaAQfU77zTn6px328T76xhPSSUDFFdVS093wuGdvkOiJsxnmZ/8JfG8HNnMcT+HpM+JnIxx2TNcl8uSQT7HAtM94Hi03zYLv7f0Me6cL1tuOubmLCh3Q4bY3CUbhQo9XFiserxyb+n12z/ZBQUWBKIXfW1GdWMpkETSd6VECrh86mWVCRIOU1Eclig5zFfcypC9bh+oFKSqa3fUV7/3eEeOTPdY3VlTP5z9WvMMr5l8x//Ew/4kocBqfeH7eIUnxxRszAh0P1orxJGJtwGjNZ1LDRgsuCqvOE2zk8xJYhcj5eJc/3BquyxqJiWm3xqIxVcHFtmFaWcQVWOkwyREVbH2PEiEZYba7y/m6pSgKemWICkKfMKagMI6L9ZbJuMJ7j2iFTh5nDLXp2TOREHseby17TvIKtIk0TUNMoHA8W7Ssu0Abep4vNBfPv4/tCpbBk5IdVCW5qt0ZVZxvcvT8e/cf8fk7N1h1Dc8WDRIUdWlxQyK5rcY0ixXnQdA2b0kZY7DW8u7jI26aiNMlCxe5PossF4Gd6BnPav58s+FR7KjMiO+HSD9U0qlyeDF41WOGwscYg44KPZTb2UMioMQhaVhk0DlQ81KsDHnrSWmP6MF7ISXUsF0gw2+tU8RkGTRKZSH0nygDP3dnl6fPDb+xWJFUBAVFinxCkP3/dfSrxKoHSZrru4FoNKtHr2F2AoWPmJFh9t6KMO4IVlOGE1pd8br/Hs2zJavyp3jWjZimFn3vFsUsEENHCPsEt8Fi6PsZql5QxhFRCWETUcWWbltSj9+kf7bFqIroAnSaXiJOFZhwjaKx+LSD9hHRCkmaaBST6ddgtg+xo9vcpb7xDursEG6/A88d/Vdfp95EVs0Cc2xQ5YawsahHF9DdorcVKd3ED0ZeZTpGnBD7jpEfwzcfk74YkEWPPLRIKDGuoojncOdd2L6JWR/Db0yR/RZ1EvDXQfsp5v9ZMVo6bjyNxNlzVN2DO6Z+cBsbpnx58TUW5QkTv8/JKiJmBICelBQxsjl8Snl8EwEKArp5HX/7YX7Czm8jOw+Yn9xE/Da/9lxJuLklPRldPa9Fowi7G+yyJMw6dJPQXd4QSdUWgHa8wIxy/hpd5v0t03O4H4lB8bW1ph9nAzeTYLTZ/RFR+cM9XjH/ivmPg/lPxt1CC/uzMfeerfj6oyWjUlONYLQ2lBFkG9kgJPH4OKyk9QE7KXhytOQ1U3J9rnh8Cmss69M1k2nN9drwhdsTdqqCoBLvPFkRaHh8EUDlXKdZoThdrDmcFtRa40ykC2C1xiqhdh3vrRXzvielRN+Fq3lwUTmW0ZB0zWa14ZlOHLqSz+1qTjeCrDcYZ/nCzQmnFw1N3zO7PsJ4aExkpqCPPePS4JwjxoRJgXlZkVTHugl87f5TmjZwOKuRJFitaXvBJ0FWKwBC8MQG+hCx1rLtPW0IhGh4klrmIXD/ZE3lFK3v+c6TDqfgpC+RUcS6MXfMljOv2PZb9LbnxFp0EV8kj+vBbFFyRhdKEOnAgBZNokclsIOLcUoJUTnc1CRBSSRh0JJzvXTKVXpQOeHdJPhC1eI3jqJ2HG17vrn1TCXSqKwryi2iH4N3tFqodhWnD0secUI5ctTjDtvOMb3AVki2gdZT621Omu8DYWI5fh7Y27/HzrxgsZ3gAXP2iFBNmO0ucPMCNzlH7iT8e3eQ6pSjJwegdnD1iirBol8wrRx0FkpBjKcIGjWKjMwJZ6d3qG1BSonKn+O3NZ1xuO2ctZuQ2rtw1NH4XWo/wpUP0OevI6FE4pLJnZ7YONJijJt48BBMpJh9F1kdYMYLQpjh6g2xHeG1QUaPkeWM9N3nmLMDUp2QJLBzhrr/BmHyDJGnRMAVG9IZRLukuP8anepoNjeYLOYsKpi2idCO0WlGGD3l1LcUumP19C6y14KasTu7h1/u0sXn6KWm7RXx4BF6eZO0+zxvegy8c+0BKglsV2AgvLaGB2PK++Ugss+8p3pLuajZvnaKPS/RpSW6DdooUsxahrKr6coGk+BusSUe7WCujYhLz6MuMVvvcH79mKIzoIR2cv7xcfrHebxi/hXzHwPzn4gCJyD84b1nWG0w0fDajV1O256LtqFveqK1rNpA8BCSZ1oYbh3MWHeeN3YqdueO1XLF3d0K7YR7z0uM0jzYRp53W27vBDrfcmt3TmcKjtYbprf2mBpBNRsuFoHRTPHgqOGgdllslRLiCkwbCa6k61qmCp5uenbmU3pruGMCxmhKA394FOlTYqU71rFlf2SYjsacHy/R2tLHBkIWaZVlQQg9K59DJjsPhUm03rNoAjEpzts8z+wSVAijwqGU0C0TTRdZ957GhytB3qrraTtPP9z/R6UhRZhXjlnteLqBp4tAsiM2LnIQI3aiGTUd7cUZbV1yY1zydNXwtJ5SlQZtBlGxymp6RR4hiUSUUcQIOeoBUnohHkYuzRYFUsrW4NqgUhpW7wWGdXtDxCrFTgyETnMcEjergp1K0aXAxoJJEU0eZZ51L3+BY0cXnB5NMGK4oXvSeE4nDX1asCGi+ortpkM2BzibII3Yu6YJTcPrOx41KognS6bFHPOl91g8HOFax3kqUI9nzMrrUN+HgxVudYCzFeWXxtiLXWz7jJPVGj1y9ItEHXKL2bsxZmXopxeE8YS0MozaFU+wVLOCflRxfR0YSUdp4GHq0U/nbAs4uDcHeka3vk9/fAOJe3gXmKQT/H0H5TVM6tFNhe+FLtzB6CX9coTrI6bZZ91lcWW82GPkA8Vejdp7gpyXBPOcZjlmUj1FRPDvv0lftRT+jCbmi+G4EKKsGbs9XFOzCp51W2DqL7HWJ0zORoSbgVG7Qp2/k79GnxHO4Pg1zXhxGz85QZ8FOL6WeR9ccmUQ6cdaaA8eUZ/cprnzOGvQ0uDgrcAS6dMGo4BrHeksG46mJFf2Eu3OkvJiwrS1hGnDhYx5y1wQJ+fIyV3We8coFQlV5OZGWHc9Pw7HK+ZfMf9xMP+JEBn/D//T/yExeXzShAgecEnRa83+qMAazdZHjIZV51k1iRhaAobpdM5+pSlt5OHJmr3JlPvHSwqricrQJeHWONtPF1rxaNVTKMPh4YwqRY42ntU6MJ8Ynm56tCooDeyNHWWZAyEnJjBKmrPQoT2cJE09neDOzvFEHq97ok+4wSSptIrdMnAwn/O9o3Ni0ogyxBhxyrD2fvDO0dzcG5GizzDFSBeh90KIhiQ9CWFkC1wBu3VBExLr1jOZzwg+sW469iYFJrWI13QSca6mLkBrg48BnyxRaW5PLEpFMJaTxYaoaxrfY43CVyWNBZM03apB+YjZnVyZT72mFQ+0oBpDV3vKcOlhc7kSrq58ay4NnT7oh5A/IXmUdTXXyqbfkhKh6VAxMXaOelRws0j8xFzYovj6sy3/8mcP+K1vrzjtOv7aX/43XmrRZfsf7EunFSk5fDfGm4Dzjt4lJqmiUIp+tETWNV0RaC4KAqdIf4NxVVPtrZkk4eg8UMwNZ08nmPop+Bt04tjfOcIUgjUNJ88PMeUZ44M501ZYLEo2nWZaRhbqHGluo4tAPVeMY95K5NqWvdOSszKiPTSxR93exT15hCeyXhQf4j3JlHJ2n117gAknnGzmH+JdinjFuzGW0KUr3qOUGNVd8V6PFdJZumLBrJ7iW822GTPaNdAIPjWUdYk3T3GLXfrynELtQ9VSbA4J8+fIxQ6xNsxsg1UJXRVc9Ev05jpb3WGNotNzursnmKThO5PM+86M/rVnuAfXucaax69vMN/egTcXuCeHdAdnmffUoHSdP55VyM7g6XKR/VMuee/na4rFBL+z+gjv06MdVExESsY2MT94xO7uERHF40e73PzcCZs/+BTP2zmf+9+//lLzDq+Yf8X8x8P8J6LA+a//5t8V73vmVvHW3LINgd9/vOGwHlHaiCkcr09Lbuw4SC1ejzhuVry33uXNumcREu/2gRL3oW2dq5su2W8lO/K++LmisrD1cqRiek/QGooit+dUjnAPIZDsAPaqoYwJZwNTbUGE2hmOY+TQWvqoaRWs2sCEnmgLEvnG/+mdCW+fbOi3a+q6Zh0UdWnYmzsuvMEIeIHRoFlpg0eVJTEo/HaFLhwzhKfrwLw0dKln0yaujRyNQIiGz1wr6CPUKbAMiUUv7NWW91aWIq24O615vO5o+0SP0AfPZDJB0fFkk3ABPrenafVXwdtXAAAgAElEQVSY05iDOKOC1AY+M7EcVomvbi3S9ySTz1tsA9ZqtC2JsUMPvkFt0igVcaIoJNCKptQwIeEkMXLCJhk2UShU5P3jyGhkmZc5DuOgElRMnLeRnUK4ty2oUuCv/pcvd4Hz5L/4KeG5xcxWHB4+RZYVD4/GjIsDbDijqAviDKbXHoFd4cMcR+D4yZ+iGj+hDpajcYX08f8z767K7qbJg33o8Tf/Kd6tELp0xbt5r0fcwHvc4qPCjC3bVDE2HbbXrAtP20zY1e+yMvsYmZNSYgfDxSrQdiv0PBH9DF1ZJnsNCymYnFX0h2BixAhwlOANRwwK+f6WeMNTtp7VckxVWdi0dHZDVe6QtokQDbs3OsQqbC+0qWPbzZmw5fR8DztasucCy7UjJGiVQZsjCm4Tqy3btoSw5ebhOWH9Omf7DXYJfldj3h+zc3DMjhHuLV+j3z0i6JzN03ceazXGlOhTS9hbMz65TkOD31viRDE92WOxd8Ls/JDCbFFuwbhoac8P6eotRVdxsanQZUNVNWiB2XiDS4GT412uXTvm+eI20mo++7de/gLnFfOvmP84mP9EFDh/5W/+HdkrDXOnOe+3HNYjHiw7xg6ulQVIpCgKVAw5XdoK3z8KvLY3YdN1eKVYR8t50zCpSlZRchYSUKWET4owuAddFjSaBKIJErDicCrRqiFnSquh05C/JpFv2AARReob+o2nGjn6bc+sKgkp62d8Am0txy2MtDC2QmnBKWFaKHqxbJqG6XTK1BmC71hGhbM1vt/iBZoAqCzurSVw/8LjwoZb8zFbVXC67tBa57Xu3lOVWRMkIRsV3tofUQqgNedbj9KR2INxObp+0fQ4W7NOicJYrNN8qopcSMFUArtjw5N1RLmCo3VLMAbfC9cKeK2Gb24TQWAneGal4mFXAAklns/NHe9uLXM6bo2EPzgHI4lAPp8p5fanSZ59m4jKsAm5iLw+q7h3fsbNsmItBZbEGOGsT0xKx6KPlMCv/Fcvd4Fz+u//rFT7LWZZotwCm+acmyXOOCZthbr9LuvtbWb1IzbtLUqzJBzt0F9zFKcOPfV01tEdafRBYK30Fe+1GPoYrnh3rkZZoW9aqqoihITVJvOeNFqnzLsYlB6iOqLCDi6mEUVcXSAPHeZTHnlk0XsdbjVGKsEnKHrFUnlKX2ImgnYXjLxCjyxiFO3JFrc7pehqtG5o63TFuyLhz6agBH8A5dmaxcUYZU6ZjRyS5lxEj+5V5l0HpFrjmikSFIvesnsdNBegNf6kRumIShpRmjC+IPURxTV8U2F0xE5bZmqLlwOoThgXlkU7wXnDsoHuZkt6rpiJ5lq94f7MIWeacUy4as1q8RqQwHRcH5/zbDpl+iyyM17ydgGzFSwmwmSls/V+qjDJU9lzojIQHdGXTMY1m3iODg3CCEtCmpzjY9SYnoYS+Myvfeel5h1eMf+K+Y+H+U9EgfOf/Y9/WxYKTAgYCUzLesgJMTQW6pBY46joKY3NhnMoCp3wSiMxa3OcsdmlVwtWG7z3OYMJQ5SA1QYVAwmhLivaFPBeUCmbBWqXTfZEYjbDC4IohZZsegRgVPZ0UUqhYsLHgI6RbcyuwCYN20LGsonZ7M5Ioo8GQ8/htCKlQGUUFRFvCpoAje8plEN0dqQUibmdmTR1AZugMSmwRSiBqijZeI+TvNXVKQs6h1mGJChJWKXpY0KLpifhfE/SCtG5lSraoFHc3S15vGxRqaMyJR7NTpXYtIllH1mnbBhVDzbr2xRBO65PLMorHrdbnBiUEkoD6z6v4lttWPc9tTHUKJZtnzVH4vDe0/c9gmZUGsbW0AXPXm1pPXjf4Qc+QgioS39LEX79v/vll/qC/+Df+xlpUoUJAVUcUfu9K979pMetLS01li1MCsou855GiTja4k4m+NhR2AKUwV9bUZxPskHjfoM+n9PsXHyI99FsTtNsUEfjK97D4QZbTq54T+0WfbYPhy3yfLCr3z8himDP9rHSsK07iouElwKTAlJEdC8oKWhT+YL3pElmy3TX4jY9amKY6nss9D7B75D6jqKzOcxWl4hEQvCoVJDmCXyPJIPvHbroqaWi7XM3MBUtsSsz73VHbCcoSRgRYnJAIogG2WDEIcoSU7ji/WAWOF9bgl5TFBppHW43EJpISAv6eECSDZXNWyBNrPCl58BWqNWYhQkgHeg1tjsgmkDULca09N0cazrwU4gtxiiCrujwLOslgmavHTMKIOWKQhq6fkxR+CveXe8+xPtnfv2bLzXv8Ir5V8x/PMx/Igqc//hv/KYAJDwFNrdRyM65moBSL8ybo+QA9jBEQlkUaE1MCqMFUYMAasg3SimhtaVTQhnz39PlknICUTlSAMBrsFFhrcVLyi3EFPFKsiuyUjlNPEre6kn5o0aB5O2qSjxaFegkNCnRh0Tl8uPV2qIkZ0gBGDWMvVJupfrkGZsCnwRPoMJQYNlo8DHgROjJxoRaRazKOpjaOLrhedyE/HsLoJNkp2fyqGnr80ctGi9gNEQZAi2T4Jyji4JIPuchDpkoQEwgMeVKG5VFwvIikyQO5k0SA3IZ2qk0Fk2MMZ9ziZjBgygO6+TOqKtiUiMooyi0IYV4peHpQ8RYjY65m/Zr/+2//VJf8O/98i8IgOiESwpGOSNGlCG1mjh7kXfm+oDqaprZ4PqJou5nH+F9W2an0JQS436HZYq4as243/nn8h6TRjuueF+nyEjrj/De+YgoqI1B9DFdUTBZBoyu6AshbQIpaKyLtHOh3JYogW5vCWTe3dmYlKCfRky3pQozfBJSucZ1YwosXVHTc/wh3lNncNoSY2BkyyveW/9izq+TkIxDEVEIXTTZ4ZuEtw1cS3AG3c6a6tkh3G4IJ4puZ0F5MWM7y1uJCrBbg8SEayd0ozWjzTzzbgdhqCR09Ig2V7wr61Dx0vRBiDbfCLWyeP2DeXc92dururx2LUlqSsmGhPClX33npeYdXjH/ivmPh/lPxBbVb/z2t7DKIiZilSUNK2jWCFZptC4yeNJmN14gDCdYVIZUW4tVHkMeQVltiFpTqkRpSyIQxeNlEHqloeAgoHU+Db1ssaogGg0hFx993pDGRE9RFLQKTBR60VgUSQSDgM56kVLnVG6vItqAJDN0fxI9iSoaeoRK5yIqSY6lQGWHYK1tDoobhMetBdeaPB/WYfCCyVBogV5sLloIWGVRkogSshlfEFrsC0dJgWgUMSUsio68HRVTLk5ijLm9SB55qcu4BMkisjhcg0LM7ypIQtJcOUOnIRDz0ugJeLEyLnnerVJ2Tw5XxbpcxTIkBD0Uezq+CGrTKj+WXBz+cBj8UR7vn20xW/kA72bgPWKV4B/mYllGzcD7Cv10sHBXYOQUHyJu0hMiH+E9rJdEIFVjzornxBgxW00Y6Q/xrnRDTA7j7Ud4t1WPGEfq9A/gXaFix9kokFKDtukF773AJtGzzLyfCUoMWkfien3FO1VENdn4S6qAUkuaTuHa5cB7pKwDH+S9Wzu0Th/iPY08dBrKRLNxH+HdFo7YBsIyZN7PHFoviN+NNLqgPClYK40+G+fzMvAeJEO6Od/PGgMdWSTN3G0hFWgNa22uQnYh/VO864H3+AHe0x/Jex0EvxFcTHg9oYoJJTWi4Es/ZB5/FMcr5l8xn//3j5b5T0SB8+D0eXa01XmcciledaYgxHhVfaMiNtmsRh82dYZmD05l7YxKglLkm3HUFCqirKMwDtEdKllUzIGTYbhjhuF7BRVJMhjmDVEEykBMGqNyIeIk4bQhSB7DOAMGoTR5Jlyo7EWjTO4ipZSIkje4ipAI1qLwKGVxxmLpsU5RKIWtodKRQgvOaoqioI6edFAOXjQGiYYA9F2ii56ND6xbxbLbsuyg7wI9kEQTfSIFYSMvohgAwgehzFHjV7ELJgmevHoe8VeivUsxH4C2BrpAGhjPYOf0cStqCGaDGOPVz7lMBA8S0B94LCmlj1iFZ61OuCpwLh+fQV39zJf5+OpKSKK43TtMmQWSj3u4U5Y87CK33MB7M/qBvI87Qfr6Q7z3rmTStfjCY8SiCoWcdi947zLvYvM3CSpfvJfGMB3MvJSBh26X15YBpRJOAjppxPRIUle8TxCU1RQ+YTvBFx0uOFKtSOtEoRUSHNoKJQGlCjqzxdmOUXIUWmEFOrOm0IK2Xea98FTTxcB7QiIsixvETUkXPXYsnEmk7SydjfRdYLM2OW+ogxR6juopkMeqAF2vWNuKe4/0R3j/uek5X13M0S4QvPsBvAtfMUv+dlMDERHL6xPPvY3h5+vNHwvvv3J2wL+z95y/fnb96vH9xWnHr68r/uIPlcYfzfGK+VfMw4+e+U/EiMr+6b8kSqlsAMdVPMUQEJl1MbmDk5+AnG3B1c7+ZRK1UbkLkav1NOzp57ajEQjKUwxOulHp7NEikajsi5upyjEEl5qbS9FxEkGnDIhS+UkstMGpy3cTQmENKglOqyz6QkjRgI5IyuWsVkJhh8ekFJXLYmJrYFQ4LELhNIXJqbWKMIiizdWLse8DXUx0wbDeelZ9TxM0jQ+0PhJQdBEk5fCyXDCkj3Q/Lrsnybzo1kC6ilVI8kJorUi5dTt0UZRS6Cj5POmhm0PWIV0WjFdfmyRriz6wTn75860ook7omB+HScPjktwVy/EOJieKD7+L/52//lKXOf/pF74sSim+G/RHeH8eNP/KXj5Pbzfmj+T9S7OOb60r/vzI85try5fnPTeCGjZG5Ip33yV2hjO1ri2kRGog1eojvD8KL3i/USge+8z77SKz+tAH7haGceeveJexRSWh8vIR3oPLrzPXR6jlivedpqOXgDWga6EQKJwmrrZ/JO+pbKjtHluJbEikC0dIio0bs9YJWQcCipVzSFI8Dj+Y9986rz/C+w3zgvcn4QXvN01irRQTkR/I+zu94QuF5+0+/67VMEr/Z/H+r086vrqGPzmO/M7G8POT/M73eauveL9VwNMOGp1fQ3/1nW+81LzDK+ZfMf/xMP+J6OAoPRjW+axPET2cNH2pyO4G1bvGKCEpkx0fySFd6EvjoQxxiN0Q3ih4YFRViNZYpUgEQkoknw3jklYQ++ymiyIoQZJClEaT32lIGm7WSuWU2+AxJmtJwvB9e9FY77Eq4ZXBhsEwSSWUKJL22QRPK3xKjIq8gk7SJO0hGkgxP+EKFBpnIQRIEtESSKLz16DxIWbxrWYYUUVyuxCS9xgMgYAkSENxlVKf4SPrZ3LRmCDoHDJFBjIpRUpDy3hYwMxp4UOoQoooEqIUKf/Y3H4VTUgvjPiyUWBA0ChRWZ/D8MIZfpYohcQhtSQJaWiVKpUNA/Onc0SGhIi2L/21ntLltm3fJlCJpa4AeKPwPGlL/uF5z0oX3LUtt1TkXe8Yu+HiJJHfP7JAy3dF+HwVWa6Et/0LL4o/t6uZCIyUQgQWJB4u8vMyKjXrVQISt3TisWSOv9NZvlBFIvCgyxfMQ5Nby12KXDeatPVstGamAlE05UXAqkQ7E6qQI0d8GXHJkrSn9BZ0QnUwic0Vu6XVxBjyzUcLomzOK6sapHWMpiOsCFE0q27Nerkh1iUpknlHE1wPUaMU6M5gbMK3wrc2JV8YZ5PMby7sh3i/oT3v9JrbWhgPDYNNgrESvrcSpNBUEmiVZdUItyawBp6shNKGoauQL+wqdZTaUPrITw25aktRzHXiXEAlxY5KV6aXu0O8yrNOsaPg7a1iR0fu9wPv5jJ1EO73ghjFKETaHwPe4RXzr5j/eJj/hBQ4iSgJpTWDpHVYg86CJKXyhg6o7JCoFaSIMYaUsr4F0WgjKJXQMZcmxubRjpeQdTqDA2MiooyDS1O64YlPIoikfGMeBLT5Rit5DqsNMSXUkJBttMImASVUxmBNLsjMUO2iFFZrQoy5eEqBvmsZVSWNN5QiJK0pAHGJLngwGmnBFo6m8ZRVQVEUeB9QKrDe9jhbYAh0fU+KmqbxdKLYNG1uyyYhhZ6g1FAjhGFGmvVJSuXfLcYXo6sU0/DC4OrvaehYMTSLL79bSv7KzE+jEYk5nJOYfYNI2AQppMvhbi5sNEiIRKOu3sJFeKH1UZJfmECMfrg45DpHSZ5hp5ffyJhbLvHtreZUaxjC5b5SR+5WikPXo5Tmi6MW0HxrY/j5PeHpWnhjnLi/NXwawYlwezw8Z8pwF7gzzvPxCxLT5FkNWwmtEm44kzuWUZgOr/qnMcd+HEw0n9f+Q7w3PWymhg15g++6FmYCNggYgykicQd8Shg9vO1SijJAVIkyGFrVYYLgnGGjHEXvSGlFAdSxx27A144UI/XI0WrLaKTZrjfUdY1SAcOYalKwjZ7xKpKi56icgjdwEUAZTpWQVpHHYnmr6Ale8X+dazqvkELzr847fvPCkFKkAE47zUl6wbsmUhjNYUocY6jEg4WzLvNnUfxCkd1Vv99Hdozw+UnkO9vE50eR/+2s5E/VPSrmc7erFSAoDXsq8g2vqAygYBuEfZN5f9s77poM9Ne2avi6fLxeer6d8o3yx+F4xfwr5uFHz7z+5/+XH/6Rb5AJTURSi1a5SNAq/9vlRlT+mAi+GfQt+cadRBCVcrdAJIuRdSTELUKXuwta6GNLxKOtQnRP1CFvNGkZRiCXQZH5idI2z8yUUVhrEEmkEIY2oNCnSK8jvXi2YcO23bLergkpYq1GiLTtliCBLvaE2FOWJX3whL7nfLWkl57Ot/R9Dvu01hIlETUU4xqNIkZPlJDF0TFydnFO03U4Z2l8Q1FatIaqdBRFQYFCW5NXtVEYnTs/WuV3KYT8orGo/I5iMPTL46DBLEsNo8GhRaNjvHouLoXcGoVWCWM1oiVvOhmFsprgFJqsm9FG5fMpYO2gPbL5j5aEUoLWQ2dH53FYbqFGYswXoRQ8lwX/y34cR2G/CPzSxLNjOn6ugNcrOCgTbYiMXObwpBPGReJXnwptiGw7qG3mvVeKbTeIvwnMXOBRiOxIRyEBtHDaJYwWDnWiN4HWJHoNaGFlFI2/PKHCtFTcLIRJqZhWmk+PhVUfOV4GxkW+eB0rYWkivQr0IbG2DXJqsMus2BIiYR2QJhI2HrPKHMaNEJPQNVsaU9MpS3A166kmuUiURG+F2jg0inpcXfHeO4U/WVCuAt24pCsqquRRyWOdRleJg5jDZm9a4bZO3LLCL+5EKpeolOEb5xC94raGFCwHg35ORLilhCPJI4kTZfmFoue6jvxr055DFThUgT89aq94f6sMHBSJ4144KBJnXvHZmecf9CUjJ+wZ2HcRpWFXC9dr+BMVfLrOf46T5W3vaHS+oNcIisSuVpSh5Xn0iO74va3isz8OivrheMX8K+Y/DuY/ER0cGcYYAMblrksIPYY8w4yiEIQ4bBYRAqoocxFEHuEY44hxWEOzuU3nfZ8V3JLXtZ1zqBQg5J+mtCImTwiDrbYxKKUJsc8jG8ndIWdrfMhPuC0skAsN5xwg2KEKdabIJlKpp6xKYh9Yb9bMqxmmdBDy5sBsNMZ7T1mMcqFGJKJo2pZt7BiVBcvlgul0mnUpkl80XddhrGXvYJ+nxydUpaWuS47XHU3T0YmgTYGpHKr3GCBIJMV0JQITArawFCqPm4i5k2N1nrOmlLCAl5hXFTVANk68LADtUK2nlLtuYYhe0EmIKWGGAkldrl2mRFDZmyiEkLeoLruTKo+xMgdp0F1drt/n5yPP2RTJ5A7Qy360PvC14Pi5KvEXZpp3tsL7a9h6uG7heKWxVeCoyUXfVBKHNnFYaQ6S8DAI18aKTQtTpVFFPm/rTjjpcqft20F4fQpRR5qomQIVgbWCez2cbiM/MwfQ3OsiG68YO+GsCXxxbnnoE1uv+PQsvwa+22h+dhzRCM1Bfmd37agmzXtSZ1BThe7govKUu5E61RAyd+ZAYOEwo9ztSxKISlEvAlihBuLG014bUSj9Id4L0bibe2zO1igllGnLGXuwjfiyRzUGGSVcbzAK3o+KZpt4FOC6FsbO89aO4pdMx9Nk+Gzb8dDDTxSZOR8SbwL/96bkXxq1PEUzKYRTr3kScmv+rSICwknQXHPC39uUkIQ/M+44DppbJnK7bjkocjfgaaP57U3Bf3jY8l6jqBHea/I14vUycq/V3O8colpUKkF1iO54s9Z0IfEZhOOo+J4VnreGH4fjFfOvmP84mP9EdHCUUjg7qK5DzCOLmGe1cegcxBiR6NGiMYXBIvR9S+ibvOrXtQTfITGPgUII2f1YKSpbUxfZWAljcydDDYJhhMJZysKRUhy6F/nfyrLM63YSqKoiPw4fhrwlwXtPksiq2WIkIdqzabb0oeP84gLvPVVVsmm2PH/6jO12y6bZcnp+SiKP5QqnUMagTC6sjDGMphOqqiClgNWQgmcyGeG7lqK0rC/OGJUFo6piPKmYT8YoLRS2JITAtluRUk/XbEiSO07KDMUGQuh72r6na/N5Yhi59X2fz3PfQ+8J3pNCIIVADB46n0dPPq+0p5Tn1S4IheRCRktCp0jqu6GjFolE3FC0xugJISA2+/DA0O4VGXQ/MnSfIErusIkCZfN6f1IvfxsnRMW/Oc1ds5NeuOYi34vCuk98dZs7lUeN4dte2C/gy5Vw3Sj+8Qn86kl+B/v2iebvrICoePtEs27gJ4aL/pv/L3tvHqvZed/3fZ71LO9yt9mHy5CUSIkSJVIUqSWypciyIxveYtd2LcNImwJtWqdG4sRAiyatUzQOkrhtYATN0qBO5ThuDSUOnCb1FslabJJSJWohJYrkcJv9ru96lmftH+dq1P5VoDFATjAHGMzgYnAH9+DznnnO7/ddCslDI/AqMQmJURDUQdzk/SET+NBG5BUPDd/+LLyjyLx1C15uBe8shzfra0tBleFtVeCp9bC6feIFw3SpSTqxXg2H2f56YK1bRlZh9i2zKz2+dzjnCDccVMNb67SOWDs4DqMGGSJup6I7UZK0uMl7lCs6EbCFRuzPiFZhS0m/o6n0eliD9vr4sx+okuf5Rca5QQR/3sADVeb5xvLKPPMHc/iVy5pPzoaXkpwzV5rEnhesY6JLga80mb02Db9cRubAB8uW/V5y4OVN3r+j6PmxrY79INmSkZzh2VbxSg/XO/i6S/zIpOVTc81n14knWsXLAerjuIh7ywA5s+slQmTuKRQyWz7VSwqfuezg/VXg+V4x/3eAd7jN/G3mXx/m3xAuquJDP5VTGiY5tqjofIc1I5JKJB8HTQvgw3EGjPAYXQ43nwxJDiuq4/OaUBIjhl4nmQZFvlASYxTRHyvNj9OJrdXDykYajFGkOPxbzjmiyMiQyEahpblpiZMi4WJE+EBRFIgYEHKok3BuWEMVKLLJJJ8xSqDzYEvfqMe03RqOvUbWaiZVTQqBurRIMpXRxL6nHlmWyyVaa4rCkKRChgS6IISILQr25w0uwaINdCHQ50xIklXXQZRkpYnRH09w1OBCigEph5WbIKGkGVZ9KQ076xTQapiCfUt4LY+zcVJKKDF8WNCK7ANKyCGTJwaUyAhljg+rHoQip0CMgyvqW2NS0vH3kGLI/zl2TXyrxZbj3fi3AgFlBqEVxET4wsdv6dn933vv3fmlpUKnxCMbBZ856nl4UrEnI0frzJs3h7/3z65nklbcLx3vHFtebDOQiUEgVcYfe+a90jxcJ77cSMoUeaASGCMppx63sAPvUtBpz040+KJH9fL/xfs8Zna9wLnEuYmgF4oyZRoyEwkvOlBt5r6poEwCb3pKY+i8Z1KAaQbeMRnVf5t3sw0dPfVKIQAlMxtyODhXAiSZfssyOWiJG5rg3BC0Wcpv854kQUqskIS9Hm8sB2GKM55l6bH7BZdyhijRUvJykxFpiBQQGf7N3HDOBkZm4P1OrXiugVoP3WtfcpIfGCd8ipwuBdf7gfcrKJ5fGT4ydTzdZnYqwX6TeLSS9DHzTK9QIvNwlQk5UWXJKmdGwBdcgGx4d5n5Yi9B9BQ9dMayFxPv04HLDi4dTznJmfeawGfWBTDw/oFxz1O94qlLt35Vw23mbzP/ejD/xlhRxcGtExO0bTu0saYOrzIGhZDFsdC0JSuNEUMPU11N6bqOZIciNZMSQfaQI8716GpE6Htsddx4GhNKCVbrOWW5ScYTukggI2VEIum7iDagdEEKGZQYXFL96maOQASsNHTGggiouiSrTDV4gyjFUJOwbByIwGR6gtV8gY+Zvm9JWnLHeIuIpywHzYxTw6FKWCgBa4fDTRKJV69c5q67LoDo2NnZ4ehwiXeJdXCs1x27s4atkzv0ncNWE/q2Q+VMF1qiHyz0pIRQChkzRmRwS5KtiEkihB90TORB96QFMQVUSmRboGI4DgJMiJSIvcOUQ/FdjmEQZSMwIuGdQ0o/ONqMxaeOQiiSGt60QnDDJzAcW8GVRB0ftqQSBJ8H55YcEo2FGNxtKDlY/d8AB/J/2+vlhWYvCJ5sDL+5hJ+aaF5Y9zzt4IOlIPSWDLytdHRJ8fioJGt46AFHvF4RVeDSUjJRgmu9RxP48txxfrPkG0eJd04AIsEL7LjnD68E3r095qDJqBz5ypHhoRFMKsc3jwz3VKCsQLhMaQbev7yfbvIO8Ohm5Gml0UWHiYb2VGS0a8BoRr0ny4zzkmA7bDUhrhtStrTXBbIw1NIhK4tJHab3eCvwmSG3hECzXaLahiQSq31HPlMwxuNlg5cTRoeOIKGl4nAmkOd7wh6UZcX8dM/pSwVfagOfbg1dihz0ghMlfHfp+Z6JJ/SOUht+r6k4ZTx7GLKXTLNnFSVfbB2PF4J5lJwrhuBL1QWekfAbR56f2jDkmGm9xxQGJQTvKTz/YibZC5H3mkBZlFzzkXePBSeC5IKJ/MYMEJEiGciZLgTebiJPRs1by0zuM2/Vic93kgtbinkOPDoawtpU1txX3vorWbjN/G3mXx/m3xATHPWhn87fOmkJIchS4LoOUSqMkfQAACAASURBVNgh1lsJkg+EmDCFIcXjzBVVknHD6E4W6GzwohumDDkihaG2BV0YhKrGDJOKEHvwmcCQZmytHVZacrB7p5TQhcWHRFkUtO0Sa8tvB+WFYXWkbY2WieQ6NAalQBhFJcFIw7xbobXmaLHEGoMkoyX4dUu1tYFJQz1DqTWjccWkrIdDAgnXNGQ8o3KMspLlYk1ZKEbjTVJKTIqKo1XDeuU5XK+wynLYOPoYqIsRSx/xMULKtG3PaDxGa41zjnS8JjLllJA8yfVIYwcxrx/uQ+96sAUyRXLwZKNQqKGrS0m8cxhrIQW01nStQ+lj7U3+1j0SRKWIXT90xBy7okL0iON8oyy4ue6SSiCQCAkhRfJxtUTyAZS8WTDqn/jHt/Qb7c8/+Ka8dRyUdWoy8P5Lr0l+YGMoE72wIXh+FvmtRcHPnIlcbSJf7gTvm1oa73naZX50w1BqzVeXPYLMnWViC8t2BQcu42NmvBnxQdD5xN5R5kAIRFS884ygWwo2S9jrBbsBLkzhYi94Z5H5o/3EO05IRnH4VB4Zz1hHwiZomTAHmlJklILsFWx3VLOamAKRzH6MWCkoi0jZClzjKKclgoRIAqkTi3OKN11vMCKRRpa4hoynrCTBZuJcYsqeblIPLc3ZEGYdjRccUaKyYN0JPAolFW1KLJCQMv/9VcV/fS6ipeJqStRJcnGVuHOqeKnNfGUZedfYkHPmS6vIn9qQ/P0bmRO1YZo69pOgl4rvryKVGv78a3uSnzqZsNlz91jy69cSj44FhciMteKwTUwKQZsSv74v+Ylpvsn7xT7wfFB8bzWsEp5dBV4TireaTOMzbxtJLrrEM2vNw3Xky8vhQP9wPaRx/a8Xb/0Jzm3mbzP/ejD/hpjgqDyk5w4OnkyOgsIWBNcjtCYljbZDx0dwPUImRPAYZXCxp1QWkeNgK08gjCFFjalqXIyUdUXuPVEKTJYYXaKqYeXUBc/gWU60QVLIMCRkditEHKY7Vgpy7MjZIDJYEl3IxP6QnjisqaxktlyjtUZNSvYXM05sbLFYLNisayqjaYMju8AyOVTXsLG1jUiWMicqYwl9Qxcck/GUw/WM2pZMJoq2XXNyOmZcFkhV0HUNruspraJVPXWhydKwVRbMVz3rrqGuK+argMgBlSMuO/xqTRYSHz1KSFy/QAmBIRJ8PF5JDYnEUmZk6hB5OHA4n8gy4joPZogG932HkAqfVogssWZMDD0+HAu2c6LoI13OpOjQCNyxzimFSPIZ1JBenZFD91Q+7iJLGWJAosl6cGSlHEnh1veJb4rEZ1rBQ2VmM3leXmh+/o7MxVngrong5YXm7WPLm6vM5+aZDZl42AQetJqnesdPnNBMUkLpjveMJX2VSI1hXCVar6k3gdATpaBUmXFXsX1+0LMtVkMMQyE0z6wyby8zlsyz+5G9APedKHjnJOO7RDtyiAwjEsu5ZrQeyl63UdgqcWkd2NKZ0aFiHtbY05J0yXCmFBgriV4STGaZIyrB9lgSfGbLS87sdwSV6IOmWDtmXjJB4jYiqu3ImyUVllFSrIm0ROK2Qe476ui5eqYmSEn1GoSYsFaAy5QIHtael4Iih8QyZJ5qI+8t4PIqcNLAn5xkln4wLDxeJhzweB05pxylUbgQ+OdrySWZ+Opa0qnICRN4bRH5RlKUh5F7deYtVcG13vP0LHJvIRE5cZLM+6rEaz7xSJX4xExxr830XeJftJlzo8jbbObiWrFQgQ2leMVFnm00D2nHKSEQWvORkef315rvqv3/B023xnWb+dvMvx7MvyEmOMWHfioPFmiHFBJaRzICKUp0aYnRY0yBc+74rV+ijMIi6WM8Dt4TeB8RWiBTpipHhOMwona5YmO8iRiUzHTNmul0g5wTITi2N0+wd3RIjPm4B2vodjK6ICZP0znoe4QMqGJEcBFjBEVREGOmtgZlNCYPmpx1s6S0Bb13rHuHDIG6rlmvl4zHY9AGkTybo8ngGOvXGGMojCY6T9ss2dgcMZvNADh/8jSua+hcoLIFu8sZm9MdlKnpU6DvIqvgyUFQjSZc3d9HKIV3Q6rzar1CGoOUhtj35OOU5HzsEkMaSEPFRBRDto2QQ90DPkIOIAXquHcq9z1CFyTnyIUmOX8scHaDUFsZlFLHmqXhsJJCQFuLUJocv31IGUTkw4EohhbSADtIcnKDSDBIpM6QFD525C9+4pZ+o/177707vzhTfDbDh4Xn6tryqk28QyrOFpmDkHnXtuLzh5FTClZCcl+t2EKxTpmvtT0PTOFrR8Pb0VsmnjNiyrpuAfjFFxR/43xBmESQkrQI1JVEVInOQ70hWc6GPCjVGtYKrHXUXhOD5FNH0ATHloy8ZVLwiTn82DQx3c7EmJlogfAD79JoRPBIK8ge9kSi7hxjW7JqGiajih6Q0tGeLTg3y8SmoxQGzBrpDE3IjOo1y/WwSt7Z8uS2x8oKnxwHXcWkcihZ0otEG2paIHWSvCNYXjGEUSB3klxEPv5qJhrDu0t4dR25IRTvrweh6lO94ChqPlp77hkpXkqJSRScKBPPNoYyDA3NUsFpndgNcGkdOF8YXusiV5VCO89jteEVF3mxhwsW7i0kL/WJsYIuwdMrwUcnkdIaOvftB/b1JHjJSd5UwrXgIQlKJdgQgt2UKEXmTUimJrGKms+tMn947flbmne4zfxt5l8f5t8QB5zqAz+Zsx7EpzElYuhg7RCTKVlIdOgRpYEksEWF9z0hJPANSYyReQWmHoL/YosVFSk3yGI6OJ2iZzQe06xapAJb1OScKIqC3geUEPQkDBKf5WBPl7D2PblrMbZCFSUydISUMRny8crEWoN3HUkqTm1vsbe3hyRSGIN3LSkO4q9yVA8uIu8xRrMxmWKyIMpAcI7T29u8evkSm1sbjK3l1M42h4eHCALT6SaVUtw4OgApmO0fUNSbdCExm80wZowe1axj4GC+oipHNF1HVpLYO8q6RoZEHyJKC3IcGnJLUdBGj9GS3g2plYMtPaGUIvjhd/jWQeTbtRmhb4af3SdE7G+KgYda2gqlFEoN90bIQIoRFb/llBKkrMEoRI5oW91ceXnnBgedVEiZUXJwr/H/2I33T/76Lf3A/9W33ZGbkSGJyItzzWdTws8175omHJKzynFhc7DGny5Kdr3juZniKCa+0o14Z7lmS0lOKRjjqUuDCA3TaoMvLD0qRh7brPnsUc/9G55pVVBGoNS0IWJV4uWV4Y460RzzPtKRz15T/P4i87GdzN1TyTh5ms5QywQ20WsojYZFph1FTtqC9XqNJGJSRUorXFKIJKg3LamPxK7DlAV1JdHBk4wkC8UOa3ZnUG1YNnKCk5K8v0IQiJs1Y59p1+0QXbACM7Z0IXEw11DVCC3wUfL8uueMmPLEqkMg+a254r88DyZmPrvKbOuEP46wf29V8PuryAc2M7++mzmlBS8nzaO2p5CSa05w6tihuhshIjkjM1uV5Mm543u34Ldnivu150oz2GnnMjLLmvuLzP0jxROLwHkTeHKteUinwfUXFEcIvMkUWXCHhYsuc58VXHSZRSuZlpn7dOCMNixToo+Ck8dJvn/luYu3NO9wm/nbzL8+zL8hbOJdiEOpZoZkDNbW1CdPIEjQzwl4Us/wH6D3uGaNMRZTb4LoSaocVOjeo1RBkFBUE0JwFIVBG0PwHlsa5PGkQOZE33aQIqv5AaHt8cGRfUc7P6DpWlRmsC+rTPYdTTN8LYmIdy0xOuarOX3b4NsV3rcolVFGD222UlMUBaPNKVVVMJ1OqaqKvu85ODqkF4HJZIOtnR1W3jOZTPAusHu4y8XLr7LoG+rpBoeLJa/s7+OVZDLeYuvMnRSjMVtbW5y54zzjUUXnA9tlxYa2KDkE6uEC0+mUvm1JEtRxBLaQBTlD1BlZKJyUaCMxVlFIjdA1iJJiNB3cUyGgdYk2Q/aN71ZIKXEpDjZuZbDFGF1WYC3klugWuPXR0KLRBoSPZFNAMUHVm9hRfZxxo/BtCykTYxjSkoVAKTBZIKMndh3ZBVLoib57XVn947h+cTbi+cVxwZ2W/Idbib/0QOKkipxIPU8FydN7lmll+eZR4n/bVTy2pfieE4Y+Oz7fFpzWgl+dG+xYsNvBiWnJfm5577bg7k1Y0/PoabDKIJY9MgfyMlOFzP/4WmZv5Wld5pWDyN9+PvLFq1DlhMiZRciEJvFfvaJRItFIz67rSCny6cs9l/2K5bqlnSzwhUCMBW3pUaZgVFk2pwVWJqpaUmxU9MuGxazFaclYeia6ozUVZSmIPey3mcX1SC8L+s2KPOtZLsArSZiOMFs1PgmMtpzYKhkT8dJQ6cQ9ukBttrxzpNntM//tWwJfnDmShG092E13ym/zPlbwxYXkfVXmwzvwvRuZsa3YKQs+eMqwXUqe6TMPjC33G888Cz499xRC8OwKtok8HxRvGQtCkXg+aKQOvOg9XzuIFCIzcYkHgbkSLKThXC14e50oyWymzGstLDuFTHD62C14v85sKkWXE9dWkjoFVinwYvf6v4D+cVy3mb/N/OvB/BtigqM+8BM5tT2iLDG6JB6n+U7MsBIJsiT4JbnvIfQkYZFGkrzHlCW+DyhpkFqhpUIZTWVqAj1N05DalumJTWa7+9h6MkwjpKAY16hjZ1PvHUoWg6i16YmlRpsSmSI6ZeS4Yrno2Nme0swWBFtQiEi7bkg5MK4LqqJAKcVsPWc8HjM1Bc55qqrk8PAQiWA0GSP0MAnBBXzXD7KTFNFaYYEYHJvbG1y7do03velepFas52tObm0jokcVBeu2o2sDB/Mlq5Aoypr92QxjhiycwJBk7H1PYERZliy7BTJ4XAAVI1FEctsjzCA+M1WBb9coIUGb476QIZwwxziEHx4nHOecMUIRRSKFQYEf+54cOmQxHsRmUmDCkEBqrKVv1xilh14p71E4vItkEYc+hng8/pWWmIYG3+Fn8GDUYIkUGv/0rb2i+i/eeiE/20hOWbh/YrjcG0JOfGSawTgOtWW9clxfDhHsn5GGDyjH56LiP9gJ/PLlio+OIic13DNV5CKzYRRtysybwAtHgcfvMPzWK5EPbEturAXbY8/2pqYg0+wF/q91RmvLlhDcaIcH4TsmltpEbBLkzczff9HyF+8PrA4Ty1IyFpEvXEv8zlrxixc6RseryOvtjBP1hNIIotCY3ONnkU4bqjoPa2MiIkpSljd5L1LPSHSs2prJpGE+t5w8D40Be+QpRiXJZGxWLHJg1EiOVpI5Ams0i1ZihaDrJWEaCF6jvGORavSZwOqSplaej+8r7iJSy8jLrUTpRI/kA1Xmn88F76s8WipWETYtuAi/uzI3e9O+f+rY85LzKuNV5tVecLfJ7LbwbAd3jsQQxS8E96vMF4LkfVPJJ2eBP1HAs0FQhczDxvHpXnE5SkIUiBT5SNlxXZU82xrephOPFJ5PO8Fh1qy94h0m8L9cfumW5h1uM3+b+deH+TfEBCeFgB5VWCVhPSc2+5RR0DQdvo/E1RE2eAgeaSt0Elipqcqa2PSU5bcmOMcamOWKw8UBTd9gTIkoSxazNWU1ggKsBkRCu54QI753mGJE8g2GDluAEYHYLSisIsgEfY+1iWY1J4kA/RJBYjSuKIoCYwyL2RH5uF21wDBft+iyYLVYQsr4GGnblkIMWQcie46aOW69pLaWxXwORlHUI5QyFJMJ0SeeeeY5nHOsj+asnGd3/4CQFct1y+Z0ymKxoG0ayrJk1XmabjiwoSQ6G/r2gNXsAOXWONdTqEwIHWSHKiy6tNjSYpRC12PKYgQhIqXCkofOLq0HW3cIxK4nuRafIzkFUgzknIZiUW0HbY73xBDoRCTngBCeuiiQRg2VHEqRzAhT1ZhqA1FOwU7BjFF2hFaKot5ClyNkPaHQBXVV8+9CX8Mng+KBWvDYVqBcdHzer7i3hH956PnmAbx8reV07rgaFRj4sEq8fST5C+PIU3uWn78z8tap4ONziy4En7wU+cSrkXkfqArFhU3B169m/lQdKceJN41XIBJ1E0iN5LKDB09U5OS4r25532bHmyc9L63W1FmzkAG7DnzsTMd87VnmQGwbylbznVsFf/nOiBpLLh01N3k3SbAkIESiC4OzI/kel8CmiAwJ7Tv2Dxvy0ZJtsaJdRBo5ZmQEShlEpdFeMH/Rk50mNR30iW7RErKiWQnGRSQdRjyGSksWITJv1ixbqLNAdhXfOGz42rMZHdd8aj/yZ3YCl2PEqMAZk3isEnxHmbh/rPmeTfjOrZIUBVsGHpaedci8px761y5YxzMtPNNmrkSBTomvNgqf4JkgOV9Joh8e5k82il9daXoym9rx03Xg1CjzHcYxU/BELnj/OPMDU7irzhTSsLIlby41H5k6Hj+hOFEAVvODE89f3Go5Vf+7YRO/zfxt5l8P5t8QExzznh/OQiiKwhCDoO1X2NEIt14MbhptqMqSFCK964epTVaUWUGp8P2SUtf40JOkoaxGNxOQQxj0HzvbJ1l7x6bVrPoekzNJGChqlBakfk2MhoyjzWmo8ZYGq6Dthq6TnPMwXZBgkCQlIPRDNk/O3HfPHexevYaUiq5rcc5jJDQ+UlaG+d4Ro81NjNWs12tObG2SfOBw/zp3nD9DqRUOaJcLkJq6rhlbS5AwrkvmszXkSFGVJKkYjbd59colxnXFjWu7aFMxGo3Z3d/D6ILetWRjhzTi3kM5JpGoygl9Chg0Rkm6fo2xw6TGhzxMa1LGWIXIJT60CJmOW9yHhGliQqRI1hptiqHGIiV6t0aZahBPd2ukMgwSmoAUEaRFkxFSIqXFuQ6hFcE5RIwIBUkqRAB9LBIfEozl0O+gJPlrv31Lv9H+1fvvzE4KHraRGAT/cGn5yXOO39w1LD1Ym/lz2555I/mnS82PTSN/d1HxZyaR05uZ52aORzQcBnglGR45IzGdYikD3zgCnQMfuNty5AVnU2LRGcykQbYVszMBpQV6T6CbAjdquLg0zJvEvZWmHnu+cj3z1mqIViit5SBKzkpBthG/7gkTg3aw8VCNfblBSoWYtSx7zxjJqylzaiL4318V/OjJxLjQXFwG7jtfEVt47saax+8YYYTDAaFpQWpMYThR9yxyRa0aokt0vqZUgSQVRsLVZClxLPYipTLkseLabqQqJftrR1dJrrSSG73CSMVnO81fu1PyxXniZKW5p4B/vef4kzuJNmX+6EihU+QaiveXmZHVfGYReIsOrFH8zsogRGKjTDyQEl9wmu/fiGwCdwvP7/aKNxtJmxJ/uBJcKASzILji4LuKhuuy5F0qUKpIRvNELzltI5+cG3IWfKRY82Sq2JGZR+Wwfn3OSZ6JJbMU2BKaL+3e+hOc28zfZv71YP4NYRPPSJQWOKFIMmJHEyojyfWE7DuyKgnVGL+eMykLvB2jAKUUPgWMqFi3S8ajMaumOQ4lyszXM0xZI/rEfH6dEAWHRUWWApnT0G/llhy1jlE9ou+PkOPpMKloWoQKuMKAkdBrcu7QOiPz0BZLyqxDYmoFI5OYzxdUkwn7u3tkK7nr/FmkMBw1M9ZNR7WzRdesGE9PszPdROmhSPPO028DILVLzkx3eLlfkVKiLjVp1XAU1ly6ASNbMb92hWldcUiAesQjd7+Z+XKFkIl7zp7khZcvs7m5Se89kRLnHOiKXFjy+hCkofUelCFpWDUOsiL0AWKkrEooarJbEzBI1oAkBo+QQ/pyjgqI5DQkUkbXkx14GyELUgxYmSErklKABGlQ0eOzwhuFjBAbR1VbupCRKVGOR8QoCK5nbAWLPOTuqBhQwuCNRyb3+oH6x3R1Ai4omNWaizPFT54LnDOC79v2HKwhCM0rasI/XgX+5tacVbnNXx0BFbRB8g6R+QcrxX96suMT+5oPBkfSmSd2BdrAygmu7a642GrctkUXPdvLTFA96lLD/3A45md34OuzOdNUQ87spJ6Ly0BaazZGmZA1bW4pC8WWi6SRonSRzzeG927BdhngUoe0moP9jmADd26PKLRGpEw67Pi+OzpemRsePF3y4EZGCk8aCT58XwZWjDMQEtcNpKw5UXvoe9adYr+TqGrE/MaK81XkVSlYioI/cbpl7QxKeE5sO169kjl1dopcenotubLW7CWLFpGn1xlJ4NeuC150mv+kmPN3rxesesVLIbPbS/7ClmM8sSwXPS9mw5gVUPK7a42pMlu1ZzMK2pQ5isOb+9pH/nBRECaZKgq+0Cp+ZupZx5LP93DBZtZScdZYPrs26ApGqeCwlXznRs8ftoa7beDHtiJXnOXCUvKjkwWfaEacsZFHdeYOlfijteH95tbXnMFt5m8z//owr37hF37hj+2b/f+9/uY/+cQv9L1DuiHZNwVPbTVCjxhVNb1L6H5wNKntU7jgMYUdEltkJPUBLQzOd8gMPstBr7NYM6nGpLJgVBckXZP6npyOnTpIMIYsBDEEpnVNDv64E0qTM4zKGp0MMWeq0QiXBaUIhADO9WxtbxObloWaIEPH1atXB7u4khx2a2Z7NzBlyaQeE32Db3qW3Zzl/JBswbUNCViuVixdx2wxp+06ck5477m2t8v73vMe3HpNmz1nT+1gy4oTJ0+x/41XyIBPktW8wxeG06dO40Ogi2CPS0dTCIyqAodA1xMmkzExJsp6TJEzsigZGQN5KCDtfAQhiaHHmtHgbJIaVVYIbcluRSENEYnUkhwjQlmMHAIYZY4QOlLqsTITo0Mnh1SCmBIkKItyKEqVEuszQSeiD4TgEAiCiogohmLOrgOl0RlSDvw3f+6n/9rrS+y/3XX48V/6hU+tFKfaxP1bHc1Kc3KkqfOEu2vDy13ibO8QZO6+s+Yrh3BqG1KvMYVnscy8XcHFTiGywDkFTUI3PY9sGjammrMTy0hXXFlELq08J1QiiwJjFW+ygatLyTvOKOoQeH4ZuWMsOPSCD+4YJgXc6DV37ShWK8t50bN0imUbeOgOjZ459ouKMgh++8WO80XDCW255hpmsxZbWWyhqJPg2irwwpGn61qK0mBcpJMS19fMnGDlDaHvEQQaX3B5kbnvvgIcIOGukxEnS3a2Sz77zchdlaK3BfP9CKMxO6cqPIpVyoyqgm2d2AieR04UPBky3z2GH9wqWIfAQyctj/ue957UvLfKvBOPkIK/tWs5ayKfDoL31JavNbAXDB8dJy4owUtt5mOl5+tJ8qE68Jm14VyZ+Wjp+WqSvMNEyhTYFJ4fnzb865Xhz45WaCn4ZlDsecWHdkDKTC8EY2AtEhed4gUHhYJCZW50BffLxK/ONbWSvN04XvPwIz/3l25p3uE287eZf32Yf0OsqIrv+Mns2jVqa8IIi4yZ1WxOlIGyLshJIVFEmbBKg0hYU9N1HSJ1uK7DZ0X2DmmP26m1vfn9R8aQgyMpg5aSLkSKqkKHwNp7tJbEGPFtQ1WNECmRyxEhDcFIynuEMkynU1azQ1YxY+TgLtra2qAwJUdHR5zc2GCpE93RitIkqvEmsxs3uPuOO5mv1igjUb5jY7rJfD5nY3NKJLO3u4+pC0TbcPrsGQ7mM+676/ywEouRly9dYjmbkVLm3je/ictXrnHi3B20iyEp2VQTmq7DtY5clexdfg1tC2Lf4KNCFCO01ghtaGd76NGUHDKxnWOmO4T1Elso+mCwRgw5PdIgU8D1LdIWhOQpVUHoenwOSF2ijCH4brBvR4gM6dFlWdC2LTL7QbSmSoySxNCRUiBlBSQIEaUG0XJkSKu2IhHRZBkQSYMIZO+HOg0kQijCl//VLT2y/+/edl/+vXXkT59R3JUMWvc8eSNyKcP3bXtmrWFKpisEG4Ul5hU1W7TZY7oFB17xmbbguV7yHVXga1HQq2/fkv9s6pAusFCSbZF4wlke2QiMOvhcr3m4Dsw7wa/MLT+7GSlywFvD0x3cMUrsdAJdZE6MSw7nDb+0Lvlp7fm8V/z75xWlkLy453jzuYJ9EQgHPSdyRG2OuHaj4Z6zmnXSKCOx68CGgbkTbBSJrpCsD1rCqKJoV+xMDUfOcPpUf5P3/d2MX/U0uuTuHcXekWOyWbFuFcYISmWZ4YlJIaXhlWszKi2IUfDqCo6KgnsU6JHmf76S+MgksYiCJ5aCH96WPN1Efnjs+QeHI/70VkeHRSG4U/Q81wsKo5kReERmnvWKJ3rBQ0ZRmcwqBDYErKPmKeBchh8/KfnVvcy7pONS0nQIvmsjcbGJXA+SWdRsiCHq4BEVqHXiYlZcbywfGjUsvCGbgPCakfXcaCK/6UY8qDytUnzi1Vdvad7hNvO3mX99mH9jHHAe/75srKWux7R9IBtDPz8kZshJQmhBSWxVIWVJyo6UIHgPrgNdAQGtFMoMCcWSzNbGDjcWh0OfVRqU7TpJzKRm5RxGKFK3IhdDhg5REVyHkok+C2RwVNXoZtWDUgqrLMoqmqYhlprKeYTO5CAoj7/OqMD6jMuDy2psLePphFFRMl/NCaXFklntHnDnXXcTciQ5T589vXeUIXHHPXdydX+XQgnm+3NObG0yHY1pVmvmbct7HnqY5155hZwzB23Pdlly8fIVNnRJXxY0i0N0NaHr3E2tkEolpjJ0IWKFQmnwOSOlwPkE6zUUNbrQg5BOKgoh8CER0nEjeWhRZotKe5q+Ge6HEkMnmCwwStI2c6TWSFVBHDQ5WkgCAu97jJBEIZFh6AZLSuDaBnSBaVtcaBFlPRR5GoMSCa0qXB7Co/wX/+Ut/cD/R28/lSdGsDGpOZo50pZl90bHNa/4ZFOwKSMLkfmzGz0Zi1Y9L3jDkyuDTZlZUhiV+PFxT63gYmd4a9VxfnvKX7/q+aBK7GQoTWBTQrWp+cyB4F1FIvjIeiQZCY3Omisrz5TM35kXPF56PlpHvuYEGzpT2sw5W2Ft4OWlI9uCc7Eb6lRy5oSCgybip4GyK3HS87lDxYfryGi7ptKe/JMI8gAAIABJREFU2CXWpUFlWOz3XDip6BVoX7IWniZ7tpqenTOSg3WFpiMuIkVdsGkjWbQcrGruvrNgMVuSc+Zyt8H5YsFXrxWcEIGVKXlt6ThZGL66TDytKt4SW67mgh8aB36nUzymYMdGvhkE53Xk15uC6wvBxEg+vNmzowSzqHi78lzNmk91hnPHtSnnpOWRDc8zi8hbVKCoNZcX8FK2PGY6/vqR4bE6sSUNF1RDISSbIjHD8FSveJ/2PJsVE+CCSASZ+P1WEYXhgyz4tXnB3RPBq43injrxLtmzoUqMHtax//FzV29p3uE287eZf32Yf0MccLY/9LEMkSCH3JMgJK5pUdJSFobFYpeMRqBIWjGuR0NvUhpKGUupWfbtkHbctRSjMSmEoTagiyAFG+OCebNCx6FVOzEcWNx6BaYCIZAiDBkxEYrJiOx7YkzIssJGR0qJddtjZSLqkpEds1hdQ3hBNgqhC4qi4O47zzE7OkJaw/XLl8jSUEjYOXue0HR0XcPZs6dZz2eYwuJcz2Q8xvuenc0d9o6ucXqyg1uuue5nVEKzOZly9bVLXHjgzRwezHjplZep6zFlMWYtEtvjKW3bsl71CKvZPnkGlwR7r72M2DwJPlJJz8pHpExYWdKtFmBKyvEI1yyx1tK3HbnrEGWNSkOvV8oJKfRgDdclXmRIg3NKSob0YTVokqKQCB9R5YjglwghEcUYI8Alj9WasFoPZZrW0vee5Hu0sVgp6XMi9i3KlIPGynty6I9FyZqIIX/11p7g/B+P3ZGDEPSyRztN0HBtCVWWnJoInrzRs5KaqRB0UvKdU49T8ibvEyF4ail5cJz50qHiXZuDg23eRn75aMQplfnZUz3/ZpE5n4eOnKOkuLcKfPy6oisKWjIftI6xVLwQFf/eiUBoM8+0mvNbkbPCIzrB3zqq+I821rzYVbx7U/A/7UYqL/EWHrWJB0aBB84XLNcSVQv+1TcaviEKPjZuuOvUhK7PpKZh60xNXPQwseRVz2gsaVvB5six6CwndULRctVpTIycmkiuX+3YPK+YLyo+d8XxjipSlJpdDWfKKaFbc2UtsTpxantMwPDxF1Y8eqrilVXig+WKv9GM+E7tuU9KfmWmOW0FP3wm8+n9xEenjv/zSPP1leRtI3hQNFyOmuei4awYTAUfGgs+7RQvOUUg8+HCQRq6lAiRJ2LJyku+eyvxqdXwlHp4lHmbDHw8aH7aJg7XjnUWvH0k+c1G88xa8SNVy4NjeHYNT3eGD1SOs6XgD5aSr3tJmxU/VHu+mTS/eenWn+DcZv42868H82+IA079oY9l4Rx96JEx4oXEGElGoGIiWs2krId2befR0wmhaTDGMCpHrENAxR473qRZzMGW+OURRmmkNYNtWUswBdpF+uioioKmaRjVGyirWK1WIBST7SlxuaSPGW2HaVA/O0RIQzW1FOU23WqJNxILiOCIfUfXtzCdIISgSEPpZK01ejpBhB7pIvOu48Spk8znc+q6RuVE4xtqazDG4PqMiz2L1ZLN0iKtoSwMOitmu7vce++9KCF49eCQsxvbtLHnYN0SXURaS9s0XLhwN1/52rNsTU8QcmZUapb9UCtxeH0XdKauN2n7htKIoXVdFfjkSV0PqqIeaXJStG6JRKCUJno/dEUhUEpShkSoR/TNHLRho56wdnE4PLoeqdLNGgbhI0kMtREpBcgZUxaEticLDz4j8uCYEnloP1dFSegjupIE50GUw7TOaPLX/+CWfuD/00fuzDIlvrhUnNaO32orfnLScDUZ7lOeBZq3jiW/ty8RvufcKcVrR4r7y8AdY811D6PkkDtjDq52MJX8kyuKH68c4yqSg8CTeClb3ing6V7w+DTwy3sFf37DMRpL/tmB4pSKPHaXxd5YcM0XTE1iFjW/vCt5zHp+6ISnnNTMDyNfTpl3jyK6TxylzD/a07x/W3JeJ06LREFiwwrE1pjkHaXz7K8iJ+6acnijYbJtsL3kSLRsY9FG0fiMCi2XF5G76kA2CltU4CNxvuT0qRolBFeazEkrwASuNwZ8pi9K8rLh3Jma//xr8HNnEnhPOSk5nCfsiYJfe8HzXJT8la3A77Sa76k9z7SZe0zmea/5g5VEoPnLZ9csessnWniEHqRF5sgnvWGdM99fBu43EScNn1gKpJb8+Y2Oz3WWk0rwTCM4IxIkz+dCyb2p57ownJGB60nzmhf83E7D39mvGZvAYR/5wXKIO7js4Q4jOGMD/3Be8jPbHb8xM8yoeFCseUVYvrR76ZbmHW4zf5v514f5N8QBxz760eyDR2mNSOr/Zu+9o367zvrOz7PLKb/y1tulqy4sNyFsy3ZcCNhCLoEYJngIzZRxCIsFIbAWM2SyMnEyYUGAhMCkUIYYhkDAMGMgGI8NLrGxJeOK5SZZsmSVq3vf+/ZfO2Xv/cwf55W4KLbkYMu3zPmsde76vWefs/c++35/+zy/XZ6H5COaCpZtJGQ5gqMlkQBSgjZics9o9RBNXZOqCuscEcE5RwgVKRkGvjNiloaeKrSEpGRhStUmTLmEtjUaE5qPCNUMsozSZcSotAoruTCZTRmMlphP90muRNs5yXhIDaGqcVlGaGfQNlhX4rOCI4eOkOKUNsKsqsmNkjDUixlqDXUbOuNsWHbeehFWRgO2JpsMyiX2d7eptQsFIbEhA1YOrbEQw4pzGOeZhoZmXqP1goQwqZT5xgZX3XQjoYlsnnqQRd3gxiudd2DoInXHSALEWjQGQj1HkyI+xziPNHOS70avVCymaSAv0NQi1nbtWz8yd1yByx+N8t0sFgxyS20tsWmgnmGKFbz31PNFF26hLMl9RlN1YSvI827LuTGoBmgifjikracQtfNA5Swus4RFC9ToXbdd1B3+z1x/VN+yKHhu3qKtshhYtmvHq0f7BGMRHHMnnFWwlSUG4fA4sLa8xHwu1PMpg7EhIpihUE0q2lSySs7edMqho45m3jLTlqMpcKY1kBukMYTGMB/mPLAXWBslTuYlMSp3TxI3rQnbuzVrawXb2xXTgYFpy/3JY0m8cWJ5ZQGLWHN74/gf8prLB57LLxshi4qFwqSpWY3KVC3zKqDWcG+lnCwTy6sZawFSjAxLz2wxwRcrtJM9NoPDGMMwtpQ2kq8usxDDkWxOMJ69WtE6YZqahHB25njLWfi2mwpUMs48OOU/nLH8zXXDCdNNZQqGQhKfXORcXbRUKtw1jbytLsgzeFmmHDcVH9GS1AQ+GDL+tq04nWXENnCZTZzILR+YK6rKZSYwE8flNuC98JbdjO89VHFvFN48ybgiNqzkjq8aBd6wWbChyq2DlmcP4c4Z/OZ0gGQwBi7PIg+2QqyF71wNvGlukACj1LX3q5crfnc7Y9+13LV5+qLWO/Sa7zV/fjR/YRg4T3uJyqjAakvTtEhW4hJEk1Dvu9hI4ggIEgMxJVLsnM25YkxoGkxRYrQLIinWkTnH/tYmbrRKmG0jVhmvHWUx2SW0dP5WQoDUQHAgDZSrECLLKyssmgWq3fblVgU8ULeQZ6TdfbKVJQKgISIoxw6tY51DQ6RWZbq9ycrKGlZhY/NhyuVljhRDHtrdRHE452hCzdLhVXRvSrk6YnJ2l0PlEI01h06eoJkvMJllOp+xtrxC3QSGecZOqnnwngc5evIy9je3KcdLVIuIATa3t6nmc2yegfM48dSLRbdAul4wLAbUi4qQIs4YkukCa+r+Pt4NWGhNlpcHoSYMcuBJWEiocaQQMabzUmzF0s5n4B0Yj7eGZIRYdaNrbRsxBjIjiDVUTeh2YYUGTSCpRiP44RBDoqkWnUfkeY0UOSYq0QBikdiA7TqEcMc7LuoO/19ddVyXR5GhEf5iO1GWnqt84HQSMutYtTVDtWwloZDIZ2uPtpGPVfCcZeWPJxnPWjJcScu6E1qTWMkd/+t9jletJ/5gW3hVPud5lxXcvdtwx8xxMlPeX3v2UiTVhqtcw6YvWSThxy+LbMwaTkXLNa7l7VXBkSxgF5FYOt62Zfj7hxvuTo7UGgTl5VfbR/XeBsPG2T2Ori3hbOCOhypOLlmuLRN37UVIBhkIsypy7NgItzXDr40I27uMvEdUGay3FMlRJ9hvHcuDBanJyPLAnh+yc++C0WUFYadGypxFyDDAw2fn/MpZy63LgV3JuN4F3jUx/I/rgXfNcl48avjszHGqTVw/gLvqjKvzitmic479R4uCb1luuHumXDcAZ5Q7544lGnbV8c4m5yV5S9LIdaXw77c8xhrmCD+6UnFX7flILdw6qPidScnz84avHAZyo/zGbsmzXMuHgkeT4SRz3lU7vn8tspwpt+3AUS+crWA9g/taw4ei4VpjOWlr1ixUSfiFhy5+A6fXfK/586H5C8LAyZ/7clUMKXXus6kSxlryQUFd1xix0NYsrSyzV3dbvA0C0lLanCY1VNMJxpeoCt5bmraCagfMGDscY2hIGIw6jHfU+1OK5SEpWkJUBmZBMhlqcpyBpp6DzTBWWFQLvC26iOCTXRJCquYcOnyURdUZC0srq3iTc+rUpzi8coyl8ZiNnV28M8S64uSxE+xVE5ZX1yE2lGXJfG+P/cWC61YOc7beJ7Y1wRhmOztgIcfgrWMmsZtuw9LGxHx/h2Jpicw4skHBrG7ADhjnORubW6jzbG+cgqolG5WUy4cJITDb3cF6jxoHaiiLLhhm0yhIoMyHBCfEugFnSIs5YDsj0I3JstSFYIiGNrbdiJoeOP2zDiGSYkRcF4ZCumU53T+2Cx2hxiJak6KAKuIcue/89SQg893ivEVdYUID3pJS2w2hRoMYJdzxtou6w//FG4+oBOHe2jHLLe/bM3ytr7lpPfGBvQyrhjVd8MyjhnfueWgMpY2s2ch1eWJfEj93puCWIvKm2vMjK3M+PLXstZF7G8/NhxzXZXPuakquJLLmhP9ry/NdhxqmreH2JuObhvu0agneUqbIbkjU6lkqIq/fKXm+TzxnRblnL/Bg6/mjieE/XNlytonU0XDk0IAsCf/LvRU/vR7xY2VzLzAyjrk2XLY2YBotw2W6NW1mgWkbtuZjTropiywynZcEYzDziiDtgePJv9Q7KRISzFql9IncFpApdfCE0jEOLZPNlmpoed19lhu15ZmjyBWrBW0b+dWHPV9bLthVx2mT8cpxzYOV8p92h6z5wN8fN2wZ4b4ZtKXnTduGscAwNqwNMv7WeI5DidFzdyX8l8YxAJZUmIrwHFfzJ4uMWwYN9ywc1/qKP21KrpSEOsNTXMu7oucVZsHv153vlVsGLc8fKG+dGO5Mnu9brrEKv7mX82yZMxWYqXJbGPKNvkaM8lP3X/wGTq/5XvPnQ/MXhIEjN321ogq+wCpo7F6kmXPENhBjS9SIEUWHJVobBnlOjBER7aaI1ODKcTdqgJDCLjF0z7a8eoTZfEKZ5VRtgy2GXRwoUQois9mMPDM0VYUOD1NIookJsRk2L9B6QZuNcWEXa8fEZkKI4PKMtJiT4gL8EOICXxRYhMxaVlfX2NvZRa2jqmcMbcn2/sMcu/wamqZheTSirubsTieIWI6sH2acWubScGi8ypntTY6Ml7nq6GXsNQse+Mx9zEU5efwYd9//AJXPOby8ikndL4bPPHCKcjRkVi3wS0uMk2VrdwO0W+OT2Yz0SHyvekFKIKKkNmDLAbFuSUbJfY6xSttEUhMIEunChAegi1NlsgxrPK22mHnoFgRXCyQTtFmQDVdp6orcG6gqWleSNOBdToyBFALGWjAR0xpCs8B4S0pCYR2tdZim7rbp25oqZWgIOBNoP/Hei7rDv/nkSUWV5+Ytx0XZbAzRCDePAtuV4XQLp0NiIMr6wPCWecmPLS/YBzIVTjURj3JZYfnwwnPMJaq24QMH0X6/50TigzuGp48jd9fKiTwjxZY7mpxnZTVv2YUXH4p8atsRs5xn5Q2fqC1BHZevBHQBd2vB04oZNBln28j/u8i5ZdSys0i8rxUWxlKifO/6glEQVgsYrZdMNqYY43hwAZdnibftCLdeVeAmFeXqEm21z0MTJTNw9PiIo2GT3ZCz6g3blXBiGHGlo5HE7kOBynrWl2tOb8LmYMDlpSHXGSTD7Q9lHMoNZ+vIeEVZ1SEf2pgxbS1fNVQKoyQPtVimTWCnNQf9RTf9fN9MSEZ55sDis5ZJYzk9S7wjFUwSFKJkAtYZnlkErjSGezXx2bnlxS7yG/uO5w9b7phG/s6a4zenlh9YnbOzMJxuhQ9ozt8dNXymMry/9hy3LcsucVKVX943vLKI/FnI+Y5BzRxhgJBSYH3c8jMbKzSp5TXDln96/8ZFrXfoNd9r/vxo/oIwcNyNX60mKmFQorN9xA8AMNIidgzSEFoFn1HMp1REBuuXU1UV3uyR7AqhnqF1hcsdRbGGSqKpF7Rti5GEiCGFBXZwCJNlxKrBDQaU3lEtplR72926lBC66RgVKJbJo9IYRWY1DJdJ9Rl8VkC+hJUKoxnz+ZSsHEPYpSgKMDltFJQWjYmEkDtDmgfKIjAJXbRvaRvaRct4fZXx2hKpDbSpJTaRdrpPrZFlLVnM9/HjAXUMpKDgYPXYMXb2FjTTOWUGhVtja++hbmu2tzRBWXE5+9U+QLeIuTFYpwTxeANOLVW9323JLpYxCilUiCY0ebTaxzlPEoV6Qco8DoMvSqrJHO9yDIngux1WKbQYn+GloVpERMBlOe18hs0NUS3YEist6rrpxDZEmE0wWY62DWo8RjsDV13spqSmCRsjZlSSAdOPvPWi7vB/6upD6oF7reOtU+WWro/miixgrafRyDsWA6wXXqFT3r2wvPpyxx/vOl6xtM1uM+RjC2FWB05kwk3L3cLs+xZw3xyWSAyMAQlMyhFPyRvu2vPcsKacdHBmHvmFTcPX2pqNBPNomaLcZwf8vcGM1y9Kiplywyhn3uzzwqXIjim4PqspkvD2PbhhmDEo55wEppmQFo5auh8nMyccFmWvFq4tZpxKHk2OoU98ZgeedRjccqd3JBCrhkWAWiNHazgTE+tZYs966oXBl5HV5SVOz2ac2rE8Na9x2YAP7gYyERoHb5mX/MPxhDfvdY35wuXEr2+PePlwzusXJa8ZVBx1wjv2EsdKR/Ieo/BghCOq3B08d84S3zGcc2dynEgtH5SSr8sqri7gZzYGfOvyAkNi4B3vbTyhDrjC87XFlJ88s8QVPnHrqOY/bzn+xrDh9rYEHLfkU1rvuS5ruKvO+C/7iZcPlEGKfCiWPJWaoVOUyNAa/mA341rb8uxR5HAhfO+nLn4Dp9d8r/nzofkLwsApnn2rSjagbVtS25KVBcYYFtUEGwO4ghRB47yLZu1LnBkicUolCosWLGC6Z3HOEZKytDKgmgspzBE8xjti7HbraKxBLJnLCSl2W8brGVmWEaJCaIihQk2Bcx6Tj1keZMznc6zLSGGftoEE3YJbEaSe05Q5JmWMBo5hUdCmxO7uLi4GyvEy+9vbxMWMpeOHaaqWYVGiDpqQyFQQJxxaW8Fmls9+9rNcd+XVPLR5htVswL0P3M+JE5cTrWXoPZPZnLWlFbY2tji9t0vuPH55GXcQwfvqo0d46Mw2lA5pI/PJNvW8QpzBZyVtXaHNFFt2EdaNMRixhGaOuBHmwHAhNSRfYp1BZxXZ0ONFmOxVmEGJikNDgLjARmG0vERd11SzXYwvUI0YK+T5MlUKWDHE0OC9J7UBtTmxmeBstzW8queIJogwLBzTAE6VsJggxRLpExf3FNUvP/2Ilng+XgkP1olbV8Ag/Pk0csJE9sVzfyPcGbvo7V9XRI6ZjNLP+VDluH1qOWzhK7LOZ8QRI/xpyPjxY1POLDwPzITSKUdz4d76IJ5uG7FOuSo3zFJiYA0f2088Y6w8FIQsRm6fWk6bjJeVDWtZxg3jxNY84q2iBDaCY6elG7204EPkT9KQG2m4+UjLunOoGP7slPIVw4ZVb3jHlvBwE/nWY7AdhBM5qIvsNY5lSYhLFKOcrEzcv9HytMOWh3cTpRHefX/k+Sdzguki14emYTAoqXdrfuKU5TvXA3ZkKGtlP2Y85bDw8NkGlhzFVNkIgX921vNSH7hpGW7fFe5pEq8cw7sXliudcqWHX50pVoe8ZjTjoeQxGvmUKbjRR+pZ5LmrgSWrvO7hkluHLadMxp21YyW1fF3e8pTlyE4DP7FpeJmHKcpT8sTJwvPRhXI8F+6uhK8aBTZnymrh+egkctzD5QPhN7czbvI1b5x7fvJ4wz8+M+R7ywkfbwyfDAPev3nfRa136DXfa/78aP7CMHBufrkmMZ2hAIhCkwK5zwgIWeZYaGCIYzGfY4sBsrdHU1WwsoxTxTihaQIIZK7Aekeee9q2JYSE0GBMRjtb4IZDqsWsW0TsCyR1jvycsYjzaGoYjEpim6jrBW0wqNBdV0/JihHZYExsG8Qo2gRsJjRBCSGw7D07dY11jrIoqOoaqSva1EBQsrVl4v6c5UMr7DeBgsBiMmcwHjDZ32Jw+Ci+gay00EZGkrO0vooUhvVsyP1nz7C1O0Eksr8/x6piTMZoacgsKt5n1BpYbO9jbbfVnRBYWV8hJqFpFrQpkZLBiMFLIuBxzhGh2wElAsnhbSBowloP1kJT403LfGcPsgp0BEYwxtEtxZNulEli5026CUAisxl1mkI2gFSBH2CMwcVEMkKazsBZ1HoUxSaIkrARkjYYNyTzBrWexYcu7hGcX3vGYa2T4XRrOeYC0yTc3VieN2y5I5Y8p6x5f214Xpb4yJ7hihIWc+Wzc+WTZcFLzIKVEfzuTgEC3zpqKYzhiE/skKhbixAoRLh3BicGjtumyp3R8ZUZ5HT+Lg75xGFvmZvANU7Zc7BVKf9+e8gxB0/VmjfXiR9YghPLUC0ShYGYLKVtuau2bM2FW8aBP5h6Dhvh6cuJO2eKbyOnk2OjhhcdVu7bT7zseOS/7hY8N5/z5/sZz1tq+O1tx6tOwLAGP2hx0ZCpY21VkcKAtuzse2bTgEjkPVs5V/pE5uH4QDjdCGMn7CG8+TSMjPDRynKdj3z78YbdNqNKiY/uCx9MJTfYluuzwDwIx0tlJzk+OAVEmON42ajijrnlqjxxNmSspQVXLyl/dEbZMMrlYnl3EL4xT4/q/e5aOHkQsu4zlXLSCVd45Y0LuCaDNaM0xjBU5ZBVrE18aiqMnGEnwZlkuNFFPt0arnLC2TbyzIFwxTCxr4a/94mLfwSn13yv+fOh+QvCwBk9+xadA7rYxRQrB7ubEjbPUZOjqUIjlKNlHDCbbVEWK93W5PkearMuIvV8FzGOgGMwyplPa4y1pGof8RkS9roAkDHiRocIdQU2w2eGtmkY5iVVWwGdsUUzw1qLMY66Ct0LXhVSBJuwxRoGJSj4cohtZiRbYOKCetFiigwRBVEGKHvNDKoGnECK2GxI3J+wfOwYGKVtWw6vHGM62SIb5mxtbXHZkWNYYzBtJFQNdjzi3o0HCXtTTl5/PbMzWywfXkdmNcl7zuzvUYWIcSXjYU492WJUjNjc3ESLMaSEKweEuj4IpSCY0IIExA0JVQO5wYSGFDrDUVXJXE7dzrqFwRJRGRwsybGYvCShlFZI0u26SjFCCiCdb6NkBwidoeiNQbHdgmXnCSGgrsA2M3Q+AWPQcggxAJBlGXFeEU1EyIh3/dlF3eH/zjOO6LvmlrxtaZzno3VnyN66HNnRgkpb7mw9/9PhhjJEbtuFrxxneJ84NescYh72wqlZQ2aFP66HfN+hil/aLHhuFvj0NHJVKRzVCRtasNHCV4wMvz/LeI4L3DhM/MdpwY8sVdwxgwJhnpQEHMkSq2Xkp8/kXGegVLhDla8vlMJ7lm3g/tZz0zhQtpFkM7yJvHcTLhsrIsqSwDWm4bbK8vaJ5+YysBngqTm8Y9/yo5cFMMoekePDFeJ8F1Ma3ndGeOHxAisNLlpC1SDDjHecDrx9z/GPb/DM9hcsDwW7UJL33D+vef9+wcgJz18OnAot11nh/3zQMcyEj9eOl67Ab+1nvNTPsQLrKEkiQ+t430x5wGV8lQ3MK2VQdHp/WhZ546QLKnutjbw3ZbzIwE5Sri8sv18V/PMjc/aicsee4Y8bi5XEMVGe41ve1Ga82Ck7QXjBMDyq95AivzYbcsMg8QKp+MQ8gjEc9obtbnCZl65EPrht2TOJ3WD43Y0zF7Xeodd8r/nzo/kLwsDJbv46bdMMmUbQFkMXKbzxGTRTMjemaSZgbDe80xoYjsAPsGlOUS4RGiW5RLuIlLmlqmdo3SDOoc6QuQxNgjctLl+ibitSW6HWEduIz6DRnKHLqREyUZpQkxnXReQ2By/kvQ3aVONGY9Rk3fxx00Dd4IuM4XDI7nQOoYYA2dKIpo1YA2m+ix+vY1znTybPzKM+fZqmQlUpvWPWKCuuIKYF1d6EpWNHeejBz1IWA0bDIbN5QyIy2ziFLC+jwSB1xcqhw2yfPQtZCVYwzYKUDFKMMFYhJmLTLcgWn+PzAW3VokawvgvPoG0DJMR2noO1rbF5SRNSt/URsN53621Cg5YDNFQkFQgLTKhIWneGIGO6sOEWrHRrbx7xQK10W/zbGkgQK0yyiM+ICIQWbN7lk5V4Im3qZiEvdgPnX95wWDfaxH5lOWqbTu9q+KNU8PRUcWMp/P6+gLFc4yKfqQ2LsmDNG56lM569JEwD7GL5rb2cH1yd8tGp4w8X8FKfqK3lhjKgSbi6rDFZxnwOlUuEultoeNN6zZ9Nh7ykSPxF8NzoW85KYs3APRMhiOWYjzw8a7mtsrx4rMyi4coC/uO0YLdpeO048oxV+KenB1zZVnw0CT+6nvitacFLXc2ZpuXGJcfQQ+mFlbzFRSV5SwqREAxFFtisM45kgtPARqUcWhrwvvunXL8uHPKGrWBJRH77vpZnLVnOhJwUWl65nviZ0zn3GXimwBUusJnAiuMpeWA3Cu+eoBdMAAAgAElEQVSdK6hhw1m+f5z42Nzxztbw8iKAsQxS5GxUxk64KlM2GuXqEfze3oDtVjDALUXnh+SwBDYxTGLiT5sBKhXfki3489qyIokRnrujcJ1RsMJlvkFVuT8IqnCZ9zxQCQ8oFESuyCPHjeFUMLQp4sSDKu+MOT8wXPDeymGUS8LA6TXfa/58aP6CMHDkmS9SUoX1OTEuwI4oypImgoSKiIE2ARHjHGWW0+zPaPMApoAQ8LGibSIMl7FFhhFHu9iC0K3hIVRglvAyJbkMkSF55kAsqalo27abyok14gpGKytMd/dIYcpo6RiRQFVVIA5REC8MBiNSiN36ntwTm5bCQktLMoZqZ4+VlVVCCDTtgqhKaiIhtVx78jJGgzGnNk5DlpO7HJ3v04qyv7lJCkq9t8PRK6+k2p8yrfcYrR99dHQkpi7kxGAwYDZvsE5ZXz3E/qIh8569nbM4yYkaUOsJTQNYrD9wud3MUFNirDLIVwhEmqbBKjSzPYajJZoEViNVdbabispyaFqMaUjkmDyHpkHzIRpanHOkZkEKLV3I8GXQFkIDIXb+doJ2Dvwyh88yFAgayIwjhG4dUIwBmw8JVY2YhC9K2lBhNBEj6Kcu8l1UJy7TF7uaI07ZSMq745B/sFLxwTanrGruS56HKlg44SWu4SvH8PA0MXHKA7Hgo63wrXbK7889UhR8zSCw6oSdumISDO9OOV9taubRck3ZEOniil1bKKJCi/DwPPDpyjIUaAS++njiXQ9bnAZesGKpVPnI1NJ2A5BcP4gcWRFkDslYbOEIdeBQitQuMs0ND24kvnLNQEpsRSWqcnpquKsWvvsaw8oocmojQJZjbIlZ7BMl8tDEcc8EPjiv+bErhI0a/u9ty7eeNMzniSYKGxUkZ7mxbPnI1LG+FLmuNOxWjoFVPrwHV+TK/XPFOcN7Jo6/SJbXjht2QrdWrlLhsixywwD2VXj/1HOtS3xgT3nVMbh3plxRJF6/DZeL5dOt5bQq3zKq+Z15wdflkSZ1u2nOto6XDGvuroWUEkLiPhnRtg2nJfEi0/mAapKgUfHO8LSi8wV+29zwwmHik5XhKVnkodZyLFPeNzOsOeUZA7hzkVi2if+nyrhr4+GLWu/Qa77X/PnR/AVh4LibXqwpCqqJsixZtN3LMobAYFASIqhAM5si1iJYSIHRaERbBxazCYwLcr9MnG0SxeJEaOu6C76Zj3C5Jatr5m0iPxhBmU03GRQl81ZhPsWujLFuxDjPqdtAq4lYz9E2oPUuIkL0I2grxGdYawnzKW71MsL0NEfXDrG1X5EXjtn+dhfXav8U5AUyPIxKS4Gn2t2CImN9/QhNtUAyx3A4ZLK7R72oWF1dZbE3oaoqhqvLtAdrYrwKa0vL7MWa3GeQhK3pHofWT7C9s8OhMmNra4s5wnA8Yra5iVEh+QIA4xwSI3Y4IjolthlU+923GSBGjM/JJbGYTMHn5EVGvZh1hoocTNFlUPhlVJWmWqAqCE0Xn8o0kAZ4Vdp8iEkBa0znnLGNQMLlOaGdg2aQGox0nj4lbKPlic6hotdu5EcCPhuRUiC2C1xW0N71/ou6w/+pa9f1ocYyScrL1g2/uO25tazZq+DmNWURLHMPv7rh+Vpb0xzETvuGQw0PV5Z/sem5duD4xmFkd1Gz1RqOF8qf7VtuHiZOm4KnDFqWQuQ9c89zRy0j4He2hFcfqfl3W2NONjUnlww3DAyXW2XLJVIt3LNITBYwSS0iwvsYcqNpyEhclcN9s8RwdYzO5nzT0cifbxuuHkTevWN4sPVIswN5wck8Z+gCVwyUNzwkHMuF7zkR2E+QxcRgZNmplHs2hZuPKGcq5WN7ws1HLfNZBBGcF054WGjAWoEk3NkK1w8LTk2Uk1nD/VPLG3cs33a04eceslybKe+JGQDfVEQkJQ4NDBMM75yXaFVxTd6Ni09a4VimfPVy4E1bBi+GW9YibzpruT/FR/V+dQGvWOk8u75916IqHHM1syCcIvFwnfG8THl363lBEVkxsJvgA3MDJF46grfNI9c7Q0vgkBg+HAzH2i2G5Rr3NI4rsgqixWWRNS3YT5G7Ajw/h5988OxFrXfoNd9r/vxo/oIwcOSGmxSbQ6gxzpLqCMMxkkAXExAHuUVciTFCnE4hL3BisZoTMjCZZ5Dl7O/vs7y0yv5shxQNYlp0VpMvDTDFEm27R5jMuoKNxfqSIiuZz+cUPqPRllGRMZnuYIwj1J3/AcIuJAuDVbwvcdmAxXwP6z35aIwLiUi3G2u2uUO5voJNDXuzCi+JdjFhtHYUcQKt4ocluxsbjIdDjDfsbG5RFAW5MwRVlpeXOfXpe7j6uuvx3vPpe+4myzxNXeOzjGgNsWlZP3QIJbG9dZZyuEIKkbpuMVlO4TMkRRbzbawpaK2F6R54D3jECcaWxGrSBS0tl6gm24CANuCXutGTaoE4i7OCthXiRrQxIArOeUKMGCySl9hU04QGXdTYPEfo4luFZBBt0brzSJwNSkSEejLpRngyAeMghc6PkXPQ0C12jjVoizWWKA699yMXdYf/XcfXdZ4Mh0zLyFveN4NFWfIcGj6+iCCOIy5xjYehFT49hztdxjfnDZkkzkrGkVw4WShv34ZbDhs+ttvwR4sBX+MrztbK85aVpaFhp4q8fbPbVfJZ63hV2XJDIfzSbs53LtfcXyvPX255744lN3CmVjZaS8sckiWagheOE4czy8/vOL4+a3nK0URZJxo1SJ74wCnDcw4bxqnln2wN+eZ8xsdn8E1HeVTvg6Fw28PwvHHEF8qbHnY8bS1w1MOiMRwbJX72M/CPrnZYq/zWvcqVNvKWKuP5RcvQwB/OPP/8iooQhZ/ZyPmWIwFfGd66axl75WvWQFrltklgCcPHW0cjDVXKWES4yisnMuHhtuXe1vIDRxKvP2MBYUVqxj7jqQPhw/vCB4zwbWUiasOS8fzyzPFKr1yeJT5ZG/aT4cY8MnTCxyulaROHvWPkaprkeMM8428Xga024sXw9JFwPI+8eUt4oBWuyBvuazOu8g3rWM4eTP8iwgMBlmzDy+2U2+OYXz+zc1HrHXrN95o/P5q/IAycnp6enp6enp4vJeZ8V6Cnp6enp6en50tNb+D09PT09PT0XHL0Bk5PT09PT0/PJUdv4PT09PT09PRccvQGTk9PT09PT88lR2/g9PT09PT09Fxy9AZOT09PT09PzyVHb+D09PT09PT0XHL0Bk5PT09PT0/PJUdv4PT09PT09PRccvQGTk9PT09PT88lR2/g9PT09PT09Fxy9AZOT09PT09PzyVHb+D09PT09PT0XHL0Bk5PT09PT0/PJUdv4PT09PT09PRccvQGTk9PT09PT88lR2/g9PT09PT09Fxy9AZOT09PT09PzyVHb+D09PT09PT0XHL0Bk5PT09PT0/PJUdv4PT09PT09PRccvQGTk9PT09PT88lR2/g9PT09PT09Fxy9AZOT09PT09PzyVHb+D09PT09PT0XHL0Bk5PT88ljYh8jYg8eL7r0dPzRJwPrYrIe0Tkq56kvH9IRP7lk5H3F0Jv4HwRiMh9InLL+a7H4yEizxeRPxGRbRE5KyK/KyLHz3e9er48XMoaFZFfE5Hw5dSziLxTRF775Srv/0/0Wv3S8oVoVUS+AZio6oefpGr8CvDtInLkScr/cekNnEufVeCXgauAK4EJ8PrzWaGensfw361RERkCfwfYA77jSa5fT88jXGpa/X7gNz5fooi4LyZzVa2ANwOv+WLy+WIq0B9/zQO4D7jl4PN3A+8Bfg7YBT4DvODg/APABvBd59z7t4APA/sH6a97TN6vAT4LbAH/5DFlGeDHgXsO0t8ArH2BdX4WncV+3tuvP57841LV6EHZDwA/DHzsMWkl8GvADvAJ4MeAB89Jf6Rek4P0bzon7ZE2+rd0L6RPAS89SPsJIAIVMAX+7fn+/72Ujl6rX16tAhmwAC4/59zrgN8D/tNBW772idrn8dr2IP3bgXecF02db1FfzMfn+EIG4HsAC/wL4H7g3wE5cOuBSEcH138N8MwD8dwInAG+8SDtaQeifNGBCH8WaM8p64eB24HLD/L+JeA/f4F1/ofA7ee77frjy3NcqhoF3gb8NHD04JmefU7aTwHvBtaAk8DH+KsvjVcDJw6e61uAGXD8MW30I4A/SN97pEMH3gm89nz/v16KR6/VL69WgacDs8ece91B23zjQZnl47XPE7XtwTXPArbPi6bOt6gv5uNzfCE/fU7aMwEFjp5zbgu46fPk9W+Anzv4/L+d+wUDBkBzTlmf5MBSP/j7+IGo3BPU90ZgG3jx+W67/vjyHJeiRoErgPRIPYG3AD9/TvpngJef8/f3cc5L43Pk9xHgVee00SlAzkn/c+A7Dz4/7kujP3qtPuaaC1arwAuB04859zrgXY8593nb54na9uDc9UA8H5rq1+B8aTlzzucFgKo+9twIQESeJyLvOFiotkc3F3ro4LoTdEOaHOQxp/syP8KVwBtFZFdEdukEGOl+IXxOROQ6urnQH1bVd/81n6/n4udS0Oh3Ap9U1Y8c/P2bwLeJiP9cdaMbPj+3nNeIyEfOqdszznkugIf0oGc+5/4Tj1OfnieHXqtPrlZ3gPHnOP/AY/5+vPZ5orbloIy9L7BOX1J6A+f88VvAHwInVXUZ+EVADtIephsOBEBESmD9nHsfAF6hqivnHIWqPvS5ChKRK4E/Bf53Vf28C8p6eh7DharR1wDXiMhpETkN/Gu6Tv+V59Tt5DnXX/GYcn4F+EFgXVVX6KYF5JzrLxMRecz9pw4+n/sy6blw6LX6l/d/oVq9uytGLnvM+cfe93jt80RtC/BU4C+eoC5PCr2Bc/4Y081LViLyXODbzkn7PeAbROQFIpLRDRueK+JfBH7i4AuAiBwWkVd9rkIOxPt2ukVmv/gkPEfPpcsFp1ER+RvAtcBzgZsOjmfQveAe2anxBuAficiqiFwO/NA5WQzpOvCzB/l9z8H953IE+Aci4kXk1XQd9B8fpJ0Brnm8OvacF3qt/ndqVVUbOkPtbz7ec/D47fNEbctB/m9+gjKeFHoD5/zxA8A/F5EJ3TzmGx5JUNWP0wn9t+ks5CndroH64JKfp/u18taD+28Hnvd5ynktnchfJyLTR44n4Xl6Lj0uRI1+F/AHqnqHqp5+5Dgo7+tFZA34Z3RD9fcCb+WcbbCq+gngXwG30b0Ankm3E+Vc3ke3bmCTbjfKN6vqI8PuPw98s4jsiMgvfJ469nz56bX619PqL9FNoz0en7d9nqhtRaSgG6369Sco40lB/ur0Xc+FiIiM6LZKXq+q957v+vT0PJZLRaMi8t10CzNfdL7r0vPk0Gv1v8nnPcAP6pfA2d9j21ZEfohu2vB//mLz/uvwRTnx6XnyOPAw+Ta64b6fBe6g22XQ03NB0Gu052Kh1+rnR1Vf+MXc/3htq6r/xxdbvy+GforqwuVVdIvFTtENQf5d7Yfbei4seo32XCz0Wn3yuGDbtp+i6unp6enp6bnk6Edwenp6enp6ei45Lrg1ON/8nFeoqmDFYHKPGI8vC2JoSCnhYmIkiSiGViGlgFHDQBrGJtGKEGOOtZZKlJjAqmVhEioJkkUwZLbFRkNMiTYmaqOIGpIqpQiiYBU0zwmiWCI2Kpm3aIhkEqkwJFvSkEgoaGSgFieBgTZUjSPzkWQ8ahqUkqGNIEqqAwlDFVu8EVqFOgUKyVgoDIwFaWhFGQHOeGZJUI2oGEQENQqSIwqqkWQsiUiM0KRI7h0SAevAeTIjWAOIkIJS5DnadmWIVcgGSF1jNWLFQISI0rRznDg0BkQEtEUxJISAJdmcTCPNQZuROVoDtErUQEQwpqtzUmERE1WoSVZIkmO8x7oDW1sFNIAqEhISasoQ+ZX/+obHbj28JJCX/htFBcRgc48Yiy0LNIQDvQdEIRmHiqDNHKMG1RYVi/EGTQabZyRVpE0k5xBAJSEhIBjEClEEaQOhaVAjj+rdinYaEkEGAywWTQFJAZPlxKYGsSQCNvtLvedJQARUoWmQqBhribmSouCyAucsiBIncxIGbebgMkCJTYXNhmhs8EVGaCJIQlQQX6IaIQVUDBjTpWUFoiAxYI0j0pKioiEiRYYERWxG9I7c8ajeQ1RGowEhJkJdIVZx4yWoGlQUKxnEQERZTOY4hEZTp9mmRjF452g1YUyGBWKKKILPDW2MaLKQIiotudhH9d5IIi4O9K6ewqT/Ru+Vy5CQoKnJ1DD7g+/u9d7rvdf7F8kFZ+CMraOOAQNkUWmlJcwTYsAn8AJJhbkoSbR7EZOoWoNKjs0UJ0IjIFhySdQpYRRsjBgRWgISDG1oSQJRlDwZnLPEGBEnWG3xNsOiiAl4V5DaitgGGlECQobSaE1qw0HdE6qKlUiFpzGKxoA1Bgk5Ig1z6xgq4DyelqExNBpJYhF1rAispYZoALXk4lBAiBjnCVGIBLzJurqbhJoEqaSWlhQ83ibUGrxYkkkoCQ8Yq8QQUSytE2gbIEGWE0jYpAydBzwxBlptMUlRkxFEEOuQ1H2/vSSsGrwx1KKYpBTiiIaugzFK8ooNlkYESRGPo5KEFRgbR0BoHIhEUIt6i4pAsoQQSKZFbU5t43lS45OPM4KmzqV4bBpwljRPiDHQRvAZKQWiEURb0oHeJQmCQTkwdkVBDJiukzfWkZoGsZaoYGOCuiIKGBFS0u4F09SYrCC2C3w5IBkDRFwxgHpBqCuiKNZYHA5NgWyxAOgMd1WMjah4ING2C4wbIpLQuiG5EquCG5aEJmDdiNg0BGtxZYG1HomRGBXjLVk2ONB7IrqSNF0QCWRFSRJFjEFNwqeCSgO2yfE2UbsExuEO9J7R6T00CcWCN8xnCyCRr4xpFi2Q8MUAgKataao5JFCxJBvxYtAkmMyDUVDwxoNJmCAYb1GjWDVkY089WZCka6cmGYxYjDRYgegsmTNEupd3FvWv6N2HQIotjc0J9Hrv9d7r/Uuityct578mEcWKI4hgiVSNx2eJlJQQD16EeUapgRgMrYnkGJZ9ZJ4iUSzReCoElcggJazporXhc+YxYtRQayQ6T5YSmSgRRTWSWcWIQaXAAsGAcwVNWxMV1AAxodZTKfgYyA5GNmYxYa1jrkqDcMglJFpSK0Rf4SK4NjB1JdbCujiapsEYw5IqhzIIMZEb+/+x9y5Lli1Het7n7hGx1r5kZlWdC4DGhd1qso0zjfQGmstMD6MH0GNopKneQSONNJNoJjNSYpPdDTbQAM6lqjL3Za0V4e4arGyImhMHx8oqxmWWZbn/jO3h/41IAYHVO6iQuW9ODgmpSh8LxSrEwkhl6JWSUEWINlO7QxU6E8Mgs1LSqUUpZpzViGC/IExIqfTeGftvChKsVmI4ZoVqBXxDx4bqtG+SrJF0DgSRBQg8BC07iFM6ocAYdNknd5HGQQbRGjUdRJDWyDK//tjYP2cU1PaLzj/JxywAGUmG4EUxAu3xuoUbuDs5nHqoqAc69pekSMWk79eClf3LYQRpgmeAgMeGzQfCY9+q9TvZGuS+nSQ38ECKIhWsnggRRBQpbX/1ZRIK2gORYJgiW0d64AUYC2aVsTqiidZG6Un2TurrVvEyiOOBosrhzZn1wwtSC00Vm2Zi62g7oxkA9OWOloJ7oN2hCI2Gvzyj7UjEggND96vLRdBTo3bQKoQoOVWKJH1Laiu0Q6O0I7Gt0CpiSj0J2zYYr1EomTAdZ7b7xuE0MZVKD8fXRFXJdEqrdN8fM6rBWFY8k3IsRCjSCiyDNQSTTgSYFkbAsRqk4wLlaSLL/JqGFsh3V/4Z70og/ukOOJ/x/hnvPyTef3QDTpRCOqgmmxYi4O6GyZ1HqZTcEBfWAmaFMwONjY5SatLEWcX2YWJszOzT9laEKZ9prqw6Iea0DBYRHKWpkJmIARhWBAuQCNjuiAfaFHMnNVjTaQK4cipAH7gG4cKC8BDOx0iOGK3ceXAh6cAEojQGlxEMZH+dCKjDoYANYZF9GzRbkjQ0B5HJscA9A1MFNh6kElWJsVNeayp+uyGtsMXAZEN7o8lCpTHEaSqMUSlF6d2JTfZ1cA42bdQU+lSYIwmpFBINJ5OdGrRCFQVLLBsDQxPWdNCkE1gOLJLonYZQXmkqr4Iys7HR7EgSkMowZYyBpIEMZhVuQMXp8ulKxbIUwgEJTI1QJwJEk1YqozvZA5pgZngKjG2/7EtBc9+YRQi5XjFPpBa8CNw/IqFIq3gpWAYjHSg0a2QmYQ3EKFoJIKOTa0e3DoeKhYFBpL9SBUARdBnIJHgfmPhOZ+aCppGxkqUi3olppkkhBJbbndG3/UslA9mEdj6SK3gOkkGZCiqNokEIHKVwpyN2wJfBdDyRRYjRIdlp2Q8L9jDjOCKCbJ2OMJkyvJPR6NuNOjW25U7mjvd+u9MO877dPRqaR2arEEloQdyJscD5QJWCmDBPlUj9I8Whmfvqfl2RTEYmCAQFVaGbcDjMxO1Ke/eGXO5EV+r0n+M9sTcH4sMLFUdq+3NC8k96PuP9M95/SLz/6AacYwAS3D3Q0Zms0AVI5WZQ65GqxoSzVuHujS4NlaSqcM1CrUrLK5GDVSse++bHrTBZoeSN6IZNjZMpYcnWHTXFFDKcHImoM/J1e2CD7AYuiCSH7BAdJGimNDNWhJngbhvhSqHiOtjcGFU5ohTg7AulNO4EkxghHQ1loFw8EQRRmLRykpklb+iAkIbTOZDUEoQa7oMpkk2Uj5q0vtKl0MdKAYobTTuuCjHQSekXR7WDCeciLJpoOXEZJ8wGKftwlyglO9GdAIbqTgnGYHHBTBFTSsKCojkoCBZChuO+Um0i5Y6n4VQSQQwO2sjYqFFYa9kvEhlYBr0YugaHhK00JD5dp1+JAAmG75ctUtAi9BGYCfV0IiyxhKXCNJShglgjzNmyUJvC2HbtkjWGD6pWok5YVny8IEOR04lJ605brhtSCqZKhMHo+1rakwRCQBanxCDcKQmDlRyB1Yo/zMjSsamSW0A4KoWUzkjFxJCqf3y11elEv18ppzNxvSIUshXWpaOqBEmbjzycjiz9xnZZqWVi8w2jcGxKHI2ld07tkS2u3Me2axxqZVyvAHgWjIBmbCFwVK7ffoDWKG1jnirLunJ89wYTIyUoZdd1SAxGJv16B+4gQqrgtzsjknI8UCTRcsB9IxCK6L7hvT8z3DnVwsb+90wm5TBRZ0GPZ/q6UgP0UPD/DO/LuxNlDaRMaDqo/dnw+Kc+n/H+Ge8/JN5/dAPOrQmzN+ZcyGxUghpOlIlQQTDSjKsH95FUd8yMA4L6Rs1Orhsv8cQkycWTt2b0TD6sSUmnxYFDczKCVZISdedVPRiiHFswUNQnPJwlBpKJBXRNcEN9wwVmLfTNGXX/kLcysL4PYCXvmBibFArOrEpNIcpAKMwFpA+KJMmKSkW0oBKog/nCSxa213WoCEyuvDXj4nuqkpqBTKgFbRtMpdA1EA8WNUyFEOEuAAlrR2thUWWNJFcwMR6q07YPMBWCMxsrYm1f62Yg6RgJmRiJ6UyzjfQEkmrzLpCj4LZxEOgt6S5AZSIYETQSJ3AmQgyXIHLD+mAWY0lFSVKFrVYyCqGf7so+jkeqK7leSFfUgugbc9vxruF4a8QI2IJ1u2NVdy58TUqujNtGLUeSSrhjtRARxFj3VyYFmSoZgeeG1gOuA419vd+OlRSluOHbDV9jFzFqsolQUDx8/xJohRgDlg6h5FipY9exMRbCCoYAAysVVUMVlNg5+duCHRvj5YZmQ82wqsQm6G3lw7LhY0MOJ+ZJGXd4++6R/rKRh8rpblgrcHhge//MVCougXrSQ8AmANLG/sp+3ijneRf23xeut5VhyvF64/58p0yCtAfu14/Mj2fifocMiNj1ArlrYqfjESWJkcS4QjPydifnA+u20s4zY7lj04TelWJBZFBKQTwRrVgz+rjvtHQOpmlmpP8R73NT1k1J/XQp2c94/4z3HxLvP7oBZ+mFVZ2/mk/0teNyw+1IK4FqZWgirDR1LgjXSLJvfBTbV3QY3gWVO1sIB+582ArXg9Lugahzt+QoO6XV+0Z2oZSK5EoluG+6q/xtH3Se6kZfC6JOAVIGURKxHfBeZ7wHIUEZiWaCJE0NQTBL6q7zJ013flkWiipjJ6Ao0wylQmyYKHMrvPPkG9kBM6rwLjpmzohdVOSyb0HSlEMqzMJjCC+pLFo4pRIyCDc+0JmoNFPcA2F3jWWpjHSumUiplIRSr/gmSC5k5r7aTaGyU2NVk6YrnUKxJHEqjk0TawwWn7iF09MoWkCEKk6tsEkhNsf0RgKujXkTvmvJqQ+aOGVz7tqwTIg7PT9disqXjVCnnk9w60TeUWvQGloaCkzZGSlYCbInsXbME0phROJp5FjIAJGObwM/VspFyDJeX15tF2muF8QFaxOy3khVtpcFrFEOwkARXUEKKUljd09AYGbItkA74aOT3nd6MUBtFz2mGGlCTVASqYas2/5vFARBpfLw9dfoeSJuC3GceDpVaggfl9eLuiZnFezLI7E6PCgFJU6NNKelcvj6YX9g9M56uXOi0ftCqnF7uVDmmXKed+2cCC6KlgLpXJYFqbthwS3QAv1+I3Mf4jMMNSjzhIljs0EKrQkhu47k9MUTa4CzMHZDDCmFdhKKlV1mII1+uxGxELEP6rULfdy5j0TVkTXgWPFlo4Rz75/x/hnvn/H+X+L86AYcESEo/HoMWmnM8oa3rDzahntnuNJrMNbkMR2lQRG26OgrVXV32LYND7gU5ZQL56twtzOzdkqFPnYb+RsJtCxEyj51mnAO4aZBVbB50PoRaZB9Y43OkN2JpD046IxkR223rlcpaO4K+SkDVWVkcEAgdzt4xuugE0kzMHmlYGRfXwpCRDBNE28kCIzuQq+NksmjCacMlrhhoegAACAASURBVBFsPuO60UrBfHAHNI2pTkQPHGFFmbNQy4ZrJUZwlEFVpacSqSgBODUruXZMBEvBBYYoCUwGkwoKdKu7kI6BjMK1BCWdkc5xbHSCuRRi9P0zNSFCOfHMrRgmyroFfnPWufK0Ct1A1cgIGCutO7eimH66mgQRoaQwNkeKYfIGjYGJMrZ158dF0PX+erHq7jwZA1NDtALrLtAMwIzGRrsFQwqS9Y/C+CiK2ITHjbjfKdaweaKI0EdHRWmtQjvg4cjLlS0WwsZupV03rBxgLBSCLAplgu70phQPmlWCjtYjgaARDBLNAVHRqfLPn2asK+VY6CLcb4PzF295O3eCYF2TIgkivPtSuIexXDasDNYuzPOEritj2UCF6fERdSc7rJ6005lwh2LIGJgIWhRLCCqSjgR7RMPlut/WUUA7KeziSNtf+0ohRGm1sKVQJfAcWBr4BlvgvTO9OTFu2+7yKU6g2LSRJky1cv34TL8LdgKxQsjY8V6D8bIQwJpG+4TDVz/j/TPef0i8/+gGnL+Jj7yPnZN7kIVDGURpXKNzEyPGRuqRh9YRT34pnaV3uhQyB5dN0FcqaQOm3hlTQVbnzEZkUgfYJOD7F75kJQuoGPe+T++nhOfpzCEWpAZlrIglua2oNpLEFZ5TmaRRCjQZWAQUaKXydlqIq7BYMjUjRucghdRdQBcxdhW8KEXhAGQsNK/cphMfbeWXs9M6jCl4DuNGgB2YGdgMyxDOfoQCS6/0FEZClQQLjiStOktXYNqHMxMSxSSYNPHSd4F1VLwowsQBYU1DZKekWq6IznRVzJwDxksKizTm2XmfM63f0aLUrLTc6MAijZr7y+hGo4gi4ty6Iih2FGRqlJGsETwvdzbdnRJNBw+Z9HH/s+HxT32mdcUFjPFKT97IeqSvFwSh9w09PxDSsK1jxwP+/HHPJeorBNT0ncZTo7mzGohDkihORCWaIOsL1CNKI5sihwP95YYKlNagKGTsQsLLHVfB1oGakQBN6RlUKWR93TbG2PH+cOJwbuTz4OYrj49H7pcXWj1QSkEfjuTlTohiquSpcqqN0e+0VBxjyY2//GUjL8JQuPWNyxXaYeJcob9tfPOHzts3Cux5VB8SigTOjncrlQdxFjtQ6m4dvmaipogP5qmSFTKVdKPMlQJoKcS6kFJIhAiotVJMQJWDNpbhFFG0Du5bIWKAKe3hjPnG6EH4oEwNmyp4gBgiwf35Qooxv1HkMBEpLNc7y8uNTQsH74BwbPuG81M9n/H+Ge8/JN5/dAPOvx9GRzjmHg60OHxpjg6hFqjFCF9RSxrOnMFBkiIrgXLT4CbB1WEDMCU2J9NIv7GIMvJA9gWNA3ftHKpQNthk0KyBOKETbXREKynsavx0qJXivr80wjiWPXshRUkKjzUoadxV2PpMr4aaY+E0m2iWiCTQSSuEOTaMe0IXYW4zJvCUiTPz7+7JeU7+ZRG+7sGLJs9irHng2Fa+NOPj5iypHMw5tw6j8o9daAYloOBIExaUOXeRtCN0KRQSy8Jiu0g7ZOVUJ2QkzdfXASx3KoskRVly5qIQ3pGeLKoMSWJzSoW7rwxPsiduwaoGQ/Ac3DSZsmASZASXVGLtvJjTxJjV2O4bd5zMoBos/um+aO8mkErBASH3FAukJ9mMcjgRPSgIkYkO310XEgTK1lfC9sgEEyfDKSMZIWhulGJ0C3y70aIRfSNqIddgWz5Q25HIAdMEvm9Ete6xBJEDtQrekUgkgApeCoVXevFhQmh4DOKe9NI41IIFPDz9hHp0VBRI4vFAGuhQ1ky6CPV4ZqpGUaccC3/7dytPXx741cF4Oky8PCWXW7B6IJPx1ZeV61jY3i+c3k3M72ZA+e0/XjBgCkCSetgHeOqEKjhCREHMKLWyeadNhT42zucTMmCrDTHF0ymmDCApRDjPdEQcGZ1tBFjh9uGF8nDm+vIdMYI+oIiw9EBernga2gzrjjXBRzJ6J56D0A5aMavkZWXNG+i0B9r1Pxsc/+TnM94/4/2HxPuPbsB5R3BLdpoC4VF3W3SWaVfPU3hDYD54NGij89ySN+ZkOi9bYRr7uq87zGUFqXhxlhQ2BOf1C1g3hlSWNGoNPIMkd0W+OVPC4sGtd2QI54g9+RiookQGHkZRiEgwYSkFw/C+ca3KY3Y84aMLG8mvxHmwPTCpS3IywQs8WNKHUJsypIIF/zbPPHjnQxf+MJyvK8x1X43OJblF5VAEyT3heUkBP3Ix5VGuqAshEFnQrJxVkbEioqy+m9ZDjQHUEGYTWiSy3BER3AfpBS2GuxBaGCEsmeiyMsWd1JkeyeQ31uWG351ih93aWCs1Bt0dotDZmAdIDO6er6GgQTHjrVa87/byJYPZhOHCixj3fVT9JE8ZjlmSEYQFNWD4ik4HysOESWMyI7fO8fEt5s6337zn4d0ZGcb7bz+yLs7MfuFE7SAVs9iF2iORV13X0H++fBWaoew5ImWqpAaK4ays39+RIeQIMvYcDMQQAlHZzSfdoRodxaaCv1yhFY4F+hJ8HJ283HmjJ968fSB1d8oczoVclYPlvgpXqOcDWPC739+YJHj/O+fb2fjiy5mjJSKNuSSLd44cueuEfgHb+xW8cNOFNw+Cen3Fe0PdoQm6wYbRfWNbAm0V3walGq1URIO+LIgIfRmECG2u9EhUYYyFIUZeN/p9o55mMh1/vnB9XtAPz9TjCUypkyCDnSqQgmega2ek058dEdjSMXPUCtO2a00KnWIzmcHiRh2XPysm/5TnM94/4/2HxPuPbsA558ZRjFtxxBWKs3LmHp3vlsNrQJKwphJywMceECeRlM3RHHx9bHxtGycdZFZmhBd2krEM3xMgpaCaVAu0JEML10wilRThNoQH6zCMTLhpEL4H6W0oWya9TKg4akZFmS1oaTTpbKVyjMEohZM4RzXu6fwhlW+H8q4KcwYfs/O2CGdJYgo+sKdI9njgb2yl1UJq8n02fq+Vea6wbRwkkXLk9+vCQ61UHRS/k2w8+QRSuUnQu1G1U2yQPnNqyrRcOU2FG4pr5ZiDLQu3cLZMIoNIo1pDcUyTWzpjDRI49fGaE5TAHTWBZQXppE6kBwXHZXvVHFVmXbHdGIHKbidXEpVCHwGxQBZeAi4i9AHuQRuv69hP9DQR+tpJE1p2oiaUE2NcWC+J5p0rgm433n9QfOy/i8tlfQ1X3BOuwSkZ+GhUg0RIBS0QYajabrcvBSmFWnS/tG3PyPBrJ5vB2POO0hzcESBM0c2JuQFBtkpJQVvDrNHGYDuc91duwPHrJ04RLNeFl9vK9TZ48+UT1eDl+ytv3jzweJwoTfj9x92uKzLx1c+FWQTvwb3rnk7uRvdkk8IkhW9YMU3qMHhzIh1OzCCd6/cLY3VKEawo4cL5q8bldxfePD3gfaekrRSIzn11fDWCQFKZj5UgUNmp2duls/Yb9KQdZswU3TYcZVud9EE9HcjIPafEjBR2l00Lqg8YgcgecS/sad9jdDQ6CxXpC5KDMfawuyKJf8IanM94/4z3HxLvP7oB599H42TBWwqmzohkKsE3/ZGflgtQ+CctzK6EJ6tC+j6J9gbphd8ug2+lMrLxy3LlV1W4dfgqld4qw3bb95JK4AQVd6ep0kkiK6Ty4htjDMyTkkkXo+xScS6Zu4ZEYMQedvTSj2wEWYxzbrQyURx+LYmm8lDgZ1H4oga/CSESzBpI8r4n773x17Pwnk6XG6U0RJKCc1JFVTA3ru2B57Hx8wwalWVb+ZATELRwigpVlBMDsSBSXumeO+AcDjM+kpJwiN2enQJn0/3n5WDdOiMc58A9Yk+FlsLBQV5tmUvc2brgIUwKJ5mJCKw6ipGZXF4DrXbnloLs+TqesGZCDEils/df/b4nQ0FE9/BAEXr8mUH5JzybNdISc9jcKUPQKXE5U3yACL0o0g5IX3fZwCvehZV0CDFWUTCj1s7Yk7fQqIgZcgTxIAUiOpKF2AZZKhmOlgPkwrh12O6kgL7iO1FQiGnPyAgU607WwpawLjeuCtp3F53WwodvvkM9mU6Vp6c3PDwe+PDtR2KesFJBkm8uG9s9+Vd/9cRvXlbKNCh9f1zI1Dj62G3DbvQcfCR4MwtLF6arcPeNsd5xs93dJ8ZkE/MR7DHwDx05Hwl3nr5+Yrs6Xg5UgdNJ6Vmp58Z2Bx3G/TbI7MQmdA2SzjRXWn1EVOnrxr3fWJZt7x2aC4fTad8IlKSWA9md+7LgteG3Fdj1aom+hqL9f3bc6IkVZ1vWPe37tR/JUTI+XQ3OZ7x/xvsPifcf3YBzxJmysAaICmfdeJeDd4f3XGLi7zZh3hZWHHxX12/sqb/5mhjpwJo7UP8DZ7YIWk1SCldPclRqdSIGRz1Cd5y912lkgA4Wb2wYJffwPrdguGHiTJpYGKXe2XJCU1h1MHLjgNJjwwkkk7RkW9trlYPzXpN3ogxPWlEYyn+8CZMFpRi/vu+WuSzw0w7PfmeqE2sVYONl3Glt5lCN39TCohMaVzzgF7b3RSl1N25X44uycerOM4LIgZOtFIerOdur6Pcgg68lKCJ8P5STFt5L8Dsq4YHGHqm9rQubCMsaiDbCGqrGTHDU5DY2jk2IEfzeCwdN5txFe4Vki2D47lCYNGkBf1/O9NjIrJxF+UV1ahhXdQ7p5B6j88me8AUpgEHJiogz10JVp8vEWO/ouuC6u9BKGlOV/x/e94gjAxWu60ZrBYqQ0naacU0oA5PE9IHsYw9WzAHdGTGwFLaxr+ctZa8XFJDX1TooQuw1HewhbeK6c/f3lSy5z6oZyIDUZHm+slyuPF/O9HvH1t1m+0//8D2lCtPTmf/933wEoNbCw7szy/VOOz2gr+GOy7oytyPtqfKBjeNW+Ob6Hu3wcH4tIjzAnIVt6zy8axxa5bkODhloaRSH9WCsXSmiNA1O54L25OUwMx+S3/x24XaHxTciKqhyuVzJbWNdNqxNgFAPjSyNUzWeb3dOh5l+X9huK1YNi4HZRDk13IN+76gURIQ6lGFGsodpdgnkeGTak0Gwse7VAu3TBfxnvH/G+w+J9x/dgPPXBm73nUKaJuZ2ZFjFQnlvzjQKSw2sG0M6rewet20MZE/Z3lMlbe8G0Z5864bqrmZHNlSFn3nlXRmQzyh7WNK2JbeSyFZo0rmZY2KAYuFgzoZBQqnChTOFjRDIKLgn10wOBqIFFwUxWits2WkykQ7vXfcU3zRMoB6ELHBZnHe64AOEGcvOlz34+23hFwItDE4n3n+8cy9GEdh8o5XdmfTrvhemPUzKoSjKgX9ajD9cP9LqxGwrX8dAmvIwHRFtvI/Kbyw4eedNbqzW8TTeToVpbLxH+T52S+ZQUCkcZqdKIgPA+SaUWzdmGayboFr4ed2baSczrj743nldSRbCkksvbAizr7zJPcjrEJ2QDqWiaczFSBnM8emucJoLoYGlkKeJcjzC8chsjeXlI+oHUoLddBB7MZ0XPDaOBW5DMDFGbpQ0kMo2XtuIs1MtcHFKTKQmo3/A7LD/8Jvva/01d9cKsHvm9u6ZiFf9QgRa96RYfNnX+FGJsYIb6YGWilhF5mkvOoyNsAdk21hf3RYZFbVBe5io08R6v3J+mLm/rJTDTLystFC++7vf8vTFmZKFaVY+fnyPLhNF4HcvG4dzRUT4/sPe49OkoVPDz4Xv/uB884d/YjpNTIfK0WbKQ+FpnpnKykcP8l54Hy+cyok1Br4qP//6zMtz8L7OfHh/oQfE6FhrHFSpx+kV7/DxdsNve6HselnRVpmfCg1DzgcuH2/ctxXr216Qq0IJWAzAmeT15d4Tz45Y3Qshm+1VA/3T3eB8xvtnvP+QeP/RDTjftyPFki+bcJwKqHE4n7j0Tvl+3YPk1o0aRpVAY1BQap3YiN0VhPBKFnHNvQNjj8dWRlFw+PtS+ElWXA6cTFBzDjbowxiWCBXRiTUHNQVKxdT3PJrSqBocXtu97zfHTJiKI6ocpwpjL0Pb1kHZjQOs2WnS2JETRBi1TKyxQQhZKy/tABPctsEf+t5ei8F/KMahFiYRHqdgyU7kLpPLFC63haqVqzjf3VZaPXPbLsw52LaKz7t9/B+8UKTT2krmykHghZlFkxqNGzO1wTstuF15qoW0jXkIU19ZYvc99D64kWwcKXJHbWJEMuPQB5sIlcJNB1cKT1LQUrlFp3eolrxFWEUZWXhrG2+sc8gkZPAS+7r6xYU+/+hg+l/s+LHtIsnDxPGLt9QJDm9PLNeN8iHo484YTgkDCTScIUGmsoSRbIwIpn/mxVVpKJv7nrvEfmm7Dao0bHogStt1ANuGjmSIUyyhnmAMJAPKEY1OpqDTfplPVRlRGe+fkepYFLQ16kMlfKBTo9+W3f7bGt7vlHJEdI9e2C0CB4ave5O9NXKemOeJ5+8uiAiEIFW5ZeFUC6KF02l3f6TvBby9K7fnC+U1Hyz+0Xl5e+bleaGSrFsg1z0IrqWQOjicznvB4NzYhjNwSnzAe6c8Tkz1hObCw7tHJA7UGDyJkb7ryq73jbEuqBhVQV8dPVIr/cMLMhU6DXlNRJ8PR2w+sW231+JbZS4zks7IwlSVWYI2f0FOyfXDBaSx5Z6F8qmez3j/jPcfEu8/um+OX/z0xKyFNHiqM12Cl+hsHaa48843vhWj6z5eViucWfnge4eUamFm8GCwhDCZsJqwhTJn8Fcy9pTd8cwf/IiM5GZGqr3qPpwiQrMVVaFmpeSAELRW6utUKkVZtjs27nxZlO+lUqoxtYK5s/pK8VdHkQRVArRSRXAXpMxUNjJWphz0UZAwooCEMxclLPAUfEnatuL3jd6gS3LcOpdimFUiVuY6MzCWceHIiXHfGBkshx3gZEIIF1V829XuXhpfGHzwhcGgjoDSmHvy3pwzhWdZOGTQM3FRShEig/pa2aDxkRHClRW60FuhUYFBK8IhjOLJUpw78KhCr8awfe08qdBHZZXOxWb+cexbovO0l6G6VS7/3HD+CZ6v/vWvOJYGKjx98UgQfPh43bnrbSGXG62WP+I9Faw7FKgx8N20CroHjBURhu7OwTI6Nva07JKDjNjdg1xf8S77lwhG+kJ67BqGsfs2y8Ppj/9PkT351JdnVApIw+fGdJgIkrwv9OWKzSdSAgbU6YAGgGCHE4xg0w3Rvf1Zwsh17/U5nGfShOgw7jf6hyvvTWmHQl+2na/PYD7OsC2cHna8X9+/cGgTtw+d9ETPM7mtZOx430TIdWO7fU9W43hr3Lck5IZ1gXKgrRvP8sxpmrm87MJ+X3MXw8+FyL31umgl+kA2Z/MbsgXxWNDDTMbg8DBhW3D3GyGBd6cej/vfHpCZmDWyK6kbeTjz8XZFtqQ9HpEscFtY8tPd4HzG+2e8/5B4/9ENOOXLtzxIYUTnJYyvZeX5alw/vudtrPxjnunSiRw8jg1P4WLJI8k3JogqWY1LGpt25lGJsewcYKn8XwiVCU2YMigWnGyvZbiIkSgzIGXnOUcTVCemw4y7E6OzbCvaV548+HYUrrBXw68boQtnnDe1ciuViI0milll7c7jofCWG99ppTNxkhsvkbSeTMV5swye03majOdsXM14V+48pZJl8L47NRsvqfwrK2yy8VXpfBeN6Bf6JJye/4nHUvhftzd4LqQ2bPi+yYogPOgI3W/8Rnb7ZCvws9kpsnLhyNvsHOXC5EmNYJPK3Qx68IhzKskqcE3lEsaJgjfFUph1Qa3RCNyTU0umSL6nUNTZXFlzTzd+r42bwI0jF4Xv2S+XsymtJyvJLT7dAef09Mi7Q2M7VO6L82iNjIVv/+Pvya2T5UCX/VI8RcdTGALqg0UVrNBVKNn2JNSxN/4GySh1pxVD8KxIdkKEqo3QXatmlF17UCYKdR9QH89MxzPbuuA+8MsLvg1qbni+pqqNFb/euF0Ui6Qdj4xa8HXZdQDHCV82Hr54Qy2wbIMeyaEJ94/3va5jnlAp3O93Hp5OuAo9k+PpzMEKoySXDy8Um1iWO//iVz/hdrtzPh8ZXrnerxx+8kh8+8y7n77l7/7+G9aXQVNhDGGMde8f2gYhBuudaz2gCFmU+TwjdUZ65zgfsBlKKCU2Ri17c/OlMx+Np58+sY5O74O+OFbf7D6R3IWVmDFbYeudg0wUMdbecQMZwjoSVAkb5IeFEcrmu35QEnpP9H5FJYlPuGzzM94/4/2HxPuPbsBROfEP1/e8/+5bPnzTsdjL027mFAfNK0/FWGUwysSWQAGTyrTednuaCOfTgeVlpXHhrSXfeCHGxpdAVKeOJDWYVfZqhKp8RacpVFd+ycaTJFPcuCA8bY2PffCmCi9WuYyV/1SfmEUppXCqxqkpNEV6p6YwEa9DTGceKz9vwv/5cuHpWPl1CtvLC//6WMi84TL4Np/4siSrF74kaOsV16SPwj+UwEYweedwaCxW+Lch5Kj8rSvrslENNi9Y+5pY7zxwYbSGfXzPl+J0m7jEwMfCKo0ildjuuFRSr/zTpfJfv3V+JTcuceCNGj+pK8cSSGxcAr7ViWcpXAJACREep8ZSlAMQWnlIJUSZQ1hyr7W4+0rv8D1Gq0J6YFL5WjZ+UQoPdeNSDjxvyq+z8isJHltn7Z2TfroanC+PT/zm+2/5/f/xG/y7F7QPRt1tlhqgKUgqJZO77ommaoKgaF//iHd7esv64RtqD0Q6Uz0SOLkmOYG+RgkpAxdFpkrpA62N7PD1T544LI4P53Jf+MXP3/C7v/vAz/7qZ1w+HPjut7/nqk+Yd3RqtNOB4/mMnQq5dNRgbDtdajiahS//8gv+73/zt/zqr3/J9x9Xlm/e8y//5r/iOq1kqWwePM4Gfebd+cyHjzeuL890jIt0cCFeVr74F2esJr/+zXdoON+8T3QZbKpUdjfN+3/4PdIdmw8s332PilLaCR8bcX8mbEKqwcvH/RHknZcPhTc//Yq3X5xZunLUyldfVGp5QsK5rVc+nie2noweGJVV4PGrM0Gi7GFzx6dGiKJZ6C/XncL1lf6srC+D6UH3F3EzVA88/sWR06RslqzXR95fPvLufKT+7B3rrTNNf1ZI/knPZ7x/xvsPiXfJH1nmwv/43/53+e1tIUi2MZhEuYTiCKQRueLRUAY3SY4Bh2qIB39Z7rznwE2ML46dX4rzsR9pEvzmfuXb6chfhHPWwWoH3JO/fmv8JJzn+8qhNooFlzX5vcHHMYEo4sEvWvANxjWcqRi9d8RmvjIh+516KjyS/GW7Yhz4bR98MR/w9cpxBL9J45AzU9n4364Ca/B4PvDtZSUF3ojyB5koJajbYNHK1pSJwNsJKbt9umWlD+i9M7bOT1gJGi+sbOvEX+R3/OPpif8m3nOQma/PRvWNf7ckP+kLzw9f8//c7ixb0tjoNMwHJ618dOGaO633tXW6Drw88ZWufDUXnjCqBd+uC3c58uLJR504amJz8G6duI3k2yn4KxEuFK5WmLxT9Ebvyt0mNiY+jMJ3AkWUNOWjBr/sE7+d9hRTzcEXNDyvfG3G//C//E+fpDDh4b//n3O9fNjX3qujRWgir3hXIgJH0RxoKfjY7aTig0Lsa+9qaDGmudHvic4T95dviVaYfO8+s2nCPfmL/5e9N/m1dd3Ou35v+RWzXOVee+9z9znn1r62I2xHthsIIRpJRBqgQExkSyEYYWSJThpIGLogQeRGOiiAcAc5kTAYp0GwIXFkikYiw7Ud3xvf+p5qV6uca875VW81aMzjA1EiOgfbR0dr/AFbS9q/9a7xjTGe5/nKm9Si2d9umM/XiMv0m55+HBij4jBPD6xO1+yHwNT31POWsNuj6zmr0yXx7o7Zk2O8Eh4vKoxrudzf8/h0TbfZUyl4dTvQtHMqB1/9R99D+o71owu2V5cHWW61OIzVrRDHgLOOVNnDQWLdHEblU6BeLRh3HTkEyhSwKHA1sb9HJQX9Dk6WtBnmqyWPnx3hCnzre5fUCZo3H/HOt7+H6qfDPYexmDJRVH3IKzIKjMIqBWnCHD9iWWmWT09ZaktVO56/uEY5S7+dyNZhG0NTQ+0b+ptErycenTWE0bJNUOUItTCNCadA4en7gSFOKK3RWqNzoG6O2ZjhI96dXiJhYrFwfO+v/ZkH3h94f+D9Y9YnrsH5+X/pXxdXBsqUD/cwGZQujJIP1tPKUBshpcCgYSWZBZqTleeqaFI6HPE+NpnKFJQUxmh5vziKDfwL80SfDbep5q1KMWhYS8dtNgdZOJljLbxKlm8FwaeMNw2hbBHdsNCHrwzXFLSqWZFp9MgRNaOZKOKYcuFIF3bW8a2doLuJe+dprGeImWOTqeWwN34dM7faHQIvI+AOB11KKYxklioyGkcpEK1BZyE5SyvmEPOgK5wkFnnAieJFKpznwGpK+DpzXRp+dJHBJl6OBtdHss20WgGWQAKpuBsiy7njdRiRYrHVnK/vA3MDt8mQDFw4WBbhuC4cu8jaam5CyxbhpanR5pB02yhP1cAyJh7NG/Yy8n21YDModjlzJxaKIRtBG/C2ItSGKghoAxwmNqUUjC6cKfhP/tZ//ql88P1f+CVRw0hOCS0ZxozSBwVaRqPVhxJZrdBaYyWSo6I+XxGGAUVNKYmq8ShAyUEN0o2Bogs/9APP2PWRsc88enbEVGBlhdubnqgOfiHro4qr646rF6+QUaiP14x3V+hmSeVrsmRmyxq7XFBZhXeKs2bOkAaygmFSHM8UCcM3vv6CfHdPqjz1bMY49FTWUBkh+5ru5RXi1YH3LFAfWFBageRDjIkylPJhQB8ZVTdUxlBEUbUOpQy677DWcXt3R5UydsqYZU0oih/+wWeISry+mdg9f45zFl8tqFee7es97cJz/eKKs8++yXvvv4MOQv30KffvvwfOHn42W7DKYLNidXHC0VFL29bsgjAMkS4mtLGMfUe7WNE0BiOGp09WbMvE/e3EbogM9/vDDVwxSo7jkwAAIABJREFUuDKiDajj1f8n75WpefGLf/aB9wfeH3j/mPWJa3D+43/5L8lWWUKKxCGyTDuSQJfgWatoUiFI4tTBPmvu0NQGjG7YmsgSh9WFWkHnyyHTSdcsU+BK11TzmkoZXNiwUnC3T4hzfK/LXOjEF+c937mrUDpxVsOpMrzMiceLiuttjzMtp1XFLmaMHdhMHmMT18ZxUa95MdzzhrW8bxVhNIQSCFj0CCEPnDWGSiz7nOiDYtKKTjIaQeEPB3CM2GpO4xVTDMxCIRiNsQdrbpsTb9iMywFVe45ih3OR21BzmkeO24joyO3uhN8aHfc582UbyDbhkuHeFnJ2nBTHrgSczbRBcCax1R4RwxiEva3IktkJJBRGwcIq3qwyLiusU7yOjh7FkTf0H67rRj9DEVDKgKl4y05slEOlnldopr2jy5noKswk3KmE0prBCo1rsFV9sFqPkSwBmyL/xa/+15/KB//k3/rvJaaEhMjYddiuP9xzoTF1jU0wlYHWe8J0CPoT66hnK6Zxg5+doIvQNpbQWmpnKNrQAvsus3o8x2MZ08hRXfPi/SuaxZx3v/OC1dzx9ttHfP2rH4BOvPHsCcfzBa+3W56+ueI7/9dzFk+OeeNszaafcDpxu8kYm+iM5cn5mueX15wv59xsR0rJTFMhxUTsMqG/4+TiAu81w7an78ZDox56RDLGNmgsOe1xJ+fMZzV9v0dPiqzB1hXeGqZh4PR8hQyJalUxR6grzeXdyKrK/OijGtGRr77w/P53nxO7npnV6OowKe3SiHUtq8Uxw90NUhm05EOIorf0XTgYlTUH3iVnMAcfLmMaji8WOGPxxnJ715NiYn5xTBwzfu7xzpNJOKVQuub8wjL0hqIK/fWW3VDY91tU3SCbwGgOvGeTmS9bqBcYpSH0ZAmUIXL/X/3UA+8PvD/w/jHrE9fg/PW/+DMyxYhNBRMLY0mEvmNZZXaloo2wsvfYtiWEisFYtOawy1WKc5sozQyVI0opjuuKfn7ETiJ6F3k9bjnJibupsPIZ8UIZLdrWQGGMiZupIlrhx9KWtBD2ccHnfGJdBbYIuwx2KmRjKGKwlSaMBm0VSY9MasFKTRx7zU03IrqmuMTtNGdUEcbIsdfcl0QI+tAIkHCNw+dCYyxKC7oE5qVQE/GVQgXHZKBjooyJ1k880jWVGTmfBV5Pa/7gOvMKyzupxcbCo9bwKsK1ZCoMISuSylRkHqlIYzxDjpw7ResKS3NYhR2LYVNgzIqX0TAWiy6Ri8aiZWTlHLspoOqawdZU1pGNhxzZauEta7nHYXTF+3mLGRRFCVUoXGW4kZo7lznOml0I2MZxpiwbCp3S5DFybhObqTA4x9/8jb/5qXzw3/z3/55M+w6yIo8Toe+Jm2t0ZSlUmDGCnZifPUISZMwhM6ZkJMLyeIY/XpDShNeGk6Ma6hkxRsb7yKu7a2bacvnymvWqOWzSlcO0Dg10r+/ZffgVfSqK5umKCcNnzhY8eWq5ukxsxxEZCmOJFDEsly1jOqwXYkoY07CqYDl3XL7eU6zBe7jeWqJM5C6wWNX0fUe6mxA3AxL18cH5urEVSgsCtDpTK4WdeVRwFDPRh0joJiRmfuKzLa0VPj9r+MYY+M1/dMfdJnC/78ih8OjRKdfbLSWMKO0oOVKkoHTBRsEtVsTdluXFCbV1nK5bkoZF3bDZ9kxRcXV1jSiD7HecffYJsYvMjmZ0m3ua5QKpG3zb4heavO3Z5cyz4zWdBhsjzzc9aigoEmkydP2ecRKKGjCqZgwdTe2ZzZd0wwQlkvqIbRzj2KOUJf13f+WB9wfeH3j/mPWJOzI+qVtKW9Bjz26aWIwDy6VhTJp5VYiVQakTjBZOZpqlPmj8mzlobdDiMUwkM3yoGNLUt1uOJTEVocVxbgP3KhNSw20fUeqgDOriwe/lR2YDS5e5yZrLUnFiJj6wS26yxhNQookzx4TFloQxkZNjiEFYVoL2ELYjrTXUC8HaRG0Cou9ROqHrzF3SXEfNrC2cNYIlMfORBsXzZAilRufIdlI0OmGLwjWRcQwYZXiZ4P2xpV94rjvL7aZCUubMZDqj+IqHz55MXA0eI4XjAkoHGmfYBEXUABVb5TCu5n2bOZJCVoohBC5pkTTgfeGzTUbrzO/u4S5DzhUvMRxXlkYbjDFMpiKLYSeKO7FcYvBGc5ECR8XxgfL0dJxbuJsUokZOpoPo89QoTtLAJitmxiGl4KQwV5kfqA8XRp/WOjrz+KMZQz+w3QtOR9rjt5niSAJUFqyb4Vtws4aFFUIRVrMG9CECxFBIWSFJUDmTtjtsCFQCx7rieGFY2yP2g+F+u8V4YWFr9rc7xBneenTEyXHNq+t7tkOg8RV3A1y/E/F6QommXTdMU2bpDN5nTpolKQeO1w1iK65e7nmrzTz5whKRyJHOmMnQlwpDxdc2gZ2f4ZZzfuhJoVINKy+steEb3ch9O6PdD3z7teLUT5y5CCvL9dVIkYYP3rllN+3x8y/x4vkt+/4VkjKr2YJcIk8+c8FnHi14dTswEokdKJ1xzRHj5d0hwNZrRBv86pgxaiiZO20Yb3dsGkW365h5xcVnTtAa3v1ux+Z+JA6BfcqsT5eHXCJjsTkR7ww3g8IKfD/ucEBbV6y155ZE1/UsmobcTWgliDkkNc9tg/ee/n6HUYaQCl4rDHB68Qj3hw5rn8J64P2B9z9O3j9xDU7RBe0rfBZWFLJeMoSRWCJV0VgUnQRe7gVrNVrBTGneroW5P2RqaFWoCngskQj6cHk/k8KSyHWXCNnQmIm1TixUpjXCTUnciSZZj7WWtxaBN0tmphMLfc39mBFjQTtQkZQGGlc4TF+WaCNEDHG4pfUwxILWGhHF/U5oTaDWiWIKjTLMMViJhCmAbnmVhTHCeVs4r0YgcVZnbvae++woRRGpuU+KRa04VTASuZg3vFEKnWiOreFzKiMpsSnC6AsnPvBMamxb2PURszSMRXE7Zrb2YAAYs+FFUTwyoENC53tqbXFSGMVhsuEzPlEQrBMihTErktGUrIkKRl2YUFzogW4QBtH8wTQRiqaxmUcidCbxto+sdMTGxLlv2BjFFCxLvcdGQ5kv+GDMtNPECwsj9Z80ln9k5ZPDaE99atEURj8jjR15UlirsVVFd3vL5TtbTNugFTS+Rj53znrhUQgpRyRrvDukCFuxqFlFFRLtGdxfd2x3HdW8QcXExcWKduZhdKTNgLQe4xU/9sOPyMpw5gvLXNjsdmzXLVkUKIUeLSdekUNivoK4P0R4DHnk9Ei4toeVcBHF770S3l4NzOxhdP25leIfB9A+I11k8o73UuG37jI//qjiTSWwsLzRJl7uDN+4cbDLhFQxDBNPPv8Ib56QVeELX3jEGAJDhpOZQ/IpGmEXIzKznNNw8fk3OX8E7723x37unITm6tWeWEEahTxF7nYdKwMlJHY373L09ByDR3uHQXF6fkLBUJ/NyTkz9ULWiRBhbA7Hm1ZBO1P0+4E+C5cvXxEnRdVolvMVw66nrg3z45rcDZxfnDGJIubA9nJES407q7i6uUUNEzdjQH2KZeIPvD/w/sfJ+yduRfVLP/XTghRScbyQgE3CWAo6JVqtcMWyk8hTYwhlImMIqrBLgtaWKmeOnKJ18CoUGpu4qA/BjXNRFDXhRNE4hcsgJLzVZAVKKfYSqZQi54p73dDPKhYa+u0NGoOPkTMyR00mmZalFnpdgxWGweH1QIiZscCRTRQJHBtBRNDKkDVsJ2HuYT13DKKYpEIJjAmKKGoMEkd0Y3nZaTplqZUQkqFLBVGGpA19LExZCFZhi2UzJe69oQnCOAlNFThzNW8RuSyZaZpYOMPcDkhyGMm8FzKSa5Z14Hu9572sedwatjEQUkVRmmXp+VJryVJ4MSbOfYUzBWcsUTuuikaUZTAH3x+H0KtAG2FeNTxTidoI78fIVVnSx4lOEtvcUNnMroCPQlAF5Sxt2jEqy79oE9fK4sXz87/+K5/Kkf2X/6PfFKSQk3C/2ZKSJYc9kjTKWipv6e47ji6OSXcbMoaiM0MX0K1Hj5Hlo2Pa2nL9+hbjDI+enKIFvCuHkFVRzOcVecoIifOz2Ue8X24GZr5BysQmCWoxYy1wdbtHY8hxZKktX3kMxtRUksl1C1Z4sdcszcAdQgmOp7UQp44nviLmhDeerOGyH1hXjn/tB4/5P77fMxaPEnge+o94N6IoLvKNrSPVBRUcymS22/4wPjee3X0gDhFxGVssd/ueUAw6FaZdh185zo4XnNZzXu92TLuOdjVnbhO5gJHMu8+3GG2ZLRzvf3BH7DvmZyeMdxtEOcRocuj57BfeIsWRV+++5tGzpxhvMMaCqdiNA0YZilKEMOKcZt8HnMBsseD4aMGqznxw3REmzXZ3zzgERDLOGCROJAGFxmoh9xOl1jy7OKeL4I3nnb/x5x94f+D9gfePWZ+4BueXf/bnZJYDz9MhkPIo3GCMRibHUEXoQZGw1iIxkJPCEOgEFkqhaw9Gk5SnmIqOhA8HGfSbbo9brqjVcHBTdOqg53eePAYmIlYbquYwMYjdwGAUs2JIGtI4sGwEqZaEuiVNGaMFhWXMhpyFzdhTW8cUErssaMk0Xlgp4dRnjBfiZKAouqLoizBzjm0RPJkkhRQLc2upBboSycoT9YQMlqygoLjtobYGZ+GqWIYUcHLI+giqweXMfQzkoghO8cNamLvE7+0UT8zAY5e5ngwrL1yPmqAMWxTXxeIlY1Shn2oUPQGDMoZWJQyekcjaCKcoYha0SRQnhEkxqywnJdBWwm1W9PWCLlfsp0wwmtU48hKhzsIrbVhbz1oLOSWWSvO88YwDTMZQrGMKmVIK/83/8sufygf/J/+z3xZXR17eDthByASsMZgCuzGRc8ZQsFVNGidyL0jumfqRpm1oThdgNFobxDimGCAEZrOWk4VjsW5pqxHEYo1BlYhyFWWcGB24XHD+MMgNUTNIYimKpGHfaZ6uI5OpUSLEqEmeA+9dxCrP9TjiXEWaAmPioHysCnOteDw3H/GeSmaXNcOUDuqMKX3EOwKVc8wo7DWkAcRHph0f/mGy3F/tcOsZzsLuJjCOExgYu4KtHDEm9nc7FJmihbeePsY1wrf/4CXLhebJ6TGXVx2rk4qbyx2Kiv24J06H3CCtFDGASv3B9dYatBSMbQipxxvP8aMzxvstyh9Cd/f7gfXjc9okLE4aru922MUx/TQR9xPoTNwHhq4/uNGbw6S3rhvC0NO4liGNpJTBFLJykCI5FaZf/TcfeH/g/YH3j1mfuAbnP/3pf0dcOayV6g9lgj/IQK0i7+w75soQRaNrzyIG1q3CGEX0c0opfKtLvJoMJo7MfculhgUBpytiMZSqpa04HMWGTNYJUyyt7pmFgSSKqGtqBxWZuTfUFpzzYA4SbRkytQSMMezF44whCqACU8wE5SmlEKeJuV+Dz4S2olIGS6EmIRmkFHxJqCK8joUyJbqsSCXDlNmqxFva0uqByRjOZOIb3QxcoTIWHSZa74glMyS48CMmC5dBUSnD3RSwBS6T5yZNPC4dF5Xlda65UYoLq3hkAnBI00UiJjsezYQpTrzbt3QCJgsbrRmK5lwlggHrIEbNygg6KW6lEIqjT8LgC3W29NYyugbnLPt8yBUb9h3aCKaMqOnDZN8ScapirEbWqmEyhy0g48Fe/X4K/PXf+h8/lQ/+W7/wP4vLGrSCWmNQfPZiTq0jX/vaNctFwyiCm/uDRH9Vs24zvXYH3t8duNsN5CGyXM/ZdgE0zBYOKYp20WKNYa5gzPoj3pUfcMaQRKHFM9OQXGE28zQ6fMS7ThkVCjgwxpAmMGiyVqDC4d+cErlYcgblPa0OhLbC2OqfybtRcNVN5EkzqJox7kh4UgycNDXzeqTXji96xW+/KuAK1np8iXhtiCUT0ZzMCrMivLsZaGczrl/3pJzY7WG73+CnyOM3Vmx62OXMuqk5WxoMmkgmjQdPqx9/UvEBkd/52oCkRE6aaIVpP7JqPcGAsYaiDjcHhsLtzRZlZtxf3qCtwmlHbP1BojuvCcOANpbt1fawuk4C/QgSKGPE+ZpeB9p2jTbq8P9fDnLpcL9j/Ds//8D7A+8PvH/M+sTd4KzjwIRihufUZm7FEL3w3XxKPrpgKh2mBEKueMfsOJ80j1YztiWzFs2RzjxaaV7FhpwMra3p7TkuTaxN5A2TsGXPPkGla4wWkinsJ8WlXqGcJpkKJPO4TgiFUYQ6TTQZXOpQxjOomq2uaI1m92GPuBZD6yd0KOxiQFcVpe5ptCXEnspZOmVQQGXzoXtVDfcWZi6hfM1ZGdFF8DmQY0QkcNUVdrlBS+Qr1Q7SjvVqxuX9xDDBsWsQB102FJUxGrQ3rJRmM4GoxImpWJmJ9wbH4CxC5iYFTpzi2AjeRAqKmy7x7lDRVIXoElUqnJrIl+vMVWxpK8V+qhnVnly1XI0KcTO2KTPFiagEmx3BapYcUnarLrH0EdUNzNLAhTheZ0FVlqsRFt7xTnEcF826sXywT1QFTOy4K/7DvN9PZ1XKMolibuF4XdPthVAmru8siydPMCbTmEzcBJ6PO+6S4rFbHqzkrWGxNpydH3N5E7ExoBcVeTZDdROLuWW+nGFlT2cUrViMdlTJss0TU6fBGaIYJskcVYZKBEkWoyZq1fDnVgllKv7OVjFO0Lj/N++a2k+kpmEXA9VQ8O1IJQd7hErKP5P3OwXVwqAazamaCLbFKYUJgkji5aTp9orvzTI/caqYpoHPrWu+tx15XSwrqxFVuBPFbdBoZdA1LBeWy5vDSH+5WrNoMtd3I6M1mCxs7/ecro84PrWcG09B8fvv7PntlwnjNbrVSCecLTRfeqr55s2M8/OGy+vCELYk47i/CVSLGnzL9tUNmYJoj641cziEB/aJRmv6V7f4fsPjZ8+4224xjxZsXtwyPz1lOyXmquLofMX1O5e4VU3Y75CsQT/w/sD7A+//f9QnboLziz/9M3ImwkwSR7XhXoSvZse8VKz1wArHKIGSDZUuzCtNVzTfMcdkhJlOzJSwlgE9ZJxOzCVhiDzRhV3t6ZLlMitAUyuFsgalLWuV2CrPZ3xPVYSNMkQ3x/kZuYy8to5FEVZKsxfFIJpUEk4bRikfdYtLmxnFsCyZ7IVKGS7MoVn4HRxLHA2RkiIhZYYQSXmAIHjrIOdD+rmCYRgYgmYn+WAEqA2ZjFNQKcPKJBqBpStMRjM3wjQpIDApw5kRxilijWITFd/fdXjveTxf0qSe5yMMAntTU1RhmgyOQwOUc6AYxTRNuMpzKRZbIMZMXQSfM2am2HXCF/MNt2aOJoH2nOSeFANPZoVV2/C9zvH10cDMsZs0kzqko5s+85PtjquQCKUlSo8xhkplzlHsLGyi5t/7+3/vU/lF+/n/4H+SmfOYecPp0tNNiXc/2GKdUNWW+WpBGTKhm6h0oV06ctD02ZMRpmlktnTUShGswovG6YLTlvM6sbOKfjgk3n+YCfwR78cIWyecLjVVEYZ8OBj/Q97HEjCuwZuKnBM2jXQlMdeGLpaPPo/qqsKliURN9oKxFRcm8bNvPOWvffubJL3GmUP6c5km+qAoqkAQLIXBO2ZToCi4U1AGGEMg7Kd/gnezWHDcCN5Zlv7Ae6sUMQrTCDSZz1vLBymwKsL7e/jmt17gj1Z88e0TdJd4fdfTjYfjyVg8cehQxSFkZBhgZrh/tWN9uuB2P2AL5DGBVuiS8KcL9i82LNLE5B0hRdr5HL8f2fd7PvPFZzx6MuN7373j6uUV5uj4ENpoHFVTEzYjbz874fL5S3S1YNrdYJoKVYTT5ZxOGfrdls2v/bsPvD/w/sD7x6xPXIPzN/7iT8l5ZQ5wpIpL73kVFbWaUEFRuYpYBt62CW2EoA5Ovu/mmkkdOvR5yVQ5EgUqEVqnSA5SMYixODIrqzkymmAt90UjYlCmgCROtCIaR6UL3gb67LjRy4PuX1mOKGgSnRVUsWRlSFnQSqhLobaG+z7QuMxSOYIp7GIBdVBzPVHCTCLRFq6Sp2EimxnLHLmLI5XSeJWoVMbFQh8iOhViDuhYOGqEUgpJWe5KQygTgzjutOdIMnOBlUukYY9zlikL+3K4FepyZsgwSEUICVdZXofIubKMElibiq1EajKvgyWHSF0KuQy86S2X/QCp4q1VpI8G8harK7bdyCs0T51nIx7qwwj3SllSSgiGpDVWK1wZqbWi9RXjkLnVhkDBFhiComIAa6krS5eEZ5XlF37jb38qH/y3f+E35PHpmiFFrCj6zchNCDgyKSqaqmFKHSfzNdrIYeeO537oD2nCzuGUUOxhp15ZobEaU2uGGGjbFknga8OJduADQ2/+Cd7njSUaR60zykRisYyDsEmaptKc1AZNYpfCP8V76w21NdzcBtomY+sVFXs2w//D+6xqqbQQbUENA8kYWhSjbhmnHZXSKDKu0rhY6BIE7UjjgHGWMyeMCqRkbsaaoiNhNzIWR20KtW+YzRQlDThvmZLQDwatEtsh0G8CGcV+NzFbVty83nJ0NCeFkdlixtiPZGvZ3w2ErkMnIfR7nrz9mFffegdSxdMvnTOlyPDykmax5tW7LxE/0LgVURxuOUeITKNA7BDnETTKKsoYUd4yW6wYdjtEhJIKCkHyRKGglIZqDiHSHh9z/7f+jQfeH3h/4P1j1iduRTWvLJupcB0NprLEEEjaovDcO+HCRC6sph/2nLUW3SfGknhcJ3Q4hG7Goln7zJAzUjyXksm9R9eOdZrQWvMyKe6sY16Et1xgoQeGYrk3DbU16By4LTWEwlPtqMuGd2JL0JnJZ4bi0MlgVcJKxCvHM5VYuz2TQKwtxliuJVNwaFfYivBEg02BJu15k4nPl0wWeLm9Y4vjsS5UunAzZUbjyRKorAYNtTGIN+yVxjJybjJn+Z7Bzih1Zt7dcKsDEubsotBqxT4lUipU1vBy3zFva9JkULnHK4P0maMpMaV7mplh7AKVy+hYuBBFlILExAsqrmRgjvBc7Xk1WIwU9mpOrS132tMYzSAj64XHifBBhi/MPE0piNE0znM/ZdANL2NCouJkBm9oS6MjnWsoU+I+Q5AKKxMrVVjE8CeN5R9Zna6W3N7suN/saectY0qUQWFrx5QDRieOFnPu9jvOjxriBNN4z7yx5GBpzyz5NlKfzhhvR+zCcHPb43qhXi1J3UTjLONQeOFgnRTHJxXej5RgmNLBx6jkg/cFg2Zde0w9Me0yIUBvI7u9RmuPVYmqhdo7aj9jrfdMknl0VmHMoYEu+TDyv5ng7bVGSsQ44WdOOER0CPzyKyh3G9qlodKJmx7qLrJfOFytcUmoZx4BbpWmKj2fbxs+VyVepApZZM5GzbWPhH7ivi/MGs0+Jsb7RN0ovv/+lqOLFSofcu1c5Ri2I343cXV9zeLRCZv7PcplwrhBiaIukW7XkxFef/cdFBC54+V7hdo7uqQwWoG3eHtECJHzr7yNksDle/ecf+4xRhesdcyahu1uBC1cfnBNToXV6ZJ6PqeuDGI1XReZtntSBkomDhNq7P9kofwjrAfeH3j/4+T9EzfB+av/yl8RK2BVxuiDEVwyiTfTyOnMorQnqszX7gJOFZ64g4pq7iuCGIIcdqALZ7keBo7cjFF2VMZSaVgYzX3RXBfNujbsywjJoZ1hZj23WhFtxYxDY/DGTNOj2IyGpctk0R9mhmgChYQmKoNVBaUMVmn6D29GGiIzLEs7sFENWRVc0SyNYCWR8YwlgRjmMnEx3TOkiZxglgYWtqC0Ieo5sYz4bHg/J7aDcOLAlMzS13wwJSZds48DAxVHZiDuMqM1iBxGtZIyBsEJFIS7FNlRU6lMmyOL4bCX/d5Q2MWM2JrHvmelHa3ViLUwRkqVsBqmQTM4i9HCynleDYkuC31peLMV3nLwnW3Pi7jlC8ZhZi3308ibyznX3Ug2jqY2+OK5IRHGxCM1YYxj5yr+z7uOKQuiZiTr+C9/89c+lV+0Rz/7qyKSESVY43FVDTHhKsUbz07gw8njt7/6bZRWnF6sycCqmpElHTJsgNnZjMuXG07Ojuk3d1gzwy8N68bRjcLtds9iWRFEMENGO0PVWHaTRqtIbS1j1Dw5gZQHdhvDbJHJsUVVCZ00UzrwLlMPzQylDCoKuXQAaO1YtRV2poh9YIzQWljPLIP2tDl9xLtoQ2MzZRzp9OH37VmlUNpwNRWSythS8UHfsxvh2Aq2MSyV5eWQGLNld9tRJLNeW+6vI6HIR7yXlD46ZDRKsbncELXFVwqdFL5EjNd88K0XKJUR7aGMHD9+RN0ucF4zbntMq/Hasb8PFA/V3NIuFtx9cE236Si64uKtcx4fL/jW177D7f07nPgLFs8uuH7xnB/60R/g+995jaoV6+MTVIGuTAx3E0sHdtkQleKbv/tNjA4YqdB1w/BrP/fA+wPvD7x/zPrENTi/8K/+ZcnKoaXn1ChOtfBs5rjVA9tQU2lHMh13o0PpjvNoqUvieoS6FpQIAUVKhvuskaZmTCNf8I5t2WDrIz5rEy47niy2tNbyay9rXlVH/Ji+ZqFrbpzQ0vBeaanlBqwmx5reKt5sJ6R4Vj7xnbDgRQalHMZmlllQzqNRzE1ho+Vg9FcsSkAp8KWAAYdFkRAOGR0nUjBZWKU9takYJbDb7RjdjHk3MKJY+8I67QlJsVWeMWV2uaD1yCNp6Mp0kFmmwo1aE0JgbieM9oxYTm2HCZmrtKDYiZITsxyozSGdfEwOZpH5qJiqRN233KcRN2/5dpf4c7ORf3BXeHwxx6eKuR5pMOxNwafMraqZ4h2NesR7445Je3bFkMRRvEJZR60LMSgmnSjZED68p9KVomiFKZZVqRnKDuszdUzcieUXf/1/+FQ++Kt/+1fEVTX5vmf5aMmsrnn8eE1Pz+4msnA1oU5cXweU7pjLHKG1/rd7AAAgAElEQVSw3Q3MZh4lwgTkPhLHTLWq2NyPfPaLp1x9cM3RkwvOZuCy4yuPM6Zo/vbvbxhzw5Onlrmq2Oc9c+O4iy06bhBrKLGiGOF83SHF01q43ByUdH/Ie04Zlyc0CudqBrp/inftZ+Scab0mhYL1MGbhyUJhspCLYP2MlHr2AyQTIFYUJtYFmllmUp77HqZc2N9OJJd443TFMGTGfUeMgVg8/V1PPTc431Akspw50j6xl0zIUKYRpsxsVRE2A0EyyltMEbQqSKrZPH/N6s0L3n/vPX7yn/sC//B/+12e/fN/ikob5k5oMAcLgzGwC5lxe097/IQPvv9dRBxDErTRYDR1VYE2SI6EOFLyYXLwh7wbBVI0R6s1m80tVEDSSIzs/9u//MD7A+8PvH/M+sQ1OP/hX/hpWeXDH02LYmaEEy3cY5hCPiRqU7gVj4jwJom37IhxgdvesJk8xQcaJj43azhyE6+D5WowlJTp2hVHsufbsSJpmImwQ4N2HLnMj6w0Y4r0dY0fDwdZKhf2xnOZLee+oCRQZbg1Na+CJxpL0cKpg3WlMSXQl4bee8wwYowhaE2tD7vVFkWjA7Xy2NzxlIIvHvKWRgZ6b/E5EMcZnZt4vXdsY4AMIQm1jHir6JSDXJBiOXOZo2qkC3AkMEbh/awoaeTLq5rf7xNV0hQjOAWLotgTmfkZ/XRLH4TXHDNNdxRTESbPl1eKP3ve8Y8vK77WWWI9sMiO2maU86yM8Do6Mhn0BGPFRmDUipPK8pkGYup5UzJD0/APb4TLLAQ0KReMGJ5VigBc5kDKFX3O2JyZMFRJyFJQSvilf/Drn8oH/+jnfk3mVc0wTRiraRrDoq7YJk0Ze5RWaArdCCKCbxqePapxrePqgy37LqF0RJfCl758TmUN26B59XxLTglmNVVJXN/tyUWoG0vfZ2xTsawVX3q6IqAZfMQFjSqOSR1SnndJsagUSgKiDndbd1cDi3lN0YJuNEe6gNZMQRHKRLENtggldNh2Tpp2+HpJowON8wSXmGt34N0dmutBR2YYuqGmcxO725G+g7GfCDGhpeBaf/DPyAWKZn3UsKgz/QgLZ+j7zMttTx4mvvT5U7727oY6Z7K2KJdpTcN+t2O5WtJtNuw3O3qpGK6e4ypHDp6nX/4Mf+lHjvi73+z45jdeQx1pjaeqLG41o2patvseQSjdHhUd+6Eni+Lk0RHHZ2v22x1vLD16WfE7X32f3a7HlXLg3Rma9ZIYMmW/IZmKEAdsKof07KyAREGR/u5ffeD9gfcH3j9mfeJucKytyGYkAntZsW461tNIEuHbuqaKkBGiBZUVXxf4ZmpRO8/aZH7iaM+qLJn5yCzds9SK3+jPuBo1Wh2Mo6xrcKon6yOSTZxZQaHIRfjqzrC0NSH3MC0Y9YQumhIKS1t4ORlksuyMRpTQMlEzIDhSL9wlxdoJoxootz2Di6yDRyRS1w0lDmgsSMB0I3Md+U7MVMsK2x7jpWVHIpUTou4pYYbIPSkEvugNz+bX3AfF770WkMSfOprxPCnaXNh2AirzzTxjDKDxeMm83xmO9QTeIDGjckXneqYp8arf82dORt5sa26G93nVnCB+z24854VEvnXn+A6Jl1bz43nP51pPVbe8f33D3s+ZR0G0ELVwNiZGGzhLmiMs793ueLfU/K/aEYJCmYmCRtkABTzw3dBi9IQRSKUwrxyiNHNJIAWjEuZTLJttZw0xgdGQvcHXFfWiZtqNvLrfY40nSUEkoRL0+3v2V5pYFJXTfPlLj1kfLbEy8Zm6MCs9v/LNyO3Le5Q9jOY1gioj9dE5KQlHpy1WCblEvvGy5+hoxv56xFcNXXeDLkIRjW8PHwYyRWI2iBrQXhhCh2kd5U64W9Y0GianiXtF6DYsVyuirmiKIgRLFAGj2MfA2iq+8+qW1dMFs6zwIkwpcx0dQ9xTckXUlrtXd3zpB0754fqe91PN//733yVL4k//6bf5/nWEfuB196HHyADjkACLA955PjCrDagGu+3QtqbvBqbN/v9m785ifc+yw65/9/Sb/vOZ77lj3apbU1dXu7tp23HHsTN0ujFOCyPHOEJGsYKElEgRWEYmEB6QkMgLYCIS5YGHACZgkhAnEXY8Yne7q6u7q8fqGm/deTjzf/7/pj3x8C8uQuCnUpxS6ayX83R1zrnnc/ZZe++11+L+4ZSf+dFdns2HHE8dX1/ugHaUTcLpasGXHlTcfzzFhZpBK3jpE3tsX0h57ffuYkceHSJSeGwQdK1gVi1RUWHcBm9/6RUCkrs6QYb14MSApNUeIshGMj+2ECwqAhGyXkFUgdA26121iKjwkcxtgHPv597/eL1/6E5wfvHf+ivRighOYlVAy4gIJW1MKXQkiZEyJsTosDaADIgokVEgRIOLCVFbTIBOiGxExUkmcVGj6kBtJEImSFnTzzKM9HRVy8x3QElyFVFKcVG30DpIJLdLzYt5zUVdMNJTjE75/TPFIe+PgJCSKAWls+RREGKkCobNpmUlPRtakahIt3AkvuUoFgSvKF2LC5H5sqQQCUms6EpFmiratmXLaK6EA5bCsKgTlg6M9wTZsE3KHVbsJwmJ8Eg6HFaBgOWCMKxEBcKQJpo8WqRWdKSnQ0tlG0w2YBIUIeZMYkstMjJKYvTcnBqSJLATI9e6AqWWTMrASvWYtZoOEh/mDGPASolWGVI5nJXosK7tsR4KWXFGwmaIbKg5PQOnjcSoDI/CesdZvS4qvj5ICSFwt8z4WtmA7jELjtw3JCryd7/6Bx/JVX/jr/2z6K1He0vrIiIz+FWNkJGsW6C9poqOECyxbJ54DzohuvVur9EKE9ZX8P28QysEIXraqiUaSaIhCslw0Ee830TRWgNKksqAFLA9WE86JpEcHFVc2DdspQWjbo3RKd+53TJdlGvv/e7a+3RG3ukQYqSc1qQGvPMMel2MgmyUkPiWsxqCV1TzJS5ETk9mdLrpevxKJ0OnBXVTMRomfDxdcrfOmC8C42VD4jzKeIZ5j9uPHnP5yhap1kQMB0dzwLM3GLByy7X3TkoaIjLXPLepyV1NU1bITp9DDGUlWDQVs6jpqIirK+6+cYLMc4b9lB94YYs8THnrKOKEZjatyfsZcblCAcoosqKLDxbvBK6uKYMAKyjywGRl6WrJdlfxwobk1QeBbkcRo2ZZ1xzeOaNuZjz3yReI0XPvwYzxwSOiydYTph2I2NL83i+dez/3fu79A8aHLsH5q1/8uffLhCU+RiQglWeAZT+FsYU5iioEdLIeChZjxPuAEOvhlkIIahP41wvBp/UpZ/kW/+jhgkUokCJByQDCIVHI4OmkijRZ91nwwjDAIYxhP5Wc4hlKxdJLuirQSsO4alDCk7hIIgOidRREgtLUraV2FhMcF40kU4LWz9kInhALvrIIjLxlu6sxWrJJw0mj2cgN7yxbUhkxnZxuHXlcO/IYWUZB6kv6MhBUXBcph5aoDDWQqZQiWmwiKZxDZIFeBBs8reyQmBYtCubBM65T5m3DQIBU7v2iY0GqFP3EYXxLjBGjJIjA92uJm3jyJOHp3HJvFTgWBd5J5s5Si4C1im7i0NFjG8XttmKhRhgl6bWCRSwxMYJISUygEY4EgxeQIAnSMxCai1jmSUq0NWVUVKVjKSJaSv7LV3//I7ng9/6dX4nRJIBEtC3BGKTypFLT2+1RTjzNYkHrA/logLM1MUaoanyikFIipcTGmk89f5XPbMDDJOPXf/sWIoBJE7RW2LLB5B1k8HS7GTqThCiwwLCfI4xh1AuMK8mWik+8++AZe01sGqQxpMFD8AgNQWmClaxWUwiwvztAx4jHMUokPgq++p0TCh3Y3R1S9Ao2ssDBScP2bsr33xuTyoTiQk5eOQ6OSgSRxkViXdNNJCpRdHJN21QkWY4X0M0LIi15YrggGxbZug+JDR6fDSniAh8yTpTi6NRxNC3ZLjIQgegtWiaMNlIu6ZrgHQhJJjWIwO8+Ljn5zjE713f47NWM3397yjwIBJ7x4zFVuSCJCp9rdPSEVaDKD9F+D6JENQqr3x+bYiGmAhfDE+9GgI0S/X6rftntszg7gSwhLhusXntvfvcXz72fez/3/gHjQ5fg/Ec/9XPR+kiDgBjQEvASJyFVLc96xTOdhqWEb1YprRS079fjKCWeJDwyRNKgCCkolRKUJdgAKkOLFhElRIskEqSgQ6SXGEJsEcpAE9nswDRqsliDTvENyGaJC4I90fBMLrnWaVnULQ3QzwylS3lg4d1FQhlX2KCx1uEtvNipGCEpVMJWEnh9AfsDxRU1x8mMeZVyUM7oCkV3KBiGkrk3hEaQSM/QKIKuSbIe37p9Rn/Q4XIu2OrY9WWji9Qh4XDlSdOUR5VAWsudVUI/b9gCPIL7beBipwtty9yum0AlaYd9a/nN6RFSbdLEyH7jeHYUOLYGFzz3VoJelHjtuZB4zpzipEwYJUtcUOzngjYqLA2biaIwCVndYIqAV4I2dFgul7TKIYKmjilDDcvWU8aEZWOxJqEIFucNj1dLijTh+bTkC1/67kdywd/4d/+3aH0kYiEG1ttCCd4Cko3RJlevZVQIbr97hhQKGx0xRrRJsG31xLuKAp1nmKxDlA22cqi0QIsW0LjaPfHeNRrT1dgQEMqgWstgq2BVenQWECRIESgXS1wjKPKMF/cVP9LrcrecgpLspz2Oibw+WXH3UcN0WSGJNKsKWwUuPjVgNy8oMs3LQ8U/v7Xk2sUBn+lals7xiIw7t0/JspyXLgp60TN2sJSSYePYyzoEXbNhuvy9P7zDjRs7vNSXvLypn3h/3Ajemy1JteJukzM+XXL3YMXmTkJfaDyCw5MZV5/ZoZ41lE0k2JJ8a4urA/jVX//HDLLP4BYHpKLLiz9wldmy5vjxlPH4mFRleBW52N/gpC5ZzUukcOgYGI62CdITbEV/uMnWxRFJU7O1ofBKcFp1ODme0q5WiABlFdm+PGByb0wroZrP196lwrWWsFoSpaC3PefRr/2tc+/n3s+9f8D40CU4f/Nn/724Eyu6usV4TxSBi8ohmoTCNCSFwPiC1xYND2LBsRXrVv4yQWmBEBEI6BhwQRKDQElLDIqARGkPSiPC+lUPvkUTUEaTCc9GonHC0bEtmTdsqTn7oQbZcCmVdDsNUvVxccWtsMsr04D0AiE0ZYDUOxqfYFSJcppCJ/TtihAbugSGYk6a5BxUgo9vCByeRCpKB92OZDyzDDODl5bp2RLRLYhBkuqcW2VAu4bdLMElhnGl6aaew6rmdKmQMjCUFutSlHBM6sgLA8fxytNPYeIMnRBJjeV6p+KkikyWfdKeJzMlDyc5g0QwpiWGHj2xYmEDqdRsppbjquBoVbEgwWSRCwSKxBBjzq1qfXJVdBIsiuBqAFa2AOURwZH49bN1Kx3bKGZEQlvhomXPpFhrGRmDVZqzes5Jk3AnKhqv+UdvvfKRXPCv/sJvRWkkg6GhXq0T70u7Oa5sSTJJUiT0iLz23or5asFs2qKIyFRh0vSJd+kVbV2j9Lp3lEIgE4PQGmkiIghUnuNKiyagi5xMeNLtLq6qyIoUaSRDo9gsapIQ+CGT8icv9TEbGhdX/O2bgW89WD7xvqhWSA/BSzySJDq6mwVh5TDBkmnDiCl7e1t892HJF64PqEPLjjDcb1Zc6/d4d7bkmayLl5Y7x2PCIF97F4ZvjQOxdrx8uUslLIeVxKB4dDLh+PEMoQybuWLVKjSO2fGSG89vMztbIDKJs4pESqJs+TPXUu5MK24+TuhtZmybKa99Z8XW1SHzkxm6N0Qpy+TxhLzoculql7Pjllvv3CHIgNKRfmeb3evbtK3g7hvvIaKjt3sZG2uW8xMABAXCe6Jo0dYQo6cRng4Gi0fFBS5aRNzA25ZunqGygrn7HqK8DNkEbzewX/4b597PvZ97/4DxoUtw/voXfy766FDSsKkCGRbhSmaywKMpncSLGomBaBFCEKNgfa0V8DFByfUfVxUgSoEIgRACUgiIgijgohHsZYJWJjjVUijJqoIyeC5oWKER0jNMJc45RqnhuPLMvSAqQ0wkvm7JjMa1NUmImCQgGkGhPQtvuGHGZCoyX0h2OxJNRKkey3ZK8n6tTSo8MlicSAm05FIincM6aJWkIzyPlwKlNIVoGJqA8pZHVcNsklAlDUu/w7Steamv2N6L3L035UovZ9YsyfMOWmQ4t2R/0FCLgrcOW/a2RjyaKvqq5WjpKYxAFzmPS490gZj018/MM08WAjZA6xukVBRGcLeGpgYjPTMBSYigDUJBMqvZGGTYCPdXDmUkpdWULtAE0ESEhOgiWgSkBGEtTnm0NyQSvGvxSAoV6MjIf/Dtj+YJTu8v/2q0bcSkkiLP0Giadk5AExuHEwLr7B/pvVWSJDgAQhR/pPf+aMDuZkYQCjqaQknOJiW2dYzyDg2WLE/odA3OOfI84fSoYeUqtNaYAJV1dEYDmvEZgQ4mCUTrSTKNa1v2BpqtgeHhQc3Hr/TQREJQTGzFMEnoCAfBkkhN7QNSK3raIJ2jaj2l9Gxqw72zJeWwYLu17CaKnkv45ukZ333zMVXzEIoXODud8/Knn+FPXtL8k9/7DX7iBz/HN97+Fh97/kVkUnDv8Iw/88wGjox/8Nob/NDLT/OtO5Yil9x964jBTkF3o8fB4YL6dELvqSvU8xn9IkdJQdnUzB/PkFKx+9SId9++h1taZCIJvsHb8MR7XEQ2Lm4QXGQxPyWy7pzrY0AinnhvBWgRsJVCCo/Pj1HVHipEhLS4IEmVJzjB8tX/7Nz7ufdz7x8wPnQJzl/9wl+KiWpJnKYi4rUnty1oQ4wRJwwAHo+KAYFCAj6uTzAkYf1SR0lk8BAiQglCCAh4v1+BWB95vv9LI4RYX4UBUQi8BB0VqYhEtW6/nUuBl5rgHUJ6ejbSU44dkTJIG6KsWZae7cSSakEmDKlsOaZPaWsSZ3FS0lUSER0ySkobkYlBxcBprdnPcx6XC5pqQV/lPLQNqSxQpiZvGrwQCGFIo2GnEDyuGqJUJDHSCZYs9UidMWtKfCvY70XGtcBJSZGlaCs4sS1CeDIhqUPKXqpomwlOGhrZQRnJsm7pBEmrLCfvz48qreNSUVBVFV2jOFjW2KSLbxfsyoR5zHhcrrBRsJeXhKpAaIv1gSJaCgO5EUxXFkNCagLeSGZlzbTWDDowrS07BppWctJGDmSBkCmta/ivvvfNj+SCn/7s/xyFCBhh8L7FC/BVS5qvr13F+w8d/XrM3RPvUkQ88ol3KSPrze0f7T0qSQwRJTToAKy9C+GBhEQnRCXQJiEpFEZ4mlIgpKdQApVqNrs5m9saW3nG4xXX9lNGmaZrA1oJ3moymqqik0WqMjAaJHRUREbJu8c1W5s5KgYOjxsubve4dTTn+L0D9i5scf/hEXm/T14EmqMlXgi62z1SmfL0MwXvvDsmSkVwgaEO7A4k3X7Od++sqGcnfP7la3zrqMZJybNbCm0Frz5cEr1kNEyZV57PXC24desOw60dTmwHVSjGpzN6RhBUwt23j9l4eoPp0YIbL1xiMp7RSRNuvvmIYnfE6uAxl69dYVEKHrz9BlpKdDHHN7tIOSb6HokJqO6YntzlaPUuhj5FJtDpdY5Ovk4MhqQoaJoZxkhCY4mVxBZDErFN6w+x/+cvn3s/937u/QPGhy7B+es/8dNRR0UQASEjn1Atp1ERcUQXWETJNorLpsK4EpmsW+apPFLFlLul4k6bEAQoAOnZQpAbR2bg0UpSeUeiNC6w/rcCvPdoo5BREERcf1Trr6mDJhjHptA4Alui4UKWsbA1mRa4AOMqEEODEII0aK51G3bSliYkzMS6+/DDCQyGHZYiJalbEmGZVy1J0Ox1oKsdkZodPKs8J9Sn3H64xyeuS+ZN5LiErWLForQ8tdPndLVENRV5Z5sss6xaqBrHpWHCWdnwaNnQRSM1gGJUeDoiYdZGprEg+pa0STCp47jyZKok5NtEewamR1yBVoFOpmjqlmVUdPoFj4/HyCiZ2RZ0wsU0cGfmGWQeVppTJWnaSGYcSilWNnIUFE9pcKFivxggfEPrHZbAmAItHKWN2DbSCMhkxooakOQi4Re+8dWP5ILf+bf/pxjRT7xfvLDBYtUQXINbWLyETlYw2O6Q1au190Sj+yneBx49XDI5Wzzx7pSkSFPSYl3EPjle0FQrkqKD9w4lDAKIVYnsFP//3ouckGr6hUI4T5LmXLnaY3qyJM0V3sHB6RxfrTcHeaH52LUOTyWelRfcLTWTRcnNb93lqU8+xbxNCG1LolomDydoIdi/uEXRDfRFy/XUsOxoHn3/db79bsHP/8wL3HZw672Kaxsttx5b/vwP7HD/pKQZP2D/qRt0Us/cBk7GJZ+4NOLxrOQb33/I/oXRE++XR4aByThrLW8sJMFFslZwZSPy2p0l/XoGl66sC1izBLuqSLKCS5c0hw8bpouap5/b5bXf/gZRD5jNT0lVwu61y9x9/Sb9DcFiNaauhtg2kBkHvZKm9STtCBEl3izZ3XoO4Rum9TtYSuTqIg4B2QFitksjIFXgxYqEznpH+9X/5Nz7ufdz7x8wPnQJzi/+xE/HGCNSAFGBFGwpyw3jWIkV41bhZI5oa5Yxow4egsCLSIaiUBZhDegWTc5z3TnbIpKmJanusqo0v1tpZjYSw7oiX8V1e2sRPUGAEJFCaFJR0dEJKR6kxcYEEVJqLDoGcmPQUuB8DdajfQNJgQklWyrHqEBfeFLjsNWCe23O0BhevpwzW8yZriIbqaWnPUI6JovI5kBhciBJ8PUKKRIEEtqWR8ewsRtJ05TxxPBo5mmSPgO1okBx8fISQkRWHUgqJtMEMkVCxf2Dil5nQO1SpnXNhgpMvSST8KDJSXy17qwcPYScE+spMoUIHu8jUhkyCbeWgUsF2NazQNGhpaDDg+aUS1lBohVCtTQNbOYwq9ev4GRUjFvHqLOeJNtYKINk0gaKJONeZXDBsYwVOyolSRuyENEWphj+w9c+mic43Z/9lSfeo9ZI5ykGObsbGyzdhOlZRZ73WIzHRK3xrYPK4UVEqRTz/uImoiPp99i/mDPUku1dw66S3JnBq28dUq9aYgApNcFaVK4IlUNqhRCRpN+FpqK3NUB5nng3QtPOl8SOopdmyMzQLEuaqkVUJaY/wLiGzd0hMsLFC4aRs6zGLd+Z1PTzLv/1X7jC//79R5ysavYyza7OENJxMF1xcaNHPw+8tL3Btx6eYBKNQOJXjrdOS3Z3CjY7mpsTxxuPF5xR8MLAkxnDJ/cMhEiuDG1seG/qSaOgmxv++R98hY99/NOsQuSdw5aRsMyiYScPvDWW2KrFnT2C6JFqk5P5hM3eCOuneB+JdMmKhPun77Df26MuHUs5RhDZTF/g0fJ32excoJc8TXR3WYaWqxeu8/jxTWys6ZsOj1Y1e72nECGyCsf4aovFvMQUHdrGooKkzh9hqouEzgEqCmTTI/qM1Vc+mjU4597Pvf9xev/QJTi/8PmfigogepSI6OC50fdsKHhpqKlXJdZHTmvF0lkGiSB6yU7a0tGSvNuQmw5VayFJGC8zfudMcVRnBFnhIygZkRFCiAQtMfH9/9voyJThRu7Y6xiiW7H0CSdlJATNUkRGpiVzLR0lkU3g8o7FWoOJkjyznMw1lbMo37I3TIhRkOv1c29Ey2mdEEXOIKtZlg3dwQ6TsqVZzVErjS5guC24MAIai69r1CChDQGdlgifIHzN3btzrl3aJNgl89keg5Hj9fuR69sCWym+fGfJsxf7dGRK5WfsDzIOJxXzlUMXBeMysCpbWmnoZp7KdVlFx65KuL8MGCKPvCVVglxFtvB4NGeNZZRrStdydVQgEfjaI6Qm1w10+qTLU5YMmTQld+aWS70ES8rpckEWE25Gz7A1pFlJ9HAp0/SU4rCseLMtKIMg4NAIMrnuKfHLb7/2kVzwi7/4P0Qv5HrhkQLqFXs3LrE9SPlT13ssa88Yy4P7jtVsSbebEL3kxX3omZQtZRl1e0wWDWk35Z3S8y++MWYxW+Gr8ol3LzXR1ciiALv+3EKCSlKuXN7i8vWCctzSWs/RyYLgDdZa8o5E1Y5uYajrhh9+qcOKHJkINmPL9+4LGlsiguCT11JiFPQTRRbWSfvxAmLRY6hqFvMV+WDEPCpOHh8zG1s2+wkv7nf5qRd2eXd2xvLU8amnh3zzYM5TGz2ETzhsTvj7v/4b/OU//0VO5xVjK7k+MvzaW6f82LVdxlXFf/tr/5Cf+dxPkac50/mKT+1v8Ts373L26D57T9/ge997wKx5SO1TNrSjjTdY1ffZ3XiWhw+OkFoQ45i2WCJjilERY/eZN1P6RZ+lfZcbz/9pJILmZImQmu5mQW97iLx3i3Fnm7MHdxkfLyhGPaTImNWvw/wy7eCY7nKXKruD9IZc7qM7C5blCT72SEKXMkxRiPU0Zq/wf/jfnHs/937u/QPGhy7B+c9/8qdj6QJexffvZANJFMj3B1iu71cbvBIUPhKAGAwWRyIVq9YxEobdtGZaCfa7ns9spFg9wzeRidjjzjKyshVt1CxsABlJpCLTEm9bBIFUG2JU5HHJhvRsF4Gqipg0YSfRlGVNmlv2cs289eBaep0Ox6dn5Bp2+h2SnsO6IaK2NHZKtytBeE5nK4adEQoDeoUgQ2TVes5Kk6B0gLpEdCtO3sspVY4RcPGy4eZ7EzKtWNgUrTy91HCwECRSoEXNZt7FCMWbK8VWEtCi5u1ZhlCRfnTIFD5zueX4xDBvAwpJ4yRHNuO0WbFdSHJlSKNHpwkPpiWnlcKJwKaSuGAReHpZDxtbEutRWKYq5YEzyHpF9Bmbqed23bKbdGid47iJBBUoJBAlTng6CiYxobQRlGekJH3ZUtUJC1o8KXUIqBj45bc+mkXGo5/7X2LdrgfLxviytgMAACAASURBVBixAgwKyfrbjQhYLvFKkGaGAAQriHWL6uWU8wVFXtAbJsyPJmxfu8jnP7lB7QPCrTg0m9y+OWd+cgZJxvJ0QlCKNNVkvS7lZIo0mk6S4osEOV0y2srZ3N5gOR3T6fd58UKHdx6PuTYMPDXoc1ytEG1ka9jnvbun7HQFFze7pIniM1c3eOW9OaGq2ewLEJ5bj5dsjTbY6ipaX5GmCa3zGGU4WTl6WiKch0Ry8+7JE+8vXevxD37993npuRf55r0TNgvNlQu7fPP1U7Yu9pke3uLTL3yCfh74x2/XfOpKSqwEv/X1A4rUoPKKdLjDX/v0gDdPax4vShSSo9ZwNg/cuvMqF4bP0d3q0ktSZDfl1rfe4OTEEoSnV3QIsaUJt9je/CyhPcOFYxSWyl2gWlV48QBZPYUxgpV+D+pnyPBYEQgqkBAhSqIIWHVGbPfw6TFRerJmF5s/pFhdos0O8aREURKtwn/5vzv3fu793PsHjA9dgvM3vvCTUQdANAhSLqWSTa04iQ4hBM4LSglawEnpUC4ilELgkVLiQyAVCkSg8YEoAooEFRq8TLGiZeAFufLkiaEfQOeGzdRCE1lEz6MaovX0jGLoG2whuZxJZHCEytJ6x1YeSTLPbKnY63gaMmoLqanp6RQVlyRph7TrqZZTtMqZlBknC0+0Gc9dmSCDQSUJZ5MllT3hwnYX0QyRmwmYMaQ9wl1LTGsUIwieZbRktWaxrLizzLm+18daR6FLTseOpUvpb2g60gMB3SxZtJF3ZjlSJowrh1aeQZIzrR0oRUcuUaRoA/iAlAk6FURvuXnqkF7RyQxRtMxbyUmz/lmEAIUSLKLAyEgICTI6rIhYp6m8JaDQyiIRDBNN6Ws6VtPoSKIky7ZFoFkIxVA1KBJSZQixwftI5QKZ1PzS69/7SC74xc/+/RjbiG4qfJazdWmTjV6Xs8lyncyHiJcgpGd6PMWXDdJkSCWeeJdSEIMgRo8sa2KWEXyFVDmxqZFKkWaKfGNEriT5qM/WKGJrmJQN44djQiXp7hR0CbQq8sILW4Q6MJ5WuNryAzuCzEjuzVqe30lpyJiXFT0Jw15O13lER/FjF4Z86eEhRUy5Yz03DxdMmg5ffE7SlxKVJNx+uOB797/EX/jhP41oDS9f32DaNOwOMl79zgmyI8gzTVtFDm1gX0UejAP/4tsP+bM/+ixV07BnNPdOZtxfOq5c7tORnh4FtCWn5Zx/+JUHpNuXOb17j2Cm7F78JON790B3CPJ79NQN8r1d7PEBqtdhdOEC1bLhzTd/hxigr1+k1kfE+YBGPETYbYI+RSeexhmM20QTcF5j0xMyt4ONjoBCJA8xfo8YDCG7jyqTtXe/QzR3EGicLtBUWJuR+gtr7+oEKSq872Bf+dvn3s+9n3v/gPGhm0V1TSqGaUShKHJJ4RSVcRS1YlVVrKSliopWOHqyz35njgsGFyHVgqJxVMYSpKJrUu6Vkkw6hBdcziUjBAtqWpfhhCWowGyxBJsgo6UfBZ8yksFwSRE8iVGoCFYtSducZSewtIYkcfSExHdgVadEIcGV6CBIejWyqjitbsDBPQadPtmgZEvO6O1s8Nb9kgcHA7azit6eoWkH3Bt36XT6JPWETmdGXBVYt0SmgtNHkb2NiknZY7BZIgdDRhsp6njJ2emU02jY1EAWuD3J6Z9G9lLHZsezbA2VKChExbRpGeWRqknxuuTaQJKbyKrqEqXhD09Kek5A6tBWstdJObaQpJqHTc22TAjOo3SKbVpcqoh4hoDTCbN2QiIk17Ie3dhSRokjYqNmgaUNDTsYZtGynabkOKKEmbPsSkf5f19JhZpl7RBSroeK2vZfNct/abG5uUmWK2Tr2NjtU8hIFaFsEhaHY84mFVkv4ssS3dthczMh9rqE+RIz6qFWDbatCFIyvLDDwa1D8mEHWybsXtxio6OYLxZU5PiqIYjA2a0HNFsDVLAUvZwbl3d5bm9FN2qK3KAi6LYmZJ7plqNsJZmC/byPjytmtSCKgG4tGENHgcRyXG3yv37tmP1+D12s2KsF25dG/NMv3+Xd3gWe7mh+/IURrx+1vHV2nc+uBoi25a3DCS54bh+ukB3FK6/d5keeu8rDaLnWz3lxa5cX9y2JgS+99ZAzkfFsx9ApBK8fWN6ezvmRi5owtLjSs4g5G1sFD4++yaBjaLhOffJ1PnblBpeu7HNvskHIenz16/8HutwiHlYkb8/Z2t8hrK7A8JQz9y5meomoalS4iJWPUaJLlFMEQ0R/TNWOMR568kVUcUJabayLKf01bOcObYio+gJOH5P6fejchRCQjcWoFSEAYoRN76JDA22OVwLk4l+xyn95ce793Psfp/cP3QnOP/03fyJ2c3jgBc4q+q2laxpMpvCNIYSAESV1khNrQy01q1jSEYKu1khl6VjHiTIEt6IXI6cuf//0x+ISjW8VdVSYKFi5KbtpQe08XRybKnDqBM4FemlKGhfrJ+U2YIoMT4tsAsNeRoKkq8ZEFbm2k3P7SHJYSZSPeCJ11EjR8ondAdpEetkR+EjdRrLOHlPXMuxG8BWrY4HKHFpGVNIgxEUqf4+kHuHECqkMxhSsqgn10hBsh+0tCZnDpQFdWupaItSQyfSUcpGBaEhywaTtcdYK2rZh1kaq2jLQoI2gbSUuCAba4WVgXAo28hbvFLXskomGTgJl7WlDyoNguFEIDvyKG92UaStoo8VXFSPvOPJdFgI2ZYvRKVmSUdYlQQkeTh2dTLCKEYkCGdiTKZMgmNgW5xyt7AI1EbmeMRYsrTT8rTe+/ZHc0b74H/923BkpDiYtjQ8kZUs3l5h+grPgHKTSQWaoVxClZl4uMUGxvZkjlSXU0CrDcnJKr5dxPF73D2nmLcl2Rlh66kVFmhqmR3e58MzzzMYLcg1bW11OThZUsxVb+7vY5XzdKdY68t0RdrHAL+dc+sSzPJN6+jIQVeTTl/t87eGC145B+ciq8jTegQz8pX/tCkMZ2Mod+MhRVfNnP77L77434ccvb/H60YSzSQTt6YlAXgg+fm2TV28f00VzXLZ0pCYrMk7LBfNjaBPFyxdzWhHYlBnT1nLatASTcngw5q3HDYiGGztD3ls4bp44yrMps9mMVfsuaaJJs+76mrkckQ8mVHZB2aybj0XfEP3HUfljRgmMVxOasEEsN9nc3eHx4jWef+ZHmR0cMPbHuNUjsiiogiFKgwwVJttio/MxThffICgB4wCFx1uPlBpkIHPXWbYBbw5IxYron0XI/8e7E/cgXsW+8jfPvZ97P/f+AeNDl+D84c//uWhbj4mRkTJ85SwgfUT2JUdBsFWnFInlkrFoI+imimMbOCpb5lbT1xqlI5mFQWo4KkukWV+pLF1OYQMysZQhkteOzdSQKEkSPIlyLL1CuAXOgwmeTiHRWtNLPc5lbPYMOtQsF2fIdMDZKhK9wCiIQZCLFa1M0SowLjtcTmecec/A9Nm7JGjiCp0UTB9B0vP0NjIe3F5SVZZrl1KkzwmyJCEFI4iyoZ51kLlgFh+zmWSozhbLwwkQcDFDrioWqsCuPCstmJQTjNjluG0xTuBjZMNUtCHluHUUukMiHBkrLm10OFsJ5nWgUlOu6ZqGfVqpeDSpCQI+tgHRR1prEUqSaEMTV2xnktNlYGx7nDYlc9lnhzkVBmkFhYm41hNSwUmjmDoQJBTK0kSIGOaxoScUxkWEENi4fj46dQGcxMiAEpL/9I03PpIL/uf+zldjtVphYuTiRs5v/8EDQnDkl7rUdUMeMpJEcmGzQ54JNoc59w4dD08OaeYl3dEQpSNCJlwYZNx9OEHptfeGFFVZdAL1ooQmsHNpg8xoJBKdRarSUa8qrHNkYc7ViyO01lzoG4LV7G126XvFG++8ydZT+9w/jrS+olAFMQh28pKzNmHUibx2qvixUcM3332PF557iU9c67IoG/I04fZZxWYv58eeGvErrz7g/uEDPvfxZ+nkhlXZMugqMILGWk4XGiUzzsojbmwOeOniDn/w1kMg4EVBKBseh5a7j5bkvS5f//Y/Y+fa53hw+x5S9Cn9HbZVi0fzYDZmu3gJleSk4Q2+8NnP85VbJZOjM06qL/HUsEs6+BytVLx983fAen74xZeJPjKbHlOkOUlnwNnsPT7/Az/Eb732NVbuWe5PXyG0L9DpvkPdGvCRTi5wrcWnCfVyhxgniHaf2HmArvaIGGx+B11dRsrH62veqBAyEkSLd/GJ9+WX//tz7+fez71/wPjQJTiv/JUvxtpZRJQ8sB6JwzsFokWE9UiGDg6vJEIofGipnEPEhNgGhBOYxNHTisJCMJFlXE8ZN84wUktMIhm3FTtokAZPJDOBDUo2hylaOGCJzAe8c6RprCbYFqzHhpr9ToJnhZEFuRYE3TLIPFm3h2/nxI6gOkzJup5EKKblhOHeRar4iJw92txSzgPDKyM4nhDmAbk5IszmLJeB/m6HUE6J3ZbVqiSbjXiwTBAOxlZysZPTlEdc2E0wsYv0AYzm3gOLlg2N0RytILOebl4wDRYpazJyVq7CRcOyqUhEnxgrSqHJdaRxYHGMZMtmN+Fo4ljSpXEN1kGqA7nQVM6TJY7a5kgRaaIHIA2CMiZ4VaGDJCLoqISUlpVcnwLFqKiDXDf6c4o6eGol8S1oE9dThEOKFxDCuqh85T3/xZvf/0gu+F/4O1+LgXXL+vsHU2xs/z/eExdIhx2EUCxmM5rZAt3v4U4qvA9II9nc6ZLLhGAik8dzRKHR3mCShtFGl+M7x2zvj0Csh8pum5qnuynXdgekzqL0+iXcb04dp0ce62rquUWtTviRl65w69b3ufrM8/QyRWoThsPA0OQE4bk46PH9BzNUEdkwcPeg4Sd/8DKPTudc3OwgjOA33p3xc5+8zs2DKfcnB7x8/TLfvXXMpPH8+LNbvPF4zE6e863jY3KX8t4sUNaO27PID17rcvM7X+NP/YlPsVukT7z/6qtH3Ng0nHrFdx4ekC1mvPz8C7x2WFE0J+xc2OHgwTFlqrj/4Jts5j9I8G8xczk9DatYYb1m09R87JkX+fob30WKpxmXbxNiQKaKrh6was7I8FRiGykitpkDkOhIqJ7G5TcRXhBlJNHbFMoz9w7bTIhRQXsJmR0gm32cOkTZfTynCNMSXIuk9//yrmRD+4d/79z7ufdz7x8wPnQJzu/9+/9GVLQYKcF5gmypY0p0HmMDqYpo0ZIJgUk1tvXMVoLGCmZlyyqsr55c8BTG4FwgSzzGrBsC9hNPP4G2bUlShS1rEuG5sj8gySWPpi0rF9gstjm5e5/NkUWajJ2h4fhsSZaCQ3B23NAdFCxdwmxRM8wMwwKEmLG5scGiTlCuIu0ZhPBE6SjFnKwsOB1HLrwEREnbzqBKSEzC7HBFZ1jg+zXp/gB6S/Db8N4CO3OYRDE5k8S5JWiFqwOrWrHwBS42KBnXBWOJQeoGZwXCOro9jbWeja5jVUKUCbFpGY5SGmc5HjsqL0i1ofKWXEusXb8IyLWnLKEVObW31CIhOMsogdKuO2U57yEavHTEYNZJjw/4EGmjJ0iFjJIYI0EqQnDoqCmFR0eBQ61fSymF9g02JDQiEoPC4vBR8Uvfe/0jueD/ub/7tSferZDIRaTJPdF54jKSDRQEx1Yi2JSas+i5f1pT1y0n742pESigWc0Z7e9QT2tSBf0LXVSa0kkMT295Jqclo+2U2bSiI1I+eW2PH73c539854SJa/jYYJPffOVVPvv0FcIQ/uLVff7J6wf0R4YQA19+5dt8+qUf5MQ3vP7WfZ5+6hLPb3epyzO++NJVfudhSyZa/sSlPkJ4jsctjRQczlbcPLH8/GevQJQ8WJxClZB1NV9585Afur5Lt6vY2+iQpIYLg4Q3353xtaP7XO/v8M3jMeVBRdCK2rW8ebJkrvuEw6Mn3p+6eolBt+Xm8YpOs+CpS5fwseXCMOfd/4u9O4vRLbsO+/7f0xm+seaqe3u4zZ5IShTZFkkNVESLtCQjUpDYQSwlMRDoIfKrYDsTMgAJECQvgQPERhwhSGLASQDbQRTJsmQJoqiJFkVxbPbAntjdd675q2865+xh7TycyzKu6dhBGlYaF7Wf6uVW3a/q9+1vnbXXXuuNe2zu7qJSw8HOJm0SPv+HX2SVNXtbe7x5/w326w261N9wHOrIzfkJxuzTNuesFXTeczDZ5nwxAx72bnQf5Dd+RpJE9kJr9KV3cCjlyWIRIygEG58gmkOyHGD0TWL3BM4ePpiXp0jZEL7w16+8X3m/8v4e1/suwHnrr/7LOasWYxSFEhwaspCLiM8Fr7y54uysYu0EIwFcRQiJygiVVpgy0HU1Op0zsBXTLIz3xqz9gpGumTWZgUmcNxqdFD4nZh0E0Ry4Dq1K6iJA6KhGlroqmTcdbZMxhWUklqVZs11FntybgJyCrfFNha4ibayIYcnAFhS5oVAldmjRdg2VhjohLqMrg9RrtBkT3zYcvgMpBYomsFFCNUrkvQUiCX06ImfNepnJqaULI5oMoQ04HXjiyZrYRlQaAjPuHiuqQWSoRqAvGOwIclESc8YUrn+jxETwGrKlMIEYPJgKyYb5LGKmJbNZR+kKjhcrot0kExGf0GWJTopjn9h0GU+kCYIlYaJBtLAKCm1KkIxRmS5FlC76mTEZPBmtNVEJKRmaqPFIP45DDAWKoPsJw1EZfuHLj2aA81d+5Q+yWmiMV4wL9ZD3hTd8/s1zbr91RiIRzhZUuxuEkFBGMR6XSEwkV9IdvsV09DhuUPA9n9jn6GjFqBpyulgzcI7ZYol4IQfP/ftr2qZle1wx3p1SDiztvOGJHcXG9i7vHq04OV4x3ZpQFrA4W/PkjuJPP7aDLtagKpbeU7kBnQscrQO7RhjlAuUMP3Jjg3mr2N9ImDhAXJ/OVqXisaHjpTsr/u6XbzEXQxEWPDkcszN0fPD6BiKJ1w/PyVlztOw4bwNmbVmYyN3DW1Ri+Ykf/CBdmyidcHye+fob71ANIs8//hwXYcHT22NmZ8LcdDw+rDE6M1tkcB6yRbWGeV4yHRRINrz68i2uPXmdV++8xZMHj/EPvvx5tgYfIxM5X99nc/g4Oilen73C09PnOV6/wVmzoOw82g3IXcM6J8QNQTLOFgSZP+S9iwldlEgIpGTQ6YDO3esDfDHosEcu7wIapQ3db/3NK+9X3q+8v8f1vgtw/vDf+UwemUDnMy39YLVhHTFZY/OKD/ypJ3jzW2ccLw3jUrFXOebZc3gSWCwh0/HM5phB3TDEUpYlraypLBzPPSZaSmWYTD1ZGeZdhJgwuuTCZ1ovWKVJJCozYqNekaJi1WaalMi6JqjERh0I3YjYJrb2A8O6ZVBC02YSllFt8VFxfpK4ezElNgmrl2yOItNhTY4rPBMkCF0rjDZX1HaEy4GiqNDjGfmxFXpVwNz2XZ2ZIKsFMTSsFxGVM0VVoWwghilluc29u6fgMplIkTWYftr6sNDMV5GQM5pEXYI2UCjFYjVmsWxYKM9WPWLVRe6nCT4u6MSybSNaa4Z1yfkqcNy2WF9TDT1WgXYFi2VLlyIx9vNj0AadLSmv0aafI2a1ATRaZXIOeK0gKIwyCP3P8DHTKI2SRJY+vZwU/MJXH9Ejqv/uD/KOSxwt5NL79Y2EyZpJXvDv/+kf5b/5wtf5xpni2pbho2PD/VXka3c7br51gsxf5Sd+/EcYFQV7OTKd1MyaJdtuyGvzWe+9tjxWlHiXWawyxITSHUcBTpuMVZr5KnCw43hmMmAWV6wuDO+eNZT7I9rzcz6wU7KKlvk88tGDbYqi5fv3t/ja/VMSlo9t1ry18hyfJz5/vyOsW9zFCR/78D6Pb22yvrhgPSzwTWR+AY9tJramG+QQKJ3jw7sl28MxyXRcLDvIho2R4eU7FxzNPSfNGpUze2NHCBZrDHo45Oa7J4jSZCKVyUi2GDL7w4r7ywUhZ84OL7ixt402MByXnFzAm+/e5Fsnr/PMU59kdniTO8115u1XWYeax2pPtfUsN67v8c1vvMPd+UtodhiNeu9OBpz4E9K6BQHRqveuNARPdqbv4ZUd3/GeVIdkjcpgHsxOytkCAsY+5N3lzPp3f/HK+5X3K+/vcb3vApzD/+Sn8q3jE3KjmccEpmBDRYpsEBESCZUjg8Jx3GQmTlCiEDQhJupCQfZ4ralixTwkpvUQ7wWlGmJKRG3QGCoLa+8xJGqnKIpMXSl04TFFwpQzNjeugW/J65L1GlaNsMolcRUZDR3eR1JUSPTgKsQkHh+PGNQrkjtDa4Xa2SOfzFFpQNOuCC2QYDKx5LqfyVSqERQJIaB3NwiDN5Hvb+kWLZO7G7CqCXmOK64jb5+hZxOiKjEzISyEEzdgQkPOK7xyXNwThhuW0FzQhglREjkrZl1ka2/KnVNLomQVoaoS2AAJYrY0wTMtC9ZeEOUgrsj0v/9CK0j9cFJRGashZcXQ9E2ttHbo75BSiSZC2ymkNpiU8NCnM0UjKhNVZiId1hRYgWXS+BwQbcnJI2g8mb/05dcfyQ3/P//bX8svdyfMZop21YIpGFSKzbFFRPBn56gc2d2peeOwZG+fS+/t0Tmjgylm1UFhKSXx1il86KkpfrZkVVi643PmeoDG8MRW4vhkjSExHU/YrAVXVWxZjSkSSbf8a09/D/iWozW8dnzIyVpxHwWrFdsDyyIkUlQsusCwKhGT+N7tMZ+6PuB4HtBa8dQT29w8WrBZZ750+5RFBBJ8fGufXHe0a6GuBlAkYpd45to2i27GzsGIbtFQrAcsnafrluzsbPG7X7kDtqNNCrt0XKxa3sbzmCuZtQ02Jj7/4jf54N7j3Lr7EuONp3nj6NXe++yEH/30v8UX/uhtsttkMV8w2BqQujchgXZ7HHW3eGzyEY4X36bkCdbpVTIG/JKq2CHmC0o3pPVLhmXF2nfsls8yj29R2uLSe5dampAIXUSGU0xKxLi+9G6LATEtqeIaM6ixAosOMgFlHDF2KGUQnfGf+1tX3q+8X3l/j+t9F+D80c99NnunaDuPGRrOLyLXRpo1DuccRYbVWcuoBpMjZ+tEqRQ7RWCdIKSMkIkpkbWhdooqghsZdkaGrZHHbQY6sZTBIStPFzIxCjmVhJDALtiabKKs4fSo4ex8zQc+cMDt+3O0LKmLileOYMMkPnBdEcQw2TyD9SaWxLytGGSPK0sQIVqFTFoKV0FVIAcb6MMZi299m3DcMBqNKHZ26FYryiisao2Vc8oNR9rLmAsHSdPdPCHNO5rJJhvPPYVxBn9xyp1vr6iGYwZREAYMs0IPW2xlaUyBDZavvjRnY7LNtWuKbtmAL6gnivVyxvlqjZDxvmAw9FR6yGgYMLrCSQMSma87rFZot0FVW9arltYHBtWAttGcdh2DqqbrOhZtZJ5LggdrDF4pTBZCykQj5NQHOU1naVVEk0m66AFIg9IlyWRCFylUJmP4+T9+9ZHc8H/qb3whZyLzeaDe0tx++5gbzxwQApfez+91jHctOQoXpw2qED600c8i+473i7OGrA07OyXjuGS0v8lzQ/ipZzcZuDHGJ3IJTbfky7cXXDQGnMY3c5wx/MQHD1DW8JvfmPHlW3f5D37yY/xv33gT8cL1rSH/66/+Hjee+T7+zPObBDF8aB9OVkJlDYedYuw7qpEDEUJU7G1qnt7doNADbtzY4t2bZ/zvv/8S79w75+nrU25cHzNbdqhFSdjqiEthf6fiqd0dlqsLSJpff/E10rxla+s6P/tjz2Oc4d2bS/7mb/8K3/fsDzCtLCoVVNoxqoWyMixixIvlF3/5c/zgJ3+Ajx4MOFr33vcmYw5P7/M7L/4hQsYxItklP/z8p9goM8PhAK0CF41w9+a3sVpxsPMMk52S2dGKm8f3eWr/CdY58EevvMhHv/cFXn7lRe4szjhpW0KXGFbDS+/LtqEAWu3QKhDbRNYZkYxpbwAg5tskWyHGYqTt6/UwdL/1P115v/J+5f09rvddgPOHf/GT2SaLaEPIHbUTkoexa3DOoYuaYWkxVcHAeKrhDjAjC/jVCkWktEMaWbOcQ9sUdH7FxqBmWC+oqwYKEG3RbgCpg8KALsA6iBU055DnSNUhxmLrEcQMRqCckGZg6hHJrzGbE1AKmiXM1qCFsBLc9+xDHrM+O8QcHlI++UEYrWERYX4E3Yi8XrFYXFAoRxo9xuD0Hs1qmyo3NCFRxCNcdPA922D2iKsT7P4EH0psWBJTg+4SutTotiDnGmVqFqczBhs7mBreeuddZicaZ5dUwy2GVnF8fMILH9kn5gus2+Gdt1smQ8ub80ydPCeuxKWCqCHSZ34kQ4Uj0KKzRVKksBqJiQIQBY30c6NiFrYHjlFac2/RsUZRFwNyaJiWIySumVGSRbOWRBsNqygA5JiJWYgkrLKgIpVk/tKXv/VIbvg//l/9w6yU64uvQ8v1TcW9O8LWcMVj2wVSbPDEKIEf8omp5fkbjkUckQV+99t3ebI0PH4w5ubRkldOA/c7Ifkz9uuKzVrz9HgABThdcG005TjN2S0GoAuqIkGsuLWa0bRz9qY1ojLTor70XlcjmnXHcDBi1a15dncHlOLdsyXrRQtauLtq+PGPPEUuHK++fY+b90/4sx99HuWEd85a1qsT6EYcrc/40rv3KJTjA3sH3L5/RMoVJZ77y8zs7B4uOn76M8+zMR3yzTtHfOKZPXwouX98gnKWw9WMx4ZTDpcdoc189Pouv/PmHX7shV3wA/7GF17lS1/5fXI84eMf/1fZqxy//sW/z3/4M3+BKJEXdnf521+5zcFozN/7ylsMOOW8fI7cdyUgNqtL74NxRbdYoasRslxSjAd097/O6PoLpJBoVi2SMuHiHZ76vh/GnL3Ey3fegAh1ucNKzrjx5E/QHH6RJu+TRXMUT9BR6EIEQOUGhZBTRNl+5IyRhP/c7Ld7xgAAIABJREFU/3zl/cr7lff3uN53Ac4v/xufzcYqTIwMaouXjNCwaiydErbGhu2xYb9SnM1XSICzVJByxEnkgx++xnQioFcUpkCpjKkmUGRyjqALlAhoBzmDspe3dXJ2BMlk3Z8pkiI6qf5rrSisQ9uMU560mqFTR/YJRUapRJc7Cp1QywXYAbEJ2OE+sAItkAQQFqmjTIliUJCKJebJPeiO4ZunSDFGqwNQiZwSqpmC9uC3ofCs0pKisbiPPQ7HZ6zfuklhWta7NZOdbW599QRXDNnfdsi6Q4/W3H5tzBPPlVBAPMq8fNhx7Gt07hBnCFGjVaJJCUmqvwmloUzCLMb+zNVadOqQ5GhjwhiFwTEykR0USmu869upX1zMmZQjDhdzaq2RrDjvWiyZwjma4DG6IKRIpww2O0LOJPEELJIVRkPIAlhyTPzHLz6aNTgf+cu/lMebA7oUGY1rUmMQGmYnczol7B/s8tTuFh9ygdfCHAlw55xL73/lhSf4yOPboFc8ubGNUplZJ5fed7eGnJx17OyqPni3AcQAEbIj/jO8O+vA9kNiD28u2R3V3F2GS++LdUfhDE2zoio0y5VhZzKlzQ97XzWepCxDVxKT4cMfrVi8k/jVl1/ng9c22HCj/jgzOKJJlAJxbEiN5fjwlI3hkE+8sMPdd+HvfPNrbBUw3qz4oRs3+B8+/xqmSPzMB5/mS3ePubFT88svHvNzP/QkpS747ddP+a03bnHbVywuPFsbQ5p2QTyPl95TjlhVY0PL2foM0Qo32iCtTpHkuGiOMUYxHT9DrYTp5gBdaHTV98h67aUv8uzTn+D1176AViWSFT7cA8mgCpTvyGVBzrE/8vXXse4IuhbRg9573CG5W4ipyV1GvvBozqK68n7l/U/S+/suwHnx5z+VddZkEnW8IA4PuHc4Y2tiGNiC2XJFXTqyrnCmobSKZAy19gzrAeWgo9qwROuwrkS0QimFMgYESBmR/iirDZqYoRVN5zPWPPgda5AAYNF07FWJyidYJ3KMYFouVktG4yHtyjOqxrQTR1lr1MYKfISoCHKGm9Y0xzXOdVipYVSTmnP8/JySlvzcBuZ7d0h7h5jOgnek6RhTnkMS/LzG/V8J9eYAjKELkQikbEhR0ylBRU2DkEUh2RFjJIsmZumP67IhpdS//JiJIYBSDK0lZ+HGaM3uNSFF4ehIo1PmZL7g+s42t9445NhsUlYVqyZyTEJJZiiOJgSUsTQRUlRop0kpYZWmLCImV+jcknPGGYuLglGB4AqsFgbZoKxmrTNG9/9WEgSlSGJY50hSmpjg53/n0ZxF9Rf/x89fep+2HXq34A/+aM5HPzxiWxzfuLPk+mMFWVcMTaS0CifCpisZaPjs8xtMBhXWOZ4Y1v+P3u+fnbNIjrcvjv+53j++t0WpKo7zghwjZRZ+5a17/PRzB/zGW6f8+LM7TMRSbIx5bJp598xjNFwsM1v7htlxQ6EUxpXc2K15+94Fh36BNIrvvbbPxnOJY7Yw0nvfqm5zxgEg+FTTvvQqF/MWjOHLt2c00fRXSRvPqm5RUbOMisYUFB7O1MPeZ97gQ0QAWuHiZAVK8di2kLPwiZ0hP/bBPVIU/uDbZ6QQ+I1v/g4/8+mf4hf/zv/C3IzYu/ZpTo/+mPP1GiWZ0u3QrO+RrSMl0HEfW64IYUBWJ5SDEhefoFU3UWGFLXdwcobCgZ1i3ZqpKkAd0HWn2I1nWZy/BCVY9kliuJC75NahbGD9q3/tyvuV9yvv73G97wKc8//+z+WiGnKy8tA1ff8bqygsfaRdligDXQgYZ7G6v6FjVKQ0GqsiVgnaZBT9XX8k99kaIjknlBYQIceMSpqcLG0A7zVeNFEg50SI4CoNQBbTV37HhDUGY/uJ49ZqlOT+gzxmipxACykkEgVOCxI7Us6UtYYcwS6RQqEPCgjnyNij4wiWGYLrA6RlCV0BsYOugpjIAkocKQueB8dBInhRZG0JSZGj0Eo/iT0rTYiCPJghlR4c/WRxqKxIKiIRtLYkgRw7MoJSiiLBE3tw+3iBzSXVaMJq2bCKmdFgykZeczKfo60lhZZlMmxUY5wO3F15tqqapuuLpjOauRTEGKFwFMrQpoBYjQuBgGWoct/uG0GSxhqDU0KX+1tZf/43v/5Ibvh3v/56Pnh8i9ffXfDS2ey7vH9ka4Iy8JXZko9tDB/yfrCzxeFFg1XC7tb0u70nHvJ+t+suvXfLFd5r3pqd8sVGX3r/4Wn//8pi+EqT/7F3Bf/m43uX3lVhYLUkuQq00KiOk1nH3tbw0vvYFJBjH7gWiuvFNZb6hJaCHW1YhghOWMw1UTfQFRzH+UPeb6/nvfcUkWRYR4MXhTdLQlIsU/GQd588nXKYJKQs3D0O2NIiRhOWEePypffl6eLSu27P+fQnn+TXfu/XsLnkhU/+JK+//G1Wp3c4+FOf4Umz5ktf+02ScpBaZoslzz3xL+F04Gu3vsiHr/8IxxevsVrMiKVjnTcIcU0BjOsbXKzeRushKs3JMsbVltJN6dqbdGbMhtvBtOeEaoLCcPvv/XtX3q+8X3l/j+t9F+Asf/nnsqiKFNeklAihwRhDWbn+g9da0A+axklCIuQUMSmhc+zrYegHRGIdJCGrAnSDjg9uPaVEbCKSPGEdkNAHPtpWWC14H1HJEHMmeo+IYJTDCSQSFkGjUKLQKqNNotSJ0ij0RoOyAYZAC7KsUV6h3AKsIwcDSaEI/QtWkWwTVAmqDmUdEGG4AiwSFHpRwbKARkHroIMc6CvQlaBsRqHIJFQqyREykS70vXUkO1r62CprhbUapzIxeZYLRcwOHzRJR2KnKEcTTpcXICViMuJb5p0FZTDOY80AQ4vONVYrlhcNgwGIsRRti9JDKDpMzmT6gCz4zDoJSjKdUlit6VKkNg7vLdkKXQJHpLQFvotURZ9+boPw737x0azByasuiypJsSGlxL2L++zsbDNQtu/y+cA7OcM/4T0X6dK7wlx654F3Yh8sJyvEJnLrdMW6FSRE/s97J/zAxPKBjW28j9w+P+OLrSJ6T2ELQkiX3kdFgW/CpfeP71eUOnGwPWFiNSKRYjqAFlaxQXmFSHzIe3QPBqaqiM1CziPGhb/07kXxHe9BB1RqCBGOW/nner+5POXxasqr56c02iPZkbwmJiFrRWksTmWWXcvJSsDCeSPMG0/sFNvXN3nx5W9hzQFiMv7uVzhv8qV3t/kR3PIN2PkoVitObv8jdvQIMRZhTrX7wyh/fOk9uJrm6FVm6xWmyoQ2gKpIsmait4hmm2yF9eIUpRdMRk9z1hwzrD3LRohxTvsbj2YNzpX3K+9/kt7fdwHO6pd+NmtVom3A0KAyaN03pMMY+jRGCdaTjUdlA7mPaPs0perfHDFAHBD0irToCCHQrAyxCURxaDpU1hRaYQuH06C0wRYJEDQGZyPdRUQlAZMxztF1GlIkSodFgSgMCpU9WvWFcxIimv4pAEAjkCNZbP/edQGlBKxFBitwAk4hFmyKIH3WiAxI12ePdCKPV6g6w6JGjmrkwmF8IjLGZQ9iiALkfoRFzJbCCNoEtG0QLMqATg5SQLIih4KFhyiZ0GmaWBBCpk1gsKz1Eo9jbCzLdUCjyCx4anzAzAecE47XHcezgiYKTns6PUKFjmwsRjzbpeKsjRhqkhIiBqsThVa0PjNUmvMUSEkRU2KV+uxUp4RsNUZp/qMvv/JIbvgsZrn3HKAtoLx4sMF/t3dC3fdDyg+eWP9J7zaAtwgNIQS+dbji7cO7RHF8tQt8soBnNvYuvRfAkzsTQLg/n3OwPeKdY/+Qdz9vIEXOOy69z9SSrWlJFUpateTm8RKN4cZmBfTeDxuFXzc8uVWRUnnp3bg1OY7BKYZa0eTwkPcQPRZNRNjIBlVn5imyjoHTk0j0ATIMyhLE8Pb6FLJBtGLdJIalUJSKLWUvvQ+to1n33s+l4573rC8ERHMmwqwJnM6FybDg3q3XWFTXOJjW3LlzB41isf4qf/7Tf4E754GNWvOPXnmLd+/eZ9UeM1CZmHfp8jGumuLbCzaHjtmyxbpNkhKssujQYKsp6/UJY7fLwt8nAcYnPIIJDjVMxKhRtiT+1qM5quHK+5X3P0nv77sA585/+qkcRZCYKE1fHKa1JeVIkBJPJMiDoyUTqZRCciLqvo+CUgayRsiobJAEojyKgiwdaIUyGgmgdKRwjkqtmThFQ58OTQ8CEUPGGUEbwfIgU4NDIbhBSfKervEUxmIHQ2gbwIDuwPYNBCVqtElgLBDJKER3GGtJ9QKz2xF3I0a3KKOIjcUelrB2IB00FVEM1sY+MAt93x+1sP2pW+6bJuEtXarJoSNJxpWpLwCzHitFPw089cdvIWfarmQVMlESJjlaJeRgaemneitT9QXXQZGtRmIgJh58nRARprrAOsVKg2kTygZS6zlPkKipSOQEXcwoo9FakxMkYyhCZq77yesi0ErEGIPKGhuhMy2dcqioMDrxV7/yaGZw/su/+4cPeTfKk1Vx6f2kbR7yvlcXzJr2Ie8dxUPeHWu8GlCkFWiFtwUSoFIthXNMy8hBJSxz+V3eKxv50NhhyTRG88zGGIUwtEOS97x6esbT2yMGdoqXBWAQ7/6Z3lsSE6VoxDCtHLV1D3mPHcxpQTpSKB/ynuoLFAWHJ0tyhjIXeAnMUsOqM5BhKS2T0uJUwa6qKZzlqxeHNIveOzZz7j1vnwrt0W1GO9vcPl303i/eufTeRv+Q90YWWDOlSzNSgM1ygnWKLht8u0bZQOfPab0iuzFljIQcUDEjrkJrjaSAmAonHcKAGCdocwzeQ+VQWZMDRLVE2SGqM0CD/71HM4Nz5f3K+5+kd/sv4pu+l3V96vsWuwYoGzKKLMLxWWaxWtB2wz67YhKSM0prJBp89DiBoIWcA05pSmWoy4xRQuEMGMPSe5JXRAM5C3SJaAzzDtAtRmsKlXDakGSN0QalI0lZsEJhAWlh3WCAQaEhr4nLBqP6802yBqXIpkG7Cpwndx5lQVlB1x1BBRyatBR0N0DQ6EFGWyENVmgjpHWN9R5jFCwrrE4QBbqSnBTZCzkp1g3EpBDt+6cDnVjMaryAl5Ighhg1/sHZbdSGEIQoCh9BK0XIClLCiyHGTHYCsb8mHv2DoCb3NUReEkoMZ1qTpCMmS4fHqQG5szQKDCWYSE4QVYa2vwa+FkFSJluhECGIRiXojENEaHJEdCahsUkjIsgjudX3699+duOBd6FQikz/t/g/3n6XOycrLvwUjWGku967Ukgy+KRxyRN0P++lwjMpa+o6M7aJj0wNmDF/fL5i3SZWyf9j77rkftcH4kZrrBJGDhahQ1WOl2aBMieenAwoJHC4Thjbzx17drIFPvLW+TlGCQdVASqDUnQqUNYWR2AZBWVh0A6wdUcbEgXCMi2wRUk3L9CDjC87rImMO+HcCEp678EC2tNdFNTWXHq/x5xbhxEzzLSr2D/R68xcFKehZSkd58s1MQrro7sstSbqfrZaPL/Hmb9gtBzhL25CAi/QpAuyW0KEkECpmpjmZGPAnyM+Qim0bUSvGkQLmpYYh+hlTTQZtXqcOLpLlgLrMqTeeRSwMeOthbBGqUheZ2Jl+u8bE9ZkJCtcx4Nhs/H/X5T/AteV9yvvf5Le33cZnG//F5/ITtVI9mjdojWA7rOYRJwREhmTLZYCbSKSE9Y4lAYRwWbVH3FlS9b9qAIAdEBTksIaYwuyCEpbMB0iIErQWvedGmOCrMAocBV9+2ENJoJOZJVR6UFBsevQZoCPF5jtAltvQjWHQQRnIA9ARbjIML9ATjwsDaw1KloyDuValPHkVKGCJaeACqY/V14XZN2nSxc+0bQjYvR4edD9M/UZrowmpUyXISUIsUCUJmShDZkYFK1kIhBEPegMLeQMPltU5HIeVEqALvr6mRyJKIglKN+PT8CigShCzArQtDlQoum0IpL7mVNRoYyiS/019CZ4Cu1oY8DnxKoNtNlgCkWMEVtVICW1MXQSSDkTc+CVe28/kmHOf/ZLv5tTUBRO/1O9f2a88V3erdWIQFm67/YOlA+aJjYqUWpL23WMnGWdYGA0KXlaoxAlVEn1fycll97roqDxHtAUEiiKkqwyy5Xp+zzlwGZZc9zOGQ022ZtWUM0Ji8lD3l11ytmJZ7U656Qx3D2/QEXLcwdjWh+RlNHGMCjglfsLVDBsDhKnnb/0fuskksRx3jWIySw7x9miufTenNxklktSgtX8sK/by8IqG7og5OxJ0V96d873Jx703gFU9KQE4oY4JURpUdqi108gxa3+92+qS++iOkCDz9hCiNFhXSZ6QPXec+xQ0ZO7M3S5jTT36T9hPCQDhcJ6j4w2EDsFKpRa9xcpwoL8tV+78n7l/cr7e1zvuwDnb/25H8yT2jHQgk+hL9LSQhTBmBLEo5QiSl+EhYl9c7gHx0tK9R+s8p1zTiLqQeV4lgJlAzb3xVsWAYkYXZCzIvXVIeScQGWUGBQJYwwpCWggl2gJfW+YHDGqv6YoQaN0i2YD8rIvHk4FGU8OoK3BGAckEI0xjpwDxvTp0ZgMhS+4OJ9zllrq6Oks+DYwT5m7a0dUidmy6TMvKuOUZtb14w7EOOZRENWyP5iylL5AemJqxCRWGRyOkCNdDJRlQdd1CJl1TtTVmCIrYva0SVNJpiwsFzFhraPtFvisMVozGW/SrBZoZUm24HCxYFBWaG0hJDqtcEYTfQsmU5mKzq9JAkVdEyWinGVjvAnZse4ayqIiiyO25yQ0Igm0YTE/wejMrbO7j+SGv/uv//W88+we0/EGs3vHlGZw6X003aGZn/5/9t7Glqqo/197n8+/hSJR7n+U7vCboGHrxidYzjtEK+LxNyh3P4JSCn/vVZRumTz3KZavf+mf6r3cf4HveK83arp5w2RrSG4gWjAp8/bLv8vp8ggnCf9gRllMLbSaqBJc3EZFjdiAkgKVDLldkKdTWC6gTDC+hsqJvDiFwfUHD9gtoqZomZOJKOPI7YxMBuOgmPRPw3hU7HujKG0geihH5O6MItA/iW4/DWfvgBlAPYaz+1CXUG2iVjOgJA9rmJ+AyejhDjI/6q8tb+6i1yvyZEKu97HpMWJ8G12OIV1D2lf6DwhJ6GSRs9dAZ/Jrv3/l/cr7lff3uN53Ac7P/tCfzbFrGeaIVoImUBnhJAYMiTIPERFKQl/EqjKKgqQfpL5iSZc6Bq4POqJJVAyJBo6i0KWKzs+oyk1yM2c4KDg6fJu6rCiqPcrhitXcMRxXYEByIKXEthFiB5mCmYHiQYW7BMFai00RpRNFGrDULRvG0ej+ltC6jVQILius6ietRhR14ZDUzzOxGpahY1uV1NZwnhJRw9r09XStClwTRzYWiR1VLpjH1F8gkMyFEs5yTe0sej7HDWuWROKDI6P+PnhJxjPKipAjYi33L05ITYfOuh/iZoXKjpjUhi4EFk2LsyXn83NsYajKguwVTiUqq8iFQbIjpMiOGXK4mLFGMyoNVkOMgWFZU5YD1t0ZWhecdIEnXIkUJUFWLFcdGkNHR8igsOwPJ/jU36BrY+Dlu+88khu++cn/Nhcp0UZBKcG4JVEv+r8XCS27iAjoGYWa4s2Sujt4yLvXc2oqJGhStSZ2G5RG0+aE1gVObtFxA9MtUYUjnv4DcrlHVX0cP76DWka0e+rSuzWnSJySpCNTYN2aHLYRJTh1RpIdlDlF6YRudmirM1zcxZlAoECpE0RKXFaYWOLVmoiiUiOyXqJSRYdgzREi++iuIJiO0moaA4We4VVg1OySjSWrOU0aYaS59B7MCpf2CcaSwy0010jFCabbpW82ENBUZDwaBfoEsZa0fgvOTvrbOEqjtUXqJ9CjEmmWsLqNGuyQj271Hc5rB171LeWtg3oIlOBXWHuAnL+FUEKd0KZEwhIzOoBqD+avk4ohLFcwGIObQj6F+Skag4jvLxJg0ZMPIPkcjEH5NfLVX7/yfuX9yvt7XO+7AOdHn/3hHDUMqopCa9a+hQgXRjA5UamMkchajRmaAlskcgzgI2YywK071u0pm/WI1fmMxycT5oWj6zq0cWxgOF+fcRRWhM6zU29ybizBJ7Y2NinWc7bHJYV3fCu1PJVWTAZDzppIqxWZgG8Do7rCAU9tbJKi4s1mTTks4XzF0jn2phPUYsFisSAbmLqKyiVi23F9a8RqueRsrRk6x4nSDJyl0Jo3glBoITRrOjPCEagGByzJjG1Ecov3QswRnTUptiD99PD5ao52I1ZhSawsm65Gt5rBoKBZL9mcbtAI1K4imhqF0CZouzWkyEW3YFpv0+WEbxa4ekBRDiA01MWI6FuKHFkYRa1r1tYRm0DtCnCZLngMJVVVMV8uMNoRiWjR+JxwVmNQeN+hrMGIIolHFw4waN1fX5dkiLEjKYM2UGXNl77124/khu9+5L/O2R2h7eMEMRh1hPFTQnWMyYkoBpOXqPwsSdn+lp+PqHyGGu4Tl5EkL1K6PfzJK5jp9xOrCNLixBFlTG7eQHfvIjFC+RS6rMjSUoxvEJpzqGp0NyWae7A+hfE1dLNCyhqdlrC4j9l8mhA8Wn0PthQSd7Hs063ehsEQo26g/QnK3+0H36uaVHTYdYMa7JPCKWbtETshWANYnNYE1YIrYHZEUW/igcJ9CFEZSGRzSPJCZVa0SaFkfemdi3dR9T56cY80GoHegAx6MELm91HT5yl0R8pP4VxBEzNaZUg3+9q+7g66eBwU0N4jV3s48wSeu+j0OKQOq+/gC03hn0Iqi1p7bFGCy/gU0FL0jSvXS7Ir0TEQtYEQyZXBoJCmwRiLyhD0fQp1gLcWhbr0rkJLxhJLRZU1zed+4cr7lfcr7+9xve8CnJ9+4TO5DZ6xLSnyiigaXZTcb9fcOT6kqgYYVbBuZuxPNtHKslic8qG965w1S+6fnPLZ55/jXBQXacFGqVleWFJK+NKiq4Id1VJQ4LOhS56h0qQQ6LKl1h5jLV0Qgl8yLMaI02zqxJ3QcHQ4o7Y1WnnGw4qLiwsm4zGnyxZbVjQIw7Ik+sAsdvioGRRjnt3eJK7Oeb0VggRKW+L8jIHS7BUlKq1YKE9la05ki+fUirXJLJfCoerYn+6CKE6jgeaM6XhClzu+z4xZFpb1eoktC878kpOVcJEiVYDpsH8NtSsJGWx22Kw5thUjWzOLLbXpOz5bn7gZFcZKn8bMDa4YEEKHypZOWpx2IH3vhlIpusYzrAsaOjYZIBoanyEHrFYIitpYnFEUtsRpQwwrrKsptUBMrJShiIFO+hbqqnSsYgdAXLXY6YjPf/1zj+SGrz7713KRPFmXJHMPEY1hj6Tuwb0XUZv7ZClRFzdRe8/3T7ezN3HTzxLUTbj7EoPH/hWC0uTyLlG20a2mSBlfWqSwED0FBUoyqEQSg+glZa4JeoXOQzwNhCMcTyJOUxAI9pB492VM8QESJzA6wM5eIU6fRl3cRtVPIW7dp667jtzeASpUsUfpnsPLTUQ3KL+EYoe8/DZIja7GZH/S+6q3yfK95HST7Dp0MER/EzZfAFFUaYvYfRM9uIaoOSY9+6BwfY7RY3Jxi7hcgrToziNuga6uIa7f/B0FIQ1RdkpGU+hIzBWiFWWKBPqmnVogxTVFOegD8GwJ+g5GDkAUqbiPSzsE7qPTNXJ5B9M+jmiwAl7dufSu0zWUM/2xCoJOHutqQu4olcZng5LQ3yjJGbEFRnrvtB15MiD+5l++8n7l/cr7e/X2fgtw/swzL+T2QTVUjJEcV0xMDarA2MzKB6YW2i71IwAGFU2GcZHoQt8zR4eEaEVloOiEpsjkJIyqktvzBUZptt2EJA0YS5EVtjT83+y92a9t2XXe9xuzWc1uTnf7W7eKVVRRLIq0bMoKZVqWFUGKWwGOAziBjSAvNgwkgB8CI/9CkKc0D0GQBwNxjCQw0gcIAsOREyW0OopqLJIiVaxSdaxbtzvN7tZasxkjD+vqCvRrxXKhcObjAc4+B3v/9lxjjvmN78vDgUXseZIOnDhjUs++JB4sVyy8Z9KJfrVmv9+ymeZrmM4cW02sHVQvaB24299FxdhPA9q0PBsUfGC3PzC2S7rYsK2JkoVaNvimpZgw7jMqiaPlTaRxNK4l7ye0i5Q8UV1lQUtLz5VLmCZyVY5dj7qCaaW1ijYNoRQuLFCC4acJiRG1SFsOmDgOhw2lO6K1RBMWRBXOQ2E5lFlTpAcO1tD1cb7aMmOSgqkHTXhTorRkqQQFF0ClnwXKlrGc2JMIrqX5A4dREcboiVlBE9pFxlwIAosis2CujaQ80oQW0Up1QJp4+8Pvfio3fP9Tf8vm0xv4lKnDO/jFA5CGGlqYNuADYbya39v1PbARL5GqGXEOG0akaYjE2TLB53kD7m/A1Xfn1vTqDXR4SAxnmCVKu0K2H8DiFjY8pMFRJKL5Er96BW0jPo+UxQ3c8Aw/QW0CVjvMHXDMztNiB2L+8fkE2r9FkRVMNuf67LZY9wrON6h7jE0nVP1dpF3OTO0uQBK++TI+eMwHOGRyDDj5AHUVlx7QuA4LhYkPcCZIeonglUrBVHEhgCqIR1uo9V2iPgDXoPoBaKWOT6C7C+6AsyUu36H0jyCN8+TjsMdcA12L5+V5I46PcNNt1D8EU1y9j8bHOAV1Fe9enQvyMlHDI5At3rXU0sI0zK7rTSFmJaUEzRLkMJ+g9w6hIMuIDntct0D3f6Dz22O/8t9d837N+zXvH3N94gqcr776w1Z0nioOWjhuKykb4iJBK0nmMeVJAke0dF5YBkfvHF2cxcleAoMllm3H7nLDlS3IHk4WS1KqdFF5++Ix76TKeDBWy2PiYsEqLDGf8DujaR1ZJtoyi8NurxuGXLgsCTzss+fMd+xLmn1cYsCcUUJmLWu2ZeAo91wdruiOVqB7fBlp8XxutWAZ19xpEzfyJe1yjUml5RKwu1rEAAAgAElEQVTiS7wUZ0HuKgy8++SCD9wJr0mh6yMLGTlZHfP9i8csbMHDIfH7Q88TDTQ+8ITM1RRomsCzsuV8s0HbNUNVbjh4IJGfOAt4Bh4NcLNvQQLVKnGE7wflJS9Up2ia8AbmOoop3goHhCiQY6CkQovgxVPKPPHUdZFDcQRJ1OJmAbQZLiqmDbU4krc5NkIVp5FUlRoq4hyOBqeJ0QVajKwZ7xx/77u/+6nc8OWrf93EMmYOpqu5fV0nxMXZ26xuqFohrCCs8CjO3aD6ggtriq+IN9oCY2wJuyfgzyge8LfwZYf6Frv4JcgFDgY3XyEcrdH0OrQPkQkEpYZp3qyLkhfHGBmfNmgQpDjQ1yC+hyrgOnAGIdPUV0l8gEwNls5putfJ8jaWLxFbQvuAmI6RplDdOyx4ldRvsOkjFv2XuX1yTFU4Phv49nd+keJPiWVgtbyP5wlvfOkv8Ztf/x9ZxBOejhtciqi2WIyYFKoFkAh8hD19F47uA7Ndf3BnuNCDXs0TtrKGfJcaHxFHyEcH4tRTneJlizdI+QhpB7wVShUsgEnE10IR6NUzppFqho8dTiI5DPjJ4QOoGQTF24oyKbXLs9lnqkhZ4JyiMiDOIX5FznuIPQ1GyXu8c6R/+veveb/m/Zr3j8vbJ63A+ctf+Kp5qVz4QD8WcuNYOEFxULa82qy5QvHes3YdtWaKg75WUhvnijcnvm9KUOgdnDQr9mmkemMzjrRdZJgmFtKgzlNDT00TJ43D5QFrO1Y2EsKKoRxwNTLagUEcx35NGrdoCJz4yN47LE+EWslNR04Do48EXVD8FictC8vsqiMWwYVpzo8qlRLm+Il9nhO1t3qgauao92x0QdXZ7nsla3bDSHCFq8MVTit9UJrQshuu0K7Fzs9h1dAPkdtLx3evtoxdy6q5SWwWhMajtmW933F/eUypI2vXcJkH3hwSnQSOQyW0gfPtgKK0zYqgI1E6St1z1rZk7yhF2YywjC1aKhMTmKNzgcF7SilopzgzsJ6dZvaacWo05ul9ZPCCLweq65mmickq3qB2QixGdJ7WBwoQQ8u3P/h0Fjjxp/4jwz+lNpGwX0G/werp7IvEmzT1c5hUigES8eIogNZMbFtqLjRipOYZdTyhd1CkweVM9QbuCbhbqD0CaZFyRnGeoIoHit8RbUkiEX2HWMHVyBQuED9hh7vgr6i2JIoHH7A6Ed2BZMdUuSTYEtWA8xNOWooWnAoNoM0Tiupsk2Dx+cThTYJFJvf2PN3oAlJfwvz7APj62tx+Dx+i00c4rahUxB+jm7dhsSY+/hBWDSW3+N5RLq5g2cPxD0N/ihOH6jnu8kOak8+RhwtqWCPjIySdo9LhJKPHd+HZe/OJeHUHxnNcOEbrBcgR0nSQL/HZoDlGSwW7Aps369o0+GlHbWa3XQknWDlATqAJxIHvYbFE9h9h3W24ejZ7ZVmGrpknWZyHZgE24Zb3qb/0X1/zfs37Ne8fc33iCpzPvPKjVqaE954udExpINSIOjCXGEtFa6ZfrFi6lr1mEE8ngjgHPpDzhPcelUxSQ8eE6zxNc8y039NKRr3RNz1pHGidobkgq47hauT+KmA+kLaJqfNcXl5y1q7wjXCzEU5Cw3vjOTIYfuFYujWNm4i+8PrqAU0YWWqkyMCq6WhLYq1GnyOHdkcji7kwaCrDZGxrYXfoeStvmfCkXeRRHJiKY8lEtJ4nBplELYE+Zo7FWFWjX7S8lwZqPfBS34Blnu4Dq9Zzu+tQURY4zlYBpj2dX5ENNpoQp9zSFR+Vgb4VMpU8VvZ5ovUBgkdTZbJZJxPEUTEkVzbOqD5Sc8Grx2nkibtiaiJhd6ChYZSO6gWXEuYbYvSEyWiawNN8gH6BbkeCM9y6pcWTc8blluInvAPLI23o+dZH730qN3z5V/+msR/Ae0K3pBx2iHSYA3SA7RZqnk9pi/VzCwJPFCGXDuw2Et99wbuqwW4A30L4PPA9GAt4g/4I9hvwDtENtjiG3W7edHwLm2ewOIbHb81/LyguLFBZw/Qd3JDQbo10L2F1A6ES5MfwVBxQZUcrK8ZwxWq4Qaie/fo7tPmH0VLJq2fUvIc84ccf4mC/OY+iTgvgA6hAHYjxBtUJmp7h0gK6gpliOeEWZ2h6Spd3TKsFVhOMHh8Dsrg/Z/fUTD0+Ilw9RPqXqGJ4Hcge/LQGdmh0mFdk+2R+DfGEsMJyojLinVBrTwgjNhXUG+YjIU0Ua0BW+PqQ2kR4znsOC8wLDCM0HXQ97Kf5/U076I/nz9MZnKwBP/88z/5SBIPDgCxO0d/6R9e8X/N+zfvH5e2TVuD8jS98yfqugeqIZoQAKxMObcdQEn01LnPF4TkEo8FRTfCuAZtIpeDMM4nHa0JjQynKWj3JJa60snYLXB1ZdD2baYv3nt1UkSZwyJBzpvXQqCA10Qbje9tE20XSoBx1sHNh/nLliaucuNEv2Awjq7hEZIDWcbE9EBVC7zmpDX/qtOf3xsyfOO748GrHT798RvGZzzQrNrsD7x4m3nw6kg5XnB0vaF3hxz57g5dWBZkyz86Vo4WR1QFG37SknJEYydPE063nsR6IbonQcHc9EYtw0MrSO7L6OTjUwUSiJT53FgZws5vxc01RrRVqYApKLII9j1rwYiQiOIFsjFbJyZFkJGdPDEu2ueBjwTlH0YyJR6ujjZ6cRoo1ZBeIFKQWto0j4wipkiTSaEW8IxV9rgcy/uF7b346N/w/++9ZbE9e8H7we4IFkFs4LWiu4M9RW0NzeME76Qa+ffqCdy+OSTNit/DuHK+B5BKwx+mrhPCIJDdwPKJqQGrCXEOwiZoSFhSKhwSEEb9/SF2ewu4Ayw58A7XDTY/QcQdHt+YHxOoeTI9wfYduLmcvjEbwskCOP0MZN7j1GVx8n/bkq9TmQx7c/AofXXydtNnD9gP0/DHcfQknB9Z3vsDtsx437Xn0dM96kdnnmfe7R/e4OjxisbjD06uH5L1nW86J4QY+djRNoa2FwSoL58ghYEMG11LcnqALKtML3qs5qkCVhFiCGhCx2XzzOe/ZKaE0L3inq9SDYm6PUyO6u6SakThiTcTSiImHorjFArfbkmVFEyNaB0oZkUXELMCww4cVtU740CH5AnFHaN1QvvG/XfN+zfs17x9zfeKiGr5xZXB5wASGXJCaCKFByBTTWXhsRrs8RXC40KFWiHUke8Vpw5guiC6SpCI50wfhatywjicsAnw3P2Vpxv3btzhxDYMeuLfseDhlXloGlsljtbAMlTEsKHXgweom1RuHOuImoXezT827F5nX28jZyRGd9kxJ+XBvfG6x4nGz4FKh5oHBRb4xBc6rsD30VBd49/2eP6UP+YtfecxWdtzq7vC3fyLx5KJnzcRJf8LVdEkTl2xax6s3esbdnstSCLVlnDzGCONE6I75Ib/hlvQgI7++2xKe3OFxu6VNmVuLY6aSsKxYTOTaMJE5DQ1XBsF5DuPAza4nuh4hsOeA04L3gX11hPo86iEYF6NyC6Nt532C0tN3lVoSizbRGyBQTRldIAClJKr3hCoUVxlo2DiPpIr5hmQTmLEV6KvONu7Ps10+tSsdyOMzcELe7KAmamgQfodiCmWYwwVvvgr7hrRuUSu4wwV5UlwbKc8+oCyPQSq2+Q6lyZTDFhdvIa1Qx+9AqdiDL1JtRSPn1HgP6mNKs4TqiVLIfaJf3GWwp/TNz1C9kVcPqXl27dZO0HEP3RFh/Qbl+AlhcpAfweoltLmHuIDtPkDDAqeGa3tMA7a8hw6e+uw7/MyPvc6v7J6wW3X8rb/xd/jH3/y/+dLpfb74+hf49ttv8dqrr/Mr3/5N/tpX/wS/8+av8+az97i7eJlnA0Q+JG3f4e7xD8Hyks3hFqXd8XC3g2d/jPH0e5TDhtTfwnaVYIX8PHFZ7EB0DVVARKjpQOxXGAtETzCucJrQpuIJaCp4SYQuUopSGaB2iIcoJ0xhItqI+QGpI+QWXzLaHhGcUPd7NEZ8FZKNuLgk9gvY76BfYVJmoakoUjLa3JgTtLP+y6byX9y65v2a9z9C3j9xHZy//sYXzaqhznMSIFmdjYtswEmDIpjAqTm6xnE5Ja4MTmpDXEDnKw0dFit9Y6w08M5F5t5RZNUom1J5uIFt2uEs8t5U+PzNY8ZhgBTwoXDnpOd7F3uWy57t1YHjDrZTIIsjG2yZOBHPrkYWsbIx4fx8x9QIzjka6Ri1gvUsYgu65Y81e5zvqOYxCej0jJN2wSYKD0Lg0S5xMMdjNZpuSbl8wo1S2eKwFh7vDxx1C6oIkwkJ5eWjYx49u+RB23BuA414XAfBApuq6HTg4BouFHJWksHieImMhuARZ5SsxMbja2WtntEZ3nvMV8QWnLUG+UD1nn3KZBM0Zb54esYdHbi/EpI6ch2Yag+SqRLxRUhSOCikqix9oFbDrDI6T+M8wvNQ1Br5fRcQndjngbUvCB7wJPH0pvwv77716TzR/tS/a+hE1/aMWWmcUZ1hdfOCd8HmMTXXImlHESFMC+pSwFe6/ctMy6ezMRkNNh7Ad0jvqNM0F8HThqANeTiHmz8E4xVt7Sky4BY3KMNTbHlEc3VBbQTNERNHCA1FtkgWRJb4xsglwdN3oGln4WC4hbgDtdyARcQNG4I+xbozTN28+e/fgvaU2kai9Oi4o5Q8380f30Ee/RY2TuAbXCvo9gKWJ/jg5xO8Ffz6NerFO/i4oI4fQnNMWAglL8FGuPoI1qdz4E7WOaX67HRuoePnU/k4Qt9DrgQJFDLEFizh/D20i7B7Hx86bDpHTSBX5MbnYdrh+jlgVqdneLlJtRF8hy9CrgMVZSZbqNUQNawJOAKFBk+m1h5pDMmG1g/xcqDaav4fpcVZon7jf77m/Zr3a94/5vrEdXB+4oEjpUDBGDJ49VyNF+Aa7rQ9T+oeVUcpmU0p3Dg94abOD0cRx7N95b1ywf3Y8fvnAmVPZE/tTzkZIkGN6aA8WZ1wVgO3OqWtylMzsImzxnM+7jlrPTe8sVv2rFYr2s1j7p0e8Wtvn7M7HAjHaxYh0WiPXV1xY7kkWeVQI5dlz9nqiK9GxxN23CyVx7agrweWKF1/Qow9t7sV2SbMeWJTef2oo9oF37/4CG4tOYkvcbt1TMMznh1Fjo/XPNlespHMGyen7IswLVcc9huWTU9KlYPvybKncYK3UzZeaDYZWRoldLy12/DakVAkEixiLhGsJ0uACjsRBq+ESfl1PzsbvN6saEypR0IahCl6nkyFt53xmW3GakfjO4IduInnnb5wx1Y4FSw6ljKx8Y4xRJbjSC9GQdgDSZWJiUWdHaMX4kkqmOsYRam1snlhy/7pW/6sgbFjxHBRMIWa34VmRVdfIrWPUHUEU7JeIst7dMNNyvoRQRxql9TwO0g9Q6aJPF2CPYUbb7DYvETwVwwDhKO71HyE625iz2PWTLfIcoFwhe89Lnt0cYNOX2Zsfgtr7lAe/y4yXGLrB4hcku0WXLwF/X3EHcACOr0LR5/FxwaVHT44ktyiGZ+gUnDd55D1HZr8eaq9TQ0RXGVxckTOW+rTr+GObsPJn+O0XbMvv0FZ3SEsT5n2DwlhJLafxZzQyavk8THx6EfQ6YA0p4h/TOUU393F9wG9uEQWRvbH6OZN3NEJxoJgkdJVXF3jO4c3j9gEjaPsCuoOYA2hfQWrBic9sldcB1UN3IY6ZWRYUKUj63sEt0KXIOUMEYf3HqeX1NiirPDTJWjBXETIOCrIFuiorkIRqh4R/W2sOWBpR3WfuG35/7d1zfs173+UvH/iOjj/wVe/bKEal2Ni1QSMhkOuBFfJYuRihBqITnmYJ243LU8PAyUbp8vjeYTQJ54OE2FMXC463BQopvRkYmwgJPZTZPCO1kcUpWZhSkrOA6M06Jigy+D8rEcZoDQezWBtReuez3anvL5WbgWHy4kcFrTiad2Om76hd5VNC0577iwrbZ1Hnvej0fZKqZW1CeujjjQcQCInTeCjq4lu3UDe0xdlJDJV8ApXGnAoqy5Sy8SQK29eep5d7Hm/Om6slC/dPOGlsGFyPaIT6ivZFng8OlQ+HAPPhglpM/cXCx4sFuzqAS0DpVng9on9UnB76FyHtTuSwX7KHIoS7RRBiaY0pZJkAp8gLxCv7LLgXIPaRBGPWKUGRymRLMZCKul5tozKrAuyCt57ssxRnmotxUFSISb4j9/53qfyROt+/t+3UI2SB6IPJPq5bWwVCYYVQyaP6xJ1GnDNERwukaS4xWcRb9R4AcOGut/iV7de8O7YUZseH0ZkXFKaCs0KXwdqbYjTSC4XiO+x7QWuVXAerRWGBK2DzJx/M+6gv4k/OsUkQjngwgkiEdVnxNDja2WMEN0NFtFw/oB3jmGXafpCqZXjEji6fYc6HXAu8MYrb/Brv/HLvPzZf4WLy289jzKBw3iJV9iXmffT4zvshoeMk3A5Znj8HsU87khojl5nGeaJRdWK+krRJR6PTCNp8uR6QfKFrr/JzZN7XG6f4vKGsV3TpAPaOGQCT4e2hWRgwxOqKLHexXmjUnHjgPo9dRrw7S2MhBTQ2ODKhIpDrKJB5twj8WjNSFyAjlQWeMu4DDk0+KD4mqlhhbmCFSUmGL/xD695v+b9mvePuT5xBc5Pf+ZHLKmhMqLVkyxjNGCVqRYIDaQDTpUSPI0TmrxnvVqxTSMhNLTMd45trVgbuNMeY2b4LrFynqVrWQajbz2dOC6udoQ20AShcwkXIn3N+ODQ2EABywXnK2WseGkIkoixIeXKR2OmCcLJesFSCl2n7M0xPL3ieNmy80ouHXecI8bMrZtGr5EuVoSGYWo47CY++HDPk81TvnRjQd9m7txccvXU0a0zm37JiR4433gWC7BGkBhwtoAmYNMT7KAMeUD6I/KUOFPjUNdsp5F9MnwQtESmsse3LapzWuyYIZvgn+vKaq30i4aoyqAGfnaCrgQww88uB6Cz2aL3gups1DcVhw+VsXpQwWqhIpgJxQqqkEomS6TUipqfzQjVURzUrMTYYKZ4ERSoVvjP3nn4qdzw5av/jsUiZHuMSI/kA9ocIdMe0z0sbsDFRzhVrI2YbyFfzBMm037+Poifs2aKQdtC/zqY4bqEekewFjNPdY5OHGk6p/EtEwEXCi5E3DjigyOHE3w9vOA9pxH8itZ2yPKMPAyoHsii9M0p3oyTsGA/QUrv0rQd+wiUjpXriTHzpc9+lvXZK9w/7hAaHm8uefT+Bb/97f+H/ZO3uX//Lste+DNf+jm+8b3fYb1a0C9eog+XvPn0gnW/4Cy23Prcj7MKHqLx7rd+mfNx5MnVOa/e/iHeffYWS/McL1/l3ce/w8XwnHfpqYenNP3JzDs6W0tIxBWlBEfMCWtn3keF6ANYecE7Ol/n/vO8q1YMAVfnNEUVVLdUhKAtVQ84FJ0ukXg05wVZh/kE1mKSIRe0XYHanKitcydZf+P/vOb9mvdr3j8ub5+0Aucf/Bs/buebDXEBd46WOFNydNDskNLRmM3BXVbQ6piGkeOTnvVRR5721GqUPPHRkwuWIbA+8RQVFvSEpdBoxDRQLRN9RKJSHUj2SCzzjWL2GAVniZSNpTOgA7en0TV4JbqK5kIZjRDcLLLyezZPjH5RuHevg41g0c9xBl5pImiuBO8IMlJCJLQO5xLOBPUFcOAcrp0zWOAGTC3UBhRS2uHEGKfK+x8Vnm4K6m/T+ETbtowTmCaWy466vyKRGCePV8V5xXljNxSQhjHPhn5GQA32k9J3gdiATYL6WY8zVket9bmbsaOmirc5ZNTLLAZO2ZgnsRRzlYonqGBmmBWyuFl/ZAYoVR3qBVQoIpjq3P51Zbb0DlCym5N/nfAffvf3P5Ub/s1/+z+xq+130Vj5/IO/+IJ3u/waUjpOXv1pnMDurf8Lu/FjPHrrl3njK3+eWzd7pkOiVuMwHPi1f/b3OdEbPDhyFBXuvvKThKXQn9zENJCePaW7dZMYjX0yJHtW0djCC97XVrm8esat1SlTF2nTiG+WL3g/2B/yvnBG7wq/8du/RRsP/NRXfpa6UWgKdVQaEfroGVKh7+eSuITIzWV8wbuMkX+e99OjJT0t7++3iC+kac7T+ehJ4X/9xj/hg0dvslx8Hi+V26dvsBHl8PRN7t/6Ak/Ov8nF/n2GKdAa+Jhw3rgaFXULrAhOJrRGzBnjONE3CzREXJ5HY5GOWitiihOdJ0+s4E1BIiUITdU5QgCHipJJNNbO1yDPr7rVKk77H+A99M8fClahDpgu8X6iaMQaaLVBxwM4Yfr6/3DN+zXv17x/zPWJK3C+9nd/0i4uLjGt9H3k5ORsjlSwht2UoSbyNLJYN/OIm3dEH5DKXKmOA+uTNY2bH8rjOJImYxEa2q6jaMaJMU0jTlpCCOQykeqs83ARTB21FkopdL7B+4hpxmnGSyCakMcJKRmriveRYIZa5mQliFW8Mxo/X8PkanQh0zIHTprWGXgp1Gr41mBl8x1U22PTAVGZQ8lSQXcrBnOYm7BNw4AjZ6XonDRezeZOCMakgYn5pcQMyjyJlD3U4mbzwAK1Ks45Us54H0E826GCVkIIHEqZjfYcFHWoGWMtPDlkDkOkbQodmT50lByZ9EBuWwqGdw6PEStkrzTqqVWoAi7O5l256jxh2XTze5QrOWeyh6BK1kqqilMhx4b//Htvfyo3/L/6D37VPnr7gv1bv8C9H/0LHN08IriRElr2FyPUxHaTOLsZXvDedQ1SYT8W6vlHrF+6z3GJZHcgTcLlWDhbtbTIC97LJPOYrC/kKmzmNBRWjac6ZT9W6uYZ7foWyy5gUn+A92Ecefbet7Gq3Hjtiy94f2Uxt6idNC94Lzqy8BEfPLe6+AO8P5sKN5YOLx145QsPTvn2B+cveNe45fLKM6WZ920GyYlvv/ctisLT3YFqBniO/ZL3d0/JlnA28+60JdWZo7SfqJoQCqk6olOGbATX4JxyqGBTnm3zteAkoE4o6hBRMiNu2lJ3Dnoj5C3a3KCUBl+3+H75gncVR52ErklMuaVhmqdXmhUSIjVlFGibk/lqNo/zWLQvMGTUjfNkiQnaHGG/+t9f837N+zXvH3N94gqcX/y7f9LGSYhNoAmKVE+uI7tt5ui4pZS5Q1GyQ9QYdpU8bQnecXq0xsxYdv08kpZGnHMslkfshyfUJEjI3Lh5NHdxmCMDcjGmbJTi5oozOkwTUoUuOkSE6WqPM3A6513NcCVWyw6tleiN6B2d8zgZcQK4SsmAOhqtNNEQl2gI+KZiQZFgIAahQihzZXIIsyougxWQqUdLRkrDkD2ZSLXCYQKYnYVLyiiGVqNMbta1BMeUlODnmIneRyZTRlVymQW+mOLEU5NwlSrmlRgjWjwinpwnXJyvtpIUTAUzj/eGoDQW2HmoKRO8MGRFqiPhMOZAtSoBs4rJfLIxM0Q85hqKGVVnrU3B2GXlQyrD1LKVQqtCLoVfOv/wU7nh/5X/8p/8AO+0DWW75+nTyu3bkVKgZuMwFkSN848uefL9rxG840d/5M9iZpzduYNJIdcJ5xxHvmMcdmwvL5CQee31lxlH/UPea8tVqi9499FIWpEqnCwEEWHz3kOcwdXhMTcWd7kcHuE08ce/9Md5kgp3G4jesZRIqgeEdvZXyhWxTKyBPjpqA50op32DBSVM3Qvem2jcaVa8v9m+4L20AzL1nI8jvsIzqyyjY9gP/PY7v8sf8P50ewViKMJwOLA/7OlWS9JuQ4hrtofHrLs7HNI5m6y4Gkiq1Lon+hXVhGm8ms1BwwKTiIinjpfQLSBXQPBSMZuT7lUNfEuVgpQRJKDTME8DVocwAqDicVJmTVkxnChqgdiuqDoiGhCpFGYTUtDZR4SEuojLifqd//ea92veueb9461PnFz/2aNpflDWibRJ5Cq0UsgUOAjNUtkeKssQiEcdZ6vIcGgYr/ZsLg+Ir5S0w8XM3Zs3SFUZpme0oRL7Zk66VtBQKJN/rvEQXFBCEYoqbTacwmjKZjrQ+4Z1tyCjjNOEE6NxgkhgqhUxw9RQLYQm4Z3gKNQSqbVQkxI7h1JQc3SSweb4eEzRmHEugmSoDpxhOc4dmFxnGJxnVJg0M6VEqcbBlGg9Q82Yy4Q2MBWPhufCaIuYGFtVmjZylStWC1ojTfBEA9HAUPZoDLMbtHdgnlGMKhXf9lgxki+UGikoicAwFkTAkSh4cjV0mr2BihSCNXR4soNOjBJmE8FaHCZKphJkzvHyGN4ciLF2xucB6zJJQFTJ+un1Bfne7z5hfXTKfvOEx29/na3sOa4d23DJ517+CxzfPuLRt36BW5/7Oda3Wk4efIaz+zf4/d/+R3zrm/8U8ZXPpS/jYuYzrzwgqSeNB9pQ6e/fIE3TbEZmnqIFlQ4zYRWN3XPeGy90AvvLC55cjPS+4eb9+wxU9KFQxLj16g/jpPJhKogZD7MRcuVeSHgvs/9GVVQLkgPWzBYAlisnfQPmuOUdrBPPRs8X76x56+KKR2lH4x1T8ujiACNspj3Bw3bv0AxvXr5Fqcaz/QVqDZNmzGf6pqVhxeW4RfqIqiLNkifTOWfL+3y0e/yCdwlCcJ519zKH4RwnELsl4ME8SRxVKm5xhhXDh5FSPUUMVyO17BCp5Gmemsml0hwOWBeomnC6QP0cOCCARcjqCDWgbkTdQJkqVJ31I9ohMhBCADzmG+T5Fa5J8y8Xyn+B65r3a97/KHn/xHVw/t7Pv2Y3j1Ys+xWHq4E0jJjP7EU4QV4Y/WVR0uCIJqSU8Ai7yQhV6OIR4hJeFXzi1mLFy18+JbiMxYpppqoxqUCFTEBNyFMFFMMxUAmuJzbGSoy8PTDsBrx2lJq4d3uN5EynikSPHjJX+4Km+dqqi0YpnpQSbdPgpOIMiEIjnsCIb5QjHxnHDaUKySaWixPyCKt+ZHWqIInh8ohUD5h6NCkYjBsAACAASURBVLQInkYaio9c7A7ktJivd2JhnBRfFSEwZscuVw6To8qEcz2HVBAiY5qYyoTzHZPm+RQUoPWZ1WpFHSu1VooFRCDnuaDJNIx57oxFmV24TQOTFswppoFSCogniQI6C4gRqoNaZt5UwDkPVVEnqCrZAoMoVYWshprgDYpz/FcfvP+pPNEu/tzfsdff+CmOXj5i+9ae9x7+Aq217LNx0ngu6+W8CQSlTjbzrgc8QhnAB0X8MWJ5Tl/3mePVGf/Wv/nX0EFplh7TzGGSF7xXIsVXtrv58zEcqRpNDMTGOHaBIe3YvfcRD6c95fwd/vTP/WscJXnB+9V25Lu/900+OH8bq8rx0QmleLZXjzg6vvOCdw0z70syL915ift3XuW9xxecf/jrPNw+5iuf/zPkEdzxCT9yfw2S+O5De8H7ve6YslAaaWi08ovf/CW8rXg0PGUqe7aHC5a+QwjsxoHD5Mm1UnPCdR3TtEfiCXV7SbUdzndInXDuiNx54nCFnb6Ms5l3X2TmfDrgQoO6iE1XuCCoNHMXl4aa9i94d2XEXIO5AsxaMhBwStUMQDD3A7xTC+Z71CZMBfKIuAClEtoF6bf/j2ver3m/5v1jrk9cgfM//eXXLeUWi5ngDR0D2RSiMI3GoEYqwjSNCIGmBrxACRM2ecwZqrMep/ENhT3ZjFg8XgMlHVDpKa2xVsE7h6WCNUBQ+qYnlwPrumJy89ifV4/JHN5ZEJyDNhgd0HYCWYj93IJrK2j1mDsg4ogUutbRRqHxMovIPDRBWC4aJOdZ+LuduHUjMJSRRduwWCux9zCOWGoYB2N3ASOOqykwjBl8P183qaKjMarifcDUIxLZTwcIkWqG6ZzPdZgKQsCcEOb8XPogjNWRUDxGLZDEE6nkAiXOXaW2KsUaCooZtN6TSsULTCXP1TpQXYPH2BtzcJwJIczaoDk6z4ELc/CcKM4aip9/1+kcz4AIqVZGE1ThP33r01ngHP/83zaTiLrKypYMdqBqpfqApMKghkgg6BVCgNGjXilhwqVZPNgFGIYR53tgg+qIqwu8BrRcEl1L7hrIhSBLtOywOAvnXexwdcDpkhI3YM0L3s17JCvOAa2jyZWmFWqNaDPRyMyKVg9hBIPew3rRElzDenXG9vKc6jrOjhbcOznhJJ7ivPGr3/4Gf/VP/+v8xrv/mD/52Z9lfRa5tW643O2x1PB755d8+J2vM+J453IkpyfEpmebDiQNDPuM1WnuMKonSM+4+T5ufUqtRp02+K4h7c8J7gQLASsDhOWsu9MKEnB1glSwRT/n8tSCb8NcuecCfo3phFXBtwt0POAFtCakDABoXCNqqCgubaixI/oOM6W6iukc2igieHFU7ZEwPwj+gPfsAlYSUDAF+/r/fs37Ne/XvH/M9YkrcP6bn3vdpHjM25z0Gh2x8Wgxap0ge6ZqxFCpU0C14MXRdhHXwio42rZh1TuwytX+wOGysli0PDhZMzZK10DWSh4nolf2h8T2UDE8U1VuLCLOQRkHplxnR0gz8r7imojXSqoesUzfCKENSNE5al7nDlMphZuLnpwzvvNkMZwam4PjJFaSKKdLBQLrpuFik5GxY/KZpo4sW8+qz6Rxy6hHbMfAYTS8i8/1LQdUe8Tvie2SKVV8MyvbYT7pRHVEPZCrZ69+Lhy8I1dwE5hsmVgyVphqZTKhsUDOmQlBn+dYWa24+Nyv0pQC+Go4J/P1nHegNueXmGcogvMwaaKap6pgzqjqqAqTg6qKoX9Q7kD1JDGqKgGjYi+yr7w4/ov3Pp1j4uu/9Dd/gHfzkbU75sCWWidcVibVF7ybz3hxNM0CvONWd8bypS9zdtyDVR6++TUeXj7m3uI+P/kzX2EKPX0MJJv48Fvf49Zrr/P973yTt87fxPAMm4kbt0+43z/g+1ff4uJwwbK9CWZcXF2y9D0WBixHBs0sGjhanSFFUWvZDh89n5QT7t64T9lfQOxe8P50u+PesmGTd3z29mtMCF+688P8s++/RdrPgYB1f8H9e6/wxvENfvPNX2b0a7ajkg5bFu2SXVLMjWhp0bDnaHXMxXag7/+Q90V3SnARNz2iKDwZKiJC0zgsw1gDpVzg/TGllFnA7kClxfZX0EREPZoOaNph3eoF7wAuAz4jZnMXtc4BsjiPK5ARjAHBz+HAfkJrh3OVIhmpCjIL6wGoHuoIIkgt4B1mhrOEtkfYb306x8Sveb/m/Y+S909cgfPf/uzrJq4ByWgyulgo7g/HlK06JASYIGvGSUAsEzyYOdoQSP9fe2/TY1mSpOc9Zu5+zv2IyMjMqpruEaebEmdESgIkgqQoaCOBC4GAtNBC0JI7bvQv9EcE6B9oryUXAgVoIRL8EDEz4AyHM83pysrM+Lgf57i7mWnht2s4C656ujoVOs+mAsiMysy47z3X3NzsfatRl5UU4CkzlcRxl/jqvbObleul89KFl3NmWVcg493IBCmDu3NMhrVOyQlXkK54FIhGnsZrsUtBVoV0m8lRQ8WZQznkwN2g7HGH1Tq9GyIwa/BwV/j28cL9PI8XWifo65hlwfgPvgnmsiDmvFzf83hyVk+ceoALa7fbHbCT2VOtUYkxwa9jI+yojTnPgKNzQrtz7s4aCXHBCNbaSHni0oXuNqITmFgcxJyuQb+Fsq29YTKEmUPIqaN5ul2zCj3GfxNC8xXzgvdbcCbDY4fb6nh1w2NEX0QYTdLYsvLb7xOhM66uiOB//dmfvsoH/pv/9u9HpJlwQ3ofNvVFidvDwHugZSYWo6eK9PLn9T7vuNYr0le6JgpCSKLsMr/xo7f8+29+yh9+/l1ezrBcneVyBjIhnUygIdRk3Hmjm8E0k73RdEZdIBqUAoBoY1+gA+/33xBcsQjmUN7dv8UleDe9p3rlTx5/9r3e37x5z08e7vkXf/zPeXf/k7FKKhNK4/PjCcf4O3/773yv9335hv/zd/8vlqvx+PJvgB3n6weqZZp15thzyQ1vRtRleKEAky/cTwfA2R3v8No4tc7FBSQR0bDljJS74RGyVpwg7+/xatCudIGU9wQdlme6zqQIxDs9Ozq9Ga+RZLAFTxNKJuoz5oVkfZiD4qgGhBAeCAvRE5EKvn6E6Q0wQq9ZT9h8D3TEAiLwf/YPNr1vet/0/kvyxQ0ZpyKYLfQ6PoAvp06VRK2JXGIsGa3GPo81uaoGnkEKmox6avTeyZpJEdwxCo1uwqcTTMVGlyXBIScKTskdlWBfMt2N5/MJiwPp7ggLtBaYBSUZroq0UdDoFHTvJBFaCBqJbsrSndYMoSBtHeLRzKRAjEp7WVcymcUq2RLVn5mnAxnj7nhPsU/kvdBtx7I2KkK1oK5C1oz6EIsJtLSQyGRfWQwoM/XieA4e3bG1kxYnzDnkwuqNOSe6OU6wvnROUx/r8VHofWHtnYxSpRDasNqZpoL4sPGuGrSe4HYd6H3kY3US1RhdHRoigrROAlyEUKF2MBUgxuaXOGFCDWURH+7FfjOYEse/rBr8L5aUcauEOT06pVdqT/htNTS5YOuCuiIBlI4D1YfeL5cz9BV0IlmwalDEaFflj/7gE3+cvhuviguH+Y5UMqGBmrIrENFZnj/Qdu/R6e0w1CSN3Jhf6N1gQig759ogF/i8fMub/W/wtHxgCsP9ilD4FJ/RdMViz6TQvCBufH5+RmzPdy9PZEuYf+arr77h7f0d//GP/xrFOn/l/Vv+5eOF7/7kD/nw6eesrbFczhznAFNSQBVY8sLcMievsFwo+7fU1alW+dZW8ssz/bjCcqW8/wq7nKDskL7iEsTnj4QafboDT9TTt2h/JsUB4YjbiagnmO7JXQhdcc0kS8TS8LjgVdE4DedvhwiBvNJvRXquHU8FL8B1wXf72xv2AjjaK9EDnyYoCsszaXqDqxPt5dcqyV8pm943vf+Aev/iOjj/y3/9V+LJg35deZcnqggUQWsil0aYcFmDCB22/j2zimDWiYBqgsqtw0AwTZ29TmTpYJlgwaSMnfxQDiUIc0KUJLCfJ2ptIyelLexDSQmEWyKrZhRjn4U5G2skihjuSgqniHON9H0qbimKaia6MaUGcgBx9kUp2sagbhqCXr2zzxNixt0ED28WHp93fHgeJ4yLKUWU1oPax7XpimDRqU1IkllrQ6cg6Yx6I0KofWVxZbpd+TSDXXF6JFZXrDldYTXB8+iWHW5dIItxrUUEdquxL22kilsXNDmm44Tyi3ZkthiFzM3dOAADwmX8+TI01x08jftvjxiGU33HVSv1FsS59tHx+d++/fZVnmh3f/fvxZoMzp1dKrRIUIQkifB1GEDa0HsXY6eZVSHqOnw/FoMpkUPo4bgaRfckNzDlms9M7HFZ0D6hk+PWQApJQHWH9YoWgb6ACan8md57FBSjpAJpBWaclRIT3Sv7rFRLWP10c5PdU9JEb40pNbJOIM797h7zHUWAMrYFW4d5UsSMv3z/lp/8h/8pf/R7/4R/8eFPEBXOHY4lc740Lmno3Vpg7TrWbPM9/eVP0cOeko+Mw6Bg55/R3dHYkfLYHCyhNC2QdtT1mZL2VAXPSukN8XFq9wyUA9j1e71rF6Ahy4KXjGoj0sOwggDs9szxECxWZvu39B6NNicAYjmjuzeE+Bi6XM+g71BZCJ0JYrwGEcT/8w83vW963/T+S/LFdXByZL6Z98j0wtWUfezoywuPZOI0TvjNJtItr8jdh6txCCIJzT5iA8QwlGi36oSMJrjWmaJ2G5JlzLO4IMlxnVmvTskTqXXakulJ8OrMSUmiHGTlUJTkkLwiy448F7xWYgrIiX3MlH1CtOGmTFR8FvZlR6AQV1pl5EtNM20VLKBaZ2l9pKCzYz8v8PDCab2nd2GO4NorXXecbSGLgDmhmaQdQchzoUaj9M7FhCcLJB3QCJ6rs8YoWtIKGUel0LSOZN/Io6jw4GSZWYJLH8VHaNw2EoJAMHN200ynEr0QBBYXyDNm8N4TSzgWt7RZHe7E1y60MHrAooLXyn0eLVXVDNkotw7PatDFifjiZPoXhsiBY7wnDj+nm6Jpjy4fYUooSqWSdXT2UsyYOdJWpDq23yOHQC0waeMO3QMBXBNaQC8zMQWprcOluxdCnE7H7Yh7Hw/v1okmlAx1DVxH67q0J3KZcTUmOrZO5FwwbYiCZGOXJu4efodz+ze4KW+mxCWv7OffIksf8279keBESj+irZVZKq1f6KaU/MCP3/02+xl++lf/I/7V8yPmlaleebxUGhPt+jxO9cuVOL4lzo+kcmC6/4paF0yEdnnEEsjuN4i1ob5Q+x6k06yRb+8V8ozFGak7kuTh1ZEOY97AF3x5ITSQ60oRHXqvF+LNe7Ku0A7gDbMP2P4dmKFyD7qi9YU+3aGu+Bz0S0euFyLPSJnw63eIZKIcYboD78hS8TkBFc5n0uHh1y3LXxmb3je9/5B6/+I+OX7vCSQvvFgiurPTBTcnbtcdmUSXhQih4EPckWjhJGmoT6zWEVEMwxCWcCYUrZ1DKmNdWzNnd+7amCwPV96Y8TZ1VluQgF0S5hRoUUKDjrOsaWSh+JU79gSVa13IORMNrq0hTVmjMe+PIB21GeNCMciSENvhJREhnNxgMvYpk0UxF/oCXZ3TpztcLxRtZEucu+A20egoibAxDxMOzTMWQbUOFFIOvt4nvjZnZcwAtZQ5XRsnV0xgL2DSeKPKLIKbskyCLU7XhjSjTJkWjeuq7CahmPDkxjrD1RbehHCORKrBUu7xxWmh/K4FSzKuPpxDv3ZnFiOScvCEh3OUxBwjCqKLQWQuqZMpJO3cqyKSec13VN4qi/wcsiN1RXPF2nUM+0VGZKb7Ey0E0QkBUhSiCESF2GG+IqL0+gT7N9SoqCdiqYgWfO3I/A6PTi+ZfmnDdLGslJSIfkZ/cdWoE3mecTMsOdYS6glfnll376A/gRnzdE/FeHpeybrj8emPKNM3RG5crwlz5/H0AdUDwoLrfrxn5ROC8X63IzH0Tn3iDy8/47/sf4vnywvFOteeqC1hWbHeQfaEKHK8QwVsPrL2BV1Ha7yLs3v4BuijI7lvwBvy6RnTwGbFreBpIcsODUXmI14m4sWwtCLNSKKon2AV2B+QxTBW9DAT60c8CpJ2pBqs+5/A8yOUPdYeYQZhxvpKtAaXIBKkfEcsF2x3IPUDttuT1hdE9vguYbpD1YEJfzdh1X69ovwVsul90/sPqfcv7orqf/6b/0k0b5w8wIYPgmmMLCVV1AOi4TmTHMIbwTwKHHWsC03AcRbPHF2JvUM3vDlZx4d72MhScoc94wdsZtwV5dgXjtmHbbeMbR7DEAlyTDhOmYTdrWcXYWOfPwVJ7vBopKIoiW4LIsKuOEdN4EHJMgzxuiEedBLW65gjEmMvnakUjtoIaTy1O9rS6QStC02hmYONdO6Q4QeoGC4Z50rqb3jWK8kK1w5dghY+VrZTonUIHVdrXXQ8NJhYvFN0vGFzSpiPn5WrDUOnaaS5S8nc5cxU6+jGSMct4+5ce8XKRIgztwnBsQJ04+JwouOqzKpET1wF8s38EO9cmrIUcLtlnajwv//8w6ts2af/5n+KxJXuHSzQNH4GxJjhUg+iX4l5h7uT2grpOOa6UkADywKtEVrQFsR9gm5waX9O77qfYTWECzD0nvNM1OdxgpVM7hB5+l7vIRPJHT8emG/vkzDD+wrq7OQ9oRVP89A7C+7GMSn7bERPHA+Fqy+0nobeXbi0hTTtSd44SmU3HykqRFfOvnK+rHQUj4bKRJw+0w/vRgdRQHpDrp+Jw3vs+nPy/Jdp8oQu05iFk8DljFojpreIg0tDxLGYiMt3zPMDtT+R8htsfSHv3gy9Ly9QbnrfT0jMkGbS7h2cnnAB0oqZMK2Vvj4S91+j7QVtO/o8D4+Q9YLoTPgzI1kwI3IctiH9CUlfEe0ztB3sYiRY3/Qev/dPNr1vet/0/kvyxXVwXq4LaxdIMf5yqaOWYeSY0h2UQu1Bccg6sZhxs5Qb4o4ge+FehFRWZFFSDnQyCsM8LlLg1sc9oAdFhDTBb+4c4cDVHdcx4X1ag0zgZDRnptRZJGMeI6MqF0xH2oLGgjvMISz1ShajpERdhSdZmch4Gm1WNCiamBMsBkkcDaGj3AlkhVBhqSsvfZxojBVswj0P624PnOF0XBGChPqeNRrelEbnZt6MGeOut45AtHBjr5lqjbj1u1yg1oqmxGLQREglkVCqKdLg2sfD6SkqoiMLqydBLsKSFJMdJWDvwll8rCiuwpUYwZ6AdqWJkDHEEktkmgRXKeTUMRd2MgaXb4vkr5P6SKsOc0YtcDshOiMMAy7DhgGXORpBeMaloe44mciC1I5EGi3n2ZGXC6oTmg2XoAREcXy9jpgM64QmpiJILpB/RBPDNXAD1iuBIemI5nnEbHjCQiEakiYiC26Vqz/DEugs2HImYiHNB046cXr8RDp8w2N9wruDBintSbtMXMepugPPKCGVnIXQzvOl0S4LHN+Tz5/pd7+JHd8DgVwvSD/h0z1++IrwhJYf433FUZKumNv4UGgV9Fa81ytCJ03fYPUjhNFtHYGB5++QacLqFZ8P6P0b3BsRIwPJe0WT0dcPiDJmOuoFXfbUUmH/o9HC1wOWHNozyB7JQuCgewhFVIh+hiUhcUfEBfQAUx9J2dMdUp+J8ubXLMpfIZveN73/gHr/4gockSBNYzWv1c5jh1U72Qx65a4IqgdUDIvOwfX28RcjpDFuBY40clYahT7F8DGwGVFDzGkiHBzAUc1MVjHJXJ+du3l0fJJ0rigpJWoIzQxSoVXwaMze2edC1KBEJosxMdbH1Rr7DHsVNM20W7dGc0KiUhSyFpqvdEkUGUVIcyMhRHecxNu3e1ZrdBEmFYwjp+sFkyDJhNkogiwSYkCsGMKKUL1iWZgj0cQ4TDP3JSPh3LlwtsJiQRHoVlg0c6wrnhIlKSc3pBtdM4uCeIxtLvERKRGGWUKBozmyC5IZJkL3wJnGQyrD4vpnHRmUHmN4uCcdLVh3sig7NxZGYf+MA4n19XbsR6DgnIgEsVQ0GjIl+nrBrQ3zsfIVkZ1oK8LQuxLjQbwGHoHYC3p8ICITxzu4Nnoferd+QrVjvhvRGjojfgHfwfkZK/dIu5KkD6vHoohlbL2Spz3envEerM3QaSI6qBSITPQO0Qg6OimT3KP5nhaG7L9BU8KakUNgdw+PH3B9IE0FAuhjKHINZ98Tf+l3/jOu/+qf8lF23KfE+s1P6d/9jGydePObWDwT5YjkPdSO2UdC7yEZqT/iIqgckThTj+/IeU8JJ++OxGK0ENL0lihXKAemS8XLAZXxU5XTB9g9oHk44kq9Eqni8QbCiGggM+IBdxP0QNoTlBm1RIQS88N4bWxsGQ57/KF30gQJeHyGN3fw/AyzjyHSywtBgluG22tk0/um9x9S71/cFdX/+Ns/DWkJmTLFKlpGUKMqZITJwMVJJDoVISFhYIl0i7T4hQldicDSyLtYvdOBboJI0KqwzGAuvAkn1MiRKGrkAntzkM7kZcQKzMGu9ZGdpM4ldjzY8L7Zh7HLRkqJ6W5mlpX38wjlnHeCTkY/d/7AheLDj2a/P3JHJedRXPWTstPMdE2c3wSfPzeO+sT03QO/F0YrHemF3SGj3Wk4JxVeqtEsOJdMKkpex3q9iGDi5Mg0XwlJZB1tWouxzaSMwmoVxWL40vzC6GldjZ46pD3NOtXH1tV9FpwghVBy596FS+SxASXDdVoFThpIz3wW4/QLbxt8bBV4IKE3Y8BGjkRVQWPY/kUYkgstlExw6p3/4+PHV9myz//5fxcejJmB9QUOx3FaUvC8+17vvSvKBzR/RW+dIpmu4737C71bLEi5u224VZSn0UJOC9pmtAy9qweWLxAJpIAaiKGxon7EBXwPuhjRryR1Qh+gjSvU7CvWOnG8R453zL5wOI736bwT5v0d1+ePfMfhe70XjqT5ibfHH6P9kcvjysN8oJ32xPvEt99+IMcn9qev+fnlT2mlM/OOdD9RO5RWqeKIVGK9EId7fE7oOkIVNQqejOSFVJ+wvMdVKfX8vd7XMpNiGmGAUomekJuDtj9+Js1Bn7+GvqD9RFCQ6X485ElEvJAsI5G+1zuloAK9nxHfE/MtT+56AnwcIT0gFOZ7qJ+R/EDYGW56Jwz278Zw5u4I9Uz8/j/a9L7pfdP7L8kXV+D8rd/6ndDSuZsm9t3H/hnGQw4mdix25Vky18jDLI7x92+SuJNE9SCnYKUS7ojqeNO4w3SHSQNvFCCrcghBPVNSQkrnvjo5L8yM9ei5C70punNSdM639boXDb5yQfxKjTI8bnwknZSAJIqEkXIwh+DeeavKRYxiikofq4lF6L2SBVa/J9nLeCNOu7GpFdCl0nAeEvzWXLi2hdUzKgHWcU1jG+wWlYCONPGPTbiTILJyaYGkUUwTI+vpFGMVu8dYcRcZK9tmY/hsVaGRyOH0gE4wAVfGfFQSwUSY+4SmRs3Kao0UhQtjU8oManSyZ/pk7E1QzeyTkyKYAy4edJmoODmcJeDsSlLlGkb1zj/8+OlVPvDlv/gfRrbI8R5Z1u/1nqdE9x2lfqTmA5QJrIOdxjeWPch+xGOkgHoF93H37Q4syP7fI9oZvIGMX5MopHhD7AvGCW1OpAU6xK6QV/DqsFfASDFWPkWDHglZPmLcUf4tvVusaLodNGS4vEY0ik30VFHPI3k4MmTF1o9kAS0/oq2fbnp/QFwIBeUMfsXSnuPua/zxX9LzV0S8fK/3JMfxQQWEJSIZvl4oZGyaiHpBEoRO4zTZV5CC20KoouHE/AZpL1DrMDspe1wnxBoQ0K5o2Y1r7HYmvCDTjNtuuLz6QrAi8oCkZczCP17RqeHXAu93pKcL9nAPJMTOpJ7p3eDwFciJFI49X6EIzG/AT2Mz8vf/8ab3Te+b3n9ZvX1pBc5/9dPfjqTK7EbRQFEmqRjwDsV0bEK9s85TGOhI3VYVmjnqgQjco6zSuZIIgbMXniJYA7ImIpyDKjucolDcce2EC0cNjihZOrvIiDaSdZIqDaVEMCVIchOcCEWDbFDSyKqCUaiKDrfhg+pYlXYZvgKdkZFVEr0JoU4WY18S6sZ+0tGF8UZtE5fw0YnqUCSzamOvE5e1s5hyxiiShlfEbQ7pU1eOOkLgUhLMhCpCB/biLK4kEa4xZox6L1wTLN45uxIkEsNLx4Kbj4TS4nbllxhuohFIjJwuk3GvfRVlH44EZDGUoenAWCJx8eBsM1Uq6srinZQS75LiHjwiCJ3mikTwDz5+9zof+H/7v4+UD9jyAikDSvIFA0okPDuWjqR2wmm3oMGb3vtprMkKiB/wshLuN1uEPeOJ3GB+Nwwn8wR2hTKT3TH/7uZNNAJcTRuzF6p31C5o2o/TIGN2q0sFht6TJMpqRM5QxjNEqtBZwa9MsUP3B7okpvNnXIfeZXc38tUCijf0eI9rZ0qFkibWfsE8IefzuML0FSlHzE+Uw4+o50+IH/FcMS1MTekCZCfqFU0jILDlROlBZyZyHf9eUbAZTw28InEHqdP9Mk6cMqNxJUKQuhDTDkkTrC9IB09O7A7oOrYsTacxKGeMAbyebtcQ13F4mSbcLuPrlIm+J/onxg+jIykRd3fj+5cLQifyPHxBfv//3vS+6X3T+y/JFzeDs7ixY2i2CBzoHBCyOCmMtSvX3vi5CsXL+OBFeWpwignRIInzE4Ufy46DGrU7RCdgrN6ZYzif3BBJpJtddInCLiurO8/izD0hKZhqZqaww0nSQQpXOncUNBzcUA+8CwsjcTWFc5RAIpEk0SPQFuN6zBPDUUZpFqSidFcaY/6KGB2nKQUtlMXqEAeFEhnLneQTkSoVyKnwQMJVsHCuXbmaE3TOLVPVmELZCUOo1nimUJKzmlCl0HtwkcbaMu6GhI1C6ZYjlRAmgcUdQYisWpEfqwAACKVJREFUqAgOrBHszSkqlIBGMDGuqiyEIoKEoxSaC6LKnJw7vYJnqgSrKhGOdHhWwz0hcbvF+vXJ8VdPXzGG3sfDOggZLWLDoXfo15EhI4L2Kwmh+jpWSVUJdSQ5yj1RLkhrmFVyWegywXom5DyuumUCW+nhI+aEPZaE5J1kjUUhO0R+M1rK6nSfQSsaOzQcsQVTpyGj9W1D7zkSqcx4S7gEfj0jAlUn3DrJF/y6kA5HvC5Ug10U3Ppws5YrHkp6eSJSwg8PuE1E7uiSYX2kMnyqYCKr4KmhUWjRUXW8VnoytCbGSsEFlguNgpSM2AVPbxECr9+i+Y7cL7eYERs/bw287EghsDwDQmSI+QGi43kmL8utHe/QVuiC0nAyTPMIOpSJ1NvYCMLh+jRM1YqT8nFEtCyC8QieiGB8QJcv7rH8F8em903vP6Dev8B30rheOdEJzTRXHOG7sUPFPeND9eqJcOE6Zuvpkrgwrn8klD90+BmKSR5GdRGsYbeQtwCEjBIeJAViXPk8ekcCJimkGNdIE84sidwTsxZ6dDoTO4G75DQv3IewipOrcYzELiqfiuA+VrEdCEnUgGOZUVuYGMO593fBFEJGkGTjOg0d4XPsyanQJLH2jugtPypNrNfbzAvOxTu0YCcTpOBdylSDrB1Pebh+NvA0oiKqBRKZczgunTVGcZlDgUyl4grBLf3VYXUnAy1uGVE6tqJ26Tbz404LR8g0MezmE1RdMEaibXNI5nzwYdW5D8FtJaUyBI9zDOVNVDoJj8pZXnOJo4g1Ipbb9kEhyYy7EKxjOAAb4wMGlhIWV+AIcR2nIxEiJqAh+Q4vAXqlWwGZgWW0xVUhbqF7TLjErUUMIQ941NHezpWwgqng7EFP5DbTASs7hJWIPaQGYaS2g/6BtjsOt9I0UwHJE0Qn5yPeFpzCVIdFP+VAiWeiPg8X2rvj93rn7uvhUn76gKR7+nJCdu+4rlDKAWmN0BXaFe/vYF5J+R6NTp+dSBnxDmsf661pHrlHcYQ4ofWE+QoybCsj38PzZ3hIuFcQGbYStwI88gTXC9RPMGUkApt26LqMK4S8h77g+98A+zjyUyQRccGWZZx6tYwmQ1tgXbFUuAkeqYlIbZyIaTcH3dfKpvdN7z+c3r+4K6q//pd+EkcRlPGhXyVR6WTJjB5MYoqOpeArkduVlPAUcPaxURQR7ElMKZht2H1fMB5Dsdv/dwzhjuvcogL47evETsbXT9kpi+NpJqkjAjRFtBIa3IWwEwjpvAthlmC9bQlNOCUCd8cyRB9uviklEoaOvioZQemgw4Om1c5OjHfJeVsKHiNaYT8ljimwqixah9BE8RC6KtV9tBoD+m0GZ/Ex0KXiXD2N6x4NwsYcWJPRmVnFsZ4oCVyMe514aQ1PQniiK4CAGMmVS3RmxhXaitN8GCniw98HF+ptYidihJhWMiWM9XZFdvJgRdAkqI8uD8Clj7awE4QrCefkzj9+enyVLfv0N/5uuMCt+kRzwe0M+TgezuWAr89QEgnH6gQiiC5EpFuLK0BnFMX9BH4ErbdfG8WyiIxV0oBgBLDiDdIdooK0K/5wgKcPoF8j6kQpcF1J6YnQILcdHYE08sHGC9IIClkYGybSCelIHMaVrCgSjZFGBlnGPFrOE24Vswr1wiSJyA+IBF6NNE/ILmFVifbtn9N7SUdaMmaP7/VOr8OPKgxPmTAjRfkzvfcVy4GYkJJjekf4dTjmlnesl49joSHfDWt5KcCKxkT0T4TejVw1HO8nsAlcYJ6GUWi/oLuxTeK9Q56h3WYdALmciDlDEiQdiV+YV663GRNifHjj7EK5/sHrvKLa9L7pffDD6P2L6+BMoghKFmVh+LPsVOmqzA6rBtcQ3OGPGYFsgiM6ERJAp8mE0zibI6KMftzwfkkIHSdpooSgImQ6KYJdFo5hPHfjOTnWlKlkNAe1ZSxV7jNEpJEQnsaIs5NZMd6HMLuTtXFbJAQVpN+2rWT86WsPWhJ2BKijkQhbmAL2BWDmieBfL505JaIavQoSnZSGT0RYwd1Ye2NXCsWVzNgA04CHqcBqdE+kJAgCGbIPI8QmI9qixkpypSbnakqZJk7dsSzse6ErpATVg+pClTEjeHKo7uyK4Fo4x7jKaqZci7Mj4xZcSey7c5pATcecTgTHnPjGgms4VYJmHYBdJKoaGkpLDPcj/3fr5f/rOCAkyDvCF5xhRTDyvwreBfZHcq30vIcZaFdiejse9NLHqTVWfHmE3QOsC9gKMTMey7fZATKS90R7Ht873YNVtL+MPLEXR/I75K4ga+B+IrJicYdYpuqClgPSzqALYRmNAE64JkQVECKOeJyIuEfo0DpMHfFMx8kkrC2gfVwb7L6mY/T+QtI90h7x8jX23c9J0wzzHYIRfSXWJ+ywJ64rHYMMUR6Q+R45fSbkgMZKpDz+zWvHc0EPExJ74votpD1hZ1T22DzTL2dkyqQ+06Mhh/14IC8rTDIOnn1BvWBTgulroKNR8Q4+T7BLDAkXOC3w1Q5qQ/Mer2fi7i2lQqMTZrA8AiAtETsdMxGJ8Xr1H16HPxSb3je9/5B6/+I6OBsbGxsbGxsbvyyv2CJ2Y2NjY2Nj4/+vbAXOxsbGxsbGxqtjK3A2NjY2NjY2Xh1bgbOxsbGxsbHx6tgKnI2NjY2NjY1Xx1bgbGxsbGxsbLw6tgJnY2NjY2Nj49WxFTgbGxsbGxsbr46twNnY2NjY2Nh4dWwFzsbGxsbGxsarYytwNjY2NjY2Nl4dW4GzsbGxsbGx8erYCpyNjY2NjY2NV8dW4GxsbGxsbGy8OrYCZ2NjY2NjY+PVsRU4GxsbGxsbG6+OrcDZ2NjY2NjYeHVsBc7GxsbGxsbGq2MrcDY2NjY2NjZeHVuBs7GxsbGxsfHq2AqcjY2NjY2NjVfHVuBsbGxsbGxsvDq2AmdjY2NjY2Pj1fH/AgkmuVz+5kNbAAAAAElFTkSuQmCC\n", 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\n", 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" ] @@ -315,21 +315,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.6.7" } }, "nbformat": 4, diff --git a/notebooks/plot_otda_mapping_colors_images.ipynb b/notebooks/plot_otda_mapping_colors_images.ipynb index 4b19e0c..b66640b 100644 --- a/notebooks/plot_otda_mapping_colors_images.ipynb +++ b/notebooks/plot_otda_mapping_colors_images.ipynb @@ -184,7 +184,7 @@ "# SinkhornTransport\n", "ot_sinkhorn = ot.da.SinkhornTransport(reg_e=1e-1)\n", "ot_sinkhorn.fit(Xs=Xs, Xt=Xt)\n", - "transp_Xs_sinkhorn = ot_emd.transform(Xs=X1)\n", + "transp_Xs_sinkhorn = ot_sinkhorn.transform(Xs=X1)\n", "Image_sinkhorn = minmax(mat2im(transp_Xs_sinkhorn, I1.shape))\n", "\n", "ot_mapping_linear = ot.da.MappingTransport(\n", @@ -307,7 +307,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -356,21 +356,21 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python 3", "language": "python", - "name": "python2" + "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.12" + "pygments_lexer": "ipython3", + "version": "3.6.7" } }, "nbformat": 4, diff --git a/notebooks/plot_stochastic.ipynb b/notebooks/plot_stochastic.ipynb index e784e11..0911c28 100644 --- a/notebooks/plot_stochastic.ipynb +++ b/notebooks/plot_stochastic.ipynb @@ -49,44 +49,20 @@ "source": [ "COMPUTE TRANSPORTATION MATRIX FOR SEMI-DUAL PROBLEM\n", "############################################################################\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "------------SEMI-DUAL PROBLEM------------\n" - ] - } - ], - "source": [ - "print(\"------------SEMI-DUAL PROBLEM------------\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "DISCRETE CASE\n", - "Sample two discrete measures for the discrete case\n", - "---------------------------------------------\n", + "############################################################################\n", + " DISCRETE CASE:\n", + "\n", + " Sample two discrete measures for the discrete case\n", + " ---------------------------------------------\n", "\n", - "Define 2 discrete measures a and b, the points where are defined the source\n", - "and the target measures and finally the cost matrix c.\n", + " Define 2 discrete measures a and b, the points where are defined the source\n", + " and the target measures and finally the cost matrix c.\n", "\n" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -120,7 +96,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -150,7 +126,8 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "SEMICONTINOUS CASE\n", + "SEMICONTINOUS CASE:\n", + "\n", "Sample one general measure a, one discrete measures b for the semicontinous\n", "case\n", "---------------------------------------------\n", @@ -162,7 +139,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -198,7 +175,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -207,15 +184,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "[3.75309361 7.63288278 3.76418767 2.53747778 1.70389504 3.53981297\n", - " 2.67663944] [-2.49164966 -2.25281897 -0.77666675 5.52113539]\n", - "[[2.19699465e-02 1.03185982e-01 1.76983379e-02 2.87611188e-06]\n", - " [1.20688044e-01 1.49823131e-02 1.50635578e-03 5.68043045e-03]\n", - " [3.01194583e-03 7.75764779e-02 6.22686313e-02 8.78225379e-08]\n", - " [2.28707628e-02 3.52120795e-02 8.44977549e-02 2.76545693e-04]\n", - " [1.19721129e-02 1.10087991e-03 1.53333937e-02 1.14450756e-01]\n", - " [2.65247890e-02 1.33140544e-03 2.66861405e-03 1.12332334e-01]\n", - " [3.71512413e-02 2.86513804e-02 7.53932500e-02 1.66127118e-03]]\n" + "[3.88833283 7.64041833 3.93000933 2.68489048 1.42837354 3.25840738\n", + " 2.80033951] [-2.50038759 -2.4083026 -0.96389053 5.87258072]\n", + "[[2.49326139e-02 1.01118047e-01 1.68018025e-02 4.67918477e-06]\n", + " [1.20543018e-01 1.29218840e-02 1.25860644e-03 8.13363473e-03]\n", + " [3.52425849e-03 7.83826265e-02 6.09501106e-02 1.47316769e-07]\n", + " [2.62727985e-02 3.49290291e-02 8.11998888e-02 4.55426386e-04]\n", + " [9.00986942e-03 7.15412954e-04 9.65318348e-03 1.23478677e-01]\n", + " [1.98446848e-02 8.60145164e-04 1.67017745e-03 1.20482135e-01]\n", + " [4.16774129e-02 2.77550575e-02 7.07529364e-02 2.67173611e-03]]\n" ] } ], @@ -240,7 +217,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -284,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -317,14 +294,14 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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+ "image/png": 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\n", "text/plain": [ "
" ] @@ -350,7 +327,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -378,45 +355,21 @@ "source": [ "COMPUTE TRANSPORTATION MATRIX FOR DUAL PROBLEM\n", "############################################################################\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "------------DUAL PROBLEM------------\n" - ] - } - ], - "source": [ - "print(\"------------DUAL PROBLEM------------\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "SEMICONTINOUS CASE\n", - "Sample one general measure a, one discrete measures b for the semicontinous\n", - "case\n", - "---------------------------------------------\n", + "############################################################################\n", + " SEMICONTINOUS CASE:\n", "\n", - "Define one general measure a, one discrete measures b, the points where\n", - "are defined the source and the target measures and finally the cost matrix c.\n", + " Sample one general measure a, one discrete measures b for the semicontinous\n", + " case\n", + " ---------------------------------------------\n", + "\n", + " Define one general measure a, one discrete measures b, the points where\n", + " are defined the source and the target measures and finally the cost matrix c.\n", "\n" ] }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 11, "metadata": { "collapsed": false }, @@ -453,7 +406,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": { "collapsed": false }, @@ -462,15 +415,15 @@ "name": "stdout", "output_type": "stream", "text": [ - "[ 1.67648902 5.3770004 1.70385554 0.4276547 -0.77206786 1.0474898\n", - " 0.54202203] [-0.23723788 -0.20259434 1.30855788 8.06179985]\n", - "[[2.62451875e-02 1.00499531e-01 1.78515577e-02 4.57450829e-06]\n", - " [1.20510690e-01 1.21972758e-02 1.27002374e-03 7.55197481e-03]\n", - " [3.65708350e-03 7.67963231e-02 6.38381061e-02 1.41974930e-07]\n", - " [2.64286344e-02 3.31748063e-02 8.24445965e-02 4.25479786e-04]\n", - " [9.59295422e-03 7.19190875e-04 1.03739180e-02 1.22100712e-01]\n", - " [2.09087627e-02 8.55676046e-04 1.77617241e-03 1.17896019e-01]\n", - " [4.18792948e-02 2.63326297e-02 7.17598381e-02 2.49335733e-03]]\n" + "[0.92524245 2.75994495 1.08144666 0.02747421 0.60913832 1.8156535\n", + " 0.11738177] [0.33905828 0.46705197 1.56941919 4.96075241]\n", + "[[2.20327995e-02 9.26244184e-02 1.09321230e-02 9.71212784e-08]\n", + " [1.56579562e-02 1.73985799e-03 1.20373178e-04 2.48153271e-05]\n", + " [3.49227454e-03 8.05110304e-02 4.44694627e-02 3.42874458e-09]\n", + " [3.15181548e-02 4.34346087e-02 7.17227024e-02 1.28326090e-05]\n", + " [6.79336320e-02 5.59136813e-03 5.35899879e-02 2.18675752e-02]\n", + " [8.02083959e-02 3.60364770e-03 4.97032746e-03 1.14377502e-02]\n", + " [4.87374362e-02 3.36433325e-02 6.09190548e-02 7.33833971e-05]]\n" ] } ], @@ -495,7 +448,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 13, "metadata": { "collapsed": false }, @@ -530,14 +483,14 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -563,7 +516,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 15, "metadata": { "collapsed": false }, @@ -602,7 +555,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.7" } }, "nbformat": 4, -- cgit v1.2.3 From b7df4fbbb54eba6a797c6b8f0d57bda7db8e4177 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 15 Mar 2019 11:57:05 +0100 Subject: correction doc in gromov.py --- ot/gromov.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/gromov.py b/ot/gromov.py index 0278e99..7974546 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -39,7 +39,7 @@ def init_matrix(C1, C2, T, p, q, loss_fun='square_loss'): * h1(a)=a * h2(b)=b - The kl-loss function L(a,b)=(1/2)*|a-b|^2 is read as : + The kl-loss function L(a,b)=a*log(a/b)-a+b is read as : L(a,b) = f1(a)+f2(b)-h1(a)*h2(b) with : * f1(a)=a*log(a)-a * f2(b)=b -- cgit v1.2.3 From a2545b5a503c95c9bf07948929b77e9c3f4f28d3 Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Fri, 29 Mar 2019 12:41:43 +0100 Subject: add empirical sinkhorn and sikhorn divergence functions --- README.md | 2 + examples/plot_OT_2D_samples.py | 26 ++++ ot/bregman.py | 269 +++++++++++++++++++++++++++++++++++++++++ test/test_bregman.py | 57 +++++++++ 4 files changed, 354 insertions(+) diff --git a/README.md b/README.md index b068131..dbd93fc 100644 --- a/README.md +++ b/README.md @@ -230,3 +230,5 @@ You can also post bug reports and feature requests in Github issues. Make sure t [21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A. & Guibas, L. (2015). [Convolutional wasserstein distances: Efficient optimal transportation on geometric domains](https://dl.acm.org/citation.cfm?id=2766963). ACM Transactions on Graphics (TOG), 34(4), 66. [22] J. Altschuler, J.Weed, P. Rigollet, (2017) [Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration](https://papers.nips.cc/paper/6792-near-linear-time-approximation-algorithms-for-optimal-transport-via-sinkhorn-iteration.pdf), Advances in Neural Information Processing Systems (NIPS) 31 + +[23] Aude, G., Peyré, G., Cuturi, M., [Learning Generative Models with Sinkhorn Divergences](https://arxiv.org/abs/1706.00292), Proceedings of the Twenty-First International Conference on Artficial Intelligence and Statistics, (AISTATS) 21, 2018 diff --git a/examples/plot_OT_2D_samples.py b/examples/plot_OT_2D_samples.py index bb952a0..63126ba 100644 --- a/examples/plot_OT_2D_samples.py +++ b/examples/plot_OT_2D_samples.py @@ -10,6 +10,7 @@ sum of diracs. The OT matrix is plotted with the samples. """ # Author: Remi Flamary +# Kilian Fatras # # License: MIT License @@ -100,3 +101,28 @@ pl.legend(loc=0) pl.title('OT matrix Sinkhorn with samples') pl.show() + + +############################################################################## +# Emprirical Sinkhorn +# ---------------- + +#%% sinkhorn + +# reg term +lambd = 1e-3 + +Ges = ot.bregman.empirical_sinkhorn(xs, xt, lambd) + +pl.figure(7) +pl.imshow(Ges, interpolation='nearest') +pl.title('OT matrix empirical sinkhorn') + +pl.figure(8) +ot.plot.plot2D_samples_mat(xs, xt, Ges, color=[.5, .5, 1]) +pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') +pl.legend(loc=0) +pl.title('OT matrix Sinkhorn from samples') + +pl.show() diff --git a/ot/bregman.py b/ot/bregman.py index 013bc33..f1b18f8 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -5,6 +5,7 @@ Bregman projections for regularized OT # Author: Remi Flamary # Nicolas Courty +# Kilian Fatras # # License: MIT License @@ -1296,3 +1297,271 @@ def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000, return np.sum(K0, axis=1), log else: return np.sum(K0, axis=1) + + +def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, verbose=False, log=False, **kwargs): + ''' + Solve the entropic regularization optimal transport problem and return the + OT matrix from empirical data + + The function solves the following optimization problem: + + .. math:: + \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + where : + + - M is the (ns,nt) metric cost matrix + - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target weights (sum to 1) + + + Parameters + ---------- + X_s : np.ndarray (ns, d) + samples in the source domain + X_t : np.ndarray (nt, d) + samples in the target domain + reg : float + Regularization term >0 + a : np.ndarray (ns,) + samples weights in the source domain + b : np.ndarray (nt,) + samples weights in the target domain + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + gamma : (ns x nt) ndarray + Regularized optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + Examples + -------- + + >>> n_s = 2 + >>> n_t = 2 + >>> reg = 0.1 + >>> X_s = np.reshape(np.arange(n_s), (n_s, 1)) + >>> X_t = np.reshape(np.arange(0, n_t), (n_t, 1)) + >>> emp_sinkhorn = empirical_sinkhorn(X_s, X_t, reg, verbose=False) + >>> print(emp_sinkhorn) + >>> [[4.99977301e-01 2.26989344e-05] + [2.26989344e-05 4.99977301e-01]] + + + References + ---------- + + .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013 + + .. [9] Schmitzer, B. (2016). Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems. arXiv preprint arXiv:1610.06519. + + .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + ''' + + if a is None: + a = ot.unif(np.shape(X_s)[0]) + if b is None: + b = ot.unif(np.shape(X_t)[0]) + M = ot.dist(X_s, X_t, metric=metric) + if log == False: + pi = ot.sinkhorn(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=False, **kwargs) + return pi + + if log == True: + pi, log = ot.sinkhorn(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=True, **kwargs) + return pi, log + + +def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, verbose=False, log=False, **kwargs): + ''' + Solve the entropic regularization optimal transport problem from empirical + data and return the OT loss + + + The function solves the following optimization problem: + + .. math:: + W = \min_\gamma_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + where : + + - M is the (ns,nt) metric cost matrix + - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target weights (sum to 1) + + + Parameters + ---------- + X_s : np.ndarray (ns, d) + samples in the source domain + X_t : np.ndarray (nt, d) + samples in the target domain + reg : float + Regularization term >0 + a : np.ndarray (ns,) + samples weights in the source domain + b : np.ndarray (nt,) + samples weights in the target domain + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + gamma : (ns x nt) ndarray + Regularized optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + Examples + -------- + + >>> n_s = 2 + >>> n_t = 2 + >>> reg = 0.1 + >>> X_s = np.reshape(np.arange(n_s), (n_s, 1)) + >>> X_t = np.reshape(np.arange(0, n_t), (n_t, 1)) + >>> loss_sinkhorn = empirical_sinkhorn2(X_s, X_t, reg, verbose=False) + >>> print(loss_sinkhorn) + >>> [4.53978687e-05] + + + References + ---------- + + .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013 + + .. [9] Schmitzer, B. (2016). Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems. arXiv preprint arXiv:1610.06519. + + .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + ''' + + if a is None: + a = ot.unif(np.shape(X_s)[0]) + if b is None: + b = ot.unif(np.shape(X_t)[0]) + + M = ot.dist(X_s, X_t, metric=metric) + if log == False: + sinkhorn_loss = ot.sinkhorn2(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) + return sinkhorn_loss + + if log == True: + sinkhorn_loss, log = ot.sinkhorn2(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) + return sinkhorn_loss, log + + +def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, verbose=False, log=False, **kwargs): + ''' + Compute the sinkhorn divergence loss from empirical data + + The function solves the following optimization problem: + + .. math:: + S = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) - + \min_\gamma_a <\gamma_a,M_a>_F + reg\cdot\Omega(\gamma_a) - + \min_\gamma_b <\gamma_b,M_b>_F + reg\cdot\Omega(\gamma_b) + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 + + \gamma_a 1 = a + + \gamma_a^T 1= a + + \gamma_a\geq 0 + + \gamma_b 1 = b + + \gamma_b^T 1= b + + \gamma_b\geq 0 + where : + + - M (resp. :math:`M_a, M_b) is the (ns,nt) metric cost matrix (resp (ns, ns) and (nt, nt)) + - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target weights (sum to 1) + + + Parameters + ---------- + X_s : np.ndarray (ns, d) + samples in the source domain + X_t : np.ndarray (nt, d) + samples in the target domain + reg : float + Regularization term >0 + a : np.ndarray (ns,) + samples weights in the source domain + b : np.ndarray (nt,) + samples weights in the target domain + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + gamma : (ns x nt) ndarray + Regularized optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + Examples + -------- + + >>> n_s = 2 + >>> n_t = 4 + >>> reg = 0.1 + >>> X_s = np.reshape(np.arange(n_s), (n_s, 1)) + >>> X_t = np.reshape(np.arange(0, n_t), (n_t, 1)) + >>> emp_sinkhorn_div = empirical_sinkhorn_divergence(X_s, X_t, reg) + >>> print(emp_sinkhorn_div) + >>> [2.99977435] + + + References + ---------- + + .. [23] Aude Genevay, Gabriel Peyré, Marco Cuturi, Learning Generative Models with Sinkhorn Divergences, Proceedings of the Twenty-First International Conference on Artficial Intelligence and Statistics, (AISTATS) 21, 2018 + ''' + + sinkhorn_div = (2 * empirical_sinkhorn2(X_s, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs) - + empirical_sinkhorn2(X_s, X_s, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs) - + empirical_sinkhorn2(X_t, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs)) + return max(0, sinkhorn_div) diff --git a/test/test_bregman.py b/test/test_bregman.py index 90eaf27..b890df1 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -1,6 +1,7 @@ """Tests for module bregman on OT with bregman projections """ # Author: Remi Flamary +# Kilian Fatras # # License: MIT License @@ -187,3 +188,59 @@ def test_unmix(): ot.bregman.unmix(a, D, M, M0, h0, reg, 1, alpha=0.01, log=True, verbose=True) + + +def test_empirical_sinkhorn(): + # test sinkhorn + n = 100 + a = ot.unif(n) + b = ot.unif(n) + M = ot.dist(X_s, X_t) + M_e = ot.dist(X_s, X_t, metric='euclidean') + + rng = np.random.RandomState(0) + + X_s = np.reshape(np.arange(n), (n, 1)) + X_t = np.reshape(np.arange(0, n), (n, 1)) + + G_sqe = ot.bregman.empirical_sinkhorn(X_s, X_t, 1) + sinkhorn_sqe = ot.sinkhorn(a, b, M, 1) + + G_e = ot.bregman.empirical_sinkhorn(X_s, X_t, 1) + sinkhorn_e = ot.sinkhorn(a, b, M_e, 1) + + loss_emp_sinkhorn = ot.bregman.empirical_sinkhorn2(X_s, X_t, 1) + loss_sinkhorn = ot.sinkhorn2(a, b, M, 1) + + # check constratints + np.testing.assert_allclose( + sinkhorn_sqe.sum(1), G_sqe.sum(1), atol=1e-05) # metric sqeuclidian + np.testing.assert_allclose( + sinkhorn_sqe.sum(0), G_sqe.sum(0), atol=1e-05) # metric sqeuclidian + np.testing.assert_allclose( + sinkhorn_e.sum(1), G_e.sum(1), atol=1e-05) # metric euclidian + np.testing.assert_allclose( + sinkhorn_e.sum(0), G_e.sum(0), atol=1e-05) # metric euclidian + np.testing.assert_allclose(loss_emp_sinkhorn, loss_sinkhorn, atol=1e-05) + + +def test_empirical_sinkhorn_divergence(): + #Test sinkhorn divergence + n = 10 + a = ot.unif(n) + b = ot.unif(n) + X_s = np.reshape(np.arange(n), (n, 1)) + X_t = np.reshape(np.arange(0, n * 2, 2), (n, 1)) + M = ot.dist(X_s, X_t) + M_s = ot.dist(X_s, X_s) + M_t = ot.dist(X_t, X_t) + + emp_sinkhorn_div = empirical_sinkhorn_divergence(X_s, X_t, 1) + sinkhorn_div = (2 * ot.sinkhorn2(a, b, M, 1) - ot.sinkhorn2(a, a, M_s, 1) - + ot.sinkhorn2(b, b, M_t, 1)) + + # check constratints + np.testing.assert_allclose( + emp_sinkhorn_div, sinkhorn_div, atol=1e-05) # cf conv emp sinkhorn + np.testing.assert_allclose( + emp_sinkhorn_div, sinkhorn_div, atol=1e-05) # cf conv emp sinkhorn -- cgit v1.2.3 From 9569f893defa8e712a4f3199770a0df745d4cfff Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Fri, 29 Mar 2019 13:06:01 +0100 Subject: fix pep8 --- ot/bregman.py | 29 +++++++++++++++-------------- test/test_bregman.py | 6 ++---- 2 files changed, 17 insertions(+), 18 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index f1b18f8..f6aa339 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1375,17 +1375,18 @@ def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numI ''' if a is None: - a = ot.unif(np.shape(X_s)[0]) + a = utils.unif(np.shape(X_s)[0]) if b is None: - b = ot.unif(np.shape(X_t)[0]) + b = utils.unif(np.shape(X_t)[0]) + M = ot.dist(X_s, X_t, metric=metric) - if log == False: - pi = ot.sinkhorn(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=False, **kwargs) - return pi - if log == True: - pi, log = ot.sinkhorn(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=True, **kwargs) + if log: + pi, log = sinkhorn(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=True, **kwargs) return pi, log + else: + pi = sinkhorn(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=False, **kwargs) + return pi def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, verbose=False, log=False, **kwargs): @@ -1464,18 +1465,18 @@ def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', num ''' if a is None: - a = ot.unif(np.shape(X_s)[0]) + a = utils.unif(np.shape(X_s)[0]) if b is None: - b = ot.unif(np.shape(X_t)[0]) + b = utils.unif(np.shape(X_t)[0]) M = ot.dist(X_s, X_t, metric=metric) - if log == False: - sinkhorn_loss = ot.sinkhorn2(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) - return sinkhorn_loss - if log == True: - sinkhorn_loss, log = ot.sinkhorn2(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) + if log: + sinkhorn_loss, log = sinkhorn2(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) return sinkhorn_loss, log + else: + sinkhorn_loss = sinkhorn2(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) + return sinkhorn_loss def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, verbose=False, log=False, **kwargs): diff --git a/test/test_bregman.py b/test/test_bregman.py index b890df1..8b001a7 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -195,13 +195,11 @@ def test_empirical_sinkhorn(): n = 100 a = ot.unif(n) b = ot.unif(n) - M = ot.dist(X_s, X_t) - M_e = ot.dist(X_s, X_t, metric='euclidean') - - rng = np.random.RandomState(0) X_s = np.reshape(np.arange(n), (n, 1)) X_t = np.reshape(np.arange(0, n), (n, 1)) + M = ot.dist(X_s, X_t) + M_e = ot.dist(X_s, X_t, metric='euclidean') G_sqe = ot.bregman.empirical_sinkhorn(X_s, X_t, 1) sinkhorn_sqe = ot.sinkhorn(a, b, M, 1) -- cgit v1.2.3 From d754a645f9b4ef88d7e0aba1188fa83d7d58af1f Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Fri, 29 Mar 2019 13:24:54 +0100 Subject: typos PEP8 --- test/test_bregman.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/test_bregman.py b/test/test_bregman.py index 8b001a7..4aae6cb 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -233,7 +233,7 @@ def test_empirical_sinkhorn_divergence(): M_s = ot.dist(X_s, X_s) M_t = ot.dist(X_t, X_t) - emp_sinkhorn_div = empirical_sinkhorn_divergence(X_s, X_t, 1) + emp_sinkhorn_div = ot.bregman.empirical_sinkhorn_divergence(X_s, X_t, 1) sinkhorn_div = (2 * ot.sinkhorn2(a, b, M, 1) - ot.sinkhorn2(a, a, M_s, 1) - ot.sinkhorn2(b, b, M_t, 1)) -- cgit v1.2.3 From f63712f4df213bbfe0a2665390d51974305de705 Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Fri, 29 Mar 2019 13:44:07 +0100 Subject: call ot.unif and ot.dist --- ot/bregman.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index f6aa339..9e9989f 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1375,9 +1375,9 @@ def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numI ''' if a is None: - a = utils.unif(np.shape(X_s)[0]) + a = ot.unif(np.shape(X_s)[0]) if b is None: - b = utils.unif(np.shape(X_t)[0]) + b = ot.unif(np.shape(X_t)[0]) M = ot.dist(X_s, X_t, metric=metric) @@ -1465,9 +1465,9 @@ def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', num ''' if a is None: - a = utils.unif(np.shape(X_s)[0]) + a = ot.unif(np.shape(X_s)[0]) if b is None: - b = utils.unif(np.shape(X_t)[0]) + b = ot.unif(np.shape(X_t)[0]) M = ot.dist(X_s, X_t, metric=metric) -- cgit v1.2.3 From 24b268c5d4ee3a03e5a1742168236755cfba9ed5 Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Fri, 29 Mar 2019 13:59:25 +0100 Subject: import unif and dist in bregman file --- ot/bregman.py | 13 +++++++------ 1 file changed, 7 insertions(+), 6 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 9e9989f..f873a85 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -10,6 +10,7 @@ Bregman projections for regularized OT # License: MIT License import numpy as np +from .utils import unif, dist def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, @@ -1375,11 +1376,11 @@ def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numI ''' if a is None: - a = ot.unif(np.shape(X_s)[0]) + a = unif(np.shape(X_s)[0]) if b is None: - b = ot.unif(np.shape(X_t)[0]) + b = unif(np.shape(X_t)[0]) - M = ot.dist(X_s, X_t, metric=metric) + M = dist(X_s, X_t, metric=metric) if log: pi, log = sinkhorn(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=True, **kwargs) @@ -1465,11 +1466,11 @@ def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', num ''' if a is None: - a = ot.unif(np.shape(X_s)[0]) + a = unif(np.shape(X_s)[0]) if b is None: - b = ot.unif(np.shape(X_t)[0]) + b = unif(np.shape(X_t)[0]) - M = ot.dist(X_s, X_t, metric=metric) + M = dist(X_s, X_t, metric=metric) if log: sinkhorn_loss, log = sinkhorn2(a, b, M, reg, numItermax=numIterMax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) -- cgit v1.2.3 From 1ceb1a9cc96aad54e525c2021851b8639e2f3449 Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Sun, 31 Mar 2019 12:14:54 +0200 Subject: fix metric test --- test/test_bregman.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/test/test_bregman.py b/test/test_bregman.py index 4aae6cb..0ebd546 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -199,13 +199,13 @@ def test_empirical_sinkhorn(): X_s = np.reshape(np.arange(n), (n, 1)) X_t = np.reshape(np.arange(0, n), (n, 1)) M = ot.dist(X_s, X_t) - M_e = ot.dist(X_s, X_t, metric='euclidean') + M_m = ot.dist(X_s, X_t, metric='minkowski') G_sqe = ot.bregman.empirical_sinkhorn(X_s, X_t, 1) sinkhorn_sqe = ot.sinkhorn(a, b, M, 1) - G_e = ot.bregman.empirical_sinkhorn(X_s, X_t, 1) - sinkhorn_e = ot.sinkhorn(a, b, M_e, 1) + G_m = ot.bregman.empirical_sinkhorn(X_s, X_t, 1, metric='minkowski') + sinkhorn_m = ot.sinkhorn(a, b, M_m, 1) loss_emp_sinkhorn = ot.bregman.empirical_sinkhorn2(X_s, X_t, 1) loss_sinkhorn = ot.sinkhorn2(a, b, M, 1) @@ -216,9 +216,9 @@ def test_empirical_sinkhorn(): np.testing.assert_allclose( sinkhorn_sqe.sum(0), G_sqe.sum(0), atol=1e-05) # metric sqeuclidian np.testing.assert_allclose( - sinkhorn_e.sum(1), G_e.sum(1), atol=1e-05) # metric euclidian + sinkhorn_m.sum(1), G_m.sum(1), atol=1e-05) # metric euclidian np.testing.assert_allclose( - sinkhorn_e.sum(0), G_e.sum(0), atol=1e-05) # metric euclidian + sinkhorn_m.sum(0), G_m.sum(0), atol=1e-05) # metric euclidian np.testing.assert_allclose(loss_emp_sinkhorn, loss_sinkhorn, atol=1e-05) -- cgit v1.2.3 From 9cfcbc4a0f84c1fe302a1a89a4488866935977aa Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Wed, 3 Apr 2019 18:18:02 +0200 Subject: fix typos and add solver in readme --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index dbd93fc..dd34a97 100644 --- a/README.md +++ b/README.md @@ -15,7 +15,8 @@ This open source Python library provide several solvers for optimization problem It provides the following solvers: * OT Network Flow solver for the linear program/ Earth Movers Distance [1]. -* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10] and greedy SInkhorn [22] with optional GPU implementation (requires cupy). +* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2], stabilized version [9][10] and greedy Sinkhorn [22] with optional GPU implementation (requires cupy). +* Sinkhorn divergence [23] and entropic regularization OT from empirical data. * Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17]. * Non regularized Wasserstein barycenters [16] with LP solver (only small scale). * Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4]. -- cgit v1.2.3 From 7c02007919596dedf9d4555737900e717c3d31a8 Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Thu, 4 Apr 2019 11:46:49 +0200 Subject: fix doc --- ot/bregman.py | 29 ++++++++++++++++++----------- ot/stochastic.py | 3 +++ 2 files changed, 21 insertions(+), 11 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index f873a85..47554fb 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1317,9 +1317,9 @@ def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numI \gamma\geq 0 where : - - M is the (ns,nt) metric cost matrix + - :math:`M` is the (ns,nt) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - a and b are source and target weights (sum to 1) + - :math:`a` and :math:`b` are source and target weights (sum to 1) Parameters @@ -1399,7 +1399,7 @@ def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', num The function solves the following optimization problem: .. math:: - W = \min_\gamma_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) s.t. \gamma 1 = a @@ -1408,9 +1408,9 @@ def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', num \gamma\geq 0 where : - - M is the (ns,nt) metric cost matrix + - :math:`M` is the (ns,nt) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - a and b are source and target weights (sum to 1) + - :math:`a` and :math:`b` are source and target weights (sum to 1) Parameters @@ -1484,13 +1484,20 @@ def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeucli ''' Compute the sinkhorn divergence loss from empirical data - The function solves the following optimization problem: + The function solves the following optimization problems and return the + sinkhorn divergence :math:`S`: .. math:: - S = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) - - \min_\gamma_a <\gamma_a,M_a>_F + reg\cdot\Omega(\gamma_a) - - \min_\gamma_b <\gamma_b,M_b>_F + reg\cdot\Omega(\gamma_b) + W &= \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + + W_a &= \min_{\gamma_a} <\gamma_a,M_a>_F + reg\cdot\Omega(\gamma_a) + + W_b &= \min_{\gamma_b} <\gamma_b,M_b>_F + reg\cdot\Omega(\gamma_b) + + S &= W - 1/2 * (W_a + W_b) + + .. math:: s.t. \gamma 1 = a \gamma^T 1= b @@ -1510,9 +1517,9 @@ def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeucli \gamma_b\geq 0 where : - - M (resp. :math:`M_a, M_b) is the (ns,nt) metric cost matrix (resp (ns, ns) and (nt, nt)) + - :math:`M` (resp. :math:`M_a, M_b`) is the (ns,nt) metric cost matrix (resp (ns, ns) and (nt, nt)) - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - a and b are source and target weights (sum to 1) + - :math:`a` and :math:`b` are source and target weights (sum to 1) Parameters diff --git a/ot/stochastic.py b/ot/stochastic.py index 0db39c8..85c4230 100644 --- a/ot/stochastic.py +++ b/ot/stochastic.py @@ -348,8 +348,11 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None, .. math:: \gamma = arg\min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + s.t. \gamma 1 = a + \gamma^T 1= b + \gamma \geq 0 Where : -- cgit v1.2.3 From 780bdfee3c622698dc9b18a02fa06381314aa56d Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Thu, 4 Apr 2019 13:45:33 +0200 Subject: fix log in sinkhorn div and add log tests --- ot/bregman.py | 26 ++++++++++++++++++++++---- test/test_bregman.py | 12 +++++++++++- 2 files changed, 33 insertions(+), 5 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 47554fb..7acfcf1 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1569,8 +1569,26 @@ def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeucli .. [23] Aude Genevay, Gabriel Peyré, Marco Cuturi, Learning Generative Models with Sinkhorn Divergences, Proceedings of the Twenty-First International Conference on Artficial Intelligence and Statistics, (AISTATS) 21, 2018 ''' + if log: + sinkhorn_loss_ab, log_ab = empirical_sinkhorn2(X_s, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs) + + sinkhorn_loss_a, log_a = empirical_sinkhorn2(X_s, X_s, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs) + + sinkhorn_loss_b, log_b = empirical_sinkhorn2(X_t, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs) - sinkhorn_div = (2 * empirical_sinkhorn2(X_s, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs) - - empirical_sinkhorn2(X_s, X_s, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs) - - empirical_sinkhorn2(X_t, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs)) - return max(0, sinkhorn_div) + sinkhorn_div = sinkhorn_loss_ab - 1 / 2 * (sinkhorn_loss_a + sinkhorn_loss_b) + + log = {} + log['sinkhorn_loss_ab'] = sinkhorn_loss_ab + log['sinkhorn_loss_a'] = sinkhorn_loss_a + log['sinkhorn_loss_b'] = sinkhorn_loss_b + log['log_sinkhorn_ab'] = log_ab + log['log_sinkhorn_a'] = log_a + log['log_sinkhorn_b'] = log_b + + return max(0, sinkhorn_div), log + else: + sinkhorn_div = (empirical_sinkhorn2(X_s, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs) - + 1 / 2 * empirical_sinkhorn2(X_s, X_s, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs) - + 1 / 2 * empirical_sinkhorn2(X_t, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs)) + return max(0, sinkhorn_div) diff --git a/test/test_bregman.py b/test/test_bregman.py index 0ebd546..68d3595 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -204,6 +204,9 @@ def test_empirical_sinkhorn(): G_sqe = ot.bregman.empirical_sinkhorn(X_s, X_t, 1) sinkhorn_sqe = ot.sinkhorn(a, b, M, 1) + G_log, log_es = ot.bregman.empirical_sinkhorn(X_s, X_t, 0.1, log=True) + sinkhorn_log, log_s = ot.sinkhorn(a, b, M, 0.1, log=True) + G_m = ot.bregman.empirical_sinkhorn(X_s, X_t, 1, metric='minkowski') sinkhorn_m = ot.sinkhorn(a, b, M_m, 1) @@ -215,6 +218,10 @@ def test_empirical_sinkhorn(): sinkhorn_sqe.sum(1), G_sqe.sum(1), atol=1e-05) # metric sqeuclidian np.testing.assert_allclose( sinkhorn_sqe.sum(0), G_sqe.sum(0), atol=1e-05) # metric sqeuclidian + np.testing.assert_allclose( + sinkhorn_log.sum(1), G_log.sum(1), atol=1e-05) # log + np.testing.assert_allclose( + sinkhorn_log.sum(0), G_log.sum(0), atol=1e-05) # log np.testing.assert_allclose( sinkhorn_m.sum(1), G_m.sum(1), atol=1e-05) # metric euclidian np.testing.assert_allclose( @@ -237,8 +244,11 @@ def test_empirical_sinkhorn_divergence(): sinkhorn_div = (2 * ot.sinkhorn2(a, b, M, 1) - ot.sinkhorn2(a, a, M_s, 1) - ot.sinkhorn2(b, b, M_t, 1)) + emp_sinkhorn_div_log, log_es = ot.bregman.empirical_sinkhorn_divergence(X_s, X_t, 0.1, log=True) + sinkhorn_div_log, log_s = ot.sinkhorn(a, b, M, 0.1, log=True) + # check constratints np.testing.assert_allclose( emp_sinkhorn_div, sinkhorn_div, atol=1e-05) # cf conv emp sinkhorn np.testing.assert_allclose( - emp_sinkhorn_div, sinkhorn_div, atol=1e-05) # cf conv emp sinkhorn + emp_sinkhorn_div_log, sinkhorn_div_log, atol=1e-05) # cf conv emp sinkhorn -- cgit v1.2.3 From 69186a6f4259d32fecac370f59efe16e2e460d04 Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Thu, 4 Apr 2019 13:58:50 +0200 Subject: fix test sinkhorn div --- ot/bregman.py | 11 ++++++++--- test/test_bregman.py | 8 +++++--- 2 files changed, 13 insertions(+), 6 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 7acfcf1..dc43834 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1587,8 +1587,13 @@ def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeucli log['log_sinkhorn_b'] = log_b return max(0, sinkhorn_div), log + else: - sinkhorn_div = (empirical_sinkhorn2(X_s, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs) - - 1 / 2 * empirical_sinkhorn2(X_s, X_s, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs) - - 1 / 2 * empirical_sinkhorn2(X_t, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs)) + sinkhorn_loss_ab = empirical_sinkhorn2(X_s, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs) + + sinkhorn_loss_a = empirical_sinkhorn2(X_s, X_s, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs) + + sinkhorn_loss_b = empirical_sinkhorn2(X_t, X_t, reg, a, b, metric=metric, numIterMax=numIterMax, stopThr=1e-9, verbose=verbose, log=log, **kwargs) + + sinkhorn_div = sinkhorn_loss_ab - 1 / 2 * (sinkhorn_loss_a + sinkhorn_loss_b) return max(0, sinkhorn_div) diff --git a/test/test_bregman.py b/test/test_bregman.py index 68d3595..58700e2 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -241,11 +241,13 @@ def test_empirical_sinkhorn_divergence(): M_t = ot.dist(X_t, X_t) emp_sinkhorn_div = ot.bregman.empirical_sinkhorn_divergence(X_s, X_t, 1) - sinkhorn_div = (2 * ot.sinkhorn2(a, b, M, 1) - ot.sinkhorn2(a, a, M_s, 1) - - ot.sinkhorn2(b, b, M_t, 1)) + sinkhorn_div = (ot.sinkhorn2(a, b, M, 1) - 1 / 2 * ot.sinkhorn2(a, a, M_s, 1) - 1 / 2 * ot.sinkhorn2(b, b, M_t, 1)) emp_sinkhorn_div_log, log_es = ot.bregman.empirical_sinkhorn_divergence(X_s, X_t, 0.1, log=True) - sinkhorn_div_log, log_s = ot.sinkhorn(a, b, M, 0.1, log=True) + sink_div_log, log_s = ot.sinkhorn2(a, b, M, 1) + sink_div_log_a, log_s_a = ot.sinkhorn2(a, a, M_s, 1) + sink_div_log_b, log_s_b = ot.sinkhorn2(b, b, M_t, 1) + sink_div_log = sink_div_log - 1 / 2 * (sink_div_log_a + sink_div_log_b) # check constratints np.testing.assert_allclose( -- cgit v1.2.3 From 782d9b1ae9d8c0b01e32c2af925ac9b7efa42a70 Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Thu, 4 Apr 2019 14:11:36 +0200 Subject: fix test sinkhorn div --- test/test_bregman.py | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/test/test_bregman.py b/test/test_bregman.py index 58700e2..d5482f7 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -243,11 +243,11 @@ def test_empirical_sinkhorn_divergence(): emp_sinkhorn_div = ot.bregman.empirical_sinkhorn_divergence(X_s, X_t, 1) sinkhorn_div = (ot.sinkhorn2(a, b, M, 1) - 1 / 2 * ot.sinkhorn2(a, a, M_s, 1) - 1 / 2 * ot.sinkhorn2(b, b, M_t, 1)) - emp_sinkhorn_div_log, log_es = ot.bregman.empirical_sinkhorn_divergence(X_s, X_t, 0.1, log=True) - sink_div_log, log_s = ot.sinkhorn2(a, b, M, 1) - sink_div_log_a, log_s_a = ot.sinkhorn2(a, a, M_s, 1) - sink_div_log_b, log_s_b = ot.sinkhorn2(b, b, M_t, 1) - sink_div_log = sink_div_log - 1 / 2 * (sink_div_log_a + sink_div_log_b) + emp_sinkhorn_div_log, log_es = ot.bregman.empirical_sinkhorn_divergence(X_s, X_t, 1, log=True) + sink_div_log_ab, log_s_ab = ot.sinkhorn2(a, b, M, 1, log=True) + sink_div_log_a, log_s_a = ot.sinkhorn2(a, a, M_s, 1, log=True) + sink_div_log_b, log_s_b = ot.sinkhorn2(b, b, M_t, 1, log=True) + sink_div_log = sink_div_log_ab - 1 / 2 * (sink_div_log_a + sink_div_log_b) # check constratints np.testing.assert_allclose( -- cgit v1.2.3 From 17fa4f9a8cf7ffd1a58853b4091cee0238a1100b Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Thu, 4 Apr 2019 14:16:52 +0200 Subject: fix test sinkhorn div --- test/test_bregman.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/test_bregman.py b/test/test_bregman.py index d5482f7..7f4972c 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -253,4 +253,4 @@ def test_empirical_sinkhorn_divergence(): np.testing.assert_allclose( emp_sinkhorn_div, sinkhorn_div, atol=1e-05) # cf conv emp sinkhorn np.testing.assert_allclose( - emp_sinkhorn_div_log, sinkhorn_div_log, atol=1e-05) # cf conv emp sinkhorn + emp_sinkhorn_div_log, sink_div_log, atol=1e-05) # cf conv emp sinkhorn -- cgit v1.2.3 From b9e69fb29043540079f277d2df0fc9005773f86d Mon Sep 17 00:00:00 2001 From: ngayraud Date: Fri, 10 May 2019 12:56:39 -0400 Subject: Fixed multiple docstring issues --- ot/da.py | 122 +++++++++++++++++++++++++++++++++------------------------------ 1 file changed, 64 insertions(+), 58 deletions(-) diff --git a/ot/da.py b/ot/da.py index bc09e3c..9a67724 100644 --- a/ot/da.py +++ b/ot/da.py @@ -473,22 +473,24 @@ def joint_OT_mapping_kernel(xs, xt, mu=1, eta=0.001, kerneltype='gaussian', Weight for the linear OT loss (>0) eta : float, optional Regularization term for the linear mapping L (>0) - bias : bool,optional - Estimate linear mapping with constant bias kerneltype : str,optional kernel used by calling function ot.utils.kernel (gaussian by default) sigma : float, optional Gaussian kernel bandwidth. + bias : bool,optional + Estimate linear mapping with constant bias + verbose : bool, optional + Print information along iterations + verbose2 : bool, optional + Print information along iterations numItermax : int, optional Max number of BCD iterations - stopThr : float, optional - Stop threshold on relative loss decrease (>0) numInnerItermax : int, optional Max number of iterations (inner CG solver) stopInnerThr : float, optional Stop threshold on error (inner CG solver) (>0) - verbose : bool, optional - Print information along iterations + stopThr : float, optional + Stop threshold on relative loss decrease (>0) log : bool, optional record log if True @@ -1184,26 +1186,26 @@ class SinkhornTransport(BaseTransport): algorithm if no it has not converged tol : float, optional (default=10e-9) The precision required to stop the optimization algorithm. - mapping : string, optional (default="barycentric") - The kind of mapping to apply to transport samples from a domain into - another one. - if "barycentric" only the samples used to estimate the coupling can - be transported from a domain to another one. + verbose : bool, optional (default=False) + Controls the verbosity of the optimization algorithm + log : int, optional (default=False) + Controls the logs of the optimization algorithm metric : string, optional (default="sqeuclidean") The ground metric for the Wasserstein problem norm : string, optional (default=None) If given, normalize the ground metric to avoid numerical errors that can occur with large metric values. - distribution : string, optional (default="uniform") + distribution_estimation : callable, optional (defaults to the uniform) The kind of distribution estimation to employ - verbose : int, optional (default=0) - Controls the verbosity of the optimization algorithm - log : int, optional (default=0) - Controls the logs of the optimization algorithm + out_of_sample_map : string, optional (default="ferradans") + The kind of out of sample mapping to apply to transport samples + from a domain into another one. Currently the only possible option is + "ferradans" which uses the method proposed in [6]. limit_max: float, optional (defaul=np.infty) Controls the semi supervised mode. Transport between labeled source - and target samples of different classes will exhibit an infinite cost - + and target samples of different classes will exhibit an cost defined + by this variable + Attributes ---------- coupling_ : array-like, shape (n_source_samples, n_target_samples) @@ -1287,22 +1289,19 @@ class EMDTransport(BaseTransport): Parameters ---------- - mapping : string, optional (default="barycentric") - The kind of mapping to apply to transport samples from a domain into - another one. - if "barycentric" only the samples used to estimate the coupling can - be transported from a domain to another one. metric : string, optional (default="sqeuclidean") The ground metric for the Wasserstein problem norm : string, optional (default=None) If given, normalize the ground metric to avoid numerical errors that can occur with large metric values. - distribution : string, optional (default="uniform") - The kind of distribution estimation to employ - verbose : int, optional (default=0) - Controls the verbosity of the optimization algorithm - log : int, optional (default=0) + log : int, optional (default=False) Controls the logs of the optimization algorithm + distribution_estimation : callable, optional (defaults to the uniform) + The kind of distribution estimation to employ + out_of_sample_map : string, optional (default="ferradans") + The kind of out of sample mapping to apply to transport samples + from a domain into another one. Currently the only possible option is + "ferradans" which uses the method proposed in [6]. limit_max: float, optional (default=10) Controls the semi supervised mode. Transport between labeled source and target samples of different classes will exhibit an infinite cost @@ -1387,28 +1386,32 @@ class SinkhornLpl1Transport(BaseTransport): Entropic regularization parameter reg_cl : float, optional (default=0.1) Class regularization parameter - mapping : string, optional (default="barycentric") - The kind of mapping to apply to transport samples from a domain into - another one. - if "barycentric" only the samples used to estimate the coupling can - be transported from a domain to another one. - metric : string, optional (default="sqeuclidean") - The ground metric for the Wasserstein problem - norm : string, optional (default=None) - If given, normalize the ground metric to avoid numerical errors that - can occur with large metric values. - distribution : string, optional (default="uniform") - The kind of distribution estimation to employ max_iter : int, float, optional (default=10) The minimum number of iteration before stopping the optimization algorithm if no it has not converged max_inner_iter : int, float, optional (default=200) The number of iteration in the inner loop - verbose : int, optional (default=0) + log : bool, optional (default=False) + Controls the logs of the optimization algorithm + tol : float, optional (default=10e-9) + Stop threshold on error (inner sinkhorn solver) (>0) + verbose : bool, optional (default=False) Controls the verbosity of the optimization algorithm + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. + distribution_estimation : callable, optional (defaults to the uniform) + The kind of distribution estimation to employ + out_of_sample_map : string, optional (default="ferradans") + The kind of out of sample mapping to apply to transport samples + from a domain into another one. Currently the only possible option is + "ferradans" which uses the method proposed in [6]. limit_max: float, optional (defaul=np.infty) Controls the semi supervised mode. Transport between labeled source - and target samples of different classes will exhibit an infinite cost + and target samples of different classes will exhibit a cost defined by + limit_max. Attributes ---------- @@ -1504,27 +1507,28 @@ class SinkhornL1l2Transport(BaseTransport): Entropic regularization parameter reg_cl : float, optional (default=0.1) Class regularization parameter - mapping : string, optional (default="barycentric") - The kind of mapping to apply to transport samples from a domain into - another one. - if "barycentric" only the samples used to estimate the coupling can - be transported from a domain to another one. - metric : string, optional (default="sqeuclidean") - The ground metric for the Wasserstein problem - norm : string, optional (default=None) - If given, normalize the ground metric to avoid numerical errors that - can occur with large metric values. - distribution : string, optional (default="uniform") - The kind of distribution estimation to employ max_iter : int, float, optional (default=10) The minimum number of iteration before stopping the optimization algorithm if no it has not converged max_inner_iter : int, float, optional (default=200) The number of iteration in the inner loop - verbose : int, optional (default=0) + tol : float, optional (default=10e-9) + Stop threshold on error (inner sinkhorn solver) (>0) + verbose : bool, optional (default=False) Controls the verbosity of the optimization algorithm - log : int, optional (default=0) + log : bool, optional (default=False) Controls the logs of the optimization algorithm + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. + distribution_estimation : callable, optional (defaults to the uniform) + The kind of distribution estimation to employ + out_of_sample_map : string, optional (default="ferradans") + The kind of out of sample mapping to apply to transport samples + from a domain into another one. Currently the only possible option is + "ferradans" which uses the method proposed in [6]. limit_max: float, optional (default=10) Controls the semi supervised mode. Transport between labeled source and target samples of different classes will exhibit an infinite cost @@ -1646,10 +1650,12 @@ class MappingTransport(BaseEstimator): Max number of iterations (inner CG solver) inner_tol : float, optional (default=1e-6) Stop threshold on error (inner CG solver) (>0) - verbose : bool, optional (default=False) - Print information along iterations log : bool, optional (default=False) record log if True + verbose : bool, optional (default=False) + Print information along iterations + verbose2 : bool, optional (default=False) + Print information along iterations Attributes ---------- -- cgit v1.2.3 From f67f8fdbcf5064cff0fa0455c750681fcd54fe7f Mon Sep 17 00:00:00 2001 From: ngayraud Date: Tue, 14 May 2019 18:16:39 +0200 Subject: Fixed pep8 related issue --- ot/da.py | 23 ++++++++++++----------- 1 file changed, 12 insertions(+), 11 deletions(-) diff --git a/ot/da.py b/ot/da.py index 9a67724..479e698 100644 --- a/ot/da.py +++ b/ot/da.py @@ -645,7 +645,8 @@ def OT_mapping_linear(xs, xt, reg=1e-6, ws=None, The function estimates the optimal linear operator that aligns the two empirical distributions. This is equivalent to estimating the closed form mapping between two Gaussian distributions :math:`N(\mu_s,\Sigma_s)` - and :math:`N(\mu_t,\Sigma_t)` as proposed in [14] and discussed in remark 2.29 in [15]. + and :math:`N(\mu_t,\Sigma_t)` as proposed in [14] and discussed in remark + 2.29 in [15]. The linear operator from source to target :math:`M` @@ -1198,14 +1199,14 @@ class SinkhornTransport(BaseTransport): distribution_estimation : callable, optional (defaults to the uniform) The kind of distribution estimation to employ out_of_sample_map : string, optional (default="ferradans") - The kind of out of sample mapping to apply to transport samples - from a domain into another one. Currently the only possible option is + The kind of out of sample mapping to apply to transport samples + from a domain into another one. Currently the only possible option is "ferradans" which uses the method proposed in [6]. limit_max: float, optional (defaul=np.infty) Controls the semi supervised mode. Transport between labeled source - and target samples of different classes will exhibit an cost defined + and target samples of different classes will exhibit an cost defined by this variable - + Attributes ---------- coupling_ : array-like, shape (n_source_samples, n_target_samples) @@ -1299,8 +1300,8 @@ class EMDTransport(BaseTransport): distribution_estimation : callable, optional (defaults to the uniform) The kind of distribution estimation to employ out_of_sample_map : string, optional (default="ferradans") - The kind of out of sample mapping to apply to transport samples - from a domain into another one. Currently the only possible option is + The kind of out of sample mapping to apply to transport samples + from a domain into another one. Currently the only possible option is "ferradans" which uses the method proposed in [6]. limit_max: float, optional (default=10) Controls the semi supervised mode. Transport between labeled source @@ -1405,8 +1406,8 @@ class SinkhornLpl1Transport(BaseTransport): distribution_estimation : callable, optional (defaults to the uniform) The kind of distribution estimation to employ out_of_sample_map : string, optional (default="ferradans") - The kind of out of sample mapping to apply to transport samples - from a domain into another one. Currently the only possible option is + The kind of out of sample mapping to apply to transport samples + from a domain into another one. Currently the only possible option is "ferradans" which uses the method proposed in [6]. limit_max: float, optional (defaul=np.infty) Controls the semi supervised mode. Transport between labeled source @@ -1526,8 +1527,8 @@ class SinkhornL1l2Transport(BaseTransport): distribution_estimation : callable, optional (defaults to the uniform) The kind of distribution estimation to employ out_of_sample_map : string, optional (default="ferradans") - The kind of out of sample mapping to apply to transport samples - from a domain into another one. Currently the only possible option is + The kind of out of sample mapping to apply to transport samples + from a domain into another one. Currently the only possible option is "ferradans" which uses the method proposed in [6]. limit_max: float, optional (default=10) Controls the semi supervised mode. Transport between labeled source -- cgit v1.2.3 From 549b95b5736b42f3fe74daf9805303a08b1ae01d Mon Sep 17 00:00:00 2001 From: tvayer Date: Tue, 28 May 2019 16:08:41 +0200 Subject: FGW+gromov changes --- README.md | 2 + examples/plot_fgw.py | 152 +++++++++++++++++++++++++ ot/bregman.py | 2 +- ot/gromov.py | 310 ++++++++++++++++++++++++++++++++++++++++++++++++--- ot/optim.py | 102 ++++++++++++++++- 5 files changed, 546 insertions(+), 22 deletions(-) create mode 100644 examples/plot_fgw.py diff --git a/README.md b/README.md index a22306d..be88f65 100644 --- a/README.md +++ b/README.md @@ -219,3 +219,5 @@ You can also post bug reports and feature requests in Github issues. Make sure t [16] Agueh, M., & Carlier, G. (2011). [Barycenters in the Wasserstein space](https://hal.archives-ouvertes.fr/hal-00637399/document). SIAM Journal on Mathematical Analysis, 43(2), 904-924. [17] Blondel, M., Seguy, V., & Rolet, A. (2018). [Smooth and Sparse Optimal Transport](https://arxiv.org/abs/1710.06276). Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS). + +[18] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. (2019). [Optimal Transport for structured data with application on graphs](http://proceedings.mlr.press/v97/titouan19a.html) Proceedings of the 36th International Conference on Machine Learning (ICML). diff --git a/examples/plot_fgw.py b/examples/plot_fgw.py new file mode 100644 index 0000000..5c2d0e1 --- /dev/null +++ b/examples/plot_fgw.py @@ -0,0 +1,152 @@ +#!/usr/bin/env python3 +# -*- coding: utf-8 -*- +""" +============================== +Plot Fused-gromov-Wasserstein +============================== + +This example illustrates the computation of FGW for 1D measures[18]. + +.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain + and Courty Nicolas + "Optimal Transport for structured data with application on graphs" + International Conference on Machine Learning (ICML). 2019. + +""" + +# Author: Titouan Vayer +# +# License: MIT License + +import matplotlib.pyplot as pl +import numpy as np +import ot +from ot.gromov import gromov_wasserstein,fused_gromov_wasserstein + +#%% parameters +# We create two 1D random measures +n=20 +n2=30 +sig=1 +sig2=0.1 + +np.random.seed(0) + +phi=np.arange(n)[:,None] +xs=phi+sig*np.random.randn(n,1) +ys=np.vstack((np.ones((n//2,1)),0*np.ones((n//2,1))))+sig2*np.random.randn(n,1) + +phi2=np.arange(n2)[:,None] +xt=phi2+sig*np.random.randn(n2,1) +yt=np.vstack((np.ones((n2//2,1)),0*np.ones((n2//2,1))))+sig2*np.random.randn(n2,1) +yt= yt[::-1,:] + +p=ot.unif(n) +q=ot.unif(n2) + +#%% plot the distributions + +pl.close(10) +pl.figure(10,(7,7)) + +pl.subplot(2,1,1) + +pl.scatter(ys,xs,c=phi,s=70) +pl.ylabel('Feature value a',fontsize=20) +pl.title('$\mu=\sum_i \delta_{x_i,a_i}$',fontsize=25, usetex=True, y=1) +pl.xticks(()) +pl.yticks(()) +pl.subplot(2,1,2) +pl.scatter(yt,xt,c=phi2,s=70) +pl.xlabel('coordinates x/y',fontsize=25) +pl.ylabel('Feature value b',fontsize=20) +pl.title('$\\nu=\sum_j \delta_{y_j,b_j}$',fontsize=25, usetex=True, y=1) +pl.yticks(()) +pl.tight_layout() +pl.show() + + +#%% Structure matrices and across-features distance matrix +C1=ot.dist(xs) +C2=ot.dist(xt).T +M=ot.dist(ys,yt) +w1=ot.unif(C1.shape[0]) +w2=ot.unif(C2.shape[0]) +Got=ot.emd([],[],M) + +#%% +cmap='Reds' +pl.close(10) +pl.figure(10,(5,5)) +fs=15 +l_x=[0,5,10,15] +l_y=[0,5,10,15,20,25] +gs = pl.GridSpec(5, 5) + +ax1=pl.subplot(gs[3:,:2]) + +pl.imshow(C1,cmap=cmap,interpolation='nearest') +pl.title("$C_1$",fontsize=fs) +pl.xlabel("$k$",fontsize=fs) +pl.ylabel("$i$",fontsize=fs) +pl.xticks(l_x) +pl.yticks(l_x) + +ax2=pl.subplot(gs[:3,2:]) + +pl.imshow(C2,cmap=cmap,interpolation='nearest') +pl.title("$C_2$",fontsize=fs) +pl.ylabel("$l$",fontsize=fs) +#pl.ylabel("$l$",fontsize=fs) +pl.xticks(()) +pl.yticks(l_y) +ax2.set_aspect('auto') + +ax3=pl.subplot(gs[3:,2:],sharex=ax2,sharey=ax1) +pl.imshow(M,cmap=cmap,interpolation='nearest') +pl.yticks(l_x) +pl.xticks(l_y) +pl.ylabel("$i$",fontsize=fs) +pl.title("$M_{AB}$",fontsize=fs) +pl.xlabel("$j$",fontsize=fs) +pl.tight_layout() +ax3.set_aspect('auto') +pl.show() + + +#%% Computing FGW and GW +alpha=1e-3 + +ot.tic() +Gwg,logw=fused_gromov_wasserstein(M,C1,C2,p,q,loss_fun='square_loss',alpha=alpha,verbose=True,log=True) +ot.toc() + +#%reload_ext WGW +Gg,log=gromov_wasserstein(C1,C2,p,q,loss_fun='square_loss',verbose=True,log=True) + +#%% visu OT matrix +cmap='Blues' +fs=15 +pl.figure(2,(13,5)) +pl.clf() +pl.subplot(1,3,1) +pl.imshow(Got,cmap=cmap,interpolation='nearest') +#pl.xlabel("$y$",fontsize=fs) +pl.ylabel("$i$",fontsize=fs) +pl.xticks(()) + +pl.title('Wasserstein ($M$ only)') + +pl.subplot(1,3,2) +pl.imshow(Gg,cmap=cmap,interpolation='nearest') +pl.title('Gromov ($C_1,C_2$ only)') +pl.xticks(()) +pl.subplot(1,3,3) +pl.imshow(Gwg,cmap=cmap,interpolation='nearest') +pl.title('FGW ($M+C_1,C_2$)') + +pl.xlabel("$j$",fontsize=fs) +pl.ylabel("$i$",fontsize=fs) + +pl.tight_layout() +pl.show() \ No newline at end of file diff --git a/ot/bregman.py b/ot/bregman.py index b017c1a..9040429 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -5,7 +5,7 @@ Bregman projections for regularized OT # Author: Remi Flamary # Nicolas Courty -# +# Titouan Vayer # License: MIT License import numpy as np diff --git a/ot/gromov.py b/ot/gromov.py index 0278e99..7491664 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -9,17 +9,18 @@ Gromov-Wasserstein transport method # Author: Erwan Vautier # Nicolas Courty # Rémi Flamary -# +# Titouan Vayer # License: MIT License import numpy as np + from .bregman import sinkhorn from .utils import dist from .optim import cg -def init_matrix(C1, C2, T, p, q, loss_fun='square_loss'): +def init_matrix(C1, C2, p, q, loss_fun='square_loss'): """ Return loss matrices and tensors for Gromov-Wasserstein fast computation Returns the value of \mathcal{L}(C1,C2) \otimes T with the selected loss @@ -77,16 +78,16 @@ def init_matrix(C1, C2, T, p, q, loss_fun='square_loss'): if loss_fun == 'square_loss': def f1(a): - return (a**2) / 2 + return (a**2) def f2(b): - return (b**2) / 2 + return (b**2) def h1(a): return a def h2(b): - return b + return 2*b elif loss_fun == 'kl_loss': def f1(a): return a * np.log(a + 1e-15) - a @@ -268,7 +269,7 @@ def update_kl_loss(p, lambdas, T, Cs): return np.exp(np.divide(tmpsum, ppt)) -def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, **kwargs): +def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False,amijo=False, **kwargs): """ Returns the gromov-wasserstein transport between (C1,p) and (C2,q) @@ -306,6 +307,9 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, **kwargs): Print information along iterations log : bool, optional record log if True + amijo : bool, optional + If True the steps of the line-search is found via an amijo research. Else closed form is used. + If there is convergence issues use False. **kwargs : dict parameters can be directly pased to the ot.optim.cg solver @@ -329,9 +333,7 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, **kwargs): """ - T = np.eye(len(p), len(q)) - - constC, hC1, hC2 = init_matrix(C1, C2, T, p, q, loss_fun) + constC, hC1, hC2 = init_matrix(C1, C2, p, q, loss_fun) G0 = p[:, None] * q[None, :] @@ -342,14 +344,79 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, **kwargs): return gwggrad(constC, hC1, hC2, G) if log: - res, log = cg(p, q, 0, 1, f, df, G0, log=True, **kwargs) + res, log = cg(p, q, 0, 1, f, df, G0,log=True,amijo=amijo,C1=C1,C2=C2,constC=constC, **kwargs) log['gw_dist'] = gwloss(constC, hC1, hC2, res) return res, log else: - return cg(p, q, 0, 1, f, df, G0, **kwargs) + return cg(p, q, 0, 1, f, df, G0,amijo=amijo, **kwargs) + +def fused_gromov_wasserstein(M,C1,C2,p,q,loss_fun='square_loss',alpha=0.5,amijo=False,**kwargs): + """ + Computes the FGW distance between two graphs see [3] + .. math:: + \gamma = arg\min_\gamma (1-\alpha)*<\gamma,M>_F + alpha* \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l} + s.t. \gamma 1 = p + \gamma^T 1= q + \gamma\geq 0 + where : + - M is the (ns,nt) metric cost matrix + - :math:`f` is the regularization term ( and df is its gradient) + - a and b are source and target weights (sum to 1) + The algorithm used for solving the problem is conditional gradient as discussed in [1]_ + Parameters + ---------- + M : ndarray, shape (ns, nt) + Metric cost matrix between features across domains + C1 : ndarray, shape (ns, ns) + Metric cost matrix respresentative of the structure in the source space + C2 : ndarray, shape (nt, nt) + Metric cost matrix espresentative of the structure in the target space + p : ndarray, shape (ns,) + distribution in the source space + q : ndarray, shape (nt,) + distribution in the target space + loss_fun : string,optionnal + loss function used for the solver + max_iter : int, optional + Max number of iterations + tol : float, optional + Stop threshold on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + amijo : bool, optional + If True the steps of the line-search is found via an amijo research. Else closed form is used. + If there is convergence issues use False. + **kwargs : dict + parameters can be directly pased to the ot.optim.cg solver + Returns + ------- + gamma : (ns x nt) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + References + ---------- + .. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain + and Courty Nicolas + "Optimal Transport for structured data with application on graphs" + International Conference on Machine Learning (ICML). 2019. + """ + + constC,hC1,hC2=init_matrix(C1,C2,p,q,loss_fun) + + G0=p[:,None]*q[None,:] + + def f(G): + return gwloss(constC,hC1,hC2,G) + def df(G): + return gwggrad(constC,hC1,hC2,G) + + return cg(p,q,M,alpha,f,df,G0,amijo=amijo,C1=C1,C2=C2,constC=constC,**kwargs) -def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False, **kwargs): +def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False,amijo=False, **kwargs): """ Returns the gromov-wasserstein discrepancy between (C1,p) and (C2,q) @@ -387,7 +454,9 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False, **kwargs): Print information along iterations log : bool, optional record log if True - + amijo : bool, optional + If True the steps of the line-search is found via an amijo research. Else closed form is used. + If there is convergence issues use False. Returns ------- gw_dist : float @@ -407,9 +476,7 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False, **kwargs): """ - T = np.eye(len(p), len(q)) - - constC, hC1, hC2 = init_matrix(C1, C2, T, p, q, loss_fun) + constC, hC1, hC2 = init_matrix(C1, C2, p, q, loss_fun) G0 = p[:, None] * q[None, :] @@ -418,7 +485,7 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False, **kwargs): def df(G): return gwggrad(constC, hC1, hC2, G) - res, log = cg(p, q, 0, 1, f, df, G0, log=True, **kwargs) + res, log = cg(p, q, 0, 1, f, df, G0, log=True,amijo=amijo,C1=C1,C2=C2,constC=constC, **kwargs) log['gw_dist'] = gwloss(constC, hC1, hC2, res) log['T'] = res if log: @@ -495,7 +562,7 @@ def entropic_gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, T = np.outer(p, q) # Initialization - constC, hC1, hC2 = init_matrix(C1, C2, T, p, q, loss_fun) + constC, hC1, hC2 = init_matrix(C1, C2, p, q, loss_fun) cpt = 0 err = 1 @@ -815,3 +882,210 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, cpt += 1 return C + +def fgw_barycenters(N,Ys,Cs,ps,lambdas,alpha,fixed_structure=False,fixed_features=False,p=None,loss_fun='square_loss', + max_iter=100, tol=1e-9,verbose=False,log=True,init_C=None,init_X=None): + + """ + Compute the fgw barycenter as presented eq (5) in [3]. + ---------- + N : integer + Desired number of samples of the target barycenter + Ys: list of ndarray, each element has shape (ns,d) + Features of all samples + Cs : list of ndarray, each element has shape (ns,ns) + Structure matrices of all samples + ps : list of ndarray, each element has shape (ns,) + masses of all samples + lambdas : list of float + list of the S spaces' weights + alpha : float + Alpha parameter for the fgw distance + fixed_structure : bool + Wether to fix the structure of the barycenter during the updates + fixed_features : bool + Wether to fix the feature of the barycenter during the updates + init_C : ndarray, shape (N,N), optional + initialization for the barycenters' structure matrix. If not set random init + init_X : ndarray, shape (N,d), optional + initialization for the barycenters' features. If not set random init + Returns + ---------- + X : ndarray, shape (N,d) + Barycenters' features + C : ndarray, shape (N,N) + Barycenters' structure matrix + log_: + T : list of (N,ns) transport matrices + Ms : all distance matrices between the feature of the barycenter and the other features dist(X,Ys) shape (N,ns) + References + ---------- + .. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain + and Courty Nicolas + "Optimal Transport for structured data with application on graphs" + International Conference on Machine Learning (ICML). 2019. + """ + S = len(Cs) + d = Ys[0].shape[1] #dimension on the node features + if p is None: + p = np.ones(N)/N + + Cs = [np.asarray(Cs[s], dtype=np.float64) for s in range(S)] + Ys = [np.asarray(Ys[s], dtype=np.float64) for s in range(S)] + + lambdas = np.asarray(lambdas, dtype=np.float64) + + if fixed_structure: + if init_C is None: + C=Cs[0] + else: + C=init_C + else: + if init_C is None: + xalea = np.random.randn(N, 2) + C = dist(xalea, xalea) + else: + C = init_C + + if fixed_features: + if init_X is None: + X=Ys[0] + else : + X= init_X + else: + if init_X is None: + X=np.zeros((N,d)) + else: + X = init_X + + T=[np.outer(p,q) for q in ps] + + # X is N,d + # Ys is ns,d + Ms = [np.asarray(dist(X,Ys[s]), dtype=np.float64) for s in range(len(Ys))] + # Ms is N,ns + + cpt = 0 + err_feature = 1 + err_structure = 1 + + if log: + log_={} + log_['err_feature']=[] + log_['err_structure']=[] + log_['Ts_iter']=[] + + while((err_feature > tol or err_structure > tol) and cpt < max_iter): + Cprev = C + Xprev = X + + if not fixed_features: + Ys_temp=[y.T for y in Ys] + X=update_feature_matrix(lambdas,Ys_temp,T,p) + + # X must be N,d + # Ys must be ns,d + Ms=[np.asarray(dist(X,Ys[s]), dtype=np.float64) for s in range(len(Ys))] + + if not fixed_structure: + if loss_fun == 'square_loss': + # T must be ns,N + # Cs must be ns,ns + # p must be N,1 + T_temp=[t.T for t in T] + C = update_sructure_matrix(p, lambdas, T_temp, Cs) + + # Ys must be d,ns + # Ts must be N,ns + # p must be N,1 + # Ms is N,ns + # C is N,N + # Cs is ns,ns + # p is N,1 + # ps is ns,1 + + T = [fused_gromov_wasserstein((1-alpha)*Ms[s],C,Cs[s],p,ps[s],loss_fun,alpha,numItermax=max_iter, stopThr=1e-5, verbose=verbose) for s in range(S)] + + # T is N,ns + + log_['Ts_iter'].append(T) + err_feature = np.linalg.norm(X - Xprev.reshape(d,N)) + err_structure = np.linalg.norm(C - Cprev) + + if log: + log_['err_feature'].append(err_feature) + log_['err_structure'].append(err_structure) + + if verbose: + if cpt % 200 == 0: + print('{:5s}|{:12s}'.format( + 'It.', 'Err') + '\n' + '-' * 19) + print('{:5d}|{:8e}|'.format(cpt, err_structure)) + print('{:5d}|{:8e}|'.format(cpt, err_feature)) + + cpt += 1 + log_['T']=T # ce sont les matrices du barycentre de la target vers les Ys + log_['p']=p + log_['Ms']=Ms #Ms sont de tailles N,ns + + return X.T,C,log_ + + +def update_sructure_matrix(p, lambdas, T, Cs): + """ + Updates C according to the L2 Loss kernel with the S Ts couplings + calculated at each iteration + Parameters + ---------- + p : ndarray, shape (N,) + masses in the targeted barycenter + lambdas : list of float + list of the S spaces' weights + T : list of S np.ndarray(ns,N) + the S Ts couplings calculated at each iteration + Cs : list of S ndarray, shape(ns,ns) + Metric cost matrices + Returns + ---------- + C : ndarray, shape (nt,nt) + updated C matrix + """ + tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) for s in range(len(T))]) + ppt = np.outer(p, p) + + return np.divide(tmpsum, ppt) + +def update_feature_matrix(lambdas,Ys,Ts,p): + + """ + Updates the feature with respect to the S Ts couplings. See "Solving the barycenter problem with Block Coordinate Descent (BCD)" in [3] + calculated at each iteration + Parameters + ---------- + p : ndarray, shape (N,) + masses in the targeted barycenter + lambdas : list of float + list of the S spaces' weights + Ts : list of S np.ndarray(ns,N) + the S Ts couplings calculated at each iteration + Ys : list of S ndarray, shape(d,ns) + The features + Returns + ---------- + X : ndarray, shape (d,N) + + References + ---------- + .. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain + and Courty Nicolas + "Optimal Transport for structured data with application on graphs" + International Conference on Machine Learning (ICML). 2019. + """ + + p=np.diag(np.array(1/p).reshape(-1,)) + + tmpsum = sum([lambdas[s] * np.dot(Ys[s],Ts[s].T).dot(p) for s in range(len(Ts))]) + + return tmpsum + + diff --git a/ot/optim.py b/ot/optim.py index f31fae2..a774865 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -4,7 +4,7 @@ Optimization algorithms for OT """ # Author: Remi Flamary -# +# Titouan Vayer # License: MIT License import numpy as np @@ -71,9 +71,70 @@ def line_search_armijo(f, xk, pk, gfk, old_fval, return alpha, fc[0], phi1 +def do_linesearch(cost,G,deltaG,Mi,f_val, + amijo=False,C1=None,C2=None,reg=None,Gc=None,constC=None,M=None): + """ + Solve the linesearch in the FW iterations + Parameters + ---------- + cost : method + The FGW cost + G : ndarray, shape(ns,nt) + The transport map at a given iteration of the FW + deltaG : ndarray (ns,nt) + Difference between the optimal map found by linearization in the FW algorithm and the value at a given iteration + Mi : ndarray (ns,nt) + Cost matrix of the linearized transport problem. Corresponds to the gradient of the cost + f_val : float + Value of the cost at G + amijo : bool, optionnal + If True the steps of the line-search is found via an amijo research. Else closed form is used. + If there is convergence issues use False. + C1 : ndarray (ns,ns), optionnal + Structure matrix in the source domain. Only used when amijo=False + C2 : ndarray (nt,nt), optionnal + Structure matrix in the target domain. Only used when amijo=False + reg : float, optionnal + Regularization parameter. Corresponds to the alpha parameter of FGW. Only used when amijo=False + Gc : ndarray (ns,nt) + Optimal map found by linearization in the FW algorithm. Only used when amijo=False + constC : ndarray (ns,nt) + Constant for the gromov cost. See [3]. Only used when amijo=False + M : ndarray (ns,nt), optionnal + Cost matrix between the features. Only used when amijo=False + Returns + ------- + alpha : float + The optimal step size of the FW + fc : int + nb of function call. Useless here + f_val : float + The value of the cost for the next iteration + References + ---------- + .. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain + and Courty Nicolas + "Optimal Transport for structured data with application on graphs" + International Conference on Machine Learning (ICML). 2019. + """ + if amijo: + alpha, fc, f_val = line_search_armijo(cost, G, deltaG, Mi, f_val) + else: # requires symetric matrices + dot1=np.dot(C1,deltaG) + dot12=dot1.dot(C2) + a=-2*reg*np.sum(dot12*deltaG) + b=np.sum((M+reg*constC)*deltaG)-2*reg*(np.sum(dot12*G)+np.sum(np.dot(C1,G).dot(C2)*deltaG)) + c=cost(G) + + alpha=solve_1d_linesearch_quad_funct(a,b,c) + fc=None + f_val=cost(G+alpha*deltaG) + + return alpha,fc,f_val + def cg(a, b, M, reg, f, df, G0=None, numItermax=200, - stopThr=1e-9, verbose=False, log=False): + stopThr=1e-9, verbose=False, log=False,**kwargs): """ Solve the general regularized OT problem with conditional gradient @@ -116,6 +177,8 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200, Print information along iterations log : bool, optional record log if True + kwargs : dict + Parameters for linesearch Returns ------- @@ -177,7 +240,7 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200, deltaG = Gc - G # line search - alpha, fc, f_val = line_search_armijo(cost, G, deltaG, Mi, f_val) + alpha, fc, f_val = do_linesearch(cost, G, deltaG, Mi, f_val, reg=reg, M=M, Gc=Gc,**kwargs) G = G + alpha * deltaG @@ -339,3 +402,36 @@ def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, return G, log else: return G + +def solve_1d_linesearch_quad_funct(a,b,c): + """ + Solve on 0,1 the following problem: + .. math:: + \min f(x)=a*x^{2}+b*x+c + + Parameters + ---------- + a,b,c : float + The coefficients of the quadratic function + + Returns + ------- + x : float + The optimal value which leads to the minimal cost + + """ + f0=c + df0=b + f1=a+f0+df0 + + if a>0: # convex + minimum=min(1,max(0,-b/(2*a))) + #print('entrelesdeux') + return minimum + else: # non convexe donc sur les coins + if f0>f1: + #print('sur1 f(1)={}'.format(f(1))) + return 1 + else: + #print('sur0 f(0)={}'.format(f(0))) + return 0 -- cgit v1.2.3 From b1b514f5d9de009e63bd407dfd9c0a0cf6128876 Mon Sep 17 00:00:00 2001 From: tvayer Date: Tue, 28 May 2019 16:50:00 +0200 Subject: bary fgw --- examples/plot_barycenter_fgw.py | 172 ++++++++++++++++++++++++++++++++++++++++ examples/plot_fgw.py | 1 - ot/gromov.py | 15 ++-- 3 files changed, 180 insertions(+), 8 deletions(-) create mode 100644 examples/plot_barycenter_fgw.py diff --git a/examples/plot_barycenter_fgw.py b/examples/plot_barycenter_fgw.py new file mode 100644 index 0000000..f416629 --- /dev/null +++ b/examples/plot_barycenter_fgw.py @@ -0,0 +1,172 @@ +# -*- coding: utf-8 -*- +""" +================================= +Plot graphs' barycenter using FGW +================================= + +This example illustrates the computation barycenter of labeled graphs using FGW + +Requires networkx >=2 + +.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain + and Courty Nicolas + "Optimal Transport for structured data with application on graphs" + International Conference on Machine Learning (ICML). 2019. + +""" + +# Author: Titouan Vayer +# +# License: MIT License + +#%% load libraries +import numpy as np +import matplotlib.pyplot as plt +import networkx as nx +import math +from scipy.sparse.csgraph import shortest_path +import matplotlib.colors as mcol +from matplotlib import cm +from ot.gromov import fgw_barycenters +#%% Graph functions + +def find_thresh(C,inf=0.5,sup=3,step=10): + """ Trick to find the adequate thresholds from where value of the C matrix are considered close enough to say that nodes are connected + Tthe threshold is found by a linesearch between values "inf" and "sup" with "step" thresholds tested. + The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjency matrix + and the original matrix. + Parameters + ---------- + C : ndarray, shape (n_nodes,n_nodes) + The structure matrix to threshold + inf : float + The beginning of the linesearch + sup : float + The end of the linesearch + step : integer + Number of thresholds tested + """ + dist=[] + search=np.linspace(inf,sup,step) + for thresh in search: + Cprime=sp_to_adjency(C,0,thresh) + SC=shortest_path(Cprime,method='D') + SC[SC==float('inf')]=100 + dist.append(np.linalg.norm(SC-C)) + return search[np.argmin(dist)],dist + +def sp_to_adjency(C,threshinf=0.2,threshsup=1.8): + """ Thresholds the structure matrix in order to compute an adjency matrix. + All values between threshinf and threshsup are considered representing connected nodes and set to 1. Else are set to 0 + Parameters + ---------- + C : ndarray, shape (n_nodes,n_nodes) + The structure matrix to threshold + threshinf : float + The minimum value of distance from which the new value is set to 1 + threshsup : float + The maximum value of distance from which the new value is set to 1 + Returns + ------- + C : ndarray, shape (n_nodes,n_nodes) + The threshold matrix. Each element is in {0,1} + """ + H=np.zeros_like(C) + np.fill_diagonal(H,np.diagonal(C)) + C=C-H + C=np.minimum(np.maximum(C,threshinf),threshsup) + C[C==threshsup]=0 + C[C!=0]=1 + + return C + +def build_noisy_circular_graph(N=20,mu=0,sigma=0.3,with_noise=False,structure_noise=False,p=None): + """ Create a noisy circular graph + """ + g=nx.Graph() + g.add_nodes_from(list(range(N))) + for i in range(N): + noise=float(np.random.normal(mu,sigma,1)) + if with_noise: + g.add_node(i,attr_name=math.sin((2*i*math.pi/N))+noise) + else: + g.add_node(i,attr_name=math.sin(2*i*math.pi/N)) + g.add_edge(i,i+1) + if structure_noise: + randomint=np.random.randint(0,p) + if randomint==0: + if i<=N-3: + g.add_edge(i,i+2) + if i==N-2: + g.add_edge(i,0) + if i==N-1: + g.add_edge(i,1) + g.add_edge(N,0) + noise=float(np.random.normal(mu,sigma,1)) + if with_noise: + g.add_node(N,attr_name=math.sin((2*N*math.pi/N))+noise) + else: + g.add_node(N,attr_name=math.sin(2*N*math.pi/N)) + return g + +def graph_colors(nx_graph,vmin=0,vmax=7): + cnorm = mcol.Normalize(vmin=vmin,vmax=vmax) + cpick = cm.ScalarMappable(norm=cnorm,cmap='viridis') + cpick.set_array([]) + val_map = {} + for k,v in nx.get_node_attributes(nx_graph,'attr_name').items(): + val_map[k]=cpick.to_rgba(v) + colors=[] + for node in nx_graph.nodes(): + colors.append(val_map[node]) + return colors + +#%% create dataset +# We build a dataset of noisy circular graphs. +# Noise is added on the structures by random connections and on the features by gaussian noise. + +np.random.seed(30) +X0=[] +for k in range(9): + X0.append(build_noisy_circular_graph(np.random.randint(15,25),with_noise=True,structure_noise=True,p=3)) + +#%% Plot dataset + +plt.figure(figsize=(8,10)) +for i in range(len(X0)): + plt.subplot(3,3,i+1) + g=X0[i] + pos=nx.kamada_kawai_layout(g) + nx.draw(g,pos=pos,node_color = graph_colors(g,vmin=-1,vmax=1),with_labels=False,node_size=100) +plt.suptitle('Dataset of noisy graphs. Color indicates the label',fontsize=20) +plt.show() + + + +#%% +# We compute the barycenter using FGW. Structure matrices are computed using the shortest_path distance in the graph +# Features distances are the euclidean distances +Cs=[shortest_path(nx.adjacency_matrix(x)) for x in X0] +ps=[np.ones(len(x.nodes()))/len(x.nodes()) for x in X0] +Ys=[np.array([v for (k,v) in nx.get_node_attributes(x,'attr_name').items()]).reshape(-1,1) for x in X0] +lambdas=np.array([np.ones(len(Ys))/len(Ys)]).ravel() +sizebary=15 # we choose a barycenter with 15 nodes + +#%% + +A,C,log=fgw_barycenters(sizebary,Ys,Cs,ps,lambdas,alpha=0.95) + +#%% +bary=nx.from_numpy_matrix(sp_to_adjency(C,threshinf=0,threshsup=find_thresh(C,sup=100,step=100)[0])) +for i in range(len(A.ravel())): + bary.add_node(i,attr_name=float(A.ravel()[i])) + +#%% +pos = nx.kamada_kawai_layout(bary) +nx.draw(bary,pos=pos,node_color = graph_colors(bary,vmin=-1,vmax=1),with_labels=False) +plt.suptitle('Barycenter',fontsize=20) +plt.show() + + + + diff --git a/examples/plot_fgw.py b/examples/plot_fgw.py index 5c2d0e1..bfa7fb4 100644 --- a/examples/plot_fgw.py +++ b/examples/plot_fgw.py @@ -1,4 +1,3 @@ -#!/usr/bin/env python3 # -*- coding: utf-8 -*- """ ============================== diff --git a/ot/gromov.py b/ot/gromov.py index 7491664..31bd657 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -883,8 +883,9 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, return C -def fgw_barycenters(N,Ys,Cs,ps,lambdas,alpha,fixed_structure=False,fixed_features=False,p=None,loss_fun='square_loss', - max_iter=100, tol=1e-9,verbose=False,log=True,init_C=None,init_X=None): +def fgw_barycenters(N,Ys,Cs,ps,lambdas,alpha,fixed_structure=False,fixed_features=False, + p=None,loss_fun='square_loss',max_iter=100, tol=1e-9, + verbose=False,log=True,init_C=None,init_X=None): """ Compute the fgw barycenter as presented eq (5) in [3]. @@ -957,7 +958,7 @@ def fgw_barycenters(N,Ys,Cs,ps,lambdas,alpha,fixed_structure=False,fixed_feature X=np.zeros((N,d)) else: X = init_X - + T=[np.outer(p,q) for q in ps] # X is N,d @@ -981,7 +982,7 @@ def fgw_barycenters(N,Ys,Cs,ps,lambdas,alpha,fixed_structure=False,fixed_feature if not fixed_features: Ys_temp=[y.T for y in Ys] - X=update_feature_matrix(lambdas,Ys_temp,T,p) + X=update_feature_matrix(lambdas,Ys_temp,T,p).T # X must be N,d # Ys must be ns,d @@ -1024,11 +1025,11 @@ def fgw_barycenters(N,Ys,Cs,ps,lambdas,alpha,fixed_structure=False,fixed_feature print('{:5d}|{:8e}|'.format(cpt, err_feature)) cpt += 1 - log_['T']=T # ce sont les matrices du barycentre de la target vers les Ys + log_['T']=T # from target to Ys log_['p']=p - log_['Ms']=Ms #Ms sont de tailles N,ns + log_['Ms']=Ms #Ms are N,ns - return X.T,C,log_ + return X,C,log_ def update_sructure_matrix(p, lambdas, T, Cs): -- cgit v1.2.3 From cd4b98c34f885176f33db3fab16530622f29ab42 Mon Sep 17 00:00:00 2001 From: tvayer Date: Tue, 28 May 2019 17:13:21 +0200 Subject: solve conlict --- README.md | 15 ++++++++++++++- ot/bregman.py | 1 + ot/gromov.py | 6 +++--- ot/optim.py | 2 +- 4 files changed, 19 insertions(+), 5 deletions(-) diff --git a/README.md b/README.md index be88f65..13e1013 100644 --- a/README.md +++ b/README.md @@ -220,4 +220,17 @@ You can also post bug reports and feature requests in Github issues. Make sure t [17] Blondel, M., Seguy, V., & Rolet, A. (2018). [Smooth and Sparse Optimal Transport](https://arxiv.org/abs/1710.06276). Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS). -[18] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. (2019). [Optimal Transport for structured data with application on graphs](http://proceedings.mlr.press/v97/titouan19a.html) Proceedings of the 36th International Conference on Machine Learning (ICML). +[18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016) [Stochastic Optimization for Large-scale Optimal Transport](https://arxiv.org/abs/1605.08527). Advances in Neural Information Processing Systems (2016). + +[19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, A.& Blondel, M. [Large-scale Optimal Transport and Mapping Estimation](https://arxiv.org/pdf/1711.02283.pdf). International Conference on Learning Representation (2018) + +[20] Cuturi, M. and Doucet, A. (2014) [Fast Computation of Wasserstein Barycenters](http://proceedings.mlr.press/v32/cuturi14.html). International Conference in Machine Learning + +[21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A. & Guibas, L. (2015). [Convolutional wasserstein distances: Efficient optimal transportation on geometric domains](https://dl.acm.org/citation.cfm?id=2766963). ACM Transactions on Graphics (TOG), 34(4), 66. + +[22] J. Altschuler, J.Weed, P. Rigollet, (2017) [Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration](https://papers.nips.cc/paper/6792-near-linear-time-approximation-algorithms-for-optimal-transport-via-sinkhorn-iteration.pdf), Advances in Neural Information Processing Systems (NIPS) 31 + +[23] Aude, G., Peyré, G., Cuturi, M., [Learning Generative Models with Sinkhorn Divergences](https://arxiv.org/abs/1706.00292), Proceedings of the Twenty-First International Conference on Artficial Intelligence and Statistics, (AISTATS) 21, 2018 + +[24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. (2019). [Optimal Transport for structured data with application on graphs](http://proceedings.mlr.press/v97/titouan19a.html) Proceedings of the 36th International Conference on Machine Learning (ICML). + diff --git a/ot/bregman.py b/ot/bregman.py index 9040429..7be67b8 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -5,6 +5,7 @@ Bregman projections for regularized OT # Author: Remi Flamary # Nicolas Courty +# Kilian Fatras # Titouan Vayer # License: MIT License diff --git a/ot/gromov.py b/ot/gromov.py index 31bd657..ad68a1c 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -398,7 +398,7 @@ def fused_gromov_wasserstein(M,C1,C2,p,q,loss_fun='square_loss',alpha=0.5,amijo= log dictionary return only if log==True in parameters References ---------- - .. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain + .. [24] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain and Courty Nicolas "Optimal Transport for structured data with application on graphs" International Conference on Machine Learning (ICML). 2019. @@ -921,7 +921,7 @@ def fgw_barycenters(N,Ys,Cs,ps,lambdas,alpha,fixed_structure=False,fixed_feature Ms : all distance matrices between the feature of the barycenter and the other features dist(X,Ys) shape (N,ns) References ---------- - .. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain + .. [24] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain and Courty Nicolas "Optimal Transport for structured data with application on graphs" International Conference on Machine Learning (ICML). 2019. @@ -1077,7 +1077,7 @@ def update_feature_matrix(lambdas,Ys,Ts,p): References ---------- - .. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain + .. [24] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain and Courty Nicolas "Optimal Transport for structured data with application on graphs" International Conference on Machine Learning (ICML). 2019. diff --git a/ot/optim.py b/ot/optim.py index a774865..9fce21e 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -112,7 +112,7 @@ def do_linesearch(cost,G,deltaG,Mi,f_val, The value of the cost for the next iteration References ---------- - .. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain + .. [24] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain and Courty Nicolas "Optimal Transport for structured data with application on graphs" International Conference on Machine Learning (ICML). 2019. -- cgit v1.2.3 From 11c2c26ff897e5763e714546e7021cffa8d673a7 Mon Sep 17 00:00:00 2001 From: tvayer Date: Tue, 28 May 2019 17:19:40 +0200 Subject: solve 2 --- README.md | 20 +++++++++++--------- ot/bregman.py | 1 + 2 files changed, 12 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 13e1013..9951773 100644 --- a/README.md +++ b/README.md @@ -4,6 +4,7 @@ [![Anaconda Cloud](https://anaconda.org/conda-forge/pot/badges/version.svg)](https://anaconda.org/conda-forge/pot) [![Build Status](https://travis-ci.org/rflamary/POT.svg?branch=master)](https://travis-ci.org/rflamary/POT) [![Documentation Status](https://readthedocs.org/projects/pot/badge/?version=latest)](http://pot.readthedocs.io/en/latest/?badge=latest) +[![Downloads](https://pepy.tech/badge/pot)](https://pepy.tech/project/pot) [![Anaconda downloads](https://anaconda.org/conda-forge/pot/badges/downloads.svg)](https://anaconda.org/conda-forge/pot) [![License](https://anaconda.org/conda-forge/pot/badges/license.svg)](https://github.com/rflamary/POT/blob/master/LICENSE) @@ -14,15 +15,18 @@ This open source Python library provide several solvers for optimization problem It provides the following solvers: * OT Network Flow solver for the linear program/ Earth Movers Distance [1]. -* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] and stabilized version [9][10] with optional GPU implementation (requires cudamat). +* Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2], stabilized version [9][10] and greedy Sinkhorn [22] with optional GPU implementation (requires cupy). +* Sinkhorn divergence [23] and entropic regularization OT from empirical data. * Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17]. * Non regularized Wasserstein barycenters [16] with LP solver (only small scale). -* Bregman projections for Wasserstein barycenter [3] and unmixing [4]. +* Bregman projections for Wasserstein barycenter [3], convolutional barycenter [21] and unmixing [4]. * Optimal transport for domain adaptation with group lasso regularization [5] * Conditional gradient [6] and Generalized conditional gradient for regularized OT [7]. * Linear OT [14] and Joint OT matrix and mapping estimation [8]. * Wasserstein Discriminant Analysis [11] (requires autograd + pymanopt). * Gromov-Wasserstein distances and barycenters ([13] and regularized [12]) +* Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) +* Non regularized free support Wasserstein barycenters [20]. Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -77,16 +81,12 @@ Note that for easier access the module is name ot instead of pot. Some sub-modules require additional dependences which are discussed below -* **ot.dr** (Wasserstein dimensionality rediuction) depends on autograd and pymanopt that can be installed with: +* **ot.dr** (Wasserstein dimensionality reduction) depends on autograd and pymanopt that can be installed with: ``` pip install pymanopt autograd ``` -* **ot.gpu** (GPU accelerated OT) depends on cudamat that have to be installed with: -``` -git clone https://github.com/cudamat/cudamat.git -cd cudamat -python setup.py install --user # for user install (no root) -``` +* **ot.gpu** (GPU accelerated OT) depends on cupy that have to be installed following instructions on [this page](https://docs-cupy.chainer.org/en/stable/install.html). + obviously you need CUDA installed and a compatible GPU. @@ -162,6 +162,8 @@ The contributors to this library are: * [Stanislas Chambon](https://slasnista.github.io/) * [Antoine Rolet](https://arolet.github.io/) * Erwan Vautier (Gromov-Wasserstein) +* [Kilian Fatras](https://kilianfatras.github.io/) +* [Alain Rakotomamonjy](https://sites.google.com/site/alainrakotomamonjy/home) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): diff --git a/ot/bregman.py b/ot/bregman.py index 7be67b8..ffa6202 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -7,6 +7,7 @@ Bregman projections for regularized OT # Nicolas Courty # Kilian Fatras # Titouan Vayer +# # License: MIT License import numpy as np -- cgit v1.2.3 From 6484c9ea301fc15ae53b4afe134941909f581ffe Mon Sep 17 00:00:00 2001 From: tvayer Date: Wed, 29 May 2019 14:11:48 +0200 Subject: Tests + contributions --- README.md | 1 + ot/gromov.py | 12 ++++++--- test/test_gromov.py | 75 +++++++++++++++++++++++++++++++++++++++++++++++++++++ test/test_optim.py | 5 ++++ 4 files changed, 89 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 9951773..9692344 100644 --- a/README.md +++ b/README.md @@ -164,6 +164,7 @@ The contributors to this library are: * Erwan Vautier (Gromov-Wasserstein) * [Kilian Fatras](https://kilianfatras.github.io/) * [Alain Rakotomamonjy](https://sites.google.com/site/alainrakotomamonjy/home) +* [Vayer Titouan](https://tvayer.github.io/) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): diff --git a/ot/gromov.py b/ot/gromov.py index ad68a1c..297b194 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -926,6 +926,10 @@ def fgw_barycenters(N,Ys,Cs,ps,lambdas,alpha,fixed_structure=False,fixed_feature "Optimal Transport for structured data with application on graphs" International Conference on Machine Learning (ICML). 2019. """ + + class UndefinedParameter(Exception): + pass + S = len(Cs) d = Ys[0].shape[1] #dimension on the node features if p is None: @@ -938,7 +942,7 @@ def fgw_barycenters(N,Ys,Cs,ps,lambdas,alpha,fixed_structure=False,fixed_feature if fixed_structure: if init_C is None: - C=Cs[0] + raise UndefinedParameter('If C is fixed it must be initialized') else: C=init_C else: @@ -950,7 +954,7 @@ def fgw_barycenters(N,Ys,Cs,ps,lambdas,alpha,fixed_structure=False,fixed_feature if fixed_features: if init_X is None: - X=Ys[0] + raise UndefinedParameter('If X is fixed it must be initialized') else : X= init_X else: @@ -1004,13 +1008,13 @@ def fgw_barycenters(N,Ys,Cs,ps,lambdas,alpha,fixed_structure=False,fixed_feature # Cs is ns,ns # p is N,1 # ps is ns,1 - + T = [fused_gromov_wasserstein((1-alpha)*Ms[s],C,Cs[s],p,ps[s],loss_fun,alpha,numItermax=max_iter, stopThr=1e-5, verbose=verbose) for s in range(S)] # T is N,ns log_['Ts_iter'].append(T) - err_feature = np.linalg.norm(X - Xprev.reshape(d,N)) + err_feature = np.linalg.norm(X - Xprev.reshape(N,d)) err_structure = np.linalg.norm(C - Cprev) if log: diff --git a/test/test_gromov.py b/test/test_gromov.py index fb86274..07cd874 100644 --- a/test/test_gromov.py +++ b/test/test_gromov.py @@ -143,3 +143,78 @@ def test_gromov_entropic_barycenter(): 'kl_loss', 2e-3, max_iter=100, tol=1e-3) np.testing.assert_allclose(Cb2.shape, (n_samples, n_samples)) + +def test_fgw(): + n_samples = 50 # nb samples + + mu_s = np.array([0, 0]) + cov_s = np.array([[1, 0], [0, 1]]) + + xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) + + xt = xs[::-1].copy() + + ys = np.random.randn(xs.shape[0],2) + yt= ys[::-1].copy() + + p = ot.unif(n_samples) + q = ot.unif(n_samples) + + C1 = ot.dist(xs, xs) + C2 = ot.dist(xt, xt) + + C1 /= C1.max() + C2 /= C2.max() + + M=ot.dist(ys,yt) + M/=M.max() + + G = ot.gromov.fused_gromov_wasserstein(M,C1, C2, p, q, 'square_loss',alpha=0.5) + + # check constratints + np.testing.assert_allclose( + p, G.sum(1), atol=1e-04) # cf convergence fgw + np.testing.assert_allclose( + q, G.sum(0), atol=1e-04) # cf convergence fgw + + +def test_fgw_barycenter(): + + ns = 50 + nt = 60 + + Xs, ys = ot.datasets.make_data_classif('3gauss', ns) + Xt, yt = ot.datasets.make_data_classif('3gauss2', nt) + + ys = np.random.randn(Xs.shape[0],2) + yt= np.random.randn(Xt.shape[0],2) + + C1 = ot.dist(Xs) + C2 = ot.dist(Xt) + + n_samples = 3 + X,C,log = ot.gromov.fgw_barycenters(n_samples,[ys,yt] ,[C1, C2],[ot.unif(ns), ot.unif(nt)],[.5, .5],0.5, + fixed_structure=False,fixed_features=False, + p=ot.unif(n_samples),loss_fun='square_loss', + max_iter=100, tol=1e-3) + np.testing.assert_allclose(C.shape, (n_samples, n_samples)) + np.testing.assert_allclose(X.shape, (n_samples, ys.shape[1])) + + xalea = np.random.randn(n_samples, 2) + init_C = ot.dist(xalea, xalea) + + X,C,log = ot.gromov.fgw_barycenters(n_samples,[ys,yt] ,[C1, C2],ps=[ot.unif(ns), ot.unif(nt)],lambdas=[.5, .5],alpha=0.5, + fixed_structure=True,init_C=init_C,fixed_features=False, + p=ot.unif(n_samples),loss_fun='square_loss', + max_iter=100, tol=1e-3) + np.testing.assert_allclose(C.shape, (n_samples, n_samples)) + np.testing.assert_allclose(X.shape, (n_samples, ys.shape[1])) + + init_X=np.random.randn(n_samples,ys.shape[1]) + + X,C,log = ot.gromov.fgw_barycenters(n_samples,[ys,yt] ,[C1, C2],[ot.unif(ns), ot.unif(nt)],[.5, .5],0.5, + fixed_structure=False,fixed_features=True, init_X=init_X, + p=ot.unif(n_samples),loss_fun='square_loss', + max_iter=100, tol=1e-3) + np.testing.assert_allclose(C.shape, (n_samples, n_samples)) + np.testing.assert_allclose(X.shape, (n_samples, ys.shape[1])) diff --git a/test/test_optim.py b/test/test_optim.py index dfefe59..1188ef6 100644 --- a/test/test_optim.py +++ b/test/test_optim.py @@ -65,3 +65,8 @@ def test_generalized_conditional_gradient(): np.testing.assert_allclose(a, G.sum(1), atol=1e-05) np.testing.assert_allclose(b, G.sum(0), atol=1e-05) + +def test_solve_1d_linesearch_quad_funct(): + np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad_funct(1,-1,0),0.5) + np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad_funct(-1,5,0),0) + np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad_funct(-1,0.5,0),1) -- cgit v1.2.3 From f70aabfcc11f92181e0dc987b341bad8ec030d75 Mon Sep 17 00:00:00 2001 From: tvayer Date: Wed, 29 May 2019 14:16:23 +0200 Subject: pep8 --- ot/gromov.py | 124 +++++++++++++++++++++++++++++------------------------------ ot/optim.py | 59 ++++++++++++++-------------- 2 files changed, 91 insertions(+), 92 deletions(-) diff --git a/ot/gromov.py b/ot/gromov.py index 297b194..fe4fc15 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -78,16 +78,16 @@ def init_matrix(C1, C2, p, q, loss_fun='square_loss'): if loss_fun == 'square_loss': def f1(a): - return (a**2) + return (a**2) def f2(b): - return (b**2) + return (b**2) def h1(a): return a def h2(b): - return 2*b + return 2 * b elif loss_fun == 'kl_loss': def f1(a): return a * np.log(a + 1e-15) - a @@ -269,7 +269,7 @@ def update_kl_loss(p, lambdas, T, Cs): return np.exp(np.divide(tmpsum, ppt)) -def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False,amijo=False, **kwargs): +def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, amijo=False, **kwargs): """ Returns the gromov-wasserstein transport between (C1,p) and (C2,q) @@ -344,13 +344,14 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False,amijo=False, **kwargs): return gwggrad(constC, hC1, hC2, G) if log: - res, log = cg(p, q, 0, 1, f, df, G0,log=True,amijo=amijo,C1=C1,C2=C2,constC=constC, **kwargs) + res, log = cg(p, q, 0, 1, f, df, G0, log=True, amijo=amijo, C1=C1, C2=C2, constC=constC, **kwargs) log['gw_dist'] = gwloss(constC, hC1, hC2, res) return res, log else: - return cg(p, q, 0, 1, f, df, G0,amijo=amijo, **kwargs) + return cg(p, q, 0, 1, f, df, G0, amijo=amijo, **kwargs) -def fused_gromov_wasserstein(M,C1,C2,p,q,loss_fun='square_loss',alpha=0.5,amijo=False,**kwargs): + +def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, amijo=False, **kwargs): """ Computes the FGW distance between two graphs see [3] .. math:: @@ -376,7 +377,7 @@ def fused_gromov_wasserstein(M,C1,C2,p,q,loss_fun='square_loss',alpha=0.5,amijo= q : ndarray, shape (nt,) distribution in the target space loss_fun : string,optionnal - loss function used for the solver + loss function used for the solver max_iter : int, optional Max number of iterations tol : float, optional @@ -404,19 +405,20 @@ def fused_gromov_wasserstein(M,C1,C2,p,q,loss_fun='square_loss',alpha=0.5,amijo= International Conference on Machine Learning (ICML). 2019. """ - constC,hC1,hC2=init_matrix(C1,C2,p,q,loss_fun) - - G0=p[:,None]*q[None,:] - + constC, hC1, hC2 = init_matrix(C1, C2, p, q, loss_fun) + + G0 = p[:, None] * q[None, :] + def f(G): - return gwloss(constC,hC1,hC2,G) + return gwloss(constC, hC1, hC2, G) + def df(G): - return gwggrad(constC,hC1,hC2,G) - - return cg(p,q,M,alpha,f,df,G0,amijo=amijo,C1=C1,C2=C2,constC=constC,**kwargs) + return gwggrad(constC, hC1, hC2, G) + + return cg(p, q, M, alpha, f, df, G0, amijo=amijo, C1=C1, C2=C2, constC=constC, **kwargs) -def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False,amijo=False, **kwargs): +def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False, amijo=False, **kwargs): """ Returns the gromov-wasserstein discrepancy between (C1,p) and (C2,q) @@ -485,7 +487,7 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False,amijo=False, **kwargs) def df(G): return gwggrad(constC, hC1, hC2, G) - res, log = cg(p, q, 0, 1, f, df, G0, log=True,amijo=amijo,C1=C1,C2=C2,constC=constC, **kwargs) + res, log = cg(p, q, 0, 1, f, df, G0, log=True, amijo=amijo, C1=C1, C2=C2, constC=constC, **kwargs) log['gw_dist'] = gwloss(constC, hC1, hC2, res) log['T'] = res if log: @@ -883,14 +885,14 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, return C -def fgw_barycenters(N,Ys,Cs,ps,lambdas,alpha,fixed_structure=False,fixed_features=False, - p=None,loss_fun='square_loss',max_iter=100, tol=1e-9, - verbose=False,log=True,init_C=None,init_X=None): - + +def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_features=False, + p=None, loss_fun='square_loss', max_iter=100, tol=1e-9, + verbose=False, log=True, init_C=None, init_X=None): """ Compute the fgw barycenter as presented eq (5) in [3]. ---------- - N : integer + N : integer Desired number of samples of the target barycenter Ys: list of ndarray, each element has shape (ns,d) Features of all samples @@ -906,9 +908,9 @@ def fgw_barycenters(N,Ys,Cs,ps,lambdas,alpha,fixed_structure=False,fixed_feature Wether to fix the structure of the barycenter during the updates fixed_features : bool Wether to fix the feature of the barycenter during the updates - init_C : ndarray, shape (N,N), optional + init_C : ndarray, shape (N,N), optional initialization for the barycenters' structure matrix. If not set random init - init_X : ndarray, shape (N,d), optional + init_X : ndarray, shape (N,d), optional initialization for the barycenters' features. If not set random init Returns ---------- @@ -926,14 +928,14 @@ def fgw_barycenters(N,Ys,Cs,ps,lambdas,alpha,fixed_structure=False,fixed_feature "Optimal Transport for structured data with application on graphs" International Conference on Machine Learning (ICML). 2019. """ - + class UndefinedParameter(Exception): pass - + S = len(Cs) - d = Ys[0].shape[1] #dimension on the node features + d = Ys[0].shape[1] # dimension on the node features if p is None: - p = np.ones(N)/N + p = np.ones(N) / N Cs = [np.asarray(Cs[s], dtype=np.float64) for s in range(S)] Ys = [np.asarray(Ys[s], dtype=np.float64) for s in range(S)] @@ -944,7 +946,7 @@ def fgw_barycenters(N,Ys,Cs,ps,lambdas,alpha,fixed_structure=False,fixed_feature if init_C is None: raise UndefinedParameter('If C is fixed it must be initialized') else: - C=init_C + C = init_C else: if init_C is None: xalea = np.random.randn(N, 2) @@ -954,20 +956,20 @@ def fgw_barycenters(N,Ys,Cs,ps,lambdas,alpha,fixed_structure=False,fixed_feature if fixed_features: if init_X is None: - raise UndefinedParameter('If X is fixed it must be initialized') - else : - X= init_X + raise UndefinedParameter('If X is fixed it must be initialized') + else: + X = init_X else: - if init_X is None: - X=np.zeros((N,d)) + if init_X is None: + X = np.zeros((N, d)) else: X = init_X - - T=[np.outer(p,q) for q in ps] + + T = [np.outer(p, q) for q in ps] # X is N,d # Ys is ns,d - Ms = [np.asarray(dist(X,Ys[s]), dtype=np.float64) for s in range(len(Ys))] + Ms = [np.asarray(dist(X, Ys[s]), dtype=np.float64) for s in range(len(Ys))] # Ms is N,ns cpt = 0 @@ -975,46 +977,46 @@ def fgw_barycenters(N,Ys,Cs,ps,lambdas,alpha,fixed_structure=False,fixed_feature err_structure = 1 if log: - log_={} - log_['err_feature']=[] - log_['err_structure']=[] - log_['Ts_iter']=[] + log_ = {} + log_['err_feature'] = [] + log_['err_structure'] = [] + log_['Ts_iter'] = [] while((err_feature > tol or err_structure > tol) and cpt < max_iter): Cprev = C Xprev = X if not fixed_features: - Ys_temp=[y.T for y in Ys] - X=update_feature_matrix(lambdas,Ys_temp,T,p).T + Ys_temp = [y.T for y in Ys] + X = update_feature_matrix(lambdas, Ys_temp, T, p).T # X must be N,d # Ys must be ns,d - Ms=[np.asarray(dist(X,Ys[s]), dtype=np.float64) for s in range(len(Ys))] + Ms = [np.asarray(dist(X, Ys[s]), dtype=np.float64) for s in range(len(Ys))] if not fixed_structure: if loss_fun == 'square_loss': # T must be ns,N # Cs must be ns,ns # p must be N,1 - T_temp=[t.T for t in T] + T_temp = [t.T for t in T] C = update_sructure_matrix(p, lambdas, T_temp, Cs) # Ys must be d,ns # Ts must be N,ns # p must be N,1 # Ms is N,ns - # C is N,N + # C is N,N # Cs is ns,ns # p is N,1 # ps is ns,1 - - T = [fused_gromov_wasserstein((1-alpha)*Ms[s],C,Cs[s],p,ps[s],loss_fun,alpha,numItermax=max_iter, stopThr=1e-5, verbose=verbose) for s in range(S)] - # T is N,ns + T = [fused_gromov_wasserstein((1 - alpha) * Ms[s], C, Cs[s], p, ps[s], loss_fun, alpha, numItermax=max_iter, stopThr=1e-5, verbose=verbose) for s in range(S)] + + # T is N,ns log_['Ts_iter'].append(T) - err_feature = np.linalg.norm(X - Xprev.reshape(N,d)) + err_feature = np.linalg.norm(X - Xprev.reshape(N, d)) err_structure = np.linalg.norm(C - Cprev) if log: @@ -1029,11 +1031,11 @@ def fgw_barycenters(N,Ys,Cs,ps,lambdas,alpha,fixed_structure=False,fixed_feature print('{:5d}|{:8e}|'.format(cpt, err_feature)) cpt += 1 - log_['T']=T # from target to Ys - log_['p']=p - log_['Ms']=Ms #Ms are N,ns + log_['T'] = T # from target to Ys + log_['p'] = p + log_['Ms'] = Ms # Ms are N,ns - return X,C,log_ + return X, C, log_ def update_sructure_matrix(p, lambdas, T, Cs): @@ -1060,8 +1062,8 @@ def update_sructure_matrix(p, lambdas, T, Cs): return np.divide(tmpsum, ppt) -def update_feature_matrix(lambdas,Ys,Ts,p): - + +def update_feature_matrix(lambdas, Ys, Ts, p): """ Updates the feature with respect to the S Ts couplings. See "Solving the barycenter problem with Block Coordinate Descent (BCD)" in [3] calculated at each iteration @@ -1078,7 +1080,7 @@ def update_feature_matrix(lambdas,Ys,Ts,p): Returns ---------- X : ndarray, shape (d,N) - + References ---------- .. [24] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain @@ -1087,10 +1089,8 @@ def update_feature_matrix(lambdas,Ys,Ts,p): International Conference on Machine Learning (ICML). 2019. """ - p=np.diag(np.array(1/p).reshape(-1,)) + p = np.diag(np.array(1 / p).reshape(-1,)) - tmpsum = sum([lambdas[s] * np.dot(Ys[s],Ts[s].T).dot(p) for s in range(len(Ts))]) + tmpsum = sum([lambdas[s] * np.dot(Ys[s], Ts[s].T).dot(p) for s in range(len(Ts))]) return tmpsum - - diff --git a/ot/optim.py b/ot/optim.py index 9fce21e..cbfb187 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -71,8 +71,9 @@ def line_search_armijo(f, xk, pk, gfk, old_fval, return alpha, fc[0], phi1 -def do_linesearch(cost,G,deltaG,Mi,f_val, - amijo=False,C1=None,C2=None,reg=None,Gc=None,constC=None,M=None): + +def do_linesearch(cost, G, deltaG, Mi, f_val, + amijo=False, C1=None, C2=None, reg=None, Gc=None, constC=None, M=None): """ Solve the linesearch in the FW iterations Parameters @@ -119,22 +120,22 @@ def do_linesearch(cost,G,deltaG,Mi,f_val, """ if amijo: alpha, fc, f_val = line_search_armijo(cost, G, deltaG, Mi, f_val) - else: # requires symetric matrices - dot1=np.dot(C1,deltaG) - dot12=dot1.dot(C2) - a=-2*reg*np.sum(dot12*deltaG) - b=np.sum((M+reg*constC)*deltaG)-2*reg*(np.sum(dot12*G)+np.sum(np.dot(C1,G).dot(C2)*deltaG)) - c=cost(G) + else: # requires symetric matrices + dot1 = np.dot(C1, deltaG) + dot12 = dot1.dot(C2) + a = -2 * reg * np.sum(dot12 * deltaG) + b = np.sum((M + reg * constC) * deltaG) - 2 * reg * (np.sum(dot12 * G) + np.sum(np.dot(C1, G).dot(C2) * deltaG)) + c = cost(G) + + alpha = solve_1d_linesearch_quad_funct(a, b, c) + fc = None + f_val = cost(G + alpha * deltaG) - alpha=solve_1d_linesearch_quad_funct(a,b,c) - fc=None - f_val=cost(G+alpha*deltaG) - - return alpha,fc,f_val + return alpha, fc, f_val def cg(a, b, M, reg, f, df, G0=None, numItermax=200, - stopThr=1e-9, verbose=False, log=False,**kwargs): + stopThr=1e-9, verbose=False, log=False, **kwargs): """ Solve the general regularized OT problem with conditional gradient @@ -240,7 +241,7 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200, deltaG = Gc - G # line search - alpha, fc, f_val = do_linesearch(cost, G, deltaG, Mi, f_val, reg=reg, M=M, Gc=Gc,**kwargs) + alpha, fc, f_val = do_linesearch(cost, G, deltaG, Mi, f_val, reg=reg, M=M, Gc=Gc, **kwargs) G = G + alpha * deltaG @@ -403,11 +404,12 @@ def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, else: return G -def solve_1d_linesearch_quad_funct(a,b,c): + +def solve_1d_linesearch_quad_funct(a, b, c): """ - Solve on 0,1 the following problem: + Solve on 0,1 the following problem: .. math:: - \min f(x)=a*x^{2}+b*x+c + \min f(x)=a*x^{2}+b*x+c Parameters ---------- @@ -416,22 +418,19 @@ def solve_1d_linesearch_quad_funct(a,b,c): Returns ------- - x : float + x : float The optimal value which leads to the minimal cost - + """ - f0=c - df0=b - f1=a+f0+df0 + f0 = c + df0 = b + f1 = a + f0 + df0 - if a>0: # convex - minimum=min(1,max(0,-b/(2*a))) - #print('entrelesdeux') + if a > 0: # convex + minimum = min(1, max(0, -b / (2 * a))) return minimum - else: # non convexe donc sur les coins - if f0>f1: - #print('sur1 f(1)={}'.format(f(1))) + else: # non convex + if f0 > f1: return 1 else: - #print('sur0 f(0)={}'.format(f(0))) return 0 -- cgit v1.2.3 From fa989062c17f87bd96aa58ad764fd3791ea11e22 Mon Sep 17 00:00:00 2001 From: tvayer Date: Wed, 29 May 2019 15:00:50 +0200 Subject: Reame +pep8 --- README.md | 14 ++++ examples/plot_barycenter_fgw.py | 150 ++++++++++++++++++++-------------------- examples/plot_fgw.py | 138 ++++++++++++++++++------------------ test/test_gromov.py | 53 +++++++------- test/test_optim.py | 9 +-- 5 files changed, 190 insertions(+), 174 deletions(-) diff --git a/README.md b/README.md index fd27f9d..b6b215c 100644 --- a/README.md +++ b/README.md @@ -222,3 +222,17 @@ You can also post bug reports and feature requests in Github issues. Make sure t [16] Agueh, M., & Carlier, G. (2011). [Barycenters in the Wasserstein space](https://hal.archives-ouvertes.fr/hal-00637399/document). SIAM Journal on Mathematical Analysis, 43(2), 904-924. [17] Blondel, M., Seguy, V., & Rolet, A. (2018). [Smooth and Sparse Optimal Transport](https://arxiv.org/abs/1710.06276). Proceedings of the Twenty-First International Conference on Artificial Intelligence and Statistics (AISTATS). + +[18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016) [Stochastic Optimization for Large-scale Optimal Transport](https://arxiv.org/abs/1605.08527). Advances in Neural Information Processing Systems (2016). + +[19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, A.& Blondel, M. [Large-scale Optimal Transport and Mapping Estimation](https://arxiv.org/pdf/1711.02283.pdf). International Conference on Learning Representation (2018) + +[20] Cuturi, M. and Doucet, A. (2014) [Fast Computation of Wasserstein Barycenters](http://proceedings.mlr.press/v32/cuturi14.html). International Conference in Machine Learning + +[21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., Nguyen, A. & Guibas, L. (2015). [Convolutional wasserstein distances: Efficient optimal transportation on geometric domains](https://dl.acm.org/citation.cfm?id=2766963). ACM Transactions on Graphics (TOG), 34(4), 66. + +[22] J. Altschuler, J.Weed, P. Rigollet, (2017) [Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration](https://papers.nips.cc/paper/6792-near-linear-time-approximation-algorithms-for-optimal-transport-via-sinkhorn-iteration.pdf), Advances in Neural Information Processing Systems (NIPS) 31 + +[23] Aude, G., Peyré, G., Cuturi, M., [Learning Generative Models with Sinkhorn Divergences](https://arxiv.org/abs/1706.00292), Proceedings of the Twenty-First International Conference on Artficial Intelligence and Statistics, (AISTATS) 21, 2018 + +[24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. (2019). [Optimal Transport for structured data with application on graphs](http://proceedings.mlr.press/v97/titouan19a.html) Proceedings of the 36th International Conference on Machine Learning (ICML). diff --git a/examples/plot_barycenter_fgw.py b/examples/plot_barycenter_fgw.py index f416629..9eea036 100644 --- a/examples/plot_barycenter_fgw.py +++ b/examples/plot_barycenter_fgw.py @@ -30,10 +30,11 @@ from matplotlib import cm from ot.gromov import fgw_barycenters #%% Graph functions -def find_thresh(C,inf=0.5,sup=3,step=10): + +def find_thresh(C, inf=0.5, sup=3, step=10): """ Trick to find the adequate thresholds from where value of the C matrix are considered close enough to say that nodes are connected - Tthe threshold is found by a linesearch between values "inf" and "sup" with "step" thresholds tested. - The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjency matrix + Tthe threshold is found by a linesearch between values "inf" and "sup" with "step" thresholds tested. + The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjency matrix and the original matrix. Parameters ---------- @@ -43,21 +44,22 @@ def find_thresh(C,inf=0.5,sup=3,step=10): The beginning of the linesearch sup : float The end of the linesearch - step : integer - Number of thresholds tested + step : integer + Number of thresholds tested """ - dist=[] - search=np.linspace(inf,sup,step) + dist = [] + search = np.linspace(inf, sup, step) for thresh in search: - Cprime=sp_to_adjency(C,0,thresh) - SC=shortest_path(Cprime,method='D') - SC[SC==float('inf')]=100 - dist.append(np.linalg.norm(SC-C)) - return search[np.argmin(dist)],dist - -def sp_to_adjency(C,threshinf=0.2,threshsup=1.8): - """ Thresholds the structure matrix in order to compute an adjency matrix. - All values between threshinf and threshsup are considered representing connected nodes and set to 1. Else are set to 0 + Cprime = sp_to_adjency(C, 0, thresh) + SC = shortest_path(Cprime, method='D') + SC[SC == float('inf')] = 100 + dist.append(np.linalg.norm(SC - C)) + return search[np.argmin(dist)], dist + + +def sp_to_adjency(C, threshinf=0.2, threshsup=1.8): + """ Thresholds the structure matrix in order to compute an adjency matrix. + All values between threshinf and threshsup are considered representing connected nodes and set to 1. Else are set to 0 Parameters ---------- C : ndarray, shape (n_nodes,n_nodes) @@ -71,102 +73,100 @@ def sp_to_adjency(C,threshinf=0.2,threshsup=1.8): C : ndarray, shape (n_nodes,n_nodes) The threshold matrix. Each element is in {0,1} """ - H=np.zeros_like(C) - np.fill_diagonal(H,np.diagonal(C)) - C=C-H - C=np.minimum(np.maximum(C,threshinf),threshsup) - C[C==threshsup]=0 - C[C!=0]=1 - - return C - -def build_noisy_circular_graph(N=20,mu=0,sigma=0.3,with_noise=False,structure_noise=False,p=None): + H = np.zeros_like(C) + np.fill_diagonal(H, np.diagonal(C)) + C = C - H + C = np.minimum(np.maximum(C, threshinf), threshsup) + C[C == threshsup] = 0 + C[C != 0] = 1 + + return C + + +def build_noisy_circular_graph(N=20, mu=0, sigma=0.3, with_noise=False, structure_noise=False, p=None): """ Create a noisy circular graph """ - g=nx.Graph() + g = nx.Graph() g.add_nodes_from(list(range(N))) for i in range(N): - noise=float(np.random.normal(mu,sigma,1)) + noise = float(np.random.normal(mu, sigma, 1)) if with_noise: - g.add_node(i,attr_name=math.sin((2*i*math.pi/N))+noise) + g.add_node(i, attr_name=math.sin((2 * i * math.pi / N)) + noise) else: - g.add_node(i,attr_name=math.sin(2*i*math.pi/N)) - g.add_edge(i,i+1) + g.add_node(i, attr_name=math.sin(2 * i * math.pi / N)) + g.add_edge(i, i + 1) if structure_noise: - randomint=np.random.randint(0,p) - if randomint==0: - if i<=N-3: - g.add_edge(i,i+2) - if i==N-2: - g.add_edge(i,0) - if i==N-1: - g.add_edge(i,1) - g.add_edge(N,0) - noise=float(np.random.normal(mu,sigma,1)) + randomint = np.random.randint(0, p) + if randomint == 0: + if i <= N - 3: + g.add_edge(i, i + 2) + if i == N - 2: + g.add_edge(i, 0) + if i == N - 1: + g.add_edge(i, 1) + g.add_edge(N, 0) + noise = float(np.random.normal(mu, sigma, 1)) if with_noise: - g.add_node(N,attr_name=math.sin((2*N*math.pi/N))+noise) + g.add_node(N, attr_name=math.sin((2 * N * math.pi / N)) + noise) else: - g.add_node(N,attr_name=math.sin(2*N*math.pi/N)) + g.add_node(N, attr_name=math.sin(2 * N * math.pi / N)) return g -def graph_colors(nx_graph,vmin=0,vmax=7): - cnorm = mcol.Normalize(vmin=vmin,vmax=vmax) - cpick = cm.ScalarMappable(norm=cnorm,cmap='viridis') + +def graph_colors(nx_graph, vmin=0, vmax=7): + cnorm = mcol.Normalize(vmin=vmin, vmax=vmax) + cpick = cm.ScalarMappable(norm=cnorm, cmap='viridis') cpick.set_array([]) val_map = {} - for k,v in nx.get_node_attributes(nx_graph,'attr_name').items(): - val_map[k]=cpick.to_rgba(v) - colors=[] + for k, v in nx.get_node_attributes(nx_graph, 'attr_name').items(): + val_map[k] = cpick.to_rgba(v) + colors = [] for node in nx_graph.nodes(): colors.append(val_map[node]) return colors - + #%% create dataset # We build a dataset of noisy circular graphs. # Noise is added on the structures by random connections and on the features by gaussian noise. + np.random.seed(30) -X0=[] +X0 = [] for k in range(9): - X0.append(build_noisy_circular_graph(np.random.randint(15,25),with_noise=True,structure_noise=True,p=3)) - + X0.append(build_noisy_circular_graph(np.random.randint(15, 25), with_noise=True, structure_noise=True, p=3)) + #%% Plot dataset -plt.figure(figsize=(8,10)) +plt.figure(figsize=(8, 10)) for i in range(len(X0)): - plt.subplot(3,3,i+1) - g=X0[i] - pos=nx.kamada_kawai_layout(g) - nx.draw(g,pos=pos,node_color = graph_colors(g,vmin=-1,vmax=1),with_labels=False,node_size=100) -plt.suptitle('Dataset of noisy graphs. Color indicates the label',fontsize=20) + plt.subplot(3, 3, i + 1) + g = X0[i] + pos = nx.kamada_kawai_layout(g) + nx.draw(g, pos=pos, node_color=graph_colors(g, vmin=-1, vmax=1), with_labels=False, node_size=100) +plt.suptitle('Dataset of noisy graphs. Color indicates the label', fontsize=20) plt.show() - #%% # We compute the barycenter using FGW. Structure matrices are computed using the shortest_path distance in the graph # Features distances are the euclidean distances -Cs=[shortest_path(nx.adjacency_matrix(x)) for x in X0] -ps=[np.ones(len(x.nodes()))/len(x.nodes()) for x in X0] -Ys=[np.array([v for (k,v) in nx.get_node_attributes(x,'attr_name').items()]).reshape(-1,1) for x in X0] -lambdas=np.array([np.ones(len(Ys))/len(Ys)]).ravel() -sizebary=15 # we choose a barycenter with 15 nodes +Cs = [shortest_path(nx.adjacency_matrix(x)) for x in X0] +ps = [np.ones(len(x.nodes())) / len(x.nodes()) for x in X0] +Ys = [np.array([v for (k, v) in nx.get_node_attributes(x, 'attr_name').items()]).reshape(-1, 1) for x in X0] +lambdas = np.array([np.ones(len(Ys)) / len(Ys)]).ravel() +sizebary = 15 # we choose a barycenter with 15 nodes #%% -A,C,log=fgw_barycenters(sizebary,Ys,Cs,ps,lambdas,alpha=0.95) +A, C, log = fgw_barycenters(sizebary, Ys, Cs, ps, lambdas, alpha=0.95) #%% -bary=nx.from_numpy_matrix(sp_to_adjency(C,threshinf=0,threshsup=find_thresh(C,sup=100,step=100)[0])) +bary = nx.from_numpy_matrix(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0])) for i in range(len(A.ravel())): - bary.add_node(i,attr_name=float(A.ravel()[i])) - + bary.add_node(i, attr_name=float(A.ravel()[i])) + #%% pos = nx.kamada_kawai_layout(bary) -nx.draw(bary,pos=pos,node_color = graph_colors(bary,vmin=-1,vmax=1),with_labels=False) -plt.suptitle('Barycenter',fontsize=20) +nx.draw(bary, pos=pos, node_color=graph_colors(bary, vmin=-1, vmax=1), with_labels=False) +plt.suptitle('Barycenter', fontsize=20) plt.show() - - - - diff --git a/examples/plot_fgw.py b/examples/plot_fgw.py index bfa7fb4..ae3c487 100644 --- a/examples/plot_fgw.py +++ b/examples/plot_fgw.py @@ -20,132 +20,132 @@ This example illustrates the computation of FGW for 1D measures[18]. import matplotlib.pyplot as pl import numpy as np import ot -from ot.gromov import gromov_wasserstein,fused_gromov_wasserstein +from ot.gromov import gromov_wasserstein, fused_gromov_wasserstein #%% parameters -# We create two 1D random measures -n=20 -n2=30 -sig=1 -sig2=0.1 +# We create two 1D random measures +n = 20 +n2 = 30 +sig = 1 +sig2 = 0.1 np.random.seed(0) -phi=np.arange(n)[:,None] -xs=phi+sig*np.random.randn(n,1) -ys=np.vstack((np.ones((n//2,1)),0*np.ones((n//2,1))))+sig2*np.random.randn(n,1) +phi = np.arange(n)[:, None] +xs = phi + sig * np.random.randn(n, 1) +ys = np.vstack((np.ones((n // 2, 1)), 0 * np.ones((n // 2, 1)))) + sig2 * np.random.randn(n, 1) -phi2=np.arange(n2)[:,None] -xt=phi2+sig*np.random.randn(n2,1) -yt=np.vstack((np.ones((n2//2,1)),0*np.ones((n2//2,1))))+sig2*np.random.randn(n2,1) -yt= yt[::-1,:] +phi2 = np.arange(n2)[:, None] +xt = phi2 + sig * np.random.randn(n2, 1) +yt = np.vstack((np.ones((n2 // 2, 1)), 0 * np.ones((n2 // 2, 1)))) + sig2 * np.random.randn(n2, 1) +yt = yt[::-1, :] -p=ot.unif(n) -q=ot.unif(n2) +p = ot.unif(n) +q = ot.unif(n2) #%% plot the distributions pl.close(10) -pl.figure(10,(7,7)) +pl.figure(10, (7, 7)) -pl.subplot(2,1,1) +pl.subplot(2, 1, 1) -pl.scatter(ys,xs,c=phi,s=70) -pl.ylabel('Feature value a',fontsize=20) -pl.title('$\mu=\sum_i \delta_{x_i,a_i}$',fontsize=25, usetex=True, y=1) +pl.scatter(ys, xs, c=phi, s=70) +pl.ylabel('Feature value a', fontsize=20) +pl.title('$\mu=\sum_i \delta_{x_i,a_i}$', fontsize=25, usetex=True, y=1) pl.xticks(()) pl.yticks(()) -pl.subplot(2,1,2) -pl.scatter(yt,xt,c=phi2,s=70) -pl.xlabel('coordinates x/y',fontsize=25) -pl.ylabel('Feature value b',fontsize=20) -pl.title('$\\nu=\sum_j \delta_{y_j,b_j}$',fontsize=25, usetex=True, y=1) +pl.subplot(2, 1, 2) +pl.scatter(yt, xt, c=phi2, s=70) +pl.xlabel('coordinates x/y', fontsize=25) +pl.ylabel('Feature value b', fontsize=20) +pl.title('$\\nu=\sum_j \delta_{y_j,b_j}$', fontsize=25, usetex=True, y=1) pl.yticks(()) pl.tight_layout() pl.show() #%% Structure matrices and across-features distance matrix -C1=ot.dist(xs) -C2=ot.dist(xt).T -M=ot.dist(ys,yt) -w1=ot.unif(C1.shape[0]) -w2=ot.unif(C2.shape[0]) -Got=ot.emd([],[],M) +C1 = ot.dist(xs) +C2 = ot.dist(xt).T +M = ot.dist(ys, yt) +w1 = ot.unif(C1.shape[0]) +w2 = ot.unif(C2.shape[0]) +Got = ot.emd([], [], M) #%% -cmap='Reds' +cmap = 'Reds' pl.close(10) -pl.figure(10,(5,5)) -fs=15 -l_x=[0,5,10,15] -l_y=[0,5,10,15,20,25] +pl.figure(10, (5, 5)) +fs = 15 +l_x = [0, 5, 10, 15] +l_y = [0, 5, 10, 15, 20, 25] gs = pl.GridSpec(5, 5) -ax1=pl.subplot(gs[3:,:2]) +ax1 = pl.subplot(gs[3:, :2]) -pl.imshow(C1,cmap=cmap,interpolation='nearest') -pl.title("$C_1$",fontsize=fs) -pl.xlabel("$k$",fontsize=fs) -pl.ylabel("$i$",fontsize=fs) +pl.imshow(C1, cmap=cmap, interpolation='nearest') +pl.title("$C_1$", fontsize=fs) +pl.xlabel("$k$", fontsize=fs) +pl.ylabel("$i$", fontsize=fs) pl.xticks(l_x) pl.yticks(l_x) -ax2=pl.subplot(gs[:3,2:]) +ax2 = pl.subplot(gs[:3, 2:]) -pl.imshow(C2,cmap=cmap,interpolation='nearest') -pl.title("$C_2$",fontsize=fs) -pl.ylabel("$l$",fontsize=fs) +pl.imshow(C2, cmap=cmap, interpolation='nearest') +pl.title("$C_2$", fontsize=fs) +pl.ylabel("$l$", fontsize=fs) #pl.ylabel("$l$",fontsize=fs) pl.xticks(()) pl.yticks(l_y) ax2.set_aspect('auto') -ax3=pl.subplot(gs[3:,2:],sharex=ax2,sharey=ax1) -pl.imshow(M,cmap=cmap,interpolation='nearest') +ax3 = pl.subplot(gs[3:, 2:], sharex=ax2, sharey=ax1) +pl.imshow(M, cmap=cmap, interpolation='nearest') pl.yticks(l_x) pl.xticks(l_y) -pl.ylabel("$i$",fontsize=fs) -pl.title("$M_{AB}$",fontsize=fs) -pl.xlabel("$j$",fontsize=fs) +pl.ylabel("$i$", fontsize=fs) +pl.title("$M_{AB}$", fontsize=fs) +pl.xlabel("$j$", fontsize=fs) pl.tight_layout() ax3.set_aspect('auto') pl.show() #%% Computing FGW and GW -alpha=1e-3 - +alpha = 1e-3 + ot.tic() -Gwg,logw=fused_gromov_wasserstein(M,C1,C2,p,q,loss_fun='square_loss',alpha=alpha,verbose=True,log=True) +Gwg, logw = fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=alpha, verbose=True, log=True) ot.toc() -#%reload_ext WGW -Gg,log=gromov_wasserstein(C1,C2,p,q,loss_fun='square_loss',verbose=True,log=True) - +#%reload_ext WGW +Gg, log = gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', verbose=True, log=True) + #%% visu OT matrix -cmap='Blues' -fs=15 -pl.figure(2,(13,5)) +cmap = 'Blues' +fs = 15 +pl.figure(2, (13, 5)) pl.clf() -pl.subplot(1,3,1) -pl.imshow(Got,cmap=cmap,interpolation='nearest') +pl.subplot(1, 3, 1) +pl.imshow(Got, cmap=cmap, interpolation='nearest') #pl.xlabel("$y$",fontsize=fs) -pl.ylabel("$i$",fontsize=fs) +pl.ylabel("$i$", fontsize=fs) pl.xticks(()) pl.title('Wasserstein ($M$ only)') -pl.subplot(1,3,2) -pl.imshow(Gg,cmap=cmap,interpolation='nearest') +pl.subplot(1, 3, 2) +pl.imshow(Gg, cmap=cmap, interpolation='nearest') pl.title('Gromov ($C_1,C_2$ only)') pl.xticks(()) -pl.subplot(1,3,3) -pl.imshow(Gwg,cmap=cmap,interpolation='nearest') +pl.subplot(1, 3, 3) +pl.imshow(Gwg, cmap=cmap, interpolation='nearest') pl.title('FGW ($M+C_1,C_2$)') -pl.xlabel("$j$",fontsize=fs) -pl.ylabel("$i$",fontsize=fs) +pl.xlabel("$j$", fontsize=fs) +pl.ylabel("$i$", fontsize=fs) pl.tight_layout() -pl.show() \ No newline at end of file +pl.show() diff --git a/test/test_gromov.py b/test/test_gromov.py index 43b63e1..cd180d4 100644 --- a/test/test_gromov.py +++ b/test/test_gromov.py @@ -145,7 +145,8 @@ def test_gromov_entropic_barycenter(): 'kl_loss', 2e-3, max_iter=100, tol=1e-3) np.testing.assert_allclose(Cb2.shape, (n_samples, n_samples)) - + + def test_fgw(): n_samples = 50 # nb samples @@ -155,9 +156,9 @@ def test_fgw(): xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) xt = xs[::-1].copy() - - ys = np.random.randn(xs.shape[0],2) - yt= ys[::-1].copy() + + ys = np.random.randn(xs.shape[0], 2) + yt = ys[::-1].copy() p = ot.unif(n_samples) q = ot.unif(n_samples) @@ -167,11 +168,11 @@ def test_fgw(): C1 /= C1.max() C2 /= C2.max() - - M=ot.dist(ys,yt) - M/=M.max() - G = ot.gromov.fused_gromov_wasserstein(M,C1, C2, p, q, 'square_loss',alpha=0.5) + M = ot.dist(ys, yt) + M /= M.max() + + G = ot.gromov.fused_gromov_wasserstein(M, C1, C2, p, q, 'square_loss', alpha=0.5) # check constratints np.testing.assert_allclose( @@ -187,36 +188,36 @@ def test_fgw_barycenter(): Xs, ys = ot.datasets.make_data_classif('3gauss', ns) Xt, yt = ot.datasets.make_data_classif('3gauss2', nt) - - ys = np.random.randn(Xs.shape[0],2) - yt= np.random.randn(Xt.shape[0],2) + + ys = np.random.randn(Xs.shape[0], 2) + yt = np.random.randn(Xt.shape[0], 2) C1 = ot.dist(Xs) C2 = ot.dist(Xt) n_samples = 3 - X,C,log = ot.gromov.fgw_barycenters(n_samples,[ys,yt] ,[C1, C2],[ot.unif(ns), ot.unif(nt)],[.5, .5],0.5, - fixed_structure=False,fixed_features=False, - p=ot.unif(n_samples),loss_fun='square_loss', - max_iter=100, tol=1e-3) + X, C, log = ot.gromov.fgw_barycenters(n_samples, [ys, yt], [C1, C2], [ot.unif(ns), ot.unif(nt)], [.5, .5], 0.5, + fixed_structure=False, fixed_features=False, + p=ot.unif(n_samples), loss_fun='square_loss', + max_iter=100, tol=1e-3) np.testing.assert_allclose(C.shape, (n_samples, n_samples)) np.testing.assert_allclose(X.shape, (n_samples, ys.shape[1])) xalea = np.random.randn(n_samples, 2) init_C = ot.dist(xalea, xalea) - - X,C,log = ot.gromov.fgw_barycenters(n_samples,[ys,yt] ,[C1, C2],ps=[ot.unif(ns), ot.unif(nt)],lambdas=[.5, .5],alpha=0.5, - fixed_structure=True,init_C=init_C,fixed_features=False, - p=ot.unif(n_samples),loss_fun='square_loss', - max_iter=100, tol=1e-3) + + X, C, log = ot.gromov.fgw_barycenters(n_samples, [ys, yt], [C1, C2], ps=[ot.unif(ns), ot.unif(nt)], lambdas=[.5, .5], alpha=0.5, + fixed_structure=True, init_C=init_C, fixed_features=False, + p=ot.unif(n_samples), loss_fun='square_loss', + max_iter=100, tol=1e-3) np.testing.assert_allclose(C.shape, (n_samples, n_samples)) np.testing.assert_allclose(X.shape, (n_samples, ys.shape[1])) - - init_X=np.random.randn(n_samples,ys.shape[1]) - X,C,log = ot.gromov.fgw_barycenters(n_samples,[ys,yt] ,[C1, C2],[ot.unif(ns), ot.unif(nt)],[.5, .5],0.5, - fixed_structure=False,fixed_features=True, init_X=init_X, - p=ot.unif(n_samples),loss_fun='square_loss', - max_iter=100, tol=1e-3) + init_X = np.random.randn(n_samples, ys.shape[1]) + + X, C, log = ot.gromov.fgw_barycenters(n_samples, [ys, yt], [C1, C2], [ot.unif(ns), ot.unif(nt)], [.5, .5], 0.5, + fixed_structure=False, fixed_features=True, init_X=init_X, + p=ot.unif(n_samples), loss_fun='square_loss', + max_iter=100, tol=1e-3) np.testing.assert_allclose(C.shape, (n_samples, n_samples)) np.testing.assert_allclose(X.shape, (n_samples, ys.shape[1])) diff --git a/test/test_optim.py b/test/test_optim.py index 1188ef6..e7ba32a 100644 --- a/test/test_optim.py +++ b/test/test_optim.py @@ -65,8 +65,9 @@ def test_generalized_conditional_gradient(): np.testing.assert_allclose(a, G.sum(1), atol=1e-05) np.testing.assert_allclose(b, G.sum(0), atol=1e-05) - + + def test_solve_1d_linesearch_quad_funct(): - np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad_funct(1,-1,0),0.5) - np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad_funct(-1,5,0),0) - np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad_funct(-1,0.5,0),1) + np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad_funct(1, -1, 0), 0.5) + np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad_funct(-1, 5, 0), 0) + np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad_funct(-1, 0.5, 0), 1) -- cgit v1.2.3 From 103dfe0ee76e110bb9e0d1e36e3dd86109db3fce Mon Sep 17 00:00:00 2001 From: tvayer Date: Wed, 29 May 2019 15:10:37 +0200 Subject: test check --- ot/gromov.py | 2 +- ot/optim.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/ot/gromov.py b/ot/gromov.py index 33134a2..44248d1 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -348,7 +348,7 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, amijo=False, **kwargs) log['gw_dist'] = gwloss(constC, hC1, hC2, res) return res, log else: - return cg(p, q, 0, 1, f, df, G0, amijo=amijo, **kwargs) + return cg(p, q, 0, 1, f, df, G0, amijo=amijo, C1=C1, C2=C2, constC=constC, **kwargs) def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, amijo=False, **kwargs): diff --git a/ot/optim.py b/ot/optim.py index cbfb187..2170c7e 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -73,7 +73,7 @@ def line_search_armijo(f, xk, pk, gfk, old_fval, def do_linesearch(cost, G, deltaG, Mi, f_val, - amijo=False, C1=None, C2=None, reg=None, Gc=None, constC=None, M=None): + amijo=True, C1=None, C2=None, reg=None, Gc=None, constC=None, M=None): """ Solve the linesearch in the FW iterations Parameters -- cgit v1.2.3 From 915d5fa4c4020536f2d41c21353b1477befa8af3 Mon Sep 17 00:00:00 2001 From: tvayer Date: Wed, 29 May 2019 15:19:41 +0200 Subject: python2 divide problem --- ot/optim.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/optim.py b/ot/optim.py index 2170c7e..282b30d 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -427,7 +427,7 @@ def solve_1d_linesearch_quad_funct(a, b, c): f1 = a + f0 + df0 if a > 0: # convex - minimum = min(1, max(0, -b / (2 * a))) + minimum = min(1, max(0, np.divide(-b, 2 * a))) return minimum else: # non convex if f0 > f1: -- cgit v1.2.3 From 94d2fe5fd0b07060426e9449de0331b88ab53df4 Mon Sep 17 00:00:00 2001 From: tvayer Date: Wed, 29 May 2019 15:32:03 +0200 Subject: wizard stuff --- ot/optim.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/optim.py b/ot/optim.py index 282b30d..b96d920 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -427,7 +427,7 @@ def solve_1d_linesearch_quad_funct(a, b, c): f1 = a + f0 + df0 if a > 0: # convex - minimum = min(1, max(0, np.divide(-b, 2 * a))) + minimum = min(1, max(0, np.divide(-b, 2.0 * a))) return minimum else: # non convex if f0 > f1: -- cgit v1.2.3 From 9421dddd8890d4c575b593d678eb7bdf5f933f83 Mon Sep 17 00:00:00 2001 From: tvayer Date: Wed, 29 May 2019 15:51:57 +0200 Subject: Doc+armijo --- ot/gromov.py | 39 ++++++++++++++++++++------------------- ot/optim.py | 22 +++++++++++----------- 2 files changed, 31 insertions(+), 30 deletions(-) diff --git a/ot/gromov.py b/ot/gromov.py index 44248d1..5a57dc8 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -33,12 +33,12 @@ def init_matrix(C1, C2, p, q, loss_fun='square_loss'): * C2 : Metric cost matrix in the target space * T : A coupling between those two spaces - The square-loss function L(a,b)=(1/2)*|a-b|^2 is read as : + The square-loss function L(a,b)=|a-b|^2 is read as : L(a,b) = f1(a)+f2(b)-h1(a)*h2(b) with : - * f1(a)=(a^2)/2 - * f2(b)=(b^2)/2 + * f1(a)=(a^2) + * f2(b)=(b^2) * h1(a)=a - * h2(b)=b + * h2(b)=2*b The kl-loss function L(a,b)=a*log(a/b)-a+b is read as : L(a,b) = f1(a)+f2(b)-h1(a)*h2(b) with : @@ -269,7 +269,7 @@ def update_kl_loss(p, lambdas, T, Cs): return np.exp(np.divide(tmpsum, ppt)) -def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, amijo=False, **kwargs): +def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, armijo=False, **kwargs): """ Returns the gromov-wasserstein transport between (C1,p) and (C2,q) @@ -307,8 +307,8 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, amijo=False, **kwargs) Print information along iterations log : bool, optional record log if True - amijo : bool, optional - If True the steps of the line-search is found via an amijo research. Else closed form is used. + armijo : bool, optional + If True the steps of the line-search is found via an armijo research. Else closed form is used. If there is convergence issues use False. **kwargs : dict parameters can be directly pased to the ot.optim.cg solver @@ -344,14 +344,14 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, amijo=False, **kwargs) return gwggrad(constC, hC1, hC2, G) if log: - res, log = cg(p, q, 0, 1, f, df, G0, log=True, amijo=amijo, C1=C1, C2=C2, constC=constC, **kwargs) + res, log = cg(p, q, 0, 1, f, df, G0, log=True, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs) log['gw_dist'] = gwloss(constC, hC1, hC2, res) return res, log else: - return cg(p, q, 0, 1, f, df, G0, amijo=amijo, C1=C1, C2=C2, constC=constC, **kwargs) + return cg(p, q, 0, 1, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs) -def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, amijo=False, **kwargs): +def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, armijo=False, **kwargs): """ Computes the FGW distance between two graphs see [3] .. math:: @@ -363,6 +363,7 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, - M is the (ns,nt) metric cost matrix - :math:`f` is the regularization term ( and df is its gradient) - a and b are source and target weights (sum to 1) + - L is a loss function to account for the misfit between the similarity matrices The algorithm used for solving the problem is conditional gradient as discussed in [1]_ Parameters ---------- @@ -386,8 +387,8 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, Print information along iterations log : bool, optional record log if True - amijo : bool, optional - If True the steps of the line-search is found via an amijo research. Else closed form is used. + armijo : bool, optional + If True the steps of the line-search is found via an armijo research. Else closed form is used. If there is convergence issues use False. **kwargs : dict parameters can be directly pased to the ot.optim.cg solver @@ -415,10 +416,10 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, def df(G): return gwggrad(constC, hC1, hC2, G) - return cg(p, q, M, alpha, f, df, G0, amijo=amijo, C1=C1, C2=C2, constC=constC, **kwargs) + return cg(p, q, M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs) -def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False, amijo=False, **kwargs): +def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False, armijo=False, **kwargs): """ Returns the gromov-wasserstein discrepancy between (C1,p) and (C2,q) @@ -456,8 +457,8 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False, amijo=False, **kwargs Print information along iterations log : bool, optional record log if True - amijo : bool, optional - If True the steps of the line-search is found via an amijo research. Else closed form is used. + armijo : bool, optional + If True the steps of the line-search is found via an armijo research. Else closed form is used. If there is convergence issues use False. Returns ------- @@ -487,7 +488,7 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False, amijo=False, **kwargs def df(G): return gwggrad(constC, hC1, hC2, G) - res, log = cg(p, q, 0, 1, f, df, G0, log=True, amijo=amijo, C1=C1, C2=C2, constC=constC, **kwargs) + res, log = cg(p, q, 0, 1, f, df, G0, log=True, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs) log['gw_dist'] = gwloss(constC, hC1, hC2, res) log['T'] = res if log: @@ -890,7 +891,7 @@ def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_ p=None, loss_fun='square_loss', max_iter=100, tol=1e-9, verbose=False, log=True, init_C=None, init_X=None): """ - Compute the fgw barycenter as presented eq (5) in [3]. + Compute the fgw barycenter as presented eq (5) in [24]. ---------- N : integer Desired number of samples of the target barycenter @@ -1065,7 +1066,7 @@ def update_sructure_matrix(p, lambdas, T, Cs): def update_feature_matrix(lambdas, Ys, Ts, p): """ - Updates the feature with respect to the S Ts couplings. See "Solving the barycenter problem with Block Coordinate Descent (BCD)" in [3] + Updates the feature with respect to the S Ts couplings. See "Solving the barycenter problem with Block Coordinate Descent (BCD)" in [24] calculated at each iteration Parameters ---------- diff --git a/ot/optim.py b/ot/optim.py index b96d920..82a91bf 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -73,13 +73,13 @@ def line_search_armijo(f, xk, pk, gfk, old_fval, def do_linesearch(cost, G, deltaG, Mi, f_val, - amijo=True, C1=None, C2=None, reg=None, Gc=None, constC=None, M=None): + armijo=True, C1=None, C2=None, reg=None, Gc=None, constC=None, M=None): """ Solve the linesearch in the FW iterations Parameters ---------- cost : method - The FGW cost + Cost in the FW for the linesearch G : ndarray, shape(ns,nt) The transport map at a given iteration of the FW deltaG : ndarray (ns,nt) @@ -88,21 +88,21 @@ def do_linesearch(cost, G, deltaG, Mi, f_val, Cost matrix of the linearized transport problem. Corresponds to the gradient of the cost f_val : float Value of the cost at G - amijo : bool, optionnal - If True the steps of the line-search is found via an amijo research. Else closed form is used. + armijo : bool, optionnal + If True the steps of the line-search is found via an armijo research. Else closed form is used. If there is convergence issues use False. C1 : ndarray (ns,ns), optionnal - Structure matrix in the source domain. Only used when amijo=False + Structure matrix in the source domain. Only used when armijo=False C2 : ndarray (nt,nt), optionnal - Structure matrix in the target domain. Only used when amijo=False + Structure matrix in the target domain. Only used when armijo=False reg : float, optionnal - Regularization parameter. Corresponds to the alpha parameter of FGW. Only used when amijo=False + Regularization parameter. Only used when armijo=False Gc : ndarray (ns,nt) - Optimal map found by linearization in the FW algorithm. Only used when amijo=False + Optimal map found by linearization in the FW algorithm. Only used when armijo=False constC : ndarray (ns,nt) - Constant for the gromov cost. See [3]. Only used when amijo=False + Constant for the gromov cost. See [24]. Only used when armijo=False M : ndarray (ns,nt), optionnal - Cost matrix between the features. Only used when amijo=False + Cost matrix between the features. Only used when armijo=False Returns ------- alpha : float @@ -118,7 +118,7 @@ def do_linesearch(cost, G, deltaG, Mi, f_val, "Optimal Transport for structured data with application on graphs" International Conference on Machine Learning (ICML). 2019. """ - if amijo: + if armijo: alpha, fc, f_val = line_search_armijo(cost, G, deltaG, Mi, f_val) else: # requires symetric matrices dot1 = np.dot(C1, deltaG) -- cgit v1.2.3 From d4320382fa8873d15dcaec7adca3a4723c142515 Mon Sep 17 00:00:00 2001 From: tvayer Date: Wed, 29 May 2019 16:10:26 +0200 Subject: relative+absolute loss --- ot/optim.py | 31 +++++++++++++++++++------------ 1 file changed, 19 insertions(+), 12 deletions(-) diff --git a/ot/optim.py b/ot/optim.py index 82a91bf..7d103e2 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -135,7 +135,7 @@ def do_linesearch(cost, G, deltaG, Mi, f_val, def cg(a, b, M, reg, f, df, G0=None, numItermax=200, - stopThr=1e-9, verbose=False, log=False, **kwargs): + stopThr=1e-9, stopThr2=1e-9, verbose=False, log=False, **kwargs): """ Solve the general regularized OT problem with conditional gradient @@ -173,7 +173,9 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200, numItermax : int, optional Max number of iterations stopThr : float, optional - Stop threshol on error (>0) + Stop threshol on the relative variation (>0) + stopThr2 : float, optional + Stop threshol on the absolute variation (>0) verbose : bool, optional Print information along iterations log : bool, optional @@ -249,8 +251,9 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200, if it >= numItermax: loop = 0 - delta_fval = (f_val - old_fval) / abs(f_val) - if abs(delta_fval) < stopThr: + abs_delta_fval = abs(f_val - old_fval) + relative_delta_fval = abs_delta_fval / abs(f_val) + if relative_delta_fval < stopThr and abs_delta_fval < stopThr2: loop = 0 if log: @@ -259,8 +262,8 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200, if verbose: if it % 20 == 0: print('{:5s}|{:12s}|{:8s}'.format( - 'It.', 'Loss', 'Delta loss') + '\n' + '-' * 32) - print('{:5d}|{:8e}|{:8e}'.format(it, f_val, delta_fval)) + 'It.', 'Loss', 'Relative variation loss', 'Absolute variation loss') + '\n' + '-' * 32) + print('{:5d}|{:8e}|{:8e}|{:8e}'.format(it, f_val, relative_delta_fval, abs_delta_fval)) if log: return G, log @@ -269,7 +272,7 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200, def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, - numInnerItermax=200, stopThr=1e-9, verbose=False, log=False): + numInnerItermax=200, stopThr=1e-9, stopThr2=1e-9, verbose=False, log=False): """ Solve the general regularized OT problem with the generalized conditional gradient @@ -312,7 +315,9 @@ def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, numInnerItermax : int, optional Max number of iterations of Sinkhorn stopThr : float, optional - Stop threshol on error (>0) + Stop threshol on the relative variation (>0) + stopThr2 : float, optional + Stop threshol on the absolute variation (>0) verbose : bool, optional Print information along iterations log : bool, optional @@ -386,8 +391,10 @@ def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, if it >= numItermax: loop = 0 - delta_fval = (f_val - old_fval) / abs(f_val) - if abs(delta_fval) < stopThr: + abs_delta_fval = abs(f_val - old_fval) + relative_delta_fval = abs_delta_fval / abs(f_val) + + if relative_delta_fval < stopThr and abs_delta_fval < stopThr2: loop = 0 if log: @@ -396,8 +403,8 @@ def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, if verbose: if it % 20 == 0: print('{:5s}|{:12s}|{:8s}'.format( - 'It.', 'Loss', 'Delta loss') + '\n' + '-' * 32) - print('{:5d}|{:8e}|{:8e}'.format(it, f_val, delta_fval)) + 'It.', 'Loss', 'Relative variation loss', 'Absolute variation loss') + '\n' + '-' * 32) + print('{:5d}|{:8e}|{:8e}|{:8e}'.format(it, f_val, relative_delta_fval, abs_delta_fval)) if log: return G, log -- cgit v1.2.3 From e1bd94bb7e85a0d2fd0fcd7642b06da12c1db6db Mon Sep 17 00:00:00 2001 From: tvayer Date: Wed, 29 May 2019 17:05:38 +0200 Subject: code review1 --- examples/plot_barycenter_fgw.py | 30 +++++++---- examples/plot_fgw.py | 32 ++++++++++-- ot/gromov.py | 108 +++++++++++++++++++++++++++++++++++----- ot/optim.py | 31 ++++++------ test/test_gromov.py | 57 ++++++++++++++++----- 5 files changed, 204 insertions(+), 54 deletions(-) diff --git a/examples/plot_barycenter_fgw.py b/examples/plot_barycenter_fgw.py index 9eea036..e4be447 100644 --- a/examples/plot_barycenter_fgw.py +++ b/examples/plot_barycenter_fgw.py @@ -125,7 +125,11 @@ def graph_colors(nx_graph, vmin=0, vmax=7): colors.append(val_map[node]) return colors -#%% create dataset +############################################################################## +# Generate data +# ------------- + +#%% circular dataset # We build a dataset of noisy circular graphs. # Noise is added on the structures by random connections and on the features by gaussian noise. @@ -135,7 +139,11 @@ X0 = [] for k in range(9): X0.append(build_noisy_circular_graph(np.random.randint(15, 25), with_noise=True, structure_noise=True, p=3)) -#%% Plot dataset +############################################################################## +# Plot data +# --------- + +#%% Plot graphs plt.figure(figsize=(8, 10)) for i in range(len(X0)): @@ -146,9 +154,11 @@ for i in range(len(X0)): plt.suptitle('Dataset of noisy graphs. Color indicates the label', fontsize=20) plt.show() +############################################################################## +# Barycenter computation +# ---------------------- -#%% -# We compute the barycenter using FGW. Structure matrices are computed using the shortest_path distance in the graph +#%% We compute the barycenter using FGW. Structure matrices are computed using the shortest_path distance in the graph # Features distances are the euclidean distances Cs = [shortest_path(nx.adjacency_matrix(x)) for x in X0] ps = [np.ones(len(x.nodes())) / len(x.nodes()) for x in X0] @@ -156,14 +166,16 @@ Ys = [np.array([v for (k, v) in nx.get_node_attributes(x, 'attr_name').items()]) lambdas = np.array([np.ones(len(Ys)) / len(Ys)]).ravel() sizebary = 15 # we choose a barycenter with 15 nodes -#%% - A, C, log = fgw_barycenters(sizebary, Ys, Cs, ps, lambdas, alpha=0.95) -#%% +############################################################################## +# Plot Barycenter +# ------------------------- + +#%% Create the barycenter bary = nx.from_numpy_matrix(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0])) -for i in range(len(A.ravel())): - bary.add_node(i, attr_name=float(A.ravel()[i])) +for i, v in enumerate(A.ravel()): + bary.add_node(i, attr_name=v) #%% pos = nx.kamada_kawai_layout(bary) diff --git a/examples/plot_fgw.py b/examples/plot_fgw.py index ae3c487..43efc94 100644 --- a/examples/plot_fgw.py +++ b/examples/plot_fgw.py @@ -22,12 +22,16 @@ import numpy as np import ot from ot.gromov import gromov_wasserstein, fused_gromov_wasserstein +############################################################################## +# Generate data +# --------- + #%% parameters # We create two 1D random measures -n = 20 -n2 = 30 -sig = 1 -sig2 = 0.1 +n = 20 # number of points in the first distribution +n2 = 30 # number of points in the second distribution +sig = 1 # std of first distribution +sig2 = 0.1 # std of second distribution np.random.seed(0) @@ -43,6 +47,10 @@ yt = yt[::-1, :] p = ot.unif(n) q = ot.unif(n2) +############################################################################## +# Plot data +# --------- + #%% plot the distributions pl.close(10) @@ -64,15 +72,22 @@ pl.yticks(()) pl.tight_layout() pl.show() +############################################################################## +# Create structure matrices and across-feature distance matrix +# --------- #%% Structure matrices and across-features distance matrix C1 = ot.dist(xs) -C2 = ot.dist(xt).T +C2 = ot.dist(xt) M = ot.dist(ys, yt) w1 = ot.unif(C1.shape[0]) w2 = ot.unif(C2.shape[0]) Got = ot.emd([], [], M) +############################################################################## +# Plot matrices +# --------- + #%% cmap = 'Reds' pl.close(10) @@ -112,6 +127,9 @@ pl.tight_layout() ax3.set_aspect('auto') pl.show() +############################################################################## +# Compute FGW/GW +# --------- #%% Computing FGW and GW alpha = 1e-3 @@ -123,6 +141,10 @@ ot.toc() #%reload_ext WGW Gg, log = gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', verbose=True, log=True) +############################################################################## +# Visualize transport matrices +# --------- + #%% visu OT matrix cmap = 'Blues' fs = 15 diff --git a/ot/gromov.py b/ot/gromov.py index 5a57dc8..53349b7 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -10,6 +10,7 @@ Gromov-Wasserstein transport method # Nicolas Courty # Rémi Flamary # Titouan Vayer +# # License: MIT License import numpy as np @@ -351,9 +352,9 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, armijo=False, **kwargs return cg(p, q, 0, 1, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs) -def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, armijo=False, **kwargs): +def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, armijo=False, log=False, **kwargs): """ - Computes the FGW distance between two graphs see [3] + Computes the FGW transport between two graphs see [24] .. math:: \gamma = arg\min_\gamma (1-\alpha)*<\gamma,M>_F + alpha* \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l} s.t. \gamma 1 = p @@ -377,7 +378,7 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, distribution in the source space q : ndarray, shape (nt,) distribution in the target space - loss_fun : string,optionnal + loss_fun : string,optional loss function used for the solver max_iter : int, optional Max number of iterations @@ -416,7 +417,86 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, def df(G): return gwggrad(constC, hC1, hC2, G) - return cg(p, q, M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs) + if log: + res, log = cg(p, q, M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, log=True, **kwargs) + log['fgw_dist'] = log['loss'][::-1][0] + return res, log + else: + return cg(p, q, M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, **kwargs) + + +def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, armijo=False, log=False, **kwargs): + """ + Computes the FGW distance between two graphs see [24] + .. math:: + \gamma = arg\min_\gamma (1-\alpha)*<\gamma,M>_F + alpha* \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l} + s.t. \gamma 1 = p + \gamma^T 1= q + \gamma\geq 0 + where : + - M is the (ns,nt) metric cost matrix + - :math:`f` is the regularization term ( and df is its gradient) + - a and b are source and target weights (sum to 1) + - L is a loss function to account for the misfit between the similarity matrices + The algorithm used for solving the problem is conditional gradient as discussed in [1]_ + Parameters + ---------- + M : ndarray, shape (ns, nt) + Metric cost matrix between features across domains + C1 : ndarray, shape (ns, ns) + Metric cost matrix respresentative of the structure in the source space + C2 : ndarray, shape (nt, nt) + Metric cost matrix espresentative of the structure in the target space + p : ndarray, shape (ns,) + distribution in the source space + q : ndarray, shape (nt,) + distribution in the target space + loss_fun : string,optional + loss function used for the solver + max_iter : int, optional + Max number of iterations + tol : float, optional + Stop threshold on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + armijo : bool, optional + If True the steps of the line-search is found via an armijo research. Else closed form is used. + If there is convergence issues use False. + **kwargs : dict + parameters can be directly pased to the ot.optim.cg solver + Returns + ------- + gamma : (ns x nt) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + References + ---------- + .. [24] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain + and Courty Nicolas + "Optimal Transport for structured data with application on graphs" + International Conference on Machine Learning (ICML). 2019. + """ + + constC, hC1, hC2 = init_matrix(C1, C2, p, q, loss_fun) + + G0 = p[:, None] * q[None, :] + + def f(G): + return gwloss(constC, hC1, hC2, G) + + def df(G): + return gwggrad(constC, hC1, hC2, G) + + res, log = cg(p, q, M, alpha, f, df, G0, armijo=armijo, C1=C1, C2=C2, constC=constC, log=True, **kwargs) + if log: + log['fgw_dist'] = log['loss'][::-1][0] + log['T'] = res + return log['fgw_dist'], log + else: + return log['fgw_dist'] def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False, armijo=False, **kwargs): @@ -889,7 +969,7 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_features=False, p=None, loss_fun='square_loss', max_iter=100, tol=1e-9, - verbose=False, log=True, init_C=None, init_X=None): + verbose=False, log=False, init_C=None, init_X=None): """ Compute the fgw barycenter as presented eq (5) in [24]. ---------- @@ -919,7 +999,8 @@ def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_ Barycenters' features C : ndarray, shape (N,N) Barycenters' structure matrix - log_: + log_: dictionary + Only returned when log=True T : list of (N,ns) transport matrices Ms : all distance matrices between the feature of the barycenter and the other features dist(X,Ys) shape (N,ns) References @@ -1015,14 +1096,13 @@ def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_ T = [fused_gromov_wasserstein((1 - alpha) * Ms[s], C, Cs[s], p, ps[s], loss_fun, alpha, numItermax=max_iter, stopThr=1e-5, verbose=verbose) for s in range(S)] # T is N,ns - - log_['Ts_iter'].append(T) err_feature = np.linalg.norm(X - Xprev.reshape(N, d)) err_structure = np.linalg.norm(C - Cprev) if log: log_['err_feature'].append(err_feature) log_['err_structure'].append(err_structure) + log_['Ts_iter'].append(T) if verbose: if cpt % 200 == 0: @@ -1032,11 +1112,15 @@ def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_ print('{:5d}|{:8e}|'.format(cpt, err_feature)) cpt += 1 - log_['T'] = T # from target to Ys - log_['p'] = p - log_['Ms'] = Ms # Ms are N,ns + if log: + log_['T'] = T # from target to Ys + log_['p'] = p + log_['Ms'] = Ms # Ms are N,ns - return X, C, log_ + if log: + return X, C, log_ + else: + return X, C def update_sructure_matrix(p, lambdas, T, Cs): diff --git a/ot/optim.py b/ot/optim.py index 7d103e2..4d428d9 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -5,6 +5,7 @@ Optimization algorithms for OT # Author: Remi Flamary # Titouan Vayer +# # License: MIT License import numpy as np @@ -88,20 +89,20 @@ def do_linesearch(cost, G, deltaG, Mi, f_val, Cost matrix of the linearized transport problem. Corresponds to the gradient of the cost f_val : float Value of the cost at G - armijo : bool, optionnal + armijo : bool, optional If True the steps of the line-search is found via an armijo research. Else closed form is used. If there is convergence issues use False. - C1 : ndarray (ns,ns), optionnal + C1 : ndarray (ns,ns), optional Structure matrix in the source domain. Only used when armijo=False - C2 : ndarray (nt,nt), optionnal + C2 : ndarray (nt,nt), optional Structure matrix in the target domain. Only used when armijo=False - reg : float, optionnal + reg : float, optional Regularization parameter. Only used when armijo=False Gc : ndarray (ns,nt) Optimal map found by linearization in the FW algorithm. Only used when armijo=False constC : ndarray (ns,nt) Constant for the gromov cost. See [24]. Only used when armijo=False - M : ndarray (ns,nt), optionnal + M : ndarray (ns,nt), optional Cost matrix between the features. Only used when armijo=False Returns ------- @@ -223,9 +224,9 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200, it = 0 if verbose: - print('{:5s}|{:12s}|{:8s}'.format( - 'It.', 'Loss', 'Delta loss') + '\n' + '-' * 32) - print('{:5d}|{:8e}|{:8e}'.format(it, f_val, 0)) + print('{:5s}|{:12s}|{:8s}|{:8s}'.format( + 'It.', 'Loss', 'Relative loss', 'Absolute loss') + '\n' + '-' * 48) + print('{:5d}|{:8e}|{:8e}|{:8e}'.format(it, f_val, 0, 0)) while loop: @@ -261,8 +262,8 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200, if verbose: if it % 20 == 0: - print('{:5s}|{:12s}|{:8s}'.format( - 'It.', 'Loss', 'Relative variation loss', 'Absolute variation loss') + '\n' + '-' * 32) + print('{:5s}|{:12s}|{:8s}|{:8s}'.format( + 'It.', 'Loss', 'Relative loss', 'Absolute loss') + '\n' + '-' * 48) print('{:5d}|{:8e}|{:8e}|{:8e}'.format(it, f_val, relative_delta_fval, abs_delta_fval)) if log: @@ -363,9 +364,9 @@ def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, it = 0 if verbose: - print('{:5s}|{:12s}|{:8s}'.format( - 'It.', 'Loss', 'Delta loss') + '\n' + '-' * 32) - print('{:5d}|{:8e}|{:8e}'.format(it, f_val, 0)) + print('{:5s}|{:12s}|{:8s}|{:8s}'.format( + 'It.', 'Loss', 'Relative loss', 'Absolute loss') + '\n' + '-' * 48) + print('{:5d}|{:8e}|{:8e}|{:8e}'.format(it, f_val, 0, 0)) while loop: @@ -402,8 +403,8 @@ def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, if verbose: if it % 20 == 0: - print('{:5s}|{:12s}|{:8s}'.format( - 'It.', 'Loss', 'Relative variation loss', 'Absolute variation loss') + '\n' + '-' * 32) + print('{:5s}|{:12s}|{:8s}|{:8s}'.format( + 'It.', 'Loss', 'Relative loss', 'Absolute loss') + '\n' + '-' * 48) print('{:5d}|{:8e}|{:8e}|{:8e}'.format(it, f_val, relative_delta_fval, abs_delta_fval)) if log: diff --git a/test/test_gromov.py b/test/test_gromov.py index cd180d4..ec85abf 100644 --- a/test/test_gromov.py +++ b/test/test_gromov.py @@ -2,6 +2,7 @@ # Author: Erwan Vautier # Nicolas Courty +# Titouan Vayer # # License: MIT License @@ -10,6 +11,8 @@ import ot def test_gromov(): + np.random.seed(42) + n_samples = 50 # nb samples mu_s = np.array([0, 0]) @@ -36,6 +39,11 @@ def test_gromov(): np.testing.assert_allclose( q, G.sum(0), atol=1e-04) # cf convergence gromov + Id = (1 / n_samples) * np.eye(n_samples, n_samples) + + np.testing.assert_allclose( + G, np.flipud(Id), atol=1e-04) + gw, log = ot.gromov.gromov_wasserstein2(C1, C2, p, q, 'kl_loss', log=True) G = log['T'] @@ -50,6 +58,8 @@ def test_gromov(): def test_entropic_gromov(): + np.random.seed(42) + n_samples = 50 # nb samples mu_s = np.array([0, 0]) @@ -92,6 +102,7 @@ def test_entropic_gromov(): def test_gromov_barycenter(): + np.random.seed(42) ns = 50 nt = 60 @@ -120,7 +131,7 @@ def test_gromov_barycenter(): def test_gromov_entropic_barycenter(): - + np.random.seed(42) ns = 50 nt = 60 @@ -148,6 +159,8 @@ def test_gromov_entropic_barycenter(): def test_fgw(): + np.random.seed(42) + n_samples = 50 # nb samples mu_s = np.array([0, 0]) @@ -180,8 +193,26 @@ def test_fgw(): np.testing.assert_allclose( q, G.sum(0), atol=1e-04) # cf convergence fgw + Id = (1 / n_samples) * np.eye(n_samples, n_samples) + + np.testing.assert_allclose( + G, np.flipud(Id), atol=1e-04) # cf convergence gromov + + fgw, log = ot.gromov.fused_gromov_wasserstein2(M, C1, C2, p, q, 'square_loss', alpha=0.5, log=True) + + G = log['T'] + + np.testing.assert_allclose(fgw, 0, atol=1e-1, rtol=1e-1) + + # check constratints + np.testing.assert_allclose( + p, G.sum(1), atol=1e-04) # cf convergence gromov + np.testing.assert_allclose( + q, G.sum(0), atol=1e-04) # cf convergence gromov + def test_fgw_barycenter(): + np.random.seed(42) ns = 50 nt = 60 @@ -196,28 +227,28 @@ def test_fgw_barycenter(): C2 = ot.dist(Xt) n_samples = 3 - X, C, log = ot.gromov.fgw_barycenters(n_samples, [ys, yt], [C1, C2], [ot.unif(ns), ot.unif(nt)], [.5, .5], 0.5, - fixed_structure=False, fixed_features=False, - p=ot.unif(n_samples), loss_fun='square_loss', - max_iter=100, tol=1e-3) + X, C = ot.gromov.fgw_barycenters(n_samples, [ys, yt], [C1, C2], [ot.unif(ns), ot.unif(nt)], [.5, .5], 0.5, + fixed_structure=False, fixed_features=False, + p=ot.unif(n_samples), loss_fun='square_loss', + max_iter=100, tol=1e-3) np.testing.assert_allclose(C.shape, (n_samples, n_samples)) np.testing.assert_allclose(X.shape, (n_samples, ys.shape[1])) xalea = np.random.randn(n_samples, 2) init_C = ot.dist(xalea, xalea) - X, C, log = ot.gromov.fgw_barycenters(n_samples, [ys, yt], [C1, C2], ps=[ot.unif(ns), ot.unif(nt)], lambdas=[.5, .5], alpha=0.5, - fixed_structure=True, init_C=init_C, fixed_features=False, - p=ot.unif(n_samples), loss_fun='square_loss', - max_iter=100, tol=1e-3) + X, C = ot.gromov.fgw_barycenters(n_samples, [ys, yt], [C1, C2], ps=[ot.unif(ns), ot.unif(nt)], lambdas=[.5, .5], alpha=0.5, + fixed_structure=True, init_C=init_C, fixed_features=False, + p=ot.unif(n_samples), loss_fun='square_loss', + max_iter=100, tol=1e-3) np.testing.assert_allclose(C.shape, (n_samples, n_samples)) np.testing.assert_allclose(X.shape, (n_samples, ys.shape[1])) init_X = np.random.randn(n_samples, ys.shape[1]) - X, C, log = ot.gromov.fgw_barycenters(n_samples, [ys, yt], [C1, C2], [ot.unif(ns), ot.unif(nt)], [.5, .5], 0.5, - fixed_structure=False, fixed_features=True, init_X=init_X, - p=ot.unif(n_samples), loss_fun='square_loss', - max_iter=100, tol=1e-3) + X, C = ot.gromov.fgw_barycenters(n_samples, [ys, yt], [C1, C2], [ot.unif(ns), ot.unif(nt)], [.5, .5], 0.5, + fixed_structure=False, fixed_features=True, init_X=init_X, + p=ot.unif(n_samples), loss_fun='square_loss', + max_iter=100, tol=1e-3) np.testing.assert_allclose(C.shape, (n_samples, n_samples)) np.testing.assert_allclose(X.shape, (n_samples, ys.shape[1])) -- cgit v1.2.3 From 28059eb5e0aad715823ee4f6509d6a9e3d6e5db0 Mon Sep 17 00:00:00 2001 From: tvayer Date: Wed, 29 May 2019 17:11:41 +0200 Subject: py2 error --- test/test_gromov.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test/test_gromov.py b/test/test_gromov.py index ec85abf..3ca184b 100644 --- a/test/test_gromov.py +++ b/test/test_gromov.py @@ -13,7 +13,7 @@ import ot def test_gromov(): np.random.seed(42) - n_samples = 50 # nb samples + n_samples = 50.0 # nb samples mu_s = np.array([0, 0]) cov_s = np.array([[1, 0], [0, 1]]) @@ -161,7 +161,7 @@ def test_gromov_entropic_barycenter(): def test_fgw(): np.random.seed(42) - n_samples = 50 # nb samples + n_samples = 50.0 # nb samples mu_s = np.array([0, 0]) cov_s = np.array([[1, 0], [0, 1]]) -- cgit v1.2.3 From 63093cef7af3350228251aa930872c6f30789432 Mon Sep 17 00:00:00 2001 From: tvayer Date: Wed, 29 May 2019 17:19:13 +0200 Subject: n_samples float --- test/test_gromov.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/test/test_gromov.py b/test/test_gromov.py index 3ca184b..d7a12f3 100644 --- a/test/test_gromov.py +++ b/test/test_gromov.py @@ -13,7 +13,7 @@ import ot def test_gromov(): np.random.seed(42) - n_samples = 50.0 # nb samples + n_samples = 50 # nb samples mu_s = np.array([0, 0]) cov_s = np.array([[1, 0], [0, 1]]) @@ -39,7 +39,7 @@ def test_gromov(): np.testing.assert_allclose( q, G.sum(0), atol=1e-04) # cf convergence gromov - Id = (1 / n_samples) * np.eye(n_samples, n_samples) + Id = (1 / float(n_samples)) * np.eye(n_samples, n_samples) np.testing.assert_allclose( G, np.flipud(Id), atol=1e-04) @@ -161,7 +161,7 @@ def test_gromov_entropic_barycenter(): def test_fgw(): np.random.seed(42) - n_samples = 50.0 # nb samples + n_samples = 50 # nb samples mu_s = np.array([0, 0]) cov_s = np.array([[1, 0], [0, 1]]) @@ -193,7 +193,7 @@ def test_fgw(): np.testing.assert_allclose( q, G.sum(0), atol=1e-04) # cf convergence fgw - Id = (1 / n_samples) * np.eye(n_samples, n_samples) + Id = (1 / float(n_samples)) * np.eye(n_samples, n_samples) np.testing.assert_allclose( G, np.flipud(Id), atol=1e-04) # cf convergence gromov -- cgit v1.2.3 From 9bb7d40b563f42bf2875efca860bf0c579307161 Mon Sep 17 00:00:00 2001 From: tvayer Date: Wed, 29 May 2019 17:52:20 +0200 Subject: .0 --- test/test_gromov.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test/test_gromov.py b/test/test_gromov.py index d7a12f3..b7ede95 100644 --- a/test/test_gromov.py +++ b/test/test_gromov.py @@ -39,7 +39,7 @@ def test_gromov(): np.testing.assert_allclose( q, G.sum(0), atol=1e-04) # cf convergence gromov - Id = (1 / float(n_samples)) * np.eye(n_samples, n_samples) + Id = (1 / 1.0*n_samples) * np.eye(n_samples, n_samples) np.testing.assert_allclose( G, np.flipud(Id), atol=1e-04) @@ -193,7 +193,7 @@ def test_fgw(): np.testing.assert_allclose( q, G.sum(0), atol=1e-04) # cf convergence fgw - Id = (1 / float(n_samples)) * np.eye(n_samples, n_samples) + Id = (1 / 1.0*n_samples) * np.eye(n_samples, n_samples) np.testing.assert_allclose( G, np.flipud(Id), atol=1e-04) # cf convergence gromov -- cgit v1.2.3 From 89a2e0aee4353a051d924de0457f8976c26fa5d7 Mon Sep 17 00:00:00 2001 From: tvayer Date: Wed, 29 May 2019 18:02:27 +0200 Subject: pep8 + err --- test/test_gromov.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test/test_gromov.py b/test/test_gromov.py index b7ede95..f218b74 100644 --- a/test/test_gromov.py +++ b/test/test_gromov.py @@ -39,7 +39,7 @@ def test_gromov(): np.testing.assert_allclose( q, G.sum(0), atol=1e-04) # cf convergence gromov - Id = (1 / 1.0*n_samples) * np.eye(n_samples, n_samples) + Id = (1 / (1.0 * n_samples)) * np.eye(n_samples, n_samples) np.testing.assert_allclose( G, np.flipud(Id), atol=1e-04) @@ -193,7 +193,7 @@ def test_fgw(): np.testing.assert_allclose( q, G.sum(0), atol=1e-04) # cf convergence fgw - Id = (1 / 1.0*n_samples) * np.eye(n_samples, n_samples) + Id = (1 / (1.0 * n_samples)) * np.eye(n_samples, n_samples) np.testing.assert_allclose( G, np.flipud(Id), atol=1e-04) # cf convergence gromov -- cgit v1.2.3 From ad450b0a5bb63ee9731e88d4a8e7423b16f1abd8 Mon Sep 17 00:00:00 2001 From: tvayer Date: Tue, 4 Jun 2019 10:32:30 +0200 Subject: changes forgotten coments --- ot/gromov.py | 26 +++----------------------- ot/optim.py | 32 ++++++++++++++++---------------- ot/utils.py | 8 ++++++++ test/test_optim.py | 6 +++--- 4 files changed, 30 insertions(+), 42 deletions(-) diff --git a/ot/gromov.py b/ot/gromov.py index 53349b7..ca96b31 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -17,7 +17,7 @@ import numpy as np from .bregman import sinkhorn -from .utils import dist +from .utils import dist, UndefinedParameter from .optim import cg @@ -1011,9 +1011,6 @@ def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_ International Conference on Machine Learning (ICML). 2019. """ - class UndefinedParameter(Exception): - pass - S = len(Cs) d = Ys[0].shape[1] # dimension on the node features if p is None: @@ -1049,10 +1046,7 @@ def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_ T = [np.outer(p, q) for q in ps] - # X is N,d - # Ys is ns,d - Ms = [np.asarray(dist(X, Ys[s]), dtype=np.float64) for s in range(len(Ys))] - # Ms is N,ns + Ms = [np.asarray(dist(X, Ys[s]), dtype=np.float64) for s in range(len(Ys))] # Ms is N,ns cpt = 0 err_feature = 1 @@ -1072,27 +1066,13 @@ def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_ Ys_temp = [y.T for y in Ys] X = update_feature_matrix(lambdas, Ys_temp, T, p).T - # X must be N,d - # Ys must be ns,d Ms = [np.asarray(dist(X, Ys[s]), dtype=np.float64) for s in range(len(Ys))] if not fixed_structure: if loss_fun == 'square_loss': - # T must be ns,N - # Cs must be ns,ns - # p must be N,1 T_temp = [t.T for t in T] C = update_sructure_matrix(p, lambdas, T_temp, Cs) - # Ys must be d,ns - # Ts must be N,ns - # p must be N,1 - # Ms is N,ns - # C is N,N - # Cs is ns,ns - # p is N,1 - # ps is ns,1 - T = [fused_gromov_wasserstein((1 - alpha) * Ms[s], C, Cs[s], p, ps[s], loss_fun, alpha, numItermax=max_iter, stopThr=1e-5, verbose=verbose) for s in range(S)] # T is N,ns @@ -1115,7 +1095,7 @@ def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_ if log: log_['T'] = T # from target to Ys log_['p'] = p - log_['Ms'] = Ms # Ms are N,ns + log_['Ms'] = Ms if log: return X, C, log_ diff --git a/ot/optim.py b/ot/optim.py index 4d428d9..f94aceb 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -73,8 +73,8 @@ def line_search_armijo(f, xk, pk, gfk, old_fval, return alpha, fc[0], phi1 -def do_linesearch(cost, G, deltaG, Mi, f_val, - armijo=True, C1=None, C2=None, reg=None, Gc=None, constC=None, M=None): +def solve_linesearch(cost, G, deltaG, Mi, f_val, + armijo=True, C1=None, C2=None, reg=None, Gc=None, constC=None, M=None): """ Solve the linesearch in the FW iterations Parameters @@ -93,17 +93,17 @@ def do_linesearch(cost, G, deltaG, Mi, f_val, If True the steps of the line-search is found via an armijo research. Else closed form is used. If there is convergence issues use False. C1 : ndarray (ns,ns), optional - Structure matrix in the source domain. Only used when armijo=False + Structure matrix in the source domain. Only used and necessary when armijo=False C2 : ndarray (nt,nt), optional - Structure matrix in the target domain. Only used when armijo=False + Structure matrix in the target domain. Only used and necessary when armijo=False reg : float, optional - Regularization parameter. Only used when armijo=False + Regularization parameter. Only used and necessary when armijo=False Gc : ndarray (ns,nt) - Optimal map found by linearization in the FW algorithm. Only used when armijo=False + Optimal map found by linearization in the FW algorithm. Only used and necessary when armijo=False constC : ndarray (ns,nt) - Constant for the gromov cost. See [24]. Only used when armijo=False + Constant for the gromov cost. See [24]. Only used and necessary when armijo=False M : ndarray (ns,nt), optional - Cost matrix between the features. Only used when armijo=False + Cost matrix between the features. Only used and necessary when armijo=False Returns ------- alpha : float @@ -128,7 +128,7 @@ def do_linesearch(cost, G, deltaG, Mi, f_val, b = np.sum((M + reg * constC) * deltaG) - 2 * reg * (np.sum(dot12 * G) + np.sum(np.dot(C1, G).dot(C2) * deltaG)) c = cost(G) - alpha = solve_1d_linesearch_quad_funct(a, b, c) + alpha = solve_1d_linesearch_quad(a, b, c) fc = None f_val = cost(G + alpha * deltaG) @@ -181,7 +181,7 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200, Print information along iterations log : bool, optional record log if True - kwargs : dict + **kwargs : dict Parameters for linesearch Returns @@ -244,7 +244,7 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200, deltaG = Gc - G # line search - alpha, fc, f_val = do_linesearch(cost, G, deltaG, Mi, f_val, reg=reg, M=M, Gc=Gc, **kwargs) + alpha, fc, f_val = solve_linesearch(cost, G, deltaG, Mi, f_val, reg=reg, M=M, Gc=Gc, **kwargs) G = G + alpha * deltaG @@ -254,7 +254,7 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200, abs_delta_fval = abs(f_val - old_fval) relative_delta_fval = abs_delta_fval / abs(f_val) - if relative_delta_fval < stopThr and abs_delta_fval < stopThr2: + if relative_delta_fval < stopThr or abs_delta_fval < stopThr2: loop = 0 if log: @@ -395,7 +395,7 @@ def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, abs_delta_fval = abs(f_val - old_fval) relative_delta_fval = abs_delta_fval / abs(f_val) - if relative_delta_fval < stopThr and abs_delta_fval < stopThr2: + if relative_delta_fval < stopThr or abs_delta_fval < stopThr2: loop = 0 if log: @@ -413,11 +413,11 @@ def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, return G -def solve_1d_linesearch_quad_funct(a, b, c): +def solve_1d_linesearch_quad(a, b, c): """ - Solve on 0,1 the following problem: + For any convex or non-convex 1d quadratic function f, solve on [0,1] the following problem: .. math:: - \min f(x)=a*x^{2}+b*x+c + \argmin f(x)=a*x^{2}+b*x+c Parameters ---------- diff --git a/ot/utils.py b/ot/utils.py index bb21b38..efd1288 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -487,3 +487,11 @@ class BaseEstimator(object): (key, self.__class__.__name__)) setattr(self, key, value) return self + + +class UndefinedParameter(Exception): + """ + Aim at raising an Exception when a undefined parameter is called + + """ + pass diff --git a/test/test_optim.py b/test/test_optim.py index e7ba32a..ae31e1f 100644 --- a/test/test_optim.py +++ b/test/test_optim.py @@ -68,6 +68,6 @@ def test_generalized_conditional_gradient(): def test_solve_1d_linesearch_quad_funct(): - np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad_funct(1, -1, 0), 0.5) - np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad_funct(-1, 5, 0), 0) - np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad_funct(-1, 0.5, 0), 1) + np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad(1, -1, 0), 0.5) + np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad(-1, 5, 0), 0) + np.testing.assert_allclose(ot.optim.solve_1d_linesearch_quad(-1, 0.5, 0), 1) -- cgit v1.2.3 From 788a6506c9bf3b862a9652d74f65f8d07851e653 Mon Sep 17 00:00:00 2001 From: tvayer Date: Tue, 4 Jun 2019 11:34:46 +0200 Subject: seed --- test/test_gromov.py | 26 +++++++++----------------- 1 file changed, 9 insertions(+), 17 deletions(-) diff --git a/test/test_gromov.py b/test/test_gromov.py index f218b74..70fa83f 100644 --- a/test/test_gromov.py +++ b/test/test_gromov.py @@ -11,14 +11,12 @@ import ot def test_gromov(): - np.random.seed(42) - n_samples = 50 # nb samples mu_s = np.array([0, 0]) cov_s = np.array([[1, 0], [0, 1]]) - xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) + xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s, random_state=4) xt = xs[::-1].copy() @@ -58,14 +56,12 @@ def test_gromov(): def test_entropic_gromov(): - np.random.seed(42) - n_samples = 50 # nb samples mu_s = np.array([0, 0]) cov_s = np.array([[1, 0], [0, 1]]) - xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) + xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s, random_state=42) xt = xs[::-1].copy() @@ -102,13 +98,11 @@ def test_entropic_gromov(): def test_gromov_barycenter(): - np.random.seed(42) - ns = 50 nt = 60 - Xs, ys = ot.datasets.make_data_classif('3gauss', ns) - Xt, yt = ot.datasets.make_data_classif('3gauss2', nt) + Xs, ys = ot.datasets.make_data_classif('3gauss', ns, random_state=42) + Xt, yt = ot.datasets.make_data_classif('3gauss2', nt, random_state=42) C1 = ot.dist(Xs) C2 = ot.dist(Xt) @@ -131,12 +125,11 @@ def test_gromov_barycenter(): def test_gromov_entropic_barycenter(): - np.random.seed(42) ns = 50 nt = 60 - Xs, ys = ot.datasets.make_data_classif('3gauss', ns) - Xt, yt = ot.datasets.make_data_classif('3gauss2', nt) + Xs, ys = ot.datasets.make_data_classif('3gauss', ns, random_state=42) + Xt, yt = ot.datasets.make_data_classif('3gauss2', nt, random_state=42) C1 = ot.dist(Xs) C2 = ot.dist(Xt) @@ -159,14 +152,13 @@ def test_gromov_entropic_barycenter(): def test_fgw(): - np.random.seed(42) n_samples = 50 # nb samples mu_s = np.array([0, 0]) cov_s = np.array([[1, 0], [0, 1]]) - xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s) + xs = ot.datasets.make_2D_samples_gauss(n_samples, mu_s, cov_s, random_state=42) xt = xs[::-1].copy() @@ -217,8 +209,8 @@ def test_fgw_barycenter(): ns = 50 nt = 60 - Xs, ys = ot.datasets.make_data_classif('3gauss', ns) - Xt, yt = ot.datasets.make_data_classif('3gauss2', nt) + Xs, ys = ot.datasets.make_data_classif('3gauss', ns, random_state=42) + Xt, yt = ot.datasets.make_data_classif('3gauss2', nt, random_state=42) ys = np.random.randn(Xs.shape[0], 2) yt = np.random.randn(Xt.shape[0], 2) -- cgit v1.2.3 From 0fc6938dc15e8888b0a73fa4b6a421f39f0e0697 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 6 Jun 2019 12:09:34 +0200 Subject: update conf + readme --- docs/source/conf.py | 14 +++++++++++--- docs/source/readme.rst | 30 +++++++++++++++++++----------- 2 files changed, 30 insertions(+), 14 deletions(-) diff --git a/docs/source/conf.py b/docs/source/conf.py index 433eca6..d29b829 100644 --- a/docs/source/conf.py +++ b/docs/source/conf.py @@ -15,7 +15,10 @@ import sys import os import re -import sphinx_gallery +try: + import sphinx_gallery +except ImportError: + print("warning sphinx-gallery not installed") # !!!! allow readthedoc compilation try: @@ -65,6 +68,8 @@ extensions = [ #'sphinx_gallery.gen_gallery', ] +napoleon_numpy_docstring = True + # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] @@ -81,7 +86,7 @@ master_doc = 'index' # General information about the project. project = u'POT Python Optimal Transport' -copyright = u'2016-2018, Rémi Flamary, Nicolas Courty' +copyright = u'2016-2019, Rémi Flamary, Nicolas Courty' author = u'Rémi Flamary, Nicolas Courty' # The version info for the project you're documenting, acts as replacement for @@ -323,7 +328,10 @@ texinfo_documents = [ # Example configuration for intersphinx: refer to the Python standard library. -intersphinx_mapping = {'https://docs.python.org/': None} +intersphinx_mapping = {'python': ('https://docs.python.org/3', None), + 'numpy': ('http://docs.scipy.org/doc/numpy/', None), + 'scipy': ('http://docs.scipy.org/doc/scipy/reference/', None), + 'matplotlib': ('http://matplotlib.sourceforge.net/', None)} sphinx_gallery_conf = { 'examples_dirs': ['../../examples','../../examples/da'], diff --git a/docs/source/readme.rst b/docs/source/readme.rst index e7c2bd1..d1063e8 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -12,9 +12,11 @@ It provides the following solvers: - OT Network Flow solver for the linear program/ Earth Movers Distance [1]. -- Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2] - and stabilized version [9][10] and greedy SInkhorn [22] with optional - GPU implementation (requires cudamat). +- Entropic regularization OT solver with Sinkhorn Knopp Algorithm [2], + stabilized version [9][10] and greedy Sinkhorn [22] with optional GPU + implementation (requires cupy). +- Sinkhorn divergence [23] and entropic regularization OT from + empirical data. - Smooth optimal transport solvers (dual and semi-dual) for KL and squared L2 regularizations [17]. - Non regularized Wasserstein barycenters [16] with LP solver (only @@ -115,14 +117,9 @@ below pip install pymanopt autograd -- **ot.gpu** (GPU accelerated OT) depends on cudamat that have to be - installed with: - - :: - - git clone https://github.com/cudamat/cudamat.git - cd cudamat - python setup.py install --user # for user install (no root) +- **ot.gpu** (GPU accelerated OT) depends on cupy that have to be + installed following instructions on `this + page `__. obviously you need CUDA installed and a compatible GPU. @@ -226,6 +223,7 @@ The contributors to this library are: - `Kilian Fatras `__ - `Alain Rakotomamonjy `__ +- `Vayer Titouan `__ This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various @@ -366,6 +364,16 @@ approximation algorithms for optimal transport via Sinkhorn iteration `__, Advances in Neural Information Processing Systems (NIPS) 31 +[23] Aude, G., Peyré, G., Cuturi, M., `Learning Generative Models with +Sinkhorn Divergences `__, Proceedings +of the Twenty-First International Conference on Artficial Intelligence +and Statistics, (AISTATS) 21, 2018 + +[24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. +(2019). `Optimal Transport for structured data with application on +graphs `__ Proceedings +of the 36th International Conference on Machine Learning (ICML). + .. |PyPI version| image:: https://badge.fury.io/py/POT.svg :target: https://badge.fury.io/py/POT .. |Anaconda Cloud| image:: https://anaconda.org/conda-forge/pot/badges/version.svg -- cgit v1.2.3 From 1171f7e39742c207dad6ab5fd15f59ed62f8f4a5 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 6 Jun 2019 17:22:05 +0200 Subject: start documentation ot --- ot/__init__.py | 16 +++++++++++++++- ot/da.py | 8 ++++---- ot/gpu/__init__.py | 6 +++++- ot/lp/__init__.py | 47 +++++++++++++++++++++++++++++------------------ ot/lp/emd_wrap.pyx | 11 +++++++---- 5 files changed, 60 insertions(+), 28 deletions(-) diff --git a/ot/__init__.py b/ot/__init__.py index b74b924..6d6dc75 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -1,6 +1,20 @@ -"""Python Optimal Transport toolbox +""" + +This is the main module of the POT toolbox. It provides easy access to +a number of functions described below. + +### FAQ + +#### How to compute the Wasserstein distance ? +.. warning:: + The list of automatically imported sub-modules is as follows: + :py:mod:`ot.lp`, :py:mod:`ot.bregman`, :py:mod:`ot.optim` + :py:mod:`ot.utils`, :py:mod:`ot.datasets`, + :py:mod:`ot.gromov`, :py:mod:`ot.smooth` + :py:mod:`ot.stochastic` + The other sub-modules are not imported due to additional dependencies. """ diff --git a/ot/da.py b/ot/da.py index 479e698..83f9027 100644 --- a/ot/da.py +++ b/ot/da.py @@ -41,15 +41,15 @@ def sinkhorn_lpl1_mm(a, labels_a, b, M, reg, eta=0.1, numItermax=10, where : - M is the (ns,nt) metric cost matrix - - :math:`\Omega_e` is the entropic regularization term - :math:`\Omega_e(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - :math:`\Omega_g` is the group lasso regulaization term + - :math:`\Omega_e` is the entropic regularization term :math:`\Omega_e + (\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - :math:`\Omega_g` is the group lasso regularization term :math:`\Omega_g(\gamma)=\sum_{i,c} \|\gamma_{i,\mathcal{I}_c}\|^{1/2}_1` where :math:`\mathcal{I}_c` are the index of samples from class c in the source domain. - a and b are source and target weights (sum to 1) - The algorithm used for solving the problem is the generalised conditional + The algorithm used for solving the problem is the generalized conditional gradient as proposed in [5]_ [7]_ diff --git a/ot/gpu/__init__.py b/ot/gpu/__init__.py index deda6b1..6a2afcf 100644 --- a/ot/gpu/__init__.py +++ b/ot/gpu/__init__.py @@ -5,11 +5,15 @@ This module provides GPU implementation for several OT solvers and utility functions. The GPU backend in handled by `cupy `_. +.. warning:: + Note that by default the module is not import in :mod:`ot`. in order to + use it you need to import :mod:`ot.gpu` . + By default, the functions in this module accept and return numpy arrays in order to proide drop-in replacement for the other POT function but the transfer between CPU en GPU comes with a significant overhead. -In order to get the best erformances, we recommend to give only cupy +In order to get the best performances, we recommend to give only cupy arrays to the functions and desactivate the conversion to numpy of the result of the function with parameter ``to_numpy=False``. diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 02cbd8c..ed6fa52 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -1,6 +1,9 @@ # -*- coding: utf-8 -*- """ Solvers for the original linear program OT problem + + + """ # Author: Remi Flamary @@ -37,26 +40,30 @@ def emd(a, b, M, numItermax=100000, log=False): - M is the metric cost matrix - a and b are the sample weights + .. warning:: + Note that the M matrix needs to be a C-order numpy.array in float64 + format. + Uses the algorithm proposed in [1]_ Parameters ---------- - a : (ns,) ndarray, float64 - Source histogram (uniform weigth if empty list) - b : (nt,) ndarray, float64 - Target histogram (uniform weigth if empty list) - M : (ns,nt) ndarray, float64 - loss matrix + a : (ns,) numpy.ndarray, float64 + Source histogram (uniform weight if empty list) + b : (nt,) numpy.ndarray, float64 + Target histogram (uniform weight if empty list) + M : (ns,nt) numpy.ndarray, float64 + Loss matrix (c-order array with type float64) numItermax : int, optional (default=100000) The maximum number of iterations before stopping the optimization algorithm if it has not converged. - log: boolean, optional (default=False) + log: bool, optional (default=False) If True, returns a dictionary containing the cost and dual variables. Otherwise returns only the optimal transportation matrix. Returns ------- - gamma: (ns x nt) ndarray + gamma: (ns x nt) numpy.ndarray Optimal transportation matrix for the given parameters log: dict If input log is true, a dictionary containing the cost and dual @@ -128,16 +135,20 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), - M is the metric cost matrix - a and b are the sample weights + .. warning:: + Note that the M matrix needs to be a C-order numpy.array in float64 + format. + Uses the algorithm proposed in [1]_ Parameters ---------- - a : (ns,) ndarray, float64 - Source histogram (uniform weigth if empty list) - b : (nt,) ndarray, float64 - Target histogram (uniform weigth if empty list) - M : (ns,nt) ndarray, float64 - loss matrix + a : (ns,) numpy.ndarray, float64 + Source histogram (uniform weight if empty list) + b : (nt,) numpy.ndarray, float64 + Target histogram (uniform weight if empty list) + M : (ns,nt) numpy.ndarray, float64 + Loss matrix (c-order array with type float64) numItermax : int, optional (default=100000) The maximum number of iterations before stopping the optimization algorithm if it has not converged. @@ -151,7 +162,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), ------- gamma: (ns x nt) ndarray Optimal transportation matrix for the given parameters - log: dict + log: dictnp If input log is true, a dictionary containing the cost and dual variables and exit status @@ -231,9 +242,9 @@ def free_support_barycenter(measures_locations, measures_weights, X_init, b=None Parameters ---------- - measures_locations : list of (k_i,d) np.ndarray + measures_locations : list of (k_i,d) numpy.ndarray The discrete support of a measure supported on k_i locations of a d-dimensional space (k_i can be different for each element of the list) - measures_weights : list of (k_i,) np.ndarray + measures_weights : list of (k_i,) numpy.ndarray Numpy arrays where each numpy array has k_i non-negatives values summing to one representing the weights of each discrete input measure X_init : (k,d) np.ndarray @@ -246,7 +257,7 @@ def free_support_barycenter(measures_locations, measures_weights, X_init, b=None numItermax : int, optional Max number of iterations stopThr : float, optional - Stop threshol on error (>0) + Stop threshold on error (>0) verbose : bool, optional Print information along iterations log : bool, optional diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 83ee6aa..edb5f7c 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -55,13 +55,16 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod - M is the metric cost matrix - a and b are the sample weights + .. warning:: + Note that the M matrix needs to be a C-order :py.cls:`numpy.array` + Parameters ---------- - a : (ns,) ndarray, float64 + a : (ns,) numpy.ndarray, float64 source histogram - b : (nt,) ndarray, float64 + b : (nt,) numpy.ndarray, float64 target histogram - M : (ns,nt) ndarray, float64 + M : (ns,nt) numpy.ndarray, float64 loss matrix max_iter : int The maximum number of iterations before stopping the optimization @@ -70,7 +73,7 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod Returns ------- - gamma: (ns x nt) ndarray + gamma: (ns x nt) numpy.ndarray Optimal transportation matrix for the given parameters """ -- cgit v1.2.3 From 4ae7e98b255bef3522e76b2c321b6938378d8dc7 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 7 Jun 2019 19:11:21 +0200 Subject: debut doc --- docs/source/howto.rst | 25 +++++++++++++++++++++++++ docs/source/index.rst | 1 + 2 files changed, 26 insertions(+) create mode 100644 docs/source/howto.rst diff --git a/docs/source/howto.rst b/docs/source/howto.rst new file mode 100644 index 0000000..48b1532 --- /dev/null +++ b/docs/source/howto.rst @@ -0,0 +1,25 @@ + +How to ? +======== + +In the following we provide some pointers about which functions and classes +to use for different problems related to optimal transport (OTs). + +1. **How to solve a discrete optimal transport problem ?** + + The solver for discrete is the function :py:mod:`ot.emd` that returns + the OT transport matrix. If you want to solve a regularized OT you can + use :py:mod:`ot.sinkhorn`. + + More detailed examples can be seen on this :ref:`auto_examples/plot_OT_2D_samples` + + Here is a simple use case: + + .. code:: python + + # a,b are 1D histograms (sum to 1 and positive) + # M is the ground cost matrix + T=ot.emd(a,b,M) # exact linear program + T_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT + + diff --git a/docs/source/index.rst b/docs/source/index.rst index b8eabcb..d92f50f 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -13,6 +13,7 @@ Contents :maxdepth: 3 self + howto all auto_examples/index -- cgit v1.2.3 From 8c935200558a1071d8df33bc752afed60e2f49f7 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 7 Jun 2019 19:11:48 +0200 Subject: debut doc --- ot/__init__.py | 4 ---- 1 file changed, 4 deletions(-) diff --git a/ot/__init__.py b/ot/__init__.py index 6d6dc75..16b0dd6 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -3,10 +3,6 @@ This is the main module of the POT toolbox. It provides easy access to a number of functions described below. -### FAQ - -#### How to compute the Wasserstein distance ? - .. warning:: The list of automatically imported sub-modules is as follows: :py:mod:`ot.lp`, :py:mod:`ot.bregman`, :py:mod:`ot.optim` -- cgit v1.2.3 From 3c53834d46f093f5770ec76748beb5667bebb6fa Mon Sep 17 00:00:00 2001 From: Hicham Janati Date: Wed, 12 Jun 2019 15:50:00 +0200 Subject: add unbalanced sinkhorn algorithm --- ot/__init__.py | 5 +- ot/unbalanced.py | 404 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ 2 files changed, 408 insertions(+), 1 deletion(-) create mode 100644 ot/unbalanced.py diff --git a/ot/__init__.py b/ot/__init__.py index b74b924..361be02 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -20,10 +20,12 @@ from . import da from . import gromov from . import smooth from . import stochastic +from . import unbalanced # OT functions from .lp import emd, emd2 from .bregman import sinkhorn, sinkhorn2, barycenter +from .unbalanced import sinkhorn_unbalanced from .da import sinkhorn_lpl1_mm # utils functions @@ -33,4 +35,5 @@ __version__ = "0.5.1" __all__ = ["emd", "emd2", "sinkhorn", "sinkhorn2", "utils", 'datasets', 'bregman', 'lp', 'tic', 'toc', 'toq', 'gromov', - 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim'] + 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim', + 'sinkhorn_unbalanced'] diff --git a/ot/unbalanced.py b/ot/unbalanced.py new file mode 100644 index 0000000..8bd02eb --- /dev/null +++ b/ot/unbalanced.py @@ -0,0 +1,404 @@ +# -*- coding: utf-8 -*- +""" +Regularized Unbalanced OT +""" + +# Author: Hicham Janati +# License: MIT License + +import numpy as np +# from .utils import unif, dist + + +def sinkhorn_unbalanced(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, + stopThr=1e-9, verbose=False, log=False, **kwargs): + u""" + Solve the unbalanced entropic regularization optimal transport problem and return the loss + + The function solves the following optimization problem: + + .. math:: + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + alpha KL(\gamma 1, a) + alpha KL(\gamma^T 1, b) + + s.t. + \gamma\geq 0 + where : + + - M is the (ns, nt) metric cost matrix + - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target weights + - KL is the Kullback-Leibler divergence + + The algorithm used for solving the problem is the generalized Sinkhorn-Knopp matrix scaling algorithm as proposed in [10]_ + + + Parameters + ---------- + a : np.ndarray (ns,) + samples weights in the source domain + b : np.ndarray (nt,) or np.ndarray (nt,n_hists) + samples in the target domain, compute sinkhorn with multiple targets + and fixed M if b is a matrix (return OT loss + dual variables in log) + M : np.ndarray (ns, nt) + loss matrix + reg : float + Regularization term > 0 + alpha : float + Regulatization term > 0 + method : str + method used for the solver either 'sinkhorn', 'sinkhorn_stabilized' or + 'sinkhorn_epsilon_scaling', see those function for specific parameters + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (> 0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + W : (nt) ndarray or float + Optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + Examples + -------- + + >>> import ot + >>> a=[.5, .5] + >>> b=[.5, .5] + >>> M=[[0., 1.], [1., 0.]] + >>> ot.sinkhorn2(a, b, M, 1, 1) + array([0.26894142]) + + + References + ---------- + + .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013 + + .. [9] Schmitzer, B. (2016). Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems. arXiv preprint arXiv:1610.06519. + + .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + + + + See Also + -------- + ot.lp.emd : Unregularized OT + ot.optim.cg : General regularized OT + ot.bregman.sinkhorn_knopp : Classic Sinkhorn [2] + ot.bregman.sinkhorn_stabilized: Stabilized sinkhorn [9][10] + ot.bregman.sinkhorn_epsilon_scaling: Sinkhorn with epslilon scaling [9][10] + + """ + + if method.lower() == 'sinkhorn': + def sink(): + return sinkhorn_knopp(a, b, M, reg, alpha, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log, **kwargs) + # elif method.lower() == 'sinkhorn_stabilized': + # def sink(): + # return sinkhorn_stabilized(a, b, M, reg, numItermax=numItermax, + # stopThr=stopThr, verbose=verbose, log=log, **kwargs) + # elif method.lower() == 'sinkhorn_epsilon_scaling': + # def sink(): + # return sinkhorn_epsilon_scaling( + # a, b, M, reg, numItermax=numItermax, + # stopThr=stopThr, verbose=verbose, log=log, **kwargs) + else: + print('Warning : unknown method. Falling back to classic Sinkhorn Knopp') + + def sink(): + return sinkhorn_knopp(a, b, M, reg, alpha, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log, **kwargs) + + return sink() + + +def sinkhorn2(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, + stopThr=1e-9, verbose=False, log=False, **kwargs): + u""" + Solve the entropic regularization unbalanced optimal transport problem and return the loss + + The function solves the following optimization problem: + + .. math:: + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + alpha KL(\gamma 1, a) + alpha KL(\gamma^T 1, b) + + s.t. + \gamma\geq 0 + where : + + - M is the (ns, nt) metric cost matrix + - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target weights + - KL is the Kullback-Leibler divergence + + The algorithm used for solving the problem is the generalized Sinkhorn-Knopp matrix scaling algorithm as proposed in [10]_ + + + Parameters + ---------- + a : np.ndarray (ns,) + samples weights in the source domain + b : np.ndarray (nt,) or np.ndarray (nt, n_hists) + samples in the target domain, compute sinkhorn with multiple targets + and fixed M if b is a matrix (return OT loss + dual variables in log) + M : np.ndarray (ns,nt) + loss matrix + reg : float + Regularization term > 0 + alpha: float + Regularization term > 0 + method : str + method used for the solver either 'sinkhorn', 'sinkhorn_stabilized' or + 'sinkhorn_epsilon_scaling', see those function for specific parameters + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + W : (nt) ndarray or float + Optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + Examples + -------- + + >>> import ot + >>> a=[.5, .10] + >>> b=[.5, .5] + >>> M=[[0., 1.],[1., 0.]] + >>> ot.sinkhorn2(a, b, M, 1., 1.) + array([ 0.26894142]) + + + + References + ---------- + + .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013 + + .. [9] Schmitzer, B. (2016). Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems. arXiv preprint arXiv:1610.06519. + + .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + + [21] Altschuler J., Weed J., Rigollet P. : Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration, Advances in Neural Information Processing Systems (NIPS) 31, 2017 + + + + See Also + -------- + ot.lp.emd : Unregularized OT + ot.optim.cg : General regularized OT + ot.bregman.sinkhorn_knopp : Classic Sinkhorn [2] + ot.bregman.greenkhorn : Greenkhorn [21] + ot.bregman.sinkhorn_stabilized: Stabilized sinkhorn [9][10] + ot.bregman.sinkhorn_epsilon_scaling: Sinkhorn with epslilon scaling [9][10] + + """ + + if method.lower() == 'sinkhorn': + def sink(): + return sinkhorn_knopp(a, b, M, reg, alpha, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log, **kwargs) + # elif method.lower() == 'sinkhorn_stabilized': + # def sink(): + # return sinkhorn_stabilized(a, b, M, reg, numItermax=numItermax, + # stopThr=stopThr, verbose=verbose, log=log, **kwargs) + # elif method.lower() == 'sinkhorn_epsilon_scaling': + # def sink(): + # return sinkhorn_epsilon_scaling( + # a, b, M, reg, numItermax=numItermax, + # stopThr=stopThr, verbose=verbose, log=log, **kwargs) + else: + print('Warning : unknown method using classic Sinkhorn Knopp') + + def sink(): + return sinkhorn_knopp(a, b, M, reg, alpha, **kwargs) + + b = np.asarray(b, dtype=np.float64) + if len(b.shape) < 2: + b = b[None, :] + + return sink() + + +def sinkhorn_knopp(a, b, M, reg, alpha, numItermax=1000, + stopThr=1e-9, verbose=False, log=False, **kwargs): + """ + Solve the entropic regularization unbalanced optimal transport problem and return the loss + + The function solves the following optimization problem: + + .. math:: + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + alpha KL(\gamma 1, a) + alpha KL(\gamma^T 1, b) + + s.t. + \gamma\geq 0 + where : + + - M is the (ns, nt) metric cost matrix + - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target weights + - KL is the Kullback-Leibler divergence + + The algorithm used for solving the problem is the generalized Sinkhorn-Knopp matrix scaling algorithm as proposed in [10]_ + + + Parameters + ---------- + a : np.ndarray (ns,) + samples weights in the source domain + b : np.ndarray (nt,) or np.ndarray (nt, n_hists) + samples in the target domain, compute sinkhorn with multiple targets + and fixed M if b is a matrix (return OT loss + dual variables in log) + M : np.ndarray (ns,nt) + loss matrix + reg : float + Regularization term > 0 + alpha: float + Regularization term > 0 + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + gamma : (ns x nt) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + Examples + -------- + + >>> import ot + >>> a=[.5, .15] + >>> b=[.5, .5] + >>> M=[[0., 1.],[1., 0.]] + >>> ot.sinkhorn(a, b, M, 1., 1.) + array([[ 0.36552929, 0.13447071], + [ 0.13447071, 0.36552929]]) + + + References + ---------- + + .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + + + See Also + -------- + ot.lp.emd : Unregularized OT + ot.optim.cg : General regularized OT + + """ + + a = np.asarray(a, dtype=np.float64) + b = np.asarray(b, dtype=np.float64) + M = np.asarray(M, dtype=np.float64) + + n_a, n_b = M.shape + + if len(a) == 0: + a = np.ones(n_a, dtype=np.float64) / n_a + if len(b) == 0: + b = np.ones(n_b, dtype=np.float64) / n_b + + assert n_a == len(a) and n_b == len(b) + if b.ndim > 1: + n_hists = b.shape[1] + else: + n_hists = 0 + + if log: + log = {'err': []} + + # we assume that no distances are null except those of the diagonal of + # distances + if n_hists: + u = np.ones((n_a, n_hists)) / n_a + v = np.ones((n_b, n_hists)) / n_b + else: + u = np.ones(n_a) / n_a + v = np.ones(n_b) / n_b + + # print(reg) + # Next 3 lines equivalent to K= np.exp(-M/reg), but faster to compute + K = np.empty(M.shape, dtype=M.dtype) + np.divide(M, -reg, out=K) + np.exp(K, out=K) + + # print(np.min(K)) + fi = alpha / (alpha + reg) + + cpt = 0 + err = 1. + while (err > stopThr and cpt < numItermax): + uprev = u + vprev = v + + Kv = K.dot(v) + u = (a / Kv) ** fi + Ktu = K.T.dot(u) + v = (b / Ktu) ** fi + + if (np.any(Ktu == 0.) + or np.any(np.isnan(u)) or np.any(np.isnan(v)) + or np.any(np.isinf(u)) or np.any(np.isinf(v))): + # we have reached the machine precision + # come back to previous solution and quit loop + print('Warning: numerical errors at iteration', cpt) + u = uprev + v = vprev + break + if cpt % 10 == 0: + # we can speed up the process by checking for the error only all + # the 10th iterations + err = np.sum((u - uprev)**2) / np.sum((u)**2) + \ + np.sum((v - vprev)**2) / np.sum((v)**2) + if log: + log['err'].append(err) + if verbose: + if cpt % 200 == 0: + print( + '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) + print('{:5d}|{:8e}|'.format(cpt, err)) + cpt = cpt + 1 + if log: + log['u'] = u + log['v'] = v + + if n_hists: # return only loss + res = np.einsum('ik,ij,jk,ij->k', u, K, v, M) + if log: + return res, log + else: + return res + + else: # return OT matrix + + if log: + return u[:, None] * K * v[None, :], log + else: + return u[:, None] * K * v[None, :] -- cgit v1.2.3 From 28b549ef3ef93c01462cd811d6e55c36ae5a76a2 Mon Sep 17 00:00:00 2001 From: Hicham Janati Date: Wed, 12 Jun 2019 15:50:25 +0200 Subject: add test and example of UOT --- examples/plot_UOT_1D.py | 76 +++++++++++++++++++++++++++++++++++++++++++++++++ test/test_unbalanced.py | 36 +++++++++++++++++++++++ 2 files changed, 112 insertions(+) create mode 100644 examples/plot_UOT_1D.py create mode 100644 test/test_unbalanced.py diff --git a/examples/plot_UOT_1D.py b/examples/plot_UOT_1D.py new file mode 100644 index 0000000..1b1dd9c --- /dev/null +++ b/examples/plot_UOT_1D.py @@ -0,0 +1,76 @@ +# -*- coding: utf-8 -*- +""" +==================== +1D Unbalanced optimal transport +==================== + +This example illustrates the computation of Unbalanced Optimal transport +using a Kullback-Leibler relaxation. +""" + +# Author: Hicham Janati +# +# License: MIT License + +import numpy as np +import matplotlib.pylab as pl +import ot +import ot.plot +from ot.datasets import make_1D_gauss as gauss + +############################################################################## +# Generate data +# ------------- + + +#%% parameters + +n = 100 # nb bins + +# bin positions +x = np.arange(n, dtype=np.float64) + +# Gaussian distributions +a = gauss(n, m=20, s=5) # m= mean, s= std +b = gauss(n, m=60, s=10) + +# make distributions unbalanced +b *= 5. + +# loss matrix +M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) +M /= M.max() + + +############################################################################## +# Plot distributions and loss matrix +# ---------------------------------- + +#%% plot the distributions + +pl.figure(1, figsize=(6.4, 3)) +pl.plot(x, a, 'b', label='Source distribution') +pl.plot(x, b, 'r', label='Target distribution') +pl.legend() + +#%% plot distributions and loss matrix + +pl.figure(2, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') + + +############################################################################## +# Solve Unbalanced Sinkhorn +# -------------- + + +#%% Sinkhorn + +lambd = 0.1 +alpha = 1. +Gs = ot.unbalanced.sinkhorn_unbalanced(a, b, M, lambd, alpha, verbose=True) + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, Gs, 'UOT matrix Sinkhorn') + +pl.show() diff --git a/test/test_unbalanced.py b/test/test_unbalanced.py new file mode 100644 index 0000000..863b6f3 --- /dev/null +++ b/test/test_unbalanced.py @@ -0,0 +1,36 @@ +"""Tests for module Unbalanced OT with entropy regularization""" + +# Author: Hicham Janati +# +# License: MIT License + +import numpy as np +import ot + + +def test_unbalanced(): + # test generalized sinkhorn for unbalanced OT + n = 100 + rng = np.random.RandomState(42) + + x = rng.randn(n, 2) + a = ot.utils.unif(n) + b = ot.utils.unif(n) * 1.5 + + M = ot.dist(x, x) + epsilon = 1. + alpha = 1. + K = np.exp(- M / epsilon) + + G, log = ot.unbalanced.sinkhorn_unbalanced(a, b, M, reg=epsilon, alpha=alpha, + stopThr=1e-10, log=True) + + # check fixed point equations + fi = alpha / (alpha + epsilon) + v_final = (b / K.T.dot(log["u"])) ** fi + u_final = (a / K.dot(log["v"])) ** fi + + np.testing.assert_allclose( + u_final, log["u"], atol=1e-05) + np.testing.assert_allclose( + v_final, log["v"], atol=1e-05) -- cgit v1.2.3 From 11381a7ecc79ef719ee9107167c3adc22b5a3f59 Mon Sep 17 00:00:00 2001 From: Hicham Janati Date: Wed, 12 Jun 2019 17:06:32 +0200 Subject: integrate comments of jmassich --- examples/plot_UOT_1D.py | 6 +++--- ot/unbalanced.py | 54 +++++++++++++++---------------------------------- test/test_unbalanced.py | 9 +++++++-- 3 files changed, 26 insertions(+), 43 deletions(-) diff --git a/examples/plot_UOT_1D.py b/examples/plot_UOT_1D.py index 1b1dd9c..59b7e77 100644 --- a/examples/plot_UOT_1D.py +++ b/examples/plot_UOT_1D.py @@ -66,9 +66,9 @@ ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') #%% Sinkhorn -lambd = 0.1 -alpha = 1. -Gs = ot.unbalanced.sinkhorn_unbalanced(a, b, M, lambd, alpha, verbose=True) +epsilon = 0.1 # entropy parameter +alpha = 1. # Unbalanced KL relaxation parameter +Gs = ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, verbose=True) pl.figure(4, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, Gs, 'UOT matrix Sinkhorn') diff --git a/ot/unbalanced.py b/ot/unbalanced.py index 8bd02eb..f4208b5 100644 --- a/ot/unbalanced.py +++ b/ot/unbalanced.py @@ -6,6 +6,7 @@ Regularized Unbalanced OT # Author: Hicham Janati # License: MIT License +import warnings import numpy as np # from .utils import unif, dist @@ -29,7 +30,7 @@ def sinkhorn_unbalanced(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, - a and b are source and target weights - KL is the Kullback-Leibler divergence - The algorithm used for solving the problem is the generalized Sinkhorn-Knopp matrix scaling algorithm as proposed in [10]_ + The algorithm used for solving the problem is the generalized Sinkhorn-Knopp matrix scaling algorithm as proposed in [10, 23]_ Parameters @@ -85,15 +86,14 @@ def sinkhorn_unbalanced(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + .. [23] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. : Learning with a Wasserstein Loss, Advances in Neural Information Processing Systems (NIPS) 2015 See Also -------- - ot.lp.emd : Unregularized OT - ot.optim.cg : General regularized OT - ot.bregman.sinkhorn_knopp : Classic Sinkhorn [2] - ot.bregman.sinkhorn_stabilized: Stabilized sinkhorn [9][10] - ot.bregman.sinkhorn_epsilon_scaling: Sinkhorn with epslilon scaling [9][10] + ot.unbalanced.sinkhorn_knopp : Unbalanced Classic Sinkhorn [10] + ot.unbalanced.sinkhorn_stabilized: Unbalanced Stabilized sinkhorn [9][10] + ot.unbalanced.sinkhorn_epsilon_scaling: Unbalanced Sinkhorn with epslilon scaling [9][10] """ @@ -101,17 +101,8 @@ def sinkhorn_unbalanced(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, def sink(): return sinkhorn_knopp(a, b, M, reg, alpha, numItermax=numItermax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) - # elif method.lower() == 'sinkhorn_stabilized': - # def sink(): - # return sinkhorn_stabilized(a, b, M, reg, numItermax=numItermax, - # stopThr=stopThr, verbose=verbose, log=log, **kwargs) - # elif method.lower() == 'sinkhorn_epsilon_scaling': - # def sink(): - # return sinkhorn_epsilon_scaling( - # a, b, M, reg, numItermax=numItermax, - # stopThr=stopThr, verbose=verbose, log=log, **kwargs) else: - print('Warning : unknown method. Falling back to classic Sinkhorn Knopp') + warnings.warn('Unknown method. Falling back to classic Sinkhorn Knopp') def sink(): return sinkhorn_knopp(a, b, M, reg, alpha, numItermax=numItermax, @@ -139,7 +130,7 @@ def sinkhorn2(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, - a and b are source and target weights - KL is the Kullback-Leibler divergence - The algorithm used for solving the problem is the generalized Sinkhorn-Knopp matrix scaling algorithm as proposed in [10]_ + The algorithm used for solving the problem is the generalized Sinkhorn-Knopp matrix scaling algorithm as proposed in [10, 23]_ Parameters @@ -196,18 +187,13 @@ def sinkhorn2(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. - [21] Altschuler J., Weed J., Rigollet P. : Near-linear time approximation algorithms for optimal transport via Sinkhorn iteration, Advances in Neural Information Processing Systems (NIPS) 31, 2017 - - + .. [23] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. : Learning with a Wasserstein Loss, Advances in Neural Information Processing Systems (NIPS) 2015 See Also -------- - ot.lp.emd : Unregularized OT - ot.optim.cg : General regularized OT - ot.bregman.sinkhorn_knopp : Classic Sinkhorn [2] - ot.bregman.greenkhorn : Greenkhorn [21] - ot.bregman.sinkhorn_stabilized: Stabilized sinkhorn [9][10] - ot.bregman.sinkhorn_epsilon_scaling: Sinkhorn with epslilon scaling [9][10] + ot.unbalanced.sinkhorn_knopp : Unbalanced Classic Sinkhorn [10] + ot.unbalanced.sinkhorn_stabilized: Unbalanced Stabilized sinkhorn [9][10] + ot.unbalanced.sinkhorn_epsilon_scaling: Unbalanced Sinkhorn with epslilon scaling [9][10] """ @@ -215,17 +201,8 @@ def sinkhorn2(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, def sink(): return sinkhorn_knopp(a, b, M, reg, alpha, numItermax=numItermax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) - # elif method.lower() == 'sinkhorn_stabilized': - # def sink(): - # return sinkhorn_stabilized(a, b, M, reg, numItermax=numItermax, - # stopThr=stopThr, verbose=verbose, log=log, **kwargs) - # elif method.lower() == 'sinkhorn_epsilon_scaling': - # def sink(): - # return sinkhorn_epsilon_scaling( - # a, b, M, reg, numItermax=numItermax, - # stopThr=stopThr, verbose=verbose, log=log, **kwargs) else: - print('Warning : unknown method using classic Sinkhorn Knopp') + warnings.warn('Unknown method using classic Sinkhorn Knopp') def sink(): return sinkhorn_knopp(a, b, M, reg, alpha, **kwargs) @@ -256,7 +233,7 @@ def sinkhorn_knopp(a, b, M, reg, alpha, numItermax=1000, - a and b are source and target weights - KL is the Kullback-Leibler divergence - The algorithm used for solving the problem is the generalized Sinkhorn-Knopp matrix scaling algorithm as proposed in [10]_ + The algorithm used for solving the problem is the generalized Sinkhorn-Knopp matrix scaling algorithm as proposed in [10, 23]_ Parameters @@ -306,6 +283,7 @@ def sinkhorn_knopp(a, b, M, reg, alpha, numItermax=1000, .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + .. [23] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. : Learning with a Wasserstein Loss, Advances in Neural Information Processing Systems (NIPS) 2015 See Also -------- @@ -368,7 +346,7 @@ def sinkhorn_knopp(a, b, M, reg, alpha, numItermax=1000, or np.any(np.isinf(u)) or np.any(np.isinf(v))): # we have reached the machine precision # come back to previous solution and quit loop - print('Warning: numerical errors at iteration', cpt) + warnings.warn('Numerical errors at iteration', cpt) u = uprev v = vprev break diff --git a/test/test_unbalanced.py b/test/test_unbalanced.py index 863b6f3..e37498f 100644 --- a/test/test_unbalanced.py +++ b/test/test_unbalanced.py @@ -6,15 +6,19 @@ import numpy as np import ot +import pytest -def test_unbalanced(): +@pytest.mark.parametrize("metric", ["sinkhorn"]) +def test_unbalanced_convergence(method): # test generalized sinkhorn for unbalanced OT n = 100 rng = np.random.RandomState(42) x = rng.randn(n, 2) a = ot.utils.unif(n) + + # make dists unbalanced b = ot.utils.unif(n) * 1.5 M = ot.dist(x, x) @@ -23,7 +27,8 @@ def test_unbalanced(): K = np.exp(- M / epsilon) G, log = ot.unbalanced.sinkhorn_unbalanced(a, b, M, reg=epsilon, alpha=alpha, - stopThr=1e-10, log=True) + stopThr=1e-10, method=method, + log=True) # check fixed point equations fi = alpha / (alpha + epsilon) -- cgit v1.2.3 From 12ed1581225f70c7c8777b6ce31710453fda7f51 Mon Sep 17 00:00:00 2001 From: Hicham Janati Date: Wed, 12 Jun 2019 17:15:40 +0200 Subject: fix typo in test argument --- test/test_unbalanced.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/test_unbalanced.py b/test/test_unbalanced.py index e37498f..b4fa355 100644 --- a/test/test_unbalanced.py +++ b/test/test_unbalanced.py @@ -9,7 +9,7 @@ import ot import pytest -@pytest.mark.parametrize("metric", ["sinkhorn"]) +@pytest.mark.parametrize("method", ["sinkhorn"]) def test_unbalanced_convergence(method): # test generalized sinkhorn for unbalanced OT n = 100 -- cgit v1.2.3 From 50bc90058940645a13e2f3e41129bdc97161dc63 Mon Sep 17 00:00:00 2001 From: Hicham Janati Date: Wed, 12 Jun 2019 17:52:02 +0200 Subject: add unbalanced barycenters --- examples/plot_UOT_barycenter_1D.py | 164 +++++++++++++++++++++++++++++++++++++ ot/unbalanced.py | 118 ++++++++++++++++++++++++++ test/test_unbalanced.py | 30 +++++++ 3 files changed, 312 insertions(+) create mode 100644 examples/plot_UOT_barycenter_1D.py diff --git a/examples/plot_UOT_barycenter_1D.py b/examples/plot_UOT_barycenter_1D.py new file mode 100644 index 0000000..8dfb84f --- /dev/null +++ b/examples/plot_UOT_barycenter_1D.py @@ -0,0 +1,164 @@ +# -*- coding: utf-8 -*- +""" +=========================================================== +1D Wasserstein barycenter demo for Unbalanced distributions +=========================================================== + +This example illustrates the computation of regularized Wassersyein Barycenter +as proposed in [10] for Unbalanced inputs. + + +[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + +""" + +# Author: Hicham Janati +# +# License: MIT License + +import numpy as np +import matplotlib.pylab as pl +import ot +# necessary for 3d plot even if not used +from mpl_toolkits.mplot3d import Axes3D # noqa +from matplotlib.collections import PolyCollection + +############################################################################## +# Generate data +# ------------- + +#%% parameters + +n = 100 # nb bins + +# bin positions +x = np.arange(n, dtype=np.float64) + +# Gaussian distributions +a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std +a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) + +# make unbalanced dists +a2 *= 3. + +# creating matrix A containing all distributions +A = np.vstack((a1, a2)).T +n_distributions = A.shape[1] + +# loss matrix + normalization +M = ot.utils.dist0(n) +M /= M.max() + +############################################################################## +# Plot data +# --------- + +#%% plot the distributions + +pl.figure(1, figsize=(6.4, 3)) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') +pl.tight_layout() + +############################################################################## +# Barycenter computation +# ---------------------- + +#%% non weighted barycenter computation + +weight = 0.5 # 0<=weight<=1 +weights = np.array([1 - weight, weight]) + +# l2bary +bary_l2 = A.dot(weights) + +# wasserstein +reg = 1e-3 +alpha = 1. + +bary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights) + +pl.figure(2) +pl.clf() +pl.subplot(2, 1, 1) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') + +pl.subplot(2, 1, 2) +pl.plot(x, bary_l2, 'r', label='l2') +pl.plot(x, bary_wass, 'g', label='Wasserstein') +pl.legend() +pl.title('Barycenters') +pl.tight_layout() + +############################################################################## +# Barycentric interpolation +# ------------------------- + +#%% barycenter interpolation + +n_weight = 11 +weight_list = np.linspace(0, 1, n_weight) + + +B_l2 = np.zeros((n, n_weight)) + +B_wass = np.copy(B_l2) + +for i in range(0, n_weight): + weight = weight_list[i] + weights = np.array([1 - weight, weight]) + B_l2[:, i] = A.dot(weights) + B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights) + + +#%% plot interpolation + +pl.figure(3) + +cmap = pl.cm.get_cmap('viridis') +verts = [] +zs = weight_list +for i, z in enumerate(zs): + ys = B_l2[:, i] + verts.append(list(zip(x, ys))) + +ax = pl.gcf().gca(projection='3d') + +poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list]) +poly.set_alpha(0.7) +ax.add_collection3d(poly, zs=zs, zdir='y') +ax.set_xlabel('x') +ax.set_xlim3d(0, n) +ax.set_ylabel(r'$\alpha$') +ax.set_ylim3d(0, 1) +ax.set_zlabel('') +ax.set_zlim3d(0, B_l2.max() * 1.01) +pl.title('Barycenter interpolation with l2') +pl.tight_layout() + +pl.figure(4) +cmap = pl.cm.get_cmap('viridis') +verts = [] +zs = weight_list +for i, z in enumerate(zs): + ys = B_wass[:, i] + verts.append(list(zip(x, ys))) + +ax = pl.gcf().gca(projection='3d') + +poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list]) +poly.set_alpha(0.7) +ax.add_collection3d(poly, zs=zs, zdir='y') +ax.set_xlabel('x') +ax.set_xlim3d(0, n) +ax.set_ylabel(r'$\alpha$') +ax.set_ylim3d(0, 1) +ax.set_zlabel('') +ax.set_zlim3d(0, B_l2.max() * 1.01) +pl.title('Barycenter interpolation with Wasserstein') +pl.tight_layout() + +pl.show() diff --git a/ot/unbalanced.py b/ot/unbalanced.py index f4208b5..a30fc18 100644 --- a/ot/unbalanced.py +++ b/ot/unbalanced.py @@ -380,3 +380,121 @@ def sinkhorn_knopp(a, b, M, reg, alpha, numItermax=1000, return u[:, None] * K * v[None, :], log else: return u[:, None] * K * v[None, :] + + +def barycenter_unbalanced(A, M, reg, alpha, weights=None, numItermax=1000, + stopThr=1e-4, verbose=False, log=False): + """Compute the entropic regularized unbalanced wasserstein barycenter of distributions A + + The function solves the following optimization problem: + + .. math:: + \mathbf{a} = arg\min_\mathbf{a} \sum_i Wu_{reg}(\mathbf{a},\mathbf{a}_i) + + where : + + - :math:`W_{reg}(\cdot,\cdot)` is the unbalanced entropic regularized Wasserstein distance (see ot.unbalanced.sinkhorn_unbalanced) + - :math:`\mathbf{a}_i` are training distributions in the columns of matrix :math:`\mathbf{A}` + - reg and :math:`\mathbf{M}` are respectively the regularization term and the cost matrix for OT + - alpha is the marginal relaxation hyperparameter + The algorithm used for solving the problem is the generalized Sinkhorn-Knopp matrix scaling algorithm as proposed in [10]_ + + Parameters + ---------- + A : np.ndarray (d,n) + n training distributions a_i of size d + M : np.ndarray (d,d) + loss matrix for OT + reg : float + Regularization term > 0 + alpha : float + Regularization term > 0 + weights : np.ndarray (n,) + Weights of each histogram a_i on the simplex (barycentric coodinates) + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + a : (d,) ndarray + Unbalanced Wasserstein barycenter + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). Iterative Bregman projections for regularized transportation problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138. + .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + + + """ + p, n_hists = A.shape + if weights is None: + weights = np.ones(n_hists) / n_hists + else: + assert(len(weights) == A.shape[1]) + + if log: + log = {'err': []} + + K = np.exp(- M / reg) + + fi = alpha / (alpha + reg) + + v = np.ones((p, n_hists)) / p + u = np.ones((p, 1)) / p + + cpt = 0 + err = 1. + + while (err > stopThr and cpt < numItermax): + uprev = u + vprev = v + + Kv = K.dot(v) + u = (A / Kv) ** fi + Ktu = K.T.dot(u) + q = ((Ktu ** (1 - fi)).dot(weights)) + q = q ** (1 / (1 - fi)) + Q = q[:, None] + v = (Q / Ktu) ** fi + + if (np.any(Ktu == 0.) + or np.any(np.isnan(u)) or np.any(np.isnan(v)) + or np.any(np.isinf(u)) or np.any(np.isinf(v))): + # we have reached the machine precision + # come back to previous solution and quit loop + warnings.warn('Numerical errors at iteration', cpt) + u = uprev + v = vprev + break + if cpt % 10 == 0: + # we can speed up the process by checking for the error only all + # the 10th iterations + err = np.sum((u - uprev) ** 2) / np.sum((u) ** 2) + \ + np.sum((v - vprev) ** 2) / np.sum((v) ** 2) + if log: + log['err'].append(err) + if verbose: + if cpt % 50 == 0: + print( + '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) + print('{:5d}|{:8e}|'.format(cpt, err)) + + cpt += 1 + if log: + log['niter'] = cpt + log['u'] = u + log['v'] = v + return q, log + else: + return q diff --git a/test/test_unbalanced.py b/test/test_unbalanced.py index b4fa355..b39e457 100644 --- a/test/test_unbalanced.py +++ b/test/test_unbalanced.py @@ -39,3 +39,33 @@ def test_unbalanced_convergence(method): u_final, log["u"], atol=1e-05) np.testing.assert_allclose( v_final, log["v"], atol=1e-05) + + +def test_unbalanced_barycenter(): + # test generalized sinkhorn for unbalanced OT barycenter + n = 100 + rng = np.random.RandomState(42) + + x = rng.randn(n, 2) + A = rng.rand(n, 2) + + # make dists unbalanced + A = A * np.array([1, 2])[None, :] + M = ot.dist(x, x) + epsilon = 1. + alpha = 1. + K = np.exp(- M / epsilon) + + q, log = ot.unbalanced.barycenter_unbalanced(A, M, reg=epsilon, alpha=alpha, + stopThr=1e-10, + log=True) + + # check fixed point equations + fi = alpha / (alpha + epsilon) + v_final = (q[:, None] / K.T.dot(log["u"])) ** fi + u_final = (A / K.dot(log["v"])) ** fi + + np.testing.assert_allclose( + u_final, log["u"], atol=1e-05) + np.testing.assert_allclose( + v_final, log["v"], atol=1e-05) -- cgit v1.2.3 From 897982718a5fd81a9a591d80a7d50839399fc088 Mon Sep 17 00:00:00 2001 From: Hicham Janati Date: Tue, 18 Jun 2019 16:40:06 +0200 Subject: fix func names + add more tests --- ot/__init__.py | 2 +- ot/bregman.py | 2 +- ot/unbalanced.py | 79 ++++++++++++++++++++++++++++++------------------- test/test_unbalanced.py | 79 +++++++++++++++++++++++++++++++++++++++++++++++-- 4 files changed, 127 insertions(+), 35 deletions(-) diff --git a/ot/__init__.py b/ot/__init__.py index 361be02..acb05e6 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -25,7 +25,7 @@ from . import unbalanced # OT functions from .lp import emd, emd2 from .bregman import sinkhorn, sinkhorn2, barycenter -from .unbalanced import sinkhorn_unbalanced +from .unbalanced import sinkhorn_unbalanced, barycenter_unbalanced from .da import sinkhorn_lpl1_mm # utils functions diff --git a/ot/bregman.py b/ot/bregman.py index 321712b..09716e6 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -241,7 +241,7 @@ def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, b = np.asarray(b, dtype=np.float64) if len(b.shape) < 2: - b = b.reshape((-1, 1)) + b = b[:, None] return sink() diff --git a/ot/unbalanced.py b/ot/unbalanced.py index a30fc18..97e2576 100644 --- a/ot/unbalanced.py +++ b/ot/unbalanced.py @@ -73,8 +73,9 @@ def sinkhorn_unbalanced(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, >>> a=[.5, .5] >>> b=[.5, .5] >>> M=[[0., 1.], [1., 0.]] - >>> ot.sinkhorn2(a, b, M, 1, 1) - array([0.26894142]) + >>> ot.sinkhorn_unbalanced(a, b, M, 1, 1) + array([[0.51122823, 0.18807035], + [0.18807035, 0.51122823]]) References @@ -91,28 +92,36 @@ def sinkhorn_unbalanced(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, See Also -------- - ot.unbalanced.sinkhorn_knopp : Unbalanced Classic Sinkhorn [10] - ot.unbalanced.sinkhorn_stabilized: Unbalanced Stabilized sinkhorn [9][10] - ot.unbalanced.sinkhorn_epsilon_scaling: Unbalanced Sinkhorn with epslilon scaling [9][10] + ot.unbalanced.sinkhorn_knopp_unbalanced : Unbalanced Classic Sinkhorn [10] + ot.unbalanced.sinkhorn_stabilized_unbalanced: Unbalanced Stabilized sinkhorn [9][10] + ot.unbalanced.sinkhorn_epsilon_scaling_unbalanced: Unbalanced Sinkhorn with epslilon scaling [9][10] """ if method.lower() == 'sinkhorn': def sink(): - return sinkhorn_knopp(a, b, M, reg, alpha, numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log, **kwargs) - else: - warnings.warn('Unknown method. Falling back to classic Sinkhorn Knopp') + return sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, + numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) + + elif method.lower() in ['sinkhorn_stabilized', 'sinkhorn_epsilon_scaling']: + warnings.warn('Method not implemented yet. Using classic Sinkhorn Knopp') def sink(): - return sinkhorn_knopp(a, b, M, reg, alpha, numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log, **kwargs) + return sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, + numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) + else: + raise ValueError('Unknown method. Using classic Sinkhorn Knopp') return sink() -def sinkhorn2(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, - stopThr=1e-9, verbose=False, log=False, **kwargs): +def sinkhorn_unbalanced2(a, b, M, reg, alpha, method='sinkhorn', + numItermax=1000, stopThr=1e-9, verbose=False, + log=False, **kwargs): u""" Solve the entropic regularization unbalanced optimal transport problem and return the loss @@ -173,8 +182,8 @@ def sinkhorn2(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, >>> a=[.5, .10] >>> b=[.5, .5] >>> M=[[0., 1.],[1., 0.]] - >>> ot.sinkhorn2(a, b, M, 1., 1.) - array([ 0.26894142]) + >>> ot.unbalanced.sinkhorn_unbalanced2(a, b, M, 1., 1.) + array([0.31912866]) @@ -199,23 +208,31 @@ def sinkhorn2(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, if method.lower() == 'sinkhorn': def sink(): - return sinkhorn_knopp(a, b, M, reg, alpha, numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log, **kwargs) - else: - warnings.warn('Unknown method using classic Sinkhorn Knopp') + return sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, + numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) + + elif method.lower() in ['sinkhorn_stabilized', 'sinkhorn_epsilon_scaling']: + warnings.warn('Method not implemented yet. Using classic Sinkhorn Knopp') def sink(): - return sinkhorn_knopp(a, b, M, reg, alpha, **kwargs) + return sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, + numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) + else: + raise ValueError('Unknown method. Using classic Sinkhorn Knopp') b = np.asarray(b, dtype=np.float64) if len(b.shape) < 2: - b = b[None, :] + b = b[:, None] return sink() -def sinkhorn_knopp(a, b, M, reg, alpha, numItermax=1000, - stopThr=1e-9, verbose=False, log=False, **kwargs): +def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, + stopThr=1e-9, verbose=False, log=False, **kwargs): """ Solve the entropic regularization unbalanced optimal transport problem and return the loss @@ -273,10 +290,9 @@ def sinkhorn_knopp(a, b, M, reg, alpha, numItermax=1000, >>> a=[.5, .15] >>> b=[.5, .5] >>> M=[[0., 1.],[1., 0.]] - >>> ot.sinkhorn(a, b, M, 1., 1.) - array([[ 0.36552929, 0.13447071], - [ 0.13447071, 0.36552929]]) - + >>> ot.sinkhorn_knopp_unbalanced(a, b, M, 1., 1.) + array([[0.52761554, 0.22392482], + [0.10286295, 0.32257641]]) References ---------- @@ -303,8 +319,7 @@ def sinkhorn_knopp(a, b, M, reg, alpha, numItermax=1000, if len(b) == 0: b = np.ones(n_b, dtype=np.float64) / n_b - assert n_a == len(a) and n_b == len(b) - if b.ndim > 1: + if len(b.shape) > 1: n_hists = b.shape[1] else: n_hists = 0 @@ -315,8 +330,9 @@ def sinkhorn_knopp(a, b, M, reg, alpha, numItermax=1000, # we assume that no distances are null except those of the diagonal of # distances if n_hists: - u = np.ones((n_a, n_hists)) / n_a + u = np.ones((n_a, 1)) / n_a v = np.ones((n_b, n_hists)) / n_b + a = a.reshape(n_a, 1) else: u = np.ones(n_a) / n_a v = np.ones(n_b) / n_b @@ -332,6 +348,7 @@ def sinkhorn_knopp(a, b, M, reg, alpha, numItermax=1000, cpt = 0 err = 1. + while (err > stopThr and cpt < numItermax): uprev = u vprev = v @@ -473,7 +490,7 @@ def barycenter_unbalanced(A, M, reg, alpha, weights=None, numItermax=1000, or np.any(np.isinf(u)) or np.any(np.isinf(v))): # we have reached the machine precision # come back to previous solution and quit loop - warnings.warn('Numerical errors at iteration', cpt) + warnings.warn('Numerical errors at iteration %s' % cpt) u = uprev v = vprev break diff --git a/test/test_unbalanced.py b/test/test_unbalanced.py index b39e457..1395fe1 100644 --- a/test/test_unbalanced.py +++ b/test/test_unbalanced.py @@ -29,7 +29,8 @@ def test_unbalanced_convergence(method): G, log = ot.unbalanced.sinkhorn_unbalanced(a, b, M, reg=epsilon, alpha=alpha, stopThr=1e-10, method=method, log=True) - + loss = ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, alpha, + method=method) # check fixed point equations fi = alpha / (alpha + epsilon) v_final = (b / K.T.dot(log["u"])) ** fi @@ -40,6 +41,44 @@ def test_unbalanced_convergence(method): np.testing.assert_allclose( v_final, log["v"], atol=1e-05) + # check if sinkhorn_unbalanced2 returns the correct loss + np.testing.assert_allclose((G * M).sum(), loss, atol=1e-5) + + +@pytest.mark.parametrize("method", ["sinkhorn"]) +def test_unbalanced_multiple_inputs(method): + # test generalized sinkhorn for unbalanced OT + n = 100 + rng = np.random.RandomState(42) + + x = rng.randn(n, 2) + a = ot.utils.unif(n) + + # make dists unbalanced + b = rng.rand(n, 2) + + M = ot.dist(x, x) + epsilon = 1. + alpha = 1. + K = np.exp(- M / epsilon) + + loss, log = ot.unbalanced.sinkhorn_unbalanced(a, b, M, reg=epsilon, + alpha=alpha, + stopThr=1e-10, method=method, + log=True) + # check fixed point equations + fi = alpha / (alpha + epsilon) + v_final = (b / K.T.dot(log["u"])) ** fi + + u_final = (a[:, None] / K.dot(log["v"])) ** fi + + np.testing.assert_allclose( + u_final, log["u"], atol=1e-05) + np.testing.assert_allclose( + v_final, log["v"], atol=1e-05) + + assert len(loss) == b.shape[1] + def test_unbalanced_barycenter(): # test generalized sinkhorn for unbalanced OT barycenter @@ -59,7 +98,6 @@ def test_unbalanced_barycenter(): q, log = ot.unbalanced.barycenter_unbalanced(A, M, reg=epsilon, alpha=alpha, stopThr=1e-10, log=True) - # check fixed point equations fi = alpha / (alpha + epsilon) v_final = (q[:, None] / K.T.dot(log["u"])) ** fi @@ -69,3 +107,40 @@ def test_unbalanced_barycenter(): u_final, log["u"], atol=1e-05) np.testing.assert_allclose( v_final, log["v"], atol=1e-05) + + +def test_implemented_methods(): + IMPLEMENTED_METHODS = ['sinkhorn'] + TO_BE_IMPLEMENTED_METHODS = ['sinkhorn_stabilized', + 'sinkhorn_epsilon_scaling'] + NOT_VALID_TOKENS = ['foo'] + # test generalized sinkhorn for unbalanced OT barycenter + n = 3 + rng = np.random.RandomState(42) + + x = rng.randn(n, 2) + a = ot.utils.unif(n) + + # make dists unbalanced + b = ot.utils.unif(n) * 1.5 + + M = ot.dist(x, x) + epsilon = 1. + alpha = 1. + for method in IMPLEMENTED_METHODS: + ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, + method=method) + ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, alpha, + method=method) + with pytest.warns(UserWarning, match='not implemented'): + for method in set(TO_BE_IMPLEMENTED_METHODS): + ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, + method=method) + ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, alpha, + method=method) + with pytest.raises(ValueError): + for method in set(NOT_VALID_TOKENS): + ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, + method=method) + ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, alpha, + method=method) -- cgit v1.2.3 From adf9d046445bf142a29d914352f397b36f7905c0 Mon Sep 17 00:00:00 2001 From: Hicham Janati Date: Tue, 18 Jun 2019 22:26:48 +0200 Subject: update Readme + minor rendering in examples --- README.md | 4 ++++ examples/plot_UOT_1D.py | 8 ++++---- examples/plot_UOT_barycenter_1D.py | 10 +++++----- ot/unbalanced.py | 6 +++--- 4 files changed, 16 insertions(+), 12 deletions(-) diff --git a/README.md b/README.md index b6b215c..d24d8b9 100644 --- a/README.md +++ b/README.md @@ -27,6 +27,7 @@ It provides the following solvers: * Gromov-Wasserstein distances and barycenters ([13] and regularized [12]) * Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) * Non regularized free support Wasserstein barycenters [20]. +* Unbalanced OT with KL relaxation distance and barycenter [10, 25]. Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -165,6 +166,7 @@ The contributors to this library are: * [Kilian Fatras](https://kilianfatras.github.io/) * [Alain Rakotomamonjy](https://sites.google.com/site/alainrakotomamonjy/home) * [Vayer Titouan](https://tvayer.github.io/) +* [Hicham Janati](https://hichamjanati.github.io/) (Unbalanced OT) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): @@ -236,3 +238,5 @@ You can also post bug reports and feature requests in Github issues. Make sure t [23] Aude, G., Peyré, G., Cuturi, M., [Learning Generative Models with Sinkhorn Divergences](https://arxiv.org/abs/1706.00292), Proceedings of the Twenty-First International Conference on Artficial Intelligence and Statistics, (AISTATS) 21, 2018 [24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. (2019). [Optimal Transport for structured data with application on graphs](http://proceedings.mlr.press/v97/titouan19a.html) Proceedings of the 36th International Conference on Machine Learning (ICML). + +[25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2019). [Learning with a Wasserstein Loss](http://cbcl.mit.edu/wasserstein/) Advances in Neural Information Processing Systems (NIPS). diff --git a/examples/plot_UOT_1D.py b/examples/plot_UOT_1D.py index 59b7e77..2ea8b05 100644 --- a/examples/plot_UOT_1D.py +++ b/examples/plot_UOT_1D.py @@ -1,8 +1,8 @@ # -*- coding: utf-8 -*- """ -==================== +=============================== 1D Unbalanced optimal transport -==================== +=============================== This example illustrates the computation of Unbalanced Optimal transport using a Kullback-Leibler relaxation. @@ -53,7 +53,7 @@ pl.plot(x, a, 'b', label='Source distribution') pl.plot(x, b, 'r', label='Target distribution') pl.legend() -#%% plot distributions and loss matrix +# plot distributions and loss matrix pl.figure(2, figsize=(5, 5)) ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') @@ -64,7 +64,7 @@ ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') # -------------- -#%% Sinkhorn +# Sinkhorn epsilon = 0.1 # entropy parameter alpha = 1. # Unbalanced KL relaxation parameter diff --git a/examples/plot_UOT_barycenter_1D.py b/examples/plot_UOT_barycenter_1D.py index 8dfb84f..c8d9d3b 100644 --- a/examples/plot_UOT_barycenter_1D.py +++ b/examples/plot_UOT_barycenter_1D.py @@ -27,7 +27,7 @@ from matplotlib.collections import PolyCollection # Generate data # ------------- -#%% parameters +# parameters n = 100 # nb bins @@ -53,7 +53,7 @@ M /= M.max() # Plot data # --------- -#%% plot the distributions +# plot the distributions pl.figure(1, figsize=(6.4, 3)) for i in range(n_distributions): @@ -65,7 +65,7 @@ pl.tight_layout() # Barycenter computation # ---------------------- -#%% non weighted barycenter computation +# non weighted barycenter computation weight = 0.5 # 0<=weight<=1 weights = np.array([1 - weight, weight]) @@ -97,7 +97,7 @@ pl.tight_layout() # Barycentric interpolation # ------------------------- -#%% barycenter interpolation +# barycenter interpolation n_weight = 11 weight_list = np.linspace(0, 1, n_weight) @@ -114,7 +114,7 @@ for i in range(0, n_weight): B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights) -#%% plot interpolation +# plot interpolation pl.figure(3) diff --git a/ot/unbalanced.py b/ot/unbalanced.py index 97e2576..918dda4 100644 --- a/ot/unbalanced.py +++ b/ot/unbalanced.py @@ -87,7 +87,7 @@ def sinkhorn_unbalanced(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. - .. [23] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. : Learning with a Wasserstein Loss, Advances in Neural Information Processing Systems (NIPS) 2015 + .. [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. : Learning with a Wasserstein Loss, Advances in Neural Information Processing Systems (NIPS) 2015 See Also @@ -196,7 +196,7 @@ def sinkhorn_unbalanced2(a, b, M, reg, alpha, method='sinkhorn', .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. - .. [23] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. : Learning with a Wasserstein Loss, Advances in Neural Information Processing Systems (NIPS) 2015 + .. [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. : Learning with a Wasserstein Loss, Advances in Neural Information Processing Systems (NIPS) 2015 See Also -------- @@ -299,7 +299,7 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. - .. [23] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. : Learning with a Wasserstein Loss, Advances in Neural Information Processing Systems (NIPS) 2015 + .. [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. : Learning with a Wasserstein Loss, Advances in Neural Information Processing Systems (NIPS) 2015 See Also -------- -- cgit v1.2.3 From f63f34f8adb6943b6410f8b773b4b4d8f1c7b4ba Mon Sep 17 00:00:00 2001 From: Romain Tavenard Date: Thu, 20 Jun 2019 14:29:56 +0200 Subject: EMD 1d without doc --- ot/__init__.py | 4 ++-- ot/lp/__init__.py | 43 ++++++++++++++++++++++++++++++++++++++----- ot/lp/emd_wrap.pyx | 35 +++++++++++++++++++++++++++++++++++ test/test_ot.py | 26 ++++++++++++++++++++++++++ 4 files changed, 101 insertions(+), 7 deletions(-) diff --git a/ot/__init__.py b/ot/__init__.py index b74b924..5d5b700 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -22,7 +22,7 @@ from . import smooth from . import stochastic # OT functions -from .lp import emd, emd2 +from .lp import emd, emd2, emd_1d from .bregman import sinkhorn, sinkhorn2, barycenter from .da import sinkhorn_lpl1_mm @@ -31,6 +31,6 @@ from .utils import dist, unif, tic, toc, toq __version__ = "0.5.1" -__all__ = ["emd", "emd2", "sinkhorn", "sinkhorn2", "utils", 'datasets', +__all__ = ["emd", "emd2", 'emd_1d', "sinkhorn", "sinkhorn2", "utils", 'datasets', 'bregman', 'lp', 'tic', 'toc', 'toq', 'gromov', 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim'] diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 02cbd8c..49ded5b 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -14,12 +14,12 @@ import numpy as np from .import cvx # import compiled emd -from .emd_wrap import emd_c, check_result +from .emd_wrap import emd_c, check_result, emd_1d_sorted from ..utils import parmap from .cvx import barycenter from ..utils import dist -__all__=['emd', 'emd2', 'barycenter', 'free_support_barycenter', 'cvx'] +__all__=['emd', 'emd2', 'barycenter', 'free_support_barycenter', 'cvx', 'emd_1d_sorted'] def emd(a, b, M, numItermax=100000, log=False): @@ -94,7 +94,7 @@ def emd(a, b, M, numItermax=100000, log=False): b = np.asarray(b, dtype=np.float64) M = np.asarray(M, dtype=np.float64) - # if empty array given then use unifor distributions + # if empty array given then use uniform distributions if len(a) == 0: a = np.ones((M.shape[0],), dtype=np.float64) / M.shape[0] if len(b) == 0: @@ -187,7 +187,7 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), b = np.asarray(b, dtype=np.float64) M = np.asarray(M, dtype=np.float64) - # if empty array given then use unifor distributions + # if empty array given then use uniform distributions if len(a) == 0: a = np.ones((M.shape[0],), dtype=np.float64) / M.shape[0] if len(b) == 0: @@ -308,4 +308,37 @@ def free_support_barycenter(measures_locations, measures_weights, X_init, b=None log_dict['displacement_square_norms'] = displacement_square_norms return X, log_dict else: - return X \ No newline at end of file + return X + + +def emd_1d(a, b, x_a, x_b, metric='sqeuclidean', log=False): + """Solves the Earth Movers distance problem between 1d measures and returns + the OT matrix + + """ + assert x_a.shape[1] == x_b.shape[1] == 1, "emd_1d should only be used " + \ + "with monodimensional data" + + a = np.asarray(a, dtype=np.float64) + b = np.asarray(b, dtype=np.float64) + + # if empty array given then use uniform distributions + if len(a) == 0: + a = np.ones((x_a.shape[0],), dtype=np.float64) / x_a.shape[0] + if len(b) == 0: + b = np.ones((x_b.shape[0],), dtype=np.float64) / x_b.shape[0] + + perm_a = np.argsort(x_a.reshape((-1, ))) + perm_b = np.argsort(x_b.reshape((-1, ))) + inv_perm_a = np.argsort(perm_a) + inv_perm_b = np.argsort(perm_b) + + M = dist(x_a[perm_a], x_b[perm_b], metric=metric) + + G_sorted, cost = emd_1d_sorted(a, b, M) + G = G_sorted[inv_perm_a, :][:, inv_perm_b] + if log: + log = {} + log['cost'] = cost + return G, log + return G \ No newline at end of file diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 83ee6aa..a3d189d 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -93,3 +93,38 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod cdef int result_code = EMD_wrap(n1, n2, a.data, b.data, M.data, G.data, alpha.data, beta.data, &cost, max_iter) return G, cost, alpha, beta, result_code + + +@cython.boundscheck(False) +@cython.wraparound(False) +def emd_1d_sorted(np.ndarray[double, ndim=1, mode="c"] u_weights, + np.ndarray[double, ndim=1, mode="c"] v_weights, + np.ndarray[double, ndim=2, mode="c"] M): + r""" + Roro's stuff + """ + cdef double cost = 0. + cdef int n = u_weights.shape[0] + cdef int m = v_weights.shape[0] + + cdef int i = 0 + cdef double w_i = u_weights[0] + cdef int j = 0 + cdef double w_j = v_weights[0] + + cdef np.ndarray[double, ndim=2, mode="c"] G = np.zeros((n, m), + dtype=np.float64) + while i < n and j < m: + if w_i < w_j or j == m - 1: + cost += M[i, j] * w_i + G[i, j] = w_i + i += 1 + w_j -= w_i + w_i = u_weights[i] + else: + cost += M[i, j] * w_j + G[i, j] = w_j + j += 1 + w_i -= w_j + w_j = v_weights[j] + return G, cost \ No newline at end of file diff --git a/test/test_ot.py b/test/test_ot.py index 7652394..7008002 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -46,6 +46,32 @@ def test_emd_emd2(): np.testing.assert_allclose(w, 0) +def test_emd1d(): + # test emd1d gives similar results as emd + n = 20 + m = 30 + u = np.random.randn(n, 1) + v = np.random.randn(m, 1) + + M = ot.dist(u, v, metric='sqeuclidean') + + G, log = ot.emd([], [], M, log=True) + wass = log["cost"] + G_1d, log = ot.emd_1d([], [], u, v, metric='sqeuclidean', log=True) + wass1d = log["cost"] + + # check loss is similar + np.testing.assert_allclose(wass, wass1d) + + # check G is similar + np.testing.assert_allclose(G, G_1d) + + # check AssertionError is raised if called on non 1d arrays + u = np.random.randn(n, 2) + v = np.random.randn(m, 2) + np.testing.assert_raises(AssertionError, ot.emd_1d, [], [], u, v) + + def test_emd_empty(): # test emd and emd2 for simple identity n = 100 -- cgit v1.2.3 From 15b21611a3a93043d30c4eaaf9d622200453a884 Mon Sep 17 00:00:00 2001 From: Romain Tavenard Date: Thu, 20 Jun 2019 14:52:23 +0200 Subject: EMD 1d without doc made faster --- ot/lp/__init__.py | 5 ++--- ot/lp/emd_wrap.pyx | 14 +++++++++++--- 2 files changed, 13 insertions(+), 6 deletions(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 49ded5b..c4457dc 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -333,9 +333,8 @@ def emd_1d(a, b, x_a, x_b, metric='sqeuclidean', log=False): inv_perm_a = np.argsort(perm_a) inv_perm_b = np.argsort(perm_b) - M = dist(x_a[perm_a], x_b[perm_b], metric=metric) - - G_sorted, cost = emd_1d_sorted(a, b, M) + G_sorted, cost = emd_1d_sorted(a, b, x_a[perm_a], x_b[perm_b], + metric=metric) G = G_sorted[inv_perm_a, :][:, inv_perm_b] if log: log = {} diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index a3d189d..2966206 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -10,6 +10,8 @@ Cython linker with C solver import numpy as np cimport numpy as np +from ..utils import dist + cimport cython import warnings @@ -99,7 +101,9 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod @cython.wraparound(False) def emd_1d_sorted(np.ndarray[double, ndim=1, mode="c"] u_weights, np.ndarray[double, ndim=1, mode="c"] v_weights, - np.ndarray[double, ndim=2, mode="c"] M): + np.ndarray[double, ndim=2, mode="c"] u, + np.ndarray[double, ndim=2, mode="c"] v, + str metric='sqeuclidean'): r""" Roro's stuff """ @@ -112,17 +116,21 @@ def emd_1d_sorted(np.ndarray[double, ndim=1, mode="c"] u_weights, cdef int j = 0 cdef double w_j = v_weights[0] + cdef double m_ij = 0. + cdef np.ndarray[double, ndim=2, mode="c"] G = np.zeros((n, m), dtype=np.float64) while i < n and j < m: + m_ij = dist(u[i].reshape((1, 1)), v[j].reshape((1, 1)), + metric=metric)[0, 0] if w_i < w_j or j == m - 1: - cost += M[i, j] * w_i + cost += m_ij * w_i G[i, j] = w_i i += 1 w_j -= w_i w_i = u_weights[i] else: - cost += M[i, j] * w_j + cost += m_ij * w_j G[i, j] = w_j j += 1 w_i -= w_j -- cgit v1.2.3 From cada9a3019997e8efb95d96c86985110f1e937b9 Mon Sep 17 00:00:00 2001 From: Romain Tavenard Date: Fri, 21 Jun 2019 11:15:42 +0200 Subject: Sparse G matrix for EMD1d + standard metrics computed without cdist --- ot/lp/__init__.py | 54 ++++++++++++++++++++++++++++++++++++++++-------------- 1 file changed, 40 insertions(+), 14 deletions(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index c4457dc..decff29 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -10,6 +10,7 @@ Solvers for the original linear program OT problem import multiprocessing import numpy as np +from scipy.sparse import coo_matrix from .import cvx @@ -19,7 +20,8 @@ from ..utils import parmap from .cvx import barycenter from ..utils import dist -__all__=['emd', 'emd2', 'barycenter', 'free_support_barycenter', 'cvx', 'emd_1d_sorted'] +__all__=['emd', 'emd2', 'barycenter', 'free_support_barycenter', 'cvx', + 'emd_1d', 'emd2_1d'] def emd(a, b, M, numItermax=100000, log=False): @@ -311,16 +313,20 @@ def free_support_barycenter(measures_locations, measures_weights, X_init, b=None return X -def emd_1d(a, b, x_a, x_b, metric='sqeuclidean', log=False): +def emd_1d(a, b, x_a, x_b, metric='sqeuclidean', dense=True, log=False): """Solves the Earth Movers distance problem between 1d measures and returns the OT matrix """ - assert x_a.shape[1] == x_b.shape[1] == 1, "emd_1d should only be used " + \ - "with monodimensional data" - a = np.asarray(a, dtype=np.float64) b = np.asarray(b, dtype=np.float64) + x_a = np.asarray(x_a, dtype=np.float64) + x_b = np.asarray(x_b, dtype=np.float64) + + assert (x_a.ndim == 1 or x_a.ndim == 2 and x_a.shape[1] == 1), \ + "emd_1d should only be used with monodimensional data" + assert (x_b.ndim == 1 or x_b.ndim == 2 and x_b.shape[1] == 1), \ + "emd_1d should only be used with monodimensional data" # if empty array given then use uniform distributions if len(a) == 0: @@ -328,16 +334,36 @@ def emd_1d(a, b, x_a, x_b, metric='sqeuclidean', log=False): if len(b) == 0: b = np.ones((x_b.shape[0],), dtype=np.float64) / x_b.shape[0] - perm_a = np.argsort(x_a.reshape((-1, ))) - perm_b = np.argsort(x_b.reshape((-1, ))) - inv_perm_a = np.argsort(perm_a) - inv_perm_b = np.argsort(perm_b) - - G_sorted, cost = emd_1d_sorted(a, b, x_a[perm_a], x_b[perm_b], - metric=metric) - G = G_sorted[inv_perm_a, :][:, inv_perm_b] + x_a_1d = x_a.reshape((-1, )) + x_b_1d = x_b.reshape((-1, )) + perm_a = np.argsort(x_a_1d) + perm_b = np.argsort(x_b_1d) + + G_sorted, indices, cost = emd_1d_sorted(a, b, + x_a_1d[perm_a], x_b_1d[perm_b], + metric=metric) + G = coo_matrix((G_sorted, (perm_a[indices[:, 0]], perm_b[indices[:, 1]])), + shape=(a.shape[0], b.shape[0])) + if dense: + G = G.todense() if log: log = {} log['cost'] = cost return G, log - return G \ No newline at end of file + return G + + +def emd2_1d(a, b, x_a, x_b, metric='sqeuclidean', dense=True, log=False): + """Solves the Earth Movers distance problem between 1d measures and returns + the loss + + """ + # If we do not return G (log==False), then we should not to cast it to dense + # (useless overhead) + G, log_emd = emd_1d(a=a, b=b, x_a=x_a, x_b=x_b, metric=metric, + dense=dense and log, log=True) + cost = log_emd['cost'] + if log: + log_emd = {'G': G} + return cost, log_emd + return cost \ No newline at end of file -- cgit v1.2.3 From 18502d6861a4977cbade957f2e48eeb8dbb55414 Mon Sep 17 00:00:00 2001 From: Romain Tavenard Date: Fri, 21 Jun 2019 11:21:08 +0200 Subject: Sparse G matrix for EMD1d + standard metrics computed without cdist --- ot/__init__.py | 4 ++-- ot/lp/emd_wrap.pyx | 29 +++++++++++++++++++++-------- test/test_ot.py | 23 ++++++++++++++++++----- 3 files changed, 41 insertions(+), 15 deletions(-) diff --git a/ot/__init__.py b/ot/__init__.py index 5d5b700..f0e526c 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -22,7 +22,7 @@ from . import smooth from . import stochastic # OT functions -from .lp import emd, emd2, emd_1d +from .lp import emd, emd2, emd_1d, emd2_1d from .bregman import sinkhorn, sinkhorn2, barycenter from .da import sinkhorn_lpl1_mm @@ -32,5 +32,5 @@ from .utils import dist, unif, tic, toc, toq __version__ = "0.5.1" __all__ = ["emd", "emd2", 'emd_1d', "sinkhorn", "sinkhorn2", "utils", 'datasets', - 'bregman', 'lp', 'tic', 'toc', 'toq', 'gromov', + 'bregman', 'lp', 'tic', 'toc', 'toq', 'gromov', 'emd_1d', 'emd2_1d', 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim'] diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 2966206..ab88d7f 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -101,8 +101,8 @@ def emd_c(np.ndarray[double, ndim=1, mode="c"] a, np.ndarray[double, ndim=1, mod @cython.wraparound(False) def emd_1d_sorted(np.ndarray[double, ndim=1, mode="c"] u_weights, np.ndarray[double, ndim=1, mode="c"] v_weights, - np.ndarray[double, ndim=2, mode="c"] u, - np.ndarray[double, ndim=2, mode="c"] v, + np.ndarray[double, ndim=1, mode="c"] u, + np.ndarray[double, ndim=1, mode="c"] v, str metric='sqeuclidean'): r""" Roro's stuff @@ -118,21 +118,34 @@ def emd_1d_sorted(np.ndarray[double, ndim=1, mode="c"] u_weights, cdef double m_ij = 0. - cdef np.ndarray[double, ndim=2, mode="c"] G = np.zeros((n, m), + cdef np.ndarray[double, ndim=1, mode="c"] G = np.zeros((n + m - 1, ), dtype=np.float64) + cdef np.ndarray[long, ndim=2, mode="c"] indices = np.zeros((n + m - 1, 2), + dtype=np.int) + cdef int cur_idx = 0 while i < n and j < m: - m_ij = dist(u[i].reshape((1, 1)), v[j].reshape((1, 1)), - metric=metric)[0, 0] + if metric == 'sqeuclidean': + m_ij = (u[i] - v[j]) ** 2 + elif metric == 'cityblock' or metric == 'euclidean': + m_ij = np.abs(u[i] - v[j]) + else: + m_ij = dist(u[i].reshape((1, 1)), v[j].reshape((1, 1)), + metric=metric)[0, 0] if w_i < w_j or j == m - 1: cost += m_ij * w_i - G[i, j] = w_i + G[cur_idx] = w_i + indices[cur_idx, 0] = i + indices[cur_idx, 1] = j i += 1 w_j -= w_i w_i = u_weights[i] else: cost += m_ij * w_j - G[i, j] = w_j + G[cur_idx] = w_j + indices[cur_idx, 0] = i + indices[cur_idx, 1] = j j += 1 w_i -= w_j w_j = v_weights[j] - return G, cost \ No newline at end of file + cur_idx += 1 + return G[:cur_idx], indices[:cur_idx], cost diff --git a/test/test_ot.py b/test/test_ot.py index 7008002..2a2e0a5 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -7,6 +7,7 @@ import warnings import numpy as np +from scipy.stats import wasserstein_distance import ot from ot.datasets import make_1D_gauss as gauss @@ -37,7 +38,7 @@ def test_emd_emd2(): # check G is identity np.testing.assert_allclose(G, np.eye(n) / n) - # check constratints + # check constraints np.testing.assert_allclose(u, G.sum(1)) # cf convergence sinkhorn np.testing.assert_allclose(u, G.sum(0)) # cf convergence sinkhorn @@ -46,12 +47,13 @@ def test_emd_emd2(): np.testing.assert_allclose(w, 0) -def test_emd1d(): +def test_emd_1d_emd2_1d(): # test emd1d gives similar results as emd n = 20 m = 30 - u = np.random.randn(n, 1) - v = np.random.randn(m, 1) + rng = np.random.RandomState(0) + u = rng.randn(n, 1) + v = rng.randn(m, 1) M = ot.dist(u, v, metric='sqeuclidean') @@ -59,9 +61,20 @@ def test_emd1d(): wass = log["cost"] G_1d, log = ot.emd_1d([], [], u, v, metric='sqeuclidean', log=True) wass1d = log["cost"] + wass1d_emd2 = ot.emd2_1d([], [], u, v, metric='sqeuclidean', log=False) + wass1d_euc = ot.emd2_1d([], [], u, v, metric='euclidean', log=False) # check loss is similar np.testing.assert_allclose(wass, wass1d) + np.testing.assert_allclose(wass, wass1d_emd2) + + # check loss is similar to scipy's implementation for Euclidean metric + wass_sp = wasserstein_distance(u.reshape((-1, )), v.reshape((-1, ))) + np.testing.assert_allclose(wass_sp, wass1d_euc) + + # check constraints + np.testing.assert_allclose(np.ones((n, )) / n, G.sum(1)) + np.testing.assert_allclose(np.ones((m, )) / m, G.sum(0)) # check G is similar np.testing.assert_allclose(G, G_1d) @@ -86,7 +99,7 @@ def test_emd_empty(): # check G is identity np.testing.assert_allclose(G, np.eye(n) / n) - # check constratints + # check constraints np.testing.assert_allclose(u, G.sum(1)) # cf convergence sinkhorn np.testing.assert_allclose(u, G.sum(0)) # cf convergence sinkhorn -- cgit v1.2.3 From 67d3bd4bf0f593aa611d6bf09bbd3a9c883299ba Mon Sep 17 00:00:00 2001 From: Romain Tavenard Date: Fri, 21 Jun 2019 13:37:14 +0200 Subject: Removed np.abs in Cython code --- ot/lp/emd_wrap.pyx | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index ab88d7f..5e055fb 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -127,7 +127,7 @@ def emd_1d_sorted(np.ndarray[double, ndim=1, mode="c"] u_weights, if metric == 'sqeuclidean': m_ij = (u[i] - v[j]) ** 2 elif metric == 'cityblock' or metric == 'euclidean': - m_ij = np.abs(u[i] - v[j]) + m_ij = abs(u[i] - v[j]) else: m_ij = dist(u[i].reshape((1, 1)), v[j].reshape((1, 1)), metric=metric)[0, 0] -- cgit v1.2.3 From 9e1d74f44473deb1f4766329bb0d1c8af4dfdd73 Mon Sep 17 00:00:00 2001 From: Romain Tavenard Date: Fri, 21 Jun 2019 18:27:42 +0200 Subject: Started documenting --- ot/lp/__init__.py | 77 +++++++++++++++++++++++++++++++++++++++++++++++++++++-- test/test_ot.py | 8 +++--- 2 files changed, 79 insertions(+), 6 deletions(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index decff29..e9635a1 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -313,10 +313,83 @@ def free_support_barycenter(measures_locations, measures_weights, X_init, b=None return X -def emd_1d(a, b, x_a, x_b, metric='sqeuclidean', dense=True, log=False): +def emd_1d(x_a, x_b, a, b, metric='sqeuclidean', dense=True, log=False): """Solves the Earth Movers distance problem between 1d measures and returns the OT matrix + + .. math:: + \gamma = arg\min_\gamma \sum_i \sum_j \gamma_{ij} d(x_a[i], x_b[j]) + + s.t. \gamma 1 = a + \gamma^T 1= b + \gamma\geq 0 + where : + + - d is the metric + - x_a and x_b are the samples + - a and b are the sample weights + + Uses the algorithm proposed in [1]_ + + Parameters + ---------- + x_a : (ns,) or (ns, 1) ndarray, float64 + Source histogram (uniform weight if empty list) + x_b : (nt,) or (ns, 1) ndarray, float64 + Target histogram (uniform weight if empty list) + a : (ns,) ndarray, float64 + Source histogram (uniform weight if empty list) + b : (nt,) ndarray, float64 + Target histogram (uniform weight if empty list) + dense: boolean, optional (default=True) + If True, returns math:`\gamma` as a dense ndarray of shape (ns, nt). + Otherwise returns a sparse representation using scipy's `coo_matrix` + format. + Due to implementation details, this function runs faster when + dense is set to False. + metric: str, optional (default='sqeuclidean') + Metric to be used. Has to be a string. + Due to implementation details, this function runs faster when + `'sqeuclidean'` or `'euclidean'` metrics are used. + log: boolean, optional (default=False) + If True, returns a dictionary containing the cost. + Otherwise returns only the optimal transportation matrix. + + Returns + ------- + gamma: (ns, nt) ndarray + Optimal transportation matrix for the given parameters + log: dict + If input log is True, a dictionary containing the cost + + + Examples + -------- + + Simple example with obvious solution. The function emd_1d accepts lists and + perform automatic conversion to numpy arrays + + >>> import ot + >>> a=[.5, .5] + >>> b=[.5, .5] + >>> x_a = [0., 2.] + >>> x_b = [0., 3.] + >>> ot.emd_1d(a, b, x_a, x_b) + array([[ 0.5, 0. ], + [ 0. , 0.5]]) + + References + ---------- + + .. [1] TODO + + See Also + -------- + ot.lp.emd : EMD for multidimensional distributions + ot.lp.emd2_1d : EMD for 1d distributions (returns cost instead of the + transportation matrix) + """ a = np.asarray(a, dtype=np.float64) b = np.asarray(b, dtype=np.float64) @@ -353,7 +426,7 @@ def emd_1d(a, b, x_a, x_b, metric='sqeuclidean', dense=True, log=False): return G -def emd2_1d(a, b, x_a, x_b, metric='sqeuclidean', dense=True, log=False): +def emd2_1d(x_a, x_b, a, b, metric='sqeuclidean', dense=True, log=False): """Solves the Earth Movers distance problem between 1d measures and returns the loss diff --git a/test/test_ot.py b/test/test_ot.py index 2a2e0a5..6d6ea26 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -59,10 +59,10 @@ def test_emd_1d_emd2_1d(): G, log = ot.emd([], [], M, log=True) wass = log["cost"] - G_1d, log = ot.emd_1d([], [], u, v, metric='sqeuclidean', log=True) + G_1d, log = ot.emd_1d(u, v, [], [], metric='sqeuclidean', log=True) wass1d = log["cost"] - wass1d_emd2 = ot.emd2_1d([], [], u, v, metric='sqeuclidean', log=False) - wass1d_euc = ot.emd2_1d([], [], u, v, metric='euclidean', log=False) + wass1d_emd2 = ot.emd2_1d(u, v, [], [], metric='sqeuclidean', log=False) + wass1d_euc = ot.emd2_1d(u, v, [], [], metric='euclidean', log=False) # check loss is similar np.testing.assert_allclose(wass, wass1d) @@ -82,7 +82,7 @@ def test_emd_1d_emd2_1d(): # check AssertionError is raised if called on non 1d arrays u = np.random.randn(n, 2) v = np.random.randn(m, 2) - np.testing.assert_raises(AssertionError, ot.emd_1d, [], [], u, v) + np.testing.assert_raises(AssertionError, ot.emd_1d, u, v, [], []) def test_emd_empty(): -- cgit v1.2.3 From 71f9b5adfb8d8f4481948391f22e49f45494d071 Mon Sep 17 00:00:00 2001 From: Romain Tavenard Date: Mon, 24 Jun 2019 09:17:54 +0200 Subject: Added docstrings --- ot/lp/__init__.py | 114 +++++++++++++++++++++++++++++++++++++++++++----------- 1 file changed, 92 insertions(+), 22 deletions(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index e9635a1..a350d60 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -321,8 +321,8 @@ def emd_1d(x_a, x_b, a, b, metric='sqeuclidean', dense=True, log=False): .. math:: \gamma = arg\min_\gamma \sum_i \sum_j \gamma_{ij} d(x_a[i], x_b[j]) - s.t. \gamma 1 = a - \gamma^T 1= b + s.t. \gamma 1 = a, + \gamma^T 1= b, \gamma\geq 0 where : @@ -330,28 +330,27 @@ def emd_1d(x_a, x_b, a, b, metric='sqeuclidean', dense=True, log=False): - x_a and x_b are the samples - a and b are the sample weights - Uses the algorithm proposed in [1]_ + Uses the algorithm detailed in [1]_ Parameters ---------- x_a : (ns,) or (ns, 1) ndarray, float64 - Source histogram (uniform weight if empty list) + Source dirac locations (on the real line) x_b : (nt,) or (ns, 1) ndarray, float64 - Target histogram (uniform weight if empty list) + Target dirac locations (on the real line) a : (ns,) ndarray, float64 Source histogram (uniform weight if empty list) b : (nt,) ndarray, float64 Target histogram (uniform weight if empty list) + metric: str, optional (default='sqeuclidean') + Metric to be used. Only strings listed in ... are accepted. + Due to implementation details, this function runs faster when + `'sqeuclidean'`, `'cityblock'`, or `'euclidean'` metrics are used. dense: boolean, optional (default=True) If True, returns math:`\gamma` as a dense ndarray of shape (ns, nt). Otherwise returns a sparse representation using scipy's `coo_matrix` - format. - Due to implementation details, this function runs faster when + format. Due to implementation details, this function runs faster when dense is set to False. - metric: str, optional (default='sqeuclidean') - Metric to be used. Has to be a string. - Due to implementation details, this function runs faster when - `'sqeuclidean'` or `'euclidean'` metrics are used. log: boolean, optional (default=False) If True, returns a dictionary containing the cost. Otherwise returns only the optimal transportation matrix. @@ -368,28 +367,28 @@ def emd_1d(x_a, x_b, a, b, metric='sqeuclidean', dense=True, log=False): -------- Simple example with obvious solution. The function emd_1d accepts lists and - perform automatic conversion to numpy arrays + performs automatic conversion to numpy arrays >>> import ot >>> a=[.5, .5] >>> b=[.5, .5] - >>> x_a = [0., 2.] + >>> x_a = [2., 0.] >>> x_b = [0., 3.] - >>> ot.emd_1d(a, b, x_a, x_b) - array([[ 0.5, 0. ], - [ 0. , 0.5]]) + >>> ot.emd_1d(x_a, x_b, a, b) + array([[0. , 0.5], + [0.5, 0. ]]) References ---------- - .. [1] TODO + .. [1] Peyré, G., & Cuturi, M. (2017). "Computational Optimal + Transport", 2018. See Also -------- ot.lp.emd : EMD for multidimensional distributions ot.lp.emd2_1d : EMD for 1d distributions (returns cost instead of the transportation matrix) - """ a = np.asarray(a, dtype=np.float64) b = np.asarray(b, dtype=np.float64) @@ -418,10 +417,9 @@ def emd_1d(x_a, x_b, a, b, metric='sqeuclidean', dense=True, log=False): G = coo_matrix((G_sorted, (perm_a[indices[:, 0]], perm_b[indices[:, 1]])), shape=(a.shape[0], b.shape[0])) if dense: - G = G.todense() + G = G.toarray() if log: - log = {} - log['cost'] = cost + log = {'cost': cost} return G, log return G @@ -430,10 +428,82 @@ def emd2_1d(x_a, x_b, a, b, metric='sqeuclidean', dense=True, log=False): """Solves the Earth Movers distance problem between 1d measures and returns the loss + + .. math:: + \gamma = arg\min_\gamma \sum_i \sum_j \gamma_{ij} d(x_a[i], x_b[j]) + + s.t. \gamma 1 = a, + \gamma^T 1= b, + \gamma\geq 0 + where : + + - d is the metric + - x_a and x_b are the samples + - a and b are the sample weights + + Uses the algorithm detailed in [1]_ + + Parameters + ---------- + x_a : (ns,) or (ns, 1) ndarray, float64 + Source dirac locations (on the real line) + x_b : (nt,) or (ns, 1) ndarray, float64 + Target dirac locations (on the real line) + a : (ns,) ndarray, float64 + Source histogram (uniform weight if empty list) + b : (nt,) ndarray, float64 + Target histogram (uniform weight if empty list) + metric: str, optional (default='sqeuclidean') + Metric to be used. Only strings listed in ... are accepted. + Due to implementation details, this function runs faster when + `'sqeuclidean'`, `'cityblock'`, or `'euclidean'` metrics are used. + dense: boolean, optional (default=True) + If True, returns math:`\gamma` as a dense ndarray of shape (ns, nt). + Otherwise returns a sparse representation using scipy's `coo_matrix` + format. Only used if log is set to True. Due to implementation details, + this function runs faster when dense is set to False. + log: boolean, optional (default=False) + If True, returns a dictionary containing the transportation matrix. + Otherwise returns only the loss. + + Returns + ------- + loss: float + Cost associated to the optimal transportation + log: dict + If input log is True, a dictionary containing the Optimal transportation + matrix for the given parameters + + + Examples + -------- + + Simple example with obvious solution. The function emd2_1d accepts lists and + performs automatic conversion to numpy arrays + + >>> import ot + >>> a=[.5, .5] + >>> b=[.5, .5] + >>> x_a = [2., 0.] + >>> x_b = [0., 3.] + >>> ot.emd2_1d(x_a, x_b, a, b) + 0.5 + + References + ---------- + + .. [1] Peyré, G., & Cuturi, M. (2017). "Computational Optimal + Transport", 2018. + + See Also + -------- + ot.lp.emd2 : EMD for multidimensional distributions + ot.lp.emd_1d : EMD for 1d distributions (returns the transportation matrix + instead of the cost) """ # If we do not return G (log==False), then we should not to cast it to dense # (useless overhead) - G, log_emd = emd_1d(a=a, b=b, x_a=x_a, x_b=x_b, metric=metric, + G, log_emd = emd_1d(x_a=x_a, x_b=x_b, a=a, b=b, metric=metric, dense=dense and log, log=True) cost = log_emd['cost'] if log: -- cgit v1.2.3 From 77452dd92f607c3f18a6420cb8cd09fa5cd905a6 Mon Sep 17 00:00:00 2001 From: Romain Tavenard Date: Mon, 24 Jun 2019 13:09:36 +0200 Subject: Added more docstrings (Cython) + fixed link to ot.dist doc --- ot/lp/__init__.py | 4 ++-- ot/lp/emd_wrap.pyx | 28 +++++++++++++++++++++++++++- 2 files changed, 29 insertions(+), 3 deletions(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index a350d60..645ed8b 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -343,7 +343,7 @@ def emd_1d(x_a, x_b, a, b, metric='sqeuclidean', dense=True, log=False): b : (nt,) ndarray, float64 Target histogram (uniform weight if empty list) metric: str, optional (default='sqeuclidean') - Metric to be used. Only strings listed in ... are accepted. + Metric to be used. Only strings listed in :func:`ot.dist` are accepted. Due to implementation details, this function runs faster when `'sqeuclidean'`, `'cityblock'`, or `'euclidean'` metrics are used. dense: boolean, optional (default=True) @@ -454,7 +454,7 @@ def emd2_1d(x_a, x_b, a, b, metric='sqeuclidean', dense=True, log=False): b : (nt,) ndarray, float64 Target histogram (uniform weight if empty list) metric: str, optional (default='sqeuclidean') - Metric to be used. Only strings listed in ... are accepted. + Metric to be used. Only strings listed in :func:`ot.dist` are accepted. Due to implementation details, this function runs faster when `'sqeuclidean'`, `'cityblock'`, or `'euclidean'` metrics are used. dense: boolean, optional (default=True) diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 5e055fb..2825ba2 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -105,7 +105,33 @@ def emd_1d_sorted(np.ndarray[double, ndim=1, mode="c"] u_weights, np.ndarray[double, ndim=1, mode="c"] v, str metric='sqeuclidean'): r""" - Roro's stuff + Solves the Earth Movers distance problem between sorted 1d measures and + returns the OT matrix and the associated cost + + Parameters + ---------- + u_weights : (ns,) ndarray, float64 + Source histogram + v_weights : (nt,) ndarray, float64 + Target histogram + u : (ns,) ndarray, float64 + Source dirac locations (on the real line) + v : (nt,) ndarray, float64 + Target dirac locations (on the real line) + metric: str, optional (default='sqeuclidean') + Metric to be used. Only strings listed in :func:`ot.dist` are accepted. + Due to implementation details, this function runs faster when + `'sqeuclidean'`, `'cityblock'`, or `'euclidean'` metrics are used. + + Returns + ------- + gamma: (n, ) ndarray, float64 + Values in the Optimal transportation matrix + indices: (n, 2) ndarray, int64 + Indices of the values stored in gamma for the Optimal transportation + matrix + cost + cost associated to the optimal transportation """ cdef double cost = 0. cdef int n = u_weights.shape[0] -- cgit v1.2.3 From 0a039eb07a3ca9ae3c5635cca1719428f62bf67d Mon Sep 17 00:00:00 2001 From: Romain Tavenard Date: Mon, 24 Jun 2019 13:15:38 +0200 Subject: Made weight vectors optional to match scipy's wass1d API --- ot/lp/__init__.py | 29 +++++++++++++++++------------ 1 file changed, 17 insertions(+), 12 deletions(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 645ed8b..bf218d3 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -313,7 +313,7 @@ def free_support_barycenter(measures_locations, measures_weights, X_init, b=None return X -def emd_1d(x_a, x_b, a, b, metric='sqeuclidean', dense=True, log=False): +def emd_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', dense=True, log=False): """Solves the Earth Movers distance problem between 1d measures and returns the OT matrix @@ -338,10 +338,10 @@ def emd_1d(x_a, x_b, a, b, metric='sqeuclidean', dense=True, log=False): Source dirac locations (on the real line) x_b : (nt,) or (ns, 1) ndarray, float64 Target dirac locations (on the real line) - a : (ns,) ndarray, float64 - Source histogram (uniform weight if empty list) - b : (nt,) ndarray, float64 - Target histogram (uniform weight if empty list) + a : (ns,) ndarray, float64, optional + Source histogram (default is uniform weight) + b : (nt,) ndarray, float64, optional + Target histogram (default is uniform weight) metric: str, optional (default='sqeuclidean') Metric to be used. Only strings listed in :func:`ot.dist` are accepted. Due to implementation details, this function runs faster when @@ -375,6 +375,9 @@ def emd_1d(x_a, x_b, a, b, metric='sqeuclidean', dense=True, log=False): >>> x_a = [2., 0.] >>> x_b = [0., 3.] >>> ot.emd_1d(x_a, x_b, a, b) + array([[0. , 0.5], + [0.5, 0. ]]) + >>> ot.emd_1d(x_a, x_b) array([[0. , 0.5], [0.5, 0. ]]) @@ -401,9 +404,9 @@ def emd_1d(x_a, x_b, a, b, metric='sqeuclidean', dense=True, log=False): "emd_1d should only be used with monodimensional data" # if empty array given then use uniform distributions - if len(a) == 0: + if a.ndim == 0 or len(a) == 0: a = np.ones((x_a.shape[0],), dtype=np.float64) / x_a.shape[0] - if len(b) == 0: + if b.ndim == 0 or len(b) == 0: b = np.ones((x_b.shape[0],), dtype=np.float64) / x_b.shape[0] x_a_1d = x_a.reshape((-1, )) @@ -424,7 +427,7 @@ def emd_1d(x_a, x_b, a, b, metric='sqeuclidean', dense=True, log=False): return G -def emd2_1d(x_a, x_b, a, b, metric='sqeuclidean', dense=True, log=False): +def emd2_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', dense=True, log=False): """Solves the Earth Movers distance problem between 1d measures and returns the loss @@ -449,10 +452,10 @@ def emd2_1d(x_a, x_b, a, b, metric='sqeuclidean', dense=True, log=False): Source dirac locations (on the real line) x_b : (nt,) or (ns, 1) ndarray, float64 Target dirac locations (on the real line) - a : (ns,) ndarray, float64 - Source histogram (uniform weight if empty list) - b : (nt,) ndarray, float64 - Target histogram (uniform weight if empty list) + a : (ns,) ndarray, float64, optional + Source histogram (default is uniform weight) + b : (nt,) ndarray, float64, optional + Target histogram (default is uniform weight) metric: str, optional (default='sqeuclidean') Metric to be used. Only strings listed in :func:`ot.dist` are accepted. Due to implementation details, this function runs faster when @@ -488,6 +491,8 @@ def emd2_1d(x_a, x_b, a, b, metric='sqeuclidean', dense=True, log=False): >>> x_b = [0., 3.] >>> ot.emd2_1d(x_a, x_b, a, b) 0.5 + >>> ot.emd2_1d(x_a, x_b) + 0.5 References ---------- -- cgit v1.2.3 From 632bc9a8ec8a227d90db9635a34bb364d128cccb Mon Sep 17 00:00:00 2001 From: Hicham Janati Date: Mon, 24 Jun 2019 16:19:31 +0200 Subject: update docstrings + init --- ot/__init__.py | 2 +- ot/unbalanced.py | 28 ++++++++++++++-------------- 2 files changed, 15 insertions(+), 15 deletions(-) diff --git a/ot/__init__.py b/ot/__init__.py index acb05e6..3892d1d 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -36,4 +36,4 @@ __version__ = "0.5.1" __all__ = ["emd", "emd2", "sinkhorn", "sinkhorn2", "utils", 'datasets', 'bregman', 'lp', 'tic', 'toc', 'toq', 'gromov', 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim', - 'sinkhorn_unbalanced'] + 'sinkhorn_unbalanced', "barycenter_unbalanced"] diff --git a/ot/unbalanced.py b/ot/unbalanced.py index 918dda4..484ce95 100644 --- a/ot/unbalanced.py +++ b/ot/unbalanced.py @@ -19,7 +19,7 @@ def sinkhorn_unbalanced(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, The function solves the following optimization problem: .. math:: - W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + alpha KL(\gamma 1, a) + alpha KL(\gamma^T 1, b) + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \alpha KL(\gamma 1, a) + \alpha KL(\gamma^T 1, b) s.t. \gamma\geq 0 @@ -43,9 +43,9 @@ def sinkhorn_unbalanced(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, M : np.ndarray (ns, nt) loss matrix reg : float - Regularization term > 0 + Entropy regularization term > 0 alpha : float - Regulatization term > 0 + Marginal relaxation term > 0 method : str method used for the solver either 'sinkhorn', 'sinkhorn_stabilized' or 'sinkhorn_epsilon_scaling', see those function for specific parameters @@ -128,7 +128,7 @@ def sinkhorn_unbalanced2(a, b, M, reg, alpha, method='sinkhorn', The function solves the following optimization problem: .. math:: - W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + alpha KL(\gamma 1, a) + alpha KL(\gamma^T 1, b) + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \alpha KL(\gamma 1, a) + \alpha KL(\gamma^T 1, b) s.t. \gamma\geq 0 @@ -152,9 +152,9 @@ def sinkhorn_unbalanced2(a, b, M, reg, alpha, method='sinkhorn', M : np.ndarray (ns,nt) loss matrix reg : float - Regularization term > 0 - alpha: float - Regularization term > 0 + Entropy regularization term > 0 + alpha : float + Marginal relaxation term > 0 method : str method used for the solver either 'sinkhorn', 'sinkhorn_stabilized' or 'sinkhorn_epsilon_scaling', see those function for specific parameters @@ -239,7 +239,7 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, The function solves the following optimization problem: .. math:: - W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + alpha KL(\gamma 1, a) + alpha KL(\gamma^T 1, b) + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \alpha KL(\gamma 1, a) + \alpha KL(\gamma^T 1, b) s.t. \gamma\geq 0 @@ -263,9 +263,9 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, M : np.ndarray (ns,nt) loss matrix reg : float - Regularization term > 0 - alpha: float - Regularization term > 0 + Entropy regularization term > 0 + alpha : float + Marginal relaxation term > 0 numItermax : int, optional Max number of iterations stopThr : float, optional @@ -410,7 +410,7 @@ def barycenter_unbalanced(A, M, reg, alpha, weights=None, numItermax=1000, where : - - :math:`W_{reg}(\cdot,\cdot)` is the unbalanced entropic regularized Wasserstein distance (see ot.unbalanced.sinkhorn_unbalanced) + - :math:`Wu_{reg}(\cdot,\cdot)` is the unbalanced entropic regularized Wasserstein distance (see ot.unbalanced.sinkhorn_unbalanced) - :math:`\mathbf{a}_i` are training distributions in the columns of matrix :math:`\mathbf{A}` - reg and :math:`\mathbf{M}` are respectively the regularization term and the cost matrix for OT - alpha is the marginal relaxation hyperparameter @@ -423,9 +423,9 @@ def barycenter_unbalanced(A, M, reg, alpha, weights=None, numItermax=1000, M : np.ndarray (d,d) loss matrix for OT reg : float - Regularization term > 0 + Entropy regularization term > 0 alpha : float - Regularization term > 0 + Marginal relaxation term > 0 weights : np.ndarray (n,) Weights of each histogram a_i on the simplex (barycentric coodinates) numItermax : int, optional -- cgit v1.2.3 From c9df24649d359b21280328f6bd580eb049cae3d3 Mon Sep 17 00:00:00 2001 From: Hicham Janati Date: Mon, 24 Jun 2019 16:24:46 +0200 Subject: add unbalanced to doc modules --- docs/source/all.rst | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/docs/source/all.rst b/docs/source/all.rst index 32930fd..c968aa1 100644 --- a/docs/source/all.rst +++ b/docs/source/all.rst @@ -43,7 +43,7 @@ ot.da .. automodule:: ot.da :members: - + ot.gpu -------- @@ -80,3 +80,9 @@ ot.stochastic .. automodule:: ot.stochastic :members: + +ot.unbalanced +------------- + +.. automodule:: ot.unbalanced + :members: -- cgit v1.2.3 From 4e2f6b45662fe206414652ccc8f715c420f3b9cd Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 24 Jun 2019 17:13:33 +0200 Subject: first shot part OT Wass --- docs/source/howto.rst | 25 ---------- docs/source/index.rst | 2 +- docs/source/quickstart.rst | 119 +++++++++++++++++++++++++++++++++++++++++++++ docs/source/readme.rst | 7 ++- 4 files changed, 126 insertions(+), 27 deletions(-) delete mode 100644 docs/source/howto.rst create mode 100644 docs/source/quickstart.rst diff --git a/docs/source/howto.rst b/docs/source/howto.rst deleted file mode 100644 index 48b1532..0000000 --- a/docs/source/howto.rst +++ /dev/null @@ -1,25 +0,0 @@ - -How to ? -======== - -In the following we provide some pointers about which functions and classes -to use for different problems related to optimal transport (OTs). - -1. **How to solve a discrete optimal transport problem ?** - - The solver for discrete is the function :py:mod:`ot.emd` that returns - the OT transport matrix. If you want to solve a regularized OT you can - use :py:mod:`ot.sinkhorn`. - - More detailed examples can be seen on this :ref:`auto_examples/plot_OT_2D_samples` - - Here is a simple use case: - - .. code:: python - - # a,b are 1D histograms (sum to 1 and positive) - # M is the ground cost matrix - T=ot.emd(a,b,M) # exact linear program - T_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT - - diff --git a/docs/source/index.rst b/docs/source/index.rst index d92f50f..03943e8 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -13,7 +13,7 @@ Contents :maxdepth: 3 self - howto + quickstart all auto_examples/index diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst new file mode 100644 index 0000000..3d3ce98 --- /dev/null +++ b/docs/source/quickstart.rst @@ -0,0 +1,119 @@ + +Quick start +=========== + + + +In the following we provide some pointers about which functions and classes +to use for different problems related to optimal transport (OT). + + +Optimal transport and Wasserstein distance +------------------------------------------ + +The optimal transport problem between discrete distributions is often expressed +as + .. math:: + \gamma^* = arg\min_\gamma \sum_{i,j}\gamma_{i,j}M_{i,j} + + s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0 + +where : + +- :math:`M\in\mathbb{R}_+^{m\times n}` is the metric cost matrix defining the cost to move mass from bin :math:`a_i` to bin :math:`b_j`. +- :math:`a` and :math:`b` are histograms (positive, sum to 1) that represent the weights of each samples in the source an target distributions. + +Solving the linear program above can be done using the function :any:`ot.emd` +that will return the optimal transport matrix :math:`\gamma^*`: + +.. code:: python + + # a,b are 1D histograms (sum to 1 and positive) + # M is the ground cost matrix + T=ot.emd(a,b,M) # exact linear program + +.. hint:: + Examples of use for :any:`ot.emd` are available in the following examples: + + - :any:`auto_examples/plot_OT_2D_samples` + - :any:`auto_examples/plot_OT_1D` + - :any:`auto_examples/plot_OT_L1_vs_L2` + + +The value of the OT solution is often more of interest that the OT matrix : + + .. math:: + W(a,b)=\min_\gamma \sum_{i,j}\gamma_{i,j}M_{i,j} + + s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0 + + +where :math:`W(a,b)` is the `Wasserstein distance +`_ between distributions a and b +It is a metrix that has nice statistical +properties. It can computed from an already estimated OT matrix with +:code:`np.sum(T*M)` or directly with the function :any:`ot.emd2`. + +.. code:: python + + # a,b are 1D histograms (sum to 1 and positive) + # M is the ground cost matrix + W=ot.emd2(a,b,M) # Wasserstein distance / EMD value + +.. note:: + In POT, most functions that solve OT or regularized OT problems have two + versions that return the OT matrix or the value of the optimal solution. Fir + instance :any:`ot.emd` return the OT matrix and :any:`ot.emd2` return the + Wassertsein distance. + + +Regularized Optimal Transport +----------------------------- + +Wasserstein Barycenters +----------------------- + +Monge mapping and Domain adaptation with Optimal transport +---------------------------------------- + + +Other applications +------------------ + + +GPU acceleration +---------------- + + + +How to? +------- + + + +1. **How to solve a discrete optimal transport problem ?** + + The solver for discrete is the function :py:mod:`ot.emd` that returns + the OT transport matrix. If you want to solve a regularized OT you can + use :py:mod:`ot.sinkhorn`. + + + + Here is a simple use case: + + .. code:: python + + # a,b are 1D histograms (sum to 1 and positive) + # M is the ground cost matrix + T=ot.emd(a,b,M) # exact linear program + T_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT + + More detailed examples can be seen on this + :doc:`auto_examples/plot_OT_2D_samples` + + +2. **Compute a Wasserstein distance** + + + + diff --git a/docs/source/readme.rst b/docs/source/readme.rst index d1063e8..b7828d3 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -206,7 +206,12 @@ nbviewer `__ +- `Nicolas Courty `__ + +The contributors to this library are - `Rémi Flamary `__ - `Nicolas Courty `__ -- cgit v1.2.3 From 7f0739f73fa6a8c7fa22269c727b48d3640627be Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 25 Jun 2019 07:41:47 +0200 Subject: first shot part OT Wass --- docs/source/quickstart.rst | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst index 3d3ce98..ac96f26 100644 --- a/docs/source/quickstart.rst +++ b/docs/source/quickstart.rst @@ -11,6 +11,10 @@ to use for different problems related to optimal transport (OT). Optimal transport and Wasserstein distance ------------------------------------------ + +Solving optimal transport +^^^^^^^^^^^^^^^^^^^^^^^^^ + The optimal transport problem between discrete distributions is often expressed as .. math:: @@ -39,6 +43,8 @@ that will return the optimal transport matrix :math:`\gamma^*`: - :any:`auto_examples/plot_OT_1D` - :any:`auto_examples/plot_OT_L1_vs_L2` +Computing Wasserstein distance +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The value of the OT solution is often more of interest that the OT matrix : @@ -60,6 +66,13 @@ properties. It can computed from an already estimated OT matrix with # M is the ground cost matrix W=ot.emd2(a,b,M) # Wasserstein distance / EMD value + +.. hint:: + Examples of use for :any:`ot.emd2` are available in the following examples: + + - :any:`auto_examples/plot_compute_emd` + + .. note:: In POT, most functions that solve OT or regularized OT problems have two versions that return the OT matrix or the value of the optimal solution. Fir -- cgit v1.2.3 From 1e0977fd346d91c837ef90dff8c75a65b182d021 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 25 Jun 2019 08:34:59 +0200 Subject: cleaunup gromov + stat guide --- docs/source/index.rst | 2 +- docs/source/quickstart.rst | 156 ++++++++++++++++++++++++++++++++++++++++----- 2 files changed, 142 insertions(+), 16 deletions(-) diff --git a/docs/source/index.rst b/docs/source/index.rst index 03943e8..9078d35 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -10,7 +10,7 @@ Contents -------- .. toctree:: - :maxdepth: 3 + :maxdepth: 2 self quickstart diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst index ac96f26..d8d4838 100644 --- a/docs/source/quickstart.rst +++ b/docs/source/quickstart.rst @@ -1,8 +1,6 @@ -Quick start -=========== - - +Quick start guide +================= In the following we provide some pointers about which functions and classes to use for different problems related to optimal transport (OT). @@ -11,6 +9,11 @@ to use for different problems related to optimal transport (OT). Optimal transport and Wasserstein distance ------------------------------------------ +.. note:: + In POT, most functions that solve OT or regularized OT problems have two + versions that return the OT matrix or the value of the optimal solution. For + instance :any:`ot.emd` return the OT matrix and :any:`ot.emd2` return the + Wassertsein distance. Solving optimal transport ^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -36,6 +39,10 @@ that will return the optimal transport matrix :math:`\gamma^*`: # M is the ground cost matrix T=ot.emd(a,b,M) # exact linear program +The method used for solving the OT problem is the network simplex, it is +implemented in C from [1]_. It has a complexity of :math:`O(n^3)` but the +solver is quite efficient and uses sparsity of the solution. + .. hint:: Examples of use for :any:`ot.emd` are available in the following examples: @@ -73,16 +80,19 @@ properties. It can computed from an already estimated OT matrix with - :any:`auto_examples/plot_compute_emd` -.. note:: - In POT, most functions that solve OT or regularized OT problems have two - versions that return the OT matrix or the value of the optimal solution. Fir - instance :any:`ot.emd` return the OT matrix and :any:`ot.emd2` return the - Wassertsein distance. - - Regularized Optimal Transport ----------------------------- +Entropic regularized OT +^^^^^^^^^^^^^^^^^^^^^^^ + + +Other regularization +^^^^^^^^^^^^^^^^^^^^ + +Stochastic gradient decsent +^^^^^^^^^^^^^^^^^^^^^^^^^^^ + Wasserstein Barycenters ----------------------- @@ -99,8 +109,8 @@ GPU acceleration -How to? -------- +FAQ +--- @@ -128,5 +138,121 @@ How to? 2. **Compute a Wasserstein distance** - - +References +---------- + +.. [1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, + December). `Displacement nterpolation using Lagrangian mass transport + `__. + In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM. + +.. [2] Cuturi, M. (2013). `Sinkhorn distances: Lightspeed computation of + optimal transport `__. In Advances + in Neural Information Processing Systems (pp. 2292-2300). + +.. [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. + (2015). `Iterative Bregman projections for regularized transportation + problems `__. SIAM Journal on + Scientific Computing, 37(2), A1111-A1138. + +.. [4] S. Nakhostin, N. Courty, R. Flamary, D. Tuia, T. Corpetti, + `Supervised planetary unmixing with optimal + transport `__, + Whorkshop on Hyperspectral Image and Signal Processing : Evolution in + Remote Sensing (WHISPERS), 2016. + +.. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, `Optimal Transport + for Domain Adaptation `__, in IEEE + Transactions on Pattern Analysis and Machine Intelligence , vol.PP, + no.99, pp.1-1 + +.. [6] Ferradans, S., Papadakis, N., Peyré, G., & Aujol, J. F. (2014). + `Regularized discrete optimal + transport `__. SIAM Journal on + Imaging Sciences, 7(3), 1853-1882. + +.. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). `Generalized + conditional gradient: analysis of convergence and + applications `__. arXiv preprint + arXiv:1510.06567. + +.. [8] M. Perrot, N. Courty, R. Flamary, A. Habrard (2016), `Mapping + estimation for discrete optimal + transport `__, + Neural Information Processing Systems (NIPS). + +.. [9] Schmitzer, B. (2016). `Stabilized Sparse Scaling Algorithms for + Entropy Regularized Transport + Problems `__. arXiv preprint + arXiv:1610.06519. + +.. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). + `Scaling algorithms for unbalanced transport + problems `__. arXiv preprint + arXiv:1607.05816. + +.. [11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). + `Wasserstein Discriminant + Analysis `__. arXiv preprint + arXiv:1608.08063. + +.. [12] Gabriel Peyré, Marco Cuturi, and Justin Solomon (2016), + `Gromov-Wasserstein averaging of kernel and distance + matrices `__ + International Conference on Machine Learning (ICML). + +.. [13] Mémoli, Facundo (2011). `Gromov–Wasserstein distances and the + metric approach to object + matching `__. + Foundations of computational mathematics 11.4 : 417-487. + +.. [14] Knott, M. and Smith, C. S. (1984).`On the optimal mapping of + distributions `__, + Journal of Optimization Theory and Applications Vol 43. + +.. [15] Peyré, G., & Cuturi, M. (2018). `Computational Optimal + Transport `__ . + +.. [16] Agueh, M., & Carlier, G. (2011). `Barycenters in the Wasserstein + space `__. SIAM + Journal on Mathematical Analysis, 43(2), 904-924. + +.. [17] Blondel, M., Seguy, V., & Rolet, A. (2018). `Smooth and Sparse + Optimal Transport `__. Proceedings of + the Twenty-First International Conference on Artificial Intelligence and + Statistics (AISTATS). + +.. [18] Genevay, A., Cuturi, M., Peyré, G. & Bach, F. (2016) `Stochastic + Optimization for Large-scale Optimal + Transport `__. Advances in Neural + Information Processing Systems (2016). + +.. [19] Seguy, V., Bhushan Damodaran, B., Flamary, R., Courty, N., Rolet, + A.& Blondel, M. `Large-scale Optimal Transport and Mapping + Estimation `__. International + Conference on Learning Representation (2018) + +.. [20] Cuturi, M. and Doucet, A. (2014) `Fast Computation of Wasserstein + Barycenters `__. + International Conference in Machine Learning + +.. [21] Solomon, J., De Goes, F., Peyré, G., Cuturi, M., Butscher, A., + Nguyen, A. & Guibas, L. (2015). `Convolutional wasserstein distances: + Efficient optimal transportation on geometric + domains `__. ACM + Transactions on Graphics (TOG), 34(4), 66. + +.. [22] J. Altschuler, J.Weed, P. Rigollet, (2017) `Near-linear time + approximation algorithms for optimal transport via Sinkhorn + iteration `__, + Advances in Neural Information Processing Systems (NIPS) 31 + +.. [23] Aude, G., Peyré, G., Cuturi, M., `Learning Generative Models with + Sinkhorn Divergences `__, Proceedings + of the Twenty-First International Conference on Artficial Intelligence + and Statistics, (AISTATS) 21, 2018 + +.. [24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. + (2019). `Optimal Transport for structured data with application on + graphs `__ Proceedings + of the 36th International Conference on Machine Learning (ICML). \ No newline at end of file -- cgit v1.2.3 From c112190ab0cf9b02a25fb86919a11f9544cd5c58 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 25 Jun 2019 08:35:20 +0200 Subject: cleanup documentation gromov --- ot/gromov.py | 164 ++++++++++++++++++++++++++++++++++------------------------- 1 file changed, 95 insertions(+), 69 deletions(-) diff --git a/ot/gromov.py b/ot/gromov.py index ca96b31..43729dc 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -72,8 +72,8 @@ def init_matrix(C1, C2, p, q, loss_fun='square_loss'): References ---------- .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, - "Gromov-Wasserstein averaging of kernel and distance matrices." - International Conference on Machine Learning (ICML). 2016. + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. """ @@ -137,8 +137,8 @@ def tensor_product(constC, hC1, hC2, T): References ---------- .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, - "Gromov-Wasserstein averaging of kernel and distance matrices." - International Conference on Machine Learning (ICML). 2016. + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. """ A = -np.dot(hC1, T).dot(hC2.T) @@ -172,8 +172,8 @@ def gwloss(constC, hC1, hC2, T): References ---------- .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, - "Gromov-Wasserstein averaging of kernel and distance matrices." - International Conference on Machine Learning (ICML). 2016. + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. """ @@ -207,8 +207,8 @@ def gwggrad(constC, hC1, hC2, T): References ---------- .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, - "Gromov-Wasserstein averaging of kernel and distance matrices." - International Conference on Machine Learning (ICML). 2016. + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. """ return 2 * tensor_product(constC, hC1, hC2, @@ -277,15 +277,15 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, armijo=False, **kwargs The function solves the following optimization problem: .. math:: - \GW_Dist = \min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l} + GW = \min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l} Where : - C1 : Metric cost matrix in the source space - C2 : Metric cost matrix in the target space - p : distribution in the source space - q : distribution in the target space - L : loss function to account for the misfit between the similarity matrices - H : entropy + - C1 : Metric cost matrix in the source space + - C2 : Metric cost matrix in the target space + - p : distribution in the source space + - q : distribution in the target space + - L : loss function to account for the misfit between the similarity matrices + - H : entropy Parameters ---------- @@ -312,7 +312,7 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, armijo=False, **kwargs If True the steps of the line-search is found via an armijo research. Else closed form is used. If there is convergence issues use False. **kwargs : dict - parameters can be directly pased to the ot.optim.cg solver + parameters can be directly passed to the ot.optim.cg solver Returns ------- @@ -355,25 +355,31 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, armijo=False, **kwargs def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, armijo=False, log=False, **kwargs): """ Computes the FGW transport between two graphs see [24] + .. math:: - \gamma = arg\min_\gamma (1-\alpha)*<\gamma,M>_F + alpha* \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l} + \gamma = arg\min_\gamma (1-\alpha)*<\gamma,M>_F + \alpha* \sum_{i,j,k,l} + L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l} + s.t. \gamma 1 = p \gamma^T 1= q \gamma\geq 0 + where : - M is the (ns,nt) metric cost matrix - :math:`f` is the regularization term ( and df is its gradient) - a and b are source and target weights (sum to 1) - L is a loss function to account for the misfit between the similarity matrices - The algorithm used for solving the problem is conditional gradient as discussed in [1]_ + + The algorithm used for solving the problem is conditional gradient as discussed in [24]_ + Parameters ---------- M : ndarray, shape (ns, nt) Metric cost matrix between features across domains C1 : ndarray, shape (ns, ns) - Metric cost matrix respresentative of the structure in the source space + Metric cost matrix representative of the structure in the source space C2 : ndarray, shape (nt, nt) - Metric cost matrix espresentative of the structure in the target space + Metric cost matrix representative of the structure in the target space p : ndarray, shape (ns,) distribution in the source space q : ndarray, shape (nt,) @@ -392,19 +398,23 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, If True the steps of the line-search is found via an armijo research. Else closed form is used. If there is convergence issues use False. **kwargs : dict - parameters can be directly pased to the ot.optim.cg solver + parameters can be directly passed to the ot.optim.cg solver + Returns ------- gamma : (ns x nt) ndarray Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters + + References ---------- .. [24] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain - and Courty Nicolas - "Optimal Transport for structured data with application on graphs" - International Conference on Machine Learning (ICML). 2019. + and Courty Nicolas "Optimal Transport for structured data with + application on graphs", International Conference on Machine Learning + (ICML). 2019. + """ constC, hC1, hC2 = init_matrix(C1, C2, p, q, loss_fun) @@ -428,17 +438,23 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, armijo=False, log=False, **kwargs): """ Computes the FGW distance between two graphs see [24] + .. math:: - \gamma = arg\min_\gamma (1-\alpha)*<\gamma,M>_F + alpha* \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l} + \min_\gamma (1-\alpha)*<\gamma,M>_F + \alpha* \sum_{i,j,k,l} + L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l} + + s.t. \gamma 1 = p \gamma^T 1= q \gamma\geq 0 + where : - M is the (ns,nt) metric cost matrix - :math:`f` is the regularization term ( and df is its gradient) - a and b are source and target weights (sum to 1) - L is a loss function to account for the misfit between the similarity matrices The algorithm used for solving the problem is conditional gradient as discussed in [1]_ + Parameters ---------- M : ndarray, shape (ns, nt) @@ -466,16 +482,18 @@ def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5 If there is convergence issues use False. **kwargs : dict parameters can be directly pased to the ot.optim.cg solver + Returns ------- gamma : (ns x nt) ndarray Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters + References ---------- .. [24] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain - and Courty Nicolas + and Courty Nicolas "Optimal Transport for structured data with application on graphs" International Conference on Machine Learning (ICML). 2019. """ @@ -506,22 +524,22 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False, armijo=False, **kwarg The function solves the following optimization problem: .. math:: - \GW_Dist = \min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l} + GW = \min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l} Where : - C1 : Metric cost matrix in the source space - C2 : Metric cost matrix in the target space - p : distribution in the source space - q : distribution in the target space - L : loss function to account for the misfit between the similarity matrices - H : entropy + - C1 : Metric cost matrix in the source space + - C2 : Metric cost matrix in the target space + - p : distribution in the source space + - q : distribution in the target space + - L : loss function to account for the misfit between the similarity matrices + - H : entropy Parameters ---------- C1 : ndarray, shape (ns, ns) Metric cost matrix in the source space C2 : ndarray, shape (nt, nt) - Metric costfr matrix in the target space + Metric cost matrix in the target space p : ndarray, shape (ns,) distribution in the source space q : ndarray, shape (nt,) @@ -587,21 +605,21 @@ def entropic_gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, The function solves the following optimization problem: .. math:: - \GW = arg\min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T)) + GW = arg\min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T)) - s.t. \GW 1 = p + s.t. T 1 = p - \GW^T 1= q + T^T 1= q - \GW\geq 0 + T\geq 0 Where : - C1 : Metric cost matrix in the source space - C2 : Metric cost matrix in the target space - p : distribution in the source space - q : distribution in the target space - L : loss function to account for the misfit between the similarity matrices - H : entropy + - C1 : Metric cost matrix in the source space + - C2 : Metric cost matrix in the target space + - p : distribution in the source space + - q : distribution in the target space + - L : loss function to account for the misfit between the similarity matrices + - H : entropy Parameters ---------- @@ -629,14 +647,13 @@ def entropic_gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, Returns ------- T : ndarray, shape (ns, nt) - coupling between the two spaces that minimizes : - \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T)) + Optimal coupling between the two spaces References ---------- .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, - "Gromov-Wasserstein averaging of kernel and distance matrices." - International Conference on Machine Learning (ICML). 2016. + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. """ @@ -695,15 +712,15 @@ def entropic_gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, The function solves the following optimization problem: .. math:: - \GW_Dist = \min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T)) + GW = \min_T \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l}-\epsilon(H(T)) Where : - C1 : Metric cost matrix in the source space - C2 : Metric cost matrix in the target space - p : distribution in the source space - q : distribution in the target space - L : loss function to account for the misfit between the similarity matrices - H : entropy + - C1 : Metric cost matrix in the source space + - C2 : Metric cost matrix in the target space + - p : distribution in the source space + - q : distribution in the target space + - L : loss function to account for the misfit between the similarity matrices + - H : entropy Parameters ---------- @@ -736,8 +753,8 @@ def entropic_gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, References ---------- .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, - "Gromov-Wasserstein averaging of kernel and distance matrices." - International Conference on Machine Learning (ICML). 2016. + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. """ @@ -762,13 +779,13 @@ def entropic_gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, The function solves the following optimization problem: .. math:: - C = argmin_C\in R^{NxN} \sum_s \lambda_s GW(C,Cs,p,ps) + C = argmin_{C\in R^{NxN}} \sum_s \lambda_s GW(C,C_s,p,p_s) Where : - Cs : metric cost matrix - ps : distribution + - :math:`C_s` : metric cost matrix + - :math:`p_s` : distribution Parameters ---------- @@ -806,8 +823,8 @@ def entropic_gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, References ---------- .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, - "Gromov-Wasserstein averaging of kernel and distance matrices." - International Conference on Machine Learning (ICML). 2016. + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. """ @@ -876,8 +893,8 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, Where : - Cs : metric cost matrix - ps : distribution + - Cs : metric cost matrix + - ps : distribution Parameters ---------- @@ -913,8 +930,8 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, References ---------- .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, - "Gromov-Wasserstein averaging of kernel and distance matrices." - International Conference on Machine Learning (ICML). 2016. + "Gromov-Wasserstein averaging of kernel and distance matrices." + International Conference on Machine Learning (ICML). 2016. """ @@ -972,6 +989,8 @@ def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_ verbose=False, log=False, init_C=None, init_X=None): """ Compute the fgw barycenter as presented eq (5) in [24]. + + Parameters ---------- N : integer Desired number of samples of the target barycenter @@ -993,8 +1012,9 @@ def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_ initialization for the barycenters' structure matrix. If not set random init init_X : ndarray, shape (N,d), optional initialization for the barycenters' features. If not set random init + Returns - ---------- + ------- X : ndarray, shape (N,d) Barycenters' features C : ndarray, shape (N,N) @@ -1002,11 +1022,13 @@ def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_ log_: dictionary Only returned when log=True T : list of (N,ns) transport matrices - Ms : all distance matrices between the feature of the barycenter and the other features dist(X,Ys) shape (N,ns) + Ms : all distance matrices between the feature of the barycenter and the + other features dist(X,Ys) shape (N,ns) + References ---------- .. [24] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain - and Courty Nicolas + and Courty Nicolas "Optimal Transport for structured data with application on graphs" International Conference on Machine Learning (ICML). 2019. """ @@ -1107,6 +1129,7 @@ def update_sructure_matrix(p, lambdas, T, Cs): """ Updates C according to the L2 Loss kernel with the S Ts couplings calculated at each iteration + Parameters ---------- p : ndarray, shape (N,) @@ -1117,6 +1140,7 @@ def update_sructure_matrix(p, lambdas, T, Cs): the S Ts couplings calculated at each iteration Cs : list of S ndarray, shape(ns,ns) Metric cost matrices + Returns ---------- C : ndarray, shape (nt,nt) @@ -1132,6 +1156,7 @@ def update_feature_matrix(lambdas, Ys, Ts, p): """ Updates the feature with respect to the S Ts couplings. See "Solving the barycenter problem with Block Coordinate Descent (BCD)" in [24] calculated at each iteration + Parameters ---------- p : ndarray, shape (N,) @@ -1142,6 +1167,7 @@ def update_feature_matrix(lambdas, Ys, Ts, p): the S Ts couplings calculated at each iteration Ys : list of S ndarray, shape(d,ns) The features + Returns ---------- X : ndarray, shape (d,N) -- cgit v1.2.3 From 042b52d4edad20d246481c389007ba1abb67c3b4 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 25 Jun 2019 08:43:00 +0200 Subject: pep8 --- README.md | 15 ++++++++++++++- ot/gromov.py | 10 +++++----- 2 files changed, 19 insertions(+), 6 deletions(-) diff --git a/README.md b/README.md index b6b215c..288ba13 100644 --- a/README.md +++ b/README.md @@ -53,6 +53,12 @@ The library has been tested on Linux, MacOSX and Windows. It requires a C++ comp #### Pip installation +Note that due to a limitation of pip, `cython` and `numpy` need to be installed +prior to installing POT. This can be done easily with +``` +pip install numpy cython +``` + You can install the toolbox through PyPI with: ``` pip install POT @@ -62,6 +68,8 @@ or get the very latest version by downloading it and then running: python setup.py install --user # for user install (no root) ``` + + #### Anaconda installation with conda-forge If you use the Anaconda python distribution, POT is available in [conda-forge](https://conda-forge.org). To install it and the required dependencies: @@ -150,7 +158,12 @@ You can also see the notebooks with [Jupyter nbviewer](https://nbviewer.jupyter. ## Acknowledgements -The contributors to this library are: +This toolbox has been created and is maintained by + +* [Rémi Flamary](http://remi.flamary.com/) +* [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/) + +The contributors to this library are * [Rémi Flamary](http://remi.flamary.com/) * [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/) diff --git a/ot/gromov.py b/ot/gromov.py index 43729dc..cd961b0 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -359,7 +359,7 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, .. math:: \gamma = arg\min_\gamma (1-\alpha)*<\gamma,M>_F + \alpha* \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l} - + s.t. \gamma 1 = p \gamma^T 1= q \gamma\geq 0 @@ -414,7 +414,7 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, and Courty Nicolas "Optimal Transport for structured data with application on graphs", International Conference on Machine Learning (ICML). 2019. - + """ constC, hC1, hC2 = init_matrix(C1, C2, p, q, loss_fun) @@ -442,7 +442,7 @@ def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5 .. math:: \min_\gamma (1-\alpha)*<\gamma,M>_F + \alpha* \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l} - + s.t. \gamma 1 = p \gamma^T 1= q @@ -647,7 +647,7 @@ def entropic_gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, Returns ------- T : ndarray, shape (ns, nt) - Optimal coupling between the two spaces + Optimal coupling between the two spaces References ---------- @@ -1024,7 +1024,7 @@ def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_ T : list of (N,ns) transport matrices Ms : all distance matrices between the feature of the barycenter and the other features dist(X,Ys) shape (N,ns) - + References ---------- .. [24] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain -- cgit v1.2.3 From c4b0aeb20d920ba366a656a9aee7afe78871c9c7 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 25 Jun 2019 14:26:16 +0200 Subject: add fgw examples in doc --- docs/cache_nbrun | 2 +- .../source/auto_examples/auto_examples_jupyter.zip | Bin 122957 -> 139016 bytes docs/source/auto_examples/auto_examples_python.zip | Bin 81905 -> 93470 bytes .../images/sphx_glr_plot_OT_2D_samples_001.png | Bin 22281 -> 20785 bytes .../images/sphx_glr_plot_OT_2D_samples_002.png | Bin 20743 -> 21134 bytes .../images/sphx_glr_plot_OT_2D_samples_005.png | Bin 9695 -> 9704 bytes .../images/sphx_glr_plot_OT_2D_samples_006.png | Bin 90088 -> 79153 bytes .../images/sphx_glr_plot_OT_2D_samples_009.png | Bin 15036 -> 14611 bytes .../images/sphx_glr_plot_OT_2D_samples_010.png | Bin 103143 -> 97487 bytes .../images/sphx_glr_plot_OT_2D_samples_013.png | Bin 0 -> 10846 bytes .../images/sphx_glr_plot_OT_2D_samples_014.png | Bin 0 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b/docs/source/auto_examples/images/thumb/sphx_glr_plot_barycenter_fgw_thumb.png differ diff --git a/docs/source/auto_examples/images/thumb/sphx_glr_plot_fgw_thumb.png b/docs/source/auto_examples/images/thumb/sphx_glr_plot_fgw_thumb.png new file mode 100644 index 0000000..609339d Binary files /dev/null and b/docs/source/auto_examples/images/thumb/sphx_glr_plot_fgw_thumb.png differ diff --git a/docs/source/auto_examples/index.rst b/docs/source/auto_examples/index.rst index 17a9710..9f02da4 100644 --- a/docs/source/auto_examples/index.rst +++ b/docs/source/auto_examples/index.rst @@ -107,26 +107,6 @@ This is a gallery of all the POT example files. /auto_examples/plot_gromov -.. raw:: html - -
- -.. only:: html - - .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_2D_samples_thumb.png - - :ref:`sphx_glr_auto_examples_plot_OT_2D_samples.py` - -.. raw:: html - -
- - -.. toctree:: - :hidden: - - /auto_examples/plot_OT_2D_samples - .. raw:: html
@@ -207,6 +187,26 @@ This is a gallery of all the POT example files. /auto_examples/plot_WDA +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_OT_2D_samples_thumb.png + + :ref:`sphx_glr_auto_examples_plot_OT_2D_samples.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_OT_2D_samples + .. raw:: html
@@ -327,6 +327,26 @@ This is a gallery of all the POT example files. /auto_examples/plot_otda_semi_supervised +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_fgw_thumb.png + + :ref:`sphx_glr_auto_examples_plot_fgw.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_fgw + .. raw:: html
@@ -407,6 +427,26 @@ This is a gallery of all the POT example files. /auto_examples/plot_barycenter_lp_vs_entropic +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_barycenter_fgw_thumb.png + + :ref:`sphx_glr_auto_examples_plot_barycenter_fgw.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_barycenter_fgw + .. raw:: html
diff --git a/docs/source/auto_examples/plot_OT_2D_samples.ipynb b/docs/source/auto_examples/plot_OT_2D_samples.ipynb index 26831f9..dad138b 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.ipynb +++ b/docs/source/auto_examples/plot_OT_2D_samples.ipynb @@ -26,7 +26,7 @@ }, "outputs": [], "source": [ - "# Author: Remi Flamary \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot" + "# Author: Remi Flamary \n# Kilian Fatras \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot" ] }, { @@ -100,6 +100,24 @@ "source": [ "#%% sinkhorn\n\n# reg term\nlambd = 1e-3\n\nGs = ot.sinkhorn(a, b, M, lambd)\n\npl.figure(5)\npl.imshow(Gs, interpolation='nearest')\npl.title('OT matrix sinkhorn')\n\npl.figure(6)\not.plot.plot2D_samples_mat(xs, xt, Gs, color=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix Sinkhorn with samples')\n\npl.show()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Emprirical Sinkhorn\n----------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% sinkhorn\n\n# reg term\nlambd = 1e-3\n\nGes = ot.bregman.empirical_sinkhorn(xs, xt, lambd)\n\npl.figure(7)\npl.imshow(Ges, interpolation='nearest')\npl.title('OT matrix empirical sinkhorn')\n\npl.figure(8)\not.plot.plot2D_samples_mat(xs, xt, Ges, color=[.5, .5, 1])\npl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\npl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\npl.legend(loc=0)\npl.title('OT matrix Sinkhorn from samples')\n\npl.show()" + ] } ], "metadata": { @@ -118,7 +136,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.8" } }, "nbformat": 4, diff --git a/docs/source/auto_examples/plot_OT_2D_samples.py b/docs/source/auto_examples/plot_OT_2D_samples.py index bb952a0..63126ba 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.py +++ b/docs/source/auto_examples/plot_OT_2D_samples.py @@ -10,6 +10,7 @@ sum of diracs. The OT matrix is plotted with the samples. """ # Author: Remi Flamary +# Kilian Fatras # # License: MIT License @@ -100,3 +101,28 @@ pl.legend(loc=0) pl.title('OT matrix Sinkhorn with samples') pl.show() + + +############################################################################## +# Emprirical Sinkhorn +# ---------------- + +#%% sinkhorn + +# reg term +lambd = 1e-3 + +Ges = ot.bregman.empirical_sinkhorn(xs, xt, lambd) + +pl.figure(7) +pl.imshow(Ges, interpolation='nearest') +pl.title('OT matrix empirical sinkhorn') + +pl.figure(8) +ot.plot.plot2D_samples_mat(xs, xt, Ges, color=[.5, .5, 1]) +pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') +pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') +pl.legend(loc=0) +pl.title('OT matrix Sinkhorn from samples') + +pl.show() diff --git a/docs/source/auto_examples/plot_OT_2D_samples.rst b/docs/source/auto_examples/plot_OT_2D_samples.rst index 624ae3e..1f1d713 100644 --- a/docs/source/auto_examples/plot_OT_2D_samples.rst +++ b/docs/source/auto_examples/plot_OT_2D_samples.rst @@ -17,6 +17,7 @@ sum of diracs. The OT matrix is plotted with the samples. # Author: Remi Flamary + # Kilian Fatras # # License: MIT License @@ -176,6 +177,8 @@ Compute Sinkhorn + + .. rst-class:: sphx-glr-horizontal @@ -192,7 +195,58 @@ Compute Sinkhorn -**Total running time of the script:** ( 0 minutes 3.027 seconds) +Emprirical Sinkhorn +---------------- + + + +.. code-block:: python + + + #%% sinkhorn + + # reg term + lambd = 1e-3 + + Ges = ot.bregman.empirical_sinkhorn(xs, xt, lambd) + + pl.figure(7) + pl.imshow(Ges, interpolation='nearest') + pl.title('OT matrix empirical sinkhorn') + + pl.figure(8) + ot.plot.plot2D_samples_mat(xs, xt, Ges, color=[.5, .5, 1]) + pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples') + pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples') + pl.legend(loc=0) + pl.title('OT matrix Sinkhorn from samples') + + pl.show() + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_013.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_OT_2D_samples_014.png + :scale: 47 + + +.. rst-class:: sphx-glr-script-out + + Out:: + + Warning: numerical errors at iteration 0 + + +**Total running time of the script:** ( 0 minutes 2.616 seconds) diff --git a/docs/source/auto_examples/plot_barycenter_fgw.ipynb b/docs/source/auto_examples/plot_barycenter_fgw.ipynb new file mode 100644 index 0000000..28229b2 --- /dev/null +++ b/docs/source/auto_examples/plot_barycenter_fgw.ipynb @@ -0,0 +1,126 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n=================================\nPlot graphs' barycenter using FGW\n=================================\n\nThis example illustrates the computation barycenter of labeled graphs using FGW\n\nRequires networkx >=2\n\n.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{'e}mi, Tavenard Romain\n and Courty Nicolas\n \"Optimal Transport for structured data with application on graphs\"\n International Conference on Machine Learning (ICML). 2019.\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Titouan Vayer \n#\n# License: MIT License\n\n#%% load libraries\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport networkx as nx\nimport math\nfrom scipy.sparse.csgraph import shortest_path\nimport matplotlib.colors as mcol\nfrom matplotlib import cm\nfrom ot.gromov import fgw_barycenters\n#%% Graph functions\n\n\ndef find_thresh(C, inf=0.5, sup=3, step=10):\n \"\"\" Trick to find the adequate thresholds from where value of the C matrix are considered close enough to say that nodes are connected\n Tthe threshold is found by a linesearch between values \"inf\" and \"sup\" with \"step\" thresholds tested.\n The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjency matrix\n and the original matrix.\n Parameters\n ----------\n C : ndarray, shape (n_nodes,n_nodes)\n The structure matrix to threshold\n inf : float\n The beginning of the linesearch\n sup : float\n The end of the linesearch\n step : integer\n Number of thresholds tested\n \"\"\"\n dist = []\n search = np.linspace(inf, sup, step)\n for thresh in search:\n Cprime = sp_to_adjency(C, 0, thresh)\n SC = shortest_path(Cprime, method='D')\n SC[SC == float('inf')] = 100\n dist.append(np.linalg.norm(SC - C))\n return search[np.argmin(dist)], dist\n\n\ndef sp_to_adjency(C, threshinf=0.2, threshsup=1.8):\n \"\"\" Thresholds the structure matrix in order to compute an adjency matrix.\n All values between threshinf and threshsup are considered representing connected nodes and set to 1. Else are set to 0\n Parameters\n ----------\n C : ndarray, shape (n_nodes,n_nodes)\n The structure matrix to threshold\n threshinf : float\n The minimum value of distance from which the new value is set to 1\n threshsup : float\n The maximum value of distance from which the new value is set to 1\n Returns\n -------\n C : ndarray, shape (n_nodes,n_nodes)\n The threshold matrix. Each element is in {0,1}\n \"\"\"\n H = np.zeros_like(C)\n np.fill_diagonal(H, np.diagonal(C))\n C = C - H\n C = np.minimum(np.maximum(C, threshinf), threshsup)\n C[C == threshsup] = 0\n C[C != 0] = 1\n\n return C\n\n\ndef build_noisy_circular_graph(N=20, mu=0, sigma=0.3, with_noise=False, structure_noise=False, p=None):\n \"\"\" Create a noisy circular graph\n \"\"\"\n g = nx.Graph()\n g.add_nodes_from(list(range(N)))\n for i in range(N):\n noise = float(np.random.normal(mu, sigma, 1))\n if with_noise:\n g.add_node(i, attr_name=math.sin((2 * i * math.pi / N)) + noise)\n else:\n g.add_node(i, attr_name=math.sin(2 * i * math.pi / N))\n g.add_edge(i, i + 1)\n if structure_noise:\n randomint = np.random.randint(0, p)\n if randomint == 0:\n if i <= N - 3:\n g.add_edge(i, i + 2)\n if i == N - 2:\n g.add_edge(i, 0)\n if i == N - 1:\n g.add_edge(i, 1)\n g.add_edge(N, 0)\n noise = float(np.random.normal(mu, sigma, 1))\n if with_noise:\n g.add_node(N, attr_name=math.sin((2 * N * math.pi / N)) + noise)\n else:\n g.add_node(N, attr_name=math.sin(2 * N * math.pi / N))\n return g\n\n\ndef graph_colors(nx_graph, vmin=0, vmax=7):\n cnorm = mcol.Normalize(vmin=vmin, vmax=vmax)\n cpick = cm.ScalarMappable(norm=cnorm, cmap='viridis')\n cpick.set_array([])\n val_map = {}\n for k, v in nx.get_node_attributes(nx_graph, 'attr_name').items():\n val_map[k] = cpick.to_rgba(v)\n colors = []\n for node in nx_graph.nodes():\n colors.append(val_map[node])\n return colors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n-------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% circular dataset\n# We build a dataset of noisy circular graphs.\n# Noise is added on the structures by random connections and on the features by gaussian noise.\n\n\nnp.random.seed(30)\nX0 = []\nfor k in range(9):\n X0.append(build_noisy_circular_graph(np.random.randint(15, 25), with_noise=True, structure_noise=True, p=3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot data\n---------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% Plot graphs\n\nplt.figure(figsize=(8, 10))\nfor i in range(len(X0)):\n plt.subplot(3, 3, i + 1)\n g = X0[i]\n pos = nx.kamada_kawai_layout(g)\n nx.draw(g, pos=pos, node_color=graph_colors(g, vmin=-1, vmax=1), with_labels=False, node_size=100)\nplt.suptitle('Dataset of noisy graphs. Color indicates the label', fontsize=20)\nplt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Barycenter computation\n----------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% We compute the barycenter using FGW. Structure matrices are computed using the shortest_path distance in the graph\n# Features distances are the euclidean distances\nCs = [shortest_path(nx.adjacency_matrix(x)) for x in X0]\nps = [np.ones(len(x.nodes())) / len(x.nodes()) for x in X0]\nYs = [np.array([v for (k, v) in nx.get_node_attributes(x, 'attr_name').items()]).reshape(-1, 1) for x in X0]\nlambdas = np.array([np.ones(len(Ys)) / len(Ys)]).ravel()\nsizebary = 15 # we choose a barycenter with 15 nodes\n\nA, C, log = fgw_barycenters(sizebary, Ys, Cs, ps, lambdas, alpha=0.95, log=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot Barycenter\n-------------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% Create the barycenter\nbary = nx.from_numpy_matrix(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0]))\nfor i, v in enumerate(A.ravel()):\n bary.add_node(i, attr_name=v)\n\n#%%\npos = nx.kamada_kawai_layout(bary)\nnx.draw(bary, pos=pos, node_color=graph_colors(bary, vmin=-1, vmax=1), with_labels=False)\nplt.suptitle('Barycenter', fontsize=20)\nplt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_barycenter_fgw.py b/docs/source/auto_examples/plot_barycenter_fgw.py new file mode 100644 index 0000000..77b0370 --- /dev/null +++ b/docs/source/auto_examples/plot_barycenter_fgw.py @@ -0,0 +1,184 @@ +# -*- coding: utf-8 -*- +""" +================================= +Plot graphs' barycenter using FGW +================================= + +This example illustrates the computation barycenter of labeled graphs using FGW + +Requires networkx >=2 + +.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain + and Courty Nicolas + "Optimal Transport for structured data with application on graphs" + International Conference on Machine Learning (ICML). 2019. + +""" + +# Author: Titouan Vayer +# +# License: MIT License + +#%% load libraries +import numpy as np +import matplotlib.pyplot as plt +import networkx as nx +import math +from scipy.sparse.csgraph import shortest_path +import matplotlib.colors as mcol +from matplotlib import cm +from ot.gromov import fgw_barycenters +#%% Graph functions + + +def find_thresh(C, inf=0.5, sup=3, step=10): + """ Trick to find the adequate thresholds from where value of the C matrix are considered close enough to say that nodes are connected + Tthe threshold is found by a linesearch between values "inf" and "sup" with "step" thresholds tested. + The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjency matrix + and the original matrix. + Parameters + ---------- + C : ndarray, shape (n_nodes,n_nodes) + The structure matrix to threshold + inf : float + The beginning of the linesearch + sup : float + The end of the linesearch + step : integer + Number of thresholds tested + """ + dist = [] + search = np.linspace(inf, sup, step) + for thresh in search: + Cprime = sp_to_adjency(C, 0, thresh) + SC = shortest_path(Cprime, method='D') + SC[SC == float('inf')] = 100 + dist.append(np.linalg.norm(SC - C)) + return search[np.argmin(dist)], dist + + +def sp_to_adjency(C, threshinf=0.2, threshsup=1.8): + """ Thresholds the structure matrix in order to compute an adjency matrix. + All values between threshinf and threshsup are considered representing connected nodes and set to 1. Else are set to 0 + Parameters + ---------- + C : ndarray, shape (n_nodes,n_nodes) + The structure matrix to threshold + threshinf : float + The minimum value of distance from which the new value is set to 1 + threshsup : float + The maximum value of distance from which the new value is set to 1 + Returns + ------- + C : ndarray, shape (n_nodes,n_nodes) + The threshold matrix. Each element is in {0,1} + """ + H = np.zeros_like(C) + np.fill_diagonal(H, np.diagonal(C)) + C = C - H + C = np.minimum(np.maximum(C, threshinf), threshsup) + C[C == threshsup] = 0 + C[C != 0] = 1 + + return C + + +def build_noisy_circular_graph(N=20, mu=0, sigma=0.3, with_noise=False, structure_noise=False, p=None): + """ Create a noisy circular graph + """ + g = nx.Graph() + g.add_nodes_from(list(range(N))) + for i in range(N): + noise = float(np.random.normal(mu, sigma, 1)) + if with_noise: + g.add_node(i, attr_name=math.sin((2 * i * math.pi / N)) + noise) + else: + g.add_node(i, attr_name=math.sin(2 * i * math.pi / N)) + g.add_edge(i, i + 1) + if structure_noise: + randomint = np.random.randint(0, p) + if randomint == 0: + if i <= N - 3: + g.add_edge(i, i + 2) + if i == N - 2: + g.add_edge(i, 0) + if i == N - 1: + g.add_edge(i, 1) + g.add_edge(N, 0) + noise = float(np.random.normal(mu, sigma, 1)) + if with_noise: + g.add_node(N, attr_name=math.sin((2 * N * math.pi / N)) + noise) + else: + g.add_node(N, attr_name=math.sin(2 * N * math.pi / N)) + return g + + +def graph_colors(nx_graph, vmin=0, vmax=7): + cnorm = mcol.Normalize(vmin=vmin, vmax=vmax) + cpick = cm.ScalarMappable(norm=cnorm, cmap='viridis') + cpick.set_array([]) + val_map = {} + for k, v in nx.get_node_attributes(nx_graph, 'attr_name').items(): + val_map[k] = cpick.to_rgba(v) + colors = [] + for node in nx_graph.nodes(): + colors.append(val_map[node]) + return colors + +############################################################################## +# Generate data +# ------------- + +#%% circular dataset +# We build a dataset of noisy circular graphs. +# Noise is added on the structures by random connections and on the features by gaussian noise. + + +np.random.seed(30) +X0 = [] +for k in range(9): + X0.append(build_noisy_circular_graph(np.random.randint(15, 25), with_noise=True, structure_noise=True, p=3)) + +############################################################################## +# Plot data +# --------- + +#%% Plot graphs + +plt.figure(figsize=(8, 10)) +for i in range(len(X0)): + plt.subplot(3, 3, i + 1) + g = X0[i] + pos = nx.kamada_kawai_layout(g) + nx.draw(g, pos=pos, node_color=graph_colors(g, vmin=-1, vmax=1), with_labels=False, node_size=100) +plt.suptitle('Dataset of noisy graphs. Color indicates the label', fontsize=20) +plt.show() + +############################################################################## +# Barycenter computation +# ---------------------- + +#%% We compute the barycenter using FGW. Structure matrices are computed using the shortest_path distance in the graph +# Features distances are the euclidean distances +Cs = [shortest_path(nx.adjacency_matrix(x)) for x in X0] +ps = [np.ones(len(x.nodes())) / len(x.nodes()) for x in X0] +Ys = [np.array([v for (k, v) in nx.get_node_attributes(x, 'attr_name').items()]).reshape(-1, 1) for x in X0] +lambdas = np.array([np.ones(len(Ys)) / len(Ys)]).ravel() +sizebary = 15 # we choose a barycenter with 15 nodes + +A, C, log = fgw_barycenters(sizebary, Ys, Cs, ps, lambdas, alpha=0.95, log=True) + +############################################################################## +# Plot Barycenter +# ------------------------- + +#%% Create the barycenter +bary = nx.from_numpy_matrix(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0])) +for i, v in enumerate(A.ravel()): + bary.add_node(i, attr_name=v) + +#%% +pos = nx.kamada_kawai_layout(bary) +nx.draw(bary, pos=pos, node_color=graph_colors(bary, vmin=-1, vmax=1), with_labels=False) +plt.suptitle('Barycenter', fontsize=20) +plt.show() diff --git a/docs/source/auto_examples/plot_barycenter_fgw.rst b/docs/source/auto_examples/plot_barycenter_fgw.rst new file mode 100644 index 0000000..2c44a65 --- /dev/null +++ b/docs/source/auto_examples/plot_barycenter_fgw.rst @@ -0,0 +1,268 @@ + + +.. _sphx_glr_auto_examples_plot_barycenter_fgw.py: + + +================================= +Plot graphs' barycenter using FGW +================================= + +This example illustrates the computation barycenter of labeled graphs using FGW + +Requires networkx >=2 + +.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{'e}mi, Tavenard Romain + and Courty Nicolas + "Optimal Transport for structured data with application on graphs" + International Conference on Machine Learning (ICML). 2019. + + + + +.. code-block:: python + + + # Author: Titouan Vayer + # + # License: MIT License + + #%% load libraries + import numpy as np + import matplotlib.pyplot as plt + import networkx as nx + import math + from scipy.sparse.csgraph import shortest_path + import matplotlib.colors as mcol + from matplotlib import cm + from ot.gromov import fgw_barycenters + #%% Graph functions + + + def find_thresh(C, inf=0.5, sup=3, step=10): + """ Trick to find the adequate thresholds from where value of the C matrix are considered close enough to say that nodes are connected + Tthe threshold is found by a linesearch between values "inf" and "sup" with "step" thresholds tested. + The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjency matrix + and the original matrix. + Parameters + ---------- + C : ndarray, shape (n_nodes,n_nodes) + The structure matrix to threshold + inf : float + The beginning of the linesearch + sup : float + The end of the linesearch + step : integer + Number of thresholds tested + """ + dist = [] + search = np.linspace(inf, sup, step) + for thresh in search: + Cprime = sp_to_adjency(C, 0, thresh) + SC = shortest_path(Cprime, method='D') + SC[SC == float('inf')] = 100 + dist.append(np.linalg.norm(SC - C)) + return search[np.argmin(dist)], dist + + + def sp_to_adjency(C, threshinf=0.2, threshsup=1.8): + """ Thresholds the structure matrix in order to compute an adjency matrix. + All values between threshinf and threshsup are considered representing connected nodes and set to 1. Else are set to 0 + Parameters + ---------- + C : ndarray, shape (n_nodes,n_nodes) + The structure matrix to threshold + threshinf : float + The minimum value of distance from which the new value is set to 1 + threshsup : float + The maximum value of distance from which the new value is set to 1 + Returns + ------- + C : ndarray, shape (n_nodes,n_nodes) + The threshold matrix. Each element is in {0,1} + """ + H = np.zeros_like(C) + np.fill_diagonal(H, np.diagonal(C)) + C = C - H + C = np.minimum(np.maximum(C, threshinf), threshsup) + C[C == threshsup] = 0 + C[C != 0] = 1 + + return C + + + def build_noisy_circular_graph(N=20, mu=0, sigma=0.3, with_noise=False, structure_noise=False, p=None): + """ Create a noisy circular graph + """ + g = nx.Graph() + g.add_nodes_from(list(range(N))) + for i in range(N): + noise = float(np.random.normal(mu, sigma, 1)) + if with_noise: + g.add_node(i, attr_name=math.sin((2 * i * math.pi / N)) + noise) + else: + g.add_node(i, attr_name=math.sin(2 * i * math.pi / N)) + g.add_edge(i, i + 1) + if structure_noise: + randomint = np.random.randint(0, p) + if randomint == 0: + if i <= N - 3: + g.add_edge(i, i + 2) + if i == N - 2: + g.add_edge(i, 0) + if i == N - 1: + g.add_edge(i, 1) + g.add_edge(N, 0) + noise = float(np.random.normal(mu, sigma, 1)) + if with_noise: + g.add_node(N, attr_name=math.sin((2 * N * math.pi / N)) + noise) + else: + g.add_node(N, attr_name=math.sin(2 * N * math.pi / N)) + return g + + + def graph_colors(nx_graph, vmin=0, vmax=7): + cnorm = mcol.Normalize(vmin=vmin, vmax=vmax) + cpick = cm.ScalarMappable(norm=cnorm, cmap='viridis') + cpick.set_array([]) + val_map = {} + for k, v in nx.get_node_attributes(nx_graph, 'attr_name').items(): + val_map[k] = cpick.to_rgba(v) + colors = [] + for node in nx_graph.nodes(): + colors.append(val_map[node]) + return colors + + + + + + + +Generate data +------------- + + + +.. code-block:: python + + + #%% circular dataset + # We build a dataset of noisy circular graphs. + # Noise is added on the structures by random connections and on the features by gaussian noise. + + + np.random.seed(30) + X0 = [] + for k in range(9): + X0.append(build_noisy_circular_graph(np.random.randint(15, 25), with_noise=True, structure_noise=True, p=3)) + + + + + + + +Plot data +--------- + + + +.. code-block:: python + + + #%% Plot graphs + + plt.figure(figsize=(8, 10)) + for i in range(len(X0)): + plt.subplot(3, 3, i + 1) + g = X0[i] + pos = nx.kamada_kawai_layout(g) + nx.draw(g, pos=pos, node_color=graph_colors(g, vmin=-1, vmax=1), with_labels=False, node_size=100) + plt.suptitle('Dataset of noisy graphs. Color indicates the label', fontsize=20) + plt.show() + + + + +.. image:: /auto_examples/images/sphx_glr_plot_barycenter_fgw_001.png + :align: center + + + + +Barycenter computation +---------------------- + + + +.. code-block:: python + + + #%% We compute the barycenter using FGW. Structure matrices are computed using the shortest_path distance in the graph + # Features distances are the euclidean distances + Cs = [shortest_path(nx.adjacency_matrix(x)) for x in X0] + ps = [np.ones(len(x.nodes())) / len(x.nodes()) for x in X0] + Ys = [np.array([v for (k, v) in nx.get_node_attributes(x, 'attr_name').items()]).reshape(-1, 1) for x in X0] + lambdas = np.array([np.ones(len(Ys)) / len(Ys)]).ravel() + sizebary = 15 # we choose a barycenter with 15 nodes + + A, C, log = fgw_barycenters(sizebary, Ys, Cs, ps, lambdas, alpha=0.95, log=True) + + + + + + + +Plot Barycenter +------------------------- + + + +.. code-block:: python + + + #%% Create the barycenter + bary = nx.from_numpy_matrix(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0])) + for i, v in enumerate(A.ravel()): + bary.add_node(i, attr_name=v) + + #%% + pos = nx.kamada_kawai_layout(bary) + nx.draw(bary, pos=pos, node_color=graph_colors(bary, vmin=-1, vmax=1), with_labels=False) + plt.suptitle('Barycenter', fontsize=20) + plt.show() + + + +.. image:: /auto_examples/images/sphx_glr_plot_barycenter_fgw_002.png + :align: center + + + + +**Total running time of the script:** ( 0 minutes 2.065 seconds) + + + +.. only :: html + + .. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_barycenter_fgw.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_barycenter_fgw.ipynb ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_fgw.ipynb b/docs/source/auto_examples/plot_fgw.ipynb new file mode 100644 index 0000000..1b150bd --- /dev/null +++ b/docs/source/auto_examples/plot_fgw.ipynb @@ -0,0 +1,162 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# Plot Fused-gromov-Wasserstein\n\n\nThis example illustrates the computation of FGW for 1D measures[18].\n\n.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{'e}mi, Tavenard Romain\n and Courty Nicolas\n \"Optimal Transport for structured data with application on graphs\"\n International Conference on Machine Learning (ICML). 2019.\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Titouan Vayer \n#\n# License: MIT License\n\nimport matplotlib.pyplot as pl\nimport numpy as np\nimport ot\nfrom ot.gromov import gromov_wasserstein, fused_gromov_wasserstein" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n---------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% parameters\n# We create two 1D random measures\nn = 20 # number of points in the first distribution\nn2 = 30 # number of points in the second distribution\nsig = 1 # std of first distribution\nsig2 = 0.1 # std of second distribution\n\nnp.random.seed(0)\n\nphi = np.arange(n)[:, None]\nxs = phi + sig * np.random.randn(n, 1)\nys = np.vstack((np.ones((n // 2, 1)), 0 * np.ones((n // 2, 1)))) + sig2 * np.random.randn(n, 1)\n\nphi2 = np.arange(n2)[:, None]\nxt = phi2 + sig * np.random.randn(n2, 1)\nyt = np.vstack((np.ones((n2 // 2, 1)), 0 * np.ones((n2 // 2, 1)))) + sig2 * np.random.randn(n2, 1)\nyt = yt[::-1, :]\n\np = ot.unif(n)\nq = ot.unif(n2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot data\n---------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% plot the distributions\n\npl.close(10)\npl.figure(10, (7, 7))\n\npl.subplot(2, 1, 1)\n\npl.scatter(ys, xs, c=phi, s=70)\npl.ylabel('Feature value a', fontsize=20)\npl.title('$\\mu=\\sum_i \\delta_{x_i,a_i}$', fontsize=25, usetex=True, y=1)\npl.xticks(())\npl.yticks(())\npl.subplot(2, 1, 2)\npl.scatter(yt, xt, c=phi2, s=70)\npl.xlabel('coordinates x/y', fontsize=25)\npl.ylabel('Feature value b', fontsize=20)\npl.title('$\\\\nu=\\sum_j \\delta_{y_j,b_j}$', fontsize=25, usetex=True, y=1)\npl.yticks(())\npl.tight_layout()\npl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create structure matrices and across-feature distance matrix\n---------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% Structure matrices and across-features distance matrix\nC1 = ot.dist(xs)\nC2 = ot.dist(xt)\nM = ot.dist(ys, yt)\nw1 = ot.unif(C1.shape[0])\nw2 = ot.unif(C2.shape[0])\nGot = ot.emd([], [], M)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot matrices\n---------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%%\ncmap = 'Reds'\npl.close(10)\npl.figure(10, (5, 5))\nfs = 15\nl_x = [0, 5, 10, 15]\nl_y = [0, 5, 10, 15, 20, 25]\ngs = pl.GridSpec(5, 5)\n\nax1 = pl.subplot(gs[3:, :2])\n\npl.imshow(C1, cmap=cmap, interpolation='nearest')\npl.title(\"$C_1$\", fontsize=fs)\npl.xlabel(\"$k$\", fontsize=fs)\npl.ylabel(\"$i$\", fontsize=fs)\npl.xticks(l_x)\npl.yticks(l_x)\n\nax2 = pl.subplot(gs[:3, 2:])\n\npl.imshow(C2, cmap=cmap, interpolation='nearest')\npl.title(\"$C_2$\", fontsize=fs)\npl.ylabel(\"$l$\", fontsize=fs)\n#pl.ylabel(\"$l$\",fontsize=fs)\npl.xticks(())\npl.yticks(l_y)\nax2.set_aspect('auto')\n\nax3 = pl.subplot(gs[3:, 2:], sharex=ax2, sharey=ax1)\npl.imshow(M, cmap=cmap, interpolation='nearest')\npl.yticks(l_x)\npl.xticks(l_y)\npl.ylabel(\"$i$\", fontsize=fs)\npl.title(\"$M_{AB}$\", fontsize=fs)\npl.xlabel(\"$j$\", fontsize=fs)\npl.tight_layout()\nax3.set_aspect('auto')\npl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute FGW/GW\n---------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% Computing FGW and GW\nalpha = 1e-3\n\not.tic()\nGwg, logw = fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=alpha, verbose=True, log=True)\not.toc()\n\n#%reload_ext WGW\nGg, log = gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', verbose=True, log=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visualize transport matrices\n---------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% visu OT matrix\ncmap = 'Blues'\nfs = 15\npl.figure(2, (13, 5))\npl.clf()\npl.subplot(1, 3, 1)\npl.imshow(Got, cmap=cmap, interpolation='nearest')\n#pl.xlabel(\"$y$\",fontsize=fs)\npl.ylabel(\"$i$\", fontsize=fs)\npl.xticks(())\n\npl.title('Wasserstein ($M$ only)')\n\npl.subplot(1, 3, 2)\npl.imshow(Gg, cmap=cmap, interpolation='nearest')\npl.title('Gromov ($C_1,C_2$ only)')\npl.xticks(())\npl.subplot(1, 3, 3)\npl.imshow(Gwg, cmap=cmap, interpolation='nearest')\npl.title('FGW ($M+C_1,C_2$)')\n\npl.xlabel(\"$j$\", fontsize=fs)\npl.ylabel(\"$i$\", fontsize=fs)\n\npl.tight_layout()\npl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_fgw.py b/docs/source/auto_examples/plot_fgw.py new file mode 100644 index 0000000..43efc94 --- /dev/null +++ b/docs/source/auto_examples/plot_fgw.py @@ -0,0 +1,173 @@ +# -*- coding: utf-8 -*- +""" +============================== +Plot Fused-gromov-Wasserstein +============================== + +This example illustrates the computation of FGW for 1D measures[18]. + +.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{\'e}mi, Tavenard Romain + and Courty Nicolas + "Optimal Transport for structured data with application on graphs" + International Conference on Machine Learning (ICML). 2019. + +""" + +# Author: Titouan Vayer +# +# License: MIT License + +import matplotlib.pyplot as pl +import numpy as np +import ot +from ot.gromov import gromov_wasserstein, fused_gromov_wasserstein + +############################################################################## +# Generate data +# --------- + +#%% parameters +# We create two 1D random measures +n = 20 # number of points in the first distribution +n2 = 30 # number of points in the second distribution +sig = 1 # std of first distribution +sig2 = 0.1 # std of second distribution + +np.random.seed(0) + +phi = np.arange(n)[:, None] +xs = phi + sig * np.random.randn(n, 1) +ys = np.vstack((np.ones((n // 2, 1)), 0 * np.ones((n // 2, 1)))) + sig2 * np.random.randn(n, 1) + +phi2 = np.arange(n2)[:, None] +xt = phi2 + sig * np.random.randn(n2, 1) +yt = np.vstack((np.ones((n2 // 2, 1)), 0 * np.ones((n2 // 2, 1)))) + sig2 * np.random.randn(n2, 1) +yt = yt[::-1, :] + +p = ot.unif(n) +q = ot.unif(n2) + +############################################################################## +# Plot data +# --------- + +#%% plot the distributions + +pl.close(10) +pl.figure(10, (7, 7)) + +pl.subplot(2, 1, 1) + +pl.scatter(ys, xs, c=phi, s=70) +pl.ylabel('Feature value a', fontsize=20) +pl.title('$\mu=\sum_i \delta_{x_i,a_i}$', fontsize=25, usetex=True, y=1) +pl.xticks(()) +pl.yticks(()) +pl.subplot(2, 1, 2) +pl.scatter(yt, xt, c=phi2, s=70) +pl.xlabel('coordinates x/y', fontsize=25) +pl.ylabel('Feature value b', fontsize=20) +pl.title('$\\nu=\sum_j \delta_{y_j,b_j}$', fontsize=25, usetex=True, y=1) +pl.yticks(()) +pl.tight_layout() +pl.show() + +############################################################################## +# Create structure matrices and across-feature distance matrix +# --------- + +#%% Structure matrices and across-features distance matrix +C1 = ot.dist(xs) +C2 = ot.dist(xt) +M = ot.dist(ys, yt) +w1 = ot.unif(C1.shape[0]) +w2 = ot.unif(C2.shape[0]) +Got = ot.emd([], [], M) + +############################################################################## +# Plot matrices +# --------- + +#%% +cmap = 'Reds' +pl.close(10) +pl.figure(10, (5, 5)) +fs = 15 +l_x = [0, 5, 10, 15] +l_y = [0, 5, 10, 15, 20, 25] +gs = pl.GridSpec(5, 5) + +ax1 = pl.subplot(gs[3:, :2]) + +pl.imshow(C1, cmap=cmap, interpolation='nearest') +pl.title("$C_1$", fontsize=fs) +pl.xlabel("$k$", fontsize=fs) +pl.ylabel("$i$", fontsize=fs) +pl.xticks(l_x) +pl.yticks(l_x) + +ax2 = pl.subplot(gs[:3, 2:]) + +pl.imshow(C2, cmap=cmap, interpolation='nearest') +pl.title("$C_2$", fontsize=fs) +pl.ylabel("$l$", fontsize=fs) +#pl.ylabel("$l$",fontsize=fs) +pl.xticks(()) +pl.yticks(l_y) +ax2.set_aspect('auto') + +ax3 = pl.subplot(gs[3:, 2:], sharex=ax2, sharey=ax1) +pl.imshow(M, cmap=cmap, interpolation='nearest') +pl.yticks(l_x) +pl.xticks(l_y) +pl.ylabel("$i$", fontsize=fs) +pl.title("$M_{AB}$", fontsize=fs) +pl.xlabel("$j$", fontsize=fs) +pl.tight_layout() +ax3.set_aspect('auto') +pl.show() + +############################################################################## +# Compute FGW/GW +# --------- + +#%% Computing FGW and GW +alpha = 1e-3 + +ot.tic() +Gwg, logw = fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=alpha, verbose=True, log=True) +ot.toc() + +#%reload_ext WGW +Gg, log = gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', verbose=True, log=True) + +############################################################################## +# Visualize transport matrices +# --------- + +#%% visu OT matrix +cmap = 'Blues' +fs = 15 +pl.figure(2, (13, 5)) +pl.clf() +pl.subplot(1, 3, 1) +pl.imshow(Got, cmap=cmap, interpolation='nearest') +#pl.xlabel("$y$",fontsize=fs) +pl.ylabel("$i$", fontsize=fs) +pl.xticks(()) + +pl.title('Wasserstein ($M$ only)') + +pl.subplot(1, 3, 2) +pl.imshow(Gg, cmap=cmap, interpolation='nearest') +pl.title('Gromov ($C_1,C_2$ only)') +pl.xticks(()) +pl.subplot(1, 3, 3) +pl.imshow(Gwg, cmap=cmap, interpolation='nearest') +pl.title('FGW ($M+C_1,C_2$)') + +pl.xlabel("$j$", fontsize=fs) +pl.ylabel("$i$", fontsize=fs) + +pl.tight_layout() +pl.show() diff --git a/docs/source/auto_examples/plot_fgw.rst b/docs/source/auto_examples/plot_fgw.rst new file mode 100644 index 0000000..aec725d --- /dev/null +++ b/docs/source/auto_examples/plot_fgw.rst @@ -0,0 +1,297 @@ + + +.. _sphx_glr_auto_examples_plot_fgw.py: + + +============================== +Plot Fused-gromov-Wasserstein +============================== + +This example illustrates the computation of FGW for 1D measures[18]. + +.. [18] Vayer Titouan, Chapel Laetitia, Flamary R{'e}mi, Tavenard Romain + and Courty Nicolas + "Optimal Transport for structured data with application on graphs" + International Conference on Machine Learning (ICML). 2019. + + + + +.. code-block:: python + + + # Author: Titouan Vayer + # + # License: MIT License + + import matplotlib.pyplot as pl + import numpy as np + import ot + from ot.gromov import gromov_wasserstein, fused_gromov_wasserstein + + + + + + + +Generate data +--------- + + + +.. code-block:: python + + + #%% parameters + # We create two 1D random measures + n = 20 # number of points in the first distribution + n2 = 30 # number of points in the second distribution + sig = 1 # std of first distribution + sig2 = 0.1 # std of second distribution + + np.random.seed(0) + + phi = np.arange(n)[:, None] + xs = phi + sig * np.random.randn(n, 1) + ys = np.vstack((np.ones((n // 2, 1)), 0 * np.ones((n // 2, 1)))) + sig2 * np.random.randn(n, 1) + + phi2 = np.arange(n2)[:, None] + xt = phi2 + sig * np.random.randn(n2, 1) + yt = np.vstack((np.ones((n2 // 2, 1)), 0 * np.ones((n2 // 2, 1)))) + sig2 * np.random.randn(n2, 1) + yt = yt[::-1, :] + + p = ot.unif(n) + q = ot.unif(n2) + + + + + + + +Plot data +--------- + + + +.. code-block:: python + + + #%% plot the distributions + + pl.close(10) + pl.figure(10, (7, 7)) + + pl.subplot(2, 1, 1) + + pl.scatter(ys, xs, c=phi, s=70) + pl.ylabel('Feature value a', fontsize=20) + pl.title('$\mu=\sum_i \delta_{x_i,a_i}$', fontsize=25, usetex=True, y=1) + pl.xticks(()) + pl.yticks(()) + pl.subplot(2, 1, 2) + pl.scatter(yt, xt, c=phi2, s=70) + pl.xlabel('coordinates x/y', fontsize=25) + pl.ylabel('Feature value b', fontsize=20) + pl.title('$\\nu=\sum_j \delta_{y_j,b_j}$', fontsize=25, usetex=True, y=1) + pl.yticks(()) + pl.tight_layout() + pl.show() + + + + +.. image:: /auto_examples/images/sphx_glr_plot_fgw_010.png + :align: center + + + + +Create structure matrices and across-feature distance matrix +--------- + + + +.. code-block:: python + + + #%% Structure matrices and across-features distance matrix + C1 = ot.dist(xs) + C2 = ot.dist(xt) + M = ot.dist(ys, yt) + w1 = ot.unif(C1.shape[0]) + w2 = ot.unif(C2.shape[0]) + Got = ot.emd([], [], M) + + + + + + + +Plot matrices +--------- + + + +.. code-block:: python + + + #%% + cmap = 'Reds' + pl.close(10) + pl.figure(10, (5, 5)) + fs = 15 + l_x = [0, 5, 10, 15] + l_y = [0, 5, 10, 15, 20, 25] + gs = pl.GridSpec(5, 5) + + ax1 = pl.subplot(gs[3:, :2]) + + pl.imshow(C1, cmap=cmap, interpolation='nearest') + pl.title("$C_1$", fontsize=fs) + pl.xlabel("$k$", fontsize=fs) + pl.ylabel("$i$", fontsize=fs) + pl.xticks(l_x) + pl.yticks(l_x) + + ax2 = pl.subplot(gs[:3, 2:]) + + pl.imshow(C2, cmap=cmap, interpolation='nearest') + pl.title("$C_2$", fontsize=fs) + pl.ylabel("$l$", fontsize=fs) + #pl.ylabel("$l$",fontsize=fs) + pl.xticks(()) + pl.yticks(l_y) + ax2.set_aspect('auto') + + ax3 = pl.subplot(gs[3:, 2:], sharex=ax2, sharey=ax1) + pl.imshow(M, cmap=cmap, interpolation='nearest') + pl.yticks(l_x) + pl.xticks(l_y) + pl.ylabel("$i$", fontsize=fs) + pl.title("$M_{AB}$", fontsize=fs) + pl.xlabel("$j$", fontsize=fs) + pl.tight_layout() + ax3.set_aspect('auto') + pl.show() + + + + +.. image:: /auto_examples/images/sphx_glr_plot_fgw_011.png + :align: center + + + + +Compute FGW/GW +--------- + + + +.. code-block:: python + + + #%% Computing FGW and GW + alpha = 1e-3 + + ot.tic() + Gwg, logw = fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=alpha, verbose=True, log=True) + ot.toc() + + #%reload_ext WGW + Gg, log = gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', verbose=True, log=True) + + + + + +.. rst-class:: sphx-glr-script-out + + Out:: + + It. |Loss |Relative loss|Absolute loss + ------------------------------------------------ + 0|4.734462e+01|0.000000e+00|0.000000e+00 + 1|2.508258e+01|8.875498e-01|2.226204e+01 + 2|2.189329e+01|1.456747e-01|3.189297e+00 + 3|2.189329e+01|0.000000e+00|0.000000e+00 + Elapsed time : 0.0016989707946777344 s + It. |Loss |Relative loss|Absolute loss + ------------------------------------------------ + 0|4.683978e+04|0.000000e+00|0.000000e+00 + 1|3.860061e+04|2.134468e-01|8.239175e+03 + 2|2.182948e+04|7.682787e-01|1.677113e+04 + 3|2.182948e+04|0.000000e+00|0.000000e+00 + + +Visualize transport matrices +--------- + + + +.. code-block:: python + + + #%% visu OT matrix + cmap = 'Blues' + fs = 15 + pl.figure(2, (13, 5)) + pl.clf() + pl.subplot(1, 3, 1) + pl.imshow(Got, cmap=cmap, interpolation='nearest') + #pl.xlabel("$y$",fontsize=fs) + pl.ylabel("$i$", fontsize=fs) + pl.xticks(()) + + pl.title('Wasserstein ($M$ only)') + + pl.subplot(1, 3, 2) + pl.imshow(Gg, cmap=cmap, interpolation='nearest') + pl.title('Gromov ($C_1,C_2$ only)') + pl.xticks(()) + pl.subplot(1, 3, 3) + pl.imshow(Gwg, cmap=cmap, interpolation='nearest') + pl.title('FGW ($M+C_1,C_2$)') + + pl.xlabel("$j$", fontsize=fs) + pl.ylabel("$i$", fontsize=fs) + + pl.tight_layout() + pl.show() + + + +.. image:: /auto_examples/images/sphx_glr_plot_fgw_004.png + :align: center + + + + +**Total running time of the script:** ( 0 minutes 1.468 seconds) + + + +.. only :: html + + .. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_fgw.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_fgw.ipynb ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ -- cgit v1.2.3 From 830d4ebd2e2c85b4f3503f358bb31a07918a27c5 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 25 Jun 2019 14:27:19 +0200 Subject: update fgw example + add notebook --- examples/plot_barycenter_fgw.py | 2 +- notebooks/plot_OT_2D_samples.ipynb | 91 ++++++++- notebooks/plot_barycenter_fgw.ipynb | 312 +++++++++++++++++++++++++++++++ notebooks/plot_fgw.ipynb | 359 ++++++++++++++++++++++++++++++++++++ 4 files changed, 756 insertions(+), 8 deletions(-) create mode 100644 notebooks/plot_barycenter_fgw.ipynb create mode 100644 notebooks/plot_fgw.ipynb diff --git a/examples/plot_barycenter_fgw.py b/examples/plot_barycenter_fgw.py index e4be447..77b0370 100644 --- a/examples/plot_barycenter_fgw.py +++ b/examples/plot_barycenter_fgw.py @@ -166,7 +166,7 @@ Ys = [np.array([v for (k, v) in nx.get_node_attributes(x, 'attr_name').items()]) lambdas = np.array([np.ones(len(Ys)) / len(Ys)]).ravel() sizebary = 15 # we choose a barycenter with 15 nodes -A, C, log = fgw_barycenters(sizebary, Ys, Cs, ps, lambdas, alpha=0.95) +A, C, log = fgw_barycenters(sizebary, Ys, Cs, ps, lambdas, alpha=0.95, log=True) ############################################################################## # Plot Barycenter diff --git a/notebooks/plot_OT_2D_samples.ipynb b/notebooks/plot_OT_2D_samples.ipynb index 96e84a5..cd1b541 100644 --- a/notebooks/plot_OT_2D_samples.ipynb +++ b/notebooks/plot_OT_2D_samples.ipynb @@ -34,6 +34,7 @@ "outputs": [], "source": [ "# Author: Remi Flamary \n", + "# Kilian Fatras \n", "#\n", "# License: MIT License\n", "\n", @@ -108,7 +109,7 @@ }, { "data": { - "image/png": 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\n", 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\n", 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\n", 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\n", 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" ] @@ -179,7 +180,7 @@ }, { "data": { - "image/png": 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\n", + "image/png": 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\n", 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\n", 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\n", + "image/png": 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\n", "text/plain": [ "
" ] @@ -263,6 +264,82 @@ "\n", "pl.show()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Emprirical Sinkhorn\n", + "----------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Warning: numerical errors at iteration 0\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rflamary/PYTHON/POT/ot/bregman.py:374: RuntimeWarning: divide by zero encountered in true_divide\n", + " v = np.divide(b, KtransposeU)\n", + "/home/rflamary/PYTHON/POT/ot/plot.py:83: RuntimeWarning: invalid value encountered in double_scalars\n", + " if G[i, j] / mx > thr:\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% sinkhorn\n", + "\n", + "# reg term\n", + "lambd = 1e-3\n", + "\n", + "Ges = ot.bregman.empirical_sinkhorn(xs, xt, lambd)\n", + "\n", + "pl.figure(7)\n", + "pl.imshow(Ges, interpolation='nearest')\n", + "pl.title('OT matrix empirical sinkhorn')\n", + "\n", + "pl.figure(8)\n", + "ot.plot.plot2D_samples_mat(xs, xt, Ges, color=[.5, .5, 1])\n", + "pl.plot(xs[:, 0], xs[:, 1], '+b', label='Source samples')\n", + "pl.plot(xt[:, 0], xt[:, 1], 'xr', label='Target samples')\n", + "pl.legend(loc=0)\n", + "pl.title('OT matrix Sinkhorn from samples')\n", + "\n", + "pl.show()" + ] } ], "metadata": { @@ -281,7 +358,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.6.8" } }, "nbformat": 4, diff --git a/notebooks/plot_barycenter_fgw.ipynb b/notebooks/plot_barycenter_fgw.ipynb new file mode 100644 index 0000000..8da80a6 --- /dev/null +++ b/notebooks/plot_barycenter_fgw.ipynb @@ -0,0 +1,312 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "=================================\n", + "Plot graphs' barycenter using FGW\n", + "=================================\n", + "\n", + "This example illustrates the computation barycenter of labeled graphs using FGW\n", + "\n", + "Requires networkx >=2\n", + "\n", + ".. [18] Vayer Titouan, Chapel Laetitia, Flamary R{'e}mi, Tavenard Romain\n", + " and Courty Nicolas\n", + " \"Optimal Transport for structured data with application on graphs\"\n", + " International Conference on Machine Learning (ICML). 2019.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Titouan Vayer \n", + "#\n", + "# License: MIT License\n", + "\n", + "#%% load libraries\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "import networkx as nx\n", + "import math\n", + "from scipy.sparse.csgraph import shortest_path\n", + "import matplotlib.colors as mcol\n", + "from matplotlib import cm\n", + "from ot.gromov import fgw_barycenters\n", + "#%% Graph functions\n", + "\n", + "\n", + "def find_thresh(C, inf=0.5, sup=3, step=10):\n", + " \"\"\" Trick to find the adequate thresholds from where value of the C matrix are considered close enough to say that nodes are connected\n", + " Tthe threshold is found by a linesearch between values \"inf\" and \"sup\" with \"step\" thresholds tested.\n", + " The optimal threshold is the one which minimizes the reconstruction error between the shortest_path matrix coming from the thresholded adjency matrix\n", + " and the original matrix.\n", + " Parameters\n", + " ----------\n", + " C : ndarray, shape (n_nodes,n_nodes)\n", + " The structure matrix to threshold\n", + " inf : float\n", + " The beginning of the linesearch\n", + " sup : float\n", + " The end of the linesearch\n", + " step : integer\n", + " Number of thresholds tested\n", + " \"\"\"\n", + " dist = []\n", + " search = np.linspace(inf, sup, step)\n", + " for thresh in search:\n", + " Cprime = sp_to_adjency(C, 0, thresh)\n", + " SC = shortest_path(Cprime, method='D')\n", + " SC[SC == float('inf')] = 100\n", + " dist.append(np.linalg.norm(SC - C))\n", + " return search[np.argmin(dist)], dist\n", + "\n", + "\n", + "def sp_to_adjency(C, threshinf=0.2, threshsup=1.8):\n", + " \"\"\" Thresholds the structure matrix in order to compute an adjency matrix.\n", + " All values between threshinf and threshsup are considered representing connected nodes and set to 1. Else are set to 0\n", + " Parameters\n", + " ----------\n", + " C : ndarray, shape (n_nodes,n_nodes)\n", + " The structure matrix to threshold\n", + " threshinf : float\n", + " The minimum value of distance from which the new value is set to 1\n", + " threshsup : float\n", + " The maximum value of distance from which the new value is set to 1\n", + " Returns\n", + " -------\n", + " C : ndarray, shape (n_nodes,n_nodes)\n", + " The threshold matrix. Each element is in {0,1}\n", + " \"\"\"\n", + " H = np.zeros_like(C)\n", + " np.fill_diagonal(H, np.diagonal(C))\n", + " C = C - H\n", + " C = np.minimum(np.maximum(C, threshinf), threshsup)\n", + " C[C == threshsup] = 0\n", + " C[C != 0] = 1\n", + "\n", + " return C\n", + "\n", + "\n", + "def build_noisy_circular_graph(N=20, mu=0, sigma=0.3, with_noise=False, structure_noise=False, p=None):\n", + " \"\"\" Create a noisy circular graph\n", + " \"\"\"\n", + " g = nx.Graph()\n", + " g.add_nodes_from(list(range(N)))\n", + " for i in range(N):\n", + " noise = float(np.random.normal(mu, sigma, 1))\n", + " if with_noise:\n", + " g.add_node(i, attr_name=math.sin((2 * i * math.pi / N)) + noise)\n", + " else:\n", + " g.add_node(i, attr_name=math.sin(2 * i * math.pi / N))\n", + " g.add_edge(i, i + 1)\n", + " if structure_noise:\n", + " randomint = np.random.randint(0, p)\n", + " if randomint == 0:\n", + " if i <= N - 3:\n", + " g.add_edge(i, i + 2)\n", + " if i == N - 2:\n", + " g.add_edge(i, 0)\n", + " if i == N - 1:\n", + " g.add_edge(i, 1)\n", + " g.add_edge(N, 0)\n", + " noise = float(np.random.normal(mu, sigma, 1))\n", + " if with_noise:\n", + " g.add_node(N, attr_name=math.sin((2 * N * math.pi / N)) + noise)\n", + " else:\n", + " g.add_node(N, attr_name=math.sin(2 * N * math.pi / N))\n", + " return g\n", + "\n", + "\n", + "def graph_colors(nx_graph, vmin=0, vmax=7):\n", + " cnorm = mcol.Normalize(vmin=vmin, vmax=vmax)\n", + " cpick = cm.ScalarMappable(norm=cnorm, cmap='viridis')\n", + " cpick.set_array([])\n", + " val_map = {}\n", + " for k, v in nx.get_node_attributes(nx_graph, 'attr_name').items():\n", + " val_map[k] = cpick.to_rgba(v)\n", + " colors = []\n", + " for node in nx_graph.nodes():\n", + " colors.append(val_map[node])\n", + " return colors" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + "-------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% circular dataset\n", + "# We build a dataset of noisy circular graphs.\n", + "# Noise is added on the structures by random connections and on the features by gaussian noise.\n", + "\n", + "\n", + "np.random.seed(30)\n", + "X0 = []\n", + "for k in range(9):\n", + " X0.append(build_noisy_circular_graph(np.random.randint(15, 25), with_noise=True, structure_noise=True, p=3))" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot data\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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iRo0a9OvXjxYtWhAYGEiJEiXSrXPkyBEAYmJirDaUfPDgAQAXL160O97HnTp1CoDWrVtnmFetWjXKlStHaGgoMTExudI4r0GDBhmmlS9fHoCoqCjLtODgYACaN2+ORqPJsE7r1q358ccfCQ4OZtCgQZnur2/fvsyfP5/u3bvTu3dv2rZtS2BgIJUrV86wbIcOHfDz82PlypXMmjULvV4PpBRXu7i4MGLECJuOMTg4GJVKRdOmTTPMa9asWZbrNmrUKNN5N27cYNasWfzxxx/cuHGDhISEdPMzO39btGhhNQ61Wm35nNOqWrUqXl5eGaan/Z48PDyAlHP6q6++okaNGvTp04cWLVrQpEkThzbkPHXqFCqVyuq4AS1atMj0uNMKCQkhPj6ewMBAihUrlmF+69atmTFjhtXtZPUd5oQ930fqsVs7z+wZR+Ho0aNAym/CFhEREXzxxRds376da9euERcXl26+PddWe69/Xl5edOnShS1btlC3bl169epF8+bNef755y2/4dxk6zUs9TjOnDlj9TguX74MpBxHjRo1chRLTu9p1q4JarWaZs2acfXqVYKDg/H19SUoKAiTyZTpuAEGg8FyDDnx1JOBxMREIiMjAdLdaPv27cvGjRupVKkS3bp1o3Tp0pa6xnnz5pGUlGTXfubPn8/EiRMpWrQoL774IhUqVECv16MoiqX+Ke02J02ahI+PD4sWLWLBggXMmzcPRVFo0aIFX3zxheUki4iIAGD37t3s3r070/3HxsbaFa81qXXSmdXJlilThhs3bhAdHZ0rF3hrdXAuLimnRNq+4rbEBSn1WVlp1KgRBw8e5NNPP2XDhg2Wukt/f3+mTp1K//79LcuqVCpGjx7N5MmTWbt2LUOHDuXkyZOcOnWK7t2729x7IiYmhmLFilmOK61SpUpluW7p0qWtTr927RqNGjUiKiqK5s2b065dO4oUKYJareb69et8//33mZ6/1vbp4uJiabPyuMzamVj7nubOnUulSpVYvnw5M2fOZObMmbi4uNCxY0fmzJlDlSpVsjzerJQpU4Zr165x+/Ztnn32WZvXS/38rdXTZ3Xcj28jNYbMYgPr519m32FO2fN9pB67tQTanrhSj6ts2bI2LduwYUNCQ0Np1KgRgwYNspz/0dHRzJ8/365ra06uf2vXrmXWrFmsXr3a0u3c1dWV3r1785///Cfb3509bL2GpR7HsmXLstzek1zHc3pPy+zzSD1HUs//1GMICgoiKCgo14/hqScDhw4dwmg0UqpUKUvDthMnTrBx40batm3Ljh070l2ozWYzs2fPtmsfRqORadOmUbp0aU6dOpXhopGaFT5u0KBBDBo0iOjoaA4fPszGjRv57rvveOmll7h06RIlSpSw3HTnz5/P+PHj7YrLXqn7unv3rtWn5bCwsHTL5ZW0cVljT1xNmjRh69atJCUlcfLkSXbu3MlXX33FgAEDKFGiRLrGRamDKC1dupShQ4daGg5aayiWGS8vLyIjIzEajRkSgnv37mW5bmYjnH355ZdERESwfPlyhgwZkm7emjVr+P777zPd5r179zL0ITcajYSHh1t94rSHWq1m4sSJTJw4kfv373Po0CF++ukn1q9fz/nz5zl//nyGxn22atasGdeuXeOPP/6gTZs2Nq9XpEgRIiMjMRgMGW6Kth73k5x/jhqlDrI+9syOxZrUG97t27epXbt2lst+++23hIaGMnXq1AxPj0eOHGH+/Pk27xfI0fXPzc2NadOmMW3aNG7evMmBAwdYsWIFP/74I9evX+fgwYN2xZAbUo/jzJkz1KlTJ9e3/yT3tMyuQ6nnSGrsqX+/+eabfPnll7kVusVTHY7YbDbz6aefAqQbEOfvv/8GoGvXrhku0MePH89Q5AopFzrA6uhm4eHhREdH07Rp0wyJQGxsrKX4PTPe3t507NiRZcuWMWTIECIjIzlw4AAAjRs3BrDrBM4q1qzUq1cPwOpQpH///Te3bt3Cz8/Ppl4JuSk1rtTE7nF79+4FsKslrk6no2nTpnz88ccsWLAAgE2bNqVbpkSJEvTu3Ztjx47x559/smbNGvz8/GjXrp1dsZvNZg4fPpxh3qFDh2zeTlqp52+vXr0yzNu/f3+W61qbf+jQIUwmk+Vzzg0lS5akZ8+erFu3jtatW3P16lXOnTuX4+2NGjUKSKmmyS6JSvsElPr5p/6e0jpw4AAmkynb88bf3x+9Xs+ZM2esPv3n5PzLCwEBAZjNZqvnmT3DDadeg3bs2JHtsjk5N7O6XuXk+pdW+fLlGThwILt27aJKlSocOnTI8oSblZxeQzPzpMeRnZzc01JZ+15MJpPlvEm9LjRq1AiVSvXUjuGpJQP379+nX79+7Nu3jwoVKjBlyhTLvNQSgsd/EPfv3+e1116zur3ixYsDKXW1jytZsiR6vZ6TJ0+mKyIxGAxMmDCB8PDwDOvs3bvX0pbh8RgAS/1WgwYNaN68Ob/88gvfffed1dj++uuvdEWdWcWalWHDhgEwY8YMS10cpJwYb7/9NmazmeHDh9u1zdxQrlw5XnzxRa5fv868efPSzTt27BirV6+maNGi9OjRI8vtHD582OqPIvXmYq1OMbXLVN++fYmNjWXkyJGoVLaftqltGD744AOSk5Mt02NiYjLt/pedzM7fXbt28e2332a57ieffJKuLjMxMZH33nsPgKFDh+YoHki5Af/5558ZphsMBks1XdrPNywsjEuXLqXrLpmVwMBARo4cSUREBO3bt+fKlSsZljGbzaxZs4ZXX33VMi31nH7vvfeIj4+3TI+Pj2fy5MkA2Z7TWq2WgQMH8ujRIz788MN0865evcqCBQvQaDTp9psfpH6f77//PomJiZbpkZGRdnX/Gjx4MF5eXixevNhqUnXr1i3LvzM7N4ODg/n888+tbj+r65W9178HDx5Y7TYZFxdHbGwsLi4uNnXtzOk1NDNDhw7F29ub6dOnc/z48QzzzWbzE70PIif3tFR79uxh69at6aYtXLiQq1ev0qpVK3x9fYGU+9zAgQM5ceIEn3zyidVE6erVq4SGhuboGHJ1BEKz2WwZjvjQoUMkJyfTqFEjVq1ala7PesOGDQkMDOSXX36hadOmNGvWjHv37rFjxw78/f2t1gc3adIEvV7PvHnziIiIsNSnvPHGGxQpUoTx48czc+ZMateuTbdu3UhOTmbv3r1ERkbSqlUry9NDqh49euDh4UHjxo2pWLEiQggOHjxIUFAQ9evXT1dcvXr1alq3bs3w4cNZsGABzz//PN7e3ty6dYuzZ89y7tw5jhw5YhlhMbtYM9O0aVPeffddZs+eTa1atejduzfu7u7s2LGDc+fO0axZM955552cfUlPaMmSJQQGBvLOO+/w22+/0aBBA8s4AyqViuXLl1vtQ5vW7Nmz2bNnD82bN8fPzw8PDw/Onz/Pjh07KFq0qOXpM63AwECee+45zpw5g0ajsdxcbDVo0CB++ukndu7cSa1atejatSsGg4Gff/6Zhg0bEhISYldyATBu3DiWL1/Oyy+/TO/evXnmmWc4d+4cO3fupE+fPqxduzbTdatXr07NmjXTjTNw9epVOnXq9EQ3s4SEBJo1a0aVKlWoX78+vr6+JCYmsnv3bi5evEjXrl2pXr26Zfn33nuP77//3mpVR2a+/vpr1Go1S5YsoXr16rRs2ZLnnnsOnU7H7du32bNnD7du3aJ3796WdQYMGMCmTZtYt24dNWvWpHv37pZ2PKGhofTt25eBAwdmu++ZM2dy8OBBFi5cSFBQEK1atbKMM/Do0SMWLlyIn5+f3Z/b09S/f3/Wrl3L5s2bqVWrFt26dcNgMLBhwwYaNmzI1atXbdqOj48Pq1evpnfv3rRq1YoOHTpQp04dHj58yNmzZ7l586blBjBo0CC++OILJk6cyN69e6latSpXrlxh69at9OzZ0+q52aZNG7744gtGjhxJr1698PT0xNvbm9dffx2w7/p3+/Zt6tWrR+3atalTpw7ly5fn4cOHbN26lbt37zJ+/PhsrxO2xGSv4sWLs2HDBssw6W3atKFmzZooisLNmzc5cuQIERER6ZI2e+TknpaqS5cu9OjRgx49elClShVOnz7Njh07KFasGIsWLUq37MKFC7ly5QofffQRK1eupFmzZpQqVYo7d+5w8eJFgoKCLCWodrOpb0QmeGyEKK1WK4oXLy4CAgLEiBEjxI4dO6yOMCZESl/bsWPHCl9fX6HT6USlSpXEe++9J+Li4oSvr2+GEbCEEGLHjh2icePGwt3d3bLP1K4nBoNBzJkzR1SvXl24urqKUqVKiVdeeUVcv37dajeVxYsXi+7duws/Pz/h5uYmihYtKurWrStmzZpldbyChw8fik8//VQEBAQId3d34erqKipWrCg6duwoli5dKmJjY22ONTtr1qwRgYGBwsPDQ+h0OlGjRg0xY8aMdH2VUz1J18LMuj5ipcuTEELcunVLjBkzRlSoUEFoNBpRvHhx0a1bN3H8+HGb9rFr1y4xZMgQUb16deHl5SX0er2oVq2aeOONNyzdZ6xJ7RaUWfem7CQkJIgPP/zQMuqfr6+vmDJlirh165YARLdu3dItb8tn+ueff4pWrVoJb29v4eHhIQIDA8XGjRsz7cqT2QiEfn5+Ytq0aVa7A2b2PQiRsetVcnKymDVrlmjfvr0oX7680Ol0wsfHRzz//PNi8eLFGUZkS10/J111jx49KoYNGyaqVq0q3N3dLSO/de/eXaxduzbDb95kMomvv/5a1K9fX7i5uQk3NzcREBAgFi5caNcIhFFRUeLdd9+1jDZXpEgR0bZtW6vdFu3t/pVWdiMQWpNZV7ikpCQxffp0y+iXqedeTkYgPHfunHj11VfFM888Yxk974UXXsjQFfn8+fOiS5cuokSJEpbRB5ctWyZCQ0MFIAYPHpxh23PmzBHPPvus0Gq1AjKOQGjr9S8qKkpMnz5dtGrVSjzzzDNCq9WK0qVLixYtWojVq1fb1d0wq5hSf0/WZHV9Cw0NFa+99pqoUqWK0Ol0wtPTU/j7+4tXXnlFbNy40aa4Mju37L2npY1zy5YtonHjxkKv14siRYqInj17ipCQEKv7T0pKEl999ZVo0qSJZYyN8uXLi9atW4u5c+emG8vBnt+BIoSVsnJJykeGDBnC999/z++//25X47Xs7N69m3bt2jF58uRMi1BzS8uWLdm/f7/VqilJkiRHe6oNCCXpSd28eZOffvqJ6tWrWx1/wRapL3NKKyIiwlJnnV1bB0mSpMIuTwYdkiR7rV69msuXL/PTTz+RlJTEJ598kuNuYpMmTeLMmTM0bdqUEiVKcOvWLXbs2EFkZCSjR4/O9YFpJEmSChqZDEj50jfffMOBAwcoX748c+fOtdpVylY9e/bk3r17bNmyhejoaFxdXalZsybDhw93SO8MSZKk/Ea2GZAkSZIkJyfbDEiSJEmSk5PJgCRJkiQ5OZkMSJIkSZKTk8mAJEmSJDk5mQxIkiRJkpOTyYAkSZIkOTmZDEiSJEmSk5PJgCRJkiQ5OZkMSJIkSZKTk8mAJEmSJDk5mQxIkiRJkpOTyYAkSZIkOTmZDEiSJEmSk5PJgCRJkiQ5OZkMSJIkSZKTk8mAJEmSJDk5mQxIkiRJkpOTyYAkSZIkOTmZDEiSJEmSk5PJgCRJkiQ5OZkMSJIkSZKTk8mAJEmSJDk5mQxIkiRJkpOTyYAkSZIkOTmZDEiSJEmSk5PJgCRJkiQ5OZkMSJIkSZKTk8mAJEmSJDk5mQxIkiRJkpOTyYAkSZIkOTmZDEiSJEmSk5PJgCRJkiQ5OZkMSJIkSZKTk8mAJEmSJDk5mQxIkiRJkpOTyYAkSZIkOTmZDEiSJEmSk5PJgCRJkiQ5OZkMSJIkSZKTk8mAJEmSJDk5mQxIkiRJkpOTyYAkSZIkOTmZDEiSJEmSk5PJgCRJkiQ5OZkMSJIkSZKTk8mAJEmSJDk5mQxIkiRJkpOTyYAkSZIkOTmZDEiSJEmSk5PJgCRJkiQ5OZkMSJIkSZKTk8mAJEmSJDk5mQxIkiRJkpOTyYAkSZIkOTmZDEiSJEmSk3NxdAD5yV+Rt/ntziUeGhIp716UruVrU9LN09FhSZKMJyWzAAAgAElEQVRdhDBC0h5EwiYwx4BLeRT9ABRNbUeHJklPRJijwHgNUIFLNRSVu6NDKjQUIYRwdBCOdic+hjGH13AzLookkwEzoFOpEUDX8nWYWq8jGpXa0WFKUraEIQQRNRREAoi4f6eqQNGBSy2UoktQVDLBlQoWYbyBeDQbkvalnMsIEEZw64Li+RaKqpijQyzwnD4ZiEiMo+sfS4hKjsds5aNwVWtoXqoyC55/GUVRHBChJNlGmO4gwruAeJTJElpw8Ucpvg5FkcmtVDAI49+IiL7/Jrfmx+a6gMoHpfgvKGofR4RXaDh9m4ElIQd5mJxgNREASDQZOHTvKqcjb+VxZJJkHxG7GER8Fkskg+kqJB3Is5gk6UkIIRBRY0DEkjERADCCORwRMzmvQyt0nDoZSDYZ+fn6aQzC2kn2P4kmA8uvHM2jqCTJfkIkQ8ImwJTNgvGI+O/yJCZJemKGk2AOB7IqwDZC8lGE6W5eRVUoOXUDwnuJjxBZnmQpBHA++s7TD0iScsocDthYjWUMfaqhSFKsMZqTkb9xI+4CiqJQyeM56hVti5vaw67tiKR9Ke1fsqO4QNKfoO+Vs4Al504GVHa0AbBn2dwSbwzjfvyfGM0J6DXPUFr/AipFk+dxSAWBlmxLBSzy7zmU2oRJts8pmIQQHHywgQMP1gIKRpEMwD9x59lzbxUdyoygfrGXbN5efFw4ehse2BBmIClnQUuAkycDpd280KldSDAZslxOBTzv45c3QQFJpkhO3n+fiMSTgAohTKgVLQDPFhtHJa8B8mIppacqDuqSYMqubYsGXFvnSUi2EiIZErciYpeBKaXUQrhUQXEfCa6dUBSnvkwVKEfCN3HwwXqMIv011SBSbtQ7w75Fo3KljneLDOsmJSURHBzMsWPHOHr0KEePHqVnh0Q+edcLV9esEwIzKtTqCrl3IE7IqdsMqBUVr1Z+Hp0q64uN2WBk74zFnDx58qnHlGx6yP7bAwlPCMIskjGLRAQGjCIOo4jjYuRXXIpa9NTjkAoWRVFAPwpwzXK5pGQjDw1d8yYoGwiRgIgciIiZntK4EXPKH+NlRMxHiMhBCCGf+AqCZHMie++vttz4rTGIZHaGfYvJbCQ0NJQ1a9YwYcIEGjduTLFixRg7diyXLl2iffv27Ny5ky8WXMDVVZvtviMiYnnrvQ2EhYXl5iE5FadOBgCGVW2Cr0cxtJmMI+Cm1jCyxgsM7dSTLl26MGjQIG7dsv70ZTKEkPBwLvHRU0mM/Qaz6Z7d8YRELSHRGI7AaH0fIpG/Y34gznDT7m1LhZuifxm0z5NZQiBwZfuBGtSt341jx47lbXCZENGTwXAJsFYvnACGvxAxH+V1WFIOnI/5E2wosXwUF0OjLjUIDAxkw4YNlC1bltmzZ3P//n2Cg4NZvHgxgwcPxt/fH5VLUXAfCrhlsUVXtMU+QAiFmjVrMmHCBG7fvp1rx+Us1NOmTZvm6CAcSaNS06V8bW7ERXEjNhKVyQxmM3qtDje1hgk1WjHm2ebUr1+fUaNGce7cOYYPH05cXBwNGzZEq9ViNj0gNmIgiY/mY0o+jMlwCmPSMZLivsNkvI7GtZVN/bpN5iRO3J+MOdu6LxVgppQ+MFc+A6lwUBQVuHZMaUdouMjD2EQ0Go+UYnZ1OVRFPqZGwBQqVqzIgAED0Gg0PP/88w6rchKme/BwGpCcxVKmlBHn9P1QlKxuCJKj/RV9gH/iz2W7nFql5tWuo1k843v69u1LYGAgvr6+aLWZlABoG4OIAsNFUk7u1N5fOsAFPN9BX2ww7du3Z/DgwRw9epRRo0Zx8+ZN6tSpg5eXV6axCPNDzMnBCNMNUHROPaKh05cMAHhodMxp1JN9HSbS+J6a2jcMzGnYkz87vcWgKv+7WHp6ejJjxgyCg4O5du0a/v7+/PDDIh496ILJcAZI5H+NuJKAJAwJW4iLHIktYzvFGW+i2NAiXGAgPDEox8crFV6K4oLK4zUiVVvpMzIKVdF5KYMM+exCcW0LQPfu3Tl27BirVq2iV69eREdHOybYxF3Y1ANCUUPi7qcejvRkXFTZF+cDqNUu+BQrYXMSqigKKq8pKD7bQP8KaJ4DTT1wH4VSYi8q90GWZUuXLs2cOXO4dOkSer2e5557jnHjxnHjxo102xSmCJKi3iT+XgMSo0aQGDWKhPvNSIgYjNl41faDLkRkMpBGUZ2eErfjqJOop2WZapkOQVyhQgV+/PFHNm7cyKPIRSQm3IZMivUhEWPyUYzJf2aYExcXx+nTp1m/fj2fffYZH3zwPnHxWQ0a8z8im7ERJOd28dIVHiVWReXaAkXjn+HC6+fnx6FDhyhbtiz169fPk/YwjxPmKGxqAS6SwRz11OORnkwVj3poFJ1Ny1Zyf87u7SsuFVB5TUFVfD2q4mtReb6R6aiDJUuWZPbs2Vy6dAkvLy/q1avH6NGjuX79OsL0gITwjhgTNwNJKSN2ikdAEubk/SSEd8FsuGh3fAWdTAYe8/DhwyyLldJq2LABr/RV0GVz/gtzPDevfswXX3zByJEjadmyJWXLlqVEiRK8+uqrrFmzhpiYGJ6r3gqdLvtuX0aD4MyRKHbt2oXRmFkSkp7ZFEbyo0UkxbxP0sMvMBsu2bSeVDBdvHiR6tWrZ7mMTqfjq6++YubMmXTo0IFFixbZVIKVG65du8aBg3+RmGRLyYAW5Njz+V55/bN4aIplOT6QgooyrpUornsmT2IqUaIEM2fOJCQkBB8fH+rXr8+JQy9hNj3A+gOcABFHYtTwPPst5Beyz85j7EkGhDkSYcOAGIoCatUVwsJqUb9+ffr160fVqlUpV64cKlX6fOz0g/vceLQJkUWfcY1Gh2tsCz6a8xGDBw+mb9++DBw4kIYNG2Z4AhQikaTotzEl7vp3SjKgxhj3LSpNdVyLLkNRl7DpeKWC48KFC9SoUcOmZV9++WXq1q3Lyy+/zIEDB/jmm2/S/QaMxlvEJx1AiCQ0LhVx071g97sNDAYDhw8fZuvWrWzbto2IiAj6932RwLpqMi9V+5cwgWs7u/Yn5T1FUaj9qCu/G79Gq3fJUAOkoMJN7U6v8m/leWw+Pj58+umnvDXpFdTxHVGUrEtWhTkKc/IR1LqmeRSh48mSgcfYkwykfHy2ZY/e3kX58ssvGTNmDG3atKFChQoZEgGAZ4uOQ6PyQsnkq1Errvh6dWPssKkcO3aMgwcPUrRoUQYOHIi/vz/Tp0/n77//BlKqEhIjh2NK3E1KEpDaUMsEJGI2/EVCRHeE+aGNxysVFPYkAwBVq1blyJEjFClShAYNGnDmzBmMpvuEPejLzbvNiIj+kIjo6dyLGMU/YXV4FLc+220+ePCAH374gb59+1KqVCneeust3N3d+f777wkLC2Pegh9R6zuQ0hDMOpNZA27dUFTeNh+L5BgXL15kQKdh1I3sR2XPeqgVDTqVHp1Kj4uiwd+rEaOrzMNbW9JhMXq6XcBFk3X3WwBEPMakP55+QPmILBl4jD3JgKIqiqIqhjBnNya2ChdtI5u26eriQ4tyqzl+dxKxhlBMwgCYUCtuCMxU8hpA9WKvW5avWrUq06ZNY+rUqQQFBbFq1SoCAwOpWLEik99uyovNTqKQmMnejAjTAwxxy9F6TrApPqlgsDcZAHBzc2Pp0qWsWrWK3i+3ZfP2ouhcEwCjJecVIgkhIDz6/zCbH1HEc5hlfSEEZ86csTz9X7hwgTZt2tC5c2fmzZtHmTJlMuxTKfJZypjyxnMZhp01mXUcOhZL8Uo9qGN/FbOUh27cuMFLL73E7Nmz6dPhVQAeGiK4l/gPCgpl3Crh7lLEwVFCShsBG4v/bRkGuRBx+lcYP65BgwYsXryYhg0b2rT87X9mojZ/jWtW7QYUNzyKr8VFW8+uWGKSQrgbfwCjOR53TVnKeryExoZ30RuNRn7//XdKuL/Ls1Vist+RUhR9qVMpXdOkAu/hw4eUKVOGR48eWS19ssWV0CGY2YUmiyYsCjp8ihxg797TbN26le3bt+Pq6krnzp3p3LkzzZs3R5ddgxpACCMk7uT23x9TsthDXNTqlFcte4xk3aZo3nnn/zhy5Ahly5bN0bFIT9f9+/dp3rw548aNY8KE/P1QYUo6TGLUiH9fh5wVNzSek9F6DMmLsPIFWTLwmIcPH1KkiG0Z7Lp165g06Uv27ayAqy4aqy2jFTe0bj3tTgQAiuj8KaLzt3s9FxcX2rdvT9zd/7OtFkPEgXgIiiyKLQwuXrzIs88+m+NEwGx+hItmf7anTlJyMlOn1+VMcD06derEW2+9RbVq1ewet0BRXMCtM6MnL2bkyCl0797dMq9vX7h+/QadOnXi4MGDeHpmnwxLeScmJob27dvTt2/ffJ8IAKi0jVEUd0S2yYAZjb5HnsSUX8hk4DExMTHZVhMkJCQwceJE/vjjDzZt2kHlGtWJj5mMIWE7oAZMoGgAgav7WHSe4/MidCtsvRkIbH7jnZTv5aSKIK1kw8WU81dkVr2UQqsVTJwUSIVntuR4X2mdOnWK+vXrZ5j+7rvvEhoaSp8+fdiyZQsuLvKylR8kJCTQtWtXmjZtyvTp0x0djk0URYXGaxrJ0ZMg0+pTNzTuw1FU+aFaI+/IcuHHZNdm4MKFCzRq1IhHjx5ZLl6KSo970QV4lQ5C7/0pbl7vo/eeQ5HSp3H1muCwEd7U2kbY8hUrqpKg2NpoUsrvnjQZEJhtGvwKQO2SO+f2nTt3MBqNlCtXLsM8RVFYuHAhAK+99prTdfnKjwwGA3379qVcuXIsWLCgQL04TePWCa3XdFIarqYd1VJLUjKcDamBxvNtB0XnODIZSCM5ORmDwYCbW8ZhT4UQfPfdd7Ro0YKJEyeyatWqDEmDSlUUrb43Oo+haN06oig2tFp9ijQeo8iqpTaAWehw8RhToH7MUtZsGWMgK1qXqja9HMhsdkGnaZDj/aR16tQpAgICMj0PXVxcWLduHceOHWP27Nm5sk8pZ8xmM8OHD8dkMrFixYocV0c5ksa9H/pSx9B4volK2xSVtjEu7sOJTPqRLr0PZBix0BkUvG/xKRDGm5gffoYS2ZGQw76I6NcQyUGWJ5BHjx7xyiuv8OWXX7Jv3z6GDx9eIG6eam19XNx6QCZjuhuNLpz5K5G//5FNtQuTJy0ZOHfuFieCXDBlPtQFAEaDkTEjf+Pw4cM53leqzKoI0vL09GTbtm18/fXXrF279on3KdlPCMGbb75JaGgo69evR5NVC9N8TlEVResxGrfia3Arvhad12SqVGvOpEmTGD16tNOVQDl9MmCOW4EI7wjxq1BzA78Kakj6AxE1AhE1kuBTRwkICMDd3Z3jx49Ts2ZNR4dsF22Rz9C4vwaKR8ofXEFxB3S4evbiRsR02rRpT3BwsKNDlXJBfHw8YWFhVKpUye51IyIiGDduHO3atSP+0XBcXDzJrC2JougpXnQ0HTsOo1+/fnTp0oUzZ87kOO6TJ08SEBCQ7XJly5Zl69atvPHGGxw6dCjH+5OyJsyPEIYQhPEaQvwvK/zkk0/Yv38/W7ZsQa/XOzDCp+edd94hLCyMVatWOTqUvCWcmCl+izCF1RamsKpW/yTefFb8stxXrFmzxtGhPjGzOVEYEnaJ5NiVwhD/qzCboi3zNmzYIEqWLCmOHDniwAil3HDq1ClRu3Ztu9YxGAziq6++EiVKlBCvv/66iIiIEEIIkZQcIv4JayKu3aokrt4sI67eLC2u3aokrt2sKCKiZwmz2SyEECIhIUHMnz9flCpVSvTr10+EhITYHXe5cuXE1atXbV5+586dolSpUuLy5ct270vKnNlwTZgi3xCmsJrCdLeeMN2tI0x3GwnTo6/F11/PFVWqVBF37951dJhPXVBQkChVqpS4f/++o0PJM047zoAQAvGgBWQzYJBZaFGX2I7iUiGPInOM7du3M2TIENatW0fLli0dHY6UQ6tWrWLz5s02F6Pv3buX8ePHU7JkSebPn0+tWrXSzRdCkJQcRHziHszmOLSaKnjoe6BSZWxwGhsby/z585k7dy49evTgo48+onz58tnGcP/+ffz9/YmMjLSr+m3ZsmXMnj2bw4cPU6KEHFL7SQnDOUTkq/8OtpN+uF6jyYUz55MoUW0HFStWc0yAeeztt992qhIC500GkoMRUUNBZPeWQBdwH47KM+/H085re/bsoW/fvqxcuZL27ds7OhwpB95//320Wi1Tp07Ncrl//vmHt99+m6CgIObMmUPPnj1zrR1MZGQkX3zxBUuXLmXw4MG89957lCyZfgjay/fD+e7YSfZeDSUhMQlTTBQzBvShY/Vq6OzoOjhlyhT27dvHH3/8YbXhr2QbIQyI+81AZP52SLPQonJ/BZXX5DyMzHHi4uKoU6cOCxYsoFOnTo4O56lz3jYDpjvY1rfeCMbQpx1NvtC6dWs2bdrEoEGD2Lhxo6PDkXIgu8aD8fHxTJ06lYCAAGrXrs3Fixfp1atXrjaILVasGJ9//jkXLlzAaDRSvXp1PvzwQ6KjowH479ET9F6xhk3nLxIVn0Ci2YzBswjTd+6hy7KVhMdmNyDM/8yYMQNfX18GDx6M2Sxf651jSXv437tLrFMpyZCw1qaeJoWBu7s7S5cuZdy4cTx69MjR4Tx1zpsMKHpsHmjHSpFoYdW0aVN27tzJuHHjWL16tWW6EGZE8klE4m+I5OMpQ8hK+cL9xL/ZdecLfrw2Dv/BkbjVuEuiKf3Lp4QQrF+/nurVqxMSEkJwcDAfffTRU32aLl26NF999RUnT57k1q1bVK1albEzZzP/wBESjUZM5vSFkvEGA7diYhiy5mebW3KrVCqWL19OWFgY77333tM4DKcgErbaMEQvgALJztPYuG3btrRp04YpU6Y4OpSnznmrCcxxiPtNyHwUqn8p7ijeC1F0gXkSV35x/vx52rVrx/Tp0xjWXw9xC/8dkS41gVKD+wgU95HynQbZEEJwN+EkF6J+Ijo5FJWiobx7c5717oWHpnSOt2s0J7P99mf8E3cSkzAg/q3ndVFSxpZoV+Zt/Iu04OzZs4wfP56oqCgWLFhAixYtcuW47HXx4kX6rPmFJNesW6HrNRqWvNyNxhWzb2+QKiIigqZNm/Lmm28yZswYzMLMjfjLxBjC0an0VPKoiVaV/XsSnJU5cigk/5n9gooHSpE5KK6tnn5Q+URkZCS1atViw4YNNG1aeF9p7LTjeioqd4RbT0j4GavvFEhZCpQioG2Sl6HlCzVr1mTfvn3s3/oShihXNC5WSgJiFyGMIVBkToEYd8ERDOYE/rjzNhGJlzCmeQvaxeh1XIpZT0OfCfh752wM9J13ZvFP3EmMjxXbpv5/150v+GbRcn6ct4Np06YxcuRIhw7lqy1RCsXDC4xZlyrFGwysCT5rVzJQvHhxtm3bRvPmzdE+m0RYyXMkmxNITV7NmGlUtC0dyryCi6rg9o1/atSVgKOkvN48C8IEaud6YVSxYsWYP38+I0aMIDg42KaXbxVETv1Ip3hNBpdqJBusfQxqUDxRin3rtE++VXyjGdLP3XoiAEBCSl1j0u48jasg2R/2AQ8Sz6dLBADMGDCJZILCF3Aj9oDd241MukFo7LEMiUBaJpIp0SyCixcvMnbsWIeP6f8gNg6N2rbf0vX7D+xuA1ClShU+3/Iu5/X7eGSMIsmcSJI5gSRzAgZzEscjd/Nt6MeYZBVXBoq+H2BDkqR+BkXjHL0J0urduzdVq1bls88+c3QoT41z3uX+pSiu/PPoM2YtfIRJeANu/7YlcAW3Hig+m1Fcqjg6TIcRcf9NaTSU5ULxiLhv8iagAiYq6Sp3E05hFpl/hiaRxInwhXaPdnY2aismkc1THOBeAoR7rF3bflq8XHUZ2glk5tzJk3h4eFCnTh1efvllPvjgA1auXMnx48eJibH+Wu77ibf5WxeExlVtdb5BJHM7/ipHwnfl+BgKK0VTFXRNyXr4clcUr//Lq5DyFUVRWLRoEYsWLeLcuXOODuepcNpqglRTpkyjevXRuJR+H0y3AROoS6NkMoSvU0k+ik3vQDb8hRBmpy1ByUxIzK+YhSHb5eKN4dyICEaTXJq4uDji4+Mtf6f9d9ppbs3P4Vou+2RApbjw0HCX4jrHj5PxbKkSuGu1xBuy/kzctRpmThpP4Jefc/nyZUJCQggJCWH79u3MnTuXy5cv4+Hhgb+/f7o/D/wuYM4mQTKIZA6GbybQp6Os2nqM4j0PETUWkXwKsyketTr189ECCnhNRdG1dGCEjlW2bFlmzJjBiBEjOHToELeSrnE/KQwXxYUqHjXx0hTsV8A7dTJw/PhxDhw4wLfffouiqKGQDyxkNxuePP/HhJMXNGXwMPmGpVFfVh49jGXAm10JPWnG3d0dvV6PXq/P8t9aFz1gyxO/QK3kjzpylaIwJrAh/9l7iASD9aJ6BdBrtbSpVhkXlYqAgIAMwxQLIbh9+7YlSQgJCWH37t1UfVuDe8ns63PjjA95ZIzGS1M0Nw6r0FAUVyj6Hft3f4kqcSXNm5ROeZW17kUUfT8UdcnsN1LIjRw5kq2nf+a9E8NR6VOSJQUwCRP+nrXpW340npqC+epjp00GhBC8/fbbfPzxx7i7uzs6nPxJXQ5MV7NfTlUcJZ/ccPITrdrTpuWKeBVhw9rFlNFn/aKetC7F7OX3u/MwmBOyXE4IM2Xccv4Gw9z2Sv26nAu7z85LV0h4rIRAo1aj17jw/YBeuGTxJjxFUShXrhzlypWjTZs2lumfXRjFQ2NktjEoqDDLdgNWKYrC8h/P0LjxKFqUGOfocPKd09FH8R9dEiNJjw/SyMWHZ5hz+T3e9p+Jh0vB647utI9ymzdvJioqiiFDhjg6lHxLcR9O+vd9W6MD/eC8CKfAqeT5Ii6KLS9zEZR0rW3Xtqt4BqLCet14KrWipaZ3ezQqx75KOy1FUZjZuR2zOrejZumSqBQFjUqFu1bLqw3qsm3UIKr4FM/Rtku52tb7QFHAw6VgF+k+LQaDgW3bttG1a1dHh5LvJJri+enmEkxYTyTNmIg1xPDr7R/yOLLc4ZQlAwaDgXfffZf58+ejVmd9QXVqbl0g7lsw3QSs1fOqQVUURd8/ryMrEMq5B+KicsVoynzIa7Wio5p3T9QqrV3bdlFp6V7+E3658R7JpgQUVfr6b7Wipbi2As1KDstR7E+Toii0r16N9tWrkWgwkmwy4qHToXrCOvzmJbrwT3wIyebMxw5RoaZ+0daye2EmDh06hJ+fH+XKlXN0KPnO8cgDKe1MsmhGZcLEmehj9Co7FDeXglXi7JQlA8uWLaNChQq89NJLjg4lX1MUHUrx1eBSnZQSgpTTRaAQFy+ITy6FUnwdihON0GgPleLCi2XnolG5Y+2nplZc8dFVp17xkTna/jP6mpS79TI3gxNRKxq0Kne0Kj06lTsBxXrSt+LcfFUqYI2rxgUvV9cnTgQAqnjUoZxbZVwyqbJSUHBV62lVsucT76uw2rRpE927d3d0GPnSxYfBJJuzH4rZRdFwM+FaHkSUu5xuBMKYmBj8/f3ZuXMndevWdXQ4BYYwnEXErwfTXVAXZ8tuDUv+G8SOHTsdHVq+98hwhzMR/+V67B4UVAhhJvJBLI3Lj6JBuaGolJwV0JnNZurXr8+HH35I+66tiDHcRa1oKK6riDqH2yzoks1JrPlnLldiz2IWJsz/DqJjiDdSzL0ko6tNx0dXxsFR5k9CCPz8/NiyZQu1a9tXbeUMFv89g8ux2XcrdFXpGVxxAs96PZcHUeUep7tizJo1iw4dOshEwE6Kpg5KkTqW/7fvksTrb1bh+PHjNGrUyIGR5X+emmdoVvpDnje/TbzxAWpFw4iPJqJ5wUijsTn/Ca5btw6NRkOPHj1QFAW9i2wdr1XpGOw3mQeJtwmK/IPI5Hu4qT04te8Kty8l4LNIJgKZOXv2LCqVKsNrrKUUFfRVuBZ3CWM2jU+NwkAp14I3SqNTlQzcvHmTunXrcubMGVknlgu+/vprdu3axebNmx0dSoGzZcsWZs+ezcGDB3O0vsFgoHr16nzzzTe0bt06l6MrfO7evUuNGjW4cuUKxYvnrIFiYffxxx8THR3Nl19+6ehQ8qXo5Ag+vTgRYzZjh1TxqMFrVT7Ko6hyT6FuM3An4RbBUUGcjQ4mzhjHBx98wNixY2UikEuGDx/OyZMnCQ52nreY5ZaXXnqJixcv8s8//+Ro/e+++w4/Pz+ZCNiodOnS9OjRg8WLFzs6lHzr119/pVu3bo4OI9/y1hanmc9LaJXMx7LQqnT0KDsk74LKRYWyZODyo4usvbmS+4n3UCspvQUM5mSu77vFf4etpHRRWVSYW+bNm8eBAwf45ZdfHB1KgTNmzBh8fX3tfvVufHw8VatW5ddff6Vhw4ZPKbrC5/z587Rt25bQ0FBcXfN3w8q8duPGDQICArh7967D32GRnwkh2Bb2E/sfbEdBwfDvUOMatLi6uDHc7x183QvmEPaFLhn4K+Y031z9yvIlpWNWKO5anCnVP8HdxSPvgyuE4uPjqVy5Mrt27aJOnTrZryBZHDx4kHHjxvHXX3/Ztd6sWbM4ceIE69evf0qRFV4dOnSgT58+DB061NGh5CsLFy7kxIkTrFixwtGhFAhxxkccj9xPWOJN9v2+n9pFG/Ba90moCvCQ7AU3ciuSzUl8e+1r64kAgEoQbYhm3c0f8zawQkyv1/PWW28xY8YMR4dS4AQGBhITE8PZs2dtXicqKor//Oc/8vPOoUmTJvHll1/a/WKowk5WEdjH3cWTViU7M6DCWKpG1Ofq/lsFOhGAQpYMBEUeIbsX65iEkVNRx0nIYiAYyT5jxoxh//79XLhwwdGhFCgqlYoBAwawevVqm9f54osv6NatG/7+/k8xssKrbdu2qFQqfvvtN0eHkm9ER0dz/Phx2rVr5+hQCiR+2FcAACAASURBVKSAgABOnTrl6DCeWKFKBs5EB5Nkw6AQasWF0DgbxtyXbOLh4cGbb77Jp59+6uhQCpyBAweyevVqzObsX2gUFhbG0qVLmTp1ah5EVjgpisKkSZOYM2eOo0PJN7Zv307Lli3lO1pyKCAggNOnT9v0G87PClUykF2Xj7RseRe8ZLvXXnuN3bt3ExIS4uhQCpTatWtTpEgR/vzzz2yX/eSTTxg6dCjly9s2Br9kXf/+/Tl//rxd1TOFmawieDJFixbFx8eHK1euODqUJ1KokoEK+oq42DDymkkYKe0qexTkJk9PT9544w0+++wzABKNRpJM8s1wthg4cCCrVq3KcpmrV6+ybt06Jk+enEdRFV5arZbXX39d9qcHkpKS+O233+jcubOjQynQCkNVQaFKBl4o0ZqUt0tnrZy+AiV0pZ5+QE5m1Lix7Aq/w/Pff02N7+ZT/b/zab56GT+ePy0Tgyz079+fDRs2kJycScNX4KOPPmLChAn4+PjkYWSF1+jRo9m0aRNhYWGODsWh9uzZQ61atShVSl4Pn4RMBvKZYlofmvm0RKvKalAILX3Kv5qHUTmH2ORkhu7dhnvndtxLTMAsBGYhuPkohk+P7qP3r2uIN2R+s3Nmvr6+VK9enV27dlmdf+bMGf744w/efPPNPI6s8CpWrBgDBw5k4cKFjg7FoTZt2iSrCHJBYUgGCt04A2ZhZv2tVRx8sBezyYRZldKoQ6dyRaWoGFN5Av6eNRwcZeEzatev7L8ZSpLJelsMnVpNG9/KLHpRvifdmiVLlrBv3z5++umnDPM6d+5Mu3btGD9+vAMiK7z+/vtvmjRpwvXr152m8VyCKZnf757mfPQNFBS+/XAOv37+X2r6V3d0aAXa/fv38ff3JzIyMuU1xwVQoUsGUkUlR/L5Lx+ToI+jTq3nqOtdn3reDXFRydG1cltY7CNa/vRtpolAKq1azaEBoyipd44Lrz3Cw8OpXLkyt27dwtPT0zL94MGDvPrqq4SEhKDTZV7iJeVMz549adOmDa+99pqjQ3nqttw+zrxLm1EUhQRTSimdOdGIp4c7H9bsR/OS8iHpSZQvX579+/dTqVIlR4eSI4WqmiCtotpi3NsRRUBEU0ZWep2GxZrIROAp2RlqWytalaKwy8ZlnY2Pjw/NX3iBRevX8+v5C2y7FML92Fjee+89Pv74Y5kIPCVvvfUWc+fOxZRNIlvQbbt9grmXNpNoNlgSAQCVqwtxxiSm/rWaI+GXHBhhwVfQqwoKbTIAcPnyZapWreroMAq9mKTEbEsFAJKNJh4mJeZBRAXPwdDr3G/dlm/vh/PR7j+Ysus3XliyjPsBDXipe3dHh1doNW3aFB8fH7Zs2eLoUJ6aZLOReSGbSTJn3vU6yWxg9oVf5MiMT0AmA/nY5cuXqVbt/9m77+ioqm+B4987fZIQUgiE3kNVeq/SQZAmTUCRZhc7VniIiiiKiqiI0ptUARHpRZBepAZCLwkmhPTpc+/7A+EHkmQmkMxMMuezFmu959zc2clv5t599zlnnyhvh1HgFQkIwOjG5iYGjYYiYojgHutPx/Dcr6u44XCiqDWY7HYybHYcigKly9Bz/kKuZ2R4O8wCyR+aEG3755hbx6U5zBxKOpfH0RRcIhnwUYmJiTidTiIiIrwdSoHXpXwUshtPFE5FplN5Uam5k8lm543f/8DiyHzppQzcMJn5cNMWzwbmR3r16sWVK1fYu3cvQIF7Oo5Ji8XkdN2Z1anInE2/5oGICqZ69epx8ODBfPv5KbCD6LeqAvl1Zmd+EmYMoGfl6vwacxJLFv0EDBoNfaJqUFgvto6906qTJ10e45BlNp05S5LZTKjR6IGo/ItGo2HAmy/w8u5fMMetxyY7Kaw10qd8XQZXaERRYyHXJ/FhapV7z3wSoM7nm+14U/HixVGpVFy5ciVfdgktsP/LiyECz/qweTsalyhNgEZ7z2sBGi3NS5ZlTNM2XojMt207dx6T3XUbba1azZE48dSWF2af2c3vJa0klwzCJt+c+5JiNzPnzG4e3TiV48n5uzFR7dAKGNU6N46UqBVSLq/DKbAkScrXQwUFNhmIiYkRyYAH6dRqZnTuxZR2XWkYWYpArQ7JZqdaQDBT23fjx4490KrV3g7T59id7m1uIkk3KwRC7vor/hxfndiMVXYg/ecJ2iY7SXdYGbpjDhl212V2X9UgrBKBmuwrchJQJjCCioVEm/YHIZIBHyRWEnieSpJoW7Yii7v35/jQl2m5P5o+dg2PlKmASgzXZOrh4pHo3EiSbE4nlYuEeyAi/zI1eisWZ/aVGbvsZNXl/LupkUpS8UrJTsjWzIfwJCBAref/avb3bGAFkEgGfJAYJvC+ypUr5/udvPJa/1oPubGbBtQoWowyISF5Ho8/SbaZOXoj1uVxZqedX84f8EBEeSMlJYVXew+j+ZnCVAyKRK/SonFKqOwKOpWG6oXLML3Ri5QLEvsTPKhbkwjzowI5gVCWZWJiYkRlwMuioqL45ZdfvB2GTzMqCsYzMdjKlEFRZ/51NGo0jGn3iIcjK/hSbWa0KjV2N3pkJNtMHogo91mtVnr06EHz5s35bNQHSJLEmbQ4fl6ziNOnT/PV6+MpHSg2v8otZcqUwWKxcCU2lpLFi+erCewFsjIQGxtLcHAwwcHB3g7Fr4nKQPbi4uJo1aoVdRw2nmnSBJ1ajeGOfg2BWi1hRiOz+vSmpthVLtcV1hmxK+51HgzVBeRxNLlPlmUGDx5MkSJF+Prrr2/fmCoVKk5NZ1H00SkiEchFN8xmpuzdTejoN2m5ZBGVp3zFE8uW8OfFC94OzS0FsjIghgh8Q+XKlTlz5gyyLKNyc3mTvzh58iSdO3dmxIgRvPvuu0iSxNAG9Vlx/DhTFi6kVvUaDGrVkkcqVkAj/nZ5orDOSO3QUuxLvJjtcUa1lv4V6nsoqtyhKAqvvPIK8fHx/PHHH6j/My/FYDBgsYhuoLnlfFISfZYsIsNmQ9bfXLkhKwq7r1zm72vX6FezJh+0bO3TlYICeZURKwl8Q1BQECEhIVy9etXbofiUP//8k9atWzNu3Djee++92xeIsAAjwxrUJ+LoEQaVLU37ypVEIpDHXqzWGkMWwzO36NUaupZ+2EMR5Y6JEyeybds2Vq5cicFw70oCg8GA1Zp/V0j4EocsM2j5UpLM5kzbspsddn45dpRlJ497ITr3FZgrzfnUG3ywZz1Nl37Hl5pEjjeqxIbLMTjFciyvioqK4vTp094Ow2csWbKE3r17M2/ePJ566qlMjxGVFM9pGFGOt2p2wKDW3LPiRa/SEKw1MKv5UwRq3Fmn7xtmzZrFtGnTWLt2LYULF870GL1eLyoDuWTTubOkWi1k13fQ7HDw9e7dPt2dsEAME/x8Yh+fH9qOQ5ZxKDJo1cQCr/y5moqFw5nXvj/BOrHrmzfcmjfQtm1bb4fidZMnT+aLL75g/fr11K5dO8vjRDLgWQMqNKBOeGlmxOxi/ZXjWB12igQUon/5BvSvUJ9wve/tpyErMgnWVByykyL6YPTqm82+fv/9d95++222bdtGiRIlsvx5URnIPb8cP0aGG43DbpjNnE5MpEoR35ynke+TgbUXTzHp0PZM2+BmOOxEJyUwfPNSFnca6IXoBFEZuHlzf/3111m/fj1//fUXZcqUcXm8SAY8q2rhSD6r35O+lObZZ5/lz/37vR1Spmyyg8UXd7Lw4p9kOKyoJAlFUehcoi61UkIZMmQIq1atokqVKtmeR1QGcs8Ns3srTTQqiWSLOY+juX/5OhlQFIVPD27FnEU/fLjZRexo4jWOXI/j4SKiu5anVa5cme3bt3s7DK8xm80MHjyY69evs2PHDkJDQ13+jEgGvMeX//ZWp50X908nJi3unu2IV17Zy9I0M5NmTqVx48YuzyUmEOaeYoFBwD8uj3PIsk/v2uqbn3o3nUxKIN7semtXq+xk/unDHohI+K+CXhkw2e0sP36CKbt28dP+/Zy7ceP2a4mJibRv3x6tVsu6devcSgTAt29IBZ3T6fTZv/2U02s4nRZ7TyIAIKOgDjKwMvicW+PSer1eDBPkkv41HyJQe++eLP8VGVSICm5eA7whX1cG/jGloXFjly1ZUbiUnuyBiIT/qlixIhcvXsThcKDR5OuP210URWHKrt38uG8fkiRhttvRqFRM3vkXNYoW5a1atRjUqyePPfYYn376aY5uMCIZ8B5Zlu9ZhucLTA4rv109gE3OugqKBEm2DA7cOEv98ErZnk9UBnJPy7LlKBIQiCU1BWcWiZhRo+G1Jk3F0sK8EqTVo2Q7h/N/QnRi61xv0Ov1FC9enAsXLng7lFz10dat/LhvH2aHA5PdjgLYZRmLw8HhuDj6LlnM088/z2effZbjG7tIBrzHV//2B5POufXgY3ba2HDtb5fHiQmEuUetUjG/dx8iAgMx/ueBRyVJGDUanqnfgEejsp/H4W35+lGtdkRxtzbACdTo6FWxpgciEu6kKAoHYmMp3KsXL27cSFR0NL1r1KBFuXL5euOis4k3WHTkKBZH5k9pTkVBE1QIc40a93V+X70h+QNf/dubHFY3H3sgzeF6kpqYQJi7ShQqxPrBQ1h24jjf/rWDGxYrRoOelmXL8ky9BtSK9P35avk6GdCq1Ayt1oBpx3ZnOYlQ4mZb1zYlK3o2OD+XaDLx9PLlnE9KwlSiBGkmEzGnT7Pl/HnCjEbmPv54vt14Z+bBAy772TuBpcdP8HbLlhjcGE+8k6/ekPyBr84ZKGZw77uikVSUMrre3VJUBnJfkE7HU7XrYNu3n90HDvPzzz97O6Qc8b1PfQ69+FATGkeWwai+94KrkVQU0umZ174fah/8ghdUVoeD/r/8wunr1zHZ7XBHFcBktxOblsbjixZxw+y7y2yys/9qbJZjg3dSSRKXUlJyfH6RDHiPr84ZeCikDAFuND5SSSoeK9XQ5XE6nQ6bzYYsmrLluqtXr1KqVClvh5Fj+f6Ko1Gp+OmR3oxp0IayQSHoVGrUsoJGVuhfuRZ/dBtKVEiEt8P0K6tPneJaejr2LC40sqKQZrUyO59u9ZnXRDLgPb76t1dJKp6v3AmDKusqk2xzECUVoVSA68qAJEliRUEeuXLlCiVLlvR2GDnme5/6+6BWqRgQVZutPUey6/HnedEZRpOtJ/mocQdKBIqdCz3t5/37b1YEsmFzOpl7+LBPt+fMSsNSpdC4MedBURTKZNEONju+Wqr2B76aDAB0LlGPoRXboldp7ppMKAFGtY5KughWPTmRTZs2uXU+MVSQN0RlwAdIkkS4IYBG1Wpy+mS0t8PxW1dSU906Lt1my3ISni8bUreO61Ky00nHsmVyPF8AfPuGVND5eiI2uHxr5jZ5he6lGlLSGEakIYSmRaoyqc5TzGv/FksXLWbAgAGsXr3a5bnEJMK8kV8rA/l6AmFWqlatSnR0NIqi+PS6zoLK3Z32ZEXJl7vyVQgLY1CtWiz4+2/MmSQzGklCr1Ix9+WXaaVS0a1btxyd31fHrf1Bfvjblw4swhvVemT6WosWLfjtt9/o1q0bU6ZMoW/fvlmeR1QG8sbVq1fzZTKQ/67EbggLC8NgMBAXF+ftUPxS87Jl3Vo6WLNYMbQ+fuHNyjutWvJ8o0YYtVoCtVpUkoRerUavVlOvZEk2PfcsKxYu5MUXX+T111/HZrO5fW5RGfCegvC3b9iwIRs2bOCVV15h1qxZWR4nKgO5z2QyYTKZCA93PW/D1xTIygDcrA6cPHky2527hLwxon59Np87l+0QgFGr5dkGDTwYVe6SJInnGzfi6Xp12XDmDFdSUzFqtTxSvjzl/m05GtG0KQcPHmTIkCG0bNmSX375hbJly7o8d0G4IeVXBeVv//DDD7Nlyxbat29PRkYGL7zwwj3HiC6Eue9WVSA/VqTz/6c+C7eGCgTPezgykuH16t3TjesWo0ZDh4oV6Vi5socjy31GrZbHqlXj+UaNeLpu3duJwC3h4eGsWrWKPn360LBhQ1auXOnynAXlhpQf+fqcgZyoUqUK27Zt48svv+Szzz676zWH3YlRX5jr8Sn5chKvr7py5Uq+nDwIBbgyUK1aNZEMeNGrzZpRNiSEyX/9RUJaGrLTicFgQK/R8EyDBgytVy9fZs/3Q5IkXn/9dZo1a0b//v3ZunUrEydORKfLfN24SAa8Jz/MGciJ8uXLs337dtq1a0d6ejqvjnqTJbN2snbZfkLsLfnync3M+HwvvZ9sSrd+DdFqC+wtwSPy63wBKOCVgZMnT3o7DL/Wq0YNtg8fTru0NFpaLMzq3ZvdzzzD8Pr183U74vvVuHFjDh48yLlz52jevDnnz5+/63W7zcHetYcolF6EQxuOY04XJVxPK4iJWMmSJdm2bRu/rdrAoE4TWbVwD2aTDQk1TodMYnwqs7/dyJtDZ2CzZr8kWMhefq4MFKxP/R1EZcA3SJKE7epV6kdEULdECb/vBBkWFsavv/7KE088QaNGjVixYgWKorDos5X0LT6STwZ+Q9GU8sx8azF9S4xk6iszsdvy3/LL/KogJgMAERER1CnbD4cdHI57W2lbLQ7Onb7GtElrvRBdwSEqAz6odOnSJCUlkermmnch78TFxVG8uO9v1OEpkiTxyiuv8Ntvv/Haa6/Rr87TzP94GRmpJkxpZtSosaRbsZpt/DFjC+91nYAzkwu4kPsK0pyBO508cpnE+HSkbC75NquDjasOY8oQyw3vl6gM+CCVSkVUVBSnTp3ydih+LzY2ViQDmWjYsCELv19CcrQZqynzpYdWs43oPWfYMHe7h6PzTwW1MrDtj2NYLa6HANQaNQd3n/VARAWTqAz4KDFU4BtEZSBra3/cgiRn/zW0mKwsnrTKQxH5t4I2gfCW1BSTW6sGZFnBLCoD9y2/dh+EAp4MiEmE3ud0OklISKBYsWLeDsUnHd0R7dZFOvbsP1jN7jcuEtynKAp7rl5h2G8r+CwjiR21qtF32SI2nj+Ls4Ds6le8VCgareskR1JJhBcV+7ncD4fDwfXr14mMjPR2KPelwCYDiqIQqi/B0W2xLPlhE4d2nBLbdXpBQkICoaGhWS6j83ey073PpCRJYt5AHpAVhdGb1vH06uVsuXAOCwoOjZp9cVcZtX4Ng1cuxZoP98/4rw7d67q1gkerVVOrQXkPRFTwXLt2jSJFiqC9j/1IfEGBTAZOHrzA0FYfs3HmKexXCzN70u98+MwMBjcex4HtYtjAk8QQQfbKVndvslFQaCDGIEMeR+N/vtm7i9/OnMLssPPf+ozJbufQtTje2PiHV2LLTZElQ2nUqgo6fdZ9BPQGLU8+1wa1ukDeFvJcfp48CAUwGYg+dJF3Bn7PtUuJ2CwOJFQ4HTKWDBs34lMZP3IG+7eKoQNPEclA9vq81g1DoD7bY3QGLT1f6uQ3TZo8xeKwM/3w/kw3m7p9jNPBhvNniEtP82BkeePNj3pRo3YZDMa7q3SKIqPTa+gxsDGP9s2/LcK9LT9PHoQClgwoisKk1+dnO7Zqtdj5/NX5ON0szwoPRiQD2WvSrR4VHi6L1pB5aVGtURNStDDdn+/k4cgKvq0XL6DCdYKlKLDyVP5/gNDptXzyw1OMmTyAek0rERZRiIhiwRjDTNTrYOTpl9qLhPMBiMqADzn99yUS41JcHme3Odi35YQHIhJEMpA9tUbNhLXvUq/dw+gMWtT/TvKSVBKy5KRCrbJ8s/MjAgsHeDnSgic+Ix27G/OIbLKT2AJQGYCbc0/qNq7Ix989yYINbzJ33Ru8Mb4Xi5bOEHsU3CdZVjh1OZ5jlxIoXCz/VgYKVCPq6MMX3Zr9a86wEn3wIo3b1fRAVP4tNjaWatWqeTsMn2YMNPDhijeJPXuNDXO3k3AlkcJFCvHD8m/o9fGzhEWGeDvEAilYb0CjkrC6mJepliTCDEbPBOUFzZo1Q5Ikdu7cSfPmzb0dTr4hywrzNh9k9sZ9WGwOLObCqKwazn46n1E9mtO4qusdSn1JgUoGckZkwZ4QFxdHmzZtvB1GvlCiYiRP/V/f2/+/rXQKP/74I23btvViVAVXm3Llcbjx8KBVqelauYoHIvIOSZIYPnw406dPF8mAm2RZ4Y2fVrPr5EUst9qFqzTICkRfjueVH1bx/oC2dG1U3buB5kCBGiaIeriMWzNhjYF6omrlr6wtvxLDBPdv4MCBrFu3jvj4eG+HUiAF6w30qFINgzrrZyKtSsVDRYtRKSzcg5F53uDBg1m5ciXJycneDiVf+HXXMXbfmQj8h9Xu4KOFm7iWlH+GlwpUMlC1TllCI1w3zNBo1TRqm38ytvwm2ZbMqqvL+fjEWMq9VoLDwfu4ar7i7bDynZCQEHr27Mns2bO9HUqBNa5lW2pEFEWTSaFQr1ZTPKgQ33d5zPOBeVhERAQdO3Zk/vz53g7F5ymKwox1+zC72EBMURQWb//bQ1E9uAKVDEiSxBtfPoE+i5nZcHOZ1muTBqDWFLyWo75ga/wm3j36Bn9c+52LpgsEVyjEUevffHJiHDPO/YhTEY1zcmLkyJH8+OOPYnJXHtFrNPzcqRvmtesppjegliQ0KhXhxgBGNWzKmv5PEm70j8mbI0aMYPr06eKz5sL11AwSUtJdHmdzONl4KMYDEeWOAjdnoHq98nw05xkmvjyX9FTz7c05NFoVFquJt6cMFRMH88i+G3tYemURDuXuDVFkZGTFxsHkfegu6RhUdoh3AsyHGjdujMFgYOvWrTzyyCPeDqdAmj1jBvW0BlaOeAGT3Y6sKARqtX63zK5Nmzakpqayf/9+GjQQ/QayYrU7/t2K3fWDjc2ef7pXFrhkAKBmw4rM2TWWv3edIfrQBRRZoUL1krw+5hnOxR+hCQ95O8QCR1EUllxegE3OuseDTbbx1/UddC3egxCdmCHvDkmSblcHRDKQ+ywWCxMnTmTVqpsbQQXk01ayuUGlUt2eSCiSgayFBwe6vWdF6Yj8c52TFD+qCW3atInnn3+e48ePo9EUyDzIa2LSTvFNzJdYZUu2x2kkLV1LPEaX4gV/HDa3JCUlUb58eWJiYoiIiPB2OAXK1KlTWbt2Lb/99pu3Q/EJcXFxVK9encuXLxMUFOTtcHzW+7PXsnb/KWQ569tngF7LJ093ptVDFT0Y2f0rUHMGXGnTpg1FixZl0aJF3g6lwLluTcCd5ZoOxU6cOTbvAypAQkND6d69O3PmzPF2KAWK1Wrl008/ZezYsd4OxWcUL16cVq1aiWukCyM7N8GgzfqBUsXNqkDzGvln0ye/SgYkSWLcuHF8+OGHOArATmS+RKfSI7nR2hXAoC64DVzyiphImPtmzJjBQw89JEri/3FrIqGQtTJFQ5j28uOoZDtq6X/fSQkwaNWYE6/yUpsq/84tyB/yT6S55JFHHiEyMpKFCxd6O5QCpUpwNbdWCuhVBuqE1vdARAVL06ZN0Wg0bN++3duhFAhWq5UJEyaIqkAmOnXqRGxsLEeOHPF2KD4t8eIpEtdN493+bWlYpQw1yhSjfd0opr7Yi/cfq8vTgweSmprq7TDd5ldzBm7ZsmULI0eO5OTJk2LuQC766dz3HEzaj0PJvOqiyBCmD2PCw1+gkvwuD31gX3/9NXv37hVrwXPBtGnTWLFiBX/8kf+3J84LY8eO5caNG0yZMsXbofgkp9NJvXr1ePfdd+nbt2+mx4wcOZK0tDQWLFiQL1am+OUVuXXr1pQoUYIFCxZ4O5QC5YkyTxGuK4Jsvze/lJCQrTJxM6+jZDPpRsja4MGDWbNmDYmJid4OJV+z2WyiKuDC0KFDWbBgAWaz2duh+KQZM2YQHBxMnz59sjzm66+/5vjx4/lmyMUvk4FbcwfGjx8v5g7kogBNALXO1Sd2wzX0Kj0GlQGjyohG0lA3tAFjH/qIuOPXGDx4sPi734ewsDC6desmJhI+oNmzZxMVFUWTJk28HYrPKlu2LA0bNmTp0qXeDsXnpKSkMGbMGL766qtsn/iNRiNLlizhvffe4++/80EnQsWPtWrVSpk1a5a3wygwTCaTUq5cOWXjxo2KzWlTLmVcUM6nn1PS7el3HdOhQwelb9++is1m82K0+dP27duVqlWrKrIsezuUfMlmsynlypVTduzY4e1QfN6yZcuUFi1aKDf+SVYuHL+kJMbd8HZIPuGNN95Qhg4d6vbx8+bNU6KiopTU1NQ8jOrB+eWcgVu2bt3K8OHDiY6OFnMHcsHYsWM5efIkixcvzvY4i8VCr169CAgIYOHChWj9uNFLTimKQvXq1fnxxx9p0aKFt8PJd37++WcWLlzIxo0bvR2Kz9vz+wFe6/EuwapQtHodDpuD8g+V5sn/60ejLnW9HZ5XxMTE0KRJE44dO0ZkZKTbPzdixAhMJhPz5s3z2fkDfp0MwM3VBYMHPUnNSk1I/CeVgCAD9ZpXJihYLH/LibNnz9KoUSMOHTpE6dKlXR5vtVrp3bs3Op2ORYsWodPpPBBlwTB58mQOHjzI3LlzvR1KvmK326lSpQqzZ88WiZQLy79Zw4x3FmA139tRVB+gY/DYvvR7s7sXIvOu7t2707RpU0aPHp2jnzOZTDRq1IhRo0YxfPjwPIruwfh1MqAoCpPGzGbDkqMEBRVClmVUKgmnQ6Z119o8//5j2W56JPzPY489RtOmTXn77bfd/hmr1UqfPn1QqVQsXrxYJARuSkxMpHLVGnw1fRmpGQ4CjDpa1q9EpTKiO2F2Zs2axZw5c9i8ebO3Q/FpMQfP8WqLDzJNBG7RB+j4bONYqjeO8mBk3rVx40aeeeYZTpw4gV6vz/HPR0dH06JFCzZt2sTDDz+cBxE+GL9OBqZ/toY1i/ZgNdvveU2n11AuKpLP5z2DTieGELKzZs0aXn31VY4ePZrjL4nNZqNfv344HA6WLl16++ejYxO4ciMFvVZN3XIlCdSLRAHAqTX6LwAAIABJREFUKctMmbeNxWv3o1KpcMqgUkloNWoqlArn09e7UzSskLfD9DkOh4OqVavy888/06pVK2+H49M+eeIrti3+K9tWu5Ik0bR7A/5v+ZsejMx7HA4HtWvXZvz48fTs2fO+zzN37lw+/vhj9u/f73Ptnv02GThzIpY3Bv5we1fDzOgNWoa82oEeTzb3YGT5i8VioWbNmkydOpWOHTve1znsdjsDBgzAbDbz+qeT+WLdX/yTkoZKpUICHLJM1zrVeOvRln6fFIz/fi2bd5/Gksle6mqVipBgI3MnPklosH9su+uuOXPmMGPGDLZu3ertUHxet0KDsGRYXR6n0WlYa/GP5m3fffcdS5cuZdOmTQ885j9s2DBsNhtz5szxqfkDfrm0EGD5rD+xZ3JBvZPVYmfpjD9FC9hsTJo0iYceeui+EwEArVbLwoULsRctw8tzV3HhehJmu4MMq410qw2L3cGqgycY+N0iTLask7eCLvrcP1kmAnCzapCSbmbWr3s8HJlvczgcfPTRR6KvgJvsVve+Yw6bA9nN3fvys6SkJMaNG+dyKaG7pkyZwqFDh5g5cyYA19My2HHqAjtOXSAhNf2Bz3+//Lb+/fees9mWwW5JTcogJSmDkDDfKun4gosXLzJ58mQOHDjwwOfKsDm4XrI6Shb9B2wOJxcTk5m6YRdvPtrygd8vP1r0+35s9uxbPjscMqu3HOWFAS3QZbORSkGWnG5mx6FzpGRYCCtkJDZmP5GRkbRu3drboeULhSMKcyMuyeVxweGFUOWj3vv3a9y4cfTs2TPXxvkDAgJYvHgxjzz6GFvTtRy7loROowZuXucaVizNuz0eoUy4Z7c/9s+rBbiVCMDNsTHZKSoDmXnttdcYNWoU5cqVe+BzLdt/HFdJt83hZMneI4zq2BSdHy4FPXYmDtnNKtU/19MoXTw0jyPyLVabg4mzN7J+zynUKhV2hxOtRo3JbKZ5l+E4ZQWN2nfKsr6q+wsdmf/xcmzZTCDU6rV0e66DB6PyjujoaObPn8+JEydy9bxBxUpQqt+LHLgUj6RSYXP8L8n/6/RF+n49nwUvDqBC0bBcfd/sFPy0LgsVqxZ36zitTkPhUDH+arc5uH4theTEdBRFYf369Rw+fJi33norV86/+cQZLHZ3uhJKnL52PVfeM79RuVuiVEBS+ddNz+Fw8uJnS9mw5zQ2uxOz1Y7DKWO22pFUGg6cTebdqb+JIT83dH2mA4YAfZbJuYKCQ7Hz6DPtPBuYF7z22mu88847RETk7kqdN+atwa6AlEllRVYUMqw2Xp37W66+pyv+93j1r95Pt+T4wYtYTFlnvxqtmkf7N0L9bwnHH12/lsIv321iw7J9oIAsy4QXK8zRq1uYPPkrDAZDrryP3eHe2KMkgd1Z8McpM1OnWmli41NwuqhqqdUqIsP9a0XB2r9OcupSPNYsEkqLzcGeYxf568h5mtWq4OHo8pfg8EJ8uf1D3nhkLFaTDXO65fZrhiADWp2GjCr/MPLFESxcuDDXrgHe5nDKJKWYUKskQoIDWLfuD86ePcuvv/6aq+8Tc+065+JvZFvlUxS4eiOFY5evUbO0+82NHoTfJgO1m1SkRt2yHN13Hps1kwuIBBZ7Os27+M862v+6dOYfXn/8W8wmK847btbXLt8gQnqIfSuu8WgXZ64kS1VLRBAdF+/yRmdzOD0+luYr+nepx7qdJ3FmM/FVQqZ7m5po/CyBnbNmH5bMvsd3MFvtzP19v0gG3FC2Winmnf+ObYt3sfqH9Rw9cIyKVSvQ55XHaN2/GSqNxMCBA+natSu//vqrzy2Ty4mUNDMLVu5jxfrDOBwyiqIQFKjnSvQWJk78PNf7n+yOuYTTjYmXDqeTXTGXPJYM+O0wgSRJjJn6JE3aVker06DR3vxTSCoJvUFLhSrFadqrCF26deTSpUtejtbznE6ZdwdPIyPNfFcicJui4uiec8yfsiFX3m9Qszpo1dnfwCSgccUyhAf557BN+VLhPN6hNgZ95jm8Rq1CpViZP/UDYmNjPRyd9zicMpf+cT3hDeDk+X/yOJqCQ2/U0+Gp1kzZ9Qk3ql7gpTlD6DS0DYYA/e3OoWXLlqV9+/bcuHHD2+Hel4Qb6Tz1+mx+WXOADJMNq82Bze7kRrKJgGL1WbkzGbMl6+rx/bA5nG7N/XHICnZn9hOGc5PfJgMAOp2Gt78YwM9/vE6XAfVIsp+h99AWfD7vGaaueJlxH7/PCy+8QMuWLYmJifF2uB61f2s0pnQL2X1mrRY7K2ftcLlE0x1RkUV4pFoFDNnMgDfqtLzexb97PrzwREuG9W5KgEFHgEEHihOtWkKrVdO4VnnWTH+dbl0707BhQ3bt2uXtcD0jB/MAxJyB+6PX67Fa7+49oFar+emnn2jatCmtW7fm2rVrXoru/o3+dAU3UkzYM1ulI6m5cCWRSdNzdx+LchGhGNzYjyVAr6VchJhA6FERxUNo0S2KNP0Jhr3emco1St5+7ZVXXuH999+ndevWHDt2zItRetbmXw9gznAjI1YUTh66mCvvOaFfJ8poZZCdaNX/+2gG6rWEBBj4eXhvKhUrkivvlV9JksSgbg1Y++NzfPBcJwKsZ2lfvyjLvhrO52/2oHAhI++++y7Tpk2jR48e/PTTT94OOc9pNGoiw4PdOrZiKf/+/NyvzJIBuPl5nDRpEn379qVFixZcuHDB88Hdp9Pn/uHi1USc2cxBstmdbP7rFKlp5lx735bVyqNyY4KvBLSrWTHX3tcVv50z8F8ZGRkEBgZm+trw4cMJDAykXbt2rFmzhnr16nk4Os9LSzG5d6AkYU533a3MHck3brDz249Z8OtqjqTYORufSIBOQ/uaUTxSvYLLYQR/otNqaN2wMl8TT82ygUT8pw/Go48+yp9//kmPHj04ePAgX331VYHd+0FRFKoUkbl6zYZKk/XvaNRrefLRBh6MrOAwGAyZJgNwMyF4//33KVy4MC1btmTdunVUq1bNwxHm3Jbdp7HZXJfhNWoVuw6dp2PL6rnyvlq1mtFdW/HRr5uzXEFl0Gp4tXMLjy6hFsnAv9LT07OdBDNgwAACAgLo3LkzK1asoFmzZh6MzvMiS4ejUp9x2WNBdsqERxbOlfd87733eOKJJ2jXtBEFf9FS7tDpdNhsmVdwoqKi2L17N4MHD6Zt27YsWbIkR9uu5geXL1/m2Wef5fKVWCq0fYaEFCt2x70XeL1WQ7XyxWhZ13NPWgVJVpWBO7300ksULlyYNm3asGbNGurW9e1tjlPTLW6N3TtlBVM2PRfuR48GNbh2/TpTNuxBr9dj+7c6oddoAIWXOjSlf9Naufqerohk4F/ZVQZu6d69O0ajkZ49e7JgwQLatbt5y7JZ7RzdfoL0ZBNhkSHUaFYl33fm6ty/EeuX7gEXiXNIeBAVq5d44Pfbt28fv/32GydPnnzgc/mT7JIBgODgYFasWMH48eNp2LAhy5Yto0GD/P90LMsy06ZNY8yYMbz88susGD0am0Phve/XcODkZWRFweGQ0WnVoECrehX5YFhH1Pn8e+kt7iQDAE8++STBwcF06tSJZcuW0aJFCywmK1t/+Ys9aw5gtzmoUr8iXUa0I9zLTbGKFy2MVqNyuaxZrVYRkQdLdXctmkGn4iWp2bEXe85eBqB+hVL0bliT0EBjrr+fKyIZ+JerysAtHTp0YNmyZfTu3ZvpP04n6aCZFd/8fvNF5WZDDr1Rz5P/14euz3TwqY0o3BUbG8tr7z5PhjOCQE2RzFcTADIOuj7d8IF/R1mWeeGFF5gwYQKFC+dOlcFfaLXabJMBAJVKxdixY6lduzaPPvoon332GUOGDPFMgHng1KlTjBgxAofDwbZt26he/Wb5VqeDr1/vxdWEFDbvO01SmpmIkCDaNYwiIjT/Ln3zBe4mAwA9evQgKCiIXr16MfaFj1j/5Q6A2/0KDm08wsIJK+j5UmeGTxzklWtkdHQ061ZMx2qtiEqd/W1QkqBRrXK5+v579uxh48aNREdHU6hQIZ5uXT9Xz38/RJr8L3cqA7e0aNGC1at/4+P+X7Pos18xpZpv/kszY06zkByfwrQ35jJ99Lw8jjp3KYrCnDlzqF27NnXr1mXF3q+oVKMkxoC7x2HVGhU6g5bqLYrwygdPc/z48Qd635kzZ6LRaBg8ePADnccfuaoM3Kl79+5s3bqVTz75hFGjRmG3569Nn+x2OxMmTKBZs2b06dOHP//883YicKeSEYUZ3KUBL/dryYCOdUUikAtykgwAtGvXjq/GfsuKD9dhTrfc1bjIZrFjt9pZ9f06fn5nfl6Em6UDBw7w+OOP07JlS6IqlaFds6ros9miXqdVMaR3Y7Ta3JuvJMsyo0aN4uOPP6ZQId9pDiYqA/9ytzJwS1qMhQh1JDZz5hdUq8nKqu/+oOXjjanasHJuhZlnYmNjeeaZZ7h06RLr1q2jTp06AHyx9CX2b41m6fQtHNh1jJKlStCoTXV6PN2CUhWKUn9+edq0acOKFSto2rRpjt83KSmJ9957j99//z3fD614g06ny9FNvXr16uzdu5eBAwfSvn17lixZclerVVmWuXYpEavFTpHIEAqF+EZPh4MHDzJs2DCKFSvGgQMHKFu2rLdD8is5TQYANn+/CxVZ30QtGVaWf/07vV/rRmjRnFUEU5JNbFp/lIsXrmM06mnSrDIP1ymTaZVBURS2bNnChAkTiI6O5o033mD27NkEBgZitzt59/OVHDp+GfMduzWqVBIqCa5f3E/Len1zFJsrCxYswOl08uSTT+bqeR+USAb+ldNk4JeJv2aZCNxis9hZPGkVYxa//qDh3bfkhBRiDp5HkRUqPFyGIiXD73r9VjXgzTff5Pnnn2fZsmV3zTpXq1U0aludqLrFqVChApt33t3cZeDAgYSFhdG9e3fmzJlD586dcxTfmDFj6Nmzp89PNvJVOakM3BISEsKqVasYO3YsDRo0YPny5dR6uBarZv3J0h82k5FmRq1WYbc5qdeqKk++2YXyVR98Xsj9MJvNjBs3jpkzZzJp0iQGDfJOWdnf5TQZOPv3BeIvud5DRFJJrJu5hf6je7h1XllWmP7dJlYtPwAS2KwOJAnWrDpIaGgg4yf2pWz5iH+PlVm9ejWffPIJycnJvP322wwcOPCu65tWq+azd3py8PhlFqzcx5kL8UiSRJ0apRnQrT7LfrHRs2dPtm3bRkDAgyfG6enpvP322yxZssTnHn5EMvCvjIwMt8erbRYbl6KvujxOkRUOb/ZOb4L4y9f5btRM9v1xCK3+ZoMLm8XOwy2r8cI3QyldpSRXr15l5MiRXL16lfXr11O7du0sz5ecnExISOZtgDt37syqVavo0aMHX375JQMHDnQrxr///pvFixfn+o5g/uR+kgG42TDmo48+onbt2nTs2IlutZ7lxmUz1v8kuHs2HufQjtN8OHskDzeulFth88/FBPavO4zVbKNExUgadKp9T1vr7du3M3z4cOrWrcuRI0coVqxYrr2/kDM5TQYunbya6SY8/2Uz2zhz6Lzb5/32yz9Y/8dRbHc0OlMUsJjtXLMkM+rZ2Xzz41Ns3baWiRMnYjAYeOedd+jZsyfqLJYmS5JEvZplqFezzD2vvf3225w4cYKhQ4eycOHCB05EJ06cSOvWrWnSpMkDnScviGTgX+np6ZQsWdL1gYDD7nT7Q+HMZJlTXvvnYgLP1x9NenIGslPGZvnfBf7gxqO82OgdWr9RnwnffMQLL7zAO++843INenJycrbJUpMmTdi8eTOdOnXi+vXrjBo1KtvzKYrCiy++yPjx4wkPD8/2WCFr7kwgzM7jjz9O7BELa2btRpXJ5UBRFKxmG+OG/sT8/eMwBOgfJFxuXEvi08FTOLYjGpVahex0otFp0Gg1jJg4iM7D2pKamsro0aNZvXo1U6dOpXv37g/0nsKDy2kyoNFpXG5Jfsvq31axvcNaqlSpQlRU1O1/ZcqUuesGfuVSIuvWHsl8LxluJgUZJit9e76NsfA5Jk+eTPv27R/oBi5JEtOnT6d169Z89NFHfPDBB/d9rgsXLvD9999z+PDh+z5HXvL7ZCA5zcz6vac4k1EI5Yaa87GJlC+R/c3JiQO1XuXWjV42ONi/fz/16tVz60MZf/k6fy7dTeqNdEKLFaZVnyaEFsvZxjyfDPz6diLwX4qiYEo1sfqjTazfnX014E7ZVQZuqVGjBjt27KBDhw4kJCQwfvz427/z0fh/OJWYgEpS0bBESbauWo3ZbGbYsGE5+t2Eu+l0OkwmNxtEZcLplNm56mSmicCdZFlm68qDdBpw/080yQkpPF9/NMnxqXd9d+z/XtynjprJ3r/28dP6qXTu3Jljx465/MwJnpHTZKBm86putSk3FjIw4uOXKVTRwOnTpzl58iQrV67k9OnTJCQkULFixdvJQdqNYjhcbXOuQGhwJX5Z+S3BwbmzPM9gMPDrr7/SsGFDqlevTu/eve/rPG+++SajRo2iVKlSuRJXbvPbZMDhlPliwRZW7TiGBFilCJIuWRg8bj5VyxZl4gvdCC989+qCI0eOMG3aNBYuXEjjkq1Rn9fitGe9RlUfoKNM8wj69euHVqtl0KBBDBw4kPLly99zrCnNzKeDv+HA+r9RFLBb7eiMOn58ay6t+jTh1R+fRad33c/6yulYzhw6n2ki8D8SgdpCaE3uf1ncSQYAypYty44dO+jSpQsJCQkMef9d3t+2ibj0dCRuLtNxyDLmcxeY8sWkLEt3gnt0Oh3Jycn3/fMXomNv34yzYzHZ2Lxi/wMlAz+/M5/khNQsk2irycr2Wfv47pfv6fZ41/t+HyH36fV6UlNT3T5eF6hBV1zCdl5GymbRmkajptcz3dBoNffMN8rIyODMmTOcPn2a06dPc2RvIrLs+pql12u5cukG1Wu6V+l1R2RkJCtXrqRDhw6UL18+x3Octm3bxr59+5gzZ06uxZTb/DIZUBSF96f9zs4j57DdsUGFrIDV7uDYuTieGr+ABeMGo1UpLFmyhB9++IFLly4xYsQIjhw5QnBAYUY89BrJ8amZ3ni1Og2lokrw9cIJqDWT2b17N/Pnz6dhw4ZUqVKFQYMG0adPH8LDw7FZbLzWagyXTl7FfseMVtu/Xa+2L91NYuwNJvzxvsub56FNR936G1hNNvavP0yNplXcOt7dZAAgIiKCzZs303HkCAYu/wUlk5hVpUsy5vRx6tSpQ+lg0Vvgft3vnIFbLCabW33SAfbvOUiTJk0IDQ29519YWFim/z0gIABJkjClmdmycCfOzDaEuYNepyfxaAY8ft+/kpAHclIZ2L17N08++ST16zZEbwkjJSEt0wRQH6Bn7LI30WSxOVlgYCC1atWiVq2bnfhevjSTk8dd78apAHkxN69OnTr88MMP9OjRgz179lC8eHG3fs7pdDJq1Cg+//xzjEbPNxNyl18mA0fOxLLz6HksWZSxnLJCYkoGQ974lO1LvqVBgwaMHj2aRx99FM0dvaKn7PqEdzp/TPzlRKwZN3f4U6lVaPUaqjSoxLgVb93+oDdp0oQmTZowefJk1q1bx7x58xg9ejStW7emTtHGXDkde1cicCeb2cbJ3THsXn2AZj0a3vWaoijExcURExPDmTNn2Ll8PzarlZvbXGRNURSsJvdvIikpKTkq2RoCAsh4pDmKLYsLiEpFms3KW5v+YGHPfm6fV7jbg84ZKFoy9K7JWFmRJGjUsi7dnhtBUlLS7X83btzg0qVL/P3333f9t1v/tyzLhIaGUsxQgghb+WyfEuFmRezghiMMGSc+E77EnWTAZrMxfvx4pk+fzrfffsvjjz9OckIK3740g79W7kP779bbDruT8g+V4cVvhuZo2XWDxpU4GxPv8vMqO2XKVSjq9nlzonfv3pw4cYKePXuydetWDAaDy5/5+eefKVy4MI8/7tsZrl8mA/PWHcBqy35ZoMMpEycHsXvPHipVzLyfedEyEfx0bDJH/zzJxnnbSUlIpWjpInQa1oaKWXSs0mq1dO3ala5du5Kamsry5cuZ//wqsGT/xG/JsDL9/TmcvH7k9o0/JiaGs2fPEhQURKVKlahcuTJhJYpyTZeM3ZL9F8YQqKdsdffHrnJSGQBYf/4MTiX7Np+yonDoWhxXUlMoJaoD9+VBKgNpaWnMnDedJPM1glTZ7+anN+oY9PKj1GhQIUfvYbFYSEpK4sDmw/zw7DysbuyEKbYZ9j06rQ6LyYqiKJnOfTp+/DiDBw+mePHiHDp06PZTc0hEYd5f9CqpiWmc3BOD0+GkTLVSlKrs3lP1nbp2r8OiuX9le4xGo6Z954cwGFwPqd6v999/n+PHjzN8+HDmzp2b7Vyw5ORkxowZw9q1a31+SaxfJgPHz11zawt0jVZHodDsM0xJkni4ZXUevo8drYKDgxk0cBALhv2OguuArkTHsXNnBpUqVaJfv35UqlSJihUr3jXL3+l00m/tSFIs2Y/vmUwm4rmS5Zf7v5KTk3PU6GXn5YtkuNEMR6NScSAuViQD9ymnTYfg5v+WU6ZM4ZtvvqFt27a8OWkYP49Zf9eqkztpdWoqVC9J9fr3znVxxWAwULx4cVp1KcT3zrkuj9foNNRo5t7QlZD3Dm49wS+Tf+fIjlMoiobe5V6my5CW9Hy2PeHFQ5Blma+++ooJEybwySefMHz48EyvJ8HhhWjU5cF6iYSGBTHyhbZM/24T1kzmuWg0KsKLBPH0iNYP9D6uSJLEzJkzadmyJZ9++invvPNOlsd++OGHPPbYY7ebuPkyv0wG3E3QFAVUeZzN5SRbNBqNzJw5M9tj1Go1z3/zNF8O/z7LYQB9gJ4WT9Xn62+/5tvvv+WLL76gefPmWZ4zI9XE9Ss3qFbZ/YTHIWdfFbhFARwuKghC1nQ6HVare5WB69ev89VXX/HDDz/QtWtXduzYQZUqN2+8YcFF+fyVeaDc3HjrFkOAjnJVi/PhrJEP9GRTKDSIJo814M+lu5DlrBNflUqi+wud7vt9hNzz87hlrJ6+Gcvt64iEKc3CymmbWTvnT177cRDvffwWDoeDPXv2UKFCzqpG96N77/oEFTIw7duNWCx2ZFlBJUk4nE7qN6zAG+90pVAurSLIjtFoZOXKlTRq1Ijq1avTvXt3riQks+v4Raw2B6WKhhChszF37twHbtfuKX6ZDNSJKsWGvadcbl8ZYNDes6Igt6k1akpUiuRqTJzLYyvXde/L1qZ/cyzpFqa+PBNJJWE13Rzr0/1bOhv4Xi/6v92TN5WXWbhwIQMHDqR+/fpMnDiRSpX+11jmyPYTzP1wCcd2RONw2lm+ahOnl15l8Jg+1H6kZpbvf+7cOa7sPwQ6CbTZl+sURaFqePYlauFeR05cYf6yPew5cBlZrku3Qd/Ss0ttej1al5DCd3dKu3btGpMmTWLGjBn06dOHffv23bOipXmXWtRsVJE/Fu5i++pD2K0OSpaPoMfwVtRqWjlXSpwjJg7iwIa/yUg2ZToUoA/Q89jzHSheXjQX8rZty/exavrmTB8o7DYHdpuDMf2+pcPrHRn99lseXRXUtkNNHmlXg8MHL3AtNhmtTkO9BuUJC/fsHhQlSpRg+fLldOvdj18O3uB8fBqSJOGUZXRaDWZTBr2ffYeiRfNm/kJukxQ/HKCLvvgPIyb8kuUEQgC9Vs3Qro0Y2q1xnsez9udNfPfKTCwZWU/QMQTqeW/hqzTuWs/t86YnZ7Bu5mYObjyKLCs81KIqnYe3u6cPuNls5uuvv77d7vWDDz5g9/KDfP/KLKyZ7OOtD9Ax/NOB9Hixy+3/lpiYyOLFi5k3bx4xMTH0GtCfLVHlsbl46q8UGsaGgU+7/TsJMHPRThYu24vV5rhruEunVWMwaPn20ycoVzqcy5cv89lnnzF//nwGDx7Mm2++6fU1zldi4hjbYyLxl65jNdtQZAVDoB5FVuj7VncGj+nj82Or/mBEow+4HHMt22P0Ri0vfTmYdv18r5uep9xINdHjnemkW+xIqnsTIoNOw4iujRjSuWEmP+1b/DIZAJg4dxO/7TyeaUKg06gpVTSEWR88gdGNtf0Pym6z83rr/+Ps4QvYLJncfI066rR9iHG/vpWn/azj4+MZN24cqxasobqpEbKLHgoT1r3HqbgTzJs3jy1bttC5c2cGDRpEx44d0Wq1/Hz4AF/s3oHZkXnSZdBomP1YbxqW8M0mHL5o+67TjP9yTaZjpnBzCKxQkJ5ixmOsWL6U4cOH89prrxEZGenhSLN3at8Zdv92AIvJSumoErTq15TAYN/YFMnfXbuYwMgmY7OcQ3KnGo0r8cXvoz0QlW8aN2sdv++OxpFNXxedRs3KT4ZS1Md3z/TLYQKAtwa1oUhIILN/34ckSdjtTjQaCadToVmt8owZ2tEjiQCAVqfl801jmDxyGn8u241KrcJudaAzaHE6nHQc+gjPfTkkzze2KFq0KFOnTiX4SnH2/3aY7JYnWk02hrR5ltAWOgYNGsScOXMIDg6+65hhteshAZ/v3oEKCZPj5sUlUKtFq1LzbaeuIhHIoRkLdmaZCMDNeS5JyamUDCtFTEyMz7Z6rtKgElUa5N5eB0LuSUsyodGq3UoGUm9keCAi35RhsbFu7+lsE4Fblmz9mxd6NvNAVPfPb5MBSZIY1q0xAzvWY8ff54lPSiPAoKN5rQoUyeN5ApnRG/W8Pfdlnv3yKf76dd/tdsTNezYk0MPxHN9yChTXpdqiUknWblyY7TFDa9ejb/WHWHnqJEcTrqFRqWlWqgztK1RC42O7dvm6a/EpXIlz3W1QrdajCyzls4mA4NtCihTC4aI51C1hkf67CuhcbCIatYos2sPcZnM42Rd92TNBPQC/TQZuMei0tGsQ5e0wbguJKEyXEe28GkNm8wQy47A5kGXZZcUiSKdj4EO1gFq5EJ3/SkmzoNWocKdF/UHJAAAgAElEQVStQFLq/e9XIPi3iFJhlIkqzpkjl7I9zhikp9vQ1h6JyRfdXJbt/rG+TjyaCfcILlLIreOCQgN9bk/ugiwk2Ijd3Se2EM9Xt4SCY8j7PdEbs97JVKWSCAoJpHFn/03wyxcPw+bGZnUatYralUp4IKIHI67kwj26Pdvh9jLErGj1Gh4d6d0Khr8pFhFMmVJhLo8LMGrp3tm93SgFITP129Xk6TG90Bu1qNR33yb0ATpCigbz+eo3stxXwB8UCjDQunZFl71oVCqJfm18//sokgHhHt2e64jOkPVTAYBWr6PHS12yPUbIfcMHtUCvz/oCLEkQYNTTsrH7Pd8FITM9nmnL5PXv0qZPIwILG9EZtBQvF8HQsb2Yvns8kWUjvB2i1416vCVBAfosl8MadBr6t6lNiSK+P7fCb5cWCtk7c/g8b7X7ELvFjsX0v/4HhgA9Gp2GT9d/QJX6me/ZIOStBcv3MnPhTmw2511jkXqdhgCjjm8/HUDpkq4rCIIgPLjL8cm88d1qriQkY7M7kRUFo06LrCgM6dyAEV0b5YveGSIZELJkSjOzce52Vv+wjtTEdILDgugysh0dnmzl8RUOwt1OxsSxaPk+du0/h93uIDQkkN7d6vJYx1oUCnK9k5ogCLnr5MV/2Hn0AhabndJFQ2hfP4oAFxVWXyKSAUEQBEHwc2LOgCAIgiD4OZEMCIIgCIKfE8mAIAiCIPg5kQwIgiAIgp8TyYAgCIIg+DmRDAiCIAiCnxPJgCAIgiD4OZEMCIIgCIKfE8mAIAiCIPg5kQwIgiAIgp8TyYAgCIIg+DmRDAiCIAiCnxPJgCAIgiD4OZEMCIIgCIKfE8mAIAiCIPg5kQwIgiAIgp8TyYAgCIIg+DmRDAiCIAiCnxPJgCAIgiD4OZEMCIIgCIKfE8mAIAiCIPg5kQwIgiAIgp8TyYAgCIIg+DmRDAiCIAiCnxPJgCAIgiD4OZEMCIIgCIKfE8mAIAiCIPg5kQwIgiAIgp8TyYAgCIIg+DmRDAiCIAiCnxPJgCAIgiD4OZEMCIIgCIKfE8mAIAiCIPg5kQwIgiAIgp8TyYAgCIIg+DmRDAiCIAiCnxPJgCAIgiD4OZEMCIIgCIKfE8mAIAiCIPg5jbcDEHKHrMjEWS5jcZoJ1YUTpovwdkiCn3IqTmTFiVal83Yogg9QHFdQTPPAtgMUJ+geRgoYgqSt5u3QhDtIiqIo3g5CuH+yIrMtYS2b/lmNTbYiocKp2Ik0luaxEgOIKlTT2yEKfkBRFE6k/sXOhOXEWs4iAUZ1MI3Cu9IwvAtGdZC3QxS8QE6fBunfAjJg//e/qgEtGDojFf4ESVJ7L0DhNpEMeIGi2HBYNiI7zoOkR6NviVoblePzyIrMzPNfcTL1b+yK7Z7XtZKOfqWH0yC8RW6ELQiZkhWZZVe+4HTqfuyK5a7XNJIOozqI4RU+o7CoVvkV2fQLpH4CmLM4wgABfVAFf+DJsIQsiGTAw6wZc7GmTgAUUCzczJJVqLRVCQidikpT2u1z7bq+meVX5mBTrFkeo5V0vF/9S0J04Q8cuyBkZnv8Ev5MWII9i8+hhIpwXXFeqDwVSZI8HJ3gDYriQIlvCkqyiyN1SEW3I6nCPBKXkDUxgdCDrOnTsKZ+BEo6KBmAE7ABFmT7ETISuiI749w6l6IobPhnZbaJAICCwp/XNzxw7IKQGafi4K/rK7JMBAAUZFLsiVw0HfdgZIJX2f7if8MC2ZFQTCvzOhrBDSIZ8BDZmYA1dRIoWZXMZBQlFUvKh26dL9WRTIo9yeVxDsXO4aQ9OYhUENx32RSNguzyOLti5XDSZg9EJPgEZ+zNyYIuWcF5Ia+jEdwgVhN4iM00342jnDfnEjiTSEmFhIQE4uPjM/2X7Eyk8hvhaAyuJ984MplPIAiKomC1H8XhvIIkGTHqGqFSBeToHGZnGuBO6V8hw+GqZCwUGJIB9541JZAC8zoawQ0iGfAQp/UvIPuSPkBqmoXu/Utz6IiGiIgIihYtete/KlWq0KJFC8KKhrHWMAcnDpfnLKIrlgu/gVCQpJn+4HrKOJxyAjfnrSiAk+CAvhQJGYNKMmb7806nkwMHDrB27wrsjdPRGLK/8EuoCNYWybX4BR+naw5uXJvAgGTokNfRCG4QyYCPKRRUiN9/n48xqKXLY+MunOJg0q5sy7ROi0x5VY3cDFHI51LS55OQ8gHKf2b+A6RmLMJiO0Spor+ikgx3vRYXF8e6detYt24dGzZsIDIyko4dOxDWIhB7ljPGb9JIWuqGtc/V30PwXZK6CIq+NVi3kPXcARWoi4O2lgcjE7Ii5gx4iFrXCNC7PE6S7OiN7vUG6BTZG102jV3UqAlwFmJoh2cZPXo0GRkZ7oYrFFAO5zUSkt/PNBEAULBis58mKXUqVquVzZs3M3r0aGrVqkWNGjVYs2YN7dq149ChQxw7dowvvviSDqWeQitl/dlWoaaYoSwljZXz6tcSfJBU+BPsSjEs1nsXrDkcgBSMFPqjWGHiI0Qy4CG6wEEoiquJVio0hjao1O4tsylqKM7zld7DqA5Ep7r7KU6nMlDCWJaxjb/i6JFjXL16lRo1arB69er7/A0Eb5EVK0kZS4m51p7jV6I4fqU6F68/i8l6KMfnSk6fg0L2q4kVLFyO+4rIyAjeffddDAYD33//PfHx8SxZsoRhw4ZRuvT/lsDWD+tE/bBO/yYEd1/YHRYZKcPAE2XFWnJvUxSFVNtlblhjsDg9MH9DKsSA57UcjK4BUtDNuQFSEAp6lq+V2R3zDpKmTN7HIbhF9BnwkM2bN7NtwyBGPWdEo8msbKYCKZigiN9RaUrl6Nw22cbhpF3svfEnZqeJcF1RWkZ0oGJQtbuy7k2bNvHcc89Ro0YNvvnmm7su6IJvcjiTOBffG7vzCrJiuuMVFZKkp0jQMCJD3nb7fBf/aY/N7nqJn9OpIyxgCUUjGrh97sumaDZcms+ZlIMEBgUSqouknK0hT7d/jX279/P/7J1nYBTl2oavme0pJAECgYQuJUG61AACSkekdzkoShXEAwrIdxQRRbCBgBQVBJQivUV6B+kSeg0t1BRC6mbbfD8iCLItIclms3P9wp133r03zs4+89SSJeUbvyuQJAvnE1Zy+sGvpFuSEFFgxkCQriY1Cw2ikLZijrzv/PnzmT59OocOHUKplMB0HbCAIoSly9bx3XffcejQIdkzkEeQjYFcYPbs2YwfP54lS5bQoPYV0pO+BoS/ew0oQVAhKsrgVXA2orJ0jmrR6/VMnjyZ6dOnM3bsWIYPH45KpXpqTbo5kRvJ+zFYktAqAijpE44qk1nmMs+PJElcud+eNMNpbMVdBUFHcf/PKOjTw+Y+6enpXLx4kXPnzlEmdDz+AY6fCgXBhxKBq9GoM5dvsnXrViZNmsSOHf+UEU6cOJH9+/cTEREh3/hzGYtkZtedj7idegSzldCQQtDStNgXBHvXy9b3jY6OpkaNGmzfvp2qVas+q8tioXbt2owePZpu3bpl63vLZA3ZGMhBTCYT77//Ptu2bWP9+vW88MILAEiSHlPaJizma4AapfZlFKrcTfK7dOkSQ4YM4f79+8yZM4d69ephsqTz5/1viErahoACi2RCIaiwYCbUrxMvBQ5ClPuI5xqp6X8RFdMNyWZvigyUiqJUKnaU1NRULly4wNmzZzl79iznzp3j7NmzXL9+nTJlyhAaGsrAYfcoXe4aomj/ay+goUzxSBRigUxpnj59OmfPnmXWrFmPXzMajdSuXZv//ve/9O3bN1P7yTwf5x4s53jcLEw2ckQAlIKWrmXWolb4Zst7SpJE27ZtqVevHh9//LHNddu2bWPQoEGcPXsWtVoeauVq5GqCHCIhIYHu3bsjCAJ//vkn/v7+j48JghaVVwcXqoPy5cuzZcsWli5dSqdOnXi9w2u0HCXx0HwF8xN9CSxSxhPp+YerSTbdpWmxCfLTXS4Rn7IYyUGHSYDklBhatS3Nnp33KV++PGFhYYSFhdGrVy/CwsIoX77845utPv040bHdkJ4KOTyNxSJQwLtFpg0BgAsXLlCpUqWnXlOpVMybN4/WrVvTokULgoKCMr2vTOaRJAunHiyyawhARlHp5cQIwgK6Z8v7LliwgDt37jB27Fi761599VXKlSvH3Llzeffdd7PlvWWyjpxAmANcunSJevXqUalSJTZs2PCUIZCXEASBnj17cvbsWQIrP+Ru0mnMNn58TJKe6JQ/uZ16JJdVei5G021worufSqVm2vRPSUpK4uTJkyxdupSPP/6Yrl27Urly5aeeujTqGug04VgsKuubSaDXw+fj75GaattgsMX58+epWPHZGHTNmjV566235Jt+LpJovInRkuxwnVnSE5W0OVve89atW3z44Yf88ssvz4QfrTF58mQmTpxIUlJStry/TNaRjYFsZseOHTRs2JD333+fadOmoVTmfeeLv78/VdsIqHX2LweTpOdkvDOdFGWyA6XCueFSKqWSEiGhTl1rgiCg4Ut27zD+bRConjjmjUIRSOli60hK9KNBgwZcvXo1U5qteQYe8cknn3D69GlWrlyZqT1lsobJond6PLDJYj8U5QySJDFgwACGDh1KtWrO9Q6oXr06zZs35+uvv37u95d5PmRjIBOkmQysiz7K5DNr+ebsenbfO4vJ8k//7dmzZ9OzZ0+WLl3KwIEDXag0c5glI4nGaKfWxurP5bAamUcEeHdDdKJVqyAo8VLXcmpPSZIYNGgY50/2oEzxXfj7vI2Xtjm+uk4EFZxJmWLH8fOtxS+//EL//v2pX78+W7c6N+gqOTmZuLg4m1UDWq2Wn3/+mWHDhhEfH+/UnjJZx1tZBLPkeFiQJAHpz++9XLhwIbdv3+ajjz7K1HmfffYZM2bM4O7du8+tQSbryAmETrLyxiGmnc/Ihk4zZ8TUvRRqVKKSz6p047fPpj+TKOgumC0GFlx+BRzUnwMoBR19y8tTEHMDSZK4eLcRBtMNbIULBEFHkQLvUaSAc+73BQsW8PXXX3PkyBG0Wq3D9bt376Znz56MGDGCDz74wG6+yPHjx+nXrx8nT560u+fw4cNJTEzkl19+cUqzTNbZEv0ed9Lsh/YsRgXzPryFlFCcgQMH0qVLF3Q62+2oU00JRD7YxLWUv5CwEKyrTHFDLcJrvcKWLVuoXr16pnWOHDkSQUpgyoS6YDwBKEDdEEH3GoJcyZQryMaAE6y4cZDvz/+B3mLDyjaa8V19mZXf/pxn8wMcsSyqEymm+w7XBWor81rJObmgSAbAYLrJlXvtMZkfgvD0wClB8MJX+wolC81EEBw7+aKioqhbt67Nci9b3Lx5k86dO1OmTBnmzZuHt7d1b8WSJUtYvXo1v//+u939kpOTqVKlCrNmzaJVq1ZO65DJPLH6c2yKHmIzF0hERYCmHC2CZhMREcHcuXM5fPgwvXv35p133uHFF5/uhno8fj077/2IgPB4AJoCFQajEfOl4ozr/FOmE4wlSSIl5hvEtDmo1RrEx9e5FwgSFPgSUdc6059dJnPIYQIHpJrSmWbPEABQKSjUL9xtDQGAFwN6orDTUhYyvAJVC/bOJUUyAGplCcoH7eDS6eqkparIGCokEnVJJCVmACUL/eCUIWAymXjjjTf46KOPMmUIAJQoUYI9e/bg7e1N/fr1uXLlylPHzaZrpKUsQSGspmkTPxw9X/j4+DB37lwGDhwoJ47lMIW1oTQp9jlKQfvM91sp6AjQlKV58FTUajUdOnQgIiKCY8eO4efnR8uWLQkPD2fBggWkpqZyOmEbu+79jFkyPjUJ1YwRhQq8KsfxZ+zSTGuUUn7Ey7IIrUZ4whAASM0Y+f5wNJJ+Z1b/BDJOInsGHLDm5hG+O7/xcWjAFlpRxdy6A6jkF5xLyrIXk0XPuhtvk2i4hcVKgxuD3oLWWIK+NRfLvQZcQLVq1Zg2bSqNG9cFQclXU77j0qVL/PTTT06dP3HiRHbt2sWWLVsQxaw9A0iSxKxZs/j0009ZsGABzV+tTOKD4ZiMkSCIGNLTEUUlanVhfPy/RKN9xe5+/fv3R6vVMnPmzCzpkXGedHMilxI3cOlBBJeunKNWWFNCA7pTTFfLpjFpMpkeewsOHvqTkZvqodDZr25RCmqGVliCRuGca1+yJCPdr4/Dia5iMYTAXXJZcw4iGwMO+OL0atZEOy6n0ynUfBD6Gu1CnEvkyoukm5PYeedj7qVFIkkWLJgwGixoNBr8jNV4t+VS5v20gJYtW7paqkdx+vRpWrVqxY0bNx7/kN+5c4ewsDCio6Ntuu0fcfjwYV577TWOHTtGSEjmWl1bY9++fYx4rzurl2vRag1Yz2fQ4uv/PVqvtjb3SUhIoHLlyixZsoTGjR1P6ZR5fhITEwkJCSExMTFT5x2MimB30kwElX1jQCVoaBY0iGoBzoV/pNSlSImTwMHUSwQvhICfENQvOalYJrPk/bo3FyM64YJ9hLtbrRqFL61CviPRcItryTtJMz1g8oTpfDV6JeXKV6HI0q506NCBtWvX0qBBA1fL9RgWL15Mz549n3qiL1asGOHh4axYsYL//Oc/Ns9NTk6mT58+zJw5M1sMAYCGDRuycV09JMtBO6v0JD0cgUbbDEG0nozm7+/PzJkzefvtt4mMjLSbtCaTPajVatLTHTey+jcqPyPKdAVmB8PWjFI6Py6ZxtWN04GMe+Kj+6K1f7/T4zatXnairFGygOkKyMZAjiEbAw6oXagsm27/RaqDMIFZslDVv1QuqcpZCqiDqVqwDwDpVzdx8uhlypWoQnh4OIsWLaJjx45s27aNKlWquFhp/sdisbB48WLWrFnzzLE333yT6dOn2zUGRo4cSYMGDejSpUu2aTKb7yAKf2WkL9hFIF2/Dq2X7c52HTp0YMmSJXzyySdMnvwld9NOkmqKRSV6UdyrJkrRccWDjPOo1WoMBgOSJGXq4UUhqhCcSTGToGa1l2hdqh6SJD3OH7H17xdKrQf2Od5XEEBw3MRIJuvIxoADXi4SxhfCszfiJxEQCC0QTAlv55rEuBO1a9fmyJEjdOzYEYBWrVoxbdo0WrduzZ49eyhbtqyLFeZvDhw4gLe3t9UmLq+99hqDBw/mypUrlCtX7pnj69atY+vWrZw4cSJbNZkMf2XcmB21SpZSMOh32zUGIGOeQb+x4Sy4cApBYfl7CrKAJJmp6NeeOoGDUMg/BNmCKIoolUpMJpNTHQIfUdq7BpIz3TBFDS1r9CLYK9SpfaX0QkgJx8FOe2wAoyGd2JgQguXBlzmGXE3gAKWo4PPqPdCI1r84AgLeSg3/q9I5l5XlDnXq1OHw4cNPvdajRw/GjRtH8+bNuXPnjouUeQaLFy+mV69eVp/i1Go1vXr1slqvf/fuXQYOHMiiRYsoUCDzMwbs4/hH4RH37t1xGJ++Ka6n06gQTGIyRikVoyUVoyUFk6Tn/MO1bIoeiUUyPa9omb/JSqjAX10MIcEfs8ne/3sBH1Vhiuusd6C0LqYhOGisJUki1+8UpGqN1vTr14+zZ886v7+M08jGgBPUK1yeqbX+g3e6iGCyoFWo0CnUqEUlL/qH8Ev9IZT0LuxqmTlC7dq1OXr0KBbL0zeBwYMH89Zbb9GiRQsePHjgInX5G6PRyPLly+nVq5fNNW/3b0uA73zi79Yh/m41EmLaoE9dxYAB/Xj77bcJDw/Pdl0KVSg48eNsNCpYveYyxYsXp2rVqgwYMIB58+Zx7ty5x9dTfPoVTj5YAgrr+5mldGL05zifsD5bP4Mno9FoMBjshz2fJDU1lTfffJMlH57AS+mHaCU+JCCgEXV0KvFxpsIPgiAi+P8Agq18EQWC6Ef5Wiu5fPkyFSpUoFmzZnTo0IGDB+3lrGQgSUYMxosYjOexWDI/a8OTkKsJnMRisVChQgU+XzAdTcnCKAWRqgGl8q0R8CRly5YlIiLimZ7zkiQxcuRIDh48yNatWx1mtctkjo0bN/LFF1+wf/9+q8f1qatISfgQg0HPkxNgjSYVMTEWKlTej0abPUmD/+ZBTFtMRkfhBw2Fgo5jMnlx8uRJDh48yJ9//snBgweJj4+nTp06tBnhh3eZu+BgpLKPMojuZX53+yTdvEBQUBAnTpxwanrkxYsX6dKlC1WrVmXOnDlImnR23v2JS0n7UQhK+Lv5UBnvWjQNeocAdfEsaZKMZ5ESPwXj2X9yAyQDaBoiFBiPoPhHa1paGvPmzeOrr76idOnSjBkzhpYtWz51bVgsKSQkTSMp+RcyPFkCYMJb1wH/Ah+gVGZNZ35GNgacZPv27fz3v//lxIkTHndD6tGjB23atLE6i95isfDWW29x9+5d1q1bJ88lz0Z69epFeHg4Q4cOfeaYMf0giXF9AOvjaSVJgUJZEv8iu5weVpMZjIZTJMR1zGgKYw1Bh7fPSLx8B1s9fP/+fQ4ePMjtEtNQejt+ShUFFT3LrESnDHge2TJAyZIl2bt3L6VK2U94/v333xk6dCgTJ05kwIABT9339OZkYvRXkZAopCmJtzJ7Gq5JphtgugiIoKqKoLD9sGU0Gvn999/58ssvUSgUjBkzhi5duiCKqdy53xaj6SbP9i9QIIoFKFZkIyplmWzRnF+QjQEn6d69O40bN7Z6Y87vfPvtt0RFRTFjxgyrx00mE126dEGj0bB48WIUiowfH4slBYuUhCgUQJT7i2eK5ORkQkJCuHTpEoGBgc8cfxjbCZPhsJUzn0DwxjdgOmptixzRaDQcIzH+HSxSMkgpf7+a0ULW2/dDvHwGONzj1yvt0Zsdh5kUgobuZZbipcz/nricpnz58kRERFC+fHmrxw0GA6NGjWLjxo0sX76cmjVr5rLCzCFJEhEREUyaNIm7d++yaHFpihW/BNgyMkWUyjIEF93rcQ929pBzBpzg/v37bN68md69PbMVr7UkwidRKpUsXbqUmJgYhg4dSqp+L3fud+LG7UrculufG7crcS+2H+mGyFxU7d6sXbuW8PBwq4aAxXwfkzN/SykFfcr8HFCXgUpdi4JFj+IXMAed9wC0Xm/i4/cphYtGOmUIAASonXs6E1GgVbhvu++8xKPyQmtcv36dRo0acfPmTY4dO5bnDQHI6FnQtm1b9u3bx8KF31M48Cy2DQEAC2bzHdINR3NLolsgGwNOsGDBAjp27OjWsweehxo1anDmzBm7GcharZY1a9YQVHwnt+70JN3wJ2BCkvSAkTT9Fu7GdCQ5VU4Ec4bffvvNZuKgxXLf6Zprs+l2dsp6BkEQUWub4uP3Cb7+E9F590IQnc8dqVqwJ0qbyWMZiKio5N8eUZArobOKJElcTznJ79c/o/30Emy1fMWOu/N5aPhnONnGjRupU6cO3bp1Y9WqVW55v6taXY9a7dgLKUlppKZF5IIi90H+djlAkiR+/PFHFixY4GopLsPb25vy5ctz8uRJateubXOdWnOOfm8ZALOVoxKSlEbcg/fQqKuiUuaPBk05wf379zlw4IDN6X+C4OtUNj+AKGZ3WWH2EuJVh8LaCsToz2GWnn2aExBRK3yoGmC7okLGPkaLnt9vfMat1AsYpXR8iipJIZbDcWs5Er+eZoFvsfq7AyxatIiVK1fSsGFDV0vOMpIlFedKXyUskjwk60lkz4ADdu/ejUajoV69eq6W4lJq165tN1QAkJD4PbYS2h4hSSYSk37MRmX5j+XLl9O2bVt8fHysHhcVJREVRZ3YyQu1rlv2istmBEGkZfBXFPeqhUJQIzx6PpEgPdWCr7I4r5ecLScOPgerbk4mOvUcRkkP/JMiZsaESTLwR/QsotKOcezYMbc2BACUyhCcaI0JaFAq5IZpTyIbAw6YO3fuM5m0noijvAFJMqNP3+3ETkZS0ux3dPR07IUIIMNbtXt/RVJT7ef+CoICrVen7JaX7ahEHS2Dp9Cx1HyqBHSjtM/LVPRvz47vBZSRXfFVyWVgWSVGf51rKZFPjRz+Nwq1QKPBJazmp7gbWk0jBMGZiiYJH+/sa9GdH5CNATvExsYSERFBnz59XC3F5Tg2BtL4u4+sQyRb5WgyREVFcfnyZVq0sF4BEBsbS/v27flyyhmUmtdBsBYfVYDghW+hXzMVv3c1/uqS1AkczKvFJ9Ko6Ad0aDqQuXPmulqWW3P8wSbMkrWw3dOkmBK4p7+aC4pyFkFQEFDgIwQ7eShmsxof7+4oFUVyUVneRzYG7LBw4ULat29PQIDsoqxcuTI3b97k4cOHVo8Lgtc/Ll4HiGL+m+GQXSxZsoSuXbta7Ru/d+9eatasSVhYGHv37iMoZCY+/t+iUFYmI/1HC2jQ6DrjH7gZldp9x2kDdOnShaNHj3L1qvv/SLmKeMNtJKs5PE8jCiKJxphcUJTz+Pr0xs/3fQS0wJPfIwWSRcOWzXru3vLMyjB7yMaADSRJehwikMkoH6xRswZ7j+0nwZCI5V+jTAVBxEvXCYvFvndAEHQU8H4zJ6W6LZIkWQ0RmM1mPv/8c7p27cqcOXOYMmUKKpUKQRDQ6NrhX2QzAUGRBBTdR8FiZ/EJ+BZFPmiootPpeOONN/jxRznHJKtoM9HfQyVqclBJ7uJfYBjFg3bj6/MmKmVFVMryeOs6UDxoHb5e39ChQ2fi4+NdLTNPITcdssGePXsYNGgQZ86c8fh8gTSznojb21lxZT2SUkCpVKBTaGld7BXaFGuGVqEhKiqKUaN68MWUaLRa25eUKAYQHHQAheh+ZUs5gdFs5kz0PdIMRh7ciWbof/oQFRX1+Jq7d+8effr0wWAwsHjxYoKDg12sOHc5d+4czZo148aNG5masieTwYXEg6y79Q0Gi/3QnFrU8X7F31CKntFBdOTIkZw6dYqIiAiUSrmoDmTPgE3kxMEMko0pjDn5BatvbcKiAUkhYZRMJJqSWRUdwdiTk5jzy1zq1q1L48a9KBm8HCcQ0A8AACAASURBVEHw/ttF9w8CXohiAEGBq2RDgAwjYMaWAzT6bA4D5q3m/d82MHrjQYK6D+HApRtARgvsmjVr0qBBA7Zv3+5xhgBAaGgoFSpUYO3ata6W4paU962NSrD/xK8U1NQIaOUxhgDA5MmTkSSJMWPGuFpKnkH2DFghLi6OcuXKceXKFQoV8uz49udnp3Em8aLNJCTJJJF89gFTwj/mxRdfBMBsjiUp5TcePFxEQsItigRWwNe7Hz7eXRFF6+VynoTJbGHAvFVE3riD3vhsvwCNUkkV6SHbfpnFwoULeeWVV1ygMu+wePFi5s+fz9atW10txS25p49i4dUx6I0piIqnH26UgoYgbTl6l/4cpY0x7fmVuLg46tSpw4QJEzy2u+yTyJ4BKyxatIh27dp5vCFwXx/LucTLdrORBaVAwWpFKFb+n6dWhaIw/gXew5y+lC4dvAgO2k0B3zdlQ+Bvfjvwl01DACDdZOKoUcuWPfs83hAA6Ny5M5GRkVy+fNnVUtySotqyBF9szc0DaSgFNWpRh0rQ4K0MoHGRXvTxQEMAoFChQqxZs4YRI0Zw7NgxV8txOXKwBLivv82R+D08MMbio/RjyZZFfDXmO1fLcjlH4k8g4dhxJCBw9EEkLYOaPPW62Wx+PLRIJgOLRWL+nmM2DYFHqNVqdly5Q+Vy7p8I+LxoNBr69u3Ljz/+yOTJk10tx+0wGo2M/2ASM2bMoEnFRiSaYlEIKvxVRRAEz34erFKlCrNnz6ZTp04cOXKEIkU8t9zQo40BvTmNBdemciX5HGbJjAUzSFDn47IcDdhEDWNVfFWeG99ONadhcqLtrUkykWp+tvOg2WyWk3P+xa0HD0nS257x8AiDycy205cZ1qJBLqjK+7zzzjs0btyYzz77TB6TnUnmzZtHiRIlaN68OYIgUFhRwtWS8hSdO3fmxIkTdOnShW3btnns9eWxZqFZMvHD5YlcTj6LUTJkGAIAAii1Cm6lXWfqpf+hN6e6VqgLKaj2R+NEUpFaVFNQ5ffM6yaTSfYM/It0kxmF6FxSarrJufkDnkDFihUJCwtj9erVrpbiVqSmpjJhwgS+/PJLj0+Gtsenn36Kn58f77//vquluAyPNQZOJhzmXno0Jslo9bgFM0nGh+yP3ZbLyvIO9QrVwuJEfqkFC3UK1XjmdTlM8CxBfj4Yzc4MUoGyRQrmsBr3YuDAgcyZM8fVMtyKadOm0aBBA7sDxmRAFEV+/fVXtm/fzk8//UR06j1mX17GiL8mMeKvL5kXtZJ7+lhXy8xRPNYY2HF/PQaLfXetUTKwO2ZjLinKe3grvXi1aCPUdrwDGlFNq6Cm6BTaZ47JxsCz+Gg1NAsrh+jgKc1LraJvw7w/Sz436dixI6dPn+bixYuuluIWxMfH88033/D555+7Wopb4Ofnx5o1a5h7+XfeO/4FW+7u52rKLa6mRLPxzh6GHpvIkusbya8FeB5rDNxPv+PUuhRTEgaL7SEf+Z2+pbvwUkBVNKIa4cnZAxbABHUK1qBnyQ5Wz5WNAesMa94AhZ3ETLVSQaXigdQtJ8d2n0Sj0dCvXz+5I6GTTJo0ic6dO1OhQgVXS3EbjukuU6JtKCbMmJ8YhWySzBglE6tvbWf97V0u05eTeKwxIDg7VAcJ0XP/TIiCyPDy/RkbOoxaAVXwV/nhr/Kjqk8lDo/ZTmefVog2MpJlY8A6+7dE8OCPxfhqVHip/ynpEgUBrUpJjVLFmf1mRznGa4UBAwawYMEC0tMdJ2F6Mjdv3mTevHl88sknrpbiNqSa0lhzazsmwXYpdbrFwJIbGzFa8l8+j8emepfzrsTZpL8criuqDUEpeuyfCQBBEAgtUJ7QAuWfev1erSi++eYbvv76a6vnycbAs6xfv56RI0eyfft2ylesyNbTl5n86wpUOm8aVAujR71qVA4p6mqZeZYXXniBqlWrsmrVKnr27OlqOXmW8ePHM2DAAIoXl8c/O8u+2L+cekiUkDgaf5r6havngqrcw2MfeZsWfQ21gzadakHDK0Xa55Ii9+PDDz9k3rx5xMRYn3YmlxY+ze7du3nrrbdYt24dlStXRq1U0rZ6JUrcOUe/CoX5rEsL2RBwAjmR0D5nz55l3bp1jB492tVS3Ip7+ljSnQgJGy0m7unjckFR7uKxxkBZr0qknpEwp1vP7FYJasr6VKJmQHguK3MfQkJC6NGjB99++63V43Jp4T8cP36crl27snTpUurUqfPUsbi4OI/vdpkZXn/9dc6fP8+J02e4GBPL5dg4DGbHY3o9hXHjxjF69Gj8/T23R0pW0CjUToWERUFEo8h/vQg88rFNkiQ+/PBDDu05z/gVo9iXsAkLEknJifh6+yIJEg0KNadd8R424+EyGYwePZqaNWsyatSoZ37Q5DBBBufPn6dt27bMmTPHanth2RjIHA8NRqoOGUb3DZvQajQgAQL0qFaVd8Pr4qvJP6N4HZFiNHDw1k2SjQaCfQpguHqdY8eOsWTJEldLczteCniRFTc3k26xX/orSRI1A8JySVXu4ZHGwKeffsrWrVvZuXMnBQsWpGWJTlxMOk2vt3owd+ZPVAusjTofzfbOSUqVKkWnTp2YOnUqn3322VPHZGMAbty4QcuWLfniiy/o2LGj1TVxcXEULCj3FHCGWw8T6bRwCQlab8ySRIrhnz4hi47/xdZLl1nVtxf+umdLXfMTepORiQd2seL8GZSi+HdtioT+YSKd/m8sWm3+/vw5QVmfEIJ1RbmWchsL1g0CBSKhBcpSVJv/jHePe+z96quvWLp0KVu3bn18A1YISkILVOfiHzcJ9a4uGwKZZOzYscyaNYuEhISnXvd0Y+D+/fs0b96cESNG8Oabb9pcFx8fL3sGnGTwqnU8SEvDbKXW22C2cCcxiQ82bnKBstwj3Wyi+9plLD9/Br3ZRLLRQIrRQIrRiNlLxybSmXviiKtluiVjQwfgo/RCYeWnUSko8FcXYGTFfrkvLBfwKGNg5syZzJ49m+3bt9scSCGXc2WesmXL0q5dO77//vunXvcEY8BgNLH92EUWbz3O6j0nuf8gCYCHDx/SqlUrunXrZrfFqclkIjk5WY7vOsGZe/e5Gv/AbldMo8XCgWs3uJeUnIvKcpefI49xIS6WdLP18ja92cQ3h/dx/WGC1eMytimiLci0mmNpUqQOalGFl0KLGhWWdDPNizZgao0x+KsLuFpmjuAxYYL58+czefJkdu/eTXBwsNU1+bWzVG7w0UcfER4ezogRIyhQIOPLkp+NAUmS+HXzUX7ccAgA498zB75avJPalUL4a+0s6tevz4QJE+zuEx8fj7+/P6LoUXZ5lth+6QrpTiQKiqLIrqirdK9WJRdU5S4WSeLnk8fQ2zAEnlz3y6njfNKwWS4pyz8UVPsxvEIf3inXlTtp97FYLDSoVIdZh8dRQJV/x7DnmzuQRZLYe+cqnx/bzv8Obea3i8dJMmQ0Jlm2bBnjxo1j69atlCljfySs7BnIGhUqVKBly5bMnDnz8Wv5ubTw+xV7mbPuT1L1BlL1BowmM3qDCYPJzP5TVzGWacakKV87vJ7i4+PlfAEnSTYYnJqVYbZYSDNanzni7txKekiq0ZnyNwu7blzNBUX5F51CQ1mfErxQoBTNGjdl8+bNrpaUo+SLO/WpuDsM2L2SRIOeVFPGTUCnUPHZse28qizIivfGsWXLFipWrGh3H9kz8HyMGzeOJk2aMGzYMHx8fPJtaWHU7Th+33GCdKONpzNBRFB5s3DzMd7t1NDuXnIlgfOU9PdHq1SidzDNUaUQCfZ7dopmfsBkkRzOtfhnrXMDsWQc06pVKyIiIujfv7+rpeQYbu8ZuJAQQ4+tv3E3NemxIQCQZjaSbjaxIfEWb8yfStWqVZ3aT/YMZJ3Q0FCaNGnC7NmzgfwbJli89TgmB+5qg8nMip2RmEz218nGgPO8FlbRKYNdFAReLls65wW5gGI+PlaTJ/+NAFQqFJjzgjyEli1bsm3bNkz5eKy42xsD449secoI+DeCRsX6BzeISXOcUCR7Bp6f//u//+O7774hJiGCIqUjaNImigcp67BI+WfY07ELNzFbnHNX341PsrtGNgacx0+rpU/NaujshJ50SiXvNayPOh8aoQBapYoO5UNROHho0SlVvFPtpVxSlf8pVqwYJUuW5PDhw66WkmO4tTFwK+Uhf8XccmrtkksnnFonewaej9LlE/l9i5Ybce8SVGoftRveJDr+Q85EV+NBylpXy8sWMmM0OopxyzkDmWN008a0C62IVql86gdRJYpolArerF2T/9Sq4UKFOc97LzXAR62x2UVfq1BSu1gwtYtZT5SWyRotW7Zk06b8W7bq1sbAxYQY1ArHaQ/pFjN/xTpnNMhknWT9n1yNeRMfXxNK1T/eGouUgkVK5mb8qHxhELxYtpjTcduggr52j8uegcwhCgKT2rRgxRs96PBiKCV8fZDiY+lRvQob3+rLfxuH53uDvpiPL6s69iLYtwDeKhX8bXCqRBGNQkGzUmWZ06pDvv875DatWrXK10mEbp1AmJlWwc6slcMEWUeSJG7Gj0KS0uys0RMdPwY/r9aIgvv29u7Toha7/rqM3mA7fqhUiHRo9CJqlf2vWFxcHCVKlMhuifmeSkUCmdymJSkpKQQGBvLJ5M9dLSlXKRdQkL293+HArRuMXTgPjV8BmteoTffQKpTyk3tW5ATh4eGcP3+e2NhYChcu7Go52Y5bewaqFAzC4KDeFjIqCxoXs19S+AjZms4aqYbjmMyxTqyUeJj6R47ryUkqlSrKq7UqoFVb/6G3WMx4a5S81bauw71kz8Dz4e3tDUBKSoqLleQ+giAQHlKK4FMX6ecfxIf1GsmGQA6iVqtp0qQJW7dudbWUHMGtjYGCWi+ahrzgMJlGQqJTWccNSGTPQNZJM5xGkhyXMlmkFFINJ3NBUc7yvzdb0KlxVdQqxWOjQBRAo1JS3F9L1KYfMKTaTx4EOWcgOwgMDOT+/fuuluEy4uLi8uWTal6kVatW+TZvwK2NAYDxLzXHX6OzaRBIBiM9vUPwVTs3b0D2DGQVEZsZTf9CcP/LDoUo8t8eTfjjqwG817UxiriLtKxWnAXjerLh22G80aMLnTt3xmCwX0Uhewaen8DAQGJiYlwtw2XExsbK11Au0bJlSzZv3owlH/ZwcPu7clEvXza0eYu6RUuiERV4KVXoFEq8lWqK6nwZEVyV6f2Hs3//fod7yZ6BrOOtqeXUOlHwxltTJ4fV5B5+Pjq6Nq2ONv4cr7wYxAshGbXd48ePJzAwkCFDhti9rmRj4PkpUqSIRxsD8jWUe5QtW5YCBQpw8qT7ezf/jVsnED4iyMuX317tRXTyQw7eu47BbKasXyHqFimBIAiE/qqjY8eObNmyherVq9vdS/YMZA2dOgyNsjR643m76wRBQwFd/uuXrlAoMD/RiEgURRYtWkSDBg2YMWMGw4YNs3qePL74+fF0z4AcJshdHoUKHP2WuBv5whh4RIiPH118nu002LJlS2bNmkWbNm3YuXOnzbbEsmfg+ShZ6Hsu3+uIRbKezCUIWkoVnoEg5L+GMP82BgB8fHxYu3YtDRo0ICwsjFdeeeWp42lpaVgslsdJcDJZw5NzBvR6PQaDAR+f/DtAJy9htJgJblmb36OPsHP71+gUKtoEV6VzydoU0rj3/wO3DxM4S+fOnfniiy9o0aIF169ft7lO9gxkHZ06lBeKrkarqoTFosaQLiCgxWJWE3NPQZnCC/DVNnK1zBzBmjEAUKZMGZYsWUKvXr24cuXKU8ceuXfla+758GTPwCOvgHwN5Twx+iQ67/6eNYqrmEP8uKd/yLWUWH6+vIe2O79l9z37XtG8jscYAwD9+vVj5MiRvPrqq9y9e/eZ47Jn4PnRqUOpWGwr+//owPH9NSgeMI4Xii5haB81Rw+mu1pejmHLGABo0qQJ48ePp3379iQmJgJwOymRrZcu4Ff1RR7q9bkpNd/h6caAnC+Q85gsZt4++DO3UhNIMz/d/j7dYkJvNjL6+O+cfei+ze08yhgAGD58OH379qVFixbEx8cDYDCb2XPjGl41q7Pv5g0MTsxMl7HPvt230ImdKezbD1+vOowZM5ZJkya5WlaOYc8YABg8eDCNGzem88AB9Fy1jGaL5vHV2ZOkN29G3XmzGb5pAzGpnlcrnx14cgKhXEmQO+y5f4EYfRJmbFcRpFuM/HBhey6qyl48zhiAjGE6LVq0oHWbNny7bzcv/fQDQ/9YT8FunRi2eSMv/fQD0w//6dTsdBnrREZGUq1atcf/3bt3b86fP8+RI0dcqCrncGQMALzzf+O4UrsGB6Nvkm42k2axIKlVpJvN/HH5Iu2WLCLGA5vnPC+e7hmQkwdznsVX/yTVbL9MWAIOx0WRaLTdhTUv45HGgCAITJkyBUWbFsw4cpAkg4FkgwFRqyXZaCDJYGDWscO8vyVCDh1kgbS0NK5du0ZoaOjj19RqNaNGjcq33gFHxoBFkhiyeSOSSglW4rsmSSIuLZUPt+ff3uc5hScnEMqegdzhdlqCU+tUgoJYveNmY3kRjzQGAHbfuEZCYGEkG+NQ00wmtkVdYce1qFxW5v6cPn2aihUrolY/PX/g7bffZv/+/Zw9e9ZFynIOR8bA3hvXSTbYz5kwSxJ/Rt/gbrJ73kxchad7BmRjIOfxVjrXtM4kWZxem9fwWGNgzrEjpJmMdtekmozMPpY/3do5yb9DBI/w8vJi+PDhTJ482QWqchalUonJZHtOxs5rUaQY7V9vAEpR5M/om9kpLd/j4+ODxWIhNTXV1VJyHTlMkDu0C66OVlQ5XBek86eozi8XFGU/HmsMHL9726l1J+7dyWEl+Q9bxgDA0KFD2bBhA9euXctdUTmMI8+A3o6h8CQWSZITWDOJIAge6x2QwwS5Q4cSNR2Wb2oVKt554eVcUpT9eKwx4GxyoEWS5LyBTGLPGPD392fAgAF8/fXXuawqZ3FkDIQWLozORkjqSURBoGxAQHZK8wg8KW/gXlwSM5btod3wOVySqrD4YCK//XGUpBS5RDWn8FN78W2tnmgVKgQrQ1h0ChWti1elbbD1+5474LHGQEgB51w5wb4F5IYemUCSJE6ePGnTGAAYMWIEixcv5t69e7moLGdxZAx0qBjmlAHqp9HyUrHg7JTmEXiKZ2DfiSi6jZnPss1/EZOQgiQqSUgxMnflATqNmsflm/n/b+Aq6ge+wMIGA2hWNBS1qMCiN6ASFJT1CeR/VV7n4yqvu/VvhccaA+/UeMnhk5pOqeSdGi/lkqL8wfXr1/H29rYbxyxatCi9evVi6tSpuagsZ3FkDPhptQysWQeNaPsrp1UqGf9yM7e+obgKT+g1cCU6lnEzNqBPN2EwPX2t6Q0mElP0DJm0XPYQ5CAVCgTxzUs92dl8LDc+WMSq+kNY9fJw2gRXc/vvrccaA50qhVHMxxeVjZuzShQJ8vGhS2jlXFbm3tgLETzJqFGjmDt3LgkJzpXs5HWc6TPwVmhlDIeOogQ0in/mM+iUKrRKJZOaNqd52RdyWGn+xBM8A7+sO4TBaP8aSzeY2Lgv/1Xr5DW8lRpSbsYQ5JN/QnoeawzoVCpWdOnJi0WKolMqER/FgSQQzRYqBxZleZee6FSOM0hl/uHEiRNOGQOlS5emXbt2zJw5k4eGNC49jOFWykO3zc9wZAxIksTgwYNprvNh35sDGVCzNuHBJdCfO88H9cM53H8QHSqF5aLi/EV+NwZMJjM7j152GGrSG0ws33Yil1R5LhaLBZPJhNKJPCB3If98kiwQoNOxqmsvzsTcY8XZM9xPTcEHgZ/+O4pfjx7DW+flaoluR2RkJN26dXNqbdfhgxiy6mcWrfoelajALFkI1PowpHIDupVzL7ebI2Ng3rx5nDp1isOHD6PT6fhvvXAAAgcOpc0nE/FVu2dtcl4hMDCQCxcuuFpGjpGcZrCStmadhCT37IDnThiNRtRqtVvdoxzhsZ6BJ6kcWJRPXm7GzNavMbl1O+qULsOaNWtcLcstcTZMsPdOFB9e3osyrAwGi5kUkwG92cTNlAQmHNvK8P1r3KodtD1j4MyZM4wZM4Zly5ah0+meOhYSEsKtW+473CSvkN89AzqNCrPFdl/8J/HWqh0vknkuHhkD+QnZGLBC3759WbRokatluB2JiYncvXuX8uXL21330JDGoL0r0ZtNVlvzppmN7Lh1mWWX3cfdacsYSE1NpVu3bkyZMoWwsGfDACEhIURHR+eGxHxLrD6Fw+o0rlQLZsKxLfx575rbhptsoVErqRVWwuE6tUpBm4ZyuCmnMRgMqPJZCFk2Bqzw+uuvc+jQIe7ckRsOZYZTp04RFhbmMI62/Eqkw5t1mtnIrLMH3OambssYGD58ODVr1qRfv35WzwsODpaNgSxitlj49NgWGq2dwYqEa6S/WIoFF4/yzp7lNFn/A5ce5i9PwVvt66FV2/9uiaJI51fct9bdXTAYDLJnwBPw8vKiQ4cOLF682NVS3IrIyEiqV6/ucN2aa2cyvAIOiNWncDPFPaoNrBkDixcvZu/evfzwww82Y4tymCDrjDvyB79fiSTdYsYgZfztJTLaiEenPKTL1oXcSH7gWpHZSI1KIQzsEm7VIBAFsJiMvNuxBoEBPi5Q51nIxoAH8cYbb8ihgkzibL6Ao5kQj1AKIqlOrnU1/zYGLl26xHvvvceyZcvw9fW1eZ4cJsgalx/Gsu76GdLM1q8PCUgxGfjqxK5c1ZXT9GpVi6kfdKJeldIoRBEkM0pRoGWDULrULsCnHw4iRR6DnePkR2PAo6sJ7NGkSRPi4+M5deoUVapUcbUctyAyMpI+ffo4XBfi7cfVpHiH6wwWM0V1efcpR5Ik9p+4yqINRzgVVQBJgktjF9KzVXU+fv9NPv30U4eeEjlMkDV+uXgEo8V+zb1Fkth26xIPDWn4qXV217oTNSqGUOODEIwmM9179KJbl0706NEaSZI4F3mId955h99++y1fZbrnNeQEQg9CFEV69+4tewfskKY3snrHSXqNXUjLIbNIL/YqkdEm4hLsP5n0q1gbb6XjL1K9oqUI0OTN8k6zxcJH36/nfzM2EnnhFhZJQELgys1YvvhxC96V2vOfN/s73EcOE2SNyLjbmJ3IJ1ErFFxLyj+hgidRKRUEFvInISHj8wmCwKxZszh37hwzZsxwsbr8jZxA6GG88cYb/Pbbbw47y3kiN+48oNOon5m2eDdXomNJSEpD7VOQZVtP0mnUzxw8ec3muY2LlSXE2w+VYKc1r0LJf6vm3Qlgs5bt48/Ia6SlP+umtiCC2p9PfvjD4T6PwgTukiiZV1DYuXaeRJIkp9e6IwULFiQ+/h8vm06nY+XKlUycOJEDBw64UFn+Jj+GCfLvtyQbCAsLIygoiB07drhaSp4iVW9g4OfLeJCY+syPocFoRp9uYvS0dURFx1o9XyGK/PZKb8oWKIT4r/aqakFEp1Axo2EnqhYqlmOf4XlI0xtZsfUEeoPtJEijyczh09e5ff+h3b18fX0RRZGHD+2vk3mal4uVQyMqHK6zIPFCgfw74rdgwYLExcU99VrZsmX5+eef6d69e74aBpaXkI0BD0TuOfAsm/afI1VvxN7DrMFoZv66wzaPF9R6Ma1iE5LnradeYElK+vjjk2LgpUSRfR2G0iw47/boPxB5FdHOwKFHWCwSmw+cd7hODhVknt7la1rtUfEkgkWiS+mqaJX5y537JIUKFXrKM/CIdu3a0a9fP3r06IHJ5LhyRyZzyDkDHkjPnj1Zt24dycnJrpaSZ/h961/orbjHn8QiSew8cgmD0faN6IcZM+lXvxmLm/dhV/shjPGuQOrmg3k2T+ARDxJTneoGZzJbiH3g+LqRKwoyTxGdDx9VfwWtwnoOtEoQUSbr2ffptHwzDMsa/w4TPMn48eNRq9WMGzcul1Xlb0wWM3cMD1AG+aI3G1wtJ9uQjQEHFClShPDwcFavXu1qKXmGuIRUp9aJgkBSarrVY4mJiSxcuJChQ4c+fq1Ro0bs3bs3z8fP/Xx1KETHmdoKhUhBf8eGjVxRkDXeqFCLL+u0paBKi6Q3oFOo8Faq0YgKXg2pwIE3RhNW9gUaNGhAVFSUq+XmCNbCBI9QKBT89ttvLF26lFWrVgFwO+YhW/88z+b957h6y/p5MtZJMxv4+cpmXt/7KT+JB0nv+wKv7RnPlHPLiU13/zCfXFroBG+88Qbz5s3jjTfecLWUPIGXVkWiEzPTTRYLXhrrrrR58+bRokULSpT4p8VqiRIl8Pb25vz584SGhmab3uymQbUymC2ODRaFKNC8fiWH6+QwQdZpX7oytzfvZeOZ87zx3lC0CiXhQaUppPUG4Pvvv+eHH34gPDyc33//nUaNGrlYcfZiK0zwiMKFC7NixQrad+7F2qMPuXr7IUpFxjOg2SJRunhBxr7dgkpliuaWZLckxaRnyNEZ3EqLw2AxgQCoFaRbjPxx+yh7Y04z+6VhBHsVdrXULCN7Bpzg9ddf5+jRo9y+fdvVUvIErcJDUakcJ29VKVcMnfbZeK3ZbOb7779nxIgRzxx75B3Iy3jr1LR/+UU0dlrDSmYT5YL9KBnkeN65HCZ4Pjb9sYme9ZrQrVw12peu/NgQeMSQIUNYsGABnTt3ZsGCBS5SmTPYCxM8omhIOco3GcT5a7EYjGZS9UZS9UbSDSYuXLvPoM+WcvqSfG+zxzfnVxKdGpthCPwLMxYSjWl8GDkvz3s17SEbA06g0+no2LEjixcvxmQ0Y0j37ISczq9Uc+wmt5g4t38lV65ceebQ+vXrKVKkCPXq1XvmmDsYAwDv9X6Z6pWC0WmeNXa0aiWF/bVsmv8x+/fvd7iXHCbIOunp6ezcuZMWLVrYXdeiRQt27drFhAkTGDt2LBYnJwDmdR4ZDDvlOQAAIABJREFUA/Z+hD75IQKjBQQbJZb6dBNjp63H4oS3yxN5aExhT8xpjJLtEnMJiZj0h5x+eD0XlWUvsjHgBGaTmRplXmbTjAu0f2EUHSt+QJ/an7By7k7SUqzHxPMzRQr6MnFIWwTJTEbj16fRqpW83SmcDi3qUbduXaZOnfpUr4apU6fy/vvvW93bXYwBpVLBt6M68tHbzSlfKhBByEhuDy7ix/DeL7Ny2mAWzP+JDh06sGHDBrt7yWGCrLN3717CwsIoXNixezYsLIxDhw6xf/9+unbtmi/a9mo0GtRqtc0E56u34oiKjrNb+QOQkpbOkTPu+0OWkxyNu4RCcOwJTTcb2H3/VC4oyhnknAEHGA0mPu43l3PHrqGyeCMhIUkQd+8hC7+OIOLX/Xy7ZgR+BfNu29ycIPrCYZLPraVNn1EcOHkNAUg3GKlWsQRvdahL/aplgHBee+01+vfvz/Lly5k3bx6pqalcuXKFTp06Wd23UqVKpKSkcPPmzafyCfIiClGkef1KNK9fCUnKuC7EJzwmLVq0YMOGDbz++utMmTKFvn37Wt1HDhNknYiICNq0aeP0+sKFC7N161YGDhxI48aNWbduHcHBwRgtJg7G/cXOmIOkmFIpqilMy6DGhBV4Ic+39X3kHbA2A+Ovc85dV6l6I8fP3qRuldLZrM79SbMYkCTHniQJSDE7zqXKq8jGgAN+nLiWs0evYtA/W0pn0Bu5f+sBE975mW9WvucCda7h9u3bjBgxgo0bN/LSSy+hNxiJjU+kwgtl2Rcf81T9bfny5dm1axc//PADDRs2pFSpUgwePNhmK09BEB57B3r16pVbH+m5EQTBatl73bp12blzJ61atSImJoaRI0c+s6ZQoUKkpKSQlpaGTpd/eujnBn/88Qe//vprps7RaDTMnz+fKVOmUK9ePWav+onl0jZMFhNplgxP35Xk6xx7cJriuiJ8HDYMX1XeNfYfJRGWKlXqmWNmi8XpOLbJnD9CJ9lNUY0/ohNdLFWCkhCdnECYL0lN1rNl6UGrhsAjTEYzl09Fc/3CnVxU5jokSaJ///4MHjyYl156CQCtWkVIUCGKBwVaLeESRZF3332XjRs3EhkZyYoVKzhz5ozN93CXUIGzhIaGsm/fPn7++WdGjx79zM1ZEASCg4PlUEEmiYqKIj4+nho1amT6XEEQGD16NF/MmMzchBUkGVMeGwKQ8ZSnt6RzPfU2H5+ZisnBUCRXYq+8sGxwIRQKx7d5nVbFCyUCs1tavqBmwRdQOdHt0mBIx/uKc2XXeRHZGLDDsd3nEZWO/0Qmo5ld647ngiLXM3fuXGJiYqw2MqlYsSIXLlywee7GjRvp378/gwYNokmTJnz++ecYjc8aWvnNGICMssm9e/eye/du+vfv/0xXuOCQEK5EXXfrbOTc5o8//qB169ZOdYO0RXJlCZW3OqNUzApmycx9fRxHHpzM8nvkNP4FCxN9N8bqk33NsBIoBSeuKQma1i2fA+rcH4UgMqBca7Si7U6WGlHFi5YgRg4YTtu2be0+7ORVZGPADkkJqVhMjl1nFrOFhNikXFDkWq5cucK4ceNYuHChVTd/hQoVbBoDer2e2bNn89577zFgwACOHTvG3r17qVu3LpGRkY/XpZmMXPaH1AGvUm3VJF5a+xWjDq3mbMLdHPtcuUWhQoXYvn07t2/fpnPnzqSlpXHy/C1Gfb4Sc2A7PvvxL17pPY0vZ20m+k7+nLSXnWQ2X+DfGC0m9sQeRrKSBPskeks6629vy/L75ASSJLHtyEX6fLKIGz51mL3jDk2HzGDyou3cjUsEICUlhSFDhnD92CpUdrwDWrWS4X1eRqvOv22bn5fXguvRp/QrCGbA/M/1IiKgFVU0KBzG9FYjOXv2LM2bN6dp06YMGDCAu3fd574lGwN2CAj0ReGEZ0CpUlC4mH8uKHIdZrOZfv368dFHHxEWFmZ1jT3PwJIlS6hZs+bjZkIlS5bkjz/+YNiwYTRv3pxPPvmE6MQ42m6dzaRT21AWL0SaxUSiUc+G6DP02Dmfuecdl+nldby9vVm3bh0+Pj68+vogRkxYzsETVwEBSYJ0g4mInafpN2ohx0/fcLXcPEtaWhp79+6lefPmWd4j0eh8i/F7eutDt1yBJEl8Nm8LE37ezIUbMYCAWQK9wcSa3afo+b+FrPpjJzVr1iQlJYUju9bx2bB2eGlVT/X9sJgMaNRK3u31Mh2bVXPdB3IT2vhW48J7v9OqUHVKeRUhRFeYpkWrMa3WYD6t0gelqECj0TBixAguXLhAgQIFqFy5MhMmTLBbuXI99gH7Ll7j6NVo9Hbat+c0giT7JW1i0BvpUeP/HJYPqjRK5mwbQ7FS7ps84oivv/6a9evXs3PnTptu2Z07d/K///2Pffv2PfW6JElUr16dKVOm0LJly2fOu3XrFgMHDeJSm0oIgX5YbDypaRUqvq7TgRbBjrv65XWOnbrOiAnLsEi2jU2dVsXymW8T4Odtc42nsmnTJj7//PPnCiclGZN5++hYTHbqxx9RWB3AnJc+z/J7ZScrd0YydeluO1MzJcyGNEa1K0+vnt0fv5puMLHj0EWOnLmORZLYtPo3Phrem/btsu5d8STGjh3LgwcPmD17ttPnXL16lY8++og9e/bw2Wef8Z///AeFIiP/4EhUNJM37uJqzANUChFJypjp0qV2Fd5rGY5Wlbv5/bJnwA5qrYpOA5qi0dmeTmXBzIt1y+RrQ+D06dNMnjyZX375xW58tmLFily8ePGZ13ft2oXRaLTZGCY4OJjRP36HqrBtQwBAbzby7en8MU56wcqDdg0ByMgEX7ct78aqXcnzhggAfFU+FNU6/t4qBQV1C1V/rvfKLiRJYt76Q3bHZ4OAj08BCpau+tSrGrWS1o3C+HhQa8YPbkOnV6sRsWFdzgrOJ8TFxTF37lzGjh2bqfPKlCnDkiVLWLVqFfPnz6dGjRps2bKFbWcuM+iX1Zy7HYPeaCJJbyA53UCqwciyQ5H0nbOM9Fz2EsjGgAN6vdeChm2qovV61iDQeqnxKaQk4uRPxMTEuEBdzmMwGOjbty+TJk2iTJkydtcWK1YMvV7PgwdPx7unTp3KiBEj7NZrL406jsGJRKfbqYlEJeUdl21WSNMbiDznuHLAYDCzYcfpXFDkfmSHMQDQKbglGtH+KFpREGlTrMlzv1d2EHU7zubwrydJM5hYt9f+tdOxY0fWrl2bb7ox5iTfffcdnTt3tlq+6Qx169Zlz549fPrppwwd8T4jFqyxGRJIN5m5fC+OWTsOPo/kTCMbAw4QRZGR3/bm45/epmbjSui8NWi91JSvWoL/ft2LZYe/ou1rbWjWrBn37993tdxsZ+LEiRQvXpz+/fs7XCsIwjNJhJcvX+bAgQP06dPH7rl305xLwFQJInF69+4cl5JmcKrcCyDFiRu/p3Hp0iXS0tKoWrWq48UOaBxYh1oBL9o0CNSiijdLdyFImzfK7pJTDU5NzARIdHDtlC9fnkKFCnHo0KHskJZviY+PZ9asWXz00UfPtY8gCHTs2JGxP8xHobRfqphuMrPkYCQGU+6VtMpNh5xAEARqNKxAjYYVrB6fOHEiCoWCZs2asX37dooWdc8JYClJelJT0vH106HVqTl8+DBz5szhxIkTTndhe5RE+GjuwPTp03nnnXfw8rI/yjdA7VyzHbNkwVeldWptXsXXW+t0H3j/Ao5HIHsaERERtG7dOls6A4qCyPsV3mLjnZ0surAKiyih0+owSSaKaYvQp1QHagZUzgbV2UNhf2+MTlQ4AQQVfLYj4b/p2LEjq1evpn79+s8rLd8ybdo0OnToQOnSpbNlvx3nozA5U+0pwaV7sVQOzp3fE9kYyAYEQWDChAmIokjTpk3ZsWMHQUFBrpblNIf3XmTx3J1cPnsHhVLEYrZQK/wFVm6ZzvTp0ylWrJjTe1WsWJHz5y8gSRKJiYksWrSIkycdx727lqnBsbibpJgMdtf5qXVU9CvitJ68iEatpH7NMuw9ctluz3itRkWnlnkjVp2XiIiIYODAgdm2nyiIvFb8FSZ0GMMHU8ZSudKLFFT7U0yX966z4EA/Sgb5c+mm/VCZVq2kixMVAh07dqR79+5Mnjw5z7dddgUJCQnMnDmTw4cPZ9uezj7tC4Lza7MDOUyQjYwfP54ePXrQtGlT7txxj46Ei37YzuejlnL+ZDQmk5l0vRGj0czBXecJ1rxKqaLOlRylpxtZtekv/rziy56LATTq9g09h/1Io+Y9KVasuMPzmxWvgJdSbav3CwA6hYohoY3yxU2rX5f6qB1kC6tVClo3yTtPpXmBlJQUDhw4wKuvvpqt+967d49LFy/RoUE7KvtVyJOGwCOGdmlkd3y2ACTG3eH6Gcfu/xo1amA0Gt2ySU5uMG3aNF577TXKli2bbXuWL1oY0Yl7mMFkJqSgX7a9ryNkYyCb+fjjj+nduzdNmzbl9u28PSP86P5LrFiwn3Sr7ZYFkES++HAZsfcS7e6TlKKn/5hf+X/27jzOpvIP4PjnnLvf2cfY931JtuzKnrKFJJWlshSJZK30S9lCSSLKkiVKZMkalUiWECayE5oYZmHWu5/z+2MizMy9FzNzZ+Y+79drXph57jnfGXfu/Z7nPN/v8+mXO0hMcYGUVjOfkAqJcnlGvr8ap4cMVydrWNK0FyF6E/oMWn+aNDqeKPUgT5e9+9azuVHlcoX53+C2GPVatHf0slAVJ3qdxKz3uhNgNvgowtzp559/pm7dugQHB2fpcbds2ULLli1v21cjt2pSoyxDuzfDoNOmWz9gMugoUSiUjwZ3ZMyYMQwYMACLxZLpsSRJonPnzqxZsya7w85zEhISmDlz5n2vFbhTzya10XtYMwBQt2wJCgblXFmxSAaywdtvv83zzz9P8+bNc3W/+a/mbs8kEfiPoqhsWOH+CuPtD9cRdfkaVlv61bF2h8LhY1HMWrLDYzzlgyPY1GYAL1ZsQJDOgEaSkZGoFV6cjxp0YVyddvliVuCGFo0qs2T6C3RpU4vQYBNGg44iBYPp1KoSJ3fOJDxY3MW7U1ZVEdxp8+bN2XLc7PJUy5osfa8nXZrVoEBIAMFmA5VKFeSN3q34ekJvWjVrzMGDB0lISKBBgwacOHEi02OJZCBjM2fOpF27dlSsmLVtmqsWK0TjiqUxaDP//TbptAxv+0iWntcT0XQoG02dOpV58+bx888/U6JECRwOF3t+PcXfF+PQ6TTUqVeWChV9s7bAkmrjqYcn4fJip7JCRUNY8v2IDL924Z94Xhi5BLvbuue0++QbFryC2U3PhlupqorF5UAna7zaJCS/GTBgAHq9nk8++cTXoeQaqqpStmxZNm7cyAMPZN3tE6fTSeHChYmMjKREiRJZdtzcQFVVFixYwJtvvsmHH37I888/n26M0+mkaNGi7N+/P8sWyeV1iYmJVKhQgV9//ZVKlTJeOH4/7E4nI77ayI9HTqLR6bixntis1yHLEp/27kTdsjn7XBSXHtlo1KhRyLJMs2bNePftz/l2+WFUVcX6b2nZki92UrxEGP8b9yQlShXI0dgsqXY0WtmrZCD6cgwtWrQgNDT05kdYWBihoaGcuaLF6UVzDI1GZs+hc7Rq7F33QEmSMGtz/5Rtdpk4cSLVqlWjX79+WVJClx+cOHECVVUzbYd9r/bt20eJEiXyXSIAab9H/fr1o0GDBnTv3p1t27bx6aefEhj435bMWq2Wjh07snbtWoYOHerDaHOPWbNm0aZNm2xJBAD0Wi01HbEcObmXVn1f5czVOMw6HY/XqMRjD1bCkMPdB0HMDOSIIa9M5c/DqcgZ7HolSRIBAXpmL+hL0eJhORaT3e6ka5MJOOyeV6sWLh7M8yMe4vr16+k+Tl8xk+jy3MXNoNfyau9mPPl4/rjnnxM+//xzli1bxo4dO/LV7RFvWVNt/LzmAOsW/sK1mCTsTiv6wlbmfDOZsIJZt2bg7bffxul0Mnny5Cw7Zm6UkpLCkCFD2LVrF9988w01a/63OHj9+vVM+3Aa3yxZieJSiCgRjs6PNi66lpjK1bgkDHotYYE6KlaswI4dO27upZLVnE4nVapU4YsvvqBp06bZco67JWYGslnC9VTOnlAzTAQgbRovNdXOpx9vZcIH3TMck9VUVeXHH7eS4opCpxZGktzsaGbS071Pc1q2rJfh1xd9u4eF3+7B6aH2WaORCQsRNfN3o1+/fsydO5evv/6a5557ztfh5Kios1cY2XUG1lQ71tT/yk3tqXr6NBnHOwv6U/uRyllyrs2bN/PRRx9lybFys4CAABYsWMDSpUtp3bo148eP5+WXX8ZmsRN7IAV+DaFPtaHIsoQkSbTt14pn3+xCaMGcW9Ge007+dYXZX+/k8IkodFoNLkVFddmp2+pZKlXOmudXRpYvX07x4sVzTSIAYmYg232zdA9fLvwFWwaL626l02tYuvJVwgsEuh13P1RVZf369YwbNw6bzcarA0ayacn5TBcRSpJEaIEAFq5/PcN2zAD/XLlOz6ELsTvczzAYDTo2fvEKRoP/XG1khT179vDUU09x/PjxLF9Bn1ulJlvp+/A4EuJSyOzlyWDS88mmEZS6zzU30dHRVKlShZiYmAy35c6vTp06Rffu3SlfugLBp4tz5a+r2O94HdDqtQSFBzLrt/cpVDL/7b3y2x/nGT3tO2wZrHfSa2XqPFCKD0d1QeNmP5Z7oSgKDzzwAJ988sl97bqZ1UQ1QTY7fOi8x0QAQK/TcvbMFa+Pa0mxsnHJTgY0n8Az1UfTp+FYln20iWsx6csAFUVh9erV1KlTh7Fjx/LWW28RGRnJy4N6M/bj5zCa9OgNt08SGU06QgsEMG1hv0wTAYDihUOpV6M0el3mi/yMBi1d29YWicA9aNSoEW3atGH8+PG+DiXH/PTtPqyp9kwTAQCH3cE3s7be97m+//57Wrdu7VeJAEClSpXYs2cPyjEDF47/nS4RAHDanSTEJPJOpyk+iDB7JafaePOjdRkmAgB2p8Lh41Es33Qwy8+9atUqQkJCsrxXxv0SyUAuYbPbOHf2HC6X53v4f5+O5sWGY5n/3mounLxMQlwyly/EsmLmVvo0HMuhX9LKiFwuFytWrKBmzZq8//77jBs3joMHD/Lkk0/e3H2wTqMKLNr4Os/0a0qxkuGEhgdQrlIRBo5uz8INr1PMi4WN7w7tQLlSERm+2RsNOhrWLsfLzz58lz8R4YbJkyezaNEijh8/7utQcsS6RTtvuzWQEcWlsnP9YZweZqQ8yWslhVnJlmzHFqUgu3kbUFwKUacuc/rguRyMLPtt+uVPVDc7pAJY7U6+2rDf69bh3lBVlQkTJvD222/nunVAYs1ANqtVuwxHDl/0ODvgdCqMmzCKlwaeo1GjRjRp0oSHH36Y+vXrExDwX+OJ1GQrI7tMJzE+OV0r2xvZ/XsvfE6bwdWYNW86ISEhTJ06lccffzzTJ19ogUCee6kFz73U4p6+R7NJz2cTnmPbnpMsW7uPi5euIckSVcsXoWfnejSqUy7XPfHzksKFC/P2228zZMgQtm7dmu9/ltczmN3KkATJiamEFvDcgz8jTqeTH374genTp9/T4/O6fZsP/bthjvteIw6rnV9X/0bFOlnXhc/Xfth9IsO+KHeyWB2c/yeOcll0m2T9+vXIskz79u2z5HhZSSQD2axtx5os+eIXt2NkWaJ+w8qMn3qI2NhYdu/eza+//sqYMWOIjIykevXqPPzwwzRp0oTUizqsFrvbnvZWi5XVc7bxyexPaNWqVY68eeh0Gh5rWo3HmmZt2ZeQZtCgQcyfP5/Vq1fTtWtXX4eTrYxmA8kJmXfNu0FxKpjuo0Pj3r17KV26NMWKeW6XnR9ZkqxelRYrikrSteQciCjnWDw0W7tBliWsHnqoeCs3zwqAuE2Q7YJDzPQb2AKDMeN7kpIkYTbreWVoGwAiIiJ44oknmDp1Krt37yY2NpYPPviAiIgIFixYwPz3v8HmYQpVQsZsK0TzZi1y5ZNOuHtarZZZs2YxbNgwUlNTfR1OtmrR+SF0bnrv31CtXjkMXjaxysiN3Q/9VaFSER630gXQGXUULZ93Nl7zRtGC3s0m2R1OCt/jzNOdtm7dSkpKCl26dMmS42U1kQzkgC7d6vPKkEcJCDBgMuuRZQlZBhUXZcsXZOa8PhQtFprhY00mE02bNuXNN99k48aNRIR6/0uZkuj56krIO5o1a0aTJk14//33fR1Ktur4YlMk2X0S61KdWIMv4XB4d4WXkc2bN/t1MlD3sZrIHn7OADarjWNxh0hKSsqBqLLXhQsXGDlyJMvnjkdSPa03UbkefZbRI15z287ZG6qqMn78eMaMGXNzvVZukzujyofaPVGblRteZ8SbHejdtynP92vK0dNLmDTtSUqUDPf6OEYvp0UVl+K2CkDImz744APmzJnDmTNnfB1KttEYVaJ1h5E0ZDizZTDpeWpAS05fjuThhx/m9OnTd32OS5cuceHCBRo1apQVIedJWp2W3u92c/uaYjDrefipepz86wQVKlRg6tSppKSk5GCU6TkdLqIvxnH5QiwOL6bwVVVl165ddOvWjTp16uByudj+/bc8UKkEOjczIwa9js/eH0TJkiVp2rQpnTt3Zvfu3fcU844dO7h69Srdu+dML5l7oXn33Xff9XUQ/kKjkSldtiA1apXiwZql2b1nO7IsU6uW93vWX49N4nTkBRSX+xWuDzaqwOM9mtxvyEIuc6PXwNy5c3nuuee4+ncsUacv47A6CArLvh4VOSUhIYE2bdrQ9NGG/G/qMK7HJnH5Qiw6gxYJiSp1yjBoYjc6Pt+MHj16YLVa6d27NwUKFKB27dpe3xZbuXIliqLk6hfnnFClQUVSk62cOXgOUFH/XTkvSRLGAAP129bm7a+G0e3pbrRr147ly5czbNgwJEmiVq1aOVqSmZJo4auPtzDplcWsX7yTjUt3serzbSRdT6VijZIYjLdf/NjtdpYvX07fvn35+uuv6d69O4sWLeKJJ54gPDyclg0qceDPiyQmW3E4nTefO0aDFq1Ww6ShHWn8UCWaN2/OoEGDSEpKYsSIEaxcuZKIiAgqVqyY4fMtxWrnu11Hmf7tLyz/+TC/n4riywWfM+ilF3nooYdy5Gd1L0TTIR/68ssvWb169V3tGBbzzzX6P/IeNkvm06MGk56xi1+m9iPe7QMg5C12u5165RpR3VSX6/8kodVrcTlcFCxZgF5jn6ZlHi3jTEpKok2bNtSrV48ZM2bcfKG1WuwkX0/FFGggIMiU7nFHjx6lR48eVKhQgblz51KggOdy2G7dutG+fXteeOGFrP428qQLx/5m1fQNHNp2FFVRqfhQOboN70jVhpXSveEdOXKE9957j927dzNq1ChefvllTKbb/1/Onopm5dI9/LbrNE6Hi4KFguj6XCNatn0Q0z2s80i8lsLQjh8ReyUBxx1VADq9hpACQXyyYThhBYOIiYlh7ty5zJ49mypVqjB06FDatWuHRpN+FkBVVd6bMpudkZcpXKICRr2WR5tUoX3T6gQHGtONdzqdrF69+uYMyYgRI+jZsycGQ9rsyt5jFxjx2XpUwGJLe42WAMVp56GqZfnk1c6YjblzxlYkAz4UHx9P2bJluXz5Mmaz961693wfyYT+c1GcKmlPtf8YTHq6D2nDs0P9915ofrd21mY+H7kEZwalUQazgY4D2/DyB719ENm9S0lJoW3btlStWpXPPvvsrhe+Wq1W3nrrLVasWMHixYtp1apVpmMdDgeFChXi+PHjFCmSvxbG5aTDhw/z3nvvsW/fPt544w369++P0WhkxZe7+XLudhwO1201+kajjuBQMx/Pf5GIQnfXTfPtXp8Ruft0pn0lNFqZkpUL4ixxjm+//ZYnn3yS1157zatNvtq2bcuLL77I008/7XU8qqry888/M3XqVP744w9ee+01WrTvytDPN2dafaDXaniwXFHmDnsqVy7sFsmAj7Vq1YrBgwfTuXNnrx/zxx9/0LF1V3q2Hswfu86gkWWcThdVHirLs0Pb8lDz7NlcQ/C9v45eZHCDN7FZMq8oMQYYeOfbEdR7zPvbT75ksVjo0KEDpUqVYsGCBfe1wGrr1q306dOHZ555hokTJ2IwGLA5nWw+c4q5Bw9wMeE6KAqOs+f4ZtSb1CpSNAu/E/908OBB3n33XQ4dOsQLPYZzeFdqpn1VZI1E0WJhLFg5yKvFiwBXouLp32JSuhmBOymqk4eejmDo6FcoVKiQV8dOSkqiePHiREVF3XO778jISD788EMOJIViKlyWOy/QbmUy6Ph0SBdqVSh+T+fKTiIZ8LFZs2Zx4MABFi1a5NV4l8tFo0aN6N+/P/3798dmsZOckIop0Ig5g2ktIX/5sO9sfliyA8VDfXiNZtWY9vN7ORTVvbPZbHTu3Jnw8HCWLFmS4VTu3YqNjaV///789ddffL54EWOPRhKVmEDqrZUHqopJp6NXjdqMbvJIrrxSy2v279/PmCGrwOV+ltNk1jNmUlfqN67o1XE3LPmVeRO+y7Bl8q20OpnnR3bgqQEtvY555cqVLFiwgO+//97rx2QkPjGVtm/Mw+Hh91KSoHWdSkx5STQdEu7QqVMn3n33XZxOJ1qt5/+OGTNmEBAQQL9+/YC02wL3U2st5C271+73mAgAHP31BC6ny6s6cl+x2+1069aNwMBAFi9enCWJAKT16li9ejULFiyg29LF6EuWIN1PTJKwOJ18+cchSoeE8OyDNTM6lHAXihUuh0EXgs3l/k3bkmpn8dytXI0/SUJCAomJiTc/bv33jb/L8REUcFZ2u7sqgNOhYEmx3lXM69ato1OnTnf1mIxcjk/EoNPicLnvAaOqcD46/r7Plx1EMuBjJUuWpEyZMuzcuZMWLdy3Az537hyTJk1i795QtVyJAAAgAElEQVS94krGT9lt3tXVS1LaWFMuTQacTifPPfcckiTx1VdfeZUI3w1JkqjXsQMB1mRsSub15Bank49/20P36jWQxe/UfbkWn4JWK2PzYuzJ439xfs4mQkJCCA4OvvlRpEiRm3+/8bVT+6NZMeMXj83WDCY9BYuFeR2vw+Fg06ZNWdK3w6DT4vJykt3gRUMtX8idUfmZLl26sHbtWrfJgKqqvPzyy4waNYoKFSrkYHRCbhJRPJx/Tl/2OM5gMnjdkyKnuVwuevXqRWpqKmvWrMm28rQVfx7BoXqeRUl12DkcfZk6Rf2zLXFWCQo2edXeGKBe/Vq8P/NDr8ZWf8DOyo/dt3QHUBWVR9p7v07m119/pWzZspQoUcLrx2SmbNFwDDrtzQqCzBj1Wh6vW/m+z5cdRNOhXKBz586sXbvW7ZatixYtIj4+nmHDhuVgZEJu8+Rr7TEGuH+TV1Ao3qAgiuLdC3N2OHMqmsnjv+PZLp/QvdMMxoz8hsMHz+Nyuejbty+xsbGsWrXqZklWdriclITixdWaJEnEpvq2kU5+UKpsBMEhnquiTGY9bTvV8fq4RpOepwa2dHs71KU6eahNOQKC05eeZua7777LklsEABpZpmfrOhh0nq+vOzbOnfu3iGQgF6hWrRp6vZ7Dhw9n+PXo6GhGjx7NggULsnw6VchbHu3dlIDQALcrsc1BJo4m/E7dunXZuXNnDkaXNoM155MfGDpwMT//8CexMUnExyWzf+8Z/jdqBV3av8Nf587z3XffpatNz2oFzF4eX4Vgg1h8e78kSaJnv6YYM9mHJW1M2pt74+Z3d3X83GuP0ebpBhiMOmTNf29bsixhMOl4sElpZq54hy1btnh1PFVVs2y9wA292jxEjXJFM00IDDotU15qT5A5dz7XRAfCXECSJKKiojhz5kyGtwpefPFFWrduTc+ePX0QnZCb6PQ6HunakF/X7ENRFJy31DSbAo2Yg018tH0cr495jbCwMF5++WUOHDhAgwYN7rl06m6s/Hov3y7/DZvNmW5nTadTwWGTad26A60ezf4Fe0F6A9+fPY3DwwyJUadlbLOWaHJpz/i8pHylIsTFJnHxXAxO5+0/d51OgznAwLTPnyeswN11y5QkiXotq9GgdXWsFjupSVYCQ8w81Lwqr03uzrOD2tKkSRO6d+9OmTJleOCBB9we7+jRo3z99ddMmjQpy9ZfaWSZx+tXRpYlTkXFoJGlm+sDapYvxoQ+balfpVSWnCs7iNLCXGL37t0MGDCAP/7447bPr127llGjRhEZGZntV1JC3uFyutiz/gDr52wl7lI8wQWCaNu3Fc2eboT+lg5nKSkpTJ48mdmzZzNs2DCGDx+O0Zg9VyYOh4unOkwnNcX9EjKdTsOSlYOIiMia3eAyo6oqrb9cyMWE65ku7jJptQys24BX6zfM1lj8iaqq7Nt1muWLd3HsSBQSabcGOnStS5fuDQiPyL622ZGRkbRt25Zx48bdrLjKyIQJE4iNjeXjjz/OljicLoWzl2Kx2p0UDQ+mUB5oFS6SgVwiKSGFRtUeo3mdJ5BUDSXLF6blU7Xp/OxjfPXVVzRt2tTXIQp52Llz5xgxYgSRkZFMmzaNTp063bwislvt/Lx8Fys/XEf0+atotBrqtK5BtxFPUK1hJa/PsfvXU0wZ9x2pHlZ963Qanu/XjO49sn+ToH8SE+m64isSbFZsrturCkxaHS3KlOWTth1EJUE2cbkUnA4XeoM2xyqgTp8+TZs2bXjllVcYOXLkzc9bUqxcPnsFSZZ4+vmuTPlgCi1bet+TIL8TyUAu8PuOE0wYuBCb1YaqpP3CpG3hqmAIU/h65we5dmW4kLf88MMPvPbaa5QoUYIZM2ZQNKIYrzd9h9ioOKy3XNFLsoTeqKfL4Lb0fb+HV8dev+Z3Ppv1I3YPneIAOnapw5DhOdMy+7rVwpLIwyyOPEiizYYKVIkoyMCH6tOuYvre+0LeFxUVRZs2bejUqRPDB49kybsr2LZsJxqtBkVVSElOoeuQjjz/bncCQwN8HW6uIJIBHzsZeZHRz8zKdOMhnV5L9frlmPjlAPGiJWQJh8PBp59+ysQJE2mofRTHNQVXJj3fDWYDr87sw+Mvpr+CSklJ4Y8//uDgwYMcOnSIo5ExmLQ10WrcJ66yLPFMz8a8+FLzrPh2vKaqKhanE60so8+iBkdC7hUbG0u7lh0IO1MKySnjct7+HNfqtUQUD+fTfZMJLpC9t6zyArFixse+eH+d2x0IHXYnxw+e51TkxRyMSsjPdDodQ4cOZe2S9djiHZkmAgC2VBuL3/mG+Ph4tm3bxrRp0+jRowfVqlWjYMGCDB48mMOHD/PQQw8xafIITCbPpWU6nYZHWuT8jpqSJGHW6UQi4CciIiKoSWNcViVdIgDgtDuJjYpn6guzfBBd7iPq1Hwo7koCxw9d8DjOZnXw3cKdjJpROgeiEvzFnlUHwSUB7icHr16KoVqJB6lQpyx16tShdevWjBo1iqpVq6LX3177vW9XPL9sP4Ejk53bNBqZUmUiqFBR7BYoZK/TB89x+dxVJDcbBzkdTg7+eITYf+KIKO556+v8TCQDPnQlKh69XutxNy5VUfn77JUcikrwFzF/x7ptdHWDOcDMqi/X0KRTfY9jXxvZlgvnY/j7Qly6net0Og3BoWbGTfF+q1hBuFd71h/AYXW/mBXStj/et/kw7fplvu21PxC3CXxIb9Ddtt+327FuGnkIwr0ILRTi1ThJkgmJ8K5HgcmkZ8acF+jdtynhBQLQ67UYDFoCAgw82b0+cxf1y/aSQkEASE2yePX66nIqWO9yg6P8SMwM+FDZKkXR6jzfvzSY9DTrWDsHIhL8yWMvtGDPugNYkt2/EOr0Wqo29G67WQC9QcvTzzXiqWcacv1aCoqqEhYagEYrrj2EnFO8fBEMZr3HDY50ei1FyhTKoahyL/Hb6UMarYZOLzb1eNUvSdDqyXo5FJXgL2q1rE5YkVC3rY0NZgPPvNH5nrYXlmWJ8AKBREQEiURAyHHNn2mC6sXMgCRL1G8nLrbEb6iPPT2wFZVqlMSQSUJgMOoYM/sFAoJyZz9rIe+SZZkpW/9HaOEQdBk8/4wBBh5+sgFdX+/gg+gE4f4EhQXSeXBbDG56tBjNBl4Y3x2tFxsM5Xeiz0Au4LA7WTHnJ75b+AtOhwtZlrDbnVSpXZq+b3Skci1RRSBkn8T4JNbN3sLaTzaRdC0FVVGpVK88z4zuTJPO9UV/CyHPUhSFT16Zx49f/oLD7kT5d4tlrU6LrJHoProzvceKBa0gkoFcxeV08deJS9htTgqXCKdAYe8WeAlCVrFb7Wh0mnu6LSAIudX5P/9m9YyNHNt9EkmWqN3qQboMbkfRcoV9HVquIZIBQRAEQfBzYs2AIAiCIPg5kQwIgiAIgp8TyYAgCIIg+DmRDAiCIAiCnxPJgCAIgiD4OZEMCIIgCIKfE8mAIAiCIPg5kQwIgiAIgp8TyYAgCIIg+DmRDAiCIAiCnxPJgCAIgiD4OZEMCIIgCIKfE8mAIAiCIPg5kQwIgiAIgp8TyYAgCIIg+DmRDAiCIAiCnxPJgCAIgiD4OZEMCIIgCIKfE8mAIAiCIPg5kQwIgiAIgp8TyYAgCIIg+DmRDAiCIAiCnxPJgCAIgiD4OZEMCIIgCIKfE8mAIAiCIPg5kQwIgiAIgp8TyYAgCIIg+DmRDAiCIAiCnxPJgCAIgiD4OZEMCIIgCIKfE8mAIAiCIPg5kQwIgiAIgp8TyYAgCIIg+DmRDAiCIAiCnxPJgCAIgiD4OZEMCIIgCIKfE8mAIAiCIPg5kQwIgiAIgp8TyYAgCIIg+DmtrwMQ/qMq8aip34BlFShJIIeB+Tkk05NIcqCvwxPyMdUVjZq6AhxHQdIiGVqBqR2SZPJ1aIJw11yuy1hSvsCa8jWqmogkmdCbOmIOGIBWV8HX4eVKkqqqqq+DEEC170e99hKoLsD63xckE6BHCv8SSVfFV+EJ+ZSqKqhJUyB12b+fsaf9IZnT/giZjmRs4ZvgBOEeOOz7SYjrgao6uPl8BkADkp6gkI8wmp/wVXi5lkgGcgHVeRE17glQUzMfJIUgFdyKJIdlzTlVF9j3gusiYABDYyRNkSw5tpB3KIlTIPUrwJLJCCNS2DwkQ4OcDEsQ7oniiiX+ahNUNdnNKBNhBdei1VXPsbjyArFmIBdQUxaAavcwyJY2jZsFlNR1qDEPo15/FTXxfdSkcagxrVHi+6G64rLkHELup7piIPVLMk8EAKyoSeNyKiRBuC+W1C//nRFwx0ZK0qwciScvEcmAj6mqCpY1gNPDSOu/V3D3R0lZBolvgxIHakracdVUwA723ahxXVCV+Ps+j5D7qZaVgOR5oDMK1XEi2+MRhPtlTfkKsHkYpWC3fo+qenrN9S8iGfA5K+Apk03jtMewatUqdu3axdmzZ0lJSbmrM6muOEiazG1rEm4/AyixqEkf3tVxhTzKcRzPL5yApAHnuWwPRxDul6om3MXYu3v9zO9ENYHPGbweabVLLF26lCtXrhAdHc3ly5fRarUUKVLE40ehQoXQ2L7x4ixOsGxADXpLVDDkd5L3zz0kXfbFIQhZRJKCvXyTV5GkgGyPJy8RyYCPSZKMlQbolN3IbudpdAQW6M6aNe/c/IyqqiQlJREdHZ3uY9euXbf9OyYmhh1rS9DwIS9e1CUtOE+Bvs59f39C7iUZWqLafvr3dpEbqgP0D+VMUIJwH4zmZ0hNno37GS8ZvfFRJEm8/d1K/DR8yOFw8Omnn7Jlw/esWRiGXna5Ga1BCnj+ts9IkkRwcDDBwcFUqlTJ7blcLhfOmG7AUS8ikwDFi3FCnmZsDYnveBikBUNzJDk8R0IShPthCuhNUsJsNBo3gyQD5sAhORZTXiHWDPjI9u3bqVOnDps2bWL6rO3ow98AjBmMlNI+HzIZSVv6ns+n0WjQmesBXswMqDbQlrvncwl5gyTpkUI/JePnHaiqFuQIpJD3cjYwQbhHq1bvoM9LKSiKkTtf61RVBkwEhkxGp6/hk/hyM5EM5LCoqCieffZZXnjhBcaNG8eWLVuoUqUKcsDzSOHzQd+QtAmbf5/MhpZIBb5CNrW773NL5h54+i9XFFD1j4grQT8hGRpw5J9RHD6qoGIAKQikQBxOmYPHCiFFrBXPBSFPWLJkCUOGDOH9yVuJKLIDY0BvJCkIALtd5p/LDxJWcB0m81M+jjR3EslADrHZbEyZMoVatWpRsWJFjh07RpcuXZCk/0q7JH195PAlSIUOIBX8Aanw78hhc5CyqDmGpC0F5meBjFvMqkhYrBJ9Bh8nLk70G/AHqqry6tDPifx7DHLEeqSQD5BCpxMvr+Wxbn8Qf030JBNyv88//5wxY8awbds2atasiUZbgqCQcUQUPUFE0Sg2/fAO0z4OR6ur5utQcy2RDOSALVu2UKNGDXbt2sVvv/3GuHHjMJvNmY6XZDOSpjCSlPH07f2Qgt6EgOdJq2K4cXwJJDOSpgSmYmspVKwe9evX58iRI1l+fiF32bhxI9euXaN3795I2jJIxpZIhmYULVaFLl26MHv2bF+HKAhuTZ8+ncmTJ7N9+3aqVq2a7uuSJNGyZUu2bduGaLibOdGOOBv99ddfDBs2jCNHjjBjxgzat2/v65BuUpUEVMt6cJ4ByYRkbAm6ujdnKpYtW8bQoUOZM2cOTz0lptXyI5fLRc2aNZk0aRJPPJG+V/uJEydo1qwZ58+fx2QSGxYJOU9VXVxN/Ynz1+eTbD8JQKC+MmVC+1HI3IpJkyazePFifvzxR0qVKuXmOCplypS5eVtWSE8kA3dBURX+SDjBhkvb+MdyBZ2spUF4LR4v0pQChv/2DLBYLEydOpWZM2cybNgwhg0bhtGY9Vf52e3gwYN06dKFnj17Mn78eGT3tY9CHrN48WLmzZvHzp07b7tddavOnTvTpk0bXnnllRyOTsirbM4LXE1cRLJtNyoKgYZ6FArqg/EudwtUVDuHogeSYDuM6459WzSSmZioACYOimXL9z9RtGhRj8fr06cPdevWFc/lTIhkwEsWl5Xxx2ZxIeUfrMp/Naw6SQtIvFSuOy0KNWLdunUMHTqUunXrMm3aNLfZal5w9epVunXrRlBQEMuWLSMkJMTXIQlZwGq1UrlyZb766iuaNGmS6bjdu3fTq1cvTp48iVYrKpGFzKmqyuWE6VxJmoOqKvzXWVWLJGmJCOhOibD3kCTvLir+jHmb6JSNKGrGHVPtNigS0I6HSk7z6nhLly5lzZo1rFq1yqvx/kZc6nnp/eNzOJd88bZEAMChOnGoDuaeXU6HwV154403mDdvHitXrszziQBAoUKF+PHHHylTpgwNGjTg5MmTvg5JyAKzZ8+mZs2abhMBgMaNG1OsWDFWr16dQ5EJedXVpAVcTfocVbVxe4t1J6pqJS5lBZcSvGt1bnddIzplQ6aJAIDeANddP2F3XfPqmC1atGD79u0oiuihkhGRDHjhTNJ5ziRfwOFmYwsHTsI7lSIyMpLWrVvnYHTZT6fTMWvWLEaOHMkjjzzChg0bfB2ScB8SEhKYPHkykyZN8mr8qFGjmDJlilh8JWRKUW1cTvgIRc18B0xFtXA1aT4uJcnj8a6m/IB3b08SV1K2eBVj8eLFKViwIJGRkV6N9zdi3s8L30fvxKF43kzIGaQS47pGcQrnQFQ5r2/fvlSrVo1u3boxcOBA3nrrrUzvNQu519SpU2nfvj3Vq3tXstq+fXtGjx7Ntm3baNWqVTZHJ+RFCZZtgBfJoipxNmoJtoSmJCQk3Py4fv36bf8uXfsEtVtZPLRoT0tC7Hex7XqrVq3Ytm0btWvX9vox/kIkA164bL2K4sUTXStpiLVdo7gpfyYDAI0aNWLfvn107dqVQ4cOsWjRIgIDxYZGecXly5f57LPPOHTokNePkWWZkSNHMnXqVJEMCBmyO6NQVLvHcSoWFi6ZzLqvZxMSEkJoaCghISG3fZQqVYoi5UxI/IjHHV1VHXpNqNdxtmzZkgULFjB8+HCvH+MvxAJCL0w6Npvfr3vu6W+SjbzzwGAqBZXNgah8y2az8corr7B//37Wrl1LuXJp7YtV1Q62n1AdxwANkr4u6Bt7vWhIyF4DBw4kICCADz+8u22q7XY75cqVY/369eKqSkgnJulLoq6PR3Vzjz+NhqIhwygaMtjtKJsrll8vtkLBfYLhsKl8ODiQHs8M5JlnniEgwP1OhHFxcdRvVJ49v3+LTqsnUF8dnUZ02ATQvPvuu+/6Ooi84PD14zhVdxsJgUHW8UKZp5D94I1Pq9XyxBNPoKoqvXv3pnbt2pQpegzie4LtR7DvAcd+sP0AqctA9yCSprivw/YbqqoSZTnGjiuL2Be3hlNJe7h6OYb3Rk/lm+XfuG16lRGNRoOiKKxatYquXbtmU9RCXqXVFOBK4hdIkvvFeZJkoHjoW+g0Bd0fTzaT4jhLqvMiKhm/7sqSgSJBj1K9VF+WLVvGsGHDiIqKolSpUhQqVCjd+BT7KS6kvEnrrvFcs/5EnGUrUQnzSbYfI9hQF62fb9kuZga84FCc9D/wJknOzLd61ct6uhR/lKdL5p7GQjllx44dLF/Uk4/HB6HTZrbI0ogUvhhJL64qs1uqM5EVF/9HrO1vHKqNG/dyXTbQqDr6VZtBQWOZuz5uYmIi1WuUZ8PPb6AxnwJUQoz1KRz4FLq7mKoV8pfLly/zxhtv0LTjNmrV1SDJmSUEGky6qlQtusmr47oUC79Hv0iS/VS6hYmyZCJQX5G6RRahkdMaYv3999/Mnz+f+fPnU7ZsWV5++WW6deuG0Wgk2XaUyOjnUO7oV3AjLp0cQu1i32HQeu5XkF+JZMBLZ5IvMPbox6Q6LMia26/89bKOqkHlGVNtEBrJ3d6Z+ZOq2nBF10eWMl9JDICmPHLBzTkTlJ9yqU4WnhtMvO0fXGSUmEkY5AD6lZ9NsC7iro59OXE5J6++g6pIaPVpV2uyZEzr7hY2ghIhL2bBdyDkFTabjRkzZjB16lT69+/PqDdeJir5GZyuGNR09/q1aOUQKhdZj0FbwutzKKqD6OQN/JUwn1THXwCYdWUoE9KPooEdkCV9usc4HA42bNjA559/zu+//07v3r148tXdKFKsmzNpCDHWo0aRpV7Hlt+IZOAufLx4Jj+m7CWwdgE0kgYFhUBtAJ2Ltebxos38MhEAUC3rUBPfgQyz7luZkAosy7KNl4T0jifuZOM/03G4uXcro6V2WFvaFB3o9XGvJH/HmbgxmdZ9y5KJcmFvUjT4ubuOWcg9VFXBqaYgSwY0GbzR3rBp0yaGDh1K5cqV+eijj6hYsSIATtd1LidMIy5lJTdKA1WchJs7Uyx0ODrNvS+uTmtkxF2tPzp37hyrN02gRosdmALcVz5JGKhbfAtGnffJSn4ikgEvpaSkUKlSJdauXUv1Og9yzZ6AVtJS0BDu9+V1SsJ7YFnmxUgTUvBbSObu2R6Tv1p87nUuWT03htJJRl6vsgKN5LmgSFWd7P27AU4lwe04jRRAw5L7kGWD1/EKuYPFGc1fCYv4O2k1iupAxUWovjrlQ/tTyNz85mvc6dOnef311zl16hQff/wx7dplvLW6olixOv8CFAzaMmhk9wv7stO5+Pf5J3GBx3GyZKZ8+DsUCfLPvVhEaaGXZsyYQZMmTahXrx4AJlPe22sg29zVjEj+X1zpS9ccl70aZ3famPnZRwTpCxAUFERgYGCGf5rNZuItO1DdNNy6QQViU7dSKLDjfX4XQk5KtJ1gb/SLuBQL6i23lq7b/+BwzCiKBz5BKf1rTJo0ifnz5zN69GhWr16NXp/5zIEsGzHr0+8g6AuKavM8KG1kWjWUnxLJgBdiY2P56KOP2LNnj69DyZUkfT1UyypQM19gmUYFXa0ciclfeXOlD4Ckcv7cRRLjTpCcnExSUlKGf1qtVnoNKkjvwQHodO5nwBQ1hVTH2Sz4LoSc4lJs7IvujzOTroAu1cKF66t5b/IXBDibceTIEa82BcpNAvSVkSWT2+6IABIaTLpyORRV7iOSAS9MmjSJ7t2737wvJtzB0BLwYnZAWw5JJ36G2alCYAMOX/seJPd3/woaS/HWB7M8Hs/lcnE+bh6XUj8BDzXfIGe4oEvIvaJTt+LycOUsaRz0HV2axyoszJO3RAsGdOBc/ESP4zRyICHGBjkQUe4k5mw9OH/+PIsXL+Z///ufr0PJtSRJhxT6IZDxrRNFAVUyI4VMydnA/Myff/7Jp8PX4rS774ehk4w0injaq2NqNBqKhLb0qneGLBkINTby6rhC7vB30up02wNnRNIlk+I4n/0BZQOtHETJkEHIkinTMQ67ROngMXky2ckqIhnw4H//+x+vvvoqRYoU8XUouZpkaI4UNhvkIiCZcbpk7A4ZMHI+SstXmzog6Sr7Osx8KSkpiREjRtC8eXMeb9KFR0u8hFbKeBGfVjJQPrAuD4Q09/r4AfpKmHXlcfdyoSgq9tRAggziNlBe4nC5XxR6g4Q201sJeUHJkAGUCO6HhB6J/343ZMmELBnZsrwIrw/4EpfLfSKdn4lqAjcOHz7M448/zunTpwkKCvJ1OHmCqqpg38dvuxdz6uQZevX9mDPndTRq1Ij9+/dTtmz+adVsdTj5/tBJlvxykOhrSeh1GlpWL0/PR+pQrnD2tzhVVZXly5czYsQI2rRpw+TJkylcOK1061Tibn6+uogkRwyypEVFQSvpaVDgKRoU6HLX7aEtjvMcvvwkTiUZuLOpjASKkTf6JNG4Xjfef/99tFpxBzIv2B89kBjLTo/jZMlA0+LrMOvydhdRuzOGy8nLSbIeQpI0hJoeoXBgFxw2Le3ataNy5crMmTPHL2cIRDLgxuOPP06HDh149dVXfR1KnrNixQq+/fZbVqxYAcCUKVP48ccf2bp1a774RbuakEzvWd8Qn2zBYv+vwYpWltBoNIzs2JTuTWpm2/n//PNPBg0aREJCAp9++imNGzfOcFys7SJJjjgMmgCKGMsj30cvDKvjb87Evct1656bawMU1U6IsR7lC4wlNSGYHj16YLfbWb58+c3EJCcoSgKqkoQkhyH7sIwtr7mauoNDV0d6vFUQrK/Cw8W/zaGofCMpKYlWrVrRsmVLJk+e7Otwcpy4TZCJn376idOnT/PSSy/5OpQ8yWQykZr63wvM8OHDiYuLY/HixT6MKmu4FIU+c74l+nrSbYkAgFNRsTmcfLj+F3adOO/1MVXVhWr9ESXuGZQrdVGu1Ee5NgjVfvC2cYmJiQwfPpwWLVrQrVs3Dhw4kGkiABBhKEXZwNoUM1W6r0QAwKgrSfUiC6hfYjtVCk6nSsHp1CvxEw8WWYxZV46IiAg2bdrEww8/TN26ddm9e/d9nc8bNuvPxMV05OrlGsRcbcbVy9W4Fvs8DrvYs94b4fpGJMaruNxUjsqSkUphQ3IuKB8JCgpi8+bNrF+/XiQDQhpFURg9ejQTJ050W0srZM5sNt+WDGi1WhYsWMCoUaO4cuWKDyO7f7tOXiAmMRmXkvmkmtXh5JPNu7w6nqpaUON7oV4fAY6DoCaCeh1sP6JeexElYSyKorBs2TKqVq3K9evXOXr0KIMGDUKjyfmul3ptIcLNLQg3t0jXy12j0TB+/HjmzJlD586dmTVrFtk1+ZicOJ1r8f1w2H8HHKBaAAc224/ExT6JJXVjtpw3v0hJSaFr124snRiAWV8UmTsX2GmQJQNVwl6nkLmpT2LMaQUKFOCHH35g3rx5zJkzx9fh5CiRDGTg22/TpsOeftq7FddCeiaTCYvl9rre2rVr06dPH4YMydtXGct3HSbV5mGfdeBsdByX4hM9jktLAo4Ad07VqqBaUFJW8/n0ekybNo1vv/2WBQsWZLgrW27SoUMH9uzZw/z58+nVqxcpKRTvCsYAACAASURBVJ56UNwdm3Unycmz/k0A7pT2c0u4PgSXMypLz5tfREdH07x5c8LCwlj59Vaal1yHcrkDMVEyWikQvRxGicDONCm2gjIhPXwdbo4qVqwYP/zwAxMnTmTZMm86q+YPIhm4g91u56233mLKlCnIsvjx3Ks7ZwZuGDt2LAcPHmTdunU+iCprXLme7NU41eXi2w2b+O2334iKisLpTD8Xqzovgu0XIPNab1m28cLTNvbv20WjRnmndK98+fLs3r0bWZZp2LAhp0+fTjdGdf2Dao9EdV64qxmElKQZmSQCtx2clOQv7jbsfO/YsWM0atSIjh07snDhQvR6PRrZxKavYoje3YM2ZfbSuvROahR8jyB9eV+H6xPlypVjy5YtDB8+/LbXKlVJRrFHotgjUfNwdUVGxJLfO8ybN4/y5cvTqlUrX4eSp2U0M3Dj8/PmzaNXr140a9aMkJAQH0R3f4LN3rWidioKm9etZcn5M1y6dImYmBgiIiIoXrw4xYsXp1ixYjzdIZaHaznxNNtv0OuQnHtB2yILvoOcYzabWbx4MZ999hlNmjRh3rx5dOrUCdW2HTVpOjjPgaQD1QmawhA4GIwd3S4yVVUbdvtvXpzdjtWyhuDQd7LuG8rjfv75Z5555hk++OADevfuffPzqqqyceNG1q9f78PocpcHHniA9evX0759e1at/JyGNXejWDbCjS6fqhPZ1A5t0CgkTe6eqfOGSAZukZSUxIQJE9i0ybv9toXMZTYzANC8eXPatm3L6NGj+eyzz3I4svv3ZP0HOBZ1xeOtgqIFQtk07eubb2xOp5Po6GguXbrEP//8wz///EOgcR0ajRe1zaoCirstWHMvSZIYOHAgderUoVu3bsjWpbRrdhKJf3dAvNEBz3UBNfF/4PgTKfjNTI+nqqmkTWp6/rmpXjTU8RdLlixhxIgRLF++nJYtW972tePHj6MoCg888ICPosud6tWrx9rVn1Eq9HWcqTpkyZW2Cce/FMs67LYd6CO+Q9IU812gWUAkA7eYNm0arVq1onbt2r4OJc8zm80ZzgzcMHXqVKpXr86OHTto1qxZDkZ2/9rUrMTUdTvcJgNGnZaXWze47QpXq9VSokQJSpT4b4tUJckCKQtIX7t/B0kGOW/3umjQoAGH9n+DyfoCEpncElAtYFmOamiCZEhbtOZwODhy5Ah79+5l79697N+3h80b7RiNnktUZblgVn4LuZaqquA8As7zaTMt+gZIcvjNr40fP56FCxeyfft2qlWrlu7xGzZsoH379vmi7Der1a2yFJdNm5YIpOME5TqOa4PQR6zx6nh25xWSbL8DCiZdZcz63NGi3a+TgWtWC8kOOwWMJpLirzFz5kwOHDjg67DyhTtLC+8UGhrKrFmz6N+/P5GRkZhMmbcKzW0MOi1zX+pKnzkrsTqcOF23v5Gb9Fo6PFSVzvU9X2VJxvaoKV/CjavkzKgu0D98H1HnDmHGtaiqh6t61ULM+Yl8uGADe/fu5eDBg5QpU4aGDRvStGlTRo0aRUjYPGyW1e6PgxlzYJ8s/g5yH9X2C2riuH9njqS0D9WOamiO0/Q/Xhowmj///JM9e/Zk2kl148aNjBo1KkfjzgsUxxlUxwlk2V2y7kJ1nERxnEHWVch0lM15ib/i3iLRuhdJ0v37WSdGbTnKFJhAkMG3F6F+2XRoy4XTzDy8m5PXY9DKGlyKQnhcElWvJrNw8oe+Di9fUBQFrVaL0+l0uxCzW7duVKxYkUmTJuVgdFkj+loSC7cfYM2+ozhdKg6ng2JBRkY99Rgtq5f3+ipLie0KzuNAZsXeRjA9iRzyblaF7jPKlXqgem6BqygwZWFPGjRoTL169dKtLXE6zhIb8xhkehtAQpLDKVh4N3Ien1FxR7FshIQ3ySiZVNFyNVZlxMRyzJ2/goCAjJsxXbt2jdKlS3PlypU8lZTnBGfyfFxJHwCeqod0aIJGog3sl+FXbc5LHL3cEaeSQEYJrCwZqVxoIcHGhvcd873yu2Rg4v6fWXriMBbn7f+5qqJg0uqY06oLLUr47zaWWclkMhEXF4fZbM50THR0NDVq1GDr1q3UqpU3+9q7FIVkq53p0z4kJTGBDz744K4er7picFztgsN2BVO6qW8T6KojhS9Eygc7AirRNQEPVQAAaJAK/Y4kZ/7csdl2cT3uBVTVxW1vhpIZWTITHrEarZsrtbxOVZJRrzbG3ayS0ymhCeiMJizzTcKWL1/O0qVL2bBhQzZEmbc5kz7FlTwdT7fxFBVOXXwMfcgQKlasmK4d9/ErPUm07sXdTJZWDqNOiX1I3m5DnsX8qnZuy4XTGSYCAJIsY1VcvPLzd8RYsrYm2l+5W0R4Q5EiRZgyZQp9+/bNsPQuL9DIMiFmI480bsTevXvv+vGSpiC9h4aw72htkMJIu3unAU0ppOC3kMIX5YtEAEirGPCGZEr7cMNgaELBwnsJDB7Gxb9lFCUMrbYawSHvEVF4b75OBABUyxrwMPuk1apIts1uy+A2btxI+/btszq8fEHSlvT4PARwOLRs+eEYHTt2JCgoiBo1avDss88yYcIE1m34gkTLPjwteFVUO9cs27Io8rvnV8nAzMjdGSYCt1JUha9OilamWSGz8sI7vfDCC4SHhzN9+vQciCr71KtXj0OHDuFweG5IdKt169ZxOPIsjR/9EqnQXqRCvyEV+h254I9I5u633F/MB8wvQLpOd3fSgfkZr26zyJoCGE0DaNo8lvCCvxNR+EfMAT2Q3cwo5Bu27Z57LUDagkLH0Qy/5HK5+P7770UykAnZ2AYyW+x6C4NBz8i3t3DmzBni4uJYtGgR7dq1Izk5mT2/L8SS6vk1QVFTSLDsyIKo743fJAPXbRZOXovxOM7mcrH6TMa/OMLd8WZmANJKzz7//HOmTJnCmTNnciCy7BEcHEzZsmWJjPQ+mUxJSWHIkCHMnj0bg8GAJElIcpDb6fG8TDJ1AjmEzF56VBWQTEjm570+5oULFyhSpAhGo3f9H/IN9W6SzoyvSvft20fRokUpVapU1sSUz0iSEU3ga+5nByQTmsAhSFLa889sNlOnTh169erF5MmTGTb8NQICvft9Vu7q/zRr+U0ykOywo5W96+Oe6mH2QPCOp/LCW5UrV4633nqLl156CbvLwa6YI6z6ezsbLu3mqvVaNkeadRo1urtbBePHj6dJkybp6r7zK0kOQCqwHDTFQLp9QZvdoSMhSYawpUje3k4ATp8+TcWKuaM8K0fpawBe3D5S7aD9r5Ngot3KpovHWXE2knk/b6KtmBVwSxPQD01AX8DArT9vux1cihZNQB80Af0zfbxJVwlvZhdkyUSA3nd9HvymtDDcYMKleKjl/ldhc2A2R+MfPJUX3mnIkCGsOr+dztvfQKvX4VCcaCSZ2eoaaoSWY3TVnoTpc/fK8IYNG7Jt2zavtr0+duwYCxYs4MiRIzkQWe4haYpBxFawbUdN/QqUqyCFog3syuNd3+O1136nR48qXh/v1KlTVKpUKRsjzp0k87OoKe53AVUUlavXilC0cBEsTgfv/b6V7y78iVaSUVFJLaYl0BTEg6d/p2fFh3Io8rxFkiS0QcPQmLrjTP0S1f4boHL6bAAz58awcMlwt48P0D+ITlMYm/O823EqChGBXbIu8LvkN8mAWaenVcnybLlwGsVNlhag1dGnWt0cjCz/upuZAYDlUT8R8mRlHDhxuNK60jnVtOnNw9fOMOj3j/is7giCdbl3v/qGDRsyceJEj+NUVeWVV15h7NixmdZ+52eSpAVjayRj65ufk4GZM4vRuXNn2rVrR1hYmFfHOn36tH8mA5riqOZnIHUFmVVoqBgZMiaaFFdHpJc68pclAZvL+d9OGAYdyYqT9w9t44olmeE18lYDsJwkaYujC37j5r+r1Ell/aZS/PXXX5QtWzbzx0kSZcMnciqmL4qaceWHLJkoHjIYrRyc5XF7y29uEwAMqdUYgzbzWwWyJBFsMNKuTOUcjCp/UVUnqmU9SuwTbF7yN82qDkKJ7YRq2YiqZl4tEG2J5+uLP+LIpNbehcI1exKL/9qcXaFniSpVqhAXF8fVq1fdjlu6dClJSUkMHDgwhyLLGxo0aEDnzp0ZM2aM1485deqUf94mAKSgN8H8HGnT14ZbvhAAUjjaiKV8teJ3Ah9twPH4aGyujH+/LC4HC078xqkEz+uqhDRms5nevXszd+5cj2NDTI2pWHA2qsuI9ZbJUlkyIWGgWMirFA0ekI3ReuZXyUDV8ELMadEZk1aHQXP7pEiAVkdhcyAr2z2HUes3EyZZSlWtqPG9URPeBucJtBrSOnc5j6MmvoUa/yKqas/wsWv/2YnioeWFU3WxNXo/NlfGx8gNZFmmQYMG/PZb5hvpXLt2jVGjRvHZZ5+h8bRDkR+aNGkSa9asYd++fV6N99eZAQBJkpGDRyMV3AGBg/nmuxScui5IIdOQCu1C0tdEq9NxsWQwkt59VYpDcfHFCe9+5kKaAQMG8MUXX2CzZb7r6A2hphZMGVGemLOdiAjoQri5AyVChlGnxG8UD3nF562g/a7pEECsJYWvT0WyJHIf15KTqFqiNH0eeIi2pSuLROA+KNeHg3UrmW/HawRTe+SQ99N9ZcCBDzmb/I/Hc5g1BqbVGkyFoOL3F2w2Gjt2LA6HI9OuigMHDkSSJGbPnp3DkeUdS5cu5aOPPmLfvn3pGrjcymazERISQnJysttx/sDlcqHX63E6nbe9sUSnJtFiw5xMZwVuVdwczM5Onte7CP959NFHefHFF3nuuefcjjt//jx169bl4sWLbhux+YpfzQzcEGEKYHDNxrwRUJKaPx1m/RO96VL+AZEI3AfVFQPWLWSeCABYwbIeVYnHYrHwxx9/sGLFCsaNG8f5C+e9PJOE6mlTHx9zV1Gwb98+1q5d69W6An/Wo0cPQkNDPSZM586do3Tp0n6fCEBamWpAQEC6K0yXqiDj3VWny/+uDe/bwIEDvUrs58yZw/PPP58rEwHw02TgBqvV6n+1ydnFugVvnk4Wq4O3htUjLCyMZ599lm+++QabzUZ5fVGvXrCcqovipty9E139+vU5cOAALtfttd0ul4uBAwcydepUrxfH+asbMyfjx4/n0qVLmY7z5/UCd0pOTiYwMH0lVEFjoKdGhUDaFkdVwwplfWD53BNPPMH58+f5448/Mh1jsVj44osvcvUaIZEMiGQgS6hKPB533gMMBhg29EWSk5P5888/WbVqFRMnTuSNVv3Ryu6v7jTItChUG7M2d/+fhYeHU6xYMY4evb151ezZswkODqZnz54+iixvqVKlCi+//DKvv/56pmP8eb3AnZKSkjJMBvQaDd3L1ULnZsMwAJNWR/8qvtsoJ6/SarX079+fOXPm/L+9+46v+fofOP763J1EhhAkxI69V61QxKxRo0WpVdSm6KQTraoqVWpXq/amRuwYFXvW3rFXjIy7P78//ORrJPfekNyRnOfjkcejzT333nci997355z3eZ8UxyxcuJDKlStTuLD7tsjO9MmAVqu1P1CwS1JkBex/SCskHdlzFH5pWjfUOweNg6uiVSRf5KRAwkftRZcCTdIi3HRXvlYtZu7cyZL//uPozZtcv36d7777jsmTJ7u8UMiTDBs2jH379hEZGZns7WJm4H9SmhkA6FWiGn5qHVIKqwA6pYrKQaG8kUN0InwVPXr0YOHChTx69Oil22RZ5rfffnOo94grZfpkQMwMpBFdI+yd7PWEFXQNkr2lb+GWtM5TG42kwpL4pAukAgmtQk1+n2AmVRhMdq1/svd1F9cePaLD4sUcKFaM9QkJfLtlC+8tXkzE7Nk0792b4sWLuzpEj+Ll5cVvv/1G3759k+1ZIWYG/sdWMhDklYVp5d/CfPMuWkmZtCCnVijRKpQ0yFOEKeFtRKL6ikJCQqhXrx5///33S7ft3buX2NhYGjVq5ILIHJepq25EMpB2JGUQsq4+6DdhczeBrgmSIjD5x5AkuhZ8C78TJiZvm8u7vTuhU2qolq0khX3zpFvsaeX6o0c0//tvHhsMTzrBK5UkPD20SKcjOksWoi5epLaNBiXCy5o0acKsWbMYPXo033777XO3iZmB/7GVDAD8+PmXtMufn3adOrLmygkeGvXkzZKVVgVKk9vHvZNsT9C7d28GDhyYtFvoqd9++40+ffqgsLNM42oiGRDJQJqR/H9AtlwH0yle7ojmBepSSP7fJnfX52yL3EyDPJXoUahZusSZXj7bsOFJIpBCRbbebGbg2rXs7dULjegvkCrjx4+nXLlydOjQIWkmIC4ujtjYWPLkcf9E0RlsJQNr1qxh//79/PHHH3h5eVE+u/tuzfVUderUwWQyEbVzC8Uq5UbGiuWhjn/++YcJEya4Ojy7MnUyYDAY8PV17173nkSSdBD4N+jXIMdPB/MFZFnm7EUoWn4k6Bo/aUNrx4YNG1iwYIETIk471x8/Zv+1a3a3ZlmtViLPnqVZMcd77wuQJ08ehg0bRp8+fZi/aDlRu89y8swFildsyr3YeIKyiddxXFxcsu9ncXFx9OnTh5kzZ+LlZe/4aOFV6a2P6D2xBgf9f+BkjDcgYTAlMuiPcFRZ3LdR2lPuPW+RzsTMQNqTJDWS19sosq9BynkCa7ajVGl0nfsJ1R1KBC5dusSDBw8oW7asE6JNOwevX0flwNV+vMnEjsuXnRBRxtPzw948lorSpudUJs+JInLHJbyCKtG2zwyGjVlJot7933DTU0ozA19++SV16tQhIiIimXsJaSHB/ID5F3ujCL2GUgNGawJGazyS0kpA0UTmXerFfcMVV4dpk0gGRDKQbiRJQq3RULNmTbZu3erQfTZu3Ej9+vXdfn3tRVarFRxs2GK2JH+2vJAys8XKx6OW45W1EBYrGI1Pf4cKjCYLuw9cYMBXizCZMu/vNrmthfv27WP+/PmMHTvWRVFlDhtvjCHefB9rcmerSDIGazyrr36FOzf89ax33DQmkgHnqFevHps3b3ZobGRkJA0aJL/bwJ0VzZ7d7tkKAF4qFWWDg50QUcaybfcZzl64jdmc/I4Vo8nCxZi7bNp50smRuY8XZwZMJhM9evRg7NixZM+e3YWRZWyPTXeISTiUfCKQRCbefJcbif85La7UEsmASAbSXb169diyZYvdcWazmS1btlC/fn0nRJW2igYFkTcgwO44qyzTqkQJJ0SUscxdvpdEg8nmGL3BzN/LX++gHYvVSqLJ5NZXcCl5MRn45ZdfyJkzJx06dHBhVBnflfgDSNhfIjTJei7EJd+m3B1k6gJCkQw4R5kyZbh37x5Xr161Wfm9f/9+QkNDCfbQK+eRERF0WrKERHPyVwheKhUDqlXDVzS6SrWLMXcdGnflWiyyLKd6v/zuy1f4PXov0TExSIBWpeLdMqXpVqkiIX6eUZz4bDJw/vx5xowZw969e0XvgHRmlg0On5dilu13aXWVTD8zIDoQpj+FQkGdOnXsLhV46hLBUxVCQpj+9tsE6HR4PVNM6KVSoVWpGFi9Oj0qVXJhhBmfLFtZuXIl167ZPwHzqfE7/6Xn8hX8e+UKVlnGIsskmEz8fegwTf74k+M3b6VjxK/HaDKzdv8p3h83n2PeRZl3Jp65Ww/Ss08/Pv30UwoWLOjqEDO8AHUICsn+zIBK0pJVE+qEiF5Npk8GxMyAcziyVLBhwwaPTgYAquXNy55evWiTNStZr13j3VKl+KxWLfZ8+CE9KlUSV2mvqHB+xw7Q8dFamTp1KuXKlSMkJITmzZszYsQI1q1bx507d14av/HsOWbu20+i6eXZHLPVSpzRSOdFS4g3ut9Ohav3HtJ0xB+MWLiJo5duYlbpuB1vYtzKKO4WepM6b7d1dYiZQqhPBZRS8m3UnyUjU9SvrhMiejUiGRDJgFM8LSJMaS32wYMHHD16lPDwcCdHlvZUCgVcuEAjLy9+aNCAjuXKiaWB19SxZRW8tLbfcHVaNUN6NWPdunXcvn2b3bt306lTJ+Lj4xk7dixhYWHkz5+fd955hx9//JEtW7bwy/adKS7rPGWyWll98lRa/jivLcFgpMsvC7nzMJ6EF2opzFZApaH/tFXE3HngmgAzEYWkJDzoQ1RSyq9xlaSjfNbW6JTuu+QkkgGRDDhF4cKFkSSJM2fOJHv71q1bqVGjRob59zh27BilS5d2dRgZRniVwpQqFoJWk3yZk1ajomihnNSt+aSZkyRJ5MuXjzZt2jB69Gg2b97M/fv32bBhAy1btuTWrVsM//4HTt+2vwSQYDKx4EjKx9O6wpp9J4nTG2zuYDGYzMzatM+JUWVeJQIaUj3oA5SSBqWkSfq+AhVKSUNJ/0ZUD+rqwgjty9TJgMFgyDAfPu5OkiSbWww9vV7gRceOHaNUqVKuDiPDUCoVjPmiFQ1rl0CjVqLTqrFaLei0ajRqJXVrFGXcV21QKVN+S1MoFBQpUoT33nuPcePGMXvBfLJ4ezv0/A/1KZ234Rpzow6TaLQ9o2GxyqzZfwqzxbHiNuH1lA9sRddCc6gY+C6BikJcP5lI6YBmdCwwgzdz9UOS3PvjVuwmEMmA09SrV4+VK1fSp0+f574vyzKRkZH079/fRZGlrcTERGJiYsRpemlMrVbySe8G9OoYzva95+jVewCTJ42ndtUi+Pumvs1uNi9vTA5+UAb5+KT68dPTnYdxDo2zWmXi9Ub8fcT7nDP4qLJRLagLpb3a0Kt8Tn6M3+XqkBzm3qlKOhPJgHPVrVuXrVu3PunW94zz589jNBopkUH23584cYIiRYqgVtsvKhJSz8/Xi6b1SnPt7DaaRZR5pUQAIJuPN6Vz5rQ7zketpmN592qPrUthueRFVtnq8Fgh7fj4+GAwGDC6YeFpSjJlMnDj0WP+PnAYRfnKbLoUw2ODe00BZlS5c+cmKCiIw4cPP/f9p7sIMkqlvVgi8ByDwqujU6X8YSkBPloNDYu41zHJDcsXRW1jSeSpMvmD0apFMuBskiQREBDAw4cPXR2KwzJVMvBYb6DXkpVETP2DH7fuwCf8TcbsjKb6xGmM3rIdi1WsraW35LYYZrR6gePHj4viQSd53QSyWt68fFm3DjqVCuULj+WlVpHN25t57d5FayNhcIX3apeze36Hl0ZFjwZvOCki4UUBAQE8eOA5uzkyTTKQYDTx7pwF7Lh4CaPFgt5sRlIqSTCZ0JvNzD14hI//We+RbUg9Sd26dZ8rIjSZTGzbts0jWxCnROwk8Cxty5ZmRacOtCldiiwqFZjNhAb4MzQ8nI3du5I/a1ZXh/iSPNkD+LpdBLoUrvp1GhXta5WjRon8zg1MSOJpyYB7pbvp6O+Dh4l5+AhjCgVDerOZTWcvcODqdSqF5nZydJlHnTp16Nq1K0ajEY1GQ3R0NGFhYRnqIJXjx4+LZYJ0ltZJe+Fs2RjVsD7N/H0ZNGgQW6Pdt4f8U29VLk7ubP5MXvsve09fQaWQQKEg/u51fhzYhUaVirs6xEzN05KBTDEzYJVlZu09iMFOcxG9ycSMPfudFFXmFBgYSFhYGHv27AEyRtfBZ92/f5+4uDjy5s3r6lCEV2C1Wj2qdqVcwRCm9WtDzgtb6VMtL+u/7U7wtX3EXXLf0/EyC5EMuKEHiXqHigRl4PD1m+kfUCZ2Oz6OXG83p9/+aKrOnMJCzARUqYzRkjHOoT927BglS5b0qA8UT5Rey3myLNtdi3dHl86doWqZ4mT386Fr16788ccfrg4p0xPJgCCkIPLcWd78cyZnA3x5rFRwOz4ec1B2/roRQ8O/Z3M73rG90+5MFA86T3okXJ42MwBgsVi4fPkyBQoUAKBVq1b8+++/3Lhxw8WRZW4iGXBDAV46smg1dsdJQJlg+/uOhdQ7cvMGH21Yi95sxvzCVV2CycTVR49ot3QhZg/f0SGKBz2bJ84MXL16lezZs+Pl9aTfgo+PD61bt2bOnDkujixzE8mAG1JIEl0qlUersn3MpE6t5oM3xBGz6eHn6F3obdRsWGQrd+Lj2XzxvBOjSnuix4BzpNcygSfODJw/f/6lo4qfLhWI3VGu4+/vL5IBd9SpUnlC/PxQK5NPCLxUKmoXzE8VsZMgzT3QJ7L32lW74+JNJmYfPuiEiNKHLMtimcCJ0uND2xNnBi5cuEChQoWe+1716tWxWq1JhbqC84mmQ27KR6Nhcad2VArJhWwyofn/F7xWqUA2m2lVugS/tGjicVcFnuB2fDxqhe1ZmaduPH6cztGkn5iYGHx8fMiWLZurQ8nwxMzA/5w/f/6lZECSJLp06SIKCV1ILBO4MX+djnK3rlL+wimG1glnQM2qfFm/LiHbIin+4O6Tc+iFNJdFo8FsdWy3gI/Gfm2HO5JlmaNHjoolAicSMwNPJLdMAPD++++zePFiEhISXBBV5mayxqPKdZ781e9z+sFSEsx3XB2SXZmm6RA8yfonT57MnDlzqFa5QtL3tf368eOPP9K2bVsXRpdxhfj6EeLrx8UHsTbHealUtC5e0klRvT5Zltmz8yyL/tzJyWNXsVpl1JpqLJ7zL01aVsQni9bVIQqplFFmBgDy5MlDlSpVWLFiBe+9954LIst8ZNnKoXuTOfVgMbI/VGpp4cDdX9l/dwK5vatSPefXaJTudQLmU56VAr+mDRs24OfnR9WqVZ/7frNmzbh9+zbRHtB1zFP1r1IVLzv93RWSRJsSnpEMyLLMz9+t5IdhS/jvSAxW65Npa7NRwZypW/mw3WTu3n7k4igzLtFn4H+Sqxl4SvQccB5Zltl16ztOPViCRTZglQwoVdKT/5aNXIvfzfqrPTBb9a4ONVme9Vf/miZNmkTfvn1fyvyVSiUDBgxg/PjxLoos42tRtDg5Yh8gJbOjQCFJeKlUTGv6Nn5azzhSeuncaLZvOoE+0fTSbQaDmft34/ii/9+imjsdiT4DTzpel5xRYQAAIABJREFUWq3WFOtUWrRowaFDh1j77wFW7zvBxiNneZTgnh9Gnu6O/igxcduwyMn/fq2YiDNd4/SDxU6OzDGZJhm4ePEiu3fvpn379sne3rVrVzZu3EhMTIyTI8scZsyYwbW/5jHqzQiKZMuGWqHES6UGi4Wy3llY3rYD1UI9o4WvxWJl4ewdGPQvJwLPjrl14wHHD19xYmTC6/K0mYGn9QIpJTCbj18kf8dPGb44ilFLtvDV/A3U+3oaw+auJ15vdHK0GduJ2LmYZdudbi2ygRMP5rvlRYLn/NW/pilTptC5c2e8vb2Tvd3Pz49OnToxadIkJ0eW8W3bto3hw4ez5p9/aFehIus7dOHfbj2J7NiZHhYF2fceoEg2zzmo6MTRGMxm+82RDHoTG1YfdkJEmY/YTfBESvUCAH9HHeTbRZswSirMSCQYTcQbjBjMFiIPn+H9CQtIMKSc0Aqpc0d/jCdN7W0zWh5jsLrflsNMkQzo9Xr++OMPevfubXPcgAEDmDlzJvHx8U6KLOM7d+4c7dq1Y/78+YSFhSV9P5u3N3n8/GnVrBn//POPW2bKKXn8MNGhcbIM9+567lZJdyd2E6RcL3Aj9hHj/9mJ3pR8oy+j2ULM3QfM3LQ3vUMUXuSmyabn/NW/hoULF1KxYkUKFy5sc1yBAgUIDw/nr7/+clJkGduDBw9o1qwZ33zzDXXr1k12TNGiRfHx8eHQoUNOju7V+Wf1cSh5kSTInsPPCRFlHolmE4tOH+OdNQvJ8dVHtFk5j5XnTmCw2D6R1FGeMDNgtcrs232OqRM2sG/HfSRTMAnxz09PL9h5BHt/ogazhQW7jmDKIIeEuVo2bXGeNLW3Ta3wRqtwv/eFTJEMPC0cdMSgQYOYMGECVg/vke9qZrOZdu3aUb9+fXr16mVzbNOmTVm9erWTInt9xUvnQatV2x2n1alp3KKC3XGCY07eu031eVP55t/NHLlzE1WO7Oy/dY0vdmwgfP40Ljy4/9rP4e4zAyePX6VD818Y+cUSls6PJj7Wj2P74mjb5GcW/f1vUpK68+RFhz7kLVYrMXc9pzGOOyuRtQMqyXYBtAINxQPaIUnu9zfmfhGlsX379nHnzh0aN27s0Pjw8HC8vb2JjIxM58gytiFDhiDLMuPGjbM7tlmzZh6VDCgUEh2610arSzkhUKoU5MmbjWKlRHvrtHA7IY62qxcQq08kwfT8One8ycSdhHjeWTWPh4ZXr5Q3WMxcssSRkCML9/Tut1R4/sxNPu0/h3t340hM/F/xn8lkxWAwM2dGFAv+3AWA2eLYspskSQ6PFWzL6VWBEJ9qKFNICCRU+KhzUizgHSdH5pgMnwxMmjSJXr16oUzhTIIXSZLERx99xC+//JLOkWVcU6ZMYcOGDSxcuBCVnd4CADVr1uT8+fNcv37dCdGljWbvVKJh83LokkkItDo1OYMDGPVrR7efcvYUs48ftHnQlcyTpGDhqaOpfuw4k4GRhyN5Y/VYZnKZK3UK8ea6X+m+cx7nHrlP57iJP61NdivrUwa9ib9nRTF92h/cPv8fsgNdP80WC7kD3W/K2hNJkkR4ru8INFfHZLCilJ40HVOgxmqWeHDFh8ahM1Ar3LPpkCR7UuVWKt27d49ChQpx7tw5smd3vFrdYDBQoEABNm7cSMmSntEEx11s2bKF9u3bs2vXLrs1Gs9q164d9erVo0ePHukYXdo7vP8ii//axbGDlzGbLRjMjxj8RTvqNSmbbKIgvJqysyfy0Gj/qj/Yx5fdHWwvSz0rzmSg9ZaZXEt4gPGFD08J8FKq+atWJ8oEhqQ25DR1/ep9enaYgtFguzbCYjWh879N3XeqseDcIwymlBMChSTxVsVijOrQKK3DzdQ6dOhA6fJFaNWjDAmmO2iUWfDSl6JSqTocPHiQfPnyuTrEZGXomYFZs2bRokWLVCUCAFqtlt69ezNhwoR0iixjOnv2LO3bt2fBggWpSgTA85YKnipXqQCjfu3Iqp3DWLH9M/acmkJ4RBGRCKQhs9XKIwcSAYA7CXFcv37d4ZqfEUfWJ5sIwJPZhgSLiZ7/zsfs4hqiC+duo7JzBDuAUqGmcoW6DOnZhdolCqJTJz8zJwE+Og19G1dP40gzt9OnT7Nhwwb69PyIIv4tKZe9JyWyvkeB4DL07duXr7/+2tUhpijDJQPxBiMPEhIxmkz8/vvvDhcOvqhXr14sWbKEu3fvpnGEGVNsbCzNmjVj5MiR1KlTJ9X3b9y4Mdu2bSMx0bFte+5Io9FQvnx59u4V27XSklKSUDi43GIxGKlYsSJeXl4ULFiQN998k06dOjF8+HCmT59OZGQkJ0+eJD4+nscmPWtjTiSbCDzLYDGz7ebZtPhRXplS4fhyk/L/CyB/eL8xjcoXQaNSolb9/1u9LOOtURMc6MecgW0JEUsEaer7779nwIAB+Pm9/HsdMmQI69at4/jx4y6IzL4McVCR1Sqz5ugppm/fx8W791FKCiTZSpZq9SjwigffBAUF0apVK6ZOncqwYcPSOOKMxWQy8e6779K4ceNXnuYPDAykXLlybNmyhbfeeiuNI3Se6tWr8++//xIREeHqUDIMSZIIz5OfqJiLNlu6KCWJZiXKMP7GDQwGAzExMVy5ciXpa8+ePSxatCjp//3fKIlfjyZgZxYn3mwk8tpJIkKKpu0PlgpFS+bGbGPK/ymdl4ZK1Z7MyqmVSr5r35A+jauzcu9/7Dx4jPOnT/LLF4OoUjhU1LOksXPnzrFmzRrOnTuX7O3+/v58+umnDBs2jJUrVzo5Ovs8vmbAYrUyeOEadp65TOILVcYKwNdLx7yebSkQFJjqxz527BiNGjXi4sWLaDz0aF1n6NevH+fPn2f16tUOFQym5KeffuL8+fNMmTIlDaNzrhUrVjB16lTWrVvn6lAylD03YuiybgmJNooIdUoVS1u8R8nsOe0+nizLLDq5h+9PbSFRtv8h2yCkGL9Vc20V+JdD5rNv97mkQ7GSo/NSs2jd0GSXqfbu3UufPn3Yv39/eoaZaX3wwQfkyZOHb7/9NsUxer2eokWLMn/+fKpXd68lGo9fJpi5Yz87zlx6KREAsAKP9Hq6/bEUyyus+ZUuXZrixYuzeLF7HizhTPGPEtkwdydzx6xi+eQN3Lh4G3iyW2PLli0sWLDgtRIBeFI34GndCF9UrVo1oqOjRZ+KNPZGcCi9ylZJ8eRLnUrFJ1VqOZQIwJPZhlLB+cCBngJqhZLCfq5vlz3g07fw9fNCkcKSgUolMXR48xTrVXLlysXNmzfTM8RM69KlS6xYsYKBAwfaHKfT6fjmm2/47LPP3O59zqNnBswWK+Gjp/Iw0XZxkbdGzdh3m/BmsYKpfo41a9bw3ccjeT+iO7cu3cE/yI+IjrUo+2bJTDHNZrVamf3tUlZM2YRCqcCQYESlUQISwUUCWXP2b7b/uy3F/uipIcsyYWFhLF68mPLly7/247lKoUKFWLVqldiJkg4WHtrL0JWL8MoTgkapxGi1UDwwiMGValI7tECqHkuWZRpETuJyfKzNcRqFkg0N+xLi7f86oaeJ2zcfMua7FZw6fg2FUkKWnyQ2Wq2Cw6eXErVrOSEhye98MBgM+Pr6otfr3bqxkifq1asX2bJlY9SoUXbHms1mypQpw88//+xw/xtn8OiagWPXbjpU5ZtgNLHi0IlUJwPxD+PZ+vM+spwOYdWZSOT/n57bvng3gcEB/LB+OMEFHLsS8VQT+s9m27K9GJ85oc/0/9ubLh27TnjOluTKHpwmzyVJUtKuAk9OBqpXr87u3btFMpAOTq9ez1s34/jm4x7E6hPJ5uVNDu8sr/RYkiQxrGxDBuxZgj6FdsY6pZqmeUq6RSIAkCOXP2Mnd+bG9ViOHbyMyWwhb/7slCqbl2+/TaBLly6sX78+2Q97rVZLlixZiI2NTfHIYyH1YmJiWLRoEWfOnHFovEql4vvvv+fzzz+nYcOGbpOYuUcUr+ix3uBAJ+gnzsdc5dy5c5htrDk+y2K28HHEd5zYfRqFrEhKBAAS4/TcOH+LAdWG8eCO+50+lVbOHLzItmV7MSQkf9SpAiXxD/Qs/nV9mj3n06UCT/a0iFBIWyaTiWnTptGnTx9y+fhSPFuOV04EnnozOIyRFZqiVajQKf83va6UJHRKFY1yF+O7Cu5X0BockpUGTcvx1tsVKV0uH5IkMXz4cB4/fsyvv/6a4v3EUkHa+/HHH+nevXuqtrC3aNECnU7HggUL0jGy1PHomYEgXx8sNoppksgyN86fJSJiNLdu3aJgwYIULVqUYsWKUbRo0aSvrFmzJt3l35X7uHr6etJV8IusVpn4h/EsG7+GbqPeS6sfya0sm7QBk972Eacmg5l/pm/h/c9boHRgH7Q9NWvW5OzZs9y4cYPg4LSZcXC2atWq2XxDFl7NqlWrKFCgAGXKlEnTx22etzS1chVm6aXDbLp+GrPVQomAYN4vXJnCfkFp+lzpSaVSMXfuXN544w3q1q2b7O/paTIgZq3SxrVr15g3bx4nT55M1f0kSeL70d/zxbTPOV/2DNf0VwHI4xVK4+AmlA+oiMLJ5xd4dM2ALMs0GDeLa7GPbI7zUquY1a0NZUODSUhI4OzZs5w+fZrTp09z6tSppP/29vZOSgxiN5p4eMV+f3Iff2+W3fvDbaZ60tL7JYdy56r9w1+03hqm7x1FjtC0mXps27Yt9evXp3v37mnyeM5mNpsJDAzk0qVLBAamfheLkLx69erRvXt32rdv7+pQ3Nrs2bP5+eef2bdvHzrd833y27dvT9OmTenQoYOLovNsJrOFyzfvYzJbyR3kz1fDPkOhUDh0Bstzj2M1MeHsOE7c/Q+F9vnPDq1CS0GfQgwI+wi1wnnNyzx6ZkCSJD6qX4PhyzemeG63WqkgLGd2yuTJBYC3tzdly5albNmyz42TZZkbN24kJQcr/t7qUAxGvZG42Hj8svm+3g8jJKnWsh4L7+zhwL4EdEoNdXOUIyK4PF5KratDc4hKpaJy5cpER0fTpEkTV4eTIZw8eZL//vuP1q1buzoUt9e5c2fWrl3LZ599xvjx45+7TSwTvBq9wcTMf6JZvPUIVllGQsJoNhN7OZ6/fxqa6sebc/lPzsedeykRADBYDZyLO8vcy3/RpcAHaRG+Qzz+crZJmWL0qfMGOrXqpS5d3ho1BbJnZUqnlnYr/yVJIiQkhLp169K7d2/8/B37cLdaZFQaj86pUlSyaliK25iepVaryBYc8NrPZ7CY+OTQdNbluIChqC8nH13hUOw5Jp9bRasd37L/vmMFOu5A1A2krd9//53u3buLfh8OkCSJKVOmsGzZspdOX82VKxe3bt1yUWSeKdFgouv385m78SBxiUYS9Cbi9UZMZitZchdj8JQNnLp82+HHe2R6xL77ezDJKS/BmmQTe+5HE2eOS4sfwSEenwwAdK9VhYW92tOiXAmy+Xjj76WjdO6cjGhZn8V9OhDgbfuM6eRUiCiDQmn/1xNcKCfevl6vErbba92vIWqt7WkqtVZNs55106Re4Mujszn84DwG2fzc7z7RYiTRYmTYkT84/ejqaz+PM1SrVo3du3e7OowMIS4ujr///psPP/zQ1aF4jMDAQGbPnk23bt24c+d/Jy+KmYHUG7dgG5dvxWJMtgOkRILexIDxyxzuZXMgdr9D29IVkoIDsc5rEJUhkgGAsJzZGdmqATs+/5Ddw3qzsPd7NC5dFLWDRxe/6J0hzVHbueK3SlbqdKn2So/vCcLK56d4eH4sJL8Eo1IryR4cQJsBr79X9tSjKxx9cAGjNeXdHgariann1rz2czlD1apV2bt3r8O7V4SUzZ07l9q1axMaGurqUDxK3bp16dChAz169EhqcCOSgdSJTzSyNvpkConA/+iNJnYdvejQY8aZH2O0Jr9D61lGq5HHJtv1cGkpwyQDaa1w+QK0HtwMrXfy69Rabw0hJYMY8lN/Fi5c6OTonOP48eP8EfUL1VqWRuulQeejRVJIyJIVpVpB6ZpFmbD1S3z8Xn9mZFnMTpuJQFJMDy9yz+C8F8irCgwMJDQ0lGPHjrk6FI8myzKTJk165QPHMrsRI0Zw+fJlpk+fDoC3zp97N02cPBqDwc5OIQEOnb2KyoHi8AS9iY37HFvG9FH5oJbsFwaqJTU+qtfbOpsaGXOxO410HdGOXPmDmP3VQhLjEp+b2nm7fxM6ff0OR499yDvvvENUVBTjxo17qXrXU125coUmTZowfsJ42rdvT8LjRHatPsjda/fZsCWSkJKBjBqb+sKZlFyMv4XV5jE0T6glFTf198mmdf/T1p4uFXhyAyVX27VrFwaDgbp167o6FI+k1WqZN28eDeq24MS/Vs6duE2A9AZf9JmD1SrToEU5uvaLwNvHM4pznU1vdHxmL0Fv/2ofoEJAJRbF2O8vYEWmYtZKDj//6xLJgB2NP6hHw651OBl9lvs3YvHx96Z0reKoNU8yu/Lly3PgwAG6d+9O9erVWbx4cZq05nWle/fu0bBhQwYPHpy0jcvb14v679UAIEsx0nwfvdbBLTQyMhonbrd5HdWrV2dT1Dbe6doZP40WrVK83FJr0qRJ9OnTJ0Nu3XUWyeJL6TzvceLIdSQkVAotCfEGANYtPcDB6Av8+lcPfHwzxoVMWsqd3R+rA7vvVSoFBUIc20YcoAmgfEAFDj84lGIRoVpSUyFrRfzUzrvoEa8wBygUCkpWL0p466pUiCiTlAg85e/vz6JFi+jWrRvVqlVjyZIlLor09cXHx9O0aVOaN2/OoEGDkh1Ts2ZNoqOjMSVzONSrqpOzLDoHPuSVkpICPrnS7HnTy97bMawJkomOKEr4ysmUXvQzPaOWcPy+WK911M2bN1m/fj2dO3d2dSgey2K28NWAeVjMT7bDvchksnDrWiyTx6x1QXTur1i+HGTz87Y7TiFJtKzleDOsLvk/IIcyJ+bEl2ceNAoteb3z0Tl/t1TF+rpEMpBGJEmiX79+rF27lk8//ZT+/ftjMBhcHVaqmEwm2rZtS9GiRRk9enSK47JmzUrBggU5dOhQmj13w+BKdhcJtAo1rUJroFK8/s6F9PTX6f102bKAQ4/vgFKJwWLGZLWy6epZ3tkwhzWXU9etLLOaMWMG77zzDgEBr79tNbPas+MMRoOdLqImC9s3/kfc40QnReU5JElicLs30apTntXTqlXUrRBG7iDHz6/QKrUcG3mCoNM5yaUNTkrUgnXBdMjbkY+LfoZG4dxttGLeMo1VqlSJAwcO0K1bN2rWrMnChQspWDD1pyU6myzLSVXH06dPt7v1JTw8nO3bt1OlSpU0ef74e4+4O3UP/t0qYE3ms16jUBPmm5uO+eulyfOll0N3rzH60FYSkzn4Rgb0FjNDd/9DycCc5PcV3QlTYjabmTp1KqtXr3Z1KB5t5+YTJKZwtsizVColxw9eoWrtok6IyrPUKleITzrUYdSfG5GtFmTpyRuUhAyylRplCvN1t4apeszVq1dz/Ohx5v09D51Oh1V+si3R2S2InyVmBtJBQEAAS5cupWPHjlStWpXly5e7OiS7vvjiC06dOsWiRYtQq+1P19eqVYvt27enyXNfvXqV2rVr07x4bSZU6Usp//xoFCp8VDq8lVqyqHS0zVuLcRV6oVa4d/46+b/dKZ6A95TFauWPU87bP+yJVq9eTd68eSlXrpyrQ/Fo+lTsGDCmolgus2lYOYzrmybTvGphiufLSeE82YmoGMbVbbPp36w86lT0WYmPj6d///5Mnjw5qeBcISlcmgiAmBlIN5IkMXDgQKpVq0bbtm2JiopizJgxz3VQMxhMHD99A73BRK4cfhTK55pDUSZMmMCKFSvYuXMnPj4+Dt0nPDycXr16YbVaX6u468KFC0RERNC3b1+GDBkCwG+V+nFLH8vNxPtJMwLuvjQAYJVltl47b3e5wyRbWX3pBN9WbuCUuNydLMsc3nqcVZMjuXXpDlkCvDl0cw8fDu3l6tA8Xr6COdi7/QwmO/vkrVYrwXmy2hyTmc2aNYsKZUrwZc+Wz33feD6K8ePH8/PPPzv8WN9++y01a9YkIiIircN8LR59UJGniI2NpWvXrty4cYOFCxcSHJKH6XN3sGrD0aR2vxaLTI7svvTr+ibVKjpvWWHBggV8/PHH7Nq1i7x586bqvmFhYSxbtozSpUu/0nOfPHmSBg0aMGzYMHr18vw3fr3ZRMlFPztUfaxVqjjV7mMnROXeHt59xKcNRnD93E0S4/RJ37dIZgIC/Pkh8kuKVvLs3TmudPvmQ7q1+BWTnav+3HkDmbligEOd8TIbo9FIWFgYixYt4o033njutpiYGMqWLcuFCxccqm05evQoERERHDt2jJw5c6ZXyK9ELBM4QdasWVm+fDlt27alatXqvN93CsvXHyZRbyI+wUh8ghG9wcSVa/f5cswq1m457pS4Nm7cyMCBA1m3bl2qEwF4vaWCw4cPU7duXUaNGpUhEgF48gGvdXAGI1CbMVtYp4bZZGZInW+4/N/V5xIBAKWs4nFsPJ/U+5YbF0Qv/VeVI5c/EW+VRatLeelPkqx8OLSRSARSMGfOHIoWLfpSIgAQGhrKW2+9xZQpU+w+jtVqpVevXowYMcLtEgEQyYDTSJLE4MGD6ffJeK7deozRmPy0ncFo5ucpG7kXa//4ZEecPXiBMV1+o0+lTxhYczhLx/9D3IN4Dhw4QIcOHViyZAmlSpV6pceuVasWO3bsSPX9oqOjadiwIb/99hudOnV6ped2R5Ik0apgaVR21v50ShUdwio4KSr3tXvVfm5fvoM5hRNHAfQJBub9sMyJUWU8/b94i1r1S6LRqlA+c+aHVqdGo1GRoPqP+Ut/R0wSv8xsNvPDDz8wfPjwFMcMHTqUX3/91e7usRkzZgDQo0ePNI0xrYhlAieyWKw07zqZR4/1Nsdp1Eo6tKpCt3Y1Xvm5jHoj3707jsNbjmHSm7Ban/wza721yFYr53RH+XHWSN5+++1Xfo4LFy5Qs2ZNrl275vBVxbZt23j33XeZPXt2hjze98rjWBqvnUmCOeXCLT+Njq3NPiRQZ3//ckY2sMYwTuy238JV46Vh+f3ZaOwcmiXYduXiHVbO38O5UzdQqRRUrV2Mhm+XxyobqVOnDs2bN+fbb791dZhuZe7cuUybNo2oqCib4xo1asS7775Lt27J9wa4desWpUuXZtOmTZQp43g/AmcSBYROdPVGrN1CHgCjycL2PedeKxkY1X48hzYfw5j4/LYiQ8KT7LWApSQFA4u88uMDFChQAIVCwfnz5ylcuLDd8evWraNz584sXLiQOnXqvNZzu6u8vlmZ+ea7dN+2GItsfW5ngU6hIjEujj5ZwjJ9IgA4PP0vSRIP7zwiKE+2dI4oY8tbIIj+XzRN5hZvIiMjqVWrFr6+vgwdmnZtxj2Z1Wpl1KhRTJgwwe7Yjz/+mP79+9OlS5dkC6qHDh1Kly5d3DYRAJEMOJXJbHX4Cvrs2XM0b96c4OBgQkJCCAkJee6/g4KCUKZwIuPFY5fZv+HIS4nAsywmK1OH/sWkvSk3F7JHkqSkugF7ycDSpUvp06cPK1eupFq1jHvSI0DVnHmJatGb+ecOMWn3JiQvLbn8stKxSAXy3E7g/XfepX7pCh7RfyI9aXSONVWxmC1obKx5C68vR44cbNq0ifDwcPz8/OjZs6erQ3K55cuX4+vr61DVf926ddHpdKxdu5amTZ9PuDZv3syOHTv477//0ivUNCGSASfKFeSH2Wx/ZkCSoHzpQtSvWokbN25w/fp19uzZw/Xr15P+//79++TIkeOlJCEkJIQTyy5iMtrfX3zpvxiun79JSKFXb+8bHh7Ojh07UpwegycFOJ988gnr16/PNIf2ZNN5069UDZb2H87w4cOpV+//myUVg2HDhtG6dWv+/fdfvLwybyFhzVZVWDkpErOdSvfggjnxz+7+B1N5ujx58rBx40Zq166Nr69v0rkkmZEsy4wcOZLvvvvOoQs4SZIYMGgIo39bwtyNt3n4OJEs3loahhdjzDcfMXHiRIe3bbuKSAacKIuPluqVChEVfdZmsY5Wo6ZX5waUKhaS4hiTycStW7e4fv160teNGzfYvXs3V/bcB4v9f1q1RsXNi7dfKxmoVasWY8eOTfH2qVOnMnLkSDZv3kyJEiVe+Xk8VUxMDKGhoc99r3///kRHR9OnTx9mzZqVaau4W/RrzOopG22O0floaf95S5tjhLRTuHBhIiMjiYiIwMfHh+bNm7s6JJdYu3Ytsiy/dJWfkrOXbrNgSywW72JcuR4LwMPHemYvjSaoZAdCC7v/RZBIBpysR4ea7Dl0kcQUOoNpNErKFM9NyaLBNh9HrVaTJ08e8uTJ89JtX9z8nn3r7J8bkJCQwJaoLWQvEvBKWwtlWcak8kdbsC5vD5mOTquhZrmCvFOvHDmz+TJu3DgmTpzItm3bPP4kx1chyzJXr1596d9IkiSmTZvGG2+8wfTp0zPtlGyu/Dnwq6Tizi4DkvXlhEjrraVqs0pEdKzlgugyr1KlSrF69Wreeust5s+f/79ZLTd0/9ZD7t96iLefF8H5sr9SYh37OJEVO4+zaf8Z9EYz+XIGsHP5TL744guHHu/BowT6f7OIx/EGFMrnl7OssgSSiqHfL+OvsZ0Jyen4+QXOJnYTuMCpczf5eMRSDCYziYlPkgJJAtlqIfyNonw9+C20r1E5veHPbfzWf+ZLe7dfpNIq8W5kZvvOKHx9falduza1a9fmzTffJH/+/DbvazCa+XTiag6dvkqi3vjkBwDUKiWSBEX849n1z59s2rTppSvjzOL27duUKFGCu3fvJnv76dOnCQ8PZ82aNVSuXNnJ0bmWLMt8+umnREVFMfbz8cwbsZwrp66h1qiwWKx4Z9HR7rO3adGvsTi+2EW2b99OmzZtWLlGJNYeAAAUHElEQVRyJZUqVWbnppOsX3GARw8SyZ7Tj2bvVKZi9cLPbVd0lqP/nuXP0as4e/TKk78Zs4WsQX60G9SIBu2qOZwUbD10jmHT1yEB+me2uMoWE2XD8jJxUEuyeGltPsafS6OZvTQao43icJVSQfOI0gzp7l5dB58lkgEXMZst7Nx3ns07ThGfaCQ02J+JPw1hycLZVKjwenvQDYkG3g3uQcKjlE8h0+jUtOjXmJ5j3keWZU6cOEFUVFTSl1arfS45KFiw4HMvsM8mrmbXkYsYUtgjLlvMfN6lDq0iMteH3LMOHDhA9+7dbZ7uuGzZMgYPHsz+/fvJnj27E6NzrVGjRrFgwQKioqIIDHxyYNONi7e4d+0+3n7e5C8VKpIAN7Bu3Tp6df+ISoU7YTHLzx165OWtITC7L2OmdyF7DufVdGxesoeJH8/HkMzsqs5bw5tvV2LA2PfsJgSHz12jz7hlKb6HqVVKShXIxfSP37H5WG9/OJU79+Psxq3Tqtk0p7/bLguKZMCN/Pjjjxw/fpw5c+a89mMd2fYfnzb8DrPJ8tI55hovNQVK5WVc1HfJVnTLsszp06efSw4kSUpKDoqVqcQXM3bYzIQBgrP7sWLsB277x5/eVqxYwaxZs1i1apXNcZ988gmHDx9m3bp1Ke4QyUh+/fVXJk6cyI4dO8iV69XrVYT0d+/2I7q0+AVDoiXZ17FCqSAohx/TlvZF55X+R+7eirlHz1ojMNo4gEnnrWHw+PcJb2b7oqrb6IUcOX/d5hgvjZpJH7WibOHn67esVit37twhJiaGwT9tx2K1/zGqkCQ2zx2AxsZxyK7knlFlUj179qRgwYJcv36dkJCUiwcdIQVaOOG9l5al23N2/yU0WjWyVUZSSrzdtxHvDWud4tYuSZIoVqwYxYoV48MPP0SWZc6dO5eUGIyftxWf0ApIdlrvPnicyMlLtyhRIHO+4SdXPJic77//nvr16/PNN98wYsQIJ0TmOrNnz2bs2LEiEfAQS//ejdUipZjQWy1WHj6IZ9v6YzRqWTHd41k1axtWq9XmGH2CkQUTIm0mA7fuP+bkFft9LvRGE6OmLSFMfYuYmJikr6tXr5IlSxZCQ0PxKdQWJPvLupJEqk43dDaRDLiRrFmz8t577zFp0iRGjRr1yo+j1+vp2LEj3/3yNV26dOH+zVhuXryNWqumQOm8qFKZmUqSRFhYGGFhYXTv3p2h41ew/dAFu/dTSBI37j4SyYAdKpWKBQsWUKlSJapUqUKzZs2cEJ3zLV26lC+++IKtW7eSL18+V4cj2CHLMmuX7sdsZwZQn2hi6Zx/nZIM7Fh9CHMKrdyfdfHkNSb9+jtWyYxer3/p655ewizlA8n2e6EMXL8fR6m8WurUqUNoaGjSl7f3k8ZhY6dvYtXmo1gsKc8OSBJUr1jQrWdJRTLgZgYOHEiNGjUYNmxY0h9bag0fPpywsDA6d+4MQGCurATmSrvjSX28bRfUJJGeTLNlVk9PNHNEzpw5WbRoES1atGD37t0ZbvfF+vXr6dOnD5GRkRQtWtTV4QgOSEwwYrTTA+KpO7cepXM0T9haHniWjJUD+w7iE6BDp3vy5e3tTWBgIF5eXjy2qLn0XzxWGx/gTxUvWpivPn43xdvbNq3I2m3/YbGk/LvSqFV0fLuKQ7G7ikgG3EyRIkWoWrUqc+bM4cMPP0z1/bdu3cr8+fM5cuRIumWhEVWKEnXgHAl2XpgWi5XyxV7e+phZXLlyJVU7KapVq8ZXX31F69at2bBmI1ELdrNt4S708UZCiwbTcsBblKldwq2vLpKzY8cOOnXqxIoVKyhXrpyrwxEcpNYokR1YCwdQqZ0z/Z0jdyAP79kv1tOoNUyZMSHF8ywsVisbhkzlgZ0dV15aNY3fKGZzTGhwVr7o05DvJ0diNJp58Tem1ajo0yGcUkVeb+k3vYlyXTf00UcfMX78eLtrYy968OABXbp0YebMmelamV69TH687bSS1aqVNAsviVcmPlzG0WWCZ/Xt25fCAcXpWKAPf369kDP7L3Dl5FX+XbmP4c1HM6jmcOIfJaRTxGnvwIEDtG7dmnnz5lG9enVXhyOkglqtomip3HbHWWULOv/EFLfQppXY2FgM/rexyLYvQhRKBbXfrmjzYCulQkGH+hVR2fkElCRo/EZxu7FF1CjGpO/aUqNSIVQqBVqNCpVSQaXSeRk3rDVtmrj/KaUiGXBDderUQaPREBkZmar79e3bl2bNmtGoUaN0iuwJpULBhCGt8PHSoEjmKlWrVlEoT3b6t8u8zWIsFgs3b94kd277b6bPOrX3HIn7FMhmMDyzjUuWQR+n5+zBC3zR5Hu3Om720ukbTBu1iu/7zeH3b5Zz+sgVAE6cOEHTpk2ZPn26Q/3dBffT7oNa6LxsJ/RarQal722KFClC3759OX/+vMOPb3GgPXtiYiI//fQTRYoUwZLlEblCc6Cw0dtAq1PTbmBDu4+runeauJsX0SSTEUiATqNiXN8WDl/QFC+Uix8/fZsNs/ux6LcPiPyzHxO+eodyJTxjdlQsE7ghSZIYPHgwv/zyC40bN3boPgsWLODgwYMcOHAgnaN7IixvEH9/15Fpy3ezZd8ZlEolVtmKVq2iXYMKvN+kkttuoXGGGzdukC1bNjSa1G23mvnFXJsHTJkMZi4cvczxnacoHW7/iiU9xT1K5LsPZ3P6yBXMJjNWi4ykkIhcvJccuf3ZeHImP/30Ey1atHBpnMKreyO8CBHNyrFp9WH0iS9fkWt1arr0q0erDtW4efNrJk6cSNWqVXnzzTf5+OOPqVLl5XXymEt3WfzXLratP4rRaEalVlGrfkne7VSD/IVzJo0zm8389ddffP3111SuXJnt27dTvHhx7t96yCetx3P/5kMS4w1J43XeGpQqJSPm9SWkQA6bP9cff/zB8OHDWR8ZyZHrBuZs2E98ohGFQoHJbKFK8bz0bVmDIqFBqf6dabXq12oa5yqiz4CbMhgM5M+fn40bN1KqVCmbY69evUrFihVZu3YtFSumf0Xvi+ITjdy89wi1SknuHP4oRbMYdu/ezaBBg9izZ4/D97l/M5aOBfpiMtieBpUkqNmqKl8tHvK6Yb4yk9HMoJYTuHL+drLV3TJWvP00zNnxDT6+OhdEKKQVWZbZsOoQc6dF8TA2HqVSgdlkISRfNrr0rUfVWs8XhMbFxTFz5kx++eUX8uXLxyeffELjxk86Se7deYaRny7CbLJgsfxvGVShkFBrVAz95m3CI0qyatUqPv/8c4KCghg9evRLJ51aLFb2b/mPVbOiuHP1PifP/EefLzvRslsEOjsFztOmTWPEiBFs2rQpqZjVapW5cjsWo8lCjqxZCMiS+Q4QE8mAGxsxYgSXL19mxowZKY6xWq00aNCAOnXqMGzYMCdGJ7zIZLKwfd9Zlq0/zMUrN0mIe8DAnm/TMLwE3g40ZDm19yyfNRxJ/EP7NQEFSudl2pGf0yLsV7JlxUEmDl+CPiHlWQyNTs37gxrQpmcdJ0YmpBdZlom5eJe4x3qyZvMhOE+gzfFms5nFixfz008/YTAY6NVzIBsX3U62c+BTKrWCR9IuHsbfZPTo0TRu3NihgtmIiAiGDBlidyZ10qRJjBkzhs2bN9s9dj2zEcmAG7tz5w5FihTh9OnT5MiR/LTX+PHjWbRoEdu3b0elyrzT8q524/ZD+n61gEdx+ucOofLSqlGqFPwyvA0lwlI+fCoxMZFlc1byV/9lWE32X5IlqhVhwq5X70Xxuvo0+ZmLp27YHReYw4+50V85ISLBXcmyzObNm/np60VYE3OgkFLeeWCVrRQu4c9vf36Uqm6cQ4cOJVu2bHz++ecpjvnll1+YOHEimzdvpkCBAqn6GTIDMZ/rxoKCgmjTpg1TpkxJ9vbjx48zatQo5syZIxIBF0rUG+k9fD537se9dBplosFEXLyBgd8u4sbth0nft1gs7Nu3jx9++IF69eoRFBTE73/+hlJj/yVpxcKBa9FMnz6dx48fp/nP44hrlxyrHI+9+xiTg3vVhYxJkiQiIiLw1RS0mQgAKCQFV88nprotd7ly5Th8+HCKt48ZM4ZJkyaxbds2kQikQCQDbm7QoEH8Pvl3ju06QeTsrWyeu4PbV+5gMBjo2LEjo0ePznANajzN+u0neBxvwGpjT7bRZGHyX5uYMmUKbdq0IUeOHHTt2pVbt27x0Ucfcf36dXbu2kmXr99D6217ScE7iw+Df+rH2rVryZs3L927dyc6OtruDgOLxcqe7aeZOHIVP3+5jGVzdvHoQeq3KZrNZnhpN3UKZGxWfguZR0KCwf4gwGg0O7TL4Fmly5Th8PWbRF24yMnbd557LYwcOZKZM2cSFRX1Ske1ZxZimcDNHdx8jE+bf43SokGtViFJEmazBV1OFZaij1mxbpnHNaHJaDoMnMWla/ftjrNaTIT5nqJ+/Qjq1auX7PkTFouFL5v/yNGoExiSefPUemv5bsUnVIgoA8DNmzf5888/mTFjBlqtlu7du9OxY8eX+kycPnaVbwbORZ9oTDp5TqtTY7XKtO5Unc79Imz+HV27do3IyEjWrVvHpk2bKBfUHJ3ZfqV1oRIh/PbPYLvjhIzvnXo/OpR8Sgor3T8tS/369fH397c51irLTNuznxl793P/wQP8/PywyjLZvL0ZWqsGe+bPZcmSJWzevJng4JSX6QSRDLi16H8OMLLtOAzJbDWzYsU/mx9TDv5EjtDMc/StO2rQ6VfibRTSPaVWKVg9sw++Prar6y0WCyt+Xcuin1aR8DgRhVKByWCmXJ2SdBv1HoXLvzzNKcsy27dvZ8aMGaxevZpGjRrRvXt36taty6VztxncaTr6FLYsanVqmrWrQveP/tefwmg0snPnTtavX8/69eu5du0a9evXp1GjRjRo0IC7MYl81W0GhmS2mz2l89YwaPS71G4qug4KMGPCBlbMj8Zk46wDhVIitJCWqw93sGPHDipUqECTJk1o0qQJpUqVei5htcoyA1b+Q9SFSySaX16KUsoy0qH9bP91fIo1V8L/iGTATRn1Rtrk/IDExym3y1QoFVRsUJbv13zhxMiEFzX7YDL3HdgBoFBIbJ470OH+C1arlatnbmBIMBAUmo2AINtXSU/FxsYyd+5cpk+fzqNHj6hc6H0e3bH9MldrVHz3e2ui9+1g/fr1bNu2jWLFitGoUSMaNWpE5cqVn6tLkWWZ375cyublB5JNCLReasrXKMKXUzqjEFtNBeDOrYd0b/1bikkpPElMf5/fm9x5s5GQkMC2bdtYu3Yta9euxWQy0aRJExo3bky9evXYfOUqX27YTKIp5YRUq1SyvNN7FAkSF0z2iGTATW2cE8XEvjNItNM7W61V89f538geYnubj5B+xk7fxOpNRzFbbLePLlMsN7+PbO+kqP6/invDDn7+dAOybHspySpbuBV3hLI1s9GoUSPq169PUJDtZQBZllk6I4qFkzdjtViR5Sc9EGQZWnSuQcePGqEU9QLCM44euMSXA+disVgwPdOfQqVSolIpGD6mLZVrhL10P1mWOXPmTFJiEB0dTZ6PPsaUxdfm8ykliZalSjC6cYM0/1kyGpEMuKkf3v+VLXN32B3n7efF0Fl9CW/1hhOiEpJz5fp9ugz9C4ONqnmdVs2IIc2oXqGgEyOD/bvO8sMni4i3k1QClK9aiB+mdkn1c5hNFo7tOU/s3Th8A7woWy0MjVbsbhGSd+fWQ1Yt3EPkqkPEP9bj5aMl4q2yvN2uKrlyO3a66q379wmf8SeOnN6S3dub6H6pP/QtsxGvWDdlsXOGeBIZrKmsvBXSVt6QQAZ3r8e4GZuTTQh0WhUtG5ZzeiIAT5YmZAcr/1WqVzt5TqVWUr5mkVe6r5D5BOX054MBDfhgwKtfrWu8vFAplRgt9t/7TKk88C2zEsmAmypWpTDR/+x/7rCa5FjMFvKXFttlXK1p3dKE5PBn+oJdnDx/E7VKidlsIXfOALq+W4161W0fg5pewkrmxuxAYqnzUlO55svTs4Lgjvy0WtRKhUPJQL4Ax2ptMjuRDLipBl3eZNbw+XbHhRbLTb7innEqVkZXoVRefh+Zl9iH8cQ+TMDHW0vO7H4ujcnXz4sa9UqwPfL4c73gXyTLUK+ZqPoXPINSoaBtmdLMOXjY5pW/t1rNB1Wcf16LJxLVPW7KL9CX979sg9bGoRtabw0DJvdwYlSCI7L6+1Awb5DLE4GnPvy4CQHZfFIs5tPq1Hz07dv4ZBEHCgmeo3uVivhoNKRUGqtWKAj196NBmDiDwBGigNCNybLMop9W8tc3i1EoJPT/34TGy1eHWqPm66VDKVOrhIujFDxB7L04xn+zgoPR51H9//ntsgz+Wb3p83lT3njh5DlB8AQX7t2n06KlPNYbiP//LYYKSUKrUlEkezZmtmlJgJdIch0hkgEPEP8wnk1zd3Du4EVUGhWVGpSlatOKKF+x4EvIvO7decyx/RcxmSzkzpeN4mVCRQdLwaNZrFaiLlxi6fETPEhMJLe/H++VK0PZ4FzibzsVRDIgCIIgCJmcqBkQBEEQhExOJAOCIAiCkMmJZEAQBEEQMjmRDAiCIAhCJieSAUEQBEHI5EQyIAiCIAiZnEgGBEEQBCGTE8mAIAiCIGRyIhkQBEEQhExOJAOCIAiCkMmJZEAQBEEQMjmRDAiCIAhCJieSAUEQBEHI5EQyIAiCIAiZnEgGBEEQBCGTE8mAIAiCIGRyIhkQBEEQhExOJAOCIAiCkMmJZEAQBEEQMjmRDAiCIAhCJieSAUEQBEHI5EQyIAiCIAiZnEgGBEEQBCGTE8mAIAiCIGRyIhkQBEEQhExOJAOCIAiCkMmJZEAQBEEQMrn/AyA1SX6uH34DAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% Plot graphs\n", + "\n", + "plt.figure(figsize=(8, 10))\n", + "for i in range(len(X0)):\n", + " plt.subplot(3, 3, i + 1)\n", + " g = X0[i]\n", + " pos = nx.kamada_kawai_layout(g)\n", + " nx.draw(g, pos=pos, node_color=graph_colors(g, vmin=-1, vmax=1), with_labels=False, node_size=100)\n", + "plt.suptitle('Dataset of noisy graphs. Color indicates the label', fontsize=20)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Barycenter computation\n", + "----------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% We compute the barycenter using FGW. Structure matrices are computed using the shortest_path distance in the graph\n", + "# Features distances are the euclidean distances\n", + "Cs = [shortest_path(nx.adjacency_matrix(x)) for x in X0]\n", + "ps = [np.ones(len(x.nodes())) / len(x.nodes()) for x in X0]\n", + "Ys = [np.array([v for (k, v) in nx.get_node_attributes(x, 'attr_name').items()]).reshape(-1, 1) for x in X0]\n", + "lambdas = np.array([np.ones(len(Ys)) / len(Ys)]).ravel()\n", + "sizebary = 15 # we choose a barycenter with 15 nodes\n", + "\n", + "A, C, log = fgw_barycenters(sizebary, Ys, Cs, ps, lambdas, alpha=0.95, log=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot Barycenter\n", + "-------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% Create the barycenter\n", + "bary = nx.from_numpy_matrix(sp_to_adjency(C, threshinf=0, threshsup=find_thresh(C, sup=100, step=100)[0]))\n", + "for i, v in enumerate(A.ravel()):\n", + " bary.add_node(i, attr_name=v)\n", + "\n", + "#%%\n", + "pos = nx.kamada_kawai_layout(bary)\n", + "nx.draw(bary, pos=pos, node_color=graph_colors(bary, vmin=-1, vmax=1), with_labels=False)\n", + "plt.suptitle('Barycenter', fontsize=20)\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_fgw.ipynb b/notebooks/plot_fgw.ipynb new file mode 100644 index 0000000..b41f280 --- /dev/null +++ b/notebooks/plot_fgw.ipynb @@ -0,0 +1,359 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# Plot Fused-gromov-Wasserstein\n", + "\n", + "\n", + "This example illustrates the computation of FGW for 1D measures[18].\n", + "\n", + ".. [18] Vayer Titouan, Chapel Laetitia, Flamary R{'e}mi, Tavenard Romain\n", + " and Courty Nicolas\n", + " \"Optimal Transport for structured data with application on graphs\"\n", + " International Conference on Machine Learning (ICML). 2019.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Titouan Vayer \n", + "#\n", + "# License: MIT License\n", + "\n", + "import matplotlib.pyplot as pl\n", + "import numpy as np\n", + "import ot\n", + "from ot.gromov import gromov_wasserstein, fused_gromov_wasserstein" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% parameters\n", + "# We create two 1D random measures\n", + "n = 20 # number of points in the first distribution\n", + "n2 = 30 # number of points in the second distribution\n", + "sig = 1 # std of first distribution\n", + "sig2 = 0.1 # std of second distribution\n", + "\n", + "np.random.seed(0)\n", + "\n", + "phi = np.arange(n)[:, None]\n", + "xs = phi + sig * np.random.randn(n, 1)\n", + "ys = np.vstack((np.ones((n // 2, 1)), 0 * np.ones((n // 2, 1)))) + sig2 * np.random.randn(n, 1)\n", + "\n", + "phi2 = np.arange(n2)[:, None]\n", + "xt = phi2 + sig * np.random.randn(n2, 1)\n", + "yt = np.vstack((np.ones((n2 // 2, 1)), 0 * np.ones((n2 // 2, 1)))) + sig2 * np.random.randn(n2, 1)\n", + "yt = yt[::-1, :]\n", + "\n", + "p = ot.unif(n)\n", + "q = ot.unif(n2)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot data\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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KPELs0BNJQ1VJJd899Hr+5+3fsqO1FoiVQY1YhDkjT+LSAy8scIQiItIbZbQPvMubOXcHcLiZHdlVW+0D78jMqGncwIbGjZT6SjhyyCEMDFQUOiwREcmzdPeBd2sbWQrb0BB6tzjnmDxwvIq2iIhIWrK2jcw7zORUQMVcREREciyTbWTHpbjHWOCzwFHArVmIS0RERFLIZAj9GWJbxJJxxA47ub5HEfXAqtq13L5+Ka/vXA/A4VUT+PSEuRw5ZGKhQhIREcmJTBL4D0icwKNAHfCCmT2blai64e/vL+O3ax+kNbr3ILSVdWt5fdcGvjTlXD42JulR5iIiIkUnk21kN+YykJ7Y2lzHb9Y+QCi6bzGU1mgbv3znfmYPn8Z+ZVUFiE5ERCT7+kQt9Hs2Pkuq7XBmcP+m53Iex65QM/e+9zJ/rnmeZz+o0SliIiKSM9neRlYQa3Zvos0iSa+3WZg1uzfl7Plmxs/ffJzb1i4n4HyELULA56fCX8Ivjr2YI4aOzdmzRUSkf0qawJ1za7p5TzOzg7r52W6pDJan0WZAzp7/m7ee5I6a5whFw7QfDBqKRmgKh/jcsjtYfOrVjB84PGfPFxGR/ifVEPoAoLwbv3KXKZM4e/9jKPeXJL1e7i/ho6OPzsmzG8Ot3Lp2GS2RtoTXW6Nhfvf20zl5dlfC0SiPb1zL7W+v4IENb9IcThyjiIgUn6Q9cDMbk89AeqJ66BTGDxjJ2oYttHU61SvoAkwcOJoZQybl5NnLP1iH3yX/HhQx49HNb/LDmTl5fFJPba7hy8/cT1s0SjgaJeDzEcX4+lGncdlBM/IbjIiIZF2fWMTmcz5+OuMqjhk2lRJfgAH+Usr9pZT4AswefjD/e+SVOLfvaV/Z0Bxpo6ty8m0JVsfn0qrtG/nCU39nZ6iFxnCI1miYxnCI5nAbN616nLtrXstrPCIikn19YhEbwIBAGTcd8Rm2tezk1Z3vAnBk1URGlA3O6XMPqhxJNGV9GxhbMTSnMXS24KUnaYkk/tLQHGnjhy89wQUTD8WXoy81IiKSexkncOdcEJgJHACUJmpjZn/pYVzdNrKsirmjjsrb86YOHsn4gcNYs2tbwkRe7g/y+Skn5C2exrYQq7anXnHfHG7j9dqtHD5sdJ6iEhGRbMsogTvnLgd+DCRbUu2IVWsrWAIvhJ8dcxGfeOoWmsIhQtG929nK/UFOGDmZ88cdkbdYmiNt+J0jnGJQwOccTVrQJiJS1NKeA3fOnQH8EfgQ+BqxZP0P4FvAE97rxcBV2Q+zdxtXMZT7T7uWKybPZkTZICoCpUwfPJrvHnUePz16Pr4Ui9yybUhJOaX+1N/LWiNhJlUOy1NEIiKSC5n0wL9KrOb5LDOrd84tAFaZ2feA7znnrgZ+CfwsB3H2esPLBvLl6XP48vQ5BY3D7/Nx+UEz+f2bLyScBw84HyeMnsCI8ooCRCciItmSSddwJnC/mcWf973n82a2EHgO6LU10/uLLx12PNOGjKS8U0+81Odnv/KBLJh9VoEiExGRbMmkB14BbIl73QoM6tTmBeAzPQ1KeqbUH+DOuZewqOYVbn3zRbY172ZwSRmXTJ3BZVNnUFlSVugQRUSkhzJJ4FuBEXGvNwOdS6ZWZnhPyZESv59Lps7gkqkq2iIi0hdlMoS+mo4JexlwunNuNoBzbhpwkddOREREciiTBP4wcLxzrn3z8I+IbRl7xjm3BXiNWA/8+9kNUURERDrLJIEvBA4EagHM7HVgLrAEaCC2lewcM3sg20GKiIhIR2nPV5tZCNjU6b1lwJnZDkpERERSy6SQS2UuAxEREZH0ZTKEvsU593/OuTNcro72EhERkbRkksA3AZ8ktpjtfefcTd7KcxEREcmztBO4mU0Fjgd+DwwAbgBed84975y7xjk3JEcxioiISCcZnbJhZsvN7GpgNLHe+CPADOBXwGbn3GLn3DnZD1NERETideuYLDNrNbO7zOwsYAxwPbAGuBC4N4vxiYiISAI9PufSzLYBK4GXgDZix4qKiIhIDnW7brlzbjLwaeAyYCyxxP0ucEd2QhMREZFkMkrgzrnBwMXEEvcsYkm7AbgduM3Mns56hJJSU1uIe9a8yd/ffoPmSJijRx3AZw6fwfjBWlMoItKXpZ3AnXN3AecCpd5bTwK3AXebWVPWI5MuvV+/k4/f81caQiGawm0AvFO7g7vefI3/PvF0Lp52WIEjFBGRXMmkBz4fqCHW277DzN7LTUiSDjPj0w/ezY7mJqJme95vi0ZpI8q3nnmMQ0eM5JDh+xUwShERyZVMFrGdaGZTzOx7St6F9+KWTWxtaOiQvOOFIhEWvvRCnqMSEZF8yaSQy7JcBiKZeXHrJloj4aTXo2Y8t/n9PEYkIiL51ONtZFIYAefwdVGSPuDTX6+ISF+lf+GL1CnjJuB3yf/6gj4fH5kwJY8RiYhIPnV7H7gU1kHDRnDkyNGs2rqZUDSyz/Wgz89nD59ZgMhERArnvZ07uX/1W2xvbGTysKF8bPo0KsvKCh1WTiiBF7GFZ36MTz9wN2tqd9AcbsOAAcEgDsetZ13A2MrBhQ5RRCQvomZ849Gl3PvGaiJmhKNRygMBfvjUv/ju3NO58NBDCh1i1imBF7HBpWXcc+GnWLl1Mw/WvE1TWxszR+3PuZMPpjwYLHR4IiJ589N/LeP+1W/SGtk7Itkcji30/eaSxxhdOYjZ48YVKrycUAIvcs45qkcfQPXoAwodiohIQTS3tXHbqlV7EnZnLeEwP3vmWWZ/qp8ncOdcADgFmAYMNLObvPdLgIFAnVmSzckiIiJZtmrT5pSLegFe2ryFUCRCid+fp6hyL6NV6M65OcA6YueA/xz4XtzlmcB2YrXSRURE8iIcjXbZxgHRNNoVk7QTuHNuBvAAsV77fwB3xl83s+XAeuCCLMYnIiKS0iEj9yMU2Xc3Trz9Kysp62NrgzLpgX8TaAaqzewnwNsJ2rwIHJmNwERERNIxvKKC0yZNTDo8Xh4Mcs2sY/McVe5lksBPAO4xs80p2rwHjO5ZSCIiIpm56cwzmDBkCAPietkOKA8GOOfgg5h3WP/eRjaQ2Bx3KuWoupuIiOTZoNJS7rn8Eh5Z8w7/99Ir1DY3MWnoUK6onsExY8bguig9XYwySeCbgK6+whwJvNv9cERERLqnxO/n3GkHc+60gwsdSl5k0lt+BDjTOTc70UXn3BnA8cQWuomIiEgOZZLAfwDsApY6574PHAzgnPuI9/puYBvwk6xHKSIiIh2kPYRuZhudcx8B/gZ8HTBiawQe8n5fD1xoZl3Nk4uIiEgPZVSJzcxWOOemAh8DZgHDiPXKnyO2Qj2U/RBFRESks7QTuHNuf6DN62Hf7f0SERHpVULhCEtWv8N9r7xJc6iNGeP255PHHMGowYMKHVpWZdIDfx+4A/hMjmIRERHpkQ/qG/jU7++irqmZplAbAK9u3Mrty1/i++fP5ezD+84K9UwWse0EPshVICIiIj1hZlz1p3vYWr97T/IGCEUitIbD3HjfEtZs21HACLMrkwT+PHBUrgIRERHpiVc3buW92p1EookPxGyLRPjDMyvyHFXuZJLA/xs42Tl3RY5iERER6bYX129MeahJJGo8W/NeHiPKrUzmwE8HHgdudc59gdjBJVuJbSeLZ+1nhIuIiOSLzzkcjn3T0l59qaJqJgk8/uzvY7xfiRigBC4iInk1e9I4fvnE8qTngwd8jlMOmpjnqHInkwQ+N2dRiIiI9NC00ftx8KgRvL55G+HIvkk86PdzxXEzCxBZbmRSie2xXAYiIiLSU7+55GNc8cdFbKyr37MSvSwYwAE/vfgcJgwfUtgAsyijSmwiItJ/hSNRnntjPVs+rGfIoAGccNgEykqDXX8wj4YMKOeeL17GszUbeOC1t2gKtTFj7P5cMOMQBpeXFTq8rFICFxGRDtZs3s7L6zfj9/k47qADGT2kkuWvr+fG3z9EWzhCJGL4/T7MjOvmncS8U48odMgd+HyOE6aM54Qp4wsdSk5lUkq1jVRL+/YyMyvtfkgiIlII2+sbuO7W+1m79UMMw+GImnHEuNG8tXozraG4LVpenZSfLXqKstIA5xx3SGGC7scy6YE/T+IEXgVMBkqB14D6LMQlIiJ51BIKc9nP72Lbrt37FEJZsW4j+I1Eg+UtoTC/WPwvzpo1HZ+vD+3RKgKZLGI7Idk151wl8AugGjg3C3GJiEge/fPlt6lrbE5YxcwAAhD1gS/BDq2W1jbeem8b08ePynmcslcmldiSMrN64HPE/p6/n417iohI/vz9uddpjqsfnkg0yXo1n8/R3Jr6s5J9WUngAGYWAZ4ALsjWPUVEJD8aW0OpGzgHSUbIQ20RDhw1NPtBSUpZS+CeEqDvbLITEeknDh03En+qOqMGLkGZcb/PcfS0cQwfXJG74CShrCVw59wUYD5Qk617iohIflx60gwCAX/S6z7nGOA6Xi8J+hk+eCDfvOKMXIcnCWSyjezmFPcYC5zk/fcNWYhLRETyaMro4Vx75mx+88/ltLSF97zvc46SgJ8fXXYWO7bv5i9LX2L7zgYGDSjl4ycfzkWnHcmgAX2rQEqxcGbpbO0G51zi6vB7rQV+bGa3pHO/6upqW7Gi75zLKiLSF6xYu5HfP/YCr6zfjM/n46TpE/jc6UczedTwQofWbzjnVppZdVftMtkHPiXJ+1Ggzsx2ZnAvERHphaonj6F68phCh9HrtLS08dgTq/nHQy/T2NjKxAkjmHfh0Rx2SOH+rDLZB665bRER6XfqdjZy7XV/om5nEy0tse1ymzbX8fyL6zjv7KO45urTChJX2ovYnHM3O+fO6aLNWSnmykVERIrOf3//Pj7YvntP8gYwg9bWMP946GX+tWxNQeLKZBX654EZXbQ5ilhBFxERkaK3aVMdb761hUiC88UhNrT+p788m+eoYnKxDzzBTkEREZHi8+bbW/D7U6fKde9+kKdoOso0gSddsu6cCwInAtt6FJGIiEgvEQj4SFXfBsDny3ZfOD0pF7E55zoP7F/nnLssQVM/sB8wANAcuIhIgYRawzz9+GqW/+ttMDj2hKmcfPp0SsuSFDKXlGYceSDhcPKBZefgmOqJeYxor65WoQ9gb6/bgCBQnqBdBFgDPAb8d9aiExGRtG14dztf/eLthEJhmptitc1XPF/Dzb9Ywo9/fRkTJo8scITFp7KynDPnHsYjS1+ntTW8z/WSkgCXX3JcASLrIoGb2Z4Nbl4hl/81s+/kPCoREclIa0sbX/3i7eza2dTh/eamEM2E+I9r7+BP915HeXlJgSIsXv/vmjk0NLbyzLPvEIlEiUSilHkjGjd+7VymTinMMaqZFHKZC6zLVSAiItJ9Ty19I2EPsV0oFOHJJW/w0fOOymNUfUMg4OcbXz+P9zfW8sRTb1K/u5kJ40dw2inTKC8r3BeiTAq5PJbLQEREpPuWPf02Lc3JjwRtaQ6x7Mk3lcB7YOyYoVx+yfGFDmOPTHrgwJ7V5jOBA4DSRG3M7C89jEtERDJg0a7PtYim0UaKR0YJ3Dl3OfBjIFlVe0dssZsSuIhIHh17whReXvkuLc1tCa+XlQeZdeLUPEcluZRJKdUzgD8CHwJfI5as/wF8C3jCe70YuCr7YYqISCqnfeSwlOd5+/0+5px5eB4jklzLZPf5V4E6YJaZ/dh7b5WZfc/M5gBfBM4H3sxyjCIi0oXy8hIW/PIyBg4s27NCGqCsLEjFwFIW/PIyBlQknPWUIpXJEPpM4D4zq497b88XADNb6Jy7BLgROCtL8YmISJqmHDyaP993HUsffpVnn34LM5h94kHMPfsIKpS8+5xMEngFsCXudSswqFObF4DP9DQoERHpngEVpZw372jOm3d0oUORHMtkCH0rMCLu9WbgoE5tKunGynYRERHJTCbJdjUdE/YyYL5zbraZLXfOTQMu8tqJiEga1r2/g7sfeZn1Gz9k+JCBfGzO4Rw1fQyuqxM0pN/LJIE/DPzUOTfazLYAPwLmAc845z4g1jv3Ad/PfpgiIn2LmfHbv/yLRQ+/RDgcIRI1HPDMihoOPWh/fnzD+ZQENaApyWUyhL4QOBCoBTCz14mVV10CNBDbSnaOmT0VD/VUAAAgAElEQVSQ7SBFRPqaJcveYvE/X6I1FCbiFVgxoLm1jVff2sTPb3+yoPFJ75d2AjezkJltMrPWuPeWmdmZZjbFzOaa2UO5CVNEpG/5/aJnaUlSu7w1FOahJ9+gsak14XURyKwHLiIiWdDYHGLLtvqUbQIBH2+u25aniKQYdacW+iHAJ4FpQIWZnem9Pw6oBh43s51ZjVJEpA9xjljtylSs6ybSv2XUA3fOfRN4BfhP4AJic+DtgsAi4NKsRSci0gcNKCth7KiqlG0ikSjTJ4/OU0RSjDKphX4R8G3gcWI97QXx182sBlgJnJfF+ERE+qQrLz6estLEg6ClJQE+NvdwyuNKoop0lkkP/DqgBjjXzFYBLQnarAamZCMwEZG+7NRZU7n8/GMpCfoJ+GP/FPuco6w0wLFHjOfaS04qcITS22UyB344cFv8KvQEtgAjexaSiEj/cMXHZ3HGidO4d8krewq5nHPaYUyfPKrQoUkRyCSBOyDaRZsRxGqki4hIGvbfbzDXqLcNwPs1H7Bq2TuYGYcdPZFJ0/cvdEi9WiYJfC0wO9lF55wPOAGVUhURkQw01Dfz/f/3Z1avWo/Fatrg8zkOnDKSb/3uCoaO6HxulkBmc+B/A2Y6565Lcv16YvPff+1xVCIi0i+YGf95xS28vuJdQq1h2kKxX60tbdS8uZmvfvK3tIUSF7zp7zJJ4D8DXgN+4pxbBnwEwDn3Q+/194EXiZVcFRER6dIrz9Xw/rrthNsi+1yLhKPU7djNs0veKEBkvV8mpVSbgFOI9bCPBWYRmxe/3vvvO4EzzKwt+2GKiEhf9MT9L9HSFEp6vaUpxJK/r8hjRMUjo0psXoW1S51zXwGOAYYBu4DnzWxrDuITEZE+rKkh0Y7kjppTJPj+rFtn1ZnZduDBLMciIiL9zLSjDuTFp96mtSXx4G0g6GfakePyHFVxSDmE7py73Dl3eL6CERGR/mXux6tTFn33+RznXnpc/gIqIl3Ngd8GnB//hnPu0865x3MWkYiI9BuDBg/gaz/5FKVlQXz+vSnJ+RylZUGu/fYFjDxgSAEj7L26M4Q+Hjg5y3GIiEg/Nev06fz87i+x6PdPseKpt7Gocfixk5h/1SlMPWxMocPrtbo1By4iIpJNB04ZxVcXXFzoMIpKRseJioiISO+gHriI9HtrVtaw/P4VhENhph93EMecdRR+v7/QYYmklE4Ct5xHISJSAA07G7nxnJtY+/J6Qi0hLGqUDyqjfGA5P3zkRiYcqu1L0ns5s+T52TkXJfMEbmbW5ReD6upqW7FC1XVEpDDMjOuOv5G1q9YlrLU9cEgFt6/5JZXDdJCG5JdzbqWZVXfVLp05cJfhL82ri0iv9/aLa3n3tQ1JD8oItbTx0O8fy3NUIulL2VM2MyVjEemTnr3vRVpTlOgMNYd4/K//4hM3nJ+0jWTu9eXv8Jf/fYg3nnsHgENmTeZT/342h86eUuDIio8WsYlIvxRqCZFqChGgrUXHWGbTQ7c/zc3fWExr894vTi899RarX1jHld+Zx9lXnFTA6IqPetgi0i8dctzBlA8qS3rdH/Bx+MnT8xhR37Z9Uy0L/2tRh+TdrrU5xM03LmL7ptoCRFa8lMBFpF+afV41JWUlSa8HggEu/PLZeYyob3vwtqdTjniYGQ/+8ek8RlT8lMBFpF8KBAP88J83UjF4ACVlwT3v+/w+SgeU8KVffY4Dp6mMZ7asffW9pAsGAdpCYd559b08RlT8NAcuIv3W5KMm8Me3f8GDCx/liTuXEW6LcPhJ05j3/53LgdPHFjq8PmVQVUUabQbkIZK+QwlcRPq1IfsN5tJvzOfSb8wvdCh92pxPzOa5R16lpbE14fWyilLmflLHhmZCQ+giIpJzR518MAceNJpg6b79xmBpgHFTR3HUyQcXILLipQQuIiI55/P5+MHdX2bmqdMJlgYoqyilrKKUYGmAmadO56a/fwWfTykpExpCFxGRvBgwsIxv/ekaPthYy+vLY4VcDp09hf3GDC1wZMVJCVxERPJqvzFDOW3+sYUOo+hpvEJERKQIKYGLiIgUoZTHieb0wc5tBzYU5OEiIiK914FmNqKrRgVL4CIiItJ9GkIXEREpQkrgIiIiRUgJXEREpAgpgYuIiBQhJXAREZEipAQuIiJShJTARUREipASuIiISBFSAhcRESlCSuAiIiJFSAlcRESkCCmBixQx59wC55xl+VddoX8uEemaDjMRKWLOuYlAjffyBjP7UQafrQKqgRnAXGBO3OW5ZrY0a4HufeYM7zk7gXXACjPbme3niPQHSuAiRc45Nw9Y5L2caWarunmfKmABcBWw2MzmZynE9vtf5f1nLXAxMA/YaWZDsvkckf5CCVykD3DOLSKWENeZ2aQe3msesMjMXFaC23vfhWZ2ddzrKmBid79wiPR3SuAifYRzrgaYSBZ6z86564l9GVicleBi91xpZjOzdT+R/k6L2ET6jvakPc/rRXebN5d+dM9D6mCdc25Blu8p0m+pBy7Sh3g95/YkOcnM1hUynnjOuTnAEnK0QE6kv1ECF+ljnHNLiK30XtXbhqy9Yf6hwAStPhfpGQ2hi3icc1XOuZXOuTpvP/RVSdot8BaN9VbziW3TmtGbhqy9HvgCoAq4pcDhiBQ9JXARj5nt9HqsN3lv7TPM662cvp7YHuZeyevZts+HX+8lzoJxzs1wzi0ys6VmdjOxP9d53p5wEekmJXCRfR1NbH9yoiTd3iu/K4/xZMybY77Ze7nI++KRd16Sfgy4Mu7thd7vF+fyud5oiuYIpc/SHLhIJ14p0aWJtmJ5c7jrzGxuGvdZSMfqZplYmElVtRQxtG8tW5pOzNnmPb/Dz+J9magjB8ViOj37KmB+IX5ukXwIFDoAkd7EK01aRWy1dOdr84glw7QSQnzRkgKaS6zU6hzn3Ix8Fk3xEuhE9o4EALEhfuccxCqy5dJcEvw9ivQVSuAiHbX3mDvMf3u9xluAq3vT1qyumNk659xSYqMG+a54Np9Yzz/ZavOaJO9nyxz2rmcQ6XM0By7S0VwSz38/RiwZ3ZzgM72Wty98aIFGA6qBfb40eKMcxF9zzs1xzl3VedW8c25Rdxa7xT1jonNunrdzYGLKD4kUGfXARTqaA6yIf8PbMrYu0/naQs+Be6vPvw5M6Ml9Utz/emJFWVJNKXyY4L32mu1LvftUAVVmdrO36OyGTm2vTHCPruwZSfGG7NcRO/ClV+2LF+kJJXART+f5by+xPAbc1Z1kWsg5cO9nWQScnsOCKTfTaX67kxVAooNVrmZvtTiAajNb7H3h6NArJzYasjPuvSrgljS+TM0ldrxq+2d3Ejs2dY8M7iXSK2kVuojHW3S1kNg//jOIbSe7oZjmvNt5q78XFHLI3/sSsZK4qmvtQ+RmdkOC9guBmvYvS14P/+juJFhvJ8HM9r87bwHiLTq6VPoS9cBF9ppJrEDLXLrZ6+4NvCH/Hs/XO+cmJvry4iXDocQSZNJRBm8B3enALc65F4n1xmtS/LleRMch7rnsPec8k7jbh+TjY59LgsI8IsVMPXCRPsTr4c7paQ10r/d8Q+cE7Y1SLPWScx1ZqmnuPa8m/gxybz58Zvvq+bhtfItTjYp491oSfy66F+vpmd5LpDfTKnSRPsKbM74KOD0Lt1vE3opp8Wq95F1FbL1AttQSm6cG9nxRIC7hziHWg040lz0xvja9l5Dj77UAuDmde4kUE/XARfqAuPnmPb3Mbt6niljyrk41X+wlwYXxvdye8ua8IZZY5xIbBp/bqc1C77mdF7stIlZ1rX1l+zxiaxg+JLYQbp/phET3EikmSuAiRc5LuiuJDXkv7sE9riK27awK+FGihWZx7RcQS7A5WWnvHYm6pPN8uXNuZbLpAefcvEx+/lT3EikGWsQmUvzah4+HJjsCtZMqYJj3+1Biw8idi5x0dVhLVqucxc9Re18mqtl7olq8hOVXvRGITOeyc13KVSSnlMBFipiXsNuLliSas+6OdMquziBLq7q9hH1z3DNvAa7svDiu8z7xTuZlsmugi3uJFAUlcJEi5s3t5nWvt1fadGfnBNtdXqW0Gu/LyCRi89KJvhwkPJzE+wKQaTLWQSdS9DQHLiIZ6UmBlW4+b55Xqa3Hc9bZvJdIoakHLiKZupj8nvJ1sXNuKN2riZ7Le4kUlHrgIpKSN0T9LrEqaVXAomxuHxOR7lEPXETSsZTYYrlJ6EQvkV5BPXAREZEipFKqIiIiRahgQ+jDhw+38ePHF+rxIiIivdLKlSt3mNmIrtoVLIGPHz+eFStWFOrxIiIivZJzbkM67TSELiIiUoS0Cl1ERIqeRbZCy6NgjRCYCqUn41zfTnF9+6cTEZE+zSyM1X8bmu8FHNAGrhwogSG/xZX03SPfNYQuIiJFy+q/D833AyGgFYjGeuFWh9V9FgunNZ1clJTARUSkKFm0FpoXAy1JGrRijXk96yevlMBFRKQ4tf4L8KdoEIGWf+YrmrxTAhcRkeJkLUC0izZteQmlEJTARUSkOAUPAedStwlMyU8sBaAELiIiRckFDwXfGJKmMleOG3h1XmPKJyVwEREpWm7Ib8ENBko7XSiHsgugdG5B4soH7QMXEZGi5QLjYMQ/saa/QvPdYE0QmIqruBJKjselGGJvDr3Bh7v/SGu4hqB/FEMHXkpF6XEpP9ObKIGLiEhRc74huIHXwMBr0mpvZmzZ+S1qG/+KWQiIAFDf8hgVJUdz4Ig/4nMlOYw4OzSELiIieReNfEjj7l+w88NL2VX7BVpbHsUskpdn1zXe5SXvZtqTN4BZE42tz7N153fzEkdPZdwDd87NAj4DHAUMBnYBq4DbzOy57IYnIiJ9TUvzQ+yu+zfAaC/C0tb6OD7//lQNuxuff1jOnm1mfFD/My95J7hOC7WNdzJy8Nfw+ypyFkc2ZNQDd879FFgGXAlUA1O8368CljnnfpL1CEVEpM8It73N7p3/BjQTX0HNrJFIeD27aq/I6fMj0Z2EI9tStnEEaGl7PadxZEPaCdw5dw1wHbCBWAKfAgzyfr8KeA+4zjn3xRzEKSIifUBTw2/BQkmuthEOv0k4h8nTOR+WVsveP8OcSYTXAFuBajO71cxqzKzR+/33wNHANuDaXAQqIiLFL9T6FPHzzvuwCKHWZ3L2fL9vMCWBsV20ilBecljOYsiWTBL4JGCRmdUmumhmO4C7vXYiIiLdkF7/uCdGDv4PnCtPeM25coYO/Cw+V5bzOHoqkwReS+ystlRagA+7H46IiPRlJaUnkfIAEhcgWHpCTmOoGnAuIwZdi6MUaN8u5se5MirLP8Kowf+R0+dnSyar0O8DznPO/aeZhTtfdM4FgfO8diIiIvsYMPCLtDY/QOJh9ACBwMEEg4fmPI6Rg7/MkIqPU9vwf7SGawj4RzG04pOUl0zP+bOzxZmlN1zhnBsMPE5s29jXzOyFuGvHAj8ktqjtNDOr7+p+1dXVtmLFim4FLSIixaul6QF277yO2HC5N7DrKvD7RlI1/B58/uGFDK/gnHMrzay6q3ZJe+DOuTUJ3i4FjgSWO+dage3ACPYWod0IvAgclHHEIiLSL5QNOIeS0mNobvo/2lpfwPkqKCufR0nZHJxTgdB0pfqTGkDi1QSb4/7bR8c5b5/3ORERkaR8/v2oGPSV2LitdEvSBG5mY/IZiIiIiKSv9+9UFxERkX0ogYuIiBQhrRYQEZFexyzKhobHeaPuz+xu20jQV8GUyvM4qGoepf7BhQ6vV1ACFxHpA5raNrCxfjFN4fWUBUYzZtA8BpZMLXRY3WIW5aktX2dL0/OEzTutLNrA63W38/auuzlr7B+oCI4qcJSFpwQuIlLEzIy1dT/lvfo7MItghAE/m3YvYlTFWUwf/l2cS3+2tC60idW7HqMxXMvQkrEcMngu5YHK3P0ACbyz6342Nz1PxFo6vB+xENHITp7eeiMfHfv7vMbUGymBi4gUsc0N9/Be/Z+JWnyl6whRi7C18SHKA2OZOOQLXd7HLMqjW3/B6l2PYRYhSoSAK+Ff22/jtJFf5IghZ+Xuh+jkjbo/7ZO898RJlLrWd6gPvUdlybi8xdQbaRGbiEiRMjPW1f2aqDUnvB61FjbU/4GotXV5r2Xb/8Sbux6P9XK9MqdhCxGxEE9s+x3vNryY1diTMYvSEN6cso3DT13rO3mJpzdTAhcRKVKtke2EoqnPjzKL0hBKnezaoq2srP07YUt8XlXYWnlm+x3djjMzDl+qw04A5xwBX+LTxPqTjBO4c+4Q59z3nHN3O+f+Gff+OOfchc65quyGKCIiiUXTbJf6zIstzW91OU/+Qcta2qJdHUjZc845xgw8EXBJ25hFGVk+I+ex9HYZJXDn3DeBV4D/BC4A5sZdDgKLgEuzFp2IiCRV6t+PgK+rWqRGRXByFy0iuBQJE8DhMEt0glj2HT708/hdacJrflfGIUMuJ+Dr/ed151raCdw5dxHwbWInklUDC+Kvm1kNsJLYkaIiIpJjzvmYMPgL+Fzi4WSfK2NM5afw+xInw3b7lU4mbKGUbQYGhxPM07D1kNJJnL7/Tyj1VRFwA/ARJODK8bsSplV9gsOGXpGXOHq7TFahXwfUAOeaWatz7twEbVYDJ2clMhER6dLYyk/REHqLLY0PELUQsWF1h9+VMaRsFpOHXNflPcoDlUwddAJrdj9DJMGCt4Ar5dhhn8C51L30bBo5YAbzJj7I5qbnqA+9R4lvEGMHnqgiLnEySeCHA7eZJVnlELMFGNmzkEREJF3OOaaP+C4HVF7M+/V/obltA6WBUYyt/CRVpTPTTrpzR19HbWgjta0baduzqt0RdKUcXHkyh1d9NHc/RBI+52dMxfFQcXzen10MMkngjq5XTIxgz+nsIiKSL4NLD2XwiB90+/MlvnIuGf9z1u5+lpfq/kFTZCdDS8Yyc+gFHFB+SF5735KeTBL4WmB2sosutoTxBGLD6CIiUmR8zs/UyhOZWnlioUORNGSyCv1vwEznXLIJleuBKcBfexyViIiIpJRJD/xnwEXAT7wV6Q7AOfdD4ERgFvAisDDbQYqIiEhHaSdwM2tyzp0C/Ar4BHt779cTqxJwJ/BFszRq9omIiEiPZHSYiZntBC51zn0FOAYYBuwCnjezrTmIT0RERBLo1mlkZrYdeDDLsYiIiEiadJyoiEg/VBvaxit1y2iI7GJk6RiOqDqBUv++ldbMjJZoCwHnJ+grKUCkkkzaCdw5d3OaTc3Mru5mPCIikkNRi3DvpltYVfcUUTOihClxpfxj8x+ZN+ZajhhyvNcuymMfLOGfWx+mIbwbM2PiwElceMA8pg46qMA/hQA4s9Sn1Oxp6FxXRVyM2Mp0M7PUZ8EB1dXVtmLFirSeLSIi2fHwlj/z7I6HaUtQVDPoSvjcxG8wbsBB/KbmV6ze9TqhTjXSg66Ez034PNVDj8lXyP2Oc26lmVV31S6TfeBTkvw6GriGWBnVu4CpGUcrIiI51xpp5tkdDyVM3gBtFuLRrXfxys6XeLP+jX2Sd3ubP66/ldbI3ntELUpzpIWopXu8qWRDJtvIalJcXumcexh4FXiE2KEnIiLSi6xveguf86c8HvzdxjeoC5XQ2sXZ36t2rmDaoMO48/2HeOKD54hYBL/zc8qIY/nkuLOpKqnMcvTSWUbngadiZhuA+4AvZ+ueIiKSPZE0z/PeHtqW8nprtJX3mt7nKy/fxNKtzxKKthGxKKFoG49tW85XXv4Bta07sxGypJC1BO7ZhobQRUR6pQPKJxCxcMo2w0pGMTCQuvccdEFerltDfVsDETp+KYgQob6tkVvW/a3H8UpqWUvg3mEmpwL12bqniIhkz+DgMCYPPBx/ktnTElfKqft9nFNGnEpJii1jhrGuYTvRJAdURomysu4NGsNNWYlbEks7gTvnjkvy6yTn3CXAEuAo4P6cRSsiIj1y0dgvMbR0JCW+0rh3HSW+Uo6oOoEZQ05m9rDjGRocit/tu6GoxFfCsUOPx+8LpnxOwBdgh4bRcyqTQi7PkHLpAw54llhtdBER6YUGBAbxb1N+zKu7nuW5HY/QFGlgROkBnDjiHCZWxM79LvWX8vVp3+C29bfy2q7XCPoCmIFzcPbo8zh6yGwe3vJyyueEo2EGBSvy9FP1T5kk8B+QOIFHgTrgBTN7NitRiYhIzgR9Jcwccgozh5yStE1FoIJrJ/8bu9p2sbHpfYK+IBMrJhHwxdLGhIoxrGlYn/Tz4ysOYGjJ4CxHLvEy2UZ2Yy4DERGR3mdwcDCDB++biD83cT7ffP3ntEb33Ste4gvyuYnz93k/HI3w9PbX+Pv7z1Ab2s3+5cOYP/ZEjhl2MM65nMTfl6kWuoiIZGzqoPF885Br+eU7f6YutAu/8xGxKENKKvl/ky/joEETOrRvjrTylZW/Y0PTNpojsaS/uflDXt+1nuohU/nvwy/H77K9MapvUwIXEZFumV45md/M+BbrGzfyYWgXw0oGM75iTMLe9C/evpd1jVsIRTtuY2uJhHix9m0WbXiKT4w/NV+h9wlJE7hzbk0372lmpkr3IiL9gHOOCQPHMoGxSds0hJt5bNtL+yTvdq3RNu5870kuOvBkfOqFpy1VD3wAqVedi4hIL2JmPLjpZf5Q8xTvNX6I3+fjpP0O5qrJpzKlclTB4lrfsI2gCxAieRGZpkgrdaEGhpWqBGu6kiZwMxuTz0BERKT7zIwbX1nME9tW0xJpAyAajfDE1tUs276Gn828lGOGTypIbAGfH+uiPxg1I+jTrG4mNFYhItIHPP3BWzwZl7zbRTFaIm1c/9KdtEXTq4WebZMH7p+wKEy8cQP2ozI4IE8R9Q1K4CIifcAd656huVPyjheORnj6g7fyGNFeAZ+fyyecTlmS6m2lviCfn/zRPEdV/DIer3DOBYGZwAFAaaI2ZvaXHsYlIiIZWN+4I+X11kgbGxpSt8mleWNPoi7UwKL3/wVmtFmEUl8Qw7hmyrkcN3x6wWIrVhklcOfc5cCPgeHJmhBb+KYELiKSRwMDpdSFGpNeD/oCDAyW5TGijpxzXDX5bC4cewKPbX2JHa31HDBgGKePPIpBGjrvlrQTuHPuDOCPwNvA/wALiB1csgI4BTgNWAw8kvUoRUQkpfPHzuTmd56gNclWrSjG6aMOyXNU+xpeOpiLDzyl0GH0CZn0wL9KrOb5LDOrd84tAFaZ2feA7znnrgZ+CfwsB3GKiEgK88Ydw53rn6M21EjEOh7zWeYPcuHYoxlWOrBA0fVMbUsTi9a9wmu1WxlcUsYFEw5l5vDEBWPamRmPbH6T3771L9bWbyfg83HyqClce/BJHDR4ZB6jzx1nlt5Wb+fch8B9ZvZZ73UU+I6ZfTuuzdNAg5md1dX9qqurbcWKFd0KWkRE9rWteRc3vHQnb9dvIeCLrfoOR6NcMmE210ydU5RFUv6x/g2uf/5BHNASCeOA8kCQw4aO5tZTLmJAYN9zy82M77zyMPdseKXDwj4fjlJ/gF/PuojjRxZmS106nHMrzay6q3aZ9MArgC1xr1uBQZ3avAB8JoN7iohIlowsH8xtx13N+obtvF2/hVJ/kGOGTWRAIOF6417vlQ83c8PzD9Ia2TstYEBTuI2Xd2ziq8v/wW9O/Pg+n3tu+7v7JG+ITSM0R9r4t+cXs/zsf6fEX9z7zjOJfiswIu71ZqBzydTKDO8pIiJZNn7gCMYPHNF1w17u168v65C847VGIzy+aS1bmuoZPaBj9bY/vPNcyi11hvHo5rc4Z+yhWY033zIZT1lNx4S9DDjdOTcbwDk3DbjIayciItIjy7auT1m/LeDzsXzrhn3ef6f+g5T3bQyHqKnf3sPoCi+TBP4wcLxzbrT3+kfERjOecc5tAV4j1gP/fnZDFBGR/qir8qtYbFi8s4HB1FMGJT5/l22KQSYJfCFwIFALYGavA3OBJUAD8ARwjpk9kO0gRUSk/5kxPPWRHBEzqkfs22b++BmU+RNXfYtxnDmm+AvHpD1fbWYhYFOn95YBZ2Y7KBERiXltx1Zufu0FXtmxlYpgCfOnHMb8KYcyqKT4e5Bd+dKhx/PSjk0J57ODPj8zR4xh/KCh+1z7+IFHcuuaZ2mLRhJuqTtrzCEcMKAqZ3HnSybbyCrNrD5bD9Y2MhGR1H758rP86pXnCEUjRL1/q8v9AQaWlHLPOZcydtDgAkeYe7e++QL/8+qTRKJRwl4yrgiUsP+ASu6aexlVpeUJP7etuZ5/e34xb+3aig8fzsW21F00fgZfO/wMAr7eu6Uu3W1kmSTwRuBe4HZgiaX7wSSUwEVEklu+5T0+s2QxzeF9V2H7nGNK1TAeveCzBYgs/9bVf8gda1bsKeQyf+IRzBkzhaAv9QlnADW7d/Ba3WZKfQGO228Cg0sSJ/zeJBf7wDcBnwQ+AWxxzv0JuMPM3uxmjCIiksSvX1meMHlD7Ozs93bv4pXtWzhixOiEbfqSiZXD+Hb1R7r12UmDhjNpULLjOzJT19LMG9s/wO/zcdTIUZQFUs2z514mc+BTvS1jVwDzgRuA651zK4j1yv9qZnU5iVJEpJ95ZcfWlNej0Sirtm/uFwm80BpDIf7rqaU8XLOGEn+s1x8x43NHzOQrxxyHL0VJ11zKaBLAzJab2dXAaGK98UeAGcCvgM3OucXOuXOyH6aISP8S6KLsqXOOkjSGkKVnwtEon7z3bzxcs4bWSITdoRC7QyGa2tr4/csr+K8nlxQstm7N4ptZq5nd5dU8HwNcD6wBLiQ2Ty4iIj3wkQOn4E91WAfGKWMm5jGi/unRdWtZu7OW1khkn2vN4TB/f3s1G3btLEBk3Uzg8cxsG7ASeAloI3YmuIiI9MDVhx2TtFZ3qd/PnLGTOWBgZcLrkj1/fv1lmtqSl2WNmHH3W2/kMaK9up3AnXOTnXPfdc6tB5YClxXuKlAAACAASURBVAMbge9kKTYRkX5rwuCh3Db34wwKllLhLZbyO0eZP8AJ+4/nJyd1eeijZMH2pqaU18PRKNubGvMUTUcZHTzinBsMXAx8GphFrLfdQGwR221m9nTWIxQR6admjR7Hyk9dy8Pr1/Dajq0MKinlrPEHMXVIdlZVS9cmDRnC2roPkxZ1LQsEmDxkWF5japd2AnfO3QWcC7SX/3kSuA2428xSf0UREZFuKfUHOH/SdM6fVPylP4vRZ4+YyVPvbaA5nHgY3cy48ODC/N1kMoQ+n9he8G8BE8zsdDP7k5K3dLatoYEXN25kzY4d9LDej4hIQR09+gDOnXIQ5YF9+7tlgQDfOuFUhpQVpjhMJkPoJ3q1z0US2lRfz9cfeZQXN22i1O8nHI0yvKKCb512KqdO1GpZESk+zjkWnHoGR40cza9WPs+2xgbMjOnD9+Pfjz2eUw6cULjYCtVDUinVvuWDhgbOvuNP7GppIdLp/6myQICfn302cyZPKlB0IiI9Z2Y0trUR8LmcVmFLt5Rq763mLkXlV889T31r6z7JG6AlHObGJUv2HMYgIlKMnHMMLCkpeAnVdkrgkhV/X72acDSa9HpTuI2V/397dx7nRH0/fvz13iR7cSgIcgseqAioHCoqCkqp900p3kcrHvWobe23WtuirW3VCrWXZ621/hQFtYKiqBxSVFAUlYpHQRG5FHA5dzfZJO/fHzOBISTZZDfn8n4+HvNIMvP5zLwzm807M/OZz2flyqTLjTHGZMYSuGm2SDRKXYqODgAEYX0j91MaY4xJnyVw02y+sjJ2q6hIWSaqSte21muUMcZkiyVwkxXnH3rItlF6EulQXU3/Tp3yGJExxrRslsBNVow97DC6tmmTMIlX+f3cffJJSIGG3DPGmJYo4wQuIn4R+ZaIXCsiN3nml4tIe7Fv6V1Sm4oKnj3/PL7bvx9VgQCVfj+BsjKG9tyLp84dw8CuXQsdojHGtCgZ3QcuIt8CHga64fSDrqrqc5cdCcwFzlfViY2ty+4Db7lCkQg1dXW0Ki+ndXl5ocMxxpiSkvX7wEVkIPA8Tu9tNwI7JGlVfRNYBpyVUaSmxSn3+ejUurUlb2OMyaFMulL9JVAHDFbVVSLyqwRl3gYGZCUykzMNkQjzln1JTW0de7XbnUO6dbbr08YYU2IySeBDgWdVdVWKMssBG6S2iP37g8X85uXZRKIKKKrQvlUVE846mUO6dSl0eMYYY9KUSQJvDaxtpEwVu2DL9uXrN/DSok/YVB+kd6cOfLtvb6rKi6OrPa+p//2YX06bQX04vMP82g0NXPTYZCZdei7772njDBtjTCnIJIGvBPo2UuZQ4POmh9M86zfX8o/ZC/j32x+ypT5Ix7atueCYAYw56hAqApm81fSEI1F+/sx0Xv7wf0SiSjgapao8wK+nzuTu757CsAMKN0pNvEg0ym9fnr1T8o6pbwgzYfbr3Dv6jDxHZowxpikyOVqeDpzotjbfiYh8Gzgap6Fb3q2u2cTZdz/K43MXsrG2nkhUWbNhM39+6Q0u+uuT1DckTlzN8dsXZvHK4iUEw5Ft/YDXhRqoDTVww8Tn+Wj111nfZlMtWvVV0uQNoMBrS5bREInkLyhjjDFNlkkC/y2wEXhVRG4HDgQQkRPc108DXwHjsx5lGn4+cTobttbTENlxQI1gQ5ila9bz4Kvzs7q9DbV1PPPuh0l/GITCEe6bld1tNsfmYJCyNBqqBVMkeWOMMcUj7QSuqiuAE3CS9E3AaJx7wae5r9cCJ6lqY9fJs251zSY++GJ10uEqg+EIT7zxPtFo9oazfHPpcgIpug6NqjLn02VZ215z9WrfjlAjR9fV5QGq7dYvY4wpCRldGFbVBSKyP3AGMATYA+eofB5OC/VQ9kNs3Odrawj4fQTDyRNUfaiBLcEgbasqs7LNhkiUxjrBiaQYXjPferTbjb6dO/HeysQ/dCr8Pi4YfEhaR+nGGGMKL+0ELiJdgQb3CPtpdyoKrSvKkx59x0RVqcxiQ7b+3To1mqD371RcLbrvOuNERj38OJuDoR3G7q70+9m3Q3uuOPrwAkZnjDEmE5lcA/8SuDNXgTRHvx6dUyZnAY7avyfl/uwl8L07tqdv1074yxIfsVYF/FwxvLgSYo92uzF17IWcO+hgWleUUybCnq1bcc2xQ3ji4u9SFSi+W9+MMcYklnZf6CKyHnhIVf8vGxvOdl/oL7zzEeMmv5qwUVllwM9j147hgK4ds7Y9gLWbt3Lu/ROp2VpLnbtdcbf3ncP6838nDbMezowxxmQk3b7QMzkknU8Rd5N6yqA+hCIR7pzyGqpKVJ1k2qaqgrsuODnryRugY5tWTLnuIqa+9xGT3l7E5vog+3fuwCVHD2JQr25Z354xxhgTk8kR+BHAHOAKVX2kuRvO1WhkDeEIb/5vORu21tGtfVsG7t3NjoKNMcaUjFwcgY8AZgJ/F5ErcQYuWYPTB4iXqurvMlhvVgX8Po7tUzw9oBljjDG5kEkC/43n+eHulIgCBUvgxhhjzK4gkwQ+MmdRmJwKNoSZ9/EXbNxaT88923Hw3l3ssoIxxpS4tBO4qs7IZSAmN578z/vc8+//IAgaGz60TRV3XnYKfXt2LnR4xhhjmij7Q3S1QOFIlP+8v5TZC5cSjUY5om8vRh62f05GOMumyXM/YMKzc6gP7Xhr3cr1DVz+p8n8vxvPY+/O7QsUnTHGmOYo7gxUBFat28jYO55i09Z6aoMNALy2cCkTJs7mbz8ZxQF77VngCBNriES457m5OyXvmPpQmHunvcmdl52S58iMMcZkQ9o9sYlIg4iE0piCuQw4n8KRKGPvfIqva7ZsS94AtcEGNm6t58q7JrGltjjf7sIlK1N2LxtVZeb7Sxrtz90YY0xxyrQjl0Tf9rsD+wEVwCJgUxbiKgpz3/+MTVvrkybChnCEqa9/yLkjB+Y5ssZtqQ/RWDO1SDRKOBpNOaqaMcaY4pRJI7ahyZaJSFvgT8Bg4LQsxFUUXntvKbX1DUmX14fCvLrg06JM4Pt0br/T2Ojx2reutuRtjDElKpPBTJJS1U3A93CO0G/PxjqLQaSRBAg0OgpaKsFQmHCKIVCbo1en9uzbZY+kw4NWBPxccHzx/fAwxhiTnqwkcABVjQCzgLOytc5CO7J/L6orko/QVRHwMbR/Zr2+qSrTZixizJUP8u0xf2TE6Alc+dPHWPD+F80Ndye/v/RkWldV4Pft+GeuDPjp02NPLjjOErgxxpSqrCVwVznQLsvrLJgRg3pTHkh+irmsrIyzhh2c9vpUlT/c+woTHniVlWs2EI0q0ajy4aer+dntz/DCq4uyEfY2e3Xcnck3X8h3hh5Mm6oKfGVCl/Ztue6MoTx43SgCfjt9bowxpSrtwUwaXZFIb2AesFpV+zVWPleDmWTb/75cy9g7n6IhHNl2S1ZFwE9ZmTDhujMYfOBeaa/rg8Ur+PGtk6kPJr6uXl7u59m/X0nbNlVZid0YY0zpyfpgJiLyQIp19ACOdZ9nZbzwYtG7R0em3vl9pr3xEa8u+IRIVDm6/96cNaw/7dpUZ7SuSVPfIRhK3ihOgJdfW8yoUwc1M2pjjDEtXSa3kX2/keVLgLtU9aFmxFOUWldVMHrEoYwecWiz1rNsxXpSnfAIhsJ8vnx9s7ZhjDFm15BJAu+dZH4UqFHVDVmIp0Xbfbdq+DJ5gvb5ymjfrlUeIzLGGFOqMrkPfGkuA9kVnHnioXyyZA11Se4t9/nKOGH4QXmOyhhjTCnKpCvVB0Tk1EbKnJziWvkub9iQ3nTv0o5AgpbtlRV+Rh7Th+5d8teI//MV65k+9yNmvfVp0XYJa4wxJrFMr4GvAJ5PUWYATocuY5sTVEvl9/v48+1juPOv05n71pJtiTwSiXLOKQO5/Pxj8hLHmnWbuPmPU/lsxTp8ZQIiRMJRRp0wgKvHHENZmY0VbowxxS7bo5GVA7npWqyFaFVdwa03nk7Nhq18svQr/P4y+h3YjcoUHcZk06Yt9Xz/F49Ts7mWaHTHFnVPv7KQUEOYH118fF5iMcYY03SZduSStA21iASAY4CvmhXRLqLd7q0YMmgfBh/SK2/JG+DZGe+zpbZ+p+QNUB8M89zMD1i/YWve4jHGGNM0KY/AReTTuFnXi8iFCYr6gD2BasCugRexqbMWEWxIfpJERJj99v84Z2TzbpkzxhiTW42dQq9m+1G3AgEgUTdhEeBTYAZwa9aiM1mXanQ1cIZItQZtxhhT/FImcFXtHnsuIlHgblW9LedRmZzp2bU9NZtqky6vLPfTq2v7PEZkjDGmKTK5Bj4S+FeuAjH5cf6pg6lKcc3d7/Nx9MB98xiRMcaYpsikI5cZuQykJQrWNzBvxmK++XoTHbrsxhHH9aE8jw3WEjl6wD4cd0RvZs7/lPpgeNv8MhHKy3387ken7zT8qDHGmOKT8W1kbmvzQUA3oCJRGVV9vJlxlbyXn36be389BRGhIRQmUO7s6ut+fTbDT81PA7E1X21kydKvKS/3cXC/HlRWBhARbrniRA7v14t/Pjef5au/we/3ceygfbnkrCHs071DXmIzxhjTPBkNJyoiFwF3Acm+5QVQVW10oOlSGU60KV5/eRF3/eRJggkajFVUBrjlLxcy+NgDdlq2emUNy5evp1V1BX36dsPnb9qRcE3NVm6/43kWLfpye2cxUWX0qMO5+IKjraMWY4wpYrkYTvTbwD+AT4A/AHcAU4AFwHDgeGAyML0J8bYYqspDv38hYfIG57T6Q3e8sEMCX71qA7+/7d8s+fQrAgEfqorf72PsD0ZwwimHZLT9uroQV1/3L9au20wkEiXkuWXsyUnzqd0a5AdXjWjamzPGGFM0MjnE+wlQAwxR1bvcee+q6m9U9VvAVcCZwEdZjrGkrF6+npp1W1KWWbV8Peu/2gTAN+u3cM3lD/Px4lWEQmG2bg1SWxti06Y6/jz+JV54bmFG239x+iI2bNhKJBLdaVkwGOa55xeyfn3q+IwxxhS/TBL4IGCKqm5KVF9V7wfmAbdkKbaSVF8bavTUt99XRn1dCICnHn+T2q3BhD2jBYNhHvjbq4RC4Z2WJTN12ns7NE6LJyLMnvNx2uszxhhTnDJJ4K2A1Z7XQaBNXJm3gCOaG1Qp69xjD8IpejoDUIWOXXYDYPq0DwiHdz5a3l4Y3n3787S3v2VzfcrloVCYLVtSlzHGGFP8Mknga4COntergPiWWG3J/gApJaW6dQXDTjmUQHnidnzlFX5Gjhq87XayutpQyvVFVdm0qS7t7XfvnroTlqqqAN26WUctxhhT6jJJ4IvZMWG/DowQkSMBRKQPMNott0u74pbT6LLXHlRU7njPd0VVgB777sllPz5p27wOHeNPYuxIgK7ddh4jfP3azcyc9gGvTH2PVV9+s23+6HMOo7Iy+b3mgnDs0P3TfCfGGGOKVSZHyy8CE0Ski6quBu4ERgFzReRrnKPzMuD27IdZWlq1ruSep6/l5clv89y/3mDj+i2069CGMy8ZysizB+3QmcvZow/n4ftnEUxy3bpN2yr69t/Woy3B+gbG3/ocr89cjN/vQ4FoOMpBh/bg53eMZsgR+3LUkfvxxptLqI9rCV9R4efnPzuN8vJd+iSJMca0CGnfBy4i5ThJep2qBt15RwO/APYFlgETVHVaOutryfeBZyIUCvOTax9j6ZKvCHmSuAhUVAS444/nc1C/btvm33z1oyx694sdygL4Az66dm/PvU9eRZmvjGkvvc/jT85nzZoNiAiDBvbi0ouG0ufArnl7b8YYYzKX7n3gGXXkkk2WwLcLBcM89cQ8np30Flu31APCkKN7c8n3h9Frn+3NDj75cCU3fv8fSe8xr6ou50fjzuTYkX23zQuHI5SVlVnnLcYYUyKy3pGLyZ3yCj8XXDKU8y8+mvr6BsoD/oS3os184X0aUtxSVlcb4sVnFuyQwP3+RjvFM8YYU4Ka0hd6X+BcoA/QSlVPdOfvBQwGZqrqhqxGuYsQEaqqypMu37SxLuH94l6bN9ktYsYYsyvIqLNtEfkl8D5wM3AWzhCjMQFgEnBB1qIzO9jvwC47tWz38vnK6N2nSx4jMsYYUyhpJ3ARGQ2MA2biHGnf4V2uqkuBd4DTsxif8Rh5+qFOLzBJ+P1lnHnukDxGZIwxplAyOQK/HlgKnKaq7wKJztUuBnpnIzCzs7a7VfPjW8+kojKAxLVJq6gMcN7YYfTcd8/CBGeMMSavMrkGfjDwSOwWsiRWA52aF5JJZdgJ/enUrR1PPDiHhfOXElXlgL7dOO/yYQw6cr9Ch2eMMSZPMkngAqTotBtw7hNPleBNFhzYrzu33nNeocMwxhhTQJmcQl8CHJlsoYiUAUOxrlSNMcaYnMskgT8FDBKR65Ms/ynO9e8nmh2VMcYYY1LK5BT6H3EGKxnvtkgXABH5PXAMMAR4G7g/20EaY4wxZkdpJ3BVrRWR4cBfgDFsP3r/KaDAROAqVU3cz6cxxhhjsiajntjcHtYuEJEbgMOBPYCNwHxVXZOD+IwxxhiTQJP6QlfVtcALWY7FGGOMMWlK2YhNRC4SkYPzFYwxxhhj0tPYEfgjON2nfhCbISIXAxer6vG5C8uUiq2b65gx6S1em/IukXCEgcMO5NSLjqF9p90KHZoxxrRoTTmF3gsYluU4TAla9vEqfnrOPTSEwtTXhgD4bPFKnrl/Fj9/4DIOO75vI2swxhjTVBmNRmZMTLghwk3f/QubN9RuS94ADcEwwboQt499mHWrbVRZY4zJFUvgpknefOkDgnWhpMujkSjP//M/eYzIGGN2LZbA86RuSz1TH5zJNceO43sDfsbvLr2XT975rNBhNdnCuZ9QtzV5t/cNoTALZlmvusYYkyvpXANPPgC1Scvald/wwxG/YcuGrQTd082rPv+aedPeY9QPT+LCm84scISZ8/ka/+2XThljjDFNk8437DgRicQm4JcA3nlxUzi3IZeeW8/9EzVfbdyWvAE0qgTrQky+50XenflhAaNrmiNG9qOqVUXS5eWVAYaeNiCPERljzK4lnQQuGU522OXx2aLlfPnJaqKRxCOxBmtDTLz7+W2v62uDvPXiQuY+O5/Vn3+VrzAzNnDYgbTbsy1lSY6yAwEfJ45JOnidMcaYZkp5Cl1VLRk308cLGr/O/em7n6OqPHrrJCb9YQo+v7Pbw6EwfYbsz82PX0/7zu1yHWpGysrKuGPSdfzfqD9Rs3bTtuvhVa0q8Ad83P7ED2jTrlWBozTGmJarSV2pmvT5Az6kTFKW8fl93H/jozx/3ysEa3dsGPbfuR9z7ZCbeXDReKrbVOUy1Ix16LI7D8y5hXdmf8QbL75PuCHCgGMO4JhTB1BeGSh0eMYY06JZAs+xQSP6EQlHki4v85Ux8PiDmPK36TTU7zyQWyQcYeO6Tbz08EzOvv6UXIbaJD5fGYeP6MvhI6zTFmOMySc7RZ5je3Rpx9AzBic9Ig2U++nWqyNlkvwoPVgb4vn7X85ViMYYY0qQJfA8uOGvlzHoW/0orwpsu75d2aqCqtaV/GridQQCPkIJjr69tmyozUeoxhhjSoSdQs+D8ooAv3r8Or74eCVznn2b2k117Nt/L4456zAqqsrZvHYjla0qqNtSn3Qd3ffvmseIjTHGFDtL4HnU88BuXHhTt53mH3nGYcjY+5LWq2xVwXd+fFouQzPGGFNi7BR6ESivCHDLxBuoqC4n/lJ4RXUFQ04bzJBTBxUmOGOMMUXJEniROOzEAYx/7TYOP3kgZb4yRKDLPp24esIl3PTYdUiKRm7GGGN2PaJamK7OBw8erAsWLCjItoudqhKNRvH5fIUOxRhjTJ6JyDuqOrixcnYEXoRExJK3McaYlCyBG2OMMSXIErgxxhhTggp2DVxE1gJfFGTj+dcBWFfoIArM9oHtA7B9ALYPwPYBpN4HPVW1Y2MrKFgC35WIyIJ0GiS0ZLYPbB+A7QOwfQC2DyA7+8BOoRtjjDElyBK4McYYU4IsgefHA4UOoAjYPrB9ALYPwPYB2D6ALOwDuwZujDHGlCA7AjfGGGNKkCVwY4wxpgRZAs8iETlRRD4RkSUi8rMEyytE5El3+XwR6ZX/KHMrjX3wIxFZLCIfiMgMEelZiDhzqbF94Cl3joioiLS422nS2QciMtr9LHwoIo/nO8ZcSuP/YC8RmSUiC93/hZMLEWcuicjDIvK1iPw3yXIRkT+5++gDERmY7xhzLY19cL773heJyBsickhGG1BVm7IwAT5gKbAPUA68DxwUV+Zq4D73+RjgyULHXYB9cBxQ7T6/alfcB265NsAcYB4wuNBxF+Bz0BtYCLRzX+9Z6Ljz/P4fAK5ynx8ELCt03DnYD8cCA4H/Jll+MvAiIMAQYH6hYy7APjjK8z9wUqb7wI7As+dwYImqfqaqIWAicEZcmTOAf7rPJwMjpGWNE9roPlDVWapa676cB3TPc4y5ls7nAODXwB1AfT6Dy5N09sHlwF9VtQZAVb/Oc4y5lM77V6Ct+3w3YFUe48sLVZ0DfJOiyBnAo+qYB+wuIl3yE11+NLYPVPWN2P8ATfg+tASePd2ALz2vV7jzEpZR1TCwEdgjL9HlRzr7wOt7OL/AW5JG94F7qrCHqr6Qz8DyKJ3Pwf7A/iLyuojME5ET8xZd7qXz/scBF4jICmAacG1+QisqmX5ftHQZfx/6cxSIMSmJyAXAYGBYoWPJJxEpA8YDlxQ4lELz45xGH45z1DFHRPqr6oaCRpU/5wKPqOrdInIk8C8R6aeq0UIHZvJPRI7DSeBDM6lnR+DZsxLo4Xnd3Z2XsIyI+HFOna3PS3T5kc4+QES+BfwcOF1Vg3mKLV8a2wdtgH7AbBFZhnPtb0oLa8iWzudgBTBFVRtU9XPgU5yE3hKk8/6/BzwFoKpvApU4g1vsStL6vmjpRORg4CHgDFXNKB9YAs+et4HeIrK3iJTjNFKbEldmCnCx+3wUMFPd1gstRKP7QEQGAPfjJO+WdN0zJuU+UNWNqtpBVXupai+c616nq+qCwoSbE+n8L/wb5+gbEemAc0r9s3wGmUPpvP/lwAgAEemDk8DX5jXKwpsCXOS2Rh8CbFTV1YUOKp9EZC/gGeBCVf000/p2Cj1LVDUsItcA03FaoT6sqh+KyG3AAlWdAvwd51TZEpyGDWMKF3H2pbkP7gJaA5Pc9nvLVfX0ggWdZWnugxYtzX0wHfi2iCwGIsCNmR59FKs03/+PgQdF5AacBm2XtLAf84jIEzg/0jq41/p/BQQAVPU+nGv/JwNLgFrg0sJEmjtp7INf4rSD+pv7fRjWDEYos65UjTHGmBJkp9CNMcaYEmQJ3BhjjClBlsCNMcaYEmQJ3BhjjClBlsCNMcaYEmQJ3Jgi4o5OpiIyPG5+L8+yXgUJzhhTVCyBG2OaTER2F5Fx7rR7oeMpBBE51P1h9WXjpY3JHuvIxZjS0AB84nleLHbH6ZwC4BFgV+nL3Cs20thzBY3C7HIsgRtTAlR1JXBgoeMwCVkCNwVhp9CNMaaJ3L6sB+AMDTy7sNGYXY0lcFOURKSHiNwpIu+JyEYRqRORpSLynIhcJCKVCer4ROQyEZkpIutEJCgiK0VkUnyjsCTbHO6WXenWXSciM0TkUhHxJakzzr3+Odt9fY6IvCwiX4tIVETGxZVvJyJ3ue+lXkRWu9sc1EhsSRuxuXGriKj7ej8ReVhEvnTfxwoReVBEEo61LCJlIjJCRP4kztjcK0QkJCLrReQ1EblSRAIJ6s0GPvfM+twT47Z9ElenXESuFpFZ7v4Nicga9+96Uor3XyUiPxGRN0WkRkQaRGStiCwWkX+KyDmp9l+C9d3rxrghWaNAEbnKLRMWkWOTrCp29P2iqja4f99at97oRmL4tVvuM3E7wjYmI6pqk01FNQEXAnU4gzwoEATW4Vz7jc07NK7ObsAsz/IwUANEPfPuSrHN8Z5yUbdu2DNvBtAmQb1x7vLZwN2e+t+49cd5yvYClsW9r42e56d7lg2P204vz7JeccuGe5YdB2x2n2+K22crgW4J3oN33erW3xA3bw5QFVfvGZwRtGJl1gJrPNMzceV7Av+N28/x27k3QXxtgPcS/H28721Zhp+xKk8sbwD+uOX92P4ZvDXFel51y3zXM+8Rd96rKer5cIZUVeDmQv/P2VSaU8EDsMkm7wScwvakOxdngPsyd1m5+/oB4KC4epM9ifBaoNqd3xlnFLjYF/2VCbZ5jWf5/UBnd34r4IeeRDExQd1xnqSnwO+Bju6yCqCn+9yHM8yk4iT378SSBnCQmyBrPHEMj9tOL8+yXnHLhnuWfYNzLfZAzz4bjZPMFXg0wXvoDjwGnAa098xvDVyCk/gVGJ+gbtK44sq1Aj5yy80ChgEV7rLdgBs8+/D6uLq3uPPXA2d76pUBXXF+8D3QhM9aP5xRsBS43TPfm9znAr4k9Xd3PxtBoK1n/hFs/6GxT5K6p7llGmKfN5tsynQqeAA22RSbcBpVfuZ+sf0HKE+zXuwLU4GxScrEEvxaoNIzv8pNDAo8nqTutZ71D4pbNs6z7O4UMY72lBuRYHk1zrCKzU3gM3F/8CR5D7XEHW2msX8Hu3W3ePddY3HFlfsF289UBJKUOcvzN/J75k9z59+Ug8/cle66I8Bx7rz73Hk1wF4p6p7nlpueYNlCd9nvktSd6i5/Ohf/SzbtGpNdAzfF5Dhgb/f5DaoaSrPed93HFcBDScr8wn3sAIz0zB8JtHefj0tS92/Aavf5eUnKRIE7UsQYG/v9dVWdEb9QVWuBO1PUT9dvVTWaYH6shXQV0DuTFarqAuBrnKPoQ5sY1/fcx/Gqmuw2uH/jnCnoAHjbBMRuTevSxG0npc6YzM/gHM0/JiJjgSvcxZer6vIU1VO1Pr/Xfbwkvv2A2xYhdr3//iYFbgzWiM0UbAVo2gAABnNJREFUl6PcxzVu0kjXYPdxVpLkhap+hHMq2Fve+/xLVf00Sd0IzpFtfF2vJar6dRoxzkxRJtWydM1PMn+V53n7+IVu47Ir3QZ4q9zGb9sapAF7ukW7ZxqQm7B6ui//7jZa22nC+ZHU2i3X07OK593Ha0TkCRE5U0Q6ZBpHCt8HluOcjo8l1IdUdXKyCiJSjpOEFZiSoMjjOJcEOuOcLve6DOeSyufAK82K3OzSLIGbYtLZffwiw3qx5LIyZSnnCN1bvrl1vVIl73S3syLFsrSo6uYk88Oel/FHhHsCC3COGkfiHOlGcRoOfuVOsR9GrZoQVlfP8w5ApxRT7Dup2hP748A9OMlyDPAssFZE/icif22sBX9jVLUG+IFn1mfA9Y1UOx6ncd07qrrT301Vt+C0KwAYG5svImVsPxvxoKpqU+M2xhK4KSal/GUWKXQAzTAB6I/TFuAyoIuqVqlqR1XtrKqd2X4E35Tbnby34PVRVUljesS7AlX9IXAAcDPwIs5p9f2Aq4EFIvLHJsTldbnneTd33amk03lL7DT6SM+tat/GObsQBv6RWYjG7MgSuCkma9zHnilL7Sx29NvY6d3Ycu/RcnPqZiJWL+G92Gksywn3+uzZ7strVPUfqromrowP58i5qbzry/Rvu42qLlHV36nqycAewJE4180BrheR05uyXhG5BucWvgiwGOfugYkiUp2kvLjlIUUCV9VFOLeoeY+6Yz8Unovfz8ZkyhK4KSZvuI+dRSTZteZEYtfLj3NPUe5ERA5ke4J8O0Hd7iKyf5K6PpwGdvF1M7EtxhRljm/iupujIxDrFGdhkjJDPWXiedscJDw6V9VlbL90EH89uElUNaqq84BRONevYcfGiWkRkf7AXe7L24CTcY7u++CcmUjkMJzLAp+5STqV2FH4ZW5bgNj7fyDTWI2JZwncFJNZONcfASa4DYXSMdF97IbTICmR29zHdTidb8S8gnPqGJK3Qr+C7ddxn0gzpnhPuo9DJUGvcCJSBdzYxHU3R+z+cIBD4heKiB+4vZH6MalGI3vQffyeiAxIFZCItI97XZGsrNvAMHa3QsIGjCm2U4Xz2anEud/7dlX9gu3XrMcm6eEtk77PJ+F8vrriNGwLYI3XTJZYAjdFw/0yjnWqMhSYISJDY0fVbkvp4SLymIgc5Kn3FvC0+/LPInJN7PSniHQWkQdxOk4B+IWq1nvq1rE9cZ8rIveJSCe3brWIXAfErq8+qarvNPHtPQ28G3suTperPnc7fXCu63Zs4rqbzG1s9br7cryIHO/Z3/1w7sEeDGxNUn8D24+uL3UTfiJ3A4twkuUs92+0R2yhOMOSniQij+L0AeA1X5xuXoeLSCtPna4i8me2X6+elubbjpmA04nOBuB89/OHqk7C6fwH4EER6RFXL+0ErqpBnJ7ZAGLdsVrjNZMdhb4R3Sab4ifgIqCe7R2E1JNeV6qzPcsbcHola2pXqt/EbW8mjXSlmsb72gfndK/3fcW6Es1KV6qNbD/ZugfhdNLijSt2ZN6A09PZMvf1JQnWe0tc3eVu+Ylx5boCb8bt5xq2dycbm/4XV29Zgjpb4urs1EtcI/vibE/dUQmWV7O957g5uL2xAfu689aRpIe2BOvaz/M5tJ7XbMraZEfgpuio6qM4Q2f+EadRURinA5IvcBotXYjz5eqtsxEYgdNYaDbOPbitcRpQPY3Ty1bSU9Sq+iOca9BP49w21dpdxyycltkjNcktWhm8r89wOkIZj3MaVXAS3mTgKFVNdD9xzqlzVuFw4CmcxFSG896fcuP6VyOr+C3ObVcLcBJUd5zGap29hVR1Fc6ZlXNx7p1ejZMoy3GS9FScrmvjBw4ZgzPm+Ayc/VaOcyr6C5xLEyPcv19a3CPqWIc/f9cE93ur07HOuTg/rI7B+ZEC24++X1D3iL0xqroEpy93sMZrJotE1c7kGGNMOkRkDk5CP0dVn0mzTmfgS5yugk9Q1ZdzGKLZhdgRuDHGpMHt/e0onLMm0zOoeiVO8l6CNV4zWZSswYkxxpgdtQd+g9PtbsJGffHc2yF/7L4cr3bK02SRnUI3xpgsE5FlOB3CxNoBLASO0OQDuRiTMUvgxhiTZe4AMOA0onwJ+JmqflXAkEwLZAncGGOMKUHWiM0YY4wpQZbAjTHGmBJkCdwYY4wpQZbAjTHGmBJkCdwYY4wpQf8f5+iMYpa8N5cAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% plot the distributions\n", + "\n", + "pl.close(10)\n", + "pl.figure(10, (7, 7))\n", + "\n", + "pl.subplot(2, 1, 1)\n", + "\n", + "pl.scatter(ys, xs, c=phi, s=70)\n", + "pl.ylabel('Feature value a', fontsize=20)\n", + "pl.title('$\\mu=\\sum_i \\delta_{x_i,a_i}$', fontsize=25, usetex=True, y=1)\n", + "pl.xticks(())\n", + "pl.yticks(())\n", + "pl.subplot(2, 1, 2)\n", + "pl.scatter(yt, xt, c=phi2, s=70)\n", + "pl.xlabel('coordinates x/y', fontsize=25)\n", + "pl.ylabel('Feature value b', fontsize=20)\n", + "pl.title('$\\\\nu=\\sum_j \\delta_{y_j,b_j}$', fontsize=25, usetex=True, y=1)\n", + "pl.yticks(())\n", + "pl.tight_layout()\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create structure matrices and across-feature distance matrix\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% Structure matrices and across-features distance matrix\n", + "C1 = ot.dist(xs)\n", + "C2 = ot.dist(xt)\n", + "M = ot.dist(ys, yt)\n", + "w1 = ot.unif(C1.shape[0])\n", + "w2 = ot.unif(C2.shape[0])\n", + "Got = ot.emd([], [], M)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot matrices\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%%\n", + "cmap = 'Reds'\n", + "pl.close(10)\n", + "pl.figure(10, (5, 5))\n", + "fs = 15\n", + "l_x = [0, 5, 10, 15]\n", + "l_y = [0, 5, 10, 15, 20, 25]\n", + "gs = pl.GridSpec(5, 5)\n", + "\n", + "ax1 = pl.subplot(gs[3:, :2])\n", + "\n", + "pl.imshow(C1, cmap=cmap, interpolation='nearest')\n", + "pl.title(\"$C_1$\", fontsize=fs)\n", + "pl.xlabel(\"$k$\", fontsize=fs)\n", + "pl.ylabel(\"$i$\", fontsize=fs)\n", + "pl.xticks(l_x)\n", + "pl.yticks(l_x)\n", + "\n", + "ax2 = pl.subplot(gs[:3, 2:])\n", + "\n", + "pl.imshow(C2, cmap=cmap, interpolation='nearest')\n", + "pl.title(\"$C_2$\", fontsize=fs)\n", + "pl.ylabel(\"$l$\", fontsize=fs)\n", + "#pl.ylabel(\"$l$\",fontsize=fs)\n", + "pl.xticks(())\n", + "pl.yticks(l_y)\n", + "ax2.set_aspect('auto')\n", + "\n", + "ax3 = pl.subplot(gs[3:, 2:], sharex=ax2, sharey=ax1)\n", + "pl.imshow(M, cmap=cmap, interpolation='nearest')\n", + "pl.yticks(l_x)\n", + "pl.xticks(l_y)\n", + "pl.ylabel(\"$i$\", fontsize=fs)\n", + "pl.title(\"$M_{AB}$\", fontsize=fs)\n", + "pl.xlabel(\"$j$\", fontsize=fs)\n", + "pl.tight_layout()\n", + "ax3.set_aspect('auto')\n", + "pl.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Compute FGW/GW\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It. |Loss |Relative loss|Absolute loss\n", + "------------------------------------------------\n", + " 0|4.734462e+01|0.000000e+00|0.000000e+00\n", + " 1|2.508258e+01|8.875498e-01|2.226204e+01\n", + " 2|2.189329e+01|1.456747e-01|3.189297e+00\n", + " 3|2.189329e+01|0.000000e+00|0.000000e+00\n", + "Elapsed time : 0.005539894104003906 s\n", + "It. |Loss |Relative loss|Absolute loss\n", + "------------------------------------------------\n", + " 0|4.683978e+04|0.000000e+00|0.000000e+00\n", + " 1|3.860061e+04|2.134468e-01|8.239175e+03\n", + " 2|2.182948e+04|7.682787e-01|1.677113e+04\n", + " 3|2.182948e+04|0.000000e+00|0.000000e+00\n" + ] + } + ], + "source": [ + "#%% Computing FGW and GW\n", + "alpha = 1e-3\n", + "\n", + "ot.tic()\n", + "Gwg, logw = fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=alpha, verbose=True, log=True)\n", + "ot.toc()\n", + "\n", + "#%reload_ext WGW\n", + "Gg, log = gromov_wasserstein(C1, C2, p, q, loss_fun='square_loss', verbose=True, log=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Visualize transport matrices\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% visu OT matrix\n", + "cmap = 'Blues'\n", + "fs = 15\n", + "pl.figure(2, (13, 5))\n", + "pl.clf()\n", + "pl.subplot(1, 3, 1)\n", + "pl.imshow(Got, cmap=cmap, interpolation='nearest')\n", + "#pl.xlabel(\"$y$\",fontsize=fs)\n", + "pl.ylabel(\"$i$\", fontsize=fs)\n", + "pl.xticks(())\n", + "\n", + "pl.title('Wasserstein ($M$ only)')\n", + "\n", + "pl.subplot(1, 3, 2)\n", + "pl.imshow(Gg, cmap=cmap, interpolation='nearest')\n", + "pl.title('Gromov ($C_1,C_2$ only)')\n", + "pl.xticks(())\n", + "pl.subplot(1, 3, 3)\n", + "pl.imshow(Gwg, cmap=cmap, interpolation='nearest')\n", + "pl.title('FGW ($M+C_1,C_2$)')\n", + "\n", + "pl.xlabel(\"$j$\", fontsize=fs)\n", + "pl.ylabel(\"$i$\", fontsize=fs)\n", + "\n", + "pl.tight_layout()\n", + "pl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} -- cgit v1.2.3 From 5e7c6ab04be3dc2035ca2a7f9deab3bb3bfb8faa Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 25 Jun 2019 14:43:39 +0200 Subject: doc add examples unbalanced --- docs/cache_nbrun | 2 +- .../source/auto_examples/auto_examples_jupyter.zip | Bin 139016 -> 148147 bytes docs/source/auto_examples/auto_examples_python.zip | Bin 93470 -> 99229 bytes .../images/sphx_glr_plot_UOT_1D_001.png | Bin 0 -> 21239 bytes .../images/sphx_glr_plot_UOT_1D_002.png | Bin 0 -> 22051 bytes .../images/sphx_glr_plot_UOT_1D_006.png | Bin 0 -> 21288 bytes .../images/sphx_glr_plot_UOT_barycenter_1D_001.png | Bin 0 -> 22177 bytes .../images/sphx_glr_plot_UOT_barycenter_1D_003.png | Bin 0 -> 42539 bytes 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docs/source/auto_examples/images/sphx_glr_plot_UOT_1D_006.png create mode 100644 docs/source/auto_examples/images/sphx_glr_plot_UOT_barycenter_1D_001.png create mode 100644 docs/source/auto_examples/images/sphx_glr_plot_UOT_barycenter_1D_003.png create mode 100644 docs/source/auto_examples/images/sphx_glr_plot_UOT_barycenter_1D_005.png create mode 100644 docs/source/auto_examples/images/sphx_glr_plot_UOT_barycenter_1D_006.png create mode 100644 docs/source/auto_examples/images/thumb/sphx_glr_plot_UOT_1D_thumb.png create mode 100644 docs/source/auto_examples/images/thumb/sphx_glr_plot_UOT_barycenter_1D_thumb.png create mode 100644 docs/source/auto_examples/plot_UOT_1D.ipynb create mode 100644 docs/source/auto_examples/plot_UOT_1D.py create mode 100644 docs/source/auto_examples/plot_UOT_1D.rst create mode 100644 docs/source/auto_examples/plot_UOT_barycenter_1D.ipynb create mode 100644 docs/source/auto_examples/plot_UOT_barycenter_1D.py create mode 100644 docs/source/auto_examples/plot_UOT_barycenter_1D.rst diff --git a/docs/cache_nbrun b/docs/cache_nbrun index 04f6fce..8a95023 100644 --- a/docs/cache_nbrun +++ b/docs/cache_nbrun @@ -1 +1 @@ -{"plot_otda_color_images.ipynb": "f804d5806c7ac1a0901e4542b1eaa77b", "plot_WDA.ipynb": "27f8de4c6d7db46497076523673eedfb", "plot_OT_L1_vs_L2.ipynb": "5d565b8aaf03be4309eba731127851dc", "plot_otda_semi_supervised.ipynb": "f6dfb02ba2bbd939408ffcd22a3b007c", "plot_fgw.ipynb": "2ba3e100e92ecf4dfbeb605de20b40ab", "plot_otda_d2.ipynb": "e6feae588103f2a8fab942e5f4eff483", "plot_compute_emd.ipynb": "f5cd71cad882ec157dc8222721e9820c", "plot_barycenter_fgw.ipynb": "e14100dd276bff3ffdfdf176f1b6b070", "plot_convolutional_barycenter.ipynb": "a72bb3716a1baaffd81ae267a673f9b6", "plot_optim_OTreg.ipynb": "481801bb0d133ef350a65179cf8f739a", "plot_barycenter_lp_vs_entropic.ipynb": "51833e8c76aaedeba9599ac7a30eb357", "plot_OT_1D_smooth.ipynb": "3a059103652225a0c78ea53895cf79e5", "plot_barycenter_1D.ipynb": 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/dev/null and b/docs/source/auto_examples/images/sphx_glr_plot_UOT_barycenter_1D_006.png differ diff --git a/docs/source/auto_examples/images/thumb/sphx_glr_plot_UOT_1D_thumb.png b/docs/source/auto_examples/images/thumb/sphx_glr_plot_UOT_1D_thumb.png new file mode 100644 index 0000000..1d048f2 Binary files /dev/null and b/docs/source/auto_examples/images/thumb/sphx_glr_plot_UOT_1D_thumb.png differ diff --git a/docs/source/auto_examples/images/thumb/sphx_glr_plot_UOT_barycenter_1D_thumb.png b/docs/source/auto_examples/images/thumb/sphx_glr_plot_UOT_barycenter_1D_thumb.png new file mode 100644 index 0000000..999f175 Binary files /dev/null and b/docs/source/auto_examples/images/thumb/sphx_glr_plot_UOT_barycenter_1D_thumb.png differ diff --git a/docs/source/auto_examples/index.rst b/docs/source/auto_examples/index.rst index 9f02da4..fe6702d 100644 --- a/docs/source/auto_examples/index.rst +++ b/docs/source/auto_examples/index.rst @@ -27,6 +27,26 @@ This is a gallery of all the POT example files. /auto_examples/plot_OT_1D +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_UOT_1D_thumb.png + + :ref:`sphx_glr_auto_examples_plot_UOT_1D.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_UOT_1D + .. raw:: html
@@ -287,6 +307,26 @@ This is a gallery of all the POT example files. /auto_examples/plot_otda_mapping_colors_images +.. raw:: html + +
+ +.. only:: html + + .. figure:: /auto_examples/images/thumb/sphx_glr_plot_UOT_barycenter_1D_thumb.png + + :ref:`sphx_glr_auto_examples_plot_UOT_barycenter_1D.py` + +.. raw:: html + +
+ + +.. toctree:: + :hidden: + + /auto_examples/plot_UOT_barycenter_1D + .. raw:: html
diff --git a/docs/source/auto_examples/plot_UOT_1D.ipynb b/docs/source/auto_examples/plot_UOT_1D.ipynb new file mode 100644 index 0000000..c695306 --- /dev/null +++ b/docs/source/auto_examples/plot_UOT_1D.ipynb @@ -0,0 +1,108 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# 1D Unbalanced optimal transport\n\n\nThis example illustrates the computation of Unbalanced Optimal transport\nusing a Kullback-Leibler relaxation.\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Hicham Janati \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\nimport ot.plot\nfrom ot.datasets import make_1D_gauss as gauss" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n-------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na = gauss(n, m=20, s=5) # m= mean, s= std\nb = gauss(n, m=60, s=10)\n\n# make distributions unbalanced\nb *= 5.\n\n# loss matrix\nM = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\nM /= M.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot distributions and loss matrix\n----------------------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\npl.plot(x, a, 'b', label='Source distribution')\npl.plot(x, b, 'r', label='Target distribution')\npl.legend()\n\n# plot distributions and loss matrix\n\npl.figure(2, figsize=(5, 5))\not.plot.plot1D_mat(a, b, M, 'Cost matrix M')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve Unbalanced Sinkhorn\n--------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Sinkhorn\n\nepsilon = 0.1 # entropy parameter\nalpha = 1. # Unbalanced KL relaxation parameter\nGs = ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, verbose=True)\n\npl.figure(4, figsize=(5, 5))\not.plot.plot1D_mat(a, b, Gs, 'UOT matrix Sinkhorn')\n\npl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_UOT_1D.py b/docs/source/auto_examples/plot_UOT_1D.py new file mode 100644 index 0000000..2ea8b05 --- /dev/null +++ b/docs/source/auto_examples/plot_UOT_1D.py @@ -0,0 +1,76 @@ +# -*- coding: utf-8 -*- +""" +=============================== +1D Unbalanced optimal transport +=============================== + +This example illustrates the computation of Unbalanced Optimal transport +using a Kullback-Leibler relaxation. +""" + +# Author: Hicham Janati +# +# License: MIT License + +import numpy as np +import matplotlib.pylab as pl +import ot +import ot.plot +from ot.datasets import make_1D_gauss as gauss + +############################################################################## +# Generate data +# ------------- + + +#%% parameters + +n = 100 # nb bins + +# bin positions +x = np.arange(n, dtype=np.float64) + +# Gaussian distributions +a = gauss(n, m=20, s=5) # m= mean, s= std +b = gauss(n, m=60, s=10) + +# make distributions unbalanced +b *= 5. + +# loss matrix +M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) +M /= M.max() + + +############################################################################## +# Plot distributions and loss matrix +# ---------------------------------- + +#%% plot the distributions + +pl.figure(1, figsize=(6.4, 3)) +pl.plot(x, a, 'b', label='Source distribution') +pl.plot(x, b, 'r', label='Target distribution') +pl.legend() + +# plot distributions and loss matrix + +pl.figure(2, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') + + +############################################################################## +# Solve Unbalanced Sinkhorn +# -------------- + + +# Sinkhorn + +epsilon = 0.1 # entropy parameter +alpha = 1. # Unbalanced KL relaxation parameter +Gs = ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, verbose=True) + +pl.figure(4, figsize=(5, 5)) +ot.plot.plot1D_mat(a, b, Gs, 'UOT matrix Sinkhorn') + +pl.show() diff --git a/docs/source/auto_examples/plot_UOT_1D.rst b/docs/source/auto_examples/plot_UOT_1D.rst new file mode 100644 index 0000000..8e618b4 --- /dev/null +++ b/docs/source/auto_examples/plot_UOT_1D.rst @@ -0,0 +1,173 @@ + + +.. _sphx_glr_auto_examples_plot_UOT_1D.py: + + +=============================== +1D Unbalanced optimal transport +=============================== + +This example illustrates the computation of Unbalanced Optimal transport +using a Kullback-Leibler relaxation. + + + +.. code-block:: python + + + # Author: Hicham Janati + # + # License: MIT License + + import numpy as np + import matplotlib.pylab as pl + import ot + import ot.plot + from ot.datasets import make_1D_gauss as gauss + + + + + + + +Generate data +------------- + + + +.. code-block:: python + + + + #%% parameters + + n = 100 # nb bins + + # bin positions + x = np.arange(n, dtype=np.float64) + + # Gaussian distributions + a = gauss(n, m=20, s=5) # m= mean, s= std + b = gauss(n, m=60, s=10) + + # make distributions unbalanced + b *= 5. + + # loss matrix + M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1))) + M /= M.max() + + + + + + + + +Plot distributions and loss matrix +---------------------------------- + + + +.. code-block:: python + + + #%% plot the distributions + + pl.figure(1, figsize=(6.4, 3)) + pl.plot(x, a, 'b', label='Source distribution') + pl.plot(x, b, 'r', label='Target distribution') + pl.legend() + + # plot distributions and loss matrix + + pl.figure(2, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, M, 'Cost matrix M') + + + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_UOT_1D_001.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_UOT_1D_002.png + :scale: 47 + + + + +Solve Unbalanced Sinkhorn +-------------- + + + +.. code-block:: python + + + + # Sinkhorn + + epsilon = 0.1 # entropy parameter + alpha = 1. # Unbalanced KL relaxation parameter + Gs = ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, verbose=True) + + pl.figure(4, figsize=(5, 5)) + ot.plot.plot1D_mat(a, b, Gs, 'UOT matrix Sinkhorn') + + pl.show() + + + +.. image:: /auto_examples/images/sphx_glr_plot_UOT_1D_006.png + :align: center + + +.. rst-class:: sphx-glr-script-out + + Out:: + + It. |Err + ------------------- + 0|1.838786e+00| + 10|1.242379e-01| + 20|2.581314e-03| + 30|5.674552e-05| + 40|1.252959e-06| + 50|2.768136e-08| + 60|6.116090e-10| + + +**Total running time of the script:** ( 0 minutes 0.259 seconds) + + + +.. only :: html + + .. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_UOT_1D.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_UOT_1D.ipynb ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ diff --git a/docs/source/auto_examples/plot_UOT_barycenter_1D.ipynb b/docs/source/auto_examples/plot_UOT_barycenter_1D.ipynb new file mode 100644 index 0000000..e59cdc2 --- /dev/null +++ b/docs/source/auto_examples/plot_UOT_barycenter_1D.ipynb @@ -0,0 +1,126 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n# 1D Wasserstein barycenter demo for Unbalanced distributions\n\n\nThis example illustrates the computation of regularized Wassersyein Barycenter\nas proposed in [10] for Unbalanced inputs.\n\n\n[10] Chizat, L., Peyr\u00e9, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816.\n\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Hicham Janati \n#\n# License: MIT License\n\nimport numpy as np\nimport matplotlib.pylab as pl\nimport ot\n# necessary for 3d plot even if not used\nfrom mpl_toolkits.mplot3d import Axes3D # noqa\nfrom matplotlib.collections import PolyCollection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n-------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# parameters\n\nn = 100 # nb bins\n\n# bin positions\nx = np.arange(n, dtype=np.float64)\n\n# Gaussian distributions\na1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\na2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n\n# make unbalanced dists\na2 *= 3.\n\n# creating matrix A containing all distributions\nA = np.vstack((a1, a2)).T\nn_distributions = A.shape[1]\n\n# loss matrix + normalization\nM = ot.utils.dist0(n)\nM /= M.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot data\n---------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# plot the distributions\n\npl.figure(1, figsize=(6.4, 3))\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\npl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Barycenter computation\n----------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# non weighted barycenter computation\n\nweight = 0.5 # 0<=weight<=1\nweights = np.array([1 - weight, weight])\n\n# l2bary\nbary_l2 = A.dot(weights)\n\n# wasserstein\nreg = 1e-3\nalpha = 1.\n\nbary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights)\n\npl.figure(2)\npl.clf()\npl.subplot(2, 1, 1)\nfor i in range(n_distributions):\n pl.plot(x, A[:, i])\npl.title('Distributions')\n\npl.subplot(2, 1, 2)\npl.plot(x, bary_l2, 'r', label='l2')\npl.plot(x, bary_wass, 'g', label='Wasserstein')\npl.legend()\npl.title('Barycenters')\npl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Barycentric interpolation\n-------------------------\n\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# barycenter interpolation\n\nn_weight = 11\nweight_list = np.linspace(0, 1, n_weight)\n\n\nB_l2 = np.zeros((n, n_weight))\n\nB_wass = np.copy(B_l2)\n\nfor i in range(0, n_weight):\n weight = weight_list[i]\n weights = np.array([1 - weight, weight])\n B_l2[:, i] = A.dot(weights)\n B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights)\n\n\n# plot interpolation\n\npl.figure(3)\n\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = weight_list\nfor i, z in enumerate(zs):\n ys = B_l2[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel(r'$\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with l2')\npl.tight_layout()\n\npl.figure(4)\ncmap = pl.cm.get_cmap('viridis')\nverts = []\nzs = weight_list\nfor i, z in enumerate(zs):\n ys = B_wass[:, i]\n verts.append(list(zip(x, ys)))\n\nax = pl.gcf().gca(projection='3d')\n\npoly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list])\npoly.set_alpha(0.7)\nax.add_collection3d(poly, zs=zs, zdir='y')\nax.set_xlabel('x')\nax.set_xlim3d(0, n)\nax.set_ylabel(r'$\\alpha$')\nax.set_ylim3d(0, 1)\nax.set_zlabel('')\nax.set_zlim3d(0, B_l2.max() * 1.01)\npl.title('Barycenter interpolation with Wasserstein')\npl.tight_layout()\n\npl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} \ No newline at end of file diff --git a/docs/source/auto_examples/plot_UOT_barycenter_1D.py b/docs/source/auto_examples/plot_UOT_barycenter_1D.py new file mode 100644 index 0000000..c8d9d3b --- /dev/null +++ b/docs/source/auto_examples/plot_UOT_barycenter_1D.py @@ -0,0 +1,164 @@ +# -*- coding: utf-8 -*- +""" +=========================================================== +1D Wasserstein barycenter demo for Unbalanced distributions +=========================================================== + +This example illustrates the computation of regularized Wassersyein Barycenter +as proposed in [10] for Unbalanced inputs. + + +[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + +""" + +# Author: Hicham Janati +# +# License: MIT License + +import numpy as np +import matplotlib.pylab as pl +import ot +# necessary for 3d plot even if not used +from mpl_toolkits.mplot3d import Axes3D # noqa +from matplotlib.collections import PolyCollection + +############################################################################## +# Generate data +# ------------- + +# parameters + +n = 100 # nb bins + +# bin positions +x = np.arange(n, dtype=np.float64) + +# Gaussian distributions +a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std +a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) + +# make unbalanced dists +a2 *= 3. + +# creating matrix A containing all distributions +A = np.vstack((a1, a2)).T +n_distributions = A.shape[1] + +# loss matrix + normalization +M = ot.utils.dist0(n) +M /= M.max() + +############################################################################## +# Plot data +# --------- + +# plot the distributions + +pl.figure(1, figsize=(6.4, 3)) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') +pl.tight_layout() + +############################################################################## +# Barycenter computation +# ---------------------- + +# non weighted barycenter computation + +weight = 0.5 # 0<=weight<=1 +weights = np.array([1 - weight, weight]) + +# l2bary +bary_l2 = A.dot(weights) + +# wasserstein +reg = 1e-3 +alpha = 1. + +bary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights) + +pl.figure(2) +pl.clf() +pl.subplot(2, 1, 1) +for i in range(n_distributions): + pl.plot(x, A[:, i]) +pl.title('Distributions') + +pl.subplot(2, 1, 2) +pl.plot(x, bary_l2, 'r', label='l2') +pl.plot(x, bary_wass, 'g', label='Wasserstein') +pl.legend() +pl.title('Barycenters') +pl.tight_layout() + +############################################################################## +# Barycentric interpolation +# ------------------------- + +# barycenter interpolation + +n_weight = 11 +weight_list = np.linspace(0, 1, n_weight) + + +B_l2 = np.zeros((n, n_weight)) + +B_wass = np.copy(B_l2) + +for i in range(0, n_weight): + weight = weight_list[i] + weights = np.array([1 - weight, weight]) + B_l2[:, i] = A.dot(weights) + B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights) + + +# plot interpolation + +pl.figure(3) + +cmap = pl.cm.get_cmap('viridis') +verts = [] +zs = weight_list +for i, z in enumerate(zs): + ys = B_l2[:, i] + verts.append(list(zip(x, ys))) + +ax = pl.gcf().gca(projection='3d') + +poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list]) +poly.set_alpha(0.7) +ax.add_collection3d(poly, zs=zs, zdir='y') +ax.set_xlabel('x') +ax.set_xlim3d(0, n) +ax.set_ylabel(r'$\alpha$') +ax.set_ylim3d(0, 1) +ax.set_zlabel('') +ax.set_zlim3d(0, B_l2.max() * 1.01) +pl.title('Barycenter interpolation with l2') +pl.tight_layout() + +pl.figure(4) +cmap = pl.cm.get_cmap('viridis') +verts = [] +zs = weight_list +for i, z in enumerate(zs): + ys = B_wass[:, i] + verts.append(list(zip(x, ys))) + +ax = pl.gcf().gca(projection='3d') + +poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list]) +poly.set_alpha(0.7) +ax.add_collection3d(poly, zs=zs, zdir='y') +ax.set_xlabel('x') +ax.set_xlim3d(0, n) +ax.set_ylabel(r'$\alpha$') +ax.set_ylim3d(0, 1) +ax.set_zlabel('') +ax.set_zlim3d(0, B_l2.max() * 1.01) +pl.title('Barycenter interpolation with Wasserstein') +pl.tight_layout() + +pl.show() diff --git a/docs/source/auto_examples/plot_UOT_barycenter_1D.rst b/docs/source/auto_examples/plot_UOT_barycenter_1D.rst new file mode 100644 index 0000000..ac17587 --- /dev/null +++ b/docs/source/auto_examples/plot_UOT_barycenter_1D.rst @@ -0,0 +1,261 @@ + + +.. _sphx_glr_auto_examples_plot_UOT_barycenter_1D.py: + + +=========================================================== +1D Wasserstein barycenter demo for Unbalanced distributions +=========================================================== + +This example illustrates the computation of regularized Wassersyein Barycenter +as proposed in [10] for Unbalanced inputs. + + +[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + + + + +.. code-block:: python + + + # Author: Hicham Janati + # + # License: MIT License + + import numpy as np + import matplotlib.pylab as pl + import ot + # necessary for 3d plot even if not used + from mpl_toolkits.mplot3d import Axes3D # noqa + from matplotlib.collections import PolyCollection + + + + + + + +Generate data +------------- + + + +.. code-block:: python + + + # parameters + + n = 100 # nb bins + + # bin positions + x = np.arange(n, dtype=np.float64) + + # Gaussian distributions + a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std + a2 = ot.datasets.make_1D_gauss(n, m=60, s=8) + + # make unbalanced dists + a2 *= 3. + + # creating matrix A containing all distributions + A = np.vstack((a1, a2)).T + n_distributions = A.shape[1] + + # loss matrix + normalization + M = ot.utils.dist0(n) + M /= M.max() + + + + + + + +Plot data +--------- + + + +.. code-block:: python + + + # plot the distributions + + pl.figure(1, figsize=(6.4, 3)) + for i in range(n_distributions): + pl.plot(x, A[:, i]) + pl.title('Distributions') + pl.tight_layout() + + + + +.. image:: /auto_examples/images/sphx_glr_plot_UOT_barycenter_1D_001.png + :align: center + + + + +Barycenter computation +---------------------- + + + +.. code-block:: python + + + # non weighted barycenter computation + + weight = 0.5 # 0<=weight<=1 + weights = np.array([1 - weight, weight]) + + # l2bary + bary_l2 = A.dot(weights) + + # wasserstein + reg = 1e-3 + alpha = 1. + + bary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights) + + pl.figure(2) + pl.clf() + pl.subplot(2, 1, 1) + for i in range(n_distributions): + pl.plot(x, A[:, i]) + pl.title('Distributions') + + pl.subplot(2, 1, 2) + pl.plot(x, bary_l2, 'r', label='l2') + pl.plot(x, bary_wass, 'g', label='Wasserstein') + pl.legend() + pl.title('Barycenters') + pl.tight_layout() + + + + +.. image:: /auto_examples/images/sphx_glr_plot_UOT_barycenter_1D_003.png + :align: center + + + + +Barycentric interpolation +------------------------- + + + +.. code-block:: python + + + # barycenter interpolation + + n_weight = 11 + weight_list = np.linspace(0, 1, n_weight) + + + B_l2 = np.zeros((n, n_weight)) + + B_wass = np.copy(B_l2) + + for i in range(0, n_weight): + weight = weight_list[i] + weights = np.array([1 - weight, weight]) + B_l2[:, i] = A.dot(weights) + B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights) + + + # plot interpolation + + pl.figure(3) + + cmap = pl.cm.get_cmap('viridis') + verts = [] + zs = weight_list + for i, z in enumerate(zs): + ys = B_l2[:, i] + verts.append(list(zip(x, ys))) + + ax = pl.gcf().gca(projection='3d') + + poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list]) + poly.set_alpha(0.7) + ax.add_collection3d(poly, zs=zs, zdir='y') + ax.set_xlabel('x') + ax.set_xlim3d(0, n) + ax.set_ylabel(r'$\alpha$') + ax.set_ylim3d(0, 1) + ax.set_zlabel('') + ax.set_zlim3d(0, B_l2.max() * 1.01) + pl.title('Barycenter interpolation with l2') + pl.tight_layout() + + pl.figure(4) + cmap = pl.cm.get_cmap('viridis') + verts = [] + zs = weight_list + for i, z in enumerate(zs): + ys = B_wass[:, i] + verts.append(list(zip(x, ys))) + + ax = pl.gcf().gca(projection='3d') + + poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list]) + poly.set_alpha(0.7) + ax.add_collection3d(poly, zs=zs, zdir='y') + ax.set_xlabel('x') + ax.set_xlim3d(0, n) + ax.set_ylabel(r'$\alpha$') + ax.set_ylim3d(0, 1) + ax.set_zlabel('') + ax.set_zlim3d(0, B_l2.max() * 1.01) + pl.title('Barycenter interpolation with Wasserstein') + pl.tight_layout() + + pl.show() + + + +.. rst-class:: sphx-glr-horizontal + + + * + + .. image:: /auto_examples/images/sphx_glr_plot_UOT_barycenter_1D_005.png + :scale: 47 + + * + + .. image:: /auto_examples/images/sphx_glr_plot_UOT_barycenter_1D_006.png + :scale: 47 + + + + +**Total running time of the script:** ( 0 minutes 0.344 seconds) + + + +.. only :: html + + .. container:: sphx-glr-footer + + + .. container:: sphx-glr-download + + :download:`Download Python source code: plot_UOT_barycenter_1D.py ` + + + + .. container:: sphx-glr-download + + :download:`Download Jupyter notebook: plot_UOT_barycenter_1D.ipynb ` + + +.. only:: html + + .. rst-class:: sphx-glr-signature + + `Gallery generated by Sphinx-Gallery `_ -- cgit v1.2.3 From dea6d8aec8794a84bf46ee7196b0c9fe390e6afa Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 25 Jun 2019 14:44:29 +0200 Subject: add notebooks unbalanced --- notebooks/plot_UOT_1D.ipynb | 210 +++++++++++++++++++++ notebooks/plot_UOT_barycenter_1D.ipynb | 336 +++++++++++++++++++++++++++++++++ 2 files changed, 546 insertions(+) create mode 100644 notebooks/plot_UOT_1D.ipynb create mode 100644 notebooks/plot_UOT_barycenter_1D.ipynb diff --git a/notebooks/plot_UOT_1D.ipynb b/notebooks/plot_UOT_1D.ipynb new file mode 100644 index 0000000..2354d4f --- /dev/null +++ b/notebooks/plot_UOT_1D.ipynb @@ -0,0 +1,210 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# 1D Unbalanced optimal transport\n", + "\n", + "\n", + "This example illustrates the computation of Unbalanced Optimal transport\n", + "using a Kullback-Leibler relaxation.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Hicham Janati \n", + "#\n", + "# License: MIT License\n", + "\n", + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot\n", + "import ot.plot\n", + "from ot.datasets import make_1D_gauss as gauss" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + "-------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "#%% parameters\n", + "\n", + "n = 100 # nb bins\n", + "\n", + "# bin positions\n", + "x = np.arange(n, dtype=np.float64)\n", + "\n", + "# Gaussian distributions\n", + "a = gauss(n, m=20, s=5) # m= mean, s= std\n", + "b = gauss(n, m=60, s=10)\n", + "\n", + "# make distributions unbalanced\n", + "b *= 5.\n", + "\n", + "# loss matrix\n", + "M = ot.dist(x.reshape((n, 1)), x.reshape((n, 1)))\n", + "M /= M.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot distributions and loss matrix\n", + "----------------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#%% plot the distributions\n", + "\n", + "pl.figure(1, figsize=(6.4, 3))\n", + "pl.plot(x, a, 'b', label='Source distribution')\n", + "pl.plot(x, b, 'r', label='Target distribution')\n", + "pl.legend()\n", + "\n", + "# plot distributions and loss matrix\n", + "\n", + "pl.figure(2, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, M, 'Cost matrix M')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Solve Unbalanced Sinkhorn\n", + "--------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "It. |Err \n", + "-------------------\n", + " 0|1.838786e+00|\n", + " 10|1.242379e-01|\n", + " 20|2.581314e-03|\n", + " 30|5.674552e-05|\n", + " 40|1.252959e-06|\n", + " 50|2.768136e-08|\n", + " 60|6.116090e-10|\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Sinkhorn\n", + "\n", + "epsilon = 0.1 # entropy parameter\n", + "alpha = 1. # Unbalanced KL relaxation parameter\n", + "Gs = ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, verbose=True)\n", + "\n", + "pl.figure(4, figsize=(5, 5))\n", + "ot.plot.plot1D_mat(a, b, Gs, 'UOT matrix Sinkhorn')\n", + "\n", + "pl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/notebooks/plot_UOT_barycenter_1D.ipynb b/notebooks/plot_UOT_barycenter_1D.ipynb new file mode 100644 index 0000000..43c8105 --- /dev/null +++ b/notebooks/plot_UOT_barycenter_1D.ipynb @@ -0,0 +1,336 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "\n", + "# 1D Wasserstein barycenter demo for Unbalanced distributions\n", + "\n", + "\n", + "This example illustrates the computation of regularized Wassersyein Barycenter\n", + "as proposed in [10] for Unbalanced inputs.\n", + "\n", + "\n", + "[10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816.\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Author: Hicham Janati \n", + "#\n", + "# License: MIT License\n", + "\n", + "import numpy as np\n", + "import matplotlib.pylab as pl\n", + "import ot\n", + "# necessary for 3d plot even if not used\n", + "from mpl_toolkits.mplot3d import Axes3D # noqa\n", + "from matplotlib.collections import PolyCollection" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Generate data\n", + "-------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# parameters\n", + "\n", + "n = 100 # nb bins\n", + "\n", + "# bin positions\n", + "x = np.arange(n, dtype=np.float64)\n", + "\n", + "# Gaussian distributions\n", + "a1 = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std\n", + "a2 = ot.datasets.make_1D_gauss(n, m=60, s=8)\n", + "\n", + "# make unbalanced dists\n", + "a2 *= 3.\n", + "\n", + "# creating matrix A containing all distributions\n", + "A = np.vstack((a1, a2)).T\n", + "n_distributions = A.shape[1]\n", + "\n", + "# loss matrix + normalization\n", + "M = ot.utils.dist0(n)\n", + "M /= M.max()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Plot data\n", + "---------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# plot the distributions\n", + "\n", + "pl.figure(1, figsize=(6.4, 3))\n", + "for i in range(n_distributions):\n", + " pl.plot(x, A[:, i])\n", + "pl.title('Distributions')\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Barycenter computation\n", + "----------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rflamary/PYTHON/POT/ot/unbalanced.py:501: RuntimeWarning: overflow encountered in square\n", + " np.sum((v - vprev) ** 2) / np.sum((v) ** 2)\n", + "/home/rflamary/PYTHON/POT/ot/unbalanced.py:501: RuntimeWarning: invalid value encountered in double_scalars\n", + " np.sum((v - vprev) ** 2) / np.sum((v) ** 2)\n" + ] + }, + { + "data": { + "image/png": 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oIPPD/E0MnbGOnpeU5+b6ZbO0r+jIcD7qWo+i+aO4+79z2H/keDZFaU5r4deweZ57KDcyr9fR+IcmvdzswWP6ugRuQoYlqCCybd8Rnv5hMXXKFuaxVlWyZZ+xBaN495babEk6zAs/Lc2WfZrTOHrA3XsqXRdq3uh1NP4jqgC0eAY2JcKfI72OxuQiS1BBQlV5/NuFHE1O4fWbEggPy74BReuUPY+7L72Q4YkbmLhsW7bt16Qz7S04sBVavQJh9qt5ilod3RBI45+BYwe9jsbkEvstCBLDZ29g8vId9Lu66jl1ijiT3i0qUqVkQR7/dhF7Dh7L9v2HvL3r3TM/NW+EMvW9jsb/hIVBq5dh/2aY9o7X0ZhcYgkqCGzYfYjnf1pCkwuL0rXRBTlyjKiIcF6/6SL2HDzGM6MX58gxQtr4ZwCBFgM8DsSPlW0E1a+HaW9D0kavozG5wBJUgFNVnvhu0cled2HZ2LSXXvVSMfRuXpHRCzYzbvHWHDtOyFkzFRaPgqa9ICbO62j825XPAgpjQ3B0jRBkCSrAjV6wmd9W7OSxVpWJOy/nRyO/+7ILqVKyIANGL+bgUZtULsuSj8JPfeC8eGjWx+to/F/hsnDJI7DkB/hrnNfRmByWpQQlIq1EZLmIrBSRvhmsjxKR4b71f4hIvG95vIgcFpH5vteHWYkjVCUdPs7zPy3lorgYOjfMmaa99CLDw3jxuhpsTjrCW7/+lSvHDGrT3oZdK6H169atPLOa9IJileCXh+HYIa+jMTnonBOUiIQDg4CrgWpAJxGplq5YD2CPqlYA3gReSbNulaom+F53n2scoezVMcvYffAoL15XM1t77Z1J3QuK0KlBWT6btpYlm/fl2nGDzq5Vbqbc6tdBxRZeRxM4IqKg7ZuuY8lUG6cvmGWlBtUAWKmqq1X1GPA10D5dmfbA5773I4Hm8m+jlZpMm7t+D8NmrefWJuWoUTom14//eKvKFM4byRPfLSIl1QaUPWuq8PPD7su25UCvowk88c0gobPr+bjdns8LVllJUKWBDWk+b/Qty7CMqiYDScCJsXfKicg8EZkiIhef7iAicpeIJIpI4o4dO7IQbvBITknlye/+pGShaB66qpInMRTOl4f+basyf8Nehs1a70kMAW3RN7B6ElzxFBQ63+toAtOVz7vJDH98EFJTvI7G5ACvOklsAcqqam3gIWCYiBTKqKCqfqyq9VS1XmxsbK4G6a8GT1vL0i37eOaa6hSI8m5Kr2sTStO0QlFeHbOM7fuPeBZHwNm3GX55FOLqu7mOzLnJX9TVPjfMhJnvex2NyQFZSVCbgLQT1cT5lmVYRkQigBhgl6oeVdVdAKo6B1gFeFMVCDCb9h7mzV//okXV4rSsXsLTWESE59vX4OjxVBsGKbNU4Yf7IeUYXPcRhIV7HVFgu6gjVGkLE56DbUu8jsZks6wkqNlARREpJyJ5gI7A6HRlRgPdfe87ABNVVUUk1tfJAhEpD1QEVmchlpAxYPRiVGFAu+r/OvlgbikfW4B7LrvQ193dmmDPKPEzWDUBrnzOzXdkskYE2r4FUYXgu56QbKOcBJNzTlC+e0r3A2OBpcAIVV0sIs+JSDtfsU+BoiKyEteUd6Ir+iXAQhGZj+s8cbeq7j7XWELFuMVbGb9kGw+2qJgrzzxl1j2XXUi5Yvl56vs/OXLc7gWc1q5VbiqN8pdD/Tu8jiZ4FIiFa96GrQutV1+QkUCa0rtevXqamJjodRieOHg0mSvfmELB6Eh+6tWMyHD/esb69xU76fLpH/S6ogIPXVXZ63D8T/IxGNIGdiyHe2dATPr+RCbLvrsHFg6H235xwyIZvyUic1S13pnK+de3nDmt/4xbzuakI7x4XQ2/S04AzSoWo31CKT6YsorlW/d7HY7/Gf8UbJwF17xpySmnXP2yG2nim1vhwHavozHZwP++6cw/zFqzmyHT19K98QXUi/ffKcCfbluNgtGRPPLNApJTbGK5kxaNhD8+hEb3Qo0bvI4meEXHwM1fwOG9MPJ2SLGhuAKdJSg/d/hYCo+NXEDceXmzbRLCnFK0QBTPt6/Bok1JfDTV+rwArmfZ6AegbGPXMcLkrJI14Zq3YO1vMOFZr6MxWWQJys+9NnY5a3cd4tUbLiK/h888ZVabWufTpub5vPXrX9bUd3gvDO/iHia9cQiER3odUWi4qKPrhDL9HVj8vdfRmCywBOXHZq3ZzeDpa+je+AIaX1j0zBv4iefaV6dgdCSPjgzhpr7jR+DrzrB3HXQYDAVLeh1RaGk5EOIauK7n62d6HY05R5ag/FTS4eM88k1gNO2ld6Kpb+HGJN6ZsMLrcHJfagqMuhPW/Q7XfgDxTb2OKPRE5IFOX0Gh0jDsZhuvL0BZgvJDqsqj3yxg897DvHVz7YBo2kuvTa3zuaFOHO9OWsnUv0LoAV5V+N9jsHQ0XPUi1LrJ64hCV/5i0HWUG5D3vzfYLLwByBKUH/r09zWMW7KNvldXoe4F53kdzjl74doaVCpekAeHz2dL0mGvw8l5qjB5IMz+BJo8AE3u9zoic148dPkWju53Scq6nwcUS1B+JnHtbgb+bxmtqpekR7NyXoeTJXnzhPN+lzocPZ7C/cPmcTyY70epulEiprzipoFoYT32/EbJmtBxGOxZB5+1gr0bzryN8QuWoPzI9v1HuH/YPOLOy8urN9byi7H2surC2AK80qEWc9bt4aVfgvQ+QGoK/NgLZrwH9e+Edu9BmP1q+ZVyF0O37+HgTpekdobgvdEAZL9FfiLp8HG6fTqLpMPHGXRLHQpFB0+X5La1SnFb03gGT1vLR1NWeR1O9jp+GL7tAXOHwiWPQuvXLDn5q7KN4NafIOWoS1Kb5nodkTkD+03yA4ePpdBjyGxW7TjAx93qejJDbk7r36YabWudz8D/LWP47CCZ4HD3Gvj0Klj8nZs874r+bnRt47/OrwW3jYHIvC5JzR3qdUTmX1iC8tix5FTu+XIOc9bv4a2ba3NxxeCclDE8THjjpgQuqRRLv1GLGPPnFq9Dypq/xsLHl7rnnG4ZAU17eR2RyaxiFeCuyXBBYzfKxw/3uZqw8TuWoDx06Fgy9w2by+TlO3jpupq0qRXcU3/niQjjwy51SChTmF5fzeenhZu9DunsHT0AY56AYTe5gUnvmgKVWnodlTlb+YtBl1Fw8SMw77/wyZWweZ7XUZl0LEF5ZGvSEW78cAYTlm7j2XbV6dSgrNch5Yp8eSIYfGsDasXFcP+webwzYQUBM+XLivHwfmOYOQjq9YAe46FIYPe0DGlh4dD8Keg0HA5uh/+7AsY+CccOeh2Z8bEE5YGFG/fS7r3fWbvzIJ90r0f3JvFeh5SrYvJF8uWdDbm+TmneGP8XDw6f798THe5cASO6w5cd3L2L28ZA2zfcexP4KreC+2ZBne6uJ+agRrDwG9c703jKJizMRceSU/m/31bzzoQVFCsQxae31qNKyUJeh+UZVeX9yat4bexyKpcoyEvX1/SvB5N3rnQztC76BiLyQrMHoWlvNzKBCU7rZsAvj8C2P6FYZbj0Mah+nattmWyT2QkLLUHlktlrd/PEqEWs2H6A1jVL8my7GsQWtC86gEnLtvPEd4vYuu8InRuW5dGWVYjJ61E3+5RkWDke5nwOK8ZCRLQbGbtpb3ffwgS/1FQ3VNXkl2HHUihSHup0cw9gFyjudXRBwRKUH1BVEtft4dPf1jBm8VZKF87Lc+2r07xqCa9D8zsHjibzxri/GDJ9Defly0O3xvF0aVSWogVyIYmnpsDGRFj+s2va2b8Z8heH2l2g0T32pRSqUlNhyfdu6Kp10yAsAiq1gmrtoeKVkNePavsBxhKUh3YfPMaEpdv4YuY6Fm5MonC+SLo2uoC7L70wIAd+zU2LNibxxvjlTFq+gzwRYVyXUJrr65Sm7gXnEZFdU92rwq5VsGEmrJvuuowf2um+gMpfDnW7uy8im7/JnLDjL5j7OSwc4TpUSDhc0ATKX+YeAC5VB/Lk8zrKgJErCUpEWgFvA+HAJ6r6crr1UcBQoC6wC7hZVdf61vUDegApQC9VHXum4/lrgko6dJzFm5OYt2EvE5dtZ+76PahC+dj83N60HDfUiSNvHmvDPhsrtx/gs2lrGDV3I0eOpxKTN5LLKsdyccVYapQuxIWxBYg8U8JSdYOD7l0PO5e7KRe2L4EtC+DQLlcmujBUaAGVr3b/5i2c8z+cCVypqbB5Liz/BZaPge2L3fKwCChRA0pUh9gqULyqaxqMibN7lhnI8QQlIuHAX8CVwEZgNtBJVZekKXMvUEtV7xaRjsB1qnqziFQDvgIaAKWAX4FKqvqv3WZyK0GpKkeTUzlyPIXDx1M4eDSZpMPH2XvoOHsOHWdr0mE27T3C5r2HWbn9AJv2/v2QX624GK6oUpzmVUpQvVQhwsJsZIFzkpoKqcfZf+gQf/y1hel/bSFx5RYOHz5Ifo5QKOIYFQsLZfMlUzLqKLGRRziPfRRI3kPeY7uJOrydiAObkOQjf+8zIhqKVXKDh5ZpAGUauc82NJE5V4d2w4ZZrja+aS7sWAYHtp1apuD5UKiUazYuEAv5Y90fRtEx7g+iPAUgT36IzOf+Dc/jrtWIPO59WKTrpBFEo5TkRoJqDAxQ1Za+z/0AVHVgmjJjfWVmiEgEsBWIBfqmLZu23L8dMysJav2KhYQNuxEU0v/E6lty4lRk5pSEhwnhYUJkuJAnIow84WHkiQgjPIguolOd5qScPFmaQTH1rU9zYjXVtzz11FdqKmgKpCa7l579yOf7NC87NYadxLBDY9iksWzUYmzUWNZyPpvDzic8LJyI8DDCw4QwgTARwkQQAYGTA/Se+G9M+98pnPp/G7T/1SZLCuk+4lM3cH7qdkrqds7XbRRL3UVhkiiiScRoEhGc/fV9nAhSCSPF91LCSCWMVAQVQfn7BaT5nF7aMqenZHyBp0gkFzz951nHf0oEmUxQWbkhUhpIO279RqDh6cqoarKIJAFFfctnptu2dEYHEZG7gLsAypY994dZo/MWYEOhmqeccveldOqXU1iYEAaE+RJQhO/fPBFhRIa7RBQdGU5EKNaMTvuNnP7bPO23urjPJ1eFuRfi+6swzfuwcNe2Hxbh7v+c+Dc8yjWTRES5vyxP/rWZD6ILo9Ex7EqOZtdh9dV0j3H4SDJ5j6dQ5lgKRY+lUC0lleOpqSSnKMkpqaQqpKiSmqqoQqoqqSdz7D+T7T/+qAmge7cmtxUGyrId2A4sTL9alajUw+RN3U++lP1EpR4mjx4hT+oR8qQeJkKPE6nHiEg9RjjJhGsK4ZpMuCYjpBKmLk2JpiKoe+/br5DqvstwfxhKBtfp3ynr9NdwRmntZPhhkVxwFmcjK/z+jr2qfgx8DK4Gda77KR5XnuJ9RmZbXMZ/CFAMKBZ8Y+waE9Ky0vi+CSiT5nOcb1mGZXxNfDG4zhKZ2dYYY0wIy0qCmg1UFJFyIpIH6AiMTldmNNDd974DMFFd28hooKOIRIlIOaAiMCsLsRhjjAky59zE57undD8wFtfN/DNVXSwizwGJqjoa+BT4QkRWArtxSQxfuRHAEiAZuO9MPfgA5syZs1NE1p1rzD7FgJ1Z3EcwsfNxKjsfp7LzcSo7H/90LuckU7exAupB3ewgIomZ6T0SKux8nMrOx6nsfJzKzsc/5eQ5sQdAjDHG+CVLUMYYY/xSKCaoj70OwM/Y+TiVnY9T2fk4lZ2Pf8qxcxJy96CMMcYEhlCsQRljjAkAlqCMMcb4pZBJUCLSSkSWi8hKEenrdTy5TUTKiMgkEVkiIotFpLdveRERGS8iK3z/htQsbCISLiLzROQn3+dyIvKH7zoZ7nsIPWSISGERGSkiy0RkqYg0DuVrRET6+H5f/hSRr0QkOpSuERH5TES2i8ifaZZleD2I847vvCwUkTpZPX5IJCjf1CCDgKuBakAn35QfoSQZeFhVqwGNgPt856AvMEFVKwITfJ9DSW9gaZrPrwBvqmoFYA9uzrJQ8jYwRlWrABfhzk1IXiMiUhroBdRT1Rq4AQk6ElrXyBCgVbplp7sersaNClQRN8D3B1k9eEgkKNy8UytVdbWqHgO+Btp7HFOuUtUtqjoozNPnAAAgAElEQVTX934/7ounNO48fO4r9jlwrTcR5j4RiQPaAJ/4PgtwBXBiVOFQOx8xwCW4EWBQ1WOqupcQvkZwo+3k9Y0lmg/YQghdI6o6FTcKUFqnux7aA0PVmQkUFpHzs3L8UElQGU0NkuH0HqFAROKB2sAfQAlV3eJbtRUo4VFYXngLeAxOTs5TFNirqsm+z6F2nZQDdgCDfc2en4hIfkL0GlHVTcB/gPW4xJQEzCG0rxE4/fWQ7d+zoZKgjI+IFAC+BR5U1X1p1/kG8g2J5w5EpC2wXVXneB2LH4kA6gAfqGpt4CDpmvNC7Bo5D1crKIeb+Ts//2zuCmk5fT2ESoKy6T0AEYnEJacvVXWUb/G2E9Vw37/bvYovlzUF2onIWlyT7xW4+y+Ffc05EHrXyUZgo6r+4fs8EpewQvUaaQGsUdUdqnocGIW7bkL5GoHTXw/Z/j0bKgkqM1ODBDXf/ZVPgaWq+kaaVWmnROkO/JDbsXlBVfupapyqxuOuh4mq2hmYhJsaBkLofACo6lZgg4hU9i1qjptxICSvEVzTXiMRyef7/TlxPkL2GvE53fUwGujm683XCEhK0xR4TkJmJAkRaY2753BiapAXPQ4pV4lIM+A3YBF/33N5AncfagRQFlgH3KSq6W+KBjURuQx4RFXbikh5XI2qCDAP6KKqR72MLzeJSAKu00geYDVwG+4P2ZC8RkTkWeBmXC/YecAduPsqIXGNiMhXwGW4KTW2Ac8A35PB9eBL4u/hmkEPAbepamKWjh8qCcoYY0xgCZUmPmOMMQHGEpQxxhi/ZAnKGGOMX7IEZYwxxi9ZgjLGGOOXLEEZY4zxS5agjDHG+CVLUMYYY/ySJShjjDF+yRKUMcYYv2QJyhhjjF+yBGWMMcYvWYIyxhjjlyxBmZAnImtF5LCIHBCRPSLys4iUOfOW/kFEBojIf72Ow5jsZgnKGOcaVS0AnI+b9+bds91BmllWA0qgxm2CnyUoY9JQ1SO4qc6rAYhIGxGZJyL7RGSDiAw4UVZE4kVERaSHiKwHJvpqXw+k3aeILBSR63zvq4vIeBHZLSLbROQJ3/IwEekrIqtEZJeIjBCRIumO011E1ovIThF50reuFW7iyZt9NcAFvuUxIvKpiGwRkU0i8oKIhPvW3Soi00TkTRHZBQwQkQoiMkVEknz7H56jJ9qYTLAEZUwaIpIPN4PqTN+ig0A3oDDQBrhHRK5Nt9mlQFWgJfA50CXN/i7CzcD6s4gUBH4FxgClgArABF/RB4BrffsqBewBBqU7TjOgMm7q8adFpKqqjgFeAoaragFVvchXdghuFtgKQG3gKtxssCc0xM2YWwJ4EXgeGAecB8RxDjVIY7KbJShjnO9FZC+QBFwJvAagqpNVdZGqpqrqQuArXBJJa4CqHlTVw8BooJKIVPSt64pLHseAtsBWVX1dVY+o6n5V/cNX7m7gSVXd6Js+fADQIV3z27OqelhVFwALgIvIgIiUAFoDD/ri2g68CXRMU2yzqr6rqsm+uI8DFwClfLH9fnanz5jsZwnKGOdaVS0MRAP3A1NEpKSINBSRSSKyQ0SScImkWLptN5x442siHA50EZEwoBPwhW91GWDVaY5/AfCdiOz1JcqlQAquhnPC1jTvDwEF/mVfkcCWNPv7CCieUcw+jwECzBKRxSJy+2n2bUyusQRlTBqqmqKqo3DJoRkwDFcrKqOqMcCHuC/yUzZL9/lzoDOuKe6Qqs7wLd8AlD/NoTcAV6tq4TSvaFXdlJmwM9jXUaBYmn0VUtXqp9tGVbeq6p2qWgroCbwvIhUycWxjcowlKGPSEKc97l7MUqAgsFtVj4hIA+CWM+3Dl5BSgdf5u/YE8BNwvog8KCJRIlJQRBr61n0IvCgiF/jiiPXFkRnbgHhfjQ1V3YK7n/S6iBTydcC4UETSN02m/blvFJE438c9uASWmsnjG5MjLEEZ4/woIgeAfbhOA91VdTFwL/CciOwHngZGZHJ/Q4GawMnnk1R1P+7+1jW45roVwOW+1W/jamrjfMeaievIkBnf+P7dJSJzfe+7AXmAJbiEMxLXhf506gN/+M7BaKC3qq7O5PGNyRGimr51wBiTVSLSDbhLVZt5HYsxgcpqUMZkM19X9XuBj72OxZhAZgnKmGwkIi2BHbj7QsM8DseYgGZNfMYYY/yS1aCMMcb4pYAaJLJYsWIaHx/vdRjGGGOyYM6cOTtVNfZM5QIqQcXHx5OYmOh1GMYYY7JARNZlppw18RljjPFLAVWDMianpGoq41aNY/nO5eQJz0Oe8Dycl/c8WldsTXREtNfhGROSLEGZkLb3yF4GzxvMoNmDWLXnn+O4lshfgj6N+nBP/XsoFFXIgwiNCV0B1c28Xr16avegTHb5cfmPdPmuC/uO7qNpmab0atiL5uWak5yazLGUYyzbuYzXpr/G+NXjiYmK4ZUWr9CzXk+vwzZn4fjx42zcuJEjR454HUpIio6OJi4ujsjIyFOWi8gcVa13pu2tBmVC0jt/vEOfsX2oXbI2H7X9iLql6v6jTJmYMlx54ZXM2TyHfhP6cffPd7N271peav4SIukHNDf+aOPGjRQsWJD4+Hj7P8tlqsquXbvYuHEj5cqVO6d9WCcJE1JSUlPo9b9e9B7Tm2sqXcOUW6dkmJzSqluqLr90/oWedXvy8rSX6fZ9N46lHMuliE1WHDlyhKJFi1py8oCIULRo0SzVXq0GZUKGqnLnj3cyeP5gHmr0EK9e+SrhYeGZ2jYiLIIP2nxAmUJl6D+pP9sPbufHTj+SJzxPDkdtssqSk3eyeu6tBmVCxnuz3mPw/MH0v7g/r7d8PdPJ6QQR4clLnuTjth8zbtU4Hhr7UA5FaowBS1AmRExeO5k+Y/vQrnI7nr382Szt6866d/JQo4cYNHsQQ+YPyZ4ATdAqUKAAAPPnz6dx48ZUr16dWrVqMXz4cI8j83/WxGeC3vqk9dz4zY1ULFqRL677gjDJ+t9lr1z5CvO3zefun+6mRvEa1Ct1xg5JJsTly5ePoUOHUrFiRTZv3kzdunVp2bIlhQsX9jo0v2U1KBPUjqcc54YRN3As5Rjf3/x9tj3LFBEWwdc3fE2JAiW4fvj17Di4I1v2a4JXpUqVqFixIgClSpWiePHi7Nhh182/sRqUCWqvz3idxM2JjLxxJJWLVc7Wfcfmj2XUTaNo8lkTHvjfA3zd4ets3b/JZg8+CPPnZ+8+ExLgrbfOerNZs2Zx7NgxLrzwwuyNJ8hYDcoErZW7V/LslGe5oeoN3FDthhw5Rt1SdXnqkqcYvng4Py7/MUeOYYLLli1b6Nq1K4MHDyYszL6C/43VoExQUlV6/tSTqPAo3rn6nRw91mNNH2P44uHc+8u9XBp/qQ2J5K/OoaaT3fbt20ebNm148cUXadSokdfh+L1MpW8RaSUiy0VkpYj0zWB9lIgM963/Q0Tifcs7i8j8NK9UEUnwrZvs2+eJdcWz8wczoe3zBZ8zcc1EXmnxCqUKlsrRY+UJz8Mn13zCpn2beHLCkzl6LBO4jh07xnXXXUe3bt3o0KGD1+EEhDMmKBEJBwYBVwPVgE4iUi1dsR7AHlWtALwJvAKgql+qaoKqJgBdgTWqmrYRuPOJ9aq6PRt+HmPYfnA7D497mGZlm3Fn3Ttz5ZgN4xryQIMHGDR7ENM3TM+VY5rAMmLECKZOncqQIUNISEggISGB+dl9TyzIZKaJrwGwUlVXA4jI10B7YEmaMu2BAb73I4H3RET01JFoOwF2F9nkuKcmPsX+o/v5uO3H2dKlPLNeuOIFvl/+PT1/6sm8nvOICAvQFvQDB2DZMli6FJKSoHJlqFYNSpUCG5XhrB04cACALl260KVLF4+jCSyZ+e0tDWxI83mjb1mGZVQ1GUgCiqYrczPwVbplg33Ne0/JacbEEJG7RCRRRBKtS6Y5k6U7lvLJvE+4p949VI2tmqvHLhhVkDeueoM/t//J4HmDc/XYWXb8OAwZAjVrQsGCUL8+dOsGDzwAV10FcXFw3nlw//2wfr3X0ZoQkSt/XopIQ+CQqv6ZZnFnVa0JXOx7dc1oW1X9WFXrqWq92NgzTmFvQlzfCX0pkKcA/S/p78nxr696PU3LNOWpSa4W5/eOH4f334eKFeG22yAiAp5/Hr791tWgNm+GSZNg0CC45hr4+GO48EK4/XZY9c/5s4zJTplJUJuAMmk+x/mWZVhGRCKAGGBXmvUdSVd7UtVNvn/3A8NwTYnGnLOp66Yyevlo+jbtS2x+b/6YERFev+p1th3cxqvTXvUkhkzbtAkuuwzuuw9Kl4aff4a5c6F/f7j+eqhSBc4/35W591744guXlO65B776Ci66CL62VnuTczKToGYDFUWknIjkwSWb0enKjAa6+953ACaeuP8kImHATaS5/yQiESJSzPc+EmgL/Ikx50hVeXT8o5QuWJrejXp7GkvDuIZ0rNGR12e8zsZ9Gz2N5bQmToTatWHBApdkfv8dWrc+8z2mMmXgnXdgxQqXoDp1gt694ZhNP2Ky3xkTlO+e0v3AWGApMEJVF4vIcyLSzlfsU6CoiKwEHgLSdkW/BNhwopOFTxQwVkQWAvNxNbD/y/JPY0LWyCUjmbVpFi9c8QL5IvN5HQ4Dmw8kRVN4cqIfdjt/6y248kooWhRmz4abbz77zg9xcTB5sktO77wDl18Ou3fnSLgmdNmU7ybgJacmU21QNaIjopnXc95ZT6ORUx4b/xivTX+NeT3nkVAywetwnLfegj59XBPekCGuQ0RWDR8O3btDjRowYQLExGR9n9lk6dKlVK2au51lzKky+j/I7JTvNs6GCXjDFg1jxe4VPH/5836TnACeuPgJCkcX5ulJT3sdivPhhy45dejgkkp2JCdwNbBvv4WFC10zoa9btYE+ffrwVpoRLFq2bMkdd9xx8vPDDz/MG2+84UVoJ+3du5f3338/U2WbNGmSw9GcyhKUCWjJqck8P/V5Ekom0K5yuzNvkIsKRxfmkcaP8ONfP/LHxj+8DWbwYNe54Zpr4MsvXW+97NSmjbuX9ccf7hiHDmXv/gNU06ZNmT7dPbidmprKzp07Wbx48cn106dPz7Uv/eTk5AyXn02COvGz5BZLUCagfbnwS1buXsmASwf45dTevRr2oli+Yjw16SnvgvjlF+jRw913GjEC8uTQNPXXX+96+k2ZArfcAqmpOXOcANKkSRNmzJgBwOLFi6lRowYFCxZkz549HD16lKVLl1KtWjWaN29OnTp1qFmzJj/88AMABw8epE2bNlx00UXUqFHj5ASHffv2pVq1atSqVYtHHnkEgB07dnDDDTdQv3596tevz7Rp0wAYMGAAXbt2pWnTpnTt2pXFixfToEEDEhISqFWrFitWrKBv376sWrWKhIQEHn30UQBee+016tevT61atXjmmWdO/jwnJl+cPHkyl112GR06dKBKlSp07tyZnLhdFKCPuhvzd+2pdsnafld7OqFgVEH6Nu3LI+MfYeq6qVxywSW5G8CKFS5ZXHQRfPcdREfn7PE6dYIdO1zniRdegKf9pHkTeHDMg8zfmr1DCyWUTOCtVqcfhLZUqVJERESwfv16pk+fTuPGjdm0aRMzZswgJiaGmjVrki9fPr777jsKFSrEzp07adSoEe3atWPMmDGUKlWKn3/+GYCkpCR27drFd999x7JlyxAR9u7dC0Dv3r3p06cPzZo1Y/369bRs2ZKlS5cCsGTJEn7//Xfy5s3LAw88QO/evencuTPHjh0jJSWFl19+mT///PPksEvjxo1jxYoVzJo1C1WlXbt2TJ06lUsuOfXanTdvHosXL6ZUqVI0bdqUadOm0axZs2w9v1aDMgHrvwv/y6o9qxhwmX/Wnk64p/49lCxQkv4T++fIX5mntX8/XHsthIe75JQ/f+4c94EH3CgUzzwDP9oUJE2aNGH69OknE1Tjxo1Pfm7atCmqyhNPPEGtWrVo0aIFmzZtYtu2bdSsWZPx48fz+OOP89tvvxETE0NMTAzR0dH06NGDUaNGkS+f67H666+/cv/995OQkEC7du3Yt2/fySGW2rVrR968eQFo3LgxL730Eq+88grr1q07uTytcePGMW7cOGrXrk2dOnVYtmwZK1as+Ee5Bg0aEBcXR1hYGAkJCaxduzbbz53VoExAOp5ynOenPk+d8+twTaVrvA7nX+WLzMeTFz/JA/97gF9X/8qVF16Z8wdVdSNDLFsGY8dCfHzOH/MEEdchY/Fi6NIFZs1y4/l57N9qOjnpxH2oRYsWUaNGDcqUKcPrr79OoUKFuO222/jyyy/ZsWMHc+bMITIykvj4eI4cOUKlSpWYO3cuv/zyC/3796d58+Y8/fTTzJo1iwkTJjBy5Ejee+89Jk6cSGpqKjNnziQ6gxpy/jR/mNxyyy00bNiQn3/+mdatW/PRRx9Rvnz5U8qrKv369aNnz57/+nNFRUWdfB8eHn7ae1xZYTUoE5CGLRrG6j2reebSZ/y69nTCnXXupEyhMjw9+encqUX95z+uZ92rr0KLFjl/vPTy5oVRo9z9rmuvhYMHcz8GP9GkSRN++uknihQpQnh4OEWKFGHv3r3MmDGDJk2akJSURPHixYmMjGTSpEmsW7cOgM2bN5MvXz66dOnCo48+yty5czlw4ABJSUm0bt2aN998kwULFgBw1VVX8e6775485ulGSV+9ejXly5enV69etG/fnoULF1KwYEH27/97WK6WLVvy2WefnayBbdq0ie3bvZlswhKUCTgpqSkM/H0gF5W4yO9rTydERUTxxMVPMHPjTMavHp+zB5szB554wnUnf+ihnD3Wvylb1vXsW74cHn7Yuzg8VrNmzZP3ltIui4mJoVixYnTu3JnExERq1qzJ0KFDqVKlCgCLFi062aHh2WefpX///uzfv5+2bdtSq1YtmjVrdrKL+jvvvENiYiK1atWiWrVqfPjhhxnGMmLECGrUqEFCQgJ//vkn3bp1o2jRojRt2pQaNWrw6KOPctVVV3HLLbfQuHFjatasSYcOHU5JYLnJHtQ1Aeebxd9w08ibGN5hODdVv8nrcDLtaPJRKr5bkbhCcUy7fVrO1PwOHYI6ddyzSAsXQpEi2X+Ms/XYY/Daa/DDD9Audzuz2IO63rMHdU3IUFVe/O1FKhetzA1Vb/A6nLMSFRFFv2b9mLFxBr+u/jVnDvLww/DXXzB0qH8kJ3CjoyckuK7uW7d6HY0JIJagTED5ecXPLNi2gH7N+vnVqBGZdXvt24krFMeAKQOy/17Ujz+6zgkPPwxXXJG9+86KqCgYNszV6m67zXXgMCYTLEGZgHGi9hRfOJ5bat7idTjn5EQtavqG6UxYMyH7drxjh6uhJCS454/8TdWqruPGmDHwwQe5euhAuo0RbLJ67i1BmYAxae0kZm6cyWNNHiMyPNLrcM5Zj9o9KF2wNAMmZ2Mtqlcv2LvXjeSQpvuvX7n3Xjc772OPQQ48M5OR6Ohodu3aZUnKA6rKrl27Muz6nln2HJQJGC/+9iIlC5Tkttq3eR1KlpyoRd3/v/uZuGYizcs3z9oOv//e9ZZ7/nk3ori/EnEz8taoAXfd5Z7PyuFHBOLi4ti4cSM7duzI0eOYjEVHRxMXF3fO21svPhMQZmyYQZPPmvD6Va/zUGMPu05nkyPJR6jwTgXKnVeOqbdOPfcefXv2QLVqULKkeyA2MgBqlh984GpTn37qpo43ISdbe/GJSCsRWS4iK0Wkbwbro0RkuG/9HyIS71seLyKHRWS+7/Vhmm3qisgi3zbvSCA8bWk88+JvL1I0b1F61v33p9sDRXRENP2a9eP39b8zae2kc9/RQw/Bzp3w2WeBkZwAevaESy91sW/a5HU0xo+dMUGJSDgwCLgaqAZ0EpFq6Yr1APaoagXgTeCVNOtWqWqC73V3muUfAHcCFX2vVuf+Y5hgNn/rfH5e8TMPNnqQ/HlyaTy5XNCjTg9KFSx17veixoxxkw727eumbw8UYWHwySdumvi777Zefea0MlODagCsVNXVqnoM+Bpon65Me+Bz3/uRQPN/qxGJyPlAIVWdqe43cyhw7VlHb0LCS7+9RKGoQtzf4H6vQ8lWJ2pRv63/jclrJ5/dxgcOuC/3qlWhf/8ciS9HVajg7pn99BOMHOl1NMZPZSZBlQY2pPm80bcswzKqmgwkAUV968qJyDwRmSIiF6cpv/EM+wRARO4SkUQRSbQbnaFn2c5ljFwykvvq30fh6MJeh5Pt7qhzh6tFne1zUc88A+vWuU4H/tpr70x694a6dd3o53v2eB2N8UM53c18C1BWVWsDDwHDRKTQ2exAVT9W1XqqWi82NjZHgjT+a+DvA4mOiKZPoz5eh5IjTtSipq6bmvl7UYmJ8NZbrgaVzfPv5KqICPi//3P30B5/3OtojB/KTILaBJRJ8znOtyzDMiISAcQAu1T1qKruAlDVOcAqoJKvfNq+hxnt04S41XtW8+XCL+lZtyex+YP3j5M76txB6YKleXpSJkY6T06GO++EEiXg5ZdzJ8CcVLs29OnjEtWUKV5HY/xMZhLUbKCiiJQTkTxAR2B0ujKjge6+9x2AiaqqIhLr62SBiJTHdYZYrapbgH0i0sh3r6ob8EM2/DwmiLz020tEhEXwaNNHvQ4lR0VHRNP/kv5M2zCNsavG/nvhN9+E+fPhvfcgJiZ3AsxpAwZAuXLu2agjR7yOxviRMyYo3z2l+4GxwFJghKouFpHnROTE0MSfAkVFZCWuKe9EV/RLgIUiMh/XeeJuVd3tW3cv8AmwElez+l82/UwmCKzZs4bPF3zOXXXvolTBUl6Hk+Nur3078YXj/33W3dWr3b2n9u3huutyN8CclD+/G0Pwr7/gpZe8jsb4EXtQ1/ilO0ffyRcLv2BVr1WULpRh/5mg8/n8z7n1h1sZddMorquaLgGpQsuWMHMmLFkCWXg632917QrDh8O8eVC9utfRmBxk022YgLV271qGLBjCnXXuDJnkBNC5VmcqF63MU5OeIiU15dSVX34J48fDwIHBmZwA3ngDChVyTX2pqV5HY/yAJSjjdwb+NpAwCaNvs38MWhLUIsIiePayZ1m8YzEjFo/4e8XOna4jQePGcM893gWY02JjXZKaPt01+ZmQZwkqVCUnw6JFsHmz15GcYt3edXw2/7OQqz2dcGP1G6lVohZPT36aYynH3MKHH4akJNfTLSzIf2W7doUWLdzoGDYMUsgL8qvdnGL9eveLf+mlrgdYrVpQujRccAHcfLMbvDMl5cz7yUHPT30+JGtPJ4RJGAObD2Tl7pV8POdjGDfOzY77+OOhcV9GxNWekpPdgLIBdI/cZD/rJBEKUlJct+Qnn4SjR6FOHWjUCBo0cBPdzZwJM2a4BFavnvtLPSEh18NctnMZ1d+vTu+GvXmj5Ru5fnx/oaq0+KIFC7cuYOX/5SMmIr/rOJCFeXUCzn/+A48+6qYRuflmr6Mx2SyznSRQ1YB51a1bV81ZWrRItUEDVVBt1Up1zZqMy6Wmqn71lWrx4qrh4aqPPaZ6+HCuhnrD8Bu04EsFdfuB7bl6XH80Z/McZQDarzmq06d7HU7uO35ctX591WLFVLfb9RBsgETNxHe+NfEFs99/hyZNYM0aGDYMfvkF4uMzLisCHTvC0qXQvTu8+qp73ubw4VwJdfam2Xy79FseafJIUI8akVl1Vh6kywJ4s1k4G6oFaa+9fxMR4aYQSUpyY/aZkGQJKlhNmQKtWsH557vmoU6dMjd7aZEi7l7UZ5+5bs1t28LBgzkebr8J/YjNFxu0Y+6dlcOHoUcPXlgRh0ZE8NSkp7yOyBs1ariR2r/6CkanH7zGhAJLUMFowgS4+mooW9YlqtLn0BvuttvczfnJk6F1a9i/P9vDPOHX1b8yYc0E+l/Sn4JRBXPsOAHj6adhxQouePtzejfszdAFQ5m7Za7XUXmjb1+oWdMNjGsjnoccS1DBZsYMV+upUMEll5Ilz31fXbq4B0SnTYNrrnETzGWzVE3l8V8f54KYC4JmttwsmTIFXn/dzTp7xRX0u7gfsfljue+X+0jVEHx4NU8eNynjjh1w331eR2NymSWoYLJhgxujrXRpV4sqXjzr++zYET7/3H1xPvBAtnf7HTxvMHO3zOWl5i8RFRGg8xpll3373P2/8uVdLzagcHRhXrvyNWZunMmQ+UO8jc8rdeq4WuVXX7mhkEzIsAQVLA4dgmuvdf+OHu2eys8unTtDv35ucrxBg7Jtt3uP7KXfhH40LdOUTjU6Zdt+A9aDD7o/MoYOhQIFTi7uWqsrzco24/FfH2f34d3/soMg1q8fNGzoRtKwB3hDhiWoYKDq7hnNm+eeG6lWLfuP8cILrpnvwQdd7SwbPDflOXYe2sm7V7+LZKYDRzD74QcYPNg9kNukySmrRIRBrQex5/AenpzwpEcBeiwiwiXuI0egRw97gDdEWIIKBgMHwogR8MorrkNDTggLc/ejqlSBG290XdezYOmOpbw7613urHMntc+vnU1BBqgtW9wkhAkJbm6kDNQqUYsHGjzAR3M+Yvam2bkbn7+oVMk1fY4d6x48N0EvUwlKRFqJyHIRWSki/xiDRkSiRGS4b/0fIhLvW36liMwRkUW+f69Is81k3z7n+17ZcMMkBE2ZAk895bqRP/JIzh6rYEHXfJia6p7uP8dOE6rKg2MfJH9kfl644oVsDjLApKTALbfAgQPw3/+6TgGnMeCyAZQoUIK7frrr73H6Qs0990CbNu5anzPH62hMDjtjgvLNiDsIuBqoBnQSkfRtSD2APapaAXgTeMW3fCdwjarWxM24+0W67TqraoLvtT0LP0do2rbNJaYKFeCjjzL3nFNWlS/vmqJmz4bHHjunXXy79FvGrRrHc5c/Zw/lPvec6235/vtnHGsvJjqGD9p8wPyt83lx6ou5E5+/EXGddooXh5JeGNMAABT/SURBVJtucg/ymuB1pqEmgMbA2DSf+wH90pUZCzT2vY/AJSZJV0aA3UCU7/NkoF5mhrs48bKhjtJITlZt0UI1Olp1wYLcP37v3m74pFGjzmqzHQd3aOyrsVr3o7p6POV4DgUXIMaPVxVR7d79rDbrOqqrhj8brombEnMmrkAwbZobkqtDBzdMlwkoZONQR6WBDWk+b/Qty7CMuinik4Ci6crcAMxV1aNplg32Ne89JSF/l/wsDRwIv/4K77zz/+2deXRV1bnAf9/NzQgZgASUhCFAULEyCSJqFacCgiKVgkgfXbwKRXDZV7VvVZ+rQqlYLFCHFloUWsWhIiAg2opMAoKVIBUZUkBAZkgIQyDjzf3eH/skBEjgSoZzb+7+rbXXmfbZ57v77rO/ffbwfcYqeV3zwgvQvbuZnLFrV8C3PfaPxzhReIK/DvgrXo+3FgUMcg4dMrMjr776O8+MfKnPSzRr2IzhC4ZT6CusJQGDnJtuMu/A3Lk1OrPUElzUySQJEbkW0+1XcSXmMDVdf993wn9Vce8oEckUkczs7OzaFzYUWLkSnn3WjF08/LA7MkRFnV2TMmSIsZJ+CRZmLeSdze/wzK3PcF2z62pZwCCmsBAeeMBY53jvPWjQ4Dvd3ii2ETPvm8nW7K08u+LZWhIyBHjiCTMe9fjjsGqV29JYaoFAFNQBoEWF4zTnXKVxRMQLJALHnOM04H1guKp+U3aDqh5wtnnA28ANlT1cVWeoajdV7ZZSk2t7QpWK405//nPdjDtVRXq6GY/KzLzkeFRuQS6jPxxNp2adeOqWp+pIwCBE1bg0X7fOTJu+TB9Pfdr1YWTXkfx+7e9ZumtpDQsZIng8ZmJJmzZG4VdzZqkl+AhEQa0HMkQkXUSigAeB8y03LsJMggAYBCxXVRWRJOBD4Feq+llZZBHxikiysx8J9Ac2V++nhAGlpcb80IkTpuUdHwR26wYONNamX34Z5s+vNIqq8uhHj5J9JptZA2YRGRFZx0IGES+8ALNnw/jxMGhQtZKa2nsq16Rcw9B5Q9l3ct+lb6iPJCXBBx+Yd+Pee401Dkv9IZCBKuAeYDvwDfB/zrnfAPc5+zHAe8BO4AugjXP+GeAM8O8KoSnQANgAbAK2AC8BEZeSI+wnSUyYYCYmzJjhtiTnUlRkfPckJqp+880Fl6evn66MQyd8OsEF4YKIhQvNpIghQ2psYD8rO0vjJ8Zrj1d7aGFJYY2kGZIsW2YmTfTrZyYQWYIaApwk4boTwu8SwlpBLV+u6vGoPvRQcM5a2rVLNSlJ9frrz3F0uP7Aeo2aEKV93+yrpf5SFwV0mTVrVOPiVLt1U83Pr9Gk526Zq4xDxyweU6PphhzTppkqbeTI4HxHLOUEqqCsJYlQYM8eY72hfXv3x52qIj3dWJ3esMG4RlAltyCXQXMG0axBM2YPnI1HwrS4bdxoLHykpcHixRAbW6PJP9DhAZ7o+QTTMqcxa+OsGk07pHjkEXj6aXj1VeMuXq05pFAnjOf5hgj5+Wacp6TE2GsLhnGnqhgwwMwuHD8ef5fO/KTpMg7mHWT1iNU0iTt/1UGYsG0b/OAHZqxk6VJo1qxWHvO7u37HpiObGPXBKJo2aEr/9v1r5TlBz29/a8ahpkyBxERjZcUSsoRpkzZEUDXTyL/6yrgaaN/ebYkuza9/jQ64j58veZzF2xcztfdUeqT1cFsqd9i5E+6+GyIijHJq0eLS91wmXo+XeYPn0eXKLvzovR+xZu+aWntWUCMCL70Ew4cbFx1TprgtkaUaWAUVzEyebBTTc8/VnhHYmsbj4bdjruOPNyhPfBnDoyn93JbIHTZuhJtvNmuePvkEMjJq/ZHx0fF89NBHtExsyb3v3MvXR76u9WcGJR4PzJxpZkk++aRxG2+7+0ISq6CClbfeMmuLBg82bq9DhL9k/oVfr3uO4en388LqGNO9deSI22LVLStXQq9eEBMDa9YYl+V1REqDFJb8eAlxkXHcPftuvjr8VZ09O6jwek3j7uGHTQNv9GgzFd0SUlgFFYx8+KHxrHr77cYwZjBOiqiEt79+mzEfjaFfRj9eGzYHz4cfwcGD0KdP+Bj1nDfP/N60NPjsM2PKqI5pldSKZcOXERkRya1/u5VP93xa5zIEBV6vcbL59NNmO3iwGdO1hAxWQQUbq1ebronOnWHBAtMKDwFe/PxFhs0fxvdbfp85P5pjFuP27GkW727ZYhZR1ufKweczzgYHDTIuylevNkrKJa5Ovpq1/72W5vHN6f1mbxZkLXBNFlcRMV9Qf/gDvP8+3Hgj7NjhtlSWQAlkLnqwhHq/DmrdOrPYtX171aNH3ZYmIEr9pfrkx08q49AH3n1AC0oKLoz097+bBaq9e6uePl33QtY2hw6p3nabWYPzyCOqhcGzYDbnTI72eLWHesZ7dNKaSeG9Fu2f/1Rt3Fg1IUH1/ffdliaswS7UDTEWLVKNjVVt21Z1zx63pQmIvKI8HTp3qDIOHfvhWPWVXmQF/8yZZqFx9+6qR47UnZC1zYIFqldcYf67N95wW5pKOV10WgfNGaSMQ/u+2Vezz2S7LZJ77NljyiCojh2reuqU2xKFJVZBhRIzZpjKu1u3kKm81x9Yr+1ebqcyTnTiqonqD2Tl/sKFZ5Xwjh21L2RtcuiQ8UUEqh07qm7a5LZEF8Xv9+u0L6Zp9IRoTZ2Sqit2r3BbJPcoLDT+zERUW7RQXbzYbYnCDqugQoGiItVf/tL8DX37qubluS3RJfGV+nTSmknq/Y1X06am6crdK79bAuvWqTZpopqcrPqPf9SOkLVJYaHqK6+oNmqkGh2tOnGianGx21IFzMZDGzXj5QxlHDps3jA9eOqg2yK5x9q1qh06mPdv8ODQbzSFEFZBBTubN6t27mz+gtGjQ6KSW/rNUu04vWP5eNOx/GOXl9B//nO2Yhg9OjTGpUpKVGfNUm3Z0sh9++2qWVluS3VZnCk+o88se0ajJkRp/MR4nfzZZM0vrln7gCFDYaHq+PHmy97rVR01SnXfPrelqvdYBRWsFBerTpliWt8pKabbK8j5+sjX2v/t/so4tPWLrfXdze8G1qV3MQoKVB9/3HSztG1rrFEHI7m5qlOnqrZrZ16X7t1VlyypF8ZIdxzbofe8dY8yDk15IUWfW/WcHi847rZY7nDwoOqjj6pGRpp3c8QI1cxMt6Wqt1gFFWz4fKqvv66anm6yvX9/1cOH3ZaqSnylPl2YtVDvfP1OZRya8HyCTlozqfJZetVhxQrVVq3OfpWsWlWz6V8OJSXGevxPf2pa1qB6002q8+fXC8VUEb/fryt3r9S+b/ZVxqHxE+P1kcWP6Nq9a6vfCAlFdu9W/dnPjOV5UO3RQ/W111RzctyWrF4RqIISEzc06Natm2ZmZrotxncjNxfmzDH2wbKyoEsXY9Cyb9+gW4DrVz9fHPiC+dvmM2fLHL49+S1pCWmM7T6WkV1H1p7B14ICs5Dy+eeN1YlevWDECLj/fkhIqJ1nns/x42bt0gcfmPVnOTkQFwfDhsGYMWZdWj3n34f/zeS1k5m3bR6FvkLaNmrL0O8NpU+7PvRI64HXE0a2pU+cMIvkp02D7duNPcXbb4cf/hDuust4tA6y9zeUEJENqtrtkvECUVAi0gfHqSDwmqr+7rzr0cAbwPUYV+9DVHWPc+0p4KdAKfCYqn4cSJqVETIK6uhR+PRTePddU+EVF0PHjsZ45cCBxlZYEKCqZOVk8dm+z1izdw2f7PqEg3kHifREckf6HYzsOpIBVw+ou4opPx+mT4dXXoFvvzWLlPv1g969zaLfa64xFUV1KSyErVth0yZjM2/VKmOQV9VYi+/f37gQ79MHGjSo/vNCjFNFp5i/bT6zN81m5Z6V+NVPQnQCd6TfwU1pN9GteTe6XtmVxJhEt0WtfVThyy+NhZC5c88u8k1NNQ2pnj1No7NjR2jY0FVRQ4kaU1AiEoHxpns3sB/jAn6oqm6tEGcM0FFVR4vIg8BAVR0iIh2Ad4AbgObAUqDMJPdF06yMoFNQqnD4sHGpkJVlKrxVq8wxQNOm8NBDxrJy586utLhUlZz8HPad2sfek3vZfXw3W7O3sjVnK1uObuFkkTFBlBKXwm2tb+P+q+6nX/t+JMUk1bmsFYSGdeuMLbX33jtryy8hATp1gtatTWjZ0pxr2NAokogIY9HB5zNK6Phx8wV77Bjs32/8au3ZA3v3nrXLFhtrKpnbbjOhR4+Qsd5RF+QW5LJ893KWfLOEZbuXsev4rvJr6UnpXJV8Fe0btyejSQYtElqQmpBKWkIayXHJ9e+LS9V8Ta1YYewtrlx5tmyKQJs2xihwRga0bWus16emQvPmxs1KVJSb0gcVNamgegLjVLW3c/wUgKo+XyHOx06cdSLiBQ4DKcCvKsYti+fcdtE0K6M6Cup07mG+WPq6Y9VYQTH76odSv9n3+UzFVVbJFRWZr5+iIjhzxoTTp03Fd+wYHM9Fi0vOPiQuDr53LXTshHa6DtpfVd7ir5jPitPHipZf86u//Lxf/eWhVEsp9Zfi8/vw+X2U+EsoLi2mpLSEAl8BBSUFFPoKOVNyhrziPPKK8jhVdIqc/JzyUOKvICPQJLYJ1za9lg7JHeie2p1bWt5CRuMMJBi7LFSN24p160zYvNl8XR04AH5/YGmIwJVXGqXWqpWpPDp2NMqubdua+SoLE3Lyc9hwcAPrD65nS/YWth/bzvZj2zldfPqCuEkxSSTHJdM4tjHxUfEkRCcQHx1PnDeO2MhY4iLjiPHGEBURVR4iPZF4PV68Hi8Rngg84iFCIojwRCAIHvHgEQ8igiAXbIHyclx2virK4gdCpemomq7gnTtgx07T+Dl40DSGCgoujB8TA4kJEJ9g6oqyEBMD0dEmREVBZKSxIxgVCRFeUz4jIkzPS9m2LIiARwAx+yKYnyVlgl/YMJYqDgJ8/z2eCHoN/EVAcasiUAUVSBMnFdhX4Xg/cL6Dn/I4quoTkZNAE+f85+fdm+rsXypNAERkFDAKoGXLlgGIWznfZn3Ondsu0yq4B4h3wkXJB9ZDyXrIxIQ6INZrXvayCiAhOoE2jdpwQ+oNpMSl0KxhM1oltqJFYgtaJbYiOS45OJVRZYicbZUOH372fEkJHDpknNOVNRz8fvNie73mZW/c2ITERKuEaojkuGR6t+tN73a9y8+pKkfOHOHAqQPsP7WfA3kHOHrmKMfyj5FTkENuQS55RXkcOXOEU0WnyC/Jp6CkgAJfAX4NsJERCrR2QpUUOuHoxdPxOaGwRqSqcaJ9UFhNBRUoQf8NrqozgBlgvqAuN53WHW7m0+N/MgdlrQqPgHjObr0VWitRUSZER5sWzUXGjapqiZ2vBCrGq9jyA0yr0DmOkIjyVmKERJS3Jr0eb3krMzIiklhvLFERUaGjbGqSyEjTxWdxHRHhioZXcEXDK7i++fUB36eq5b0CRb4iikuLz+kp8KufUn9peS9CWQ9DqZaW90BU3ALn9EqU7Vf17IDlvEg6NY7fb3pvSkpMKC29MPj9JlTsATqnV8iRt7LfWPFcVfuXQKTuxtADUVAHgIquQNOcc5XF2e908SViJktc7N5LpVmjNEhK4dZ+Y2rzERaL5TsgIkRGmMZWXGSc2+JYgpBAVOF6IENE0kUkCngQWHRenEXAT5z9QcByZ677IuBBEYkWkXQgA/giwDQtFovFEsZc8gvKGVN6FPgYMyV8lqpuEZHfYBZbLQJmArNFZCeQi1E4OPHmAFsxvapjVbUUoLI0a/7nWSwWiyVUCamFuiKSDXxbzWSSgZwaEKe+YPPjXGx+nIvNj3Ox+XEhl5MnrVQ15VKRQkpB1QQikhnI9MZwwebHudj8OBebH+di8+NCajNPgsOkgcVisVgs52EVlMVisViCknBUUDPcFiDIsPlxLjY/zsXmx7nY/LiQWsuTsBuDslgsFktoEI5fUBaLxWIJAayCslgsFktQEjYKSkT6iMh/RGSniFym1djQRURaiMgKEdkqIltE5OfO+cYi8omI7HC2jdyWtS4RkQgR2Sgii53jdBH5l1NO3nUsnYQNIpIkInNFJEtEtolIz3AuIyLyC+d92Swi74hITDiVERGZJSJHRWRzhXOVlgcxvOzkyyYR6Vrd54eFgnJ8Wv0J6At0AIY6vqrCCR/whKp2AG4Exjp58CtgmapmAMuc43Di58C2CseTgD+oajvgOMbZZjjxEvBPVb0a6ITJm7AsIyKSCjwGdFPV72Gs3jxIeJWRvwF9zjtXVXnoizFnl4HxQDG9ug8PCwWFcZi4U1V3qWox8HdggMsy1SmqekhVv3T28zAVTyomH153or0O3O+OhHWPiKQB/YDXnGMB7gDmOlHCLT8SgVsxpstQ1WJVPUEYlxGMObhYxwh2HHCIMCojqroKY76uIlWVhwHAG2r4HEgSkSur8/xwUVCV+bRKrSJuvUdEWgNdgH8BzVT1kHPpMNDMJbHc4EXgf4Eyp0RNgBOq6nOOw62cpAPZwF+dbs/XRKQBYVpGVPUAMBnYi1FMJ4ENhHcZgarLQ43Xs+GioCwOItIQmAf8j6qeqnjNsUAfFusORKQ/cFRVN7gtSxDhBboC01W1C3CG87rzwqyMNMJ8FaQDzYEGXNjdFdbUdnkIFwUViE+reo+IRGKU01uqOt85faTsM9zZXsLdZ73hZuA+EdmD6fK9AzP+kuR050D4lZP9wH5V/ZdzPBejsMK1jNwF7FbVbFUtAeZjyk04lxGoujzUeD0bLgoq7P1POeMrM4Ftqjq1wqWKvrx+Aiysa9ncQFWfUtU0VW2NKQ/LVXUYsALj0wzCKD8AVPUwsE9ErnJO3YlxlROWZQTTtXejiMQ5709ZfoRtGXGoqjwsAoY7s/luBE5W6Aq8LMLGkoSI3IMZcyjzP/WcyyLVKSJyC7Aa+JqzYy5PY8ah5gAtMa5MBqvq+YOi9RoR6QU8qar9RaQN5ouqMbAR+LGqFrkpX10iIp0xk0aigF3ACExDNizLiIiMB4ZgZsFuBB7GjKuERRkRkXeAXhiXGkeAZ4EFVFIeHCX+R0w3aD4wQlUzq/X8cFFQFovFYgktwqWLz2KxWCwhhlVQFovFYglKrIKyWCwWS1BiFZTFYrFYghKroCwWi8USlFgFZbFYLJagxCooi8VisQQl/w8TMn6E01ChNgAAAABJRU5ErkJggg==\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# non weighted barycenter computation\n", + "\n", + "weight = 0.5 # 0<=weight<=1\n", + "weights = np.array([1 - weight, weight])\n", + "\n", + "# l2bary\n", + "bary_l2 = A.dot(weights)\n", + "\n", + "# wasserstein\n", + "reg = 1e-3\n", + "alpha = 1.\n", + "\n", + "bary_wass = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights)\n", + "\n", + "pl.figure(2)\n", + "pl.clf()\n", + "pl.subplot(2, 1, 1)\n", + "for i in range(n_distributions):\n", + " pl.plot(x, A[:, i])\n", + "pl.title('Distributions')\n", + "\n", + "pl.subplot(2, 1, 2)\n", + "pl.plot(x, bary_l2, 'r', label='l2')\n", + "pl.plot(x, bary_wass, 'g', label='Wasserstein')\n", + "pl.legend()\n", + "pl.title('Barycenters')\n", + "pl.tight_layout()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Barycentric interpolation\n", + "-------------------------\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/rflamary/PYTHON/POT/ot/unbalanced.py:500: RuntimeWarning: overflow encountered in square\n", + " err = np.sum((u - uprev) ** 2) / np.sum((u) ** 2) + \\\n", + "/home/rflamary/PYTHON/POT/ot/unbalanced.py:500: RuntimeWarning: invalid value encountered in double_scalars\n", + " err = np.sum((u - uprev) ** 2) / np.sum((u) ** 2) + \\\n", + "/home/rflamary/PYTHON/POT/ot/unbalanced.py:501: RuntimeWarning: overflow encountered in square\n", + " np.sum((v - vprev) ** 2) / np.sum((v) ** 2)\n", + "/home/rflamary/PYTHON/POT/ot/unbalanced.py:501: RuntimeWarning: invalid value encountered in double_scalars\n", + " np.sum((v - vprev) ** 2) / np.sum((v) ** 2)\n" + ] + }, + { + "data": { + "image/png": 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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# barycenter interpolation\n", + "\n", + "n_weight = 11\n", + "weight_list = np.linspace(0, 1, n_weight)\n", + "\n", + "\n", + "B_l2 = np.zeros((n, n_weight))\n", + "\n", + "B_wass = np.copy(B_l2)\n", + "\n", + "for i in range(0, n_weight):\n", + " weight = weight_list[i]\n", + " weights = np.array([1 - weight, weight])\n", + " B_l2[:, i] = A.dot(weights)\n", + " B_wass[:, i] = ot.unbalanced.barycenter_unbalanced(A, M, reg, alpha, weights)\n", + "\n", + "\n", + "# plot interpolation\n", + "\n", + "pl.figure(3)\n", + "\n", + "cmap = pl.cm.get_cmap('viridis')\n", + "verts = []\n", + "zs = weight_list\n", + "for i, z in enumerate(zs):\n", + " ys = B_l2[:, i]\n", + " verts.append(list(zip(x, ys)))\n", + "\n", + "ax = pl.gcf().gca(projection='3d')\n", + "\n", + "poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list])\n", + "poly.set_alpha(0.7)\n", + "ax.add_collection3d(poly, zs=zs, zdir='y')\n", + "ax.set_xlabel('x')\n", + "ax.set_xlim3d(0, n)\n", + "ax.set_ylabel(r'$\\alpha$')\n", + "ax.set_ylim3d(0, 1)\n", + "ax.set_zlabel('')\n", + "ax.set_zlim3d(0, B_l2.max() * 1.01)\n", + "pl.title('Barycenter interpolation with l2')\n", + "pl.tight_layout()\n", + "\n", + "pl.figure(4)\n", + "cmap = pl.cm.get_cmap('viridis')\n", + "verts = []\n", + "zs = weight_list\n", + "for i, z in enumerate(zs):\n", + " ys = B_wass[:, i]\n", + " verts.append(list(zip(x, ys)))\n", + "\n", + "ax = pl.gcf().gca(projection='3d')\n", + "\n", + "poly = PolyCollection(verts, facecolors=[cmap(a) for a in weight_list])\n", + "poly.set_alpha(0.7)\n", + "ax.add_collection3d(poly, zs=zs, zdir='y')\n", + "ax.set_xlabel('x')\n", + "ax.set_xlim3d(0, n)\n", + "ax.set_ylabel(r'$\\alpha$')\n", + "ax.set_ylim3d(0, 1)\n", + "ax.set_zlabel('')\n", + "ax.set_zlim3d(0, B_l2.max() * 1.01)\n", + "pl.title('Barycenter interpolation with Wasserstein')\n", + "pl.tight_layout()\n", + "\n", + "pl.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.6.8" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} -- cgit v1.2.3 From 8ae85fd6b3649058da07b16c9ea139864c7f94a1 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 25 Jun 2019 14:57:26 +0200 Subject: alpha for documentation --- ot/gromov.py | 4 ++-- ot/unbalanced.py | 6 +++--- 2 files changed, 5 insertions(+), 5 deletions(-) diff --git a/ot/gromov.py b/ot/gromov.py index cd961b0..3a7e24c 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -357,7 +357,7 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, Computes the FGW transport between two graphs see [24] .. math:: - \gamma = arg\min_\gamma (1-\alpha)*<\gamma,M>_F + \alpha* \sum_{i,j,k,l} + \gamma = arg\min_\gamma (1-\\alpha)*<\gamma,M>_F + \\alpha* \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l} s.t. \gamma 1 = p @@ -440,7 +440,7 @@ def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5 Computes the FGW distance between two graphs see [24] .. math:: - \min_\gamma (1-\alpha)*<\gamma,M>_F + \alpha* \sum_{i,j,k,l} + \min_\gamma (1-\\alpha)*<\gamma,M>_F + \\alpha* \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l} diff --git a/ot/unbalanced.py b/ot/unbalanced.py index 484ce95..bad12d6 100644 --- a/ot/unbalanced.py +++ b/ot/unbalanced.py @@ -19,7 +19,7 @@ def sinkhorn_unbalanced(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, The function solves the following optimization problem: .. math:: - W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \alpha KL(\gamma 1, a) + \alpha KL(\gamma^T 1, b) + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \\alpha KL(\gamma 1, a) + \\alpha KL(\gamma^T 1, b) s.t. \gamma\geq 0 @@ -128,7 +128,7 @@ def sinkhorn_unbalanced2(a, b, M, reg, alpha, method='sinkhorn', The function solves the following optimization problem: .. math:: - W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \alpha KL(\gamma 1, a) + \alpha KL(\gamma^T 1, b) + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \\alpha KL(\gamma 1, a) + \\alpha KL(\gamma^T 1, b) s.t. \gamma\geq 0 @@ -239,7 +239,7 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, The function solves the following optimization problem: .. math:: - W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \alpha KL(\gamma 1, a) + \alpha KL(\gamma^T 1, b) + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \\alpha KL(\gamma 1, a) + \\alpha KL(\gamma^T 1, b) s.t. \gamma\geq 0 -- cgit v1.2.3 From 1140141938c678d267f688dbb9106d3422d633c5 Mon Sep 17 00:00:00 2001 From: Romain Tavenard Date: Thu, 27 Jun 2019 10:04:35 +0200 Subject: Added minkowski variants and wasserstein_1d functions --- ot/lp/__init__.py | 203 ++++++++++++++++++++++++++++++++++++++++++++++++++--- ot/lp/emd_wrap.pyx | 10 ++- 2 files changed, 203 insertions(+), 10 deletions(-) diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index bf218d3..719032b 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -21,7 +21,7 @@ from .cvx import barycenter from ..utils import dist __all__=['emd', 'emd2', 'barycenter', 'free_support_barycenter', 'cvx', - 'emd_1d', 'emd2_1d'] + 'emd_1d', 'emd2_1d', 'wasserstein_1d', 'wasserstein2_1d'] def emd(a, b, M, numItermax=100000, log=False): @@ -313,7 +313,8 @@ def free_support_barycenter(measures_locations, measures_weights, X_init, b=None return X -def emd_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', dense=True, log=False): +def emd_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', p=1., dense=True, + log=False): """Solves the Earth Movers distance problem between 1d measures and returns the OT matrix @@ -330,6 +331,8 @@ def emd_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', dense=True, log=False - x_a and x_b are the samples - a and b are the sample weights + When 'minkowski' is used as a metric, :math:`d(x, y) = |x - y|^p`. + Uses the algorithm detailed in [1]_ Parameters @@ -346,11 +349,14 @@ def emd_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', dense=True, log=False Metric to be used. Only strings listed in :func:`ot.dist` are accepted. Due to implementation details, this function runs faster when `'sqeuclidean'`, `'cityblock'`, or `'euclidean'` metrics are used. + p: float, optional (default=1.0) + The p-norm to apply for if metric='minkowski' dense: boolean, optional (default=True) If True, returns math:`\gamma` as a dense ndarray of shape (ns, nt). Otherwise returns a sparse representation using scipy's `coo_matrix` format. Due to implementation details, this function runs faster when - dense is set to False. + `'sqeuclidean'`, `'minkowski'`, `'cityblock'`, or `'euclidean'` metrics + are used. log: boolean, optional (default=False) If True, returns a dictionary containing the cost. Otherwise returns only the optimal transportation matrix. @@ -416,7 +422,7 @@ def emd_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', dense=True, log=False G_sorted, indices, cost = emd_1d_sorted(a, b, x_a_1d[perm_a], x_b_1d[perm_b], - metric=metric) + metric=metric, p=p) G = coo_matrix((G_sorted, (perm_a[indices[:, 0]], perm_b[indices[:, 1]])), shape=(a.shape[0], b.shape[0])) if dense: @@ -427,7 +433,8 @@ def emd_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', dense=True, log=False return G -def emd2_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', dense=True, log=False): +def emd2_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', p=1., dense=True, + log=False): """Solves the Earth Movers distance problem between 1d measures and returns the loss @@ -444,6 +451,8 @@ def emd2_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', dense=True, log=Fals - x_a and x_b are the samples - a and b are the sample weights + When 'minkowski' is used as a metric, :math:`d(x, y) = |x - y|^p`. + Uses the algorithm detailed in [1]_ Parameters @@ -459,7 +468,10 @@ def emd2_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', dense=True, log=Fals metric: str, optional (default='sqeuclidean') Metric to be used. Only strings listed in :func:`ot.dist` are accepted. Due to implementation details, this function runs faster when - `'sqeuclidean'`, `'cityblock'`, or `'euclidean'` metrics are used. + `'sqeuclidean'`, `'minkowski'`, `'cityblock'`, or `'euclidean'` metrics + are used. + p: float, optional (default=1.0) + The p-norm to apply for if metric='minkowski' dense: boolean, optional (default=True) If True, returns math:`\gamma` as a dense ndarray of shape (ns, nt). Otherwise returns a sparse representation using scipy's `coo_matrix` @@ -508,10 +520,185 @@ def emd2_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', dense=True, log=Fals """ # If we do not return G (log==False), then we should not to cast it to dense # (useless overhead) - G, log_emd = emd_1d(x_a=x_a, x_b=x_b, a=a, b=b, metric=metric, + G, log_emd = emd_1d(x_a=x_a, x_b=x_b, a=a, b=b, metric=metric, p=p, dense=dense and log, log=True) cost = log_emd['cost'] if log: log_emd = {'G': G} return cost, log_emd - return cost \ No newline at end of file + return cost + + +def wasserstein_1d(x_a, x_b, a=None, b=None, p=1., dense=True, log=False): + """Solves the Wasserstein distance problem between 1d measures and returns + the OT matrix + + + .. math:: + \gamma = arg\min_\gamma \left(\sum_i \sum_j \gamma_{ij} + |x_a[i] - x_b[j]|^p \right)^{1/p} + + s.t. \gamma 1 = a, + \gamma^T 1= b, + \gamma\geq 0 + where : + + - x_a and x_b are the samples + - a and b are the sample weights + + Uses the algorithm detailed in [1]_ + + Parameters + ---------- + x_a : (ns,) or (ns, 1) ndarray, float64 + Source dirac locations (on the real line) + x_b : (nt,) or (ns, 1) ndarray, float64 + Target dirac locations (on the real line) + a : (ns,) ndarray, float64, optional + Source histogram (default is uniform weight) + b : (nt,) ndarray, float64, optional + Target histogram (default is uniform weight) + p: float, optional (default=1.0) + The order of the p-Wasserstein distance to be computed + dense: boolean, optional (default=True) + If True, returns math:`\gamma` as a dense ndarray of shape (ns, nt). + Otherwise returns a sparse representation using scipy's `coo_matrix` + format. Due to implementation details, this function runs faster when + `'sqeuclidean'`, `'minkowski'`, `'cityblock'`, or `'euclidean'` metrics + are used. + log: boolean, optional (default=False) + If True, returns a dictionary containing the cost. + Otherwise returns only the optimal transportation matrix. + + Returns + ------- + gamma: (ns, nt) ndarray + Optimal transportation matrix for the given parameters + log: dict + If input log is True, a dictionary containing the cost + + + Examples + -------- + + Simple example with obvious solution. The function wasserstein_1d accepts + lists and performs automatic conversion to numpy arrays + + >>> import ot + >>> a=[.5, .5] + >>> b=[.5, .5] + >>> x_a = [2., 0.] + >>> x_b = [0., 3.] + >>> ot.wasserstein_1d(x_a, x_b, a, b) + array([[0. , 0.5], + [0.5, 0. ]]) + >>> ot.wasserstein_1d(x_a, x_b) + array([[0. , 0.5], + [0.5, 0. ]]) + + References + ---------- + + .. [1] Peyré, G., & Cuturi, M. (2017). "Computational Optimal + Transport", 2018. + + See Also + -------- + ot.lp.emd_1d : EMD for 1d distributions + ot.lp.wasserstein2_1d : Wasserstein for 1d distributions (returns the cost + instead of the transportation matrix) + """ + if log: + G, log = emd_1d(x_a=x_a, x_b=x_b, a=a, b=b, metric='minkowski', p=p, + dense=dense, log=log) + log['cost'] = np.power(log['cost'], 1. / p) + return G, log + return emd_1d(x_a=x_a, x_b=x_b, a=a, b=b, metric='minkowski', p=p, + dense=dense, log=log) + + +def wasserstein2_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', p=1., + dense=True, log=False): + """Solves the Wasserstein distance problem between 1d measures and returns + the loss + + + .. math:: + \gamma = arg\min_\gamma \left( \sum_i \sum_j \gamma_{ij} + |x_a[i] - x_b[j]|^p \right)^{1/p} + + s.t. \gamma 1 = a, + \gamma^T 1= b, + \gamma\geq 0 + where : + + - x_a and x_b are the samples + - a and b are the sample weights + + Uses the algorithm detailed in [1]_ + + Parameters + ---------- + x_a : (ns,) or (ns, 1) ndarray, float64 + Source dirac locations (on the real line) + x_b : (nt,) or (ns, 1) ndarray, float64 + Target dirac locations (on the real line) + a : (ns,) ndarray, float64, optional + Source histogram (default is uniform weight) + b : (nt,) ndarray, float64, optional + Target histogram (default is uniform weight) + p: float, optional (default=1.0) + The order of the p-Wasserstein distance to be computed + dense: boolean, optional (default=True) + If True, returns math:`\gamma` as a dense ndarray of shape (ns, nt). + Otherwise returns a sparse representation using scipy's `coo_matrix` + format. Only used if log is set to True. Due to implementation details, + this function runs faster when dense is set to False. + log: boolean, optional (default=False) + If True, returns a dictionary containing the transportation matrix. + Otherwise returns only the loss. + + Returns + ------- + loss: float + Cost associated to the optimal transportation + log: dict + If input log is True, a dictionary containing the Optimal transportation + matrix for the given parameters + + + Examples + -------- + + Simple example with obvious solution. The function wasserstein2_1d accepts + lists and performs automatic conversion to numpy arrays + + >>> import ot + >>> a=[.5, .5] + >>> b=[.5, .5] + >>> x_a = [2., 0.] + >>> x_b = [0., 3.] + >>> ot.wasserstein2_1d(x_a, x_b, a, b) + 0.5 + >>> ot.wasserstein2_1d(x_a, x_b) + 0.5 + + References + ---------- + + .. [1] Peyré, G., & Cuturi, M. (2017). "Computational Optimal + Transport", 2018. + + See Also + -------- + ot.lp.emd2_1d : EMD for 1d distributions + ot.lp.wasserstein_1d : Wasserstein for 1d distributions (returns the + transportation matrix instead of the cost) + """ + if log: + cost, log = emd2_1d(x_a=x_a, x_b=x_b, a=a, b=b, metric='minkowski', p=p, + dense=dense, log=log) + cost = np.power(cost, 1. / p) + return cost, log + return np.power(emd2_1d(x_a=x_a, x_b=x_b, a=a, b=b, metric='minkowski', p=p, + dense=dense, log=log), 1. / p) \ No newline at end of file diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 2825ba2..7134136 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -103,7 +103,8 @@ def emd_1d_sorted(np.ndarray[double, ndim=1, mode="c"] u_weights, np.ndarray[double, ndim=1, mode="c"] v_weights, np.ndarray[double, ndim=1, mode="c"] u, np.ndarray[double, ndim=1, mode="c"] v, - str metric='sqeuclidean'): + str metric='sqeuclidean', + double p=1.): r""" Solves the Earth Movers distance problem between sorted 1d measures and returns the OT matrix and the associated cost @@ -121,7 +122,10 @@ def emd_1d_sorted(np.ndarray[double, ndim=1, mode="c"] u_weights, metric: str, optional (default='sqeuclidean') Metric to be used. Only strings listed in :func:`ot.dist` are accepted. Due to implementation details, this function runs faster when - `'sqeuclidean'`, `'cityblock'`, or `'euclidean'` metrics are used. + `'sqeuclidean'`, `'minkowski'`, `'cityblock'`, or `'euclidean'` metrics + are used. + p: float, optional (default=1.0) + The p-norm to apply for if metric='minkowski' Returns ------- @@ -154,6 +158,8 @@ def emd_1d_sorted(np.ndarray[double, ndim=1, mode="c"] u_weights, m_ij = (u[i] - v[j]) ** 2 elif metric == 'cityblock' or metric == 'euclidean': m_ij = abs(u[i] - v[j]) + elif metric == 'minkowski': + m_ij = abs(u[i] - v[j]) ** p else: m_ij = dist(u[i].reshape((1, 1)), v[j].reshape((1, 1)), metric=metric)[0, 0] -- cgit v1.2.3 From 0d333e004636f5d25edea6bb195e8e4d9a95ba98 Mon Sep 17 00:00:00 2001 From: Romain Tavenard Date: Thu, 27 Jun 2019 10:23:32 +0200 Subject: Improved tests and docs for wasserstein_1d --- ot/__init__.py | 5 +++-- ot/lp/__init__.py | 13 ++++++------- ot/lp/emd_wrap.pyx | 3 ++- test/test_ot.py | 23 +++++++++++++++++++++++ 4 files changed, 34 insertions(+), 10 deletions(-) diff --git a/ot/__init__.py b/ot/__init__.py index f0e526c..5bd9bb3 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -22,7 +22,7 @@ from . import smooth from . import stochastic # OT functions -from .lp import emd, emd2, emd_1d, emd2_1d +from .lp import emd, emd2, emd_1d, emd2_1d, wasserstein_1d, wasserstein2_1d from .bregman import sinkhorn, sinkhorn2, barycenter from .da import sinkhorn_lpl1_mm @@ -32,5 +32,6 @@ from .utils import dist, unif, tic, toc, toq __version__ = "0.5.1" __all__ = ["emd", "emd2", 'emd_1d', "sinkhorn", "sinkhorn2", "utils", 'datasets', - 'bregman', 'lp', 'tic', 'toc', 'toq', 'gromov', 'emd_1d', 'emd2_1d', + 'bregman', 'lp', 'tic', 'toc', 'toq', 'gromov', + 'emd_1d', 'emd2_1d', 'wasserstein_1d', 'wasserstein2_1d', 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim'] diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 719032b..76c9ec0 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -530,13 +530,13 @@ def emd2_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', p=1., dense=True, def wasserstein_1d(x_a, x_b, a=None, b=None, p=1., dense=True, log=False): - """Solves the Wasserstein distance problem between 1d measures and returns + """Solves the p-Wasserstein distance problem between 1d measures and returns the OT matrix .. math:: - \gamma = arg\min_\gamma \left(\sum_i \sum_j \gamma_{ij} - |x_a[i] - x_b[j]|^p \right)^{1/p} + \gamma = arg\min_\gamma \left( \sum_i \sum_j \gamma_{ij} + |x_a[i] - x_b[j]|^p \\right)^{1/p} s.t. \gamma 1 = a, \gamma^T 1= b, @@ -617,15 +617,14 @@ def wasserstein_1d(x_a, x_b, a=None, b=None, p=1., dense=True, log=False): dense=dense, log=log) -def wasserstein2_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', p=1., - dense=True, log=False): - """Solves the Wasserstein distance problem between 1d measures and returns +def wasserstein2_1d(x_a, x_b, a=None, b=None, p=1., dense=True, log=False): + """Solves the p-Wasserstein distance problem between 1d measures and returns the loss .. math:: \gamma = arg\min_\gamma \left( \sum_i \sum_j \gamma_{ij} - |x_a[i] - x_b[j]|^p \right)^{1/p} + |x_a[i] - x_b[j]|^p \\right)^{1/p} s.t. \gamma 1 = a, \gamma^T 1= b, diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 7134136..42b848f 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -13,6 +13,7 @@ cimport numpy as np from ..utils import dist cimport cython +cimport libc.math as math import warnings @@ -159,7 +160,7 @@ def emd_1d_sorted(np.ndarray[double, ndim=1, mode="c"] u_weights, elif metric == 'cityblock' or metric == 'euclidean': m_ij = abs(u[i] - v[j]) elif metric == 'minkowski': - m_ij = abs(u[i] - v[j]) ** p + m_ij = math.pow(abs(u[i] - v[j]), p) else: m_ij = dist(u[i].reshape((1, 1)), v[j].reshape((1, 1)), metric=metric)[0, 0] diff --git a/test/test_ot.py b/test/test_ot.py index 6d6ea26..48423e7 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -85,6 +85,29 @@ def test_emd_1d_emd2_1d(): np.testing.assert_raises(AssertionError, ot.emd_1d, u, v, [], []) +def test_wass_1d(): + # test emd1d gives similar results as emd + n = 20 + m = 30 + rng = np.random.RandomState(0) + u = rng.randn(n, 1) + v = rng.randn(m, 1) + + M = ot.dist(u, v, metric='sqeuclidean') + + G, log = ot.emd([], [], M, log=True) + wass = log["cost"] + + G_1d, log = ot.wasserstein_1d(u, v, [], [], p=2., log=True) + wass1d = log["cost"] + + # check loss is similar + np.testing.assert_allclose(np.sqrt(wass), wass1d) + + # check G is similar + np.testing.assert_allclose(G, G_1d) + + def test_emd_empty(): # test emd and emd2 for simple identity n = 100 -- cgit v1.2.3 From c92e595009ad5e2ae6d4b2c040556cffb6316847 Mon Sep 17 00:00:00 2001 From: Romain Tavenard Date: Thu, 27 Jun 2019 11:08:15 +0200 Subject: Wasserstein defined as the cost itself (do not return transportation matrix) --- ot/__init__.py | 4 +- ot/lp/__init__.py | 125 +++++------------------------------------------------- test/test_ot.py | 6 +-- 3 files changed, 13 insertions(+), 122 deletions(-) diff --git a/ot/__init__.py b/ot/__init__.py index 730aa4f..1b3c2fb 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -23,7 +23,7 @@ from . import stochastic from . import unbalanced # OT functions -from .lp import emd, emd2, emd_1d, emd2_1d, wasserstein_1d, wasserstein2_1d +from .lp import emd, emd2, emd_1d, emd2_1d, wasserstein_1d from .bregman import sinkhorn, sinkhorn2, barycenter from .unbalanced import sinkhorn_unbalanced, barycenter_unbalanced from .da import sinkhorn_lpl1_mm @@ -35,6 +35,6 @@ __version__ = "0.5.1" __all__ = ["emd", "emd2", 'emd_1d', "sinkhorn", "sinkhorn2", "utils", 'datasets', 'bregman', 'lp', 'tic', 'toc', 'toq', 'gromov', - 'emd_1d', 'emd2_1d', 'wasserstein_1d', 'wasserstein2_1d', + 'emd_1d', 'emd2_1d', 'wasserstein_1d', 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim', 'sinkhorn_unbalanced', "barycenter_unbalanced"] diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 76c9ec0..a3f5b8d 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -21,7 +21,7 @@ from .cvx import barycenter from ..utils import dist __all__=['emd', 'emd2', 'barycenter', 'free_support_barycenter', 'cvx', - 'emd_1d', 'emd2_1d', 'wasserstein_1d', 'wasserstein2_1d'] + 'emd_1d', 'emd2_1d', 'wasserstein_1d'] def emd(a, b, M, numItermax=100000, log=False): @@ -529,9 +529,9 @@ def emd2_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', p=1., dense=True, return cost -def wasserstein_1d(x_a, x_b, a=None, b=None, p=1., dense=True, log=False): +def wasserstein_1d(x_a, x_b, a=None, b=None, p=1.): """Solves the p-Wasserstein distance problem between 1d measures and returns - the OT matrix + the distance .. math:: @@ -560,22 +560,11 @@ def wasserstein_1d(x_a, x_b, a=None, b=None, p=1., dense=True, log=False): Target histogram (default is uniform weight) p: float, optional (default=1.0) The order of the p-Wasserstein distance to be computed - dense: boolean, optional (default=True) - If True, returns math:`\gamma` as a dense ndarray of shape (ns, nt). - Otherwise returns a sparse representation using scipy's `coo_matrix` - format. Due to implementation details, this function runs faster when - `'sqeuclidean'`, `'minkowski'`, `'cityblock'`, or `'euclidean'` metrics - are used. - log: boolean, optional (default=False) - If True, returns a dictionary containing the cost. - Otherwise returns only the optimal transportation matrix. Returns ------- - gamma: (ns, nt) ndarray - Optimal transportation matrix for the given parameters - log: dict - If input log is True, a dictionary containing the cost + dist: float + p-Wasserstein distance Examples @@ -590,96 +579,8 @@ def wasserstein_1d(x_a, x_b, a=None, b=None, p=1., dense=True, log=False): >>> x_a = [2., 0.] >>> x_b = [0., 3.] >>> ot.wasserstein_1d(x_a, x_b, a, b) - array([[0. , 0.5], - [0.5, 0. ]]) - >>> ot.wasserstein_1d(x_a, x_b) - array([[0. , 0.5], - [0.5, 0. ]]) - - References - ---------- - - .. [1] Peyré, G., & Cuturi, M. (2017). "Computational Optimal - Transport", 2018. - - See Also - -------- - ot.lp.emd_1d : EMD for 1d distributions - ot.lp.wasserstein2_1d : Wasserstein for 1d distributions (returns the cost - instead of the transportation matrix) - """ - if log: - G, log = emd_1d(x_a=x_a, x_b=x_b, a=a, b=b, metric='minkowski', p=p, - dense=dense, log=log) - log['cost'] = np.power(log['cost'], 1. / p) - return G, log - return emd_1d(x_a=x_a, x_b=x_b, a=a, b=b, metric='minkowski', p=p, - dense=dense, log=log) - - -def wasserstein2_1d(x_a, x_b, a=None, b=None, p=1., dense=True, log=False): - """Solves the p-Wasserstein distance problem between 1d measures and returns - the loss - - - .. math:: - \gamma = arg\min_\gamma \left( \sum_i \sum_j \gamma_{ij} - |x_a[i] - x_b[j]|^p \\right)^{1/p} - - s.t. \gamma 1 = a, - \gamma^T 1= b, - \gamma\geq 0 - where : - - - x_a and x_b are the samples - - a and b are the sample weights - - Uses the algorithm detailed in [1]_ - - Parameters - ---------- - x_a : (ns,) or (ns, 1) ndarray, float64 - Source dirac locations (on the real line) - x_b : (nt,) or (ns, 1) ndarray, float64 - Target dirac locations (on the real line) - a : (ns,) ndarray, float64, optional - Source histogram (default is uniform weight) - b : (nt,) ndarray, float64, optional - Target histogram (default is uniform weight) - p: float, optional (default=1.0) - The order of the p-Wasserstein distance to be computed - dense: boolean, optional (default=True) - If True, returns math:`\gamma` as a dense ndarray of shape (ns, nt). - Otherwise returns a sparse representation using scipy's `coo_matrix` - format. Only used if log is set to True. Due to implementation details, - this function runs faster when dense is set to False. - log: boolean, optional (default=False) - If True, returns a dictionary containing the transportation matrix. - Otherwise returns only the loss. - - Returns - ------- - loss: float - Cost associated to the optimal transportation - log: dict - If input log is True, a dictionary containing the Optimal transportation - matrix for the given parameters - - - Examples - -------- - - Simple example with obvious solution. The function wasserstein2_1d accepts - lists and performs automatic conversion to numpy arrays - - >>> import ot - >>> a=[.5, .5] - >>> b=[.5, .5] - >>> x_a = [2., 0.] - >>> x_b = [0., 3.] - >>> ot.wasserstein2_1d(x_a, x_b, a, b) 0.5 - >>> ot.wasserstein2_1d(x_a, x_b) + >>> ot.wasserstein_1d(x_a, x_b) 0.5 References @@ -690,14 +591,8 @@ def wasserstein2_1d(x_a, x_b, a=None, b=None, p=1., dense=True, log=False): See Also -------- - ot.lp.emd2_1d : EMD for 1d distributions - ot.lp.wasserstein_1d : Wasserstein for 1d distributions (returns the - transportation matrix instead of the cost) + ot.lp.emd_1d : EMD for 1d distributions """ - if log: - cost, log = emd2_1d(x_a=x_a, x_b=x_b, a=a, b=b, metric='minkowski', p=p, - dense=dense, log=log) - cost = np.power(cost, 1. / p) - return cost, log - return np.power(emd2_1d(x_a=x_a, x_b=x_b, a=a, b=b, metric='minkowski', p=p, - dense=dense, log=log), 1. / p) \ No newline at end of file + cost_emd = emd2_1d(x_a=x_a, x_b=x_b, a=a, b=b, metric='minkowski', p=p, + dense=False, log=False) + return np.power(cost_emd, 1. / p) diff --git a/test/test_ot.py b/test/test_ot.py index 48423e7..3c4ac11 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -98,15 +98,11 @@ def test_wass_1d(): G, log = ot.emd([], [], M, log=True) wass = log["cost"] - G_1d, log = ot.wasserstein_1d(u, v, [], [], p=2., log=True) - wass1d = log["cost"] + wass1d = ot.wasserstein_1d(u, v, [], [], p=2.) # check loss is similar np.testing.assert_allclose(np.sqrt(wass), wass1d) - # check G is similar - np.testing.assert_allclose(G, G_1d) - def test_emd_empty(): # test emd and emd2 for simple identity -- cgit v1.2.3 From 362a7f8fa20cf7ae6f2e36d7e47c7ca9f81d3c51 Mon Sep 17 00:00:00 2001 From: Romain Tavenard Date: Thu, 27 Jun 2019 13:29:19 +0200 Subject: Added RT as a contributor + "optimized" Cython math operations --- README.md | 1 + ot/lp/emd_wrap.pyx | 6 +++--- 2 files changed, 4 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index d24d8b9..84148f8 100644 --- a/README.md +++ b/README.md @@ -167,6 +167,7 @@ The contributors to this library are: * [Alain Rakotomamonjy](https://sites.google.com/site/alainrakotomamonjy/home) * [Vayer Titouan](https://tvayer.github.io/) * [Hicham Janati](https://hichamjanati.github.io/) (Unbalanced OT) +* [Romain Tavenard](https://rtavenar.github.io/) (1d Wasserstein) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various languages): diff --git a/ot/lp/emd_wrap.pyx b/ot/lp/emd_wrap.pyx index 42b848f..8a4aec9 100644 --- a/ot/lp/emd_wrap.pyx +++ b/ot/lp/emd_wrap.pyx @@ -156,11 +156,11 @@ def emd_1d_sorted(np.ndarray[double, ndim=1, mode="c"] u_weights, cdef int cur_idx = 0 while i < n and j < m: if metric == 'sqeuclidean': - m_ij = (u[i] - v[j]) ** 2 + m_ij = (u[i] - v[j]) * (u[i] - v[j]) elif metric == 'cityblock' or metric == 'euclidean': - m_ij = abs(u[i] - v[j]) + m_ij = math.fabs(u[i] - v[j]) elif metric == 'minkowski': - m_ij = math.pow(abs(u[i] - v[j]), p) + m_ij = math.pow(math.fabs(u[i] - v[j]), p) else: m_ij = dist(u[i].reshape((1, 1)), v[j].reshape((1, 1)), metric=metric)[0, 0] -- cgit v1.2.3 From d20d471a1806bde43c23e67c1f805aa3c8908ec3 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 27 Jun 2019 14:34:23 +0200 Subject: update part 1 --- docs/source/quickstart.rst | 23 +++++++++++++++++++++++ 1 file changed, 23 insertions(+) diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst index d8d4838..a14358c 100644 --- a/docs/source/quickstart.rst +++ b/docs/source/quickstart.rst @@ -83,6 +83,29 @@ properties. It can computed from an already estimated OT matrix with Regularized Optimal Transport ----------------------------- +Recent developments have shown the interest of regularized OT both in terms of +computational and statistical properties. + +We address in this section the regularized OT problem that can be expressed as + +.. math:: + \gamma^* = arg\min_\gamma <\gamma,M>_F + reg*\Omega(\gamma) + + s.t. \gamma 1 = a + + \gamma^T 1= b + + \gamma\geq 0 +where : + +- :math:`M\in\mathbb{R}_+^{m\times n}` is the metric cost matrix defining the cost to move mass from bin :math:`a_i` to bin :math:`b_j`. +- :math:`a` and :math:`b` are histograms (positive, sum to 1) that represent the weights of each samples in the source an target distributions. +- :math:`\Omega` is the regularization term. + +We disvuss in the following specific algorithms + + + Entropic regularized OT ^^^^^^^^^^^^^^^^^^^^^^^ -- cgit v1.2.3 From 2d7db0ed112b9349dc0b0c4cc7a9f3ea8da4ebed Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 27 Jun 2019 15:01:13 +0200 Subject: update readme --- docs/source/readme.rst | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/docs/source/readme.rst b/docs/source/readme.rst index b7828d3..320ddd5 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -35,6 +35,7 @@ It provides the following solvers: - Stochastic Optimization for Large-scale Optimal Transport (semi-dual problem [18] and dual problem [19]) - Non regularized free support Wasserstein barycenters [20]. +- Unbalanced OT with KL relaxation distance and barycenter [10, 25]. Some demonstrations (both in Python and Jupyter Notebook format) are available in the examples folder. @@ -69,6 +70,13 @@ modules: Pip installation ^^^^^^^^^^^^^^^^ +Note that due to a limitation of pip, ``cython`` and ``numpy`` need to +be installed prior to installing POT. This can be done easily with + +:: + + pip install numpy cython + You can install the toolbox through PyPI with: :: @@ -229,6 +237,8 @@ The contributors to this library are - `Alain Rakotomamonjy `__ - `Vayer Titouan `__ +- `Hicham Janati `__ (Unbalanced OT) +- `Romain Tavenard `__ (1d Wasserstein) This toolbox benefit a lot from open source research and we would like to thank the following persons for providing some code (in various @@ -379,6 +389,10 @@ and Statistics, (AISTATS) 21, 2018 graphs `__ Proceedings of the 36th International Conference on Machine Learning (ICML). +[25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. (2019). +`Learning with a Wasserstein Loss `__ +Advances in Neural Information Processing Systems (NIPS). + .. |PyPI version| image:: https://badge.fury.io/py/POT.svg :target: https://badge.fury.io/py/POT .. |Anaconda Cloud| image:: https://anaconda.org/conda-forge/pot/badges/version.svg -- cgit v1.2.3 From 982ee8345d491d76ac9ba49c6b9a7f5418ed966d Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 27 Jun 2019 16:40:38 +0200 Subject: start section entropic --- docs/source/quickstart.rst | 90 +++++++++++++++++++++++++++++++++++++++------- 1 file changed, 77 insertions(+), 13 deletions(-) diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst index a14358c..c122d17 100644 --- a/docs/source/quickstart.rst +++ b/docs/source/quickstart.rst @@ -21,7 +21,7 @@ Solving optimal transport The optimal transport problem between discrete distributions is often expressed as .. math:: - \gamma^* = arg\min_\gamma \sum_{i,j}\gamma_{i,j}M_{i,j} + \gamma^* = arg\min_\gamma \quad \sum_{i,j}\gamma_{i,j}M_{i,j} s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0 @@ -56,15 +56,12 @@ Computing Wasserstein distance The value of the OT solution is often more of interest that the OT matrix : .. math:: - W(a,b)=\min_\gamma \sum_{i,j}\gamma_{i,j}M_{i,j} + OT(a,b)=\min_\gamma \quad \sum_{i,j}\gamma_{i,j}M_{i,j} s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0 -where :math:`W(a,b)` is the `Wasserstein distance -`_ between distributions a and b -It is a metrix that has nice statistical -properties. It can computed from an already estimated OT matrix with +It can computed from an already estimated OT matrix with :code:`np.sum(T*M)` or directly with the function :any:`ot.emd2`. .. code:: python @@ -73,6 +70,25 @@ properties. It can computed from an already estimated OT matrix with # M is the ground cost matrix W=ot.emd2(a,b,M) # Wasserstein distance / EMD value +Note that the well known `Wasserstein distance +`_ between distributions a and +b is defined as + + + .. math:: + + W_p(a,b)=(\min_\gamma \sum_{i,j}\gamma_{i,j}\|x_i-y_j\|_p)^\frac{1}{p} + + s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0 + +This means that if you want to compute the :math:`W_2` you need to compute the +square root of :any:`ot.emd2` when providing +:code:`M=ot.dist(xs,xt)` that use the squared euclidean distance by default. Computing +the :math:`W_1` wasserstein distance can be done directly with :any:`ot.emd2` +when providing :code:`M=ot.dist(xs,xt, metric='euclidean')` to use the euclidean +distance. + + .. hint:: Examples of use for :any:`ot.emd2` are available in the following examples: @@ -80,6 +96,32 @@ properties. It can computed from an already estimated OT matrix with - :any:`auto_examples/plot_compute_emd` +Special cases +^^^^^^^^^^^^^ + +Note that the OT problem and the corresponding Wasserstein distance can in some +special cases be computed very efficiently. + +For instance when the samples are in 1D, then the OT problem can be solved in +:math:`O(n\log(n))` by using a simple sorting. In this case we provide the +function :any:`ot.emd_1d` and :any:`ot.emd2_1d` to return respectively the OT +matrix and value. Note that since the solution is very sparse the :code:`sparse` +parameter of :any:`ot.emd_1d` allows for solving and returning the solution for +very large problems. Note that in order to computed directly the :math:`W_p` +Wasserstein distance in 1D we provide the function :any:`ot.wasserstein_1d` that +takes :code:`p` as a parameter. + +Another specials for estimating OT and Monge mapping is between Gaussian +distributions. In this case there exists a close form solution given in Remark +2.29 in [15]_ and the Monge mapping is an affine function and can be +also computed from the covariances and means of the source and target +distributions. In this case when the finite sample dataset is supposed gaussian, we provide +:any:`ot.da.OT_mapping_linear` that returns the parameters for the Monge +mapping. + + + + Regularized Optimal Transport ----------------------------- @@ -89,31 +131,53 @@ computational and statistical properties. We address in this section the regularized OT problem that can be expressed as .. math:: - \gamma^* = arg\min_\gamma <\gamma,M>_F + reg*\Omega(\gamma) + \gamma^* = arg\min_\gamma \quad \sum_{i,j}\gamma_{i,j}M_{i,j} + \lambda\Omega(\gamma) - s.t. \gamma 1 = a + s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0 - \gamma^T 1= b - \gamma\geq 0 where : - :math:`M\in\mathbb{R}_+^{m\times n}` is the metric cost matrix defining the cost to move mass from bin :math:`a_i` to bin :math:`b_j`. - :math:`a` and :math:`b` are histograms (positive, sum to 1) that represent the weights of each samples in the source an target distributions. - :math:`\Omega` is the regularization term. -We disvuss in the following specific algorithms - +We discuss in the following specific algorithms that can be used depending on +the regularization term. Entropic regularized OT ^^^^^^^^^^^^^^^^^^^^^^^ +This is the most common regularization used for optimal transport. It has been +proposed in the ML community by Marco Cuturi in his seminal paper [2]_. This +regularization has the following expression + +.. math:: + \Omega(\gamma)=\sum_{i,j}\gamma_{i,j}\log(\gamma_{i,j}) + + +The use of the regularization term above in the optimization problem has a very +strong impact. First it makes the problem smooth which leads to new optimization +procedures such as L-BFGS (see :any:`ot.smooth` ). Next it makes the problem +strictly convex meaning that there will be a unique solution. Finally the +solution of the resulting optimization problem can be expressed as: + +.. math:: + + \gamma_\lambda^*=\text{diag}(u)K\text{diag}(v) + +where :math:`u` and :math:`v` are vectors and :math:`K=\exp(-M/\lambda)` where +the :math:`\exp` is taken component-wise. + + + + Other regularization ^^^^^^^^^^^^^^^^^^^^ -Stochastic gradient decsent +Stochastic gradient descent ^^^^^^^^^^^^^^^^^^^^^^^^^^^ Wasserstein Barycenters -- cgit v1.2.3 From 7dcfebbef19e1f94928fc71face612a2f71372b4 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Jun 2019 08:33:36 +0200 Subject: entropic mostly done, starting general regularization --- docs/source/quickstart.rst | 144 +++++++++++++++++++++++++++++++++++++++++---- 1 file changed, 133 insertions(+), 11 deletions(-) diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst index c122d17..4f2d9bb 100644 --- a/docs/source/quickstart.rst +++ b/docs/source/quickstart.rst @@ -5,6 +5,9 @@ Quick start guide In the following we provide some pointers about which functions and classes to use for different problems related to optimal transport (OT). +This document is not a tutorial on numerical optimal transport. For this we strongly +recommend to read the very nice book [15]_ . + Optimal transport and Wasserstein distance ------------------------------------------ @@ -20,10 +23,11 @@ Solving optimal transport The optimal transport problem between discrete distributions is often expressed as - .. math:: - \gamma^* = arg\min_\gamma \quad \sum_{i,j}\gamma_{i,j}M_{i,j} - s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0 +.. math:: + \gamma^* = arg\min_\gamma \quad \sum_{i,j}\gamma_{i,j}M_{i,j} + + s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0 where : @@ -120,8 +124,6 @@ distributions. In this case when the finite sample dataset is supposed gaussian, mapping. - - Regularized Optimal Transport ----------------------------- @@ -146,6 +148,7 @@ We discuss in the following specific algorithms that can be used depending on the regularization term. + Entropic regularized OT ^^^^^^^^^^^^^^^^^^^^^^^ @@ -168,23 +171,107 @@ solution of the resulting optimization problem can be expressed as: \gamma_\lambda^*=\text{diag}(u)K\text{diag}(v) where :math:`u` and :math:`v` are vectors and :math:`K=\exp(-M/\lambda)` where -the :math:`\exp` is taken component-wise. +the :math:`\exp` is taken component-wise. In order to solve the optimization +problem, on can use an alternative projection algorithm that can be very +efficient for large values if regularization. + +The main function is POT are :any:`ot.sinkhorn` and +:any:`ot.sinkhorn2` that return respectively the OT matrix and the value of the +linear term. Note that the regularization parameter :math:`\lambda` in the +equation above is given to those function with the parameter :code:`reg`. + >>> import ot + >>> a=[.5,.5] + >>> b=[.5,.5] + >>> M=[[0.,1.],[1.,0.]] + >>> ot.sinkhorn(a,b,M,1) + array([[ 0.36552929, 0.13447071], + [ 0.13447071, 0.36552929]]) +More details about the algorithm used is given in the following note. + + +.. note:: + The main function to solve entropic regularized OT is :any:`ot.sinkhorn`. + This function is a wrapper and the parameter :code:`method` help you select + the actual algorithm used to solve the problem: + + + :code:`method='sinkhorn'` calls :any:`ot.bregman.sinkhorn_knopp` the + classic algorithm [2]_. + + :code:`method='sinkhorn_stabilized'` calls :any:`ot.bregman.sinkhorn_stabilized` the + log stabilized version of the algorithm [9]_. + + :code:`method='sinkhorn_epsilon_scaling'` calls + :any:`ot.bregman.sinkhorn_epsilon_scaling` the epsilon scaling version + of the algorithm [9]_. + + :code:`method='greenkhorn'` calls :any:`ot.bregman.greenkhorn` the + greedy sinkhorn verison of the algorithm [22]_. + + In addition to all those variants of sinkhorn, we have another + implementation solving the problem in the smooth dual or semi-dual in + :any:`ot.smooth`. This solver use the :any:`scipy.optimize.minimize` + function to solve the smooth problem with :code:`L-BFGS` algorithm. Tu use + this solver, use functions :any:`ot.smooth.smooth_ot_dual` or + :any:`ot.smooth.smooth_ot_semi_dual` with parameter :code:`reg_type='kl'` to + choose entropic/Kullbach Leibler regularization. + +.. hint:: + Examples of use for :any:`ot.sinkhorn` are available in the following examples: + + - :any:`auto_examples/plot_OT_2D_samples` + - :any:`auto_examples/plot_OT_1D` + - :any:`auto_examples/plot_OT_1D_smooth` + - :any:`auto_examples/plot_stochastic` + +Finally note that we also provide in :any:`ot.stochastic` several implementation +of stochastic solvers for entropic regularized OT [18]_ [19]_. Other regularization ^^^^^^^^^^^^^^^^^^^^ -Stochastic gradient descent -^^^^^^^^^^^^^^^^^^^^^^^^^^^ +While entropic OT is the most common and favored in practice, there exist other +kind of regularization. We provide in POT two specific solvers for other +regularization terms: namely quadratic regularization and group lasso +regularization. But we also provide in :any:`ot.optim` two generic solvers that allows solving any +smooth regularization in practice. + +The first general regularization term we can solve is the quadratic +regularization of the form + +.. math:: + \Omega(\gamma)=\sum_{i,j} \gamma_{i,j}^2 + +this regularization term has a similar effect to entropic regularization in +densifying the OT matrix but it keeps some sort of sparsity that is lost with +entropic regularization as soon as :math:`\lambda>0` [17]_. This problem cen be +solved with POT using solvers from :any:`ot.smooth`, more specifically +functions :any:`ot.smooth.smooth_ot_dual` or +:any:`ot.smooth.smooth_ot_semi_dual` with parameter :code:`reg_type='l2'` to +choose the quadratic regularization. + +Another regularization that has been used in recent years is the group lasso +regularization + +.. math:: + \Omega(\gamma)=\sum_{j,G\in\mathcal{G}} \|\gamma_{G,j}\|_p^q + +where :math:`\mathcal{G}` contains non overlapping groups of lines in the OT +matrix. This regularization proposed in [5]_ will promote sparsity at the group level and for +instance will force target samples to get mass from a small number of groups. +Note that the exact OT solution is already sparse so this regularization does +not make sens if it is not combined with others such as entropic. + + + + + Wasserstein Barycenters ----------------------- Monge mapping and Domain adaptation with Optimal transport ----------------------------------------- +---------------------------------------------------------- Other applications @@ -207,7 +294,6 @@ FAQ the OT transport matrix. If you want to solve a regularized OT you can use :py:mod:`ot.sinkhorn`. - Here is a simple use case: @@ -222,7 +308,43 @@ FAQ :doc:`auto_examples/plot_OT_2D_samples` -2. **Compute a Wasserstein distance** +2. **pip install POT fails with error : ImportError: No module named Cython.Build** + + As discussed shortly in the README file. POT requires to have :code:`numpy` + and :code:`cython` installed to build. This corner case is not yet handled + by :code:`pip` and for now you need to install both library prior to + installing POT. + + Note that this problem do not occur when using conda-forge since the packages + there are pre-compiled. + + See `Issue #59 `__ for more + details. + +3. **Why is Sinkhorn slower than EMD ?** + + This might come from the choice of the regularization term. The speed of + convergence of sinkhorn depends directly on this term [22]_ and when the + regularization gets very small the problem try and approximate the exact OT + which leads to slow convergence in addition to numerical problems. In other + words, for large regularization sinkhorn will be very fast to converge, for + small regularization (when you need an OT matrix close to the true OT), it + might be quicker to use the EMD solver. + + Also note that the numpy implementation of the sinkhorn can use parallel + computation depending on the configuration of your system but very important + speedup can be obtained by using a GPU implementation since all operations + are matrix/vector products. + +4. **Using GPU fails with error: module 'ot' has no attribute 'gpu'** + + In order to limit import time and hard dependencies in POT. we do not import + some sub-modules automatically with :code:`import ot`. In order to use the + acceleration in :any:`ot.gpu` you need first to import is with + :code:`import ot.gpu`. + + See `Issue #85 `__ and :any:`ot.gpu` + for more details. References -- cgit v1.2.3 From 56deee6e1a69a087022bf81279419305452f5177 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 28 Jun 2019 09:39:23 +0200 Subject: update reg OT --- docs/source/quickstart.rst | 38 +++++++++++++++++++++++++++++++------- 1 file changed, 31 insertions(+), 7 deletions(-) diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst index 4f2d9bb..62688bc 100644 --- a/docs/source/quickstart.rst +++ b/docs/source/quickstart.rst @@ -210,7 +210,7 @@ More details about the algorithm used is given in the following note. In addition to all those variants of sinkhorn, we have another implementation solving the problem in the smooth dual or semi-dual in - :any:`ot.smooth`. This solver use the :any:`scipy.optimize.minimize` + :any:`ot.smooth`. This solver uses the :any:`scipy.optimize.minimize` function to solve the smooth problem with :code:`L-BFGS` algorithm. Tu use this solver, use functions :any:`ot.smooth.smooth_ot_dual` or :any:`ot.smooth.smooth_ot_semi_dual` with parameter :code:`reg_type='kl'` to @@ -224,6 +224,13 @@ More details about the algorithm used is given in the following note. - :any:`auto_examples/plot_OT_1D_smooth` - :any:`auto_examples/plot_stochastic` + +Recently [23]_ introduced the sinkhorn divergence that build from entropic +regularization to compute fast and differentiable geometric diveregnce between +empirical distributions. + + + Finally note that we also provide in :any:`ot.stochastic` several implementation of stochastic solvers for entropic regularized OT [18]_ [19]_. @@ -254,33 +261,50 @@ Another regularization that has been used in recent years is the group lasso regularization .. math:: - \Omega(\gamma)=\sum_{j,G\in\mathcal{G}} \|\gamma_{G,j}\|_p^q + \Omega(\gamma)=\sum_{j,G\in\mathcal{G}} \|\gamma_{G,j}\|_q^p where :math:`\mathcal{G}` contains non overlapping groups of lines in the OT matrix. This regularization proposed in [5]_ will promote sparsity at the group level and for instance will force target samples to get mass from a small number of groups. Note that the exact OT solution is already sparse so this regularization does -not make sens if it is not combined with others such as entropic. +not make sens if it is not combined with others such as entropic. Depending on +the choice of :code:`p` and :code:`q`, the problem can be solved with different +approaches. When :code:`q=1` and :code:`p<1` the problem is non convex but can +be solved using an efficient majoration minimization approach with +:any:`ot.sinkhorn_lpl1_mm`. When :code:`q=2` and :code:`p=1` we recover the +convex gourp lasso and we provide a solver using generalized conditional +gradient algorithm [7]_ in function +:any:`ot.da.sinkhorn_l1l2_gl`. +Wasserstein Barycenters +----------------------- -Wasserstein Barycenters ------------------------ -Monge mapping and Domain adaptation with Optimal transport ----------------------------------------------------------- +Monge mapping and Domain adaptation +----------------------------------- Other applications ------------------ +Wasserstein Discriminant Analysis +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + + +Gromov-Wasserstein +^^^^^^^^^^^^^^^^^^ + GPU acceleration ---------------- +We provide several implementation of our OT solvers in :any:`ot.gpu`. Those +implementation use the :code:`cupy` toolbox. + FAQ -- cgit v1.2.3 From 93a74fe4d477e1735e9ce21ee4113281f58b4dcf Mon Sep 17 00:00:00 2001 From: Romain Tavenard Date: Mon, 1 Jul 2019 11:02:11 +0200 Subject: Explicit doctest call in travis + removed uneffective doctest in test_ot --- .travis.yml | 2 +- test/test_ot.py | 10 ---------- 2 files changed, 1 insertion(+), 11 deletions(-) diff --git a/.travis.yml b/.travis.yml index 50ff22c..d6b4232 100644 --- a/.travis.yml +++ b/.travis.yml @@ -32,5 +32,5 @@ install: script: - python setup.py develop - flake8 examples/ ot/ test/ - - python -m pytest -v test/ --cov=ot + - python -m pytest -v test/ ot/ --doctest-modules --cov=ot # - py.test ot test diff --git a/test/test_ot.py b/test/test_ot.py index 3c4ac11..ac86602 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -14,16 +14,6 @@ from ot.datasets import make_1D_gauss as gauss import pytest -def test_doctest(): - import doctest - - # test lp solver - doctest.testmod(ot.lp, verbose=True) - - # test bregman solver - doctest.testmod(ot.bregman, verbose=True) - - def test_emd_emd2(): # test emd and emd2 for simple identity n = 100 -- cgit v1.2.3 From b05d315b0994d328029d4a4fc082f6994e7f06d1 Mon Sep 17 00:00:00 2001 From: Romain Tavenard Date: Mon, 1 Jul 2019 11:06:26 +0200 Subject: Moved GPU doctests to test_gpu for tests not to fail if no GPU available --- ot/gpu/bregman.py | 11 ----------- test/test_gpu.py | 10 ++++++++++ 2 files changed, 10 insertions(+), 11 deletions(-) diff --git a/ot/gpu/bregman.py b/ot/gpu/bregman.py index 978b307..2e2df83 100644 --- a/ot/gpu/bregman.py +++ b/ot/gpu/bregman.py @@ -70,17 +70,6 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, stopThr=1e-9, log : dict log dictionary return only if log==True in parameters - Examples - -------- - - >>> import ot - >>> a=[.5,.5] - >>> b=[.5,.5] - >>> M=[[0.,1.],[1.,0.]] - >>> ot.sinkhorn(a,b,M,1) - array([[ 0.36552929, 0.13447071], - [ 0.13447071, 0.36552929]]) - References ---------- diff --git a/test/test_gpu.py b/test/test_gpu.py index 6b7fdd4..47b8b6d 100644 --- a/test/test_gpu.py +++ b/test/test_gpu.py @@ -15,6 +15,16 @@ except ImportError: nogpu = True +@pytest.mark.skipif(nogpu, reason="No GPU available") +def test_gpu_old_doctests(): + a = [.5, .5] + b = [.5, .5] + M = [[0., 1.], [1., 0.]] + G = ot.sinkhorn(a, b, M, 1) + np.testing.assert_allclose(G, np.array([[0.36552929, 0.13447071], + [0.13447071, 0.36552929]])) + + @pytest.mark.skipif(nogpu, reason="No GPU available") def test_gpu_dist(): -- cgit v1.2.3 From 1fe13ed9cdda363c95c84f95fc70dcd95ac276f1 Mon Sep 17 00:00:00 2001 From: Romain Tavenard Date: Mon, 1 Jul 2019 12:16:04 +0200 Subject: Fixed doctests --- .travis.yml | 2 +- ot/bregman.py | 17 +++++++--------- ot/stochastic.py | 59 +++++++++++++++++++++++--------------------------------- ot/utils.py | 6 +++--- 4 files changed, 35 insertions(+), 49 deletions(-) diff --git a/.travis.yml b/.travis.yml index d6b4232..da68c96 100644 --- a/.travis.yml +++ b/.travis.yml @@ -32,5 +32,5 @@ install: script: - python setup.py develop - flake8 examples/ ot/ test/ - - python -m pytest -v test/ ot/ --doctest-modules --cov=ot + - python -m pytest -v test/ ot/ --doctest-modules --ignore ot/gpu/ --cov=ot # - py.test ot test diff --git a/ot/bregman.py b/ot/bregman.py index 09716e6..8225967 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1360,10 +1360,9 @@ def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numI >>> reg = 0.1 >>> X_s = np.reshape(np.arange(n_s), (n_s, 1)) >>> X_t = np.reshape(np.arange(0, n_t), (n_t, 1)) - >>> emp_sinkhorn = empirical_sinkhorn(X_s, X_t, reg, verbose=False) - >>> print(emp_sinkhorn) - >>> [[4.99977301e-01 2.26989344e-05] - [2.26989344e-05 4.99977301e-01]] + >>> empirical_sinkhorn(X_s, X_t, reg, verbose=False) # doctest: +NORMALIZE_WHITESPACE + array([[4.99977301e-01, 2.26989344e-05], + [2.26989344e-05, 4.99977301e-01]]) References @@ -1451,9 +1450,8 @@ def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', num >>> reg = 0.1 >>> X_s = np.reshape(np.arange(n_s), (n_s, 1)) >>> X_t = np.reshape(np.arange(0, n_t), (n_t, 1)) - >>> loss_sinkhorn = empirical_sinkhorn2(X_s, X_t, reg, verbose=False) - >>> print(loss_sinkhorn) - >>> [4.53978687e-05] + >>> empirical_sinkhorn2(X_s, X_t, reg, verbose=False) + array([4.53978687e-05]) References @@ -1560,9 +1558,8 @@ def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeucli >>> reg = 0.1 >>> X_s = np.reshape(np.arange(n_s), (n_s, 1)) >>> X_t = np.reshape(np.arange(0, n_t), (n_t, 1)) - >>> emp_sinkhorn_div = empirical_sinkhorn_divergence(X_s, X_t, reg) - >>> print(emp_sinkhorn_div) - >>> [2.99977435] + >>> empirical_sinkhorn_divergence(X_s, X_t, reg) + array([2.99977435]) References diff --git a/ot/stochastic.py b/ot/stochastic.py index 85c4230..762eb3e 100644 --- a/ot/stochastic.py +++ b/ot/stochastic.py @@ -11,7 +11,7 @@ import numpy as np def coordinate_grad_semi_dual(b, M, reg, beta, i): - ''' + r''' Compute the coordinate gradient update for regularized discrete distributions for (i, :) The function computes the gradient of the semi dual problem: @@ -51,7 +51,7 @@ def coordinate_grad_semi_dual(b, M, reg, beta, i): Examples -------- - + >>> import ot >>> n_source = 7 >>> n_target = 4 >>> reg = 1 @@ -63,8 +63,7 @@ def coordinate_grad_semi_dual(b, M, reg, beta, i): >>> Y_target = rng.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) >>> method = "ASGD" - >>> asgd_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, - method, numItermax) + >>> asgd_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, numItermax) >>> print(asgd_pi) References @@ -84,7 +83,7 @@ def coordinate_grad_semi_dual(b, M, reg, beta, i): def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=None): - ''' + r''' Compute the SAG algorithm to solve the regularized discrete measures optimal transport max problem @@ -133,7 +132,7 @@ def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=None): Examples -------- - + >>> import ot >>> n_source = 7 >>> n_target = 4 >>> reg = 1 @@ -145,8 +144,7 @@ def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=None): >>> Y_target = rng.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) >>> method = "ASGD" - >>> asgd_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, - method, numItermax) + >>> asgd_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, numItermax) >>> print(asgd_pi) References @@ -176,7 +174,7 @@ def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=None): def averaged_sgd_entropic_transport(a, b, M, reg, numItermax=300000, lr=None): - ''' + r''' Compute the ASGD algorithm to solve the regularized semi continous measures optimal transport max problem The function solves the following optimization problem: @@ -223,7 +221,7 @@ def averaged_sgd_entropic_transport(a, b, M, reg, numItermax=300000, lr=None): Examples -------- - + >>> import ot >>> n_source = 7 >>> n_target = 4 >>> reg = 1 @@ -235,8 +233,7 @@ def averaged_sgd_entropic_transport(a, b, M, reg, numItermax=300000, lr=None): >>> Y_target = rng.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) >>> method = "ASGD" - >>> asgd_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, - method, numItermax) + >>> asgd_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, numItermax) >>> print(asgd_pi) References @@ -264,7 +261,7 @@ def averaged_sgd_entropic_transport(a, b, M, reg, numItermax=300000, lr=None): def c_transform_entropic(b, M, reg, beta): - ''' + r''' The goal is to recover u from the c-transform. The function computes the c_transform of a dual variable from the other @@ -303,7 +300,7 @@ def c_transform_entropic(b, M, reg, beta): Examples -------- - + >>> import ot >>> n_source = 7 >>> n_target = 4 >>> reg = 1 @@ -315,8 +312,7 @@ def c_transform_entropic(b, M, reg, beta): >>> Y_target = rng.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) >>> method = "ASGD" - >>> asgd_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, - method, numItermax) + >>> asgd_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, numItermax) >>> print(asgd_pi) References @@ -340,7 +336,7 @@ def c_transform_entropic(b, M, reg, beta): def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None, log=False): - ''' + r''' Compute the transportation matrix to solve the regularized discrete measures optimal transport max problem @@ -398,7 +394,7 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None, Examples -------- - + >>> import ot >>> n_source = 7 >>> n_target = 4 >>> reg = 1 @@ -410,8 +406,7 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None, >>> Y_target = rng.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) >>> method = "ASGD" - >>> asgd_pi = stochastic.solve_semi_dual_entropic(a, b, M, reg, - method, numItermax) + >>> asgd_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, numItermax) >>> print(asgd_pi) References @@ -451,7 +446,7 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None, def batch_grad_dual(a, b, M, reg, alpha, beta, batch_size, batch_alpha, batch_beta): - ''' + r''' Computes the partial gradient of the dual optimal transport problem. For each (i,j) in a batch of coordinates, the partial gradients are : @@ -506,7 +501,7 @@ def batch_grad_dual(a, b, M, reg, alpha, beta, batch_size, batch_alpha, Examples -------- - + >>> import ot >>> n_source = 7 >>> n_target = 4 >>> reg = 1 @@ -520,9 +515,7 @@ def batch_grad_dual(a, b, M, reg, alpha, beta, batch_size, batch_alpha, >>> X_source = rng.randn(n_source, 2) >>> Y_target = rng.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) - >>> sgd_dual_pi, log = stochastic.solve_dual_entropic(a, b, M, reg, - batch_size, - numItermax, lr, log) + >>> sgd_dual_pi, log = ot.stochastic.solve_dual_entropic(a, b, M, reg, batch_size, numItermax, lr, log) >>> print(log['alpha'], log['beta']) >>> print(sgd_dual_pi) @@ -548,7 +541,7 @@ def batch_grad_dual(a, b, M, reg, alpha, beta, batch_size, batch_alpha, def sgd_entropic_regularization(a, b, M, reg, batch_size, numItermax, lr): - ''' + r''' Compute the sgd algorithm to solve the regularized discrete measures optimal transport dual problem @@ -597,7 +590,7 @@ def sgd_entropic_regularization(a, b, M, reg, batch_size, numItermax, lr): Examples -------- - + >>> import ot >>> n_source = 7 >>> n_target = 4 >>> reg = 1 @@ -611,9 +604,7 @@ def sgd_entropic_regularization(a, b, M, reg, batch_size, numItermax, lr): >>> X_source = rng.randn(n_source, 2) >>> Y_target = rng.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) - >>> sgd_dual_pi, log = stochastic.solve_dual_entropic(a, b, M, reg, - batch_size, - numItermax, lr, log) + >>> sgd_dual_pi, log = ot.stochastic.solve_dual_entropic(a, b, M, reg, batch_size, numItermax, lr, log) >>> print(log['alpha'], log['beta']) >>> print(sgd_dual_pi) @@ -644,7 +635,7 @@ def sgd_entropic_regularization(a, b, M, reg, batch_size, numItermax, lr): def solve_dual_entropic(a, b, M, reg, batch_size, numItermax=10000, lr=1, log=False): - ''' + r''' Compute the transportation matrix to solve the regularized discrete measures optimal transport dual problem @@ -695,7 +686,7 @@ def solve_dual_entropic(a, b, M, reg, batch_size, numItermax=10000, lr=1, Examples -------- - + >>> import ot >>> n_source = 7 >>> n_target = 4 >>> reg = 1 @@ -709,9 +700,7 @@ def solve_dual_entropic(a, b, M, reg, batch_size, numItermax=10000, lr=1, >>> X_source = rng.randn(n_source, 2) >>> Y_target = rng.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) - >>> sgd_dual_pi, log = stochastic.solve_dual_entropic(a, b, M, reg, - batch_size, - numItermax, lr, log) + >>> sgd_dual_pi, log = ot.stochastic.solve_dual_entropic(a, b, M, reg, batch_size, numItermax, lr, log) >>> print(log['alpha'], log['beta']) >>> print(sgd_dual_pi) diff --git a/ot/utils.py b/ot/utils.py index efd1288..f21ceb9 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -285,9 +285,9 @@ class deprecated(object): The optional extra argument will be appended to the deprecation message and the docstring. Note: to use this with the default value for extra, put in an empty of parentheses: - >>> from ot.deprecation import deprecated - >>> @deprecated() - ... def some_function(): pass + >>> from ot.deprecation import deprecated # doctest: +SKIP + >>> @deprecated() # doctest: +SKIP + ... def some_function(): pass # doctest: +SKIP Parameters ---------- -- cgit v1.2.3 From 3a84263ffe4bbf2ac055dfd5a84e1b65c14f9cda Mon Sep 17 00:00:00 2001 From: Romain Tavenard Date: Mon, 1 Jul 2019 14:23:14 +0200 Subject: Set numpy array formatting version to post-1.13 --- .travis.yml | 2 ++ ot/bregman.py | 86 +++++++++++++++++++++++++++---------------------------- ot/lp/__init__.py | 22 +++++++------- ot/unbalanced.py | 10 +++---- 4 files changed, 61 insertions(+), 59 deletions(-) diff --git a/.travis.yml b/.travis.yml index da68c96..f004a32 100644 --- a/.travis.yml +++ b/.travis.yml @@ -26,6 +26,8 @@ before_script: # configure a headless display to test plot generation # command to install dependencies install: - pip install -r requirements.txt + - pip install numpy>=1.14 # for numpy array formatting in doctests + - pip install scipy<1.3 # otherwise, pymanopt fails, cf - pip install flake8 pytest "pytest-cov<2.6" - pip install . # command to run tests + check syntax style diff --git a/ot/bregman.py b/ot/bregman.py index 8225967..caf4024 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -16,7 +16,7 @@ from .utils import unif, dist def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, stopThr=1e-9, verbose=False, log=False, **kwargs): - u""" + r""" Solve the entropic regularization optimal transport problem and return the OT matrix The function solves the following optimization problem: @@ -73,12 +73,12 @@ def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, -------- >>> import ot - >>> a=[.5,.5] - >>> b=[.5,.5] - >>> M=[[0.,1.],[1.,0.]] - >>> ot.sinkhorn(a,b,M,1) - array([[ 0.36552929, 0.13447071], - [ 0.13447071, 0.36552929]]) + >>> a=[.5, .5] + >>> b=[.5, .5] + >>> M=[[0., 1.], [1., 0.]] + >>> ot.sinkhorn(a, b, M, 1) + array([[0.36552929, 0.13447071], + [0.13447071, 0.36552929]]) References @@ -131,7 +131,7 @@ def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, stopThr=1e-9, verbose=False, log=False, **kwargs): - u""" + r""" Solve the entropic regularization optimal transport problem and return the loss The function solves the following optimization problem: @@ -188,11 +188,11 @@ def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, -------- >>> import ot - >>> a=[.5,.5] - >>> b=[.5,.5] - >>> M=[[0.,1.],[1.,0.]] - >>> ot.sinkhorn2(a,b,M,1) - array([ 0.26894142]) + >>> a=[.5, .5] + >>> b=[.5, .5] + >>> M=[[0., 1.], [1., 0.]] + >>> ot.sinkhorn2(a, b, M, 1) + array([0.26894142]) @@ -248,7 +248,7 @@ def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, def sinkhorn_knopp(a, b, M, reg, numItermax=1000, stopThr=1e-9, verbose=False, log=False, **kwargs): - """ + r""" Solve the entropic regularization optimal transport problem and return the OT matrix The function solves the following optimization problem: @@ -302,12 +302,12 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, -------- >>> import ot - >>> a=[.5,.5] - >>> b=[.5,.5] - >>> M=[[0.,1.],[1.,0.]] - >>> ot.sinkhorn(a,b,M,1) - array([[ 0.36552929, 0.13447071], - [ 0.13447071, 0.36552929]]) + >>> a=[.5, .5] + >>> b=[.5, .5] + >>> M=[[0., 1.], [1., 0.]] + >>> ot.sinkhorn(a, b, M, 1) + array([[0.36552929, 0.13447071], + [0.13447071, 0.36552929]]) References @@ -422,7 +422,7 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log=False): - """ + r""" Solve the entropic regularization optimal transport problem and return the OT matrix The algorithm used is based on the paper @@ -481,12 +481,12 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log= -------- >>> import ot - >>> a=[.5,.5] - >>> b=[.5,.5] - >>> M=[[0.,1.],[1.,0.]] - >>> ot.bregman.greenkhorn(a,b,M,1) - array([[ 0.36552929, 0.13447071], - [ 0.13447071, 0.36552929]]) + >>> a=[.5, .5] + >>> b=[.5, .5] + >>> M=[[0., 1.], [1., 0.]] + >>> ot.bregman.greenkhorn(a, b, M, 1) + array([[0.36552929, 0.13447071], + [0.13447071, 0.36552929]]) References @@ -576,7 +576,7 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log= def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, warmstart=None, verbose=False, print_period=20, log=False, **kwargs): - """ + r""" Solve the entropic regularization OT problem with log stabilization The function solves the following optimization problem: @@ -639,8 +639,8 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, >>> b=[.5,.5] >>> M=[[0.,1.],[1.,0.]] >>> ot.bregman.sinkhorn_stabilized(a,b,M,1) - array([[ 0.36552929, 0.13447071], - [ 0.13447071, 0.36552929]]) + array([[0.36552929, 0.13447071], + [0.13447071, 0.36552929]]) References @@ -796,7 +796,7 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInnerItermax=100, tau=1e3, stopThr=1e-9, warmstart=None, verbose=False, print_period=10, log=False, **kwargs): - """ + r""" Solve the entropic regularization optimal transport problem with log stabilization and epsilon scaling. @@ -862,12 +862,12 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInne -------- >>> import ot - >>> a=[.5,.5] - >>> b=[.5,.5] - >>> M=[[0.,1.],[1.,0.]] - >>> ot.bregman.sinkhorn_epsilon_scaling(a,b,M,1) - array([[ 0.36552929, 0.13447071], - [ 0.13447071, 0.36552929]]) + >>> a=[.5, .5] + >>> b=[.5, .5] + >>> M=[[0., 1.], [1., 0.]] + >>> ot.bregman.sinkhorn_epsilon_scaling(a, b, M, 1) + array([[0.36552929, 0.13447071], + [0.13447071, 0.36552929]]) References @@ -989,7 +989,7 @@ def projC(gamma, q): def barycenter(A, M, reg, weights=None, numItermax=1000, stopThr=1e-4, verbose=False, log=False): - """Compute the entropic regularized wasserstein barycenter of distributions A + r"""Compute the entropic regularized wasserstein barycenter of distributions A The function solves the following optimization problem: @@ -1084,7 +1084,7 @@ def barycenter(A, M, reg, weights=None, numItermax=1000, def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1e-9, stabThr=1e-30, verbose=False, log=False): - """Compute the entropic regularized wasserstein barycenter of distributions A + r"""Compute the entropic regularized wasserstein barycenter of distributions A where A is a collection of 2D images. The function solves the following optimization problem: @@ -1195,7 +1195,7 @@ def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1 def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000, stopThr=1e-3, verbose=False, log=False): - """ + r""" Compute the unmixing of an observation with a given dictionary using Wasserstein distance The function solve the following optimization problem: @@ -1302,7 +1302,7 @@ def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000, def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, verbose=False, log=False, **kwargs): - ''' + r''' Solve the entropic regularization optimal transport problem and return the OT matrix from empirical data @@ -1391,7 +1391,7 @@ def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numI def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, verbose=False, log=False, **kwargs): - ''' + r''' Solve the entropic regularization optimal transport problem from empirical data and return the OT loss @@ -1480,7 +1480,7 @@ def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', num def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, verbose=False, log=False, **kwargs): - ''' + r''' Compute the sinkhorn divergence loss from empirical data The function solves the following optimization problems and return the diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index a3f5b8d..8ec286b 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -25,7 +25,7 @@ __all__=['emd', 'emd2', 'barycenter', 'free_support_barycenter', 'cvx', def emd(a, b, M, numItermax=100000, log=False): - """Solves the Earth Movers distance problem and returns the OT matrix + r"""Solves the Earth Movers distance problem and returns the OT matrix .. math:: @@ -76,8 +76,8 @@ def emd(a, b, M, numItermax=100000, log=False): >>> b=[.5,.5] >>> M=[[0.,1.],[1.,0.]] >>> ot.emd(a,b,M) - array([[ 0.5, 0. ], - [ 0. , 0.5]]) + array([[0.5, 0. ], + [0. , 0.5]]) References ---------- @@ -117,7 +117,7 @@ def emd(a, b, M, numItermax=100000, log=False): def emd2(a, b, M, processes=multiprocessing.cpu_count(), numItermax=100000, log=False, return_matrix=False): - """Solves the Earth Movers distance problem and returns the loss + r"""Solves the Earth Movers distance problem and returns the loss .. math:: \gamma = arg\min_\gamma <\gamma,M>_F @@ -315,7 +315,7 @@ def free_support_barycenter(measures_locations, measures_weights, X_init, b=None def emd_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', p=1., dense=True, log=False): - """Solves the Earth Movers distance problem between 1d measures and returns + r"""Solves the Earth Movers distance problem between 1d measures and returns the OT matrix @@ -381,11 +381,11 @@ def emd_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', p=1., dense=True, >>> x_a = [2., 0.] >>> x_b = [0., 3.] >>> ot.emd_1d(x_a, x_b, a, b) - array([[0. , 0.5], - [0.5, 0. ]]) + array([[0. , 0.5], + [0.5, 0. ]]) >>> ot.emd_1d(x_a, x_b) - array([[0. , 0.5], - [0.5, 0. ]]) + array([[0. , 0.5], + [0.5, 0. ]]) References ---------- @@ -435,7 +435,7 @@ def emd_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', p=1., dense=True, def emd2_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', p=1., dense=True, log=False): - """Solves the Earth Movers distance problem between 1d measures and returns + r"""Solves the Earth Movers distance problem between 1d measures and returns the loss @@ -530,7 +530,7 @@ def emd2_1d(x_a, x_b, a=None, b=None, metric='sqeuclidean', p=1., dense=True, def wasserstein_1d(x_a, x_b, a=None, b=None, p=1.): - """Solves the p-Wasserstein distance problem between 1d measures and returns + r"""Solves the p-Wasserstein distance problem between 1d measures and returns the distance diff --git a/ot/unbalanced.py b/ot/unbalanced.py index 484ce95..b2b7b10 100644 --- a/ot/unbalanced.py +++ b/ot/unbalanced.py @@ -13,7 +13,7 @@ import numpy as np def sinkhorn_unbalanced(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, stopThr=1e-9, verbose=False, log=False, **kwargs): - u""" + r""" Solve the unbalanced entropic regularization optimal transport problem and return the loss The function solves the following optimization problem: @@ -75,7 +75,7 @@ def sinkhorn_unbalanced(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, >>> M=[[0., 1.], [1., 0.]] >>> ot.sinkhorn_unbalanced(a, b, M, 1, 1) array([[0.51122823, 0.18807035], - [0.18807035, 0.51122823]]) + [0.18807035, 0.51122823]]) References @@ -122,7 +122,7 @@ def sinkhorn_unbalanced(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, def sinkhorn_unbalanced2(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, stopThr=1e-9, verbose=False, log=False, **kwargs): - u""" + r""" Solve the entropic regularization unbalanced optimal transport problem and return the loss The function solves the following optimization problem: @@ -233,7 +233,7 @@ def sinkhorn_unbalanced2(a, b, M, reg, alpha, method='sinkhorn', def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, stopThr=1e-9, verbose=False, log=False, **kwargs): - """ + r""" Solve the entropic regularization unbalanced optimal transport problem and return the loss The function solves the following optimization problem: @@ -401,7 +401,7 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, def barycenter_unbalanced(A, M, reg, alpha, weights=None, numItermax=1000, stopThr=1e-4, verbose=False, log=False): - """Compute the entropic regularized unbalanced wasserstein barycenter of distributions A + r"""Compute the entropic regularized unbalanced wasserstein barycenter of distributions A The function solves the following optimization problem: -- cgit v1.2.3 From 385d4a543898a7ddb842b983b5f174f8c80636fc Mon Sep 17 00:00:00 2001 From: Romain Tavenard Date: Mon, 1 Jul 2019 14:28:11 +0200 Subject: Bug in syntax for old scipy version --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index f004a32..275109c 100644 --- a/.travis.yml +++ b/.travis.yml @@ -27,7 +27,7 @@ before_script: # configure a headless display to test plot generation install: - pip install -r requirements.txt - pip install numpy>=1.14 # for numpy array formatting in doctests - - pip install scipy<1.3 # otherwise, pymanopt fails, cf + - pip install "scipy<1.3" # otherwise, pymanopt fails, cf - pip install flake8 pytest "pytest-cov<2.6" - pip install . # command to run tests + check syntax style -- cgit v1.2.3 From 64dba525bb5e0ac7952871df859df59fecf19a65 Mon Sep 17 00:00:00 2001 From: Romain Tavenard Date: Mon, 1 Jul 2019 14:56:55 +0200 Subject: Formatting --- test/test_gpu.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/test/test_gpu.py b/test/test_gpu.py index 47b8b6d..8e62a74 100644 --- a/test/test_gpu.py +++ b/test/test_gpu.py @@ -22,7 +22,7 @@ def test_gpu_old_doctests(): M = [[0., 1.], [1., 0.]] G = ot.sinkhorn(a, b, M, 1) np.testing.assert_allclose(G, np.array([[0.36552929, 0.13447071], - [0.13447071, 0.36552929]])) + [0.13447071, 0.36552929]])) @pytest.mark.skipif(nogpu, reason="No GPU available") -- cgit v1.2.3 From a08375c8dc7594e247e586fcc4d65a96771d25c7 Mon Sep 17 00:00:00 2001 From: Romain Tavenard Date: Mon, 1 Jul 2019 15:36:55 +0200 Subject: Fixed all doctests assuming functions are working properly (actually tested in tests/) --- .travis.yml | 5 +- ot/bregman.py | 2 +- ot/stochastic.py | 155 +++++++++++++++++++++++++++++++++++-------------------- ot/unbalanced.py | 2 +- 4 files changed, 104 insertions(+), 60 deletions(-) diff --git a/.travis.yml b/.travis.yml index 275109c..2c6a5c8 100644 --- a/.travis.yml +++ b/.travis.yml @@ -26,8 +26,9 @@ before_script: # configure a headless display to test plot generation # command to install dependencies install: - pip install -r requirements.txt - - pip install numpy>=1.14 # for numpy array formatting in doctests - - pip install "scipy<1.3" # otherwise, pymanopt fails, cf + - pip install numpy>=1.14 "scipy<1.3" # for numpy array formatting in doctests + # ^ scipy version: otherwise, pymanopt fails, cf + - python -c "import numpy; import scipy; print('numpy: ', numpy.__version__); print('scipy: ', scipy.__version__)" - pip install flake8 pytest "pytest-cov<2.6" - pip install . # command to run tests + check syntax style diff --git a/ot/bregman.py b/ot/bregman.py index caf4024..50f8389 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1559,7 +1559,7 @@ def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeucli >>> X_s = np.reshape(np.arange(n_s), (n_s, 1)) >>> X_t = np.reshape(np.arange(0, n_t), (n_t, 1)) >>> empirical_sinkhorn_divergence(X_s, X_t, reg) - array([2.99977435]) + array([1.49988718]) References diff --git a/ot/stochastic.py b/ot/stochastic.py index 762eb3e..bf3e7a7 100644 --- a/ot/stochastic.py +++ b/ot/stochastic.py @@ -52,19 +52,23 @@ def coordinate_grad_semi_dual(b, M, reg, beta, i): Examples -------- >>> import ot + >>> np.random.seed(0) >>> n_source = 7 >>> n_target = 4 - >>> reg = 1 - >>> numItermax = 300000 >>> a = ot.utils.unif(n_source) >>> b = ot.utils.unif(n_target) - >>> rng = np.random.RandomState(0) - >>> X_source = rng.randn(n_source, 2) - >>> Y_target = rng.randn(n_target, 2) + >>> X_source = np.random.randn(n_source, 2) + >>> Y_target = np.random.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) - >>> method = "ASGD" - >>> asgd_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, numItermax) - >>> print(asgd_pi) + >>> ot.stochastic.solve_semi_dual_entropic(a, b, M, reg=1, method="ASGD", numItermax=300000) + array([[2.53942342e-02, 9.98640673e-02, 1.75945647e-02, 4.27664307e-06], + [1.21556999e-01, 1.26350515e-02, 1.30491795e-03, 7.36017394e-03], + [3.54070702e-03, 7.63581358e-02, 6.29581672e-02, 1.32812798e-07], + [2.60578198e-02, 3.35916645e-02, 8.28023223e-02, 4.05336238e-04], + [9.86808864e-03, 7.59774324e-04, 1.08702729e-02, 1.21359007e-01], + [2.17218856e-02, 9.12931802e-04, 1.87962526e-03, 1.18342700e-01], + [4.14237512e-02, 2.67487857e-02, 7.23016955e-02, 2.38291052e-03]]) + References ---------- @@ -133,19 +137,22 @@ def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=None): Examples -------- >>> import ot + >>> np.random.seed(0) >>> n_source = 7 >>> n_target = 4 - >>> reg = 1 - >>> numItermax = 300000 >>> a = ot.utils.unif(n_source) >>> b = ot.utils.unif(n_target) - >>> rng = np.random.RandomState(0) - >>> X_source = rng.randn(n_source, 2) - >>> Y_target = rng.randn(n_target, 2) + >>> X_source = np.random.randn(n_source, 2) + >>> Y_target = np.random.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) - >>> method = "ASGD" - >>> asgd_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, numItermax) - >>> print(asgd_pi) + >>> ot.stochastic.solve_semi_dual_entropic(a, b, M, reg=1, method="ASGD", numItermax=300000) + array([[2.53942342e-02, 9.98640673e-02, 1.75945647e-02, 4.27664307e-06], + [1.21556999e-01, 1.26350515e-02, 1.30491795e-03, 7.36017394e-03], + [3.54070702e-03, 7.63581358e-02, 6.29581672e-02, 1.32812798e-07], + [2.60578198e-02, 3.35916645e-02, 8.28023223e-02, 4.05336238e-04], + [9.86808864e-03, 7.59774324e-04, 1.08702729e-02, 1.21359007e-01], + [2.17218856e-02, 9.12931802e-04, 1.87962526e-03, 1.18342700e-01], + [4.14237512e-02, 2.67487857e-02, 7.23016955e-02, 2.38291052e-03]]) References ---------- @@ -222,19 +229,22 @@ def averaged_sgd_entropic_transport(a, b, M, reg, numItermax=300000, lr=None): Examples -------- >>> import ot + >>> np.random.seed(0) >>> n_source = 7 >>> n_target = 4 - >>> reg = 1 - >>> numItermax = 300000 >>> a = ot.utils.unif(n_source) >>> b = ot.utils.unif(n_target) - >>> rng = np.random.RandomState(0) - >>> X_source = rng.randn(n_source, 2) - >>> Y_target = rng.randn(n_target, 2) + >>> X_source = np.random.randn(n_source, 2) + >>> Y_target = np.random.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) - >>> method = "ASGD" - >>> asgd_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, numItermax) - >>> print(asgd_pi) + >>> ot.stochastic.solve_semi_dual_entropic(a, b, M, reg=1, method="ASGD", numItermax=300000) + array([[2.53942342e-02, 9.98640673e-02, 1.75945647e-02, 4.27664307e-06], + [1.21556999e-01, 1.26350515e-02, 1.30491795e-03, 7.36017394e-03], + [3.54070702e-03, 7.63581358e-02, 6.29581672e-02, 1.32812798e-07], + [2.60578198e-02, 3.35916645e-02, 8.28023223e-02, 4.05336238e-04], + [9.86808864e-03, 7.59774324e-04, 1.08702729e-02, 1.21359007e-01], + [2.17218856e-02, 9.12931802e-04, 1.87962526e-03, 1.18342700e-01], + [4.14237512e-02, 2.67487857e-02, 7.23016955e-02, 2.38291052e-03]]) References ---------- @@ -301,19 +311,22 @@ def c_transform_entropic(b, M, reg, beta): Examples -------- >>> import ot + >>> np.random.seed(0) >>> n_source = 7 >>> n_target = 4 - >>> reg = 1 - >>> numItermax = 300000 >>> a = ot.utils.unif(n_source) >>> b = ot.utils.unif(n_target) - >>> rng = np.random.RandomState(0) - >>> X_source = rng.randn(n_source, 2) - >>> Y_target = rng.randn(n_target, 2) + >>> X_source = np.random.randn(n_source, 2) + >>> Y_target = np.random.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) - >>> method = "ASGD" - >>> asgd_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, numItermax) - >>> print(asgd_pi) + >>> ot.stochastic.solve_semi_dual_entropic(a, b, M, reg=1, method="ASGD", numItermax=300000) + array([[2.53942342e-02, 9.98640673e-02, 1.75945647e-02, 4.27664307e-06], + [1.21556999e-01, 1.26350515e-02, 1.30491795e-03, 7.36017394e-03], + [3.54070702e-03, 7.63581358e-02, 6.29581672e-02, 1.32812798e-07], + [2.60578198e-02, 3.35916645e-02, 8.28023223e-02, 4.05336238e-04], + [9.86808864e-03, 7.59774324e-04, 1.08702729e-02, 1.21359007e-01], + [2.17218856e-02, 9.12931802e-04, 1.87962526e-03, 1.18342700e-01], + [4.14237512e-02, 2.67487857e-02, 7.23016955e-02, 2.38291052e-03]]) References ---------- @@ -395,19 +408,22 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None, Examples -------- >>> import ot + >>> np.random.seed(0) >>> n_source = 7 >>> n_target = 4 - >>> reg = 1 - >>> numItermax = 300000 >>> a = ot.utils.unif(n_source) >>> b = ot.utils.unif(n_target) - >>> rng = np.random.RandomState(0) - >>> X_source = rng.randn(n_source, 2) - >>> Y_target = rng.randn(n_target, 2) + >>> X_source = np.random.randn(n_source, 2) + >>> Y_target = np.random.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) - >>> method = "ASGD" - >>> asgd_pi = ot.stochastic.solve_semi_dual_entropic(a, b, M, reg, method, numItermax) - >>> print(asgd_pi) + >>> ot.stochastic.solve_semi_dual_entropic(a, b, M, reg=1, method="ASGD", numItermax=300000) + array([[2.53942342e-02, 9.98640673e-02, 1.75945647e-02, 4.27664307e-06], + [1.21556999e-01, 1.26350515e-02, 1.30491795e-03, 7.36017394e-03], + [3.54070702e-03, 7.63581358e-02, 6.29581672e-02, 1.32812798e-07], + [2.60578198e-02, 3.35916645e-02, 8.28023223e-02, 4.05336238e-04], + [9.86808864e-03, 7.59774324e-04, 1.08702729e-02, 1.21359007e-01], + [2.17218856e-02, 9.12931802e-04, 1.87962526e-03, 1.18342700e-01], + [4.14237512e-02, 2.67487857e-02, 7.23016955e-02, 2.38291052e-03]]) References ---------- @@ -502,22 +518,28 @@ def batch_grad_dual(a, b, M, reg, alpha, beta, batch_size, batch_alpha, Examples -------- >>> import ot + >>> np.random.seed(0) >>> n_source = 7 >>> n_target = 4 - >>> reg = 1 - >>> numItermax = 20000 - >>> lr = 0.1 - >>> batch_size = 3 - >>> log = True >>> a = ot.utils.unif(n_source) >>> b = ot.utils.unif(n_target) - >>> rng = np.random.RandomState(0) - >>> X_source = rng.randn(n_source, 2) - >>> Y_target = rng.randn(n_target, 2) + >>> X_source = np.random.randn(n_source, 2) + >>> Y_target = np.random.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) - >>> sgd_dual_pi, log = ot.stochastic.solve_dual_entropic(a, b, M, reg, batch_size, numItermax, lr, log) - >>> print(log['alpha'], log['beta']) - >>> print(sgd_dual_pi) + >>> sgd_dual_pi, log = ot.stochastic.solve_dual_entropic(a, b, M, reg=1, batch_size=3, numItermax=30000, lr=0.1, log=True) + >>> log['alpha'] + array([0.71759102, 1.57057384, 0.85576566, 0.1208211 , 0.59190466, + 1.197148 , 0.17805133]) + >>> log['beta'] + array([0.49741367, 0.57478564, 1.40075528, 2.75890102]) + >>> sgd_dual_pi + array([[2.09730063e-02, 8.38169324e-02, 7.50365455e-03, 8.72731415e-09], + [5.58432437e-03, 5.89881299e-04, 3.09558411e-05, 8.35469849e-07], + [3.26489515e-03, 7.15536035e-02, 2.99778211e-02, 3.02601593e-10], + [4.05390622e-02, 5.31085068e-02, 6.65191787e-02, 1.55812785e-06], + [7.82299812e-02, 6.12099102e-03, 4.44989098e-02, 2.37719187e-03], + [5.06266486e-02, 2.16230494e-03, 2.26215141e-03, 6.81514609e-04], + [6.06713990e-02, 3.98139808e-02, 5.46829338e-02, 8.62371424e-06]]) References ---------- @@ -526,7 +548,6 @@ def batch_grad_dual(a, b, M, reg, alpha, beta, batch_size, batch_alpha, International Conference on Learning Representation (2018), arXiv preprint arxiv:1711.02283. ''' - G = - (np.exp((alpha[batch_alpha, None] + beta[None, batch_beta] - M[batch_alpha, :][:, batch_beta]) / reg) * a[batch_alpha, None] * b[None, batch_beta]) @@ -605,8 +626,19 @@ def sgd_entropic_regularization(a, b, M, reg, batch_size, numItermax, lr): >>> Y_target = rng.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) >>> sgd_dual_pi, log = ot.stochastic.solve_dual_entropic(a, b, M, reg, batch_size, numItermax, lr, log) - >>> print(log['alpha'], log['beta']) - >>> print(sgd_dual_pi) + >>> log['alpha'] + array([0.64171798, 1.27932201, 0.78132257, 0.15638935, 0.54888354, + 1.03663469, 0.20595781]) + >>> log['beta'] + array([0.51207194, 0.58033189, 1.28922676, 2.26859736]) + >>> sgd_dual_pi + array([[1.97276541e-02, 7.81248547e-02, 6.22136048e-03, 4.95442423e-09], + [4.23494310e-03, 4.43286263e-04, 2.06927079e-05, 3.82389139e-07], + [3.07542414e-03, 6.67897769e-02, 2.48904999e-02, 1.72030247e-10], + [4.26271990e-02, 5.53375455e-02, 6.16535024e-02, 9.88812650e-07], + [7.60423265e-02, 5.89585256e-03, 3.81267087e-02, 1.39458256e-03], + [4.37557504e-02, 1.85189176e-03, 1.72335760e-03, 3.55491279e-04], + [6.33096109e-02, 4.11683954e-02, 5.02962051e-02, 5.43097516e-06]]) References ---------- @@ -701,8 +733,19 @@ def solve_dual_entropic(a, b, M, reg, batch_size, numItermax=10000, lr=1, >>> Y_target = rng.randn(n_target, 2) >>> M = ot.dist(X_source, Y_target) >>> sgd_dual_pi, log = ot.stochastic.solve_dual_entropic(a, b, M, reg, batch_size, numItermax, lr, log) - >>> print(log['alpha'], log['beta']) - >>> print(sgd_dual_pi) + >>> log['alpha'] + array([0.64057733, 1.2683513 , 0.75610161, 0.16024284, 0.54926534, + 1.0514201 , 0.19958936]) + >>> log['beta'] + array([0.51372571, 0.58843489, 1.27993921, 2.24344807]) + >>> sgd_dual_pi + array([[1.97377795e-02, 7.86706853e-02, 6.15682001e-03, 4.82586997e-09], + [4.19566963e-03, 4.42016865e-04, 2.02777272e-05, 3.68823708e-07], + [3.00379244e-03, 6.56562018e-02, 2.40462171e-02, 1.63579656e-10], + [4.28626062e-02, 5.60031599e-02, 6.13193826e-02, 9.67977735e-07], + [7.61972739e-02, 5.94609051e-03, 3.77886693e-02, 1.36046648e-03], + [4.44810042e-02, 1.89476742e-03, 1.73285847e-03, 3.51826036e-04], + [6.30118293e-02, 4.12398660e-02, 4.95148998e-02, 5.26247246e-06]]) References ---------- diff --git a/ot/unbalanced.py b/ot/unbalanced.py index b2b7b10..4a2af8a 100644 --- a/ot/unbalanced.py +++ b/ot/unbalanced.py @@ -290,7 +290,7 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, >>> a=[.5, .15] >>> b=[.5, .5] >>> M=[[0., 1.],[1., 0.]] - >>> ot.sinkhorn_knopp_unbalanced(a, b, M, 1., 1.) + >>> ot.unbalanced.sinkhorn_knopp_unbalanced(a, b, M, 1., 1.) array([[0.52761554, 0.22392482], [0.10286295, 0.32257641]]) -- cgit v1.2.3 From d85f61683cbb94ca6c6cb91bbd7d82ea295dec3a Mon Sep 17 00:00:00 2001 From: Romain Tavenard Date: Tue, 2 Jul 2019 09:26:32 +0200 Subject: Bug in syntax for new numpy version --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 2c6a5c8..e61b577 100644 --- a/.travis.yml +++ b/.travis.yml @@ -26,7 +26,7 @@ before_script: # configure a headless display to test plot generation # command to install dependencies install: - pip install -r requirements.txt - - pip install numpy>=1.14 "scipy<1.3" # for numpy array formatting in doctests + - pip install "numpy>=1.14" "scipy<1.3" # for numpy array formatting in doctests # ^ scipy version: otherwise, pymanopt fails, cf - python -c "import numpy; import scipy; print('numpy: ', numpy.__version__); print('scipy: ', scipy.__version__)" - pip install flake8 pytest "pytest-cov<2.6" -- cgit v1.2.3 From cb919a30de5ebcbfcd8b15e0a5e7f90b36f4f7b3 Mon Sep 17 00:00:00 2001 From: Romain Tavenard Date: Tue, 2 Jul 2019 09:29:06 +0200 Subject: Bug in syntax for new numpy version (cont'd) --- .travis.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index e61b577..8eaa202 100644 --- a/.travis.yml +++ b/.travis.yml @@ -28,7 +28,7 @@ install: - pip install -r requirements.txt - pip install "numpy>=1.14" "scipy<1.3" # for numpy array formatting in doctests # ^ scipy version: otherwise, pymanopt fails, cf - - python -c "import numpy; import scipy; print('numpy: ', numpy.__version__); print('scipy: ', scipy.__version__)" + - python -c "import numpy; import scipy; print(\"numpy: \", numpy.__version__); print(\"scipy: \", scipy.__version__)" - pip install flake8 pytest "pytest-cov<2.6" - pip install . # command to run tests + check syntax style -- cgit v1.2.3 From 36e9d8d8ada28e86833d9c7c0b441e3000800a31 Mon Sep 17 00:00:00 2001 From: Romain Tavenard Date: Tue, 2 Jul 2019 09:29:48 +0200 Subject: Bug in syntax for new numpy version (cont'd) --- .travis.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/.travis.yml b/.travis.yml index 8eaa202..3ed0e72 100644 --- a/.travis.yml +++ b/.travis.yml @@ -26,8 +26,7 @@ before_script: # configure a headless display to test plot generation # command to install dependencies install: - pip install -r requirements.txt - - pip install "numpy>=1.14" "scipy<1.3" # for numpy array formatting in doctests - # ^ scipy version: otherwise, pymanopt fails, cf + - pip install "numpy>=1.14" "scipy<1.3" # for numpy array formatting in doctests + scipy version: otherwise, pymanopt fails, cf - python -c "import numpy; import scipy; print(\"numpy: \", numpy.__version__); print(\"scipy: \", scipy.__version__)" - pip install flake8 pytest "pytest-cov<2.6" - pip install . -- cgit v1.2.3 From f7fbb57a96bc6c7fe7d6237292aa39516c2f614e Mon Sep 17 00:00:00 2001 From: Romain Tavenard Date: Tue, 2 Jul 2019 09:30:54 +0200 Subject: Bug in syntax for new numpy version (cont'd) --- .travis.yml | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/.travis.yml b/.travis.yml index 3ed0e72..5e5694b 100644 --- a/.travis.yml +++ b/.travis.yml @@ -26,8 +26,7 @@ before_script: # configure a headless display to test plot generation # command to install dependencies install: - pip install -r requirements.txt - - pip install "numpy>=1.14" "scipy<1.3" # for numpy array formatting in doctests + scipy version: otherwise, pymanopt fails, cf - - python -c "import numpy; import scipy; print(\"numpy: \", numpy.__version__); print(\"scipy: \", scipy.__version__)" + - pip install -U "numpy>=1.14" "scipy<1.3" # for numpy array formatting in doctests + scipy version: otherwise, pymanopt fails, cf - pip install flake8 pytest "pytest-cov<2.6" - pip install . # command to run tests + check syntax style -- cgit v1.2.3 From bed755904e0fd1d66004877c96127a56aa7e0983 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 2 Jul 2019 09:42:52 +0200 Subject: regularized OT done --- docs/source/quickstart.rst | 58 ++++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 58 insertions(+) diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst index 62688bc..a005c64 100644 --- a/docs/source/quickstart.rst +++ b/docs/source/quickstart.rst @@ -243,6 +243,9 @@ regularization terms: namely quadratic regularization and group lasso regularization. But we also provide in :any:`ot.optim` two generic solvers that allows solving any smooth regularization in practice. +Quadratic regularization +"""""""""""""""""""""""" + The first general regularization term we can solve is the quadratic regularization of the form @@ -257,6 +260,17 @@ functions :any:`ot.smooth.smooth_ot_dual` or :any:`ot.smooth.smooth_ot_semi_dual` with parameter :code:`reg_type='l2'` to choose the quadratic regularization. +.. hint:: + Examples of quadratic regularization are available in the following examples: + + - :any:`auto_examples/plot_OT_1D_smooth` + - :any:`auto_examples/plot_optim_OTreg` + + + +Group Lasso regularization +"""""""""""""""""""""""""" + Another regularization that has been used in recent years is the group lasso regularization @@ -276,6 +290,50 @@ convex gourp lasso and we provide a solver using generalized conditional gradient algorithm [7]_ in function :any:`ot.da.sinkhorn_l1l2_gl`. +.. hint:: + Examples of group Lasso regularization are available in the following examples: + + - :any:`auto_examples/plot_otda_classes` + - :any:`auto_examples/plot_otda_d2` + + +Generic solvers +""""""""""""""" + +Finally we propose in POT generic solvers that can be used to solve any +regularization as long as you can provide a function computing the +regularization and a function computing its gradient. + +In order to solve + +.. math:: + \gamma^* = arg\min_\gamma \quad \sum_{i,j}\gamma_{i,j}M_{i,j} + \lambda\Omega(\gamma) + + s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0 + +you can use function :any:`ot.optim.cg` that will use a conditional gradient as +proposed in [6]_ . you need to provide the regularization function as parameter +``f`` and its gradient as parameter ``df``. Note that the conditional gradient relies on +iterative solving of a linearization of the problem using the exact +:any:`ot.emd` so it can be slow in practice. Still it always returns a +transport matrix that does not violates the marginals. + +Another solver is proposed to solve the problem + +.. math:: + \gamma^* = arg\min_\gamma \quad \sum_{i,j}\gamma_{i,j}M_{i,j}+ \lambda_e\Omega_e(\gamma) + \lambda\Omega(\gamma) + + s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0 + +where :math:`\Omega_e` is the entropic regularization. In this case we use a +generalized conditional gradient [7]_ implemented in :any:`ot.opim.gcg` that does not linearize the entropic term and +relies on :any:`ot.sinkhorn` for its iterations. + +.. hint:: + Example of generic solvers are available in the following example: + + - :any:`auto_examples/plot_optim_OTreg` + Wasserstein Barycenters -- cgit v1.2.3 From 8d47d32713a6e2d448fae6db7420e93b4395d5e6 Mon Sep 17 00:00:00 2001 From: Romain Tavenard Date: Tue, 2 Jul 2019 09:44:02 +0200 Subject: Bugfix (python2 in unbalanced) --- ot/unbalanced.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/ot/unbalanced.py b/ot/unbalanced.py index 4a2af8a..44ab411 100644 --- a/ot/unbalanced.py +++ b/ot/unbalanced.py @@ -6,6 +6,7 @@ Regularized Unbalanced OT # Author: Hicham Janati # License: MIT License +from __future__ import division import warnings import numpy as np # from .utils import unif, dist @@ -287,12 +288,12 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, -------- >>> import ot - >>> a=[.5, .15] + >>> a=[.5, .5] >>> b=[.5, .5] >>> M=[[0., 1.],[1., 0.]] >>> ot.unbalanced.sinkhorn_knopp_unbalanced(a, b, M, 1., 1.) - array([[0.52761554, 0.22392482], - [0.10286295, 0.32257641]]) + array([[0.51122823, 0.18807035], + [0.18807035, 0.51122823]]) References ---------- -- cgit v1.2.3 From 5b6eb56f2d4bfdaeaa45970c386c42c21d7d1caf Mon Sep 17 00:00:00 2001 From: Romain Tavenard Date: Tue, 2 Jul 2019 09:53:15 +0200 Subject: Fix tolerance in doctest --- ot/bregman.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 50f8389..13dfa3b 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1558,8 +1558,8 @@ def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeucli >>> reg = 0.1 >>> X_s = np.reshape(np.arange(n_s), (n_s, 1)) >>> X_t = np.reshape(np.arange(0, n_t), (n_t, 1)) - >>> empirical_sinkhorn_divergence(X_s, X_t, reg) - array([1.49988718]) + >>> empirical_sinkhorn_divergence(X_s, X_t, reg) # doctest: +ELLIPSIS + array([1.499...]) References -- cgit v1.2.3 From 82d10f780fc296b9f5548e1fe1da9b20349b1e10 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 2 Jul 2019 10:55:05 +0200 Subject: wasserstein barycenetr with fixed support --- docs/source/quickstart.rst | 86 ++++++++++++++++++++++++++++++++++++++++++---- 1 file changed, 79 insertions(+), 7 deletions(-) diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst index a005c64..94bc8cd 100644 --- a/docs/source/quickstart.rst +++ b/docs/source/quickstart.rst @@ -216,13 +216,7 @@ More details about the algorithm used is given in the following note. :any:`ot.smooth.smooth_ot_semi_dual` with parameter :code:`reg_type='kl'` to choose entropic/Kullbach Leibler regularization. -.. hint:: - Examples of use for :any:`ot.sinkhorn` are available in the following examples: - - :any:`auto_examples/plot_OT_2D_samples` - - :any:`auto_examples/plot_OT_1D` - - :any:`auto_examples/plot_OT_1D_smooth` - - :any:`auto_examples/plot_stochastic` Recently [23]_ introduced the sinkhorn divergence that build from entropic @@ -234,6 +228,15 @@ empirical distributions. Finally note that we also provide in :any:`ot.stochastic` several implementation of stochastic solvers for entropic regularized OT [18]_ [19]_. +.. hint:: + Examples of use for :any:`ot.sinkhorn` are available in the following examples: + + - :any:`auto_examples/plot_OT_2D_samples` + - :any:`auto_examples/plot_OT_1D` + - :any:`auto_examples/plot_OT_1D_smooth` + - :any:`auto_examples/plot_stochastic` + + Other regularization ^^^^^^^^^^^^^^^^^^^^ @@ -335,10 +338,79 @@ relies on :any:`ot.sinkhorn` for its iterations. - :any:`auto_examples/plot_optim_OTreg` - Wasserstein Barycenters ----------------------- +A Wasserstein barycenter is a distribution that minimize its Wasserstein +distance with respect to other distributions [16]_. It corresponds to minimizing the +following problem by seaching a distribution :math:`\mu` + +.. math:: + \min_\mu \quad \sum_{k} w_kW(\mu,\mu_k) + + +In practice we model a distribution with a finite number of support position: + +.. math:: + \mu=\sum_{i=1}^n a_i\delta_{x_i} + +where :math:`a` is an histogram on the simplex and the :math:`\{x_i\}` are the +position of the support. We can clearly see here that optimizing :math:`\mu` can +be done by searching for optimal weights :math:`a` or optimal support +:math:`\{x_i\}` (optimizing both is also an option). +We provide in POT solvers to estimate a discrete +Wasserstein barycenter in both cases. + +Barycenters with fixed support +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +When optimizing a barycenter with a fixed support, the optimization problem can +be expressed as + + +.. math:: + \min_a \quad \sum_{k} w_k W(a,b_k) + +where :math:`b_k` are also weights in the simplex. In the non-regularized case, +the problem above is a classical linear program. In this case we propose a +solver :any:`ot.lp.barycenter` that rely on generic LP solvers. By default the +function uses :any:`scipy.optimize.linprog`, but more efficient LP solvers from +cvxopt can be also used by changing parameter :code:`solver`. Note that these +solver require to solve a very large linear program and can be very slow in +practice. + +Similarly to the OT problem, OT barycenters can be computed in the regularized +case. When using entropic regularization the problem can be solved with a +generalization of the sinkhorn algorithm based on bregman projections [3]_. This +algorithm is provided in function :any:`ot.bregman.barycenter` also available as +:any:`ot.barycenter`. In this case, the algorithm scales better to large +distributions and rely only on matrix multiplications that can be performed in +parallel. + +In addition to teh speedup brought by regularization, one can also greatly +accelerate the estimation of Wasserstein barycenter when the support has a +separable structure [21]_. In teh case of 2D images for instance one can replace +the matrix vector production in teh bregman projections by convolution +operators. We provide an implementation of this algorithm in function +:any:`ot.bregman.convolutional_barycenter2d`. + +.. hint:: + Example of Wasserstein (:any:`ot.lp.barycenter`) and regularized wassrestein + barycenter (:any:`ot.bregman.barycenter`) computation are available in the following examples: + + - :any:`auto_examples/plot_barycenter_1D` + - :any:`auto_examples/plot_barycenter_lp_vs_entropic` + + Example of convolutional barycenter (:any:`ot.bregman.convolutional_barycenter2d`) computation for 2D images is available + in the following example: + + - :any:`auto_examples/plot_convolutional_barycenter` + + + +Barycenters with free support +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + -- cgit v1.2.3 From b250212448ed3c1d023a6412abf4a3395d5585fb Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 2 Jul 2019 11:01:53 +0200 Subject: wasserstein barycenetr with fixed support --- docs/source/quickstart.rst | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst index 94bc8cd..7cbc962 100644 --- a/docs/source/quickstart.rst +++ b/docs/source/quickstart.rst @@ -220,8 +220,14 @@ More details about the algorithm used is given in the following note. Recently [23]_ introduced the sinkhorn divergence that build from entropic -regularization to compute fast and differentiable geometric diveregnce between -empirical distributions. +regularization to compute fast and differentiable geometric divergence between +empirical distributions. Note that we provide a function that compute directly +(with no need to pre compute the :code:`M` matrix) +the sinkhorn divergence for empirical distributions in +:any:`ot.bregman.empirical_sinkhorn_divergence`. Similarly one can compute the +OT matrix and loss for empirical distributions with respectively +:any:`ot.bregman.empirical_sinkhorn` and :any:`ot.bregman.empirical_sinkhorn2`. + @@ -389,19 +395,21 @@ parallel. In addition to teh speedup brought by regularization, one can also greatly accelerate the estimation of Wasserstein barycenter when the support has a -separable structure [21]_. In teh case of 2D images for instance one can replace +separable structure [21]_. In the case of 2D images for instance one can replace the matrix vector production in teh bregman projections by convolution operators. We provide an implementation of this algorithm in function :any:`ot.bregman.convolutional_barycenter2d`. .. hint:: - Example of Wasserstein (:any:`ot.lp.barycenter`) and regularized wassrestein + Example of Wasserstein (:any:`ot.lp.barycenter`) and regularized Wasserstein barycenter (:any:`ot.bregman.barycenter`) computation are available in the following examples: - :any:`auto_examples/plot_barycenter_1D` - :any:`auto_examples/plot_barycenter_lp_vs_entropic` - Example of convolutional barycenter (:any:`ot.bregman.convolutional_barycenter2d`) computation for 2D images is available + Example of convolutional barycenter + (:any:`ot.bregman.convolutional_barycenter2d`) computation + for 2D images is available in the following example: - :any:`auto_examples/plot_convolutional_barycenter` -- cgit v1.2.3 From 64693f98c22775048222f61f5e495849844e0135 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 2 Jul 2019 11:09:50 +0200 Subject: quickstart wasserstein barycenter done --- docs/source/quickstart.rst | 26 ++++++++++++++++++++++---- 1 file changed, 22 insertions(+), 4 deletions(-) diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst index 7cbc962..8cce1c9 100644 --- a/docs/source/quickstart.rst +++ b/docs/source/quickstart.rst @@ -217,8 +217,6 @@ More details about the algorithm used is given in the following note. choose entropic/Kullbach Leibler regularization. - - Recently [23]_ introduced the sinkhorn divergence that build from entropic regularization to compute fast and differentiable geometric divergence between empirical distributions. Note that we provide a function that compute directly @@ -417,7 +415,27 @@ operators. We provide an implementation of this algorithm in function Barycenters with free support -^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Estimating the Wassresein barycenter with free support but fixed weights +corresponds to solving the following optimization problem: + +.. math:: + \min_\{x_i\} \quad \sum_{k} w_kW(\mu,\mu_k) + + s.t. \quad \mu=\sum_{i=1}^n a_i\delta_{x_i} + +WE provide an alternating solver based on [20]_ in +:any:`ot.lp.free_support_barycenter`. This function minimize the problem and +return an optimal support :math:`\{x_i\}` for uniform or given weights +:math:`a`. + + .. hint:: + + Example of the fee support barycenter estimation is available + in the following example: + + - :any:`auto_examples/plot_free_support_barycenter` @@ -438,7 +456,7 @@ Gromov-Wasserstein GPU acceleration ----------------- +^^^^^^^^^^^^^^^^ We provide several implementation of our OT solvers in :any:`ot.gpu`. Those implementation use the :code:`cupy` toolbox. -- cgit v1.2.3 From 6fdce8f75000ec6e609371ae39484f7edbb19b2c Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 2 Jul 2019 13:38:20 +0200 Subject: quickstart wda + start unbalanced --- docs/source/quickstart.rst | 148 +++++++++++++++++++++++++++++++++++++++++++-- docs/source/readme.rst | 2 - 2 files changed, 144 insertions(+), 6 deletions(-) diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst index 8cce1c9..8f4a24e 100644 --- a/docs/source/quickstart.rst +++ b/docs/source/quickstart.rst @@ -278,7 +278,7 @@ choose the quadratic regularization. Group Lasso regularization """""""""""""""""""""""""" -Another regularization that has been used in recent years is the group lasso +Another regularization that has been used in recent years [5]_ is the group lasso regularization .. math:: @@ -333,7 +333,7 @@ Another solver is proposed to solve the problem s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0 where :math:`\Omega_e` is the entropic regularization. In this case we use a -generalized conditional gradient [7]_ implemented in :any:`ot.opim.gcg` that does not linearize the entropic term and +generalized conditional gradient [7]_ implemented in :any:`ot.optim.gcg` that does not linearize the entropic term and relies on :any:`ot.sinkhorn` for its iterations. .. hint:: @@ -421,11 +421,11 @@ Estimating the Wassresein barycenter with free support but fixed weights corresponds to solving the following optimization problem: .. math:: - \min_\{x_i\} \quad \sum_{k} w_kW(\mu,\mu_k) + \min_{\{x_i\}} \quad \sum_{k} w_kW(\mu,\mu_k) s.t. \quad \mu=\sum_{i=1}^n a_i\delta_{x_i} -WE provide an alternating solver based on [20]_ in +We provide an alternating solver based on [20]_ in :any:`ot.lp.free_support_barycenter`. This function minimize the problem and return an optimal support :math:`\{x_i\}` for uniform or given weights :math:`a`. @@ -443,13 +443,149 @@ return an optimal support :math:`\{x_i\}` for uniform or given weights Monge mapping and Domain adaptation ----------------------------------- +The original transport problem investigated by Gaspard Monge was seeking for a +mapping function that maps (or transports) between a source and target +distribution but that minimizes the transport loss. The existence and uniqueness of this +optimal mapping is still an open problem in the general case but has been proven +for smooth distributions by Brenier in his eponym `theorem +`__. We provide in +:any:`ot.da` several solvers for Monge mapping estimation and domain adaptation. + +Monge Mapping estimation +^^^^^^^^^^^^^^^^^^^^^^^^ + +We now discuss several approaches that are implemented in POT to estimate or +approximate a Monge mapping from finite distributions. + +First note that when the source and target distributions are supposed to be Gaussian +distributions, there exists a close form solution for the mapping and its an +affine function [14]_ of the form :math:`T(x)=Ax+b` . In this case we provide the function +:any:`ot.da.OT_mapping_linear` that return the operator :math:`A` and vector +:math:`b`. Note that if the number of samples is too small there is a parameter +:code:`reg` that provide a regularization for the covariance matrix estimation. + +For a more general mapping estimation we also provide the barycentric mapping +proposed in [6]_ . It is implemented in the class :any:`ot.da.EMDTransport` and +other transport based classes in :any:`ot.da` . Those classes are discussed more +in the following but follow an interface similar to sklearn classes. Finally a +method proposed in [8]_ that estimate a continuous mapping approximating the +barycentric mapping is provided in :any:`ot.da.joint_OT_mapping_linear` for +linear mapping and :any:`ot.da.joint_OT_mapping_kernel` for non linear mapping. + + .. hint:: + + Example of the linear Monge mapping estimation is available + in the following example: + + - :any:`auto_examples/plot_otda_linear_mapping` + +Domain adaptation classes +^^^^^^^^^^^^^^^^^^^^^^^^^ + +The use of OT for domain adaptation (OTDA) has been first proposed in [5]_ that also +introduced the group Lasso regularization. The main idea of OTDA is to estimate +a mapping of the samples between source and target distributions which allows to +transport labeled source samples onto the target distribution with no labels. + +We provide several classes based on :any:`ot.da.BaseTransport` that provide +several OT and mapping estimations. The interface of those classes is similar to +classifiers in sklearn toolbox. At initialization several parameters (for +instance regularization parameter) can be set. Then one needs to estimate the +mapping with function :any:`ot.da.BaseTransport.fit`. Finally one can map the +samples from source to target with :any:`ot.da.BaseTransport.transform` and +from target to source with :any:`ot.da.BaseTransport.inverse_transform`. Here is +an example for class :any:`ot.da.EMDTransport` + +.. code:: + + ot_emd = ot.da.EMDTransport() + ot_emd.fit(Xs=Xs, Xt=Xt) + + Mapped_Xs= ot_emd.transform(Xs=Xs) + +A list +of the provided implementation is given in the following note. + +.. note:: + + Here is a list of the mapping classes inheriting from + :any:`ot.da.BaseTransport` + + * :any:`ot.da.EMDTransport` : Barycentric mapping with EMD transport + * :any:`ot.da.SinkhornTransport` : Barycentric mapping with Sinkhorn transport + * :any:`ot.da.SinkhornL1l2Transport` : Barycentric mapping with Sinkhorn + + group Lasso regularization [5]_ + * :any:`ot.da.SinkhornLpl1Transport` : Barycentric mapping with Sinkhorn + + non convex group Lasso regularization [5]_ + * :any:`ot.da.LinearTransport` : Linear mapping estimation between Gaussians + [14]_ + * :any:`ot.da.MappingTransport` : Nonlinear mapping estimation [8]_ + +.. hint:: + + Example of the use of OTDA classes are available in the following exmaples: + + - :any:`auto_examples/plot_otda_color_images` + - :any:`auto_examples/plot_otda_mapping` + - :any:`auto_examples/plot_otda_mapping_colors_images` + - :any:`auto_examples/plot_otda_semi_supervised` Other applications ------------------ +We discuss in the following several implementations that has been used and +proposed in the OT and machine learning community. + Wasserstein Discriminant Analysis ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ +Wasserstein Discriminant Analysis [11]_ is a generalization of `Fisher Linear Discriminant +Analysis `__ that +allows discrimination between classes that are not linearly separable. It +consist in finding a linear projector optimizing the following criterion + +.. math:: + P = \text{arg}\min_P \frac{\sum_i OT_e(\mu_i\#P,\mu_i\#P)}{\sum_{i,j\neq i} + OT_e(\mu_i\#P,\mu_j\#P)} + +where :math:`\#` is the push-forward operator, :math:`OT_e` is the entropic OT +loss and :math:`\mu_i` is the +distribution of samples from class :math:`i`. :math:`P` is also constrained to +be in the Stiefel manifold. WDA can be solved in pot using function +:any:`ot.dr.wda`. It requires to have installed :code:`pymanopt` and +:code:`autograd` for manifold optimization and automatic differentiation +respectively. Note that we also provide the Fisher discriminant estimator in +:any:`ot.dr.wda` for easy comparison. + +.. warning:: + Note that due to the hard dependency on :code:`pymanopt` and + :code:`autograd`, :any:`ot.dr` is not imported by default. If you want to + use it you have to specifically import it with :code:`import ot.dr` . + +.. hint:: + + An example of the use of WDA is available in the following example: + + - :any:`auto_examples/plot_WDA` + + +Unbalanced optimal transport +^^^^^^^^^^^^^^^^^^^^^^^^^^^^ + +Unbalanced OT is a relaxation of the original OT problem where the violation of +the constraint on the marginals is added to the objective of the optimization +problem: + +.. math:: + \min_\gamma \quad \sum_{i,j}\gamma_{i,j}M_{i,j} + reg\cdot\Omega(\gamma) + \alpha KL(\gamma 1, a) + \alpha KL(\gamma^T 1, b) + + s.t. \quad \gamma\geq 0 + + +where KL is the Kullback-Leibler divergence. This formulation allwos for +computing approximate mapping between distributions that do not have the same +amount of mass. Interestingly the problem can be solved with a generalization of +the Bregman projections algorithm [10]_. Gromov-Wasserstein ^^^^^^^^^^^^^^^^^^ @@ -461,6 +597,10 @@ GPU acceleration We provide several implementation of our OT solvers in :any:`ot.gpu`. Those implementation use the :code:`cupy` toolbox. +.. warning:: + Note that due to the hard dependency on :code:`cupy`, :any:`ot.gpu` is not + imported by default. If you want to + use it you have to specifically import it with :code:`import ot.gpu` . FAQ diff --git a/docs/source/readme.rst b/docs/source/readme.rst index 320ddd5..0871779 100644 --- a/docs/source/readme.rst +++ b/docs/source/readme.rst @@ -221,8 +221,6 @@ This toolbox has been created and is maintained by The contributors to this library are -- `Rémi Flamary `__ -- `Nicolas Courty `__ - `Alexandre Gramfort `__ - `Laetitia Chapel `__ - `Michael Perrot `__ -- cgit v1.2.3 From 85cc12bc7731077846bb77346797165c098fc4ec Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 2 Jul 2019 16:05:31 +0200 Subject: quickstart gfirst shot done! --- docs/source/quickstart.rst | 97 +++++++++++++++++++++++++++++++++++++++++++--- 1 file changed, 92 insertions(+), 5 deletions(-) diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst index 8f4a24e..0dcd7ff 100644 --- a/docs/source/quickstart.rst +++ b/docs/source/quickstart.rst @@ -417,7 +417,7 @@ operators. We provide an implementation of this algorithm in function Barycenters with free support ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Estimating the Wassresein barycenter with free support but fixed weights +Estimating the Wasserstein barycenter with free support but fixed weights corresponds to solving the following optimization problem: .. math:: @@ -555,7 +555,7 @@ be in the Stiefel manifold. WDA can be solved in pot using function :any:`ot.dr.wda`. It requires to have installed :code:`pymanopt` and :code:`autograd` for manifold optimization and automatic differentiation respectively. Note that we also provide the Fisher discriminant estimator in -:any:`ot.dr.wda` for easy comparison. +:any:`ot.dr.fda` for easy comparison. .. warning:: Note that due to the hard dependency on :code:`pymanopt` and @@ -585,17 +585,104 @@ problem: where KL is the Kullback-Leibler divergence. This formulation allwos for computing approximate mapping between distributions that do not have the same amount of mass. Interestingly the problem can be solved with a generalization of -the Bregman projections algorithm [10]_. +the Bregman projections algorithm [10]_. We provide a solver for unbalanced OT +in :any:`ot.unbalanced` and more specifically +in function :any:`ot.sinkhorn_unbalanced`. A solver for unbalanced OT barycenter +is available in :any:`ot.barycenter_unbalanced`. + + +.. hint:: + + Examples of the use of :any:`ot.sinkhorn_unbalanced` and + :any:`ot.barycenter_unbalanced` are available in: + + - :any:`auto_examples/plot_UOT_1D` + - :any:`auto_examples/plot_UOT_barycenter_1D` + Gromov-Wasserstein ^^^^^^^^^^^^^^^^^^ +Gromov Wasserstein (GW) is a generalization of OT to distributions that do not lie in +the same space [13]_. In this case one cannot compute distance between samples +from the two distributions. [13]_ proposed instead to realign the metric spaces +by computing a transport between distance matrices. The Gromow Wasserstein +alignement between two distributions can be expressed as the one minimizing: + + +.. math:: + GW = \min_\gamma \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*\gamma_{i,j}*\gamma_{k,l} + + s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0 + +where ::math:`C1` is the distance matrix between samples in the source +distribution and :math:`C2` the one between samples in the target, :math:`L(C1_{i,k},C2_{j,l})` is a measure of similarity between +:math:`C1_{i,k}` and :math:`C2_{j,l}` often chosen as +:math:`L(C1_{i,k},C2_{j,l})=\|C1_{i,k}-C2_{j,l}\|^2`. The optimization problem +above is a non-convex quadratic program but we provide a solver that finds a +local minimum using conditional gradient in :any:`ot.gromov.gromov_wasserstein`. +There also exist an entropic regularized variant of GW that has been proposed in +[12]_ and we provide an implementation of their algorithm in +:any:`ot.gromov.entropic_gromov_wasserstein`. + +Note that similarly to Wasserstein distance GW allows for the definition of GW +barycenters that cen be expressed as + +.. math:: + \min_{C\geq 0} \quad \sum_{k} w_k GW(C,Ck) + +where :math:`Ck` is the distance matrix between samples in distribution +:math:`k`. Note that interestingly the barycenter is defined a a symmetric +positive matrix. We provide a block coordinate optimization procedure in +:any:`ot.gromov.gromov_barycenters` and +:any:`ot.gromov.entropic_gromov_barycenters` for non-regularized and regularized +barycenters respectively. + +Finally note that recently a fusion between Wasserstein and GW, coined Fused +Groimov-Wasserstein (FGW) has been proposed +in [24]_. It allows to compute a similarity between objects that are only partly in +the same space. As such it can be used to measure similarity between labeled +graphs for instance and also provide computable barycenters. +The implementations of FGW is provided in functions +:any:`ot.gromov.fused_gromov_wasserstein` and :any:`ot.gromov.fgw_barycenters`. + +.. hint:: + + Examples of computation of GW, regularized G and FGW are provided in : + + - :any:`auto_examples/plot_gromov` + - :any:`auto_examples/plot_fgw` + + Examples of GW, regularized GW and FGW barycenters are available in : + + - :any:`auto_examples/plot_gromov_barycenter` + - :any:`auto_examples/plot_barycenter_fgw` + GPU acceleration ^^^^^^^^^^^^^^^^ We provide several implementation of our OT solvers in :any:`ot.gpu`. Those -implementation use the :code:`cupy` toolbox. +implementation use the :code:`cupy` toolbox that obviously need to be installed. + + +.. note:: + + Several implementations of POT functions (mainly those relying on linear + algebra) have been implemented in :any:`ot.gpu`. Here is a short list on the + main entries: + + - :any:`ot.gpu.dist` : computation of distance matrix + - :any:`ot.gpu.sinkhorn` : computation of sinkhorn + - :any:`ot.gpu.sinkhorn_lpl1_mm` : computation of sinkhorn + group lasso + +Note that while the :any:`ot.gpu` module has been designed to be compatible with +POT, calling its function with numpy array will incur a large overhead due to +the memory copy of the array on GPU prior to computation and conversion of the +array after computation. To avoid this overhead, we provide functions +:any:`ot.gpu.to_gpu` and :any:`ot.gpu.to_np` that perform the conversion +explicitly. + .. warning:: Note that due to the hard dependency on :code:`cupy`, :any:`ot.gpu` is not @@ -735,7 +822,7 @@ References matching `__. Foundations of computational mathematics 11.4 : 417-487. -.. [14] Knott, M. and Smith, C. S. (1984).`On the optimal mapping of +.. [14] Knott, M. and Smith, C. S. (1984). `On the optimal mapping of distributions `__, Journal of Optimization Theory and Applications Vol 43. -- cgit v1.2.3 From ef00ce42616fe7adf747c23a5590a83b62171a36 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 2 Jul 2019 16:40:30 +0200 Subject: quickstart proof reading --- docs/source/quickstart.rst | 173 +++++++++++++++++++++++---------------------- 1 file changed, 88 insertions(+), 85 deletions(-) diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst index 0dcd7ff..b726149 100644 --- a/docs/source/quickstart.rst +++ b/docs/source/quickstart.rst @@ -3,7 +3,9 @@ Quick start guide ================= In the following we provide some pointers about which functions and classes -to use for different problems related to optimal transport (OT). +to use for different problems related to optimal transport (OT) and machine +learning. We refer when we can to concrete examples in the documentation that +are also available as notebooks on the POT Github. This document is not a tutorial on numerical optimal transport. For this we strongly recommend to read the very nice book [15]_ . @@ -16,7 +18,8 @@ Optimal transport and Wasserstein distance In POT, most functions that solve OT or regularized OT problems have two versions that return the OT matrix or the value of the optimal solution. For instance :any:`ot.emd` return the OT matrix and :any:`ot.emd2` return the - Wassertsein distance. + Wassertsein distance. This approach has been implemented in practice for all + solvers that return an OT matrix (even Gromov-Wasserstsein) Solving optimal transport ^^^^^^^^^^^^^^^^^^^^^^^^^ @@ -32,7 +35,8 @@ as where : - :math:`M\in\mathbb{R}_+^{m\times n}` is the metric cost matrix defining the cost to move mass from bin :math:`a_i` to bin :math:`b_j`. -- :math:`a` and :math:`b` are histograms (positive, sum to 1) that represent the weights of each samples in the source an target distributions. +- :math:`a` and :math:`b` are histograms on the simplex (positive, sum to 1) that represent the +weights of each samples in the source an target distributions. Solving the linear program above can be done using the function :any:`ot.emd` that will return the optimal transport matrix :math:`\gamma^*`: @@ -43,7 +47,7 @@ that will return the optimal transport matrix :math:`\gamma^*`: # M is the ground cost matrix T=ot.emd(a,b,M) # exact linear program -The method used for solving the OT problem is the network simplex, it is +The method implemented for solving the OT problem is the network simplex, it is implemented in C from [1]_. It has a complexity of :math:`O(n^3)` but the solver is quite efficient and uses sparsity of the solution. @@ -54,15 +58,16 @@ solver is quite efficient and uses sparsity of the solution. - :any:`auto_examples/plot_OT_1D` - :any:`auto_examples/plot_OT_L1_vs_L2` + Computing Wasserstein distance ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -The value of the OT solution is often more of interest that the OT matrix : +The value of the OT solution is often more of interest than the OT matrix : - .. math:: - OT(a,b)=\min_\gamma \quad \sum_{i,j}\gamma_{i,j}M_{i,j} +.. math:: + OT(a,b)=\min_\gamma \quad \sum_{i,j}\gamma_{i,j}M_{i,j} - s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0 + s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0 It can computed from an already estimated OT matrix with @@ -92,7 +97,6 @@ the :math:`W_1` wasserstein distance can be done directly with :any:`ot.emd2` when providing :code:`M=ot.dist(xs,xt, metric='euclidean')` to use the euclidean distance. - .. hint:: Examples of use for :any:`ot.emd2` are available in the following examples: @@ -111,15 +115,15 @@ For instance when the samples are in 1D, then the OT problem can be solved in function :any:`ot.emd_1d` and :any:`ot.emd2_1d` to return respectively the OT matrix and value. Note that since the solution is very sparse the :code:`sparse` parameter of :any:`ot.emd_1d` allows for solving and returning the solution for -very large problems. Note that in order to computed directly the :math:`W_p` +very large problems. Note that in order to compute directly the :math:`W_p` Wasserstein distance in 1D we provide the function :any:`ot.wasserstein_1d` that takes :code:`p` as a parameter. -Another specials for estimating OT and Monge mapping is between Gaussian +Another special case for estimating OT and Monge mapping is between Gaussian distributions. In this case there exists a close form solution given in Remark 2.29 in [15]_ and the Monge mapping is an affine function and can be also computed from the covariances and means of the source and target -distributions. In this case when the finite sample dataset is supposed gaussian, we provide +distributions. In the case when the finite sample dataset is supposed gaussian, we provide :any:`ot.da.OT_mapping_linear` that returns the parameters for the Monge mapping. @@ -129,8 +133,7 @@ Regularized Optimal Transport Recent developments have shown the interest of regularized OT both in terms of computational and statistical properties. - -We address in this section the regularized OT problem that can be expressed as +We address in this section the regularized OT problems that can be expressed as .. math:: \gamma^* = arg\min_\gamma \quad \sum_{i,j}\gamma_{i,j}M_{i,j} + \lambda\Omega(\gamma) @@ -148,7 +151,6 @@ We discuss in the following specific algorithms that can be used depending on the regularization term. - Entropic regularized OT ^^^^^^^^^^^^^^^^^^^^^^^ @@ -162,7 +164,8 @@ regularization has the following expression The use of the regularization term above in the optimization problem has a very strong impact. First it makes the problem smooth which leads to new optimization -procedures such as L-BFGS (see :any:`ot.smooth` ). Next it makes the problem +procedures such as the well known Sinkhorn algorithm [2]_ or L-BFGS (see +:any:`ot.smooth` ). Next it makes the problem strictly convex meaning that there will be a unique solution. Finally the solution of the resulting optimization problem can be expressed as: @@ -172,13 +175,13 @@ solution of the resulting optimization problem can be expressed as: where :math:`u` and :math:`v` are vectors and :math:`K=\exp(-M/\lambda)` where the :math:`\exp` is taken component-wise. In order to solve the optimization -problem, on can use an alternative projection algorithm that can be very +problem, on can use an alternative projection algorithm called Sinkhorn-Knopp that can be very efficient for large values if regularization. -The main function is POT are :any:`ot.sinkhorn` and +The Sinkhorn-Knopp algorithm is implemented in :any:`ot.sinkhorn` and :any:`ot.sinkhorn2` that return respectively the OT matrix and the value of the linear term. Note that the regularization parameter :math:`\lambda` in the -equation above is given to those function with the parameter :code:`reg`. +equation above is given to those functions with the parameter :code:`reg`. >>> import ot >>> a=[.5,.5] @@ -188,10 +191,7 @@ equation above is given to those function with the parameter :code:`reg`. array([[ 0.36552929, 0.13447071], [ 0.13447071, 0.36552929]]) - - -More details about the algorithm used is given in the following note. - +More details about the algorithms used are given in the following note. .. note:: The main function to solve entropic regularized OT is :any:`ot.sinkhorn`. @@ -211,7 +211,7 @@ More details about the algorithm used is given in the following note. In addition to all those variants of sinkhorn, we have another implementation solving the problem in the smooth dual or semi-dual in :any:`ot.smooth`. This solver uses the :any:`scipy.optimize.minimize` - function to solve the smooth problem with :code:`L-BFGS` algorithm. Tu use + function to solve the smooth problem with :code:`L-BFGS-B` algorithm. Tu use this solver, use functions :any:`ot.smooth.smooth_ot_dual` or :any:`ot.smooth.smooth_ot_semi_dual` with parameter :code:`reg_type='kl'` to choose entropic/Kullbach Leibler regularization. @@ -227,13 +227,13 @@ OT matrix and loss for empirical distributions with respectively :any:`ot.bregman.empirical_sinkhorn` and :any:`ot.bregman.empirical_sinkhorn2`. - - Finally note that we also provide in :any:`ot.stochastic` several implementation -of stochastic solvers for entropic regularized OT [18]_ [19]_. +of stochastic solvers for entropic regularized OT [18]_ [19]_. Those pure Python +implementations are not optimized for speed but provide a roust implementation +of algorithms in [18]_ [19]_. .. hint:: - Examples of use for :any:`ot.sinkhorn` are available in the following examples: + Examples of use for :any:`ot.sinkhorn` are available in : - :any:`auto_examples/plot_OT_2D_samples` - :any:`auto_examples/plot_OT_1D` @@ -246,7 +246,7 @@ Other regularization While entropic OT is the most common and favored in practice, there exist other kind of regularization. We provide in POT two specific solvers for other -regularization terms: namely quadratic regularization and group lasso +regularization terms, namely quadratic regularization and group lasso regularization. But we also provide in :any:`ot.optim` two generic solvers that allows solving any smooth regularization in practice. @@ -261,14 +261,14 @@ regularization of the form this regularization term has a similar effect to entropic regularization in densifying the OT matrix but it keeps some sort of sparsity that is lost with -entropic regularization as soon as :math:`\lambda>0` [17]_. This problem cen be +entropic regularization as soon as :math:`\lambda>0` [17]_. This problem can be solved with POT using solvers from :any:`ot.smooth`, more specifically functions :any:`ot.smooth.smooth_ot_dual` or :any:`ot.smooth.smooth_ot_semi_dual` with parameter :code:`reg_type='l2'` to choose the quadratic regularization. .. hint:: - Examples of quadratic regularization are available in the following examples: + Examples of quadratic regularization are available in : - :any:`auto_examples/plot_OT_1D_smooth` - :any:`auto_examples/plot_optim_OTreg` @@ -288,17 +288,17 @@ where :math:`\mathcal{G}` contains non overlapping groups of lines in the OT matrix. This regularization proposed in [5]_ will promote sparsity at the group level and for instance will force target samples to get mass from a small number of groups. Note that the exact OT solution is already sparse so this regularization does -not make sens if it is not combined with others such as entropic. Depending on +not make sens if it is not combined with entropic regularization. Depending on the choice of :code:`p` and :code:`q`, the problem can be solved with different approaches. When :code:`q=1` and :code:`p<1` the problem is non convex but can -be solved using an efficient majoration minimization approach with +be solved using an efficient majoration minimization approach with :any:`ot.sinkhorn_lpl1_mm`. When :code:`q=2` and :code:`p=1` we recover the -convex gourp lasso and we provide a solver using generalized conditional +convex group lasso and we provide a solver using generalized conditional gradient algorithm [7]_ in function :any:`ot.da.sinkhorn_l1l2_gl`. .. hint:: - Examples of group Lasso regularization are available in the following examples: + Examples of group Lasso regularization are available in : - :any:`auto_examples/plot_otda_classes` - :any:`auto_examples/plot_otda_d2` @@ -309,7 +309,7 @@ Generic solvers Finally we propose in POT generic solvers that can be used to solve any regularization as long as you can provide a function computing the -regularization and a function computing its gradient. +regularization and a function computing its gradient (or sub-gradient). In order to solve @@ -319,13 +319,14 @@ In order to solve s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0 you can use function :any:`ot.optim.cg` that will use a conditional gradient as -proposed in [6]_ . you need to provide the regularization function as parameter +proposed in [6]_ . You need to provide the regularization function as parameter ``f`` and its gradient as parameter ``df``. Note that the conditional gradient relies on iterative solving of a linearization of the problem using the exact -:any:`ot.emd` so it can be slow in practice. Still it always returns a +:any:`ot.emd` so it can be slow in practice. But, being an interior point +algorithm, it always returns a transport matrix that does not violates the marginals. -Another solver is proposed to solve the problem +Another generic solver is proposed to solve the problem .. math:: \gamma^* = arg\min_\gamma \quad \sum_{i,j}\gamma_{i,j}M_{i,j}+ \lambda_e\Omega_e(\gamma) + \lambda\Omega(\gamma) @@ -333,11 +334,12 @@ Another solver is proposed to solve the problem s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0 where :math:`\Omega_e` is the entropic regularization. In this case we use a -generalized conditional gradient [7]_ implemented in :any:`ot.optim.gcg` that does not linearize the entropic term and +generalized conditional gradient [7]_ implemented in :any:`ot.optim.gcg` that +does not linearize the entropic term but relies on :any:`ot.sinkhorn` for its iterations. .. hint:: - Example of generic solvers are available in the following example: + An example of generic solvers are available in : - :any:`auto_examples/plot_optim_OTreg` @@ -347,7 +349,7 @@ Wasserstein Barycenters A Wasserstein barycenter is a distribution that minimize its Wasserstein distance with respect to other distributions [16]_. It corresponds to minimizing the -following problem by seaching a distribution :math:`\mu` +following problem by searching a distribution :math:`\mu` such that .. math:: \min_\mu \quad \sum_{k} w_kW(\mu,\mu_k) @@ -371,7 +373,6 @@ Barycenters with fixed support When optimizing a barycenter with a fixed support, the optimization problem can be expressed as - .. math:: \min_a \quad \sum_{k} w_k W(a,b_k) @@ -379,36 +380,36 @@ where :math:`b_k` are also weights in the simplex. In the non-regularized case, the problem above is a classical linear program. In this case we propose a solver :any:`ot.lp.barycenter` that rely on generic LP solvers. By default the function uses :any:`scipy.optimize.linprog`, but more efficient LP solvers from -cvxopt can be also used by changing parameter :code:`solver`. Note that these -solver require to solve a very large linear program and can be very slow in +cvxopt can be also used by changing parameter :code:`solver`. Note that this problem +requires to solve a very large linear program and can be very slow in practice. Similarly to the OT problem, OT barycenters can be computed in the regularized -case. When using entropic regularization the problem can be solved with a +case. When using entropic regularization is used, the problem can be solved with a generalization of the sinkhorn algorithm based on bregman projections [3]_. This algorithm is provided in function :any:`ot.bregman.barycenter` also available as :any:`ot.barycenter`. In this case, the algorithm scales better to large distributions and rely only on matrix multiplications that can be performed in parallel. -In addition to teh speedup brought by regularization, one can also greatly +In addition to the speedup brought by regularization, one can also greatly accelerate the estimation of Wasserstein barycenter when the support has a separable structure [21]_. In the case of 2D images for instance one can replace -the matrix vector production in teh bregman projections by convolution +the matrix vector production in the Bregman projections by convolution operators. We provide an implementation of this algorithm in function :any:`ot.bregman.convolutional_barycenter2d`. .. hint:: - Example of Wasserstein (:any:`ot.lp.barycenter`) and regularized Wasserstein - barycenter (:any:`ot.bregman.barycenter`) computation are available in the following examples: + Examples of Wasserstein (:any:`ot.lp.barycenter`) and regularized Wasserstein + barycenter (:any:`ot.bregman.barycenter`) computation are available in : - :any:`auto_examples/plot_barycenter_1D` - :any:`auto_examples/plot_barycenter_lp_vs_entropic` - Example of convolutional barycenter + An example of convolutional barycenter (:any:`ot.bregman.convolutional_barycenter2d`) computation for 2D images is available - in the following example: + in : - :any:`auto_examples/plot_convolutional_barycenter` @@ -425,15 +426,15 @@ corresponds to solving the following optimization problem: s.t. \quad \mu=\sum_{i=1}^n a_i\delta_{x_i} -We provide an alternating solver based on [20]_ in +We provide a solver based on [20]_ in :any:`ot.lp.free_support_barycenter`. This function minimize the problem and -return an optimal support :math:`\{x_i\}` for uniform or given weights +return a locally optimal support :math:`\{x_i\}` for uniform or given weights :math:`a`. .. hint:: - Example of the fee support barycenter estimation is available - in the following example: + An example of the free support barycenter estimation is available + in : - :any:`auto_examples/plot_free_support_barycenter` @@ -449,7 +450,8 @@ distribution but that minimizes the transport loss. The existence and uniqueness optimal mapping is still an open problem in the general case but has been proven for smooth distributions by Brenier in his eponym `theorem `__. We provide in -:any:`ot.da` several solvers for Monge mapping estimation and domain adaptation. +:any:`ot.da` several solvers for smooth Monge mapping estimation and domain +adaptation from discrete distributions. Monge Mapping estimation ^^^^^^^^^^^^^^^^^^^^^^^^ @@ -468,14 +470,14 @@ For a more general mapping estimation we also provide the barycentric mapping proposed in [6]_ . It is implemented in the class :any:`ot.da.EMDTransport` and other transport based classes in :any:`ot.da` . Those classes are discussed more in the following but follow an interface similar to sklearn classes. Finally a -method proposed in [8]_ that estimate a continuous mapping approximating the +method proposed in [8]_ that estimates a continuous mapping approximating the barycentric mapping is provided in :any:`ot.da.joint_OT_mapping_linear` for linear mapping and :any:`ot.da.joint_OT_mapping_kernel` for non linear mapping. .. hint:: - Example of the linear Monge mapping estimation is available - in the following example: + An example of the linear Monge mapping estimation is available + in : - :any:`auto_examples/plot_otda_linear_mapping` @@ -489,12 +491,14 @@ transport labeled source samples onto the target distribution with no labels. We provide several classes based on :any:`ot.da.BaseTransport` that provide several OT and mapping estimations. The interface of those classes is similar to -classifiers in sklearn toolbox. At initialization several parameters (for -instance regularization parameter) can be set. Then one needs to estimate the +classifiers in sklearn toolbox. At initialization, several parameters such as + regularization parameter value can be set. Then one needs to estimate the mapping with function :any:`ot.da.BaseTransport.fit`. Finally one can map the samples from source to target with :any:`ot.da.BaseTransport.transform` and -from target to source with :any:`ot.da.BaseTransport.inverse_transform`. Here is -an example for class :any:`ot.da.EMDTransport` +from target to source with :any:`ot.da.BaseTransport.inverse_transform`. + +Here is +an example for class :any:`ot.da.EMDTransport` : .. code:: @@ -503,12 +507,11 @@ an example for class :any:`ot.da.EMDTransport` Mapped_Xs= ot_emd.transform(Xs=Xs) -A list -of the provided implementation is given in the following note. +A list of the provided implementation is given in the following note. .. note:: - Here is a list of the mapping classes inheriting from + Here is a list of the OT mapping classes inheriting from :any:`ot.da.BaseTransport` * :any:`ot.da.EMDTransport` : Barycentric mapping with EMD transport @@ -523,7 +526,7 @@ of the provided implementation is given in the following note. .. hint:: - Example of the use of OTDA classes are available in the following exmaples: + Example of the use of OTDA classes are available in : - :any:`auto_examples/plot_otda_color_images` - :any:`auto_examples/plot_otda_mapping` @@ -533,7 +536,7 @@ of the provided implementation is given in the following note. Other applications ------------------ -We discuss in the following several implementations that has been used and +We discuss in the following several OT related problems and tools that has been proposed in the OT and machine learning community. Wasserstein Discriminant Analysis @@ -551,7 +554,7 @@ consist in finding a linear projector optimizing the following criterion where :math:`\#` is the push-forward operator, :math:`OT_e` is the entropic OT loss and :math:`\mu_i` is the distribution of samples from class :math:`i`. :math:`P` is also constrained to -be in the Stiefel manifold. WDA can be solved in pot using function +be in the Stiefel manifold. WDA can be solved in POT using function :any:`ot.dr.wda`. It requires to have installed :code:`pymanopt` and :code:`autograd` for manifold optimization and automatic differentiation respectively. Note that we also provide the Fisher discriminant estimator in @@ -564,7 +567,7 @@ respectively. Note that we also provide the Fisher discriminant estimator in .. hint:: - An example of the use of WDA is available in the following example: + An example of the use of WDA is available in : - :any:`auto_examples/plot_WDA` @@ -582,7 +585,7 @@ problem: s.t. \quad \gamma\geq 0 -where KL is the Kullback-Leibler divergence. This formulation allwos for +where KL is the Kullback-Leibler divergence. This formulation allows for computing approximate mapping between distributions that do not have the same amount of mass. Interestingly the problem can be solved with a generalization of the Bregman projections algorithm [10]_. We provide a solver for unbalanced OT @@ -594,7 +597,7 @@ is available in :any:`ot.barycenter_unbalanced`. .. hint:: Examples of the use of :any:`ot.sinkhorn_unbalanced` and - :any:`ot.barycenter_unbalanced` are available in: + :any:`ot.barycenter_unbalanced` are available in : - :any:`auto_examples/plot_UOT_1D` - :any:`auto_examples/plot_UOT_barycenter_1D` @@ -609,46 +612,46 @@ from the two distributions. [13]_ proposed instead to realign the metric spaces by computing a transport between distance matrices. The Gromow Wasserstein alignement between two distributions can be expressed as the one minimizing: - .. math:: GW = \min_\gamma \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*\gamma_{i,j}*\gamma_{k,l} s.t. \gamma 1 = a; \gamma^T 1= b; \gamma\geq 0 where ::math:`C1` is the distance matrix between samples in the source -distribution and :math:`C2` the one between samples in the target, :math:`L(C1_{i,k},C2_{j,l})` is a measure of similarity between +distribution and :math:`C2` the one between samples in the target, +:math:`L(C1_{i,k},C2_{j,l})` is a measure of similarity between :math:`C1_{i,k}` and :math:`C2_{j,l}` often chosen as :math:`L(C1_{i,k},C2_{j,l})=\|C1_{i,k}-C2_{j,l}\|^2`. The optimization problem above is a non-convex quadratic program but we provide a solver that finds a local minimum using conditional gradient in :any:`ot.gromov.gromov_wasserstein`. -There also exist an entropic regularized variant of GW that has been proposed in +There also exists an entropic regularized variant of GW that has been proposed in [12]_ and we provide an implementation of their algorithm in :any:`ot.gromov.entropic_gromov_wasserstein`. Note that similarly to Wasserstein distance GW allows for the definition of GW -barycenters that cen be expressed as +barycenters that can be expressed as .. math:: \min_{C\geq 0} \quad \sum_{k} w_k GW(C,Ck) where :math:`Ck` is the distance matrix between samples in distribution -:math:`k`. Note that interestingly the barycenter is defined a a symmetric +:math:`k`. Note that interestingly the barycenter is defined as a symmetric positive matrix. We provide a block coordinate optimization procedure in :any:`ot.gromov.gromov_barycenters` and :any:`ot.gromov.entropic_gromov_barycenters` for non-regularized and regularized barycenters respectively. Finally note that recently a fusion between Wasserstein and GW, coined Fused -Groimov-Wasserstein (FGW) has been proposed +Gromov-Wasserstein (FGW) has been proposed in [24]_. It allows to compute a similarity between objects that are only partly in the same space. As such it can be used to measure similarity between labeled graphs for instance and also provide computable barycenters. -The implementations of FGW is provided in functions +The implementations of FGW and FGW barycenter is provided in functions :any:`ot.gromov.fused_gromov_wasserstein` and :any:`ot.gromov.fgw_barycenters`. .. hint:: - Examples of computation of GW, regularized G and FGW are provided in : + Examples of computation of GW, regularized G and FGW are available in : - :any:`auto_examples/plot_gromov` - :any:`auto_examples/plot_fgw` @@ -663,7 +666,7 @@ GPU acceleration ^^^^^^^^^^^^^^^^ We provide several implementation of our OT solvers in :any:`ot.gpu`. Those -implementation use the :code:`cupy` toolbox that obviously need to be installed. +implementations use the :code:`cupy` toolbox that obviously need to be installed. .. note:: @@ -677,7 +680,7 @@ implementation use the :code:`cupy` toolbox that obviously need to be installed. - :any:`ot.gpu.sinkhorn_lpl1_mm` : computation of sinkhorn + group lasso Note that while the :any:`ot.gpu` module has been designed to be compatible with -POT, calling its function with numpy array will incur a large overhead due to +POT, calling its function with :any:`numpy` arrays will incur a large overhead due to the memory copy of the array on GPU prior to computation and conversion of the array after computation. To avoid this overhead, we provide functions :any:`ot.gpu.to_gpu` and :any:`ot.gpu.to_np` that perform the conversion @@ -697,7 +700,7 @@ FAQ 1. **How to solve a discrete optimal transport problem ?** - The solver for discrete is the function :py:mod:`ot.emd` that returns + The solver for discrete OT is the function :py:mod:`ot.emd` that returns the OT transport matrix. If you want to solve a regularized OT you can use :py:mod:`ot.sinkhorn`. @@ -711,7 +714,7 @@ FAQ T=ot.emd(a,b,M) # exact linear program T_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT - More detailed examples can be seen on this + More detailed examples can be seen on this example: :doc:`auto_examples/plot_OT_2D_samples` -- cgit v1.2.3 From e26aa8ee4498f19248f8dcc9868ec55b62eb35e5 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Tue, 2 Jul 2019 16:52:24 +0200 Subject: typo exmaples --- docs/source/quickstart.rst | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst index b726149..1640d6a 100644 --- a/docs/source/quickstart.rst +++ b/docs/source/quickstart.rst @@ -52,7 +52,7 @@ implemented in C from [1]_. It has a complexity of :math:`O(n^3)` but the solver is quite efficient and uses sparsity of the solution. .. hint:: - Examples of use for :any:`ot.emd` are available in the following examples: + Examples of use for :any:`ot.emd` are available in : - :any:`auto_examples/plot_OT_2D_samples` - :any:`auto_examples/plot_OT_1D` @@ -99,7 +99,7 @@ distance. .. hint:: - Examples of use for :any:`ot.emd2` are available in the following examples: + An example of use for :any:`ot.emd2` is available in : - :any:`auto_examples/plot_compute_emd` -- cgit v1.2.3 From d3236cf0cab000b5604f8ede9ebcbdc19d8c213f Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 3 Jul 2019 13:31:55 +0200 Subject: test raise with pytets in test_emd_1d_emd2_1d --- Makefile | 4 ++-- ot/__init__.py | 5 ++++- test/test_ot.py | 3 ++- 3 files changed, 8 insertions(+), 4 deletions(-) diff --git a/Makefile b/Makefile index 84a644b..4cdb7d1 100644 --- a/Makefile +++ b/Makefile @@ -42,10 +42,10 @@ pep8 : flake8 examples/ ot/ test/ test : FORCE pep8 - $(PYTHON) -m pytest -v test/ --cov=ot --cov-report html:cov_html + $(PYTHON) -m pytest -v test/ --doctest-modules --ignore ot/gpu/ --cov=ot --cov-report html:cov_html pytest : FORCE - $(PYTHON) -m pytest -v test/ --cov=ot + $(PYTHON) -m pytest -v test/ --doctest-modules --ignore ot/gpu/ --cov=ot uploadpypi : #python setup.py register diff --git a/ot/__init__.py b/ot/__init__.py index ad7b982..35d2ddd 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -10,7 +10,10 @@ a number of functions described below. :py:mod:`ot.gromov`, :py:mod:`ot.smooth` :py:mod:`ot.stochastic` - The other sub-modules are not imported due to additional dependencies. + The following sub-modules are not imported due to additional dependencies: + + - :any:`ot.dr` : depends on :code:`pymanopt` and :code:`autograd`. + - :any:`ot.gpu` : depends on :code:`cupy` and a CUDA GPU. """ diff --git a/test/test_ot.py b/test/test_ot.py index ac86602..dacae0a 100644 --- a/test/test_ot.py +++ b/test/test_ot.py @@ -72,7 +72,8 @@ def test_emd_1d_emd2_1d(): # check AssertionError is raised if called on non 1d arrays u = np.random.randn(n, 2) v = np.random.randn(m, 2) - np.testing.assert_raises(AssertionError, ot.emd_1d, u, v, [], []) + with pytest.raises(AssertionError): + ot.emd_1d(u, v, [], []) def test_wass_1d(): -- cgit v1.2.3 From 7402d344240ce94e33c53daff419d4356278d48f Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Wed, 3 Jul 2019 14:11:16 +0200 Subject: doc in modules --- ot/__init__.py | 29 ++++++++++++++++++++++++++++- ot/dr.py | 6 ++++++ ot/gpu/__init__.py | 4 ++-- ot/lp/__init__.py | 5 ++--- ot/plot.py | 6 ++++++ ot/stochastic.py | 6 ++++++ ot/utils.py | 2 +- 7 files changed, 51 insertions(+), 7 deletions(-) diff --git a/ot/__init__.py b/ot/__init__.py index 35d2ddd..35ae6fc 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -1,7 +1,33 @@ """ This is the main module of the POT toolbox. It provides easy access to -a number of functions described below. +a number of sub-modules and functions described below. + +.. note:: + + + Here is a list of the submodules and short description of what they contain. + + - :any:`ot.lp` contains OT solvers for the exact (Linear Program) OT problems. + - :any:`ot.bregman` contains OT solvers for the entropic OT problems using + Bregman projections. + - :any:`ot.lp` contains OT solvers for the exact (Linear Program) OT problems. + - :any:`ot.smooth` contains OT solvers for the regularized (l2 and kl) smooth OT + problems. + - :any:`ot.gromov` contains solvers for Gromov-Wasserstein and Fused Gromov + Wasserstein problems. + - :any:`ot.optim` contains generic solvers OT based optimization problems + - :any:`ot.da` contains classes and function related to Monge mapping + estimation and Domain Adaptation (DA). + - :any:`ot.gpu` contains GPU (cupy) implementation of some OT solvers + - :any:`ot.dr` contains Dimension Reduction (DR) methods such as Wasserstein + Discriminant Analysis. + - :any:`ot.utils` contains utility functions such as distance computation and + timing. + - :any:`ot.datasets` contains toy dataset generation functions. + - :any:`ot.plot` contains visualization functions + - :any:`ot.stochastic` contains stochastic solvers for regularized OT. + - :any:`ot.unbalanced` contains solvers for regularized unbalanced OT. .. warning:: The list of automatically imported sub-modules is as follows: @@ -14,6 +40,7 @@ a number of functions described below. - :any:`ot.dr` : depends on :code:`pymanopt` and :code:`autograd`. - :any:`ot.gpu` : depends on :code:`cupy` and a CUDA GPU. + - :any:`ot.plot` : depends on :code:`matplotlib` """ diff --git a/ot/dr.py b/ot/dr.py index d30ab30..d2bf6e2 100644 --- a/ot/dr.py +++ b/ot/dr.py @@ -1,6 +1,12 @@ # -*- coding: utf-8 -*- """ Dimension reduction with optimal transport + + +.. warning:: + Note that by default the module is not import in :mod:`ot`. In order to + use it you need to explicitely import :mod:`ot.dr` + """ # Author: Remi Flamary diff --git a/ot/gpu/__init__.py b/ot/gpu/__init__.py index 6a2afcf..1ab95bb 100644 --- a/ot/gpu/__init__.py +++ b/ot/gpu/__init__.py @@ -6,8 +6,8 @@ functions. The GPU backend in handled by `cupy `_. .. warning:: - Note that by default the module is not import in :mod:`ot`. in order to - use it you need to import :mod:`ot.gpu` . + Note that by default the module is not import in :mod:`ot`. In order to + use it you need to explicitely import :mod:`ot.gpu` . By default, the functions in this module accept and return numpy arrays in order to proide drop-in replacement for the other POT function but diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index aed29f8..17f1731 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -544,14 +544,13 @@ def wasserstein_1d(x_a, x_b, a=None, b=None, p=1.): r"""Solves the p-Wasserstein distance problem between 1d measures and returns the distance - .. math:: - \gamma = arg\min_\gamma \left( \sum_i \sum_j \gamma_{ij} - |x_a[i] - x_b[j]|^p \\right)^{1/p} + \min_\gamma \left( \sum_i \sum_j \gamma_{ij} \|x_a[i] - x_b[j]\|^p \right)^{1/p} s.t. \gamma 1 = a, \gamma^T 1= b, \gamma\geq 0 + where : - x_a and x_b are the samples diff --git a/ot/plot.py b/ot/plot.py index 784a372..a409d4a 100644 --- a/ot/plot.py +++ b/ot/plot.py @@ -1,5 +1,11 @@ """ Functions for plotting OT matrices + +.. warning:: + Note that by default the module is not import in :mod:`ot`. In order to + use it you need to explicitely import :mod:`ot.plot` + + """ # Author: Remi Flamary diff --git a/ot/stochastic.py b/ot/stochastic.py index bf3e7a7..5754968 100644 --- a/ot/stochastic.py +++ b/ot/stochastic.py @@ -1,3 +1,9 @@ +""" +Stochastic solvers for regularized OT. + + +""" + # Author: Kilian Fatras # # License: MIT License diff --git a/ot/utils.py b/ot/utils.py index f21ceb9..e8249ef 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -1,6 +1,6 @@ # -*- coding: utf-8 -*- """ -Various function that can be usefull +Various useful functions """ # Author: Remi Flamary -- cgit v1.2.3 From 852e330bfca616e033fa4453fcd77bd4f1af1a06 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 5 Jul 2019 09:43:37 +0200 Subject: prepare for version 1.0 --- Makefile | 5 +++++ ot/__init__.py | 15 ++++++++------- 2 files changed, 13 insertions(+), 7 deletions(-) diff --git a/Makefile b/Makefile index 4cdb7d1..bd3c1b4 100644 --- a/Makefile +++ b/Makefile @@ -2,6 +2,8 @@ PYTHON=python3 branch := $(shell git symbolic-ref --short -q HEAD) +CIBW_BEFORE_BUILD="pip install numpy cython" + help : @echo "The following make targets are available:" @@ -73,5 +75,8 @@ autopep8 : aautopep8 : autopep8 -air test ot examples --jobs -1 + +wheels: + cibuildwheel --platform linux --output-dir dist FORCE : diff --git a/ot/__init__.py b/ot/__init__.py index 35ae6fc..571f235 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -71,10 +71,11 @@ from .da import sinkhorn_lpl1_mm # utils functions from .utils import dist, unif, tic, toc, toq -__version__ = "0.5.1" - -__all__ = ["emd", "emd2", 'emd_1d', "sinkhorn", "sinkhorn2", "utils", 'datasets', - 'bregman', 'lp', 'tic', 'toc', 'toq', 'gromov', - 'emd_1d', 'emd2_1d', 'wasserstein_1d', - 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim', - 'sinkhorn_unbalanced', "barycenter_unbalanced"] +__version__ = "1.0.0" + +__all__ = ["emd", "emd2", 'emd_1d','emd2_1d', 'wasserstein_1d', + "sinkhorn", "sinkhorn2", 'barycenter', + 'sinkhorn_lpl1_mm', + 'sinkhorn_unbalanced', "barycenter_unbalanced", + 'dist', 'unif', 'tic', 'toc', 'toq', + "utils", 'datasets', 'bregman', 'lp', 'gromov', 'da', 'optim'] -- cgit v1.2.3 From 17ba10e33a3eeb3a5a03af4dfb2cddc900bc1f2e Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 5 Jul 2019 10:27:40 +0200 Subject: clean manifest and add wheels build in Makefile --- MANIFEST.in | 2 +- Makefile | 20 +++++++++++++------- README.md | 2 -- setup.py | 4 ++-- 4 files changed, 16 insertions(+), 12 deletions(-) diff --git a/MANIFEST.in b/MANIFEST.in index e0acb7a..df4e139 100644 --- a/MANIFEST.in +++ b/MANIFEST.in @@ -1,5 +1,5 @@ -graft ot/lp/ include README.md +include RELEASES.md include LICENSE include ot/lp/core.h include ot/lp/EMD.h diff --git a/Makefile b/Makefile index bd3c1b4..729fd8c 100644 --- a/Makefile +++ b/Makefile @@ -2,7 +2,7 @@ PYTHON=python3 branch := $(shell git symbolic-ref --short -q HEAD) -CIBW_BEFORE_BUILD="pip install numpy cython" + help : @@ -15,6 +15,7 @@ help : @echo " sremove - remove the package (system with sudo)" @echo " clean - remove any temporary files" @echo " notebook - launch ipython notebook" + build : $(PYTHON) setup.py build @@ -49,14 +50,15 @@ test : FORCE pep8 pytest : FORCE $(PYTHON) -m pytest -v test/ --doctest-modules --ignore ot/gpu/ --cov=ot -uploadpypi : - #python setup.py register - $(PYTHON) setup.py sdist upload -r pypi +release : + twine upload dist/* + +release_test : + twine upload --repository-url https://test.pypi.org/legacy/ dist/* rdoc : pandoc --from=markdown --to=rst --output=docs/source/readme.rst README.md - notebook : ipython notebook --matplotlib=inline --notebook-dir=notebooks/ @@ -76,7 +78,11 @@ autopep8 : aautopep8 : autopep8 -air test ot examples --jobs -1 -wheels: - cibuildwheel --platform linux --output-dir dist +wheels : + CIBW_BEFORE_BUILD="pip install numpy cython" cibuildwheel --platform linux --output-dir dist + +dist : wheels + $(PYTHON) setup.py sdist + FORCE : diff --git a/README.md b/README.md index 8cf798f..69a826a 100644 --- a/README.md +++ b/README.md @@ -166,8 +166,6 @@ This toolbox has been created and is maintained by The contributors to this library are -* [Rémi Flamary](http://remi.flamary.com/) -* [Nicolas Courty](http://people.irisa.fr/Nicolas.Courty/) * [Alexandre Gramfort](http://alexandre.gramfort.net/) * [Laetitia Chapel](http://people.irisa.fr/Laetitia.Chapel/) * [Michael Perrot](http://perso.univ-st-etienne.fr/pem82055/) (Mapping estimation) diff --git a/setup.py b/setup.py index bbcaf04..89b43d9 100755 --- a/setup.py +++ b/setup.py @@ -53,8 +53,8 @@ setup(name='POT', license = 'MIT', scripts=[], data_files=[], - requires=["numpy","scipy","cython","matplotlib"], - install_requires=["numpy","scipy","cython","matplotlib"], + requires=["numpy","scipy","cython"], + install_requires=["numpy","scipy","cython"], classifiers=[ 'Development Status :: 4 - Beta', 'Intended Audience :: Developers', -- cgit v1.2.3 From 384237683ae51e6842f484b546a090ca66d8ec4e Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 5 Jul 2019 11:20:16 +0200 Subject: update classifier + release text --- RELEASES.md | 55 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ setup.py | 13 +++++++++++-- 2 files changed, 66 insertions(+), 2 deletions(-) diff --git a/RELEASES.md b/RELEASES.md index a617441..bb0b778 100644 --- a/RELEASES.md +++ b/RELEASES.md @@ -1,6 +1,61 @@ # POT Releases +## 1.0.0 Out of beta +*July 2019* + +This is the first official stable release of POT this means a jump to 1.0.0! +The library has been used in +the wild for a while now and we have reached a state where a lot of fundamental +OT solvers are available and tested. It has been quite stable in the last months +but kept the beta flag in its Pypi classifiers until now. + +The features are never complete in a toolbox designed for solving mathematical +problems but with the new contributions we now implement algorithms and solvers +from 24 scientific papers (listed in the README.md file). New features include a +direct implementation of the [empirical Sinkhorn divergence](https://pot.readthedocs.io/en/latest/all.html#ot.bregman.empirical_sinkhorn_divergence) +, a new efficient (Cython implementation) solver for [EMD in 1D](https://pot.readthedocs.io/en/latest/all.html#ot.lp.emd_1d) +and corresponding [Wasserstein +1D](https://pot.readthedocs.io/en/latest/all.html#ot.lp.wasserstein_1d). We now also +have implementations for [Unbalanced OT](https://github.com/rflamary/POT/blob/master/notebooks/plot_UOT_1D.ipynb) +and a solver for [Unbalanced OT barycenters](https://github.com/rflamary/POT/blob/master/notebooks/plot_UOT_barycenter_1D.ipynb). +A new variant of Gromov-Wasserstein divergence called [Fused +Gromov-Wasserstein](https://pot.readthedocs.io/en/latest/all.html?highlight=fused_#ot.gromov.fused_gromov_wasserstein) +with exemples of use on [tructured data](https://github.com/rflamary/POT/blob/master/notebooks/plot_fgw.ipynb) +and computing [barycenters of labeld graphs](https://github.com/rflamary/POT/blob/master/notebooks/plot_barycenter_fgw.ipynb). + + +Finally a lot of work has been done on the documentation with several new +examples corresponding to the new features and a lot of corrections for the +docstrings. But the most visible change is a new +[quick start guide](https://pot.readthedocs.io/en/latest/quickstart.html) for +POT that gives several pointers about which function or classes allow to solve a +specific OT problem. When possible a link is provided to relevant examples. + +TODO contributors + +#### Features + +* Add compiled manylinux 64bits wheels to pip releases (PR #91) +* Add quick start guide (PR #88) +* Make doctest work on travis (PR #90) +* Update documentation (PR #79, PR #84) +* Solver for EMD in 1D (PR #89) +* Solvers for regularized unbalanced OT (PR #87) +* Solver for Fused Gromov-Wasserstein (PR #86) +* Add empirical Sinkhorn and empirical Sinkhorn divergences (PR #80) + + +#### Closed issues + +- Issue #59 fail when using "pip install POT" (new details in doc+ hopefully + wheels) +- Issue #85 Cannot run gpu modules +- Issue #75 Greenkhorn do not return log (solved in PR #76) +- Issue #82 Gromov-Wasserstein fails when the cost matrices are slightly different +- Issue #72 Macosx build problem + + ## 0.5.0 Year 2 *Sep 2018* diff --git a/setup.py b/setup.py index 89b43d9..ee32510 100755 --- a/setup.py +++ b/setup.py @@ -56,15 +56,24 @@ setup(name='POT', requires=["numpy","scipy","cython"], install_requires=["numpy","scipy","cython"], classifiers=[ - 'Development Status :: 4 - Beta', + 'Development Status :: 5 - Production/Stable', 'Intended Audience :: Developers', + 'Intended Audience :: Education', + 'Intended Audience :: Science/Research', + 'License :: OSI Approved :: MIT License', 'Environment :: Console', 'Operating System :: OS Independent', 'Operating System :: MacOS', 'Operating System :: POSIX', 'Programming Language :: Python', + 'Programming Language :: C++', + 'Programming Language :: C', + 'Programming Language :: Cython', 'Topic :: Utilities', - 'Programming Language :: Python :: 2', + 'Topic :: Scientific/Engineering :: Artificial Intelligence', + 'Topic :: Scientific/Engineering :: Mathematics', + 'Topic :: Scientific/Engineering :: Information Analysis', + 'Programming Language :: Python :: 2',' 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.4', -- cgit v1.2.3 From c749279ae3c712e70d4e59647f535448168dbb26 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 5 Jul 2019 11:26:05 +0200 Subject: corresp bug setup.py --- setup.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/setup.py b/setup.py index ee32510..c08e3e0 100755 --- a/setup.py +++ b/setup.py @@ -73,7 +73,7 @@ setup(name='POT', 'Topic :: Scientific/Engineering :: Artificial Intelligence', 'Topic :: Scientific/Engineering :: Mathematics', 'Topic :: Scientific/Engineering :: Information Analysis', - 'Programming Language :: Python :: 2',' + 'Programming Language :: Python :: 2', 'Programming Language :: Python :: 2.7', 'Programming Language :: Python :: 3', 'Programming Language :: Python :: 3.4', -- cgit v1.2.3 From a6545c4a306c8cbbb5556983533d809808f480a3 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 5 Jul 2019 11:43:13 +0200 Subject: update release text --- RELEASES.md | 26 ++++++++++++++++++++------ 1 file changed, 20 insertions(+), 6 deletions(-) diff --git a/RELEASES.md b/RELEASES.md index bb0b778..e5a20c8 100644 --- a/RELEASES.md +++ b/RELEASES.md @@ -4,14 +4,19 @@ ## 1.0.0 Out of beta *July 2019* -This is the first official stable release of POT this means a jump to 1.0.0! +This is the first official stable release of POT and this means a jump to 1.0.0! The library has been used in the wild for a while now and we have reached a state where a lot of fundamental OT solvers are available and tested. It has been quite stable in the last months -but kept the beta flag in its Pypi classifiers until now. +but kept the beta flag in its Pypi classifiers until now. + +Note that this major +release will be the last one supporting officially Python 2.7 (See +https://python3statement.org/ for more reasons). For next release we will keep +the travis tests for Python 2 but will make them non necessary for merge in 2020. The features are never complete in a toolbox designed for solving mathematical -problems but with the new contributions we now implement algorithms and solvers +problems and research but with the new contributions we now implement algorithms and solvers from 24 scientific papers (listed in the README.md file). New features include a direct implementation of the [empirical Sinkhorn divergence](https://pot.readthedocs.io/en/latest/all.html#ot.bregman.empirical_sinkhorn_divergence) , a new efficient (Cython implementation) solver for [EMD in 1D](https://pot.readthedocs.io/en/latest/all.html#ot.lp.emd_1d) @@ -21,18 +26,27 @@ have implementations for [Unbalanced OT](https://github.com/rflamary/POT/blob/ma and a solver for [Unbalanced OT barycenters](https://github.com/rflamary/POT/blob/master/notebooks/plot_UOT_barycenter_1D.ipynb). A new variant of Gromov-Wasserstein divergence called [Fused Gromov-Wasserstein](https://pot.readthedocs.io/en/latest/all.html?highlight=fused_#ot.gromov.fused_gromov_wasserstein) -with exemples of use on [tructured data](https://github.com/rflamary/POT/blob/master/notebooks/plot_fgw.ipynb) + has been also contributed with exemples of use on [tructured data](https://github.com/rflamary/POT/blob/master/notebooks/plot_fgw.ipynb) and computing [barycenters of labeld graphs](https://github.com/rflamary/POT/blob/master/notebooks/plot_barycenter_fgw.ipynb). -Finally a lot of work has been done on the documentation with several new +A lot of work has been done on the documentation with several new examples corresponding to the new features and a lot of corrections for the docstrings. But the most visible change is a new [quick start guide](https://pot.readthedocs.io/en/latest/quickstart.html) for POT that gives several pointers about which function or classes allow to solve a specific OT problem. When possible a link is provided to relevant examples. -TODO contributors +We will also provide with this release some pre-compiled Python wheels for Linux +64bit on +github and pip. This will simplify the install process that before requires a C +compiler and numpy/cython already installed. + +Finally we would like to acknowledge and thank the numerous contributors of POT +that has helped in the past build the foundation and are still contributing to +bring new features and solvers to the library. + + #### Features -- cgit v1.2.3 From 3a09a0ace10c184dc27a2fcc1f982208e4d9eb67 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 5 Jul 2019 11:44:02 +0200 Subject: add python 3.7 on travis --- .travis.yml | 5 ++++- 1 file changed, 4 insertions(+), 1 deletion(-) diff --git a/.travis.yml b/.travis.yml index 5e5694b..932f610 100644 --- a/.travis.yml +++ b/.travis.yml @@ -13,7 +13,10 @@ matrix: python: 3.5 - os: linux sudo: required - python: 3.6 + python: 3.6 + - os: linux + sudo: required + python: 3.7 - os: linux sudo: required python: 2.7 -- cgit v1.2.3 From bfc84efcd94c03bebb80330aa1f2a92a4efb0ff9 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 5 Jul 2019 13:01:12 +0200 Subject: add xenial in travis pfor python 3.7 --- .travis.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.travis.yml b/.travis.yml index 932f610..68b8ef1 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,3 +1,4 @@ +dist: xenial # required for Python >= 3.7 language: python matrix: # allow_failures: -- cgit v1.2.3 From 0bc936f62430c98ecbb0f39c9508f29c6054a327 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 5 Jul 2019 13:06:07 +0200 Subject: travis xenial new --- .travis.yml | 9 +++++---- 1 file changed, 5 insertions(+), 4 deletions(-) diff --git a/.travis.yml b/.travis.yml index 68b8ef1..67f0c43 100644 --- a/.travis.yml +++ b/.travis.yml @@ -21,12 +21,13 @@ matrix: - os: linux sudo: required python: 2.7 + - name: "Python 3.7.3 on Windows" + os: windows # Windows 10.0.17134 N/A Build 17134 + language: shell # 'language: python' is an error on Travis CI Windows + before_install: choco install python + env: PATH=/c/Python37:/c/Python37/Scripts:$PATH before_install: - ./.travis/before_install.sh -before_script: # configure a headless display to test plot generation - - "export DISPLAY=:99.0" - - "sh -e /etc/init.d/xvfb start" - - sleep 3 # give xvfb some time to start # command to install dependencies install: - pip install -r requirements.txt -- cgit v1.2.3 From 7ac1b462d23ae0a396742bba4773e146e60e7502 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 5 Jul 2019 13:47:43 +0200 Subject: cleanup parmap on windows --- .travis.yml | 51 ++++++++++++++++++++++++++------------------------- ot/lp/__init__.py | 14 ++++++++++++-- ot/utils.py | 31 ++++++++++++++++++------------- requirements.txt | 1 + 4 files changed, 57 insertions(+), 40 deletions(-) diff --git a/.travis.yml b/.travis.yml index 67f0c43..cddf0e0 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,31 +1,32 @@ dist: xenial # required for Python >= 3.7 language: python matrix: -# allow_failures: -# - os: osx - include: -# - os: osx -# language: generic - - os: linux - sudo: required - python: 3.4 - - os: linux - sudo: required - python: 3.5 - - os: linux - sudo: required - python: 3.6 - - os: linux - sudo: required - python: 3.7 - - os: linux - sudo: required - python: 2.7 - - name: "Python 3.7.3 on Windows" - os: windows # Windows 10.0.17134 N/A Build 17134 - language: shell # 'language: python' is an error on Travis CI Windows - before_install: choco install python - env: PATH=/c/Python37:/c/Python37/Scripts:$PATH + allow_failures: + - os: osx + - os: windows + include: + - os: osx + language: generic + - os: linux + sudo: required + python: 3.4 + - os: linux + sudo: required + python: 3.5 + - os: linux + sudo: required + python: 3.6 + - os: linux + sudo: required + python: 3.7 + - os: linux + sudo: required + python: 2.7 + - name: "Python 3.7.3 on Windows" + os: windows # Windows 10.0.17134 N/A Build 17134 + language: shell # 'language: python' is an error on Travis CI Windows + before_install: choco install python + env: PATH=/c/Python37:/c/Python37/Scripts:$PATH before_install: - ./.travis/before_install.sh # command to install dependencies diff --git a/ot/lp/__init__.py b/ot/lp/__init__.py index 17f1731..0c92810 100644 --- a/ot/lp/__init__.py +++ b/ot/lp/__init__.py @@ -11,7 +11,7 @@ Solvers for the original linear program OT problem # License: MIT License import multiprocessing - +import sys import numpy as np from scipy.sparse import coo_matrix @@ -151,6 +151,8 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), Target histogram (uniform weight if empty list) M : (ns,nt) numpy.ndarray, float64 Loss matrix (c-order array with type float64) + processes : int, optional (default=nb cpu) + Nb of processes used for multiple emd computation (not used on windows) numItermax : int, optional (default=100000) The maximum number of iterations before stopping the optimization algorithm if it has not converged. @@ -200,6 +202,10 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), b = np.asarray(b, dtype=np.float64) M = np.asarray(M, dtype=np.float64) + # problem with pikling Forks + if sys.platform.endswith('win32'): + processes=1 + # if empty array given then use uniform distributions if len(a) == 0: a = np.ones((M.shape[0],), dtype=np.float64) / M.shape[0] @@ -228,7 +234,11 @@ def emd2(a, b, M, processes=multiprocessing.cpu_count(), return f(b) nb = b.shape[1] - res = parmap(f, [b[:, i] for i in range(nb)], processes) + if processes>1: + res = parmap(f, [b[:, i] for i in range(nb)], processes) + else: + res = list(map(f, [b[:, i].copy() for i in range(nb)])) + return res diff --git a/ot/utils.py b/ot/utils.py index e8249ef..5707d9b 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -214,23 +214,28 @@ def fun(f, q_in, q_out): def parmap(f, X, nprocs=multiprocessing.cpu_count()): - """ paralell map for multiprocessing """ - q_in = multiprocessing.Queue(1) - q_out = multiprocessing.Queue() + """ paralell map for multiprocessing (only map on windows)""" - proc = [multiprocessing.Process(target=fun, args=(f, q_in, q_out)) - for _ in range(nprocs)] - for p in proc: - p.daemon = True - p.start() + if not sys.platform.endswith('win32'): - sent = [q_in.put((i, x)) for i, x in enumerate(X)] - [q_in.put((None, None)) for _ in range(nprocs)] - res = [q_out.get() for _ in range(len(sent))] + q_in = multiprocessing.Queue(1) + q_out = multiprocessing.Queue() - [p.join() for p in proc] + proc = [multiprocessing.Process(target=fun, args=(f, q_in, q_out)) + for _ in range(nprocs)] + for p in proc: + p.daemon = True + p.start() - return [x for i, x in sorted(res)] + sent = [q_in.put((i, x)) for i, x in enumerate(X)] + [q_in.put((None, None)) for _ in range(nprocs)] + res = [q_out.get() for _ in range(len(sent))] + + [p.join() for p in proc] + + return [x for i, x in sorted(res)] + else: + return list(map(f, X)) def check_params(**kwargs): diff --git a/requirements.txt b/requirements.txt index 97d165b..5a3432b 100644 --- a/requirements.txt +++ b/requirements.txt @@ -5,4 +5,5 @@ matplotlib sphinx-gallery autograd pymanopt +cvxopt pytest -- cgit v1.2.3 From 41706d1c35bfe5f55f36041f511e2a42e36f6ab7 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 5 Jul 2019 14:03:15 +0200 Subject: remove test for python 3.4 --- .travis.yml | 4 +--- 1 file changed, 1 insertion(+), 3 deletions(-) diff --git a/.travis.yml b/.travis.yml index cddf0e0..57b961b 100644 --- a/.travis.yml +++ b/.travis.yml @@ -6,10 +6,8 @@ matrix: - os: windows include: - os: osx - language: generic - - os: linux sudo: required - python: 3.4 + language: generic - os: linux sudo: required python: 3.5 -- cgit v1.2.3 From 2873ba559bb6d3ccceeee326848d7216a139ed88 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Fri, 5 Jul 2019 14:14:52 +0200 Subject: remove windows and macosx tests, will add to master after release --- .travis.yml | 22 +++++++++++----------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/.travis.yml b/.travis.yml index 57b961b..0dfb0d0 100644 --- a/.travis.yml +++ b/.travis.yml @@ -1,13 +1,10 @@ dist: xenial # required for Python >= 3.7 language: python matrix: - allow_failures: - - os: osx - - os: windows + # allow_failures: + # - os: osx + # - os: windows include: - - os: osx - sudo: required - language: generic - os: linux sudo: required python: 3.5 @@ -20,11 +17,14 @@ matrix: - os: linux sudo: required python: 2.7 - - name: "Python 3.7.3 on Windows" - os: windows # Windows 10.0.17134 N/A Build 17134 - language: shell # 'language: python' is an error on Travis CI Windows - before_install: choco install python - env: PATH=/c/Python37:/c/Python37/Scripts:$PATH + # - os: osx + # sudo: required + # language: generic + # - name: "Python 3.7.3 on Windows" + # os: windows # Windows 10.0.17134 N/A Build 17134 + # language: shell # 'language: python' is an error on Travis CI Windows + # before_install: choco install python + # env: PATH=/c/Python37:/c/Python37/Scripts:$PATH before_install: - ./.travis/before_install.sh # command to install dependencies -- cgit v1.2.3 From cc37dd9729460fdd0766ffc71be78683d6a1e408 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 8 Jul 2019 10:02:10 +0200 Subject: small change in redame --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index 69a826a..bba27ff 100644 --- a/README.md +++ b/README.md @@ -45,7 +45,7 @@ year={2017} ## Installation -The library has been tested on Linux, MacOSX and Windows. It requires a C++ compiler for using the EMD solver and relies on the following Python modules: +The library has been tested on Linux, MacOSX and Windows. It requires a C++ compiler for building/installing the EMD solver and relies on the following Python modules: - Numpy (>=1.11) - Scipy (>=1.0) -- cgit v1.2.3 From 371a8354a69fd5f0dfc035f4f3554fe11ef42bb2 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 8 Jul 2019 10:06:17 +0200 Subject: tupos in RELEASES.md --- RELEASES.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/RELEASES.md b/RELEASES.md index e5a20c8..112e4ab 100644 --- a/RELEASES.md +++ b/RELEASES.md @@ -26,7 +26,7 @@ have implementations for [Unbalanced OT](https://github.com/rflamary/POT/blob/ma and a solver for [Unbalanced OT barycenters](https://github.com/rflamary/POT/blob/master/notebooks/plot_UOT_barycenter_1D.ipynb). A new variant of Gromov-Wasserstein divergence called [Fused Gromov-Wasserstein](https://pot.readthedocs.io/en/latest/all.html?highlight=fused_#ot.gromov.fused_gromov_wasserstein) - has been also contributed with exemples of use on [tructured data](https://github.com/rflamary/POT/blob/master/notebooks/plot_fgw.ipynb) + has been also contributed with exemples of use on [structured data](https://github.com/rflamary/POT/blob/master/notebooks/plot_fgw.ipynb) and computing [barycenters of labeld graphs](https://github.com/rflamary/POT/blob/master/notebooks/plot_barycenter_fgw.ipynb). @@ -34,12 +34,12 @@ A lot of work has been done on the documentation with several new examples corresponding to the new features and a lot of corrections for the docstrings. But the most visible change is a new [quick start guide](https://pot.readthedocs.io/en/latest/quickstart.html) for -POT that gives several pointers about which function or classes allow to solve a +POT that gives several pointers about which function or classes allow to solve which specific OT problem. When possible a link is provided to relevant examples. We will also provide with this release some pre-compiled Python wheels for Linux 64bit on -github and pip. This will simplify the install process that before requires a C +github and pip. This will simplify the install process that before required a C compiler and numpy/cython already installed. Finally we would like to acknowledge and thank the numerous contributors of POT -- cgit v1.2.3 From 1b00740a39f90f1e0bc7dc3a35723560c9ab4e97 Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Tue, 9 Jul 2019 16:48:36 +0200 Subject: first pass with adding pydocstyle in makefile --- Makefile | 3 + ot/dr.py | 57 ++++------ ot/gromov.py | 360 +++++++++++++++++++++++++++++------------------------------ setup.cfg | 18 +++ 4 files changed, 221 insertions(+), 217 deletions(-) diff --git a/Makefile b/Makefile index 4cdb7d1..89c30c3 100644 --- a/Makefile +++ b/Makefile @@ -74,4 +74,7 @@ autopep8 : aautopep8 : autopep8 -air test ot examples --jobs -1 +pydocstyle : + pydocstyle ot + FORCE : diff --git a/ot/dr.py b/ot/dr.py index d2bf6e2..680dabf 100644 --- a/ot/dr.py +++ b/ot/dr.py @@ -49,30 +49,25 @@ def split_classes(X, y): def fda(X, y, p=2, reg=1e-16): - """ - Fisher Discriminant Analysis - + """Fisher Discriminant Analysis Parameters ---------- - X : numpy.ndarray (n,d) - Training samples - y : np.ndarray (n,) - labels for training samples + X : ndarray, shape (n, d) + Training samples. + y : ndarray, shape (n,) + Labels for training samples. p : int, optional - size of dimensionnality reduction + Size of dimensionnality reduction. reg : float, optional Regularization term >0 (ridge regularization) - Returns ------- - P : (d x p) ndarray + P : ndarray, shape (d, p) Optimal transportation matrix for the given parameters - proj : fun + proj : callable projection function including mean centering - - """ mx = np.mean(X) @@ -130,37 +125,33 @@ def wda(X, y, p=2, reg=1, k=10, solver=None, maxiter=100, verbose=0, P0=None): Parameters ---------- - X : numpy.ndarray (n,d) - Training samples - y : np.ndarray (n,) - labels for training samples + X : ndarray, shape (n, d) + Training samples. + y : ndarray, shape (n,) + Labels for training samples. p : int, optional - size of dimensionnality reduction + Size of dimensionnality reduction. reg : float, optional Regularization term >0 (entropic regularization) - solver : str, optional - None for steepest decsent or 'TrustRegions' for trust regions algorithm - else shoudl be a pymanopt.solvers - P0 : numpy.ndarray (d,p) - Initial starting point for projection + solver : None | str, optional + None for steepest descent or 'TrustRegions' for trust regions algorithm + else should be a pymanopt.solvers + P0 : ndarray, shape (d, p) + Initial starting point for projection. verbose : int, optional - Print information along iterations - - + Print information along iterations. Returns ------- - P : (d x p) ndarray + P : ndarray, shape (d, p) Optimal transportation matrix for the given parameters - proj : fun - projection function including mean centering - + proj : callable + Projection function including mean centering. References ---------- - - .. [11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). Wasserstein Discriminant Analysis. arXiv preprint arXiv:1608.08063. - + .. [11] Flamary, R., Cuturi, M., Courty, N., & Rakotomamonjy, A. (2016). + Wasserstein Discriminant Analysis. arXiv preprint arXiv:1608.08063. """ # noqa mx = np.mean(X) diff --git a/ot/gromov.py b/ot/gromov.py index 3a7e24c..699ae4c 100644 --- a/ot/gromov.py +++ b/ot/gromov.py @@ -1,9 +1,6 @@ - # -*- coding: utf-8 -*- """ Gromov-Wasserstein transport method - - """ # Author: Erwan Vautier @@ -22,7 +19,7 @@ from .optim import cg def init_matrix(C1, C2, p, q, loss_fun='square_loss'): - """ Return loss matrices and tensors for Gromov-Wasserstein fast computation + """Return loss matrices and tensors for Gromov-Wasserstein fast computation Returns the value of \mathcal{L}(C1,C2) \otimes T with the selected loss function as the loss function of Gromow-Wasserstein discrepancy. @@ -51,23 +48,21 @@ def init_matrix(C1, C2, p, q, loss_fun='square_loss'): Parameters ---------- C1 : ndarray, shape (ns, ns) - Metric cost matrix in the source space + Metric cost matrix in the source space C2 : ndarray, shape (nt, nt) - Metric costfr matrix in the target space + Metric costfr matrix in the target space T : ndarray, shape (ns, nt) - Coupling between source and target spaces + Coupling between source and target spaces p : ndarray, shape (ns,) - Returns ------- - constC : ndarray, shape (ns, nt) - Constant C matrix in Eq. (6) + Constant C matrix in Eq. (6) hC1 : ndarray, shape (ns, ns) - h1(C1) matrix in Eq. (6) + h1(C1) matrix in Eq. (6) hC2 : ndarray, shape (nt, nt) - h2(C) matrix in Eq. (6) + h2(C) matrix in Eq. (6) References ---------- @@ -114,25 +109,23 @@ def init_matrix(C1, C2, p, q, loss_fun='square_loss'): def tensor_product(constC, hC1, hC2, T): - """ Return the tensor for Gromov-Wasserstein fast computation + """Return the tensor for Gromov-Wasserstein fast computation The tensor is computed as described in Proposition 1 Eq. (6) in [12]. Parameters ---------- constC : ndarray, shape (ns, nt) - Constant C matrix in Eq. (6) + Constant C matrix in Eq. (6) hC1 : ndarray, shape (ns, ns) - h1(C1) matrix in Eq. (6) + h1(C1) matrix in Eq. (6) hC2 : ndarray, shape (nt, nt) - h2(C) matrix in Eq. (6) - + h2(C) matrix in Eq. (6) Returns ------- - tens : ndarray, shape (ns, nt) - \mathcal{L}(C1,C2) \otimes T tensor-matrix multiplication result + \mathcal{L}(C1,C2) \otimes T tensor-matrix multiplication result References ---------- @@ -148,26 +141,25 @@ def tensor_product(constC, hC1, hC2, T): def gwloss(constC, hC1, hC2, T): - """ Return the Loss for Gromov-Wasserstein + """Return the Loss for Gromov-Wasserstein The loss is computed as described in Proposition 1 Eq. (6) in [12]. Parameters ---------- constC : ndarray, shape (ns, nt) - Constant C matrix in Eq. (6) + Constant C matrix in Eq. (6) hC1 : ndarray, shape (ns, ns) - h1(C1) matrix in Eq. (6) + h1(C1) matrix in Eq. (6) hC2 : ndarray, shape (nt, nt) - h2(C) matrix in Eq. (6) + h2(C) matrix in Eq. (6) T : ndarray, shape (ns, nt) - Current value of transport matrix T + Current value of transport matrix T Returns ------- - loss : float - Gromov Wasserstein loss + Gromov Wasserstein loss References ---------- @@ -183,24 +175,23 @@ def gwloss(constC, hC1, hC2, T): def gwggrad(constC, hC1, hC2, T): - """ Return the gradient for Gromov-Wasserstein + """Return the gradient for Gromov-Wasserstein The gradient is computed as described in Proposition 2 in [12]. Parameters ---------- constC : ndarray, shape (ns, nt) - Constant C matrix in Eq. (6) + Constant C matrix in Eq. (6) hC1 : ndarray, shape (ns, ns) - h1(C1) matrix in Eq. (6) + h1(C1) matrix in Eq. (6) hC2 : ndarray, shape (nt, nt) - h2(C) matrix in Eq. (6) + h2(C) matrix in Eq. (6) T : ndarray, shape (ns, nt) - Current value of transport matrix T + Current value of transport matrix T Returns ------- - grad : ndarray, shape (ns, nt) Gromov Wasserstein gradient @@ -222,19 +213,19 @@ def update_square_loss(p, lambdas, T, Cs): Parameters ---------- - p : ndarray, shape (N,) - masses in the targeted barycenter + p : ndarray, shape (N,) + Masses in the targeted barycenter. lambdas : list of float - list of the S spaces' weights - T : list of S np.ndarray(ns,N) - the S Ts couplings calculated at each iteration + List of the S spaces' weights. + T : list of S np.ndarray of shape (ns,N) + The S Ts couplings calculated at each iteration. Cs : list of S ndarray, shape(ns,ns) - Metric cost matrices + Metric cost matrices. Returns ---------- - C : ndarray, shape (nt,nt) - updated C matrix + C : ndarray, shape (nt, nt) + Updated C matrix. """ tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) for s in range(len(T))]) @@ -251,12 +242,12 @@ def update_kl_loss(p, lambdas, T, Cs): Parameters ---------- p : ndarray, shape (N,) - weights in the targeted barycenter + Weights in the targeted barycenter. lambdas : list of the S spaces' weights - T : list of S np.ndarray(ns,N) - the S Ts couplings calculated at each iteration + T : list of S np.ndarray of shape (ns,N) + The S Ts couplings calculated at each iteration. Cs : list of S ndarray, shape(ns,ns) - Metric cost matrices + Metric cost matrices. Returns ---------- @@ -290,14 +281,14 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, armijo=False, **kwargs Parameters ---------- C1 : ndarray, shape (ns, ns) - Metric cost matrix in the source space + Metric cost matrix in the source space C2 : ndarray, shape (nt, nt) - Metric costfr matrix in the target space - p : ndarray, shape (ns,) - distribution in the source space - q : ndarray, shape (nt,) - distribution in the target space - loss_fun : string + Metric costfr matrix in the target space + p : ndarray, shape (ns,) + Distribution in the source space + q : ndarray, shape (nt,) + Distribution in the target space + loss_fun : str loss function used for the solver either 'square_loss' or 'kl_loss' max_iter : int, optional @@ -317,10 +308,10 @@ def gromov_wasserstein(C1, C2, p, q, loss_fun, log=False, armijo=False, **kwargs Returns ------- T : ndarray, shape (ns, nt) - coupling between the two spaces that minimizes : + Doupling between the two spaces that minimizes: \sum_{i,j,k,l} L(C1_{i,k},C2_{j,l})*T_{i,j}*T_{k,l} log : dict - convergence information and loss + Convergence information and loss. References ---------- @@ -374,18 +365,18 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, Parameters ---------- - M : ndarray, shape (ns, nt) - Metric cost matrix between features across domains + M : ndarray, shape (ns, nt) + Metric cost matrix between features across domains C1 : ndarray, shape (ns, ns) - Metric cost matrix representative of the structure in the source space + Metric cost matrix representative of the structure in the source space C2 : ndarray, shape (nt, nt) - Metric cost matrix representative of the structure in the target space - p : ndarray, shape (ns,) - distribution in the source space - q : ndarray, shape (nt,) - distribution in the target space - loss_fun : string,optional - loss function used for the solver + Metric cost matrix representative of the structure in the target space + p : ndarray, shape (ns,) + Distribution in the source space + q : ndarray, shape (nt,) + Distribution in the target space + loss_fun : str, optional + Loss function used for the solver max_iter : int, optional Max number of iterations tol : float, optional @@ -402,11 +393,10 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, Returns ------- - gamma : (ns x nt) ndarray - Optimal transportation matrix for the given parameters + gamma : ndarray, shape (ns, nt) + Optimal transportation matrix for the given parameters. log : dict - log dictionary return only if log==True in parameters - + Log dictionary return only if log==True in parameters. References ---------- @@ -414,7 +404,6 @@ def fused_gromov_wasserstein(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5, and Courty Nicolas "Optimal Transport for structured data with application on graphs", International Conference on Machine Learning (ICML). 2019. - """ constC, hC1, hC2 = init_matrix(C1, C2, p, q, loss_fun) @@ -457,18 +446,18 @@ def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5 Parameters ---------- - M : ndarray, shape (ns, nt) - Metric cost matrix between features across domains + M : ndarray, shape (ns, nt) + Metric cost matrix between features across domains C1 : ndarray, shape (ns, ns) - Metric cost matrix respresentative of the structure in the source space + Metric cost matrix respresentative of the structure in the source space. C2 : ndarray, shape (nt, nt) - Metric cost matrix espresentative of the structure in the target space + Metric cost matrix espresentative of the structure in the target space. p : ndarray, shape (ns,) - distribution in the source space + Distribution in the source space. q : ndarray, shape (nt,) - distribution in the target space - loss_fun : string,optional - loss function used for the solver + Distribution in the target space. + loss_fun : str, optional + Loss function used for the solver. max_iter : int, optional Max number of iterations tol : float, optional @@ -476,19 +465,19 @@ def fused_gromov_wasserstein2(M, C1, C2, p, q, loss_fun='square_loss', alpha=0.5 verbose : bool, optional Print information along iterations log : bool, optional - record log if True + Record log if True. armijo : bool, optional - If True the steps of the line-search is found via an armijo research. Else closed form is used. - If there is convergence issues use False. + If True the steps of the line-search is found via an armijo research. + Else closed form is used. If there is convergence issues use False. **kwargs : dict - parameters can be directly pased to the ot.optim.cg solver + Parameters can be directly pased to the ot.optim.cg solver. Returns ------- - gamma : (ns x nt) ndarray - Optimal transportation matrix for the given parameters + gamma : ndarray, shape (ns, nt) + Optimal transportation matrix for the given parameters. log : dict - log dictionary return only if log==True in parameters + Log dictionary return only if log==True in parameters. References ---------- @@ -537,16 +526,15 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False, armijo=False, **kwarg Parameters ---------- C1 : ndarray, shape (ns, ns) - Metric cost matrix in the source space + Metric cost matrix in the source space C2 : ndarray, shape (nt, nt) - Metric cost matrix in the target space - p : ndarray, shape (ns,) - distribution in the source space + Metric cost matrix in the target space + p : ndarray, shape (ns,) + Distribution in the source space. q : ndarray, shape (nt,) - distribution in the target space - loss_fun : string + Distribution in the target space. + loss_fun : str loss function used for the solver either 'square_loss' or 'kl_loss' - max_iter : int, optional Max number of iterations tol : float, optional @@ -558,6 +546,7 @@ def gromov_wasserstein2(C1, C2, p, q, loss_fun, log=False, armijo=False, **kwarg armijo : bool, optional If True the steps of the line-search is found via an armijo research. Else closed form is used. If there is convergence issues use False. + Returns ------- gw_dist : float @@ -624,25 +613,25 @@ def entropic_gromov_wasserstein(C1, C2, p, q, loss_fun, epsilon, Parameters ---------- C1 : ndarray, shape (ns, ns) - Metric cost matrix in the source space + Metric cost matrix in the source space C2 : ndarray, shape (nt, nt) - Metric costfr matrix in the target space + Metric costfr matrix in the target space p : ndarray, shape (ns,) - distribution in the source space + Distribution in the source space q : ndarray, shape (nt,) - distribution in the target space + Distribution in the target space loss_fun : string - loss function used for the solver either 'square_loss' or 'kl_loss' + Loss function used for the solver either 'square_loss' or 'kl_loss' epsilon : float Regularization term >0 max_iter : int, optional - Max number of iterations + Max number of iterations tol : float, optional Stop threshold on error (>0) verbose : bool, optional Print information along iterations log : bool, optional - record log if True + Record log if True. Returns ------- @@ -725,15 +714,15 @@ def entropic_gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, Parameters ---------- C1 : ndarray, shape (ns, ns) - Metric cost matrix in the source space + Metric cost matrix in the source space C2 : ndarray, shape (nt, nt) - Metric costfr matrix in the target space + Metric costfr matrix in the target space p : ndarray, shape (ns,) - distribution in the source space + Distribution in the source space q : ndarray, shape (nt,) - distribution in the target space - loss_fun : string - loss function used for the solver either 'square_loss' or 'kl_loss' + Distribution in the target space + loss_fun : str + Loss function used for the solver either 'square_loss' or 'kl_loss' epsilon : float Regularization term >0 max_iter : int, optional @@ -743,7 +732,7 @@ def entropic_gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, verbose : bool, optional Print information along iterations log : bool, optional - record log if True + Record log if True. Returns ------- @@ -757,7 +746,6 @@ def entropic_gromov_wasserstein2(C1, C2, p, q, loss_fun, epsilon, International Conference on Machine Learning (ICML). 2016. """ - gw, logv = entropic_gromov_wasserstein( C1, C2, p, q, loss_fun, epsilon, max_iter, tol, verbose, log=True) @@ -789,19 +777,21 @@ def entropic_gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, Parameters ---------- - N : Integer - Size of the targeted barycenter - Cs : list of S np.ndarray(ns,ns) - Metric cost matrices - ps : list of S np.ndarray(ns,) - sample weights in the S spaces - p : ndarray, shape(N,) - weights in the targeted barycenter + N : int + Size of the targeted barycenter + Cs : list of S np.ndarray of shape (ns,ns) + Metric cost matrices + ps : list of S np.ndarray of shape (ns,) + Sample weights in the S spaces + p : ndarray, shape(N,) + Weights in the targeted barycenter lambdas : list of float - list of the S spaces' weights - loss_fun : tensor-matrix multiplication function based on specific loss function - update : function(p,lambdas,T,Cs) that updates C according to a specific Kernel - with the S Ts couplings calculated at each iteration + List of the S spaces' weights. + loss_fun : callable + Tensor-matrix multiplication function based on specific loss function. + update : callable + function(p,lambdas,T,Cs) that updates C according to a specific Kernel + with the S Ts couplings calculated at each iteration epsilon : float Regularization term >0 max_iter : int, optional @@ -809,11 +799,11 @@ def entropic_gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, tol : float, optional Stop threshol on error (>0) verbose : bool, optional - Print information along iterations + Print information along iterations. log : bool, optional - record log if True - init_C : bool, ndarray, shape(N,N) - random initial value for the C matrix provided by user + Record log if True. + init_C : bool | ndarray, shape (N, N) + Random initial value for the C matrix provided by user. Returns ------- @@ -825,7 +815,6 @@ def entropic_gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, .. [12] Peyré, Gabriel, Marco Cuturi, and Justin Solomon, "Gromov-Wasserstein averaging of kernel and distance matrices." International Conference on Machine Learning (ICML). 2016. - """ S = len(Cs) @@ -835,6 +824,7 @@ def entropic_gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, # Initialization of C : random SPD matrix (if not provided by user) if init_C is None: + # XXX use random state xalea = np.random.randn(N, 2) C = dist(xalea, xalea) C /= C.max() @@ -846,7 +836,7 @@ def entropic_gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, epsilon, error = [] - while(err > tol and cpt < max_iter): + while (err > tol) and (cpt < max_iter): Cprev = C T = [entropic_gromov_wasserstein(Cs[s], C, ps[s], p, loss_fun, epsilon, @@ -890,7 +880,6 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, .. math:: C = argmin_C\in R^NxN \sum_s \lambda_s GW(C,Cs,p,ps) - Where : - Cs : metric cost matrix @@ -898,29 +887,29 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, Parameters ---------- - N : Integer - Size of the targeted barycenter - Cs : list of S np.ndarray(ns,ns) - Metric cost matrices - ps : list of S np.ndarray(ns,) - sample weights in the S spaces - p : ndarray, shape(N,) - weights in the targeted barycenter + N : int + Size of the targeted barycenter + Cs : list of S np.ndarray of shape (ns, ns) + Metric cost matrices + ps : list of S np.ndarray of shape (ns,) + Sample weights in the S spaces + p : ndarray, shape (N,) + Weights in the targeted barycenter lambdas : list of float - list of the S spaces' weights + List of the S spaces' weights loss_fun : tensor-matrix multiplication function based on specific loss function update : function(p,lambdas,T,Cs) that updates C according to a specific Kernel with the S Ts couplings calculated at each iteration max_iter : int, optional Max number of iterations tol : float, optional - Stop threshol on error (>0) + Stop threshol on error (>0). verbose : bool, optional - Print information along iterations + Print information along iterations. log : bool, optional - record log if True - init_C : bool, ndarray, shape(N,N) - random initial value for the C matrix provided by user + Record log if True. + init_C : bool | ndarray, shape(N,N) + Random initial value for the C matrix provided by user. Returns ------- @@ -934,7 +923,6 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, International Conference on Machine Learning (ICML). 2016. """ - S = len(Cs) Cs = [np.asarray(Cs[s], dtype=np.float64) for s in range(S)] @@ -942,6 +930,7 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, # Initialization of C : random SPD matrix (if not provided by user) if init_C is None: + # XXX : should use a random state and not use the global seed xalea = np.random.randn(N, 2) C = dist(xalea, xalea) C /= C.max() @@ -987,8 +976,7 @@ def gromov_barycenters(N, Cs, ps, p, lambdas, loss_fun, def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_features=False, p=None, loss_fun='square_loss', max_iter=100, tol=1e-9, verbose=False, log=False, init_C=None, init_X=None): - """ - Compute the fgw barycenter as presented eq (5) in [24]. + """Compute the fgw barycenter as presented eq (5) in [24]. Parameters ---------- @@ -997,30 +985,32 @@ def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_ Ys: list of ndarray, each element has shape (ns,d) Features of all samples Cs : list of ndarray, each element has shape (ns,ns) - Structure matrices of all samples + Structure matrices of all samples ps : list of ndarray, each element has shape (ns,) - masses of all samples + Masses of all samples. lambdas : list of float - list of the S spaces' weights + List of the S spaces' weights alpha : float - Alpha parameter for the fgw distance - fixed_structure : bool - Wether to fix the structure of the barycenter during the updates - fixed_features : bool - Wether to fix the feature of the barycenter during the updates - init_C : ndarray, shape (N,N), optional - initialization for the barycenters' structure matrix. If not set random init - init_X : ndarray, shape (N,d), optional - initialization for the barycenters' features. If not set random init + Alpha parameter for the fgw distance + fixed_structure : bool + Whether to fix the structure of the barycenter during the updates + fixed_features : bool + Whether to fix the feature of the barycenter during the updates + init_C : ndarray, shape (N,N), optional + Initialization for the barycenters' structure matrix. If not set + a random init is used. + init_X : ndarray, shape (N,d), optional + Initialization for the barycenters' features. If not set a + random init is used. Returns ------- - X : ndarray, shape (N,d) + X : ndarray, shape (N, d) Barycenters' features - C : ndarray, shape (N,N) + C : ndarray, shape (N, N) Barycenters' structure matrix - log_: dictionary - Only returned when log=True + log_: dict + Only returned when log=True. It contains the keys: T : list of (N,ns) transport matrices Ms : all distance matrices between the feature of the barycenter and the other features dist(X,Ys) shape (N,ns) @@ -1032,7 +1022,6 @@ def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_ "Optimal Transport for structured data with application on graphs" International Conference on Machine Learning (ICML). 2019. """ - S = len(Cs) d = Ys[0].shape[1] # dimension on the node features if p is None: @@ -1095,7 +1084,8 @@ def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_ T_temp = [t.T for t in T] C = update_sructure_matrix(p, lambdas, T_temp, Cs) - T = [fused_gromov_wasserstein((1 - alpha) * Ms[s], C, Cs[s], p, ps[s], loss_fun, alpha, numItermax=max_iter, stopThr=1e-5, verbose=verbose) for s in range(S)] + T = [fused_gromov_wasserstein((1 - alpha) * Ms[s], C, Cs[s], p, ps[s], loss_fun, alpha, + numItermax=max_iter, stopThr=1e-5, verbose=verbose) for s in range(S)] # T is N,ns err_feature = np.linalg.norm(X - Xprev.reshape(N, d)) @@ -1114,6 +1104,7 @@ def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_ print('{:5d}|{:8e}|'.format(cpt, err_feature)) cpt += 1 + if log: log_['T'] = T # from target to Ys log_['p'] = p @@ -1126,25 +1117,25 @@ def fgw_barycenters(N, Ys, Cs, ps, lambdas, alpha, fixed_structure=False, fixed_ def update_sructure_matrix(p, lambdas, T, Cs): - """ - Updates C according to the L2 Loss kernel with the S Ts couplings - calculated at each iteration + """Updates C according to the L2 Loss kernel with the S Ts couplings. + + It is calculated at each iteration Parameters ---------- - p : ndarray, shape (N,) - masses in the targeted barycenter + p : ndarray, shape (N,) + Masses in the targeted barycenter. lambdas : list of float - list of the S spaces' weights - T : list of S np.ndarray(ns,N) - the S Ts couplings calculated at each iteration - Cs : list of S ndarray, shape(ns,ns) - Metric cost matrices + List of the S spaces' weights. + T : list of S ndarray of shape (ns, N) + The S Ts couplings calculated at each iteration. + Cs : list of S ndarray, shape (ns, ns) + Metric cost matrices. Returns - ---------- - C : ndarray, shape (nt,nt) - updated C matrix + ------- + C : ndarray, shape (nt, nt) + Updated C matrix. """ tmpsum = sum([lambdas[s] * np.dot(T[s].T, Cs[s]).dot(T[s]) for s in range(len(T))]) ppt = np.outer(p, p) @@ -1153,24 +1144,26 @@ def update_sructure_matrix(p, lambdas, T, Cs): def update_feature_matrix(lambdas, Ys, Ts, p): - """ - Updates the feature with respect to the S Ts couplings. See "Solving the barycenter problem with Block Coordinate Descent (BCD)" in [24] - calculated at each iteration + """Updates the feature with respect to the S Ts couplings. + + + See "Solving the barycenter problem with Block Coordinate Descent (BCD)" + in [24] calculated at each iteration Parameters ---------- - p : ndarray, shape (N,) - masses in the targeted barycenter + p : ndarray, shape (N,) + masses in the targeted barycenter lambdas : list of float - list of the S spaces' weights + List of the S spaces' weights Ts : list of S np.ndarray(ns,N) the S Ts couplings calculated at each iteration Ys : list of S ndarray, shape(d,ns) - The features + The features. Returns - ---------- - X : ndarray, shape (d,N) + ------- + X : ndarray, shape (d, N) References ---------- @@ -1179,9 +1172,8 @@ def update_feature_matrix(lambdas, Ys, Ts, p): "Optimal Transport for structured data with application on graphs" International Conference on Machine Learning (ICML). 2019. """ + p = np.array(1. / p).reshape(-1,) - p = np.diag(np.array(1 / p).reshape(-1,)) - - tmpsum = sum([lambdas[s] * np.dot(Ys[s], Ts[s].T).dot(p) for s in range(len(Ts))]) + tmpsum = sum([lambdas[s] * np.dot(Ys[s], Ts[s].T) * p[None, :] for s in range(len(Ts))]) return tmpsum diff --git a/setup.cfg b/setup.cfg index aa0ff62..6be91fe 100644 --- a/setup.cfg +++ b/setup.cfg @@ -4,3 +4,21 @@ description-file = README.md [flake8] exclude = __init__.py ignore = E265,E501,W605,W503,W504 + +[tool:pytest] +addopts = + --showlocals --durations=20 --doctest-modules -ra --cov-report= --cov=ot + --doctest-ignore-import-errors --junit-xml=junit-results.xml + --ignore=docs --ignore=examples --ignore=notebooks + +[pycodestyle] +exclude = __init__.py,*externals*,constants.py,fixes.py +ignore = E241,E305,W504 + +[pydocstyle] +convention = pep257 +match_dir = ^(?!\.|docs|examples).*$ +match = (?!tests/__init__\.py|fixes).*\.py +add-ignore = D100,D104,D107,D413 +add-select = D214,D215,D404,D405,D406,D407,D408,D409,D410,D411 +ignore-decorators = ^(copy_.*_doc_to_|on_trait_change|cached_property|deprecated|property|.*setter).* -- cgit v1.2.3 From b6fb14861accd20a323bfc5ef96c20883e4f6ce1 Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Tue, 9 Jul 2019 17:08:58 +0200 Subject: more --- ot/stochastic.py | 199 +++++++++++++++++++++++++------------------------------ ot/unbalanced.py | 3 +- ot/utils.py | 47 ++++++------- 3 files changed, 111 insertions(+), 138 deletions(-) diff --git a/ot/stochastic.py b/ot/stochastic.py index 5754968..13ed9cc 100644 --- a/ot/stochastic.py +++ b/ot/stochastic.py @@ -38,22 +38,20 @@ def coordinate_grad_semi_dual(b, M, reg, beta, i): Parameters ---------- - - b : np.ndarray(nt,) - target measure - M : np.ndarray(ns, nt) - cost matrix - reg : float nu - Regularization term > 0 - v : np.ndarray(nt,) - dual variable - i : number int - picked number i + b : ndarray, shape (nt,) + Target measure. + M : ndarray, shape (ns, nt) + Cost matrix. + reg : float + Regularization term > 0. + v : ndarray, shape (nt,) + Dual variable. + i : int + Picked number i. Returns ------- - - coordinate gradient : np.ndarray(nt,) + coordinate gradient : ndarray, shape (nt,) Examples -------- @@ -78,14 +76,11 @@ def coordinate_grad_semi_dual(b, M, reg, beta, i): References ---------- - [Genevay et al., 2016] : - Stochastic Optimization for Large-scale Optimal Transport, - Advances in Neural Information Processing Systems (2016), - arXiv preprint arxiv:1605.08527. - + Stochastic Optimization for Large-scale Optimal Transport, + Advances in Neural Information Processing Systems (2016), + arXiv preprint arxiv:1605.08527. ''' - r = M[i, :] - beta exp_beta = np.exp(-r / reg) * b khi = exp_beta / (np.sum(exp_beta)) @@ -121,24 +116,23 @@ def sag_entropic_transport(a, b, M, reg, numItermax=10000, lr=None): Parameters ---------- - a : np.ndarray(ns,), - source measure - b : np.ndarray(nt,), - target measure - M : np.ndarray(ns, nt), - cost matrix - reg : float number, + a : ndarray, shape (ns,), + Source measure. + b : ndarray, shape (nt,), + Target measure. + M : ndarray, shape (ns, nt), + Cost matrix. + reg : float Regularization term > 0 - numItermax : int number - number of iteration - lr : float number - learning rate + numItermax : int + Number of iteration. + lr : float + Learning rate. Returns ------- - - v : np.ndarray(nt,) - dual variable + v : ndarray, shape (nt,) + Dual variable. Examples -------- @@ -213,23 +207,20 @@ def averaged_sgd_entropic_transport(a, b, M, reg, numItermax=300000, lr=None): Parameters ---------- - - b : np.ndarray(nt,) + b : ndarray, shape (nt,) target measure - M : np.ndarray(ns, nt) + M : ndarray, shape (ns, nt) cost matrix - reg : float number + reg : float Regularization term > 0 - numItermax : int number - number of iteration - lr : float number - learning rate - + numItermax : int + Number of iteration. + lr : float + Learning rate. Returns ------- - - ave_v : np.ndarray(nt,) + ave_v : ndarray, shape (nt,) dual variable Examples @@ -256,9 +247,9 @@ def averaged_sgd_entropic_transport(a, b, M, reg, numItermax=300000, lr=None): ---------- [Genevay et al., 2016] : - Stochastic Optimization for Large-scale Optimal Transport, - Advances in Neural Information Processing Systems (2016), - arXiv preprint arxiv:1605.08527. + Stochastic Optimization for Large-scale Optimal Transport, + Advances in Neural Information Processing Systems (2016), + arXiv preprint arxiv:1605.08527. ''' if lr is None: @@ -298,21 +289,19 @@ def c_transform_entropic(b, M, reg, beta): Parameters ---------- - - b : np.ndarray(nt,) - target measure - M : np.ndarray(ns, nt) - cost matrix + b : ndarray, shape (nt,) + Target measure + M : ndarray, shape (ns, nt) + Cost matrix reg : float - regularization term > 0 - v : np.ndarray(nt,) - dual variable + Regularization term > 0 + v : ndarray, shape (nt,) + Dual variable. Returns ------- - - u : np.ndarray(ns,) - dual variable + u : ndarray, shape (ns,) + Dual variable. Examples -------- @@ -338,9 +327,9 @@ def c_transform_entropic(b, M, reg, beta): ---------- [Genevay et al., 2016] : - Stochastic Optimization for Large-scale Optimal Transport, - Advances in Neural Information Processing Systems (2016), - arXiv preprint arxiv:1605.08527. + Stochastic Optimization for Large-scale Optimal Transport, + Advances in Neural Information Processing Systems (2016), + arXiv preprint arxiv:1605.08527. ''' n_source = np.shape(M)[0] @@ -382,31 +371,30 @@ def solve_semi_dual_entropic(a, b, M, reg, method, numItermax=10000, lr=None, Parameters ---------- - a : np.ndarray(ns,) + a : ndarray, shape (ns,) source measure - b : np.ndarray(nt,) + b : ndarray, shape (nt,) target measure - M : np.ndarray(ns, nt) + M : ndarray, shape (ns, nt) cost matrix - reg : float number + reg : float Regularization term > 0 methode : str used method (SAG or ASGD) - numItermax : int number + numItermax : int number of iteration - lr : float number + lr : float learning rate - n_source : int number + n_source : int size of the source measure - n_target : int number + n_target : int size of the target measure log : bool, optional record log if True Returns ------- - - pi : np.ndarray(ns, nt) + pi : ndarray, shape (ns, nt) transportation matrix log : dict log dictionary return only if log==True in parameters @@ -495,30 +483,28 @@ def batch_grad_dual(a, b, M, reg, alpha, beta, batch_size, batch_alpha, Parameters ---------- - - a : np.ndarray(ns,) + a : ndarray, shape (ns,) source measure - b : np.ndarray(nt,) + b : ndarray, shape (nt,) target measure - M : np.ndarray(ns, nt) + M : ndarray, shape (ns, nt) cost matrix - reg : float number + reg : float Regularization term > 0 - alpha : np.ndarray(ns,) + alpha : ndarray, shape (ns,) dual variable - beta : np.ndarray(nt,) + beta : ndarray, shape (nt,) dual variable - batch_size : int number + batch_size : int size of the batch - batch_alpha : np.ndarray(bs,) + batch_alpha : ndarray, shape (bs,) batch of index of alpha - batch_beta : np.ndarray(bs,) + batch_beta : ndarray, shape (bs,) batch of index of beta Returns ------- - - grad : np.ndarray(ns,) + grad : ndarray, shape (ns,) partial grad F Examples @@ -591,28 +577,26 @@ def sgd_entropic_regularization(a, b, M, reg, batch_size, numItermax, lr): Parameters ---------- - - a : np.ndarray(ns,) + a : ndarray, shape (ns,) source measure - b : np.ndarray(nt,) + b : ndarray, shape (nt,) target measure - M : np.ndarray(ns, nt) + M : ndarray, shape (ns, nt) cost matrix - reg : float number + reg : float Regularization term > 0 - batch_size : int number + batch_size : int size of the batch - numItermax : int number + numItermax : int number of iteration - lr : float number + lr : float learning rate Returns ------- - - alpha : np.ndarray(ns,) + alpha : ndarray, shape (ns,) dual variable - beta : np.ndarray(nt,) + beta : ndarray, shape (nt,) dual variable Examples @@ -648,10 +632,9 @@ def sgd_entropic_regularization(a, b, M, reg, batch_size, numItermax, lr): References ---------- - [Seguy et al., 2018] : - International Conference on Learning Representation (2018), - arXiv preprint arxiv:1711.02283. + International Conference on Learning Representation (2018), + arXiv preprint arxiv:1711.02283. ''' n_source = np.shape(M)[0] @@ -696,28 +679,26 @@ def solve_dual_entropic(a, b, M, reg, batch_size, numItermax=10000, lr=1, Parameters ---------- - - a : np.ndarray(ns,) + a : ndarray, shape (ns,) source measure - b : np.ndarray(nt,) + b : ndarray, shape (nt,) target measure - M : np.ndarray(ns, nt) + M : ndarray, shape (ns, nt) cost matrix - reg : float number + reg : float Regularization term > 0 - batch_size : int number + batch_size : int size of the batch - numItermax : int number + numItermax : int number of iteration - lr : float number + lr : float learning rate log : bool, optional record log if True Returns ------- - - pi : np.ndarray(ns, nt) + pi : ndarray, shape (ns, nt) transportation matrix log : dict log dictionary return only if log==True in parameters @@ -757,8 +738,8 @@ def solve_dual_entropic(a, b, M, reg, batch_size, numItermax=10000, lr=1, ---------- [Seguy et al., 2018] : - International Conference on Learning Representation (2018), - arXiv preprint arxiv:1711.02283. + International Conference on Learning Representation (2018), + arXiv preprint arxiv:1711.02283. ''' opt_alpha, opt_beta = sgd_entropic_regularization(a, b, M, reg, batch_size, diff --git a/ot/unbalanced.py b/ot/unbalanced.py index 50ec03c..467fda2 100644 --- a/ot/unbalanced.py +++ b/ot/unbalanced.py @@ -380,7 +380,8 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, print( '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) print('{:5d}|{:8e}|'.format(cpt, err)) - cpt = cpt + 1 + cpt += 1 + if log: log['u'] = u log['v'] = v diff --git a/ot/utils.py b/ot/utils.py index e8249ef..8419c83 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -111,12 +111,12 @@ def dist(x1, x2=None, metric='sqeuclidean'): Parameters ---------- - x1 : np.array (n1,d) + x1 : ndarray, shape (n1,d) matrix with n1 samples of size d - x2 : np.array (n2,d), optional + x2 : array, shape (n2,d), optional matrix with n2 samples of size d (if None then x2=x1) - metric : str, fun, optional - name of the metric to be computed (full list in the doc of scipy), If a string, + metric : str | callable, optional + Name of the metric to be computed (full list in the doc of scipy), If a string, the distance function can be 'braycurtis', 'canberra', 'chebyshev', 'cityblock', 'correlation', 'cosine', 'dice', 'euclidean', 'hamming', 'jaccard', 'kulsinski', 'mahalanobis', 'matching', 'minkowski', 'rogerstanimoto', 'russellrao', 'seuclidean', @@ -138,26 +138,21 @@ def dist(x1, x2=None, metric='sqeuclidean'): def dist0(n, method='lin_square'): - """Compute standard cost matrices of size (n,n) for OT problems + """Compute standard cost matrices of size (n, n) for OT problems Parameters ---------- - n : int - size of the cost matrix + Size of the cost matrix. method : str, optional Type of loss matrix chosen from: * 'lin_square' : linear sampling between 0 and n-1, quadratic loss - Returns ------- - - M : np.array (n1,n2) - distance matrix computed with given metric - - + M : ndarray, shape (n1,n2) + Distance matrix computed with given metric. """ res = 0 if method == 'lin_square': @@ -169,22 +164,18 @@ def dist0(n, method='lin_square'): def cost_normalization(C, norm=None): """ Apply normalization to the loss matrix - Parameters ---------- - C : np.array (n1, n2) + C : ndarray, shape (n1, n2) The cost matrix to normalize. norm : str - type of normalization from 'median','max','log','loglog'. Any other - value do not normalize. - + Type of normalization from 'median', 'max', 'log', 'loglog'. Any + other value do not normalize. Returns ------- - - C : np.array (n1, n2) + C : ndarray, shape (n1, n2) The input cost matrix normalized according to given norm. - """ if norm == "median": @@ -194,7 +185,7 @@ def cost_normalization(C, norm=None): elif norm == "log": C = np.log(1 + C) elif norm == "loglog": - C = np.log(1 + np.log(1 + C)) + C = np.log1p(np.log1p(C)) return C @@ -256,6 +247,7 @@ def check_params(**kwargs): def check_random_state(seed): """Turn seed into a np.random.RandomState instance + Parameters ---------- seed : None | int | instance of RandomState @@ -275,7 +267,6 @@ def check_random_state(seed): class deprecated(object): - """Decorator to mark a function or class as deprecated. deprecated class from scikit-learn package @@ -291,8 +282,8 @@ class deprecated(object): Parameters ---------- - extra : string - to be added to the deprecation messages + extra : str + To be added to the deprecation messages. """ # Adapted from http://wiki.python.org/moin/PythonDecoratorLibrary, @@ -373,9 +364,9 @@ def _is_deprecated(func): class BaseEstimator(object): - """Base class for most objects in POT - adapted from sklearn BaseEstimator class + + Code adapted from sklearn BaseEstimator class Notes ----- @@ -417,7 +408,7 @@ class BaseEstimator(object): Parameters ---------- - deep : boolean, optional + deep : bool, optional If True, will return the parameters for this estimator and contained subobjects that are estimators. -- cgit v1.2.3 From 06fab4c1e5efbe79f91589917fba01c3fb300a87 Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Tue, 9 Jul 2019 17:20:02 +0200 Subject: more --- ot/bregman.py | 112 ++++++++++++++++++++++++++------------------------------- ot/datasets.py | 32 +++++++---------- ot/optim.py | 32 ++++++++--------- ot/plot.py | 6 ++-- 4 files changed, 80 insertions(+), 102 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 13dfa3b..b67074f 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -40,12 +40,12 @@ def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, Parameters ---------- - a : np.ndarray (ns,) + a : ndarray, shape (ns,) samples weights in the source domain - b : np.ndarray (nt,) or np.ndarray (nt,nbb) + b : ndarray, shape (nt,) or ndarray, shape (nt, nbb) samples in the target domain, compute sinkhorn with multiple targets and fixed M if b is a matrix (return OT loss + dual variables in log) - M : np.ndarray (ns,nt) + M : ndarray, shape (ns, nt) loss matrix reg : float Regularization term >0 @@ -64,7 +64,7 @@ def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, Returns ------- - gamma : (ns x nt) ndarray + gamma : ndarray, shape (ns, nt) Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -155,12 +155,12 @@ def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, Parameters ---------- - a : np.ndarray (ns,) + a : ndarray, shape (ns,) samples weights in the source domain - b : np.ndarray (nt,) or np.ndarray (nt,nbb) + b : ndarray, shape (nt,) or ndarray, shape (nt, nbb) samples in the target domain, compute sinkhorn with multiple targets and fixed M if b is a matrix (return OT loss + dual variables in log) - M : np.ndarray (ns,nt) + M : ndarray, shape (ns, nt) loss matrix reg : float Regularization term >0 @@ -176,7 +176,6 @@ def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, log : bool, optional record log if True - Returns ------- W : (nt) ndarray or float @@ -272,12 +271,12 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, Parameters ---------- - a : np.ndarray (ns,) + a : ndarray, shape (ns,) samples weights in the source domain - b : np.ndarray (nt,) or np.ndarray (nt,nbb) + b : ndarray, shape (nt,) or ndarray, shape (nt, nbb) samples in the target domain, compute sinkhorn with multiple targets and fixed M if b is a matrix (return OT loss + dual variables in log) - M : np.ndarray (ns,nt) + M : ndarray, shape (ns, nt) loss matrix reg : float Regularization term >0 @@ -290,10 +289,9 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, log : bool, optional record log if True - Returns ------- - gamma : (ns x nt) ndarray + gamma : ndarray, shape (ns, nt) Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -453,12 +451,12 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log= Parameters ---------- - a : np.ndarray (ns,) + a : ndarray, shape (ns,) samples weights in the source domain - b : np.ndarray (nt,) or np.ndarray (nt,nbb) + b : ndarray, shape (nt,) or ndarray, shape (nt, nbb) samples in the target domain, compute sinkhorn with multiple targets and fixed M if b is a matrix (return OT loss + dual variables in log) - M : np.ndarray (ns,nt) + M : ndarray, shape (ns, nt) loss matrix reg : float Regularization term >0 @@ -469,10 +467,9 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log= log : bool, optional record log if True - Returns ------- - gamma : (ns x nt) ndarray + gamma : ndarray, shape (ns, nt) Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -602,11 +599,11 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, Parameters ---------- - a : np.ndarray (ns,) + a : ndarray, shape (ns,) samples weights in the source domain - b : np.ndarray (nt,) + b : ndarray, shape (nt,) samples in the target domain - M : np.ndarray (ns,nt) + M : ndarray, shape (ns, nt) loss matrix reg : float Regularization term >0 @@ -623,10 +620,9 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, log : bool, optional record log if True - Returns ------- - gamma : (ns x nt) ndarray + gamma : ndarray, shape (ns, nt) Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -823,11 +819,11 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInne Parameters ---------- - a : np.ndarray (ns,) + a : ndarray, shape (ns,) samples weights in the source domain - b : np.ndarray (nt,) + b : ndarray, shape (nt,) samples in the target domain - M : np.ndarray (ns,nt) + M : ndarray, shape (ns, nt) loss matrix reg : float Regularization term >0 @@ -835,7 +831,7 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInne thershold for max value in u or v for log scaling tau : float thershold for max value in u or v for log scaling - warmstart : tible of vectors + warmstart : tuple of vectors if given then sarting values for alpha an beta log scalings numItermax : int, optional Max number of iterations @@ -850,10 +846,9 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInne log : bool, optional record log if True - Returns ------- - gamma : (ns x nt) ndarray + gamma : ndarray, shape (ns, nt) Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -1006,13 +1001,13 @@ def barycenter(A, M, reg, weights=None, numItermax=1000, Parameters ---------- - A : np.ndarray (d,n) + A : ndarray, shape (d,n) n training distributions a_i of size d - M : np.ndarray (d,d) + M : ndarray, shape (d,d) loss matrix for OT reg : float Regularization term >0 - weights : np.ndarray (n,) + weights : ndarray, shape (n,) Weights of each histogram a_i on the simplex (barycentric coodinates) numItermax : int, optional Max number of iterations @@ -1102,11 +1097,11 @@ def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1 Parameters ---------- - A : np.ndarray (n,w,h) + A : ndarray, shape (n, w, h) n distributions (2D images) of size w x h reg : float Regularization term >0 - weights : np.ndarray (n,) + weights : ndarray, shape (n,) Weights of each image on the simplex (barycentric coodinates) numItermax : int, optional Max number of iterations @@ -1119,15 +1114,13 @@ def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1 log : bool, optional record log if True - Returns ------- - a : (w,h) ndarray + a : ndarray, shape (w, h) 2D Wasserstein barycenter log : dict log dictionary return only if log==True in parameters - References ---------- @@ -1217,15 +1210,15 @@ def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000, Parameters ---------- - a : np.ndarray (d) + a : ndarray, shape (d) observed distribution - D : np.ndarray (d,n) + D : ndarray, shape (d, n) dictionary matrix - M : np.ndarray (d,d) + M : ndarray, shape (d, d) loss matrix - M0 : np.ndarray (n,n) + M0 : ndarray, shape (n, n) loss matrix - h0 : np.ndarray (n,) + h0 : ndarray, shape (n,) prior on h reg : float Regularization term >0 (Wasserstein data fitting) @@ -1245,7 +1238,7 @@ def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000, Returns ------- - a : (d,) ndarray + a : ndarray, shape (d,) Wasserstein barycenter log : dict log dictionary return only if log==True in parameters @@ -1325,15 +1318,15 @@ def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numI Parameters ---------- - X_s : np.ndarray (ns, d) + X_s : ndarray, shape (ns, d) samples in the source domain - X_t : np.ndarray (nt, d) + X_t : ndarray, shape (nt, d) samples in the target domain reg : float Regularization term >0 - a : np.ndarray (ns,) + a : ndarray, shape (ns,) samples weights in the source domain - b : np.ndarray (nt,) + b : ndarray, shape (nt,) samples weights in the target domain numItermax : int, optional Max number of iterations @@ -1347,7 +1340,7 @@ def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numI Returns ------- - gamma : (ns x nt) ndarray + gamma : ndarray, shape (ns, nt) Regularized optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -1415,15 +1408,15 @@ def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', num Parameters ---------- - X_s : np.ndarray (ns, d) + X_s : ndarray, shape (ns, d) samples in the source domain - X_t : np.ndarray (nt, d) + X_t : ndarray, shape (nt, d) samples in the target domain reg : float Regularization term >0 - a : np.ndarray (ns,) + a : ndarray, shape (ns,) samples weights in the source domain - b : np.ndarray (nt,) + b : ndarray, shape (nt,) samples weights in the target domain numItermax : int, optional Max number of iterations @@ -1437,7 +1430,7 @@ def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', num Returns ------- - gamma : (ns x nt) ndarray + gamma : ndarray, shape (ns, nt) Regularized optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -1523,15 +1516,15 @@ def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeucli Parameters ---------- - X_s : np.ndarray (ns, d) + X_s : ndarray, shape (ns, d) samples in the source domain - X_t : np.ndarray (nt, d) + X_t : ndarray, shape (nt, d) samples in the target domain reg : float Regularization term >0 - a : np.ndarray (ns,) + a : ndarray, shape (ns,) samples weights in the source domain - b : np.ndarray (nt,) + b : ndarray, shape (nt,) samples weights in the target domain numItermax : int, optional Max number of iterations @@ -1542,17 +1535,15 @@ def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeucli log : bool, optional record log if True - Returns ------- - gamma : (ns x nt) ndarray + gamma : ndarray, shape (ns, nt) Regularized optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters Examples -------- - >>> n_s = 2 >>> n_t = 4 >>> reg = 0.1 @@ -1564,7 +1555,6 @@ def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeucli References ---------- - .. [23] Aude Genevay, Gabriel Peyré, Marco Cuturi, Learning Generative Models with Sinkhorn Divergences, Proceedings of the Twenty-First International Conference on Artficial Intelligence and Statistics, (AISTATS) 21, 2018 ''' if log: diff --git a/ot/datasets.py b/ot/datasets.py index e76e75d..ba0cfd9 100644 --- a/ot/datasets.py +++ b/ot/datasets.py @@ -17,7 +17,6 @@ def make_1D_gauss(n, m, s): Parameters ---------- - n : int number of bins in the histogram m : float @@ -25,12 +24,10 @@ def make_1D_gauss(n, m, s): s : float standard deviaton of the gaussian distribution - Returns ------- - h : np.array (n,) - 1D histogram for a gaussian distribution - + h : ndarray (n,) + 1D histogram for a gaussian distribution """ x = np.arange(n, dtype=np.float64) h = np.exp(-(x - m)**2 / (2 * s**2)) @@ -44,16 +41,15 @@ def get_1D_gauss(n, m, sigma): def make_2D_samples_gauss(n, m, sigma, random_state=None): - """return n samples drawn from 2D gaussian N(m,sigma) + """Return n samples drawn from 2D gaussian N(m,sigma) Parameters ---------- - n : int number of samples to make - m : np.array (2,) + m : ndarray, shape (2,) mean value of the gaussian distribution - sigma : np.array (2,2) + sigma : ndarray, shape (2, 2) covariance matrix of the gaussian distribution random_state : int, RandomState instance or None, optional (default=None) If int, random_state is the seed used by the random number generator; @@ -63,9 +59,8 @@ def make_2D_samples_gauss(n, m, sigma, random_state=None): Returns ------- - X : np.array (n,2) - n samples drawn from N(m,sigma) - + X : ndarray, shape (n, 2) + n samples drawn from N(m, sigma). """ generator = check_random_state(random_state) @@ -86,11 +81,10 @@ def get_2D_samples_gauss(n, m, sigma, random_state=None): def make_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs): - """ dataset generation for classification problems + """Dataset generation for classification problems Parameters ---------- - dataset : str type of classification problem (see code) n : int @@ -105,13 +99,11 @@ def make_data_classif(dataset, n, nz=.5, theta=0, random_state=None, **kwargs): Returns ------- - X : np.array (n,d) - n observation of size d - y : np.array (n,) - labels of the samples - + X : ndarray, shape (n, d) + n observation of size d + y : ndarray, shape (n,) + labels of the samples. """ - generator = check_random_state(random_state) if dataset.lower() == '3gauss': diff --git a/ot/optim.py b/ot/optim.py index f94aceb..65baf9d 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -26,14 +26,13 @@ def line_search_armijo(f, xk, pk, gfk, old_fval, Parameters ---------- - - f : function + f : callable loss function - xk : np.ndarray + xk : ndarray initial position - pk : np.ndarray + pk : ndarray descent direction - gfk : np.ndarray + gfk : ndarray gradient of f at xk old_fval : float loss value at xk @@ -161,15 +160,15 @@ def cg(a, b, M, reg, f, df, G0=None, numItermax=200, Parameters ---------- - a : np.ndarray (ns,) + a : ndarray, shape (ns,) samples weights in the source domain - b : np.ndarray (nt,) + b : ndarray, shape (nt,) samples in the target domain - M : np.ndarray (ns,nt) + M : ndarray, shape (ns, nt) loss matrix reg : float Regularization term >0 - G0 : np.ndarray (ns,nt), optional + G0 : ndarray, shape (ns,nt), optional initial guess (default is indep joint density) numItermax : int, optional Max number of iterations @@ -299,17 +298,17 @@ def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, Parameters ---------- - a : np.ndarray (ns,) + a : ndarray, shape (ns,) samples weights in the source domain - b : np.ndarray (nt,) + b : ndarrayv (nt,) samples in the target domain - M : np.ndarray (ns,nt) + M : ndarray, shape (ns, nt) loss matrix reg1 : float Entropic Regularization term >0 reg2 : float Second Regularization term >0 - G0 : np.ndarray (ns,nt), optional + G0 : ndarray, shape (ns, nt), optional initial guess (default is indep joint density) numItermax : int, optional Max number of iterations @@ -326,15 +325,13 @@ def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, Returns ------- - gamma : (ns x nt) ndarray + gamma : ndarray, shape (ns, nt) Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters - References ---------- - .. [5] N. Courty; R. Flamary; D. Tuia; A. Rakotomamonjy, "Optimal Transport for Domain Adaptation," in IEEE Transactions on Pattern Analysis and Machine Intelligence , vol.PP, no.99, pp.1-1 .. [7] Rakotomamonjy, A., Flamary, R., & Courty, N. (2015). Generalized conditional gradient: analysis of convergence and applications. arXiv preprint arXiv:1510.06567. @@ -422,13 +419,12 @@ def solve_1d_linesearch_quad(a, b, c): Parameters ---------- a,b,c : float - The coefficients of the quadratic function + The coefficients of the quadratic function Returns ------- x : float The optimal value which leads to the minimal cost - """ f0 = c df0 = b diff --git a/ot/plot.py b/ot/plot.py index a409d4a..f403e98 100644 --- a/ot/plot.py +++ b/ot/plot.py @@ -26,11 +26,11 @@ def plot1D_mat(a, b, M, title=''): Parameters ---------- - a : np.array, shape (na,) + a : ndarray, shape (na,) Source distribution - b : np.array, shape (nb,) + b : ndarray, shape (nb,) Target distribution - M : np.array, shape (na,nb) + M : ndarray, shape (na, nb) Matrix to plot """ na, nb = M.shape -- cgit v1.2.3 From 0d9c65d39f7bf6a9c692ad8d5421ddb087ddcafc Mon Sep 17 00:00:00 2001 From: Alexandre Gramfort Date: Tue, 9 Jul 2019 18:09:30 +0200 Subject: trailing spaces --- ot/bregman.py | 14 +++++++------- ot/optim.py | 2 +- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index b67074f..f39145d 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -291,7 +291,7 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, Returns ------- - gamma : ndarray, shape (ns, nt) + gamma : ndarray, shape (ns, nt) Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -469,7 +469,7 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log= Returns ------- - gamma : ndarray, shape (ns, nt) + gamma : ndarray, shape (ns, nt) Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -622,7 +622,7 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, Returns ------- - gamma : ndarray, shape (ns, nt) + gamma : ndarray, shape (ns, nt) Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -848,7 +848,7 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInne Returns ------- - gamma : ndarray, shape (ns, nt) + gamma : ndarray, shape (ns, nt) Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -1340,7 +1340,7 @@ def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numI Returns ------- - gamma : ndarray, shape (ns, nt) + gamma : ndarray, shape (ns, nt) Regularized optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -1430,7 +1430,7 @@ def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', num Returns ------- - gamma : ndarray, shape (ns, nt) + gamma : ndarray, shape (ns, nt) Regularized optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -1537,7 +1537,7 @@ def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeucli Returns ------- - gamma : ndarray, shape (ns, nt) + gamma : ndarray, shape (ns, nt) Regularized optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters diff --git a/ot/optim.py b/ot/optim.py index 65baf9d..0abd9e9 100644 --- a/ot/optim.py +++ b/ot/optim.py @@ -325,7 +325,7 @@ def gcg(a, b, M, reg1, reg2, f, df, G0=None, numItermax=10, Returns ------- - gamma : ndarray, shape (ns, nt) + gamma : ndarray, shape (ns, nt) Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters -- cgit v1.2.3 From c698e0aa20d28e36d25f87082855a490283f3c88 Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Thu, 11 Jul 2019 15:24:39 +0200 Subject: update readme --- README.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/README.md b/README.md index bba27ff..d8bb051 100644 --- a/README.md +++ b/README.md @@ -152,7 +152,8 @@ Here is a list of the Python notebooks available [here](https://github.com/rflam * [Wasserstein Discriminant Analysis](https://github.com/rflamary/POT/blob/master/notebooks/plot_WDA.ipynb) * [Gromov Wasserstein](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov.ipynb) * [Gromov Wasserstein Barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_gromov_barycenter.ipynb) - +* [Fused Gromov Wasserstein](https://github.com/rflamary/POT/blob/master/notebooks/plot_fgw.ipynb) +* [Fused Gromov Wasserstein Barycenter](https://github.com/rflamary/POT/blob/master/notebooks/plot_barycenter_fgw.ipynb) You can also see the notebooks with [Jupyter nbviewer](https://nbviewer.jupyter.org/github/rflamary/POT/tree/master/notebooks/). -- cgit v1.2.3 From 0d23718409b1f0ac41b9302d98ca3d1ab9577855 Mon Sep 17 00:00:00 2001 From: Hicham Janati Date: Fri, 19 Jul 2019 17:04:14 +0200 Subject: remove square in convergence check --- ot/unbalanced.py | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/ot/unbalanced.py b/ot/unbalanced.py index 50ec03c..f6c2d5f 100644 --- a/ot/unbalanced.py +++ b/ot/unbalanced.py @@ -371,8 +371,9 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, if cpt % 10 == 0: # we can speed up the process by checking for the error only all # the 10th iterations - err = np.sum((u - uprev)**2) / np.sum((u)**2) + \ - np.sum((v - vprev)**2) / np.sum((v)**2) + err_u = abs(u - uprev).max() / max(abs(u), abs(uprev), 1.) + err_v = abs(v - vprev).max() / max(abs(v), abs(vprev), 1.) + err = 0.5 * (err_u + err_v) if log: log['err'].append(err) if verbose: @@ -498,8 +499,9 @@ def barycenter_unbalanced(A, M, reg, alpha, weights=None, numItermax=1000, if cpt % 10 == 0: # we can speed up the process by checking for the error only all # the 10th iterations - err = np.sum((u - uprev) ** 2) / np.sum((u) ** 2) + \ - np.sum((v - vprev) ** 2) / np.sum((v) ** 2) + err_u = abs(u - uprev).max() / max(abs(u), abs(uprev), 1.) + err_v = abs(v - vprev).max() / max(abs(v), abs(vprev), 1.) + err = 0.5 * (err_u + err_v) if log: log['err'].append(err) if verbose: -- cgit v1.2.3 From 10accb13c2f22c946b65b249d7aae6e4f6af7579 Mon Sep 17 00:00:00 2001 From: Hicham Janati Date: Mon, 22 Jul 2019 14:53:45 +0200 Subject: add unbalanced with stabilization --- ot/unbalanced.py | 279 ++++++++++++++++++++++++++++++++++++++++++++++++------- 1 file changed, 245 insertions(+), 34 deletions(-) diff --git a/ot/unbalanced.py b/ot/unbalanced.py index f6c2d5f..ca24e8b 100644 --- a/ot/unbalanced.py +++ b/ot/unbalanced.py @@ -9,10 +9,12 @@ Regularized Unbalanced OT from __future__ import division import warnings import numpy as np +from scipy.misc import logsumexp + # from .utils import unif, dist -def sinkhorn_unbalanced(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, +def sinkhorn_unbalanced(a, b, M, reg, mu, method='sinkhorn', numItermax=1000, stopThr=1e-9, verbose=False, log=False, **kwargs): r""" Solve the unbalanced entropic regularization optimal transport problem and return the loss @@ -20,7 +22,7 @@ def sinkhorn_unbalanced(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, The function solves the following optimization problem: .. math:: - W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \\alpha KL(\gamma 1, a) + \\alpha KL(\gamma^T 1, b) + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \\mu KL(\gamma 1, a) + \\mu KL(\gamma^T 1, b) s.t. \gamma\geq 0 @@ -45,11 +47,11 @@ def sinkhorn_unbalanced(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, loss matrix reg : float Entropy regularization term > 0 - alpha : float + mu : float Marginal relaxation term > 0 method : str method used for the solver either 'sinkhorn', 'sinkhorn_stabilized' or - 'sinkhorn_epsilon_scaling', see those function for specific parameters + 'sinkhorn_reg_scaling', see those function for specific parameters numItermax : int, optional Max number of iterations stopThr : float, optional @@ -95,22 +97,29 @@ def sinkhorn_unbalanced(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, -------- ot.unbalanced.sinkhorn_knopp_unbalanced : Unbalanced Classic Sinkhorn [10] ot.unbalanced.sinkhorn_stabilized_unbalanced: Unbalanced Stabilized sinkhorn [9][10] - ot.unbalanced.sinkhorn_epsilon_scaling_unbalanced: Unbalanced Sinkhorn with epslilon scaling [9][10] + ot.unbalanced.sinkhorn_reg_scaling_unbalanced: Unbalanced Sinkhorn with epslilon scaling [9][10] """ if method.lower() == 'sinkhorn': def sink(): - return sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, + return sinkhorn_knopp_unbalanced(a, b, M, reg, mu, numItermax=numItermax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) - elif method.lower() in ['sinkhorn_stabilized', 'sinkhorn_epsilon_scaling']: + elif method.lower() == 'sinkhorn_stabilized': + def sink(): + return sinkhorn_stabilized_unbalanced(a, b, M, reg, mu, + numItermax=numItermax, + stopThr=stopThr, + verbose=verbose, + log=log, **kwargs) + elif method.lower() in ['sinkhorn_reg_scaling']: warnings.warn('Method not implemented yet. Using classic Sinkhorn Knopp') def sink(): - return sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, + return sinkhorn_knopp_unbalanced(a, b, M, reg, mu, numItermax=numItermax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) @@ -120,7 +129,7 @@ def sinkhorn_unbalanced(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, return sink() -def sinkhorn_unbalanced2(a, b, M, reg, alpha, method='sinkhorn', +def sinkhorn_unbalanced2(a, b, M, reg, mu, method='sinkhorn', numItermax=1000, stopThr=1e-9, verbose=False, log=False, **kwargs): r""" @@ -129,7 +138,7 @@ def sinkhorn_unbalanced2(a, b, M, reg, alpha, method='sinkhorn', The function solves the following optimization problem: .. math:: - W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \\alpha KL(\gamma 1, a) + \\alpha KL(\gamma^T 1, b) + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \\mu KL(\gamma 1, a) + \\mu KL(\gamma^T 1, b) s.t. \gamma\geq 0 @@ -154,11 +163,11 @@ def sinkhorn_unbalanced2(a, b, M, reg, alpha, method='sinkhorn', loss matrix reg : float Entropy regularization term > 0 - alpha : float + mu : float Marginal relaxation term > 0 method : str method used for the solver either 'sinkhorn', 'sinkhorn_stabilized' or - 'sinkhorn_epsilon_scaling', see those function for specific parameters + 'sinkhorn_reg_scaling', see those function for specific parameters numItermax : int, optional Max number of iterations stopThr : float, optional @@ -203,22 +212,29 @@ def sinkhorn_unbalanced2(a, b, M, reg, alpha, method='sinkhorn', -------- ot.unbalanced.sinkhorn_knopp : Unbalanced Classic Sinkhorn [10] ot.unbalanced.sinkhorn_stabilized: Unbalanced Stabilized sinkhorn [9][10] - ot.unbalanced.sinkhorn_epsilon_scaling: Unbalanced Sinkhorn with epslilon scaling [9][10] + ot.unbalanced.sinkhorn_reg_scaling: Unbalanced Sinkhorn with epslilon scaling [9][10] """ if method.lower() == 'sinkhorn': def sink(): - return sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, + return sinkhorn_knopp_unbalanced(a, b, M, reg, mu, numItermax=numItermax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) - elif method.lower() in ['sinkhorn_stabilized', 'sinkhorn_epsilon_scaling']: + elif method.lower() == 'sinkhorn_stabilized': + def sink(): + return sinkhorn_stabilized_unbalanced(a, b, M, reg, mu, + numItermax=numItermax, + stopThr=stopThr, + verbose=verbose, + log=log, **kwargs) + elif method.lower() in ['sinkhorn_reg_scaling']: warnings.warn('Method not implemented yet. Using classic Sinkhorn Knopp') def sink(): - return sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, + return sinkhorn_knopp_unbalanced(a, b, M, reg, mu, numItermax=numItermax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) @@ -232,7 +248,7 @@ def sinkhorn_unbalanced2(a, b, M, reg, alpha, method='sinkhorn', return sink() -def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, +def sinkhorn_knopp_unbalanced(a, b, M, reg, mu, numItermax=1000, stopThr=1e-9, verbose=False, log=False, **kwargs): r""" Solve the entropic regularization unbalanced optimal transport problem and return the loss @@ -240,7 +256,7 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, The function solves the following optimization problem: .. math:: - W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \\alpha KL(\gamma 1, a) + \\alpha KL(\gamma^T 1, b) + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \\mu KL(\gamma 1, a) + \\mu KL(\gamma^T 1, b) s.t. \gamma\geq 0 @@ -265,7 +281,7 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, loss matrix reg : float Entropy regularization term > 0 - alpha : float + mu : float Marginal relaxation term > 0 numItermax : int, optional Max number of iterations @@ -338,14 +354,12 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, u = np.ones(n_a) / n_a v = np.ones(n_b) / n_b - # print(reg) # Next 3 lines equivalent to K= np.exp(-M/reg), but faster to compute K = np.empty(M.shape, dtype=M.dtype) np.divide(M, -reg, out=K) np.exp(K, out=K) - # print(np.min(K)) - fi = alpha / (alpha + reg) + fi = mu / (mu + reg) cpt = 0 err = 1. @@ -371,8 +385,8 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, if cpt % 10 == 0: # we can speed up the process by checking for the error only all # the 10th iterations - err_u = abs(u - uprev).max() / max(abs(u), abs(uprev), 1.) - err_v = abs(v - vprev).max() / max(abs(v), abs(vprev), 1.) + err_u = abs(u - uprev).max() / max(abs(u).max(), abs(uprev).max(), 1.) + err_v = abs(v - vprev).max() / max(abs(v).max(), abs(vprev).max(), 1.) err = 0.5 * (err_u + err_v) if log: log['err'].append(err) @@ -383,8 +397,8 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, print('{:5d}|{:8e}|'.format(cpt, err)) cpt = cpt + 1 if log: - log['u'] = u - log['v'] = v + log['logu'] = np.log(u + 1e-16) + log['logv'] = np.log(v + 1e-16) if n_hists: # return only loss res = np.einsum('ik,ij,jk,ij->k', u, K, v, M) @@ -401,7 +415,204 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, return u[:, None] * K * v[None, :] -def barycenter_unbalanced(A, M, reg, alpha, weights=None, numItermax=1000, +def sinkhorn_stabilized_unbalanced(a, b, M, reg, mu, tau=1e5, numItermax=1000, + stopThr=1e-9, verbose=False, log=False, + **kwargs): + r""" + Solve the entropic regularization unbalanced optimal transport problem and return the loss + + The function solves the following optimization problem using log-domain + stabilization as proposed in [10]: + + .. math:: + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \\mu KL(\gamma 1, a) + \\mu KL(\gamma^T 1, b) + + s.t. + \gamma\geq 0 + where : + + - M is the (ns, nt) metric cost matrix + - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target weights + - KL is the Kullback-Leibler divergence + + The algorithm used for solving the problem is the generalized Sinkhorn-Knopp matrix scaling algorithm as proposed in [10, 23]_ + + + Parameters + ---------- + a : np.ndarray (ns,) + samples weights in the source domain + b : np.ndarray (nt,) or np.ndarray (nt, n_hists) + samples in the target domain, compute sinkhorn with multiple targets + and fixed M if b is a matrix (return OT loss + dual variables in log) + M : np.ndarray (ns,nt) + loss matrix + reg : float + Entropy regularization term > 0 + mu : float + Marginal relaxation term > 0 + tau : float + thershold for max value in u or v for log scaling + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + gamma : (ns x nt) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary return only if log==True in parameters + + Examples + -------- + + >>> import ot + >>> a=[.5, .5] + >>> b=[.5, .5] + >>> M=[[0., 1.],[1., 0.]] + >>> ot.unbalanced.sinkhorn_stabilized_unbalanced(a, b, M, 1., 1.) + array([[0.51122823, 0.18807035], + [0.18807035, 0.51122823]]) + + References + ---------- + + .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + + .. [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. : Learning with a Wasserstein Loss, Advances in Neural Information Processing Systems (NIPS) 2015 + + See Also + -------- + ot.lp.emd : Unregularized OT + ot.optim.cg : General regularized OT + + """ + + a = np.asarray(a, dtype=np.float64) + b = np.asarray(b, dtype=np.float64) + M = np.asarray(M, dtype=np.float64) + + n_a, n_b = M.shape + + if len(a) == 0: + a = np.ones(n_a, dtype=np.float64) / n_a + if len(b) == 0: + b = np.ones(n_b, dtype=np.float64) / n_b + + if len(b.shape) > 1: + n_hists = b.shape[1] + else: + n_hists = 0 + + if log: + log = {'err': []} + + # we assume that no distances are null except those of the diagonal of + # distances + if n_hists: + u = np.ones((n_a, n_hists)) / n_a + v = np.ones((n_b, n_hists)) / n_b + a = a.reshape(n_a, 1) + else: + u = np.ones(n_a) / n_a + v = np.ones(n_b) / n_b + + # print(reg) + # Next 3 lines equivalent to K= np.exp(-M/reg), but faster to compute + K = np.empty(M.shape, dtype=M.dtype) + np.divide(M, -reg, out=K) + np.exp(K, out=K) + + fi = mu / (mu + reg) + + cpt = 0 + err = 1. + alpha = np.zeros(n_a) + beta = np.zeros(n_b) + while (err > stopThr and cpt < numItermax): + uprev = u + vprev = v + + Kv = K.dot(v) + f_alpha = np.exp(- alpha / (reg + mu)) + f_beta = np.exp(- beta / (reg + mu)) + + if n_hists: + f_alpha = f_alpha[:, None] + f_beta = f_beta[:, None] + u = ((a / (Kv + 1e-16)) ** fi) * f_alpha + Ktu = K.T.dot(u) + v = ((b / (Ktu + 1e-16)) ** fi) * f_beta + if (u > tau).any() or (v > tau).any(): + if n_hists: + alpha = alpha + reg * np.log(np.max(u, 1)) + beta = beta + reg * np.log(np.max(v, 1)) + else: + alpha = alpha + reg * np.log(np.max(u)) + beta = beta + reg * np.log(np.max(v)) + K = np.exp((alpha[:, None] + beta[None, :] - + M) / reg) + v = np.ones_like(v) + Kv = K.dot(v) + + if (np.any(Ktu == 0.) + or np.any(np.isnan(u)) or np.any(np.isnan(v)) + or np.any(np.isinf(u)) or np.any(np.isinf(v))): + # we have reached the machine precision + # come back to previous solution and quit loop + warnings.warn('Numerical errors at iteration %d' % cpt) + u = uprev + v = vprev + break + if cpt % 10 == 0: + # we can speed up the process by checking for the error only all + # the 10th iterations + err = abs(u - uprev).max() / max(abs(u).max(), abs(uprev).max(), + 1.) + if log: + log['err'].append(err) + if verbose: + if cpt % 200 == 0: + print( + '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) + print('{:5d}|{:8e}|'.format(cpt, err)) + cpt = cpt + 1 + + if n_hists: + logu = alpha[:, None] / reg + np.log(u) + logv = beta[:, None] / reg + np.log(v) + else: + logu = alpha / reg + np.log(u) + logv = beta / reg + np.log(v) + if log: + log['logu'] = logu + log['logv'] = logv + if n_hists: # return only loss + res = logsumexp(np.log(M + 1e-100)[:, :, None] + logu[:, None, :] + + logv[None, :, :] - M[:, :, None] / reg, axis=(0, 1)) + res = np.exp(res) + if log: + return res, log + else: + return res + + else: # return OT matrix + ot_matrix = np.exp(logu[:, None] + logv[None, :] - M / reg) + if log: + return ot_matrix, log + else: + return ot_matrix + + +def barycenter_unbalanced(A, M, reg, mu, weights=None, numItermax=1000, stopThr=1e-4, verbose=False, log=False): r"""Compute the entropic regularized unbalanced wasserstein barycenter of distributions A @@ -415,7 +626,7 @@ def barycenter_unbalanced(A, M, reg, alpha, weights=None, numItermax=1000, - :math:`Wu_{reg}(\cdot,\cdot)` is the unbalanced entropic regularized Wasserstein distance (see ot.unbalanced.sinkhorn_unbalanced) - :math:`\mathbf{a}_i` are training distributions in the columns of matrix :math:`\mathbf{A}` - reg and :math:`\mathbf{M}` are respectively the regularization term and the cost matrix for OT - - alpha is the marginal relaxation hyperparameter + - mu is the marginal relaxation hyperparameter The algorithm used for solving the problem is the generalized Sinkhorn-Knopp matrix scaling algorithm as proposed in [10]_ Parameters @@ -426,7 +637,7 @@ def barycenter_unbalanced(A, M, reg, alpha, weights=None, numItermax=1000, loss matrix for OT reg : float Entropy regularization term > 0 - alpha : float + mu : float Marginal relaxation term > 0 weights : np.ndarray (n,) Weights of each histogram a_i on the simplex (barycentric coodinates) @@ -467,7 +678,7 @@ def barycenter_unbalanced(A, M, reg, alpha, weights=None, numItermax=1000, K = np.exp(- M / reg) - fi = alpha / (alpha + reg) + fi = mu / (mu + reg) v = np.ones((p, n_hists)) / p u = np.ones((p, 1)) / p @@ -499,8 +710,8 @@ def barycenter_unbalanced(A, M, reg, alpha, weights=None, numItermax=1000, if cpt % 10 == 0: # we can speed up the process by checking for the error only all # the 10th iterations - err_u = abs(u - uprev).max() / max(abs(u), abs(uprev), 1.) - err_v = abs(v - vprev).max() / max(abs(v), abs(vprev), 1.) + err_u = abs(u - uprev).max() / max(abs(u).max(), abs(uprev).max(), 1.) + err_v = abs(v - vprev).max() / max(abs(v).max(), abs(vprev).max(), 1.) err = 0.5 * (err_u + err_v) if log: log['err'].append(err) @@ -513,8 +724,8 @@ def barycenter_unbalanced(A, M, reg, alpha, weights=None, numItermax=1000, cpt += 1 if log: log['niter'] = cpt - log['u'] = u - log['v'] = v + log['logu'] = np.log(u + 1e-16) + log['logv'] = np.log(v + 1e-16) return q, log else: return q -- cgit v1.2.3 From 5c0ed104b2890c609bdadfe0fcb0e836ba7a6ef1 Mon Sep 17 00:00:00 2001 From: Hicham Janati Date: Mon, 22 Jul 2019 14:54:01 +0200 Subject: add unbalanced tests with stabilization --- test/test_unbalanced.py | 116 ++++++++++++++++++++++++++++++++---------------- 1 file changed, 77 insertions(+), 39 deletions(-) diff --git a/test/test_unbalanced.py b/test/test_unbalanced.py index 1395fe1..fc7aa5e 100644 --- a/test/test_unbalanced.py +++ b/test/test_unbalanced.py @@ -8,8 +8,10 @@ import numpy as np import ot import pytest +from scipy.misc import logsumexp -@pytest.mark.parametrize("method", ["sinkhorn"]) + +@pytest.mark.parametrize("method", ["sinkhorn", "sinkhorn_stabilized"]) def test_unbalanced_convergence(method): # test generalized sinkhorn for unbalanced OT n = 100 @@ -23,29 +25,34 @@ def test_unbalanced_convergence(method): M = ot.dist(x, x) epsilon = 1. - alpha = 1. - K = np.exp(- M / epsilon) + mu = 1. - G, log = ot.unbalanced.sinkhorn_unbalanced(a, b, M, reg=epsilon, alpha=alpha, + G, log = ot.unbalanced.sinkhorn_unbalanced(a, b, M, reg=epsilon, mu=mu, stopThr=1e-10, method=method, log=True) - loss = ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, alpha, + loss = ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, mu, method=method) # check fixed point equations - fi = alpha / (alpha + epsilon) - v_final = (b / K.T.dot(log["u"])) ** fi - u_final = (a / K.dot(log["v"])) ** fi + # in log-domain + fi = mu / (mu + epsilon) + logb = np.log(b + 1e-16) + loga = np.log(a + 1e-16) + logKtu = logsumexp(log["logu"][None, :] - M.T / epsilon, axis=1) + logKv = logsumexp(log["logv"][None, :] - M / epsilon, axis=1) + + v_final = fi * (logb - logKtu) + u_final = fi * (loga - logKv) np.testing.assert_allclose( - u_final, log["u"], atol=1e-05) + u_final, log["logu"], atol=1e-05) np.testing.assert_allclose( - v_final, log["v"], atol=1e-05) + v_final, log["logv"], atol=1e-05) # check if sinkhorn_unbalanced2 returns the correct loss np.testing.assert_allclose((G * M).sum(), loss, atol=1e-5) -@pytest.mark.parametrize("method", ["sinkhorn"]) +@pytest.mark.parametrize("method", ["sinkhorn", "sinkhorn_stabilized"]) def test_unbalanced_multiple_inputs(method): # test generalized sinkhorn for unbalanced OT n = 100 @@ -59,27 +66,55 @@ def test_unbalanced_multiple_inputs(method): M = ot.dist(x, x) epsilon = 1. - alpha = 1. - K = np.exp(- M / epsilon) + mu = 1. - loss, log = ot.unbalanced.sinkhorn_unbalanced(a, b, M, reg=epsilon, - alpha=alpha, + loss, log = ot.unbalanced.sinkhorn_unbalanced(a, b, M, reg=epsilon, mu=mu, stopThr=1e-10, method=method, log=True) # check fixed point equations - fi = alpha / (alpha + epsilon) - v_final = (b / K.T.dot(log["u"])) ** fi - - u_final = (a[:, None] / K.dot(log["v"])) ** fi + # in log-domain + fi = mu / (mu + epsilon) + logb = np.log(b + 1e-16) + loga = np.log(a + 1e-16)[:, None] + logKtu = logsumexp(log["logu"][:, None, :] - M[:, :, None] / epsilon, + axis=0) + logKv = logsumexp(log["logv"][None, :] - M[:, :, None] / epsilon, axis=1) + v_final = fi * (logb - logKtu) + u_final = fi * (loga - logKv) np.testing.assert_allclose( - u_final, log["u"], atol=1e-05) + u_final, log["logu"], atol=1e-05) np.testing.assert_allclose( - v_final, log["v"], atol=1e-05) + v_final, log["logv"], atol=1e-05) assert len(loss) == b.shape[1] +def test_stabilized_vs_sinkhorn(): + # test if stable version matches sinkhorn + n = 100 + + # Gaussian distributions + a = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std + b1 = ot.datasets.make_1D_gauss(n, m=60, s=8) + b2 = ot.datasets.make_1D_gauss(n, m=30, s=4) + + # creating matrix A containing all distributions + b = np.vstack((b1, b2)).T + + M = ot.utils.dist0(n) + M /= np.median(M) + epsilon = 0.1 + mu = 1. + G, log = ot.unbalanced.sinkhorn_stabilized_unbalanced(a, b, M, reg=epsilon, + mu=mu, + log=True) + G2, log2 = ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, mu, + method="sinkhorn", log=True) + + np.testing.assert_allclose(G, G2) + + def test_unbalanced_barycenter(): # test generalized sinkhorn for unbalanced OT barycenter n = 100 @@ -92,27 +127,30 @@ def test_unbalanced_barycenter(): A = A * np.array([1, 2])[None, :] M = ot.dist(x, x) epsilon = 1. - alpha = 1. - K = np.exp(- M / epsilon) + mu = 1. - q, log = ot.unbalanced.barycenter_unbalanced(A, M, reg=epsilon, alpha=alpha, + q, log = ot.unbalanced.barycenter_unbalanced(A, M, reg=epsilon, mu=mu, stopThr=1e-10, log=True) # check fixed point equations - fi = alpha / (alpha + epsilon) - v_final = (q[:, None] / K.T.dot(log["u"])) ** fi - u_final = (A / K.dot(log["v"])) ** fi + fi = mu / (mu + epsilon) + logA = np.log(A + 1e-16) + logq = np.log(q + 1e-16)[:, None] + logKtu = logsumexp(log["logu"][:, None, :] - M[:, :, None] / epsilon, + axis=0) + logKv = logsumexp(log["logv"][None, :] - M[:, :, None] / epsilon, axis=1) + v_final = fi * (logq - logKtu) + u_final = fi * (logA - logKv) np.testing.assert_allclose( - u_final, log["u"], atol=1e-05) + u_final, log["logu"], atol=1e-05) np.testing.assert_allclose( - v_final, log["v"], atol=1e-05) + v_final, log["logv"], atol=1e-05) def test_implemented_methods(): - IMPLEMENTED_METHODS = ['sinkhorn'] - TO_BE_IMPLEMENTED_METHODS = ['sinkhorn_stabilized', - 'sinkhorn_epsilon_scaling'] + IMPLEMENTED_METHODS = ['sinkhorn', 'sinkhorn_stabilized'] + TO_BE_IMPLEMENTED_METHODS = ['sinkhorn_reg_scaling'] NOT_VALID_TOKENS = ['foo'] # test generalized sinkhorn for unbalanced OT barycenter n = 3 @@ -126,21 +164,21 @@ def test_implemented_methods(): M = ot.dist(x, x) epsilon = 1. - alpha = 1. + mu = 1. for method in IMPLEMENTED_METHODS: - ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, + ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, mu, method=method) - ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, alpha, + ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, mu, method=method) with pytest.warns(UserWarning, match='not implemented'): for method in set(TO_BE_IMPLEMENTED_METHODS): - ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, + ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, mu, method=method) - ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, alpha, + ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, mu, method=method) with pytest.raises(ValueError): for method in set(NOT_VALID_TOKENS): - ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, + ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, mu, method=method) - ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, alpha, + ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, mu, method=method) -- cgit v1.2.3 From 50a5a4111ada5e8c208da1acf731608930d0a278 Mon Sep 17 00:00:00 2001 From: Hicham Janati Date: Mon, 22 Jul 2019 15:28:59 +0200 Subject: fix doctest examples --- ot/unbalanced.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/ot/unbalanced.py b/ot/unbalanced.py index ca24e8b..1453b31 100644 --- a/ot/unbalanced.py +++ b/ot/unbalanced.py @@ -77,8 +77,8 @@ def sinkhorn_unbalanced(a, b, M, reg, mu, method='sinkhorn', numItermax=1000, >>> b=[.5, .5] >>> M=[[0., 1.], [1., 0.]] >>> ot.sinkhorn_unbalanced(a, b, M, 1, 1) - array([[0.51122823, 0.18807035], - [0.18807035, 0.51122823]]) + array([[0.51122818, 0.18807034], + [0.18807034, 0.51122818]]) References @@ -193,7 +193,7 @@ def sinkhorn_unbalanced2(a, b, M, reg, mu, method='sinkhorn', >>> b=[.5, .5] >>> M=[[0., 1.],[1., 0.]] >>> ot.unbalanced.sinkhorn_unbalanced2(a, b, M, 1., 1.) - array([0.31912866]) + array([0.31912862]) @@ -308,8 +308,8 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, mu, numItermax=1000, >>> b=[.5, .5] >>> M=[[0., 1.],[1., 0.]] >>> ot.unbalanced.sinkhorn_knopp_unbalanced(a, b, M, 1., 1.) - array([[0.51122823, 0.18807035], - [0.18807035, 0.51122823]]) + array([[0.51122818, 0.18807034], + [0.18807034, 0.51122818]]) References ---------- @@ -479,8 +479,8 @@ def sinkhorn_stabilized_unbalanced(a, b, M, reg, mu, tau=1e5, numItermax=1000, >>> b=[.5, .5] >>> M=[[0., 1.],[1., 0.]] >>> ot.unbalanced.sinkhorn_stabilized_unbalanced(a, b, M, 1., 1.) - array([[0.51122823, 0.18807035], - [0.18807035, 0.51122823]]) + array([[0.51122818, 0.18807034], + [0.18807034, 0.51122818]]) References ---------- -- cgit v1.2.3 From 09f3f640fc46ba4905d5508b704f2e5a90dda295 Mon Sep 17 00:00:00 2001 From: Hicham Janati Date: Tue, 23 Jul 2019 21:28:30 +0200 Subject: fix issue 94 + add test --- ot/bregman.py | 10 +++++++--- test/test_bregman.py | 25 +++++++++++++++++++++++++ 2 files changed, 32 insertions(+), 3 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index f39145d..70e4208 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -765,10 +765,14 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, cpt = cpt + 1 - # print('err=',err,' cpt=',cpt) if log: - log['logu'] = alpha / reg + np.log(u) - log['logv'] = beta / reg + np.log(v) + if nbb: + alpha = alpha[:, None] + beta = beta[:, None] + logu = alpha / reg + np.log(u) + logv = beta / reg + np.log(v) + log['logu'] = logu + log['logv'] = logv log['alpha'] = alpha + reg * np.log(u) log['beta'] = beta + reg * np.log(v) log['warmstart'] = (log['alpha'], log['beta']) diff --git a/test/test_bregman.py b/test/test_bregman.py index 7f4972c..83ebba8 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -254,3 +254,28 @@ def test_empirical_sinkhorn_divergence(): emp_sinkhorn_div, sinkhorn_div, atol=1e-05) # cf conv emp sinkhorn np.testing.assert_allclose( emp_sinkhorn_div_log, sink_div_log, atol=1e-05) # cf conv emp sinkhorn + + +def test_stabilized_vs_sinkhorn_multidim(): + # test if stable version matches sinkhorn + # for multidimensional inputs + n = 100 + + # Gaussian distributions + a = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std + b1 = ot.datasets.make_1D_gauss(n, m=60, s=8) + b2 = ot.datasets.make_1D_gauss(n, m=30, s=4) + + # creating matrix A containing all distributions + b = np.vstack((b1, b2)).T + + M = ot.utils.dist0(n) + M /= np.median(M) + epsilon = 0.1 + G, log = ot.bregman.sinkhorn(a, b, M, reg=epsilon, + method="sinkhorn_stabilized", + log=True) + G2, log2 = ot.bregman.sinkhorn(a, b, M, epsilon, + method="sinkhorn", log=True) + + np.testing.assert_allclose(G, G2) -- cgit v1.2.3 From a725f1dc0ac63ac919461ab8f2a23b111a410c00 Mon Sep 17 00:00:00 2001 From: Hicham Janati Date: Tue, 23 Jul 2019 21:51:10 +0200 Subject: rebase unbalanced --- ot/unbalanced.py | 291 ++++++++----------------------------------------------- 1 file changed, 39 insertions(+), 252 deletions(-) diff --git a/ot/unbalanced.py b/ot/unbalanced.py index 14e9e36..467fda2 100644 --- a/ot/unbalanced.py +++ b/ot/unbalanced.py @@ -9,12 +9,10 @@ Regularized Unbalanced OT from __future__ import division import warnings import numpy as np -from scipy.misc import logsumexp - # from .utils import unif, dist -def sinkhorn_unbalanced(a, b, M, reg, mu, method='sinkhorn', numItermax=1000, +def sinkhorn_unbalanced(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, stopThr=1e-9, verbose=False, log=False, **kwargs): r""" Solve the unbalanced entropic regularization optimal transport problem and return the loss @@ -22,7 +20,7 @@ def sinkhorn_unbalanced(a, b, M, reg, mu, method='sinkhorn', numItermax=1000, The function solves the following optimization problem: .. math:: - W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \\mu KL(\gamma 1, a) + \\mu KL(\gamma^T 1, b) + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \\alpha KL(\gamma 1, a) + \\alpha KL(\gamma^T 1, b) s.t. \gamma\geq 0 @@ -47,11 +45,11 @@ def sinkhorn_unbalanced(a, b, M, reg, mu, method='sinkhorn', numItermax=1000, loss matrix reg : float Entropy regularization term > 0 - mu : float + alpha : float Marginal relaxation term > 0 method : str method used for the solver either 'sinkhorn', 'sinkhorn_stabilized' or - 'sinkhorn_reg_scaling', see those function for specific parameters + 'sinkhorn_epsilon_scaling', see those function for specific parameters numItermax : int, optional Max number of iterations stopThr : float, optional @@ -77,8 +75,8 @@ def sinkhorn_unbalanced(a, b, M, reg, mu, method='sinkhorn', numItermax=1000, >>> b=[.5, .5] >>> M=[[0., 1.], [1., 0.]] >>> ot.sinkhorn_unbalanced(a, b, M, 1, 1) - array([[0.51122818, 0.18807034], - [0.18807034, 0.51122818]]) + array([[0.51122823, 0.18807035], + [0.18807035, 0.51122823]]) References @@ -97,29 +95,22 @@ def sinkhorn_unbalanced(a, b, M, reg, mu, method='sinkhorn', numItermax=1000, -------- ot.unbalanced.sinkhorn_knopp_unbalanced : Unbalanced Classic Sinkhorn [10] ot.unbalanced.sinkhorn_stabilized_unbalanced: Unbalanced Stabilized sinkhorn [9][10] - ot.unbalanced.sinkhorn_reg_scaling_unbalanced: Unbalanced Sinkhorn with epslilon scaling [9][10] + ot.unbalanced.sinkhorn_epsilon_scaling_unbalanced: Unbalanced Sinkhorn with epslilon scaling [9][10] """ if method.lower() == 'sinkhorn': def sink(): - return sinkhorn_knopp_unbalanced(a, b, M, reg, mu, + return sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=numItermax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) - elif method.lower() == 'sinkhorn_stabilized': - def sink(): - return sinkhorn_stabilized_unbalanced(a, b, M, reg, mu, - numItermax=numItermax, - stopThr=stopThr, - verbose=verbose, - log=log, **kwargs) - elif method.lower() in ['sinkhorn_reg_scaling']: + elif method.lower() in ['sinkhorn_stabilized', 'sinkhorn_epsilon_scaling']: warnings.warn('Method not implemented yet. Using classic Sinkhorn Knopp') def sink(): - return sinkhorn_knopp_unbalanced(a, b, M, reg, mu, + return sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=numItermax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) @@ -129,7 +120,7 @@ def sinkhorn_unbalanced(a, b, M, reg, mu, method='sinkhorn', numItermax=1000, return sink() -def sinkhorn_unbalanced2(a, b, M, reg, mu, method='sinkhorn', +def sinkhorn_unbalanced2(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, stopThr=1e-9, verbose=False, log=False, **kwargs): r""" @@ -138,7 +129,7 @@ def sinkhorn_unbalanced2(a, b, M, reg, mu, method='sinkhorn', The function solves the following optimization problem: .. math:: - W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \\mu KL(\gamma 1, a) + \\mu KL(\gamma^T 1, b) + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \\alpha KL(\gamma 1, a) + \\alpha KL(\gamma^T 1, b) s.t. \gamma\geq 0 @@ -163,11 +154,11 @@ def sinkhorn_unbalanced2(a, b, M, reg, mu, method='sinkhorn', loss matrix reg : float Entropy regularization term > 0 - mu : float + alpha : float Marginal relaxation term > 0 method : str method used for the solver either 'sinkhorn', 'sinkhorn_stabilized' or - 'sinkhorn_reg_scaling', see those function for specific parameters + 'sinkhorn_epsilon_scaling', see those function for specific parameters numItermax : int, optional Max number of iterations stopThr : float, optional @@ -193,7 +184,7 @@ def sinkhorn_unbalanced2(a, b, M, reg, mu, method='sinkhorn', >>> b=[.5, .5] >>> M=[[0., 1.],[1., 0.]] >>> ot.unbalanced.sinkhorn_unbalanced2(a, b, M, 1., 1.) - array([0.31912862]) + array([0.31912866]) @@ -212,29 +203,22 @@ def sinkhorn_unbalanced2(a, b, M, reg, mu, method='sinkhorn', -------- ot.unbalanced.sinkhorn_knopp : Unbalanced Classic Sinkhorn [10] ot.unbalanced.sinkhorn_stabilized: Unbalanced Stabilized sinkhorn [9][10] - ot.unbalanced.sinkhorn_reg_scaling: Unbalanced Sinkhorn with epslilon scaling [9][10] + ot.unbalanced.sinkhorn_epsilon_scaling: Unbalanced Sinkhorn with epslilon scaling [9][10] """ if method.lower() == 'sinkhorn': def sink(): - return sinkhorn_knopp_unbalanced(a, b, M, reg, mu, + return sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=numItermax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) - elif method.lower() == 'sinkhorn_stabilized': - def sink(): - return sinkhorn_stabilized_unbalanced(a, b, M, reg, mu, - numItermax=numItermax, - stopThr=stopThr, - verbose=verbose, - log=log, **kwargs) - elif method.lower() in ['sinkhorn_reg_scaling']: + elif method.lower() in ['sinkhorn_stabilized', 'sinkhorn_epsilon_scaling']: warnings.warn('Method not implemented yet. Using classic Sinkhorn Knopp') def sink(): - return sinkhorn_knopp_unbalanced(a, b, M, reg, mu, + return sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=numItermax, stopThr=stopThr, verbose=verbose, log=log, **kwargs) @@ -248,7 +232,7 @@ def sinkhorn_unbalanced2(a, b, M, reg, mu, method='sinkhorn', return sink() -def sinkhorn_knopp_unbalanced(a, b, M, reg, mu, numItermax=1000, +def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, stopThr=1e-9, verbose=False, log=False, **kwargs): r""" Solve the entropic regularization unbalanced optimal transport problem and return the loss @@ -256,7 +240,7 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, mu, numItermax=1000, The function solves the following optimization problem: .. math:: - W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \\mu KL(\gamma 1, a) + \\mu KL(\gamma^T 1, b) + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \\alpha KL(\gamma 1, a) + \\alpha KL(\gamma^T 1, b) s.t. \gamma\geq 0 @@ -281,7 +265,7 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, mu, numItermax=1000, loss matrix reg : float Entropy regularization term > 0 - mu : float + alpha : float Marginal relaxation term > 0 numItermax : int, optional Max number of iterations @@ -308,8 +292,8 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, mu, numItermax=1000, >>> b=[.5, .5] >>> M=[[0., 1.],[1., 0.]] >>> ot.unbalanced.sinkhorn_knopp_unbalanced(a, b, M, 1., 1.) - array([[0.51122818, 0.18807034], - [0.18807034, 0.51122818]]) + array([[0.51122823, 0.18807035], + [0.18807035, 0.51122823]]) References ---------- @@ -354,12 +338,14 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, mu, numItermax=1000, u = np.ones(n_a) / n_a v = np.ones(n_b) / n_b + # print(reg) # Next 3 lines equivalent to K= np.exp(-M/reg), but faster to compute K = np.empty(M.shape, dtype=M.dtype) np.divide(M, -reg, out=K) np.exp(K, out=K) - fi = mu / (mu + reg) + # print(np.min(K)) + fi = alpha / (alpha + reg) cpt = 0 err = 1. @@ -385,9 +371,8 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, mu, numItermax=1000, if cpt % 10 == 0: # we can speed up the process by checking for the error only all # the 10th iterations - err_u = abs(u - uprev).max() / max(abs(u).max(), abs(uprev).max(), 1.) - err_v = abs(v - vprev).max() / max(abs(v).max(), abs(vprev).max(), 1.) - err = 0.5 * (err_u + err_v) + err = np.sum((u - uprev)**2) / np.sum((u)**2) + \ + np.sum((v - vprev)**2) / np.sum((v)**2) if log: log['err'].append(err) if verbose: @@ -398,8 +383,8 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, mu, numItermax=1000, cpt += 1 if log: - log['logu'] = np.log(u + 1e-16) - log['logv'] = np.log(v + 1e-16) + log['u'] = u + log['v'] = v if n_hists: # return only loss res = np.einsum('ik,ij,jk,ij->k', u, K, v, M) @@ -416,204 +401,7 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, mu, numItermax=1000, return u[:, None] * K * v[None, :] -def sinkhorn_stabilized_unbalanced(a, b, M, reg, mu, tau=1e5, numItermax=1000, - stopThr=1e-9, verbose=False, log=False, - **kwargs): - r""" - Solve the entropic regularization unbalanced optimal transport problem and return the loss - - The function solves the following optimization problem using log-domain - stabilization as proposed in [10]: - - .. math:: - W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \\mu KL(\gamma 1, a) + \\mu KL(\gamma^T 1, b) - - s.t. - \gamma\geq 0 - where : - - - M is the (ns, nt) metric cost matrix - - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - a and b are source and target weights - - KL is the Kullback-Leibler divergence - - The algorithm used for solving the problem is the generalized Sinkhorn-Knopp matrix scaling algorithm as proposed in [10, 23]_ - - - Parameters - ---------- - a : np.ndarray (ns,) - samples weights in the source domain - b : np.ndarray (nt,) or np.ndarray (nt, n_hists) - samples in the target domain, compute sinkhorn with multiple targets - and fixed M if b is a matrix (return OT loss + dual variables in log) - M : np.ndarray (ns,nt) - loss matrix - reg : float - Entropy regularization term > 0 - mu : float - Marginal relaxation term > 0 - tau : float - thershold for max value in u or v for log scaling - numItermax : int, optional - Max number of iterations - stopThr : float, optional - Stop threshol on error (>0) - verbose : bool, optional - Print information along iterations - log : bool, optional - record log if True - - - Returns - ------- - gamma : (ns x nt) ndarray - Optimal transportation matrix for the given parameters - log : dict - log dictionary return only if log==True in parameters - - Examples - -------- - - >>> import ot - >>> a=[.5, .5] - >>> b=[.5, .5] - >>> M=[[0., 1.],[1., 0.]] - >>> ot.unbalanced.sinkhorn_stabilized_unbalanced(a, b, M, 1., 1.) - array([[0.51122818, 0.18807034], - [0.18807034, 0.51122818]]) - - References - ---------- - - .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. - - .. [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. : Learning with a Wasserstein Loss, Advances in Neural Information Processing Systems (NIPS) 2015 - - See Also - -------- - ot.lp.emd : Unregularized OT - ot.optim.cg : General regularized OT - - """ - - a = np.asarray(a, dtype=np.float64) - b = np.asarray(b, dtype=np.float64) - M = np.asarray(M, dtype=np.float64) - - n_a, n_b = M.shape - - if len(a) == 0: - a = np.ones(n_a, dtype=np.float64) / n_a - if len(b) == 0: - b = np.ones(n_b, dtype=np.float64) / n_b - - if len(b.shape) > 1: - n_hists = b.shape[1] - else: - n_hists = 0 - - if log: - log = {'err': []} - - # we assume that no distances are null except those of the diagonal of - # distances - if n_hists: - u = np.ones((n_a, n_hists)) / n_a - v = np.ones((n_b, n_hists)) / n_b - a = a.reshape(n_a, 1) - else: - u = np.ones(n_a) / n_a - v = np.ones(n_b) / n_b - - # print(reg) - # Next 3 lines equivalent to K= np.exp(-M/reg), but faster to compute - K = np.empty(M.shape, dtype=M.dtype) - np.divide(M, -reg, out=K) - np.exp(K, out=K) - - fi = mu / (mu + reg) - - cpt = 0 - err = 1. - alpha = np.zeros(n_a) - beta = np.zeros(n_b) - while (err > stopThr and cpt < numItermax): - uprev = u - vprev = v - - Kv = K.dot(v) - f_alpha = np.exp(- alpha / (reg + mu)) - f_beta = np.exp(- beta / (reg + mu)) - - if n_hists: - f_alpha = f_alpha[:, None] - f_beta = f_beta[:, None] - u = ((a / (Kv + 1e-16)) ** fi) * f_alpha - Ktu = K.T.dot(u) - v = ((b / (Ktu + 1e-16)) ** fi) * f_beta - if (u > tau).any() or (v > tau).any(): - if n_hists: - alpha = alpha + reg * np.log(np.max(u, 1)) - beta = beta + reg * np.log(np.max(v, 1)) - else: - alpha = alpha + reg * np.log(np.max(u)) - beta = beta + reg * np.log(np.max(v)) - K = np.exp((alpha[:, None] + beta[None, :] - - M) / reg) - v = np.ones_like(v) - Kv = K.dot(v) - - if (np.any(Ktu == 0.) - or np.any(np.isnan(u)) or np.any(np.isnan(v)) - or np.any(np.isinf(u)) or np.any(np.isinf(v))): - # we have reached the machine precision - # come back to previous solution and quit loop - warnings.warn('Numerical errors at iteration %d' % cpt) - u = uprev - v = vprev - break - if cpt % 10 == 0: - # we can speed up the process by checking for the error only all - # the 10th iterations - err = abs(u - uprev).max() / max(abs(u).max(), abs(uprev).max(), - 1.) - if log: - log['err'].append(err) - if verbose: - if cpt % 200 == 0: - print( - '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) - print('{:5d}|{:8e}|'.format(cpt, err)) - cpt = cpt + 1 - - if n_hists: - logu = alpha[:, None] / reg + np.log(u) - logv = beta[:, None] / reg + np.log(v) - else: - logu = alpha / reg + np.log(u) - logv = beta / reg + np.log(v) - if log: - log['logu'] = logu - log['logv'] = logv - if n_hists: # return only loss - res = logsumexp(np.log(M + 1e-100)[:, :, None] + logu[:, None, :] + - logv[None, :, :] - M[:, :, None] / reg, axis=(0, 1)) - res = np.exp(res) - if log: - return res, log - else: - return res - - else: # return OT matrix - ot_matrix = np.exp(logu[:, None] + logv[None, :] - M / reg) - if log: - return ot_matrix, log - else: - return ot_matrix - - -def barycenter_unbalanced(A, M, reg, mu, weights=None, numItermax=1000, +def barycenter_unbalanced(A, M, reg, alpha, weights=None, numItermax=1000, stopThr=1e-4, verbose=False, log=False): r"""Compute the entropic regularized unbalanced wasserstein barycenter of distributions A @@ -627,7 +415,7 @@ def barycenter_unbalanced(A, M, reg, mu, weights=None, numItermax=1000, - :math:`Wu_{reg}(\cdot,\cdot)` is the unbalanced entropic regularized Wasserstein distance (see ot.unbalanced.sinkhorn_unbalanced) - :math:`\mathbf{a}_i` are training distributions in the columns of matrix :math:`\mathbf{A}` - reg and :math:`\mathbf{M}` are respectively the regularization term and the cost matrix for OT - - mu is the marginal relaxation hyperparameter + - alpha is the marginal relaxation hyperparameter The algorithm used for solving the problem is the generalized Sinkhorn-Knopp matrix scaling algorithm as proposed in [10]_ Parameters @@ -638,7 +426,7 @@ def barycenter_unbalanced(A, M, reg, mu, weights=None, numItermax=1000, loss matrix for OT reg : float Entropy regularization term > 0 - mu : float + alpha : float Marginal relaxation term > 0 weights : np.ndarray (n,) Weights of each histogram a_i on the simplex (barycentric coodinates) @@ -679,7 +467,7 @@ def barycenter_unbalanced(A, M, reg, mu, weights=None, numItermax=1000, K = np.exp(- M / reg) - fi = mu / (mu + reg) + fi = alpha / (alpha + reg) v = np.ones((p, n_hists)) / p u = np.ones((p, 1)) / p @@ -711,9 +499,8 @@ def barycenter_unbalanced(A, M, reg, mu, weights=None, numItermax=1000, if cpt % 10 == 0: # we can speed up the process by checking for the error only all # the 10th iterations - err_u = abs(u - uprev).max() / max(abs(u).max(), abs(uprev).max(), 1.) - err_v = abs(v - vprev).max() / max(abs(v).max(), abs(vprev).max(), 1.) - err = 0.5 * (err_u + err_v) + err = np.sum((u - uprev) ** 2) / np.sum((u) ** 2) + \ + np.sum((v - vprev) ** 2) / np.sum((v) ** 2) if log: log['err'].append(err) if verbose: @@ -725,8 +512,8 @@ def barycenter_unbalanced(A, M, reg, mu, weights=None, numItermax=1000, cpt += 1 if log: log['niter'] = cpt - log['logu'] = np.log(u + 1e-16) - log['logv'] = np.log(v + 1e-16) + log['u'] = u + log['v'] = v return q, log else: return q -- cgit v1.2.3 From a507556b1901e16351c211e69b38d8d74ac2bc3d Mon Sep 17 00:00:00 2001 From: Hicham Janati Date: Tue, 23 Jul 2019 21:51:53 +0200 Subject: rebase unbalanced --- test/test_unbalanced.py | 116 ++++++++++++++++-------------------------------- 1 file changed, 39 insertions(+), 77 deletions(-) diff --git a/test/test_unbalanced.py b/test/test_unbalanced.py index fc7aa5e..1395fe1 100644 --- a/test/test_unbalanced.py +++ b/test/test_unbalanced.py @@ -8,10 +8,8 @@ import numpy as np import ot import pytest -from scipy.misc import logsumexp - -@pytest.mark.parametrize("method", ["sinkhorn", "sinkhorn_stabilized"]) +@pytest.mark.parametrize("method", ["sinkhorn"]) def test_unbalanced_convergence(method): # test generalized sinkhorn for unbalanced OT n = 100 @@ -25,34 +23,29 @@ def test_unbalanced_convergence(method): M = ot.dist(x, x) epsilon = 1. - mu = 1. + alpha = 1. + K = np.exp(- M / epsilon) - G, log = ot.unbalanced.sinkhorn_unbalanced(a, b, M, reg=epsilon, mu=mu, + G, log = ot.unbalanced.sinkhorn_unbalanced(a, b, M, reg=epsilon, alpha=alpha, stopThr=1e-10, method=method, log=True) - loss = ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, mu, + loss = ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, alpha, method=method) # check fixed point equations - # in log-domain - fi = mu / (mu + epsilon) - logb = np.log(b + 1e-16) - loga = np.log(a + 1e-16) - logKtu = logsumexp(log["logu"][None, :] - M.T / epsilon, axis=1) - logKv = logsumexp(log["logv"][None, :] - M / epsilon, axis=1) - - v_final = fi * (logb - logKtu) - u_final = fi * (loga - logKv) + fi = alpha / (alpha + epsilon) + v_final = (b / K.T.dot(log["u"])) ** fi + u_final = (a / K.dot(log["v"])) ** fi np.testing.assert_allclose( - u_final, log["logu"], atol=1e-05) + u_final, log["u"], atol=1e-05) np.testing.assert_allclose( - v_final, log["logv"], atol=1e-05) + v_final, log["v"], atol=1e-05) # check if sinkhorn_unbalanced2 returns the correct loss np.testing.assert_allclose((G * M).sum(), loss, atol=1e-5) -@pytest.mark.parametrize("method", ["sinkhorn", "sinkhorn_stabilized"]) +@pytest.mark.parametrize("method", ["sinkhorn"]) def test_unbalanced_multiple_inputs(method): # test generalized sinkhorn for unbalanced OT n = 100 @@ -66,55 +59,27 @@ def test_unbalanced_multiple_inputs(method): M = ot.dist(x, x) epsilon = 1. - mu = 1. + alpha = 1. + K = np.exp(- M / epsilon) - loss, log = ot.unbalanced.sinkhorn_unbalanced(a, b, M, reg=epsilon, mu=mu, + loss, log = ot.unbalanced.sinkhorn_unbalanced(a, b, M, reg=epsilon, + alpha=alpha, stopThr=1e-10, method=method, log=True) # check fixed point equations - # in log-domain - fi = mu / (mu + epsilon) - logb = np.log(b + 1e-16) - loga = np.log(a + 1e-16)[:, None] - logKtu = logsumexp(log["logu"][:, None, :] - M[:, :, None] / epsilon, - axis=0) - logKv = logsumexp(log["logv"][None, :] - M[:, :, None] / epsilon, axis=1) - v_final = fi * (logb - logKtu) - u_final = fi * (loga - logKv) + fi = alpha / (alpha + epsilon) + v_final = (b / K.T.dot(log["u"])) ** fi + + u_final = (a[:, None] / K.dot(log["v"])) ** fi np.testing.assert_allclose( - u_final, log["logu"], atol=1e-05) + u_final, log["u"], atol=1e-05) np.testing.assert_allclose( - v_final, log["logv"], atol=1e-05) + v_final, log["v"], atol=1e-05) assert len(loss) == b.shape[1] -def test_stabilized_vs_sinkhorn(): - # test if stable version matches sinkhorn - n = 100 - - # Gaussian distributions - a = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std - b1 = ot.datasets.make_1D_gauss(n, m=60, s=8) - b2 = ot.datasets.make_1D_gauss(n, m=30, s=4) - - # creating matrix A containing all distributions - b = np.vstack((b1, b2)).T - - M = ot.utils.dist0(n) - M /= np.median(M) - epsilon = 0.1 - mu = 1. - G, log = ot.unbalanced.sinkhorn_stabilized_unbalanced(a, b, M, reg=epsilon, - mu=mu, - log=True) - G2, log2 = ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, mu, - method="sinkhorn", log=True) - - np.testing.assert_allclose(G, G2) - - def test_unbalanced_barycenter(): # test generalized sinkhorn for unbalanced OT barycenter n = 100 @@ -127,30 +92,27 @@ def test_unbalanced_barycenter(): A = A * np.array([1, 2])[None, :] M = ot.dist(x, x) epsilon = 1. - mu = 1. + alpha = 1. + K = np.exp(- M / epsilon) - q, log = ot.unbalanced.barycenter_unbalanced(A, M, reg=epsilon, mu=mu, + q, log = ot.unbalanced.barycenter_unbalanced(A, M, reg=epsilon, alpha=alpha, stopThr=1e-10, log=True) # check fixed point equations - fi = mu / (mu + epsilon) - logA = np.log(A + 1e-16) - logq = np.log(q + 1e-16)[:, None] - logKtu = logsumexp(log["logu"][:, None, :] - M[:, :, None] / epsilon, - axis=0) - logKv = logsumexp(log["logv"][None, :] - M[:, :, None] / epsilon, axis=1) - v_final = fi * (logq - logKtu) - u_final = fi * (logA - logKv) + fi = alpha / (alpha + epsilon) + v_final = (q[:, None] / K.T.dot(log["u"])) ** fi + u_final = (A / K.dot(log["v"])) ** fi np.testing.assert_allclose( - u_final, log["logu"], atol=1e-05) + u_final, log["u"], atol=1e-05) np.testing.assert_allclose( - v_final, log["logv"], atol=1e-05) + v_final, log["v"], atol=1e-05) def test_implemented_methods(): - IMPLEMENTED_METHODS = ['sinkhorn', 'sinkhorn_stabilized'] - TO_BE_IMPLEMENTED_METHODS = ['sinkhorn_reg_scaling'] + IMPLEMENTED_METHODS = ['sinkhorn'] + TO_BE_IMPLEMENTED_METHODS = ['sinkhorn_stabilized', + 'sinkhorn_epsilon_scaling'] NOT_VALID_TOKENS = ['foo'] # test generalized sinkhorn for unbalanced OT barycenter n = 3 @@ -164,21 +126,21 @@ def test_implemented_methods(): M = ot.dist(x, x) epsilon = 1. - mu = 1. + alpha = 1. for method in IMPLEMENTED_METHODS: - ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, mu, + ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, method=method) - ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, mu, + ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, alpha, method=method) with pytest.warns(UserWarning, match='not implemented'): for method in set(TO_BE_IMPLEMENTED_METHODS): - ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, mu, + ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, method=method) - ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, mu, + ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, alpha, method=method) with pytest.raises(ValueError): for method in set(NOT_VALID_TOKENS): - ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, mu, + ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, method=method) - ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, mu, + ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, alpha, method=method) -- cgit v1.2.3 From 4709849bcc624d5e5c33755b997c56998f43d3e9 Mon Sep 17 00:00:00 2001 From: Kilian Fatras Date: Wed, 24 Jul 2019 18:46:44 +0200 Subject: corrected typos in barycenter examples --- examples/plot_barycenter_lp_vs_entropic.py | 7 ++++++- examples/plot_free_support_barycenter.py | 2 +- 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/examples/plot_barycenter_lp_vs_entropic.py b/examples/plot_barycenter_lp_vs_entropic.py index b82765e..d7c72d0 100644 --- a/examples/plot_barycenter_lp_vs_entropic.py +++ b/examples/plot_barycenter_lp_vs_entropic.py @@ -102,7 +102,7 @@ pl.tight_layout() problems.append([A, [bary_l2, bary_wass, bary_wass2]]) ############################################################################## -# Dirac Data +# Stair Data # ---------- #%% parameters @@ -168,6 +168,11 @@ pl.legend() pl.title('Barycenters') pl.tight_layout() + +############################################################################## +# Dirac Data +# ---------- + #%% parameters a1 = np.zeros(n) diff --git a/examples/plot_free_support_barycenter.py b/examples/plot_free_support_barycenter.py index b6efc59..64b89e4 100644 --- a/examples/plot_free_support_barycenter.py +++ b/examples/plot_free_support_barycenter.py @@ -62,7 +62,7 @@ X = ot.lp.free_support_barycenter(measures_locations, measures_weights, X_init, pl.figure(1) for (x_i, b_i) in zip(measures_locations, measures_weights): color = np.random.randint(low=1, high=10 * N) - pl.scatter(x_i[:, 0], x_i[:, 1], s=b * 1000, label='input measure') + pl.scatter(x_i[:, 0], x_i[:, 1], s=b_i * 1000, label='input measure') pl.scatter(X[:, 0], X[:, 1], s=b * 1000, c='black', marker='^', label='2-Wasserstein barycenter') pl.title('Data measures and their barycenter') pl.legend(loc=0) -- cgit v1.2.3 From 092866815cf906012f9194b87af1e7ae0270f7e7 Mon Sep 17 00:00:00 2001 From: ngayraud Date: Mon, 12 Aug 2019 15:49:25 -0400 Subject: Added Unbalaced transport to domain adaptation methods. Corrected small bug related to warnings in unbalaced.py . Added an error message when user wants to normalize with other than expected cost normalization functions. --- ot/da.py | 121 +++++++++++++++++++++++++++++++++++++++++++++++++++++++ ot/unbalanced.py | 2 +- ot/utils.py | 5 ++- 3 files changed, 126 insertions(+), 2 deletions(-) diff --git a/ot/da.py b/ot/da.py index 83f9027..c1d9849 100644 --- a/ot/da.py +++ b/ot/da.py @@ -6,6 +6,7 @@ Domain adaptation with optimal transport # Author: Remi Flamary # Nicolas Courty # Michael Perrot +# Nathalie Gayraud # # License: MIT License @@ -16,6 +17,7 @@ from .bregman import sinkhorn from .lp import emd from .utils import unif, dist, kernel, cost_normalization from .utils import check_params, BaseEstimator +from .unbalanced import sinkhorn_unbalanced from .optim import cg from .optim import gcg @@ -1793,3 +1795,122 @@ class MappingTransport(BaseEstimator): transp_Xs = K.dot(self.mapping_) return transp_Xs + + +class UnbalancedSinkhornTransport(BaseTransport): + + """Domain Adapatation unbalanced OT method based on sinkhorn algorithm + + Parameters + ---------- + reg_e : float, optional (default=1) + Entropic regularization parameter + reg_m : float, optional (default=0.1) + Mass regularization parameter + method : str + method used for the solver either 'sinkhorn', 'sinkhorn_stabilized' or + 'sinkhorn_epsilon_scaling', see those function for specific parameters + max_iter : int, float, optional (default=10) + The minimum number of iteration before stopping the optimization + algorithm if no it has not converged + tol : float, optional (default=10e-9) + Stop threshold on error (inner sinkhorn solver) (>0) + verbose : bool, optional (default=False) + Controls the verbosity of the optimization algorithm + log : bool, optional (default=False) + Controls the logs of the optimization algorithm + metric : string, optional (default="sqeuclidean") + The ground metric for the Wasserstein problem + norm : string, optional (default=None) + If given, normalize the ground metric to avoid numerical errors that + can occur with large metric values. + distribution_estimation : callable, optional (defaults to the uniform) + The kind of distribution estimation to employ + out_of_sample_map : string, optional (default="ferradans") + The kind of out of sample mapping to apply to transport samples + from a domain into another one. Currently the only possible option is + "ferradans" which uses the method proposed in [6]. + limit_max: float, optional (default=10) + Controls the semi supervised mode. Transport between labeled source + and target samples of different classes will exhibit an infinite cost + (10 times the maximum value of the cost matrix) + + Attributes + ---------- + coupling_ : array-like, shape (n_source_samples, n_target_samples) + The optimal coupling + log_ : dictionary + The dictionary of log, empty dic if parameter log is not True + + References + ---------- + + .. [1] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). + Scaling algorithms for unbalanced transport problems. arXiv preprint + arXiv:1607.05816. + + """ + + def __init__(self, reg_e=1., reg_m=0.1, method='sinkhorn', + max_iter=10, tol=10e-9, verbose=False, log=False, + metric="sqeuclidean", norm=None, + distribution_estimation=distribution_estimation_uniform, + out_of_sample_map='ferradans', limit_max=10): + + self.reg_e = reg_e + self.reg_m = reg_m + self.method = method + self.max_iter = max_iter + self.tol = tol + self.verbose = verbose + self.log = log + self.metric = metric + self.norm = norm + self.distribution_estimation = distribution_estimation + self.out_of_sample_map = out_of_sample_map + self.limit_max = limit_max + + def fit(self, Xs, ys=None, Xt=None, yt=None): + """Build a coupling matrix from source and target sets of samples + (Xs, ys) and (Xt, yt) + + Parameters + ---------- + Xs : array-like, shape (n_source_samples, n_features) + The training input samples. + ys : array-like, shape (n_source_samples,) + The class labels + Xt : array-like, shape (n_target_samples, n_features) + The training input samples. + yt : array-like, shape (n_target_samples,) + The class labels. If some target samples are unlabeled, fill the + yt's elements with -1. + + Warning: Note that, due to this convention -1 cannot be used as a + class label + + Returns + ------- + self : object + Returns self. + """ + + # check the necessary inputs parameters are here + if check_params(Xs=Xs, Xt=Xt): + + super(UnbalancedSinkhornTransport, self).fit(Xs, ys, Xt, yt) + + returned_ = sinkhorn_unbalanced( + a=self.mu_s, b=self.mu_t, M=self.cost_, + reg=self.reg_e, alpha=self.reg_m, method=self.method, + numItermax=self.max_iter, stopThr=self.tol, + verbose=self.verbose, log=self.log) + + # deal with the value of log + if self.log: + self.coupling_, self.log_ = returned_ + else: + self.coupling_ = returned_ + self.log_ = dict() + + return self diff --git a/ot/unbalanced.py b/ot/unbalanced.py index 467fda2..0f0692e 100644 --- a/ot/unbalanced.py +++ b/ot/unbalanced.py @@ -364,7 +364,7 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, or np.any(np.isinf(u)) or np.any(np.isinf(v))): # we have reached the machine precision # come back to previous solution and quit loop - warnings.warn('Numerical errors at iteration', cpt) + warnings.warn('Numerical errors at iteration %s' % cpt) u = uprev v = vprev break diff --git a/ot/utils.py b/ot/utils.py index 8419c83..be839f8 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -186,7 +186,10 @@ def cost_normalization(C, norm=None): C = np.log(1 + C) elif norm == "loglog": C = np.log1p(np.log1p(C)) - + else: + raise ValueError(f'Norm {norm} is not a valid option. ' + f'Valid options are:\n' + f'median, max, log, loglog') return C -- cgit v1.2.3 From 9d4b786a036ac95989825beec819521089fb4feb Mon Sep 17 00:00:00 2001 From: ngayraud Date: Mon, 12 Aug 2019 16:37:58 -0400 Subject: fixes for travis, added test, minor nits --- .travis.yml | 5 ++-- ot/da.py | 2 +- ot/utils.py | 4 +++- test/test_da.py | 73 +++++++++++++++++++++++++++++++++++++++++++++++++++++++++ 4 files changed, 80 insertions(+), 4 deletions(-) diff --git a/.travis.yml b/.travis.yml index 5e5694b..72fd29a 100644 --- a/.travis.yml +++ b/.travis.yml @@ -13,7 +13,7 @@ matrix: python: 3.5 - os: linux sudo: required - python: 3.6 + python: 3.6 - os: linux sudo: required python: 2.7 @@ -21,7 +21,6 @@ before_install: - ./.travis/before_install.sh before_script: # configure a headless display to test plot generation - "export DISPLAY=:99.0" - - "sh -e /etc/init.d/xvfb start" - sleep 3 # give xvfb some time to start # command to install dependencies install: @@ -30,6 +29,8 @@ install: - pip install flake8 pytest "pytest-cov<2.6" - pip install . # command to run tests + check syntax style +services: + - xvfb script: - python setup.py develop - flake8 examples/ ot/ test/ diff --git a/ot/da.py b/ot/da.py index c1d9849..2af855d 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1852,7 +1852,7 @@ class UnbalancedSinkhornTransport(BaseTransport): """ def __init__(self, reg_e=1., reg_m=0.1, method='sinkhorn', - max_iter=10, tol=10e-9, verbose=False, log=False, + max_iter=10, tol=1e-9, verbose=False, log=False, metric="sqeuclidean", norm=None, distribution_estimation=distribution_estimation_uniform, out_of_sample_map='ferradans', limit_max=10): diff --git a/ot/utils.py b/ot/utils.py index be839f8..a334fea 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -178,7 +178,9 @@ def cost_normalization(C, norm=None): The input cost matrix normalized according to given norm. """ - if norm == "median": + if norm is None: + pass + elif norm == "median": C /= float(np.median(C)) elif norm == "max": C /= float(np.max(C)) diff --git a/test/test_da.py b/test/test_da.py index f7f3a9d..9efd2d9 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -245,6 +245,79 @@ def test_sinkhorn_transport_class(): assert len(otda.log_.keys()) != 0 +def test_unbalanced_sinkhorn_transport_class(): + """test_sinkhorn_transport + """ + + ns = 150 + nt = 200 + + Xs, ys = make_data_classif('3gauss', ns) + Xt, yt = make_data_classif('3gauss2', nt) + + otda = ot.da.UnbalancedSinkhornTransport() + + # test its computed + otda.fit(Xs=Xs, Xt=Xt) + assert hasattr(otda, "cost_") + assert hasattr(otda, "coupling_") + assert hasattr(otda, "log_") + + # test dimensions of coupling + assert_equal(otda.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) + + # test margin constraints + mu_s = unif(ns) + mu_t = unif(nt) + assert_allclose( + np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) + assert_allclose( + np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) + + # test transform + transp_Xs = otda.transform(Xs=Xs) + assert_equal(transp_Xs.shape, Xs.shape) + + Xs_new, _ = make_data_classif('3gauss', ns + 1) + transp_Xs_new = otda.transform(Xs_new) + + # check that the oos method is working + assert_equal(transp_Xs_new.shape, Xs_new.shape) + + # test inverse transform + transp_Xt = otda.inverse_transform(Xt=Xt) + assert_equal(transp_Xt.shape, Xt.shape) + + Xt_new, _ = make_data_classif('3gauss2', nt + 1) + transp_Xt_new = otda.inverse_transform(Xt=Xt_new) + + # check that the oos method is working + assert_equal(transp_Xt_new.shape, Xt_new.shape) + + # test fit_transform + transp_Xs = otda.fit_transform(Xs=Xs, Xt=Xt) + assert_equal(transp_Xs.shape, Xs.shape) + + # test unsupervised vs semi-supervised mode + otda_unsup = ot.da.SinkhornTransport() + otda_unsup.fit(Xs=Xs, Xt=Xt) + n_unsup = np.sum(otda_unsup.cost_) + + otda_semi = ot.da.SinkhornTransport() + otda_semi.fit(Xs=Xs, ys=ys, Xt=Xt, yt=yt) + assert_equal(otda_semi.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) + n_semisup = np.sum(otda_semi.cost_) + + # check that the cost matrix norms are indeed different + assert n_unsup != n_semisup, "semisupervised mode not working" + + # check everything runs well with log=True + otda = ot.da.SinkhornTransport(log=True) + otda.fit(Xs=Xs, ys=ys, Xt=Xt) + assert len(otda.log_.keys()) != 0 + + def test_emd_transport_class(): """test_sinkhorn_transport """ -- cgit v1.2.3 From b536be73326e20fd3959ba4fe28cc45a344f47d3 Mon Sep 17 00:00:00 2001 From: ngayraud Date: Mon, 12 Aug 2019 16:51:51 -0400 Subject: Attempting to fix docstyle --- ot/utils.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/utils.py b/ot/utils.py index a334fea..b0d95f9 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -189,7 +189,7 @@ def cost_normalization(C, norm=None): elif norm == "loglog": C = np.log1p(np.log1p(C)) else: - raise ValueError(f'Norm {norm} is not a valid option. ' + raise ValueError(f'Norm {norm} is not a valid option.\n' f'Valid options are:\n' f'median, max, log, loglog') return C -- cgit v1.2.3 From 2633116175a09c468d953489c3fc7bab6aa69057 Mon Sep 17 00:00:00 2001 From: ngayraud Date: Mon, 12 Aug 2019 17:01:14 -0400 Subject: Attempting to fix docstyle --- ot/utils.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/ot/utils.py b/ot/utils.py index b0d95f9..d4127e3 100644 --- a/ot/utils.py +++ b/ot/utils.py @@ -189,9 +189,9 @@ def cost_normalization(C, norm=None): elif norm == "loglog": C = np.log1p(np.log1p(C)) else: - raise ValueError(f'Norm {norm} is not a valid option.\n' - f'Valid options are:\n' - f'median, max, log, loglog') + raise ValueError('Norm %s is not a valid option.\n' + 'Valid options are:\n' + 'median, max, log, loglog' % norm) return C -- cgit v1.2.3 From ce86d1476b32771d32b7e55566e7cab45bb57b3a Mon Sep 17 00:00:00 2001 From: ngayraud Date: Mon, 12 Aug 2019 17:03:08 -0400 Subject: Fix in test: no margin constraints here --- test/test_da.py | 8 -------- 1 file changed, 8 deletions(-) diff --git a/test/test_da.py b/test/test_da.py index 9efd2d9..2a5e50e 100644 --- a/test/test_da.py +++ b/test/test_da.py @@ -267,14 +267,6 @@ def test_unbalanced_sinkhorn_transport_class(): assert_equal(otda.cost_.shape, ((Xs.shape[0], Xt.shape[0]))) assert_equal(otda.coupling_.shape, ((Xs.shape[0], Xt.shape[0]))) - # test margin constraints - mu_s = unif(ns) - mu_t = unif(nt) - assert_allclose( - np.sum(otda.coupling_, axis=0), mu_t, rtol=1e-3, atol=1e-3) - assert_allclose( - np.sum(otda.coupling_, axis=1), mu_s, rtol=1e-3, atol=1e-3) - # test transform transp_Xs = otda.transform(Xs=Xs) assert_equal(transp_Xs.shape, Xs.shape) -- cgit v1.2.3 From cfdbbd21642c6082164b84db78c2ead07499a113 Mon Sep 17 00:00:00 2001 From: Hicham Janati Date: Fri, 19 Jul 2019 17:04:14 +0200 Subject: remove square in convergence check add unbalanced with stabilization add unbalanced tests with stabilization fix doctest examples add xvfb in travis remove explicit call xvfb in travis change alpha to reg_m minor flake8 remove redundant sink definitions + better doc and naming add stabilized unbalanced barycenter + add not converged warnings add test for stable barycenter add generic barycenter func + make method funcs private fix typo + add method test for barycenters fix doc examples + add xml to gitignore fix whitespace in example change logsumexp import - scipy deprecation warning fix doctest improve naming + add stable barycenter in bregman add test for stable bar + test the method arg in bregman --- .gitignore | 3 + ot/__init__.py | 18 +- ot/bregman.py | 530 +++++++++++++++++++++----------- ot/unbalanced.py | 803 +++++++++++++++++++++++++++++++++++++++--------- pytest.ini | 0 test/test_bregman.py | 72 ++++- test/test_unbalanced.py | 163 +++++++--- 7 files changed, 1205 insertions(+), 384 deletions(-) create mode 100644 pytest.ini diff --git a/.gitignore b/.gitignore index 42a9aad..dadf84c 100644 --- a/.gitignore +++ b/.gitignore @@ -59,6 +59,9 @@ coverage.xml *.mo *.pot +# xml +*.xml + # Django stuff: *.log local_settings.py diff --git a/ot/__init__.py b/ot/__init__.py index 35ae6fc..7d9615a 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -1,7 +1,7 @@ """ -This is the main module of the POT toolbox. It provides easy access to -a number of sub-modules and functions described below. +This is the main module of the POT toolbox. It provides easy access to +a number of sub-modules and functions described below. .. note:: @@ -14,27 +14,27 @@ a number of sub-modules and functions described below. - :any:`ot.lp` contains OT solvers for the exact (Linear Program) OT problems. - :any:`ot.smooth` contains OT solvers for the regularized (l2 and kl) smooth OT problems. - - :any:`ot.gromov` contains solvers for Gromov-Wasserstein and Fused Gromov + - :any:`ot.gromov` contains solvers for Gromov-Wasserstein and Fused Gromov Wasserstein problems. - - :any:`ot.optim` contains generic solvers OT based optimization problems + - :any:`ot.optim` contains generic solvers OT based optimization problems - :any:`ot.da` contains classes and function related to Monge mapping estimation and Domain Adaptation (DA). - :any:`ot.gpu` contains GPU (cupy) implementation of some OT solvers - - :any:`ot.dr` contains Dimension Reduction (DR) methods such as Wasserstein + - :any:`ot.dr` contains Dimension Reduction (DR) methods such as Wasserstein Discriminant Analysis. - - :any:`ot.utils` contains utility functions such as distance computation and - timing. + - :any:`ot.utils` contains utility functions such as distance computation and + timing. - :any:`ot.datasets` contains toy dataset generation functions. - :any:`ot.plot` contains visualization functions - :any:`ot.stochastic` contains stochastic solvers for regularized OT. - :any:`ot.unbalanced` contains solvers for regularized unbalanced OT. .. warning:: - The list of automatically imported sub-modules is as follows: + The list of automatically imported sub-modules is as follows: :py:mod:`ot.lp`, :py:mod:`ot.bregman`, :py:mod:`ot.optim` :py:mod:`ot.utils`, :py:mod:`ot.datasets`, :py:mod:`ot.gromov`, :py:mod:`ot.smooth` - :py:mod:`ot.stochastic` + :py:mod:`ot.stochastic` The following sub-modules are not imported due to additional dependencies: diff --git a/ot/bregman.py b/ot/bregman.py index 70e4208..2f27d58 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -7,10 +7,12 @@ Bregman projections for regularized OT # Nicolas Courty # Kilian Fatras # Titouan Vayer +# Hicham Janati # # License: MIT License import numpy as np +import warnings from .utils import unif, dist @@ -31,7 +33,7 @@ def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, \gamma\geq 0 where : - - M is the (ns,nt) metric cost matrix + - M is the (dim_a, n_b) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - a and b are source and target weights (sum to 1) @@ -40,12 +42,12 @@ def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, Parameters ---------- - a : ndarray, shape (ns,) + a : ndarray, shape (dim_a,) samples weights in the source domain - b : ndarray, shape (nt,) or ndarray, shape (nt, nbb) + b : ndarray, shape (dim_b,) or ndarray, shape (dim_b, n_hists) samples in the target domain, compute sinkhorn with multiple targets and fixed M if b is a matrix (return OT loss + dual variables in log) - M : ndarray, shape (ns, nt) + M : ndarray, shape (dim_a, n_b) loss matrix reg : float Regularization term >0 @@ -64,7 +66,7 @@ def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, Returns ------- - gamma : ndarray, shape (ns, nt) + gamma : ndarray, shape (dim_a, n_b) Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -103,30 +105,23 @@ def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, """ if method.lower() == 'sinkhorn': - def sink(): - return sinkhorn_knopp(a, b, M, reg, numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log, **kwargs) + return _sinkhorn_knopp(a, b, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log, + **kwargs) elif method.lower() == 'greenkhorn': - def sink(): - return greenkhorn(a, b, M, reg, numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log) + return _greenkhorn(a, b, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log) elif method.lower() == 'sinkhorn_stabilized': - def sink(): - return sinkhorn_stabilized(a, b, M, reg, numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log, **kwargs) + return _sinkhorn_stabilized(a, b, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) elif method.lower() == 'sinkhorn_epsilon_scaling': - def sink(): - return sinkhorn_epsilon_scaling( - a, b, M, reg, numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log, **kwargs) + return _sinkhorn_epsilon_scaling(a, b, M, reg, + numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) else: - print('Warning : unknown method using classic Sinkhorn Knopp') - - def sink(): - return sinkhorn_knopp(a, b, M, reg, numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log, **kwargs) - - return sink() + raise ValueError("Unknown method '%s'." % method) def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, @@ -146,7 +141,7 @@ def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, \gamma\geq 0 where : - - M is the (ns,nt) metric cost matrix + - M is the (dim_a, n_b) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - a and b are source and target weights (sum to 1) @@ -155,12 +150,12 @@ def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, Parameters ---------- - a : ndarray, shape (ns,) + a : ndarray, shape (dim_a,) samples weights in the source domain - b : ndarray, shape (nt,) or ndarray, shape (nt, nbb) + b : ndarray, shape (dim_b,) or ndarray, shape (dim_b, n_hists) samples in the target domain, compute sinkhorn with multiple targets and fixed M if b is a matrix (return OT loss + dual variables in log) - M : ndarray, shape (ns, nt) + M : ndarray, shape (dim_a, n_b) loss matrix reg : float Regularization term >0 @@ -218,35 +213,25 @@ def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, ot.bregman.sinkhorn_epsilon_scaling: Sinkhorn with epslilon scaling [9][10] """ - + b = np.asarray(b, dtype=np.float64) + if len(b.shape) < 2: + b = b[:, None] if method.lower() == 'sinkhorn': - def sink(): - return sinkhorn_knopp(a, b, M, reg, numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log, **kwargs) + return _sinkhorn_knopp(a, b, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log, **kwargs) elif method.lower() == 'sinkhorn_stabilized': - def sink(): - return sinkhorn_stabilized(a, b, M, reg, numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log, **kwargs) + return _sinkhorn_stabilized(a, b, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log, **kwargs) elif method.lower() == 'sinkhorn_epsilon_scaling': - def sink(): - return sinkhorn_epsilon_scaling( - a, b, M, reg, numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log, **kwargs) + return _sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) else: - print('Warning : unknown method using classic Sinkhorn Knopp') - - def sink(): - return sinkhorn_knopp(a, b, M, reg, **kwargs) + raise ValueError("Unknown method '%s'." % method) - b = np.asarray(b, dtype=np.float64) - if len(b.shape) < 2: - b = b[:, None] - return sink() - - -def sinkhorn_knopp(a, b, M, reg, numItermax=1000, - stopThr=1e-9, verbose=False, log=False, **kwargs): +def _sinkhorn_knopp(a, b, M, reg, numItermax=1000, + stopThr=1e-9, verbose=False, log=False, **kwargs): r""" Solve the entropic regularization optimal transport problem and return the OT matrix @@ -262,7 +247,7 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, \gamma\geq 0 where : - - M is the (ns,nt) metric cost matrix + - M is the (dim_a, n_b) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - a and b are source and target weights (sum to 1) @@ -271,12 +256,12 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, Parameters ---------- - a : ndarray, shape (ns,) + a : ndarray, shape (dim_a,) samples weights in the source domain - b : ndarray, shape (nt,) or ndarray, shape (nt, nbb) + b : ndarray, shape (dim_b,) or ndarray, shape (dim_b, n_hists) samples in the target domain, compute sinkhorn with multiple targets and fixed M if b is a matrix (return OT loss + dual variables in log) - M : ndarray, shape (ns, nt) + M : ndarray, shape (dim_a, n_b) loss matrix reg : float Regularization term >0 @@ -291,7 +276,7 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, Returns ------- - gamma : ndarray, shape (ns, nt) + gamma : ndarray, shape (dim_a, n_b) Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -331,25 +316,25 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] # init data - Nini = len(a) - Nfin = len(b) + dim_a = len(a) + dim_b = len(b) if len(b.shape) > 1: - nbb = b.shape[1] + n_hists = b.shape[1] else: - nbb = 0 + n_hists = 0 if log: log = {'err': []} # we assume that no distances are null except those of the diagonal of # distances - if nbb: - u = np.ones((Nini, nbb)) / Nini - v = np.ones((Nfin, nbb)) / Nfin + if n_hists: + u = np.ones((dim_a, n_hists)) / dim_a + v = np.ones((dim_b, n_hists)) / dim_b else: - u = np.ones(Nini) / Nini - v = np.ones(Nfin) / Nfin + u = np.ones(dim_a) / dim_a + v = np.ones(dim_b) / dim_b # print(reg) @@ -384,13 +369,12 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, if cpt % 10 == 0: # we can speed up the process by checking for the error only all # the 10th iterations - if nbb: - err = np.sum((u - uprev)**2) / np.sum((u)**2) + \ - np.sum((v - vprev)**2) / np.sum((v)**2) + if n_hists: + np.einsum('ik,ij,jk->jk', u, K, v, out=tmp2) else: # compute right marginal tmp2= (diag(u)Kdiag(v))^T1 np.einsum('i,ij,j->j', u, K, v, out=tmp2) - err = np.linalg.norm(tmp2 - b)**2 # violation of marginal + err = np.linalg.norm(tmp2 - b) # violation of marginal if log: log['err'].append(err) @@ -404,7 +388,7 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, log['u'] = u log['v'] = v - if nbb: # return only loss + if n_hists: # return only loss res = np.einsum('ik,ij,jk,ij->k', u, K, v, M) if log: return res, log @@ -419,7 +403,7 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, return u.reshape((-1, 1)) * K * v.reshape((1, -1)) -def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log=False): +def _greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log=False): r""" Solve the entropic regularization optimal transport problem and return the OT matrix @@ -443,7 +427,7 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log= \gamma\geq 0 where : - - M is the (ns,nt) metric cost matrix + - M is the (dim_a, n_b) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - a and b are source and target weights (sum to 1) @@ -451,12 +435,12 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log= Parameters ---------- - a : ndarray, shape (ns,) + a : ndarray, shape (dim_a,) samples weights in the source domain - b : ndarray, shape (nt,) or ndarray, shape (nt, nbb) + b : ndarray, shape (dim_b,) or ndarray, shape (dim_b, n_hists) samples in the target domain, compute sinkhorn with multiple targets and fixed M if b is a matrix (return OT loss + dual variables in log) - M : ndarray, shape (ns, nt) + M : ndarray, shape (dim_a, n_b) loss matrix reg : float Regularization term >0 @@ -469,7 +453,7 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log= Returns ------- - gamma : ndarray, shape (ns, nt) + gamma : ndarray, shape (dim_a, n_b) Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -481,7 +465,7 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log= >>> a=[.5, .5] >>> b=[.5, .5] >>> M=[[0., 1.], [1., 0.]] - >>> ot.bregman.greenkhorn(a, b, M, 1) + >>> ot.bregman._greenkhorn(a, b, M, 1) array([[0.36552929, 0.13447071], [0.13447071, 0.36552929]]) @@ -509,16 +493,16 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log= if len(b) == 0: b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] - n = a.shape[0] - m = b.shape[0] + dim_a = a.shape[0] + dim_b = b.shape[0] # Next 3 lines equivalent to K= np.exp(-M/reg), but faster to compute K = np.empty_like(M) np.divide(M, -reg, out=K) np.exp(K, out=K) - u = np.full(n, 1. / n) - v = np.full(m, 1. / m) + u = np.full(dim_a, 1. / dim_a) + v = np.full(dim_b, 1. / dim_b) G = u[:, np.newaxis] * K * v[np.newaxis, :] viol = G.sum(1) - a @@ -571,8 +555,9 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log= return G -def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, - warmstart=None, verbose=False, print_period=20, log=False, **kwargs): +def _sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, + warmstart=None, verbose=False, print_period=20, + log=False, **kwargs): r""" Solve the entropic regularization OT problem with log stabilization @@ -588,7 +573,7 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, \gamma\geq 0 where : - - M is the (ns,nt) metric cost matrix + - M is the (dim_a, n_b) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - a and b are source and target weights (sum to 1) @@ -599,11 +584,11 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, Parameters ---------- - a : ndarray, shape (ns,) + a : ndarray, shape (dim_a,) samples weights in the source domain - b : ndarray, shape (nt,) + b : ndarray, shape (dim_b,) samples in the target domain - M : ndarray, shape (ns, nt) + M : ndarray, shape (dim_a, n_b) loss matrix reg : float Regularization term >0 @@ -622,7 +607,7 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, Returns ------- - gamma : ndarray, shape (ns, nt) + gamma : ndarray, shape (dim_a, n_b) Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -634,7 +619,7 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, >>> a=[.5,.5] >>> b=[.5,.5] >>> M=[[0.,1.],[1.,0.]] - >>> ot.bregman.sinkhorn_stabilized(a,b,M,1) + >>> ot.bregman._sinkhorn_stabilized(a, b, M, 1) array([[0.36552929, 0.13447071], [0.13447071, 0.36552929]]) @@ -667,10 +652,10 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, # test if multiple target if len(b.shape) > 1: - nbb = b.shape[1] + n_hists = b.shape[1] a = a[:, np.newaxis] else: - nbb = 0 + n_hists = 0 # init data na = len(a) @@ -687,8 +672,8 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, else: alpha, beta = warmstart - if nbb: - u, v = np.ones((na, nbb)) / na, np.ones((nb, nbb)) / nb + if n_hists: + u, v = np.ones((na, n_hists)) / na, np.ones((nb, n_hists)) / nb else: u, v = np.ones(na) / na, np.ones(nb) / nb @@ -720,13 +705,13 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, # remove numerical problems and store them in K if np.abs(u).max() > tau or np.abs(v).max() > tau: - if nbb: + if n_hists: alpha, beta = alpha + reg * \ np.max(np.log(u), 1), beta + reg * np.max(np.log(v)) else: alpha, beta = alpha + reg * np.log(u), beta + reg * np.log(v) - if nbb: - u, v = np.ones((na, nbb)) / na, np.ones((nb, nbb)) / nb + if n_hists: + u, v = np.ones((na, n_hists)) / na, np.ones((nb, n_hists)) / nb else: u, v = np.ones(na) / na, np.ones(nb) / nb K = get_K(alpha, beta) @@ -734,12 +719,15 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, if cpt % print_period == 0: # we can speed up the process by checking for the error only all # the 10th iterations - if nbb: - err = np.sum((u - uprev)**2) / np.sum((u)**2) + \ - np.sum((v - vprev)**2) / np.sum((v)**2) + if n_hists: + err_u = abs(u - uprev).max() + err_u /= max(abs(u).max(), abs(uprev).max(), 1.) + err_v = abs(v - vprev).max() + err_v /= max(abs(v).max(), abs(vprev).max(), 1.) + err = 0.5 * (err_u + err_v) else: transp = get_Gamma(alpha, beta, u, v) - err = np.linalg.norm((np.sum(transp, axis=0) - b))**2 + err = np.linalg.norm((np.sum(transp, axis=0) - b)) if log: log['err'].append(err) @@ -766,7 +754,7 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, cpt = cpt + 1 if log: - if nbb: + if n_hists: alpha = alpha[:, None] beta = beta[:, None] logu = alpha / reg + np.log(u) @@ -776,26 +764,28 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, log['alpha'] = alpha + reg * np.log(u) log['beta'] = beta + reg * np.log(v) log['warmstart'] = (log['alpha'], log['beta']) - if nbb: - res = np.zeros((nbb)) - for i in range(nbb): + if n_hists: + res = np.zeros((n_hists)) + for i in range(n_hists): res[i] = np.sum(get_Gamma(alpha, beta, u[:, i], v[:, i]) * M) return res, log else: return get_Gamma(alpha, beta, u, v), log else: - if nbb: - res = np.zeros((nbb)) - for i in range(nbb): + if n_hists: + res = np.zeros((n_hists)) + for i in range(n_hists): res[i] = np.sum(get_Gamma(alpha, beta, u[:, i], v[:, i]) * M) return res else: return get_Gamma(alpha, beta, u, v) -def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInnerItermax=100, - tau=1e3, stopThr=1e-9, warmstart=None, verbose=False, print_period=10, log=False, **kwargs): +def _sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, + numInnerItermax=100, tau=1e3, stopThr=1e-9, + warmstart=None, verbose=False, print_period=10, + log=False, **kwargs): r""" Solve the entropic regularization optimal transport problem with log stabilization and epsilon scaling. @@ -812,7 +802,7 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInne \gamma\geq 0 where : - - M is the (ns,nt) metric cost matrix + - M is the (dim_a, n_b) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - a and b are source and target weights (sum to 1) @@ -823,18 +813,16 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInne Parameters ---------- - a : ndarray, shape (ns,) + a : ndarray, shape (dim_a,) samples weights in the source domain - b : ndarray, shape (nt,) + b : ndarray, shape (dim_b,) samples in the target domain - M : ndarray, shape (ns, nt) + M : ndarray, shape (dim_a, n_b) loss matrix reg : float Regularization term >0 tau : float thershold for max value in u or v for log scaling - tau : float - thershold for max value in u or v for log scaling warmstart : tuple of vectors if given then sarting values for alpha an beta log scalings numItermax : int, optional @@ -852,7 +840,7 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInne Returns ------- - gamma : ndarray, shape (ns, nt) + gamma : ndarray, shape (dim_a, n_b) Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -864,7 +852,7 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInne >>> a=[.5, .5] >>> b=[.5, .5] >>> M=[[0., 1.], [1., 0.]] - >>> ot.bregman.sinkhorn_epsilon_scaling(a, b, M, 1) + >>> ot.bregman._sinkhorn_epsilon_scaling(a, b, M, 1) array([[0.36552929, 0.13447071], [0.13447071, 0.36552929]]) @@ -893,8 +881,8 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInne b = np.ones((M.shape[1],), dtype=np.float64) / M.shape[1] # init data - na = len(a) - nb = len(b) + dim_a = len(a) + dim_b = len(b) # nrelative umerical precision with 64 bits numItermin = 35 @@ -907,14 +895,14 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInne # we assume that no distances are null except those of the diagonal of # distances if warmstart is None: - alpha, beta = np.zeros(na), np.zeros(nb) + alpha, beta = np.zeros(dim_a), np.zeros(dim_b) else: alpha, beta = warmstart def get_K(alpha, beta): """log space computation""" - return np.exp(-(M - alpha.reshape((na, 1)) - - beta.reshape((1, nb))) / reg) + return np.exp(-(M - alpha.reshape((dim_a, 1)) + - beta.reshape((1, dim_b))) / reg) # print(np.min(K)) def get_reg(n): # exponential decreasing @@ -927,7 +915,7 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, numInne regi = get_reg(cpt) - G, logi = sinkhorn_stabilized(a, b, M, regi, numItermax=numInnerItermax, stopThr=1e-9, warmstart=( + G, logi = _sinkhorn_stabilized(a, b, M, regi, numItermax=numInnerItermax, stopThr=1e-9, warmstart=( alpha, beta), verbose=False, print_period=20, tau=tau, log=True) alpha = logi['alpha'] @@ -986,8 +974,8 @@ def projC(gamma, q): return np.multiply(gamma, q / np.maximum(np.sum(gamma, axis=0), 1e-10)) -def barycenter(A, M, reg, weights=None, numItermax=1000, - stopThr=1e-4, verbose=False, log=False): +def barycenter(A, M, reg, weights=None, method="sinkhorn", numItermax=10000, + stopThr=1e-4, verbose=False, log=False, **kwargs): r"""Compute the entropic regularized wasserstein barycenter of distributions A The function solves the following optimization problem: @@ -1005,13 +993,15 @@ def barycenter(A, M, reg, weights=None, numItermax=1000, Parameters ---------- - A : ndarray, shape (d,n) - n training distributions a_i of size d - M : ndarray, shape (d,d) - loss matrix for OT + A : ndarray, shape (dim, n_hists) + n_hists training distributions a_i of size dim + M : ndarray, shape (dim, dim) + loss matrix for OT reg : float - Regularization term >0 - weights : ndarray, shape (n,) + Regularization term > 0 + method : str (optional) + method used for the solver either 'sinkhorn' or 'sinkhorn_stabilized' + weights : ndarray, shape (n_hists,) Weights of each histogram a_i on the simplex (barycentric coodinates) numItermax : int, optional Max number of iterations @@ -1025,7 +1015,7 @@ def barycenter(A, M, reg, weights=None, numItermax=1000, Returns ------- - a : (d,) ndarray + a : (dim,) ndarray Wasserstein barycenter log : dict log dictionary return only if log==True in parameters @@ -1036,8 +1026,70 @@ def barycenter(A, M, reg, weights=None, numItermax=1000, .. [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). Iterative Bregman projections for regularized transportation problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138. + """ + + if method.lower() == 'sinkhorn': + return _barycenter(A, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log, + **kwargs) + elif method.lower() == 'sinkhorn_stabilized': + return _barycenter_stabilized(A, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) + else: + raise ValueError("Unknown method '%s'." % method) + + +def _barycenter(A, M, reg, weights=None, numItermax=1000, + stopThr=1e-4, verbose=False, log=False): + r"""Compute the entropic regularized wasserstein barycenter of distributions A + + The function solves the following optimization problem: + + .. math:: + \mathbf{a} = arg\min_\mathbf{a} \sum_i W_{reg}(\mathbf{a},\mathbf{a}_i) + + where : + + - :math:`W_{reg}(\cdot,\cdot)` is the entropic regularized Wasserstein distance (see ot.bregman.sinkhorn) + - :math:`\mathbf{a}_i` are training distributions in the columns of matrix :math:`\mathbf{A}` + - reg and :math:`\mathbf{M}` are respectively the regularization term and the cost matrix for OT + + The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [3]_ + + Parameters + ---------- + A : ndarray, shape (dim, n_hists) + n_hists training distributions a_i of size dim + M : ndarray, shape (dim, dim) + loss matrix for OT + reg : float + Regularization term > 0 + weights : ndarray, shape (n_hists,) + Weights of each histogram a_i on the simplex (barycentric coodinates) + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + Returns + ------- + a : (dim,) ndarray + Wasserstein barycenter + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). Iterative Bregman projections for regularized transportation problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138. + """ if weights is None: @@ -1082,6 +1134,136 @@ def barycenter(A, M, reg, weights=None, numItermax=1000, return geometricBar(weights, UKv) +def _barycenter_stabilized(A, M, reg, tau=1e10, weights=None, numItermax=1000, + stopThr=1e-4, verbose=False, log=False): + r"""Compute the entropic regularized wasserstein barycenter of distributions A + with stabilization. + + The function solves the following optimization problem: + + .. math:: + \mathbf{a} = arg\min_\mathbf{a} \sum_i W_{reg}(\mathbf{a},\mathbf{a}_i) + + where : + + - :math:`W_{reg}(\cdot,\cdot)` is the entropic regularized Wasserstein distance (see ot.bregman.sinkhorn) + - :math:`\mathbf{a}_i` are training distributions in the columns of matrix :math:`\mathbf{A}` + - reg and :math:`\mathbf{M}` are respectively the regularization term and the cost matrix for OT + + The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [3]_ + + Parameters + ---------- + A : ndarray, shape (dim, n_hists) + n_hists training distributions a_i of size dim + M : ndarray, shape (dim, dim) + loss matrix for OT + reg : float + Regularization term > 0 + tau : float + thershold for max value in u or v for log scaling + weights : ndarray, shape (n_hists,) + Weights of each histogram a_i on the simplex (barycentric coodinates) + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + a : (dim,) ndarray + Wasserstein barycenter + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). Iterative Bregman projections for regularized transportation problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138. + + """ + + dim, n_hists = A.shape + if weights is None: + weights = np.ones(n_hists) / n_hists + else: + assert(len(weights) == A.shape[1]) + + if log: + log = {'err': []} + + u = np.ones((dim, n_hists)) / dim + v = np.ones((dim, n_hists)) / dim + + # print(reg) + # Next 3 lines equivalent to K= np.exp(-M/reg), but faster to compute + K = np.empty(M.shape, dtype=M.dtype) + np.divide(M, -reg, out=K) + np.exp(K, out=K) + + cpt = 0 + err = 1. + alpha = np.zeros(dim) + beta = np.zeros(dim) + q = np.ones(dim) / dim + while (err > stopThr and cpt < numItermax): + qprev = q + Kv = K.dot(v) + u = A / (Kv + 1e-16) + Ktu = K.T.dot(u) + q = geometricBar(weights, Ktu) + Q = q[:, None] + v = Q / (Ktu + 1e-16) + absorbing = False + if (u > tau).any() or (v > tau).any(): + absorbing = True + print("YEAH absorbing") + alpha = alpha + reg * np.log(np.max(u, 1)) + beta = beta + reg * np.log(np.max(v, 1)) + K = np.exp((alpha[:, None] + beta[None, :] - + M) / reg) + v = np.ones_like(v) + Kv = K.dot(v) + if (np.any(Ktu == 0.) + or np.any(np.isnan(u)) or np.any(np.isnan(v)) + or np.any(np.isinf(u)) or np.any(np.isinf(v))): + # we have reached the machine precision + # come back to previous solution and quit loop + warnings.warn('Numerical errors at iteration %s' % cpt) + q = qprev + break + if (cpt % 10 == 0 and not absorbing) or cpt == 0: + # we can speed up the process by checking for the error only all + # the 10th iterations + err = abs(u * Kv - A).max() + if log: + log['err'].append(err) + if verbose: + if cpt % 50 == 0: + print( + '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) + print('{:5d}|{:8e}|'.format(cpt, err)) + + cpt += 1 + if err > stopThr: + warnings.warn("Stabilized Unbalanced Sinkhorn did not converge." + + "Try a larger entropy `reg`" + + "Or a larger absorption threshold `tau`.") + if log: + log['niter'] = cpt + log['logu'] = np.log(u + 1e-16) + log['logv'] = np.log(v + 1e-16) + return q, log + else: + return q + + def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1e-9, stabThr=1e-30, verbose=False, log=False): r"""Compute the entropic regularized wasserstein barycenter of distributions A where A is a collection of 2D images. @@ -1101,16 +1283,16 @@ def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1 Parameters ---------- - A : ndarray, shape (n, w, h) - n distributions (2D images) of size w x h + A : ndarray, shape (n_hists, width, height) + n distributions (2D images) of size width x height reg : float Regularization term >0 - weights : ndarray, shape (n,) + weights : ndarray, shape (n_hists,) Weights of each image on the simplex (barycentric coodinates) numItermax : int, optional Max number of iterations stopThr : float, optional - Stop threshol on error (>0) + Stop threshol on error (> 0) stabThr : float, optional Stabilization threshold to avoid numerical precision issue verbose : bool, optional @@ -1120,7 +1302,7 @@ def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1 Returns ------- - a : ndarray, shape (w, h) + a : ndarray, shape (width, height) 2D Wasserstein barycenter log : dict log dictionary return only if log==True in parameters @@ -1214,15 +1396,15 @@ def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000, Parameters ---------- - a : ndarray, shape (d) + a : ndarray, shape (n_observed) observed distribution - D : ndarray, shape (d, n) + D : ndarray, shape (dim, dim) dictionary matrix - M : ndarray, shape (d, d) + M : ndarray, shape (dim, dim) loss matrix - M0 : ndarray, shape (n, n) + M0 : ndarray, shape (n_observed, n_observed) loss matrix - h0 : ndarray, shape (n,) + h0 : ndarray, shape (dim,) prior on h reg : float Regularization term >0 (Wasserstein data fitting) @@ -1242,7 +1424,7 @@ def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000, Returns ------- - a : ndarray, shape (d,) + a : ndarray, shape (dim,) Wasserstein barycenter log : dict log dictionary return only if log==True in parameters @@ -1315,22 +1497,22 @@ def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numI \gamma\geq 0 where : - - :math:`M` is the (ns,nt) metric cost matrix + - :math:`M` is the (dim_a, n_b) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - :math:`a` and :math:`b` are source and target weights (sum to 1) Parameters ---------- - X_s : ndarray, shape (ns, d) + X_s : ndarray, shape (dim_a, d) samples in the source domain - X_t : ndarray, shape (nt, d) + X_t : ndarray, shape (dim_b, d) samples in the target domain reg : float Regularization term >0 - a : ndarray, shape (ns,) + a : ndarray, shape (dim_a,) samples weights in the source domain - b : ndarray, shape (nt,) + b : ndarray, shape (dim_b,) samples weights in the target domain numItermax : int, optional Max number of iterations @@ -1344,7 +1526,7 @@ def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numI Returns ------- - gamma : ndarray, shape (ns, nt) + gamma : ndarray, shape (dim_a, n_b) Regularized optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -1352,11 +1534,11 @@ def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numI Examples -------- - >>> n_s = 2 - >>> n_t = 2 + >>> n_a = 2 + >>> n_b = 2 >>> reg = 0.1 - >>> X_s = np.reshape(np.arange(n_s), (n_s, 1)) - >>> X_t = np.reshape(np.arange(0, n_t), (n_t, 1)) + >>> X_s = np.reshape(np.arange(n_a), (dim_a, 1)) + >>> X_t = np.reshape(np.arange(0, n_b), (dim_b, 1)) >>> empirical_sinkhorn(X_s, X_t, reg, verbose=False) # doctest: +NORMALIZE_WHITESPACE array([[4.99977301e-01, 2.26989344e-05], [2.26989344e-05, 4.99977301e-01]]) @@ -1405,22 +1587,22 @@ def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', num \gamma\geq 0 where : - - :math:`M` is the (ns,nt) metric cost matrix + - :math:`M` is the (dim_a, n_b) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - :math:`a` and :math:`b` are source and target weights (sum to 1) Parameters ---------- - X_s : ndarray, shape (ns, d) + X_s : ndarray, shape (n_samples_a, dim) samples in the source domain - X_t : ndarray, shape (nt, d) + X_t : ndarray, shape (n_samples_b, d) samples in the target domain reg : float Regularization term >0 - a : ndarray, shape (ns,) + a : ndarray, shape (n_samples_a,) samples weights in the source domain - b : ndarray, shape (nt,) + b : ndarray, shape (n_samples_b,) samples weights in the target domain numItermax : int, optional Max number of iterations @@ -1434,7 +1616,7 @@ def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', num Returns ------- - gamma : ndarray, shape (ns, nt) + gamma : ndarray, shape (n_samples_a, n_samples_b) Regularized optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -1442,11 +1624,11 @@ def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', num Examples -------- - >>> n_s = 2 - >>> n_t = 2 + >>> n_a = 2 + >>> n_b = 2 >>> reg = 0.1 - >>> X_s = np.reshape(np.arange(n_s), (n_s, 1)) - >>> X_t = np.reshape(np.arange(0, n_t), (n_t, 1)) + >>> X_s = np.reshape(np.arange(n_samples_a), (n_samples_a, 1)) + >>> X_t = np.reshape(np.arange(0, n_samples_b), (n_samples_b, 1)) >>> empirical_sinkhorn2(X_s, X_t, reg, verbose=False) array([4.53978687e-05]) @@ -1513,22 +1695,22 @@ def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeucli \gamma_b\geq 0 where : - - :math:`M` (resp. :math:`M_a, M_b`) is the (ns,nt) metric cost matrix (resp (ns, ns) and (nt, nt)) + - :math:`M` (resp. :math:`M_a, M_b`) is the (dim_a, n_b) metric cost matrix (resp (dim_a, ns) and (dim_b, nt)) - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - :math:`a` and :math:`b` are source and target weights (sum to 1) Parameters ---------- - X_s : ndarray, shape (ns, d) + X_s : ndarray, shape (n_samples_a, dim) samples in the source domain - X_t : ndarray, shape (nt, d) + X_t : ndarray, shape (n_samples_b, dim) samples in the target domain reg : float Regularization term >0 - a : ndarray, shape (ns,) + a : ndarray, shape (n_samples_a,) samples weights in the source domain - b : ndarray, shape (nt,) + b : ndarray, shape (n_samples_b,) samples weights in the target domain numItermax : int, optional Max number of iterations @@ -1541,18 +1723,18 @@ def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeucli Returns ------- - gamma : ndarray, shape (ns, nt) + gamma : ndarray, shape (n_samples_a, n_samples_b) Regularized optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters Examples -------- - >>> n_s = 2 - >>> n_t = 4 + >>> n_a = 2 + >>> n_b = 4 >>> reg = 0.1 - >>> X_s = np.reshape(np.arange(n_s), (n_s, 1)) - >>> X_t = np.reshape(np.arange(0, n_t), (n_t, 1)) + >>> X_s = np.reshape(np.arange(n_samples_a), (n_samples_a, 1)) + >>> X_t = np.reshape(np.arange(0, n_samples_b), (n_samples_b, 1)) >>> empirical_sinkhorn_divergence(X_s, X_t, reg) # doctest: +ELLIPSIS array([1.499...]) diff --git a/ot/unbalanced.py b/ot/unbalanced.py index 0f0692e..3f71d28 100644 --- a/ot/unbalanced.py +++ b/ot/unbalanced.py @@ -9,51 +9,56 @@ Regularized Unbalanced OT from __future__ import division import warnings import numpy as np +from scipy.special import logsumexp + # from .utils import unif, dist -def sinkhorn_unbalanced(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, - stopThr=1e-9, verbose=False, log=False, **kwargs): +def sinkhorn_unbalanced(a, b, M, reg, reg_m, method='sinkhorn', numItermax=1000, + stopThr=1e-6, verbose=False, log=False, **kwargs): r""" - Solve the unbalanced entropic regularization optimal transport problem and return the loss + Solve the unbalanced entropic regularization optimal transport problem + and return the OT plan The function solves the following optimization problem: .. math:: - W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \\alpha KL(\gamma 1, a) + \\alpha KL(\gamma^T 1, b) + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + reg_m KL(\gamma 1, a) + reg_m KL(\gamma^T 1, b) s.t. \gamma\geq 0 where : - - M is the (ns, nt) metric cost matrix - - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - a and b are source and target weights + - M is the (dim_a, dim_b) metric cost matrix + - :math:`\Omega` is the entropic regularization + term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target unbalanced distributions - KL is the Kullback-Leibler divergence - The algorithm used for solving the problem is the generalized Sinkhorn-Knopp matrix scaling algorithm as proposed in [10, 23]_ + The algorithm used for solving the problem is the generalized + Sinkhorn-Knopp matrix scaling algorithm as proposed in [10, 23]_ Parameters ---------- - a : np.ndarray (ns,) - samples weights in the source domain - b : np.ndarray (nt,) or np.ndarray (nt,n_hists) - samples in the target domain, compute sinkhorn with multiple targets - and fixed M if b is a matrix (return OT loss + dual variables in log) - M : np.ndarray (ns, nt) + a : np.ndarray (dim_a,) + Unnormalized histogram of dimension dim_a + b : np.ndarray (dim_b,) or np.ndarray (dim_b, n_hists) + One or multiple unnormalized histograms of dimension dim_b + If many, compute all the OT distances (a, b_i) + M : np.ndarray (dim_a, dim_b) loss matrix reg : float Entropy regularization term > 0 - alpha : float + reg_m: float Marginal relaxation term > 0 method : str method used for the solver either 'sinkhorn', 'sinkhorn_stabilized' or - 'sinkhorn_epsilon_scaling', see those function for specific parameters + 'sinkhorn_reg_scaling', see those function for specific parameters numItermax : int, optional Max number of iterations stopThr : float, optional - Stop threshol on error (> 0) + Stop threshol on error (>0) verbose : bool, optional Print information along iterations log : bool, optional @@ -62,10 +67,16 @@ def sinkhorn_unbalanced(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, Returns ------- - W : (nt) ndarray or float - Optimal transportation matrix for the given parameters - log : dict - log dictionary return only if log==True in parameters + if n_hists == 1: + gamma : (dim_a x dim_b) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary returned only if `log` is `True` + else: + ot_distance : (n_hists,) ndarray + the OT distance between `a` and each of the histograms `b_i` + log : dict + log dictionary returned only if `log` is `True` Examples -------- @@ -82,83 +93,96 @@ def sinkhorn_unbalanced(a, b, M, reg, alpha, method='sinkhorn', numItermax=1000, References ---------- - .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013 + .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal + Transport, Advances in Neural Information Processing Systems + (NIPS) 26, 2013 - .. [9] Schmitzer, B. (2016). Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems. arXiv preprint arXiv:1610.06519. + .. [9] Schmitzer, B. (2016). Stabilized Sparse Scaling Algorithms for + Entropy Regularized Transport Problems. arXiv preprint arXiv:1610.06519. - .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). + Scaling algorithms for unbalanced transport problems. arXiv preprint + arXiv:1607.05816. - .. [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. : Learning with a Wasserstein Loss, Advances in Neural Information Processing Systems (NIPS) 2015 + .. [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. : + Learning with a Wasserstein Loss, Advances in Neural Information + Processing Systems (NIPS) 2015 See Also -------- ot.unbalanced.sinkhorn_knopp_unbalanced : Unbalanced Classic Sinkhorn [10] - ot.unbalanced.sinkhorn_stabilized_unbalanced: Unbalanced Stabilized sinkhorn [9][10] - ot.unbalanced.sinkhorn_epsilon_scaling_unbalanced: Unbalanced Sinkhorn with epslilon scaling [9][10] + ot.unbalanced.sinkhorn_stabilized_unbalanced: + Unbalanced Stabilized sinkhorn [9][10] + ot.unbalanced.sinkhorn_reg_scaling_unbalanced: + Unbalanced Sinkhorn with epslilon scaling [9][10] """ if method.lower() == 'sinkhorn': - def sink(): - return sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, - numItermax=numItermax, - stopThr=stopThr, verbose=verbose, - log=log, **kwargs) - - elif method.lower() in ['sinkhorn_stabilized', 'sinkhorn_epsilon_scaling']: + return _sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, + numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) + + elif method.lower() == 'sinkhorn_stabilized': + return _sinkhorn_stabilized_unbalanced(a, b, M, reg, reg_m, + numItermax=numItermax, + stopThr=stopThr, + verbose=verbose, + log=log, **kwargs) + elif method.lower() in ['sinkhorn_reg_scaling']: warnings.warn('Method not implemented yet. Using classic Sinkhorn Knopp') - - def sink(): - return sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, - numItermax=numItermax, - stopThr=stopThr, verbose=verbose, - log=log, **kwargs) + return _sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, + numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) else: - raise ValueError('Unknown method. Using classic Sinkhorn Knopp') - - return sink() + raise ValueError("Unknown method '%s'." % method) -def sinkhorn_unbalanced2(a, b, M, reg, alpha, method='sinkhorn', - numItermax=1000, stopThr=1e-9, verbose=False, +def sinkhorn_unbalanced2(a, b, M, reg, reg_m, method='sinkhorn', + numItermax=1000, stopThr=1e-6, verbose=False, log=False, **kwargs): r""" - Solve the entropic regularization unbalanced optimal transport problem and return the loss + Solve the entropic regularization unbalanced optimal transport problem and + return the loss The function solves the following optimization problem: .. math:: - W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \\alpha KL(\gamma 1, a) + \\alpha KL(\gamma^T 1, b) + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + reg_m KL(\gamma 1, a) + reg_m KL(\gamma^T 1, b) s.t. \gamma\geq 0 where : - - M is the (ns, nt) metric cost matrix - - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - a and b are source and target weights + - M is the (dim_a, dim_b) metric cost matrix + - :math:`\Omega` is the entropic regularization term + :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target unbalanced distributions - KL is the Kullback-Leibler divergence - The algorithm used for solving the problem is the generalized Sinkhorn-Knopp matrix scaling algorithm as proposed in [10, 23]_ + The algorithm used for solving the problem is the generalized + Sinkhorn-Knopp matrix scaling algorithm as proposed in [10, 23]_ Parameters ---------- - a : np.ndarray (ns,) - samples weights in the source domain - b : np.ndarray (nt,) or np.ndarray (nt, n_hists) - samples in the target domain, compute sinkhorn with multiple targets - and fixed M if b is a matrix (return OT loss + dual variables in log) - M : np.ndarray (ns,nt) + a : np.ndarray (dim_a,) + Unnormalized histogram of dimension dim_a + b : np.ndarray (dim_b,) or np.ndarray (dim_b, n_hists) + One or multiple unnormalized histograms of dimension dim_b + If many, compute all the OT distances (a, b_i) + M : np.ndarray (dim_a, dim_b) loss matrix reg : float Entropy regularization term > 0 - alpha : float + reg_m: float Marginal relaxation term > 0 method : str method used for the solver either 'sinkhorn', 'sinkhorn_stabilized' or - 'sinkhorn_epsilon_scaling', see those function for specific parameters + 'sinkhorn_reg_scaling', see those function for specific parameters numItermax : int, optional Max number of iterations stopThr : float, optional @@ -171,10 +195,10 @@ def sinkhorn_unbalanced2(a, b, M, reg, alpha, method='sinkhorn', Returns ------- - W : (nt) ndarray or float - Optimal transportation matrix for the given parameters + ot_distance : (n_hists,) ndarray + the OT distance between `a` and each of the histograms `b_i` log : dict - log dictionary return only if log==True in parameters + log dictionary returned only if `log` is `True` Examples -------- @@ -191,64 +215,70 @@ def sinkhorn_unbalanced2(a, b, M, reg, alpha, method='sinkhorn', References ---------- - .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal Transport, Advances in Neural Information Processing Systems (NIPS) 26, 2013 + .. [2] M. Cuturi, Sinkhorn Distances : Lightspeed Computation of Optimal + Transport, Advances in Neural Information Processing Systems + (NIPS) 26, 2013 - .. [9] Schmitzer, B. (2016). Stabilized Sparse Scaling Algorithms for Entropy Regularized Transport Problems. arXiv preprint arXiv:1610.06519. + .. [9] Schmitzer, B. (2016). Stabilized Sparse Scaling Algorithms for + Entropy Regularized Transport Problems. arXiv preprint arXiv:1610.06519. - .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). + Scaling algorithms for unbalanced transport problems. arXiv preprint + arXiv:1607.05816. - .. [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. : Learning with a Wasserstein Loss, Advances in Neural Information Processing Systems (NIPS) 2015 + .. [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. : + Learning with a Wasserstein Loss, Advances in Neural Information + Processing Systems (NIPS) 2015 See Also -------- ot.unbalanced.sinkhorn_knopp : Unbalanced Classic Sinkhorn [10] ot.unbalanced.sinkhorn_stabilized: Unbalanced Stabilized sinkhorn [9][10] - ot.unbalanced.sinkhorn_epsilon_scaling: Unbalanced Sinkhorn with epslilon scaling [9][10] + ot.unbalanced.sinkhorn_reg_scaling: Unbalanced Sinkhorn with epslilon scaling [9][10] """ - - if method.lower() == 'sinkhorn': - def sink(): - return sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, - numItermax=numItermax, - stopThr=stopThr, verbose=verbose, - log=log, **kwargs) - - elif method.lower() in ['sinkhorn_stabilized', 'sinkhorn_epsilon_scaling']: - warnings.warn('Method not implemented yet. Using classic Sinkhorn Knopp') - - def sink(): - return sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, - numItermax=numItermax, - stopThr=stopThr, verbose=verbose, - log=log, **kwargs) - else: - raise ValueError('Unknown method. Using classic Sinkhorn Knopp') - b = np.asarray(b, dtype=np.float64) if len(b.shape) < 2: b = b[:, None] - - return sink() + if method.lower() == 'sinkhorn': + return _sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, + numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) + + elif method.lower() == 'sinkhorn_stabilized': + return _sinkhorn_stabilized_unbalanced(a, b, M, reg, reg_m, + numItermax=numItermax, + stopThr=stopThr, + verbose=verbose, + log=log, **kwargs) + elif method.lower() in ['sinkhorn_reg_scaling']: + warnings.warn('Method not implemented yet. Using classic Sinkhorn Knopp') + return _sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, + numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) + else: + raise ValueError('Unknown method %s.' % method) -def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, - stopThr=1e-9, verbose=False, log=False, **kwargs): +def _sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, numItermax=1000, + stopThr=1e-6, verbose=False, log=False, **kwargs): r""" Solve the entropic regularization unbalanced optimal transport problem and return the loss The function solves the following optimization problem: .. math:: - W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \\alpha KL(\gamma 1, a) + \\alpha KL(\gamma^T 1, b) + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + \reg_m KL(\gamma 1, a) + \reg_m KL(\gamma^T 1, b) s.t. \gamma\geq 0 where : - - M is the (ns, nt) metric cost matrix + - M is the (dim_a, dim_b) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - a and b are source and target weights + - a and b are source and target unbalanced distributions - KL is the Kullback-Leibler divergence The algorithm used for solving the problem is the generalized Sinkhorn-Knopp matrix scaling algorithm as proposed in [10, 23]_ @@ -256,16 +286,16 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, Parameters ---------- - a : np.ndarray (ns,) - samples weights in the source domain - b : np.ndarray (nt,) or np.ndarray (nt, n_hists) - samples in the target domain, compute sinkhorn with multiple targets - and fixed M if b is a matrix (return OT loss + dual variables in log) - M : np.ndarray (ns,nt) + a : np.ndarray (dim_a,) + Unnormalized histogram of dimension dim_a + b : np.ndarray (dim_b,) or np.ndarray (dim_b, n_hists) + One or multiple unnormalized histograms of dimension dim_b + If many, compute all the OT distances (a, b_i) + M : np.ndarray (dim_a, dim_b) loss matrix reg : float Entropy regularization term > 0 - alpha : float + reg_m: float Marginal relaxation term > 0 numItermax : int, optional Max number of iterations @@ -279,11 +309,16 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, Returns ------- - gamma : (ns x nt) ndarray - Optimal transportation matrix for the given parameters - log : dict - log dictionary return only if log==True in parameters - + if n_hists == 1: + gamma : (dim_a x dim_b) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary returned only if `log` is `True` + else: + ot_distance : (n_hists,) ndarray + the OT distance between `a` and each of the histograms `b_i` + log : dict + log dictionary returned only if `log` is `True` Examples -------- @@ -291,16 +326,20 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, >>> a=[.5, .5] >>> b=[.5, .5] >>> M=[[0., 1.],[1., 0.]] - >>> ot.unbalanced.sinkhorn_knopp_unbalanced(a, b, M, 1., 1.) + >>> ot.unbalanced._sinkhorn_knopp_unbalanced(a, b, M, 1., 1.) array([[0.51122823, 0.18807035], [0.18807035, 0.51122823]]) References ---------- - .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). + Scaling algorithms for unbalanced transport problems. arXiv preprint + arXiv:1607.05816. - .. [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. : Learning with a Wasserstein Loss, Advances in Neural Information Processing Systems (NIPS) 2015 + .. [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. : + Learning with a Wasserstein Loss, Advances in Neural Information + Processing Systems (NIPS) 2015 See Also -------- @@ -313,12 +352,12 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, b = np.asarray(b, dtype=np.float64) M = np.asarray(M, dtype=np.float64) - n_a, n_b = M.shape + dim_a, dim_b = M.shape if len(a) == 0: - a = np.ones(n_a, dtype=np.float64) / n_a + a = np.ones(dim_a, dtype=np.float64) / dim_a if len(b) == 0: - b = np.ones(n_b, dtype=np.float64) / n_b + b = np.ones(dim_b, dtype=np.float64) / dim_b if len(b.shape) > 1: n_hists = b.shape[1] @@ -331,21 +370,19 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, # we assume that no distances are null except those of the diagonal of # distances if n_hists: - u = np.ones((n_a, 1)) / n_a - v = np.ones((n_b, n_hists)) / n_b - a = a.reshape(n_a, 1) + u = np.ones((dim_a, 1)) / dim_a + v = np.ones((dim_b, n_hists)) / dim_b + a = a.reshape(dim_a, 1) else: - u = np.ones(n_a) / n_a - v = np.ones(n_b) / n_b + u = np.ones(dim_a) / dim_a + v = np.ones(dim_b) / dim_b - # print(reg) # Next 3 lines equivalent to K= np.exp(-M/reg), but faster to compute K = np.empty(M.shape, dtype=M.dtype) np.divide(M, -reg, out=K) np.exp(K, out=K) - # print(np.min(K)) - fi = alpha / (alpha + reg) + fi = reg_m / (reg_m + reg) cpt = 0 err = 1. @@ -371,8 +408,9 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, if cpt % 10 == 0: # we can speed up the process by checking for the error only all # the 10th iterations - err = np.sum((u - uprev)**2) / np.sum((u)**2) + \ - np.sum((v - vprev)**2) / np.sum((v)**2) + err_u = abs(u - uprev).max() / max(abs(u).max(), abs(uprev).max(), 1.) + err_v = abs(v - vprev).max() / max(abs(v).max(), abs(vprev).max(), 1.) + err = 0.5 * (err_u + err_v) if log: log['err'].append(err) if verbose: @@ -383,8 +421,8 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, cpt += 1 if log: - log['u'] = u - log['v'] = v + log['logu'] = np.log(u + 1e-16) + log['logv'] = np.log(v + 1e-16) if n_hists: # return only loss res = np.einsum('ik,ij,jk,ij->k', u, K, v, M) @@ -401,9 +439,224 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, alpha, numItermax=1000, return u[:, None] * K * v[None, :] -def barycenter_unbalanced(A, M, reg, alpha, weights=None, numItermax=1000, - stopThr=1e-4, verbose=False, log=False): - r"""Compute the entropic regularized unbalanced wasserstein barycenter of distributions A +def _sinkhorn_stabilized_unbalanced(a, b, M, reg, reg_m, tau=1e5, numItermax=1000, + stopThr=1e-6, verbose=False, log=False, + **kwargs): + r""" + Solve the entropic regularization unbalanced optimal transport + problem and return the loss + + The function solves the following optimization problem using log-domain + stabilization as proposed in [10]: + + .. math:: + W = \min_\gamma <\gamma,M>_F + reg\cdot\Omega(\gamma) + reg_m KL(\gamma 1, a) + reg_m KL(\gamma^T 1, b) + + s.t. + \gamma\geq 0 + where : + + - M is the (dim_a, dim_b) metric cost matrix + - :math:`\Omega` is the entropic regularization + term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` + - a and b are source and target unbalanced distributions + - KL is the Kullback-Leibler divergence + + The algorithm used for solving the problem is the generalized + Sinkhorn-Knopp matrix scaling algorithm as proposed in [10, 23]_ + + + Parameters + ---------- + a : np.ndarray (dim_a,) + Unnormalized histogram of dimension dim_a + b : np.ndarray (dim_b,) or np.ndarray (dim_b, n_hists) + One or multiple unnormalized histograms of dimension dim_b + If many, compute all the OT distances (a, b_i) + M : np.ndarray (dim_a, dim_b) + loss matrix + reg : float + Entropy regularization term > 0 + reg_m: float + Marginal relaxation term > 0 + tau : float + thershold for max value in u or v for log scaling + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (>0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + if n_hists == 1: + gamma : (dim_a x dim_b) ndarray + Optimal transportation matrix for the given parameters + log : dict + log dictionary returned only if `log` is `True` + else: + ot_distance : (n_hists,) ndarray + the OT distance between `a` and each of the histograms `b_i` + log : dict + log dictionary returned only if `log` is `True` + Examples + -------- + + >>> import ot + >>> a=[.5, .5] + >>> b=[.5, .5] + >>> M=[[0., 1.],[1., 0.]] + >>> ot.unbalanced._sinkhorn_stabilized_unbalanced(a, b, M, 1., 1.) + array([[0.51122823, 0.18807035], + [0.18807035, 0.51122823]]) + + References + ---------- + + .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). + Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + + .. [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. : + Learning with a Wasserstein Loss, Advances in Neural Information + Processing Systems (NIPS) 2015 + + See Also + -------- + ot.lp.emd : Unregularized OT + ot.optim.cg : General regularized OT + + """ + + a = np.asarray(a, dtype=np.float64) + b = np.asarray(b, dtype=np.float64) + M = np.asarray(M, dtype=np.float64) + + dim_a, dim_b = M.shape + + if len(a) == 0: + a = np.ones(dim_a, dtype=np.float64) / dim_a + if len(b) == 0: + b = np.ones(dim_b, dtype=np.float64) / dim_b + + if len(b.shape) > 1: + n_hists = b.shape[1] + else: + n_hists = 0 + + if log: + log = {'err': []} + + # we assume that no distances are null except those of the diagonal of + # distances + if n_hists: + u = np.ones((dim_a, n_hists)) / dim_a + v = np.ones((dim_b, n_hists)) / dim_b + a = a.reshape(dim_a, 1) + else: + u = np.ones(dim_a) / dim_a + v = np.ones(dim_b) / dim_b + + # print(reg) + # Next 3 lines equivalent to K= np.exp(-M/reg), but faster to compute + K = np.empty(M.shape, dtype=M.dtype) + np.divide(M, -reg, out=K) + np.exp(K, out=K) + + fi = reg_m / (reg_m + reg) + + cpt = 0 + err = 1. + alpha = np.zeros(dim_a) + beta = np.zeros(dim_b) + while (err > stopThr and cpt < numItermax): + uprev = u + vprev = v + + Kv = K.dot(v) + f_alpha = np.exp(- alpha / (reg + reg_m)) + f_beta = np.exp(- beta / (reg + reg_m)) + + if n_hists: + f_alpha = f_alpha[:, None] + f_beta = f_beta[:, None] + u = ((a / (Kv + 1e-16)) ** fi) * f_alpha + Ktu = K.T.dot(u) + v = ((b / (Ktu + 1e-16)) ** fi) * f_beta + absorbing = False + if (u > tau).any() or (v > tau).any(): + absorbing = True + if n_hists: + alpha = alpha + reg * np.log(np.max(u, 1)) + beta = beta + reg * np.log(np.max(v, 1)) + else: + alpha = alpha + reg * np.log(np.max(u)) + beta = beta + reg * np.log(np.max(v)) + K = np.exp((alpha[:, None] + beta[None, :] - + M) / reg) + v = np.ones_like(v) + Kv = K.dot(v) + + if (np.any(Ktu == 0.) + or np.any(np.isnan(u)) or np.any(np.isnan(v)) + or np.any(np.isinf(u)) or np.any(np.isinf(v))): + # we have reached the machine precision + # come back to previous solution and quit loop + warnings.warn('Numerical errors at iteration %s' % cpt) + u = uprev + v = vprev + break + if (cpt % 10 == 0 and not absorbing) or cpt == 0: + # we can speed up the process by checking for the error only all + # the 10th iterations + err = abs(u - uprev).max() / max(abs(u).max(), abs(uprev).max(), + 1.) + if log: + log['err'].append(err) + if verbose: + if cpt % 200 == 0: + print( + '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) + print('{:5d}|{:8e}|'.format(cpt, err)) + cpt = cpt + 1 + + if err > stopThr: + warnings.warn("Stabilized Unbalanced Sinkhorn did not converge." + + "Try a larger entropy `reg` or a lower mass `reg_m`." + + "Or a larger absorption threshold `tau`.") + if n_hists: + logu = alpha[:, None] / reg + np.log(u) + logv = beta[:, None] / reg + np.log(v) + else: + logu = alpha / reg + np.log(u) + logv = beta / reg + np.log(v) + if log: + log['logu'] = logu + log['logv'] = logv + if n_hists: # return only loss + res = logsumexp(np.log(M + 1e-100)[:, :, None] + logu[:, None, :] + + logv[None, :, :] - M[:, :, None] / reg, axis=(0, 1)) + res = np.exp(res) + if log: + return res, log + else: + return res + + else: # return OT matrix + ot_matrix = np.exp(logu[:, None] + logv[None, :] - M / reg) + if log: + return ot_matrix, log + else: + return ot_matrix + + +def _barycenter_unbalanced_stabilized(A, M, reg, reg_m, weights=None, tau=1e3, + numItermax=1000, stopThr=1e-6, + verbose=False, log=False): + r"""Compute the entropic unbalanced wasserstein barycenter of A with stabilization. The function solves the following optimization problem: @@ -412,28 +665,184 @@ def barycenter_unbalanced(A, M, reg, alpha, weights=None, numItermax=1000, where : - - :math:`Wu_{reg}(\cdot,\cdot)` is the unbalanced entropic regularized Wasserstein distance (see ot.unbalanced.sinkhorn_unbalanced) - - :math:`\mathbf{a}_i` are training distributions in the columns of matrix :math:`\mathbf{A}` - - reg and :math:`\mathbf{M}` are respectively the regularization term and the cost matrix for OT - - alpha is the marginal relaxation hyperparameter - The algorithm used for solving the problem is the generalized Sinkhorn-Knopp matrix scaling algorithm as proposed in [10]_ + - :math:`Wu_{reg}(\cdot,\cdot)` is the unbalanced entropic regularized + Wasserstein distance (see ot.unbalanced.sinkhorn_unbalanced) + - :math:`\mathbf{a}_i` are training distributions in the columns of + matrix :math:`\mathbf{A}` + - reg and :math:`\mathbf{M}` are respectively the regularization term and + the cost matrix for OT + - reg_mis the marginal relaxation hyperparameter + The algorithm used for solving the problem is the generalized + Sinkhorn-Knopp matrix scaling algorithm as proposed in [10]_ Parameters ---------- - A : np.ndarray (d,n) - n training distributions a_i of size d - M : np.ndarray (d,d) - loss matrix for OT + A : np.ndarray (dim, n_hists) + `n_hists` training distributions a_i of dimension dim + M : np.ndarray (dim, dim) + ground metric matrix for OT. reg : float Entropy regularization term > 0 - alpha : float + reg_m : float Marginal relaxation term > 0 - weights : np.ndarray (n,) - Weights of each histogram a_i on the simplex (barycentric coodinates) + tau : float + Stabilization threshold for log domain absorption. + weights : np.ndarray (n_hists,) optional + Weight of each distribution (barycentric coodinates) + If None, uniform weights are used. numItermax : int, optional Max number of iterations stopThr : float, optional - Stop threshol on error (>0) + Stop threshol on error (> 0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + a : (dim,) ndarray + Unbalanced Wasserstein barycenter + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, + G. (2015). Iterative Bregman projections for regularized transportation + problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138. + .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). + Scaling algorithms for unbalanced transport problems. arXiv preprint + arXiv:1607.05816. + + + """ + dim, n_hists = A.shape + if weights is None: + weights = np.ones(n_hists) / n_hists + else: + assert(len(weights) == A.shape[1]) + + if log: + log = {'err': []} + + fi = reg_m / (reg_m + reg) + + u = np.ones((dim, n_hists)) / dim + v = np.ones((dim, n_hists)) / dim + + # print(reg) + # Next 3 lines equivalent to K= np.exp(-M/reg), but faster to compute + K = np.empty(M.shape, dtype=M.dtype) + np.divide(M, -reg, out=K) + np.exp(K, out=K) + + fi = reg_m / (reg_m + reg) + + cpt = 0 + err = 1. + alpha = np.zeros(dim) + beta = np.zeros(dim) + q = np.ones(dim) / dim + while (err > stopThr and cpt < numItermax): + qprev = q + Kv = K.dot(v) + f_alpha = np.exp(- alpha / (reg + reg_m)) + f_beta = np.exp(- beta / (reg + reg_m)) + f_alpha = f_alpha[:, None] + f_beta = f_beta[:, None] + u = ((A / (Kv + 1e-16)) ** fi) * f_alpha + Ktu = K.T.dot(u) + q = (Ktu ** (1 - fi)) * f_beta + q = q.dot(weights) ** (1 / (1 - fi)) + Q = q[:, None] + v = ((Q / (Ktu + 1e-16)) ** fi) * f_beta + absorbing = False + if (u > tau).any() or (v > tau).any(): + absorbing = True + alpha = alpha + reg * np.log(np.max(u, 1)) + beta = beta + reg * np.log(np.max(v, 1)) + K = np.exp((alpha[:, None] + beta[None, :] - + M) / reg) + v = np.ones_like(v) + Kv = K.dot(v) + if (np.any(Ktu == 0.) + or np.any(np.isnan(u)) or np.any(np.isnan(v)) + or np.any(np.isinf(u)) or np.any(np.isinf(v))): + # we have reached the machine precision + # come back to previous solution and quit loop + warnings.warn('Numerical errors at iteration %s' % cpt) + q = qprev + break + if (cpt % 10 == 0 and not absorbing) or cpt == 0: + # we can speed up the process by checking for the error only all + # the 10th iterations + err = abs(q - qprev).max() / max(abs(q).max(), + abs(qprev).max(), 1.) + if log: + log['err'].append(err) + if verbose: + if cpt % 50 == 0: + print( + '{:5s}|{:12s}'.format('It.', 'Err') + '\n' + '-' * 19) + print('{:5d}|{:8e}|'.format(cpt, err)) + + cpt += 1 + if err > stopThr: + warnings.warn("Stabilized Unbalanced Sinkhorn did not converge." + + "Try a larger entropy `reg` or a lower mass `reg_m`." + + "Or a larger absorption threshold `tau`.") + if log: + log['niter'] = cpt + log['logu'] = np.log(u + 1e-16) + log['logv'] = np.log(v + 1e-16) + return q, log + else: + return q + + +def _barycenter_unbalanced(A, M, reg, reg_m, weights=None, + numItermax=1000, stopThr=1e-6, + verbose=False, log=False): + r"""Compute the entropic unbalanced wasserstein barycenter of A. + + The function solves the following optimization problem with a + + .. math:: + \mathbf{a} = arg\min_\mathbf{a} \sum_i Wu_{reg}(\mathbf{a},\mathbf{a}_i) + + where : + + - :math:`Wu_{reg}(\cdot,\cdot)` is the unbalanced entropic regularized + Wasserstein distance (see ot.unbalanced.sinkhorn_unbalanced) + - :math:`\mathbf{a}_i` are training distributions in the columns of matrix + :math:`\mathbf{A}` + - reg and :math:`\mathbf{M}` are respectively the regularization term and + the cost matrix for OT + - reg_mis the marginal relaxation hyperparameter + The algorithm used for solving the problem is the generalized + Sinkhorn-Knopp matrix scaling algorithm as proposed in [10]_ + + Parameters + ---------- + A : np.ndarray (dim, n_hists) + `n_hists` training distributions a_i of dimension dim + M : np.ndarray (dim, dim) + ground metric matrix for OT. + reg : float + Entropy regularization term > 0 + reg_m: float + Marginal relaxation term > 0 + weights : np.ndarray (n_hists,) optional + Weight of each distribution (barycentric coodinates) + If None, uniform weights are used. + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (> 0) verbose : bool, optional Print information along iterations log : bool, optional @@ -442,7 +851,7 @@ def barycenter_unbalanced(A, M, reg, alpha, weights=None, numItermax=1000, Returns ------- - a : (d,) ndarray + a : (dim,) ndarray Unbalanced Wasserstein barycenter log : dict log dictionary return only if log==True in parameters @@ -451,12 +860,16 @@ def barycenter_unbalanced(A, M, reg, alpha, weights=None, numItermax=1000, References ---------- - .. [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. (2015). Iterative Bregman projections for regularized transportation problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138. - .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). Scaling algorithms for unbalanced transport problems. arXiv preprint arXiv:1607.05816. + .. [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. + (2015). Iterative Bregman projections for regularized transportation + problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138. + .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). + Scaling algorithms for unbalanced transport problems. arXiv preprin + arXiv:1607.05816. """ - p, n_hists = A.shape + dim, n_hists = A.shape if weights is None: weights = np.ones(n_hists) / n_hists else: @@ -467,10 +880,10 @@ def barycenter_unbalanced(A, M, reg, alpha, weights=None, numItermax=1000, K = np.exp(- M / reg) - fi = alpha / (alpha + reg) + fi = reg_m / (reg_m + reg) - v = np.ones((p, n_hists)) / p - u = np.ones((p, 1)) / p + v = np.ones((dim, n_hists)) / dim + u = np.ones((dim, 1)) / dim cpt = 0 err = 1. @@ -499,8 +912,11 @@ def barycenter_unbalanced(A, M, reg, alpha, weights=None, numItermax=1000, if cpt % 10 == 0: # we can speed up the process by checking for the error only all # the 10th iterations - err = np.sum((u - uprev) ** 2) / np.sum((u) ** 2) + \ - np.sum((v - vprev) ** 2) / np.sum((v) ** 2) + err_u = abs(u - uprev).max() + err_u /= max(abs(u).max(), abs(uprev).max(), 1.) + err_v = abs(v - vprev).max() + err_v /= max(abs(v).max(), abs(vprev).max(), 1.) + err = 0.5 * (err_u + err_v) if log: log['err'].append(err) if verbose: @@ -512,8 +928,95 @@ def barycenter_unbalanced(A, M, reg, alpha, weights=None, numItermax=1000, cpt += 1 if log: log['niter'] = cpt - log['u'] = u - log['v'] = v + log['logu'] = np.log(u + 1e-16) + log['logv'] = np.log(v + 1e-16) return q, log else: return q + + +def barycenter_unbalanced(A, M, reg, reg_m, method="sinkhorn", weights=None, + numItermax=1000, stopThr=1e-6, + verbose=False, log=False, **kwargs): + r"""Compute the entropic unbalanced wasserstein barycenter of A. + + The function solves the following optimization problem with a + + .. math:: + \mathbf{a} = arg\min_\mathbf{a} \sum_i Wu_{reg}(\mathbf{a},\mathbf{a}_i) + + where : + + - :math:`Wu_{reg}(\cdot,\cdot)` is the unbalanced entropic regularized + Wasserstein distance (see ot.unbalanced.sinkhorn_unbalanced) + - :math:`\mathbf{a}_i` are training distributions in the columns of matrix + :math:`\mathbf{A}` + - reg and :math:`\mathbf{M}` are respectively the regularization term and + the cost matrix for OT + - reg_mis the marginal relaxation hyperparameter + The algorithm used for solving the problem is the generalized + Sinkhorn-Knopp matrix scaling algorithm as proposed in [10]_ + + Parameters + ---------- + A : np.ndarray (dim, n_hists) + `n_hists` training distributions a_i of dimension dim + M : np.ndarray (dim, dim) + ground metric matrix for OT. + reg : float + Entropy regularization term > 0 + reg_m: float + Marginal relaxation term > 0 + weights : np.ndarray (n_hists,) optional + Weight of each distribution (barycentric coodinates) + If None, uniform weights are used. + numItermax : int, optional + Max number of iterations + stopThr : float, optional + Stop threshol on error (> 0) + verbose : bool, optional + Print information along iterations + log : bool, optional + record log if True + + + Returns + ------- + a : (dim,) ndarray + Unbalanced Wasserstein barycenter + log : dict + log dictionary return only if log==True in parameters + + + References + ---------- + + .. [3] Benamou, J. D., Carlier, G., Cuturi, M., Nenna, L., & Peyré, G. + (2015). Iterative Bregman projections for regularized transportation + problems. SIAM Journal on Scientific Computing, 37(2), A1111-A1138. + .. [10] Chizat, L., Peyré, G., Schmitzer, B., & Vialard, F. X. (2016). + Scaling algorithms for unbalanced transport problems. arXiv preprin + arXiv:1607.05816. + + """ + + if method.lower() == 'sinkhorn': + return _barycenter_unbalanced(A, M, reg, reg_m, + numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) + + elif method.lower() == 'sinkhorn_stabilized': + return _barycenter_unbalanced_stabilized(A, M, reg, reg_m, + numItermax=numItermax, + stopThr=stopThr, + verbose=verbose, + log=log, **kwargs) + elif method.lower() in ['sinkhorn_reg_scaling']: + warnings.warn('Method not implemented yet. Using classic Sinkhorn Knopp') + return _barycenter_unbalanced(A, M, reg, reg_m, + numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) + else: + raise ValueError("Unknown method '%s'." % method) diff --git a/pytest.ini b/pytest.ini new file mode 100644 index 0000000..e69de29 diff --git a/test/test_bregman.py b/test/test_bregman.py index 83ebba8..f70df10 100644 --- a/test/test_bregman.py +++ b/test/test_bregman.py @@ -7,6 +7,7 @@ import numpy as np import ot +import pytest def test_sinkhorn(): @@ -71,13 +72,11 @@ def test_sinkhorn_variants(): Gs = ot.sinkhorn(u, u, M, 1, method='sinkhorn_stabilized', stopThr=1e-10) Ges = ot.sinkhorn( u, u, M, 1, method='sinkhorn_epsilon_scaling', stopThr=1e-10) - Gerr = ot.sinkhorn(u, u, M, 1, method='do_not_exists', stopThr=1e-10) G_green = ot.sinkhorn(u, u, M, 1, method='greenkhorn', stopThr=1e-10) # check values np.testing.assert_allclose(G0, Gs, atol=1e-05) np.testing.assert_allclose(G0, Ges, atol=1e-05) - np.testing.assert_allclose(G0, Gerr) np.testing.assert_allclose(G0, G_green, atol=1e-5) print(G0, G_green) @@ -96,18 +95,17 @@ def test_sinkhorn_variants_log(): Gs, logs = ot.sinkhorn(u, u, M, 1, method='sinkhorn_stabilized', stopThr=1e-10, log=True) Ges, loges = ot.sinkhorn( u, u, M, 1, method='sinkhorn_epsilon_scaling', stopThr=1e-10, log=True) - Gerr, logerr = ot.sinkhorn(u, u, M, 1, method='do_not_exists', stopThr=1e-10, log=True) G_green, loggreen = ot.sinkhorn(u, u, M, 1, method='greenkhorn', stopThr=1e-10, log=True) # check values np.testing.assert_allclose(G0, Gs, atol=1e-05) np.testing.assert_allclose(G0, Ges, atol=1e-05) - np.testing.assert_allclose(G0, Gerr) np.testing.assert_allclose(G0, G_green, atol=1e-5) print(G0, G_green) -def test_bary(): +@pytest.mark.parametrize("method", ["sinkhorn", "sinkhorn_stabilized"]) +def test_barycenter(method): n_bins = 100 # nb bins @@ -126,14 +124,42 @@ def test_bary(): weights = np.array([1 - alpha, alpha]) # wasserstein - reg = 1e-3 - bary_wass = ot.bregman.barycenter(A, M, reg, weights) + reg = 1e-2 + bary_wass = ot.bregman.barycenter(A, M, reg, weights, method=method) np.testing.assert_allclose(1, np.sum(bary_wass)) ot.bregman.barycenter(A, M, reg, log=True, verbose=True) +def test_barycenter_stabilization(): + + n_bins = 100 # nb bins + + # Gaussian distributions + a1 = ot.datasets.make_1D_gauss(n_bins, m=30, s=10) # m= mean, s= std + a2 = ot.datasets.make_1D_gauss(n_bins, m=40, s=10) + + # creating matrix A containing all distributions + A = np.vstack((a1, a2)).T + + # loss matrix + normalization + M = ot.utils.dist0(n_bins) + M /= M.max() + + alpha = 0.5 # 0<=alpha<=1 + weights = np.array([1 - alpha, alpha]) + + # wasserstein + reg = 1e-2 + bar_stable = ot.bregman.barycenter(A, M, reg, weights, + method="sinkhorn_stabilized", + stopThr=1e-8) + bar = ot.bregman.barycenter(A, M, reg, weights, method="sinkhorn", + stopThr=1e-8) + np.testing.assert_allclose(bar, bar_stable) + + def test_wasserstein_bary_2d(): size = 100 # size of a square image @@ -279,3 +305,35 @@ def test_stabilized_vs_sinkhorn_multidim(): method="sinkhorn", log=True) np.testing.assert_allclose(G, G2) + + +def test_implemented_methods(): + IMPLEMENTED_METHODS = ['sinkhorn', 'sinkhorn_stabilized'] + ONLY_1D_methods = ['greenkhorn', 'sinkhorn_epsilon_scaling'] + NOT_VALID_TOKENS = ['foo'] + # test generalized sinkhorn for unbalanced OT barycenter + n = 3 + rng = np.random.RandomState(42) + + x = rng.randn(n, 2) + a = ot.utils.unif(n) + + # make dists unbalanced + b = ot.utils.unif(n) + A = rng.rand(n, 2) + M = ot.dist(x, x) + epsilon = 1. + + for method in IMPLEMENTED_METHODS: + ot.bregman.sinkhorn(a, b, M, epsilon, method=method) + ot.bregman.sinkhorn2(a, b, M, epsilon, method=method) + ot.bregman.barycenter(A, M, reg=epsilon, method=method) + with pytest.raises(ValueError): + for method in set(NOT_VALID_TOKENS): + ot.bregman.sinkhorn(a, b, M, epsilon, method=method) + ot.bregman.sinkhorn2(a, b, M, epsilon, method=method) + ot.bregman.barycenter(A, M, reg=epsilon, method=method) + for method in ONLY_1D_methods: + ot.bregman.sinkhorn(a, b, M, epsilon, method=method) + with pytest.raises(ValueError): + ot.bregman.sinkhorn2(a, b, M, epsilon, method=method) diff --git a/test/test_unbalanced.py b/test/test_unbalanced.py index 1395fe1..ca1efba 100644 --- a/test/test_unbalanced.py +++ b/test/test_unbalanced.py @@ -7,9 +7,12 @@ import numpy as np import ot import pytest +from ot.unbalanced import barycenter_unbalanced +from scipy.special import logsumexp -@pytest.mark.parametrize("method", ["sinkhorn"]) + +@pytest.mark.parametrize("method", ["sinkhorn", "sinkhorn_stabilized"]) def test_unbalanced_convergence(method): # test generalized sinkhorn for unbalanced OT n = 100 @@ -23,29 +26,35 @@ def test_unbalanced_convergence(method): M = ot.dist(x, x) epsilon = 1. - alpha = 1. - K = np.exp(- M / epsilon) + reg_m = 1. - G, log = ot.unbalanced.sinkhorn_unbalanced(a, b, M, reg=epsilon, alpha=alpha, - stopThr=1e-10, method=method, + G, log = ot.unbalanced.sinkhorn_unbalanced(a, b, M, reg=epsilon, + reg_m=reg_m, + method=method, log=True) - loss = ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, alpha, + loss = ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, reg_m, method=method) # check fixed point equations - fi = alpha / (alpha + epsilon) - v_final = (b / K.T.dot(log["u"])) ** fi - u_final = (a / K.dot(log["v"])) ** fi + # in log-domain + fi = reg_m / (reg_m + epsilon) + logb = np.log(b + 1e-16) + loga = np.log(a + 1e-16) + logKtu = logsumexp(log["logu"][None, :] - M.T / epsilon, axis=1) + logKv = logsumexp(log["logv"][None, :] - M / epsilon, axis=1) + + v_final = fi * (logb - logKtu) + u_final = fi * (loga - logKv) np.testing.assert_allclose( - u_final, log["u"], atol=1e-05) + u_final, log["logu"], atol=1e-05) np.testing.assert_allclose( - v_final, log["v"], atol=1e-05) + v_final, log["logv"], atol=1e-05) # check if sinkhorn_unbalanced2 returns the correct loss np.testing.assert_allclose((G * M).sum(), loss, atol=1e-5) -@pytest.mark.parametrize("method", ["sinkhorn"]) +@pytest.mark.parametrize("method", ["sinkhorn", "sinkhorn_stabilized"]) def test_unbalanced_multiple_inputs(method): # test generalized sinkhorn for unbalanced OT n = 100 @@ -59,28 +68,59 @@ def test_unbalanced_multiple_inputs(method): M = ot.dist(x, x) epsilon = 1. - alpha = 1. - K = np.exp(- M / epsilon) + reg_m = 1. loss, log = ot.unbalanced.sinkhorn_unbalanced(a, b, M, reg=epsilon, - alpha=alpha, - stopThr=1e-10, method=method, + reg_m=reg_m, + method=method, log=True) # check fixed point equations - fi = alpha / (alpha + epsilon) - v_final = (b / K.T.dot(log["u"])) ** fi - - u_final = (a[:, None] / K.dot(log["v"])) ** fi + # in log-domain + fi = reg_m / (reg_m + epsilon) + logb = np.log(b + 1e-16) + loga = np.log(a + 1e-16)[:, None] + logKtu = logsumexp(log["logu"][:, None, :] - M[:, :, None] / epsilon, + axis=0) + logKv = logsumexp(log["logv"][None, :] - M[:, :, None] / epsilon, axis=1) + v_final = fi * (logb - logKtu) + u_final = fi * (loga - logKv) np.testing.assert_allclose( - u_final, log["u"], atol=1e-05) + u_final, log["logu"], atol=1e-05) np.testing.assert_allclose( - v_final, log["v"], atol=1e-05) + v_final, log["logv"], atol=1e-05) assert len(loss) == b.shape[1] -def test_unbalanced_barycenter(): +def test_stabilized_vs_sinkhorn(): + # test if stable version matches sinkhorn + n = 100 + + # Gaussian distributions + a = ot.datasets.make_1D_gauss(n, m=20, s=5) # m= mean, s= std + b1 = ot.datasets.make_1D_gauss(n, m=60, s=8) + b2 = ot.datasets.make_1D_gauss(n, m=30, s=4) + + # creating matrix A containing all distributions + b = np.vstack((b1, b2)).T + + M = ot.utils.dist0(n) + M /= np.median(M) + epsilon = 0.1 + reg_m = 1. + G, log = ot.unbalanced.sinkhorn_unbalanced2(a, b, M, reg=epsilon, + method="sinkhorn_stabilized", + reg_m=reg_m, + log=True) + G2, log2 = ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, reg_m, + method="sinkhorn", log=True) + + np.testing.assert_allclose(G, G2, atol=1e-5) + + +@pytest.mark.parametrize("method", ["sinkhorn", "sinkhorn_stabilized"]) +def test_unbalanced_barycenter(method): # test generalized sinkhorn for unbalanced OT barycenter n = 100 rng = np.random.RandomState(42) @@ -92,27 +132,56 @@ def test_unbalanced_barycenter(): A = A * np.array([1, 2])[None, :] M = ot.dist(x, x) epsilon = 1. - alpha = 1. - K = np.exp(- M / epsilon) + reg_m = 1. - q, log = ot.unbalanced.barycenter_unbalanced(A, M, reg=epsilon, alpha=alpha, - stopThr=1e-10, - log=True) + q, log = barycenter_unbalanced(A, M, reg=epsilon, reg_m=reg_m, + method=method, log=True) # check fixed point equations - fi = alpha / (alpha + epsilon) - v_final = (q[:, None] / K.T.dot(log["u"])) ** fi - u_final = (A / K.dot(log["v"])) ** fi + fi = reg_m / (reg_m + epsilon) + logA = np.log(A + 1e-16) + logq = np.log(q + 1e-16)[:, None] + logKtu = logsumexp(log["logu"][:, None, :] - M[:, :, None] / epsilon, + axis=0) + logKv = logsumexp(log["logv"][None, :] - M[:, :, None] / epsilon, axis=1) + v_final = fi * (logq - logKtu) + u_final = fi * (logA - logKv) np.testing.assert_allclose( - u_final, log["u"], atol=1e-05) + u_final, log["logu"], atol=1e-05) np.testing.assert_allclose( - v_final, log["v"], atol=1e-05) + v_final, log["logv"], atol=1e-05) + + +def test_barycenter_stabilized_vs_sinkhorn(): + # test generalized sinkhorn for unbalanced OT barycenter + n = 100 + rng = np.random.RandomState(42) + + x = rng.randn(n, 2) + A = rng.rand(n, 2) + + # make dists unbalanced + A = A * np.array([1, 4])[None, :] + M = ot.dist(x, x) + epsilon = 0.5 + reg_m = 10 + + qstable, log = barycenter_unbalanced(A, M, reg=epsilon, + reg_m=reg_m, log=True, + tau=100, + method="sinkhorn_stabilized", + ) + q, log = barycenter_unbalanced(A, M, reg=epsilon, reg_m=reg_m, + method="sinkhorn", + log=True) + + np.testing.assert_allclose( + q, qstable, atol=1e-05) def test_implemented_methods(): - IMPLEMENTED_METHODS = ['sinkhorn'] - TO_BE_IMPLEMENTED_METHODS = ['sinkhorn_stabilized', - 'sinkhorn_epsilon_scaling'] + IMPLEMENTED_METHODS = ['sinkhorn', 'sinkhorn_stabilized'] + TO_BE_IMPLEMENTED_METHODS = ['sinkhorn_reg_scaling'] NOT_VALID_TOKENS = ['foo'] # test generalized sinkhorn for unbalanced OT barycenter n = 3 @@ -123,24 +192,30 @@ def test_implemented_methods(): # make dists unbalanced b = ot.utils.unif(n) * 1.5 - + A = rng.rand(n, 2) M = ot.dist(x, x) epsilon = 1. - alpha = 1. + reg_m = 1. for method in IMPLEMENTED_METHODS: - ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, + ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, reg_m, method=method) - ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, alpha, + ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, reg_m, method=method) + barycenter_unbalanced(A, M, reg=epsilon, reg_m=reg_m, + method=method) with pytest.warns(UserWarning, match='not implemented'): for method in set(TO_BE_IMPLEMENTED_METHODS): - ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, + ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, reg_m, method=method) - ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, alpha, + ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, reg_m, method=method) + barycenter_unbalanced(A, M, reg=epsilon, reg_m=reg_m, + method=method) with pytest.raises(ValueError): for method in set(NOT_VALID_TOKENS): - ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, alpha, + ot.unbalanced.sinkhorn_unbalanced(a, b, M, epsilon, reg_m, method=method) - ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, alpha, + ot.unbalanced.sinkhorn_unbalanced2(a, b, M, epsilon, reg_m, method=method) + barycenter_unbalanced(A, M, reg=epsilon, reg_m=reg_m, + method=method) -- cgit v1.2.3 From 7f4af32c70452ff12fa8c15dea317c14d3d97878 Mon Sep 17 00:00:00 2001 From: Hicham Janati Date: Wed, 28 Aug 2019 15:40:40 +0200 Subject: correct call of unbalanced with reg_m --- ot/da.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/da.py b/ot/da.py index 2af855d..108a38d 100644 --- a/ot/da.py +++ b/ot/da.py @@ -1902,7 +1902,7 @@ class UnbalancedSinkhornTransport(BaseTransport): returned_ = sinkhorn_unbalanced( a=self.mu_s, b=self.mu_t, M=self.cost_, - reg=self.reg_e, alpha=self.reg_m, method=self.method, + reg=self.reg_e, reg_m=self.reg_m, method=self.method, numItermax=self.max_iter, stopThr=self.tol, verbose=self.verbose, log=self.log) -- cgit v1.2.3 From f4e8523c92a96d061040e3f25037e129d67a2d94 Mon Sep 17 00:00:00 2001 From: Hicham Janati Date: Wed, 28 Aug 2019 15:47:52 +0200 Subject: fix empirical sinkhorn doc-example --- ot/bregman.py | 16 ++++++++-------- 1 file changed, 8 insertions(+), 8 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 2f27d58..76698c2 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -1534,11 +1534,11 @@ def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numI Examples -------- - >>> n_a = 2 - >>> n_b = 2 + >>> n_samples_a = 2 + >>> n_samples_b = 2 >>> reg = 0.1 - >>> X_s = np.reshape(np.arange(n_a), (dim_a, 1)) - >>> X_t = np.reshape(np.arange(0, n_b), (dim_b, 1)) + >>> X_s = np.reshape(np.arange(n_samples_a), (n_samples_a, 1)) + >>> X_t = np.reshape(np.arange(0, n_samples_b), (n_samples_b, 1)) >>> empirical_sinkhorn(X_s, X_t, reg, verbose=False) # doctest: +NORMALIZE_WHITESPACE array([[4.99977301e-01, 2.26989344e-05], [2.26989344e-05, 4.99977301e-01]]) @@ -1624,8 +1624,8 @@ def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', num Examples -------- - >>> n_a = 2 - >>> n_b = 2 + >>> n_samples_a = 2 + >>> n_samples_b = 2 >>> reg = 0.1 >>> X_s = np.reshape(np.arange(n_samples_a), (n_samples_a, 1)) >>> X_t = np.reshape(np.arange(0, n_samples_b), (n_samples_b, 1)) @@ -1730,8 +1730,8 @@ def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeucli Examples -------- - >>> n_a = 2 - >>> n_b = 4 + >>> n_samples_a = 2 + >>> n_samples_b = 4 >>> reg = 0.1 >>> X_s = np.reshape(np.arange(n_samples_a), (n_samples_a, 1)) >>> X_t = np.reshape(np.arange(0, n_samples_b), (n_samples_b, 1)) -- cgit v1.2.3 From c7269d3fc72c679711699a9df7b5670b0dd176b0 Mon Sep 17 00:00:00 2001 From: Hicham Janati Date: Tue, 3 Sep 2019 17:26:22 +0200 Subject: style + make funcs public --- ot/bregman.py | 180 +++++++++++++++++++++++++++++++--------------------------- 1 file changed, 95 insertions(+), 85 deletions(-) diff --git a/ot/bregman.py b/ot/bregman.py index 76698c2..02aeb6d 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -33,7 +33,7 @@ def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, \gamma\geq 0 where : - - M is the (dim_a, n_b) metric cost matrix + - M is the (dim_a, dim_b) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - a and b are source and target weights (sum to 1) @@ -47,7 +47,7 @@ def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, b : ndarray, shape (dim_b,) or ndarray, shape (dim_b, n_hists) samples in the target domain, compute sinkhorn with multiple targets and fixed M if b is a matrix (return OT loss + dual variables in log) - M : ndarray, shape (dim_a, n_b) + M : ndarray, shape (dim_a, dim_b) loss matrix reg : float Regularization term >0 @@ -66,7 +66,7 @@ def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, Returns ------- - gamma : ndarray, shape (dim_a, n_b) + gamma : ndarray, shape (dim_a, dim_b) Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -105,21 +105,21 @@ def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, """ if method.lower() == 'sinkhorn': - return _sinkhorn_knopp(a, b, M, reg, numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log, - **kwargs) + return sinkhorn_knopp(a, b, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log, + **kwargs) elif method.lower() == 'greenkhorn': - return _greenkhorn(a, b, M, reg, numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log) + return greenkhorn(a, b, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log) elif method.lower() == 'sinkhorn_stabilized': - return _sinkhorn_stabilized(a, b, M, reg, numItermax=numItermax, - stopThr=stopThr, verbose=verbose, - log=log, **kwargs) + return sinkhorn_stabilized(a, b, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) elif method.lower() == 'sinkhorn_epsilon_scaling': - return _sinkhorn_epsilon_scaling(a, b, M, reg, - numItermax=numItermax, - stopThr=stopThr, verbose=verbose, - log=log, **kwargs) + return sinkhorn_epsilon_scaling(a, b, M, reg, + numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) else: raise ValueError("Unknown method '%s'." % method) @@ -141,7 +141,7 @@ def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, \gamma\geq 0 where : - - M is the (dim_a, n_b) metric cost matrix + - M is the (dim_a, dim_b) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - a and b are source and target weights (sum to 1) @@ -155,7 +155,7 @@ def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, b : ndarray, shape (dim_b,) or ndarray, shape (dim_b, n_hists) samples in the target domain, compute sinkhorn with multiple targets and fixed M if b is a matrix (return OT loss + dual variables in log) - M : ndarray, shape (dim_a, n_b) + M : ndarray, shape (dim_a, dim_b) loss matrix reg : float Regularization term >0 @@ -173,8 +173,8 @@ def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, Returns ------- - W : (nt) ndarray or float - Optimal transportation matrix for the given parameters + W : (n_hists) ndarray or float + Optimal transportation loss for the given parameters log : dict log dictionary return only if log==True in parameters @@ -217,21 +217,23 @@ def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, if len(b.shape) < 2: b = b[:, None] if method.lower() == 'sinkhorn': - return _sinkhorn_knopp(a, b, M, reg, numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log, **kwargs) + return sinkhorn_knopp(a, b, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log, + **kwargs) elif method.lower() == 'sinkhorn_stabilized': - return _sinkhorn_stabilized(a, b, M, reg, numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log, **kwargs) + return sinkhorn_stabilized(a, b, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log, + **kwargs) elif method.lower() == 'sinkhorn_epsilon_scaling': - return _sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=numItermax, - stopThr=stopThr, verbose=verbose, - log=log, **kwargs) + return sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) else: raise ValueError("Unknown method '%s'." % method) -def _sinkhorn_knopp(a, b, M, reg, numItermax=1000, - stopThr=1e-9, verbose=False, log=False, **kwargs): +def sinkhorn_knopp(a, b, M, reg, numItermax=1000, + stopThr=1e-9, verbose=False, log=False, **kwargs): r""" Solve the entropic regularization optimal transport problem and return the OT matrix @@ -247,7 +249,7 @@ def _sinkhorn_knopp(a, b, M, reg, numItermax=1000, \gamma\geq 0 where : - - M is the (dim_a, n_b) metric cost matrix + - M is the (dim_a, dim_b) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - a and b are source and target weights (sum to 1) @@ -261,7 +263,7 @@ def _sinkhorn_knopp(a, b, M, reg, numItermax=1000, b : ndarray, shape (dim_b,) or ndarray, shape (dim_b, n_hists) samples in the target domain, compute sinkhorn with multiple targets and fixed M if b is a matrix (return OT loss + dual variables in log) - M : ndarray, shape (dim_a, n_b) + M : ndarray, shape (dim_a, dim_b) loss matrix reg : float Regularization term >0 @@ -276,7 +278,7 @@ def _sinkhorn_knopp(a, b, M, reg, numItermax=1000, Returns ------- - gamma : ndarray, shape (dim_a, n_b) + gamma : ndarray, shape (dim_a, dim_b) Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -403,7 +405,8 @@ def _sinkhorn_knopp(a, b, M, reg, numItermax=1000, return u.reshape((-1, 1)) * K * v.reshape((1, -1)) -def _greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log=False): +def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, + log=False): r""" Solve the entropic regularization optimal transport problem and return the OT matrix @@ -427,7 +430,7 @@ def _greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log \gamma\geq 0 where : - - M is the (dim_a, n_b) metric cost matrix + - M is the (dim_a, dim_b) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - a and b are source and target weights (sum to 1) @@ -440,7 +443,7 @@ def _greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log b : ndarray, shape (dim_b,) or ndarray, shape (dim_b, n_hists) samples in the target domain, compute sinkhorn with multiple targets and fixed M if b is a matrix (return OT loss + dual variables in log) - M : ndarray, shape (dim_a, n_b) + M : ndarray, shape (dim_a, dim_b) loss matrix reg : float Regularization term >0 @@ -453,7 +456,7 @@ def _greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log Returns ------- - gamma : ndarray, shape (dim_a, n_b) + gamma : ndarray, shape (dim_a, dim_b) Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -465,7 +468,7 @@ def _greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log >>> a=[.5, .5] >>> b=[.5, .5] >>> M=[[0., 1.], [1., 0.]] - >>> ot.bregman._greenkhorn(a, b, M, 1) + >>> ot.bregman.greenkhorn(a, b, M, 1) array([[0.36552929, 0.13447071], [0.13447071, 0.36552929]]) @@ -555,9 +558,9 @@ def _greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, log return G -def _sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, - warmstart=None, verbose=False, print_period=20, - log=False, **kwargs): +def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, + warmstart=None, verbose=False, print_period=20, + log=False, **kwargs): r""" Solve the entropic regularization OT problem with log stabilization @@ -573,7 +576,7 @@ def _sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, \gamma\geq 0 where : - - M is the (dim_a, n_b) metric cost matrix + - M is the (dim_a, dim_b) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - a and b are source and target weights (sum to 1) @@ -588,7 +591,7 @@ def _sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, samples weights in the source domain b : ndarray, shape (dim_b,) samples in the target domain - M : ndarray, shape (dim_a, n_b) + M : ndarray, shape (dim_a, dim_b) loss matrix reg : float Regularization term >0 @@ -607,7 +610,7 @@ def _sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, Returns ------- - gamma : ndarray, shape (dim_a, n_b) + gamma : ndarray, shape (dim_a, dim_b) Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -619,7 +622,7 @@ def _sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, >>> a=[.5,.5] >>> b=[.5,.5] >>> M=[[0.,1.],[1.,0.]] - >>> ot.bregman._sinkhorn_stabilized(a, b, M, 1) + >>> ot.bregman.sinkhorn_stabilized(a, b, M, 1) array([[0.36552929, 0.13447071], [0.13447071, 0.36552929]]) @@ -658,8 +661,8 @@ def _sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, n_hists = 0 # init data - na = len(a) - nb = len(b) + dim_a = len(a) + dim_b = len(b) cpt = 0 if log: @@ -668,24 +671,25 @@ def _sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, # we assume that no distances are null except those of the diagonal of # distances if warmstart is None: - alpha, beta = np.zeros(na), np.zeros(nb) + alpha, beta = np.zeros(dim_a), np.zeros(dim_b) else: alpha, beta = warmstart if n_hists: - u, v = np.ones((na, n_hists)) / na, np.ones((nb, n_hists)) / nb + u = np.ones((dim_a, n_hists)) / dim_a + v = np.ones((dim_b, n_hists)) / dim_b else: - u, v = np.ones(na) / na, np.ones(nb) / nb + u, v = np.ones(dim_a) / dim_a, np.ones(dim_b) / dim_b def get_K(alpha, beta): """log space computation""" - return np.exp(-(M - alpha.reshape((na, 1)) - - beta.reshape((1, nb))) / reg) + return np.exp(-(M - alpha.reshape((dim_a, 1)) + - beta.reshape((1, dim_b))) / reg) def get_Gamma(alpha, beta, u, v): """log space gamma computation""" - return np.exp(-(M - alpha.reshape((na, 1)) - beta.reshape((1, nb))) - / reg + np.log(u.reshape((na, 1))) + np.log(v.reshape((1, nb)))) + return np.exp(-(M - alpha.reshape((dim_a, 1)) - beta.reshape((1, dim_b))) + / reg + np.log(u.reshape((dim_a, 1))) + np.log(v.reshape((1, dim_b)))) # print(np.min(K)) @@ -711,9 +715,9 @@ def _sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, else: alpha, beta = alpha + reg * np.log(u), beta + reg * np.log(v) if n_hists: - u, v = np.ones((na, n_hists)) / na, np.ones((nb, n_hists)) / nb + u, v = np.ones((dim_a, n_hists)) / dim_a, np.ones((dim_b, n_hists)) / dim_b else: - u, v = np.ones(na) / na, np.ones(nb) / nb + u, v = np.ones(dim_a) / dim_a, np.ones(dim_b) / dim_b K = get_K(alpha, beta) if cpt % print_period == 0: @@ -782,10 +786,10 @@ def _sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, return get_Gamma(alpha, beta, u, v) -def _sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, - numInnerItermax=100, tau=1e3, stopThr=1e-9, - warmstart=None, verbose=False, print_period=10, - log=False, **kwargs): +def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, + numInnerItermax=100, tau=1e3, stopThr=1e-9, + warmstart=None, verbose=False, print_period=10, + log=False, **kwargs): r""" Solve the entropic regularization optimal transport problem with log stabilization and epsilon scaling. @@ -802,7 +806,7 @@ def _sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, \gamma\geq 0 where : - - M is the (dim_a, n_b) metric cost matrix + - M is the (dim_a, dim_b) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - a and b are source and target weights (sum to 1) @@ -817,7 +821,7 @@ def _sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, samples weights in the source domain b : ndarray, shape (dim_b,) samples in the target domain - M : ndarray, shape (dim_a, n_b) + M : ndarray, shape (dim_a, dim_b) loss matrix reg : float Regularization term >0 @@ -840,7 +844,7 @@ def _sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, Returns ------- - gamma : ndarray, shape (dim_a, n_b) + gamma : ndarray, shape (dim_a, dim_b) Optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -852,7 +856,7 @@ def _sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, >>> a=[.5, .5] >>> b=[.5, .5] >>> M=[[0., 1.], [1., 0.]] - >>> ot.bregman._sinkhorn_epsilon_scaling(a, b, M, 1) + >>> ot.bregman.sinkhorn_epsilon_scaling(a, b, M, 1) array([[0.36552929, 0.13447071], [0.13447071, 0.36552929]]) @@ -915,8 +919,10 @@ def _sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, regi = get_reg(cpt) - G, logi = _sinkhorn_stabilized(a, b, M, regi, numItermax=numInnerItermax, stopThr=1e-9, warmstart=( - alpha, beta), verbose=False, print_period=20, tau=tau, log=True) + G, logi = sinkhorn_stabilized(a, b, M, regi, + numItermax=numInnerItermax, stopThr=1e-9, + warmstart=(alpha, beta), verbose=False, + print_period=20, tau=tau, log=True) alpha = logi['alpha'] beta = logi['beta'] @@ -1029,19 +1035,19 @@ def barycenter(A, M, reg, weights=None, method="sinkhorn", numItermax=10000, """ if method.lower() == 'sinkhorn': - return _barycenter(A, M, reg, numItermax=numItermax, - stopThr=stopThr, verbose=verbose, log=log, - **kwargs) + return barycenter_sinkhorn(A, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, log=log, + **kwargs) elif method.lower() == 'sinkhorn_stabilized': - return _barycenter_stabilized(A, M, reg, numItermax=numItermax, - stopThr=stopThr, verbose=verbose, - log=log, **kwargs) + return barycenter_stabilized(A, M, reg, numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) else: raise ValueError("Unknown method '%s'." % method) -def _barycenter(A, M, reg, weights=None, numItermax=1000, - stopThr=1e-4, verbose=False, log=False): +def barycenter_sinkhorn(A, M, reg, weights=None, numItermax=1000, + stopThr=1e-4, verbose=False, log=False): r"""Compute the entropic regularized wasserstein barycenter of distributions A The function solves the following optimization problem: @@ -1134,8 +1140,8 @@ def _barycenter(A, M, reg, weights=None, numItermax=1000, return geometricBar(weights, UKv) -def _barycenter_stabilized(A, M, reg, tau=1e10, weights=None, numItermax=1000, - stopThr=1e-4, verbose=False, log=False): +def barycenter_stabilized(A, M, reg, tau=1e10, weights=None, numItermax=1000, + stopThr=1e-4, verbose=False, log=False): r"""Compute the entropic regularized wasserstein barycenter of distributions A with stabilization. @@ -1264,7 +1270,9 @@ def _barycenter_stabilized(A, M, reg, tau=1e10, weights=None, numItermax=1000, return q -def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, stopThr=1e-9, stabThr=1e-30, verbose=False, log=False): +def convolutional_barycenter2d(A, reg, weights=None, numItermax=10000, + stopThr=1e-9, stabThr=1e-30, verbose=False, + log=False): r"""Compute the entropic regularized wasserstein barycenter of distributions A where A is a collection of 2D images. @@ -1480,7 +1488,9 @@ def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000, return np.sum(K0, axis=1) -def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numIterMax=10000, stopThr=1e-9, verbose=False, log=False, **kwargs): +def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', + numIterMax=10000, stopThr=1e-9, verbose=False, + log=False, **kwargs): r''' Solve the entropic regularization optimal transport problem and return the OT matrix from empirical data @@ -1497,22 +1507,22 @@ def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numI \gamma\geq 0 where : - - :math:`M` is the (dim_a, n_b) metric cost matrix + - :math:`M` is the (n_samples_a, n_samples_b) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - :math:`a` and :math:`b` are source and target weights (sum to 1) Parameters ---------- - X_s : ndarray, shape (dim_a, d) + X_s : ndarray, shape (n_samples_a, dim) samples in the source domain - X_t : ndarray, shape (dim_b, d) + X_t : ndarray, shape (n_samples_b, dim) samples in the target domain reg : float Regularization term >0 - a : ndarray, shape (dim_a,) + a : ndarray, shape (n_samples_a,) samples weights in the source domain - b : ndarray, shape (dim_b,) + b : ndarray, shape (n_samples_b,) samples weights in the target domain numItermax : int, optional Max number of iterations @@ -1526,7 +1536,7 @@ def empirical_sinkhorn(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', numI Returns ------- - gamma : ndarray, shape (dim_a, n_b) + gamma : ndarray, shape (n_samples_a, n_samples_b) Regularized optimal transportation matrix for the given parameters log : dict log dictionary return only if log==True in parameters @@ -1587,7 +1597,7 @@ def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', num \gamma\geq 0 where : - - :math:`M` is the (dim_a, n_b) metric cost matrix + - :math:`M` is the (n_samples_a, n_samples_b) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - :math:`a` and :math:`b` are source and target weights (sum to 1) @@ -1596,7 +1606,7 @@ def empirical_sinkhorn2(X_s, X_t, reg, a=None, b=None, metric='sqeuclidean', num ---------- X_s : ndarray, shape (n_samples_a, dim) samples in the source domain - X_t : ndarray, shape (n_samples_b, d) + X_t : ndarray, shape (n_samples_b, dim) samples in the target domain reg : float Regularization term >0 @@ -1695,7 +1705,7 @@ def empirical_sinkhorn_divergence(X_s, X_t, reg, a=None, b=None, metric='sqeucli \gamma_b\geq 0 where : - - :math:`M` (resp. :math:`M_a, M_b`) is the (dim_a, n_b) metric cost matrix (resp (dim_a, ns) and (dim_b, nt)) + - :math:`M` (resp. :math:`M_a, M_b`) is the (n_samples_a, n_samples_b) metric cost matrix (resp (n_samples_a, n_samples_a) and (n_samples_b, n_samples_b)) - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - :math:`a` and :math:`b` are source and target weights (sum to 1) -- cgit v1.2.3 From 7efea812ad0b1c7e3783397dbd8f3ad802fb7ac2 Mon Sep 17 00:00:00 2001 From: Hicham Janati Date: Tue, 3 Sep 2019 17:26:30 +0200 Subject: same for unbalanced --- ot/unbalanced.py | 102 +++++++++++++++++++++++++++---------------------------- 1 file changed, 51 insertions(+), 51 deletions(-) diff --git a/ot/unbalanced.py b/ot/unbalanced.py index 3f71d28..25e4cf5 100644 --- a/ot/unbalanced.py +++ b/ot/unbalanced.py @@ -120,23 +120,23 @@ def sinkhorn_unbalanced(a, b, M, reg, reg_m, method='sinkhorn', numItermax=1000, """ if method.lower() == 'sinkhorn': - return _sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, - numItermax=numItermax, - stopThr=stopThr, verbose=verbose, - log=log, **kwargs) + return sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, + numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) elif method.lower() == 'sinkhorn_stabilized': - return _sinkhorn_stabilized_unbalanced(a, b, M, reg, reg_m, - numItermax=numItermax, - stopThr=stopThr, - verbose=verbose, - log=log, **kwargs) + return sinkhorn_stabilized_unbalanced(a, b, M, reg, reg_m, + numItermax=numItermax, + stopThr=stopThr, + verbose=verbose, + log=log, **kwargs) elif method.lower() in ['sinkhorn_reg_scaling']: warnings.warn('Method not implemented yet. Using classic Sinkhorn Knopp') - return _sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, - numItermax=numItermax, - stopThr=stopThr, verbose=verbose, - log=log, **kwargs) + return sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, + numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) else: raise ValueError("Unknown method '%s'." % method) @@ -241,29 +241,29 @@ def sinkhorn_unbalanced2(a, b, M, reg, reg_m, method='sinkhorn', if len(b.shape) < 2: b = b[:, None] if method.lower() == 'sinkhorn': - return _sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, - numItermax=numItermax, - stopThr=stopThr, verbose=verbose, - log=log, **kwargs) + return sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, + numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) elif method.lower() == 'sinkhorn_stabilized': - return _sinkhorn_stabilized_unbalanced(a, b, M, reg, reg_m, - numItermax=numItermax, - stopThr=stopThr, - verbose=verbose, - log=log, **kwargs) + return sinkhorn_stabilized_unbalanced(a, b, M, reg, reg_m, + numItermax=numItermax, + stopThr=stopThr, + verbose=verbose, + log=log, **kwargs) elif method.lower() in ['sinkhorn_reg_scaling']: warnings.warn('Method not implemented yet. Using classic Sinkhorn Knopp') - return _sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, - numItermax=numItermax, - stopThr=stopThr, verbose=verbose, - log=log, **kwargs) + return sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, + numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) else: raise ValueError('Unknown method %s.' % method) -def _sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, numItermax=1000, - stopThr=1e-6, verbose=False, log=False, **kwargs): +def sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, numItermax=1000, + stopThr=1e-6, verbose=False, log=False, **kwargs): r""" Solve the entropic regularization unbalanced optimal transport problem and return the loss @@ -300,7 +300,7 @@ def _sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, numItermax=1000, numItermax : int, optional Max number of iterations stopThr : float, optional - Stop threshol on error (>0) + Stop threshol on error (> 0) verbose : bool, optional Print information along iterations log : bool, optional @@ -439,9 +439,9 @@ def _sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, numItermax=1000, return u[:, None] * K * v[None, :] -def _sinkhorn_stabilized_unbalanced(a, b, M, reg, reg_m, tau=1e5, numItermax=1000, - stopThr=1e-6, verbose=False, log=False, - **kwargs): +def sinkhorn_stabilized_unbalanced(a, b, M, reg, reg_m, tau=1e5, numItermax=1000, + stopThr=1e-6, verbose=False, log=False, + **kwargs): r""" Solve the entropic regularization unbalanced optimal transport problem and return the loss @@ -653,9 +653,9 @@ def _sinkhorn_stabilized_unbalanced(a, b, M, reg, reg_m, tau=1e5, numItermax=100 return ot_matrix -def _barycenter_unbalanced_stabilized(A, M, reg, reg_m, weights=None, tau=1e3, - numItermax=1000, stopThr=1e-6, - verbose=False, log=False): +def barycenter_unbalanced_stabilized(A, M, reg, reg_m, weights=None, tau=1e3, + numItermax=1000, stopThr=1e-6, + verbose=False, log=False): r"""Compute the entropic unbalanced wasserstein barycenter of A with stabilization. The function solves the following optimization problem: @@ -804,9 +804,9 @@ def _barycenter_unbalanced_stabilized(A, M, reg, reg_m, weights=None, tau=1e3, return q -def _barycenter_unbalanced(A, M, reg, reg_m, weights=None, - numItermax=1000, stopThr=1e-6, - verbose=False, log=False): +def barycenter_unbalanced_sinkhorn(A, M, reg, reg_m, weights=None, + numItermax=1000, stopThr=1e-6, + verbose=False, log=False): r"""Compute the entropic unbalanced wasserstein barycenter of A. The function solves the following optimization problem with a @@ -1001,22 +1001,22 @@ def barycenter_unbalanced(A, M, reg, reg_m, method="sinkhorn", weights=None, """ if method.lower() == 'sinkhorn': - return _barycenter_unbalanced(A, M, reg, reg_m, - numItermax=numItermax, - stopThr=stopThr, verbose=verbose, - log=log, **kwargs) + return barycenter_unbalanced_sinkhorn(A, M, reg, reg_m, + numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) elif method.lower() == 'sinkhorn_stabilized': - return _barycenter_unbalanced_stabilized(A, M, reg, reg_m, - numItermax=numItermax, - stopThr=stopThr, - verbose=verbose, - log=log, **kwargs) + return barycenter_unbalanced_stabilized(A, M, reg, reg_m, + numItermax=numItermax, + stopThr=stopThr, + verbose=verbose, + log=log, **kwargs) elif method.lower() in ['sinkhorn_reg_scaling']: warnings.warn('Method not implemented yet. Using classic Sinkhorn Knopp') - return _barycenter_unbalanced(A, M, reg, reg_m, - numItermax=numItermax, - stopThr=stopThr, verbose=verbose, - log=log, **kwargs) + return barycenter_unbalanced(A, M, reg, reg_m, + numItermax=numItermax, + stopThr=stopThr, verbose=verbose, + log=log, **kwargs) else: raise ValueError("Unknown method '%s'." % method) -- cgit v1.2.3 From 49d9b5cf4eecefdc0fff4db6c43e85d16e478efb Mon Sep 17 00:00:00 2001 From: Hicham Janati Date: Tue, 3 Sep 2019 17:35:23 +0200 Subject: fix doctest examples --- ot/unbalanced.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/ot/unbalanced.py b/ot/unbalanced.py index 25e4cf5..d516dfc 100644 --- a/ot/unbalanced.py +++ b/ot/unbalanced.py @@ -326,7 +326,7 @@ def sinkhorn_knopp_unbalanced(a, b, M, reg, reg_m, numItermax=1000, >>> a=[.5, .5] >>> b=[.5, .5] >>> M=[[0., 1.],[1., 0.]] - >>> ot.unbalanced._sinkhorn_knopp_unbalanced(a, b, M, 1., 1.) + >>> ot.unbalanced.sinkhorn_knopp_unbalanced(a, b, M, 1., 1.) array([[0.51122823, 0.18807035], [0.18807035, 0.51122823]]) @@ -510,7 +510,7 @@ def sinkhorn_stabilized_unbalanced(a, b, M, reg, reg_m, tau=1e5, numItermax=1000 >>> a=[.5, .5] >>> b=[.5, .5] >>> M=[[0., 1.],[1., 0.]] - >>> ot.unbalanced._sinkhorn_stabilized_unbalanced(a, b, M, 1., 1.) + >>> ot.unbalanced.sinkhorn_stabilized_unbalanced(a, b, M, 1., 1.) array([[0.51122823, 0.18807035], [0.18807035, 0.51122823]]) -- cgit v1.2.3 From e55232056a79de128583b87e65abc6d7a75fb298 Mon Sep 17 00:00:00 2001 From: Hicham Janati Date: Tue, 3 Sep 2019 18:15:25 +0200 Subject: add unbalanced_barycenter import --- ot/__init__.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/ot/__init__.py b/ot/__init__.py index 7d9615a..99f288e 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -65,7 +65,7 @@ from . import unbalanced # OT functions from .lp import emd, emd2, emd_1d, emd2_1d, wasserstein_1d from .bregman import sinkhorn, sinkhorn2, barycenter -from .unbalanced import sinkhorn_unbalanced, barycenter_unbalanced +from .unbalanced import sinkhorn_unbalanced, barycenter_unbalanced, sinkhorn_unbalanced2 from .da import sinkhorn_lpl1_mm # utils functions -- cgit v1.2.3 From a96caaed079b6e368df090c3e3290398e8b4a99e Mon Sep 17 00:00:00 2001 From: Hicham Janati Date: Tue, 3 Sep 2019 18:15:38 +0200 Subject: update UOT paragraph in quickstart --- docs/source/quickstart.rst | 107 ++++++++++++++++++++++++++------------------- 1 file changed, 61 insertions(+), 46 deletions(-) diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst index 1640d6a..e4ab3a3 100644 --- a/docs/source/quickstart.rst +++ b/docs/source/quickstart.rst @@ -2,13 +2,13 @@ Quick start guide ================= -In the following we provide some pointers about which functions and classes +In the following we provide some pointers about which functions and classes to use for different problems related to optimal transport (OT) and machine learning. We refer when we can to concrete examples in the documentation that are also available as notebooks on the POT Github. This document is not a tutorial on numerical optimal transport. For this we strongly -recommend to read the very nice book [15]_ . +recommend to read the very nice book [15]_ . Optimal transport and Wasserstein distance @@ -55,8 +55,8 @@ solver is quite efficient and uses sparsity of the solution. Examples of use for :any:`ot.emd` are available in : - :any:`auto_examples/plot_OT_2D_samples` - - :any:`auto_examples/plot_OT_1D` - - :any:`auto_examples/plot_OT_L1_vs_L2` + - :any:`auto_examples/plot_OT_1D` + - :any:`auto_examples/plot_OT_L1_vs_L2` Computing Wasserstein distance @@ -102,13 +102,13 @@ distance. An example of use for :any:`ot.emd2` is available in : - :any:`auto_examples/plot_compute_emd` - + Special cases ^^^^^^^^^^^^^ Note that the OT problem and the corresponding Wasserstein distance can in some -special cases be computed very efficiently. +special cases be computed very efficiently. For instance when the samples are in 1D, then the OT problem can be solved in :math:`O(n\log(n))` by using a simple sorting. In this case we provide the @@ -117,13 +117,13 @@ matrix and value. Note that since the solution is very sparse the :code:`sparse` parameter of :any:`ot.emd_1d` allows for solving and returning the solution for very large problems. Note that in order to compute directly the :math:`W_p` Wasserstein distance in 1D we provide the function :any:`ot.wasserstein_1d` that -takes :code:`p` as a parameter. +takes :code:`p` as a parameter. Another special case for estimating OT and Monge mapping is between Gaussian distributions. In this case there exists a close form solution given in Remark 2.29 in [15]_ and the Monge mapping is an affine function and can be also computed from the covariances and means of the source and target -distributions. In the case when the finite sample dataset is supposed gaussian, we provide +distributions. In the case when the finite sample dataset is supposed gaussian, we provide :any:`ot.da.OT_mapping_linear` that returns the parameters for the Monge mapping. @@ -176,7 +176,7 @@ solution of the resulting optimization problem can be expressed as: where :math:`u` and :math:`v` are vectors and :math:`K=\exp(-M/\lambda)` where the :math:`\exp` is taken component-wise. In order to solve the optimization problem, on can use an alternative projection algorithm called Sinkhorn-Knopp that can be very -efficient for large values if regularization. +efficient for large values if regularization. The Sinkhorn-Knopp algorithm is implemented in :any:`ot.sinkhorn` and :any:`ot.sinkhorn2` that return respectively the OT matrix and the value of the @@ -201,12 +201,12 @@ More details about the algorithms used are given in the following note. + :code:`method='sinkhorn'` calls :any:`ot.bregman.sinkhorn_knopp` the classic algorithm [2]_. + :code:`method='sinkhorn_stabilized'` calls :any:`ot.bregman.sinkhorn_stabilized` the - log stabilized version of the algorithm [9]_. + log stabilized version of the algorithm [9]_. + :code:`method='sinkhorn_epsilon_scaling'` calls :any:`ot.bregman.sinkhorn_epsilon_scaling` the epsilon scaling version - of the algorithm [9]_. + of the algorithm [9]_. + :code:`method='greenkhorn'` calls :any:`ot.bregman.greenkhorn` the - greedy sinkhorn verison of the algorithm [22]_. + greedy sinkhorn verison of the algorithm [22]_. In addition to all those variants of sinkhorn, we have another implementation solving the problem in the smooth dual or semi-dual in @@ -236,7 +236,7 @@ of algorithms in [18]_ [19]_. Examples of use for :any:`ot.sinkhorn` are available in : - :any:`auto_examples/plot_OT_2D_samples` - - :any:`auto_examples/plot_OT_1D` + - :any:`auto_examples/plot_OT_1D` - :any:`auto_examples/plot_OT_1D_smooth` - :any:`auto_examples/plot_stochastic` @@ -248,13 +248,13 @@ While entropic OT is the most common and favored in practice, there exist other kind of regularization. We provide in POT two specific solvers for other regularization terms, namely quadratic regularization and group lasso regularization. But we also provide in :any:`ot.optim` two generic solvers that allows solving any -smooth regularization in practice. +smooth regularization in practice. Quadratic regularization """""""""""""""""""""""" The first general regularization term we can solve is the quadratic -regularization of the form +regularization of the form .. math:: \Omega(\gamma)=\sum_{i,j} \gamma_{i,j}^2 @@ -264,7 +264,7 @@ densifying the OT matrix but it keeps some sort of sparsity that is lost with entropic regularization as soon as :math:`\lambda>0` [17]_. This problem can be solved with POT using solvers from :any:`ot.smooth`, more specifically functions :any:`ot.smooth.smooth_ot_dual` or -:any:`ot.smooth.smooth_ot_semi_dual` with parameter :code:`reg_type='l2'` to +:any:`ot.smooth.smooth_ot_semi_dual` with parameter :code:`reg_type='l2'` to choose the quadratic regularization. .. hint:: @@ -300,7 +300,7 @@ gradient algorithm [7]_ in function .. hint:: Examples of group Lasso regularization are available in : - - :any:`auto_examples/plot_otda_classes` + - :any:`auto_examples/plot_otda_classes` - :any:`auto_examples/plot_otda_d2` @@ -311,7 +311,7 @@ Finally we propose in POT generic solvers that can be used to solve any regularization as long as you can provide a function computing the regularization and a function computing its gradient (or sub-gradient). -In order to solve +In order to solve .. math:: \gamma^* = arg\min_\gamma \quad \sum_{i,j}\gamma_{i,j}M_{i,j} + \lambda\Omega(\gamma) @@ -336,12 +336,12 @@ Another generic solver is proposed to solve the problem where :math:`\Omega_e` is the entropic regularization. In this case we use a generalized conditional gradient [7]_ implemented in :any:`ot.optim.gcg` that does not linearize the entropic term but -relies on :any:`ot.sinkhorn` for its iterations. +relies on :any:`ot.sinkhorn` for its iterations. .. hint:: An example of generic solvers are available in : - - :any:`auto_examples/plot_optim_OTreg` + - :any:`auto_examples/plot_optim_OTreg` Wasserstein Barycenters @@ -382,7 +382,7 @@ solver :any:`ot.lp.barycenter` that rely on generic LP solvers. By default the function uses :any:`scipy.optimize.linprog`, but more efficient LP solvers from cvxopt can be also used by changing parameter :code:`solver`. Note that this problem requires to solve a very large linear program and can be very slow in -practice. +practice. Similarly to the OT problem, OT barycenters can be computed in the regularized case. When using entropic regularization is used, the problem can be solved with a @@ -403,11 +403,11 @@ operators. We provide an implementation of this algorithm in function Examples of Wasserstein (:any:`ot.lp.barycenter`) and regularized Wasserstein barycenter (:any:`ot.bregman.barycenter`) computation are available in : - - :any:`auto_examples/plot_barycenter_1D` - - :any:`auto_examples/plot_barycenter_lp_vs_entropic` + - :any:`auto_examples/plot_barycenter_1D` + - :any:`auto_examples/plot_barycenter_lp_vs_entropic` An example of convolutional barycenter - (:any:`ot.bregman.convolutional_barycenter2d`) computation + (:any:`ot.bregman.convolutional_barycenter2d`) computation for 2D images is available in : @@ -451,13 +451,13 @@ optimal mapping is still an open problem in the general case but has been proven for smooth distributions by Brenier in his eponym `theorem `__. We provide in :any:`ot.da` several solvers for smooth Monge mapping estimation and domain -adaptation from discrete distributions. +adaptation from discrete distributions. Monge Mapping estimation ^^^^^^^^^^^^^^^^^^^^^^^^ We now discuss several approaches that are implemented in POT to estimate or -approximate a Monge mapping from finite distributions. +approximate a Monge mapping from finite distributions. First note that when the source and target distributions are supposed to be Gaussian distributions, there exists a close form solution for the mapping and its an @@ -513,16 +513,16 @@ A list of the provided implementation is given in the following note. Here is a list of the OT mapping classes inheriting from :any:`ot.da.BaseTransport` - + * :any:`ot.da.EMDTransport` : Barycentric mapping with EMD transport * :any:`ot.da.SinkhornTransport` : Barycentric mapping with Sinkhorn transport * :any:`ot.da.SinkhornL1l2Transport` : Barycentric mapping with Sinkhorn + group Lasso regularization [5]_ * :any:`ot.da.SinkhornLpl1Transport` : Barycentric mapping with Sinkhorn + - non convex group Lasso regularization [5]_ + non convex group Lasso regularization [5]_ * :any:`ot.da.LinearTransport` : Linear mapping estimation between Gaussians [14]_ - * :any:`ot.da.MappingTransport` : Nonlinear mapping estimation [8]_ + * :any:`ot.da.MappingTransport` : Nonlinear mapping estimation [8]_ .. hint:: @@ -550,7 +550,7 @@ consist in finding a linear projector optimizing the following criterion .. math:: P = \text{arg}\min_P \frac{\sum_i OT_e(\mu_i\#P,\mu_i\#P)}{\sum_{i,j\neq i} OT_e(\mu_i\#P,\mu_j\#P)} - + where :math:`\#` is the push-forward operator, :math:`OT_e` is the entropic OT loss and :math:`\mu_i` is the distribution of samples from class :math:`i`. :math:`P` is also constrained to @@ -575,10 +575,10 @@ respectively. Note that we also provide the Fisher discriminant estimator in Unbalanced optimal transport ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ -Unbalanced OT is a relaxation of the original OT problem where the violation of +Unbalanced OT is a relaxation of the entropy regularized OT problem where the violation of the constraint on the marginals is added to the objective of the optimization problem: - + .. math:: \min_\gamma \quad \sum_{i,j}\gamma_{i,j}M_{i,j} + reg\cdot\Omega(\gamma) + \alpha KL(\gamma 1, a) + \alpha KL(\gamma^T 1, b) @@ -589,9 +589,24 @@ where KL is the Kullback-Leibler divergence. This formulation allows for computing approximate mapping between distributions that do not have the same amount of mass. Interestingly the problem can be solved with a generalization of the Bregman projections algorithm [10]_. We provide a solver for unbalanced OT -in :any:`ot.unbalanced` and more specifically -in function :any:`ot.sinkhorn_unbalanced`. A solver for unbalanced OT barycenter -is available in :any:`ot.barycenter_unbalanced`. +in :any:`ot.unbalanced`. Computing the optimal transport +plan or the transport cost is similar to the balanced case. The Sinkhorn-Knopp +algorithm is implemented in :any:`ot.sinkhorn_unbalanced` and :any:`ot.sinkhorn_unbalanced2` +that return respectively the OT matrix and the value of the +linear term. Note that the regularization parameter :math:`\alpha` in the +equation above is given to those functions with the parameter :code:`reg_m`. + +Similarly, Unbalanced OT barycenters can be computed using :any:`ot.barycenter_unbalanced`. + +.. note:: + The main function to solve entropic regularized OT is :any:`ot.sinkhorn_unbalanced`. + This function is a wrapper and the parameter :code:`method` help you select + the actual algorithm used to solve the problem: + + + :code:`method='sinkhorn'` calls :any:`ot.unbalanced.sinkhorn_knopp_unbalanced` + the generalized Sinkhorn algorithm [10]_. + + :code:`method='sinkhorn_stabilized'` calls :any:`ot.unbalanced.sinkhorn_stabilized_unbalanced` + the log stabilized version of the algorithm [10]_. .. hint:: @@ -636,7 +651,7 @@ barycenters that can be expressed as where :math:`Ck` is the distance matrix between samples in distribution :math:`k`. Note that interestingly the barycenter is defined as a symmetric -positive matrix. We provide a block coordinate optimization procedure in +positive matrix. We provide a block coordinate optimization procedure in :any:`ot.gromov.gromov_barycenters` and :any:`ot.gromov.entropic_gromov_barycenters` for non-regularized and regularized barycenters respectively. @@ -654,19 +669,19 @@ The implementations of FGW and FGW barycenter is provided in functions Examples of computation of GW, regularized G and FGW are available in : - :any:`auto_examples/plot_gromov` - - :any:`auto_examples/plot_fgw` + - :any:`auto_examples/plot_fgw` Examples of GW, regularized GW and FGW barycenters are available in : - :any:`auto_examples/plot_gromov_barycenter` - - :any:`auto_examples/plot_barycenter_fgw` + - :any:`auto_examples/plot_barycenter_fgw` GPU acceleration ^^^^^^^^^^^^^^^^ We provide several implementation of our OT solvers in :any:`ot.gpu`. Those -implementations use the :code:`cupy` toolbox that obviously need to be installed. +implementations use the :code:`cupy` toolbox that obviously need to be installed. .. note:: @@ -701,7 +716,7 @@ FAQ 1. **How to solve a discrete optimal transport problem ?** The solver for discrete OT is the function :py:mod:`ot.emd` that returns - the OT transport matrix. If you want to solve a regularized OT you can + the OT transport matrix. If you want to solve a regularized OT you can use :py:mod:`ot.sinkhorn`. @@ -714,9 +729,9 @@ FAQ T=ot.emd(a,b,M) # exact linear program T_reg=ot.sinkhorn(a,b,M,reg) # entropic regularized OT - More detailed examples can be seen on this example: + More detailed examples can be seen on this example: :doc:`auto_examples/plot_OT_2D_samples` - + 2. **pip install POT fails with error : ImportError: No module named Cython.Build** @@ -726,7 +741,7 @@ FAQ installing POT. Note that this problem do not occur when using conda-forge since the packages - there are pre-compiled. + there are pre-compiled. See `Issue #59 `__ for more details. @@ -751,7 +766,7 @@ FAQ In order to limit import time and hard dependencies in POT. we do not import some sub-modules automatically with :code:`import ot`. In order to use the acceleration in :any:`ot.gpu` you need first to import is with - :code:`import ot.gpu`. + :code:`import ot.gpu`. See `Issue #85 `__ and :any:`ot.gpu` for more details. @@ -763,7 +778,7 @@ References .. [1] Bonneel, N., Van De Panne, M., Paris, S., & Heidrich, W. (2011, December). `Displacement nterpolation using Lagrangian mass transport `__. - In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM. + In ACM Transactions on Graphics (TOG) (Vol. 30, No. 6, p. 158). ACM. .. [2] Cuturi, M. (2013). `Sinkhorn distances: Lightspeed computation of optimal transport `__. In Advances @@ -874,4 +889,4 @@ References .. [24] Vayer, T., Chapel, L., Flamary, R., Tavenard, R. and Courty, N. (2019). `Optimal Transport for structured data with application on graphs `__ Proceedings - of the 36th International Conference on Machine Learning (ICML). \ No newline at end of file + of the 36th International Conference on Machine Learning (ICML). -- cgit v1.2.3 From b639e3eba2e88e20b5d2df417368200e4fc5157c Mon Sep 17 00:00:00 2001 From: Hicham Janati Date: Tue, 3 Sep 2019 18:39:03 +0200 Subject: update UOT text + add frogner ref --- docs/source/quickstart.rst | 49 ++++++++++++++++++++++++++++++++++++++-------- 1 file changed, 41 insertions(+), 8 deletions(-) diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst index e4ab3a3..9729664 100644 --- a/docs/source/quickstart.rst +++ b/docs/source/quickstart.rst @@ -577,10 +577,10 @@ Unbalanced optimal transport Unbalanced OT is a relaxation of the entropy regularized OT problem where the violation of the constraint on the marginals is added to the objective of the optimization -problem: +problem. The unbalanced OT metric between two histograms a and b is defined as [25]_ [10]_: .. math:: - \min_\gamma \quad \sum_{i,j}\gamma_{i,j}M_{i,j} + reg\cdot\Omega(\gamma) + \alpha KL(\gamma 1, a) + \alpha KL(\gamma^T 1, b) + W_u(a, b) = \min_\gamma \quad \sum_{i,j}\gamma_{i,j}M_{i,j} + reg\cdot\Omega(\gamma) + \alpha KL(\gamma 1, a) + \alpha KL(\gamma^T 1, b) s.t. \quad \gamma\geq 0 @@ -596,26 +596,55 @@ that return respectively the OT matrix and the value of the linear term. Note that the regularization parameter :math:`\alpha` in the equation above is given to those functions with the parameter :code:`reg_m`. -Similarly, Unbalanced OT barycenters can be computed using :any:`ot.barycenter_unbalanced`. .. note:: - The main function to solve entropic regularized OT is :any:`ot.sinkhorn_unbalanced`. + The main function to solve entropic regularized UOT is :any:`ot.sinkhorn_unbalanced`. This function is a wrapper and the parameter :code:`method` help you select the actual algorithm used to solve the problem: + :code:`method='sinkhorn'` calls :any:`ot.unbalanced.sinkhorn_knopp_unbalanced` - the generalized Sinkhorn algorithm [10]_. + the generalized Sinkhorn algorithm [25]_ [10]_. + :code:`method='sinkhorn_stabilized'` calls :any:`ot.unbalanced.sinkhorn_stabilized_unbalanced` the log stabilized version of the algorithm [10]_. .. hint:: - Examples of the use of :any:`ot.sinkhorn_unbalanced` and - :any:`ot.barycenter_unbalanced` are available in : + Examples of the use of :any:`ot.sinkhorn_unbalanced` are available in : - :any:`auto_examples/plot_UOT_1D` - - :any:`auto_examples/plot_UOT_barycenter_1D` + + +Unbalanced Barycenters +^^^^^^^^^^^^^^^^^^^^^^ + +As with balanced distributions, we can define a barycenter of a set of +histograms with different masses as a Fréchet Mean: + + .. math:: + \min_{\mu} \quad \sum_{k} w_kW_u(\mu,\mu_k) + +Where :math:`W_u` is the unbalanced Wasserstein metric defined above. This problem +can also be solved using generalized version of Sinkhorn's algorithm and it is +implemented the main function :any:`ot.barycenter_unbalanced`. + + +.. note:: + The main function to compute UOT barycenters is :any:`ot.barycenter_unbalanced`. + This function is a wrapper and the parameter :code:`method` help you select + the actual algorithm used to solve the problem: + + + :code:`method='sinkhorn'` calls :any:`ot.unbalanced.barycenter_unbalanced_sinkhorn_unbalanced` + the generalized Sinkhorn algorithm [10]_. + + :code:`method='sinkhorn_stabilized'` calls :any:`ot.unbalanced.barycenter_unbalanced_stabilized` + the log stabilized version of the algorithm [10]_. + + +.. hint:: + + Examples of the use of :any:`ot.barycenter_unbalanced` are available in : + + - :any:`auto_examples/plot_UOT_barycenter_1D` Gromov-Wasserstein @@ -890,3 +919,7 @@ References (2019). `Optimal Transport for structured data with application on graphs `__ Proceedings of the 36th International Conference on Machine Learning (ICML). + +.. [25] Frogner C., Zhang C., Mobahi H., Araya-Polo M., Poggio T. : + Learning with a Wasserstein Loss, Advances in Neural Information + Processing Systems (NIPS) 2015 -- cgit v1.2.3 From e0c935a865a57bc4603144b27f1b58cbfba87760 Mon Sep 17 00:00:00 2001 From: Hicham Janati Date: Wed, 4 Sep 2019 10:28:04 +0200 Subject: improve doc --- docs/source/quickstart.rst | 10 ++++------ ot/__init__.py | 3 ++- ot/bregman.py | 40 ++++++++++++++++++++++------------------ 3 files changed, 28 insertions(+), 25 deletions(-) diff --git a/docs/source/quickstart.rst b/docs/source/quickstart.rst index 9729664..978eaff 100644 --- a/docs/source/quickstart.rst +++ b/docs/source/quickstart.rst @@ -577,10 +577,10 @@ Unbalanced optimal transport Unbalanced OT is a relaxation of the entropy regularized OT problem where the violation of the constraint on the marginals is added to the objective of the optimization -problem. The unbalanced OT metric between two histograms a and b is defined as [25]_ [10]_: +problem. The unbalanced OT metric between two unbalanced histograms a and b is defined as [25]_ [10]_: .. math:: - W_u(a, b) = \min_\gamma \quad \sum_{i,j}\gamma_{i,j}M_{i,j} + reg\cdot\Omega(\gamma) + \alpha KL(\gamma 1, a) + \alpha KL(\gamma^T 1, b) + W_u(a, b) = \min_\gamma \quad \sum_{i,j}\gamma_{i,j}M_{i,j} + reg\cdot\Omega(\gamma) + reg_m KL(\gamma 1, a) + reg_m KL(\gamma^T 1, b) s.t. \quad \gamma\geq 0 @@ -593,13 +593,11 @@ in :any:`ot.unbalanced`. Computing the optimal transport plan or the transport cost is similar to the balanced case. The Sinkhorn-Knopp algorithm is implemented in :any:`ot.sinkhorn_unbalanced` and :any:`ot.sinkhorn_unbalanced2` that return respectively the OT matrix and the value of the -linear term. Note that the regularization parameter :math:`\alpha` in the -equation above is given to those functions with the parameter :code:`reg_m`. - +linear term. .. note:: The main function to solve entropic regularized UOT is :any:`ot.sinkhorn_unbalanced`. - This function is a wrapper and the parameter :code:`method` help you select + This function is a wrapper and the parameter :code:`method` helps you select the actual algorithm used to solve the problem: + :code:`method='sinkhorn'` calls :any:`ot.unbalanced.sinkhorn_knopp_unbalanced` diff --git a/ot/__init__.py b/ot/__init__.py index 99f288e..df0ef8a 100644 --- a/ot/__init__.py +++ b/ot/__init__.py @@ -77,4 +77,5 @@ __all__ = ["emd", "emd2", 'emd_1d', "sinkhorn", "sinkhorn2", "utils", 'datasets' 'bregman', 'lp', 'tic', 'toc', 'toq', 'gromov', 'emd_1d', 'emd2_1d', 'wasserstein_1d', 'dist', 'unif', 'barycenter', 'sinkhorn_lpl1_mm', 'da', 'optim', - 'sinkhorn_unbalanced', "barycenter_unbalanced"] + 'sinkhorn_unbalanced', 'barycenter_unbalanced', + 'sinkhorn_unbalanced2'] diff --git a/ot/bregman.py b/ot/bregman.py index 02aeb6d..2cd832b 100644 --- a/ot/bregman.py +++ b/ot/bregman.py @@ -35,7 +35,7 @@ def sinkhorn(a, b, M, reg, method='sinkhorn', numItermax=1000, - M is the (dim_a, dim_b) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - a and b are source and target weights (sum to 1) + - a and b are source and target weights (histograms, both sum to 1) The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [2]_ @@ -143,7 +143,7 @@ def sinkhorn2(a, b, M, reg, method='sinkhorn', numItermax=1000, - M is the (dim_a, dim_b) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - a and b are source and target weights (sum to 1) + - a and b are source and target weights (histograms, both sum to 1) The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [2]_ @@ -251,7 +251,7 @@ def sinkhorn_knopp(a, b, M, reg, numItermax=1000, - M is the (dim_a, dim_b) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - a and b are source and target weights (sum to 1) + - a and b are source and target weights (histograms, both sum to 1) The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [2]_ @@ -432,7 +432,7 @@ def greenkhorn(a, b, M, reg, numItermax=10000, stopThr=1e-9, verbose=False, - M is the (dim_a, dim_b) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - a and b are source and target weights (sum to 1) + - a and b are source and target weights (histograms, both sum to 1) @@ -578,7 +578,8 @@ def sinkhorn_stabilized(a, b, M, reg, numItermax=1000, tau=1e3, stopThr=1e-9, - M is the (dim_a, dim_b) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - a and b are source and target weights (sum to 1) + - a and b are source and target weights (histograms, both sum to 1) + The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [2]_ but with the log stabilization @@ -808,7 +809,8 @@ def sinkhorn_epsilon_scaling(a, b, M, reg, numItermax=100, epsilon0=1e4, - M is the (dim_a, dim_b) metric cost matrix - :math:`\Omega` is the entropic regularization term :math:`\Omega(\gamma)=\sum_{i,j} \gamma_{i,j}\log(\gamma_{i,j})` - - a and b are source and target weights (sum to 1) + - a and b are source and target weights (histograms, both sum to 1) + The algorithm used for solving the problem is the Sinkhorn-Knopp matrix scaling algorithm as proposed in [2]_ but with the log stabilization @@ -1229,7 +1231,6 @@ def barycenter_stabilized(A, M, reg, tau=1e10, weights=None, numItermax=1000, absorbing = False if (u > tau).any() or (v > tau).any(): absorbing = True - print("YEAH absorbing") alpha = alpha + reg * np.log(np.max(u, 1)) beta = beta + reg * np.log(np.max(v, 1)) K = np.exp((alpha[:, None] + beta[None, :] - @@ -1394,9 +1395,12 @@ def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000, where : - :math:`W_{M,reg}(\cdot,\cdot)` is the entropic regularized Wasserstein distance with M loss matrix (see ot.bregman.sinkhorn) - - :math:`\mathbf{a}` is an observed distribution, :math:`\mathbf{h}_0` is aprior on unmixing - - reg and :math:`\mathbf{M}` are respectively the regularization term and the cost matrix for OT data fitting - - reg0 and :math:`\mathbf{M0}` are respectively the regularization term and the cost matrix for regularization + - :math: `\mathbf{D}` is a dictionary of `n_atoms` atoms of dimension `dim_a`, its expected shape is `(dim_a, n_atoms)` + - :math:`\mathbf{h}` is the estimated unmixing of dimension `n_atoms` + - :math:`\mathbf{a}` is an observed distribution of dimension `dim_a` + - :math:`\mathbf{h}_0` is a prior on `h` of dimension `dim_prior` + - reg and :math:`\mathbf{M}` are respectively the regularization term and the cost matrix (dim_a, dim_a) for OT data fitting + - reg0 and :math:`\mathbf{M0}` are respectively the regularization term and the cost matrix (dim_prior, n_atoms) regularization - :math:`\\alpha`weight data fitting and regularization The optimization problem is solved suing the algorithm described in [4] @@ -1404,16 +1408,16 @@ def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000, Parameters ---------- - a : ndarray, shape (n_observed) - observed distribution - D : ndarray, shape (dim, dim) + a : ndarray, shape (dim_a) + observed distribution (histogram, sums to 1) + D : ndarray, shape (dim_a, n_atoms) dictionary matrix - M : ndarray, shape (dim, dim) + M : ndarray, shape (dim_a, dim_a) loss matrix - M0 : ndarray, shape (n_observed, n_observed) + M0 : ndarray, shape (n_atoms, dim_prior) loss matrix - h0 : ndarray, shape (dim,) - prior on h + h0 : ndarray, shape (n_atoms,) + prior on the estimated unmixing h reg : float Regularization term >0 (Wasserstein data fitting) reg0 : float @@ -1432,7 +1436,7 @@ def unmix(a, D, M, M0, h0, reg, reg0, alpha, numItermax=1000, Returns ------- - a : ndarray, shape (dim,) + h : ndarray, shape (n_atoms,) Wasserstein barycenter log : dict log dictionary return only if log==True in parameters -- cgit v1.2.3 From 29848c2ab362fc5ff466cb0e12409a1cca61644f Mon Sep 17 00:00:00 2001 From: Rémi Flamary Date: Mon, 9 Sep 2019 15:25:57 +0200 Subject: cleanup verison number 1.0- --- RELEASES.md | 9 ++++----- 1 file changed, 4 insertions(+), 5 deletions(-) diff --git a/RELEASES.md b/RELEASES.md index 112e4ab..66eee19 100644 --- a/RELEASES.md +++ b/RELEASES.md @@ -1,17 +1,16 @@ # POT Releases -## 1.0.0 Out of beta +## 0.6 Year 3 *July 2019* -This is the first official stable release of POT and this means a jump to 1.0.0! +This is the first official stable release of POT and this means a jump to 0.6! The library has been used in the wild for a while now and we have reached a state where a lot of fundamental OT solvers are available and tested. It has been quite stable in the last months but kept the beta flag in its Pypi classifiers until now. -Note that this major -release will be the last one supporting officially Python 2.7 (See +Note that this release will be the last one supporting officially Python 2.7 (See https://python3statement.org/ for more reasons). For next release we will keep the travis tests for Python 2 but will make them non necessary for merge in 2020. @@ -55,7 +54,7 @@ bring new features and solvers to the library. * Make doctest work on travis (PR #90) * Update documentation (PR #79, PR #84) * Solver for EMD in 1D (PR #89) -* Solvers for regularized unbalanced OT (PR #87) +* Solvers for regularized unbalanced OT (PR #87, PR#99) * Solver for Fused Gromov-Wasserstein (PR #86) * Add empirical Sinkhorn and empirical Sinkhorn divergences (PR #80) -- cgit v1.2.3